From 5229414fb9f23aac80150b98a37a338201741a51 Mon Sep 17 00:00:00 2001
From: Eytan Bakshy <ebakshy@gmail.com>
Date: Mon, 22 Sep 2025 08:54:38 -0500
Subject: [PATCH 1/2] add mtpfn codebase

add mtpfn codebase
---
 private_multitask_pfn/.gitignore              |   18 +
 private_multitask_pfn/1d.ipynb                | 1005 ++++++++
 private_multitask_pfn/1d_hist.py              |   60 +
 private_multitask_pfn/PFNs/LICENSE            |  201 ++
 private_multitask_pfn/PFNs/README.md          |   99 +
 .../PFNs/Tutorial_Training_for_BO.ipynb       | 1027 ++++++++
 .../PFNs/pfns/bar_distribution.py             |  578 +++++
 private_multitask_pfn/PFNs/pfns/config.json   |   56 +
 private_multitask_pfn/PFNs/pfns/decoders.py   |   35 +
 private_multitask_pfn/PFNs/pfns/encoders.py   |  642 +++++
 .../PFNs/pfns/hierarchical.py                 |  335 +++
 .../PFNs/pfns/initializers.py                 |   11 +
 private_multitask_pfn/PFNs/pfns/layer.py      |  475 ++++
 .../PFNs/pfns/pfn_bo_bayesmark.py             |  508 ++++
 .../PFNs/pfns/positional_encodings.py         |  344 +++
 .../PFNs/pfns/priors/__init__.py              |    3 +
 .../pfns/priors/condition_on_area_of_opt.py   |   63 +
 .../condition_on_area_of_opt_continuous.py    |   76 +
 .../PFNs/pfns/priors/fast_gp.py               |  203 ++
 private_multitask_pfn/PFNs/pfns/priors/gp.py  |   69 +
 .../PFNs/pfns/priors/hebo_prior.py            |  613 +++++
 .../pfns/priors/hyperparameter_sampling.py    |  113 +
 .../PFNs/pfns/priors/input_warping.py         |   93 +
 .../PFNs/pfns/priors/nonmyopic_bo.py          |  159 ++
 .../PFNs/pfns/priors/normalize_with_style.py  |   59 +
 .../PFNs/pfns/priors/prior.py                 |   99 +
 .../PFNs/pfns/priors/prior_bag.py             |   45 +
 .../PFNs/pfns/priors/simple_mlp.py            |  126 +
 .../PFNs/pfns/priors/utils.py                 |  438 ++++
 .../pfns/scripts/acquisition_functions.py     |  506 ++++
 .../PFNs/pfns/scripts/tabpfn_interface.py     |  540 +++++
 .../PFNs/pfns/scripts/tabpfn_model_builder.py |   54 +
 .../PFNs/pfns/scripts/tabular_metrics.py      |  226 ++
 .../PFNs/pfns/scripts/tune_input_warping.py   |  336 +++
 private_multitask_pfn/PFNs/pfns/train.py      |  878 +++++++
 .../PFNs/pfns/transformer.py                  |  835 +++++++
 private_multitask_pfn/PFNs/pfns/utils.py      |  520 +++++
 private_multitask_pfn/PFNs/pyproject.toml     |   43 +
 private_multitask_pfn/PFNs/setup.py           |    4 +
 .../benchmark_time_mtgp_samples.csv           |   11 +
 .../benchmark_time_mtgp_task.csv              |    6 +
 .../benchmark_time_scaml_samples.csv          |   11 +
 .../benchmark_time_scaml_task.csv             |    6 +
 private_multitask_pfn/eval.py                 | 1070 +++++++++
 private_multitask_pfn/eval_axial.py           |   85 +
 private_multitask_pfn/eval_axial_multitask.py |  131 ++
 private_multitask_pfn/eval_f.py               |  284 +++
 private_multitask_pfn/eval_fcnet.py           |  125 +
 private_multitask_pfn/eval_hpobench.py        |  201 ++
 private_multitask_pfn/eval_plot_util.py       |  315 +++
 private_multitask_pfn/eval_task.py            |  139 ++
 private_multitask_pfn/eval_vis.py             |  215 ++
 private_multitask_pfn/gen_axial_batch.py      |   96 +
 private_multitask_pfn/gen_batch.py            | 1083 +++++++++
 private_multitask_pfn/gen_task_batch.py       |  275 +++
 private_multitask_pfn/lmc.py                  |  316 +++
 .../load_pfn4bo_checkpoints.py                |   91 +
 private_multitask_pfn/mtgp_nuts.py            |  323 +++
 ...l Evaluation on Discrete MF Problems.ipynb | 1843 +++++++++++++++
 .../multifidelity/borehole_res.pkl            |  Bin 0 -> 267 bytes
 .../multifidelity/mf lily.ipynb               | 1662 +++++++++++++
 .../multifidelity/multifidelity.py            |  448 ++++
 .../multifidelity/multifidelity_fixed.py      |  492 ++++
 .../multifidelity/wing_weight_res.pkl         |  Bin 0 -> 267 bytes
 .../paper_figures/benchmark_time.ipynb        |  273 +++
 .../paper_figures/benchmark_time_samples.csv  |   11 +
 .../paper_figures/benchmark_time_task.csv     |   17 +
 .../paper_figures/figures_bayesian.py         |  428 ++++
 .../paper_figures/figures_benchmarks.py       |  449 ++++
 .../paper_figures/figures_boxplot_data.pkl    |  Bin 0 -> 1118 bytes
 .../paper_figures/figures_finetune.ipynb      |  833 +++++++
 .../paper_figures/figures_hpobench.py         |  420 ++++
 .../paper_figures/figures_uncorr.py           |  537 +++++
 .../paper_figures/joint_data.py               |  483 ++++
 private_multitask_pfn/paper_figures/nlls.pkl  |  Bin 0 -> 686 bytes
 .../pd1_gp_surrogates.pickle                  |  Bin 0 -> 2152569 bytes
 private_multitask_pfn/pd1_gp_surrogates.pth   |  Bin 0 -> 53658 bytes
 private_multitask_pfn/pfn.yml                 |  199 ++
 private_multitask_pfn/scaml.py                |  435 ++++
 private_multitask_pfn/task_embedding.ipynb    | 2065 +++++++++++++++++
 private_multitask_pfn/train.py                |  638 +++++
 private_multitask_pfn/train_continue.py       |   46 +
 private_multitask_pfn/utils.py                |  781 +++++++
 83 files changed, 28338 insertions(+)
 create mode 100644 private_multitask_pfn/.gitignore
 create mode 100644 private_multitask_pfn/1d.ipynb
 create mode 100644 private_multitask_pfn/1d_hist.py
 create mode 100644 private_multitask_pfn/PFNs/LICENSE
 create mode 100644 private_multitask_pfn/PFNs/README.md
 create mode 100644 private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb
 create mode 100644 private_multitask_pfn/PFNs/pfns/bar_distribution.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/config.json
 create mode 100644 private_multitask_pfn/PFNs/pfns/decoders.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/encoders.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/hierarchical.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/initializers.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/layer.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/positional_encodings.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/__init__.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/fast_gp.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/gp.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/input_warping.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/prior.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/prior_bag.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/simple_mlp.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/priors/utils.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/scripts/acquisition_functions.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/scripts/tabpfn_interface.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/scripts/tabpfn_model_builder.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/scripts/tabular_metrics.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/scripts/tune_input_warping.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/train.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/transformer.py
 create mode 100644 private_multitask_pfn/PFNs/pfns/utils.py
 create mode 100644 private_multitask_pfn/PFNs/pyproject.toml
 create mode 100644 private_multitask_pfn/PFNs/setup.py
 create mode 100644 private_multitask_pfn/benchmark_time_mtgp_samples.csv
 create mode 100644 private_multitask_pfn/benchmark_time_mtgp_task.csv
 create mode 100644 private_multitask_pfn/benchmark_time_scaml_samples.csv
 create mode 100644 private_multitask_pfn/benchmark_time_scaml_task.csv
 create mode 100644 private_multitask_pfn/eval.py
 create mode 100644 private_multitask_pfn/eval_axial.py
 create mode 100644 private_multitask_pfn/eval_axial_multitask.py
 create mode 100644 private_multitask_pfn/eval_f.py
 create mode 100644 private_multitask_pfn/eval_fcnet.py
 create mode 100644 private_multitask_pfn/eval_hpobench.py
 create mode 100644 private_multitask_pfn/eval_plot_util.py
 create mode 100644 private_multitask_pfn/eval_task.py
 create mode 100644 private_multitask_pfn/eval_vis.py
 create mode 100644 private_multitask_pfn/gen_axial_batch.py
 create mode 100644 private_multitask_pfn/gen_batch.py
 create mode 100644 private_multitask_pfn/gen_task_batch.py
 create mode 100644 private_multitask_pfn/lmc.py
 create mode 100644 private_multitask_pfn/load_pfn4bo_checkpoints.py
 create mode 100644 private_multitask_pfn/mtgp_nuts.py
 create mode 100644 private_multitask_pfn/multifidelity/Model Evaluation on Discrete MF Problems.ipynb
 create mode 100644 private_multitask_pfn/multifidelity/borehole_res.pkl
 create mode 100644 private_multitask_pfn/multifidelity/mf lily.ipynb
 create mode 100644 private_multitask_pfn/multifidelity/multifidelity.py
 create mode 100644 private_multitask_pfn/multifidelity/multifidelity_fixed.py
 create mode 100644 private_multitask_pfn/multifidelity/wing_weight_res.pkl
 create mode 100644 private_multitask_pfn/paper_figures/benchmark_time.ipynb
 create mode 100644 private_multitask_pfn/paper_figures/benchmark_time_samples.csv
 create mode 100644 private_multitask_pfn/paper_figures/benchmark_time_task.csv
 create mode 100644 private_multitask_pfn/paper_figures/figures_bayesian.py
 create mode 100644 private_multitask_pfn/paper_figures/figures_benchmarks.py
 create mode 100644 private_multitask_pfn/paper_figures/figures_boxplot_data.pkl
 create mode 100644 private_multitask_pfn/paper_figures/figures_finetune.ipynb
 create mode 100644 private_multitask_pfn/paper_figures/figures_hpobench.py
 create mode 100644 private_multitask_pfn/paper_figures/figures_uncorr.py
 create mode 100644 private_multitask_pfn/paper_figures/joint_data.py
 create mode 100644 private_multitask_pfn/paper_figures/nlls.pkl
 create mode 100644 private_multitask_pfn/pd1_gp_surrogates.pickle
 create mode 100644 private_multitask_pfn/pd1_gp_surrogates.pth
 create mode 100644 private_multitask_pfn/pfn.yml
 create mode 100644 private_multitask_pfn/scaml.py
 create mode 100644 private_multitask_pfn/task_embedding.ipynb
 create mode 100644 private_multitask_pfn/train.py
 create mode 100644 private_multitask_pfn/train_continue.py
 create mode 100644 private_multitask_pfn/utils.py

diff --git a/private_multitask_pfn/.gitignore b/private_multitask_pfn/.gitignore
new file mode 100644
index 0000000..8dbdd78
--- /dev/null
+++ b/private_multitask_pfn/.gitignore
@@ -0,0 +1,18 @@
+__pycache__
+.ipynb_checkpoints
+pfn_fb
+ckpt
+*.tar
+eval_plot
+eval_plot_cache
+eval_plot_cache_
+eval_plot_cache__
+wandb
+slurm
+wandb_links
+datasets
+output
+figures
+*.png
+*.pdf
+final_models
\ No newline at end of file
diff --git a/private_multitask_pfn/1d.ipynb b/private_multitask_pfn/1d.ipynb
new file mode 100644
index 0000000..e706496
--- /dev/null
+++ b/private_multitask_pfn/1d.ipynb
@@ -0,0 +1,1005 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/utils.py:293: FutureWarning: Decorating classes is deprecated and will be disabled in future versions. You should only decorate functions or methods. To preserve the current behavior of class decoration, you can directly decorate the `__init__` method and nothing else.\n",
+      "  @torch.no_grad()\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "\n",
+    "import botorch\n",
+    "import gpytorch\n",
+    "import torch\n",
+    "\n",
+    "from utils import *\n",
+    "import traceback\n",
+    "import matplotlib.pyplot as plt\n",
+    "from gpytorch.kernels import RBFKernel\n",
+    "# from torch.distributions import MultivariateNormal\n",
+    "import os\n",
+    "from botorch.models import SingleTaskGP\n",
+    "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
+    "from gpytorch.distributions import MultivariateNormal\n",
+    "\n",
+    "default_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def get_test_function(n_samples, lengthscale=None, seed=0):\n",
+    "    with botorch.manual_seed(seed):        \n",
+    "        train_x = torch.rand(n_samples, 1)\n",
+    "        test_x = torch.linspace(0, 1, 1000).view(-1, 1)\n",
+    "        all_x = torch.cat([train_x, test_x], dim=0)\n",
+    "        \n",
+    "        rbf = gpytorch.kernels.RBFKernel()\n",
+    "        if lengthscale:\n",
+    "            rbf.lengthscale = lengthscale\n",
+    "        else:\n",
+    "            rbf.lengthscale = torch.distributions.Gamma(3, 6).sample()\n",
+    "        \n",
+    "        \n",
+    "        mean = torch.zeros(all_x.size(0))\n",
+    "        covar = rbf(all_x)\n",
+    "        \n",
+    "        y = MultivariateNormal(mean, covar).sample().unsqueeze(1)\n",
+    "        train_y = y[:n_samples]\n",
+    "        test_y = y[n_samples:]\n",
+    "        \n",
+    "    return train_x, train_y, test_x, test_y\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def plot(test_info, pfn_infos):\n",
+    "    n_plots = len(pfn_infos) + 1\n",
+    "    fig, axs = plt.subplots(1, n_plots, figsize=(n_plots * 5, 4))\n",
+    "    \n",
+    "    queried = []\n",
+    "    for i, pfn_info in enumerate(pfn_infos):        \n",
+    "        pfn_id, model, model_dim = pfn_info\n",
+    "        train_x, train_y, test_x, test_y = test_info\n",
+    "        train_task_id = torch.zeros(train_x.size(0))\n",
+    "        \n",
+    "        if model_dim > 1:\n",
+    "            # pad train and test with 0s\n",
+    "            padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)\n",
+    "            padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)\n",
+    "        else:\n",
+    "            padded_train_x = train_x\n",
+    "            padded_test_x = test_x\n",
+    "            \n",
+    "        # train_x, train_task_id, train_y, test_x, \n",
+    "        posterior = pfn_predict(model, train_task_id.to(default_device), padded_train_x.to(default_device), train_y.to(default_device), padded_test_x.to(default_device))\n",
+    "        mean = posterior.mean.cpu()\n",
+    "        # std = posterior.variance.sqrt().cpu()\n",
+    "        lower, upper = posterior.lower.cpu(), posterior.upper.cpu()\n",
+    "        \n",
+    "        args = train_task_id, padded_train_x, train_y, None, padded_test_x, test_y\n",
+    "        args = [arg.to(default_device) if arg is not None else None for arg in args]\n",
+    "        next_x, next_y = pfn_get_best(model, *args)\n",
+    "        next_x, next_y = next_x[0].cpu().item(), next_y.cpu().item()\n",
+    "        queried.append((next_x, next_y))\n",
+    "        \n",
+    "        ax = axs[i]\n",
+    "        ax.plot(test_x, test_y, label=\"true\", color=\"C0\")\n",
+    "        ax.scatter(train_x, train_y, label=\"train\", color=\"C0\")\n",
+    "        ax.plot(test_x, mean, label=\"mean\", color=\"C1\")\n",
+    "        ax.fill_between(test_x.flatten(), lower.flatten(), upper.flatten(), alpha=0.2, color=\"C1\")\n",
+    "        ax.set_title(f\"PFN {pfn_id}\")\n",
+    "        ax.axvline(next_x, color=\"C3\", linestyle=\"--\", label=\"query\")\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "    gp = SingleTaskGP(train_x, train_y)\n",
+    "    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)\n",
+    "    fit_gpytorch_mll(mll)\n",
+    "    gp.eval()\n",
+    "    gp_pred = gp.posterior(test_x)\n",
+    "    gp_mean = gp_pred.mean.cpu().detach()\n",
+    "    gp_std = gp_pred.variance.sqrt().cpu().detach()\n",
+    "    \n",
+    "    args = train_task_id, train_x, train_y, torch.zeros_like(test_y), test_x.unsqueeze(1), test_y.unsqueeze(1)\n",
+    "    next_x, next_y = gp_get_best(*args)\n",
+    "    next_x, next_y = next_x.cpu().item(), next_y.cpu().item()\n",
+    "    queried.append((next_x, next_y))\n",
+    "    \n",
+    "    ax = axs[-1]\n",
+    "    ax.plot(test_x, test_y, label=\"true\", color=\"C0\")\n",
+    "    ax.scatter(train_x, train_y, label=\"train\", color=\"C0\")\n",
+    "    ax.plot(test_x, gp_mean, label=\"gp mean\", color=\"C2\")\n",
+    "    ax.fill_between(test_x.flatten(), gp_mean.flatten() - 2 * gp_std.flatten(), gp_mean.flatten() + 2 * gp_std.flatten(), alpha=0.2, color=\"C2\")\n",
+    "    ax.legend()\n",
+    "    ax.set_title(\"GP\")\n",
+    "    ax.axvline(next_x, color=\"C3\", linestyle=\"--\", label=\"query\")\n",
+    "    \n",
+    "    ax_min_y = min([ax.get_ylim()[0] for ax in axs])\n",
+    "    ax_max_y = max([ax.get_ylim()[1] for ax in axs])\n",
+    "    for ax in axs:\n",
+    "        ax.set_ylim(ax_min_y, ax_max_y)\n",
+    "    \n",
+    "    plt.show()\n",
+    "    \n",
+    "    return queried\n",
+    "    # return fig, axs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
+      "Using a Transformer with 18.38 M parameters\n",
+      "Loaded model\n",
+      "Using a Transformer with 17.35 M parameters\n",
+      "Loaded model\n",
+      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
+      "Using a Transformer with 18.38 M parameters\n",
+      "Loaded model\n",
+      "Using a Transformer with 17.35 M parameters\n",
+      "Loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_names = [\"fresh-snowball-475\", \"young-grass-475\", \"fluent-durian-477\", \"wild-serenity-478\"]\n",
+    "models = [load_model(f\"/home/yl9959/mtpfn/wandb_links/{name}\", best=True).to(default_device) for name in model_names]\n",
+    "\n",
+    "pfn_infos = [\n",
+    "    (\"bar 0.2\", models[0], 1),\n",
+    "    (\"gauss 0.2\", models[1], 1),\n",
+    "    (\"bar\", models[2], 1),\n",
+    "    (\"gauss\", models[3], 1),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a Transformer with 25.77 M parameters\n",
+      "Loaded model\n",
+      "Using a Transformer with 25.77 M parameters\n",
+      "Loaded model\n",
+      "Using a Transformer with 25.77 M parameters\n",
+      "Loaded model\n",
+      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
+      "Using a Transformer with 26.79 M parameters\n",
+      "Loaded model\n",
+      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
+      "Using a Transformer with 26.79 M parameters\n",
+      "Loaded model\n",
+      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
+      "Using a Transformer with 26.79 M parameters\n",
+      "Loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_names = [\n",
+    "    \"sparkling-microwave-480\", \n",
+    "    \"wobbly-donkey-484\", \n",
+    "    \"likely-donkey-479\", \n",
+    "    \"fallen-cherry-483\",\n",
+    "    \"stilted-night-482\",\n",
+    "    \"youthful-wildflower-481\",\n",
+    "]\n",
+    "models = [load_model(f\"/home/yl9959/mtpfn/wandb_links/{name}\", best=True).to(default_device) for name in model_names]\n",
+    "\n",
+    "pfn_infos = [\n",
+    "    (\"G 0.\", models[0], 3),\n",
+    "    (\"G 0.1\", models[1], 3),\n",
+    "    (\"G 0.2\", models[2], 3),\n",
+    "    (\"B 0.\", models[3], 3),\n",
+    "    (\"B 0.1\", models[4], 3),\n",
+    "    (\"B 0.2\", models[5], 3),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Seed 0\n",
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Seed 1\n",
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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efvppNm/ezOOPP95s2wsvvJDPP/9cB1NERLpQMmZDRUUFf//73wGYN28eAP/4xz/IysoiKyuLK6+8sl2PRUREopOMmTFjxgy+++47brjhBubOncvcuXObvte/f3+mTZvWrsciIiIdN3r0aJ5//nnOPfdcxowZw/nnn8/48eMxTZNNmzbx/PPP43A4GDx4sN2lirSbmmtFYuDqq6/mr3/9KxUVFVFt369fP+bPn88jjzzCCy+8wMyZM6mtraVPnz4ccsghPPfcc5x99tlt3s6ECRP46KOP+P3vf88111xDeno6v/jFL5g1a1ZnH5KIiMRAsubDK6+8wtNPP9309dKlS5uWgxo8eLCaa0VE4iAZM2PniRjr169v9YDJp59+quZaEZEu1N4sAZg9ezazZ88GwOl0MmDAAH75y19y5513kpqa2ub1//Of/3DLLbfwzDPPUFZWxgEHHMDbb7/NkUce2eHHISIisZNs2VBWVsYtt9zS7LL77rsPgGHDhqm5VkQkjpItM3a+L3X33Xe3+N5RRx2l5loRkS526qmnsmLFCu677z4+/PBDnnjiCQzDYNiwYZx44olcfvnlbU41F0lEhqlxASIiIiIiIiIiIiIiIiIiIiIiIiIiIgA47C5AREREREREREREREREREREREREREQkUai5VkREREREREREREREREREREREREREpJGaa0VERERERERERERERERERERERERERBqpuVZERERERERERERERERERERERERERKSRmmtFREREREREREREREREREREREREREQaqblWRERERERERERERERERERERERERESkkcvuAmItEomQm5tLeno6hmHYXY6ISEIyTZOqqioGDhyIw9Fzz7NQZoiItE2ZYVFmiIi0TZlhUWaIiLRNmWFRZoiItE2ZYVFmiIi0TZlhUWaIiLQt2szods21ubm5DBkyxO4yRESSwrZt2xg8eLDdZdhGmSEiEj1lhjJDRCRaygxlhohItJQZygwRkWgpM5QZIiLRUmYoM0REotVWZnS75tr09HTAeuAZGRk2VyMikpgqKysZMmRI09/MnkqZISLSNmWGRZkhItI2ZYZFmSEi0jZlhkWZISLSNmWGRZkhItI2ZYZFmSEi0rZoM6PbNdfuHGmekZHRrpCI1NWx6cwzARjx0ks4UlLiUp+ISCLp6ctAKDNERKKnzFBmiIhES5nRscwQEUkmsdrPVWYoM0Sk+1NmxIYyQ0R6AmVGbOh4hohI9NrKjG7XXNthpklgw8amz0VERHZLmSEiItFSZoiIiIhId6T9XBERiZYyQ0REoqXMsJeefxGRFhx2FyAiIiIiIiIiIiIiIiIiIiIiIiIiIpIo1FwrIiIiIiIiIiIiIiIiIiIiIiIiIiLSyGV3ASIiIiIiIiIiIiIiIiIiIiIiIiIiiSgcDhMMBu0uQ6LkdrtxOp2dvh0114qIiIiIiIiIiIiIiIiIiIiIiIiIfI9pmuTn51NeXm53KdJOWVlZ5OTkYBhGh29DzbUiIiIiIiIiIiIiIiIiIiIiIiIiIt+zs7G2X79++P3+TjVqStcwTZPa2loKCwsBGDBgQIdvS821OxkG7oEDmz4XERHZLWWGiIhES5khIiIiIt2R9nNFRCRaygwREYmWMsNeev5FWgiHw02Ntb1797a7HGmHlJQUAAoLC+nXrx9Op7NDt6Pm2kaOlBRGffKx3WWIiEgSUGaIiEi0lBkiIiIi0h1pP1dERKKlzBARkWgpM+yl51+kpWAwCIDf77e5EumInT+3YDDY4eZaRywLEhERERERERERERERERERERERERHpDgxNc05Ksfi5qblWRERERERERERERERERERERERERESkkZprG0Xq69l0xplsOuNMIvX1dpcjIhKdYD2Ypt1V9DjKDBERiZYyQ0RERES6I+3niohItJQZIiISLWWGvfT8i4i05LK7gIQRiVC/cmXT5yIiCa+6EEo3Qb+x4Muwu5qeRZkhIiLRUmaIiIiISHek/VwREYmWMkNERKKlzLCXnn+RbmXKlCkceOCB/PWvf7W7lKSm5loRkWRUVwaFayBYC2hyrYiIiIiIiIiIiIiIiIiIiIiItM00TcLhMC6X2kf3xGF3ASIi0k4N1VCwCuor7a5EREREREREREREREREREREREQSxMUXX8znn3/Ogw8+iGEYGIbBU089hWEYvPfeexx88MF4vV7mzp3LxRdfzGmnndbs+ldffTVTpkxp+joSiTBr1ixGjBhBSkoK48eP5+WXX+7aB2UTtR6LiCSTYD0Uroa6ckjvD7UldlckIiIiIiIiIiIiIiIiIiIiItLtmaZJXTBsy32nuJ0YhtHmdg8++CDr1q1jv/3244477gBg1apVANx4443ce++97LXXXvTq1Suq+501axbPPvssDz/8MKNHj2bOnDlccMEF9O3bl6OOOqrjDygJqLlWRCRZhENQ9C1U5UPGQMC0uyIRERERERERERERERERERERkR6hLhhm3K0f2HLfq++Yjt/TdrtnZmYmHo8Hv99PTk4OAGvXrgXgjjvuYNq0aVHfZ0NDA3fddRcfffQRkyZNAmCvvfZi7ty5PPLII2quFRGRBGCaUPodVGy1JtY6nBAJ2V2ViIiIiIiIiIiIiIiIiIiIiIgkgUMOOaRd22/YsIHa2toWDbmBQICDDjoolqUlJDXXfo8zylHHIiJdrnIHlGyAlGxweuyuRlBmiIhI9JQZIiIiItIdaT9XRESipcwQEZFoKTPspedfpG0pbier75hu2313VmpqarOvHQ4Hptl85exgMNj0eXV1NQDvvPMOgwYNarad1+vtdD2JTs21jRx+P3svmG93GSIiLdWWQtFacKeAx293NYIyQ0REoqfMEBEREZHuSPu5IiISLWWGiIhES5lhLz3/ItExDAO/J/FbLj0eD+FwuM3t+vbty8qVK5td9s033+B2uwEYN24cXq+XrVu3ctRRR8Wl1kSW+D9pEZGeLFALhWshHIT03nZXIyIiIiIiIiIiIiIiIiIiIiIiCWz48OEsXLiQzZs3k5aWRiQSaXW7Y445hnvuuYf//Oc/TJo0iWeffZaVK1dy0EEHAZCens51113HNddcQyQS4YgjjqCiooJ58+aRkZHBRRdd1JUPq8s57C5ARER2IxKG4vVQVwJp/eyuRkREREREREREREREREREREREEtx1112H0+lk3Lhx9O3bl61bt7a63fTp07nlllu44YYbOPTQQ6mqquLCCy9sts2dd97JLbfcwqxZsxg7diwzZszgnXfeYcSIEV3xUGylybWNIvX1bLv0MgCG/PtRHD6fzRWJSI9XvtX6SO8Hhs6FSCTKDBERiZYyQ0RERES6I+3niohItJQZIiISLWWGvfT8i3Qve++9NwsWLGh22cUXX9zqtjNnzmTmzJm7vS3DMLjqqqu46qqrYlliUlBz7U6RCLWLFjV9LiJiq5pia2ptSiY4PXZXIz+kzBARkWgpM0RERESkO9J+roiIREuZISIi0VJm2EvPv4hICxqFKCKSaIJ1ULQWiIA33e5qREREREREREREREREREREREREehQ114qIJJJIBEo2Qm0ppPa1uxoREREREREREREREREREREREZEeR821IiKJpCoXyjZDWj8w9CdaRERERERERERERERERERERESkq6lzS0QkUTRUQfF6cKeAy2t3NSIiIiIiIiIiIiIiIiIiIiIiIj2SmmtFRBJBJAwlGyBQA/5su6sREZHuJlgPecsh1GB3JSIiIiIiIiIiIiIiIiIiIgnPZXcBicRISbG7BBHpqSp3QMV2SOtvdyUSJWWGiCSVim1QW2o112o6epdTZoiIiIhId6T9XBERiZYyQ0REoqXMsJeefxGR5tRc28jh97PP0iV2lyEiPVFDFRRvBE8aON12VyNRUGaISFIJ1kNlLoQDYBh2V9PjKDNEREREpDvSfq6IiERLmSEiItFSZthLz7+ISEsOuwsQEenRIhEo+Q6CNZCSZXc1IiLSHVXlQW2J3VWIiIiIiIiIiIiIiIiIiIgkDTXXiojYqSoPKrdDWl+7KxERke4oWAdlW8CbZnclIiIiIiIiIiIiIiIiIiKSZIYPH85f//pXu8uwRVyba+fMmcPJJ5/MwIEDMQyD119/vc3rfPbZZ0yYMAGv18uoUaN46qmn4llik0hDA1t/9Su2/upXRBoauuQ+RaSHC9RCyQZwecHpsbsa2ykzRETioCofGqrAm2F3JTGlzBARkWglU2aIiCST7rifq8wQEYkPZYZFmSEi0jZlhkXHM0QkFqZMmcLVV18dk9tatGgRl112WUxuK9nEtbm2pqaG8ePH89BDD0W1/aZNmzjxxBM5+uij+eabb7j66qv55S9/yQcffBDPMi3hMDWfz6Hm8zkQDsf//kSkZzNNKNsM9RWQkm13NQlBmSEiEmM7p9b60sAw7K4mppQZIiISraTKDBGRZNIN93OVGSIicaLMUGaIiERLmaHjGSLdWDhismBjCW98s4MFG0sIR0xb6zFNk1AoFNW2ffv2xe/3x7mixOSK540ff/zxHH/88VFv//DDDzNixAjuu+8+AMaOHcvcuXN54IEHmD59erzKFBHperUlUL4FUnt3u4anjlJmiIjEWFU+NFRC5mCIBO2uJqaUGSIiEi1lhoiIREuZISIi0VJmiIhItJQZIgLw/so8Zr61mryK+qbLBmT6uO3kcczYb0DM7+/iiy/m888/5/PPP+fBBx8E4Mknn+SSSy7h3Xff5eabb2bFihV8+OGHDBkyhGuvvZYvv/ySmpoaxo4dy6xZs5g6dWrT7Q0fPpyrr766aRKuYRj8+9//5p133uGDDz5g0KBB3HfffZxyyikxfyx2i+vk2vZasGBBsx8MwPTp01mwYMFur9PQ0EBlZWWzDxGRhBYOQelG63N3zzyzIxaUGSIiexCsh/Kt4EvXSRwoM0REJHrKDBERiZYyQ0REoqXMEBGRaCkzRLqf91fmccWzS5o11gLkV9RzxbNLeH9lXszv88EHH2TSpElceuml5OXlkZeXx5AhQwC48cYb+fOf/8yaNWs44IADqK6u5oQTTuDjjz9m6dKlzJgxg5NPPpmtW7fu8T5mzpzJWWedxfLlyznhhBM4//zzKS0tjfljsVtCNdfm5+fTv3//Zpf179+fyspK6urqWr3OrFmzyMzMbPrY+R9BRCRhVeVCVSGk9rG7kqSmzBAR2YPqAmtqrTfT7koSgjJDRESipcwQEZFoKTNERCRaygwREYmWMkOkewlHTGa+tRqzle/tvGzmW6sJR1rbouMyMzPxeDz4/X5ycnLIycnB6XQCcMcddzBt2jRGjhxJdnY248eP51e/+hX77bcfo0eP5s4772TkyJG8+eabe7yPiy++mHPPPZdRo0Zx1113UV1dzVdffRXTx5EIEqq5tiNuuukmKioqmj62bdtmd0kiIrsXqIGS78CbBg6X3dX0OMoMEekRQg3W1FpPqqbWdoIyQ0REoqXMEBGRaCkzREQkWsoMERGJljJDJHF9tam0xcTa7zOBvIp6vtrUdRNfDznkkGZfV1dXc9111zF27FiysrJIS0tjzZo1bU6uPeCAA5o+T01NJSMjg8LCwrjUbKeE6uzKycmhoKCg2WUFBQVkZGSQkpLS6nW8Xi9er7cryhMR6bzyrdYkwczBdleS9JQZIiK7UV0IdWWQOcjuShKGMkNERKKlzBARkWgpM0REJFrKDBERiZYyQ6R7KazafWNtR7aLhdTU1GZfX3fddcyePZt7772XUaNGkZKSwhlnnEEgENjj7bjd7mZfG4ZBJBKJeb12S6jm2kmTJvHuu+82u2z27NlMmjTJpopERGKottRqrvX31iTBGFBmiIi0IhyE8i3g8YOR9ItUxIwyQ0REoqXMEBGRaCkzREQkWsoMERGJljJDpHvpl+6L6Xbt4fF4CIfDbW43b948Lr74Yn7yk58A1iTbzZs3x7yeZBXX5trq6mo2bNjQ9PWmTZv45ptvyM7OZujQodx0003s2LGD//znPwBcfvnl/OMf/+CGG27g5z//OZ988gkvvvgi77zzTjzLBMDh9zN27Zq434+I9FCRCJRugkjYaniSFpQZIiIxUFMEdeWQnmN3JXGlzBARkWglU2aIiCST7rifq8wQEYkPZYYyQ0QkWsoMHc8Q6W4mjshmQKaP/Ip6zFa+bwA5mT4mjsiO+X0PHz6chQsXsnnzZtLS0nY7VXb06NG8+uqrnHzyyRiGwS233NItJ9B2VFzHWS1evJiDDjqIgw46CIBrr72Wgw46iFtvvRWAvLw8tm7d2rT9iBEjeOedd5g9ezbjx4/nvvvu47HHHmP69OnxLFNEJP5qCqE6H1J7211JwlJmiIh0UiQMFdvA6QaH0+5q4kqZISIi0VJmiIhItJQZIiISLWWGiIhES5kh0rM5HQa3nTwOsBppv2/n17edPA6nI/arX1933XU4nU7GjRtH3759m/2t+b7777+fXr16MXnyZE4++WSmT5/OhAkTYl5PsjJM02ytMTppVVZWkpmZSUVFBRkZGXaXIyJiLdG9fTE0VEJav9jdbiQENcUwbDL4Mtt1Vf2ttOh5EJFupboQti+yssbxgwUqwgGoq4Bhk8Cb3q6b1d9Ki54HEekxQgHrZI1ew9t9sob+Vlr0PIiItE1/Ky16HkRE2qa/lRY9DyIibdPfSoueB5HYqa+vZ9OmTYwYMQKfz9fh23l/ZR4z31pNXkV902UDMn3cdvI4Zuw3IBalSiv29POL9m+la7ff6WEiDQ3k3vB7AAbe/RccXq/NFYlIt1GVbzXBZigQuwtlhogkHNOEiu1gGC0ba8VWygwRSTplm6wTNtJzwJNqdzUiIpKgtJ8rIiLRUmaIiEi0lBn20vMvEj8z9hvAtHE5fLWplMKqevql+5g4IjsuE2slthx2F5AwwmGqPviAqg8+gHDY7mpEpLsI1kPZZvD4u/0S3T2KMkNEEk1dmdUIlZJtdyXyQ8oMEUkmwXrr5MBQPS0XqRIREfke7eeKiEi0lBkiIhItZYa99PyLxJXTYTBpZG9OPXAQk0b2VmNtklBzrYhIPFXlWQ1PKVl2VyIiIt1ZZR6YIXDpLGIREemEylyoLbG7ChEREREREREREREREdupuVZEJF4CtdbUWl8GGPpzKyIicdJQBdV54MuyuxIREUlmgRoo3wKeVLsrERERERERERERERERsZ26vURE4qVih9Xw5M2wuxIREenOqgshUKdmKBER6ZyKXOv1i0+vX0RERERERERERERERNRcKyISDw3VULENUrLAMOyuRkREuqtQA1RsB1+a3ZWIiEgya6iCiq3W6xcRERERERERERERERFRc62ISFxU5kKwBrzpdlciIiLdWU0RNFRqSrqIiHROxQ4I1ur1i4iIiIiIiIiIiIiISCM114qIxFpD1a6ptSIiIvESCUP5dnD5wNBuvYiIdFB9JVRu1+sXERERERERERERERGR73HZXUCiMFJSGLPk66bPRUQ6bOfUJ3+23ZVInCgzRCQh1JZCXSmk9bW7EtkDZYaIJLzKHRCoA39vuysREZEkov1cERGJljJDRESipcywl55/EZGW1FzbyDAMDL/f7jJEJNk1VGnqUw+gzBCRhFCZCxjg0C59IlNmiEhCq6+Aiu3g72V3JSIikmS0nysiItFSZoiISLSUGfbS8y8i0pLWjxURiaWKXAjWgSfN7kpERKQ7q6+AmkJIybC7EhERSWaVuRBqAE+q3ZWIiIiIiIiIiIiIiIgkFDXXNooEAuTeeBO5N95EJBCwuxwRSUYN1dbUWl+m3ZVInCkzRMR2NcUQqge3ziBOdMoMEUlYO6fWpmhqrYiItJ/2c0VEJFrKDBERiZYyw156/kVEWlJz7U6hEBWvv07F669DKGR3NSKSjCrzIFAD3nS7K5F4U2aIiJ1CDVYzlFdT0pOCMkNEElXT1FqdqCEiIh2g/VwREYmWMkNERKKlzLCXnn+RbqWqqorzzz+f1NRUBgwYwAMPPMCUKVO4+uqrm7YZPnw4d955J+eeey6pqakMGjSIhx56aI+3e/HFF3Paaadx11130b9/f7KysrjjjjsIhUJcf/31ZGdnM3jwYJ588slm19u2bRtnnXUWWVlZZGdnc+qpp7J58+am7y9atIhp06bRp08fMjMzOeqoo1iyZEmz2zAMg8cee4yf/OQn+P1+Ro8ezZtvvtnp52pP1FwrIhILgZrGqU9ZdlciIiLdXU0x1FeCN8PuSkREJFk1Ta3NsrsSEREREREREREREZGkYZomtcFaWz5M04y6zmuvvZZ58+bx5ptvMnv2bL744osWzaoA99xzD+PHj2fp0qXceOONXHXVVcyePXuPt/3JJ5+Qm5vLnDlzuP/++7nttts46aST6NWrFwsXLuTyyy/nV7/6Fdu3bwcgGAwyffp00tPT+eKLL5g3bx5paWnMmDGDQOOk7KqqKi666CLmzp3Ll19+yejRoznhhBOoqqpqdt8zZ87krLPOYvny5Zxwwgmcf/75lJaWRv28tJcrbrcsItKTVBVAoAoyB9tdiYiIdGeRiDVp0OUBQ+fJiYhIB1XmQrAeUvvYXYmIiIiIiIiIiIiISNKoC9Vx2POH2XLfC89biN/d9mp0VVVVPP300zz//PMce+yxADz55JMMHDiwxbY/+tGPuPHGGwHYe++9mTdvHg888ADTpk3b7e1nZ2fzt7/9DYfDwZgxY7j77rupra3lD3/4AwA33XQTf/7zn5k7dy7nnHMOL7zwApFIhMceewzDMJrqycrK4rPPPuO4447jmGOOaXYfjz76KFlZWXz++eecdNJJTZdffPHFnHvuuQDcdddd/O1vf+Orr75ixowZbT4vHaEj8iIinRWsh/Kt4EuHxhAQERGJi/pya3KtJg2KiEhH1VdC5Q7wZ9ldiYiIiIiIiIiIiIiIxNh3331HMBhk4sSJTZdlZmYyZsyYFttOmjSpxddr1qzZ4+3vu+++OBy72k779+/P/vvv3/S10+mkd+/eFBYWArBs2TI2bNhAeno6aWlppKWlkZ2dTX19PRs3bgSgoKCASy+9lNGjR5OZmUlGRgbV1dVs3bq12X0fcMABTZ+npqaSkZHRdD/xoMm1IiKdVV0ADZWaWisiIvFXXQCEwemxuxIREUlWVfkQrAN/b7srERERERERERERERFJKimuFBaet9C2+04Ebre72deGYbR6WSQSAaC6upqDDz6Y5557rsVt9e3bF4CLLrqIkpISHnzwQYYNG4bX62XSpEkEAoE273vn/cSDmmtFRDojHLSm1npSNbVWRETiK1ALlfngzbC7EhERSVYN1VCxHXyZdlciIiIiIiIiIiIiIpJ0DMPA7/bbXcYe7bXXXrjdbhYtWsTQoUMBqKioYN26dRx55JHNtv3yyy9bfD127NiY1jNhwgReeOEF+vXrR0ZG68e6582bxz//+U9OOOEEALZt20ZxcXFM6+gIR9ubiIjIblUXQl25Dk6LiEj81ZZAoAo8aXZXIiIiyaoyDwI14E23uxIREREREREREREREYmD9PR0LrroIq6//no+/fRTVq1axS9+8QscDgfGDwYHzps3j7vvvpt169bx0EMP8dJLL3HVVVfFtJ7zzz+fPn36cOqpp/LFF1+wadMmPvvsM37729+yfft2AEaPHs0zzzzDmjVrWLhwIeeffz4pKfZP6tXk2kZGSgqj589r+lxEpE2RMFRsA7cXHE67q5EupMwQkS4XiUDFDnCnaFJ6klFmiEjCCNRC5XbwaQK6iIh0nvZzRUQkWsoMERGJljLDXnr+RbqX+++/n8svv5yTTjqJjIwMbrjhBrZt24bP52u23e9+9zsWL17MzJkzycjI4P7772f69OkxrcXv9zNnzhx+//vfc/rpp1NVVcWgQYM49thjmybZPv7441x22WVMmDCBIUOGcNddd3HdddfFtI6OUHNtI8MwcGVn212GiCST2hLrI62f3ZVIF1NmiEiXqyuDulJI7WN3JdJOygwRSRjVBdBQBZmD7a5ERES6Ae3niohItJQZIiISLWWGvfT8i3Qv6enpPPfcc01f19TUMHPmTC677LJm22VkZPDiiy9GfbtPPfVUi8s+++yzFpdt3ry52dc5OTk8/fTTu73dgw46iEWLFjW77Iwzzmj2tWmaLa5XXl6+29uMBTXXioh0hGlC5Q5reqBDf0pFRCTOqvLBjIDTbXclIiKSjIL1UL4VfOmagC4iIiIiIiIiIiIi0s0tXbqUtWvXMnHiRCoqKrjjjjsAOPXUU22uLLmoI6xRJBCg8M9/BqDfjTfi8HhsrkhEElpdGVQVQIrO3OqJlBki0qUCtda0QS3jnZSaMiMSpt+NN+H4wVIrIiJdoqYQ6is0tVZERGJG742IiEi0lBkiIhItZYa99PyLdD/33nsv3377LR6Ph4MPPpgvvviCPn20Ump7qLl2p1CIsuf/C0C/3/0OFBIisifVBRAJgctrdyVih+9nxnXXKTNEJL5qSyBQrYaoZPX9zLjkJzDsQHvrEZGeJxyEsi3gSdXUWhERiR29NyIiItFSZoiISLSUGfbS8y/SrRx00EF8/fXXe9xm8+bNXVNMEnPYXUBCKtlodwUiksgCNVCZBymZdlciIiLdXSQClTvAnaKGqO6gvhxM0+4qRKSnqS6EunLw6fWLiIiIiIiIiIiIiIhItNRc25q6Uh30FpHdqy60Jgh60uyuREREurv6cqgtA1+G3ZVILNRXWD9TEZGuEglD+TZwe8HhtLsaEREREREREREREZGkY6qPMCnF4uem5trWNFRaHyIiPxQOQsV28KqxVkREukB1IRAGp5be6RYaqqG62O4qRKQnqSmG2mJI6WV3JSIiIiIiIiIiIiIiScXtdgNQW1trcyXSETt/bjt/jh3hilUx3UpDNdSWaslEEWmpptiaOpeeY3clIiLS3QXroSoPvJpa2204HFCVC72GgstrdzUi0t1FItaJgQ4nOPT2j4iIiIiIiIiIiIhIezidTrKysigsLATA7/djGIbNVUlbTNOktraWwsJCsrKycDo7vrKfjq60JhKCyjzIGqplE0VkF9OEyh3gdOtvg+zSUA1+v91ViEh3VFsCgWrIGGR3JRIr3jRoqLJ+thkD7a5GRLq7ujJrArpfU2tFRERERERERERERDoiJ8cavrezwVaSR1ZWVtPPr6PUXNsabxrUl0NdOaT2trsaEUkUdWVQU6QlVaW5onWQ1Rd0dpKIxJJpWlNrnR79felODAc4XdaJfOkD9LMVkfiqzAUimpQtIiIiIiIiIiIiItJBhmEwYMAA+vXrRzAYtLsciZLb7e7UxNqdHDGopU0PPfQQw4cPx+fzcdhhh/HVV1/tdtunnnoKwzCaffh8vrjXaPh8jPzoI0b+9x8YPh8QgdriuN+viCSR6gIwwzo4HWdJlRn/uQcjXAG1pXG/TxHpYeorrOmmvky7K0loSZUZz9yL4XFbP9OaIutkPhGReKmvgOp8SMmyu5KEkAx5ISKSbJr2cz/6qPH99O5BmSEiEnvKDGWGiEi0lBn2ZkZ3ff5FYsXpdOLz+fSRJB+xaKyFLmiufeGFF7j22mu57bbbWLJkCePHj2f69Ol7HJWckZFBXl5e08eWLVviXSaGw4Fn8CA8Of0wHA7wpEFVPoQa4n7fIpIEArXWlDlvht2VdGtJlxmDBmEEqqFsM0Qicb9fEelBaksgHNAJHXuQdJmR09d6neHyQiQE1UVxv28R6cEq8yBYD26/3ZXYLlnyQkQk2TTt5w4eZO3ndgPKDBGR+FBmWJQZIiJtU2ZY7MqM7vj8i4h0Vtz/Gt5///1ceumlXHLJJYwbN46HH34Yv9/PE088sdvrGIZBTk5O00f//v3jXWZL3nRoqNQ0QhGx1BZDoNpqvJe4ScrM8KZZU4017VxEYiUctJbyVubsUVJmxk7edKjcYTW+iYjE2s4TA1M0/RySPC9ERKRLKTNERCRaygwREYmWMkNEJLnFtbk2EAjw9ddfM3Xq1F136HAwdepUFixYsNvrVVdXM2zYMIYMGcKpp57KqlWrdrttQ0MDlZWVzT46wgwEKLj7HgoefgYzGALDAQ6XtYyiiPRskTCUbwePHwzD7mq6raTMjEeew4wY1v+Lsi3W/xURkc6qLbVO8vKm211JwkrKzHj0f9brDGg8ka8aajS9VkTioLoAAlU6SYOuyQuIXWaIiCSTpv3cu+/BDATsLqfTlBkiIvGjzLAoM0RE2qbMsNiVGd3t+RcRiYW4NtcWFxcTDodbnEXRv39/8vNbb1odM2YMTzzxBG+88QbPPvsskUiEyZMns3379la3nzVrFpmZmU0fQ4YM6VCtZihE6RNPUPrCW5jhxuYoXwbUFEO9XqyI9Gi1pVBfBj5NfoqnpMyMF9+xMiO1D9QUWo0MIiKdVV0IGOBw2l1JwkrKzHjpvV2vMwwD3D6o2K4TM0QktkIB62+LN00nBtI1eQGxywwRkWTStJ/7xBOYoZDd5XSaMkNEJH6UGcoMEZFoKTPszYzu9vyLiMRCXJtrO2LSpElceOGFHHjggRx11FG8+uqr9O3bl0ceeaTV7W+66SYqKiqaPrZt2xa7Ytx+CNZBbUnsblNEkk9VPlaTk8vuSuQHEiYzHC5wuqF0s7Wcu4hIRwVqrGZ9X4bdlXQ7CZMZO6VkQV2pXmuISGzVFEFduU4M7IT25gV0QWaIiEhCUmaIiEi0lBkiIhItZYaISGKJa6dYnz59cDqdFBQ0n+RXUFBATk5OVLfhdrs56KCD2LBhQ6vf93q9eL3eTte6W55UqNwBmUPAqcY6kR6noRpqCnRwugskfWakZENlrvXRa1h87kNEur/aEgjWQkovuytJaEmfGdB40o5h5UZqX02YFJHOi4Shcju4PGAk3LnUtuiKvIAuyAwREYk7ZYaIiERLmSEiItFSZoiIJL+4Hm3xeDwcfPDBfPzxx02XRSIRPv74YyZNmhTVbYTDYVasWMGAAQPiVeae+TKgvgLqyuy5fxGxV00RBGrB47e7km4v6TPD4QRvOpRttv7PiIi0VyRiNVq6fGq0bEPSZ8ZO/l5QXWi93hAR6azaUqgp0Qka39Nt8kJEROJOmSEiItFSZoiISLSUGSIiyS/uo1ivvfZaLrroIg455BAmTpzIX//6V2pqarjkkksAuPDCCxk0aBCzZs0C4I477uDwww9n1KhRlJeXc88997BlyxZ++ctfxrvU1u2cKFWVD2l97alBROwRDlmTqz2pdlfSYyR9ZvgyoWK79dF3b3tqEJHkVV9undDlz7a7kqSQ9JkBViN1uMR6rZGSZV8dIpL8TNM6QQPA6ba3lgTTLfJCRES6hDJDRESipcwQEZFoKTNERJJb3Jtrzz77bIqKirj11lvJz8/nwAMP5P3336d///4AbN26FYdj1wDdsrIyLr30UvLz8+nVqxcHH3ww8+fPZ9y4cfEudfd8mday8A3DramEItIz1JVak+TS+tldSY+R9JlhGNYUwvIt1v8bNUqJSHvUlFhLejs9dleSFJI+M3byZVgNcVlDdEKPiHRcfQVUF2j/sxXdJi9ERCTulBkiIhItZYaIiERLmSEiktwM0zRNu4uIpcrKSjIzM6moqCAjIyPq60Vqa/l2wsEAjHnxzzj6DG2+QcV26L8fZI+IZbkiksjyVkDFNshI0CUWIiGoKYZhk62TANqho38ru5uYZMZLd+PoPfgHN5wLGYMg5wD43oshEZHdCgVg6wIgAr6s2N9+OAB1FTBsUrtPFlNmWGKSGS/fiyN7YMuNyrdZrzV67xWrckWkpylaB0XfQtbgtrdtS6geGqph2I/A42/XVZUZFj0PItITNNvPXfI1Dr8yoyP0PIhIT6DMiA09DyLSEygzYiMmxzM68PyLiCSTaP9Wxn1ybbIwfD72eutNKFqP0dqwME+q1WCbOVhLLIr0BA3V1sRqX8/d6Zbda8qMwrUYnlYywd8bKndAeo71ISLSlroyCFRDWn+7K5EYa8qM/BWtZwaAL73xhJ6B4PZ1bYEikvyCdda+Z0r7TrgTERHpjKb93MbPRUREdkeZISIi0VJm2EvPv4hIS2qubWQ4HHhHj4a0eqja0XIDbwZU5VtTIhN1iqWIxE5tCQTqrCZJkR9oygx/NdQWtdzA5bVOxCj5DlJ6WV+LiOxJdQFggMNpdyUSY02Z4SuDhsrWN/JmWify1RRC1tDWtxER2Z3qQmiosk4GFhER6SJN+7kiIiJtUGaIiEi0lBn20vMvItKS1qqOlsMJTpe11Ldp2l2NiMRTJGz9rntS7K5Ekpm/N9QWW81SIiJ7EqiBmiJNS+/JDMNaKaNsC4QCdlcjIskkHLImX3v81t8SERERERERERERERERiQlNrm1kBgIUP/IoVBfQ55TDaPWQlC/LmiZVVwb+7C6uUES6TF259Xue2sfuSiRBNWVGVR59TvtR65lhOCAlC0q/szIjpVcXVykiSaO2FIK1+jvRTTVlRuUO+px+VOuZAeDLhMo86/WGpk+KSLRqi6G+AtL62V2JiIj0ME37uUCfX12G4fHYXJGIiCQqZYaIiERLmWEvPf8iIi2pubaRGQpR/NBDAPQ+8ZDWD3q7vGCGrYPeaq4V6b5qCgETnG67K5EE1SwzTj58941S3nRrid6S72DggVruXURaikSgKtfaz9TEwW6pWWacesTuM8PhtKbml22FtP7aDxGRtpkmVOywTupy6O0dERHpWs32c3/xcx10FRGR3VJmiIhItJQZ9tLzLyLSksPuApKOLwuq86xmKRHpfoJ1UJVvNUWKxEJqX6jKg8oddlciIomooQJqy8CbYXclkgh8WVBXCtWFdlciIsmgvhxqijT5XEREREREREREREREJA7UXNtenlQI1EJVgd2ViEg81JZCoBo8aXZXIt2F021lR/FGqK+0uxoRSTS1pRAOWpNrRRxO6/9C+VYIh+yuRkQSXVUBRELKEBERERERERERERERkThQc21H+DKgYps14VJEug/TtCaMOj1amltiKyULgjVQuhEiYburEZFEEQ5aU629OqFDviclC2pLoFon84nIHgRqoTLPen9CREREREREREREREREYk7NtR3hzYCGKmvpeBHpPuorrGYWX6bdlUh3lNYPKnZAxXa7KxGRRFFXZk209qbbXYkkEofLmkJZtsVqwBYRaU1NEQSqtOKGiIiIiIiIiIiIiIhInKi5tiMMw5owVr4NQg12VyMisVJbCuGAllWV+HC6rewo2WA11ImIVBeC4QCH0+5KJNGk9NL0WhHZvXDIWk3H49eKGyIiIiIiIiIiIiIiInGi5tqO8mVCfbkOeIt0F+GQtTS3J9XuSqQ782VaJ2UUr4dQwO5qRMROgVqrudanqbXSCocTPClQtlnTa0WkpdoSqCvXihsiIiIiIiIiIiIiIiJx5LK7gERheL0Mf+lFKN6A4XZHcQWH1YRXtgXScsDliX+RIhI/dWXQUAlp/eyuRJJAU2YUfhtdZnxfWl+ozAPvd9B3jKaNifRUdaUQqIGUQXZXInHWlBkFq9qXGb4sqMqDqnzIGhK3+kQkyZgmVOWCwwEOvaUjIiL2adrPbfxcRERkd5QZIiISLWWGvfT8i4i0pCMxjQynk5T994c8oGpHdFdKyYKKHVCdD1lD41meiMRbdaH1rw5QSxSaMmNHCGqL2ndlhwtSe0Ppd9a0sYwB8SlSRBKXaVpN9i6PGux7gKbMyKqzTuSJlsMJ7sbptWn9wKU3skQEqK+A6iJI6WV3JSIi0sM17eeKiIi0QZkhIiLRUmbYS8+/iEhLDrsLSGrfn14barC7GhHpqGCd1Vzr1dLc0kXcfqupruhbqG9Ho5WIdA/1FdbkWi3nLW1J6WVN16/Ks7sSEUkU1YXW+w8un92ViIiIiIiIiIiIiIiIdGtqrm1kBgKUPP44Jf97EzMYiv6KKVmNB7zz41abiMRZbSkEq8GTZnclkiSaMuOFt9qXGd/n7w3BGqvBNhSIbYEikthqSxsbozSJtCdoyowX321/ZhgO6+Sfsi3WyUAi0rMF66EyF3wZdlciIiKyaz/38ccxA3pfQ0REdk+ZISIi0VJm2EvPv4hIS1r/vJEZClF4z70A9Dp2P6JeoLfpgPdma7lWd0q8ShSReDBNqzneqaW5JXrNMmPaQdFnxg+l9bOWhnf7od9YcOicF5FuLxyCqlxr9QPpEZplxvRD258Zvkyo2G7lRe+9Yl6fiCSR2mJoqITMwXZXIiIi0nw/99xzMTwemysSEZFEpcwQEZFoKTPspedfRKQldfHEgi/TWt63MtfuSkSkvRoqobZE05/EHg4XpPWF8s1Qsc3uakSkK9SXQ32lckeiZxjW642yLRCosbsaEbFLJGI12rt9OilQRERERERERERERESkC6i5NhYMA1KyrAPeDVV2VyMi7dG0NLfP7kqkp3L5rAmWxeugptjuakQk3qoLwYxYzfXSM5jmrs+D9R27DW86BKqgYkdsahKR5FNXar128WXZXYmIiIiIiIiIiIiIiEiPoObaVtSHzLY3+iFvujVJqmxL8wPoIpK4ImFriWVPit2VSBILRmLwN9+XaTXbFa6BhurO356IJKZgPVQXaGptTxMONn1qrnmzY7dhGODvBRVbrcnHItLzVOUDJjjddlciXWBzcQ2m3lsSEZE2mKbJ5mKtbiEiIm1TZoiIiIiIdIyaa1sxN7eDV0ztbS3TWFsa03pECAWsRoqaYqgqsBpCq/KhugjqyiFYp6bujqgrt5bn9mXaXYkkscUFMfrdS+0L9RVQtNb6nReR7qeu1DoZy5NmdyXShczvNcKZa9+1phd3hCfNatAu3xajykQkaTRUQ3W+Xrf0AJGIyfEPfsGUez9jQ6FOuhMRkd0LhCIce9/nTLn3M7aX1dpdjoiIJLCahhA/vvtTjrnvM0qqG+wuR0REElhpza5j1NUNIRsrERFJHGqubcX7WzrYKOVOAUwo/Q7CChrpBNO0muzKt8KOJbBlPmxdYH1sXwQ7vobti2HbQtiywPr+lvlQuNZqug3oDdWo1BQBWppbOuf9TTFqrjUMSO8PlblQvB4ikdjcrogkBtO0To5xuqzfd+kxjO/9vB2RICx6vOM35u8FlTugriwGlYl8TzgEDVVQU/K9k/kKrK/rK/X61m41RdZrPE+q3ZVInDkcBjkZXgDeXp5nczUiIpLIPC4HfdKszHh3hTJDRER2L9XrIjPFTcSE91fl212OiIgksF7+XcNCPl3bwUEhIiLdjJprW/HOJpOqhg42NaX2sZb7rdIbWtIBkbDVHLtjCWxdCHnLrAOpDgekZEHGIMj8wUdaH3D7IFQPpRutptutX0LecuuAuA6Ety4UsH5XNT1QOumVDREC4Rg12DpckNYXyjdDhSYTinQrDVXW5FpNHZT1H0DRtx27rtsPkSCUb9eqBdJ5gRpr5ZW85bBlnnXS3rYFsGNx48l8i2Br40l+W+ZZ21XssK4nXScctH5OaqztMU4ePxCA5xZusbkSERFJdCcfaGXGs19utbkSERFJdDtfZygzRERkT74/LOSFxTpWLSICaq5tYni9DH36ae6e+huCTjd/nl/ZsRtyuMDjh5KNOugo0TNNqxF254Hs2iLwpUPmYEjrB950cHpan3TncFmNFv5syBhofbg81gHYHYth25fWBNyQlnpppq7UanTypttdiSQhw+tlyNNPccfRl1PvcPO3r6pid+Mun9X0XbwOaopjd7siYq/aEgjWWb/j0qMYXi+DnnySm4+8nDc53Lrwy392vDnWnw2V26G2NHZFSs8RiVj7F3krrKbZHUutacgAvgxIb3w9sfNEvoxB1kl+hmFtt/MkwPxVmqDcVWpLoaFSJ2f0IDP2y8FhQHF1gNeWbre7HBGR3dr5fvrQp5/G8HrtLqdHOnH/AQBsLa3VVCkRSWjKDPvtzIw1eZUs3qz3lEQkcSkz7GV4vfDAP7nhR5fz5fZqVud2sG9KRKQbUXNtI8PpJPWwiWQcvC8Rw8GHG+sJdnQSoS8LAlVQukkTpaRtDdWQv9Jqqq0rt5pp0/p3vPnGMKypRhkDrNsK1UPuMusgePlWa/KRWM3MDgcY+jMo7Wc4HKSlFzBr0FNkGdW88W0dkVj+vfdlgBmBwjU6UUOkO4iEraY0TR3skQynk4xJhxMZuw93h86lAbe1OsHWBR27QZfPeo1RsVWvNSR6pmk11eYuhW1fWRPyXSmQNRjSc6x9D5e35cl8hmGd5OdNt7bLHAQuN5Rvsm4nf6V1wprEh2lCVa71c3A47a5Guojf42JEH2uf4ZkFml4rIolr5/vpqYdNxHAqp+yQneqhT5rVcPDEvE02VyMisnvKDPsN7pWCx2UdD3vsC2WGiCQuZYa9DKeT0ccdxYq+o4gYDh6fq8wQEVFX2Q/ce4SDbB8U1Ub4eHN9x27EMCC1j3XAsrogtgVK92GaUJkL2xdD+RZrCllaP2sSbaw4XODvDZkDwQxZjRzbF1uNpT25GSNQA7XF4M2wuxJJVqYJc+5heOg7bvC8zLbKMPO2xXg6dGpfqK+wJtiGQ7G9bRHpWnVlUF9pNa9Jj/XUyZkUO/rwZGiGdcHix60TKTrCnw2VeZpwLtEJ1EDBKti+CKoLIbW3dSKex9/+2zIMa8J+xiCr4bZ0E2xbDGVbrBMJJLYaKqG6yDqBV3qU/152OIYBS7aWszZfE0JERGT3Xvv1ZAC+WF/MttJam6sREZFEZRgGb115BAAfrSmgsLKDx8BFRKTbczkdvHKF9Trj7eW5VNRqeJuI9Gxqrm1kBgKU/vFXVN1xIVcPWg/A5e+UsWB7B5ulXD5rskzxemsJYJHvC4egaB3kfmM1vWYMtCZFxYvhgJRekD7AOkCbu8Q6wB7ooW+41pZaTQbuDjQUiABmOExp4DhK1/s5k4/Z29jGz14v5eu8QOzuxDCshvvybVbjiogkr+pCq4kylifQSNIwg0FKn3uO8PufcMpwk4dDJ1FtpkDJRtg0p2M36vKCgbUqQaSDDbrS/X3/ZL6yTeDLhIwcaxJtLLgbJ986gPwVkL9cE/djrboIwg3Wcy09Sr90HzP2zQHgzH8tYE2eGmxFJPHs3M8tfe45zKAOttplSLafH4/uA8BpD81jQ6FWFRCRxKPMSAxjctI5eFgvQhGT0x6ax+ZivYYXkcSjzLDXzud/xBfvMq6vn4ZQhJ/8cx7by3poX4mICGqubWIGAhQ8O4eCRX7OyX+QvpQDcO6rJdSHOjjh098H6sqh5LuePSVUmgvWQ8EKaxplSqY1WfaHy6/Gi8NpNeulZFkH2Lcvhqr8nvX/0zStx+z2dd3zLt2OGQxS8M/nKfg6CyMCt7ieAUx++lIxkVj+Pjnd1nTC0g3WxGkRST7BOuv3V1NreywzGKTgzj9S8I9nuHiMSTnpPBY+3vrm4ic7Pu3T3xuq86GmKHbFSvcRCkDRWshdCpGgNWk2Xg2avqzGE4K2W/dXUxKf++lpQg1QuQO8aXZXIjb5xREjAKhqCHH8g1/YXI2ISEtN+7l3/lEHvW22MzNKagJMvb+DJ/CJiMSRMiNx/PxHVmbkVtRz0t/n2lyNiEhLygx7NT3/f/wjl0wcBMB3xTWc++8vba5MRMQ+aq7dybHrqXA1lPGU/0E8WGG9pKOTCA0D0vpA+RaoyotFlZLsAjWQv8yaRJne377JqS6fdYA93AA7llhTdMM9ZOe0vgLqSsGrJieJEYeTHztXcqxjCQBrikOxvX1PauMk9HWaBieSjGpLIVhtLaMuPd643tZrjsdDJ1BuplqvEzZ+3LEbc3qsfCjf2vEGXemeGqogb5m1ikpKr645mc/phsxBjff9jTUxVzqntgTqK/W6pQc7ZHh2s69La2K4SoaIiHQrU8b0a/Z1ZX0PeZ9XRETa7YT9c5o+r24IURuI8fEMERHpNo7fv2/T59tK62gI6TiEiPRMaq5tjSuFfSPfcofrScDkvNc6MXnH5QOXxzqw2aAlmXq0hirIW24tDZ0x0DoAbSfDgNQ+1vTc4m+t2hqq7a2pK9SWQjhgLacsEgt7W9MH/5/rOdyEOP+14tjfx85J6MUbtPy3SDIxTesEK6dH09Klyf9O700Vfh4JnWxd8PVTEOnggYyUbKgu0PRa2aW2FHK/gZoCyBgQv2m1rTEMSG88SJe33Gr87kkrZMSSaULFDuu9BENv2/Rk/77wkKbPb3h5mY2ViIhIopt1+v5Nn//lvbU2ViIiIonMMAyunz6m6etHPv/OxmpERCSRbaxcx4WThjV9/eyXW22sRkTEPjpK05pJvwbDwTmuzzjP+Unnby8l22qsLFoHYZ0B2CM1VEP+SqgrsRprHU67K9rF7bcOvFflWsu41pbaXVH8hEPW47RrYrB0T+NOhZRe7OXI50Lnh5TXx6GJxDAgvR9UbIPK7bG/fRGJj/oKa/KgL9PuSiSBHD7YOsHnqfBxFJkZ1oTP9bM7dmNOt/VRvkXTawWqCqzG2kA1pA8Eh8ueOvzZ4PZZr3/Kt6jBtiPqypQfAsC0cf2bPv9oTaGNlYiISKI7+5AhTZ+/s0Kr6ImIyO79/Ecjmj7/fJ1O2BYRkdYV1xXz00N39VV8s63cvmJERGzUJc21Dz30EMOHD8fn83HYYYfx1Vdf7XH7l156iX322Qefz8f+++/Pu+++2xVl7jLgADj0UgBuc/2HscYWtld2oinWMCC9v9XUV74lRkVK0gjUQsFK6+Bo+oDEnDzkcEHGIOtAfO43UJVvd0XxUV9uLa3q09KqiSzpMsPta8qMq1yv0otKCmvi0ODk9IAnFYo3Wg17IpL4aks0LT3Oki4zGg3NcFKHj0dDJ1kXfPN8x5tj/dlQU6zptT1dZS7kLwci1vRYu6dl+zKt/ZaC1dbJQdI+1YXWRGvlR8wka14ApLh3nZxbpWW+RUTiLlkzw+HYtf9XXhukLqCT70RE4i1ZMyPFs+s1xjfbygmEtFqeiEi8JWNmOA0nFeFdQ5/eWpZLKKzMEJGeJ+5dfi+88ALXXnstt912G0uWLGH8+PFMnz6dwsLWJ27Mnz+fc889l1/84hcsXbqU0047jdNOO42VK1fGu9Tmxp9NePDheI0g/3D/jVs/6uTZ3g4XpGRB8XpropD0DKEGKFwN1UWNB7kTsLF2p6ZlXCOQtwzKu+FB8JoiMCP2TfGSNiVtZuw9nVD2KDKMWq51vcydX1TG535SsiBUA8UbNKFQJNGFg1azmyfN7kq6raTNDOCFn/YB4PnwsdQ60qzmw81fdOzGHC7rQ9Nre67KXGtKrOEAf2+7q9nFlwEef2OD7Q67q0kewTqoygNvut2VdBvJnBcAb/3fEU2f/3uOlmwVEYmnZM+MN6/8UdPnz36pIR8iIvGU7Jnx/KWHNX3+6hKtliciEk/Jmhmp7jQiRLjxlKymy95f1U2HtImI7EHcO/3uv/9+Lr30Ui655BLGjRvHww8/jN/v54knnmh1+wcffJAZM2Zw/fXXM3bsWO68804mTJjAP/7xj3iX2pzhwHnMjZQY2Yx05HFSwSN8/F1d527TkwYOJxSthYaq2NQpiSsShqJ11sHujBzrZ58M/L3B5bEO0Jdt7j7LuAbrrYm8mlqb0JI2MxxOXJOvBOBc5yesXr+B+dsa4nNfqf2shotKNamIJLTaUmioVHNUHCVtZgAD0p2kug1qSOHRwHHWhUuf7fh+l6bX9lxV+VCwCpwu6/9BovFlWq8tdp5wKG2rLbHeL1B+xEwy5wXAyL6pTZ//7ZMNrNyhVSxEROIl2TNjv4GZTZ//6d01bCjUMQgRkXhJ9sw4dPiu9xBufHUFW0tqbalDRKQnSObM6J3Sm2H9d2XElc8vJa+ik31TIiJJJq7NtYFAgK+//pqpU6fuukOHg6lTp7JgwYJWr7NgwYJm2wNMnz59t9s3NDRQWVnZ7KMjDI+HwQ//i8F33Yjhbpxq6csi/fjbCJkOTnfO5Z1332BRbiebpfy9IVAFhWshFOjcbUniMk0o+Q7KN0N6/+SblOrLAm/jMq7dpcG2rhQaqq3laSUhJWVm/On6XZkx8ECCQybjMiLc4HqB814rYWVhHJZtdbqt/8fFG3Wihkgiq8q3psIny8k1SSYpM+OP1+zKDODzi/oB8GRoBjWmF0o2wLaFHboPTa/toaqLrMZaw4CUXnZXs3spvazVIwpXQ72aAvcoErFOoHL7rJ+rdFpX5AXELjNaYxgGX9xwdNPXJ/19rg5iiEhCaNrPffhfGB6P3eV0WnfIDIfD4MNrjmz6eur9cyit0TEIEbGfMiMBMqO2FFa/0fSl2+ng1V9Pbvr6yHs+pboh1PHbFxGJEWWGvZlheDwM+Nc/qL7rKnC7cBgOctL78LuTd/0sJs36hIaQjkOISM8R1+ba4uJiwuEw/fv3b3Z5//79yc9vfVx4fn5+u7afNWsWmZmZTR9DhgzpUK2Gy0X6lCmkT5qA4dzVBOEZPJ5lg88D4Hb303yzoZNLYxgGpPW3pg4Wr7cOnkn3U7kDStZDSjY4k3Snz5sO3jQoXGM12CYz07SanFxua7lcSUhJmRmHH9QsM9yH/4oIDqY7F3Ow8S3zt8dpem1KFgRrrCZ+5YhI4mmogppCa2KjxEVSZsZhBzbLjD5+Jz8/MJUK0ng23PhGWWen11YXaXptT1FXbjXWRkLWCZyJLq0fBKqhYA0E1RS4W3VlUFOq/IihrsgLiF1mABAOwoaPm100JNvPTycMbvp60eayjt++iEiMNO3nTpmC4UqywQKtSMrMaMXe/dOZOrZf09fLtpXH9PZFRDpCmWFzZlTlw98PhpcusYbqNJowtBeHDNt1su6avNid8CEi0lHKDHszw3C5SD3qSEKHjweXdTzD4/Rw8IhUBvba1WexsbCmQ7cvIpKMkr7L7KabbqKioqLpY9u2bTG/j4OPv5gdKWPIMOoYu+p+Pl2bz/LtFYQ72s/kcFkHF8s2WdOlpHupLYWib61pQx6/3dV0TlOD7Vooj/3vVpdpqLKWV/Vm2F2J2CzumdFrGI59TrDuy/1f7ppbwWfrijqXGbuT2gcqt0N1QYxvWEQ6raYYQvXgTvL9gB6uK15n3HpkJqfvk8JjoRNoMF1QsJKNKxZ0LDMcLmu6efk2Ta/t7gI11sGwYK31ujJZpOdYJx4UrdP/0d2pKQTCyXuCZg8Ws8wI1MDDR8CzP4UdS5p9676zxnPcOOvAyv97bQVvfLODBRtLCEe6wUozIiI9SFe8znjsokM5YlQfAP6gzBARSVoxy4z0HBg2GcwwvHllsxO7X75iMuOHZAHwh1eVGSIiySrerzMyvZn86VwfQ3pbDc96nSEiPUlcT/Xo06cPTqeTgoLmjT8FBQXk5OS0ep2cnJx2be/1evF6vZ2u1QwGqXjrbajYTuZhe9FsAUaHi29GXUH28hs4wrmKP3/8GnMj+9MnzctlR45g8sg+7b9Dlxd8GY1NmCnWCxtJfsF662caCkBGN/mZetN3LePqcELGQLsrar+6UqvJKbUDv6vSZZIyM8q2kDl5TPPMOPhiQt9+yCGOdRznWMwN74+kn1HeucxojctrNVGVbrSmFbo6/7ikmwiHrL/bYE3rdib/mb1JJRxqXNJbjbXxlJSZUfodmUfsxw8Xeh+eGuRVevFS+CgucH1M2bynuPNrd8cyI6WX1ZxXUwzp/dveXpJPKGCd+FZXChkD7K6mfQyH9f+yfKt1Al/vkXZXlFgCtVCZpxMCY6wr8gJilxl4UmHAgVC0Ft6+Gi79DBy7zovvn2HdR1V9iKv+9w0AAzJ93HbyOGbsl2R/E0Qk6TXt5wKZJ5+E4XbbXFHnJF1mtKFvunWyTl5FvTJDRGynzEiAzDjuT7D+Q9jxNXzzXzjovF21ZXhZBqwvrFZmiIjtlBn2ZoYZDPLcA7+kNlRL8NjDGdt/fwamDcQwDPr4+5Dm/w5KXHyzrVyZISI9Rlwn13o8Hg4++GA+/njXcnaRSISPP/6YSZMmtXqdSZMmNdseYPbs2bvdPlbMYJC8P/yBvL/8EzMUava9+RuLeXhxNX8OnQvAVa7XGGgUU1LdwKx31zJ/Y3HH7tSbbjUrFq6xlvWU5BaJQMkGayne9CSaIBUNX6Y1Da1wtdWskUwiYajItZrYJaElZWbc80jLzMiHl4OTAfi963+UYC3r2+nMaI2/tzUtO5knS0vnBGqgqgBKvoMdS2HzXNgyr/nH5rnW90o2Wk07DVVWZkl81JVa+3U+NUfFU1Jmxr2Ptfo6Y/aSdQA8Gj6JiGlwiHMdKTXbOpYZTrf1+qJim37Pu6NIBIrXWw386f2tZtVk4/SAvxcUr7PyS3apLYFANXjS7K6kW0mmvGgybSa4UiBvGSx7vuni91fm8cyXW1tsnl9RzxXPLuH9lXldU5+ISKOm/dw//AEzGLS7nE5LyswwTevEpR94f2Uery3NbXG5MkNE7KLMSIDMyBoCB5zVeMe3QH0FYGXGB6tavj5XZoiIXZQZ9mZGoKGWw574iqP/s5Jnlj/J7z7/HVd+fCWPrXiM11d+zbdF21tcR5khIt1d3I/GXXvttfz73//m6aefZs2aNVxxxRXU1NRwySWXAHDhhRdy0003NW1/1VVX8f7773Pfffexdu1abr/9dhYvXsyVV14Z71JbFY7Ao3M2YQJvhCcxL7wvKUaAq1yvAtYB60fnbOr4ct+pfSBYZzXYButiVbbYoXIHlG+xlmZNxgPdbfFnW42qBauSqxm8rgzqy60GYUl43SUzXgkfQamZxkhHHmc7P8M0YeeCGJ3KjB8yHJCSBeWbm94Mkx4gWA8VO2D717B1AWxfBEWrrWmV4SAYhtVg53Rbn4eD1vcK11iTCbYsgG0LoXST9ffc1HItMVWVb/1uOjQxON66S2aYgIcgW83+fBg5BIBTnPOBDmZGSi+oLoTaJDshStpWsc3K/LS+yf03xpNq1V+0Fhqq7a4mMUTCULHdOiHQ+OF8a+mspMuL9Bw40Dq5m9m3QX0l4YjJzLdWt7r5zj25mW+t1jJ8IiKdlFSZUV8JL/4MHjkSKnc10iozRES6RlJlxk7jfgJpOdZ7Rh/focwQEekiyZQZke8dMxzVazRuh5uS+hI+2vIRL265j5RBL+LK/BoIAyY46jEdDeCo5bZ3FpJbnU9BTQFFtUWU15dTF6rD1HFIEUlycT8id/bZZ1NUVMStt95Kfn4+Bx54IO+//z79+1vLlG7duhXH95a4mzx5Ms8//zw333wzf/jDHxg9ejSvv/46++23X7xLbdWq3AqKqxsAGEgJ1wd/xWzH9ezr2Mp0x2Lej0ykuLqBVbkVHDC4g8176f2tN8CK1kL//axmGEkudeXWFCm3v0uWZg9HrP+bpbUBsv0e9h2YibMr+nnT+llTDwvXwIDx4EmCJa9riqzl0ZO5AaEH6T6Z4eN/oSn82v02V7te4bLwVYRwY0LnM+OHvOlWc3/pZhhwgBoyurNALVTlWZOKA1XW9D9vGqRk7/HnviszDCsz+vlwBquhYCU43NaJPhmDrH+1D9I5DVVWI7NO6OgS3SczYDh5rGMoj4eOZ4ZzEcc4vuEZplFcTfszw+kBA+tvRWpf5UJ3UVNiTXv1pILLF7e76bLXGf7e1v5LyXrovz84e/i+el2ZNfk8ta/dlXRLSZkX434Ca9+B6gL4bBZfjb6OvIr63W5uYi37/dWmUiaN7N11dUqPFY6YfLWplMKqevql+5g4IhunQ/sckvySKjNcPmtqbV0ZvH4FXPAaOBx8talUmSEJRZkh3VVSZcZOTjccfgV8dBssfpIVOWcoMyShKDOku0qmzPA6PU2f/+7g3+FKTWVV8Sre2zCHZSWLcHhKSRn4EpE+H9NQfAxmKBUwwIhQHDR5fTWM6Z+OgQEGeJ1eUt2p9PP3I8ubhd+dBP0lkhSUGdKVuuQI1pVXXrnbsyg+++yzFpedeeaZnHnmmXGuKjqltYGmzx2GSYGZxb2hs7jN/QwXuz5kYWAsZaQ3267dDAek9bcOgLtSoO8YHQRPJuEglGyAYC1kDor73c3fWMw/P99McXUAl2GNMuuT5uWyI0cweWSfuN8/6f2hIrexwfaAxG7ECtZBZb6W5k4y3SUzPowcwgmRRQx3FDDd+TXvRA5vdbuY8PexGlQyBlhN8NK9hALWz7dss9W86cuwmmGj2FfYmRkl1QGcLTJjMIQarGWoq/KtaZe9hln7JIn8tz2R1RRb2ePXm81dpbtkhpsQAIvMMayODGWcYysnOL/if+GjO5YZKdmN02tLrMZ5SW7BOihaA2YYfPH7ec7fWMxDn2+mtLoBp2FNE4jb6wzDsPKmYjt4M6H3XrG9/WRTlW/9q/yNm6TLC6cbJl4Kn/wRvnqU2tQTorpaYdXuD4yLxMr7K/OY+dbqZo0YAzJ93HbyOGbsN8DGykRiI2kyw+WB0x+zJtd+9xks/BdM+k3UWaDMkK6gzJDuLmky4/sGTYChk2HrfAZ+eTvw2zavosyQrqDMkO4uKTMD8Dg9HNT/ICrLB7Ng6X64M1bhylza2GT7MqHaIQRKjsAMZQMGhLLISbMGCETMCIFwgKpAFcV1xfhdfgakDWBA6gB8cRwgId2fMkO6Wjdcuz62sv2eZl+HcfF0eDrLInuRZtRzmeudVrdrN2fj5LiSDdYZ55I8yrdZk4fT+7f7quEILN9ewWfrili+vaL5sr9mBLMyj/9+OJff/uNlXnj1ZdbP+R+fvvcKgZoyCuhFxLQaq0qqG5j17lrmb+yC5X8NB2TkWI1eResgEqv17eOgtsSa7uhJs7sS6SG+nwUhXNwTOhuAn7k+Is2saXW7mHB5weGAsk0QDsX2tsVeNcWwYwnkryRswvKqdD7bGmD5jsoWS8Wbpsntn1cw/G+53PpZOfM2FHHXO2uZX53Dtwxh56orzTLD5bUastNzIFQHO5bC9sVWk08i/31PROFg45LeOutWovP9LNjVK2/wcOgUAE50fombYMcyw+UFTKjYBlpyKblFIlC8AWrbN9V0j68zsM7qvvHjcob/LZdZcyuYt6GIP73zLfOrB7B+d5kRa063Nem7dKM1mbenCtRY00l1QqD80KCDYdAhEAlx8KpZ7FqYdff6pevAhLRfOGKyYGMJb3yzgwUbS1os+xsMR7j6f0sZfuM7/Oa5r7ni2SUtJpzlV9RzxbNLeH9lXleWLiJ994aJl1mff3Q75K+MOguUGdIRbWVGfTDM5c98zagb3+C6/y5UZogkqkm/BoeTfkVfcqRjWZubKzOkI9rKjNpAiIuf/IrhN77DDS8vU2aIJLjsVA+YXoIVE6jdehENRcdiRly4/NtIGfQSrrQ1gGlt18hhOPC5fGSnZJOTmoPT4WRj+UaWFS2joKYAU8cupFFbmVFZH+ScRxcw/MZ3+H+vrVBmSJfr4Wsvtm3fgZn0SfNSUt2ACXgJ0ICHm4K/5E3PzfzYuZLFno3sO/CIzt+ZO8Va3rvoW+tzTR9MfLWl1sHglCxwtO/Xaf7GYh6ds4ni6gYiJkRwkOpxcPOIdfiKljOoZg0ZRg3nAud6gGLr4/99r7+iyMxgVWQ4SyOjWBTZh0fnbOKwEX3is3Tr9zlckNbXauTz+CF7RJzvsAMiEavp2eXVJGjpMj/MjA8iB/NNZC8OdHzHma4veCo8g95pXvYdGIcl4/29oaoAqvMhc3Dsb1+6VjgIpZuh7DvAZH6xm0e/WN+UGWGcBDxZXDmpD/MLXby1ro5AeNfV/7O8lv8sB9g1CbAWH36znsaVWJpnhsNp/R/yhaxs2/E1ZAy2/r6r2Sc6taXQUGlNYhSJwg8zwyCCiYN3IxO52cykn1HBif417Dvw6I7dgT/byoWsMutzSU6V26F8i/Xa0IhuJ/+HrzNCOAl6srhych8+z7Uy4/tvTT2ypIZHALD26UO4qMdDCoHG/5vE73WGN91qLi1eB94JjY3hPUxNsfUcpPSyuxJJREdcBS9dQlb+PM5K+zEvVR/QaoutAeRkWsufibRHa5M+0n0urjtub778rpT3VuY32/6dFfk/vAmApryY+dZqpo3L0TJ8Il1p1FTYMg92LIZXL2XiLz9hQKaP/Ip6ZYbEVGuZ0dcX5q4JVYQ3foa/dA17OfL4G2V4fGH4Fu7weikxM1hvDmK1OYx5kf1YFBlDGJcyQ8ROmYNh7Kmw6lXu8D7L1Lr9COFssZkyQzpqd68zfjdtbz5fV8Sn3xY12/7FxdtbvR29zhBJHPsOzKR3mpuyujJMI0SgeBrBign4BryCK/U7vH0/ISVjOyP7Hdrq9Q3DIM2TRqo7lYqGClaXrKYiUMGIjBG4tZpXj7a7zLh66kg+XlPA/E25GK4aDCOAw+Pm+cXVmKRiJcQuygyJJzXXtsHpgMuOHMGsd9diAMPIZx1DWW0O57HwiVzueotfu97CGT4LHCmdv0NfhrWEa+EacPnU0JLIwkEoWW9NiWzncrvzNxYz6921mECJmc5IRx6/dL7Hj43lOLc0vu1pQMB0ss3sR4HZi3o8mBhkGdX0o5whjiL6GpVMcS5ninM5QdPJwvp92LKsmr0OnBL/htKd/z+L14EnNfGawevLrcm1OkgtXeiHmTGcfP4cOo//ef7IKc4FPB8+hsuO3Cc+DfAOF7h9ULrJmmzXE5tTuotADRSutSaE+7OZv62OWe9ZmVFkZlFA45uZAfjd50EgGNXNbmIg/Sill1mF2whTXN3AqtwKDhj8vWZvh8v6ex5qsCZe1pZCn5GQMchqwJXWmSZU5VqNb3qeJEo/zIy9yGUjgwnh4rHQCfzB/V/O9X2J07iCH75JEBWXFyIhqNih5tpkVVcOxevBmxp1rn//dUaB2YsiGveFAyZ/+3wb442N3OjazECjhL5GBW5CRDCoNFPJM7PZZObwTWQUK80R9DKryKIKlxFpPTNiJbWvdVJc6SboO6ZnnRgXCVu/ox5/z3rcEr3MIbDvT2DFS9zueYY3+RMNeFo0S5nAbSeP05vG0i7vr8zjimeXtPj/VFUf4rY3V7f79kwgr6KerzaVMmlk75jUKCJRMAwYf461Il7hapwfz+S2k6/iimeXYNBy7rkyQzqieWaYHGp8y1nOzzjBXEjqkgZro1beDvEbDfiNIoZQxDF8w5W8QaWZwnvhw/hf5dF89d14Jo1q37EVEYmRQy6B9R8yPLCDs52f8nx4qjJDYmJPrzNuf0uvM0SSVW2omrMOS+fhj+swQ73AUY8Z7E3d1l/izv4Cb78PiHjXcfuCW7n+0Ovp6299FTbDMMjyZdEQbmBL5RbqQ/WMyhqFX6tC9kitZYbhLqXev4p7lz2Kw7eD9DE1La5nhn2E6wcQrhlNqGYMkfqBQAgTtzJD4kLNtY0Mj4dBf30AyrZiuJs/LZNH9uGmE/Zpmv6zt7mVdQzlr6HTOdHxJUMaiuCb5+DQX8ammNS+VoNG0VoYMF4NUomqfBtUFUJGTruuFo5Yk59MYISRyyz3u+zv2Nz0/SWRUbwXnsjCyFhWm8MI7ebXNJU69jM2c4RzBcc6ljLOsYUjnKtg0Sr47jk46AIYcWTUE646xJsOofrEbAavLrQOVuv3R+KgKTNKN7WZGcWRdD4Nj+do5zIucs1m8shj4ldYSi+rQaMyNzEnSkvbakuhcLXVUJUxgDAuHp2zGhMoNjN3NdZ2UCHZFJJNX7OMvpRTWhtofUOXFzIHWXXkLYPaMugzyjqZQlqqr4DqImuSvcgPNGVG8YY9ZoZZ3cBIczsbGcz/wsdwtetVUmu2Qv5y6zVBR/izoSoPsgbrhKNkEw5ajbWhesgYGN1Vvvc6o9DMoohe7G1s46fOORznWMwIR0HUd19vuvkicgDvhQ9lcWQ0adTtPjM6y+GE1N5Wc21KL0jvQRPAa0utkwLTWn+zWQSAQ34OGz7CX7uDNycs4aKNU1osewYwY78BNhQnySocMZn51upWp1oC+KnnIMd6Rhm5DDMKyDRq8BAkgItSM4N8M5vV5jBWRYZRSVqz6+ZX1MX/AUhCaNrPbfxcbJS9Fxz6C/jiPlj4L2aMnsa/LpjQYvrPTsoMaY9dmWFylGM5/+d6jUMc65q+n2tmMy+8H4vMMWyMDCTX7EMtXkwgw6hlAKXs49jKgY4NHOVYTh+jkrNdn3G26zNK33kFTrgNRh2rk826OWVGAvKmw4QL4ct/clvqa3zJsWysbP576DCUGdI+bb3O2MlDkCFGIWnU4SJMLT4KzF6UkYZJ68e1Cyr1OqOnUGbYy/B4yLn/XjaWbwS3i4gZobiuGI/DwznjD2dclsEf3/628XVGBHAQLD0Kb3gEmSOeY2vVVm6eezM3TLyBkVkjd3s/XqeX/v7+FNUVEYqEGJM9hlS3jkH2JM0zI4IzbS2eXgtwpa1vsa0Z9mJGPBiOIDgaMJz1uFI34UrdhJcPCdf3J1g+kWD5wYCDwqqWr4NFOkPNtY0Ml4uMGTMgbwVU7Wjx/ckj+3DYiD6syq2gtDbA/DyDR5fBH0MX8IjnAVj2Aow53prs1uliDEjLsZqj3H7oNw4ccWyQlParK4PSjZCSaU35a4dVuRUEqkq4zv0uU5zLAag1vbwQnsJT4elsMfvjI8CtR2aSk+lnv35u3ttQz22fVzS7nRpSWGiOZWFoLPdxFmONLfzM+SFneRbgKtkAH91uTX6a/Fvov2+sHnlL/j6NzeDfNjaDJ8BObrDOaiRJpGZf6VaaMmPHUqgtavH9H2bG8nUXcNSO5ZziXEDptjVkDxkbp8Ic4EuHsi3W0vQeneWXVKoKoGA1hOusRirDYNX2CnZURSikLxWkN9t8IMUc6NjAQY4NTOtXSZZZgd8IUF4fYVO1kyIzk1yzD6siw1hpjmCjOZCd0y+L6IWJQUGdk+pAhDTPbvYzUrKs/0flW6GhEvruowag1lQXWtN+XXpupKWmzNj2lfV79AM/zIxX1pu8tTGV18M/4jzXJ7Dq9Y4317pTrOa9ilw11yab8q3W/mxG9AexVuVWsL0qQgH9ONixnr+7/sFhjrVN328wXawxh5KSMwZP9lAyevXmy3x469taehlVDDBK2MfYxoGOjfQzypnm/Jppzq+pMP28HT6c8vIc6oK9SXHH4bWp2w8N1dbKIL4M6/9uT1Dd2PDczteU0sO4U2DiZfD5X9h73aPM/c2lfFWaSkFlHS8s2s6C70oACIQieFx670ii89Wm0hYNd72p4CTnl5zkXMCBxkbcRrjN24mYBt+YI/kkfBCvR45gu9mXpdvKGZiVwoFDs/C6tKpDd9a0nyv2czhg9HGQtxw2zIbXr2DGFQuY9vtj+GpTKQWVdTw6ZxOr86zXI5GIiUNTCCVKX20qJa1yA/91P8UkpzVxsMF081r4R7wYnsIScxQ5lHLlxF6cMyiTTL+Hf3xVxWvrg1SaaWynH4vC+/BM+DgMIhxirOMc16ec6PiS7LLl8NxPiQyeiOPEezv+2lcSnjIjQe37E1j1Gp6qPGZPXsTCkb8lv6KOez9cx47yOiJtdUiK/MDC70paPbHHRwPHOJbyY8cKDnesZpijCAeRFttVmiksi4xkUWQf3o8cyjpzMDuPaWwsqmHhdyUcOjxb+zHdnDLDXobLRdqM4wjmL8YwoLCmkCxvFiOzRpLly2LI/jBj30GN7yvUcdubq6iqDxGqHcqfjvgT9y66l82Vm/njl3/khkNvYGzv3R8Tdzqc9PP3o6imiLUla9mn9z5qsO1BvmzMDKd/I95+7+NM2db0vVDNSELV+xAJ9OLEfUcwYVgvwOCVhXWs2BrG4SnCmbIVZ9o6XKnrcfoKcOa8hafPJwRKjuTr3F70SXMyeWR/DJ3EJzFgmKbZrXaNKysryczMpKKigoyMDjTW7WyuTWt7Ws7J/ytiRWGAL3PuJad8KQz7EUz/Uweq3o1QA9QUW42RmkCYOMIhyPvGOhAa5RSpncxIhCdffJkzKp4iw6glZDp4JjyNv4V+goMI2VThNazlva+bPoYpe+9q0smtDPPz/60hUl9NBAcbGNzqfWRRxZ19PubkhrchWGtdOHo6TL7SOhM1HiIhqMyD7JHQb6z9zeDl2yD3G2vqYncOy0jI+hsxbDL42rdEb6f/VnYTnX4edjbXprbdUDf78VuYFv6Col4H0ffMBzpQbZRMEyq2Q9+x0Hd0/O5HYqtihzWxFiDVWg4vHDG5+LV8vtixa1dtLyOXM5xzmO5YxEhHXrvuIt/sxSfhA/kgMpEvIvsTaTwD/CdjUnhgehtNd6YJNUWAAX33hsyh9v+tTxTBOtj6ZWNzewL/PQ0HoK4Chk1q9/6AMsPS6edhZ3Otv+2laA5/PJ9etZt4z3sTGE44/8WorteqQK21TzhkYrv3F8QmtaWw42triniUv6/BsMm5L+cTLlzH/3M/1zRJKmQ6mBM5gHnhcSw3R1KHt8XrjE1lIa54aS3UV9GAi00MZB9jG9Mdizjd+QXDHIWANc12ca8TOOKUS8CXFfOHjRmx8rD3KOs1RXfejwermXjbQuvnnGiT4UP1Vn3DftTuk7WUGZZOPw9bFkCobteJEaYJr19hrXA07jQ46+nGi00OuP1DqhpCvHz5JA4Z3rlVDqRnqA+GOe2heazNrwJgpLGDX7ve5BTH/GYNtYVmJs7sEfTuPwi8GUQMF68vy8UMNjDUKGSIUUSOo6xp+4hpMDeyH/8On8gXkf25Ysoofj9jny5/fJI8lBmWTj8Pm74AM2Ttn1UXwdvXQOV2OOBsOP3Rps2C4Qj73voBgXCE9676MWMH9NznXKJXW1vNGw/8hjMCb+E2wtSbbp4JT+PR0ImkUkc2lQQMN2A0e50RjsDZTywlv9YgjVpqSGEbzY+59aWcy1xv8zPnbHxG0HpfZeJlcMzN8TuOIUlLmWHp1PMQCcPmudZr7e//jm2aA7NvBacX/u9ryBpCdUOI/W//ANOEL244miHZGuIhbauqD3LcA3OaNdfuZeTyC+d7nOycT4bxg8mzrhTwpmEaTiqrq8ik5bLf6yODeDY8lVfCP6Ya6//hzFP25aLJw+P5UCTJKTMsnXkegpEgi/MXU95QzpD0IYzMGkmKq/VhCEVVDRz6p48AeOJX/cj0O7hn0T2sKlmFx+HhukOv44C+B+zx/iJmhKKaIrJ8WeyTvQ9+t3KnuyuvDXD0/e9Sm/4GnqzFAJgRN4HSyYRqRgFhMD0Q9nPbSRM4cf8hOAwHwXCE4//6BWU1QXBWYppeiKTgzvwGT/ZcHB5rCEEk0Iv6/FO5+dgZXDxpnBpsu7mKhgqqg9UMSmv/MNRo/1ZqPEojMxSi6qOPoGwr6QcOIppfrYMHeFhRGOQvXMwDxnLYMs86ODbksNgUtfNgavF68KRpUlyiqMq1Gkkzctp1tbLKGuY89yd+7pwPBiyNjOL64GV8Zw5kBLmkGM2XWc32N58AOzDDya3H5jDrXWv6VG+zglLSGUku2+lLPV4Ayknn/4pPI2PaT8j79BHOcn2OY/0HsGMxHHkdDJ3UiQe/Gw6X9f+zfLM1NTNraOzvI1qRsNVY6PZ1/wPyYpumzCjdRPqE4VFlxqIB53PUtvn0LVsK2xfD4EPiU5xhWNNGK7Zaf6f0ZnTiq9gOBavA6W5qoCisCTPx8Z3Ld5sc4VjJr51vMLlxQghA2HTwnZnDBnMg4/fdn4EDB1uTzQwDgvVs2rqVlWvXMNLIZYSRT45RxnmuTzmPT9lu9uGF0BSeDU/ltW/hmBE+/u/9MoZkOHn7nL5k+n7QOGsYkNYP6iutWoN1VvOT091FT1ICqymGhirIbP2kF5GmzCjeQPqho6PKjAkDPLy7YRgbvfswsmEtrHkbDr6oYwV4/FazZmWemmuTQTgIJRusE/oaT7Zoy/bKEDOe2sz1rhf4mecjHIZJvenm2fBUPg4dSJXRvHHzh68zRvRyNb7OKMdNmAyzhvXmICJh+Fv4JxzlWMavXW8y0fEtR5S/QcPzn3Bz3bmsyDyWV87qS+rupp+3l+GwHnPZZquZPL3tE16TWm0xBGrAr2ZIiYJhwBHXwOuXw+rXrQPgI47EMAwOGtaLOeuKePnr7WqulTZtKq7h6Hs/A6ympt+7/8fpji9wGNYJfRsiA1gUGcM34ZFspy83TpxA76F9wHDgAHJ6Fze+L2XiJkS1mcJE57ec4/yE/RxbONK5giOdK1gaGcV9c87kP5nncOsbq5gwNIv/Xna4Jtl2M037uUD61KkYLh1qsF1aXzj8cvjwFlj+Aux/FoyeCoDb6eCAwZks3lLGy19v55aTxtlcrCS671YvpuF/l3CuYysY8EH4EO4M/YwK089Q8jEMgwC7Xlt8/3WG0wHXHj2k6VhGJjWUmHUEcDOSHXzLMIrI4k+hC3gsdAI3u5/lZOeXsPBhCha/Se+L/oNr6KFd/pglfpQZCWz4j6H/flCw0mqyPfNJ0rwuxvRPZ21+FS8t3sa1x42xu0pJcMu2lXPqQ/Oavh5iFHCt62VOcczH2fhao8DMYllkL9ZEhjLj6KMYM2IvcLkxDAdrvivmbx+sYriRzxhjG0MdRfzIsYrRjh3MdDzN71wv8njoBB4Ln8Bd766hrDbAXz9az4x9c/jn+RM0ybabUWbYywyFqP7wQ3yVmxl45CT27rU3HufuVy7um+5laLafraW1vLushIuPGMzvJ/6eB75+gKWFS7ln0T384bA/7HGCrcNw0De1L4U1hWwo38A+2fvs8T4luS38roTznvkfvkHP43FXYpoGwfKJBEonYThrMMPpmKE0iPgAB3v3HkqflF3DZ+469XCueHYJRFKhcQp6sOxQwnX9cXhL8fadjcNThn/oU/x12RK+ybuINxYF+emEwdx75gFqtO1miuuKWV+2HpfD1aHm2mhp5FgjMxBgx9XXsGPmA5jBUFTXOXtf64yJd4r6Ehx3unXh/H9YB0RjxZcBmNZ0kkDLM7akizVUQ8lG8Ka1a+nOr7/dQt7zv+ZU53yCppN7gmdxRuA2NpiDGWdsbtZYawB90rzsO7Bl88PkkX246YR96J3mZYBRwlg24zMCHJpW3GLbi2ab3Bi6jNMbZrIxMgBqS+D9m2DuA9b0ulhz+ayJS0XfQk1J7G8/WrUlUFdqNReKxElTZtzxt6gz47gDR/BseBoAkYWPWtPZ4sWbbk0prNgRv/uQ2KjMbWys9TQ11n62ub6psXaCsY4XPHfyrGcWk52rCZsGX4XHcHfwTM4L3MS1wV/zgu9s+h9xEYw61ppkPXQSjDyaEUdfRO/jruNu7/9xTsMfuDlwEe+EJ1JNCoONYn7nfpm53qv4veu/3Pb+FgC2VYY59tlCGkK7WdjAl2HVWbweClZDsOUSTz1KOGRNS/f4dUKH7FZTZvzxoagz46xx1uuMJwPWgXDWvGVNrO8ofxZU7rAawSWxlW+DqnxIa7ux1jRNZn9Xz2+fnssH3t9zkWs2DsPk5fCRHNfwZ14P/6hZY220rzOGUMgYtuAxQkxOy+ezyEGcFbiViwK/Z01kKN5QFfe4H2Vm9W38+sXVhGK5TqQ7BZwuKFnfvTMmHLJOrkm0ibWS2PqOgb2Ptz5/93rr/xFwzqFDAJi/sYRutjiVxJBpmry3Iq+xsdbkPOfHfOz9HWc45+AwTBaF9+au4DncGryY/4aPYTUjcKf1ZtyQftbJD4125YWPAG7chFgSHsm9nt/w+YH38URoBvWmm4McG3jWM4t+7/6SwUYRS7aWc+l/viaitYW7lab93KuvwQzE4b1G6Zhhk2H0cdbnb19lvZ/d6KzGzJi3oViZIbtlmiZL3nqYgS/MYKxjKyVmOpcGruVXwWsJmE6GGQXNDkjv7nXG919jAIwgjzFsZUC6iweP3nWydgHZ/F/wt/wscCM7zN70D+diPDEdc+6D1vR+6RaUGQnMMGDybwEDVr0K2xYBu15nzNto4/E+SXiRiMkrX29vaqz1EORK52vM9tzAT5zzcBomS8MjuSdwBlcFfs0/Qqex1P8jRu1zEKRkgtsPLh+H7z2YK084mNzUcbwUmcK9wTO5sOE6XnIcT5W3PxlGHde4X2GO92rOND/gwY++BeD9VfncN/tb7dd0M8oMe5mBAPnXXofv9n+wT8bIqJpcz27MjPV5DioaKvA4PfzukN9xUL+DCEaC3L3objZXbN7jbTgMB339fSmsLWRTxSbCkfAet5fkE4mY/O+rLVzw0n2kDHsUh7uSSEMf6rb8ikDp4WC6iNQPxQz0g4gfAwcDMn1MHNF8mMCM/QbwrwsmkJNpNd9aHy76eUdz+aHHU7PxWgIlP8Y0HUT8y/mo7A6cKZt4Zcl2npy32YZHLvFgmia51bmsLllNeUM5JvHdF9BpHp2wT28XI7KcbCoPc2XhKTyS8hFUbIOVr8D4c2J3R6l9rQap4nXQf3/rYKN0PdOE0k3WG5JZ0U+nW7xsGSO+vJnejiqKzAx+HbiaRaa1LF5/Spttu/MtqcuOHIFzN63vk0f24bARfViVW0FpbYBsv4d9B2ZiYvLVjgDnvdb8he435ihOCMzikX6vMqXyLVj9htWsPfV2SB8Q9eOIii8TqgugaA24J7R7CdFOM02rcQSjXc3PIl3hwBwPN3tO58zw56SXrIPvPoORx8TvDlN6WZmUMTCxl6rvyaoKvjexNguAN76t5aoPykmnlt+7/ssFro8BaDDdvB6ezPvhQynC2rb9mbEv2f7TSennI7jpcxqWvkha5QaucL3F+c6PeSD0U54JT6O41sWseZXcftRuJly6UyA9B8q3WCdr9B/Xc5uDdp7Q0d2nK0qXO3ywl14+By/WHcqNqZmk1RZbS/ftNaVjN+hJg9ptUJkPfTXRPGHVlUPZd1YmRLEv++zyGrbM/S8veP6H2wizJdKPm0K/ZH5kPwZT2GzbzrzOCIQjLMkPcv5r45kb2I9LnO9zretlDnOsZWzNDbz34ZWcPOOETj30Zvy9rde/ZZug7z7d8+SFulLr5638kPaa+Ctram3RWlj0GBx+OUeM7kOqx8nW0lru+eBbbpixj91VSgL628cbeOCjdfSmgr+4H2WqcykAyyJ78XzoaDaZA6jFB7SdGbvLC6cDDjnwIJZuuoC1Hz/Dz5yzmeFcxFGOZfw5dC7/WTeNx+du4tIj9+qiRy3SQxkOOOxXkLvEOpnnkzvh+L8AcMw+/fC4HKzNr+KROd9x+VEjbS5WEk4kwpxHruaogqfBgM/DB3Bd8PKm96JSaX4CXGcy4+i9I3ydF+CSN61jJF9EDuD4hlnc5X6Ck5xfwke3QsEKOOXv1ntRIhI/ffe2Bjds+Ajeux4u/ZRp++bwx3fW8PWWMp5fuJXzDrNxxUpJWP/336W8syIPgJHGDv7h/jtjHVsBWBEezqvhI/jGHEkQdwczYwZOI0LNqvdhyX/oXZ/PH91P8hPnXG4K/pJ15hAe+nQj4wZkcuIBMT7mLSK4HdGtXjl93xzu/fBblm0JMmdtDScckIbL4eKag6/hroV3sbZ0LbMWzuL2ybczIG33v6tOh5PeKb3ZXrUdv8vPkIwhsXookgDOfnQ+y+ufxJezEIBgxQHU5/0UTC8YDda/jXZmxm0nj8PZynTyGfsNYNq4HL7aVEphVT390q0mXKfD4LxD92H2t3sx893xpAz6Lw5PCSnDHqWh8HjueNtkVL9Ujty7X1c8ZImTcCTM1qqtbK7YTIo7BXcXrLSrybWdYBgG/3eodXD6g21Otoz5hfWNJc9AfXks78g66Fax3WpkEXtUF0Lldmt5rSiZ279mny9/T2+jihWR4ZzS8CcWmfvw6pl9uHqCizFpdc22753m5aYT9mHyyD1PqnI64IDBmUzZuy8HDLbejHI5DCYP8fLm2S2v24CHiwvP4dF+N2N6M6zpsq9eBtu+ivqxRC21H9SWQfG3TZN0ukx9ufVz8vfq2vsViYLLYXD+xEE8GjoRgMCXj3VuCmFbPKkQamhsOJeEU1tqTX6Fpom14YjJVR+Uc7hjNbO91zc11pYOnc4ze/+Nd3wnNR3MgE5khseLe8xxpJ39b9YdMpOVkeFkGLXc5n6Gdzx/4GDjW55aVsP/+7ScQHg3Z3k53ZAxwJqumLeiZ07DNE1r38zh1AkdEnM+l8GvDk4lgJunGqZYF65+o3M3mpJl/Z/VahiJKRK2VsgI1lsT6NsQDDSQPv8ubnY/h9sI82Z4Ej8Jz+L/nX001xzsYmRa89VUOvM6I8Xt4EdDvDx1SjZhnDwWPpHjAn/h68hoMoxaTt56N0temkUoGKNJEoYDUntD2WaoKYrNbSaaqnwwlB/SASmZcMgl1uef/glqisnwufn5ESMA+OdnGyms7MZTn6VDGkJhHvhoHROMdbznvYmpzqUEcFEw5gLmjfl/5KeMaWqshegyo7W8AEj1OJg0ZjA5x13NCYFZfBkZS4oRYKb7af7j/jOPvzuXBz9arwm2IvHmz4aJl1mfL3yk6T3gPmleLjhsGAB/fm8tFXUxXIFPkl8kTOi1X1uNtcA/Q6dwcfAGvOnZfHB+X64/xMWw9OZ/vzuTGRleB0cP93HftKymbStJ48rg/3Fz8BKCphNWvIT55PHWe/4iEl+HXwEuL+QuhRUvMSgrhdMnWEvr/uG1FdQHNUFQmquqDzY11p7umMNbnpsZ69hKyJPJpzk/5yHP/2fvrMOjuLo4/M7MWjayccPd3Sm0VCjUhbq3lFJ3gbp+dXel1L2FGi0tUEpxdydA3HWzNjPfHzdCQhIiG2Xe58nDsruZvbvZmXPvPb/zO9ewSu+NFyF4qXfMkGQC+59K4GWfsarjVAp1G8PkXfxiuZ+pym+Azk1frOWrlQea4F0bGBhURffoICb3iwXgg7+9ZDlF8ZRFsXDviHvpHNKZPE8ez616jkJPYU2HwqJYCLYGsy9/H1nFhnt6WyElr4BN3jewhK1A1yVcaacR67uMT6cN5rYTuxEXUtHwKdZh4+3LhjK5f01ibIkx3SI4a3A7xnSLKBPhxjtCuHLkYG4/dhxF+27FmzsUSdKxxfyGLe5brpi1lJ/WJTbq+zVoPHyajz15e9ibu5dgazDBlqYxFDIyOQ3kzF4B3Dk/F4BfpfHcGDFHtLBc8wkcc6v/XqjU1S5zl0i2BhlK+ibF54HsvSLRa7Ie+flAzo4lBP3zKEGSj//UfnwRN5NZ42LoGWFCliSGxkWjjo2usmq7IQyMsbBpeiwz/s7F5dP5O8Fd9tj/DvRhf6fneEp7RTjdzJsBx9wOfc9s2IseyqFicHMQRHZvOrepvGRQvWCyHfm5BgbNwIX97AxedCpXmOYTVZQM236Bfmc33gsGhIlzMSReOEsbtAzcBcKxVnUJB1ggMd/HlT9lcqPyE3eZvkWRdDxB7TAddzfh7YZwLXD1cfg3ZkgSPYceR27vscz++QfOLficXiTyreVx3lNP5+VNU5CAJ48Prfr3ZRM44qEgRQhsY/sfXS7JxTlQlF4mjjYw8DdXDgzkmf8K+MJ3IjeZfkZKXifEhmGd63dAS5CICQVpEGE4xrU4ClLEzxHWebquk5iZR+qP93O2shWvrjAvZir9jz2fNWEmJEmiX1Q06hj/rzMmdLax+toY7pqfS7ClIxfueojbTd9zozKXoTl/cPCbRDpMeQpsoQ17IRBtAd2FkLlbzGFquQZrFbgLRLePoylmGviXvmfDtp9FTJj/CJz9Jtcd25XXF+wGYM3+HE4ZYDj2GIh2e/uznZzw4iIuUBbypOkjLJKKJ7gj5jHXE9N+GDearEw/rpvfY8Yp3QMYds1w7pjXhUR1HqflfMZ4ZTO/yzO5dcHNvG++gOmGY6aBQePS/UTY+w/sXwK/3AHX/QOKiVtO6M5H/+0DYMPBXI7tWXsjCYO2i+rzUfTNNEJ2/oBPl5nhm0aXwcfzb/8I2oWIdUavsdGoo/2/zpjSx86odhbu/DOX+GCFn3YU85k6kT16PG+ZXyUseR18NAmumAuhhnuZgUGjYY+AgRfB2tkw/2HofTp3T+rFN6uF+GRLch7DOoUf4SAGRwM+VWNvZhEnv7wYCY27Td9wk2kuAHpMf0xjb+H4yB4cq8v+jRmyiRGTL+dg0gls++NFRvjW8pD5M8bKW7jHO50ZP2wiwKJw1uB2/nmjBgYGdeL+U/vw++ZUVA1Sc8Fhc2Ez2bCb7cwYNYMHlzxISlEKr659lRkjZ6DISrXHCjQH4vK52Ju3F7vZToDJ6GLQWvGqGltTsrhoznWYQ3ajawojQy/l0mMnMLxdTxxWB+O7wq0n9qrShbYh3HpCP07sHcfd3zvwqR1JkediDl2LbMnkju88BFjHMalvvJ/eqUFT4FW97M7dTVJBEhH2CCyKpcle23CubSAmWeLO0UIJ/dyyIrIGThcPbJ0jktf+xBIkRIoZOwzHqaYmL1G4JtkjavX01auWYl/0KGZ8zFNHcI33Hl4+ox29I83IhwhNq6vabijBVpk3Tw3nwzMj2H1zHBO7lotNP98fQs+DM8nvOBF0DZa8BMvfEbf9hWKGgHDI2i1EAk2BKx8KkstaqxsYtEQsisTlQyN41XcuANqa2eB1NuIL2oV7ba5RfdVi8LogfSu48yBItIKes8PJSR8f5N6iF7jX/A2KpKP3mITlgg+R2w0p+9XGihmhdjNXXnghwZd/jtZjMrKkc73pZ+ZYHmLZ5l30ejOZ/bm+ql1sJRmC40R769RN4lp8tJCfDLratgRfBi2KALPMZQPsJBPJfLXkWrDlp/ofUJKEmC/3AHiLj/x8g6bD4xSuteYAMY+uge/XpVD4/S2MYCsFegBXe+/l9LMupmu4GakJ1hmRdoXZZ0XwxinhbLupA98GXsI13nvI1wPoULSFpE+mkZ64p3rn87oQGAnOTMhpY91bijLFOWixN/dIDForsgLj7hC3138GB1cSbDNzzhCRQLzh87WGq5QBAB8u2ccJLyzgfuUznjO/j0VSocMoLJOfROo0pmwe21gxIzpQ4fMp0Zx3wRUo573HZq0LYVIhs83PkvfnM4x7+i/S8114VT/uRxkYGJQjyTDmRtHZKG0zrP4IgLBACyf3FfsRU2evwmecgwa6zrb3riZk5w94dYVbvLfwp3IcNx0TS3tH06wz2oeY+Oa8SF6ZFMaOG0WR0DKtH+d4HiNRj4TsvagfTMSXvss/L2hgYFA1gy8Be6TI6y19jehgG+O6C5fRq2etQjW6Dxgg3O9PfnkxJny8Yn6rTFhLn7OQJj0FUb1AkhstZnRo144RV78Io29Cl82cqKzjZ+sD9JH2c9tX6znv7aXkOj3G99XAoInpEG5ncIdQAF76xUN2cU7ZY6HWUO4ZcQ9WxcqmzE18uvXTIx4v3BZOvjuf/fn70fypYzFoUu7/YT0X/XQjpqDd6KqFSOc13Dn+FMZ27I/DWm4MVp0LbUPpFx/ObzefwOcX3cItg25FVwNQ7AcI6PQO13+xkCs/WkGe02vEjFaAW3WzI2cHSYVJRNojm1RYC4a41i9cNSiw7PaYP+Khw2ghtljxnv9fLDAKinOFg49mJEuaBFc+5OwTjkk1VNCUoqdspO/ax7BKQlh7k/dWFl3dAYvSRO6tlTDJEq9Pruiq58HMwJ1X8Q7nizs2fgV/Pw6qn1q5gkgWmyyQvkO4+zU2+clCtGYJPPJzDQyakenDgvhKPZ4ELQbZlQObvmvcFwwIE8JzV17jvo7BkdFU4UBfkAZBsSBJ6LrOY38c4EvLU0xSVuPWzRSOuRvp+JlN78JtDUY+fgbuE54gQw+ht3yQuZYHmagv47hP0jnty2pac0uycEcuzoHUzcKRr63jyhebzDbDtdagcbl1pCji+0Q9Wdyx68+GCWOtIeDOFy3pDVoOuftFnD6CE7bmzKHfyvvoIx8kTQ/lQu/DvD1tYoVkd1NiViTmXhTFIm0w53geJ0GLoR0ZWH+9hce+WdrwF5BkUdyYs08IUtsCqlcUblqDmnskBq2duEHQfaK4PecmUL3cdXLPsodv+XJdMw3MoKWgaTpP/7aFZ03vM830GwDevufBhJkQ1rHpOgyVYInoTNylb/Gl73hkSede8zfMcD7H+P/9zk2fr23SsRgYHFWExMPAC8XthU9CoVjX33dKbwC8qs4jc7c01+gMWgjaomfpnz4XVZe42XsLW4NGs+ra5nPBt5okll0tBOAJehznux9hjxaHUpiC892JIjdmYGDQOJisMKrERGrJK5CfzIySmJHv8vH6AkPgfrSjajofLNmHgsor5jc5S1mKLptg5HWiqKepOihKEgw8H+nst/DYY2knZfGd5VFOktewen8Ogx+fz/9+29Y0YzEwMChjZknMyCzwsXS7QoGnPF/YKaQTNw+5GYB5CfP45+A/NR5LkiTCA8JJLkwm3ZneeIM2aDTcXi8/p76IKXg7umYisvhq3rxwIr3CejWpG7EkSUTbo7mk3wXc0O9+NK8DxZqBvfPb/JuwlUGP/2nMcVo4xb5itmdvJ7UolSh7FOYjmNQ0Boa41g+EWGVGtxOqaI+GWHhIMiQshtSN/n0xSYLgaMg7KFynDBoXXRetFr3FtWrbmbp/B8W/zMAuuVmkDuJW781suL4dccFHFuU2JjaTxO+XRDG+46HuehLPuM6haNxM0dp77yL44wHwufz3wvZw8BVD+vbGdUhz5UN+IthDG+81DAz8RHiAQscwGy/4LhB3bPhKFE00FoZ7bcshJ0H8BMeArJCQ6+O273fwveURhsi7ydGDkE9/kaABpzfrMK3dx5Mx+X22mPsTKLl5w/I6j5hmsy/bhctXTeWeJEFIHLiyhcC2rTvsF6SKeGm4Dho0MtGBCsEWif+0fuzXooXb+d5F9T+gJIE1uMS91o9zPoP6U5Ql/h6BETUKnfamZHLgy9vLhLUXex7kl5tGE2xt3iW9wyrz3XkRhMV24WzP46zWeuKQnDyQ/zjagRUNfwGLXXTYyNoNPj8WAjYXziwhcLceeW1pYHBExt4srumZO+G/V2gfVj4vWZ2Q3YwDM2hudqQWcMarC3nd/BoXmP5B1SX0EdMxj762Wbv9RAQH0OmMGbwRMB2PrnC6soJPLU+zYusedN1wCDEwaDQGXgihnUQx19+PAdAtqrzQZ/nerOYamUELIHHRB8j/PA3AQ75r+EMbyaIrY7GamqeAr5S4YIWPzwxnQLSZFCK4wPMw27SOhKg5MPsMIzdmYNCYdD9JOI/6imH+I/RvVy6W/G93Gyl8NagXGxNzGfTYn8hovGx+i9OVFeiyCWnsbTDwgubp8BbZA8v575MVOoBAyc175pe4WvkdEJ08DAwMmpZRXcu7QO9JNlHoLqzgOjsidgTn9xTmbx9u+pCDBQdrPJ5FsWAz2UjIS6DI28bzjm2MNfuzGfjmDZhDNqLrCsWJl/PORSfRK7xHswgjAYItwUwdNpkruz+A7ItGNudh7/QOsjWJV/4yxLUtFafXyY7sHWQ6M4m2R2OSTc0yDkNcW4JkNhP3v/8Rd9+NSKa6/zGePym07PZ6Tzvodar4z/K3hUDTnygWkUDJ2g1OI2HSqBSmC2ehwMgjP7coE33eTOy6kxVab6733k77UBtBlpZxmvWJNPPp2RGc37diFUi/vwawqM+j6CYbJK6C3+71rygqOEa0c83YDqrPf8c9lNyDJa61hgOUQdNQFjPumV6vmPH65DB+1UaxSesshFLrjtz+okEY7rXNT0GqED7YQ0GxoOk6F32yjTszH6GLnMZBLYpHQ5/C3G5g7Y6nesTfszAd8pIgL1k4eOcllf/kpwgRj8cphEl1oG+nGPpd+Qo/288F4GrTH3xkfp4Rb+3hp+1OfFW1x5BkCI4T1/y0zW237bynSMwNmqoK36DVU77OuAFJc4NWt/nQ+6eHoyPztXq8uGP7Lw0bkM0hxH2FaQ07jkHD0VThyqppYK5erO91Ocmfcy+d1f2k66Fc4nmA7l26IjeTY21lhsdb+e78SOIiw7jcM4NF6iDskhvf7/ezbumfDW+pFBhVEu9q3mxt8ei6cD2X5Fp1RDEwOCI2B4y+Udxe/Dxk7+P9K4YDkOP0kpzbRudiBjXi9Pg45ZVFXJf9PKcpK3HrJj6NuBVp4HlN3xmjCsZ2sHLz5Rdzi3Q/+bqdkfIOfrA8wvj7P2bB9jQ0ow1fq6Jsnvu//yGZmyc5ZlALTFYYdb24ve5TSFwNwMsXDgJgT0YRGQXu5hqdQTNSsOs/ohfeC8BbvjP5Qj2RqwYFNltnjMpM6Gzj54uiAMjCwWWemezW4qEgmeL3T0XPT2nmERrUBSNmtCIkCcbeKm5v+gaS1vLYmf0AWJWQQ66zDRS+GtSZlLxiznzjPwrdPh4wfc6ZyjJUqURY2/tUYeLUXFiDiTjvZeh9OrKk84j5U+4wfQfodJ7xK6sTso1ivlaGETOal4Z+/vdM6gXA4p35WKQQ8twVc9Pn9DiHAZED8GgeXl3zKq4jGL85rA6KfEUcyD9QQahr0HLZm1HIRV89jyVsBbou4Uq6iCuHjaV7WPdmE0aWYjPZuOPYk/jwtGdRizsgmZzYO36IbE2i84xf2ZSYZ8SMFkSpsDbLlUV0YDRKM+ZVWobqrwUgmc2EnnsOoZMnIJnq/gdpF6IQZhMf54wFuejDrhKb1unbGuYuVR22ECFUzNwpHAkN/I/qhey9IvF5pGo7bzHOX2YQJ2WzW4tnmudOrhsZwR+XRjfNWOvAgCjLYfddtaYr95ofAHOgcFv+9U7/ifAkGYJihAA2a7cQD/gTZ3aJa63Rmtug6SiPGcfVK2Z0CzMRYFZ4xncxAPrWOUII2ViUutfmJzfeaxhUj7sAMnaIDaaSIoDlO5L40vIkneU0DmhRLBr4DC+dfwRhreoVYtncg+DMKRGzxkJMX4gfBPFDof1waDcUYgdAeFfxej63EPfmJ4ux1HZRIJvY1/NqrvPcgVO3cqyyie8sj/L8n7u49MdqnG1KBbaF6ZC2tW3OUQpSwVMoCp0MDGpBWcy49Fqk8E6Qn1ongW2/KDNmGb5Vj0VFhrQtkN0A5wVJAkugcNJui+doa6IgRfwE1VDIp/nI+vUxBst7ydaDuNjzAJeN78M7p7W8ue8xHawUY2Oa9y7mqGOxSCoDNj3DO9/92rADy4pwWsze27qLS115ohVzM7pGGrRBek6GuEHiej73VoZ2KC/+efLXrc04MIPm4vvVB3ncNIuzlKV4dYWl3W7ninNOh2ZyBamO4C5DmeJ5lCQ9gm5yCt9ZHuOp2XO47ev1zT00gzpQNs899xwj6d3S6TgaOo8Tt3+9CzSVUV3KXaWem7e9mQZm0GwUpiN9eyUWSeVXdSTP+y7g+ZNCeeTYltdhYWJXURyShYNLPfdzQIsioOgg6W9OguKcZh6dQW0xYkYrI6YfdJ0gbv9+L+O6l8cMw9nt6OSDf8Ve5DXK70w1CWdYedT0EmFtHXJkmioMZ1z54hruzBadHd0FolNcfQVNsgnG38WXtosAuM30A4+YPkFC47x3lvHkr9vqd1yDZsGIGc1LQz//43pGld3+e6OC2+fGd0g+RJZkbh5yM2HWMBILE/lo80dHFDOG28JJKUohw5lR5/EYND0Pz/8Ga4zICbjTT2XGCScxc/KwZhfWlmKWzQyJHkJU0Q2ozo4VBLZnvLGEl+fvbO4hGgBF3iK2Z28n25VNtD0aWWpeeashrvUTsiTx2TlicbE908fizCAYJCZwrHxfCGL8TVCJg0/2Pv+74xoIV7qiDLBH1Pw8XcM1/0nsebvJ1EO42nsP867uxp2jQzArLaPK+1Au6m9n5jGHb5J9m9ONz9s/hM8SIkRgv94pFjf+QDEL99/sPZC73z/HBLEIy94n/q3B7cvAoKVhM0nMPiuc/7QB/Kv2R9J8sPqjxn3RgFDIT/LfeW1QO1QvZOwET0FZPDmYlk30onvpUiKstZ/7KpeP7YYiVxMzfG4hvi7KFEUQcYNEYqzTMeJ2eFdwtIeQOCG2DYmHsE4Q3bvkeWOg/QjxPE0V8c2ZJW4fgeuGBjFk9Amc73mYND2UXnIiP1kfxpm8nQ9WpLExMQ+1cs2ErIhx5CdBeiO6ljcH3mLRetDW8pJNBq0Ai10kJ0LioCCt1vP3YKvMB2eEk0EYf6tDxJ3bGyhWtDmE0K8gtWHHMag/XpeYx5oDqnf30HVy/n6V2KwVuHQz13ru5ttrh3L14KAW41p7KLePCubescF4MXG790a+9R2LSdKYnvMSi/76ueqYUVuswSKmZu1pnLV1U1CYIZznW4BzpEEbQpJg/N0gmyFhMRH75vLmJUMB+G1TKgeynM08QIOmZM3+HAp+e5jLTH+j6RLeEddz/PGTkFuYsBbgofEOzh7dh3Pcj7NDa0+slMM3lsfZs3Ep7y/ew7I9WQ13PjcwMChHkmDUDWLumbIe1n9BfGgAz00RRb4/rEsiPb9mxyiDNoTqI/eTSwnyZLBLa8e93ulsnh7H+X3tLca19lCePsHBPWNEgXMa4VzifYBUPYwY935S3zmb5TuTjZhhYNAYjL5RdFJNXEW39Pk8fHpfAL5YcYB8VytdlxvUi983pfDhkn1MklfyoOkzcefAC5H6nV07Ya3PLfLtuYmik5a3ZM6h2IQBgGIR+6TuwvLufM7sOnf/QpI44dxp/NPuOkB043vB/C4yGh8u2cfspfuMdYaBQRPQv52DOyf2BODbVRmEmCPIcVUsiHJYHdw69FYkJBYnLmZJ0pIaj2lRLFgUC/vz9+NWDcOQlswbS5aw3vUmkqSj5o3ky8sv4oqRAzHLLWtvSpEVPr36eKZ0vKeSwDaZ1xbs5rPlCUbMaEaKvEVsz9pOriuX6MDmF9aCIa4tQ/f5KFi0iIJla9HVI4tNqqJflJlzewcAcOWcbLQBF5S34t7xmz+HK5AVCIwQSVmjrat/cRdCdgLYgo+4MMhf8Rm2xP9w62au89zJ7Sf1Ij645bb5tCgS04cFVVmF/sC2jlxfMJU8gkTi+te7RLWgPzAHiKR4+nbhuugP8pPF+RVYg9uXgUEjUBYzlq+rd8wYEW/l5K42nvWJQgx991/C3bmxsAQKYaDRMq1pyU4Q16qgGJAk9mc5SflxJt3lZJL1cPaOe57I6Piqf1fzCeFbcQ6EtIMOI8VPWCchlq5tRbg5AIKiIboPdBwD8UPAZBffBWcW1NBGxWaSuGF4MKeOHsjZ7ifYpnUkSsrjS8uT/LVqE/f+sIWps1exdE9mxV+UTRAcIwoqMnf637W8uchPEXHRaohrDWpPWcxYtAhdMkFUbyFuLap9lfVxnWyM62DhS/UEcceuPxvmOivJIi7kHjDca5uL3IPi+h4QXu1T0ld+S9i+n9F0idu8N3H7maMJD2i564wgi8yNw4O5flgQOjL3+a7je3UcJknjmD2vMOenb6qOGbV+gSjh9JuX6N+BNwU+t1i3WIOaeyQGbZHQDjDkUnF73gxO6xHA4A6hAFz24YrmG5dBk7I5KY+f3nuUG01zAdjf62rsg85qcY61pThsMjeNCGbSgHZc6HmIjVoXIqQCvrQ8yW+//8zF7y9n3LMLmLfZWL+2ZCrMc31tqKiyreJoB/3OFbcXPAmeIs4f3p7u0UGoms51n65p3vEZNBmpP95PaPpKCnUb13tv5+sL2hFobbnrjEi7wk0jgstyb4l6NFd67iNfDyA2bz1Zn17J+Gf+MmJGC8eIGa2QoGgYcL64/eeDXD0yhtgQGx5V446v1jfr0AyajqW7M7nh87UMknbzqvlNZEmHbifCsKuPvNbwOoUBR3GO2AuNGyjyE53GQufx0Hlsibv+McJIpNNY6DAKovuCKUAYjOUni7xWLYkJUjjutEv4IPR2vLrCFOVfnje/g4zGI3O3GuuMVoIRM5oXf3z+Nx3fnWCrCadH5ZPFbnRdx6N6KjynT0Qfzut5HgCzNs8iq7iajpklOKwO8j35JBUm1WtMBo3P71sSeGvrw0iKB19RV56deA19I3tiUQ7vrN0S6BgexGOnHksP6eYygW1Axw+RLek8+NMWI2Y0E6XC2jx3HlGBUS1CWAuGuLYM3eMh8fobSLz/GXRv/YP0qHblF4b5ByUYcrn4z9pPRDsDf2O2g2KCzF3gKfL/8Y9GdB1y9ouWz7bQKp+iarAxMY8Ny/8maOMsAB7yXcWwwUOY0qd1OKheNSiQlyaY6ULFNvEL9SHc655Krh4IWbvgt7v9J7C1BoPFBulbG96e3pUvhIiWwBabLDJou5TFjAeeb1DMGBZnYbPelZ/V0Ujowum8MbE5ID9RFBAYND6F6ZC9B9UWxsbkIhbtSCPtl8cZKe8gX7ezoPfjTOjfuerfLc4VzpaBUcJ1Nm6gKCSoS4ulqjDbhACjwwhoNwQUq6gE99T8nbhheBD3TIjlDs/1LFX7EiS5mG1+lk5yGhkFHp7+bfvhYinFIoRQ2Xsgpw247Je51gYL15/WSGt1e2zllMWM629A93iEuC6qt3jwCOfeofSPtvCPNohkPRzc+ZBQczX3EQkIFZvbRpFe0+PKg7z94m9Q6XpSus5Yt/Ifwje8C8D/fJcwetxJHNupdTie3js2mJcmmGlHBvd4r+dH9RjMksoM81d0LNpUdcyoDbJJzGWy97S+9q9FmeK8tQY390gM2iqDLxGdDJxZ8OcD9IgWQu4D2U7DvbYNo2o6y/ZkMWd9Et98NYtHTbMB2BR/Pl3GXSjm4w1FU0XRXw0FeQ3hkeMcPDIhnLs917FS64VDcvKZ5X+MkTeTkufihs/WGkmMFsxh81yDls+giyAwGgpTYenrSJJEu1AhWFx/MJeMAqPwrq1SGjP+m/8dsZvFOuMe73TunNiL/rGtY53x7ImhvHy8mTgy2aF35HrvHXh0hdOUlVzt/MiIGS0cI2a0UoZcJoqC85OQlr5GsE103vl7e7rhXtuGOXSdce/3G4kgj7ctr2CTvBA3GI65XeQZqj2AR+Qc3EUQ2lkIZtuNEKYh9vCSLk6VJCqKSeR7g6Igoit0HCV+z9FB5Kjzk+pkEHDFlLNY1/M2VF1mirKE50scbAFjndEKMGJG8+KPz1+RJSjZ9v55fSYOU9Rh7rUAZ3c/m26h3XD6nLyz4R20GvYeZEnGYXWQVJBEnjuvXuMy8C+HxouluzO5/9+HUawZaN4Qbhw8jWO79sfewrtPy5LM19dM5Jped6G6YpFNRQR0/ADJnA1opBoxo0lpqcJaMMS1fmdi1/LJ5PRfc1jjmCjc6pxZsOWnxnlRe4RILmbtbTvOcM2JMwvyDgpX4CpYuieTqbNX8dKP/9Jxw8vI6Hzlm0BI35N4YLyjiQdbfzRdYu6aBAIlFz05UHa/iok1ek8e8F5DPoGQsQN+u7dO4o8asYWKxHjqZrG4qg+qVwjKvUXCHdrAoJVyVi+RwHjBdwFeXYGDKyB5XeO9oDUYPE7DvbYp8DghYwerD+Qy9cutzPxhE3v+nsVI93I8usLvne7isuP6H/57mk8UH+gIQW38ECGq9beYUzELAUaHkRDVR7Riyk+tttWSXhIzTJLGk96Lma8Owyp5ecP8BkPlnQC8t3jf4e2+TTZxnc7Y0TqdBg8lP6VEGNV6Yn0ZqgeWvQn/PC0EEgbNT1AUhHcDZ06t/yaX9LejIfOtOgGA4k0/N2wMpe61OfvBZ2xSNhm6DjkHxHW3ktCydJ3xwg//0WHdi5hQmaOOJXTwmVw9uPU4npbGDIdURFeSuNt7PXPVMZgllfvNX9JbOlB1zKgNthCRTMnaA2orca7QNFHcpFjEeWdg0BgoFjj2HnF73Wfc0r1cwH7s8wtJzDEEtm2NeZtTGPfsAi5+fzlvfv0z9+Q/gyLp7HaMYcDJV4HJWveDaqooAClIEfs1eUmiYLAwU3TUOPQ+T5FfiuckJOasScAiqTzmuZzF6gACJTezzM9zvLwWgMd+3mq04TMw8BfWYFGQAfDfq1CQyl0n9yx7eMRTfxkC2zZIacy4/v2/6LpEzBc+953IcSOHcFrv1tOZR5Yk5qxOIELKpwtJLNX6c59XtP6eZvqNy5U/jJhhYOBvzAEw6npxe8nLPH1SeT5u4KN/kuc0BLZtjUPXGbd9tZ6UnELeML9OvJQNIfFw3AxhPlEdrlwozCjJPYyA2P5CUFtZTHskZEXkRWIHiByGoyM4s0UnsFqsQxRJ4q2EOJ7znY+qS0xR/uVZ03tIlG9GGTHDwKBxef68QWW3L37rIKqq4KpkRqjICjcNvgmLbGFT5ibm759f4zHtZjtezUtiQWKNQlyDxqdyvLjy+5fxBaxD12WuH3wNFw4eiaOV5FPNJoXvVqgUJ52D5glDNudj7/ghkqkQvSRuGDGj8XF6nezI3tEihbVgiGv9TniAwrVDAsv+//xKNwy/Wvxn/Rf+EygeiiSJ9hx5B0TllkH90VTI3gdoYtFYiaV7Mnn6t+3kFhYxw/w1YVIhm7XOPOK7iiWb9ta/xWkzsCU5j8xCsVlqkXxEk132WCZhbNE6c7/nanzmYMjYBr/fJ8Ri/sAeLqoQUzcJQUddEjKaJoS1+UlCuH60Y4ikmp8GTN5jgxQu7mdnvx7LF6Wtvle817gOn7YQIXL01/lscDiaBll7WLtjH48tSCez0M0IeQdTTfMAuNc7nY93BhweM7zFQsAZFAPth0Jox4Y71R4JkxWiekC7YaKoJD+lyu/GoTFDliQe9F7FD+o4zJLKY+ZPmCCvJ7PQzZbkKqpFLYFgCYD0bcKNtzXiKYLc/eL8aW2utfkpMPcW2DpHxN39S5t7RAalhHYUrlHO2s0fOzpMnNkzgG98x6HpEgEZ6xsuWi9zr01t2HEMao8zSwgtKxXyla4zcgqd3Gv5mkgpn21aB2Z4r+W3Nbtb7TrDJnlxUMSd3htYqA7CJnl52PwpQUX7q44ZtSEoWlzb8g76cdSNSHEOFGWJ883AoDGJGwQ9JgHQcelMTuwZXvbQd2taeZGTQQXmbU7hhs/WkpLnIoQiPjC/QLBUzHKtDw+lTWDpwdq3TQVEIVZBqvhBAkcn8X3qMAo6jRE/HceIjhqxA0R3DZ9bzEOc2Q1aEx8aMyQJ7vRO5w91ODbJy7vml5kkryAlz8XKfdlHOJKBgUGt6X0aRHQXrZoXPMnA9qEM71Qulvp1YwO7jRm0KMpjRjFPmT8kTspmjxbHk75L+Wx5YqtdZwRKboIp4kdtPM95LwDgYdOndC5YbcQMAwN/02OiMGfwuRi+/UV6xpQX/y7Y0Ur3eg2q5NB1Ril3mr5ljLKVYt3C5m7XQkhs1b+s62I9oanCNCR2oH/MkSRJHCd2ALQbCqYAsQ5RazYKKI0Z/2kDeM53AT5d5nzTYp4yfQjo6GCsMwwMGpnJ/WOJcwhjQrdPIzUrhFx37mHPiw+K55I+ogDw862fk1xY83ok3BZOmjONzOLWM49ta1SOF7ItEWvMrwC40ycTqg8k2h7dnEOsEyv3ZZOa50H3dKA4+Xw0TyiyJYuADh+C7EJHM2JGI+P0OtmevZ0cV06LFNaCIa5tFG4aHoxSortYl+qhqOOJENpJuJ1t/KZxXtRkBbMdMneDK79xXuNooCBFiAsCow57yKvqvLlwNzpwufIXveWD5Ol2rvfeTjh5mCW1/g5MzUC2s+LCI5JculIuzk4gjn+1gSzp+5BwNUjbAn/MhEoVRfUmIEwImFM3CUfD2jimaaoQ1mbvFY5vssk/Y2mNeJ2w8n2Y/1C1TpMGTYSq1qkdTWXuGC2qfF/3nYtTtwoxe8K//hrd4ViDwVNgtAFvTApS8GYn8NqKXHQk2kvp3GMW8f9936ms0npikrSKMcOVLxLTkT1EItvWxNV89nDhkhvVu2QsWRUerhwzIsjnDe8ZzPZNRJZ07jJ/xwh5O4l51ZwLtlCxEZa+tfW18oaSNlYFYG09ji4AJCyBH6aJOGsJFs4CXcY396gMSjFZRJszTat1HJlxTAhJRLFYGwiAtu3Xho3BcK9tWjStpLAM4exdwqHrjEuUBfSX95Ov25nuvZNIclEkvVWvM2LJoj3p3OC9nVVaT4KlYh4xf0Ju+oFqjnAEZJModsjeA8W5DR9wY1OQIoRn/mjPbmBwJMbcAJYgyNjB8x2Wld3907ok9MYs4DNoMjw+jft/3IT4a+o8Z36PjnIGB7QoXvGcTaEUWPuYoWtQlCmcpQKjof1wIaKN7QehHcS+S0CYKA6wh0NwjGjl2m4odBwt1i2KRcyV61m8WTlmRJHHs94LmKuOwSKpvGF+nTPk/0jONYpDDQz8hmIuNwNZ9xmkbuaZKQPKHv5mtVGQ0VY4NGacLf/H6coKvLrC7d6biCMTSaqmC1Fd0HURA5xZJQVwJS7n+SVO5658v+1dV44Z7UmnA2m8pZ7FD+o4TJLG2+ZXyU/e6ZfXM/Azu/4sv23sP7QuJAnG3yH+3foT740vzxF+saKe63qDFkfFdYZgjLyFGxTROesT9SRe2BRUdcwoFdZagiC+1DTEzxIUSYLgWGESEtpJmHh4qy8qPDRm/KcN4AWvcLC9xLSQh0yfQck7TcuvY2GigYFBnXj7smFlt//c4MKm2CjyFh32vJM7n8yAyAF4NA/vbni3Rldas2LGLJtJLEjEqxkO6k2Nquk8OndLebyQPNjiv0KSVLz5/VALevDmXzmtyuU1vaB0biOjFXemOPkcNF8gii0Ne8dZIHsB3YgZjYTT62Rnzk5yXDlEB0a3SGEtGOLaRiEsQGbHTXGEWCXcKsxcVAjDrxEPbvym8UQlAaHgKxLiw9bSIrMl4S2GrL1C8FlJtLl0TyZXzVpJXrGPAdIezlH+A+Ae73QK9QAipAJ0qN61rwUSbq+Y3JUlsEtugihPWOQRxL/FXeHU58EcCCkb4I8HGiQkrIAtRCRoMndC8lqR0Kku4ecpgrSt4rn2sAqChKMKXYfdf8PXV8DGr4XQePeC5h7V0Y2jQ83f3SMQHaiw/cY48mQH76uniTtXvt94omlJEm17cktaUhv4F3chK1ev4NbvtpPpkrDpLmaaviJQcrNM7ct7vlMJkworxgxntohBMf2EuFUxN8/YFbMQ97YbApJJJENK3LEPjxk6dsnL076L+dB3CgCPmD9D2ft39ccPjBTfubQt4G4EJ//GwpUvugMEhLYe11rNB8vegj8fFF0TovvCOW+LzUeDlkVglIgjtXSvjQ9W2DQ9li/V4wEo3vp7w+OF4V7bdBSmCaHlIa61h64z+kl7OV9ZDMB93mkU6xYckrMNrDN0giQXPhSmeu5mm9aBKCmfQRufPKyYo9bYQsSaJGt3y177ugvEuWW41ho0FbZQGDkNgPBVL7L2diGWSshy8tny/c04MAN/MG9zCqOf/pvsIpE8ulL5k8nKKjy6wrPeiyiUAmsfM3xuMd832cQcMX6ISFabalkIYAkUQtv2I8Qaxp1f6xath1I5ZiiSjl3ycLv3Rr5Tj8UkabxifouwHY1kVmBgcLTScQy0HwnoMP8hukcHs+Q+scbYmpLPrxtTmnd8Bg3m0JgRTj4Pmz8B4FXfueTrAdglT8PWGapXXPfzksBXLARVYV0gpq/Y34rsLQo3QIhs81OEWUQDqCpmhFAESMz0Xst6rRuhUhEjl90g5uEGLQN3Icy9Fb69qvw+Z0azDcegnkT2hB4nA9B55aPMv20sAKsScvhvt+Ec2NqpvM4ACKWAl8xvI0s6i9X+/K6OJLPIU3XMKEwTpi5xA0XOtzGx2EviTA+xp1RNbKkcM5boA3jNdzYAU02/c6fpWwCcnlZSyX404XPD30+U/9/VOvZDDapmcIdQfrhRxIx/d2VTUBhKvjv/sAJwWZKZPmg6NsXGjpwd/H2ghlwjEGoLJduVTbozvdHGblA1byzYTWp+uVbIGv0rijUTzRuCO+sYNF8EqXneVuXyGh18qO5IRivugSv5HHSfHSXgIAHtZ4PkoxXphVsNxb5idubsJMuV1aKFtWCIaxsNkyyR7xZn19ydxdDlWLH48Llg3eeN98JBMZCf3HpaZLYkchPBlQsBFSf+pS1a84q9BOHkDvOPyJLOF77j+VMbQQQVJ3WVK6hbKv3iHUQGWaksE+pIRWHFhxs9bNa7wSnPiqRL0hr46xGxgeYPzAEQEi8++8RVkLRWtPQozhGCpqJMyNoDB1dCbgIERwuX5qOR/CT4/V5Y8IQQ4QTHwbH3QM+Tm3tkRzdhncEa1KAFns0k4dWEq2mWHiyu4Tvm+W+MlbE6xHgN91r/omn8t3I57/+9kYMuK6Bznfl3OsnpJOvh3Oy9hVAqVmQW5qQJx6i4gRDepfnFm2UV4EOEU1V+MqieamNGV1J4wndZmcB2Usq7pK+eU/3xg2PE9T1ju/8KNRoTXS8XoluDm3s0taMwTSQtNpUIEAZcAGe8KtqoG7Q8JEk4wynWWju+BVtl/taGkqGHEOjLhf3Ljvg7NY/hUPfaVnBetlZUL+QkiEKGkiKKyuuMO80/IEs6X/km8Ls2inAqdiRp7euMHhwknyCu8MzggBaFw5OG5+d76p/4DooWYuXcFuyUU5ghzm1LYHOPxOBoos8ZENEDPIWEL3m07O6H5mxpvjEZNJjSlnvZRSIWDJD2cr9J7C/+z3cpCXrFuV6NMaM4R+y1hHcVbrUhcfV3ljLbIKqncKhSrGL9UIfCn+piRk8Oco/3Or7wnYAi6Zyw83HyFr9dvzEaGBgcjiTDsKuEwcSeBbBnAe3Dyvdbb/pibfONzaDBVI4ZD5k/JVwqZJvWkXfUMwikouNSndYZmk/sOxRliO4+8YOFWLvjaIjpI2JLeBeI7C72ljqNFUJuRwcxL85LrHdnvKpihiRBL/bjxsJ1njtJ00MJc+7D/dXVonOIQfOSuBreHQ9rZ8Ohf7mguGYbkkEDGHWDyM2lb6VH4vdld1/6wYpmHJRBQ6kcMwQ6z5g/IE7KJkUL5yPfZHwIU6rDYoYzW6wDYvo1XTc+xQSRvcSPM6dKB9uqYsbf2jDe8J4BwK2mn7hBmcv9P24ir9hwvmwxpG2F90+AFYes/Zo7Z2bQYIZ0CC27ff2sBALNgRR6DzfgiQyI5MLeFwLwxbYvyHZVL86UJZlAcyCJ+Ym4/NV12eCIzNucwst/lXeJUAJ3YAkX8wBXyvngjQZdFDeUu8G2fEZ2CSfOYTskZsioRX0oTjkbXbViCtyLrd0X3PnNWorcLdjoo5Xh8rnYmb2TrOIsou0tW1gLhri2UTmpS7nCfeF+d5l7CFvnNJ6g6dAWmc7WUw3Q7BTnCuGmPazCJE3VRGuk0nZ7N5vmEC3lskeL4wnf5QBYqTjprlwNVyc0nxCUFqYLIWVeUolYOqn8pzBNjLeB4gdFhuuO7QJU2NZAlqA/ewmlPMl9+lcZLHR1h8nPiEXSgeWiasxfzpqyIoTh9nAhGk1eB/uXw/6lcHC5mExLEoS0Ozrbqape0abt26uFAFkxw7CrYcqHhgthS8AaCBHdRBV+A86JvpEmCrHzuu8ccceaWfXebD4ikiREHrkHjDZcfkTNS2TOPyvJ0B2AxAnyOk5W1uDTZW7y3EYWDiyHxIwwCggNsEDsQFFk0JKwOSB2UFmLJUUtriZm6PRnH6/4zi0T2EavfZm9S3+s+ril4t38ZMjY2bLdBkHMpfISG7/i3l8cWAbfT4P0reIcP/kJGHNj87khG9SOgFAxx6lDd4tgm4Xv1eMAKNg41z9jKM41ii4ak4JUIWSyC9fayuuMW0w/EVOyznjcdwUAJtQKh2jQOqMJqW6doUg6/dhLIQFc5r2fdD0US95eCn+ZWb+1jWwS8aqlrn19blEw1VqKMwzaDpIM4+8CJNj8PaOkbWUP7U43XNxaI6qm89jPW8ta7gXj5A3za1glH/PUEXysTkKqlHCsMmZoPrGvpEtCDBXdVxQ8+4PgGLE/ERxb4k5Yu1Z51cUMk6TRl/084ruCWb5JADgWzCDtjxf9M14DAwOI7g3dThS3/378MOfphMzD27UatHwqx4xj5Q2co/yHpkvM8F6LDxNyJY1KrdcZrjyxrgkIE87l7UeIYtGaCskUsyjejhsAHUYKd1tnjsh/1NDut8pDVRMzzJJKX/aRiYPpnjtx62as++aT++vDdTq+gR/RNFjyCnw0SXTeC2kHZ7xW/riuVvurBi2YgFAYKvYr+PsJHJQLoxJzGuZMbdA8VI4ZpVyiLCjrkPGu71QyCS17rELM8BSB6hFziqbeP5dliOgufoqyDttXqi5mzNNGMcsrDIvuM3/FFcofDHrsT5JzjVbfzYqmwbI34b0JkLZZOOKXYjYK1ls7lfcrbHoMhZ5CtCrmgpM6T6JbaDeKfcV8vPnjGo8bbAmmwFtAapHRka8pKI0ZpUhKEbb47wDwZI9FLeqBrpafuxXdYFs2iizxyBl9gUNjhoJa2BdX2mnomglz8DZs8d/S75HfySw0TGoaisvnYmfOTjKLM1u8Y20pLX+ETYRkNhPz0IPE3HoNksnkl2O+eHJo2e2r52aTFzkU4gaD5oU1s/3yGlViCxFiqazd/nMXbctomnCRUj0VJ2vAluS8sovjifI6xilb8OoKt3tvohgbdoqRJbHskIDIICv94utYmad6ReuK3EQoLGmfEhglWifFDRTfmfghEDdItFUKbicSya58IfgpTK+30HZst0hmntqbiCBrhfsjg628dVpEhftunZeDFjcIJj0JshkSFsPCp8tahvsFk1U4UDnai003e7hwZw1tLxLnR2N1WupG+GEarPpAfEfbDYXzZsGwK2vfrtHA75TFjIceRDKbITheJBGL6t/SavbZ4pz7Qj2Rg1qUuC5s/v4Iv9UAAkKFY3SR0TLDL7gL2b5lPUlFEj5MxJHFDaZfAHjJdx7r9B4EU1R2GQujkIhAM70GjxGJ6JaI2XZIi6VsxnawVRsz3j0trIKDbdfNr6Jv+7nq48om8Z5z9om5Skt1EtFUMUZd95/ooLHQfLDiXZg3U7TkjeoF534Ancc398gMqCJmVIWjnZgH1dK99qcLIvlKnQBAYOpqfPkNFMVKsmirZrjXNg4+t1hvWAJEURkV1xnHyRs5RtmKR1e4zXsTTmw4KCyLGXVeZ2iqcIM9tGCvqp/CNJEgb4Q1Y3XrjKhgK++fGswBPYYrPfeRrwcQlLVZCDrqU6RkDRaFGlm7Wl7BUFGmWLPZQpp7JAZHI9G9oZeYl82O/hIT4vw66aXFh7XeM2j5rNyXTUpeaeGlzjPm9+gkp3NQi+Je7zTCDimOrjZm+NxC9BoUA+2Hin2X+rrVVoc1SBQORvYQRUPFubX6tepjhoUPJgfymO8K3vEJh6mYZY/DYkNg21Ko1TzXoOUiyTD4YtGpLHkdbPuZT64ZWfbwyS8vbsbBGdSXQ2NGAC6eMn0EwCx1Mhv07kSQW/bcWq8zdA3yU8V8PaY/xA8Te/gla5taY3OIfaZ2w4QgNy+pzuvP6mJGdLCF9062sl7vzgzvtQCErnkdNn5TtzEaNJzCDPj8PNH9UPNBn7PgnHeRAoKJue5cYm640IgZrZkB5wknalcuP/ZZWHb3lLeXNuOgDOpLxXWGoLuUyEOmTwH4yHcKa/WeQBUxQ/WKOX94d5HHbQ5kWaw9wruIPbBK+0rVxYx/7BPZ1e5cAB43z+Z8ZRFvLdrdVKM2qExeEnx6NvxxP6huiBuMdN57xNx4ETE3XGTEjGagMdZ5j5/Vr+z2fd8cIMQSQr4n/7DnyZLMdQOvQ5EUVqauZFXqqurHKUkEW4JJKkyiyGsUBjY2lWOGNfYnZFMBqjsad/opQHmuN85hY2SXVmJaVMLk/nG8fdlQYh2HioJNRDKG4yKvRtdlzI71WGPm8ubCbdUex+DIuFU3u3J2ke5MJ8oe1SqEtQD+UZG2ASSzmfBLL4WUTVCQ5JdjOqwyw+LMrEkRycoVSR5OHjkN5twEO+fBoAuFI1xjEBQlNs2zEyCqR+O8RluhqCTpbI847KHS9hYR5HOd6VcAXvadxya9KwCdqChkuO7YLii1Pfd9rhKHJUlUe0d0FxtMlmDR0qImNA08hUJAU5Am3F5VrxDL1bHl6NhukYzqEsmW5DyynR7C7Rb6xTtQZHh1kpPb/sgFoMCjM/LDNBZePozgiY/Bnw/Bnr9F9flx94oNWX8iK0AdN+jaEq58WPkubBffO2yhMOYm6H7S0SkybmGUxYxDCe8iBLHe4noJ8aLsCj3CTezKhhd85/Oq5S1Y/wX0PqNxRBmSLBIoOQcgKPbI1x2D6tE0yNpDQX4OeQRhwsdd5u8IkDws1/rwjnomAO0RQuZgnJhQOWvSKSghsc058iNT2mJJsULGdsa2C2TUlSOqjBnPnljEfX9fBsBU0+9I/76IWwNrvzOqOK4FAiOF26DJKs6flkZBivgJbuF/o6IM4SafulH8v9+5MPr6o9PpvYVSZcyojM0hCjVy9wmR6xHoFGqiKKAdy7x9GaNsJW3dr8Qdd03DBhoQKjYzC1IhrJHWKEcr+cki4eBoV3ZX6TojlMKydcbrvnPYrHdFQqMdFQt2arXO8BSKOaSuizVBYJT4uyrWEgdrCdBFssHnFgLc4hzh8KH7xO9Yg0URhB+oaZ3x6HGFPPpPJ6713M2nlmew7v8P9Z8XUCbcV/e5blCU+IxtCSKp0hLmypoqXGvNVv+vkwwMasuo6yHhX2x5u5lq/oN3vacBsCejkO7RhqNya+LQVnqXKX9xmrISj65ws/cWnNjoycEKzz8sZnidUJQtWnVH9mzcQl2TBaJ6i3iSsUOIsYKjj3gtrClmzCgo4Jn/LsKFmdtNP8CCx1G9xSgnPNAyrvlHMRI+wjukiIIS8xHmuwYtk9BO0GMSbJsDC55k/A3l4iiPqpGcW0x8aAsvNjWowKEx407Td3SQM0jUI3nRdz5WPMRSsePDEdcZmk+sEe1REN1L5DEagiSJgmtbCGTtEUXNNkeduj3UFDNuzc3ntZXj6eU7yPWmX/D+cCOKoxNyp1ENG7dB7dj7jzAKKUwDUwCc+LDojFWchRQWT/i5k0VxpCGUar3IJhh7M/x+H10Svqa3NIjtekfS8t3kFHkICzT2I1sTlVt2W/HwmvlNAiQPS9W+zFeHoB0yjy+LGbomznNHR7Gv35xzclkRRhOqR+T6g+MqFH9UGzOk4Sz/zsfonLk8Y3qf21Zaudur8fx5Aw9z2TRoRLbOgbm3CiMgxQLDroJBFyNJEuFnHF9ifGLEjKamVvmMOnLB8A48PGcLADvTioi0dWdf/g40i3aYsK5TSCdO73Y6c3bPYdbmWfSL6IfdXHXOJMgSRGphKimFKXQP6+7XMRtU5NCYYQragjlkE7ou40q+AHSZQ309HzmjL0rldhmtgMn945jYN5aV+7JJL3ARHSxEwm71WC772sNO3ydYwpfz+c4AvHOu5smzhjT3kFsdHtXDrpxdpDnTiLZHo9S1YLMZMbI8VeEtFhVOh7r6OLPExKyOvHdauSJ/aaJHVOZ2HCsmnqs/9uOgKyGbwB4GOXvFBqdB1fg8kLVXfF4m62EPl7a3uN70M4GSm3Vad95Rz8CCh77sRZFEBYYjwMTMU3sztlvkkV9T9ZQk2PMgpL1oh9RhFIR1FptTtRG4ybLYgHK0F06mHUaKtvQ+V0nFd91aySsyDGzvYELPKAa2d5RtqJ3Vy07CrfHMPEYI+zKdGsM/SCUpfKTYGJFkIRRf8sphrcMMGkDCf/DtVeXC2t6nwwWfQI+JRuKoJWOPEOdkA9oSf1biXjtXG0uarYtoq7P+c3+N8HACQqE4u0GOuwYIAWbeQQLDRIX2JcoCessHydUDucNzI9aymKETgJsYm8qVZ0/m+BGDm3fctUWWxSZZbH/wFqO4c6qMGRf2CyTh1nbkDL6+zMHW+t+L5G+oxsHWHFCSdN8uYldLwuMUSR6z3W8is0YhcRV8f60Q1prtcNKjcMythrC2teKIF90BajmP++EQ99qgvb83vJuAJAvHuZwE8NZtLmlQA95i4QhsC64gLCpdZ0w3/YJDcrJF68Tb6pkE4qQvCWXdMWq1znAXQO5BsbYJ7SzWBp3GinVCeFch6g2KFiLUoGgIiRfX9biB0OkY6DgKovuJ719hukiQ+MnNtrp1xlWDgki4NZ4+A4Zzs/cWVF1C2TWP4qXv1v1FZEXMw7L3iPG3BJxZ4qeh4gMDg4ZgC4GR0wC4z/ojMSVimiW7jD2i1kZpK71+UkKZi9SzvotJ0GPoxYGaY4bPJdpvR/WC6D5N0wFHksr3q0qLd2oRV6qLGdcPCybh1nbs73Y5z3ovEs/993lcvz9o7EU1F5oGG76C14fBgidgxTtCTG3Q+pAk4V5rCYLMHUibvuGP248te/i/3UbMaG2UxowB0l6uUX4H4EHvNVjx0J3Esq3lWq0zVK8wcHG0h/hB/p3bmgMguq9wwnUX1drtvJTqYsado0NIuDWeNe2vZL46DDNeCmZfQFF6gv/GbnA4qg8WPAmfnCXWk1G94eIvIKonePLFmlQxxFFthg6joONoJF3ju04/AmI+tmxvVvOOy6DOVG7ZfYvpR/rK+8nWg/jMezxuSeTNK8QMXS8puogU53hLMG1RzGKtExQtxlap1XyVMUOSGH3eHdD7NBRJ52XzW+Sum8NtX62n2OPHjq0GVeMuhDk3wzdXCGFtWBc483UYfImRB2+j2MwKP9w4tuz/CWkKDquDXHdulc+f0mMKsfZYsl3ZfL+r5i6vIdYQUp2pFHgKanyeQcMoixlyMdbYnwDwZB2L5o7gUMO8O07qweT+zeRo7gcUWWJMtwjOGtyOMd0iUGQJu9nOpxfewJV9LwfAGrmQr3d8yb3fr8XlNWJGbSkV1qYWpRJlj2pVwlowxLVl6KpK0YqVFK3bhK7YhctPTD+RcIzuIzaYirKEgKYO7Soj7Arvny4EtnN3FKPpOoyYKh7cuxAydzXG2xFYggBdvIbR3rVq8pOE62sVrrUA/eIdTLLvZIyyDa+uMMN7LXZcdCMZuWwjyszHV486srBW14XQuShTJLQ7jBTfr8DIhrXik0qcb6P7QPuRQqTrzBWbGA0VWZQwbWi5G65bhWNmpbMxcAwcfz8gwba5sOwNI6nRUFx5YhPqzweE4DG0I5z5Ghx7t9FOtoVRFjNWrERXS84zSRJ/M3OAELnUg5gghVdODkVH5jmfSBqy5QdxPjcGsklsPOQd8Nv14qjD44Ss3WC20bdDFCPtaUxR/gVghncaRVjpTAqyBGa8dLS5eO76KUwYOayZB15HSr/fsQPEhrmz+s3S20eH8ITvMmb5JgEQtPwlUtdUI7C1BovilvStLUcQpeuQvU9ck1uqKEpTYfUs+O1eMc6I7nDue9B1QnOPzKAKqowZVWELFZvAzpxaHbdDiIkx404iT7cT7M2A5LUNH6wtVHynClIafiwDQX6y6DZhrdhqtV+8g8n2HYxXNuPTZe71TicIJ51IOyThfYR1hs8NuYniuhzVRyS4YvuJ71EVhYNVopjAHg4RXaHjGNGi1R4pCm+qaKnnb+4fF8J8bTgzfEIAGLDlK3JWfFn3A1nsQmSbuVMUJzUnui7EzpLUsgs0DI4Oep8Okb2QfU6+6PAjAHM3JDfzoAzqysgu4XQL0XnD/CpWycd8dRi/qCNpR0bNMUP1iNbMEd3EfLGpN60DwiB+MIR2EEnuBu5NPnm8g7fVM3ncK5IZtpVvUDTnLmMvqqnZtxjeOw5+nI6em0RRUXuKetyHHtq1uUdmUF+CY0W8AFj4NL0iLdx2ouiEZ8SM1sfILuG0DzHxjPl9FElnjjqWnVo74sms/ToDhLC2IFW4G0f3rVeXriNSWswdN0CsO/y4L/TsxHBu897ENq0jDi2XA2+cQVa2IfxrFPKSYPYZsPh5QIfBl8HZ74g9U1kR1xhJRlc1ijbsoGjjzpr3RgxaB2NvBcVMUOoKXuy7D4C5642Y0doY2SWcuJL2132lBK5XxB7+V74JHJBEN7fDYkZRhtAfxDRSbKgv5gBROG6PEIUhtVkjSBKMvwtnpxMwSypvml8la9Mf9Hl4HnnF/ik6N6iCpDXw7rGw7lNAgj5nwNlviYLQEnRVo2jjDoo27jBiRjNQ63xGHRnaMYyLR3YE4JeNabQPbo/b50atIj9tUSxc1f8qAObtm0diQWK1x7Wb7bh9blKKjJxGYzKySzixIVas0b8imwvQ3JF4c4eAXp6HiHPYuPmEttlVPdAcyPTB0zmp3TkA2GJ+58ddc+j90DyK3I2bQ2kLeFUvu3J2kVKUQpQ9ClMrzJsY4toSdLebA1deyYE7n0CPGVzi8tNFCEkiugnRYoeRov1NfopoqVZLjuskLijZLo092T5xvG4nigdXf9gI7+YQAqPERDd7r7HZXBl3gRDP2ByHJRhUDTYm5rF0ewJTpbkAvKuezk69PR1IQ5E0JERj1ZuO74ZZOUIVlc8NeYmiBXv8UNEKxx7u/+orW4gQhbcfLhL4+cl1+q5WhyxJfD2logD5zK8zcXc+kYMDbhZ3bP4ebeX7xvesviT8K9xqd/8lXM0GXQznvg+xA5t7ZAZVUBYzrrwS3X1IgtAWIlrhuPLqfS6c0EVsZnxf2B9P9CCxmd2YTucBYUL0X4NY0qAadB2y96IW57IxS+G/HUncbPoBRdL5UT2G+dow2pOBIukoaERL+Zxz4lgsUd1bb/Wto50Q2EK1zvgmWeLDMyJ4zHcFs3yTkCWd6NUvoW77nY2JeSzamcHGxDzU0gLygDBAh7QtDXJ+9huF6ZB7QBS/tMS/kzMbfrsH1s4GdLH5dNabwknGoEVSbcyojCRBSDtAr7Wg8bhuDn5UxwHg3fpLwwcrScJhNWe/cFw1aBjuQsg5INYbJdeT0nXGsm17uQaxznhbPZMdenvakYks6UdeZ+h6iTNqtlizdhgBUT2E83BDUEwi8Rk/FNoNF26D+anCQaKRsJokXpoYyrfqBJ4pcSMM2/Au6vZ5VceMmrBHCserzN3NWzTkzIaidAgIP/JzDQwaG0kWxZqSTLeMv5kgr2ftgVw8vtqcVAbNjarpLNuTxS8bk3kh6DO6yGkk6RE85L2KSPKRJaqPGZoKBWlCFBXRo2FF3Q3BHCBcCcO7CaFvHTstHUqgRebR40L4SD2FB7zXiPvWf4g29zaW7c5gzvoklu3JQtWMfalGIWMHfHGhEFClbgRrCPr4+zjws8aBJz5BdzV8/9GgGRl4vlib5x2ANR9zQu9oAP7dlWmcU62EQ2PG/eGL6CfvJ0cP4iXvFMKlAqSaYkZlNF+5sDamb+0L9+qLoz3EDQbZ4jeBbXiAwvWjo5jquZsM3UEf+QCbXzufZbvSjXjhT3b9Be+MgwNLwRIMZ70Fw6+GvIMi/2ULLXuq7vFw4L4XOTDzlZr3RgxaByHx0P88AE5PfRMbbuZtSUU38oOtgkNjxjHdIlFQedb8HiZJY43WnT+14UhIh8cMV55YY0b3FXtdLQ1rkMiR2xwlDra1EdjK2Cfez7ag0VglH++bX2K4tJ3z3l7Ksj1ZRszwJ5oK/74IH54suk/ZI0SX3HF3HibUFjHjJQ7MeNmIGc1ArfMZ9aB0nfHD2iQibBFEBkSS467abGRw9GCGxwxH1VVmbZ5VY4wJtYWSWpRKnjvPr+M1qBgzenRKwxK2GgBX+qnoqgOQy9YZj5zRF0VugXlVPxFiCeGRcXcSwwkAWGN/xBS8gatmrTBiRg20BWEtNLK4Njs7m0svvZSQkBBCQ0OZOnUqhYWFNf7OhAkTkCSpws/111/fmMM8HGvw4ffJshBZxA+GyF7CUcpT83spxXLIRsXEzzPILlbFAlOS4cBySN3kp4FXgVQy7ux9jed82BrRdchOEMLTSo6gS/dkMnX2Ku7/cRMF/7yF3ZfLXi2W133n0IUUlJJ2exFB1iO3TgKRjC7KFC1Z2w+HkLjGTWpIkmj52m4oRPaE4jy/iOZGtbPywenhDIgub99z3qxNTF8Vw5veMwCQN3zBgb/eafBrHVW48uDvJ+DPh6A4R2xanvUmjJre+BuXLYxWGzMq42gnNhPrOYEPsZZeHyTOPzhF3Nz1hyiSaAwUs7hu5B40xPF1pTCdNRs3cMOPB7n/p82kLnyXcHcSGbqDx7xX0IWUshatPQOLuOrkkYwbfUzTO0b5m5A4iBkAklJt0uPELjbeOTWcDwOu5mPfyUIs9u9zzP9pNi/8sYP7f9zE1NmrWLqnRKAbGAW+YiGwdTXj4tdbDFm7RJxuSZX3pSSvg++vFQ6lJhsc/wCMv8uIF601XlSFPUII8mp5HsQEyXytHg+Avm8JxQV+EKhbHcJpNd+o9G4w+cngKShbbxy6ziha/CZ2NY9dWruSdUZymZ6/xnWG5hPHlS0QP0QkDapavzYEWYbgGIgfJjptaJS09Pb493VKOKd3AK9PDuNz5Uze950q7lz8HN//9F3VMaM6JAmCo0UiN/dAo4z1iOi6KKrUtdZ9bdbUOrfmbam06ZhRWyJ7QN+zAHjCNAsbbno++LshsG3hzNucwrhnF3Dx+8v585t3GJL9O6oucbvnJhwU1BwzSlu1BsdBdO/mb9WqmMU4wruWCGzrn5y7YmAgr04K5XP1JO7xXoemS8jrZpP08dXc8dVaLn5/OeOeXcC8zcY8xm8UZsAvd8JbY2DnPLEOHDENrv5dmFGUorX+a8pRHTPsEdD3bHF78fN0Ci7fHxr99N/4alXpZNBcHBoznvnqb45LEYYu//NdQhDlwvda5TN0TRTYhbQTnfIUc/XP9SdBUWLt4UeB7Y3Dg7jn5O5c57kTt27mONawf/Y0bvtqnREvGorqEzmNz6eIDnxxg+DyH4VZUUGq2DtsiftqfuSojhmlDLsS7BFYnSncYBKOp6e9tgTNEJS0aA6NGbd9tZ7v1iZyrfIbA+QECnUbH3hPwYfp8Jjhc4tOQVG9xDW7pWILEeYgliAoTK3d78gmuk55nNSwYdglN7Msz2PL2Fj2GRkxww/kJcLsM+Hvx8XeZvuRcObr0O34lmlw4meMmFFOpwh72e1rPl5LfGA8qqbiq8Zs5Ip+V2CWzWzJ2sKKlBXVHtdmsuFTfYZ7rZ+pEDO+Xsla53sAeLJHoxbHgSYMw2IdNt6+bCiT+8c153CbhFBbKLPOvp/ugccgSTq2dl+zNmM5F7+/xIgZVeBVvezM2UlyUXKrFtZCI4trL730UrZs2cL8+fP55ZdfWLx4Mdddd90Rf2/atGmkpKSU/Tz33HONOcy6oZghqqeoynLl17rlZGRA+Uc99P00tJB20OsUcceqDxpX0GQOEOPO3CVasRgIN9+8g0J4fAhL92Ty9G/bySx001dK4FRlJQD3+67Fipdpo2O4e1Iv/nfOAD68csQRNqJKkhmaKjYXGqt9UnWYrGKREz8EJJNIwjfQwemkrjbmXhiJqeTrvKk4kq105UP1VN73ie9zx31fk7Dgg4aO/uhgX4lb7Z6/hRB+8CWirXd0n+YeWbPQZmKGxQ5hncBV0OBr+wa9O7+pI8XG9spGPK8CwsXGdUtwDW0t+NwsX72S1xfuI7lIp7uUxBRlCQAPlrgoTR0dy92TevHcqe155qIxjBl9TOsW2hxKcIzYpKoh6TG5ewD/XhXDo74rme2biIzOHeYfGSbtBCCr0M3Tv20vF0sFxQpBX+omMcdqajQNsvaIAqrAIxTONDW6Bus+g1/vEkmLsM5w7rvQY2Jzj6xZaDPxoipkRbj2eJy1iiGyJLFN78QGrSsWSeWFT75v+BgkSWxE5+6v9VrHoArcBWK9ERAKVFxn9JYOMElZA8BM71SCcHLN6PgjrzN8LiF6Do4ThXQhcY27Aa2YxJymw3BRPFSY0SjXZ0mSOKNnABumx/E/3yX8oI5DQWOG+WtiEXOTw2JGtWO2iO9v1u5qHdYbleIcKEgRLk2tlbxE4ZC+8MlGE1Q3JW06ZtSFkdPAHkEHOYNbTT8CMPaZv5t5UAbVMW9zCjd8tpaUPBdxZPE/s1gLvqmexX49iqtGt685ZhRlCLem6N4tZ/0hK2KPKqyLWD/U0qX/sMNIEmf1srP3lji+VSdwu/cmfLrMeaZ/edX8BiZ8pOa5uOGztUYio6F4i4Wz1GtDROc1XYVep8G1f8GQS8VcUW1bLlJHfczodw4ExUBRBo6N5d32MgrcnPnGf804MIOaODRmADxg/oxAyc1KrRcr1Z5cNrpT3fIZ+SmiADq6D5gsTfQuSgiMgNj+IJv8Mpc3yRLn9LZz5+S+3Oa9CU2XuMi0iDtMYt1sxIt6UpAKn54N/74g/j98Kpz7gTCZ8TqFo2krTprXlqM+ZoAo/B99AwDXKz/TQUpja0o+V3+8qpkHZlAdlWMGQGcphTtM3wGwO+4MLpx07OExQ9fEHD60E4S0gu5pAaEinpgDxTWrFlitFmLP+R+5jj4ES8V8YnmGXlJ54bYRMxrA5h/g7bGwf4m4boyYBic/IeLFUYIRM8o5VFz7765Mnv8tWbjXuqp2r422R3NmtzMB+GTrJ7hq6IbjsDlId6Yb7rV+onLMsET+hWzJRvM68OSM5LYJw3j1osF8OW00S+474agQ1pbSwRHDO6c8RpfAgUiSRkD7z1ACjJhRmVJhbWt3rC2l0cS127ZtY968eXzwwQeMGjWKcePG8frrr/PVV1+RnJxc4+/a7XZiY2PLfkJCQmp8fpMjSaL9ZmQv4eZSC8eFvy6PrvD/ZYkeGHolyGZI2QBJqxtpsCXYI8TiNmtPm3ASaBA+j/gcZKVCkkHV4L3F+9ABEz5uNs0B4GvfBJZrfYkjkz+2pDG+exQD2ztQajp7NFWIWa3Boq1qaIfmacEnSSLp3m6IEArlp4gW8w1A0yWCpIoTl2wcfO2bwGyfEPl03v0Z2kY/iDvaKq5cUZ03v8StNqyzaJk08rqWk/hqYtpczAiOE8nMejpwrpwaU3b7Bd8F6JIs2mulbvTXCCtisgJaidOaUVleG9Ts/fz03wayCMaEj9tNP6BIGj+ro/lDG0Es2SJmdAqkb7QNJbpXy2yV1BCCoiBugBAyVeOOr+kSdtnHI76r+MQ3EVnSecT8KeOljZR+095bvE+0+5Ykce648iB1sxCmNSX5SZCTIN6X1Extc6vClQvzZpQUY2nQcxKc/bbYzDwKaXPxoioCI4UDei27ZPxzRXSZe+1FyiJ2Z/lBDGcNKRGH1vyZGtRAXqIQSVuDK6wzZNQyR5dvfMexWu9NDDlHXmd4CqEoSzhQxg0Ure6aCmswxA4ULrmqp6S1nv/XlLouoQD3eq9joTqIAMnDi5Z3iCfj8JhxpPHqKmRsb1qBuK4Lh1/NJ5IFrQ1dg03fwXdTIX2r+Dunb23uUTWIoyJm1BazHcbeCsA05Vd6SgfJLPSQlFvczAMzqIyq6Tz281Z0QELjRfPbOCQn67VuvOY7l0jya44ZnkJAgqje/nc2byiKSQhsQ9o1OJbouigumauN5SbvrXh0hTOU5bxlfhUzYt/rsZ+3Gq346oOmwYav4PXhYu/KUyAK9y//CU5+UhT75SWJOWtAKy4mqYQRMxAFSv1FByVp6ev8dG3/soe2puSTVdi2xNRtgUNjBsB4eSOnKyvw6TIPe68mCFft8xkgijOsIRDTV5gHNAdBURBT8t3zgwmAqsGHSxKYp43kId/VANxm+oHLlPlln5sRL+rAvsXwznhI+Fe4Qp7znhDXZu0S+4NB0UeFA6ERMw6h24kQOwCr5OUh02cA/LMzgwJXw/KQBv6ncswAsd541vw+NsnLFq0jb2YMrjpmFGWKIuKI7s2T664P9nBhDmKy1doRXZWtzCi6hLVad8KkQj6zPE0XSQijjJhRD1z58NON8N3VIucT0R1OeRYGX3xU5cKNmFERq0nhrUuHlv3/2zVJRNhiQQdPNUX+Z3U/i6iAKLJd2fy0+6dqj20z2VA1lZSiFHQj190gKscM2ZKOJUIYTLlSz0L3hvHN6mROHxjPmG4RKHLbn/9VJjIgmtQ9Z+Er6I0k+wjo8DFKwD7AiBkgzue2JKyFRhTXLlu2jNDQUIYPH15230knnYQsy6xYUb1lN8Dnn39OZGQk/fv3Z+bMmTid1butut1u8vPzK/w0CZIk2pmFdRabDkdwBA21ybwxOazs//f+lctuT1hZa75Gd6+VJFFxnHdAiEeOZvKTShYCERXu3pKcR2bJJuF58mI6yhlk6CH8z3cJ8WRgllQyC91sST6CWK60VWtQDMQNbhmuRTYHxA4qb8tTQ1XPkdiSnIdNPTxJvZsOzPadzJe+CQDIy1+HbT/X+3XaLPsWw7dXw54FJW61l5W41fZu7pE1K20uZpht4nxz18+9NjpQ4ekThBBzrx7PL7IQTbHivcaLFQFhok2OK7dxjt+WcGazc/sm9hZZ0ZG5QPmHznIamXoIj3ivogNpmCSV7MJidu1LEO5MwW20Yi8wUgiuFFuVVeBbkvMI0goAiYd9V/Gp7yRkSece87ccI21Gh4qxVZIgOBZc2ZDShA62zmzI3AmWwJa1sZO0Fr67Fg6uFEmK4+6DCTPbfGu9mmiqeAHNuM4wB0BIiZNzLegUaqL7iEk4dSs95CTe+m0FB/Pr5wZXhiQJl4e8A00vdG8LuPKEuNYu1n+HrjNOlVfSTU4hVw/kGd/FdCIFRdJqXme4C4QjfnRfUeDZVK1ZD0VWRIFpu2FgdYj1TgOL9iqzJTkPB/n4MHGj9zbWaD1wSE4et3xCiF54eMyoicBo4USeuVO0LG0KinPEWjMw4sjPbWnkJ8Evd8CyN4QLYdxgOO1l8W8r5qiIGXWh63HQfgRmSeVJ80dIaNz0+VpS8gyBbUti5b7sMleQi5SFjFW24tSt3Oa9iQ6kI0t69ddCTRXXvvCuLbdVq8kiRFv2yFq7SFXFluQ8whDn2R/aSKZ7Rcvvk5U1vGd+EQseUvJcrNxndGepE/sWw/sT4MfpkJ8Ijg5w7vtw8ddgCoC0zWJd4mgn/m1DGDGjhN6nCwG8K5fBSV9zyaiOZQ/d8uU60gvqv6ds4H8OjRkWvDxqmg3AbHUSkq4iSXWYP7vyxF51TF8htG5OgmNKCvu89TYuKKV0LeagkM/Vk3jZKwTkj5s+5lR5OToY8aI2aBr88zx8chYUpUN0P7jqVzHnyN4jcmDN/b1pQoyYcQiSBOPuAEnhZGUNx8kbALj9q/VGUUYL49CYUcqlyt+Mkrfj0s185J1EilM/PGZ4naIoLrKnyHu1JkoFtkr13fcOZUtyHolFEjd7bmGL1okoKY/PLU/RXsoAMGJGXdi7SLjVrv8ckIQe5pTnRKfflmRs0gQYMeNwTh0Qx9hu5funT/+ciE0OJ7ea/LRFsXBlvysB+GXvL6QUVu8GGmINId2ZTr6nZb731kLFmKFjjZ2LJGn4CvqguuJADzjqr4cr92WTnhNIcdKF+Aq7IykeAjrOQrHvBfSjOma4VXebE9ZCI4prU1NTiY6u6NZqMpkIDw8nNbX6zdNLLrmEzz77jIULFzJz5kw+/fRTLrvssmqf//TTT+NwOMp+OnTo4Lf3cERkWTgHBUYJge0ROL1nAK9OCgUgqUDlpM8yRBstkw0ydkDCksYdr8kq3Eoydzd4U6LV4sqH7H1ioS8rFR7KdopqmPZSOheY/gHgMe+V5BFEKAWHPa9KVK9whw3tICbsTekodSTMNrEpFdlDuF55ap6AVUe200M4+bQjnR4cqPDYXtrxge8UvveNE3f8+xLsnNfQkbcNXLnw12Mw/+Fyt9qz34KR17a5hER9aJMxo4HutRf3D+TJ44XA9omicynWLSKRtX+pP0dZjjlAXMMMl8KaUX2QvZe8omKc2GgvpXO+shiAh71XkUsQIYgChCjySNcdYqO5Lbs2BEYIF0WzXbTCPkQAnu30EEFeScw4yMO+q/jcdyKypHOv+WuOkTaVPa8MSRbnT3G2cPcvrrodjN/wFAlnPtVd1r692VG9sOJd+PUucGYKsf4570CvU5p7ZM1OU8ULaOZ1RmA0INdavHjNyGgORIj51+iCvxj/ce0cGWrEGiw20PMSG36so43cRNHdxBIIlF/jwijgMpNow/6c7yIKCCCIclFblesMVz64C0WSO7xL8zuE2MNFVwxHSdGet35riqrIdnqIJpd2pNOedK7x3MNOrR2xUg5PWGZj14vLnndEJEkk5XMPQvbexnfm13XRHltXW5drra6J9nzfTRUx12QTidHJT7dcYV4dOGpiRl0YfyeYrIyUd3C+8g/rD+Zy2muNvBdlUCdKhWsxZDPT9AUgupmk66HYKU+GV3ktLMoQe5ShHQ9/rCVhDhBxzRxY77bf2U4PMWSXrTMWakOY6r2bYt3CBGUjH5mfIwCXIQSsLamb4LPzYPYZIh5YQ+CkR+G6f4SAKmktuHNF69aW5ojsJ4yYUYLFXuZey7I3eGpye64YI7qmLN2TxcXvLW/GwRlU5tBr3FTld7rJKaTroXzsOxlFqrg3UyPeYrHmiOoliqhbAiHxIlZ4ixtU8Fn63mPJoj3p/KqOKCv8ftn8FmPkLQBGvKiJoiz4/DxY+KRYPwy+DC7+SqxVi9JE58TWtAbyA0bMqER4V+hzBgAvBH6GBS9/b0/nuk/XNPPADA6l8nUunkxmmL4E4Cd1LDsR360KMUPXxDUgrItwpm6NBEaKfL1sPuLao/S9B+HkPs+1HNSiiJey+dz8FPGU/64RM2rAXShyGp+cBXkHISgWjr8fRt/YMszImgEjZlTNp1NHcUr/WAB+2ZjCm38UIksybrXqwoxhMcMYFDUIn+bjs22fVXtcm8mGT/WRWlT/Yl6Ditc5U/BmTIG70TUTrswJ4Aup8nlHG2XvXQ+gOHkKvuL2SLKHgA6zUOx7KPWvPdo+I5fPxY7sHaQWpbYpYS3UQ1w7Y8YMJEmq8Wf79u31HtB1113HpEmTGDBgAJdeeimffPIJP/74I3v27Kny+TNnziQvL6/s5+DBg/V+7XphsopqLdlUq0X+GT0rOo0Vmxww4Dzxn9UfHtEBt8EEhILPCZm7/O401OLRNCGs9TqrrKINt1uEc4tpLhZJZYE6mF+00fTkAIc6mYfbqxFCql6RYA7tJDaeW2IFn2ISbleRvYRYqZbthg8l3G5BlnTCpEKsko9e7K/w+B468KTvUtI7nQbo8M9zsPtvP72BVkqpW+3ehRXdaqPavlvtUR0zGuheC3BRP9GCLZ0wPlIniztXvd94sSIgFApTms4ttDVSkAz5KdjDYgGdG0y/YJZU/lKH8Js2ip4cRJLEBowHEwFxfYQ7U1untArcHAgFaWXf+XC7BeWQmNGDRB70Xc0XvhNQJJ17zd8wWNpDaEAlF0ZJFokUdyEkb6h166Y643VB2lYozhUbPS2BvESYezNs+BLQofcZcM67YqO6DdPS4gU08zojIFQ4itfSvRag65izAThdWU4QTv+0mwkIE9/Jo7Uwrz4U54pYEVDetaR0/XCl6U+CJBfrta58pR5Pd5Iq1F4cts5wF4gCgJh+Yo3RUgo1zAFiTFG9xfv10/dDxAytLGZEk8MVnhkk6pF0k1N4wPIlhbqNsMoxozoUsygAydrV+CLxogyxFrS3Itfa/BT45U5Y+probBI/BM6bJdxEWsp3rRqMmNEAguNg8KUAzDR9STj5ZBd50I7SFmUtkehgG6DzhHkWIVIx67VufKxOogspNccMjxOQREF1a1h/2BwQ3Ud0f6pH8Xe43YKpLGZ46U4iS7QBXOW5j0LdxjHKVmZbniHW1kTu5a2VnP3ww3TR4nv3fLG/PWIa3LwaBpwvCnyz90CAQ3ToqmRU0BowYkY96HVqiXttHtKKd7lrYq+yh/ZkFBkxowUhYgaEkc8NpjkAPO29mFAqriOrzWeAuA4XZUJEN+FW3ZJwdBBrjtJ1UT0ofe9mSSVUKsQm+Zjtm8iv6kisko/3zC/RT9rH6oRGLuxurRxYAe+Ohz1/CwfzM9+A4+6BzB3gK4bgeBE72ghGzGgAI6eBzUGUN4lrlN8BWLM/x2jL3YIojRkCnf+ZPyRIcrFLi+cHdRx6iWSkQswoyhQdJ8I6N+lY/U5gpNjHgho7N5a+d4ukgiTxoPdqUrQwOsnp/GR9mH6SaPe94WD1xziq2b8U3jlGdGsG6DFJFG93P7FldQv0E0bMaBiKLPHAaX3K/v/vrhyi7dHkVbPPLEkSV/S7AlmSWZO2hs2Zm6s9tsPmIM2ZRp7byGnUl7KYIXmwxvwCgCfrWPBGAMrhzzsKqfDe1TBcSRfic3ZEkr0EdPgYJXA3AFuTjx7NhdPrZHv2djKcGUTbo9uUsBagzu/mrrvu4qqrrqrxOV27diU2Npb09IpCCJ/PR3Z2NrGxtRcvjBo1CoDdu3fTrVu3wx63Wq1YrQ0PyJLJRPQ9d5fdrhP2cAjvJjYczfYaNxplSeLU7jZ+2y0U6pf/lM13Z14IW36CnAQhQux5cj3fRS0JihZtPG2hENm9cV+rJVGYKqqkqqm+7hfvYErAegZoCRTpVh70XkMnUrFIYiNeAiKCrPSLdxz+y5rvEGFtCxdSybL4u8sKZGwXAqg6uE70i3cQGWQlq9CNjtiYsuvFOCkXjufgYErSZfzdAyy7foWFTwl31i7jG+ENtWBcefDfq7Bngfh/WGeYMOOoENWWctTHjOBYcW1354vkYR0xyRIj4i2sSvbwru8MrrQsICgnAXb92TgOlpZAIbwvSD2qWnnVGnchZO0BaxD92odypn0zg9S9FOsWHvVdRReSMUkqJnyESE4yA3sytHfbFkRWwB4uHGxTN4vvUHDsYTHDIvmw6D4e8F0D6FxiWsjj5o95Z4FC70svxWY6RDEgSRASKwRLKRuEg4qjg/8EPz43pG0RYw2JbX4hka7Drj9E3PAWi9g8/m7RwvkooKXFC2jmdYasCIF5yoZai/Us8f3ZRzu6SEmcoSzjiX+jePS4useeigcNFOLJ3IMQE9L850lrIC9ROGEfsuboF+/gGHsiJ6nr0HSJB73X0IVkFEkDqllneJ2i2CWmnyjWaWmfvWISAi6zDdK3gzOrwcLSyjHDKnnZpzu4wjODby2PMUjey8uWd3h79cO8Fu/AotTiMzHbQfWIdY/J1jhurKoPcvaJv1FrSBboGmz7GZa/LUS1JhuMml4iqm0drfnacsxoEgZfArv/Iiz3AA+YP+cu7w28tmAXt5/Us7lHZgCM7BLOJUHrONm3Bq+ucJ93Gt1IQi5xIKwyZuiauA5H9mw5joO1IThG7KdmbBfXzzoINyvHDJvkQdI1Vuh9uMIzg48tzzJS3sn+ny5EvflXlKBWVPzQFBRlwb8viuJdtcSdrN+5cMKDokAoe58oFjLbwdG+2nmIZDIRfe15oPqQzC0zaWLEjHpgtgkzkP9ehWVv4hh1fYWHP/pvH9eOP4r2OlowI7uEE+ewMa3oE0KkYjZpnTmoRaBL4npaYz6jlMJ0UXwT0b3lrTkkSeyna6qIFbJSZ4fUyvECwCZ5ucN7E2EUMlbZyieWZ7hghYWnzDL3n9oHqaV9Ds2BrsOyN+CvR0XuK6IHTPlQFDCmbQFLUJ33jiWTieipU0DXkEy1LJhsYoyY0QAsgTDyOlj8PLeYfuRHdRxphPPVqoNcPLKFd1U4SiiNGSl5Ls5TFjNB2YBHN/GR72ScBBweMzxOsc6I7N4yzaTqSnAMqL0hdaPIV5vthz2lcszIIoSZ3qncZ/6aPvJBvrE8zk3eW/noPwgPtHDT8d2NmAFi/3LhU6ITH7rQoQybCp1G1ysvCqUx41zQMWJGCS0+n1EP2odVPA837DMTH6vg8rmwVTHnaxfUjpM7ncy8hHl8suUTnjn2GeQq9jJtJht5rjxSi1JxWBuYHzlKKY0ZWZZ5yOY8NE8Y3vx+6Kro1CcBsQ4bI7scnY7UUP4Zpea50AHdG4Ur+XxsMb9iCt5OQPvZuJIv4t3FEB8awJVjOzf3kBuVQk8hO3N2kuPKITowuspzs7VT53cUFRVF7969a/yxWCyMGTOG3Nxc1qwpb/uwYMECNE0ru/jXhvXr1wMQFxdX16HWCcliIWLqVCKmTkWy1EMY6Wgv2rc6s4741OdPCi27vTrFw/YCGwy+WNyxZlbjO8rKJrCHCeeBwozGfa2WgrdYCKLMtmoTnkpxJpdKoqLyBd8FuDERLInWo6VT4+uO7YJS+azRfML1J7RDyxfWliJJoq1sdB9R9V2H1kqKLD4HKP9c4jn8e3+wUKfXpovxdZsoFmB/PQq75vth8K2Eff/Ct1cJYa0kC3ego8St9lCO+phhDhCCGFf925d9cLqYmOYTyCvus8Sdqz8SwsDGwOYQwqB6OkK0WXRdJBjdhRAQiuLJ4yrpNwBe9Z2LR1cIlMTfJErKJ1WP4Lozj0ORj7LNlYAw4WBrC4F8IRyrHDPakYGOzAO+qcz2TUSWdG50f8Bb776J26sdfszAKLF5n7JRuMx6/dBCw+MUIuD8JCGsbe7queIc+OsRWPSMmLPEDRJJi6NEWAttN15AA9YZ9nARR7y1dHOTJKKGijZ8FyoL+XhDESkFfnA6t4eLc6XYcPI5IsU5oogxoOKmkoLKjbY/APhCPYEsPYQASQhZqlxn+NxQlC1EUmGdW16SuxRJEvOcuIGAIooVGuBKU9U6owPp7NXjucIzk1w9kCHybqanPc5JH+7DV1vXNFuoWI+klziV+5v8JChIbx2utQWp8NvdsORlIayNGwTnfQj9zmk1wlpo2zGjWnRd7D34w/lJNsG4O9GRmKL8yxh5C6/8tYuswkZaXxjUCcWdy0PyLADeUs+kWLdglcQ+YbV7U4XprddRKqyzSHQXptXp16qKGR0Rx1ir9+RSzwPk6oF0cm0j5cVj0NJ3+nHQrRiPExa/AK8NhuVvCmFtl2Nh2kI4+20hYEtaI/4eQTFiHljDPEQym4g4fzIRUyYimVtm0vuojBn+oPdpIt/hzoflb/HLLePKHnry123ku46yjngtFEWWmDHaxmWK2HP/zjcelyRyHzXmM0px5QmxamR3se/SEpEk0cknopvIZdVxP7SqeAEQSxbXee9ko9aFCKmATy1P89u/K7nw3eWG06YzG766FP58UMw/+0+BK38Rt7N2i9hQD1MGETMmiZhhaZnfNyNmNJBep0BULwIlN/ebvwBg5g+bKPY0csdWg1qhyBI3Hd+NaHJ4yPQpAD+ro9iidz08Zmiq0DuEdRF7820FR3tRTFKUVaUWo6qYkUkot3tu4F+1P4GSmw/NLzBV+Y0X/tzBjZ+vbcLBt1C2/QxvjoIV7wA69JgIJz0B3SbUW1gLJTHjvElEnHeyETNKaBX5jHrwzmXDym7P+G4HEbYYct251T5/Ss8pBJoDOVBwgEUHF1X7PIfNQboznXzP0eMa6k8UWeKCMVYsEf8C4M4Zje6NAqSy6+MjZ/Q9+nLgh6DIEo+c0Rcojxm6N4ri5Cl48/shyT5s7T7HHLqcR+Zu4cGfNjXfYBuZXFcuW7O2kuvObbPCWqiHuLa29OnTh8mTJzNt2jRWrlzJf//9x80338xFF11EfHw8AElJSfTu3ZuVK1cCsGfPHp544gnWrFlDQkICc+fO5YorruDYY49l4MCBjTVU/2CyQERXMeE8wgI/0CLz0PjyxeeMv3OFO0BAGBSkwI7fGnmwiMpSEO1b6tF+rVWh68I5sjjnsET3oXiXvIZZLWa91pXZ6iSCKC57LCLIysxTezO2WyUHEF2D/FThLBbVSoS1pZRWfkf3EWKxOghsx3aLZOapvYkIEpt1NslDX/bRwVzxGDoyvbZcAd1PAl0VlWsbv/Xnu2h5uPJgwZMw/yHxnQvrDGe9JdriKK3o+9HEtOmYERQDFjt4Cuv16w6bzI3DxTX7U3UiWXKkcPLc8qM/R1mONViMtaBuyc02T2E65CeWOd65lr6LxVfADq09H6inElgSM4JxEmAP4tYLJjN5QPvmHHHzERAKsQOFyCg/hbFdwirEDLvkpg/7aGd28ojvKl7xnQvAnebv+OaDZ0RsrYzNIVp7Z++BpLXi71HfZIczG5LXCwFcSFzzC2v3LhLFGPsWg6TA8GvgtJdEhbfBYbTpeFEZa7Bwn6vDHC2o32R8mBgs76WPtJ9XVtS/uKMMc4BIouUe9I+oq62i65CbCJpXfGaHkL3xdxxF+8jX7bzkOx875UUCh60zNJ+4xoV1FgnkliqsPZTgWIgfJNaYBSlVX8drSeV1RqDkojcJ5JgiudTzADl6EEPk3byq/Y+zP02o/YGDokXhUNrmOp1TR8RdAFl7wRbU/PGkJjQVNn8v4k3SWlCsMPYWOP1l0fa5jdImYoanSAi485NFS878ZMhLanghXPxgpO4nAfCk6SMsePlwyT4/DNigoeTPuY8ATxa7tXje9J2NDU/ZY1XuTbkLxPUnqlfrdJQyWSCyF5jsYp5eByrHjGCpmF7sJ85cxCa9K+d7HuGgFkV7PYXityfA3n8a4Q20EnweWPUhvDYEFjwhBJOxA+CyH+Cyn4RrZeIq4Qxptol1UksV2jUSrT5m+HuarlhgwPni9vK36B+uMbhDaNnD365O9PMLGtSHDQdzkRY8gUVSWawOYIPWldLUbrX5jFJUrzADiOgm8lItGVkWzqlhXcRaSfPV6dcrxwuAEMlJ/8B83omcyS6tHfFSNp9anmZvwj7u+naDv99B6yFhCbwzDnb8Kq4Dp70Ik56GrF1irRcSV2f34LZIq48ZzmyxnvD3Ho8kw7i7AImzlKWMkrYB8OfWVP++jkG9WLonkwd/2sxT5g9xSE72arF8rU5AQz48ZhSlC0Ox8C6tY1+qtkiS6JwR2lEUH1cRT6qKGWZJ423zlSyyHIci6Txk/oxXzG+ycPN+XvhjR1O+g5ZD7kH48mL4+jLR8SKkPUx4AEZMg6ieraOzUxPR6mNGIzO5fyyH6jNTMuzYFBvOagxHgi3BTOkxBYCvd3xNsa+4yufZTDY8mofUIiMG1Yc/t6Ty3paXkSQVX2EvfHkDQRc6l1iHjbcvG8rk/m2kOKgBTO4fx9uXDSXWccj8WAvGXjCFAPcIJEnHFvcTlsg/+Wz5fj5ZltBsY20sMosz2Zq1FafPSbS97QprARo1+/P5559z8803c+KJJyLLMlOmTOG1114re9zr9bJjxw6cTnFxtFgs/PXXX7zyyisUFRXRoUMHpkyZwoMPPtiYwwRAV1VcW7cCYOvbF0mpfSuyMgKjRGIq/+ARE1Tn97XzxL+iUmJ9mhdVsaEMuRyWvgZrP4Gekxt/4hEYKRIzWXtEu1G5jX7RizKFuDYwovpFQMISzPsX49NlZnqnoSFz38Su2BUf4XYL/eIdVVd4F6SKzzG6T+tMYkgShHYSt9PFQhdrcK1+dWy3SEZ1iWRLch7ZTk/Z5yRLOrfMy+GXXUI0oKJwasq1fNvLQeCO74UzRnG2aBHTlhZlIDaf/n1JvD9JhkEXwdArjUVELWmzMcMaJAT4WXvKCxvqyOUDAnlrdSFuLDznOY9nTe/A+s+Fk0gtz9k6YQuB3ANiw7SSQOioxOcWfz9JFudz6iZsu0UhzP3eqfgw8fAp3ZFVFzFSLj0GjUUJ79y8Y25ubCEiWZu+BQpSGNslllFdRhwWMyRJ58qfLuXh5GAeN8/mcuVPlsx20mfKA0QEV/rumWzgaCfieuIa4RjvaC/EvLXB5xZilOw9QvzmiG9elz5XHix5BfYuFP8P7woTZoo26wY10priBTRwnREUIwSbul67eVNAKMXtxxKcuJgLlYV8kOindq32iBJBenxZkYFBJYpzxGZy5cS014l17YcAvO47m2xCeOP0WDw+7+HrDF0XTnHBcWIjug7tsZsde7hwsE3bKrp6BMfWe/zVrTOgB7d/8TCPFz3OEHk3TxY/yU0/PsT/Tu2Iw1qL63lwrPgbpW6B2P5ijtYQNFU4N3mLRHxqqWTvg8XPC+deEPH5uPtEDD0KaG0xowxdK3EjjRDJP3ukELppPlFol3tAJMQbUowz5ia8B1bSzZPCTaY5/LPXuL43O3sXEbL9awDu807Dg5mnz+pNgbuKmAFCGFWcJ65pga3APbs6AkJF3EtZL1y16yDaqS5maLrO+I9lzi56nPcsLzFM3oX6yTn4jn8Y67G3tb39qOpQvbDhS/jnecg7IO4L7QgnPCycCN15kLZRrJNMVhHP6rBG0lUN14594PNiaz8KqZXrcVttzAgIh9yE2q+Na0uvU2DTd5B3EJa9ya0nTuOaj1cDsGpfNlPHdfHv6xnUmT/m/869yjI0XeIZ38U8fU5fcoqriRmVKUoX67uQVjInVEoKSfSSws+Q+DqtN6qLF4oM7ryXSfzqRrrKqXxieYaL1j7IPZLEE2f3x2ZuRWuyhqD64J9n4d8XxDw0ojuc+4G4rqSsF88JiW9Q/BQxIwE0H7YOY5BaeRFHq4wZkizOeXOAKNAqzhHxPyDMf8WiUT1F/NjxG4+aP+Z0z/9YlZDNWYNb8Jr5KOGVv3ZxpryUicpaPLoCo27gxsC+h8cMVz7I5rYrkFRMIqev+cr3OivFk2pjhjQc74ZvkVa8w9nKUnpKSVy36A7yXV4ePK0vFlMb1VkcitcFy98S3TC8ReLaMeB86HaS6BBoCfTLy5TFDHQRM1rTHmkVtLaY4RfdVB2446SevDhfdJtZf8DJuaPj2Ju7F7vZXuXzT+58Mn/u/5PUolR+2v0TF/e+uMrnhVhCSHemEx8YT1A98/NHK4/+/TUmx050TcGVegafX30ymYVeooNtjOwSflQ71lZmcv84JvaNZeW+bNILXGWfUbZrAs+tfJF5+3/FGrUA2ZzHw3NVknKKuXdy71b/Geq6TnJRMnty9yBLMlH2tr+vLOltrMdJfn4+DoeDvLw8QkJq35pEczrZMVTYjvdauwbZXvXF+ogU54pKf3NAjRMIXdfp8npK2f/fOiWMU7sq8PXlIqE66nohzGtsfG4hUokdAGGdGv/1mhqfWzjzuPOEOKEqPEV4vroSiyuTt3xn8pxPfO77bolDqmmzoDBdbPjHDfL/5mVTU+rum75NJJkbKNbblunllC8yKtx3Xm8bL0TPg5XviTt6TIJj72obbq5FmfDfa5CwWPw/tKMQSEX3adzX1XzitTuNrXN7jfpeK9saTRYzinPg4EohOKxHdb/bp9PrLREzZDQ2RT5IYGECDL5ECNX9ja5DXqKIDeFGsoTM3UKQ4mgHukbRV9cSWJjAl77jmembBkDCrfFiMyYoBuIGi00aA+GOn75NOK4Fx1R5zV+b4uHcbzM5U/6PF83vYJZUDth60/H8p6t3T/G5RPsmxSI+8+BYcX5VFoNrmnBiLs4RIpTiHBGzG0OUXlt0HfYsgGVviPFIMgy+FIZe0bjuUKpHiC86janz+zdihqBZ1hneYti/HBSl1n+3/D0rCPn7PvJ0OyPdb/HF+fEMi/PDfKsgRbhWxA9pXaLPpkDXIWWjEB5UElnunf8uXfd9SYIWw8me5/BgFjGjKooyQLFBuyHNe51qCJ4iSNsiihAbwR18yQE3T81Zy+eWpwiXCtmptWNB3ye4/thaCsl1TcRrexTE9mvY55y9D1I3l8S3FpgYVj2w7nNRkKX5wGyHUdOhzxlHFk75XKK7SadjRAeGOmDEDEGDP4f9y0T3l+i+QrxemcJ0ca6pngYJbNPX/kL06hfw6AqneZ7m9dsupnfs0ft3a1Y8TgpfHUlQ0UE+8U3kYd/VANXHDF0XgsjQ9hAzoPWvP3RdFGhk76mzYKo6/txTzHW/5mDFwzPm9zlH+U880GMSnPNO1edWW0H1waZvhVAqp8SVOigGxt8Fw64S8TD3IOTuF3unQVH12p/Til3sOO0aAHqtWIrsqJv7pREzBA3+HAozIHGliAf+dtLf9jP8+yJYgth0wX+c8cGWsof+vfd4OoTXM4di0GA+X55Al18vZqyyle/VcdzlvbH6mFEZVy5oOrQfLvZSWhNeF6RuFDm0EP8VTf+5bjdDVt5FlJTHaq0nV3hmMOOsYVwxprNfjt+iyT0A318LB1eI/w++DE5+XBRN5uwTeQc/rE8rxIxVy5GDjXxGffDb5+Bxin3JvIMijljsYh/WHwVIrjy8X1yK2VfII94rma1OYvWDJxEZ1AaFmq2Ed/7Zwwe/r2C+9R7CpELe5jxumHbj4ddQ1QMF6aJ4r63nhTxOSNsk1tbBcXVaf/y5ZClDtjxDlJRPnm5npvdaTpgynfOGtZKClfqg67BtLvz5kFhDgMiDDb1SfH5BkX6dh1aIGatXIgcZ+Yz60Oy6qVry07okbv96fdn/Vz04nl15m1FkpVpR7OrU1byw+gXMspkXJ7xItL3qvbHUwlQ6h3SmW1i3xhh6m+TpeRv47ODNyJZc3JnH0910Lr/dNqG5h9UqyXHlcMsvb7G+8GskSUd1dqQ46TLevPA4Th1Qy7VbC8Sn+TiQf4CE/ATsZjvBlubPZeV78lEkhZGxI+v+u7W8Vh4FJTRNTECocIBx5tT4NEmSeGNy+Ybjjb/nkFasiA1OgPVfNLzNX20wWYWYMnNnnduvtQpy9oskdWANSvmV72NxZZKgxfBqSWvq07rbahbWuvLEoiO6T+sX1oJYMId1Fu/HXVDv9vWl9Ik0s3F6LN+dV+6c8t12F88Wnc7SbrejSzLs+gN+ubN1f+90DbbOgW+uFMJaSRZix3Pfb3xhrUHrwhYqkhzO3Hr9utUk8eLEUAA0ZG7JPk88sOk7cY3zN5IkYkPuAZFoO5opzhEbyQGh4hzf+C2BhQlk6cE8W1KMMXVwoLh2KhbRTq+1J7b9icUuNuNCO0FBmhDrVGJonIUN02O54Mwzucp7L3m6nY6u7eR9NZ1fl2/hQF4V7f5KXWytgUK4m7hKiFD2L4OUTSIxn7IBDiyFA8vFbdVT8jvNOMnPS4Tf7hatWItzROw9+y0YMbVlCrMMmh9zgBA7uGs/NwvpOoJCazQOyclkeSVTvs2kyKM1fCz2SCGYbIy409opzikRH1dyDSxMI37vdwA87bsYD2YeGl/N4txTKDaqo3u3XmEtiALT2AElHV1ShGOeHxnX0cqX00axY8wLpOjh9JSTOGPbPcxauJUvNheRmH+EFrGSLJLwzkwRL4pz6zeQglSxhg5wtMzrd9oW+OE6WDtbCGs7joXzP4a+ZzWva7tB7YnoJooZqhP/BUWLc01W6v89BqIHn8qB4CFYJJVnze9x6iv/4Pap9T6eQQNY9DRBRQdJ1sN5znchAG+fWoNQsShDrFEie7aN9YckQWR38d0uTPfLISd2tbFuWizPT47hDu+NPOi9Grdugl1/UPTaGDK3LvLL67QoNBU2fgtvjYKfrhdr2cAomPQ/uG0DDJ8qPt+DKyFju+jC5WjXNgrfj2YCwsAaIpzm/E3PSeDoCJ5C+u//lJmn9C57aPxzC1G1NuXb0qqYP/dzxipbcesmXvKezw/nR9buFzUfuAohomvrE9aCuG5F94WACLHP5CfvoImDu2E543k8ip3h8k4+sTzD83NW8dKfO/hm1UHS8w/fz2oTbPkR3h4nhLXWEJjyIUx+WnToyN4rYkhrXp8aVI/FLuYA7YZD/GCQFFG45Y81vM2BadRUAO4yfUsEeQx/8i/amNdXq0HTdJ75fTuPmWcRJhWyVevEpDMvOXxvQNeF0Dq0vTAQautY7BDTX1zn8lPEPLqWTDxmDKYp75Fu745DcvKW5TXUH2/i9Xnr+Xb1QTIL21hOLWUDfHw6fHOFENYGxcCx98K4O8QaLiTW/wVeBkcVZwyK59YTyzs6nvbqStoHt6fAU1Bt7BgWM4x+Ef3wal6+3P5ltccOtgaT6kylyNsEuqs2gMenMWvzLGRLLprXgSfzeD6dOqa5h9VqCbOF8ebpt3DzwLswEYBiP4C98+vc/P0cXvxzG9+tSSSnyNPcw6wTxb5idmTvYG/eXhxWR4sQ1jYVRlalMXC0F5OyIwgUT+8ZQLew8snGqmQP9JgIjg7gzoeN3zT2SAU2h2iPnL5duGO1FQozxAaAPaL6BGLaFiGOBO73TcWN2Ew+r28NFTjeYiFwiOrVsBaMLY1SgW1UH+Fs53E26HAhVpnh8VbmX1oubH57TSGXbBnJFe570c2BkLYZ17fT2LVlLaofNB9NSvY+mHsrLHlZtL6I6iNEtSOva5utUgwahiQJkQma2MSuB1P62AmxCtH/Am0IuaH9hFhwzWw/DvQQbA5RSFCY1jjHbw1oKmTtFZ+zNRgKUtDXfAzAU95LyUVMGE/tZhGihvCu1TutHs2YrBDTDyJ7CLfZKuZHDqvMuI5WZlxwPOd4HmevFovDm85xG+7iyU9/xe3T2ZiYx6KdGWxMzCuPGWa7cEYMiROv43WKlt+5+4XwSfWKZFFoByFOaS5Bkc8Naz6G766GpDVCiDX8Gjj3PYjqfcRfNzjKCYwCdFHUUxskmaD+pwFwkbIIgC0ZfkiOKGbxk53gd8Fkq0bXhfObrh7mTq8ufw+b5GW51oc/tBEAHNuxinmi6hUFOBE92sb6whxwSGFFqt8LdUJtMmMG9ibtxFfZo8XRTsrirJ338s3CNZzwSTo+tZqYUYoki7jhyoXkdWKMdUnyFWWKIg659o7STYa7AJa8AnNuFp1JbKFw4sMw6am28d06mgiKPnI7xcBIMY/wOuu/lyPLdDzlDgr0AIbKu7lMmc+utIYV2xrUg+T16MveAOBB7zUUIvakBsVUI3h0FwCSpPKMkgABAABJREFU+Pv7qe1mi8BkFe/JbPdLMY8kSYQFyJzZM4BZZ0bwmTqRcz2Pk6DFEOhKJfybs9HmPcDyncnMWZ/Esj1ZrVcoqPpgw9fw9lj44VohigoIg5MeFaLa0TeKvczkNZC8XuxLONqD0RqzbaCYxN+zMYw6FAsMvAAAacXbTB9Rcc/jYHbD9o8N6kd6npP7TF8BMFudRBJRdAuvpailoMTxNaQVu+tZgyCmr4iBRf4pyJAkCUd8Tyynv1AmsP3c8hSfLFjHvd9v5OqPV6FqOsv2ZLX+mAGiQPT7afDtVaLrY/sRMH2x6JCXtEbk1kLijTzH0YBiEvumHUYIUWVBqlhfNBCpz5kQ3pUQycm9pq8ByGhrgsNWQkJWEafIKzhNWYlPl7nHO50OUVXkMIqzxR5HRI+jp2OVJRBiB4qORAUptc7dSZJEWGQsURe/zeb489B0iQtNizh16UXM/v4nbvtqXduIGbkHYc5N8O5xsH+J2PccPhVOfUF03WpuIxODNoMiS9w5sWfZ/9ML3NiVcILNwRR4Cqr8HUmSuKLfFUhILEtexo7sHVU+L9AcSLGvmHSnf+aMbZ1/923HEvEPAO6000G3EB5oFOM2BIcthKsGXsjzx/2PIDkG2VyAvfO7vLN+Nnd/u44ZP2xsNTEjx5XDlswtpBSlEGWPwlaPbs2tGUNc2xhYg4WIqhbuIfeOLZ903DwvB4+uCPcyEO27XEc+hl8IihHuPZm761Sd1WLxuiBzF0hU38ZS9cLi5wGdb33HslTrT3yQwjunhjGhUzWbBppPJFMjugkRdFtDkkSrj8heYoPFD4voHhFmnphQscXPv9pAJhU9wn4tGps7iw5L7mP2rDdZursVOKF5imD52/DDNEjbLMQDY2+Fs94Q34ta4PLp7M6uWZRS4NZ49J881qR4+GBdIVfOySKtsA2cm0cz9gjhqtAAZ6nHjis9lySuSTtf3NzxW3kbFn8iyeL6mbMffK2raspv5CcLoWap+/l/ryOpbpZrffhBG0/PcBPvnx7OMEeBeE5bjAv+QjGJ2BLdVzikFFft8D8g2sJFx/TmHM/j/Kf2I0hy8Z7lZb5+/2lu/2EHz83bwf0/bmLq7FUs3ZNZ/oul31d7uBCjBMeKfwNCmzcRoOuwd5FIVqz5WMw92o+A82bB0Ctq5RDl9Grsyal5Yy+nWOPhRblsTvfwxqoCrpmbRU5xa6taMaiWgDAwB9bJvZZep6AhM0bZSmcphQu+z/KPQ4g9XIhdClIbfqy2gjNbxAt7JdfatC0oe/9G0yWe8F7O4BgLs84Mp0dEJZdTXRcOcqHtIaxT0427sTFZRcI7vKv4zjRCEefg7h34a8DzrNe6ES4V8rXlCSbzH0PfTuCWH3bzfHUxA8oFtroKSesgY6dYQ9aErou/dcpGUZxa+W/enOg67JyH9vUVsPUnQBdOcxfMhm4nVGjtmV6k8vCiXHZkeXlhWT7TfsmmwG3EjFZLSDsI6yL2KWpbhFGZ0I5siBcdGe41fc2Nb8314wANjojqhbk3I+kac9UxLNCGMrqdhc/OjiA+uIqktuoVBdGRPYS7fVsjIFTED6Rq1wz14fjONqYPC2KL3pnTPU/xre9YZHTk5W8Q+dmJfPj191z8/nLGPbuAeZtT/Pa6jY7XBas+gNeHwo/XCTdamwNOeBBu2yjcpFSPiF2Jq8S8JSQW7OHke3T259a8zkjM9/HQwlz25fp4YnEeN/6WjcvXMpM8Rz32cDH/aozuQz0niaIpTxEsfZ1bT+he9tAl7y/3/+sZHJHPP3yJPvIB8nU766PP5uspETistUj1uXJFAUNk99bveh4QKhwHZQs4s/x33Ji+WM56lSI5mIHyPr60PEkUOWxJzqfvw/O4+P3l3PbV+tYZM0rZNR/eGgObvhHrovF3weVzxPUjeb1YIznaVRDX5bk0DlbVWeoQ9uWKmHEwz8eDC3O5dV4OXtWIGa0GSyDEDBCmQs6ckmKuBiArYh4CXGhaxCBpN9fOXu2HgRrUlVs/+ovHzbMA+CPwDJ6YMgSzUqlzq7dYrDMie4oChqMJi110hQmOK+nAVPtcmKSY6XfaTbwb9zjJejjd5BR+tDzCyIR3GPzIz603ZhRmwO8zxBpj3WeADr1OhbPegq4TxLwzOK5at9rsYvWInaV2Znl5aGEuyQUq9/2Vy93zc1qsmMyg6bhoRHmO9d5vt9E+pD1F3iK0ava7OoV04vgOxwPwydZPqn1esCWYlMIUnH7QvbR1bpv/KJLsw1fYnePaj+Pnm8fV3O3boFbYTDYmdJzAe5NfIVgbgCSp2GJ+JaDDx/y5fTf9HmnZ6wxVUzmYf5DNmZsp8hURExiDyU+O5blFKpkFNceMPWlu3v4ri8wCHy/+msGbf/op31lHWvkKugUTEg/5iWLTqQYXiZO72jirVwBzdohk4+pkD2O7HCsqw7J2wfovYfQNjT9eSRYilNz9YuIc3qXxX7Ox0HXR9syZKTYBqmPDl5CTQKYewlO+SwH4+/IoAszVbETpeklldzuI6F4hQdmmkKQSkagOmTvALgkBaQO4fGAgFgXu+zuv7L6dWnvO8DzFa+bXmaBs5Br1W/6Zv51l3rsJDA4l2+kh3G6hX7wDpSWUAega7PwDVr5XnuDpdAwcc1udXaD+tySPTzY6uX5YEDuyvCxKcFN6+f/inAjGdrDy0ooCPt5QxMcbyl0nRn2Uxu2jglmY4GJDmpefLwhjQBsyqWnzyIoQziStFd+nejhont0rgG+3Olma6GGt3pP8uLGEpCyFlR/CyY/7f8y2ULGhUJQuXFCOJjxFwv3cEgiKGT1hCdKBpXh0hQe812CSJeZeFIUNNzg1cd00GdV7NSLLYn5hsoqkb0GqKO6pFE+vGxqESY7nysX3ca/+NdeZfuUK5U8GSbu5yzsdG16yCt08/dt27pvcm5AAc8uLGSDc8Ze/LQoxQIiwxtwEXY+v0xziwYV5/LC9mJtHBLE+1cOSg+UbfD+cH8nQOAuPL87jxx3FfLKxfHNg1Iep3DQimP+zd9ZhUlxZH36rql3HFRtcBg8QIBAgJEHi7snGPZuNbjay+Ta62bhs3N1l4wkhBkGDuwww7treVd8fd4SBGRjp8XqfZx5murqrbzfdde6953d+58utXrYUh/jujCgGH8CYX6cTYzAJB4XiHU1v2+lIQOo9AfYs4TTlZ/4dOoM95WH6uFu5/JMNYLIKR0xHgu5io2lQthvQ6r0Xmqqy86vH6Q98GJ5OrqU/i0+Jw7Rv8gK6tzOIYoSEYeJ11TjoRdhh8bKpvXhWvp+CVQ9wpLKSJ0xP80zoOB5ST8OGnzRyDhwzbLGiY0fhZjHfie4Ltrj6BZqaJjrLlGaK9bLB1LlcYIu2we+PQ+5aZGCbmsKagVfybuFQlj5fBYj1xFdnxjM83sjNP5SycJe/XsyY9UY+542y894GD5nlYX4+203fnlV43nWpKZD1FAnRnL2JLaH34dA5Z7Dq5d8YI23lTvkliipOJdapfwjaA3XRU8i5aynRHNwdPI+0KIU3TojdP+FdQ01BRndu1epIEJ0vcteJz7UtJiKn/ftUFxLw7Aq4KXQ536gTuN/4EgPlbD423cWL4Xk8UXYSV7y5kqfPGku03Ux+hY8Ep4WJaTEocifaB/SVw/KXYfHTda6NtjixhzzhYiE681dA/kYoyxSCKXtcvfnKVV+V8OsePzdOdvLjTh9/5tYVgS84N4H+0Qau+rqE1XlB3lhbFzM2FuZz8jAbr66qoqLCy6ft9JJ1DoLZBdYYMbeM9DxFMcLIU+HX/8CS57jm2it5YoE4lF3mo8ofwm7W00ztQSCkcvozP/N46asgw2fmY3jyhDQUQxPefzUkip2TRwoRfnfAHiviRc5q0X0rUq8rbhD2k5+k/OPrGcYePjXfyUWBm9gUqh97c8t8XSNm1OArh+/+AStfF3/HDoITnxUFO/kbhGujPa7BXNBfPi9iVV6QmyY7+XqbjzX5ImYYZVh4fgKpTgMXfFbErrJwvZixoyTE0QMsPLuiEvw+PmyXF6rTYhSDEFcqJvGZkKTWudwnjSQ8YDbK9h+42/gqJ2b2JxhWMXaazdvujScQYt7jv3Jd5fPEK+VkKqnMPfE8ZPs+33FNFcWacYOEYUVPxGgVAlvFLPQFzdjzlCSJK46bxu1f92VyxlPMV5ZyneET5qlLuUW6hJWacOPsEjHDVwaLnoTFz4iOrQC9D4UxZ4uYazCLvb2D5DbP/LiI7SUhbp7i4pNNHjYWCtGUyyzx47kJxNsUTv2wkDK/Vi9m7C4LM7W3mSeXVRAtBXi9zV6oTmfln8eN4N1lewBYsCmfZy2jyTHnUOYvI9rScNfQ04acxqLsRWwv3c6irEUc1uuw/e5jN9rJrcql0FtIH2M33ktpBWXeIDOefhopfiOaJjPCeirPnjMJo2I8+IN1moRBNpAeN4KPTn2Yc999nlzlMwyOLdj6PyZcgoNjEM6RnStmVAWryCjLIKcqB6fJiSOCHZA0TeNvb+VQXBni/OnRfL+2kl2FYp0R61B4+i8pOC0K174uhMb/+7Ou+KugIsTARBPvLC4j1qHwn3Nd2Ns4XSlpHSHpbUPKy8txu92UlZXhcjUx+QyoHg+bx40HYMjKFci2CKgP8jdC0fYDCzyBBTt9XPhFce3fVx3i4KZeG+GbW8Qi5oy36lzz2hp/hahQSx4jkvhdkfJsUWFrjWpcFFq6G+3Di5DUINcGruJzdSrLL04kznaAhHZVofj/SB3XM9ocqCoUbhEOwI64/drcNhdN09hVFuaoN3KEQ3M1Eio3Ke9xmeFLFEklV4vm8eCJrNX6AxDnMHPp9DSmDGhZkjAi5KwWAqmCTeJvd28hkOpzaO1dwqrGO+s9fL/Dx5H9Lfyyy08vl8KR/S2syAmwKjfAyAQT5X6Vl1YduE3bkf0tfL/jIM5Z1WT8RRbtmpq5edjSa2V3o91jRtAHu/8Qc6MWbvh+usnDX78rBWCglMl35luRUeHYJyB5VIvOeUA8RWCwidZQPWUSrWlCGFmyUxRUhP2E3j0fgyePp0PH8VDoDFZfliRcQUozhaAhcUT3LbpoCzzF4j32lYn5xj4Vbqqmsas0zKw38pglr+Q/xueIliqp1Cw8F5rPQnU0GjKyBHsXNHeKmFG8QyQqdiwUfxssMOp0GH26cIgBQqrGq6urWLTHzxFpFhbs9DEgxsDMfhaWZwdYmRtgUqqJrPL6SYmGmNnPzE8ZTXMnyrjQCH0nN3seo8cMQYeuMyrzYc9SscndVAHmzl/h+zvI16KY4n+CEAbumObiorGtXPxqKpRliU3nrlyQFwkqC4QLXI1TWDUZy7+l38r7qdLMzA4+wg+XDsNuamDjOegVjvYpY4WLandFVcW6uGiLuP5EeC0VVjV2lwb4+t2nudIgHDd/CI/lhuAVRFGBUxKFrAeMGZoq/i8CVSJ5aHHVJRH9ZSIRHfSJZEIrCw+bSzCs8fzKSv7MDTIrzcz3O3wMjTUyIzmIedVrjCr8HzIqAcnMw4GTeDk8l2AjddTTepv5dc/BY4bLLLHmHKMoJmysE0wj6DFD0O7vQ3m2KOJzxDfJGb8h3v5pJadsuQmTFOaqwLVMPf4SzpqkJx3alKLthJ+ZjBL287fA5fxgPJwlFyY2XvTtKxPX1F6HNL3gpitTni3WDJoq9kUjsN4KqRp7ysLMfEMIUqOo4G7jaxyvLAIgU4vjzuAFLNTG1YsZyW4Ldx07nDnpHRyvyzJh6Quw4hXxeQDR2n3qtTD2XHySmWd+2MiW7CIOT/TzzY4Ao5IsTOrjYGVugD8y/cwbZGVlToCPNx3YVX5yLxOLMw/s3GUO+fn0f/8AYMiSRcjuhhOejaHHDEHE3ofSPZD9p2jvHWnCQdFCvjQDpv6V+4Jn8PwvO2oPP3LaaE4a18OKsjuAD5bvYfUnD3OP8RWKcGM/5RksMQfOPdVSniOupSlju75r7b6U7BLxwuKKaDFfsCQL7aubMFVlU6lZuDp4LQvVMfvdb991RqeJGXuz6Uv46mZhCIQEh16J57Bbefz7zWQVFDE1McTXWWbGJ5sZk2Tkz9wgizP9nDzUxs+7fHy57cC5igkpJpZlNyNmLPsD2annM1pCu7wPmgbFO4XA1hbTujWwpwjfW+dg0bzcHLyE98Mz+e/Z45g7shN9P7opz/+ynd++eY/XTQ+iIhGedTfGgdP3v2NlPpicIvdt7OEFluEgFGwWn39bTLP2QwJhjazyMA++9T/+ZXyFeKkMVZN4PXwkj4ROpRwRnzplzPBXim4Yvz9WZy6VPBoOuRAcyaCFKDfE8sgyH2U+lUmpJr7c6mNybxNDY42szgvw+54AZ6Xb+GyLl593HXjfaUyikVV5B+7uWi9mLF+K7NDzGS2hU+mmmsilry/nuw15tX8/csZA3NGZxNviURrJi3y67VPe3fQuMZYYHp35KGZlf4VdeaAcSZMYkzCmx7Wybwr3fLWad7KuRTYVY/Uezhfn/pPEFhbw6xycQk8pn25ayCPLnkGxCOFoqHIQvtwT0IJRgPisd2TMCKth8j35ZJRn4Al6iLPFNdmtttwb5vVfSwipMCTZzO+bqxjf30qS28C2vACrMnwcO97JFysr2JTdeMwwGyV6RRvZnn9wV/nDh5u46ig3E5MmNvk11o63iddKXVxbTZsECW+JSIKbnQddfMx6PY8dpXUt35dflEDcD38VbmfDjoNpf2v9eJpKVYGozkoZ2/U26/0VkLkCtFDjbTo1Ff53PeSsZmF4NBcEb2ZSqpn3Tj5AgAhUiXOnjOu6ouOWoIarBbbbIiKwXZNZxnUfb2En+2/6TZNW8YDpJVIl0cbpu/A4XgrNpQorEvD3eUPbXyxVsAmWvSREEwBGGzv7n8X8tdP556w45g6wUB5QWZIZ4G/fl7bv2KqZkgyvXzwVgz2qWY/TFxaCDokZBVuFO9pBCi8aQ9M0xr+QR7FPtLe41/ASZxt+rHMbiLTrnRoWG/Cp41o85i5HZb6IJbYocd1b+iKsepNMLY7Z/oc4cnAUT86JFu30NKDXhJ7XLikSBKogfxOUZ1WLlep/j9ZklnHtx1vJIIVkinjM9DSTZFHksEIdyO3BCynRnMRJZfUe12Exo3gHrHgNdv5cO5Ks5Nmcsft4bjpyAIf3NeMJaPyY4eP2n8oOeKq2Ym4/iafPPwzZ2rzrvh4zBB26zggFYPdi8XtT5+dqCO2tU5G8JVwS+Bvfq4cAsOnKZCyGVopTfGViVd9nYsSdSLsMqirEC1V5ohVaDSE/vrfPweIr4D/BU/GOPIc7pjeQNKwRKccOFO6u3b1AQ9OE43HBJhFbrVERPf2azDKu/ng7Y+Vt/Nv4AmYpSKYWx7WBq9mpJeHFTAzlxEj1W1vuFzM0DYKeupaIEkKoaLS1uaj2s80ebvmxjMePjmJSqhl/SOOLLV7u+a283v0MhDhb+ZHrDB8RI1UC8GV4IvcEzyWHRtbALeC0wTIPnj0dydy877geMwTt/j6oKuSuFteVFs7ZQ2GVp557ir8aPqZAc3Gk/yGW3Xua7izVVqgqvHYs7PqNX8IjOS94K7dOdXH5+EaShmpYiE17WnFLZYEQdvgrGizKawlrMsu46uMd7KFuf2+m/Cf/Mr5CL6kQgK/DE7g7eB65e11XJeC/54xr/8S3pol9qT+egQ2fi3bdwA4thcCh15Iw9VxCkoF3F+/gkQU723Voeye9B//xO0pU81yG9ZghiNj74K8UawaTbb/1dUTY9CX88hAY7fiv/pMh99dv7739vnmdx3mtm/LKT+s5ZuFc4qUyVvY+n3Fzzm9aZ6xAlejW0OuQiLmBdyo0TRTzFWyqdtaLnGBi/c4sAt/cyVhlO2FN4uHQqfw3fBwaB37fOyxm7INWuoddb11Dv4KfAAi7+1Ix5wme3RHHs7/uatex6OLayNBu74OqihxG4VZRDNyKOVhg5buYlj9LkeZkpv9hynGw8/55epvpNubBz1dy5vLT6CMXsCvpKPrOv2l/85SgF7xl0Gt85+rQ05GoYSjaIboLG0yiM0ATP6trMsu4/OMMqrByu+FNTjX8AkCR5uQ/odN4LzwTtYH40WExw1cmurUufkZ0PwCKrP14wHcSZ86eQi+rD4M1isf+DNXrftQe6OLayNCpdFNNpNwXZNQ/v6t326d/S6UsUEacteGcXyAc4G8L/0aht5BTB5/KyYNP3u8+mqaRW5XLkOgh9Ha1QTFiF+e4N+9iZ/hj1KCLm8f8m/PGT9HjdBvz69Y8zn3lB0zRf2KK+xFJDqGpBgLFUwkUT4Gwixon271pj5hR6islszKT3Kpc7EY7LvP+1w9V03j15xIWbqzirpMScNsUzAaJFxeW8MO6yjYbWz2kALI5F8WSg2zJYUxvJ++d/EizT9PUa2U3K1FtOZLBQNxVV9X+HhEsUeBIEqKRgyTi/nm4m/M+q3OvfXhJJfdPvAS+uE5sXI06rf1actvixIZ9wUZIGt11KtXCQSjYAoFKcKU0fBcVcpZ8RK+c1QQkM7eHLgQk+h6oRa4aEg578cN6lrAWhEgvbnB1per26hZyLf88FHsC2CU/6ewgrElspC4h9Ks2hjn+B7nZ8C7nGn7gKGUlE+TNvB06gu/U8Ty1YBuT0uLap913/gZY9TZk/AaAJil4B87jWe1knlgtXIBu/qGUm39oh7Hsw70z3fxjL1HWohz4YXMRc8ZFtf9gejCtihmOeOGIGvS2SKQhSRL/N8PN1d+ICtKHQ6dysukPLEVbYfPXMOyYZp/zgMiKiAMlu8CR2P3cLfYl5Bcb8rJMWLawbdN6Bq1+Bxm4O3gePsykOhURG/yVkDRKF9a2FJNduC2bHFCyQ3wn9iqMKfYEcEg+0tlBSJM5I3A75yrfc4vhXcbL2/jMdAevhufwemg2bqpq97g0aL+YoWlCXLfuI9j1e+3Nvt7TeFY7mce2iU3Ja6u/r+3NHdNc/OvXOlHW1xkav+8sZdrwnruh1BFEZJ1R04a+eEfTxbWyAWnwHFj9DqcrP9WKa99eV8WFY1p53bK4hTNW6R5IGNq6c3VVPIVQmQf2WMIqrM8uo9gTYGj2pyT5CsjSYnkhPJ8bHY0UvVQVisR2TP/uL6yFutb1Ne0lqwoi2p2l2BPAJXnYrqXwt8Bl3Gj8gDQ5j/dN/8fDodN4LnwM2cTj00wkU9R4zJAkEZ/aWDSuaRqSJOENqpT4NB74vZzPtwgHwcu+bCxmaBwlL+dWwzv0l3MB2KamcHfoPH5VW9+94IZDnTz8R534+P0tKqftLuOQQT1UQN/VkGWIThNdJ/wVLXKINigyxjFnsGXNEgbLWdxpfIMv10zjhLE9pMCunQirGkt3FmNZ+yZjd/2GDzO3hS4CJBIO1E3JWyL261w97P/DEQ/GcWK/ryJbzEFa6YBe7Anglqpws4OAZmALffhJHcuR/mFcZ/iEi5UvmassY5q8lqdDJ/BK+Gh8mNGAv3+8liOHJ7WPgDBQBes/RVv2IlL2ytqbN1lG80j5LH5Qx6P+LMPPPx/gJG3DZePsPLeyirCs8OaQIwE4u8jH8Kh2H4rO3pgdQgDiKWwbce3A2bD2QyjZiXnJU1ww5QxeXZRRe3jh5nyOGNbD9s7bmJqYUdMGNLjoGeKlMnar8ZSnzW2asFbTqnMbQ7unsBaq1xr9QQ0KIaAzscVO/vtSEDTxWOhcruVTZimruNn4PofKG7k+eCVFNC4ObfeYUfO81euMiioPgcXPY/v9AfppXoKawvPh+TyZdyK+16qAA3fWiyRnpdt4e52nXsy4sCzAgB7QHLJLI8uiGDjgEXMwV2qL9y5MY04hZ9X/SA5l8jfDh/wzdAFLdhZzaP/IFYfq7B8zopc/Sh+5gGwthsr08/cX1mqq2JuKHdh+3XO7ArICcQPBbBcxpSyz2sX24HsjxZ4AMVIFMVTwbOgYPlWncrfhNQbK2dxvfImzlR95KHQ6P6uj2Fsw1e4xw1MMf/wXbcmzSH6ROwi4+vFE4FieKZmEiswHXwQAGSg/4KkizbGDrXyxxVsbMyTgiqoQqXoKrl1pE91UE3FZjEzuH8viHUW1t1VWRqMaSgiGgxgb6LBqUkycNfQsnvjzCT7f/jkz+8wkxlJ/3itJEnajneyqbBLtiZgiNFfsquwdM2RjCTuCnyPJ4M+fz6G9h+jC2naguCoEahTB0omEfEmYY3/DYN+OOe5nTFHL8BfNIFg6CVQT7RUzKgOVZFdlk1uVS1gNE2+Lr3WrrV1n+MIUVYS5++M88stF8fe1r+dEdBz7oyEZypHNObVCWoMlB8lYCFKdl+z6EjdFlX5iHfu7V0eCbq6QaTqSyUT8NVdH+KSScAwpzxLCzwO0057cy8zEFBNLq1unFHlUYbnfexLsWQJLnoOj/hXZ8R1o3M4kIbBVNotW151dTFXTpqQ8S1RRNnDBX7S9kPd+Xs29wZdAggcDp5CpiQXDjZMPsJKvzBMLx57kDLI3sgLxQ8TvrRTYxtjqJiqKpDFC20EJTrIR/w8V2LgjdCGfhafwgPFFBsrZXGX8nOPVRbwZOIIPliVyxqQ0lmT5ibcp9I+u+1zuLWqIsZkYkeJunqhKDcPuP2DNu5C7VtykSaxwHM7L8il8vbb1m49us0SZX2NUgpF3Torly60+1uQHCIbh9mkuNhYGue+3coIq3DXdxfB4I+d+WsSfuaI9xv2z3JyZbmd6HzMBFV5aWU6c0cec4botf3vTqphhcYsEYUVOix3QZve3MDrRyOq8IMW4+Db6DI4vekm0bul/eMTbLWONgoo8cT3s7u61JbuhqoBFhSae/2Up1weeRZbD/BAey/eqqNS8YrwDPAVC6NZIMYdOE1GMED8YLE7Rcqk8W7yvsqFezDBIKsO0DL4Pj2eBOo57DC8zQ1nNpYYvOVX5mWdDx/JxeCqJUikA5b4Q7y/fzZkT+/D7Hj+9XQp93BGMGUEPbFsgRLUlwh1K1SQ2OCfzVPgkvtna+s9FtEWmxKcypZeJl46N4aNNXjYUiHhwxzQXy3MCPPh7BYoM/5rhpm+UgRPfK6jthPD40VEcP8TGEWkWVA2eWlrGUFeQaQOa16pVp/VEbJ1hixXifzXcdJfyIfNg9TvMVFaRFCwil1iyK8IHf1yTxhMDZXtE4tLawz5XalgUncgyizLKef6XnRRW+ommgudMH4i1RvAM/Jg4K70BYUPQI5IYcYO7TiFjpHCnimt/3gYozwVnQtNEAQdh75ixiyRuDFzKZcavOEpZwS3GdzlCWcktwUvYrqVSjBunVkVfSbQY2ztm/LzLx8AYA6nOCMaMfbjsy2K+3e7jsvEOvtnmZVfZwb+TY6Wt3Gp8p9bBvUBz8VjoFN4NzySMUhszjupv4fGjo3hvg4fNRSFMssQ/prn4eZefh/8ox2GUuXeWm3ibzBFvFFBS3Qnh+fnRHDXAyvxBYm760O8lHJasckjfqJa/UJ32xxoF7r6iUNrkaFHy+8yxsdy55TIe99/FScpvfL71Wxh7YeTH2kP5Zl0Od3+xgXBZDj+YHwAJHgqeSqYmCrJqvoP7oYYh6IOE4aLgpqdhdoo9Ums0FG0TRfX2hBZ3bdk7ZpikEMO1nRTjIpdYHgidyafhqdxnfJFx8jZuMb7LBYZveDJ0Ih+ED6fEA08t2Mq1Rwxi4eYChqe4SHTVxfJ9RQ0T02Kal+zQNMhaAStfR1v3MVKgAgkISSa+kqbxX+9sNvr6tuh1175+i0yxT+WkoVbunenmrXUethWHsBslbjvMxdfbfDy+tIJYq8z9R0RhN0pMeimPmnTFGyfEMK2PhVOH25AliX/9PJ/j+0sM79NNRXtdDWei2B9vCwxmGHmKcK9d+gJXXXQJCzbZ2F0snMx2FbWvo1l3pyZm5JT5AHBTya/mj0EShfYPD2qiw6C3WFw/o7q5Q5csi65e4aDomOFIEJ/ZVhJjMxHCwCOhk1mv9uFSw1dMV9byjXwrdwYv4Gt1Ig05SgGUeII8tWAr18waxE+b8xndO4q4vRK9rY4Ze6GqGvOe+JVNueU8PT6f9PUP0ZdsAJarg7kteBFbtOZ/BmpixnmjbNwyxcWbaz3sKAkRbZW5ZYqTjzZ5eWZ5JakOhfuPcCNLElNeqWvl/P7JsUxIMXHeKDsmReIfP87jLyMUBiRHteh16rQzilHkBoMeUbjRUgGmbEA57FpYeDPnKt/zbngWu4tH6eLaCLJvzBgm7eIL0/8AuDP4F55Pa2Cv2lMk4kNMWs8o+m4OkiTyPhY3lO4WAltvifj7AGvtvdcZVilAserk3MAtzFWW81fDR6TLGbxmepCV6kAeDZ3Cr+pIamJITcy4auZAftpcwCF9o4m2150vIjGjcBsseRZt1dtIwSokoNiWxr89x/BB/kTCtK4rZpRFotSnceUhDq4Y7+DNtVXsLA2T5JC5fpKTt9d5eGFlJWnRBh48IgpvUOPw1/NrH//56XGMTDByxXgHZoPELd/P46/jjKTG60Yh7U2b6Kaawb9OSOf05xZTVCV0U6WVBvqlxFPoLSTe1nAsmpwyma8zvmZryVbe3/w+l4++fL/7OEwO8qvyKfQWkuLoubndfWOGJfUNjK4Qoao0QuWjGJygx+f2IMEp9pK0sB2tajBefwIG2w5Mcb+imPOxJH6FOW4BgZLJBIunoIXtUO1+XhMzrpgxkIWb85nUPxa3tU6L2NyYURGoIL8qn1xPLr6QjyhLFJZqPVggpHLWU3vwBjUunhHNW4tK8Qa0Rs/VVOxmiSq/xvnTopg72snXqyvIKgkR7w4wcVgl/1u3jZVZGZhtuWDOpirYsBuu3eAi1d6HzNxYZg7qT4y97fZNJU3TWv/KOxGdzuZdVSFrmbDWb8LC4/31Hm7+sRSAqw5xcNOwYvjwQpF8PeYxSBnTpsOtRzggxFRxQ4TwpTNPrsuyIHeNcPJqoDJ/0fZC7v9qEzcb3mWaso41ahonBv6PMAqD2MPd8wc03D7aUwySItqhN9UlrLuihqFwi5h822NbJAoMq3DRa8soqvSz94UnU4unlPpiQCMhztqn3WmeFs221BO5csehlOEg41ox8Vm0vbBW1FBDnMPMpdPTDt4WvHQPbPkGbcu3SB7R/k+TDXwYmMJz4WPYpjXNMfrScXYO72vh34vKWZ0nxE8fnhLLsDgjizMDZFaEOHekHU9Qw2luejZe0zTeXe9hVKKJEfH7CPTVkKgs7TtFLOqaQae7VnYQHfY+VORB1vJaEWFLeWVVJXf/Uo6BEEuj/kGMbw+MPBUmXxXBwVZTVQhGO/SecMBikS6NpxiyVrAks4p7vtvDNHk1Nxs/wKcZmR14iEwtgSHs5u45vTk0SRHt9Bx6VXfE8JWLOFOeDbYYwgZ7gzFjp5ZEFTYOl1fzD8ObDJZF4rBcs/FueAavhY4mlxhGmvIYN2wQL68WbhytjhlqCLJWwtbv0DJ+QwqJhaeqWHjLfxivho9mu9Y08fm1Ex1MSjVz/Xcl5FcJYdMXZ8QxMNrATxl+Sv0qpw+34Q1pOExNjxlhVePtdR4OTTUxKHaf72k4INp89Z3cbAG+HjMEHf4+hAKwa5GYkzdnXvrFdZCzmqVJZ3BaxnEA3H24m/NHR8CNsjwbXL2EC3VnXitEmvIcyF7J4jyZ+77ZVnuNukb5hKMNK/hTHciJgbt5+ggT80fsEyc0Vaxd4gYJ96ie9L7tjbcE8jaKZH8E2nw3vM7QGCXv4O+Gd3FKXvyakcdDJ/F8eD4hDLiopBwHRkKkm/Lo3W8gn2/xIgE7I7HOqKbMr/LVVi8nDrWxuSjI8e8VNvl1jZa28UT8p/QtF46FPs3IC+H5zDzufAYlRfH1Ni8hDU4YYsXXzJgRDIuYMb2vmbSofd7/kE849PedKlpLN4MOv1Z2EjrsfQh6Yc9SMW9pqTudGmbJhw8zqfQrcrVo/pjzJSdMHhHZcfZAvlmXwxVvrkQD/mt8lLnKMlap/Tkp8H+oyDx7pJE5wxpZW3iKwGCF3hO771qsqVQViRatlflCUN6CwtLG9qY2an1rE8oSKifIv/M3w4f0lgsA0c71nfAsPjMcTWzKAP7YUUSM3cTKO4QL374JKoBkt4W7jh1+4HZ9mgZ562HDp4TXfYJSvK32UIaayPvhGbwbnkkxB7+W3DndxfA4I2d8XOfy89npcYyIN/LxRi8Wo8TcARYCYQ17M2KGL6Tx7voqjkiz0Nu1T8wIeiDoF3tTzSwa0mOGIKLvg78Sdi8W8bst3GuDPvj8KlH0N/lqOPpe7vxsHa8vFu3lnzl7HPNGtnNL427I3jGjhlsM73CF4Qs2qr3JmXI3s0b1P/iJ1JDYg0wZ2/2L5WsIByF/kyiEjoCD7b4xo6+Uy22Gd0iVxXX22/Ah3Bm8gDwanne5LAbinWa2F1SRFmfnpxtnAK2IGXuRX+5j4ZYCThqbyk+bC3j0jQ+5zfAWhynrASjUXNVtyGegNdCG/L5ZbgZGGzjto7qY8fnpcaQnGHl7nYc4m8ysfhZCqobN2PSYURVQeX+Dh7kDrSTt28klUAnhEPQ7rNlzGj1mCDrkfajIE3ui1obzr03muzsg41eWqEM5PXAHr/xlIjOHNLFQQKdR9o0ZMiofm+5ijLydr8ITsRz+V2YN30dEFvKBp0Tkvp1J7T7mLoevHCpyhcg2UCnmWWbXftexxtYZ67T+xFLGFYbPOUf5AYskcshr1X68ET6Sz8NT8GHGbTGgAhW+EKN7R/HZVVOBVsaMcAi2L8C36FksGT/W3rxe7cuToRP5Vj2kwRixL48cGUVvt8KpH9bFjP+dEcfweCOvra6iX5SBqb3NhFUNazNiRplf5aONHo4dbCV+304u/gqxVup3WLMLK/WYIejq78Plb6zgm/Wie9eLF4zAaN+F0+zErDRcQLW1ZCt3/H4HEhL3TbuPNPf+xnml/lJMsokx8WMadMHt7uwbMxT7Fmx9XkbTZDw7r+Y/x8/mpHE91HCwnQmrGoc9uIDcMl9dzJD8oBkwuFdijv0Z2VytXdJkQpVDCZaNJlyRDsi4LUbKfCEApg2K442LJgFNjxnBcJCyQBkFngIKvYUEwgFcZhc2o43skiBbcv0cPtTO//6s4Nkfi5v12m49Lp4Yu8LN7+TW3vbEecn0izfx2Ypy+sUZ6ZsYJLMik3xfNlkVWWRWZpJVkUWJv+GufrIkk2hLxhROZXxqf4bEptHX1ZcoSxQA5YFyFElhYtLEZo0Vmn6t1MW11WiqSmD7dgBMAwYgyRHsI1yWJRYe7pSDuvIs2Onjwi/qPpw7rklG/v0x2PCZcDY68dmIOPs0maBHTLATR3Re51ZPMWSvEsVdDbhmhVW48NWlDPCu5k7jW4Q0meMD97Be68dA9mCVgsQ6zLx0/oT6DkQhnzh38pies/l0MNSwaINRtA1s0S1aSNcInYHaQOHTjGynF06q8GImSN1kxoGHi5SvOc/wHbGSaFMa1BR+UUdxyJTZbFP6c/ECmSos9CYPqySqmGpkCn+fN7R+4jvkQ8vfSOm2Jci7F+P27Ko9VKI5eDc8k9fDc8jRDu7ANiTWwCvHxbIs28+8gVaMisSavADHVSfMd16T3La2+bq4ttV0WMwIh0SyIxxoVUu2L7Z4uaa63fx0eTWvmx4UBQGnvgxRrXOz2Y/uviEfDkH2n4Qr87nwo0y8VeU8a3qMGKmSh4Kn8XT4BAaxG4sUYri9nPsuOh4laUTPFUW1FeEgFGcIp3RJYlEu3P/1FqAuZng0MztIwU0lXswcpyzmcuULBsrZtadZFB7OJ+phLAyPoYAoAFZeksim7BIu/7IYPyZ6k1e7kdVozPCWoGWuoGLbIsw5yzCH6tpl71CTeCt8BB+EZ1DOwUWKoxONPD03mlW5QeYOtKDIEj/v8nH+Z2LeVyP+bTN0cW2r6RTrjNz1ULpLdGpoKtt/gh/vxmuMYlTFEwSrm5dE5DMX8guRZOp4UTDSEwiHIHM5YV8ZF324q1Z0mSbl8LjxGWRJ4yT/P/FoZpKchv3XGFUFYg6dekjPc63dl0CVSHyXZ0XEWaqhdUalZiGAkYeMzzO1Osm8We3F/4XO5Xd1ZKPnWnVpEuszi7jsqzJCKPQmD7MkNqsaixk1Wyubi0KsyQ+SaJd5f72HL7f59j39QTkzYTd32j/GmrNU3CDJ7EqYzem7jiWX2LaPGbq4ttV06PtQukfskbiSWixcf3VJNtP//Bv95VzeCc3kzHs+jegQexo1G+Y5ZT6OlpfynOkxQprMMYH72KT1YRgZJDqN+8cMEPsw5TmQOhbcTSv+7faEAiKxXbJDzEXs8c0W6DQUMyo0K7tIJoYyiqtbfRsJcabyI5cavqSXVFccsVQdwpfhQ1mgjuGrO8/h162FXPn2n/s9T03M+O854+olMTRfGWT8TtG67zHv/BFnVd3elFcz8ZU6kfdDM1miDaUxR8QaZvQ188/D3WwpCnJkfwuSJPHq6kr++bNo4dqWMaN2nhsKYDrsJCSzHjNaQsTfh8wVwmGwrebnm76CX/4tRP/XreaJpeU88v2W2sMZD8xvm+ftIewdM2pIoIRfzH/FIgV5PHgif9qmNBwz9qUiB2zxQjzVQrfvLkk4KDpllO5qk3WGkSAnKb9xhrIQoxTGq5l4ITyP50LHUsWBDUnW3300Czblcc07q/Y71mjMqF5nrM0qY1NOBbEOE+8t28N3G4RD7HApg2sMnzBXWQaAXzPwcnguT4eOp5L61+V5A83cMNnNrtIQs9LEevSRP8p5YqkwN2nzmLFtG6ghTNNOQTI27/9FjxmCDnkfNA0KNoncoLtXy/fDK/MIvnMuRi3AtYGr+FydqseMVtJQzDhP+Zb/M75GuWblNv+FVDr71Y8Zmibm0jH9Re5fz280nYBH7OuVZYG/TOwRGs1gsot5kSQ1uM4o1kQH1zhKkYDLDV9w9l4i2zLNxvfqIXwTnsCv6kj8iMKQTf+aw7frc7ju3dX7DeWAMUNT2fznL0jrPqZfzleYfWIdo2oSC9QxvByeyyJ1BAdbZ5wyzMpl4xzkVIaZ3lfEjDsXlvL6GtGtoO1jxlbQNBEzDHpBRkvoFPmMVnD7p2t584/dAMTaTbx9VR8yKzJJcjReFPDkyif5Pft3hscO545D79hPp6FqKnlVeaTHpZNk71nFBfvFDCmEPe1RZHMRgaLDCBRNI8mRyG+3zGpxNwWd5lEjdgbYT7Qpl2Ow78YU8wuKbXftzWrIRrhyKKHKYYSqBoEqrs+b75nD/1Znc8MHa/Z7HvG/qfLomcOYMshFqb+UEm8JlcFKZFkmq8BCfpmE0yLzw/pKlmzzNvu1HDPWyZxRTsq9YUb3taJqKvf+byvLdmdhshRx8tQQeVV55HnyyK3KxRtq/DliLDGkOlJJdaTSx9WHvq6+9Hb2xnSAwkldXNsCWhokVI+HzeNEu+chK1cg2yJY2R3yi3bzqFCtnG6MUp/KmOfrFNwPzY7i1DQ/vHsOBKtgxt9h8NGRG1tT8FeAvwqSR3a+jXx/JeSsAn85OBsWGbyzdA+fLNnMM6bHiZMq+G/oWB4MnUk8JSRKdcr3+04cyahe1QLFGkepmAGQOFxfXOyNqgpxbdFW4ZpmcjT7FA05QIWRkTSVmlhdoEXVq/g2E+AE5XfOU75jhLyr3vkytTg2qb3ZoaWQq8UQRMasBbHhw2QyoIUCjLYVEefbzWBpDwZJrX1sSJP5WR3Nh+Hp/KiOI0DDE/TPTo+jl1Mhr0pla3GQI9IsjTpDPbO8gmSHwolD28AhYm90cW2r6dCYUbwT8ta16rqaXxVm4kt1rbaW9nqchMIlwtFozoORv3Z1Z/fa6v+PdzcFeXNpFhcrX3KCYTHb1WTmBh4ghnLipDKceJBRufb8s5k0tE9Hj7r7UlkgXGw9RfyRL/Hs7zn7xQxZU2s/4vmam7HyDs5XvmWKsqHeqTaofflTHchmKY3NoSRyiKVAc+PHhIpMPMWkkUu8VEaC2YcxWMlUWyb9/JvpK+fXO1ex5uCL8GQ+CU9jlTaAxjagvjoznkS7TE6lyvaSIEcPsGIx7H9fTdN4bEkFQ2KNzGusDXCk0MW1raZTrDMq8iBzmRDXNrXgTg3B22eAp7A2eQHw9omxTOnd+jaZVOaJAreU8aC0zn20S1CWBdl/sqbMym2fieuNpmn80/gGE5QtfB6ezAPBM4ipLgqrt8bQnUH2JxQQCbqSneLa1MpuIY2tM2QtzBHKKi42fIVLEhs334fHc1/oLHZq+68jY60yRV613m0SGm4qiKcUMyEC5mi2+KMZnWis7VxhlCGo7ne6RrEYJL44PY5oq0xuRZDK7X8wsegz5JxqgZYkw6CjYNy5qM4U/rO4gvHJJo5Ia2Nhti6ubTUd+j6EQ5C9QhQLt/Bas6UoyD/e/pUPzP8HwM45b5J26LGRHGWPYvH2Is584Q9cVPGD+SYSpFKeDJ3Aw6HT6EU+UdXdeurFjBqqCkVRhu5auz/eUuGeWZ4NFmez90UajxlinaFpkE0cJbhQCDNbXsEFyndM3me9kU8My8ID2aalslNNphA3pZqdEAYMhLASoJ+5jJhgPpPsuaT4tjFQyqq3N+XXjCxUR/NVeCI/quP2E0MBxFk0PphvwOFyk+ORySgLM2+gBUMDSa9gWOOhxeVM62NmWp+2ixmq18fm+RcCMGTJImT3wQvV90aPGYKIvw9lmcLoI6r5LeCbRMAjumMUbYVDr2T1iFs4/unfaw//eMPhDIhv/n6xjqAmZuzNPYaXOMfwIxvUPtwVPA8vloZjxt6EfOI62WsC2JvWcaFbEQ5CwWaxzrDFtNrJuaGY0UfK46+Gj2s7KhVpTl4Mzeet8BGNFmGnRFnILj1w8Z3DbKDSH2JggoNt+Q23PwWNidImLjZ8xVHKCkCIpv6nHsq/Q6djjOnH6yenYK7MJLvUS2bIwbwhLuQG9op9IY2HFpVz1AALk1IjsEfQCPVixrI/kJ16PqMldGiHjMzlEPaDreXtonf/9DJ9tr5OrhbNLP/DLPj7fJLcPbzwuBXsGzOSKOJ78804JS/PB+fyhToZDbl+zPAUgWwUXflMEehq1RNRw0KX4CsXHTX8ZcLdX5LB7GDRHi/P/1pXkK9pEJYUFC1cu86owspRynLOUX6gT3WnDBDrgjVaGivUweRZB7G8MpYMLYmKBtYHAHazQsDvZ3pUIfEVG5ggb+JweQ1xUnntfYo0J5+GD+P18JHs0vbfI0iPN/LcMdEYJImsijB5VWHmDLA0aB5VGVD5z+IKjh9iZWxS27Xerhczli9Fduj5jJbQKfIZrWDBpjwufHV57d+/3TqZXVUbMSkmbI3M7Qq9hVz/0/UE1SA3HHIDE5Im7HefEl8JVsXKqIRRGOWes8+yb8wwxf6EOeFb1JADz55z0Xy9AZl3LjmUyQNaHut1mkdDTrO1yF5QypEVD0bXBgzuP5ENdesDTZNQ/cmEPX1xSv0pLolHDcSBVpOnU0H2IBnLkCQvJuLx+dy4bVDmadl4R/a2cMO8WLwhD9uL8smpLCIhppIiXxFF3iKKfcUUeYso9BYSVIMHPFe8NZ5ezl61Qtqa3xv7fh+I9hDX9oDsZyfAYBbiqfwNBxXXRllkfj4vgcNfF2KOm34o5dRrU2DsObD0OVj2AqRNB2MbCzD2xuwUE8W8DcIRsTlOWW1J0Af5G4VblqthF8VF2wt5a8kurjJ8Q5xUwQ41icdCJwPgpP4Vo9gTqPujqlBsusT214W1+yLLEDtQOOAUbBKfjWYmMKYMiGNSWhzrs8so9gSIsZlYl1XG20vrqi7ipVLitFLWI1pc+THxXngm74VnMkDK4lhlMdPlNYySdtBLKqSXUgjs7xICiCtdAGo6W+RpUSxRh/FDeBw/q6Mpo+EN36snOJjWx8yEFFPtxlOsTWF4/IEnWlce0vy2hDo9EHucuJYHPM0WLdSQYFf44Zx4Zr8pFt+nZZ3GQusK0Qp2zx/QZ3IkRyzEU+U5ogVOWyVpOgJfGRTvYGmOENamSTkcq4gFxl2hCwhgxFEtqnVKHjZofcn19SDHj47AES/mH0XbOVTZzYRT0lhfaqDYG2wwZiRIZWSqsZyl3k5qsIATld84UlnBaHkHw+VdDK8pytjnv03VJGRpnzozIxAEZHF8s9aLheoYfgyP5U9tUG2r2H25daqT8ckmDkk21W48xdoU0hMajxmSJHH9oT13c0enBVjcYuM7UNV0kbRsgOHHwfKXeTBpAZ9nC3HtWZ8URcZhwBYL5bnCFak7xYaGCAVEctZoYUlGae3Ng+UsJihb8GlGHgyegYO6qtvaNYamicKBmP7gSGzngXdiDCZIGAZmuxDZVuYJB8IWdms50DpjgTqWpYEhnKX8xHxlCUcqK5gp/8mn6mE8GTqhXoJhX2EtgIZEKS5Ka9pyV+fVa4S10HRh7b9muBkeb2R8skkkKbf9SNza96G0OrZJMgw8EsadW1uIJQM3T9Fjhk4TUAyiULiqWAhrDM1PVg+ONXLXqYfy6sdHcYHhO4xf/xXGzgSzLpZqCd9vEEX0fze8TYJUynY1madCJwCiW08N9falQOy3BH2QMFwX1jaENQpSxoA9VsSQilzhUNjEGHKwvSlJglQKSdaK2EAa36oT+VadSFKwiHnKUuYoSxkjbSNBKma+svTgT2ik3t7UTjWR39V0flfT+UUd1ajb4SPToH+cjTG93LV7lPFOGHWA6YRRkbjtsObt1el0IyxRYr8p6Gm1oLBBTDZIPwl+fhCWv8zoqdfxyZVTOPGZRQCc/N9FrLrzqMg/bw+hJmbU0FfK5XRlIQCfhSbjRcT1/WLGvniKwN23VSK4Lo1iFOsMxSSKty3hZhca703DMaMPf1uawGHyWv5i+I5EqZRbjO9yleFT3g/P4P3wDDZp9QvzDyasBaj0i44ZDQlrXVRyjLKEc5XvGCbvAcTe1RfqZJ4KncD1Z8znvxYfI8154NkMDhcJcamMOcDzWQwSd0zXY4bOQTBaIXYAZP8pCvgP4Bx2IPpMOxtf5rckefO40vAZZ78Yy483zIjsWHsQ+8aMu42v4ZS8rFAH8X14HFr1vLg2ZoR8whAsZbgurG0NsiJyZdZoiO4rcnz+cqEtqCxgSnyQSSf3YUOJTJGfBtcZDrz8Eh7JC+H5TJQ2c7SyjKOVZaRKRUyQtjBB3iJyFNU1D6WanRLNQSlOPJoZgxTGQoAkiok3lyH7NPb2jqrQrPysjuKT8GH8rI4m1IAk6JXjYki01895JzoOnPdymGT+ebgeM3Tah1lDE3nzokmc89ISAP727mYeODOZHaU7sBqsDQrA46xxHNP/GD7Z9glvbXiLsQljMezT1cltdpNflU+Rt6hHudfuHTMkQymmuAUA+PPmoQXiqNmsyK9ofhc2nZYzJz2ZI4cnsXRnMfkVPhKcFpbsKOKxH7eCagXVihoK4i+KwZ8/B8W2C4NjAwbnRmRTEYolG8WSjZ/F2KNB02S0QAxqMBot5EINudBCDjTVTEj1oigmKlUjilUCtGoPKQ1QkeQAyEEk2Q9yAEn2IykeJKWKQSlB/FoFZeFK/vprOWEtXPcishp+bYqkEG+NJ9GeSKItcb9/D+RE2xnRxbXthT1OJDSC3oMKY/tGGXj52Bgu/EK0CT7p/QJeO+ZEnBs/ExvFa96H8ee3x6jrsEaJDZm8dWKT2tnBCeFwEAo2iiS+K7lBAWxYhed/2Um6tJO5iqhq+Xvwktp2Cmbqb0DF2Kq/vAGPcK6NG9y+IuauhCxDTJpI2uVvFJ+NZm7UKTL1qutHpLj5Yk02Fb5Q7W2SBIO0PZTiIICxVgS7XUvlsdApPMYp2PEySt7BACmbAVI2sVI5TjxYpQB+zYgPE/laFJlaPBlaEqvUAfUccffmo1PjGJdk5LYFZWSUhbhojINoa8e0OtDpAZidYE+Esj0tFtcCDIwx8u/ZUdz8QykZWjLf2I5hTuWnsPhp0aY7khMTWQGTFUoyhDDI0LUmPQ2ihqFwG+GAl2cWFyKhcpXhcxRJ44vwofxW3TLaRJBoKijSXORqsSQ49Yr6NsdoEe7x9liUwq2McpZAcjwYLI3GjIFaJuXYeD18FE+FTySWMg6VN5Iu72SElEFvKZ9kqbi25VKNsFbVJMqws6c6VmxTU/lTG8hqdUCjbiP/OyOOEfFGrvu2lDK/ytnpdpxmPWbotDFGC9jioHxP8xKDQ4+BlW9gLd7Iv0fncvNqsWl04/cl3DMzqkFn5SYjG0QcK95RVzjSXanIAU8xYWcqC7dsA8BAiGsNnwDwYngeWcQzgh21D6ldY/hKwewSc2i9eK8+sgzR/URHjIJNwh24Fe1bD7TOqMTG8+H5fKVO4HzleyYrGzlF+YUT5N/4TJ3Ky6G5rNf6ReZ1VWNW4Isz4hkYY+CyL0XnlFOHWbGUboVfv4RtP4ouNSA6BAw7BtJPbrtWzjo9A1usEGaX7gJ3w8XIByM9ycbng84mc8dKekmFLH3xWkZf9gJmg15k1hzCqsanq7KZLK/nTMNPANy6197U3u6ltTGjBl8p2KL168GBkJXqGOIU+4Rl2eBKEvOTJtCUvSlZ0kjTsvBgoRw7ucTycnguL4fnYsHPWHkbI6QMBkjZ9JPyiJIqiJKqMBAihAGfZiSfaHK0GLarKazX+rFe7dfg3lSUGT6dp9HbCRf8qOCyGjlmVAwmRZ876DQDkx2sMeApbBtxLUC/6bD+EyFa/O1Rxs59kPmjkvlyTQ6lniD//mYT1x85GKOir5GbQ03M2Ju/GT7EKIX5KTya5dqQ2tv3ixl7E/SAbIKoVrRv7w7ICsQNEkLbgk1CUGaLbfF70ljM+M03isWBERwpr+BE5XdS5SIuNHzDhYZvWK/25cvwofysjmaD1geN5n8nUihkirKeOfJSpstrMEkike3FjJp+GqZpV/PWJ0WMdEgc7dqN4iuGoBlcKeI90NGJFI4k0UG0Ikd8vlqCwYzlsKvh+zu4RPmK9wpn8PRPvbj88AF6C+pmsm/MOEpextHKcoKawl3B8+s1XIuxmUTeu6bou5FOsDotxGQTP84kURzpLUEpz2Yk+YAKNleD6wyDpNJHy2OXlsD9oTP5v9C59JNyGS9tZZy8lYFyFv2lHOKlMqKkKqKkKiCvwSGUazbWqGms1gbwqzqKFepggnvJgPpHKbx7chwus8wZHxUyLN7IjL7mBsWJOjqdicMGxZEWZ2dnYRVLM4r5fnUsIwfYqQhU4DI3bEJw/MDj+WnPT+R6cvk241vm959f77gsyZgUE9mV2cRZ4/YT33ZH9o0Z5sQvkeQgIU8/QuXp7K3O1/Pf7Y8iS/XcgiemxfDq4gxKPdWmHpoRwm5AJeztRdjTD3/+MUiGMhTrLhRbBoolE9mch6T4kcyFyObCiI4xo2r/25xGJ7HWWGIsMcRaY+t+t8QSZ40jzhqH0o3WI93/StFZMLtEsrkip0kJ51l7tXlcmRtk5IvF/HzYhfRdfh+sfgeGzhOOPu2JLbZaYLsW0DqulWk4CPmboHSPGEMjAW99dhnllZXcZ/wUgHdCM1miDcNJFbGUoezlVBfnMDMixS1EVp4iiB+qJy8OhiRBVB8h3MurFjo7klq1OXXNrIHc99WmerebpSBJiCT03+fFMGVAHCFV49CX8ij0qlRhZbE6gtUMwIupwQ2qPi6J3eUaDx8ZxU2JRo5+q4CwBqMSjDw3P4ZkZ/2L+v1HRLXoNejoNBtnIpTtFi27WzF5P3WYlZt/KAXgxsJjGG/+ifiyTFGMMfacCA22Gmu0SFhW5Iiq3K5OeRZU5LCh3ExhpZ8j5ZUMlfdQqVm4J3gObiqJoQyzFMQghdmtJhHvdjAxrWGRvk6EkSQR680uIdwr3Q2KEcUW22DMsEgBrNXFM3+fN5QpA4bhDw1l1HM5+Gv3rTTiKSGEggENLyaqsNTGj5qY8ey8aG6LMjD3beEMPSHFxH/nRRNnqx8znpjTvNanOjqtxh4rihw0renzLlsM9J8B277n5PA33MwFAHy40cuHG738dF4CaVGtWBpao0X72dI9ED+45efpzAS94n03O1mfU0GZV2xszJWXkibnUaC5eSc0k/5k1f63uK0GscYIB0UBX/Jo3RnkQNjjwDQeCreJ+ZHBIj5brdzob2idkaklcG/obAaHMzlTWcAEZQsnK79ysvIry9XBvB46ku/UQ/BhxoGHSqzUy0xVk+qQyKrUePW4GGKsMse9JzatDutt4um5Mbgt9dcmLxxWBTt/gc8XiFbmNThTYMQJMHS+/hnRiQySBNF9oDJXuJ238HN1zfReXL75Et4y3c/Ego847Y7hPHLzlfSK7piWgF2RpTuLqaqq5D7TiwC8EZrNMm0osZQSRZ0jXW3MqEENi9gTN0R3rW0K9lgwjhX7heVZ1X83/3Pa2N6UXfLjwE8CZdXrjDiKPGHGv5jHYnUEixkBQCylFBHV4LkTbRIFXo13jnejaAFO+Uw43c/qpfHkdLDbLGLP2GAFSeKNk5s9fB0dgSSJfe3yRuxjIoHZDiNOrHavfQWmXscd84fz5ZocAJ5ZuJ1nFm5n6W1HkODSk7NNZenOYoqr6gxBhksZHK8IR+CvQ4fUFmbsFzP2xVMC0WliLt3TkSRR4Gi0CYFtRbYwDIiAiGLvmBFG4Rt1It+ohzBF2sAcw3JGSTsYIe9ihLyLm3mPAs3FanUAG7S+7FBTyCOaIs1FEAMhZGz4iaKKOKmMdHMevcN7ONKdhbliV73nVROGI485G+vYs0Vr96oC3j9aBW8phMxiv7kHiER0OoCawtiqwlZ146PfYXjiRmMrXM3thre47NtEHvp2M3/ecSTR9m5g5tFO7B0zHHi42/gaAB+EpxPUZIzVRXy1MaMqv7pj64CeXXjR1hgtYEwWOQ1PsdgvLc9GkSWumZnGfV9vrXd3p+TFhZf42nXGeLaXjOaIN/Jr7+PAwzApA5CJkiqxEiCIQgADBVoUVaZY8sIuPpwnMaxS46EFYi/quEFmHj4qBuM+hXqfnt7O+hIdnVby8GmjOam6S8b9X4nv0JMXmXCYHMgNdK6xGCycNuQ0nl/zPB9v/ZjpvabjNNU3KnGb3RR4Cij2FZNg6/6aoL1jhmLfitG1Fk2TCBROQ7iWiutEjN2o5787AYos8cBJI7n8zZX7HJGRNDFXeubssUwb4mL57nwueHFj9XENyVCBbNmFpASQDeVIhnIkQ2W1K60fRQmCFCDBpRAMQ1FlGJBwmBVSoxxYDRYsigWzwYxFsWA32XGZXHU/ZhdOkxOXydXlnGdbi77Cai8kSSTMyrPFBnkTFNqXjbPz3Mo6Cfjhv40go+8IyFsPfzwLR9zRliNumBqBbe5a8Tpa6IDSYmqEtSU7xSbBARIMxZ4AZyoLSJGLyNfc3Bc6GxmVXuTXE9YCXDo9DUUGKgrAniAWiDpNw5kkBLb5G8TnuxWbN1MGxHHbvKE8/8tOCiv9tbfHOsxcOj2NKQPiADDIEh+eGsfOkhDPLthAWZUfgxQmqClIaOQRQwl11Uo/nZeMLGm1FXjbr4lAC2QdnUhgjREtvn3lYmOjhUiSxJnpNt5Z56ESG/cGz+Yx0zOw8nUYMKvlleQNPpks2sGWZIhkTVd2KPSVQ+F2MNsp8gVxUcVfDN8C8GjoFApxMYQ9yJJGnFRBlhZHAW6ePXa4Xknf3phskDhCzEOKtkFZJlP6xjcpZpgNEl+eGU92hcpTP6ynosqHIqkENAUFjWzi6hVm7BszMq7VY4ZOJ8MSJb4TQW/zEhnpJ8G271F2/MTpaWfw3s66JPcFnxXx8/mt6EwhScJZr3SXiA3WqJafq7NSmgm+MnD3otgjBJROPJxpWAjAo6GTiKKyXn5ixuAEscYoLxCuIJGMx90Vo7X6eh9dLbLNAkd8i11sa2hsnVFsH0Bw+mxwFsLa92HHzxwib+EQ0xZ8mFgcHsZidTgrwgPxYCGTBCqqHc0VVH65oNeBY0bQK7q/ZK2EPUtEoUgNihHSDheC2uTRTW5jrqPTZCxucPcWroItFNc6zQpyyljeyZ3JmYafeND4PDe9M453rpwV4cF2X/IrfPzV8BFpch55WhQPhs7AXN2+s8GYUYO3RIiiHB3cOaorYbJD8ijxb8lOEbet0aJYoxk0dW8q1qbw5ZnxVPhVHv5mA1UeD7KkEa+VYCXAdlIpr+7AZJUCLDrNhiyDJPtANpBxeZToBqMLoHTaAmuUmFc1oYtei+k3DTZ8JgSLvz1K4tx/MzLVzdqsstq73PbJOl48/5C2ef5uyL4tWG80vA/AH+EhbKF37e37xYy9CVSJuXNU70bu0ENxJor1c8FmkcNoRaeMvdk/Zsgs0tLZYh7P1RPsHBJYDtmrIH8D8aFyZit/Mps/D35iDdGZtwKQFEgZCwOPgBEnIUf1ETGuZJdwqA54xF6tK1l3qtVpe2wxwnSncKuILy0RaUoS5sP/SuijizhaWc7U8Fp+V0dy71cb+c+poyM/5m7K3jHjRsP7JEvF7FIT+DY8vlZYC9UxI+wFVYXYQV07n9OVkCRR8GeLEfuBRduYElfAHUf25r+L8w+4zhgQbeDjU+OQgHu/XI/X48GLhYCm4NdkttIHL2KNEy17WX5BMrKiICkmhkoSGekd8YJ1dNqO9BQ3UTZjnYsn8MMqIydPLiHW2nBn5Rm9Z/BdxndklGfw4ZYP+Uv6X+odV2QFo2IkuzKbWEtst3LXbIjamCGFsCR+BkCwdDxqIAmomxOfOCZVz393EuakJ/PsOeO4+4sN5JTVxfwkt4W7jh3OnHThQj9joIP3L4tCkcNc+uYiir1BtJALLayihayEPVOhWpBrMvl5/5ohSHvlM3Sajr57157YYoTzmr+iSQnnvx/m5tapLtKezKm+RYIp18Inl8P2H0USLnVcmw65QWyxYpM/dw2EAxDVV1QstjUhf7VjbXXS/iBK+NTgHqYpvwPwj+CFVGAjmvL9hLVnT+ojJqyBKpHQjBvUPVqdtye2GLHBU+sQ0vKWwFMGxDEpLY712WUUewLE2EyMSHHvt2HYL8pAvygD5hl9uL/aUcRY3RIpRSskmSLKsXP1tJTqx+oBQqcTohjAlSpEFw20hGwO98+K4t6Zbvo/mcOn6lROCy9kChvg9ydgzv2RrUa2uEXFbVkWxA2M3HnbEzUsNgFDVeBKJcZWxiWGr3BJXjaqfXg1fDRRVCJLGna8+DQje7QErp89uHbCqtPOSJJIFFjcULwTSncxJcnIpPMOYX1O+QFjxsAYIwNjwLBXzKhpo9dLyyeVAsqxc930VD1m6HR+TDYhFKnKb564NmGY6M5QsIkHU3/jvvnnMuApsc7YVRaOwLgc4C0TYhbz6PZZH7QXvnLhpGqNAkmqbcF6uvITLsnDRrUPP4bHES+V1XvYpP6xYu2nmCC2v57obCqyLNrZW6LE9b5sj3jvbLGteg8PvM6IgyPuhMlFsOFz2PotlopcZiqrmamsRjVI7NSS2KqmsFtLZJuWyryxfVHKq787qir+rz1F4rtZvFMUg5RkiA4FNUiKWMOnHQ5p08HScPsyHZ2I4U6F8kwIVIrrdAt446QEfPnnkfPJatLkPGZmvwjo4tqm0i+4nfnKlwD8I3gRldhIpGi/5dmk/nslhEJ+sd+WmK7vTzUXxQjxQ8S+VMkucV0OFTa+HpYQcdpoFa6G1YUOTd2bGhEviv6vn9m7dp1hkDSCGOmt5aNRSBl2bp6RguLSHaJ02hGTQ8ylfCVtJ6IxO2D4CfDzA7DiVaSpf+WLaw6jqNLP+Ht+AODnLfkHPodOPfZuwTpR2sgsZRUhTeaT0GGE9krl1YsZ++ItFS2/9Xnm/pidkDRKFF2U7BTriwh8Pw4cM4bDmLOgqghy10HZHvKzd5CVnUU0lbilShRUDITxYqZEc+KOiSe+73CRp4obDMljRHwLeERc271Y/K4oYHaL16Gj055E9YGK3OpCpqgWnUKJTYPhx8KGz7jL8DrzAvezYldJZMfZzamJGWOkbZynfA/A86F5+3VRmJQWLdyGYwfqHVs7AkkSReMWN5TuZpIpg0MSFTaUWyjya42uM8Yli3Xg3/ZaZ5ikMCoG+mvZqMiUY+f2WSkoZr2zjE73xmSQWXXnUWzILmfeE78CkFUkE1bDBMNBjA2Y8cmSzDnDz+GeP+7h+13fc1Tfo0h11jcNdJvdFHmLKPIVdXv32pqYYYr5BdlciBpyECidgBas3w1j9vAO6lyu0yBz0pM5cngSS3cWk1/hI8FpYWJazH4C6Bq34XuPn8gVb65EC8WgSUEkxYdkKEIy+EA1c970aCqD5diMNgySLhVtLvo71p4oxjoRVRMXHPsqxn3Rg7EMPx42fAq/PwYnv9Qx7eGs0SJ5mL8BQj6IGdC2G/7+SlEFX55d7ZR6kNccDjBw49NIksoP4bF8r04AIJHieneLc5g57ZA+QmTlKYb4YaKSTKf51DqE2IQjU8jf8oW1DKN6HaC11V5MGRDH3/dxFJEkiHeYuH16r9pKPx2dTos9TmzqtqaVUjVybcyQuCP0F34w/h1pzx+Q8asQcEQKSRLf79LdwvnB7DzoQzodpbvr3LaBEepmFGU1YU3iluAlhFFIoBgJFbdUxRatFw53DFfPGtTBA9cRLrbDRbwu3IpSkcmopAQwHDxu6DFDp9vQ0jav6SfBT/fB+k9QRp1e75CmRaBa1RFf7TSaJMTw3QFNEzEj6K11mR+R4makvYxjgksA+FfoHGIor32IhHCdGJHshIosSBiut2RtCWYHJKWLz3vxDqjIEcIna1SLXV4Pus6wxcIhf4HxF0DBRrJXfIW2ZxmpUh4DpBwGyDl1911f/XMw7AlCUJsyDvocqgsddNoXsxNcvURRWQvFtQCW+L78I3ghL5v+w0XKV7BnGfSeEMGBdlPCIUatvB1JUvk+PI4f1PEAxDYUM2rae2sqVBVAVD+x/6XTfCRJrLNt1UUuQY/ohFXT6rBmvqOGIRQAf5kopPFki71NazQoxgjtTRm5fXqqvs7QaX8kScyhKnPb9nn6ThFz3fwN8NsjMP9hYvZq6R0Mawd4sM6+TEyLIdltIafMy23GtwH4VU1nE32ABmLGvoR81TmobrIWawsMJlF4KhtEMZw1qsUO/3tzwJghG8T+ozMRwkES0qvYsy2b/y7NoMgTIqQphFFw2y2cN6UfA9NixTpUU0ELi3xiOCS+1wZz9ZooWm/rrtNxmGwQ0w9y1oj1RkuLYCdcjLbtRwYHsjhH+YH/+Y6P6DC7OxPTYujlMnC/70VkSePj8FQ2qH1rPSNqY0ZUCMwxovBCv250HAaTMKpxxKMU72CknCUKPKyuA/p8NLbOSHQYuUNfZ+j0MPrF1eXQAyGZBFsseZ48Eu0Nd/xJj0tnfOJ4VuSt4M2Nb3LLxFvqHTfIBhRZ6RHutRPTYkiM8VIV9xMA/vyj0YLR1MgFJYQjao1IU6fzoMgSkwc0Tb82Jz2Z/9a63UpoIRPgItFm5MY5vRnf30Kht5AibxFoYDfZsRltyHpHvSahi2urkQwGYi68sPb3NsMeK0RUIV+T25J9dnocx78nWo9+u93L8RMugp0/iyTv2g9E5WtHYHaKjYHCrcL1NW6QqLyKNJUFQljrK6tubdOE/58VryEVbydgdHKr79Lamw3VrTBq5qmXTk8T1WAVBWLDMbpv5Mffk1CMwhHN7ISCLVCeI97XNp6MNNVRREcnUkQ0ZpidQnRRntVqcS3AeyfHcvpHRWzXUsnoewppO9+FRU9C6iEROX8tZqdokV2yWwgdu9LGjLcEireDxSmuW0Evyu+PAPBqeA5rtAEAKJJGNJWUaQ5ytHgeP3a43g6jsyBJQmxgdgnBVekukWCwxhz0s6jHDJ32pk3WGRZ3s9cUAAyYBUtfFM6aW7/nleNm85fPRfHZ0uwAk1Jb2RZTMYnvYvF2IUSNQJvNDsdTLNzabXUbS4oM1zsXYCgRYqlFajrp0g5gn3WGtxBscXpL1tYgSSIJbYsR4tqSXVCWDSarcGNrq3WGJEHCcFLmDieswoadGWg5q4kLZJEQykWqyhNCLH+FuK8kC+GiLVb8RPcVBaixA0W86krzJJ3uhytFOEC3pphPkjlx1lQ+/vkPTlJ+w/fR5ViuXtQ9rvNtyR9PI+WsJqDY+bvvktqb5eqOSvvtTYEQ1lqjIXaAfu1oLZIkChqaUtQQ9Ip1YlmWmCfJhmY7pvfEdYZkMBBzytGghpCMepqh02Fxi/l5yN9212uLS7jX5m+Ala/DYdcjuXtx74np/OOTdQBkFFbRL6714sWegCJLXDo9jWVfvsIYeTtVmpmPQ4cRRmk4ZuyLp0SYu1ii2mnEXRRZEY6wihHyNwJaq4qQmoViBCWK8elRjBk+nPVZpZRUeYi1yAxLtIuui5omYphsAMkMFmPTcmKdHMlgIObko0BTkQwdYBikE1lcqWJt7i0Gewvd+c1OpAkXw++Pcb3hQz6vnEJ+uY8EVzP2uXowiizxn16/MWzHboo1Bw8FTyNWqgDq1hmXTU1F0UJib8Kov6+dAosbkkaL703hNqjIBkfiAa/zPXadcfKR1b/rMaO9aTfdVDOxmQxcfFgaL/62kzWZZbiNwyiWi/EEPdiMDe93nT3sbFblr+LP/D9ZU7CGUfGj6h2PMkdR5C2i2FdMvK37dptRZIm4vl/h8QUJefoRKh8JmiiKrIkZd+k58G7Bwdxuezt7Ux4op8RbQoG3gLyqPIyKEZfJhekgneN7Op3natjBSCYTiTff1PZPZHaJzVlPATiaNpEdnWji3JE23ljr4bpvS/l6gIWnJlyG4ZcHxKbVwCPExKsjMFqF4LUyD3wVEDdALKoikeQMBUTytEQkqnGlNC2xkLceVr8DwLVVF1GIEPxKqLV3iXWYuXR6mqjoClSJZGjsIL3dXiSQJNHG1WSHwi3CGdIeKyqq25DmOIro6LSWiMcMZ5JIeKuhVm+WTko1c9JQKx9v8jJn4xx+d/xEXFUerHgVJl8ZmfHWYI8V43YmdR3X71BAFIWEAuCqHvOKV6Ail0wtjodDpwJgIISBECYpRKljEI8fN4k56brzR6fDZIPEEUJ0VbhVxBxHwkEd7vWYodOetMk6w+QQm7G+suaJa2UDjDwV/nga1rzHzFPncvQAC99u93H6R0WcOcLGvbPcezmhtwBbjBCmlOyG+C7u9q2GRbtQTavXLjR/8xISSlYQ1BTuD52FBX/tsdp1Rl+nEObG9tfFZ5FAMYrWk45EsfYs3S3aUMqK+C60VbtjRMwYPqAfDOjXZs+ho9OmWFzgTBHXs1YU2x07PJrL1p7HtNK1xJdu45cXb2LaZY+33vW8u1K0XbjFA7f5zqndm7Lhrb1Lvb0pEKJ9SRFdlSJZGKlzcIxW8eNIEgLn4p1ibWGLbpbgqqetMySjgcRLToWgH4x60rvTYXaJz2+gqm3no30OhcR04a7568NwzKOcPakvH67I5M/dpcz4z0KunDGAm44eoseMg7Ams5T7vljLd6b3AHgrdAS7EC7m+8WMfVFD4t+m5lB6OrIsXByRoGCjuK29BLbVKDKM6h0F+7Rw766ImHGKcOE16TGjy6MYISYNsleKDgEt7bA67FjY8Bnukp3cYPiAife5uPGowXr3uCbw5+o/Gb39WZDg3uA5+DADQlxbEzMmx/tFJxN792533uWQZZFHt7ghf5MwqrLHHXBvq0euMy4+RezJ6jGj3Wk33VQL+Mf8YXyzPpfMEi+H3f8HVx6RyLjBpVgN1gbXGimOFI7qdxRf7/yaNza8wQPTHqjnUFvjXptVmUWMJabbutc+tfgLdvuWoWky/twTQFMAodBPclu469jheg68G3Egt1uDbCDGEkOMJYbert6U+ErI9+RT7CsmpIZwmV2NitV7Orq4tr2RJCFGrcgRbV2aaLF87SQnb6z1APDNdh/n+sbzTtIoyF0jHAmPuqctR31gZIPYtPGWijYglfkQ3U+IiFuykaNWt8Ar3iH+bc5GdtArkheaynLHDL4pnFh76NFZNqJMQ+pXdKlhkfSOH9p1hGFdBWs0JI8F83aRlAh4Wv6Z0NHp7thixULaXxGRttF/O9TJx5u8+DFxo+d8XjX9G9Z9CIOPElXKkcJoBW8ZlGSINmqdfdGhaWKsFbl1LfIKt8DaDwG4PfgXPAiR2mOzncQF47DGjiV9/DQUpZO/tp6MJIl5iNkFRVuFo7LVLdyVdXS6K5IkRIYVec1/7ND5sPI1URyxaxE3TT6Ub7f7AHhnvQezQeKfh7dis1aSRUwozQBHXETiWodRkSt+HHslIdQw3t+eBuDN8Gx2aCk8e5QVi+yov84oy4Kovh1XBNldMZiFyNaZDJ4iIXzyFEFVoXBhMTmE4Fxfc+jo1MedAuWZzXc834erpvfm9o/+wnOmx5iS8wbvfHYkZ51wbAQH2k1QVfjiOgj5WCGl82F4eu2hJ+bEYMC1v9tQyC/Wg0np+v5URyLLwjHdGi0K/ou3Q9Ankt06Ol0NWRZz0YJNbfs8ZicMP16Ia1e+AYddD1F9uPnooZz5wh8APLNwO4kuC+dP6de2Y+niXPjqMs5UfiRNzqNAczNi2vEMtsQ0zaHOWyquXXt13NA5CJIkxIFQLbCV9eIWHZ3m4EgQcaYqX6zRW4KswNS/wv+u40xlAW+HZ/Gf7yAlyspJ43pFdLjdCk2j4sNrsCoBfg+P4CN1Gm/MtxMOx9bFjFAlBBGddeRubHHalTE7IXk0mB2iONPsbFrXDR2dHowkSdx09BCue3cVAM/8mMeNVifW/mVEmaMafMzJg07m18xf2VOxhwV7FnBk3yPrHa9xry3yFZFg637FCP6Qn/+uexjZBMHiKaj+JF487xCqAqH9XE11ehYmxUSiPZEEWwLlgXIKPAXkefIo85fhNDmxG+16gexe6LOpajRVJZCZRSAzC01VD/6A1mCLFUk/f2WTHxJvU7huYp3AdHFWEG3qX0XyOuM32LEw8uNsLtYosQFdVQB7lkHOKpHwDweb9vhQQCSvs1dC1nLwlwuxTHMqhpc8B+VZaPZ4bvWcW3vz/EEWTkiPYcbgeEb12msjylMoWi9E9236c+g0HYNJCJdTx4FiFiKDcKCjR6Wj02oiHjMUg6hU9Ve1/lxAL5eBi8eKlnsL1TF8GZ4oCjp+/nedk0WksMeKgpGK3Miety2ozBObFLZYURiihuDnh0BT+VqbzEJ1LABnpts4ZqCRQwcmMXrkaF1Y21UwOyBplBAkhPziM6m18ZxOR6cJtNk6w+IWziDNnVuZbKJ1K8DqdxgYbeDM9LoE4qurIxCLzE4xrqIdopitKxL0iWI/o6WeA0tw/ef0De+mTLPxWOhkLhvvYM7Q6PrrDF8ZGO0Q008XebYVilE456eMhd6TRDLCEiWc2cqy6kS3AU/X/Qzq6EQSS5Rw5PSUtOo0o5KtmNIm87/wJAySypiV/2j6nk9PYsXLkPErmmLhBt9F1DTZu2myk9mDo/ffm9I0sZcW1RfcfTps2Dp7YTAJB/6UsSAZqtcWWkePqtOhqSqB3EICeUVtv5+u0zKsUWI+2pbzIUkS87GkkaAG4Zf/ADB5QCwzhtS1Vn1ywda2G0M3IL/Ch6+ylOsMHwOwvddJTB05aP+Y0RCaJgpo3BHqKNiTqBHYxg4W7e2Dno4eUbdFjxndEFkRRkuaJK5BLSVlNFq/6ciSxgPGF1AI8+zP2yM2zO5IweI3ma6sxa8Z+UfoQh4/OpppA6LqYoakibVfVD8xF9DpvNTk0ZPSxffIU9TRI+oUaKpKIK+QQF6hHjM6gHbVTbWAY0el0D/OXvv3grUavqCPUCP5b4fJwSmDTwHg3U3vUu4vr3fcIBswyAayK7MbPUdX5r5FTyGbilCDTvyFs3nuvNHMHp7I8WNSmTwgVhfW6iBJEm6zm4HRAxmTMIYBUQMIq2Fyq3KpDDRd09jd0cW11Wg+H9tnz2b77NlovlYsApqCwSwc8/wVzXrYXyc5+eS0OqeEQmtfGHOW+OP3x8FXGsFBthDZIJKctmghtspcBrsWQf7G6gRnsXjdgSrxr6dYJD/zNsDuxZC5vFrwGieqHpuzGZS5DDZ8CsBVVZeyzSPaJ1w9wcH9s6L2v3/AAxrCxVFv1dp2SJL4TPQaL1ymKgs6x2dVR6cVtEnMsMUKAU/Qe/D7NoHbDnPx5gnC8eifwQvQTE7h0rr63YicvxbFKMZdvCNiY28T/JVQsFkImWtcKNZ8AEVbKdXs3OE/D4AbDnVy52HO6s2nvl3bcbEnIisiKZIyDsxuMffQBR86HUybrTPMLvHTjIK9WtJPBNkI+Rsgby3/muHmmbl11zs1EuIRezxUZIvvYVekdA94S+rFgXBVCZ5FLwDwn9BpXDk1hb9N2sclWw2JdU5MP91Buz2QJOHqEd0Xek2APpOh9wSIHSQEziGfKK6pEdxWFYr/n5C/o0euo9O+SJIQ3KC1em70yJwEwmPOo1hzMFzehfbbo5EZY3ehZBd8fxcA//SdToYmHMzvmObisvGNFI9XFYh4EztAd5XqbDgTIWW0KPwvz9aL9/ZB8wfYfv6tbL/oTjSfHls7JWaX+PwGIlPM3fjzOGHY8eL3VW+JrkHAc+eO5x/zhgFQWKkbLjSGNxBm4r0/cpnhf8RKFZSZU5g489gmdz0kWCXmvjbdZbtFSJKIwbGDRL4qoAts2wLNH2D7Bbex/cI72j4Hq9N+2GKFSVJV6wSB0mHXoRltjJQz+IvyDVvydCFHY1QU5yJ9exsAT4RO5NLDB3PcYGv9O3lLRFF+lO7+2yWQJCFUTxolCmZ0gW11zPgH2/9yux4zOoB21U21AFmW+PLaaVxQ3RVjU46XeFs8Jb7GC8qP7Hsk/Vz9qApW8famt/c7Xute6+1e378V2Zv4aPsbAPjz53PbvMEcNSy1g0el05mxG+2kudNqRbYhNUROZQ7eUCfWgLQT+o5tR2GPB0lpVlJDkiTGJplq/57wYh4/RJ8mJlzeElj0VBsMtIUYzKINiDMJ0IToKmulENDuWlT3s3uxcKotyRD3cyaJNiKK6SBPsA+eYvjpfgC+tc7lK98IAK46xMGNk124zPt81DVVTE6j+4EjHp12wGQXDgbJo0SLxPIc3UVKR2dvLC5RWOAri8jpZEliam9xLS0gir9VniMOrHitNskRMawxIg6V7IrseSNFOCiEtf4KseEHUJKBtuJlAO4NnU0hbm47zMU1E51YQ6UiuR2lu0Z1WeyxkDJGOH9V5umJEZ3uSU3b4pYUNthiYfDR4vc/38QgS8zuX9cmvP+TOSzJaqVAQjGK+V/R9pYJgDsSbwmUZoiCweqEdlVA5X9vP4Vb8rBB7cvw6Sdx2XgHZsM+ld1VhaJI0K0nMNodSRIu5s4kiB8MfQ6FvlOE4DZlLMQNEfFd08BXXi24zRLrQl1sq9MTsMaI9Ya3de61RkVixsj+/DN4PgDBBQ+wcfWSSIyw66Np8MW1EKhkhTaU18Oi1eDjR0dx0VgHhobcQAIe8bi4IXor6s6KLUY4pFtjhJGA7mCr05UwmITgsq3FtZIEvSdC0mhRbFbtXms2KMwbVdcqvN+tX7IuKzL7Xt2F/Aofw+78hkSKuVj5CgD3Iacj22KafhJfucip6HGk5cgyxA2GuKFiruQrP/hjdHR0qkWBfUROtzXOz7ZYpEOvAOBvhg/pJeXT79Yv2ZzbPJOq7k5miYfvH72IOKmcTWpvDjvqZM4a5azfsjkcFP8XMf3BaG38ZDqdD1eyyKPrAlsdnYNiNSkcPyYFAE8gzHEP7yGvlEYFgIqscOHICwFYuGchm4s373fcZDCRWZFJUO0ehj3b8ss559ObkeQwocrB3DfvSC6cMqx+zNDRaQSb0VYrsu3n6ocn6CGvKo9gDza00sW1HYUlSiT2/K1bpF/8VSWvRV0tkr7bfhCC1c6ErIjqOFeKcElxJokWFGan+NeZJBLPrmRxv5a0LdJUWHg/eIvxOvtyXclptYeuP7QRt6iqQrE5Ht2vJa9Kp6XIshCrpY4Twqfy7M7tdKmj0944k4XoPELC870nyJ+oh7EgPEa06Pv5wciK2yVJfKdLMsT1tTOhaVC8U4hnnInV7RBDsPB+pHCQBeExfBA+HICLx9pFG/OQXzhWGC0HOblOp8Zkg8QRIjHiKwNvaUePSEcn8lii6q5rzWX0GWINsWcp5G/ApNTfVDn9oyLe39BKYbolShQ2FO8UxVVdATUMRTtEPDDVOQyuWruGY9UFANwZPJ8z0htYZ9TMa2MGCHGxTsciSSKRZIsRa9G4gdDrEOg7VQhve00QMcJoF3GiNFPECt2VUKe7Isvg6gWhQKs/5y67hc/VKXwfHodJCuP/6HK+XZMZoYF2YZa+ADsWEpJN3BC4BK1623U/J6ka1LBImsb01wu/OzsWFySPFHMbXWCr09Wwx4rrflvPccxOGH6c+H3V22INAMQ76neMO+bJ3/h9WyfbO+pAvlqTA8CtxnewSgFRbFFTCNkUapKbzsQ2GF0PQ5bFmiEpXewNVubr13sdnaZgjQZXKlQVt+48Q48hlJCOTfJzn+ElQOPox37hz92tKw7sTqz68X1OUn4jrEk8bLyEyYMauPZX5Yv/D1dK+w9Qp/U4kyAxXawV9UIPHZ0D0jumfmHZLW9VsT6rGK2R+dvg6MHM6D0DgJfXvUx4nzx5lDmKEn8JhZ7usVa5/7fXMNh3oqlGYv0nMXvoQAyyoaOHpdPFsBvtDIweyOj40STYEij2FVPiK0HtgTkUXVzbUciyEJS2wEnth3Pqb7jftTEFRlYLSn/5D/hKIzDANkKSRQWjwSz+bWprowOx+h3IXAaKmadcN+BDbNi9d3Jsw64gQa+YlMYO0oVTHYU1GpLHQlx1q6XO/JnV0WlPbLEiGeGPXEX2l2fWxAyJ24IXUaFZIX8jrP0gYs8BgNEGaFC4TSTsOwvl2VC0TSSTahYNq96Ggs1USQ5uDV4CSHxyWhyyJEFlgdh4ciR16LB1IoRiEImR5FFCfFiZ39Ej0tGJLBZ3y9u8unvBoKPE7yteBeDjU+u3EX1mWSvjkSQJsVDpLiFE6QqUZ4ux2vdac2kqiaufQpY0Pg4fxj9Pn7Z/hbemiQKTqL51Luk6nRPFIERSzkQRI3pPEj+JI8RcoTxbuFXpiXSd7og9XsSOVnbLkCSJd06M5fbghZRrNsbIO8j97pEIDbKLkrkCqtuzvmw4nQxNODV+f3Z8464gVfnC7Ty6b3uNUqc1mJ3CScrsEt0xdHS6CpYoUUzUGkfBpiBJongpeQxo4Vr3WpNB5pULJtS762uLMtp2LF2I1//YxSHSJk5UfkdDgkMuaF7Owlcq1h/WZjjd6jROTWvulLFir7MsU+9yoaPTFKJ6C6OD1uQ1JAnDjFsIS0amK2s5Qf4dgPeX60V8AJqvjEPW/QuAl8LzuOukCfvfyVcKBpswDmmJmZVO58CVDPFDxPcp5Ovo0ejodFriHGYeOmVUvdtW7zRS6i9t9DFnDT0Lu9HOrvJdfLfru3rHZEnGarCyp2IPgXAnynO3gLzKfJaUvgGAv+AoHjplLNHm6A4elU5Xxm12Mzx2OCNiR2BWzORV5TXqFN1d0cW1HYktViw2mimwHRhjJOPaFO6f5a69bfiyOYTdfcBbDD8/1HMSgbnrYNlLAGSMuJKntwtBwLUTHUxKNe9//72T3o6E9hypzr4YTBA/VLTWC4f0SnAdHRAud+5eEIhc++wR8SJm3DLFSS6x/Ct0DgDa8pehdHfEngcQyfqqfOFg2xnwFEPBJpGUMFZXMBZtg5WvA3Cb/zzyieYfh7kYm2QSmxVGC0SniSIYne6BJInvVfIYUMxQnqvHG53ug2IQ7T9b2uZ13Ll17rV56xmXbCLj2hQuG2cHIKMszNRX8giEW/GdMZjFtbVoG/gjF9/aBH+FGKfJXs95dt1vnzMwtI0KzYo06TLSExpwpfWWCMFadD9x3dHpOsiy6KoS21+IbJNHg6RAWZaexNDpfhhMIvEdgevx5N4WllzRn29jxPri9IrXuejhd1DVHjjP8hTDB+eDGiQ7ZiL3lc8B4Mk50QyKbcTJ3F8BslG0oDY0sH+l0zmxuEUxhmISsV9HpytgtIiC4/aYi5udMPx48fvqd6BoOwAzhyaQ8cB8Zg8T+/Hfbcjj1Gc7WQe+DuDNP3aRUVDB3cbXAJD6zxAC5aaiaRD0iT0PfR8rsjjiIXW82COsKhRO8/peko5O41hc4O5T3Q2mFd+VqN4o48T64l/mN4inlHeW7ubCV5dFZpxdmA2v30AShexSExh9xJn0it6nO4YaAl+l2NuwuBs+iU7Xwd1HdDipLIhsF0odnW7GqYf0JuOB+fSOEdfEj5d5eOqbykbFsS6zizOGngHA+5vfp8RXst/xcn85eVVdu6D2gi/+jqR4CXtTuXP2XEYn92u88FtHp4nIkkyiPZFR8aNIc6VRFaiiwFPQY1xs9RV3R2J2gC0e/C1zDJnVr66C2aOZuN90ndiY3/U7bPw8UqPsvHhL4cf/A00l1H8Wp66dCMDoRCN/ndRAm1YQ4mNrNMSk6UnvzoAkicReylgwWKtb6/WMi6+OTqPY44QAMMJijjkDxcLi/fAMfgmPRAoHYME9de3jIoGsiMKR4h0d7xDqr4C89aKtt7W6Gi8cgIX3gxriB20Cn6lTmdzLxMVj7WKDwlsqNs2tUR05cp22whEPKaPB4oSKbD3e6HQfbNUORS35TLtSYVB1y9GVr9XefOzgupZKWRVhnl/ZykS8NUa0MivaDmon/e6pKhTtEELlveJAcUkJyRteBuAr12mcMLbX/o8NB0V3jNgBonhSp+tiMEFUHyFqiOkvBHO6eEqnu+FIEEUEkSjoM9roO2Ymv4RHYpGCXFX2H95dsqP15+1KqCp8chmU7SHkSGFuzqWAxPFDrBw72NrIY0Ji7REzoC6O63QdbDGiWDzkb1FHMh2dDsEeJ649bS0OlCRIHQcp44R77c8P1jt89qQ6p+5lGSV8vbaLdLdoA3YVVXH7p+s4Q/mJEfIuEZvHX1DXdakp+MuFm7Yt7uD31Wk+JpsoqEgdJ/JuZZkR7Tamo9PtcKdUd+VrZSv7sWdDdBpOrYL7jC8BGgs25fP7tu7Rprsl7F75PSOyRSfC33pdzKTBqfvfqbJAOJ66Gti30ul6yDLEDhRdl/SuGTo6B+XqmQNrf/9ja5jluxqPGUf0OYL+7v54Q17e2PBGvWOyJGM328mszMTT1p0/2ojnl39CZmApmiYzOfY0Zg8ejM2o5yx0IodZMTMgegAj40cSZY4iryqvy35fmoMuru1onIliI74FyfBEh8J5o+ouhC/uSWZd2l/EH4ufFuKm7ooagh/vFg6J7l7M2XUuBV6xOfjsvBjR2ntfQn7xoye9Ox/2uGrBUzSU6wJbnR6O2SUEqt7SiJ42LcrAcYOtgMRNwcso0RxQuAWWvxzR58FkE8mUgs0dl2gM+iB/o2h360isu33pi1C0nWLNyS3+CwGJp+dGi2o9T5Fw3nX37pgx67QP1mjhSGiN1eONTvfB7BLu3JFwr81dB0B6gpHpfepc9P6zuILVea1ohSRJ4EyAsj1Q3knb+ZVnifE54mtv0jSN7995nFipnC1qKvNOOLvhCu+qAnAmix+d7oHJBonDRczQNJHI0J2qdLoLJjs4ksRcOQJM6B/Pa46LKNesjJO3UfDlPWzL7+RO5ZHk90dh63doionjiq6mTLNhN0o8MOsAblGV1XEjqk/7jVMnsrhSRGGmp0h3ktLpGliixJqhPRJeZhcMP0H8vuZ9UfhczfTB8fSNrduXv+KtlWSV9qxWkgC+YJjDH1qIm0puNr4vbhxxUvPjgq9cPMZoOfh9dVqGLItrfq8JED9MFO6XZbbPd0lHp6thsotuPr7y1q2fZQPMuh1NUjhSWcHJ8q8AnP3iEgoq/JEZaxeiorwE9dOrAPhCnsXpR03b3zzKXyE6MMX0F12udLoHBhPEDRHmVBHOF+rodDeOH5Na79J470cBsstKG7yvLMlcNPIiJCQWZS9iRd6KesedRidVwSpyq3LbcMRtw7bCHB5f/RAAVu80rjl8PAk2vZu3TtsQY4lhROwIBkQNwBvydnsXW11cW4PBQPRZZxJ91plgaMeJpzWmupKvZRWv/zcjSjjuVXPM+sPJizlELPJ//D/hoNQd+eNZyP6TkGLlzeS/s62qLvmf7FT2v7+mieSFu49IIul0PixuSB4lKu11wZNOZ6ctY4YkiU1bNRzxJN0Tc6I5Y4SNPGK4NXgJANrqdyH7z4g+D/Z44fJWuBXCocie+2CEAlCwESpywZlUt9G0ewmsFQmLG4OXUYRIdsdYFRErVVUUXxhM7TtenfbH7ISkkaKwQ483Ou1BW68zjBZRlNFSB0JXCgyZK35f8mxtAuT1E2KZP6guSXv8e4X8sqsVruqKCcx2ERs6mxOorwwKt4lEkCLiwBdbvLzxzW+cblgIwKOmy3BaG4gR/grxmNj+wsFdp/sgSaLFbvKY6i4busBWpxvhSgYUsXfUSiTFwEunDeAN58UAXK18wt8ffY4lO4pafe5Oz85fRDcQ4CPX+WzQ+gEwPN6I1djIlquvTKw5Ygfoie+ujCSJrlj2BFH431NRFKKPnUn0/Olg0OdBnRqTTawZ2sN1U5KEiUKfKYBWe50EUGSJhTfOYGRqXQHC1AcWsHJ3J1sftCHvLt3NDe+vBuAGwwdEUSHmnKNOa16nvUBldcGMnixvF0w2iB8kRLbR/cFfCeXZEZlL9RgUhehjZlTHDH0O1G1xJotcn6+0deeJHYA07nwA/ml6nWTE2mLCvT+wLisyRYKdHU3TeGNxBiuevYx+ch6ZWhyrep+DwbRPd4yarhjRaXpXjO6INQriBglThVAPEpcrCtHHHE70/MP1mNERdJRuqhVYjArb752HSanbi7nk+VK25jZcEDUgagDz+88H4KW1L9Vz3ZQkCbfZTXZVNuWBVrqxtxOapvHybzs54+NbkA0VhP0JTI6fS5q7H4qes9BpQ4yKkTR3Gulx6bhMLvKq8vBFuDtzZ0EX11Yjm0wk3XknSXfeiWxqR2GNwSQWG/6Wu3rcPs3N5eMd1X9JzM++iJA5Gkoy4JeHul8CcMt3sO5DAK7yXs7tq6JrD718bCMLB2+JENPE9hfVxjqdE7MDktKF6Lwit/t9dnW6DW0eM2yx4poViHzS44EjojhlmJVv1Qm8HZqJhEb4x3tFRXmkkCQhbC3bDcU72++7HA4Kx9rSPUIwULNg8BShLrwfgFdCR7NAHQfAeyfHirFVFQqnD3t8Y2fW6W6YHbrAVqfdaJd1hj1OFGS09Ho7/gJQzJC3DjJ+q7356bkxHN63rojtvM+KKfK0ovDDEiU2ggu3dJ4N4XBQCH5DVWLDGsirDHPjN3lM2/UUAG+EZnP5/EP3f6warm7r3V84Y+t0T+yxogjQZIfKrudYoKPTIJYoETsi5X5jdnLVMVP43nA4iqTxqOkZLnn+R6r87Vxo155U5MKHF4Gm8kFoOjfmzKg9dM/MRlxrQ34hbIsdqCe+uwMGs0h0S4aWFzl1cWSTkaSrzybpitPbdz9dp2XY44UApz32aMwuGH686JCx+SvRJaMaSZL44prDiLYZa2876ZlFBMPdf12+cncJt368li/X5pAu7eAc5QdxYPwFYh+wOXjLhImI2XHw++pEDosbkkYIka0jSRi6VBXoLuZNQMSMs0i68gw9ZnRnjBaI7ity3q3dbx17FsQNwomHB43PAyJ+HfPkb2g9IHf4y9ZCfv/iFWZ4vkXVJP4WuIKLJjfgcF5ZILrkRvdt/0HqtA+uVNFxsbKgx+TNZZORpKvOIumqM/WY0QF0mG6qlciyxJZ759a77ca3Gy+GPXXIqSTZkij2FfPmxjfrHbMZbQRCAbIqsrpEzPliTQ73/fwOfvOfaJqML/tULpgyGLf5AF2VdHQiSIwlhvS4dPq5+lHhr6DEV9IlvjvNoc1Uhvfeey9TpkzBZrMRFRXVpMdomsadd95JcnIyVquV2bNns3Xr1rYaYufBHgdS6xxDbpxct/lSiJvHnDeKc25fUOvU1y3IXQO/CivzhTGn8q06ofZQb5fCrLQGWiCFAxDyQdxAkRDV6dyYHWKDyuTs2e4fPQw9ZuyDwSRcK1pReHEg7p0ZBcC/QueyXU1G8RbCL/+O7MJcMQqhfNFW0S6trQkHIX8TlO4Sm0lydTWlpsJP9yP7Stmo9uGB0JkAjEowMinVDN5isTEek9Y8hxCdro/JDonpQsxekdNjNqa6A3rMaACLG4zWlnetsMfDqFPF70ufE0n3av47r75o9PU1VS0dpcCRIBxAC7cJ1/CORNOgOEOI7B2JtTdf+20JVxs+JU3OI1eL5tek8xid2MBGoqdQONa5e7ffmHU6Bmu0KAI0WEVRjk6XQY8ZjSDL4E4VXR8iVWTkiGfi0WeToSbSSyrkPuNLfLh8T2TO3dkIh+DDC6Eqnx1SH+4I/QUQa4ljBlkYEmvc/zGaCpX5ENVXdFXS6R7YY4VLmKdEL9jrBnT7mGGNat2aoTlIEiQMg/4zxd8//t9+a+7v/3Z4vb+/Xte9i5g0TeOCl4XIWELlHuMryJIGfSZD/xnNO1nILwrKXcmRH6hO07DFQMpYSB0PRodwsW0PZ2idTkO3jxmtwZki1tCt7VokG2Dm7aAYma6s5Wzlx9pDv2/r3l0yVFXj5pe/4QHjCwD8N3ws48aMJcW1j4Okv0LkYeIGi391uieyLDQOFlfn6wam0yT0mNG+LLxxRu3voTCs2tNwzDArZi4bfRkAC3YvYG3h2nrHo63R5FblUuTr3DEnFFa57oOfsSR9BkCgcAZnjB9OeqJedKHTvpgUEwOiBjA8bjhG2UieJ4+Q2n2MF9pMXBsIBDj11FO54oormvyYf//73zzxxBM8++yzLFmyBLvdztFHH43P1/a2wZqmESouJlRc3P4KakuU2NxqxeLbIEt8eWad496Luf1hytXijyXPQdaK1o2xM1C6G769HcJB8uMn85fs42sP/XdedL3XX4/KAnD1Egs6na6BxQ2JI8TiubXtY3S6BHrMaAB7nHARbIOkh9kg8dGpcXixcF3wKgIYhFPh6ncj+0QmG5iswk22og2TJCE/5K2Hkp1CWKvsJYBa9TZkLcermbg6eA1+TLxwTAxvnxQrHhf0Cecok63txqfTealxTLdE6Y7pXQg9ZjSAyQ6W6Na5po0+U3wXyjJh4/9qb7YZZd46Mbb27y+3tvI9kxVwxENpBpR1sOiqIheKtonEaHVRxiurKinN3sblyhcAFI65mseO7bX/Y4Mecc2IHSCKYnS6P7YYSBguxFN64rzL0NViRrtiixXJOX+EOljIBtxxyeSPu46gpnCM8gfBxf+NzLk7Gz/dA7t+JyBbudh3LT6Ey/ubJ8Ty0Oyohh9TkSeKWeIG6R2VuhvRfcAW1yOLLzRNI1RaQaisols4knT7mGGyiyLoNuiS1CBmJww9BmQjZPwqTED2Is5h5qmzxtb+/fHKdijM7iA0TeO2T9ZS7hOJxTOUnxgjb0cz2mDiJXUF4k3FWyyK/PTuGR2LLAuBc+8Jong7HITyrHrFqjp16DGji8WM1mAwCSOLgLf134fovnDIRQDcbXmHNCkHgA9WdNMiPoSw9so3lvKo8RmipCpWq/2ZPOcs/jZln2u+GhKdSGIH6vGgJ2Cyiz3IkK9VZm1dBT1m9IB8RhvSL87Ov44fUfv3D2s9BBr53gyLHcaRfY8E4PnVz+MJemqPmRQTiqywu3w3wXCwbQfdQkJhldOfX4wl6WMkg4ewL4Wbps/n2pnDMCl6zkKn/ZEkiQRbAiPjRpJkS6LAU0BlN+n21Ga7uXfffTfXX389I0eObNL9NU3jscce4/bbb+f4449n1KhRvP7662RnZ/Ppp5+21TDrnt/rZeuUqWydMhXN2w7V23sjy+BKEQuNVjAi3sibJ4jEty+k0e/HiXjSjhLJv+/vFK25uyqeYvj6FvCXE4wdxvQ9l6JVf3xfOCaGuQOtuMwNfJy9pdUTzv568qKrYY+FuCEQ8IjFgk63Ro8ZDWB2CfFRGwnMxyebeHJONOu0/vwzeD4A2rIXIPvPyD6RJUpcf/PWiYRypPFXQu5aUYDhTKovrM1chrbsJQDuCp3Pdi2Vt0+M5cj+FhxGSSQ/3b3BqTt99GjMTpEEMdl1x/Qugh4zGsGRIIoGWorJLtqRAqx4tZ54cGpvc22L620lIQY+lU25vxXubAaLeL7CLaIQriPwlkLBJjAYawss1uUHuPeXYv5tfB6jFKY0aQrpk47AZtxnHaFpUFUE0f1EMYxOz8GZKBxhfOWt+77ptBtdLWa0KwazaC8ZyW4ZZicTh6XxR8p5AJxf8SLn//NxvIFu1Cp58zfw26MAXO+7hB2aKOT+3xnxHNbHjHXfmAHCXchggYShwjVSp3thMIt9RzXU42KD5vOz9fTr2Xr2rWjerr931yNihiNRCADbI0kuScJlbfDR4u8G3GuPGZXCxYelAbBwcwGT7/+RQKj7uUB/vyGPd5YKIVgM5dzj+BAAKf1kiOnfvJPV/P9F9dY7MHUWFKOIA70mgCNZdEbpJgnkSKL5/Gw94wa2nnVL++dg24AeETNagyNJ5DY8xa0/16jTIDEdQ9jLG67/YiLIZ6uyOfKRnwmrXU/0dTA++TOLMVufZIqygSrNTOyMKxnXPwmTss81v6pA7FG4GygI1+meOFPEGr6j9lLbEc3nZ+uZN7L1zJv0mNGd8xltyLmT+zF7WAIAP60Pc8ObOaiNdNE7a9hZxFnjKPAW8Or6V+sdi7ZEU+wtJqcqp62H3CLe+GMXayq+wODchKYauGLcWcwaNIBEPWeh08HYjDaGxgxlSPQQAuEABZ4C1C7e8anTqA137txJbm4us2fPrr3N7XYzadIkFi9e3Ojj/H4/5eXl9X66JLZYMFqEkLAVTEjZuwJBYuzGs9ASR0KgCr6+WUy2uxqBSvjmVtGy2ZXCOb4bal1BAI7sb2n4ceGgeGzsQCGc0el6uHuJDcbKAlC7UTJOp9X0iJghSWKxrGlt5ngwvY+4lr4dnsWH4elImor2w/9F3nHHVu14mLsGyrIil8SpKhRi4Ioc4RSxd+ujihy0H/+FhMbboZm8HxatCKf0ro4fvlIwOUS1r158oWONEgJb2Qiezt3iRaf59IiYAcL532BpXVHSsGMgqo+4Ri5/ud6hWf3q5twhFWa+nt+6JIbFLYoA8zeAr6zl52kJAY9wVQ96a2OUqmkc824hVymfMVreQalmJ+qI6xt+vKdIuIJE99MT2j2RqL7ipzJfbwHeDekxMaMGR7yIHcHW7UXVw55AUvpMPg9PxiiFeUh7hEue/gK1OyS+S3bBJ6Jl4Cuho/lSPRQAp0kiPaGRNqxBr/iJH6w7SnVn7AnCOKEHutf2ZLpkzLBGg9EW2ev+gTA7Ycg88Zw5q2DDZ/vd5ZjRdd3mcsp8XPrG8i7pkNUYgZDKpW/UdRW81/khir9MzCdHn9n8E3qLhRO6Lfbg99VpXywuSB4tul34K/X9JZ16dMmY0RoUA0SnibxGa102JRlm3wUmB73827jFILrvbc2v5NaP1nSrmOENhPn+oxe43CC6Kb1ov4xeg8fuf8eAByQFYgbWz4nodG9kWbhCG616R6VuTo+LGW3EaYf0rv19R77Gcz/lNhgzrAYrV4+9GgmJXzJ/YVH2otpjsiTjNDvZU7GHivbqANJEKnxB/vX9t5gTvgEghWOZ0judXq5eSHrOQqcToMgKvV29SY9Lx2l0kleV16iLdFeg06hJcnNFu+jExMR6tycmJtYea4j7778ft9td+9O7d+9G79upMTvEpoi/dYlls6H+hdKPiTG7riHo7COEtV/f0rUmXAGPGHPhFrC4WTrqXywpqmvdfd1ER+OPrcwX4kxXSuP30encSJIQ19oTuqYwXKfN6DExwxYrxEe+tlkAuS010wCJ24N/YaPaB8lXQuDbuyLfWsYWK1qB56yB4h0QboVgOByEoh2QtVIkhFyp9Vvohfzw3Z1I/nJWqf35Z+gCAP7vcHfd8aBXOKiYDxBHdHoW9liRAAmHutZcSeeg9JiYYXZWt/duhUOObICp14nfN3wm5uDVJDvqLx2LvCoDnsqhwNOKAih7vCiGy9sgigHbg1BACGtrHD6qeWONh5HSDq42fArAjuFXi/Ht93i/iEOxA3X3wZ6KLIs5hC1GF1F1Q3pMzKjB4hbrbW8EixxkhbTUeG4JXsImtTcJUil/LbmXobd9Rqmn626gEvDAe2eDr5Qc6yDuC51de+i5+TENP0ZTxXUipr9Ys+h0X2RZFN0YzO03p9HpcLpkzDDZxP5Me655o/vA0Pni9wX3iLn0XgxJrG+KsXBzAWl//4pKf9sUmrc3d32+vvb3cdIW5gZ/EH9MvKS2g0aTCQeEAUVUX7HHptP5UAxirZA8BlCEi203Ev7ptJwuGTNaiz1eONhGQmhuj4fDbwbgIsPXzJJXAvDBikxG/fO7btMp455XP+U/xmcBeD40n9OPmbP/9V5TxXsa1VfsZ+v0LCxuIVz3luoF392YHhkz2oD0VHe9v/+3MsAFz+0hGNp/bjY0ZignDjoRgBfXvEiht27P12Fy4A/52V2+m3AnMoM7/fmfsKa+gySFCZanc/20mfRz98OsmA/+YB2ddiTaEk16fDq9nb0p8ZVQ7u+awv9miWtvvfVWJEk64M+mTZvaaqwN8ve//52ysrLanz179rTr80cUZxKoaqsnQw/Mqh8oynAws/AGCokSgqavb+4abWmCXuFYm7cezE5+GXEvp/1Ql8AeEmvgykMacaT1lYLRDjED9I2mro7BDHGDRBWmnqDoUugxIwIoBlEk0Iaf/TunuwDwYeby4F8p12yYCtez4LX/IxSO8OLcGg0Wp3ApzFkt2qM2B00TTtbZKyFvnXB8dyTUdw3UVFj4ABRtpVBzcUXgegIYGZ9s5KyRtrpzuHoJZ2Adnb1xJUP8ECFoD3bNdjddFT1mRABJEuuJYCtb8qaOh/4zxfX0t0dr1yaSJHHT5P3n3hNezOPMjwtRW5IwrBmzp0jM+dv6excOCmFteZb4vktiOfzGmiru+7mAR4z/xSiFWWKewpipR+//eE0TotyoPmLcOj0XoxXiBovvR3s5v+nUoseMCONKASLbLcNgsnLhGEft+uIQeQsPGZ9j7P99y+VvrOh67lKaBp9fA7lr8RrcnFxyDUFEcd8xgyxM7mVq+HFVhWCLE+Ja3TWk+2ONBndv0f64q33GuzF6zGgAR4K45rfX59TkgEFHgSUKirbCilfrHbaaFP4ytd9+D0u/61tu/WhNuwyxLVBVjYe+3cQ7S3cDoBDmKfeb4mDadOg7pfknrSoWQrWGigB1OheuZEgdIwpgK7J1AVQXQY8ZEUaWxf6JhihUbi1p02HYcQD8x/gsiRQDUOEPMezOb7jvq42tf44OIhRWue/jP/hL5u04JB9/qMMoGX4uSVENFGHs3U1Jp2fiShGfgebm13Qiih4zOj8pUVaOG10/D1xYoXLCo7t4d3Hpfvc/edDJDIoahCfk4ak/n6onpI21xZJblUu+J7+th31QAiGV69/9kx3SK8imYtRANEemnMbQ+H7EWvWiC53OiVkxMyh6EMNihqFqKnlVeZ1KrN4UDAe/Sx033HADF1xwwQHv079//xYNJClJJCfz8vJITk6uvT0vL48xY8Y0+jiz2YzZ3E3U99YY4TjlrxCVRy3kjHQ7Z6TbufabEj7fIhLUmVo85/pv4UPrvdjzN8JXN8G8h8TmVmfEXwHf/kO0EDfa+Sjtbm74vc4JZHaahRePbcQZJBwUjl3Jo8XmhU7Xxx4rFooFm0QiW+o0pts6B0CPGRHCHg8muyiKaINr9oVjHFw4xsHFXxTxw84kLg/+ldeMDzIr9Bs/f/YCh590WWSf0GQXovnKXNHKzt0LHIkiySI38t0OBcRGQVkmVOaJxLQrub5bbQ3LXoQdPxHQFK4OXksOsZw8zMrDR1a3YPUUidgQO6Dx59Pp2UT3Exu+hVuFq6XeWqtd0GNGhLC4xWc2HAClEaFPU5h8Jez5QwhRN38FQ48B4KoJTq6a4OT0jwpZklXnPrg4M8DTyyq5ZmIjhW8HQpKFULUiB3IlSBzRfAenphAOQv4mKN0lvtvVMaRmzXS74T0GyVlUKNFMOv2WhmOEtxjMbl0kpSOwx4uYUbgV3BZ9jdKO6DEjwthixH6UtxTscRE77U3TE7hhdIg7P7+euzwPcLyyiN1aAg+vP403/tjFeZP7Rey52pxFT8K6DwmhcH7VtWQj3qerJzi4cXIj+04hn3AXjB0g1j86PYOo3mJO4y8Ta1ydDkePGQ1giQKjTRQImezt85xRvSH9JFj+Mvx0H4w8RYhCqrnr2BH8fe4w5jz2CzsK6wrM3122h8kDYjl+TNdz/579yM/1XstrI9eQsnWbeM8PvaL5c8egV6xBovvp+1ldBWu0yFHlrhMOtnsVeOp0TvSY0QbY48Rnv+Y70FqmXA25a4gpyeAl6+Oc7P0HfsT+1/O/7GBy/1hmDk1o/fO0M+P/71seUR9koJJNjhbDnlHXccuUBoq6a7opJY0SxiM6PROjBWLSIHuVKJhqKE+m0+boMaNr8MSZY7nnxHSmPrCACl9dUfkbv5UyqreF4b3qrqWKrHD12Ku59ddb2VS8ibc3vc25w88FwCAbsBqtZJRn4DK7sBvbaR21D5qmMfj2rzHF/oQ5YR2aJnNUyrlcMLE/vZy9OmRMOjpNRZZkkh3J2E12dpbuJN+TT7QlGouha8xpmhVt4+PjiY9vm6rYtLQ0kpKS+PHHH2uDQnl5OUuWLOGKK65ok+fsdBhMwkWvcHOrxLU1PDEnmgeOcDP8v8IefqPWl9O8t/Gx/T7MNQLbOQ9E5LkiiqcIvroZireD0Y4690FueL9OSDsqwchTc6Mbf3xlvhBr6S33uhfRfaEqX3w+9Or8LoEeMyKEyS5cKUp3tmlBxIvHxlLmUxn9PNweupAHjS9weOE7/PlrL0ZOnYdBjqCISDaI6tqAB4q2Q8kuEYtscWCyCqdqNCEO85UL559gldiAtsU0npze+D9Y9TYAtwYv4Q91OFN6mbh/VpQ4HvKJDaiU4WDupMUlOh2PJIl27yGvEHS7UvTkRzugx4wIYXaD2SUK1WytqFK2x8P4C+GPp+GPZ6H3pHrzr/dOjqPAE2bCi3m1tz38RwUj4o0c3teM0tyYISvgrE62oELCcFF0GCmCPijYXCesrRYeV/x/e/cdX3dZ/n/8dfbIONmre7e0zLJaQFBREERw8NWfXxWcIE5UEFHEjXsPXF/EiaKiiIiDKXuUAgXaUrqb0aRpds7+/P640pbSpk3Sc3LW+/l45GFIT07uT8w51+e+7+u+rliam9cOc5r7cd7l/QcAoZdfvv/5UTK2J4ZkI/lXCo/LZa34BrvsXiWDSYlyYIoZGeb2WNJT6+NWVS2D9z3uyia+cE6a7vUfpubRb/AB71/Y7DTw6b/C3PpyTpxdizuT84xsWPMP+M/VAHw28VYedhYB8PpFIT58wiixyklbt4ya2VYhUkqHv8wS39qfAn+FumnlAcWM/fCHba7Q3zp5ybXeIMx+GTx/B+zcCPd8Hc744t7D8rq542On8XznAC//xt27v/6hG1YytTrMMdOrcBXIAbct3UN7Jda+/7hyTlrzI/uPI/+fzX3Ga3CHJdKERyk4IvkpUAFNh1tcUIJt3lPMyAKXyyr797XbmsqhHjrz+OGVn4ebLmZJ/DkePOJWjn7yXMDiw9t/8Qi3fOBkFrdUFkzMWNvRz7tTv+Pl3seJOj7+Pf1S3rJswb4P3NWRr3qmFSuR0lbeBOX1tial7lo5oZhROCqDPp68+pWs2LyT1//ogd1fv+x37fzw7S1Mr/XtjhmNZY2898j38s3Hvsnf1/+d2ZHZnDTlJAAigQjtA+1s7N3IwpqFeHIw3398Sw+e8mfw1/8LgCXhN/CGIw9jVmQWPrcKBUlhqPRXcljtYWzp38Lm/s0MJYeoDlTn/b1b1mZxmzdvZuXKlWzevJlUKsXKlStZuXIlAwMDux+zcOFCbrrpJsDajH74wx/mC1/4AjfffDNPPfUUb3vb22hpaeG8887L1jDzT1mtLbym4gd/7BiEfW5WXdzEmxbbBvDTzkxeN3glfZRZJaq/vh/6WjPyszKiZ5ONqft5CFXzzLKvM/sPey8Y/eWNdQS9o7ywhnvAVwY1c7SAXWy8Afv/NZXI2OtD8odixkFUNlvCaSbaJx1AJOjmqYua6Jp2JtcmzwFg8TPf5Opf/YtYMgvtAv1hiEyBUJUlz+5Ya6dttz0G21ZA21O22eN224JRRdPoC3CbH8S595sAfDv5Ov6cfgkAv3ltLX6Pa88Gd9XMiW1iSGnxeKF+oSV89+e+zYvsTTHjANwjVWDjw4f+XEtea6+D+ADc84192sbWhz08cVETRzTsWbR5x9+6ed2NXSTTE4gZbo/Fu8FOiwWDXYd4ASOifdYNo2eT/W5GEmtvWzfM4T9up4kdfNM3stG9+LV4Zy7b9zkcxw7wVc1QDJG9+YIjBzJimqPkKcWMMSqrt8MZ0b7MPq/LDZVN1Mw+iugRVu3jGu/PON39GG/+2UP8v58+SHoiMWOybH4IbrwQnDS/S76UX6VesfufvvGK6tEPIA52WvJT7RxVOi9FlVPsNTW0I9cjkXEqqZhR3gjp9OS2qq9ohMPfaJ8/9GM7bL0fc+rLefyqV+D37Nm6ev2P7ued1z+K4+RxzBhx3X0bOOWrd+7+77pyPx/jV7jiA3bo4vDzx/+k0b6R5P0ZiiuFKFBuCbbhWuhv32duLYWppGLGoQrXWvGCTN0bRabByz4FLhfVa//A02dt3uufX/29e/nA7x7PzM/Ksu/e/hzf/c5XeL/3rwB82/9u3nbGctz7e68f7rbD4LXqpiTY/kXNHPs8kYF1YMkqxYzcc7lcLJ1Rw6OfOn2vr19yXSvf+9fe8en45uM5d+65APz4iR+zqW/T7n+rC9fRNthG68Dk51d98e/P8Iaf3USo5fe4XA4V8ZO46MSTmVM9h0q/unlLYfF5fMyKzGJx7WIC7gAdgx3E83x/JWt14j/96U9z/fXX7/7vo48+GoA777yT0047DYA1a9bQ29u7+zGXX345g4ODvOc976Gnp4eTTz6Z2267jWBwEsoAe71EdgUjbw7L5werbAE+2pux6pzlfjfvOrqMG54eAizB9vWxq7kh9FVqe7fAX98Hr/yCtWDNpU33wx1fsJZUlS0MnP5Vzvrt3vnf//7f+v1PKsA2M+OD1monqABSlMobbRLe12r/K0VDMeMgQtUjm3NdWT+VXBFwc+mJFZxzwxtpcXXxGs8DXBX9Oj/8W5h3nXUSFYEsnMvxBg7t1Pq2x3D+dRUuJ82fUifz7eTrAbj7bQ17TjkNdlqipDa4Zax8IWhYCNtWWkVCVYfJG4oZBxGqsgrhqQR4DuG0stsLp10Bf343bHkQ1t4GC16110MiATefPKWSN/5pzwLUEx0Jrrqzl0+/pJKQb5wxw+2xTh6D262CYu1c2zTxTOD35jjWlrnrOYj3273jyOG77YMpLr51Jx5SfM//PWpcA8Sr5+E/cZST/0NdFotrtIEh+1HeaJ1T+raqe0oeKriYkSvegL3fdqyyOJJJbi+UNxBc8Erau9ppav03P/B9h3ckLuO+DYfz5dtW89FXzifgzbMD0ttXw2//B5JRbk8dzaeS72BXRaxH33WAOVm0zw5G1i+0+0kpPV6/3TNseywzFdrymcdD5BXLIZ2CfHsNT0BJxYxwjSVrxgcz2zHiQDw+mLoUphwL2x6Ff38a3vSb/T60uszPN994JO//7Z7kqDtWb+cHd67jolPn4PPkZ+XPtR39fPZvz+z1tbve4IEbbgRccMJ7x/+ekE7ZXlHjksn7/0oyL1AOjYeNHCTtLM3K9h4PkdOX2Vw9l3uwGVJSMeNQuVzWKWOg3fZ/fRnoBjR9mXVcevTnlN35KX55yvd423+rdv/zLU+2cdzMjbzlxBnj7640SVZu6eFf/7mNG/0/BuDa5Kv54Ftfs//CUYkhSMahZfHkVZ2X/Beuhch0K1gWmVpca5a7YgaKGSWxnzGJ6soDfPrVh/G5W/bcs//zyQEWTQlw+uLy3XvKb1zwRjb0buDJzif52iNf43MnfY6aYA1et5cKfwUb+jZQ5i+jJjg5+4b3PtfFzx5YSXjm/+HyxEgOzuLK089jWsU0GsOqZi6FyeVyUR+up8xXxqa+TbQOtBL2hakM5Geun8sphOO+49DX10ckEqG3t5fKyvz8pR9UzxabZEemZPRG6PmdSV7+qz3V1xrYyR/KvsbM1EZb+D/xvbDk9ZN/85VOwopfwYpfAg7pxiP489TL+dh9ey+SXbasgvcdN1rLPccSLiPT7BSwOz8X2CQDhrph6yO2IJWJSXghSyetqtuM5ftvX3wARfFemQEF9Xvoa7PNuYqmSanM/WxXgnN+28a1vm9xuudx+p0Q3669mitfd0JeLUjFtj6B+7bL8aVj/Cu1lEsSHyKJl8+cWsmFR5aPPKjfWoJPOUYtm2X8erdZ1ctAZWG3gk/FYbgXZiwb94ZcQb1XZlHB/B7SKdj8gP1/Hqo+9Od74gZ46FrrDnH+dfvdBHyiI865v9+70uy5C0J88xVVE48Z0T77qGyG6pH2p2Odp0R7Yedm6N1iCS4h+96BeJpfPjnIV+/vB+By7w1c4r2ZuCeM//yf7j8xMjEEw30w5Wi1WZPRDffYHMUbKI6kh2QUYgMw46Rxx76Cea/MsoL8PcQGYMtD9r7pL8/88yeGYKCL7nt/Rk3bPQw5Ad4S/wQrnPn87wnT+dy5S/JnnrHjedLXnY17oI0V6bm8Of5Jolgy1A9eVc3Z80ZJmk1Gbc2icbG1a5XSlU5bC/DeLba+W8wSQ5CI2dqUb3wbvQX5XpkFOfs9tD9t3R0qJ7EzQzoFWx6Ff10JTgredjPMPnXUh9+9tpML/u/hvb528alzuOyMBfkTM4Cdg3Guvft5fnzP+r2+/ou3Hclpd7wWutbCnJfDyz5pVd3HY7ATfOUw9ViL0VLYBjqhbaV1VCnFAjHxAUglYebJ4z4MrJhhCvr30P407FxvSYCZ4Dhw++dg/Z04vhAPnnQd/++2vSuyX3bGAi4+dU5exYyugRjfvf057nnwQf7o/yx1rj7uSh1J6IyrOGH2fvYu0inb/66bD/ULiiuBUg5dfBC2jNwrZfqgbK7F+u11PvPkce+JFvR7ZQbp97B/juPwx8e2ctkfn9zr65ecXsOZR1bsjhkD8QGuuu8q2gbbmF4xnc8s/wzhkdyUruEuwt4wi2sX7/5aNnT0RfnaP9fwx8fXEp55LZ7AdlKxei454gOcNnceC2oW4HMfQoEVkTyRdtJ0DHawoXcD0WSU2nAtXvfYk/v74n14XB6Obzp+3D97rO+VykDMR+HakZPjAwd/7DjMqfay6uIm3nGUnWrbTjVnD17FLakTbTHrge/Dv6+a3LZlvVvhrx+AFdcDDhx2Hj9s/Mw+ibXffmUV7z32AJs7w922iVk7R4m1xS5cY4kPQztzPRKRyVVWZ5PjaM+k/LhFdT5WXDSVu+Z+gvtSi6lwDfPhHZ/lzT+4g8/e03vwJ5gMrSvhtivwpWPclTqS9yc+SBIv155VzQVHjJzgTiUsobB2rhJrZWIqW6zF0tAOO9Qgku/cHqhohvhQZp7v8POh4TBIDMIdn9/v6+DIRj8r39PEa+bvSTb665ph5ny/jW8+OMEW48FKqGyyzcetj0Dbk9DfYRXg9ieVhMEdtlmz5WFLFAjX2NxqZOPh03f17k6sPcv9IJd4bwbAd9pl+0+sTafsOWtmZr1yvBS4UBVUzbQk2+I6vyylJFBu9z3DWbrX94WhrJaak99JfOoywq4Yv/B/lSNd6/jNQ5uZc+Wt/OSe/bcIn1Q7nodfvBr3QBvPpafwjvhluxNrf31e7eiJtemkxayqmVY9SEqb2233D76gbQqL5KPyesCxe97J4vZAw3yYf6b9998/Ovr9PXDq/Hoe+eTpHD9rT0Woa+9+njlX3sqvH9w06vdNtrf8/KF9Emv/cNEyTtvxe0usDUbg2HeOP7E2Fbd1rdrZSqwtFuX1UDfP9v4O8LcvUpSqptmcIFP3Ri6XHVpoPgpXYphlD17MYxdNZV7Dnr3kr/1zDXOuvJWbHt+amZ+ZAWd/97/844GV/Mr3ZepcfTydnkn1Kz6y/8RasDlGeYO6Kcn++cvsbyM2oL0LkTFyuVycf+w07rviNCKhPYnbP/xPN6/5xibuXzsIQLm/nE+c8AkigQib+zfzzce+ubttfW2wlr54H8/3PE8ilcjaWJddczt/fHwd4Wm/wBPYDskIFx/xXk6cOY3ZkdlKrJWi4Xa5aS5v5oj6I2gsa6RrqIu+2AT3FrNEWYgjHMchPTREemiInBfz9Ydt8zaa+T+Wcr+bjy/fk209SIj3Jz7A1YkLSLm8sPFeuPFCa/uazd9DMmbVav/4Tuh81m7+XvYp7px6MV9/OLrXQ49r8XPewjDu0SYNyag9X9082wyS4lc1zapCxQdzPRIpUTmJGR4fVE23ZKlJ+pmVATdXnFrHuxMf3Z1ge73/y2x88l5+8cQAnUOTuAHzYhvvI3XrZQTSw9yXWsxFiUuJ4+PseUHOnBuy1h2OY4lYVVOhekbuxiqFzeWCmlmWeNffoaQpGbecxIxglW3cZmJR1e2xzQpfmVVge+Tn+31YVdDNp07Z91Tndx8e4BdPDLBzOL2f7zrYz/Zagm0oAn3bLMl20/2w9THoXAvd66FrHWx7HDbdB1sehJ4N1oY7MmWvlqs3rx3mz6uHAVjs2sg3fNfaPxx+Pq45L93/zx/cDmXawJAxqppqiRPDOgQoBayiyeYdyejBHzsR/jII1+A/8SKG64+i0jXEr/3XcLzrWQC+dOtqrr9/I33R7G1MHFDXc3DdWdDfypr0VP5f/FP0YNWoL19ewcnTR2nlvWveUdlia1M69C1gMaF6th28mMzkxUnkOA7paIx0NJb79XQZv2CV3eNP9vpqsAoWnWPdJXY8B/d/94APr68IcOVZi/b5+qf+sopfPrCRoXjuEkkcx+G3D23m6da993G+fv6RHF/VD3d/zb5w5P+DSMv4f8Bgl8WWsn27h0gBi0y3j8HOklpjUswQgpVQNWPkUOoE1oj2x+2FM75khRGiPdTe+Dq+fMq+Vc4u/f0T/OqBjUQTubsncxyHX9y3AVdfKzf4v8A0dycb043sXHYFR84dZe8i2gten1Wt1SELGU3lFJvLD3TmeiQZo5iRW3mVN5VFU6rK+Px5S/b5+hf/2smtK/tIphwawg1ccfwVBD1BVnWt4uuPfp14Km7t7EP1dAx1sL53PakMz/nTaYef/Xc9adcw4ek/xxPejJMK8rYF7+W46VOZVz0vqxVzRXKl3F/OwpqFLKpdhOM4tA+0705qzzWt9o5whodZc8xS1hyzFGd4ONfDgYrGzG2Iv0jA6+JP59fxnTOqRr7i4vrUGZwT/Rxb/bPt1OBdX4a/vBe2rcjsD0+nYN3tcOPb4dGfQyoGLcew/uU/4YPrlvL2m7v3+ZblUw8wYXDSI5VBZliFLikNwYi1jhnuyfVIpETlLGaUNVhr+kk8qVPud3Pd66ew87Qv8e/UMQRdCX7i+ybP3vtXvv9wZiusj1Xrw38l9a+r8KQT/Du1lHckLiOGxYrjWl4QMwY7rZJc3fxxt40R2YvHB/XzrUr+ZFb4l6KQk5gRjNjfayxD79OVU+DUy+zzJ34Hmx/Y78Mayjz85rW1fPfM6r2+/pm7+/jJikMYizdo7Wormuz9fHiHbcR3PA1daywJFqySR+UUS94a8WRHnLfc1MUHb7OExzp6+Yn/G4RccTqql8IJF+3/Z8b6wT3y2veOkkwl8kK+kCVix4dULUQKV7AKyptgaN+1mYwJVEBZLaGXfJCdtceMHOD7Ci9xPwHA1Tc/za8eyEE1wrYnSPz8TBhoZ3V6Gm+Of5IuIrv/eWHtAaqBDHZa7K1fqE1v2VtkKpTVF+0cwonGWHPu+1jzho/gDGcpKV+yx+u3PYjJrq7sctmhpKPfav99z9ehe8MBv+WoaVX87G3H8p03HbXX1z/916f5/SNbsjTQA3tw/Q7O+u69XHnTU/v826y6MrjtCkgOWxeQRa8Z/w+ID9l8pGqGDm0UG7fbOjAGItaRsUQ40RhrzvsAa15/aX7swUpuRKZCqDqz8w1/GM7+ur1fDnWx9I638JtXh/n2G4/a62FX/fVpbn6iNXM/dxzuWdvJy75xNz+75W7+4P8cc9xtbHXqeEviEzRNnbP/b0rGbF2vdp51ZhIZjcc70tnXWzRFqZxojDWv/SBrXvchxYwcyLu8qSx6zZFT+fYbj+Cjr9q7evgP/t3NA+usM+CsyCwuP/5yAp4AT3Y+yVcf+SqxVAyP20NtqJat/VvZ0LuBdAYOjjiOw3+e6eDkr9zBF297lPD0n+EJbcFJhhna/G4WNUxjXvU8IoHIwZ9MpEB53B5ayls4suFIWspb6In20B3tzshr7FDse3xL8kOo2j6ivdbKNMOWNvtZ2uznga1xbnjaAsMzzkxO6/sMf5h3B0e23oCnczX8/SPQdDgsfh3MOsVuzCYiPgDr7oAnf29Vp4BEoIbnZr+VzqZTueCmKLB3cH7r4WGCXhfvPbZi9Ocd7LJJRe0cVZMqNZUt0LvVFhr9OpkjJcIXtMrNHU/b5u0kOWFKAKYEeOeaj9Pf9j1e57mXr/h+ynXPbOY3Ne9jYX2Qpc3Z20ROpeHp1l52Dgwxa90vmd76DwD+mHoJH0+8mxQeLjyyjJDXxf8uGUmoivUDLtvgfkGSlciEBSqgfgG0Pq7YI/nP47XkqK41dsggE2afBotfC0/fBHdeA6+91u7HXuSkaZaI+sNH+1ndtSfB8EePDTC10sPieh9HNU0wZrg99loMjD4/2BUzuofiDCS9vP8/e+YYAeJc6/8WU1w76PZPof6cz+x/fpOKW1v0piXawJDxqWiG8m22UViuCmNSgFwuq/zdt83eCz1ZuscPVIDLRfVpH4BHf0Zo03381PcNLktczM3p5Xztn2uoLfOzZEqEJVOyN+9JpR0e3tCN8/wdHPfQB/GlhngmPYO3xD9BN1aN/e1HlVEXcvPSmaMctIgPWtW5+oXqpiT78vptzXLrY1YR2hvM9YhE9lZWb90g0smJr/tPhL8cph4HU46x4h7/uBze/IcDru+fflgjAB+6YeVeX//s356hzO9lyZQIh7Xs20kjU3bFjO39UboG4nz+lmf2ecw7TprFlOoQS4fvhzW3gssDS98+sfWD4W6onav5SLHyh63afesKS6DTgU4pFb6g3Rtty/DffqgaXv1t29PeuYGT7r0A3vhrPvyih13+xydxAUdOq2J+4wH2ng/RC2PG1p3DfO2fa5jr2sqv/V9himsHG9ON/GXmJ7lkyhzm7u8Qn+PYAb6qmRCZlrVxShEJ10D1LOsW7A2q2IzIOJx39DQSqSa+8Y9/7fX1L9/cSfxVDvOb/RxWexhXHH8FX3n4K6zqWsXnHvgclx17GVXBKqqCVWzs24gLFzMjM/GM8/X3wpjx/PYBvnvHOtz+7YRn/gK3v5t0soyjg5ew+OQpHDdlAXWhuoM/qUgRKPOVsaBmAbWhWjb3baZjsIMKfwXl/tysvyq5Nl+5Pbah0brSWiRlKXH0s6dGdifXAiTx8rrnXkkdJ3Bt8y0cufM2fO1PWQvYUDXMOAlmLIfGxQdO7HIcGOqyCdLmB619ayoGQMJXwc2p5fyu73i2P17NZvatbDAz4uHzL6068ODjI9Wv6hZYlSApLcEIVLTAzg1KcJLSUt4IOzda8ugBkouy4Ttn1bPk2veyPt3Mx3w38nbvP3no/k18MP5ezjt2Fn9ZM8y0Sg+/e10trgzFrfuf7+In92zANdDOh3w3Md29HoCvJ87n+6nzABdHNvr4zKkviEnJGET7LFaV12dkHCKAVc2smQOdq22jfDI3H0XGK1yzpxNGpv5WT3wvbH8GOtfAP6+Ec79vG+P78ctzazn+5x17fe2Td/YCcOkJFfx21SCL633832syd5BwV8zoGoix0ylnG3uSGz2k+L7vuxzrXgv+MmrOuwaC+4mjjgP92+0wS9X0jI1NSoTHC9UzYdtj2U1MFMmmUI0lWw1nOUncXw4VLjj+IgbTfsq23Ml3/d9nVqKN76RexxV/tkqAl52xgOvu28CJs2v5/puPydiPv21VG5/92zMc3/8fvub7MT5XivtSi7k4cSn92BrDa+aHuPolB1j7Sqcsmb5+keYdMrqyeruv6N5g1TpF8kmwytaW4gP2+WQqr4cj32z7Ds/9C1b9CQ5/w0G/7a/vO4lzf3DfXl+7/E9PAvCJVy3kR3c/zxmHNfGVNxyRsaHuihltvaNXaH7riTP49DmHWVz44aX2xQVnQctR4/+BsX7wlSmhqtiVN0LlVOjdYnuBIqWivMne33o3WyXbTAmPJNj+4zLoWgu/ei23nfYlzrxr759x2R8tZnzq7EV85z/P8YZjp3L1OYszNoz9xYyT3U/xQ9+3qXQN83y6mX/P/RQffvlho+/9D3VZXK6do+rlMnbVVr2ZoS6LMSIyZj6Pj2/8z+F89A97d6X45j+6AHjPy2r45X/DvOTI9/NU/Fqe73meT977ST523MeYFZlFdbCaDX0bSDpJZkdm4/McoPvRC+wvZnjKnyHU8gdcnijpeA0vq7+I84+dyvzq+TSW6bUtpcXtctMQbqAqUEXbQBtbB7bSPthOVaCK4CQfYFdGQD4L19lmQ2Jw1E3rQxXwuvjZq2voGEwxnHD4wr3WaryLCG9o+18aeRVv9t7BW7y3Uzu8E1bfYh9giY2VTbZI7PEDLlv4Ge7G2bkJV7Rn7x9WNYMN9adx+arpbHPqaGX/Gw+XLavgoqUHud50EoZ2agOj1EVaoG8rJIaVYC2lI1BuLa+7npv05Npyv5sfvqqGgcTbuWXrPE7b8E1OcK/mH4EruHrFhWxLn8y2/hRn/KaT2dVeLl5aPqbqhC+sMlgT9rO4JYLHbUlS19z6LMe61nCp/09UuoYZcIJcmriEf6ePBeDql1RywZEvqEybTsLAdmvLXDUjW78KKWU1syDWBwMd+63aKZI3ghGLE7GBzFWv9fjhlV+Am95rBz3+81k485r9Ju82lHn49ivt5z7bleDHK/a0JfvWQ9Z6tmMwxmtu6GR6xMMlx1ZwWP3BF50OHDNW0+5U08mLNyYdrvH+jFd4VuB4/LjOuGb0xNnBTvt91c1TlQeZmLIG2ywc6IDK5lyPRmT83G5L8Bhot+TRbL4X+ssAF2UnvoPVTh0Lt97Ipb4/MdvdxicS72KIIF/75xoAbnmyjdae+5hRW8b7XjqHuQ0Hnwu9sPpHQ0WQ42fV4HG7uG1VG+//9SNc7r2B9/j/DsDfUify0cR7iWOx6PtnVnP2vIMs0g522mu+WvMOOQCXyw5eDHRCtGfyExhFDsTjhfJm6Fo9+X+b3oC9NpacD0/8Fm79GMw8BSoOvGF85LQqvnDeEuorAvzr6Q7+tGLr7n+75h+rAfj9o1vYsGOQGTVh3v+yucyoPXhHowPFjPf+egXOAb73/y48lpctHBn3Py4fuQ+cCkf/L4xxY303x4HhHtv3UEX04uZ22xrTUJcVCQhmr/KySF5xu6F2th3mG95phZ0yJRSBV38Hbv8sbHmQhQ9ezs0LLqDr+Mv508rt/P2ptt0P/cLfnwXguvs28mxbH7Pry3n/S+fSUnXwvcaxxwyHCzz/4irvr/C60jyUXojnlEu5+LD5oyfWJoYglYKmeSosJOPj9VvV+22PWYcVdXQUGZfXHT2N7X0xIhVD/P7hVp7YtKcF/U/u6AbgX4/WsWjGh0iV/Ywd0Q4+de+neNPCN3H27LOpDdWypX8LsVSM2ZHZu6trHihmXPzrFXsG4IoTaPw7/uqHAEgOzeT9x7ybo6bWMr96PvVh5URJ6fJ7/MyIzKA2VMu2gW10DHXQG+ulOliNf5IKnCi5Np/5w3ayqGdj1pJrAU6fvWez4C1HlHHa9R20D1qw6KCGbyXfwPeT53Gi+xnOcD/CW2rXQO9W6G+1j/1wAWnHxfNOM4+l57EmcCS1LSfxsyeGGHZcxNj3D3wuW4mE/Vx0TDNe90EqHu5KaKmZOdHLlmIQrLKN675tSq6V0lLZYlUNcjBBPmveyGvtsFcy2LWIFX/8HMe4n+Nb/h/xP6m7+Wzybazuns7a7iS3PR9l4wcPnHx4//Nd/OjuDXQOJPC6LPbUlQc4cu4M7n1yNZ/w3sByj7Xaeyw9j0sTl7DZaWQ+W6gIB3jbES3sDhmOY/GhonkkKUqnuiULPD77+4r2Z34BWCSTPF57P+xcnbnkWrCDdWd+CW7+IGx9BO75Bpx6mVXJfZHzFtomwHnAJcdWcORP2vd5zJPbEzy5PcG/no+y9v1jiRkb6RqI43lBzFg0azq/WTXEkDOd5D5TXIeve6/lDd7/4rjcuF5+NTSPUsVqV2eM+gVagJaJc7stUWSwUy3ApXCV1dt8O9qb/XbU/jC43Sxc/hpomwH3fotzPfezxLWB9yc+yLPOnsTVFZt7WLG5h3vXdfHIJ08/4NPetqqNq29+mo6+2O6vNUeCvHxhA3c8+iS/9H1/9zzjR8lz+GryjTi4WcRGysMhXjW35cBNpOJDFvvq5tompsiBBMotiaTtSVvjVQcMySdltbDDC6nE+BNBD1W4Fua+ArY9alUGb7kU3vSbg3bxe8uJFhvOWNzEx86Yz7Jr7tjnMQ9v6ObhDd08sbWHf1166gGfb7SY8ZJ5dfz58W0HTKytL/dz6vyRSu/P/BWeutHiw3HvnljVuFgvBCpVybRUBCutjXfHKosV+5lXixSlQIUlAbY9YesvmUyK8IfgjC/C/d+DZ/7CEZuuh/QaXvaGn3P5mQs49Wt37fMtD67v5sH13azrGOAPFy874NPftqqNq//6NB39e8eMZbNruPmJtt0xo5JBvuL7Ca/yPALAvanF/Mr7en64aIFtou9POgWDO6BuviqPysSU11vhmc7Vth6lwgEiY+ZyuXjvafNIO2lOmVfDfzes48rfDe3zuGc3VYD7Ilrm3US/+yl+8+xveLjtYd62+G3Mjsyma6iLgfgAMyIzWLkhzeduXrNPzDh2RjX/WLVrr8TBW/EUgca/4/ZZ5794z1GEYseydFoTC2rmUR3UPqQIQLm/3Ko4hxtpG2xj+9B2Uk4Kj9tDyJvdfDGt5OW7iibo2TxpLSWDXhf/vbCRl/5qO1v7Uru/nsDLf9NH8N/0EfxoyMNhNUOc39hOXbqLJWW9+N1pSKdY3+/ixqeHeNRZwFPOLBrZSQc1pJIeEitSQGDUn+0jQWw4zjNtfRwx9QBt94a6rS1S3fzJX/CT/OJy2UJj3za1XZXSEqy06uE7N+Q0+aesbhqHveNHfPvnP+Fi900s8zzD392f4G/pZfw4eQ7POjM46boOZld5OXtekKDXxZlzQwS9tnp077pOvnDrOjbSRAwf050O2qnBGdjGzFXf4YPe+/G4HGKOj+8kX8uPU+eQwhYDLGYkeLq1d0/MGNwOgQg0LLQKKCLZEoxA/TxoXQm+sP7eJH+Fa8DlyfxGed18eNmn4N+fhrX/sMSoZe8/4CZ4JOjm6YubWHztvgm2APE0nHRdB3NrvLx6Xgi/B141N4TfY895z3OdfOkf61hPC0k8THM6aKeWNf0u7npytHmGw1XeX/MG738B2LrkfUybedL+B5iKw1APNC7Obht0KQ3hGmtvuXOjEiOkMHl81sa+7Uk7SHSQJKdD5g2OVCo8El71NbjnK8wZaOMv/k/zneTr+GnqbBIvWMLs7I+x/JrbWdBUwWuOasHjdnPWkia8HktI+dsT2/jA71bu82PaeofpfeQGbvVdR5VrkEEnwMcSF/OP9Al7Lt2VZnh4cO95xos5aRjaAfULoawuk78JKWaVU+zgxcB2VTaX/BKsssN4sT5Ldp1MLhdUNsKx74R/fQrW/N2SU4/4nzE/RXMkxGOfOp2lX/jPfv99bccAy6+5nUXNlbzmqBa8bjevWtKEe+Sk9s0rt/HBG1bu831tvVF+/+jWfb7+Yp0DcR7e0M2y6j746wfsiwvPgRknjj9+plN2kLfpcFUrLCWRKVbEZqhb9xVSWiqn2N99z2Z7HWRyzuH2wMkftgTeB38AWx6Ea09hxllf48ErzubEL+97KAPg4Y3dnPTlO1jUXMl5R7fgdbs4Y3ETrpGx/XXlVj50wxP7fF9bb5Q/P76nGNWp7if4ou/nTHV1EXc8fDn5/1iVnsFQ0n/gecbAdpsX1czO/hxMilf1TKsMrXmHyIS4XW6mVU7jlfNDVFz4HB/4Rfe+D0qHaV3zZmqbV5CM/JXnep7jqvuu4rjG4zhr9lm0eFv4xSP38/O7dpCOTYXdRy/ctPVG+duTbeBK4q1chb/mv3hC2+xpE1VE287D5UrSlwwwPNBMdYsSa0VeyOVyURWsIhKI0FTWRPtgO53DnbjJ7kFFJdfmu1A1lNVYK6BJ2uT1eVzce2Ej92yK0hNz+OBtO/f69239Kbb1B/h36wxgBkvqfVxxUiWX/aeHtoHUXo/dxOg3bRUMMoUuYvhwAI/Lgkr3UHz0wSWGIRmD5iPVJkdMqMaq6gx3KxFCSkvVVFt4jQ/ldME96Pfy4fdewv3Pns20Z3/KtK57Oc9zP+d57ueR9Hz+OnQSt/cfwxVbbIPm2KeG+MiJFbz7lm4GEw5g1UZa6OIUzyrO9dzHce61u5//1tTxfCn5vww4QeaxhSgB3KR3ry3tjhnDO636T8NhdvJdJNsqWqC6B7rXWwKVFjwlHwUjds8c68985cGZJ8OpH4e7roFVf7LEqOPedcDXQpnfzYYPNPOfDTHaB1JcdVfvXv9u84wUd2+yk9w3PjPEJcdW8Oabdow8Yk/1wgPNM2ropYFu3uO9ldd57wPg+4nXsKT2JUzb3zc4aVtwrpqu1t6SGS6XJSb2t6kVnxSu8ka7r471T876i8dvHTpcbjjrG/Dozwmsv5PLfb/n3VWP8JGdr+fO9FHsKvPU2hultTfKnWs6AfjrwgbeefIs3vyzh/b79Atdm7nS+xte4nkKgFXpmXw4cQnDjp9FbGSQID6Sux9/wLWpwU5bh1DMkPHweC1RYqhbsUHyi9ttSU2tKyc/uRbswGrNLDjijbDy13Drx2yuUXngrhYvVFse4PkvncVtq9rZuGOQr/1zzV7/vitm3L56OwBnH97Mm0+Yzv+OEjPGq6unF/5zoVWdrZsPSy+Y2CHcoR0QrrOEMykd3gDUzLE23iogIqXEPdIFItZn99fZ2N9b9GrrTnTnF+3w65/eSdP8M3nusq9w21Y/azv6+d4d6/b6lm09w2zrGeY/z3YA8LpjpvC6o6fylp8fPGbUs5NP+H7H6zz3AtCeruYryTfybHoa3oPtgUd7rCNG7Tx1xpBD4/Xb/ci2xyDap3wKkQmqC9Xx8jlh/nrpBm5dtY1NnW5uWxl7wSNc7GhbiqtzHoH6f+KNrOCRjkd4pOMR0vEqkgOLcAengsuBdBBwcHkHcAc68IQ24S1fjctjz+ekvcR7jyaxcykud5p0sg4nUU3fkLoaiIzG5XJRHaymKlBFS7yFRDqR1Z+n5NpdPB4qzjhj9+d5w+2GyqnQv902fSexLcxLZgRxHIcVbWX84onBUR+3qjPBW/6yY9R/fzEfCRrpJsIgLhd42TshtyY8yqQhnYLBLmvFPI7FNSlyuxaAB9rtb0QtLmQy5EPMCEZssb17fV5Us1i+aAapBZ/nO/9YwezNN3KW+yGOc6/lOPdavuC7jm1OLc+mp9PaWceKv4W5BIcK7zBTXF0scG9hqqtr93OlHBf/SJ/Aj5KvYa0zhWZ2MIVOXC4oZ3ivn1sT9tuGfyJmbb7LcrARJKXJ7YbaOZbYna0FYCkOuYwZbg9UNMP2p4EstPWef4a1vb/3W7DyN5AYguUfOOCcxeVy8YrZQeIph0da49y8dnjUx967Jc69W8Y3z2ihi2pXPx/03sTLPE+Qdlx8L3ku/04fy0tGm2cMbIdQrc0zdC8pmRKMQGSatThWApUUIl/I1qM6V0/eZpzbY+s9g51w/EUw+zS4/3tUD67nOv/XeCI9m/9Lnsk/08cRfVHF8ttXb9+dNLWHw3GuNVzovY1XuR/B7XKIOV5+mDyX/0udQQs7qHYNAFDJ3q3+Rl2bivUDbosZ6l4g4xWusUpSnastobDQD+h53FScshTSafBo06+ghWpsbSlXid9l9TD35bD1Ybt3+tO74G03W1L6GHncLs4+opm+aIIH1+/gv891jfrYvz/Vxt+fasvEyAE4bs3XrLV5oBJO+vDEDjYmY7a2XTtbSVWlqLzR9jh6txVv5wuPm4qTjwHHya89WMktf5klv7Y+bvfZ2SiaUTcPzv0BPPp/8MxfYe1t+J6/g3OOexc7j3k/D62v4eGN+6lKOOLPK7bx5xXbDvgjIgxwsfdvXOj5JyFXnJTj4vb0MfwxeTKt1O1OrIVR5hnxIftoOiLzh+OlNIVrrHJz+yqb2xdaJ+BdMQMUM3IhH/bA80TYF+bwhkU0nljLytYNrGmNs2G7s9djnGQl0bbzce94Cb6a+/FFVuD29+CveeCgz59OVJDoX0RqeCakQrjwkY7VQNra2zdUBLNxWSJFxeVyEQmM0hUgkz/HcRzn4A8rHH19fUQiEXp7e6msLJKTOMkYbH7QkmtDVTkbxjtv3sHtG2PMrvKwvid18G94ERdpDmPjqOvGLuyU+c8vOG7/67F9rbbQ1nyUFphkb6mEvUbSCav2XCrSSUs4n7HcNu/HoSjfKyeg4H8P0T7bePAGwF+e69Hs5aI/rGFq5z28Mfgws5PP43WlD/j4pOPmKWc2t6RO4JbUMrqpYD5bDh4z3rwYT6zHKtbWzMr4dYgcVH8HtK6wTbQ8SHQfVSoOw70wY9m4F6oL/r0yQwr29zDcY/dJoarsJQE9fRPc913AgTkvh9OuGNei7Vm/3c4zXUlOnxXgPxtiB/+GFwkTZRatuFxQxjCf9P2WI9wbSDluvpN8LXemjx59njHcA04KWo7RBoZkXnwQNj9sBzIKqVJIMgqxAZhx0rhjW8G+V2ZY0fweor2w5WH7O/BN8n1OfNAqfHqDsPYf8MQNdogDGHCCrI+cwJ92zGStM5VWp5ZhJ4CPJHWuXua6WjnKvY6XeR5nimvPIY1bUifys+SrqHH66HJVkmbfTaIDrk0lY5b427hEcw+ZuEQUtj4CqVhuqoRmWmLIDrvOWA6+8W38Fc175SHKm99D21PQuzl3RS1SCWh9Av7zafu7OvXj8NIrD+kpj/n8v+kejPPuU2bx0/9uyNBA93AB7yy/j08lf2D/derlMP/MiSXO926zg1nNRxR+4r1MzHCPxQdfMO/WeTMmPgCppFWnHmeiV968V+ZY0f4eujdAx9NWOMObxUSi9qfgwR/B9mfsv70hOOJ8nKXvYMGP2ognHS49fT7f+s/aAz8PAA5LXBt4s+cOzvPcR9hl61lr01P4Y+oUHkvPJ8aevexR5xmJYatcXrfQKvkqBkimpFPQ/qTdY1S2FN7fVqzfDmTMPHncxRCK9r1ynPR7yKxYKkbHYAebe7fxlh/YoYwLX1LJL+7p2/uBrjiesnV4y9bhDrTh9nfjcsfBceGkwqQTtaTjNaTjVaQTteD4cBIVOKkITioMuHABTZEg9378ZXjcBfbaFSkwY32vVOXaQuAN2MJKx6qcJtd+7RVVrNqe4JTpAf72XJSfrRjgipMqd7dpnVHpwte3mV7KqWQQH0m8rjTbnSoGCTGd9t33bS7ghVndu0LCe14ya/+JtUPdtpFTv0CJtbIvj89acrc/VVrJtSLBSqsm1bUWfGV5NTn+/NlzWdc9k/nT3s2fnuxi5VNPcsHMXv7xxCbKGaYy4CYWi7LRaaLbKWdduoWky0e7U0MUP/PYetCYcfFJLXiGuy02VKklq+RIeQNUz7LXoTegqpeSfwKVEK62AxnZSq5d/Fr7OXd+CZ6/HQY64PTPQFndmL795+fUsqk3yYlTA1z/xCB/WzvMh04o561/sUWqhTVuUt2b6KGcKgbwkMLrStPq1JLAyzQ6cLlglquNT3hvoMW9gyEnwDWJN7HSmQeMMs9IDNmHKoNItvjLrG18xyo72JBH92oiYxKMWAX0nRshMsnJtf4y21gf2gFzT4cFr4b1d5J8+q+U923miL67OcJ390GfZtAJcHvqaH6behkbnBam0852l60bjGttyklbpfPqWZp7yKHxBa0DxrYVlrCtCsiSLyoaoWeTHeZ352DbyOODhgVw3Lvg/u/C3V+1pO3Zp034KW+6ZDk7BuMcM72asN/Lis07eeuJM3jPrx4D4KhpVazc0jOm59pfzDjOtZorUz+2Lyw+D+a9YmL3e9Fe2/uoman7xVIWqoKqmdD57Eh1c1UElxJSNcMqt3Y/D5XN2YtDTYfDOd+B9XfBE7+zroArfolrxS95unoavdNeRl3DIKHjynm8t5yzj53L+3/7OACnTA/TuuU5lrg2cIz7OV7ueXyvbnwb0w38PXUi96UPo58yHPa8n486z0hGYXCHVdetnaMYIJnl9lj12uFeGO4ujoN9IjkU8ASYXjmdhnADv7loG+0DXdRVRdnU7aZ/2MVxc4L86F+D4PhZUnskT7ZV4aTDuLw94ErhcsVJx6bicsdx+3bgpEM4yQpIhXCcPesCuyLB1eccpsRakTyiyrWFIjYAWx4aqU6Yfy0ltw+mqA66efcvH2XHQIz9/VHtOpX3rpNn8bN7N9A1sKcqVV15gPe8ZBbL5+xnAz4xZMkAzUfm7uS85L/drxF/8Z7sfjFVrj1kRfF72F0RzTXuv4Nc6RhIURty865DiBkXnTyVZfVJqJkDDQuV0Ci5lYzBtschuhMqmnI9mv1T5dpDVtC/h57N0LoSqqZl9+dsfQT+8xmLTaEaOP1qu4efIMdx2D6YHkPMcHhd+AkucP6KO51guxPhs4m3sslpGn2ekUpAfzvUzbdDGtrAkGxJRG2ekk4WThK3KtcesqL6PQx1299wqDp3SYCpuI0jnbLDHMM7oO1Jq8KzcxP0teKkYpaEUlbPk/3lPJ2YwlpnCpvTDXhdKQacEH2EceGa2NpUX7v9DlqOGnd1TpF9pNN2QLxvC1QWePtvVa49ZHnze0glYcuDdh+Qy+SLge3w8M9g3b+si9177rKiChnkOA7b+2PUlQc4+St30N4bHXVtqikS5KqzD+Pzf3+Gtt7o7n87prKXG7gSf3wnTDvB5j4TqfKeTlqMaVJVdMHWl7Y+YpUsx3hYtaCocu0hK+rfQyph9/h92yAyJfsJ5sk4bH4A1t4G2x6zOceL+cpw3B6cdBL3SBeNF4o5PlamZ/Pf9OE8np6Lr6yGd50ye2zzjGTMYl7dPFub0v6GZEtfK7Q+bmtS2awMnWmqXHvI9HvIrrSTpj/ez2BikN5YL/2JfmLJODsHk1SEXFz+xyfYOZjAcTw4jgfSAZx0CNJ+SAdoqqzY7zyjORLk6nMO48wlzTm8OpHSocq145QeGmLNMUsBWLDiMdzhPGvrGyiH8ibYuSEvk2sbyuym5j0vmcU1t64+YPWP5XPqWDanjqdbe+keilMT9rO4JbL/irXpJAyOVCWsUACRA9jrNVIiybWSM3kVM/xlVtmi/amRimj5X9WgsfwQY0ZjGM9gu1WNql+ghSfJPW/AFkK3PWYbBYpD8gJ5ETNCNZYglxjKblvvqcfBa38M/7rK7sn+9mE4/A1w3DsntHjrcrkOGjMa6OES780cm7KWfc60E+lc8D7OTwZHn2c4aauuWzXNKjgosVayyRe0RIm2JyAd0X2LFJ5QNZQ1wOD23B0i8vjtZ6eTtsHm9sGUpZbM5/GD22NN85w0pJMk1rdz151rSDlu+gkx7ATY1VYPJrA2NbTD4ljDAiXWSma43TaPH+q0v+lxHn7LF+nhKGvOfhcACx66H3dEr4+C5vFa97y2J3KbXFtWD0e+CXY8Z3OK370J3vHPjO6JuFwuGivt7/Xqcw7jvb9eMera1K6N7TOWNPHwhm6290dpDiQ47o434ercCTWz4SUfm/g8q7/DCopEsnwQUgqDN2CFBLY9VnTVzS1mvAeABY88iLuiMIpEyCTy+KBhEaQT0NeW/Tb2Xj/MPhVmnWL3+5sftOTeno3Qs8UOmyQGcbEnJqQ8QdYnatjoNPJceiqPp+fSTQVxrOPqJ06dPbZ5xq7E2prZUDtP6wSSXRXNUNVtlaEjUwtiD9FixkUALHj0YdzlhTlfKlR5sZ9RANwuN5FAhEggQkt5C2knTSKdIJlOknbSfPLlU7j0hieBkeTakWhyoHlGQ0WQ42fVqGKtSB5Scm0hqWyGvq12Q5+nJ4uWz6njE2ct5Cf37H0qr/ZFp/I8bjhi6kEmz45jp7YrW2yCoU1vOZiKJmtflorbBptIqahsgd6tMLyzoFq7TChm7Ko0WDXDKtZ6dCsjeaKs1hK+O58Fb0iLopJfAuUQroP+tuwm14It0p73Q7j/e7DmVnjqRth0H5zwXqs0MMF7+hfHDD8JXu15kP/nvYsQMUu0OvYduI58I4sPtkjc32G/j7oFiiMyOSqaoXebteErq8/1aETGx+Wy9/aB9ty1Ct/F7bVk31C1JdKm4jY/SCUAx8bq8XLswtnEg3X85J4NDB/q2lS0zyqsNS+2nyuSKcGItf/e/owlDRbAJreUgLI6+3vM5aFRlwuqZ8DyD8Ltn7XD5DddDOdfb4npGXbmkmZ+9JZj+Ozf9q4Y1fSiilEet4tlc2otIeq3b7S5f6gaTv34xO/vhneCrwzq5mpeInuUN1qydTFUNxcZL38YGg6zgx4D7ZNTdGmk+wWLzrGPdNI60MT6bQ6fTllsClbhCVayY1Mfv72vlc7BxO6neHFl2gPOM1JxS6zdVThE7/+SbS4X1M6BaI91Qy1vyPWIRIqS2+Um4AkQ8NjhqPOOrCDoKRv7PENE8pru2ApJqNom1n1tUJmnLYexje8TZo2x+seBDHRAuFrJUzJ2oWpLbor2atNaSos3YJPj1sdtY3mcbbVyaVwxIxW3hKiqGXaKvYCuU0pEzUyIdsNAZ17fq0mJqmiygxhOOvvJG74QnHq5VQC55+vWfuzfV0HjEjjqzTD9xAmNYfmcOk6YGqb9kb9Q+/yfCMZ22D80LoFTL7P4cDCDXTa+hoXjbncvMmEeH1TPhNYVBXevJgLYAb5gtc218+Uwn8ttB89HOXyekbWp+KAlmDUutgPvIplWNc0SR4a6i7P9txQef1l+dAZze6BpCSz/ANx1DTx7M9zxeTj96qz8uDOXNPOKw8ZQMSqdgj+/B9bfafHnlI9aYtREJGOQGIamIyzZXmQXt9sSzAu8urnIhAUr7f67/SkrsjHZ3TPcXjukHijf7xzgxAURjps3bWLzjF0Va3fvb2jvWyaJLwR180c67w1pTVRkkox5niEieU93bYVkV7WQvta8r8w5puofBzLcYxuO9Ysy2vJJipzbbae5+zus8rGqHUspKW+0DZCBkYrfBWRMMePFC0/e/I2BUsI8PmvlFX1MGyCSf0I19jcZ65+8zdtpJ1iFqSd+B0/+ATpWwT+vtPu1uafD7NMs4fBg92zpJLSuhA334Fl3O1MSg/b1sgY49u0w/4yxJetG+yy5uGGRqg/K5CtvsPu1we2TU31HJJM8XksCbH3c3j8LpMLmIa1NJaMwtNNixlgOb4hMxK72360riq79txSwiibo2Zz7/QePzzpfLH0HPPITuPebEKqCkz6UnR93sIpR6RTc/EF45i+WeLX8QzBj+cR+mJPe0w5clUllf0JVe6qb+8LqjiSlJ1wDTYePVLDdnneVNic0z4j1w3Cv3fupYq3kQnmDVUzuWmvzDsUWkUmhyrQixUF3boUmVPOCDbkirYgWH7JT281HWBVSkfEI10GgciRxpDLXoxGZPG4P1M62VkW5bN+XDcmoVQKtmT2y8KRqb5LHwjX2t9rxjJ0Iz2XrZJEX8votoa9r7eRWRvKH4bh3wmHnwlN/hNW3QN82WHG9fYSqreVfZIoly3qDgAOxPnvv715vY07uaZ1EZBosfi0sPHvsSSiJIbs/bFpSvPMoyW9ujyWTD3ba3/Mo1TZF8lZ5g8WPaJ8lfBSzVNxiUN08qJmlg7uSXeWNUDkVerfY/ZBIroWqrZLycHfuk5m8ATj8ddbG+Kk/wL8/bQcGj33H5I4jGYM/vcsq6LrccMJ7Yf4rJn7YZGiHrR3UzrFiESL7UzXd5g5DO3L/WhTJhV0Jtu2roL/NCosU6n35ULfNMRoXW2VqJTVKrtTMsgJnOvgtIiIyLtrtLzRu90jLsI7ibCeZitvCXd1CndqWifEFrWpn11ol10rpCVXbydPtz4A3VByLNPFBGN5pG9u183SiWwpD1XTb/Bjogkol8UkeKauzZNVcVEYrq4MTL4alb4MN/4X1d8PWR+w9ftN9B//+YMQqV81+KUw5Znwb2am4bWTUzlf1QcmtcK0lUPVsUgKVFB5vwO5x2p+y9+RC3dg+mHTSuuFUzbD5RzHMqSS/ud22yT20wxIIg1W5HpGUOrfbuucNtFu11ly/D3qD1q0iOQzP/g1uuRSScZtbTIaBTvjj22Hjf8HtgxMvgcPOmfhB2mTUfq+1c+1ArshovH6LD9seU3VzKV1ldVaIqeNp6+pa0VhYhQzSSdvP95ZB0xFaB5Dc8wagfr51zoj2Tm4BBhERkQJWQHegslu4zk6qDnYWV9WlXRsY1bOs+mKxbtRI9pXX5y5xRCTXqqbDYFdxxIhoD8SHraJh9SxV85DC4fHZRlm01yplBipyPSIRE6q25L5cVqHyhWH+GfaRjMGOddC5GvrbLXalEuA49ropq7ON/YZFFt8mUhlqrznGHM0xJLdcLqieDoMdig9SmMobwb/ROmUU499vOgV9bXbYu2GRDvbJ5AlWWgJV+1PWhaaQkkakOJXVWaJ3tNcqB+aaLwTHX2xzhbW3wW0ft7nDyz6V3fv7zQ/CjRdaxURvEE76EMx7xcRfo45ja3bVsyymihxMWYPdl/RtVTEaKV3hGjtk3fGM3auX1eX/4QTHgVgvRPutOmj9AhUDkvwRrrG9i/an7LXk8ed6RCIiInlPK3W7eDyUnfqS3Z/nNbfbNpcHtlsVpmK46XEc2/SuaLYTU7k+ES+FLVhlE2y1TJJsyeeY4fVD3VyrahAfsI25QuM4tkniclvrp8hUJUNJ4QnXQM0c6Fhli1TaIC9d+RQzXC6r8D/QDk564m1MM8UbsJZ4jYuz8/xOGvra7Zrr5ytJSvJDMAKRGdD5rN2n6R5HCok/bNWWutYUX3Kt41h8rGiExsNsXiUymSJTrT3rYFfhHJT1uCk7/nBIp8Gjw7BFxeOz7nltT9oBvXy4XwmUwfIPgr8MVv0J/vt12P4snPdDCFVl9mfFBuCOL8BD1wKOdR446YMwZemh7VsMd0MgAjUqLCJj9MLq5oV+OM/jpuy4JXbPleu1ESk8/jKrYOsvg50bIBXL32r/uw55+8v27G0UWxdaKXyRaTDcCz2bbY6fb/clu2IGKGbkQj7tZ4iI5AntLo5wBwJM//GPcz2MsQvX2enmge3F0W64v8MWwRoWqdKoHLpdiSN9bfmROCJFJ+9jRlmdJfVtf8YqaxRSUl86ZZva/gqrWFten+sRiUxc1TQY6rI2kpXNuR6N5EjexYyyOnuPjfUXd+svx7FquGV1mmNI/qmaahXQhnfmRzU4kfGoaIKeTRAftA3jYjHQbglPDYflfyUsKU4en83jh3ZCfMiS2fOc2+9n+uc/BIkYBHSvVXTKGy2RL9aXP/MGfxhOuMgSqlZcD2v+Dj85Fc79Icw86dCfP5WElb+Bu78CfdvsazNOhhMvtuSoQ5GMQSIKUw4riNe35JFgxKodd6yyTjAFWpjGYsYH7XWmmCET4Q3Y+k6gArrWQu82K66TL4mrjmOHKOLDtg5cv6CwE+KluLk9VqQn2msHOMrqcj2ivbj9fqZ/7gMj3c0UMyZb3u1niIjkAWWcFSq3G6pnggtIRnM9mkMzsN02LhoXQ6AAKyxKfgrXWpuVWH+uRyKSG9UzrF1Yf4dNQAtBMgp9rdbyrOVoJdZK4fP4rMWSx6d4JPnDG7D4EC3yv8nB7VYVtPGw4kr+kuLgC0HtbEgMW1UbkUISrLQEo+GduR5J5gx2gXfXupQ2wCWHyupsvXdoR+HM46V4+UJW1Szal+uR7M3jh6P/F07/LJTVw86N8Iuz4C+XwM5NE3vO4R548Efw/WPhbx+0xNqyBnjJx+HlVx16Yu2uDk3VM61zn8h4Raba3/vQjlyPRCS3XC4rZjBlqb2f9rfnx5prMmbJvp4AtBwFzUdpXiH5z19mnb5SSUgM5Xo0IiIiea2AStnJPsI11pKoZ5OV7C9EQ912OqpxsbWYEskUbwAqWqBzdf5UVxCZTB6fTYzjA1Y5syzPE1WjPRAbtEo9dXNVYVCKR7jG/q47VtnmZCFVkpbiVdFgbfQSw8VZnW9oB7j90LhE94GSv8qbrCLcYKeSLKTwVDRD79aCqa55QEPdlvTUcJgqSUt+qJ5hsSEPK0hJCdpVrTzf2tG73FaptnYOPHQtrL/LKs4++XtY/Fo44k0w6yXg9Y/+HIM7YMPdsPoWWPOPPUklgUpY9BpY8lor3pAJQzvseWtm51/bZSkMXr8d3t72aHHcf4kcqlAVNB9paz47N9hBkLK6yd9TSMasWq0DVE23fQ0d8JZCUt4INbOsGnRloGCro4uIiGSbdvdHpIeGWHvSyQDMv+9e3OECmJy6XCMLrtvzb4FrLKK9kEpA8xGqTijZUV4PO9fbBFeJepJBBRMzAhVQvxDaVtoCU7Ay1yPaVzplFcy9AVsQq5xi1dlFiknVNNtMG+i0tmBSUvIyZgQjtnjat634kmujPZBOQ/MSKMvQZrhINni8toExtKN4E92leIWq7DDrzvWFndwR7bHq0U2HQ0VjrkcjYnwhqJsH21bk/XpWejjK2tddAsD8e+7CHQnmeESScYFyW6fpei4/9x4qmuDlV8P8M+HxX9uh1qdutA9vEFqOsYTWsjrbS4kN2Byocw10P7/3c0WmwdzTYcGrrM14piRjtgfSuKSwY6bkXlkdVI8kQPmClmReQCxmvB+A+ffeg7tCB2HlEHm8lsxaVgs9W6xyLA6Eqy0GZFM6aZ08knErflU51Q5kaF9DCo3LZfdKsV7bp8uTvQuLGR8ARtbTy/PwPrSI5eV+hohIjim59gWc4eFcD2H8gpXWTqjjaTsNVygT6li/nbBtWgKVLbkejRSrYATCdTDUaQkkIhlUMDGjohGSC6B9lS04+fJoEhQftEpRFY1QN18VzKV4eXy22Bvrzd9Ed8mqvIwZlS22sZxK2N9oMYj1Q3x4JEmqKdejETm4cK0lcuxcb4krqmQmhaRqKvS3WqcMf3muRzN+Qzus/WXjYq1LSf4pb7TqZzs35H18cGLxXA9Bsq2yGXq35G+1TJcLpp8I006A1hWw5jbY8hDE+mDz/fYxmsop0Hw0zFxuLcYznczuOHbItmq65idy6Fwu2wvcXd288ArWKGZIVoSqIVhl9/S9W+x9N9lpSei+sB1cysTeeTJmVc7jQ/Z8oRponGH3bUqqlULm9dv+XOxxSxrPk306xYzcysv9DBGRHFJybTGITIX+tsKZUMcHbOO74TDbSBTJFpfLJtT9rVYdU+0spFRVzbBT1J1roNyd/ZPbB5NOwVCXtUuqX2gLwwdq1SdSDELVUDPHEt39YXDrNlxyLFxrlW+Gd2a2MlOuxIcseb1xsc2PRAqBywU1M22DPNqTNxsYImMSjEDVTOh8FnxleZ38t4+BDvAEoHlx3lTmEdnLrgpSwztH1nvrcj0iKWXByEi18g35mVy7i8tlCbJTltrhiR3PwfZnoH+7HXQF8Pjt9VTRAo2H2Twom3Pz4Z1W8bd2TmHFSclfvuCe6ubqfiGyh8tl7+/hWjtcMdwzMs/ut89dWIEqb8hiwVjfk5MxKxCSGLL5gz8MdVMhXGPzd+05SrEIVUP9fGh7wmJLrvcQRURE8ox29YuBNwC1cwuiXRjxARjutcTa6plaVJLsC9dCoNISukNVuR6NSG64XLaQ76RgxzpbaMrV5Dg2sqBVVm+LwdoklFISmWbVmgfarEKOSC653fY3ObDd2tkVcsJ3Mmob13XzNceQwuMvg9rZ0PqEJV8U8mtRSs+u6rXDO22DOd85jiXWeoPQdIS1kBXJV/7wngSqZFQb3JJbkRbo21o4yXweLzQsso9cSSUsGavlaAgUYIV3yV/ljVZIoXudHSwtlG6WIpPB5bJDIcEIVM+ARBSivXZYaajLDmWn4jYvcGHzb5fbvs9xAMcKg+z63BOwOXv1LJs7+CtUpVaKV+UUy+Hoft4KVyl5XEREZDft2hSLQmgX9sLE2ppZ+TlGKT5evyWOdKxScq2UNrfHko4AdjxvJ1Ens+JIKm4tmbwBiwNV01WtVkqPx2sb5NGRCgqKS5JrZfXWxm64p3APOyRjFl/q5qkilBSuihao3G5/y5VqGSwFxBey6pptT1gSkceX6xGNLp2Evna7/2pcXBjJwCK7E6ieh8gUJVBJ7gSrbM+hZyP4dFD0oBwH+jvsEEqFKqRLhrlcdjgvutMqc5Y35npEIvnLF7SPikZLmk0M2UGRZMz2K5IxSCcgnbbXlttrCbVevx1s8oetS4cSaqUUuFxQNxcSA9DfbvMPERERAZRcWzzyvV1YrN8+lFgruVBWZ5t+iSHw5XH7MpFs25Vg6/LCjrW2gJTt5L500ip1ppM2Ga+epYRCKW3BSlukan9ypMVSHncckOLn8Volj20rbJOh0CoSpBJWebdmNtTOK7zxi+zi8drf8VC3HUr1q7qZFJDKFkvs6G+zz/NROmWJtZXNVsXQX5brEYmMza4N7mivEqgkt1wuq5DZ16r11bGI9lq1Ws1RJFt8IahfCFsftZb1urcROTi3x7rFBCpyPRKR/OUNWHyJD9kalQ6lioiIAJC1o1Zf/OIXWb58OeFwmKqqqjF9z4UXXojL5drr48wzz8zWEIvPrnZhqaTd9OSLaA/EBpVYK7kTrISyBhjuy/VIZBSKGZPI7bHNuaYjwEnbJnM6lfmfk07ZYY++dghUwpSl0HSkEmtFwCr+RKbbBrnj5Ho0BUcxI8PKGyBca4f0Ckk6aVUUqmZA/QJLThQpZOEaO4Q0tNPu0SQjFDMmgdtjaz3egFXnzzephCWDVTQosVYKkzdg67247ACGZI1ixkGEqmwuO9Sd65Hkt2TMXqu1cy3BViRbyuqgZs6eogYyqRQzRKRoBSNWpCcZy698kwKmmCEiUviytgMZj8c5//zzWbZsGT//+c/H/H1nnnkm11133e7/DgQmqZqX2034uON2f16wyhutHWrnamtb4c7xJvPQDmun0bQEqqbldixS2iqboW+rLTTl+nUh+1DMmGQul70n+8tgx3PQ1wbBCkuCPdQDEKmEHapIjlTFrV9osSmfW8SKTDa32+7Xoj0w1AVl9bkeUUFRzMgwj8+q17auKJz7pHTSYlfVNEuUUoyRYlE93eLCYJclvsshK7iYUahC1Zbc0fG0JQLmS2X++AAM9Vi8qFtgh9JFClF5vb3Gtj9rrYrz5d7H7SZ8xHxIO+Au/GIKihljUDUNBtqtQ50q/+0rnbLOGtWzLBFZJNtqZkKsb08HgXwubON2Ez58PuDk59rIOClmiEhRq2yxyuhda6yggcc/uT9fMQPQfoaISD7J2s7pZz/7WQB+8YtfjOv7AoEATU1NWRjRgbmDQWb86peT/nMzzuWyiiGx/txOqB3HFto8IWheAhWT//+pyF5CNRCstrZc4dpcj0ZeRDEjR8I1EDgGerdC90b732ClbZC4xjFhSqdsoh3rt+8rq4XKqZYUki+bfiL5xh+2E+CtK9TCb5wUM7KgvNGSvIe68z+hb1dibeUUqF9kBwpFioU3YIcvtj2mlssZUmgxo6BVzbB7mu71EJkyvvlENsQHYbgXGg+zsanCuRS6mlkQ77d5e+WUvEigcgf8zPja5ZCIQTCY6+EcMsWMMQhW2nvq9mfAX54Xf4d5Zdfh2fr5SkSQyeHxQcNCu+8Z2mHVbPOUxYyPWedNxYwsjEhEJINcLqiZDYlh6Nk8MsefvPs+d8DPjK9+1PJNFDOyMKIDK4j9DBGRSZZ3M/y77rqLhoYGFixYwHvf+1527NhxwMfHYjH6+vr2+ih5uybUoSro75j8n59OQt82q4DYcqQSayU/eLxQNdVaWKgFd9FQzMgAj8826aYfDw2HAS5LWuprheEemzynk3teN+nUnhZ3Qzugd5u15XYcSwaZdgJMOc4m20qsFTmwikaomWstwNXCL+sUMw7A47XqSrve4/NVOmkxp7LFkqV8hb+4KrKPsnqongmDO8BJ53o0JUsxYwJ2VeYP1+VmLeqFYv02l6lbYBuCSqyVYuD2jLSZr4Th7lyPRl6g5GJGZIr9HUZ7cz2S/LJrzbl2bv5UcJfS4C+DunnWTUztu/NeycUMESlcHi/UzbV8k6GuXI+mJClmiIjkj7xKrj3zzDP55S9/ye23385XvvIV7r77bl71qleRSqVG/Z5rrrmGSCSy+2PatGmTOOI85i+DxsXgC1krosmSGLakrIoWaD7KqiKK5IuyeqvIGR/I9UgkAxQzMsxfZhPl6SfC1GMtycrttappg13Q32oHJwa2Q7TPqgwEKqwd97QTYMZy+7ysVtU5RMajZqYlCg506PBHFilmjEFZPVQ228GJfLQrsba8ac88R6QYuVx2Hxaus3swmXSKGYfAF7LD3r6Q3dtMtnTKfm4iarGibq6qKkpxCVRA/YKRBCqtbeWDkowZvtBIK/p+HRLdJZWweVTNnLyuHCpFrKLJEruHdtjfo+SlkowZIlLY/GU2/3Acu/eTSaOYISKSX8aV/XLFFVfgcrkO+LF69eoJD+ZNb3oTr3nNazj88MM577zzuOWWW3jkkUe46667Rv2eT3ziE/T29u7+2LJly4R+dnpoiLXLlrN22XLSQ0VyujNUDU2Hg9uf/QRbx7GJ+3CPtThuPgIC5dn9mSLj5QtZ67yoTmpNBsWMAuUN2IJs42Ew4ySYvhymL7P/nbbMkm9nLLN/m3aCVacqr1dbbpGJ8visZaS/In+TGieBYkYecLstoc/jz7/F0l2JtRXN0LREibVS/HxBSwp00qo+tR/FHDOKQrgGmo4AT3ByE2xTCeu+EYjAlGOsO4cSa6UYVTRZVebhXkjFczqU9HCUtf/zYda++eOkh4ZzOpbRKGZkSeUUKG8o6Tnsbo5jc5WqGVYtXbFHcmFX++6q6SMdxvKvA0Z6OMraN36Etf/v8rxdG1HMEBHZj/IGy/2I9k1axzOLGR9l7Zs+ppih/QwRkbwwrr5oH/3oR7nwwgsP+JjZs2cfynj2ea66ujrWrVvHy1/+8v0+JhAIEAhkps1OaufOjDxPXimrhebDoX3VSEXZRnBluKJgMmYVdQIV0HKYbXprEUnyVXkD7NxoVZaVmJFVihlFwO0GfxgI53okIsVtVwWq1schPmgnwkuMYkaeCFVZgu32Z8AXtvbDuZZOQl+7VdVVxVopJeUNVv2sc7UdfsqH12OeKPaYURTKau2wd9sTMNhp1dGzKRmFgU6omgYNh6kdtxS/6hl2GKpnM0Sm5HQdNtWb3xV0FTOyxOOzRL5tj9neQCm/7w53QzACdfOsfbJIrni8trYUH7IDThXNuR7RPhQz9n2ukogZIlL4qmbYvkX3+pH5R/a7V6b6FDNe/FzazxARyZ1xzfbr6+upr8/ygvgLbN26lR07dtDcnH+TwIJSVgctR0LHs1bFo7zBKlIdKicNQ922gBaZZtULVa1W8l0wAuWN9lpQckZWKWaIiIxDRRPUzoPtz9pGZSbu1QqIYkYeqZpuB+cGO+3vMpdScejvgMhUS5byBXM7HpHJVj0TojstabAyx6/HPKKYUSDKaq3aePvT2Tvs7TiW1JSIWjJ6/Xy7jxIpdm6PJfLF+mBwu61zyX4pZmRRWT1UTrUiBjlO8s6ZxBAk41ZwxK+D6ZIHfCFoWGiHt4e6raOAjJlihojIKNxuywOJ9dtabaXetxQzRERKS9aOlWzevJmVK1eyefNmUqkUK1euZOXKlQwM7DllsnDhQm666SYABgYGuOyyy3jwwQfZuHEjt99+O+eeey5z587ljDPOyNYwS0eoGlqOgsjIZvlQt21CTISThuEe6N1mk/Upx1hFEiXWSiFwuaCyBXCsbaTkBcUMERGsfXHVdFugysMWfvlCMSPLvH5rR+/yQDyHFQKSMXstVM2AxiVKrJXS5PXbwQuvD6K9uR5NQVLMyLHyBjvsHaq2NaRYf2ae13HsNdG7Fdw+aDkaGhYpsVZKiz9sh4/cXlvnlUOmmDFOLpfNYYOVdtCh1CSGYbAbaufmZYVQKWHhGosPqQREe3I9mqKlmCEiJWfXAQ5vCIZ25Ho0BUUxQ0Sk8GWtT82nP/1prr/++t3/ffTRRwNw5513ctpppwGwZs0aenttg8jj8fDkk09y/fXX09PTQ0tLC6985Sv5/Oc/r5YXmeIPWxJsWS3seN42IQIV9jGWFpPJmFVESESt+mfT4ZakWMptn6QwhWvtdTC80zb7JOcUM0REsPux+vm2SdffPnIYRF5MMWMSlNVZNYLtz4AnMPnJSvFBO8ynKoQitjleOxfanwJvUPPvcVLMyAPhGjuUvXMz9G6Cni0Qrgb/BA5op1O2iZeKg78CGhZDpEVdaaR0ldVaAlXbk5a8HqjI9YgKmmLGBATKrYpy60qr4uorkeqt8QGI9tmhxNo5pVm1V/JbZTOkk9CxCui1/TzJKMUMESlJoWpoXGTzj6jiy1gpZoiIFD6X40y0fGl+6uvrIxKJ0NvbS2Vl5Zi/Lz00xJpjlgKwYMVjuMNFvhCUiEJ/G/RusYUgsGpQHv9Ioq3LKqalEpCK2f96/BCqgsop1vZJm3pSyPpaYdtjVllgLMnl+SidtErUM5aPewIz0ffKYqOYISJ5Kdprm5OpmN1zZUIqDsO9MGPZuDfdFTNMycWMVBI6noKerZa4lOlW3qMZ7rEE87r5UDPb2o6JlLp0CjqegZ6NdvAi26/HZBRiAzDjpHG3OFbMMPo9jCLWD31t0LPJDnCHq8eWiOWk7f4oNmD3RlXT7dCsqpqLmO4NlkA1yeu16eEoa85+BwALHrofd6R6XN+v90pT8L8Hx4HONdC1FiJTiz/RNJ2EvnZoPAyqZ2m+IvmtZ7MlQJXV2kG9HNorZjzyIO4K7WdMhH4PIpIXshxf9ooZjz6Mu1z7GRNRcvsZIiITMNb3yqxVrpU85wta26bIVKveOdwLwzss6TaVsMe43FYlKlwHoQgEKu1DC0ZSDMrq7YRdtMc25URERPJFMGKtjduesETDUFWuRySlyOOFugUQH4b+Dqt8k02OA4PbrbVx8xF2oK/YN+ZFxsrtscposX4Y2A4VTbkekcjEBCqgvgLK662C7UAHDO6wZEBv0GKPa+Twazpph4MSQ+CMfG/T4RYfvP6cXoZI3qmaYcnnOzdAZMrkHYoSAbtnr5pm3VeivcU9f3Uci12Vzfa60z6J5LvKqRDth+7n7e/WrS1hERHJgL3iS0vhFrESEREZI82kdnG7CS5ZsvvzkuHxQXmDfTDPKlSlk4BjGxoenza1pTh5fLYI2vq4Jdlq40HGo1RjhohMnvKGkQTbp8CtFq8FrZBjhj9sFZnanrBN5PLG7PycVBz6t1sFw/pFVvVARPbmC0H9AmhdodZ7UvhC1SOHXfvswOvgDoj12YFvJ22PcXkt6baiZc/jlVQrsn9utx3CiA9agmNF8+Ss57rdBOfPtNetW+vHJc1fBrWzrYKZv8zWXYvR4HbwV1iXDY+21qQAuN1QN886U/S1Tm5Xmv2MJThvBuAU3tqIiIjsbdf8IzFk84/KlszOP3bHDBQzcqGQ9zNERLJEKwAj3MEgs/54Y66HkXserxaGpHSUN4xs6PXa/4qMkWKGiEyKyinWUaDjaTv0NM7W3JIfCj5mhKqgcQm0P2mLpeWNmV0s3dXmu2q6bfrp71xkdGW1lmDb/tSeSp8ihSxYaR9V0yGdssMW6ZTFGbcXPH4d+BYZK18IGhdB60pLAMzWoagXcAf8zPrepyARg6BiUsmrnAJDO6B3a/F1odhVsdYbtLlRsHTbC0sB8vrt8HY6AX1tVsE2Bwm2FjM+aQV+FDNERAqfNwANC+0AR397RrueuQN+Zn33SrsHU8yYdAW/nyEikgU6aiAipcsbsE282MCe6jgiIiL5wuWC6plQtwCGd9pJcJFcKKuF5iPBV2bVbtKpQ3/OVHzPczUtsQ8l1oocXOVUqJ4FA50jXWdEioTbY8mBgXKreugNFFdilshkCEYs8c/th8HOXI9GSo3bA7XzIFhlCd7FIhmD3m3gL7c5kbpsSCHyhy0+hGszN6cXEREJVIys6ZZZgq2IiEiRUnKtiJS28kbbfIj25nokIiIi+3K5oHaOtZ0c6laCreROuAZajoLyetuMiw9O7HnSSRjYbomBlS0w9VhLInd7MjlakeLldkPtXKsI199hVTxERER2KauFpsPB5bV7LpHJFCiHhsNGEry7cj2aQ+OkLUl9sMuKM0w5xuZEIoUqUA7NR1g3v75W65QkIiJyqELVNv/whaG/LdejERERyQol145IDw+z7mUvZ93LXk56eDjXwxGRyeILQvUMVa+VcVHMEJFJtSuRqm4BDO2ceFKj5ERRxYxgJTQfDfULIT5kG3KJMV5TMmYJHv0dEKiEKUuh6Ug75CQi4+P1W+u9UBUMqDKIiIi8SHm9JVC5vTDck7Ufk47GWPe2j7PuHVcV/n2uZE5ZLTQutnXWQp67DnSANzQybzncKrKJFDp/mc3Dq6ZZhcFJrGBrMeMTrHv7pxQzRESKTbjG7pc8ISsQcojS0RjrLriSdRdeqZiRA0W1nyEikiHeXA8gbzgOidbW3Z+LSAmpaIaeLbbhoAoEMhaKGSIy2XYl2OKCrjW2URmoyPWoZCyKLWZ4/VA/H8rq7P5poAMGd9jXfSGrQOtyQzoN6YQl1abi4PFbC8rKKVBWDx5NRUUOib/MKsO1PgFDO+z1JSIisktZHdQvgNaV4A3YfVqmOQ6Jjh0jn2f+6aWAVTRCdBZ0rgZvsPC6VER7bP7SuFhrxVJ8fEGoX2Tz9P526yjjcmX/5zoOie07dn8uIiJFJlwDDSPzj/jgoR1MUszIrWLbzxARyQDtaIqIeANWvbbtCUhHCm/BV0RESoPbDXVzLSmxc7W18NNGn+RKuMbafsX67IDSYBfEByARxbIr3Pa3Wt5ojw1GrGLtZGzaiZSKcA00LrJ5TLRXlaBFRGRvlVMg1g9dz0Fls1WyFZks1TNguNu6V1Q253o0YxcftC4dTUdovi3Fa1eCbXIkwbaiSXN1ERE5dBXNUDcE25+1vXZvMNcjEhERyQitqImIAJQ3QWgrDO+06h4iIiL5yOWCmll2MGT7auhvs+RFlzvXI5NS5HJZMl8wYpvn6ZRVv3Ec+5v0+C0pXESyp6LJXnftq2zjwl+e6xGJiEi+cLms+0ViGPq22Wa3DpTLZPEGoG6+VS8b6i6MRNX4kB0crF8Ikam5Ho1IdgUroWkJtD2pBFsREcmMXXsXyRh0Pw/lDXZPKCIiUuC00ykiAtbKuGambUynk7kejYiIyIFVtkDLURCogr5Wi18iueb2WMthf9gq4SixVmRyRKZZEki0z5JCREREdvH4oGGRbWz3t4GTzvWIpJSEa6BhoXVdifbkejQHFu2zogt1C6BmtpIMpTSEqqH5CAhU2NqSYoSIiBwqtwfqR+6nBrZboq2IiEiB026niMgu5Y32MdiZ65GIiIgcXLjGEmwj02Gg0zYDRUSk9LhcUD0Taudb++WEEmxFROQFfCFoWAzhOuhTgq1MssoWaFxs7eeHduR6NPty0jDQYYkfjYuhbq4OCUppCVVD85EQrh05vJ3I9YhERKTQebx2CLxmju1b6CC4iIgUOK0SiIjs4vbYpjRua5knIiKS7/xha+PXuBjSCdsIUQV2EZHS43ZD7RxLsB3aqfmMiIjsLVAOTYdDWR30as4gk6xqGjQdAXigd1t+/P05jh1Q7d0G/kqYcoy1MVbFWilFwcqRw9tTLdk81p/rEYmISKHzeK2DQf1CiPbCcE+uRyQiIjJh3lwPIG+4XPjnztn9uYiUqHCttVXtft4Wk/R+IPujmCEi+cTtsU3AUBXseB762y3pNlil96h8oJghIpPF7Ya6eYADO56zCue+cK5HJSIi+SJQbgmO25+Fvm1Q3gDewMSfz+XCP70FSINuc+VgKpttntr1nM1ZfUGbs7o9kzuOdNISB2MD4K+AxiUQmXJorwWRYuALQePhEIjAjnUQH4Cyhsy9Rl0u/NObd38uIiIlwO2xrgD+EHSusS4a5WOILYoZuaX9DBGRfSi5doQ7FGLOLbfkehgikmsuF1TPgMHtdpIuVJXrEUkeUswQkbwUqobmo6C8DbrXQ+9Wi2OBilyPrKQpZojIpHK7oW4+uNyWYOukwV+e61GJiEi+8Ietgq03ADs3QDAy4fmCOxhgzk8/B4kYhEIZHqgUpWDE5qwV7bBzoyXZut32N+gL2/1LpjlpSMYgMQSJqK39BirtdVDeAP6yzP9MkULl8ULtbHut7njOuiMFK+2/D5E7GGDOTz4LqaRihohIKXG5rJiVvwy61lpsCVcfcK3KHQww58efsU4DihmTTvsZIiL7UnKtiMiLBcqtpWrrE/a5W2+VIiJSIDxea7kZrrX2lr2boWeLbaKLiEhpcLuhdq7NYzpXQzqVkQ1xEREpEl4/NBw2ssH9HCSjUFaf61FJqfB4LcGivBGGdliC7VA3DLeNJFBMPOF7NycNA9vtHgisSq6vDKpm2gHUYJWNQ0T2r6zWkmp7t0L3Rvvfiibtk4iIyMSFqqH5aAhugp6NEO2z+8HJ7mIgIiIyAZoJiYjsT+UUGOiEgXaobMn1aERERMbHH4b6edZ6s6/NNkK0UCUiUjrcbqiZZYkj25+15JVwba5HJSIi+WJ3nPBD20pLQtR8QSaTx2fJehVNkBiG2AAMd0PnWkv8PpQqtrF+q4RbPdOeyxcaqYyrtrYiY+bxWZwoq4eOVTC8UwcxRETk0Hj9tmdRVmtVbId2WCcBERGRPKfk2hHp4WE2nH8+ALNuvBG3SsyLlDa3x6rXRnfa6blgZa5HJHlEMUNECkag3BasIlMg1mfVemRSKWaISM64XFA13RKntj8Lfe1Q0ZCdlssiIlKYyuot6TAxNO5qoelojA0XfxpIM+uPf8btC2ZnjFL8fCH7CFRYq+D44KFVr40PQvUsqJ6RuTGKlKpAuRUiad1+SE9jMeNqAGb9+S+4y32ZGJ2IiBSicA1EpsFgp3UueNEBKIsZnwFg1k034y4rz8EgS5f2M0RE9qXk2l0ch/i653d/LiJCqApq5kD7Klvg9WjBR0YoZohIofGH7UMmn2KGiORaRRN4A7B9NfRug4pGS7gVERHx+q2yed+28SczOg7xza0jn2d+aFKCfEFrD9y9fuLJtY4DTtpaD4tIZoSqbU0pPjTxtSXHIb65bffnIiJS4kLVew75+V9UEEQxI7e0nyEisg+VKxEROZCq6VA1FQa26wZSREREREQKU6gaWo6CqhnQv93aJYuIiIBVjkolcj0KEVPeYNXL0smJfX8yCt7goVW+FZG9BcqhrAGivbkeiYiIFAt/2LpoxPpyPRIREZGDUnKtiMiBuD1QO88WZId25Ho0IiIiIiIiE+MLQdMS+0jGoL8d0qlcj0pERHItGLFkxGQ01yMRsQNBoRoY7pnY98cHIFi1bwU0ETk0FU1WFXqiie8iIiIvVt4A6bTFFxERkTym5FoRkYMJlEP9Als4ig/kejQiIiIiIiIT4/ZAzSxoOcYST/paIT6Y61GJiEgu+cshWKl4IPnB7YHIVEhEJ9ZFLBGFikarfisimROqgVCVqteKiEjmhGqsuJW6K4mISJ5Tcq2IyFhUNEHdfBjqsSpPIiIiIiIihaqsFqYcA/ULLZmqv11VqERESpXLBeWNkBjO9UhETFm9JXzHxpnElxi2Sv3BqqwMS6SkebxQNR3iQxNLfBcREXkxrx8qWiCmwlYiIpLflFwrIjJW1TPtY2C7Np5FRERERKSweQNQPx+mHAvhOujvgKFubZaLiJSiUBW4vZCK53okIuALWhJftH989yXRXgjXWgU0Ecm8sgYIVEKsL9cjERGRYlFeZ/MQFbYSEZE85s31APKGy4WvpWX35yIi+3B7bPM5FYO+Noi0gEtnFEqSYoaIiIyVYoaI5LuyWghGYKAdujdYFVslpYiIlJZAxD5i/ZacOBYuF77GWkt+1G2uZFpFE/RshmgPhKoP/vh0yj4qWzTvEsmWXYnvHassyXY8rzWXC19DLeDoNSoiInsEq6xrwVCnddMAxYxc036GiMg+lFw7wh0KMfeO23M9DBHJd94ANCyCVAL626xdhW4sS45ihoiIjJVihogUBI8XIlOtgm3vVhjutsohIiJSGtxuqGy2hKmxfkswwNxffgUSMQiFsjg4KUm+ENTMgtYn7NDPwe5Loj1WgXmsyeEiMjEVTdCzyarXBiNj/jaLGddAKqmYISIie7hcEJlie+7pJLi9FjOu/5Id4lPMmHTazxAR2ZdKLoqIjJe/DJqWQLAG+lrBSed6RCIiIiIiIofOF4S6uTDtePD6cz0aERGZTKFqcPvUklXyR0WLJX0PbD/w49JJiA9D9Uzw+CZlaCIlyx+GqhkQ7dO+iIiIZEa4zuYi0d5cj0RERGS/lFwrIjIRgQpLsA3VQF+btR0TEREREREREREpRIFKCFdDrD/XIxExHi/UzgVvCIa6R3/cwHaoaITypskbm0gpq2yxJKjhnbkeiYiIFAOPF6qmQ3xIBzdERCQvKbl2RDoaZcMbzmfDG84nHY3mejgiUgiCldB8OJTX72lXISVBMUNERMZKMUNERERECoLbbZVCk1FrwXoQ6VicDR/4Ahsu/YrucyV7QlXQsBBScYj27PvvQ93gDVoSrsc72aMTKU2+INTMhkTUXptjYDHji2z48JcVM0REZF/ljSMHN3osZnzwS2z40DWKGTmg/QwRkX1ptWGXdJroqlW7PxcRGZNABTQdAZ2roWcLlNWCL5zrUUm2KWaIiMhYKWaIiIiISKEI19i6VmII/GUHfmw6TXTtxpHPD56MKzJhlS3WNaxzNfS1QrAKXFjrYLcPGpfY366ITJ6KJohMgb5tUDnl4I9Pp4k+t2n35yIiInvx+qF6BrSuBF9AMSOXtJ8hIrIPJdeKiBwqX8gWcb0h6F4PiWEI1YDLleuRiYiIiIiIiIiIjI2/zKpG9Ww6eHKtyGSqmmZ/kzs3jrSid6CsAapnWbEDEZlcbg/UzIGhHntNhqpzPSIRESl05U12YGqgN9cjERER2Ys7W0+8ceNG3vnOdzJr1ixCoRBz5szh6quvJh4/cIuQaDTK+973PmpraykvL+f1r389HR0d2RqmiEhmeHxQvwBajgKX16ooJGO5HlXBUMwQEZGxULwQEZGxUswQEZmgiibABalErkcyaRQzCkS4BlqOhhnLYcZJMGWpEmtFcilYCfXzID5kBUdKhGKGiEiWeP12cCpRPPvrihkiIsUha8m1q1evJp1O8+Mf/5inn36ab33rW1x77bVceeWVB/y+Sy+9lL/97W/ceOON3H333bS2tvK6170uW8MUEckcl8valE09FiLTYGgHDGy3tmVyQIoZIiIyFooXIiIyVooZIiITFKq2JMZoT65HMmkUMwqIy2VdxHwhdQ0TyQeVU6B2Dgx2QTqZ69FMCsUMEZEsqmiC8oZcjyJjFDNERIqDy3EcZ7J+2Ne+9jV+9KMfsX79+v3+e29vL/X19fz2t7/lDW94A2ABZ9GiRTzwwAOceOKJB/0ZfX19RCIRent7qaysHPPY0kNDrDlmKQALVjyGOxwe8/eKiOwjnYbB7dC9wRaW/CEIRsDtzeDPSNpzz1huzz0OE32vnEyKGSIi+SHfY8ZkxAtQzBARGQvFDJPvvwcRkYPq3Qqtj9shctf+63Okh6OsOfsdACx46H7ckfG1BM/390rFDBGRMUoloGMV9GyByub97oHsFTMeeRB3RXHtZyhmiIhkTnrHNtacdDoACx59GHd5xbi+P9/fK/M9Zmg/Q0RKyVjfK7NWuXZ/ent7qampGfXfH3vsMRKJBKeffvrury1cuJDp06fzwAMP7Pd7YrEYfX19e32IiOSc222n66YeC1OOBm/Yqtj2t0NiKNejKwiKGSIiMhbZiBegmCEiUowUM0RExqisAQIRiPbmeiQ5o5ghIjJGHh/UL4KKZuhrK5kKti+kmCEikkGh8R3aKzSKGSIihWfSkmvXrVvH9773PS666KJRH9Pe3o7f76eqqmqvrzc2NtLe3r7f77nmmmuIRCK7P6ZNmzbhMXqqq/FUF3ewFpFJ5vFBZCpMOx6mLLWE2/iQVQAZ6IBYf0kuNh2MYoaIiIxFtuIFKGaIiBSbQokZIiJ5weuHqmkQG4ADNL7zRMrxVJZP4sAmh2KGiMg4+YLQtMQqnve1QSq+z0MUMxQzRETGylNdjacqAm5ProeSUYUSM7SfISKyt3En115xxRW4XK4DfqxevXqv79m2bRtnnnkm559/Pu9+97szNniAT3ziE/T29u7+2LJly4Sexx0OM/+B+5n/wP0qbS4imefxWmJty9Ew/UT73/ImSKdgoBN6t0HfNhjshOEeS7pNDEEyCsnYCz6ilpwb6y+I6iGKGSIiMhb5Fi9AMUNEJF8Vc8wQEckrFc0QHL16rTsUZP4fvs38334Fdzg0yYMbG8UMEZFJ5AtB0+FQNQP6t9sexgh3KMj833+T+b/7at6ujShmiIjkh93r6Q8+qJgxDtrPEBHJHu94v+GjH/0oF1544QEfM3v27N2ft7a28tKXvpTly5fzk5/85IDf19TURDwep6enZ6+TGB0dHTQ1Ne33ewKBAIFAYMzjFxHJuUCFfUSmWsJsfBASw5Y0Gx9Jqk0l7cNJ2fc4Drhc9oEbXG7wBCBUY/+bpxQzRERkLPItXk/l0AIAABR6SURBVIBihohIvlLMEBGZJL4gVE2H9qcgWGlrUQVGMUNEZJJ5A1bBNlAB3c9bBfTyenCPezt60ilmiIjIWClmiIiUlnHPZurr66mvrx/TY7dt28ZLX/pSli5dynXXXYfbfeAFuKVLl+Lz+bj99tt5/etfD8CaNWvYvHkzy5YtG+9QRUTynzdgHy+WSkI6CU7aPnZxjSTWuj22IOVyTd5YJ0AxQ0RExkLxQkRExkoxQ0RkElW2QO9WGN4J4dpcj2bcFDNERHLA7YHa2RCqgh3PQ387eIPg8eV6ZAekmCEiImOlmCEiUlqydtx827ZtnHbaaUyfPp2vf/3rdHZ20t7eTnt7+16PWbhwIQ8//DAAkUiEd77znXzkIx/hzjvv5LHHHuPtb387y5Yt48QTT8zWUAFIR6Nseuvb2PTWt5GORrP6s0REDsrjtQoh/jAEyvd8+MP2dY8v7xNrx0MxQ0RExqLQ4gUoZoiI5EohxgwRkbzjDUDNLEhE7RD4C6RjcTZd9lU2XfHtgr/PVcwQEcmCcA20HA0tx5BO+9h02dfYdMW3FDMUM0REDqiY1tMLMWYU0+9fRCRTstaH49///jfr1q1j3bp1TJ06da9/cxwHgEQiwZo1axgaGtr9b9/61rdwu928/vWvJxaLccYZZ/DDH/4wW8PcI51m6JFHdn8uIiKTRzFDRETGouDiBShmiIjkSEHGDBGRfFTeBOWNMNgJFc17vp5OM/Tk2pHPndyMLUMUM0REssTjhcgUcFcw9PT6kS9mre7TpFDMEBHJsiJaTy/ImFFEv38RkUxxObvetYtEX18fkUiE3t5eKisrx/x96aEh1hyzFIAFKx7DHQ5na4giIjk30ffKYqOYISJycIoZRjFDROTgFDOMfg8iUnQGd8C2R8E/0lUJSA9HWXP2OwBY8ND9uCPV43pKvVca/R5EpBQc6tqI3iuNfg8iUgoUMzJD+xkiIgc31vfKwj4eKCIiIiIiIiIiIiIi2VNWC1UzYGgHOKpeJCIiIiIiIiIipUHJtSIiIiIiIiIiIiIiMrqaWVBWDwPbcz0SERERERERERGRSaHkWhERERERERERERERGZ03AHXzweWBWH+uRyMiIiIiIiIiIpJ1Sq4VEREREREREREREZEDK6uFunkQ7YNkPNejERERERERERERySpvrgeQT1yhUK6HICIiBUIxQ0RExkoxQ0RERESKRtV0iA1A2xpcAX+uRyMiIgVCayMiIjJWihm5pd+/iMjelFw7wh0Os/DxFbkehoiIFADFDBERGSvFDBEREREpKm4P1M/HnYqx8FefgnAthLX5KiIio9PaiIiIjJViRm7p9y8isi8l14qIiIiIiIiIiIiIyNh4A9CwCJIxSMZzPRoREREREREREZGscOd6ACIiIiIiIiIiIiIiUkD8ZdC0BCqbwePL9WhEREREREREREQyTsm1I9KxGJsvuojNF11EOhbL9XBERCSPKWaIiMhYKWaIiIiISDFKx2Js/tDlbL7qW6QTyVwPR0RE8pjWRkREZKwUM3JLv38RkX15cz2AvJFKMXj3Pbs/FxERGZVihoiIjJVihoiIiIgUI93niojIWClmiIjIWClm5JZ+/yIi+1DlWhERERERERERERERERERERERERERkRFKrhURERERERERERERERERERERERERERmh5FoREREREREREREREREREREREREREZERSq4VEREREREREREREREREREREREREREZoeRaERERERERERERERERERERERERERGREd5cDyDTHMcBoK+vb1zflx4aYiCV2v297mQy42MTEckXu94jd71nlirFDBGRg1PMMIoZIiIHp5hhJhozREQKyaHe5ypmGMUMESkFihmZoZghIqVAMSMztJ8hInJwY40ZRZdc29/fD8C0adMm/iTNzRkajYhIfuvv7ycSieR6GDmjmCEiMnaKGYoZIiJjpZiRgZghIlJIDuE+VzFDMUNESoxixoQpZohIyVHMmDDtZ4iIjN3BYobLKbIjG+l0mtbWVioqKnC5XOP63r6+PqZNm8aWLVuorKzM0gjzS6lds663uOl6x85xHPr7+2lpacHtdmdphPlPMWPsSu16ofSuWddb3BQzDp1ixtiV2vVC6V2zrre4KWYcOsWMsSu164XSu2Zdb3FTzDh0ihljp+stfqV2zbresVPMMIoZY6frLX6lds263rFTzDCKGWOn6y1+pXbNut6xG2vMKLrKtW63m6lTpx7Sc1RWVpbEH9gLldo163qLm653bEr5tN4uihnjV2rXC6V3zbre4qaYMXGKGeNXatcLpXfNut7ippgxcYoZ41dq1wuld8263uKmmDFxihnjp+stfqV2zbresVHMUMyYCF1v8Su1a9b1jo1ihmLGROh6i1+pXbOud2zGEjNK96iGiIiIiIiIiIiIiIiIiIiIiIiIiIjIiyi5VkREREREREREREREREREREREREREZISSa18gEAhw9dVXEwgEcj2USVNq16zrLW66XplMpfb7L7XrhdK7Zl1vcSu16803pfb7L7XrhdK7Zl1vcSu16803pfb7L7XrhdK7Zl1vcSu16803pfb71/UWv1K7Zl2vTKZS+/3reotfqV2zrlcmU6n9/nW9xa/UrlnXm3kux3GcrD27iIiIiIiIiIiIiIiIiIiIiIiIiIhIAVHlWhERERERERERERERERERERERERERkRFKrhURERERERERERERERERERERERERERmh5FoREREREREREREREREREREREREREZERSq4VEREREREREREREREREREREREREREZUXLJtT/4wQ+YOXMmwWCQE044gYcffviAj7/xxhtZuHAhwWCQww8/nFtvvXWSRpoZ47nen/70p5xyyilUV1dTXV3N6aefftDfTz4a7//Hu9xwww24XC7OO++87A4ww8Z7vT09Pbzvfe+jubmZQCDA/PnzC+rverzX++1vf5sFCxYQCoWYNm0al156KdFodJJGe2juuecezjnnHFpaWnC5XPzlL3856PfcddddHHPMMQQCAebOncsvfvGLrI+zmClmKGaMRjGjMChmHJhiRmYpZihmjEYxozAoZhyYYkZmKWYUd8wotXgBihmKGXtTzMgsxQzFjP0p1JhRavECSidmKF7kB8UMxYz9UcwoHIoZo1PMyDzFDMWM/VHMKByKGaPLSsxwSsgNN9zg+P1+5//+7/+cp59+2nn3u9/tVFVVOR0dHft9/H333ed4PB7nq1/9qvPMM884n/rUpxyfz+c89dRTkzzyiRnv9b75zW92fvCDHziPP/648+yzzzoXXnihE4lEnK1bt07yyCduvNe8y4YNG5wpU6Y4p5xyinPuuedOzmAzYLzXG4vFnGOPPdY566yznHvvvdfZsGGDc9dddzkrV66c5JFPzHiv9ze/+Y0TCASc3/zmN86GDRucf/7zn05zc7Nz6aWXTvLIJ+bWW291PvnJTzp//vOfHcC56aabDvj49evXO+Fw2PnIRz7iPPPMM873vvc9x+PxOLfddtvkDLjIKGYoZoxGMUMxIx8pZuSWYoZixmgUMxQz8pFiRm4pZhR3zCi1eOE4ihmKGXtTzMgsxQzFjP0p1JhRavHCcUorZihe5J5ihmLG/ihmKGbkI8WM3FPMUMzYH8UMxYx8lC8xo6SSa48//njnfe973+7/TqVSTktLi3PNNdfs9/H/8z//45x99tl7fe2EE05wLrrooqyOM1PGe70vlkwmnYqKCuf666/P1hAzbiLXnEwmneXLlzs/+9nPnAsuuKCgAsV4r/dHP/qRM3v2bCcej0/WEDNqvNf7vve9z3nZy16219c+8pGPOCeddFJWx5kNYwkUl19+ubN48eK9vvbGN77ROeOMM7I4suKlmKGYsT+KGYVDMeOmAz5GMSOzFDMUM/ZHMaNwKGbcdMDHKGZklmJGcceMUosXjqOYoZixN8WMzFLMUMx4sUKOGaUWLxyndGOG4kVuKGYoZryYYkZhUcwYnWJG5ilmKGa8mGJGYVHMGF22Yob70GvfFoZ4PM5jjz3G6aefvvtrbreb008/nQceeGC/3/PAAw/s9XiAM844Y9TH55OJXO+LDQ0NkUgkqKmpydYwM2qi1/y5z32OhoYG3vnOd07GMDNmItd78803s2zZMt73vvfR2NjIkiVL+NKXvkQqlZqsYU/YRK53+fLlPPbYY7tLoK9fv55bb72Vs846a1LGPNkK+T0r3yhmKGaMRjFDMaNYFPJ7Vr5RzFDMGI1ihmJGsSjk96x8o5hR3DGj1OIFKGaAYsaLFfJ7Vr5RzFDM2J9CjRmlFi9AMeNgCvn9Kh8pZihm7I9ihmJGsSjk96t8pJihmLE/ihmKGcUiW+9X3kP67gLS1dVFKpWisbFxr683NjayevXq/X5Pe3v7fh/f3t6etXFmykSu98U+/vGP09LSss8fXr6ayDXfe++9/PznP2flypWTMMLMmsj1rl+/njvuuIP//d//5dZbb2XdunVccsklJBIJrr766skY9oRN5Hrf/OY309XVxcknn4zjOCSTSS6++GKuvPLKyRjypBvtPauvr4/h4WFCoVCORlZ4FDOMYsbeFDMUM4qJYkbmKGYYxYy9KWYoZhQTxYzMUcwwxRozSi1egGLGLooZeyhmZI5ihlHM2KOQY0apxQtQzDgYxYvMUswwihl7KGYoZhQTxYzMUswwihl7KGYoZhSTbMWMkqlcK+Pz5S9/mRtuuIGbbrqJYDCY6+FkRX9/P29961v56U9/Sl1dXa6HMynS6TQNDQ385Cc/YenSpbzxjW/kk5/8JNdee22uh5YVd911F1/60pf44Q9/yIoVK/jzn//M3//+dz7/+c/nemgiRUUxozgpZihmiGSDYkZxUsxQzBDJhmKPGaUYL0AxQzFDJDsUM4pPqcULUMwQmSyKGcVHMUMxQyRbFDOKj2KGYsZElEzl2rq6OjweDx0dHXt9vaOjg6ampv1+T1NT07gen08mcr27fP3rX+fLX/4y//nPfzjiiCOyOcyMGu81P//882zcuJFzzjln99fS6TQAXq+XNWvWMGfOnOwO+hBM5P/j5uZmfD4fHo9n99cWLVpEe3s78Xgcv9+f1TEfiolc71VXXcVb3/pW3vWudwFw+OGHMzg4yHve8x4++clP4nYX1/mC0d6zKisrdWpvnBQzjGLGHooZRjGjeChmZI5ihlHM2EMxwyhmFA/FjMxRzDDFGjNKLV6AYsYuihl7KGZkjmKGUcwwhR4zSi1egGLGwSheZJZihlHMMIoZRjFDMUP2TzHDKGYYxQyjmKGYcTDF8xs6CL/fz9KlS7n99tt3fy2dTnP77bezbNmy/X7PsmXL9no8wL///e9RH59PJnK9AF/96lf5/Oc/z2233caxxx47GUPNmPFe88KFC3nqqadYuXLl7o/XvOY1vPSlL2XlypVMmzZtMoc/bhP5//ikk05i3bp1uwMiwNq1a2lubs77IDGR6x0aGtonEOwKko7jZG+wOVLI71n5RjFDMePFFDOMYkbxKOT3rHyjmKGY8WKKGUYxo3gU8ntWvlHMKO6YUWrxAhQzQDHjxQr5PSvfKGYoZrxQoceMUosXoJhxMIX8fpWPFDMUM15IMcMoZhSPQn6/ykeKGYoZL6SYYRQzikfW3q+cEnLDDTc4gUDA+cUvfuE888wzznve8x6nqqrKaW9vdxzHcd761rc6V1xxxe7H33fffY7X63W+/vWvO88++6xz9dVXOz6fz3nqqadydQnjMt7r/fKXv+z4/X7nj3/8o9PW1rb7o7+/P1eXMG7jveYXu+CCC5xzzz13kkZ76MZ7vZs3b3YqKiqc97///c6aNWucW265xWloaHC+8IUv5OoSxmW813v11Vc7FRUVzu9+9ztn/fr1zr/+9S9nzpw5zv/8z//k6hLGpb+/33n88cedxx9/3AGcb37zm87jjz/ubNq0yXEcx7niiiuct771rbsfv379eiccDjuXXXaZ8+yzzzo/+MEPHI/H49x22225uoSCppihmKGYoZihmCFjpZihmKGYoZihmCFjpZhR3DGj1OKF4yhmKGYoZmSTYoZiRjHFjFKLF45TWjFD8SL3FDMUMxQzFDMUM2SsFDMUMxQzFDMUM8anpJJrHcdxvve97znTp093/H6/c/zxxzsPPvjg7n879dRTnQsuuGCvx//hD39w5s+f7/j9fmfx4sXO3//+90ke8aEZz/XOmDHDAfb5uPrqqyd/4IdgvP8fv1ChBQrHGf/13n///c4JJ5zgBAIBZ/bs2c4Xv/hFJ5lMTvKoJ24815tIJJzPfOYzzpw5c5xgMOhMmzbNueSSS5ydO3dO/sAn4M4779zva3LXNV5wwQXOqaeeus/3HHXUUY7f73dmz57tXHfddZM+7mKimKGYoZihmKGYIWOlmKGYoZihmKGYIWOlmFHcMaPU4oXjKGYoZpy6z/coZmSOYoZixmgKMWaUWrxwnNKJGYoX+UExQzFjNIoZhUEx4wLHcRQzJotihmLGaBQzCoNixgWO40xezHA5TpHV+BUREREREREREREREREREREREREREZkgd64HICIiIiIiIiIiIiIiIiIiIiIiIiIiki+UXCsiIiIiIiIiIiIiIiIiIiIiIiIiIjJCybUiIiIiIiIiIiIiIiIiIiIiIiIiIiIjlFwrIiIiIiIiIiIiIiIiIiIiIiIiIiIyQsm1IiIiIiIiIiIiIiIiIiIiIiIiIiIiI5RcKyIiIiIiIiIiIiIiIiIiIiIiIiIiMkLJtSIiIiIiIiIiIiIiIiIiIiIiIiIiIiOUXCsiIiIiIiIiIiIiIiIiIiIiIiIiIjJCybUiIiIiIiIiIiIiIiIiIiIiIiIiIiIjlFwrIiIiIiIiIiIiIiIiIiIiIiIiIiIyQsm1IiIiIiIiIiIiIiIiIiIiIiIiIiIiI5RcKyIiIiIiIiIiIiIiIiIiIiIiIiIiMuL/A3Slh60L1UqTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Seed 2\n",
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Seed 3\n",
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Seed 4\n",
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "G 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.1\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
+      "B 0.2\n",
+      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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l7TE+FBUV8corrwAwd+5cAF599VUSExNJTEzk9ttvb9JnERGRxmmPMWP06NFs3ryZ++67jzlz5jBnzpzK97p3786oUaOa9FlERKR5Bg4cyIcffsgVV1zB4MGDueqqqzjkkEOwLIu0tDQ+/PBD7HY7vXv3DnVTRZpFCbMidbjrrrt48cUXKSoqatT2KSkpzJs3jzfffJNPPvmEcePGUV5eTteuXTnyyCP54IMPuOyyyxo8zuGHH860adP429/+xt13301cXBw33ngjTz755L5+JBERCYL2Gh+++OILJkyYUPl86dKllcsw9e7dWwmzIiItoD3GjN2TKzZs2FDnAMj06dOVMCsi0oqaGksApk6dytSpUwFwOBz06NGDP/7xjzz++OPExMQ0uP/777/PQw89xP/+9z8KCgo4+OCDmTx5MieeeGKzP4eIiARXe4sPBQUFPPTQQzVee+655wDo16+fEmZFRFpQe4sZu+9NPf3007XeO+mkk5QwKyLSis477zxWrlzJc889x08//cR///tfbDYb/fr14+yzz+bWW29tsPK4SFtls1QGQEREREREREREREREREREREREREREOjB7qBsgIiIiIiIiIiIiIiIiIiIiIiIiIiLSkpQwKyIiIiIiIiIiIiIiIiIiIiIiIiIiHZoSZkVEREREREREREREREREREREREREpENTwqyIiIiIiIiIiIiIiIiIiIiIiIiIiHRoSpgVEREREREREREREREREREREREREZEOTQmzIiIiIiIiIiIiIiIiIiIiIiIiIiLSoTlD3YDGCAQCZGRkEBcXh81mC3VzRETaJMuyKCkpoWfPntjtnXc+hGKGiEj9FC+qKGaIiNRPMaOKYoaISP0UM6ooZoiI1E8xw1C8EBFpmGKGoZghItKwpsSMdpEwm5GRQZ8+fULdDBGRdmHbtm307t071M0IGcUMEZHG6ezxAhQzREQaSzFDMUNEpLEUMxQzREQaq7PHDMULEZHGU8xQzBARaazGxIx2kTAbFxcHmA8UHx8f4taIiLRNxcXF9OnTp/Ka2VkpZoiI1E/xoopihohI/RQzqihmiIjUTzGjimKGiEj9FDMMxQsRkYYpZhiKGSIiDWtKzGgXCbO7S4rHx8fr4i/SCQQqKki75BIA9vvsM+xRUSFuUfvS2ZdhUMwQ6TwUL/ZNZ48XoJgh0lkoXuw7xQzFDBFpms4cexQzFDNEZO86c3yoS2ePGYoXIrIvOltMUcxQzBCRltER40ljYka7SJgVkU7GsvBs3FT5WEREpE6KFyIi0hiKFyIi0toUe0REpC6KDyIiEiyKKSIiEgydNJ7YQ90AERERERERERERERERERERERERERGRlqSEWRERERERERERERERERERERERERER6dCcoW6AiIiIiIiIiIiIiIiIiIiIiIiINI/f78fr9Ya6GdJIYWFhOByOUDdDpFNSwqyIiIiIiIiIiIiIiIiIiIiIiEg7Y1kWWVlZFBYWhrop0kSJiYmkpqZis9lC3RSRTkUJsyIiIiIiIiIiIiIiIiIiIiIiIu3M7mTZlJQUoqOjlXzZDliWRXl5OdnZ2QD06NEjxC0S6VyUMCsibY/NRljPnpWPRURE6qR4ISIijaF4ISIirU2xR0RE6qL4ICIiwaKYIrv4/f7KZNkuXbqEujnSBFFRUQBkZ2eTkpKCw+EIcYukU+qk8UQJsyLS5tijojjgl59D3QwREWnjFC9ERKQxFC9ERKS1KfaIiEhdFB9ERCRYFFNkN6/XC0B0dHSIWyLNsfvv5vV6lTArIdFZ44k91A0QERERERERERERERERERERERGRprN1osqQHYn+biKhoYRZERERERERERERERERERERERERERHp0JQwKyJtTsDlIu3iS0i7+BICLleomyMiIm2U4oWIiDSG4oWIiLQ2xR4REamL4oOIiASLYoqIiARDZ40nzlA3QEQ6uIAfvOXgrQCfC7wu89zvMe8B2B1gD4OwKPPjA9eqVbv2D4Su7SIi0rYFAooXIiLSMMULERFpbYo9IiJSl+rxIX0RxERBfG+ITQEtxysiIk2hPod0ACNHjuTQQw/lxRdfDHVTRDqvThpPlDArIsHnKQd3MVQUQFmuSZb1ewALbI5dCbIOwGZuAlkBkzwb8IPlB5e36lj5aWClQlTirn1ERKTD8ZSDpwy8ZeDe9V+/FwgAdnBGQFg0hMfs+omFsMhQt1pEREIh4Dcxw+cGvxsCPrAs857NDo4wcESYOOGMArsW1hERERERkRCyLNi+CFZ/DWt/rnr9/XPAuasvE5MCA0fBEddDn6NC0kwRERGRtsayLPx+P06nUttEJLh0VRGR4PB5oCIfSnZCeZ6pImuzQ3gURMaDI7zxM6TDKqoe56yD0q0QmQgJvSCmG4RHt8hHEBGRVuR1mXhRutNMsPC5zKw1h9MkO9kcYMMMKnjLoSx7V2VyG4TFQHQSOOJC/SlERKQ1eCugohDK86Eizzyvnii7Jxu7kmajzMAzEa3YWBEREREREcxk8OUfwfw3IPt385rPBvQwjxP7gt0HJZnmvteyD8zPfifBOS9AlwEha7qIiIhISxs7diwzZ85k5syZvPTSSwCMHz+e66+/nu+++44HH3yQlStX8tNPP/Hee+9RWFjI119/Xbn/XXfdxbJly5gxYwYAgUCAp556irfeeousrCwGDRrEQw89xMUXXxyCTycibZ0SZkVk37iKoSwHiraDu8RUcIqINxVhm7uEUPX94ntAhBNcRZC5HMLjIKE3JPQ0VQZFRKR9cZdAcSYU7wBPqZlQER4LUUmNixtWwCTQlmRC2aaax43WhAoRkQ7DssyEipIsMynPU2pWnAiPhsgEU318r/sGzAoX3grIXQ9uT9V7rkKIitJypyIiIiIi0jIsC1Z9AdP/BfmbzWvOSBh2HvQ9BT4fZ17783xzL8vrgm3zYcWn5idtJrw2As5+Fg6/NnSfQ0RERNoty7Ko8PpDcu6oMAe2Rtx7femll1i/fj3Dhw/nscceA+D3380ko/vvv59nn32W/fffn6SkpEad98knn2TixIm88cYbDBw4kFmzZnH11VfTrVs3TjrppOZ/IBHpkJQwKyJNt3vwujjDJCx5KyAyziS32lpgyVO7E6K7QFSySYjKWQPF2yGpP8T3Amd48M8pIiLB5Sk3kyuK0k3ciIg31/CmJizZ7CbBNjwWHNUqku9YBN4BZlJFRGxw2y4iIq3Hskwl2aJ0kyxr+U3MSGhCzLDZzYC0M9JMyCgvr3pv20Jw9YX43qaPYW+B/ouIiIiIiHROuRth8l2wZbZ5Ht0VjrsTDr+mWt9kXM19wiJh/5Hm56T7YPLdsOkX+Pb/zATAUY9rwp+IiIg0SYXXz7CHfwzJuVc/dgbR4Q2noiUkJBAeHk50dDSpqakArF27FoDHHnuMUaNGNfqcbrebJ554gmnTpjFixAgA9t9/f+bMmcObb76phFkRqUUJsyLSNBUFULgdSjIg4IXIJIjp2jrnttkgMh4i4sBdDFkrzSB6lwEQ0003jURE2qKA30ywyN9sqoVHJ5kEpWCoft13RkDeBlO9Nrm/SZx1hAXnPCIi0jrcJZC/xUyOsyyITq6/kmxjVZ/UF5m4q9J5JsSlQmI/cx71JUREREREpLkCAZj/H/j5cfC7wRkFJ/wF/vDnpk3sTuoPV38JM5+CGU/CvFfM60qaFRERkU7kyCOPbNL2GzdupLy8vFaSrcfj4bDDDgtm00Skg2hSwuyTTz7Jl19+ydq1a4mKiuLYY4/lqaeeYvDgwfXu99lnn/HQQw+xZcsWBg4cyFNPPcVZZ521Tw0XkVbmKobCbWYJ7YDXzIYOi2qx0zkS4vb+ps1mlmENj4XyPNi+2CRHJe8fnAF1CQrFDBHBVWQqa5RkmiW0E3oH/eZ+ZbwIi4GoLuacWaugdCd0OaD1JnXIPlHMEOnk/D6TJJuXBt4yiOliqsMGUWW8cIZDVDz43GbyXWk2JPSBpH6qUN5OKGaISHvhaOSykdJyFDNEpFWU5sDXt8LGaeb5gFPg7Ocheb86N28wPthsMPJ+M8Fv0p0maTamm6lUKy1GMUNE2iP1OUKjPcSMqDAHqx87o0WO3Zhz76uYmJgaz+12O5Zl1XjN6/VWPi4tLQVgypQp9OrVq8Z2ERHKHxFpSGeMJ01ae3DmzJncdtttzJ8/n6lTp+L1ejn99NMpKyvb6z7z5s3jiiuu4MYbb2Tp0qWcf/75nH/++axatWqfGy8ircDrMslO2xdCQRpExEB8zxZNlrVHRTLoqzcZ9NWb2KPqGSi3OyA2xQx4526AHUvM8q3tmdcFPk+oWxEUihkinVggAEXbYfsSKM3cda1OCnqybJ3xIjIB4nuYiug7FkPOBvB76z+QhJxihkgn5iqGzOWQuRLsNkjoFfRk2TrjhTPCxIvIeMjfZPo7hemmMrq0aYoZItIe2KOjGfTrPAb9Og97dHSom9NpKWaISIvbMhfeON4kyzoj4ZwXTIXYvSTLNik+HDEWznjCPJ72KGz6JahNl5oUM0SkvVGfI3TaQ8yw2WxEhztD8mNrwlhgeHg4fn/D92O7detGZmZmjdeWLVtW+XjYsGFERESQnp7OAQccUOOnT58+jW6PSGfUWeOJzdozDb8JcnJySElJYebMmZx44ol1bnPZZZdRVlbG5MmTK1/7wx/+wKGHHsobb7zRqPMUFxeTkJBAUVER8fHxzW2uiDRFwG8qAuanmaSjqESIqKfqa6hZAVNN0BEOXQdBfG+wN2lOQOiV5kDOWpOQ3GVAk3dv69dKxQyRTsLnNhMtCtJMVdmoEM5I85SZiRRxPaDbIJNMK+3iOqmYIdIJWJbpb+SsM1VlY7uDvUmL4AS3La4iEzfiekDXAxQzdmkP10nFDBGRtqE9XCcVM0QkqBa/B1PugYAPug2Bi8dD92HBPYdlwbe3w9KJEJUMf54Pcd2De44QaA/XydaIGe3h9yAiEmrt4VoZ6pjhcrlIS0tjv/32IzIyuIUIWtrNN9/MsmXL+PTTT4mNjWXFihWceuqpFBQUkJiYWLndjz/+yJlnnsl7773HiBEjmDhxIi+++CKHHXYYM2bMAODBBx/kjTfe4LnnnuP444+nqKiIuXPnEh8fz3XXXReaD9gI7fnvJ9LWNCVm7FM2WVFREQDJycl73ebXX3/ltNNOq/HaGWecwa+//rovpxaRllSeDxlLIWMZ+N1mCe22nCwLYLObwW27AzJXQO669lNR0LJMNavMZVCcEerWtBjFDJFOYHeVwPxNZjntUCbLAoTHmOqBpTtNFfLiDHPNlTZPMUOkg/P7zAoRGcvMxLf4XqFLlgVTAT0q0VREL82E7Yt3VZsNhK5N0miKGSIi0liKGSISFH4ffH8/TLrTJMsOvwhumh78ZFkwfZWznoPUg6EiHybfpXtbrUQxQ0REGksxo/nuvfdeHA4Hw4YNo1u3bqSnp9e53RlnnMFDDz3Efffdx1FHHUVJSQnXXnttjW0ef/xxHnroIZ588kmGDh3K6NGjmTJlCvvtV3flfxHp3Jo9IhUIBLjrrrs47rjjGD58+F63y8rKonv3mrMdu3fvTlZW1l73cbvduN3uyufFxcXNbaaINIXXBQVboXCrudETF5oqTwG3h233PwVAn3//DXtEeON3jkw0Sx/lbgBPOaQMNRUO26qAH/I2Q956k9gVHmOSfzsYxQyRTqA0B7JXg6fUVMq2O1r8lI2KF3anWd67PA8ylkOXMuiyf6u0T5pHMUOkg/NWmFUVCreZyRVhLf9dvdH9C0eYSd6tKDST8CqKzMoPbbk/0ckpZohIWxVwudh2080A9Hn7LeyqEhNyihkiEhSeMvj0Otg41Tw/+R9w4l9NYmsjNCs+hEXCBW/AmyfBuu9g+Udw6JXN/QTSCC0VMxQvRCSY1OdoGxQz9s2gQYNqJQ2PHTu2zm3HjRvHuHHj9nosm83GnXfeyZ133hnMJop0eJ01njQ7E+62225j1apVzJkzJ5jtAeDJJ5+s90InIkEWCEBpFuRtgooCiE42iZshbE/58jVVbWsqZ6SpKFiSYarMdh/aNpdU9fsgd735vUcnm4F4d2moW9UiFDNEOjDLgqLtJvnJCphq340cJNhnTYkX0V3MoEbOWvCWQ7fBZsBB2hzFDJEOzFUMO1dDeQ7EpZoE1dbQ1P5FVCKERUHhFnAXQdfBENutJVsozaSYISJtViBA+cKFlY8l9BQzRGSflefDh5fC9oXgjDJJrAee37RjNDc+dD8QTn4Afn4Mfvw7DBptxhSkRbRUzFC8EJGgUp+jTVDMEJF2r5PGk2aVMbz99tuZPHky06dPp3fv3vVum5qays6dO2u8tnPnTlJTU/e6zwMPPEBRUVHlz7Zt25rTTBFpDFcxZK00S1X73ZDQO7TJssFid5oKh+W5pqJgeX6oW1ST32eStvI2QmzXDl21SjFDpAMLBCA/DXauAofTLGXdWsmyzREeA3EpppJ61gpwl4S6RbIHxQyRDqw8HzKXm+/n8T1bL1m2uZwRptqspwwylpp4F/CHulVSjWKGiIg0lmKGiOyz4gwYf5ZJlo1MhOu+bXqy7L469k5IGWaKnkx/onXP3Ym0ZMxQvBAR6VgUM0RE2q8mJcxalsXtt9/OV199xS+//MJ+++3X4D4jRozg559/rvHa1KlTGTFixF73iYiIID4+vsaPiASZ32cGfbcvgqJtENPVVN9ry4lOTWWzm8F4X7lJmi3Z2fA+rWF3smz+ZpNc5uyYFQ4VM0Q6OL8PctdB9mqIiDWDBe2BI9zEhtJsyFjW9iZUdFKKGSIdXGm2+T7uKTXXYFuz5u62PpvNfF8Pi4Sdv5vquN6KULeq01PMEBGRxlLMEJGgyNsE754BOWvMyko3/AB9jm79djiccOZT5vGidyFrVeu3oQNrjZiheCEi0jEoZoiItH9NGqW67bbbmDhxIh9++CFxcXFkZWWRlZVFRUXVgNG1117LAw88UPn8zjvv5IcffuC5555j7dq1PProoyxatIjbb789eJ9CRJqmLA8ylpiKgHY7JPQyFZQ6IpsNYruD5TPVBIt2hLY9lcmyabuSZTvo7x3FDJEOzeeB7DWQu8Es/xYeG+oWNY3dYRK2PCWm2mFpdqhb1OkpZoh0YMWZ5nu45YW41PY5QS8iDmK7QUGaJlu0AYoZIiLSWIoZIrLPctbBf0dDUTokD4AbfoSUoaFrz34nwrDzwArA1IdC144OSDFDREQaSzFDRKT9a1LC7Ouvv05RUREjR46kR48elT+ffPJJ5Tbp6elkZmZWPj/22GP58MMPeeuttzjkkEP4/PPP+frrrxk+fHjwPoWINI7XBdnrYMcikzQblwqRCaFuVeuI6Qp2pxmsL9gKltX6bQj4IW+DGWiP7dahk2VBMUOkw/K5Ifv3XdeyFAiLCnWLmsdmM1VBAj6TNBvqCRWdnGKGSAdVtAOyVprHMd1C25Z95Qg3Ew0rCk3SbNGO0PQpRDFDREQaTTFDRPZJ9lp472woy4buB5lk2aR+oW4VnDbOjHVs+gW2zAl1azoMxQwREWksxQwRkfbP2ZSNrUYMBs2YMaPWa5dccgmXXHJJU04lIsEUCEDpTsjbCBUFEN0FwqND3arWF5UIrmKznGrAD0n9TYXd1hAImKWbcjdCbNcOnywLihkiHZK3wlxDizPMpAtHWKhbtO9iuprkp6wVEPBCYr/2Wf2wnVPMEOmAirabmOEIg6ikULcmOGx2iE81farM5eApg+T9zdKo0moUM0REpLEUM0Sk2bLXwIQxUJYDqQfBtd+aVZbaguT94PDrYNG78PPjcMMPupcVBIoZIiLSWIoZIiLtXytliolIyLiKTFWnHYvB5zJVkdpBsqwtMgJbZAsklUbGQ2Qc5KwxCcQBf/DPsSfLMpUY8zZATDI4I1v+nCIiweYpM0mlJZkQ36PNJMsGJV5EJUJYtEnsyt9sJjmIiEjzFe3YlSwb3maSZYPav4hKMit15KyFnSvBUx6c44qISIdii4rCFtVOV+QQEenMstfAe+fsSpY9OOjJskGJDyf+1YwzbJsPG6YGp2EiItLuqM8hIiLB0BnjicqgiHRUPo+p6lSQZioCxrSfqqb2qEiGfDe+5U4QHmuqQ+WsA78Xug1qucQvy4KCLWYwPTLBJGSJiLQ37hLIWgXleRDXA+yOULcICHK8iIw3nytnrZlM0WVAm/mcIiLtStEO2LlqV7JsYqhbA7RQ/yI82vQhCreBpwJShrSdilMiIhJy9uhohixdEupmiIhIU+1cbSrLludCj0Pgmq+D+j0/aPEhvgccfRPMewV+eQwGjlKVWRGRzsRbgX3Npwy5d4BZBemlIZDYFw44BY68EZL6hbqFIiLSTnTWe1hKmBXpaAIBKN0J+Wnmpk5UogZu6xIWDbEOyN8EAQ90GwphLVD5tTDdJF9FxEF4TPCPLyLS0ioKTeKTq9DcjLd14AUKwmPM4ELOOrD80GWgltkWEWmK4kzIXm0SSdtIsmyLcoSZFTxKsyFjKXQbAvE9NVAtIiIiItIe5W6E989rsWTZoDvublg03qwwuP4HGHxmqFskIiItzbJg9Tfw/d+gNKvmeztXmp/5b8DJf4dj7wB7Bx7PERER2QeKkCIdSXk+ZC2HjCXgLTWDtxFxoW5V2+WMgLhUUxUqazm4ioN3bMsyybI7V5vkXP0dRKQ9Ks+HzBXm+hjXs2Mny+4WFg2xXSF3g5nw4PeGukUiIu1DyU4zwcJmg6ikULem9djspk9hs5mKHrkbwO8LdatERERERKQpCtNNsmxZNqQeBNd+07aTZQFiusBRN5rHs54xYxIiItJxeSvgq1vhs+tMsmxCXzj1EfjjL3DbArh4PPQ/AfxumPYIfH2rxjekU+nfvz8vvvhiqJshIu1EJ8h6EOkEPGWQvQa2L4LiDIjpCjHd2m1iU8DjIf2Bp0l/4GkCHk/LnswRZqpAleVAxjJTHWpfWRYUbDHLl4dHm2W+RUTam9Jsk/jjLatKBGpjWixeOCMhNgXyN5uJD74WjkUiIu1dWS7s/N08ju4S2rbUoVX6F1FJEJlgJlvsXGn6aCIi0mkF3G7Sb7mF9FtuIeB2h7o5IiJSn5KdJlm2eDt0HQRXf9VikwCDHh9G3G7uY+1YDJtn7PvxRESkbSrPh/fOhhUfg80BJ95H4KY5pE9cR/rjbxGI7w/DL4TrJsE5L5ptVnwC396hCRXSpo0cOZK77rorKMdauHAhN998c1COJdKZdNZ7WFpjVqQ987qgJBPyt5iKslHJJkGzvfMHKPttWeXjFmd3mMqJ5blmKdUuAyGxb/OW4Q74IW8T5K43ibKqLCsi7VFxxq7EJ8sky7ZVLRkvKquQbwXLDynDICwyuOcQEekIyvPNEqABr5ls0Ba1Vv8iPNpMyCvcbhJmuw01VZ9ERKTz8fspmzmr8rGIiLRR5fnwv/PNpOnEvnDN1xDbreXOF+z4EJsCR4yF396A2c/BgJP3/ZgiItK2lOWaiR07V5kJHZdMgP1PgvLy2jHFZoMjr4fY7vDJ1bD8Q+g2GI6/K2TNl/bFH7BYkJZPdomLlLhIjt4vGYc9dAV1LMvC7/fjdDact9GtWwt+hxPpyDrpPaz2WX5SpLPzecwSQdsWmMFpuw0SeneMZNlQsdlMVd6wKMj+3VRVrCho2jE8ZaaqbO4602FRsqyItDeWBflp5hpod5jrYmfmCIP4HlC0w9yM8pSHukUiIm1LRYHpj/hcbTdZtrU5wiChF3hKIWMJFGyFQCtMAhQRERERkaZxFcPEiyB7NcSmwrXfmO/y7c2xd4A9DLbMhvT5oW6NiIgEk6uoKlk2tjtc/4NJlm3IkLPgzKfM418eNwWjRBrww6pMjn/qF654ez53fryMK96ez/FP/cIPqzJb5Hxjx45l5syZvPTSS9hsNmw2G++99x42m43vv/+eI444goiICObMmcOmTZs477zz6N69O7GxsRx11FFMmzatxvH69+/Piy++WPncZrPxzjvvcMEFFxAdHc3AgQP59ttvW+SziEj7o4RZkfbE74Wi7bB9gUlmCnjNDZzIhFC3rOOIiDMVBUuzYPsiyF4L7pL69/H7dv1dFplE5tgUJS+LSPvj90HuBlNZNjymxZaea3fsTohPheJMs8S2uzTULRIRaRtcxWaymKfc3LCXKjab+Z04wsyARvZqszqIiIiIiIi0DZ5y+OhyM8ktKtkkyybvH+pWNU9CLzj0SvN41rOhbYuIiASPzwOfXmvuLcWkwNgpkDKk8fsf9UcYei4EfPDlzbo3JfX6YVUmf5q4hMyimv+fZBW5+NPEJS2SNPvSSy8xYsQIbrrpJjIzM8nMzKRPnz4A3H///fz73/9mzZo1HHzwwZSWlnLWWWfx888/s3TpUkaPHs2YMWNIT0+v9xzjxo3j0ksvZcWKFZx11llcddVV5OfnB/2ziEj7o4RZkfZgd6LstoWwY6mp4BTXA6KTwaZ/xkFnd0J8T1NtNm8DbPsNMleYJcorCk0lWXcplOWZSozb5kPGMtPhSOgFjvBQfwIRkabxukyibO46iFaF7FrsTlNptjTHxANXUahbJCISWu5SkyzrKoK47iZBVGqLTIDoLmZ514xlZrlXERHp+AJ+2PBT1fNnB8NT/eE/x8A3t8HGn1V9XEQklHxu+PQa2DoXIuLhmq+aloDUFh1/lxkr2jhVVQRFRDoCy4JJd8LmGRAWA1d9Bl0HNu0YNhuMeclUUc9dD/NeaZGmSvvnD1iMm7Qaq473dr82btJq/IG6tmi+hIQEwsPDiY6OJjU1ldTUVBwOBwCPPfYYo0aNYsCAASQnJ3PIIYdwyy23MHz4cAYOHMjjjz/OgAEDGqwYO3bsWK644goOOOAAnnjiCUpLS1mwYEFQP4eItE/KtBNpy3xuKNxmEjZ3LAVfuUnYie5ilsqWlhUeAwm9wRkFxdthxxJI/xW2zjM/2+abWX0+l0kUiE5WsoCItD8VhZC5rKpCdpgqZNfJ7jAx2FVgkmaV9CQinZWn3HwHrsg310V9/62fM8JMxqsoMNWrCraaRCoREel4AgFY8j945Qj4bGzV654SEwdy1sLSiTDxQnjrJDP4LSIirSvghy9vgo3TzD2wqz6DnoeGulX7Lnl/OOgS83j2c6Fti4iI7Lt5r8DyD8HmgEsnND9WRSfDGf8yj2c/Z/IORPawIC2/VmXZ6iwgs8jFgrTWGxc78sgjazwvLS3l3nvvZejQoSQmJhIbG8uaNWsarDB78MEHVz6OiYkhPj6e7OzsFmmziLQvzlA3QETq4K2AkixTVbaiAMKjzWC0kmRDIzza/ICpIrt7gNsRpgq/ItJ+WZapnJ2zHnwVijONYbObCu+lOyFzOXQ/0CQZi4h0Ft4KU5G8NGdXsqy+CzeK3QHxqaYib9YKM1mlywCIiG29NrhLzUoYTq2GISLSInauhkl3wPaF5nlkUtV7N8+EqEizStGGn2DFpyYevH8eHH0zjHrMrHIkIiIty7Jg8l2w+hvz3fiyidD3D6FuVfAc/xdY8QmsmQTZayBlaKhbJCIizZE2C6Y9Yh6f+RQMHLVvxxt+ESz6r6msPu1RuPjdfW6idCzZJXtPlm3OdsEQExNT4/m9997L1KlTefbZZznggAOIiori4osvxuPx1HucsLCwGs9tNhsBrfgiIqjCrEjb4iqGnA2QPh+yVkLACwm9VFG2LbE7TZUoZ4QSBESk/fK6THWjzOVAQMmyTWGzQVyqidGZy6FoR+ja4io2lR5FRFqDz22SgUoyFTeaKzLBTLQoTIcdi00MaekbtLsnyGQsMX87EREJLsuCxRPgrZEmWTY8Dk7/J9y+qGqbrgOh22AYPBrOeR7uXA5H3WTeW/AWvHcOlOWGpPkiIp3KtEdgyfvmvv5F78IBp4a6RcGVMgSGnmsez3omtG0REZHmKdoBn10PVgAOuQKO+uO+H9Nmg9FPmservjCT4UWqSYmLDOp2TREeHo7f3/BqXHPnzmXs2LFccMEFHHTQQaSmprJly5agt0dEOg9VmBUJtUDAVJEtyTQ/PhdExENC7067vKk9KpKhv3wY6maIiHQ8lgVlOZC7EcpzIaZru65kFNJ4EdPNxO/MFeD3QGI/sLfiRAp3qalKFZMC3Qa13nlFpHPyuSF7NRTvaJfJsm2qf+EIN5MiK/IhYykk5ELyfhAZH/xz+TyQv9n8+Cogab/gn0NEpDPzeWDK3bB0onk+8HQY8xLE98QODF27pu79YrrA2c/CoNHwxY2wYxG8ezpc+zUk9m2t1ouIdC5zXoC5L5nHY16GYeeGpBn26Oi9x4dgOOk+WPMtrPoSTvyrqsyKiLQnPg98dp0Zu+l+EJz9fL25Ak2KKT0OgWHnw+qvYfoTcPkHQWmydAxH75dMj4RIsopcWHW8bwNSEyI5er/koJ+7f//+/Pbbb2zZsoXY2Ni9Vn8dOHAgX375JWPGjMFms/HQQw+pUqxIkLR4H6WNUnlEkVDxe6E401QW2rYACrZAWLRJlI2M77TJsiIi0kLcpaYy4PbF4CkxyTrtOFm2TYhKgogYk0SWuw78vtY5r89jKgSX7tTS2iLS8nwec50r3LYrWVbzbveZzWZWEYnpCkXbYPsis0y3zx28c5TlmqqyueshKgEcEepjiogEk7sEPrzEJMva7HDqw3DFJxDfs/HHGHga3PgTJPSF/E0wYYy5VygiIsG1aLxZghpMFfDDrwlpc1pU6kEwdAxgwcynQ90aERFpip8eNKtWRCbAZe9DeHRwj3/y303fZe1kk58gsovDbuORMcMAkxxb3e7nj4wZhsMe/HuL9957Lw6Hg2HDhtGtWzfS09Pr3O75558nKSmJY489ljFjxnDGGWdw+OGHB709ItJ5aKRLpLV5yqA0G4q2g6vQVBiKSTb/FRERCTZvhRl0LdgK3lKToKNE2eCJiDPJY7kbTZX4bkNa9vcb8EPOOrO8ts1J7dsXIiJB5PNA9hqTLBuXqmTZYHNGmAksrmLIWmn6iEn9IbZ78ydEeMqgMN38WAGTuGV3AAXBbLmISOdWstMky2Yuh7AYuPR9k/zaHN0Gw40/wvgzzWT698+Fsd9BbLegNllEpNNa9QVMvts8PuEeOPb/Qtue1nDS/bBmEvz+lak4qyqzIiJt35pJsOBN8/iCtyB5/+Cfo9tgOPgyWP4RzHoWrvgo+OeQdmv08B68fvXhjJu0mswiV+XrqQmRPDJmGKOH92iR8w4aNIhff/21xmtjx46ttV3//v355Zdfarx222231Xi+ZcuWGs8tq3a93MLCwma1U0Q6Ho12ibQGyzLLNpfuNElLnlKIiIW49recaWsIeDxkPPEaAD3//mfs4UomFhFpMlcxlOWYJCd3salentA71K0KqjYTL8KiIK67+V17XZAyxFSfDbZAAPI2mYH02BRTPVBEpKVUVpZNN8myjrBQt6jZ2ky82JvIeNM/rCiAzGUQmWhidkxXCI9tuDKsZZlKh6XZJhZ5SswEmWBXIhEREXOdnXCO+U4e3RWu+gx61a5qE3C7ybjvbwD0fPop7BERez9mfE+49luTNJu7Hj6+Aq6bpImOIiL7asM0+PIWwIIjb4BTHgp1i5oWH5ordTgMPRfWfAsz/g2XTgj+OUREJHgKtsI3uxL/jv0/GDy6Ubs1K6accA8s/xjWfWdWJOw+rLmtlg5o9PAejBqWyoK0fLJLXKTERXL0fsktUllWRNqOVumjtEH2UDdApEPz+6AkC3YsgW0LzPJqzjAz+BmVpGTZvfEHKJm1gJJZC8AfCHVrRETaB8syVeWKMyBjKWz7DXb+bgqQJvQ2y/h0NG0pXjjCTJXAigLYsRSKdpi/SbBYFhSkQd4GU5ne2Tk6KyISIj43ZP/eIZJlgbYVL/bGZjdJrnE9IOA1FWfT55u+ZMFWM0nCXWIqx3srwF0KZXnmb7RjiYn72avBbjdxX8myIiLBV7gN3jvbJMsm9Ycbf6ozWRYAv5+SH3+k5Mcfwe9v+NhJ/eCar82kie0L4es/mwlzIiLSPOnz4ZOrzXfr4RfBWc82PBGtNTQ1PjTXyPvNf1d/be4PiohI2+TzwOfXg6sIeh8Fpz7S+H2bE1O6DoShY8zjuS82ubnS8TnsNkYM6MJ5h/ZixIAuSpYV6Qxaq4/SxqjCrEhL8Lmrqvu4CszgZ1SSkltERCQ4An7we8DnMhVNveWmmqyn1CTROMIgIs5UppPWY7NDfI+qCoHuYkjaD8Ii9+24uyvL5q6DqEQIUxKUiLQgr8sMqBbv6BjJsu2N3WH6jlFJJqaX50JJBmAzfwu7wzwO+CHgAQvFfRGR1lC03VSWLdxqvuOPnWImzAVTt0Fw2UT43/nw+5fQZQCc8mBwzyEi0hlkrYQPLgVfBRwwCs5/o/MVL+l+IAw73yTMznjSxBcREWl7fh4HOxabgicXvds69+FO+IupQr7yczj572YyoIiISCejhFmRYPKUQ+lOU+XHVWSq+sSmgF3/1EREOhXL2pXQ6ga/11SzCPh2/fjNjxXY9bNHFdK6ql0E/GD5zf4+d9WxfB6wfIDNJGWGRUFUctuomNGZRSWBMxJyN0B5AXQ9AGK6Ne/v4vOY4xRsNn9bVQwUkZbkKTPLsZVkmgkA6seEVlhU1XLclrXr+8SuGd42uxlEsWnhIBGRFle0vWZl2bGTg58su9t+J8CYl8ySrLOegW5D4KCLW+ZcIiIdUd4m+N+F4C6CviPg0vfBGR7qVoXGyPtNQtSaSWYFxD5Hh7pFIiJS3bof4NdXzePzXjOrTrSGnofB/ifD5ukw7xU4+7nWOa+IiEgbotEvkWBwl0JxprmB7imByDhz41yDlyIinYO3wiQZecrAVWxuyvu8pvJbwAuWDVMCDvNfm31X8uSuBMrdiZQ1kmetPd63mWoYNrtJYAqLgsh4JTO1VWFR5rtAWS5sXwyJfSCxr/mbNVZFAeRuNIlrsd1MEq6ISEtxFZvKsuV5EN+z81VgautsNnCEg/4sIiKtq2gHvHdOtWTZKZDQu2XPedjVZtLc3Bfhm9uh22BIPahlzyki0hEUZ5gq3WXZ5rp5xcede+JxylA49EpYOhF+ehBu+FGT7EVE2oqiHfD1rebxMbfC0HNa9/wn/MUkzC6dCCf9zRQAExER6USUYSGyLzzl5gtt0TaTJBUZb26a66aDiEjHFgiYpFhXEZTmgLsEvOWAZRJYnRGmeoUjxjxXXOicbHZzo8nnMgPspVkQ3wtiu0NkItj3MrHGXWIm4hRuNZWKVeVRRFpaWR5krzZxLb6HJv6JiIjArmTZs6EgDRL7wXWTWz5ZdrdTH4asFbDpF/j4Krh5BkQnt865RUTao/J8+N8FZvW/5AFw9ZcQlRjqVoXeyf+AlV/Att9Mpdlh54a6RSIi4vfBFzeaghk9DoVRj7V+G/qfAL2OhB2LYP5rcNqjrd8GERGRENIomEhz+NyQn2ZuMuSsAYcTEnubhFklRYmIdFzuUijYaq7/6b9B5kpwFZgE2fgeZvA0LhWikiA8ZtdSyYoLnZ4z0lSbdUaYpQG3/Qbp8yFnvalOX5JlEmQLtphqtOnzIWcthEXuqvKoZFkRaUHFGZC5HLxl5pqjZFkRERETHyecU5UsO3aKWTWitdgdcNG75tyFW+HzGyDgb73zi4i0JxWFprJszlqI6wnXfq1KebvF94RjbzePpz0CPk9o2yMiIjDjCUj/FcLj4JLxZtygtdlspsoswMJ3TSwVERHpRDQSJtIUAb+pLrFtAWStNF8mE3pDRFyoWyYiIi3FskyViqxVsG1+VVJRdJKZLBHTzSzvpgQjaUh4rEmcjU4GXwXkbYCMZbB9EexYbP4fK881/z8l9jHbi4i0lIAf8jabuIZlql9rkoeIiIiZzPbeOZC/GRL7wtjJrZssu1t0Mlz+IYRFm+VSfw5B5SkRkbbOXQIfXGz6NdFdTbJsYt9Qt6ptOe5Oc/8yfzMsejfUrRER6dzW/wSznzePz30ZkvcPXVsGnQndhoK7GBa+E7p2iIiIhIDKVYk0VkWBGVAuzTIzvRJ6KTmqhdgiIxg85b+Vj0VEQmJ3omxRuqkAGvCbyrHRXULdMtml3cYLR7iWUxWR0PK6IHeDqVgXldDhE/TbbbwQEZHWV5IFE8ZA/iZI6LursmzTE69sUVEMXrK48nGzpQ6Hc18xS7bOfRF6HALDL2z+8UREOhJPOXx4GWxfaO7ZXfsNdBsc6lbVK2jxoSki4uDkf8Dku2D6EzD8IlXgFREJhfzN8OUfAQuOvHGfv9fvc0yx202V2S9vgvmvwR/+bIp5iIhIpxKSPkoboGw/kYb4PGb55O2LTLJsTDeTLKVk2RZjs9mwR0Vij4rEpipXIhIKrmJT7XP7QlNdKCrJTJTQzYI2RfFCRKQZyvNNdeuCNIjp2uGTZUHxQkREGqlkp0mWzdsACX1g7KRmVym02WzYo6OxR0fve+w56GI49g7z+JvbYOfv+3Y8EZGOwOuCj6+ArXMhIh6u+cpMMmjjghofmuLwa6HHoaaK4NSHW++8IiJieMrhk2vBVQS9joTRT+7zIYMSUw68EBL7QXkeLHl/n9skIiLtT8j6KCGmjD+R+pTnQ+ZScyPaGQHxPcERFupWiYhIS/H7ID/NTJLYXXUvvoeJASIiIu1ZIACF6ZCx1KyekdBL8U1ERGS30hx4/1zIXQ/xveG6SZDUP9StqnLqI7D/SPCWwyfXmIF2EZHOyueBT6+FzTMgLAau+hx6HhbqVrVtdgec/Txgg+UfwdZ5oW6RiEjnYVmmyvfOlaYw16Xvt517cg4nHHeneTzvFRNjRUREOgElzIrUJeA3CVM7FkNZvkmUjYgLdas6jYDHS8ZTb5Dx1BsEPN4WPVdOuZ+529xYltWi5xGRdqCi0CQR7VxllqJJ6AXOyFC3SurRmvECIKvUz6/b3S1+HhGRoPOUQdZKyFxhYlx8aqdaMaO14wXA9mIfCzMUM0RE2oWyXFNZNmctxPU0lWWT99unQwY8HjLuf4CM+x8g4Kl/0HlrXhlL0gvqP6DDCRf91yTz5m8ylWZ1L0tEOiO/D764ATb8aO7bXfkJ9D0m1K1qtKbEh7psyill5fZmTprofYSpNAsw5R7wt07fSESk01vwNqz4BGwOuOQ9M/YUBA3FlHVZJazOKG74QIdeBbHdoXg7rPw0KG0TEZH2I+Dx8Psd97D2rnub1UdprzrPCJlIY3nKzTLcWat2VZVNNbNvpfX4/RT9OIuiH2eB37/Ph9tR4sPjr3sQYeSEbK76Ko9paRrMFum0AgEo3LZrkkQ2xKVCZEKoWyWNEeR4ASbByReoO2b84b87ueLLPH7boZghIu1EIADFGbsqp6dDTFeITAx1q1pfC8QLgG1FPvx7iRnHv5fNJZ/nsTpHg9AiIm1aSRa8dw7krIG4HjB2MiTvv+/H9fko+vprir7+Gnw+tuWXE9hLzDjpmRlc+No8tuWX13/MmC6mGpU9DNZMgl9f3fd2ioi0J34vfHGjuQY6wuHyD2G/E0LdqqbZIz7sybIs0vPK91rg49TnZjLm1Tnkljbz3tRpj0JUEmSvhgVvNe8YIiLSeBt/hh/uN49HPQb9jw/aoS2vd68xxeMLcMaLszjr5dmUe2rHmxrCImHE7ebxnBdMYTGRVlJSUsJVV11FTEwMPXr04IUXXmDkyJHcddddldv079+fxx9/nCuuuIKYmBh69erFf/7zn3qPO3bsWM4//3yeeOIJunfvTmJiIo899hg+n4+//vWvJCcn07t3b8aPH19jv23btnHppZeSmJhIcnIy5513Hlu2bKl8f+HChYwaNYquXbuSkJDASSedxJIlS2ocw2az8c4773DBBRcQHR3NwIED+fbbb/f5dyWyL+rrZxSVVmD/6TusH6a0WsGRtkAJsyLVledDxhKzDHdciqrKdgBLszwcN94kxe727tJSbpqcz3Hjd1LmNQHhw1VlNYJDfoWfIleg1dsrIq3M54HsNZC1u+JeT7A7Q90qCZE56W6Ofy+bmybnV772+qIS/jgpj+Pf21n52ldrK2rEjJxyP8VuxQwRaWPcJaaq7I4lEPDuqpzeRpZ76wB+2FTBCROyufunQsDccHrxtxKu/zaPE6rFjB82VdTYL7vMT5lHMUNEpE0oTIfxZ1Yly143GboMCPppvlq6gxOens6D36wCIBCweObHtVz73wWc9Mz0yu1mbcipsV9WkYsKzx6D1b2PgNFPmsdTH4Etc4PeXhGRNsnngc/GwuqvzcSBS9+HA04Ndav2jWWZJGCvyxRy8ZQxYfZ6TnxmOv+esgr8Xnw+P098t4Zr3v2NE57+pXLXpemFNQ61o7ACl7cRCU7RySZpFmD6k2aCpYiItIzstSZ2WX445AoYcVtQD//mrM01nrt9fh6btJor357Psf+uihmbsstqbLe9oByPb497U0debybZ522ENUrs6wgsy6LcWx6Sn6as7PuXv/yFuXPn8u233zJ16lRmz55dKwEV4JlnnuGQQw5h6dKl3H///dx5551MnTq13mP/8ssvZGRkMGvWLJ5//nkeeeQRzjnnHJKSkvjtt9+49dZbueWWW9i+fTsAXq+XM844g7i4OGbPns3cuXOJjY1l9OjReHZV3SwpKeG6665jzpw5zJ8/n4EDB3LWWWdRUlJS49zjxo3j0ksvZcWKFZx11llcddVV5Ofn12qjSGv59w9rOfGZ6UyYtwWAMrePR75ZxaVv/srx1WJGZrGrxn7b8svx+TvmeIYyQkTA3Jgo3gE568DvMYPJnWiJ0o7s8zWmOsfCDPMlJrfcz+Ozay8/MX2Lm/HLy7j24BiemFPMf5eZzsP6P/dgbVYx+eUekqPDObBnAg79ryHSMbhLzZKbxRkQm6IkIuH9FebaP32LqdKxpdDHU/NKam338e/lHN4jnPMGRfHozCI++r2cMDus/XNPfs8oUswQkdDye6FoO+RvAW+ZqSqrGBd0ry8qBeDb9RW8PDqJFdleXvytdsx4eUEpQ7qEcVK/CB74pYhv11eQFGln0R9TFTNEREIpbxNMONcsO5rYD679BpL3a5FTvfrLRgA+/C2dJy44iLmbcvnP9E21tvvHV6vonRTNob0Tueez5Uxbs5M+yVHMuPdkFqTlk13iIiUukqOPuBHHtgVmudTPr4dbZkNc9xZpu4hIm+Bzw6fXwfrvTWXZyybCoDNC3arG8fvAV2E+g88FJUVV76X/CuFOsAJAACyLf35vxjHenJPOA0Nz+SHN4q1ZFbUOe9P7i/j81hH07xrDHR8tZd6mPIb1iGfS/x1fM2bsl4zDbqu582HXwtKJsH0h/PAAXDqhBX8BIiKdVGkOfHgpuIuh77Ew5iWw2Rrerwle+nkDJ+9+UpzB52uK+e/czFrbjXl1DlP+fBTJ8bHcOnEJy7cXcfR+yXx00x9qxIxjjr4Z+6ynYfbzMOz8oLdXWleFr4JjPjwmJOf+7crfiA6LbnC7kpISJkyYwIcffsipp5qJUOPHj6dnz561tj3uuOO4/35TrXnQoEHMnTuXF154gVGjRu31+MnJybz88svY7XYGDx7M008/TXl5OX//+98BeOCBB/j3v//NnDlzuPzyy/nkk08IBAK888472Hb9/z9+/HgSExOZMWMGp59+OqecckqNc7z11lskJiYyc+ZMzjnnnMrXx44dyxVXXAHAE088wcsvv8yCBQsYPXp0g78XkZbw5kwzyeLRSasZe9x+TPh1CxN+3QpARLUVkU57fibf/+10nHY7Y99bwOacMk4dksJb1x7ZcD+jnVHCrEggAAVpkLveDCTHpYa6RRJEMWE1L9Klnr3PaHpsVjE2qEyWBbjh/cUUllXNougaG8HNJ+7HsQO6Br2tItKKKgpg5+/mv/E9VFVWgNoxY3cV8rrcN62Q7cU+PvrdTMzwBuCG9xaSV1a1JJ5ihoi0KsuC0mzIT4OybIiMNxMBpUXsGTPcvr3HjD9/X8A1B0Xz7Xoz0F3gCnDjhIU1llFVzBARaUU7f4f3zzfxsstAkyzbgjEzOtwBZVXVOLz1VOa499PlHL1/MtPWmGrl2/IrOP6pX8gsqro31SMhksfOvJ9RWStNddzPbzCfwaF+rYh0QF4XfHI1bJwKzki4/AM44LRQt6pugQB4SsFrqsVSUWie+9ymUAuAy1O1veUHezjYwszKV9iIDs+n2L2rb+Fw4vW79jxLpYe/WExSTATztpiJe6szi+uMGY+MGcbo4T2qdrTb4ZwX4M2TTMXeDVNh4N6TTUREpIm8FfDJVWZF26T9zESPYE1m93nAVWhiTHWZK6jICwfqTl56+ptFZLvsrMkz1cgXpOXXihmD4w9kiiMaZ9YK2PBT+5mcIu3W5s2b8Xq9HH300ZWvJSQkMHjw4FrbjhgxotbzF198sd7jH3jggdjtVRUKunfvzvDhwyufOxwOunTpQnZ2NgDLly9n48aNxMXVXIXa5XKxaZOZ9Lpz504efPBBZsyYQXZ2Nn6/n/LyctLT02vsc/DBB1c+jomJIT4+vvI8Im1BYbl3r+/9Z/pG5m3Kq4wRP6/Nblw/o53RXTTp3Pw+yNtglheISoLwmFC3SILE7bOIcNqIDa/6EhSwLFz1DGQDvDAvDwirfL6uNIoUXJWT6PJK3Tz53VoeOGuIBrNF2quyXDNA6imH+F6aJSuVMSMmvOb/Cw3FjHcWFwGOyufrSiPpaqtKflLMEJFWU1EABelm1Qy7A+J7mv/KvrEscBVBSab5HbtL8VUU4wy4udhdwcEOH16csL4vUcVRHGhzsNlKpYLIWof6ZFUpUNU32VASRpJihohI69s6Dz66wgwydz8IrvkKYru16CmjI5xA1UBEreVPq8kpdTNlRc2qUNUHJACyilzc/PFa3j/vOU6YfilsnQO/PAajHgtqu0VEQs5TDh9fCZungzMKrvwY9h8Z6lZVCQTAU2JWsaoohIp8kyTlc5v7jc5wk+QbGW8q49psEF7tmh4RDxGRWJaFxw8RThtx4XaK3SaZifBY/E474Knr7KzO8UBOzfcyi8qp3u/IKnLxp4lLeP3qw2sOZqceBH/4E/z6Kky5B/48H8IbrgQnIiIN8HtNVfRtv0FEAlz5KcR02ffjVhRCaQ5W8Q68FaWEOyCs+kpF8T0Ij/EDRXXuPnOHBfhrvFZclA9EsTvJdn1xGO86T+EW52T45Z8w8HSNn7VjUc4ofrvyt5Cduy0ICwur8dxms9X5WiBg+uilpaUcccQRfPDBB7WO1a2buW9w3XXXkZeXx0svvUS/fv2IiIhgxIgReDw1v5PVdx6R1mJZFl6/Rbiz9tJ2kWF7Hz/6YsmOWq/VdW+qzn5GO6KEWem8fB7IWWeqy8Z2MzcupN1bl+fly7UVvLu0lPHnJlPirvriUey2mLKh9vJF1RX7an55ySGJeMqI2nVTKmDBNlJ4YGouv+zXVcumirQ3pdmQtQoCXohXRfHOblW2l8/XlPPByjI+vqgrJdWqkLt9Fl+sKa93/3J/zc5EFl2Is8qIsPkAEzPS6c4/puYwTTFDRFqCuwSKtpsfnxtiugavYkVn4yqC3F2TKXM3mH5iSaYZ8K5m902UC6Fqnt0MOAiYsutXvy3Qjd+t/vwWGMJvgaGssfrisWoGgR2kEGtVEGYzgxV+y0Y63Xl4WjY/KmaIiLSMVV/AV7eaKn+9j4arPjUT6INsaXoBH81cx/W7npe7fVVvFu3g3Rnr9+n4u3stt/9UypKLXsXx+ViY+5L5TEPPqW9XEZH2ozwfPrrcJByFxZhrdv/jQ90qU/HWVWR+yrJNJVmfx0xYDI8yybGN7JNZlsXiTA/vLCllZrqbqVd3o9RTM5HitYUlTWygjS4UkU8cFvbKmPG3L1YyalhqzWVTR94Pv39lKiDOfhZOfbiJ5xIRkRoCAfj6T7DhR5N3cOXH0G3Qvh2zPB+KdmAVZ7Aww8Nrq8NYkm3n52tS8AZqVrR8dn5xkw5dQQQD2UY6qbgJxwLe8J3D1c5pxGStgDWTYNi5+9Z+CRmbzUZ0WNueDLP//vsTFhbGwoUL6du3LwBFRUWsX7+eE088sca28+fPr/V86NChQW3P4YcfzieffEJKSgrx8fF1bjN37lxee+01zjrrLAC2bdtGbm5uUNshsq/8AYtFW/J5Ydp6NmaX8vNfRtZ43+138/LPG/bpHLv7GQ98WUc/o51Qwqx0Tj43ZK+BwnSI625m90q7l1Xq54wPciqfX/N1fo33y70WrywsbfJxfbuqBwYsGzkkUkwsxR74PaOIg3sn7FujRaT1lGZD1kqz3FlsSqhbIyG2ucDHOR9XxYyLPqvZoXX5LD5cVX/CbF18OIjAR8CykUkXSoihRDFDRILNWwFFO0x/xlsGUckmWVYaryQLMpebn4xlUJKx1009kV1ZUx5PkRVDMdGUW2aypQ2LCJuXc/p4Wb09lx62PLrYSuhjz6EPOYx2LAQgw0pmiv8PfO0/jt+t/SqP68dBGH4Clo1tpFBKNBvd0YoZIiLBZlkw7xWY+pB5PuQcuOgdCAt+1Zt5G3O58p3fiPC5KxNmN+aUViZPBXYsZeH24AwiFFX4WBB1IiP+8GeY/5oZnE8ZCl0GBOX4IiIhU5wBEy+C7NUQmQBXfgZ9jwlNWywLPKW7EmRzTeKSd9f9ovBo075mTlqclubmlp+r7j0dN77mMr0ev8XmQv+euzXAxjBbGnkkstVKoQyTKFNU4WVBWj4jBlSrchgRB2c+BZ9cDXNfhoMuhZQhzfosIiKdnmXBD3+DlZ+B3QmXvg/9jm3+8TxlkL8FireD38tn6THcN93H7iqxR72zk+rRp9QboNhd/4p5ewrgoJ8tmzirgnX0oYxoCojnHd+Z3On8CqY/AUPO1ipW0mLi4uK47rrr+Otf/0pycjIpKSk88sgj2O12bHtUN547dy5PP/00559/PlOnTuWzzz5jypQpQW3PVVddxTPPPMN5553HY489Ru/evdm6dStffvkl9913H71792bgwIH873//48gjj6S4uJi//vWvREW1jYq6IruNn5vGP6esqXx+6OM/1Xj/500LsIdnYwvPxR6WT7i/rPI9Z9xKvIHeBNwpVF+5Ym8KyuvoZ7QTSpiVzsfnNjdaCrdBXCo4whreR1qVLTKCgV++Ufm4MVw+ixu+zat3m3Jv88rcb6UHsVY5pdSchbWlwM3wXhblXovYcJWAEmnTynJNZVnLDzEtu9ymtJ7mxAuAMk+ASz+vf8Znvqt5MSONXnXGjO1Fbg7saVHhU8wQkX3gc5vB44Kt4C6GqESI7h3qVrV5tsgIBn72CmStxLbwP7BjoUmY3VN8T+gyELoOgi77Q3wvisJSOOTd/NrbVnPw8Smc874Z3E6imIG2HRxu38Af7Gs40r6OnrZ8bnJ+x03O71gcGMh432i+DxzNRnoTY5VXDmLvllnsZljAwuO3iA5TzBAR2Sd+L/xwPyx8xzw/5k9wxr9aZNA3r6iUK98xS166HeFcfuajlY9320p3ILuOvZtnR2E53lMexb59MY7tv8Gn18KNU7Wstoi0X3mb4P3zoSgd4nrA1V9C92Gt24bdSbIVhabf4Co0kxad4RAeA1E9wNa87+m772XllPk58/Oyepe63pjv2+t79ZljHUwCZbX7GUUVuH1+LKvaEqxDzoFBo2H9DzDlLzB2ipbfFhFpKsuCqQ/DgrcAG5z/Bgw6o3nHCvjNike5G8FTAtHJbK8I577ptfsQ1fsc75Y0Ly7NsA4lBleNmPGu7yz+FPUz4Tlr8K74nLBDL2vWsUUa4/nnn+fWW2/lnHPOIT4+nvvuu49t27YRGVlzdeh77rmHRYsWMW7cOOLj43n++ec544xm/jvbi+joaGbNmsXf/vY3LrzwQkpKSujVqxennnpqZcXZd999l5tvvpnDDz+cPn368MQTT3DvvfcGtR0i+yItt6xGsiyYMIXNgzN2Dc74VTyyII2YAaU1NrjxTtM/sEd9RozNhuUPx1c2GF/xwfhKhmLSSwPUlUS7s7iCCo8fp8NGWDtaOk8Js9K5+DxVybLxPcwML2lzbDYbzsS6y9zvzTtLS1mdW/8NpNzy5iU/AbUSnwDumO7jzVW5/J7jZc7YFJx2G1FOGwmR7ScIiHQKFQUmWTbgVWXZDqY58QLghd9KyK2oPybsLG1qBY8qdcWMe2b6eHZJDpsLfCz8Y3e8fogLtxEXoZghIo3g95qb5QVbTVyLjIOE3hrIbIinHLYvwJY2G2f6fFONdzebHboNhh6Hmp/UAyE8ttYhxv1U0OBpsqrFjALiWWDFs8A/lDf85xKBh5Psyxnj+JUz7As5wr6BI8I3sCHQi2d9l/Jj4Mhax3tonp/HfstmZ5mfJTelUuQOkBxpJ0YTLkREmqY0Bz67DrbOBWxwxhMw4s/BPYdlmaqDpTn89bNNVa/bbBRF1I4r+9LPqMsrv2zkX1PWkOAby/S4zdh2rsL/7R04Lnpb3xNEpP3JWGYqy5bnQvIAuOYrSOrXOufea5JshKnEGqQVPXbfy/rTjzkNXqcLmzmZ28JOIXG1Xn979mYe/HoVkWEOFvz9VLYXVNAjMZKIM5+GzTNNvFz+ERx6ZbPOKyLSKVkW/Ph3s+IDwFnPwMGXNO9Y5fmQn2buAYZHQ3wvsNm4+sOddW9frc9R5GneKX04KaJmv6WYGD4Lv4CrvBPI+uYReg+/gLQCD72Togl36t6UBFdcXBwffPBB5fOysjLGjRvHzTffXGO7+Ph4Pv3000Yf97333qv12owZM2q9tmXLlhrPU1NTmTBhwl6Pe9hhh7Fw4cIar1188cU1nltW7WrPhYWFez2mSDCd9+qcGs9tYbmEd5lNWPxSbA4TLFx+sAJhBNwpBLzJWP4o8rBhs7uxUYgjIgObw0NY/ErC4lcS8MbjyT8eb8EIsGrHgU8XbeeuT5YzqHssP951Iptzy+iXHI2zjSfPKltQOg+/F7LXVFWWVbJsh7Imx9vgNjn7kDC7N7/vOu/J72fjDYANSLujZ9DPIyLN5C4xybLeCohPDXVrpI1YsbN2zIjGxVDbVvrZdtLNVkTXlV4ecFbgx0GOlUCG1ZXfrX5st7phrvZNU+GDzQVmYsfJE7Ip9VrER9hYcUuPff04ItKRBfxQutMswVaRB2HRkNCr2dWMOoWKAjPQu2UO7Fhs+oG7RSVBv+Oh/3GQenCjqu8tzmx4xKG+foabcH4KHMVPgaPoRiFXOadxneMnBtp38Gb4C/wWGMLfvTeyyepVuU92ucXuJfZOmpBNgStArzgHc6/v3mBbRERkl4yl8PHVZgnT8Di48C0Yclbwju/3QnkeFO0wiV0+D/MzGo7Pwb43tTXPLOVdQAK3uv6P/1iP4lz1GfQ5Co65JajnEhFpUeu+h89vNJPcehwCV30BsS28SpRlmSWvXYVQshMq8sHnAkd4UJNk67KsjntTe8prYLJ3U63JLAGg3OPnxKenk1Hk4qj+SXx267Ew8n6Y9gj89KCpOBudHNRzi4h0SJYF39+3q7IscPbzcNSNTT+OpxwKt5ochoDPFH6ptkLulqKGJ92VuIMbM/6VdxJnRHxFHzJ54t8P81bpCZw8uBvjrz86qOcRWbp0KWvXruXoo4+mqKiIxx57DIDzzjsvxC0TaX8qfBUUu8xYtC08h4huP+GMW4XNZpK4A54kvMWHcP7QY/h0Vhx7TxkNYI/cgTNuFWGJS7CHFRPZ/TvCk37FnX02vpLhNbaet8msBL5+ZykHj/uJEpePMYf05JUrDmupjxoUyhiUziHgh5x15stmXPcaXzKl7Ql4vGS/PhGAlD9djT18738vj99i+hYXhY3oCIybWRS0Nu7Ju+v0FmbWkE1VPERCz+syVcXdRRCnRPaOqCnxAsDls5ix1UWxJ0AMFYywr2akfRl/sK9hf1smdlu1WZ/b4IA6vilnWsnM8B/C1/7jWWANxqpj6YmGlHrNeYrdtWeZiogAEAiY5JuCNFMdzxlhliNtgeWjO4TiDJMgu2W2mShDtetrfC8CvY8je3Y5RCWTctk1DcYLgHJvgFlb3ZR5G75W/3N24/oZOSTyou9i3vWdxc3Oydzo+J5j7Gv5LvwBXvWdz+v+c/HtcZumYFdVqR0lwa1IKCLSYVkWLHwHfvwH+N3Q5QC4/CPoNig4x/eUQ1m2Gcx2FYHdQakjkTl5Fi5/VVXyML+Pm1Z9C8Dbw8/F6zDX9yfnFAenHXX4sewAnnRcwUNhH5gqV6kHQ78RLXY+EZGgsCxTle/HfwAW7D8SLv0fRDZ9RaFGn89Taq7hu5NkKyvJxrZokixAYYmb9S+8z5+3umvEh7r8e27LxYyMIhcAC7fsil0jboPlH0POGpM4e+4rLXZuEZEOwe+Fb//PVObGBmNegiOua+IxfFCSAXmbwV0M0V1qTO7Or/CzMGPvE7mr9zleiL6A5hT62JtyInnddy4PhU3kBu/H/I+jmL4uJ2jHF6nu2WefZd26dYSHh3PEEUcwe/ZsunZt2e9kIh2JL+BjbfYOZm7cCo4yIrr+TFjSfGw2M7bgKxmCJ/8E/OX7Azamltfcv657WAFXHzyuPnhyRxEWv5TwbtOwhxcQ1Xsi3qLDcGWdC4GoWm0p2ZWwO2l5hhJmRULOsiBvk1nCIDbFzA6Wts3vp+CbqQCk3HwFsPcB7VcXlvDygtJGHbah5bfjKaOYmEY3c298AQhTLoNIaPl9ZqJEyU6I76mlKDuqJsQLgGdm5ZC1eg73OmZzYsQKwm01k4+yrCQ2BHqRTSL5Vjx+7ITjI8VWQF9bNkNt6fSw5XOFczpXOKezNtCH13znMjkwgkAzEmdFROpUUQAFW00SqN1uJvxpdYyaLAvyNu5Kkp0D+Ztqvt91EPQ/HvqfAEn9weWm4OEbAEi55UoaihcAf/+liK/XVTSqOTvLmtbPKCGa53yX8pHvFP4Z9l9OcSzjnrDPOdGxgjs8/0cmXRp1XhER2UN5PnxzO6ybYp4PGm0qy0Ym7NtxLWtXYlWWic+eUgiP2bWClYP/+zaP6VvcNXaxW37GpM0D4N0Dz2b3bfiM0vonQOzrval3/WdxqH0TY5gPn10Ht8wy7RQRaYv8XlOZb9F/zfMjrjdLWQe74IllmVWoXEVmBY+KgmqVZFs+Sba6Wyfl8MiM6YyhZnyoS0OT5uIooyQI4xmA+Z2f8wKMHw1L3odDr4K+fwjOsUVEOhp3CXx6LWz6BWwOOO9VOPTKxu9vWVCWA/mbzUT5iBhI6F1rHOvSz/PYuGvVurrU6nM4I/a6bTQuyolsfBuB//lHMdbxI33sOfzR8R2v+C9s0v4ijXHYYYexePHierfZsmVL6zRGpB0qcBWQXpzONW9to9y5gpj9v8XuLANMoqw7ZzQBd837QgVlNY+xt3tYAFhOvEVH4S0+hPCu0wnvMoOwhKU4otKp2HYdAU9KC366lqVRN+n4CrZA7nqISa73i6K0Hwt2uLHb4MhUJzPX7GCIrYhkWzExuLDvqiZVSiT5Vjy5Vjw5JNLwrDqLeEprDEpMOC+Z677Jb3L7XD6LMIeS80RCxrLMjYbCrZUDmNJ5zdvmJrl8M0N2TuHuDT8TG16V/LQ1kMKMwCHMChzM8sAB5FL/QHokbo60r2eMfR7nOOYzxL6Nl8P/wx8D3+E65k4und30TkHAsrAroVtEwFQ1KtgKRdvA5zaDtuq/VAn4TPXYLXNg6xyTtLSbzW6Wbe1/AvQ/DmK7N+sUM7a46BrtYHhKWKOTZRsSho8IPFCtn/GfM5O47fsCMujKDd6/cp5/Lo+Hjeco+3qmRDzAnd7bmR04OCjnFxHpNNJmwZe3mApN9jAY9Rgcc6uZfNJcgQCU50HxDpNg5feaiocJvZmW5qZPQoDBXRy1kmWbKwI3DmomRz19aiL3/VzYhKPY+Jv3ZsakFpkqgZ9eB9dNAqcKCIhIG1OeD5/fAJunAzY4/Z+mymmw7pEEAuApgYrCXUmyhSZJ1hkBEXGtmiT7w8YKBnYJY0CSk6VZ3qAcM5ra/ZV/HB/Pv/alknm/EXDYNbD0fzD5bjPpQqs1inQOPjdsX2RW7CvYYiaIBXwQlWTusXQbau67xHYLdUtDr2QnfHAxZK2AsGi4ZAIMOr3x+7uKq+7/2e0Qn1o5Ud6yLKZscHFw9zD6JjjrTZZtijjK8FFznOyWw2N4c0nZXvYwPITxlO9yXg1/hVudk/jYf0pQ2iMiIvuuwuvmg0WrSU4swh5WiCf5Q6Li1gDgd3XHvfMc/OUD9/k8kWF2Th7cle9XZePJOQNf6RCien6MPTyP6P7/oWLHlfjLBu/zeUJBCbPSsRVnmgqDkfHmS6u0XxWFsPN3PFlryF26ngG2DCznTr4JeKGBPIJSolgb6MOywABmBw7mt8AQXNV2SnB6uahPGcMPGMRfpprlVL+/shupMc1Lsiv3WsQpt0EkdIozTNW5mC66qduZBXwUrZ2JY9ZnDLGvBSDWBtutrnztP46v/cex0erdpEO6iGCDcyDrevWhPOVW8hZ9yc3OyRxsT8NaeDf3Os/lBd/F+Gl8/Cj3WsSGK2FWpFML+KEk06yK4SqC6ORWHbxt03xu2LEY0mZD+jzz+9nNEQ69j4L9ToC+I/a5euCmAh9jvzWT5bbc0XOfjrVb38hyTuvhwZ7Yi3eWmnWOpl+bgsdvVdvKxjeB41nqGch/wl7iIPsWxoc9zcO+6/nQf2qN4/kCFk67YoaISA2uIpj6MCx+zzzvMhAuftcM6DeX32sqPhVtg7I881pUIoSZpeaW7/Twx8nBjRmDoks5vruP/LBUvl5vlsmefV0K2eX1VzGvSzmR+C55H+e7p8K2+fDj3+HsZ4PSThGRoMhYBp9eA4XpZtzmondhyFn7ftxAANxFVUmyriLwe0ySbGQcOFs/0Wt2uotbvysAghczDoot5uiUAGs9Kczdbpbqnnd9d9blNS8Z1+cP4HTsmmAy6jFYO8Ukzc1/DY67MyhtFpE2KBCAzb/A4gmwcRp4yxvep+tgGDwahpxj7sl0tkIQ2xfDJ1ebSXrRXeGqT6HXEY3b1+cxE/HyN5vfdUy3WhPlp2xwcfsPwY0ZR8YXcEhXG7MLE1ifbxJwf7uhOzO3Nm7S3+TAH/hj4DsOtW/iLucX+PxXVMUMaXMsy2p4I2lz9HeTpsp35fPm7N958+diohPXEt/3M8LiyrAsB57ck/HkjmTPdNCuseHklnoaPPYpg1OYn1lOQbnpW8z52yl8snAb36/KBiBQ0Y/yLbcR2esDnDFpRPWZgGvH5fhKahcAqdHPaIPabstE9lV5PmSvBYfTzBhuBZZlsTHfiz/QtKD2c5qLjfnBmVnc4cx6Dj6+Ev53Pvz0D8JXTOQsxwIG27djC3jxWzZyrHjWB3qxJHAAiwMDWRwYyLpAb3KsePyWjVgqONK+nj86v2dC+FOsirqFl8Je5Xj7SmwEOGtIAg+PGcYp+0Vht8GB3cIY2jWMqLCqjl4quY1u8jH/3ckTc4oa3lBEgq+iwEyUCIts8xMlLMtiQ56XQBM6QpZl8cOmCtIKgzOzuEPyuWDl5/DR5STMeZxj7GvxWg62dT2By9wPcYL7RZ71XbbXZNl3xyTXeH79oTWXtrv44K48PGYYZx2Ywqv+C7gn6RUYeAY2LG53fsMn4Y9zMBsb3dzhb2Tx4m8lTf+cItIxVBSaweKMZaZyR0Jvs8TzHgLNjBlTNlSwrbidxQxXIaz/AX56CN4/zyT5rP/eDHZHxMHAM2DU43DtN3DGv8xy2/u61DbU6I9N2+xq1D7/PLnmec8ZWHNpu2uPTOXhMcO45qBYAE7pH8F+iU6inFX9jF6YG03pVncu8ozjC/8JOG0Bngh7l/udHwFVf/Nhr2Xy9pLSJn0uEel8/AGLjdklTRpw8QcsJi3PILMoONW1W4VlwZrJ8J9jqpJlj7gebpnZ/GRZb4WpqJU+3wyGVxSaSSzxPSqTZQFWZlfFjDnpjRtovuPo2BrPR/SuWfH1luN68/CYYdxypLmHee6gKPokOAmvNhdvd8xojOGvbGL6gf80Txa+DUs/aPS+ItJ5+PyBJscMjy/AN8t2kF3SuO/MtSz9AP57hkmWTdoPbpy6b8myAb8ZC8rbbK7f6fMha6WpjhiZYPpYMd3A2bRlqINlYYan2uOGY0ZChI2Lh0bVeO2ApJqD3Xef3J+HzhnGX0bEA3DF8Gh6xtWcvN2kmPHIj3y1dLt5Ep1sqv0CzPi3+TuJSMdiWbBhKrx5Aky8CNZ8uyuBMwUGnwUjboeTH4RTHjSPD7wQugzEwga562DuS/DuKPM9fN6r5hq8Fy6vn2+W7SC/rOEEnTZvyfswfrRJlu0yEP44tXHJspYFpdlmQnjWSlNNNqF3natKzd1WFSdW7Gz4d7ZnfABIjKyZxPyP0QN56JxhPHiCiRk3HRZD91hHjXuMPcmp5yw2/uW9CoDLHb9w7iPv8sOqrHq2l1AICzPFg8rLG5H4Lm3O7r/b7r9jR+D2+dmc07T72OUeH18v3UFRhfKm9sYb8JJWlMaq3FXM31RCeNefcPR4jzJvGf6KXpSn/R+e3NOoq3bq4NQ49u9We+xpT/+56nCevPAgAO4+bRBdYyMI1Mh/C2D5Y6lIvxFv0aHYbAEie32EM35ZrWMd+MiPzFpfX4wJLVWYlY7JU26SZf0usxx3K5m4spyHZhRx4ZAonj89qVH7LM70cOOk4FalaJesAOz8Hbb9Bpt/q3p90zRw7roAJ/WnIH4Ir2zsxiarJ5usnmRYXQjUk/sfho/ll3l44LMlHGNfwwWxvxPlyuY8xzzOc8xjZ0R/4npdC4GTSIx0sOKWVCJ3DWBHVLvHFImHA9lMJl0oIA6rgfkGby0po2ecg7GHxNa7nYgEkdcF2etMNbr41rv2N9fri0t5el4J1x0cw7iRjUv0mbHVza1TgjvDuMPwVsC6r2DlpyZxGvBEJPF62cl86DuFnduTGziAcVhqVYf0vXOTWZRZdWPq5sNjuO0oc13vHmtiRkyYDewP4Op5DJ4Zz3CkfT3vRDzHI55r+dUaThExDcaMF38rITXGzuXDG+6oiEgH4feZqnX5m0z8ik2ptyr6s7+W8NqiUm47Mpa/HhvfqFN8va6Cu38qBNpBzCjeAVvmwta5ZvDAqlZNL6Yb9D8e+p8APQ6uXKYu2Krf89ldNbAhp/SPBMxEuQ8v6MJX66puSt9yRCzXHmKu6/0Snay4JbWyoni4o2rwIo5yDmQz20ihlGju8d7K1kB3/hL2Obc6JxFDBQ/7xmJhxxOAf80pplu0nfOHtO2JQSISOo98u4qJ89N54Mwh3HLSgEbt879ft/DopNXERzpZ8egZLdzCINi52kyo2DzdPE8eAOe+bOJFc7hLoDgLirebx+ExJknWXvfKEf5qYerqr/MadYrzBkfz8gIzWPTFJV15ZUHVpLnbjozl/MEmOWpo1zCW35JK/K6YUT2HLYFSEiglnVTKiaz3fpjLG+D6eV2ZetjtDFzzqllaO2Uo9Dq8Ue0Vkc7h3s+W8/WyDP51wXCuOqZfo/Z5bcZGXpy2gT7JUcy+rwlLM3td8MP9sHi8eT5oNFzwpqng3VQBv5lQV1FgVutwlZgJiOHRJuHTEd7wMVpJ9Zhxyed5DS2WhwWMPSSGz9eYSSyTLu/KfdMKK9+/8+hYTu5vjnJEj3CW35xKfISJGdX7NEm2UhKsUrbQAzfh9a6G5PIFuPuT5SREhXHKkO5w6JWw7APTP/vuPrjio85XRVKkoyrNNt8L1042zyPi4ZArzL/7HofU+2/9/96dDpt+5v790+i9c7pJnv3pHzD9X2bi2rH/Z75DV/P0D+v479w0DuqVwKT/a+Z39VDzVsAPD1TFr8FnwwVvmNVtG+Ipg/w0M/nA7oD4nnvtYwD4qn35P/eThgs57Tnf5Ycru+3qn5g3/joijsNSTUw8sV9kzZhRbb9kWwmJVilp9MBDWK2YsdAawg/+oxjtWMg9tg+4cWJPPvjjMRx3gFbIaiscDgeJiYlkZ5sJM9HR0dgUu9s8y7IoLy8nOzubxMREHI7mrX7cFl3zzgIWbMln/PVHcfLglEbt89DXv/PFku0cf0BXJv7xmBZuYftT5C5iS/EWcspycNgdZIW/R0S3NQB48kfg3nk29aWAWhb846yh3DhhEQC/3HMSpzw3s85tRw/vwfKHTych2oxZ1awXacfEGRuujIvAchCWuJjInp9QEQjHXzqscku3L8C1/13AZ7eO4Kj+jRunb01KmJWOx+8zX9LL8yChdQeGX11obnZ/ubaiwYRZfwB+zyji89XtqHpHsAX8sHMVbJ4BabPM3wzAZwN2daoOvhT6HWFu6kfEsWabm/+ua9xgBIAXJ9Hd+/L3sfsR4bQRFWHju/nLyV06hQscc+ju3gIzH4PVg+G4u4lNGVK5b/Uvkt2iHbgqoIeVRw/y+J39Gzz3ozOLlTAr0loCAbOUdVm2uenQDjw9z8SMCSvKGkyY3R0zPlvZiWNGQz69DgLF5nFcKhxyJTPDTuKF78uadJjkKAe/Xt+dmHAb8RF2lmZVJcz+/fiaf6f4iKoB6rCBJ3P6T0mMD3uaAfZMngl/m3Geq1lt9W9UzLj/lyIlzIp0FhWFkLfRDOxGxENClwZ3eW2RSbD5z6LSBhNmK2PGqjZcUcAKQM7aqiTZgi01308eAP2Pg37HQddBrTI46/I1femrnnEO5l6fQkKEndhwO5PWV8XpB46r+XeqHjOqr2TRLTaMwjI3fSxzQ/t39udl/4VkksxTzre5xjkNB37+4buxcgLGXT8VKmFWRPZq4nxTCe6ZH9c1mDDrD1gsSMvnowXbACh2tfGq5CU7YeZTZrDaCpiEqBG3w0n31agA2yiWZRKtijOhNNNMvt9dibCBuONvxnKJA5KczL4uhS7RdqLD7Dir5bruGdsTqsWMpKiqx91iI8gvc9PPMhWdGtPPOH3pH0g7eJOp1v7JNXDzDIht/SXJRaRt+npZBgCv/rKxwYTZ3THj44UmZmzLb8I9quw18PmNkP07YIORD8CJfwV7ExbBDATAXWT6U8UZ4CquSpKN6VLvBMRQ8jdjhd3hKeHMvDaF7rEOIp02wuxVcenuP+wRMyKrfoepsVVJFl1jI8grdbOflQk0Lmbc8N4itvz7bBMHz34e3jjOxI+1U2DoOU3/ICLStmyaDp/fABX5YA+DP9wKx//FTDRohMkbyoERbHOfyTf3vA6rPodF/zUTn+f/x6xqcPi1cNL9+KO7siAtn08Wmr7Jyh3tdFXOzBXw5U3mHhY2OPnvcMK9DcevgN/c98vbZCZ4xDau0nn1SRaNUT3ELLsplai4MKotasRtR9Vcgbd6zOgbX5UmtDtm7G+Z7wV1xYynfJdzqn0JpzqWcqx/FVe9g4kZ0makpppCQruTZqX9SExMrPz7dRQLtpiCFB/MT28wYXZ3P+OLJWbFgzkbG7/yc2cQsAJklWWRVpSGx++h3F/OSwtfotyRjRVw4sq6AF9RIyqeA6cO7c4v95xE76Rowp127LY9k2Gr7E6WBRiQUjV+3SMhkqwiFxYOsJfjyrwIgLDExUT1+ojy9D8SqKjZt7zkjV/bZMxQwqx0LJYF+ZuhcLupLmhrwg2XJvD6LW6Zks/wlDD+Uu0GhdPeuIHceZtyeWtWGrmlbnKtBKBL5evHDujgs7ECfshaAZtnmiTZimrVm8JjoM8xBLodDp9/CMDspAs5rmcKzl1VmPIqmthb2KV7tZtFCb2H8ueFKTzru4QVx82DFZ+ZJdy//hMMvxCOvrlyKYzx5yazs9RP36hwnvxuLXab6YA4LH+9s7IrP65lYdcMLpGWV5IBBWnmxkM9M3RDweWzuGlSPiP6hPPnI6tuUITZwduIS1r1mJFpJQOJla93+JhRH1chLPmk6rmnhPK4nkQedQ32QaPA7iRvVdOSZXfrUW0Zu0FdGjfg4rDb+PtZQ1hT/iJd1zxKQuEaxoW/z8Pesfwe2A9QLBDp9AIBU0k1d4NZ6i4utVa11BJ3gJun5HP6/pFcf2jTJ15VjxnbrBQgtvL1kMcMVxFsX2RWlNi+sLIaOGD6bT0OhX7HmiTZPSqSBNOUNYWUYKdXfDjH75+IY1eXMb+Z/YxecVV/w2HdGhczEiLsvHRGIk67jSR7zX7G7tGOz/wj8VpOngt7nSud07EBD/j+iOKJiOyWV+rmTx8s4cLDenH50X1rve/b2x3vXX5Ylcm4SavJLHLVen308Ja7DjdLcSbMfREWvwe+Xe0dei6MGgfJDScA1eD3mQnbxTtMhS3LB5GJEF1zAos/AJNW5+Py+ugRVzNmlHqakf0E9EmoihmHpoYzLa3hpbn7xDt55rREEiPtxFh1x4z6WNjhwjfh7VPMhJ3Pr4drvgaHhgVEOpPMogr+78OlXP2Hfpx/WK9a73sbyOpsdsywLFj0Lvz4D3P9jukG578BA09rXMNrJMlmmscBH4S1rSRZfwC+XpVPIOAjJbZmzGgoHu9p95yMfolV1+mjeoazIrvhpWkP7BbGv05OoHe8gzBf02MGYMZv7A5IGQLH3gFznofv74P9R0KEioOItEuWBQveMlVSLT90P8hUSE0dXufmW3LLuOez5dx0wn51XuN9/oCprnrkDaay7MafYfazkP4rLHwH39KPeMc6jxfLRuGqVle7TfYz9ibgh1//Az8/BgEvxHaH81+DAxoRv8rzTVXZkkwzqW+PCXn+AHyxKh+H5aNbTDjH1YgZTWtm9Ut7xK5M2WP7RPDl2griwuu/f3Rcn3AePSmewV3CsNwNx4w0qwcT/adxvfNHHnJO5GzPE01rrLQ4m81Gjx49SElJwevVkvbtRVhYWLuuLLsuq4T7v1zBnacOZGQdibG+QP0XtnZ1byoESt1lvDpzMaVWNt3j4nBEb+adlW/jDXhxBrpQtOUqAu7GFRLb3c/Yv1vVd/qRg1P4ZW02vZPqn4R+1vAe/OMsF4f3SySnxM2fJi7BBlgBk/fgyrwQm6MMZ9xaontPoGzLbVjehgvFhJrujEnHUpxhbv7GJLfYUp0AS7M8/LLFzS9b3PzpiFiiwsw32bA98nPX53lZm+fj3EHmAuMLWHyxIof/zVpfOc5Z/Tvnk9+t5YGzhoR+MDvYKpNkZ+xKkq02OB4ea6pH7T8Seh3BvC3FPPfLNraN+jsAOdO89J69nqdGdaFHl0T+74eCOk/RFMf2DueRE+MZ1KUL9BkEQ8fAb2/Chp9g1ReQuQxOfRgS+3Fy/90z/mJ44KwhlQkIA9hBIbFkU//MS48fInWlFWlZrmLI2WAqSjRilm5rm7HVxextbmZvc3Pz4bGVkyucdhveajfNf8/xkl7k48wDTMzw+i2+WpnL+7PWVcuNqbrJ0WFjRkPK82DFp7D6G2xeFwPOcbDZSuUu/5+ZXDCCfjNK+FdYIV0SE7j/l32fuX7WAZH8/fh4Du3e8CDM6AFRQBQMfZHCr+4hsWAVj4VNoMITziJrCDsbiBki0oF5XaafUrjVDO4m1B6kBpi8oYJft3v4dbuHsYfE7HXprGVZHnLLA5y2v4l7bp/Ft6tyeX/2usr+Rcj7GVYActabBNltv5lqHFa1G2RhUdDnGJMg2/cPEBG392PtI1tEOEVPPsIrc7OZN9uHZbMDPg6IyOCx01KIj4vn8dnF+3yeK4ZHU+IJcGzvhhZaNUtzG1E1+hkHsI0SotlJF74OHA9+O885/sMVzunkEc+zvsv2uZ0i0jF8vHAbC9LyWZCWX2fC7G6LtuRT6vZVDly4vH4+XpDOuEmr68yd+dPEJbx+9eFtY2AiYxkseBtWfgb+XcmlvY+G0x6B/k1c0tVdAmW5ULQd3MVm0DoquXLCdHXzNuXy6LSdrHfvXj3Kx5DIHTx8anciouN5fn5Jg6fzOMK4bte9LU8dCV03HRaLDRjZv+E+7CXDdseMyD3uTW2nlCh27pqI3yPWTmZpHYNRkQlw2QfwzqmwZTZMfRhGa4BbpDN5Z3Yai7YWsGhrQZ0Js7sHsudtygULjt21xHK5x8dni7bx6LfNiBllefDt/8G6Keb5AafB+a9DbANLsu41STbKVEF0hDfhk7e8eZtyeXBqDps9u1cl8nFg5Db+cWoPbBFxvLO05mTuhuJDXe4ZEUdipJ0zBjQcM646aHf1p5oxY392UE4kWbtixqBkJ+vz66gsX7AVuuyajHLiX814SeFWmPEknPGvRrVXRNoQy4Jpj8Dcl8zzQ66Ac16EsL1fT17+eQOLtxaweGtBndXgfLsmWcxcn0N0uIOjBp4GA0+jYt0vlE/5B12KV3MrH3JexA88472UrwLHY2FvW/2M+mSthEl3wQ6zZDWDz4ZzX4aYBu6puUugIB2Kt5tx8diUWhM75m3K5W8/5bHNu/semI+Do9K5/5SeeByxfLu+aSsMep1hDPjQ/G1tESY+PnpSAv0TnYwZVH/yk81mq7ZKakSNmLEfGbgIJxPzmQ/pHsbynV5e8l3IhY7ZDLWnc7FjJjCmSe2V1uFwONp1Aqa0L098t4al6YWMHb+QDU+cTrm3nApf1bWs0FXCqtxVLNzkomtsOIf2TSLcHo7H6+SX1QU8+u26tn9vKgQsy+KzZat56OvfcbtjgDAiun9BePKvAPSLGc6qJRdCYN9WoXv2kkP4369bueDQHqReNw0AW2Tt7wh2u42bTqyasP761YfXTHS2eanYcSXR/d7GEbWNqN4TKd/yJ7DaVt9tT01O45o1axbPPPMMixcvJjMzk6+++orzzz9/r9vPmDGDk08+udbrmZmZHa6stIRYRaEZkHVGmEHoFlR9wvWGfB8Hdzf/0B17VJg9/YMcACIdcPqAKP46tZCv1vnoQTxdMAOyVrXkJ79l461ZaRyzX9fK2WTtVsBnlqrYPMPcjK+eJBsRB/2O35Uke3hlh2Heplwem7KBtfSDaqtSb/PEceUUDxCcJQRsNlvNil3RXcwyGgNOgRn/NktkfPVnU6mk95GVmx07oCvH7NeV3zOKyC/3kBwdzrlfVt3wuumwGN7e4waY228R6ax/Fl9HppghLc7vM5X6fGUQX3fyUahVv5xvLfIzIMl8/XLucZ0/+yMTMz6+0M4fekfw5+8LmLrZSy9iScIsxV2909ChYkZjlGbDik9gzSTwewDYaPXk2fBL+SlwJJZlfglpnoRdMSMnKKe12WzcfHgTK2g4I0i84Fms7/5GdNZyXoj9H5uOe4Yzf6ja5Mrh0Xy4x3LpvoDV6Gr1HY3ihXRoFYUmWbQ029wsryMxZ7fq14CdZYEaS2pWd/6nZlmi76/sxtCuYVz3TR7zd3jpSzTxNnNtqd7PCLRWzNhdRTZ9vqki6yqs+X5Sf5Mk2+cYSD2o1SpC/ZqWzyO/FbLBWXNp8o3uxF0xIzjLPDntthrV5Burvn7Gz/bj+LRLgCvyX+N25zdkW4m87z8jKO1trxQzRIwwR9V1vsTlJS6y7mvqxW+Ym+mz7zuZPsnRXPT6PH7P2PskAQsYN2k1o4al1rrP1SpcReY7/+IJsH1B1et9R8BJfzP3khq7ko/Pbe5HlWRBWQ54K8zqRrEpe51oP29TLg9P2cxGetd4fa0rqUkxw7LZyY7Z+4S5CKet1hKpjVFfzIgOs3Nsbyfztntq7hTwm0qBF7wBn1xtlszdfyQMOr3J529vFDNEjOpXTZ8/gHOPToHfb+HxBbjy7d8AWPbwKBKjwxn94mzS82veu6iuzphhWSbJ8vu/QXmuSXA9bRwcc+vel7BuZ0myu83blMtDU9LYTM17kr+7uuyKGXm19mkoPtQlOszO/x0d3JjRJdrOQJxs2DNpNne9KUoQl2r+e9az8OElMP91OORy04/roBQzpMOxLPjpQfj1VfP8tHFw3J0NfpduqDK2LxCgqNzLdf8139XX//NMwp12jvvUT0HZ3xlj/5X7wj6hty2X58PfYGzgRx73XsNCa0ho+xkNcZfAzKdNZVnLD+FxcMY/4fDr6v+decqhaAcUpZvHMcl15inM25TLP6ZsYRs1KwGuqOi6K2bk19qnIRY2wlO71XgtPsLOHUGOGX0TnKQX+SlwxfGy7wIeCvuAvzo/BfcjLToBvi1TzBAxXN6q75JLspZQ4a/Ab/krX3P7vGzIyWPcV2ac+51bs7Hb7Nz0ZgX+ANgiMrD5o7ECkRCoStQM+b2pEPL6vXy4ZDHjpiwl4O6LzVlMVK8PcERvBcCdcwqr1pxGzQyEhll1pCYnx4Rz52kDdz1r/Fj46OE9GDUslQVp+WSXuEiJi+SKt+dTsf1qkga+ApGZRPb4ElfGZbTlFfOanDBbVlbGIYccwg033MCFF17Y6P3WrVtHfHzV0vUpKQ3MIhVpCp8HcteBrxziG1dyel9UX3qtwmexMd/L5gJfrQqzu01Nc3H6gCi+WmdmU2STVGfC7AZ6Yy/dxu8ZRRzcO6HOY7VpAR9kLt9VSXZ2zQHyiDjofwLsfxL0PLzW4Lg/AP/8OdMkyzZTuMNUdG22vn+Ai9+FaY+aGYTf3wcn3ANDqmZQOuzU+Nu8Ojqc238o4OnTErl0WHSthNkiV4CEiM6QyVY3xQxpcUXbTHXx+LbboSz3VosZXou1uV4yS/27kqJqfzmdle7mD70jmLrZzMrKJaFawmxVzNhMT+ylO9pvzGiskkxY9iGs+8EsfwRY3YbySM5JvO85geZ+0e4aZSd3j+W3kyKDeL12RmIb/SRMuoPwvI0MXfZPXhj5DHfP8POfM5M4e2BUrYTZYneA5KjOOfNX8UI6rOJMkyzrrTD9FHv9/8Y9/pr9jFXZHgpce1+2aMEOD0O7hjF/h0mMySeeeGoPaKeRiq00M/gxI+CD7NUmSXbbQvNZq8e2sCjodURVkmxD1aRagD8Aj07byQb6NPsY+yU6SCus2dHYPzG41+s9+xmPnuTg0ZnFvDw6iZP7X8qz/8nh3rDPeNT5PjutJNy+8yqX2+tsFDNEDKva5bbC62djdikub82Y4fVXPV+5o4g+ydH1JsvullnkYkFaPiMGtNLyaeX5sHk6rPoSNkytqiZrD4MDL4Cjb4LeRzUuUdbvNUm3ZbkmUdZTYuJvZEKDVaH8AXhoWjab9kiWbYqDUsJYucey2YenBneCyJ4x468j7DzzawlPnJLAod3DGfxaZo3tvTvXE9Z9sFlh6Zg/wW+vwzd/hj/9CrHd9jx8h6KYIWLYqw0yu30Blm4rpPq4sy9gUe6pGuzelFPKEf2S602W3a1GzCjaDlPugfW7Zgx3GwoXvgU9Dq69o2WZit/l+eZ67Soy933aeJLsbv4A/GNqDmk0fwL/0T3DWZBRc5LDMb2C+7n3jBl/PtLGa4tK+fvx8fSKc3D42ztrbO/DjjNnnUn2iow3kyuGnQerv4HJd8MNP+098bmdU8yQDufncVXJsmc/B0f9sVG7VY8PVmkuv6YVEVNtnNPv91NSVjUWujWvjIHd48gv8wB2vg0cx4/uo7je8QO3Ob/hYHsan0U8xnf+o3my+IrW7Wc0ht8Li98zBZXKd02OG3YejH4K4uupbOh1mbGLgi0m2TYqERLr7kf4A3D/1DzSaX4Ow2GpYSzN2qOf0aNlY8bYQ+D9FWXceXQs9x0bxwnvZfO+/wyudkxjP/tOArNfwH7aw0FtQ3uhmCGdncfvIacihzJfadWLNlizw8JFBuHJK7GF55MXVcr4NX6i+roBG++v7kZqbDL2BCe4emJZFrbIfLCc+MuG1DhHq9+bagOK3EVsKtjMk99tJODqiyMqjcjeH2B3lmL5I6nIuBR/6bAGjzOsRzyrM2ve/zu4d2JQ2+qw22r8bS4+ojdfL7VxxeCxTFz3OmEJy/BX9MVbcCwAgYBVo1/aFjQ5YfbMM8/kzDPPbPKJUlJSSExMbPJ+Ig2yLMjfBCU7g54s+/S8YpIj7fxxj8pyJe6qAQeP3+K0ifVXsUsv8tcY/AbT7CJi8VB109y763F++R6VINoynxsylsCWeaaSbI0k2XizRN7uSrJ7qd4B8HtGEatd5oLqDPi4bvX3AEwYdia+evbb7Q+9wvnggi58vqacqZvdPHFKAhNXlnFuA0tO1BLdxXQcZz4NG6fBrGdMJcMDL6hz83MGRXHa/pF7rSJ7+gc5LL2pO1F7y6bu4BQzpEVVFJjrf2R8vdeX1mBZFv+cXUz/RCfXHBxT472SapMsPH6Lcz6uvyJRepEPl692zCjcI2a4MBUK21XMaIrCdFj2gRkw372Ed49D4LBrWGkdwPtflTcrXgCcPziK50YlMnFlOfO3uxk3MoH/rSjj4qFBrlAfHg2jn4Sv/wSF6Vyw/VnOvPVJIsPrbucFn+byw5XdOmXMULyQDicQMMtW5qwDh7PGDfaAZfHwjCIO6R5ebYllo8RTs59RV8wIVMuQSi/yUeYJ7PG+jSJi8Fbr7pdjvhMHJWYUZ5jqsdsXwo6l4P1/9s4zvI0ya8P3jLosy7bc7Ti9994rISSETgi9974L7NJ3gY9lYQuwuyx16b1DIIUeIAmEVNJ7j3uTLdnqmu/Ha1tWLLnXMPd1cSFLI3nsJPPM+57nPCe8YYyEXsIc230CpA5ttxTZaGzLKWO/K5artn8ONE0vAK4dHcPdU6z8b0MF24t83DvFyptbKjh/aNtONbl8hIXzh8TUrDP+GziTFMnOpdqveVL3LPd92I2HF4xWNaMJqJqh0hXxBYL86dOtTO6bxOkjwve8nJ6QscnlDXDWMz/VelUhjRL8u7/lEs1XJErldF/9Kd5dMv/U5QDgVTSUYcGuWLBjoUCJJ0dJJEdJwoGZAoe77X4wVylkb4Aja2Dft5C9PnTPD5A8EIYthFGXQGxqw59XbZKtLBFFa69TLKIMsWKvUGrctXJbThn7PPHN+5mAP0+3csWIGP71i4P8Mi/X7FrGziI/Yy+9sNmf2RhuGhfLVaMsGLVS2H1CNTd/so9/naXBlNYPTnxQNLoX7hCj0i94p/GJvV0QVTNUfku4fQHu/3QrcwanMndIeGO51x+6xhY6PCysSh+vxh8M4ql1zP7CCnolNT5hqKC8EtZ8DN88VNWooIPpf4Cpt4P2GDOPtxJcJaK50FUKfrfYPzEndHqTbG22Zts54G16M2L1XtaJvYxMOP1C/rbWhduncOXIGD7cUckVI2Ma/pAWcOdkK7eOj8WolcLqXNXc9bOWv4x1YCraLfbhNDqY9xjs/VasATe8BmOvaNNz7ChUzVA5rlj7Iqx8Ujw+9UkYe2XYy+VuHw8u2sbpIzOY2S9JNDB4ysFVirc8NO3z8PbVXPhx+L2iz+vGe3h9zdd7DhwkTgpvwvKg57nA6XwYmMFt2g85X/Md8zVrmC1v4PDPmyHzIdHQ1pEE/LDtE/j+UVFnArD1gbl/hQHzor+v2ihrPyzWIEYrxHWr955681E7h73NT2L98JxExqTreeCHMkw6mYWDTHy228VVQ43kP/cWAClXnYeka9062QPTrdw9xYpRK5HvFI3kPrQ86r+QF/RPElj1FN4Rl2JM7tmq37croGqGym+JYqeHhxdv57xx3RnXK47CykKOOI5Q7inH6w8iaZxo4zbyyM972V++B0kKYqjaynEBBxygrbrF3Vi4HwrBWLVcURSJoCeFQGUvlICZoDsLEZSkIGkqyCuvAI5/w6w/6CfXmcshxyE2HCrF7YpHl7ASQ+pSJClIwJ2K6+glKL76G8EBfvzjLLJsJu76aDOZ8WZOGpLKF1vzuG5G76jvUbxeCv71bwBSfv87JH3T12X/OGc4fzljCNkVh8h2HOHbnEUYUpYSqOhD0JvK41/v4uZZ/TDpO09wVLu5S0aOHInH42Ho0KE8+OCDTJkypb2+tcrxjiMXSg6IlIgGEpsaQyAoNsi3Fnh4Zp0oPlw1Kgap1o1u7YTZPy0vi/I5oWOOlge47avSsNftWMgmcteQzdzJN4acBWLM6uGfRYGjOvkDhEm21zRhks0Y1WgTW+3ivSYY4Jy9PwDw5sCTIha0/3VSPCf3NfHUWgdvbankryfEo5ElzhsSw3lDhOLePtFa532NQqOHWfcJ8+zm92CVEIdoptloZlkAt19h0LN53Dre0vzz+Q2iaoZKgwT8ULxPmPatHXejXK0Zq7PdvPSr2DCoa5gNbT7f85094ufULmoeLQ9w7eLa43ckirGSR+Sb4E6vGU2leB9sfFMUcatTCjPHwuhLxEY9ULxLbNo1Ri8AXjglgZk9jTy6qpzPd7m4a7IVjSxx2YgYLhsh/rzumNRG1+iYZFFY+PQmOLoW45Y3YczlEQ89VBZg0LN53DPFynVjGl+Y+i2j6oVKpyTgh+K9YrPdEFszmqxaM77a7+bNLQHe3FJZxzBbe53xuy/D1w/VeGo1VRx1BLjok/Axn4XEU0hCxPc2SzO8FZCzUaTIHl0jDLO1MVih21iR/Jc5pkNSZOujwOlptF4AvHVWIhMy9dy/vIzvDrq5fowFWZLCrsttphnHEL7OkHjIfyk9pTyma7bwO/vfmPzsw9w+M6vOvYdKZFTNUOlKBIIKaw6U8OnGo7y37ijvrj1SxzDrcIcMsze9vYEkypirWcskeRsT5R0kSeXwHjxc3beQLf47pxFbZ+WKGc33WbCtpzCcxqaL0czV/7ekisKw1hi5MBwMiLHalcVQUQilB4Q2Fu8TyeTFe+u+J3kg9J8njLKpQxo2cXorQ8mEFYXCJIsC+hihRc1oqswu9zR8UC0+WpjEiFQdt31lZ0Oul3MHm5EkidsmWgm63Oz62zJ6A8n3XNDkc2kq1ZohR/i9fXlYYtC/9/DEqS7OnjoCFrwI/5sFu5eJJsVRF7f5+XU1VM1Q6UpUa8Ybqw+ydEseH64/ysHHTgk7pnZ67PVvrj/2I/AFFNy+0ESFvYVO/vnVrkZ9/z5SNrN+ehwKqz6323g4/SlIqZUQFQyI67UzX/znrQCtoUpLumbS9f7SpjUjLjoviSHJOm7+NJdzPvsB9gK/P497poTWFu29ztBp6mrGRztdfLRT4rmZOcwzxEFyP3EvcML98MXd8M0DMPDU4z6hvCmomqHS6dj1BSz9o3g8674ws2y1Zjz3w15+2F3ExxuzOXiDLZTyrdFRqy+Pq7/XAf6wj892gqeWxWTvwYP8cfHhmq9jcFGJAQWZIuK4z38VrwfmcJ/2LaZrttBv7yvwn89g1r0w+nLRaN6eeCvFPfBP/xGmVxB7+DPuEvv20Rq//Z6qRNlD4vdliG3QKFvNruKmacZn5yUxKEnHZYuKKXEHGZmmR5Ik/m9mfM0xt0/UEXS5yXl/CQDJly1odcOsJEkYqz5SV2sd+VVwLKuDg5go72Dpf27AdunrTOun6kJjUDVDpStRrRn//GoX6w+V8umvOXx2RxYFlQWYdCa8ipdS03vE9N2EJAc44BCXxKDXRsCdSdCTgiYYx3njU3lzVRlIQSYNDLDmYD6yvhDZmIOsK0NjzEdjzEdvW03Qa8NXPgJ/2QgkfQlOUsmr0GMz2tB3oea6plDmKeNQ+SEKKwux6C3klWowZryDLu5XAHxlI3DnLgAl8s+/+Jap9E+N5bwXRFNktwQTkiTx93NG1BwzKL3+dYbi91Py8ssAJN98U7MMs5IkYdRr6a7tzik9z+bL3TvRWnZhzHyHyoM38/TyfTy9fB8f3TCJMT1sTf78tqDN70DS09N57rnnGDt2LB6PhxdffJGZM2fyyy+/MHr06Ijv8Xg8eDyhDcry8oZHhan8RvE4oGiP6FLWNTFJNAI/7SvihR8PUOT0UKkYoGqczoq9RUyvdaNX2/x0sCxw7McAIp22mmxHgGxH6LgAmqhmWTd6ntkU4ErJw/hMkR5YUBHg+fVOLhwWQ5+E8H+21YX3kkovNrOeIRlxaFo7ZMjjgNzNolCeszHUaVdNTAp0nwi9pkPGyEYXJRbtquT7gx6SzDJf7PU3/IZanD7AhCxJ/GGSlTsmxoYZmlsFSYIJ14sUkk3vCNOswQp9Zzfr4/6zxqkaZhuBqhkqjabsiNiciE1r+Ng2orZmlClmIK3m+cl9QubW2uanXcWRr3VPrQmNrDhaHqDIFRqt40Ef1SzrwsAzmwJcr/UyMk3cvOY4Ary00cnlI2LIiusAzWguBTuEUfbQqtBzPaaI4m3KIN7bVsHaraVY9DJf7I2svdGY09uIJEk8MD2OP0+ztr5mNERiX5h2B3z/V1j/GqQMhqzxUQ9/dFW5aphtgOboBaiaodIOBHxQsBPsB0XzVdUapbZmFCtWqLqu16cZO4sia8b/NoYSXY+U+dle6zgnZpxETj6txMizmwLcYvAxOFls/h+y+3l9cwXXjLaQZqna+VaCULSb4OG1VO7/GbN9F7JS67oraSBtqGhmyBoHif1apXGxNXl1k5NdRX4kCb7a1zTNmJIl1mCPzY4XI6E6UepeAA23+G5hkfQnesr5PK37D5d+f7dqmG0AVTNUuhpfbM3loc+3k1vmrvP8vKGhxHKnx4+GAPPktZxXsJwphq1opJCO+BQNjpgs1jkSKVDiMcensKu0piUNAz7ipAripQricZAmlZIhFWGTnFilSrDvEv/ViwT6GBStEX8gAAE/MgHkgBtJqZtaF0ZCL9Fo0Wu62GuJizy+tAa/BzxOkVxYUSSK1L5KsW+jjxEm3ibqkaIovLChgqPlflx+ha/3N21vakzVCNSn5iV0Os2IxO2Lj3L2iDSh47PuE6anL++Dfid1uoaXjkLVDJWuRlM0o5qdeY6In7V0S17N4+055eQ30ESgw891ms+5RfsJhkI/QV0M8okPwrir2Z7n5JMl27l5SjpxShmUZVdNppMI6GPZ5pApcfmwmT0MyTB2nr2peggqCk+tcWL3BCmuDLL8YOM1QyPBiFShGc+cbGPXf9vqLJtGfcMqbv1Rw+5e+8SIcUsyjLsGfn0b8jbDV/fD2c+323l2VtRahkqnpHgffHyN2NsZdQlM/2PNS9WaUVpWhoQCGAFYu+cI4wb0qEn59irFgNhL2VMS+Vq3Ijd0AVlXYqLCH/p7XUHdev0upTuX+u5mVuBXHrW8R1rlYVhyB6z8F9kDr+Ad/wxuOGkkMYZj6xnCrFXgcJMSa2R8Lxua5o5zzvlVpGRv+VA03gGYk2Di9aIebIiSAOtzgSMvlChrsEBcZr1TLPxBhSdWOwgEFQ7YA6w60njNsBllhldpxltnibCYzrDO0Ib93iUe9l3C5/r7OEPzE9d9+CHT7rmhw86tK6BqhkpXI3ydoSBSX+HrXXsZ2T2O17e9zs85P6MYFCQg4OqGr2wUfucgFF+4GVJ2JeB3iHAQZ4ERT0Fo7SJpy9DE7EEbsxtt7E5kfQmGpOUYkpYTqOzOe7/KmAxesuLjSLekc6hAy8o9Zdw+pz8GbWgPqFX1op3wBX3kOnM5YD/MW6sqiDeZ2FFwkP28iC4uD0WR8eTPx1c6herf/7H0TophaKZIbP/4hslAx2uGTtbR19YLd845mHv/C40xD0Pyl3gKRFPnX5bs4JMbO0ezQJsbZgcMGMCAAQNqvp48eTL79u3jySef5I033oj4nkcffZSHHnqorU9NpasTDIhECo9DdLi2kJ/2FfHo0p01hYNgrYvOHcsK+Lcs1RSzjx2VHYnnN1Q0eEwk9ird2LvPzRf73Px4WQrd47Tc8bWdFYc9vLutgmfmmPkhW+LCYWYKSstqCu/VJFkMXDu9V1jhvUkoikjmKNgBBdvEAqJoD6GSCoAk0j66TxJGWVvvJo9v21Hk43df2pt1ih8vTApLzGizi74kwfhrIeCFrR/B94+B2SaSc1XaBFUzVBqFuwxK9osNjGakBrUGx2qGUlszluTw+Ck0STOe/CVUrChyNVBYrsU+JZN9VZrxy5WppFo0XLekhC0FPpbsruCR6SZW5spcPiKG7GJ762tGS1EUyN0kjLLZ66qelERK+aiLIbEPAGtzPNz1beRE94ZYfH5SmE502EKh/0mQtwV2fg7f/QUWvtIx53Gc0By9AFUzVNoYv0fcQ9sPC9OJVhgv69OMPy7J5h+1NMPla1gznlgd0oxozXuR2K9ksH+fm2X73Gy8Jo0Ek8xFnxRz1BHglwMlPNx3F8rRdYzwbEDjLkEGqq372cFEdmgHkDl0GoNGzxAjUzsp3x1w8+AP4RvIhka8z2aUeXdBeGp9R28uRaIMC9f47uAT/Z+ZrNnO3co7wF0dfVqdGlUzVLoSX2zN5YY3NxBJDa5/cwPPXTxaGKACPsYWfsyt+rfoLhfWHPNrsA/fBEazOjiIzUofvJ5QOpLFocUZaLhQa8JNhlRMplREhlTM/82yoa/M54f1W0hUSsjU2IlXqovsCnidSF4nEXOYjHGiCB3fXdzb2/pAUn+xrxJTz6SQgF+YYX0ukULoKgG3A/yVovivNYAuBkwJTd6Pqs2iXS4eXdX0omOfBC0vnRZeAOqMmhGRgu0ieGDSzWKvK2+zSAw85+WOPrNOgaoZKl2JRmsG4PI2vG742xc7ax5vPGyv99iR0l4e0/2PgfIRAJYHRnCf+yqWj7kYbTDI/P+sAODAgb3cNhI+Oajn5olJbM8t54Ufd3SuvalG8ubmyrD9u8YyMVPP4yfFt/4JtQL1mQhkWRIaW7RLmMN0Jjj1X/DibNj8Loy8EHrPaL+T7YSotQyVToe3At67RJhBu0+CU5+suVf+YmsuN7+5llSKGS3ls1PpjqvKMPvHb8r5q6G85jrsCTS8N/XXlaF76A25jU1PlVgeHMXU8mFcoPmO/4tbjFR2hMxf/o9rFRO/7jmBlCmX8H5hFr+bM5CVewrrNIWkxxl54LTBYU0hUQkGIXcj7FgMOxdD0e7Qawm9YNJNogYRLZDLXQ6OfCg/KrwIjTDKVvPceifPrHM2eNyxnNLPyAPT42q+7kxrDP0xqeTblJ58GJjOudofuNX3CijXt2htdryjaoZKVyJsnSH5qmxCOiDI878sx7L/J3yKuPbr3MOx504n6I7eBP3yD6FJejuywxv9FH8c/rKx+MvGguRFG7sDnXUjGssuNObDHOJVHlhl4+Lh8xiePJKbXhQ19EKHk3PH9GL5rkKGZFh5dNnO5utFO6MoCiXuEo44jlDsKubTNRJfbfKjtazGmPEBssZN0G/BffQiAq5eUT/ngvFZ3Dk3NNWjM2mG1WBGCcTizl2AOet1dLaV+MqHEXR3R9OJzrNDXCbjx49n5cqVUV+/5557uP3222u+Li8vJysrqz1OTaUrYT8M9qMQm9riG7BAEF748UDY5lLtQnYhCTz/wwGGdbOh10isyWna6ITmMv21Ai4bomHVYR8gU+GDm5cW4iCGN7Y4GagcqLMhVuz08OjSndwzf2DDm0xBv+iKKzsCRXuhcKco8rtK6h4blyXSYzNGQcZo0VkcBYcnyIIPipjVy8hNYy0UVAToaxOlkwpvkPW5Xm7+IvKI2YY4f4iZ0entGLcuSTDxRmEiPvCj6J4+6/mGk08i8NJGJ1eOjOlUYtUVUDVDJYxgUJhl/S6wZnbIKTSkGbkk1WiGTiPxS3b7aMaEl/O5ZpiGLQWiCJJXCTd/UYoLIx/vcNLd10LNaE2CfnFN3fweFIrUqiAy/j5zKOh3Ht269waEnqzL9XLFZxF0qRHcNNbC0JRONKJj8s1QuEM0/PzwD+AWonUFvrG5Qk0MbCIN6QWomqHShvg9kL8Nyo6K9POq8W2RNKN2Y142yTWaoZFhdXbTRkFXNsJgG4lR/8vjriF2ZlWuYbZuI5Pc2zFsC6WbVyp6fg32ZUOwLxuDfcnHhuQD1sI9yZVM7tMxhtkcR4ALPy7iomExnD3QRIVPoXtVmnqZO8iaHC/XLK5fMzIo5AB17+VvnWChf2KUsXudjD1KN2733cAL+ie5WruMFSvGM23arI4+rS6FqhkqnZFAUOGhz7dHND5V8+Bn25ghbcLw7X2cX7IXZChRLLwRmMNHgekcVlKjvrd2umB9uDCKxjxFrLfe/Qaum96b5z37a46RCWLCQ594ifIyO0a8iGxZDX5k3IoBOxaeOmd89OKEoohk9oBH6KjfI8ajespEsd/vFq9DaLKUsfEpsgfsfi77tJhrx1g4qbcRb0Chm1VoRqkryC/ZHn7/lb3ez0ilmHzqGntvmxBLz/iOad5sKZ/urORM7U5IHwGn/wf+d4Iwzg4/D/rP7ejT65SomqHSGWmsZkzrl0xQUVi1r7hJnx9NM0y4+YP2A67QfIEsKRQrsTzku5TPgpMBiQH3f8FoYx7VU5hWHvXzzVEDEGB7SQmO3L2dZ2+qFjuKfFy7uITfT4hlSpYBRYH0WKE3RZUBVh/18ucf6m/kTqSMYuLqPH/bxFgyY7ueZrj9CktzLcxPLhaJlSmDodsYGHcVrH0Rlt0F169s/1HqnRy1lqHSYSgKLL5dBCHFpMA5r9Tam1J4/LO1DJYOkCaVUKEYcRPaMz9IOs/9cICRWYn4FaXJNXBnE/em/Gh5I3ASi8pP4FzdSi4IfE4fOZcp5Utg2RKuVeLYun0035f1JS7Yh2LS8Va15+WVubnhzQ08W6sppObnd+aLWkPuJjj8s/jPVasWrdHDoNNg9GXQcxrIEYyvAb+okZfnQkWBWJMYYkVNuKq2uzHPy83LSrlvqpURaTq0kkRq1dSmgooAPx/18M+f62+wsOKkvKZNPcRtE2JJielcU5yqiZRK/k//uZyiWc2Q4C62fPkyw+Zd1f4n1oVRNUOlMxK2zpA9yPo8gp50JI0dU+bbaMyH8SnQP34g5w+4hD++2rRrlttXz4uKHn/5CPzlI5B0JegTVqOLX4usL+HtnW/z9rbP0Nlm4CudwCeb9vDxhoKqNwaB8ItUVL1oZ37aV8QfP9jMX84cSt8UC0HJg1spIK8ij5KKIIfyzHy+vhBDypfoE38EIFDZA1f2RSj+qunVcgUE69aL7z55EHGmzlnP0GnFn0fAORiffTS6+A0Y0z+i8sAtrDtUyoo9hUyrNeG9o+iQlcyvv/5Kenr0v5QGgwGDoTEZMCq/WVylULIPjNaaG/6WsC2nLKyrGYRxpzY/ODMZ9nwe7c1r2wJhZ1JZ1fHnC0ooETw21YHoL/x4gAm9ksQ4I7cd7EdEAd9+WBhk7UegPFuYlo5FkkVqbPIgSB9elfzR+M2qz3a72F3iZ3eJk893uchxBrhmVAzfHHBzwN60kajH8vsJUUZitCWyRoyqqywWRoivH4AznwatsUkf8/CKcrSyxHlDzBi1qmm2saiaoRKGIxfKcyCm426iImmGcozpsaM0439bwq+x1V3iZd4maEZb4q2EnUtg64diAwvwSzre803n2cBpOPamUrZN4epRZSw/6GFfadNGoh7L1aPqbjp1KFqD0JNProUjqzlPM4z3ApENTn/6vgyLXmJ+XxMGVTMaRUN6AapmqLQRfg/kbRX31tb0sPTzzqYZqZQwX/MLp2h+Yey+3dSOAzwcTObb4Gi+CY6mQjHgP2a7oN01IwLPrHNwsCzAIyvL+cdP5SjAxcNjWHnYE3VM4LHEEXkSyDmDOm9qbiS+Co7jRf/JXK1dxtDtj7MyqS/j+3erk/ihEhlVM1Q6I2sOlNQZqV2bWCr5o+sZTB+IglqRYuUp/1m8F5iJu1FZ2s3n+R9DZlkJhSAyFZjYbFeIxVqVNSshVamchEIMbv712WrmdJ+MhqDYfwr6wOcWhWdfJfh94rmAVxS5JcQ9s9Yo0mm1zf+5/raqnMPlAe5fXsb9y8uI0UmcM8jMyiONX2ck4IhomD2xd9P2gzoTv18BaaZCxmp2ok0fLprEf/4vLP0D9JoePWHrN4yqGSqdkYY0AyCv3MOQB75ste85Xd7EI9qXyapKNv84MJWHfRdTijXsuA3utJrHtZsF1+d66RfhczvDOuNPy8s4Uh7gjq/tAMQbxX7ML9neRmmGhmBUw+yY9gz/aGVuXGZn0QIbQ5WDaEzxwjA26z7RaFG4Q4w2H6eao2qj1jJUOowtH4r0Z0kjJptZq/4eBoP8umUraY5t6PGSryQQQFNnb+pHZyaDn8tt11Mu92t50T+Tl5jOBHknZ8orma9ZQ7JURnLlcibqlgPgV2RylESKiaNYicWDHt3HGpStiUhuO1QUiZqRJ0Jjg94CfU8URtl+c8Qa41gURUw1dJWKOrq7TNTJTXERa+O3fVlKtiPAjcuEGTctRmZqdwMb8nzsb4RmmCQ/KUppRMNs74TO24QQKZW8gASe85/GHboPsfz0NzYPPoOh3RJFSrlKg6iaodIZqVlnyC5kQy6SphKNxokp4yMkbSVKwIinYD7rd4xl/c9td+Ou+Gx4CubjKTwRXfw69Ik/IOvKMKYuQZ/4Pb7S8XhLZkLQAHIlksaL4reAIu69q9cYD32+nTmD0+qdrNCWXPv6epweP1e8uhaAtHiZrCTILpbIKQ0gaXMx9XgHrfkgAN7iqXgKTgaEEVmrcxGQ7CjeuoZZq7Hzaoa+1qLOXXCKSAw25qNP/BFv8Qlc8tIaPr95KkMzrR0aNtjk36DT6WTv3r01Xx84cIBff/0Vm81G9+7dueeee8jOzub1118H4F//+he9evViyJAhuN1uXnzxRb777ju++uqr1vspVH5bBHxQtAf8XrDWM76tCZRU1u2WC9ABOzNRkGr1XUtR+sa1+EmXSugmFZEpFZLpLsL94YvEuHLF+I1oaPQiPTahJ6QMFCbZpL5NNoOGnUutX12OU5i3/rcxcmE6Eo+dEA+L6z6faJJJs3RQZ53WACc+CB9fK8zaK56EmXc3Od34gR/KWLbXxbsLOveIqdZC1QyVVsXnEumyWoO4dnUQkTTj2CaLzkP1kqD+I4qcHrbllDG8W4QNo9bAWQDbPoEdn4m0KBCbU4PP5FXPifxlfdW13SM07sUmaEY0BiZqSTB1wj8XWy8YdzWsfpY/ad/gp+BgjkRJArvtKztL9rh58Zhxr8cjql6odFn8XjHaOIJZFiJrRvMyYZtPLJWcqvmZszQrGS/vCnttXbA/XwfG8G1wFHuVTKo1Y6i0P8IntZNm1ENtRfOK6Uu88mvTNOOh0wdzxtLwBpPp3Q1Y9J1QMxrgb/4LGCfvYoS8H8MPD3Pv4b/yz7nH/zpD1QyV45UCR3Tj00hpL0/r/02mVExAkXg5cDL/8Z+Ng/Y3+0sEUdDUfDVW2hlWcJdQEHUIBdkZZM96FwPTapmpZFnopawDrRZkk1jftfIG+bF6W+FTeG1z4zXj7bMS0QXNLFxUGfb8gkGmLt8Eff5XGi4/nMODp1ph1r1irWY/DD89BTPu7OjTa1VUzVA5XqlPM1qbOJz8WfcGCzQrADiqJHG/70q+D45sxLtD18tAPVtUHb3OCCjhqmF3K7y9tTLK0XX5YGEKFZUWLlkS/p4bx1rQdnHT0BkflXHrKB23G/eAwQpmG8y8B5bdCcsfgWHnRDagdUFUzVDpspRlw9I7xOMZd0LPqeJxwAfFe/Fn/0oAiXxCe8zHGmY7EgWZ1cHBrA4O5s/+Kxgl7WWyZhsT5e0MlA4TJ1XSXSqkO4WhNwWBncd8kCSLWnfyIOg+EXpMFlMVIgVvKYqonbvLREiLyy4a4g0WMdlWjm6h8QXDv86rCPLhDlejf96PzkvnaGks134Z/p4HpluRu+CE0pcC87lc+yW95HzueO4fDJx3HddM793Rp9XmqJqhcrxS4HCDXIlsyEHSeNGYD2BIXAVAwJUpkk997VizVPT4SifjKx2PLn4D+sTvkfUlGJKXo0v4BV/JdLz2YciGXNDpUfyxKIFYlIAJBQ25ZW7WHChhUp/W8ZU1FY8/vA6RZw+SZxePNZadGNM/QNZWoAQMuHMX4ncMDTv+yQuzKLEP44FFe8Oef+ai0Z16qrWudhdkIAZP/mmYMt9Fn/QdPscwFG8yp/13JY+dPYzzx3fvsPNssmF23bp1zJoVSsKqjgG/7LLLePXVV8nNzeXw4cM1r3u9Xu644w6ys7Mxm80MHz6cb775JuwzVFSahP0wOPJC3XGtgM0sjFc+RUM2ySTg6FSLhdok4KCHlE9fOYeh0gEGSEfIkIpJk0rQSMeUA+y1HsekQHyWMMfGdw89tqSIRUQLWX7Qjc0kMyJVT0ykuQxN4IwBJnZFeP4PkzogXbY2Mckw+8+w5A7Y8yWkD4OBp0Y81GaUeWZ+Aud/XHfc1ep2Gs/eGVA1Q6VVKT0kunzj6o5Rbk+qNcOjaMklCRvlnVYzJJSac8tTEkilFBcG9PjQSuE7O5FMXS1CUSB7PWxfBIdWgVL1/eKy2JZ+JlL/uQxOsyD/6gTqaexoJjeP62DNqI+h58Chn7DkinSWR2Lv545JVq5dUlrn0G8OtF8RrCNR9UKlSxLwi1Fv9iMQmxZxM71aM9yKjjwSSaSMhhoZWgOJIBPlHSzU/MDJ8hpMUugavzbYnyWBiSwLjA8rltSmQIknGTuVGDHiRdPWmlEPS/e46GfT0i9RR0wrmFqHZcYBJWHPXTWqbod2Z2VadwNnDDDxh6/t+NBys+8WlujvZZy8m5/3vQn8vqNPsc1RNUPleCUlNnLj8lnyCh7TvYhB8nEgmModvhvYoPRv57MLcezaZ5fSjWTsVGDChBuNpBBURNasgkyelMTAuLafEKIoCp/tdjE8VU+veC0xupbp7eQsA4qiB8LNTxcO7TqacfZAE0NTdPzfj3XXW6/ulHhw8j5hcjrpYfjwSljxBIy4QOwZHieomqFyvBJNM1qbufJa/qJ7mWSpjKAi8VrgJP7pP5cKGpdGXbta4UdLiRJLAg4qMGLGg3xMPaO91hlBReHTnS7GZerJsmqxtLCeMTpdT6VPy7GacXr/rpPafcMYCwatxL9+qTtG/D8b/dw+oirIJn0EjL0S1r4IRbvhx3/ASX/pgDNufVTNUOmSBIOw6CZh/MwYDdOqjLN+j2jwth/BnJBKOWU4FSNFxJN6zJ5IZ8GIBzcGflEG8Yt/UNWzCinYyZIKSJTKsUkOdIgU13PGZDGiX08wJ4IlVUxO1UXRR0URgSzeCmGUdRaI//s94j31TLfwBxU+2eliapaB9FgNFn3L1hmDU3QkxViBcMPs7F5dZ4rFn6dbOVIe4JVfK6jEyAv+U7lH9w63aD/hvB9O+E0YZlXNUDleiTF6kY05ENShjdmC3vYLAN7SCXjyTwOlo1JNtfjs4/HZx6CN+xVD0rfCOJvyBTrbj/jsY/A5+yDJHiSdHSloQPHHoQTM5JVXQoTpQW2B1x/k041HGdcnBjTlGHTgO3YAt+TFkLoEfYL43Qbc6biOXoTiCw/CiDfLzO47jFx7EAg3zI7r2bmDlmon+j594Wg+3WRlZfkGtJbdGNMW4Tp8FSDx2s+HupZhdubMmShK9EycV199NezrO++8kzvvPL4601U6kMoSkS5oTqi3u6upDMmII8liYKfDiBMzTsxoadkY6JaixU9/6ShD5QMMk/bTX86mj5RDkhTdVFSpGDiqJJGtJJGtJDJt7Ggyeg7gqJRGr+SGjUO+gMK/fnEwvYeBCZmNj/jfnO/lis/EAuvgrRktHpskGfT0funvzHmrAK9Gx5kDTFw3xsLAxE4QK54xCsZdA2ueh5/+K762ZtY5LIhS7+iMzUfLGJIR1yEjptoTVTNUWo3KEtEwYU5s9eShplKtGdsc5hrN0HDs3W7T0UhVaRutiFIr+baIBIpIAITGDORw2LE2sx6PXyHXGaBnfMPXW7df4d+/ODipj5FRabUSfz0O2P2lMMqWHQk9nzEKhi1kJaO4eFEp/FrOwVstLSpkezU6er/0d/H4M/HcNaNiOGewmf62TqAZ0ZA1MP2PeN69gumaLaz0r6Jf4ulRD/8taIaqFypdjmAQivdA6UGRPBEprYKQZmxxWGo0QyYY8dimoJPrJloAmHGzQPMjV2i+oLecV/P87mAm7wdmsjgwkbxGbA4VYKOgykxrwEs/joa9bjPrcfsVCioCdI9r+Hrr8AT57zonp/UTxp3GsnSPixuXlRKjk9h2Q3qzixJejY7rTvgDX1+UgmQIadbdU2I5oaeR/omNP6eORlEUBtY63yNKKvf4ruFp/X+4SbOIPVsn03vweFUzaqFqhkpXYXwvG+lxxlojthVu1XzC7boPAfgqMIbbfDc22qRUjUEDnpYvV2pQjpmukUMKOaQAQod6kxP2us2sp8IbpNQdpJu1Yc0odQV5dr2DcwaZm3R9fmdbJfd+V0a6RebnK9NaXMgGwtI6Hp8Tz4hUHX1tjTun6r2t6scdgaLAkOTo57sjt5z+2p1o+p8MPaaIRsev/wQLX22/k2xjVM1QOV6pqxmtg1mvodIbIJEyHtK9xqma1QDsDWZwp+/aJjds+Ai/BuWQTA6iicJCJT3JC3vdZtZT7gni9CpkxDY8aa6gIsCLG51cMDSGXo3Yy6rmhQ1OHlvloL9Ny1cXpxDTCppRO338ldNt9IjTRqwPdAZ9iEQQGF7PWm2z3ciQQDYaY5yYUHjSI/D2Qlj9HIy5AhL7tN/JthGqZqh0Sda9BPuXg9YEZ78g9qd8bsjfJqYhxaYx2KojyXKIg474mr2p1sCik3D6Wq+g4SZSXVqigASKFCuKIoWtReaPmEhZuhW3P0CqtcpsqigQ8Ir//J6QSdZVSk6pk1c2e7h0EGQl1G+Src0/f3bw3HonY9P1fLgwqcWNeQCmWpqx+PxkYvVSo/bXjqVFmhIMAIoI1WpisFZQgWG11hlvBOZwrXYxPeV8TvQv5+d9Yxnfy9ZhI9DbA1UzVI5HHF4HFmshCZYgFdqf0MVtAsCdfzK+kum0JAgk1qDF4WkN/5UGf9kY/GUj0Vo3YUj6DtlQhCFpBfr49XhLpuEtG4asLxGps4oWJ0kcdUj4fUZMGjOpcQ3r4KHiCt5cfYirp/UOaUwDuP1u7vlkM5+sL2REDy23ztdi0ks43aFrhcZ0EGP6h8iGIgC8xVPxFM4Fpe59uC8gYdaZ0cqhaUkr7hQm++TYxnu5GoNkNNL7889qHrcmQUVhVFYq33x7JjG9n0Absxdt7Db8jqE43T4CQaXD9KITOwlUVI4h4BNF6aAP9K07ZlIjw7XTe3HrkoKa5/yt8M/j4K0ZbCnwctq7RXVem9nDwIUDZT7d7WHpgSBWKhgn72SivINx8i4GSYcxSL6In3tUSWJfMIO9Sib7lAz2K+nsC6Zjwo2WIDGSh0SLgbNGjqXfc7mAg1vGKQxK0jG/X/TCyltbK3h6nZOn1zk5eGtGo3/OZXtDG3NHy/14W+j4kmQZQ69u5MbLKEHxuxqU1ImK2CPOgyOrIXcTLH8UTvu3MD/VQlHAUM+Ivns/2UKSxcC103sxuc/xPzZVRaVFBANQcgCCftC3/8jRY6nWjBuWhDqxAzS8id8Q+27JYPVRT8Rk6nMGmTipO3yw08PXh1putPKjpVwxo8OPWfKSaDHQPSmWAc/kAnDHxFgGJumY0zv6TfGz6xw8u97Js+udHLwlXXSs71oKe78Ff5Uu6MzQfy4MPkOMQwI+/TqUolpQEWiRSViRZAy9Mqoei8L85CwDA7qC8SmuG0/5z+QPug+4wfsa7sAJUQ9VNUNFpZOhKFB6AIr3giVJjJGOQrVmXLOkrOa5IC13Mu65OYPlB901TWsplHKl9gsu0HxLnCRSjSolE5/4JvN+YAablD40d0PLg54yJQYDXkySj0SLgYyEWAZWacY9U6wMSNQys2d0zfjnzw5e21zB8+ubts54Z5v4WSp8CiWuAHpN834GRZI5bE2r0YxqxmUYupRZFkRKl/GYpeqS4ERODKznLM0qTCv/zg1rbuPyGQNUzVBR6WJoZIkHThvM9W9uABTu1r7L9drPAfiv/wwe9y+sY1ZtDLtuyuDz3S5u+aLuNIP7p1rpZvbx9nY3Px5tebG7EiN2JQYTXoxVmpEcZ2HIc8IQ9adpVgYl6ZicFX1j/88/lPH5bhcvbqxg/y2N14zXN4sCQq4zSLkniKGZmhGN4SmNN8tCaG+rI1Gg3lHgd36Rw+CYXZw2M8DUeY/BCzNg2yciObDX9PY7URUVlSYTrhmtx/aH5vLTZy8wYMPDJEoO/Mg87z+V//jP5rFpem5QKnhtn5mVuS2/xjoxU6pYiMGNQfKTaDEQb4lh+PNCM/5vRhyDkrWMy4iuGXd8ZWfFEQ8f7XCx/pq0Rn/vF9YLzdhd4sflC9a7j99Yao/RHpioIz2K4bcz6ENEFAVfMPq9wL2LdpAVE+SqCRWMnRQH/eZAn9mw71vY/QVMuqkdT1ZFRQWAsmz45kHxeM5DkNQv3CxrTQdZiwaxN/X9ktZN8d56QzovbXTy8AoR9NQ3QcveUmGGev2MRMorKnlru5ufc1q+zgiioTdHicGDJEGcxUyy5zAj/k/UBP4y08rQRBiZpEAgAEGv8BWggKQBrYFrvgmwrUhiea6Wby5pfNLgc+udAKzL9eINKK2iGaZaptusOA1xhubtFTZJU4J+8DrBU1H9ZhFOowRFMIBWBwYr6BrRoKmIvbpqKjHyvP9U7tW9w7XKR8z+3ySS4yw8cNpg5g1tvYnBKioqbUelr5LdJbtx+srp3WsrO8s3oSgy7pxz8ZePbPHnb3loLk99u4fHv94NwMiseH49Ygfg85unsj23nHfXHGZj1XMNo8FfPhp/+Qi01s0icdZQhCHlS/S2FXjtY/E7BpNoMdAjScu3e3bwwPtCB2+bm8KorCRGdU/CqDWik+vu9Vz4v1/Itrv49YidD66fHPEM/EE/Lr+LCl8Fpe5SSt2lfLJe7L1tOuQn2ZyJQZsDBEGuxJDyBfqENQAEfXG4cxYSqOwb9Sd0VyWm2GJC9ae0OCO6NkjKkGQZQ79+rf65IPam3F4FxWfDWzIdQ9J3GFKW4ncO4EhpkKl/+67D9EI1zKp0HeyHwZEP1sZvlh9LvjPAW1sruGBITJ0Ni8l9khjb088XB6MbkfrbtOwuaVrnw7AUPWadRGXVjeNzpyTw7tZK/nGileSKfZyUtoqt2SsYLB2qM4KoXDGzNdiTLUovdgR7sFfJYL+SQSXRi9Fa/AziMBdP6smsN/Jrnn9qrbihXxqvZXBV11eFN8gVn5UwPlPPHyZZ2V8a+tmOlPnJitDNFlQU9pT4sepl/vFzOZePiKn52QCmvlpAbAs6sgcmhb7nD5elsrnAy0n1GLY6BEmGmXfDh1dB/lbY/C6MvCjsEJtJbrA4U+z08OjSndwzf6BazFZRqQ9HHjhywZLSrt/2aLmf97dXcunwGJLMdTVjcIafn3Oia0afBC37SpumGRO7hRcB/j03ns92ufjT9DjiDDInDYCe/8mJ8u6mcZg09PgYwBEunNiTcS+GUj0eXy3Gr628PKUmBarMHeSyRcXM6W3kpnGx7Cr2k0wpZ2tW4n9vFdryWom1Cb1gyJkEep/I3go9RkniX1+Wcu0YS5hmjH8pH20L7uvHZ4QWCSsuS2FnsZ+ZPVq3q64teSFwKmdqVtFXzsG1+UXgwqjHqpqhotKJKDsKhbvAlABacZ960O7n452VXDnSQrwx/MI2uU8SPWw+tpdELxBkxmrIdjQt/m9WTyPJ2LlB+xkXab6tabYLxmYiD1uAecA87nvW3rSfLQpHSMWIh35kc/6Enox9KaQZj64ShZFN16YRV/WzF1YGuGJRCWcPMnHlSAtbC0NFmYKKACkxdYvHvoDCAbsfWYKn1zq5eXwslbVidEf/L7/Oe5rCCT1D+rD80hQO2v2MSe88iU6NxaqXiVTHfsB3GRPlHXSTizjN/TmPLlVUzVBR6aTsyXeweHMu10zvjcUQvu8yb2g6qbEGrnC9wvXaxQD8n+8SXg6cXHOM1SBT7mlcE131rsRp/U1hhtn/mxHHz0c9XDoiBr1GYt7A1ltnHCWVGFz0JpeF43sy6ZXQ9bu6mL735vQaI2e2w8/Vn5dw2fAYzh8aw8Y8oRlBBUpcAWymuprhDSgcLvPjCcBLG53cNjGWSm/o4lhttmouZwwIFYm/viiZvIoA/bpYgwWARS/h9ke//1CQ2Vdh4tWlP+A76wxmVY/YXnYXXLcCNGrpQEWlo9mWU8bX2/O5fkYfjLrw6+G8oekYdXJNIbWlDIwLwIdXMHnbJyDBjmB3/iJfz/SRvZhbrHD60EQ0ugzmjG49zcgmhVgq6Ek+C8b1ZPrroUCTP/8gmg5rN9ztL/Vz49ISbhhr4YwBZtbkeAAodgUp8wQjGo7cfoVsR4ByT5A3Nldw12QrFbV+ZyNeyMPbgiT2i4eFmvuXXpBMmScY1SzbmTHr5LA9u0gcrZB5+ru9XK+NYcLUOXDy36CyGLpPbKezVFFRCWPZncIAmTURxl3DrweLWPHrdq7va0cXnx42qVXsDbTOtRtgTLq4N75yZEzNPb5OA7dNiOWow8+07nokycCpg1tPM/bTjWRKGcwhzh2Wwomvhz73/u/FORy8NkYYPzVmdpTA77+yc9vEWOb1MbGtSBy/t9RPhTdIjL6uZrh8QfIqghRUBHh/eyX3TbWGvT755XyKXI1fix17Vb1+jAUQTW2fnZeEN6A02yzbaPxuqCwVxlhjnDBWG6ygM4p6dzAAvkpwFkBFkZj2aEqoN7zGoJXqaMYbgTlco11CD7mAszQr+bBsJje8uYFnLx6tmmZVVDohaw+WsOZACTfM6INf8bHXvpdSTymL9i1iZ/kaYZbNvgC/Y1iLv9cJA0V9/+YT+tYYZq0mHdfP6EOl18+wbnEM6xbHeeOy6Hn3kiZ+ugZ/+Sj85cPDjbNJP6JPWMvotBOxVwypMcsCPPllAVDAazdZMGqMmHQmDhdqeGJZHr+f051p/ZPItruqfk+l5DtLMGhl/Ioff9CPx+/B6XNSWFFOgcNNnt3H2r0KV8+KDzuzG17OJrvUgy5+PfrkL5G1omHBax+LJ38+BEPXWUkSOS21uX2OmO4Ra9Tx0Q2T0Mpym5hl2xq9RqaiasHlLZqJLm4dsr4EvW0l3pKp5JW5Okwv1F0vla5BZQmU7AdTfJ0kz6Zw07JS1uV6+Wq/my8uTKHCG+Sub+2c3NfEKf1MxJgMgCvq+88aaOJvPzlqvn77rEQmddPjCcCLG53882cHY9J1xBlk7q11E20zyVT6xGiDeZb9zEv8Cj5ZKTYTgKFV17V9wXR+CQ5idXAQm5Q+HFZSmpwe4kfLPfMH8vZumYKKugm1q454uO2rUtx+hclZBtbkeFmT4+UPk6xItVKn5r1dyLYbwi9Ipa4gV35ezMa80Ocu3u3i3CHhN80Ob+O6Bd9bkMjDP5aztVB83jcXJ5MRq0Hx+Sl661N0wNyLzgwbgddpiE2HybfAD3+Dda9Cz2kQ353Xz7Dx+GoHf5sdj6Gev6peRYNeCiABL/x4gAm9ko7rsakqKs3G5xbXf50x6rjrtuKyRSXsK/WzNsfLO2cnYXcHue87O2cPMjO7l5E4kx6IPvruxN5G9lV1HwMsOi+J4Sk6Kn0KT69z8sw6J5O66TFrJf40Pa7mOL2Gmo36MwaYOWNA+DX2s/OSOP29usnlzcGLjnvmD+TZLVLE0d6rs7089Ukxeo3EgEQtv+b72Jbv4qakjdxY/BmDDRvQSkEoR5jGes+AAadA2jAKXUEu+aiYncWhVMWfjnoYmRZuTvI3sq6z+Pxkrvy8mIIK8YavL0qmmxkKXxUjartddCZZcZ2swaIB/ntKCq+svpZHKh7EuGcJg6XJbFd61jnOp2jQqZqhotI5qCiCwp0ibUEfU/P0gg+KKHYF2Vvi55n5NgoqAjzwfRkXDjMzrbsRi1EHRE/ymNRNz4c7QuuQry9Kpl+iDocnyOOrHby6qYJpWQZMOok/TbeCxwEb3+BHw6eYpKrPTRsGIy5A7j6xZpTa+ws0nPtR3eTy5uDGwD3zB/L3dZHv9dfmeLl/uZ1Es4ZEk8zWQh9bC31cOdISts64dFExX1wY3gST7wyw8MMiDpeHKtU7in0Ym5kOuPzSFE54vaCmMPHluQlYly6h8FVIuuhMesVrmzSytTPw77nxvLqpgj9Nj8McYfxfORbu9F3LG/rHOE27ml+CA3nhR4OqGSoqnZC5//qRoAKFTg9/PWsYR0sreWTJDq6Y0ovxvWxcKi2uMcve57uStwIn1rzXooNxyQG+PRq6Dqy4LIWsOC1l7iCPrirn3W2VzOppwKSV+HOtdUZtLh0Rw6UjYsKee+V0W01yeUupwMQ98wdy/6rIDqR1OV5uXFpKj3gNErCjyM/d35Vx/tCYsDz0m5aV8s7Z4cb/bIefM94roqgytJDIdTZ/csWaq1IZ/1LI1PvFhcn0qTVCu1+irllm2eq9LRDaI+ka0B0lKEbG+t1V41ERKfY6U6NGxdbm77PjeW97BbdNiMVTjwnMr8i4JT0+Rcv7X/7I9JtvR7P1IzE9ZMOrMO7qJn1fFRWV1ueU/6wEwBcI8se5A9lX6OSfX+7iuhl9GJkVj6aeffOeiWbizfqa5CaANffNJiXWSLHTw0Ofb+ezTTnMHpjCiMBWbrL/A7Zlg6ThX74zeNp/JrEGDW+Nt9W5Dv13XgI3R0gubw4OYrhn/kBu/S5y0/m6HA+XLSpheKqOgoog+0r9/O5LO2cMMFfVDYQA3PWNnedOsYW990iZn7lvF4aZejwBJawBrSlm2W3Xp9WkpmskWHxBMv1soev74OSG9aLJ+tDGPDQjjiV7XFw5MoY8Z/RfRkCR0UhBirHy8aotjB3UF03qIGG8UlFRaX92LoWdi4Up9tQnAYUzn/sFAJPGytVjtOwo8vGfXxzcMj62wevT6DQdxa4gh8pC14HqxuiCigB/+r6ML/e5Oam3kViDxP1TxTqjdv1WAX43IbbOZz92Qhx3f1dW5/nmUEgC181P5bJlkffYfi4ycumnxUztbmBLgY+iyiDXLynl4K3hqal/WVHOo7Pjw547YPczq1bjBoDpmDTZxpplAfbfkk6vp8R0prQYmVfOSKR/Lc0YntryJu4GNaWyGPxesGaKxGGTLUpTnE0c4y4T4TUl+0EJgCH8z/PuKbH8cMjDwsFmtheGexBcGHnefxr3697iVs3HfBKYSgAtD32+nTmD0zps3LaKikpkFj73MwDJsXqG965k9YE83t32JYXy90hIuLLPj2qWnTM4lZ/3FeP0hO7ftz00lxiDltwyF3d/tIUfdhcyb0ga8WYd98wfBByjGYrC3ScPrPPZ95w8kEeX7WzGTxTZOPtT4eesyv0WfeIUfPYJKAFLzTv25Rj4v0+KGN9Xy/r9fgJBuPOD3Tx99eGwT35oyTrOmahFqVp3SJJEQZnE3W9Vhh2XmVD7uqiQ79uEufeXaAxCWwKeFDy5ZxFw9apz9m/cHMfFTwmtHJYZx9/PGc6A1NA1eEwPW533tCaK10vR8y8AkHTdtUj6lmvU72b3Y+MRO7MHpWDQyry08gAoejwF8zFlvos+6Tt8ZaNRgjJSMLZD9KJrVYhUfpsE/FC8T/w/pmXpOOtyxQ30ziI/+0r9fHfAzeI94r9le10s3lOP8amXkStGWthb6mdmDyNj0vVkVHUKG7Vw87hYLhkeg1Uv1TF4JihlnK75hgWaFbAoN/SCzgRZE6D7ZIKZo3nkW4nvDnpa9DMCvLVbZkmUn+WRleU1jw+VhS7iiqJQ+7QrInQTn/hmAcXHLAa8QQg0s4ldK0v0StDWGGarR9sFXV6KXv8YgMTzTu3wTaOo9J8H+5fDkTWw8l9wyuNM72Fkeo+QYevZOSZu+LquCXs3PRjKfhSgyOlhW04Zw7tFLmSpqPymKc8GVynEZbb7t65Oh/35qJc9xT4W73GxZK+bJXvdnDXAxBf7omvGBUPN3DLOQoEzwLy+Rkak6kmzCM2I0UvcOdnKdaMtWA11NUMjReo9DjE8Vc/+W9JZ+GER63PrNkY0lU8PaFlxuDLia3/42g6ATJAk+yb+qv2ZkzVr4GsnwwEkWBfsz/uBGfz98rPCuo6nvJJfp+iQXxEkUM94t/rQacRYp4IKoeX9EnUEXW4OdgW9iMJJfUyc1GcmfDMLaf9yXk95m7H593Ds2PQ9ZDGYg6pmqKh0NB4HFOwQhhZTfNhL1ffIKw572Fnk48MdlSzb52bZPjfnDDKxJie6Wfb3E2K5dLgZXxDO6G9iaIquJoE11iDz4Iw4bpsQK9JbgwHYtRTW/A885Zgk2BDsy+P+hbx12klwjKaMzzSw/5Z05rxZ2OTU80h8cUTLr/mRNePqxcJklVdRd3FQ+7R2FoWfh6IoTHi5bnrsnmJ/owrOkdDKMCBJy84iPxoJ+sVJ7HqznfVCUcDvqjI/eUIt4hJVBqgYsR5sZHPgsU00/z7ByO++C78XWREczpv+2Vys/Zbf6T7mJmc3VTNUVDoh1bfDq/cXsy2njLd+OcyyrXks25rHI312cJP3FQAe9V0QZpYFuGuclpMG2HhklYOFg00MTNKRXDUNI84o89jseO6ZYq1J/G4Ks3oa2XdzOhNeanxqUn38kKtjvz2y/p3/sWjmKMmr+31q74v/fDT8/UFFYcorBRzLgRZonE6WSDbLFFYGSTbLDExqnUZNxe9v3N6W3wNuO/h9ohlHbxEaQVDce7jsYnyqKT6sWac+zh1iDmtuf2KWkduX112/HiCDfhylBAvGyhI27zvCqJn3wobXIWVw035gFRWVNmXdwVK2HC3jxZX7azRj4ZhuNUk9kfjdif0Y28PG37/cxYXju9M/1UKiRRhfEy0G/nPBKB4+dQBxa/4JK54AFIjvAdP/yL/eFwVZSZYjmvZP7W/i5L5G+j+d2+yGhdr8UqinyBVZM875UGjGsZoA4bsnx+7TefwK016rqxlHywONbt4+Fq0soZVF83c/m5ZBzdCMRutDO3HZiBguq2qiiTXI/GOmkT9+X1czDpFGb3IIIrO3wsjunVsZZElq94lgKioqgMcJS/8oHk+6GVIGiTp6FZsLg2zK9/LUGgffHPCwbJ+bBYNMUT5McN0YC31tOp5cLSaL9rXpatYUKTEanj/FRpk7WO86I9ruxvlDY1g42Eyf/+ZGOaJpbLYb8AUja8YFVeuM5Q3U2r/Y5+bR2aGvnd5gHbMsUG8jQUPUrvv0SmieZjREVE0J+sGZDzoLZAyB2LSG958kSaw5jHEiHCV/m2jIr7UGuX5MLNePEQau0el6Hpth5O4fQprxZuBErtMuJksu4izNSj4IzCS3zM2aAyVM6pPYqj+7iopK67D2UA72YDlvbVxNifErAHpwAVscw6O+5/LJPbl9Tn+eXr6Xq6b2ok+KhZiqCUrpcSZeu3I8ZZU+4szRr3vRAvOum9GHq6f1ps+9S5v5E1UbZ0egtW4ipfv3OMjHkPI1+uTv8JcPwWefQKCyN//3idCMNXtDe0puH6RZ0oCDNc9tPaxw4+yUmnMucfq5+62jdb5zWWUAJD9a66/obSvQGEXNQ/Gb8RTPwlcyiWgWzUG2QcBqAHokmhmUbo14XFuh+P0UPf00AIlXXdkqhtnbqhJyAQy1xs36y0cQSPgZjfkQ+uSv8eTPQyHQIXrRtVwFKr9Nyo6AM0/czLUis98Iv/GtzywLYsPeqJV4fE5C1GPqjE0oOQBbPmBR4GtkXZWpSWuEXtOh72zIGCWKpYAMPDY7wKubKli0y9Xkkay1iWaWrY8Vhz117pWPlPvJsmpZedjDxZ9GT6byH5sP3kj0GvjzdCvBoMKFwxq36d+pkCSYcht8cDnkbIA9X0P/k8IOMWmiF20UJbQ+KamMbqJQUfnN4i6H0oNikS51bDTanLcKw77+ZFf0NHIQmmHRyzw5tx7NiLK59NwpCVyzuISHZ0Y3t8iSxFPzbLyxuYLXNlc0ODKtPt7fHtn4BAojpX2crvmJUzSrSZXsNa/4DDaW62fwWNEU9itiNN4tbj1Zevh6v5trFkdPpmpuUUInS/xzTgIP/1jGlaO6oGbUx4Tr4NBKksq2Mkdez9fBsWEvB5FVzVBR6Wj8XijcJZIWrBlRD3N4Fea9Ha4ZtZNjIyFLYDNp+HdDmlGwE1Y+AUVibBEJPVnX/UouXNeHf55ki7rxLUsSL59m473tlTyzzhn2ml4WDXCN5fXN0TQjOnuKfXWKJkfL/XSzavlst4tboyRTmXUS/mY2Wehliefm2/jbqnJuGGsBWmdMbaMI+kWzj88DepNI44jrDlo9IEHAJ4xR7jJwlYjCgzG+0cbZauL0kdeLj/gv4gT5VzLkYq7QfklJ5YgW/0gqKiptw/7CiprkQIDR0m4WHv0rSPCyfx7PB06t8x6tMYbUWC3/mdf0dcYTc+K5+zs7z5wcPZVCI0u8uyCRj3e66mhGnEGizNP46/LzGyoafWw1B+3+OpqR7fCTGavlvW0V3PVt5GSqWINMqbt513qdBt46K5EnVjv4fYRErDbD7xGjUUGYjawZYu2rq2VmCAbBUwaOfLE/6rZDTHLNXmJjsRkia4YH8TkKMiWKBXfhfph6Joy7qkUTvlRUVFqfXw6UcNp/V4Y998H6ukXa2hi1GrJsZp66YFTkA+xHiPvgcsheJ74efCYMOwckmYdnWHh4pZOn6tEbjSyx+IJkluxx8d+14ZoRqwdHE7Yu/r3G2fBBx3C03F/nFjrXESA9VsOrm5w8+EN5xPdZDXI9ber1o9PAp+cm8fRaJ3dOad8idhi1m/MC3lChQaMHnTlcS5pIsjGyZlQSCgnxoMfurgq6McZXrXVUVFTajR8eg/KjEN8dZtwF5TmhvSJg6V4Xn+0O34v6qIG9KaNWok+Clv/Ws1aIts64d6qVJ1Y7+OsJ8VHfq5ElPjsvia/3u3mqhZrx2CpHwwcdQ/4xxtdKX5A8Z4A0i4Zn1znCpsvWpjmNiLX5aGESL210hk2lbXOqm+6smZDcv05KbINIEiT0FE37hTsBKSwopTZppvDfqxsDL/hP4T7d29yi+YSPA9MIoKHA0XTfgoqKSvuwZFMxnu27MWV9hAR4Ck9gS1F0syyAUSczKN3Kfy8cHfWYaGbZ383uxws/7ufe+XXTZavRyBIfXj+JFXuK+Pe3e8JeizVqcbgb0zQt4y8fRc5WYZzV235CYzqCLm4zurjNBL0J+B1D8ZUPI+juBrWmfpdVhl/bXD6F0ooANouWN1aW8u7Px+5NKcjGoxxRthHTdy2yVuyHKQED3tJJeItnQrD+CamJpkTevmYCb60+zJ9PO/4amAudtRtZJNwF84np+Sy6uPX4iqejBH0o/oR21wvVMKvSuXGXidh/Q6wYK9FCjFoJt7952yETuzVh0V+yH9a+BIdWAeLyGkweiDz4DOg1I+qNZUqMhjsnW9lf6g8zzN4yzlJnAXHzOEudjaiWcOmiEs4YEL6RMu3VAi4ZZub7Q/V34n2wvf6FVjREioeGp+e3bYR4m2JNhzGXipSv1U9D9wmi+64Km1lPIkUUU9f4to9M+ijZSJI4TkVFpRaKAvbDYuPX3PWuESNbME5nRg8j265PR9fAGOqMWA13TbGyLtfL2lrJhQ/PjONP34ffrF8yzMwbWxo2OckEGS3tZo5mPSfLa+guh0xfdiWGZYHxfB6cxGr3YMZmGNmvhL7vtFcLuG6MhTc2118Yb0hToqHTiJ/52VO63t+HBolNg2Hnwq9v8Sftm3zvHYnvmNv0A6TTS8lVNUNFpSNQFCjZJwoQ1vQmGxsbYkBiA+ucgBfWvwqb3hXptvoYGHMFDDmTsbKWbWOVBjWjR7yWOydb+WKvi/320DrjPycncP2ScMPqqf2MDTYTNoU5bxXS3Rpuupn6agG3T4zlidXRixwOr8KOouYlBuo0kGrR1mhG0NUOGy3BgBh1F/CJySgp3cCUED0N0FsBFUVQegjKjoIlWTRXNhKbWU8sdhyEf74LI/f6ruRVwz84WbOWfa7dQHILfjAVFZX2IBk7z+j/jV4K8EVgHA/7LyZSRlOPuObvjZ09yMxp/U0NakZfm447J+t4c0sF5bUMsv871ca5H4U3U0/LMrDiSMsnJVVzzgdF2D3hxtcprxRw/1Qrf1kZ2fgEsKekZQmz/RN1dcZ4txmKAhWFQjes6RCXJUajyhGK8bIstMSUINYMpQegLFvskxobX3S3mfXEUE4FdQ1UR5QUsqQCXBiIN2qELpnV9CcVleOBLFvkGgQA+5bDh1eKBi5jHEy9AxJ7V11f4rhkBJw/NLZBzRiUpGNQkq5OneLts5M47d2isOeGpejYUtDySUnVXL6opE4D+eRX8nnkhLioZlkQwSHNRZYkhqboO25vKhioas5zg84oUsljM4RRNuAFT7kwSlUUgTlBvN5EbGY9Rpy4qZssnK0kkSmJP9eYhFShZ2VHxd8dFRWV9qFoL6x+Vjye/08IeESDt9YAiL2P5gRWZMY2v1nq2tEWrhgR06BmDE/VMzxVX6feHUkzsqwajpQ3P1jqWG45plnbExCa8cSc+KhmWWjYaNwQY9L1jElvx3WGI0/4KVKHCkO1ppnrR0kCW2/xmcW7xdJUV/e+wmbWo6MSHyFj3FuBE7lB+xnd5UJOlX9mUXAqKbGN3+9SUVFpX1xKETGZbyNJQXz2UXiL5jT4nvS45jdn3TanPzef0Bedpv6GhLE9bYztaatjmH3r6gmc/t9VYc/Vb6KVqxJnRyEbstElrEFn3YisL0WfuAJ94gqC/hgClT0JVPYm6M7giS+D1DbQFpYHuOy5o9x7Roowy0p+ZH0BsjEHrXk/mpi9yLpyDvnEJTjos+ItmYLPPqFBo2xtJvdJYnKflk1c76wcqwNBVw98jsHoYrejT/4Sd/4pIPnaXS9Uw6xK5yUYEMZTv0t0QbUCRi00quGgFt2tGv4yK45p3etuENTBkQtrX4a93wCKSETsORWGnYucOqTRxfU/T4/D7g6yOlsYkeb3M7Hf7g9Ljr15XCxL9rg4YG+9BcOiCImJb2ypJEbXuqaAahpaPHUZhp8He74RhYv1r8KU39W8NCQjjiGW3ax2mvES3knjxkAlRnpYFIZkqGNSVVTCqCwRG65dzCw7MlXHnZOtTGpKk0UEmnJ9/PuJ8dz9rZ1fqjRjZg8D07sb+LFWAeDuKVaW7nXXjAyvjREP0+QtzJHXc4JmI0lSqKhQoRj4KjiWzwOTWBEcHmbijDRe/Pn1rdfIcSx6+TjRjGiMvAhl11K6uwq4VPMVLwXmh71ciQkfOjIssqoZKirtTXm2SM6xJLdKE181J/Q0cN0YCxMy61lnFO2G5X8ViesAfU+ESTcJ40wVTdGMZ+bb+PP3ZTXX8DHpekan6diQFypcPzIrnq/251HPdNcmczhCkaM+s2xL0bW3Zngc4CoTRllbL5H+11Aynz5G/GdJEZNJSg9Upc027ho/JCOOkZY9rHDWNeR+r4xiqX8c87Vr6b3jWRgyNsInqKiodBZ0+Hla/2/SpFL2BDO5w3c9CuGFg3MGmbhgaAxj0ttvnfHGmYn8349lrM8VGjEkWUc/mzbMnPqvufGMeTG/RedUm6II6xWgXrNsS9G1Z5BqwCfSYs0JkNRf6EVjG3FM8WAYIZL8CnYIM20jjVBDMuIYZdnDSmfdolYZFrophSTH6unfu7eY8uXIhbhujf+5VFRUOhXXTe/N/GHpDM2McF8ZDMLKx+G7RwAFUgbDlN8LE4wlJSzBuima8f6CRB5eUV5jiB2UpCPdIpPrDF3XXzzVxoSXW08z9pbWLfQowL3fRU4j7/K4y8Q0LnOSGL9ujBfrh9o6oihibeLIh9L94K1smtYgNGOMZQ+rnHUnq5RiJUMpJjlWz5BMG/h04vvEJDWpkUNFRaUFfHW/mGzTb64IaMrZCH63aMQip8kfd88UK1OyDPS1RR+b3Riats6w8fCP5eyuWlcMTdYRq5dweEUThFaGd85OZOqrBfV9TJOIVMsIKvD7r+yt9j06HEcexCcLbY9phQY4WYakvqAExP5knLHOJMghGXGMi9nLTxXpNc9VYuRF/3zu1L3PrdpPWGs8gfG9uladT0XleMcfrLqPlryYur2JpHETqOyBO3cBkZq3q/n7guEMz4ojI775hlmgQbNsbf536VgeXrydwyUiFGpYZhySJG57ATLjTbx59QRm/fP7Bj8r6MnEk3cWnvxT0Fp2o43dgtayE1lbgWzdhs66DYBdgKWfmWAgBiVgBkUGSeFfm7zE9HUgaSuQpPD9KyWow+8Ygq9sFIGKvoA6tac243vZSI8zklsW8rt5C+aitexAZ92Gt2Q6ibFyu+uFaphV6bw4ckVqgqX10nAsehm7u/7K70un2TDrJC74WKRmDE/VMb1HA072gA82vwcb3hDdfAC9Z8LYKyC+R5PPMyNWw7sLkiiqDFBQEWRQko5/z02gm9XB8+udzOppwKiVWH5pKkfL/aRbNPT5b26Tv09jacmo7/rQdeyE9dZD1sKUW2HxbbB9kRhflSD+3DUyXD+jFwVL9rCTun8XDpDBI9P1NOG+QEXl+CcYFMYgRWnRCLGWotfQoFnojTNt6GSJ86s0Y2SanslZjWiwaEV6xWt5b0ES+c4AZZ4gWXFaXjzNxoM/lPH21kpO7WckRi+z7upUjjoCZMTInPzMWqbKW5ksb2WKvA2TFNowsisxfBccxdeBMSwPjoyYKNERtGshuyPQm5HGXgUr/skt2k94NzCrTgLUftJ5cLpZ1QwVlfbEZYfC3aIQ2YT0z0i8tyARRaFGM0al6aObZRUFtn8KPz8DQZ8wyE67HXpOa9E5DEzS8f45SWQ7/Lj9kGzW8PbZSdz9rZ1Pd7m4YKiZOKPMrhvTyXYESInR0P/ptltntBXt1pgXDICzUCR2pA6B+CzQNLHIpDOJgrfBAgU7RdNQIxqGNDLcMKMneUv2sYesOq8/4L+ME8170ZcfhQ2vw4D5ET5FRUWlM3CP9m3Gy7soV0xc67udCkx8fLoBn9/HeUvFBvyoNH2LzbJNZUSqno8WJnO4zI+iQIxe5tNzk/j9V3a+3u/m2tExJJo1HLglnaOOAHEGmeHP57XrObYGcisnx0fF64TKUqEVSf2jJ5DXhyyL8aiKAoU7ADnqFKvaaGS4cUZPcpccYB91QwmOkMJ90xPQaLVCl4r3i5TZDlyPq6ioNJ7Pb56K2x9g4XM/AzCqewIjsuLrHuitgI+vhZ2LxdcDT4Vh54DBKtYbLbgejs808Pn5yewv9aPTgFaW+OKiFG5eWsqKIx7umBhLqkXD/lvSyXEE0EgSk15pPfNsa2LQiOTBToPfI5JcdRZIGybCZbRR7gkkSRhXjVbRaFG4U0xKiU1ruKGvCo0MN83oSc6Sgxygrmk2h0Tum54k9qYMsWA/KiaEqYZZFZW2Z993sHuZqEnOeRiK94rrQ1zjQ6eWXZhMhTfIOR+KvanR6TqGprTMLNtUpnU38tXFRvaW+IjRyUiSxA+XpXDt4lLW5Xq5b6qVblYt+25OJ68igMuncOKbhQ1/cAfQ2km4LcKcCOkjWvd6LEli/VFRKNLLLSlhL2tkuHlmD7KXHOIQIdPsG4GTuE67mD5yLk+POoLmeA9DUVHpYmQ7sgEFY/onaIx5BP0WXNkXcqx98OvbplNa6ePc58U6Y2zPBHonN32CQUuYMziVOYNT2ZlXjs2sR5Ik1tx7Ipe9vIbtueXcNqc/vZJi2PvIyRQ4PBQ6PJzx9Kr6P1TR43cMxe8YCviRTdkiKdZ8EFmfj6y3I2kr0WjrTm6tvpopASMBdzpBdxZ+Zz8Crp6g1K+nfdMk9ua1je+qs6ORJR44bTA3vLmB6t9A0JuKr2wM+vh1GJK/oI/pair9TmL1se12XqphVqVz4q0UKU46U1hXc0sZmqzjaD03rk+fnMDsXqIQ/sIpCbyxuZL7pzWQ8JO3GX78pxgdDpA+EibeAMkDWny+SWYNSWaxkaGVJe6ZYmXhIBPdrKF/urUftxVtddkOHk96kDEKekyBQ6tg9TNw8t9qXprcJ4k/nwJ//y6bAy4jZYRf5Cf1VkfdqaiEUVEgUm1iOnbswNh0PT8drdt5XM0rp9uY1l1oxlPzEvhgeyW3jGvfhUJtUi0aUi1CM/QaiUdmxXH5iBh6xmvBkYeUvYGs7PWQs4GvDOEjiI4qSXwdGMNXwbGsDQ7A3wlvEbW/hU2VAfNg87vElx3lHuPH/NN9OvZamuFHe9yO41BR6ZT4vVC0q1ZSR3SGJuvYWhh9vOg7ZyfWmGP/cWI8S/a4uHxEFKOM1ynWF/u/F1/3mAIz7mx08mhjyIwNXeeNWoknT4rnhrEW+iSI5yVJapd1RlvRLo15fg84C0SxIKl/y1LxJUmMypO1kLdVGLVN8Q2+bXKfJB46Bf76bS5H3fowzSjEhn7m7fDVn2DTO5AyBDJGNv8cVVRUWo3MeBPZdjHhZ7a8niu1XwBwu+9GDijpfHqmiZEJHohN4+GZFfxwyM2CQQ2bItuK7nEhPYjRyzx/SgJ7Svz0raUZWV1YM9qFikIwGkTaU0LP5o9GhVDROhgQ9ymS1Chj6+Q+STx8Cvzl2zxy3LowzSgnJrTOMNlEgIHLrhpmVVQ6AXqtjLee2dpf/n46A9LEv+f75g9i/aFSThyUUvfA8lx45zzI3STqLeOvg6wJIqxE23qN0r0TQte3OIPMa2fa2FPip59NPC9XrTP8nbgw0KnMspXFYt0R30NMsjA0oXhsSQadUaSSl+eINW0jJ6ZM7pPEI6fA/32bT65bG1bPKMUavjfVXo0nKiq/dQJ++OJe8Xj8taA3Qe4esSchNW4T5IdLU+gRL64Dt0+MZW+Jv92b8mpTO9XWZtLw7oJE9pX66V+lGRpZIjNWi9MbXQc7Gre/g/XMVxF6nDasbZoXdEbhecjZKFLMj9GiyX2SePQUeOCbfPI9Osqx4MDMq4F5/E77MaP2Pw/By0Xzn4qKSodT4i7hkOMQuvg16OI2oigy7uwLUPzhtYdf7p1NqlXUwG+Y2Ydip4deSc1o/G0lBqaFrm/JsQYW3TyFA0UV9EsRdXmtRiYj3tQMX5OWoKsHXlcPKK56SvIg60uRNBVImkqEW0pCCepQArEofvFffWm8kZAUA+Bu8LjjlXlD03n24tE89Pn2qqRZBW/hieisv6KNOcDaw3vJdvZnQMIApHZaY6i7mSqdD0URIyk95aJbthU4ZPfzh2/srI0wcgHgxrEWbpsQG5ZEdFIfEyf1qWdjOOCD9a/Cr28DiujCnnijGJPahv+Ao43F+N14C/9e0/hR2BMz9azOjm4Eay5Wg0S5J1yKesdr2G+vu9Okb6/kp/Zi4g1w5JfQf1kTal6a3CeJj3olsS2njHMXVVA76LjCp2DRH2e/CxWV5hLwQ8lBsYHbig0TTWFviY87vrazKT+y8enOybFcP8YSlkR0Wn8Tp/XvRMVEdxlSzkb6Z6+H7A1inHgtfJKen/0DWBkcysrgMLYrPajvxr5vgjbiqLuWYtZJdVLMbUaZEnfdTTBtZ9xPUYKN3pBsFLIWxlwB3z3MRfrvGTXnQk75PPyQoKK0XwqWispvGUWBkv1ilKW1bqpONTuLfPzha3tUs+xfZsVx0VBz2AJ/4WAzCwdHMT6V58AX94D9EEgamHg9DD2nzYuQkiQxIDHyOuPCoWbe3lq3mzoatcfotSY6GXzHyINE5Oa+Nk+u8DjEOFRbb0jq13omA2uG0JbczSKpthHpg5P7JLGoap1x+scV4S/2nCZGNB74Adb+Dwad0jrnqaKi0iw2H7Vz10dbasyyKZTyD93zALzoP5nTz76Y/yXmIHnKwZIGwCXDY7hkeMcVJCIh16MZZw4w8ekuVzufUV2i6UOHoTWKRuvWmqIly5DYBxQ/FO1ptAlqcp8kPo+mGdVIUtV9R6f6Daqo/OZYe7CEP326NapZ9oVLxjBncGrYOuOa6b25JtLBeVvh7XPF3pAxXkyuSBkskujaeJ0RTTO0ssTMHga+P+Rp0+/fZQn6xVpUb4GMISIhtjl/VoZYSBsu9q4ceWK90cjPmdwniSW9ktiabeeMTxq/HlRRUWkj1r8iJgyYbDDhenEPaIgBrYEVh908sqI86ltfOd3GzB6GMM24dXz7pbc1Fq0cWTMsepkRqbqo9ZqOpEMNs95KcNX6c9e1bDJWvVhSILEv5G8Te1XH1EQm90nii15JbDpq5+xPhWa87J/HVZqlWAp3imTkgeqelIpKR+MNeHl/w05eW30AQ6qYOuEpmEugsk/NMS9cNpQ5A7uHacZd8wa2+7k2hE4j0z+1rpalW410t5k5XNKC+1fFQNCT1oKzi0wgcLyPUm2YeUPTmTM4jTUHSjhcUsFdH23BVzoJfeIKDMnfkOscRbIpmURT+wQOqoZZlc5HRaFIa23FDZsZrxfU+/rlI2KaNrbTfgS+exiKdouv+8+DSTc1rcO3lbltopUzBpg54Y36f9Zqyjxt05E3Nl3PdwfDN7rePCuJxXtcjE7Tsa/Uj8uv4PQqZMRGFgVJr6fnMw/XPO4yxHWDIWfDlvfF+NzMMWEFC40Mw7vFkWBykesM/f5X7i/DKPuxmfUMsSioUqnym8aZJ3TA2vo3oo0hqDQ83ufS4TGdz7DodwtzTfYGyF4vxjHVLnBKshj3nDEaMsegSx1MYonEC+80PMrIqJXIdrRNvMa4DD0/HFMc+fS8JL7c52Zkmo7DZQGKKoNoJDBHiAtsd70IBkRDj7dqoSVJwlSHItKXjPGNHnEXlT6z4Ne3kEr2M6RwGXp5PrWb6Ffss6MEVc1QUWlznAWiiS8mMeq/a7dfYd7b9V9HLzzGLFsveZvhy/vFdSYmCeb8nyhkdzB/PSGeswaaWPhhcYPHtlWDBcCETD0rj4Q3/H1zSQrLD7gZlqojvyLIgVI/NlMb64WrVKQ8pQ4RSU+tnZBhzRSfn79dNA9pGh6NWL3OgHDz05qDdvxpFzH+yDq0pQdg45twwv2te74qKiqNwl7p5fT/hkbCSQR5QvcMNsnJ1mBP/u4/n92pxeAsbZKZpbPxr7kJnDnAxOWflTR47Ak9DXX2j1qLyVl6Vh2jGV9cmMyqIx4GJeko9wTZXOAT0zjaCElW6Pm3m8GUgNRjLMS0IIk8ErIMtj7gcYIzH2Ib9/cmmmZsPGynzO1T1xkqKp2AHLuLhc/9XO8xJw1p5L7ZkbXw5gLwlImJBlN+L1LiGtGY1da8crqNpXvd3LSstMFjLx5m5s0tbWPaHJ+hZ80xQSuLz09iXa6XfjYdlT6FX7I9DGulkeUNrk18lVBRDLHp4s+qpWmBOiMkDwSfS+y7xtY/PaU2GhlGZMUD4b/7jUfslLlUzVBRaTdcpbD8r+LxzLvFnpXfDdYM9pb4uOTT+u+9Z/VsQyNlO/HpuUl8sN3Fnd/aGzz21vEW/tOEgKmmMCpNx8a8kHHX5Vf4eGES2wp9dLOKq+H3Bz1MzmrjWoWvElwlSOmD6fn+eyBJSIbWS4yPiDWzahJFqfBwHINGhtHd46nWjDIsvB44iRu1n2Ff9n/s0E5gfO/Etm9yV1FRicrXO3fz2GeFmHu+g0b24Xf2xVcyLeyYkwb16KCzax1kWeKHP87k1Z8O8tDn2xs8/vwpWt5d1Tb1jBSrhoJa089dvgDvXTuRQ8WVxJt1GHQavtqWx4mDU9vk+zcFyWCg5wfv1zxuSzSyxKQ+iUzqk8hdH23BWzwdXcJqNKYjfLV3PVuPBBidOpyJvZPbXDNUw6xK58LvheL9YtO3lcZ+NWZUQ4yuCf/QDv0E3z0ixhwYYmHaHdB7ZvNPsBXpnaDlwC3pXPxpMQ6PwuaCyN1207sbKKqMbn7acE0qZ75XxOHyphmk5vc1cvZAc52CR0ashmtHizj0sRkNX2AljYxpYJ8Gj+uUjL4Edn8hUsH2fA0DTq5zyPVjYnngh7LQ11+5GMhBtFKQwTHlnHJiKjMm9mrPs1ZR6Rz4PcKcpDc1ekRYa1NU2bBmmJuiGW2FEhRd5Nnr4Og6kRYSPOaan9ALMoVBlvQRdYohQ5Jh383pLPywCG+AqOmIp/U3snRP9BER669OZe7bhY363dXm9P4mpmTVNcx2j9NyTZVmjIse6gi0s1647GJMujFemI8NseLvaTAg7gnKssWIQ6NFHNNcJBnGXglf3Q9bP+SeMXN5aG2o9HDZUheDOIBGUhgc4+CUOWnMmNC1F7AqKp0Ob6VojNNoQBd9BHZOI5oJGt1gsfdb+P4xcS1P6g9z/ypMs52EcRkGdt+UzpnvFSJJEtuiaMZJfYzsXRe9KLHu6lSmv1ZQJ128IRYONtEzTlvHMNsnQUufBEuD7281vXAWCAN12nCIa51pKHWoHrXtKRfa0gTj3E1jLTxd6/d/7mcVDKaQuZqTuFT3LYddSYxom7NWUVFpgIPF4UaT6zSLmarZRqVi4FbfzXjRgSNXJIW25gSDDmBmTyPbb0jj9HeLMGija8aMHsaohtlks8zi85OZ8mo+9Uwij8jFw8xoZSnMMGvQwMAkHQOTQmaneX3bcEKItwLJXYpp/DSRRN5Ke5x10OohZSDkuEXjqSXCGPYoXD4ihlc3hUyzCz8tZwCHkSQYHOPg5LlZzGpoQaaiotIm7MyLnhLYJA6uEsmyXqdo9pp0CyT27rCJTsciSRKn9DMxNcvA6e8VopUl9kVpvpvczRDVMDsoScsLp9qY9mrjgkRqc+3oGLKPqYGkW2SGpugZmhL6Pc3p3Xpms3rXJq5S8LnFmtDWW1znWwODRfwdyPlVfA9TQpPefvZAEx/vDCXIX/BJKX3IrtGMefN6cMLYji/0q6gct6x4AlwlkDxITJEp2i3WDcD63NafJNoZkSSJc4eYmdnTwFnvF+EPKuRXRF4oTMkyRDXMzuhh4E/TrA2GpkTi1vEWVh8N/333t2kZna5ndHroet3mBmVvJbjtkDQAKbEvprR2Wj/qjOI+IudXkYQepYY3rbuBFYfFOu8l/8lcoVlGfNlOnnv5eW6PncgDpw1m3tDGN2+oqKi0DsWuYr7dlYs++Vs0pmyUgAl37kKga+9BRUKSJK6Y0osTB6Vy3vM/U+byUeGNXMuZO7AP767aFfG12UNiOGOMlVtfz23yOdw8O4vPNxZTu/Gsd3IME3onMqF3qOlgRv9WmkTUQiSNBtOwYe3+fQekxrIrH3ylE9EnrmDR7m/wFPkJugtIi0ltc804/v72q3Rtyo5CRUHEzqTm0phUvEaZnxQFNrwBX94njDFpw2DBS53GLFuNJEm8dVYSHy6MXmBXoMaMFAmbScO3l9TdZJ/bx8jMHgZuHGvh4K0ZPDQjruY1i07imfk2Yg2h3+W5g828dkYrJ2h0dgyxMPJC8Xj9qxCou1i9bETdDv6d9MSjaLFX+vjToq18sbXpwqui0uUpz2nWpm1rcrQRjQIdli7rq4T938O3D8MbZ8En18Ga/0HORmGwsqTCgPkiOe7ij2DhKzD5FugxOWpyiEaW+PjcZN48K7ruKgr8YVL0BPVEs4afrqi7KX75iBimZOm5Y2IsB2/N4O4poc/oFa/hP/MSiNWHbkUXDjbx7tntM2KhSQT9YmwhCONx94li/KklBcw2MVY1oSdkjYf04RAMijF3SgvGMfWYItI//G6ukJfWeXkHvfAqGkorvdz36TZVM1RUWhNFgZL9VWkJ9RtWsx2t1Hm8fRF89xdxLe81HU7/T6cyy1aj10gsvTCF50+JrtOKAtePib7OSDJr2HBN3TSsOybGMj5Dz/1TrRy8NYPfTwhpxoRMPf84MVwzFgwy8XE9651WR1HEtV2jh7QRbWeWrUbWQGI/sbZwNZy4VU1dvZbYTi+WBcbyJ++l3LDCrGqGikoHkVcWakAbIe3lDu0HADzgv4z9SpUp0ZLSYY2DrY1ZJ/PNJSn8c0581GMUFM4aENlIKkuQatGw5bq6mvGXWXGMTdfzl1lxHLw1g+tq6c6p/Yz8ZVY81lp7U2cPNPHRwnYsQLjLwV0mDA2pQ9rOLFuNIVYkEAaDYs3YSB6YHp5Y6EfLTnoQUCTslT7u+WiLqhkqKh1EaUUrjJw+sEIky3qdkD4Spt4uDPydxCxbmzijzA+Xpda5LtVGASZ1i3zuGkkiy6pl2/V1NePJk+IZnabj8TnxHLglnctr7ctfNTKGe6fGYTWG1hlnDzTx+hkdtDdVUSiastNHiOt6a5llqzHbxN8BbwUEmvZ37Fg9d2NgL5kEFYnSSi93fahqhopKm1F2FH55Xjye/kcoOwKm+Jp1g6dtQvE6LSkxGlZdkcofJkXXDI0EveMjZ19rJehr00VcZzw7P4GRqTqePjmBA7ekc/6QUBP93VNiuX2ilfhjNOPZ+e1cA/d7oLIEEvtDYt/Wn3rUELHpoh5SGT3V+IVa+4bFxPFW4EQAfq/9iLwyFze8uUHVDBWVdsYX8HGo/BAl/v3oE78HwJ17Noo/rt73dXWybGZ+umc2N53QN+ox3ayp6KNMIvcF3cRZHTx9ZV3NuXGugV4pErfO1/LajbHMGBja+3ni3BH8Yc5wbJbQ/fyC0d34xzlqlMWxvHH1eAC8xdNRgjo0pmyQgqAtJq/c2eaaoRpmVToP7jIoPVB1o996Q1yO7RCORINjUgM+UcRe9xKgwKDT4ZQnmpTc0N5EmFwdQoEzoxQlat4fQRgenhnHq2ckcudkIQq1jZ+jqrrnxmXomd3TwNWjYvj7ifHM6NH0TjrF56f43c8pfvdzFF8XXO0NOUsYLJz5sGNxnZcDUdJR8kisGaD+0OfbCQRbYLZSUelq+FxgPyyKfR2YqNSYJot2xe+GPV+JZo3Xz4RvHoR93wrN1JmFsXLK7+C8N+GCd2HGndD3xCY3nhjqkV2FyEb/2kRaTNw2MZa3zkrilvHCvHP9mJCJZ1hVSsec3kamZum5dbyFf5yYwMRuTRvz0OZ64feI1FhLmkjqje8efTS2RgcJPSBjFOgtLTPNShKMvhQAZfsirNTtiC8ktPGkaoaKSiviyBOTAiwpDaZ6Nmad0SC/vgUrnwQUcQ954oOg7dyj8oza6L8XBbh9YvQmi2jvv3pUDO+fk8TVVU19tQ2zg5PFdXfhYBMTMvXcOTmWx+ckhKV3NESL9EJRwJEvdDd9hCgMtAcGi2jQ8LsbXdAOKpH/bIqIx4P4PaqaoaLSMWTbRSqbCTdP6p5BJwVYHJjIB4EZoYO0bTxCswOob3KaosCf6jFHAZgibG6dN9jMhwuTuHiYWKPcMyX0Gf0TxbXuqlEWxqTreHCGlSdOSmBoK43RrhdFCY3HTR2CEptF8SuvUvzSSyjeNk7+ikkGW08xwjvYuPuTSJoRQEMpserelIpKB1OtGc0mdxO8cwH4XdBtHEy+FWy9WrXm0hbU16CuKPD32fH1vj9GX1czzhhg4uNzk1kwyIwkSTxYKwCkd4Iwm90xMZaRqTr+NjuOJ05KoF9i22pGnbWJEhR7T7JOrDfiMhs9YaLJWDMgNkPoVVPOOYJmeDBQTshMpmqGikob8f2jEPCIOkB8d7E/YAjtmWQ7O1k9o52od50BPHVy/UbWWENdzTi5r4lPz0vmlH4mJEnisVq6090qNOOBGVZGpOr499x4njgpgR7x7djw6PeI67ett0h6lWUUr5fil15qnzUHiHuJuO4iZCQYeX9Nd8wfzgv+U/EoOkbJ+5goi/HoqmaoqLQvOc4c8ivy2eZ6D0lS8NlH43e0f5poR1HfOkOrkXn+0rERX7MarKSYUsiIqxsQctrwTF6/ehiXjB3N2PQxvHrZrJrXMuOFD+vxhSMY3i2O5y8Zw+PnjiAjvo0bqltAu+tJFQatWKMqgVh8pRPEc7ZVSFIAtHagbTXj+IgtUOn6BINiDLffJTpdW5EWm5+8lfD1n8XYa0kDU38Pg05rlXNrS+ozASsoUV+vnQj1wHQrD/0oRkD9e248KTF1N9U+PTeJ1zdXcHdVgUKWJF46vWVd2IrfT8EL7wCQcMYcJF0Xu1RpDTD6EmF+2PgGDDg5LFFkW05ZxLc5iMGl6FGA3DI3aw6UMKlPJ0xbVFFpC8pzhQk0rluHnkarpQW2lMJdsHOJGNHtC43JxJohxi71mAQpg1stgSpa9xyIokS0xcQfayXZ/XFSLP/42YEswXPzE4iLsOn0/oJE3tteyX1ThWboNBJvntX8hMA21YvqDaiEXk1L9jDbRAp93haRmm9p5ki67pPA1gepZB+Xar7mv4Gzwl4uxYpHET+vqhkqKq2EtxKK94p7uUaYlnJaWpRY9zJseF08HnUJjL2y7QqjrcixG9+1UYiuKbWnU9w41sIz65wYtRIvnmqLaIh640wbi3a5uKPKgGvSyby3oHma0Wy9qDbLGiyQNrT9U/Cri9mOXHEP0ADR1hlFJOBTNOo6Q0WlA8kuFeanu7Tv0lvOI1exca/vSqDzX/dbQn0/nQJYI6wZgJpGbRCTK17dVIHVIPHK6YkRG7xfOs3GV/vcXFvVeBFnkNs3VVYJCr3QWyBlIFhSUCorKfjHPwFIuOACJH0bpjpKkiicu8ugsqhRa5BompFHEoOVwyiIZGRVM1RU2p+clhhmS/ZXJcs6hPlywvWiubcDm9MbS31LIQWRRBuJW8aH6hkLB5v4YLuLJJPMa2cmRtzPevrkBH466uHcwcLsmWTW8Ol57acZYWuT02cjVZaCyQapg0WYTFsia0RTnqtEJM1GmUh1LNE04yipDFCOAOo6Q0WlTSjYCb++LR5PvEE0eVvDk1GzyztJPaOdqXedoUCsPvIRV40Kacb8vkaW7nWTFiPz9tmR95uemBPP5gIf8/qK5vbMWC2L2lEzagj4QmbZ5AE1TTCK399+a45qLCkQkyJSZiMEi23PLQ/7uoAE3gvM5FLt1/xe8yHnBYeomqGi0o6Ueco44jzC90e/x00hQV8s7vzO73VqTRraebMaI9cKrp4ymEGJ8QDM6O/jh92FdEsw8cH1k0iPq2t+/etZwzhUXMH4XsLv1jvZwmc3T23JqbcbHaInwOaj9prH3uIZ6BJWozEfRpL9oC0h6LeSW0abaUYXc6GpHLc488RYiVZI63H5gpz1fhGDk3U8cVICuQ0Usqs3RiJ/mB2+uBsKd4qkp5MeFl3ZXYyPFiZR4gryh69LKfMozOwZPbXqzskh89MVIy1cNiKm3q6LkWl6RqZ1vlFOHc6A+bDpXVHc3vYxjLyo5qWSyuhdGfuVDDSSiKAtcLijHqeiclzhrYDSQ2CMa3ejULknyJnvFTI5y8BfZsWT64gSAV3F5Q0krbYIJQiHV4tNsPytoedj06DfScIoa+vdJr8jTS3z06fnJlHqDnLFZ2KszuSs6KaxG8eGNphuGhfLDWMt9WrG+EwD4zO7QHJWwFu1AdVHbEBpmnjLbIoXhubcjeC2gzG+6ecgSTDyQvjuYa7ULuPlwMlUEq7fR5TUmpWeqhkqKi1EUUQDn6s0avNGcWWAM98vYn5fI/dMjSOngca8m8bW7TyuYcMbIbPshOthxPnNPfN2J6ZW0WHx+UnY3QoXf1oMwNh6Ul8vHR5ad9052cofJsXWqxnTuhuZ1r0D03Y72iwLYrReQk+oKBKGbn09a1fqX2fkEtpQUjVDRaXtybG7WPDsT5w/rju/O7EfuWUupshbuFz7FQB/9F1HOSGdqN2IdjyRGRtqvF58fjIOb5ALPhaaMTRZhzZKE8aCQaHr3YMz4vjzdGu9mjG7l5HZvTpIMwI+oRfmxPYxO0VDaxDrxaPrRMptA4n19WlGESHDsqoZKiptz4GiCs5/4Weuntqba6b3Jqcs+r87vUbm3vkDI7/oKhVm2YpCsZ8x4UZxL9nJk2WrGZQU2ntZdF4S/iAs+KAIgH42LTG6yDowt0+oWP332fH8bXZ8vZpxSj8Tp/TrJOlO5XmQlAmpQ8W6oz0wxUNcFhTvabRhtj7NKCf0GapmqKi0Mt89LGoG/U8WjVnAjhKFSz/N43cTYrl4WEy9NXCzTuLB6cfnuO3xmWL/SS/D22eLproz3hOakWnVYIkyhrV2rePpkxNQqD958OxBZs4e1Hrn3SyCfmGWju8ByQObXqtobWSNSEN35Im/n8c05UTSjOf8p3GB5jsmaHYx0r+XX5W+qmaoqLQD6w4Wc/Xra5g9Jp9vir8AwJN/BgTr3gub9Rr+tmB4e59iu3DCwBQeXbaTJIuepy4YTaxRy6lPrQQgwRx5b8oWo2dkVnzN169eITxi9YUWXjihe+ue+G+AYqen5rFImZ2IPnElhqTvcRfNANkHAX2baYZqmFXpeHxuKN4nNnY1LTdefrXfzc5iPzuL/Xy808W4jOifuei8JEakRnndZYfFv4fSg2Cwwsl/g5SOvituGpuuTcPhDdKtalTElxel8Eu2p2ZD6OOFSazO9rB0j5uthWLM57ELg/oWCir1oNHBmCvg+7/Cr+/AoNNrxqTYzHq6kc1R6nbe1SYltnOP41VRaTXKckTyRQeky364o5L99gD77ZW8uaWSkanRx659c3EyfW1tMJZNUeDgCpE0WHpQPCfroNd0GDgfMka1SxLIhmtS8fghvaqoveLyFH7N83FKP3Etem9BIpvyfbzyq5NcpzAWH7swOC40I+gXBe/qZNnmbkDFJEJSf8jdDFpT80bs9p6JZ/WL2CpzuUDzHS8F5kc9VNUMFZUWUlEI9sMQkxS1MeF/Gys4Uh7g+Q0VPL+hghH1aMbPV6TWXE/rsPkDWPeSeNzFzLIAWlli7dWpKAo1Eyi+vzSF7YU+TuwlrnVvnGljb4mfv6woJ1A1LafLaYYzXxSQO8osW43ZBvFZIv24AcOszawnjVzyqJuMUvu3rWqGikrb8+TXu8ktc/PkN7t58pvdjE2V+Y/ueQBe989hZTA0+m7TdWkRpzMcD8ToZX65MhWtDIlmoRnfXJzMvlJ/TSPdS6fZyHMGuG955PQ66MSa4XeDs1Bcp5MGNHidbnNikkUhvXR/g+trm1lPCvkUUHfSl6oZKirty0OfbyO/3MMjS3fwyNId9E+Nbpzc/n9z0WoiaEYwAB9dIxJmLakw5feQ1LfLmGUBbCYNq65IIUYnE1+VJrvswmRyHQEGJom117PzE3B6Ff74jR2oO5JbkqSuld0emwZpw9tfP+K6QXkOeBxho92jYTPrSaaAQiKti0LjUVXNUFFpRQ7/AjsXi7rAqIvB44S4TP74aRGFlUHuX17GA9+X0c0a/Tq/5bq0sKCM44luVi0rLk8hziDXTK34/Pwk7O4gmbFiP//fc+MB+N2XdgBSY8L1s0toRtAvpjNaM8Uki442y1ZjTgSjFTzldcJCbGY9iRRRTMisnUMSnwamMEOziVSpBBRVM1RU2oMb3lqPvdLHsiPvoTEFkSuH4XcMjXjslgfnHrea0S81lh/+OJNEiwGLQVxHP75xMh5fkHiznniznr+fMxyrUcf1b64HoFtCuKm4PqOsSvNJtYb/nr0l09El/IzGfAjZUEDQ3QNoO83oJKqq8pvGfrgqySmzVT4uvyI8HXBtTngnk1YGf9UhRm2UC5u7DJbcIYxL5iQ49XGx4dzFiDPKYaOK0iwazhgQ2nwZna5ndLqeeKPMvd+V1ZsIpdIM+s6GTW+Lv0dbPoSxVwAwJCOOvrE+Epz7KVZiKSUWV1VqoISCBKTHGWvi2lVUjms8Tig7ItINOuBm89h0wF/zfWFfxxkkyjxi4zeqZrSEvK3wy7OQv018rYuBwafD0AXCtNWO2Ezhm2tZVi1Z1tCt4oRMAxMyDSgKPLqqnFk9u0BSbFNRFJHuYc1smVm2Gms3qCwVf8ebc58ja9CNvghW/pMbtYv4OjCKAmy4Eb97SRJ/N1XNUFFpIX4PFO0VaZ666ElD2cdoxqZjNCM1Rq5Zi0TVjB2fw+qnxeOxV3Y5s2w1yeZwzegZr6VnfOiaKdJhoagyyNPrnJwxoJMkODUWZz7ozB1vlq2mkQXtIRlxDIz1kOTYTxFWSrHiQazx1HWGikr7crikMuzrC0qeJkNTwoFgKo/6LyDNDHlVh5jaYp3RiUi1hGtGX5surBGxOhl2V7GP1zdXcsmwDjadNhZ3mZiWktQPEvt1juK1JInR684CcX7G6IliQzLiGBy7mxTHfoqIo4g4/FWlAlUzVFTal0PF4ZqxO98Z9nWa1UheuUjUiWiWBVj+V9j7tWjWnXQLpA7qUmbZaqpNTtUMStIxKCmkGSf3FeuKVUc8fLrLxQ1j2imVtTUJ1lpXpgzumGYLg0WsMQp3NsowOyQjjiGxuyl02CnCSj42jh1uq2qGikoroijwzYPi8dAFIGkgJh4kiYN2f81hAQUOlYXvVWVYNORUpc4er8anamrXLgCGpYTXuKvr4Yt2ufjuoIcrRrbh9MC2QAmKWkVsOqQOaV4gR1uhNUBsBhTtqmOYHZIRx7DY3RQ4SijGSn7VxKOH/RfzWWAiO5ReqmaoqLQDnoCHQodPmA9NR1ECRsqzTws7Jj3OSG7VdIvjXTN6JIZrwOju4Xv+547NAmBsjwTWHSrl/HFqWmx7ML6XLezvoeK34isbiz7hF3TWLQTKxpHWhppxfMYXqHQdKkvAfgjMCa2SnhcIKvx1ZXm9x2TU2qjXRbrwexyw5A9Qsg9MNjj1yS5plm0K5w8x89HCJF47Q705bVVkDYy+TDze+pEopAAaGa6d3guARMlBLKFN0eyq1NkHTht83N+YqKgAUJYNvopGbc62Nt6AwosbK+o9JqNWOqBe04r/Jt1lsPxR+OxmYZbVGmH0pXDRezDhunY3yzaFq0fF0CO47AABAABJREFU8OE5iTw7/zjUDGe+uCdJGQjaVmgikWVI7CMSCt325n3EgLl4DIkkSg7O0azAgqvmtf2KMOGqmqGi0kLsR6CyqN5rb6UvyOe7XVFfB0iqZSLVRapNH/oJVj4pHo+8EEZd0pyz7VLcNjGWD89J5O+z4zv6VBqPswA0BkgbJtJdOwOGWDE21WWv97DqdYYkQbJUjonQSKPditj0UzVDRaXtKav08cuBkpqv58prWaBZQUCRuMN3Ay6MJJlC/w6jTAv9zfGnaXF8eE4if+7sY2MVRYwfDfhE4TqpFRrtWhODBWw9wV0ebso6hnDNKMNAKHBgr7rOUFFpNwrK3Rwoqn9vKtHSwP7E7i9hxT/F47FXQdZYkDvRdakN+NvseD48J5HbJrb/fmKLqB6rXY2uA81PsWmiYdRb2eCh1ZohSwopUhkGQs2j6t6UikobsPcbOPyTqBkMOVP407VGDpf5cfqUet+aYFIXF8fyzHwbHy1M4ppRXajJQgmKZFlLMqQOBl0nTGM124S3I+gPe7paMzRVmqFBrEnKsbBXEVMwVM1QUWl7Vh3Yj6RxYEj+GgBPwXwUvzXsmASzGqZ3LK9fNZ6PbpjM+eOyOvpUfhNoZIkHThuMRKgdz1s8HUWR0ZqykQ25baoZ6l2TSscRDEDJAQh4Qd86N6mFlcGoryWbZWL1En89Ib7mOf2xhWy/G5bdBcV7RJLQqU+KsWrHObIkMSZdT4xevSS0Or2mC8O11wnbPql5enKfJO6ZP5BEiwGJ8AXuw2cMZd7Q9PY+UxWV9sfjgPKqdNkOYFexL+prqTEycQaJOyeHFg8RmyyaiqLA3m/h/ctgz5eABAPmw3lviqTBVtLDtkQjS4zNMLRN4m5H4i4Dja4q3aMVu80NFrD1FmnK9RSso6LRYRh1LgBn637m2C3RR84comqGikpLcNmh9EBV0nn0e+GfjnijvpYaI5NokrlmVOjaUafJomAnfPt/YsN7wHwYd02HJKu3N9oqzTB0Fc2oLBbmgtShnccsW401Q+iT11nvYbXXGUqt1KcAGv56trrOUFFpD5Zuza15bKOcR3QvAfBc4DSyzQNJMyssHBIy+Khj3QQ6jdAMXWs2KrY2QT+UZ4t1W+YYSOgpmuQ6G9ZMsa/ZQNNeNM1wYubRBcNUzVBRaQfeWXMk6mupVgNZNhMnDkqN/gHOAvj0RvF4wHzoP1cYrI5zDFqhGdquZLapHqsdm9bRZyIwWkVqoau0UYeHa0aIYuL42zmqZqiotBqKIlLDAYafC0GlpsH7+Q3R9wNSY2T62bSMSddFPea3ilErauBdxqCpBMGRC+ZEsT/VmrWK1sQUL7TEXTfILNo6I5ck/n7OcFUzVFTaGIfXwdPfHsaQsgxJ4yHg6obPPrbm9ZRYA0MzrfRK6qTXlw7ErNcypkcCclfRjOOAeUPTefbi0aTFiXWs4kvEXz6CgDudm2f3bFPN6IQ7eiq/GRx54oYvJrlVPk5RFNz+6J11d0628uu1aQxKCnVXh22oBP3w7cNQsF0k+JzyuBhjpqLSEmQNjLpYPN78PvhCHduT+yTx0mXjmNo/fJNscp/OmyypotKqlGWD19UhJtGgohCI3mPBgzPi2HBNGt1iG0gLbAreClj+CHz3sChcJvSCM5+GGXd26kTZ3wR+T9U41f5tY5CyZoA5CVwlDR8biYGngM5EN/K5IDU77KUZ/VNa4QRVVH6jBIPCLOv31Jt0Hggqdczqtfn7ifGsuSqV5JjakyxqHVCeA1/eI5rzuo2Dabf/JsyyXQ5XqShMpQ4VCR6dDYNFGKBcZQ0eWr3O6J0SntI4c4CqGSoqbY0/EESpJRp/1r1OklTOjmAW//Yv4OnpAVZdlkScUd2S7XJ4HGIvMzYDMkZ1vsaK2mj1wszrdTXYtFetGcnW8JHgs1TNUFFpc3z1bUwBr1w+nu//MAtzndSPKhQFPrtFTMtI6AkjLwJjJ0/p/q1SbZa1ZopG7c6CNUM0DPo9DR9LSDOM+vA0shMG1GPqVlFRaRp7voacDSIBuvcJYi9A1uINKPWaOj44J4kvL0pu3Sl5Ku1PdbKsKRHShoo//86KrBG65o2clF+tGcFj/uaeMFDVDBWVtkRRFPaXHsYl7UcXvwEAd94Z1LYGLrl1Gp/fPFU1hap0GuYNTWflXSfUfO3OPZPKA7dyxZgZbfp9j++5LCqdF58LSvaLEQKa5nW7+QIKsiSS7nwBhVu+KOWLfe6oxxu0EhpZQhtpsaAo8NNTcGiVOJ+5fxVpcCodgqTX0/2J+2sed3n6zIINr0HZUdj+GYw4v+YljQwTsmL4aLe95jmHJ0AnLruoqLQO7nIoPwrm+Hb5dt6AglYWidq+gMKVn5ew4nD0zWCDRmhGbT9TixJmC6uSBctzRILh6MvEOO5maqCKoFX0QgmKRBZbb7B2a8Wzq4VGJ5pwsjeIIklTRyPqLSIpZutHnKcs478MqXnJ7Q9y/GfHqKi0Ec480bxxjDnS41fQa0Tiny+gcN5HRWzIi55KXqMZtZ6rSQv0OsUEC1cpJPaFEx867sejdkYa1Au3XYzWThsGsZ144z42DeyHxXpaZ6r3UI0M03qa2FgQSqBRFAV1G1RFpXXx+APoNTKSJOH1Bzn9vyvZmecAYKa8kTM1PxFQJO70XYcXHUaTEY3OgCQ1PP5YpZMQDEBFIUgaSBkC8d1B07CWSwYD3V97reZxuxObJlKpXKUNNmhqZJjVy8yrm+ofC6+iotIyamuG2xdg7r9+5FBxdD0w6uQ6e1NhrH8Fdn8h9hzGXyfMjyqdj9pm2dQhSLKuY/WhNqYEEWhTUdDo5FuNDHP6mPhoh6uNT05F5TeIosD3Il3WP+hstLIGjHE4vUFOfKOAvIrojRZGrYQsSeqavytTO1k2bVijzLIdvuYwJYBGL6YJa+rut2lkmNPbyNf7o/s3VFRUWo7HH8CgFU12+0vyufjZ/QTSP0UDeO1jCbrDJ2qb9BokVTNUatHheoLw/U3oZeOXAyWgtM85qJU6lY6h9LDYsI1rnjHFH1Q46a0CYnQyj82OZ8EHhXgamHJsrGrENtUaB2o1VD3e9A5sXwRIcMKfxI2oSochaWRiRnaiTuuWImtFh/8Pf4PN78GQM8NGY509yMSd39prvi5zq4ZZld8A5VXpsubENv9Wbr/CCa8X0M2q4c7JsVz4STHehjSjSivC0gKbmzC7+ytY8Q9hxLGkVunM0GZ+mEptWkUvKopEOlRin7YdpxqTIgrVlSVgaUZa09AFsPVjsso30E86yh5F3EOVuwOqYVZFpTn4PaKBT2sI29B1eILMer2A4ak6Lh8RwxWflRCoL16WkGb0ij9mea0E4btHoOyIKELOewz05gifoNLW1KsXHgd43UKbO7vJwBgn7iXKcxo0zAJcN9rCf9aEDLOVviDqoC0Vldaj2OnhhMd/YFq/JE4dnsH1b66veS0GF4/oXgbgpcB8tiiiKdtoFonmQ5LVxrkugdcJlaXi/j2xb5Mmg0gaDTETxrfhyTVAddNeTuOa9m6bGBtmmA0q9aeYqaioNI0cu4s5T/zAaSMymNDbxm3vbWrwPcaqjaixPSPsFJccgC/vE4+HnQfdxqpTLDojNWbZDEgdDDojEnSsPtRGkiAuUzSTNqHB+94pVtUwq6LSFuz5CnI2EtSamLFhKnNdZnonVXL/8oYnzRiq9qamZBn430a1CarLUW2WNdqalCzb4WsOg1X853FGncDxl5lxqmFWRaUN2ZpdxoJnf+La6b2JMWh4bNkudPFrMBpzUAJGvAXz6rzHqBWr/dmDUvhsUw5xJnWP6rdOh+tJFY+fO4Kpf1te83WQ+qeytBTVMKvS/lSWgP2QuHFq5iZOriPAAXsACHDD0pIGzbIQKmTrNRLLLkwmEASzToZDP8Ga/4mDJt8CvaY365xUVOql3xzY8LpY8Oz4HIYtrHlJK4u/kye/XQhAuadtL/wqKh2Ou1wYZtspXXZ7oY8cZ4AcZ4BrFpc2aJaF0AZTnEHms/OSMFR1aDeJYADWviiaMgB6TIGZd9c79lulnfFWis79pAGNMh61CI1WpFFlrxd/N+QmOrCtGdBzGhz8kXf6LWfs7ksAoRnqsFQVlWZQdlSsS+Iyw55edcRDkSvIdwc9fHewcWMpq9cZ6bEaPl5Ya8z2+lfh8M/CkHvSX5pkslFpJ7xOsamfMgjisxo+vqORJKEH5UcbVdCO0cu8c3YiF3xcDIgmC9Uwq6LSeizdmkeZy8fizbks3pwb9toftO+TKRVzOJjMk/4FNc9XrzP62nS8vyCR1JjmduWptClBf61U2cHCeNoVp4NYUsU4V5e9wfuQOIPM86ckcN2SUgCcniDWdjhFFZXfCu+sOUyFN8C7a4/w7tojjXpPtWF2dPcE3r5mAt1tVc13igKLbwNfJaQOheELu+Y16nin2iwbmy7+nHSdtN3ZnCTMTu7yqGanY0k0a/jrCXHc+50w8fmDilpsVlFpKYoC3z8KwArLXLKdcby8xQNE3puSgNr93caq6aozehh440wb/WyqLnQZggFw5FUlyw7tWvUjWRYJ5QXbox6SatFwx8RY/p+9+w6To7oSv/+tztPT05OTcs5CgAiSQOQMDhjnjI2zvbZxer3rtX8Oa3u9Nqx3nXDE6xwBYzAm5xwEEiAJ5SxNDp27q94/zkRN6pmpzufzPPOoOkz3lTTTp+rec8/5zuM9WRyYUqXjuru2E0ua/O+9O+QORxRP/Z0AxFouxEoFKPdCaEg4cTll/eLVa2ZQ5fewolmv/lV+mFXt590b5nHjo3sAMK3M5k3pRnGVXWZKdj+bSfBMfaluaJWn/d2jZz795IoafvmawQv8MvdgotPyOjerGtySuHvvfwAWrHgtrHrdlMek7GMlk7TffCftN9+JlUzmejj26K8yC/D876Wq2RDL69wsq5Vppe50MsCVKmTdh6S6rCe9XbLT5XEOfv53REc/sfr962r5waXVA7eHViM/odHD0tpJTjCl4nDPlweTZU96O1z01cKa7CgA04oXZgrCbVCzYEQ79owpr5c2RdHOqX3/Ca8HoO7g3ayukEoB3VGNGUpNWrQbOvZAWRUYwy+JA56JL5H//uZ6/uuCqoHbQ2PGyc0eFla7YPeDslkKYOOnoX6pDQNXUzVqvIiHIdIF9cugel5Oxzcp/hooq4HoxBVmANbP8lLTl8St1xlK2SvoGz015GRjO+9yyuKE57Jv8JnTB6uLD40Zp830Mvf46uQq96Kd0H1EEohmngJ1i6aUiGYlErT/5je0/+Y3WImE/eNMh9MF1XMgEZWqVRO4eGEZ3r4cbo0ZStmrYoyYMdST/3Y+Hz5n4cDtsiGtjjYsrGNWdV88eeGPsOs+2Zh3ynulC4HKL0MryzYNT5bNi/gwlNMlmwfjk6tI+Yblg+c3PTo3pdT0bf8nHHoOXGU8WzWyGuDxnv9AE29YMVgAwtMXMgzDYOMcH00B3ZhXEMyUxIvyOul+O8n1o7yIKb5K2eBtjr0+8841gzkhsaQWjVLKTgHv8OsMT+39OFxhUrEGEh3rAPjFB5rZuHhkx1fDMDh7ST31Fd5sDFXlsbyIJ33ef5Z06XI5Daq91RM8e3p0VlZlV89h+ZpKK+IhIskJ+qICF8z38uSh+MDtat9xi9/xXvjnFyARguY1sOGj0xqTso+VSHL0f24EoOriszBcRfJRteRiSZwIHYPtd8CK1wx7OOjtW8geI6FPqaIQ65GqaFmqLgsQm6iXNrBulpd7dg+2hRkRMyYjEYY7/10qiTrccM7nYNEFU389NaZpxYtQC5Q3ZDdJyumGytlw5AWpGjDZqsWNqyXprmUbb3LexWZeqwvZSk2WZUHnPkhERq2ek7ImjhmrGtxsbRucNKgqOy5mdOwdqArCqtfDkoumNWQ1fSPihRWHSIdUGK+ZX1jtax1OqYx8aJMkzqYx9qDXoD2qmyyUspvbOfKawUWSr7t/hsOw+HPqLF7fPIvo/o6Bxyu9OaxdYFmQiknyZCoOZmJ4WarBJyI1q5CKQYZTzmOdbrm+cXoK63MzXfGQxAZ3AJpXQ3CWJBFNkZVIcPSrXwOg6sorMdw5qvJV3gC+vsqBZVUTPj3oddASNumOFskGdqXyRIVv4s+AhgofkcTg+ZrPPUrMCLXBPz8vxyteK59XKr8MTZZtXDmio1HexIeh/HXg8cumQo9/4ucDbqeB320QTlh0x5JkdilbqSJnWfDAN+V4+atwpyoZ40R9QNDrIJwYfI5RjOfnxW5YJfKVaX/+DpUXMcUXlOI48bAcj6LCYwxURe6JpdDUPKXsU1k2+HtvuDrx1DwMQOzYJYCTKr/Bguo5xBKv5GiEqhDkRTzpE+z7mU6mLOJJKPNk7r20wqzKnkQE2nbJbtpptgiKJCZeyDYMA5dj8AKhZuhCtmVKZdmu/VLt7YL/N2E7S6WmzemGNW+W4+d/P2K3XdArP69dupCtilnXQYkHWaouC+nFDIDEkMTamuOTn9IV7YK/f0qSZV0+uPSbmiybj+JhqSpZtwhcGTzTHk15vSzCx3sn/72GAavfAMDlybtwkdSYodRkhdv6rgFG7qiG9DbmwfDYEvQMWZRIxuDu/yexrvlEWPfBaQxWZUQyAuF2qFsCtQsLM+nLXysdWxLhtJ7evzFPY4ZS9oomRv5OvcN5F8sc++mwAnwt8TYItRA2Bhc+3c4sf+ZYpiRKdh+Sa7FYr8xNVDRB3TKpYjTjRJh5MsxcK3/OOEnub1wBVfMl4dLphVRy8LU6D8jr9R6DSKecX49TUSivxcOD16m1i2H2qbKpbhrJsnnF5YGqObJ5NQ0aM5TKjGRq/AIJ/fEhPGRT7KjJT3f+m1zTVM2FE96oaxr5ZiBZdiY0rhqRLJu3vAGJ92l2sejXfy3cFdGYodS07Lq/r7qsDxaci9M7fuLkjL7qseE01z1UHkrGJF5UzZZrrykky+YNp1vmqcaZo3IYBoH+mKEb85SylWvIPJO3/k4MR5JkaD6p3uUANFV6qPXVEorr754qDOUeJ/1pfl2RzFa71atplT0de6WtWeWsab9UdAoL2VVDqwU+/3vY95hUxbjoa9IeWZWOZLRvMSchi0eGUy5EPeVSsSmTll0Gz/5SKi3vegAWnT/w0ECFWa0WqIpVrAe6D6ZV1cZO6SY/DZ1g8rqmsJAd64HbPgVtO8AbhEv/ExqWT/51VGZZpiwu1S+TVkfZ5vFDsBnad0y6xRIA88+Gsu9THengYsfTdMdm2z9GpYqVmYKOPXLs8o36lGiaiw2hxOCC97CF7Me+Bx275fri/H/P7QJ2MiYVBFNx+btbfeeYhkO++qsEujyltdAe7oSZK6F2kVROLESecgg0SrVkT/mET+/fmKcVZpWyV+S4hNl6Ovmk688AfCv5JjqpAH8V4WQOWpklY1It1UzJZsWqeVJZ3VsBbv/UPv+ScalQm+z/ikoCbbxXbse65f2wZG7F6QVX31e+xRnLlOu3WI+cE1TPk/nKLF+rZk15f+XA0IRxQ2OGUplxfMw4XtKU65BxF7L3PALP/w4wYO3Vkpyi8sewSoErpHBMIQk0yPWFZcr1YhqCXgdHQqauZyg1XY/8t/y5+CII1BFNjb82kbT6Y4YmzBakeC9EuqBmgWzmznZBj0woq5L50HEEvQ564im9zlDKZv0N8xzeQ7gqnwMgduwy+jsHOQw3hmEQjuvvnioMhmEQLHPTGU7QHU3QVJm566oCXR1SBSfcLhfb/lpbKvikmzC7ulEq2TaVO3D0v+/RF+Gpn8rxGf8irY1V8TNTkiDVuV8qqrjKoGKmLBqVN8hzeo5IdRQzgycMLh+sukqON/128CwGTZhVJaD7sOwyzWJ1WUg/YfbMOdIIZlH1FBZz4yG4/TOSLFtWDa/6ribL5qtQq7Saq56buzEEGgCnJLFNltMNy18NwDtdd9IdHb9KjVJqiN6j8jXOwnK61xmXLZJKQSc2DumcsfM+ePlWwIBz/y37C9gDVQQPS6W8aLeca3oCUkmwap5UCgzOls9Bp1sSnHpb+qoEHpVJe6vIP1dqFkLd4sJNlu0XaJD/qzSuXSr1OkOpjIgct9jwOffvCRoRnjcX8IfUuXKnx88bV0i1oDNnZ2EhtL9SUbhd5jpmnQJz10PTStm05Q1M/fPP5ZGE2/I6qJwpVbpnngTzzoS5G2DOeph9GjSdAJVzZd4lmZDz766DUpW257DMzcR7s1+R1rLkfXsOy7+RhVTZnX06NK0q3mRZkP+3QFNalQM1ZiiVGdHE+OfY/VPEV58xH4BLVjYNf0IqCf/4rBwvOh/mnF6YnRKK1fFttQulsuxQZdUSL9KsSA5Q2VekplurBSo1dYc2SYVZwwkLz4WyaibKJ+wvWv6x02Sd5fXLC/Azp1SF2yDaCw0r5KsYkmVB4ofTI9ejY6gc2JinMUMpO0X6Nml7G/6BYVgkutZgRgcL7ViWnK997hLJiXr3hnlZH6NSk1VZJutu3VphVhU8MwXtu2XSwKaWAv/zZHoX7ZVeB5ve34Svv1JgrAfu+YosLC48D5Zebst4VB6zLKlsHAuBv1ra6/lrJHFg6KRiMi4XKh17pL1goH7MymPTtvJKqQbQvhP2PykTnMDGOV78Zi/rZk9cJUqpghPrha4D4KvM+lv/5yPdaT2vodzJs+9rJOCZ5AJyIgz/+By0bJXKspd/B2rmT2GkKuMSETBNqFskla5yxVclMSnaPbUqt8tfhfncrzndsZWywCHbh6dUUUrGoX2PJIk63WM+7Yv3p9eCck6li2euaRzY8ET3QXjw23J84tskQSlbUnFph51K9FURnNt3vlsuVQTHailtWTKRnQjLxo9wG4Q7IHJYkqm8lYXdEm6ooW3hahcWfrIsyIK2r1KucSdI8rpooY95ZRFWNxXJ/6dSeeJrt708cHyysZ3XOx8E4EuJd2MOqVGwrM7N09c0Du98ZLf+LgqpBARnQOVsKKvJzuedYUgVveMr6VmWxKhEpK8ibUTmZuI9EI9AtBWsJGBIbHZ5wGFj5fP+909G+roMmRIbg7OgvF5iZS6vCbKtogk698r87Dj/vpctLmNVMMqyek28UMpO1921Pa3nrZ1bzVP/dgG15ccl0DzzCzi6BTwVcMIbCzMhs1j1J8sGZ/YlyxZYZdl+Lq/EirZX0p6/fc3SMtbVxZhfU6B/Z6XywSPflT/nb5RzeIeLG57pHfdb+quSb5zj48n3NlLnz8I5v5mUc+pUTDZxYAJGX2cJT19nCV/aFapLjpmUgk1uP8zo28xYTDwV8hXvHfMa6w0r/HR0djGrqoSuwZTKgj88eRBn+XZcgVewLCexlouHPZ4yZZfFJauaefJfz6e+Qn8HVf5762lzCMWSNFRk9jpDE2ZV5vUclq+KRttectPR0TPJF1W72NExfGfSwIKEZcED35LKScEZsPFTugu72CWjUjHLVwkzTpQJn7ESJFweuUDx10DrDujYJcfuDCwqeytg+avghT/C878dSJg9f76P82uAxuxW31QqK7oPSzJO1aysv/XBntG3ZM+scI54rKbMObkXN5Nw15f6Fi0CcPm3pZWOyj+WJdWtahbKAnkuORxyLnJo09S+v7wOx/yzYNd9nHDsFuDVdo5OqeLUewTCrfK7N474GIWfKjwGPce1uqv198UMMwn3fBUSIWhcBae824YBp8FMyueaZcnnWuWsySX/DE1w8tdA1WxIRKWFd+8xCB2T5CtvBfiChbvoEesZXlGvGJJlQa5rAk3Qum3ChNnXLPVDUwfM0I15SmWCA5OvuG8E4A/Jc9hkLRrxnDr/JK8zJiMR6euiUANNi6SybD581hlG38L5KHGpf8NGIirjj3bJ4moyCrHuwerZBuBwy0K8wymVtwxH33yeAVhSKdZK9VXdTkrScCopz3F6JM5Vz5eNBmVVpZtkVlYNvmr5tx6nCv4bV/hhZgc06yYLpXJlxCJ2uB3u/Zocr3ydzjvlk6HJsk2rCn8jhr9OumdNsLmi39tXl0NXJzRqzFBqStp3wUs3y/GiC+V8LQ2pIXNXDeUZvM6wLDk3j/WA4ZJN1f66vs3ZbjlnT8bk8URENnODzCMdX7SolCXCEGqTNer6pTkpKpNxDofkgRx7acynXH1iALq6oLZEr8eUyoDuaC9g4m34BwCJ9vVYiZphz+nfZAHQENRNTqowfODshVl5H02YVZmViEDbTpmMtqE6xL6uJC8cTTAj4ORQ78gEqNctL+Nbj/bQUD7KwsBLt8Ceh2Qc539Rqkqo4mRZEGmXSmI1C6XSY7rVsVxeaaPudEPrdih3Zmaia/UbYMtf4fDzcGSLTKgpVazi4b7qssGsvu3OjiSvtCXwuw3CiZEttt+00s91j/ewqHqK8cmypJrggadk9/Rl34K6JdMctcqYSLtUdq2Znx+TdWU1EpsS4altzlj9eplEPf2D9o9NqWKTiErHC29Akm1Gsa0twf6usXvevWmln58+F+KU5lFapT33674q4xVynWFHVbzxWJYktSYiMtleNVcSX+xIjnL7wN0sG8liPZKA1blPOjC4y2RSP9N/PztFOmTxpn55rkeSGeW10OaU5LBxKicrpey15WAXrb2DrSbf4ryXVY49dFl+vpV8E29YBH/aAefNy0LSTH88qFssCVSFkqjTn0g7dK20v/J5MtpXGTYmf8ZD8ndMxWVR3kohFa0sJGnWkCRahxO8fnCXg7evyrq7TP4cI/6XFIcTKmfCkRdyPRKlSsqm/Z2EYmO3Hr5oRSN3vnSUK0+aOfaL3PtV6aBWPQ+WX1FY5+PFLBWHnqPFkywLcr3nDUqnsAk25SmlbPDo92TT18xToH4pTx1JEk+NHTPWzfTw+ME4r1+RhST1WI8kwHqDMqfir5Hj0boYWZacr8d7ZR6p5yh0HZQ5prKq0o1bljXYBaRuSd/12ijzisXCVyXXZWluulBKTc+jO1vZ03kIV8WLOH2HsVJeYm3nDjy+rCnA1iO9XHHC+AVElCplGq1UZnXslcoFlfZUFDzrl8fGffx9JwVYWO3ipKbjTjg798PjP5Tj0z4A9ctsGY/KDMPjZtbXPzNwPClmUqoIuwMwYwVUNE8+McrhkFatqbjs8AzOsH9xpbweFl8E226H538HTf9h7+srlU96jkjbTZtiQbrO/9X4MeNDawMsqXFx2swpTlI8cyNsv0MmAS74EjSsmNrrqClLO170L7g3L8+f9uLegCTNho5NLWG2caVMQgUabB+aUkWn+9CE1yQX/6Zl3Jf4zPoga5s9bJh13CJoy1Z49v/k+IxPZP53Mhnr66AQ7Oug0Jy5JCBvhXwFZ8pnVcc+iekuryTs5/Pkt2VBqEX+bZpOwChvZNaP5HrQ8BTR4oSvSha1Y93jVgtUStnriv99eOA4SIhPuf4IwHXJN9BOkK+cX895S5KcOTuDiTNDP+eaT5R5i3zYFDYdQyufj8ayBhNmLQtJmKWv4qxD4lIe/RsYHk/+xZ6yatnsmYiUbqVdpbLINC1e+/1Hxnw86HNx3ZtO5MHtLZyzdIxOOIefh6d/Icdr3grldRkYqZq0/uruVXOl+MYkEqDyMj70c7rkGrNlqybMKpVpvS2w6TdyvORiIq4q3vDnsdczZgWd3HBFDY/uj3HuvAxWCDSTkvDq9kkXpWDzxOeNhiFz7h6/zIvVzJfPyK4D0sHI4ZLz0FLa6JuKQ88xmbNpWg2Bxoxcq+RVTPEFpbJwPJz14jlKlZrW3hhv/ckTgIl/wV0AxNvPhJQUDFzc6Od371vPE7vbOG+ZfV3AVfHKq3iSRXm8wqUKXrgdOvfKwp0NJ4FbjsXHfOzedzTgdoDbaXDxwuNO3M0U3P8NSMVg5lpYfdW0x6Iyy3A6qVh30uS/sX+iKjhD2lp4K6Y+CIdTKrQkQnLhGmya+muNZc2bYds/YO8jkpir7bRUMUpEpDKdL5jVxcunDsXGfOyut9VT7nHgdhpcsmiKi4Tb/gHP/lKOz/wkzFk/tddR05J2vOhtgcrZMumfTwKN0H1QFvzzaHFfqaISD8kmPl/lmL9n9+2Jjvnt/3hrPTU+B16XwaXHx4xkDO77ulQDWXAuLDrfzpGPFO1rg1c9VzZ3ZatjhssjycaBpr7E2b2yeDKQOJtnVfv6N9B5gtC4AsrrMICKc87J9cjs53DI4tXRLbkeiVIl46bnDgy7/WHXLdQYvbxizuQNV1zBx4IuyrxuLl2UwcVgy5INDJ5y2URVKslThtFX0aowprMNlyv/Yo+3QjbthVs1YVapLPjZw7vHfOyWj5zBnBo/Aa+Ly1aPMVdhWfCPzwEWzD0D5q7XuYN8kIxKa+26xVC7ePRqi+PIy/gwlL8GDO1ioVTGPflj+TypWwLNa7j+qdCYT73pjXUsqHZR6XWMnJuyUzwka/vBmTLvNNXEeXcZVM2WtdpQixS1Ch2TTW7+GnAWcSKOZUlV+HgYquZA3aKMzt/lVUxxusFfJ7khmjCrVEZ9+daXAHAFn8fpPYaVKiPefiblXvh/r2vkoiWrqSzzcMmqPFsTVXkrr+JJFhXGDKMqPGZKEgBN07ZKbv/zZO+Yjy0Yr532C3+AYy9JS7azPysn5Kr49CcQ1C2G2kX2TOa4vDLpFX1GWol4AtN/zaGq5sD8s2D3A/D87+Hcf7X39ZXKBz1HpOpZlqvLfvH+rjEfW1w7zc+Hwy/Ag9+W4xPfDstfNb3XU5kV7ZIKrjXz7WlXbqeyahlbIpy9xDelSk3XQalyXjV7zKd89B8dYz62vG6cmPHkT2RTSFkNnPmJaQxyAv1VBA2HJEZVzc3N55nTJYsd5Q2SkNqxF7oPg6dMPs/y4TqrfwNdRbNsoCuFCfqyKnC4pXpJMS86KZUnPvmH5weOZxnHuNp5BwBfT76VX1TGoGqMCoF2sSzoOSztUJtWyeevUukyDKjQTXtKZct/3P7ymI+tmV018Qu8/DfY9xg4vXDCm6ZXHMIultnXxScuG9XMJGD1fab0V/x2yty8wy3np8X0WZOISHvtukVQtzT/5pns4KuUrkjxkFaZVSpTElF4+udyvOQS8Nfw42fHri47oqtqJoTbZWN4/XKZR5/kZoBROZxQ0STzSKEW6Nov80kYkjjrymBHjlxIRqVwh7e/K9SM4owT4/HXSH6IXmsolVG3Pn8ISOGtvxuAeNtZYJbhMOD8pQuo9BXZ56tSGVJiUVplTfchSZIKTK/KRSRh8tyROJZlEfBM4cSqfddgy6INH5VKbirvWckknXc8QOcdD2AlkxN/Q6hFLuSaVstElZ07n/01UDUPwh19Lf9sduJb5M8dd8vvjFLFJBmTRCJvRVYujnvjJs8flWrk9f4MVbvrPQZ3f0nagC44F059b2beR6VlwnhhJmUzRc38/Eya8vglzsTH3hSklJqGaDd07QP/yGSirpg50MFiduUUYsah52DLn+X47M/KomImWKYkRrl80nI7H5L/nS6onAmzT5UFAKdXEpPD7TLeXLAsWbiOdMgGuuY1wz73rUSCzr/eROdfb8JKJHIzxkzxVsrPX6wn1yNRqmh1hOK8dKgbgNrywcXqz7l+j9dI8nBqJfeZJ057DiwtPUdkAbb5BE2WzXN5G3vKquU6JBHJ9UiUKkqtvTG2HRn/vKyyLI2562Qc7v5/crzscmhYNv3BTYVlSZW83mPSWrv7MMT65jA85ZIEFZwFlXMkMchfK9cHqaRsYO4+JNcKoVb53MnE/Hq2xENyzVG7SCpCTvG6LG/jQz+HU7qLxMeudqmUmqYtf4ZwKyl/PTuCp4Nj7OTU+VUZ7upjWX1JrMgcT90ie5Jlh3I4ZNPWjJNh1mmSRBvp6NvkHrb3vXKhv9NRuEM6ic4+VQrIZGH+Lu9iii8oG+v1WkMp2x3qjLCndfD8zFX5LA5PG2aynHj7BgBm13ip9upckZq8vIsnWaIVZpX94mFJVPX4xz3JT8fH7ujk7t2jt0htLHewtNbN+08eo+pnKiEtUs0EzNkgu/RUQbASSQ5/6wYAgmefjuEa4+fIMqUdrNsv7VYDDZkZUNVsSVSIdEhik53ql8HMtXDwGXjhj7DqKntfX6lc6j0qk+OVM7Pydu+9tZ0nDsZHfWxprYuGcif/cto0KkUnY3DXF+WzoGZhX9Vy3SWbSxPGi95WmeSvHLuyZM6VN0DngYmfp5SavM79EI/Iou1x3vrXNl5sGf3C/8RGN0Gvg0+tH6OCUyIMD/ynHC97FcxZZ9eIhzNTcg7qr5Nz3Uwl5U6V0y3nyYFG6D0iFWe7Dsp1oK9KFlqzIRmTCh6+IMxYIdVlj4vPViLB4X+Vbg7BSy7GcBdRa1GHQxabjm7J9UiUKlqv+f4j7GsfvpB7kvEKr3I+jonBHXXv5o7TnbK5IZNCLTL/0bQ6/2KCGiFvY4+nXM6Neo/a1hVMKTXoousfpD00+tzUhoW1eF0OvnDFiolf6OmfyxpLWTWseG32OwnEw9IxykxKa21/HZTXyWeIu0xi3njn+6mEVNpLRCTBNtwiGxrDbbJm5A1IR8BCmVcb6G63TNqUTyMJKm/jw1D+Gvm/MZPTXuNTSh3HsuDxHwHwne4L+MHNBnBoxNM2zvHiccD/OzuD5/393Ss8FdLRqHzk/JmtHA4I1Es8iXT0Fd86LBuwvQGZ18mH7kXpskz5e8Qj8veqWQDl9VmNbXkXUzzl4KuGcKteayhlI8uy2PDNe4fck8Rbdw8A8bazWTsviEWKb77uBIxCOb9WeSXv4kmW6JWOspdlQceevgSp6bffHitZFmBNo4cfXzFO8uJzv4a2HVJ546xPFc7ki0qPmYTuI3Jh1bA8s+2B3D6omQeHnwez0v7F/xPfKgmzW2+DRRfa+9pK5UoyLokznvKsTXKMlSwLcOoMD187t2rqL25Z8PB10LJV4spFX5MFApW/4iH5vK5daP+ueDv5KiXOJCL6M6WUnSKd0HNozM1OYyXLApw7z8fHTx+n3elTP+/rptEI6z40zYGOwUxJ9aaKBmhYKQsH+crlgao5fYmzRyX+dx8Gt1cW+DO1wGomZcHdsqB6nlTfzed/p0zyVcq/cyphb7cNpRTAiGRZsPiC+9cA7Gq8hK+d3QzBDFeXjfXI513jCm2PrKZPN+0plTFjJcsCXHXyLK5am8aaSaRzcIPeyiuhcoY9g5uImZT3TkQkyaWiSc7xfZUyvzcZTrd8eSukyEXNfJmniXXLZrdwG0QOyZyhryJ/k2ctUzas4JBksup5+TlOu/mC4AlIRyRfVa5Ho1Rx2fsIHN0MTi+/iZ475tPetsrPJYsyOFdsWcO7V2RzQ55hyHydvwaq50pc6Novc0lOl3zuuPK4nbhlSR5ErEfmvWYul3iZz2sQ2RRokGRopZRtkubwLg3uqqdxeDoxExUkOtZx9gaTi1fMY0lNhorLKVWkNHIre4XbpP12eW3GJw7mjdeGomMPbPqNHJ/5iVGrSqkCloxJG6jgTGlHNdkJu6kINIFvr1wE2V1ldsbJUL8UWrbBK3fC3PX2vr5SuRA6BtFOCGZpUn8Cs4LTTHR/6WbY/k+ZyL/gSxBstmVcKkMsU3am1y+z/zPbbp5ymQSMdmjCrFJ26jog54zlk09gaq4YJ2Ycewm2/EWON16bmWoJltmXLNsIjasKpyKDy9uXONski8pd+2XRA2TB1e235xoxlZAKHqmEVPConp/1Ch55x1cpyQjxXm3RrlQWXOZ4grWOVwhbXg7Nfz2LbOiwNK5kTCrbNa7MXGcdVVp0055SOTG3Ns3z+oevg0i7FCRZdkXmK3ym4oMb0fy1MpdSVm3vZjTDkNfzBmSuMBGR5NxQq1w7hA/I9YQvmPmK7elKhCHUJtV165dmvvJiPnG6oWIGtG7ThFml7Pb4DwGw5m+ka8vYn7NzKjP82R9qkcT4XHev8FbIV+UsiUXdh2RsZkKSeb2B/Ko6G+2WdWJfpfzbBWfkd3JvLviCEkdS8exXyFeqSMWS5uANI4mnTqrNxtvOBcvDzBovMwL5sR6vVCHRhFlln1QC2ncCliyGZshXz6nkuSNxPjFW1SfLhAe/LTui52yABWPv0FMFKBGGULssjNcvlYpW2eDyyE7HQ5tkwtDOBXnDgBPfJq3ed9wJZ33avtdWKhdSCejYJwtvOZ7M+MKZQba3J3n3mmlM8rduh8d+IMenfwBmrrVncCpzQq2yoFE9N9cjmZhhSOJDz5Fcj0Sp4hHpgO6Dk06Y//T6Cg73pnjdsjESR1IJeOC/AEu6Asw+ffpjPZ5lSbJsoF4SowolWXYolwcqZ0pFqnC7fL6FjkG4Qx7rb+M6mXMEMyXXAbEeoK8SSdUcqZCnFTykonp5oyxoa8KsUhnlJsnnXL8H4Mn613PWbE9mf+8sUzYM1yyAqgI4t1WFQTftKZVV7z1zPm6ng7Vz04gXnfsGWnWz+k2Z3QRsJmX+xLKkMl7VbEmYtbu72mjcZfIVbIZ4WK7heo5IslSype9zKpj5ZOHR9P+7ANQtlnUId54k8WaTv0au2cxkbv4flCpGHXuk0yQQn38hbBn+8MwKJ+fM81Lvd7KiPoPdY6Jd8vudT90rXB6JCRVNUoil95jMj3UfkqTLXG6osCzZoBzplCTjhhUy76Xn0aPzBCSOx8NQpgmzStkhlkgNHLsrn8Hh7sZpVnLWjHOpWZpiTfNMKjzjdMxTSo1Kr3KUfboPyglsRdO0XyqRsnCOsn76obUB3nFCOe84YZyKoi//HY5ukRPnMz9e2pWGik20Wy5K6pdKi+1sTN4NVd4gOxpj3fbvuJx3JlTOlkpcW/4CTavsfX2lsinUApE2qMhOFdZ4ysI9Ssz4941B3nvSNKthxENw95dlR/PcM2D1G6f3eirzEhEwTahbVDi7u31BSfjSVtpKTZ9lSYthMznqxHUsaeEZ5RTyugureN3yCZJTn/8ddOyW88ANH7FpwMfpOSqLFY0rs9NFIZMcTkn8DdRLPI10yPVipFO+LAtcbln4cLj6zu0NwJIEsVRSqlEk4+BwSJvWqnlSebesOvvXAvnO37epz0zpv41SNoklU3iOm5x6o/N+5jqOQXk955x/eV/FowzOO4VaJXmpdqF8Fiplh4FNe4dzPRKlikYsmcI5Sjz464c3cPKcSWysuOerkIpBw0pYfIGNIzxOrEfOySuaZbOxvy53ccbjl6/gDJl3D7fLWlPP0ey35h74d2mE6gXSsaRU15f6u1jEevMnoU6pQvfkTwALs+lEorXLgd5hD//qtbUsqM5w6kYyKr/XTaun1JUp4wxD5nzKqmXDYLhNzlnD7bKhwl0m12DZSJ41k33r0iFJAq1fJrHKzgrsxcjhlGvYjt0aP5SyQSyZGlJhNoWn9n4AXrfkNVy6sJJ4Kk5T+fTzs5QqRZowq+wR64G23XKSOM3dprGkxRk3HmVGYOQi34bZE+xECrXCEzfI8anXyM5oVRzCbbL427hKqknlYqLK7YPgTGjdan/CrOGANW+BR78rCVNKFSozJdUwXN6sJGuE4ibrfn6UVQ0jkwzXzZrmZLplwUPfkUn6QCOc/bnSnSQvFJYl5wJ1i6U9d6HwBmXSLR7SSSSlpqu/uuwo1f46Iibrf3GUM2ePjA+nz5rgOqNjLzz7Kzne8LHMtKUMtcrEf+NKWZgsJp5y+aqcJRsbYr2QCElVk1ivnP+mkoAJGHJu7HRLVSNfpXxGeoPZ6y5RiPr/neIh2YihlJqWI11RNn7rXi5bPbgJ0Eucj7lukhsnvUOS+zP5+xYPy/lt3RKtXqTsp61SlbLN7tYQ533nfl574swRj62eOYk55EObYPMf5fjEt2Xms98yZaM7DqmQVz0vfzo2GIac0/oqpbBEqEWu7UKtcr3gLZfrpExUO433QqRL5v8bVkgScalvaHa6ZHNF6ys6V6WUHWK9A/NK79l3IVWPJUc8JePJspYJvS1Qs1A+Z/Od2yeVXPs3VEQ6ofeIfF6nWmUNylMOrjL71m0sq2/eqkuOfZXS7SPQUPgb27OprArazAmfppQa3zN727nqh49x5UlyneGqfA6HpwMzGeBViy+gPdLOvOA8Ah5N5FdqKvLkSlgVNMuC9j2y4Fk5clJosl5sSdAaNmkNDz+R8jhhw0TJT4/+r4yjfimsvHLaY1F5wLKg+4jsFmxeZUsF42mpaJBdccmo/TsYF18oF15z1tn7ukplU6hFEtwDDVl5u4f3x+iJWzx2ID7s/qZyByvqpnmas/U22HkvGE44/4uafFIIwq2SJFc9r7CSmx1OSfBt36mLEEpNh2VB5/4xq8v+/ZUI0aTF3bujw+5fUediZsU4McMy4aFvS7Xx2afDwvPtHrlUrLAsaFie2dbe+aC//SpDNjakErLpBgsw5HPR4Sqsz/Jcc7qlMljnHj1nUcoGv31iL4mUxS2bDg3c9w7nXTQZHbJgO3OtVHbOFDMl11V1S6RSt1J2G7ZpTxNmlZqOH92/E8uCm547OOz+1544A/dobfTGcu/X5M+5Z8CstTaOsI+ZlHl2fy3UL8nPyoL9nK7hrbnD7dKWu/eYXDd5yqdfvGVY5cByqeYenKnn0kP1X5tapmxqVEpN3fO/g1gXR5zNPGCuwdo2fG7qmpOykIwZapWNyYXWvWLohoqqOUOSZ49JYmu4Q57n9kny7GSLuaTikAhDPAJWSuasKprly1+XPxtLComnvG9znnbUU2o6vnjLi0D/dYaJt/Z+AE6ovIiEmcDn8tEU0OqySk2VRng1fb1HpY28TRMsjjHWRP99YyXOsR4E2Pso7H5ALtw3flrbUBYww+Nm5hf/BSwTI9oCgVrZ2e2vyfXQZEHBXwvhFgjYnDDrdBdfNTFVWkxTEpX6k1yywDFGIs2Xzq7EmE6STfsuePR/5Pi090m1P5V3BuIFYBhJSSyoW1yYFbjKqmQBwrI0QUypqYp0SJu28tpRHx7rV+sLGyeo+rTtDjiyWTZLbbzW/t/RZFQqGjWuktafpcjpzugEuuHxMPO/rx84Llr+GjmH0Vii1LQdfy0RIMyHXbfIjZPeIb9jbn/mBhBqkU2INfMz9x4qo/I+9jic0kmldXvxb9ZRKsPGyjm69sKl6b/Ivsdhx12ytnHCGyXZx05mEroPy6aPhhXgyWAMs9PQ1tzV8yRBKtwuFQZ7W2QeyO2TeSCXd/z5SMvqS4qKSJJsfwJWzQLZxJylFtt5Hx+G8gbBXS6JZFq5TKmpM0144kcA3OO7CCs0MnB8+JQM/44lYzL3XLtYPjcL1fHJs4mwdMGN9kCkTbp0RDvl7zqwIds5POnfTElirNk3F+/ySBypmiubIn2VBVNNNm9jirtc/k0TEU2YVWoahq6Bu4KbcXhbsZJ+/mX9FfTEuplfNZ9yd2F8Xqn8lrfxJMM0YVZNTzIGbTtkd5VNkzgHe1Kj3n9S0zi/mMkoPPJdOT7hTZIsowqW4XQSPPNEmfQKzoSGZflzcWIYsru9+5DurFbqeOFWWdjNYoWKvV0jWxcBLKubxkV4Ki5VPVJxqSR4whun/loqowynk+A56+TzuOugtJMKFGiymTcoSRfJSGaTL5QqVv3VZa3UmF0AdneMHjPmV41zWRzthidvkONTrrb/M8ZMFlY7vAJluFwEL7kk18PIPF8QPGUaS5SywY5jvcNuv8d5BzVGL6mq+TgbV2X2micekrmG2sX2J0yprCmI2FNWJfNcOr+l1LRsPdIz6v21gTQXGi0L7vmqHM8/2/5N22ZKkmUrmuW1C3GTMfR156mVr9oFEO2Sr1CLtDqP9sj14MDmMavvG4dsgnH2JUXVLpKiGGXVWa8cWBDxoZ/bJ8ljvUc0YVap6dhxt6ylu/38InLWqE+p8GT4XCzUAlXzstYZMCsMQ9aOPeV93UkXS4Jm/1cqBomo5BCYSYkPDgcYLpk79JTJn+4y+YwrwEJceRtTnC4pEBVqAbRyu1JTYVkWmw929d0y8dTeC0Cq80wcDlOqy/q1uqyyR97GkwzThFk1PR37ZEdv5UxbXi5lWnzkHx0j7v/R5dWsahgn+em530il20AjrH2XLWNRORTtlipbNQsl+dmVZ7sYympkx3k8pBVhlerXX13WIGs7RqNJi6891D3i/t9eWTt+8tNEnv6FVGfzVcHZn9OFw0Iw0E5qQeFW1HOXSUyJdmmSk1JT0V9ddoyOBD0xk59tCo24/+9vrqO5YpwJ8ad+Ir+X1fNg1VU2DXaI3mMyqV+3uLDa4an85CmX85dIu8YSpaahtTfGbZsPD9wOEuIa120AOE98iySxOjM0T2GZMs9Wv3zMiulK2cYbBJdfKwcqNUWWZXGgI8Jz+zpHPHb3tWdR7k1zbmrX/bD3YamOesIb7e/a1HtUOlk0rSrcZNnjOZxy7eevkWrsiahsGkvGpP2zmezbDGDIvJ7DBU5vXzVaf0EmReWMv0Y6TCqlpu6JHwLQMfMcdmwd+Tl83zsbcDszOKcd7ZKKnzXzCnfuPF3usuKJdYWurFqKTymlJs2yLDbt7xy47ap4CafvKFbKy7eueB298V7mV83Hr/O/Sk3LpFfkHnzwQV71qlcxY8YMDMPg5ptvnvB77r//fk4++WS8Xi+LFi3ixhtvnMJQVd4Jt0PnHtlhakMy0Tce7uLEHx8Z9bH1M8epqNF1AJ7/fd8TPzJmRSlVACwLeo9hxcJ0b2mj+9l9WJP/mMo8t0+Ss2OjVw9QgzRmlJBIuyT9lI2eqGS3L9zXyQk3HB71sVNnTGPx+vDzgzHlrE+PmXil8oOVStF994N0P/wsVtWCwp4MMwxpAZiI5nokOaHxQk3LBNVlr72zgxNuGP06Y1XDODHj2FZ4+e9yfMYn7F+4jnRIkkr90vzbIFZkrGSS7jvuoPuOO7CSo1caLhrl9ZCI5XoUGaUxQ2XS+//vaU752t3D7nuH8y6CRkSq0TUsz+w1QqgV/HVQPSdz76GyoiBij9sHZZXSurZIacxQmWJZFu/42ZNs/NZ9oz6+qCHNIguWBff2VZddeB7ULbFphH1CLZIkVb+ssOdMJuL2SWJORRNUzZYk2tqFULNANj9WzoJAvWxUznGybEHEh6E8AanGaBbAWKdJY4bKiNYdsPNeTAxete2iUZ8yreIfEzFTUqSoZr4WACpCeR1T3H7AknOdIqQxQ2VKMmVy2f88zJU/eLTvHgtPrVxzJDo20Fjpxufy0egv0G6bKi/ldTzJoElnooVCIdasWcP3v//9tJ6/e/duLr/8cs4991w2bdrEJz7xCa655hr++c9/TnqwKo+kklJ9L5WwrQLBDc+G6ImPftIU8Iyz4+2x74OZgJmnwLyNtoxF5UAqIe203X6supUc/MI3OHjttVjxeK5HNrryOjnJN1O5Hkle05hRIixLfn+xstYy9Nebw8TH+PWb8m7seAju+zpgwdLLYN6ZUx6fyg4rGuXg13/EwW/9H5anKtfDmT5vhVSYLMHYovFCTcsE1WX/ujXCpKdmzRQ8cj1gwaILYcaJ0xzkcZJRSU6pXwy+SntfW41gxeMc/MQnOfiJT+bv9YVdfEFpfZdK5HokGaMxQ2XSnS8dHXa7jCjvdd0uN1ZeCR6//Rso+iWjEn9qF2btukplTsHEHn8tJPN4fNOkMUNlSixp8vCO1um/0LZ/wMFn5HN/9RvtTeaM9cicXeMKvebIIwUTH/p5g3L+Ex/ZsaXYaMxQGfH0zwC4N3UiB6yG7L9/uE021lbOyv57q4zL65ji8Ut191SejcsmGjNUpmw+2MXLhwe7qzrLt+MsO4hlekh0nElPrIfmQDPl7vIcjlIVm7yOJxk06RneSy+9lEsvvTTt5//oRz9i/vz5fOc73wFg+fLlPPzww1x//fVcfPHFk317lS+6D0L3YQg2ZeXtnI4xkp/2Pgr7HpPFijM+VpitJFKJvjZBib4Emb7dVoYBGDJJ5nD1tQ3yFObfcSKxHoh0QdUs2UWfLIC/o69KEpviIVmUVqPSmFEiBhKVqnM9kul59H/72tQ1w/qP5no0Kh2hllyPwF6egFSbTEZKriWqxgs1Zf3VZc2kvZ0mtv4dWrZJNaZ1H7TvdUFag/a2SLWj4Ex7X1spTwV4yiERAmdVrkeTERozVKYkU+aI+97ivI8ao5e9ZgNzG1fJXECmhNqgai4EcrCQrkqXt0LmHM1k5pLBc0hjhsqUWHJkzJg004T7/kOOF10k1f/skorLfHvjSo0ranqcLql+37m36BOvNWYo28VD8NxvAPhVavTqshmViss5Xs0CcLqz//6qtLn9siEoGSvKDaEaM1SmGMNygSy8dfcCkOhYB6lyrS6rlI0y3uv8scce44ILLhh238UXX8xjjz2W6bdWmRLrgbZd4AvkdiI1GYPHvifHq98giwqFIBmTHX1dB+Ur3C73GQ45efQGpX2QNyi3DQckE9Iyo+ewfE/3Ifm+RLiwWxmYKfk7JWPQtAqaTpDF3ULg8shkY7w31yMpKhozClTXQfsTlbJt90Ow/Q7AgHM/L7tfVX6LdBb2z9xo3D7ZhFHELVHtovFCDejftFFeO+rD1lTOlSOd8NRP5fjU90jlNTuFWqUabu3C4twMp3LL6ZLqMfFIrkeSNzRmqHQdn/zkIcH7XX8H4IepV2e2jXO0S+aAauZpbFDZVUKVA9OhMUOlK5a0oTPM1r/D0S3y+b/6KvtijGVCz1GonitfSk2XvxqsVGGvRWWAxgw1oc1/hlgXCX8jD5qrs//+oTYINMkcgVLZ5nDKtUYymuuR5AWNGSpdQ+sIOv27cfr3Ypku4m0bMQxo8jdpdVmlbJLxbMcjR47Q2Dg8w72xsZHu7m4ikQhlZWUjvicWixGLxQZud3d3j3iOyhHTlGTZRAgqc1wN6YU/SuKovw5OekduxzIRMymLH/Gw7KLyBqFqHngD4C6TlgROj7RhPp5l9VWhjUmLtGQEYr2SHJAIyYK+ZUmSjSdQOLu0Yj0y9kCDVJUdo4VuXiurAXbJJKSR8f0HJUFjRgGaoA12QYh2wkPfluMT3yLJ+yq/JaOyoFu7JNcjsV95rfxOqXFNJV6AxoyiY1kTbtqIT2Ud+4kb5Fy1dhGseM30xni8RFjOHeuWyHWAUplQVg3WziGdS0qbxgyVruMTZq9yPkiT0cFhq4a/pjbyzbKqzLyxZcom6abVkpSrVDaVUOXAdGjMUOmKJaZZYdayBueiFl0IVXOmP6h+oVbZ9Fe3OHMbPVRp8QblmjsZ1evYIXQtQ43LsuCpnwDQMfNcrPYsryEmo7JuWTN/9LVnpbKhrEq6FiuNGSptidTgBiVP7QNyX9darFQFOKCxXKvLKmWXvDxD+sY3vkFlZeXA1+zZs3M9JNWv5zB07Yfyuqy95YKqUSZ1eo7Ac7+W43Ufyt9KgKmEtPfuPSYTCs0nwJx1MPt0qFsEFU0yGe32jX3BYhhSzdRbIUk0lbOgYZm8zpwNMOtUaFgu7WKj3dB1QN4zHsrPHb+phCQ6J2PQsAJmnFy4iXa+SmmdrVU4ckpjRo51HZLWPlmcLE2ZY3+2rWmcQmuhx34gmxpqFsDaq6cxMpUVZkpamVfPh4oZuR6N/TwBif2mDdVq1AgaM4pMtBN6Do17LhlLjR0zzpztGXnnsZdh+z/6nvBJeztqWKZU96ier21RVWZ5K+QaM6lVZqdDY0bpGVot0IHJB5xSXfYnycs5f543cxtlw+0Sy4I53piuSld/5UA1ZRozSs/xmyyGetMpafz/77wHDj8vxS9WXWVfjIn3daSrW6yJjco+nnK5xkhoR6Tp0nhRQg48BUc2g9PDsVkXjfm0a07KUJXAcJtcX5RVZ+b1lUqHO0/zNwqExozS1D835fAewRXYhmUZxNs2AvC2DTUEPIFcDk+popLxhNmmpiaOHj067L6jR48SDAbH3JH9+c9/nq6uroGv/fv3Z3qYKh3xMLTtkIU3m6uYjtUq9Xevq+XmN43SKuKJG6TiavMaWHierWOxhWVCqEUSZctqYeZamL0OqudJkqUdu/kMQxKFAw3SznXOOvmacZK02EjGJTG1+7BUyLKmuet9uszk4L9JoAlmnSJJw65REhUKhdsnScyaMGsbjRkFJtoliUpZnnQZK/npT6+v5bdXTrJt9oGn4JU7AQPO+jQ4p5Bwq7LHsmRTSEWTxJBi3B3vCchmDG1VNK6pxAvQmFF0Og+AmRh3ITiaHD1m/Pn1tfzkiuMSbS0LHvu+HC+5GBpX2jVSEWqVCm7V8+x9XaWO5/b3LWhrwixozFDpiwwpS36h4xnmOY7SYQV41flnc/0lGdo4biZlA2LNgsKeH1GFzVsxWDmwxGnMUOmKjNHK4i8f2sBXX7tq4hd48Dvy54Jzocqm5AfLkgSp6nm6QU/ZyzCkpbvGiWF0LUON66mfyp9z1tHtbRr1KX95Qx2f2xC0/70TYXB4JL5o1xmVS+4yWXNLxXM9kpzTmKHS1RtNAuCpkeqyyZ5VWIk6vvR6P5+8YEUuh6ZU0bGxXM7o1q9fz+233z7svrvuuov169eP+T1erxevt0DaypcKy4L2XRDrtrXiRShuct+eGA/ti436+PpZo/wcHH0Rdt0HGLDhY/l3sh/vhXCHLIY39lWPykbrI8MAX1C+KmfJ4mi0SxbmQy1SlRdkN7Cn3N5qWeMxkxDplIqy/lponAeBxuJJcvLXQsfeXI+iaGjMKDD91aKzVHW8J2bywN4YN28bvZrBqTMm+XOQiMBD18nxqtdJ1WuV38KtklBav0w278SLsLKF2wfegFTO9GSowkARmEq8AI0ZRSXSId0vykavLtsZNXlkf4yfPNs76uOnjBYzdj8AR7dIwsip19g5Wok5pikb3dw+e19bqeMZBpQ3QM8x0GIeGjPUhNp6Yzyxu52v3/7ywH3vcUm18d+mzuMjc6rBk6E5lFCr/L4GtKWeyiFPQDpXxcNyHlTCNGaoiRzrjvLsvg4++YfnRzxWWeZm7dw0NpXvfRT2PSrz86uusm99I9IOviqonmvP6yk1lLcCLGStMN/W5HJE1zLUmHpb4MWbAHgscCFvvblzxFMWVbtY25yhDXORDqiaB2VVmXl9pdLlLgOnV9YRnaW9QVRjhprI/vYw24708P5fPYPh6sRVKdcb8bazWT7TwYYFM6j2VeZ4lEoVl0nP9vb29rJjx46B27t372bTpk3U1NQwZ84cPv/5z3Pw4EH+7//+D4APfvCDfO973+Ozn/0s73nPe7j33nv54x//yG233Wbf30JlXs8R6NwnSVE2Xgz/631d3LJtElVvLAse/6EcL70UahfZNpZps0ypnmo4oX65TEzlsjqIu0y+KprkRDTSKZNmvcfky7LkcU+57RWDAdnBGO2SxAB/rSSiBRrSqt5ouN00f/3rA8d5zRfsa3caLflFhdFozChisR7oPghl2Ts5/9gdHdy/d/QNFlPyzI2SbFXeAKe8177XVZkR7ZKJ+Ybl8tlLgcWLyfDXSiXdEqLxQk1a10GpTjBGddmr/9bGc0cS6b9eMgZP/EiO17xZqufYZaDS0wKt9JQjRRsvxuOtkE2KZio7GzizSGOGstsbb3iMnS2DnWNWGbs43bGVhOXk/5IX8ZFMLUik4hIjqucW3e+pKrDY0185sHVbrkdiO40Zym6X/c/DtPaOPjdljtFFb4SH+qrLztsoG+rskIxKx7kZK8ftwKFyr6Diw1DeCvnZSkSk82ER0pihbPPcryAV5wVzAW95avTP+Yz1BE2EweGGSvuKX6n8lfcxxemWPIRop8SRIqIxQ9lt47fuGzj21DyEYZgkQwsxo7MAg6by0auVK2WHvI8nGTLphNmnn36ac889d+D2tddeC8C73vUubrzxRg4fPsy+ffsGHp8/fz633XYbn/zkJ/nud7/LrFmz+OlPf8rFF19sw/BVViQi0LZDTmpsTggcLVl2SY2L7e3J0b9hz0NS9cnphVOutnUs05KMyY7B8jqoWwLlk2wJnmkuL1Q0ylftIkk6inZBz1GIdsv4Hc7BJFunZ/KJ0WYKkhGpRpGKy89KxQyoaJbEH2f6HzeG203V666c5F8yRzwB+YqHNGF2FBozilj3YYkP/ux93o2WLFvtc9ARncL0Uut22PwnOd74yaKd6C0a8ZDEl6ZVw5LNCipeTIY3IHG4hKp2aLxQkxLplA0P/rGrN00qWRZgy19lk6C/Dk540/TGd7xIB3iDUDO/ZH6n803RxovxeCvA7ZfFsiJblNCYoew2NFkW4D2uOwC4zTydo9RkrkNPuE0qy/qz07FDZVfBxR5fX6xIN+GvQGjMUHYbK1k2bYeegx13g+GA1W+QP+3Q2yrXGxW6kJ7vCi4+9HOXyXVFtKto51E1ZihbmCl4+hcA/F/qwrGfl6lzrnAHVM+DsjQqnquCVxAxxVdVlMVBNGaojHGEcVc/BUh1WQC3w03QE8zlqFSRK4h4kgGTnvE955xzsMY5ibvxxhtH/Z7nnntusm+l8oFlQdsuWeStnGXby8aSFl7X6IvF37moirfd1Man1h33oZ9KwBM3yPGaN9lb9Wk64qHBC5C6xfnfYtXplsTe8jqong/xXqkU2V+BNtYjCbQgC/pOtywOGY7BCTzLAisFZlL+XyxTHnP55f+lvF5afRTZwuyoDEOSt469PPFzS5DGjCIV64Wu/ZCl1g/jxYwbLq/mvbe28+9nTWIsZhIe+C/57FpwLswZv7WiyrFkVM5D6pZB5excjyY7POWyeSUZLZnKMBov1KR0HZTfj/KRCUbjxYxfvqaGj/6jg2+cVzX8gUgHPPdrOT7tGnt/71IJ2WAy48SiXVRUecrllQWy3qNFd12mMUPZJZZM4XUNr+zaQAevcjwGwO+4jJ9fnqEFiWRMuidUzZFq0ErlmicgsSMZzfVIbKUxQ9lltJjR77o3ruFLt7zIDe9YO/ELPXSd/DlnHTQss2lwPXKtUT1XN+ipzPLXSvfCIqUxQ9li533QtQ88AW6NDl93cBrwlXMq+eYj3XznogwktMZDcj5XVSJz6KoweMtzPYKM0Jih7GBZFvGUOew6w1P9GIYjDvFm3n7yWv78ZA9fuGIphp7nK2W7DJVIUEWj5wh07oVAvW2TLbdsC/Pxf3by+TNGLjq8cYWf1Q0eNr2/Ccfx7/fyrX3tv6vhhDfbMpZpi3ZJxbv6ZVC7oPBa6Dkc0tbaF5T2HKYpFYgSEakWm4hKQm0yBmZicMejYYDDK7vCPIG+yrR+SfCxIWHYSibpffhhAAJnnonhyvOPqv6kwf7EYaWKXfdhmXzJwsTL77aE+Py9XXzl7JEJsR9cG+C0mV6e/8AoMWM8m/8Mba9I8siGj9k4WmW7ZAxCrVIdvXbhiHORgosX6XL7wV0u8bhEEmaVSlu0C3oOQVnNiId++lwvX3uom29dUDXisS+cGeTsub7RY8YzN0IiJJ0iFl9k73hDrdJxoaLZ3tdVk1K08WIi/hrZ5KSUGuG7d7/C9Xdv53/fctKw+9/puhO3kYKmE/jd2QtwVAYyM4BwOwSbR938oYpDwcUeT7nM8SVCEz9XqRLzjdtf5scP7eKn7zxlxGO/ePepnLusgdeeOBOHY4K5qZZtssYBsMqm6rKWKYUwGlYU3SapYlVw8WEoT995UQl1RFJqsp6/7QesAQ7XrSfW7Rn22K+vrGX9LC9vWeWf3HpGusIdMoeepUInKvcKIqa4y8BwSiEbpdQwn/zDJm7ffITff2Cd3GEkcNc8CsDrlryacxakeP1pc1lVNzOHo1SloCDiSQaUxt9STU08DG07pMKoja3mP/7PTgC+8Uj3iMfef7LsMhpxoRDrkYVsgLVX50d1pkgHJOPSHrpydnFMEDgc0gbaO8qCkGXJBBwARkYroFjxOAc++CEAlj77TP5/IHsCfYlN4cFJI6WKVX912bKqrLzd5+/tAuCLD3SNeOxdJ4wRM8bTfWigJRKnf0gSSVR+SsakakXNQkliGyXuFFy8SJdhyM9m+85cj0Sp/NN9SD4fRkkw+tpDcn3x2bs7Rzz2xpVy/TAiZrTvHly0Xvdhezc/xUPSqaFmfuFtrCsyRRsvJuKtAMOlixJKjeL6u7cD8LHfDVZ38RHjrc575Mbii3D4qzLz5skYYEDlnOKYS1KjKrjYYxjgr4PWtlyPRKm8c8ODuwB47y+fHvHYucsaACZOloW+6rIWzDwFmlfbM7hQi/zuajXBglFw8WEob0AKppRQRySlJiXaxdKOB8GAD+w+a8TD62d5gUmuZ6Srv9q4xoOSUhAxxe2XPJMi62ShlB1u3nQIgNf9QJJk3ZXP4HCFMONVvGbZBrpiXcwINGt1WZVxBRFPMqD4SyHGeqB1x2BlTJUe04T2XRDtlDYrWeJ3j/Ejuem3EOuGqrmw7LKsjWdM4XYwU9C0WtrnlUKQMgxZ6Hc4tV3g8dw+KKuUJHOlil33Yak8nQdVK/zuSX72WpYsTqRiMOMkWHppZgampi8RkUWfmoVQv7Q0E828FXI+ppQaFO3u6zhRNelvHTNmPPFD2RQ2byPMOHFawxvGsuSaoWqObs5QueOpkAWzuFYLVCodVzofpsboheAMaDxBFvYyIdwGwSaNDyr/+Pq7gek8ulK269gDm/8kx6uuko1105WIyHVH3WJpwa1Uprn9fdXIdR1EqVG9eDM+I8Er5kxesBZk730tS6qNV83Ni3UbpYZxecFT1rdxVCk1NhNP7UMAxNs30pPooaashmpfdY7HpVTxKv6st0gH9B6WxE+Vvp7DMolTXp/VZNByzyjv1XMEtvxZjk//gD2TSdMR6ZBk2caVUKnlz1Uff61UHFaqmMVD0HUga9VlJ1I22YTZV+6Cg09L5fSNnyqNzQ6FKNYjSWa1i6FhGThLYxfbCJ5y+bunErkeiVL5o/sQxCPy+zFJrtEqPu1/AvY/KdcXp3/AhgEOEe2UFnhVc+x9XaUmw+mS6xRd0FYqDRbvcd4hh0sugYxVl41KNfOquXo9ovJPf+XAlFYmV8p2j3wXrL4CHLNPtec1Q61SrXyU7htKZYRhyJphQqsEKjWq538HwF9SG4EsnutHu2QOStesVb4qq9EKs0pNwFWxBYenDTPpJ9F5KqZpMqN8Bg47O+IppYYp/t8uV5mcKPYcyfVICkesB1pfkZYqWdyZ/On1FVR6R/mRfOqnkiwy4ySYsz5r4xlVtFuSIhtXSsURpfp5K6QCorY7VcWs6xDEe6SSQI594cwgHuckJp0infDY9+T45HdD5axMDEtNV7hNErMbV0LdktKsLNvPUy7nsTqRpJSI9UD3gSklMH31nMqRd5pJePyHcrzydfbGBTMJsRDULJDqnkrlUlm1bPhUSo3rbMcLLHYcJOH0w+x1mavMFO6Aimb53VQq37j94J78xiSlStX/vOWk9J7YcwSe+7Ucr7zSnoIg0W6Zn6vWDRgqy/rPkbSrp1LDte+CfY9hWgY3p84Y8fANl2fo/N8yZc6seq6s6yuVjzzl2sRCqXFZeGofACDRsZ7PXFFFlbeKGp92JlIqk4o/YRbAtKQaUaw31yPJf2YK2nZIu+0st4b76KmjLEa0bIMdd8vx6R/M7eRPPNSXxLNCd+mpkTwBWVhIRHI9EqUyo7+6rK8yLybirzl5kkm7j30fYt1QsxDWvCkzg8oXZko+i2I9kigcbpdE1HC73I71SKW5fErwN5PSZt1wQfOJUDMfHKVxmjomp1taompcUUp0HZLfhyls2njHCaMkfmy9TTpqeINw8jumP76hwm1Q0SAJUUrlmjcADjektBuGUuN5r/N2ANxLL4Ty2sxc8yRjgCGbNPLgmkqpEQwD/HX686lUml69Js2CGo/+r5yL1S2FORum/8aWJUViqufJuZ5S2eT2S6GdlLbWVmqY5/8AwMPmKo5QO+LhixdmKJk10imb8XQOSuUzd5l0WtHNFkqNyunfibPsIJbpJtGxgWWzTJoDzThLuaiQUllQGj1uDQPiYdnJ612U69Hkt8590HkAKhqz+rb+0dpqW9Zg1adFF0L90qyOaZhkTKqANCyHytm5G4fKXy6PJBKGjmWuEo1SudRfXTYPKrMuq5vk6cv+J2HHXYABZ33ankoe+cRMSUJzIgSplFRldXkkOcbplr+v4ZDd5mayL6E2KtVI+ivOOV3g8vV9Za+6PCCLPLEemdSrWyJJokqUVcumL6VKXX91Wd8olWIncPFC38g7473w9C/keO277T13S8bANKF6vny2KpVrnoBU8kiEcj0SpfLWIuMAZzk3Y+LAMf+cKcWbtEQ7IdAg7SiVyle+oFwX6mK2UuP6ymtWpvfEcPvgtceKV4N7lOuTyepvvR3U5CiVA26/JD4lohIvlFIyD/T87wD4S2rjiIe/f2mGqsuaKSmMUbc4+3P6Sk2G2y8xI65zU0r1s4Zccw9Ul+08hX99dQOV3gC1ZSM3Xyil7FU6K3i+IHTth+AMbYs5llCbVJctq5QEmyz6/BmjJMfsewwOb5KxnHZNVsczjJmE3mPSUrVmvlZZUGPz10gFTqWKTaxXYmhZVV58Bn5stIrkY0lE4OHr5HjV62TjQ7GIh2SRxLIk2atyjvwf9U8+OD2jJ2uZKalskozJn4mwJM9GO+X1UnEwnDL57S7L3GRbPCwLR55yaFwlG1I0uWw4T19VTMvKi989pXKm+7B8Vvknv2nj3WtGqS773G/kM69ytixa2yncBsFZUF5v7+sqNVUOJ/hrZWOfUmpU73HeAUBoxgYqgk2Z2WBnpiCVlHnJUu+koPKbp1yuA5PRXI9Eqbx22eo0k1Wf+JFsXKqeDwvOmf4b97feblqtrbdVbjgcsvmnY4/MQyqlZD27cy+4fPwzeuqIhy9akKHk8kgHlNVqdVmV/1xeOW/RhFmlBiRNSZh1eA/hCryCZRnE2zcyvynJjMAM3I7s5mspVYpKJyvBWyGJbL1HJelRDZeIQut2SQ711mXsbaxRqhO8akkZb1hxXBKzmZTJJIBVr4dAdiveDrAs6DkqCxp1S2SxUWWc4XbT+O9fGDguGN4KmTAyU/qzoopL9yGpxleV3QrbKXNkzHjdsrLRqwWO5ZlfSIX5QCOc+l4bR5dD0W758vihaq783XyVUlU2HQ4nOMpGLqz0V6rtT8QNt8r7JGPyPZ4ycE0zgdYyJQE72i1VVWoWQPWcKVd3LNh4ka7+hOVUXKsEqNIV65XruAmq/cVTI2PGm1f6WTfzuM/G7sOw+c9yvO5D9iZFxUOyWaF6jia555mijxcTKauSn02tFqgUAJF4auC4ih6udD4MQGD5BVLhPxOinfK7WJ65OTeVXwo29rj98qUJs0oB0BVJDLvtdhq854z51AXSuEaPdg+ucSx/1eCm2OmIdsu1kSZHFayCjQ9D+YIyx6eUEn3VZaPNpxF9ZTA+1JU5eNtqP25nBuaIzKTM2zesyHoRLJU/CiamGIZcD4dacz0SpfLG3rYwAJ7aBwFwR9fw5tNmUuEpp65M545UdhVMPLFZ6STMGgb4KqBzn0wm2NH6pliYplSWDbVA5cyMvU1PzGRXZ3LYfbODTv73klEWI7beJv9X3iCc9LaMjWlC4VYZQ/3S9BOR1LQZbjc1b8vh//tUecrB5YdkRFqfKlUMYj2D1WWzqCdm8vTh+LD71ja7ue6iSSxgt2wbTIo681pZ9Ctkib6KrO6AVMqtaAKvjZ81DqdMePuC0tbPNCVROt4LkU6pmhjtglTfYpHLK19OjyScjbZRwEwOVrNNRCRRxxOQuFrROO12twUbL9Ll9su/cTKiCbOqdKWxaaMnZnLXruFJHZcs9PHN86tGPvnJH4OZgJknw5z19o3TsuQzum5J5pKt1JQVfbyYiCcA7nKJyUqVMMuy6Ikl+eNT+wfue4vzPsqMONQtxahdlJnWwpYp3RXqluhidgkp2NjjcEixiUK/flZqmizLojeW5McP7hx2/8fPX8xHz1uc3os8/XOZRwnOhEXn2zGoIdVldX2rUBVsfBjKUy7dqczkxM9VqtjFw1gv3oQB/Dx27rCHPntGkDceXzDKLpEO6SYTaMjM66uCUFAxxRPQDoNKAaZpEU6k+NebXsBwt+MKvgDAm1e8hrVzTWaUz8Dj1LwklV0FFU9sVFpRyVsp1Yl6DmuV2aG6D0iriEADGJlpC/enl8J85u5O3rJy+IXBKMUDZRHhmRvleO27c5d4GO+VZKHmZVOufKdKjMsriWaRNk2YVcWj6+CU22BP1S+fD/GlB7q4cunwCqipyRQuMJPw4LdlcXrh+TDndHsHmU1mSjZwWEDNQqiaY2+i7FgcjiEJtDOGV6CNh2RSLhGWxZpUoq+yhIEMlL7qig5wuaUybcUMSSIrq9Lkz3Q5nOCrgp5DuR6JUrkR651w08YPnu7hvx7t4fz5wxeMRyk4C0e2wK77AAPWfdjeKrCxbvBUZL0au1Jp8QSkanmsJ9cjUSqnvn3nNn54/05On18LgIsk73TdKQ8uuQTKpreZa0yxHtmMXa6L2apAaCVkpfjyrS/xq8f3srx5+LpA2nNTiQg89n05Xnb5tDcMA3LN4Q1qdVmVe+5yub5IRHI9EqVyb+ttGPFe9lv13NiydNhD5qiL4DYwk5CMQ+M83ZCnCoe7DJw+BtaPlCpRH/vdc9yz9SjRhIm38WEMwyTZu5gKZyPlbhd1fr0eVypbSith1jAk8VGrzA4Kt0PrdmnrnMHklc/c3QnA714MT/zk538nSTiVs6RVUS6kElJNr2Gl7s7LASuVIvz0MwD4T1mL4RylamG+8tdIUr5SxSDa1ZeolN1KeV96oAuAm7ZNY9J185+h7RVZSNjwUZtGlgPJKPS2QHk91C6ShctctfkeWoG2XyopY0zFJaHWSknFE8OQShNOFzi9MhmSgXEXdLxIl69Szl2VKkXdhyRBf5wk1G89KgmAd++eoG2wZQ4uWC+9VD5T7WKZ0hq1abU9bVaV7UoiXozH4ZDKM1nuGKBUvvn+fVIl8LFdbQBc6niSZqOdHmc1Fc1rMrfxNdotrVJ1HrKklHzsUarA3fjoHgC2HOwedr+VbpLHs7+C0DHZLLHkoukPyLIknjSu1HhS4IoiPrg8spk/0pHrkSiVe8//DoC/ps7kWGz4QxlLCwy3yzpBeX2m3kEViIKKKS6fxI/kBHO4ShW52zZLHonhDOGuegqAeNvZhBNhmsqXUOYqG+/blcqIgoonNiqthFmQpAOtMisSEWlXnYqDP/u7kj0O+O7FVcPvDLXAC3+U49Pen5udcZYFvUclYbdqTvbfX2HFYux717sAWPrsMxj+AmoD17/A1p8wplQh6zwAyVheVJfxuw2+dm5Vek/uPgRP/0KO132ocFtjRzv7WrcuhpoF+VmV1ekCZ+4qahd0vEhXfytUjSuq1MR6JqwuO5ag1+D/OyM4/M6d90HLyzI5e+p77Rljv0inxBqt9JS3SiJeTKR+Sa5HoFTeudp1BwDW0kulumwmzrViPTJPUNFk/2urvKaxR6niUxfw8vZ1cyd+YjIOj3xXjpdeCmW103/zWJcUg9FrjoJXNPGhrBp6juR6FErlVvfhvk5G8NfUxmEPNQccXL44A0lPqYR8Vc/T9vaqsGKKu0zWuJLxXI9EqZwZWnncXf0YhiNBKjKTWvcS1s4vp8GvhfxUbhRUPLFR6Z1JGYZURuvYA4FGqaxaiswUtGyXBNXKmRl/uzKXQSQ5GAA2zvHyy9fU4Dh+MeKpn0MqBo2rYN5GciLSLhUJaxfpxYaaPI9fdvknI4NJTkoVokgHdB8Ef+6TTV+9pIz/vrhqZMwYjWXBQ9+RWDLzZGmtWmgsS+Kz4YCmE2QDhyZKlq7+iaRUTBL9lCoV3YchEQb/rEl921tW+fmPcyuHx4xkDJ64QY5PfJtU2rSLmZLNDTOWaKUnpZQqICcZr3CyYweW001w8Zn2tMoeTaQT6pdLFTallFIFwbJG1gT8+PmL+fj5i3E40pif2fxH6D4AvipJmJ3unI5lQrSnr6OFzjerPKFrH0rBC38Ay+Qpcwl7rcENcv96ZpBrTipPbz1jssJtkt+g1WVVoXE4oXJ25q69lSoA3dGEHBhx3NWPAnDezMt5zaVuZgebKHdr9zqlssmR6wHkhDfYV7HoYK5Hkjsde6S9b0WjJORkWGP5yPcYcaHQthO2S3UP1n84N8lByagsqNctlh3bSk2W2w/uckhoSwlVwCwLOvZJBfIcTH7W+4fHDI9zlJgxllf+CQefAacHzry28BJNLUuqM7h8MOMkaUNeaH8HZS+3X34etFWRKiX91WXTmEAtcw3/jAx6jJExY/OfBtuhnvBGO0cqm+3K67RyoFJK5bmhVTxgsLqsseA8KK8FRwY2TMd7wVMOQa0GqJRShSQUT424r8LnSi9Z1kzBQ9fJ8ZJL7EloinbJtZFWl1X5xOOX5Cdz5O+LUiXBsuD53wHwl9RZwx6qGG1uyg6puLxv9Vz5/VOq0FQ0asEBVdLaQ1Jh2V31NA5XGDNew8LgCXidHhrKtbqsUtlWmgmzhiGtPbv2QbQ716PJvu7DUl3WXyUJRRl26/YIe7qGXzQ7R7tOeOJHgAULzoWGFRkf1wiWBb0tUDVXJ5/U1BkGlNVI4rVShSrcBt2HJAEoy/7ycpiWsDnsvrQnlyId8NgP5Hjtu6UyayGxTOg5JJXwm9fk5N9f5SGHQxbGNK6oUtJ1UKq2TrCB7TebQ8O6WAC4j7/QCLfBpt/I8Wnvk4rNdjGTfW3w5oLTbd/rKqWUspVlWfzwgZ0Dt5to4zLHE3JjwbnSUjgTwh0QnKnVZZVSqoBYlsV3794+4v6gL83z/Rdvgvadci2z/IrpJzSZSYiH5JpDE0xUPunf4K1UqTq8CVq2ksDN7anThz1U4clQ+kWor7qsX9cNlFKq0MSTJv95x1YghafmIbmvfSOGI06Dv4GgJ5jbASpVgkozYRZkwiIRkSqro7TYKVqRDji2FVxu8GR+wv75o3E+dkfHiPtHbMbe/yQceEoqepz2voyPa1ThNlkkqV2o1fzU9PgqdGe1KlymCR17wbDsTSpKw727o3zqrs4R96dTwAOAx74PsW75HLe7gmCmWRb0HAZfjSTLllXlekQqn/iCkErmehRKZUe0S6rL+sdPXvrjS2H+7b6uEfePiBlP/0Ku++qXw6LzbRwocv1QXi+LFUoppfLWD+7fyX/9c9vA7Xe67sJlmBwJniAdHTKxmTzWI9VlK2fa/9pKKaUy5su3vsRPHto94v6AL41K5KYJD31HjhddZE9RjnAbBBogWGCbwlXxc7q1S6MqbZukuuwdqbV0M7yFdtCbgTXmZAww+qrLlm56h1JKFaq3/fRx/vniUVzBLTg8HZjJchKdpxDwOmn06/qCUrlQ2mdU/hroOgDh9lyPJDviITj6srT09ddm/O0iCZPX/KF11MeGLWSbqb7qssDKKyE4I+NjGyEZlepQdYvBXZb991fFxe2XCaNUItcjUWryeo9K4maWdyn3xk3ec+vo8TithNl9T8COu8FwwFmfyUxL1UwZSJatgubVOtmsRnL75c9S2uSlSlfnAVkE8JSP+ZSumMln7+4c9bFhIaNtJ2y7XY7Xf1hihF1ScVkQr9I2eEoplc86QvFhybI+YrzFeS8A2+ovylx12UhnX3VZPbdXSqlC0dYb48ZH94z6mNuZxrXE9n/AsZfkGt6O6rLJmFxzVM8HZwHNc6nSUVZt73W2UoUiGcd84Y8A/CV11oiHA5moMBtuh2BTVtb3lVJK2etIV5Sn9nQAFp7a+wFIdGwAy01zsIoqb1UOR6dU6SrtKxl3GWBCx+7irwaZjMOxl2VHckVDVt7yot+0jPmYMbSC6/Z/QvsuqXh70juyMLLjWBaEWqFqjlaHUvbwlMvnSzKa65EoNTmpBHTsAZcn662lz7zx6JiPTZgwmwjDw9fJ8aqroH6ZfQPLhtAx8FRAkybLqjG4y6TyWSqe65EolVmRTug5NGHy0tqfHBnzMUf/dYZlweM/AMuEBefIZ6ydQu1y7VBeb+/rKqWUso1lWZz01buG3Xel82GqjV72mfUcrjgBPH773zjeK6+r1WWVUqpgxJIp1n7t7jEfn3BuyrLgwf+S40UXSAXz6Qq3QbBZrzlU/vKU93Uo0w3eqrREXvoHjmgHx6wqHjJHzje57d5X3V9dtnK2dkhVSqkC09ITY9037gHAWb4Dp+8wlukm3rEOgLlVjcNzp5RSWaPbUsvrpapbd6M9kxj5KJWElm3QfUgmWLK043N/99hJyKc097W7S0Tg6Z/L8cnvlJbD2RbpAG8QaubrhUaeMFwuGj7z6YHjgtPfjijUoslvqrB0H4Jwqz0t4yapMzr2xOoJDRO0SH36F1IZN9AIp1xt88gyLNwODg80rgRfZa5HU3AKPl6ky+2XBYhkrG8hQqkiZFnQuR9SMSgfv8p50hz7sWV1fZ8F+x+Hg8+Aww2nvd/GgdK3UIG2wSsgJRMvlFLDxFPHBwyL9zjvAODG1CVcXJ+h6/Vwp3Qw0vmAkqaxR6nC0hkev1PYzOoJutLtvAcOPQdOLyx/1fQ7HyUiYDilo4WuWRSVoooPbj+4fYPXyEqViPizv6UMuDl1BilGZsfWltmcMRtug+AsrS6rRiiqmKJUkXrhQOfAsafmAQASnadBSjrszQpmt+OrUqMp1XhSOn/TsThcclHXvgv8NeO2/ixIpgltO6BzDwQact6i+q631fPAvhjvPKHv33nznwaTs1a+NvsDSiVk8mnGicX3f1/ADI+H2ve+N9fDmJ6yakk+VKpQxMNSXdYTyJvW0re9pZ4nD8a4avk4ixLHtsKWv8jxxmsHW9cXgnivVAxtXgPlOtk1FUURL9LhcA5uxCAHm4uUyoZIB3QfhLKacZ+WMkffYHHLm+p44WiCixb4wEzC4z+UB1a/HoIz7B2rLlQUnJKJF0qpYWLH7bA4y/ECix0HSbn8LF+9kdPnVdn/pvGQJI7YHXtUwdHYo1RhSYzYZCF+e83pHO6KsqxpnGtxy4IH+qvLngfV86Y/oHC7vM4E3TdU4Smq+OApB1eZrLEpVSrC7QT3SaXAv6Q2DnvoxlfX0B23mFFh4/pKMtq3gUKry6qRiiqmKFWknH2tKhy+g7gCO7AsB/G2M/noJW5mB+dS5dciOSr3SjWeaMIs9CW2HYT23VLhrVhOOC0LOnZLwqy/NqsVyR7d2Trq/S2dXVxzUt8uiXAbbPqtHJ/2Pmk1nG2hFknWzUE1RVXkPOXyWWJZxfOZoopb5z6IdkHlrKy/9SM7Wka9v6u7i6tPHGdnnZmEh/5L2m0vugBmn56hEWZAMgaRLmhYoYvpKj2+Kt2IoYqXZUHnXvk8d49fuenBHaNfZ4R6u3nHCX0x46VbJa75KuGkt9k71kRENiHqQoVSSuW9O7YcHnb7GuftABxr3Mgb1tRm5nM80gk1C3LTQUkppdSU3LHlMP9+85ZRH9uwKI2KT3sfkQ4XDjcsf7V0H5uOWI9svqiao9ccKr85nFA5U7tmqZLy0p0/Y4WZYIs5j23WnGGPnTPPZ/8bhttl07ZuoFBKqYJzx5bD/OtNm4HB6rLJ7hNwmFWcu6yWNQ0Lczk8pUqe9o8EmXQor4Ou/dLSuRhYllQKbNkKZVUTLjzb6dGdrXzj9q0j7neR5Bu3bx1Mpn3mRtkZV78cFpybtfENiPdKkm7tgryppqiElUoR2byZyObNWKlUroczNW6//HyltB2RKgCRDomB/pqsT8RLzNg24n4/0eExYzQv/BHadoI3COs/ksFR2swyobdF2upVz831aApaUcSLdHkKqHqyUpMVaoXuwxKHxvHozla+dccrI+6vpHcwZsR64OlfyAOnvEcqp9sp0g4VM3ShosCUVLxQSgGyKPHZP28euL3C2MNZzs0kLQf/s3smjx5M2v+miYjMA+iGOIXGHqUKxR1bDvOhXz9LS298zMcn9GBfddkFZ0Hd4ukPKtIFlXN080WRKrr4EJyh3RtVybhjy2ESz0ohqL8eV112LkfGX8uYimQUDIcUOdENFGoURRdTlCoi/dcZ7aE4hrsNV1DmqOJtZ4F3F68ccuHKcXdwpfqVajzRhNl+Lp8kTba+UhztQ7r2w7GXpX1vFi9WUyb86IHdtFsjF6bncgSAHz+4m1TbHth6mzyw7kPZP9G3TAh3QNU8XezOQ1Ysxp43vJE9b3gjVqxAE07dfvlcSURzPRKlxmeaUmE9Fc/65GZ/zGgbpcX8TI4BfTFjtK54XQdk4wXA+g8X1md571HZqFO3WDdsTFNRxIt09Z+rmhlI7lAql8yUbPQzjHE7YqRM+OEDe0aNGU20ARIzzGd/BbFuaV+67HJ7x5oIg8MDVbpQUWhKKl4opUiZFl/624vD7rvGJdVlHzZXctSq4ccP7xv9OmM6wu3SwaisyuYXVoVIY49S+S9lWnz51pewxnnOl299iZQ5zjMOPA277peEphVXSjeK6Yh2y6a/ypnTex2VtzQ+KFWYUqbFjbfcyRrHLpKWg1tSGwYec5IiQHjstYypCrXJpu0JNpir0qUxRan8NPw6I4Wn5mEMwyLZuwQzXo9lwX//88j41xlKZVGpxhNNmB3KXydV9tp2SgJRoercB0dflEpk3oqsvvWLh7rY3uvjIA3D7l/KXsqMOBbQ2hsj9ND3JWl13pnQfEJWxwjI/3NZtbRSVSoTHA75GUtqwqzKc71HoOewJHBm2YuHuni5189hhr/3MvbgNZIDMePFQ13Dv9Gy4OHrJMl35lpYfHH2Bj1d0W5p0Ve/VNrrKZWu/o0YGldUsek9CqFjE8ahFw918WJvOccYvkiwnN24jRQW4Oo9BFv+Kg+s+9D0F6uPF+6A4MzC2qShlFIl6Mnd7RztHpzcbaaNVzkeA+BBcw0dlI9+nTEdydhgW2KllFIF4cnd7RzuGv8a+3BXlCd3t4/9hAe/LX/OPRMalk1/UNEuWbPQip1KKZVXntzdztmRuwC4zzyRNioHHlvKXgxjjLWMqUqEwemWmKCbtpVSqqAMvc4wnFHcVU8DEG87G4f/FaxUNUe6kuNfZyilMk4TZocyDAg0SIWj7oO5Hs3kWRZ07IUjL4K7DHyVE3+PzdrDcY5QO+y+BRzEbQyWbV5t7CJ47CkwnHDaB7I9REglZCGjdqEmK6nM8gUhpZUAVR5LRGWTiMsrrUOzrD0cp4XhSUeL2I/LMEc8b5jtd8DBZ8HphY3XFs6EUSohCbO1i3RXuJo8l0fO75Kls7NPlYBkHNr3yALABMmt7eE47Qy/vlnMPpzG4C7sq1134rCSMOtUmH26vWONhyReVs2y93WVUkrZ7ljP8OSnq1134DZSbDIXsM9sIIEbGOU6YzrC7VDRpJsqlFKqgBwfLyb9vMMvwPZ/AAassqG6bKxHqstWNE3vdZRSStnuWHeIK50PA/DX1MaB+5ezB8eQ5QnbrjH6N23rOoJSShWcodcP7urHMBwJUpFZmJaJYXmwkhUjnqeUyj5NmD2eyyu7d1u3SxXSQmFZkujbX1k2B8myABU+94j7yhhM7DAwea/rDrmx4tW5qfAaaoVAEwQas//eqrS4y8FhSDVlpfJRx97Bits54HOPjBk+IzHivhr/kGTeSAc8/kM5XvtumTQqFL3HJNGqak6uR6IKla9KE2bzSTIuC5pq6noOQ7gVyiae/DccI2OG1xjcmLTS2M0ZzhexcEh1WbuFO6ByVs6us5RSSqXPGtLRroIwb3HeC8A9qZPoZLBi37DrjOlI9S2KB2cVzmY+pZRSNFSkV0xjzOc99B35c846aFw1vcFYFkQ6oXI2eAPTey2llFK2W9z7LE1GB51WOfeYJwPgIIXzuOIftlxjxMOSr1Cpm7aVUqoQDVw/GHE81dLxKN5+Bg5nDDNRBZZ7+POUUjmhCbOjKauSBfBjWyERyfVoJmamoPUVSZb1BqSqZQ5EEiZX3Rwecf/QtYJzHC+wyHEIy10OJ78ri6PrkwhLi7ya+fKnUpnkLtP22YXILJEE51AbdO4Bfy0Y2T8d6IqZvPnWkTFjKAOoC3hZOWNIctKj/wuxviqtJ7whs4O0U6RTNuTULtL4o6bOU66bMPJJ5z449rJWk5+qeFg2/HkrJvxcbAmneNftY8cMA5NrXP8AwFp2OdQssHOkfZWe/LpQoZRSBeBAR5hP/GHTwO23OO+hwoiwx2xkmzWLKN7RrzOmI9IhHav8tRM/VymlVN6oLR8/qckAmit9nDZ/lA1+LdvgpVvkeNXrpGvGdMS65dqosoA2hiulVAlp3nMTALem1hPv61hhDUmzsPUaI9wuhULKqqb/WkoppbKuv/K4u+opDFcYM15LsmcFmB6sZHD86wylVNZowuxYKhog1AItW6WFcr5KxmWhvmUb+KtlUiVH7t0zfsUzLwne6boLAOOkt+bmRD/cLru0tYWFygZ3Gbi0fXZB6TkKh56TjQjFLJWA9h1gpSQBKAdu3jr+hpT+vRbvP2s+zv6zlb2Pws57JcH3rM9Mv9VdtqQS0sq7dlFO47QqAm6/7ETSpNn84PJIMny4LdcjKUxdByDalVbF1l8+Hxr38fMcm1jsOETSWYbj1PfYNcJBA5We9DNcKaXy3ffu3TFw7CbJ1a5/AnBnai1dVmD064zpMJOyeaZyNjh0mlUppQrJf96xdczH+uPFl161AqdjlOrhD10HWDDzFGheM/3BRLuham7O5umUUkqNI9qNf6ds1P5L6qyBu62+aGHrNUasBzxlumlbKaUKmGzkTuGpeQiAeNtGwImZrMKwZNPemNcZSqms0ZncsRgOqGiCzv1SvTUfk6divXDkBWjfBYF6SaLIoZQ18j4Hgwkdbyl7gnqjCwKNsOr1WRxZn2gXuAPaCltlj2FIq/uEVpgtGKk4RNqh91iuR5JZnfslObi8PmdDSJojg4abwSqNtQEvn79sGRsW1skd8RA8fL0cr34j1C/NxjDtETomFUKCM3I9ElXo3D5wegfb/qrccrjks6nrwPDez2pikU7o3Csb/tJoXZ0Y5ULDi2xI8hHj3e67AXCd8i4597JTrAc8Af0MV0qpAhFPDs5DvcrxKM1GO8esKjZZCwjhG3mdMV2RTqksq9VllVKq4ESTY6/5NFX6+OHbT+aSVc0jH2zfBZv/JMcrXgvOabbfjvVIR5lAw/ReRymlVGa8/De8xNhpNrPJWjhwdxU9wChrGVNlWX2btufkrJusUkqp6QvHk7iCm3F4OjGTARJdazGcPVjJ4PjXGUqprCqQ0mw54nRLImr7LlkQr12UP9Uievuq30a7INicF1X2YsnBhewVdS5et8zPkmAS01xKgzPM8gcfABM49RpwebM7OMuUXdpNq8EbyO57q0kzXC7qPvKRgeOC5q3QSoCFxOmWFtHtu2XB0zXNCe98FOmA9p1S5TsPYgfAmbM9nDvPx7LKJInkUmr8HlbOqBy+G/vJH0vl9+AMOOXduRrq5MV6pNJ0zYIJW46rySuqeJEOV5mcQyVj4PLlejSqX+iYfLZqB4P0mCZ07IVUDMrTW0gwhiTVXr7YxynNXpZXJogmAiw/8Ceqd3RDxQxphWqn/oWK+uV6DVHgSi5eKFXKjP4/TD4TvBOiEF9wEW9rPpGKqvqR1xnTYZmQjELDcnDqZ4saTmOPUvnPMeQ6453r5+J2OljeXMHMKj+nza8Zu+LTA9+Srk3Na2D2qdMfSLSrryuRXnOUAo0PShWgTb8D4C+pjYDBNSeV4yPBifXVNASa7LvGiHXLmqJWl1Vp0piiVH6yMPHUPgDA0vLzaDzJ4ISZczmxacX41xlK5UipxpPS+ZtOlcsnlY9at8vtXCfNppLQuQ/adgCWJA6lUZUpGzqig0mBZ8z2cs3JQyZ4HvoOJCJSEXDR+dkfXKRDqk1pZaiCYHg81H/so7kehj085fI7aqY0Wa5QGA4It0L3QaiZn+vR2CuVgNYd8meaSUqZ0hUbjBmnz/Ty3pPGWRQ48gK8dIscn/WZwkkUNFOSaNW4Kq2W42ryiipepMPhAE+FJGiq/OB0y+9610FNmE1XqEVirD/9ODS0wuxpM7y8a0253Og9Cg/dLMfrPjj9yk7Hi/X0LVToNUShK7l4oVQJM/oyZs93PEdzdBd4ypm15jxm1S+Qaz07RbvBG8xp5w6VvzT2KJX/XEMWqs9ZWs95yxon/qbWV+CFP8jxqqumXxgkEZHrmAqtMFUqND4oVWA69sDehzExuDl1JgAXLvBx+kyb5/uHFn7y5LajrCocGlOUyj9JM4mrfBuW7zCW6eG1S85jXoObExvWEPRo9XCVn0o1nuRJudQ85/b3Jc1uk69UcuLvyYRoNxx+Ho6+KC15Aw15kywLsL9r8N/lw6dUDD7Qvhu23ibH6z5i/wLFRMwkJKKS+JbtyrZKucskuS8Zy/VIVLoMB/gqZCIk1pvr0dirfQ/0HJbq6Tl2oFva3pW5jMHEp9EkY/DAf8nxssthxklZGJ1Nwm2yeK47wpWdfJVgJnI9CjWUvxp6j0iCvBpfMt7XvcM5qfPyAz0SMxrKHbxhRdngA0/8GFJxqew0b6O9Y7UsqfRUNVc2QCmllCoIR7ujgMWnfH+TO5Zc2jd/loG5qFgPVM7WuSallCpQnRG5tj5pThVnLU5zruz+b0pS08y1MPv06Q8i0gmBJt1orZRS+ep52STxvGMFh6jj7LleTp2Rgc6EkU4p/KQbKJRSqqB1RDtIBu8BoMlxJjNrocHfoMmySuUhrTCbLrcfyh2ygzgZk0qp7rKJv88OqSR0H4C2XZAMQ0WjVLPKE3fsiHAsZPKrzWEAvnVBFdVlQxYinviRTCLN2wjNJ2R/gOF2SQ4LNGX/vdWUWKZJfOdOADwLF2LksqrzdLl80kI7GdFdoYXEWynVAjv2QOPKvNqcMGW9LdCxSyogOnIX/m/dHqEjYvLXrREAvnNhFUHvOL/jz/0KuvZDWQ2c/sEsjdIGyZhUnaxZAK4MTKApoMjiRbrcZWBN/DSVRW4/hDtkQ0JZVa5Hk9+6D0qF2TS6PliWxS3bInTFLP65MwrAt86vwu/u+z0/+iLsvAcwYP1H7Y/VsS6pGhjUhYpiUJLxQqkSkjItbn7uIN3RBA/vaOUMxxaWm6/I9fiCczOThBQPyTV+oMH+11ZFQWOPUvkpkTK5+bmDdIYTPLevE4D/eO1qXOn00j76Emz5ixzbUV3W7CtAkkddBFXmaXxQqnBE4wkST/6KCuBXUdmo/dVzKnHY/ZltpiARhrrFUjBLqTRpTFEqf4TjSW7ZdJB799+D5d2DZTr56IYrcDlcNPk1T0nlt1KNJ5owOxkuH1Q0SeJOPAz1SzLb0tqyZEG5Yzf0HANfAIIzM/d+U7D5WJwP3t4x7L7Th+6sO/A07H8CDCec/oEsjw6pOGUmoWoeOPXHvVBY0Si7XvVqAJY++wyGv4ATTQ1DKs+1d+Z6JGoyDAMCddC5XxZAC30RNB6WCumQ0yp5j+6P8bE7hseMtc3jJJO27YRNv5PjMz8hbbELRbgVgrML/2cnzxVVvEiX2yfnVWaOOh6o0ZVVSTJocCb4dKfwqKLd0nnCVykVZidwx84on7izc+C204ATm/pihmXCY9+T46WXyoKCnSwLoj3SBi9bmyRVRpVkvFCqhPz5mf187i+bB25/3H2LHCy+WDadpxF3Ji3SCdXzwBuw/7VVUdDYo1R++sUju/n67VsHblf73SxqSPOz/IFvAhbMPs2eDkjRbrl+9NdM/7VUwdD4oFThuPnmP/Pm8H56LR//ME+jOeBgVjAD1xbRTqkuq4Wf1CRpTFEqf3zj9q386vG9lM2+DVcAPNHTCPqc1JXVUenVbhIqv5VqPCmNtGA7Od2y4zfeDQefgZbtkIjY+x6WBaE2OLxJEk4jHRBsAl+Vve9jg5dbRyZrzK3qS0w1U/D4D+V45Wtz05I61C4XGOW5bz+uSpgnIIkdqrC4fOBwQOsOqRZaqMyUVEePdOT8s/C5I/ER9zUGxphgMpPw4LfASsG8s2D+WRkenY3ivfLzUzNPK4Qo+7nKpIpNcuTvk8ohTznEI5I0q0YyTUmWTYTTTih+/MDwn/Eqn4MqX9/l64574NjLksx66nvtHq0sVPgqtQ2eUkoViPu3tQwcrzW2cZrxEjjcsOj8zFR/TyXAcGicUEqpAvTPF48Ou728OYjHlcYy2eEX4KVbAANWvM6ejXXxkGy6zMTGDqWUUtNWue33ANyaWk8EH6saPBmoLpuUOcXqedqpTimlCtgfn96Pw3cAV2A7luVgif9CHA4HzYFmDF0rViovacLsVBgOCDTKwnjLVtj/JHTshUR0eq+bjEP3YTj4rLxm92HZXRxozGn77LFYlsUX7u0c+wnb/wntOyVZ8OR3Zm1cA5JRMJCLjBIpGa3ylKdckubMVK5HoiarvE4qhbbvls0MhahjL3Tuk0qnOTwhNy2L/3qsJ/1v2PJXaNkmvz9nfjxzA7ObZUm1qcq5mWn9qpTLK1+pAk7kL1b+aug6IFWC1HC9R6H7QNrdOZKmxS9fCA27z9EfwhIRePIGOT7x7eCvtXGgyCanWEiuIbQNnlJK5b1YMsU/thwZuP1R181ysOgCCDZnZj4t1i0VoDKRjKuUUipjwvEkz+wd3vnIm06yLMD935A/526AGWumP5h4GDz+nG9uV0opNbrurnbOST4KwJ9SZwNQ5cvA+kq4XebLtLqsUkoVrPZQnFjSxFN7HwDJrjVUeoLUldVR7a3O8eiUUmPJvyzMQuIpB7dfKhAdfl5aRVc0y4mtt0KSGcZjWbLgG+uBSDv0HpNJd6dbFtwn+v4c29aWJD5W0cxEGJ7+mRyf/I7cJA2F26FytrY0UrnnLpNqk6kYOEqjfHnRMBxQXgsdu2VBtKIx1yOanJ4j0LoN/FXgzO3u5CcOjqyG6Rxrfqn7IDzVF0PWfcj+ZKhMinaBNwhVOaiqrkqDYch5Vdf+XI9EHc9TLgnznfuhaWWuR5M/EhFo2yFxKM3rm3/uHLkRcWAd+4U/QKhVNhWufr2NA+0T6ZAEqApdqFBKqULwp6cPDByvNHZzrvN5UjhwLr5QruHsZlmS5FS3RCsCKqVUgfnpQ7tH3OdMp9DGwWdg2+0yT7jitfZUl412SXVZb2D6r6WUUsp29//1Bl5txNlhzuBZazEAbkcGqsuaSdm07dSUDaWUKlRfvvVFHJ6juIMvYlkG8bZz8c2EpvImrS6rVB7Ts6/pMgyZgPdVSeJr+w6pqur2S9KsNygLw/0VLSyzr71CWCZFkmGpTGsYUok1OEMmXgrAtrbEiPtW1bvl4Pk/QLgNKmbAyiuzPDIkYdfhhqrZ2g5b5Z7LJ1/JmHw2qMLi9kuLtNbtMontKc/1iNIT6ZB21U63xJcc292ZHHHfhtmjJE5ZJjzwLUkwn3ESLL08C6OziZmSc4HmNfYsnig1Fm9Aq5bnK3+1JP1XzshMkk6hsSzo2CMxqTL9jQT7u0fGjFNneGWD4fPSDo91H7J/g6GZkgTfuqV5v3lRKaWU2N8RHjj+iOsWALZUnMmaqtmZqS6b6KsIWEib+pRSSgGwty084r6VM4ITf+N9fdVl5220Z3OkmQQsWQtSSimVl5Yf/hsAf0ydjbQzhaW1bnvfZKC6bIO9r6uUUiqrthzsxFN3PwDJnpWY8QYWNQSp8WlhP6XymSbM2sUwwBeULzMlE+iRDmk/2t/G2zDk2AAMp1RZcpdBWU1BJnXu6RxM1PjeJdU8eiDGtesq5O/cv5B9+vtzU9Uw3AHV8zVRQeWH/sT6zj25HomaKn8ddB2E1legaXX+VxKK9cLRl2RDRjA/KuTtGZIw+/1Lq3n8QIxPrx9lUeLFm6Rqu8sHZ322sOJjpEOqmlc053okqti5NCE7b7n9UmW2Y59sqCukz7BMCLVA+x6p1j6Jf4v93YPXGd+/tJqnDsX59PoKePCrkIxKLJ5/tv3jjXRAWa1Wl1VKqQJytEuqki8yDnCZ80kAlq67PHPzQbFuqJhZOBsplVJKDeiODhYA+e6bT+Slw918+NyF43/T/idhx12ynrP81fYUQ4h2ga9aO+MppVS+atnG4vhLJC0HN6U28p0Lq9jdmeRtq20siJNKyFfV3Pxfb1JKKTUu03UUV/B5AK5c/GoiM1y86ZRFOAqkUKJSpUoTZjPB4eyrLluR65FkzA+e7uH6J3oA+NS6Cq5YUsYVS/qSN+7+kVQGbF6TmYXsicRDUhFK22GrfOKtANPM9SjUVBkGVDRKm21PAGoX5m8SVCIqlWUj7XlTqeK6x7v58bMhAP59Y5DLF5dx+eJREv66DsATP5bjdR+CYAElnppJqSLdsAJcOdgookqL2ycV0xKRXI9EjcZfK1Vmg82lXSEiEYXWHeBwTGpR+T8e6uI3m6Xy03+eXzkYMw4+C7vuk24cZ3zc/jhsJiUZt2GFVGdXSimV975w82Zu3nQIgBsXPgAHgLln4GtYkJnqsmZKNsLrxgqllCoolmXxqT89z10vHQXg5+8+hfOWNfKaE2dO/M33fk3+XHA2NC63YzDSfbB2sSZIKaVUHjJNi3t++x0uBO4zT+J7r53D6XMy0Dky0g6BRiivt/+1lVJKZUUiZfK+/3uSQ9bdeAyTxZWruWTVDCo9ldRqdVml8p4mzKpJ6Y2b/PS5EP/dlywLMKdyyMTOoU2DC9kbPpabhLJIh0w4+Sqz/97KFobLRc173jNwXBTcfqkubZny+6EKj9MtrbZbt0t18Mo0JtWzLRmDoy9Cz2FJls3xz1pX1OTnm3r5nyd7B+6bUznG77RlwgP/KRsuZp4My1+VpVHaZKB9UmOuR1JSijJepMPlA6cXYj0TP1dln8srSaIdu6WThLOEfjb7WZb8/cOtacfL1nCKG58P8ZPnQgP3zQ72/duZSXj0f+R4xWugdpHdI+77HK/Xz/EiVbLxQqkidbQ7yg/u28GvH98HwELjIDMP3C4PLr0sg9Vle2QDpVYEVGnQ2KNUftjfHuabd2zlthcOD9w3pybNKuF7HobdD8gmjBU2VZdNhKVKeXnd9F9LFSSND0rlr92tIb74l2e4rv0fYMAfU2fzleoMdPpKJWQzXtUc3TyhpkVjilK5s/1oDx/77XNsbztA+cKnAXjtoleTMlPMDMzEqZ/vqoCUajwpnb+pssV3Hu/hF5tCw+6bNbCQnYLH/leOl12RmYXsicR6ZOIqHxPZVNoMj4fGz34m18Owl7tMEpySMTlWhclTDqk4tLwMTg8E8mj3bzIGx14arGqYByfiX36wi79uHV4Bc3ZwjHFt+Qsc2Sy/H2d9NufJvpPS3z6pel5pJsblUFHGi3S4vFJlVuUvfy10H4GeQzL5XWp6j0LHHlkETvPz/NN3dXL/3tiw+2b3b8zb8ld5PV8lnPIee8cK+jleAko2XihVpD7wq2fYtL9z4PbHXX/FwII5G6B+SWaqywLEeqFhuVYiV2nR2KNUfnj7z55gb1t42H2z0kl+siy47+tyvOBcqFtqz4AinXLd4UkzaVcVHY0PSuWv137/Ec6O3U+9p5vDVg0PWifRGMjAOkWkHcobtLqsmjaNKUrlzkXXPwiAt+FBDEcKKzqfuVXNVHoqqdHqsqrAlGo80dVAlRbLsrj+iZHJsjCkwuzWv0PbTqm2cWoGFrLTEemC+qXgrcjN+ys1FpdPE2aLRVk19B6TSq6OE/KjulAiAkeHJsvmNrynTItvP9YzIlkWxkiY7dwPT/5Ejtd9uPBanGr7JJUL3mCuR6DG43CBNyDnxmU1clwq4iFo2S7/Bmmc88SSFtc93j0iWRagOeCEcBs8c6Pccdr7M3OeH27Tz3GllCoAoViS6+/aPixZdpFxgCscj8uNZVdkrrpsMiaJsloRUCmlCkJXJMF3735lRLIsgM+dxibzXffD3kfks3/Fa+2Zz00lZENhRfP0X0sppZRt2npjfO++HXRFErzDcxcAv02eR6XfjcPuTqpmElLJvuqyBVQ0RCmlFABHuqL86IGdABiubtzVTwBQFb+QlJliRmCGVpdVqkDomZhKy/17Y8Naag9VV+aQyq5P/UzuOOU94KvK3uD69bfGC87I/nsrW1mmSfzAQeIHDmKZZq6HYw+HQ34vktFcj0TZIdAAyYhURA2353YssR448kLeJMsC3Lwtwg+fGT1mlHuOO/UwU/DAN6Vy78xTZJG7kGj7pJwqyniRLm8gL37f1Th8lVKJrmOPVCcqBWYKWndAtEuq7KbhV5tD3PDsyE15AC6HAU/cIG1L65fD0kvtHK1I9iXqVs/Vz/EiVtLxQqki8oP7d/DTh3cPu+9fXDfhMCyYe0Zmq8v2xzZfZWZeXxUdjT1K5dZ/3rGVnz+ye8T9dQHPxN88tLrswvOgfrE9g4p2ysb7TG3uUAVB44NS+ecLN2/hF4/sYbmxl1Md20lYTn6fOpfasgykUYTbpXOhbsRTNtCYolT2feIPz3Hjo3sA8NQ+gOFIkgrPpdq5iBpfDbW+9NZFlMonpRpPdJVdpaUlPPYvhWEY8PQvINYt7YRWvDp7A+tnWdLOqH55aVXwKlJWNMrOCy4AYOmzz2D4/TkekU28FZJIoopDoBF6j8DhF6BpVW4mOEJtcOwlWbwNzsibRJ/DvZP4Od/yZ6nW6/bD2Z8Bu3dsZ5pWJcypoo0X6XD5wKXtgPOaYUCgDjr3SYwotOrZU9G5T74qGtL+PN/fNU7MOPICvHInYMAZH5dqTHYLt8uGE78uVBSzko4XShWRnceGb7BYPLS67NLLM5eAZFmywSLYXHjXKypnNPYolVvbjvSMen9tuXfib95xNxx4EpxeWHkVONNIsp2IZUIiBg2ztaJgidP4oFT+2XywC4C3O+8G4J/mqbRQzbIym9dbLBOScaicnTdrOaqwaUxRKvue29cJ9FWXrZLqsrGWC6iYbWh1WVWwSjWe6JW5SsvxBQGHad8FL90sxxs+lptqZ7Ee8FRApVaXVXnM3RdYSqXKXLEzDGmhlorB4U3QdSB7/7emCR174dCzkAjlVbIsgMeZ5iJy+2546qdyvP7DknhaSFIJmeTS9kkqF9w+WbxLJXI9EjUelw+cLmh9BRKRXI8ms3pb5O9ZVjmpBeWxuqHWeE145LtyY9nl0LDMhkEeJxGRJNyqufo5rpRSBcDjGv5Z/S+uv+IwLB50nAYNSzN3TRTvlY5GaVZPV0oplXse5+jn93UVE1yrWBbc9x9yvOgCqF1gz4BiPeALakVBpZTKQx6XgwrCvNb5MAC/Sl4IQK3f5rmiaLd0rNBN20opVbD656Y8tfdjOJIkw3NJhRfRUFGm1WWVKjBTOtP7/ve/z7x58/D5fJx++uk8+eSTYz73xhtvxDCMYV8+n2/KA1a5MXbykwWPfk8ShuZthJlrszouGYIl1RWr54CnPPvvr1S63GXg8krr+RJS9DEj0ACGUyrNtmyTHcKZFOuFI5vly+mWJNM8q3LkHuPsIuAeMs5UHO77miT7zT5dKkIVmkg7lDfoBJfKDVeZJCUmo7keia2KMmb466QTQtvO4t00E+uFlq2AJRX1J2Gs64z3eO6WfzNvBZx2jQ2DHEW4HYIztSWqUgWqKGOGGpd7SPLTYuMAlzukkset/ivBV5W5N452y3WXuyxz76GUyiiNGaXH7Rp9cmrCCrPbbodDz8nmx1VX2lccJNYj1x6uNCrcKqVySmNG6fE4HbzO+RDlRozt5kyesGTTdm2ZzQmzsR6omg0uGyqXK6XygsaM0uNxOjBcXbir5P863nIhYDCnqkqryypVYCZ9pveHP/yBa6+9li996Us8++yzrFmzhosvvphjx46N+T3BYJDDhw8PfO3du3dag1bZN9rSvtsBt57yglQ4dHpg3YezPi4AYt2ykF7RnJv3Vypd/QmzRZbcNJ6SiRllVVJRr3U7HH4OQq32v0cqIVVlDzwFXfshUC+7kfOQc5QEXo8DfnxFzeAdT/1cEqF8lXD2Z/Mu6XdCZhJSSa0uq3LH5ZHzL7N4KswWbcwwDPnM7twL3YdyPRr7pRKyYSTaBeX1k/52xygf/3McbXzI/IPcOPV9mUmCiofkvKxqduHFIKVU8cYMlbaP91WXvcM8jfeetShz1WVTCalGXlFg3TCUUgM0ZpQma5TNin6Pk09dtGTsbzJNuO8bcrzoQqieb89gEmFJwA002PN6SqmM0ZhRmhLJFO9w3gXAr1KS+FThMXjfyQH73iQeAo9/SnNnSqn8pDGjNIXiySHVZeeRCi8kWObgnevGuc5QSuWlSWd4XHfddbzvfe/j6quvZsWKFfzoRz/C7/fz85//fMzvMQyDpqamga/GRp1kLjQf+UfHsNsb53h58T0VrN75Y7njpLdDMAcJq5YllT6q5siFhlL5zOEEbxCSsVyPJGtKKma4/RCcIdXqDjwNR16U5KHpSsah6yDsfwqOvCD3Vc6cVLvrbPviA8P/3q9bVsaWDzWzYXZfFY1Dz8ELfYlQZ32mMFubhtslAU5b6alccnrB4c71KGxT1DHD5ZUF0pbt9sSGfGGa0LYDug9KItEUEk+/91TvsNsfOLmc+xf9HmcqAo2rYPkVdo12kGXJ53jlbNn0opQqOEUdM9SY/vLsAQCWGPu5rK+67PkXX8myGTXjfdv0xLqlErlWI1eqYGnMKD2WZfHQK8M3s//3m05k0xcvYm7tOB3qXv4bHN0sc3yrr7JvM0akEwJN4Ava83pKqYzRmFF6EimThvanWOQ4RK/l46bUmfzmylqeeV8TTQEbN+X1xwKvjUm4Sqmc0phReiLxFDGrY0h12Qv4ypt83HHtydQFtCuRUoVmUgmz8XicZ555hgsuuGDwBRwOLrjgAh577LExv6+3t5e5c+cye/ZsXvOa1/Diiy+O+z6xWIzu7u5hXyo3btoa5puPDP/3r/Y5+NJZQTzP/gwiHbLQvObNuRmgVpdVhcZXKRVqSkBJxgyHEyr6JsA7dsP+J+HIFuhtmdz/u2nK52vbLtj/BBx8FhIh+azL48Se328Jcf3jw//9G8odfHJdxWDL7VgP3Pd1wIJlV8C8M7M/0Okyk/JVOSdzlayUSkfTKqhflutR2KIkYoa/Rj7LW7YVz+aZzr1SLTxQP+lWpTc+38sPnu4Zdt/MCicfrtuEY98jYDhh47VS1c9usW7wVEh1WaVUwSmJmKGGueGBnfz0oV0Dt//FdRMOwyIx+wzcjUsyd05uWRAPy4ZFPe9XqiBpzCg93737FX728O5h9y1trODilU14XONcW5gpuL+vuuzii6VAhx1SccAhm+yVUnktGzFD40X+sCyLb92xlR8/uGuguuxNqTOZXx/k1BmewfUMO6TifV0rdC1bqWKhMaO0mKbF1/7+Ev9150t91WVTJEPzWVC5mFUz6mjU4kpKFaRJrWq2traSSqVG7HRobGxk69ato37P0qVL+fnPf84JJ5xAV1cX3/72t9mwYQMvvvgis2bNGvV7vvGNb/DlL395MkNTNkuZFm+/uY3HDsRHPPbc+5vg2FZ46Ra548xP5qbSYX912caVWl1WFQ63HxjZEqwYlXTMcJfJomo8LMlEnXvBEwB/nSTTussksah/0dUyJaE2GYVYL4Rb5U8zId8XbM7rBdp4yuItf23lmcMjk4KffG/T4A3Lgoeug1ALBGfC+g9ncZQ2inRAWY22T1K558rfStOTVTIxo6IJug/J+UDDCnBkIBk0W7oOyjWBLyjVc9MUTphc9adWXm5NDru/wmPwyNsC8Kf/lTvWvAVqFtg5YmGZcg3RtBo841SXUkrlrZKJGYqucIKL//tBjnRHB+5bYuznMqdUl3WvuAJ8VZkbQCLUdx1XgB0xlFKAxoxScqw7yoZv3kvSHD7vetnqJn7wtrUTv8CWv0LLVrlGWH2VfRv3Ip1QXqOVypUqANmIGRov8sP+9jAbv3UfADNp4UHvUwD0zL2QWy9vsP8NI51QXquxQKkiojGjdOw41sMF1z0IgOHqpHyhVJc9u/nVvGltGXOCs3Dm8Rq+UmpskysDNAXr169n/fr1A7c3bNjA8uXLueGGG/jqV7866vd8/vOf59prrx243d3dzezZU6/+E01B+ku4CmBPV2rUZFlAdls/fB1gwaILYObJWR3bgFiPVpctVi4X1W99y8BxUXGXSYJ5MibtmdUwRRczPH75MpMQD0HXXuhIAYYkwBr9CbMpeQ6GtLN2l4G/OjebEaZg05H4qMmyI+y4G3bdJ4sO5/1bXwJ5gTFTkIhBw0pwFtnnUyEq5nihJpTrmGFZFrHJxgyHEwIN0LFHPgNrM5AQmg29x+DYS+D2yvn4JDy4LzYiWRbA6zTgqZ9BqBUqZsDJ77BrtMNFOmSBQis8lRaNFyUvL2JG0sTn1gn0yfj75kPDkmUBPu76Cw4smHsm1GewuixAtAuqF+gGCzU1GnsKVj7EjHjKxOvSmDEZv39q/4hkWYB40pz4m1MJuO8/5HjJZfZdK1gmJOMQnFXYmyWVvTQ+FJXJxgy71zJM0yJhasyYrKHdK97puhOnYfFQahX7vQvtfzONBSqTNKYUlFzHjJRpkTKt8bsuqBH+++5XBo699Xf1VZddgDcwm7qyOmp8NTkcnVI2KdF4Mqm/aV1dHU6nk6NHjw67/+jRozQ1NY3xXcO53W5OOukkduzYMeZzvF4vXq89iWT/+9BBrnsA/vyGOGubCyPxKNeiSYurb2kb+wkv3QKt22XBYF2OqgNalixcaHXZouTweGj64hdzPYzMcPulClsqXvQJs4UYM75x7wF++jj8/S0Jlte5bXlNQKrJ+irlC+QzzErJZAlIAqnhlGTZAtMTM3nvre0TP7H7MDz833J88ruksmIhinZCWZUkvKmcK+p4UWIKMWZ88Z8H+P0muPPtSeZXTeKyyuWVqqwt2+S4cqYt48maUCsc2SLHk6yM0REx+eBtHaM+ttzaAS/eJDc2XpuZ8yQzCYko1C8r+vMwNZzGi+JSiDHjU398ntu3HOb+T59LU6Vu6U7HsZ4o/3bTlmH3rTJ2cbnzSUwMHMsuy2x12VQCcEp1eKWmQGNPfijEmPGBXz3DYzvbePCz51JdrusZ6TjYGeG6u7aP+lgsnYTZ534NHbtl3s7O6rKxHrn20xatagiND/krGzHDzngB8I6fP8HLh3t48LPnEvCWTnLDdOxpDfHLx/YC4CfKW5xSafbnqUupsTKQeBzt1ligMkZjSu4UYsy46oePcqQrygOfPUc3WqTplaM9/P2FwwA4PEdxVT4LQOzYJSRqYUZgBg67rh2UyqFSjSeT+u31eDysXbuWe+65Z+A+0zS55557hu2GGE8qlWLz5s00N2enKuh3HjiIhcGX7u/KyvsVumcPx1n2g8Ps606N+ngDHVL5CeC094M/RzsmYj3gqdCFC1V4nC6pxJaMTvzcAleIMeOGx46SsuDbj3Zn9o0MQ5JonR75crgKMln20f0xVt9whJ74yAoew6QScM9XpKVp40o46W3ZGaDdLBPiYaieC04bE6qVUgUZM371bCsJ0+CGZ3on/83eCqnOeuwl6Dk68fPzRagNjmwGMzHpif779kQ56SdHRn3MQ4IvWj9ioIPFrFNsGOwowm0QqIeAXkMoVcgKMWb89bmDRBMmv31yX1ber9D9Y/NhTvuPe0bc/xnXHwG423GGbH7IaHXZTun6kcmkXKVUxhVizLjzpaP0xJL8ffPhrLxfofvrswc445v3jvn4hAmziQg88C05Xv5qCDSO//zJiPVK9wzdrKdUQSjEmPHIjjbaQ3Ee3dGalfcrdL99Yh/nfPv+gdtXOR8kaITZZTZxv7mGWHKCdY6piPVA5WyNBUoVmUKLGSnTYtP+To50R3npUIbXwIvETx/axYXXPzhw21N/J4ZhkeheiRmdg9vh1+qyShW4Sae7X3vttfzkJz/hl7/8JS+//DIf+tCHCIVCXH311QC8853v5POf//zA87/yla9w5513smvXLp599lne/va3s3fvXq655hr7/hZp2NKSIJ7KwIlukXnbTeNUlsXij02/koSn+mWw7IqsjWuEaBdUzda2eEXKsiyS7e0k29uxrCL8vfVVSguWElCoMeOePTFSo7RxU8O9ddyYAf95fl9F3ad+Ai0vS4LYef8uCcKFKNYtv7/lWl02XxR9vCgxhRozfv9ieGo/f/3VWY++CL0t9g4qE3pb4MgLkIpNqcr21X8buxr5x1w3sdg4IElJ6z86jUGOIxkD04Tq+bKBSZUUjRfFp1Bjxs8f3p3V9ytUH/rNsyPuO914mbOdL5CwnFSceKV0fcgUy5KK5MGZ2jZVTZnGnvxRqDHjN4/vzer7Fapr//j8uI9/6sIl47/AUz+FnkNQXg8rXm3fhvZkTOa/AlpRUA2n8SG/FVLMSB5+kT94vsJi4wB/fPpAxt+vGPzrTZsHjg1MrnbeAcAvUpdg4eADawP2vmG8V9axtVudyhCNKblVSDEjFE8OHN+y6VDG368YfO22lweOHb59uIMvYlkG8ZaLAHj3uoUYBVgMS6nRlGo8mfRK4Zve9CZaWlr44he/yJEjRzjxxBO54447aGyUnbf79u3DMWQyuaOjg/e9730cOXKE6upq1q5dy6OPPsqKFdlvxfx/z4e45mSbT3aLTGSc3XO/XbOZedsel7bhZ306s5U8xtNfXTaYnR2aKvusSIRXNpwBwNJnn8Hw+3M8Ipu5/UBpBJpCjhk3b4tw1fIi+9nLov+5pJpXLymDvY/CC1IJirM/V7iVwS1LKoM0rAS3ttHNF0UfL0pMIceMe/fEOH/+FD4byuug95hUbW1alb8T6N2HpRqumbS34hLSXvsj7r/JqdGZn8xcAlS4FYKzZSFclRyNF8WnUGNGbyzJs/s6OHlOdVbft/BZfNb9ewBaZl3I+pUL7WuXPZp4r8w7adtUNQ0ae/JHIcWMZGqwGurWIz3saullQb2uZ0zVHz+wntPmj1P1KdoND10nxyteC34bP/ejXeCv1UrlagSND/mtkGKGdd/XOd2xlf9y/4irXv4y7aE4NeWejL9vsTjH8TwLHEfotvz8JXUWt7ypjtUNNv/7Rbpk47ZXY7nKDI0puVVIMSMUG0yYvfHRPfzrZcvxuHSDcHosvA2ywSLZdTJmvJEfXj2LE2dprpIqHqUaT6ZUWuejH/0oH/3o6JV/7r///mG3r7/+eq6//vqpvI3tXm5L5HoIBetVc2Js2PsjuXHS26F2Ue4GE+mE+uVaXVYVLneZVBgwk4VbaXMSCjVm7OxITvwkNarXLSuTZNneY3D/N+XOVVfBvDNzO7DpiPeCuxwq7E0UU0oNV6gxY1/XNGJGoAFCfdVbG1ZAcIZ9A5suy4LOfdCyVTbL2ZzQ6yHBz4I/wREzYcG5sOBsW19/QLwXnF6onmtfxSilVM4VSswYmvwEcKQrmpNxFLLzHc+y1vEKCcPLjJMvl64PmRTrhpqFcu2ulCoKhRIzQvHUsNttoTgLdL/XlHz1NSvHT5YFePwHEGmXiuJLL7PvWsEyIRWXgh96/aFUwSmUmNF21lfxb7uXEx27uMZ5Oz3R8zVhdhLe4/wHAL9Lncs3L2pgTaPN/3apuBSg0uJPShW1QokZQxNmQTZ017g0ZqTDWf4KrvJdWKaTWMuFfPbVfs6YP1+ryypVBEpq20DKlFLC1z/eza3bI7keTkH5tv/XEO2E6nmSMJsrsR7wBPQCQxU2tx9cPkjqQmk+S5kWpmXxn490c+dOjRmT8eWzKyUh/N6vymJz3RI4/QO5Htb0RLokiU03ayilGJn8lDIhaVr8x0Nd3L9nCvG9vB4w4PAL0L4bTHPCb8m4VAJatkn1W5dPqiPZ7KOum2iM7ZWqS2d83PbXByTpN9wBVfMy275bKaXGcHzyU9K0iCZSfOXWl3h0Z2uORlU4HJh82iUdKxzLLsv85odkTBa2ba6orpRS6Th+ITuZsuiNJfl/f3uRZ/a252hUhemtp88d/wmhNnj0e3K88krw21j9PdYraxh2VqxVSqnj9Hrq+WpS1muvdf2ZxNFtdITifOmWLWw+0JXj0eW3JcZ+Njq3kLIM/i95Ea9amoE5/0gnlGulcaVUfuiNDZ+b6o0mOdYT5Yu3bGHbkZ4cjaoQmHjr/wlAomM9VrKKC5fNIugJ5nhcSik7lFbCrAVPHIzz3Sd7+dgdHbkeTsE4z/Es3j33SLu7sz8HTnfuBhPtgspZ2r5CFTaXBzx+WYhTeeP45KekCXfsiPLDZ3p5/20aMyajwuuAp38uSVZuP1zwJXAW8E7FRBjcPt2soZQaEDpugillwe9fDPOT50K8+29TXMj218j5wbGX4NjLuT1PiHZL8m7rdhmXz/4JoFXGLj7s/JvcOPMTmUtmjbTL4kTVrMy8vlJKTeD45KeUafLjB3fx80d289afPJGjURWO1zsfYLljP92WH+eSizISk4aJdkJ5A5TZmDillFJpOj5mRBMpvnPnNm58dA9X/fCxHI2q8MysKsPpmGBzxSPXQ7xHWmUvudjeAcR6pGrt/8/efYe3UWUNHP7NqFru3U7vvZEQIPQAgUDove/SYWFpCyxlgaUtLJ2l89H70ntn6QRCKIGEJKR39271mfn+uI5LbCd2IlmydN7n0RNZHknXTqIzc++55zjckX1dIYRopSFg8LKxB18YE3BpIQo/u5jr3vqVp2av4qD7vo718OLaKTbVWvsDcyr5hUWRrxJoWU2VxvuCnlSpGEKIOLXpdUadP8QlL//K07NXMes/X8VoVPHPnvkztpR1WIaLYOWebD/EQd/0vrEelhAiQhL6LC20SfKTaVqUeeOgWlMvkkEjNzkeV1+MPwoKRsduMMEGlXglCUsiEbizpcJsnOko+Wl9vdHJ0WKzVnwJvzyv7u9+qZoY6s18NZBWFP3Wr0KIXqMhuEnyk2WxpjbcydHd4EpXCapVy2DDL+Dt4SpSpgm1a2Hdj9CwQZ13R6EddQp+7nHcj10zYcie6hYNZhhCPsgdIm21hRAxs+mihC9osry8IUaj6V1S8XFpU3XZh63DVHXZaDLDYBqQ2VdaaAshYqKhg4XsP0ql4lN3uexbWPaqWQ3fP6Lujz9SXYdFStivCo6kFUTuNYUQogPqOkPj8tAZ1FsppJf/zMiVz8d6WHEvmzoOs6mE4ifCM3HZbZF/k43dUqPQrUkIIbaGus5oyZOq94eYt6YGUJ2QRFuWZYEWbK4uG6ycjmWkken24HF4Yjw6IUSkJHTC7KaLEuvrAqyq9MZoNPFtXX0YX6h9MvE/HU9RrFWpZKftT4nByFrx1qhxRHICS4hYcaaqXaYibmya/LSqyse6Wklq7siaujCBcMf/fodq6+Dzm9UX44+CodN7cGRREA4AOmT0ifVIhBBxZNPrjIWljZTVByPz4na3+sxprFKJq5XLIByh194cf62qDL5hHlhmUxUM+za/7OraMEGjbcz4h/05huob2GDlwK4Xb/N7dKqhAtKL1U0IIWJk0+Sn2csrKK+XbiMdWVnR2Kbzxzn2t8jXallhFvKqvp+qxB5N/lq1SU5aaAshYmTTzdxfLamgxhuK0Wjil2VZrKhoxOhkcd+5pYTZT68HIwAFY2HoXpEdnL9WxRHZdC2EiLKN1xkbyOVf4eMBOCXwDIO0DbEcVtzZGDPMpphxsu1j3FqIX83BzLVG4rJFYaNcoF7N7UmlcSFEnPip/GtSh9yJ7loHwDvzV7TrvCpUAcbl5Q3U+f04c79Ed9RhBrMJVu0CQKZbkmWFSCTbvgIaxzZdlJhfFmR+WRBwAOokOeJtFnqhPypD7PtcebvHD9Rnc7jtawxLwzb9SrV4HyvBRnCmSMKSSByOFNBsqnqNHoUdrKLbNk1+mr3Gx2drnIDEidZ+2hDk8JcrOvxeGl4edtylqvkVT4Qdz+rh0UWBvwbS8qUlqxCijU2vMz5d5sVLBM+VdRtkFKkJ9tIFUL8BcoaoFtG2CF/CBRuhdh3UrlbVkFLzweaMyEt/vTrAiW9Utnlshj6XE+yfAnBZ+GyeiVZr7ZBXtb3LHiznWkKImNo0+entebKA3ZEP5m/g7Gd/am6h3U8r5wzbewDcHD4emycysalTlgVBL+QOj3ysFUKILtr0OuOVH9fGaCTx7ZUf13LpK7+Sk9pxbHA5NnP+v+5H+O1lQINJJ0a2E4VpgBFWaxiy7iSEiLLW6xkvGHtxmP41O9gW82/H/3Fs8B8xHFl8eeKblVz/zu8MyPGQgp8/2VW1wIfDBwEaLnuEP6/DAak0LoSIOz9VfYbuqsBV9Da+VWfx3PcrwZTkz03d99lS7vz4D/rn+3DmfgFAoGx/sFR+WYrDEcvhCSEiLKFngD9cUNLm6xBtP8C+WlrB7sPze3JIcen5+e2r7hZRyU2OxwB4xX04xxSO7elhteWrgdyhEK0FdRFf7HYyDz20+X5CcnhUEno4EP0qOaJLPvm9tM3Xflxtvv52WQU7D5VKQ4/+3Fn7WIvbHQ8zTF8PqXmw97URqUwYU80tWfuppCsRf5IhXoi49PnisjZfb5osG7GY4UpXVem9VbDuJ9XKLau/qli0LVUqLEttCGgoh9q1Kmk2JTPireLumdO2fWw+1fzboVqfPhyeRc6wqRF9v2aWpSr05g2DVGl/J5B4IWLqqyXtNyi39sH8DcwcJ5Ww//XeIoDmaoGX25/HpYX41hjDR+b2/HloBBOaOhKoV3FXFrZFpEjsEVvhu+Udb1DeSGKGcs2bCwCoauy4E8ceIzpZ87Es+OhqdX/QrtB/+8gOLFDfVKlcrkHEZkh8EBEyZ0VVq680Lg7/hQ/1y9hRX8Qptg/5YP72EjOA69/5HYDVVV5Otn1BjtbAKrOAD0w1JzWtX4Q35vlrwJMP7qzIvq4QHZGYIrooL3goljkbu2cl9vTfCNdPaPN9uc5Q7vz4DwDK7W/j0EMY3oGE68c3f3/KQCmuJBJUksaThP1JDdPigc+WbfaY//tqFbsMzceW5Dkw6+rbVjvRMLnd8RCZmpdfzCHsfcSZMRpZk6AX7C6pLptEdKeTPrfcHOthRJfd1ZQw65OE2ThgmBaPfrV8s8c8/MUKdhycJzFjk5ix0V9sbzHT9gOW7kCbcT14cnp4ZFHgrwV3trRkjWNJES9E3DFMi1e+XcRDjnu5N3wYC6xB7Y555MsIxgxNVxsRzDD4qmH9z+BMh/RCSMlRyT2OlC1XMDLCEGyAQB3Ul4KvCoyQ2pCW1S8CA21vXV1LzNAwucPxEDlaAwvMgdwRPpof94xSm1JfddPPNTA6ry96HYkXIlYM0+LFH9Zs9pjr3v6dGWOKmiurJqt1Nb7m+3vo8zjQ9j2GpXFD+CRA47Kd06M7gEAd5I2KbKVBkdQk9ojuMkyL135at9ljJGYovlDHc1MbnTt9aMffWPQurPpGddSYdIKqABhJgXooHAf2KFdFF72axAcRCYZp8e5vbTtXrLXyuTl8PDc6nuAy+4v8+c2pzBhzctLHjI1sGM0dLP7PmIWBqkZ+8oTUyL2JVBoXPUxiiugKw7T4aqFBMH13XPmf4ip8j3DD6OaqqSDXGa3p7rU4sn4CwF96IK070R46qW+MRiVEdCVrPEnYtJ85K6qobNphnIqvw2MWN7hZsL62J4cVd0obDD5e7m/z2Cm2D9nVtgCf5eTi0F/IS3N18uwe4quG9D5qd7YQiULTICVLVZgVMTdnRRVV3hAAabSvug2wpMGZ9DFjbV2YeaWhNo+lOTSOcP/IJfaXANB2uQAKxsRieJFlmRD0qUqO0pJVCNHKnBVVnBF6jpm2H3jOeROjtVXtjllWb498zNDtkJrfMuletRzW/gCrZ8Pq76BkAVStgJo1ULtOVY6tXgUVS1V12lXfqGM3zAN/tTq3zuyrEm6jYFVNmPUNLQvZ59reZHfbb/gsJ+eHziOIg3RXFC5HjRCEfKo7hWxKEkLE2JwVVdT6Qps9ZkOtf5PqUMlnaVlDc2VZNwFusD8OwBPGTBZaavODxxHFKcxgo9rQml4YvfcQQogtmLOiijp/eLPHSMyARSV17R6bPrKlomxOqhOX3db+iUYIPr5G3R8xE/JHRHZgQa/adJEqm66FENE3Z0UV9U0xo/V6xrPGPnxuTMSthbgqcCc/LCvt7CWSwm9rW+bmDtC/p79eTr2ewcvGHgCMyrVjj2RymL9WzbdJLBBCxJE5K6poCBgEK/fADGWgO2pw5nzZ5hi5zoCfV1cDJu7CtwEI1U5ityGDmr+/2/A8dEkoFiKhJGzCbFm9SgLVMHDRcWueMnKo8nb8vWRx7vvVbb4epy3n7/YXALhbO4kzpo+OxbBahHxqx3emVJdNJpZlYXq9mF4vlmXFejjR40pXSXki5jbGDB2TFDpOYt5AbtLHjNPebn+xNMRcwa22+9A1ixV9ZsHoA2MwsigI1KnqhKmdtPETcSFp4oWIK2X1fu4MH8XP5lCytEaec97EKG11m2NKonmdoenq8ymjj7o53BDyQu1qKF2gEmLX/wzrf4GS36BiEXibWrt6ciGzn/pss0d3U9xxr1U2399Zn8/F9lcAWDP+r9S4+/PwrCi1L2osh/RidROiicQLESsbrzMidVyiOuz+b5rvn2d/gwF6ORVaLkN2OYrcFJ1nDo1y9wpfTdNG7Yzovo9IKhJ7RHdJzOiamXd/1e4xu03nnmMnkZvq5OGTpnT8xO8ehKplKpFp0gnquiqS/DWQViSxRGyRxAcRCS3rGQYZNLb6jsZloTOpttIYr68k+4e7YjPAOHHQfV833bM4264SoGan7cv1e+WRl6Jz+4ysyL5hsFHNu0W6grkQnZCYIrqi+frBshOsUBsGnHmfo9lrOz4uCYUMk8Me+BZ75k/YPKuwTCeBsv1Jddn4+wFDyE93ce1BCVAsSohOJGs8SdiE2YJ0Nyn4ed15LWfZ30an46S07JTkPmmdu6FlIT+DRh5w3INLC8PAXbj89BM4blwEW1FsDV+1WuxOidKCuohLls/H4slTWDx5Cpav4wrRCcHhURXiJGk25grS3QDsrP3GdvqSDo+x0JM+ZiyubFvppJAqHrbfjs0IYPXdnsEHXBSjkUWBv0FNbjncsR6J2IykiRcirhSku6nHw9+CZ7PCLCRHa+A5502M0FpabodwkOPpgTacmqbOJzw5kF6kKsZuesvoqxJk3Rk9OmG/sbpsIVXc41AbK141pzNi54OYe3oh+w2NQsvrQL3abJc7BPQOqkqJpCXxQsTKxuuMSB2XqOoD6jpjmLaWM23vAHCP7WT2Gj+AuacXstuAKP5+Ql7ZqC2iQmKP6C6JGVuvzhfikEl9mfuPfZg6qINNFnXr4fNb1P3xx0S+ong4AOhqQ6MQWyDxQUTCxljQT6tgFG03cZeRzT9CpwIwYsn/wdq5PT6+eBA2WtbddtXnM1Zfhddy8alnJseMS+OH0wsZVxDBubtgAzhTIa0gcq8pxBZITBFdsTFmaI5KjHAWYe9AND2Eq+CDDo9LRt6gAboXV8H7AATK98YKZ+Kxp3HO7qOZc+XeDCuITqc+IeJBssaThE2Y3WFwDuemfsYkfTln2d/jKvuz2Gnf0uiiz4Lc+m0dZhJlSQPUBkwG/Wd9q0csbnU8wgC9nDVmPux5OZoe438eYb9qPZvZN7bjECJaHG5V3S3ccUVT0XN2GJzDfukreMJ5O2fZ3yGFjnfR/f3LIHd9V5dUO2sAyr3GJjEDUvDzqPN2irUqyB6ENuOf6jM7EQQbwZkik1tCiA7tMDiH4kw3XlK4K3wEv5qDydXqed55E8O1tc3HXfFVkAfm1sdwpLGxti7cHDPshLnXeS/5Wh2/mwO5MvgnADQtCq2LTENVCMwZIpvthBBxY2PM2NKn3i3vL+Sxr1f0yJjiydKyBgZd/i6gYsYdjodwagYfG1N435gKuj06MaM1X7VKcJLYIYSIsa7GjFs/XMSz363qkTHFk/nraptjxqbqmtqSdxozPvoHhBohdxiMPyLyg/PXqvbbEkuEED2kdczQtfZrFe+aO/GGsTOaZVD//ClqvjuJzF1ZxbCr3m/6yuKv9tcBeNGYTomZBURhbspXqwpAOWNciEoIITaxMWZgpILhIFB6EACOzJ/RU1quK+7+5A/emre+s5dJWF8vqWDidR/hKvgI3d6IESggVLUrAKahuvRFfW5KCBETCZswa9M1hh16OU+E9wPgNPsHPOu8mTzalhZfXhPmgbkNvLMkuUqMP/tr24ujU2wfMNP2A0HLxrmh81Wr+FjzVqk2RjLRJBKVPQXsbkmYjQM2XeMv26ndxFP0pbzhvIa+lLc7blFlmHvmNPD1muT6O3tgbkObr20Y3O14gPH6SiqtdNjvX+BMi9HoosBfA2nF8RELhRBxx6Zrze13/Lg5N/hXfjMHkafV8bzzRoZq6wBYUB7i1m/rmV8WiuVwe9zN39Q137/a/gw76Iups1L4S+h8AkSx6q63AlILILN/9N5DCCG6aWPMcBHcbALUvLW13PDO76yrSZ4d/ABXvf5b8/2/2N5ior6cGiuVq0KnUtsTl1whL+hO2agthIgLNl3jhpn9uc3xEH2p6PS4n1fX8I835tMYaF8cJJGd/+LPnX6vzreZa64VX8L8V0HTYYezVPGCSLJMCAfV5otYFyARQiSN1nNTGnRYAOSa0J/ZYOWQ7l2N9dHVPTzC2Dr1yR+a70/Tf2dHfREBy8HD4QOpDUShGErIp7o6SaVxIUQcao4ZpgfCeZjBPEI1UwBwF70OqE5x36+o4vwXOj/nTlQnPvY9unstjqzvAQiUHAKo7nUNfukSLEQiS+gr+Jnj+zHw0Gt4Sj+MRsvFTvpC3nFdyWTtj3bHrqlNrgkmX7jlgmCKtpgr7M8DcFP4RHIHjonVsFqEA2oSK6u/ajUrRCLSdXBlgpFcyZfxauIBZ/Lbzv+hHg8j9bW85foHU7VFHR67oT65TpC9wdaTSBY32h9nP9tcApaDp4uuSKyJoHAANBtkFMd6JEKIODZzXDE3HzGeLI+DFAKcEryUBeZA8rU6XmyVNAtQ4TViONKe19gUM060fcyf7B9jWhp/C53DSquYE8Z7ovOmIS9YQO5QsEcxKVcIIbbCzIJafsn+O8ek/bLFY2u8wegPKI40NCV7jdOWN1d9uiZ0CmVkc9p2PbAhz1sNGX1lo7YQIm7ss/RmjrR9yf+l3IOLzceExmByrWfU+Tr/eU/YaUDH3zBC8O4l6v6Q6dBv+8gPzF8H7gxVYVYIIXrQzHHFXH/IWLJSHR0W/6gjjUtDZwGgzX0Mln7S00OMmY2Vx8HiIvsrADxv7EUpORwxOgpzU74aSCsEd2bkX1sIISJg5rhiHjxxMgWeIjRbHYGyA7DCHmzuEhw537Q5Ntm6rIKJu+gNNM0iVDsJwzu0+TuzJshasRCJLKETZgH2GlXIiUcewdo97qTK1ZcirZoXnTdwou1j1KqqEkqS3Kf3l/rY77kyFparXdd9qOAh5104NYN3jB15ytiXe2fGwUKBtwrSpbqsSALuDDCSa4I7nk3eeR88s27Gl6Haaz/nvIljbf9rd1zITI6LhdcWepn5XBkralr+jV5if4nj7J9hWBrnh87j7AN2iuEIo8BXrSoUSvwRQmzB9JEF3Hz4eG4/fAz7jMzlhOCVLDQHkK/V8rzzJgZrGwAIJ8l1xnO/NbL/82WsrzeYpi/gn/anALgtfAwfm2ph+urdorBwYJnQWAnZgyAtP/KvL4QQ22ru47h9pdxs3MGH+1ax16jOP6vCRnJcZzz61XIOvPcrKhuCuAhyp+NBHE3zUm+Z0wC4eKcod3sINqoqg1n9ovs+QgjRHTOug5QcxljL+HLce0wZ2PnchC+YHBvz7v10CYfc/w11/s6ryJ6525COvzH7PqhYrDoI7XhmdApzBBpUl4tIV64VQogu2GNEPrccPp47Dx/F6Jz2n3Ffm+N5Mryv+uKNv6i11wR26weLOOqhb5u/3kWfzw76YgKWgwfDBwNw/LgIJ8waTfEpo68UgBJCxLWZ44r55u/78MRJM8hOScVftj8ArvyP0ezVzcf5kyRx6p9vLeDYR77FkfMNtpS1WIaLQNkBbY45aEICFYwSQrST8AmzADZNY+SosayYfh/vGDvi1AxudDzBHY6HcKMqO4abkp8WlIf4Zk2AxqDJO3/4aAgmVkA4571qFleG+XRlADcBHnHeSb5Wx+/mwKadhhppzhj/swgHAE1NNMnFhUh0Do/6d550u7Xily0tn5QjHmBDwW44NYNbHI/ycNYz2GlJGt2Y/DSvNMh3awPUBVTM8CXYRcTFH9ewqDLMnPWqqsmptvc5z/4mAFeGT+dDcyopjgQ6lTDDYBqQ2U/ijxCiS2yaxoR+mYwpSqeGdI4PXslq2wAKtRpecN7IIG1D83XGjxuC/LghSI3f5N0lPgLhxIr9V31Wy8KKMIHqtTzouBu7ZvK6sQsPGgc1H+O2R+GztbECPHkqYVYIIeLRzJth1IFolsHIry7gcMd3zd/aZVhum0MDTRca3y+vZN6aGioaArz32wZCRmJdZ9z47kLmr6ujpM7PtfanGKGvo9zK5OrQKaimsuC0Rfl83FutzvulCpQQIp5kDVBJs5pO4dKXOK7VJu4dBue0OXRjle5vllYwf10tZXV+Ppi/ASPBNnnf8fEfzFtTQ7CTnYi6BnZbB3NTFUvh81vU/UknqMp/kRZsBGeKbNwTQsTUxrmpwvSWjjtTih3N928JH0cotRgaSil//iwWb6hjfY2PDxeUJFwVwQc+X8YPKzcmfVlcaH8VUNVly8imb7oNmx7h6wxftaoy7snd8rFCCBFjNl1jzxH9yEvzEK7dnrB3EJoewl30FhuLDW68zvjk91KWlTewpsrLpwtLEy5mPPntSr5fsxRX/kcABMpmYYUzmr8/oV8meqRjhhAirthjPYCe5HSncl7ofH4x3+Mq5wscYfuK0dpqzgpdSMhMw7IsZr2g2lYMzbazrDrMrGFu7j8gZwuv3BtZ3Op4hHH6SiqsDM4IXowPN4ePSon1wMBXpSaw5OJCJANHCticYASlEkE8caTw+4Qrefb9J7jU8RL7+d/nKcdqzg2dTw3phEyLkGFxyH8rABiUaWNlrcFx4zzcvFdWbMceJUfZPucaxzMA3Bo6mv8a07loxyhXfeppvhpIyZH4I4ToNodN/VlNBhXTb8P30d8Yqa/lBedN/N5wG43BwRzxsooZ/TJsrK0zOGtKGlfskrGZV+198qjlKce/ydIa+dkcxuWhM9iY+HTHjKzIv2HIqyrM5g0Dhzvyry+EEJGg22DfG8FfAyu/5oAl13KofjZvmLty+m5D+GZpZfOhjYEwFQ0BjnlEJdXmpbmoaAjw95mjOGfPoZ28Qe91mP4Vx9s/w7Q0LgidSzUqLj59SJTn4QL1KsEpo29030cIIbbGoN1g7GEw/1UO3XA3z2hXM88axsnTBjJnRUtlwMaAwZoqLyc8+j0AaS47DYEwNx02jhN2HBir0fe4N8/dtf2Dpglv/RXCfigcD+OOjM6b+2sga7CqYCuEEDHmaLXh7Jgxqfy4oQYAPy5WTbmcoV9dTP7aj7jzvn/ygrE3APcdvx0HJmj1vD30X5mq/4HfcvBAU3XZJw6O8HWGGVYVZjP7g55AhUWEEAkvxe4ANAIbDsM59D/Y0xdiT19AuH4ctX4/q6saOf3puW2e8/SpO7D7iMTYKKaSfy3cxa+i6SHCjUMJ1Uxtc8x/jt0uNoMTQvSYpDp7c+gaoPGoMYvSPW6lwspgjL6Kt53/YNWvX1PS0LJLeVm12jnx7lJ/jEYbeTd+Vdt8/xL7Sxxsm03IsnFO8ELWoYLb/sNinDBrBAFN7aaX6n7Jy2Yjfb/9SN9vP7DZYj2a6HKkqETZcOJ81iQKh03jfuNQzgxehGl3s4ttAW84r2G4tpZXfveyqral4uzKWtUG75XfvbEabsRd8b+a5vtH6F/yb/v/AfB4eCYPGIcAMH1QAiV5WyaEfJDVH2xJtZ+od0umeCHiWutFCWdaDicEr2KJ2ZdirYrt5l7G6rVrmr+/tk7FjHf+8PX4OKPlgg+rScXHE85/M0gvZY2Zz5nBiwnQUt1k534RjhmWCY2VkD0YUhNjok5EkcQLEWu6HXY4E4ZMR8fkTseDHGv7H6nOtued7/y6gT9K6pu/rmhQXZE+XFDSo8ONFsuyOP0pteAyTFvLTY7HAbgnfDjfmuOaj5sW6ZjRdhBqo1zmQHAn1sYVEWck9oitpekw8kDoOwW7FeJh510UUdkuZnyysJSFG+qav95YCerzxeU9OtxosSyLEx79bovHje/XQaXwHx+H1d+qOdddL4zOPE84AJodMooj/9oisUl8EFHSunmox9F2ffXjhiH82u94AK6xP8NQbR0A3y2vJBGEDZPDH/im+WsdkyvszwPwtLEv5WQDMCLX0eHzt9rGAhwyLyViRWKK2EpOuwoaZrCQEanTAXAVvgW6j08WreTn1TXtntPRY71RIGyw711f4Miagz11OZbpwL/hcDYW/thoUF5qbAYoRCwkaTxJqoyQNGfLh1zm4MlM/+AmHnLezSR9GQ/ZbuXeZ/5A50jMBM0jfvTnRgBOtH3c3FL7qvCp/GCNItutU+032b7YubmXiD5vFaQWSHW/JKe7XPS75+5YD6Nn6DZwpkNjWaxHIjaR6Vax4CNzKv5ZY6l8/QoG6aW85ryWC6v/wj7PTmn3HEe024b2oBfmq+Tfw/SvuM3xMLpm8Ux4H64Pn0SqQ6cxZDEsJ4FOIwJ1qh1rWkGsRyK6IanihYhruSkt1w990nUqyOT44FW84LyRYazH+/Hf6K9dxRqrpQ2oI0EuOUzL4r3F9TzuuIvxTd0rTgpdTjlZgGqRalqQ64nwD9xYrhYkcgbLRjuxRRIvRFzQ7bDXP1hWb2No+Sfc4niU2kVZwHZsnJR/9ae1vPrT2nZPdXbUaroX8odMPllYSgaNPOS4G48W4CtjHPcahzUf49TBHs0fN1CrKgFmSnVZEV0Se8Q2sTthjyuoevUCigLreNx5O3bPjDaHPPLlch7p4KnOqH6I9px1Nb42Vdg7kpPawVpGzRr4+Bp1f+KxkBulCu2+arWOkZIdndcXCUvig4iWfhktyQ3F6W0THf79bT0a+/GU4xd2t/3GvY77OCx4HY4Euc74bV0tP7VK5DrS9gWj9DXUWKncF1bFPwZkRDj5wzQg5IeCMVKAQ8SMxBSxtQbkpPLDymoAdi3aj98X/ozurMRd+A63vHtUh89JlOuMzxeXsbRqHalD3gMgULYfVqhtbtKYYtlgLZJLssaTpDqD65dh58zJqaTYNVIcOiXkcnTwGq6yP8uf7B/zV/sbTNaWcH7oPCpp2ZkcCFu47L17EXZljdphvp/+A9fbnwTgztCRvNTUUvvsKWn4w1ZzglhMGCFV6SNLWleIJJOSBXXrYj0KsYkJBQ5OnuChT7oNT2EfdgnewIPOe9hJX8j/Oe7ktvAxPGgcROsdZxoQMqxenzi7qCIEwOH6l9zelCz7bHhvrgn/mVv3yebgESkEDQtPomR7AfgboHCsqj4ihBDdtMdAF8eM8TA6305OipqALyeL44JX8aLzRobqG3jFeR0nBa/gD6s/ABYQNi3seu+OGT+u9XKP4z52s82n0XJxSvAyVlqqytLTh+SwfR8nlgXOSMbGYAOgQd5w+dwWQvQuuo2BB13BRy+ns2/962R+fzv/sk/n6vCpGHS+gGtaFoZpYevlMeOrJeXYCXOf4z8M09ez3srhwtC5mOi8cXQeQ7Pt2HXQorURwjLBXw9F48Hpic57CCFEpKTlkbrftdS9cyljWIX15V+xcfJm4wWAaVqYpoXey2PGt5tJlv3skj3JSXXi2nTR3rLg7Qsg2Ah5I2DSidEZnBlWMSWzn2zeE0LEjfOmprOmzuDgESlMKGhfSdVC52+hs/lAv5wx+ious/+X1eY/sCwreuffPaR1dfUU/PzN/jIA94YP46M/D8LttLerurvN/LVqbU8KcAgheqGrZo2mMRDmmKn9Kcyt5z+fHkXKwIdxZP1IqH4sRsOYds8xTDMhYsab85bj7vNfNFsAwzuAUPXOzd/74ap9sOsaae6kSqMTImklUKZL11y5ayYX7aR2BNywZyZBHFwbPoXzg+fitVzsYlvAu64rmaItbn7OPs+WYZgWCytCTHu8hJd6Qcttw4RfVtfwzOyVPDN7FXs+XcY0fQH3OO5D1yyeD+/Ff5oqeEwodOCya7FNlgXwVakqUZ682I5DiJ7mkIW6eKRpGtfvmcXZU9IBOHFqH04MXsHT4RnomsXfHS9yj+N+3ASan9MYspj1QjmWZfFzSZCdHivpFS23N40ZM58v5xTb+9zpfKg5ZlwdPgULnTF5dtx2jQxXAp1CBBvBmQJp0jpJCLF1dE3j3/tk8eeJaQAcOToFgHKyOTb4DxaZ/SnUanjJeT2TtT8AWFVrcOTLFQB8uybAjo+V8Mlyf2x+gG5oHTOe/XYZZe9cxwG2OQQsO2eHLuI3a0jzsaPyHHgcOqnOCMYMMwzeGsgZCqly3SCE6H3sdhv7HncBTD0DNI3j7Z/xiONO0ul8rmnuqmr+/MQcAP63qJSd/vUp3y6t6KkhbzXDtPhmSQW3f7iI2z9czJnPzOWf9qfY3fYbjZaL04OXNG9YH53nIN2lkxLNTXm+GlUJMF3aZwshegdX0Qgy9r0SbE60pR9zT/qzqK13nXt/fgnnPPcjAO/+uoGd/vUpP66q7oHRbpvWMeO2DxZx2au/dnrs4LxUMlMcuB2bJA/PeQSWfQo2B+z6N/VnNPhrVZci6ZInhIgj6S6dBw7IYeawFBw2jXRn+4SmcrK5NHQWAKfZ32fV92/yt5fnAfDKj2uZdvOn/L6+rkfHvTVax4x/v7+Iez5d0vy9M2zvUajVsMos4BljBkUZTrLcemQ3cluWWlPI6h+9WCOEEFGUk+rkoZOmMH1UAcNzB2D4BhGq2g0Ad/FrYGts95zbP/qDa95cAMAzs1ey882fsry8oUfHvTVax4xr3p7DJ+vewJ66Ast04lt/NK1T5vLTXWSnOhOmArsQYvOSOjU+w9VycvyWuQu/BwfykONuhunr+a/zBm4OH8djxgGsqTOo8ptc+nENGxpMLvukhqPHxG+C27fLKrj3f0up94ebH5um+3jccRtuLcTHxhSuDp/CxqqIHe007HFGSLWvyBog1WUFptfL4smq3f3In35E98Tv/7eIcKSoi2ojGOuRiM3IdOuEsXNN+BQWW/35p/0pDrF9yyhtNeeGzmep1Q+AP6rCBA04+90qShtNzvugmgNHpMR49J3bNGZYlsXF9h853/4GAI+F9+fG8AlYTRcMg7IS8NTBXwNZg1VrVtGrJF28EL1GTkrL+Ww52RwTvJrHnbcxRV/Cs86bOTt0IV+aE/mlVFX0PumNSgwLTn+nipXn94nVsLeodcywYXCJ/WV2s81vTpb9ypzQ5visaGzIqy+FjGLIHhj51xYJS+KFiEvbnQCpeQQ/v529bT/zhnY1Z4T+xnKr4zjw1RKVIHvqk3MBOP7R71l5y6weG253fTB/A5e/9hs13lDzY2fY3uVE+6eYlsYFofP43RrU/D1nhDuktmMaEPRCnxHgcEf5zYSQ2CMiaMCOsPN58NVdHBj6kNV2F7eGj93sUz5cUArAuc//BMAZT8/lp6tnRH2oW6ujmNFtZQvh42vU/UknQcHIyAxuU5al4knuMGnBLbaKxAfRU4rSbNRXhds9/j9zMk+E9+MU+4fc7niI/X8aBEdP4pKmxNmLX/qFDy7cvYdH23WbixnFVHK2/W0Abg0fi9PhjM4gAvVqLSGtMDqvL0QXSUwRkeDQHWSk2Kkrn4EtbRE2Vxnuwjfxrz++3bHPfLeKGw4dx9VNibPXvrWAZ07bsaeH3GVtYoYWwpa2gJS+HwHgLzkIK9RSkGNQrvz/EckrWeNJUmcm9klrO6Gx1OrHwcEbecuYhl0zudrxHA867iYdL3UBi5C5+d3b8eDbZRX8671FbZJlR7Kaxxy3k6IF+Z8xiXND5ze3bvr0pAJyPdFelegCX7WqLitVokQycnjA7oZwYMvHipgZkNESM54z9uHE4JWUWVmM1NfytvMfHGX7nI1VPmoDJgGj98UMO2HOtb/VnCx7a+hobgif2Jws+/WfC0iLZJXAeBAOgGZXyVdCCBEhQ7PbbkirJY0Tg1fwhTEBjxbgUcftHKZ/BYAvZNILQkabmOEkxOX2F9nNNp+gZeOc0IV8Zm7X5vg5pxVGtnoHqGsGRyrkDZcKHkKIxDBiP94ecRPrrRyG6ht4w3k1e+k/dXp4yDB7cHBb74P5Gzj72Z/aLGIfa/sfVzmeB+Bf4eP5xJzS/L1fziyKfks/X5Wac0oviu77CCFENIw6CLY/FYC/2N/iL7Y3t/gUo9Vahj9kRG1o26qjmLE5v/1z3/YPhgPw6ukQ9kPRBLUpJVqCDeBMg1RpwS2EiG+TizpPFr0lfBwLzf7kaXXc4XgQ02iJE4Fw/F5zbClmXOV4Fo8W4AdzBO+aOzLn9CgltPprIbO/KoYjhBAJYKfBuWA58K8/GsvScWT+ij3j5w6PtayW64xAqLfEDBPduR53wYdomkmobhzh2u3bHh/Hm0WEENGRYFkv3eOyfBRR2eYxL27OD53H1aE/E7Rs7G/7gXedV3DHG98S7/myhgkPf7G8+WvT0hihreF651N4tACfGxM5J3QhQdTi8imTUhmaHQe7oM2wqjCbNQD0OEjeFaKn2exqN2o4/tswJ7NCp58Cqpq//t4azQGBm/nSGE+KFuQ2xyPc5XiAVHyc9351r4sZaZaXfzqe5gD7DxiWxpWh03jAOJSN1cj/OjWNfhlxEDMizVcF6YWqNasQQkTIiIwgudS2ecyHm9NDl/CWMQ2nZnCX80Eusf+XKz7tDe1RW2JGKj6udzzJNNtCApaDc0IX8j9zcpvj/75zOgWpET6vD/tVJaf84ar9qRBCJIhBQ0ZwYuBy5pgjydB8POq4g4vsL2OjfXLT7R8ujsEIu8cwLf751oI2jx2kf8u/7I8B8FD4IB41Dmj+3g17ZkanInlrZhjCQVWdXDZcCCF6I02D7U5k3aAjALjM8V9Osn202ac89MWy5vumFZ+TVB3FjM2546iJpLs7+Bz/9Hoona/mV/e8PLprDP46yOgDzuSouCOE6L0OHQpuOi7SEsDJX0N/xW852N32Gz+9dFPz94w4XdjYUszYRf+NA23fY1ga14RO4aFZOXgcUbjOCNSrjRNSgEMIkUCOnNIPsDD9/QhW7AWAu+h1NGd5u2Pfmre++X6vuc6wV+Eq+ATdWY0ZysBfchgb178BnjltB9wOyVMSItkkdcJslTdInlZLP8oobJUEBRrPGPtyVPBa1lp5DNDLuTd4LbPqXuxwwSJeLFhfS2VjS0v3yfoS/u14lFQtwJfGeM4KXUSAlh2FcVMk0FsFafmqwqwQycqdBUZwi4eJ2Kn2hSjQauhDefNmiwoy+VPo79waOoawpXOY7Rved16OXvILdYH4vEjYqHXMGKSVcIfzYSbpy6mzUjgtdCnPG3u3OT7iVQLjgRlWrfQy+qrFJyGEiJBaX4hirZJiKiiiovnxEHYuCJ3L/eGDATjP/ib7rvh3pwsY8WJjzMilllsd/8c4fRV1loeTg5fzaasKgRtFPGaYBjSUQ/ZgSO+4VbkQQvRW9f4QHi3IxcGzeCu8E7pmcYH9dZ533rTJXBU8/OXyTl4lfsxZUUVJXUtcO0D/jjsdD6JrFs+G9+aW8LG0XpRw9sR6xMZ5J2mZKoTozTSNJQOP5q3wTgDc4HiSI/QvOz38tlabLOI096ldzNgSp72DBY2F78Ds+9T9nc5VyazREg6ojRfpEk+EEPEvEAoxlHXkU00e7TdrL7X6cUP4JAAmLLqbsdoKIH6TnzYXMxyEud7+JABPG/uy0BqIK1rrGb6m6rLO1Oi8vhBCxIAvZKDmasKEaiYTbhyCZguS0vd50NpW9b7gxV+a7/eGmKHZa3GkL8aetgTL0vGvOw6Mtp/hTlu8JE4JIXpSUv/Pz/Go5NEsrYF8rYZs6tp8f541jP0Dt/C6sQs2zeJC+2u87LyOAVppLIa7RVXejcl2FofZvuZG55M4NIO3jGmcHrqkTbJs3DDD6pYp1WVFknN6ID7PKUWTjTEjR6snT6slgwYALHQeMA7h2OA/mjdZvOi8kWvtT5FC/FYNVjHDYqY+hzscD9FXr2SlWchhwev53JzU7viE3FjnqwZPrroJIUQEbYwZuVodeVodaXibv2ehc1v4WC4Onk3QsnGAbQ4vO6+jf5xeY4CKGWO1ldztfJCBehklVjZHBa/he2t0h8dHPGG2oVy1PM0bBnpSX8IKIRJQy9yUl4fDB3J58HQaLDc76ot4z3UFe+odt8CLV2X1LddAR9k+517HvTg0g1eNXbk6fAqtk2UBHHqUN64ZoaauRoNk3kkI0evlpLp5xDiAj8Oqw8Ntjoc5VP96y0+M0znH1jGjKxybLmRXLoM3zlb3R+wPI2dGaGSd8FWDJ08VPhBCiDiX43GiaVCoVVNANS7aF2x5ztibD43tcWoG9zn+Qxpe4jT3abMx41Tb+wzVN1BuZXBX+EggStcZgXqVKCvVZYUQCaYg3d10z66SSjccjhlOxebegKvwnU6fF6chozlmaLZG7Gm/4Sp8D4BA2f4YvsHtjnd0tDFPCJHwkvp//tg+meSluZqn6vtqFQxlbZtj6vFwUehczg+eS52VwmR9Ke85r4D5r6lKR3Ekx+PERZC/2V/hNPsHADwW3p8LQucSpH2ropDZ0yPsgLdKJSqlFcR6JELEliMFdLtayBNxadOY0Z8yBtPSdmKuNYqZgVt4PqxaVZxi/5D3nVewuz4vBqPdsjxHkMvtL3Ke4y1cWpjPjYkcGryeZVbfDo/XSLAKrJYJoYDaDS4L50KICNs0ZgykhIFsaHPMa+buHB+8ikornfH6St51XsV++pyeH+yWWBbDyz/hJsfjZGsNrDCLOCLwTxZbAzp9SkQ73vlqwO6EgpFgd0XwhYUQIj60jhmaBr+Zgzg7eD7zzUHkavU86byNy+3PYycc66F2ycZFllNs73Ob4xFsmsXz4elcGjobq4NpSEe0O1l4KyC9CFLzovs+QgjRA1TMSOE/xqF8bkxA1yzucDzIIVtImo3Xyk8tC/Nd47S3ihkhH7x0skpeyhsBu14Y2cFtyjTULVO6FAkheofW1xm6BsNYS3GrLkiKxmWhM1lr5TFYL+UWx/9hmfGweNxeZzGjmErOt78GwM2h46lDVQ2MSgEQX41aT3ClReHFhRAidnYYnENxplutZxgZoAUIlM0AwJn9PfaMnzp8Xrx2sihId4PuR09ZjjP/f2iaQahuPKGqXTs8XirMCpGckvp/vk2HM3dXOwg2TnHY6TgJ9i1zF/YP3ML35ijSND98+x94669QFT/t8Mame7nT9SjTbfMIWzrXhU7ihvCJbRYkWi/Uh2MdwTZWl80aKMlKQjg8YHeD0X6Xr4gPm8YMTWsfMxrwcGX4dE4O/p31Vg6D9FKedv4bProa6jd08KoxsuIrxn5zPrvaFhC2dO4NHcIpoUupIb3NYQMoab4f85gRaf5aSMmC1PxYj0QIkYC6EjNAbbY4MPAvfjSHk6F5edh5N3xzD4TjpEK5vxY+uZa+Cx7Grpl8YYzn1ODfWEf7z87+tFTI1SO1gBzyQcgL+SMhJTsyrymEEHGmo5ixzirg8OB1PBHeD4Cz7e/wmvNahmlrN/NK8WGHgRnc6nmWax3PAPB/4QO4Mnw6Zqu5qT6UN993RnM6KBxQ5U5k3kkIkSBaYobOneEj+MIYj02zuNPxIAfr33T6vHhNmN1hcA5FGV3fFOe0NX2WWxa8cxGUzgdXBux9bfQ31/lrwZ0pXYqEEL1GV+emaknjvOD5BC0bB9q+53Dj/R4eadd0HDMsbnI8RqoW4AdzBD+YI5q/E/HuR4F6cKZJdVkhREKy6RrXHjQGAA0dK5SP4R1CoEIViXIXv4buXt3ueVacXmeM6+cmL7sEV8EH6HYvRiAf/4Yj2bTr0UZOqTArRFJK+v/5Ow/N44oDRpGbpk6yO0uYBVhHPscG/8E/QqdgOTxQ9jvGK2fCnP+DoLfT50WdZcGi9zBePpWBrKfKSuPE0JU8YexP6w/9LOpJ13zNX8e8wuzG6rKSrCQ2ZbORusfupO6xO9iSZFHL5pBdqb1A+5jRcZWnL82J7Bu4lUfD+xO2dFj5FcEX/sR3r90H/rqeHHJb9SXw0TXw8dVo3krK7cWcFryEO4xj2lV7yqOGDK0ltsU8ZkSSZUGgATL7qaqFovdKxngheo1NY4ajk5ixgVyOCV7NQ+ED1QMLXqfkqT/z2meze2qoHVvzPbxyKqz4EgMb72afyEWhc1hB+0rkRVSSTmPz16FIbLIww9BYATlDIKPj6udCdJnECxHnOooZQRxcF/4TZwUvpMZKZYK+gnedV3GG7R101Ml5YyDMsY/M5rGvV8Ry+M0sfy0r7j2Io03V6u7W0DHcFD6B1nNTfSkjo1XMsEWzSp+3WlWXleQmEQsSe0SUbIwZOWke7ggfwf+Midg0i7scD3Cw/m2HzzEtqPEGOeqhb3n++/YL3bFgmhbXv72AGWMKu/yc5qrkX98F815Q2V+7X6qqvkaTZUGwEbL6qzlcIbaFxAfRg7o6N/WLNYxbwscDcIHxJFV/zOaIB7/l9Z/jY8Ne2DD5xxu/ccD4tsmqh+jfsJftFwKWnWdDe+GipYOjQ4/gdYZlSXVZEZ8kpogImjmumAdPnExRphssJ5aRRrB8H0L1Y9D0MCn9n0Gz17Z5jmlZrKnyctgD3/D+b/FROKraW8/5/51NWtEX2FyVmGEPvjV/ArPzDXYOqTArkl2SxhN7rAcQD3YemseOg/NYsL6WKm+Q5xZafLyq44sGC51njRkct+c+rP/gTmbYfoRfnoPF78H2p8LI/VVb9Z5SvwG+uRdWf4sT+MEcwd/Nv7LcbL8Y0N/ZyBUzRnH8u6qCpRHL5CczrFrPZw0Em/wzFG3pLhcDHn441sPoeSnZ0tKrF9g0Zjz6m8nX69pvtmjAw43hk/ivMZ3/Fj1HTvU8dqp4BV54HyYeA2MPA1d6B+8QBYF6+PlZmP8amCEszcb9oQO5138Y2Jx0tFdkgKuRS/ZpHTPic5fgVgnUq999WtcXZUR8Stp4IXqNTWPGfT+bzC1t/6Ebxs4t4eOZbY7lwbTHKAqWcPiSK8B2EEw9tWerqzaUwez7YcUXAIQyBnBo+Vks2DC4k/3X0N/t48K9RnLCe2phYpuvMywL6kshow/kDJXzI7HNJF6I3mDTmHHrDwa/V5p8aO7Az4Hh/NvxCNNt87jK8TwzbD9yeegMnvx2Jd8tr+K75VWctuvg2P4AG+YRfOFPDKtbgc9ycnHoHN43d2x3WH+3n7P3HM6fP1AxIxytaiRhv8rTzRoIuix8iJ4nsUdEU+uYUVk/kve+fogD+IK7HPdjheBtc+d2z7nn0yX8sLKaH1ZWc/yOA2Iw6ra+WlrBU7NXbfaYbI+DKw8YxaWv/AaA3abBgjfg0+vUAdufBoN3i/JIgVCjqioohT9EBEh8ED2tdcyoaAxw9TcG6xran4M/bsxkB30RM20/YL30J5Y03MBFq6o5bLt+MRh1W2/NW88Lc9a0eSyXWq51PA3APeHDqXL149xpQ7joM3WdEdFOFoFaVdE82hs0hOgmiSki0maOK2bGmCLmrKhiXXUjN73/O9Xrj0Ef+CA2dwkp/Z7Gu/rM5uRT04TLXvmVn1fXcM5zP7HyllkxHb835OWez3/m24pXceYuxDJt+NeejBXKa3NctsfBudOHcuO7i4BWG/OESFLJGk8kU7GJTYcJ/TIB2G2YhS9s8XNJkEGZdnZ7qqzd8RV6LmeELmaG8SOP5L2EVrcWvroD5r8Kk06AodOjmzgb8sGvL6lkXSOIpdv5d+BIHjEObNPmrrV/HTae8YUOYD0Q4/ba3ipIzZNJJiFac3jA5kL1jBTxrHXM2GWoRdCwmLM+yMhcBzs/Udrm2CVWPz4d8y8++uxTLra/wujQapj7OPzyPIyYCeOOUBUqoqGhTCXJLnpbVcIA6LMd9ZP/wu2vpKqvOymsfu9xk+mbYWNjzEioCrOBOsgbBU5PrEcihEgCrWPGzkMtQobFd+uCDM+xs/sm1xlfmBN5btR9pP30MMfZP1Of38v+B9udCOMOA7s7egMN1KtrmXkvNiUZ6TD2cNaNOIUFz6ud452doTz556mkOjVaYsY2nss0lqt2p/mjpBK4ECKptI4Z04ZYhE2Lb9cGGZSZz4znLuMY83Outj/DDvpiPnD+ne8XnUAKe+IjivFhSywL5jwCH/0DlxFkvZXDWcGL+c0a0uHhz562IzYd+EBVHgl33uhp2zRWqgpQnpwovYEQQsRWS8zIxD/0akKfazhWfs5/XA9gBTTeMae1Ob60zh+bgXaizhfa4jFz/zGDYNhsTph1lfwMH5+lvjliprpO6gm+WtX5wpnaM+8nhBAR1vY6wyRswjdrA+S4dY5+tbLpKI3LQmcyzraKfuFS7nA8zJmhi2I36FaqGoPtHrvO8RQ5WgMLzIE8YhzI4nMGUOU34DO1PmOPVIVZywR/PRSNl/UEIURSsOka04bmArnMGJfP7xWL+WDh6bxfeje+lHWk9Hsa35o/g+UgaARZV9NxIcKeVh+s54+qP5hT9SbO3K8B8G84CsM3qN2xc/8xg7XV3uaEWaddNloLkYwkYbYDNl0jzamx24DOFxxW1hiAxsfm9gQPm4nrj7fgp6eheiV8dpNKhppwNAyfoXYfR0rQC7+/QeiX/+IIqoVro2gSK8aey0Pvdj5hc9w4D+MK2v51xyz5yQyrm1SXFaItRwrYXWDEx4ml6BqHTcNh05g+qPOYsbTG4GNzez4JTmbFrCVN8WIF/P6GuhVPhKF7wZA9wJ21bQMyQqqN9tJPYMVXYKkV6Ar3QHL2OBtf0Q78URkGKjt9iXOmpDUly7aI6SaLSAo2qv9r6VJdVgjR85w2DadNY+/BbqxOKur9VuPirfAZvGbsxst9X4SKP2DOw/Dri2qTxZhDwZ0RuUF5K+H3t1SybLABgA1pYyja92Ia0oewprT9okRrF+2Y3pQs2yK8LdcZ/hqVrFswWtrcCSGSmsuu4UJjxhA3vpAJaPzXmM435lhusD/BdNs8dit9mo9d73Fn6EgwZ4Lesy2zvpw9m2FzrqFP9RwANmRN5oCSs6ih404aV+6aoaoDthKV64yQT7XMzuovVcqFEEnB7bTDjGvgE9BWfM7djvuxhQzeNHdtPmbemtrNvEL0vfnLOhoCYU7YcSDVjUEqGwKbPf76Q8Zi0zXcDrV4PUJbw8hP/6U29xVPgt0u6YFRA0YQNJt0KRJCJIyUps/VmUNTWLVJklMdqVxiXchTXMMM249cYL0GHNTjY3xp7ho04Kjt+1PREKDG23aTxWH6Vxxo+46wpXNZ6Ez+tXcuNh089pZzfzNSnSz8tWpTd3pxZF5PCCF6kUx3ChMKh6PrAdJSz+GpRfdgT12Gu+8L+NeegC8cYF1VbNePn/luFXZbgCH9qvnvwrdYY3wEgL90FuG6Se2Ov+fYSdh0DZe9ZQ7NIZ2JhEhKkq24la79omWCKaTZcY0/EkbsB7+/Cb+9DPUb4Jt74LsHYfAeMHwfNZFjd3X/zSwTSn+Hxe/B8s8g5MMBrDQLWTniFG4vmcz8dzefYPev6ZlomywSDM2O0V+/two8uVJdVnTK9Hr5Yxc1oTvim6/RPUmya9PhUZXjQt5Yj0RE2MM/qgQkCx1zyJ7oQ/aE9T/Bb6/C6tmwYZ66fXMP5I+EPlOgeLyqXuHJ2/wibzgANaugbCGs/RHW/9hSTRageCKnrdqH//m3425/Dne9WMHK2s2Xcfr7Lu0TsQZkJMgpg68GsgdHNtlMxEzSxguREDY9N9/orT98APxgjYLDHoIlH8GPT0J9idqU9/Nzqu3o8H2hz3YqIai7Qj5YO1dtrlj5dfPmCrIHcW7pwbxbsSNPN+Zy0etlVPo2n/16wY7tk6KK07cyYSvkVRsEiyaobhRCRIjEC9HbuVst/K61CjgldBn7GnO5xvEM/bQK7nQ+BPd/AnteAWMPi37irL8OZt/Hjp/fiUsLYepObgoew2MlM4HOr13OnNx+I0SeJwpj9VVB1iBIyY78awvRRRJ7RI/TdNjnGsIfmdhXfck9zgfICHl5xtgXgHU1vpgNzTAtLnjxFwB2G5bP7rd9tsXnnDxtEKCumwZopTzjvFkVEMkdBvve0HOFOPx14MmWmCIiRuKDiCcue/tz9+8Cg/iH7VRuczzChfbXYMHB6hqjhzQGwlz2yq8ATB9VwPY3ftLm+4O0DdzoeByAu8NHsMAazNFjVUGpFEfLz9P6/lYzDQg0QPEEcMSwq4cQnZCYInpCqiOVodlDWVzSiG/tn0jp/wSO9N+hzyusW38k0LObt1tbX+Pl6jfmA3DcAXP4eM1bAATK9iNUtVuHzzlkUl8APK6WcW+6uVuIZJOs8SRBsl+i68TxHp79rfMEtpABlmWx2pfCgEknoI0/Eha9BwvfUhVnl36sbna3WtQuGAMFoyCzn0oa1Vv9NViWaodauwYql0LJfFj7g6q0tFHWAC4qm8Vb5s4Y823AlqtRtl6Qf/WoPD5a5uOsyTFoISTVZUUXWb7YTeLGjN3ZlFSfIJU8k9Ss4W7eXdJ5m7uQAU4brE6dyID9JqM1lsGyz2Dpp1C5RCW+li2EX5qe4EyDtAJIyQJHKmCBaaq44KuGhlK1saI1Ty4M3VtVOc8bzqf/US2yL/iwpts/zwuH5/LlqgDHjUuAE6OwXyWWZfaJ9UhEBCVlvBAJY1o/J7PXbqaCq6ZjDt+PNXnTGVD5FdqvL0DlMpXouvQTVTG7eJKqVJ4zGLIHqUrlrTfphQMqVtSth/JFULpAbdIwWr1v4ThVvXbIHrx7bwkAf/2gmtpA985Jnjokh7nrgxw4fCsWEYyg2liXN1JdJwkRYRIvRG+maRrDc+wsqdo4/6PxkTmVLwMT+LPtQ86yv0N25VJ49TTCH12LfYfTYPLJkd984KuBn56Cr+8GXxUuDb40xvOQ8zS+9RZ066UemZXNwoowu/R3RnaMQS/YXCqWSHVZEWMSe0SP03Rs+1zDC4/exnHah9zgeJJMGrnPOJRNNzSEDZMNtX7650R/vqcx2LJ+8dcXfurek2vW8H72HaR6ayCzP+z/b3B1XMk84ixLbTbMHwVSdUpEkMQHES8KUzv+bHvZ2JOR2hpOt78Pr59DKHMQJZ6RPRIz6v0tMePc59rGDBdB/uO4j1QtwHfmaB4wDmnzfV3TuGNGFrUBk77pEViD9lWpgibpsp4g4pfEFNET8lLy2G3IUAyvD/+643D3ew5H5s+ghfCvO5aNaWc1/ho89gzKGwL0zUqJ6piCRpDfy5cDFs68j3lnxf8ACFRMJ1g5fYvPz3A7uPnw8ZiWRbp7K4qSCJFgkjGeSMZiF9w4PYsTx6cy8/nyDr8/e22ADQ0GN3xVx1mTU7li10wYd7jacVe+CP74AFZ+A94KVUlw9exWz9bAmaqSZjVN7Vi2Oqj850iFQbvAqFlQNIHX792w1T/PlGInU4ojvCDRVd4qtWCT1r2FFCGShiNF3USvdf/+OZwzJciBL1Z0+P25G4LMXR/kru/ruWRaOudNLYSJx6pbQxms+xHW/aRacNeuUe2xqxo2/6auDFVho88k6Le9SjaKUFWpaf1cTOu3FdXR45G3GjL6qmQyIYSIAy8cnsfPJUEOe6njmDG/LMgbi308+nMj/9xjJ/58+N5QvlhdXyz/XG2eaHd9gdqop2lqo5oR6uilIb0IBu0GI/dXFc2BkNGSINvdZFmAPQa62WPgViTLmmGoL1UVwHOGSIKTEEJ04OMTC/hhfYCjXqlsfsyPi4eMg3nW2IfPxn+CZ9k7pNavhU+vg8/+BUOnw5hD1We9J2fr3tg01EbueS/Cr/9t7ohipBVzXtXRvG/uAKHuf27vOzSFfYdu3ZA2y1et4klKVhReXAgh4p9ms3PcmZez+uN0Bqx8hUscL5OtNXBj+AQsVGLUsvIG7vz4D979dQMPnTiFmeOKojqmhlbJT/PW1m7myE1ULYenDiLVu1atJxxwm9ok3lOCDeBMl+4XQoiEpWkaK8/vw/tLfZzzXnWb790cPp5h2nr2ZB51TxzJ4Y3XcffpM9llWHQ/ExsCLfNY36+oavUdi5sdjzJBX0G1lcaFwb9g0j7h94jREUrqNYIQDkLhYFXsRgghktzAzP58cKmP5+fYeP5HcPd9HkfGfDT9aXzrTgDTxecrfuXRTw3mr/Xyxrm7MKl/VsTHYVkWVf4qVtWtYklVKa6iN3Bmfw9AoGwGwcq9On1ucWbbtYvjdhgQ8fEJIXoPSZjtolF5ne8q+Mv7LRcRD//UyKxBMLZPJjZdw8ofBfmj0Ha5UFWMXf+zWuSu+AMaStQCdrCDRChPLuQOhdwRWP2m8KM5gmF5KWS5u7+T+a59s7r9nKgwQuqWNTD67QGF6K2KJ8Z6BCICxhV0PoFywustC9y3z65n92KzKWaAlZqPNnJ/taANalKmdi14K1U1p5AX0FQikTtTtYNLL1K7nJuSi0zL4of1QUbnaWS4dIJG9xKeHjtoKxfS4104AGhSaUoIEXe2K+o8ZrTefHHjV3VMzjUY22cUtoJRWDv/Fa1qOaydq64tqldAzRq1+S68SaVzRwqkFULucCgY3VSRdgiGBT+sDzIuzSTNqdMY6l7MePHwCCxWW6ZKlk0vhvwR0oVCCCE2Y2qfjjeyNeBh6m8H42ImB9lmc6r9I8aYK2DJR7DkIyw0tMIxMHBX1U40f5Sac3JntT03Nk117VGzSs1frZ0Lyz6FxpYN5GZ6X/SxB7Oh30G8/2xNl8f+9rE9kGwUbFBV1rOkUrkQIslpGgP2PY/r7k/hWscznGZ/n2Ktkr+FzsaHm73v+KL50FveX0hmioMdBueo9QzLatOtbmsEwyZzV1UxeUA2boeNhsCWO+S19snFu0P5H/D0QVBfoua+Drhd/dmT/LWQN0KKGwghEt5+Q9tvfjawcX7oPF7XrmEoG3ja+W+ufy8PfdbUiMaMQNjgx1XVbD8wB6ddp87fccw4y/YOh9u+JmzpnBf6KyWoOamv/hSlAk2NFZDRR82nCSGEQNd0hmYN5cCJQZ7+wsC39s+k9Hsae9ofeAY+iG/tyVz4VMvxN7//K+fvNZqdhuRFLGZUNNbxvyXLyc2qwWc28M6ap3BmL8KyNAIlhxCq2Wmzz3/j3F226f2FEIlFViO74bixHl5Y4N3icae+to5s1zpKrSzqgrDbABdPH5KDljcc8oa3HGhZqipUoE4tSmCp1tspWWBrWTj/ZLmfM96pojC1nu9PK2JVTdcmmF48PJfhOXZyPXGSnOqrUhcWqfmxHokQ8UsS+RLGgcPdvLPEv8XjTnttLRmudZRZWdQHYeZQNw/NakpatTlVpb2myn9d8foiH3/7uIYROXY+OrGA38o6qSy4iRcOz2V0nmOrNmb0Chtj0NZW1hJCiCiaPsjFZysDmz0mbKqYkeZcT5mVSWMIDh+Vx537HtdykGVBqFEt7ALoDlVt1pXe4TnG0/MauO7LOqYUO3j1qHzmrg92abzPH5bL2AIHma4IxIz6UkjJUYm89gSpaC6EEFE0tY+THzr5vA7g5BVjD14x9mCa9hv7O35mR20BI7U1ULpA3VrTbGoOStNVJdlgg9q0tymHh0Up2/HPiumkpEziiQl5fLOgcYtjzffo3LNfNhMKHaQ5e+A6w1utEpvcmdF/LyGE6AW+yz6IC6vSudXxMAfY5tBfK+OM4N+ak4wAVlZ6Oe7/viPVZSMYNgkZFmftMYQr9h+91e976weLePTrFRwyqQ/3HLsdny4s2+JzxvfN5PL9R7HdgCw8JT/Ci8epTRwZfVWybEbxVo9nqxhB0OyyliGESAq6ptE/w8aaurYdUOtI5ZTQZbzivI7R+mourriak/7vCmxOD96QgU3T+Otew7lgn+GdvPKWXf3GfF6au5aTdhrIDYeO491f23dYPUT/mr/bXwTguvDJuPtN4rkpWUwpduK2R2FNLdio5tSyB4GeoOslQgixFRw2B+Pyh5GRso66xuF4V51BSv9nsLlL8Ay6H//6YzEaVUz4fnk9J67+ELeegS9ow+3QuWTfkZy+W9fXvDdqCDZQ7ivnspeW8MOyEHtNKWOF9TQVvgosw4l//TGEG8Z2+Nyjt+/HIZP6MnWQ2pghhBAbySdCN/xrr65NuFeQxZKASpYF+Gp1gIs/qmFN7SaJrpqmqgNmDYScpvajaQVtkmUBPlruA6C00eSLVX7um1u/xTHMHOpmp36u+EmWNYIqKThbqssKIZLDvTOzu3RcOdksC6hkWYAPlvm5/NMaShqMzT+xE28uVjHjj6owX6zyc/f3W44Zx4zxMK2fK3GTZY0QWEBWf0lKF0LEpa5W9y4nmxVBlSwL8NoiH1d8WkOltylmaJragJfRV93SCsCd0eln30u/q82AP24I8cUqP7fNrtviGE7fLpWd+7sikyzbUAbOVCgcA660bX89IYRIAl2t7j3bGs81wZPZL/Bvtvc/yF+C5/Nt2n6E88eqyrKgqpJ7K1UFWV9VU7Js01xV8UQYdwTscx2c9Bon1P+V78wxfLY6yJer/Nz09ZZjxkkTVMzokWRZf51qm50p1WWFEGKjt4/N5w1zV44PXkWllc54fSVvuq5mO21Ju2MbAwahpi5FD3+xnKte/w1vsHuVYTd69OsVALz5y3q+WlLOvz9YtMXnHD65L7sMy8Pzx5vw1EEqPmUPhgPv6vlkWVCbED3ZLTFTCCES3P9O6rhS62qrkJODl1NneZiq/8Ejjjsxgl4sC8KmxV2f/ME1b84nGDa36n1fmrsWgGe+W8U3Syt4rCmGbLS//j13Oh5E1yyeDO/LM8YMDhiZzi79XdFJlrUs8FZB1gApviGEEB3wODx8cdmuAJj+AXhXnIfh64tub8Qz4DFchW+DphYwrHA2vqDKDfKHTG58dyHXvb0Aw9xypztf2Ee5t5yFlQv5pfwXltcs54dlPpz5HzCn8S4qfBWYwVy8K8/tNFkWYJ/RhewyLE+SZYUQ7UiF2W7QNI0zJ6fyyE+N7NDHyZwuVmACeH2xj4WVIT44vvutIex6ywn/n96s2uLxN+yZySEj46xNkLeyqbJfD7TgE0KIOKBpWnNl8n0Gu/hkxeYrB7b24gIva+rCPHdY9z8zHa3O97sSM26cnslh8RYzIs1XpSqCSAwSQsQpXdOYNczNu0v9HD4qhdcW+br83BcWeKkPmty3f/cn8R3dvM64ea9MDhvl6fb7dMhbCbodCseq6oZCCCG6xKZr7D7AxZerAxw71sOLXeiEVEEm75k78V7FTvxpQirXHZYJQS/4q8FXC1iq2qzTo86b7e52my1aryuc3IWYces+WRwyooeuMyxLJTYVjpMNGEII0Ypd15hU6GBu6SieHPhvZq28mVH6Gl5yXs9t4aP5P2MWVic1VZ77fjV5aS4umjGi2++ra7BxDfykx+Zs8fg7jprIwRMK4fNb4POb1YN9JsM+/1QbAHuaZUI4AAX9pLKgECJpOGwaQ7PtLKsO8+eJqTw5r6WjxCJrAKcEL+UZ5y3sbvuNJ7VbOS14CY2o8/2nZ69iWEEaJ08btE1jOOHR79t8fbD+DXc4HsKmWbwU3oPrwidzz37ZHDg8itcZviq1gTB7YPTeQwghernslHTy0pxUNATZd2wuH/56Fq6C93HmzMaZ8w321MX4Sw/EaBzV7rlPfLOS4lw/syYU4dAd6JqOhoZpmYTNMH7DT22glsZQI/6wH13XSXems6puFZ4hj2Nzqe4Vodrt8JccDGbnMeH+4yczY0xh1H4PQojeTRJmu+mSaRnsOdDN5GInox5o3xZicxZVhHnkpwbOnLzlyfsF5SG+WaMWP5x693bInTQhtVvHR1040FTZb4BMMImu0XU8U6c23xeit7p2j0wOGpHC9n2cjLi/ezHjmzVBnvm1sUuf6fNKg8xZF+SE8R4ctu7FjBPHx1nMiDQzDEZYVZeVz5PEI/FCJJDbZ2RxwvgQU/s4u5UwC/DOEj+7D/Ry9JjNJ7NalsWPG4L8UhLixAmpOLrZ+OG4cRGKGb4aldxUNA5SZTOD6AESL0SCeeCAbH4tDTG52NmlhNnWnvq1ke37ODlohEclyGb07fA4y7L4bl2QhRUhTp6Q2u3rjC3FpIjy14A7EzL69Nx7CrElEntEnHj60Fx+Lw8xIreQ3f74J/92/B8H2r7jSscL7KIv4G+hc6ig485693y6hHF9M7e4yGxZFl8vrWB5eSMn7TQQh00n0MVKg3lpLo4Y6YIXjoLln6kHh8+E3S4Cu6tbP2vEBOrAlaE2kQgRaRIfRBx79ag8FleG6Jtua5MwC/CjNZKTg3/ncedt7KQv5HnnTZwZvJhS1Abua95cwOC8VHYbvvnPTtO0+OKPctbX+jh+hwGdHGXxF9tbXOb4LwCvG7twefgMJhS6OGRkFK8zjBCE/JA/GhwJXmREJAaJKSKGPrxwd+ZvKKMquI4P54UJlB5CuGEU7uJX0F0VeAY8SbhhOMHKPTG8Q4CWeaV/vVWCqVUxoo8Ni6addtrGPzScNiduu5tUezqvzf+Z7yveZb1/ITYXmOFUAiWHEa4ft9nx7Te2kFkTYtCpQojeKEnjiWZZ1pbrXcdYXV0dmZmZ1NbWkpHRzR3FDeWw5nvI7HgBYFsM+s/6rXpeUarOkWM8XDKt5WcxLYtVtQaDMm28vsjHxR/XbNVrn7t9GpfuHINd15tTt0G1TSqeJK2whYiibfqsTCDb9HuoL4G1P0Jm5BdatzZm9E23cfw4D+dOTW9+rHXMePY3L1d/XrtVr339HpmcPDHBE2YbylTlwr7bg97NzDAhEpTEixbb9LuoWw/rfoqr64y+6TZO2y6VUye1bNAzTIs1dQaDsuw89GM9t3xTv1Wv/dCsbGYOjcBigb9WLT4UjY/K704IEVkSM1ps0++iZg2U/BrxhE7Lshh8b/c25m3UJ83G+TukcWyrzRAhw2J9vcHALDt3flfHf+Y0bNVrv3ZUHpOLnVv13G4zDajfAMUT1UZtIUTMSMxosU2/i+pVUPJbxM+VG4MmYx8qASyOtX3GP+1P4dZCVFtp3Bg6kVfN3Wi9iN1an0w3f99/FIdMahlTMGxSWuenf46H69/+nce/WdHhc7fk+6MtCj+7RH2W21ww9VQYfxRoMVykq12rkqXyhsVuDEIkOIkZyjb/HqqWQ+nvEY8ZZY0GOzxW2uH3xmnLedp5CzlaA6VWFmcHL+Jna3jz9/tkuvnnwWPZd2xR82OBsEFZXYD+OR4uf/VXXvxhTafvnUEj/3I8xoG27wB4JDyLm8PHYaEz57RCClKjOMdft0F1S+0zSdYShIgjEjOUeP09/La+jIP+80PLA7ofZ+5nOHO/RtMMAAx/H0K12xGuG48Vzmo+ND/dxgUz89huUMu6QyBksryqhCX1c3lt4ef4NbVWYlk2QlU7E6jYa7NVZTf66eoZ5KT20NyUECJudOezUirMxkBJo8l9PzTwyQo/509N5/t1QZZVh/h6TZArd83gX1/Xdev1rt8jk+u+rGVKsZOLd0rf8hN6UtivJreyBkqyrBBCbIV19Qa3za7nw2V+/jI1jW9WB1hcGWbO+iA3Tc/sdrLszXtlcsX/apk+yMVJE3qw6lMsmGF1yxwgE1xCiKSwrt7g+i/reOcPH3/ZPp3PV/n5rSzEvNIQ9+yX1e1k2dtnZHHJxzUcPCIlMsmygXoI+qBorCTLCiFEBGjbMM+yvsHg8v/V8spCH3/ZPo1PV/r5aUOQhRVhHjsop9vJsvfsl8UFH9Zw4nhPzyXLAngrwZMH6VI1RAghNqelUrjGi8Ze/GQO5y7HA4zVV3GH8yEONb7muvDJLLX6tXvu+lo/F7z4C89+t4pzpw/jo99L+W55JcvLG3nxzJ26nSz74AmTufy5L3m2/5sUvvWuejCjL+xxmdoAEUthv0rcTZPqskKI5LW55hLzrSEcEryBRx13MFJfy0vO67kvfCj3G4cQxs76Wj9nPvMjOwzK4dy9hvHhghK+WFzOuhofb5+362aTZafpC7jV/gj99XJClo0bwiey6x77Yf0PLp2WHt1k2aBXrSHkDJa1BCGE6AaH7m77gOkmWL4/oZodceZ8hSNrLjb3emzu9VD4LmYwB8PXHzOUQ004nes+1umTozG8b4BlVRuoCK4Ae416LQ0s00GoZnuCVbthhXI2O5bH/7w9pz45l+sPGSvJskKILZKE2RhaVBHmL+9Xt3msu8myAIOybCz7a5y2nfNWQuZASMmO9UiEEKJX+7UsxNnvto0ZN29FzBiR62Dl+XEaMyLNWwWeXGmhJ4RIOj+VhDj9nao2j92zFVUCx+VHMGYE6iHYCIWjIbN/ZF5TCCHENpu7Icipb7eNGY/81P2YMbnI2fPXGeGAqjCbMwRsjp59byGE6GUcmxRs/cPqzyHBGzjd9h4X2l9lN9t8PtT/zsvGHtwVPrK5xXZrP6ys5s9P/NDmsee/X929cRBm9+pXmJd9B5RXAxoM3xd2Okd1CIo1X42qLuiKn6pdQgjR01Icm9+Yt8Yq5PDgdfzb8QgH2r7nIser7Geby7/Cx/O1OQ7QmLOyijmPz2nzvDd+Wdfh6w3USrjM/iKzbOr4VWYB54fOY541jCtHFrFyXJSrjlumWs/OGwGezSdjCSGEaKuzxFQrlEOg9BCCFftgz/gVe8Y8bCmr0J1V6M6281BVwPcVTV/YwbJ0CpzDWLN6LKG68WB2rQDUXqMKWXnLrG34aYQQyUQSZrfB5ycX8NSPlTyxwIjpOJyb2+oXS0Gv2o2d1V+qy4puMb1elu69DwDDPv0E3ZPgVTBFUvjkxHye+amKp36PXMxoCFndfo4zWTZHb6wumzUQbHK6k6gkXohE9f7x+Tz/SxXPRDBmLK8Od/s5EbvO2Jgsmz9KOk+ImJB4IRLZ28fm8eK8ap5bGLmY8f26YLefE5O5KW8FZPSXDXIiLknsEfFG0zRePSqPV36r5oVFKmaEsfOQcTDvmztwhf0FZtp+4Fj75xxm+5o3jF151DiAJR1UnG3trXnru/T+bgIcafuSM2zvkvq/MvVgZj+YegYM2jU+qvmZYZU0ldFXrllE1Eh8EL2Bx6Hz4uG5vL6ghv8u7vg6o5EUzgudz4fGbK53PMkYfRXPOm9mjjmSZ8P78KE5lQBtk6ge+7qlIrmdMLvoCzjK9jn763OwaRaGpfG0sS93ho+iHvV/w2nvgc9jb6VKlM0eFP33EiKCJKaIeFCQ7uaZ03bg5blreGvehnbft4xUQtXTCFVPA92PLWU1Nvd6NHstmgDgRIQAAD0JSURBVL0esAAdK5yOGcrC9PfB8PUnZHcT2op1cCFE9yVrPJEMkm0wKMvOtXsXMn1AOTd/VkqlH8o62Hkdbf5wnAYKbxXkDYuPneGi1zGqq7d8kBC9yLAcB9fto2LGvz8vpcKvUU7PV99Omul+qS6bNCReiEQ0Os/BDfsUsteAcm79vJRyv04FWT0+Dkck1qz9tWojXcFotfAgC88iRiReiEQ1vsDJ+BmFTO9fzh1fllLqt1FFZo+Po8cTZv21YPdAziDQo1xxSoitJLFHxJspxU6mFKu5qbu+LKXEb6eaDFZZRZwduogp4cX83fEiO+iLOcb+OcfYP+cncxivG7vygTG12/NYGibbaUs5yDabQ23fkK2pCuaWOwtt3BEw9lBwpUfhJ91K/jpVWdaTG+uRiAQn8UH0Bjv1c7FTPxUz7vmqlHV+J3WkbXKUxtvmznwTGMe59jc50fYxO+iL2cG5mIDlYI45koXWQNZY+QRw4CRMkVbFaG01U/Q/yNIam1/pf8Ykbg0fyyJrQJt30KM9jxT2q64VucPA4d7y8ULEGYkpIh7sNjyf3Ybns8uw1dz83iJqfAGgg7ka043ROAKjccQWXzMgybJC9KhkjCeSMBsBuw/PZ5eh+SxYX8vBrzVu+QkRZtfjcNE5UA/OVLVLXAghRLM9R+Sz27DYxYykSJmV6rJCiAQxfUQ+u8cwZti2dVHCV6MWHorGQaZ0nRBCiGjaZ1Q+00fELmb0aCcLIwT+ehVf3D2fHCyEEL3dvqPy2buDmPGjNZKjg9cyWfuDM+zvsq8+l8n6UibrS7nB8SR/mH35zhzDH1Y/llp9KbOyqLNSCWIjhSDpmpf+WjlDtPVM0ZcwVV9EvlbX/PqrzAIeM/bnuuNmQXpBLH70zQs2QtEQmUsSQohWZo7OZ8bIzV9nVJHBDeGTeCQ8i2Ntn3GU/Qv6aRXsZpvPbszv9LUrrAzeM3bkOWNvFm+SKNsjLAsaKyB7MKQV9vz7CyFEgjlm6gCOnNKf2csqOfGx72M9HCGE2Cy58o8Qmw4T+mUyKi/AoorutzvdGjOGuOmbbmPn/s4tH9yTLEstjheMia8d4kIIESc2xowMl5e6QM/skDtgmJt+GTbG5idB6PdWqsqyaXG4+CKESDAWhAMqSR8LNB10B9gcEXuHjTEDei756eARKQzMtNEnfRuyn7yVYJpQNF420QkhRA+JRcw4fFQKQ7PtpDp7sMprQxlk9FGbMYQQQmyVzcWMn6wRnBMaQT7VHGybzUG2b5mgrWCEvo4R+rpuvU+9lcJH5hTeNqaRN3QKo4rS0NI3rVAYB4KN4PRIpyIhhOhAV68zSsnhHuMI7jEOZ6i2nmn67wzRNlCsVeIgjIGNUiub5VYxP5nDWWANwqDjuaejxqQwsTDK69++alVZPGewbPIWQogIsekauw7P69H3PHr7fuwwWLpECCG6Z6tms++//34GDRqE2+1mxx13ZM6cOZs9/uWXX2bUqFG43W7Gjx/Pe++9t1WD7Q3eODqfjQVfp/ZxcuveWRF/jzMnp/L8Ybk8PCubf+6RGf12FN3lr1EVPjL7xnokQog4IDGjc9+d2rJrefogFzfsGfnqSJfvks6Lh+dy//7ZXLlrJlq8xYxIM4KqhVLWQNB7ssyVECISel3M0O2qs0I4BEYYQn7wVkHtWqhbr1p6WmZE3mr+2UXN9w8ekcI1u2dE5HVbu31GFi8fmcs9+2Xxt2lb+fqWBQ2lgA7FEyRZVggRNb0uZvSgeWe1xIzjxnm4fJfIb2Z+/KAcXj0qjzv3zebcqT24WdpbCc40yB8hFQCFEF0mMaNzP53RMjd15uRULpnW8pleTjaPGQdwaPBGJgce4qzghTwcnsWnxnasNAupszzNx4YtnTrLw0JzAB8YU7k1dAxHBq5hSuAhBh50JRcdNYPb9y/i9O3iMFkW1LVbaiG44nR8Qoge0ytjRsgLjeVQtwHqS1RRIzPyxZ3mnNYSMy7aMZ3zd+jsM1NjmdWXZ40ZXB8+mXNCF3F66FLOCl3MNeFTeNKYya/W0A6TZd85No93js3jtn2yOXF8asR/hmZGUHVFyh2qOqYKIcRW6JUxo4d8/ffpzfevPnAMf9lzaMTf45OL9+D9C3bj1iMncuQUWYcQQnRPtxNm//vf/3LxxRdz7bXX8tNPPzFx4kT2228/ysrKOjz+22+/5bjjjuO0007j559/5tBDD+XQQw9l/vzOWzD0Zm67xu/nFHP//tk8elAOR4/1cOm0jhcOTt9u607Ax+Q52Lm/K/4SZUElKQUbIXsQOFJiPRohRIxJzNg8j0Pn93OKuG9mNvfOzOakCan8eWLHseHMyVsXM0bmOtipnyvxE2U38lZCWpFUBBGiF+p1McOTC323hwHTYNAuMGhXGLgLDJymHs8apJJHa9erSnhGaJveLs2p89tZKmbcuk8Wp05KY9Ywd4fHnj1l6xZ5h2XbmdpnG2KGZapEYbsHiidCetGWnyOEEFuh18WMHpbp0pl3ZhH375/NP3fP5Owp6Wxf3HF1pq2NGYOy7Ezp5DWjJuSFcFAly0pHIyFEF0nM2LycFBs/nl7I/ftnc9nOGZw3NZ2Bme0TmGpIZ8h2e3Fz+AROC13KnsG7mBB4lCH+Zxnuf5phgWeZEHiU/YO3cHboIh4wDmGuNYogDgbnuKJfJXBbbOwYItcvQiS9XhczTAPCPgh6ISUHcodB5gC1wbuhTN1MI2JvV5BqY85phTx4QDZ/3SGNi3fKwG3veA5pq+emchyMK+iBmNFQDhl9Ib1P9N9LCJGQel3M6GH9sj18e/lePHzSFE7dZRCXzRzV6bFn7j5kq95jWEEao4sjX1hECJEcup0we+edd3LGGWdwyimnMGbMGB566CE8Hg+PP/54h8ffc889zJw5k0svvZTRo0dzww03MHnyZO67775tHny8cts1Zg1PIdOlfr2Fae0nmOacVsjfd+78wzvHrbc5trWDRsRxIqqvClJyVWs8IUTSk5ixZR6HzoEjUkhramHar4P213NOK+S8zVRtaj0pNfuUtjFjz4GuCI20FwgHwNIgeyDoPdgSVggREb0uZthdkJavKhDZnWBzgMOtOi1kFEPRWJU822+KWrRorFBVPrYhcTbdpWLGxs/9wdntK+v9cHphp5svNvXVnwqa7zttMKloGxYkjJBKlvXkQZ+JkCotkIQQ0dPrYkYMZLp1Zg1PwdUUM0bkto0ZOW6duacXcuTors0x/e+klpgxJNvOkA5iUFSZYWishJwhkF7cs+8thOjVemXMsCx1fh0OqD8tK6pvl+uxMWt4Cvam1nnj8h1tvj8ix85PZxSyfwcb9kx0QrSNCe8c29KCdfcBLnI9cd4ByF8L7mzw5MR6JEKIGOt1McOZCrkjoP+O0G97tbGsaKza3N13Crgy1FxNyBuxtyxItbH/sJTmok7bFbWNGbv0d/LLmUXs0cV1iWcObfnsPX6cp9ME3Ijy1YAjVVWXlXUEIcRW6nUxIwb6ZKWw39ii5gIdmya3HjyxD/Ou3ZcdBm35PDw/3cV9x2/X/PX5ew2L7GCFEEmnW2eBwWCQH3/8kX322aflBXSdffbZh9mzZ3f4nNmzZ7c5HmC//fbr9HiAQCBAXV1dm1tvdujIFP46NY29B6mLg+PGeihIteGwtT3pT3W0fP3PPTMpTtO5bOd0ClJbJpR26e/EpsdplUAjpCp95AxWCQNCbC1dxz1uHO5x4+RitReTmLF1TpyQyl+2T2PX/ipp6czJqRSk2kh3tv3sT2sVM+6YkUVhqs4/98iguFXC7aEjU5Knsiyo6rIZfVTVR5EcJF4kjISNGXaX+lzqO0UtXKTmq+oejeURqfBxzpQ0zp6SxtQ+KmZcvFM6+R4beZ62/x8yXC2x4KFZ2eR7dG7dO4v+mS0L26d0Mcm2QyEv1JdCZj+VLOvO3PrXEiIaJF4klISNGVH2950zOGtyKuML1HzNeTukkeextZlzgrbXGf93YA55Hp3/zMxukyB7/FgPPa6+VCXK5gyBZLrGEb2XxJ640Gtjhq6Dtwr8derPuvVQs1bNexjBbXvtLrhuz0zOnJzK8Bz12X/G5DRyUtrHjNbLG48dpGLG/x2Yw9hWCbdHjolBzOgOy1KVGTP7gh7nib0iMUh8iFs9ETMiHi8y+jTNw2xSoMlmV1Wz+06BvBEqQdRfs23v1Ym79s3mjO1SGZChPkNPGp9KlltvFzNae2RWNnkenWcPzWW7Vpu3e6RglBFUnVLzhknXCtH7SUyJmV4ZM+LAIydN4YzdBpOfrvKmjt6+P5kpDgoyOt9kcc+xk8hLc/HQiZPbJNYeNFEK+AkRMUkaT7pVDqKiogLDMCgsbFu9rrCwkEWLFnX4nJKSkg6PLykp6fR9br75Zq677rruDC2u2XWNv03LwLQsFlaEGZHT8a/95SPzOOCFcgDG5Nn59pTCdolOUd5Mvm0aK9TiRVrhlo8VYjN0t5vBr7wc62GIbSQxY+u47RqX7ZyBYVosqgwzqqkSVOt44NThqUNzOeLlCgC2K3Ly3akdxIyeG3bsBb2gOyB7gCygJxGJF4kj4WOGrkNagUrobyiDqhVq0dudsU3JpalOnct3ySBsWiyuDDM6T8UMe6sNdn3Tbdw4PZNT3qoCYFo/F3NOi2DM8FVDyK8WYHKHqkUZIeKMxIvEkvAxI0oy3TpX7JpJyLD4oyrMmKaY0Xpj3qRCByeMT+XST2oA1a3ih0jGjK3VWAHONFUxyx7HLb2FaEViT3zolTEjrVB95tkcoOlgGapIRbABGkqhsQqwVEcHW3Q+E/M8Nq7cNZO/7WSxvKZlbio3pWXhbL+hbiYVOvj3t/UA7D3Y3XHMiOsFDSDUqCo0puZt+VghIkDiQ/zqiZjR49cYdifkj1Sfc2UL1caLCBebKEqzcdVumfxtWgYra8KMbIoZ+a02c5803oPLrvHoz40A7Ds0hRlD3GiaRtBoiRNRDxmWpeblMgdARt8ov5kQ0ScxJXYSMmb0gP45Hq6aNYaLZoxgbbWPEYVq48LGBFqAC/cZzsqKRt74ZT0Ah0zqy8ET+6BpGjXels2DerwWGRSiF0rWeBKXqcFXXHEFtbW1zbc1a9bEekgRoWsaY/Md7SrLAgzPsVOc1rLbLtWh966qgCGvmsDLGZxUGedCiNiLeMwww2rHtbdKJQEF6lULvB5m01XM6Kiq+A59nWS2qhaY6dJ6V8yIBl8VZPWHlOxYj0QIEcdifp2h2yCjWFWbLRoPpgm167Y5ztibYobeQSzYoY8TV6vrj3RnhGKGaUDdBpU1VTxBJTFJsqwQIoHELGZYpooL4YCqfhThVWOHTcWMjbGgdUzYsa8Th9722JhfZ/hr1e+kYLRUMBdCxK2IxgyHWyXDujPAlaY++9Ly1bx7vx2g/w7qmqKhXCU+RTG7yGXXGJ3XEjNar2tM7dO+E17MY8bW8Nc1JSlvQ8cNIYToophcY2iamjcvngBoKmE0Ctx2jVGtYkbrjXlT+7avHNgcW3pySdlXBc50teFb1rKFEHEu5msZUeRx2puTZQHy0lrixE5DcjE3ucTZGDNcdukIIYSInG6taObl5WGz2SgtLW3zeGlpKUVFRR0+p6ioqFvHA7hcLlyuzstuJ5IjRqfw6kIf526fRrpLQ9fAtCDX0/GJ+phW7YziircKsoeAJ2fLxwohkkKvjBmaDTzZahJJd6hEJjOkJs+NgEp0cmWCMzbt5PYd4uaj5X7+PDGNfhkqhDt08Dg6XpDorKJ5wvHXgiMNMvvHeiRCiK3UK2PGtrA71YJ3ah5UrYTaNSrGeHIj1gZ0535Ovl0b5OixnuZriGy33mFSLcCgrG7EjJAXGishvVBVlpXNCkKIHpSQMSMcUOe0RlBtRtYd6prEMltab+t2lczj8ES8ZfSEAge/loU4cEQKhU2tU/tndP4e/TbzvYgKNqpb4TgVc4QQopsSLmboukqmTclWSZ4VS6B+vbqv98wc0JAsG8trDKYPcuPQ4V9f1zV3uuhI4WZacsecGQY01bZcCJH0eiJmxHReKr1IrX+UzFdJs2kFUX07TdPI9+iUe02mFjsZnmPn0Z8b2aGPs91xG3W2Nh4RIZ+q2N5njNqQIoQQ2yDhY0YPc9h03A4df8hkZGE6f9p5IG/NW89eo9rGKqe9JU6kuZJkDVwIETXdOvN0Op1MmTKFTz/9tPkx0zT59NNPmTZtWofPmTZtWpvjAT7++ONOj082t+6dxecnF3DoKA92XeOXM4uYd1YRzk2q0L5zbB5/2T6Ni3ZM7+SVYmhjolL2wFiPRCQI0+dj6V57s3SvvTF9vlgPR2ylXhkzUvOg/04wcFcYsBMM2hkG7qLu950C6X0h7IOatSqJtofbyt2/fzZfnFzAPkPcuO0qZvx8ZlG7Ch6vHZXHeVPTOG27JJj4sUz1d5EzSCa6kpDEi8TRK2NGJLjSoWicijHOdKhbryqbR8CTh+Ty1Z8KmNbPRaZL56czCvnmlPaLIS8ensv5O6Rx9JgubAaxTLWo4q9TibLF20myrOgVJF4kloSKGUZQffb7qiElB4onwoBpMHAaDNxZ3R+wMxRPUi1DTQPqS9Rz/HXqczkCXj4yj6/+XMD4AicFqTbmnl7Ixye2jxlPH5LDxTulM3OoOyLvu1lhv/q95I2ErAHRfz8hIkxiT3xIqJjRmq5DRh/oOxlSC1XnByO45edFwHvHF/DNKQUMzbYzINPOnNMKeePo/HbHPXpgDn/fOZ0d+zo7eJU44a9TlXzlmkb0IIkP8SthY0ZraflQPF5tsvBWRv3tvvhTAbNPKaQ43cboPAffn1rIc4fltjvu/v2zuWb3DEbmRqlolGlAY4XavJ5eHJ33ECIGJKbETlLEjB72w1X78P2Ve5Od6mTKwBy+u2JvHjlpSptjbLrGXcdM5MZDx1GY0QNzU0IkiWSNJ91Ou7/44ov505/+xPbbb88OO+zA3XffTWNjI6eccgoAJ598Mn379uXmm28G4IILLmCPPfbgjjvuYNasWbz44ovMnTuXRx55JLI/SS9l07U21ZwyXB3nMI8rcDKuIA4nl0xDTSwVjZdEJRE5lkVo/frm+6L36nUxQ9NU1b/WbHawZagJ9Iw+KpGpoQyqV6s22p7sHmvb5rBpDGwVM7LcHceMycVOJhfHYcyIBm+Vqm6e0SfWIxGxIPEiofS6mBEpmqaq5qVkQ+1aqF6h4ktqHti3fge506bRP7MlZuSkdFzZaad+Lnbq14X3CTY2febmQd4wSM1XYxeiN5B4kXASImZ4KyHkh8y+kDVQxYFOP1dzVRvVjZVoGyvUNUndBlVtNiV7m2KGy67RP6MlZuR5Oo4Zuw90s/vAnkiWDaifMXcY5AyReCN6J4k9cSMhYkZnXOlqXt7mhJpV6hrCkRLVt3TbNfqmt8SMgk4qyO4zxM0+Q+J8ETvYqOJMhCu3C7FZEh/iWkLHjI1S86BgDGyYp9Y6XNEr1ORx6Hha5cAWpnX8eTtreHRjF41lah5Lri1EopGYElNJETN6ULrbQbq7JWgUZXZ8LXHYdv16akhCJI8kjSfdTpg95phjKC8v55prrqGkpIRJkybxwQcfUFioWqOtXr0aXW9J4Nl55515/vnn+cc//sGVV17J8OHDeeONNxg3blzkfgoRO95KdZGR0TfWIxFCxKGEjBmudHVLL4KaNVCzWm0cSMvvsfZ3ookRVIvpheO2KUFACBEfEjJmdIfdCblD1MJF9SqVPKuhvo5lfDGCKmnJ5oT8UaqrhHzmCiFirFfHjI1VYp2pqjJgWpGqFNgVdpdqnZpWALlDVQXWuvUqedYMq01+zrTevQgc9kNDOeQMVdXMu/q7EUKITvTqmNEVDjcUjlUxonIppGSqWCA2L9igYnGUW5ILIXqXhI8ZG2UUq/Pu0gVgc4A9zjc4bItAPegOyB8p81lCiIhKmpghhBAJSrOs+E8PrqurIzMzk9raWjIyMrr35IZyWPO9qtghIiscUFWm+k5RVbGEiBDT62XxZFVif+RPP6J7utAiWGzbZ2UC6fHfg7cKKpdBQwm4M6O6I1tsonadiu9FE2UhPUlJvNg6Ei9axO3vwrJUkmr1SlUFQ7erato9mThrhMBXBaapFlKyB0mrUtFrSbzYenH7ORkD2/S7qFkDJb+qpJy6DerPgtHq+mFbWZZKnN1YcTbYVCXKnQFaLztHDnmhsUpVfsofqbp9CNFLJWPskZjRIia/C9OEqhVQsVglgrqT++9gi2rXqXhTOCbWIxFJJhnjQ0ckZigx/T2YJpQthKplat4nEYuBGCGoL4WicZAzONajESLikiWmSMxQ5PcghIiWRIon3fmsTMCzX9FjGitU20DZhS2ESGaeHHBlqEqzVctUO7e0gt63ON3bBOrVjvDsQZIsK4RIPJqmKpd7clQCVM0q9aduA3dWdCtihAMq8coyIbUAsgaojhLyWSuEENvGDKuE1ow+qgWqM0ITj5qm4oUnR31mN5Sqa5O69apSVEpW71j89tWohNn8kap6rrTHFkKI7tF11bHCZlMJUJapYoBoL+RTFRUzimM9EiGEiB1dh7zhEPapDhgZfXp3p4pNWZaaS8vqp66ThBBCCCGEaKUXzJiLuOSvBYdHtWRNpAsoIYTYGja7WpRwZ0LFH1C7XiU6SYuf6LBMtaCeP1qqHQohEptuU4u4aQXQWA61a9WfpqGqB7rSIrNBwzRURcJAo0qqSi2AzH6QmicJS0IIEQlmWC3YZvZTbbMdUWp56vSoykkZfVS8qFmjKirpNnXeHI/XJ2YYGirA7oCiCep3JPNMQgixdTStaWOxXSXNNparzW+iLV8VZA5UmxGFECKZ2Z2QP0ptJGgohfSiWI8ocrwVqtBJ7nCZ2xJCCCGEEO1IwqzoPjMM/jooGi+tnYQQorXUXHBuBxVLoGalSqB1pcd6VImnsQI8eZAtO8OFEElCt6lFi9QC8NeoChn1JepmWSpBypECNlfXkowsU1WSDflUJRE0cKZB3khIy1MLx5KsJIQQkWNzQkbf6CbLtmZ3qcTTtCLwVjZtuChTGyRSsiNX3XZb+evUhuz0QrWQ7cmJ9YiEECIxZPZTsad0gbpmSCuU8/uNQl7QnarioPxOhBBCbcYuHAvrfwFvVWKckwcbwDSheJT6+YQQQgghhNiEJMyK7mssV4sZmf1iPRKRqDQN57ChzfeF6FUcbjXB5EpT1WbDAVWhT0RG2K8W+nOHxmeFLNGzJF6IZKPrLW23c4ao5FlfjUqCCjRAuFIdpwGaTVWf1TSVVGuZ6vNzI4cbHKmQOUC1ak3JUm1JhUhEEi9ErGX0URsferqykc2u5m/SCsBXDXXroX6DSqJ1patN0JGoVN5dIS94q1XnoqLxTYldEoNEgpHYI2ItrUBVmi35TSXNphfJv0VQ8Sd7sHQsErEj8UHEI08OFIyCDfNUsqmzFyeZmuGW7nRpBbEejRDRJTFFCCFEJCRpPJGEWdE9Qa9afM8ZJosZImr0lBSGvvNOrIchxNbTddUK1eFRLfDq1jctTMRgMTqRWBY0lEPOUJnsEoDEC5Hk7E71WZhWALnDVPJRyKc2FhhBCPnBCquKGroOukMlydqcYHerirQOT1Jd/IrkJfFCxJymqbmUWL7/xg0X2QPVOXXtGqjboJJq3ZkqNkRbsFFt9tCdKlkpe4B05BAJS2KPiAueHCieACXz1YaJ9OLkPv8PNqrN11lSCETEjsQHEbcy+qi5pLIFag6pNxarsKymTSJ9IHtQrEcjRNRJTBFCCBEJyRpPJGFWdJ1lqiokeSNV23EhhBCbl16okpNKm5Jm0wpls8G28FaqCiC5Q5J7gUcIITal66qyubSZE0IIsSWudHXL7KfOr+vWqz/DFeBMAVdGZK9ZTAMC9erm9EDWYMjsI5X9hBCip6RktyTN1q2HjOLk3dDtq1abDd2ZsR6JEELEp+xBajN21TIVL/RelkbgrQBXJuSPVBsDhRBCCCGE6IScLYqua6wAT56qRiKEEKJr3JnQZyKUL4KaNZCW3zPVmxJNyKfaKeUNV1URhRBCCCGEEFvP7lSL4OlFEKgDb5WqONtYCZahKko5POrcu7ub1YwQhBpVlyLLUtdEhePUtZBUlBVCiJ7nzoTiiVDym/qsz+iTfBuRA/UqrmX2j/VIhBAifuk65I8AMwg1a9VGt96yySLoVZv1ikbKhnIhhBBCCLFFkjAruibkVe1c84apaolCRJHp87HiqKMAGPzyy+gpkhwnejlHilogtruhanlLVSfRNZapNm3kDlNVeoVoIvFCCCFEV0i8EGIzNE0lUrkzIWsA+GvVrbFcJRf5qtVxuk1VntUdaiGdpoVzy1A3IwxGQCXI6nZwpkLOUNUOPCVbOm2IpCOxR8QdVxoUjYMN85oqzSZR0qxlga8G8kdJEpWIOYkPIu7ZHKrTaNAH9aVqk128M8Oqa0b+KEgriPVohOgxElOEEEJEQrLGE0mYFVtmmarCSN5wSM2P9WhEMrAsgkuXNd8XIiFsnGiyu1W1WSOkFo/FljWUqfiTMyR5FnNE10i8EEII0RUSL4ToGt2mrlE8OZAzWFVpCjaqTdSBBgjWQzigkmMtC9DU+bluh5R0cGWoRFmnR93XbbH+iYSIHYk9Ih650qFoApT8CvUbIL04OeZZArXqZ8/sF+uRCCHxQfQOTg8UjoH1v6i5+XhOQrUsqC+BjL6yfiCSj8QUIYQQkZCk8UQSZsWWNZaDJw+yB8uFhhBCbAtdVwvPdjeULVQTOWmF8tm6Of46tQCfN0IqnAshhBBCCNGTnB51a800VQUnmiZPNV2dr8s1jRBC9A7uDCgaDxt+bao0W9x72m1vDcsEf736mTeNaUIIITqXkgWFY6Fknqre6smN9Yja25gsm5INBaPAJmkPQgghhBCiaxJ4JkRERLAR0FR1WUlUEkKIyMgohj6TwJmmFifMcKxHFJ/CAQjUQe4wSI3DCTkhhBBCCCGSja6D3Ql2l7rZHJIsK4QQvY07E4onQkoO1G1QSaWJyletEqky+sR6JEII0fuk5UPBWLVpzlcT69G011imOlwUjlN/CiGEEEII0UWSMCs6Z4bBWwXZQ9RFkRBCiMjx5ECf7VSF2boNKjlUtLBMaCiHrEGQNSDWoxFCCCGEEEIIIYRIHO4MKB7fkjRrGrEeUeSZYQj6mro9uWI9GiGE6J0yiqFwDIT94K+J9WhaNJSB7lLJsilZsR6NEEIIIYToZSRhVnSuvlTtvM4ZFOuRCCFEYnKlQfEEyB6skkODDbEeUfyoL4XUfFXhXLfFejRCCCGEEEIIIYQQicWVrpJm0/KhfkPidUBqKIf0IkgrivVIhBCid8vspxJTQ4HYV5q1LKgvAZtTxTDpTCeEEEIIIbaCJMyKjnmrVPuKvOGqvZ4QQojosLvUDu3CMeBvAG9lrEcUe40V4EiBglHgcMd6NEIIIYQQQgghhBCJyZUORRNUUmldAiXNhnygaZAzBGz2WI9GCCF6v6z+UDQejFDs1jAsU23wcKRC8URIzYvNOIQQQgghRK8nMwWivZBPtQYvngjuzFiPRiQjTcPRp0/zfSESnm6D3KHg8ED5IqhbD2mFyVlZ1V+rdokXjJFWSmLLJF4IIYToCokXQggheprEHtGbOFKgaJz6t1q3XrXf1nvx0pFlqc3YOUPBkxPr0QjRlsQH0Ztl9gVNh7IF0FAKqQU99+/YCEJDGXjyoGC0rB0IARJThBBCREaSxpNePOsRQZYFlqHua7q6JSszrCaT8kZARp9Yj0YkKT0lhWH/+zTWwxCi52UUq0WK5qTZAlWBNlkEGyHoVYs06YWxHo3oBSReCCGE6AqJF0IIIXqaxB7R6zhSVLttNKhb17uTZr2VkJINOYOTarFP9A4SH0Svl1EMNieU/a7iRXpR9OOFrwaCDZA5APJHqJglhJCYIoQQIiKSNZ700hmPbWSGIdCgKqlaJmgANvWnZaobmkpScqYmz4m3ZUF9KWT0VZUOZTJJCCF6XkoW9NkOKpZAzSpVdTYZdkuHvGriq2A0ZPaP9WiEEEIIIYQQQgghkovDrTYx6zrUrFFJUDZHrEfVPWG/ahdeOA6cnliPRgghElNqrlrDKF+sCn+406PTsTTkg8ZKcKZB0QTI7JecXfmEEEIIIUTEJVfCbNgP3mrAAlcG5AwBVzrY3S0n2JYJ4YA6CfeWg7+u6WQ8RZ3s99Zd1V3RWKZ+xvyRvW8iTAghEondBQVj1GdyxZKWarOJGoNCPmisUvEnW6p/CCGEEEIIIYQQQsSE3QUFY9X9mjWqA5DNGdsxdZVlQkM55AxVyb5CCCGix5UGxRPBkwNVK6B2HXiyVQGQbRXyga8aNJuqFp49UK3nCyGEEEIIESEJmnmzCcuC+hJ1P6MvZPRRLXlsW/jxcwarFg/eKnWi31CmkpU8OYmXtOSrUT9TwRh1kSNEDJl+P6tOPAmAgc8+g+52x3hEQsSArkNWfzURVLEE6jaoCSdngn1Gh7xqM0vecFXdXNdjPSLRi0i8EEII0RUSL4QQQvQ0iT2iV7M7VYVWzQ7VK9Qmbrsr1qPassZy8ORJ9zwR1yQ+iIRis6u19NQ8qF4N9etVYQx3ulrX0Lox17+xO2ygQcWczP6qomxKtnymC9EJiSlCCCEiIVnjSYJlfXbEUifWKdmqoqwnp+sn1pqmTuhd6SrR1luh2mPXl6r2RCnZ3TvZj1fBBrVbr3iCaqMhRKyZJv7585vvC5HUUrKgzySoyYGq5eBvgLS8xNi4EWwAXy3kjWhKlpV2SqKbJF4IIYToCokXQggheprEHtHb2RxQMFqtf1QvV8lQ9jheNAs2ABrkj1BrN0LEK4kPIhG50qFoLGT1g/oylThbX6IKWjlS1Dq9zdG0pq4BFpgGmKGmrq/+ljX5gtEq5rgzJVFWiC2RmCKEECISkjSeJEC2zRa4MqBoHKQWbLmi7ObY7KqNT2q+OsmvWt7SXqI3V/sL+VSyUsEYlRQshBAi/tgcKqE0JRsql6o45EoDd1asR7b1/DVqIqxgDGQPksqyQgghhBBCCCGEEPHEZoeCUWrOpmIppOZEptV2pJlh1b2oYIxKshJCCBEb7kx1yx6o5v99NdBYAWE/BBubEjAslQir29XNlQHZQ8CdoZ5rc8T4hxBCCCGEEMkg8RNmHW5w9Inc6+k2yOzb0l6iZiX46yEtv/dV+wsHwFsJucNVspLs1BNCiPjmyQHXZKhdA1UroWatqgzuSIn1yLrOslSLPE1X7f0y+0n8EUIIIYQQQgghhIhHuk11BtJsUPEHpFjgTI31qFpYluoImNFXrXEIIYSIPbsT0grULW84GEF1M8NNBzQlzNqc6lghhBBCCCF6WC/L8IwjdhfkD1eJSlXLoX6D2gXnzoj1yLomHICGMsgZoi5WpLKfEEL0DjY75AxWFc+rV6nkWV+NSqa1u2I9us0zQmoRIyWrpbWSEEIIIYQQQgghhIhfug1yh6nNz+WLwDJV2+x44KtSHQDzR2xbh0EhhBDRoWlq3SLe1y6EEEIIIURSkRmEbeXJUYmynlyoWgZ1G+K/2uzGZNnswZA/Sk14CSGE6F1caVA4BjKKoWaVij8akBKnibP+Wgg0QFY/VZkknqqRCCGEEEIIIYQQQojO6TrkDlVrCWW/qwQoZ1psxxT0QjgEfcbGTwKvEEIIIYQQQggh4l4cZ3X2Ihur/aVkQcVSqCsBT1bsJ4w6EvZDQ7mqLJs/UnZdCyFEb6ZpauOGOwsy+kHNarUhAgtSMsHhifUI1SaNxgo1lqLxkNlPNmoIIYQQQgghhBBC9DaaBtmDwLKgfKH6M1aJqkYIvJWQPxrSi2IzBiGEEEIIIYQQQvRKki0ZSSnZ0GeSSliqXAaBekgtiJ/EoGCjatudO0xV95NkWRHHbNnZsR6CEL2Hrqvq5p5c8FVD3XpoKAFvU1s6V3rPxyIzrBYuTBMy+6uNJe6Mnh2DSAoSL4QQQnSFxAshhBA9TWKPSEiapuZ4NA3KFqrHejpp1jKhvhSy+reMRYheROKDEEKISJGYIoQQIhKSMZ5IxmSk2RyqNZE7CyqXqKQlT3bsq836aiDkg7yRkDskfpJ4heiA7vEwYva3sR6GEL2PrkNqrrr5B6jKrrVroaEU0MCVBs5U0PTojSEcUEm7lqk2jWQPhNR8WbwQUSHxQgghRFdIvBBCCNHTJPaIhLax0ixA2SKwDLUe0hMsC+pLIDVPrXVIURDRy0h8EEIIESkSU4QQQkRCssYTmU2IltRctbO6ehVUrwB/A6Tlgd7Dv3LLVO25bY6WVtiStCSEEInPnaluWQNUpdnGcnWrL1GLC04PODxgd237exlBCDRAyAs2p0qUzeynFi9kg4YQQgghhBBCCCFEYtlYaVa3Q9nvqsuQJzf679tQAs50KByj5raEEEIIIYQQQgghukkSZqPJ7oT84Sp5tnKpSlJypYErs2eSVsN+aCgHTx4UjAJPTvTfUwghRHyxOSC9UN1CfvDXqltjGfjrVEVYXQPdoZJnbQ612KHb21eiNQ0ww+oWDqibZajnutIhe7CKNe4einNCCCGEEEIIIYQQInay+jclzS6EuhJIL4hOZyPLUh2U7B4oGqfmnoQQQgghhBBCCCG2giTM9gRPDrgmQ906qFqu/kzJVm2xo8Ey1Y5uIww5QyB3KDhSovNeQkSB6fez5owzAej/f4+gu90xHpEQCcLhVrf0QsgdBsGmqrDBRpVEG2yAUADMBpUcaxktixyWCZqtKZnWoarTpheDOwOcaeDKAD0KCyJCbIbECyGEEF0h8UIIIURPk9gjkkpGsdqEXb4YatdBWkFkOhptZJmqGIkzXSXLSmEQ0YtJfBBCCBEpElOEEEJEQrLGE0mY7Sk2O2QPVO2pa9ZA7Rrw1UBKVuQSZy0LArXgr1eTRkXDIK1QqvyJ3sc08f7wQ/N9IUQU6LpKdnVntDxmmmAE1c0MqwUJy1Lf03T1HN0ONpeqoi5ErEm8EEII0RUSL4QQQvQ0iT0i2XhyoM8kqFg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+      "text/plain": [
+       "<Figure size 3500x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_samples = 16\n",
+    "lengthscale = 0.2\n",
+    "for seed in range(5):\n",
+    "    print(f\"Seed {seed}\")\n",
+    "    test_info = get_test_function(n_samples, lengthscale, seed=seed)\n",
+    "\n",
+    "    for _ in range(5):\n",
+    "        train_x, train_y, test_x, test_y = test_info\n",
+    "        queries = plot(test_info, pfn_infos)\n",
+    "        gp_next_x, gp_next_y = queries[-1]\n",
+    "        train_x = torch.cat([train_x, torch.tensor([[gp_next_x]])])\n",
+    "        train_y = torch.cat([train_y, torch.tensor([[gp_next_y]])])\n",
+    "        test_info = train_x, train_y, test_x, test_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pfn",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/private_multitask_pfn/1d_hist.py b/private_multitask_pfn/1d_hist.py
new file mode 100644
index 0000000..64ec2e5
--- /dev/null
+++ b/private_multitask_pfn/1d_hist.py
@@ -0,0 +1,60 @@
+
+from gen_batch import (
+    combine_batch,
+    get_hpo_batch_fn,
+    get_icm,
+    get_lmc,
+    get_mtgp_batch,
+    get_pd1_surrogate_batch_fn,
+    get_pd1_eval_batch_fn,
+    toy_no_memory_batch,
+    get_trios_batch,
+)
+
+from PFNs.pfns.priors.hebo_prior import get_batch as get_hebo_batch
+import matplotlib.pyplot as plt
+
+
+mtgp_batch = get_mtgp_batch(
+    batch_size=200,
+    seq_len=50,
+    num_features=1,
+    max_num_tasks=2,
+    num_tasks=1,
+    lengthscale=None,
+    hyperparameters={},
+    device="cpu"
+)
+hebo_batch = get_hebo_batch(
+    batch_size=200,
+    seq_len=50,
+    num_features=1
+)
+plt.hist(mtgp_batch.x.flatten(), density=True, alpha=0.5, label="mtgp")
+plt.hist(hebo_batch.x.cpu().flatten(), density=True, alpha=0.5, label="hebo")
+plt.legend()
+plt.savefig("1x.png")
+plt.close()
+plt.hist(mtgp_batch.y.flatten(), density=True, alpha=0.5, label="mtgp")
+plt.hist(hebo_batch.y.cpu().flatten(), density=True, alpha=0.5, label="hebo")
+plt.legend()
+plt.savefig("1y.png")
+
+
+# def get_batch(
+#     batch_size,
+#     seq_len,
+#     num_features,
+#     device=default_device,
+#     hyperparameters=None,
+#     batch_size_per_gp_sample=None,
+#     single_eval_pos=None,
+#     fix_to_range=None,
+#     equidistant_x=False,
+#     verbose=False,
+#     **kwargs,
+
+
+# plt.savefig("1hebo.png")
+# plt.close()
+# plt.savefig("1heboy.png")
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/LICENSE b/private_multitask_pfn/PFNs/LICENSE
new file mode 100644
index 0000000..7095386
--- /dev/null
+++ b/private_multitask_pfn/PFNs/LICENSE
@@ -0,0 +1,201 @@
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+                        http://www.apache.org/licenses/
+
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+   9. Accepting Warranty or Additional Liability. While redistributing
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+      on Your own behalf and on Your sole responsibility, not on behalf
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+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
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+   Copyright 2023, Samuel Mueller, Noah Hollmann, ML Lab Freiburg
+
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+
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+
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diff --git a/private_multitask_pfn/PFNs/README.md b/private_multitask_pfn/PFNs/README.md
new file mode 100644
index 0000000..2818f2a
--- /dev/null
+++ b/private_multitask_pfn/PFNs/README.md
@@ -0,0 +1,99 @@
+# PFNs
+
+Prior-data Fitted Networks (PFNs, https://arxiv.org/abs/2112.10510) are transformer encoders trained to perform supervised in-context learning on datasets randomly drawn from a prior.
+Our priors can in general be described by a function that samples a datasets, or more generally a batch of datasets.
+The PFN is then trained to predict a hold-out set of labels, given the rest of the dataset.
+
+The pseudo code for a simple prior that would yield a PFN that does 1d ridge regression on datasets with 100 elements, could be something like this:
+
+```python
+def get_dataset_sample():
+    x = RandomUniform(100,1)
+    a = RandomNormal()
+    b = RandomNormal()
+    y = a * x + b
+    return x, y
+```
+
+Check out our [tutorial](https://colab.research.google.com/drive/12YpI99LkuFeWcuYHt_idl142DqX7AaJf) to train your own ridge regression PFN.
+
+### Install with pip
+
+This way of installing allows you to use the package everywhere and still be able to edit files.
+You should use a python version **>=3.9 and <=3.11**.
+```bash
+git clone https://github.com/automl/PFNs.git
+cd PFNs
+pip install -e .
+```
+
+### Get Started
+
+Check out our [Getting Started Colab](https://colab.research.google.com/drive/12YpI99LkuFeWcuYHt_idl142DqX7AaJf).
+
+### Tabular Data
+
+
+For loading the pretrained TabPFN transformer model for classification and use it for evaluation, you can download the model like this
+
+```python
+import torch
+from pfns.scripts.tabpfn_interface import TabPFNClassifier
+# Load pretrained-model
+classifier = TabPFNClassifier(base_path='.', model_string="prior_diff_real_checkpoint_n_0_epoch_42.cpkt")
+
+train_xs = torch.rand(100,2)
+test_xs = torch.rand(100,2)
+train_ys = train_xs.mean(1) > .5
+# Fit and evaluate
+task_type = 'multiclass'
+classifier.fit(train_xs, train_ys)
+if task_type == 'multiclass':
+    prediction_ = classifier.predict_proba(test_xs) # For survival [:, 1:]
+else:
+    prediction_ = classifier.predict(test_xs)
+```
+
+
+### BO
+
+There is a BO version of this repo, with pretrained models at [github.com/automl/PFNs4BO](github.com/automl/PFNs4BO).
+The two repos share a lot of the code, but the other is not anymore actively maintained.
+You can also train your own models with our tutorial notebook [here](Tutorial_Training_for_BO.ipynb).
+
+
+### Cite the work
+
+PFNs were introduced in
+```
+@inproceedings{
+    muller2022transformers,
+    title={Transformers Can Do Bayesian Inference},
+    author={Samuel M{\"u}ller and Noah Hollmann and Sebastian Pineda Arango and Josif Grabocka and Frank Hutter},
+    booktitle={International Conference on Learning Representations},
+    year={2022},
+    url={https://openreview.net/forum?id=KSugKcbNf9}
+}
+```
+
+Training PFNs on tabular data (TabPFN) was enhanced in
+```
+@inproceedings{
+  hollmann2023tabpfn,
+  title={Tab{PFN}: A Transformer That Solves Small Tabular Classification Problems in a Second},
+  author={Noah Hollmann and Samuel M{\"u}ller and Katharina Eggensperger and Frank Hutter},
+  booktitle={The Eleventh International Conference on Learning Representations},
+  year={2023},
+  url={https://openreview.net/forum?id=cp5PvcI6w8_}
+}
+```
+
+The BO version of PFNs was introduced in
+```
+@article{muller2023pfns,
+  title={PFNs4BO: In-Context Learning for Bayesian Optimization},
+  author={M{\"u}ller, Samuel and Feurer, Matthias and Hollmann, Noah and Hutter, Frank},
+  journal={arXiv preprint arXiv:2305.17535},
+  year={2023}
+}
+```
diff --git a/private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb b/private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb
new file mode 100644
index 0000000..4c2422c
--- /dev/null
+++ b/private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb
@@ -0,0 +1,1027 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f32e2ce2",
+   "metadata": {},
+   "source": [
+    "## Train your own models\n",
+    "To train you simply need to call `train.train`.\n",
+    "We give all necessary code. The most important bits are in the `priors` dir, e.g. `hebo_prior`, it stores the priors\n",
+    "with which we train our models.\n",
+    "\n",
+    "### Training the HEBO+ model, `model_hebo_morebudget_9_unused_features_3.pt`\n",
+    "You can train this model on 8 GPUs using `torchrun` or `submitit`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3d41a7e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4949ecba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/utils.py:293: FutureWarning: Decorating classes is deprecated and will be disabled in future versions. You should only decorate functions or methods. To preserve the current behavior of class decoration, you can directly decorate the `__init__` method and nothing else.\n",
+      "  @torch.no_grad()\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from pfns import priors, encoders, utils, bar_distribution, train\n",
+    "from ConfigSpace import hyperparameters as CSH\n",
+    "\n",
+    "config_heboplus = {\n",
+    "     'priordataloader_class_or_get_batch': priors.get_batch_to_dataloader(\n",
+    "         priors.get_batch_sequence(\n",
+    "             priors.hebo_prior.get_batch,\n",
+    "             priors.utils.sample_num_features_get_batch,\n",
+    "         )\n",
+    "     ),\n",
+    "     'encoder_generator': encoders.get_normalized_uniform_encoder(encoders.get_variable_num_features_encoder(encoders.Linear)),\n",
+    "     'emsize': 512,\n",
+    "     'nhead': 4,\n",
+    "     'warmup_epochs': 5,\n",
+    "     'y_encoder_generator': encoders.Linear,\n",
+    "     'batch_size': 128,\n",
+    "     'scheduler': utils.get_cosine_schedule_with_warmup,\n",
+    "     'extra_prior_kwargs_dict': {'num_features': 18,\n",
+    "      'hyperparameters': {\n",
+    "       'lengthscale_concentration': 1.2106559584074301,\n",
+    "       'lengthscale_rate': 1.5212245992840594,\n",
+    "       'outputscale_concentration': 0.8452312502679863,\n",
+    "       'outputscale_rate': 0.3993553245745406,\n",
+    "       'add_linear_kernel': False,\n",
+    "       'power_normalization': False,\n",
+    "       'hebo_warping': False,\n",
+    "       'unused_feature_likelihood': 0.3,\n",
+    "       'observation_noise': True}},\n",
+    "     'epochs': 50,\n",
+    "     'lr': 0.0001,\n",
+    "     'seq_len': 60,\n",
+    "     'single_eval_pos_gen': utils.get_uniform_single_eval_pos_sampler(50, min_len=1), #<function utils.get_uniform_single_eval_pos_sampler.<locals>.<lambda>()>,\n",
+    "     'aggregate_k_gradients': 2,\n",
+    "     'nhid': 1024,\n",
+    "     'steps_per_epoch': 1024,\n",
+    "     'weight_decay': 0.0,\n",
+    "     'train_mixed_precision': False,\n",
+    "     'efficient_eval_masking': True,\n",
+    "     'nlayers': 12}\n",
+    "\n",
+    "\n",
+    "config_heboplus_userpriors = {**config_heboplus,\n",
+    "    'priordataloader_class_or_get_batch': priors.get_batch_to_dataloader(\n",
+    "                              priors.get_batch_sequence(\n",
+    "                                  priors.hebo_prior.get_batch,\n",
+    "                                  priors.condition_on_area_of_opt.get_batch,\n",
+    "                                  priors.utils.sample_num_features_get_batch,\n",
+    "                              )),\n",
+    "    'style_encoder_generator': encoders.get_normalized_uniform_encoder(encoders.get_variable_num_features_encoder(encoders.Linear))\n",
+    "}\n",
+    "\n",
+    "config_bnn = {'priordataloader_class_or_get_batch': priors.get_batch_to_dataloader(\n",
+    "         priors.get_batch_sequence(\n",
+    "             priors.simple_mlp.get_batch,\n",
+    "             priors.input_warping.get_batch,\n",
+    "             priors.utils.sample_num_features_get_batch,\n",
+    "         )\n",
+    "     ),\n",
+    "     'encoder_generator': encoders.get_normalized_uniform_encoder(encoders.get_variable_num_features_encoder(encoders.Linear)),\n",
+    "     'emsize': 512,\n",
+    "     'nhead': 4,\n",
+    "     'warmup_epochs': 5,\n",
+    "     'y_encoder_generator': encoders.Linear,\n",
+    "     'batch_size': 128,\n",
+    "     'scheduler': utils.get_cosine_schedule_with_warmup,\n",
+    "     'extra_prior_kwargs_dict': {'num_features': 18,\n",
+    "      'hyperparameters': {'mlp_num_layers': CSH.UniformIntegerHyperparameter('mlp_num_layers', 8, 15),\n",
+    "       'mlp_num_hidden': CSH.UniformIntegerHyperparameter('mlp_num_hidden', 36, 150),\n",
+    "       'mlp_init_std': CSH.UniformFloatHyperparameter('mlp_init_std',0.08896049884896237, 0.1928554813280186),\n",
+    "       'mlp_sparseness': 0.1449806273312999,\n",
+    "       'mlp_input_sampling': 'uniform',\n",
+    "       'mlp_output_noise': CSH.UniformFloatHyperparameter('mlp_output_noise', 0.00035983014290491186, 0.0013416342770574585),\n",
+    "       'mlp_noisy_targets': True,\n",
+    "       'mlp_preactivation_noise_std': CSH.UniformFloatHyperparameter('mlp_preactivation_noise_std',0.0003145707276259681, 0.0013753183831259406),\n",
+    "       'input_warping_c1_std': 0.9759720822120248,\n",
+    "       'input_warping_c0_std': 0.8002534583197192,\n",
+    "       'num_hyperparameter_samples_per_batch': 16}\n",
+    "                                 },\n",
+    "     'epochs': 50,\n",
+    "     'lr': 0.0001,\n",
+    "     'seq_len': 60,\n",
+    "     'single_eval_pos_gen': utils.get_uniform_single_eval_pos_sampler(50, min_len=1), \n",
+    "     'aggregate_k_gradients': 1,\n",
+    "     'nhid': 1024,\n",
+    "     'steps_per_epoch': 1024,\n",
+    "     'weight_decay': 0.0,\n",
+    "     'train_mixed_precision': True,\n",
+    "     'efficient_eval_masking': True,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# now let's add the criterions, where we decide the border positions based on the prior\n",
+    "def get_ys(config, device='cuda:0'):\n",
+    "    bs = 128\n",
+    "    all_targets = []\n",
+    "    for num_hps in [2,8,12]: # a few different samples in case the number of features makes a difference in y dist\n",
+    "        b = config['priordataloader_class_or_get_batch'].get_batch_method(\n",
+    "            bs,1000,  num_hps, epoch=0, device=device, hyperparameters={**config['extra_prior_kwargs_dict']['hyperparameters'],\n",
+    "                                                                        'num_hyperparameter_samples_per_batch': -1,})\n",
+    "        all_targets.append(b.target_y.flatten())\n",
+    "    return torch.cat(all_targets,0)\n",
+    "\n",
+    "def add_criterion(config, device='cuda:0'):\n",
+    "    return {**config, 'criterion': bar_distribution.FullSupportBarDistribution(\n",
+    "        bar_distribution.get_bucket_limits(1000,ys=get_ys(config,device).cpu())\n",
+    "    )}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c8789428",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore', category=UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4dec5326",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
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+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
+      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
+      "  model = botorch.models.SingleTaskGP(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]]), mean = tensor([[0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.],\n",
+      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
+      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using 384000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "train() missing 3 required positional arguments: 'train_loader', 'eval_loader', and 'task_encoder_generator'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Now let's train either with\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m train\u001b[38;5;241m.\u001b[39mtrain(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39madd_criterion(config_heboplus,device\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu:0\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
+      "\u001b[0;31mTypeError\u001b[0m: train() missing 3 required positional arguments: 'train_loader', 'eval_loader', and 'task_encoder_generator'"
+     ]
+    }
+   ],
+   "source": [
+    "# Now let's train either with\n",
+    "train.train(**add_criterion(config_heboplus,device='cpu:0'))\n",
+    "# or\n",
+    "#train.train(**add_criterion(config_heboplus_userpriors))\n",
+    "# or\n",
+    "#train.train(**add_criterion(config_bnn))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c427f1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pfn",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/private_multitask_pfn/PFNs/pfns/bar_distribution.py b/private_multitask_pfn/PFNs/pfns/bar_distribution.py
new file mode 100644
index 0000000..9f9f9c9
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/bar_distribution.py
@@ -0,0 +1,578 @@
+"""
+The distributions here are piece-wise continuous distribution (BarDistribution)
+and a piece-wise continuous distribution with half-normals on each side, s.t. it has support for (-inf,inf), named FullSupportBarDistribution.
+Both of these distributions are described in the "Transformers Can Do Bayesian Inference" paper as Riemann Distribution.
+"""
+
+import torch
+from torch import nn
+
+from .utils import print_once
+
+
+class BarDistribution(nn.Module):
+    def __init__(
+        self, borders: torch.Tensor, smoothing=0.0, ignore_nan_targets=True
+    ):  # here borders should start with min and end with max, where all values lie in (min,max) and are sorted
+        """
+        :param borders:
+        :param smoothing:
+        :param append_mean_pred: Whether to predict the mean of the other positions as a last output in forward,
+        is enabled when additionally y has a sequence length 1 shorter than logits, i.e. len(logits) == 1 + len(y)
+        """
+        super().__init__()
+        assert len(borders.shape) == 1
+        self.register_buffer("borders", borders)
+        self.register_buffer("smoothing", torch.tensor(smoothing))
+        self.register_buffer("bucket_widths", self.borders[1:] - self.borders[:-1])
+        full_width = self.bucket_widths.sum()
+
+        assert (
+            1 - (full_width / (self.borders[-1] - self.borders[0]))
+        ).abs() < 1e-2, f"diff: {full_width - (self.borders[-1] - self.borders[0])} with {full_width} {self.borders[-1]} {self.borders[0]}"
+        assert (
+            self.bucket_widths >= 0.0
+        ).all(), "Please provide sorted borders!"  # This also allows size zero buckets
+        self.num_bars = len(borders) - 1
+        self.ignore_nan_targets = ignore_nan_targets
+        self.to(borders.device)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        self.__dict__.setdefault("append_mean_pred", False)
+
+    def map_to_bucket_idx(self, y):
+        target_sample = torch.searchsorted(self.borders, y) - 1
+        target_sample[y == self.borders[0]] = 0
+        target_sample[y == self.borders[-1]] = self.num_bars - 1
+        return target_sample
+
+    def ignore_init(self, y):
+        ignore_loss_mask = torch.isnan(y)
+        if ignore_loss_mask.any():
+            if not self.ignore_nan_targets:
+                raise ValueError(f"Found NaN in target {y}")
+            print_once("A loss was ignored because there was nan target.")
+        y[ignore_loss_mask] = self.borders[
+            0
+        ]  # this is just a default value, it will be ignored anyway
+        return ignore_loss_mask
+
+    def compute_scaled_log_probs(self, logits):
+        # this is equivalent to log(p(y)) of the density p
+        bucket_log_probs = torch.log_softmax(logits, -1)
+        scaled_bucket_log_probs = bucket_log_probs - torch.log(self.bucket_widths)
+        return scaled_bucket_log_probs
+
+    def forward(
+        self, logits, y, mean_prediction_logits=None
+    ):  # gives the negative log density (the _loss_), y: T x B, logits: T x B x self.num_bars
+        y = y.clone().view(*logits.shape[:-1])  # no trailing one dimension
+        ignore_loss_mask = self.ignore_init(y)
+        target_sample = self.map_to_bucket_idx(y)
+        assert (target_sample >= 0).all() and (
+            target_sample < self.num_bars
+        ).all(), f"y {y} not in support set for borders (min_y, max_y) {self.borders}"
+        assert (
+            logits.shape[-1] == self.num_bars
+        ), f"{logits.shape[-1]} vs {self.num_bars}"
+
+        scaled_bucket_log_probs = self.compute_scaled_log_probs(logits)
+        nll_loss = -scaled_bucket_log_probs.gather(
+            -1, target_sample[..., None]
+        ).squeeze(
+            -1
+        )  # T x B
+
+        if mean_prediction_logits is not None:
+            if not self.training:
+                print("Calculating loss incl mean prediction loss for nonmyopic BO.")
+            scaled_mean_log_probs = self.compute_scaled_log_probs(
+                mean_prediction_logits
+            )
+            nll_loss = torch.cat(
+                (nll_loss, self.mean_loss(logits, scaled_mean_log_probs)), 0
+            )
+
+        smooth_loss = -scaled_bucket_log_probs.mean(dim=-1)
+        smoothing = self.smoothing if self.training else 0.0
+        loss = (1.0 - smoothing) * nll_loss + smoothing * smooth_loss
+        loss[ignore_loss_mask] = 0.0
+        return loss
+
+    def mean_loss(self, logits, scaled_mean_logits):
+        assert (len(logits.shape) == 3) and (len(scaled_mean_logits.shape) == 2), (
+            len(logits.shape),
+            len(scaled_mean_logits.shape),
+        )
+        means = self.mean(logits).detach()  # T x B
+        target_mean = self.map_to_bucket_idx(means).clamp_(
+            0, self.num_bars - 1
+        )  # T x B
+        return (
+            -scaled_mean_logits.gather(1, target_mean.T).mean(1).unsqueeze(0)
+        )  # 1 x B
+
+    def mean(self, logits):
+        bucket_means = self.borders[:-1] + self.bucket_widths / 2
+        p = torch.softmax(logits, -1)
+        return p @ bucket_means
+
+    def median(self, logits):
+        return self.icdf(logits, 0.5)
+
+    def icdf(self, logits, left_prob):
+        """
+        Implementation of the quantile function
+        :param logits: Tensor of any shape, with the last dimension being logits
+        :param left_prob: float: The probability mass to the left of the result.
+        :return: Position with `left_prob` probability weight to the left.
+        """
+        probs = logits.softmax(-1)
+        cumprobs = torch.cumsum(probs, -1)
+        idx = (
+            torch.searchsorted(
+                cumprobs,
+                left_prob * torch.ones(*cumprobs.shape[:-1], 1, device=logits.device),
+            )
+            .squeeze(-1)
+            .clamp(0, cumprobs.shape[-1] - 1)
+        )  # this might not do the right for outliers
+        cumprobs = torch.cat(
+            [torch.zeros(*cumprobs.shape[:-1], 1, device=logits.device), cumprobs], -1
+        )
+
+        rest_prob = left_prob - cumprobs.gather(-1, idx[..., None]).squeeze(-1)
+        left_border = self.borders[idx]
+        right_border = self.borders[idx + 1]
+        return left_border + (right_border - left_border) * rest_prob / probs.gather(
+            -1, idx[..., None]
+        ).squeeze(-1)
+
+    def quantile(self, logits, center_prob=0.682):
+        side_probs = (1.0 - center_prob) / 2
+        return torch.stack(
+            (self.icdf(logits, side_probs), self.icdf(logits, 1.0 - side_probs)), -1
+        )
+
+    def ucb(self, logits, best_f, rest_prob=(1 - 0.682) / 2, maximize=True):
+        """
+        UCB utility. Rest Prob is the amount of utility above (below) the confidence interval that is ignored.
+        Higher rest_prob is equivalent to lower beta in the standard GP-UCB formulation.
+        :param logits: Logits, as returned by the Transformer.
+        :param rest_prob: The amount of utility above (below) the confidence interval that is ignored.
+        The default is equivalent to using GP-UCB with `beta=1`.
+        To get the corresponding `beta`, where `beta` is from
+        the standard GP definition of UCB `ucb_utility = mean + beta * std`,
+        you can use this computation: `beta = math.sqrt(2)*torch.erfinv(torch.tensor(2*(1-rest_prob)-1))`.
+        :param maximize:
+        :return: utility
+        """
+        if maximize:
+            rest_prob = 1 - rest_prob
+        return self.icdf(logits, rest_prob)
+
+    def mode(self, logits):
+        density = logits.softmax(-1) / self.bucket_widths
+        mode_inds = density.argmax(-1)
+        bucket_means = self.borders[:-1] + self.bucket_widths / 2
+        return bucket_means[mode_inds]
+
+    def ei(
+        self, logits, best_f, maximize=True
+    ):  # logits: evaluation_points x batch x feature_dim
+        bucket_diffs = self.borders[1:] - self.borders[:-1]
+        assert maximize
+        if not torch.is_tensor(best_f) or not len(best_f.shape):
+            best_f = torch.full(logits[..., 0].shape, best_f, device=logits.device)
+
+        best_f = best_f[..., None].repeat(*[1] * len(best_f.shape), logits.shape[-1])
+        clamped_best_f = best_f.clamp(self.borders[:-1], self.borders[1:])
+
+        # bucket_contributions = (best_f[...,None] < self.borders[:-1]).float() * bucket_means
+        # true bucket contributions
+        bucket_contributions = (
+            (self.borders[1:] ** 2 - clamped_best_f**2) / 2
+            - best_f * (self.borders[1:] - clamped_best_f)
+        ) / bucket_diffs
+
+        p = torch.softmax(logits, -1)
+        return torch.einsum("...b,...b->...", p, bucket_contributions)
+
+    def pi(
+        self, logits, best_f, maximize=True
+    ):  # logits: evaluation_points x batch x feature_dim
+        """
+        Acquisition Function: Probability of Improvement
+        :param logits: as returned by Transformer
+        :param best_f: best evaluation so far (the incumbent)
+        :param maximize: whether to maximize
+        :return: utility
+        """
+        assert maximize is True
+        if not torch.is_tensor(best_f) or not len(best_f.shape):
+            best_f = torch.full(logits[..., 0].shape, best_f, device=logits.device)
+        p = torch.softmax(logits, -1)
+        border_widths = self.borders[1:] - self.borders[:-1]
+        factor = 1.0 - ((best_f[..., None] - self.borders[:-1]) / border_widths).clamp(
+            0.0, 1.0
+        )
+        return (p * factor).sum(-1)
+
+    def mean_of_square(self, logits):
+        """
+        Computes E[x^2].
+        :param logits: Output of the model.
+        """
+        left_borders = self.borders[:-1]
+        right_borders = self.borders[1:]
+        bucket_mean_of_square = (
+            left_borders.square()
+            + right_borders.square()
+            + left_borders * right_borders
+        ) / 3.0
+        p = torch.softmax(logits, -1)
+        return p @ bucket_mean_of_square
+
+    def variance(self, logits):
+        return self.mean_of_square(logits) - self.mean(logits).square()
+
+    def pi(
+        self, logits, best_f, maximize=True
+    ):  # logits: evaluation_points x batch x feature_dim
+        """
+        Acquisition Function: Probability of Improvement
+        :param logits: as returned by Transformer
+        :param best_f: best evaluation so far (the incumbent)
+        :param maximize: whether to maximize
+        :return: utility
+        """
+        assert maximize is True
+        p = torch.softmax(logits, -1)
+        border_widths = self.borders[1:] - self.borders[:-1]
+        factor = 1.0 - ((best_f - self.borders[:-1]) / border_widths).clamp(0.0, 1.0)
+        return (p * factor).sum(-1)
+
+    def mean_of_square(self, logits):
+        """
+        Computes E[x^2].
+        :param logits: Output of the model.
+        """
+        left_borders = self.borders[:-1]
+        right_borders = self.borders[1:]
+        bucket_mean_of_square = (
+            left_borders.square()
+            + right_borders.square()
+            + left_borders * right_borders
+        ) / 3.0
+        p = torch.softmax(logits, -1)
+        return p @ bucket_mean_of_square
+
+    def variance(self, logits):
+        return self.mean_of_square(logits) - self.mean(logits).square()
+
+
+class FullSupportBarDistribution(BarDistribution):
+    @staticmethod
+    def halfnormal_with_p_weight_before(range_max, p=0.5):
+        s = range_max / torch.distributions.HalfNormal(torch.tensor(1.0)).icdf(
+            torch.tensor(p)
+        )
+        return torch.distributions.HalfNormal(s)
+
+    def forward(
+        self, logits, y, mean_prediction_logits=None
+    ):  # gives the negative log density (the _loss_), y: T x B, logits: T x B x self.num_bars
+        assert self.num_bars > 1
+        y = y.clone().view(len(y), -1)  # no trailing one dimension
+        ignore_loss_mask = self.ignore_init(y)  # alters y
+        target_sample = self.map_to_bucket_idx(y)  # shape: T x B (same as y)
+        target_sample.clamp_(0, self.num_bars - 1)
+
+        assert (
+            logits.shape[-1] == self.num_bars
+        ), f"{logits.shape[-1]} vs {self.num_bars}"
+        assert (target_sample >= 0).all() and (
+            target_sample < self.num_bars
+        ).all(), f"y {y} not in support set for borders (min_y, max_y) {self.borders}"
+        assert (
+            logits.shape[-1] == self.num_bars
+        ), f"{logits.shape[-1]} vs {self.num_bars}"
+        # ignore all position with nan values
+
+        scaled_bucket_log_probs = self.compute_scaled_log_probs(logits)
+
+        assert len(scaled_bucket_log_probs) == len(target_sample), (
+            len(scaled_bucket_log_probs),
+            len(target_sample),
+        )
+        log_probs = scaled_bucket_log_probs.gather(
+            -1, target_sample.unsqueeze(-1)
+        ).squeeze(-1)
+
+        side_normals = (
+            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
+            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
+        )
+
+        log_probs[target_sample == 0] += side_normals[0].log_prob(
+            (self.borders[1] - y[target_sample == 0]).clamp(min=0.00000001)
+        ) + torch.log(self.bucket_widths[0])
+        log_probs[target_sample == self.num_bars - 1] += side_normals[1].log_prob(
+            (y[target_sample == self.num_bars - 1] - self.borders[-2]).clamp(
+                min=0.00000001
+            )
+        ) + torch.log(self.bucket_widths[-1])
+
+        nll_loss = -log_probs
+
+        if mean_prediction_logits is not None:
+            assert (
+                not ignore_loss_mask.any()
+            ), "Ignoring examples is not implemented with mean pred."
+            if not self.training:
+                print("Calculating loss incl mean prediction loss for nonmyopic BO.")
+            if not torch.is_grad_enabled():
+                print(
+                    "Warning: loss is not correct in absolute terms, only the gradient is right, when using `append_mean_pred`."
+                )
+            scaled_mean_log_probs = self.compute_scaled_log_probs(
+                mean_prediction_logits
+            )
+            nll_loss = torch.cat(
+                (nll_loss, self.mean_loss(logits, scaled_mean_log_probs)), 0
+            )
+            # ignore_loss_mask = torch.zeros_like(nll_loss, dtype=torch.bool)
+
+        if self.smoothing:
+            smooth_loss = -scaled_bucket_log_probs.mean(dim=-1)
+            smoothing = self.smoothing if self.training else 0.0
+            nll_loss = (1.0 - smoothing) * nll_loss + smoothing * smooth_loss
+
+        if ignore_loss_mask.any():
+            nll_loss[ignore_loss_mask] = 0.0
+
+        return nll_loss
+
+    def mean(self, logits):
+        bucket_means = self.borders[:-1] + self.bucket_widths / 2
+        p = torch.softmax(logits, -1)
+        side_normals = (
+            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
+            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
+        )
+        bucket_means[0] = -side_normals[0].mean + self.borders[1]
+        bucket_means[-1] = side_normals[1].mean + self.borders[-2]
+        return p @ bucket_means.to(logits.device)
+
+    def mean_of_square(self, logits):
+        """
+        Computes E[x^2].
+        :param logits: Output of the model.
+        """
+        left_borders = self.borders[:-1]
+        right_borders = self.borders[1:]
+        bucket_mean_of_square = (
+            left_borders.square()
+            + right_borders.square()
+            + left_borders * right_borders
+        ) / 3.0
+        side_normals = (
+            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
+            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
+        )
+        bucket_mean_of_square[0] = (
+            side_normals[0].variance
+            + (-side_normals[0].mean + self.borders[1]).square()
+        )
+        bucket_mean_of_square[-1] = (
+            side_normals[1].variance
+            + (side_normals[1].variance + self.borders[-2]).square()
+        )
+        p = torch.softmax(logits, -1)
+        return p @ bucket_mean_of_square
+
+    def pi(
+        self, logits, best_f, maximize=True
+    ):  # logits: evaluation_points x batch x feature_dim
+        """
+        Acquisition Function: Probability of Improvement
+        :param logits: as returned by Transformer (evaluation_points x batch x feature_dim)
+        :param best_f: best evaluation so far (the incumbent)
+        :param maximize: whether to maximize
+        :return: utility
+        """
+        assert maximize is True
+        if not torch.is_tensor(best_f) or not len(best_f.shape):
+            best_f = torch.full(
+                logits[..., 0].shape, best_f, device=logits.device
+            )  # evaluation_points x batch
+        assert (
+            best_f.shape == logits[..., 0].shape
+        ), f"best_f.shape: {best_f.shape}, logits.shape: {logits.shape}"
+        p = torch.softmax(logits, -1)  # evaluation_points x batch
+        border_widths = self.borders[1:] - self.borders[:-1]
+        factor = 1.0 - ((best_f[..., None] - self.borders[:-1]) / border_widths).clamp(
+            0.0, 1.0
+        )  # evaluation_points x batch x num_bars
+
+        side_normals = (
+            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
+            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
+        )
+        position_in_side_normals = (
+            -(best_f - self.borders[1]).clamp(max=0.0),
+            (best_f - self.borders[-2]).clamp(min=0.0),
+        )  # evaluation_points x batch
+        factor[..., 0] = 0.0
+        factor[..., 0][position_in_side_normals[0] > 0.0] = side_normals[0].cdf(
+            position_in_side_normals[0][position_in_side_normals[0] > 0.0]
+        )
+        factor[..., -1] = 1.0
+        factor[..., -1][position_in_side_normals[1] > 0.0] = 1.0 - side_normals[1].cdf(
+            position_in_side_normals[1][position_in_side_normals[1] > 0.0]
+        )
+        return (p * factor).sum(-1)
+
+    def ei_for_halfnormal(self, scale, best_f, maximize=True):
+        """
+        This is the EI for a standard normal distribution with mean 0 and variance `scale` times 2.
+        Which is the same as the half normal EI.
+        I tested this with MC approximation:
+        ei_for_halfnormal = lambda scale, best_f: (torch.distributions.HalfNormal(torch.tensor(scale)).sample((10_000_000,))- best_f ).clamp(min=0.).mean()
+        print([(ei_for_halfnormal(scale,best_f), FullSupportBarDistribution().ei_for_halfnormal(scale,best_f)) for scale in [0.1,1.,10.] for best_f in [.1,10.,4.]])
+        :param scale:
+        :param best_f:
+        :param maximize:
+        :return:
+        """
+        assert maximize
+        mean = torch.tensor(0.0)
+        u = (mean - best_f) / scale
+        normal = torch.distributions.Normal(torch.zeros_like(u), torch.ones_like(u))
+        try:
+            ucdf = normal.cdf(u)
+        except ValueError:
+            print(f"u: {u}, best_f: {best_f}, scale: {scale}")
+            raise
+        updf = torch.exp(normal.log_prob(u))
+        normal_ei = scale * (updf + u * ucdf)
+        return 2 * normal_ei
+
+    def ei(
+        self, logits, best_f, maximize=True
+    ):  # logits: evaluation_points x batch x feature_dim
+        if torch.isnan(logits).any():
+            raise ValueError(f"logits contains NaNs: {logits}")
+        bucket_diffs = self.borders[1:] - self.borders[:-1]
+        assert maximize
+        if not torch.is_tensor(best_f) or not len(best_f.shape):
+            best_f = torch.full(logits[..., 0].shape, best_f, device=logits.device)
+        assert (
+            best_f.shape == logits[..., 0].shape
+        ), f"best_f.shape: {best_f.shape}, logits.shape: {logits.shape}"
+
+        best_f_per_logit = best_f[..., None].repeat(
+            *[1] * len(best_f.shape), logits.shape[-1]
+        )
+        clamped_best_f = best_f_per_logit.clamp(self.borders[:-1], self.borders[1:])
+
+        # true bucket contributions
+        bucket_contributions = (
+            (self.borders[1:] ** 2 - clamped_best_f**2) / 2
+            - best_f_per_logit * (self.borders[1:] - clamped_best_f)
+        ) / bucket_diffs
+
+        # extra stuff for continuous
+        side_normals = (
+            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
+            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
+        )
+        position_in_side_normals = (
+            -(best_f - self.borders[1]).clamp(max=0.0),
+            (best_f - self.borders[-2]).clamp(min=0.0),
+        )  # evaluation_points x batch
+
+        bucket_contributions[..., -1] = self.ei_for_halfnormal(
+            side_normals[1].scale, position_in_side_normals[1]
+        )
+
+        bucket_contributions[..., 0] = self.ei_for_halfnormal(
+            side_normals[0].scale, torch.zeros_like(position_in_side_normals[0])
+        ) - self.ei_for_halfnormal(side_normals[0].scale, position_in_side_normals[0])
+
+        p = torch.softmax(logits, -1)
+        return torch.einsum("...b,...b->...", p, bucket_contributions)
+
+
+def get_bucket_limits(
+    num_outputs: int,
+    full_range: tuple = None,
+    ys: torch.Tensor = None,
+    verbose: bool = False,
+):
+    assert (ys is None) != (
+        full_range is None
+    ), "Either full_range or ys must be passed."
+
+    if ys is not None:
+        ys = ys.flatten()
+        ys = ys[~torch.isnan(ys)]
+        if len(ys) % num_outputs:
+            ys = ys[: -(len(ys) % num_outputs)]
+        print(
+            f"Using {len(ys)} y evals to estimate {num_outputs} buckets. Cut off the last {len(ys) % num_outputs} ys."
+        )
+        ys_per_bucket = len(ys) // num_outputs
+        if full_range is None:
+            full_range = (ys.min(), ys.max())
+        else:
+            assert (
+                full_range[0] <= ys.min() and full_range[1] >= ys.max()
+            ), f"full_range {full_range} not in range of ys {ys.min(), ys.max()}"
+            full_range = torch.tensor(full_range)
+        ys_sorted, ys_order = ys.sort(0)
+        bucket_limits = (
+            ys_sorted[ys_per_bucket - 1 :: ys_per_bucket][:-1]
+            + ys_sorted[ys_per_bucket::ys_per_bucket]
+        ) / 2
+        if verbose:
+            print(
+                f"Using {len(ys)} y evals to estimate {num_outputs} buckets. Cut off the last {len(ys) % num_outputs} ys."
+            )
+            print(full_range)
+        bucket_limits = torch.cat(
+            [full_range[0].unsqueeze(0), bucket_limits, full_range[1].unsqueeze(0)], 0
+        )
+
+    else:
+        class_width = (full_range[1] - full_range[0]) / num_outputs
+        bucket_limits = torch.cat(
+            [
+                full_range[0] + torch.arange(num_outputs).float() * class_width,
+                torch.tensor(full_range[1]).unsqueeze(0),
+            ],
+            0,
+        )
+
+    assert (
+        len(bucket_limits) - 1 == num_outputs
+    ), f"len(bucket_limits) - 1 == {len(bucket_limits) - 1} != {num_outputs} == num_outputs"
+    assert full_range[0] == bucket_limits[0], f"{full_range[0]} != {bucket_limits[0]}"
+    assert (
+        full_range[-1] == bucket_limits[-1]
+    ), f"{full_range[-1]} != {bucket_limits[-1]}"
+
+    return bucket_limits
+
+
+def get_custom_bar_dist(borders, criterion):
+    # Tested that a bar_dist with borders 0.54 (-> softplus 1.0) yields the same bar distribution as the passed one.
+    borders_ = torch.nn.functional.softplus(borders) + 0.001
+    borders_ = torch.cumsum(
+        torch.cat([criterion.borders[0:1], criterion.bucket_widths]) * borders_, 0
+    )
+    criterion_ = criterion.__class__(
+        borders=borders_, handle_nans=criterion.handle_nans
+    )
+    return criterion_
diff --git a/private_multitask_pfn/PFNs/pfns/config.json b/private_multitask_pfn/PFNs/pfns/config.json
new file mode 100644
index 0000000..34a6469
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/config.json
@@ -0,0 +1,56 @@
+{
+    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-sobol-gpu": [
+        "pfn_bo_bayesmark.py",
+        {
+            "pfn_file" : "final_models/model_hebo_morebudget_9_unused_features_3.pt",
+            "minimize": 1,
+            "device": "cuda:0",
+            "fit_encoder_from_step": null,
+            "sample_only_valid": 1,
+            "pre_sample_size": 1000,
+            "num_candidates": 10,
+            "sobol_sampler": 1
+        }
+    ],
+    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-max1id0-gpu": [
+        "pfn_bo_bayesmark.py",
+        {
+            "pfn_file" : "final_models/model_hebo_morebudget_9_unused_features_3.pt",
+            "minimize": 1,
+            "device": "cuda:0",
+            "fit_encoder_from_step": null,
+            "sample_only_valid": 1,
+            "pre_sample_size": 1000,
+            "num_candidates": 10,
+            "max_initial_design": 1,
+            "fixed_initial_guess": 0.0
+        }
+    ],
+    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-max1id0.5-gpu": [
+        "pfn_bo_bayesmark.py",
+        {
+            "pfn_file" : "final_models/model_hebo_morebudget_9_unused_features_3.pt",
+            "minimize": 1,
+            "device": "cuda:0",
+            "fit_encoder_from_step": null,
+            "sample_only_valid": 1,
+            "pre_sample_size": 1000,
+            "num_candidates": 10,
+            "max_initial_design": 1,
+            "fixed_initial_guess": 0.0
+        }
+    ],
+    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-bnn-sobol-gpu": [
+        "pfn_bo_bayesmark.py",
+        {
+            "pfn_file" : "final_models/model_sampled_warp_simple_mlp_for_hpob_46.pt",
+            "minimize": 1,
+            "device": "cuda:0",
+            "fit_encoder_from_step": null,
+            "sample_only_valid": 1,
+            "pre_sample_size": 1000,
+            "num_candidates": 10,
+            "sobol_sampler": 1
+        }
+    ],
+}
diff --git a/private_multitask_pfn/PFNs/pfns/decoders.py b/private_multitask_pfn/PFNs/pfns/decoders.py
new file mode 100644
index 0000000..5cef6fd
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/decoders.py
@@ -0,0 +1,35 @@
+import random
+
+import torch
+from torch import nn
+
+
+class ScaledDecoder(nn.Module):
+    def __init__(self, ninp, nhid, nout):
+        super().__init__()
+        self.linear = nn.Linear(ninp, nhid)
+        self.linear1 = nn.Linear(nhid, nout)
+        self.linear2 = nn.Linear(nhid, 10)
+
+    def forward(self, x):
+        # return torch.cat([self.linear1(x), self.linear2(x)], -1)
+        x = self.linear(x)
+        x = nn.GELU()(x)
+        temps = self.linear2(x).softmax(-1) @ torch.tensor(
+            [1.0, 1.4, 1.7, 2.0, 5.0, 10.0, 20.0, 40.0, 80.0, 160.0], device=x.device
+        )
+        if random.random() > 0.99:
+            print(temps.shape, temps[:, :2])
+        return self.linear1(x) / temps.unsqueeze(-1)
+
+
+class FixedScaledDecoder(nn.Module):
+    def __init__(self, ninp, nhid, nout):
+        super().__init__()
+        self.mapper = nn.Sequential(
+            nn.Linear(ninp, nhid), nn.GELU(), nn.Linear(nhid, nout)
+        )
+        self.T = nn.Parameter(torch.ones(10000) / 10000)
+
+    def forward(self, x):
+        return self.mapper(x) / self.T.sum()
diff --git a/private_multitask_pfn/PFNs/pfns/encoders.py b/private_multitask_pfn/PFNs/pfns/encoders.py
new file mode 100644
index 0000000..d5f6288
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/encoders.py
@@ -0,0 +1,642 @@
+import math
+
+import torch
+import torch.nn as nn
+
+from .utils import normalize_data
+
+
+class StyleEncoder(nn.Module):
+    def __init__(self, num_hyperparameters, em_size):
+        super().__init__()
+        self.em_size = em_size
+        self.embedding = nn.Linear(num_hyperparameters, self.em_size)
+
+    def forward(self, hyperparameters):  # B x num_hps
+        return self.embedding(hyperparameters)
+
+
+class StyleEmbEncoder(nn.Module):
+    def __init__(self, num_hyperparameters, em_size, num_embeddings=100):
+        super().__init__()
+        assert num_hyperparameters == 1
+        self.em_size = em_size
+        self.embedding = nn.Embedding(num_embeddings, self.em_size)
+
+    def forward(self, hyperparameters):  # B x num_hps
+        return self.embedding(hyperparameters.squeeze(1))
+
+
+class _PositionalEncoding(nn.Module):
+    def __init__(self, d_model, dropout=0.0):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        self.d_model = d_model
+        self.device_test_tensor = nn.Parameter(torch.tensor(1.0))
+
+    def forward(self, x):  # T x B x num_features
+        assert self.d_model % x.shape[-1] * 2 == 0
+        d_per_feature = self.d_model // x.shape[-1]
+        pe = torch.zeros(*x.shape, d_per_feature, device=self.device_test_tensor.device)
+        # position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        interval_size = 10
+        div_term = (
+            (1.0 / interval_size)
+            * 2
+            * math.pi
+            * torch.exp(
+                torch.arange(
+                    0, d_per_feature, 2, device=self.device_test_tensor.device
+                ).float()
+                * math.log(math.sqrt(2))
+            )
+        )
+        # print(div_term/2/math.pi)
+        pe[..., 0::2] = torch.sin(x.unsqueeze(-1) * div_term)
+        pe[..., 1::2] = torch.cos(x.unsqueeze(-1) * div_term)
+        return self.dropout(pe).view(x.shape[0], x.shape[1], self.d_model)
+
+
+Positional = lambda _, emsize: _PositionalEncoding(d_model=emsize)
+
+
+class EmbeddingEncoder(nn.Module):
+    def __init__(self, num_features, em_size, num_embs=100):
+        super().__init__()
+        self.num_embs = num_embs
+        self.embeddings = nn.Embedding(num_embs * num_features, em_size, max_norm=True)
+        self.init_weights(0.1)
+        self.min_max = (-2, +2)
+
+    @property
+    def width(self):
+        return self.min_max[1] - self.min_max[0]
+
+    def init_weights(self, initrange):
+        self.embeddings.weight.data.uniform_(-initrange, initrange)
+
+    def discretize(self, x):
+        split_size = self.width / self.num_embs
+        return (x - self.min_max[0] // split_size).int().clamp(0, self.num_embs - 1)
+
+    def forward(self, x):  # T x B x num_features
+        x_idxs = self.discretize(x)
+        x_idxs += (
+            torch.arange(x.shape[-1], device=x.device).view(1, 1, -1) * self.num_embs
+        )
+        # print(x_idxs,self.embeddings.weight.shape)
+        return self.embeddings(x_idxs).mean(-2)
+
+
+class Normalize(nn.Module):
+    def __init__(self, mean, std):
+        super().__init__()
+        self.mean = mean
+        self.std = std
+
+    def forward(self, x):
+        return (x - self.mean) / self.std
+
+
+class NormalizeMultitask(nn.Module):
+    def __init__(self, num_tasks, mean, std):
+        super().__init__()
+        self.num_tasks = num_tasks
+        self.mean = mean
+        self.std = std
+
+    def forward(self, x):
+        features = x[..., self.num_tasks :]
+        features = (features - self.mean) / self.std
+        x[..., self.num_tasks :] = features
+        return x
+
+
+class SqueezeBetween0and1(nn.Module):  # take care of test set here
+    def forward(self, x):
+        width = x.max(0).values - x.min(0).values
+        result = (x - x.min(0).values) / width
+        result[(width == 0)[None].repeat(len(x), *[1] * (len(x.shape) - 1))] = 0.5
+        return result
+
+
+def get_normalized_uniform_encoder(encoder_creator):
+    """
+    This can be used to wrap an encoder that is fed uniform samples in [0,1] and normalizes these to 0 mean and 1 std.
+    For example, it can be used as `encoder_creator = get_normalized_uniform_encoder(encoders.Linear)`, now this can
+    be initialized with `encoder_creator(feature_dim, in_dim)`.
+    :param encoder:
+    :return:
+    """
+    return lambda in_dim, out_dim: nn.Sequential(
+        Normalize(0.5, math.sqrt(1 / 12)), encoder_creator(in_dim, out_dim)
+    )
+
+
+def get_normalized_uniform_multitask_encoder(encoder_creator):
+    """
+    This can be used to wrap an encoder that is fed uniform samples in [0,1] and normalizes these to 0 mean and 1 std.
+    For example, it can be used as `encoder_creator = get_normalized_uniform_encoder(encoders.Linear)`, now this can
+    be initialized with `encoder_creator(feature_dim, in_dim)`.
+    :param encoder:
+    :return:
+    """
+    return lambda feature_dim, task_dim, out_dim: nn.Sequential(
+        NormalizeMultitask(task_dim, 0.5, math.sqrt(1 / 12)),
+        encoder_creator(feature_dim, task_dim, out_dim),
+    )
+    
+    
+class AxialNormalizedUniformEncoder(nn.Module):
+    def __init__(self, in_dim, out_dim):
+        super().__init__()
+        self.normalize = Normalize(0.5, math.sqrt(1 / 12))
+        self.unsqueeze = lambda x: x.unsqueeze(-1)  # Custom unsqueeze
+        self.linear = nn.Linear(in_dim, out_dim)
+
+    def forward(self, x):
+        # Start with N x B x D
+        x = self.normalize(x).unsqueeze(-1)   # Normalize and add a dimension at the last axis
+        x = self.linear(x)      # Apply the linear transformation 
+        return x.permute(0, 2, 1, 3)    # Permute the axes to get N x D x B x E
+
+    
+def get_axial_normalized_uniform_encoder():
+    return lambda in_dim, out_dim: AxialNormalizedUniformEncoder(in_dim, out_dim)
+
+def get_normalized_encoder(encoder_creator, data_std):
+    return lambda in_dim, out_dim: nn.Sequential(
+        Normalize(0.0, data_std), encoder_creator(in_dim, out_dim)
+    )
+
+
+def get_log_dims(x, eps=1e-10):
+    logged_x = ((x + eps).log() - math.log(eps)) / (math.log(1.0 + eps) - math.log(eps))
+    return logged_x
+
+
+def add_log_neglog_dims(x, eps=1e-10):
+    logged_x = get_log_dims(x, eps) / 2.0
+    neglogged_x = 1 - get_log_dims(1 - x, eps) / 2.0
+    logged_x[x > 0.5] = neglogged_x[x > 0.5]
+    return torch.stack([x, logged_x], -1).view(*x.shape[:-1], -1)
+
+
+class AddLogNegLogDims(nn.Module):
+    def __init__(self, eps=1e-10):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x):
+        return add_log_neglog_dims(x, self.eps)
+
+
+def get_logdim_encoder(encoder_creator, eps=1e-10):
+    return lambda in_dim, out_dim: nn.Sequential(
+        AddLogNegLogDims(eps), encoder_creator(in_dim * 2, out_dim)
+    )
+
+
+class ZNormalize(nn.Module):
+    def forward(self, x):
+        std = x.std(-1, keepdim=True)
+        std[std == 0.0] = 1.0
+        return (x - x.mean(-1, keepdim=True)) / std
+
+
+class ZNormalizePerDataset(nn.Module):
+    def forward(self, x):
+        std = x.std(0, keepdim=True)
+        std[std == 0.0] = 1.0
+        return (x - x.mean(0, keepdim=True)) / std
+
+
+class AppendEmbeddingEncoder(nn.Module):
+    def __init__(self, base_encoder, num_features, emsize):
+        super().__init__()
+        self.num_features = num_features
+        self.base_encoder = base_encoder
+        self.emb = nn.Parameter(torch.zeros(emsize))
+
+    def forward(self, x):
+        if (x[-1] == 1.0).all():
+            append_embedding = True
+        else:
+            assert (x[-1] == 0.0).all(), (
+                "You need to specify as last position whether to append embedding. "
+                "If you don't want this behavior, please use the wrapped encoder instead."
+            )
+            append_embedding = False
+        x = x[:-1]
+        encoded_x = self.base_encoder(x)
+        if append_embedding:
+            encoded_x = torch.cat(
+                [encoded_x, self.emb[None, None, :].repeat(1, encoded_x.shape[1], 1)], 0
+            )
+        return encoded_x
+
+
+def get_append_embedding_encoder(encoder_creator):
+    return lambda num_features, emsize: AppendEmbeddingEncoder(
+        encoder_creator(num_features, emsize), num_features, emsize
+    )
+
+
+class VariableNumFeaturesMultitaskEncoder(nn.Module):
+    def __init__(self, base_encoder, num_features, num_tasks):
+        super().__init__()
+        self.base_encoder = base_encoder
+        self.num_features = num_features
+        self.num_tasks = num_tasks
+
+    def forward(self, x):
+        x_num_features = x.shape[-1] - self.num_tasks
+        if x_num_features > self.num_features:
+            raise ValueError(
+                f"The model was trained using {self.num_features} features, but got {x_num_features}."
+            )
+        rescale_factor = self.num_features / x_num_features
+        x[..., -x_num_features:] = x[..., -x_num_features:] * rescale_factor
+        # pad with zeros
+        x = torch.cat(
+            (
+                x,
+                torch.zeros(
+                    *x.shape[:-1], self.num_features - x_num_features, device=x.device
+                ),
+            ),
+            -1,
+        )
+        return self.base_encoder(x)
+
+
+def get_variable_num_features_multitask_encoder(encoder_creator):
+    return lambda num_features, num_tasks, emsize: VariableNumFeaturesMultitaskEncoder(
+        encoder_creator(num_features + num_tasks, emsize), num_features, num_tasks
+    )
+    
+    
+def get_axial_num_features_multitask_encoder(encoder_creator):
+    return lambda num_features, num_columns, num_tasks, emsize: encoder_creator(num_features, num_columns, emsize)
+
+
+class VariableNumFeaturesEncoder(nn.Module):
+    def __init__(self, base_encoder, num_features):
+        super().__init__()
+        self.base_encoder = base_encoder
+        self.num_features = num_features
+
+    def forward(self, x):
+        x = x * (self.num_features / x.shape[-1])
+        x = torch.cat(
+            (
+                x,
+                torch.zeros(
+                    *x.shape[:-1], self.num_features - x.shape[-1], device=x.device
+                ),
+            ),
+            -1,
+        )
+        return self.base_encoder(x)
+
+
+def get_variable_num_features_encoder(encoder_creator):
+    return lambda num_features, emsize: VariableNumFeaturesEncoder(
+        encoder_creator(num_features, emsize), num_features
+    )
+
+
+class NoMeanEncoder(nn.Module):
+    """
+    This can be useful for any prior that is translation invariant in x or y.
+    A standard GP for example is translation invariant in x.
+    That is, GP(x_test+const,x_train+const,y_train) = GP(x_test,x_train,y_train).
+    """
+
+    def __init__(self, base_encoder):
+        super().__init__()
+        self.base_encoder = base_encoder
+
+    def forward(self, x):
+        return self.base_encoder(x - x.mean(0, keepdim=True))
+
+
+def get_no_mean_encoder(encoder_creator):
+    return lambda num_features, emsize: NoMeanEncoder(
+        encoder_creator(num_features, emsize)
+    )
+
+
+MLP = lambda num_features, emsize: nn.Sequential(
+    nn.Linear(num_features, emsize * 2), nn.ReLU(), nn.Linear(emsize * 2, emsize)
+)
+
+
+class NanHandlingEncoder(nn.Module):
+    def __init__(self, num_features, emsize, keep_nans=True):
+        super().__init__()
+        self.num_features = 2 * num_features if keep_nans else num_features
+        self.emsize = emsize
+        self.keep_nans = keep_nans
+        self.layer = nn.Linear(self.num_features, self.emsize)
+
+    def forward(self, x):
+        if self.keep_nans:
+            x = torch.cat(
+                [
+                    torch.nan_to_num(x, nan=0.0),
+                    normalize_data(
+                        torch.isnan(x) * -1
+                        + torch.logical_and(torch.isinf(x), torch.sign(x) == 1) * 1
+                        + torch.logical_and(torch.isinf(x), torch.sign(x) == -1) * 2
+                    ),
+                ],
+                -1,
+            )
+        else:
+            x = torch.nan_to_num(x, nan=0.0)
+        return self.layer(x)
+
+
+class Linear(nn.Linear):
+    def __init__(self, num_features, emsize, replace_nan_by_zero=False):
+        super().__init__(num_features, emsize)
+        self.num_features = num_features
+        self.emsize = emsize
+        self.replace_nan_by_zero = replace_nan_by_zero
+
+    def forward(self, x):
+        if self.replace_nan_by_zero:
+            x = torch.nan_to_num(x, nan=0.0)
+        return super().forward(x)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        self.__dict__.setdefault("replace_nan_by_zero", True)
+        
+        
+# class AxialLinear(nn.Linear):
+#     def __init__(self, emsize, replace_nan_by_zero=False):
+#         super().__init__(emsize)
+#         self.emsize = emsize
+#         self.replace_nan_by_zero = replace_nan_by_zero
+
+#     def forward(self, x):
+#         if self.replace_nan_by_zero:
+#             x = torch.nan_to_num(x, nan=0.0)
+#         return super().forward(x)
+
+#     def __setstate__(self, state):
+#         super().__setstate__(state)
+#         self.__dict__.setdefault("replace_nan_by_zero", True)
+
+
+
+class Conv(nn.Module):
+    def __init__(self, input_size, emsize):
+        super().__init__()
+        self.convs = torch.nn.ModuleList(
+            [nn.Conv2d(64 if i else 1, 64, 3) for i in range(5)]
+        )
+        self.linear = nn.Linear(64, emsize)
+
+    def forward(self, x):
+        size = math.isqrt(x.shape[-1])
+        assert size * size == x.shape[-1]
+        x = x.reshape(*x.shape[:-1], 1, size, size)
+        for conv in self.convs:
+            if x.shape[-1] < 4:
+                break
+            x = conv(x)
+            x.relu_()
+        x = nn.AdaptiveAvgPool2d((1, 1))(x).squeeze(-1).squeeze(-1)
+        return self.linear(x)
+
+
+class CanEmb(nn.Embedding):
+    def __init__(
+        self, num_features, num_embeddings: int, embedding_dim: int, *args, **kwargs
+    ):
+        assert embedding_dim % num_features == 0
+        embedding_dim = embedding_dim // num_features
+        super().__init__(num_embeddings, embedding_dim, *args, **kwargs)
+
+    def forward(self, x):
+        lx = x.long()
+        assert (lx == x).all(), "CanEmb only works with tensors of whole numbers"
+        x = super().forward(lx)
+        return x.view(*x.shape[:-2], -1)
+
+
+def get_Canonical(num_classes):
+    return lambda num_features, emsize: CanEmb(num_features, num_classes, emsize)
+
+
+def get_Embedding(num_embs_per_feature=100):
+    return lambda num_features, emsize: EmbeddingEncoder(
+        num_features, emsize, num_embs=num_embs_per_feature
+    )
+
+
+class SelfAttentionTaskOld(nn.Module):
+    
+    def __init__(self, num_tasks, emsize):
+        super(SelfAttentionTask, self).__init__()
+        self.num_tasks = num_tasks
+        
+        self.task_embedding = nn.Embedding(num_tasks, emsize)
+        # Multi-head self-attention
+        self.attention = nn.MultiheadAttention(embed_dim=emsize, num_heads=1, batch_first=False)
+        # Fully connected layers for downstream task
+        self.fc = nn.Sequential(
+            nn.Linear(emsize, 128),
+            nn.ReLU(),
+            nn.Linear(128, emsize)  # Example: regression output
+        )
+        
+    def forward(self, task_ids, context):
+        # remove the last dimension
+        x = x.squeeze(-1)
+        
+        task_embeddings = self.task_embedding(x.long())
+        attn_output, _ = self.attention(task_embeddings, task_embeddings, task_embeddings)
+        
+        output = self.fc(attn_output)
+        return output
+
+
+
+class SelfAttentionTask(nn.Module):
+    def __init__(self, num_tasks, emsize, num_heads=4):
+        super(SelfAttentionTask, self).__init__()
+        self.num_tasks = num_tasks
+        self.embedding_dim = emsize
+        
+        # Multi-head self-attention layer
+        self.attention = nn.MultiheadAttention(embed_dim=emsize, num_heads=num_heads)
+        
+        # Linear transformation to produce task embeddings
+        self.context_transform = nn.Linear(emsize, emsize)
+        
+        # Task ID embedding layer
+        self.task_id_embedding = nn.Embedding(num_tasks, emsize)
+
+    def _ssforward(self, task_ids, context):
+        """
+        Args:
+            task_ids: Tensor of shape (N, B, 1), representing task IDs.
+            context: Tensor of shape (N, D, B, E), representing the context for tasks.
+
+        Returns:
+            task_embeddings: Tensor of shape (N, B, E), task embeddings.
+        """
+        # average across D dimension
+        context = context.mean(dim=1)
+        
+        # Embed task IDs
+        task_id_embeds = self.task_id_embedding(task_ids.squeeze(-1).long())  # Shape: (N, B, E)
+        context_with_task_ids = context + task_id_embeds
+        
+        # Self-attention: Query, Key, and Value are the context points with task embeddings
+        attn_output, _ = self.attention(context_with_task_ids, context_with_task_ids, context_with_task_ids)
+        # task_embedding is average per task
+        
+        # Apply linear transformation to the aggregated context
+        task_embeddings = self.context_transform(attn_output)  # Shape: (N, B, E)
+        
+        return task_embeddings
+    
+    def nobatchforward(self, task_ids, context):
+        """
+        Args:
+            task_ids: Tensor of shape (N, B, 1), representing task IDs.
+            context: Tensor of shape (N, D, B, E), representing the context for tasks.
+
+        Returns:
+            task_embeddings: Tensor of shape (N, B, E), task embeddings.
+        """
+        N, D, B, E = context.shape
+        task_embeddings = torch.zeros(N, B, E, device=context.device)
+        task_embedding_lookup = torch.zeros(self.num_tasks, E, device=context.device)
+        
+        # average across D dimension
+        context = context.mean(dim=1)
+        # Self-attention: Query, Key, and Value are the context points with task embeddings
+        attn_output, _ = self.attention(context, context, context)
+        # print(attn_output.shape, "ATTN") # (N, B, E)
+        
+        for task in task_ids.long().unique():
+            task_mask = task == task_ids
+            attn_tasks = attn_output[task_mask.squeeze(-1)]
+            # print(attn_tasks.shape)
+            task_embedding_lookup[task] = attn_tasks.sum(dim=0) / task_mask.sum()
+        
+        # expanded_task_ids = task_ids.expand(-1, -1, E)
+        # import pdb; pdb.set_trace()
+        task_embeddings = task_embedding_lookup[task_ids.long().squeeze(-1)]
+        # print(task_embeddings.shape, "TASK")
+        
+        return task_embeddings
+    
+    
+    def forward(self, task_ids, context):
+        """
+        Args:
+            task_ids: Tensor of shape (N, B, 1), representing task IDs.
+            context: Tensor of shape (N, D, B, E), representing the context for tasks.
+
+        Returns:
+            task_embeddings: Tensor of shape (N, B, E), task embeddings.
+        """
+        N, D, B, E = context.shape
+        task_embeddings = torch.zeros(N, B, E, device=context.device)
+        task_embedding_lookup = torch.zeros(self.num_tasks, B, E, device=context.device)
+        
+        # average across D dimension
+        context = context.mean(dim=1)
+        # Self-attention: Query, Key, and Value are the context points with task embeddings
+        attn_output, _ = self.attention(context, context, context)
+
+        # # Flatten task_ids for advanced indexing
+        # task_ids_flat = task_ids.reshape(-1)  # Shape: (N * B,)
+        
+        # # Create task-specific mask matrix for each batch and task (N, B)
+        # task_mask = task_ids_flat.unsqueeze(1) == torch.arange(self.num_tasks, device=context.device).view(1, -1)  # Shape: (N * B, T)
+
+        # # Repeat attn_output to match task_ids (N, B, E)
+        # attn_output_flat = attn_output.view(N * B, E)  # Shape: (N * B, E)
+
+        # # Gather attention outputs based on task_mask (task_mask is (N * B, T))
+        # # Sum the attention outputs for each task across the batch
+        # # The matmul gives us the task-wise embeddings as (T, N * B, E)
+        # task_embedding_lookup = torch.matmul(task_mask.float().T, attn_output_flat)  # Shape: (T, N * B, E)
+
+        
+        # # Take the mean across N dimension (the number of samples for each task) 
+        # task_embedding_lookup = task_embedding_lookup.mean(dim=2)  # Shape: (T, B, E)
+
+        # # Normalize task embeddings by the number of occurrences per task
+        # task_counts = task_mask.sum(dim=0).view(self.num_tasks)  # Shape: (T,)
+        # task_embedding_lookup = task_embedding_lookup / task_counts.view(-1, 1).float()  # Shape: (T, B, E)
+
+        # # Lookup task embeddings using task_ids for each batch
+        # task_embeddings = task_embedding_lookup[task_ids_flat].view(N, B, E)
+
+        # return task_embeddings
+        
+        
+        
+        # Iterate over batches
+        for batch in range(B):
+            # Get unique task_ids in this batch
+            batch_task_ids = task_ids[:, batch].long().unique()  # Shape: (T,)
+            
+            # For each task in the batch, compute its embedding
+            for task in batch_task_ids:
+                # Create mask for this task in the batch
+                task_mask = (task_ids[:, batch] == task).squeeze(-1)  # Shape: (N,)
+                # print(task_mask.shape, attn_output.shape, batch)
+                
+                # Get attention output for this task (flattening across N dimension)
+                attn_tasks = attn_output[task_mask, batch]  # Shape: (D, E)
+                
+                # Aggregate the task-specific embeddings by averaging
+                task_embedding = attn_tasks.sum(dim=0) / task_mask.sum().float()  # Shape: (E)
+                
+                # Store this task embedding in the task_embeddings tensor
+                task_embedding_lookup[task, batch] = task_embedding
+                
+            task_embeddings[:, batch] = task_embedding_lookup[task_ids[:, batch].long().squeeze(-1), batch]
+            
+        return task_embeddings
+
+
+# class SelfAttentionTask(nn.Module):
+    
+#     def __init__(self, num_tasks, emsize):
+#         super(SelfAttentionTask, self).__init__()
+        
+#         self.attention = nn.MultiheadAttention(embed_dim=emsize, num_heads=4)
+#         self.context_transform = nn.Linear(emsize, emsize)
+        
+#     def forward(self, task_ids, context):
+#         N, D, B, E = context.shape
+#         context = context.view(N * D, B, E)
+#         attn_output = self.attention(context, context, context)[0]
+#         attn_output = attn_output.view(N, D, B, E)
+        
+#         task_embeddings = torch.zeros(N, D, B, E, device=context.device)
+        
+#         for task_id in torch.unique(task_ids):
+#             task_mask = task_id == task_ids
+#             task_embedding = self.task_embedding(task_id)
+#             task_embedding = task_embedding.repeat(N, D, 1, 1)
+#             task_embedding = task_embedding * task_mask.unsqueeze(-1)
+#             task_embeddings += task_embedding
+        
+        
+        
+
+
+def get_self_attention_task_encoder():
+    return lambda num_tasks, emsize: SelfAttentionTask(num_tasks, emsize)
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/hierarchical.py b/private_multitask_pfn/PFNs/pfns/hierarchical.py
new file mode 100644
index 0000000..85801af
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/hierarchical.py
@@ -0,0 +1,335 @@
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import Module
+
+from torch.nn.modules.transformer import (
+    _get_activation_fn,
+    Dropout,
+    LayerNorm,
+    Linear,
+    Module,
+    MultiheadAttention,
+    Optional,
+    Tensor,
+)
+from .positional_encodings import RotaryPositionalEmbeddings
+from .utils import bool_mask_to_att_mask, SeqBN
+
+
+class HierarchicalTaskEncoder(Module):
+    r"""TransformerEncoder is a stack of N encoder layers
+
+    Args:
+        encoder_layer_creator: a function generating objects of TransformerEncoderLayer class without args (required).
+        nlayers: the number of sub-encoder-layers in the encoder (required).
+        norm: the layer normalization component (optional).
+    """
+
+    __constants__ = ["norm"]
+
+    def __init__(
+        self,
+        global_with_target_points=False,
+        local_with_target_points=False,
+        **kwargs):
+        super().__init__()
+        
+        global_kwargs = kwargs.copy()
+        global_kwargs["global_with_target_points"] = global_with_target_points
+        global_kwargs.pop("nlayers")
+        
+        single_kwargs = kwargs.copy()
+        single_kwargs["local_with_target_points"] = local_with_target_points
+        single_kwargs.pop("nlayers")
+        
+        layers = []
+        for i in range(kwargs.get("nlayers", 24)):
+            if (i+1) % 2 == 1:
+                layers.append(GlobalTaskEncoderLayer(**global_kwargs))
+            else:
+                layers.append(SingleTaskEncoderLayer(**single_kwargs))
+                
+        self.layers = nn.ModuleList(layers)
+        self.meta_embedding = nn.Embedding(1, kwargs.get("d_model", 512))
+        
+        self.norm = kwargs.get("norm", None)
+        self.meta_tokens = kwargs.get("meta_tokens", 1)
+
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layers in turn.
+
+        Args:
+            src: the sequence to the encoder (required).
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """        
+        # all task ids should be the same across batches
+        assert (task_ids.min(dim=1).values == task_ids.max(dim=1).values).all()
+        squeezed_task_id = task_ids[:, 0] # N x 1
+        unique_task_ids = torch.unique(squeezed_task_id)
+        
+        # add meta-token task id for each task in front of the sequence
+        meta_task_ids_summary = unique_task_ids.repeat_interleave(self.meta_tokens)
+        meta_task_ids = torch.cat([meta_task_ids_summary.unsqueeze(-1), squeezed_task_id], dim=0)
+        
+        # add meta-token embeddings
+        meta_output = self.meta_embedding(torch.zeros(1, device=src.device).long())
+        meta_output = meta_output.repeat(len(meta_task_ids_summary), src.shape[1], 1)
+        meta_output = torch.cat([meta_output, src], dim=0)
+        
+        # move eval position to account for meta-tokens
+        src_mask = src_mask + len(unique_task_ids) * self.meta_tokens
+        for mod in self.layers:
+            meta_output = mod(
+                meta_output, meta_task_ids, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask
+            )
+
+        if self.norm is not None:
+            meta_output = self.norm(meta_output)
+
+        return meta_output[self.meta_tokens * len(unique_task_ids):]
+
+
+class SingleTaskEncoderLayer(Module):
+            
+    def __init__(
+        self,
+        meta_tokens,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        local_with_target_points=False,
+        batch_first=False,
+        pre_norm=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+    
+        self.pre_norm = pre_norm
+        
+        self.attn = MultiheadAttention(
+            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
+        )
+        self.norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.dropout = Dropout(dropout)
+        
+        self.ff_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
+        self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
+        self.dropout1 = Dropout(dropout)
+        self.dropout2 = Dropout(dropout)
+        
+        self.activation = _get_activation_fn(activation)
+        
+        # each source task attends to itself and the target task
+        self.local_with_target_points = local_with_target_points
+        if local_with_target_points:
+            self.source_embedding = nn.Embedding(2, d_model) # embed target vs source
+        
+    def single_task_forward(
+        self,
+        src: Tensor,
+        single_eval_position: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        src_ = src
+        
+        if self.pre_norm:
+            src_ = self.norm(src)
+        else:
+            src_ = src
+            
+        # have each task attend within itself
+        src_train = src_[:single_eval_position]
+        src_eval = src_[single_eval_position:]
+        src_left = self.attn(src_train, src_train, src_train)[0]
+        src_right = self.attn(src_eval, src_train, src_train)[0]
+        src2 = torch.cat([src_left, src_right], dim=0)
+        
+        src = src + self.dropout(src2)
+        if not self.pre_norm:
+            src = self.norm(src)            
+
+        # feedforward
+        if self.pre_norm:
+            src_ff = self.ff_norm(src)
+        else:
+            src_ff = src
+        src_ff = self.linear2(self.dropout1(self.activation(self.linear1(src_ff))))
+        src = src + self.dropout2(src_ff)
+        if not self.pre_norm:
+            src = self.ff_norm(src)    
+            
+        return src
+    
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        assert isinstance(src_mask, int)
+        single_eval_position = src_mask
+        
+        single_tasks_encoding = torch.zeros_like(src)
+        train_task_id = task_ids[:single_eval_position]
+        train_x = src[:single_eval_position]
+        
+        # attend to source and target task points
+        if self.local_with_target_points:
+            target_task_mask = (task_ids == 0).squeeze()
+            target_embedding = self.source_embedding(torch.zeros(1, device=src.device).long())
+            src[target_task_mask] = src[target_task_mask] + target_embedding
+            source_embedding = self.source_embedding(torch.ones(1, device=src.device).long())
+            src[~target_task_mask] = src[~target_task_mask] + source_embedding
+        
+        # Optimize this when scaling
+        for task in torch.unique(task_ids):
+            source_task_mask = (task_ids == task).squeeze()
+            if self.local_with_target_points:
+                # each task attends to itself and the target task
+                task_mask = target_task_mask | source_task_mask
+            else:
+                task_mask = source_task_mask
+            task_src = src[task_mask]
+            
+            train_task_mask = (train_task_id == task).squeeze()
+            task_train_length = len(train_x[train_task_mask])
+            
+            task_single_tasks_encoding = self.single_task_forward(task_src, task_train_length)
+            single_tasks_encoding[task_mask] = task_single_tasks_encoding
+        
+        return single_tasks_encoding
+
+
+class GlobalTaskEncoderLayer(Module):
+            
+    def __init__(
+        self,
+        meta_tokens,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        batch_first=False,
+        pre_norm=False,
+        device=None,
+        dtype=None,
+        task_position_embedding=False,
+        recompute_attn=False,
+        save_trainingset_representations=False,
+        global_with_target_points=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.pre_norm = pre_norm
+        
+        meta_d_model = d_model * meta_tokens
+        self.attn = MultiheadAttention(
+            meta_d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
+        )
+        self.norm = LayerNorm(meta_d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.dropout = Dropout(dropout)
+        
+        self.ff_norm = LayerNorm(meta_d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.linear1 = Linear(meta_d_model, dim_feedforward, **factory_kwargs)
+        self.linear2 = Linear(dim_feedforward, meta_d_model, **factory_kwargs)
+        self.dropout1 = Dropout(dropout)
+        self.dropout2 = Dropout(dropout)
+        
+        self.activation = _get_activation_fn(activation)
+        self.meta_tokens = meta_tokens
+        
+        self.global_with_target_points = global_with_target_points
+        
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        assert isinstance(src_mask, int)
+        assert src_key_padding_mask is None
+        
+        meta_end = self.meta_tokens * len(torch.unique(task_ids))
+        
+        # get summary tokens with T x B x (M x E), add summary embedding
+        summary_src = src[:meta_end].reshape(len(torch.unique(task_ids)), src.shape[1], -1)
+
+        if self.global_with_target_points:
+            # make global layer also attend to target points
+            task_src = src[(task_ids == 0).squeeze()][self.meta_tokens:].repeat(1, 1, self.meta_tokens)
+            global_src_ = torch.cat([summary_src, task_src], dim=0)
+        else:
+            # only attend to summary tokens
+            global_src_ = summary_src
+        
+        if self.pre_norm:
+            global_src_ = self.norm(global_src_)
+            summary_src_ = self.norm(summary_src)
+        else:
+            global_src_ = global_src_
+            
+        summary_src_ = self.attn(summary_src, global_src_, global_src_)[0]
+        
+        summary_src_ = summary_src + self.dropout(summary_src_)
+        if not self.pre_norm:
+            summary_src_ = self.norm(summary_src_)
+
+        # feedforward
+        if self.pre_norm:
+            summary_src_ff = self.ff_norm(summary_src_)
+        else:
+            summary_src_ff = summary_src_
+        summary_src_ff = self.linear2(self.dropout1(self.activation(self.linear1(summary_src_ff))))
+        summary_src_ = summary_src_ + self.dropout2(summary_src_ff)
+        if not self.pre_norm:
+            summary_src_ = self.ff_norm(summary_src_)
+            
+        # reshape back
+        summary_src = summary_src_.reshape(-1, src.shape[1], src.shape[2])
+            
+        original_tokens = src[meta_end:]
+        combined_src = torch.cat([summary_src, original_tokens], dim=0)
+        return combined_src
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/initializers.py b/private_multitask_pfn/PFNs/pfns/initializers.py
new file mode 100644
index 0000000..ec93119
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/initializers.py
@@ -0,0 +1,11 @@
+import torch
+from torch import nn
+
+
+def get_NormalInitializer(std):
+    def initializer(m):
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, 0, std)
+            nn.init.normal_(m.bias, 0, std)
+
+    return initializer
diff --git a/private_multitask_pfn/PFNs/pfns/layer.py b/private_multitask_pfn/PFNs/pfns/layer.py
new file mode 100644
index 0000000..caa3370
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/layer.py
@@ -0,0 +1,475 @@
+from functools import partial
+
+import torch
+from torch import nn
+from torch.nn.modules.transformer import (
+    _get_activation_fn,
+    Dropout,
+    LayerNorm,
+    Linear,
+    Module,
+    MultiheadAttention,
+    Optional,
+    Tensor,
+)
+
+from torch.utils.checkpoint import checkpoint
+
+
+class AxialAttentionModule(Module):
+    r"""
+    AxialAttention is a module that applies self-attention to rows and columns of a 2D input tensor.
+    """
+
+    __constants__ = ["batch_first"]
+
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        batch_first=False,
+        pre_norm=False,
+        device=None,
+        dtype=None,
+        recompute_attn=False,
+        save_trainingset_representations=False,
+        sequential_axial=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        
+        self.seq_axial = sequential_axial
+        self.pre_norm = pre_norm
+        
+        self.self_attn_row = MultiheadAttention(
+            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
+        )
+        self.row_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.row_dropout = Dropout(dropout)
+        self.self_attn_col = MultiheadAttention(
+            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
+        )
+        self.col_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.col_dropout = Dropout(dropout)
+        
+    def __setstate__(self, state):
+        if "activation" not in state:
+            state["activation"] = F.relu
+        super().__setstate__(state)
+        self.__dict__.setdefault("save_trainingset_representations", False)
+
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        N, D, B, E = src.shape
+        
+        assert isinstance(src_mask, int)
+        assert src_key_padding_mask is None
+        single_eval_position = src_mask
+        
+        def self_attn_forward(attn_module, src, row_first):                
+            if row_first:
+                src = src.reshape(N, D*B, E)
+                attn_mask = torch.zeros(N, N, device=src.device).bool()
+                attn_mask[single_eval_position:, single_eval_position:] = True
+            else:
+                # column first
+                src = src.permute(1, 0, 2, 3).reshape(D, N*B, E)    
+                attn_mask = torch.zeros(D, D, device=src.device).bool()
+                            
+            src_full = attn_module(
+                src,
+                src,
+                src,
+                attn_mask=attn_mask,
+            )[0]
+            
+            if row_first:
+                return src_full.reshape(N, D, B, E)
+            else:
+                src_full = src_full.reshape(D, N, B, E).permute(1, 0, 2, 3)
+            return src_full.reshape(N, D, B, E)
+        
+        # attend to rows
+        if self.pre_norm:
+            src_row = self.row_norm(src)
+        else:
+            src_row = src
+        src_row = self_attn_forward(self.self_attn_row, src_row, row_first=True)
+        src_row = self.row_dropout(src_row)
+        
+        # pass row as input to col
+        if self.seq_axial:
+            src = src + src_row
+            if not self.pre_norm:
+                src = self.row_norm(src)
+            
+        # attend to cols
+        if self.pre_norm:
+            src_col = self.col_norm(src)
+        else:
+            src_col = src
+        src_col = self_attn_forward(self.self_attn_col, src_col, row_first=False)
+        src_col = self.col_dropout(src_col)
+        
+        if self.seq_axial:
+            src = src + src_col
+            if not self.pre_norm:
+                src = self.col_norm(src)
+        else:
+            # combine row and col
+            src = src + src_row + src_col
+            if not self.pre_norm:
+                src = self.col_norm(src)
+
+        return src
+
+
+class BasicTaskAttentionLayer(Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        batch_first=False,
+        pre_norm=False,
+        device=None,
+        dtype=None,
+        recompute_attn=False,
+        save_trainingset_representations=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        
+        self.pre_norm = pre_norm
+        
+        self.attn = MultiheadAttention(
+            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
+        )
+        self.norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.dropout = Dropout(dropout)
+
+    def __setstate__(self, state):
+        if "activation" not in state:
+            state["activation"] = F.relu
+        super().__setstate__(state)
+        self.__dict__.setdefault("save_trainingset_representations", False)
+
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        assert isinstance(src_mask, int)
+        assert src_key_padding_mask is None
+        single_eval_position = src_mask
+        
+        if self.pre_norm:
+            src_ = self.norm(src)
+        else:
+            src_ = src
+            
+        src_train = src_[:single_eval_position]
+        src_eval = src_[single_eval_position:]
+        
+        src_left = self.attn(
+            src_train,
+            src_train,
+            src_train,
+        )[0]
+        src_right = self.attn(
+            src_eval, src_train, src_train
+        )[0]
+        src2 = torch.cat([src_left, src_right], dim=0)
+        
+        src = src + self.dropout(src2)
+        if not self.pre_norm:
+            src = self.norm(src)
+            
+        return src
+    
+    
+
+class TaskAttention(Module):
+    def __init__(
+        self,
+        num_tasks,
+        shared,
+        optimized,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        batch_first=False,
+        pre_norm=False,
+        device=None,
+        dtype=None,
+        recompute_attn=False,
+        save_trainingset_representations=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        
+        self.pre_norm = pre_norm
+        
+        self.attn = MultiheadAttention(
+            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
+        )
+        self.norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.dropout = Dropout(dropout)
+        
+        self.optimized = optimized
+        self.num_tasks = num_tasks
+        
+        kwargs = {
+            "d_model": d_model,
+            "nhead": nhead,
+            "dim_feedforward": dim_feedforward,
+            "dropout": dropout,
+            "activation": activation,
+            "layer_norm_eps": layer_norm_eps,
+            "batch_first": batch_first,
+            "pre_norm": pre_norm,
+            "device": device,
+            "dtype": dtype,
+            "recompute_attn": recompute_attn,
+            "save_trainingset_representations": save_trainingset_representations,
+        }
+            
+        if not self.optimized:
+            if shared:
+                self.task_attn_module = BasicTaskAttentionLayer(**kwargs)
+                self.task_attn_modules = [self.task_attn_module for _ in range(num_tasks)]
+            else:
+                self.task_attn_modules = nn.ModuleList([BasicTaskAttentionLayer(**kwargs) for _ in range(num_tasks)])
+        else:
+            self.task_attn = MultiheadAttention(
+                d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
+            )
+            self.task_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+            self.task_dropout = Dropout(dropout)
+
+    def __setstate__(self, state):
+        if "activation" not in state:
+            state["activation"] = F.relu
+        super().__setstate__(state)
+        self.__dict__.setdefault("save_trainingset_representations", False)
+
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        src_ = src
+        if not self.optimized:
+            combined_task_src = torch.zeros_like(src_)
+            for task_id in range(self.num_tasks):
+                task_src = src_[task_ids == task_id]
+                task_id_src = task_ids[task_ids == task_id]
+                combined_task_src[task_ids == task_id] = self.task_attn_modules[task_id](task_src, task_id_src, src_mask, src_key_padding_mask)
+                
+            src = combined_task_src
+        else:
+            single_eval_position = src_mask
+            
+            if self.pre_norm:
+                src_ = self.task_norm(src_)
+            
+            # Train attends to train with same task
+            src_train = src_[:single_eval_position]
+            task_id_train = task_ids[:single_eval_position]
+            # true when tasks are different
+            train_src_mask = task_id_train[:, None] != task_id_train[None, :]         
+            src_train, _ = self.task_attn(src_train, src_train, src_train, attn_mask=train_src_mask)
+            
+            # Eval attends to train with same task
+            src_eval = src_[single_eval_position:]
+            task_id_eval = task_ids[single_eval_position:]
+            eval_src_mask = task_id_eval[:, None] != task_id_train[None, :]
+            src_eval, _ = self.task_attn(src_eval, src_train, src_train, attn_mask=eval_src_mask)
+            
+            src2 = torch.cat([src_train, src_eval], dim=0)
+            src = src + self.dropout(src2)
+            
+            if not self.pre_norm:
+                src = self.norm(src)
+            
+        return src
+            
+
+class TransformerAttentionLayer(Module):
+            
+    def __init__(
+        self,
+        num_tasks,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        batch_first=False,
+        pre_norm=False,
+        device=None,
+        dtype=None,
+        recompute_attn=False,
+        save_trainingset_representations=False,
+        attn_type="standard",
+        task_embed_type="task_attn"
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        
+        kwargs = {
+            "d_model": d_model,
+            "nhead": nhead,
+            "dim_feedforward": dim_feedforward,
+            "dropout": dropout,
+            "activation": activation,
+            "layer_norm_eps": layer_norm_eps,
+            "batch_first": batch_first,
+            "pre_norm": pre_norm,
+            "device": device,
+            "dtype": dtype,
+            "recompute_attn": recompute_attn,
+            "save_trainingset_representations": save_trainingset_representations,
+        }
+            
+        self.num_tasks = num_tasks
+        self.pre_norm = pre_norm
+        
+        if attn_type == "standard":
+            if task_embed_type == "task_attn":
+                self.task_attn = TaskAttention(num_tasks=num_tasks, shared=False, optimized=False, **kwargs)
+            elif task_embed_type == "task_attn_shared":
+                self.task_attn = TaskAttention(num_tasks=num_tasks, shared=True, optimized=False, **kwargs)
+            elif task_embed_type == "task_attn_opt":
+                self.task_attn = TaskAttention(num_tasks=num_tasks, shared=True, optimized=True, **kwargs)
+            else:
+                self.task_attn = None
+                
+            self.all_attn = BasicTaskAttentionLayer(**kwargs)
+        elif "axial" in attn_type:
+            sequential_axial = "parallel" not in attn_type
+            args = *args, sequential_axial
+            
+            if task_embed_type == "task_attn":
+                self.task_attn = nn.ModuleList([AxialAttentionModule(**kwargs) for _ in range(num_tasks)])
+            elif task_embed_type == "task_attn_shared":
+                self.task_attn_one = AxialAttentionModule(**kwargs)
+                self.task_attn = [self.task_attn_one for _ in range(num_tasks)]
+            else:
+                self.task_attn = None
+                
+            self.all_attn = AxialAttentionModule(**kwargs)
+        else:
+            raise ValueError(f"Unknown attn_type: {attn_type}")
+        
+        self.ff_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
+        self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
+        self.dropout1 = Dropout(dropout)
+        self.dropout2 = Dropout(dropout)
+        
+        self.recompute_attn = recompute_attn
+        self.save_trainingset_representations = save_trainingset_representations
+        self.saved_src_to_attend_to = None
+        
+        self.activation = _get_activation_fn(activation)
+        
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        
+        src_ = src
+            
+        assert isinstance(src_mask, int)
+        assert src_key_padding_mask is None
+        
+        # attend to all, contains skip connection
+        all_src = self.all_attn(src_, task_ids, src_mask, src_key_padding_mask)
+        
+        # attend to tasks
+        if self.task_attn is not None:
+            assert torch.all(task_ids[:, 0] == task_ids.max(1)[0]) and torch.all(task_ids[:, 0] == task_ids.min(1)[0])
+            task_ids = task_ids[:, 0].squeeze()
+            
+            task_src = self.task_attn(src_, task_ids, src_mask, src_key_padding_mask)
+            
+            # combine task and all
+            src = all_src + task_src
+        else:
+            src = all_src
+
+        # feedforward
+        if self.pre_norm:
+            src_ff = self.ff_norm(src)
+        else:
+            src_ff = src
+        src_ff = self.linear2(self.dropout1(self.activation(self.linear1(src_ff))))
+        src = src + self.dropout2(src_ff)
+        if not self.pre_norm:
+            src = self.ff_norm(src)    
+            
+        return src
diff --git a/private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py b/private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py
new file mode 100644
index 0000000..97a7de5
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py
@@ -0,0 +1,508 @@
+import logging
+import os
+from copy import deepcopy
+
+import numpy as np
+import torch
+from bayesmark.abstract_optimizer import AbstractOptimizer
+from bayesmark.experiment import experiment_main
+from bayesmark.space import JointSpace
+from scipy.special import expit, logit
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+
+# from scipy.stats import qmc
+from torch.quasirandom import SobolEngine
+
+from .scripts import acquisition_functions, tune_input_warping
+
+
+class PFNOptimizer(AbstractOptimizer):
+    # Used for determining the version number of package used
+    # primary_import = ""
+
+    def __init__(
+        self,
+        api_config,
+        pfn_file,
+        minimize=True,
+        acqf_optimizer_name="lbfgs",
+        sobol_sampler=False,
+        device="cpu:0",
+        fit_encoder_from_step=None,
+        verbose=False,
+        rand_bool=False,
+        sample_only_valid=False,
+        round_suggests_to=4,
+        min_initial_design=0,
+        max_initial_design=None,
+        rand_sugg_after_x_steps_of_stagnation=None,
+        fixed_initial_guess=None,
+        minmax_encode_y=False,
+        **acqf_kwargs,
+    ):
+        """Build wrapper class to use optimizer in benchmark.
+
+        Parameters
+        ----------
+        api_config : dict-like of dict-like
+            Configuration of the optimization variables. See API description.
+        """
+        assert not "fit_encoder" in acqf_kwargs
+        AbstractOptimizer.__init__(self, api_config)
+        # Do whatever other setup is needed
+        # ...
+
+        self.space_x = JointSpace(api_config)
+        self.bounds = self.space_x.get_bounds()
+        self.device = device
+
+        self.model = (
+            torch.load(pfn_file)
+            if pfn_file.startswith("/")
+            else torch.load(os.path.dirname(__file__) + "/" + pfn_file)
+        )
+
+        self.X = []
+        self.y = []
+        self.api_config = {key: value for key, value in sorted(api_config.items())}
+        self.hp_names = list(self.api_config.keys())
+        # self.model.encoder.num_features = 18
+
+        self.epsilon = 1e-8
+        self.minimize = minimize
+        self.sobol_sampler = sobol_sampler
+        self.create_scaler()
+        self.sobol = SobolEngine(len(self.max_values), scramble=True)
+        self.acqf_optimizer_name = acqf_optimizer_name
+        self.acqf_kwargs = acqf_kwargs
+        self.fit_encoder_from_step = fit_encoder_from_step
+        assert not (rand_bool and sample_only_valid)
+        self.rand_bool = rand_bool
+        self.sample_only_valid = sample_only_valid
+        self.verbose = verbose
+        self.round_suggests_to = round_suggests_to
+        self.min_initial_design = min_initial_design
+        self.max_initial_design = max_initial_design
+        self.fixed_initial_guess = fixed_initial_guess
+        self.minmax_encode_y = minmax_encode_y
+        self.rand_sugg_after_x_steps_of_stagnation = (
+            rand_sugg_after_x_steps_of_stagnation
+        )
+        self.model.eval()
+
+        print(api_config)
+
+    def create_scaler(self):
+
+        list_of_scalers = []
+        self.min_values = []
+        self.max_values = []
+        self.spaces = []
+        self.types = []
+
+        for i, feature in enumerate(self.api_config):
+            # list_of_scalers.append((feature, MinMaxScaler(feature_range),i))
+            self.spaces.append(self.api_config[feature].get("space", "bool"))
+            self.types.append(self.api_config[feature]["type"])
+
+            if self.types[-1] == "bool":
+                feature_range = [0, 1]
+            else:
+                feature_range = list(self.api_config[feature]["range"])
+
+            feature_range[0] = self.transform_feature(feature_range[0], -1)
+            feature_range[1] = self.transform_feature(feature_range[1], -1)
+
+            self.min_values.append(feature_range[0])
+            self.max_values.append(feature_range[1])
+
+        self.column_scaler = ColumnTransformer(list_of_scalers)
+        self.max_values: np.array = np.array(self.max_values)
+        self.min_values: np.array = np.array(self.min_values)
+
+    def transform_feature_inverse(self, x, feature_index):
+
+        if self.spaces[feature_index] == "log":
+            x = np.exp(x)
+        elif self.spaces[feature_index] == "logit":
+            x = expit(x)
+        if self.types[feature_index] == "int":
+            if self.rand_bool:
+                x = int(x) + int(np.random.rand() < (x - int(x)))
+            else:
+                x = int(np.round(x))
+        elif self.types[feature_index] == "bool":
+            if self.rand_bool:
+                x = np.random.rand() < x
+            else:
+                x = bool(np.round(x))
+
+        return x
+
+    def transform_feature(self, x, feature_index):
+
+        if np.isinf(x) or np.isnan(x):
+            return 0
+
+        if self.spaces[feature_index] == "log":
+            x = np.log(x)
+
+        elif self.spaces[feature_index] == "logit":
+            x = logit(x)
+
+        elif self.types[feature_index] == "bool":
+            x = int(x)
+        return x
+
+    def random_suggest(self):
+        self.rand_prev = True
+
+        if self.sobol_sampler:
+
+            # sampler = qmc.Sobol(d=len(self.max_values), scramble=False)
+            # temp_guess = sampler.random_base2(m=len(self.max_values))
+            temp_guess = self.sobol.draw(1).numpy()[0]
+            temp_guess = (
+                temp_guess * (self.max_values - self.min_values) + self.min_values
+            )
+
+            x_guess = {}
+            for j, feature in enumerate(self.api_config):
+                x = self.transform_feature_inverse(temp_guess[j], j)
+                x_guess[feature] = x
+            x_guess = [x_guess]
+
+        else:
+            x_guess = {}
+            for i, feature in enumerate(self.api_config):
+                temp_guess = np.random.uniform(
+                    self.min_values[i], self.max_values[i], 1
+                )[0]
+                temp_guess = self.transform_feature_inverse(temp_guess, i)
+
+                x_guess[feature] = temp_guess
+            x_guess = [x_guess]
+        return x_guess
+
+    def transform_back(self, x_guess):
+        if self.round_suggests_to is not None:
+            x_guess = np.round(
+                x_guess, self.round_suggests_to
+            )  # make sure very similar values are actually the same
+        x_guess = x_guess * (self.max_values - self.min_values) + self.min_values
+        x_guess = x_guess.tolist()
+        return self.transform_inverse(x_guess)
+
+    def min_max_encode(self, temp_X):
+        # this, combined with transform is the inverse of transform_back
+        temp_X = (temp_X - self.min_values) / (self.max_values - self.min_values)
+        temp_X = torch.tensor(temp_X).to(torch.float32)
+        temp_X = torch.clamp(temp_X, min=0.0, max=1.0)
+        return temp_X
+
+    @torch.no_grad()
+    def suggest(self, n_suggestions=1):
+        """Get suggestion from the optimizer.
+
+        Parameters
+        ----------
+        n_suggestions : int
+            Desired number of parallel suggestions in the output
+
+        Returns
+        -------
+        next_guess : list of dict
+            List of `n_suggestions` suggestions to evaluate the objective
+            function. Each suggestion is a dictionary where each key
+            corresponds to a parameter being optimized.
+        """
+        assert n_suggestions == 1, "Only one suggestion at a time is supported"
+        # Do whatever is needed to get the parallel guesses
+        # ...
+        # scaler = MinMaxScaler()
+        # scaler.fit(self.X)
+        try:
+            num_initial_design = max(len(self.bounds), self.min_initial_design)
+            if self.max_initial_design is not None:
+                num_initial_design = min(num_initial_design, self.max_initial_design)
+            if len(self.X) < num_initial_design:
+                if len(self.X) == 0 and self.fixed_initial_guess is not None:
+                    x_guess = [
+                        self.transform_back(
+                            np.array(
+                                [
+                                    self.fixed_initial_guess
+                                    for _ in range(len(self.bounds))
+                                ]
+                            )
+                        )
+                    ]
+                else:
+                    x_guess = self.random_suggest()
+                return x_guess
+            else:
+                temp_X = np.array(self.X)
+                temp_X = self.min_max_encode(temp_X)
+                if self.minmax_encode_y:
+                    temp_y = (
+                        MinMaxScaler()
+                        .fit_transform(np.array(self.y).reshape(-1, 1))
+                        .reshape(-1)
+                    )
+                else:
+                    temp_y = np.array(self.y)
+                temp_y = torch.tensor(temp_y).to(torch.float32)
+                if (
+                    self.rand_sugg_after_x_steps_of_stagnation is not None
+                    and len(self.y) > self.rand_sugg_after_x_steps_of_stagnation
+                    and not self.rand_prev
+                ):
+                    if (
+                        temp_y[: -self.rand_sugg_after_x_steps_of_stagnation].max()
+                        == temp_y.max()
+                    ):
+                        print(
+                            f"Random suggestion after >= {self.rand_sugg_after_x_steps_of_stagnation} steps of stagnation"
+                        )
+                        x_guess = self.random_suggest()
+                        return x_guess
+                if self.verbose:
+                    from matplotlib import pyplot as plt
+
+                    print(f"{temp_X=}, {temp_y=}")
+                    if temp_X.shape[1] == 2:
+                        from scipy.stats import rankdata
+
+                        plt.title("Observations, red -> blue.")
+                        plt.scatter(
+                            temp_X[:, 0], temp_X[:, 1], cmap="RdBu", c=rankdata(temp_y)
+                        )
+                        plt.show()
+
+                temp_X = temp_X.to(self.device)
+                temp_y = temp_y.to(self.device)
+
+                if self.fit_encoder_from_step and self.fit_encoder_from_step <= len(
+                    self.X
+                ):
+                    with torch.enable_grad():
+                        w = tune_input_warping.fit_input_warping(
+                            self.model, temp_X, temp_y
+                        )
+                    temp_X_warped = w(temp_X).detach()
+                else:
+                    temp_X_warped = temp_X
+
+                with torch.enable_grad():
+                    if self.acqf_optimizer_name == "lbfgs":
+
+                        def rand_sample_func(n):
+                            pre_samples = torch.rand(
+                                n, temp_X_warped.shape[1], device="cpu"
+                            )
+                            back_transformed_samples = [
+                                self.transform_back(sample) for sample in pre_samples
+                            ]
+                            samples = np.array(
+                                [
+                                    self.transform(deepcopy(bt_sample))
+                                    for bt_sample in back_transformed_samples
+                                ]
+                            )
+                            samples = self.min_max_encode(samples)
+                            return samples.to(self.device)
+
+                        if self.sample_only_valid:
+                            rand_sample_func = rand_sample_func
+                            # dims with bool or int are not continuous, thus no gradient opt is applied
+                            dims_wo_gradient_opt = [
+                                i for i, t in enumerate(self.types) if t != "real"
+                            ]
+                        else:
+                            rand_sample_func = None
+                            dims_wo_gradient_opt = []
+
+                        x_guess, x_options, eis, x_rs, x_rs_eis = (
+                            acquisition_functions.optimize_acq_w_lbfgs(
+                                self.model,
+                                temp_X_warped,
+                                temp_y,
+                                device=self.device,
+                                verbose=self.verbose,
+                                rand_sample_func=rand_sample_func,
+                                dims_wo_gradient_opt=dims_wo_gradient_opt,
+                                **{"apply_power_transform": True, **self.acqf_kwargs},
+                            )
+                        )
+
+                    elif self.acqf_optimizer_name == "adam":
+                        x_guess = (
+                            acquisition_functions.optimize_acq(
+                                self.model,
+                                temp_X_warped,
+                                temp_y,
+                                apply_power_transform=True,
+                                device=self.device,
+                                **self.acqf_kwargs,
+                            )
+                            .detach()
+                            .cpu()
+                            .numpy()
+                        )
+                    else:
+                        raise ValueError(
+                            "Optimizer not recognized, set `acqf_optimizer_name` to 'lbfgs' or 'adam'"
+                        )
+
+                back_transformed_x_options = [self.transform_back(x) for x in x_options]
+                opt_X = np.array(
+                    [
+                        self.transform(deepcopy(transformed_x_options))
+                        for transformed_x_options in back_transformed_x_options
+                    ]
+                )
+                opt_X = self.min_max_encode(opt_X)
+                opt_new = ~(opt_X[:, None] == temp_X[None].cpu()).all(-1).any(1)
+                for i, x in enumerate(opt_X):
+                    if opt_new[i]:
+                        if self.verbose:
+                            print(
+                                f"New point at pos {i}: {back_transformed_x_options[i], x_options[i]}"
+                            )
+                        self.rand_prev = False
+                        return [back_transformed_x_options[i]]
+                print("backup from initial rand search")
+                back_transformed_x_options = [self.transform_back(x) for x in x_rs]
+                opt_X = np.array(
+                    [
+                        self.transform(deepcopy(transformed_x_options))
+                        for transformed_x_options in back_transformed_x_options
+                    ]
+                )
+                opt_X = self.min_max_encode(opt_X)
+                opt_new = ~(opt_X[:, None] == temp_X[None].cpu()).all(-1).any(1)
+                for i, x in enumerate(opt_X):
+                    if opt_new[i]:
+                        if self.verbose:
+                            print(
+                                f"New point at pos {i}: {back_transformed_x_options[i], x_rs[i]} with ei {x_rs_eis[i]}"
+                            )
+                        self.rand_prev = False
+                        return [back_transformed_x_options[i]]
+                print("No new points found, random suggestion")
+                return self.random_suggest()
+        except Exception as e:
+            raise e
+
+    def transform(self, X_dict):
+        X_tf = []
+        for i, feature in enumerate(X_dict.keys()):
+            X_dict[feature] = self.transform_feature(X_dict[feature], i)
+            X_tf.append(X_dict[feature])
+        return X_tf
+
+    def transform_inverse(self, X_list):
+        X_tf = {}
+        for i, hp_name in enumerate(self.hp_names):
+            X_tf[hp_name] = self.transform_feature_inverse(X_list[i], i)
+        return X_tf
+
+    def observe(self, X, y):
+        """Feed an observation back.
+
+        Parameters
+        ----------
+        X : list of dict-like
+            Places where the objective function has already been evaluated.
+            Each suggestion is a dictionary where each key corresponds to a
+            parameter being optimized.
+        y : array-like, shape (n,)
+            Corresponding values where objective has been evaluated
+        """
+        # Update the model with new objective function observations
+        # ...
+        # No return statement needed
+        if np.isinf(y) and y > 0:
+            y[:] = 1e10
+
+        if not np.isnan(y) and not np.isinf(y):
+            assert (
+                len(y) == 1 and len(X) == 1
+            ), "Only one suggestion at a time is supported"
+            X = {key: value for key, value in sorted(X[0].items())}
+            assert (
+                list(X.keys())
+                == list(self.api_config.keys())
+                == list(self.hp_names)
+                == list(self.space_x.param_list)
+            )
+            if self.verbose:
+                print(f"{X=}, {y=}")
+            X = self.transform(X)
+            if self.verbose:
+                print(f"transformed {X=}")
+            self.X.append(X)
+            if self.minimize:
+                self.y.append(-y[0])
+            else:
+                self.y.append(y[0])
+        else:
+            assert False
+
+
+def test():
+    from bayesmark.experiment import _build_test_problem, OBJECTIVE_NAMES, run_study
+
+    # function_instance = _build_test_problem(model_name='ada', dataset='breast', scorer='nll', path=None)
+    function_instance = _build_test_problem(
+        model_name="kNN", dataset="boston", scorer="mse", path=None
+    )
+
+    # Setup optimizer
+    api_config = function_instance.get_api_config()
+    import os
+
+    # check is file
+
+    config = {
+        "pfn_file": "final_models/model_hebo_morebudget_9_unused_features_3.pt",
+        "minimize": 1,
+        "device": "cpu:0",
+        "fit_encoder_from_step": None,
+        "pre_sample_size": 1_000,
+        "num_grad_steps": 15_000,
+        "num_candidates": 10,
+        "rand_bool": True,
+    }
+    opt = PFNOptimizer(api_config, verbose=True, **config)
+
+    function_evals, timing, suggest_log = run_study(
+        opt,
+        function_instance,
+        50,
+        1,
+        callback=None,
+        n_obj=len(OBJECTIVE_NAMES),
+    )
+
+
+if __name__ == "__main__":
+    import uuid
+
+    import bayesmark.cmd_parse as cmd
+    import bayesmark.constants as cc
+    from bayesmark.cmd_parse import CmdArgs
+    from bayesmark.serialize import XRSerializer
+
+    description = "Run a study with one benchmark function and an optimizer"
+    args = cmd.parse_args(cmd.experiment_parser(description))
+
+    run_uuid = uuid.UUID(args[CmdArgs.uuid])
+
+    # set global logging level
+    logging.basicConfig(level=logging.DEBUG)
+    # This is the entry point for experiments, so pass the class to experiment_main to use this optimizer.
+    # This statement must be included in the wrapper class file:
+
+    experiment_main(PFNOptimizer, args=args)
diff --git a/private_multitask_pfn/PFNs/pfns/positional_encodings.py b/private_multitask_pfn/PFNs/pfns/positional_encodings.py
new file mode 100644
index 0000000..3f8ace4
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/positional_encodings.py
@@ -0,0 +1,344 @@
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional
+
+import torch
+from torch import nn
+
+import math
+
+# import torch
+# from torch import nn
+
+
+# Protocol for positonal encodings.
+# __init__(d_model, max_len=..[, more optionals])
+# forward(x: (seq_len, bs, d_model)) -> Tensor of shape (*x.shape[:2],d_model) containing pos. embeddings
+
+
+class NoPositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=None):
+        super(NoPositionalEncoding, self).__init__()
+        pass
+
+    def forward(self, x):
+        return x  # * math.sqrt(x.shape[-1])
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose(0, 1)
+        self.register_buffer("pe", pe)
+
+    def forward(self, x):
+        x = self.pe[: x.size(0), :] + x  # * math.sqrt(x.shape[-1])
+        return x
+
+
+class LearnedPositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(LearnedPositionalEncoding, self).__init__()
+        self.max_seq_len = max_len
+        # self.positional_embeddings = nn.Embedding(max_len, d_model)
+        self.positional_embeddings = nn.Parameter(torch.empty(max_len, d_model))
+        nn.init.normal_(self.positional_embeddings, mean=0, std=d_model**-0.5)
+
+    def forward(self, x):
+        seq_len, bs, d_model = x.shape
+        assert seq_len <= len(
+            self.positional_embeddings
+        ), "seq_len can be at most max_len."
+        pos_emb = self.positional_embeddings[:seq_len]
+        return (
+            pos_emb.unsqueeze(1).expand(seq_len, bs, d_model) + x
+        )  # * math.sqrt(x.shape[-1])
+
+
+class PairedScrambledPositionalEncodings(LearnedPositionalEncoding):
+    # TODO check whether it is a problem to use the same perm. for full batch
+    def forward(self, x):
+        seq_len, bs, d_model = x.shape
+        assert seq_len <= len(
+            self.positional_embeddings
+        ), "seq_len can be at most max_len."
+        assert (
+            len(self.positional_embeddings) % 2 == 0
+        ), "Please specify an even max_len."
+
+        paired_embs = self.positional_embeddings.view(
+            len(self.positional_embeddings), -1, 2
+        )
+        pos_emb = paired_embs[torch.randperm(len(paired_embs))].view(
+            *self.positional_embeddings.shape
+        )[:seq_len]
+
+        return (
+            pos_emb.unsqueeze(1).expand(seq_len, bs, d_model) + x
+        )  # * math.sqrt(x.shape[-1])
+
+
+class RotaryPositionalEmbeddings(nn.Module):
+    """
+    This class implements Rotary Positional Embeddings (RoPE)
+    proposed in https://arxiv.org/abs/2104.09864.
+
+    Reference implementation (used for correctness verfication)
+    can be found here:
+    https://github.com/meta-llama/llama/blob/main/llama/model.py#L80
+
+    In this implementation we cache the embeddings for each position upto
+    ``max_seq_len`` by computing this during init.
+
+    Args:
+        dim (int): Embedding dimension. This is usually set to the dim of each
+            head in the attention module computed as ``embed_dim // num_heads``
+        max_seq_len (int): Maximum expected sequence length for the
+            model, if exceeded the cached freqs will be recomputed
+        base (int): The base for the geometric progression used to compute
+            the rotation angles
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        max_seq_len: int = 4096,
+        base: int = 10_000,
+    ) -> None:
+        super().__init__()
+        self.dim = dim
+        self.base = base
+        self.max_seq_len = max_seq_len
+        self.rope_init()
+
+    def rope_init(self):
+        theta = 1.0 / (
+            self.base
+            ** (torch.arange(0, self.dim, 2)[: (self.dim // 2)].float() / self.dim)
+        )
+        self.register_buffer("theta", theta, persistent=False)
+        self.build_rope_cache(self.max_seq_len)
+
+    def build_rope_cache(self, max_seq_len: int = 4096) -> None:
+        # Create position indexes `[0, 1, ..., max_seq_len - 1]`
+        seq_idx = torch.arange(
+            max_seq_len, dtype=self.theta.dtype, device=self.theta.device
+        )
+
+        # Outer product of theta and position index; output tensor has
+        # a shape of [max_seq_len, dim // 2]
+        idx_theta = torch.einsum("i, j -> ij", seq_idx, self.theta).float()
+
+        # cache includes both the cos and sin components and so the output shape is
+        # [max_seq_len, dim // 2, 2]
+        cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
+        self.register_buffer("cache", cache, persistent=False)
+
+    def forward(
+        self, x: torch.Tensor, *, input_pos: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """
+        Args:
+            x (torch.Tensor): input tensor with shape
+                ``[b, s, n_h, h_d]``
+            input_pos (Optional[torch.Tensor]): Optional tensor which contains the position ids
+                of each token. During training, this is used to indicate the positions
+                of each token relative to its sample when packed, shape [b, s].
+                During inference, this indicates the position of the current token.
+                If none, assume the index of the token is its position id. Default is None.
+
+        Returns:
+            torch.Tensor: output tensor with shape ``[b, s, n_h, h_d]``
+
+        Notation used for tensor shapes:
+            - b: batch size
+            - s: sequence length
+            - n_h: num heads
+            - h_d: head dim
+        """
+        # input tensor has shape [b, s, n_h, h_d]
+        seq_len = x.size(1)
+
+        # extract the values based on whether input_pos is set or not
+        rope_cache = (
+            self.cache[:seq_len] if input_pos is None else self.cache[input_pos]
+        )
+
+        # reshape input; the last dimension is used for computing the output.
+        # Cast to float to match the reference implementation
+        # tensor has shape [b, s, n_h, h_d // 2, 2]
+        xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+
+        # reshape the cache for broadcasting
+        # tensor has shape [b, s, 1, h_d // 2, 2] if packed samples,
+        # otherwise has shape [1, s, 1, h_d // 2, 2]
+        rope_cache = rope_cache.view(-1, xshaped.size(1), 1, xshaped.size(3), 2)
+
+        # tensor has shape [b, s, n_h, h_d // 2, 2]
+        x_out = torch.stack(
+            [
+                xshaped[..., 0] * rope_cache[..., 0]
+                - xshaped[..., 1] * rope_cache[..., 1],
+                xshaped[..., 1] * rope_cache[..., 0]
+                + xshaped[..., 0] * rope_cache[..., 1],
+            ],
+            -1,
+        )
+
+        # tensor has shape [b, s, n_h, h_d]
+        x_out = x_out.flatten(3)
+        return x_out.type_as(x)
+
+
+class VisionRotaryPositionalEmbeddings(nn.Module):
+    """
+    This class implements two-dimensional Rotary Positional Embeddings (RoPE) for images
+    based on the axial frequency 2D RoPE described in https://arxiv.org/pdf/2403.13298.
+
+    The position embedding is simply applied to the x-axis and y-axis separately.
+
+    Note: This module assumes the CLS token embedding is appended at the end of the sequence.
+
+    Args:
+        patch_size (int): The size of each patch. Used to divide the tiles into patches.
+            E.g. for ``patch_size=40``, a tile of shape (400, 400) will have 10x10 grid of patches.
+        tile_size (int): The size of your image tiles, if the image was tile-cropped in advance. Otherwise,
+            the size of the full input image. In this case, the function will consider your image as a single tile.
+        dim (int): Embedding dimension. Unlike :class:`~torchtune.modules.RotaryPositionalEmbeddings`, this is
+            usually set to the dim of each head in the attention module divided by 2, computed as
+            ``embed_dim // num_heads // 2``. The divide by 2 accounts for x and y positions.
+        base (int): The base for the geometric progression used to compute
+            the rotation angles
+        append_cls_token (bool): Set to True if CLS token embedding is at the end of the sequence in the vision transformer,
+            False if is in the beginning of the sequence. RoPE is zeroed out for the CLS token. Default is True.
+    """
+
+    def __init__(
+        self,
+        patch_size: int,
+        tile_size: int,
+        dim: int,
+        base: int = 10_000,
+        append_cls_token: bool = True,
+    ) -> None:
+        super().__init__()
+        self.patch_grid_size = tile_size // patch_size
+        self.dim = dim
+        self.base = base
+        self.append_cls_token = append_cls_token
+        self.rope_init()
+
+    def rope_init(self):
+        theta = 1.0 / (
+            self.base
+            ** (torch.arange(0, self.dim, 2)[: (self.dim // 2)].float() / self.dim)
+        )
+        self.register_buffer("theta", theta, persistent=False)
+        self.build_rope_cache()
+
+    def build_rope_cache(self) -> None:
+        # Create position indices for each patch in the tile
+        patches_per_tile = self.patch_grid_size**2
+        patch_idx = torch.arange(
+            patches_per_tile, dtype=self.theta.dtype, device=self.theta.device
+        )
+        # Add a placeholder index for CLS token - will not be used in RoPE
+        if self.append_cls_token:
+            patch_idx = torch.cat(
+                [
+                    patch_idx,
+                    -1 * torch.ones(1, dtype=patch_idx.dtype, device=patch_idx.device),
+                ]
+            )
+        else:
+            patch_idx = torch.cat(
+                [
+                    -1 * torch.ones(1, dtype=patch_idx.dtype, device=patch_idx.device),
+                    patch_idx,
+                ]
+            )
+        # Encode x and y positions of each patch in the tile
+        patch_x_pos = patch_idx % self.patch_grid_size
+        patch_y_pos = patch_idx // self.patch_grid_size
+
+        # Outer product of theta and position index; output tensor has
+        # a shape of [patches_per_tile + 1, dim // 2]
+        x_theta = torch.einsum("i, j -> ij", patch_x_pos + 1, self.theta).float()
+        y_theta = torch.einsum("i, j -> ij", patch_y_pos + 1, self.theta).float()
+
+        # Shape: [patches_per_tile + 1, dim]
+        freqs = torch.cat([x_theta, y_theta], dim=-1)
+        # Zero out CLS token position frequencies
+        freqs = freqs.masked_fill(patch_idx.unsqueeze(-1) < 0, 0)
+
+        # cache includes both the cos and sin components and so the output shape is
+        # [patches_per_tile + 1, dim, 2]
+        cache = torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1)
+        self.register_buffer("cache", cache, persistent=False)
+
+    def forward(
+        self, x: torch.Tensor, *, input_pos: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """
+        Args:
+            x (torch.Tensor): input tensor with shape
+                ``[b, s, n_h, h_d]``
+            input_pos (Optional[torch.Tensor]): Optional tensor which contains the position ids
+                of each token. During training, this is used to indicate the positions
+                of each token relative to its sample when packed, shape [b, s].
+                During inference, this indicates the position of the current token.
+                If none, assume the index of the token is its position id. Default is None.
+
+        Returns:
+            torch.Tensor: output tensor with shape ``[b, s, n_h, h_d]``
+
+        Notation used for tensor shapes:
+            - b: batch size
+            - s: sequence length
+            - n_h: num heads
+            - h_d: head dim
+        """
+        # input tensor has shape [b, s, n_h, h_d]
+        seq_len = x.size(1)
+
+        # extract the values based on whether input_pos is set or not
+        rope_cache = (
+            self.cache[:seq_len] if input_pos is None else self.cache[input_pos]
+        )
+
+        # reshape input; the last dimension is used for computing the output.
+        # Cast to float to match the reference implementation
+        # tensor has shape [b, s, n_h, h_d // 2, 2]
+        xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+
+        # reshape the cache for broadcasting
+        # tensor has shape [b, s, 1, h_d // 2, 2] if packed samples,
+        # otherwise has shape [1, s, 1, h_d // 2, 2]
+        rope_cache = rope_cache.view(-1, xshaped.size(1), 1, xshaped.size(3), 2)
+
+        # tensor has shape [b, s, n_h, h_d // 2, 2]
+        x_out = torch.stack(
+            [
+                xshaped[..., 0] * rope_cache[..., 0]
+                - xshaped[..., 1] * rope_cache[..., 1],
+                xshaped[..., 1] * rope_cache[..., 0]
+                + xshaped[..., 0] * rope_cache[..., 1],
+            ],
+            -1,
+        )
+
+        # tensor has shape [b, s, n_h, h_d]
+        x_out = x_out.flatten(3)
+        return x_out.type_as(x)
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/priors/__init__.py b/private_multitask_pfn/PFNs/pfns/priors/__init__.py
new file mode 100644
index 0000000..59640bf
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/__init__.py
@@ -0,0 +1,3 @@
+from . import fast_gp, nonmyopic_bo, utils, normalize_with_style, simple_mlp, hyperparameter_sampling, input_warping, hebo_prior, condition_on_area_of_opt
+from .utils import get_batch_sequence as get_batch_sequence, get_batch_to_dataloader as get_batch_to_dataloader
+from .prior import Batch
diff --git a/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py b/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py
new file mode 100644
index 0000000..9e99759
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py
@@ -0,0 +1,63 @@
+import torch
+
+from .prior import Batch
+from ..utils import default_device
+
+
+@torch.no_grad()
+def get_batch(batch_size, seq_len, num_features, get_batch, epoch, device=default_device, hyperparameters={}, **kwargs):
+    """
+    This function assumes that every x is in the range [0, 1].
+    Style shape is (batch_size, 3*num_features) under the assumption that get_batch returns a batch
+    with shape (seq_len, batch_size, num_features).
+    The style is build the following way: [prob_of_feature_1_in_range, range_min_of_feature_1, range_max_of_feature_1, ...]
+
+
+
+    :param batch_size:
+    :param seq_len:
+    :param num_features:
+    :param get_batch:
+    :param epoch:
+    :param device:
+    :param hyperparameters:
+    :param kwargs:
+    :return:
+    """
+
+    max_num_divisions = hyperparameters.get('condition_on_area_max_num_divisions', 5)
+    maximize = hyperparameters.get('condition_on_area_maximize', True)
+    remove_correct_from_rand = hyperparameters.get('condition_on_area_remove_correct_from_rand', False)
+    assert remove_correct_from_rand is False, 'implement it'
+
+    batch: Batch = get_batch(batch_size=batch_size, seq_len=seq_len,
+                             num_features=num_features, device=device,
+                             hyperparameters=hyperparameters,
+                             epoch=epoch, **kwargs)
+    assert batch.style is None
+
+    d = batch.x.shape[2]
+
+    prob_correct = torch.rand(batch_size, d, device=device)
+    correct_opt = torch.rand(batch_size, d, device=device) < prob_correct
+    division_size = torch.randint(1, max_num_divisions + 1, (batch_size, d), device=device, dtype=torch.long)
+
+    optima_inds = batch.target_y.argmax(0).squeeze() if maximize else batch.target_y.argmin(0).squeeze()  # batch_size
+    optima = batch.x[optima_inds, torch.arange(batch_size, device=device)]  # shape: (batch_size, d)
+
+    optima_sections = torch.min(torch.floor(optima * division_size).long(), division_size - 1)
+    random_sections = torch.min(torch.floor(torch.rand(batch_size, batch.x.shape[2], device=device) * division_size).long(), division_size - 1)
+
+    sections = torch.where(correct_opt, optima_sections, random_sections).float()  # shape: (batch_size, d)
+    sections /= division_size.float()
+    assert tuple(sections.shape) == (batch_size, d)
+    batch.style = torch.stack([prob_correct, sections, sections + 1 / division_size], 2).view(batch_size, -1)  # shape: (batch_size, 3*d)
+
+    return batch
+
+
+
+
+
+
+
diff --git a/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py b/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py
new file mode 100644
index 0000000..a9baa7b
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py
@@ -0,0 +1,76 @@
+import torch
+
+from .prior import Batch
+from ..utils import default_device
+
+
+@torch.no_grad()
+def get_batch(batch_size, seq_len, num_features, get_batch, epoch, device=default_device, hyperparameters={}, **kwargs):
+    """
+    This is not part of the paper, but feel welcome to use this to write a better version of our user prior.
+
+
+    This function assumes that every x is in the range [0, 1].
+    Style shape is (batch_size, 3*num_features) under the assumption that get_batch returns a batch
+    with shape (seq_len, batch_size, num_features).
+    The style is build the following way: [prob_of_feature_1_in_range, range_min_of_feature_1, range_max_of_feature_1, ...]
+
+
+
+    :param batch_size:
+    :param seq_len:
+    :param num_features:
+    :param get_batch:
+    :param epoch:
+    :param device:
+    :param hyperparameters:
+    :param kwargs:
+    :return:
+    """
+
+    maximize = hyperparameters.get('condition_on_area_maximize', True)
+    size_range = hyperparameters.get('condition_on_area_size_range', (0.1, 0.5))
+    distribution = hyperparameters.get('condition_on_area_distribution', 'uniform')
+    assert distribution in ['uniform']
+
+
+    batch: Batch = get_batch(batch_size=batch_size, seq_len=seq_len,
+                             num_features=num_features, device=device,
+                             hyperparameters=hyperparameters,
+                             epoch=epoch, **kwargs)
+    assert batch.style is None
+
+    d = batch.x.shape[2]
+
+    prob_correct = torch.rand(batch_size, d, device=device)
+    correct_opt = torch.rand(batch_size, d, device=device) < prob_correct
+    division_size = torch.rand(batch_size, d, device=device) * (size_range[1] - size_range[0]) + size_range[0]
+
+    optima = batch.target_y.argmax(0).squeeze() if maximize else batch.target_y.argmin(0).squeeze()  # batch_size, d
+
+    optima_hints = batch.x[optima, torch.arange(batch_size, device=device)] - division_size/2 + torch.rand(batch_size, d, device=device)*division_size # shape: (batch_size, d)
+    optima_hints = optima_hints.clamp(0, 1)
+
+    optima_division_lower_bound = (optima_hints - division_size/2).clamp(0, 1)
+    optima_division_upper_bound = (optima_hints + division_size/2).clamp(0, 1)
+
+    random_hints = torch.rand(batch_size, d, device=device) - division_size/2 + torch.rand(batch_size, d, device=device)*division_size # shape: (batch_size, d)
+    random_hints = random_hints.clamp(0, 1)
+
+    random_division_lower_bound = (random_hints - division_size/2).clamp(0, 1)
+    random_division_upper_bound = (random_hints + division_size/2).clamp(0, 1)
+
+
+    lower_bounds = torch.where(correct_opt, optima_division_lower_bound, random_division_lower_bound)
+    upper_bounds = torch.where(correct_opt, optima_division_upper_bound, random_division_upper_bound)
+
+    batch.style = torch.stack([prob_correct, lower_bounds, upper_bounds], 2).view(batch_size, -1)  # shape: (batch_size, 3*d)
+
+    return batch
+
+
+
+
+
+
+
diff --git a/private_multitask_pfn/PFNs/pfns/priors/fast_gp.py b/private_multitask_pfn/PFNs/pfns/priors/fast_gp.py
new file mode 100644
index 0000000..9496b98
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/fast_gp.py
@@ -0,0 +1,203 @@
+import time
+
+import torch
+from torch import nn
+import gpytorch
+
+from .prior import Batch
+from .utils import get_batch_to_dataloader
+from ..utils import default_device
+
+
+# We will use the simplest form of GP model, exact inference
+class ExactGPModel(gpytorch.models.ExactGP):
+    def __init__(self, train_x, train_y, likelihood):
+        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
+        self.mean_module = gpytorch.means.ConstantMean()
+        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
+
+    def forward(self, x):
+        mean_x = self.mean_module(x)
+        covar_x = self.covar_module(x)
+        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
+
+
+def get_model(x, y, hyperparameters):
+    likelihood = gpytorch.likelihoods.GaussianLikelihood(noise_constraint=gpytorch.constraints.GreaterThan(1.e-9))
+    model = ExactGPModel(x, y, likelihood)
+    model.likelihood.noise = torch.ones_like(model.likelihood.noise) * hyperparameters["noise"]
+    model.covar_module.outputscale = torch.ones_like(model.covar_module.outputscale) * hyperparameters["outputscale"]
+    model.covar_module.base_kernel.lengthscale = torch.ones_like(model.covar_module.base_kernel.lengthscale) * \
+                                                 hyperparameters["lengthscale"]
+    return model, likelihood
+
+
+@torch.no_grad()
+def get_batch(batch_size, seq_len, num_features, device=default_device, hyperparameters=None,
+              equidistant_x=False, fix_x=None, **kwargs):
+    if isinstance(hyperparameters, (tuple, list)):
+        hyperparameters = {"noise": hyperparameters[0]
+            , "outputscale": hyperparameters[1]
+            , "lengthscale": hyperparameters[2]
+            , "is_binary_classification": hyperparameters[3]
+            # , "num_features_used": hyperparameters[4]
+            , "normalize_by_used_features": hyperparameters[5]
+            , "order_y": hyperparameters[6]
+            , "sampling": hyperparameters[7]
+                           }
+    elif hyperparameters is None:
+        hyperparameters = {"noise": .1, "outputscale": .1, "lengthscale": .1}
+
+    if 'verbose' in hyperparameters and hyperparameters['verbose']:
+        print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
+                  , "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size, 'sampling': hyperparameters['sampling']})
+    observation_noise = hyperparameters.get("observation_noise", True)
+
+    # hyperparameters = {k: hyperparameters[k]() if callable(hyperparameters[k]) else hyperparameters[k] for k in
+    #      hyperparameters.keys()}
+    assert not (equidistant_x and (fix_x is not None))
+
+    with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations', (True, True, True))):
+        if equidistant_x:
+            assert num_features == 1
+            x = torch.linspace(0, 1., seq_len).unsqueeze(0).repeat(batch_size, 1).unsqueeze(-1)
+        elif fix_x is not None:
+            assert fix_x.shape == (seq_len, num_features)
+            x = fix_x.unsqueeze(0).repeat(batch_size, 1, 1).to(device)
+        else:
+            if hyperparameters.get('sampling','uniform') == 'uniform':
+                x = torch.rand(batch_size, seq_len, num_features, device=device)
+            elif hyperparameters.get('sampling','uniform') == 'normal':
+                x = torch.randn(batch_size, seq_len, num_features, device=device)
+            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith('uniform_'):
+                left_border, right_border = [float(v) for v in hyperparameters['sampling'][len('uniform_'):].split('_')]
+                x = torch.rand(batch_size, seq_len, num_features, device=device) * (right_border - left_border) + left_border
+            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith('clustered_'):
+                dist_std, local_dist_std, base_likelihood = [float(v) for v in hyperparameters['sampling'][len('clustered_'):].split('_')]
+
+                def first_sample(dist):
+                    return dist().unsqueeze(0)
+
+                def append_sample(samples, dist, local_dist, base_likelihood):
+                    if samples is None:
+                        return first_sample(dist)
+                    num_samples, batch_size, num_features = samples.shape
+                    use_base = torch.rand(batch_size) < base_likelihood
+                    sample_mean = torch.where(use_base[:, None].repeat(1, num_features),
+                                              torch.zeros(batch_size, num_features),
+                                              samples[torch.randint(num_samples, (batch_size,)),
+                                              torch.arange(batch_size), :])
+                    return torch.cat((samples, (local_dist() + sample_mean).unsqueeze(0)), 0)
+
+                def create_sample(num_samples, dist, local_dist, base_likelihood):
+                    samples = None
+                    for i in range(num_samples):
+                        samples = append_sample(samples, dist, local_dist, base_likelihood)
+
+                    return samples[torch.randperm(num_samples)]
+
+                x = create_sample(seq_len, lambda: torch.randn(batch_size, num_features)*dist_std,
+                                  lambda: torch.rand(batch_size, num_features)*local_dist_std, base_likelihood)\
+                    .transpose(0,1).to(device)
+            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith(
+                    'gmix_'):
+                blob_width, n_centers_max, stddev = [float(v) for v in
+                                                             hyperparameters['sampling'][len('gmix_'):].split('_')]
+                n_centers_max = int(n_centers_max)
+                def get_x(batch_size, n_samples, num_features, blob_width, n_centers_max, stddev, device):
+                    n_centers = torch.randint(1, n_centers_max, tuple(), device=device)
+                    centers = torch.rand((batch_size, n_centers, num_features), device=device) * blob_width - blob_width / 2
+                    center_assignments = torch.randint(n_centers, (batch_size, n_samples,), device=device)
+                    noise = torch.randn((batch_size, n_samples, num_features), device=device) * stddev
+                    return centers.gather(1, center_assignments[..., None].repeat(1, 1,
+                                                                                  num_features)) + noise  # centers: (b, m, f), ass: (b,n)
+                x = get_x(batch_size, seq_len, num_features, blob_width, n_centers_max, stddev, device)
+            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith(
+                    'grep_'):
+                stddev, = [float(v) for v in hyperparameters['sampling'][len('grep_'):].split('_')]
+                x = torch.randn(batch_size, seq_len//2, num_features, device=device) * stddev
+                x = x.repeat(1,2,1)
+                x = x[:,torch.randperm(x.shape[1]),:]
+            else:
+                x = torch.randn(batch_size, seq_len, num_features, device=device) * hyperparameters.get('sampling', 1.)
+        model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
+        model.to(device)
+        # trained_model = ExactGPModel(train_x, train_y, likelihood).cuda()
+        # trained_model.eval()
+        successful_sample = False
+        while not successful_sample:
+            try:
+                with gpytorch.settings.prior_mode(True):
+                    model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
+                    model.to(device)
+
+                    d = model(x)
+                    if observation_noise:
+                        target_sample = sample = likelihood(d).sample().transpose(0, 1)
+                    else:
+                        target_sample = d.sample().transpose(0, 1)  # this will be the target for the loss
+                        sample = likelihood(target_sample).sample()  # this will be the input to the Transformer
+                    successful_sample = True
+            except RuntimeError: # This can happen when torch.linalg.eigh fails. Restart with new init resolves this.
+                print('GP Sampling unsuccessful, retrying.. ')
+                print(x)
+                print(hyperparameters)
+
+    if bool(torch.any(torch.isnan(x)).detach().cpu().numpy()):
+        print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
+                  , "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size})
+
+    if hyperparameters.get('improvement_classification', False):
+        single_eval_pos = kwargs['single_eval_pos']
+        max_so_far = sample[:single_eval_pos].max(0).values
+        sample[single_eval_pos:] = (sample > max_so_far).float()[single_eval_pos:]
+
+    return Batch(x=x.transpose(0, 1), y=sample, target_y=target_sample)
+
+DataLoader = get_batch_to_dataloader(get_batch)
+
+def get_model_on_device(x,y,hyperparameters,device):
+    model, likelihood = get_model(x, y, hyperparameters)
+    model.to(device)
+    return model, likelihood
+
+
+@torch.no_grad()
+def evaluate(x, y, y_non_noisy, use_mse=False, hyperparameters={}, get_model_on_device=get_model_on_device, device=default_device, step_size=1, start_pos=0):
+    start_time = time.time()
+    losses_after_t = [.0] if start_pos == 0 else []
+    all_losses_after_t = []
+
+    with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations',(True,True,True))), gpytorch.settings.fast_pred_var(False):
+        for t in range(max(start_pos, 1), len(x), step_size):
+            loss_sum = 0.
+            model, likelihood = get_model_on_device(x[:t].transpose(0, 1), y[:t].transpose(0, 1), hyperparameters, device)
+
+
+            model.eval()
+            # print([t.shape for t in model.train_inputs])
+            # print(x[:t].transpose(0,1).shape, x[t].unsqueeze(1).shape, y[:t].transpose(0,1).shape)
+            f = model(x[t].unsqueeze(1))
+            l = likelihood(f)
+            means = l.mean.squeeze()
+            varis = l.covariance_matrix.squeeze()
+            # print(l.variance.squeeze(), l.mean.squeeze(), y[t])
+
+            assert len(means.shape) == len(varis.shape) == 1
+            assert len(means) == len(varis) == x.shape[1]
+
+            if use_mse:
+                c = nn.MSELoss(reduction='none')
+                ls = c(means, y[t])
+            else:
+                ls = -l.log_prob(y[t].unsqueeze(1))
+
+            losses_after_t.append(ls.mean())
+            all_losses_after_t.append(ls.flatten())
+        return torch.stack(all_losses_after_t).to('cpu'), torch.tensor(losses_after_t).to('cpu'), time.time() - start_time
+
+if __name__ == '__main__':
+    hps = (.1,.1,.1)
+    for redo_idx in range(1):
+        print(
+            evaluate(*get_batch(1000, 10, hyperparameters=hps, num_features=10), use_mse=False, hyperparameters=hps))
diff --git a/private_multitask_pfn/PFNs/pfns/priors/gp.py b/private_multitask_pfn/PFNs/pfns/priors/gp.py
new file mode 100644
index 0000000..8de7aac
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/gp.py
@@ -0,0 +1,69 @@
+import time
+import random
+
+import torch
+from torch import nn
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import RBF, DotProduct, WhiteKernel
+from .prior import Batch
+from .utils import get_batch_to_dataloader
+
+
+length_scale_sampling_gp = .6
+
+def get_gp(length_scale=None):
+    return GaussianProcessRegressor(
+        kernel=RBF(length_scale=length_scale or length_scale_sampling_gp, length_scale_bounds='fixed'),
+        random_state=0, optimizer=None)
+
+
+def get_batch(batch_size, seq_len, num_features, noisy_std=None, **kwargs):
+    # m = torch.normal(0.,.1,size=(batch_size,num_features))
+    # m2 = torch.rand(batch_size,num_features)
+    # b = 0 # torch.rand(batch_size)
+    x_t = torch.rand(batch_size, seq_len, num_features)
+    # gp_b = TensorGP(kernel=TensorRBF(noisy_std))
+    # y_t = gp_b.sample_from_GP_prior(x_t).detach()
+
+    gpr = get_gp(noisy_std)
+    y_t = torch.zeros(batch_size, seq_len)
+
+    for i in range(len(y_t)):
+        y_t[i] += gpr.sample_y(x_t[i], random_state=random.randint(0, 2 ** 32)).squeeze()
+    x, y = x_t.transpose(0, 1), y_t.transpose(0, 1)
+    # x, _ = torch.sort(x,dim=0)
+    return Batch(x=x, y=y, target_y=y)
+
+
+DataLoader = get_batch_to_dataloader(get_batch)
+
+def evaluate(x, y, y_non_noisy, use_mse=False, length_scale=length_scale_sampling_gp):
+    start_time = time.time()
+    losses_after_t = [.0]
+    for t in range(1, len(x)):
+        loss_sum = 0.
+        for b_i in range(x.shape[1]):
+            gpr = get_gp(length_scale).fit(x[:t, b_i], y[:t, b_i])
+            means, stds = gpr.predict(x[t, b_i].unsqueeze(0), return_std=True)
+            assert len(means) == 1 == len(stds)
+            if use_mse:
+                c = nn.MSELoss()
+                l = c(torch.tensor(means), y[t, b_i].unsqueeze(-1))
+            else:
+                c = nn.GaussianNLLLoss(full=True)
+                l = c(torch.tensor(means), y[t, b_i].unsqueeze(-1),
+                      var=torch.tensor(stds) ** 2)
+            loss_sum += l
+
+
+        losses_after_t.append(loss_sum / x.shape[1])
+
+    return torch.tensor(losses_after_t), time.time()-start_time
+
+if __name__ == '__main__':
+    ls = .1
+    for alpha in set([ls, ls * 1.1, ls * .9]):
+        print(alpha)
+        for redo_idx in range(1):
+            print(
+                evaluate(*get_batch(1000, 10, noisy_std=ls, num_features=10), use_mse=False, length_scale=alpha))
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py b/private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py
new file mode 100644
index 0000000..9bd387f
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py
@@ -0,0 +1,613 @@
+import random
+import warnings
+
+import botorch
+import gpytorch
+
+import numpy as np
+import torch
+from gpytorch.means import ZeroMean
+from gpytorch.priors.torch_priors import GammaPrior, LogNormalPrior, UniformPrior
+from torch import nn
+from botorch.models.transforms.input import *
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from gpytorch.constraints import GreaterThan
+
+from ..utils import default_device, to_tensor
+
+from . import utils
+from .prior import Batch
+from .utils import get_batch_to_dataloader
+
+
+class Warp(gpytorch.Module):
+    r"""A transform that uses learned input warping functions.
+
+    Each specified input dimension is warped using the CDF of a
+    Kumaraswamy distribution. Typically, MAP estimates of the
+    parameters of the Kumaraswamy distribution, for each input
+    dimension, are learned jointly with the GP hyperparameters.
+
+    for each output in batched multi-output and multi-task models.
+
+    For now, ModelListGPs should be used to learn independent warping
+    functions for each output.
+    """
+
+    _min_concentration_level = 1e-4
+
+    def __init__(
+        self,
+        indices: List[int],
+        transform_on_train: bool = True,
+        transform_on_eval: bool = True,
+        transform_on_fantasize: bool = True,
+        reverse: bool = False,
+        eps: float = 1e-7,
+        concentration1_prior: Optional[Prior] = None,
+        concentration0_prior: Optional[Prior] = None,
+        batch_shape: Optional[torch.Size] = None,
+    ) -> None:
+        r"""Initialize transform.
+
+        Args:
+            indices: The indices of the inputs to warp.
+            transform_on_train: A boolean indicating whether to apply the
+                transforms in train() mode. Default: True.
+            transform_on_eval: A boolean indicating whether to apply the
+                transform in eval() mode. Default: True.
+            transform_on_fantasize: A boolean indicating whether to apply the
+                transform when called from within a `fantasize` call. Default: True.
+            reverse: A boolean indicating whether the forward pass should untransform
+                the inputs.
+            eps: A small value used to clip values to be in the interval (0, 1).
+            concentration1_prior: A prior distribution on the concentration1 parameter
+                of the Kumaraswamy distribution.
+            concentration0_prior: A prior distribution on the concentration0 parameter
+                of the Kumaraswamy distribution.
+            batch_shape: The batch shape.
+        """
+        super().__init__()
+        self.register_buffer("indices", torch.tensor(indices, dtype=torch.long))
+        self.transform_on_train = transform_on_train
+        self.transform_on_eval = transform_on_eval
+        self.transform_on_fantasize = transform_on_fantasize
+        self.reverse = reverse
+        self.batch_shape = batch_shape or torch.Size([])
+        self._X_min = eps
+        self._X_range = 1 - 2 * eps
+        if len(self.batch_shape) > 0:
+            # Note: this follows the gpytorch shape convention for lengthscales
+            # There is ongoing discussion about the extra `1`.
+            # https://github.com/cornellius-gp/gpytorch/issues/1317
+            batch_shape = self.batch_shape + torch.Size([1])
+        else:
+            batch_shape = self.batch_shape
+        for i in (0, 1):
+            p_name = f"concentration{i}"
+            self.register_parameter(
+                p_name,
+                nn.Parameter(torch.full(batch_shape + self.indices.shape, 1.0)),
+            )
+        if concentration0_prior is not None:
+
+            def closure(m):
+                # print(m.concentration0)
+                return m.concentration0
+
+            self.register_prior(
+                "concentration0_prior",
+                concentration0_prior,
+                closure,
+                lambda m, v: m._set_concentration(i=0, value=v),
+            )
+        if concentration1_prior is not None:
+
+            def closure(m):
+                # print(m.concentration1)
+                return m.concentration1
+
+            self.register_prior(
+                "concentration1_prior",
+                concentration1_prior,
+                closure,
+                lambda m, v: m._set_concentration(i=1, value=v),
+            )
+        for i in (0, 1):
+            p_name = f"concentration{i}"
+            constraint = GreaterThan(
+                self._min_concentration_level,
+                transform=None,
+                # set the initial value to be the identity transformation
+                initial_value=1.0,
+            )
+            self.register_constraint(param_name=p_name, constraint=constraint)
+
+    def _set_concentration(self, i: int, value: Union[float, Tensor]) -> None:
+        if not torch.is_tensor(value):
+            value = torch.as_tensor(value).to(self.concentration0)
+        self.initialize(**{f"concentration{i}": value})
+
+    def _transform(self, X: Tensor) -> Tensor:
+        r"""Warp the inputs through the Kumaraswamy CDF.
+
+        Args:
+            X: A `input_batch_shape x (batch_shape) x n x d`-dim tensor of inputs.
+                batch_shape here can either be self.batch_shape or 1's such that
+                it is broadcastable with self.batch_shape if self.batch_shape is set.
+
+        Returns:
+            A `input_batch_shape x (batch_shape) x n x d`-dim tensor of transformed
+                inputs.
+        """
+        X_tf = expand_and_copy_tensor(X=X, batch_shape=self.batch_shape)
+        k = Kumaraswamy(
+            concentration1=self.concentration1, concentration0=self.concentration0
+        )
+        # normalize to [eps, 1-eps]
+        X_tf[..., self.indices] = k.cdf(
+            torch.clamp(
+                X_tf[..., self.indices] * self._X_range + self._X_min,
+                self._X_min,
+                1.0 - self._X_min,
+            )
+        )
+        return X_tf
+
+    def _untransform(self, X: Tensor) -> Tensor:
+        r"""Warp the inputs through the Kumaraswamy inverse CDF.
+
+        Args:
+            X: A `input_batch_shape x batch_shape x n x d`-dim tensor of inputs.
+
+        Returns:
+            A `input_batch_shape x batch_shape x n x d`-dim tensor of transformed
+                inputs.
+        """
+        if len(self.batch_shape) > 0:
+            if self.batch_shape != X.shape[-2 - len(self.batch_shape) : -2]:
+                raise BotorchTensorDimensionError(
+                    "The right most batch dims of X must match self.batch_shape: "
+                    f"({self.batch_shape})."
+                )
+        X_tf = X.clone()
+        k = Kumaraswamy(
+            concentration1=self.concentration1, concentration0=self.concentration0
+        )
+        # unnormalize from [eps, 1-eps] to [0,1]
+        X_tf[..., self.indices] = (
+            (k.icdf(X_tf[..., self.indices]) - self._X_min) / self._X_range
+        ).clamp(0.0, 1.0)
+        return X_tf
+
+    def transform(self, X: Tensor) -> Tensor:
+        r"""Transform the inputs.
+
+        Args:
+            X: A `batch_shape x n x d`-dim tensor of inputs.
+
+        Returns:
+            A `batch_shape x n x d`-dim tensor of transformed inputs.
+        """
+        return self._untransform(X) if self.reverse else self._transform(X)
+
+    def untransform(self, X: Tensor) -> Tensor:
+        r"""Un-transform the inputs.
+
+        Args:
+            X: A `batch_shape x n x d`-dim tensor of inputs.
+
+        Returns:
+            A `batch_shape x n x d`-dim tensor of un-transformed inputs.
+        """
+        return self._transform(X) if self.reverse else self._untransform(X)
+
+    def equals(self, other: InputTransform) -> bool:
+        r"""Check if another input transform is equivalent.
+
+        Note: The reason that a custom equals method is defined rather than
+        defining an __eq__ method is because defining an __eq__ method sets
+        the __hash__ method to None. Hashing modules is currently used in
+        pytorch. See https://github.com/pytorch/pytorch/issues/7733.
+
+        Args:
+            other: Another input transform.
+
+        Returns:
+            A boolean indicating if the other transform is equivalent.
+        """
+        other_state_dict = other.state_dict()
+        return (
+            type(self) == type(other)
+            and (self.transform_on_train == other.transform_on_train)
+            and (self.transform_on_eval == other.transform_on_eval)
+            and (self.transform_on_fantasize == other.transform_on_fantasize)
+            and all(
+                torch.allclose(v, other_state_dict[k].to(v))
+                for k, v in self.state_dict().items()
+            )
+        )
+
+    def preprocess_transform(self, X: Tensor) -> Tensor:
+        r"""Apply transforms for preprocessing inputs.
+
+        The main use cases for this method are 1) to preprocess training data
+        before calling `set_train_data` and 2) preprocess `X_baseline` for noisy
+        acquisition functions so that `X_baseline` is "preprocessed" with the
+        same transformations as the cached training inputs.
+
+        Args:
+            X: A `batch_shape x n x d`-dim tensor of inputs.
+
+        Returns:
+            A `batch_shape x n x d`-dim tensor of (transformed) inputs.
+        """
+        if self.transform_on_train:
+            # We need to disable learning of bounds here.
+            # See why: https://github.com/pytorch/botorch/issues/1078.
+            if hasattr(self, "learn_bounds"):
+                learn_bounds = self.learn_bounds
+                self.learn_bounds = False
+                result = self.transform(X)
+                self.learn_bounds = learn_bounds
+                return result
+            else:
+                return self.transform(X)
+        return X
+
+    def forward(self, X: Tensor) -> Tensor:
+        r"""Transform the inputs to a model.
+
+        Args:
+            X: A `batch_shape x n x d`-dim tensor of inputs.
+
+        Returns:
+            A `batch_shape x n' x d`-dim tensor of transformed inputs.
+        """
+        if self.training:
+            if self.transform_on_train:
+                return self.transform(X)
+        elif self.transform_on_eval:
+            if fantasize.off() or self.transform_on_fantasize:
+                return self.transform(X)
+        return X
+
+
+def constraint_based_on_distribution_support(
+    prior: torch.distributions.Distribution, device, sample_from_path
+):
+    if sample_from_path:
+        return None
+
+    if hasattr(prior.support, "upper_bound"):
+        return gpytorch.constraints.Interval(
+            to_tensor(prior.support.lower_bound, device=device),
+            to_tensor(prior.support.upper_bound, device=device),
+        )
+    else:
+        return gpytorch.constraints.GreaterThan(
+            to_tensor(prior.support.lower_bound, device=device)
+        )
+
+
+loaded_things = {}
+
+
+def torch_load(path):
+    """
+    Cached torch load. Caution: This does not copy the output but keeps pointers.
+    That means, if you modify the output, you modify the output of later calls to this function with the same args.
+    :param path:
+    :return:
+    """
+    if path not in loaded_things:
+        print(f"loading {path}")
+        with open(path, "rb") as f:
+            loaded_things[path] = torch.load(f)
+    return loaded_things[path]
+
+
+def get_model(x, y, hyperparameters: dict, sample=True):
+    sample_from_path = hyperparameters.get("sample_from_extra_prior", None)
+    device = x.device
+    num_features = x.shape[-1]
+    likelihood = gpytorch.likelihoods.GaussianLikelihood(
+        noise_constraint=gpytorch.constraints.Positive()
+    )
+    likelihood.register_prior(
+        "noise_prior",
+        LogNormalPrior(
+            torch.tensor(
+                hyperparameters.get("hebo_noise_logmean", -4.63), device=device
+            ),
+            torch.tensor(hyperparameters.get("hebo_noise_std", 0.5), device=device),
+        ),
+        "noise",
+    )
+    lengthscale_prior = (
+        GammaPrior(
+            torch.tensor(hyperparameters["lengthscale_concentration"], device=device),
+            torch.tensor(hyperparameters["lengthscale_rate"], device=device),
+        )
+        if hyperparameters.get("lengthscale_concentration", None)
+        else UniformPrior(
+            torch.tensor(0.0, device=device), torch.tensor(1.0, device=device)
+        )
+    )
+    covar_module = gpytorch.kernels.MaternKernel(
+        nu=3 / 2,
+        ard_num_dims=num_features,
+        lengthscale_prior=lengthscale_prior,
+        lengthscale_constraint=constraint_based_on_distribution_support(
+            lengthscale_prior, device, sample_from_path
+        ),
+    )
+    # ORIG DIFF: orig lengthscale has no prior
+    # covar_module.register_prior("lengthscale_prior",
+    # UniformPrior(.000000001, 1.),
+    # GammaPrior(concentration=hyperparameters.get('lengthscale_concentration', 1.),
+    #           rate=hyperparameters.get('lengthscale_rate', .1)),
+    # skewness is controllled by concentration only, want somthing like concetration in [0.1,1.], rate around [.05,1] seems reasonable
+    # "lengthscale")
+    outputscale_prior = GammaPrior(
+        concentration=hyperparameters.get("outputscale_concentration", 0.5),
+        rate=hyperparameters.get("outputscale_rate", 1.0),
+    )
+    covar_module = gpytorch.kernels.ScaleKernel(
+        covar_module,
+        outputscale_prior=outputscale_prior,
+        outputscale_constraint=constraint_based_on_distribution_support(
+            outputscale_prior, device, sample_from_path
+        ),
+    )
+
+    if random.random() < float(hyperparameters.get("add_linear_kernel", True)):
+        # ORIG DIFF: added priors for variance and outputscale of linear kernel
+        var_prior = UniformPrior(
+            torch.tensor(0.0, device=device), torch.tensor(1.0, device=device)
+        )
+        out_prior = UniformPrior(
+            torch.tensor(0.0, device=device), torch.tensor(1.0, device=device)
+        )
+        lincovar_module = gpytorch.kernels.ScaleKernel(
+            gpytorch.kernels.LinearKernel(
+                variance_prior=var_prior,
+                variance_constraint=constraint_based_on_distribution_support(
+                    var_prior, device, sample_from_path
+                ),
+            ),
+            outputscale_prior=out_prior,
+            outputscale_constraint=constraint_based_on_distribution_support(
+                out_prior, device, sample_from_path
+            ),
+        )
+        covar_module = covar_module + lincovar_module
+
+    if hyperparameters.get("hebo_warping", False):
+        # initialize input_warping transformation
+        warp_tf = Warp(
+            indices=list(range(num_features)),
+            # use a prior with median at 1.
+            # when a=1 and b=1, the Kumaraswamy CDF is the identity function
+            concentration1_prior=LogNormalPrior(
+                torch.tensor(0.0, device=device),
+                torch.tensor(
+                    hyperparameters.get("hebo_input_warping_c1_std", 0.75),
+                    device=device,
+                ),
+            ),
+            concentration0_prior=LogNormalPrior(
+                torch.tensor(0.0, device=device),
+                torch.tensor(
+                    hyperparameters.get("hebo_input_warping_c0_std", 0.75),
+                    device=device,
+                ),
+            ),
+        )
+    else:
+        warp_tf = None
+    # assume mean 0 always!
+    if len(y.shape) < len(x.shape):
+        y = y.unsqueeze(-1)
+    model = botorch.models.SingleTaskGP(
+        x, y, likelihood=likelihood, covar_module=covar_module, input_transform=warp_tf
+    )
+    model.mean_module = ZeroMean(x.shape[:-2])
+    model.to(device)
+    likelihood.to(device)
+
+    if sample:
+        model = model.pyro_sample_from_prior()
+        if sample_from_path:
+            parameter_sample_distribution = torch_load(
+                sample_from_path
+            )  # dict with entries for each parameter
+            idx_for_len = {}
+            for (
+                parameter_name,
+                parameter_values,
+            ) in parameter_sample_distribution.items():
+                assert len(parameter_values.shape) == 1
+                try:
+                    p = eval(parameter_name)
+                    if len(parameter_values) in idx_for_len:
+                        idx = idx_for_len[len(parameter_values)].view(p.shape)
+                    else:
+                        idx = torch.randint(len(parameter_values), p.shape)
+                        idx_for_len[len(parameter_values)] = idx
+                    new_sample = parameter_values[idx].to(device).view(p.shape)  # noqa
+                    assert new_sample.shape == p.shape
+                    with torch.no_grad():
+                        p.data = new_sample
+                except AttributeError:
+                    utils.print_once(
+                        f"could not find parameter {parameter_name} in model for `sample_from_extra_prior`"
+                    )
+            model.requires_grad_(False)
+            likelihood.requires_grad_(False)
+        return model, model.likelihood
+    else:
+        assert not (hyperparameters.get("sigmoid", False)) and not (
+            hyperparameters.get("y_minmax_norm", False)
+        ), "Sigmoid and y_minmax_norm can only be used to sample models..."
+        return model, likelihood
+
+
+@torch.no_grad()
+def get_batch(
+    batch_size,
+    seq_len,
+    num_features,
+    device=default_device,
+    hyperparameters=None,
+    batch_size_per_gp_sample=None,
+    single_eval_pos=None,
+    fix_to_range=None,
+    equidistant_x=False,
+    verbose=False,
+    **kwargs,
+):
+    """
+    This function is very similar to the equivalent in .fast_gp. The only difference is that this function operates over
+    a mixture of GP priors.
+    :param batch_size:
+    :param seq_len:
+    :param num_features:
+    :param device:
+    :param hyperparameters:
+    :param for_regression:
+    :return:
+    """
+    hyperparameters = hyperparameters or {}
+    with gpytorch.settings.fast_computations(
+        *hyperparameters.get("fast_computations", (True, True, True))
+    ):
+        batch_size_per_gp_sample = batch_size_per_gp_sample or max(batch_size // 4, 1)
+        assert batch_size % batch_size_per_gp_sample == 0
+
+        total_num_candidates = batch_size * (2 ** (fix_to_range is not None))
+        num_candidates = batch_size_per_gp_sample * (2 ** (fix_to_range is not None))
+        unused_feature_likelihood = hyperparameters.get(
+            "unused_feature_likelihood", False
+        )
+        if equidistant_x:
+            assert num_features == 1
+            assert not unused_feature_likelihood
+            x = (
+                torch.linspace(0, 1.0, seq_len)
+                .unsqueeze(0)
+                .repeat(total_num_candidates, 1)
+                .unsqueeze(-1)
+            )
+        else:
+            x = torch.rand(total_num_candidates, seq_len, num_features, device=device)
+        samples = []
+        samples_wo_noise = []
+        for i in range(0, total_num_candidates, num_candidates):
+            local_x = x[i : i + num_candidates]
+            if unused_feature_likelihood:
+                r = torch.rand(num_features)
+                unused_feature_mask = r < unused_feature_likelihood
+                if unused_feature_mask.all():
+                    unused_feature_mask[r.argmin()] = False
+                used_local_x = local_x[..., ~unused_feature_mask]
+            else:
+                used_local_x = local_x
+            get_model_and_likelihood = lambda: get_model(
+                used_local_x,
+                torch.zeros(num_candidates, x.shape[1], device=device),
+                hyperparameters,
+            )
+            model, likelihood = get_model_and_likelihood()
+            if verbose:
+                print(
+                    list(model.named_parameters()),
+                    (
+                        (
+                            list(model.input_transform.named_parameters()),
+                            model.input_transform.concentration1,
+                            model.input_transform.concentration0,
+                        )
+                        if model.input_transform is not None
+                        else None
+                    ),
+                )
+
+            # trained_model = ExactGPModel(train_x, train_y, likelihood).cuda()
+            # trained_model.eval()
+            successful_sample = 0
+            throwaway_share = 0.0
+            while successful_sample < 1:
+                with gpytorch.settings.prior_mode(True):
+                    # print(x.device, device, f'{model.covar_module.base_kernel.lengthscale=}, {model.covar_module.base_kernel.lengthscale.device=}')
+                    d = model(used_local_x)
+                    try:
+                        with warnings.catch_warnings():
+                            warnings.simplefilter("ignore")
+                            sample_wo_noise = d.sample()
+                            d = likelihood(sample_wo_noise)
+                    except (RuntimeError, ValueError) as e:
+                        successful_sample -= 1
+                        model, likelihood = get_model_and_likelihood()
+                        if successful_sample < -100:
+                            print(
+                                f"Could not sample from model {i} after {successful_sample} attempts. {e}"
+                            )
+                            raise e
+                        continue
+                    sample = d.sample()  # bs_per_gp_s x T
+                    if fix_to_range is None:
+                        # for k, v in model.named_parameters(): print(k,v)
+                        samples.append(sample.transpose(0, 1))
+                        samples_wo_noise.append(sample_wo_noise.transpose(0, 1))
+                        break
+                    smaller_mask = sample < fix_to_range[0]
+                    larger_mask = sample >= fix_to_range[1]
+                    in_range_mask = ~(smaller_mask | larger_mask).any(1)
+                    throwaway_share += (
+                        ~in_range_mask[:batch_size_per_gp_sample]
+                    ).sum() / batch_size_per_gp_sample
+                    if in_range_mask.sum() < batch_size_per_gp_sample:
+                        successful_sample -= 1
+                        if successful_sample < 100:
+                            print(
+                                "Please change hyper-parameters (e.g. decrease outputscale_mean) it"
+                                "seems like the range is set to tight for your hyper-parameters."
+                            )
+                        continue
+
+                    x[i : i + batch_size_per_gp_sample] = local_x[in_range_mask][
+                        :batch_size_per_gp_sample
+                    ]
+                    sample = sample[in_range_mask][:batch_size_per_gp_sample]
+                    samples.append(sample.transpose(0, 1))
+                    samples_wo_noise.append(sample_wo_noise.transpose(0, 1))
+                    successful_sample = True
+
+        if random.random() < 0.01:
+            print(
+                "throwaway share",
+                throwaway_share / (batch_size // batch_size_per_gp_sample),
+            )
+
+        # print(f'took {time.time() - start}')
+        sample = torch.cat(samples, 1)[..., None]
+        sample_wo_noise = torch.cat(samples_wo_noise, 1)[..., None]
+        x = x.view(-1, batch_size, seq_len, num_features)[0]
+        # TODO think about enabling the line below
+        # sample = sample - sample[0, :].unsqueeze(0).expand(*sample.shape)
+        x = x.transpose(0, 1)
+        assert x.shape[:2] == sample.shape[:2]
+    return Batch(
+        x=x,
+        y=sample,
+        target_y=(
+            sample
+            if hyperparameters.get("observation_noise", True)
+            else sample_wo_noise
+        ),
+    )
+
+
+DataLoader = get_batch_to_dataloader(get_batch)
diff --git a/private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py b/private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py
new file mode 100644
index 0000000..f4d9b02
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py
@@ -0,0 +1,113 @@
+# this is a wrapper prior that samples hyperparameters which are set to be ConfigSpace parameters
+# from fblearner.flow.projects.ae.benchmarks.pfn.ConfigSpace import hyperparameters as CSH
+# import fblearner.flow.projects.ae.benchmarks.pfn.ConfigSpace as CS
+from copy import deepcopy
+
+import torch
+
+from .prior import Batch
+
+
+def list_all_hps_in_nested(config):
+    if isinstance(config, CSH.Hyperparameter):
+        return [config]
+    elif isinstance(config, dict):
+        result = []
+        for k, v in config.items():
+            result += list_all_hps_in_nested(v)
+        return result
+    else:
+        return []
+
+
+def create_configspace_from_hierarchical(config):
+    cs = CS.ConfigurationSpace()
+    for hp in list_all_hps_in_nested(config):
+        cs.add_hyperparameter(hp)
+    return cs
+
+
+def fill_in_configsample(config, configsample):
+    # config is our dict that defines config distribution
+    # configsample is a CS.Configuration
+    hierarchical_configsample = deepcopy(config)
+    for k, v in config.items():
+        if isinstance(v, CSH.Hyperparameter):
+            hierarchical_configsample[k] = configsample[v.name]
+        elif isinstance(v, dict):
+            hierarchical_configsample[k] = fill_in_configsample(v, configsample)
+    return hierarchical_configsample
+
+
+def sample_configspace_hyperparameters(hyperparameters):
+    cs = create_configspace_from_hierarchical(hyperparameters)
+    cs_sample = cs.sample_configuration()
+    return fill_in_configsample(hyperparameters, cs_sample)
+
+
+def get_batch_old(batch_size, *args, hyperparameters, get_batch, **kwargs):
+    num_models = min(
+        hyperparameters.get("num_hyperparameter_samples_per_batch", 1), batch_size
+    )
+    if num_models == -1:
+        num_models = batch_size
+    assert batch_size % num_models == 0, "batch_size must be a multiple of num_models"
+    cs = create_configspace_from_hierarchical(hyperparameters)
+    sub_batches = []
+    for i in range(num_models):
+        cs_sample = cs.sample_configuration()
+        hyperparameters_sample = fill_in_configsample(hyperparameters, cs_sample)
+        sub_batch = get_batch(
+            batch_size // num_models,
+            *args,
+            hyperparameters=hyperparameters_sample,
+            **kwargs
+        )
+        sub_batches.append(sub_batch)
+
+    # concat x, y, target (and maybe style)
+    # assert 3 <= len(sub_batch) <= 4
+    # return tuple(torch.cat([sb[i] for sb in sub_batches], dim=(0 if i == 3 else 1)) for i in range(len(sub_batch)))
+    assert all(
+        not b.other_filled_attributes(set_of_attributes=("x", "y", "target_y"))
+        for b in sub_batches
+    )
+    return Batch(
+        x=torch.cat([b.x for b in sub_batches], dim=1),
+        y=torch.cat([b.y for b in sub_batches], dim=1),
+        target_y=torch.cat([b.target_y for b in sub_batches], dim=1),
+    )
+
+
+def get_batch(batch_size, *args, hyperparameters, get_batch, **kwargs):
+    num_models = min(
+        hyperparameters.get("num_hyperparameter_samples_per_batch", 1), batch_size
+    )
+    if num_models == -1:
+        num_models = batch_size
+    assert batch_size % num_models == 0, "batch_size must be a multiple of num_models"
+    cs = create_configspace_from_hierarchical(hyperparameters)
+    sub_batches = []
+    for i in range(num_models):
+        cs_sample = cs.sample_configuration()
+        hyperparameters_sample = fill_in_configsample(hyperparameters, cs_sample)
+        sub_batch = get_batch(
+            batch_size // num_models,
+            *args,
+            hyperparameters=hyperparameters_sample,
+            **kwargs
+        )
+        sub_batches.append(sub_batch)
+
+    # concat x, y, target (and maybe style)
+    # assert 3 <= len(sub_batch) <= 4
+    # return tuple(torch.cat([sb[i] for sb in sub_batches], dim=(0 if i == 3 else 1)) for i in range(len(sub_batch)))
+    assert all(
+        not b.other_filled_attributes(set_of_attributes=("x", "y", "target_y"))
+        for b in sub_batches
+    )
+    return Batch(
+        x=torch.cat([b.x for b in sub_batches], dim=1),
+        y=torch.cat([b.y for b in sub_batches], dim=1),
+        target_y=torch.cat([b.target_y for b in sub_batches], dim=1),
+    )
diff --git a/private_multitask_pfn/PFNs/pfns/priors/input_warping.py b/private_multitask_pfn/PFNs/pfns/priors/input_warping.py
new file mode 100644
index 0000000..c99e8f9
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/input_warping.py
@@ -0,0 +1,93 @@
+import torch
+from torch.distributions import Kumaraswamy
+
+
+def exp_in_prev_range(x,factor):
+    mini, maxi = x.min(0)[0], x.max(0)[0]
+    expx = (factor*x).exp()
+    expx_01 = (expx - expx.min(0)[0]) / (expx.max(0)[0] - expx.min(0)[0])
+    return expx_01 * (maxi - mini) + mini
+
+
+@torch.no_grad()
+def get_batch(*args, hyperparameters, get_batch, **kwargs):
+    """
+    This `get_batch` can be used to wrap another `get_batch` and apply a Kumaraswamy transform to the input.
+    The x's have to be in [0,1] for this to work!
+    """
+    returns = get_batch(*args, hyperparameters=hyperparameters, **kwargs)
+
+    input_warping_type = hyperparameters.get('input_warping_type', 'kumar')
+    # controls what part of the batch ('x', 'y' or 'xy') to apply the warping to
+    input_warping_groups = hyperparameters.get('input_warping_groups', 'x')
+    # whether to norm inputs between 0 and 1 before warping, as warping is only possible in that range.
+    input_warping_norm = hyperparameters.get('input_warping_norm', False)
+    use_icdf = hyperparameters.get('input_warping_use_icdf', False)
+
+    def norm_to_0_1(x):
+        eps = .00001
+        maxima = torch.max(x, 0)[0]
+        minima = torch.min(x, 0)[0]
+        normed_x = (x - minima) / (maxima - minima + eps)
+
+        def denorm(normed_x):
+            return normed_x * (maxima - minima + eps) + minima
+
+        return normed_x, denorm
+
+    def warp_input(x):
+        if input_warping_norm:
+            x, denorm = norm_to_0_1(x)
+
+        if input_warping_type == 'kumar':
+            if 'input_warping_c_std' in hyperparameters:
+                assert 'input_warping_c0_std' not in hyperparameters and 'input_warping_c1_std' not in hyperparameters
+                hyperparameters['input_warping_c0_std'] = hyperparameters['input_warping_c_std']
+                hyperparameters['input_warping_c1_std'] = hyperparameters['input_warping_c_std']
+            inside = 0
+            while not inside:
+                c1 = (torch.randn(*x.shape[1:], device=x.device) * hyperparameters.get('input_warping_c1_std', .75)).exp()
+                c0 = (torch.randn(*x.shape[1:], device=x.device) * hyperparameters.get('input_warping_c0_std', .75)).exp()
+                if not hyperparameters.get('input_warping_in_range', False):
+                    inside = True
+                elif (c1 < 10).all() and (c1 > 0).all() and (c0 < 10).all() and (c0 > 0).all():
+                    inside = True
+                else:
+                    inside -= 1
+                    if inside < -100:
+                        print('It seems that the input warping is not working.')
+            if c1_v := hyperparameters.get('fix_input_warping_c1', False):
+                c1[:] = c1_v
+            if c0_v := hyperparameters.get('fix_input_warping_c0', False):
+                c0[:] = c0_v
+            if hyperparameters.get('verbose', False):
+                print(f'c1: {c1}, c0: {c0}')
+            k = Kumaraswamy(concentration1=c1, concentration0=c0)
+            x_transformed = k.icdf(x) if use_icdf else k.cdf(x)
+        elif input_warping_type == 'exp':
+            transform_likelihood = hyperparameters.get('input_warping_transform_likelihood', 0.2)
+            to_be_transformed = torch.rand_like(x[0,0]) < transform_likelihood
+            transform_factors = torch.rand_like(x[0,0]) * hyperparameters.get('input_warping_transform_factor', 1.)
+            log_direction = torch.rand_like(x[0,0]) < 0.5
+            exp_x = exp_in_prev_range(x, transform_factors)
+            minus_exp_x = 1.-exp_in_prev_range(1.-x, transform_factors)
+            exp_x = torch.where(log_direction, exp_x, minus_exp_x)
+            x_transformed = torch.where(to_be_transformed[None,None,:], exp_x, x)
+        elif input_warping_type is None or input_warping_type == 'none':
+            x_transformed = x
+        else:
+            raise ValueError(f"Unknown input_warping_type: {input_warping_type}")
+
+        if input_warping_norm:
+            x_transformed = denorm(x_transformed)
+        return x_transformed
+
+    if 'x' in input_warping_groups:
+        returns.x = warp_input(returns.x)
+    if 'y' in input_warping_groups:
+        returns.y = warp_input(returns.y)
+        returns.target_y = warp_input(returns.target_y)
+
+    return returns
+
+
diff --git a/private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py b/private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py
new file mode 100644
index 0000000..4ac8e44
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py
@@ -0,0 +1,159 @@
+import torch
+
+from .prior import Batch
+from ..utils import default_device
+
+
+loaded_models = {}
+
+def get_model(model_name, device):
+    if model_name not in loaded_models:
+        import submitit
+        group, index = model_name.split(':')
+        ex = submitit.get_executor()
+        model = ex.get_group(group)[int(index)].results()[0][2]
+        model.to(device)
+        loaded_models[model_name] = model
+    return loaded_models[model_name]
+
+@torch.no_grad()
+def get_batch(batch_size, seq_len, num_features, get_batch, model, single_eval_pos, epoch, device=default_device, hyperparameters=None, **kwargs):
+    """
+    Important Assumptions:
+        'inf_batch_size', 'max_level', 'sample_only_one_level', 'eval_seq_len' and 'epochs_per_level' in hyperparameters
+
+    You can train a new model, based on an old one to only sample from a single level.
+    You specify `level_0_model` as a group:index string and the model will be loaded from the checkpoint.
+
+
+
+    :param batch_size:
+    :param seq_len:
+    :param num_features:
+    :param get_batch:
+    :param model:
+    :param single_eval_pos:
+    :param epoch:
+    :param device:
+    :param hyperparameters:
+    :param kwargs:
+    :return:
+    """
+    if level_0_model := hyperparameters.get('level_0_model', None):
+        assert hyperparameters['sample_only_one_level'], "level_0_model only makes sense if you sample only one level"
+        assert hyperparameters['max_level'] == 1, "level_0_model only makes sense if you sample only one level"
+        level_0_model = get_model(level_0_model, device)
+        model = level_0_model
+
+    # the level describes how many fantasized steps are possible. This starts at 0 for the first epochs.
+    epochs_per_level = hyperparameters['epochs_per_level']
+    share_predict_mean_distribution = hyperparameters.get('share_predict_mean_distribution', 0.)
+    use_mean_prediction = share_predict_mean_distribution or\
+                          (model.decoder_dict_once is not None and 'mean_prediction' in model.decoder_dict_once)
+    num_evals = seq_len - single_eval_pos
+    level = min(min(epoch // epochs_per_level, hyperparameters['max_level']), num_evals - 1)
+    if level_0_model:
+        level = 1
+    eval_seq_len = hyperparameters['eval_seq_len']
+    add_seq_len = 0 if use_mean_prediction else eval_seq_len
+    long_seq_len = seq_len + add_seq_len
+
+    if level_0_model:
+        styles = torch.ones(batch_size, 1, device=device, dtype=torch.long)
+    elif hyperparameters['sample_only_one_level']:
+        styles = torch.randint(level + 1, (1, 1), device=device).repeat(batch_size, 1)  # styles are sorted :)
+    else:
+        styles = torch.randint(level + 1, (batch_size,1), device=device).sort(0).values # styles are sorted :)
+
+    predict_mean_distribution = None
+    if share_predict_mean_distribution:
+        max_used_level = max(styles)
+        # below code assumes epochs are base 0!
+        share_of_training = epoch / epochs_per_level
+        #print(share_of_training, (max_used_level + 1. - share_predict_mean_distribution), max_used_level, level, epoch)
+        predict_mean_distribution = (share_of_training >= (max_used_level + 1. - share_predict_mean_distribution)) and (max_used_level < hyperparameters['max_level'])
+
+    x, y, targets = [], [], []
+
+    for considered_level in range(level+1):
+        num_elements = (styles == considered_level).sum()
+        if not num_elements:
+            continue
+        returns: Batch = get_batch(batch_size=num_elements, seq_len=long_seq_len,
+                                   num_features=num_features, device=device,
+                                   hyperparameters=hyperparameters, model=model,
+                                   single_eval_pos=single_eval_pos, epoch=epoch,
+                                   **kwargs)
+        levels_x, levels_y, levels_targets = returns.x, returns.y, returns.target_y
+        assert not returns.other_filled_attributes(), f"Unexpected filled attributes: {returns.other_filled_attributes()}"
+
+        assert levels_y is levels_targets
+        levels_targets = levels_targets.clone()
+        if len(levels_y.shape) == 2:
+            levels_y = levels_y.unsqueeze(2)
+            levels_targets = levels_targets.unsqueeze(2)
+        if considered_level > 0:
+
+            feed_x = levels_x[:single_eval_pos + 1 + add_seq_len].repeat(1, num_evals, 1)
+            feed_x[single_eval_pos, :] = levels_x[single_eval_pos:seq_len].reshape(-1, *levels_x.shape[2:])
+            if not use_mean_prediction:
+                feed_x[single_eval_pos + 1:] = levels_x[seq_len:].repeat(1, num_evals, 1)
+
+            feed_y = levels_y[:single_eval_pos + 1 + add_seq_len].repeat(1, num_evals, 1)
+            feed_y[single_eval_pos, :] = levels_y[single_eval_pos:seq_len].reshape(-1, *levels_y.shape[2:])
+            if not use_mean_prediction:
+                feed_y[single_eval_pos + 1:] = levels_y[seq_len:].repeat(1, num_evals, 1)
+
+            model.eval()
+            means = []
+            for feed_x_b, feed_y_b in zip(torch.split(feed_x, hyperparameters['inf_batch_size'], dim=1),
+                                          torch.split(feed_y, hyperparameters['inf_batch_size'], dim=1)):
+                with torch.cuda.amp.autocast():
+                    style = torch.zeros(feed_x_b.shape[1], 1, dtype=torch.int64, device=device) + considered_level - 1
+                    if level_0_model is not None and level_0_model.style_encoder is None:
+                        style = None
+                    out = model(
+                        (style, feed_x_b, feed_y_b),
+                        single_eval_pos=single_eval_pos+1, only_return_standard_out=False
+                    )
+                    if isinstance(out, tuple):
+                        output, once_output = out
+                    else:
+                        output = out
+                        once_output = {}
+
+                if once_output and 'mean_prediction' in once_output:
+                    mean_pred_logits = once_output['mean_prediction'].float()
+                    assert tuple(mean_pred_logits.shape) == (feed_x_b.shape[1], model.criterion.num_bars),\
+                        f"{tuple(mean_pred_logits.shape)} vs {(feed_x_b.shape[1], model.criterion.num_bars)}"
+                    means.append(model.criterion.icdf(mean_pred_logits, 1.-1./eval_seq_len))
+                else:
+                    logits = output['standard'].float()
+                    means.append(model.criterion.mean(logits).max(0).values)
+            means = torch.cat(means, 0)
+            levels_targets_new = means.view(seq_len-single_eval_pos, *levels_y.shape[1:])
+            levels_targets[single_eval_pos:seq_len] = levels_targets_new #- levels_targets_new.mean(0)
+            model.train()
+
+        levels_x = levels_x[:seq_len]
+        levels_y = levels_y[:seq_len]
+        levels_targets = levels_targets[:seq_len]
+
+        x.append(levels_x)
+        y.append(levels_y)
+        targets.append(levels_targets)
+
+    x = torch.cat(x, 1)
+    # if predict_mean_distribution: print(f'predict mean dist in b, {epoch=}, {max_used_level=}')
+    return Batch(x=x, y=torch.cat(y, 1), target_y=torch.cat(targets, 1), style=styles,
+                 mean_prediction=predict_mean_distribution.item() if predict_mean_distribution is not None else None)
+
+
+
+
+
+
+
+
+
+
diff --git a/private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py b/private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py
new file mode 100644
index 0000000..d1bc349
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py
@@ -0,0 +1,59 @@
+import torch
+
+from ..utils import default_device
+
+
+@torch.no_grad()
+def get_batch(batch_size, seq_len, num_features, get_batch, model, single_eval_pos, epoch, device=default_device, hyperparameters={}, **kwargs):
+    if hyperparameters.get('normalize_x', False):
+        uniform_float = torch.rand(tuple()).clamp(.1,1.).item()
+        new_hyperparameters = {**hyperparameters, 'sampling': uniform_float * hyperparameters['sampling']}
+    else:
+        new_hyperparameters = hyperparameters
+    returns = get_batch(batch_size=batch_size, seq_len=seq_len,
+                              num_features=num_features, device=device,
+                              hyperparameters=new_hyperparameters, model=model,
+                              single_eval_pos=single_eval_pos, epoch=epoch, **kwargs)
+
+    style = []
+
+    if normalize_x_mode := hyperparameters.get('normalize_x', False):
+        returns.x, mean_style, std_style = normalize_data_by_first_k(returns.x, single_eval_pos if normalize_x_mode == 'train' else len(returns.x))
+        if hyperparameters.get('style_includes_mean_from_normalization', True) or normalize_x_mode == 'train':
+             style.append(mean_style)
+        style.append(std_style)
+
+    if hyperparameters.get('normalize_y', False):
+        returns.y, mean_style, std_style = normalize_data_by_first_k(returns.y, single_eval_pos)
+        style += [mean_style, std_style]
+
+    returns.style = torch.cat(style,1) if style else None
+    return returns
+
+
+def normalize_data_by_first_k(x, k):
+    # x has shape seq_len, batch_size, num_features or seq_len, num_features
+    # k is the number of elements to normalize by
+    unsqueezed_x = False
+    if len(x.shape) == 2:
+        x.unsqueeze_(2)
+        unsqueezed_x = True
+
+    if k > 1:
+        relevant_x = x[:k]
+        mean_style = relevant_x.mean(0)
+        std_style = relevant_x.std(0)
+        x = (x - relevant_x.mean(0, keepdim=True)) / relevant_x.std(0, keepdim=True)
+    elif k == 1:
+        mean_style = x[0]
+        std_style = torch.ones_like(x[0])
+        x = (x - x[0])
+    else: # it is 0
+        mean_style = torch.zeros_like(x[0])
+        std_style = torch.ones_like(x[0])
+
+    if unsqueezed_x:
+        x.squeeze_(2)
+
+    return x, mean_style, std_style
+
diff --git a/private_multitask_pfn/PFNs/pfns/priors/prior.py b/private_multitask_pfn/PFNs/pfns/priors/prior.py
new file mode 100644
index 0000000..2fbc123
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/prior.py
@@ -0,0 +1,99 @@
+from abc import ABCMeta, abstractmethod
+from typing import Set, Optional
+from dataclasses import dataclass, fields
+import torch
+from torch.utils.data import DataLoader
+
+@dataclass
+class Batch:
+    """
+    A batch of data, with non-optional x, y, and target_y attributes.
+    All other attributes are optional.
+
+    If you want to add an attribute for testing only, you can just assign it after creation like:
+    ```
+        batch = Batch(x=x, y=y, target_y=target_y)
+        batch.test_attribute = test_attribute
+    ```
+    """
+    # Required entries
+    x: torch.Tensor
+    y: torch.Tensor
+    target_y: torch.Tensor
+    task_id: Optional[torch.Tensor] = None
+
+    # Optional Batch Entries
+    style: Optional[torch.Tensor] = None
+    style_hyperparameter_values: Optional[torch.Tensor] = None
+    single_eval_pos: Optional[torch.Tensor] = None
+    causal_model_dag: Optional[object] = None
+    mean_prediction: Optional[bool] = None  # this controls whether to do mean prediction in bar_distribution for nonmyopic BO
+
+    def other_filled_attributes(self, set_of_attributes: Set[str] = frozenset(('x', 'y', 'target_y'))):
+        return [f.name for f in fields(self)
+                if f.name not in set_of_attributes and
+                getattr(self, f.name) is not None]
+
+
+def safe_merge_batches_in_batch_dim(*batches, ignore_attributes=[]):
+    """
+    Merge all supported non-None fields in a pre-specified (general) way,
+    e.g. mutliple batch.x are concatenated in the batch dimension.
+    :param ignore_attributes: attributes to remove from the merged batch, treated as if they were None.
+    :return:
+    """
+    not_none_fields = [f.name for f in fields(batches[0]) if f.name not in ignore_attributes and getattr(batches[0], f.name) is not None]
+    assert all([set(not_none_fields) == set([f.name for f in fields(b) if f.name not in ignore_attributes and getattr(b, f.name) is not None]) for b in batches]), 'All batches must have the same fields!'
+    merge_funcs = {
+        'x': lambda xs: torch.cat(xs, 1),
+        'y': lambda ys: torch.cat(ys, 1),
+        'target_y': lambda target_ys: torch.cat(target_ys, 1),
+        'style': lambda styles: torch.cat(styles, 0),
+    }
+    assert all(f in merge_funcs for f in not_none_fields), f'Unknown fields encountered in `safe_merge_batches_in_batch_dim`.'
+    return Batch(**{f: merge_funcs[f]([getattr(batch, f) for batch in batches]) for f in not_none_fields})
+
+
+def merge_batches(*batches, ignore_attributes=[]):
+    assert False, "TODO: isn't this broken!? because catting in dim 0 seems wrong!?"
+    def merge_attribute(attr_name, batch_sizes):
+        attr = [getattr(batch, attr_name) for batch in batches]
+        if type(attr[0]) is list:
+            def make_list(sublist, i):
+                if sublist is None:
+                    return [None for _ in range(batch_sizes[i])]
+                return sublist
+            return sum([make_list(sublist, i) for i, sublist in enumerate(attr)], [])
+        elif type(attr[0]) is torch.Tensor:
+            return torch.cat(attr, 0)
+        else:
+            assert all(a is None for a in attr), f'Unknown type encountered in `merge_batches`.'\
+                                                 f'To ignore this, please add `{attr}` to the `ignore_attributes`.'\
+                                                 f'The following values are the problem: {attr_name}.'
+            return None
+    batch_sizes = [batch.x.shape[0] for batch in batches]
+    return Batch(**{f.name: merge_attribute(f.name, batch_sizes) for f in fields(batches[0]) if f.name not in ignore_attributes})
+
+
+
+class PriorDataLoader(DataLoader, metaclass=ABCMeta):
+    @abstractmethod
+    def __init__(self, num_steps, batch_size, eval_pos_seq_len_sampler, seq_len_maximum, device, **kwargs):
+        """
+
+        :param num_steps: int, first argument, the number of steps to take per epoch, i.e. iteration of the DataLoader
+        :param batch_size: int, number of datasets per batch
+        :param eval_pos_seq_len_sampler: callable, it takes no arguments and returns a tuple (single eval pos, bptt)
+        :param kwargs: for future compatibility it is good to have a final all catch, as new kwargs might be introduced
+        """
+        pass
+
+    # A class or object variable `num_features`: int
+    # Optional: `validate` function that accepts a transformer model
+
+    # The DataLoader iter should return batches of the form ([style], x, y), target_y, single_eval_pos
+    # We follow sequence len (s) first, batch size (b) second. So x: (s,b,num_features), y,target_y: (s,b)
+    # and style: Optional[(b,num_style_params)], style can be omitted or set to None, if it is not intended to be used.
+
+    # For more references, see `priors/utils.py` for a pretty general implementation of a DataLoader
+    # and `train.py` for the only call of it.
diff --git a/private_multitask_pfn/PFNs/pfns/priors/prior_bag.py b/private_multitask_pfn/PFNs/pfns/priors/prior_bag.py
new file mode 100644
index 0000000..6d954b6
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/prior_bag.py
@@ -0,0 +1,45 @@
+from typing import List
+import torch
+
+import utils
+from .prior import Batch
+from .utils import get_batch_to_dataloader
+from ..utils import default_device
+
+def get_batch(batch_size, seq_len, num_features, device=default_device
+              , hyperparameters=None, batch_size_per_gp_sample=None, **kwargs):
+    batch_size_per_gp_sample = batch_size_per_gp_sample or (min(64, batch_size))
+    num_models = batch_size // batch_size_per_gp_sample
+    assert num_models * batch_size_per_gp_sample == batch_size, f'Batch size ({batch_size}) not divisible by batch_size_per_gp_sample ({batch_size_per_gp_sample})'
+
+    args = {'device': device,
+            'seq_len': seq_len,
+            'num_features': num_features,
+            'batch_size': batch_size_per_gp_sample}
+
+    prior_bag_priors_get_batch = hyperparameters['prior_bag_get_batch']
+    prior_bag_priors_p = [1.0] + [hyperparameters[f'prior_bag_exp_weights_{i}'] for i in range(1, len(prior_bag_priors_get_batch))]
+
+    weights = torch.tensor(prior_bag_priors_p, dtype=torch.float)  # create a tensor of weights
+    batch_assignments = torch.multinomial(torch.softmax(weights, 0), num_models, replacement=True).numpy()
+
+    if 'verbose' in hyperparameters and hyperparameters['verbose']:
+        print('PRIOR_BAG:', weights, batch_assignments, num_models, batch_size_per_gp_sample, batch_size)
+
+    sample: List[Batch] = \
+        [prior_bag_priors_get_batch[int(prior_idx)](hyperparameters=hyperparameters, **args, **kwargs) for prior_idx in batch_assignments]
+
+    def merge(sample, k):
+        x = [getattr(x_,k) for x_ in sample]
+        if torch.is_tensor(x[0]):
+            return torch.cat(x, 1).detach()
+        else:
+            return [*x]
+    utils.print_once('prior bag, merging attributes', [s.other_filled_attributes([]) for s in sample])
+    sample = {k: merge(sample, k) for k in sample[0].other_filled_attributes([])}
+    if hyperparameters.get('verbose'):
+        print({k: v.shape for k,v in sample.items()})
+
+    return Batch(**sample)
+
+DataLoader = get_batch_to_dataloader(get_batch)
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/priors/simple_mlp.py b/private_multitask_pfn/PFNs/pfns/priors/simple_mlp.py
new file mode 100644
index 0000000..0cd4d55
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/simple_mlp.py
@@ -0,0 +1,126 @@
+import random
+import math
+
+import torch
+from torch import nn
+import time
+import numpy as np
+
+from ..utils import default_device
+from .prior import Batch
+from .utils import get_batch_to_dataloader
+
+
+class MLP(torch.nn.Module):
+    def __init__(self, num_inputs, num_layers, num_hidden, num_outputs, init_std=None, sparseness=0.0,
+                 preactivation_noise_std=0.0, activation='tanh'):
+        super(MLP, self).__init__()
+        self.linears = nn.ModuleList(
+            [nn.Linear(num_inputs, num_hidden)] + \
+            [nn.Linear(num_hidden,num_hidden) for _ in range(num_layers-2)] + \
+            [nn.Linear(num_hidden,num_outputs)]
+        )
+
+        self.init_std = init_std
+        self.sparseness = sparseness
+        self.reset_parameters()
+
+        self.preactivation_noise_std = preactivation_noise_std
+        self.activation = {
+            'tanh': torch.nn.Tanh(),
+            'relu': torch.nn.ReLU(),
+            'elu': torch.nn.ELU(),
+            'identity': torch.nn.Identity(),
+        }[activation]
+
+    def reset_parameters(self, init_std=None, sparseness=None):
+        init_std = init_std if init_std is not None else self.init_std
+        sparseness = sparseness if sparseness is not None else self.sparseness
+        for linear in self.linears:
+            linear.reset_parameters()
+
+        with torch.no_grad():
+            if init_std is not None:
+                for linear in self.linears:
+                    linear.weight.normal_(0, init_std)
+                    linear.bias.normal_(0, init_std)
+
+            if sparseness > 0.0:
+                for linear in self.linears[1:-1]:
+                    linear.weight /= (1. - sparseness) ** (1 / 2)
+                    linear.weight *= torch.bernoulli(torch.ones_like(linear.weight) * (1. - sparseness))
+
+    def forward(self, x):
+        for linear in self.linears[:-1]:
+            x = linear(x)
+            x = x + torch.randn_like(x) * self.preactivation_noise_std
+            x = self.activation(x)
+        x = self.linears[-1](x)
+        return x
+
+
+def sample_input(input_sampling_setting, batch_size, seq_len, num_features, device=default_device):
+    if input_sampling_setting == 'normal':
+        x = torch.randn(batch_size, seq_len, num_features, device=device)
+        x_for_mlp = x
+    elif input_sampling_setting == 'uniform':
+        x = torch.rand(batch_size, seq_len, num_features, device=device)
+        x_for_mlp = (x - .5)/math.sqrt(1/12)
+    else:
+        raise ValueError(f"Unknown input_sampling: {input_sampling_setting}")
+    return x, x_for_mlp
+
+
+@torch.no_grad()
+def get_batch(batch_size, seq_len, num_features, hyperparameters, device=default_device, num_outputs=1, **kwargs):
+    if hyperparameters is None:
+        hyperparameters = {
+            'mlp_num_layers': 2,
+            'mlp_num_hidden': 64,
+            'mlp_init_std': 0.1,
+            'mlp_sparseness': 0.2,
+            'mlp_input_sampling': 'normal',
+            'mlp_output_noise': 0.0,
+            'mlp_noisy_targets': False,
+            'mlp_preactivation_noise_std': 0.0,
+        }
+
+    x, x_for_mlp = sample_input(hyperparameters.get('mlp_input_sampling', 'normal'), batch_size, seq_len, num_features,
+                     device=device)
+
+    model = MLP(num_features, hyperparameters['mlp_num_layers'], hyperparameters['mlp_num_hidden'],
+                num_outputs, hyperparameters['mlp_init_std'], hyperparameters['mlp_sparseness'],
+                hyperparameters['mlp_preactivation_noise_std'], hyperparameters.get('activation', 'tanh')).to(device)
+
+    no_noise_model = MLP(num_features, hyperparameters['mlp_num_layers'], hyperparameters['mlp_num_hidden'],
+                num_outputs, hyperparameters['mlp_init_std'], hyperparameters['mlp_sparseness'],
+                0., hyperparameters.get('activation', 'tanh')).to(device)
+
+    ys = []
+    targets = []
+    for x_ in x_for_mlp:
+        model.reset_parameters()
+        y = model(x_ / math.sqrt(num_features))
+        ys.append(y.unsqueeze(1))
+        if not hyperparameters.get('mlp_preactivation_noise_in_targets', True):
+            assert not hyperparameters['mlp_noisy_targets']
+            no_noise_model.load_state_dict(model.state_dict())
+            target = no_noise_model(x_ / math.sqrt(num_features))
+            targets.append(target.unsqueeze(1))
+
+    y = torch.cat(ys, dim=1)
+    targets = torch.cat(targets, dim=1) if targets else y
+
+    noisy_y = y + torch.randn_like(y) * hyperparameters['mlp_output_noise']
+
+    #return x.transpose(0, 1), noisy_y, (noisy_y if hyperparameters['mlp_noisy_targets'] else targets)
+    return Batch(x.transpose(0, 1), noisy_y, (noisy_y if hyperparameters['mlp_noisy_targets'] else targets))
+
+DataLoader = get_batch_to_dataloader(get_batch)
+
+
+
+
+
+
+
diff --git a/private_multitask_pfn/PFNs/pfns/priors/utils.py b/private_multitask_pfn/PFNs/pfns/priors/utils.py
new file mode 100644
index 0000000..0504042
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/priors/utils.py
@@ -0,0 +1,438 @@
+import inspect
+import math
+import random
+import time
+import types
+from functools import partial
+
+import matplotlib.gridspec as gridspec
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.stats as stats
+
+import torch
+from torch import nn
+
+from ..utils import normalize_data, set_locals_in_self
+from .prior import Batch, PriorDataLoader
+
+
+def get_batch_to_dataloader(get_batch_method_):
+    # DL = partial(DL, get_batch_method=get_batch_method_)
+    class DL(PriorDataLoader):
+        get_batch_method = get_batch_method_
+
+        # Caution, you might need to set self.num_features manually if it is not part of the args.
+        def __init__(self, num_steps, **get_batch_kwargs):
+            set_locals_in_self(locals())
+
+            # The stuff outside the or is set as class attribute before instantiation.
+            self.num_features = (
+                get_batch_kwargs.get("num_features") or self.num_features
+            )
+            self.num_tasks = get_batch_kwargs.get("hyperparameters")["num_tasks"]
+            self.epoch_count = 0
+            # print("DataLoader.__dict__", self.__dict__)
+
+        @staticmethod
+        def gbm(*args, eval_pos_seq_len_sampler, **kwargs):
+            epoch = kwargs["epoch"]
+            model = kwargs["model"]
+            kwargs["single_eval_pos"], kwargs["seq_len"] = eval_pos_seq_len_sampler(epoch, model)
+            
+            batch: Batch = get_batch_method_(*args, **kwargs)
+            if batch.single_eval_pos is None:
+                batch.single_eval_pos = kwargs["single_eval_pos"]
+            return batch
+
+        def __len__(self):
+            return self.num_steps
+
+        def get_test_batch(self, **kwargs):  # does not increase epoch_count
+            return self.gbm(
+                **self.get_batch_kwargs,
+                epoch=self.epoch_count,
+                model=self.model if hasattr(self, "model") else None,
+                **kwargs,
+            )
+
+        def __iter__(self):
+            assert hasattr(
+                self, "model"
+            ), "Please assign model with `dl.model = ...` before training."
+            self.epoch_count += 1
+            return iter(
+                self.gbm(
+                    **self.get_batch_kwargs,
+                    epoch=self.epoch_count - 1,
+                    model=self.model,
+                )
+                for _ in range(self.num_steps)
+            )
+
+    return DL
+
+
+def plot_features(data, targets, fig=None, categorical=True, plot_diagonal=True):
+    import seaborn as sns
+
+    if torch.is_tensor(data):
+        data = data.detach().cpu().numpy()
+        targets = targets.detach().cpu().numpy()
+
+    fig2 = fig if fig else plt.figure(figsize=(8, 8))
+    spec2 = gridspec.GridSpec(ncols=data.shape[1], nrows=data.shape[1], figure=fig2)
+    for d in range(0, data.shape[1]):
+        for d2 in range(0, data.shape[1]):
+            if d > d2:
+                continue
+            sub_ax = fig2.add_subplot(spec2[d, d2])
+            sub_ax.set_xticks([])
+            sub_ax.set_yticks([])
+            if d == d2:
+                if plot_diagonal:
+                    if categorical:
+                        sns.histplot(
+                            data[:, d],
+                            hue=targets[:],
+                            ax=sub_ax,
+                            legend=False,
+                            palette="deep",
+                        )
+                    else:
+                        sns.histplot(data[:, d], ax=sub_ax, legend=False)
+                sub_ax.set(ylabel=None)
+            else:
+                if categorical:
+                    sns.scatterplot(
+                        x=data[:, d],
+                        y=data[:, d2],
+                        hue=targets[:],
+                        legend=False,
+                        palette="deep",
+                    )
+                else:
+                    sns.scatterplot(
+                        x=data[:, d], y=data[:, d2], hue=targets[:], legend=False
+                    )
+                # plt.scatter(data[:, d], data[:, d2],
+                #               c=targets[:])
+            # sub_ax.get_xaxis().set_ticks([])
+            # sub_ax.get_yaxis().set_ticks([])
+    plt.subplots_adjust(wspace=0.05, hspace=0.05)
+    fig2.show()
+
+
+def plot_prior(prior, samples=1000, buckets=50):
+    s = np.array([prior() for _ in range(0, samples)])
+    count, bins, ignored = plt.hist(s, buckets, density=True)
+    print(s.min())
+    plt.show()
+
+
+trunc_norm_sampler_f = lambda mu, sigma: lambda: stats.truncnorm(
+    (0 - mu) / sigma, (1000000 - mu) / sigma, loc=mu, scale=sigma
+).rvs(1)[0]
+beta_sampler_f = lambda a, b: lambda: np.random.beta(a, b)
+gamma_sampler_f = lambda a, b: lambda: np.random.gamma(a, b)
+uniform_sampler_f = lambda a, b: lambda: np.random.uniform(a, b)
+uniform_int_sampler_f = lambda a, b: lambda: round(np.random.uniform(a, b))
+
+
+def zipf_sampler_f(a, b, c):
+    x = np.arange(b, c)
+    weights = x ** (-a)
+    weights /= weights.sum()
+    return lambda: stats.rv_discrete(name="bounded_zipf", values=(x, weights)).rvs(1)
+
+
+scaled_beta_sampler_f = lambda a, b, scale, minimum: lambda: minimum + round(
+    beta_sampler_f(a, b)() * (scale - minimum)
+)
+
+
+def normalize_by_used_features_f(
+    x, num_features_used, num_features, normalize_with_sqrt=False
+):
+    if normalize_with_sqrt:
+        return x / (num_features_used / num_features) ** (1 / 2)
+    return x / (num_features_used / num_features)
+
+
+def order_by_y(x, y):
+    order = torch.argsort(y if random.randint(0, 1) else -y, dim=0)[:, 0, 0]
+    order = order.reshape(2, -1).transpose(0, 1).reshape(-1)  # .reshape(seq_len)
+    x = x[
+        order
+    ]  # .reshape(2, -1).transpose(0, 1).reshape(-1).flip([0]).reshape(seq_len, 1, -1)
+    y = y[order]  # .reshape(2, -1).transpose(0, 1).reshape(-1).reshape(seq_len, 1, -1)
+
+    return x, y
+
+
+def randomize_classes(x, num_classes):
+    classes = torch.arange(0, num_classes, device=x.device)
+    random_classes = torch.randperm(num_classes, device=x.device).type(x.type())
+    x = ((x.unsqueeze(-1) == classes) * random_classes).sum(-1)
+    return x
+
+
+@torch.no_grad()
+def sample_num_features_get_batch(
+    batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
+):
+    if (
+        hyperparameters.get("sample_num_features", True) and kwargs.get("epoch", 0) > 0
+    ):  # don't sample on test batch
+        num_features = random.randint(1, num_features)
+    # print("features", num_features)
+
+    return get_batch(
+        batch_size=batch_size,
+        seq_len=seq_len,
+        num_features=num_features,
+        hyperparameters=hyperparameters,
+        **kwargs,
+    )
+
+
+# @torch.no_grad()
+# def set_num_tasks_get_batch(
+#     batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
+# ):
+#     max_num_tasks = hyperparameters["num_tasks"]
+#     if hyperparameters.get("sample_num_tasks", True) and kwargs.get("epoch", 0) > 0:
+#         num_tasks = random.randint(1, max_num_tasks)
+#     else:
+#         num_tasks = max_num_tasks
+#     # print("tasks", num_tasks, max_num_tasks)
+
+#     return get_batch(
+#         batch_size=batch_size,
+#         seq_len=seq_len,
+#         num_features=num_features,
+#         max_num_tasks=max_num_tasks,
+#         num_tasks=num_tasks,
+#         hyperparameters=hyperparameters,
+#         **kwargs,
+#     )
+
+
+# @torch.no_grad()
+# def set_lengthscale_get_batch(
+#     batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
+# ):
+#     lengthscale = hyperparameters.get("lengthscale", None)
+#     if not lengthscale:
+#         lengthscale = torch.distributions.Gamma(3.0, 6.0).sample().item()
+#     print("lengthscale", lengthscale)
+
+#     return get_batch(
+#         batch_size,
+#         seq_len,
+#         num_features,
+#         hyperparameters=hyperparameters,
+#         lengthscale=lengthscale,
+#         **kwargs,
+#     )
+
+
+@torch.no_grad()
+def set_properties_get_batch(
+    batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
+):
+    epoch = kwargs.get("epoch", 0)
+    if (
+        hyperparameters.get("sample_num_features", True) and epoch > 0
+    ):  # don't sample on test batch
+        num_features = random.randint(1, num_features)
+
+    max_num_tasks = hyperparameters.get("max_num_tasks", hyperparameters["num_tasks"])
+    
+    num_tasks = max_num_tasks
+    if epoch > 0:
+        if hyperparameters.get("sample_num_tasks_curriculum", False):
+            if epoch < 50:
+                num_tasks = 1
+            else:
+                # slowly increase the number of tasks over time
+                additional = (max_num_tasks - 1) * (epoch - 50) / 50
+                epoch_max_tasks = min(max_num_tasks, 1 + int(additional))
+                num_tasks = random.randint(1, epoch_max_tasks)
+        elif hyperparameters.get("sample_num_tasks", True) and epoch > 0:
+            num_tasks = random.randint(1, max_num_tasks)
+            
+
+    lengthscale = hyperparameters.get("lengthscale", None)
+    if not lengthscale:
+        lengthscale = torch.distributions.Gamma(3.0, 6.0).sample().item()
+
+    return get_batch(
+        batch_size,
+        seq_len,
+        num_features,
+        max_num_tasks=max_num_tasks,
+        num_tasks=num_tasks,
+        lengthscale=lengthscale,
+        hyperparameters=hyperparameters,
+        **kwargs,
+    )
+
+
+@torch.no_grad()
+class CategoricalActivation(nn.Module):
+    def __init__(
+        self,
+        categorical_p=0.1,
+        ordered_p=0.7,
+        keep_activation_size=False,
+        num_classes_sampler=zipf_sampler_f(0.8, 1, 10),
+    ):
+        self.categorical_p = categorical_p
+        self.ordered_p = ordered_p
+        self.keep_activation_size = keep_activation_size
+        self.num_classes_sampler = num_classes_sampler
+
+        super().__init__()
+
+    def forward(self, x):
+        # x shape: T, B, H
+
+        x = nn.Softsign()(x)
+
+        num_classes = self.num_classes_sampler()
+        hid_strength = (
+            torch.abs(x).mean(0).unsqueeze(0) if self.keep_activation_size else None
+        )
+
+        categorical_classes = torch.rand((x.shape[1], x.shape[2])) < self.categorical_p
+        class_boundaries = torch.zeros(
+            (num_classes - 1, x.shape[1], x.shape[2]), device=x.device, dtype=x.dtype
+        )
+        # Sample a different index for each hidden dimension, but shared for all batches
+        for b in range(x.shape[1]):
+            for h in range(x.shape[2]):
+                ind = torch.randint(0, x.shape[0], (num_classes - 1,))
+                class_boundaries[:, b, h] = x[ind, b, h]
+
+        for b in range(x.shape[1]):
+            x_rel = x[:, b, categorical_classes[b]]
+            boundaries_rel = class_boundaries[:, b, categorical_classes[b]].unsqueeze(1)
+            x[:, b, categorical_classes[b]] = (x_rel > boundaries_rel).sum(
+                dim=0
+            ).float() - num_classes / 2
+
+        ordered_classes = torch.rand((x.shape[1], x.shape[2])) < self.ordered_p
+        ordered_classes = torch.logical_and(ordered_classes, categorical_classes)
+        x[:, ordered_classes] = randomize_classes(x[:, ordered_classes], num_classes)
+
+        x = x * hid_strength if self.keep_activation_size else x
+
+        return x
+
+
+class QuantizationActivation(torch.nn.Module):
+    def __init__(self, n_thresholds, reorder_p=0.5) -> None:
+        super().__init__()
+        self.n_thresholds = n_thresholds
+        self.reorder_p = reorder_p
+        self.thresholds = torch.nn.Parameter(torch.randn(self.n_thresholds))
+
+    def forward(self, x):
+        x = normalize_data(x).unsqueeze(-1)
+        x = (x > self.thresholds).sum(-1)
+
+        if random.random() < self.reorder_p:
+            x = randomize_classes(x.unsqueeze(-1), self.n_thresholds).squeeze(-1)
+        # x = ((x.float() - self.n_thresholds/2) / self.n_thresholds)# * data_std + data_mean
+        x = normalize_data(x)
+        return x
+
+
+class NormalizationActivation(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, x):
+        x = normalize_data(x)
+        return x
+
+
+class PowerActivation(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        # self.exp = torch.nn.Parameter(0.5 * torch.ones(1))
+        self.shared_exp_strength = 0.5
+        # TODO: Somehow this is only initialized once, so it's the same for all runs
+
+    def forward(self, x):
+        # print(torch.nn.functional.softplus(x), self.exp)
+        shared_exp = torch.randn(1)
+        exp = torch.nn.Parameter(
+            (
+                shared_exp * self.shared_exp_strength
+                + shared_exp * torch.randn(x.shape[-1]) * (1 - self.shared_exp_strength)
+            )
+            * 2
+            + 0.5
+        ).to(x.device)
+        x_ = torch.pow(torch.nn.functional.softplus(x) + 0.001, exp)
+        if False:
+            print(
+                x[0:3, 0, 0].cpu().numpy(),
+                torch.nn.functional.softplus(x[0:3, 0, 0]).cpu().numpy(),
+                x_[0:3, 0, 0].cpu().numpy(),
+                normalize_data(x_)[0:3, 0, 0].cpu().numpy(),
+                self.exp.cpu().numpy(),
+            )
+        return x_
+
+
+def lambda_time(f, name="", enabled=True):
+    if not enabled:
+        return f()
+    start = time.time()
+    r = f()
+    print("Timing", name, time.time() - start)
+    return r
+
+
+def pretty_get_batch(get_batch):
+    """
+    Genereate string representation of get_batch function
+    :param get_batch:
+    :return:
+    """
+    if isinstance(get_batch, types.FunctionType):
+        return f"<{get_batch.__module__}.{get_batch.__name__} {inspect.signature(get_batch)}"
+    else:
+        return repr(get_batch)
+
+
+class get_batch_sequence(list):
+    """
+    This will call the get_batch_methods in order from the back and pass the previous as `get_batch` kwarg.
+    For example for `get_batch_methods=[get_batch_1, get_batch_2, get_batch_3]` this will produce a call
+    equivalent to `get_batch_3(*args,get_batch=partial(partial(get_batch_2),get_batch=get_batch_1,**kwargs))`.
+    get_batch_methods: all priors, but the first, muste have a `get_batch` argument
+    """
+
+    def __init__(self, *get_batch_methods):
+        if len(get_batch_methods) == 0:
+            raise ValueError("Must have at least one get_batch method")
+        super().__init__(get_batch_methods)
+
+    def __repr__(self):
+        s = ",\n\t".join([f"{pretty_get_batch(get_batch)}" for get_batch in self])
+        return f"get_batch_sequence(\n\t{s}\n)"
+
+    def __call__(self, *args, **kwargs):
+        """
+
+        Standard kwargs are: batch_size, seq_len, num_features
+        This returns a priors.Batch object.
+        """
+        final_get_batch = self[0]
+        for get_batch in self[1:]:
+            final_get_batch = partial(get_batch, get_batch=final_get_batch)
+        return final_get_batch(*args, **kwargs)
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/acquisition_functions.py b/private_multitask_pfn/PFNs/pfns/scripts/acquisition_functions.py
new file mode 100644
index 0000000..9fae1eb
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/scripts/acquisition_functions.py
@@ -0,0 +1,506 @@
+import contextlib
+
+from .. import transformer
+from .. import bar_distribution
+import torch
+import scipy
+import math
+from sklearn.preprocessing import power_transform, PowerTransformer
+
+def log01(x, eps=.0000001, input_between_zero_and_one=False):
+    logx = torch.log(x + eps)
+    if input_between_zero_and_one:
+        return (logx - math.log(eps)) / (math.log(1 + eps) - math.log(eps))
+    return (logx - logx.min(0)[0]) / (logx.max(0)[0] - logx.min(0)[0])
+
+def log01_batch(x, eps=.0000001, input_between_zero_and_one=False):
+    x = x.repeat(1, x.shape[-1] + 1, 1)
+    for b in range(x.shape[-1]):
+        x[:, b, b] = log01(x[:, b, b], eps=eps, input_between_zero_and_one=input_between_zero_and_one)
+    return x
+
+def lognormed_batch(x, eval_pos, eps=.0000001):
+    x = x.repeat(1, x.shape[-1] + 1, 1)
+    for b in range(x.shape[-1]):
+        logx = torch.log(x[:, b, b]+eps)
+        x[:, b, b] = (logx - logx[:eval_pos].mean(0))/logx[:eval_pos].std(0)
+    return x
+
+def _rank_transform(x_train, x):
+    assert len(x_train.shape) == len(x.shape) == 1
+    relative_to = torch.cat((torch.zeros_like(x_train[:1]),x_train.unique(sorted=True,), torch.ones_like(x_train[-1:])),-1)
+    higher_comparison = (relative_to < x[...,None]).sum(-1).clamp(min=1)
+    pos_inside_interval = (x - relative_to[higher_comparison-1])/(relative_to[higher_comparison] - relative_to[higher_comparison-1])
+    x_transformed = higher_comparison - 1 + pos_inside_interval
+    return x_transformed/(len(relative_to)-1.)
+
+def rank_transform(x_train, x):
+    assert x.shape[1] == x_train.shape[1], f"{x.shape=} and {x_train.shape=}"
+    # make sure everything is between 0 and 1
+    assert (x_train >= 0.).all() and (x_train <= 1.).all(), f"{x_train=}"
+    assert (x >= 0.).all() and (x <= 1.).all(), f"{x=}"
+    return_x = x.clone()
+    for feature_dim in range(x.shape[1]):
+        return_x[:, feature_dim] = _rank_transform(x_train[:, feature_dim], x[:, feature_dim])
+    return return_x
+
+
+
+def general_power_transform(x_train, x_apply, eps, less_safe=False):
+    if eps > 0:
+        try:
+            pt = PowerTransformer(method='box-cox')
+            pt.fit(x_train.cpu()+eps)
+            x_out = torch.tensor(pt.transform(x_apply.cpu()+eps), dtype=x_apply.dtype, device=x_apply.device)
+        except ValueError as e:
+            print(e)
+            x_out = x_apply - x_train.mean(0)
+    else:
+        pt = PowerTransformer(method='yeo-johnson')
+        if not less_safe and (x_train.std() > 1_000 or x_train.mean().abs() > 1_000):
+            x_apply = (x_apply - x_train.mean(0)) / x_train.std(0)
+            x_train = (x_train - x_train.mean(0)) / x_train.std(0)
+            print('inputs are LAARGEe, normalizing them')
+        try:
+            pt.fit(x_train.cpu().double())
+        except ValueError as e:
+            print('caught this errrr', e)
+            if less_safe:
+                x_train = (x_train - x_train.mean(0)) / x_train.std(0)
+                x_apply = (x_apply - x_train.mean(0)) / x_train.std(0)
+            else:
+                x_train = x_train - x_train.mean(0)
+                x_apply = x_apply - x_train.mean(0)
+            pt.fit(x_train.cpu().double())
+        x_out = torch.tensor(pt.transform(x_apply.cpu()), dtype=x_apply.dtype, device=x_apply.device)
+    if torch.isnan(x_out).any() or torch.isinf(x_out).any():
+        print('WARNING: power transform failed')
+        print(f"{x_train=} and {x_apply=}")
+        x_out = x_apply - x_train.mean(0)
+    return x_out
+
+
+
+#@torch.inference_mode()
+def general_acq_function(model: transformer.TransformerModel, x_given, y_given, x_eval, apply_power_transform=True,
+                    rand_sample=False, znormalize=False, pre_normalize=False, pre_znormalize=False, predicted_mean_fbest=False,
+                    input_znormalize=False, max_dataset_size=10_000, remove_features_with_one_value_only=False,
+                    return_actual_ei=False, acq_function='ei', ucb_rest_prob=.05, ensemble_log_dims=False,
+                    ensemble_type='mean_probs', # in ('mean_probs', 'max_acq')
+                    input_power_transform=False, power_transform_eps=.0, input_power_transform_eps=.0,
+                    input_rank_transform=False, ensemble_input_rank_transform=False,
+                    ensemble_power_transform=False, ensemble_feature_rotation=False,
+                    style=None, outlier_stretching_interval=0.0, verbose=False, unsafe_power_transform=False,
+                         ):
+    """
+    Differences to HEBO:
+        - The noise can't be set in the same way, as it depends on the tuning of HPs via VI.
+        - Log EI and PI are always used directly instead of using the approximation.
+
+    This is a stochastic function, relying on torch.randn
+
+    :param model:
+    :param x_given: torch.Tensor of shape (N, D)
+    :param y_given: torch.Tensor of shape (N, 1) or (N,)
+    :param x_eval: torch.Tensor of shape (M, D)
+    :param kappa:
+    :param eps:
+    :return:
+    """
+    assert ensemble_type in ('mean_probs', 'max_acq')
+    if rand_sample is not False \
+        and (len(x_given) == 0 or
+             ((1 + x_given.shape[1] if rand_sample is None else max(2, rand_sample)) > x_given.shape[0])):
+        print('rando')
+        return torch.zeros_like(x_eval[:,0]) #torch.randperm(x_eval.shape[0])[0]
+    y_given = y_given.reshape(-1)
+    assert len(y_given) == len(x_given)
+    if apply_power_transform:
+        if pre_normalize:
+            y_normed = y_given / y_given.std()
+            if not torch.isinf(y_normed).any() and not torch.isnan(y_normed).any():
+                y_given = y_normed
+        elif pre_znormalize:
+            y_znormed = (y_given - y_given.mean()) / y_given.std()
+            if not torch.isinf(y_znormed).any() and not torch.isnan(y_znormed).any():
+                y_given = y_znormed
+        y_given = general_power_transform(y_given.unsqueeze(1), y_given.unsqueeze(1), power_transform_eps, less_safe=unsafe_power_transform).squeeze(1)
+        if verbose:
+            print(f"{y_given=}")
+        #y_given = torch.tensor(power_transform(y_given.cpu().unsqueeze(1), method='yeo-johnson', standardize=znormalize), device=y_given.device, dtype=y_given.dtype,).squeeze(1)
+    y_given_std = torch.tensor(1., device=y_given.device, dtype=y_given.dtype)
+    if znormalize and not apply_power_transform:
+        if len(y_given) > 1:
+            y_given_std = y_given.std()
+        y_given_mean = y_given.mean()
+        y_given = (y_given - y_given_mean) / y_given_std
+
+    if remove_features_with_one_value_only:
+        x_all = torch.cat([x_given, x_eval], dim=0)
+        only_one_value_feature = torch.tensor([len(torch.unique(x_all[:,i])) for i in range(x_all.shape[1])]) == 1
+        x_given = x_given[:,~only_one_value_feature]
+        x_eval = x_eval[:,~only_one_value_feature]
+
+    if outlier_stretching_interval > 0.:
+        tx = torch.cat([x_given, x_eval], dim=0)
+        m = outlier_stretching_interval
+        eps = 1e-10
+        small_values = (tx < m) & (tx > 0.)
+        tx[small_values] = m * (torch.log(tx[small_values] + eps) - math.log(eps)) / (math.log(m + eps) - math.log(eps))
+
+        large_values = (tx > 1. - m) & (tx < 1.)
+        tx[large_values] = 1. - m * (torch.log(1 - tx[large_values] + eps) - math.log(eps)) / (
+                    math.log(m + eps) - math.log(eps))
+        x_given = tx[:len(x_given)]
+        x_eval = tx[len(x_given):]
+
+    if input_znormalize: # implementation that relies on the test set, too...
+        std = x_given.std(dim=0)
+        std[std == 0.] = 1.
+        mean = x_given.mean(dim=0)
+        x_given = (x_given - mean) / std
+        x_eval = (x_eval - mean) / std
+
+    if input_power_transform:
+        x_given = general_power_transform(x_given, x_given, input_power_transform_eps)
+        x_eval = general_power_transform(x_given, x_eval, input_power_transform_eps)
+
+    if input_rank_transform is True or input_rank_transform == 'full': # uses test set x statistics...
+        x_all = torch.cat((x_given,x_eval), dim=0)
+        for feature_dim in range(x_all.shape[-1]):
+            uniques = torch.sort(torch.unique(x_all[..., feature_dim])).values
+            x_eval[...,feature_dim] = torch.searchsorted(uniques,x_eval[..., feature_dim]).float() / (len(uniques)-1)
+            x_given[...,feature_dim] = torch.searchsorted(uniques,x_given[..., feature_dim]).float() / (len(uniques)-1)
+    elif input_rank_transform is False:
+        pass
+    elif input_rank_transform == 'train':
+        x_given = rank_transform(x_given, x_given)
+        x_eval = rank_transform(x_given, x_eval)
+    elif input_rank_transform.startswith('train'):
+        likelihood = float(input_rank_transform.split('_')[-1])
+        if torch.rand(1).item() < likelihood:
+            print('rank transform')
+            x_given = rank_transform(x_given, x_given)
+            x_eval = rank_transform(x_given, x_eval)
+    else:
+        raise NotImplementedError
+
+
+    # compute logits
+    criterion: bar_distribution.BarDistribution = model.criterion
+    x_predict = torch.cat([x_given, x_eval], dim=0)
+
+
+    logits_list = []
+    for x_feed in torch.split(x_predict, max_dataset_size, dim=0):
+        x_full_feed = torch.cat([x_given, x_feed], dim=0).unsqueeze(1)
+        y_full_feed = y_given.unsqueeze(1)
+        if ensemble_log_dims == '01':
+            x_full_feed = log01_batch(x_full_feed)
+        elif ensemble_log_dims == 'global01' or ensemble_log_dims is True:
+            x_full_feed = log01_batch(x_full_feed, input_between_zero_and_one=True)
+        elif ensemble_log_dims == '01-10':
+            x_full_feed = torch.cat((log01_batch(x_full_feed)[:, :-1], log01_batch(1. - x_full_feed)), 1)
+        elif ensemble_log_dims == 'norm':
+            x_full_feed = lognormed_batch(x_full_feed, len(x_given))
+        elif ensemble_log_dims is not False:
+            raise NotImplementedError
+
+        if ensemble_feature_rotation:
+            x_full_feed = torch.cat([x_full_feed[:, :, (i+torch.arange(x_full_feed.shape[2])) % x_full_feed.shape[2]] for i in range(x_full_feed.shape[2])], dim=1)
+
+        if ensemble_input_rank_transform == 'train' or ensemble_input_rank_transform is True:
+            x_full_feed = torch.cat([rank_transform(x_given, x_full_feed[:,i,:])[:,None] for i in range(x_full_feed.shape[1])] + [x_full_feed], dim=1)
+
+        if ensemble_power_transform:
+            assert apply_power_transform is False
+            y_full_feed = torch.cat((general_power_transform(y_full_feed, y_full_feed, power_transform_eps), y_full_feed), dim=1)
+
+
+        if style is not None:
+            if callable(style):
+                style = style()
+
+            if isinstance(style, torch.Tensor):
+                style = style.to(x_full_feed.device)
+            else:
+                style = torch.tensor(style, device=x_full_feed.device).view(1, 1).repeat(x_full_feed.shape[1], 1)
+
+
+        logits = model(
+            (style,
+             x_full_feed.repeat_interleave(dim=1, repeats=y_full_feed.shape[1]),
+             y_full_feed.repeat(1,x_full_feed.shape[1])),
+            single_eval_pos=len(x_given)
+        )
+        if ensemble_type == 'mean_probs':
+            logits = logits.softmax(-1).mean(1, keepdim=True).log_()  # (num given + num eval, 1, num buckets)
+
+        logits_list.append(logits)  # (< max_dataset_size, 1 , num_buckets)
+    logits = torch.cat(logits_list, dim=0) # (num given + num eval, 1 or (num_features+1), num buckets)
+    del logits_list, x_full_feed
+    if torch.isnan(logits).any():
+        print('nan logits')
+        print(f"y_given: {y_given}, x_given: {x_given}, x_eval: {x_eval}")
+        print(f"logits: {logits}")
+        return torch.zeros_like(x_eval[:,0])
+
+    #logits = model((torch.cat([x_given, x_given, x_eval], dim=0).unsqueeze(1),
+    #               torch.cat([y_given, torch.zeros(len(x_eval)+len(x_given), device=y_given.device)], dim=0).unsqueeze(1)),
+    #               single_eval_pos=len(x_given))[:,0] # (N + M, num_buckets)
+    logits_given = logits[:len(x_given)]
+    logits_eval = logits[len(x_given):]
+
+    #tau = criterion.mean(logits_given)[torch.argmax(y_given)] # predicted mean at the best y
+    if predicted_mean_fbest:
+        tau = criterion.mean(logits_given)[torch.argmax(y_given)].squeeze(0)
+    else:
+        tau = torch.max(y_given)
+    #log_ei = torch.stack([criterion.ei(logits_eval[:,i], noisy_best_f[i]).log() for i in range(len(logits_eval))],0)
+
+    def acq_ensembling(acq_values): # (points, ensemble dim)
+        return acq_values.max(1).values
+
+    if isinstance(acq_function, (dict,list)):
+        acq_function = acq_function[style]
+
+    if acq_function == 'ei':
+        acq_value = acq_ensembling(criterion.ei(logits_eval, tau))
+    elif acq_function == 'ei_or_rand':
+        if torch.rand(1).item() < 0.5:
+            acq_value = torch.rand(len(x_eval))
+        else:
+            acq_value = acq_ensembling(criterion.ei(logits_eval, tau))
+    elif acq_function == 'pi':
+        acq_value = acq_ensembling(criterion.pi(logits_eval, tau))
+    elif acq_function == 'ucb':
+        acq_function = criterion.ucb
+        if ucb_rest_prob is not None:
+            acq_function = lambda *args: criterion.ucb(*args, rest_prob=ucb_rest_prob)
+        acq_value = acq_ensembling(acq_function(logits_eval, tau))
+    elif acq_function == 'mean':
+        acq_value = acq_ensembling(criterion.mean(logits_eval))
+    elif acq_function.startswith('hebo'):
+        noise, upsi, delta, eps = (float(v) for v in acq_function.split('_')[1:])
+        noise = y_given_std * math.sqrt(2 * noise)
+        kappa = math.sqrt(upsi * 2 * ((2.0 + x_given.shape[1] / 2.0) * math.log(max(1, len(x_given))) + math.log(
+            3 * math.pi ** 2 / (3 * delta))))
+        rest_prob = 1. - .5 * (1 + torch.erf(torch.tensor(kappa / math.sqrt(2), device=logits.device)))
+        ucb = acq_ensembling(criterion.ucb(logits_eval, None, rest_prob=rest_prob)) \
+              + torch.randn(len(logits_eval), device=logits_eval.device) * noise
+        noisy_best_f = tau + eps + \
+                       noise * torch.randn(len(logits_eval), device=logits_eval.device)[:, None].repeat(1, logits_eval.shape[1])
+
+        log_pi = acq_ensembling(criterion.pi(logits_eval, noisy_best_f).log())
+        # log_ei = torch.stack([criterion.ei(logits_eval[:,i], noisy_best_f[i]).log() for i in range(len(logits_eval))],0)
+        log_ei = acq_ensembling(criterion.ei(logits_eval, noisy_best_f).log())
+
+        acq_values = torch.stack([ucb, log_ei, log_pi], dim=1)
+
+        def is_pareto_efficient(costs):
+            """
+            Find the pareto-efficient points
+            :param costs: An (n_points, n_costs) array
+            :return: A (n_points, ) boolean array, indicating whether each point is Pareto efficient
+            """
+            is_efficient = torch.ones(costs.shape[0], dtype=bool, device=costs.device)
+            for i, c in enumerate(costs):
+                if is_efficient[i]:
+                    is_efficient[is_efficient.clone()] = (costs[is_efficient] < c).any(
+                        1)  # Keep any point with a lower cost
+                    is_efficient[i] = True  # And keep self
+            return is_efficient
+
+        acq_value = is_pareto_efficient(-acq_values)
+    else:
+        raise ValueError(f'Unknown acquisition function: {acq_function}')
+
+    max_acq = acq_value.max()
+
+    return acq_value if return_actual_ei else (acq_value == max_acq)
+
+
+def optimize_acq(model, known_x, known_y, num_grad_steps=10, num_random_samples=100, lr=.01, **kwargs):
+    """
+    intervals are assumed to be between 0 and 1
+    only works with ei
+    recommended extra kwarg: ensemble_input_rank_transform=='train'
+
+    :param model: model to optimize, should already handle different num_features with its encoder
+    You can add this simply with `model.encoder = encoders.VariableNumFeaturesEncoder(model.encoder, model.encoder.num_features)`
+    :param known_x: (N, num_features)
+    :param known_y: (N,)
+    :param num_grad_steps: int
+    :param num_random_samples: int
+    :param lr: float
+    :param kwargs: will be given to `general_acq_function`
+    :return:
+    """
+    x_eval = torch.rand(num_random_samples, known_x.shape[1]).requires_grad_(True)
+    opt = torch.optim.Adam(params=[x_eval], lr=lr)
+    best_acq, best_x = -float('inf'), x_eval[0].detach()
+    for grad_step in range(num_grad_steps):
+        acq = general_acq_function(model, known_x, known_y, x_eval, return_actual_ei=True, **kwargs)
+        max_acq = acq.detach().max().item()
+        if max_acq > best_acq:
+            best_x = x_eval[acq.argmax()].detach()
+            best_acq = max_acq
+
+        (-acq.mean()).backward()
+        assert (x_eval.grad != 0.).any()
+        if torch.isfinite(x_eval.grad).all():
+            opt.step()
+        opt.zero_grad()
+        with torch.no_grad():
+            x_eval.clamp_(min=0., max=1.)
+
+    return best_x
+
+
+def optimize_acq_w_lbfgs(model, known_x, known_y, num_grad_steps=15_000, num_candidates=100, pre_sample_size=100_000, device='cpu',
+                         verbose=False, dims_wo_gradient_opt=[], rand_sample_func=None, **kwargs):
+    """
+    intervals are assumed to be between 0 and 1
+    only works with deterministic acq
+    recommended extra kwarg: ensemble_input_rank_transform=='train'
+
+    :param model: model to optimize, should already handle different num_features with its encoder
+    You can add this simply with `model.encoder = encoders.VariableNumFeaturesEncoder(model.encoder, model.encoder.num_features)`
+    :param known_x: (N, num_features)
+    :param known_y: (N,)
+    :param num_grad_steps: int: how many steps to take inside of scipy, can be left high, as it stops most of the time automatically early
+    :param num_candidates: int: how many candidates to optimize with LBFGS, increases costs when higher
+    :param pre_sample_size: int: how many settings to try first with a random search, before optimizing the best with grads
+    :param dims_wo_gradient_opt: int: which dimensions to not optimize with gradients, but with random search only
+    :param rand_sample_func: function: how to sample random points, should be a function that takes a number of samples and returns a tensor
+    For example `lambda n: torch.rand(n, known_x.shape[1])`.
+    :param kwargs: will be given to `general_acq_function`
+    :return:
+    """
+    num_features = known_x.shape[1]
+    dims_w_gradient_opt = sorted(set(range(num_features)) - set(dims_wo_gradient_opt))
+    known_x = known_x.to(device)
+    known_y = known_y.to(device)
+    pre_sample_size = max(pre_sample_size, num_candidates)
+    rand_sample_func = rand_sample_func or (lambda n: torch.rand(n, num_features, device=device))
+    if len(known_x) < pre_sample_size:
+        x_initial = torch.cat((rand_sample_func(pre_sample_size-len(known_x)).to(device), known_x), 0)
+    else:
+        x_initial = rand_sample_func(pre_sample_size)
+    x_initial = x_initial.clamp(min=0., max=1.)
+    x_initial_all = x_initial
+    model.to(device)
+
+    with torch.no_grad():
+        acq = general_acq_function(model, known_x, known_y, x_initial.to(device), return_actual_ei=True, **kwargs)
+        if verbose:
+            import matplotlib.pyplot as plt
+            if x_initial.shape[1] == 2:
+                plt.title('initial acq values, red -> blue')
+                plt.scatter(x_initial[:, 0][:100], x_initial[:, 1][:100], c=acq.cpu().numpy()[:100], cmap='RdBu')
+        x_initial = x_initial[acq.argsort(descending=True)[:num_candidates].cpu()].detach() # num_candidates x num_features
+
+    x_initial_all_ei = acq.cpu().detach()
+
+    def opt_f(x):
+        x_eval = torch.tensor(x).view(-1, len(dims_w_gradient_opt)).float().to(device).requires_grad_(True)
+        x_eval_new = x_initial.clone().detach().to(device)
+        x_eval_new[:, dims_w_gradient_opt] = x_eval
+
+        assert x_eval_new.requires_grad
+        assert not torch.isnan(x_eval_new).any()
+        model.requires_grad_(False)
+        acq = general_acq_function(model, known_x, known_y, x_eval_new, return_actual_ei=True, **kwargs)
+        neg_mean_acq = -acq.mean()
+        neg_mean_acq.backward()
+        #print(neg_mean_acq.detach().numpy(), x_eval.grad.detach().view(*x.shape).numpy())
+        with torch.no_grad():
+            x_eval.grad[x_eval.grad != x_eval.grad] = 0.
+        return neg_mean_acq.detach().cpu().to(torch.float64).numpy(), \
+            x_eval.grad.detach().view(*x.shape).cpu().to(torch.float64).numpy()
+
+    # Optimize best candidates with LBFGS
+    if num_grad_steps > 0 and len(dims_w_gradient_opt) > 0:
+        # the columns not in dims_wo_gradient_opt will be optimized with gradients
+        x_initial_for_gradient_opt = x_initial[:, dims_w_gradient_opt].detach().cpu().flatten().numpy() # x_initial.cpu().flatten().numpy()
+        res = scipy.optimize.minimize(opt_f, x_initial_for_gradient_opt, method='L-BFGS-B', jac=True,
+                                      bounds=[(0, 1)]*x_initial_for_gradient_opt.size,
+                                      options={'maxiter': num_grad_steps})
+        results = x_initial.cpu()
+        results[:, dims_w_gradient_opt] = torch.tensor(res.x).float().view(-1, len(dims_w_gradient_opt))
+
+    else:
+        results = x_initial.cpu()
+
+    results = results.clamp(min=0., max=1.)
+
+    # Recalculate the acq values for the best candidates
+    with torch.no_grad():
+        acq = general_acq_function(model, known_x, known_y, results.to(device), return_actual_ei=True, verbose=verbose, **kwargs)
+        #print(acq)
+        if verbose:
+            from scipy.stats import rankdata
+            import matplotlib.pyplot as plt
+            if results.shape[1] == 2:
+                plt.scatter(results[:, 0], results[:, 1], c=rankdata(acq.cpu().numpy()), marker='x', cmap='RdBu')
+                plt.show()
+        best_x = results[acq.argmax().item()].detach()
+
+    acq_order = acq.argsort(descending=True).cpu()
+    all_order = x_initial_all_ei.argsort(descending=True).cpu()
+
+    return best_x.detach(), results[acq_order].detach(), acq.cpu()[acq_order].detach(), x_initial_all.cpu()[all_order].detach(), x_initial_all_ei.cpu()[all_order].detach()
+
+from ..utils import to_tensor
+
+class TransformerBOMethod:
+
+    def __init__(self, model, acq_f=general_acq_function, device='cpu:0', fit_encoder=None, **kwargs):
+        self.model = model
+        self.device = device
+        self.kwargs = kwargs
+        self.acq_function = acq_f
+        self.fit_encoder = fit_encoder
+
+    @torch.no_grad()
+    def observe_and_suggest(self, X_obs, y_obs, X_pen, return_actual_ei=False):
+        # assert X_pen is not None
+        # assumptions about X_obs and X_pen:
+        # X_obs is a numpy array of shape (n_samples, n_features)
+        # y_obs is a numpy array of shape (n_samples,), between 0 and 1
+        # X_pen is a numpy array of shape (n_samples_left, n_features)
+        X_obs = to_tensor(X_obs, device=self.device).to(torch.float32)
+        y_obs = to_tensor(y_obs, device=self.device).to(torch.float32).view(-1)
+        X_pen = to_tensor(X_pen, device=self.device).to(torch.float32)
+
+        assert len(X_obs) == len(y_obs), "make sure both X_obs and y_obs have the same length."
+
+        self.model.to(self.device)
+
+        if self.fit_encoder is not None:
+            w = self.fit_encoder(self.model, X_obs, y_obs)
+            X_obs = w(X_obs)
+            X_pen = w(X_pen)
+
+        with (torch.cuda.amp.autocast() if self.device[:3] != 'cpu' else contextlib.nullcontext()):
+            acq_values = self.acq_function(self.model, X_obs, y_obs,
+                                           X_pen, return_actual_ei=return_actual_ei, **self.kwargs).cpu().clone()  # bool array
+            acq_mask = acq_values.max() == acq_values
+        possible_next = torch.arange(len(X_pen))[acq_mask]
+        if len(possible_next) == 0:
+            possible_next = torch.arange(len(X_pen))
+
+        r = possible_next[torch.randperm(len(possible_next))[0]].cpu().item()
+
+
+        if return_actual_ei:
+            return r, acq_values
+        else:
+            return r
+
+
+
+
+
+
+
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_interface.py b/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_interface.py
new file mode 100644
index 0000000..449e4a1
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_interface.py
@@ -0,0 +1,540 @@
+import torch
+import random
+import pathlib
+
+from torch.utils.checkpoint import checkpoint
+
+from pfns.utils import normalize_data, to_ranking_low_mem, remove_outliers
+from pfns.utils import NOP, normalize_by_used_features_f
+
+from sklearn.preprocessing import PowerTransformer, QuantileTransformer, RobustScaler
+
+import numpy as np
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
+from sklearn.utils.multiclass import unique_labels
+from sklearn.utils.multiclass import check_classification_targets
+from sklearn.utils import column_or_1d
+from sklearn.preprocessing import LabelEncoder
+from pathlib import Path
+from pfns.scripts.tabpfn_model_builder import load_model_only_inference
+import os
+import pickle
+import io
+
+def load_model_workflow(i, e, add_name, base_path, device='cpu', eval_addition='', only_inference=True):
+    """
+    Workflow for loading a model and setting appropriate parameters for diffable hparam tuning.
+
+    :param i:
+    :param e:
+    :param eval_positions_valid:
+    :param add_name:
+    :param base_path:
+    :param device:
+    :param eval_addition:
+    :return:
+    """
+    def get_file(e):
+        """
+        Returns the different paths of model_file, model_path and results_file
+        """
+        model_file = f'models_diff/prior_diff_real_checkpoint{add_name}_n_{i}_epoch_{e}.cpkt'
+        model_path = os.path.join(base_path, model_file)
+        # print('Evaluate ', model_path)
+        results_file = os.path.join(base_path,
+                                    f'models_diff/prior_diff_real_results{add_name}_n_{i}_epoch_{e}_{eval_addition}.pkl')
+        return model_file, model_path, results_file
+
+    def check_file(e):
+        model_file, model_path, results_file = get_file(e)
+        if not Path(model_path).is_file():  # or Path(results_file).is_file():
+            print('We have to download the TabPFN, as there is no checkpoint at ', model_path)
+            print('It has about 100MB, so this might take a moment.')
+            import requests
+            url = 'https://github.com/automl/TabPFN/raw/main/tabpfn/models_diff/prior_diff_real_checkpoint_n_0_epoch_42.cpkt'
+            r = requests.get(url, allow_redirects=True)
+            os.makedirs(os.path.dirname(model_path), exist_ok=True)
+            open(model_path, 'wb').write(r.content)
+        return model_file, model_path, results_file
+
+    model_file = None
+    if e == -1:
+        for e_ in range(100, -1, -1):
+            model_file_, model_path_, results_file_ = check_file(e_)
+            if model_file_ is not None:
+                e = e_
+                model_file, model_path, results_file = model_file_, model_path_, results_file_
+                break
+    else:
+        model_file, model_path, results_file = check_file(e)
+
+    if model_file is None:
+        model_file, model_path, results_file = get_file(e)
+        raise Exception('No checkpoint found at '+str(model_path))
+
+
+    #print(f'Loading {model_file}')
+    # if only_inference:
+    #     # print('Loading model that can be used for inference only')
+    #     model, c = load_model_only_inference(base_path, model_file, device)
+    # else:
+    #     #until now also only capable of inference
+    #     model, c = load_model(base_path, model_file, device, eval_positions=[], verbose=False)
+    # #model, c = load_model(base_path, model_file, device, eval_positions=[], verbose=False)
+    
+    model, c = load_model_only_inference(base_path, model_file, device)
+
+    return model, c, results_file
+
+
+class TabPFNClassifier(BaseEstimator, ClassifierMixin):
+
+    models_in_memory = {}
+
+    def __init__(self, device='cpu', base_path=pathlib.Path(__file__).parent.parent.resolve(), model_string='',
+                 N_ensemble_configurations=3, no_preprocess_mode=False, multiclass_decoder='permutation',
+                 feature_shift_decoder=True, only_inference=True, seed=0, no_grad=True, batch_size_inference=32,
+                 subsample_features=False):
+        """
+        Initializes the classifier and loads the model. 
+        Depending on the arguments, the model is either loaded from memory, from a file, or downloaded from the 
+        repository if no model is found.
+        
+        Can also be used to compute gradients with respect to the inputs X_train and X_test. Therefore no_grad has to be 
+        set to False and no_preprocessing_mode must be True. Furthermore, X_train and X_test need to be given as 
+        torch.Tensors and their requires_grad parameter must be set to True.
+        
+        
+        :param device: If the model should run on cuda or cpu.
+        :param base_path: Base path of the directory, from which the folders like models_diff can be accessed.
+        :param model_string: Name of the model. Used first to check if the model is already in memory, and if not, 
+               tries to load a model with that name from the models_diff directory. It looks for files named as 
+               follows: "prior_diff_real_checkpoint" + model_string + "_n_0_epoch_e.cpkt", where e can be a number 
+               between 100 and 0, and is checked in a descending order. 
+        :param N_ensemble_configurations: The number of ensemble configurations used for the prediction. Thereby the 
+               accuracy, but also the running time, increases with this number. 
+        :param no_preprocess_mode: Specifies whether preprocessing is to be performed.
+        :param multiclass_decoder: If set to permutation, randomly shifts the classes for each ensemble configuration. 
+        :param feature_shift_decoder: If set to true shifts the features for each ensemble configuration according to a 
+               random permutation.
+        :param only_inference: Indicates if the model should be loaded to only restore inference capabilities or also 
+               training capabilities. Note that the training capabilities are currently not being fully restored.
+        :param seed: Seed that is used for the prediction. Allows for a deterministic behavior of the predictions.
+        :param batch_size_inference: This parameter is a trade-off between performance and memory consumption.
+               The computation done with different values for batch_size_inference is the same,
+               but it is split into smaller/larger batches.
+        :param no_grad: If set to false, allows for the computation of gradients with respect to X_train and X_test. 
+               For this to correctly function no_preprocessing_mode must be set to true.
+        :param subsample_features: If set to true and the number of features in the dataset exceeds self.max_features (100),
+                the features are subsampled to self.max_features.
+        """
+
+        # Model file specification (Model name, Epoch)
+        i = 0
+        model_key = model_string+'|'+str(device)
+        if model_key in self.models_in_memory:
+            model, c, results_file = self.models_in_memory[model_key]
+        else:
+            model, c, results_file = load_model_workflow(i, -1, add_name=model_string, base_path=base_path, device=device,
+                                                         eval_addition='', only_inference=only_inference)
+            self.models_in_memory[model_key] = (model, c, results_file)
+            if len(self.models_in_memory) == 2:
+                print('Multiple models in memory. This might lead to memory issues. Consider calling remove_models_from_memory()')
+        #style, temperature = self.load_result_minimal(style_file, i, e)
+
+        self.device = device
+        self.model = model
+        self.c = c
+        self.style = None
+        self.temperature = None
+        self.N_ensemble_configurations = N_ensemble_configurations
+        self.base__path = base_path
+        self.base_path = base_path
+        self.i = i
+        self.model_string = model_string
+
+        self.max_num_features = self.c['num_features']
+        self.max_num_classes = self.c['max_num_classes']
+        self.differentiable_hps_as_style = self.c['differentiable_hps_as_style']
+
+        self.no_preprocess_mode = no_preprocess_mode
+        self.feature_shift_decoder = feature_shift_decoder
+        self.multiclass_decoder = multiclass_decoder
+        self.only_inference = only_inference
+        self.seed = seed
+        self.no_grad = no_grad
+        self.subsample_features = subsample_features
+
+        assert self.no_preprocess_mode if not self.no_grad else True, \
+            "If no_grad is false, no_preprocess_mode must be true, because otherwise no gradient can be computed."
+
+        self.batch_size_inference = batch_size_inference
+
+    def remove_models_from_memory(self):
+        self.models_in_memory = {}
+
+    def _validate_targets(self, y):
+        y_ = column_or_1d(y, warn=True)
+        check_classification_targets(y)
+        cls, y = np.unique(y_, return_inverse=True)
+        if len(cls) < 2:
+            raise ValueError(
+                "The number of classes has to be greater than one; got %d class"
+                % len(cls)
+            )
+
+        self.classes_ = cls
+
+        return np.asarray(y, dtype=np.float64, order="C")
+
+    def fit(self, X, y, overwrite_warning=False):
+        """
+        Validates the training set and stores it.
+
+        If clf.no_grad (default is True):
+        X, y should be of type np.array
+        else:
+        X should be of type torch.Tensors (y can be np.array or torch.Tensor)
+        """
+        if self.no_grad:
+            # Check that X and y have correct shape
+            X, y = check_X_y(X, y, force_all_finite=False)
+        # Store the classes seen during fit
+        y = self._validate_targets(y)
+        self.label_encoder = LabelEncoder()
+        y = self.label_encoder.fit_transform(y)
+
+        self.X_ = X
+        self.y_ = y
+
+        if (X.shape[1] > self.max_num_features):
+            if self.subsample_features:
+                print('WARNING: The number of features for this classifier is restricted to ', self.max_num_features, ' and will be subsampled.')
+            else:
+                raise ValueError("The number of features for this classifier is restricted to ", self.max_num_features)
+        if len(np.unique(y)) > self.max_num_classes:
+            raise ValueError("The number of classes for this classifier is restricted to ", self.max_num_classes)
+        if X.shape[0] > 1024 and not overwrite_warning:
+            raise ValueError("⚠️ WARNING: TabPFN is not made for datasets with a trainingsize > 1024. Prediction might take a while, be less reliable. We advise not to run datasets > 10k samples, which might lead to your machine crashing (due to quadratic memory scaling of TabPFN). Please confirm you want to run by passing overwrite_warning=True to the fit function.")
+
+
+        # Return the classifier
+        return self
+
+    def predict_proba(self, X, normalize_with_test=False, return_logits=False):
+        """
+        Predict the probabilities for the input X depending on the training set previously passed in the method fit.
+
+        If no_grad is true in the classifier the function takes X as a numpy.ndarray. If no_grad is false X must be a
+        torch tensor and is not fully checked.
+        """
+        # Check is fit had been called
+        check_is_fitted(self)
+
+        # Input validation
+        if self.no_grad:
+            X = check_array(X, force_all_finite=False)
+            X_full = np.concatenate([self.X_, X], axis=0)
+            X_full = torch.tensor(X_full, device=self.device).float().unsqueeze(1)
+        else:
+            assert (torch.is_tensor(self.X_) & torch.is_tensor(X)), "If no_grad is false, this function expects X as " \
+                                                                    "a tensor to calculate a gradient"
+            X_full = torch.cat((self.X_, X), dim=0).float().unsqueeze(1).to(self.device)
+
+            if int(torch.isnan(X_full).sum()):
+                print('X contains nans and the gradient implementation is not designed to handel nans.')
+
+        y_full = np.concatenate([self.y_, np.zeros(shape=X.shape[0])], axis=0)
+        y_full = torch.tensor(y_full, device=self.device).float().unsqueeze(1)
+
+        eval_pos = self.X_.shape[0]
+
+        prediction = transformer_predict(self.model[2], X_full, y_full, eval_pos,
+                                         device=self.device,
+                                         style=self.style,
+                                         inference_mode=True,
+                                         preprocess_transform='none' if self.no_preprocess_mode else 'mix',
+                                         normalize_with_test=normalize_with_test,
+                                         N_ensemble_configurations=self.N_ensemble_configurations,
+                                         softmax_temperature=self.temperature,
+                                         multiclass_decoder=self.multiclass_decoder,
+                                         feature_shift_decoder=self.feature_shift_decoder,
+                                         differentiable_hps_as_style=self.differentiable_hps_as_style,
+                                         seed=self.seed,
+                                         return_logits=return_logits,
+                                         no_grad=self.no_grad,
+                                         batch_size_inference=self.batch_size_inference,
+                                         **get_params_from_config(self.c))
+        prediction_, y_ = prediction.squeeze(0), y_full.squeeze(1).long()[eval_pos:]
+
+        return prediction_.detach().cpu().numpy() if self.no_grad else prediction_
+
+    def predict(self, X, return_winning_probability=False, normalize_with_test=False):
+        p = self.predict_proba(X, normalize_with_test=normalize_with_test)
+        y = np.argmax(p, axis=-1)
+        y = self.classes_.take(np.asarray(y, dtype=np.intp))
+        if return_winning_probability:
+            return y, p.max(axis=-1)
+        return y
+
+import time
+def transformer_predict(model, eval_xs, eval_ys, eval_position,
+                        device='cpu',
+                        max_features=100,
+                        style=None,
+                        inference_mode=False,
+                        num_classes=2,
+                        extend_features=True,
+                        normalize_with_test=False,
+                        normalize_to_ranking=False,
+                        softmax_temperature=0.0,
+                        multiclass_decoder='permutation',
+                        preprocess_transform='mix',
+                        categorical_feats=[],
+                        feature_shift_decoder=False,
+                        N_ensemble_configurations=10,
+                        batch_size_inference=16,
+                        differentiable_hps_as_style=False,
+                        average_logits=True,
+                        fp16_inference=False,
+                        normalize_with_sqrt=False,
+                        seed=0,
+                        no_grad=True,
+                        return_logits=False,
+                        **kwargs):
+    """
+
+    :param model:
+    :param eval_xs:
+    :param eval_ys:
+    :param eval_position:
+    :param rescale_features:
+    :param device:
+    :param max_features:
+    :param style:
+    :param inference_mode:
+    :param num_classes:
+    :param extend_features:
+    :param normalize_to_ranking:
+    :param softmax_temperature:
+    :param multiclass_decoder:
+    :param preprocess_transform:
+    :param categorical_feats:
+    :param feature_shift_decoder:
+    :param N_ensemble_configurations:
+    :param average_logits:
+    :param normalize_with_sqrt:
+    :param metric_used:
+    :return:
+    """
+    num_classes = len(torch.unique(eval_ys))
+
+    def predict(eval_xs, eval_ys, used_style, softmax_temperature, return_logits):
+        # Initialize results array size S, B, Classes
+
+        # no_grad disables inference_mode, because otherwise the gradients are lost
+        inference_mode_call = torch.inference_mode() if inference_mode and no_grad else NOP()
+        with inference_mode_call:
+            start = time.time()
+            output = model(
+                    (used_style.repeat(eval_xs.shape[1], 1) if used_style is not None else None, eval_xs, eval_ys.float()),
+                    single_eval_pos=eval_position)[:, :, 0:num_classes]
+
+            output = output[:, :, 0:num_classes] / torch.exp(softmax_temperature)
+            if not return_logits:
+                output = torch.nn.functional.softmax(output, dim=-1)
+            #else:
+            #    output[:, :, 1] = model((style.repeat(eval_xs.shape[1], 1) if style is not None else None, eval_xs, eval_ys.float()),
+            #               single_eval_pos=eval_position)
+
+            #    output[:, :, 1] = torch.sigmoid(output[:, :, 1]).squeeze(-1)
+            #    output[:, :, 0] = 1 - output[:, :, 1]
+
+        #print('RESULTS', eval_ys.shape, torch.unique(eval_ys, return_counts=True), output.mean(axis=0))
+
+        return output
+
+    def preprocess_input(eval_xs, preprocess_transform):
+        import warnings
+
+        if eval_xs.shape[1] > 1:
+            raise Exception("Transforms only allow one batch dim - TODO")
+
+        if eval_xs.shape[2] > max_features:
+            eval_xs = eval_xs[:, :, sorted(np.random.choice(eval_xs.shape[2], max_features, replace=False))]
+
+        if preprocess_transform != 'none':
+            if preprocess_transform == 'power' or preprocess_transform == 'power_all':
+                pt = PowerTransformer(standardize=True)
+            elif preprocess_transform == 'quantile' or preprocess_transform == 'quantile_all':
+                pt = QuantileTransformer(output_distribution='normal')
+            elif preprocess_transform == 'robust' or preprocess_transform == 'robust_all':
+                pt = RobustScaler(unit_variance=True)
+
+        # eval_xs, eval_ys = normalize_data(eval_xs), normalize_data(eval_ys)
+        eval_xs = normalize_data(eval_xs, normalize_positions=-1 if normalize_with_test else eval_position)
+
+        # Removing empty features
+        eval_xs = eval_xs[:, 0, :]
+        sel = [len(torch.unique(eval_xs[0:eval_ys.shape[0], col])) > 1 for col in range(eval_xs.shape[1])]
+        eval_xs = eval_xs[:, sel]
+
+        warnings.simplefilter('error')
+        if preprocess_transform != 'none':
+            eval_xs = eval_xs.cpu().numpy()
+            feats = set(range(eval_xs.shape[1])) if 'all' in preprocess_transform else set(
+                range(eval_xs.shape[1])) - set(categorical_feats)
+            for col in feats:
+                try:
+                    pt.fit(eval_xs[0:eval_position, col:col + 1])
+                    trans = pt.transform(eval_xs[:, col:col + 1])
+                    # print(scipy.stats.spearmanr(trans[~np.isnan(eval_xs[:, col:col+1])], eval_xs[:, col:col+1][~np.isnan(eval_xs[:, col:col+1])]))
+                    eval_xs[:, col:col + 1] = trans
+                except:
+                    pass
+            eval_xs = torch.tensor(eval_xs).float()
+        warnings.simplefilter('default')
+
+        eval_xs = eval_xs.unsqueeze(1)
+
+        # TODO: Caution there is information leakage when to_ranking is used, we should not use it
+        eval_xs = remove_outliers(eval_xs, normalize_positions=-1 if normalize_with_test else eval_position) \
+                if not normalize_to_ranking else normalize_data(to_ranking_low_mem(eval_xs))
+        # Rescale X
+        eval_xs = normalize_by_used_features_f(eval_xs, eval_xs.shape[-1], max_features,
+                                               normalize_with_sqrt=normalize_with_sqrt)
+
+        return eval_xs.to(device)
+
+    eval_xs, eval_ys = eval_xs.to(device), eval_ys.to(device)
+    eval_ys = eval_ys[:eval_position]
+
+    model.to(device)
+
+    model.eval()
+
+    import itertools
+    if not differentiable_hps_as_style:
+        style = None
+
+    if style is not None:
+        style = style.to(device)
+        style = style.unsqueeze(0) if len(style.shape) == 1 else style
+        num_styles = style.shape[0]
+        softmax_temperature = softmax_temperature if softmax_temperature.shape else softmax_temperature.unsqueeze(
+            0).repeat(num_styles)
+    else:
+        num_styles = 1
+        style = None
+        softmax_temperature = torch.log(torch.tensor([0.8]))
+
+    styles_configurations = range(0, num_styles)
+    def get_preprocess(i):
+        if i == 0:
+            return 'power_all'
+#            if i == 1:
+#                return 'robust_all'
+        if i == 1:
+            return 'none'
+
+    preprocess_transform_configurations = ['none', 'power_all'] if preprocess_transform == 'mix' else [preprocess_transform]
+
+    if seed is not None:
+        torch.manual_seed(seed)
+
+    feature_shift_configurations = torch.randperm(eval_xs.shape[2]) if feature_shift_decoder else [0]
+    class_shift_configurations = torch.randperm(len(torch.unique(eval_ys))) if multiclass_decoder == 'permutation' else [0]
+
+    ensemble_configurations = list(itertools.product(class_shift_configurations, feature_shift_configurations))
+    #default_ensemble_config = ensemble_configurations[0]
+
+    rng = random.Random(seed)
+    rng.shuffle(ensemble_configurations)
+    ensemble_configurations = list(itertools.product(ensemble_configurations, preprocess_transform_configurations, styles_configurations))
+    ensemble_configurations = ensemble_configurations[0:N_ensemble_configurations]
+    #if N_ensemble_configurations == 1:
+    #    ensemble_configurations = [default_ensemble_config]
+
+    output = None
+
+    eval_xs_transformed = {}
+    inputs, labels = [], []
+    start = time.time()
+    for ensemble_configuration in ensemble_configurations:
+        (class_shift_configuration, feature_shift_configuration), preprocess_transform_configuration, styles_configuration = ensemble_configuration
+
+        style_ = style[styles_configuration:styles_configuration+1, :] if style is not None else style
+        softmax_temperature_ = softmax_temperature[styles_configuration]
+
+        eval_xs_, eval_ys_ = eval_xs.clone(), eval_ys.clone()
+
+        if preprocess_transform_configuration in eval_xs_transformed:
+            eval_xs_ = eval_xs_transformed[preprocess_transform_configuration].clone()
+        else:
+            eval_xs_ = preprocess_input(eval_xs_, preprocess_transform=preprocess_transform_configuration)
+            if no_grad:
+                eval_xs_ = eval_xs_.detach()
+            eval_xs_transformed[preprocess_transform_configuration] = eval_xs_
+
+        eval_ys_ = ((eval_ys_ + class_shift_configuration) % num_classes).float()
+
+        eval_xs_ = torch.cat([eval_xs_[..., feature_shift_configuration:],eval_xs_[..., :feature_shift_configuration]],dim=-1)
+
+        # Extend X
+        if extend_features:
+            eval_xs_ = torch.cat(
+                [eval_xs_,
+                 torch.zeros((eval_xs_.shape[0], eval_xs_.shape[1], max_features - eval_xs_.shape[2])).to(device)], -1)
+        inputs += [eval_xs_]
+        labels += [eval_ys_]
+
+    inputs = torch.cat(inputs, 1)
+    inputs = torch.split(inputs, batch_size_inference, dim=1)
+    labels = torch.cat(labels, 1)
+    labels = torch.split(labels, batch_size_inference, dim=1)
+    #print('PREPROCESSING TIME', str(time.time() - start))
+    outputs = []
+    start = time.time()
+    for batch_input, batch_label in zip(inputs, labels):
+        #preprocess_transform_ = preprocess_transform if styles_configuration % 2 == 0 else 'none'
+        import warnings
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore",
+                                    message="None of the inputs have requires_grad=True. Gradients will be None")
+            warnings.filterwarnings("ignore",
+                                    message="torch.cuda.amp.autocast only affects CUDA ops, but CUDA is not available.  Disabling.")
+            if device == 'cpu':
+                output_batch = checkpoint(predict, batch_input, batch_label, style_, softmax_temperature_, True)
+            else:
+                with torch.cuda.amp.autocast(enabled=fp16_inference):
+                    output_batch = checkpoint(predict, batch_input, batch_label, style_, softmax_temperature_, True)
+        outputs += [output_batch]
+    #print('MODEL INFERENCE TIME ('+str(batch_input.device)+' vs '+device+', '+str(fp16_inference)+')', str(time.time()-start))
+
+    outputs = torch.cat(outputs, 1)
+    for i, ensemble_configuration in enumerate(ensemble_configurations):
+        (class_shift_configuration, feature_shift_configuration), preprocess_transform_configuration, styles_configuration = ensemble_configuration
+        output_ = outputs[:, i:i+1, :]
+        output_ = torch.cat([output_[..., class_shift_configuration:],output_[..., :class_shift_configuration]],dim=-1)
+
+        #output_ = predict(eval_xs, eval_ys, style_, preprocess_transform_)
+        if not average_logits and not return_logits:
+            # transforms every ensemble_configuration into a probability -> equal contribution of every configuration
+            output_ = torch.nn.functional.softmax(output_, dim=-1)
+        output = output_ if output is None else output + output_
+
+    output = output / len(ensemble_configurations)
+    if average_logits and not return_logits:
+        output = torch.nn.functional.softmax(output, dim=-1)
+
+    output = torch.transpose(output, 0, 1)
+
+    return output
+
+def get_params_from_config(c):
+    return {'max_features': c['num_features']
+        , 'rescale_features': c["normalize_by_used_features"]
+        , 'normalize_to_ranking': c["normalize_to_ranking"]
+        , 'normalize_with_sqrt': c.get("normalize_with_sqrt", False)
+            }
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_model_builder.py b/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_model_builder.py
new file mode 100644
index 0000000..11917ca
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_model_builder.py
@@ -0,0 +1,54 @@
+from functools import partial
+import pfns.encoders as encoders
+
+from pfns.transformer import TransformerModel
+import torch
+import math
+
+import subprocess as sp
+import os
+
+def load_model_only_inference(path, filename, device='cpu'):
+    """
+    Loads a saved model from the specified position. This function only restores inference capabilities and
+    cannot be used for further training.
+    """
+
+    model_state, optimizer_state, config_sample = torch.load(os.path.join(path, filename), map_location='cpu')
+
+    if (('nan_prob_no_reason' in config_sample and config_sample['nan_prob_no_reason'] > 0.0) or
+        ('nan_prob_a_reason' in config_sample and config_sample['nan_prob_a_reason'] > 0.0) or
+        ('nan_prob_unknown_reason' in config_sample and config_sample['nan_prob_unknown_reason'] > 0.0)):
+        encoder = encoders.NanHandlingEncoder
+    else:
+        encoder = partial(encoders.Linear, replace_nan_by_zero=True)
+
+    n_out = config_sample['max_num_classes']
+
+    device = device if torch.cuda.is_available() else 'cpu:0'
+    encoder = encoder(config_sample['num_features'], config_sample['emsize'])
+
+    nhid = config_sample['emsize'] * config_sample['nhid_factor']
+    y_encoder_generator = encoders.get_Canonical(config_sample['max_num_classes']) \
+        if config_sample.get('canonical_y_encoder', False) else encoders.Linear
+
+    assert config_sample['max_num_classes'] > 2
+    loss = torch.nn.CrossEntropyLoss(reduction='none', weight=torch.ones(int(config_sample['max_num_classes'])))
+
+    model = TransformerModel(encoder, config_sample['emsize'], config_sample['nhead'], nhid,
+                             config_sample['nlayers'], y_encoder=y_encoder_generator(1, config_sample['emsize']),
+                             dropout=config_sample['dropout'], decoder_dict={'standard': (None, n_out)},
+                             efficient_eval_masking=config_sample['efficient_eval_masking'])
+
+    # print(f"Using a Transformer with {sum(p.numel() for p in model.parameters()) / 1000 / 1000:.{2}f} M parameters")
+
+    model.criterion = loss
+    module_prefix = 'module.'
+    model_state = {k.replace(module_prefix, ''): v for k, v in model_state.items()}
+    for key in list(model_state.keys()):
+        model_state[key.replace( 'decoder','decoder_dict.standard')] = model_state.pop(key)
+    model.load_state_dict(model_state)
+    model.to(device)
+    model.eval()
+    
+    return (float('inf'), float('inf'), model), config_sample # no loss measured
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tabular_metrics.py b/private_multitask_pfn/PFNs/pfns/scripts/tabular_metrics.py
new file mode 100644
index 0000000..b702f5a
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/scripts/tabular_metrics.py
@@ -0,0 +1,226 @@
+"""
+===============================
+Metrics calculation
+===============================
+Includes a few metric as well as functions composing metrics on results files.
+
+"""
+
+import numpy as np
+import torch
+from sklearn.metrics import roc_auc_score, accuracy_score, balanced_accuracy_score, average_precision_score, mean_squared_error, mean_absolute_error, r2_score
+from scipy.stats import rankdata
+import pandas as pd
+
+def root_mean_squared_error_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    return torch.sqrt(torch.nn.functional.mse_loss(target, pred))
+
+def mean_squared_error_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    return torch.nn.functional.mse_loss(target, pred)
+
+def mean_absolute_error_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    return torch.tensor(mean_absolute_error(target, pred))
+
+"""
+===============================
+Metrics calculation
+===============================
+"""
+def auc_metric(target, pred, multi_class='ovo', numpy=False):
+    lib = np if numpy else torch
+    try:
+        if not numpy:
+            target = torch.tensor(target) if not torch.is_tensor(target) else target
+            pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+        if len(lib.unique(target)) > 2:
+            if not numpy:
+                return torch.tensor(roc_auc_score(target, pred, multi_class=multi_class))
+            return roc_auc_score(target, pred, multi_class=multi_class)
+        else:
+            if len(pred.shape) == 2:
+                pred = pred[:, 1]
+            if not numpy:
+                return torch.tensor(roc_auc_score(target, pred))
+            return roc_auc_score(target, pred)
+    except ValueError as e:
+        print(e)
+        return np.nan if numpy else torch.tensor(np.nan)
+
+def accuracy_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    if len(torch.unique(target)) > 2:
+        return torch.tensor(accuracy_score(target, torch.argmax(pred, -1)))
+    else:
+        return torch.tensor(accuracy_score(target, pred[:, 1] > 0.5))
+
+def brier_score_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    target = torch.nn.functional.one_hot(target, num_classes=len(torch.unique(target)))
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    diffs = (pred - target)**2
+    return torch.mean(torch.sum(diffs, axis=1))
+
+def ece_metric(target, pred):
+  import torchmetrics
+  target = torch.tensor(target) if not torch.is_tensor(target) else target
+  pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+  return torchmetrics.functional.calibration_error(pred, target)
+
+
+def average_precision_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    if len(torch.unique(target)) > 2:
+        return torch.tensor(average_precision_score(target, torch.argmax(pred, -1)))
+    else:
+        return torch.tensor(average_precision_score(target, pred[:, 1] > 0.5))
+
+def balanced_accuracy_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    if len(torch.unique(target)) > 2:
+        return torch.tensor(balanced_accuracy_score(target, torch.argmax(pred, -1)))
+    else:
+        return torch.tensor(balanced_accuracy_score(target, pred[:, 1] > 0.5))
+
+def cross_entropy(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    if len(torch.unique(target)) > 2:
+        ce = torch.nn.CrossEntropyLoss()
+        return ce(pred.float(), target.long())
+    else:
+        bce = torch.nn.BCELoss()
+        return bce(pred[:, 1].float(), target.float())
+
+def r2_metric(target, pred):
+    target = torch.tensor(target) if not torch.is_tensor(target) else target
+    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
+    return torch.tensor(neg_r2(target, pred))
+
+def neg_r2(target, pred):
+    return -r2_score(pred.float(), target.float())
+
+def is_classification(metric_used):
+    if metric_used.__name__ in ["auc_metric", "cross_entropy"]:
+        return True
+    return False
+
+def time_metric():
+    """
+    Dummy function, will just be used as a handler.
+    """
+    pass
+
+def count_metric(x, y):
+    """
+    Dummy function, returns one count per dataset.
+    """
+    return 1
+
+"""
+===============================
+Metrics composition
+===============================
+"""
+def calculate_score_per_method(metric, name:str, global_results:dict, ds:list, eval_positions:list, aggregator:str='mean'):
+    """
+    Calculates the metric given by 'metric' and saves it under 'name' in the 'global_results'
+
+    :param metric: Metric function
+    :param name: Name of metric in 'global_results'
+    :param global_results: Dicrtonary containing the results for current method for a collection of datasets
+    :param ds: Dataset to calculate metrics on, a list of dataset properties
+    :param eval_positions: List of positions to calculate metrics on
+    :param aggregator: Specifies way to aggregate results across evaluation positions
+    :return:
+    """
+    aggregator_f = np.nanmean if aggregator == 'mean' else np.nansum
+    for pos in eval_positions:
+        valid_positions = 0
+        for d in ds:
+            if f'{d[0]}_outputs_at_{pos}' in global_results:
+                preds = global_results[f'{d[0]}_outputs_at_{pos}']
+                y = global_results[f'{d[0]}_ys_at_{pos}']
+
+                preds, y = preds.detach().cpu().numpy() if torch.is_tensor(
+                    preds) else preds, y.detach().cpu().numpy() if torch.is_tensor(y) else y
+
+                try:
+                    if metric == time_metric:
+                        global_results[f'{d[0]}_{name}_at_{pos}'] = global_results[f'{d[0]}_time_at_{pos}']
+                        valid_positions = valid_positions + 1
+                    else:
+                        global_results[f'{d[0]}_{name}_at_{pos}'] = aggregator_f(
+                            [metric(y[split], preds[split]) for split in range(y.shape[0])])
+                        valid_positions = valid_positions + 1
+                except Exception as err:
+                    print(f'Error calculating metric with {err}, {type(err)} at {d[0]} {pos} {name}')
+                    global_results[f'{d[0]}_{name}_at_{pos}'] = np.nan
+            else:
+                global_results[f'{d[0]}_{name}_at_{pos}'] = np.nan
+
+        if valid_positions > 0:
+            global_results[f'{aggregator}_{name}_at_{pos}'] = aggregator_f([global_results[f'{d[0]}_{name}_at_{pos}'] for d in ds])
+        else:
+            global_results[f'{aggregator}_{name}_at_{pos}'] = np.nan
+
+    for d in ds:
+        metrics = [global_results[f'{d[0]}_{name}_at_{pos}'] for pos in eval_positions]
+        metrics = [m for m in metrics if not np.isnan(m)]
+        global_results[f'{d[0]}_{aggregator}_{name}'] = aggregator_f(metrics) if len(metrics) > 0 else np.nan
+
+    metrics = [global_results[f'{aggregator}_{name}_at_{pos}'] for pos in eval_positions]
+    metrics = [m for m in metrics if not np.isnan(m)]
+    global_results[f'{aggregator}_{name}'] = aggregator_f(metrics) if len(metrics) > 0 else np.nan
+
+
+def calculate_score(metric, name, global_results, ds, eval_positions, aggregator='mean', limit_to=''):
+    """
+    Calls calculate_metrics_by_method with a range of methods. See arguments of that method.
+    :param limit_to: This method will not get metric calculations.
+    """
+    for m in global_results:
+        if limit_to not in m:
+            continue
+        calculate_score_per_method(metric, name, global_results[m], ds, eval_positions, aggregator=aggregator)
+
+
+def make_metric_matrix(global_results, methods, pos, name, ds):
+    result = []
+    for m in global_results:
+        try:
+            result += [[global_results[m][d[0] + '_' + name + '_at_' + str(pos)] for d in ds]]
+        except Exception as e:
+            #raise(e)
+            result += [[np.nan]]
+    result = np.array(result)
+    result = pd.DataFrame(result.T, index=[d[0] for d in ds], columns=[k for k in list(global_results.keys())])
+
+    matrix_means, matrix_stds, matrix_per_split = [], [], []
+
+    for method in methods:
+        matrix_means += [result.iloc[:, [c.startswith(method+'_time') for c in result.columns]].mean(axis=1)]
+        matrix_stds += [result.iloc[:, [c.startswith(method+'_time') for c in result.columns]].std(axis=1)]
+        matrix_per_split += [result.iloc[:, [c.startswith(method+'_time') for c in result.columns]]]
+
+    matrix_means = pd.DataFrame(matrix_means, index=methods).T
+    matrix_stds = pd.DataFrame(matrix_stds, index=methods).T
+
+    return matrix_means, matrix_stds, matrix_per_split
+
+
+def make_ranks_and_wins_table(matrix):
+    for dss in matrix.T:
+        matrix.loc[dss] = rankdata(-matrix.round(3).loc[dss])
+    ranks_acc = matrix.mean()
+    wins_acc = (matrix == 1).sum()
+
+    return ranks_acc, wins_acc
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tune_input_warping.py b/private_multitask_pfn/PFNs/pfns/scripts/tune_input_warping.py
new file mode 100644
index 0000000..e1a57b1
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/scripts/tune_input_warping.py
@@ -0,0 +1,336 @@
+import random
+from collections import OrderedDict
+import torch
+
+from .. import utils
+from ..priors.hebo_prior import Warp
+from gpytorch.priors import LogNormalPrior
+
+from botorch.optim import module_to_array, set_params_with_array
+import scipy
+from scipy.optimize import Bounds
+from typing import OrderedDict
+import numpy as np
+from functools import partial
+
+device = 'cpu:0'
+
+
+def fit_lbfgs(x, w, nll, num_grad_steps=10, ignore_prior=True, params0=None):
+    bounds_ = {}
+    if hasattr(w, "named_parameters_and_constraints"):
+        for param_name, _, constraint in w.named_parameters_and_constraints():
+            if constraint is not None and not constraint.enforced:
+                bounds_[param_name] = constraint.lower_bound, constraint.upper_bound
+    params0_, property_dict, bounds_ = module_to_array(
+        module=w, bounds=bounds_, exclude=None
+    )
+    if params0 is None: params0 = params0_
+    bounds = Bounds(lb=bounds_[0], ub=bounds_[1], keep_feasible=True)
+
+    def loss_f(params, w):
+        w = set_params_with_array(w, params, property_dict)
+        w.requires_grad_(True)
+        loss = 0.
+        if not ignore_prior:
+            for name, module, prior, closure, _ in w.named_priors():
+                prior_term = prior.log_prob(closure(module))
+                loss -= prior_term.sum(dim=-1)
+        negll = nll(w(x.to(torch.float64)).to(torch.float)).sum()
+        #if loss != 0.:
+        #    print(loss.item(), negll.item())
+        loss = loss + negll
+        return w, loss
+
+    def opt_f(params, w):
+        w, loss = loss_f(params, w)
+
+        w.zero_grad()
+        loss.backward()
+        grad = []
+        param_dict = OrderedDict(w.named_parameters())
+
+        for p_name in property_dict:
+            t = param_dict[p_name].grad
+            if t is None:
+                # this deals with parameters that do not affect the loss
+                grad.append(np.zeros(property_dict[p_name].shape.numel()))
+            else:
+                grad.append(t.detach().view(-1).cpu().double().clone().numpy())
+        w.zero_grad()
+        # print(neg_mean_acq.detach().numpy(), x_eval.grad.detach().view(*x.shape).numpy())
+        return loss.item(), np.concatenate(grad)
+
+    if num_grad_steps:
+        return scipy.optimize.minimize(partial(opt_f, w=w), params0, method='L-BFGS-B', jac=True, bounds=bounds,
+                                       options={'maxiter': num_grad_steps})
+    else:
+        with torch.no_grad():
+            return loss_f(params0, w), params0
+
+
+def log_vs_nonlog(x, w, *args, **kwargs):
+    if "true_nll" in kwargs:
+        true_nll = kwargs["true_nll"]
+        del kwargs["true_nll"]
+    else:
+        true_nll = None
+    params, property_dict, _ = module_to_array(module=w)
+    no_log = np.ones_like(params)
+    log = np.array([1.9, 0.11] * (int(len(property_dict) / 2)))
+    loss_no_log = fit_lbfgs(x, w, *args, **{**kwargs, 'num_grad_steps': 0}, params0=no_log)
+    loss_log = fit_lbfgs(x, w, *args, **{**kwargs, 'num_grad_steps': 0}, params0=log)
+    print("loss no log", loss_no_log[0][1], "loss log", loss_log[0][1])
+    if loss_no_log[0][1] < loss_log[0][1]:
+        set_params_with_array(module=w, x=loss_no_log[1], property_dict=property_dict)
+    if true_nll:
+        best_params, _, _ = module_to_array(module=w)
+        print("true nll", fit_lbfgs(x, w, true_nll, **{**kwargs, 'num_grad_steps': 0}, params0=best_params))
+
+
+def fit_lbfgs_with_restarts(x, w, *args, old_solution=None, rs_size=50, **kwargs):
+    if "true_nll" in kwargs:
+        true_nll = kwargs["true_nll"]
+        del kwargs["true_nll"]
+    else:
+        true_nll = None
+    rs_results = []
+    if old_solution:
+        rs_results.append(fit_lbfgs(x, old_solution, *args, **{**kwargs, 'num_grad_steps': 0}))
+    for i in range(rs_size):
+        with torch.no_grad():
+            w.concentration0[:] = w.concentration0_prior()
+            w.concentration1[:] = w.concentration1_prior()
+        rs_results.append(fit_lbfgs(x, w, *args, **{**kwargs, 'num_grad_steps': 0}))
+    best_r = min(rs_results, key=lambda r: r[0][1])
+    print('best r', best_r)
+    with torch.set_grad_enabled(True):
+        r = fit_lbfgs(x, w, *args, **kwargs, params0=best_r[1])
+    _, property_dict, _ = module_to_array(module=w)
+    set_params_with_array(module=w, x=r.x, property_dict=property_dict)
+    print('final r', r)
+    if true_nll:
+        print("true nll", fit_lbfgs(x, w, true_nll, **{**kwargs, 'num_grad_steps': 0}, params0=r.x))
+    return r
+
+
+# use seed 0 for sampling indices, and reset seed afterwards
+old_seed = random.getstate()
+random.seed(0)
+one_out_indices_sampled_per_num_obs = [None]+[random.sample(range(i), min(10, i)) for i in range(1, 100)]
+random.setstate(old_seed)
+
+# use seed 0 for sampling subsets
+old_seed = random.getstate()
+random.seed(0)
+subsets = [None]+[[random.sample(range(i), i//2) for _ in range(10)] for i in range(1, 100)]
+neg_subsets = [None]+[[list(set(range(i)) - set(s)) for s in ss] for i, ss in enumerate(subsets[1:], 1)]
+random.setstate(old_seed)
+
+
+
+def fit_input_warping(model, x, y, nll_type='fast', old_solution=None, opt_method="lbfgs", **kwargs):
+    """
+
+    :param model:
+    :param x: shape (n, d)
+    :param y: shape (n, 1)
+    :param nll_type:
+    :param kwargs: Possible kwargs: `num_grad_steps`, `rs_size`
+    :return:
+    """
+    device = x.device
+    assert y.device == device, y.device
+
+    model.requires_grad_(False)
+
+    w = Warp(range(x.shape[1]),
+             concentration1_prior=LogNormalPrior(torch.tensor(0.0, device=device), torch.tensor(.75, device=device)),
+             concentration0_prior=LogNormalPrior(torch.tensor(0.0, device=device), torch.tensor(.75, device=device)),
+             eps=1e-12)
+    w.to(device)
+
+    def fast_nll(x):  # noqa actually used with `eval` below
+        model.requires_grad_(False)
+        if model.style_encoder is not None:
+            style = torch.zeros(1, 1, dtype=torch.int64, device=device)
+            utils.print_once("WARNING: using style 0 for input warping, this is set for nonmyopic BO setting.")
+        else:
+            style = None
+        logits = model(x[:, None], y[:, None], x[:, None], style=style, only_return_standard_out=True)
+        loss = model.criterion(logits, y[:, None]).squeeze(1)
+        return loss
+
+    def true_nll(x): # noqa actually used with `eval` below
+        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
+        model.requires_grad_(False)
+        total_nll = 0.
+        for cutoff in range(len(x)):
+            logits = model(x[:cutoff, None], y[:cutoff, None], x[cutoff:cutoff + 1, None])
+            total_nll = total_nll + model.criterion(logits, y[cutoff:cutoff + 1, None]).squeeze()
+        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
+        return total_nll
+
+    def repeated_true_nll(x): # noqa actually used with `eval` below
+        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
+        model.requires_grad_(False)
+        total_nll = 0.
+        for i in range(5):
+            rs = np.random.RandomState(i)
+            shuffle_idx = rs.permutation(len(x))
+            x_ = x.clone()[shuffle_idx]
+            y_ = y.clone()[shuffle_idx]
+            for cutoff in range(len(x)):
+                logits = model(x_[:cutoff, None], y_[:cutoff, None], x_[cutoff:cutoff + 1, None])
+                total_nll = total_nll + model.criterion(logits, y_[cutoff:cutoff + 1, None]).squeeze()
+        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
+        return total_nll
+
+    def repeated_true_100_nll(x): # noqa actually used with `eval` below
+        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
+        model.requires_grad_(False)
+        total_nll = 0.
+        for i in range(100):
+            rs = np.random.RandomState(i)
+            shuffle_idx = rs.permutation(len(x))
+            x_ = x.clone()[shuffle_idx]
+            y_ = y.clone()[shuffle_idx]
+            for cutoff in range(len(x)):
+                logits = model(x_[:cutoff, None], y_[:cutoff, None], x_[cutoff:cutoff + 1, None])
+                total_nll = total_nll + model.criterion(logits, y_[cutoff:cutoff + 1, None]).squeeze()
+        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
+        return total_nll / 100
+
+    def batched_repeated_chunked_true_nll(x): # noqa actually used with `eval` below
+        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
+        assert len(x.shape) == 2 and len(y.shape) == 1
+        model.requires_grad_(False)
+        n_features = x.shape[1] if len(x.shape) > 1 else 1
+        batch_size = 10
+
+        X = []
+        Y = []
+
+        for i in range(batch_size):
+            #if i == 0:
+            #    shuffle_idx = list(range(len(x)))
+            #else:
+            rs = np.random.RandomState(i)
+            shuffle_idx = rs.permutation(len(x))
+            X.append(x.clone()[shuffle_idx])
+            Y.append(y.clone()[shuffle_idx])
+        X = torch.stack(X, dim=1).view((x.shape[0], batch_size, n_features))
+        Y = torch.stack(Y, dim=1).view((x.shape[0], batch_size, 1))
+
+        total_nll = 0.
+        batch_indizes = sorted(list(set(np.linspace(0, len(x), 10, dtype=int))))
+
+        for chunk_start, chunk_end in zip(batch_indizes[:-1], batch_indizes[1:]):
+            X_cutoff = X[:chunk_start]
+            Y_cutoff = Y[:chunk_start]
+            X_after_cutoff = X[chunk_start:chunk_end]
+            Y_after_cutoff = Y[chunk_start:chunk_end]
+
+            pending_x = X_after_cutoff.reshape(X_after_cutoff.shape[0], batch_size, n_features)  # n_pen x batch_size x n_feat
+            observed_x = X_cutoff.reshape(X_cutoff.shape[0], batch_size, n_features)  # n_obs x batch_size x n_feat
+            X_tmp = torch.cat((observed_x, pending_x), dim=0)  # (n_obs+n_pen) x batch_size x n_feat
+
+            logits = model((X_tmp, Y_cutoff), single_eval_pos=int(chunk_start))
+            total_nll = total_nll + model.criterion(logits, Y_after_cutoff).sum()
+        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
+        return total_nll
+
+    def batched_repeated_true_nll(x): # noqa actually used with `eval` below
+        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
+        model.requires_grad_(False)
+        n_features = x.shape[1] if len(x.shape) > 1 else 1
+        batch_size = 10
+
+        X = []
+        Y = []
+
+        for i in range(batch_size):
+            #if i == 0:
+            #    shuffle_idx = list(range(len(x)))
+            #else:
+            rs = np.random.RandomState(i)
+            shuffle_idx = rs.permutation(len(x))
+            X.append(x.clone()[shuffle_idx])
+            Y.append(y.clone()[shuffle_idx])
+        X = torch.cat(X, dim=1).reshape((x.shape[0], batch_size, n_features))
+        Y = torch.cat(Y, dim=1).reshape((x.shape[0], batch_size, 1))
+
+        total_nll = 0.
+
+        for cutoff in range(0, len(x)):
+            X_cutoff = X[:cutoff]
+            Y_cutoff = Y[:cutoff]
+            X_after_cutoff = X[cutoff:cutoff+1]
+            Y_after_cutoff = Y[cutoff:cutoff+1]
+
+            pending_x = X_after_cutoff.reshape(X_after_cutoff.shape[0], batch_size, n_features)  # n_pen x batch_size x n_feat
+            observed_x = X_cutoff.reshape(X_cutoff.shape[0], batch_size, n_features)  # n_obs x batch_size x n_feat
+            X_tmp = torch.cat((observed_x, pending_x), dim=0)  # (n_obs+n_pen) x batch_size x n_feat
+
+            pad_y = torch.zeros((X_after_cutoff.shape[0], batch_size, 1))  # (n_obs+n_pen) x batch_size
+            Y_tmp = torch.cat((Y_cutoff, pad_y), dim=0)
+
+            logits = model((X_tmp, Y_tmp), single_eval_pos=cutoff)
+            total_nll = total_nll + model.criterion(logits, Y_after_cutoff).sum()
+        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
+        return total_nll
+
+    def one_out_nll(x):  # noqa actually used with `eval` below
+        assert model.style_encoder is None, "one out nll not implemented for style encoder, see above for an example impl"
+        # x shape: (n, d)
+        # iterate over a pre-defined set of
+        model.requires_grad_(False)
+        #indices = one_out_indices_sampled_per_num_obs[len(x)]
+        indices = list(range(x.shape[0]))
+
+        # create batch by moving the one out index to the end
+        eval_x = x[indices][None] # shape (1, 10, d)
+        eval_y = y[indices][None] # shape (1, 10, 1)
+        # all other indices are used for training
+        train_x = torch.stack([torch.cat([x[:i], x[i + 1:]]) for i in indices], 1)
+        train_y = torch.stack([torch.cat([y[:i], y[i + 1:]]) for i in indices], 1)
+
+        logits = model(train_x, train_y, eval_x)
+        return model.criterion(logits, eval_y).squeeze(0)
+
+    def subset_nll(x):  # noqa actually used with `eval` below
+        assert model.style_encoder is None, "subset nll not implemented for style encoder, see above for an example impl"
+        # x shape: (n, d)
+        # iterate over a pre-defined set of
+        model.requires_grad_(False)
+        eval_indices = torch.tensor(subsets[len(x)])
+        train_indices = torch.tensor(neg_subsets[len(x)])
+
+        # batch by using all eval_indices
+        eval_x = x[eval_indices.flatten()].view(eval_indices.shape + (-1,)) # shape (10, n//2, d)
+        eval_y = y[eval_indices.flatten()].view(eval_indices.shape + (-1,)) # shape (10, n//2, 1)
+        # all other indices are used for training
+        train_x = x[train_indices.flatten()].view(train_indices.shape + (-1,)) # shape (10, n//2, d)
+        train_y = y[train_indices.flatten()].view(train_indices.shape + (-1,)) # shape (10, n//2, 1)
+
+        logits = model(train_x.transpose(0, 1), train_y.transpose(0, 1), eval_x.transpose(0, 1))
+        return model.criterion(logits, eval_y.transpose(0, 1))
+
+    if opt_method == "log_vs_nolog":
+        log_vs_nonlog(x, w, eval(nll_type + '_nll'),
+            ignore_prior=True, # true_nll=repeated_true_100_nll,
+            **kwargs)
+    elif opt_method == "lbfgs":
+        fit_lbfgs_with_restarts(
+            x, w, eval(nll_type + '_nll'),
+            ignore_prior=True, old_solution=old_solution, # true_nll=repeated_true_100_nll,
+            **kwargs)
+    elif opt_method == "lbfgs_w_prior":
+        fit_lbfgs_with_restarts(
+            x, w, eval(nll_type + '_nll'),
+            ignore_prior=False, old_solution=old_solution,  # true_nll=repeated_true_100_nll,
+            **kwargs)
+    else:
+        raise ValueError(opt_method)
+
+    return w
diff --git a/private_multitask_pfn/PFNs/pfns/train.py b/private_multitask_pfn/PFNs/pfns/train.py
new file mode 100644
index 0000000..972a7b1
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/train.py
@@ -0,0 +1,878 @@
+from __future__ import annotations
+
+import copy
+
+import inspect
+
+import itertools
+import logging
+import time
+from contextlib import nullcontext
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import yaml
+from torch import nn
+from tqdm import tqdm
+
+from . import positional_encodings, priors, utils
+from .bar_distribution import (
+    BarDistribution,
+    FullSupportBarDistribution,
+    get_bucket_limits,
+    get_custom_bar_dist,
+)
+from .priors import prior
+from .transformer import TransformerModel
+from .utils import (
+    get_cosine_schedule_with_warmup,
+    get_openai_lr,
+    get_uniform_single_eval_pos_sampler,
+    get_weighted_single_eval_pos_sampler,
+    init_dist,
+    StoreDictKeyPair,
+)
+import wandb
+import os
+import json
+
+class ProbabilisticRankingLoss(nn.Module):
+    
+    def __init__(self, reduction="none"):
+        super().__init__()
+        self.reduction = reduction
+    
+    def forward(self, output, y_true, task_ids):
+        mu = output[..., 0]
+        sigma = output[..., 1].exp().sqrt()
+        
+        assert torch.all(task_ids[:, 0] == task_ids.max(1)[0]) and torch.all(task_ids[:, 0] == task_ids.min(1)[0])
+        task_ids = task_ids[:, 0].squeeze()
+        
+        results = torch.zeros_like(y_true)
+        
+        for task_id in task_ids.unique():
+            task_mask = (task_ids == task_id)
+            task_mu = mu[task_mask]
+            task_sigma = sigma[task_mask]
+            task_y_true = y_true[task_mask]
+            
+            # Compute pairwise differences
+            y_diff = task_y_true.unsqueeze(0) - task_y_true.unsqueeze(1)  # Shape: (N, N)
+            mu_diff = task_mu.unsqueeze(0) - task_mu.unsqueeze(1)        # Shape: (N, N)
+            sigma_combined = torch.sqrt(task_sigma.unsqueeze(0)**2 + task_sigma.unsqueeze(1)**2)  # Shape: (N, N)
+
+            # Mask to ignore self-comparisons and identical targets
+            valid_mask = y_diff != 0  # Exclude y_diff == 0 (no ranking required)
+
+            # Compute probabilistic scores
+            prob = 0.5 * (1.0 + torch.erf(mu_diff / (sigma_combined * torch.sqrt(torch.tensor(2.0)))))
+
+            # Loss for pairs where y_true[i] > y_true[j]
+            positive_loss = -torch.log(prob + 1e-8) * (y_diff > 0)
+
+            # Loss for pairs where y_true[i] < y_true[j]
+            negative_loss = -torch.log(1.0 - prob + 1e-8) * (y_diff < 0)
+
+            # Combine losses and apply valid mask
+            total_loss = (positive_loss + negative_loss) * valid_mask
+    
+            loss = total_loss.sum(1) / valid_mask.sum(1)
+            results[task_mask] = loss
+            
+        if self.reduction == "mean":
+            return results.mean(0)
+
+        return loss
+    
+
+class PairwiseRankingLoss(nn.Module):
+    def __init__(self, bar_loss):
+        """
+        Initializes the PairwiseRankingLoss module.
+        """
+        super().__init__()
+        self.criterion = bar_loss
+
+    def forward(self, output, y_true, task_ids):
+        """
+        Compute the pairwise ranking loss based on incorrect orderings.
+
+        Args:
+            output (torch.Tensor): Predicted scores for the samples.
+            target (torch.Tensor): Ground truth values for the samples.
+
+        Returns:
+            torch.Tensor: The computed pairwise ranking loss (fraction of incorrectly ordered pairs).
+        """
+        mu = self.criterion.mean(output)
+        
+        assert torch.all(task_ids[:, 0] == task_ids.max(1)[0]) and torch.all(task_ids[:, 0] == task_ids.min(1)[0])
+        task_ids = task_ids[:, 0].squeeze()
+        
+        results = torch.zeros_like(y_true)
+       
+        for task_id in task_ids.unique():
+            task_mask = (task_ids == task_id)
+            task_mu = mu[task_mask]
+            task_y_true = y_true[task_mask]
+
+            # Compute pairwise differences
+            y_diff = task_y_true.unsqueeze(0) - task_y_true.unsqueeze(1)  # Shape: (N, N)
+            mu_diff = task_mu.unsqueeze(0) - task_mu.unsqueeze(1)        # Shape: (N, N)
+
+            # Mask to ignore self-comparisons and identical targets
+            valid_mask = y_diff != 0  # Exclude y_diff == 0 (no ranking required)
+
+            # Loss for pairs where y_true[i] > y_true[j]
+            positive_loss = (mu_diff <= 0).float() * (y_diff > 0)
+
+            # Loss for pairs where y_true[i] < y_true[j]
+            negative_loss = (mu_diff >= 0).float() * (y_diff < 0)
+
+            # Combine losses and apply valid mask
+            total_loss = (positive_loss + negative_loss) * valid_mask
+
+            # Aggregate losses for the task
+            task_loss = total_loss.sum() / valid_mask.sum()
+            results[task_mask] = task_loss
+
+        return results
+    
+
+class GaussianRankingLoss(nn.Module):
+    def __init__(self, gaussian_weight=1.0, ranking_weight=1.0):
+        super().__init__()
+        self.gaussian_weight = gaussian_weight
+        self.ranking_weight = ranking_weight
+        self.gaussian_loss = nn.GaussianNLLLoss(full=True, reduction="none")
+        self.ranking_loss = ProbabilisticRankingLoss(reduction="none")
+        
+    def forward(self, output, y_true, task_ids):
+        mu = output[..., 0]
+        var = output[..., 1].exp()
+        
+        gaussian = self.gaussian_loss(mu, y_true, var)
+        ranking = self.ranking_loss(output, y_true, task_ids)
+        
+        return self.gaussian_weight * gaussian + self.ranking_weight * ranking
+
+
+class BarRankingLoss(nn.Module):
+    def __init__(self, bar_loss, bar_weight=1.0, ranking_weight=1.0):
+        super().__init__()
+        self.bar_weight = bar_weight
+        self.ranking_weight = ranking_weight
+        self.bar_loss = bar_loss
+        self.ranking_loss = PairwiseRankingLoss(bar_loss)
+        
+    def forward(self, output, y_true, task_ids):
+        bar = self.bar_loss(output, y_true)
+        ranking = self.ranking_loss(output, y_true, task_ids)
+        
+        return self.bar_weight * bar + self.ranking_weight * ranking
+
+
+    
+class Losses:
+    gaussian = nn.GaussianNLLLoss(full=True, reduction="none")
+    mse = nn.MSELoss(reduction="none")
+    ce = lambda num_classes: nn.CrossEntropyLoss(
+        reduction="none", weight=torch.ones(num_classes)
+    )
+    bce = nn.BCEWithLogitsLoss(reduction="none")
+    get_BarDistribution = BarDistribution
+    prob = ProbabilisticRankingLoss()
+
+        
+    
+    def __repr__(self):
+        return f"ProbabilisticRankingLoss"
+
+
+def train(
+    train_loader: prior.PriorDataLoader | callable,
+    eval_loader: prior.PriorDataLoader | callable,
+    criterion,
+    encoder_generator,
+    task_encoder_generator,
+    emsize=200,
+    nhid=200,
+    nlayers=6,
+    nhead=2,
+    dropout=0.0,
+    epochs=10,
+    steps_per_epoch=100,
+    batch_size=200,
+    seq_len=10,
+    lr=None,
+    weight_decay=0.0,
+    warmup_epochs=10,
+    input_normalization=False,
+    y_encoder_generator=None,
+    pos_encoder_generator=None,
+    decoder_dict={},
+    train_extra_prior_kwargs_dict={},
+    eval_extra_prior_kwargs_dict={},
+    scheduler_generator=get_cosine_schedule_with_warmup,
+    load_weights_from_this_state_dict=None,
+    validation_period=10,
+    single_train_pos_gen=None,
+    gpu_device="cuda:0",
+    aggregate_k_gradients=1,
+    verbose=True,
+    style_encoder_generator=None,
+    epoch_callback=None,
+    step_callback=None,
+    continue_model=None,
+    continue_scheduler=None,
+    continue_optimizer=None,
+    continue_epoch=None,
+    initializer=None,
+    initialize_with_model=None,
+    train_mixed_precision=False,
+    efficient_eval_masking=True,
+    border_decoder=None,
+    num_global_att_tokens=0,
+    progress_bar=False,
+    print_every=5,
+    return_model=False,
+    num_features=None,
+    num_tasks=None,
+    attn_type="axial",
+    dirname=None,
+    target_aware=False,
+    task_embed_type="linear",
+    save_val_results=False,
+    **model_extra_args,
+):
+    device = gpu_device if torch.cuda.is_available() else "cpu:0"
+    # print(f"Using {device} device")
+    using_dist, rank, device = init_dist(device)
+    single_train_pos_gen = (
+        single_train_pos_gen
+        if callable(single_train_pos_gen)
+        else lambda: single_train_pos_gen
+    )
+    def train_pos_seq_len_sampler(*args):
+        single_train_pos = single_train_pos_gen(*args)
+        return single_train_pos, seq_len
+
+    # # LILY: what should this be
+    # single_eval_pos_gen = utils.get_uniform_single_eval_pos_sampler(
+    #     seq_len - num_tasks, min_len=num_tasks
+    # )
+    def eval_pos_seq_len_sampler(*args):
+        single_eval_pos = single_train_pos_gen(*args)
+        return single_eval_pos, seq_len
+    
+    if inspect.isclass(train_loader) and issubclass(
+        train_loader, prior.PriorDataLoader
+    ):
+        train_data_class = train_loader
+    else:
+        train_data_class = priors.utils.get_batch_to_dataloader(
+            train_loader
+        )
+    
+    if inspect.isclass(eval_loader) and issubclass(
+        eval_loader, prior.PriorDataLoader
+    ):
+        eval_loader_class = eval_loader
+    else:
+        eval_loader_class = priors.utils.get_batch_to_dataloader(
+            eval_loader
+        )
+
+
+    train_dl = train_data_class(
+        num_steps=steps_per_epoch,
+        batch_size=batch_size,
+        eval_pos_seq_len_sampler=train_pos_seq_len_sampler,
+        seq_len_maximum=seq_len,
+        device=device,
+        **train_extra_prior_kwargs_dict,
+    )
+    
+    eval_dl = eval_loader_class(
+        num_steps=steps_per_epoch,
+        batch_size=batch_size,
+        eval_pos_seq_len_sampler=eval_pos_seq_len_sampler,
+        seq_len_maximum=seq_len,
+        device=device,
+        **eval_extra_prior_kwargs_dict,
+    )
+
+    test_batch: prior.Batch = train_dl.get_test_batch()
+    style_def = test_batch.style
+    # print(
+    #     f"Style definition of first 3 examples: {style_def[:3] if style_def is not None else None}"
+    # )
+    style_encoder = (
+        style_encoder_generator(style_def.shape[1], emsize)
+        if (style_def is not None)
+        else None
+    )
+    pos_encoder = (pos_encoder_generator or positional_encodings.NoPositionalEncoding)(
+        emsize, seq_len * 2
+    )
+    if isinstance(criterion, nn.GaussianNLLLoss):
+        n_out = 2
+    elif (
+        isinstance(criterion, BarDistribution)
+        or "BarDistribution" in criterion.__class__.__name__
+    ):  # TODO remove this fix (only for dev)
+        n_out = criterion.num_bars
+    elif isinstance(criterion, nn.CrossEntropyLoss):
+        n_out = criterion.weight.shape[0]
+    elif isinstance(criterion, ProbabilisticRankingLoss):
+        n_out = 2
+    elif isinstance(criterion, GaussianRankingLoss):
+        n_out = 2
+    elif isinstance(criterion, BarRankingLoss):
+        n_out = criterion.bar_loss.num_bars
+    else:
+        n_out = 1
+
+    # border_decoder = None if border_decoder is None else border_decoder(emsize, criterion.num_bars + 1).to(device)
+
+    if continue_model:
+        model = continue_model
+    else:
+        decoder_dict = decoder_dict if decoder_dict else {"standard": (None, n_out)}
+
+        decoder_once_dict = {}
+        if test_batch.mean_prediction is not None:
+            decoder_once_dict["mean_prediction"] = decoder_dict["standard"]
+
+        if "axial" in attn_type:
+            encoder = encoder_generator(1, emsize)
+        else:
+            if task_embed_type == "feature":
+                encoder = encoder_generator(train_dl.num_features, train_dl.num_tasks, emsize)
+            else:
+                encoder = encoder_generator(train_dl.num_features, emsize)
+            
+        if task_embed_type == "linear":
+            task_encoder = task_encoder_generator(1, emsize)
+        elif task_embed_type == "onehot_linear":
+            task_encoder = task_encoder_generator(train_dl.num_tasks, emsize)
+        elif task_embed_type in ["feature", "task_attn", "task_attn_shared", "task_attn_opt", "hier", "hier_single"]:
+            task_encoder = None
+        else:
+            raise ValueError("Invalid task_embed_type")
+        
+        # transformer_class = HierarchicalTransformerModel if "hier" in task_embed_type else TransformerModel
+        model = TransformerModel(
+            encoder=encoder,
+            nhead=nhead,
+            ninp=emsize,
+            nhid=nhid,
+            nlayers=nlayers,
+            dropout=dropout,
+            style_encoder=style_encoder,
+            y_encoder=y_encoder_generator(1, emsize),
+            input_normalization=input_normalization,
+            pos_encoder=pos_encoder,
+            decoder_dict=decoder_dict,
+            init_method=initializer,
+            efficient_eval_masking=efficient_eval_masking,
+            decoder_once_dict=decoder_once_dict,
+            num_global_att_tokens=num_global_att_tokens,
+            num_features=num_features,
+            num_tasks=num_tasks,
+            attn_type=attn_type,
+            task_encoder=task_encoder,
+            task_embed_type=task_embed_type,
+            include_global=(not "single" in task_embed_type),
+            hierarchical=("hier" in task_embed_type),
+            **model_extra_args,
+        )
+    model.criterion = criterion
+    if load_weights_from_this_state_dict is not None:
+        model.load_state_dict(load_weights_from_this_state_dict)
+    if initialize_with_model is not None:
+        model.init_from_small_model(initialize_with_model)
+
+    print(
+        f"Using a Transformer with {sum(p.numel() for p in model.parameters())/1000/1000:.{2}f} M parameters"
+    )
+
+    try:
+        for (k, v), (k2, v2) in zip(
+            model.state_dict().items(), initialize_with_model.state_dict().items()
+        ):
+            print(k, ((v - v2) / v).abs().mean(), v.shape)
+    except Exception:
+        pass
+
+    # learning rate
+    if continue_optimizer:
+        optimizer = continue_optimizer
+        scheduler = continue_scheduler
+    else:
+        if lr is None:
+            lr = get_openai_lr(model)
+            print(f"Using OpenAI max lr of {lr}.")
+        optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
+        scheduler = scheduler_generator(
+            optimizer, warmup_epochs, epochs if epochs is not None else 100
+        )  # when training for fixed time lr schedule takes 100 steps
+
+    if return_model:
+        return model, optimizer, scheduler
+    
+    wandb.watch(model, log="all")
+    model.to(device)
+    # optimizer is on device
+    for state in optimizer.state.values():
+        for k, v in state.items():
+            if isinstance(v, torch.Tensor):
+                state[k] = v.to(device)
+                
+    if using_dist:
+        print("Distributed training")
+        model = torch.nn.parallel.DistributedDataParallel(
+            model,
+            device_ids=[rank],
+            output_device=rank,
+            broadcast_buffers=False,
+            find_unused_parameters=test_batch.mean_prediction is not None,
+        )
+        train_dl.model = (
+            model.module
+        )  # use local model, should not use multi-gpu functionality..
+        eval_dl.model = model.module
+    else:
+        train_dl.model = model
+        eval_dl.model = model
+
+    scaler = torch.amp.GradScaler("cuda") if train_mixed_precision else None
+
+    # check that everything uses up-to-date APIs
+    utils.check_compatibility(train_dl)
+
+    best_val_score = 1e9
+    best_model = None
+    best_train_score = 1e9
+    best_model_train = None
+
+    def run_epoch(train_mode=True, dl=train_dl, epoch=0):
+        if train_mode:
+            model.train()
+        else:
+            model.eval()
+        total_loss = 0.0
+        total_positional_losses = 0.0
+        total_positional_losses_recorded = 0
+        nan_steps = 0
+        ignore_steps = 0
+        before_get_batch = time.time()
+        
+        n_batches = len(dl)
+        if not train_mode:
+            n_batches = min(512, n_batches)
+            
+        if train_mode:
+            assert (
+                len(dl) % aggregate_k_gradients == 0
+            ), "Please set the number of steps per epoch s.t. `aggregate_k_gradients` divides it."
+            tqdm_iter = (
+                tqdm(range(n_batches), desc="Training Epoch %d" % epoch)
+                if rank == 0 and progress_bar
+                else None
+            )  # , disable=not verbose
+        else:
+            tqdm_iter = (
+                tqdm(range(n_batches), desc="Eval Epoch %d" % epoch)
+                if rank == 0 and progress_bar
+                else None
+            )
+
+        for batch, full_data in enumerate(dl):
+            if batch > n_batches:
+                break
+            
+            data = (
+                full_data.style.to(device) if full_data.style is not None else None,
+                full_data.x.to(device),
+                full_data.y.to(device),
+                full_data.task_id.to(device) if full_data.task_id is not None else None,
+            )
+            targets = full_data.target_y.to(device)
+            single_eval_pos = full_data.single_eval_pos
+            # print("epoch", epoch, "single_eval_pos", single_eval_pos)
+
+            def get_metrics():
+                return (
+                    total_loss / n_batches,
+                    (
+                        total_positional_losses / total_positional_losses_recorded
+                    ).tolist(),
+                    time_to_get_batch,
+                    forward_time,
+                    step_time,
+                    nan_steps.cpu().item() / (batch + 1),
+                    ignore_steps.cpu().item() / (batch + 1),
+                    n_batches * batch_size,
+                )
+
+            tqdm_iter.update() if tqdm_iter is not None else None
+            if using_dist and not (
+                batch % aggregate_k_gradients == aggregate_k_gradients - 1
+            ):
+                cm = model.no_sync()
+            else:
+                cm = nullcontext()
+            with cm:
+                time_to_get_batch = time.time() - before_get_batch
+                before_forward = time.time()
+                try:
+                    metrics_to_log = {}
+                    with torch.amp.autocast("cuda", enabled=scaler is not None):
+                        # If style is set to None, it should not be transferred to device
+                        if train_mode:
+                            out = model(
+                                tuple(
+                                    e.to(device) if torch.is_tensor(e) else e for e in data
+                                ),
+                                single_eval_pos=single_eval_pos,
+                                only_return_standard_out=False,
+                            )
+                        else:
+                            with torch.no_grad():
+                                out = model(
+                                    tuple(
+                                        e.to(device)
+                                        if torch.is_tensor(e)
+                                        else e
+                                        for e in data
+                                    ),
+                                    single_eval_pos=single_eval_pos,
+                                    only_return_standard_out=False,
+                                )
+
+                        # this handling is for training old models only, this can be deleted soon(ish)
+                        # to only support models that return a tuple of dicts
+                        out, output_once = (
+                            out if isinstance(out, tuple) else (out, None)
+                        )
+                        output = out["standard"] if isinstance(out, dict) else out
+
+                        forward_time = time.time() - before_forward
+
+                        # only evaluate on target_task
+                        # x is seq, batch, feature
+
+                        if single_eval_pos is not None:
+                            targets = targets[single_eval_pos:]
+                            target_task_ids = full_data.task_id[single_eval_pos:]
+                        else:
+                            target_task_ids = full_data.task_id
+                        
+                        if len(targets.shape) == len(output.shape):
+                            # this implies the prior uses a trailing 1 dimesnion
+                            # below we assume this not to be the case
+                            targets = targets.squeeze(-1)
+                        assert targets.shape == output.shape[:-1], (
+                            f"Target shape {targets.shape} "
+                            "does not match output shape {output.shape}"
+                        )
+                        if isinstance(criterion, nn.GaussianNLLLoss):
+                            assert (
+                                output.shape[-1] == 2
+                            ), "need to write a little bit of code to handle multiple regression targets at once"
+
+                            mean_pred = output[..., 0]
+                            var_pred = output[..., 1].exp()
+                            losses = criterion(mean_pred, targets, var=var_pred)
+                        elif isinstance(criterion, (nn.MSELoss, nn.BCEWithLogitsLoss)):
+                            targets[torch.isnan(targets)] = -100
+                            losses = criterion(output.flatten(), targets.flatten())
+                        elif isinstance(criterion, nn.CrossEntropyLoss):
+                            targets[torch.isnan(targets)] = -100
+                            print(f"{targets.min()=}, {targets.max()=}")
+                            losses = criterion(
+                                output.reshape(-1, n_out), targets.long().flatten()
+                            )
+                        elif isinstance(criterion, ProbabilisticRankingLoss):
+                            losses = criterion(output, targets, target_task_ids)
+                        elif isinstance(criterion, GaussianRankingLoss):
+                            losses = criterion(output, targets, target_task_ids)
+                        elif isinstance(criterion, BarRankingLoss):
+                            losses = criterion(output, targets, target_task_ids)
+                        elif border_decoder is not None:
+
+                            def apply_batch_wise_criterion(i):
+                                output_, targets_, borders_ = (
+                                    output_adaptive[:, i],
+                                    targets[:, i],
+                                    borders[i],
+                                )
+                                criterion_ = get_custom_bar_dist(
+                                    borders_, criterion
+                                ).to(device)
+                                return criterion_(output_, targets_)
+
+                            output_adaptive, borders = (
+                                out["adaptive_bar"],
+                                output_once["borders"],
+                            )
+                            losses_adaptive_bar = torch.stack(
+                                [
+                                    apply_batch_wise_criterion(i)
+                                    for i in range(output_adaptive.shape[1])
+                                ],
+                                1,
+                            )
+                            losses_fixed_bar = criterion(output, targets)
+                            losses = (losses_adaptive_bar + losses_fixed_bar) / 2
+
+                            metrics_to_log = {
+                                **metrics_to_log,
+                                **{
+                                    "loss_fixed_bar": losses_fixed_bar.mean()
+                                    .cpu()
+                                    .detach()
+                                    .item(),
+                                    "loss_adaptive_bar": losses_adaptive_bar.mean()
+                                    .cpu()
+                                    .detach()
+                                    .item(),
+                                },
+                            }
+                        elif (
+                            isinstance(criterion, BarDistribution)
+                            and full_data.mean_prediction
+                        ):
+                            assert "mean_prediction" in output_once
+                            utils.print_once("Using mean prediction for loss")
+                            losses = criterion(
+                                output,
+                                targets,
+                                mean_prediction_logits=output_once["mean_prediction"],
+                            )
+                            # the mean pred loss appears as the last per sequence
+                        else:
+                            losses = criterion(output, targets)
+                        losses = losses.flatten()
+                        loss, nan_share = utils.torch_nanmean(
+                            losses.mean(0, keepdim=True), return_nanshare=True
+                        )
+                        loss_scaled = loss / aggregate_k_gradients
+
+                    if scaler:
+                        loss_scaled = scaler.scale(loss_scaled)
+                        
+                    if train_mode:
+                        loss_scaled.backward()
+
+                        if batch % aggregate_k_gradients == aggregate_k_gradients - 1:
+                            if scaler:
+                                scaler.unscale_(optimizer)
+                            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                            if scaler:
+                                scaler.step(optimizer)
+                                scaler.update()
+                            else:
+                                optimizer.step()
+                            optimizer.zero_grad()
+
+                    step_time = time.time() - before_forward
+
+                    if not torch.isnan(loss):
+                        total_loss += loss.cpu().detach().item()
+                        total_positional_losses += (
+                            losses.mean(1).cpu().detach()
+                            if single_eval_pos is None
+                            else nn.functional.one_hot(
+                                torch.tensor(single_eval_pos), seq_len
+                            )
+                            * utils.torch_nanmean(
+                                losses[: seq_len - single_eval_pos].mean(0)
+                            )
+                            .cpu()
+                            .detach()
+                        )
+
+                        total_positional_losses_recorded += (
+                            torch.ones(seq_len)
+                            if single_eval_pos is None
+                            else nn.functional.one_hot(
+                                torch.tensor(single_eval_pos), seq_len
+                            )
+                        )
+
+                        metrics_to_log = {
+                            **metrics_to_log,
+                            **{f"train_loss": loss, "single_eval_pos": single_eval_pos},
+                        }
+                        # wandb.log(metrics_to_log)
+                        if step_callback is not None and rank == 0:
+                            step_callback(metrics_to_log)
+                        nan_steps += nan_share
+                        ignore_steps += (targets == -100).float().mean()
+                except Exception as e:
+                    print("Invalid step encountered, skipping...")
+                    print(e)
+                    raise (e)
+
+            # total_loss, total_positional_losses, time_to_get_batch, forward_time, step_time, nan_share, ignore_share = get_metrics()
+            if tqdm_iter:
+                tqdm_iter.set_postfix(
+                    {
+                        "loss": total_loss / (batch + 1),
+                        "data": time_to_get_batch,
+                        "step": step_time,
+                    }
+                )
+
+            before_get_batch = time.time()
+        metrics = get_metrics()
+        return metrics
+
+    total_loss = float("inf")
+    total_positional_losses = float("inf")
+    # Initially test the epoch callback function
+    if epoch_callback is not None and rank == 0:
+        epoch_callback(model, 1, data_loader=train_dl, scheduler=scheduler)
+    # start_epochs = 1 if continue_epoch is None else continue_epoch + 1
+    start_epochs = 0 if continue_epoch is None else continue_epoch
+    
+    # for epoch in range(start_epochs, epochs + 1) if epochs is not None else itertools.count(1):
+    epoch = start_epochs
+    while True:
+        epoch += 1
+        if epochs is not None and epoch > epochs:
+            break
+        epoch_start_time = time.time()
+        try:
+            (
+                total_loss,
+                total_positional_losses,
+                time_to_get_batch,
+                forward_time,
+                step_time,
+                nan_share,
+                ignore_share,
+                data_size,
+            ) = run_epoch(train_mode=True, dl=train_dl, epoch=epoch)
+            
+            wandb.log({
+                "epoch": epoch, 
+                "data_size": data_size * epoch,
+                "train_loss": total_loss,
+            })
+            # save checkpoint
+            torch.save(model.state_dict(), os.path.join(dirname, "final_model.pth"))
+            torch.save({
+                "epoch": epoch,
+                "model_state_dict": model.state_dict(),
+                "optimizer_state_dict": optimizer.state_dict(),
+                "scheduler_state_dict": scheduler.state_dict(),
+                "total_loss": total_loss,
+                "seed": torch.initial_seed(),
+            }, os.path.join(dirname, "checkpoint.pth")
+            )
+            with open(os.path.join(dirname, 'result.json'), 'w') as f:
+                json.dump({"epoch": epoch, "train_loss": total_loss}, f)
+            
+            if total_loss < best_train_score:
+                best_train_score = total_loss
+                torch.save(model.state_dict(), os.path.join(dirname, "best_train_model.pth"))
+            
+        except Exception as e:
+            print("Invalid epoch encountered, skipping...")
+            print(e)
+            raise (e)
+        # if hasattr(dl, "validate") and epoch % validation_period == 0:
+        if epoch % validation_period == 0:
+            with torch.no_grad():
+                val_score = run_epoch(train_mode=False, dl=eval_dl, epoch=epoch)[0]
+                wandb.log({
+                    "eval_loss": val_score,
+                }, step=wandb.run.step)
+                if val_score < best_val_score:
+                    best_val_score = val_score
+                    torch.save(model.state_dict(), os.path.join(dirname, "best_model.pth"))
+                if save_val_results:
+                    torch.save(model.state_dict(), os.path.join(dirname, f"model_{epoch}.pth"))
+
+        else:
+            val_score = None
+
+        if verbose and epoch % print_every == 0:
+            if val_score is not None:
+                text = (
+                    "-" * 89
+                    + "\n"
+                    + f"| end of epoch {epoch:3d} | time: {(time.time() - epoch_start_time):5.2f}s | train loss {total_loss:5.2f} | val loss {val_score:5.2f} |\n"
+                    # f"pos losses {','.join([f'{l:5.2f}' for l in total_positional_losses])}, lr {scheduler.get_last_lr()[0]}"
+                    f" data time {time_to_get_batch:5.2f} step time {step_time:5.2f}"
+                    f" forward time {forward_time:5.2f}"
+                    # f' nan share {nan_share:5.2f} ignore share (for classification tasks) {ignore_share:5.4f}'
+                    # + (f" val score {val_score:5.2f}" if val_score is not None else "")
+                    + "\n"
+                    + "-" * 89
+                    + "\n"
+                )
+            else:
+                text = (
+                    "-" * 89
+                    + "\n"
+                    + f"| end of epoch {epoch:3d} | time: {(time.time() - epoch_start_time):5.2f}s | train loss {total_loss:5.2f} |\n"
+                    # f"pos losses {','.join([f'{l:5.2f}' for l in total_positional_losses])}, lr {scheduler.get_last_lr()[0]}"
+                    f" data time {time_to_get_batch:5.2f} step time {step_time:5.2f}"
+                    f" forward time {forward_time:5.2f}"
+                    # f' nan share {nan_share:5.2f} ignore share (for classification tasks) {ignore_share:5.4f}'
+                    # + (f" val score {val_score:5.2f}" if val_score is not None else "")
+                    + "\n"
+                    + "-" * 89
+                    + "\n"
+                )
+            logging.info(text)
+            # print("-" * 89)
+            # print(
+            #     f"| end of epoch {epoch:3d} | time: {(time.time() - epoch_start_time):5.2f}s | mean loss {total_loss:5.2f} | "
+            #     # f"pos losses {','.join([f'{l:5.2f}' for l in total_positional_losses])}, lr {scheduler.get_last_lr()[0]}"
+            #     f" data time {time_to_get_batch:5.2f} step time {step_time:5.2f}"
+            #     f" forward time {forward_time:5.2f}"
+            #     # f' nan share {nan_share:5.2f} ignore share (for classification tasks) {ignore_share:5.4f}'
+            #     + (f" val score {val_score:5.2f}" if val_score is not None else "")
+            # )
+            # print("-" * 89)
+
+        # stepping with wallclock time based scheduler
+        if epoch_callback is not None and rank == 0:
+            epoch_callback(model, epoch, data_loader=train_dl, scheduler=scheduler)
+        scheduler.step()
+
+    if rank == 0:  # trivially true for non-parallel training
+        if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+            model = model.module
+            train_dl = None
+        return (
+            total_loss,
+            total_positional_losses,
+            model.to("cpu"),
+            train_dl,
+            best_val_score,
+            best_model,
+        )
+
+
+def _parse_args(config_parser, parser):
+    # Do we have a config file to parse?
+    args_config, remaining = config_parser.parse_known_args()
+    if args_config.config:
+        with open(args_config.config, "r") as f:
+            cfg = yaml.safe_load(f)
+            parser.set_defaults(**cfg)
+
+    # The main arg parser parses the rest of the args, the usual
+    # defaults will have been overridden if config file specified.
+    args = parser.parse_args(remaining)
+
+    # Cache the args as a text string to save them in the output dir later
+    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
+    return args, args_text
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/transformer.py b/private_multitask_pfn/PFNs/pfns/transformer.py
new file mode 100644
index 0000000..06c611e
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/transformer.py
@@ -0,0 +1,835 @@
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import Module, TransformerEncoder
+
+# from .layer import _get_activation_fn, TransformerEncoderLayer, TransformerAxialEncoderLayer, TransformerTaskEncoderLayer, TransformerAttentionLayer
+from .layer import TransformerAttentionLayer
+from .hierarchical import HierarchicalTaskEncoder
+from .utils import bool_mask_to_att_mask, SeqBN
+
+
+class TransformerModel(nn.Module):
+    def __init__(
+        self,
+        encoder,
+        ninp,
+        nhead,
+        nhid,
+        nlayers,
+        dropout=0.0,
+        style_encoder=None,
+        y_encoder=None,
+        pos_encoder=None,
+        decoder_dict=None,
+        input_normalization=False,
+        init_method=None,
+        pre_norm=False,
+        activation="gelu",
+        recompute_attn=False,
+        num_global_att_tokens=0,
+        full_attention=False,
+        all_layers_same_init=False,
+        efficient_eval_masking=True,
+        decoder_once_dict=None,
+        return_all_outputs=False,
+        save_trainingset_representations=False,
+        num_features=None,
+        num_tasks=None,
+        attn_type="axial",
+        task_encoder=None,
+        task_embed_type="linear",
+        hierarchical=False,
+        **model_extra_args,
+    ):
+        super().__init__()
+        self.model_type = "Transformer"
+        if hierarchical:
+            global_with_target_points = model_extra_args.get("global_with_target_points", False)
+            local_with_target_points = model_extra_args.get("local_with_target_points", False)
+            meta_tokens = model_extra_args.get("meta_tokens", 1)
+            self.transformer_encoder = HierarchicalTaskEncoder(
+                global_with_target_points=global_with_target_points,
+                local_with_target_points=local_with_target_points,                
+                nlayers=nlayers,
+                meta_tokens=meta_tokens,
+                d_model=ninp,
+                nhead=nhead,
+                dim_feedforward=nhid,
+                dropout=dropout,
+                activation=activation,
+                pre_norm=pre_norm,
+            )
+        else:
+            encoder_layer_creator = lambda: TransformerAttentionLayer(
+                num_tasks,
+                ninp,
+                nhead,
+                nhid,
+                dropout,
+                activation=activation,
+                pre_norm=pre_norm,
+                recompute_attn=recompute_attn,
+                save_trainingset_representations=save_trainingset_representations,
+                attn_type=attn_type,
+                task_embed_type=task_embed_type,
+            )
+            self.transformer_encoder = TransformerTaskEncoder(encoder_layer_creator, nlayers)
+        self.ninp = ninp
+        self.encoder = encoder
+        self.task_encoder = task_encoder
+        self.task_embed_type = task_embed_type
+        self.y_encoder = y_encoder
+        self.pos_encoder = pos_encoder
+        self.return_all_outputs = return_all_outputs
+        self.num_features = num_features
+        self.num_tasks = num_tasks
+        self.attn_type = attn_type
+
+        def make_decoder_dict(decoder_description_dict):
+            if decoder_description_dict is None or len(decoder_description_dict) == 0:
+                return None
+            initialized_decoder_dict = {}
+            for decoder_key in decoder_description_dict:
+                decoder_model, decoder_n_out = decoder_description_dict[decoder_key]
+                if decoder_model is None:
+                    initialized_decoder_dict[decoder_key] = nn.Sequential(
+                        nn.Linear(ninp, nhid), nn.GELU(), nn.Linear(nhid, decoder_n_out)
+                    )
+                else:
+                    initialized_decoder_dict[decoder_key] = decoder_model
+                # print(
+                #     "Initialized decoder for",
+                #     decoder_key,
+                #     "with",
+                #     decoder_description_dict[decoder_key],
+                #     " and nout",
+                #     decoder_n_out,
+                # )
+            return torch.nn.ModuleDict(initialized_decoder_dict)
+
+        self.decoder_dict = make_decoder_dict(decoder_dict)
+        self.decoder_dict_once = make_decoder_dict(decoder_once_dict)
+
+        # N(0,1) is the initialization as the default of nn.Embedding
+        self.decoder_dict_once_embeddings = (
+            torch.nn.Parameter(torch.randn((len(self.decoder_dict_once), 1, ninp)))
+            if self.decoder_dict_once is not None
+            else None
+        )
+        # nn.Embedding(len(self.decoder_dict.keys()), nhid)
+        self.input_ln = SeqBN(ninp) if input_normalization else None
+        self.style_encoder = style_encoder
+        self.init_method = init_method
+        if num_global_att_tokens is not None:
+            assert not full_attention
+        self.global_att_embeddings = (
+            nn.Embedding(num_global_att_tokens, ninp) if num_global_att_tokens else None
+        )
+        self.full_attention = full_attention
+        self.efficient_eval_masking = efficient_eval_masking
+
+        self.nhid = nhid
+
+        self.init_weights()
+        self.encode_x_y = None
+        
+        self.target_aware = model_extra_args.get("target_aware", False)
+        self.target_encoder = nn.Embedding(2, ninp) if self.target_aware else None
+        # self.from_tabpfn()
+        
+    def from_tabpfn(self):
+        
+        import einops
+
+        from tabpfn.base import (
+            create_inference_engine,
+            determine_precision,
+            initialize_tabpfn_model,
+        )
+        from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
+        
+        import os
+        model_path = "/home/lily_l/private_multitask_pfn/TabPFN/tabpfn-v2-regressor.ckpt"
+        if not os.path.exists(model_path):
+            model_path = "/home/yl9959/mtpfn/TabPFN/tabpfn-v2-regressor.ckpt"
+            
+
+        checkpoint = torch.load(model_path, map_location="cpu", weights_only=None)
+
+        state_dict = checkpoint["state_dict"]
+        config = _preprocess_config(checkpoint["config"])
+
+        encoder=get_encoder(
+            num_features=config.features_per_group,
+            embedding_size=config.emsize,
+            remove_empty_features=config.remove_empty_features,
+            remove_duplicate_features=config.remove_duplicate_features,
+            nan_handling_enabled=config.nan_handling_enabled,
+            normalize_on_train_only=config.normalize_on_train_only,
+            normalize_to_ranking=config.normalize_to_ranking,
+            normalize_x=config.normalize_x,
+            remove_outliers=config.remove_outliers,
+            normalize_by_used_features=config.normalize_by_used_features,
+            encoder_use_bias=config.encoder_use_bias,
+        )
+        encoder.load_state_dict({"5.layer.weight": state_dict["encoder.5.layer.weight"]})
+        y_encoder=get_y_encoder(
+            num_inputs=1,
+            embedding_size=config.emsize,
+            nan_handling_y_encoder=config.nan_handling_y_encoder,
+            max_num_classes=config.max_num_classes,
+        )
+        y_encoder.load_state_dict({
+            "1.layer.weight": state_dict['y_encoder.1.layer.weight'], 
+            "1.layer.bias": state_dict['y_encoder.1.layer.bias']
+        })
+        self.encoder = encoder.to("cuda")
+        self.y_encoder = y_encoder.to("cuda")
+        
+        def encoder_forward(x, y, single_eval_pos_):
+            
+            if isinstance(x, dict):
+                assert "main" in set(x.keys()), f"Main must be in input keys: {x.keys()}."
+            else:
+                x = {"main": x}
+                y = {"main": y}
+            seq_len, batch_size, num_features = x["main"].shape
+
+
+            for k in x:
+                num_features_ = x[k].shape[2]
+
+                # pad to multiple of features_per_group
+                missing_to_next = (
+                    config.features_per_group - (num_features_ % config.features_per_group)
+                ) % config.features_per_group
+
+                if missing_to_next > 0:
+                    x[k] = torch.cat(
+                        (
+                            x[k],
+                            torch.zeros(
+                                seq_len,
+                                batch_size,
+                                missing_to_next,
+                                device=x[k].device,
+                                dtype=x[k].dtype,
+                            ),
+                        ),
+                        dim=-1,
+                    )
+
+            # Splits up the input into subgroups
+            for k in x:
+                x[k] = einops.rearrange(
+                    x[k],
+                    "s b (f n) -> b s f n",
+                    n=config.features_per_group,
+                )  # s b f -> b s #groups #features_per_group
+                
+
+            for k in y:
+                if y[k].ndim == 1:
+                    y[k] = y[k].unsqueeze(-1)
+                if y[k].ndim == 2:
+                    y[k] = y[k].unsqueeze(-1)  # s b -> s b 1
+
+                y[k] = y[k].transpose(0, 1)  # s b 1 -> b s 1
+                # print(y[k].shape, x["main"].shape)
+
+                if y[k].shape[1] < x["main"].shape[1]:
+                    assert (
+                        y[k].shape[1] == single_eval_pos_
+                        or y[k].shape[1] == x["main"].shape[1]
+                    )
+                    assert k != "main" or y[k].shape[1] == single_eval_pos_, (
+                        "For main y, y must not be given for target"
+                        " time steps (Otherwise the solution is leaked)."
+                    )
+                    if y[k].shape[1] == single_eval_pos_:
+                        y[k] = torch.cat(
+                            (
+                                y[k],
+                                torch.nan
+                                * torch.zeros(
+                                    y[k].shape[0],
+                                    x["main"].shape[1] - y[k].shape[1],
+                                    y[k].shape[2],
+                                    device=y[k].device,
+                                    dtype=y[k].dtype,
+                                ),
+                            ),
+                            dim=1,
+                        )
+
+                y[k] = y[k].transpose(0, 1)  # b s 1 -> s b 1
+
+            # making sure no label leakage ever happens
+            y["main"][single_eval_pos_:] = torch.nan
+
+            embedded_y = y_encoder(
+                y,
+                single_eval_pos=single_eval_pos_,
+            ).transpose(0, 1)
+
+
+
+            for k in x:
+                x[k] = einops.rearrange(x[k], "b s f n -> s (b f) n")
+                        
+            # embedded_x = encoder(x, single_eval_pos=single_eval_pos_)
+
+            embedded_x = einops.rearrange(
+                encoder(
+                    x,
+                    single_eval_pos=len(x["main"]),
+                ),
+                "s (b f) e -> b s f e",
+                b=embedded_y.shape[0],
+            )  # b s f 1 -> b s f e
+            
+            # mean over f dimension
+            embedded_x = embedded_x.mean(dim=2)
+            
+            return embedded_x.transpose(0, 1) + embedded_y.transpose(0, 1)
+            
+        self.encode_x_y = encoder_forward
+
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        self.__dict__.setdefault("efficient_eval_masking", False)
+        if not hasattr(self, "decoder_dict_once"):
+            self.__dict__.setdefault("decoder_dict_once", None)
+        if hasattr(self, "decoder") and not hasattr(self, "decoder_dict"):
+            self.add_module("decoder_dict", nn.ModuleDict({"standard": self.decoder}))
+        self.__dict__.setdefault("return_all_outputs", False)
+
+        def add_approximate_false(module):
+            if isinstance(module, nn.GELU):
+                module.__dict__.setdefault("approximate", "none")
+
+        self.apply(add_approximate_false)
+
+    @staticmethod
+    def generate_square_subsequent_mask(sz):
+        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
+        return bool_mask_to_att_mask(mask)
+
+    @staticmethod
+    def generate_D_q_matrix(sz, query_size):
+        train_size = sz - query_size
+        mask = torch.zeros(sz, sz) == 0
+        mask[:, train_size:].zero_()
+        mask |= torch.eye(sz) == 1
+        return bool_mask_to_att_mask(mask)
+
+    @staticmethod
+    def generate_global_att_query_matrix(
+        num_global_att_tokens, seq_len, num_query_tokens
+    ):
+        train_size = seq_len + num_global_att_tokens - num_query_tokens
+        sz = seq_len + num_global_att_tokens
+        mask = torch.zeros(num_query_tokens, sz) == 0
+        mask[:, train_size:].zero_()
+        mask[:, train_size:] |= torch.eye(num_query_tokens) == 1
+        return bool_mask_to_att_mask(mask)
+
+    @staticmethod
+    def generate_global_att_trainset_matrix(
+        num_global_att_tokens, seq_len, num_query_tokens
+    ):
+        train_size = seq_len + num_global_att_tokens - num_query_tokens
+        trainset_size = seq_len - num_query_tokens
+        mask = torch.zeros(trainset_size, num_global_att_tokens) == 0
+        # mask[:,num_global_att_tokens:].zero_()
+        # mask[:,num_global_att_tokens:] |= torch.eye(trainset_size) == 1
+        return bool_mask_to_att_mask(mask)
+
+    @staticmethod
+    def generate_global_att_globaltokens_matrix(
+        num_global_att_tokens, seq_len, num_query_tokens
+    ):
+        mask = (
+            torch.zeros(
+                num_global_att_tokens,
+                num_global_att_tokens + seq_len - num_query_tokens,
+            )
+            == 0
+        )
+        return bool_mask_to_att_mask(mask)
+
+
+    def init_weights(self):
+        try:
+            # lily
+            # nn.init.normal_(self.encoder[1].base_encoder.weight, mean=0, std=1.0 / math.sqrt(self.ninp))
+            # if self.y_encoder is not None:
+            #     nn.init.normal_(self.y_encoder.weight, mean=0, std=1.0 / math.sqrt(self.ninp))
+            
+            # lily
+            for n, p in self.transformer_encoder.named_parameters():
+                if "bias" in n:
+                    nn.init.zeros_(p)
+                elif "attn" in n:
+                    nn.init.xavier_uniform_(p)
+                elif "norm" in n:
+                    nn.init.ones_(p)
+                elif "linear1" in n:
+                    nn.init.kaiming_normal_(p, nonlinearity='relu')
+                elif "linear2" in n:
+                    nn.init.xavier_uniform_(p)
+                    
+            # # decoder dict
+            # for k, v in self.decoder_dict.items():
+            #     for n, p in v.named_parameters():
+            #         if "bias" in n:
+            #             nn.init.zeros_(p)
+            #         elif "weight" in n:
+            #             nn.init.xavier_uniform_(p)
+            #             import pdb; pdb.set_trace()
+            #             print(p)
+        except:
+            self.init_weights_old()
+
+
+    def init_weights_old(self):
+        initrange = 1.0
+        # if isinstance(self.encoder,EmbeddingEncoder):
+        #    self.encoder.weight.data.uniform_(-initrange, initrange)
+        # self.decoder.bias.data.zero_()
+        # self.decoder.weight.data.uniform_(-initrange, initrange)
+        if self.init_method is not None:
+            self.apply(self.init_method)
+        for layer in self.transformer_encoder.layers:
+            nn.init.zeros_(layer.linear2.weight)
+            nn.init.zeros_(layer.linear2.bias)
+            if isinstance(layer, TransformerAttentionLayer):
+                pass
+                # nn.init.zeros_(layer.all_attn.out_proj.weight)
+                # nn.init.zeros_(layer.all_attn.out_proj.bias)
+                
+                # TODO: update initialization
+                
+                
+            # if isinstance(layer, TransformerEncoderLayer):
+            #     attns = (
+            #         layer.self_attn
+            #         if isinstance(layer.self_attn, nn.ModuleList)
+            #         else [layer.self_attn]
+            #     )
+            #     for attn in attns:
+            #         nn.init.zeros_(attn.out_proj.weight)
+            #         nn.init.zeros_(attn.out_proj.bias)
+            # elif isinstance(layer, TransformerAxialEncoderLayer):
+            #     nn.init.zeros_(layer.self_attn_col.out_proj.weight)
+            #     nn.init.zeros_(layer.self_attn_col.out_proj.bias)
+
+    def forward(self, *args, **kwargs):
+        """
+        This will perform a forward-pass (possibly recording gradients) of the model.
+        We have multiple interfaces we support with this model:
+
+        model(train_x, train_y, test_x, src_mask=None, style=None, only_return_standard_out=True)
+        model((x,y), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
+        model((style,x,y), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
+        """
+        if len(args) == 5:
+            # case model(train_x, train_task_id, train_y, test_x, test_task_id=None, src_mask=None, style=None, only_return_standard_out=True)
+            assert all(
+                kwarg in {"src_mask", "style", "only_return_standard_out"}
+                for kwarg in kwargs.keys()
+            ), f"Unrecognized keyword argument in kwargs: {set(kwargs.keys()) - {'src_mask', 'style', 'only_return_standard_out'}}"
+            x = args[0]
+            task_id = args[1]
+            y = args[2]
+            # combine train and test x along sequence dimension
+            if args[3] is not None: # test_x
+                x = torch.cat((x, args[3]), dim=0)
+                
+                if args[4] is None: # test_task_id
+                    # assume test_task_id is all zeros
+                    test_task_id = torch.zeros(args[3].shape[:-1]).unsqueeze(-1).to(task_id)
+                else:
+                    test_task_id = args[4]
+                task_id = torch.cat((task_id, test_task_id), dim=0)
+            style = kwargs.pop("style", None)
+            # forward(style, x, y),  "single_eval_pos" corresponds to where test x starts
+            return self._forward(
+                (style, x, y, task_id), single_eval_pos=len(args[0]), **kwargs
+            )
+        # during training (x, y, task_id) is passed with single_eval_pos
+        elif len(args) == 1 and isinstance(args, tuple):
+            # case model((x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
+            # case model((style,x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
+            assert all(
+                kwarg in {"src_mask", "single_eval_pos", "only_return_standard_out"}
+                for kwarg in kwargs.keys()
+            ), f"Unrecognized keyword argument in kwargs: {set(kwargs.keys()) - {'src_mask', 'single_eval_pos', 'only_return_standard_out'}}"
+            return self._forward(*args, **kwargs)
+
+    def _forward(
+        self, src, src_mask=None, single_eval_pos=None, only_return_standard_out=True
+    ):
+        assert isinstance(
+            src, tuple
+        ), "inputs (src) have to be given as (x,y,task_id) or (style,x,y,task_id) tuple"
+
+        if len(src) == 3:  # (x,y,task_id) and no style
+            src = (None,) + src
+
+        style_src, x_src, y_src, task_id = src
+
+        if single_eval_pos is None:
+            single_eval_pos = x_src.shape[0]
+                
+        if self.encode_x_y is not None:
+            src = self.encode_x_y(x_src, y_src, single_eval_pos)
+        else:
+            # x_src is N x B x D
+            if self.task_embed_type == "feature" and task_id is not None:
+                onehot_task_ids = torch.nn.functional.one_hot(
+                    task_id.squeeze(-1), num_classes=self.num_tasks
+                ).to(x_src)
+                x_src = torch.cat([onehot_task_ids, x_src], dim=-1)
+            
+            
+            
+            x_src = self.encoder(x_src)
+            if "axial" in self.attn_type:
+                N, D, B, E = x_src.shape
+            else:
+                N, B, E = x_src.shape
+                
+            if task_id is not None and self.task_encoder is not None:
+                assert task_id.shape == (N, B, 1)
+                
+                if self.task_embed_type == "linear":
+                    task_id = self.task_encoder(task_id.to(x_src.dtype))
+                elif self.task_embed_type == "onehot_linear":
+                    task_id = torch.nn.functional.one_hot(
+                        task_id.squeeze(-1), num_classes=self.num_tasks
+                    ).to(x_src.dtype)
+                    task_id = self.task_encoder(task_id)
+                elif self.task_embed_type == "self_attn":
+                    task_id = self.task_encoder(task_id.to(x_src.dtype), x_src)
+                else:
+                    raise ValueError(
+                        f"task_embed_type {self.task_embed_type} not recognized"
+                    )
+                
+                if len(x_src.shape) == 4:
+                    task_id = task_id.unsqueeze(1).repeat(1, D, 1, 1)
+                else:
+                    assert len(x_src.shape) == 3
+            
+                x_src = x_src + task_id
+                
+            # distinguish between target and source inputs
+            if self.target_aware:
+                target_mask = task_id.squeeze(-1) == 0
+                target_embedding = self.target_encoder(torch.zeros(1, device=x_src.device).long())
+                x_src[target_mask] = x_src[target_mask] + target_embedding
+                source_embedding = self.target_encoder(torch.ones(1, device=x_src.device).long())
+                x_src[~target_mask] = x_src[~target_mask] + source_embedding
+
+
+            if self.decoder_dict_once is not None:
+                x_src = torch.cat(
+                    [x_src, self.decoder_dict_once_embeddings.repeat(1, B, 1)],
+                    dim=0,
+                )
+
+            # y_src = y_src.unsqueeze(1) if len(y_src.shape) < len(x_src.shape) else y_src
+            y_src = self.y_encoder(y_src.unsqueeze(-1) if len(y_src.shape) < len(x_src.shape) else y_src) if y_src is not None else None
+
+        if self.style_encoder:
+            assert (
+                style_src is not None
+            ), "style_src must be given if style_encoder is used"
+            style_src = self.style_encoder(style_src).unsqueeze(0)
+        else:
+            style_src = torch.tensor([], device=x_src.device)
+        global_src = (
+            torch.tensor([], device=x_src.device)
+            if self.global_att_embeddings is None
+            else self.global_att_embeddings.weight.unsqueeze(1).repeat(
+                1, x_src.shape[1], 1
+            )
+        )
+
+        if src_mask is not None:
+            assert self.global_att_embeddings is None or isinstance(src_mask, tuple)
+
+        if src_mask is None:
+            if self.global_att_embeddings is None:
+                full_len = len(x_src) + len(style_src)
+                if self.full_attention:
+                    # all tokens attend to all tokens
+                    src_mask = bool_mask_to_att_mask(
+                        torch.ones((full_len, full_len), dtype=torch.bool)
+                    ).to(x_src.device)
+                elif self.efficient_eval_masking:
+                    # shortcut? used in layer.py line 112
+                    src_mask = single_eval_pos + len(style_src)
+                else:
+                    # attend to self + train set
+                    src_mask = self.generate_D_q_matrix(
+                        full_len, len(x_src) - single_eval_pos
+                    ).to(x_src.device)
+            else:
+                src_mask_args = (
+                    self.global_att_embeddings.num_embeddings,
+                    len(x_src) + len(style_src),
+                    len(x_src) + len(style_src) - single_eval_pos,
+                )
+                src_mask = (
+                    self.generate_global_att_globaltokens_matrix(*src_mask_args).to(
+                        x_src.device
+                    ),
+                    self.generate_global_att_trainset_matrix(*src_mask_args).to(
+                        x_src.device
+                    ),
+                    self.generate_global_att_query_matrix(*src_mask_args).to(
+                        x_src.device
+                    ),
+                )
+
+        if self.encode_x_y is None:
+            train_embeddings = x_src[:single_eval_pos]
+            # combine embeddings for train
+            if y_src is not None:
+                if "axial" in self.attn_type:
+                    y_src = y_src.repeat(1, D, 1, 1)
+                train_embeddings = train_embeddings + y_src[:single_eval_pos]
+            # src is concatenated (train_x, train_y) and test_x
+            src = torch.cat([global_src, style_src, train_embeddings, x_src[single_eval_pos:]], 0)
+
+        if self.input_ln is not None:
+            src = self.input_ln(src)
+
+        if self.pos_encoder is not None:
+            src = self.pos_encoder(src)
+
+        # if self.task_embed_type == "task_attn" or self.task_embed_type == "task_attn_shared":
+        #     output = self.transformer_encoder(src, task_id, src_mask)
+        # else:
+        #     output = self.transformer_encoder(src, src_mask)
+        output = self.transformer_encoder(src, task_id, src_mask)
+
+        num_prefix_positions = len(style_src) + (
+            self.global_att_embeddings.num_embeddings
+            if self.global_att_embeddings
+            else 0
+        )
+        if self.return_all_outputs:
+            out_range_start = num_prefix_positions
+        else:
+            out_range_start = single_eval_pos + num_prefix_positions
+
+        # In the line below, we use the indexing feature, that we have `x[i:None] == x[i:]`
+        out_range_end = (
+            -len(self.decoder_dict_once_embeddings)
+            if self.decoder_dict_once is not None
+            else None
+        )
+
+        # take care the output once are counted from the end
+        output_once = (
+            {
+                k: v(output[-(i + 1)])
+                for i, (k, v) in enumerate(self.decoder_dict_once.items())
+            }
+            if self.decoder_dict_once is not None
+            else {}
+        )
+
+        output_dict = {}
+        for k, v in self.decoder_dict.items():
+            if "axial" in self.attn_type:
+                # mean-pool across column dimension
+                output = output.mean(dim=1)
+            # decode only test points
+            output_dict[k] = v(output[out_range_start:out_range_end])
+        output = output_dict
+
+        if only_return_standard_out:
+            return output["standard"]
+
+        if output_once:
+            return output, output_once
+        return output
+
+    @torch.no_grad()
+    def init_from_small_model(self, small_model):
+        assert (
+            isinstance(self.decoder, nn.Linear)
+            and isinstance(self.encoder, (nn.Linear, nn.Sequential))
+            and isinstance(self.y_encoder, (nn.Linear, nn.Sequential))
+        )
+
+        def set_encoder_weights(my_encoder, small_model_encoder):
+            my_encoder_linear, small_encoder_linear = (
+                (my_encoder, small_model_encoder)
+                if isinstance(my_encoder, nn.Linear)
+                else (my_encoder[-1], small_model_encoder[-1])
+            )
+            small_in_dim = small_encoder_linear.out_features
+            my_encoder_linear.weight.zero_()
+            my_encoder_linear.bias.zero_()
+            my_encoder_linear.weight[:small_in_dim] = small_encoder_linear.weight
+            my_encoder_linear.bias[:small_in_dim] = small_encoder_linear.bias
+
+        set_encoder_weights(self.encoder, small_model.encoder)
+        set_encoder_weights(self.y_encoder, small_model.y_encoder)
+
+        small_in_dim = small_model.decoder.in_features
+
+        self.decoder.weight[:, :small_in_dim] = small_model.decoder.weight
+        self.decoder.bias = small_model.decoder.bias
+
+        for my_layer, small_layer in zip(
+            self.transformer_encoder.layers, small_model.transformer_encoder.layers
+        ):
+            small_hid_dim = small_layer.linear1.out_features
+            my_in_dim = my_layer.linear1.in_features
+
+            # packed along q,k,v order in first dim
+            my_in_proj_w = my_layer.self_attn.in_proj_weight
+            small_in_proj_w = small_layer.self_attn.in_proj_weight
+
+            my_in_proj_w.view(3, my_in_dim, my_in_dim)[
+                :, :small_in_dim, :small_in_dim
+            ] = small_in_proj_w.view(3, small_in_dim, small_in_dim)
+            my_layer.self_attn.in_proj_bias.view(3, my_in_dim)[:, :small_in_dim] = (
+                small_layer.self_attn.in_proj_bias.view(3, small_in_dim)
+            )
+
+            my_layer.self_attn.out_proj.weight[:small_in_dim, :small_in_dim] = (
+                small_layer.self_attn.out_proj.weight
+            )
+            my_layer.self_attn.out_proj.bias[:small_in_dim] = (
+                small_layer.self_attn.out_proj.bias
+            )
+
+            my_layer.linear1.weight[:small_hid_dim, :small_in_dim] = (
+                small_layer.linear1.weight
+            )
+            my_layer.linear1.bias[:small_hid_dim] = small_layer.linear1.bias
+
+            my_layer.linear2.weight[:small_in_dim, :small_hid_dim] = (
+                small_layer.linear2.weight
+            )
+            my_layer.linear2.bias[:small_in_dim] = small_layer.linear2.bias
+
+            my_layer.norm1.weight[:small_in_dim] = (
+                math.sqrt(small_in_dim / my_in_dim) * small_layer.norm1.weight
+            )
+            my_layer.norm2.weight[:small_in_dim] = (
+                math.sqrt(small_in_dim / my_in_dim) * small_layer.norm2.weight
+            )
+
+            my_layer.norm1.bias[:small_in_dim] = small_layer.norm1.bias
+            my_layer.norm2.bias[:small_in_dim] = small_layer.norm2.bias
+
+
+class TransformerEncoderDiffInit(Module):
+    r"""TransformerEncoder is a stack of N encoder layers
+
+    Args:
+        encoder_layer_creator: a function generating objects of TransformerEncoderLayer class without args (required).
+        num_layers: the number of sub-encoder-layers in the encoder (required).
+        norm: the layer normalization component (optional).
+    """
+
+    __constants__ = ["norm"]
+
+    def __init__(self, encoder_layer_creator, num_layers, norm=None):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [encoder_layer_creator() for _ in range(num_layers)]
+        )
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        src: Tensor,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layers in turn.
+
+        Args:
+            src: the sequence to the encoder (required).
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        output = src
+
+        for mod in self.layers:
+            output = mod(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
+
+
+
+class TransformerTaskEncoder(Module):
+    r"""TransformerEncoder is a stack of N encoder layers
+
+    Args:
+        encoder_layer_creator: a function generating objects of TransformerEncoderLayer class without args (required).
+        num_layers: the number of sub-encoder-layers in the encoder (required).
+        norm: the layer normalization component (optional).
+    """
+
+    __constants__ = ["norm"]
+
+    def __init__(self, encoder_layer_creator, num_layers, norm=None):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [encoder_layer_creator() for _ in range(num_layers)]
+        )
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        src: Tensor,
+        task_ids: Tensor,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layers in turn.
+
+        Args:
+            src: the sequence to the encoder (required).
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        output = src
+
+        for mod in self.layers:
+            output = mod(
+                output, task_ids, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
diff --git a/private_multitask_pfn/PFNs/pfns/utils.py b/private_multitask_pfn/PFNs/pfns/utils.py
new file mode 100644
index 0000000..b6ec085
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pfns/utils.py
@@ -0,0 +1,520 @@
+import argparse
+import datetime
+import itertools
+import math
+import os
+import random
+
+import numpy as np
+
+import torch
+from torch import nn
+from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingWarmRestarts, ChainedScheduler, LinearLR, CosineAnnealingLR
+
+
+def get_restarting_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps):    
+    warmup_scheduler = LinearLR(
+        optimizer,
+        start_factor=0.1,  # Start at lr * 0.1
+        end_factor=1.0,    # End at lr
+        total_iters=num_warmup_steps,
+    )
+
+    cosine_scheduler = CosineAnnealingWarmRestarts(
+        optimizer,
+        T_0=20,
+        T_mult=2,
+        # eta_min=1e-7
+    )
+
+    scheduler = ChainedScheduler([warmup_scheduler, cosine_scheduler])
+    return scheduler
+
+
+def get_curriculum_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps):    
+    warmup_onetask_scheduler = LinearLR(
+        optimizer,
+        start_factor=0.1,  # Start at lr * 0.1
+        end_factor=1.0,    # End at lr
+        total_iters=num_warmup_steps,
+    )
+
+    cosine_onetask_scheduler = CosineAnnealingLR(
+        optimizer,
+        T_max=50,
+    )
+    
+    warmup_scheduler = LinearLR(
+        optimizer,
+        start_factor=0.1,  # Start at lr * 0.1
+        end_factor=1.0,    # End at lr
+        total_iters=num_warmup_steps,
+    )
+    
+    cosine_scheduler = CosineAnnealingLR(
+        optimizer,
+        T_max=num_training_steps - 2 * num_warmup_steps - 50,
+    )
+
+    scheduler = ChainedScheduler([
+        warmup_onetask_scheduler, 
+        cosine_onetask_scheduler, 
+        warmup_scheduler,
+        cosine_scheduler])
+    return scheduler
+
+
+# copied from huggingface
+def get_cosine_schedule_with_warmup(
+    optimizer, num_warmup_steps, num_training_steps, num_cycles=0.5, last_epoch=-1
+):
+    """Create a schedule with a learning rate that decreases following the
+    values of the cosine function between 0 and `pi * cycles` after a warmup
+    period during which it increases linearly between 0 and 1.
+    """
+
+    def lr_lambda(current_step):
+        if current_step < num_warmup_steps:
+            return float(current_step) / float(max(1, num_warmup_steps))
+        progress = float(current_step - num_warmup_steps) / float(
+            max(1, num_training_steps - num_warmup_steps)
+        )
+        return max(
+            0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
+        )
+
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+# copied from huggingface
+def get_restarting_cosine_schedule_with_warmup_(
+    optimizer,
+    num_warmup_steps,
+    num_training_steps,
+    steps_per_restart=20,
+    num_cycles=0.5,
+    last_epoch=-1,
+):
+    assert num_training_steps % steps_per_restart == 0
+
+    def inner_lr_lambda(current_step, num_warmup_steps, num_training_steps):
+        if current_step < num_warmup_steps:
+            return float(current_step) / float(max(1, num_warmup_steps))
+        progress = float(current_step - num_warmup_steps) / float(
+            max(1, num_training_steps - num_warmup_steps)
+        )
+        return max(
+            0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
+        )
+
+    def lr_lambda(current_step):
+        inner_step = current_step % steps_per_restart
+        return inner_lr_lambda(
+            inner_step,
+            num_warmup_steps if current_step < steps_per_restart else 0,
+            steps_per_restart,
+        )
+
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+# copied from huggingface
+def get_slow_restarting_cosine_schedule_with_warmup(
+    optimizer,
+    num_warmup_steps,
+    num_training_steps,
+    steps_per_restart=50,
+    num_cycles=0.5,
+    last_epoch=-1,
+):
+    assert num_training_steps % steps_per_restart == 0
+
+    def inner_lr_lambda(current_step, num_warmup_steps, num_training_steps):
+        if current_step < num_warmup_steps:
+            return float(current_step) / float(max(1, num_warmup_steps))
+        progress = float(current_step - num_warmup_steps) / float(
+            max(1, num_training_steps - num_warmup_steps)
+        )
+        return max(
+            0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
+        )
+
+    def lr_lambda(current_step):
+        inner_step = current_step % steps_per_restart
+        return inner_lr_lambda(
+            inner_step,
+            num_warmup_steps if current_step < steps_per_restart else 0,
+            steps_per_restart,
+        )
+
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+# copied from huggingface
+def get_linear_schedule_with_warmup(
+    optimizer, num_warmup_steps, num_training_steps, last_epoch=-1
+):
+    """
+    Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
+    a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
+
+    Args:
+        optimizer (:class:`~torch.optim.Optimizer`):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (:obj:`int`):
+            The number of steps for the warmup phase.
+        num_training_steps (:obj:`int`):
+            The total number of training steps.
+        last_epoch (:obj:`int`, `optional`, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+
+    def lr_lambda(current_step: int):
+        if current_step < num_warmup_steps:
+            return float(current_step) / float(max(1, num_warmup_steps))
+        return max(
+            0.0,
+            float(num_training_steps - current_step)
+            / float(max(1, num_training_steps - num_warmup_steps)),
+        )
+
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+def get_openai_lr(transformer_model):
+    num_params = sum(p.numel() for p in transformer_model.parameters())
+    return 0.003239 - 0.0001395 * math.log(num_params)
+
+
+def get_exponential_decay_single_eval_pos_sampler(max_len, min_len=0):
+    """
+    This gives a sampler that can be used for `single_eval_pos` which yields good performance for all positions p,
+    where p <= `max_len`. At most `max_len` - 1 examples are shown to the Transformer.
+    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
+    """
+    probs = torch.logspace(2.0, 1.0, max_len - min_len)
+    sample = torch.distributions.Categorical(probs).sample().item() + min_len
+
+    return sample
+
+
+def get_weighted_single_eval_pos_sampler(max_len, min_len=0, p=1.0):
+    """
+    This gives a sampler that can be used for `single_eval_pos` which yields good performance for all positions p,
+    where p <= `max_len`. At most `max_len` - 1 examples are shown to the Transformer.
+    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
+    """
+    def sample(epoch, model):
+        return random.choices(
+            range(min_len, max_len),
+            [1 / math.pow(((max_len - min_len) - i), p) for i in range(max_len - min_len)],
+        )[0]
+        
+    return sample
+
+
+def get_uniform_single_eval_pos_sampler(max_len, min_len=0):
+    """
+    Just sample any evaluation position with the same weight
+    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
+    """
+    def sample(epoch, model):
+        return random.choices(range(min_len, max_len))[0]
+    
+    return sample
+
+
+def get_curriculum_single_eval_pos_sampler(max_len, min_len=0):
+    """
+    Just sample any evaluation position with the same weight
+    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
+    """
+    def sample(epoch, model):
+        # decrease max_len from 100 to min_len over the course of the training
+        # slowly force model to learn on min lengths
+        if epoch < 50:
+            epoch_min = int(max_len - (max_len - min_len) * ((epoch+1) / 50))
+        else:
+            epoch_min = min_len
+        return random.choices(range(epoch_min, max_len))[0]
+    
+    return sample
+
+
+class SeqBN(nn.Module):
+    def __init__(self, d_model):
+        super().__init__()
+        self.bn = nn.BatchNorm1d(d_model)
+        self.d_model = d_model
+
+    def forward(self, x):
+        assert self.d_model == x.shape[-1]
+        flat_x = x.view(-1, self.d_model)
+        flat_x = self.bn(flat_x)
+        return flat_x.view(*x.shape)
+
+
+def set_locals_in_self(locals):
+    """
+    Call this function like `set_locals_in_self(locals())` to set all local variables as object variables.
+    Especially useful right at the beginning of `__init__`.
+    :param locals: `locals()`
+    """
+    self = locals["self"]
+    for var_name, val in locals.items():
+        if var_name != "self":
+            setattr(self, var_name, val)
+
+
+default_device = "cuda:0" if torch.cuda.is_available() else "cpu:0"
+
+
+# Copied from StackOverflow, but we do an eval on the values additionally
+class StoreDictKeyPair(argparse.Action):
+    def __init__(self, option_strings, dest, nargs=None, **kwargs):
+        self._nargs = nargs
+        super(StoreDictKeyPair, self).__init__(
+            option_strings, dest, nargs=nargs, **kwargs
+        )
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        my_dict = {}
+        for kv in values:
+            k, v = kv.split("=")
+            try:
+                my_dict[k] = eval(v)
+            except NameError:
+                my_dict[k] = v
+        setattr(namespace, self.dest, my_dict)
+        print("dict values: {}".format(my_dict))
+
+
+def get_nan_value(v, set_value_to_nan=1.0):
+    if random.random() < set_value_to_nan:
+        return v
+    else:
+        return random.choice([-999, 0, 1, 999])
+
+
+def to_ranking(data):
+    x = data >= data.unsqueeze(-3)
+    x = x.sum(0)
+    return x
+
+
+# TODO: Is there a better way to do this?
+#   1. Cmparing to unique elements: When all values are different we still get quadratic blowup
+#   2. Argsort(Argsort()) returns ranking, but with duplicate values there is an ordering which is problematic
+#   3. Argsort(Argsort(Unique))->Scatter seems a bit complicated, doesn't have quadratic blowup, but how fast?
+def to_ranking_low_mem(data):
+    x = torch.zeros_like(data)
+    for col in range(data.shape[-1]):
+        x_ = data[:, :, col] >= data[:, :, col].unsqueeze(-2)
+        x_ = x_.sum(0)
+        x[:, :, col] = x_
+    return x
+
+
+def nan_handling_missing_for_unknown_reason_value(nan_prob=1.0):
+    return get_nan_value(float("nan"), nan_prob)
+
+
+def nan_handling_missing_for_no_reason_value(nan_prob=1.0):
+    return get_nan_value(float("-inf"), nan_prob)
+
+
+def nan_handling_missing_for_a_reason_value(nan_prob=1.0):
+    return get_nan_value(float("inf"), nan_prob)
+
+
+def torch_nanmean(x, axis=0, return_nanshare=False):
+    num = torch.where(torch.isnan(x), torch.full_like(x, 0), torch.full_like(x, 1)).sum(
+        axis=axis
+    )
+    value = torch.where(torch.isnan(x), torch.full_like(x, 0), x).sum(axis=axis)
+    if return_nanshare:
+        return value / num, 1.0 - num / x.shape[axis]
+    return value / num
+
+
+def torch_nanstd(x, axis=0):
+    num = torch.where(torch.isnan(x), torch.full_like(x, 0), torch.full_like(x, 1)).sum(
+        axis=axis
+    )
+    value = torch.where(torch.isnan(x), torch.full_like(x, 0), x).sum(axis=axis)
+    mean = value / num
+    mean_broadcast = torch.repeat_interleave(
+        mean.unsqueeze(axis), x.shape[axis], dim=axis
+    )
+    return torch.sqrt(
+        torch.nansum(torch.square(mean_broadcast - x), axis=axis) / (num - 1)
+    )
+
+
+def normalize_data(data, normalize_positions=-1, return_scaling=False):
+    if normalize_positions > 0:
+        mean = torch_nanmean(data[:normalize_positions], axis=0)
+        std = torch_nanstd(data[:normalize_positions], axis=0) + 0.000001
+    else:
+        mean = torch_nanmean(data, axis=0)
+        std = torch_nanstd(data, axis=0) + 0.000001
+    data = (data - mean) / std
+    data = torch.clip(data, min=-100, max=100)
+
+    if return_scaling:
+        return data, (mean, std)
+    return data
+
+
+def remove_outliers(X, n_sigma=4, normalize_positions=-1):
+    # Expects T, B, H
+    assert len(X.shape) == 3, "X must be T,B,H"
+    # for b in range(X.shape[1]):
+    # for col in range(X.shape[2]):
+    data = X if normalize_positions == -1 else X[:normalize_positions]
+    data_clean = data[:].clone()
+    data_mean, data_std = torch_nanmean(data, axis=0), torch_nanstd(data, axis=0)
+    cut_off = data_std * n_sigma
+    lower, upper = data_mean - cut_off, data_mean + cut_off
+
+    data_clean[torch.logical_or(data_clean > upper, data_clean < lower)] = np.nan
+    data_mean, data_std = (
+        torch_nanmean(data_clean, axis=0),
+        torch_nanstd(data_clean, axis=0),
+    )
+    cut_off = data_std * n_sigma
+    lower, upper = data_mean - cut_off, data_mean + cut_off
+
+    X = torch.maximum(-torch.log(1 + torch.abs(X)) + lower, X)
+    X = torch.minimum(torch.log(1 + torch.abs(X)) + upper, X)
+    # print(ds[1][data < lower, col], ds[1][data > upper, col], ds[1][~np.isnan(data), col].shape, data_mean, data_std)
+    return X
+
+
+def bool_mask_to_att_mask(mask):
+    return (
+        mask.float()
+        .masked_fill(mask == 0, float("-inf"))
+        .masked_fill(mask == 1, float(0.0))
+    )
+
+
+def print_on_master_only(is_master):
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def init_dist(device):
+    # print("init dist")
+    if "LOCAL_RANK" in os.environ:
+        # launched with torch.distributed.launch
+        rank = int(os.environ["LOCAL_RANK"])
+        print("torch.distributed.launch and my rank is", rank)
+        torch.cuda.set_device(rank)
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
+        torch.distributed.init_process_group(
+            backend="nccl",
+            init_method="env://",
+            timeout=datetime.timedelta(seconds=20),
+            world_size=torch.cuda.device_count(),
+            rank=rank,
+        )
+        torch.distributed.barrier()
+        print_on_master_only(rank == 0)
+        print(
+            f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
+            "only I can print, but when using print(..., force=True) it will print on all ranks."
+        )
+        return True, rank, f"cuda:{rank}"
+    elif "SLURM_PROCID" in os.environ and torch.cuda.device_count() > 1:
+        # this is for multi gpu when starting with submitit
+        assert device != "cpu:0"
+        rank = int(os.environ["SLURM_PROCID"])
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = "12355"
+        torch.cuda.set_device(rank)
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
+        print("distributed submitit launch and my rank is", rank)
+        torch.distributed.init_process_group(
+            backend="nccl",
+            init_method="env://",
+            timeout=datetime.timedelta(seconds=20),
+            world_size=torch.cuda.device_count(),
+            rank=rank,
+        )
+        torch.distributed.barrier()
+        print_on_master_only(rank == 0)
+        print(
+            f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
+            "only I can print, but when using print(..., force=True) it will print on all ranks."
+        )
+
+        return True, rank, f"cuda:{rank}"
+    else:
+        # print("Not using distributed")
+        # will not change any of the behavior of print, but allows putting the force=True in the print calls
+        print_on_master_only(True)
+        return False, 0, device
+
+
+# NOP decorator for python with statements (x = NOP(); with x:)
+class NOP:
+    def __enter__(self):
+        pass
+
+    def __exit__(self, type, value, traceback):
+        pass
+
+
+def check_compatibility(dl):
+    if hasattr(dl, "num_outputs"):
+        print(
+            "`num_outputs` for the DataLoader is deprecated. It is assumed to be 1 from now on."
+        )
+        assert dl.num_outputs != 1, (
+            "We assume num_outputs to be 1. Instead of the num_ouputs change your loss."
+            "We specify the number of classes in the CE loss."
+        )
+
+
+def product_dict(dic):
+    keys = dic.keys()
+    vals = dic.values()
+    for instance in itertools.product(*vals):
+        yield dict(zip(keys, instance))
+
+
+def to_tensor(x, device=None):
+    if isinstance(x, torch.Tensor):
+        return x.to(device)
+    else:
+        return torch.tensor(x, device=device)
+
+
+printed_already = set()
+
+
+def print_once(*msgs: str):
+    msg = " ".join([repr(m) for m in msgs])
+    if msg not in printed_already:
+        print(msg)
+        printed_already.add(msg)
+
+
+def normalize_by_used_features_f(
+    x, num_features_used, num_features, normalize_with_sqrt=False
+):
+    if normalize_with_sqrt:
+        return x / (num_features_used / num_features) ** (1 / 2)
+    return x / (num_features_used / num_features)
diff --git a/private_multitask_pfn/PFNs/pyproject.toml b/private_multitask_pfn/PFNs/pyproject.toml
new file mode 100644
index 0000000..7894419
--- /dev/null
+++ b/private_multitask_pfn/PFNs/pyproject.toml
@@ -0,0 +1,43 @@
+[build-system]
+requires = ["hatchling==1.18.0"]
+build-backend = "hatchling.build"
+
+[project]
+name = "pfns"
+version = "0.1.5"
+authors = [
+{name="Samuel Muller"},
+{name="Matthias Feurer"},
+{name="Noah Hollmann"},
+{name="Frank Hutter"},
+]
+
+description = "PFNs made ready for BO"
+readme = "README.md"
+requires-python = ">=3.9"
+dependencies=[
+        'torch>=1.9.0',
+        'scikit-learn>=0.24.2,<1.2',
+        'pyyaml>=5.4.1',
+        'numpy>=1.21.2,<2.0',
+        'requests>=2.23.0',
+        'gpytorch>=1.5.0',
+        'configspace>=0.4.21',
+        #'seaborn==0.11',
+        'tqdm>=4.62.1',
+        'botorch>=0.7.2',
+        'bayesmark>=0.0.8',
+]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Operating System :: OS Independent",
+]
+
+[tool.hatch.build]
+exclude = [
+  "*.gz",
+  "*.pt",
+]
+
+[project.urls]
+"Homepage" = "https://github.com/automl/PFNs4BO"
diff --git a/private_multitask_pfn/PFNs/setup.py b/private_multitask_pfn/PFNs/setup.py
new file mode 100644
index 0000000..1abbd06
--- /dev/null
+++ b/private_multitask_pfn/PFNs/setup.py
@@ -0,0 +1,4 @@
+import setuptools
+
+if __name__ == "__main__":
+    setuptools.setup()
diff --git a/private_multitask_pfn/benchmark_time_mtgp_samples.csv b/private_multitask_pfn/benchmark_time_mtgp_samples.csv
new file mode 100644
index 0000000..3d7391f
--- /dev/null
+++ b/private_multitask_pfn/benchmark_time_mtgp_samples.csv
@@ -0,0 +1,11 @@
+n_tasks,n_samples_per_task,model,time
+5,31,mtgp,0.47720147343352437
+5,40,mtgp,0.3310064523946494
+5,52,mtgp,0.28340951893478633
+5,68,mtgp,0.6167415243573486
+5,87,mtgp,0.6738561020698398
+5,113,mtgp,0.5061305745504796
+5,146,mtgp,0.9458495335187763
+5,189,mtgp,2.4895689885597676
+5,244,mtgp,4.875412699021399
+5,316,mtgp,11.058217211114243
diff --git a/private_multitask_pfn/benchmark_time_mtgp_task.csv b/private_multitask_pfn/benchmark_time_mtgp_task.csv
new file mode 100644
index 0000000..590853b
--- /dev/null
+++ b/private_multitask_pfn/benchmark_time_mtgp_task.csv
@@ -0,0 +1,6 @@
+n_tasks,n_samples_per_task,model,time
+3,50,mtgp,0.20609497535042465
+4,50,mtgp,0.27077632481232283
+7,50,mtgp,0.7451002229936421
+11,50,mtgp,5.2983047076035295
+17,50,mtgp,
diff --git a/private_multitask_pfn/benchmark_time_scaml_samples.csv b/private_multitask_pfn/benchmark_time_scaml_samples.csv
new file mode 100644
index 0000000..544786d
--- /dev/null
+++ b/private_multitask_pfn/benchmark_time_scaml_samples.csv
@@ -0,0 +1,11 @@
+n_tasks,n_samples_per_task,model,time
+5,31,scaml,0.5437628486193716
+5,40,scaml,0.3764890623278916
+5,52,scaml,0.4885109560098499
+5,68,scaml,0.47688916362822054
+5,87,scaml,0.4667881939560175
+5,113,scaml,0.7805409024003893
+5,146,scaml,0.9741538344416767
+5,189,scaml,1.9188368205912412
+5,244,scaml,5.80243408754468
+5,316,scaml,9.938315148325637
diff --git a/private_multitask_pfn/benchmark_time_scaml_task.csv b/private_multitask_pfn/benchmark_time_scaml_task.csv
new file mode 100644
index 0000000..50df5ea
--- /dev/null
+++ b/private_multitask_pfn/benchmark_time_scaml_task.csv
@@ -0,0 +1,6 @@
+n_tasks,n_samples_per_task,model,time
+3,50,scaml,0.3683895048219711
+4,50,scaml,0.20750538865104318
+7,50,scaml,0.8733225882053375
+11,50,scaml,7.345934889977798
+17,50,scaml,
diff --git a/private_multitask_pfn/eval.py b/private_multitask_pfn/eval.py
new file mode 100644
index 0000000..be0fbba
--- /dev/null
+++ b/private_multitask_pfn/eval.py
@@ -0,0 +1,1070 @@
+import itertools
+
+import multiprocessing as mp
+
+import botorch
+import torch
+import pickle
+from eval_fcnet import (
+    get_torch_format_fcnet,
+)
+from eval_hpobench import (
+    get_torch_format_hpobench,
+)
+import json
+
+from gen_batch import get_mtgp_for_eval
+from utils import *
+import datetime
+import warnings
+import os
+import argparse
+import traceback
+import matplotlib.pyplot as plt
+from collections import defaultdict
+from eval_plot_util import plot_function, plot_all, plot_bo_function, plot_bo_all
+
+warnings.filterwarnings("ignore")
+
+FUNCTION_SEED_OFFSET = 10
+TRIAL_SEED_OFFSET = 1000
+
+def model_predict(
+    model_dir,
+    model_name,
+    train_id,
+    train_x,
+    train_y,
+    test_target_x,
+    test_target_y,
+    standardize,
+    trial_seed,
+    rerun,
+    model=None,
+):
+    os.makedirs(model_dir, exist_ok=True)
+    
+    # check if results exist
+    try:
+        if rerun:
+            raise FileNotFoundError
+        # if model_name == "mtgp_nuts":# or model_name == "gp":
+        #     raise FileNotFoundError
+        with open(os.path.join(model_dir, "results.json"), "r") as f:
+            results = json.load(f)
+            trial_seed_key = str(trial_seed)
+            if trial_seed_key in results:
+                return results[trial_seed_key]
+    except FileNotFoundError as e:
+        results = {}
+    except Exception as e:
+        traceback.print_exc()
+        results = {}
+
+    print("\tPredicting", model_name)
+    with botorch.manual_seed(trial_seed):
+        try:
+            # standardize output to mean 0 std 1
+            if standardize:
+                assert (0.0 <= train_x).all() and (train_x <= 1.0).all()
+                processed_y, processed_test_y = standardize_by_task(train_y, train_id, test_target_y)
+            else:
+                processed_y = train_y
+            assert not processed_y.isnan().any()
+
+            args = (
+                train_id,
+                train_x,
+                processed_y,
+                test_target_x,
+            )
+            if "PFN" in model_name:
+                outputs = pfn_predict(model.to(train_x.device), *args)
+            elif model_name == "mtgp":
+                outputs = mtgp_predict(*args)
+            elif model_name == "gp":
+                outputs = gp_predict(*args)
+            elif model_name == "scaml":
+                outputs = scaml_predict(*args)
+            elif model_name == "lmc":
+                outputs = lmc_predict(*args)
+            elif model_name == "mtgp_nuts":
+                outputs = mtgp_nuts_predict(*args)
+            else:
+                raise ValueError(
+                    "model_type must be one of ['pfn', 'mtgp', 'gp', 'scaml', 'lmc']"
+                )
+                
+            if model_name == "mtgp_nuts":
+                mean, std = outputs.mixture_mean, outputs.mixture_variance.sqrt()
+            else:
+                mean, std = outputs.mean.squeeze(), outputs.variance.sqrt().squeeze()
+            processed_test_y = processed_test_y.squeeze()
+            mse = ((mean - test_target_y) ** 2).mean().item()
+            nll = -torch.distributions.Normal(mean, std).log_prob(processed_test_y).mean().item()
+                
+            # save predictions
+            results[trial_seed] = {
+                "mse": mse,
+                "nll": nll,
+                "mean": mean.detach().squeeze().tolist(),
+                "std": std.detach().squeeze().tolist(),
+                "true": processed_test_y.squeeze().tolist(),
+            }
+            
+            del outputs
+            
+            # print(model_type, results[trial_seed]["mse"], results[trial_seed]["nll"])
+                
+            with open(os.path.join(model_dir, "results.json"), "w") as f:
+                json.dump(results, f)
+
+        except KeyboardInterrupt as e:
+            raise e
+        except Exception as e:
+            # raise e
+            # print(e)
+            traceback.print_exc()
+            print(model_name, "failed")
+            return None
+        
+    return results[trial_seed]
+
+
+
+def bo_loop(
+    model_dir,
+    model_name,
+    init_id,
+    init_x,
+    init_y,
+    possible_id,
+    possible_x,
+    possible_y,
+    iters,
+    standardize,
+    trial_seed,
+    rerun,
+    model=None
+):
+    os.makedirs(model_dir, exist_ok=True)
+    start_iter = 1
+    original_len = len(init_x)
+    
+    # check if results exist
+    try:
+        if rerun:
+            raise FileNotFoundError
+        # if model_name == "gp":
+        #     raise FileNotFoundError
+        # read length of results
+        with open(os.path.join(model_dir, "bo_results.json"), "r") as f:
+            results = json.load(f)
+            # check if results are complete
+            if len(results["train_y"]) < iters:
+                print("\tResults are incomplete for %s" % model_dir)
+            
+            if results["completed_iters"] >= iters:
+                return {
+                    "iters": iters,
+                    "train_y": results["train_y"][:iters],
+                    "train_x": results["train_x"][:iters],
+                    "task_id": results["task_id"][:iters],
+                }
+            else:
+                # continue from last checkpoint
+                half_id = torch.tensor(results["task_id"], device=init_id.device)
+                half_x = torch.tensor(results["train_x"], device=init_x.device)
+                half_y = torch.tensor(results["train_y"], device=init_y.device)
+                trial_seed = results["rng_seed"]
+                
+                init_id = torch.cat((init_id, half_id), 0)
+                init_x = torch.cat((init_x, half_x), 0)
+                init_y = torch.cat((init_y, half_y), 0)
+                start_iter = results["completed_iters"] + 1
+    except FileNotFoundError as e:
+        results = {}
+    except Exception as e:
+        traceback.print_exc()
+        results = {}
+                
+    task_id, train_x, train_y = init_id, init_x, init_y
+    print("\tBO Loop for", model_name)
+    
+    if model:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        model.to(device)
+        task_id = task_id.to(device)
+        train_x = train_x.to(device)
+        train_y = train_y.to(device)
+        possible_id = possible_id.to(device)
+        possible_x = possible_x.to(device)
+        possible_y = possible_y.to(device)
+
+    with botorch.manual_seed(trial_seed):
+        try:
+            for i in range(start_iter, iters + 1):
+                # print(model_type, "epoch", i)
+                # standardize output to mean 0 std 1
+                if standardize:
+                    assert (0.0 <= train_x).all() and (train_x <= 1.0).all()
+                    processed_y = standardize_by_task(train_y, task_id)
+                else:
+                    processed_y = train_y
+                assert not processed_y.isnan().any()
+
+                args = (
+                    task_id,
+                    train_x,
+                    processed_y,
+                    possible_id,
+                    possible_x,
+                    possible_y,
+                )
+                if "PFN" in model_name:
+                    best_x, best_y = pfn_get_best(model, *args)
+                elif model_name == "mtgp":
+                    best_x, best_y = mtgp_get_best(*args)
+                elif model_name == "gp":
+                    best_x, best_y = gp_get_best(*args)
+                elif model_name == "scaml":
+                    best_x, best_y = scaml_get_best(*args)
+                elif model_name == "random":
+                    random_index = torch.randint(0, possible_x.shape[0], ())
+                    best_x, best_y = possible_x[random_index], possible_y[random_index]
+                elif model_name == "lmc":
+                    best_x, best_y = lmc_get_best(*args)
+                elif model_name == "mtgp_nuts":
+                    best_x, best_y = mtgp_nuts_get_best(*args)
+                else:
+                    raise ValueError(
+                        "model_type must be one of ['pfn', 'mtgp', 'gp', 'scaml', 'random', 'lmc']"
+                    )
+
+                task_id = torch.cat(
+                    (task_id, torch.tensor([0.0], device=task_id.device)), 0
+                )
+                train_x = torch.cat((train_x, best_x.unsqueeze(0)), 0)
+                train_y = torch.cat((train_y, best_y.unsqueeze(0)), 0)
+                
+                # save results
+                seed = torch.seed()
+                results = {
+                    "completed_iters": i,
+                    "train_y": train_y[original_len:].cpu().numpy().tolist(),
+                    "train_x": train_x[original_len:].cpu().numpy().tolist(),
+                    "task_id": task_id[original_len:].cpu().numpy().tolist(),
+                    "rng_seed": seed,
+                }
+                
+                with open(os.path.join(model_dir, "bo_results.json"), "w") as f:
+                    json.dump(results, f)
+
+        except KeyboardInterrupt as e:
+            raise e
+        except Exception as e:
+            print(e)
+            traceback.print_exc()
+            print(model_name, "failed")
+            return None
+    
+    return results
+                
+
+def eval_metrics(
+    n_trials,
+    bo_iters,
+    seed,
+    baselines,
+    pfns,
+    data,
+    n_target,
+    n_source,
+    exp_dir,
+    cache_dir,
+    rerun,
+    standardize=True,
+):
+    # pfns = []
+    # if pfn_ids is not None:
+    #     for pfn_id in pfn_ids:
+    #         try:
+    #             pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % pfn_id
+    #             model = load_model(pfn_dir)
+    #             pfns.append((pfn_id, model))
+    #         except Exception as e:
+    #             traceback.print_exc()
+    #             print("failed to load", pfn_id)
+    #             continue
+        
+    xs, ys, sources_xs, sources_ys = data
+    
+    results = defaultdict(dict)
+    bo_results = defaultdict(dict)
+    info = defaultdict(dict)
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print("\n!!!!!!", cache_dir, "!!!!!!", flush=True)
+    for trial in range(n_trials):
+        print("Running trial", trial)
+        trial_key = "trial_%d" % trial
+        trial_seed = seed + trial * 1000
+        
+        # generate data
+        with botorch.manual_seed(trial_seed):
+            try:
+                trial_data = torch.load(os.path.join(cache_dir, "data", "trial_%d.pt" % trial), map_location=torch.device('cpu'))
+            except FileNotFoundError as e:
+                trial_data = create_train_test(
+                    xs, ys, sources_xs, sources_ys, n_target, n_source
+                )
+                os.makedirs(os.path.join(cache_dir, "data"), exist_ok=True)
+                torch.save(trial_data, os.path.join(cache_dir, "data", "trial_%d.pt" % trial))
+                
+            # trial_data = [tensor.to(device) for tensor in trial_data]
+            train_id, train_x, train_y, test_id, test_x, test_y = trial_data
+            
+            args = (
+                train_id,
+                train_x,
+                train_y,
+                # test_id,
+                test_x,
+                test_y,
+                standardize,
+                trial_seed,
+                rerun,
+            )
+
+            # for model_type in ["scaml", "mtgp", "gp", "random"]:#, "lmc"]:#, "mtgp_nuts"]:
+            for model_name in baselines:
+                model_dir = os.path.join(cache_dir, model_name)
+                if model_name != "random":
+                    results[model_name][trial_key] = model_predict(model_dir, model_name, *args)
+                
+                bo_model_dir = os.path.join(cache_dir, trial_key, model_name)
+                bo_results[model_name][trial_key] = bo_loop(
+                    bo_model_dir,
+                    model_name,
+                    train_id,
+                    train_x,
+                    train_y,
+                    test_id,
+                    test_x,
+                    test_y,
+                    bo_iters,
+                    standardize,
+                    trial_seed,
+                    rerun,
+                )
+            for pfn_name, pfn in pfns:
+                model_dir = os.path.join(cache_dir, pfn_name)
+                results[pfn_name][trial_key] = model_predict(model_dir, "PFN: " + pfn_name, *args, model=pfn)
+                # os.symlink(model_dir, os.path.join(exp_dir, pfn_name))
+                
+                bo_model_dir = os.path.join(cache_dir, trial_key, pfn_name)
+                bo_results[pfn_name][trial_key] = bo_loop(
+                    bo_model_dir,
+                    "PFN: " + pfn_name,
+                    train_id,
+                    train_x,
+                    train_y,
+                    test_id,
+                    test_x,
+                    test_y,
+                    bo_iters,
+                    standardize,
+                    trial_seed,
+                    rerun,
+                    model=pfn,
+                )
+            
+            info[trial_key]["best_possible"] = test_y.max().item()
+            info[trial_key]["best_init"] = train_y[train_id == 0].max().item()
+            info[trial_key]["init_id"] = train_id.cpu().numpy().tolist()
+            info[trial_key]["init_x"] = train_x.cpu().numpy().tolist()
+            info[trial_key]["init_y"] = train_y.cpu().numpy().tolist()
+
+                
+    # save results
+    with open(os.path.join(exp_dir, "results.json"), "w") as f:
+        json.dump(results, f)
+    with open(os.path.join(exp_dir, "bo_results.json"), "w") as f:
+        json.dump(bo_results, f)
+    with open(os.path.join(exp_dir, "info.json"), "w") as f:
+        json.dump(info, f)
+                
+    return results
+                   
+
+def get_eval_suite(eval_type):
+    if eval_type == "num_samples":
+        # Test impact of number of targets and sources
+        # Varying correlations between tasks
+        # Varying lengthscales
+        # NUM_FEATURES = [3]
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0]
+        CORR_INIT_OPTIONS = [0.8]
+        LENGTHSCALE_OPTIONS = [None]
+        NUM_TASKS_OPTIONS = [4]
+        # NUM_TARGETS = [2, 5, 10, 20]
+        # NUM_SOURCES = [10, 20, 50, 100]
+        NUM_TARGETS = [2, 4, 8, 16, 32, 64]
+        NUM_SOURCES = [20, 50]
+        NUM_SAMPLES = []
+        for target in NUM_TARGETS:
+            for source in NUM_SOURCES:
+                if target <= source:
+                    NUM_SAMPLES.append((target, source))
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+
+    elif eval_type == "corr":
+        # Test impact of number of targets and sources
+        # Varying correlations between tasks
+        # Varying lengthscales
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0]
+        CORR_INIT_OPTIONS = [0.3, 0.6, 0.9]
+        LENGTHSCALE_OPTIONS = [None, 0.2]
+        NUM_TASKS_OPTIONS = [4]
+        NUM_SAMPLES = [(2, 20), (5, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+
+    elif eval_type == "uncorr":
+        # Test number of uncorrelated tasks
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0, 1, 2, 3]
+        CORR_INIT_OPTIONS = [None, 0.6, 0.8]
+        LENGTHSCALE_OPTIONS = [None, 0.1, 0.2]
+        NUM_TASKS_OPTIONS = [4]
+        # NUM_SAMPLES = [(2, 20), (5, 20), (10, 20)]
+        NUM_SAMPLES = [(2, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+
+    elif eval_type == "num_tasks":
+        # Test number of tasks
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0, 1]
+        CORR_INIT_OPTIONS = [None]
+        LENGTHSCALE_OPTIONS = [None]
+        NUM_TASKS_OPTIONS = [2, 3, 4, 5, 6]
+        NUM_SAMPLES = [(2, 20), (5, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+        
+    elif eval_type == "one_task":
+        # Test number of tasks
+        NUM_FEATURES = [1]
+        NUM_UNCORR_TASKS = [0]
+        CORR_INIT_OPTIONS = [None]
+        LENGTHSCALE_OPTIONS = [None, 0.2]
+        NUM_TASKS_OPTIONS = [1]
+        NUM_SAMPLES = [(4, 20), (8, 20), (16, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+        
+    elif eval_type == "uncorr_final":
+        # Test number of uncorrelated tasks
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0, 1, 2, 3]
+        CORR_INIT_OPTIONS = [0.8]
+        LENGTHSCALE_OPTIONS = [None]
+        NUM_TASKS_OPTIONS = [4]
+        NUM_SAMPLES = [(2, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+        
+    elif eval_type == "nuts_0":
+        # Test number of uncorrelated tasks
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0]
+        CORR_INIT_OPTIONS = [0.3]
+        LENGTHSCALE_OPTIONS = [0.2, None]
+        NUM_TASKS_OPTIONS = [4]
+        NUM_SAMPLES = [(2, 20), (5, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+        
+    elif eval_type == "nuts_1":
+        # Test number of uncorrelated tasks
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0]
+        CORR_INIT_OPTIONS = [0.6]
+        LENGTHSCALE_OPTIONS = [0.2, None]
+        NUM_TASKS_OPTIONS = [4]
+        NUM_SAMPLES = [(2, 20), (5, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+        
+    elif eval_type == "nuts_2":
+        # Test number of uncorrelated tasks
+        NUM_FEATURES = [3]
+        NUM_UNCORR_TASKS = [0]
+        CORR_INIT_OPTIONS = [0.9]
+        LENGTHSCALE_OPTIONS = [0.2, None]
+        NUM_TASKS_OPTIONS = [4]
+        NUM_SAMPLES = [(2, 20), (5, 20)]
+
+        return list(
+            itertools.product(
+                NUM_FEATURES,
+                NUM_TASKS_OPTIONS,
+                LENGTHSCALE_OPTIONS,
+                CORR_INIT_OPTIONS,
+                NUM_UNCORR_TASKS,
+                NUM_SAMPLES,
+            )
+        )
+        
+
+
+def run_hpobench(
+    n_trials: int,
+    bo_iters: int,
+    seed: int,
+    baselines: list,
+    pfns: list,
+    hpobench_task: str,
+    hpobench_n_tasks: int,
+    hpobench_n_features: int,
+    n_functions: int,
+    exp_dir: str,
+    cache_dir: str,
+    rerun: bool,
+    standardize=True,
+    **kwargs,
+):
+    num_features = hpobench_n_features
+    top_level_cache_dir = os.path.join(cache_dir, "hpobench")
+
+    torch.manual_seed(seed)
+    
+    try:
+        problem_dfs = pickle.load(open(f"/scratch/yl9959/mtpfn/datasets/hpobench_{hpobench_task}.pkl", "rb"))
+    except FileNotFoundError as e:
+        problem_dfs = pickle.load(open(f"/home/lily_l/private_multitask_pfn/datasets/hpobench_{hpobench_task}.pkl", "rb"))
+    except Exception as e:
+        raise e
+    possible_ids = list(problem_dfs.keys())
+    ids = []
+    for _ in range(n_functions):
+        id_indices = torch.randperm(len(possible_ids))[:hpobench_n_tasks]
+        function_ids = [possible_ids[i] for i in id_indices]
+        ids.append((function_ids[0], tuple(function_ids[1:])))
+
+    num_samples = [
+        (2, 20),
+        (5, 20),
+        (20, 20),
+        (2, 100),
+        (20, 100),
+    ]
+    for n_target, n_source in num_samples:
+        for function, (target_id, source_ids) in enumerate(ids):
+            source_ids_str = "_".join([str(id) for id in source_ids])
+            exp_exp_name = "n_target_%d__n_source_%d" % (n_target, n_source)
+            cache_experiment_name = "%s_features_%d_n_target_%d__n_source_%d__target_%d__source_%s" % (hpobench_task, num_features, n_target, n_source, target_id, source_ids_str)
+            
+            function_seed = seed + function * FUNCTION_SEED_OFFSET
+            
+            target_df = problem_dfs[target_id]
+            source_dfs = [problem_dfs[source_id] for source_id in source_ids]
+
+            data = get_torch_format_hpobench(
+                target_df, source_dfs, num_features, hpobench_task
+            )
+            
+            exp_function_dir = os.path.join(exp_dir, exp_exp_name, "function_%d" % function)
+            os.makedirs(exp_function_dir, exist_ok=True)
+            
+            cache_function_dir = os.path.join(top_level_cache_dir, cache_experiment_name)
+            os.makedirs(cache_function_dir, exist_ok=True)
+                    
+            with botorch.manual_seed(seed):
+                eval_metrics(
+                    n_trials=n_trials,
+                    bo_iters=bo_iters,
+                    seed=function_seed,
+                    baselines=baselines,
+                    pfns=pfns,
+                    data=data,
+                    n_target=n_target,
+                    n_source=n_source,
+                    exp_dir=exp_function_dir,
+                    cache_dir=cache_function_dir,
+                    standardize=standardize,
+                    rerun=rerun,
+                )
+                
+            plot_function(exp_function_dir, n_trials, "HPOBench %s: %s\nDataset %d (%d Trials)" % (hpobench_task, exp_exp_name, function + 1, n_trials))
+            plot_bo_function(exp_function_dir, "HPOBench %s: %s\nDataset %d (%d Trials)" % (hpobench_task, exp_exp_name, function + 1, n_trials))
+            
+        pfn_ids = [pfn_id for pfn_id, _ in pfns]
+        plot_all(os.path.join(exp_dir, exp_exp_name), n_functions, "HPOBench %s: %s\n Summary over %d datasets and %d trials" % (hpobench_task, exp_exp_name, n_functions, n_trials))
+        plot_bo_all(os.path.join(exp_dir, exp_exp_name), pfn_ids, "HPOBench %s: %s\n Summary over %d datasets and %d trials" % (hpobench_task, exp_exp_name, n_functions, n_trials))
+
+
+def run_hpob(
+    n_trials: int,
+    bo_iters: int,
+    seed: int,
+    baselines: list,
+    pfns: list,
+    hpob_n_tasks: int,
+    n_functions: int,
+    exp_dir: str,
+    cache_dir: str,
+    rerun: bool,
+    standardize=True,
+    hpo_n_features=3,
+    **kwargs,
+):
+    torch.manual_seed(seed)
+
+    try:
+        test_data = json.load(open("/home/yl9959/mtpfn/datasets/hpob-data/meta-validation-dataset.json", "r"))
+    except FileNotFoundError as e:
+        test_data = json.load(open("/home/lily_l/private_multitask_pfn/datasets/hpob-data/meta-validation-dataset.json", "r"))
+    top_level_cache_dir = os.path.join(cache_dir, "hpob")
+
+    ids = []
+    for domain in test_data:
+        valid_runs = []
+        for hpo_run in test_data[domain]:
+            x_shape = torch.tensor(test_data[domain][hpo_run]["X"]).shape
+            if x_shape[0] > 512 and x_shape[-1] <= hpo_n_features:
+                valid_runs.append(hpo_run)
+        if len(valid_runs) >= hpob_n_tasks:
+            for i in range(n_functions):
+                # randomly select runs
+                selected_indices = torch.randperm(len(valid_runs))[:hpob_n_tasks]
+                selected_runs = [valid_runs[i] for i in selected_indices]
+                ids.append((domain, (selected_runs[0], tuple(selected_runs[1:]))))
+
+    num_samples = [
+        (2, 20),
+        (5, 20),
+        (20, 20),
+        (2, 100),
+        (20, 100),
+    ]
+
+    for n_target, n_source in num_samples:
+        param_name = "features_%d__n_target_%d__n_source_%d" % (hpo_n_features, n_target, n_source)
+        
+        for i, (domain, (target_id, source_ids)) in enumerate(ids):        
+            source_ids_str = "_".join([str(id) for id in source_ids])
+            
+            function_seed = seed + i * FUNCTION_SEED_OFFSET
+            
+            data_xs = []
+            data_ys = []
+            for id in [target_id] + list(source_ids):
+                xs = torch.tensor(test_data[domain][id]["X"])
+                ys = torch.tensor(test_data[domain][id]["y"])
+                max_indices = 2000
+                if xs.shape[0] > max_indices:
+                    indices = torch.randperm(xs.shape[0])[:max_indices]
+                    xs = xs[indices]
+                    ys = ys[indices]
+
+                data_xs.append(xs)
+                data_ys.append(ys)
+
+            target_xs, sources_xs = data_xs[0], data_xs[1:]
+            target_ys, sources_ys = data_ys[0], data_ys[1:]
+            data = (target_xs, target_ys, sources_xs, sources_ys)
+        
+            exp_domain_dir = os.path.join(exp_dir, param_name, "domain_%s" % domain, "function_%d" % i)
+            os.makedirs(exp_domain_dir, exist_ok=True)
+            
+            cache_experiment_name = "features_%d__domain_%s__target_%s__source_%s" % (hpo_n_features, domain, target_id, source_ids_str)
+            cache_domain_dir = os.path.join(top_level_cache_dir, param_name, cache_experiment_name)
+            os.makedirs(cache_domain_dir, exist_ok=True)
+                    
+            with botorch.manual_seed(seed):
+                eval_metrics(
+                    n_trials=n_trials,
+                    bo_iters=bo_iters,
+                    seed=function_seed,
+                    baselines=baselines,
+                    pfns=pfns,
+                    data=data,
+                    n_target=n_target,
+                    n_source=n_source,
+                    exp_dir=exp_domain_dir,
+                    cache_dir=cache_domain_dir,
+                    rerun=rerun,
+                    standardize=standardize,
+                )
+                
+            plot_function(exp_domain_dir, n_trials, "HPO-B: N Target %d, N Source %d\nDomain %s (%d Trials)" % (n_target, n_source, domain, n_trials))
+            plot_bo_function(exp_domain_dir, "HPO-B: N Target %d, N Source %d\nDomain %s (%d Trials)" % (n_target, n_source, domain, n_trials))
+            
+        pfn_ids = [pfn_id for pfn_id, _ in pfns]
+        plot_all(os.path.join(exp_dir, param_name), n_functions, "HPO-B: Domain %s\n Summary over %d datasets and %d trials" % (domain, n_functions, n_trials))
+        plot_bo_all(os.path.join(exp_dir, param_name), pfn_ids, "HPO-B: Domain %s\n Summary over %d datasets and %d trials" % (domain, n_functions, n_trials))
+
+
+def run_fcnet(
+    n_trials: int,
+    bo_iters: int,
+    seed: int,
+    baselines: list,
+    pfns: list,
+    n_functions: int,
+    fcnet_n_features: int,
+    exp_dir: str,
+    cache_dir: str,
+    rerun: bool,
+    standardize=True,
+    **kwargs,
+):
+    num_features = fcnet_n_features
+    torch.manual_seed(seed)
+    top_level_cache_dir = os.path.join(cache_dir, "fcnet")
+
+
+    problems = [
+        "fcnet_naval_propulsion_data",
+        "fcnet_parkinsons_telemonitoring_data",
+        "fcnet_protein_structure_data",
+        "fcnet_slice_localization_data",
+    ]
+    try:
+        datasets = {
+            problem: pickle.load(open(f"/home/yl9959/mtpfn/datasets/fcnet_tabular_benchmarks/{problem}.pkl", "rb"))
+            for problem in problems
+        }
+    except FileNotFoundError as e:
+        datasets = {
+            problem: pickle.load(open(f"/home/lily_l/private_multitask_pfn/datasets/fcnet_tabular_benchmarks/{problem}.pkl", "rb"))
+            for problem in problems
+        }
+    except Exception as e:
+        raise e
+
+    num_samples = [
+        (2, 20),
+        (5, 20),
+        (20, 20),
+        (2, 100),
+        (20, 100),
+    ]
+    params = itertools.product(problems, num_samples)
+
+    for n_target, n_source in num_samples:
+        param_name = "features_%d__n_target_%d__n_source_%d" % (num_features, n_target, n_source)
+        for problem in problems:
+
+            target_result = datasets[problem]
+            source_results = [datasets[key] for key in datasets if key != problem]
+
+            target_xs, target_ys, sources_xs, sources_ys = get_torch_format_fcnet(
+                target_result, source_results, num_features
+            )
+            
+            exp_problem_dir = os.path.join(exp_dir, param_name, problem)
+            os.makedirs(exp_problem_dir, exist_ok=True)
+            
+            cache_problem_dir = os.path.join(top_level_cache_dir, param_name, problem)
+            os.makedirs(cache_problem_dir, exist_ok=True)
+            
+            with botorch.manual_seed(seed):
+                eval_metrics(
+                    n_trials=n_trials,
+                    bo_iters=bo_iters,
+                    seed=seed,
+                    baselines=baselines,
+                    pfns=pfns,
+                    data=(target_xs, target_ys, sources_xs, sources_ys),
+                    n_target=n_target,
+                    n_source=n_source,
+                    exp_dir=exp_problem_dir,
+                    cache_dir=cache_problem_dir,
+                    rerun=rerun,
+                    standardize=standardize,
+                )
+                
+            plot_function(exp_problem_dir, n_trials, "FCNet %s: N Target %d, N Source %d (%d Trials)" % (problem, n_target, n_source, n_trials))
+            plot_bo_function(exp_problem_dir, "FCNet %s: N Target %d, N Source %d (%d Trials)" % (problem, n_target, n_source, n_trials))
+        
+        pfn_ids = [pfn_id for pfn_id, _ in pfns]
+        plot_all(os.path.join(exp_dir, param_name), n_functions, "FCNet: N Target %d, N Source %d\n Summary over all datasets and %d trials" % (n_target, n_source, n_trials))
+        plot_bo_all(os.path.join(exp_dir, param_name), pfn_ids, "FCNet: N Target %d, N Source %d\n Summary over all datasets and %d trials" % (n_target, n_source, n_trials))
+
+
+def run_test(
+    n_trials: int,
+    bo_iters: int,
+    seed: int,
+    baselines: list,
+    pfns: list,
+    suite: str,
+    n_functions: int,
+    exp_dir: str,
+    cache_dir: str,
+    rerun: bool,
+    standardize=True,
+    **kwargs,
+):
+    torch.manual_seed(seed)
+
+    eval_suite = get_eval_suite(suite)
+    
+    top_level_cache_dir = os.path.join(cache_dir, "synthetic")
+
+    results = {}
+    for param in eval_suite:
+        if param in results:
+            continue
+        
+        results[param] = {}
+        (
+            num_features,
+            num_tasks,
+            lengthscale,
+            task_corr,
+            num_uncorr_tasks,
+            (n_target, n_source),
+        ) = param
+        
+        param_key = "features_%d__tasks_%d__lengthscale_%s__task_corr_%s__uncorr_tasks_%d__n_target_%d__n_source_%d" % (
+            num_features,
+            num_tasks,
+            str(lengthscale),
+            str(task_corr),
+            num_uncorr_tasks,
+            n_target,
+            n_source,
+        )
+        exp_title = "Synthetic Data: Features: %d, Tasks: %d\nLengthscale: %s, Task Corr: %s, Uncorr Tasks: %d\nN Target: %d, N Source: %d" % (
+            num_features,
+            num_tasks,
+            str(lengthscale),
+            str(task_corr),
+            num_uncorr_tasks,
+            n_target,
+            n_source,
+        )
+        # replace special characters
+        param_dirname = param_key.replace("[", "").replace("]", "").replace(".", "_")
+        seed_dirname = "seed_%d" % seed
+        
+        for function in range(n_functions):
+            function_seed = seed + function * FUNCTION_SEED_OFFSET
+            exp_function_dir = os.path.join(exp_dir, param_dirname + "__" + seed_dirname, "function_%d" % function)
+            os.makedirs(exp_function_dir, exist_ok=True)
+            
+            cache_function_dir = os.path.join(top_level_cache_dir, param_dirname, seed_dirname, "function_%d" % function)
+            os.makedirs(cache_function_dir, exist_ok=True)
+
+            with botorch.manual_seed(function_seed):
+                data = get_mtgp_for_eval(
+                    num_features,
+                    num_tasks,
+                    lengthscale,
+                    task_corr,
+                    num_uncorr_tasks,
+                )
+                # break
+                
+            eval_metrics(
+                n_trials=n_trials,
+                bo_iters=bo_iters,
+                seed=function_seed,
+                baselines=baselines,
+                pfns=pfns,
+                data=data,
+                n_target=n_target,
+                n_source=n_source,
+                exp_dir=exp_function_dir,
+                cache_dir=cache_function_dir,
+                rerun=rerun,
+                standardize=standardize,
+            )
+            
+            plot_function(exp_function_dir, n_trials, exp_title + "\nDataset %d (%d Trials)" % (function + 1, n_trials))
+            plot_bo_function(exp_function_dir, exp_title + "\nDataset %d (%d Trials)" % (function + 1, n_trials))
+            # break
+            
+        pfn_ids = [pfn_id for pfn_id, _ in pfns]
+        plot_all(os.path.join(exp_dir, param_dirname + "__" + seed_dirname), n_functions, exp_title + "\n Summary over %d datasets and %d trials" % (n_functions, n_trials))
+        plot_bo_all(os.path.join(exp_dir, param_dirname + "__" + seed_dirname), pfn_ids, exp_title + "\n Summary over %d datasets and %d trials" % (n_functions, n_trials))
+        # break
+
+
+# main function
+if __name__ == "__main__":
+    argparser = argparse.ArgumentParser()
+    
+    argparser.add_argument("--benchmark", type=str, default="synthetic", help="{synthetic, hpobench, hpob, fcnet}")
+    argparser.add_argument("--bo_iters", type=int, default=20)
+    argparser.add_argument("--n_trials", type=int, default=5)
+    argparser.add_argument("--seed", type=int, default=0)
+    argparser.add_argument("--n_functions", type=int, default=3)
+    
+    # baselines
+    argparser.add_argument("--baselines", nargs="*", type=str, help="{mtgp, gp, scaml, lmc, mtgp_nuts}", 
+                           default=["mtgp", "gp", "scaml", "random"])
+    
+    # pfn wandb ids
+    argparser.add_argument("--pfn_ids", nargs="*", type=str, help="wandb ids for pfn models")
+    argparser.add_argument("--ckpt_dirs", nargs="*", type=str, help="directories")
+    
+    # synthetic
+    argparser.add_argument("--suite", type=str, default="corr", help="{num_samples, corr, uncorr, num_tasks}")
+    
+    # hpobench
+    argparser.add_argument("--hpobench_task", type=str, default="rf", help="{rf, xgb, svm, mlp, nn}")
+    argparser.add_argument("--hpobench_n_tasks", type=int, default=4)
+    argparser.add_argument("--hpobench_n_features", type=int, default=3)
+    
+    # hpob
+    argparser.add_argument("--hpob_n_tasks", type=int, default=4)
+    argparser.add_argument("--hpob_n_features", type=int, default=3)
+    
+    # fcnet
+    argparser.add_argument("--fcnet_n_features", type=int, default=3)
+    
+    argparser.add_argument("--rerun", action="store_true", default=False)
+    
+    if os.path.exists("/home/yl9959/mtpfn/"):
+        home_dir = "/home/yl9959/mtpfn/"
+    else:
+        home_dir = "/home/lily_l/private_multitask_pfn/"
+    argparser.add_argument("--home_dir", type=str, default=home_dir)
+    # argparser.add_argument("--exp_dir", type=str, default="/home/yl9959/mtpfn/eval_plot/")
+    # argparser.add_argument("--cache_dir", type=str, default="/home/yl9959/mtpfn/eval_plot_cache/")
+
+    args = argparser.parse_args()
+    
+    current_time = datetime.datetime.now().strftime("%y-%m-%d_%H-%M-%S")
+    dirname = current_time + "__" + args.benchmark + "__trials_%d__seed_%d" % (args.n_trials, args.seed)
+    
+    if args.benchmark == "synthetic":
+        dirname += "__" + args.suite
+    elif args.benchmark == "hpobench":
+        dirname += "__" + args.hpobench_task
+        
+    args.exp_dir = os.path.join(args.home_dir, "eval_plot", dirname)
+    os.makedirs(args.exp_dir, exist_ok=True)
+    args.cache_dir = os.path.join(args.home_dir, "eval_plot_cache")
+    
+    # save args json
+    with open(os.path.join(args.exp_dir, "args.json"), "w") as f:
+        json.dump(vars(args), f)
+        
+        
+    pfns = []
+    if args.pfn_ids is not None:
+        for pfn_id in args.pfn_ids:
+            try:
+                pfn_dir = os.path.join(args.home_dir, "wandb_links", pfn_id)
+                model = load_model(pfn_dir)
+                pfns.append((pfn_id, model))
+            except FileNotFoundError as e:
+                pfn_dir = os.path.join(args.home_dir, "final_models", pfn_id)
+                model = load_model(pfn_dir)
+                pfns.append((pfn_id, model))
+            except Exception as e:
+                traceback.print_exc()
+                print("failed to load", pfn_id)
+                continue
+    if args.ckpt_dirs is not None:
+        for pfn_dir in args.ckpt_dirs:
+            try:
+                model = load_model(os.path.join(args.home_dir, pfn_dir))
+                pfns.append((pfn_dir, model))
+            except FileNotFoundError as e:
+                print("failed to load", pfn_dir)
+                continue
+    
+    if args.benchmark == "synthetic":
+        run_test(**vars(args), pfns=pfns)
+    elif args.benchmark == "hpobench":
+        run_hpobench(**vars(args), pfns=pfns)
+    elif args.benchmark == "hpob":
+        run_hpob(**vars(args), pfns=pfns)
+    elif args.benchmark == "fcnet":
+        run_fcnet(**vars(args), pfns=pfns)
+    else:
+        raise ValueError("Invalid benchmark")
+    
+# python eval.py --benchmark synthetic --bo_iters 10 --n_trials 10 --n_functions 3 --suite uncorr --pfn_ids treasured-lion-279 royal-firebrand-281 different-dust-282 exalted-wave-278 peach-plasma-280
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_axial.py b/private_multitask_pfn/eval_axial.py
new file mode 100644
index 0000000..ad2c25b
--- /dev/null
+++ b/private_multitask_pfn/eval_axial.py
@@ -0,0 +1,85 @@
+import torch
+from gen_axial_batch import axial_train_batch, axial_test_batch
+import json
+import os
+from train import train as load_model_from_train
+
+def load_model(ckpt_dir, best=True):
+# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_14-36-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_standard"
+    args_json = f"{ckpt_dir}/args.json"
+    with open(args_json, "r") as f:
+        args = json.load(f)
+
+    model = load_model_from_train(**args, return_model=True)
+    if best:
+        model.load_state_dict(torch.load(f"{ckpt_dir}/best_model.pth", weights_only=True))
+    else:
+        model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True))
+    
+    print("Loaded model")
+    return model
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# standard attention
+ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_14-36-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_standard"
+model = load_model(ckpt_dir).to(device)
+
+# axial attention
+ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_15-19-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_axial"
+axial_model = load_model(ckpt_dir).to(device)
+
+torch.manual_seed(0)
+
+num_features = 3
+num_train = 10
+num_test = 3
+
+x = torch.rand(num_train + num_test, 1, num_features, device=device)
+y = torch.zeros(num_train + num_test, 1, 1, device=device)
+for feature in range(num_features):
+    y += torch.pow(x[:, :, feature:feature+1], feature + 1)
+
+x_train = x[:num_train]
+y_train = y[:num_train]
+x_test = x[num_train:]
+y_test = y[num_train:]
+
+with torch.no_grad():
+    y_pred = model(x_train, y_train, x_test)
+    y_pred_axial = axial_model(x_train, y_train, x_test)
+y_mean, y_var = y_pred[..., 0], y_pred[..., 1].exp()
+y_mean_axial, y_var_axial = y_pred_axial[..., 0], y_pred_axial[..., 1].exp()
+
+criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
+loss = criterion(y_mean, y_test.squeeze(1), var=y_var)
+loss_axial = criterion(y_mean_axial, y_test.squeeze(1), var=y_var_axial)
+
+for i in range(num_test):
+    print(f"ID Test {i}")
+    print(f"x_test: {x_test[i].squeeze()}")
+    print(f"y_test: {y_test[i].item()}")
+    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (y_mean[i].item(), y_var[i].item(), loss[i].item()))
+    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (y_mean_axial[i].item(), y_var_axial[i].item(), loss_axial[i].item()))
+    print()
+    
+# reversed x in feature dimension
+reversed_x = torch.flip(x, dims=[2])
+reversed_x_train = reversed_x[:num_train]
+reversed_x_test = reversed_x[num_train:]
+
+with torch.no_grad():
+    reversed_y_pred = model(reversed_x_train, y_train, reversed_x_test)
+    reversed_y_pred_axial = axial_model(reversed_x_train, y_train, reversed_x_test)
+reversed_y_mean, reversed_y_var = reversed_y_pred[..., 0], reversed_y_pred[..., 1].exp()
+reversed_y_mean_axial, reversed_y_var_axial = reversed_y_pred_axial[..., 0], reversed_y_pred_axial[..., 1].exp()
+
+reversed_loss = criterion(reversed_y_mean, y_test.squeeze(1), var=reversed_y_var)
+reversed_loss_axial = criterion(reversed_y_mean_axial, y_test.squeeze(1), var=reversed_y_var_axial)
+
+for i in range(num_test):
+    print(f"OOD Test {i}")
+    print(f"Reversed x_test: {reversed_x_test[i].squeeze()}")
+    print(f"y_test: {y_test[i].item()}")
+    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean[i].item(), reversed_y_var[i].item(), reversed_loss[i].item()))
+    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean_axial[i].item(), reversed_y_var_axial[i].item(), reversed_loss_axial[i].item()))
+    print()
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_axial_multitask.py b/private_multitask_pfn/eval_axial_multitask.py
new file mode 100644
index 0000000..d2c5abe
--- /dev/null
+++ b/private_multitask_pfn/eval_axial_multitask.py
@@ -0,0 +1,131 @@
+import torch
+from gen_axial_batch import axial_train_batch, axial_test_batch
+import json
+import os
+from train import train as load_model_from_train
+import matplotlib.pyplot as plt
+
+def load_model(ckpt_dir, best=True):
+# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_14-36-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_standard"
+    args_json = f"{ckpt_dir}/args.json"
+    with open(args_json, "r") as f:
+        args = json.load(f)
+
+    model = load_model_from_train(**args, return_model=True)
+    if best:
+        model.load_state_dict(torch.load(f"{ckpt_dir}/best_model.pth", weights_only=True))
+    else:
+        model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True))
+    
+    print("Loaded model")
+    return model
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# standard attention
+ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_22-11-25__prior_toy_multitask__features_3__tasks_2__epochs_10__seqlen_200__attn_standard_completed"
+model = load_model(ckpt_dir).to(device)
+
+# axial attention
+# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_22-07-45__prior_toy_multitask__features_3__tasks_2__epochs_10__seqlen_200__attn_axial_completed"
+ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_22-23-28__prior_toy_multitask__features_3__tasks_2__epochs_30__seqlen_200__attn_axial_completed"
+axial_model = load_model(ckpt_dir).to(device)
+
+torch.manual_seed(1)
+
+num_features = 3
+num_train = 50
+num_test = 100
+batch_size = 1
+seq_len = num_train + num_test
+num_tasks = 1
+
+x = torch.rand(seq_len, batch_size, num_features, device=device)
+y = torch.zeros(seq_len, batch_size, 1, device=device)
+task_id = torch.randint(0, num_tasks, (seq_len, batch_size, 1), device=device)
+
+for task in range(num_tasks):
+    for feature in range(num_features):
+        constant = task + 1 #torch.randn(1, device=device)
+        y += constant * torch.pow(x[:, :, feature:feature+1], feature + 1) * (task_id == task).float()
+    
+x_train = x[:num_train]
+task_id_train = task_id[:num_train]
+y_train = y[:num_train]
+x_test = x[num_train:]
+y_test = y[num_train:]
+
+with torch.no_grad():
+    y_pred = model(x_train, task_id_train, y_train, x_test)
+    y_pred_axial = axial_model(x_train, task_id_train, y_train, x_test)
+print(y_pred_axial)
+y_mean, y_var = y_pred[..., 0], y_pred[..., 1].exp()
+y_mean_axial, y_var_axial = y_pred_axial[..., 0], y_pred_axial[..., 1].exp()
+
+criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
+loss = criterion(y_mean, y_test.squeeze(1), var=y_var)
+loss_axial = criterion(y_mean_axial, y_test.squeeze(1), var=y_var_axial)
+
+for i in range(num_test):
+    print(f"ID Test {i}")
+    print(f"x_test: {x_test[i].squeeze()}")
+    print(f"y_test: {y_test[i].item()}")
+    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (y_mean[i].item(), y_var[i].item(), loss[i].item()))
+    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (y_mean_axial[i].item(), y_var_axial[i].item(), loss_axial[i].item()))
+    print()
+    
+# reversed x in feature dimension
+reversed_x = torch.flip(x, dims=[2])
+reversed_task_id = torch.flip(task_id, dims=[2])
+reversed_x_train = reversed_x[:num_train]
+reversed_task_id_train = reversed_task_id[:num_train]
+reversed_x_test = reversed_x[num_train:]
+
+with torch.no_grad():
+    reversed_y_pred = model(reversed_x_train, reversed_task_id_train, y_train, reversed_x_test)
+    reversed_y_pred_axial = axial_model(reversed_x_train, reversed_task_id_train, y_train, reversed_x_test)
+reversed_y_mean, reversed_y_var = reversed_y_pred[..., 0], reversed_y_pred[..., 1].exp()
+reversed_y_mean_axial, reversed_y_var_axial = reversed_y_pred_axial[..., 0], reversed_y_pred_axial[..., 1].exp()
+
+reversed_loss = criterion(reversed_y_mean, y_test.squeeze(1), var=reversed_y_var)
+reversed_loss_axial = criterion(reversed_y_mean_axial, y_test.squeeze(1), var=reversed_y_var_axial)
+
+for i in range(num_test):
+    print(f"OOD Test {i}")
+    print(f"Reversed x_test: {reversed_x_test[i].squeeze()}")
+    print(f"y_test: {y_test[i].item()}")
+    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean[i].item(), reversed_y_var[i].item(), reversed_loss[i].item()))
+    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean_axial[i].item(), reversed_y_var_axial[i].item(), reversed_loss_axial[i].item()))
+    print()
+    
+min_loss = min(loss.min().item(), loss_axial.min().item())
+max_loss = max(loss.max().item(), loss_axial.max().item())
+plt.hist(loss.cpu().numpy(), bins=20, alpha=0.5, label="Standard", range=(min_loss, max_loss))
+plt.hist(loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Axial", range=(min_loss, max_loss))
+plt.legend()
+plt.xlabel("NLL")
+plt.title("Columns in Train Order")
+plt.savefig("hist_loss.png")
+plt.clf()
+
+min_loss = min(reversed_loss.min().item(), reversed_loss_axial.min().item())
+max_loss = max(reversed_loss.max().item(), reversed_loss_axial.max().item())
+plt.hist(reversed_loss.cpu().numpy(), bins=20, alpha=0.5, label="Standard", range=(min_loss, max_loss))
+plt.hist(reversed_loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Axial", range=(min_loss, max_loss))
+plt.legend()
+plt.xlabel("NLL")
+plt.title("Columns in Reverse Order")
+plt.savefig("hist_reversed.png")
+plt.clf()
+
+# plot all 
+min_loss = min(loss.min().item(), loss_axial.min().item(), reversed_loss.min().item(), reversed_loss_axial.min().item())
+max_loss = max(loss.max().item(), loss_axial.max().item(), reversed_loss_axial.max().item())
+
+plt.hist(loss.cpu().numpy(), bins=20, alpha=0.5, label="Standard", range=(min_loss, max_loss))
+plt.hist(loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Axial", range=(min_loss, max_loss))
+plt.hist(reversed_loss.cpu().numpy(), bins=20, alpha=0.5, label="Reversed Standard", range=(min_loss, max_loss))
+plt.hist(reversed_loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Reversed Axial", range=(min_loss, max_loss))
+plt.legend()
+plt.xlabel("NLL")
+plt.title("All")
+plt.savefig("hist_all.png")
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_f.py b/private_multitask_pfn/eval_f.py
new file mode 100644
index 0000000..f7bdd30
--- /dev/null
+++ b/private_multitask_pfn/eval_f.py
@@ -0,0 +1,284 @@
+import botorch
+import torch
+from botorch.acquisition.analytic import ExpectedImprovement
+from botorch.fit import fit_gpytorch_mll
+from botorch.models import SingleTaskGP
+from botorch.models.model import Model
+from botorch.models.multitask import MultiTaskGP
+from botorch.optim.optimize import optimize_acqf
+from botorch.posteriors import Posterior
+from fblearner.flow import api as flow
+from fblearner.flow.api import ResourceRequirements
+from fblearner.flow.external_api import WorkflowRun
+from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns.bar_distribution import (
+    BarDistribution,
+)
+from fblearner.flow.projects.ae.benchmarks.pfn.utils import (
+    load_model,
+    to_gp_format,
+    to_mtgp_format,
+    to_pfn_format,
+)
+from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
+from torch.distributions import Uniform
+
+
+def branin_source(x):
+    x1, x2 = x[..., 0], x[..., 1]
+    a, b, c, r, s, t = (
+        Uniform(
+            torch.tensor([0.5, 0.1, 1, 5, 8, 0.03]),
+            torch.tensor([1.5, 0.15, 2, 7, 12, 0.05]),
+        )
+        .sample()
+        .tolist()
+    )
+    first = a * (x2 - b * x1**2 + c * x1 - r)
+    second = s * (1 - t) * torch.cos(x1) + s
+    return first + second
+
+
+def hartmann_source(x):
+    alphas = (
+        Uniform(
+            torch.tensor([1.0, 1.18, 2.8, 3.2]), torch.tensor([1.02, 1.20, 3.0, 3.4])
+        )
+        .sample()
+        .to(device=x.device)
+    )
+    A = torch.tensor([[3.0, 10, 30], [0.1, 10, 35], [3.0, 10, 30], [0.1, 10, 35]]).to(
+        device=x.device
+    )
+    P = (
+        torch.tensor(
+            [
+                [3689, 1170, 2673],
+                [4699, 4387, 7470],
+                [1091, 8732, 5547],
+                [381, 5743, 8828],
+            ]
+        ).to(device=x.device)
+        * 1e-4
+    )
+    inner = -(A * (x.unsqueeze(-2) - P) ** 2)
+    # return negative for maximization
+    return (alphas * torch.exp(inner.sum(-1))).sum(-1).unsqueeze(-1)
+
+
+def pfn_gaussian_get_best(pfn, train_x, train_y, bounds):
+    target_indices = train_x[..., 0] > 0
+    best_f = train_y[target_indices].max()
+
+    pfn_model = PFNGaussian(pfn, train_x, train_y)
+    ei = ExpectedImprovement(pfn_model, best_f=best_f)
+    candidates, _ = optimize_acqf(
+        ei, bounds=bounds, q=1, num_restarts=10, raw_samples=512
+    )
+    return candidates
+
+
+def gp_get_best(train_x, train_y, bounds):
+    train_x, train_y = from_pfn_to_gp_format(train_x, train_y)
+    train_yvar = torch.ones_like(train_y) * 1e-5
+
+    best_f = train_y.max()
+
+    gp = SingleTaskGP(
+        train_x,
+        train_y,
+        train_Yvar=train_yvar,
+        outcome_transform=None,
+    )
+    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
+    fit_gpytorch_mll(mll)
+
+    ei = ExpectedImprovement(gp, best_f=best_f)
+    candidates, _ = optimize_acqf(
+        ei, bounds=bounds, q=1, num_restarts=10, raw_samples=512
+    )
+    return candidates
+
+
+def pfn_bar_get_best(pfn, train_x, train_y, bounds):
+    target_indices = train_x[..., 0] > 0
+    logits = pfn(train_x, train_y, possible_x)
+    eis = pfn.criterion.ei(logits, train_y[target_indices].max())
+    best_index = eis.argmax()
+    return possible_x[best_index], possible_y[best_index]
+
+
+def pfn_get_best(pfn, train_x, train_y, bounds):
+    if isinstance(pfn.criterion, BarDistribution):
+        return pfn_bar_get_best(pfn, train_x, train_y, bounds)
+    else:
+        return pfn_gaussian_get_best(pfn, train_x, train_y, bounds)
+
+
+def mtgp_get_best(task_id, train_x, train_y, bounds):
+    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
+    train_yvar = torch.ones_like(train_y) * 1e-5
+
+    best_f = train_y.max()
+
+    mtgp = MultiTaskGP(
+        train_x,
+        train_y,
+        task_feature=-1,
+        train_Yvar=train_yvar,
+        outcome_transform=None,
+        output_tasks=[0],
+    )
+    mll = ExactMarginalLogLikelihood(mtgp.likelihood, mtgp)
+    fit_gpytorch_mll(mll)
+
+    ei = ExpectedImprovement(mtgp, best_f=best_f)
+    candidates, _ = optimize_acqf(
+        ei, bounds=bounds, q=1, num_restarts=10, raw_samples=512
+    )
+    return candidates
+
+
+# @flow.flow_async(resource_requirements=ResourceRequirements(cpu=1))
+# @flow.typed()
+def bo_loop_continuous(
+    model_type, f, init_id, init_x, init_y, iters, bounds, model=None
+):
+    print("STARTING", model_type)
+    train_x = init_x.clone()
+    train_y = init_y.clone()
+
+    for i in range(iters):
+        if model_type == "mtgp":
+            best_x = mtgp_get_best_continuous(train_id, train_x, train_y, bounds)
+        elif model_type == "gp":
+            best_x = gp_get_best_continuous(train_id, train_x, train_y, bounds)
+        elif model_type == "pfn":
+            if model is None:
+                raise ValueError("model must be provided for pfn")
+            best_x = pfn_get_best_continuous(model, train_id, train_x, train_y, bounds)
+        else:
+            raise ValueError("model_type must be one of ['pfn', 'mtgp', 'gp']")
+
+        best_y = f(best_x)
+
+        n_tasks = train_x.shape[-1] - best_x.shape[-1]
+        best_x_pad = torch.zeros(*best_x.shape[:-1], n_tasks, device=best_x.device)
+        best_x_pad[..., 0] = 1
+        best_x_pad = torch.cat((best_x_pad, best_x), -1)
+
+        # # pad with zeros and 1 hot target encoding
+        # n_features = train_x.shape[-1]
+        # best_x_pad = torch.zeros(1, n_features, device=train_x.device)
+        # best_x_pad[:, 0] = 1
+        # best_x_pad[:, -best_x.shape[-1] :] = best_x
+
+        train_x = torch.cat((train_x, best_x_pad.unsqueeze(0)), 0)
+        train_y = torch.cat((train_y, best_y.unsqueeze(0)), 0)
+
+    return train_x, train_y
+
+
+@flow.flow_async(resource_requirements=ResourceRequirements(cpu=1, gpu=1, memory="16g"))
+@flow.typed()
+def run_bo_loop_continuous(
+    f, train_id, train_x, train_y, num_features, bo_iters, run_ids
+):
+    train_id.to(device="cuda")
+    train_x.to(device="cuda")
+    train_y.to(device="cuda")
+
+    info = {}
+    pfns = []
+    for run in run_ids:
+        workflow_run = WorkflowRun(run)
+        results = workflow_run.get_results()["output"]
+        pfn = load_model(results, num_tasks=4)
+        pfns.append(pfn)
+        info[run] = results["config"]
+
+    results = {}
+
+    bounds = torch.stack(
+        [
+            torch.zeros(num_features, device=train_x.device),
+            torch.ones(num_features, device=train_x.device),
+        ]
+    )
+
+    results["mtgp"] = bo_loop_continuous(
+        "mtgp", f, train_id, train_x, train_y, bo_iters, bounds
+    )
+    results["gp"] = bo_loop_continuous(
+        "gp", f, train_id, train_x, train_y, bo_iters, bounds
+    )
+    for run_id, pfn in zip(run_ids, pfns):
+        results[run_id] = bo_loop_continuous(
+            "pfn", f, train_id, train_x, train_y, bo_iters, bounds, pfn
+        )
+
+    return {
+        "results": results,
+        "info": info,
+    }
+
+
+@flow.registered(owners=["oncall+ae"])
+@flow.typed()
+def run_hartmann(runs: list, n_trials, bo_iters, num_tasks=4, seed=0, device="cpu"):
+    torch.manual_seed(seed)
+
+    def hartmann(x):
+        return botorch.test_functions.Hartmann(dim=3, negate=True)(
+            x[..., -3:]
+        ).unsqueeze(-1)
+
+    num_features = 3
+    max_num_tasks = 4
+
+    params = [
+        ("hartmann", 1, 20),
+        ("hartmann", 5, 20),
+        ("hartmann", 5, 50),
+        ("hartmann", 20, 50),
+    ]
+
+    results = {}
+    for param in params:
+        print("RUNNING", param)
+        results[param] = {}
+        name, num_target, num_source = param
+
+        for trial in range(n_trials):
+            train_id = []
+            train_x = []
+            train_y = []
+
+            for i in range(num_tasks):
+                task_x = torch.rand(num_source, num_features, device=device)
+
+                if i == 0:
+                    task_id = torch.zeros(num_target)
+                    task_y = hartmann(task_x)
+                else:
+                    task_id = torch.ones(num_source) * i
+                    task_y = hartmann_source(task_x)
+
+                train_id.append(task_id)
+                train_x.append(task_x)
+                train_y.append(task_y)
+
+            train_id = torch.cat(train_id)
+            train_x = torch.cat(train_x)
+            train_y = torch.cat(train_y)
+
+            result = run_bo_loop_continuous(
+                hartmann,
+                train_id,
+                train_x,
+                train_y,
+                num_features,
+                bo_iters=bo_iters,
+                run_ids=runs,
+            )
+            results[param][trial] = result
+    return results
diff --git a/private_multitask_pfn/eval_fcnet.py b/private_multitask_pfn/eval_fcnet.py
new file mode 100644
index 0000000..7418b7e
--- /dev/null
+++ b/private_multitask_pfn/eval_fcnet.py
@@ -0,0 +1,125 @@
+import h5py
+import numpy as np
+import torch
+# from ax.fb.utils.storage.manifold import AEManifoldUseCase
+# from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
+import pickle
+
+MANIFOLD_KEYS = {
+    "fcnet_naval_propulsion_data": "tree/trbo_dev/22a02b98-85a3-11ef-a873-472b54035c6c",
+    "fcnet_parkinsons_telemonitoring_data": "tree/trbo_dev/2ec159c4-85a3-11ef-a873-472b54035c6c",
+    "fcnet_protein_structure_data": "tree/trbo_dev/3b527894-85a3-11ef-a873-472b54035c6c",
+    "fcnet_slice_localization_data": "tree/trbo_dev/47f999c4-85a3-11ef-a873-472b54035c6c",
+}
+
+
+def save_to_manifold():
+    datasets = [
+        "fcnet_naval_propulsion_data",
+        "fcnet_parkinsons_telemonitoring_data",
+        "fcnet_protein_structure_data",
+        "fcnet_slice_localization_data",
+    ]
+
+    for dataset in datasets:
+        lut = {}
+        metric_name = "valid_loss"
+        with h5py.File(
+            f"/home/yl9959/mtpfn/datasets/fcnet_tabular_benchmarks/{dataset}.hdf5"
+        ) as fh:
+            for _, (k, v) in enumerate(fh.items()):
+                lut[k] = float(np.mean(v[metric_name][()][:, -1]))
+                # if i > 10000:
+                #     break
+
+        filter_rules = {
+            "activation_fn_1": "relu",
+            "activation_fn_2": "relu",
+            "lr_schedule": "cosine",
+        }
+        xs = []
+        ys = []
+        for config_string in lut:
+            keep = True
+            config = eval(config_string)
+            for filter_key in filter_rules:
+                if config[filter_key] != filter_rules[filter_key]:
+                    keep = False
+                    break
+
+            if keep:
+                result = [
+                    config["batch_size"],
+                    config["dropout_1"],
+                    config["dropout_2"],
+                    config["init_lr"],
+                    config["n_units_1"],
+                    config["n_units_2"],
+                ]
+                xs.append(result)
+                ys.append(lut[config_string])
+
+        xs = torch.tensor(xs)
+        for index in [0, 3, 4, 5]:
+            xs[:, index] = xs[:, index].log()
+        xs -= xs.min(0, keepdim=True)[0]
+        xs /= xs.max(0, keepdim=True)[0]
+
+        ys = -torch.tensor(ys)
+        results = {
+            "x": xs,
+            "y": ys,
+        }
+
+        pickle.dump(results, open(f"/home/yl9959/mtpfn/datasets/fcnet_tabular_benchmarks/{dataset}.pkl", "wb"))
+        # client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
+        # final_key = client.torch_save(data=results)
+        # print(dataset, final_key)
+        
+if __name__ == "__main__":
+    save_to_manifold()
+
+
+def get_torch_format_fcnet(target_result, source_results, max_num_features):
+    target_xs = target_result["x"]
+    target_ys = target_result["y"]
+
+    dropped_columns = []
+    dropped_values = []
+    for _ in range(target_xs.shape[-1] - max_num_features):
+        max_rows = 0
+        best_value = None
+        best_column = None
+        for column in range(target_xs.shape[-1]):
+            for unique_value in torch.unique(target_xs[:, column]):
+                num_rows = len(target_ys[target_xs[:, column] == unique_value].unique())
+                if num_rows > max_rows:
+                    max_rows = num_rows
+                    best_value, best_column = unique_value, column
+        mask = target_xs[:, best_column] == best_value
+        target_xs = target_xs[mask]
+        target_ys = target_ys[mask]
+        target_xs = torch.cat(
+            (target_xs[:, :best_column], target_xs[:, best_column + 1 :]), dim=-1
+        )
+        dropped_columns.append(best_column)
+        dropped_values.append(best_value)
+
+    sources_xs = []
+    sources_ys = []
+    for source in source_results:
+        source_xs = source["x"]
+        source_ys = source["y"]
+
+        for column, value in zip(dropped_columns, dropped_values):
+            mask = source_xs[:, column] == value
+            source_xs = source_xs[mask]
+            source_ys = source_ys[mask]
+            source_xs = torch.cat(
+                (source_xs[:, :column], source_xs[:, column + 1 :]), dim=-1
+            )
+
+        sources_xs.append(source_xs)
+        sources_ys.append(source_ys.unsqueeze(-1))
+
+    return target_xs, target_ys.unsqueeze(-1), sources_xs, sources_ys
diff --git a/private_multitask_pfn/eval_hpobench.py b/private_multitask_pfn/eval_hpobench.py
new file mode 100644
index 0000000..c47ba42
--- /dev/null
+++ b/private_multitask_pfn/eval_hpobench.py
@@ -0,0 +1,201 @@
+import os
+import pickle
+import pickle
+import numpy as np
+import pandas as pd
+import torch
+# from ax.fb.utils.storage.manifold import AEManifoldUseCase
+# from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
+
+HPOBENCH_MANIFOLD_KEYS = {
+    "rf": "tree/trbo_dev/839dd652-8544-11ef-94ac-4d9af618463b",
+    "lr": "tree/trbo_dev/ef4ea9d8-858c-11ef-a657-0908f1ce571c",
+    "svm": "tree/trbo_dev/bc90dacc-8544-11ef-94ac-4d9af618463b",
+    "xgb": "tree/trbo_dev/d58888be-858e-11ef-a657-0908f1ce571c",
+}
+
+default_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def drop_columns(df, relevant_columns, num_final_features):
+    df = df.copy()
+    # fixes one value per column by choosing the value
+    # that leads to the most diverse ys
+    dropped_columns = []
+    dropped_values = []
+    for _ in range(len(relevant_columns) - num_final_features):
+        # filter
+        max_rows = 0
+        best_value = None
+        best_column = None
+        for column in relevant_columns:
+            # Iterate through each unique value in the column
+            for value in df[column].unique():
+                # Filter the DataFrame
+                filtered_df = df[df[column] == value]
+                num_rows = filtered_df["function_value"].nunique()
+
+                # Check if this value retains the most rows
+                if num_rows > max_rows:
+                    max_rows = num_rows
+                    best_value, best_column = value, column
+
+        df = df[df[best_column] == best_value]
+        df = df.drop(best_column, axis=1)
+        
+        relevant_columns.remove(best_column)
+        dropped_columns.append(best_column)
+        dropped_values.append(best_value)
+
+    final_columns = [
+        column for column in relevant_columns if column not in dropped_columns
+    ]
+
+    return df, final_columns, dropped_columns, dropped_values
+
+
+def get_torch_format_hpobench(target_df, source_dfs, max_num_features, problem, device=default_device):
+    processed_dfs = []
+    for df in [target_df] + source_dfs:
+        df = df.copy()
+        df["function_value"] = df["result.function_value"]
+        if problem == "rf":
+            relevant_columns = [
+                "max_depth",
+                "max_features",
+                "min_samples_leaf",
+                "min_samples_split",
+            ]
+            df["max_depth"] = np.log(df["max_depth"])
+            df["min_samples_split"] = np.log(df["min_samples_split"])
+        elif problem == "lr":
+            relevant_columns = ["alpha", "eta0"]
+            df["alpha"] = np.log(df["alpha"])
+            df["eta0"] = np.log(df["eta0"])
+        elif problem == "svm":
+            relevant_columns = ["C", "gamma"]
+            df["C"] = np.log(df["C"])
+            df["gamma"] = np.log(df["gamma"])
+        elif problem == "xgb":
+            relevant_columns = ["colsample_bytree", "eta", "max_depth", "reg_lambda"]
+            df["eta"] = np.log(df["eta"])
+            df["max_depth"] = np.log(df["max_depth"])
+            df["reg_lambda"] = np.log(df["reg_lambda"])
+        elif problem == "nn":
+            print(df.columns)
+            relevant_columns = ["alpha", "batch_size", "depth", "learning_rate_init", "width"]
+            df["alpha"] = np.log(df["alpha"])
+            df["batch_size"] = np.log(df["batch_size"])
+            df["depth"] = (df["depth"] - df["depth"].min()) / (df["depth"].max() - df["depth"].min())
+            df["learning_rate_init"] = np.log(df["learning_rate_init"])
+            df["width"] = np.log(df["width"])
+
+        df = df.groupby(relevant_columns, as_index=False).mean(numeric_only=True)
+        processed_dfs.append(df)
+
+    # eventually remove when we train on more features
+    # drops columns to get maximum output diversity in target task
+    target_df = processed_dfs[0]
+    target_df, final_columns, dropped_columns, dropped_values = drop_columns(
+        target_df, relevant_columns, max_num_features
+    )
+
+    result_xs = []
+    result_ys = []
+    for df in processed_dfs:
+        # limit to non-dropped columns
+        for column, value in zip(dropped_columns, dropped_values):
+            df = df[df[column] == value]
+
+        # normalize to [0, 1]
+        xs = torch.tensor(df[final_columns].values).double()
+        xs = xs - xs.min(0, keepdim=True)[0]
+        max_values = xs.max(dim=0, keepdim=True)[0]
+        max_values[max_values == 0] = 1
+        xs = xs / max_values
+
+        # NEGATE VALID_LOSS
+        ys = -torch.tensor(df["function_value"].values).unsqueeze(-1)
+        # standardize to mean 0, std 1
+        std = torch.where(ys.std(0).isnan(), torch.tensor(1.0), ys.std(0))
+        ys = (ys - ys.mean()) / std
+
+        result_xs.append(xs.float().to(device))
+        result_ys.append(ys.float().to(device))
+
+    return result_xs[0], result_ys[0], result_xs[1:], result_ys[1:]
+
+
+def create_train_test(
+    target_xs, target_ys, sources_xs, sources_ys, n_target, n_source, max_num_task
+):
+    train_id = []
+    train_x = []
+    train_y = []
+    for i, (source_xs, source_ys) in enumerate(zip(sources_xs, sources_ys)):
+        random_indices = torch.randperm(len(source_xs))[:n_source]
+        train_x.append(source_xs[random_indices])
+        train_y.append(source_ys[random_indices])
+        train_id.append(torch.ones(n_source) * i + 1)
+
+    # add target task
+    random_indices = torch.randperm(len(target_xs))
+    train_indices = random_indices[:n_target]
+    test_indices = random_indices[n_target:]
+    train_x.append(target_xs[train_indices])
+    train_y.append(target_ys[train_indices])
+    train_id.append(torch.zeros(n_target))
+
+    train_x = torch.concat(train_x, 0)
+    train_id = torch.concat(train_id, 0)
+
+    train_y = torch.concat(train_y, 0)
+
+    test_id = torch.zeros(len(test_indices))
+    test_x = target_xs[test_indices]
+    test_y = target_ys[test_indices]
+
+    return (
+        train_id,
+        train_x,
+        train_y,
+        test_id,
+        test_x,
+        test_y,
+    )
+
+
+def save_to_pickle():
+    result = {}
+    # client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
+    for task in ["rf", "lr", "svm", "xgb", "nn"]:
+        for folder in os.listdir(
+            f"/home/yl9959/mtpfn/datasets/hpobench/{task}"
+        ):
+            if folder.endswith(".zip"):
+                continue
+            task_id = int(folder)
+            print("\tReading", folder)
+            df = pd.read_parquet(
+                f"/home/yl9959/mtpfn/datasets/hpobench/{task}/{task_id}/{task}_{task_id}_data.parquet.gzip"
+            )
+            if task == "rf":
+                df = df[(df["n_estimators"] == 512) & (df["subsample"] == 1)]
+            elif task == "lr":
+                df = df[(df["iter"] == 1000) & (df["subsample"] == 1.0)]
+            elif task == "svm":
+                df = df[(df["subsample"] == 1)]
+            elif task == "xgb":
+                df = df[(df["n_estimators"] == 2000) & (df["subsample"] == 1)]
+            elif task == "nn":
+                df = df[(df["iter"] == 243) & (df["seed"] == 8916)]
+
+            result[task_id] = df
+
+        # key = client.torch_save(result)
+        pickle.dump(result, open(f"/scratch/yl9959/mtpfn/datasets/hpobench_{task}.pkl", "wb"))
+        print(f"Saved {task} to /scratch/yl9959/mtpfn/datasets/hpobench_{task}.pkl", len(result))
+
+
+if __name__ == "__main__":
+    save_to_pickle()
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_plot_util.py b/private_multitask_pfn/eval_plot_util.py
new file mode 100644
index 0000000..21a8030
--- /dev/null
+++ b/private_multitask_pfn/eval_plot_util.py
@@ -0,0 +1,315 @@
+import itertools
+
+import multiprocessing as mp
+
+import botorch
+import torch
+import pickle
+from eval_fcnet import (
+    get_torch_format_fcnet,
+)
+from eval_hpobench import (
+    get_torch_format_hpobench,
+)
+import json
+
+from gen_batch import get_mtgp_for_eval
+from utils import (
+    create_train_test,
+    load_model,
+    pfn_predict,
+    mtgp_predict,
+    gp_predict,
+    scaml_predict,
+    standardize_by_task,
+    pfn_get_best,
+    mtgp_get_best,
+    gp_get_best,
+    scaml_get_best,
+)
+import time
+import datetime
+import warnings
+import os
+import hashlib
+import argparse
+import traceback
+import matplotlib.pyplot as plt
+from collections import defaultdict
+
+
+model_to_label = {
+    # "different-dust-282": "PFN (0.5 uncorr)",
+    # "royal-firebrand-281": "PFN (0.25 uncorr)",
+    # "peach-plasma-280": "PFN (1.0 uncorr)",
+    # "treasured-lion-279": "PFN (0.0 uncorr)",
+    # "exalted-wave-278": "PFN (0.75 uncorr)",
+    "wobbly-donkey-484": "wobbly-donkey-484 gaussian 0.1",
+    "sparkling-microwave-480": "sparkling-microwave-480 gaussian 0",
+    "likely-donkey-479": "likely-donkey-479 gaussian 0.2",
+    "fallen-cherry-483": "fallen-cherry-483 bar 0",
+    "stilted-night-482": "stilted-night-482 bar 0.1",
+    "youthful-wildflower-481": "youthful-wildflower-481 bar 0.2",
+}
+
+baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+
+def get_label(model):
+    if model in model_to_label:
+        return model_to_label[model]
+    return model
+
+
+def get_labels(models):
+    return [get_label(model) for model in models]
+
+
+def plot_all(exp_dir, n_functions, title):
+    fig, axs = plt.subplots(1, 2, figsize=(12, 7))
+    # one plot for boxplots of mse, one plot for boxplots of nll
+    
+    model_mses = defaultdict(list)
+    model_nlls = defaultdict(list)
+    
+    for function_dir in os.listdir(exp_dir):
+        # check if directory
+        if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+            continue
+        with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
+            results = json.load(f)
+            
+        models = list(results.keys())
+        for model in models:
+            mses, nlls = [], []
+            try:
+                for trial_key in results[model]:
+                    mses.append(results[model][trial_key]["mse"])
+                    nlls.append(results[model][trial_key]["nll"])
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                traceback.print_exc()
+                continue
+            model_mses[model].extend(mses)
+            model_nlls[model].extend(nlls)
+            
+    # plot mse boxplots
+    ax = axs[0]
+    ax.boxplot([model_mses[model] for model in model_mses], showfliers=False)
+    ax.set_xticklabels(get_labels(model_mses.keys()))
+    # rotate x labels
+    for tick in ax.get_xticklabels():
+        tick.set_rotation(45)
+    ax.set_title("MSE")
+    
+    # plot nll boxplots
+    ax = axs[1]
+    ax.boxplot([model_nlls[model] for model in model_nlls], showfliers=False)
+    ax.set_xticklabels(get_labels(model_nlls.keys()))
+    for tick in ax.get_xticklabels():
+        tick.set_rotation(45)
+    ax.set_title("NLL")
+    
+    plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(os.path.join(exp_dir, "plot.png"))
+
+
+def plot_function(function_dir, n_trials, title):
+    max_scatters = 50
+    with open(os.path.join(function_dir, "results.json"), "r") as f:
+        results = json.load(f)
+        
+    models = list(results.keys())
+    mse_dict = {}
+    nll_dict = {}
+        
+    fig, axs = plt.subplots(1, len(models) + 2, figsize=(6 * (len(models) + 2), 7))
+    axs = axs.flatten()
+    for i, model in enumerate(models):
+        try:
+            mses, nlls = [], []
+            for trial in range(n_trials):
+                trial_key = "trial_%d" % trial
+                mses.append(results[model][trial_key]["mse"])
+                nlls.append(results[model][trial_key]["nll"])
+            mse_dict[model] = mses
+            nll_dict[model] = nlls
+            
+            ax = axs[i]
+            # visualize predictions for one trial
+            mean = torch.tensor(results[model]["trial_0"]["mean"]).squeeze()
+            std = 2 * torch.tensor(results[model]["trial_0"]["std"]).squeeze()
+            true = torch.tensor(results[model]["trial_0"]["true"]).squeeze()
+            
+            ax.scatter(true[:max_scatters], mean[:max_scatters])
+            ax.errorbar(true[:max_scatters], mean[:max_scatters], yerr=std[:max_scatters], fmt="o")
+            ax.plot([true[:max_scatters].min(), true[:max_scatters].max()], [true[:max_scatters].min(), true[:max_scatters].max()], "k--")
+            ax.set_title(get_label(model) + " Predictions")
+            
+        except KeyboardInterrupt as e:
+            raise e
+        except Exception as e:
+            traceback.print_exc()
+            continue
+        
+    all_mses = [result for model in mse_dict for result in mse_dict[model]]
+    all_nlls = [result for model in nll_dict for result in nll_dict[model]]
+    all_mses = torch.tensor(all_mses)
+    all_nlls = torch.tensor(all_nlls)
+    
+    
+    lower_mse, upper_mse = torch.quantile(all_mses, torch.tensor([0.2, 0.8]))
+    diff = upper_mse - lower_mse
+    lower_mse = (lower_mse - 3 * diff)
+    upper_mse = (upper_mse + 3 * diff)
+    all_mses = all_mses[all_mses < upper_mse]
+    all_mses = all_mses[all_mses > lower_mse]
+    lower_mse, upper_mse = all_mses.min().item(), all_mses.max().item()
+    
+    lower_nll, upper_nll = torch.quantile(all_nlls, torch.tensor([0.2, 0.8]))
+    
+    ax = axs[-2]
+    for model in mse_dict:
+        ax.hist(mse_dict[model], bins=8, alpha=0.5, label=get_label(model), range=(lower_mse, upper_mse), density=True)
+    ax.set_title("MSE")
+    ax.legend()
+    
+    ax = axs[-1]
+    for model in nll_dict:
+        ax.hist(nll_dict[model], bins=8, alpha=0.5, label=get_label(model), range=(lower_nll.item(), upper_nll.item()), density=True)
+    ax.set_title("NLL")
+    ax.legend()
+                
+    plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(os.path.join(function_dir, "plot.png"))
+
+
+def plot_bo_function(function_dir, title):
+    plt.figure(figsize=(8, 6))
+        
+    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
+        bo_results = json.load(f)
+    with open(os.path.join(function_dir, "info.json"), "r") as f:
+        info = json.load(f)
+        
+        for model in bo_results:
+            model_results = []
+            try:
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    model_results.append(regret)
+                
+                # remove shorter trials
+                max_length = max([len(result) for result in model_results])
+                model_results = [result for result in model_results if len(result) == max_length]
+                
+                model_results = torch.stack(model_results)
+                
+                mean = model_results.nanmean(0)
+                std = model_results.std(0) / len(model_results)# ** 0.5
+                lower = mean - std
+                upper = mean + std
+                # mean = model_results.mean(0)
+                # lower = model_results.quantile(0.25, 0)
+                # upper = model_results.quantile(0.75, 0)
+                linestyle = "--" if model in baselines else "-"
+                plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
+                plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                print(e)
+                # import pdb; pdb.set_trace()
+                # raise e
+                continue
+                    
+    plt.legend()
+    plt.xlabel("BO Iterations")
+    plt.ylabel("Normalized Regret")
+    plt.title(title)
+    plt.tight_layout()
+    plt.savefig(os.path.join(function_dir, "bo_loop.png"))
+
+
+def plot_bo_all(exp_dir, models, title):
+    plt.figure(figsize=(8, 6))
+    
+    if models is None:
+        models = []
+        
+    for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                if not torch.isnan(function_results.mean(0)).any():
+                    model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except FileNotFoundError as e:
+                continue
+            except Exception as e:
+                # print(e)
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # mean = model_results.mean(0)
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        
+        mean = model_results.nanmean(0)
+        std = model_results.std(0) / len(model_results)# ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
+        plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
+
+                    
+    plt.legend()
+    plt.xlabel("BO Iterations")
+    plt.ylabel("Normalized Regret")
+    plt.title(title)
+    plt.tight_layout()
+    plt.savefig(os.path.join(exp_dir, "bo_loop.png"))
+
+
+
diff --git a/private_multitask_pfn/eval_task.py b/private_multitask_pfn/eval_task.py
new file mode 100644
index 0000000..649be68
--- /dev/null
+++ b/private_multitask_pfn/eval_task.py
@@ -0,0 +1,139 @@
+import torch
+from gen_task_batch import task_invariant_batch, task_invariant_eval_batch
+import json
+import os
+from utils import load_model
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# standard attention
+# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-18_15-48-06__prior_toy_task_invariant__features_3__tasks_2__epochs_10__seqlen_200__attn_axial"
+ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-18_17-39-47__prior_toy_task_invariant__features_3__tasks_2__epochs_10__seqlen_200__attn_axial" # use batch and seq len task encoding
+
+model = load_model(ckpt_dir).to(device)
+
+torch.manual_seed(0)
+
+num_features = 3
+num_train = 50
+num_test = 20
+
+
+batch_size = 1
+seq_len = num_train + num_test
+
+
+# x = torch.randint(5, (batch_size, seq_len, num_features), device=device)
+x = torch.rand(batch_size, seq_len, num_features, device=device)
+y = torch.zeros(batch_size, seq_len, 1, device=device)
+
+task_id = torch.randint(2, (batch_size, seq_len, 1), device=device)
+opposite_task_id = (task_id + 1) % 2
+
+correct_y = torch.zeros(batch_size, seq_len, 1, device=device)
+incorrect_y = torch.zeros(batch_size, seq_len, 1, device=device)
+
+# ID Task 1: y = x_1 + x_2^2 + ... + x_d^d
+for feature in range(num_features):
+    task_mask = task_id == 0
+    correct_y += 0 * task_mask
+    incorrect_y += 0 * ~task_mask
+    
+for feature in range(num_features):
+    task_mask = task_id == 1
+    correct_y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * task_mask
+    incorrect_y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * ~task_mask
+
+train_x = x.transpose(0, 1)[:num_train]
+train_correct_y = correct_y.transpose(0, 1)[:num_train]
+train_incorrect_y = incorrect_y.transpose(0, 1)[:num_train]
+train_original_task_id = task_id.transpose(0, 1)[:num_train]
+train_opposite_task_id = opposite_task_id.transpose(0, 1)[:num_train]
+
+
+test_x = x.transpose(0, 1)[num_train:]
+test_correct_y = correct_y.transpose(0, 1)[num_train:]
+test_incorrect_y = incorrect_y.transpose(0, 1)[num_train:]
+test_original_task_id = task_id.transpose(0, 1)[num_train:]
+test_opposite_task_id = opposite_task_id.transpose(0, 1)[num_train:]
+
+
+# Original
+pred_original_y = model(train_x, train_original_task_id, train_correct_y, test_x, test_original_task_id)
+# Opposite
+pred_opposite_y = model(train_x, train_opposite_task_id, train_correct_y, test_x, test_opposite_task_id)
+
+
+original_y_mean, original_y_var = pred_original_y[..., 0], pred_original_y[..., 1].exp()
+opposite_y_mean, opposite_var = pred_opposite_y[..., 0], pred_opposite_y[..., 1].exp()
+
+criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
+original_loss = criterion(original_y_mean, test_correct_y, var=original_y_var)
+opposite_loss = criterion(opposite_y_mean, test_correct_y, var=opposite_var)
+
+for i in range(num_test):
+    print("\nTest Point", i)
+    print("Original Y", test_correct_y[i].item())
+    print("Original Prediction: N({:.2f}, {:.2f})".format(original_y_mean[i].item(), original_y_var[i].sqrt().item()))
+    print("Opposite Prediction: N({:.2f}, {:.2f})".format(opposite_y_mean[i].item(), opposite_var[i].sqrt().item()))
+    
+    print("Incorrect Y", test_incorrect_y[i].item())
+    
+
+
+lily
+
+
+
+
+
+
+
+
+
+
+
+
+
+id_batch = task_invariant_batch(
+    batch_size=1,
+    seq_len=num_train+num_test,
+    num_features=num_features,
+    max_num_tasks=2,
+    num_tasks=2,
+    lengthscale=1.0,
+    hyperparameters=None,
+    device=device,
+)
+
+ood_batch = task_invariant_eval_batch(
+    batch_size=1,
+    seq_len=num_train+num_test,
+    num_features=num_features,
+    max_num_tasks=2,
+    num_tasks=2,
+    lengthscale=1.0,
+    hyperparameters=None,
+    device=device,
+)
+
+batches = [id_batch, ood_batch]
+batch_type = ["ID", "OOD"]
+
+for name, batch in zip(batch_type, batches):
+    train_x = batch.x[:num_train]
+    train_y = batch.y[:num_train]
+    train_task_id = batch.task_id[:num_train]
+
+    test_task_id_mask = (batch.task_id[num_train:] == 0).squeeze(-1)
+    test_x = batch.x[num_train:][test_task_id_mask].unsqueeze(1)
+    test_y = batch.y[num_train:][test_task_id_mask].unsqueeze(1)
+
+    y_pred = model(train_x, train_task_id, train_y, test_x)
+    y_mean, y_var = y_pred[..., 0], y_pred[..., 1].exp()
+
+    criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
+    loss = criterion(y_mean, test_y.squeeze(1), var=y_var)
+
+    print(f"Test Loss: {loss.mean().item()}", name)
+
diff --git a/private_multitask_pfn/eval_vis.py b/private_multitask_pfn/eval_vis.py
new file mode 100644
index 0000000..cf5eab5
--- /dev/null
+++ b/private_multitask_pfn/eval_vis.py
@@ -0,0 +1,215 @@
+import argparse
+
+import botorch
+import gpytorch
+import torch
+
+from utils import *
+import traceback
+import matplotlib.pyplot as plt
+from gpytorch.kernels import RBFKernel
+from gpytorch.distributions import MultivariateNormal
+import os
+
+default_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def plot(ax, test_info, pfn_info):
+    pfn_id, model, model_dim = pfn_info
+    train_x, train_y, test_x, test_y = test_info
+    train_task_id = torch.zeros(train_x.size(0))
+    
+    if model_dim > 1:
+        # pad train and test with 0s
+        padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)
+        padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)
+    else:
+        padded_train_x = train_x
+        padded_test_x = test_x
+        
+    # train_x, train_task_id, train_y, test_x, 
+    posterior = pfn_predict(model, train_task_id.to(default_device), padded_train_x.to(default_device), train_y.to(default_device), padded_test_x.to(default_device))
+    mean = posterior.mean.cpu()
+    std = posterior.variance.sqrt().cpu()
+    
+    ax.plot(test_x, test_y, label="true", color="C0")
+    ax.scatter(train_x, train_y, label="train", color="C0")
+    ax.plot(test_x, mean, label="mean", color="C1")
+    ax.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
+
+
+def get_test_function(seed):
+    with botorch.manual_seed(seed):
+        n_samples = torch.randint(5, 20, (1,)).item()
+        
+        train_x = torch.rand(n_samples, 1)
+        test_x = torch.linspace(0, 1, 100).view(-1, 1)
+        all_x = torch.cat([train_x, test_x], dim=0)
+        
+        rbf = gpytorch.kernels.RBFKernel()
+        # rbf.lengthscale = torch.tensor(0.2)
+        rbf.lengthscale = torch.distributions.Gamma(3, 6).sample()
+        
+        covar = rbf(all_x)
+        covar = covar + 1e-4 * torch.eye(covar.size(0))
+        
+        y = MultivariateNormal(torch.zeros(all_x.size(0)), covar).sample()
+        train_y = y[:n_samples]
+        test_y = y[n_samples:]
+        
+    return train_x, train_y, test_x, test_y
+
+
+
+def plot_multitask(ax_multi, ax_single, test_info, pfn_info):
+    pfn_id, model, model_dim = pfn_info
+    train_x, train_task_id, train_y, test_x, test_task_id, test_y = test_info
+    
+    if model_dim > 1:
+        # pad train and test with 0s
+        padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)
+        padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)
+    else:
+        padded_train_x = train_x
+        padded_test_x = test_x
+        
+    # train_x, train_task_id, train_y, test_x
+    posterior = pfn_predict(model, train_task_id.to(default_device), padded_train_x.to(default_device), train_y.to(default_device), padded_test_x.to(default_device))
+    mean = posterior.mean.cpu()
+    std = posterior.variance.sqrt().cpu()
+    nll = -torch.distributions.Normal(mean.squeeze(), std.squeeze()).log_prob(test_y.squeeze()).mean()
+    
+    ax_multi.set_title(f"Multi NLL: {nll.item():.2f}")
+    ax_multi.plot(test_x, test_y, label="true", color="C0")
+    ax_multi.plot(test_x, mean, label="mean", color="C1")
+    ax_multi.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
+    
+    for i in train_task_id.unique():
+        mask = train_task_id == i
+        marker = "x" if i.item() == 0 else "o"
+        size = 100 if i.item() == 0 else 50
+        ax_multi.scatter(train_x[mask], train_y[mask], label=f"train task {i.item()}", color=f"C{i}", marker=marker, s=size)
+
+
+    # train_x, train_task_id, train_y, test_x,
+    target_mask = train_task_id == 0
+    posterior = pfn_predict(model, train_task_id[target_mask].to(default_device), padded_train_x[target_mask].to(default_device), train_y[target_mask].to(default_device), padded_test_x.to(default_device))
+    mean = posterior.mean.cpu()
+    std = posterior.variance.sqrt().cpu()
+    nll = -torch.distributions.Normal(mean.squeeze(), std.squeeze()).log_prob(test_y.squeeze()).mean()
+    
+    ax_single.set_title(f"Single NLL: {nll.item():.2f}")
+    ax_single.plot(test_x, test_y, label="true", color="C0")
+    ax_single.plot(test_x, mean, label="mean", color="C1")
+    ax_single.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
+    
+    for i in [0]:#train_task_id.unique():
+        mask = train_task_id == i
+        # marker = "x" if i.item() == 0 else "o"
+        # size = 100 if i.item() == 0 else 50
+        marker = "x"
+        size = 100
+        ax_single.scatter(train_x[mask], train_y[mask], label=f"train task {i}", color=f"C{i}", marker=marker, s=size)
+
+
+def get_multitask_test_function(seed, n_target, n_source):
+    n_features = 1
+    n_tasks = 2
+    with botorch.manual_seed(seed):
+        if n_target is not None:
+            n_samples = n_target + n_source * (n_tasks - 1)
+            target_task_id = torch.zeros(n_target, 1).long()
+            source_task_ids = [torch.ones(n_source, 1).long() * i for i in range(1, n_tasks)]
+            train_task_id = torch.cat([target_task_id] + source_task_ids, dim=0)
+        else:
+            n_samples = torch.randint(10, 40, (1,)).item()
+            train_task_id = torch.randint(n_tasks, size=(n_samples,)).unsqueeze(1).long()
+        
+        train_xs = torch.rand(n_samples, n_features)
+        test_xs = torch.linspace(0, 1, 100).view(-1, n_features)
+        test_task_id = torch.zeros(test_xs.size(0), 1).long()
+        xs = torch.cat([train_xs, test_xs], dim=0)
+        task_id = torch.cat([train_task_id, test_task_id], dim=0)
+        
+        rbf = RBFKernel()
+        rbf.lengthscale = torch.distributions.Gamma(3, 6).sample()
+        covar_x = rbf(xs)
+        task_covar_matrix = torch.ones(n_tasks, n_tasks) * 0.9
+        task_covar_matrix += torch.eye(n_tasks) * 0.1
+        
+        covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
+        covar = covar_x.mul(covar_t)
+        covar = covar + 1e-4 * torch.eye(covar.size(0))
+        ys = MultivariateNormal(torch.zeros(covar.shape[-1]), covar).sample()
+
+        train_y = ys[:n_samples]
+        test_y = ys[n_samples:]
+        
+    return train_xs, train_task_id.squeeze(), train_y, test_xs, test_task_id.squeeze(), test_y
+
+
+# main function
+if __name__ == "__main__":
+    argparser = argparse.ArgumentParser()
+    # pfn wandb ids
+    argparser.add_argument("--pfn_ids", nargs="*", type=str, help="wandb ids for pfn models")
+    argparser.add_argument("--dirs", nargs="*", type=str, help="checkpoints for pfn models")
+    argparser.add_argument("--seed", type=int, default=0, help="seed for test function")
+    argparser.add_argument("--n_target", type=int, default=None, help="samples of target function")
+    argparser.add_argument("--n_source", type=int, default=None, help="samples of source function")
+    args = argparser.parse_args()
+    
+    pfn_dirs = []
+    if args.pfn_ids is not None:
+        # pfn_dirs += ["/home/yl9959/mtpfn/wandb_links/%s" % pfn_id for pfn_id in args.pfn_ids]
+        if os.path.exists("/home/lily_l/private_multitask_pfn/wandb_links"):
+            pfn_dirs += [(pfn_id, "/home/lily_l/private_multitask_pfn/wandb_links/%s" % pfn_id) for pfn_id in args.pfn_ids]
+        else:
+            pfn_dirs += [(pfn_id, "/home/yl9959/mtpfn/wandb_links/%s" % pfn_id) for pfn_id in args.pfn_ids]
+    if args.dirs is not None:
+        pfn_dirs += [("pfn_%d" % i, dir) for i, dir in enumerate(args.dirs)]
+
+    for pfn_id, pfn_dir in pfn_dirs:
+        try:
+            pfn_args_json = f"{pfn_dir}/args.json"
+            with open(pfn_args_json, "r") as f:
+                pfn_args = json.load(f)
+                
+            if not pfn_args["sample_num_features"]:
+                model_dim = pfn_args["num_features"]
+            else:
+                model_dim = 1
+            
+            model = load_model(pfn_dir, best=True).to(default_device)
+            pfn_info = (pfn_id, model, model_dim)
+                        
+            fig, axs = plt.subplots(1, 5, figsize=(20, 4))
+            
+            for i in range(5):
+                seed = args.seed + i
+                test_info = get_test_function(seed)
+                plot(axs[i], test_info, pfn_info)
+            
+            plt.legend()    
+            plt.suptitle(pfn_info[0])
+            plt.tight_layout()
+            plt.savefig(f"figures/pfn_{pfn_info[0]}.png")
+            plt.close()
+            
+            fig, axs = plt.subplots(2, 5, figsize=(20, 8))
+            # axs = axs.flatten()
+            for i in range(5):
+                seed = args.seed + i
+                test_info = get_multitask_test_function(seed, n_target=args.n_target, n_source=args.n_source)
+                plot_multitask(axs[0][i], axs[1][i], test_info, pfn_info)
+            
+            plt.legend()
+            plt.suptitle(pfn_info[0])
+            plt.tight_layout()
+            plt.savefig(f"figures/pfn_{pfn_info[0]}_multitask.png")
+            plt.close()
+        except Exception as e:
+            traceback.print_exc()
+            print("failed to load", pfn_dir)
+            continue
+            
+        
\ No newline at end of file
diff --git a/private_multitask_pfn/gen_axial_batch.py b/private_multitask_pfn/gen_axial_batch.py
new file mode 100644
index 0000000..669cb94
--- /dev/null
+++ b/private_multitask_pfn/gen_axial_batch.py
@@ -0,0 +1,96 @@
+import torch
+from PFNs.pfns.priors import Batch
+
+
+default_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# batch_size: number of datasets
+# seq_len: number of training samples per dataset
+def axial_train_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    """
+    Generate x_i = [x_1, x_2, ..., x_d] and y_i = x_1 + x_2^2 + ... + x_d^d
+    """
+    x = torch.rand(batch_size, seq_len, num_features, device=device)
+    y = torch.zeros(batch_size, seq_len, 1, device=device)
+    
+    for feature in range(num_features):
+        y += torch.pow(x[:, :, feature:feature+1], feature + 1)
+        
+    return Batch(
+        x=x.transpose(0, 1),
+        y=y.transpose(0, 1),
+        target_y=y.transpose(0, 1).clone(),
+    )
+
+
+def axial_test_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    x = torch.rand(batch_size, seq_len, num_features, device=device)
+    y = torch.zeros(batch_size, seq_len, 1, device=device)
+    
+    for feature in range(num_features):
+        y += torch.pow(x[:, :, feature:feature+1], num_features - feature)
+        
+    return Batch(
+        x=x.transpose(0, 1),
+        y=y.transpose(0, 1),
+        target_y=y.transpose(0, 1).clone(),
+    )
+
+
+# batch_size: number of datasets
+# seq_len: number of training samples per dataset
+def multitask_line_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    # permuted columns
+    x = torch.rand(batch_size, seq_len, num_features, device=device)
+    y = torch.zeros(batch_size, seq_len, 1, device=device)
+    task_id = torch.randint(0, num_tasks, (batch_size, seq_len, 1), device=device)
+    
+    for task in range(num_tasks):
+        for feature in range(num_features):
+            constant = torch.randn(1, device=device)
+            y += constant * torch.pow(x[:, :, feature:feature+1], feature + 1) * (task_id == task).float()
+            
+    # shuffle x columns
+    column_order = torch.randperm(num_features, device=device)
+    x = x[:, :, column_order]
+    
+    return Batch(
+        x=x.transpose(0, 1),
+        y=y.transpose(0, 1),
+        target_y=y.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+
+
+# multitask_line_batch(batch_size=2, seq_len=10, num_features=2, max_num_tasks=2, num_tasks=2, lengthscale=0.1)
+# axial_test_batch(batch_size=2, seq_len=3, num_features=2, max_num_tasks=2, num_tasks=2, lengthscale=0.1)
\ No newline at end of file
diff --git a/private_multitask_pfn/gen_batch.py b/private_multitask_pfn/gen_batch.py
new file mode 100644
index 0000000..398d0f7
--- /dev/null
+++ b/private_multitask_pfn/gen_batch.py
@@ -0,0 +1,1083 @@
+#!/usr/bin/env python3
+import gpytorch
+
+import torch
+from PFNs.pfns.priors import Batch
+from gpytorch.distributions.multivariate_normal import MultivariateNormal
+from gpytorch.kernels import RBFKernel
+from gpytorch.kernels.kernel import Kernel
+from gpytorch.means.constant_mean import ConstantMean
+from gpytorch.priors.lkj_prior import LKJCovariancePrior
+from linear_operator.operators import InterpolatedLinearOperator
+import json
+import pickle
+from botorch.models import SingleTaskGP
+from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
+from botorch.exceptions import ModelFittingError
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from eval_hpobench import get_torch_format_hpobench
+import os
+
+default_device = "cuda:0" if torch.cuda.is_available() else "cpu:0"
+
+
+class IndexKernelFixed(Kernel):
+    def __init__(
+        self,
+        covar_matrix,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.covar_matrix = covar_matrix
+
+    def forward(self, i1, i2, **params):
+        i1, i2 = i1.long(), i2.long()
+        covar_matrix = self.covar_matrix
+        batch_shape = torch.broadcast_shapes(
+            i1.shape[:-2], i2.shape[:-2], self.batch_shape
+        )
+
+        res = InterpolatedLinearOperator(
+            base_linear_op=covar_matrix,
+            left_interp_indices=i1.expand(batch_shape + i1.shape[-2:]),
+            right_interp_indices=i2.expand(batch_shape + i2.shape[-2:]),
+        )
+        return res
+
+
+def sample_task_ids(batch_size: int, seq_len: int, num_tasks: int, device: str, same_tasks_across_batch=False):
+    # split data into target and source tasks with Dirichlet / Categorical sampling
+    alphas = torch.ones(num_tasks).to(device) * 2.0
+    # alphas[0] = 1.0
+    
+    
+    # B x N
+    if same_tasks_across_batch:
+        probabilities = torch.distributions.Dirichlet(alphas).sample()
+        task_id = torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
+        task_id = task_id.unsqueeze(0).repeat(batch_size, 1)
+    else:
+        probabilities = torch.distributions.Dirichlet(alphas).sample((batch_size,))
+        task_id = (
+            torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
+        )
+    task_id[:, :num_tasks] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
+    task_id[:, -num_tasks:] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
+
+    return task_id
+
+
+def sample_task_ids_target_aware(eval_pos: int, batch_size: int, seq_len: int, num_tasks: int, device: str, same_tasks_across_batch=False):
+    # split data into target and source tasks with Dirichlet / Categorical sampling
+    alphas = torch.ones(num_tasks).to(device) * 2.0
+    alphas[0] = 1.0 # target task is less likely to be sampled
+    
+    # B x N
+    if same_tasks_across_batch:
+        probabilities = torch.distributions.Dirichlet(alphas).sample()
+        task_id = torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
+        task_id = task_id.unsqueeze(0).repeat(batch_size, 1)
+    else:
+        probabilities = torch.distributions.Dirichlet(alphas).sample((batch_size,))
+        task_id = (
+            torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
+        )
+    # ensure every task is observed during training
+    task_id[:, :num_tasks] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
+    # only test on target task
+    task_id[:, eval_pos:] = 0
+
+    return task_id
+
+
+def sample_task_ids_even_eval(eval_pos: int, batch_size: int, seq_len: int, num_tasks: int, device: str, same_tasks_across_batch=False):
+    # split data into target and source tasks with Dirichlet / Categorical sampling
+    alphas = torch.ones(num_tasks).to(device) * 2.0
+    
+    # B x N
+    if same_tasks_across_batch:
+        probabilities = torch.distributions.Dirichlet(alphas).sample()
+        task_id = torch.distributions.Categorical(probabilities).sample((seq_len,)).to(device)
+        task_id = task_id.unsqueeze(0).repeat(batch_size, 1)
+    else:
+        probabilities = torch.distributions.Dirichlet(alphas).sample((batch_size,))
+        task_id = (
+            torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
+        )
+    # ensure every task is observed during training
+    task_id[:, :num_tasks] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
+    # evenly test on all tasks
+    n_eval = seq_len - eval_pos
+    even_spread = torch.arange(num_tasks).repeat(batch_size, n_eval // num_tasks + 1)[:, :n_eval]
+    task_id[:, eval_pos:] = even_spread
+
+    return task_id
+
+
+def get_uncorr_task_ids(
+    num_tasks: int,
+    hyperparameters: dict,
+):    
+    if "uncorr_prob" in hyperparameters:
+        uncorr_prob = hyperparameters["uncorr_prob"]
+        uncorr_tasks = (
+            torch.where(torch.rand(num_tasks - 1) < uncorr_prob)[0] + 1
+        ).tolist()
+    elif "num_uncorr_tasks" in hyperparameters:
+        num_uncorr_tasks = hyperparameters["num_uncorr_tasks"]
+        uncorr_tasks = torch.arange(1, num_tasks)[:num_uncorr_tasks].tolist()
+    else:
+        uncorr_tasks = []
+    
+    return uncorr_tasks
+
+
+
+def resample_uncorr_tasks(task_id, xs, ys, num_tasks, hyperparameters):
+    if "uncorr_prob" in hyperparameters:
+        uncorr_prob = hyperparameters["uncorr_prob"]
+        uncorr_tasks = (
+            torch.where(torch.rand(num_tasks - 1) < uncorr_prob)[0] + 1
+        ).tolist()
+    elif "num_uncorr_tasks" in hyperparameters:
+        num_uncorr_tasks = hyperparameters["num_uncorr_tasks"]
+        uncorr_tasks = torch.arange(1, num_tasks)[:num_uncorr_tasks].tolist()
+    else:
+        uncorr_tasks = []
+
+    # randomly sample uncorrelated tasks
+    kernel = RBFKernel().to(xs.device)
+    for task in uncorr_tasks:
+        assert task > 0
+        task_mask = task_id == task
+        task_x = xs[task_mask]
+        kernel.lengthscale = torch.distributions.Gamma(12, 6).sample().to(xs.device)
+        ys[task_mask] = (
+            MultivariateNormal(
+                torch.zeros(task_x.shape[:-1], device=xs.device), kernel(task_x)
+            ).sample()
+        ).unsqueeze(-1)
+
+    return ys
+
+
+def draw_mtgp_samples_original(xs, task_id, input_covar_module, task_covar_matrix):
+    task_covar_module = IndexKernelFixed(task_covar_matrix)
+
+    mean_x = ConstantMean()(xs).to(xs.device)
+    covar_x = input_covar_module(xs)
+    covar_t = task_covar_module(task_id.unsqueeze(-1))
+    covar = covar_x.mul(covar_t)
+
+    return MultivariateNormal(mean_x, covar).sample().unsqueeze(-1)
+
+
+def draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_tasks):
+    CORR_LENGTHSCALE_A, CORR_LENGTHSCALE_B = 3.0, 6.0
+    # UNCORR_LENGTHSCALE_A, UNCORR_LENGTHSCALE_B = 12.0, 6.0
+    UNCORR_LENGTHSCALE_A, UNCORR_LENGTHSCALE_B = 3.0, 6.0
+    
+    B, N, D = xs.shape
+    num_tasks = task_covar_matrix.shape[-1]
+    n_ind = 1 + len(uncorr_tasks)
+    
+    if num_tasks == 1:
+        mean_x = ConstantMean()(xs).to(xs.device)
+        
+        lengthscale_prior = gpytorch.priors.GammaPrior(CORR_LENGTHSCALE_A, CORR_LENGTHSCALE_B)
+        input_covar_module = RBFKernel().to(xs.device)
+        input_covar_module.lengthscale = lengthscale_prior.sample()
+        covar_x = input_covar_module(xs)
+
+        return MultivariateNormal(mean_x, covar_x).sample().unsqueeze(-1)
+    
+
+    # Add additional "batch" dimension for independent tasks
+    xs_expand = xs.unsqueeze(0).expand(n_ind, B, N, D)
+    task_id_expand = task_id.unsqueeze(0).unsqueeze(-1).expand(n_ind, B, N, 1)
+
+    mean_x = ConstantMean()(xs_expand).to(xs_expand.device)
+
+    # input kernel: different lengthscale per indepedent task and batch
+    a = torch.tensor([CORR_LENGTHSCALE_A] + [UNCORR_LENGTHSCALE_A] * len(uncorr_tasks)).to(xs)
+    b = torch.tensor([CORR_LENGTHSCALE_B] + [UNCORR_LENGTHSCALE_B] * len(uncorr_tasks)).to(xs)
+    lengthscale_prior = gpytorch.priors.GammaPrior(a, b)
+    input_covar_module = RBFKernel(ard_num_dims=D, batch_shape=(n_ind, B)).to(xs.device)
+    # final shape: n_ind x B x 1 x D
+    input_covar_module.lengthscale = lengthscale_prior.sample((B, D)).unsqueeze(-2).permute(-1, 0, 2, 1)
+    covar_x = input_covar_module(xs_expand)
+
+    # task kernel: different correlation per batch
+    # todo: different correlation per each batch
+    task_covar_matrix = task_covar_matrix.unsqueeze(0).unsqueeze(0).expand(n_ind, B, num_tasks, num_tasks)
+    task_covar_module = IndexKernelFixed(task_covar_matrix)
+    covar_t = task_covar_module(task_id_expand)
+
+    samples = MultivariateNormal(mean_x, covar_x.mul(covar_t)).sample().unsqueeze(-1)
+    
+    # combine correlated and uncorrelated samples
+    ys = samples[0]
+    for i, task in enumerate(uncorr_tasks):
+        task_mask = task_id == task
+        ys[task_mask] = samples[i+1][task_mask]
+    
+    return ys
+
+
+def get_mtgp_for_eval(
+    num_features,
+    num_tasks,
+    lengthscale,
+    task_corr,
+    num_uncorr_tasks,
+    device: str = "cpu",
+):
+    TARGET_TRAIN = 100
+    TARGET_TEST = 500
+    SOURCE_TRAIN = 500
+
+    # generate task ids
+    task_lengths = [TARGET_TRAIN + TARGET_TEST] + [SOURCE_TRAIN] * (num_tasks - 1)
+    task_id = []
+    for task, task_length in enumerate(task_lengths):
+        task_id.append(torch.ones(task_length) * task)
+    task_id = torch.cat(task_id).to(device)
+
+    # generate x values
+    xs = torch.rand(len(task_id), num_features).to(device)
+
+    # generate task and input covariances
+    input_covar_module = RBFKernel().to(device)
+    if lengthscale is None:
+        lengthscale = torch.distributions.Gamma(1, 6).sample().to(device)
+    input_covar_module.lengthscale = lengthscale
+
+    # task covariance matrix with fixed correlation
+    if task_corr is not None:
+        task_covar_matrix = torch.full((num_tasks, num_tasks), task_corr, device=device)
+        task_covar_matrix.fill_diagonal_(1.0)
+    else:
+        if num_tasks > 1:
+            # sample from LKJ prior
+            task_covar_matrix = (
+                LKJCovariancePrior(
+                    n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
+                )
+                .sample()
+                .to(device)
+            )
+        else:
+            task_covar_matrix = torch.eye(num_tasks).to(device)
+
+    ys = draw_mtgp_samples_original(xs, task_id, input_covar_module, task_covar_matrix)
+    ys = resample_uncorr_tasks(
+        task_id, xs, ys, num_tasks, {"num_uncorr_tasks": num_uncorr_tasks}
+    )
+
+    result_xs = []
+    result_ys = []
+    for task in range(num_tasks):
+        task_mask = task_id == task
+        result_xs.append(xs[task_mask])
+        result_ys.append(ys[task_mask])
+
+    return result_xs[0], result_ys[0], result_xs[1:], result_ys[1:]
+
+
+# batch_size: number of datasets
+# seq_len: number of training samples per dataset
+def get_mtgp_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    eval_pos = kwargs["single_eval_pos"]
+    seq_len 
+    xs = torch.rand(batch_size, seq_len, num_features, device=device)
+    task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
+
+    if num_tasks > 1:
+        # sample task covariance matrix
+        lkj_covar_matrix = (
+            LKJCovariancePrior(
+                n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
+            )
+            .sample()
+            .to(device)
+        )
+        
+        corr_init = hyperparameters.get("corr_init", None)
+        if corr_init is not None:
+            sampled_corr = torch.distributions.Uniform(corr_init, 1.0).sample().to(device)
+            task_covar_matrix = torch.full((num_tasks, num_tasks), sampled_corr).to(lkj_covar_matrix)
+            task_covar_matrix.fill_diagonal_(1.0)
+            
+            blend = torch.rand((1,)).to(device)
+            task_covar_matrix = blend * task_covar_matrix + (1 - blend) * lkj_covar_matrix
+        else:
+            task_covar_matrix = lkj_covar_matrix     
+    else:
+        task_covar_matrix = torch.eye(num_tasks).to(device)
+        
+    uncorr_ids = get_uncorr_task_ids(num_tasks, hyperparameters)
+    ys = draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_ids)
+    task_id = task_id.unsqueeze(-1)
+    
+    noise = torch.distributions.Gamma(1, 5).sample((num_tasks,)).to(device)
+    noise_by_task = noise[task_id]
+    # noisy_ys = ys + torch.randn_like(ys) * noise_by_task
+    noisy_ys = ys.clone()
+
+    return Batch(
+        x=xs.transpose(0, 1),
+        y=noisy_ys.transpose(0, 1),
+        target_y=ys.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+    
+def standardize_per_task(ys, task_id, single_eval_pos, true_ys=None):
+    # all task ids should be the same across batches
+    assert (task_id.min(dim=0).values == task_id.max(dim=0).values).all(), "task_id is not the same across batches"
+    task_id = task_id[0]
+        
+    standardized_ys = ys.clone()
+    if true_ys is not None:
+        standardized_true_ys = true_ys.clone()
+    
+    train_ys = ys[:, :single_eval_pos]
+    train_task_id = task_id[:single_eval_pos]
+    
+    train_mask = torch.zeros_like(ys, dtype=torch.bool)
+    train_mask[:, :single_eval_pos] = True
+        
+    # For each task, compute mean and std based on training data only
+    for t in task_id.unique():
+        train_task_mask = train_task_id == t
+        all_task_mask = task_id == t
+        
+        # Compute mean and std for training data of this task
+        task_train_values = train_ys[:, train_task_mask]
+        # batch x seq_len x 1
+        task_mean = task_train_values.mean(dim=1, keepdim=True)
+        task_std = task_train_values.std(dim=1, keepdim=True)
+        
+        # Numerical check for 0 or nan
+        task_std = torch.where(task_std == 0, torch.ones_like(task_std), task_std)
+        task_std = torch.where(task_std.isnan(), torch.ones_like(task_std), task_std)
+            
+        # Apply standardization to all data points of this task (both train and test)
+        standardized_ys[:, all_task_mask] = (ys[:, all_task_mask] - task_mean) / task_std
+        if true_ys is not None:
+            standardized_true_ys[:, all_task_mask] = (true_ys[:, all_task_mask] - task_mean) / task_std
+
+    if true_ys is not None:
+        return standardized_ys, standardized_true_ys
+    else:
+        return standardized_ys
+    
+    
+def new_standardize_per_task(
+    *,
+    noisy_ys,
+    ys,
+    task_id,
+    single_eval_pos,
+    global_norm="z",
+    task_norm=None,
+):
+    # all task ids should be the same across batches
+    assert (
+        task_id.min(dim=0).values == task_id.max(dim=0).values
+    ).all(), "task_id is not the same across batches, you can set same_tasks_across_batch=True"
+    task_id = task_id[0, :]  # shape: seq_len
+
+    def normalize(
+        ys_to_fit_on: torch.Tensor, ys_to_transform: torch.Tensor, norm_type: str
+    ):
+        # both are a tensor of shape batch_size x ?, we want to parallelize across batch_size
+        mean = ys_to_fit_on.mean(1, keepdim=True)  # (batch_size, )
+        std = ys_to_fit_on.std(1, keepdim=True)  # (batch_size, )
+        std = std.where(std.isfinite(), 1.0)
+        if norm_type == "z":
+            return (ys_to_transform - mean) / std
+        if norm_type == "safe_z_0.1":
+            safe_std = std.clamp_min(0.1)
+            return (ys_to_transform - mean) / safe_std
+        elif norm_type == "mean":
+            return ys_to_transform - mean
+        else:
+            raise ValueError(f"Unknown norm type {norm_type}")
+
+    if task_norm is not None:
+        # print("num tasks", len(np.unique(task_id.cpu().numpy())))
+        # print(
+        #     "min train task size",
+        #     np.min(
+        #         np.unique(task_id[:single_eval_pos].cpu().numpy(), return_counts=True)[
+        #             1
+        #         ]
+        #     ),
+        # )
+        for tid in task_id.unique():
+            local_noisy_ys_for_fitting = noisy_ys[:, :single_eval_pos][
+                :, (task_id == tid)[:single_eval_pos]
+            ].clone()
+            local_noisy_ys = noisy_ys[:, (task_id == tid)]
+            local_ys = ys[:, (task_id == tid)]
+
+            ys[:, (task_id == tid)] = normalize(
+                local_noisy_ys_for_fitting, local_ys, task_norm
+            )
+            noisy_ys[:, (task_id == tid)] = normalize(
+                local_noisy_ys_for_fitting, local_noisy_ys, task_norm
+            )
+
+    if global_norm is not None:
+        # print("doing global norm with", global_norm)
+        noisy_ys_for_fitting = noisy_ys[:, :single_eval_pos].clone()
+
+        ys[:] = normalize(noisy_ys_for_fitting, ys, global_norm)
+        noisy_ys[:] = normalize(noisy_ys_for_fitting, noisy_ys, global_norm)
+
+    return noisy_ys, ys
+
+    
+def target_aware_mtgp_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    single_eval_pos = kwargs.get("single_eval_pos", seq_len // 2)
+    xs = torch.rand(batch_size, seq_len, num_features, device=device)
+    task_id = sample_task_ids_target_aware(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
+
+    if num_tasks > 1:
+        # sample task covariance matrix
+        lkj_covar_matrix = (
+            LKJCovariancePrior(
+                n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
+            )
+            .sample()
+            .to(device)
+        )
+        
+        corr_init = hyperparameters.get("corr_init", None)
+        if corr_init is not None:
+            # bias towards correlations between corr_init and 1.0
+            sampled_corr = torch.distributions.Uniform(corr_init, 1.0).sample().to(device)
+            task_covar_matrix = torch.full((num_tasks, num_tasks), sampled_corr).to(lkj_covar_matrix)
+            task_covar_matrix.fill_diagonal_(1.0)
+            
+            blend = torch.rand((1,)).to(device)
+            task_covar_matrix = blend * task_covar_matrix + (1 - blend) * lkj_covar_matrix
+        else:
+            task_covar_matrix = lkj_covar_matrix     
+    else:
+        task_covar_matrix = torch.eye(num_tasks).to(device)
+        
+    # sample number of uncorrelated tasks uniformly from 0 to T-1
+    if num_tasks > 1:
+        n_uncorr = torch.randint(num_tasks - 1, (1,)).item()
+        uncorr_ids = torch.randperm(num_tasks - 1)[:n_uncorr]
+        uncorr_ids = (uncorr_ids + 1).tolist() # task 0 cannot be uncorr
+    else:
+        uncorr_ids = []
+    
+    ys = draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_ids)
+    
+    sigma = torch.distributions.LogNormal(-4.0, 1.0).sample(ys.shape).to(device)
+    noisy_ys = ys.clone() + torch.randn_like(ys) * sigma
+    
+    noisy_ys, ys = new_standardize_per_task(
+        noisy_ys=noisy_ys,
+        ys=ys,
+        task_id=task_id,
+        single_eval_pos=single_eval_pos,
+        global_norm=hyperparameters.get("global_y_norm", None),
+        task_norm=hyperparameters.get("task_y_norm", "safe_z_0.1"),
+    )
+    
+    task_id = task_id.unsqueeze(-1)
+
+    return Batch(
+        x=xs.transpose(0, 1),
+        y=noisy_ys.transpose(0, 1),
+        target_y=ys.transpose(0, 1),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+    
+def gen_mtgp_equal_eval_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    single_eval_pos = kwargs.get("single_eval_pos", seq_len // 2)
+    xs = torch.rand(batch_size, seq_len, num_features, device=device)
+    task_id = sample_task_ids_even_eval(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
+
+    if num_tasks > 1:
+        # sample task covariance matrix
+        lkj_covar_matrix = (
+            LKJCovariancePrior(
+                n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
+            )
+            .sample()
+            .to(device)
+        )
+        
+        corr_init = hyperparameters.get("corr_init", None)
+        if corr_init is not None:
+            # bias towards correlations between corr_init and 1.0
+            sampled_corr = torch.distributions.Uniform(corr_init, 1.0).sample().to(device)
+            task_covar_matrix = torch.full((num_tasks, num_tasks), sampled_corr).to(lkj_covar_matrix)
+            task_covar_matrix.fill_diagonal_(1.0)
+            
+            blend = torch.rand((1,)).to(device)
+            task_covar_matrix = blend * task_covar_matrix + (1 - blend) * lkj_covar_matrix
+        else:
+            task_covar_matrix = lkj_covar_matrix     
+    else:
+        task_covar_matrix = torch.eye(num_tasks).to(device)
+        
+    # sample number of uncorrelated tasks uniformly from 0 to T-1
+    if num_tasks > 1:
+        n_uncorr = torch.randint(num_tasks - 1, (1,)).item()
+        uncorr_ids = torch.randperm(num_tasks - 1)[:n_uncorr]
+        uncorr_ids = (uncorr_ids + 1).tolist() # task 0 cannot be uncorr
+    else:
+        uncorr_ids = []
+    
+    ys = draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_ids)
+    
+    sigma = torch.distributions.LogNormal(-4.0, 1.0).sample(ys.shape).to(device)
+    noisy_ys = ys.clone() + torch.randn_like(ys) * sigma
+    
+    noisy_ys, ys = new_standardize_per_task(
+        noisy_ys=noisy_ys,
+        ys=ys,
+        task_id=task_id,
+        single_eval_pos=single_eval_pos,
+        global_norm=hyperparameters.get("global_y_norm", None),
+        task_norm=hyperparameters.get("task_y_norm", "safe_z_0.1"),
+    )
+
+    task_id = task_id.unsqueeze(-1)
+    
+    return Batch(
+        x=xs.transpose(0, 1),
+        y=noisy_ys.transpose(0, 1),
+        target_y=ys.transpose(0, 1),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+    
+def gen_mtgp_low_rank(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters: dict,
+    device: str = default_device,
+    **kwargs,
+):
+    single_eval_pos = kwargs.get("single_eval_pos", seq_len // 2)
+    xs = torch.rand(batch_size, seq_len, num_features, device=device)
+    if hyperparameters.get("target_only_loss", False):
+        task_id = sample_task_ids_target_aware(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
+    else:
+        task_id = sample_task_ids_even_eval(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
+
+    rank = torch.randint(2, hyperparameters.get("max_rank", 5)+1, (1,)).item()
+    alpha = hyperparameters.get("decay_alpha", 0.2)
+    if num_tasks > 1:
+        weights = torch.exp(-alpha * torch.arange(1, num_tasks).float())
+        n_ind = torch.multinomial(weights, 1).item()
+    else:
+        n_ind = 0
+    B = batch_size
+    D = num_features
+
+    # Sample mixing weights from Dirichlet
+    concentration = 1.0
+    concentration_vec = torch.full((rank,), concentration, device=device)
+
+    W_full = torch.zeros((num_tasks, rank + n_ind), device=device)
+    # split weights between rank
+    W_rank = torch.distributions.Dirichlet(concentration_vec).sample((num_tasks,)).to(device)
+    W_full[:, :rank] = W_rank
+    # randomly non-target tasks to be independent
+    independent_tasks = torch.randperm(num_tasks - 1)[:n_ind] + 1
+    for i, ind in enumerate(independent_tasks):
+        W_full[ind, :] = 0.0
+        W_full[ind, -(i+1)] = 1.0
+
+    # Create expanded inputs: batch, (rank), seq, features)
+    xs_expanded = xs.unsqueeze(1).expand((B, rank + n_ind, seq_len, D))
+
+    # Create task IDs for all latent functions
+    latent_task_ids = torch.arange(rank, device=device).unsqueeze(0).unsqueeze(-1)
+    latent_task_ids = latent_task_ids.expand(batch_size, rank, seq_len)
+    latent_task_ids_mega = latent_task_ids.reshape(batch_size * rank, seq_len)
+
+    CORR_LENGTHSCALE_A, CORR_LENGTHSCALE_B = 3.0, 6.0
+    UNCORR_LENGTHSCALE_A, UNCORR_LENGTHSCALE_B = 3.0, 6.0
+    a = torch.tensor([CORR_LENGTHSCALE_A] + [UNCORR_LENGTHSCALE_A] * n_ind).to(xs)
+    b = torch.tensor([CORR_LENGTHSCALE_B] + [UNCORR_LENGTHSCALE_B] * n_ind).to(xs)
+
+    # Create lengthscale prior
+    lengthscale_prior = gpytorch.priors.GammaPrior(a, b)
+    # original B x D x (1 + n_ind) => B x (1 + n_ind) x D
+    lengthscale_sample = lengthscale_prior.sample((B, D)).transpose(1, 2)
+
+    kernel = RBFKernel(ard_num_dims=D, batch_shape=torch.Size((B, rank + n_ind))).to(xs.device)
+    # lengthscale should have shape B x (rank + n_ind) x 1 x D
+    lengthscale = torch.ones((B, rank+n_ind, 1, D), device=xs.device)
+    # make sure rank lengthscales are the same
+    rank_lengthscales = lengthscale_sample[:, 0].unsqueeze(1).unsqueeze(1).expand(B, rank, 1, D).clone()
+    lengthscale[:, :rank] = rank_lengthscales
+    # use remaining lengthscales for independent draws
+    lengthscale[:, rank:] = lengthscale_sample[:, 1:].unsqueeze(-2)
+
+    kernel.lengthscale = lengthscale
+    K = kernel(xs_expanded)
+    mean_x = ConstantMean().to(xs.device)(xs_expanded)
+    # size B x (rank + n_ind) x seq_len
+    latent_functions = MultivariateNormal(mean_x, K).sample()
+
+    # Apply mixing (same across all batches since task IDs are same)
+    task_id_seq = task_id[0]  # Pattern from first batch
+    mixing_weights_seq = W_full[task_id_seq].T  # rank + n_ind x num_tasks
+
+    # Mix latent functions
+    ys = torch.sum(latent_functions * mixing_weights_seq.unsqueeze(0), dim=1).unsqueeze(-1)
+    
+    sigma = torch.distributions.LogNormal(-4.0, 1.0).sample(ys.shape).to(device)
+    noisy_ys = ys.clone() + torch.randn_like(ys) * sigma
+    
+    noisy_ys, ys = new_standardize_per_task(
+        noisy_ys=noisy_ys,
+        ys=ys,
+        task_id=task_id,
+        single_eval_pos=single_eval_pos,
+        global_norm=hyperparameters.get("global_y_norm", None),
+        task_norm=hyperparameters.get("task_y_norm", "safe_z_0.1"),
+    )
+
+    task_id = task_id.unsqueeze(-1)
+    
+    return Batch(
+        x=xs.transpose(0, 1),
+        y=noisy_ys.transpose(0, 1),
+        target_y=ys.transpose(0, 1),
+        task_id=task_id.transpose(0, 1),
+    )
+
+
+
+def get_hpo_batch_fn(
+    train=True,
+    max_features=6,
+    min_num_tasks=6,
+    seq_len=200,
+    device=default_device,
+    **kwargs,
+):
+    DATA_PATHS = {
+        "test": "/home/yl9959/mtpfn/datasets/hpob-data/meta-test-dataset.json",
+        "train-augmented": "/home/yl9959/mtpfn/datasets/hpob-data/meta-train-dataset-augmented.json",
+    }
+
+    if train:
+        data = json.load(open(DATA_PATHS["train-augmented"], "r"))
+    else:
+        data = json.load(open(DATA_PATHS["test"], "r"))
+
+    domain_weight = []
+    domain_data = []
+    for domain in data:
+        hpo_runs_weight = []
+        hpo_runs_x = []
+        hpo_runs_y = []
+        for hpo_run in data[domain]:
+            x = torch.tensor(data[domain][hpo_run]["X"], device=device)
+            # skip this domain if it has more than max_features features
+            if x.shape[-1] > max_features:
+                break
+
+            if x.shape[0] < seq_len:
+                continue
+
+            hpo_runs_weight.append(len(x))
+            hpo_runs_x.append(x.to(device))
+            hpo_runs_y.append(torch.tensor(data[domain][hpo_run]["y"], device=device))
+
+        # skip this domain if it doesn't have enough tasks
+        if len(hpo_runs_weight) <= min_num_tasks:
+            continue
+        domain_weight.append(sum(hpo_runs_weight))
+        domain_data.append(
+            (torch.tensor(hpo_runs_weight, device=device), hpo_runs_x, hpo_runs_y)
+        )
+        
+    domain_weight = torch.tensor(domain_weight)
+    domain_weight = domain_weight / domain_weight.sum()
+    # add uniform probability
+    domain_weight = domain_weight * 0.5 + 1 / len(domain_weight) * 0.5
+
+    def get_batch(
+        batch_size: int,
+        seq_len: int,
+        num_features: int,
+        max_num_tasks: int,
+        num_tasks: int,
+        hyperparameters=None,
+        device: str = default_device,
+        **kwargs,
+    ):
+        domain_idx = torch.multinomial(domain_weight, 1)
+        task_weight, task_x, task_y = domain_data[domain_idx]
+        task_data_indices = torch.multinomial(task_weight.float(), num_tasks)
+
+        task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))
+        # task_id = sample_task_ids(batch_size, seq_len, num_tasks, device)
+
+        xs = torch.zeros(batch_size, seq_len, task_x[0].shape[-1], device=device)
+        ys = torch.zeros(batch_size, seq_len, 1, device=device)
+        for i in range(num_tasks):
+            relevant_indices = task_id == i
+            possible_xs, possible_ys = (
+                task_x[task_data_indices[i]],
+                task_y[task_data_indices[i]],
+            )
+            # randomly select per batch
+            for b in range(batch_size):
+                relevant_batch_indices = relevant_indices[b]
+                indices = torch.randperm(len(possible_xs))[
+                    : relevant_batch_indices.sum()
+                ]
+                xs[b, relevant_batch_indices] = possible_xs[indices].to(xs.device)
+                ys[b, relevant_batch_indices] = possible_ys[indices].to(ys.device)
+
+        return Batch(
+            x=xs.transpose(0, 1).to(device),
+            y=ys.transpose(0, 1).to(device),
+            target_y=ys.transpose(0, 1).clone().to(device),
+            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
+        )
+
+    return get_batch
+
+
+def get_pd1_surrogate_batch_fn(
+    train=True,
+    max_features=6,
+    min_num_tasks=6,
+    seq_len=200,
+    device=default_device,
+    **kwargs,
+):
+    
+    data = pickle.load(open("/home/yl9959/mtpfn/datasets/pd1.pickle", "rb"))
+
+    if not os.path.exists("/home/yl9959/mtpfn/pd1_gp_surrogates.pth"):
+        
+        # group by data
+        data_grouped = data.groupby("study_group")
+        x_cols = [
+            'hps.lr_hparams.decay_steps_factor',
+            'hps.lr_hparams.initial_value',
+            'hps.lr_hparams.power',
+            'hps.opt_hparams.momentum', 
+        ]
+        y_col = 'best_valid/error_rate'
+
+        x_bounds = torch.tensor([
+            data[x_cols].min().values,
+            data[x_cols].max().values
+        ])
+
+        # for each group
+        gps = []
+        for group_name, group_data in data_grouped:
+            print("Fitting GP for group", group_name)
+            train_x = torch.tensor(group_data[x_cols].values)
+            train_x = (train_x - x_bounds[0]) / (x_bounds[1] - x_bounds[0])
+            train_y = torch.tensor(group_data[y_col].values).unsqueeze(-1)
+            
+            train_yvar = torch.ones_like(train_y) * 1e-5
+            gp = SingleTaskGP(train_x, train_y, train_yvar, outcome_transform=None)
+            mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
+            try:
+                fit_gpytorch_mll(mll)
+            except ModelFittingError:
+                fit_gpytorch_mll_torch(mll)
+            gps.append((group_name, gp))
+            
+        state_dicts = {name: gp.state_dict() for name, gp in gps}
+        torch.save(state_dicts, "/home/yl9959/mtpfn/pd1_gp_surrogates.pth")
+
+    gp_state_dicts = torch.load("/home/yl9959/mtpfn/pd1_gp_surrogates.pth")
+    gps = []
+    for data_name, state_dict in gp_state_dicts.items():
+        if "cifar" in data_name:
+            continue
+        gp = SingleTaskGP(torch.zeros(1, 4), torch.zeros(1, 1), torch.ones(1, 1) * 1e-5, outcome_transform=None)
+        gp.load_state_dict(state_dict)
+        gps.append(gp)
+
+
+    def get_batch(
+        batch_size: int,
+        seq_len: int,
+        num_features: int,
+        max_num_tasks: int,
+        num_tasks: int,
+        hyperparameters=None,
+        device: str = default_device,
+        **kwargs,
+    ):
+        task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))
+
+        xs = torch.rand(batch_size, seq_len, 4, device=device)
+        ys = torch.zeros(batch_size, seq_len, 1, device=device)
+        
+        surrogate_gps = torch.randperm(len(gps))[:num_tasks]
+        for i in range(num_tasks):
+            task_mask = task_id == i
+            task_xs = xs[task_mask]
+            gp = gps[surrogate_gps[i]].to(device)
+            with torch.no_grad():
+                ys[task_mask] = gp.posterior(task_xs).sample()
+            
+        return Batch(
+            x=xs.transpose(0, 1).to(device),
+            y=ys.transpose(0, 1).to(device),
+            target_y=ys.transpose(0, 1).clone().to(device),
+            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
+        )
+
+    return get_batch
+
+
+def get_pd1_eval_batch_fn(
+    train=True,
+    max_features=6,
+    min_num_tasks=6,
+    seq_len=200,
+    device=default_device,
+    **kwargs,
+):
+
+    data = pickle.load(open("/home/yl9959/mtpfn/datasets/pd1.pickle", "rb"))
+
+    # group by data
+    data_grouped = data.groupby("study_group")
+    x_cols = [
+        'hps.lr_hparams.decay_steps_factor',
+        'hps.lr_hparams.initial_value',
+        'hps.lr_hparams.power',
+        'hps.opt_hparams.momentum', 
+    ]
+    y_col = 'best_valid/error_rate'
+    
+    x_bounds = torch.tensor([
+        data[x_cols].min().values,
+        data[x_cols].max().values
+    ])
+    
+    all_xs = []
+    all_ys = []
+    for group_name, group_data in data_grouped:
+        if "cifar" in group_name: # eval on cifar
+            train_x = torch.tensor(group_data[x_cols].values)
+            train_x = (train_x - x_bounds[0]) / (x_bounds[1] - x_bounds[0])
+            train_y = torch.tensor(group_data[y_col].values).unsqueeze(-1)
+            all_xs.append(train_x)
+            all_ys.append(train_y)
+    
+    def get_batch(
+        batch_size: int,
+        seq_len: int,
+        num_features: int,
+        max_num_tasks: int,
+        num_tasks: int,
+        hyperparameters=None,
+        device: str = default_device,
+        **kwargs,
+    ):
+        assert num_tasks <= 4
+        task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))
+        
+        # dummy values to be replaced
+        xs = torch.zeros(batch_size, seq_len, num_features, device=device)
+        ys = torch.zeros(batch_size, seq_len, 1, device=device)
+
+        for b in range(batch_size):
+            for task in range(num_tasks):
+                task_mask = task_id[b] == task
+                task_x = all_xs[task]
+                task_y = all_ys[task]
+                indices = torch.randperm(len(task_x))[:seq_len]
+                size = xs[b, task_mask].shape[0]
+                xs[b, task_mask] = task_x[indices][:size].to(xs)
+                ys[b, task_mask] = task_y[indices][:size].to(ys)
+                
+        return Batch(
+            x=xs.transpose(0, 1).to(device),
+            y=ys.transpose(0, 1).to(device),
+            target_y=ys.transpose(0, 1).clone().to(device),
+            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
+        )
+
+    return get_batch
+
+    
+def combine_batch(functions, weight):
+    def get_batch(
+        *args,
+        **kwargs,
+    ):
+        random_index = torch.multinomial(weight, 1)[0]
+        return functions[random_index](*args, **kwargs)
+
+    return get_batch
+    
+
+# batch_size: number of datasets
+# seq_len: number of training samples per dataset
+def get_trios_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    xs = torch.rand(batch_size, seq_len, num_features, device=device)
+    ys = xs.sum(-1, keepdim=True)
+    
+    assert hyperparameters.get("same_tasks_across_batch", False)
+    task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))[0]
+    
+    task_order = torch.randperm(num_tasks)
+    # assert same tasks across batch
+
+    num_trios = num_tasks // 3
+    
+    for trio in range(num_trios):
+        task_one = task_order[trio * 3]
+        task_two = task_order[trio * 3 + 1]
+        task_three = task_order[trio * 3 + 2]
+        task_mask = (task_id == task_one) | (task_id == task_two) | (task_id == task_three)
+        
+        task_covar_matrix = torch.full((num_tasks, num_tasks), 0.9).to(xs)
+        task_covar_matrix.fill_diagonal_(1.0)
+            
+        y_trio = draw_mtgp_samples(xs[:, task_mask], task_id[task_mask], task_covar_matrix, uncorr_tasks=[])
+        ys[:, task_mask] = y_trio
+        
+    remaining = num_tasks - num_trios * 3
+    if remaining > 0:
+        task_mask = torch.zeros_like(task_id, dtype=torch.bool)
+        for task_id in task_order[num_trios * 3:]:
+            task_mask |= task_id == task_id
+        task_covar_matrix = torch.full((remaining, remaining), 0.1).to(xs)
+        task_covar_matrix.fill_diagonal_(1.0)
+        ys[:, task_mask] = draw_mtgp_samples(xs[:, task_mask], task_id[task_mask], task_covar_matrix, uncorr_tasks=[])
+        
+    # add back batch dimension
+    task_id = task_id.unsqueeze(-1).unsqueeze(0).repeat(batch_size, 1, 1)
+    
+    noise = torch.distributions.Gamma(1, 5).sample((num_tasks,)).to(device)
+    noise_by_task = noise[task_id]
+    # noisy_ys = ys + torch.randn_like(ys) * noise_by_task
+    noisy_ys = ys.clone()
+
+    return Batch(
+        x=xs.transpose(0, 1),
+        y=noisy_ys.transpose(0, 1),
+        target_y=ys.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+    
+def get_hpobench_batch_fn(
+    hpobench_task="lr",
+    train=True,
+    device=default_device,
+    **kwargs,
+):
+    
+    data = pickle.load(open(f"/home/lily_l/private_multitask_pfn/datasets/hpobench_{hpobench_task}.pkl", "rb"))
+    if hpobench_task == "lr":
+        eval_keys = [146822, 146818, 168908, 53]
+        dim = 2
+    elif hpobench_task == "svm":
+        eval_keys = [146822, 146818, 168908, 53]
+        dim = 2
+    else:
+        raise ValueError("Unknown hpobench task")
+        
+    if train:
+        batch_keys = data.keys() - eval_keys # train keys
+    else:
+        batch_keys = eval_keys
+
+    results_x, results_y = get_torch_format_hpobench(data[eval_keys[0]], [data[key] for key in batch_keys], dim, hpobench_task)[2:]
+    results_x = results_x
+    results_y = results_y
+
+    def get_batch(
+        batch_size: int,
+        seq_len: int,
+        num_features: int,
+        max_num_tasks: int,
+        num_tasks: int,
+        hyperparameters=None,
+        device: str = default_device,
+        **kwargs,
+    ):
+        assert num_tasks == 1
+        task_id = torch.zeros(batch_size, seq_len, device=device).long()
+        
+        train_task = torch.randint(len(results_x), (1,))
+        all_xs, all_ys = results_x[train_task], results_y[train_task]
+        
+        indices = torch.stack([torch.randperm(len(all_xs))[:seq_len] for _ in range(batch_size)])
+        xs = all_xs[indices]
+        ys = all_ys[indices]
+            
+        return Batch(
+            x=xs.transpose(0, 1).to(device),
+            y=ys.transpose(0, 1).to(device),
+            target_y=ys.transpose(0, 1).clone().to(device),
+            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
+        )
+
+    return get_batch
+
diff --git a/private_multitask_pfn/gen_task_batch.py b/private_multitask_pfn/gen_task_batch.py
new file mode 100644
index 0000000..36c95b1
--- /dev/null
+++ b/private_multitask_pfn/gen_task_batch.py
@@ -0,0 +1,275 @@
+import torch
+from PFNs.pfns.priors import Batch
+import gpytorch
+import numpy as np
+import matplotlib.pyplot as plt
+
+    
+default_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# batch_size: number of datasets
+# seq_len: number of training samples per dataset
+def task_invariant_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    assert num_tasks == 2, "num_tasks must be 2 for task_invariant_batch"
+    x = torch.rand(batch_size, seq_len, num_features, device=device)
+    y = torch.zeros(batch_size, seq_len, 1, device=device)
+    task_id = torch.randint(0, num_tasks, (batch_size, seq_len, 1), device=device)
+    
+    # task 1 is always 0
+    # task 1: y = x_1 + x_2^2 + ... + x_d^d
+    # task_one_mask = task_id == 0
+    # for feature in range(num_features):
+    #     y += torch.pow(x[:, :, feature:feature+1], feature + 1) * task_one_mask
+    
+    # task 2: y = x_1^d + x_2^(d-1) + ... + x_d
+    if torch.rand((1,)) < 0.5:
+        task_two_mask = task_id == 1
+    else:
+        task_two_mask = task_id == 0
+    for feature in range(num_features):
+        y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * task_two_mask
+        
+    return Batch(
+        x=x.transpose(0, 1),
+        y=y.transpose(0, 1),
+        target_y=y.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+
+# opposite of task_invariant_batch
+def task_invariant_eval_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    assert num_tasks == 2, "num_tasks must be 2 for task_invariant_batch"
+    x = torch.rand(batch_size, seq_len, num_features, device=device)
+    y = torch.zeros(batch_size, seq_len, 1, device=device)
+    task_id = torch.zeros((batch_size, seq_len, 1), device=device)
+    
+    # task 2: y = x_1 + x_2^2 + ... + x_d^d
+    task_two_mask = task_id == 1
+    for feature in range(num_features):
+        y += torch.pow(x[:, :, feature:feature+1], feature + 1) * task_two_mask
+    
+    # task 1: y = x_1^d + x_2^(d-1) + ... + x_d
+    task_one_mask = task_id == 0
+    for feature in range(num_features):
+        y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * task_one_mask
+        
+    return Batch(
+        x=x.transpose(0, 1),
+        y=y.transpose(0, 1),
+        target_y=y.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+class BatchedMultidimensionalExactGPModel(gpytorch.models.ExactGP):
+    def __init__(self, train_x, train_y, likelihood):
+        super().__init__(train_x, train_y, likelihood)
+        self.mean_module = gpytorch.means.ConstantMean(batch_shape=torch.Size([train_y.shape[0]]))
+        self.covar_module = gpytorch.kernels.ScaleKernel(
+            gpytorch.kernels.RBFKernel(ard_num_dims=train_x.shape[-1], batch_shape=torch.Size([train_y.shape[0]])),
+            batch_shape=torch.Size([train_y.shape[0]])
+        )
+        # self.covar_module = gpytorch.kernels.RBFKernel(ard_num_dims=train_x.shape[-1], batch_shape=torch.Size([train_y.shape[0]]))
+    
+    def forward(self, x):
+        mean_x = self.mean_module(x)
+        covar_x = self.covar_module(x)
+        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
+
+
+def generate_correlated_gp_samples(x, batch_size, base_samples, correlations):
+    """
+    Generate multiple GP samples with specified correlations to a base sample
+    
+    Args:
+        x: Input tensor of shape (n_points, num_features)
+        base_sample: Optional base GP sample to correlate with
+        correlations: List of correlation values
+    """
+    n_points = x.shape[0]
+    n_features = x.shape[1]
+    
+    # Expand x to include batch dimension
+    x_batched = x.unsqueeze(0).expand(batch_size, -1, -1)
+    
+    tolerance = 1e-6
+    while tolerance < 1e-3:
+        try:
+            # Initialize likelihood and model
+            likelihood = gpytorch.likelihoods.GaussianLikelihood(batch_shape=torch.Size([batch_size]))
+            model = BatchedMultidimensionalExactGPModel(x_batched, torch.zeros(batch_size, n_points), likelihood).to(x.device)
+            
+            # Set the kernel parameters
+            model.covar_module.base_kernel.lengthscale = torch.ones(batch_size, n_features).to(x) * 0.2
+            model.covar_module.outputscale = torch.ones(batch_size).to(x)
+            
+            # Get the kernel matrices and their Cholesky decompositions
+            K = model.covar_module(x_batched).evaluate() + torch.eye(n_points).to(x.device) * tolerance
+            L = torch.linalg.cholesky(K)
+        
+            break
+        except Exception as e:
+            print("Cholesky decomposition failed, retrying...")
+            tolerance *= 10
+            # raise e
+    
+    # Generate the base GP samples if not provided
+    if base_samples is None:
+        z_base = torch.randn(batch_size, n_points).to(x.device)
+        base_samples = torch.bmm(L, z_base.unsqueeze(-1)).squeeze(-1)
+    
+    # Transform base samples back to standard normal
+    z_base = torch.linalg.solve(L, base_samples.unsqueeze(-1)).squeeze(-1)
+    
+    # Generate correlated samples
+    correlated_samples = []
+    for correlation in correlations:
+        z_new = correlation * z_base + torch.sqrt(torch.tensor(1 - correlation**2)).to(x.device) * torch.randn(batch_size, n_points).to(x.device)
+        f_new = torch.bmm(L, z_new.unsqueeze(-1)).squeeze(-1)
+        correlated_samples.append(f_new)
+    
+    return base_samples, correlated_samples
+
+    
+def task_corr_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):
+    x = torch.rand(seq_len, num_features, device=device)
+    y = torch.zeros(batch_size, seq_len, 1, device=device)
+    task_id = torch.randint(0, num_tasks, (seq_len, 1), device=device).expand(batch_size, -1, -1)
+    
+    correlations = torch.linspace(0.95, 0.0, max_num_tasks - 1)
+    if hyperparameters.get("permute_tasks", False):
+        correlations = np.random.permutation(correlations)
+        
+    base_sample = None
+    base_sample, correlated_samples = generate_correlated_gp_samples(x, batch_size, base_sample, correlations)
+
+    all_samples = [base_sample] + correlated_samples
+    
+    for i, samples in enumerate(all_samples):
+        task_mask = task_id == i
+        # print(y.shape, samples.shape, task_mask.squeeze().shape)
+        y += samples.unsqueeze(-1) * (task_mask)
+        
+    x = x.unsqueeze(0).expand(batch_size, -1, -1)
+        
+    return Batch(
+        x=x.transpose(0, 1),
+        y=y.transpose(0, 1),
+        target_y=y.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+
+        
+        
+    return Batch(
+        x=x.transpose(0, 1),
+        y=y.transpose(0, 1),
+        target_y=y.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+    
+    
+# main
+if __name__ == "__main__":
+    import torch
+    import gpytorch
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from itertools import product
+
+    # Set random seed for reproducibility
+    torch.manual_seed(1)
+
+    def generate_grid_inputs(num_points_per_dim, num_features):
+        """Generate a grid of input points across multiple dimensions"""
+        # Create linearly spaced points for each dimension
+        points_per_dim = [torch.linspace(0, 1, num_points_per_dim) for _ in range(num_features)]
+        
+        # Create a meshgrid of all points
+        grid_points = torch.tensor(list(product(*points_per_dim)))
+        
+        return grid_points
+
+
+    def plot_2d_samples(x, samples, correlations, title):
+        """Plot samples for 2D inputs"""
+        num_points = int(np.sqrt(x.shape[0]))
+        X = x[:, 0].reshape(num_points, num_points)
+        Y = x[:, 1].reshape(num_points, num_points)
+        
+        fig = plt.figure(figsize=(15, 3*((len(samples)+1)//3 + 1)))
+        
+        # Plot base sample
+        ax = fig.add_subplot(((len(samples)+1)//3 + 1), 3, 1, projection='3d')
+        Z = samples[0].reshape(num_points, num_points)
+        surf = ax.plot_surface(X.detach(), Y.detach(), Z.detach(), cmap='viridis')
+        ax.set_title('Original GP Draw')
+        fig.colorbar(surf, ax=ax)
+        
+        # Plot correlated samples
+        for i, (sample, corr) in enumerate(zip(samples[1:], correlations)):
+            ax = fig.add_subplot(((len(samples)+1)//3 + 1), 3, i+2, projection='3d')
+            Z = sample.reshape(num_points, num_points)
+            surf = ax.plot_surface(X.detach(), Y.detach(), Z.detach(), cmap='viridis')
+            ax.set_title(f'Correlation: {corr}')
+            fig.colorbar(surf, ax=ax)
+        
+        plt.tight_layout()
+        plt.savefig("correlated_gp_samples.png")
+
+    # Parameters
+    num_features = 2  # Number of input dimensions
+    num_points_per_dim = 20  # Number of points per dimension
+    correlations = [0.9, 0.7, 0.5, 0.3, 0.1]
+
+    # Generate input points
+    x = generate_grid_inputs(num_points_per_dim, num_features)
+
+    # Generate samples
+    base_sample = None
+    base_sample, correlated_samples = generate_correlated_gp_samples(x, 1, base_sample, correlations)
+
+    # Combine all samples for plotting
+    all_samples = [base_sample] + correlated_samples
+
+    # Plot results
+    if num_features == 2:
+        plot_2d_samples(x, all_samples, correlations, 'GP Samples with Different Correlations')
+        
+    # Print achieved correlations
+    for i, corr in enumerate(correlations):
+        empirical_corr = np.corrcoef(base_sample.detach().numpy(), correlated_samples[i].detach().numpy())[0,1]
+        print(f"Target correlation: {corr:.2f}, Achieved correlation: {empirical_corr:.2f}")
+        
+    
\ No newline at end of file
diff --git a/private_multitask_pfn/lmc.py b/private_multitask_pfn/lmc.py
new file mode 100644
index 0000000..f72f3a2
--- /dev/null
+++ b/private_multitask_pfn/lmc.py
@@ -0,0 +1,316 @@
+
+from __future__ import annotations
+
+import math
+from typing import Any, Optional, Union
+
+import torch
+from botorch.acquisition.objective import PosteriorTransform
+from botorch.exceptions.errors import UnsupportedError
+from botorch.models.gpytorch import GPyTorchModel, MultiTaskGPyTorchModel
+from botorch.models.model import FantasizeMixin
+from botorch.models.transforms.input import InputTransform
+from botorch.models.transforms.outcome import OutcomeTransform, Standardize
+from botorch.models.utils.gpytorch_modules import (
+    get_covar_module_with_dim_scaled_prior,
+    get_gaussian_likelihood_with_lognormal_prior,
+)
+from botorch.utils.datasets import MultiTaskDataset, SupervisedDataset
+from botorch.utils.types import _DefaultType, DEFAULT
+from gpytorch.distributions.multivariate_normal import MultivariateNormal
+from gpytorch.kernels.index_kernel import IndexKernel
+from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
+from gpytorch.likelihoods.likelihood import Likelihood
+from gpytorch.means.constant_mean import ConstantMean
+from gpytorch.models.exact_gp import ExactGP
+from gpytorch.module import Module
+from gpytorch.priors.lkj_prior import LKJCovariancePrior
+from gpytorch.priors.prior import Prior
+from torch import Tensor
+
+from botorch.models.multitask import get_task_value_remapping
+
+
+class LMCGP(ExactGP, MultiTaskGPyTorchModel, FantasizeMixin):
+    r"""Multi-Task exact GP model using an ICM (intrinsic co-regionalization model)
+    kernel. See [Bonilla2007MTGP]_ and [Swersky2013MTBO]_ for a reference on the
+    model and its use in Bayesian optimization.
+
+    The model can be single-output or multi-output, determined by the `output_tasks`.
+    This model uses relatively strong priors on the base Kernel hyperparameters, which
+    work best when covariates are normalized to the unit cube and outcomes are
+    standardized (zero mean, unit variance) - this standardization should be applied in
+    a stratified fashion at the level of the tasks, rather than across all data points.
+
+    If the `train_Yvar` is None, this model infers the noise level. If you have
+    known observation noise, you can set `train_Yvar` to a tensor containing
+    the noise variance measurements. WARNING: This currently does not support
+    different noise levels for the different tasks.
+    """
+
+    def __init__(
+        self,
+        train_X: Tensor,
+        train_Y: Tensor,
+        task_feature: int,
+        num_covars: int,
+        train_Yvar: Optional[Tensor] = None,
+        mean_module: Optional[Module] = None,
+        covar_modules: Optional[Module] = None,
+        likelihood: Optional[Likelihood] = None,
+        task_covar_prior: Optional[Prior] = None,
+        output_tasks: Optional[list[int]] = None,
+        rank: Optional[int] = None,
+        all_tasks: Optional[list[int]] = None,
+        outcome_transform: Optional[Union[OutcomeTransform, _DefaultType]] = DEFAULT,
+        input_transform: Optional[InputTransform] = None,
+    ) -> None:
+        r"""Multi-Task GP model using an LMC kernel.
+
+        Args:
+            train_X: A `n x (d + 1)` or `b x n x (d + 1)` (batch mode) tensor
+                of training data. One of the columns should contain the task
+                features (see `task_feature` argument).
+            train_Y: A `n x 1` or `b x n x 1` (batch mode) tensor of training
+                observations.
+            task_feature: The index of the task feature (`-d <= task_feature <= d`).
+            train_Yvar: An optional `n` or `b x n` (batch mode) tensor of observed
+                measurement noise. If None, we infer the noise.
+                Note that the inferred noise is common across all tasks.
+            mean_module: The mean function to be used. Defaults to `ConstantMean`.
+            covar_module: The module for computing the covariance matrix between
+                the non-task features. Defaults to `RBFKernel`.
+            likelihood: A likelihood. The default is selected based on `train_Yvar`.
+                If `train_Yvar` is None, a standard `GaussianLikelihood` with inferred
+                noise level is used. Otherwise, a FixedNoiseGaussianLikelihood is used.
+            output_tasks: A list of task indices for which to compute model
+                outputs for. If omitted, return outputs for all task indices.
+            rank: The rank to be used for the index kernel. If omitted, use a
+                full rank (i.e. number of tasks) kernel.
+            task_covar_prior : A Prior on the task covariance matrix. Must operate
+                on p.s.d. matrices. A common prior for this is the `LKJ` prior.
+            all_tasks: By default, multi-task GPs infer the list of all tasks from
+                the task features in `train_X`. This is an experimental feature that
+                enables creation of multi-task GPs with tasks that don't appear in the
+                training data. Note that when a task is not observed, the corresponding
+                task covariance will heavily depend on random initialization and may
+                behave unexpectedly.
+            outcome_transform: An outcome transform that is applied to the
+                training data during instantiation and to the posterior during
+                inference (that is, the `Posterior` obtained by calling
+                `.posterior` on the model will be on the original scale). We use a
+                `Standardize` transform if no `outcome_transform` is specified.
+                Pass down `None` to use no outcome transform. NOTE: Standardization
+                should be applied in a stratified fashion, separately for each task.
+            input_transform: An input transform that is applied in the model's
+                forward pass.
+
+        Example:
+            >>> X1, X2 = torch.rand(10, 2), torch.rand(20, 2)
+            >>> i1, i2 = torch.zeros(10, 1), torch.ones(20, 1)
+            >>> train_X = torch.cat([
+            >>>     torch.cat([X1, i1], -1), torch.cat([X2, i2], -1),
+            >>> ])
+            >>> train_Y = torch.cat(f1(X1), f2(X2)).unsqueeze(-1)
+            >>> model = MultiTaskGP(train_X, train_Y, task_feature=-1)
+        """
+        with torch.no_grad():
+            transformed_X = self.transform_inputs(
+                X=train_X, input_transform=input_transform
+            )
+        self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)
+        (
+            all_tasks_inferred,
+            task_feature,
+            self.num_non_task_features,
+        ) = self.get_all_tasks(transformed_X, task_feature, output_tasks)
+        if all_tasks is not None and not set(all_tasks_inferred).issubset(all_tasks):
+            raise UnsupportedError(
+                f"The provided {all_tasks=} does not contain all the task features "
+                f"inferred from the training data {all_tasks_inferred=}. "
+                "This is not allowed as it will lead to errors during model training."
+            )
+        all_tasks = all_tasks or all_tasks_inferred
+        self.num_tasks = len(all_tasks)
+        self.num_covars = num_covars
+        if outcome_transform == DEFAULT:
+            outcome_transform = Standardize(m=1, batch_shape=train_X.shape[:-2])
+        if outcome_transform is not None:
+            train_Y, train_Yvar = outcome_transform(Y=train_Y, Yvar=train_Yvar)
+
+        # squeeze output dim
+        train_Y = train_Y.squeeze(-1)
+        if output_tasks is None:
+            output_tasks = all_tasks
+        else:
+            if set(output_tasks) - set(all_tasks):
+                raise RuntimeError("All output tasks must be present in input data.")
+        self._output_tasks = output_tasks
+        self._num_outputs = len(output_tasks)
+
+        # TODO (T41270962): Support task-specific noise levels in likelihood
+        if likelihood is None:
+            if train_Yvar is None:
+                likelihood = get_gaussian_likelihood_with_lognormal_prior()
+            else:
+                likelihood = FixedNoiseGaussianLikelihood(noise=train_Yvar.squeeze(-1))
+
+        # construct indexer to be used in forward
+        self._task_feature = task_feature
+        self._base_idxr = torch.arange(self.num_non_task_features)
+        self._base_idxr[task_feature:] += 1  # exclude task feature
+
+        super().__init__(
+            train_inputs=train_X, train_targets=train_Y, likelihood=likelihood
+        )
+        self.mean_module = mean_module or ConstantMean()
+        if covar_modules is None:
+            self.covar_modules = [get_covar_module_with_dim_scaled_prior(
+                ard_num_dims=self.num_non_task_features
+            )] * num_covars
+        else:
+            assert len(covar_modules) == num_covars
+            self.covar_modules = covar_modules
+
+        self._rank = rank if rank is not None else self.num_tasks
+        self.task_covar_module = IndexKernel(
+            num_tasks=self.num_tasks, rank=self._rank, prior=task_covar_prior
+        )
+        task_mapper = get_task_value_remapping(
+            task_values=torch.tensor(
+                all_tasks, dtype=torch.long, device=train_X.device
+            ),
+            dtype=train_X.dtype,
+        )
+        self.register_buffer("_task_mapper", task_mapper)
+        self._expected_task_values = set(all_tasks)
+        if input_transform is not None:
+            self.input_transform = input_transform
+        if outcome_transform is not None:
+            self.outcome_transform = outcome_transform
+        self.to(train_X)
+
+    def _split_inputs(self, x: Tensor) -> tuple[Tensor, Tensor]:
+        r"""Extracts base features and task indices from input data.
+
+        Args:
+            x: The full input tensor with trailing dimension of size `d + 1`.
+                Should be of float/double data type.
+
+        Returns:
+            2-element tuple containing
+
+            - A `q x d` or `b x q x d` (batch mode) tensor with trailing
+            dimension made up of the `d` non-task-index columns of `x`, arranged
+            in the order as specified by the indexer generated during model
+            instantiation.
+            - A `q` or `b x q` (batch mode) tensor of long data type containing
+            the task indices.
+        """
+        batch_shape, d = x.shape[:-2], x.shape[-1]
+        x_basic = x[..., self._base_idxr].view(batch_shape + torch.Size([-1, d - 1]))
+        task_idcs = (
+            x[..., self._task_feature]
+            .view(batch_shape + torch.Size([-1, 1]))
+            .to(dtype=torch.long)
+        )
+        task_idcs = self._map_tasks(task_values=task_idcs)
+        return x_basic, task_idcs
+
+    def forward(self, x: Tensor) -> MultivariateNormal:
+        if self.training:
+            x = self.transform_inputs(x)
+        x_basic, task_idcs = self._split_inputs(x)
+        # Compute base mean and covariance
+        mean_x = self.mean_module(x_basic)
+        
+        # Compute task covariances (sum of covariances)
+        covar_i = self.task_covar_module(task_idcs)
+        
+        covar = self.covar_modules[0](x_basic).mul(covar_i)
+        for covar_module in self.covar_modules[1:]:
+            covar_x = covar_module(x_basic)
+            # Combine the two in an ICM fashion
+            covar += covar_x.mul(covar_i)
+            
+        return MultivariateNormal(mean_x, covar)
+
+    @classmethod
+    def get_all_tasks(
+        cls,
+        train_X: Tensor,
+        task_feature: int,
+        output_tasks: Optional[list[int]] = None,
+    ) -> tuple[list[int], int, int]:
+        if train_X.ndim != 2:
+            # Currently, batch mode MTGPs are blocked upstream in GPyTorch
+            raise ValueError(f"Unsupported shape {train_X.shape} for train_X.")
+
+        d = train_X.shape[-1] - 1
+        if not (-d <= task_feature <= d):
+            raise ValueError(f"Must have that -{d} <= task_feature <= {d}")
+        task_feature = task_feature % (d + 1)
+        all_tasks = (
+            train_X[..., task_feature].unique(sorted=True).to(dtype=torch.long).tolist()
+        )
+        return all_tasks, task_feature, d
+
+    @classmethod
+    def construct_inputs(
+        cls,
+        training_data: Union[SupervisedDataset, MultiTaskDataset],
+        task_feature: int,
+        output_tasks: Optional[list[int]] = None,
+        task_covar_prior: Optional[Prior] = None,
+        prior_config: Optional[dict] = None,
+        rank: Optional[int] = None,
+    ) -> dict[str, Any]:
+        r"""Construct `Model` keyword arguments from a dataset and other args.
+
+        Args:
+            training_data: A `SupervisedDataset` or a `MultiTaskDataset`.
+            task_feature: Column index of embedded task indicator features.
+            output_tasks: A list of task indices for which to compute model
+                outputs for. If omitted, return outputs for all task indices.
+            task_covar_prior: A GPyTorch `Prior` object to use as prior on
+                the cross-task covariance matrix,
+            prior_config: Configuration for inter-task covariance prior.
+                Should only be used if `task_covar_prior` is not passed directly. Must
+                contain `use_LKJ_prior` indicator and should contain float value `eta`.
+            rank: The rank of the cross-task covariance matrix.
+        """
+        if task_covar_prior is not None and prior_config is not None:
+            raise ValueError(
+                "Only one of `task_covar_prior` and `prior_config` arguments expected."
+            )
+
+        if prior_config is not None:
+            if not prior_config.get("use_LKJ_prior"):
+                raise ValueError("Currently only config for LKJ prior is supported.")
+
+            num_tasks = training_data.X[task_feature].unique().numel()
+            sd_prior = GammaPrior(1.0, 0.15)
+            sd_prior._event_shape = torch.Size([num_tasks])
+            eta = prior_config.get("eta", 0.5)
+            if not isinstance(eta, float) and not isinstance(eta, int):
+                raise ValueError(f"eta must be a real number, your eta was {eta}.")
+            task_covar_prior = LKJCovariancePrior(num_tasks, eta, sd_prior)
+
+        # Call Model.construct_inputs to parse training data
+        base_inputs = super().construct_inputs(training_data=training_data)
+        if (
+            isinstance(training_data, MultiTaskDataset)
+            # If task features are included in the data, all tasks will have
+            # some observations and they may have different task features.
+            and training_data.task_feature_index is None
+        ):
+            all_tasks = list(range(len(training_data.datasets)))
+            base_inputs["all_tasks"] = all_tasks
+        if task_covar_prior is not None:
+            base_inputs["task_covar_prior"] = task_covar_prior
+        if rank is not None:
+            base_inputs["rank"] = rank
+        base_inputs["task_feature"] = task_feature
+        base_inputs["output_tasks"] = output_tasks
+        return base_inputs
+
diff --git a/private_multitask_pfn/load_pfn4bo_checkpoints.py b/private_multitask_pfn/load_pfn4bo_checkpoints.py
new file mode 100644
index 0000000..8db2ce4
--- /dev/null
+++ b/private_multitask_pfn/load_pfn4bo_checkpoints.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+import torch
+from ax.fb.utils.storage.manifold import AEManifoldUseCase
+from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
+
+from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns import (
+    bar_distribution,
+    encoders,
+)
+from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns.positional_encodings import (
+    NoPositionalEncoding,
+)
+from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns.transformer import (
+    TransformerModel,
+)
+
+# works with https://github.com/automl/PFNs/commit/fd212b187a22d5a07959484d03fcd82e060e9b21
+
+STATE_DICT_KEY = {
+    "hebo": "tree/trbo_dev/f380d01c-9861-11ef-9e69-84160c3811b6",
+    "bnn": "tree/trbo_dev/0e6ad5d4-9862-11ef-b27f-84160c3811b6",
+}
+
+
+def load_bnn_pfn():
+    # Load BNN PFN
+    num_features = 18
+    emsize = 512
+    encoder_generator = encoders.get_normalized_uniform_encoder(
+        encoders.get_variable_num_features_encoder(encoders.Linear)
+    )
+    encoder = encoder_generator(num_features, emsize)
+    seq_len = 60
+
+    bnn_model = TransformerModel(
+        encoder,
+        ninp=emsize,
+        nhead=4,
+        nhid=1024,
+        nlayers=6,
+        style_encoder=None,
+        y_encoder=encoders.Linear(1, emsize),
+        pos_encoder=NoPositionalEncoding(emsize, seq_len * 2),
+        decoder_dict={"standard": (None, 1000)},
+    )
+    # check if it's full or not
+    bnn_model.criterion = bar_distribution.FullSupportBarDistribution(
+        torch.arange(1001).float()
+    )
+    bnn_model.decoder = bnn_model.decoder_dict.standard
+
+    client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
+    bnn_state_dict = client.torch_load(STATE_DICT_KEY["bnn"])
+    bnn_model.load_state_dict(bnn_state_dict)
+
+    bnn_model.borders = bnn_model.criterion.borders
+    return bnn_model
+
+
+def load_hebo_pfn():
+    # Load HEBO+ PFN
+    num_features = 18
+    emsize = 512
+    encoder_generator = encoders.get_normalized_uniform_encoder(
+        encoders.get_variable_num_features_encoder(encoders.Linear)
+    )
+    encoder = encoder_generator(num_features, emsize)
+    seq_len = 60
+
+    hebo_model = TransformerModel(
+        encoder,
+        ninp=emsize,
+        nhead=4,
+        nhid=1024,
+        nlayers=12,
+        style_encoder=None,
+        y_encoder=encoders.Linear(1, emsize),
+        pos_encoder=NoPositionalEncoding(emsize, seq_len * 2),
+        decoder_dict={"standard": (None, 1000)},
+    )
+    # check if it's full or not
+    hebo_model.criterion = bar_distribution.FullSupportBarDistribution(
+        torch.arange(1001).float()
+    )
+
+    client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
+    hebo_state_dict = client.torch_load(STATE_DICT_KEY["hebo"])
+    hebo_model.load_state_dict(hebo_state_dict)
+
+    hebo_model.borders = hebo_model.criterion.borders
+    return hebo_model
diff --git a/private_multitask_pfn/mtgp_nuts.py b/private_multitask_pfn/mtgp_nuts.py
new file mode 100644
index 0000000..c22cc56
--- /dev/null
+++ b/private_multitask_pfn/mtgp_nuts.py
@@ -0,0 +1,323 @@
+
+from collections.abc import Mapping
+from typing import Any, NoReturn, Optional
+
+import pyro
+import pyro.distributions
+import torch
+from botorch.models.fully_bayesian import (
+    # matern52_kernel,
+    MIN_INFERRED_NOISE_LEVEL,
+    reshape_and_detach,
+    PyroModel
+)
+from gpytorch.likelihoods.likelihood import Likelihood
+from gpytorch.means.mean import Mean
+from torch import Tensor
+from gpytorch.kernels.kernel import dist
+from gpytorch.constraints import GreaterThan
+from gpytorch.kernels import RBFKernel, ScaleKernel
+from gpytorch.kernels.kernel import dist, Kernel
+from gpytorch.likelihoods.gaussian_likelihood import (
+    FixedNoiseGaussianLikelihood,
+    GaussianLikelihood,
+)
+from gpytorch.likelihoods.likelihood import Likelihood
+from gpytorch.means.constant_mean import ConstantMean
+from gpytorch.means.mean import Mean
+from torch import Tensor
+from math import sqrt
+from torch.nn.parameter import Parameter
+
+
+def rbf_kernel(X: Tensor, lengthscale: Tensor) -> Tensor:
+    """RBF kernel."""
+    dist = compute_dists(X=X, lengthscale=lengthscale)
+    return torch.exp(-0.5 * (dist ** 2))
+
+
+def compute_dists(X: Tensor, lengthscale: Tensor) -> Tensor:
+    """Compute kernel distances."""
+    scaled_X = X / lengthscale
+    return dist(scaled_X, scaled_X, x1_eq_x2=True)
+
+
+
+class RBFPyroModel(PyroModel):
+    r"""Implementation of the sparse axis-aligned subspace priors (SAAS) model.
+
+    The SAAS model uses sparsity-inducing priors to identify the most important
+    parameters. This model is suitable for high-dimensional BO with potentially
+    hundreds of tunable parameters. See [Eriksson2021saasbo]_ for more details.
+
+    `SaasPyroModel` is not a standard BoTorch model; instead, it is used as
+    an input to `SaasFullyBayesianSingleTaskGP`. It is used as a default keyword
+    argument, and end users are not likely to need to instantiate or modify a
+    `SaasPyroModel` unless they want to customize its attributes (such as
+    `covar_module`).
+    """
+
+    def set_inputs(
+        self, train_X: Tensor, train_Y: Tensor, train_Yvar: Optional[Tensor] = None
+    ) -> None:
+        super().set_inputs(train_X, train_Y, train_Yvar)
+        self.ard_num_dims = self.train_X.shape[-1]
+
+    def sample(self) -> None:
+        r"""Sample from the SAAS model.
+
+        This samples the mean, noise variance, outputscale, and lengthscales according
+        to the SAAS prior.
+        """
+        tkwargs = {"dtype": self.train_X.dtype, "device": self.train_X.device}
+        outputscale = self.sample_outputscale(concentration=2.0, rate=0.15, **tkwargs)
+        mean = self.sample_mean(**tkwargs)
+        noise = self.sample_noise(**tkwargs)
+        lengthscale = self.sample_lengthscale(dim=self.ard_num_dims, **tkwargs)
+        if self.train_Y.shape[-2] > 0:
+            # Do not attempt to sample Y if the data is empty.
+            # This leads to errors with empty data.
+            K = rbf_kernel(X=self.train_X, lengthscale=lengthscale)
+            K = outputscale * K + noise * torch.eye(self.train_X.shape[0], **tkwargs)
+            pyro.sample(
+                "Y",
+                pyro.distributions.MultivariateNormal(
+                    loc=mean.view(-1).expand(self.train_X.shape[0]),
+                    covariance_matrix=K,
+                ),
+                obs=self.train_Y.squeeze(-1),
+            )
+
+    def sample_outputscale(
+        self, concentration: float = 2.0, rate: float = 0.15, **tkwargs: Any
+    ) -> Tensor:
+        r"""Sample the outputscale."""
+        return pyro.sample(
+            "outputscale",
+            pyro.distributions.Gamma(
+                torch.tensor(concentration, **tkwargs),
+                torch.tensor(rate, **tkwargs),
+            ),
+        )
+
+    def sample_mean(self, **tkwargs: Any) -> Tensor:
+        r"""Sample the mean constant."""
+        return pyro.sample(
+            "mean",
+            pyro.distributions.Normal(
+                torch.tensor(0.0, **tkwargs),
+                torch.tensor(1.0, **tkwargs),
+            ),
+        )
+
+    def sample_noise(self, **tkwargs: Any) -> Tensor:
+        r"""Sample the noise variance."""
+        if self.train_Yvar is None:
+            return MIN_INFERRED_NOISE_LEVEL + pyro.sample(
+                "noise",
+                pyro.distributions.Gamma(
+                    torch.tensor(0.9, **tkwargs),
+                    torch.tensor(10.0, **tkwargs),
+                ),
+            )
+        else:
+            return self.train_Yvar
+
+    def sample_lengthscale(
+        self, dim: int, alpha: float = 0.1, **tkwargs: Any
+    ) -> Tensor:
+        r"""Sample the lengthscale."""
+        return pyro.sample(
+            "lengthscale",
+            pyro.distributions.LogNormal(
+                loc=sqrt(2) + torch.log(torch.ones(dim, **tkwargs) * dim) * 0.5,
+                scale=sqrt(3),
+            ),
+        )
+
+    def postprocess_mcmc_samples(
+        self, mcmc_samples: dict[str, Tensor]
+    ) -> dict[str, Tensor]:
+        r"""Post-process the MCMC samples.
+
+        This computes the true lengthscales and removes the inverse lengthscales and
+        tausq (global shrinkage).
+        """
+        return mcmc_samples
+
+    def load_mcmc_samples(
+        self, mcmc_samples: dict[str, Tensor]
+    ) -> tuple[Mean, Kernel, Likelihood]:
+        r"""Load the MCMC samples into the mean_module, covar_module, and likelihood."""
+        tkwargs = {"device": self.train_X.device, "dtype": self.train_X.dtype}
+        num_mcmc_samples = len(mcmc_samples["mean"])
+        batch_shape = torch.Size([num_mcmc_samples])
+
+        mean_module = ConstantMean(batch_shape=batch_shape).to(**tkwargs)
+        covar_module = ScaleKernel(
+            base_kernel=RBFKernel(
+                ard_num_dims=self.ard_num_dims,
+                batch_shape=batch_shape,
+            ),
+            batch_shape=batch_shape,
+        ).to(**tkwargs)
+        if self.train_Yvar is not None:
+            likelihood = FixedNoiseGaussianLikelihood(
+                # Reshape to shape `num_mcmc_samples x N`
+                noise=self.train_Yvar.squeeze(-1).expand(
+                    num_mcmc_samples, len(self.train_Yvar)
+                ),
+                batch_shape=batch_shape,
+            ).to(**tkwargs)
+        else:
+            likelihood = GaussianLikelihood(
+                batch_shape=batch_shape,
+                noise_constraint=GreaterThan(MIN_INFERRED_NOISE_LEVEL),
+            ).to(**tkwargs)
+            likelihood.noise_covar.noise = reshape_and_detach(
+                target=likelihood.noise_covar.noise,
+                new_value=mcmc_samples["noise"].clamp_min(MIN_INFERRED_NOISE_LEVEL),
+            )
+        covar_module.base_kernel.lengthscale = reshape_and_detach(
+            target=covar_module.base_kernel.lengthscale,
+            new_value=mcmc_samples["lengthscale"],
+        )
+        covar_module.outputscale = reshape_and_detach(
+            target=covar_module.outputscale,
+            new_value=mcmc_samples["outputscale"],
+        )
+        mean_module.constant.data = reshape_and_detach(
+            target=mean_module.constant.data,
+            new_value=mcmc_samples["mean"],
+        )
+        return mean_module, covar_module, likelihood
+
+
+
+class MultitaskRBFPyroModel(RBFPyroModel):
+    r"""
+    Implementation of the multi-task sparse axis-aligned subspace priors (SAAS) model.
+
+    The multi-task model uses an ICM kernel. The data kernel is same as the single task
+    SAAS model in order to handle high-dimensional parameter spaces. The task kernel
+    is a Matern-5/2 kernel using learned task embeddings as the input.
+    """
+
+    def set_inputs(
+        self,
+        train_X: Tensor,
+        train_Y: Tensor,
+        train_Yvar: Optional[Tensor],
+        task_feature: int,
+        task_rank: Optional[int] = None,
+    ) -> None:
+        """Set the training data.
+
+        Args:
+            train_X: Training inputs (n x (d + 1))
+            train_Y: Training targets (n x 1)
+            train_Yvar: Observed noise variance (n x 1). If None, we infer the noise.
+                Note that the inferred noise is common across all tasks.
+            task_feature: The index of the task feature (`-d <= task_feature <= d`).
+            task_rank: The num of learned task embeddings to be used in the task kernel.
+                If omitted, use a full rank (i.e. number of tasks) kernel.
+        """
+        super().set_inputs(train_X, train_Y, train_Yvar)
+        # obtain a list of task indicies
+        all_tasks = train_X[:, task_feature].unique().to(dtype=torch.long).tolist()
+        self.task_feature = task_feature
+        self.num_tasks = len(all_tasks)
+        self.task_rank = task_rank or self.num_tasks
+        # assume there is one column for task feature
+        self.ard_num_dims = self.train_X.shape[-1] - 1
+
+    def sample(self) -> None:
+        r"""Sample from the SAAS model.
+
+        This samples the mean, noise variance, outputscale, and lengthscales according
+        to the SAAS prior.
+        """
+        tkwargs = {"dtype": self.train_X.dtype, "device": self.train_X.device}
+        base_idxr = torch.arange(self.ard_num_dims, **{"device": tkwargs["device"]})
+        base_idxr[self.task_feature :] += 1  # exclude task feature
+        task_indices = self.train_X[..., self.task_feature].to(
+            device=tkwargs["device"], dtype=torch.long
+        )
+
+        outputscale = self.sample_outputscale(concentration=2.0, rate=0.15, **tkwargs)
+        mean = self.sample_mean(**tkwargs)
+        noise = self.sample_noise(**tkwargs)
+
+        lengthscale = self.sample_lengthscale(dim=self.ard_num_dims, **tkwargs)
+        K = rbf_kernel(X=self.train_X[..., base_idxr], lengthscale=lengthscale)
+
+        # compute task covar matrix
+        task_latent_features = self.sample_latent_features(**tkwargs)[task_indices]
+        task_lengthscale = self.sample_task_lengthscale(**tkwargs)
+        task_covar = rbf_kernel(
+            X=task_latent_features, lengthscale=task_lengthscale
+        )
+        K = K.mul(task_covar)
+        K = outputscale * K + noise * torch.eye(self.train_X.shape[0], **tkwargs)
+        pyro.sample(
+            "Y",
+            pyro.distributions.MultivariateNormal(
+                loc=mean.view(-1).expand(self.train_X.shape[0]),
+                covariance_matrix=K,
+            ),
+            obs=self.train_Y.squeeze(-1),
+        )
+
+    def sample_latent_features(self, **tkwargs: Any):
+        return pyro.sample(
+            "latent_features",
+            pyro.distributions.Normal(
+                torch.tensor(0.0, **tkwargs),
+                torch.tensor(1.0, **tkwargs),
+            ).expand(torch.Size([self.num_tasks, self.task_rank])),
+        )
+
+    def sample_task_lengthscale(
+        self, concentration: float = 6.0, rate: float = 3.0, **tkwargs: Any
+    ):
+        return pyro.sample(
+            "task_lengthscale",
+            pyro.distributions.Gamma(
+                torch.tensor(concentration, **tkwargs),
+                torch.tensor(rate, **tkwargs),
+            ).expand(torch.Size([self.task_rank])),
+        )
+
+    def load_mcmc_samples(
+        self, mcmc_samples: dict[str, Tensor]
+    ) -> tuple[Mean, Kernel, Likelihood, Kernel, Parameter]:
+        r"""Load the MCMC samples into the mean_module, covar_module, and likelihood."""
+        tkwargs = {"device": self.train_X.device, "dtype": self.train_X.dtype}
+        num_mcmc_samples = len(mcmc_samples["mean"])
+        batch_shape = torch.Size([num_mcmc_samples])
+
+        mean_module, covar_module, likelihood = super().load_mcmc_samples(
+            mcmc_samples=mcmc_samples
+        )
+
+        task_covar_module = RBFKernel(
+            ard_num_dims=self.task_rank,
+            batch_shape=batch_shape,
+        ).to(**tkwargs)
+        task_covar_module.lengthscale = reshape_and_detach(
+            target=task_covar_module.lengthscale,
+            new_value=mcmc_samples["task_lengthscale"],
+        )
+        latent_features = Parameter(
+            torch.rand(
+                batch_shape + torch.Size([self.num_tasks, self.task_rank]),
+                requires_grad=True,
+                **tkwargs,
+            )
+        )
+        latent_features = reshape_and_detach(
+            target=latent_features,
+            new_value=mcmc_samples["latent_features"],
+        )
+        return mean_module, covar_module, likelihood, task_covar_module, latent_features
+
diff --git a/private_multitask_pfn/multifidelity/Model Evaluation on Discrete MF Problems.ipynb b/private_multitask_pfn/multifidelity/Model Evaluation on Discrete MF Problems.ipynb
new file mode 100644
index 0000000..932d790
--- /dev/null
+++ b/private_multitask_pfn/multifidelity/Model Evaluation on Discrete MF Problems.ipynb	
@@ -0,0 +1,1843 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528209127,
+    "executionStopTime": 1743528217022,
+    "language": "python",
+    "originalKey": "a4c56e12-55d0-4b3e-a2dd-bec3739a135a",
+    "outputsInitialized": true,
+    "requestMsgId": "61357428-9500-4edb-8e7d-2a6608607fae",
+    "serverExecutionDuration": 6.2562888488173,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "import torch\n",
+    "from botorch.test_functions.synthetic import SyntheticTestFunction\n",
+    "from torch import Tensor\n",
+    "\n",
+    "\n",
+    "class WingWeightMultiFidelity(SyntheticTestFunction):\n",
+    "    \"\"\"Wing Weight Design Problem from [Chen2024]_.\n",
+    "\n",
+    "    Design variables (physical units):\n",
+    "      1. s_w   in [150,   200]   (wing area)\n",
+    "      2. w_fw  in [220,   300]   (fuel weight)\n",
+    "      3. A     in [6,     10]    (aspect ratio)\n",
+    "      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)\n",
+    "      5. q     in [16,    45]    (dynamic pressure)\n",
+    "      6. lam   in [0.5,   1.0]   (taper ratio)\n",
+    "      7. t_c   in [0.08,  0.18]   (thickness-to-chord)\n",
+    "      8. N_z   in [2.5,   6.0]    (ultimate load factor)\n",
+    "      9. w_dg  in [1700,  2500]   (design gross weight)\n",
+    "      10. w_pp in [0.025, 0.08]    (weight per unit area)\n",
+    "\n",
+    "    Fidelity parameter (stored as the 11th input):\n",
+    "      0: High fidelity (HF)\n",
+    "      1: Low fidelity 1 (LF1)\n",
+    "      2: Low fidelity 2 (LF2)\n",
+    "      3: Low fidelity 3 (LF2)\n",
+    "\n",
+    "    The HF model is given by:\n",
+    "      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *\n",
+    "           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *\n",
+    "           (N_z*w_dg)^0.49 + s_w*w_pp\n",
+    "\n",
+    "    LF models use slightly altered exponents and additive biases.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dim = 11\n",
+    "    _num_fidelities = 1\n",
+    "    _bounds = [\n",
+    "        (150.0, 200.0),  # s_w\n",
+    "        (220.0, 300.0),  # w_fw\n",
+    "        (6.0, 10.0),  # A\n",
+    "        (-10.0, 10.0),  # Lambda_deg\n",
+    "        (16.0, 45.0),  # q\n",
+    "        (0.5, 1.0),  # lam\n",
+    "        (0.08, 0.18),  # t_c\n",
+    "        (2.5, 6.0),  # N_z\n",
+    "        (1700.0, 2500.0),  # w_dg\n",
+    "        (0.025, 0.08),  # w_pp\n",
+    "        (0, 3),\n",
+    "    ]\n",
+    "    fidelities = [0, 1, 2, 3]\n",
+    "    _optimal_value = 123.25\n",
+    "\n",
+    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.\n",
+    "        s_w = X[..., 0]\n",
+    "        w_fw = X[..., 1]\n",
+    "        A = X[..., 2]\n",
+    "        Lambda_deg = X[..., 3]\n",
+    "        Lambda_rad = Lambda_deg * math.pi / 180.0\n",
+    "\n",
+    "        q = X[..., 4]\n",
+    "        lam = X[..., 5]\n",
+    "        t_c = X[..., 6]\n",
+    "        N_z = X[..., 7]\n",
+    "        w_dg = X[..., 8]\n",
+    "        w_pp = X[..., 9]\n",
+    "        fidelity = X[..., 10]\n",
+    "        cos_val = torch.cos(Lambda_rad)\n",
+    "        y = torch.zeros_like(s_w)\n",
+    "        # High fidelity (fidelity == 0)\n",
+    "        mask = fidelity == 0\n",
+    "        if mask.any():\n",
+    "            hf = (\n",
+    "                0.036\n",
+    "                * (s_w**0.758)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + s_w * w_pp\n",
+    "            )\n",
+    "            y[mask] = hf[mask]\n",
+    "        # Low fidelity 1 (fidelity == 1)\n",
+    "        mask = fidelity == 1\n",
+    "        if mask.any():\n",
+    "            lf1 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.758)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + w_pp\n",
+    "            )\n",
+    "            y[mask] = lf1[mask]\n",
+    "        # Low fidelity 2 (fidelity == 2)\n",
+    "        mask = fidelity == 2\n",
+    "        if mask.any():\n",
+    "            lf2 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.8)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + w_pp\n",
+    "            )\n",
+    "            y[mask] = lf2[mask]\n",
+    "        # Low fidelity 3 (fidelity == 3)\n",
+    "        mask = fidelity == 3\n",
+    "        if mask.any():\n",
+    "            lf3 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.9)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "            )\n",
+    "            y[mask] = lf3[mask]\n",
+    "        return y\n",
+    "\n",
+    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        fidelity = X[..., 10]\n",
+    "        c = torch.zeros_like(fidelity)\n",
+    "        c[fidelity == 0] = 1000.0\n",
+    "        c[fidelity == 1] = 100.0\n",
+    "        c[fidelity == 2] = 10.0\n",
+    "        c[fidelity == 3] = 1.0\n",
+    "        return c\n",
+    "\n",
+    "\n",
+    "class BoreholeMultiFidelity(SyntheticTestFunction):\n",
+    "    \"\"\"Borehole Problem from [Chen2024]_.\n",
+    "\n",
+    "    This problem models water flow through a borehole with 8 design variables:\n",
+    "          1. r_w   in [0.05,   0.15]   (borehole radius)\n",
+    "          2. r     in [100,    50000]  (radius of influence)\n",
+    "          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)\n",
+    "          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)\n",
+    "          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)\n",
+    "          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)\n",
+    "          7. L     in [1120,   1680]   (length of borehole)\n",
+    "          8. K_w   in [9855,   12045]  (hydraulic conductivity)\n",
+    "\n",
+    "        The fidelity index (9th input) is categorical:\n",
+    "          0: High fidelity (HF)\n",
+    "          1: Low fidelity 1 (LF1)\n",
+    "          2: Low fidelity 2 (LF2)\n",
+    "          3: Low fidelity 3 (LF3)\n",
+    "          4: Low fidelity 4 (LF4)\n",
+    "\n",
+    "        The HF model is defined by:\n",
+    "          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]\n",
+    "\n",
+    "        The low-fidelity models modify exponents and add a bias.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dim = 9\n",
+    "    _num_fidelities = 1\n",
+    "    _bounds = [\n",
+    "        (0.05, 0.15),  # r_w\n",
+    "        (100.0, 10000.0),  # r\n",
+    "        (100.0, 1000.0),  # T_u\n",
+    "        (10.0, 500.0),  # T_l\n",
+    "        (990.0, 1110.0),  # H_u\n",
+    "        (700.0, 820.0),  # H_l\n",
+    "        (1000.0, 2000.0),  # L\n",
+    "        (6000.0, 12000.0),  # K_w\n",
+    "        (0, 4),  # fidelity\n",
+    "    ]\n",
+    "    fidelities = [0, 1, 2, 3, 4]\n",
+    "    _optimal_value = 3.98\n",
+    "\n",
+    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        r_w = X[..., 0]\n",
+    "        r = X[..., 1]\n",
+    "        T_u = X[..., 2]\n",
+    "        T_l = X[..., 3]\n",
+    "        H_u = X[..., 4]\n",
+    "        H_l = X[..., 5]\n",
+    "        L = X[..., 6]\n",
+    "        K_w = X[..., 7]\n",
+    "        fidelity = X[..., 8]\n",
+    "\n",
+    "        log_term = torch.log(r / r_w)\n",
+    "        numer = 2.0 * math.pi * T_u * (H_u - H_l)\n",
+    "        y = torch.zeros_like(r_w)\n",
+    "\n",
+    "        # HF (fidelity 0)\n",
+    "        mask = fidelity == 0\n",
+    "        if mask.any():\n",
+    "            hf_denom = log_term * (\n",
+    "                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            hf = numer / hf_denom\n",
+    "            y[mask] = hf[mask]\n",
+    "\n",
+    "        # LF1 (fidelity 1): add bias.\n",
+    "        mask = fidelity == 1\n",
+    "        if mask.any():\n",
+    "            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)\n",
+    "            lf1_denom = log_term * (\n",
+    "                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf1 = lf1_numer / lf1_denom\n",
+    "            y[mask] = lf1[mask]\n",
+    "\n",
+    "        # LF2 (fidelity 2): modify the exponent on log_term and add bias.\n",
+    "        mask = fidelity == 2\n",
+    "        if mask.any():\n",
+    "            lf2_denom = log_term * (\n",
+    "                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l\n",
+    "            )\n",
+    "            lf2 = numer / lf2_denom\n",
+    "            y[mask] = lf2[mask]\n",
+    "\n",
+    "        # LF3 (fidelity 3): modify r_w exponent slightly.\n",
+    "        mask = fidelity == 3\n",
+    "        if mask.any():\n",
+    "            lf3_log_term = torch.log(4 * r / r_w)\n",
+    "            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)\n",
+    "            lf3_denom = lf3_log_term * (\n",
+    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf3 = lf3_numer / lf3_denom\n",
+    "            y[mask] = lf3[mask]\n",
+    "        # LF4 (fidelity 4): further bias.\n",
+    "        mask = fidelity == 4\n",
+    "        if mask.any():\n",
+    "            lf4_log_term = torch.log(2 * r / r_w)\n",
+    "            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)\n",
+    "            lf4_denom = lf4_log_term * (\n",
+    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf4 = lf4_numer / lf4_denom\n",
+    "            y[mask] = lf4[mask]\n",
+    "\n",
+    "        return y\n",
+    "\n",
+    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        fidelity = X[..., 8]\n",
+    "        c = torch.zeros_like(fidelity)\n",
+    "        c[fidelity == 0] = 1000.0\n",
+    "        c[fidelity == 1] = 100.0\n",
+    "        c[fidelity == 2] = 10.0\n",
+    "        c[fidelity == 3] = 100.0\n",
+    "        c[fidelity == 4] = 10.0\n",
+    "        return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528318060,
+    "executionStopTime": 1743528318381,
+    "language": "python",
+    "originalKey": "11f17363-b3c0-4b55-b211-436797c48a1e",
+    "outputsInitialized": true,
+    "requestMsgId": "961e03e5-ed26-47c2-8e06-8106aa186b53",
+    "serverExecutionDuration": 1.8204906955361,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll\n",
+    "from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP\n",
+    "from botorch.models.gp_regression import SingleTaskGP\n",
+    "\n",
+    "from botorch.models.multitask import MultiTaskGP\n",
+    "\n",
+    "from botorch.models.transforms.input import (\n",
+    "    ChainedInputTransform,\n",
+    "    LatentCategoricalEmbedding,\n",
+    "    LatentCategoricalSpec,\n",
+    "    Normalize,\n",
+    ")\n",
+    "from botorch.models.transforms.outcome import Standardize, StratifiedStandardize\n",
+    "from botorch.utils.transforms import unnormalize\n",
+    "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
+    "from torch.distributions import Normal\n",
+    "\n",
+    "\n",
+    "tkwargs = {\"dtype\": torch.double}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528129775,
+    "executionStopTime": 1743528132881,
+    "language": "python",
+    "originalKey": "e02d0748-e092-4001-b8f0-59666e0e7b29",
+    "outputsInitialized": true,
+    "requestMsgId": "f59e8b19-0f9f-4f93-b9ad-277685684230",
+    "serverExecutionDuration": 1.955185085535,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "def eval_model(model, test_X, test_Y):\n",
+    "    with torch.no_grad():\n",
+    "        posterior = model.posterior(test_X, observation_noise=True)\n",
+    "        # compute sum of LL of each point in test set (using only marginal variances)\n",
+    "        var = posterior.variance\n",
+    "        mean = posterior.mean\n",
+    "        nll = (\n",
+    "            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))\n",
+    "            .log_prob(test_Y.view(-1))\n",
+    "            .sum(dim=-1)\n",
+    "            .mean()  # take average over MCMC samples (if needed)\n",
+    "            .item()\n",
+    "        )\n",
+    "        mse = (mean - test_Y).pow(2).mean().item()\n",
+    "    return nll, mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528939339,
+    "executionStopTime": 1743528940127,
+    "language": "python",
+    "originalKey": "a194f315-36d3-4980-9506-c01343153a81",
+    "outputsInitialized": true,
+    "requestMsgId": "dbaeb34d-8bc8-4943-891a-fae5a0fc7212",
+    "serverExecutionDuration": 3.0471379868686,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):\n",
+    "    res = {}\n",
+    "    # test STGP on target task\n",
+    "    target_mask = train_X[:, -1] == 0\n",
+    "    model = SingleTaskGP(\n",
+    "        train_X[target_mask],\n",
+    "        train_Y[target_mask],\n",
+    "        input_transform=Normalize(\n",
+    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
+    "        ),\n",
+    "    )\n",
+    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
+    "    _ = fit_gpytorch_mll(mll)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"STGP - target only\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "\n",
+    "    # Test MTGP with ICM\n",
+    "    model = MultiTaskGP(\n",
+    "        train_X,\n",
+    "        train_Y,\n",
+    "        input_transform=Normalize(\n",
+    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
+    "        ),\n",
+    "        outcome_transform=StratifiedStandardize(\n",
+    "            stratification_idx=problem.dim - 1,\n",
+    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
+    "        ),\n",
+    "        task_feature=problem.dim - 1,\n",
+    "    )\n",
+    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
+    "    _ = fit_gpytorch_mll(mll)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"MTGP - ICM - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "    # Test Fully Bayesian MTGP with Latent Embeddings\n",
+    "    model = SaasFullyBayesianMultiTaskGP(\n",
+    "        train_X,\n",
+    "        train_Y,\n",
+    "        input_transform=Normalize(\n",
+    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
+    "        ),\n",
+    "        task_feature=problem.dim - 1,\n",
+    "        outcome_transform=StratifiedStandardize(\n",
+    "            stratification_idx=problem.dim - 1,\n",
+    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
+    "        ),\n",
+    "    )\n",
+    "    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"MTGP - Latent Embeddings - FB\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "\n",
+    "    # test LVGP with MAP estimation\n",
+    "    d = train_X.shape[-1]\n",
+    "    cat_dims = [problem.dim - 1]\n",
+    "    # construct input transform\n",
+    "    input_transform = ChainedInputTransform(\n",
+    "        normalize=Normalize(d=d, indices=list(range(d - 1))),\n",
+    "        latent_emb=LatentCategoricalEmbedding(\n",
+    "            [\n",
+    "                LatentCategoricalSpec(\n",
+    "                    idx=i,\n",
+    "                    num_categories=len(problem.fidelities),\n",
+    "                    latent_dim=2,\n",
+    "                )\n",
+    "                for i in cat_dims\n",
+    "            ],\n",
+    "            dim=d,\n",
+    "        ).to(train_X),\n",
+    "    )\n",
+    "    model = SingleTaskGP(\n",
+    "        train_X,\n",
+    "        train_Y,\n",
+    "        input_transform=input_transform,\n",
+    "    )\n",
+    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
+    "    _ = fit_gpytorch_mll(mll)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"MTGP - Latent Embeddings - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "customInput": null,
+    "language": "markdown",
+    "originalKey": "1f8d148e-a0e7-4915-952f-7a405ec8a480",
+    "showInput": false
+   },
+   "source": [
+    "# Wing weight\n",
+    "### Generate initial training set as in Chen et al 2024. Generate test set for target fidelity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528960203,
+    "executionStopTime": 1743529095209,
+    "language": "python",
+    "originalKey": "fd812914-49d4-4023-926e-2d41c4157ded",
+    "outputsInitialized": true,
+    "requestMsgId": "097e842b-186a-44f5-88c3-cd12b0240b33",
+    "serverExecutionDuration": 5.1055490039289,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "problem = WingWeightMultiFidelity()\n",
+    "torch.manual_seed(0)\n",
+    "# define training and test set\n",
+    "N_TEST = 100\n",
+    "fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}\n",
+    "total_n = sum(fidelity_to_n.values())\n",
+    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
+    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
+    "# set fidelities\n",
+    "start = 0\n",
+    "for fidelity, n in fidelity_to_n.items():\n",
+    "    end = start + n\n",
+    "    train_X[start:end, -1] = fidelity\n",
+    "    start = end\n",
+    "\n",
+    "train_Y = problem(train_X).unsqueeze(-1)\n",
+    "\n",
+    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
+    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
+    "test_X[:, -1] = 0  # target fidelity\n",
+    "test_Y = problem(test_X).unsqueeze(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743529122103,
+    "executionStopTime": 1743529259563,
+    "language": "python",
+    "originalKey": "16660295-7704-4fc8-86a6-865e818007df",
+    "output": {
+     "id": "644333568388982"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "e061f189-346b-4b58-9716-bcee85df522f",
+    "serverExecutionDuration": 136496.86142337,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\rWarmup:   0%|          | 0/768 [00:00, ?it/s]\n",
+      "\rWarmup:   0%|          | 1/768 [00:01,  1.10s/it, step size=1.17e+00, acc. prob=0.000]\n",
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+      "\rWarmup:   1%|▏         | 10/768 [00:02,  5.88it/s, step size=7.02e-02, acc. prob=0.665]\n",
+      "\rWarmup:   1%|▏         | 11/768 [00:02,  5.92it/s, step size=1.30e-01, acc. prob=0.695]\n",
+      "\rWarmup:   2%|▏         | 12/768 [00:02,  6.34it/s, step size=2.48e-02, acc. prob=0.660]\n",
+      "\rWarmup:   2%|▏         | 13/768 [00:02,  6.29it/s, step size=4.59e-02, acc. prob=0.685]\n",
+      "\rWarmup:   2%|▏         | 14/768 [00:02,  6.26it/s, step size=7.41e-02, acc. prob=0.704]\n",
+      "\rWarmup:   2%|▏         | 15/768 [00:02,  6.16it/s, step size=1.02e-01, acc. prob=0.716]\n",
+      "\rWarmup:   2%|▏         | 16/768 [00:03,  5.99it/s, step size=7.58e-02, acc. prob=0.715]\n",
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+      "\rWarmup:   4%|▎         | 27/768 [00:04,  6.37it/s, step size=4.11e-02, acc. prob=0.737]\n",
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+      "\rWarmup:   5%|▍         | 36/768 [00:06,  6.19it/s, step size=4.00e-02, acc. prob=0.749]\n",
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+      "\rWarmup:   5%|▌         | 39/768 [00:06,  6.25it/s, step size=2.52e-02, acc. prob=0.747]\n",
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+      "\rWarmup:   5%|▌         | 42/768 [00:06,  6.20it/s, step size=6.64e-02, acc. prob=0.759]\n",
+      "\rWarmup:   6%|▌         | 43/768 [00:07,  6.22it/s, step size=4.63e-02, acc. prob=0.756]\n",
+      "\rWarmup:   6%|▌         | 44/768 [00:07,  6.08it/s, step size=4.01e-02, acc. prob=0.755]\n",
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+      "\rWarmup:   6%|▌         | 47/768 [00:07,  8.05it/s, step size=1.28e-01, acc. prob=0.768]\n",
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+      "\rWarmup:   8%|▊         | 58/768 [00:09,  6.60it/s, step size=1.05e-01, acc. prob=0.770]\n",
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+      "\rWarmup:   9%|▊         | 67/768 [00:10,  6.44it/s, step size=7.93e-02, acc. prob=0.771]\n",
+      "\rWarmup:   9%|▉         | 69/768 [00:10,  7.23it/s, step size=5.98e-02, acc. prob=0.769]\n",
+      "\rWarmup:   9%|▉         | 71/768 [00:10,  7.67it/s, step size=5.40e-02, acc. prob=0.769]\n",
+      "\rWarmup:   9%|▉         | 72/768 [00:10,  7.34it/s, step size=7.95e-02, acc. prob=0.772]\n",
+      "\rWarmup:  10%|▉         | 73/768 [00:11,  7.03it/s, step size=6.83e-02, acc. prob=0.771]\n",
+      "\rWarmup:  10%|▉         | 74/768 [00:11,  7.31it/s, step size=2.95e-02, acc. prob=0.766]\n",
+      "\rWarmup:  10%|▉         | 75/768 [00:11,  6.73it/s, step size=4.47e-02, acc. prob=0.769]\n",
+      "\rWarmup:  10%|▉         | 76/768 [00:11,  6.59it/s, step size=6.20e-02, acc. prob=0.772]\n",
+      "\rWarmup:  10%|█         | 77/768 [00:11,  6.52it/s, step size=2.23e-02, acc. prob=0.765]\n",
+      "\rWarmup:  10%|█         | 78/768 [00:11,  6.45it/s, step size=3.42e-02, acc. prob=0.768]\n",
+      "\rWarmup:  10%|█         | 79/768 [00:12,  6.37it/s, step size=5.11e-02, acc. prob=0.771]\n",
+      "\rWarmup:  10%|█         | 80/768 [00:12,  6.34it/s, step size=7.70e-02, acc. prob=0.774]\n",
+      "\rWarmup:  11%|█         | 81/768 [00:12,  6.33it/s, step size=2.53e-02, acc. prob=0.767]\n",
+      "\rWarmup:  11%|█         | 82/768 [00:12,  6.32it/s, step size=3.79e-02, acc. prob=0.770]\n",
+      "\rWarmup:  11%|█         | 83/768 [00:12,  5.88it/s, step size=5.67e-02, acc. prob=0.772]\n",
+      "\rWarmup:  11%|█         | 84/768 [00:12,  5.62it/s, step size=8.55e-02, acc. prob=0.775]\n",
+      "\rWarmup:  11%|█         | 85/768 [00:13,  5.46it/s, step size=9.20e-02, acc. prob=0.776]\n",
+      "\rWarmup:  11%|█         | 86/768 [00:13,  5.65it/s, step size=9.22e-02, acc. prob=0.776]\n",
+      "\rWarmup:  11%|█▏        | 87/768 [00:13,  5.80it/s, step size=1.28e-01, acc. prob=0.778]\n",
+      "\rWarmup:  12%|█▏        | 89/768 [00:13,  7.73it/s, step size=1.08e-01, acc. prob=0.777]\n",
+      "\rWarmup:  12%|█▏        | 90/768 [00:13,  7.17it/s, step size=1.07e-01, acc. prob=0.777]\n",
+      "\rWarmup:  12%|█▏        | 91/768 [00:13,  6.88it/s, step size=4.15e-02, acc. prob=0.772]\n",
+      "\rWarmup:  12%|█▏        | 92/768 [00:14,  6.67it/s, step size=5.55e-02, acc. prob=0.774]\n",
+      "\rWarmup:  12%|█▏        | 94/768 [00:14,  8.39it/s, step size=3.28e-02, acc. prob=0.771]\n",
+      "\rWarmup:  12%|█▏        | 95/768 [00:14,  7.76it/s, step size=4.57e-02, acc. prob=0.773]\n",
+      "\rWarmup:  12%|█▎        | 96/768 [00:14,  7.30it/s, step size=6.65e-02, acc. prob=0.776]\n",
+      "\rWarmup:  13%|█▎        | 97/768 [00:14,  6.94it/s, step size=4.17e-02, acc. prob=0.773]\n",
+      "\rWarmup:  13%|█▎        | 98/768 [00:14,  6.72it/s, step size=5.10e-02, acc. prob=0.774]\n",
+      "\rWarmup:  13%|█▎        | 99/768 [00:15,  4.09it/s, step size=1.16e+00, acc. prob=0.777]\n",
+      "\rWarmup:  13%|█▎        | 102/768 [00:15,  6.96it/s, step size=2.30e-02, acc. prob=0.754]\n",
+      "\rWarmup:  13%|█▎        | 103/768 [00:15,  6.81it/s, step size=2.59e-02, acc. prob=0.757]\n",
+      "\rWarmup:  14%|█▎        | 104/768 [00:15,  6.68it/s, step size=3.61e-02, acc. prob=0.759]\n",
+      "\rWarmup:  14%|█▎        | 105/768 [00:16,  6.57it/s, step size=5.62e-02, acc. prob=0.761]\n",
+      "\rWarmup:  14%|█▍        | 106/768 [00:16,  6.50it/s, step size=8.73e-02, acc. prob=0.763]\n",
+      "\rWarmup:  14%|█▍        | 107/768 [00:16,  6.29it/s, step size=1.50e-01, acc. prob=0.765]\n",
+      "\rWarmup:  14%|█▍        | 108/768 [00:16,  6.28it/s, step size=5.83e-02, acc. prob=0.763]\n",
+      "\rWarmup:  14%|█▍        | 109/768 [00:16,  6.24it/s, step size=1.09e-01, acc. prob=0.765]\n",
+      "\rWarmup:  14%|█▍        | 110/768 [00:16,  6.00it/s, step size=1.97e-01, acc. prob=0.767]\n",
+      "\rWarmup:  14%|█▍        | 111/768 [00:17,  6.09it/s, step size=6.53e-02, acc. prob=0.764]\n",
+      "\rWarmup:  15%|█▍        | 112/768 [00:17,  6.16it/s, step size=1.25e-01, acc. prob=0.766]\n",
+      "\rWarmup:  15%|█▍        | 113/768 [00:17,  6.19it/s, step size=1.47e-02, acc. prob=0.761]\n",
+      "\rWarmup:  15%|█▍        | 114/768 [00:17,  6.23it/s, step size=2.84e-02, acc. prob=0.763]\n",
+      "\rWarmup:  15%|█▍        | 115/768 [00:17,  6.26it/s, step size=5.22e-02, acc. prob=0.765]\n",
+      "\rWarmup:  15%|█▌        | 116/768 [00:17,  5.97it/s, step size=6.78e-02, acc. prob=0.766]\n",
+      "\rWarmup:  15%|█▌        | 117/768 [00:18,  5.99it/s, step size=8.32e-02, acc. prob=0.766]\n",
+      "\rWarmup:  15%|█▌        | 118/768 [00:18,  6.07it/s, step size=6.22e-02, acc. prob=0.766]\n",
+      "\rWarmup:  15%|█▌        | 119/768 [00:18,  6.12it/s, step size=1.13e-01, acc. prob=0.767]\n",
+      "\rWarmup:  16%|█▌        | 120/768 [00:18,  6.17it/s, step size=1.30e-01, acc. prob=0.768]\n",
+      "\rWarmup:  16%|█▌        | 121/768 [00:18,  6.20it/s, step size=9.87e-02, acc. prob=0.767]\n",
+      "\rWarmup:  16%|█▌        | 122/768 [00:18,  6.24it/s, step size=7.51e-02, acc. prob=0.767]\n",
+      "\rWarmup:  16%|█▌        | 123/768 [00:18,  6.25it/s, step size=9.39e-03, acc. prob=0.761]\n",
+      "\rWarmup:  16%|█▌        | 124/768 [00:19,  6.22it/s, step size=1.77e-02, acc. prob=0.763]\n",
+      "\rWarmup:  16%|█▋        | 125/768 [00:19,  6.24it/s, step size=3.18e-02, acc. prob=0.765]\n",
+      "\rWarmup:  16%|█▋        | 126/768 [00:19,  6.27it/s, step size=5.77e-02, acc. prob=0.767]\n",
+      "\rWarmup:  17%|█▋        | 127/768 [00:19,  6.26it/s, step size=9.44e-02, acc. prob=0.768]\n",
+      "\rWarmup:  17%|█▋        | 128/768 [00:19,  6.27it/s, step size=2.00e-02, acc. prob=0.764]\n",
+      "\rWarmup:  17%|█▋        | 129/768 [00:19,  5.83it/s, step size=3.64e-02, acc. prob=0.766]\n",
+      "\rWarmup:  17%|█▋        | 130/768 [00:20,  5.56it/s, step size=6.57e-02, acc. prob=0.768]\n",
+      "\rWarmup:  17%|█▋        | 131/768 [00:20,  5.74it/s, step size=1.04e-01, acc. prob=0.769]\n",
+      "\rWarmup:  17%|█▋        | 132/768 [00:20,  5.90it/s, step size=1.84e-01, acc. prob=0.771]\n",
+      "\rWarmup:  17%|█▋        | 133/768 [00:20,  5.99it/s, step size=3.20e-01, acc. prob=0.772]\n",
+      "\rWarmup:  17%|█▋        | 134/768 [00:20,  6.07it/s, step size=4.77e-02, acc. prob=0.767]\n",
+      "\rWarmup:  18%|█▊        | 135/768 [00:20,  6.12it/s, step size=8.27e-02, acc. prob=0.769]\n",
+      "\rWarmup:  18%|█▊        | 136/768 [00:21,  6.17it/s, step size=3.62e-02, acc. prob=0.767]\n",
+      "\rWarmup:  18%|█▊        | 137/768 [00:21,  6.18it/s, step size=6.24e-02, acc. prob=0.768]\n",
+      "\rWarmup:  18%|█▊        | 138/768 [00:21,  6.17it/s, step size=1.08e-01, acc. prob=0.770]\n",
+      "\rWarmup:  18%|█▊        | 139/768 [00:21,  6.17it/s, step size=1.70e-01, acc. prob=0.771]\n",
+      "\rWarmup:  18%|█▊        | 140/768 [00:21,  6.17it/s, step size=2.88e-01, acc. prob=0.773]\n",
+      "\rWarmup:  18%|█▊        | 141/768 [00:21,  6.19it/s, step size=1.01e-01, acc. prob=0.770]\n",
+      "\rWarmup:  18%|█▊        | 142/768 [00:22,  6.11it/s, step size=1.71e-01, acc. prob=0.772]\n",
+      "\rWarmup:  19%|█▊        | 143/768 [00:22,  6.10it/s, step size=1.31e-01, acc. prob=0.771]\n",
+      "\rWarmup:  19%|█▉        | 144/768 [00:22,  6.12it/s, step size=2.18e-01, acc. prob=0.773]\n",
+      "\rWarmup:  19%|█▉        | 145/768 [00:22,  6.15it/s, step size=1.96e-01, acc. prob=0.772]\n",
+      "\rWarmup:  19%|█▉        | 146/768 [00:22,  6.14it/s, step size=4.92e-02, acc. prob=0.769]\n",
+      "\rWarmup:  19%|█▉        | 147/768 [00:22,  6.14it/s, step size=1.95e-02, acc. prob=0.766]\n",
+      "\rWarmup:  19%|█▉        | 148/768 [00:23,  6.14it/s, step size=3.19e-02, acc. prob=0.768]\n",
+      "\rWarmup:  19%|█▉        | 149/768 [00:23,  5.61it/s, step size=2.12e-01, acc. prob=0.769]\n",
+      "\rWarmup:  20%|█▉        | 150/768 [00:23,  5.45it/s, step size=4.98e-01, acc. prob=0.764]\n",
+      "\rWarmup:  20%|█▉        | 151/768 [00:23,  5.35it/s, step size=5.12e-02, acc. prob=0.759]\n",
+      "\rWarmup:  20%|█▉        | 152/768 [00:23,  5.29it/s, step size=5.32e-02, acc. prob=0.761]\n",
+      "\rWarmup:  20%|█▉        | 153/768 [00:24,  5.25it/s, step size=7.18e-02, acc. prob=0.762]\n",
+      "\rWarmup:  20%|██        | 154/768 [00:24,  5.22it/s, step size=1.10e-01, acc. prob=0.764]\n",
+      "\rWarmup:  20%|██        | 155/768 [00:24,  5.20it/s, step size=1.81e-01, acc. prob=0.765]\n",
+      "\rWarmup:  20%|██        | 156/768 [00:24,  5.19it/s, step size=3.13e-01, acc. prob=0.767]\n",
+      "\rWarmup:  20%|██        | 157/768 [00:24,  5.18it/s, step size=7.39e-02, acc. prob=0.764]\n",
+      "\rWarmup:  21%|██        | 158/768 [00:25,  5.17it/s, step size=5.01e-02, acc. prob=0.763]\n",
+      "\rWarmup:  21%|██        | 159/768 [00:25,  5.16it/s, step size=9.29e-02, acc. prob=0.765]\n",
+      "\rWarmup:  21%|██        | 160/768 [00:25,  5.16it/s, step size=1.57e-01, acc. prob=0.766]\n",
+      "\rWarmup:  21%|██        | 161/768 [00:25,  5.16it/s, step size=2.98e-01, acc. prob=0.768]\n",
+      "\rWarmup:  21%|██        | 162/768 [00:25,  5.15it/s, step size=3.08e-02, acc. prob=0.763]\n",
+      "\rWarmup:  21%|██        | 163/768 [00:26,  5.15it/s, step size=5.87e-02, acc. prob=0.765]\n",
+      "\rWarmup:  21%|██▏       | 164/768 [00:26,  5.15it/s, step size=1.10e-01, acc. prob=0.766]\n",
+      "\rWarmup:  21%|██▏       | 165/768 [00:26,  5.15it/s, step size=1.97e-01, acc. prob=0.767]\n",
+      "\rWarmup:  22%|██▏       | 166/768 [00:26,  5.15it/s, step size=3.81e-02, acc. prob=0.764]\n",
+      "\rWarmup:  22%|██▏       | 167/768 [00:26,  5.14it/s, step size=7.15e-02, acc. prob=0.766]\n",
+      "\rWarmup:  22%|██▏       | 168/768 [00:26,  5.15it/s, step size=1.29e-01, acc. prob=0.767]\n",
+      "\rWarmup:  22%|██▏       | 169/768 [00:27,  5.14it/s, step size=1.16e-01, acc. prob=0.767]\n",
+      "\rWarmup:  22%|██▏       | 170/768 [00:27,  5.15it/s, step size=1.81e-01, acc. prob=0.768]\n",
+      "\rWarmup:  22%|██▏       | 171/768 [00:27,  5.15it/s, step size=2.76e-01, acc. prob=0.769]\n",
+      "\rWarmup:  22%|██▏       | 172/768 [00:27,  5.15it/s, step size=5.05e-02, acc. prob=0.766]\n",
+      "\rWarmup:  23%|██▎       | 173/768 [00:27,  5.15it/s, step size=8.57e-02, acc. prob=0.767]\n",
+      "\rWarmup:  23%|██▎       | 174/768 [00:28,  5.15it/s, step size=1.53e-01, acc. prob=0.768]\n",
+      "\rWarmup:  23%|██▎       | 175/768 [00:28,  5.14it/s, step size=2.59e-01, acc. prob=0.769]\n",
+      "\rWarmup:  23%|██▎       | 176/768 [00:28,  5.14it/s, step size=8.81e-02, acc. prob=0.767]\n",
+      "\rWarmup:  23%|██▎       | 177/768 [00:28,  5.14it/s, step size=1.52e-01, acc. prob=0.769]\n",
+      "\rWarmup:  23%|██▎       | 178/768 [00:28,  5.13it/s, step size=5.10e-02, acc. prob=0.767]\n",
+      "\rWarmup:  23%|██▎       | 179/768 [00:29,  5.12it/s, step size=8.99e-02, acc. prob=0.768]\n",
+      "\rWarmup:  23%|██▎       | 180/768 [00:29,  5.12it/s, step size=1.58e-01, acc. prob=0.769]\n",
+      "\rWarmup:  24%|██▎       | 181/768 [00:29,  5.13it/s, step size=2.34e-01, acc. prob=0.770]\n",
+      "\rWarmup:  24%|██▎       | 182/768 [00:29,  5.14it/s, step size=3.42e-01, acc. prob=0.771]\n",
+      "\rWarmup:  24%|██▍       | 183/768 [00:29,  5.14it/s, step size=2.58e-01, acc. prob=0.770]\n",
+      "\rWarmup:  24%|██▍       | 184/768 [00:30,  5.15it/s, step size=3.17e-01, acc. prob=0.771]\n",
+      "\rWarmup:  24%|██▍       | 185/768 [00:30,  5.15it/s, step size=2.08e-01, acc. prob=0.770]\n",
+      "\rWarmup:  24%|██▍       | 186/768 [00:30,  5.15it/s, step size=1.72e-01, acc. prob=0.770]\n",
+      "\rWarmup:  24%|██▍       | 187/768 [00:30,  5.15it/s, step size=2.87e-01, acc. prob=0.771]\n",
+      "\rWarmup:  24%|██▍       | 188/768 [00:30,  5.16it/s, step size=2.54e-01, acc. prob=0.771]\n",
+      "\rWarmup:  25%|██▍       | 189/768 [00:31,  5.16it/s, step size=3.20e-01, acc. prob=0.772]\n",
+      "\rWarmup:  25%|██▍       | 191/768 [00:31,  6.42it/s, step size=1.28e-01, acc. prob=0.770]\n",
+      "\rWarmup:  25%|██▌       | 192/768 [00:31,  6.05it/s, step size=2.14e-01, acc. prob=0.771]\n",
+      "\rWarmup:  25%|██▌       | 193/768 [00:31,  5.79it/s, step size=2.65e-01, acc. prob=0.772]\n",
+      "\rWarmup:  25%|██▌       | 194/768 [00:31,  5.59it/s, step size=4.03e-01, acc. prob=0.773]\n",
+      "\rWarmup:  25%|██▌       | 195/768 [00:32,  5.71it/s, step size=1.66e-01, acc. prob=0.771]\n",
+      "\rWarmup:  26%|██▌       | 196/768 [00:32,  5.89it/s, step size=2.05e-01, acc. prob=0.772]\n",
+      "\rWarmup:  26%|██▌       | 197/768 [00:32,  6.03it/s, step size=1.47e-01, acc. prob=0.771]\n",
+      "\rWarmup:  26%|██▌       | 198/768 [00:32,  6.12it/s, step size=2.37e-01, acc. prob=0.772]\n",
+      "\rWarmup:  26%|██▌       | 199/768 [00:32,  6.16it/s, step size=8.93e-02, acc. prob=0.770]\n",
+      "\rWarmup:  26%|██▌       | 200/768 [00:32,  6.18it/s, step size=1.35e-01, acc. prob=0.771]\n",
+      "\rWarmup:  26%|██▌       | 201/768 [00:32,  6.22it/s, step size=2.11e-01, acc. prob=0.772]\n",
+      "\rWarmup:  26%|██▋       | 203/768 [00:33,  7.11it/s, step size=3.46e-01, acc. prob=0.773]\n",
+      "\rWarmup:  27%|██▋       | 204/768 [00:33,  6.90it/s, step size=4.25e-01, acc. prob=0.774]\n",
+      "\rWarmup:  27%|██▋       | 206/768 [00:33,  7.51it/s, step size=8.48e-02, acc. prob=0.771]\n",
+      "\rWarmup:  27%|██▋       | 207/768 [00:33,  7.23it/s, step size=1.34e-01, acc. prob=0.772]\n",
+      "\rWarmup:  27%|██▋       | 208/768 [00:33,  6.97it/s, step size=1.90e-01, acc. prob=0.773]\n",
+      "\rWarmup:  27%|██▋       | 209/768 [00:34,  6.80it/s, step size=2.88e-01, acc. prob=0.774]\n",
+      "\rWarmup:  27%|██▋       | 210/768 [00:34,  6.69it/s, step size=1.81e-01, acc. prob=0.773]\n",
+      "\rWarmup:  27%|██▋       | 211/768 [00:34,  6.58it/s, step size=1.69e-01, acc. prob=0.773]\n",
+      "\rWarmup:  28%|██▊       | 212/768 [00:34,  6.53it/s, step size=2.15e-01, acc. prob=0.773]\n",
+      "\rWarmup:  28%|██▊       | 213/768 [00:34,  6.47it/s, step size=1.15e-01, acc. prob=0.772]\n",
+      "\rWarmup:  28%|██▊       | 214/768 [00:34,  6.37it/s, step size=1.71e-01, acc. prob=0.773]\n",
+      "\rWarmup:  28%|██▊       | 215/768 [00:35,  6.36it/s, step size=6.96e-02, acc. prob=0.771]\n",
+      "\rWarmup:  28%|██▊       | 216/768 [00:35,  6.36it/s, step size=1.08e-01, acc. prob=0.772]\n",
+      "\rWarmup:  28%|██▊       | 217/768 [00:35,  6.35it/s, step size=1.65e-01, acc. prob=0.773]\n",
+      "\rWarmup:  28%|██▊       | 218/768 [00:35,  6.37it/s, step size=2.02e-01, acc. prob=0.774]\n",
+      "\rWarmup:  29%|██▊       | 219/768 [00:35,  6.38it/s, step size=2.32e-01, acc. prob=0.774]\n",
+      "\rWarmup:  29%|██▊       | 220/768 [00:35,  6.37it/s, step size=8.07e-02, acc. prob=0.772]\n",
+      "\rWarmup:  29%|██▉       | 221/768 [00:36,  5.92it/s, step size=1.23e-01, acc. prob=0.773]\n",
+      "\rWarmup:  29%|██▉       | 222/768 [00:36,  5.66it/s, step size=1.86e-01, acc. prob=0.774]\n",
+      "\rWarmup:  29%|██▉       | 223/768 [00:36,  5.48it/s, step size=1.37e-01, acc. prob=0.773]\n",
+      "\rWarmup:  29%|██▉       | 224/768 [00:36,  5.38it/s, step size=5.42e-02, acc. prob=0.771]\n",
+      "\rWarmup:  29%|██▉       | 225/768 [00:36,  5.31it/s, step size=8.26e-02, acc. prob=0.772]\n",
+      "\rWarmup:  29%|██▉       | 226/768 [00:36,  5.26it/s, step size=1.25e-01, acc. prob=0.773]\n",
+      "\rWarmup:  30%|██▉       | 227/768 [00:37,  5.22it/s, step size=6.39e-02, acc. prob=0.772]\n",
+      "\rWarmup:  30%|██▉       | 228/768 [00:37,  5.20it/s, step size=9.64e-02, acc. prob=0.773]\n",
+      "\rWarmup:  30%|██▉       | 229/768 [00:37,  5.19it/s, step size=1.44e-01, acc. prob=0.774]\n",
+      "\rWarmup:  30%|██▉       | 230/768 [00:37,  5.48it/s, step size=2.15e-01, acc. prob=0.775]\n",
+      "\rWarmup:  30%|███       | 231/768 [00:37,  5.67it/s, step size=1.62e-01, acc. prob=0.774]\n",
+      "\rWarmup:  30%|███       | 232/768 [00:38,  5.85it/s, step size=2.09e-01, acc. prob=0.775]\n",
+      "\rWarmup:  30%|███       | 233/768 [00:38,  5.64it/s, step size=3.07e-01, acc. prob=0.776]\n",
+      "\rWarmup:  30%|███       | 234/768 [00:38,  5.17it/s, step size=1.43e-01, acc. prob=0.774]\n",
+      "\rWarmup:  31%|███       | 235/768 [00:38,  4.89it/s, step size=2.04e-01, acc. prob=0.775]\n",
+      "\rWarmup:  31%|███       | 236/768 [00:38,  4.70it/s, step size=2.89e-01, acc. prob=0.776]\n",
+      "\rWarmup:  31%|███       | 237/768 [00:39,  4.57it/s, step size=1.29e-01, acc. prob=0.774]\n",
+      "\rWarmup:  31%|███       | 238/768 [00:39,  4.50it/s, step size=1.87e-01, acc. prob=0.775]\n",
+      "\rWarmup:  31%|███       | 239/768 [00:39,  4.46it/s, step size=2.66e-01, acc. prob=0.776]\n",
+      "\rWarmup:  31%|███▏      | 240/768 [00:39,  4.42it/s, step size=1.53e-01, acc. prob=0.775]\n",
+      "\rWarmup:  31%|███▏      | 241/768 [00:40,  4.40it/s, step size=2.24e-01, acc. prob=0.776]\n",
+      "\rWarmup:  32%|███▏      | 242/768 [00:40,  4.38it/s, step size=3.24e-01, acc. prob=0.777]\n",
+      "\rWarmup:  32%|███▏      | 243/768 [00:40,  4.37it/s, step size=1.01e-01, acc. prob=0.774]\n",
+      "\rWarmup:  32%|███▏      | 244/768 [00:40,  4.37it/s, step size=1.42e-01, acc. prob=0.775]\n",
+      "\rWarmup:  32%|███▏      | 245/768 [00:40,  4.36it/s, step size=6.75e-02, acc. prob=0.774]\n",
+      "\rWarmup:  32%|███▏      | 246/768 [00:41,  4.36it/s, step size=9.82e-02, acc. prob=0.774]\n",
+      "\rWarmup:  32%|███▏      | 247/768 [00:41,  4.36it/s, step size=1.41e-01, acc. prob=0.775]\n",
+      "\rWarmup:  32%|███▏      | 248/768 [00:41,  4.36it/s, step size=1.60e-01, acc. prob=0.776]\n",
+      "\rWarmup:  32%|███▏      | 249/768 [00:41,  4.23it/s, step size=8.32e-01, acc. prob=0.776]\n",
+      "\rWarmup:  33%|███▎      | 252/768 [00:42,  7.23it/s, step size=8.21e-02, acc. prob=0.770]\n",
+      "\rWarmup:  33%|███▎      | 253/768 [00:42,  6.90it/s, step size=1.03e-01, acc. prob=0.771]\n",
+      "\rWarmup:  33%|███▎      | 254/768 [00:42,  6.75it/s, step size=1.47e-01, acc. prob=0.772]\n",
+      "\rWarmup:  33%|███▎      | 255/768 [00:42,  6.62it/s, step size=2.36e-01, acc. prob=0.772]\n",
+      "\rWarmup:  33%|███▎      | 257/768 [00:42,  8.28it/s, step size=8.49e-02, acc. prob=0.771]\n",
+      "\rWarmup:  34%|███▎      | 258/768 [00:42,  7.72it/s, step size=1.44e-01, acc. prob=0.772]\n",
+      "\rWarmup:  34%|███▎      | 259/768 [00:43,  6.60it/s, step size=1.32e-01, acc. prob=0.772]\n",
+      "\rWarmup:  34%|███▍      | 260/768 [00:43,  5.80it/s, step size=2.35e-01, acc. prob=0.773]\n",
+      "\rWarmup:  34%|███▍      | 261/768 [00:43,  5.36it/s, step size=1.75e-01, acc. prob=0.773]\n",
+      "\rWarmup:  34%|███▍      | 262/768 [00:43,  5.03it/s, step size=2.75e-01, acc. prob=0.773]\n",
+      "\rWarmup:  34%|███▍      | 263/768 [00:44,  4.82it/s, step size=1.87e-01, acc. prob=0.773]\n",
+      "\rWarmup:  34%|███▍      | 264/768 [00:44,  5.54it/s, step size=3.30e-01, acc. prob=0.774]\n",
+      "\rWarmup:  35%|███▍      | 265/768 [00:44,  5.14it/s, step size=4.11e-02, acc. prob=0.771]\n",
+      "\rWarmup:  35%|███▍      | 266/768 [00:44,  4.86it/s, step size=7.59e-02, acc. prob=0.772]\n",
+      "\rWarmup:  35%|███▍      | 267/768 [00:44,  4.75it/s, step size=1.38e-01, acc. prob=0.773]\n",
+      "\rWarmup:  35%|███▍      | 268/768 [00:45,  4.61it/s, step size=6.81e-02, acc. prob=0.772]\n",
+      "\rWarmup:  35%|███▌      | 269/768 [00:45,  4.52it/s, step size=1.27e-01, acc. prob=0.773]\n",
+      "\rWarmup:  35%|███▌      | 270/768 [00:45,  5.29it/s, step size=8.20e-02, acc. prob=0.772]\n",
+      "\rWarmup:  35%|███▌      | 271/768 [00:45,  4.96it/s, step size=1.51e-01, acc. prob=0.773]\n",
+      "\rWarmup:  35%|███▌      | 272/768 [00:45,  4.77it/s, step size=2.49e-02, acc. prob=0.771]\n",
+      "\rWarmup:  36%|███▌      | 273/768 [00:46,  4.62it/s, step size=4.66e-02, acc. prob=0.772]\n",
+      "\rWarmup:  36%|███▌      | 274/768 [00:46,  4.54it/s, step size=8.62e-02, acc. prob=0.773]\n",
+      "\rWarmup:  36%|███▌      | 275/768 [00:46,  4.48it/s, step size=1.52e-01, acc. prob=0.773]\n",
+      "\rWarmup:  36%|███▌      | 276/768 [00:46,  4.44it/s, step size=7.71e-02, acc. prob=0.773]\n",
+      "\rWarmup:  36%|███▌      | 277/768 [00:46,  4.85it/s, step size=1.39e-01, acc. prob=0.773]\n",
+      "\rWarmup:  36%|███▌      | 278/768 [00:47,  5.21it/s, step size=2.34e-01, acc. prob=0.774]\n",
+      "\rWarmup:  36%|███▋      | 279/768 [00:47,  5.50it/s, step size=2.26e-01, acc. prob=0.774]\n",
+      "\rWarmup:  36%|███▋      | 280/768 [00:47,  5.73it/s, step size=2.30e-01, acc. prob=0.774]\n",
+      "\rWarmup:  37%|███▋      | 281/768 [00:47,  5.88it/s, step size=1.26e-01, acc. prob=0.774]\n",
+      "\rWarmup:  37%|███▋      | 282/768 [00:47,  6.71it/s, step size=2.22e-01, acc. prob=0.774]\n",
+      "\rWarmup:  37%|███▋      | 283/768 [00:47,  6.13it/s, step size=3.25e-01, acc. prob=0.775]\n",
+      "\rWarmup:  37%|███▋      | 284/768 [00:48,  5.78it/s, step size=7.80e-02, acc. prob=0.773]\n",
+      "\rWarmup:  37%|███▋      | 285/768 [00:48,  5.57it/s, step size=7.25e-02, acc. prob=0.773]\n",
+      "\rWarmup:  37%|███▋      | 286/768 [00:48,  5.43it/s, step size=1.26e-01, acc. prob=0.774]\n",
+      "\rWarmup:  37%|███▋      | 287/768 [00:48,  5.31it/s, step size=2.11e-01, acc. prob=0.775]\n",
+      "\rWarmup:  38%|███▊      | 289/768 [00:48,  6.41it/s, step size=1.45e-01, acc. prob=0.774]\n",
+      "\rWarmup:  38%|███▊      | 290/768 [00:49,  6.36it/s, step size=2.46e-01, acc. prob=0.775]\n",
+      "\rWarmup:  38%|███▊      | 291/768 [00:49,  5.72it/s, step size=5.19e-02, acc. prob=0.773]\n",
+      "\rWarmup:  38%|███▊      | 292/768 [00:49,  5.45it/s, step size=8.71e-02, acc. prob=0.774]\n",
+      "\rWarmup:  38%|███▊      | 293/768 [00:49,  5.67it/s, step size=1.47e-01, acc. prob=0.774]\n",
+      "\rWarmup:  38%|███▊      | 294/768 [00:49,  5.47it/s, step size=7.48e-02, acc. prob=0.773]\n",
+      "\rWarmup:  38%|███▊      | 295/768 [00:50,  5.35it/s, step size=1.24e-01, acc. prob=0.774]\n",
+      "\rWarmup:  39%|███▊      | 297/768 [00:50,  5.92it/s, step size=3.23e-01, acc. prob=0.776]\n",
+      "\rWarmup:  39%|███▉      | 299/768 [00:50,  6.24it/s, step size=1.85e-01, acc. prob=0.775]\n",
+      "\rWarmup:  39%|███▉      | 300/768 [00:50,  5.95it/s, step size=3.00e-01, acc. prob=0.776]\n",
+      "\rWarmup:  39%|███▉      | 302/768 [00:51,  6.25it/s, step size=2.11e-01, acc. prob=0.775]\n",
+      "\rWarmup:  39%|███▉      | 303/768 [00:51,  5.93it/s, step size=3.37e-01, acc. prob=0.776]\n",
+      "\rWarmup:  40%|███▉      | 304/768 [00:51,  5.69it/s, step size=3.46e-01, acc. prob=0.776]\n",
+      "\rWarmup:  40%|███▉      | 305/768 [00:51,  5.51it/s, step size=7.05e-02, acc. prob=0.774]\n",
+      "\rWarmup:  40%|███▉      | 306/768 [00:51,  5.38it/s, step size=1.13e-01, acc. prob=0.775]\n",
+      "\rWarmup:  40%|███▉      | 307/768 [00:52,  5.23it/s, step size=1.81e-01, acc. prob=0.775]\n",
+      "\rWarmup:  40%|████      | 308/768 [00:52,  5.15it/s, step size=3.51e-02, acc. prob=0.773]\n",
+      "\rWarmup:  40%|████      | 309/768 [00:52,  5.13it/s, step size=5.64e-02, acc. prob=0.774]\n",
+      "\rWarmup:  40%|████      | 310/768 [00:52,  5.12it/s, step size=8.98e-02, acc. prob=0.774]\n",
+      "\rWarmup:  40%|████      | 311/768 [00:52,  5.10it/s, step size=1.25e-01, acc. prob=0.775]\n",
+      "\rWarmup:  41%|████      | 312/768 [00:53,  5.09it/s, step size=1.93e-01, acc. prob=0.776]\n",
+      "\rWarmup:  41%|████      | 313/768 [00:53,  5.10it/s, step size=1.90e-01, acc. prob=0.776]\n",
+      "\rWarmup:  41%|████      | 314/768 [00:53,  5.09it/s, step size=1.89e-01, acc. prob=0.776]\n",
+      "\rWarmup:  41%|████      | 315/768 [00:53,  5.08it/s, step size=8.54e-02, acc. prob=0.775]\n",
+      "\rWarmup:  41%|████      | 316/768 [00:53,  5.08it/s, step size=1.19e-01, acc. prob=0.775]\n",
+      "\rWarmup:  41%|████▏     | 317/768 [00:54,  5.08it/s, step size=1.84e-01, acc. prob=0.776]\n",
+      "\rWarmup:  42%|████▏     | 319/768 [00:54,  7.18it/s, step size=2.78e-01, acc. prob=0.776]\n",
+      "\rWarmup:  42%|████▏     | 321/768 [00:54,  8.88it/s, step size=1.54e-01, acc. prob=0.776]\n",
+      "\rWarmup:  42%|████▏     | 322/768 [00:54,  7.64it/s, step size=1.69e-01, acc. prob=0.776]\n",
+      "\rWarmup:  42%|████▏     | 323/768 [00:54,  6.84it/s, step size=2.07e-01, acc. prob=0.776]\n",
+      "\rWarmup:  42%|████▏     | 324/768 [00:55,  6.29it/s, step size=2.14e-01, acc. prob=0.776]\n",
+      "\rWarmup:  42%|████▏     | 326/768 [00:55,  7.51it/s, step size=3.37e-01, acc. prob=0.777]\n",
+      "\rWarmup:  43%|████▎     | 327/768 [00:55,  6.74it/s, step size=4.74e-01, acc. prob=0.778]\n",
+      "\rWarmup:  43%|████▎     | 329/768 [00:55,  7.80it/s, step size=1.64e-01, acc. prob=0.776]\n",
+      "\rWarmup:  43%|████▎     | 330/768 [00:55,  6.97it/s, step size=2.31e-01, acc. prob=0.777]\n",
+      "\rWarmup:  43%|████▎     | 331/768 [00:55,  6.37it/s, step size=9.04e-02, acc. prob=0.775]\n",
+      "\rWarmup:  43%|████▎     | 332/768 [00:56,  5.99it/s, step size=1.37e-01, acc. prob=0.776]\n",
+      "\rWarmup:  43%|████▎     | 333/768 [00:56,  5.72it/s, step size=8.54e-02, acc. prob=0.775]\n",
+      "\rWarmup:  43%|████▎     | 334/768 [00:56,  5.54it/s, step size=1.28e-01, acc. prob=0.776]\n",
+      "\rWarmup:  44%|████▎     | 335/768 [00:56,  5.42it/s, step size=1.88e-01, acc. prob=0.777]\n",
+      "\rWarmup:  44%|████▍     | 336/768 [00:56,  5.32it/s, step size=2.39e-01, acc. prob=0.777]\n",
+      "\rWarmup:  44%|████▍     | 337/768 [00:57,  5.21it/s, step size=8.09e-02, acc. prob=0.775]\n",
+      "\rWarmup:  44%|████▍     | 338/768 [00:57,  5.18it/s, step size=1.21e-01, acc. prob=0.776]\n",
+      "\rWarmup:  44%|████▍     | 339/768 [00:57,  5.16it/s, step size=1.65e-01, acc. prob=0.777]\n",
+      "\rWarmup:  44%|████▍     | 340/768 [00:57,  5.14it/s, step size=2.22e-01, acc. prob=0.777]\n",
+      "\rWarmup:  44%|████▍     | 341/768 [00:57,  5.13it/s, step size=2.88e-01, acc. prob=0.778]\n",
+      "\rWarmup:  45%|████▍     | 343/768 [00:58,  7.21it/s, step size=3.89e-01, acc. prob=0.778]\n",
+      "\rWarmup:  45%|████▍     | 344/768 [00:58,  6.55it/s, step size=1.47e-01, acc. prob=0.777]\n",
+      "\rWarmup:  45%|████▌     | 346/768 [00:58,  6.68it/s, step size=2.41e-01, acc. prob=0.777]\n",
+      "\rWarmup:  45%|████▌     | 349/768 [00:58,  9.16it/s, step size=2.85e-01, acc. prob=0.778]\n",
+      "\rWarmup:  46%|████▌     | 350/768 [00:58,  7.99it/s, step size=1.61e-01, acc. prob=0.777]\n",
+      "\rWarmup:  46%|████▌     | 351/768 [00:59,  7.15it/s, step size=2.35e-01, acc. prob=0.778]\n",
+      "\rWarmup:  46%|████▌     | 353/768 [00:59,  7.03it/s, step size=2.35e-01, acc. prob=0.778]\n",
+      "\rWarmup:  46%|████▌     | 355/768 [00:59,  8.49it/s, step size=3.60e-01, acc. prob=0.778]\n",
+      "\rWarmup:  46%|████▋     | 357/768 [00:59,  9.02it/s, step size=1.62e-01, acc. prob=0.777]\n",
+      "\rWarmup:  47%|████▋     | 358/768 [00:59,  7.87it/s, step size=6.46e-02, acc. prob=0.776]\n",
+      "\rWarmup:  47%|████▋     | 359/768 [01:00,  7.06it/s, step size=9.28e-02, acc. prob=0.776]\n",
+      "\rWarmup:  47%|████▋     | 360/768 [01:00,  6.43it/s, step size=8.58e-02, acc. prob=0.776]\n",
+      "\rWarmup:  47%|████▋     | 361/768 [01:00,  6.04it/s, step size=1.22e-01, acc. prob=0.777]\n",
+      "\rWarmup:  47%|████▋     | 362/768 [01:00,  5.77it/s, step size=1.74e-01, acc. prob=0.778]\n",
+      "\rWarmup:  47%|████▋     | 363/768 [01:00,  5.57it/s, step size=1.83e-01, acc. prob=0.778]\n",
+      "\rWarmup:  47%|████▋     | 364/768 [01:01,  5.42it/s, step size=2.57e-01, acc. prob=0.778]\n",
+      "\rWarmup:  48%|████▊     | 365/768 [01:01,  5.30it/s, step size=3.46e-01, acc. prob=0.779]\n",
+      "\rWarmup:  48%|████▊     | 366/768 [01:01,  5.66it/s, step size=1.94e-01, acc. prob=0.778]\n",
+      "\rWarmup:  48%|████▊     | 367/768 [01:01,  5.48it/s, step size=2.75e-01, acc. prob=0.778]\n",
+      "\rWarmup:  48%|████▊     | 369/768 [01:02,  5.99it/s, step size=2.20e-01, acc. prob=0.778]\n",
+      "\rWarmup:  48%|████▊     | 370/768 [01:02,  5.73it/s, step size=6.49e-02, acc. prob=0.776]\n",
+      "\rWarmup:  48%|████▊     | 371/768 [01:02,  5.54it/s, step size=9.19e-02, acc. prob=0.777]\n",
+      "\rWarmup:  48%|████▊     | 372/768 [01:02,  5.43it/s, step size=1.23e-01, acc. prob=0.777]\n",
+      "\rWarmup:  49%|████▊     | 373/768 [01:02,  5.34it/s, step size=1.58e-01, acc. prob=0.778]\n",
+      "\rWarmup:  49%|████▉     | 375/768 [01:03,  5.90it/s, step size=7.50e-02, acc. prob=0.777]\n",
+      "\rWarmup:  49%|████▉     | 376/768 [01:03,  5.69it/s, step size=9.98e-02, acc. prob=0.777]\n",
+      "\rWarmup:  49%|████▉     | 377/768 [01:03,  5.54it/s, step size=1.37e-01, acc. prob=0.778]\n",
+      "\rWarmup:  49%|████▉     | 378/768 [01:03,  5.41it/s, step size=1.93e-01, acc. prob=0.778]\n",
+      "\rWarmup:  49%|████▉     | 380/768 [01:03,  5.97it/s, step size=9.13e-02, acc. prob=0.777]\n",
+      "\rWarmup:  50%|████▉     | 381/768 [01:04,  5.74it/s, step size=1.27e-01, acc. prob=0.778]\n",
+      "\rWarmup:  50%|████▉     | 382/768 [01:04,  5.58it/s, step size=1.63e-01, acc. prob=0.778]\n",
+      "\rWarmup:  50%|█████     | 384/768 [01:04,  6.07it/s, step size=2.76e-01, acc. prob=0.779]\n",
+      "\rWarmup:  50%|█████     | 385/768 [01:04,  5.82it/s, step size=2.11e-01, acc. prob=0.779]\n",
+      "\rWarmup:  50%|█████     | 387/768 [01:05,  6.20it/s, step size=3.15e-01, acc. prob=0.779]\n",
+      "\rWarmup:  51%|█████     | 389/768 [01:05,  7.12it/s, step size=1.27e-01, acc. prob=0.778]\n",
+      "\rWarmup:  51%|█████     | 390/768 [01:05,  6.60it/s, step size=1.11e-01, acc. prob=0.778]\n",
+      "\rWarmup:  51%|█████     | 391/768 [01:05,  6.19it/s, step size=1.41e-01, acc. prob=0.778]\n",
+      "\rWarmup:  51%|█████     | 392/768 [01:05,  5.88it/s, step size=6.88e-02, acc. prob=0.777]\n",
+      "\rWarmup:  51%|█████     | 393/768 [01:06,  5.66it/s, step size=9.50e-02, acc. prob=0.778]\n",
+      "\rWarmup:  51%|█████▏    | 394/768 [01:06,  5.51it/s, step size=1.29e-01, acc. prob=0.778]\n",
+      "\rWarmup:  51%|█████▏    | 395/768 [01:06,  5.40it/s, step size=1.76e-01, acc. prob=0.779]\n",
+      "\rWarmup:  52%|█████▏    | 397/768 [01:06,  5.97it/s, step size=1.41e-01, acc. prob=0.779]\n",
+      "\rWarmup:  52%|█████▏    | 398/768 [01:06,  5.73it/s, step size=1.06e-01, acc. prob=0.778]\n",
+      "\rWarmup:  52%|█████▏    | 399/768 [01:07,  5.55it/s, step size=4.05e-02, acc. prob=0.777]\n",
+      "\rWarmup:  52%|█████▏    | 400/768 [01:07,  5.42it/s, step size=5.55e-02, acc. prob=0.777]\n",
+      "\rWarmup:  52%|█████▏    | 401/768 [01:07,  5.33it/s, step size=2.67e-02, acc. prob=0.776]\n",
+      "\rWarmup:  52%|█████▏    | 402/768 [01:07,  5.24it/s, step size=3.68e-02, acc. prob=0.776]\n",
+      "\rWarmup:  52%|█████▏    | 403/768 [01:07,  5.18it/s, step size=4.84e-02, acc. prob=0.777]\n",
+      "\rWarmup:  53%|█████▎    | 404/768 [01:08,  5.15it/s, step size=6.53e-02, acc. prob=0.777]\n",
+      "\rWarmup:  53%|█████▎    | 405/768 [01:08,  5.14it/s, step size=8.28e-02, acc. prob=0.778]\n",
+      "\rWarmup:  53%|█████▎    | 406/768 [01:08,  5.12it/s, step size=9.99e-02, acc. prob=0.778]\n",
+      "\rWarmup:  53%|█████▎    | 407/768 [01:08,  5.11it/s, step size=1.36e-01, acc. prob=0.779]\n",
+      "\rWarmup:  53%|█████▎    | 409/768 [01:09,  3.35it/s, step size=2.44e-01, acc. prob=0.780]\n",
+      "\rWarmup:  53%|█████▎    | 410/768 [01:09,  3.66it/s, step size=2.09e-01, acc. prob=0.780]\n",
+      "\rWarmup:  54%|█████▎    | 412/768 [01:10,  4.98it/s, step size=1.28e-01, acc. prob=0.779]\n",
+      "\rWarmup:  54%|█████▍    | 414/768 [01:10,  5.55it/s, step size=1.71e-01, acc. prob=0.779]\n",
+      "\rWarmup:  54%|█████▍    | 415/768 [01:10,  5.45it/s, step size=1.99e-01, acc. prob=0.780]\n",
+      "\rWarmup:  54%|█████▍    | 416/768 [01:10,  5.37it/s, step size=1.01e-01, acc. prob=0.779]\n",
+      "\rWarmup:  54%|█████▍    | 417/768 [01:10,  5.31it/s, step size=1.33e-01, acc. prob=0.779]\n",
+      "\rWarmup:  54%|█████▍    | 418/768 [01:11,  5.26it/s, step size=1.78e-01, acc. prob=0.780]\n",
+      "\rWarmup:  55%|█████▍    | 419/768 [01:11,  5.22it/s, step size=2.30e-01, acc. prob=0.780]\n",
+      "\rWarmup:  55%|█████▍    | 420/768 [01:11,  5.19it/s, step size=2.26e-01, acc. prob=0.780]\n",
+      "\rWarmup:  55%|█████▍    | 422/768 [01:11,  7.19it/s, step size=3.83e-01, acc. prob=0.781]\n",
+      "\rWarmup:  55%|█████▌    | 424/768 [01:11,  8.14it/s, step size=1.35e-01, acc. prob=0.779]\n",
+      "\rWarmup:  55%|█████▌    | 426/768 [01:12,  7.61it/s, step size=2.10e-01, acc. prob=0.780]\n",
+      "\rWarmup:  56%|█████▌    | 427/768 [01:12,  7.43it/s, step size=2.66e-01, acc. prob=0.781]\n",
+      "\rWarmup:  56%|█████▌    | 428/768 [01:12,  6.74it/s, step size=2.42e-01, acc. prob=0.780]\n",
+      "\rWarmup:  56%|█████▌    | 429/768 [01:12,  6.22it/s, step size=1.15e-01, acc. prob=0.779]\n",
+      "\rWarmup:  56%|█████▌    | 430/768 [01:12,  5.88it/s, step size=1.52e-01, acc. prob=0.780]\n",
+      "\rWarmup:  56%|█████▋    | 432/768 [01:13,  7.17it/s, step size=2.13e-01, acc. prob=0.780]\n",
+      "\rWarmup:  57%|█████▋    | 434/768 [01:13,  7.05it/s, step size=2.73e-01, acc. prob=0.781]\n",
+      "\rWarmup:  57%|█████▋    | 436/768 [01:13,  8.52it/s, step size=2.47e-01, acc. prob=0.781]\n",
+      "\rWarmup:  57%|█████▋    | 438/768 [01:13,  8.41it/s, step size=3.61e-01, acc. prob=0.781]\n",
+      "\rWarmup:  57%|█████▋    | 441/768 [01:13, 10.43it/s, step size=2.39e-01, acc. prob=0.781]\n",
+      "\rWarmup:  58%|█████▊    | 443/768 [01:14,  8.05it/s, step size=9.58e-02, acc. prob=0.779]\n",
+      "\rWarmup:  58%|█████▊    | 444/768 [01:14,  7.34it/s, step size=1.27e-01, acc. prob=0.780]\n",
+      "\rWarmup:  58%|█████▊    | 445/768 [01:14,  6.76it/s, step size=1.53e-01, acc. prob=0.780]\n",
+      "\rWarmup:  58%|█████▊    | 447/768 [01:14,  7.70it/s, step size=2.34e-01, acc. prob=0.781]\n",
+      "\rWarmup:  58%|█████▊    | 448/768 [01:14,  8.08it/s, step size=2.98e-01, acc. prob=0.781]\n",
+      "\rWarmup:  59%|█████▊    | 450/768 [01:15,  8.10it/s, step size=1.80e-01, acc. prob=0.781]\n",
+      "\rWarmup:  59%|█████▉    | 452/768 [01:15,  8.74it/s, step size=2.04e-01, acc. prob=0.781]\n",
+      "\rWarmup:  59%|█████▉    | 453/768 [01:15,  7.66it/s, step size=2.66e-01, acc. prob=0.781]\n",
+      "\rWarmup:  59%|█████▉    | 454/768 [01:15,  6.89it/s, step size=1.44e-01, acc. prob=0.780]\n",
+      "\rWarmup:  59%|█████▉    | 455/768 [01:16,  6.36it/s, step size=7.85e-02, acc. prob=0.779]\n",
+      "\rWarmup:  59%|█████▉    | 456/768 [01:16,  5.98it/s, step size=1.03e-01, acc. prob=0.780]\n",
+      "\rWarmup:  60%|█████▉    | 457/768 [01:16,  5.72it/s, step size=1.27e-01, acc. prob=0.780]\n",
+      "\rWarmup:  60%|█████▉    | 458/768 [01:16,  5.51it/s, step size=1.65e-01, acc. prob=0.781]\n",
+      "\rWarmup:  60%|█████▉    | 459/768 [01:16,  5.39it/s, step size=1.30e-01, acc. prob=0.780]\n",
+      "\rWarmup:  60%|█████▉    | 460/768 [01:16,  5.29it/s, step size=1.31e-01, acc. prob=0.780]\n",
+      "\rWarmup:  60%|██████    | 461/768 [01:17,  5.08it/s, step size=6.64e-01, acc. prob=0.781]\n",
+      "\rWarmup:  61%|██████    | 465/768 [01:17, 10.76it/s, step size=4.15e-01, acc. prob=0.779]\n",
+      "\rWarmup:  61%|██████    | 468/768 [01:17, 13.97it/s, step size=1.60e-01, acc. prob=0.778]\n",
+      "\rWarmup:  61%|██████    | 470/768 [01:17, 12.71it/s, step size=1.14e-01, acc. prob=0.778]\n",
+      "\rWarmup:  61%|██████▏   | 472/768 [01:17, 11.95it/s, step size=2.52e-01, acc. prob=0.779]\n",
+      "\rWarmup:  62%|██████▏   | 474/768 [01:18, 10.52it/s, step size=8.49e-02, acc. prob=0.778]\n",
+      "\rWarmup:  62%|██████▏   | 476/768 [01:18,  9.11it/s, step size=1.75e-02, acc. prob=0.777]\n",
+      "\rWarmup:  62%|██████▏   | 478/768 [01:18,  7.44it/s, step size=6.42e-02, acc. prob=0.778]\n",
+      "\rWarmup:  62%|██████▏   | 479/768 [01:18,  6.89it/s, step size=1.13e-01, acc. prob=0.779]\n",
+      "\rWarmup:  63%|██████▎   | 481/768 [01:19,  7.74it/s, step size=3.34e-01, acc. prob=0.779]\n",
+      "\rWarmup:  63%|██████▎   | 483/768 [01:19,  7.90it/s, step size=1.06e-01, acc. prob=0.779]\n",
+      "\rWarmup:  63%|██████▎   | 485/768 [01:19,  7.54it/s, step size=1.58e-01, acc. prob=0.779]\n",
+      "\rWarmup:  63%|██████▎   | 487/768 [01:19,  8.77it/s, step size=3.32e-02, acc. prob=0.778]\n",
+      "\rWarmup:  64%|██████▎   | 488/768 [01:20,  7.71it/s, step size=6.08e-02, acc. prob=0.778]\n",
+      "\rWarmup:  64%|██████▎   | 489/768 [01:20,  6.97it/s, step size=1.08e-01, acc. prob=0.779]\n",
+      "\rWarmup:  64%|██████▍   | 490/768 [01:20,  6.44it/s, step size=1.69e-01, acc. prob=0.779]\n",
+      "\rWarmup:  64%|██████▍   | 492/768 [01:20,  6.61it/s, step size=7.77e-02, acc. prob=0.779]\n",
+      "\rWarmup:  64%|██████▍   | 494/768 [01:20,  7.59it/s, step size=2.16e-01, acc. prob=0.779]\n",
+      "\rWarmup:  65%|██████▍   | 496/768 [01:21,  8.34it/s, step size=2.77e-01, acc. prob=0.780]\n",
+      "\rWarmup:  65%|██████▍   | 498/768 [01:21,  8.90it/s, step size=2.62e-01, acc. prob=0.780]\n",
+      "\rWarmup:  65%|██████▌   | 501/768 [01:21,  9.45it/s, step size=1.07e-01, acc. prob=0.779]\n",
+      "\rWarmup:  65%|██████▌   | 502/768 [01:21,  8.28it/s, step size=1.59e-01, acc. prob=0.779]\n",
+      "\rWarmup:  66%|██████▌   | 504/768 [01:21,  9.48it/s, step size=4.49e-01, acc. prob=0.780]\n",
+      "\rWarmup:  66%|██████▌   | 506/768 [01:22,  9.01it/s, step size=1.32e-01, acc. prob=0.779]\n",
+      "\rWarmup:  66%|██████▌   | 508/768 [01:22,  9.37it/s, step size=2.62e-01, acc. prob=0.780]\n",
+      "\rWarmup:  66%|██████▋   | 510/768 [01:22, 10.40it/s, step size=3.88e-01, acc. prob=0.780]\n",
+      "\rWarmup:  67%|██████▋   | 512/768 [01:22, 11.25it/s, step size=1.67e-01, acc. prob=0.780]\n",
+      "\rSample:  67%|██████▋   | 514/768 [01:22, 10.96it/s, step size=1.67e-01, acc. prob=0.919]\n",
+      "\rSample:  67%|██████▋   | 516/768 [01:23, 10.75it/s, step size=1.67e-01, acc. prob=0.952]\n",
+      "\rSample:  67%|██████▋   | 518/768 [01:23, 10.63it/s, step size=1.67e-01, acc. prob=0.903]\n",
+      "\rSample:  68%|██████▊   | 520/768 [01:23, 10.54it/s, step size=1.67e-01, acc. prob=0.816]\n",
+      "\rSample:  68%|██████▊   | 522/768 [01:23, 10.50it/s, step size=1.67e-01, acc. prob=0.822]\n",
+      "\rSample:  68%|██████▊   | 524/768 [01:23, 10.46it/s, step size=1.67e-01, acc. prob=0.784]\n",
+      "\rSample:  68%|██████▊   | 526/768 [01:24, 10.42it/s, step size=1.67e-01, acc. prob=0.802]\n",
+      "\rSample:  69%|██████▉   | 528/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.826]\n",
+      "\rSample:  69%|██████▉   | 530/768 [01:24, 10.37it/s, step size=1.67e-01, acc. prob=0.841]\n",
+      "\rSample:  69%|██████▉   | 532/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.837]\n",
+      "\rSample:  70%|██████▉   | 534/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.850]\n",
+      "\rSample:  70%|██████▉   | 536/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.860]\n",
+      "\rSample:  70%|███████   | 538/768 [01:25, 10.36it/s, step size=1.67e-01, acc. prob=0.868]\n",
+      "\rSample:  70%|███████   | 540/768 [01:25, 10.33it/s, step size=1.67e-01, acc. prob=0.860]\n",
+      "\rSample:  71%|███████   | 542/768 [01:25, 10.35it/s, step size=1.67e-01, acc. prob=0.862]\n",
+      "\rSample:  71%|███████   | 544/768 [01:25, 11.22it/s, step size=1.67e-01, acc. prob=0.866]\n",
+      "\rSample:  71%|███████   | 546/768 [01:25, 10.93it/s, step size=1.67e-01, acc. prob=0.872]\n",
+      "\rSample:  71%|███████▏  | 548/768 [01:26, 10.70it/s, step size=1.67e-01, acc. prob=0.870]\n",
+      "\rSample:  72%|███████▏  | 550/768 [01:26, 10.55it/s, step size=1.67e-01, acc. prob=0.876]\n",
+      "\rSample:  72%|███████▏  | 552/768 [01:26, 10.50it/s, step size=1.67e-01, acc. prob=0.880]\n",
+      "\rSample:  72%|███████▏  | 554/768 [01:26, 10.46it/s, step size=1.67e-01, acc. prob=0.885]\n",
+      "\rSample:  72%|███████▏  | 556/768 [01:26, 10.40it/s, step size=1.67e-01, acc. prob=0.887]\n",
+      "\rSample:  73%|███████▎  | 558/768 [01:27, 10.39it/s, step size=1.67e-01, acc. prob=0.880]\n",
+      "\rSample:  73%|███████▎  | 560/768 [01:27, 10.38it/s, step size=1.67e-01, acc. prob=0.881]\n",
+      "\rSample:  73%|███████▎  | 562/768 [01:27, 10.39it/s, step size=1.67e-01, acc. prob=0.873]\n",
+      "\rSample:  73%|███████▎  | 564/768 [01:27, 10.34it/s, step size=1.67e-01, acc. prob=0.877]\n",
+      "\rSample:  74%|███████▎  | 566/768 [01:27, 10.32it/s, step size=1.67e-01, acc. prob=0.880]\n",
+      "\rSample:  74%|███████▍  | 568/768 [01:28, 10.30it/s, step size=1.67e-01, acc. prob=0.884]\n",
+      "\rSample:  74%|███████▍  | 570/768 [01:28, 10.30it/s, step size=1.67e-01, acc. prob=0.874]\n",
+      "\rSample:  74%|███████▍  | 572/768 [01:28, 10.33it/s, step size=1.67e-01, acc. prob=0.873]\n",
+      "\rSample:  75%|███████▍  | 574/768 [01:28, 10.34it/s, step size=1.67e-01, acc. prob=0.876]\n",
+      "\rSample:  75%|███████▌  | 576/768 [01:28, 10.34it/s, step size=1.67e-01, acc. prob=0.879]\n",
+      "\rSample:  75%|███████▌  | 578/768 [01:28, 10.35it/s, step size=1.67e-01, acc. prob=0.882]\n",
+      "\rSample:  76%|███████▌  | 580/768 [01:29, 11.20it/s, step size=1.67e-01, acc. prob=0.881]\n",
+      "\rSample:  76%|███████▌  | 582/768 [01:29, 10.94it/s, step size=1.67e-01, acc. prob=0.876]\n",
+      "\rSample:  76%|███████▌  | 584/768 [01:29, 11.65it/s, step size=1.67e-01, acc. prob=0.869]\n",
+      "\rSample:  76%|███████▋  | 586/768 [01:29, 11.18it/s, step size=1.67e-01, acc. prob=0.871]\n",
+      "\rSample:  77%|███████▋  | 588/768 [01:29, 11.45it/s, step size=1.67e-01, acc. prob=0.872]\n",
+      "\rSample:  77%|███████▋  | 590/768 [01:29, 11.80it/s, step size=1.67e-01, acc. prob=0.870]\n",
+      "\rSample:  77%|███████▋  | 592/768 [01:30, 12.04it/s, step size=1.67e-01, acc. prob=0.866]\n",
+      "\rSample:  77%|███████▋  | 594/768 [01:30, 12.22it/s, step size=1.67e-01, acc. prob=0.858]\n",
+      "\rSample:  78%|███████▊  | 596/768 [01:30, 12.37it/s, step size=1.67e-01, acc. prob=0.861]\n",
+      "\rSample:  78%|███████▊  | 598/768 [01:30, 12.35it/s, step size=1.67e-01, acc. prob=0.859]\n",
+      "\rSample:  78%|███████▊  | 600/768 [01:30, 12.50it/s, step size=1.67e-01, acc. prob=0.861]\n",
+      "\rSample:  78%|███████▊  | 602/768 [01:30, 12.54it/s, step size=1.67e-01, acc. prob=0.864]\n",
+      "\rSample:  79%|███████▊  | 604/768 [01:31, 12.57it/s, step size=1.67e-01, acc. prob=0.866]\n",
+      "\rSample:  79%|███████▉  | 606/768 [01:31, 12.59it/s, step size=1.67e-01, acc. prob=0.869]\n",
+      "\rSample:  79%|███████▉  | 608/768 [01:31, 12.62it/s, step size=1.67e-01, acc. prob=0.871]\n",
+      "\rSample:  79%|███████▉  | 610/768 [01:31, 12.65it/s, step size=1.67e-01, acc. prob=0.873]\n",
+      "\rSample:  80%|███████▉  | 612/768 [01:31, 12.65it/s, step size=1.67e-01, acc. prob=0.875]\n",
+      "\rSample:  80%|███████▉  | 614/768 [01:31, 12.66it/s, step size=1.67e-01, acc. prob=0.872]\n",
+      "\rSample:  80%|████████  | 616/768 [01:32, 12.64it/s, step size=1.67e-01, acc. prob=0.867]\n",
+      "\rSample:  80%|████████  | 618/768 [01:32, 12.58it/s, step size=1.67e-01, acc. prob=0.869]\n",
+      "\rSample:  81%|████████  | 620/768 [01:32, 13.52it/s, step size=1.67e-01, acc. prob=0.868]\n",
+      "\rSample:  81%|████████  | 622/768 [01:32, 13.23it/s, step size=1.67e-01, acc. prob=0.868]\n",
+      "\rSample:  81%|████████▏ | 624/768 [01:32, 12.83it/s, step size=1.67e-01, acc. prob=0.869]\n",
+      "\rSample:  82%|████████▏ | 626/768 [01:32, 12.78it/s, step size=1.67e-01, acc. prob=0.872]\n",
+      "\rSample:  82%|████████▏ | 628/768 [01:32, 12.72it/s, step size=1.67e-01, acc. prob=0.871]\n",
+      "\rSample:  82%|████████▏ | 630/768 [01:33, 11.86it/s, step size=1.67e-01, acc. prob=0.873]\n",
+      "\rSample:  82%|████████▏ | 632/768 [01:33, 11.37it/s, step size=1.67e-01, acc. prob=0.875]\n",
+      "\rSample:  83%|████████▎ | 634/768 [01:33, 11.08it/s, step size=1.67e-01, acc. prob=0.876]\n",
+      "\rSample:  83%|████████▎ | 636/768 [01:33, 10.88it/s, step size=1.67e-01, acc. prob=0.877]\n",
+      "\rSample:  83%|████████▎ | 638/768 [01:34,  9.66it/s, step size=1.67e-01, acc. prob=0.872]\n",
+      "\rSample:  83%|████████▎ | 640/768 [01:34, 10.44it/s, step size=1.67e-01, acc. prob=0.873]\n",
+      "\rSample:  84%|████████▎ | 642/768 [01:34, 11.85it/s, step size=1.67e-01, acc. prob=0.874]\n",
+      "\rSample:  84%|████████▍ | 644/768 [01:34, 12.15it/s, step size=1.67e-01, acc. prob=0.873]\n",
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+      "\rSample:  97%|█████████▋| 744/768 [01:42, 11.05it/s, step size=1.67e-01, acc. prob=0.851]\n",
+      "\rSample:  97%|█████████▋| 746/768 [01:42, 11.49it/s, step size=1.67e-01, acc. prob=0.849]\n",
+      "\rSample:  97%|█████████▋| 748/768 [01:43, 11.78it/s, step size=1.67e-01, acc. prob=0.846]\n",
+      "\rSample:  98%|█████████▊| 750/768 [01:43, 12.07it/s, step size=1.67e-01, acc. prob=0.848]\n",
+      "\rSample:  98%|█████████▊| 752/768 [01:43, 12.23it/s, step size=1.67e-01, acc. prob=0.848]\n",
+      "\rSample:  98%|█████████▊| 754/768 [01:43, 12.39it/s, step size=1.67e-01, acc. prob=0.848]\n",
+      "\rSample:  98%|█████████▊| 756/768 [01:43, 12.40it/s, step size=1.67e-01, acc. prob=0.848]\n",
+      "\rSample:  99%|█████████▊| 758/768 [01:43, 12.48it/s, step size=1.67e-01, acc. prob=0.849]\n",
+      "\rSample:  99%|█████████▉| 760/768 [01:44, 12.50it/s, step size=1.67e-01, acc. prob=0.846]\n",
+      "\rSample:  99%|█████████▉| 762/768 [01:44, 13.37it/s, step size=1.67e-01, acc. prob=0.844]\n",
+      "\rSample:  99%|█████████▉| 764/768 [01:44, 12.39it/s, step size=1.67e-01, acc. prob=0.845]\n",
+      "\rSample: 100%|█████████▉| 766/768 [01:44, 12.48it/s, step size=1.67e-01, acc. prob=0.845]\n",
+      "\rSample: 100%|██████████| 768/768 [01:44, 12.56it/s, step size=1.67e-01, acc. prob=0.845]\n",
+      "\rSample: 100%|██████████| 768/768 [01:44,  7.34it/s, step size=1.67e-01, acc. prob=0.845]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743529130077,
+    "executionStopTime": 1743529260021,
+    "language": "python",
+    "originalKey": "76750e01-4e21-4bba-9624-fb214f9072aa",
+    "output": {
+     "id": "679472244421591"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "1e7896ca-a923-47da-a3c8-32c28f5d34e9",
+    "serverExecutionDuration": 3.9172596298158,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'STGP - target only': {'NLL': 850.4604260314654, 'MSE': 2518.2767430780013},\n",
+       " 'MTGP - ICM - MAP': {'NLL': 311.31438266858777, 'MSE': 28.66387567450522},\n",
+       " 'MTGP - Latent Embeddings - FB': {'NLL': 444.5109303242923,\n",
+       "  'MSE': 39.09479567046496},\n",
+       " 'MTGP - Latent Embeddings - MAP': {'NLL': 525.5468145697394,\n",
+       "  'MSE': 61.380014810268186}}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wing_weight_res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "customInput": null,
+    "language": "markdown",
+    "originalKey": "3a4f34ce-9fdf-45cc-af1b-0be72caa11a2",
+    "showInput": false
+   },
+   "source": [
+    "## Borehole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528945358,
+    "executionStopTime": 1743528946169,
+    "language": "python",
+    "originalKey": "02708c2d-b43a-4a8f-8aca-a99fb32c615d",
+    "outputsInitialized": true,
+    "requestMsgId": "c216c09b-5424-4008-88bd-56f191aad912",
+    "serverExecutionDuration": 5.6033371947706,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "problem = BoreholeMultiFidelity()\n",
+    "torch.manual_seed(0)\n",
+    "# define training and test set\n",
+    "N_TEST = 100\n",
+    "fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}\n",
+    "total_n = sum(fidelity_to_n.values())\n",
+    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
+    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
+    "# set fidelities\n",
+    "start = 0\n",
+    "for fidelity, n in fidelity_to_n.items():\n",
+    "    end = start + n\n",
+    "    train_X[start:end, -1] = fidelity\n",
+    "    start = end\n",
+    "\n",
+    "train_Y = problem(train_X).unsqueeze(-1)\n",
+    "\n",
+    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
+    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
+    "test_X[:, -1] = 0  # target fidelity\n",
+    "test_Y = problem(test_X).unsqueeze(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528947285,
+    "executionStopTime": 1743529094868,
+    "language": "python",
+    "originalKey": "0d43b937-8f09-4d49-90d5-f4077ee2e646",
+    "output": {
+     "id": "2112932599153367"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "fba253a7-c60a-412a-89be-3b4468f04999",
+    "serverExecutionDuration": 146576.36261638,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\rWarmup:   0%|          | 0/768 [00:00, ?it/s]\n",
+      "\rWarmup:   0%|          | 1/768 [00:01,  1.22s/it, step size=7.33e-01, acc. prob=0.125]\n",
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+      "\rWarmup:   5%|▌         | 42/768 [00:07,  5.84it/s, step size=6.78e-02, acc. prob=0.766]\n",
+      "\rWarmup:   6%|▌         | 43/768 [00:07,  5.67it/s, step size=1.08e-01, acc. prob=0.771]\n",
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+      "\rWarmup:   7%|▋         | 56/768 [00:09,  7.60it/s, step size=1.17e-01, acc. prob=0.776]\n",
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+      "\rWarmup:   8%|▊         | 59/768 [00:09,  7.92it/s, step size=4.87e-02, acc. prob=0.770]\n",
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+      "\rWarmup:   9%|▊         | 66/768 [00:10,  5.82it/s, step size=1.41e-01, acc. prob=0.780]\n",
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+      "\rWarmup:  10%|▉         | 74/768 [00:11,  8.94it/s, step size=9.21e-02, acc. prob=0.778]\n",
+      "\rWarmup:  10%|▉         | 76/768 [00:11, 10.24it/s, step size=1.87e-01, acc. prob=0.783]\n",
+      "\rWarmup:  10%|█         | 78/768 [00:11,  9.54it/s, step size=8.15e-02, acc. prob=0.778]\n",
+      "\rWarmup:  10%|█         | 80/768 [00:12,  7.58it/s, step size=6.92e-02, acc. prob=0.777]\n",
+      "\rWarmup:  11%|█         | 81/768 [00:12,  7.01it/s, step size=1.03e-01, acc. prob=0.780]\n",
+      "\rWarmup:  11%|█         | 83/768 [00:12,  6.98it/s, step size=7.53e-02, acc. prob=0.778]\n",
+      "\rWarmup:  11%|█         | 84/768 [00:12,  6.56it/s, step size=9.80e-02, acc. prob=0.780]\n",
+      "\rWarmup:  11%|█         | 86/768 [00:13,  7.62it/s, step size=1.11e-01, acc. prob=0.781]\n",
+      "\rWarmup:  11%|█▏        | 88/768 [00:13,  8.43it/s, step size=5.18e-02, acc. prob=0.777]\n",
+      "\rWarmup:  12%|█▏        | 89/768 [00:13,  7.52it/s, step size=2.65e-02, acc. prob=0.773]\n",
+      "\rWarmup:  12%|█▏        | 90/768 [00:13,  6.83it/s, step size=3.92e-02, acc. prob=0.776]\n",
+      "\rWarmup:  12%|█▏        | 91/768 [00:13,  6.35it/s, step size=5.79e-02, acc. prob=0.778]\n",
+      "\rWarmup:  12%|█▏        | 92/768 [00:14,  6.04it/s, step size=7.73e-02, acc. prob=0.780]\n",
+      "\rWarmup:  12%|█▏        | 94/768 [00:14,  7.39it/s, step size=1.03e-01, acc. prob=0.782]\n",
+      "\rWarmup:  12%|█▎        | 96/768 [00:14,  7.26it/s, step size=5.55e-02, acc. prob=0.779]\n",
+      "\rWarmup:  13%|█▎        | 97/768 [00:14,  6.71it/s, step size=8.00e-02, acc. prob=0.781]\n",
+      "\rWarmup:  13%|█▎        | 99/768 [00:15,  4.81it/s, step size=2.65e-01, acc. prob=0.784]\n",
+      "\rWarmup:  13%|█▎        | 100/768 [00:15,  4.89it/s, step size=8.29e-01, acc. prob=0.778]\n",
+      "\rWarmup:  13%|█▎        | 102/768 [00:15,  5.67it/s, step size=9.39e-02, acc. prob=0.772]\n",
+      "\rWarmup:  13%|█▎        | 103/768 [00:16,  5.56it/s, step size=1.26e-01, acc. prob=0.774]\n",
+      "\rWarmup:  14%|█▎        | 104/768 [00:16,  5.49it/s, step size=1.95e-01, acc. prob=0.776]\n",
+      "\rWarmup:  14%|█▎        | 105/768 [00:16,  5.43it/s, step size=1.40e-01, acc. prob=0.776]\n",
+      "\rWarmup:  14%|█▍        | 106/768 [00:16,  5.39it/s, step size=2.06e-01, acc. prob=0.777]\n",
+      "\rWarmup:  14%|█▍        | 107/768 [00:16,  5.36it/s, step size=6.74e-02, acc. prob=0.774]\n",
+      "\rWarmup:  14%|█▍        | 108/768 [00:16,  5.34it/s, step size=1.24e-01, acc. prob=0.776]\n",
+      "\rWarmup:  14%|█▍        | 109/768 [00:17,  5.32it/s, step size=1.62e-01, acc. prob=0.777]\n",
+      "\rWarmup:  14%|█▍        | 110/768 [00:17,  5.31it/s, step size=2.67e-01, acc. prob=0.779]\n",
+      "\rWarmup:  14%|█▍        | 111/768 [00:17,  5.30it/s, step size=4.13e-02, acc. prob=0.774]\n",
+      "\rWarmup:  15%|█▍        | 112/768 [00:17,  5.28it/s, step size=7.81e-02, acc. prob=0.776]\n",
+      "\rWarmup:  15%|█▍        | 113/768 [00:17,  5.26it/s, step size=1.47e-01, acc. prob=0.778]\n",
+      "\rWarmup:  15%|█▍        | 114/768 [00:18,  5.24it/s, step size=2.50e-01, acc. prob=0.779]\n",
+      "\rWarmup:  15%|█▍        | 115/768 [00:18,  5.23it/s, step size=1.97e-01, acc. prob=0.779]\n",
+      "\rWarmup:  15%|█▌        | 116/768 [00:18,  5.25it/s, step size=2.17e-01, acc. prob=0.779]\n",
+      "\rWarmup:  15%|█▌        | 117/768 [00:18,  5.26it/s, step size=5.51e-02, acc. prob=0.775]\n",
+      "\rWarmup:  15%|█▌        | 118/768 [00:18,  5.28it/s, step size=1.03e-01, acc. prob=0.777]\n",
+      "\rWarmup:  15%|█▌        | 119/768 [00:19,  5.29it/s, step size=1.88e-01, acc. prob=0.779]\n",
+      "\rWarmup:  16%|█▌        | 120/768 [00:19,  5.28it/s, step size=2.03e-01, acc. prob=0.779]\n",
+      "\rWarmup:  16%|█▌        | 121/768 [00:19,  5.26it/s, step size=6.64e-02, acc. prob=0.776]\n",
+      "\rWarmup:  16%|█▌        | 122/768 [00:19,  5.26it/s, step size=1.22e-01, acc. prob=0.778]\n",
+      "\rWarmup:  16%|█▌        | 123/768 [00:19,  5.28it/s, step size=2.12e-01, acc. prob=0.780]\n",
+      "\rWarmup:  16%|█▌        | 124/768 [00:20,  5.29it/s, step size=4.78e-02, acc. prob=0.776]\n",
+      "\rWarmup:  16%|█▋        | 125/768 [00:20,  5.29it/s, step size=8.68e-02, acc. prob=0.777]\n",
+      "\rWarmup:  16%|█▋        | 126/768 [00:20,  5.30it/s, step size=1.51e-01, acc. prob=0.779]\n",
+      "\rWarmup:  17%|█▋        | 127/768 [00:20,  5.31it/s, step size=2.36e-01, acc. prob=0.780]\n",
+      "\rWarmup:  17%|█▋        | 128/768 [00:20,  5.30it/s, step size=2.15e-01, acc. prob=0.780]\n",
+      "\rWarmup:  17%|█▋        | 129/768 [00:20,  5.28it/s, step size=4.67e-02, acc. prob=0.776]\n",
+      "\rWarmup:  17%|█▋        | 130/768 [00:21,  5.30it/s, step size=8.30e-02, acc. prob=0.778]\n",
+      "\rWarmup:  17%|█▋        | 131/768 [00:21,  5.31it/s, step size=1.43e-01, acc. prob=0.779]\n",
+      "\rWarmup:  17%|█▋        | 132/768 [00:21,  5.29it/s, step size=1.49e-01, acc. prob=0.780]\n",
+      "\rWarmup:  17%|█▋        | 133/768 [00:21,  5.26it/s, step size=3.68e-02, acc. prob=0.776]\n",
+      "\rWarmup:  17%|█▋        | 134/768 [00:21,  5.25it/s, step size=6.44e-02, acc. prob=0.777]\n",
+      "\rWarmup:  18%|█▊        | 135/768 [00:22,  5.24it/s, step size=1.07e-01, acc. prob=0.779]\n",
+      "\rWarmup:  18%|█▊        | 136/768 [00:22,  5.23it/s, step size=1.72e-01, acc. prob=0.780]\n",
+      "\rWarmup:  18%|█▊        | 137/768 [00:22,  5.22it/s, step size=2.72e-01, acc. prob=0.782]\n",
+      "\rWarmup:  18%|█▊        | 138/768 [00:22,  5.21it/s, step size=1.04e-01, acc. prob=0.779]\n",
+      "\rWarmup:  18%|█▊        | 139/768 [00:22,  5.22it/s, step size=5.60e-02, acc. prob=0.777]\n",
+      "\rWarmup:  18%|█▊        | 140/768 [00:23,  5.23it/s, step size=9.28e-02, acc. prob=0.779]\n",
+      "\rWarmup:  18%|█▊        | 141/768 [00:23,  5.23it/s, step size=1.56e-01, acc. prob=0.781]\n",
+      "\rWarmup:  18%|█▊        | 142/768 [00:23,  5.23it/s, step size=7.33e-02, acc. prob=0.778]\n",
+      "\rWarmup:  19%|█▊        | 143/768 [00:23,  5.22it/s, step size=1.08e-01, acc. prob=0.780]\n",
+      "\rWarmup:  19%|█▉        | 144/768 [00:23,  5.22it/s, step size=1.68e-01, acc. prob=0.781]\n",
+      "\rWarmup:  19%|█▉        | 145/768 [00:24,  5.23it/s, step size=2.23e-01, acc. prob=0.782]\n",
+      "\rWarmup:  19%|█▉        | 146/768 [00:24,  5.24it/s, step size=6.15e-02, acc. prob=0.778]\n",
+      "\rWarmup:  19%|█▉        | 147/768 [00:24,  5.26it/s, step size=9.91e-02, acc. prob=0.780]\n",
+      "\rWarmup:  19%|█▉        | 148/768 [00:24,  5.26it/s, step size=1.62e-01, acc. prob=0.781]\n",
+      "\rWarmup:  19%|█▉        | 149/768 [00:24,  5.04it/s, step size=7.25e-01, acc. prob=0.778]\n",
+      "\rWarmup:  20%|█▉        | 152/768 [00:25,  7.36it/s, step size=5.27e-02, acc. prob=0.766]\n",
+      "\rWarmup:  20%|█▉        | 153/768 [00:25,  6.77it/s, step size=6.48e-02, acc. prob=0.767]\n",
+      "\rWarmup:  20%|██        | 154/768 [00:25,  6.33it/s, step size=9.58e-02, acc. prob=0.769]\n",
+      "\rWarmup:  20%|██        | 155/768 [00:25,  6.01it/s, step size=1.56e-01, acc. prob=0.770]\n",
+      "\rWarmup:  20%|██        | 156/768 [00:25,  5.80it/s, step size=2.73e-01, acc. prob=0.772]\n",
+      "\rWarmup:  20%|██        | 157/768 [00:26,  5.65it/s, step size=3.79e-01, acc. prob=0.773]\n",
+      "\rWarmup:  21%|██        | 158/768 [00:26,  5.54it/s, step size=1.14e-01, acc. prob=0.771]\n",
+      "\rWarmup:  21%|██        | 159/768 [00:26,  5.47it/s, step size=2.06e-01, acc. prob=0.772]\n",
+      "\rWarmup:  21%|██        | 160/768 [00:26,  5.42it/s, step size=1.00e-01, acc. prob=0.771]\n",
+      "\rWarmup:  21%|██        | 161/768 [00:26,  5.38it/s, step size=1.90e-01, acc. prob=0.772]\n",
+      "\rWarmup:  21%|██        | 162/768 [00:26,  5.36it/s, step size=3.57e-01, acc. prob=0.773]\n",
+      "\rWarmup:  21%|██        | 163/768 [00:27,  5.34it/s, step size=3.60e-01, acc. prob=0.774]\n",
+      "\rWarmup:  21%|██▏       | 164/768 [00:27,  5.30it/s, step size=6.80e-01, acc. prob=0.775]\n",
+      "\rWarmup:  21%|██▏       | 165/768 [00:27,  5.26it/s, step size=1.09e-01, acc. prob=0.772]\n",
+      "\rWarmup:  22%|██▏       | 166/768 [00:27,  5.25it/s, step size=2.06e-01, acc. prob=0.773]\n",
+      "\rWarmup:  22%|██▏       | 167/768 [00:27,  5.25it/s, step size=3.79e-01, acc. prob=0.774]\n",
+      "\rWarmup:  22%|██▏       | 168/768 [00:28,  5.27it/s, step size=1.98e-01, acc. prob=0.773]\n",
+      "\rWarmup:  22%|██▏       | 169/768 [00:28,  5.27it/s, step size=3.06e-01, acc. prob=0.774]\n",
+      "\rWarmup:  22%|██▏       | 170/768 [00:28,  5.28it/s, step size=5.51e-01, acc. prob=0.775]\n",
+      "\rWarmup:  22%|██▏       | 171/768 [00:28,  5.28it/s, step size=7.67e-02, acc. prob=0.771]\n",
+      "\rWarmup:  22%|██▏       | 172/768 [00:28,  5.28it/s, step size=1.42e-01, acc. prob=0.773]\n",
+      "\rWarmup:  23%|██▎       | 173/768 [00:29,  5.29it/s, step size=1.42e-01, acc. prob=0.773]\n",
+      "\rWarmup:  23%|██▎       | 174/768 [00:29,  5.29it/s, step size=2.59e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 175/768 [00:29,  5.30it/s, step size=1.41e-01, acc. prob=0.773]\n",
+      "\rWarmup:  23%|██▎       | 176/768 [00:29,  5.28it/s, step size=2.52e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 177/768 [00:29,  5.22it/s, step size=1.91e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 178/768 [00:30,  5.25it/s, step size=3.11e-01, acc. prob=0.775]\n",
+      "\rWarmup:  23%|██▎       | 179/768 [00:30,  5.26it/s, step size=2.12e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 180/768 [00:30,  5.27it/s, step size=2.46e-01, acc. prob=0.775]\n",
+      "\rWarmup:  24%|██▎       | 181/768 [00:30,  5.27it/s, step size=1.13e-01, acc. prob=0.773]\n",
+      "\rWarmup:  24%|██▎       | 182/768 [00:30,  5.27it/s, step size=1.99e-01, acc. prob=0.774]\n",
+      "\rWarmup:  24%|██▍       | 183/768 [00:30,  5.28it/s, step size=3.45e-01, acc. prob=0.775]\n",
+      "\rWarmup:  24%|██▍       | 184/768 [00:31,  5.28it/s, step size=5.73e-01, acc. prob=0.777]\n",
+      "\rWarmup:  24%|██▍       | 185/768 [00:31,  5.27it/s, step size=1.50e-01, acc. prob=0.774]\n",
+      "\rWarmup:  24%|██▍       | 186/768 [00:31,  5.26it/s, step size=2.57e-01, acc. prob=0.775]\n",
+      "\rWarmup:  24%|██▍       | 187/768 [00:31,  5.25it/s, step size=3.37e-01, acc. prob=0.776]\n",
+      "\rWarmup:  24%|██▍       | 188/768 [00:31,  5.26it/s, step size=3.87e-01, acc. prob=0.776]\n",
+      "\rWarmup:  25%|██▍       | 189/768 [00:32,  5.25it/s, step size=3.86e-01, acc. prob=0.776]\n",
+      "\rWarmup:  25%|██▍       | 190/768 [00:32,  5.25it/s, step size=1.51e-01, acc. prob=0.774]\n",
+      "\rWarmup:  25%|██▍       | 191/768 [00:32,  5.26it/s, step size=2.45e-01, acc. prob=0.775]\n",
+      "\rWarmup:  25%|██▌       | 192/768 [00:32,  5.25it/s, step size=4.10e-01, acc. prob=0.777]\n",
+      "\rWarmup:  25%|██▌       | 193/768 [00:32,  5.26it/s, step size=9.62e-02, acc. prob=0.774]\n",
+      "\rWarmup:  25%|██▌       | 194/768 [00:33,  5.26it/s, step size=1.62e-01, acc. prob=0.775]\n",
+      "\rWarmup:  25%|██▌       | 195/768 [00:33,  5.26it/s, step size=2.45e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 196/768 [00:33,  5.27it/s, step size=1.59e-01, acc. prob=0.775]\n",
+      "\rWarmup:  26%|██▌       | 197/768 [00:33,  5.27it/s, step size=2.51e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 198/768 [00:33,  5.27it/s, step size=2.70e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 199/768 [00:34,  5.28it/s, step size=1.23e-01, acc. prob=0.775]\n",
+      "\rWarmup:  26%|██▌       | 200/768 [00:34,  5.28it/s, step size=2.00e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 201/768 [00:34,  5.28it/s, step size=3.04e-01, acc. prob=0.777]\n",
+      "\rWarmup:  26%|██▋       | 202/768 [00:34,  5.28it/s, step size=1.41e-01, acc. prob=0.775]\n",
+      "\rWarmup:  26%|██▋       | 203/768 [00:34,  5.27it/s, step size=2.25e-01, acc. prob=0.776]\n",
+      "\rWarmup:  27%|██▋       | 204/768 [00:34,  5.27it/s, step size=3.46e-01, acc. prob=0.777]\n",
+      "\rWarmup:  27%|██▋       | 205/768 [00:35,  5.27it/s, step size=4.07e-01, acc. prob=0.778]\n",
+      "\rWarmup:  27%|██▋       | 206/768 [00:35,  5.27it/s, step size=1.61e-01, acc. prob=0.776]\n",
+      "\rWarmup:  27%|██▋       | 207/768 [00:35,  5.27it/s, step size=2.52e-01, acc. prob=0.777]\n",
+      "\rWarmup:  27%|██▋       | 208/768 [00:35,  5.26it/s, step size=2.01e-01, acc. prob=0.776]\n",
+      "\rWarmup:  27%|██▋       | 209/768 [00:35,  5.27it/s, step size=2.58e-01, acc. prob=0.777]\n",
+      "\rWarmup:  27%|██▋       | 210/768 [00:36,  5.28it/s, step size=3.58e-01, acc. prob=0.778]\n",
+      "\rWarmup:  27%|██▋       | 211/768 [00:36,  5.29it/s, step size=1.99e-01, acc. prob=0.776]\n",
+      "\rWarmup:  28%|██▊       | 212/768 [00:36,  5.29it/s, step size=2.37e-01, acc. prob=0.777]\n",
+      "\rWarmup:  28%|██▊       | 213/768 [00:36,  5.29it/s, step size=3.68e-01, acc. prob=0.778]\n",
+      "\rWarmup:  28%|██▊       | 214/768 [00:36,  5.29it/s, step size=5.21e-01, acc. prob=0.779]\n",
+      "\rWarmup:  28%|██▊       | 215/768 [00:37,  5.29it/s, step size=2.29e-01, acc. prob=0.777]\n",
+      "\rWarmup:  28%|██▊       | 216/768 [00:37,  5.28it/s, step size=3.32e-01, acc. prob=0.778]\n",
+      "\rWarmup:  28%|██▊       | 217/768 [00:37,  5.28it/s, step size=4.86e-01, acc. prob=0.779]\n",
+      "\rWarmup:  28%|██▊       | 218/768 [00:37,  5.26it/s, step size=5.62e-01, acc. prob=0.779]\n",
+      "\rWarmup:  29%|██▊       | 219/768 [00:37,  5.22it/s, step size=2.64e-01, acc. prob=0.778]\n",
+      "\rWarmup:  29%|██▊       | 220/768 [00:37,  5.23it/s, step size=3.69e-01, acc. prob=0.778]\n",
+      "\rWarmup:  29%|██▉       | 221/768 [00:38,  5.24it/s, step size=1.62e-01, acc. prob=0.777]\n",
+      "\rWarmup:  29%|██▉       | 222/768 [00:38,  5.26it/s, step size=2.32e-01, acc. prob=0.777]\n",
+      "\rWarmup:  29%|██▉       | 223/768 [00:38,  5.26it/s, step size=3.55e-01, acc. prob=0.778]\n",
+      "\rWarmup:  29%|██▉       | 224/768 [00:38,  5.26it/s, step size=4.69e-01, acc. prob=0.779]\n",
+      "\rWarmup:  29%|██▉       | 225/768 [00:38,  5.27it/s, step size=2.17e-01, acc. prob=0.777]\n",
+      "\rWarmup:  29%|██▉       | 226/768 [00:39,  5.28it/s, step size=2.69e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|██▉       | 227/768 [00:39,  5.29it/s, step size=2.65e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|██▉       | 228/768 [00:39,  5.29it/s, step size=2.82e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|██▉       | 229/768 [00:39,  5.28it/s, step size=1.47e-01, acc. prob=0.777]\n",
+      "\rWarmup:  30%|██▉       | 230/768 [00:39,  5.28it/s, step size=1.48e-01, acc. prob=0.777]\n",
+      "\rWarmup:  30%|███       | 231/768 [00:40,  5.28it/s, step size=2.23e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|███       | 232/768 [00:40,  5.29it/s, step size=2.84e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|███       | 233/768 [00:40,  5.24it/s, step size=1.03e-01, acc. prob=0.776]\n",
+      "\rWarmup:  30%|███       | 234/768 [00:40,  5.26it/s, step size=1.52e-01, acc. prob=0.777]\n",
+      "\rWarmup:  31%|███       | 235/768 [00:40,  5.26it/s, step size=1.92e-01, acc. prob=0.778]\n",
+      "\rWarmup:  31%|███       | 236/768 [00:41,  5.27it/s, step size=2.60e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███       | 237/768 [00:41,  5.28it/s, step size=3.12e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███       | 238/768 [00:41,  5.29it/s, step size=3.40e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███       | 239/768 [00:41,  5.28it/s, step size=3.16e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███▏      | 240/768 [00:41,  5.27it/s, step size=4.06e-01, acc. prob=0.780]\n",
+      "\rWarmup:  31%|███▏      | 241/768 [00:41,  5.28it/s, step size=4.46e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 242/768 [00:42,  5.28it/s, step size=4.45e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 243/768 [00:42,  5.28it/s, step size=4.68e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 244/768 [00:42,  5.28it/s, step size=3.77e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 245/768 [00:42,  5.27it/s, step size=2.38e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 246/768 [00:42,  5.26it/s, step size=2.53e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 247/768 [00:43,  5.25it/s, step size=2.04e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 248/768 [00:43,  5.25it/s, step size=2.76e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 249/768 [00:43,  5.13it/s, step size=7.70e-01, acc. prob=0.780]\n",
+      "\rWarmup:  33%|███▎      | 252/768 [00:43,  7.93it/s, step size=1.22e-01, acc. prob=0.774]\n",
+      "\rWarmup:  33%|███▎      | 253/768 [00:43,  7.12it/s, step size=1.32e-01, acc. prob=0.775]\n",
+      "\rWarmup:  33%|███▎      | 254/768 [00:44,  6.55it/s, step size=1.80e-01, acc. prob=0.776]\n",
+      "\rWarmup:  33%|███▎      | 256/768 [00:44,  6.74it/s, step size=2.60e-01, acc. prob=0.776]\n",
+      "\rWarmup:  34%|███▎      | 258/768 [00:44,  7.31it/s, step size=1.44e-01, acc. prob=0.776]\n",
+      "\rWarmup:  34%|███▎      | 259/768 [00:44,  6.78it/s, step size=2.63e-01, acc. prob=0.777]\n",
+      "\rWarmup:  34%|███▍      | 260/768 [00:45,  6.37it/s, step size=2.00e-01, acc. prob=0.777]\n",
+      "\rWarmup:  34%|███▍      | 261/768 [00:45,  6.01it/s, step size=1.45e-01, acc. prob=0.776]\n",
+      "\rWarmup:  34%|███▍      | 262/768 [00:45,  5.79it/s, step size=2.40e-01, acc. prob=0.777]\n",
+      "\rWarmup:  34%|███▍      | 264/768 [00:45,  7.74it/s, step size=3.18e-01, acc. prob=0.777]\n",
+      "\rWarmup:  35%|███▍      | 266/768 [00:45,  8.60it/s, step size=3.40e-02, acc. prob=0.775]\n",
+      "\rWarmup:  35%|███▍      | 267/768 [00:45,  7.57it/s, step size=6.49e-02, acc. prob=0.776]\n",
+      "\rWarmup:  35%|███▍      | 268/768 [00:46,  6.86it/s, step size=1.19e-01, acc. prob=0.776]\n",
+      "\rWarmup:  35%|███▌      | 269/768 [00:46,  6.36it/s, step size=2.19e-01, acc. prob=0.777]\n",
+      "\rWarmup:  35%|███▌      | 271/768 [00:46,  7.63it/s, step size=3.87e-01, acc. prob=0.778]\n",
+      "\rWarmup:  35%|███▌      | 272/768 [00:46,  7.47it/s, step size=5.14e-01, acc. prob=0.778]\n",
+      "\rWarmup:  36%|███▌      | 274/768 [00:46,  9.18it/s, step size=2.04e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▌      | 275/768 [00:46,  7.85it/s, step size=3.64e-01, acc. prob=0.778]\n",
+      "\rWarmup:  36%|███▌      | 276/768 [00:47,  7.03it/s, step size=1.64e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▌      | 277/768 [00:47,  6.48it/s, step size=2.74e-01, acc. prob=0.778]\n",
+      "\rWarmup:  36%|███▌      | 278/768 [00:47,  6.10it/s, step size=1.79e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▋      | 279/768 [00:47,  5.83it/s, step size=1.17e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▋      | 280/768 [00:47,  5.65it/s, step size=2.07e-01, acc. prob=0.778]\n",
+      "\rWarmup:  37%|███▋      | 282/768 [00:48,  6.22it/s, step size=8.80e-02, acc. prob=0.777]\n",
+      "\rWarmup:  37%|███▋      | 283/768 [00:48,  5.97it/s, step size=1.53e-01, acc. prob=0.777]\n",
+      "\rWarmup:  37%|███▋      | 285/768 [00:48,  6.38it/s, step size=5.91e-02, acc. prob=0.776]\n",
+      "\rWarmup:  37%|███▋      | 286/768 [00:48,  6.10it/s, step size=1.02e-01, acc. prob=0.777]\n",
+      "\rWarmup:  37%|███▋      | 287/768 [00:49,  5.85it/s, step size=1.77e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 288/768 [00:49,  5.71it/s, step size=2.83e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 289/768 [00:49,  5.59it/s, step size=4.48e-01, acc. prob=0.779]\n",
+      "\rWarmup:  38%|███▊      | 291/768 [00:49,  6.14it/s, step size=3.57e-01, acc. prob=0.779]\n",
+      "\rWarmup:  38%|███▊      | 293/768 [00:49,  6.46it/s, step size=1.48e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 294/768 [00:50,  6.18it/s, step size=2.47e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 295/768 [00:50,  5.95it/s, step size=4.09e-01, acc. prob=0.779]\n",
+      "\rWarmup:  39%|███▊      | 297/768 [00:50,  6.40it/s, step size=1.19e-01, acc. prob=0.777]\n",
+      "\rWarmup:  39%|███▉      | 298/768 [00:50,  6.12it/s, step size=1.97e-01, acc. prob=0.778]\n",
+      "\rWarmup:  39%|███▉      | 299/768 [00:51,  5.90it/s, step size=3.21e-01, acc. prob=0.779]\n",
+      "\rWarmup:  39%|███▉      | 301/768 [00:51,  7.24it/s, step size=1.55e-01, acc. prob=0.778]\n",
+      "\rWarmup:  39%|███▉      | 302/768 [00:51,  6.71it/s, step size=2.15e-01, acc. prob=0.778]\n",
+      "\rWarmup:  40%|███▉      | 304/768 [00:51,  7.87it/s, step size=2.83e-01, acc. prob=0.779]\n",
+      "\rWarmup:  40%|███▉      | 306/768 [00:51,  9.40it/s, step size=1.88e-01, acc. prob=0.778]\n",
+      "\rWarmup:  40%|████      | 308/768 [00:51,  9.82it/s, step size=2.71e-01, acc. prob=0.779]\n",
+      "\rWarmup:  40%|████      | 310/768 [00:52,  8.76it/s, step size=2.07e-01, acc. prob=0.779]\n",
+      "\rWarmup:  40%|████      | 311/768 [00:52,  7.81it/s, step size=2.03e-01, acc. prob=0.779]\n",
+      "\rWarmup:  41%|████      | 312/768 [00:52,  7.10it/s, step size=2.71e-01, acc. prob=0.779]\n",
+      "\rWarmup:  41%|████      | 314/768 [00:52,  8.14it/s, step size=5.25e-01, acc. prob=0.780]\n",
+      "\rWarmup:  41%|████      | 316/768 [00:53,  7.77it/s, step size=2.32e-01, acc. prob=0.779]\n",
+      "\rWarmup:  41%|████▏     | 317/768 [00:53,  7.10it/s, step size=3.18e-01, acc. prob=0.780]\n",
+      "\rWarmup:  42%|████▏     | 319/768 [00:53,  7.09it/s, step size=1.71e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 320/768 [00:53,  6.63it/s, step size=2.56e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 321/768 [00:53,  6.28it/s, step size=1.53e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 322/768 [00:54,  6.00it/s, step size=2.28e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 324/768 [00:54,  7.33it/s, step size=3.47e-01, acc. prob=0.780]\n",
+      "\rWarmup:  42%|████▏     | 325/768 [00:54,  6.67it/s, step size=8.82e-02, acc. prob=0.778]\n",
+      "\rWarmup:  42%|████▏     | 326/768 [00:54,  6.20it/s, step size=1.34e-01, acc. prob=0.779]\n",
+      "\rWarmup:  43%|████▎     | 328/768 [00:54,  6.48it/s, step size=8.93e-02, acc. prob=0.778]\n",
+      "\rWarmup:  43%|████▎     | 329/768 [00:55,  6.18it/s, step size=1.31e-01, acc. prob=0.779]\n",
+      "\rWarmup:  43%|████▎     | 330/768 [00:55,  5.95it/s, step size=1.94e-01, acc. prob=0.779]\n",
+      "\rWarmup:  43%|████▎     | 331/768 [00:55,  5.76it/s, step size=2.86e-01, acc. prob=0.780]\n",
+      "\rWarmup:  43%|████▎     | 332/768 [00:55,  6.00it/s, step size=9.37e-02, acc. prob=0.778]\n",
+      "\rWarmup:  43%|████▎     | 333/768 [00:55,  5.72it/s, step size=1.30e-01, acc. prob=0.779]\n",
+      "\rWarmup:  43%|████▎     | 334/768 [00:56,  5.61it/s, step size=1.96e-01, acc. prob=0.780]\n",
+      "\rWarmup:  44%|████▎     | 335/768 [00:56,  5.52it/s, step size=2.90e-01, acc. prob=0.780]\n",
+      "\rWarmup:  44%|████▍     | 337/768 [00:56,  7.09it/s, step size=2.81e-01, acc. prob=0.780]\n",
+      "\rWarmup:  44%|████▍     | 338/768 [00:56,  6.56it/s, step size=4.17e-01, acc. prob=0.781]\n",
+      "\rWarmup:  44%|████▍     | 340/768 [00:56,  6.75it/s, step size=1.24e-01, acc. prob=0.779]\n",
+      "\rWarmup:  44%|████▍     | 341/768 [00:57,  6.31it/s, step size=1.82e-01, acc. prob=0.780]\n",
+      "\rWarmup:  45%|████▍     | 342/768 [00:57,  6.01it/s, step size=8.06e-02, acc. prob=0.778]\n",
+      "\rWarmup:  45%|████▍     | 343/768 [00:57,  5.80it/s, step size=1.17e-01, acc. prob=0.779]\n",
+      "\rWarmup:  45%|████▍     | 344/768 [00:57,  5.63it/s, step size=1.70e-01, acc. prob=0.780]\n",
+      "\rWarmup:  45%|████▍     | 345/768 [00:57,  5.52it/s, step size=2.49e-01, acc. prob=0.780]\n",
+      "\rWarmup:  45%|████▌     | 346/768 [00:58,  5.46it/s, step size=3.05e-01, acc. prob=0.781]\n",
+      "\rWarmup:  45%|████▌     | 348/768 [00:58,  6.11it/s, step size=1.32e-01, acc. prob=0.779]\n",
+      "\rWarmup:  46%|████▌     | 350/768 [00:58,  6.47it/s, step size=1.91e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 351/768 [00:58,  6.18it/s, step size=2.19e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 352/768 [00:58,  5.96it/s, step size=1.39e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 353/768 [00:59,  5.79it/s, step size=2.01e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 354/768 [00:59,  5.66it/s, step size=2.48e-01, acc. prob=0.781]\n",
+      "\rWarmup:  46%|████▌     | 355/768 [00:59,  5.58it/s, step size=3.46e-01, acc. prob=0.781]\n",
+      "\rWarmup:  46%|████▋     | 357/768 [00:59,  7.08it/s, step size=2.98e-01, acc. prob=0.781]\n",
+      "\rWarmup:  47%|████▋     | 359/768 [00:59,  7.12it/s, step size=3.60e-01, acc. prob=0.781]\n",
+      "\rWarmup:  47%|████▋     | 360/768 [01:00,  6.65it/s, step size=1.28e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 361/768 [01:00,  6.28it/s, step size=1.82e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 362/768 [01:00,  6.02it/s, step size=1.51e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 363/768 [01:00,  5.82it/s, step size=1.17e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 364/768 [01:00,  5.68it/s, step size=1.64e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 365/768 [01:01,  5.59it/s, step size=5.38e-02, acc. prob=0.778]\n",
+      "\rWarmup:  48%|████▊     | 366/768 [01:01,  5.52it/s, step size=7.69e-02, acc. prob=0.779]\n",
+      "\rWarmup:  48%|████▊     | 367/768 [01:01,  5.45it/s, step size=1.09e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 368/768 [01:01,  5.41it/s, step size=1.53e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 369/768 [01:01,  5.36it/s, step size=1.74e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 370/768 [01:02,  5.33it/s, step size=1.90e-01, acc. prob=0.781]\n",
+      "\rWarmup:  48%|████▊     | 371/768 [01:02,  5.34it/s, step size=2.07e-01, acc. prob=0.781]\n",
+      "\rWarmup:  48%|████▊     | 372/768 [01:02,  5.33it/s, step size=1.38e-01, acc. prob=0.780]\n",
+      "\rWarmup:  49%|████▊     | 373/768 [01:02,  5.33it/s, step size=1.92e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 375/768 [01:02,  6.05it/s, step size=3.33e-01, acc. prob=0.782]\n",
+      "\rWarmup:  49%|████▉     | 376/768 [01:03,  5.86it/s, step size=2.25e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 377/768 [01:03,  5.72it/s, step size=2.27e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 378/768 [01:03,  5.62it/s, step size=2.21e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 379/768 [01:03,  5.54it/s, step size=2.57e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 380/768 [01:03,  5.48it/s, step size=3.10e-01, acc. prob=0.782]\n",
+      "\rWarmup:  50%|████▉     | 381/768 [01:03,  5.88it/s, step size=1.54e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|████▉     | 382/768 [01:04,  5.71it/s, step size=2.15e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|████▉     | 383/768 [01:04,  5.60it/s, step size=2.89e-01, acc. prob=0.782]\n",
+      "\rWarmup:  50%|█████     | 385/768 [01:04,  6.21it/s, step size=1.88e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|█████     | 386/768 [01:04,  5.95it/s, step size=2.08e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|█████     | 387/768 [01:04,  5.77it/s, step size=2.75e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 389/768 [01:05,  6.29it/s, step size=2.98e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 391/768 [01:05,  8.00it/s, step size=2.90e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 392/768 [01:05,  7.24it/s, step size=3.76e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 393/768 [01:05,  6.68it/s, step size=1.75e-01, acc. prob=0.781]\n",
+      "\rWarmup:  51%|█████▏    | 395/768 [01:06,  6.88it/s, step size=1.56e-01, acc. prob=0.781]\n",
+      "\rWarmup:  52%|█████▏    | 396/768 [01:06,  6.48it/s, step size=2.15e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 397/768 [01:06,  6.17it/s, step size=2.93e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 399/768 [01:06,  6.55it/s, step size=1.35e-01, acc. prob=0.781]\n",
+      "\rWarmup:  52%|█████▏    | 400/768 [01:06,  6.24it/s, step size=1.85e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 401/768 [01:07,  5.99it/s, step size=2.37e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 402/768 [01:07,  5.81it/s, step size=3.04e-01, acc. prob=0.782]\n",
+      "\rWarmup:  53%|█████▎    | 404/768 [01:07,  6.74it/s, step size=2.56e-01, acc. prob=0.782]\n",
+      "\rWarmup:  53%|█████▎    | 406/768 [01:07,  6.90it/s, step size=3.04e-01, acc. prob=0.783]\n",
+      "\rWarmup:  53%|█████▎    | 407/768 [01:07,  6.49it/s, step size=1.49e-01, acc. prob=0.781]\n",
+      "\rWarmup:  53%|█████▎    | 408/768 [01:08,  6.19it/s, step size=1.89e-01, acc. prob=0.782]\n",
+      "\rWarmup:  53%|█████▎    | 409/768 [01:08,  5.94it/s, step size=2.44e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▎    | 411/768 [01:08,  6.32it/s, step size=1.73e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▎    | 412/768 [01:08,  6.00it/s, step size=2.31e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 413/768 [01:09,  5.77it/s, step size=1.09e-01, acc. prob=0.781]\n",
+      "\rWarmup:  54%|█████▍    | 414/768 [01:09,  5.62it/s, step size=1.47e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 415/768 [01:09,  5.53it/s, step size=1.96e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 416/768 [01:09,  5.47it/s, step size=2.38e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 417/768 [01:09,  5.41it/s, step size=2.19e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 418/768 [01:09,  5.38it/s, step size=2.87e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▍    | 420/768 [01:10,  7.51it/s, step size=4.32e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▍    | 422/768 [01:10,  8.87it/s, step size=2.35e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▌    | 423/768 [01:10,  7.73it/s, step size=2.25e-01, acc. prob=0.782]\n",
+      "\rWarmup:  55%|█████▌    | 425/768 [01:10,  7.51it/s, step size=3.00e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▌    | 426/768 [01:10,  6.91it/s, step size=2.53e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 427/768 [01:11,  6.46it/s, step size=3.30e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 429/768 [01:11,  8.31it/s, step size=3.31e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 430/768 [01:11,  7.40it/s, step size=2.79e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 431/768 [01:11,  6.77it/s, step size=3.37e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▋    | 432/768 [01:11,  6.34it/s, step size=4.47e-01, acc. prob=0.784]\n",
+      "\rWarmup:  56%|█████▋    | 433/768 [01:12,  6.04it/s, step size=2.45e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 434/768 [01:12,  5.84it/s, step size=2.78e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 436/768 [01:12,  7.33it/s, step size=4.32e-01, acc. prob=0.784]\n",
+      "\rWarmup:  57%|█████▋    | 438/768 [01:12,  7.30it/s, step size=1.98e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 439/768 [01:12,  7.10it/s, step size=2.62e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 441/768 [01:12,  8.60it/s, step size=2.95e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 443/768 [01:13,  7.59it/s, step size=1.88e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 444/768 [01:13,  6.65it/s, step size=2.49e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 445/768 [01:13,  5.95it/s, step size=2.57e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 446/768 [01:13,  5.52it/s, step size=1.71e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 447/768 [01:14,  5.22it/s, step size=2.11e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 448/768 [01:14,  5.00it/s, step size=2.41e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 449/768 [01:14,  5.71it/s, step size=2.79e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▊    | 450/768 [01:14,  6.37it/s, step size=3.11e-01, acc. prob=0.784]\n",
+      "\rWarmup:  59%|█████▊    | 451/768 [01:14,  5.68it/s, step size=1.63e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▉    | 452/768 [01:15,  5.27it/s, step size=2.15e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▉    | 453/768 [01:15,  5.02it/s, step size=2.46e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▉    | 454/768 [01:15,  5.77it/s, step size=2.94e-01, acc. prob=0.784]\n",
+      "\rWarmup:  59%|█████▉    | 455/768 [01:15,  6.46it/s, step size=3.47e-01, acc. prob=0.784]\n",
+      "\rWarmup:  59%|█████▉    | 456/768 [01:15,  7.05it/s, step size=3.09e-01, acc. prob=0.784]\n",
+      "\rWarmup:  60%|█████▉    | 457/768 [01:15,  7.58it/s, step size=2.65e-01, acc. prob=0.784]\n",
+      "\rWarmup:  60%|█████▉    | 458/768 [01:15,  7.22it/s, step size=2.51e-01, acc. prob=0.784]\n",
+      "\rWarmup:  60%|█████▉    | 459/768 [01:16,  7.01it/s, step size=1.81e-01, acc. prob=0.783]\n",
+      "\rWarmup:  60%|█████▉    | 460/768 [01:16,  6.84it/s, step size=1.96e-01, acc. prob=0.783]\n",
+      "\rWarmup:  60%|██████    | 461/768 [01:16,  6.55it/s, step size=7.77e-01, acc. prob=0.783]\n",
+      "\rWarmup:  61%|██████    | 465/768 [01:16, 12.95it/s, step size=1.81e-01, acc. prob=0.780]\n",
+      "\rWarmup:  61%|██████    | 467/768 [01:16, 14.10it/s, step size=4.04e-01, acc. prob=0.781]\n",
+      "\rWarmup:  61%|██████    | 470/768 [01:16, 17.20it/s, step size=4.15e-02, acc. prob=0.780]\n",
+      "\rWarmup:  61%|██████▏   | 472/768 [01:17, 10.61it/s, step size=1.39e-01, acc. prob=0.781]\n",
+      "\rWarmup:  62%|██████▏   | 474/768 [01:17, 11.46it/s, step size=4.78e-01, acc. prob=0.782]\n",
+      "\rWarmup:  62%|██████▏   | 476/768 [01:17, 10.86it/s, step size=1.55e-01, acc. prob=0.781]\n",
+      "\rWarmup:  62%|██████▏   | 478/768 [01:17, 11.72it/s, step size=6.61e-02, acc. prob=0.781]\n",
+      "\rWarmup:  62%|██████▎   | 480/768 [01:17,  9.13it/s, step size=1.82e-01, acc. prob=0.781]\n",
+      "\rWarmup:  63%|██████▎   | 482/768 [01:18,  9.87it/s, step size=5.35e-01, acc. prob=0.782]\n",
+      "\rWarmup:  63%|██████▎   | 484/768 [01:18, 11.45it/s, step size=2.22e-01, acc. prob=0.781]\n",
+      "\rWarmup:  63%|██████▎   | 486/768 [01:18, 11.67it/s, step size=4.37e-01, acc. prob=0.782]\n",
+      "\rWarmup:  64%|██████▎   | 489/768 [01:18, 10.93it/s, step size=2.15e-01, acc. prob=0.782]\n",
+      "\rWarmup:  64%|██████▍   | 491/768 [01:18, 11.24it/s, step size=3.84e-01, acc. prob=0.782]\n",
+      "\rWarmup:  64%|██████▍   | 494/768 [01:18, 14.15it/s, step size=7.27e-02, acc. prob=0.781]\n",
+      "\rWarmup:  65%|██████▍   | 496/768 [01:19, 12.22it/s, step size=1.64e-01, acc. prob=0.781]\n",
+      "\rWarmup:  65%|██████▍   | 498/768 [01:19, 13.33it/s, step size=2.96e-01, acc. prob=0.782]\n",
+      "\rWarmup:  65%|██████▌   | 500/768 [01:19, 12.45it/s, step size=1.60e-01, acc. prob=0.781]\n",
+      "\rWarmup:  65%|██████▌   | 502/768 [01:19, 13.58it/s, step size=6.46e-02, acc. prob=0.781]\n",
+      "\rWarmup:  66%|██████▌   | 504/768 [01:19,  9.80it/s, step size=1.64e-01, acc. prob=0.782]\n",
+      "\rWarmup:  66%|██████▌   | 506/768 [01:20, 10.76it/s, step size=3.28e-01, acc. prob=0.782]\n",
+      "\rWarmup:  66%|██████▋   | 509/768 [01:20, 11.10it/s, step size=1.29e-01, acc. prob=0.781]\n",
+      "\rWarmup:  67%|██████▋   | 511/768 [01:20, 10.98it/s, step size=1.91e-01, acc. prob=0.781]\n",
+      "\rWarmup:  67%|██████▋   | 513/768 [01:20, 10.89it/s, step size=1.91e-01, acc. prob=0.808]\n",
+      "\rSample:  67%|██████▋   | 515/768 [01:20, 10.82it/s, step size=1.91e-01, acc. prob=0.711]\n",
+      "\rSample:  67%|██████▋   | 517/768 [01:21, 10.77it/s, step size=1.91e-01, acc. prob=0.821]\n",
+      "\rSample:  68%|██████▊   | 519/768 [01:21, 10.74it/s, step size=1.91e-01, acc. prob=0.868]\n",
+      "\rSample:  68%|██████▊   | 521/768 [01:21, 10.72it/s, step size=1.91e-01, acc. prob=0.850]\n",
+      "\rSample:  68%|██████▊   | 523/768 [01:21, 10.70it/s, step size=1.91e-01, acc. prob=0.833]\n",
+      "\rSample:  68%|██████▊   | 525/768 [01:21, 10.69it/s, step size=1.91e-01, acc. prob=0.802]\n",
+      "\rSample:  69%|██████▉   | 528/768 [01:22, 12.33it/s, step size=1.91e-01, acc. prob=0.785]\n",
+      "\rSample:  69%|██████▉   | 530/768 [01:22, 12.81it/s, step size=1.91e-01, acc. prob=0.806]\n",
+      "\rSample:  69%|██████▉   | 532/768 [01:22, 12.13it/s, step size=1.91e-01, acc. prob=0.814]\n",
+      "\rSample:  70%|██████▉   | 534/768 [01:22, 11.69it/s, step size=1.91e-01, acc. prob=0.826]\n",
+      "\rSample:  70%|██████▉   | 536/768 [01:22, 11.38it/s, step size=1.91e-01, acc. prob=0.840]\n",
+      "\rSample:  70%|███████   | 538/768 [01:22, 12.10it/s, step size=1.91e-01, acc. prob=0.849]\n",
+      "\rSample:  70%|███████   | 540/768 [01:23, 11.64it/s, step size=1.91e-01, acc. prob=0.859]\n",
+      "\rSample:  71%|███████   | 542/768 [01:23, 11.35it/s, step size=1.91e-01, acc. prob=0.866]\n",
+      "\rSample:  71%|███████   | 544/768 [01:23, 11.14it/s, step size=1.91e-01, acc. prob=0.872]\n",
+      "\rSample:  71%|███████   | 546/768 [01:23, 11.91it/s, step size=1.91e-01, acc. prob=0.878]\n",
+      "\rSample:  71%|███████▏  | 548/768 [01:23, 11.49it/s, step size=1.91e-01, acc. prob=0.883]\n",
+      "\rSample:  72%|███████▏  | 550/768 [01:23, 11.24it/s, step size=1.91e-01, acc. prob=0.874]\n",
+      "\rSample:  72%|███████▏  | 552/768 [01:24, 11.06it/s, step size=1.91e-01, acc. prob=0.879]\n",
+      "\rSample:  72%|███████▏  | 554/768 [01:24, 11.03it/s, step size=1.91e-01, acc. prob=0.883]\n",
+      "\rSample:  72%|███████▏  | 556/768 [01:24, 11.64it/s, step size=1.91e-01, acc. prob=0.888]\n",
+      "\rSample:  73%|███████▎  | 558/768 [01:24, 12.11it/s, step size=1.91e-01, acc. prob=0.892]\n",
+      "\rSample:  73%|███████▎  | 560/768 [01:24, 13.37it/s, step size=1.91e-01, acc. prob=0.892]\n",
+      "\rSample:  73%|███████▎  | 562/768 [01:24, 13.20it/s, step size=1.91e-01, acc. prob=0.889]\n",
+      "\rSample:  73%|███████▎  | 564/768 [01:25, 13.26it/s, step size=1.91e-01, acc. prob=0.890]\n",
+      "\rSample:  74%|███████▎  | 566/768 [01:25, 13.27it/s, step size=1.91e-01, acc. prob=0.887]\n",
+      "\rSample:  74%|███████▍  | 568/768 [01:25, 12.29it/s, step size=1.91e-01, acc. prob=0.885]\n",
+      "\rSample:  74%|███████▍  | 570/768 [01:25, 11.73it/s, step size=1.91e-01, acc. prob=0.884]\n",
+      "\rSample:  74%|███████▍  | 572/768 [01:25, 11.37it/s, step size=1.91e-01, acc. prob=0.869]\n",
+      "\rSample:  75%|███████▍  | 574/768 [01:25, 12.10it/s, step size=1.91e-01, acc. prob=0.870]\n",
+      "\rSample:  75%|███████▌  | 576/768 [01:26, 11.61it/s, step size=1.91e-01, acc. prob=0.872]\n",
+      "\rSample:  75%|███████▌  | 578/768 [01:26, 11.29it/s, step size=1.91e-01, acc. prob=0.873]\n",
+      "\rSample:  76%|███████▌  | 580/768 [01:26, 11.06it/s, step size=1.91e-01, acc. prob=0.875]\n",
+      "\rSample:  76%|███████▌  | 582/768 [01:26, 10.93it/s, step size=1.91e-01, acc. prob=0.878]\n",
+      "\rSample:  76%|███████▌  | 584/768 [01:26, 11.74it/s, step size=1.91e-01, acc. prob=0.880]\n",
+      "\rSample:  76%|███████▋  | 586/768 [01:26, 12.38it/s, step size=1.91e-01, acc. prob=0.878]\n",
+      "\rSample:  77%|███████▋  | 588/768 [01:27, 11.80it/s, step size=1.91e-01, acc. prob=0.881]\n",
+      "\rSample:  77%|███████▋  | 590/768 [01:27, 11.40it/s, step size=1.91e-01, acc. prob=0.879]\n",
+      "\rSample:  77%|███████▋  | 592/768 [01:27, 11.15it/s, step size=1.91e-01, acc. prob=0.881]\n",
+      "\rSample:  77%|███████▋  | 594/768 [01:27, 11.91it/s, step size=1.91e-01, acc. prob=0.882]\n",
+      "\rSample:  78%|███████▊  | 596/768 [01:27, 11.48it/s, step size=1.91e-01, acc. prob=0.884]\n",
+      "\rSample:  78%|███████▊  | 598/768 [01:27, 11.20it/s, step size=1.91e-01, acc. prob=0.886]\n",
+      "\rSample:  78%|███████▊  | 600/768 [01:28, 11.02it/s, step size=1.91e-01, acc. prob=0.888]\n",
+      "\rSample:  78%|███████▊  | 602/768 [01:28, 11.81it/s, step size=1.91e-01, acc. prob=0.890]\n",
+      "\rSample:  79%|███████▊  | 604/768 [01:28, 12.44it/s, step size=1.91e-01, acc. prob=0.891]\n",
+      "\rSample:  79%|███████▉  | 606/768 [01:28, 11.83it/s, step size=1.91e-01, acc. prob=0.891]\n",
+      "\rSample:  79%|███████▉  | 608/768 [01:28, 11.43it/s, step size=1.91e-01, acc. prob=0.892]\n",
+      "\rSample:  79%|███████▉  | 610/768 [01:29, 11.16it/s, step size=1.91e-01, acc. prob=0.894]\n",
+      "\rSample:  80%|███████▉  | 612/768 [01:29, 10.99it/s, step size=1.91e-01, acc. prob=0.895]\n",
+      "\rSample:  80%|███████▉  | 614/768 [01:29, 10.87it/s, step size=1.91e-01, acc. prob=0.896]\n",
+      "\rSample:  80%|████████  | 616/768 [01:29,  9.43it/s, step size=1.91e-01, acc. prob=0.896]\n",
+      "\rSample:  80%|████████  | 617/768 [01:29,  9.35it/s, step size=1.91e-01, acc. prob=0.897]\n",
+      "\rSample:  80%|████████  | 618/768 [01:29,  9.20it/s, step size=1.91e-01, acc. prob=0.898]\n",
+      "\rSample:  81%|████████  | 620/768 [01:30, 10.12it/s, step size=1.91e-01, acc. prob=0.899]\n",
+      "\rSample:  81%|████████  | 622/768 [01:30,  9.76it/s, step size=1.91e-01, acc. prob=0.898]\n",
+      "\rSample:  81%|████████  | 623/768 [01:30,  9.62it/s, step size=1.91e-01, acc. prob=0.898]\n",
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+      "\rSample:  81%|████████▏ | 625/768 [01:30,  9.37it/s, step size=1.91e-01, acc. prob=0.898]\n",
+      "\rSample:  82%|████████▏ | 626/768 [01:30,  9.19it/s, step size=1.91e-01, acc. prob=0.898]\n",
+      "\rSample:  82%|████████▏ | 628/768 [01:30, 10.19it/s, step size=1.91e-01, acc. prob=0.898]\n",
+      "\rSample:  82%|████████▏ | 629/768 [01:31,  9.89it/s, step size=1.91e-01, acc. prob=0.899]\n",
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+      "\rSample:  83%|████████▎ | 638/768 [01:31, 11.32it/s, step size=1.91e-01, acc. prob=0.901]\n",
+      "\rSample:  83%|████████▎ | 640/768 [01:31, 11.09it/s, step size=1.91e-01, acc. prob=0.902]\n",
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+      "\rSample:  84%|████████▍ | 644/768 [01:32, 10.81it/s, step size=1.91e-01, acc. prob=0.903]\n",
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+      "\rSample:  88%|████████▊ | 677/768 [01:35, 11.98it/s, step size=1.91e-01, acc. prob=0.892]\n",
+      "\rSample:  88%|████████▊ | 679/768 [01:35, 11.56it/s, step size=1.91e-01, acc. prob=0.892]\n",
+      "\rSample:  89%|████████▊ | 681/768 [01:35, 11.25it/s, step size=1.91e-01, acc. prob=0.890]\n",
+      "\rSample:  89%|████████▉ | 683/768 [01:35, 11.03it/s, step size=1.91e-01, acc. prob=0.891]\n",
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+      "\rSample:  91%|█████████ | 697/768 [01:37, 11.33it/s, step size=1.91e-01, acc. prob=0.890]\n",
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+      "\rSample:  91%|█████████▏| 701/768 [01:37, 10.88it/s, step size=1.91e-01, acc. prob=0.892]\n",
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+      "\rSample:  92%|█████████▏| 710/768 [01:38, 12.14it/s, step size=1.91e-01, acc. prob=0.895]\n",
+      "\rSample:  93%|█████████▎| 712/768 [01:38, 12.65it/s, step size=1.91e-01, acc. prob=0.895]\n",
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+      "\rSample:  94%|█████████▍| 720/768 [01:39, 11.63it/s, step size=1.91e-01, acc. prob=0.898]\n",
+      "\rSample:  94%|█████████▍| 722/768 [01:39, 11.30it/s, step size=1.91e-01, acc. prob=0.895]\n",
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+      "\rSample:  95%|█████████▌| 730/768 [01:39, 11.31it/s, step size=1.91e-01, acc. prob=0.898]\n",
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+      "\rSample:  96%|█████████▋| 740/768 [01:40, 11.53it/s, step size=1.91e-01, acc. prob=0.899]\n",
+      "\rSample:  97%|█████████▋| 742/768 [01:41, 11.22it/s, step size=1.91e-01, acc. prob=0.900]\n",
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+      "\rSample:  97%|█████████▋| 748/768 [01:41, 12.15it/s, step size=1.91e-01, acc. prob=0.900]\n",
+      "\rSample:  98%|█████████▊| 750/768 [01:41, 12.36it/s, step size=1.91e-01, acc. prob=0.901]\n",
+      "\rSample:  98%|█████████▊| 752/768 [01:41, 12.56it/s, step size=1.91e-01, acc. prob=0.901]\n",
+      "\rSample:  98%|█████████▊| 754/768 [01:41, 12.72it/s, step size=1.91e-01, acc. prob=0.901]\n",
+      "\rSample:  98%|█████████▊| 756/768 [01:42, 12.85it/s, step size=1.91e-01, acc. prob=0.900]\n",
+      "\rSample:  99%|█████████▊| 758/768 [01:42, 12.84it/s, step size=1.91e-01, acc. prob=0.900]\n",
+      "\rSample:  99%|█████████▉| 760/768 [01:42, 12.69it/s, step size=1.91e-01, acc. prob=0.900]\n",
+      "\rSample:  99%|█████████▉| 762/768 [01:42, 12.69it/s, step size=1.91e-01, acc. prob=0.897]\n",
+      "\rSample:  99%|█████████▉| 764/768 [01:42, 12.70it/s, step size=1.91e-01, acc. prob=0.897]\n",
+      "\rSample: 100%|█████████▉| 766/768 [01:42, 13.76it/s, step size=1.91e-01, acc. prob=0.896]\n",
+      "\rSample: 100%|██████████| 768/768 [01:43, 13.19it/s, step size=1.91e-01, acc. prob=0.896]\n",
+      "\rSample: 100%|██████████| 768/768 [01:43,  7.45it/s, step size=1.91e-01, acc. prob=0.896]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743529103501,
+    "executionStopTime": 1743529104461,
+    "language": "python",
+    "originalKey": "f2fc99ee-52d0-420f-8f1a-0f1293689aad",
+    "output": {
+     "id": "660736130231383"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "76d49bc3-3046-4c1a-8538-bd0190d04489",
+    "serverExecutionDuration": 3.7160157226026,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'STGP - target only': {'NLL': 820.1311610482774, 'MSE': 1658.1030020306143},\n",
+       " 'MTGP - ICM - MAP': {'NLL': 298.84299270373793, 'MSE': 11.273867204848827},\n",
+       " 'MTGP - Latent Embeddings - FB': {'NLL': 525.3912920530319,\n",
+       "  'MSE': 20.109351468055014},\n",
+       " 'MTGP - Latent Embeddings - MAP': {'NLL': 438.331706460182,\n",
+       "  'MSE': 53.53423246625931}}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "borehole_res "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "customInput": null,
+    "language": "python",
+    "originalKey": "471f2a59-d882-46f1-b8d5-2cd3575a9399",
+    "showInput": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "bento_stylesheets": {
+   "bento/extensions/flow/main.css": true,
+   "bento/extensions/kernel_selector/main.css": true,
+   "bento/extensions/kernel_ui/main.css": true,
+   "bento/extensions/new_kernel/main.css": true,
+   "bento/extensions/system_usage/main.css": true,
+   "bento/extensions/theme/main.css": true
+  },
+  "kernelspec": {
+   "display_name": "ae_binary (local)",
+   "isCinder": true,
+   "language": "python",
+   "metadata": {
+    "cinder_runtime": true,
+    "default_tagged_version": null,
+    "is_prebuilt": false,
+    "kernel_name": "ae_binary_local",
+    "kernel_override_version": null,
+    "kernel_version": null
+   },
+   "name": "ae_binary_local"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  },
+  "last_base_url": "https://bento.edge.x2p.facebook.net/",
+  "last_kernel_id": "a682d8c7-e233-4792-8769-ce2b92d6bc01",
+  "last_msg_id": "cbc97cad-5a66beaedc4295bba571018a_4963",
+  "last_server_session_id": "9609c3ef-402e-4a60-9386-29209d9ba159"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/private_multitask_pfn/multifidelity/borehole_res.pkl b/private_multitask_pfn/multifidelity/borehole_res.pkl
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diff --git a/private_multitask_pfn/multifidelity/mf lily.ipynb b/private_multitask_pfn/multifidelity/mf lily.ipynb
new file mode 100644
index 0000000..15de8ef
--- /dev/null
+++ b/private_multitask_pfn/multifidelity/mf lily.ipynb	
@@ -0,0 +1,1662 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528209127,
+    "executionStopTime": 1743528217022,
+    "language": "python",
+    "originalKey": "a4c56e12-55d0-4b3e-a2dd-bec3739a135a",
+    "outputsInitialized": true,
+    "requestMsgId": "61357428-9500-4edb-8e7d-2a6608607fae",
+    "serverExecutionDuration": 6.2562888488173,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "import torch\n",
+    "from botorch.test_functions.synthetic import SyntheticTestFunction\n",
+    "from torch import Tensor\n",
+    "\n",
+    "\n",
+    "class WingWeightMultiFidelity(SyntheticTestFunction):\n",
+    "    \"\"\"Wing Weight Design Problem from [Chen2024]_.\n",
+    "\n",
+    "    Design variables (physical units):\n",
+    "      1. s_w   in [150,   200]   (wing area)\n",
+    "      2. w_fw  in [220,   300]   (fuel weight)\n",
+    "      3. A     in [6,     10]    (aspect ratio)\n",
+    "      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)\n",
+    "      5. q     in [16,    45]    (dynamic pressure)\n",
+    "      6. lam   in [0.5,   1.0]   (taper ratio)\n",
+    "      7. t_c   in [0.08,  0.18]   (thickness-to-chord)\n",
+    "      8. N_z   in [2.5,   6.0]    (ultimate load factor)\n",
+    "      9. w_dg  in [1700,  2500]   (design gross weight)\n",
+    "      10. w_pp in [0.025, 0.08]    (weight per unit area)\n",
+    "\n",
+    "    Fidelity parameter (stored as the 11th input):\n",
+    "      0: High fidelity (HF)\n",
+    "      1: Low fidelity 1 (LF1)\n",
+    "      2: Low fidelity 2 (LF2)\n",
+    "      3: Low fidelity 3 (LF2)\n",
+    "\n",
+    "    The HF model is given by:\n",
+    "      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *\n",
+    "           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *\n",
+    "           (N_z*w_dg)^0.49 + s_w*w_pp\n",
+    "\n",
+    "    LF models use slightly altered exponents and additive biases.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dim = 11\n",
+    "    _num_fidelities = 1\n",
+    "    _bounds = [\n",
+    "        (150.0, 200.0),  # s_w\n",
+    "        (220.0, 300.0),  # w_fw\n",
+    "        (6.0, 10.0),  # A\n",
+    "        (-10.0, 10.0),  # Lambda_deg\n",
+    "        (16.0, 45.0),  # q\n",
+    "        (0.5, 1.0),  # lam\n",
+    "        (0.08, 0.18),  # t_c\n",
+    "        (2.5, 6.0),  # N_z\n",
+    "        (1700.0, 2500.0),  # w_dg\n",
+    "        (0.025, 0.08),  # w_pp\n",
+    "        (0, 3),\n",
+    "    ]\n",
+    "    fidelities = [0, 1, 2, 3]\n",
+    "    _optimal_value = 123.25\n",
+    "\n",
+    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.\n",
+    "        s_w = X[..., 0]\n",
+    "        w_fw = X[..., 1]\n",
+    "        A = X[..., 2]\n",
+    "        Lambda_deg = X[..., 3]\n",
+    "        Lambda_rad = Lambda_deg * math.pi / 180.0\n",
+    "\n",
+    "        q = X[..., 4]\n",
+    "        lam = X[..., 5]\n",
+    "        t_c = X[..., 6]\n",
+    "        N_z = X[..., 7]\n",
+    "        w_dg = X[..., 8]\n",
+    "        w_pp = X[..., 9]\n",
+    "        fidelity = X[..., 10]\n",
+    "        cos_val = torch.cos(Lambda_rad)\n",
+    "        y = torch.zeros_like(s_w)\n",
+    "        # High fidelity (fidelity == 0)\n",
+    "        mask = fidelity == 0\n",
+    "        if mask.any():\n",
+    "            hf = (\n",
+    "                0.036\n",
+    "                * (s_w**0.758)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + s_w * w_pp\n",
+    "            )\n",
+    "            y[mask] = hf[mask]\n",
+    "        # Low fidelity 1 (fidelity == 1)\n",
+    "        mask = fidelity == 1\n",
+    "        if mask.any():\n",
+    "            lf1 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.758)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + w_pp\n",
+    "            )\n",
+    "            y[mask] = lf1[mask]\n",
+    "        # Low fidelity 2 (fidelity == 2)\n",
+    "        mask = fidelity == 2\n",
+    "        if mask.any():\n",
+    "            lf2 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.8)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + w_pp\n",
+    "            )\n",
+    "            y[mask] = lf2[mask]\n",
+    "        # Low fidelity 3 (fidelity == 3)\n",
+    "        mask = fidelity == 3\n",
+    "        if mask.any():\n",
+    "            lf3 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.9)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "            )\n",
+    "            y[mask] = lf3[mask]\n",
+    "        return y\n",
+    "\n",
+    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        fidelity = X[..., 10]\n",
+    "        c = torch.zeros_like(fidelity)\n",
+    "        c[fidelity == 0] = 1000.0\n",
+    "        c[fidelity == 1] = 100.0\n",
+    "        c[fidelity == 2] = 10.0\n",
+    "        c[fidelity == 3] = 1.0\n",
+    "        return c\n",
+    "\n",
+    "\n",
+    "class BoreholeMultiFidelity(SyntheticTestFunction):\n",
+    "    \"\"\"Borehole Problem from [Chen2024]_.\n",
+    "\n",
+    "    This problem models water flow through a borehole with 8 design variables:\n",
+    "          1. r_w   in [0.05,   0.15]   (borehole radius)\n",
+    "          2. r     in [100,    50000]  (radius of influence)\n",
+    "          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)\n",
+    "          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)\n",
+    "          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)\n",
+    "          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)\n",
+    "          7. L     in [1120,   1680]   (length of borehole)\n",
+    "          8. K_w   in [9855,   12045]  (hydraulic conductivity)\n",
+    "\n",
+    "        The fidelity index (9th input) is categorical:\n",
+    "          0: High fidelity (HF)\n",
+    "          1: Low fidelity 1 (LF1)\n",
+    "          2: Low fidelity 2 (LF2)\n",
+    "          3: Low fidelity 3 (LF3)\n",
+    "          4: Low fidelity 4 (LF4)\n",
+    "\n",
+    "        The HF model is defined by:\n",
+    "          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]\n",
+    "\n",
+    "        The low-fidelity models modify exponents and add a bias.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dim = 9\n",
+    "    _num_fidelities = 1\n",
+    "    _bounds = [\n",
+    "        (0.05, 0.15),  # r_w\n",
+    "        (100.0, 10000.0),  # r\n",
+    "        (100.0, 1000.0),  # T_u\n",
+    "        (10.0, 500.0),  # T_l\n",
+    "        (990.0, 1110.0),  # H_u\n",
+    "        (700.0, 820.0),  # H_l\n",
+    "        (1000.0, 2000.0),  # L\n",
+    "        (6000.0, 12000.0),  # K_w\n",
+    "        (0, 4),  # fidelity\n",
+    "    ]\n",
+    "    fidelities = [0, 1, 2, 3, 4]\n",
+    "    _optimal_value = 3.98\n",
+    "\n",
+    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        r_w = X[..., 0]\n",
+    "        r = X[..., 1]\n",
+    "        T_u = X[..., 2]\n",
+    "        T_l = X[..., 3]\n",
+    "        H_u = X[..., 4]\n",
+    "        H_l = X[..., 5]\n",
+    "        L = X[..., 6]\n",
+    "        K_w = X[..., 7]\n",
+    "        fidelity = X[..., 8]\n",
+    "\n",
+    "        log_term = torch.log(r / r_w)\n",
+    "        numer = 2.0 * math.pi * T_u * (H_u - H_l)\n",
+    "        y = torch.zeros_like(r_w)\n",
+    "\n",
+    "        # HF (fidelity 0)\n",
+    "        mask = fidelity == 0\n",
+    "        if mask.any():\n",
+    "            hf_denom = log_term * (\n",
+    "                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            hf = numer / hf_denom\n",
+    "            y[mask] = hf[mask]\n",
+    "\n",
+    "        # LF1 (fidelity 1): add bias.\n",
+    "        mask = fidelity == 1\n",
+    "        if mask.any():\n",
+    "            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)\n",
+    "            lf1_denom = log_term * (\n",
+    "                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf1 = lf1_numer / lf1_denom\n",
+    "            y[mask] = lf1[mask]\n",
+    "\n",
+    "        # LF2 (fidelity 2): modify the exponent on log_term and add bias.\n",
+    "        mask = fidelity == 2\n",
+    "        if mask.any():\n",
+    "            lf2_denom = log_term * (\n",
+    "                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l\n",
+    "            )\n",
+    "            lf2 = numer / lf2_denom\n",
+    "            y[mask] = lf2[mask]\n",
+    "\n",
+    "        # LF3 (fidelity 3): modify r_w exponent slightly.\n",
+    "        mask = fidelity == 3\n",
+    "        if mask.any():\n",
+    "            lf3_log_term = torch.log(4 * r / r_w)\n",
+    "            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)\n",
+    "            lf3_denom = lf3_log_term * (\n",
+    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf3 = lf3_numer / lf3_denom\n",
+    "            y[mask] = lf3[mask]\n",
+    "        # LF4 (fidelity 4): further bias.\n",
+    "        mask = fidelity == 4\n",
+    "        if mask.any():\n",
+    "            lf4_log_term = torch.log(2 * r / r_w)\n",
+    "            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)\n",
+    "            lf4_denom = lf4_log_term * (\n",
+    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf4 = lf4_numer / lf4_denom\n",
+    "            y[mask] = lf4[mask]\n",
+    "\n",
+    "        return y\n",
+    "\n",
+    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        fidelity = X[..., 8]\n",
+    "        c = torch.zeros_like(fidelity)\n",
+    "        c[fidelity == 0] = 1000.0\n",
+    "        c[fidelity == 1] = 100.0\n",
+    "        c[fidelity == 2] = 10.0\n",
+    "        c[fidelity == 3] = 100.0\n",
+    "        c[fidelity == 4] = 10.0\n",
+    "        return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class WingWeightMultiFidelitySmall(SyntheticTestFunction):\n",
+    "    \"\"\"Wing Weight Design Problem from [Chen2024]_.\n",
+    "\n",
+    "    Design variables (physical units):\n",
+    "      1. s_w   in [150,   200]   (wing area)\n",
+    "      2. w_fw  in [220,   300]   (fuel weight)\n",
+    "      3. A     in [6,     10]    (aspect ratio)\n",
+    "      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)\n",
+    "      5. q     in [16,    45]    (dynamic pressure)\n",
+    "      6. lam   in [0.5,   1.0]   (taper ratio)\n",
+    "      7. t_c   in [0.08,  0.18]   (thickness-to-chord)\n",
+    "      8. N_z   in [2.5,   6.0]    (ultimate load factor)\n",
+    "      9. w_dg  in [1700,  2500]   (design gross weight)\n",
+    "      10. w_pp in [0.025, 0.08]    (weight per unit area)\n",
+    "\n",
+    "    Fidelity parameter (stored as the 11th input):\n",
+    "      0: High fidelity (HF)\n",
+    "      1: Low fidelity 1 (LF1)\n",
+    "      2: Low fidelity 2 (LF2)\n",
+    "      3: Low fidelity 3 (LF2)\n",
+    "\n",
+    "    The HF model is given by:\n",
+    "      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *\n",
+    "           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *\n",
+    "           (N_z*w_dg)^0.49 + s_w*w_pp\n",
+    "\n",
+    "    LF models use slightly altered exponents and additive biases.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dim = 6\n",
+    "    _num_fidelities = 1\n",
+    "    _bounds = [\n",
+    "        (150.0, 200.0),  # s_w\n",
+    "        (220.0, 300.0),  # w_fw\n",
+    "        (6.0, 10.0),  # A\n",
+    "        (-10.0, 10.0),  # Lambda_deg\n",
+    "        (16.0, 45.0),  # q\n",
+    "        (0.5, 1.0),  # lam\n",
+    "        # (0.08, 0.18),  # t_c\n",
+    "        # (2.5, 6.0),  # N_z\n",
+    "        # (1700.0, 2500.0),  # w_dg\n",
+    "        # (0.025, 0.08),  # w_pp\n",
+    "        (0, 3), # fidelity\n",
+    "    ]\n",
+    "    fidelities = [0, 1, 2, 3]\n",
+    "    _optimal_value = 123.25\n",
+    "\n",
+    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.\n",
+    "        s_w = X[..., 0]\n",
+    "        w_fw = X[..., 1]\n",
+    "        A = X[..., 2]\n",
+    "        Lambda_deg = X[..., 3]\n",
+    "        Lambda_rad = Lambda_deg * math.pi / 180.0\n",
+    "\n",
+    "        q = X[..., 4]\n",
+    "        lam = X[..., 5]\n",
+    "        t_c = 0.13 #X[..., 6]\n",
+    "        N_z = 4.5 #X[..., 7]\n",
+    "        w_dg = 2000 #X[..., 8]\n",
+    "        w_pp = 0.05 #X[..., 9]\n",
+    "        fidelity = X[..., -1]\n",
+    "        cos_val = torch.cos(Lambda_rad)\n",
+    "        y = torch.zeros_like(s_w)\n",
+    "        # High fidelity (fidelity == 0)\n",
+    "        mask = fidelity == 0\n",
+    "        if mask.any():\n",
+    "            hf = (\n",
+    "                0.036\n",
+    "                * (s_w**0.758)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + s_w * w_pp\n",
+    "            )\n",
+    "            y[mask] = hf[mask]\n",
+    "        # Low fidelity 1 (fidelity == 1)\n",
+    "        mask = fidelity == 1\n",
+    "        if mask.any():\n",
+    "            lf1 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.758)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + w_pp\n",
+    "            )\n",
+    "            y[mask] = lf1[mask]\n",
+    "        # Low fidelity 2 (fidelity == 2)\n",
+    "        mask = fidelity == 2\n",
+    "        if mask.any():\n",
+    "            lf2 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.8)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "                + w_pp\n",
+    "            )\n",
+    "            y[mask] = lf2[mask]\n",
+    "        # Low fidelity 3 (fidelity == 3)\n",
+    "        mask = fidelity == 3\n",
+    "        if mask.any():\n",
+    "            lf3 = (\n",
+    "                0.036\n",
+    "                * (s_w**0.9)\n",
+    "                * (w_fw**0.0035)\n",
+    "                * ((A / (cos_val**2)) ** 0.6)\n",
+    "                * (q**0.006)\n",
+    "                * (lam**0.04)\n",
+    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
+    "                * ((N_z * w_dg) ** 0.49)\n",
+    "            )\n",
+    "            y[mask] = lf3[mask]\n",
+    "        return y\n",
+    "\n",
+    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        fidelity = X[..., 10]\n",
+    "        c = torch.zeros_like(fidelity)\n",
+    "        c[fidelity == 0] = 1000.0\n",
+    "        c[fidelity == 1] = 100.0\n",
+    "        c[fidelity == 2] = 10.0\n",
+    "        c[fidelity == 3] = 1.0\n",
+    "        return c\n",
+    "\n",
+    "\n",
+    "class BoreholeMultiFidelitySmall(SyntheticTestFunction):\n",
+    "    \"\"\"Borehole Problem from [Chen2024]_.\n",
+    "\n",
+    "    This problem models water flow through a borehole with 8 design variables:\n",
+    "          1. r_w   in [0.05,   0.15]   (borehole radius)\n",
+    "          2. r     in [100,    50000]  (radius of influence)\n",
+    "          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)\n",
+    "          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)\n",
+    "          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)\n",
+    "          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)\n",
+    "          7. L     in [1120,   1680]   (length of borehole)\n",
+    "          8. K_w   in [9855,   12045]  (hydraulic conductivity)\n",
+    "\n",
+    "        The fidelity index (9th input) is categorical:\n",
+    "          0: High fidelity (HF)\n",
+    "          1: Low fidelity 1 (LF1)\n",
+    "          2: Low fidelity 2 (LF2)\n",
+    "          3: Low fidelity 3 (LF3)\n",
+    "          4: Low fidelity 4 (LF4)\n",
+    "\n",
+    "        The HF model is defined by:\n",
+    "          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]\n",
+    "\n",
+    "        The low-fidelity models modify exponents and add a bias.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dim = 7\n",
+    "    _num_fidelities = 1\n",
+    "    _bounds = [\n",
+    "        (0.05, 0.15),  # r_w\n",
+    "        (100.0, 10000.0),  # r\n",
+    "        (100.0, 1000.0),  # T_u\n",
+    "        (10.0, 500.0),  # T_l\n",
+    "        (990.0, 1110.0),  # H_u\n",
+    "        # (700.0, 820.0),  # H_l\n",
+    "        # (1000.0, 2000.0),  # L\n",
+    "        # (6000.0, 12000.0),  # K_w\n",
+    "        (0, 4),  # fidelity\n",
+    "    ]\n",
+    "    fidelities = [0, 1, 2, 3, 4]\n",
+    "    _optimal_value = 3.98\n",
+    "\n",
+    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        r_w = X[..., 0]\n",
+    "        r = X[..., 1]\n",
+    "        T_u = X[..., 2]\n",
+    "        T_l = X[..., 3]\n",
+    "        H_u = X[..., 4]\n",
+    "        H_l = X[..., 5]\n",
+    "        L = 760 # X[..., 6]\n",
+    "        K_w = 1500 #X[..., 7]\n",
+    "        fidelity = 9000 #X[..., 8]\n",
+    "\n",
+    "        log_term = torch.log(r / r_w)\n",
+    "        numer = 2.0 * math.pi * T_u * (H_u - H_l)\n",
+    "        y = torch.zeros_like(r_w)\n",
+    "\n",
+    "        # HF (fidelity 0)\n",
+    "        mask = fidelity == 0\n",
+    "        if mask.any():\n",
+    "            hf_denom = log_term * (\n",
+    "                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            hf = numer / hf_denom\n",
+    "            y[mask] = hf[mask]\n",
+    "\n",
+    "        # LF1 (fidelity 1): add bias.\n",
+    "        mask = fidelity == 1\n",
+    "        if mask.any():\n",
+    "            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)\n",
+    "            lf1_denom = log_term * (\n",
+    "                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf1 = lf1_numer / lf1_denom\n",
+    "            y[mask] = lf1[mask]\n",
+    "\n",
+    "        # LF2 (fidelity 2): modify the exponent on log_term and add bias.\n",
+    "        mask = fidelity == 2\n",
+    "        if mask.any():\n",
+    "            lf2_denom = log_term * (\n",
+    "                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l\n",
+    "            )\n",
+    "            lf2 = numer / lf2_denom\n",
+    "            y[mask] = lf2[mask]\n",
+    "\n",
+    "        # LF3 (fidelity 3): modify r_w exponent slightly.\n",
+    "        mask = fidelity == 3\n",
+    "        if mask.any():\n",
+    "            lf3_log_term = torch.log(4 * r / r_w)\n",
+    "            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)\n",
+    "            lf3_denom = lf3_log_term * (\n",
+    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf3 = lf3_numer / lf3_denom\n",
+    "            y[mask] = lf3[mask]\n",
+    "        # LF4 (fidelity 4): further bias.\n",
+    "        mask = fidelity == 4\n",
+    "        if mask.any():\n",
+    "            lf4_log_term = torch.log(2 * r / r_w)\n",
+    "            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)\n",
+    "            lf4_denom = lf4_log_term * (\n",
+    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
+    "            )\n",
+    "            lf4 = lf4_numer / lf4_denom\n",
+    "            y[mask] = lf4[mask]\n",
+    "\n",
+    "        return y\n",
+    "\n",
+    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
+    "        fidelity = X[..., 8]\n",
+    "        c = torch.zeros_like(fidelity)\n",
+    "        c[fidelity == 0] = 1000.0\n",
+    "        c[fidelity == 1] = 100.0\n",
+    "        c[fidelity == 2] = 10.0\n",
+    "        c[fidelity == 3] = 100.0\n",
+    "        c[fidelity == 4] = 10.0\n",
+    "        return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528318060,
+    "executionStopTime": 1743528318381,
+    "language": "python",
+    "originalKey": "11f17363-b3c0-4b55-b211-436797c48a1e",
+    "outputsInitialized": true,
+    "requestMsgId": "961e03e5-ed26-47c2-8e06-8106aa186b53",
+    "serverExecutionDuration": 1.8204906955361,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll\n",
+    "from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP\n",
+    "from botorch.models.gp_regression import SingleTaskGP\n",
+    "\n",
+    "from botorch.models.multitask import MultiTaskGP\n",
+    "\n",
+    "from botorch.models.transforms.input import (\n",
+    "    ChainedInputTransform,\n",
+    "    # LatentCategoricalEmbedding,\n",
+    "    # LatentCategoricalSpec,\n",
+    "    Normalize,\n",
+    ")\n",
+    "from botorch.models.transforms.outcome import Standardize, StratifiedStandardize\n",
+    "from botorch.utils.transforms import unnormalize\n",
+    "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
+    "from torch.distributions import Normal\n",
+    "\n",
+    "\n",
+    "tkwargs = {\"dtype\": torch.double}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528129775,
+    "executionStopTime": 1743528132881,
+    "language": "python",
+    "originalKey": "e02d0748-e092-4001-b8f0-59666e0e7b29",
+    "outputsInitialized": true,
+    "requestMsgId": "f59e8b19-0f9f-4f93-b9ad-277685684230",
+    "serverExecutionDuration": 1.955185085535,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "def eval_model(model, test_X, test_Y):\n",
+    "    with torch.no_grad():\n",
+    "        posterior = model.posterior(test_X, observation_noise=True)\n",
+    "        # compute sum of LL of each point in test set (using only marginal variances)\n",
+    "        var = posterior.variance\n",
+    "        mean = posterior.mean\n",
+    "        nll = (\n",
+    "            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))\n",
+    "            .log_prob(test_Y.view(-1))\n",
+    "            .sum(dim=-1)\n",
+    "            .mean()  # take average over MCMC samples (if needed)\n",
+    "            .item()\n",
+    "        )\n",
+    "        mse = (mean - test_Y).pow(2).mean().item()\n",
+    "    return nll, mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y):\n",
+    "    task_id = train_X[:, -1].long()\n",
+    "    train_X = train_X[:, :-1]\n",
+    "    \n",
+    "    \n",
+    "    with torch.no_grad():\n",
+    "        posterior = pfn_predict(pfn, task_id, train_X, train_Y, test_X[:, :-1])\n",
+    "        # compute sum of LL of each point in test set (using only marginal variances)\n",
+    "        var = posterior.variance\n",
+    "        mean = posterior.mean\n",
+    "        nll = (\n",
+    "            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))\n",
+    "            .log_prob(test_Y.view(-1))\n",
+    "            .sum(dim=-1)\n",
+    "            .mean()  # take average over MCMC samples (if needed)\n",
+    "            .item()\n",
+    "        )\n",
+    "        mse = (mean - test_Y).pow(2).mean().item()\n",
+    "    return nll, mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a Transformer with 25.77 M parameters\n",
+      "Loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import load_model, pfn_predict\n",
+    "# pfn = load_model(\"/home/yl9959/mtpfn/final_models/revived-frog-499\")\n",
+    "pfn = load_model(\"/home/yl9959/mtpfn/final_models/vibrant-breeze-498\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Trying to create tensor with negative dimension -4: [170, 1, -4]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)\n",
+      "Cell \u001b[0;32mIn[29], line 7\u001b[0m, in \u001b[0;36meval_pfn_on_problem\u001b[0;34m(pfn, problem, train_X, train_Y, test_X, test_Y)\u001b[0m\n\u001b[1;32m      3\u001b[0m train_X \u001b[38;5;241m=\u001b[39m train_X[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m----> 7\u001b[0m     posterior \u001b[38;5;241m=\u001b[39m pfn_predict(pfn, task_id, train_X, train_Y, test_X[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;66;03m# compute sum of LL of each point in test set (using only marginal variances)\u001b[39;00m\n\u001b[1;32m      9\u001b[0m     var \u001b[38;5;241m=\u001b[39m posterior\u001b[38;5;241m.\u001b[39mvariance\n",
+      "File \u001b[0;32m~/mtpfn/utils.py:344\u001b[0m, in \u001b[0;36mpfn_predict\u001b[0;34m(pfn, task_id, train_x, train_y, possible_x)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    343\u001b[0m     pfn_model \u001b[38;5;241m=\u001b[39m pfn_gaussian_fit(pfn, task_id, train_x, train_y)\n\u001b[0;32m--> 344\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pfn_model\u001b[38;5;241m.\u001b[39mposterior(possible_x\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m))\n",
+      "File \u001b[0;32m~/mtpfn/utils.py:110\u001b[0m, in \u001b[0;36mPFNGaussian.posterior\u001b[0;34m(self, X, output_indices, observation_noise, posterior_transform)\u001b[0m\n\u001b[1;32m    108\u001b[0m original_shape \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    109\u001b[0m X_reshape \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mreshape(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m--> 110\u001b[0m pfn_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpfn(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_task_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_y, X_reshape, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    111\u001b[0m mean \u001b[38;5;241m=\u001b[39m pfn_outputs[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    112\u001b[0m variance \u001b[38;5;241m=\u001b[39m pfn_outputs[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mexp()\n",
+      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/mtpfn/PFNs/pfns/transformer.py:457\u001b[0m, in \u001b[0;36mTransformerModel.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    455\u001b[0m     style \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    456\u001b[0m     \u001b[38;5;66;03m# forward(style, x, y),  \"single_eval_pos\" corresponds to where test x starts\u001b[39;00m\n\u001b[0;32m--> 457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward(\n\u001b[1;32m    458\u001b[0m         (style, x, y, task_id), single_eval_pos\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(args[\u001b[38;5;241m0\u001b[39m]), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    459\u001b[0m     )\n\u001b[1;32m    460\u001b[0m \u001b[38;5;66;03m# during training (x, y, task_id) is passed with single_eval_pos\u001b[39;00m\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    462\u001b[0m     \u001b[38;5;66;03m# case model((x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)\u001b[39;00m\n\u001b[1;32m    463\u001b[0m     \u001b[38;5;66;03m# case model((style,x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)\u001b[39;00m\n",
+      "File \u001b[0;32m~/mtpfn/PFNs/pfns/transformer.py:497\u001b[0m, in \u001b[0;36mTransformerModel._forward\u001b[0;34m(self, src, src_mask, single_eval_pos, only_return_standard_out)\u001b[0m\n\u001b[1;32m    490\u001b[0m     onehot_task_ids \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39mone_hot(\n\u001b[1;32m    491\u001b[0m         task_id\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), num_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_tasks\n\u001b[1;32m    492\u001b[0m     )\u001b[38;5;241m.\u001b[39mto(x_src)\n\u001b[1;32m    493\u001b[0m     x_src \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat([onehot_task_ids, x_src], dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 497\u001b[0m x_src \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoder(x_src)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maxial\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattn_type:\n\u001b[1;32m    499\u001b[0m     N, D, B, E \u001b[38;5;241m=\u001b[39m x_src\u001b[38;5;241m.\u001b[39mshape\n",
+      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/container.py:250\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    249\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 250\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m module(\u001b[38;5;28minput\u001b[39m)\n\u001b[1;32m    251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
+      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[0;32m~/mtpfn/PFNs/pfns/encoders.py:294\u001b[0m, in \u001b[0;36mVariableNumFeaturesEncoder.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m    290\u001b[0m     x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m*\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_features \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m    291\u001b[0m     x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(\n\u001b[1;32m    292\u001b[0m         (\n\u001b[1;32m    293\u001b[0m             x,\n\u001b[0;32m--> 294\u001b[0m             torch\u001b[38;5;241m.\u001b[39mzeros(\n\u001b[1;32m    295\u001b[0m                 \u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_features \u001b[38;5;241m-\u001b[39m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], device\u001b[38;5;241m=\u001b[39mx\u001b[38;5;241m.\u001b[39mdevice\n\u001b[1;32m    296\u001b[0m             ),\n\u001b[1;32m    297\u001b[0m         ),\n\u001b[1;32m    298\u001b[0m         \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m    299\u001b[0m     )\n\u001b[1;32m    300\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbase_encoder(x)\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Trying to create tensor with negative dimension -4: [170, 1, -4]"
+     ]
+    }
+   ],
+   "source": [
+    "eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528939339,
+    "executionStopTime": 1743528940127,
+    "language": "python",
+    "originalKey": "a194f315-36d3-4980-9506-c01343153a81",
+    "outputsInitialized": true,
+    "requestMsgId": "dbaeb34d-8bc8-4943-891a-fae5a0fc7212",
+    "serverExecutionDuration": 3.0471379868686,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):\n",
+    "    res = {}\n",
+    "    # test STGP on target task\n",
+    "    target_mask = train_X[:, -1] == 0\n",
+    "    model = SingleTaskGP(\n",
+    "        train_X[target_mask],\n",
+    "        train_Y[target_mask],\n",
+    "        input_transform=Normalize(\n",
+    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
+    "        ),\n",
+    "    )\n",
+    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
+    "    _ = fit_gpytorch_mll(mll)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"STGP - target only\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "\n",
+    "    # Test MTGP with ICM\n",
+    "    model = MultiTaskGP(\n",
+    "        train_X,\n",
+    "        train_Y,\n",
+    "        input_transform=Normalize(\n",
+    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
+    "        ),\n",
+    "        outcome_transform=StratifiedStandardize(\n",
+    "            stratification_idx=problem.dim - 1,\n",
+    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
+    "        ),\n",
+    "        task_feature=problem.dim - 1,\n",
+    "    )\n",
+    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
+    "    _ = fit_gpytorch_mll(mll)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"MTGP - ICM - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "    # Test Fully Bayesian MTGP with Latent Embeddings\n",
+    "    model = SaasFullyBayesianMultiTaskGP(\n",
+    "        train_X,\n",
+    "        train_Y,\n",
+    "        input_transform=Normalize(\n",
+    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
+    "        ),\n",
+    "        task_feature=problem.dim - 1,\n",
+    "        outcome_transform=StratifiedStandardize(\n",
+    "            stratification_idx=problem.dim - 1,\n",
+    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
+    "        ),\n",
+    "    )\n",
+    "    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"MTGP - Latent Embeddings - FB\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "\n",
+    "    # test LVGP with MAP estimation\n",
+    "    d = train_X.shape[-1]\n",
+    "    cat_dims = [problem.dim - 1]\n",
+    "    # construct input transform\n",
+    "    input_transform = ChainedInputTransform(\n",
+    "        normalize=Normalize(d=d, indices=list(range(d - 1))),\n",
+    "        # latent_emb=LatentCategoricalEmbedding(\n",
+    "        #     [\n",
+    "        #         LatentCategoricalSpec(\n",
+    "        #             idx=i,\n",
+    "        #             num_categories=len(problem.fidelities),\n",
+    "        #             latent_dim=2,\n",
+    "        #         )\n",
+    "        #         for i in cat_dims\n",
+    "        #     ],\n",
+    "        #     dim=d,\n",
+    "        # ).to(train_X),\n",
+    "    )\n",
+    "    model = SingleTaskGP(\n",
+    "        train_X,\n",
+    "        train_Y,\n",
+    "        input_transform=input_transform,\n",
+    "    )\n",
+    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
+    "    _ = fit_gpytorch_mll(mll)\n",
+    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
+    "    res[\"MTGP - Latent Embeddings - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "customInput": null,
+    "language": "markdown",
+    "originalKey": "1f8d148e-a0e7-4915-952f-7a405ec8a480",
+    "showInput": false
+   },
+   "source": [
+    "# Wing weight\n",
+    "### Generate initial training set as in Chen et al 2024. Generate test set for target fidelity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528960203,
+    "executionStopTime": 1743529095209,
+    "language": "python",
+    "originalKey": "fd812914-49d4-4023-926e-2d41c4157ded",
+    "outputsInitialized": true,
+    "requestMsgId": "097e842b-186a-44f5-88c3-cd12b0240b33",
+    "serverExecutionDuration": 5.1055490039289,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "ename": "InputDataError",
+     "evalue": "Expected the bounds to match the dimensionality of the domain. Got self.dim=6 and len(self._bounds)=7.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInputDataError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m problem \u001b[38;5;241m=\u001b[39m WingWeightMultiFidelitySmall()\n\u001b[1;32m      2\u001b[0m torch\u001b[38;5;241m.\u001b[39mmanual_seed(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# define training and test set\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/botorch/test_functions/synthetic.py:84\u001b[0m, in \u001b[0;36mSyntheticTestFunction.__init__\u001b[0;34m(self, noise_std, negate, bounds, dtype)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bounds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds \u001b[38;5;241m=\u001b[39m bounds\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(noise_std\u001b[38;5;241m=\u001b[39mnoise_std, negate\u001b[38;5;241m=\u001b[39mnegate, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bounds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m         \u001b[38;5;66;03m# Ensure at least one optimizer lies within the custom bounds\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/botorch/test_functions/base.py:49\u001b[0m, in \u001b[0;36mBaseTestProblem.__init__\u001b[0;34m(self, noise_std, negate, dtype)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegate \u001b[38;5;241m=\u001b[39m negate\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdim:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m InputDataError(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected the bounds to match the dimensionality of the domain. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdim\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     52\u001b[0m     )\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregister_buffer(\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbounds\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     55\u001b[0m     torch\u001b[38;5;241m.\u001b[39mtensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds, dtype\u001b[38;5;241m=\u001b[39mdtype)\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m),\n\u001b[1;32m     56\u001b[0m )\n",
+      "\u001b[0;31mInputDataError\u001b[0m: Expected the bounds to match the dimensionality of the domain. Got self.dim=6 and len(self._bounds)=7."
+     ]
+    }
+   ],
+   "source": [
+    "problem = WingWeightMultiFidelitySmall()\n",
+    "torch.manual_seed(0)\n",
+    "# define training and test set\n",
+    "N_TEST = 100\n",
+    "fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}\n",
+    "total_n = sum(fidelity_to_n.values())\n",
+    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
+    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
+    "# set fidelities\n",
+    "start = 0\n",
+    "for fidelity, n in fidelity_to_n.items():\n",
+    "    end = start + n\n",
+    "    train_X[start:end, -1] = fidelity\n",
+    "    start = end\n",
+    "\n",
+    "train_Y = problem(train_X).unsqueeze(-1)\n",
+    "\n",
+    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
+    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
+    "test_X[:, -1] = 0  # target fidelity\n",
+    "test_Y = problem(test_X).unsqueeze(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743529122103,
+    "executionStopTime": 1743529259563,
+    "language": "python",
+    "originalKey": "16660295-7704-4fc8-86a6-865e818007df",
+    "output": {
+     "id": "644333568388982"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "e061f189-346b-4b58-9716-bcee85df522f",
+    "serverExecutionDuration": 136496.86142337,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yl9959/.local/lib/python3.12/site-packages/linear_operator/utils/interpolation.py:71: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:651.)\n",
+      "  summing_matrix = cls(summing_matrix_indices, summing_matrix_values, size)\n",
+      "Sample: 100%|██████████| 768/768 [07:22,  1.73it/s, step size=1.59e-01, acc. prob=0.842]\n",
+      "/home/yl9959/.local/lib/python3.12/site-packages/botorch/models/utils/assorted.py:264: InputDataWarning: Data (input features) is not contained to the unit cube. Please consider min-max scaling the input data.\n",
+      "  check_min_max_scaling(\n"
+     ]
+    }
+   ],
+   "source": [
+    "wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743529130077,
+    "executionStopTime": 1743529260021,
+    "language": "python",
+    "originalKey": "76750e01-4e21-4bba-9624-fb214f9072aa",
+    "output": {
+     "id": "679472244421591"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "1e7896ca-a923-47da-a3c8-32c28f5d34e9",
+    "serverExecutionDuration": 3.9172596298158,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'wing_weight_res' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m wing_weight_res\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'wing_weight_res' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "wing_weight_res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "customInput": null,
+    "language": "markdown",
+    "originalKey": "3a4f34ce-9fdf-45cc-af1b-0be72caa11a2",
+    "showInput": false
+   },
+   "source": [
+    "## Borehole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528945358,
+    "executionStopTime": 1743528946169,
+    "language": "python",
+    "originalKey": "02708c2d-b43a-4a8f-8aca-a99fb32c615d",
+    "outputsInitialized": true,
+    "requestMsgId": "c216c09b-5424-4008-88bd-56f191aad912",
+    "serverExecutionDuration": 5.6033371947706,
+    "showInput": true
+   },
+   "outputs": [],
+   "source": [
+    "problem = BoreholeMultiFidelity()\n",
+    "torch.manual_seed(0)\n",
+    "# define training and test set\n",
+    "N_TEST = 100\n",
+    "fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}\n",
+    "total_n = sum(fidelity_to_n.values())\n",
+    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
+    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
+    "# set fidelities\n",
+    "start = 0\n",
+    "for fidelity, n in fidelity_to_n.items():\n",
+    "    end = start + n\n",
+    "    train_X[start:end, -1] = fidelity\n",
+    "    start = end\n",
+    "\n",
+    "train_Y = problem(train_X).unsqueeze(-1)\n",
+    "\n",
+    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
+    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
+    "test_X[:, -1] = 0  # target fidelity\n",
+    "test_Y = problem(test_X).unsqueeze(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743528947285,
+    "executionStopTime": 1743529094868,
+    "language": "python",
+    "originalKey": "0d43b937-8f09-4d49-90d5-f4077ee2e646",
+    "output": {
+     "id": "2112932599153367"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "fba253a7-c60a-412a-89be-3b4468f04999",
+    "serverExecutionDuration": 146576.36261638,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\rWarmup:   0%|          | 0/768 [00:00, ?it/s]\n",
+      "\rWarmup:   0%|          | 1/768 [00:01,  1.22s/it, step size=7.33e-01, acc. prob=0.125]\n",
+      "\rWarmup:   0%|          | 3/768 [00:01,  2.82it/s, step size=7.87e-02, acc. prob=0.367]\n",
+      "\rWarmup:   1%|          | 5/768 [00:01,  4.07it/s, step size=1.26e-01, acc. prob=0.599]\n",
+      "\rWarmup:   1%|          | 7/768 [00:01,  5.47it/s, step size=2.73e-02, acc. prob=0.593]\n",
+      "\rWarmup:   1%|          | 8/768 [00:01,  5.42it/s, step size=4.89e-02, acc. prob=0.644]\n",
+      "\rWarmup:   1%|          | 9/768 [00:02,  5.37it/s, step size=8.65e-02, acc. prob=0.682]\n",
+      "\rWarmup:   1%|▏         | 10/768 [00:02,  5.33it/s, step size=4.47e-02, acc. prob=0.673]\n",
+      "\rWarmup:   1%|▏         | 11/768 [00:02,  5.31it/s, step size=8.07e-02, acc. prob=0.701]\n",
+      "\rWarmup:   2%|▏         | 13/768 [00:02,  6.78it/s, step size=4.64e-02, acc. prob=0.702]\n",
+      "\rWarmup:   2%|▏         | 14/768 [00:02,  6.34it/s, step size=8.68e-02, acc. prob=0.723]\n",
+      "\rWarmup:   2%|▏         | 16/768 [00:03,  7.60it/s, step size=2.46e-02, acc. prob=0.706]\n",
+      "\rWarmup:   2%|▏         | 17/768 [00:03,  6.92it/s, step size=4.68e-02, acc. prob=0.723]\n",
+      "\rWarmup:   2%|▏         | 18/768 [00:03,  6.44it/s, step size=8.46e-02, acc. prob=0.738]\n",
+      "\rWarmup:   3%|▎         | 20/768 [00:03,  6.69it/s, step size=2.26e-02, acc. prob=0.721]\n",
+      "\rWarmup:   3%|▎         | 21/768 [00:03,  6.31it/s, step size=4.24e-02, acc. prob=0.734]\n",
+      "\rWarmup:   3%|▎         | 22/768 [00:04,  6.04it/s, step size=7.84e-02, acc. prob=0.746]\n",
+      "\rWarmup:   3%|▎         | 23/768 [00:04,  5.84it/s, step size=8.43e-02, acc. prob=0.749]\n",
+      "\rWarmup:   3%|▎         | 24/768 [00:04,  5.69it/s, step size=1.46e-01, acc. prob=0.759]\n",
+      "\rWarmup:   3%|▎         | 25/768 [00:04,  5.58it/s, step size=3.59e-02, acc. prob=0.741]\n",
+      "\rWarmup:   3%|▎         | 26/768 [00:04,  5.50it/s, step size=6.53e-02, acc. prob=0.751]\n",
+      "\rWarmup:   4%|▎         | 27/768 [00:05,  5.44it/s, step size=7.84e-02, acc. prob=0.754]\n",
+      "\rWarmup:   4%|▍         | 29/768 [00:05,  7.00it/s, step size=1.54e-01, acc. prob=0.765]\n",
+      "\rWarmup:   4%|▍         | 31/768 [00:05,  9.15it/s, step size=3.39e-02, acc. prob=0.749]\n",
+      "\rWarmup:   4%|▍         | 33/768 [00:05,  7.24it/s, step size=1.02e-01, acc. prob=0.764]\n",
+      "\rWarmup:   5%|▍         | 35/768 [00:05,  8.11it/s, step size=2.12e-01, acc. prob=0.773]\n",
+      "\rWarmup:   5%|▍         | 37/768 [00:06,  8.34it/s, step size=4.71e-02, acc. prob=0.759]\n",
+      "\rWarmup:   5%|▍         | 38/768 [00:06,  7.51it/s, step size=4.66e-02, acc. prob=0.759]\n",
+      "\rWarmup:   5%|▌         | 39/768 [00:06,  6.89it/s, step size=7.88e-02, acc. prob=0.765]\n",
+      "\rWarmup:   5%|▌         | 40/768 [00:06,  6.43it/s, step size=4.84e-02, acc. prob=0.761]\n",
+      "\rWarmup:   5%|▌         | 41/768 [00:06,  6.08it/s, step size=4.16e-02, acc. prob=0.760]\n",
+      "\rWarmup:   5%|▌         | 42/768 [00:07,  5.84it/s, step size=6.78e-02, acc. prob=0.766]\n",
+      "\rWarmup:   6%|▌         | 43/768 [00:07,  5.67it/s, step size=1.08e-01, acc. prob=0.771]\n",
+      "\rWarmup:   6%|▌         | 45/768 [00:07,  7.13it/s, step size=1.39e-01, acc. prob=0.774]\n",
+      "\rWarmup:   6%|▌         | 47/768 [00:07,  8.15it/s, step size=4.01e-02, acc. prob=0.763]\n",
+      "\rWarmup:   6%|▋         | 48/768 [00:07,  7.28it/s, step size=6.57e-02, acc. prob=0.768]\n",
+      "\rWarmup:   6%|▋         | 49/768 [00:08,  6.68it/s, step size=9.64e-02, acc. prob=0.772]\n",
+      "\rWarmup:   7%|▋         | 51/768 [00:08,  6.84it/s, step size=9.43e-02, acc. prob=0.773]\n",
+      "\rWarmup:   7%|▋         | 53/768 [00:08,  6.91it/s, step size=7.90e-02, acc. prob=0.772]\n",
+      "\rWarmup:   7%|▋         | 54/768 [00:08,  6.50it/s, step size=8.69e-02, acc. prob=0.773]\n",
+      "\rWarmup:   7%|▋         | 56/768 [00:09,  7.60it/s, step size=1.17e-01, acc. prob=0.776]\n",
+      "\rWarmup:   8%|▊         | 58/768 [00:09,  9.06it/s, step size=3.07e-02, acc. prob=0.766]\n",
+      "\rWarmup:   8%|▊         | 59/768 [00:09,  7.92it/s, step size=4.87e-02, acc. prob=0.770]\n",
+      "\rWarmup:   8%|▊         | 60/768 [00:09,  7.11it/s, step size=7.26e-02, acc. prob=0.773]\n",
+      "\rWarmup:   8%|▊         | 61/768 [00:09,  6.52it/s, step size=8.40e-02, acc. prob=0.775]\n",
+      "\rWarmup:   8%|▊         | 63/768 [00:10,  6.74it/s, step size=5.08e-02, acc. prob=0.772]\n",
+      "\rWarmup:   8%|▊         | 64/768 [00:10,  6.35it/s, step size=6.41e-02, acc. prob=0.774]\n",
+      "\rWarmup:   8%|▊         | 65/768 [00:10,  6.05it/s, step size=9.75e-02, acc. prob=0.777]\n",
+      "\rWarmup:   9%|▊         | 66/768 [00:10,  5.82it/s, step size=1.41e-01, acc. prob=0.780]\n",
+      "\rWarmup:   9%|▉         | 68/768 [00:10,  6.73it/s, step size=9.06e-02, acc. prob=0.777]\n",
+      "\rWarmup:   9%|▉         | 69/768 [00:11,  6.32it/s, step size=7.58e-02, acc. prob=0.776]\n",
+      "\rWarmup:   9%|▉         | 70/768 [00:11,  6.02it/s, step size=1.05e-01, acc. prob=0.778]\n",
+      "\rWarmup:   9%|▉         | 72/768 [00:11,  7.34it/s, step size=1.05e-01, acc. prob=0.779]\n",
+      "\rWarmup:  10%|▉         | 74/768 [00:11,  8.94it/s, step size=9.21e-02, acc. prob=0.778]\n",
+      "\rWarmup:  10%|▉         | 76/768 [00:11, 10.24it/s, step size=1.87e-01, acc. prob=0.783]\n",
+      "\rWarmup:  10%|█         | 78/768 [00:11,  9.54it/s, step size=8.15e-02, acc. prob=0.778]\n",
+      "\rWarmup:  10%|█         | 80/768 [00:12,  7.58it/s, step size=6.92e-02, acc. prob=0.777]\n",
+      "\rWarmup:  11%|█         | 81/768 [00:12,  7.01it/s, step size=1.03e-01, acc. prob=0.780]\n",
+      "\rWarmup:  11%|█         | 83/768 [00:12,  6.98it/s, step size=7.53e-02, acc. prob=0.778]\n",
+      "\rWarmup:  11%|█         | 84/768 [00:12,  6.56it/s, step size=9.80e-02, acc. prob=0.780]\n",
+      "\rWarmup:  11%|█         | 86/768 [00:13,  7.62it/s, step size=1.11e-01, acc. prob=0.781]\n",
+      "\rWarmup:  11%|█▏        | 88/768 [00:13,  8.43it/s, step size=5.18e-02, acc. prob=0.777]\n",
+      "\rWarmup:  12%|█▏        | 89/768 [00:13,  7.52it/s, step size=2.65e-02, acc. prob=0.773]\n",
+      "\rWarmup:  12%|█▏        | 90/768 [00:13,  6.83it/s, step size=3.92e-02, acc. prob=0.776]\n",
+      "\rWarmup:  12%|█▏        | 91/768 [00:13,  6.35it/s, step size=5.79e-02, acc. prob=0.778]\n",
+      "\rWarmup:  12%|█▏        | 92/768 [00:14,  6.04it/s, step size=7.73e-02, acc. prob=0.780]\n",
+      "\rWarmup:  12%|█▏        | 94/768 [00:14,  7.39it/s, step size=1.03e-01, acc. prob=0.782]\n",
+      "\rWarmup:  12%|█▎        | 96/768 [00:14,  7.26it/s, step size=5.55e-02, acc. prob=0.779]\n",
+      "\rWarmup:  13%|█▎        | 97/768 [00:14,  6.71it/s, step size=8.00e-02, acc. prob=0.781]\n",
+      "\rWarmup:  13%|█▎        | 99/768 [00:15,  4.81it/s, step size=2.65e-01, acc. prob=0.784]\n",
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+      "\rWarmup:  13%|█▎        | 102/768 [00:15,  5.67it/s, step size=9.39e-02, acc. prob=0.772]\n",
+      "\rWarmup:  13%|█▎        | 103/768 [00:16,  5.56it/s, step size=1.26e-01, acc. prob=0.774]\n",
+      "\rWarmup:  14%|█▎        | 104/768 [00:16,  5.49it/s, step size=1.95e-01, acc. prob=0.776]\n",
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+      "\rWarmup:  14%|█▍        | 106/768 [00:16,  5.39it/s, step size=2.06e-01, acc. prob=0.777]\n",
+      "\rWarmup:  14%|█▍        | 107/768 [00:16,  5.36it/s, step size=6.74e-02, acc. prob=0.774]\n",
+      "\rWarmup:  14%|█▍        | 108/768 [00:16,  5.34it/s, step size=1.24e-01, acc. prob=0.776]\n",
+      "\rWarmup:  14%|█▍        | 109/768 [00:17,  5.32it/s, step size=1.62e-01, acc. prob=0.777]\n",
+      "\rWarmup:  14%|█▍        | 110/768 [00:17,  5.31it/s, step size=2.67e-01, acc. prob=0.779]\n",
+      "\rWarmup:  14%|█▍        | 111/768 [00:17,  5.30it/s, step size=4.13e-02, acc. prob=0.774]\n",
+      "\rWarmup:  15%|█▍        | 112/768 [00:17,  5.28it/s, step size=7.81e-02, acc. prob=0.776]\n",
+      "\rWarmup:  15%|█▍        | 113/768 [00:17,  5.26it/s, step size=1.47e-01, acc. prob=0.778]\n",
+      "\rWarmup:  15%|█▍        | 114/768 [00:18,  5.24it/s, step size=2.50e-01, acc. prob=0.779]\n",
+      "\rWarmup:  15%|█▍        | 115/768 [00:18,  5.23it/s, step size=1.97e-01, acc. prob=0.779]\n",
+      "\rWarmup:  15%|█▌        | 116/768 [00:18,  5.25it/s, step size=2.17e-01, acc. prob=0.779]\n",
+      "\rWarmup:  15%|█▌        | 117/768 [00:18,  5.26it/s, step size=5.51e-02, acc. prob=0.775]\n",
+      "\rWarmup:  15%|█▌        | 118/768 [00:18,  5.28it/s, step size=1.03e-01, acc. prob=0.777]\n",
+      "\rWarmup:  15%|█▌        | 119/768 [00:19,  5.29it/s, step size=1.88e-01, acc. prob=0.779]\n",
+      "\rWarmup:  16%|█▌        | 120/768 [00:19,  5.28it/s, step size=2.03e-01, acc. prob=0.779]\n",
+      "\rWarmup:  16%|█▌        | 121/768 [00:19,  5.26it/s, step size=6.64e-02, acc. prob=0.776]\n",
+      "\rWarmup:  16%|█▌        | 122/768 [00:19,  5.26it/s, step size=1.22e-01, acc. prob=0.778]\n",
+      "\rWarmup:  16%|█▌        | 123/768 [00:19,  5.28it/s, step size=2.12e-01, acc. prob=0.780]\n",
+      "\rWarmup:  16%|█▌        | 124/768 [00:20,  5.29it/s, step size=4.78e-02, acc. prob=0.776]\n",
+      "\rWarmup:  16%|█▋        | 125/768 [00:20,  5.29it/s, step size=8.68e-02, acc. prob=0.777]\n",
+      "\rWarmup:  16%|█▋        | 126/768 [00:20,  5.30it/s, step size=1.51e-01, acc. prob=0.779]\n",
+      "\rWarmup:  17%|█▋        | 127/768 [00:20,  5.31it/s, step size=2.36e-01, acc. prob=0.780]\n",
+      "\rWarmup:  17%|█▋        | 128/768 [00:20,  5.30it/s, step size=2.15e-01, acc. prob=0.780]\n",
+      "\rWarmup:  17%|█▋        | 129/768 [00:20,  5.28it/s, step size=4.67e-02, acc. prob=0.776]\n",
+      "\rWarmup:  17%|█▋        | 130/768 [00:21,  5.30it/s, step size=8.30e-02, acc. prob=0.778]\n",
+      "\rWarmup:  17%|█▋        | 131/768 [00:21,  5.31it/s, step size=1.43e-01, acc. prob=0.779]\n",
+      "\rWarmup:  17%|█▋        | 132/768 [00:21,  5.29it/s, step size=1.49e-01, acc. prob=0.780]\n",
+      "\rWarmup:  17%|█▋        | 133/768 [00:21,  5.26it/s, step size=3.68e-02, acc. prob=0.776]\n",
+      "\rWarmup:  17%|█▋        | 134/768 [00:21,  5.25it/s, step size=6.44e-02, acc. prob=0.777]\n",
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+      "\rWarmup:  18%|█▊        | 136/768 [00:22,  5.23it/s, step size=1.72e-01, acc. prob=0.780]\n",
+      "\rWarmup:  18%|█▊        | 137/768 [00:22,  5.22it/s, step size=2.72e-01, acc. prob=0.782]\n",
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+      "\rWarmup:  18%|█▊        | 139/768 [00:22,  5.22it/s, step size=5.60e-02, acc. prob=0.777]\n",
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+      "\rWarmup:  18%|█▊        | 142/768 [00:23,  5.23it/s, step size=7.33e-02, acc. prob=0.778]\n",
+      "\rWarmup:  19%|█▊        | 143/768 [00:23,  5.22it/s, step size=1.08e-01, acc. prob=0.780]\n",
+      "\rWarmup:  19%|█▉        | 144/768 [00:23,  5.22it/s, step size=1.68e-01, acc. prob=0.781]\n",
+      "\rWarmup:  19%|█▉        | 145/768 [00:24,  5.23it/s, step size=2.23e-01, acc. prob=0.782]\n",
+      "\rWarmup:  19%|█▉        | 146/768 [00:24,  5.24it/s, step size=6.15e-02, acc. prob=0.778]\n",
+      "\rWarmup:  19%|█▉        | 147/768 [00:24,  5.26it/s, step size=9.91e-02, acc. prob=0.780]\n",
+      "\rWarmup:  19%|█▉        | 148/768 [00:24,  5.26it/s, step size=1.62e-01, acc. prob=0.781]\n",
+      "\rWarmup:  19%|█▉        | 149/768 [00:24,  5.04it/s, step size=7.25e-01, acc. prob=0.778]\n",
+      "\rWarmup:  20%|█▉        | 152/768 [00:25,  7.36it/s, step size=5.27e-02, acc. prob=0.766]\n",
+      "\rWarmup:  20%|█▉        | 153/768 [00:25,  6.77it/s, step size=6.48e-02, acc. prob=0.767]\n",
+      "\rWarmup:  20%|██        | 154/768 [00:25,  6.33it/s, step size=9.58e-02, acc. prob=0.769]\n",
+      "\rWarmup:  20%|██        | 155/768 [00:25,  6.01it/s, step size=1.56e-01, acc. prob=0.770]\n",
+      "\rWarmup:  20%|██        | 156/768 [00:25,  5.80it/s, step size=2.73e-01, acc. prob=0.772]\n",
+      "\rWarmup:  20%|██        | 157/768 [00:26,  5.65it/s, step size=3.79e-01, acc. prob=0.773]\n",
+      "\rWarmup:  21%|██        | 158/768 [00:26,  5.54it/s, step size=1.14e-01, acc. prob=0.771]\n",
+      "\rWarmup:  21%|██        | 159/768 [00:26,  5.47it/s, step size=2.06e-01, acc. prob=0.772]\n",
+      "\rWarmup:  21%|██        | 160/768 [00:26,  5.42it/s, step size=1.00e-01, acc. prob=0.771]\n",
+      "\rWarmup:  21%|██        | 161/768 [00:26,  5.38it/s, step size=1.90e-01, acc. prob=0.772]\n",
+      "\rWarmup:  21%|██        | 162/768 [00:26,  5.36it/s, step size=3.57e-01, acc. prob=0.773]\n",
+      "\rWarmup:  21%|██        | 163/768 [00:27,  5.34it/s, step size=3.60e-01, acc. prob=0.774]\n",
+      "\rWarmup:  21%|██▏       | 164/768 [00:27,  5.30it/s, step size=6.80e-01, acc. prob=0.775]\n",
+      "\rWarmup:  21%|██▏       | 165/768 [00:27,  5.26it/s, step size=1.09e-01, acc. prob=0.772]\n",
+      "\rWarmup:  22%|██▏       | 166/768 [00:27,  5.25it/s, step size=2.06e-01, acc. prob=0.773]\n",
+      "\rWarmup:  22%|██▏       | 167/768 [00:27,  5.25it/s, step size=3.79e-01, acc. prob=0.774]\n",
+      "\rWarmup:  22%|██▏       | 168/768 [00:28,  5.27it/s, step size=1.98e-01, acc. prob=0.773]\n",
+      "\rWarmup:  22%|██▏       | 169/768 [00:28,  5.27it/s, step size=3.06e-01, acc. prob=0.774]\n",
+      "\rWarmup:  22%|██▏       | 170/768 [00:28,  5.28it/s, step size=5.51e-01, acc. prob=0.775]\n",
+      "\rWarmup:  22%|██▏       | 171/768 [00:28,  5.28it/s, step size=7.67e-02, acc. prob=0.771]\n",
+      "\rWarmup:  22%|██▏       | 172/768 [00:28,  5.28it/s, step size=1.42e-01, acc. prob=0.773]\n",
+      "\rWarmup:  23%|██▎       | 173/768 [00:29,  5.29it/s, step size=1.42e-01, acc. prob=0.773]\n",
+      "\rWarmup:  23%|██▎       | 174/768 [00:29,  5.29it/s, step size=2.59e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 175/768 [00:29,  5.30it/s, step size=1.41e-01, acc. prob=0.773]\n",
+      "\rWarmup:  23%|██▎       | 176/768 [00:29,  5.28it/s, step size=2.52e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 177/768 [00:29,  5.22it/s, step size=1.91e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 178/768 [00:30,  5.25it/s, step size=3.11e-01, acc. prob=0.775]\n",
+      "\rWarmup:  23%|██▎       | 179/768 [00:30,  5.26it/s, step size=2.12e-01, acc. prob=0.774]\n",
+      "\rWarmup:  23%|██▎       | 180/768 [00:30,  5.27it/s, step size=2.46e-01, acc. prob=0.775]\n",
+      "\rWarmup:  24%|██▎       | 181/768 [00:30,  5.27it/s, step size=1.13e-01, acc. prob=0.773]\n",
+      "\rWarmup:  24%|██▎       | 182/768 [00:30,  5.27it/s, step size=1.99e-01, acc. prob=0.774]\n",
+      "\rWarmup:  24%|██▍       | 183/768 [00:30,  5.28it/s, step size=3.45e-01, acc. prob=0.775]\n",
+      "\rWarmup:  24%|██▍       | 184/768 [00:31,  5.28it/s, step size=5.73e-01, acc. prob=0.777]\n",
+      "\rWarmup:  24%|██▍       | 185/768 [00:31,  5.27it/s, step size=1.50e-01, acc. prob=0.774]\n",
+      "\rWarmup:  24%|██▍       | 186/768 [00:31,  5.26it/s, step size=2.57e-01, acc. prob=0.775]\n",
+      "\rWarmup:  24%|██▍       | 187/768 [00:31,  5.25it/s, step size=3.37e-01, acc. prob=0.776]\n",
+      "\rWarmup:  24%|██▍       | 188/768 [00:31,  5.26it/s, step size=3.87e-01, acc. prob=0.776]\n",
+      "\rWarmup:  25%|██▍       | 189/768 [00:32,  5.25it/s, step size=3.86e-01, acc. prob=0.776]\n",
+      "\rWarmup:  25%|██▍       | 190/768 [00:32,  5.25it/s, step size=1.51e-01, acc. prob=0.774]\n",
+      "\rWarmup:  25%|██▍       | 191/768 [00:32,  5.26it/s, step size=2.45e-01, acc. prob=0.775]\n",
+      "\rWarmup:  25%|██▌       | 192/768 [00:32,  5.25it/s, step size=4.10e-01, acc. prob=0.777]\n",
+      "\rWarmup:  25%|██▌       | 193/768 [00:32,  5.26it/s, step size=9.62e-02, acc. prob=0.774]\n",
+      "\rWarmup:  25%|██▌       | 194/768 [00:33,  5.26it/s, step size=1.62e-01, acc. prob=0.775]\n",
+      "\rWarmup:  25%|██▌       | 195/768 [00:33,  5.26it/s, step size=2.45e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 196/768 [00:33,  5.27it/s, step size=1.59e-01, acc. prob=0.775]\n",
+      "\rWarmup:  26%|██▌       | 197/768 [00:33,  5.27it/s, step size=2.51e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 198/768 [00:33,  5.27it/s, step size=2.70e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 199/768 [00:34,  5.28it/s, step size=1.23e-01, acc. prob=0.775]\n",
+      "\rWarmup:  26%|██▌       | 200/768 [00:34,  5.28it/s, step size=2.00e-01, acc. prob=0.776]\n",
+      "\rWarmup:  26%|██▌       | 201/768 [00:34,  5.28it/s, step size=3.04e-01, acc. prob=0.777]\n",
+      "\rWarmup:  26%|██▋       | 202/768 [00:34,  5.28it/s, step size=1.41e-01, acc. prob=0.775]\n",
+      "\rWarmup:  26%|██▋       | 203/768 [00:34,  5.27it/s, step size=2.25e-01, acc. prob=0.776]\n",
+      "\rWarmup:  27%|██▋       | 204/768 [00:34,  5.27it/s, step size=3.46e-01, acc. prob=0.777]\n",
+      "\rWarmup:  27%|██▋       | 205/768 [00:35,  5.27it/s, step size=4.07e-01, acc. prob=0.778]\n",
+      "\rWarmup:  27%|██▋       | 206/768 [00:35,  5.27it/s, step size=1.61e-01, acc. prob=0.776]\n",
+      "\rWarmup:  27%|██▋       | 207/768 [00:35,  5.27it/s, step size=2.52e-01, acc. prob=0.777]\n",
+      "\rWarmup:  27%|██▋       | 208/768 [00:35,  5.26it/s, step size=2.01e-01, acc. prob=0.776]\n",
+      "\rWarmup:  27%|██▋       | 209/768 [00:35,  5.27it/s, step size=2.58e-01, acc. prob=0.777]\n",
+      "\rWarmup:  27%|██▋       | 210/768 [00:36,  5.28it/s, step size=3.58e-01, acc. prob=0.778]\n",
+      "\rWarmup:  27%|██▋       | 211/768 [00:36,  5.29it/s, step size=1.99e-01, acc. prob=0.776]\n",
+      "\rWarmup:  28%|██▊       | 212/768 [00:36,  5.29it/s, step size=2.37e-01, acc. prob=0.777]\n",
+      "\rWarmup:  28%|██▊       | 213/768 [00:36,  5.29it/s, step size=3.68e-01, acc. prob=0.778]\n",
+      "\rWarmup:  28%|██▊       | 214/768 [00:36,  5.29it/s, step size=5.21e-01, acc. prob=0.779]\n",
+      "\rWarmup:  28%|██▊       | 215/768 [00:37,  5.29it/s, step size=2.29e-01, acc. prob=0.777]\n",
+      "\rWarmup:  28%|██▊       | 216/768 [00:37,  5.28it/s, step size=3.32e-01, acc. prob=0.778]\n",
+      "\rWarmup:  28%|██▊       | 217/768 [00:37,  5.28it/s, step size=4.86e-01, acc. prob=0.779]\n",
+      "\rWarmup:  28%|██▊       | 218/768 [00:37,  5.26it/s, step size=5.62e-01, acc. prob=0.779]\n",
+      "\rWarmup:  29%|██▊       | 219/768 [00:37,  5.22it/s, step size=2.64e-01, acc. prob=0.778]\n",
+      "\rWarmup:  29%|██▊       | 220/768 [00:37,  5.23it/s, step size=3.69e-01, acc. prob=0.778]\n",
+      "\rWarmup:  29%|██▉       | 221/768 [00:38,  5.24it/s, step size=1.62e-01, acc. prob=0.777]\n",
+      "\rWarmup:  29%|██▉       | 222/768 [00:38,  5.26it/s, step size=2.32e-01, acc. prob=0.777]\n",
+      "\rWarmup:  29%|██▉       | 223/768 [00:38,  5.26it/s, step size=3.55e-01, acc. prob=0.778]\n",
+      "\rWarmup:  29%|██▉       | 224/768 [00:38,  5.26it/s, step size=4.69e-01, acc. prob=0.779]\n",
+      "\rWarmup:  29%|██▉       | 225/768 [00:38,  5.27it/s, step size=2.17e-01, acc. prob=0.777]\n",
+      "\rWarmup:  29%|██▉       | 226/768 [00:39,  5.28it/s, step size=2.69e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|██▉       | 227/768 [00:39,  5.29it/s, step size=2.65e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|██▉       | 228/768 [00:39,  5.29it/s, step size=2.82e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|██▉       | 229/768 [00:39,  5.28it/s, step size=1.47e-01, acc. prob=0.777]\n",
+      "\rWarmup:  30%|██▉       | 230/768 [00:39,  5.28it/s, step size=1.48e-01, acc. prob=0.777]\n",
+      "\rWarmup:  30%|███       | 231/768 [00:40,  5.28it/s, step size=2.23e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|███       | 232/768 [00:40,  5.29it/s, step size=2.84e-01, acc. prob=0.778]\n",
+      "\rWarmup:  30%|███       | 233/768 [00:40,  5.24it/s, step size=1.03e-01, acc. prob=0.776]\n",
+      "\rWarmup:  30%|███       | 234/768 [00:40,  5.26it/s, step size=1.52e-01, acc. prob=0.777]\n",
+      "\rWarmup:  31%|███       | 235/768 [00:40,  5.26it/s, step size=1.92e-01, acc. prob=0.778]\n",
+      "\rWarmup:  31%|███       | 236/768 [00:41,  5.27it/s, step size=2.60e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███       | 237/768 [00:41,  5.28it/s, step size=3.12e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███       | 238/768 [00:41,  5.29it/s, step size=3.40e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███       | 239/768 [00:41,  5.28it/s, step size=3.16e-01, acc. prob=0.779]\n",
+      "\rWarmup:  31%|███▏      | 240/768 [00:41,  5.27it/s, step size=4.06e-01, acc. prob=0.780]\n",
+      "\rWarmup:  31%|███▏      | 241/768 [00:41,  5.28it/s, step size=4.46e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 242/768 [00:42,  5.28it/s, step size=4.45e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 243/768 [00:42,  5.28it/s, step size=4.68e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 244/768 [00:42,  5.28it/s, step size=3.77e-01, acc. prob=0.780]\n",
+      "\rWarmup:  32%|███▏      | 245/768 [00:42,  5.27it/s, step size=2.38e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 246/768 [00:42,  5.26it/s, step size=2.53e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 247/768 [00:43,  5.25it/s, step size=2.04e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 248/768 [00:43,  5.25it/s, step size=2.76e-01, acc. prob=0.779]\n",
+      "\rWarmup:  32%|███▏      | 249/768 [00:43,  5.13it/s, step size=7.70e-01, acc. prob=0.780]\n",
+      "\rWarmup:  33%|███▎      | 252/768 [00:43,  7.93it/s, step size=1.22e-01, acc. prob=0.774]\n",
+      "\rWarmup:  33%|███▎      | 253/768 [00:43,  7.12it/s, step size=1.32e-01, acc. prob=0.775]\n",
+      "\rWarmup:  33%|███▎      | 254/768 [00:44,  6.55it/s, step size=1.80e-01, acc. prob=0.776]\n",
+      "\rWarmup:  33%|███▎      | 256/768 [00:44,  6.74it/s, step size=2.60e-01, acc. prob=0.776]\n",
+      "\rWarmup:  34%|███▎      | 258/768 [00:44,  7.31it/s, step size=1.44e-01, acc. prob=0.776]\n",
+      "\rWarmup:  34%|███▎      | 259/768 [00:44,  6.78it/s, step size=2.63e-01, acc. prob=0.777]\n",
+      "\rWarmup:  34%|███▍      | 260/768 [00:45,  6.37it/s, step size=2.00e-01, acc. prob=0.777]\n",
+      "\rWarmup:  34%|███▍      | 261/768 [00:45,  6.01it/s, step size=1.45e-01, acc. prob=0.776]\n",
+      "\rWarmup:  34%|███▍      | 262/768 [00:45,  5.79it/s, step size=2.40e-01, acc. prob=0.777]\n",
+      "\rWarmup:  34%|███▍      | 264/768 [00:45,  7.74it/s, step size=3.18e-01, acc. prob=0.777]\n",
+      "\rWarmup:  35%|███▍      | 266/768 [00:45,  8.60it/s, step size=3.40e-02, acc. prob=0.775]\n",
+      "\rWarmup:  35%|███▍      | 267/768 [00:45,  7.57it/s, step size=6.49e-02, acc. prob=0.776]\n",
+      "\rWarmup:  35%|███▍      | 268/768 [00:46,  6.86it/s, step size=1.19e-01, acc. prob=0.776]\n",
+      "\rWarmup:  35%|███▌      | 269/768 [00:46,  6.36it/s, step size=2.19e-01, acc. prob=0.777]\n",
+      "\rWarmup:  35%|███▌      | 271/768 [00:46,  7.63it/s, step size=3.87e-01, acc. prob=0.778]\n",
+      "\rWarmup:  35%|███▌      | 272/768 [00:46,  7.47it/s, step size=5.14e-01, acc. prob=0.778]\n",
+      "\rWarmup:  36%|███▌      | 274/768 [00:46,  9.18it/s, step size=2.04e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▌      | 275/768 [00:46,  7.85it/s, step size=3.64e-01, acc. prob=0.778]\n",
+      "\rWarmup:  36%|███▌      | 276/768 [00:47,  7.03it/s, step size=1.64e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▌      | 277/768 [00:47,  6.48it/s, step size=2.74e-01, acc. prob=0.778]\n",
+      "\rWarmup:  36%|███▌      | 278/768 [00:47,  6.10it/s, step size=1.79e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▋      | 279/768 [00:47,  5.83it/s, step size=1.17e-01, acc. prob=0.777]\n",
+      "\rWarmup:  36%|███▋      | 280/768 [00:47,  5.65it/s, step size=2.07e-01, acc. prob=0.778]\n",
+      "\rWarmup:  37%|███▋      | 282/768 [00:48,  6.22it/s, step size=8.80e-02, acc. prob=0.777]\n",
+      "\rWarmup:  37%|███▋      | 283/768 [00:48,  5.97it/s, step size=1.53e-01, acc. prob=0.777]\n",
+      "\rWarmup:  37%|███▋      | 285/768 [00:48,  6.38it/s, step size=5.91e-02, acc. prob=0.776]\n",
+      "\rWarmup:  37%|███▋      | 286/768 [00:48,  6.10it/s, step size=1.02e-01, acc. prob=0.777]\n",
+      "\rWarmup:  37%|███▋      | 287/768 [00:49,  5.85it/s, step size=1.77e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 288/768 [00:49,  5.71it/s, step size=2.83e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 289/768 [00:49,  5.59it/s, step size=4.48e-01, acc. prob=0.779]\n",
+      "\rWarmup:  38%|███▊      | 291/768 [00:49,  6.14it/s, step size=3.57e-01, acc. prob=0.779]\n",
+      "\rWarmup:  38%|███▊      | 293/768 [00:49,  6.46it/s, step size=1.48e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 294/768 [00:50,  6.18it/s, step size=2.47e-01, acc. prob=0.778]\n",
+      "\rWarmup:  38%|███▊      | 295/768 [00:50,  5.95it/s, step size=4.09e-01, acc. prob=0.779]\n",
+      "\rWarmup:  39%|███▊      | 297/768 [00:50,  6.40it/s, step size=1.19e-01, acc. prob=0.777]\n",
+      "\rWarmup:  39%|███▉      | 298/768 [00:50,  6.12it/s, step size=1.97e-01, acc. prob=0.778]\n",
+      "\rWarmup:  39%|███▉      | 299/768 [00:51,  5.90it/s, step size=3.21e-01, acc. prob=0.779]\n",
+      "\rWarmup:  39%|███▉      | 301/768 [00:51,  7.24it/s, step size=1.55e-01, acc. prob=0.778]\n",
+      "\rWarmup:  39%|███▉      | 302/768 [00:51,  6.71it/s, step size=2.15e-01, acc. prob=0.778]\n",
+      "\rWarmup:  40%|███▉      | 304/768 [00:51,  7.87it/s, step size=2.83e-01, acc. prob=0.779]\n",
+      "\rWarmup:  40%|███▉      | 306/768 [00:51,  9.40it/s, step size=1.88e-01, acc. prob=0.778]\n",
+      "\rWarmup:  40%|████      | 308/768 [00:51,  9.82it/s, step size=2.71e-01, acc. prob=0.779]\n",
+      "\rWarmup:  40%|████      | 310/768 [00:52,  8.76it/s, step size=2.07e-01, acc. prob=0.779]\n",
+      "\rWarmup:  40%|████      | 311/768 [00:52,  7.81it/s, step size=2.03e-01, acc. prob=0.779]\n",
+      "\rWarmup:  41%|████      | 312/768 [00:52,  7.10it/s, step size=2.71e-01, acc. prob=0.779]\n",
+      "\rWarmup:  41%|████      | 314/768 [00:52,  8.14it/s, step size=5.25e-01, acc. prob=0.780]\n",
+      "\rWarmup:  41%|████      | 316/768 [00:53,  7.77it/s, step size=2.32e-01, acc. prob=0.779]\n",
+      "\rWarmup:  41%|████▏     | 317/768 [00:53,  7.10it/s, step size=3.18e-01, acc. prob=0.780]\n",
+      "\rWarmup:  42%|████▏     | 319/768 [00:53,  7.09it/s, step size=1.71e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 320/768 [00:53,  6.63it/s, step size=2.56e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 321/768 [00:53,  6.28it/s, step size=1.53e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 322/768 [00:54,  6.00it/s, step size=2.28e-01, acc. prob=0.779]\n",
+      "\rWarmup:  42%|████▏     | 324/768 [00:54,  7.33it/s, step size=3.47e-01, acc. prob=0.780]\n",
+      "\rWarmup:  42%|████▏     | 325/768 [00:54,  6.67it/s, step size=8.82e-02, acc. prob=0.778]\n",
+      "\rWarmup:  42%|████▏     | 326/768 [00:54,  6.20it/s, step size=1.34e-01, acc. prob=0.779]\n",
+      "\rWarmup:  43%|████▎     | 328/768 [00:54,  6.48it/s, step size=8.93e-02, acc. prob=0.778]\n",
+      "\rWarmup:  43%|████▎     | 329/768 [00:55,  6.18it/s, step size=1.31e-01, acc. prob=0.779]\n",
+      "\rWarmup:  43%|████▎     | 330/768 [00:55,  5.95it/s, step size=1.94e-01, acc. prob=0.779]\n",
+      "\rWarmup:  43%|████▎     | 331/768 [00:55,  5.76it/s, step size=2.86e-01, acc. prob=0.780]\n",
+      "\rWarmup:  43%|████▎     | 332/768 [00:55,  6.00it/s, step size=9.37e-02, acc. prob=0.778]\n",
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+      "\rWarmup:  44%|████▎     | 335/768 [00:56,  5.52it/s, step size=2.90e-01, acc. prob=0.780]\n",
+      "\rWarmup:  44%|████▍     | 337/768 [00:56,  7.09it/s, step size=2.81e-01, acc. prob=0.780]\n",
+      "\rWarmup:  44%|████▍     | 338/768 [00:56,  6.56it/s, step size=4.17e-01, acc. prob=0.781]\n",
+      "\rWarmup:  44%|████▍     | 340/768 [00:56,  6.75it/s, step size=1.24e-01, acc. prob=0.779]\n",
+      "\rWarmup:  44%|████▍     | 341/768 [00:57,  6.31it/s, step size=1.82e-01, acc. prob=0.780]\n",
+      "\rWarmup:  45%|████▍     | 342/768 [00:57,  6.01it/s, step size=8.06e-02, acc. prob=0.778]\n",
+      "\rWarmup:  45%|████▍     | 343/768 [00:57,  5.80it/s, step size=1.17e-01, acc. prob=0.779]\n",
+      "\rWarmup:  45%|████▍     | 344/768 [00:57,  5.63it/s, step size=1.70e-01, acc. prob=0.780]\n",
+      "\rWarmup:  45%|████▍     | 345/768 [00:57,  5.52it/s, step size=2.49e-01, acc. prob=0.780]\n",
+      "\rWarmup:  45%|████▌     | 346/768 [00:58,  5.46it/s, step size=3.05e-01, acc. prob=0.781]\n",
+      "\rWarmup:  45%|████▌     | 348/768 [00:58,  6.11it/s, step size=1.32e-01, acc. prob=0.779]\n",
+      "\rWarmup:  46%|████▌     | 350/768 [00:58,  6.47it/s, step size=1.91e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 351/768 [00:58,  6.18it/s, step size=2.19e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 352/768 [00:58,  5.96it/s, step size=1.39e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 353/768 [00:59,  5.79it/s, step size=2.01e-01, acc. prob=0.780]\n",
+      "\rWarmup:  46%|████▌     | 354/768 [00:59,  5.66it/s, step size=2.48e-01, acc. prob=0.781]\n",
+      "\rWarmup:  46%|████▌     | 355/768 [00:59,  5.58it/s, step size=3.46e-01, acc. prob=0.781]\n",
+      "\rWarmup:  46%|████▋     | 357/768 [00:59,  7.08it/s, step size=2.98e-01, acc. prob=0.781]\n",
+      "\rWarmup:  47%|████▋     | 359/768 [00:59,  7.12it/s, step size=3.60e-01, acc. prob=0.781]\n",
+      "\rWarmup:  47%|████▋     | 360/768 [01:00,  6.65it/s, step size=1.28e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 361/768 [01:00,  6.28it/s, step size=1.82e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 362/768 [01:00,  6.02it/s, step size=1.51e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 363/768 [01:00,  5.82it/s, step size=1.17e-01, acc. prob=0.780]\n",
+      "\rWarmup:  47%|████▋     | 364/768 [01:00,  5.68it/s, step size=1.64e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 365/768 [01:01,  5.59it/s, step size=5.38e-02, acc. prob=0.778]\n",
+      "\rWarmup:  48%|████▊     | 366/768 [01:01,  5.52it/s, step size=7.69e-02, acc. prob=0.779]\n",
+      "\rWarmup:  48%|████▊     | 367/768 [01:01,  5.45it/s, step size=1.09e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 368/768 [01:01,  5.41it/s, step size=1.53e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 369/768 [01:01,  5.36it/s, step size=1.74e-01, acc. prob=0.780]\n",
+      "\rWarmup:  48%|████▊     | 370/768 [01:02,  5.33it/s, step size=1.90e-01, acc. prob=0.781]\n",
+      "\rWarmup:  48%|████▊     | 371/768 [01:02,  5.34it/s, step size=2.07e-01, acc. prob=0.781]\n",
+      "\rWarmup:  48%|████▊     | 372/768 [01:02,  5.33it/s, step size=1.38e-01, acc. prob=0.780]\n",
+      "\rWarmup:  49%|████▊     | 373/768 [01:02,  5.33it/s, step size=1.92e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 375/768 [01:02,  6.05it/s, step size=3.33e-01, acc. prob=0.782]\n",
+      "\rWarmup:  49%|████▉     | 376/768 [01:03,  5.86it/s, step size=2.25e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 377/768 [01:03,  5.72it/s, step size=2.27e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 378/768 [01:03,  5.62it/s, step size=2.21e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 379/768 [01:03,  5.54it/s, step size=2.57e-01, acc. prob=0.781]\n",
+      "\rWarmup:  49%|████▉     | 380/768 [01:03,  5.48it/s, step size=3.10e-01, acc. prob=0.782]\n",
+      "\rWarmup:  50%|████▉     | 381/768 [01:03,  5.88it/s, step size=1.54e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|████▉     | 382/768 [01:04,  5.71it/s, step size=2.15e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|████▉     | 383/768 [01:04,  5.60it/s, step size=2.89e-01, acc. prob=0.782]\n",
+      "\rWarmup:  50%|█████     | 385/768 [01:04,  6.21it/s, step size=1.88e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|█████     | 386/768 [01:04,  5.95it/s, step size=2.08e-01, acc. prob=0.781]\n",
+      "\rWarmup:  50%|█████     | 387/768 [01:04,  5.77it/s, step size=2.75e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 389/768 [01:05,  6.29it/s, step size=2.98e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 391/768 [01:05,  8.00it/s, step size=2.90e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 392/768 [01:05,  7.24it/s, step size=3.76e-01, acc. prob=0.782]\n",
+      "\rWarmup:  51%|█████     | 393/768 [01:05,  6.68it/s, step size=1.75e-01, acc. prob=0.781]\n",
+      "\rWarmup:  51%|█████▏    | 395/768 [01:06,  6.88it/s, step size=1.56e-01, acc. prob=0.781]\n",
+      "\rWarmup:  52%|█████▏    | 396/768 [01:06,  6.48it/s, step size=2.15e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 397/768 [01:06,  6.17it/s, step size=2.93e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 399/768 [01:06,  6.55it/s, step size=1.35e-01, acc. prob=0.781]\n",
+      "\rWarmup:  52%|█████▏    | 400/768 [01:06,  6.24it/s, step size=1.85e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 401/768 [01:07,  5.99it/s, step size=2.37e-01, acc. prob=0.782]\n",
+      "\rWarmup:  52%|█████▏    | 402/768 [01:07,  5.81it/s, step size=3.04e-01, acc. prob=0.782]\n",
+      "\rWarmup:  53%|█████▎    | 404/768 [01:07,  6.74it/s, step size=2.56e-01, acc. prob=0.782]\n",
+      "\rWarmup:  53%|█████▎    | 406/768 [01:07,  6.90it/s, step size=3.04e-01, acc. prob=0.783]\n",
+      "\rWarmup:  53%|█████▎    | 407/768 [01:07,  6.49it/s, step size=1.49e-01, acc. prob=0.781]\n",
+      "\rWarmup:  53%|█████▎    | 408/768 [01:08,  6.19it/s, step size=1.89e-01, acc. prob=0.782]\n",
+      "\rWarmup:  53%|█████▎    | 409/768 [01:08,  5.94it/s, step size=2.44e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▎    | 411/768 [01:08,  6.32it/s, step size=1.73e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▎    | 412/768 [01:08,  6.00it/s, step size=2.31e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 413/768 [01:09,  5.77it/s, step size=1.09e-01, acc. prob=0.781]\n",
+      "\rWarmup:  54%|█████▍    | 414/768 [01:09,  5.62it/s, step size=1.47e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 415/768 [01:09,  5.53it/s, step size=1.96e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 416/768 [01:09,  5.47it/s, step size=2.38e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 417/768 [01:09,  5.41it/s, step size=2.19e-01, acc. prob=0.782]\n",
+      "\rWarmup:  54%|█████▍    | 418/768 [01:09,  5.38it/s, step size=2.87e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▍    | 420/768 [01:10,  7.51it/s, step size=4.32e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▍    | 422/768 [01:10,  8.87it/s, step size=2.35e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▌    | 423/768 [01:10,  7.73it/s, step size=2.25e-01, acc. prob=0.782]\n",
+      "\rWarmup:  55%|█████▌    | 425/768 [01:10,  7.51it/s, step size=3.00e-01, acc. prob=0.783]\n",
+      "\rWarmup:  55%|█████▌    | 426/768 [01:10,  6.91it/s, step size=2.53e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 427/768 [01:11,  6.46it/s, step size=3.30e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 429/768 [01:11,  8.31it/s, step size=3.31e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 430/768 [01:11,  7.40it/s, step size=2.79e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▌    | 431/768 [01:11,  6.77it/s, step size=3.37e-01, acc. prob=0.783]\n",
+      "\rWarmup:  56%|█████▋    | 432/768 [01:11,  6.34it/s, step size=4.47e-01, acc. prob=0.784]\n",
+      "\rWarmup:  56%|█████▋    | 433/768 [01:12,  6.04it/s, step size=2.45e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 434/768 [01:12,  5.84it/s, step size=2.78e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 436/768 [01:12,  7.33it/s, step size=4.32e-01, acc. prob=0.784]\n",
+      "\rWarmup:  57%|█████▋    | 438/768 [01:12,  7.30it/s, step size=1.98e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 439/768 [01:12,  7.10it/s, step size=2.62e-01, acc. prob=0.783]\n",
+      "\rWarmup:  57%|█████▋    | 441/768 [01:12,  8.60it/s, step size=2.95e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 443/768 [01:13,  7.59it/s, step size=1.88e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 444/768 [01:13,  6.65it/s, step size=2.49e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 445/768 [01:13,  5.95it/s, step size=2.57e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 446/768 [01:13,  5.52it/s, step size=1.71e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 447/768 [01:14,  5.22it/s, step size=2.11e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 448/768 [01:14,  5.00it/s, step size=2.41e-01, acc. prob=0.783]\n",
+      "\rWarmup:  58%|█████▊    | 449/768 [01:14,  5.71it/s, step size=2.79e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▊    | 450/768 [01:14,  6.37it/s, step size=3.11e-01, acc. prob=0.784]\n",
+      "\rWarmup:  59%|█████▊    | 451/768 [01:14,  5.68it/s, step size=1.63e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▉    | 452/768 [01:15,  5.27it/s, step size=2.15e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▉    | 453/768 [01:15,  5.02it/s, step size=2.46e-01, acc. prob=0.783]\n",
+      "\rWarmup:  59%|█████▉    | 454/768 [01:15,  5.77it/s, step size=2.94e-01, acc. prob=0.784]\n",
+      "\rWarmup:  59%|█████▉    | 455/768 [01:15,  6.46it/s, step size=3.47e-01, acc. prob=0.784]\n",
+      "\rWarmup:  59%|█████▉    | 456/768 [01:15,  7.05it/s, step size=3.09e-01, acc. prob=0.784]\n",
+      "\rWarmup:  60%|█████▉    | 457/768 [01:15,  7.58it/s, step size=2.65e-01, acc. prob=0.784]\n",
+      "\rWarmup:  60%|█████▉    | 458/768 [01:15,  7.22it/s, step size=2.51e-01, acc. prob=0.784]\n",
+      "\rWarmup:  60%|█████▉    | 459/768 [01:16,  7.01it/s, step size=1.81e-01, acc. prob=0.783]\n",
+      "\rWarmup:  60%|█████▉    | 460/768 [01:16,  6.84it/s, step size=1.96e-01, acc. prob=0.783]\n",
+      "\rWarmup:  60%|██████    | 461/768 [01:16,  6.55it/s, step size=7.77e-01, acc. prob=0.783]\n",
+      "\rWarmup:  61%|██████    | 465/768 [01:16, 12.95it/s, step size=1.81e-01, acc. prob=0.780]\n",
+      "\rWarmup:  61%|██████    | 467/768 [01:16, 14.10it/s, step size=4.04e-01, acc. prob=0.781]\n",
+      "\rWarmup:  61%|██████    | 470/768 [01:16, 17.20it/s, step size=4.15e-02, acc. prob=0.780]\n",
+      "\rWarmup:  61%|██████▏   | 472/768 [01:17, 10.61it/s, step size=1.39e-01, acc. prob=0.781]\n",
+      "\rWarmup:  62%|██████▏   | 474/768 [01:17, 11.46it/s, step size=4.78e-01, acc. prob=0.782]\n",
+      "\rWarmup:  62%|██████▏   | 476/768 [01:17, 10.86it/s, step size=1.55e-01, acc. prob=0.781]\n",
+      "\rWarmup:  62%|██████▏   | 478/768 [01:17, 11.72it/s, step size=6.61e-02, acc. prob=0.781]\n",
+      "\rWarmup:  62%|██████▎   | 480/768 [01:17,  9.13it/s, step size=1.82e-01, acc. prob=0.781]\n",
+      "\rWarmup:  63%|██████▎   | 482/768 [01:18,  9.87it/s, step size=5.35e-01, acc. prob=0.782]\n",
+      "\rWarmup:  63%|██████▎   | 484/768 [01:18, 11.45it/s, step size=2.22e-01, acc. prob=0.781]\n",
+      "\rWarmup:  63%|██████▎   | 486/768 [01:18, 11.67it/s, step size=4.37e-01, acc. prob=0.782]\n",
+      "\rWarmup:  64%|██████▎   | 489/768 [01:18, 10.93it/s, step size=2.15e-01, acc. prob=0.782]\n",
+      "\rWarmup:  64%|██████▍   | 491/768 [01:18, 11.24it/s, step size=3.84e-01, acc. prob=0.782]\n",
+      "\rWarmup:  64%|██████▍   | 494/768 [01:18, 14.15it/s, step size=7.27e-02, acc. prob=0.781]\n",
+      "\rWarmup:  65%|██████▍   | 496/768 [01:19, 12.22it/s, step size=1.64e-01, acc. prob=0.781]\n",
+      "\rWarmup:  65%|██████▍   | 498/768 [01:19, 13.33it/s, step size=2.96e-01, acc. prob=0.782]\n",
+      "\rWarmup:  65%|██████▌   | 500/768 [01:19, 12.45it/s, step size=1.60e-01, acc. prob=0.781]\n",
+      "\rWarmup:  65%|██████▌   | 502/768 [01:19, 13.58it/s, step size=6.46e-02, acc. prob=0.781]\n",
+      "\rWarmup:  66%|██████▌   | 504/768 [01:19,  9.80it/s, step size=1.64e-01, acc. prob=0.782]\n",
+      "\rWarmup:  66%|██████▌   | 506/768 [01:20, 10.76it/s, step size=3.28e-01, acc. prob=0.782]\n",
+      "\rWarmup:  66%|██████▋   | 509/768 [01:20, 11.10it/s, step size=1.29e-01, acc. prob=0.781]\n",
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+      "\rSample: 100%|██████████| 768/768 [01:43, 13.19it/s, step size=1.91e-01, acc. prob=0.896]\n",
+      "\rSample: 100%|██████████| 768/768 [01:43,  7.45it/s, step size=1.91e-01, acc. prob=0.896]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "customInput": null,
+    "executionStartTime": 1743529103501,
+    "executionStopTime": 1743529104461,
+    "language": "python",
+    "originalKey": "f2fc99ee-52d0-420f-8f1a-0f1293689aad",
+    "output": {
+     "id": "660736130231383"
+    },
+    "outputsInitialized": true,
+    "requestMsgId": "76d49bc3-3046-4c1a-8538-bd0190d04489",
+    "serverExecutionDuration": 3.7160157226026,
+    "showInput": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'STGP - target only': {'NLL': 820.1311610482774, 'MSE': 1658.1030020306143},\n",
+       " 'MTGP - ICM - MAP': {'NLL': 298.84299270373793, 'MSE': 11.273867204848827},\n",
+       " 'MTGP - Latent Embeddings - FB': {'NLL': 525.3912920530319,\n",
+       "  'MSE': 20.109351468055014},\n",
+       " 'MTGP - Latent Embeddings - MAP': {'NLL': 438.331706460182,\n",
+       "  'MSE': 53.53423246625931}}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "borehole_res "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "customInput": null,
+    "language": "python",
+    "originalKey": "471f2a59-d882-46f1-b8d5-2cd3575a9399",
+    "showInput": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
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+}
diff --git a/private_multitask_pfn/multifidelity/multifidelity.py b/private_multitask_pfn/multifidelity/multifidelity.py
new file mode 100644
index 0000000..3b0f739
--- /dev/null
+++ b/private_multitask_pfn/multifidelity/multifidelity.py
@@ -0,0 +1,448 @@
+
+import math
+
+import torch
+from botorch.test_functions.synthetic import SyntheticTestFunction
+from torch import Tensor
+
+
+class WingWeightMultiFidelity(SyntheticTestFunction):
+    """Wing Weight Design Problem from [Chen2024]_.
+
+    Design variables (physical units):
+      1. s_w   in [150,   200]   (wing area)
+      2. w_fw  in [220,   300]   (fuel weight)
+      3. A     in [6,     10]    (aspect ratio)
+      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)
+      5. q     in [16,    45]    (dynamic pressure)
+      6. lam   in [0.5,   1.0]   (taper ratio)
+      7. t_c   in [0.08,  0.18]   (thickness-to-chord)
+      8. N_z   in [2.5,   6.0]    (ultimate load factor)
+      9. w_dg  in [1700,  2500]   (design gross weight)
+      10. w_pp in [0.025, 0.08]    (weight per unit area)
+
+    Fidelity parameter (stored as the 11th input):
+      0: High fidelity (HF)
+      1: Low fidelity 1 (LF1)
+      2: Low fidelity 2 (LF2)
+      3: Low fidelity 3 (LF2)
+
+    The HF model is given by:
+      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *
+           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *
+           (N_z*w_dg)^0.49 + s_w*w_pp
+
+    LF models use slightly altered exponents and additive biases.
+    """
+
+    dim = 11
+    _num_fidelities = 1
+    _bounds = [
+        (150.0, 200.0),  # s_w
+        (220.0, 300.0),  # w_fw
+        (6.0, 10.0),  # A
+        (-10.0, 10.0),  # Lambda_deg
+        (16.0, 45.0),  # q
+        (0.5, 1.0),  # lam
+        (0.08, 0.18),  # t_c
+        (2.5, 6.0),  # N_z
+        (1700.0, 2500.0),  # w_dg
+        (0.025, 0.08),  # w_pp
+        (0, 3),
+    ]
+    fidelities = [0, 1, 2, 3]
+    _optimal_value = 123.25
+
+    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
+        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.
+        s_w = X[..., 0]
+        w_fw = X[..., 1]
+        A = X[..., 2]
+        Lambda_deg = X[..., 3]
+        Lambda_rad = Lambda_deg * math.pi / 180.0
+
+        q = X[..., 4]
+        lam = X[..., 5]
+        t_c = X[..., 6]
+        N_z = X[..., 7]
+        w_dg = X[..., 8]
+        w_pp = X[..., 9]
+        fidelity = X[..., 10]
+        cos_val = torch.cos(Lambda_rad)
+        y = torch.zeros_like(s_w)
+        # High fidelity (fidelity == 0)
+        mask = fidelity == 0
+        if mask.any():
+            hf = (
+                0.036
+                * (s_w**0.758)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+                + s_w * w_pp
+            )
+            y[mask] = hf[mask]
+        # Low fidelity 1 (fidelity == 1)
+        mask = fidelity == 1
+        if mask.any():
+            lf1 = (
+                0.036
+                * (s_w**0.758)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+                + w_pp
+            )
+            y[mask] = lf1[mask]
+        # Low fidelity 2 (fidelity == 2)
+        mask = fidelity == 2
+        if mask.any():
+            lf2 = (
+                0.036
+                * (s_w**0.8)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+                + w_pp
+            )
+            y[mask] = lf2[mask]
+        # Low fidelity 3 (fidelity == 3)
+        mask = fidelity == 3
+        if mask.any():
+            lf3 = (
+                0.036
+                * (s_w**0.9)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+            )
+            y[mask] = lf3[mask]
+        return y
+
+    def cost(self, X: torch.Tensor) -> torch.Tensor:
+        fidelity = X[..., 10]
+        c = torch.zeros_like(fidelity)
+        c[fidelity == 0] = 1000.0
+        c[fidelity == 1] = 100.0
+        c[fidelity == 2] = 10.0
+        c[fidelity == 3] = 1.0
+        return c
+
+
+class BoreholeMultiFidelity(SyntheticTestFunction):
+    """Borehole Problem from [Chen2024]_.
+
+    This problem models water flow through a borehole with 8 design variables:
+          1. r_w   in [0.05,   0.15]   (borehole radius)
+          2. r     in [100,    50000]  (radius of influence)
+          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)
+          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)
+          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)
+          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)
+          7. L     in [1120,   1680]   (length of borehole)
+          8. K_w   in [9855,   12045]  (hydraulic conductivity)
+
+        The fidelity index (9th input) is categorical:
+          0: High fidelity (HF)
+          1: Low fidelity 1 (LF1)
+          2: Low fidelity 2 (LF2)
+          3: Low fidelity 3 (LF3)
+          4: Low fidelity 4 (LF4)
+
+        The HF model is defined by:
+          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]
+
+        The low-fidelity models modify exponents and add a bias.
+    """
+
+    dim = 9
+    _num_fidelities = 1
+    _bounds = [
+        (0.05, 0.15),  # r_w
+        (100.0, 10000.0),  # r
+        (100.0, 1000.0),  # T_u
+        (10.0, 500.0),  # T_l
+        (990.0, 1110.0),  # H_u
+        (700.0, 820.0),  # H_l
+        (1000.0, 2000.0),  # L
+        (6000.0, 12000.0),  # K_w
+        (0, 4),  # fidelity
+    ]
+    fidelities = [0, 1, 2, 3, 4]
+    _optimal_value = 3.98
+
+    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
+        r_w = X[..., 0]
+        r = X[..., 1]
+        T_u = X[..., 2]
+        T_l = X[..., 3]
+        H_u = X[..., 4]
+        H_l = X[..., 5]
+        L = X[..., 6]
+        K_w = X[..., 7]
+        fidelity = X[..., 8]
+
+        log_term = torch.log(r / r_w)
+        numer = 2.0 * math.pi * T_u * (H_u - H_l)
+        y = torch.zeros_like(r_w)
+
+        # HF (fidelity 0)
+        mask = fidelity == 0
+        if mask.any():
+            hf_denom = log_term * (
+                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            hf = numer / hf_denom
+            y[mask] = hf[mask]
+
+        # LF1 (fidelity 1): add bias.
+        mask = fidelity == 1
+        if mask.any():
+            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)
+            lf1_denom = log_term * (
+                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            lf1 = lf1_numer / lf1_denom
+            y[mask] = lf1[mask]
+
+        # LF2 (fidelity 2): modify the exponent on log_term and add bias.
+        mask = fidelity == 2
+        if mask.any():
+            lf2_denom = log_term * (
+                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l
+            )
+            lf2 = numer / lf2_denom
+            y[mask] = lf2[mask]
+
+        # LF3 (fidelity 3): modify r_w exponent slightly.
+        mask = fidelity == 3
+        if mask.any():
+            lf3_log_term = torch.log(4 * r / r_w)
+            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)
+            lf3_denom = lf3_log_term * (
+                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            lf3 = lf3_numer / lf3_denom
+            y[mask] = lf3[mask]
+        # LF4 (fidelity 4): further bias.
+        mask = fidelity == 4
+        if mask.any():
+            lf4_log_term = torch.log(2 * r / r_w)
+            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)
+            lf4_denom = lf4_log_term * (
+                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            lf4 = lf4_numer / lf4_denom
+            y[mask] = lf4[mask]
+
+        return y
+
+    def cost(self, X: torch.Tensor) -> torch.Tensor:
+        fidelity = X[..., 8]
+        c = torch.zeros_like(fidelity)
+        c[fidelity == 0] = 1000.0
+        c[fidelity == 1] = 100.0
+        c[fidelity == 2] = 10.0
+        c[fidelity == 3] = 100.0
+        c[fidelity == 4] = 10.0
+        return c
+
+import numpy as np
+import torch
+from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll
+from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
+from botorch.models.gp_regression import SingleTaskGP
+
+from botorch.models.multitask import MultiTaskGP
+
+from botorch.models.transforms.input import (
+    ChainedInputTransform,
+    # LatentCategoricalEmbedding,
+    # LatentCategoricalSpec,
+    Normalize,
+)
+from botorch.models.transforms.outcome import Standardize, StratifiedStandardize
+from botorch.utils.transforms import unnormalize
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from torch.distributions import Normal
+
+
+tkwargs = {"dtype": torch.double}
+
+
+def eval_model(model, test_X, test_Y):
+    with torch.no_grad():
+        posterior = model.posterior(test_X, observation_noise=True)
+        # compute sum of LL of each point in test set (using only marginal variances)
+        var = posterior.variance
+        mean = posterior.mean
+        nll = (
+            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))
+            .log_prob(test_Y.view(-1))
+            .sum(dim=-1)
+            .mean()  # take average over MCMC samples (if needed)
+            .item()
+        )
+        mse = (mean - test_Y).pow(2).mean().item()
+    return nll, mse
+
+
+def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):
+    res = {}
+    # test STGP on target task
+    target_mask = train_X[:, -1] == 0
+    model = SingleTaskGP(
+        train_X[target_mask],
+        train_Y[target_mask],
+        input_transform=Normalize(
+            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
+        ),
+    )
+    mll = ExactMarginalLogLikelihood(model.likelihood, model)
+    _ = fit_gpytorch_mll(mll)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["STGP - target only"] = {"NLL": nll, "MSE": mse}
+
+    # Test MTGP with ICM
+    model = MultiTaskGP(
+        train_X,
+        train_Y,
+        input_transform=Normalize(
+            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
+        ),
+        outcome_transform=StratifiedStandardize(
+            stratification_idx=problem.dim - 1,
+            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
+        ),
+        task_feature=problem.dim - 1,
+    )
+    mll = ExactMarginalLogLikelihood(model.likelihood, model)
+    _ = fit_gpytorch_mll(mll)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["MTGP - ICM - MAP"] = {"NLL": nll, "MSE": mse}
+    # Test Fully Bayesian MTGP with Latent Embeddings
+    model = SaasFullyBayesianMultiTaskGP(
+        train_X,
+        train_Y,
+        input_transform=Normalize(
+            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
+        ),
+        task_feature=problem.dim - 1,
+        outcome_transform=StratifiedStandardize(
+            stratification_idx=problem.dim - 1,
+            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
+        ),
+    )
+    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["MTGP - Latent Embeddings - FB"] = {"NLL": nll, "MSE": mse}
+
+    # test LVGP with MAP estimation
+    d = train_X.shape[-1]
+    cat_dims = [problem.dim - 1]
+    # construct input transform
+    input_transform = ChainedInputTransform(
+        normalize=Normalize(d=d, indices=list(range(d - 1))),
+        # latent_emb=LatentCategoricalEmbedding(
+        #     [
+        #         LatentCategoricalSpec(
+        #             idx=i,
+        #             num_categories=len(problem.fidelities),
+        #             latent_dim=2,
+        #         )
+        #         for i in cat_dims
+        #     ],
+        #     dim=d,
+        # ).to(train_X),
+    )
+    model = SingleTaskGP(
+        train_X,
+        train_Y,
+        input_transform=input_transform,
+    )
+    mll = ExactMarginalLogLikelihood(model.likelihood, model)
+    _ = fit_gpytorch_mll(mll)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["MTGP - Latent Embeddings - MAP"] = {"NLL": nll, "MSE": mse}
+    return res
+
+
+# # Wing weight
+# ### Generate initial training set as in Chen et al 2024. Generate test set for target fidelity
+
+
+problem = WingWeightMultiFidelity()
+torch.manual_seed(0)
+# define training and test set
+N_TEST = 100
+fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}
+total_n = sum(fidelity_to_n.values())
+train_X = torch.rand(total_n, problem.dim, **tkwargs)
+train_X = unnormalize(train_X, bounds=problem.bounds)
+# set fidelities
+start = 0
+for fidelity, n in fidelity_to_n.items():
+    end = start + n
+    train_X[start:end, -1] = fidelity
+    start = end
+
+train_Y = problem(train_X).unsqueeze(-1)
+
+test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
+test_X = unnormalize(test_X, bounds=problem.bounds)
+test_X[:, -1] = 0  # target fidelity
+test_Y = problem(test_X).unsqueeze(-1)
+
+
+wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
+
+
+wing_weight_res
+
+# ## Borehole
+
+
+problem = BoreholeMultiFidelity()
+torch.manual_seed(0)
+# define training and test set
+N_TEST = 100
+fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}
+total_n = sum(fidelity_to_n.values())
+train_X = torch.rand(total_n, problem.dim, **tkwargs)
+train_X = unnormalize(train_X, bounds=problem.bounds)
+# set fidelities
+start = 0
+for fidelity, n in fidelity_to_n.items():
+    end = start + n
+    train_X[start:end, -1] = fidelity
+    start = end
+
+train_Y = problem(train_X).unsqueeze(-1)
+
+test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
+test_X = unnormalize(test_X, bounds=problem.bounds)
+test_X[:, -1] = 0  # target fidelity
+test_Y = problem(test_X).unsqueeze(-1)
+
+
+borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
+
+
+borehole_res 
+
+
+
+
+
diff --git a/private_multitask_pfn/multifidelity/multifidelity_fixed.py b/private_multitask_pfn/multifidelity/multifidelity_fixed.py
new file mode 100644
index 0000000..d15dee5
--- /dev/null
+++ b/private_multitask_pfn/multifidelity/multifidelity_fixed.py
@@ -0,0 +1,492 @@
+from utils import load_model, pfn_predict
+
+import numpy as np
+import torch
+from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll
+from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
+from botorch.models.gp_regression import SingleTaskGP
+
+from botorch.models.multitask import MultiTaskGP
+
+from botorch.models.transforms.input import (
+    ChainedInputTransform,
+    # LatentCategoricalEmbedding,
+    # LatentCategoricalSpec,
+    Normalize,
+)
+from botorch.models.transforms.outcome import Standardize, StratifiedStandardize
+from botorch.utils.transforms import unnormalize, normalize
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from torch.distributions import Normal
+import pickle
+
+import math
+
+import torch
+from botorch.test_functions.synthetic import SyntheticTestFunction
+from torch import Tensor
+
+def eval_model(model, test_X, test_Y):
+    with torch.no_grad():
+        posterior = model.posterior(test_X, observation_noise=True)
+        # compute sum of LL of each point in test set (using only marginal variances)
+        var = posterior.variance
+        mean = posterior.mean
+        nll = (
+            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))
+            .log_prob(test_Y.view(-1))
+            .sum(dim=-1)
+            .mean()  # take average over MCMC samples (if needed)
+            .item()
+        )
+        mse = (mean - test_Y).pow(2).mean().item()
+    return nll, mse
+
+def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):
+    res = {}
+    # test STGP on target task
+    target_mask = train_X[:, -1] == 0
+    model = SingleTaskGP(
+        train_X[target_mask],
+        train_Y[target_mask],
+        input_transform=Normalize(
+            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
+        ),
+    )
+    mll = ExactMarginalLogLikelihood(model.likelihood, model)
+    _ = fit_gpytorch_mll(mll)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["STGP - target only"] = {"NLL": nll, "MSE": mse}
+
+    # Test MTGP with ICM
+    model = MultiTaskGP(
+        train_X,
+        train_Y,
+        input_transform=Normalize(
+            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
+        ),
+        outcome_transform=StratifiedStandardize(
+            stratification_idx=problem.dim - 1,
+            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
+        ),
+        task_feature=problem.dim - 1,
+    )
+    mll = ExactMarginalLogLikelihood(model.likelihood, model)
+    _ = fit_gpytorch_mll(mll)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["MTGP - ICM - MAP"] = {"NLL": nll, "MSE": mse}
+    # Test Fully Bayesian MTGP with Latent Embeddings
+    model = SaasFullyBayesianMultiTaskGP(
+        train_X,
+        train_Y,
+        input_transform=Normalize(
+            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
+        ),
+        task_feature=problem.dim - 1,
+        outcome_transform=StratifiedStandardize(
+            stratification_idx=problem.dim - 1,
+            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
+        ),
+    )
+    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["MTGP - Latent Embeddings - FB"] = {"NLL": nll, "MSE": mse}
+
+    # test LVGP with MAP estimation
+    d = train_X.shape[-1]
+    cat_dims = [problem.dim - 1]
+    # construct input transform
+    input_transform = ChainedInputTransform(
+        normalize=Normalize(d=d, indices=list(range(d - 1))),
+        # latent_emb=LatentCategoricalEmbedding(
+        #     [
+        #         LatentCategoricalSpec(
+        #             idx=i,
+        #             num_categories=len(problem.fidelities),
+        #             latent_dim=2,
+        #         )
+        #         for i in cat_dims
+        #     ],
+        #     dim=d,
+        # ).to(train_X),
+    )
+    model = SingleTaskGP(
+        train_X,
+        train_Y,
+        input_transform=input_transform,
+    )
+    mll = ExactMarginalLogLikelihood(model.likelihood, model)
+    _ = fit_gpytorch_mll(mll)
+    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
+    res["MTGP - Latent Embeddings - MAP"] = {"NLL": nll, "MSE": mse}
+    return res
+
+class WingWeightMultiFidelitySmall(SyntheticTestFunction):
+    """Wing Weight Design Problem from [Chen2024]_.
+
+    Design variables (physical units):
+      1. s_w   in [150,   200]   (wing area)
+      2. w_fw  in [220,   300]   (fuel weight)
+      3. A     in [6,     10]    (aspect ratio)
+      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)
+      5. q     in [16,    45]    (dynamic pressure)
+      6. lam   in [0.5,   1.0]   (taper ratio)
+      7. t_c   in [0.08,  0.18]   (thickness-to-chord)
+      8. N_z   in [2.5,   6.0]    (ultimate load factor)
+      9. w_dg  in [1700,  2500]   (design gross weight)
+      10. w_pp in [0.025, 0.08]    (weight per unit area)
+
+    Fidelity parameter (stored as the 11th input):
+      0: High fidelity (HF)
+      1: Low fidelity 1 (LF1)
+      2: Low fidelity 2 (LF2)
+      3: Low fidelity 3 (LF2)
+
+    The HF model is given by:
+      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *
+           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *
+           (N_z*w_dg)^0.49 + s_w*w_pp
+
+    LF models use slightly altered exponents and additive biases.
+    """
+
+    dim = 7
+    _num_fidelities = 1
+    _bounds = [
+        (150.0, 200.0),  # s_w
+        (220.0, 300.0),  # w_fw
+        (6.0, 10.0),  # A
+        (-10.0, 10.0),  # Lambda_deg
+        (16.0, 45.0),  # q
+        (0.5, 1.0),  # lam
+        # (0.08, 0.18),  # t_c
+        # (2.5, 6.0),  # N_z
+        # (1700.0, 2500.0),  # w_dg
+        # (0.025, 0.08),  # w_pp
+        (0, 3), # fidelity
+    ]
+    fidelities = [0, 1, 2, 3]
+    _optimal_value = 123.25
+
+    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
+        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.
+        s_w = X[..., 0]
+        w_fw = X[..., 1]
+        A = X[..., 2]
+        Lambda_deg = X[..., 3]
+        Lambda_rad = Lambda_deg * math.pi / 180.0
+
+        q = X[..., 4]
+        lam = X[..., 5]
+        t_c = 0.13 #X[..., 6]
+        N_z = 4.5 #X[..., 7]
+        w_dg = 2000 #X[..., 8]
+        w_pp = 0.05 #X[..., 9]
+        fidelity = X[..., -1]
+        cos_val = torch.cos(Lambda_rad)
+        y = torch.zeros_like(s_w)
+        # High fidelity (fidelity == 0)
+        mask = fidelity == 0
+        if mask.any():
+            hf = (
+                0.036
+                * (s_w**0.758)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+                + s_w * w_pp
+            )
+            y[mask] = hf[mask]
+        # Low fidelity 1 (fidelity == 1)
+        mask = fidelity == 1
+        if mask.any():
+            lf1 = (
+                0.036
+                * (s_w**0.758)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+                + w_pp
+            )
+            y[mask] = lf1[mask]
+        # Low fidelity 2 (fidelity == 2)
+        mask = fidelity == 2
+        if mask.any():
+            lf2 = (
+                0.036
+                * (s_w**0.8)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+                + w_pp
+            )
+            y[mask] = lf2[mask]
+        # Low fidelity 3 (fidelity == 3)
+        mask = fidelity == 3
+        if mask.any():
+            lf3 = (
+                0.036
+                * (s_w**0.9)
+                * (w_fw**0.0035)
+                * ((A / (cos_val**2)) ** 0.6)
+                * (q**0.006)
+                * (lam**0.04)
+                * ((100.0 * t_c / cos_val) ** (-0.3))
+                * ((N_z * w_dg) ** 0.49)
+            )
+            y[mask] = lf3[mask]
+        return y
+
+    def cost(self, X: torch.Tensor) -> torch.Tensor:
+        fidelity = X[..., 10]
+        c = torch.zeros_like(fidelity)
+        c[fidelity == 0] = 1000.0
+        c[fidelity == 1] = 100.0
+        c[fidelity == 2] = 10.0
+        c[fidelity == 3] = 1.0
+        return c
+
+
+class BoreholeMultiFidelitySmall(SyntheticTestFunction):
+    """Borehole Problem from [Chen2024]_.
+
+    This problem models water flow through a borehole with 8 design variables:
+          1. r_w   in [0.05,   0.15]   (borehole radius)
+          2. r     in [100,    50000]  (radius of influence)
+          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)
+          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)
+          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)
+          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)
+          7. L     in [1120,   1680]   (length of borehole)
+          8. K_w   in [9855,   12045]  (hydraulic conductivity)
+
+        The fidelity index (9th input) is categorical:
+          0: High fidelity (HF)
+          1: Low fidelity 1 (LF1)
+          2: Low fidelity 2 (LF2)
+          3: Low fidelity 3 (LF3)
+          4: Low fidelity 4 (LF4)
+
+        The HF model is defined by:
+          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]
+
+        The low-fidelity models modify exponents and add a bias.
+    """
+
+    dim = 7
+    _num_fidelities = 1
+    _bounds = [
+        (0.05, 0.15),  # r_w
+        (100.0, 10000.0),  # r
+        (100.0, 1000.0),  # T_u
+        (10.0, 500.0),  # T_l
+        (990.0, 1110.0),  # H_u
+        (700.0, 820.0),  # H_l
+        # (1000.0, 2000.0),  # L
+        # (6000.0, 12000.0),  # K_w
+        (0, 4),  # fidelity
+    ]
+    fidelities = [0, 1, 2, 3, 4]
+    _optimal_value = 3.98
+
+    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
+        r_w = X[..., 0]
+        r = X[..., 1]
+        T_u = X[..., 2]
+        T_l = X[..., 3]
+        H_u = X[..., 4]
+        H_l = X[..., 5]
+        L = 760 # X[..., 6]
+        K_w = 1500 #X[..., 7]
+        fidelity = X[..., -1]
+
+        log_term = torch.log(r / r_w)
+        numer = 2.0 * math.pi * T_u * (H_u - H_l)
+        y = torch.zeros_like(r_w)
+
+        # HF (fidelity 0)
+        mask = fidelity == 0
+        if mask.any():
+            hf_denom = log_term * (
+                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            hf = numer / hf_denom
+            y[mask] = hf[mask]
+
+        # LF1 (fidelity 1): add bias.
+        mask = fidelity == 1
+        if mask.any():
+            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)
+            lf1_denom = log_term * (
+                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            lf1 = lf1_numer / lf1_denom
+            y[mask] = lf1[mask]
+
+        # LF2 (fidelity 2): modify the exponent on log_term and add bias.
+        mask = fidelity == 2
+        if mask.any():
+            lf2_denom = log_term * (
+                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l
+            )
+            lf2 = numer / lf2_denom
+            y[mask] = lf2[mask]
+
+        # LF3 (fidelity 3): modify r_w exponent slightly.
+        mask = fidelity == 3
+        if mask.any():
+            lf3_log_term = torch.log(4 * r / r_w)
+            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)
+            lf3_denom = lf3_log_term * (
+                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            lf3 = lf3_numer / lf3_denom
+            y[mask] = lf3[mask]
+        # LF4 (fidelity 4): further bias.
+        mask = fidelity == 4
+        if mask.any():
+            lf4_log_term = torch.log(2 * r / r_w)
+            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)
+            lf4_denom = lf4_log_term * (
+                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
+            )
+            lf4 = lf4_numer / lf4_denom
+            y[mask] = lf4[mask]
+
+        return y
+
+    def cost(self, X: torch.Tensor) -> torch.Tensor:
+        fidelity = X[..., 8]
+        c = torch.zeros_like(fidelity)
+        c[fidelity == 0] = 1000.0
+        c[fidelity == 1] = 100.0
+        c[fidelity == 2] = 10.0
+        c[fidelity == 3] = 100.0
+        c[fidelity == 4] = 10.0
+        return c
+    
+
+
+def eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y):
+    task_id = train_X[:, -1].long()
+    # normalize
+    train_X = normalize(train_X, bounds=problem.bounds)
+    test_X = normalize(test_X, bounds=problem.bounds)
+    
+    train_X = train_X[:, :-1]  # remove fidelity column
+    test_X = test_X[:, :-1]  # remove fidelity column
+    
+    # standardize Y
+    train_y_mean, train_y_std = train_Y.mean(), train_Y.std()
+    train_Y = (train_Y - train_y_mean) / train_y_std
+    
+    with torch.no_grad():
+        posterior = pfn_predict(pfn, task_id, train_X.float(), train_Y.float(), test_X.float())
+        # compute sum of LL of each point in test set (using only marginal variances)
+        var = posterior.variance
+        mean = posterior.mean
+        # import pdb; pdb.set_trace()
+        unstand_mean, unstand_var = (
+            mean * train_y_std + train_y_mean,
+            var * train_y_std**2,
+        )
+        nll = (
+            -Normal(loc=unstand_mean.squeeze(-1), scale=unstand_var.squeeze(-1))
+            .log_prob(test_Y.view(-1))
+            .sum(dim=-1)
+            .mean()  # take average over MCMC samples (if needed)
+            .item()
+        )
+        mse = (unstand_mean - test_Y).pow(2).mean().item()
+    return nll, mse
+
+# print(pickle.load(open("wing_weight_res.pkl", "rb")))
+# print(pickle.load(open("borehole_res.pkl", "rb")))  # load the results
+# lily
+
+tkwargs = {"dtype": torch.float}
+problem = WingWeightMultiFidelitySmall()
+torch.manual_seed(0)
+# define training and test set
+N_TEST = 100
+fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}
+total_n = sum(fidelity_to_n.values())
+train_X = torch.rand(total_n, problem.dim, **tkwargs)
+train_X = unnormalize(train_X, bounds=problem.bounds)
+# set fidelities
+start = 0
+for fidelity, n in fidelity_to_n.items():
+    end = start + n
+    train_X[start:end, -1] = fidelity
+    start = end
+
+train_Y = problem(train_X).unsqueeze(-1)
+
+test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
+test_X = unnormalize(test_X, bounds=problem.bounds)
+test_X[:, -1] = 0  # target fidelity
+test_Y = problem(test_X).unsqueeze(-1)
+
+
+wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
+
+# for pfn_id in ["vibrant-breeze-498", "revived-frog-499"]:
+#     pfn = load_model(f"/home/yl9959/mtpfn/final_models/{pfn_id}")
+#     nll, mse = eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)
+#     wing_weight_res[pfn_id] = {"NLL": nll, "MSE": mse}
+    
+print("Wing Weight Results")
+for k, v in wing_weight_res.items():
+    print(k)
+    print(v)
+    print()
+# Save the results
+pickle.dump(wing_weight_res, open("wing_weight_res.pkl", "wb"))
+
+
+
+problem = BoreholeMultiFidelitySmall()
+torch.manual_seed(0)
+# define training and test set
+N_TEST = 100
+fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}
+total_n = sum(fidelity_to_n.values())
+train_X = torch.rand(total_n, problem.dim, **tkwargs)
+train_X = unnormalize(train_X, bounds=problem.bounds)
+# set fidelities
+start = 0
+for fidelity, n in fidelity_to_n.items():
+    end = start + n
+    train_X[start:end, -1] = fidelity
+    start = end
+
+train_Y = problem(train_X).unsqueeze(-1)
+
+test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
+test_X = unnormalize(test_X, bounds=problem.bounds)
+test_X[:, -1] = 0  # target fidelity
+test_Y = problem(test_X).unsqueeze(-1)
+
+borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
+
+# for pfn_id in ["vibrant-breeze-498", "revived-frog-499"]:
+#     pfn = load_model(f"/home/yl9959/mtpfn/final_models/{pfn_id}")
+#     nll, mse = eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)
+#     borehole_res[pfn_id] = {"NLL": nll, "MSE": mse}
+    
+print("Borehole Results")
+for k, v in borehole_res.items():
+    print(k)
+    print(v)
+    print()
+# Save the results
+pickle.dump(borehole_res, open("borehole_res.pkl", "wb"))
\ No newline at end of file
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diff --git a/private_multitask_pfn/paper_figures/benchmark_time.ipynb b/private_multitask_pfn/paper_figures/benchmark_time.ipynb
new file mode 100644
index 0000000..cd1f606
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/benchmark_time.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "      <th></th>\n",
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+       "      <th>mtgp</th>\n",
+       "      <th>scaml</th>\n",
+       "      <th>pfn</th>\n",
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+       "      <th>1</th>\n",
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+       "      <td>0.000186</td>\n",
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+       "      <td>0.000248</td>\n",
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+       "      <td>9</td>\n",
+       "      <td>316</td>\n",
+       "      <td>15.475098</td>\n",
+       "      <td>1.954615</td>\n",
+       "      <td>0.000378</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  n_samples_per_task       mtgp     scaml       pfn\n",
+       "0           0                  31   0.525355  0.641527  0.000156\n",
+       "1           1                  40   0.475192  0.789012  0.000167\n",
+       "2           2                  52   0.316957  0.571510  0.000171\n",
+       "3           3                  68   0.433492  1.640374  0.000186\n",
+       "4           4                  87   0.892945  0.781131  0.000200\n",
+       "5           5                 113   1.074306  0.666595  0.000217\n",
+       "6           6                 146   1.437731  0.942868  0.000248\n",
+       "7           7                 189   2.241446  1.490375  0.000279\n",
+       "8           8                 244   4.005497  1.585110  0.000320\n",
+       "9           9                 316  15.475098  1.954615  0.000378"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(8, 3))\n",
+    "\n",
+    "df = pd.read_csv(\"/home/lily_l/private_multitask_pfn/benchmark_time_samples.csv\")\n",
+    "n_samples_per_task = df[\"n_samples_per_task\"]\n",
+    "mtgp = df[\"mtgp\"]\n",
+    "scaml = df[\"scaml\"]\n",
+    "pfn = df[\"pfn\"]\n",
+    "\n",
+    "axs[0].plot(n_samples_per_task, mtgp, label=\"Joint (ICM)\")\n",
+    "axs[0].plot(n_samples_per_task, scaml, label=\"Ensemble (ScaML)\")\n",
+    "axs[0].plot(n_samples_per_task, pfn, label=\"MTPFN\")\n",
+    "\n",
+    "axs[0].set_xlabel(\"Number of samples per task\")\n",
+    "axs[0].set_ylabel(\"Time (s)\")\n",
+    "axs[0].set_ylim(-0.5, 13)\n",
+    "axs[0].set_title(\"5 Tasks\")\n",
+    "\n",
+    "df = pd.read_csv(\"/home/lily_l/private_multitask_pfn/benchmark_time_task.csv\")\n",
+    "n_tasks = df[\"n_tasks\"]\n",
+    "mtgp = df[\"mtgp\"]\n",
+    "scaml = df[\"scaml\"]\n",
+    "pfn = df[\"pfn\"]\n",
+    "\n",
+    "\n",
+    "axs[1].plot(n_tasks, mtgp, label=\"Joint (ICM)\")\n",
+    "axs[1].plot(n_tasks, scaml, label=\"Ensemble (ScaML)\")\n",
+    "axs[1].plot(n_tasks, pfn, label=\"MTPFN\")\n",
+    "\n",
+    "axs[1].set_title(\"50 Samples per Task\")\n",
+    "axs[1].set_xlabel(\"Number of tasks\")\n",
+    "axs[1].set_ylabel(\"Time (s)\")\n",
+    "axs[1].set_xscale(\"log\")\n",
+    "\n",
+    "axs[1].set_ylim(-0.5, 13)\n",
+    "\n",
+    "# for axis set grid\n",
+    "for ax in axs:\n",
+    "    ax.grid(True, which='major', linestyle='--', linewidth=0.5)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.savefig(\"benchmark_time.pdf\", bbox_inches='tight')\n",
+    "\n",
+    "\n",
+    "# plt.legend()\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pfn",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/private_multitask_pfn/paper_figures/benchmark_time_samples.csv b/private_multitask_pfn/paper_figures/benchmark_time_samples.csv
new file mode 100644
index 0000000..2e0b278
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/benchmark_time_samples.csv
@@ -0,0 +1,11 @@
+,n_samples_per_task,mtgp,scaml,pfn
+0,31,0.5253551891073585,0.641526885703206,0.00015585944056510924
+1,40,0.47519186716526746,0.7890122389420867,0.0001666948199272156
+2,52,0.3169569944962859,0.5715096918866038,0.0001706913113594055
+3,68,0.4334919206798077,1.6403738755732775,0.0001862967386841774
+4,87,0.8929445460438729,0.781130695156753,0.00020006559789180755
+5,113,1.074306179396808,0.6665953608229757,0.0002172837033867836
+6,146,1.4377311939373612,0.9428675603121519,0.0002484273165464401
+7,189,2.241445547528565,1.490374975092709,0.0002793321385979652
+8,244,4.005497383885086,1.5851096855476499,0.00031972844153642653
+9,316,15.475097639858722,1.954615351371467,0.0003778502345085144
diff --git a/private_multitask_pfn/paper_figures/benchmark_time_task.csv b/private_multitask_pfn/paper_figures/benchmark_time_task.csv
new file mode 100644
index 0000000..3cd4bf3
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/benchmark_time_task.csv
@@ -0,0 +1,17 @@
+,n_tasks,mtgp,scaml,pfn
+0,3,0.2848466670140624,0.2343419875949621,0.00014029312878847123
+1,4,0.25992622077465055,0.7537569304928183,0.00015438385307788848
+2,7,0.8310501158237458,1.5789503464475274,0.00020955316722393036
+3,11,13.401715995557606,1.616787961870432,0.0003231516107916832
+4,17,,2.5916220061481,0.0005841787904500961
+5,26,,17.287783980555833,0.0011530579999089242
+6,40,,25.625626320578156,0.002393585816025734
+7,61,,,0.00520332083106041
+8,94,,,0.011591497063636779
+9,143,,,0.026707475446164607
+10,219,,,0.06632151622325182
+11,335,,,0.16265095062553883
+12,513,,,0.4161450076848269
+13,784,,,0.9698941947892308
+14,1199,,,2.2640723291784526
+15,1832,,,5.259221996180713
diff --git a/private_multitask_pfn/paper_figures/figures_bayesian.py b/private_multitask_pfn/paper_figures/figures_bayesian.py
new file mode 100644
index 0000000..c041a72
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/figures_bayesian.py
@@ -0,0 +1,428 @@
+from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
+import torch
+import json
+
+
+import pickle
+import pdb
+import gpytorch
+import torch
+from botorch.models import SingleTaskGP
+from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
+from botorch.exceptions import ModelFittingError
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from tqdm import tqdm
+import botorch
+import einops
+
+from tabpfn.base import (
+    create_inference_engine,
+    determine_precision,
+    initialize_tabpfn_model,
+)
+from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
+
+from tabpfn import TabPFNClassifier, TabPFNRegressor
+import matplotlib.pyplot as plt
+import os
+
+import traceback
+from collections import defaultdict
+
+
+colors = {
+    "MTPFN": "deepskyblue",
+    "ScaML": "#E78C35",
+    "ICM": "#DA2222",
+    "ICM (NUTS)": "C2",
+    "GP": "#95211B",
+}
+
+baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"] #+ ["random"] 
+
+# model_to_label = {
+#     "different-dust-282": "PFN (0.5 uncorr)",
+#     "royal-firebrand-281": "PFN (0.25 uncorr)",
+#     "peach-plasma-280": "PFN (1.0 uncorr)",
+#     "treasured-lion-279": "PFN (0.0 uncorr)",
+#     "exalted-wave-278": "PFN (0.75 uncorr)",
+# }
+
+# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+
+def get_label(model):
+    if model in model_to_label:
+        return model_to_label[model]
+    return model
+
+
+def get_labels(models):
+    return [get_label(model) for model in models]
+
+
+def get_color(model):
+    return colors[get_label(model)]
+
+
+
+
+
+def plot_bo_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
+        # linestyle 
+        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
+
+                    
+    plt.legend()
+    plt.xlabel("BO Iterations")
+    plt.ylabel("Normalized Regret")
+    plt.title(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    
+    
+    
+
+def plot_bo_ax(ax, exp_dir, models, title, path=None):    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                if torch.isnan(function_results).any():
+                    continue
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results) #0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        # linestyle 
+        xs = torch.linspace(0, 40, len(mean))
+        ax.plot(xs, mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
+
+    ax.set_title(title)
+    ax.set_ylabel("Normalized Regret")
+    ax.set_xlabel("BO Iterations")
+    # ax.set_ylim(0.05, 0.7)
+    # ax.set_yscale("log")
+    # plt.legend()
+    # plt.xlabel("BO Iterations")
+    # plt.ylabel("Normalized Regret")
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+    
+    
+    
+    
+
+def plot_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    # one plot for boxplots of mse, one plot for boxplots of nll
+    
+    model_mses = defaultdict(list)
+    model_nlls = defaultdict(list)
+    
+    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+    for model_order in all_models:
+        for function_dir in os.listdir(exp_dir):
+            # check if directory
+            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                continue
+            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
+                results = json.load(f)
+                
+            models = list(results.keys())
+            for model in models:
+                if model != model_order:
+                    continue
+                mses, nlls = [], []
+                try:
+                    for trial_key in results[model]:
+                        mses.append(results[model][trial_key]["mse"])
+                        nlls.append(results[model][trial_key]["nll"])
+                except KeyboardInterrupt as e:
+                    raise e
+                except Exception as e:
+                    traceback.print_exc()
+                    continue
+                model_mses[model].extend(mses)
+                model_nlls[model].extend(nlls)
+    
+    # plot nll boxplots
+    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("NLL")
+    plt.title(title)
+    plt.ylabel("NLL")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    plt.clf()
+    
+    # plot mse boxplots
+    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("MSE")
+    plt.title(title)
+    plt.ylabel("MSE")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path.replace(".png", "_mse.png"))
+    plt.clf()
+    
+
+def plot_bo_function_ax(ax, function_dir, models, title):
+    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
+        bo_results = json.load(f)
+    with open(os.path.join(function_dir, "info.json"), "r") as f:
+        info = json.load(f)
+        
+    for model in baselines + models:
+        for model_try in bo_results:
+            if model_try != model:
+                continue
+            model_results = []
+            try:
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    model_results.append(regret)
+                
+                # remove shorter trials
+                max_length = max([len(result) for result in model_results])
+                model_results = [result for result in model_results if len(result) == max_length]
+                
+                model_results = torch.stack(model_results)
+                
+                mean = model_results.nanmean(0)
+                std = model_results.std(0) /  len(model_results) #** 0.5
+                lower = mean - std
+                upper = mean + std
+                # mean = model_results.mean(0)
+                # lower = model_results.quantile(0.25, 0)
+                # upper = model_results.quantile(0.75, 0)
+                linestyle = "--" if model in baselines else "-"
+                
+                xs = torch.linspace(0, 40, len(mean))
+                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
+                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
+                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                print(e)
+                # import pdb; pdb.set_trace()
+                # raise e
+                continue
+                    
+    # plt.legend()
+    ax.set_xlabel("BO Iterations")
+    ax.set_ylabel("Normalized Regret")
+    ax.set_title(title)
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+
+
+
+# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
+# plot_bo_all("/home/yl9959/mtpfn/eval_plot/25-01-28_11-31-32__synthetic__trials_5__seed_0__num_samples/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0", models, "All")
+
+# fig, axs = plt.subplots(1, 5, figsize=(12, 2.5))
+plt.figure(figsize=(6, 4))
+ax = plt.gca()
+
+# # add grid
+# for ax in axs:
+#     # y grid only
+#     ax.yaxis.grid(True,  linestyle='-', linewidth=0.5, alpha=0.5)
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_23-38-18__synthetic__trials_3__seed_0__nuts_1/features_3__tasks_4__lengthscale_0_2__task_corr_0_6__uncorr_tasks_0__n_target_2__n_source_20__seed_0/function_0"
+models = ["stilted-night-482"]
+model_to_label = {
+    "stilted-night-482": "MTPFN",
+    "mtgp": "ICM",
+    "scaml": "ICM (NUTS)",
+    "gp": "GP",
+    "mtgp_nuts": "ScaML",
+}
+title = ""
+plot_bo_function_ax(ax, dir, models, title)
+
+plt.legend()
+plt.savefig("full.pdf", bbox_inches='tight')
+lily
+
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__xgb/n_target_5__n_source_20"
+model_to_label = {
+    "stilted-night-482": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["stilted-night-482"]
+title = "XGB"
+plot_bo_ax(axs[1], dir, models, title)
+
+
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__rf/n_target_2__n_source_20"
+model_to_label = {
+    "stilted-night-482": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["stilted-night-482"]
+title = "RF"
+plot_bo_ax(axs[2], dir, models, title)
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__lr/n_target_2__n_source_20"
+model_to_label = {
+    "fallen-cherry-483": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["fallen-cherry-483"]
+title = "LR"
+plot_bo_ax(axs[3], dir, models, title)
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__nn/n_target_2__n_source_20"
+model_to_label = {
+    "stilted-night-482": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["stilted-night-482"]
+title = "NN"
+plot_bo_ax(axs[4], dir, models, title)
+
+handles, labels = axs[0].get_legend_handles_labels()
+# legend above plot
+fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=4, handles=handles, labels=labels)
+# plt.legend()
+plt.tight_layout()
+plt.savefig("hpobench.pdf", bbox_inches='tight')
diff --git a/private_multitask_pfn/paper_figures/figures_benchmarks.py b/private_multitask_pfn/paper_figures/figures_benchmarks.py
new file mode 100644
index 0000000..6bc1a91
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/figures_benchmarks.py
@@ -0,0 +1,449 @@
+from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
+import torch
+import json
+
+
+import pickle
+import pdb
+import gpytorch
+import torch
+from botorch.models import SingleTaskGP
+from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
+from botorch.exceptions import ModelFittingError
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from tqdm import tqdm
+import botorch
+import einops
+
+from tabpfn.base import (
+    create_inference_engine,
+    determine_precision,
+    initialize_tabpfn_model,
+)
+from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
+
+from tabpfn import TabPFNClassifier, TabPFNRegressor
+import matplotlib.pyplot as plt
+import os
+
+import traceback
+from collections import defaultdict
+
+
+#4477AA', '#EE6677', '#228833', '#CCBB44', '#66CCEE', '#AA3377', '#BBBBBB'
+# colors = {
+#     "PFN (0%)": "#4477AA",
+#     "PFN (10%)": "#66CCEE",
+#     "PFN (20%)": "#228833",
+#     "ScaML": "#CCBB44",
+#     "ICM": "#EE6677",
+#     "GP": "#AA3377",
+# }
+##2166AC', '#4393C3', '#92C5DE '#F4A582', '#D6604D', '#B2182B'
+colors = {
+    # "PFN (0%)": "#C3A8D1",
+    "PFN": "deepskyblue",
+    "PFN (10%)": "#6059A9",
+    "PFN (20%)": "#4E96BC",
+    "ScaML": "#E78C35",
+    "ICM": "#DA2222",
+    "GP": "#95211B",
+    "Random": "#B2B2B2",
+}
+
+marker = {
+    "PFN (0%)": "o",
+    "PFN (10%)": "^",
+    "PFN (20%)": "s",
+    "ScaML": "1",
+    "ICM": "+",
+    "GP": "x",
+}
+
+# marker = {
+#     "PFN (0%)": "1",
+#     "PFN (10%)": "2",
+#     "PFN (20%)": "3",
+#     "ScaML": "+",
+#     "ICM": "x",
+#     "GP": "o",
+# }
+
+##CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE', '#882255', '#44AA99', '#999933', '#AA4499'
+# colors = {
+#     "PFN (0%)": "#CC6677",
+#     "PFN (10%)": "#332288",
+#     "PFN (20%)": "#DDCC77",
+#     "ScaML": "#117733",
+#     "ICM": "#88CCEE",
+#     "GP": "#882255",
+# }
+
+baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]# + ["random"] 
+
+# model_to_label = {
+#     "different-dust-282": "PFN (0.5 uncorr)",
+#     "royal-firebrand-281": "PFN (0.25 uncorr)",
+#     "peach-plasma-280": "PFN (1.0 uncorr)",
+#     "treasured-lion-279": "PFN (0.0 uncorr)",
+#     "exalted-wave-278": "PFN (0.75 uncorr)",
+# }
+
+# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+
+def get_label(model):
+    if model in model_to_label:
+        return model_to_label[model]
+    return model
+
+
+def get_labels(models):
+    return [get_label(model) for model in models]
+
+
+def get_color(model):
+    return colors[get_label(model)]
+
+
+
+
+
+def plot_bo_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                traceback.print_exc()
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
+        # linestyle 
+        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
+
+                    
+    plt.legend()
+    plt.xlabel("BO Iterations")
+    plt.ylabel("Normalized Regret")
+    plt.title(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    
+    
+    
+
+def plot_bo_ax(ax, exp_dir, models, title, path):    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
+        # linestyle 
+        ax.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        ax.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
+
+    ax.set_title(title)
+    ax.set_ylabel("Normalized Regret")
+    ax.set_xlabel("BO Iterations")
+    # ax.set_ylim(0.05, 0.7)
+    # ax.set_yscale("log")
+    # plt.legend()
+    # plt.xlabel("BO Iterations")
+    # plt.ylabel("Normalized Regret")
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+    
+    
+    
+    
+
+def plot_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    # one plot for boxplots of mse, one plot for boxplots of nll
+    
+    model_mses = defaultdict(list)
+    model_nlls = defaultdict(list)
+    
+    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+    for model_order in all_models:
+        for function_dir in os.listdir(exp_dir):
+            # check if directory
+            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                continue
+            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
+                results = json.load(f)
+                
+            models = list(results.keys())
+            for model in models:
+                if model != model_order:
+                    continue
+                mses, nlls = [], []
+                try:
+                    for trial_key in results[model]:
+                        mses.append(results[model][trial_key]["mse"])
+                        nlls.append(results[model][trial_key]["nll"])
+                except KeyboardInterrupt as e:
+                    raise e
+                except Exception as e:
+                    traceback.print_exc()
+                    continue
+                model_mses[model].extend(mses)
+                model_nlls[model].extend(nlls)
+    
+    # plot nll boxplots
+    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("NLL")
+    plt.title(title)
+    plt.ylabel("NLL")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    plt.clf()
+    
+    # plot mse boxplots
+    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("MSE")
+    plt.title(title)
+    plt.ylabel("MSE")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path.replace(".png", "_mse.png"))
+    plt.clf()
+    
+
+
+def plot_bo_function_ax(ax, function_dir, title):
+    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
+        bo_results = json.load(f)
+    with open(os.path.join(function_dir, "info.json"), "r") as f:
+        info = json.load(f)
+        
+    for model in baselines + models:
+        for model_try in bo_results:
+            if model_try != model:
+                continue
+            model_results = []
+            try:
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    model_results.append(regret)
+                
+                # remove shorter trials
+                max_length = max([len(result) for result in model_results])
+                model_results = [result for result in model_results if len(result) == max_length]
+                
+                model_results = torch.stack(model_results)
+                
+                mean = model_results.nanmean(0)
+                std = model_results.std(0) / len(model_results)# ** 0.5
+                lower = mean - std
+                upper = mean + std
+                # mean = model_results.mean(0)
+                # lower = model_results.quantile(0.25, 0)
+                # upper = model_results.quantile(0.75, 0)
+                linestyle = "--" if model in baselines else "-"
+                
+                xs = torch.linspace(0, 40, len(mean))
+                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
+                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
+                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                print(e)
+                # import pdb; pdb.set_trace()
+                # raise e
+                continue
+                    
+    # plt.legend()
+    ax.set_xlabel("BO Iterations")
+    ax.set_ylabel("Normalized Regret")
+    ax.set_title(title)
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+
+
+
+
+# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
+
+
+#### !!!!!!!!!  FC-Net   !!!!!!!!!! ####
+
+fig, axs = plt.subplots(1, 4, figsize=(12, 3))
+
+# add grid
+for ax in axs:
+    ax.grid(True, which='both', linestyle='-', linewidth=0.5, alpha=0.5)
+
+
+# fcnet naval
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_5__n_source_20/fcnet_naval_propulsion_data"
+models = ["likely-donkey-479"]
+model_to_label = {
+    "likely-donkey-479": "PFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+title = "Naval Propulsion"
+plot_bo_function_ax(axs[0], dir, title)
+
+title = "Parkinson's"
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_2__n_source_100/fcnet_parkinsons_telemonitoring_data"
+models = ["wobbly-donkey-484"]
+model_to_label = {
+    "wobbly-donkey-484": "PFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+plot_bo_function_ax(axs[1], dir, title)
+
+title = "Protein Structure"
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_2__n_source_100/fcnet_protein_structure_data"
+models = ["fallen-cherry-483"]
+model_to_label = {
+    "fallen-cherry-483": "PFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+plot_bo_function_ax(axs[2], dir, title)
+
+title = "Slice Localization"
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_2__n_source_100/fcnet_slice_localization_data"
+models = ["stilted-night-482"]
+model_to_label = {
+    "stilted-night-482": "PFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+plot_bo_function_ax(axs[3], dir, title)
+
+# plt.legend()
+plt.tight_layout()
+plt.savefig("fcnet.pdf")
\ No newline at end of file
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diff --git a/private_multitask_pfn/paper_figures/figures_finetune.ipynb b/private_multitask_pfn/paper_figures/figures_finetune.ipynb
new file mode 100644
index 0000000..3c54e9a
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/figures_finetune.ipynb
@@ -0,0 +1,833 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lily_l/private_multitask_pfn/PFNs/pfns/priors/utils.py:293: FutureWarning: Decorating classes is deprecated and will be disabled in future versions. You should only decorate functions or methods. To preserve the current behavior of class decoration, you can directly decorate the `__init__` method and nothing else.\n",
+      "  @torch.no_grad()\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "from utils import load_model_from_epoch, load_model\n",
+    "from gen_batch import get_hpobench_batch_fn, get_mtgp_batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_batch_fn = get_hpobench_batch_fn('lr', train=False)\n",
+    "\n",
+    "batches = []\n",
+    "for i in range(20):\n",
+    "    batch = lr_batch_fn(\n",
+    "        batch_size=256,\n",
+    "        seq_len=20,\n",
+    "        num_features=2,\n",
+    "        max_num_tasks=10,\n",
+    "        num_tasks=1,\n",
+    "    )\n",
+    "    batches.append(batch)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "svm_batch_fn = get_hpobench_batch_fn('svm', train=False)\n",
+    "\n",
+    "svm_batches = []\n",
+    "for i in range(20):\n",
+    "    batch = svm_batch_fn(\n",
+    "        batch_size=256,\n",
+    "        seq_len=20,\n",
+    "        num_features=2,\n",
+    "        max_num_tasks=10,\n",
+    "        num_tasks=1,\n",
+    "    )\n",
+    "    svm_batches.append(batch)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtgp_batches = []\n",
+    "for i in range(20):\n",
+    "    batch = get_mtgp_batch(\n",
+    "        batch_size=256,\n",
+    "        seq_len=20,\n",
+    "        num_features=2,\n",
+    "        max_num_tasks=10,\n",
+    "        num_tasks=1,\n",
+    "        lengthscale=None,\n",
+    "        hyperparameters={},\n",
+    "    )\n",
+    "    mtgp_batches.append(batch)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_nll(model, batches, n_train=10):\n",
+    "    nlls = []\n",
+    "    for batch in batches:\n",
+    "        tensor_args = batch.x[:n_train], batch.task_id[:n_train], batch.y[:n_train], batch.x[n_train:], batch.task_id[n_train:]\n",
+    "        tensor_args = [t.cuda() for t in tensor_args]\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            output = model(*tensor_args)\n",
+    "        mean = output[..., 0].squeeze()\n",
+    "        var = output[..., 1].exp().squeeze()\n",
+    "        nll = -torch.distributions.Normal(mean, var.sqrt()).log_prob(batch.y[n_train:].squeeze()).mean()\n",
+    "        del output\n",
+    "        nlls.append(nll)\n",
+    "    nlls = torch.stack(nlls)\n",
+    "    return nlls.mean().item(),\n",
+    "\n",
+    "def get_nlls(model, batches, n_train=10):\n",
+    "    nlls = []\n",
+    "    for batch in batches:\n",
+    "        tensor_args = batch.x[:n_train], batch.task_id[:n_train], batch.y[:n_train], batch.x[n_train:], batch.task_id[n_train:]\n",
+    "        tensor_args = [t.cuda() for t in tensor_args]\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            output = model(*tensor_args)\n",
+    "        mean = output[..., 0].squeeze()\n",
+    "        var = output[..., 1].exp().squeeze()\n",
+    "        nll = -torch.distributions.Normal(mean, var.sqrt()).log_prob(batch.y[n_train:].squeeze()).mean()\n",
+    "        del output\n",
+    "        nlls.append(nll)\n",
+    "    nlls = torch.stack(nlls)\n",
+    "    return nlls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "home_dir = \"/home/lily_l/private_multitask_pfn/ckpt/25-01-29_04-46-15__prior_hpobench_lr__features_2__tasks_1__epochs_200__seqlen_50__attn_standard__task_hier__seed_0__continued__37634\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "2\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "3\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "4\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "5\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "6\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "7\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "8\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "9\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "10\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "11\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "12\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "13\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "14\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "15\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "16\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "17\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "18\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n",
+      "19\n",
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "nlls = []\n",
+    "svm_nlls = []\n",
+    "gp_nlls = []\n",
+    "for epoch in range(1, 20):\n",
+    "    print(epoch)\n",
+    "    model = load_model_from_epoch(home_dir, epoch * 5).to('cuda')\n",
+    "    nll = get_nll(model, batches)\n",
+    "    nlls.append(nll)\n",
+    "    svm_nll = get_nll(model, svm_batches)\n",
+    "    svm_nlls.append(svm_nll)\n",
+    "    gp_nll = get_nll(model, mtgp_batches)\n",
+    "    gp_nlls.append(gp_nll)\n",
+    "    del model\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "original_dir = \"/home/lily_l/private_multitask_pfn/final_models/playful-tree-486\"\n",
+    "original_model = load_model(original_dir, best=False).to('cuda')\n",
+    "nll = get_nll(original_model, batches)\n",
+    "nlls = [nll] + nlls\n",
+    "svm_nll = get_nll(original_model, svm_batches)\n",
+    "svm_nlls = [svm_nll] + svm_nlls\n",
+    "gp_nll = get_nll(original_model, mtgp_batches)\n",
+    "gp_nlls = [gp_nll] + gp_nlls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.plot(nlls, label='nll')\n",
+    "plt.plot(svm_nlls, label='svm_nll')\n",
+    "plt.plot(gp_nlls, label='gp_nll')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "svm_nlls\n",
+    "\n",
+    "svm_nlls_updated = [(1.38,), (1.64,)] + svm_nlls[2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1.0934847593307495,),\n",
+       " (0.045990969985723495,),\n",
+       " (-0.052184343338012695,),\n",
+       " (-0.08173080533742905,),\n",
+       " (-0.021465713158249855,),\n",
+       " (-0.09919952601194382,),\n",
+       " (0.18788771331310272,),\n",
+       " (-0.019634241238236427,),\n",
+       " (-0.08407261967658997,),\n",
+       " (-0.0778316855430603,),\n",
+       " (-0.19331598281860352,),\n",
+       " (-0.06958013027906418,),\n",
+       " (-0.17593057453632355,),\n",
+       " (-0.09505557268857956,),\n",
+       " (-0.09145152568817139,),\n",
+       " (-0.15969513356685638,),\n",
+       " (-0.07935347408056259,),\n",
+       " (-0.2318514883518219,),\n",
+       " (-0.09641797840595245,),\n",
+       " (-0.12503884732723236,)]"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nlls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = nlls, svm_nlls_updated, gp_nlls\n",
+    "with open('nlls.pkl', 'wb') as f:\n",
+    "    pickle.dump(data, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with open('nlls.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "    \n",
+    "nlls, svm_nlls_updated, gp_nlls = data\n",
+    "\n",
+    "xs = torch.arange(20) * 16 * 32\n",
+    "\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "plt.grid(alpha=0.5)\n",
+    "# plt.ylim(-0.5, 3.5)\n",
+    "plt.xlim(0, 10000)\n",
+    "plt.plot(xs, nlls, label=\"LR (In Domain)\", linewidth=2, color=\"C0\")\n",
+    "plt.plot(xs, svm_nlls_updated, label=\"SVM (Out of Domain)\", linestyle=\"--\", linewidth=2, color=\"C1\")\n",
+    "plt.plot(xs, gp_nlls, label=\"GP Draw (Out of Domain)\", linestyle=\"--\", linewidth=2, color=\"C2\")\n",
+    "plt.xlabel(\"# of LR Train Datasets for Fine-Tuning\", fontsize=12)\n",
+    "plt.ylabel(\"Negative Log Likelihood\", fontsize=12)\n",
+    "# legend above plot\n",
+    "plt.legend(fontsize=12, loc='upper left')\n",
+    "plt.savefig(\"finetune.pdf\", bbox_inches='tight')\n",
+    "# plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=3, fontsize=10)\n",
+    "# legend to right of plot\n",
+    "# plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "# plt.plot(svm_nlls, label='svm_nll')\n",
+    "# plt.plot(gp_nlls, label='gp_nll')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TransformerModel(\n",
+       "  (transformer_encoder): HierarchicalTaskEncoder(\n",
+       "    (layers): ModuleList(\n",
+       "      (0): SingleTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (1): GlobalTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (2): SingleTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (3): GlobalTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (4): SingleTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (5): GlobalTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (6): SingleTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (7): GlobalTaskEncoderLayer(\n",
+       "        (attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
+       "        )\n",
+       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (meta_embedding): Embedding(1, 256)\n",
+       "  )\n",
+       "  (encoder): Sequential(\n",
+       "    (0): Normalize()\n",
+       "    (1): VariableNumFeaturesEncoder(\n",
+       "      (base_encoder): Linear(in_features=2, out_features=256, bias=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (y_encoder): Linear(in_features=1, out_features=256, bias=True)\n",
+       "  (pos_encoder): NoPositionalEncoding()\n",
+       "  (decoder_dict): ModuleDict(\n",
+       "    (standard): Sequential(\n",
+       "      (0): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "      (1): GELU(approximate='none')\n",
+       "      (2): Linear(in_features=1024, out_features=2, bias=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (criterion): GaussianNLLLoss()\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finetuned_model = load_model_from_epoch(home_dir, 195).to('cuda')\n",
+    "finetuned_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a Transformer with 6.58 M parameters\n",
+      "Loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "original_dir = \"/home/lily_l/private_multitask_pfn/final_models/playful-tree-486\"\n",
+    "original_model = load_model(original_dir, best=False).to('cuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_nlls_mtpfn(model, aux_data, batches, n_train=10):\n",
+    "    nlls = []\n",
+    "    for batch in batches:\n",
+    "        tensor_args = batch.x[:n_train], batch.task_id[:n_train], batch.y[:n_train], batch.x[n_train:], batch.task_id[n_train:]\n",
+    "        tensor_args = [t.cuda() for t in tensor_args]\n",
+    "        \n",
+    "        train_x, task_id, train_y, test_x, test_task_id = tensor_args\n",
+    "        B = train_x.shape[1]\n",
+    "        for task, aux_batch in enumerate(aux_data):\n",
+    "            aux_x = aux_batch.x.cuda().repeat(1, B, 1)\n",
+    "            aux_y = aux_batch.y.cuda().repeat(1, B, 1)\n",
+    "            train_x = torch.cat([train_x, aux_x], dim=0)\n",
+    "            train_y = torch.cat([train_y, aux_y], dim=0)\n",
+    "            task_id = torch.cat([task_id, torch.ones_like(aux_y).long() * (task+1)], dim=0)\n",
+    "            \n",
+    "        tensor_args = train_x, task_id, train_y, test_x, test_task_id\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            output = model(*tensor_args)\n",
+    "        mean = output[..., 0].squeeze()\n",
+    "        var = output[..., 1].exp().squeeze()\n",
+    "        nll = -torch.distributions.Normal(mean, var.sqrt()).log_prob(batch.y[n_train:].squeeze()).mean()\n",
+    "        del output\n",
+    "        nlls.append(nll)\n",
+    "    nlls = torch.stack(nlls)\n",
+    "    return nlls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a Transformer with 6.59 M parameters\n",
+      "Loaded model\n",
+      "i 0\n",
+      "i 1\n",
+      "i 2\n",
+      "i 3\n",
+      "i 4\n",
+      "i 5\n",
+      "i 6\n",
+      "i 7\n",
+      "i 8\n",
+      "i 9\n",
+      "i 10\n",
+      "i 11\n",
+      "i 12\n",
+      "i 13\n",
+      "i 14\n",
+      "i 15\n",
+      "i 16\n",
+      "i 17\n",
+      "i 18\n",
+      "i 19\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor(0.1844, device='cuda:0')"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mtpfn = load_model(\"/home/lily_l/private_multitask_pfn/wandb_links/upbeat-darkness-493\", best=False).to('cuda')\n",
+    "\n",
+    "lr_batch_fn_train = get_hpobench_batch_fn('lr', train=True)\n",
+    "    \n",
+    "best_lr_data = None\n",
+    "best_nlls = 1e10\n",
+    "for i in range(20):\n",
+    "    print(\"i\", i)\n",
+    "    aux_data = []\n",
+    "    for j in range(3):\n",
+    "        aux_batch = lr_batch_fn_train(\n",
+    "            batch_size=1,\n",
+    "            seq_len=50,\n",
+    "            num_features=2,\n",
+    "            max_num_tasks=10,\n",
+    "            num_tasks=1,\n",
+    "        )\n",
+    "        aux_data.append(aux_batch)\n",
+    "    mtpfn_nlls = get_nlls_mtpfn(mtpfn, aux_data, batches)\n",
+    "    quartile = torch.median(mtpfn_nlls)\n",
+    "    if quartile < best_nlls:\n",
+    "        best_nlls = quartile\n",
+    "        best_lr_data = aux_data\n",
+    "        \n",
+    "best_nlls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot([\n",
+    "    get_nlls_mtpfn(mtpfn, best_aux_data, batches).cpu(),\n",
+    "    # get_nlls_mtpfn(mtpfn, best_aux_data, svm_batches).cpu(),\n",
+    "    # get_nlls_mtpfn(mtpfn, best_aux_data, mtgp_batches).cpu(),\n",
+    "])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtpfn_nlls = get_nlls_mtpfn(mtpfn, best_aux_data, batches).cpu().numpy()\n",
+    "mtpfn_svm_nlls = get_nlls_mtpfn(mtpfn, best_aux_data, svm_batches).cpu().numpy()\n",
+    "mtpfn_gp_nlls = get_nlls_mtpfn(mtpfn, [], mtgp_batches).cpu().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "[(mtpfn_nlls, mtpfn_svm_nlls, mtpfn_gp_nlls),\n",
+    "(finetuned_nlls, finetuned_svm_nlls, finetuned_gp_nlls),\n",
+    "(original_nlls, original_svm_nlls, original_gp_nlls)] = pickle.load(open('figures_boxplot_data.pkl', 'rb'))\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 3, figsize=(8, 4))\n",
+    "\n",
+    "box_colors = ['skyblue', 'C5', 'C4']\n",
+    "bp = axs[0].boxplot([mtpfn_nlls, finetuned_nlls, original_nlls], showfliers=False, widths=0.6, patch_artist=True)\n",
+    "for i, patch in enumerate(bp['boxes']):\n",
+    "    patch.set_facecolor(box_colors[i])\n",
+    "# change center line\n",
+    "for i, median in enumerate(bp['medians']):\n",
+    "    median.set(color='black', linewidth=1)\n",
+    "axs[0].set_title(\"LR (In Domain)\")\n",
+    "\n",
+    "bp = axs[1].boxplot([mtpfn_svm_nlls, finetuned_svm_nlls, original_svm_nlls], showfliers=False, widths=0.6, patch_artist=True)\n",
+    "for i, patch in enumerate(bp['boxes']):\n",
+    "    patch.set_facecolor(box_colors[i])\n",
+    "# change center line\n",
+    "for i, median in enumerate(bp['medians']):\n",
+    "    median.set(color='black', linewidth=1)\n",
+    "axs[1].set_title(\"SVM (Out of Domain)\")\n",
+    "\n",
+    "bp = axs[2].boxplot([mtpfn_gp_nlls,finetuned_gp_nlls, original_gp_nlls], showfliers=False, widths=0.6, patch_artist=True)\n",
+    "for i, patch in enumerate(bp['boxes']):\n",
+    "    patch.set_facecolor(box_colors[i])\n",
+    "# change center line\n",
+    "for i, median in enumerate(bp['medians']):\n",
+    "    median.set(color='black', linewidth=1)\n",
+    "axs[2].set_title(\"GP Draw (Out of Domain)\")\n",
+    "axs[0].set_ylabel(\"Negative Log Likelihood\", fontsize=12)\n",
+    "\n",
+    "# add legend\n",
+    "from matplotlib.patches import Patch\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "legend_elements = [\n",
+    "    Patch(facecolor=box_colors[0], edgecolor='black', label='MTPFN'),\n",
+    "    Patch(facecolor=box_colors[1], edgecolor='black', label='Fine-Tuned Single-Task'),\n",
+    "    Patch(facecolor=box_colors[2], edgecolor='black', label='Original Single-Task'),\n",
+    "]\n",
+    "# legend over all subplots\n",
+    "\n",
+    "# fig.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=3, fontsize=12)\n",
+    "fig.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=(0.54, 0), ncol=3, fontsize=12)\n",
+    "\n",
+    "\n",
+    "for ax in axs:\n",
+    "    ax.set_xticklabels([\"MT\", \"FT\", \"ST\"], fontsize=12)\n",
+    "    # ax.grid(alpha=0.5, axis='y')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"finetune_nll.pdf\", bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGdCAYAAABO2DpVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAYhElEQVR4nO3dcWyU9R348U/VUNtxrSIiEop1EbxDho7qDOlm1KkLWZzsj8U4mzGjxJmic2SJ6T86s2x1WbZoMsOY2WQJYzqXoIuJEmcCZFE2OEKirpWy2ciGqFtmW2h3GtrfH/7ofvwE5Y7v0+u1r1dywTue5/l+SKq8fe655+rGxsbGAgAggdOqPQAAMHUICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASOaMiV5wdHQ0Dhw4ELlcLurq6iZ6eQCgAmNjYzE0NBTz5s2L00478XmJCQ+LAwcOREtLy0QvCwAksH///pg/f/4Jf3/CwyKXy0XEh4M1NTVN9PIAQAUGBwejpaVl/O/xE5nwsDj69kdTU5OwAIAa80mXMZR98eY///nP6OjoiHPOOScaGhriM5/5TOzataviAQGAqaOsMxb/+c9/or29Pa655pp47rnn4txzz42+vr44++yzs5oPAKghZYXFj370o2hpaYnHH398/LULL7ww+VAAQG0q662QP/zhD3H55ZfH1772tZgzZ0589rOfjccee+xj9ymVSjE4OHjMAwCYmsoKi7///e+xbt26WLhwYWzZsiXuuuuuuOeee+LXv/71Cffp7u6O5ubm8YePmgLA1FU3NjY2drIbz5gxIy6//PJ46aWXxl+75557YufOnfHyyy8fd59SqRSlUmn8+dGPqwwMDPhUCADUiMHBwWhubv7Ev7/LOmNx/vnnx+LFi495rVAoxJtvvnnCferr68c/WuojpgAwtZUVFu3t7fH6668f89revXvjggsuSDoUAFCbygqL73znO7Fjx4744Q9/GPv27YtNmzbFL37xi+js7MxqPgCghpQVFldccUVs3rw5fvvb38aSJUvi+9//fjz88MNx6623ZjUfAFBDyrp4M4WTvfgDAJg8Mrl4EwDg40z4l5ABtWF4eDh6e3vL2mdkZCT6+/ujtbU1GhoaTnq/fD4fjY2N5Y4ITELCAjiu3t7eaGtrm5C1isViLFu2bELWArIlLIDjyufzUSwWy9qnp6cnOjo6YuPGjVEoFMpaC5gahAVwXI2NjRWfRSgUCs5AwDTl4k0AIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkygqL733ve1FXV3fMI5/PZzUbAFBjzih3h0suuST++Mc//u8AZ5R9CABgiiq7Cs4444yYO3duFrMAADWu7Gss+vr6Yt68efHpT386br311njzzTezmAsAqEFlnbG48sorY8OGDXHxxRfHW2+9FQ8++GB84QtfiFdffTVyudxx9ymVSlEqlcafDw4OntrEAMCkVVZYrFixYvyfly5dGldeeWVccMEF8bvf/S5uv/324+7T3d0dDz744KlNCQDUhFP6uOlZZ50VixYtin379p1wm66urhgYGBh/7N+//1SWBAAmsVMKi0OHDsXf/va3OP/880+4TX19fTQ1NR3zAACmprLC4rvf/W5s27Yt+vv746WXXoqvfvWrcfrpp8ctt9yS1XwAQA0p6xqLf/zjH3HLLbfEv//97zj33HPj85//fOzYsSPOPffcrOYDAGpIWWHxxBNPZDUHADAF+K4QACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMmUdYMsoLb19fXF0NBQZsfv6ek55tcs5HK5WLhwYWbHB06NsIBpoq+vLxYtWjQha3V0dGR6/L1794oLmKSEBUwTR89UbNy4MQqFQiZrjIyMRH9/f7S2tkZDQ0Py4/f09ERHR0emZ12AUyMsYJopFAqxbNmyzI7f3t6e2bGByc/FmwBAMsICAEjGWyEA1Izh4eHo7e096e0rve4nn89HY2NjJSNOe8ICgJrR29sbbW1tma9TLBYzvRZpKhMWANSMfD4fxWLxpLc/+kmicj8Nlc/nKxmPEBYA1JDGxsaKziRk/Wko/sfFmwBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIz7WMA0MndmXTS8tzfiQG3+P0XDe3tj7sy6ao8BfAxhAdPInW0zorD9zojt1Z6kMoX48M8ATF7CAqaR9cX34+b7N0ShRm9X3NPbG+t/8vX4SrUHAU5IWMA0cvDQWIyctShi3mXVHqUiIwdH4+ChsWqPAXyM2nyjFQCYlIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMqcUFg899FDU1dXFvffem2gcAKCWVRwWO3fujPXr18fSpUtTzgMA1LCKwuLQoUNx6623xmOPPRZnn3126pkAgBpVUVh0dnbGl7/85bjuuus+cdtSqRSDg4PHPACAqemMcnd44oknYvfu3bFz586T2r67uzsefPDBsgcDAGpPWWcs9u/fH9/+9rfjN7/5TZx55pkntU9XV1cMDAyMP/bv31/RoADA5FfWGYtisRjvvPNOLFu2bPy1I0eOxPbt2+NnP/tZlEqlOP3004/Zp76+Purr69NMC1RseHg4IiJ2796d2RojIyPR398fra2t0dDQkPz4PT09yY8JpFVWWHzxi1+MV1555ZjXbrvttsjn83Hfffd9JCqAyaO3tzciIlavXl3lSU5dLper9gjACZQVFrlcLpYsWXLMa5/61KfinHPO+cjrwOSycuXKiIjI5/PR2NiYyRo9PT3R0dERGzdujEKhkMkauVwuFi5cmMmxgVNX9sWbQG2aPXt23HHHHROyVqFQOOYtU2D6OOWw2Lp1a4IxAICpwHeFAADJCAsAIBlhAQAkIywAgGSEBQCQjI+bAsc1PDw8flOtk3X0zpjl3iEzy3trABNLWADH1dvbG21tbRXt29HRUdb2xWLRfS9gihAWwHHl8/koFotl7VPpd4Xk8/lyxwMmKWEBHFdjY2NFZxHa29szmAaoFS7eBACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJBMWWGxbt26WLp0aTQ1NUVTU1MsX748nnvuuaxmAwBqTFlhMX/+/HjooYeiWCzGrl274tprr42bbropXnvttazmAwBqyBnlbHzjjTce8/wHP/hBrFu3Lnbs2BGXXHJJ0sEAgNpTVlj8v44cORJPPfVUHD58OJYvX37C7UqlUpRKpfHng4ODlS4JAExyZV+8+corr8TMmTOjvr4+vvWtb8XmzZtj8eLFJ9y+u7s7mpubxx8tLS2nNDAAMHmVHRYXX3xx7NmzJ/785z/HXXfdFatWrYq//vWvJ9y+q6srBgYGxh/79+8/pYEBgMmr7LdCZsyYERdddFFERLS1tcXOnTvjkUceifXr1x93+/r6+qivrz+1KQGAmnDK97EYHR095hoKAGD6KuuMRVdXV6xYsSIWLFgQQ0NDsWnTpti6dWts2bIlq/kAgBpSVli888478Y1vfCPeeuutaG5ujqVLl8aWLVvi+uuvz2o+AKCGlBUWv/zlL7OaAwCYAnxXCACQjLAAAJIRFgBAMsICAEim4u8KAYBT1dfXF0NDQ5kdv6en55hfs5LL5WLhwoWZrlErhAUAVdHX1xeLFi2akLU6OjoyX2Pv3r3iIoQFAFVy9EzFxo0bo1AoZLLGyMhI9Pf3R2trazQ0NGSyRk9PT3R0dGR65qWWCAsAqqpQKMSyZcsyO357e3tmx+ajXLwJACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkU1ZYdHd3xxVXXBG5XC7mzJkTK1eujNdffz2r2QCAGlNWWGzbti06Oztjx44d8cILL8QHH3wQN9xwQxw+fDir+QCAGnJGORs///zzxzzfsGFDzJkzJ4rFYlx11VVJBwMAak9ZYfH/GxgYiIiIWbNmnXCbUqkUpVJp/Png4OCpLAkATGIVX7w5Ojoa9957b7S3t8eSJUtOuF13d3c0NzePP1paWipdEgCY5CoOi87Oznj11VfjiSee+Njturq6YmBgYPyxf//+SpcEACa5it4KWbNmTTz77LOxffv2mD9//sduW19fH/X19RUNBwDUlrLCYmxsLO6+++7YvHlzbN26NS688MKs5gIAalBZYdHZ2RmbNm2KZ555JnK5XBw8eDAiIpqbm6OhoSGTAQGA2lHWNRbr1q2LgYGBuPrqq+P8888ffzz55JNZzQcA1JCy3woBADgR3xUCACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJFN2WGzfvj1uvPHGmDdvXtTV1cXTTz+dwVgAQC0qOywOHz4cl156aTz66KNZzAMA1LAzyt1hxYoVsWLFiixmAQBqXNlhUa5SqRSlUmn8+eDgYNZLAgBVkvnFm93d3dHc3Dz+aGlpyXpJAKBKMj9j0dXVFWvXrh1/Pjg4KC4AiIiIuTProuG9vREHavdDig3v7Y25M+uqPcakkXlY1NfXR319fdbLAFCD7mybEYXtd0Zsr/YklSvEh38OPpR5WADAiawvvh83378hCvl8tUepWE9vb6z/ydfjK9UeZJIoOywOHToU+/btG3/+xhtvxJ49e2LWrFmxYMGCpMMBMLUdPDQWI2ctiph3WbVHqdjIwdE4eGis2mNMGmWHxa5du+Kaa64Zf370+olVq1bFhg0bkg0GANSessPi6quvjrExZQYAfFTtXoYLAEw6wgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyfjadACqYnh4OCIidu/endkaIyMj0d/fH62trdHQ0JDJGj09PZkct1YJCwCqore3NyIiVq9eXeVJ0sjlctUeYVIQFgBUxcqVKyMiIp/PR2NjYyZr9PT0REdHR2zcuDEKhUIma0R8GBULFy7M7Pi1RFgAUBWzZ8+OO+64Y0LWKhQKsWzZsglZa7pz8SYAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAyvitkkhgeHh7/pr+TcSpfBZzlF/4AML0Ji0mit7c32traJmStYrHoy3gAyISwmCTy+XwUi8WT3v5Uvgo4n8+XOx4AnBRhMUk0NjZWdBbBVwED00m5bxv39PQc8+vJ8pZx5YQFADWj0reNOzo6ytreW8aVExYA1Ixy3zau9EJ3bxlXTlgAUDMqedu4vb09o2k4HvexAACSccYiI319fTE0NJTZ8Su9IKlcuVwuFi5cmOkaAEwdwiIDfX19sWjRoglZq9wLkiqxd+9ecQHASREWGTh6pqKSe0ycrFO58+bJOnqvjCzPvAAwtQiLDGV9jwkXJAEw2bh4EwBIRlgAAMkICwAgmYrC4tFHH43W1tY488wz48orr4y//OUvqecCAGpQ2WHx5JNPxtq1a+OBBx6I3bt3x6WXXhpf+tKX4p133sliPgCghpQdFj/96U9j9erVcdttt8XixYvj5z//eTQ2NsavfvWrLOYDAGpIWWHx/vvvR7FYjOuuu+5/BzjttLjuuuvi5ZdfPu4+pVIpBgcHj3kAAFNTWWHxr3/9K44cORLnnXfeMa+fd955cfDgwePu093dHc3NzeOPlpaWyqcFACa1zG+Q1dXVFWvXrh1/Pjg4OC3iYu7Mumh4b2/Egdr94E3De3tj7sy6ao8BQA0pKyxmz54dp59+erz99tvHvP7222/H3Llzj7tPfX191NfXVz5hjbqzbUYUtt8Zsb3ak1SuEB/+OQDgZJUVFjNmzIi2trZ48cUXY+XKlRERMTo6Gi+++GKsWbMmi/lq1vri+3Hz/RuikM9Xe5SK9fT2xvqffD2+Uu1BAKgZZb8Vsnbt2li1alVcfvnl8bnPfS4efvjhOHz4cNx2221ZzFezDh4ai5GzFkXMu6zao1Rs5OBoHDw0Vu0xAKghZYfFzTffHO+++27cf//9cfDgwbjsssvi+eef/8gFnQDA9FPRxZtr1qzx1gcA8BG1+5EFAGDSERYAQDLCAgBIRlgAAMlkfufN6Wh4eDgiInbv3p3ZGiMjI9Hf3x+tra3R0NCQyRo9PT2ZHBeAqUtYZKC3tzciIlavXl3lSdLI5XLVHgGAGiEsMnD0rqT5fD4aGxszWaOnpyc6Ojpi48aNUSgUMlkj4sOoWLhwYWbHB2BqERYZmD17dtxxxx0TslahUIhly5ZNyFoA8ElcvAkAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGTOqPYAfGh4eDh6e3tPevuenp5jfi1HPp+PxsbGsvcDgE8iLCaJ3t7eaGtrK3u/jo6OsvcpFouxbNmysvcDgE8iLCaJfD4fxWLxpLcfGRmJ/v7+aG1tjYaGhrLXAoAs1I2NjY1N5IKDg4PR3NwcAwMD0dTUNJFLAwAVOtm/v128CQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDIT/rXpR79MdXBwcKKXBgAqdPTv7U/6UvQJD4uhoaGIiGhpaZnopQGAUzQ0NBTNzc0n/P26sU9Kj8RGR0fjwIEDkcvloq6ubiKXnlIGBwejpaUl9u/fH01NTdUeByLCzyWTj5/JdMbGxmJoaCjmzZsXp5124ispJvyMxWmnnRbz58+f6GWnrKamJv+yMOn4uWSy8TOZxsedqTjKxZsAQDLCAgBIRljUqPr6+njggQeivr6+2qPAOD+XTDZ+JifehF+8CQBMXc5YAADJCAsAIBlhAQAkIywAgGSERY3Zvn173HjjjTFv3ryoq6uLp59+utojMc11d3fHFVdcEblcLubMmRMrV66M119/vdpjMc2tW7culi5dOn5jrOXLl8dzzz1X7bGmBWFRYw4fPhyXXnppPProo9UeBSIiYtu2bdHZ2Rk7duyIF154IT744IO44YYb4vDhw9UejWls/vz58dBDD0WxWIxdu3bFtddeGzfddFO89tpr1R5tyvNx0xpWV1cXmzdvjpUrV1Z7FBj37rvvxpw5c2Lbtm1x1VVXVXscGDdr1qz48Y9/HLfffnu1R5nSJvy7QoCpbWBgICI+/I84TAZHjhyJp556Kg4fPhzLly+v9jhTnrAAkhkdHY1777032tvbY8mSJdUeh2nulVdeieXLl8d///vfmDlzZmzevDkWL15c7bGmPGEBJNPZ2Rmvvvpq/OlPf6r2KBAXX3xx7NmzJwYGBuL3v/99rFq1KrZt2yYuMiYsgCTWrFkTzz77bGzfvj3mz59f7XEgZsyYERdddFFERLS1tcXOnTvjkUceifXr11d5sqlNWACnZGxsLO6+++7YvHlzbN26NS688MJqjwTHNTo6GqVSqdpjTHnCosYcOnQo9u3bN/78jTfeiD179sSsWbNiwYIFVZyM6aqzszM2bdoUzzzzTORyuTh48GBERDQ3N0dDQ0OVp2O66urqihUrVsSCBQtiaGgoNm3aFFu3bo0tW7ZUe7Qpz8dNa8zWrVvjmmuu+cjrq1atig0bNkz8QEx7dXV1x3398ccfj29+85sTOwz8X7fffnu8+OKL8dZbb0Vzc3MsXbo07rvvvrj++uurPdqUJywAgGTceRMASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJPN/AGmNkwmy2yb1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "finetuned_nlls = get_nlls(finetuned_model, batches).cpu().numpy()\n",
+    "finetuned_svm_nlls = get_nlls(finetuned_model, svm_batches).cpu().numpy()\n",
+    "finetuned_gp_nlls = get_nlls(finetuned_model, mtgp_batches).cpu().numpy()\n",
+    "\n",
+    "# box plot of nlls\n",
+    "plt.boxplot([finetuned_nlls, finetuned_svm_nlls, finetuned_gp_nlls], showfliers=False)\n",
+    "plt.show()\n",
+    "\n",
+    "original_nlls = get_nlls(original_model, batches).cpu().numpy()\n",
+    "original_svm_nlls = get_nlls(original_model, svm_batches).cpu().numpy()\n",
+    "original_gp_nlls = get_nlls(original_model, mtgp_batches).cpu().numpy()\n",
+    "\n",
+    "plt.boxplot([finetuned_nlls, finetuned_svm_nlls, finetuned_gp_nlls], showfliers=False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pfn",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/private_multitask_pfn/paper_figures/figures_hpobench.py b/private_multitask_pfn/paper_figures/figures_hpobench.py
new file mode 100644
index 0000000..25c8f45
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/figures_hpobench.py
@@ -0,0 +1,420 @@
+from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
+import torch
+import json
+
+
+import pickle
+import pdb
+import gpytorch
+import torch
+from botorch.models import SingleTaskGP
+from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
+from botorch.exceptions import ModelFittingError
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from tqdm import tqdm
+import botorch
+import einops
+
+from tabpfn.base import (
+    create_inference_engine,
+    determine_precision,
+    initialize_tabpfn_model,
+)
+from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
+
+from tabpfn import TabPFNClassifier, TabPFNRegressor
+import matplotlib.pyplot as plt
+import os
+
+import traceback
+from collections import defaultdict
+
+
+colors = {
+    "MTPFN": "deepskyblue",
+    "ScaML": "#E78C35",
+    "ICM": "#DA2222",
+    "GP": "#95211B",
+}
+
+baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"] #+ ["random"] 
+
+# model_to_label = {
+#     "different-dust-282": "PFN (0.5 uncorr)",
+#     "royal-firebrand-281": "PFN (0.25 uncorr)",
+#     "peach-plasma-280": "PFN (1.0 uncorr)",
+#     "treasured-lion-279": "PFN (0.0 uncorr)",
+#     "exalted-wave-278": "PFN (0.75 uncorr)",
+# }
+
+# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+
+def get_label(model):
+    if model in model_to_label:
+        return model_to_label[model]
+    return model
+
+
+def get_labels(models):
+    return [get_label(model) for model in models]
+
+
+def get_color(model):
+    return colors[get_label(model)]
+
+
+
+
+
+def plot_bo_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
+        # linestyle 
+        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
+
+                    
+    plt.legend()
+    plt.xlabel("BO Iterations")
+    plt.ylabel("Normalized Regret")
+    plt.title(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    
+    
+    
+
+def plot_bo_ax(ax, exp_dir, models, title, path=None):    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                if torch.isnan(function_results).any():
+                    continue
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results) ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        # linestyle 
+        xs = torch.linspace(0, 40, len(mean))
+        ax.plot(xs, mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
+
+    ax.set_title(title)
+    ax.set_ylabel("Normalized Regret")
+    ax.set_xlabel("BO Iterations")
+    # ax.set_ylim(0.05, 0.7)
+    # ax.set_yscale("log")
+    # plt.legend()
+    # plt.xlabel("BO Iterations")
+    # plt.ylabel("Normalized Regret")
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+    
+    
+    
+    
+
+def plot_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    # one plot for boxplots of mse, one plot for boxplots of nll
+    
+    model_mses = defaultdict(list)
+    model_nlls = defaultdict(list)
+    
+    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+    for model_order in all_models:
+        for function_dir in os.listdir(exp_dir):
+            # check if directory
+            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                continue
+            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
+                results = json.load(f)
+                
+            models = list(results.keys())
+            for model in models:
+                if model != model_order:
+                    continue
+                mses, nlls = [], []
+                try:
+                    for trial_key in results[model]:
+                        mses.append(results[model][trial_key]["mse"])
+                        nlls.append(results[model][trial_key]["nll"])
+                except KeyboardInterrupt as e:
+                    raise e
+                except Exception as e:
+                    traceback.print_exc()
+                    continue
+                model_mses[model].extend(mses)
+                model_nlls[model].extend(nlls)
+    
+    # plot nll boxplots
+    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("NLL")
+    plt.title(title)
+    plt.ylabel("NLL")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    plt.clf()
+    
+    # plot mse boxplots
+    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("MSE")
+    plt.title(title)
+    plt.ylabel("MSE")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path.replace(".png", "_mse.png"))
+    plt.clf()
+    
+
+def plot_bo_function_ax(ax, function_dir, models, title):
+    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
+        bo_results = json.load(f)
+    with open(os.path.join(function_dir, "info.json"), "r") as f:
+        info = json.load(f)
+        
+    for model in baselines + models:
+        for model_try in bo_results:
+            if model_try != model:
+                continue
+            model_results = []
+            try:
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    model_results.append(regret)
+                
+                # remove shorter trials
+                max_length = max([len(result) for result in model_results])
+                model_results = [result for result in model_results if len(result) == max_length]
+                
+                model_results = torch.stack(model_results)
+                
+                mean = model_results.nanmean(0)
+                std = model_results.std(0) / len(model_results) ** 0.5
+                lower = mean - std
+                upper = mean + std
+                # mean = model_results.mean(0)
+                # lower = model_results.quantile(0.25, 0)
+                # upper = model_results.quantile(0.75, 0)
+                linestyle = "--" if model in baselines else "-"
+                
+                xs = torch.linspace(0, 40, len(mean))
+                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
+                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
+                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                print(e)
+                # import pdb; pdb.set_trace()
+                # raise e
+                continue
+                    
+    # plt.legend()
+    ax.set_xlabel("BO Iterations")
+    ax.set_ylabel("Normalized Regret")
+    ax.set_title(title)
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+
+
+
+# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
+# plot_bo_all("/home/yl9959/mtpfn/eval_plot/25-01-28_11-31-32__synthetic__trials_5__seed_0__num_samples/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0", models, "All")
+
+fig, axs = plt.subplots(1, 5, figsize=(12, 2.5))
+
+# add grid
+for ax in axs:
+    # y grid only
+    ax.yaxis.grid(True,  linestyle='-', linewidth=0.5, alpha=0.5)
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__svm/n_target_2__n_source_20/"
+models = ["likely-donkey-479"]
+model_to_label = {
+    "likely-donkey-479": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+title = "SVM"
+plot_bo_ax(axs[0], dir, models, title)
+
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__xgb/n_target_5__n_source_20"
+model_to_label = {
+    "stilted-night-482": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["stilted-night-482"]
+title = "XGB"
+plot_bo_ax(axs[1], dir, models, title)
+
+
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__rf/n_target_2__n_source_20"
+model_to_label = {
+    "stilted-night-482": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["stilted-night-482"]
+title = "RF"
+plot_bo_ax(axs[2], dir, models, title)
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__lr/n_target_2__n_source_20"
+model_to_label = {
+    "fallen-cherry-483": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["fallen-cherry-483"]
+title = "LR"
+plot_bo_ax(axs[3], dir, models, title)
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__nn/n_target_2__n_source_20"
+model_to_label = {
+    "stilted-night-482": "MTPFN",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["stilted-night-482"]
+title = "NN"
+plot_bo_ax(axs[4], dir, models, title)
+
+handles, labels = axs[0].get_legend_handles_labels()
+# legend above plot
+fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=4, handles=handles, labels=labels)
+# plt.legend()
+plt.tight_layout()
+plt.savefig("hpobench.pdf", bbox_inches='tight')
diff --git a/private_multitask_pfn/paper_figures/figures_uncorr.py b/private_multitask_pfn/paper_figures/figures_uncorr.py
new file mode 100644
index 0000000..011119d
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/figures_uncorr.py
@@ -0,0 +1,537 @@
+from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
+import torch
+import json
+
+
+import pickle
+import pdb
+import gpytorch
+import torch
+from botorch.models import SingleTaskGP
+from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
+from botorch.exceptions import ModelFittingError
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from tqdm import tqdm
+import botorch
+import einops
+
+from tabpfn.base import (
+    create_inference_engine,
+    determine_precision,
+    initialize_tabpfn_model,
+)
+from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
+
+from tabpfn import TabPFNClassifier, TabPFNRegressor
+import matplotlib.pyplot as plt
+import os
+
+import traceback
+from collections import defaultdict
+
+
+#4477AA', '#EE6677', '#228833', '#CCBB44', '#66CCEE', '#AA3377', '#BBBBBB'
+# colors = {
+#     "PFN (p=0.0)": "#4477AA",
+#     "PFN (p=0.1)": "#66CCEE",
+#     "PFN (p=0.2)": "#228833",
+#     "ScaML": "#CCBB44",
+#     "ICM": "#EE6677",
+#     "GP": "#AA3377",
+# }
+##2166AC', '#4393C3', '#92C5DE '#F4A582', '#D6604D', '#B2182B'
+colors = {
+    # "PFN (p=0.0)": "#C3A8D1",
+    "MTPFN (p=0.0)": "#8C4E99",
+    "MTPFN (p=0.1)": "#6059A9",
+    "MTPFN (p=0.2)": "#4E96BC",
+    "ScaML": "#E78C35",
+    "ICM": "#DA2222",
+    "GP": "#95211B",
+}
+
+marker = {
+    "MTPFN (p=0.0)": "o",
+    "MTPFN (p=0.1)": "^",
+    "MTPFN (p=0.2)": "s",
+    "ScaML": "1",
+    "ICM": "+",
+    "GP": "x",
+}
+
+# marker = {
+#     "PFN (p=0.0)": "1",
+#     "PFN (p=0.1)": "2",
+#     "PFN (p=0.2)": "3",
+#     "ScaML": "+",
+#     "ICM": "x",
+#     "GP": "o",
+# }
+
+##CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE', '#882255', '#44AA99', '#999933', '#AA4499'
+# colors = {
+#     "PFN (p=0.0)": "#CC6677",
+#     "PFN (p=0.1)": "#332288",
+#     "PFN (p=0.2)": "#DDCC77",
+#     "ScaML": "#117733",
+#     "ICM": "#88CCEE",
+#     "GP": "#882255",
+# }
+
+baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"] #+ ["random"] 
+
+# model_to_label = {
+#     "different-dust-282": "PFN (0.5 uncorr)",
+#     "royal-firebrand-281": "PFN (0.25 uncorr)",
+#     "peach-plasma-280": "PFN (1.0 uncorr)",
+#     "treasured-lion-279": "PFN (0.0 uncorr)",
+#     "exalted-wave-278": "PFN (0.75 uncorr)",
+# }
+
+# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+
+def get_label(model):
+    if model in model_to_label:
+        return model_to_label[model]
+    return model
+
+
+def get_labels(models):
+    return [get_label(model) for model in models]
+
+
+def get_color(model):
+    return colors[get_label(model)]
+
+
+
+
+
+def plot_bo_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
+        # linestyle 
+        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
+
+                    
+    plt.legend()
+    plt.xlabel("BO Iterations")
+    plt.ylabel("Normalized Regret")
+    plt.title(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    
+    
+    
+
+def plot_bo_ax(ax, exp_dir, models, title, path=None):    
+    if models is None:
+        models = []
+        
+    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
+    for model in baselines + models:
+        model_results = []
+        for function_dir in os.listdir(exp_dir):
+            try:
+                function_results = []
+                # check if directory
+                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                    continue
+                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
+                    bo_results = json.load(f)
+                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
+                    info = json.load(f)
+                
+                
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    function_results.append(regret)
+                
+                function_results = torch.stack(function_results)
+                model_results.append(function_results.mean(0))
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                continue
+            
+        if len(model_results) == 0:
+            continue
+        model_results = torch.stack(model_results)
+            
+        # import pdb; pdb.set_trace()
+        # mean = model_results.median(0).values
+        # lower = model_results.quantile(0.25, 0)
+        # upper = model_results.quantile(0.75, 0)
+        mean = model_results.mean(0)
+        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
+        lower = mean - std
+        upper = mean + std
+        linestyle = "--" if model in baselines else "-"
+        # linestyle = "-" if model in baselines else "--"
+        # linestyle = "-"
+        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
+        # linestyle 
+        xs = torch.linspace(0, 40, len(mean))
+        ax.plot(xs, mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+        ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
+
+    ax.set_title(title)
+    # ax.set_ylabel("Normalized Regret")
+    ax.set_xlabel("BO Iterations")
+    # ax.set_ylim(0.05, 0.7)
+    # ax.set_yscale("log")
+    # plt.legend()
+    # plt.xlabel("BO Iterations")
+    # plt.ylabel("Normalized Regret")
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+    
+    
+    
+    
+
+def plot_all(exp_dir, models, title, path):
+    plt.figure(figsize=(8, 6))
+    # one plot for boxplots of mse, one plot for boxplots of nll
+    
+    model_mses = defaultdict(list)
+    model_nlls = defaultdict(list)
+    
+    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
+
+    for model_order in all_models:
+        for function_dir in os.listdir(exp_dir):
+            # check if directory
+            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
+                continue
+            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
+                results = json.load(f)
+                
+            models = list(results.keys())
+            for model in models:
+                if model != model_order:
+                    continue
+                mses, nlls = [], []
+                try:
+                    for trial_key in results[model]:
+                        mses.append(results[model][trial_key]["mse"])
+                        nlls.append(results[model][trial_key]["nll"])
+                except KeyboardInterrupt as e:
+                    raise e
+                except Exception as e:
+                    traceback.print_exc()
+                    continue
+                model_mses[model].extend(mses)
+                model_nlls[model].extend(nlls)
+    
+    # plot nll boxplots
+    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("NLL")
+    plt.title(title)
+    plt.ylabel("NLL")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path)
+    plt.clf()
+    
+    # plot mse boxplots
+    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
+    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
+    # for tick in plt.gca().get_xticklabels():
+    #     tick.set_rotation(45)
+    # plt.title("MSE")
+    plt.title(title)
+    plt.ylabel("MSE")
+    # plt.yscale("log")
+    
+    # plt.suptitle(title)
+    plt.tight_layout()
+    plt.savefig(path.replace(".png", "_mse.png"))
+    plt.clf()
+    
+
+def plot_bo_function_ax(ax, function_dir, models, title):
+    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
+        bo_results = json.load(f)
+    with open(os.path.join(function_dir, "info.json"), "r") as f:
+        info = json.load(f)
+        
+    for model in baselines + models:
+        for model_try in bo_results:
+            if model_try != model:
+                continue
+            model_results = []
+            try:
+                for trial in bo_results[model]:
+                    best_init = info[trial]["best_init"]
+                    best_possible = info[trial]["best_possible"]
+                    
+                    results = bo_results[model][trial]
+                        
+                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
+                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
+                    # average normalized regret
+                    regret = (cummax - best_possible) / (best_init - best_possible)
+                    
+                    model_results.append(regret)
+                
+                # remove shorter trials
+                max_length = max([len(result) for result in model_results])
+                model_results = [result for result in model_results if len(result) == max_length]
+                
+                model_results = torch.stack(model_results)
+                
+                mean = model_results.nanmean(0)
+                std = model_results.std(0) / len(model_results)# ** 0.5
+                lower = mean - std
+                upper = mean + std
+                # mean = model_results.mean(0)
+                # lower = model_results.quantile(0.25, 0)
+                # upper = model_results.quantile(0.75, 0)
+                linestyle = "--" if model in baselines else "-"
+                
+                xs = torch.linspace(0, 40, len(mean))
+                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
+                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
+                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
+                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
+            except KeyboardInterrupt as e:
+                raise e
+            except Exception as e:
+                print(e)
+                # import pdb; pdb.set_trace()
+                # raise e
+                continue
+                    
+    # plt.legend()
+    ax.set_xlabel("BO Iterations")
+    ax.set_ylabel("Normalized Regret")
+    ax.set_title(title)
+    # plt.title(title)
+    # plt.tight_layout()
+    # plt.savefig(path)
+
+
+
+# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
+# plot_bo_all("/home/yl9959/mtpfn/eval_plot/25-01-28_11-31-32__synthetic__trials_5__seed_0__num_samples/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0", models, "All")
+
+fig, axs = plt.subplots(1, 4, figsize=(12, 3))
+
+# add grid
+for ax in axs:
+    ax.grid(True, which='both', linestyle='-', linewidth=0.5, alpha=0.5)
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0"
+models = ["fallen-cherry-483", "wobbly-donkey-484", "youthful-wildflower-481"]
+model_to_label = {
+    "fallen-cherry-483": "MTPFN (p=0.0)",
+    "wobbly-donkey-484": "MTPFN (p=0.1)",
+    "youthful-wildflower-481": "MTPFN (p=0.2)",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+title = "0 Unrelated Tasks"
+plot_bo_ax(axs[0], dir, models, title)
+
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_1__n_target_2__n_source_20__seed_0/function_4"
+model_to_label = {
+    "sparkling-microwave-480": "MTPFN (p=0.0)",
+    "stilted-night-482": "MTPFN (p=0.1)",
+    "youthful-wildflower-481": "MTPFN (p=0.2)",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["sparkling-microwave-480", "stilted-night-482", "youthful-wildflower-481"]
+title = "1 Unrelated Task"
+plot_bo_function_ax(axs[1], dir, models, title)
+
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_2__n_target_2__n_source_20__seed_0/"
+model_to_label = {
+    "sparkling-microwave-480": "MTPFN (p=0.0)",
+    "wobbly-donkey-484": "MTPFN (p=0.1)",
+    "youthful-wildflower-481": "MTPFN (p=0.2)",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["sparkling-microwave-480", "wobbly-donkey-484", "youthful-wildflower-481"]
+title = "2 Unrelated Tasks"
+plot_bo_ax(axs[2], dir, models, title)
+
+
+dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_3__n_target_2__n_source_20__seed_0/"
+model_to_label = {
+    "likely-donkey-479": "MTPFN (p=0.0)",
+    "fallen-cherry-483": "MTPFN (p=0.1)",
+    "stilted-night-482": "MTPFN (p=0.2)",
+    "mtgp": "ICM",
+    "gp": "GP",
+    "scaml": "ScaML",
+}
+models = ["likely-donkey-479", "fallen-cherry-483", "stilted-night-482"]
+title = "3 Unrelated Tasks"
+plot_bo_ax(axs[3], dir, models, title)
+
+
+handles, labels = axs[0].get_legend_handles_labels()
+# legend above plot
+fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=6, handles=handles, labels=labels)
+# plt.legend()
+plt.tight_layout()
+plt.savefig("uncorr.pdf", bbox_inches='tight')
+
+# original
+# # uncorr 0
+# # dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_0__n_target_2__n_source_20__seed_0"
+# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_21-12-23__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_0__n_target_2__n_source_20__seed_0"
+# models = ["fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481"]
+# model_to_label = {
+#     "fallen-cherry-483": "MTPFN (p=0.0)",
+#     "stilted-night-482": "PFN (p=0.1)",
+#     "youthful-wildflower-481": "PFN (p=0.2)",
+#     "mtgp": "ICM",
+#     "gp": "GP",
+#     "scaml": "ScaML",
+#     # "random": "Random",
+# }
+# path = "uncorr_0.png"
+# # title = "4 Total Tasks, 0/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
+# title = "0 Unrelated Tasks"
+# plot_bo_ax(axs[0], dir, models, title, path)
+# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
+
+
+# # uncorr 1
+# # dir = "/home/yl9959/mtpfn/eval_plot/25-01-28_13-31-07__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_1__n_target_2__n_source_20__seed_0"
+# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_1__n_target_2__n_source_20__seed_0"
+# models = ["sparkling-microwave-480", "youthful-wildflower-481", "wobbly-donkey-484"]
+# model_to_label = {
+#     "sparkling-microwave-480": "PFN (p=0.0)",
+#     "youthful-wildflower-481": "PFN (p=0.1)",
+#     "wobbly-donkey-484": "PFN (p=0.2)",
+#     "mtgp": "ICM",
+#     "gp": "GP",
+#     "scaml": "ScaML",
+#     # "random": "Random",
+# }
+# path = "uncorr_1.png"
+# title = "4 Total Tasks, 1/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
+# title = "1 Unrelated Task"
+# plot_bo_ax(axs[1], dir, models, title, path)
+# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
+
+# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_2__n_target_2__n_source_20__seed_0"
+# path = "uncorr_2.png"
+# title = "4 Total Tasks, 2/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
+# models = ["stilted-night-482",  "wobbly-donkey-484", "youthful-wildflower-481"]
+# # models = ["youthful-wildflower-481"]
+# model_to_label = {
+#     "stilted-night-482": "PFN (p=0.0)",
+#     "wobbly-donkey-484": "PFN (p=0.1)",
+#     "youthful-wildflower-481": "PFN (p=0.2)",
+#     "mtgp": "ICM",
+#     "gp": "GP",
+#     "scaml": "ScaML",
+#     # "random": "Random",
+# }
+# title = "2 Unrelated Tasks"
+# plot_bo_ax(axs[2], dir, models, title, path)
+# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
+
+# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_3__n_target_2__n_source_20__seed_0"
+# path = "uncorr_3.png"
+# models = ["fallen-cherry-483", "youthful-wildflower-481", "stilted-night-482"]
+# model_to_label = {
+#     "fallen-cherry-483": "PFN (p=0.0)",
+#     # "wobbly-donkey-484": "PFN (p=0.2)",
+#     "youthful-wildflower-481": "PFN (p=0.1)",
+#     "stilted-night-482": "PFN (p=0.2)",
+#     "mtgp": "GP",
+#     "gp": "ICM",
+#     "scaml": "ScaML",
+#     # "random": "Random",
+# }
+# title = "4 Total Tasks, 3/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
+# title = "3 Unrelated Tasks"
+# plot_bo_ax(axs[3], dir, models, title, path)
+# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
+
+# plt.legend()
+# plt.tight_layout()
+# plt.savefig("uncorr.pdf")
\ No newline at end of file
diff --git a/private_multitask_pfn/paper_figures/joint_data.py b/private_multitask_pfn/paper_figures/joint_data.py
new file mode 100644
index 0000000..9dd0cab
--- /dev/null
+++ b/private_multitask_pfn/paper_figures/joint_data.py
@@ -0,0 +1,483 @@
+#!/usr/bin/env python3
+import gpytorch
+
+from PFNs.pfns.priors import Batch
+import torch
+from gpytorch.distributions.multivariate_normal import MultivariateNormal
+from gpytorch.kernels import RBFKernel
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import timeit
+
+import warnings
+warnings.filterwarnings("ignore")
+import pandas as pd
+        
+def get_mtgp_batch(n_samples, n_features, n_tasks):
+    xs = torch.rand(n_samples, n_features)
+    task_id = torch.randint(n_tasks, size=(n_samples,)).unsqueeze(1).long()
+    
+    rbf = RBFKernel()
+    rbf.lengthscale = torch.tensor(0.2)
+    covar_x = rbf(xs)
+    task_covar_matrix = torch.eye(n_tasks)
+    # randomly choose two tasks to have a correlation
+    related_tasks = torch.randperm(n_tasks)[:2]
+    task_covar_matrix[related_tasks[0], related_tasks[1]] = 0.9
+    task_covar_matrix[related_tasks[1], related_tasks[0]] = 0.9
+    
+    covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
+    covar = covar_x.mul(covar_t)
+    ys = MultivariateNormal(torch.zeros(n_samples), covar).sample()
+
+    return xs, task_id, ys
+
+
+def draw_paired(n, target_corr, samples):
+    
+    target_train = torch.linspace(0, 0.25, n)
+    target_test = torch.linspace(0.25, 1, n)
+    
+    source1_train = torch.linspace(0.4, 1, n)
+    source2_train = torch.linspace(0.0, 0.6, n)
+    
+    xs = torch.cat([target_train, source1_train, source2_train, target_test], dim=0).view(-1, 1)
+    task_id = torch.cat([torch.zeros(n), torch.ones(n), torch.ones(n) * 2, torch.zeros(n)], dim=0).long()
+    task_id = task_id.unsqueeze(1)
+    
+    rbf = RBFKernel()
+    rbf.lengthscale = torch.tensor(0.1)
+    covar_x = rbf(xs)
+    task_covar_matrix = torch.eye(3)
+    task_covar_matrix[0, 1] = target_corr
+    task_covar_matrix[1, 0] = target_corr
+    task_covar_matrix[0, 2] = target_corr
+    task_covar_matrix[2, 0] = target_corr
+    task_covar_matrix[1, 2] = 1.0
+    task_covar_matrix[2, 1] = 1.0
+    
+    covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
+    covar = covar_x.mul(covar_t)
+    # covar = covar_x
+    ys = MultivariateNormal(torch.zeros(4 * n), covar).sample(torch.Size([samples]))
+    
+    # add batch dimension to xs
+    return xs.unsqueeze(0).repeat(samples, 1, 1), task_id.unsqueeze(0).repeat(samples, 1, 1), ys
+
+
+def get_constructed(n, pairs, samples=10):
+    source_xs = []
+    source_ys = []
+    target_train_xs = []
+    target_train_ys = []
+    target_test_xs = []
+    target_test_ys = []
+    
+    for i in range(pairs):
+        if i == 0:
+            corr = 0.95
+        else:
+            corr = 1.0
+        xs, task_id, ys = draw_paired(n, corr, samples)
+        target_train, source1_train, source2_train, target_test = xs[:, :n], xs[:, n:2 * n], xs[:, 2 * n:3 * n], xs[:, 3 * n:]
+        target_y, source1_y, source2_y, target_test_y = ys[:, :n], ys[:, n:2 * n], ys[:, 2 * n:3 * n], ys[:, 3 * n:]
+        
+        source_xs.extend([source1_train, source2_train])
+        source_ys.extend([source1_y, source2_y])
+        target_train_xs.append(target_train)
+        target_train_ys.append(target_y)
+        target_test_xs.append(target_test)
+        target_test_ys.append(target_test_y)
+
+    target_train_xs = target_train_xs[0]
+    # target_train_ys = torch.stack(target_train_ys, dim=0).sum(dim=0)
+    target_train_ys = target_train_ys[0]
+    target_train_ids = torch.zeros(samples, n).long()
+    target_test_xs = target_test_xs[0]
+    # target_test_ys = torch.stack(target_test_ys, dim=0).sum(dim=0)
+    target_test_ys = target_test_ys[0]
+    target_test_ids = torch.zeros(samples, n).long()
+    
+    source_xs = torch.cat(source_xs, dim=1)
+    source_ys = torch.cat(source_ys, dim=1)
+    source_ids = torch.cat([torch.ones(samples, n) * i for i in range(pairs * 2)], dim=1).long() + 1
+    
+    xs = torch.cat([target_train_xs, source_xs, target_test_xs], dim=1)
+    task_id = torch.cat([target_train_ids, source_ids, target_test_ids], dim=1).unsqueeze(-1)
+    ys = torch.cat([target_train_ys, source_ys, target_test_ys], dim=1)
+    
+    return xs, task_id, ys.unsqueeze(-1)
+
+
+default_device = "cuda:0" if torch.cuda.is_available() else "cpu:0"
+def get_joint_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    max_num_tasks: int,
+    num_tasks: int,
+    lengthscale: float,
+    hyperparameters=None,
+    device: str = default_device,
+    **kwargs,
+):    
+    xs, task_id, ys = get_constructed(seq_len // 4, 1, batch_size)
+    perm = torch.randperm(xs.size(0))
+    # randomly permute the data
+    xs = xs[perm]
+    task_id = task_id[perm]
+    ys = ys[perm]
+    
+    return Batch(
+        x=xs.transpose(0, 1),
+        y=ys.transpose(0, 1),
+        target_y=ys.transpose(0, 1).clone(),
+        task_id=task_id.transpose(0, 1),
+    )
+
+    
+
+def mtgp_predict(task_id, train_x, train_y, possible_x):
+    mtgp = mtgp_fit(task_id.double(), train_x.double(), train_y.double())
+    covar_factor = mtgp.task_covar_module.covar_factor.detach()
+    v = mtgp.task_covar_module.raw_var.detach()
+    cov_matrix = covar_factor @ covar_factor.t() + torch.eye(covar_factor.size(0)) * np.exp(v)
+    # to correlation
+    std_devs = np.sqrt(np.diagonal(cov_matrix))
+    # Outer product of the standard deviations to get normalization factor
+    # print(cov_matrix)
+    corr_matrix = cov_matrix / (std_devs[:, None] * std_devs[None, :])
+    print(corr_matrix)
+    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
+    return mtgp.posterior(possible_x)
+
+
+
+def joint_visualization():
+    
+
+
+    for seed in range(10, 20):
+        torch.manual_seed(seed)
+
+        n = 50
+        n_test = n
+        pairs = 1
+        xs, tasks, ys = get_constructed(n, pairs)
+        print(xs.shape, tasks.shape, ys.shape)
+        train_xs, train_tasks, train_ys = xs[0, :-n_test], tasks[0, :-n_test].squeeze(), ys[0, :-n_test]
+        test_xs, test_tasks, test_ys = xs[0, -n_test:], tasks[0, -n_test:].squeeze(), ys[0, -n_test:]
+
+
+        fig, axs = plt.subplots(1, 3, figsize=(12, 4))
+        # fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+
+        def plot(ax, pred, title, legend=False):
+            if "NUTS" in title:
+                pred_mean, pred_std = pred.mixture_mean.detach().squeeze(), pred.mixture_variance.sqrt().detach().squeeze()
+            else:
+                pred_mean = pred.mean.detach().squeeze()
+                pred_std = pred.variance.sqrt().detach().squeeze()
+            
+            print(title, pred_mean.shape, pred_std.shape, test_ys.shape)
+            nll = -torch.distributions.Normal(pred_mean, pred_std).log_prob(test_ys.squeeze()).mean()
+            colors = plt.cm.tab10.colors + ("darkgrey",)
+            # colors = colors[:1] + colors[2:] + ("darkgrey",)
+            for i in range(pairs * 2 + 1):
+                if i == 0:
+                    label = "Target"
+                else:
+                    label = f"Auxiliary {i}"
+                ax.scatter(train_xs[train_tasks == i], train_ys[train_tasks == i], label=label, color=colors[i], marker="x")
+            ax.plot(test_xs.squeeze(), test_ys.squeeze(), color=colors[0], label="True")
+            ax.plot(test_xs.squeeze(), pred_mean, color=colors[-1])
+            ax.fill_between(test_xs.squeeze(), pred_mean - 2 * pred_std, pred_mean + 2 * pred_std, alpha=0.2, color=colors[-1], label=r"$\mu \pm 2\sigma$")
+            ax.axvline(0.6, color="black", linestyle="--", alpha=0.5, label="End of Aux 1")
+            if legend:
+                ax.legend()
+            ax.set_title(title + " NLL: {:.2f}".format(nll.item()))
+            
+            
+        # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "dutiful-darkness-309"
+        # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "mild-valley-277"
+        # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "major-monkey-325"
+        try:
+            pfn_dir = "/home/yl9959/mtpfn/ckpt/25-01-15_22-04-28__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_80__attn_standard__task_hier__seed_0__13187" 
+            pfn = load_model(pfn_dir)
+        except FileNotFoundError:
+            pfn_dir = "/home/lily_l/private_multitask_pfn/ckpt/25-01-15_22-04-28__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_80__attn_standard__task_hier__seed_0__13187"
+            pfn = load_model(pfn_dir)
+        except e:
+            raise e
+        pred = pfn_predict(pfn, train_tasks, train_xs, train_ys, test_xs)
+        plot(axs[0], pred, "MTPFN")
+        
+        pred = mtgp_predict(train_tasks, train_xs, train_ys, test_xs)
+        plot(axs[1], pred, "Joint (ICM)")
+
+        pred = scaml_predict(train_tasks, train_xs, train_ys, test_xs)
+        plot(axs[2], pred, "Ensemble (ScaML)")
+        
+        min_y = np.min([ax.get_ylim()[0] for ax in axs])
+        max_y = np.max([ax.get_ylim()[1] for ax in axs])
+        for ax in axs:
+            ax.set_ylim(min_y, max_y)
+        
+        
+        # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # pred = mtgp_nuts_predict(train_tasks.to(device), train_xs.to(device), train_ys.to(device), test_xs.to(device))
+        # plot(axs[2], pred, "MTGP NUTS")
+
+        # 3 column on top
+        h, l = axs[0].get_legend_handles_labels()
+        fig.legend(h, l, ncol=3, loc='lower center', bbox_to_anchor=(0.5, 1.0))
+        # plt.tight_layout()
+        plt.savefig("joint_data_{}.png".format(seed), bbox_inches="tight")
+        plt.savefig("joint_data_{}.pdf".format(seed), bbox_inches="tight")
+        
+        # break
+        
+import signal
+
+class TimeoutException(Exception):
+    pass
+
+def timeout_handler(signum, frame):
+    raise TimeoutException("Function timed out!")
+
+def long_running_function():
+    signal.signal(signal.SIGALRM, timeout_handler)  # Set the signal handler
+    signal.alarm(600)  # Set the alarm for 600 seconds (10 minutes)
+    try:
+        # Your long-running code here
+        while True:
+            print("Running...")
+            # Simulate work
+    except TimeoutException:
+        print("Function terminated after 10 minutes.")
+    finally:
+        signal.alarm(0)  # Disable the alarm
+
+# long_running_function()
+
+
+def time_up_to(func, number, max_time=600):
+    signal.signal(signal.SIGALRM, timeout_handler)  # Set the signal handler
+    signal.alarm(max_time)  # Set the alarm for 600 seconds (10 minutes)
+    time = None
+    try:
+        time = timeit.timeit(func, number=number) / number
+    except TimeoutException:
+        print("Function terminated after 10 minutes.")
+    finally:
+        signal.alarm(0)  # Disable the alarm
+    return time
+
+        
+def get_times(n_tasks, n_samples_per_task, pfn, device, benchmark_repeats):
+    
+    print("n_samples_per_task", n_samples_per_task, "n_tasks", n_tasks, flush=True)
+
+    def gen_data(n_tasks, n_samples_per_task):
+        n_samples = n_tasks * n_samples_per_task
+        xs = torch.rand(n_samples, 1)
+        task_id = torch.randint(n_tasks, size=(n_samples,)).long()#.unsqueeze(0)
+        ys = torch.randn(n_samples, 1)#.unsqueeze(0)
+        
+        return task_id.to(device), xs.to(device), ys.to(device)
+    
+    def benchmark_mtgp():
+        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
+        mtgp_fit(task_id, xs, ys)
+        
+    def benchmark_scaml():
+        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
+        for _ in range(n_tasks):
+            gp_fit(task_id, xs, ys)
+        
+    def benchmark_pfn(pfn):
+        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
+        pfn_gaussian_fit(pfn, task_id, xs, ys)
+    
+    mtgp_time = time_up_to(benchmark_mtgp, number=benchmark_repeats) if n_tasks < 15 else None
+    scaml_times = time_up_to(benchmark_scaml, number=benchmark_repeats) if n_tasks < 100 else None
+    pfn_times = time_up_to(lambda: benchmark_pfn(pfn), number=benchmark_repeats)
+    
+    return mtgp_time, scaml_times, pfn_times
+        
+        
+        
+def benchmark_time():    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    benchmark_repeats = 5
+    
+    # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "dutiful-darkness-309"
+    # pfn_dir = "/home/yl9959/mtpfn/ckpt/25-01-15_22-04-28__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_80__attn_standard__task_hier__seed_0__13187"
+    pfn_dir = "/home/yl9959/mtpfn/ckpt/25-01-16_11-13-16__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_200__attn_standard__task_hier__seed_0__98742"
+    pfn = load_model(pfn_dir)
+    
+    mtgp_times = []
+    scaml_times = []
+    pfn_times = []
+    
+    n_samples_per_task = 50
+    n_taskss = torch.logspace(0.5, 4, 20).int()
+    for n_tasks in n_taskss:
+        mtgp_time, scaml_time, pfn_time = get_times(n_tasks, n_samples_per_task, pfn, device, benchmark_repeats)
+        mtgp_times.append(mtgp_time)
+        scaml_times.append(scaml_time)
+        pfn_times.append(pfn_time)
+        
+        plt.plot(n_taskss[:len(mtgp_times)], mtgp_times, label="Joint (ICM)")
+        plt.plot(n_taskss[:len(mtgp_times)], scaml_times, label="Ensemble (ScaML)")
+        plt.plot(n_taskss[:len(mtgp_times)], pfn_times, label="PFN")
+        plt.xlabel("Tasks")
+        plt.title(f"{n_samples_per_task} Samples per Task")
+        
+        # save csv
+        df = pd.DataFrame({
+            "n_tasks": n_taskss[:len(mtgp_times)],
+            "mtgp": mtgp_times,
+            "scaml": scaml_times,
+            "pfn": pfn_times
+        })
+        df.to_csv("benchmark_time_task.csv")
+        
+        plt.xscale("log")
+        plt.ylabel("Time (s)")
+        plt.yscale("log")
+        plt.legend()
+        plt.savefig("benchmark_time_task.png")
+        plt.savefig("benchmark_time_task.pdf")
+        plt.clf()
+        
+    return
+        
+    
+    mtgp_times = []
+    scaml_times = []
+    pfn_times = []
+    
+    n_tasks = 5
+    n_samples_per_tasks = torch.logspace(1.5, 2.5, 10).int()
+    for n_samples_per_task in n_samples_per_tasks:
+        mtgp_time, scaml_time, pfn_time = get_times(n_tasks, n_samples_per_task, pfn, device, benchmark_repeats)
+        mtgp_times.append(mtgp_time)
+        scaml_times.append(scaml_time)
+        pfn_times.append(pfn_time)
+        
+        plt.plot(n_samples_per_tasks[:len(mtgp_times)], mtgp_times, label="Joint (ICM)")
+        plt.plot(n_samples_per_tasks[:len(mtgp_times)], scaml_times, label="Ensemble (ScaML)")
+        plt.plot(n_samples_per_tasks[:len(mtgp_times)], pfn_times, label="PFN")
+        plt.xlabel("Samples per Task")
+        plt.title(f"5 Tasks")
+        
+        # save csv
+        df = pd.DataFrame({
+            "n_samples_per_task": n_samples_per_tasks[:len(mtgp_times)],
+            "mtgp": mtgp_times,
+            "scaml": scaml_times,
+            "pfn": pfn_times
+        })
+        df.to_csv("benchmark_time_samples.csv")
+        
+        plt.xscale("log")
+        plt.ylabel("Time (s)")
+        plt.yscale("log")
+        plt.legend()
+        plt.savefig("benchmark_time_samples.png")
+        plt.savefig("benchmark_time_samples.pdf")
+        plt.clf()
+    
+        
+def get_specific_times(model_name, n_tasks, n_samples_per_task, device, benchmark_repeats):
+    
+    print(model_name, "n_samples_per_task", n_samples_per_task, "n_tasks", n_tasks, flush=True)
+
+    def gen_data(n_tasks, n_samples_per_task):
+        n_samples = n_tasks * n_samples_per_task
+        xs = torch.rand(n_samples, 1)
+        task_id = torch.randint(n_tasks, size=(n_samples,)).long()#.unsqueeze(0)
+        ys = torch.randn(n_samples, 1)#.unsqueeze(0)
+        
+        return task_id.to(device), xs.to(device), ys.to(device)
+    
+    def benchmark_mtgp():
+        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
+        mtgp_fit(task_id, xs, ys)
+        
+    def benchmark_scaml():
+        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
+        for _ in range(n_tasks):
+            gp_fit(task_id, xs, ys)
+        
+    def benchmark_pfn(pfn):
+        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
+        pfn_gaussian_fit(pfn, task_id, xs, ys)
+    
+    if model_name == "mtgp":
+        benchmark = benchmark_mtgp
+    elif model_name == "scaml":
+        benchmark = benchmark_scaml
+    time = time_up_to(benchmark_mtgp, number=benchmark_repeats)
+    
+    return time
+        
+        
+def benchmark_time_slurm(model_name, vary_tasks=True):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    benchmark_repeats = 20
+
+    result_times = []
+    df = pd.DataFrame()
+    if vary_tasks:
+        n_samples_per_task = 50
+        n_taskss = torch.logspace(0.5, 4, 20).int()
+        for n_tasks in n_taskss:
+            times = get_specific_times(model_name, n_tasks, n_samples_per_task, device, benchmark_repeats)
+            result_times.append(times)
+            
+            df = pd.DataFrame({
+                "n_tasks": n_taskss[:len(result_times)],
+                "n_samples_per_task": [n_samples_per_task] * len(result_times),
+                "model": [model_name] * len(result_times),
+                "time": result_times,
+            })
+            df.to_csv(f"benchmark_time_{model_name}_task.csv", index=False)
+            
+    else:
+        n_tasks = 5
+        n_samples_per_tasks = torch.logspace(1.5, 2.5, 10).int()
+        
+        for n_samples_per_task in n_samples_per_tasks:
+            times = get_specific_times(model_name, n_tasks, n_samples_per_task, device, benchmark_repeats)
+            result_times.append(times)
+            
+            df = pd.DataFrame({
+                "n_tasks": [n_tasks] * len(result_times),
+                "n_samples_per_task": n_samples_per_tasks[:len(result_times)],
+                "model": [model_name] * len(result_times),
+                "time": result_times,
+            })
+            df.to_csv(f"benchmark_time_{model_name}_samples.csv", index=False)
+        
+
+
+if __name__ == "__main__":
+    from utils import *#mtgp_fit, to_mtgp_format, scaml_predict, mtgp_nuts_predict, pfn_predict, load_model
+    import sys
+    
+    # # benchmark time
+    # arg = sys.argv[1]
+    # arg_options = [
+    #     ("mtgp", True),
+    #     ("scaml", True),
+    #     ("mtgp", False),
+    #     ("scaml", False),
+    # ]
+    # args = arg_options[int(arg)]
+    # benchmark_time_slurm(*args)
+    
+    # visualization
+    joint_visualization()
\ No newline at end of file
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diff --git a/private_multitask_pfn/pd1_gp_surrogates.pth b/private_multitask_pfn/pd1_gp_surrogates.pth
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diff --git a/private_multitask_pfn/pfn.yml b/private_multitask_pfn/pfn.yml
new file mode 100644
index 0000000..a074b58
--- /dev/null
+++ b/private_multitask_pfn/pfn.yml
@@ -0,0 +1,199 @@
+name: pfn
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - _sysroot_linux-64_curr_repodata_hack=3=haa98f57_10
+  - binutils_impl_linux-64=2.40=h5293946_0
+  - binutils_linux-64=2.40.0=hc2dff05_1
+  - bzip2=1.0.8=h5eee18b_6
+  - ca-certificates=2024.9.24=h06a4308_0
+  - comm=0.2.2=pyhd8ed1ab_0
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - expat=2.6.3=h6a678d5_0
+  - gcc_impl_linux-64=11.2.0=h1234567_1
+  - gcc_linux-64=11.2.0=h5c386dc_1
+  - gxx_impl_linux-64=11.2.0=h1234567_1
+  - gxx_linux-64=11.2.0=hc2dff05_1
+  - ipykernel=6.29.5=pyh3099207_0
+  - jupyter_client=8.6.0=py312h06a4308_0
+  - jupyter_core=5.7.2=py312h06a4308_0
+  - kernel-headers_linux-64=3.10.0=h57e8cba_10
+  - ld_impl_linux-64=2.40=h12ee557_0
+  - libffi=3.4.4=h6a678d5_1
+  - libgcc=14.2.0=h77fa898_1
+  - libgcc-devel_linux-64=11.2.0=h1234567_1
+  - libgcc-ng=14.2.0=h69a702a_1
+  - libgomp=14.2.0=h77fa898_1
+  - libsodium=1.0.18=h7b6447c_0
+  - libstdcxx=14.2.0=hc0a3c3a_1
+  - libstdcxx-devel_linux-64=11.2.0=h1234567_1
+  - libstdcxx-ng=14.2.0=h4852527_1
+  - libuuid=1.41.5=h5eee18b_0
+  - ncurses=6.4=h6a678d5_0
+  - nest-asyncio=1.6.0=py312h06a4308_0
+  - openssl=3.4.0=hb9d3cd8_0
+  - prompt_toolkit=3.0.43=hd3eb1b0_0
+  - ptyprocess=0.7.0=pyhd3eb1b0_2
+  - pure_eval=0.2.2=pyhd3eb1b0_0
+  - python=3.12.0=h996f2a0_0
+  - python-dateutil=2.9.0post0=py312h06a4308_2
+  - readline=8.2=h5eee18b_0
+  - setuptools=75.1.0=py312h06a4308_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - sqlite=3.45.3=h5eee18b_0
+  - stack_data=0.2.0=pyhd3eb1b0_0
+  - sysroot_linux-64=2.17=h57e8cba_10
+  - tk=8.6.14=h39e8969_0
+  - tornado=6.4.1=py312h5eee18b_0
+  - traitlets=5.14.3=py312h06a4308_0
+  - tzdata=2024b=h04d1e81_0
+  - wheel=0.44.0=py312h06a4308_0
+  - xz=5.4.6=h5eee18b_1
+  - zeromq=4.3.5=h6a678d5_0
+  - zlib=1.2.13=h5eee18b_1
+  - pip:
+      - anyio==4.6.2.post1
+      - argon2-cffi==23.1.0
+      - argon2-cffi-bindings==21.2.0
+      - arrow==1.3.0
+      - asttokens==2.4.1
+      - async-lru==2.0.4
+      - attrs==24.2.0
+      - babel==2.16.0
+      - beautifulsoup4==4.12.3
+      - bleach==6.2.0
+      - botorch==0.12.0
+      - certifi==2024.8.30
+      - cffi==1.17.1
+      - charset-normalizer==3.4.0
+      - click==8.1.7
+      - contourpy==1.3.0
+      - cycler==0.12.1
+      - debugpy==1.8.8
+      - defusedxml==0.7.1
+      - docker-pycreds==0.4.0
+      - executing==2.1.0
+      - fastjsonschema==2.20.0
+      - filelock==3.16.1
+      - fonttools==4.54.1
+      - fqdn==1.5.1
+      - fsspec==2024.10.0
+      - gitdb==4.0.11
+      - gitpython==3.1.43
+      - gpytorch==1.13
+      - h11==0.14.0
+      - httpcore==1.0.6
+      - httpx==0.27.2
+      - huggingface-hub==0.26.2
+      - idna==3.10
+      - ipython==8.29.0
+      - ipywidgets==8.1.5
+      - isoduration==20.11.0
+      - jaxtyping==0.2.19
+      - jedi==0.19.2
+      - jinja2==3.1.4
+      - joblib==1.4.2
+      - json5==0.9.28
+      - jsonpointer==3.0.0
+      - jsonschema==4.23.0
+      - jsonschema-specifications==2024.10.1
+      - jupyter==1.1.1
+      - jupyter-client==8.6.3
+      - jupyter-console==6.6.3
+      - jupyter-events==0.10.0
+      - jupyter-lsp==2.2.5
+      - jupyter-server==2.14.2
+      - jupyter-server-terminals==0.5.3
+      - jupyterlab==4.2.5
+      - jupyterlab-pygments==0.3.0
+      - jupyterlab-server==2.27.3
+      - jupyterlab-widgets==3.0.13
+      - kiwisolver==1.4.7
+      - linear-operator==0.5.3
+      - markupsafe==3.0.2
+      - matplotlib==3.9.2
+      - matplotlib-inline==0.1.7
+      - mistune==3.0.2
+      - mpmath==1.3.0
+      - multipledispatch==1.0.0
+      - nbclient==0.10.0
+      - nbconvert==7.16.4
+      - nbformat==5.10.4
+      - networkx==3.4.2
+      - notebook==7.2.2
+      - notebook-shim==0.2.4
+      - numpy==2.1.3
+      - nvidia-cublas-cu12==12.4.5.8
+      - nvidia-cuda-cupti-cu12==12.4.127
+      - nvidia-cuda-nvrtc-cu12==12.4.127
+      - nvidia-cuda-runtime-cu12==12.4.127
+      - nvidia-cudnn-cu12==9.1.0.70
+      - nvidia-cufft-cu12==11.2.1.3
+      - nvidia-curand-cu12==10.3.5.147
+      - nvidia-cusolver-cu12==11.6.1.9
+      - nvidia-cusparse-cu12==12.3.1.170
+      - nvidia-nccl-cu12==2.21.5
+      - nvidia-nvjitlink-cu12==12.4.127
+      - nvidia-nvtx-cu12==12.4.127
+      - opt-einsum==3.4.0
+      - overrides==7.7.0
+      - packaging==24.2
+      - pandocfilters==1.5.1
+      - parso==0.8.4
+      - pexpect==4.9.0
+      - pillow==11.0.0
+      - pip==24.3.1
+      - platformdirs==4.3.6
+      - prometheus-client==0.21.0
+      - prompt-toolkit==3.0.48
+      - protobuf==5.28.3
+      - psutil==6.1.0
+      - pure-eval==0.2.3
+      - pycparser==2.22
+      - pygments==2.18.0
+      - pyparsing==3.2.0
+      - pyro-api==0.1.2
+      - pyro-ppl==1.9.1
+      - python-json-logger==2.0.7
+      - pyyaml==6.0.2
+      - pyzmq==26.2.0
+      - referencing==0.35.1
+      - regex==2024.11.6
+      - requests==2.32.3
+      - rfc3339-validator==0.1.4
+      - rfc3986-validator==0.1.1
+      - rpds-py==0.21.0
+      - safetensors==0.4.5
+      - scikit-learn==1.5.2
+      - scipy==1.14.1
+      - send2trash==1.8.3
+      - sentry-sdk==2.18.0
+      - setproctitle==1.3.3
+      - smmap==5.0.1
+      - sniffio==1.3.1
+      - soupsieve==2.6
+      - stack-data==0.6.3
+      - sympy==1.13.1
+      - terminado==0.18.1
+      - threadpoolctl==3.5.0
+      - tinycss2==1.4.0
+      - tokenizers==0.20.3
+      - torch==2.5.1
+      - tqdm==4.67.0
+      - transformers==4.46.2
+      - triton==3.1.0
+      - typeguard==4.4.1
+      - types-python-dateutil==2.9.0.20241003
+      - typing-extensions==4.12.2
+      - uri-template==1.3.0
+      - urllib3==2.2.3
+      - wandb==0.18.6
+      - wcwidth==0.2.13
+      - webcolors==24.11.1
+      - webencodings==0.5.1
+      - websocket-client==1.8.0
+      - widgetsnbextension==4.0.13
+prefix: /ext3/miniconda3/envs/pfn
diff --git a/private_multitask_pfn/scaml.py b/private_multitask_pfn/scaml.py
new file mode 100644
index 0000000..89323a4
--- /dev/null
+++ b/private_multitask_pfn/scaml.py
@@ -0,0 +1,435 @@
+#!/usr/bin/env python3
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+"""Scalable Meta Learning with Gaussian Processes.
+
+Code adapted from https://github.com/boschresearch/Scalable-Meta-Learning-with-Gaussian-Processes/blob/main/scamlgp/model.py"""
+
+import copy
+from itertools import compress
+from typing import Dict, Hashable, List, Optional, Tuple, Type, Union
+
+import torch
+from botorch.fit import fit_gpytorch_mll
+from botorch.models import SingleTaskGP
+from botorch.models.transforms import Standardize
+from botorch.utils.datasets import SupervisedDataset
+from gpytorch import Module
+from gpytorch.constraints import GreaterThan, Interval
+from gpytorch.distributions import MultivariateNormal
+from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
+from gpytorch.likelihoods import GaussianLikelihood, Likelihood
+from gpytorch.means import ZeroMean
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from gpytorch.priors import GammaPrior, LogNormalPrior
+from linear_operator import to_linear_operator
+from linear_operator.operators import PsdSumLinearOperator
+
+
+def validate_meta_data(meta_data: Dict[Hashable, SupervisedDataset]):
+    """Validate the metadata passed to meta-learning algorithms"""
+    if len(meta_data) == 0:
+        raise ValueError("Empty meta data. Needs at least one source task.")
+    task_id_source_0, data_source_0 = list(meta_data.items())[0]
+    X_shape = data_source_0.X.shape
+    Y_shape = data_source_0.Y.shape
+    if X_shape[:-2] != Y_shape[:-2]:
+        raise ValueError(
+            f"The X and Y batch sizes of task {task_id_source_0} are not equal."
+        )
+    for task_id, task_data in meta_data.items():
+        if (
+            task_data.X.shape[:-2] != X_shape[:-2]
+            or task_data.Y.shape[:-2] != Y_shape[:-2]
+            or task_data.X.shape[-1] != X_shape[-1]
+        ):
+            raise ValueError(
+                f"Dimensions of tasks {task_id_source_0} and {task_id} do not match."
+            )
+        if task_data.Y.shape[-1] != 1:
+            raise ValueError(
+                f"The output dimension of task {task_id} is {task_data.Y.shape[-1]} "
+                f"but must be one"
+            )
+
+
+def _get_default_likelihood_noise(
+    batch_shape: torch.Size | None = None,
+) -> GaussianLikelihood:
+    noise_prior = LogNormalPrior(-8.0, 2.0)
+    return GaussianLikelihood(
+        noise_prior=noise_prior,
+        noise_constraint=Interval(1e-8, 1e-2, initial_value=1e-3),
+        batch_shape=batch_shape,
+    )
+
+
+def _get_default_likelihood(
+    batch_shape: torch.Size | None = None,
+) -> GaussianLikelihood:
+    # noise_prior = LogNormalPrior(-8.0, 2.0)
+    return GaussianLikelihood(
+        # noise_prior=noise_prior,
+        noise_constraint=Interval(1e-8, 1e-5, initial_value=1e-6),
+        batch_shape=batch_shape,
+    )
+
+
+def _get_kernel_source_gp(
+    base_kernel: Union[Type[RBFKernel], Type[MaternKernel]],
+    ard_num_dims: int = 1,
+    batch_shape: torch.Size | None = None,
+) -> ScaleKernel:
+    lengthscale_prior = GammaPrior(3.0, 6.0)  # Same prior as `SingleTaskGP`
+    outputscale_prior = GammaPrior(2.0, 0.15)  # Same prior as `SingleTaskGP`
+
+    return ScaleKernel(
+        base_kernel(
+            ard_num_dims=ard_num_dims,
+            lengthscale_prior=lengthscale_prior,
+            # If the outputscale is zero, lengthscales tend to be arbitrary. This
+            # avoids numerical instability when lengthscales get very small/large.
+            # Since our domain is in [0, 1] the lengthscale ranges allow for
+            # anything between
+            lengthscale_constraint=Interval(
+                lower_bound=torch.tensor(1e-4),
+                upper_bound=torch.tensor(1e2),
+                initial_value=torch.full((ard_num_dims,), 0.5),
+            ),
+            batch_shape=batch_shape,
+        ),
+        outputscale_prior=outputscale_prior,
+        # Avoid numerical instability: prior.log_prob is nan for output_scale = 0
+        # If the scale gets too large then we have to add more noise to make the
+        # matrix PSD. Since our model acts on normalized data the maximum output
+        # scale should be ~1.
+        outputscale_constraint=Interval(
+            lower_bound=torch.tensor(1e-4),
+            upper_bound=torch.tensor(1e2),
+            initial_value=torch.tensor(1.0),
+        ),
+        batch_shape=batch_shape,
+    )
+
+
+def _get_default_kernel(
+    base_kernel: Union[Type[RBFKernel], Type[MaternKernel]],
+    ard_num_dims: int = 1,
+    batch_shape: torch.Size | None = None,
+) -> ScaleKernel:
+    # The lengthscale prior is not as restrictive as for `SingleTaskGP`
+    # since the difference signal can have a broad range of lengthscales depending on
+    # how successful the transfer is. This prior is inspired by our study on
+    # meta-learning priors.
+    lengthscale_prior = LogNormalPrior(0.5, 1.5)
+    # The outputscale parameter can attain a broad range of values because the target
+    # y-values are normalized wrt all target and meta observations. This prior is
+    # inspired by our study on meta-learning priors.
+    outputscale_prior = LogNormalPrior(-2.0, 3.0)
+    return ScaleKernel(
+        base_kernel(
+            ard_num_dims=ard_num_dims,
+            lengthscale_prior=lengthscale_prior,
+            lengthscale_constraint=Interval(
+                lower_bound=torch.tensor(1e-4),
+                upper_bound=torch.tensor(1e2),
+                initial_value=torch.full((ard_num_dims,), 1.0),
+            ),
+            batch_shape=batch_shape,
+        ),
+        outputscale_prior=outputscale_prior,
+        outputscale_constraint=Interval(
+            lower_bound=torch.tensor(1e-4),
+            upper_bound=torch.tensor(1e2),
+            initial_value=torch.tensor(0.1),
+        ),
+        batch_shape=batch_shape,
+    )
+
+
+def _compute_target_prior(
+    x: torch.Tensor, source_gps: List[SingleTaskGP], weights: torch.Tensor
+) -> Tuple[torch.Tensor, PsdSumLinearOperator]:
+    """Compute the prior distribution of the target GP.
+
+    Args:
+        x: A `n x d` or `batch_shape x n x d` (batch mode) tensor of features.
+        source_gps: Dictionary containing the source GPs (order needs to match weights).
+        weights: The source weights (order needs to match source_gps).
+
+    Returns:
+        Tuple containing:
+            - The mean function as `n x 1` or `batch_shape x n x 1` tensor.
+            - The covariance function as `n x n` or `batch_shape x n x n` lazy tensor.
+    """
+    if len(source_gps) != len(weights):
+        raise ValueError(
+            f"The number of source GPs, {len(source_gps)}, does not "
+            f"equal the number of weights, {len(weights)}"
+        )
+    posteriors = [task_gp.posterior(x) for task_gp in source_gps]
+    weighted_post_mean_source = [p.mvn.mean * w for p, w in zip(posteriors, weights)]
+    weighted_post_cov_source = [
+        p.mvn.lazy_covariance_matrix * w**2 for p, w in zip(posteriors, weights)
+    ]
+    prior_mean_target = sum(weighted_post_mean_source, torch.zeros(*x.shape[:-1]).to(x))
+    prior_cov_target = PsdSumLinearOperator(*weighted_post_cov_source)
+    return prior_mean_target.unsqueeze(-1), prior_cov_target
+
+
+def meta_fit_scamlgp(
+    meta_data: Dict[Hashable, SupervisedDataset],
+    likelihood: Optional[Likelihood] = None,
+    covar_module: Optional[Module] = None,
+    num_restarts_log_likelihood: int = 5,
+    seed: Optional[int] = None,
+) -> Dict[Hashable, SingleTaskGP]:
+    """Train the source GPs on the given meta-data.
+
+    Args:
+        meta_data: Dictionary containing the source datasets.
+        likelihood: A likelihood. If omitted, use a standard
+            `GaussianLikelihood` with inferred noise level.
+        covar_module: The kernel of the GPs in the stack. If omitted, use an
+            `RBFKernel`.
+        num_restarts_log_likelihood: The number of restarts for the log-likelihood
+            optimization of the model parameters. The first restart corresponds to
+            warm-starting, while the rest of the initial conditions are sampled
+            from the parameters' prior.
+        seed: A seed to make the optimization reproducible.
+
+    Returns:
+        A dictionary of the trained source GPs and corresponding task IDs.
+
+    """
+    if seed is not None:
+        torch.manual_seed(seed=seed)
+    validate_meta_data(meta_data)
+    source_gp_0 = list(meta_data.values())[0]
+    ard_num_dims = source_gp_0.X.shape[-1]
+    batch_shape = source_gp_0.X.shape[:-2]
+    if likelihood is None:
+        likelihood = _get_default_likelihood(batch_shape=batch_shape)
+    if covar_module is None:
+        covar_module = _get_kernel_source_gp(
+            base_kernel=RBFKernel, ard_num_dims=ard_num_dims, batch_shape=batch_shape
+        )
+    source_gps = {}
+    for task_id, task_data in meta_data.items():
+        likelihood = copy.deepcopy(likelihood)
+        covar_module = copy.deepcopy(covar_module)
+        task_gp = SingleTaskGP(
+            train_X=task_data.X,
+            train_Y=task_data.Y,
+            likelihood=likelihood,
+            covar_module=covar_module,
+            mean_module=ZeroMean(batch_shape=batch_shape),
+            outcome_transform=Standardize(1, batch_shape=batch_shape),
+        )
+        task_mll = ExactMarginalLogLikelihood(task_gp.likelihood, task_gp)
+
+        fit_gpytorch_mll(
+            task_mll,
+            num_restarts=num_restarts_log_likelihood,
+            pick_best_of_all_attempts=True,
+        )
+        source_gps[task_id] = task_gp
+    return source_gps
+
+
+def significant_weights_mask(
+    weights: torch.Tensor, std_Y_vals: torch.Tensor, threshold: float
+) -> torch.Tensor:
+    r"""Boolean mask of weights that when rescaled exceed the threshold.
+
+    Args:
+        weights: The weights of the models. shape = (n_source_tasks,)
+        std_Y_vals: The standard deviation of the models' Y-values.
+            shape = (n_source_tasks,)
+        threshold: threshold for weight pruning, $\tau$, according to the criterion
+            $$
+                \frac{w_i \sigma_i}{\sum_j w_j \sigma_j}n_w < \tau,
+            $$
+            where $w_i$ is the weight of task $i$, $\sigma_i$ is the standard
+            deviation of the Y-values from task $i$, and $n_w$ denotes the number of
+            weights.
+
+    Returns:
+        Mask containing True for significant weights and False otherwise.
+    """
+    num_weights = len(weights)
+    w_times_sigma = weights * std_Y_vals
+    norm_weights = w_times_sigma * num_weights / w_times_sigma.sum()
+    return norm_weights >= threshold
+
+
+class ScaMLGP(SingleTaskGP):
+    def __init__(
+        self,
+        train_X: torch.Tensor,
+        train_Y: torch.Tensor,
+        source_gps: Dict[Hashable, SingleTaskGP],
+        likelihood: Optional[Likelihood] = None,
+        covar_module: Optional[Module] = None,
+        weight_pruning_threshold: float = 1e-3,
+    ) -> None:
+        r"""Scalable and Modular Kernel for Transfer Learning with Gaussian Processes.
+
+        Args:
+            train_X: A `batch_shape x n x d` tensor of training features.
+            train_Y: A `batch_shape x n x m` tensor of training observations.
+            source_gps: Dictionary containing the source GPs.
+            likelihood: A likelihood. If omitted, use a standard
+                `GaussianLikelihood` with inferred noise level.
+            covar_module: The kernel of the target GP. If omitted, use an `RBFKernel`.
+            weight_pruning_threshold: Relative threshold for weight pruning during
+                prediction. Weights satisfying the following condition are pruned:
+                `weights * num_weights/ weights.sum() < weight_pruning_threshold`.
+                Defaults to 1e-3. Note that the absolute threshold decreases linearly
+                with `num_weights`. Usually, it is not required to change this.
+
+        Example:
+            >>> pi = torch.pi
+            >>> meta_X = torch.distributions.Uniform(-pi, pi).sample((20, 1))
+            >>> meta_Y1 = torch.sin(meta_X)
+            >>> meta_Y2 = torch.cos(meta_X)
+            >>> meta_data = {
+            >>>     0: SupervisedDataset(meta_X, meta_Y1),
+            >>>     1: SupervisedDataset(meta_X, meta_Y2),
+            >>>}
+            >>> source_models = meta_fit_scamlgp(meta_data)
+            >>> X = torch.distributions.Uniform(-torch.pi, torch.pi).sample((7, 1))
+            >>> Y = torch.sin(X) * X/10
+            >>> model = ScaMLGP(X, Y, source_models)
+        """
+        self._weight_pruning_threshold = weight_pruning_threshold
+
+        batch_shape = train_Y.shape[:-2]
+        n_source_tasks = len(source_gps)
+        # Note that, since we normalize with respect to all meta- and target data, the
+        # target data alone does generally not have mean = 0 and std = 1. This may lead
+        # to BoTorch raising warnings about Y-normalization and can be safely ignored.
+        Y_meta = torch.cat(
+            [
+                gp.outcome_transform.untransform(gp.train_targets.unsqueeze(-1))[0]
+                for gp in list(source_gps.values())
+            ],
+            dim=-2,
+        )
+        Y_all = torch.cat([Y_meta, train_Y], dim=-2)
+        outcome_transform = Standardize(1, batch_shape=batch_shape)
+        outcome_transform(Y_all)
+        # Make sure parent constructor doesn't re-learn the normalizer and only consumes
+        # current state
+        outcome_transform.eval()
+
+        # Cache the posterior mean of the source models
+        if train_Y.shape[-2] > 0:
+            with torch.no_grad():
+                posteriors = [gp.posterior(train_X) for gp in source_gps.values()]
+                self.source_means = torch.stack(
+                    [p.mvn.mean for p in posteriors], dim=-1
+                )
+                # No need to deal with LazyTensor since the target typically has little
+                # data
+                self.source_covs = torch.stack(
+                    [p.mvn.covariance_matrix for p in posteriors], dim=-1
+                )
+
+        if covar_module is None:
+            covar_module = _get_default_kernel(
+                base_kernel=RBFKernel,
+                ard_num_dims=train_X.shape[-1],
+                batch_shape=batch_shape,
+            )
+
+        self.source_gps = source_gps
+        batch_shape = train_Y.shape[:-2]
+        if likelihood is None:
+            likelihood = _get_default_likelihood(batch_shape=batch_shape)
+        # We normalize to zero-mean unit-variance
+        mean_module = ZeroMean(batch_shape=batch_shape)
+        if outcome_transform is None:
+            outcome_transform = Standardize(1, batch_shape=batch_shape)
+        # If input is empty, do not standardize the data
+        if train_Y.nelement() == 0:
+            outcome_transform = None
+        super().__init__(
+            train_X,
+            train_Y,
+            likelihood=likelihood,
+            covar_module=covar_module,
+            mean_module=mean_module,
+            outcome_transform=outcome_transform,
+        )
+
+        # Add weight parameters
+        self.register_parameter(
+            "raw_weights",
+            torch.nn.Parameter(torch.full((n_source_tasks,), 1.0 / n_source_tasks)),
+        )
+        # Use a generic distribution that is constant for `w \lesssim 1` and decays
+        # quickly for `w \gtrsim 1`. This prior is best for tasks functions that live on
+        # relatively similar scales.
+        self.register_prior(
+            "weights_prior",
+            GammaPrior(1.0, 1.0),
+            lambda m: m.weights,
+            lambda m, v: m._set_weights(v),  # pylint: disable=protected-access
+        )
+        # For unrelated tasks, the weights can reach a value of zero and may conflict
+        # with a strictly positive prior. We therefore bound the weights from below.
+        weights_constraint = GreaterThan(1e-10, transform=None)
+        self.register_constraint(
+            "raw_weights",
+            weights_constraint,
+        )
+        self.to(train_X)
+
+    @property
+    def weights(self):
+        # When accessing the parameter, apply the constraint transform
+        return self.raw_weights_constraint.transform(self.raw_weights)
+
+    @weights.setter
+    def weights(self, value):
+        self._set_weights(value)
+
+    def _set_weights(self, value):
+        if not torch.is_tensor(value):
+            value = torch.as_tensor(value).to(self.raw_weights)
+        # When setting the parameter, transform the actual value to a raw one by
+        # applying the inverse transform
+        self.initialize(
+            raw_weights=self.raw_weights_constraint.inverse_transform(value)
+        )
+
+    def forward(self, x: torch.Tensor) -> MultivariateNormal:
+        if self.training:
+            # Use cached values
+            mean = (self.source_means @ self.weights).unsqueeze(-1)
+            cov = self.source_covs @ self.weights**2
+        else:
+            std_Y_vals = torch.tensor(
+                [m.outcome_transform.stdvs.flatten() for m in self.source_gps.values()]
+            ).to(x)
+            mask = significant_weights_mask(
+                self.weights, std_Y_vals, self._weight_pruning_threshold
+            )
+            significant_weights = self.weights[mask]
+            significant_models = list(compress(self.source_gps.values(), mask))
+            mean, cov = _compute_target_prior(
+                x=x, source_gps=significant_models, weights=significant_weights
+            )
+        if hasattr(self, "outcome_transform"):
+            # Transform the source terms to the target space
+            self.outcome_transform.eval()
+            mean = self.outcome_transform(mean)[0]
+            cov /= self.outcome_transform.stdvs**2
+            if self.training:  # return to training mode, if relevant
+                self.outcome_transform.train()
+        cov = to_linear_operator(cov) + self.covar_module(x)
+        # print(mean.squeeze(-1).shape, cov.to_dense().shape)#,squeeze(-1), cov.to_dense())
+        # if mean.shape[0] == 1:
+        #     return torch.distributions.Normal(mean.squeeze(-1), cov)
+        return MultivariateNormal(mean.squeeze(-1), cov)
\ No newline at end of file
diff --git a/private_multitask_pfn/task_embedding.ipynb b/private_multitask_pfn/task_embedding.ipynb
new file mode 100644
index 0000000..6e79e6e
--- /dev/null
+++ b/private_multitask_pfn/task_embedding.ipynb
@@ -0,0 +1,2065 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import gpytorch\n",
+    "\n",
+    "import torch\n",
+    "from PFNs.pfns.priors import Batch\n",
+    "from gpytorch.distributions.multivariate_normal import MultivariateNormal\n",
+    "from gpytorch.kernels import RBFKernel\n",
+    "from gpytorch.kernels.kernel import Kernel\n",
+    "from gpytorch.means.constant_mean import ConstantMean\n",
+    "from gpytorch.priors.lkj_prior import LKJCovariancePrior\n",
+    "from linear_operator.operators import InterpolatedLinearOperator\n",
+    "\n",
+    "import torch\n",
+    "from gen_batch import (\n",
+    "    combine_batch,\n",
+    "    get_hpo_batch_fn,\n",
+    "    get_icm,\n",
+    "    get_lmc,\n",
+    "    get_mtgp_batch,\n",
+    ")\n",
+    "from PFNs.pfns import (\n",
+    "    bar_distribution,\n",
+    "    encoders,\n",
+    "    priors,\n",
+    "    utils,\n",
+    ")\n",
+    "from PFNs.pfns.train import (\n",
+    "    train as train_pfn,\n",
+    ")\n",
+    "import sys\n",
+    "import logging\n",
+    "import os\n",
+    "import datetime\n",
+    "import json\n",
+    "import argparse\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.is_available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "default_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def train(\n",
+    "    get_batch_fn,\n",
+    "    num_features = 2,\n",
+    "    num_tasks = 1,\n",
+    "    seq_len = 20,\n",
+    "    epochs = 20,\n",
+    "    sample_num_features = False\n",
+    "):\n",
+    "    batch_fn_hypers = {\n",
+    "        \"num_tasks\": num_tasks,\n",
+    "        \"sample_num_tasks\": False,\n",
+    "        \"sample_num_features\": sample_num_features,\n",
+    "    }\n",
+    "    get_batch_fn = priors.get_batch_sequence(\n",
+    "        get_batch_fn,\n",
+    "        priors.utils.set_properties_get_batch,\n",
+    "    )\n",
+    "    criterion = torch.nn.GaussianNLLLoss(reduction=\"none\", full=True)\n",
+    "\n",
+    "    single_eval_pos_gen = utils.get_uniform_single_eval_pos_sampler(\n",
+    "        seq_len - num_tasks, min_len=num_tasks\n",
+    "    )\n",
+    "    \n",
+    "    encoder_generator = encoders.get_normalized_uniform_multitask_encoder(\n",
+    "        encoders.get_variable_num_features_multitask_encoder(encoders.Linear)\n",
+    "    )\n",
+    "\n",
+    "    return train_pfn(\n",
+    "        priordataloader_class_or_get_batch = get_batch_fn,\n",
+    "        criterion = criterion,\n",
+    "        encoder_generator=encoder_generator,\n",
+    "        single_eval_pos_gen=single_eval_pos_gen,\n",
+    "        extra_prior_kwargs_dict = {\n",
+    "            \"num_features\": num_features,\n",
+    "            \"hyperparameters\": batch_fn_hypers,\n",
+    "        },\n",
+    "        num_features=num_features, \n",
+    "        num_tasks=num_tasks, \n",
+    "        seq_len=seq_len, \n",
+    "        epochs=epochs,\n",
+    "        progress_bar=True,\n",
+    "        y_encoder_generator= encoders.Linear,\n",
+    "        steps_per_epoch = 256,\n",
+    "        print_every=1,\n",
+    "        validation_period=2,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def sample_data(xs):\n",
+    "    # randomly sample uncorrelated tasks\n",
+    "    kernel = RBFKernel().to(xs.device)\n",
+    "    kernel.lengthscale = 0.2\n",
+    "    return MultivariateNormal(\n",
+    "                torch.zeros(xs.shape[:-1], device=xs.device), kernel(xs)\n",
+    "            ).sample()\n",
+    "    \n",
+    "    \n",
+    "def gen_batch(\n",
+    "    batch_size: int,\n",
+    "    seq_len: int,\n",
+    "    num_features: int,\n",
+    "    max_num_tasks: int,\n",
+    "    num_tasks: int,\n",
+    "    lengthscale: float,\n",
+    "    hyperparameters=None,\n",
+    "    device: str = default_device,\n",
+    "    **kwargs\n",
+    "):\n",
+    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
+    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
+    "    \n",
+    "    xs = torch.cat((x1, x2), -1)\n",
+    "    ys = sample_data(x1)\n",
+    "    ys += x2.squeeze(-1)\n",
+    "    task_ids = torch.ones(batch_size, seq_len, 1, device=device)\n",
+    "    task_xs = torch.cat((task_ids, xs), -1).to(device)\n",
+    "\n",
+    "    return Batch(\n",
+    "        x=task_xs.transpose(0, 1),\n",
+    "        y=ys.transpose(0, 1),\n",
+    "        target_y=ys.transpose(0, 1).clone(),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Toy Problem\n",
+    "\n",
+    "2 features: $x_1$ and $x_2$\n",
+    "\n",
+    "$f \\sim \\mathcal{GP}(0, k_\\text{RBF})$\n",
+    "\n",
+    "$y = f(x_1) + x_2$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(11, 5))\n",
+    "fig.suptitle(\"Sample data\")\n",
+    "\n",
+    "x1 = torch.linspace(0, 1, 50).unsqueeze(-1)\n",
+    "x2 = torch.linspace(0, 1, 50).unsqueeze(-1)\n",
+    "\n",
+    "f_x1 = sample_data(x1)\n",
+    "ys = f_x1.expand(50, 50) + x2.expand(50, 50)\n",
+    "axs[0].contourf(x1.squeeze(), x2.squeeze(), ys, levels=20)\n",
+    "axs[0].set_title(\"Contour\")\n",
+    "axs[0].set_xlabel(\"x1\")\n",
+    "axs[0].set_ylabel(\"x2\")\n",
+    "axs[0].axis(\"equal\")\n",
+    "\n",
+    "for x2 in torch.linspace(1, 0, 5):\n",
+    "    ys = f_x1 + x2\n",
+    "    axs[1].plot(x1.squeeze(), ys.squeeze(), label=\"x2=%.2f\" % x2)\n",
+    "axs[1].set_title(\"Line plot\")\n",
+    "axs[1].set_xlabel(\"x1\")\n",
+    "axs[1].set_ylabel(\"y\")\n",
+    "axs[1].legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train single-task PFN on problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using cuda:0 device\n",
+      "init dist\n",
+      "Not using distributed\n",
+      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7ff916b3c9a0>, 'seq_len_maximum': 20, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 1, 'sample_num_tasks': False, 'sample_num_features': False}}, 'num_features': 2, 'num_tasks': 1, 'epoch_count': 0}\n",
+      "Style definition of first 3 examples: None\n",
+      "Initialized decoder for standard with (None, 2)  and nout 2\n",
+      "Using a Transformer with 1.49 M parameters\n",
+      "Using OpenAI max lr of 0.0012557515027448725.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:286: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with autocast(enabled=scaler is not None):\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 36.05it/s, data_time=0.00251, step_time=0.0207, mean_loss=1.76]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.63it/s, data_time=0.00492, step_time=0.0216, mean_loss=0.567]\n",
+      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with autocast(enabled=scaler is not None):\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 84.63it/s, data_time=0.00575, step_time=0.00705, mean_loss=-0.14]  \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.35it/s, data_time=0.003, step_time=0.0204, mean_loss=-0.181]    \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.29it/s, data_time=0.00241, step_time=0.0203, mean_loss=-0.324]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 98.59it/s, data_time=0.00231, step_time=0.0069, mean_loss=0.00138]   \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.04it/s, data_time=0.00327, step_time=0.0209, mean_loss=-0.318]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.90it/s, data_time=0.00235, step_time=0.0204, mean_loss=-0.292]  \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 98.21it/s, data_time=0.00234, step_time=0.00609, mean_loss=-0.437] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.08it/s, data_time=0.00241, step_time=0.0214, mean_loss=-0.566]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 36.41it/s, data_time=0.00248, step_time=0.0211, mean_loss=-0.258]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 99.01it/s, data_time=0.00235, step_time=0.0067, mean_loss=-0.685] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.85it/s, data_time=0.00243, step_time=0.0213, mean_loss=-0.257]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.46it/s, data_time=0.0031, step_time=0.0215, mean_loss=-0.359] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 99.38it/s, data_time=0.00319, step_time=0.00607, mean_loss=-0.606] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.17it/s, data_time=0.00249, step_time=0.0211, mean_loss=-0.376]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.02it/s, data_time=0.0025, step_time=0.0211, mean_loss=-0.493] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.69it/s, data_time=0.00223, step_time=0.00608, mean_loss=-0.254]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.79it/s, data_time=0.00249, step_time=0.0212, mean_loss=-0.547]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.99it/s, data_time=0.00249, step_time=0.0213, mean_loss=-0.734]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.82it/s, data_time=0.00286, step_time=0.00623, mean_loss=-1.2] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.02it/s, data_time=0.00249, step_time=0.0232, mean_loss=-0.946]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.91it/s, data_time=0.00251, step_time=0.0215, mean_loss=-1.02] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.66it/s, data_time=0.0029, step_time=0.00668, mean_loss=-1.09] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.89it/s, data_time=0.00249, step_time=0.0216, mean_loss=-1.21]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.83it/s, data_time=0.0025, step_time=0.0212, mean_loss=-1.24] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.54it/s, data_time=0.0024, step_time=0.00603, mean_loss=-1.34] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.28it/s, data_time=0.0025, step_time=0.0223, mean_loss=-1.53] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.75it/s, data_time=0.00232, step_time=0.0213, mean_loss=-1.6] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.66it/s, data_time=0.00359, step_time=0.00626, mean_loss=-1.65]\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_batch, num_features=2, num_tasks=1, seq_len=20, epochs=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HERE\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(1)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "TRAIN_CONST = 0.5\n",
+    "train_x2 = torch.ones_like(train_x1) * TRAIN_CONST\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "train_y = f + train_x2\n",
+    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
+    "task_train_x = torch.cat((torch.ones_like(train_x1).unsqueeze(-1), train_x), -1)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.sort()\n",
+    "sorted_f = f[indices]\n",
+    "sorted_train_x2 = train_x2[indices]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:10], train_y[:10], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10], color=\"grey\", alpha=0.5)\n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Train Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model is able to fit this setup and make reasonable predictions for varied $x_1$ and constant $x_2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HERE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(1)\n",
+    "N_POINTS = 5\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "train_y = f + train_x2\n",
+    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
+    "task_train_x = torch.cat((torch.ones_like(train_x1).unsqueeze(-1), train_x), -1)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.sort()\n",
+    "sorted_f = f[indices]\n",
+    "sorted_train_x2 = train_x2[indices]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
+    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model is able to fit this setup and make reasonable predictions for varied $x_1$ and $x_2$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing Data\n",
+    "\n",
+    "How does the model do when entire features ($x_1$ or $x_2$) are missing?\n",
+    "\n",
+    "Each input is now the concatentation of\n",
+    "1. Task description: 2-bit vector indicating whether feature is present\n",
+    "2. Features: $\\left[x_1\\right]$, $\\left[x_2\\right]$ or $\\left[x_1, x_2\\right]$\n",
+    "\n",
+    "The input is variable-length: could be 3 or 4 dimensional depending on number of missing columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_missing_batch(\n",
+    "    batch_size: int,\n",
+    "    seq_len: int,\n",
+    "    num_features: int,\n",
+    "    max_num_tasks: int,\n",
+    "    num_tasks: int,\n",
+    "    lengthscale: float,\n",
+    "    hyperparameters=None,\n",
+    "    device: str = default_device,\n",
+    "    delete_x1=None,\n",
+    "    delete_x2=None,\n",
+    "    **kwargs\n",
+    "):\n",
+    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
+    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
+    "    \n",
+    "    ys = sample_data(x1)\n",
+    "    ys += x2.squeeze(-1)\n",
+    "    \n",
+    "    task_ids = torch.ones(batch_size, seq_len, 1, device=device)\n",
+    "    \n",
+    "    task_info = torch.ones(batch_size, seq_len, 2, device=device)\n",
+    "    if delete_x1 is None:\n",
+    "        delete_x1 = np.random.rand() < 0.2\n",
+    "    if delete_x2 is None:\n",
+    "        delete_x2 = np.random.rand() < 0.2 and not delete_x1\n",
+    "        \n",
+    "    # get accurate task embedding\n",
+    "    if delete_x1:\n",
+    "        task_info[:, :, 0] = 0\n",
+    "    if delete_x2:\n",
+    "        task_info[:, :, 1] = 0\n",
+    "        \n",
+    "    if delete_x1:\n",
+    "        xs = torch.cat((task_ids, task_info, x2), -1)\n",
+    "    elif delete_x2:\n",
+    "        xs = torch.cat((task_ids, task_info, x1), -1)\n",
+    "    else:\n",
+    "        xs = torch.cat((task_ids, task_info, x1, x2), -1)\n",
+    "    \n",
+    "    xs = xs.to(device)\n",
+    "\n",
+    "    return Batch(\n",
+    "        x=xs.transpose(0, 1),\n",
+    "        y=ys.transpose(0, 1),\n",
+    "        target_y=ys.transpose(0, 1).clone(),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using cuda:0 device\n",
+      "init dist\n",
+      "Not using distributed\n",
+      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7efbdc328040>, 'seq_len_maximum': 20, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 3, 'sample_num_tasks': False, 'sample_num_features': True}}, 'num_features': 2, 'num_tasks': 3, 'epoch_count': 0}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Style definition of first 3 examples: None\n",
+      "Initialized decoder for standard with (None, 2)  and nout 2\n",
+      "Using a Transformer with 1.49 M parameters\n",
+      "Using OpenAI max lr of 0.0012557141533970579.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:286: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with autocast(enabled=scaler is not None):\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.33it/s, data_time=0.0026, step_time=0.0267, mean_loss=1.88] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.51it/s, data_time=0.00259, step_time=0.0267, mean_loss=0.959]\n",
+      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with autocast(enabled=scaler is not None):\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:04<00:04, 57.34it/s, data_time=0.00263, step_time=0.0196, mean_loss=0.71]  \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:14<00:00, 17.15it/s, data_time=0.00261, step_time=0.0263, mean_loss=0.737]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.36it/s, data_time=0.00347, step_time=0.0263, mean_loss=0.645]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.42it/s, data_time=0.00262, step_time=0.00761, mean_loss=0.523]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 29.72it/s, data_time=0.00255, step_time=0.0269, mean_loss=0.599]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.90it/s, data_time=0.00302, step_time=0.0264, mean_loss=0.624]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 71.39it/s, data_time=0.0027, step_time=0.00755, mean_loss=0.432] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.19it/s, data_time=0.00258, step_time=0.0271, mean_loss=0.7]  \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.16it/s, data_time=0.00259, step_time=0.0267, mean_loss=0.677]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.94it/s, data_time=0.00266, step_time=0.00857, mean_loss=0.61] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.19it/s, data_time=0.00259, step_time=0.0267, mean_loss=0.637]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.38it/s, data_time=0.00318, step_time=0.027, mean_loss=0.586]  \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 72.30it/s, data_time=0.00313, step_time=0.00765, mean_loss=0.632]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.25it/s, data_time=0.00257, step_time=0.0269, mean_loss=0.597]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.30it/s, data_time=0.00255, step_time=0.0271, mean_loss=0.741]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 73.30it/s, data_time=0.00319, step_time=0.00787, mean_loss=0.472]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.29it/s, data_time=0.0026, step_time=0.0265, mean_loss=0.472] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.22it/s, data_time=0.00255, step_time=0.027, mean_loss=0.316] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.94it/s, data_time=0.00267, step_time=0.00764, mean_loss=0.445]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.23it/s, data_time=0.00259, step_time=0.027, mean_loss=0.252] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 30.38it/s, data_time=0.00344, step_time=0.0276, mean_loss=0.142]   \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.94it/s, data_time=0.00354, step_time=0.0078, mean_loss=-0.16]    \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:11<00:00, 21.86it/s, data_time=0.0208, step_time=0.0627, mean_loss=-0.0567] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 30.48it/s, data_time=0.00257, step_time=0.0265, mean_loss=-0.0749] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.53it/s, data_time=0.00296, step_time=0.0081, mean_loss=-0.109]   \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.89it/s, data_time=0.00253, step_time=0.0264, mean_loss=-0.431]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.27it/s, data_time=0.00255, step_time=0.0283, mean_loss=-0.409]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.54it/s, data_time=0.00349, step_time=0.00799, mean_loss=-0.482]\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_missing_batch, num_features=2, num_tasks=3, seq_len=20, epochs=20, sample_num_features=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Missing $x_1$\n",
+    "\n",
+    "Only $x_2$ and $y$ are present in the training data, where the full model is $f \\sim GP, y=f(x_1) + x_2$. This means that the model has no information about the shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
+      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
+      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
+      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
+      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(1)\n",
+    "N_POINTS = 5\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "train_y = f + train_x2\n",
+    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "task_info[:, 0] = 0\n",
+    "\n",
+    "task_id = torch.ones(1000, 1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_id, task_info, train_x2.unsqueeze(-1)), -1)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.sort()\n",
+    "sorted_f = f[indices]\n",
+    "sorted_train_x2 = train_x2[indices]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_task_info = torch.ones(100, 2)\n",
+    "    test_task_info[:, 0] = 0\n",
+    "    test_task_id = torch.ones(100, 1)\n",
+    "    task_test_x = torch.cat((test_task_id, test_task_info, test_x2), -1)\n",
+    "    \n",
+    "    print(task_train_x[:N_POINTS].shape, train_y[:N_POINTS].shape, task_test_x.shape)\n",
+    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
+    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "plt.suptitle(\"Missing X1\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Missing $x_2$\n",
+    "\n",
+    "Only $x_2$ and $y$ are present in the training data, where the full model is $f \\sim GP, y=f(x_1) + x_2$. This means the model has shape information but doesn't know about the shift."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(1)\n",
+    "N_POINTS = 15\n",
+    "train_x1 = torch.rand(1000)\n",
+    "# train_x2 = torch.randn_like(train_x1)\n",
+    "train_x2 = torch.ones_like(train_x1) * 0.2\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "train_y = f + train_x2\n",
+    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "task_info[:, 1] = 0\n",
+    "\n",
+    "task_id = torch.ones(1000, 1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_id, task_info, train_x1.unsqueeze(-1)), -1)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.sort()\n",
+    "sorted_f = f[indices]\n",
+    "sorted_train_x2 = train_x2[indices]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_task_info = torch.ones(100, 2)\n",
+    "    test_task_info[:, 1] = 0\n",
+    "    test_task_id = torch.ones(100, 1)\n",
+    "    task_test_x = torch.cat((test_task_id, test_task_info, test_x1), -1)\n",
+    "    \n",
+    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
+    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-4, 4)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "plt.suptitle(\"Missing X2\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(1)\n",
+    "N_POINTS = 15\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "train_y = f + train_x2\n",
+    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "task_info[:, 1] = 0\n",
+    "\n",
+    "task_id = torch.ones(1000, 1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_id, task_info, train_x1.unsqueeze(-1)), -1)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.sort()\n",
+    "sorted_f = f[indices]\n",
+    "sorted_train_x2 = train_x2[indices]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_task_info = torch.ones(100, 2)\n",
+    "    test_task_info[:, 1] = 0\n",
+    "    test_task_id = torch.ones(100, 1)\n",
+    "    task_test_x = torch.cat((test_task_id, test_task_info, test_x1), -1)\n",
+    "    \n",
+    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
+    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-4, 4)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "plt.suptitle(\"Missing X2\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transfer Learning\n",
+    "\n",
+    "Shared:\n",
+    "$f \\sim \\mathcal{GP}(0, k_\\text{RBF}), c \\sim \\text{Uniform}(0, 10)$\n",
+    "\n",
+    "Source: $y = -f(x_1) + cx_2$\n",
+    "\n",
+    "Target: $y = f(x_1) + cx_2$\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_transfer_batch(\n",
+    "    batch_size: int,\n",
+    "    seq_len: int,\n",
+    "    num_features: int,\n",
+    "    max_num_tasks: int,\n",
+    "    num_tasks: int,\n",
+    "    lengthscale: float,\n",
+    "    hyperparameters=None,\n",
+    "    device: str = default_device,\n",
+    "    **kwargs\n",
+    "):\n",
+    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
+    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
+    "    c = torch.rand(batch_size, device=device) * 10\n",
+    "    task_ids = torch.randint(0, 2, (batch_size, seq_len), device=device).float()\n",
+    "    task_id[:2] = torch.tensor([0, 1], device=device).unsqueeze(-1)\n",
+    "    task_ids[-2:] = torch.tensor([0, 1], device=device).unsqueeze(-1)\n",
+    "    \n",
+    "    f = sample_data(x1)\n",
+    "    source_ys = f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
+    "    target_ys = -f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
+    "    \n",
+    "    task_xs = torch.cat((task_ids.unsqueeze(-1), x1, x2), -1).to(device)\n",
+    "    ys = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "    return Batch(\n",
+    "        x=task_xs.transpose(0, 1),\n",
+    "        y=ys.transpose(0, 1),\n",
+    "        target_y=ys.transpose(0, 1).clone(),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train without missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using cuda:0 device\n",
+      "init dist\n",
+      "Not using distributed\n",
+      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7efb5cfe9bc0>, 'seq_len_maximum': 30, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 1, 'sample_num_tasks': False, 'sample_num_features': False}}, 'num_features': 2, 'num_tasks': 1, 'epoch_count': 0}\n",
+      "Style definition of first 3 examples: None\n",
+      "Initialized decoder for standard with (None, 2)  and nout 2\n",
+      "Using a Transformer with 1.49 M parameters\n",
+      "Using OpenAI max lr of 0.0012557515027448725.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 30.88it/s, data_time=0.00285, step_time=0.0271, mean_loss=25.5]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.75it/s, data_time=0.00285, step_time=0.0266, mean_loss=2.25]\n",
+      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with autocast(enabled=scaler is not None):\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.53it/s, data_time=0.0028, step_time=0.00757, mean_loss=1.47] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.74it/s, data_time=0.00325, step_time=0.0266, mean_loss=1.39]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.67it/s, data_time=0.00288, step_time=0.0271, mean_loss=1.14]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.30it/s, data_time=0.00313, step_time=0.00871, mean_loss=1.09]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.62it/s, data_time=0.00286, step_time=0.0268, mean_loss=1.06]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.68it/s, data_time=0.00286, step_time=0.0269, mean_loss=0.925]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.35it/s, data_time=0.00279, step_time=0.00777, mean_loss=0.919]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.64it/s, data_time=0.00286, step_time=0.0275, mean_loss=0.941]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:12<00:00, 20.87it/s, data_time=0.00285, step_time=0.0268, mean_loss=0.821]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.83it/s, data_time=0.00293, step_time=0.00872, mean_loss=1.06]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.83it/s, data_time=0.00285, step_time=0.0264, mean_loss=0.77] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:11<00:00, 21.37it/s, data_time=0.0106, step_time=0.0471, mean_loss=0.722] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:04<00:04, 55.79it/s, data_time=0.00293, step_time=0.00857, mean_loss=0.816]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 23.57it/s, data_time=0.00327, step_time=0.0337, mean_loss=0.695]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 25.62it/s, data_time=0.00319, step_time=0.0263, mean_loss=0.708]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 65.45it/s, data_time=0.00909, step_time=0.0177, mean_loss=0.403] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.75it/s, data_time=0.00286, step_time=0.0279, mean_loss=0.594]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 29.69it/s, data_time=0.00314, step_time=0.0271, mean_loss=0.472]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 65.45it/s, data_time=0.00295, step_time=0.00849, mean_loss=0.259]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.60it/s, data_time=0.00372, step_time=0.0273, mean_loss=0.371]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.74it/s, data_time=0.00286, step_time=0.0272, mean_loss=0.135]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.30it/s, data_time=0.00291, step_time=0.00825, mean_loss=0.0601]   \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.70it/s, data_time=0.00288, step_time=0.0264, mean_loss=0.0139]   \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 23.60it/s, data_time=0.00331, step_time=0.0406, mean_loss=-0.197]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:06<00:06, 37.71it/s, data_time=0.0029, step_time=0.0176, mean_loss=-0.204]  \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 23.51it/s, data_time=0.00291, step_time=0.0427, mean_loss=-0.534]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.71it/s, data_time=0.00417, step_time=0.0276, mean_loss=-0.592]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:07<00:07, 35.97it/s, data_time=0.00294, step_time=0.00872, mean_loss=-0.643]\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_transfer_batch, num_features=2, num_tasks=1, seq_len=30, epochs=20, sample_num_features=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without transfer learning, there is a large amount of uncertainty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yC7799ltMnjwZsVgM27Ztw3vvvYcvv/wSEyZMwMyZM6FWq3HOOefg5ptvhtfrxauvvoq8vLwDFoGS6cknn8R5552HyZMn47rrroPD4cBf//pXjBo1ar+J3K+99lq8+eab2LNnD/r163fQfQ4cOBDvvPMOLr30UgwfPhxXX301Ro0ahXA4jO+//x7vv/8+rr322sT6RzJnyoIFC3DHHXfgoosuwuDBgxEOh7F48WJ88MEHmDBhAq688srEuitWrMD06dPx8MMP45FHHgEQbxn2+OOP4/bbb8fFF1+M0047DYsXL8a///1v/P73v0dWVlZi+9tuuw2vvvoqzjrrLNx7771QqVR4/vnnkZ+fj3vuueewYyciIiIiSkcsphARERERHUJ2djY+/fRT3HPPPXjooYdgs9lw5ZVXYsaMGQedt6QzN998M/7zn//g+eefh9frRUlJCe644w489NBDnW47YsQIrFq1Co8++ijeeOMNtLa2Ii8vD+PGjcPvfve7I4pnX1988QW++OKL/Zb369cPo0aNwkcffYQXXngBb731Fj788EPo9XoMGDAAd955J4YMGQIAGDp0KGbNmoWHHnoI9957LwoKCnDrrbciNzcX119//VHH2J3OOecc/Pe//8UjjzyCX/3qVxg8eDDeeOMNvPnmm9i8eXOHdb1eL3Q6HaxWa6f7Pffcc7FhwwY8++yzmDNnDl5++WVoNBqMGTMGzz33HG688cajinv06NGYPn065syZg/r6esiyjIEDB+J3v/sd7rvvPqjV6k73cdttt0GlUuG5557Dxx9/jNLSUrzwwgu48847O6xnMpmwYMEC3HXXXXjiiScgSRKmTZuGF154Abm5uUf1PoiIiIiI0oUg9+Tsi0RERERERGlo7NixyM3Nxbx58xLL8vPzcfXVV+PZZ59NYmRERERERJQMnDOFiIiIiIj6rEgkgmg02mHZggULsH79ekybNi2xbPPmzQgEAnjggQd6OUIiIiIiIkoFHJlCRERERER9VmVlJU455RRceeWVKCoqwrZt2/D3v/8dFosFmzZtQnZ2drJDJCIiIiKiFMA5U4iIiIiIqM+y2Ww49thj8dprr6G5uRkGgwFnnXUWnnrqKRZSiIiIiIgogSNTiIiIiIiIiIiIiIiIDoFzplDKmz9/Pq6//noMGTIEer0eAwYMwM9//nPU19cnJR6n04mbbroJubm5MBgMmD59OtasWdPl7bdu3YrTTz8dRqMRWVlZuOqqq9Dc3NyDERNROsiUXCdJEt544w2ce+65KC0thcFgwKhRo/DEE08gGAzut74gCAe8PfXUUz3xtogojWRKXgSAa6+99oC5btiwYT0cNRGlg0zKdwf7bCcIAk499dTEepWVlQdd79133+2pt0ZEaSKV8mJ9fT1+9atfYfr06TCZTBAEAQsWLDisfdTW1uKSSy6B1WqF2WzGeeedh4qKip4JmHoUR6ZQypswYQLsdjsuvvhiDB48GBUVFfjrX/8KvV6PdevWoaCgoNdikSQJJ598MtavX4/77rsPOTk5eOmll1BdXY3Vq1dj8ODBh9y+pqYG48aNg8ViwR133AGv14s//vGPKCsrw4oVK6BWq3vpnRBRqsmUXOf1emEymXD88cfj7LPPRl5eHpYuXYo333wTU6ZMwTfffANBEBLrt3+xvvrqqzvsZ9y4cRg5cmSPvUciSn2ZkheBeDHl3XffxWuvvdZhucViwTnnnNOToRNRGsikfPfvf/97v2WrVq3Cn/70JzzzzDO47777AMSLKf3798fPfvYznHnmmR3WP/nkk1FeXt59b4qI0k4q5cUFCxZg+vTpGDx4MHJycrB06VJ8++23mDZtWpe293q9GD9+PFwuF+655x6oVCq88MILkGUZ69atY1vZdCMTpbiFCxfKsVhsv2UA5N/85je9Gsv//vc/GYD8/vvvJ5Y1NTXJVqtV/tnPftbp9rfeequs0+nkqqqqxLJ58+bJAORXXnmlR2ImovSQKbkuFArJ33333X7LH330URmAPG/evA7LAci333579wRORBklU/KiLMvyNddcIxsMhp4MkYjSWCbluwO54YYbZEEQ5Orq6sSyPXv2yADkZ5999qhjJqLMk0p50e12y62trbIsy/L7778vA5C//fbbLm//9NNPywDkFStWJJZt3bpVVigU8oMPPtjd4VIPY5svSopAIIBhw4Zh2LBhCAQCieV2ux2FhYU48cQTEYvFAABTpkyBKHb8VZ0yZQqysrKwdevWXo171qxZyM/Px4UXXphYlpubi0suuQRz5sxBKBQ65PazZ8/G2WefjbKyssSyU045BUOGDMF7773XY3ETUXL0xVynVqtx4okn7rf8ggsuAICDvpdAIHDANmBElFn6Yl7cVywWg9vt7qkwiSiF9PV81y4UCmH27NmYOnUqSkpKDriOz+dDOBw+qriJKPWla140mUzIyso64u1nzZqFiRMnYuLEiYllw4YNw4wZM3guMA2xmEJJodPp8Oabb2LXrl34zW9+k1h+++23w+Vy4Y033oBCoTjo9l6vF16vFzk5OZ0ey+/3o6WlpdObw+HodF9r167F+PHj90vokyZNgt/vx44dOw66bW1tLZqamjBhwoT9Xps0aRLWrl3b6fGJKL30xVx3MA0NDQBwwPfyxhtvwGAwQKfTYcSIEXjnnXcOe/9ElB76cl70+/0wm82wWCzIysrC7bffDq/X2+l2RJSe+nK+29dnn30Gp9OJK6644oCvP/roozAajdBqtZg4cSK++uqrw9o/EaWPdM2LR0OSJGzYsOGg5wJ3794Nj8fTozFQ91ImOwDqu4477jjcf//9ePrpp3HBBRegsbER7777Ll588UUMGTLkkNu++OKLCIfDuPTSSzs9zjPPPINHH3200/XKy8tRWVl5yHXq6+sxZcqU/ZYXFhYCAOrq6jB69OiDbrvvuj/e3m63IxQKQaPRdBorEaWPvpbrDhWf2WzGGWec0WH5iSeeiEsuuQT9+/dHXV0d/va3v+GKK66Ay+XCrbfeeljHIKL00BfzYmFhIe6//36MHz8ekiThiy++wEsvvYT169djwYIFUCr5tYwoE/XFfPdj//nPf6DRaHDRRRd1WC6KImbOnIkLLrgAxcXFqKiowPPPP48zzjgDH3/8Mc4666wuH4OI0kc65sWj0X6u72DnAoF4Xh06dGiPxUDdi5/aKakeeeQRfPrpp7jmmmvg9XoxdepU3HHHHYfcZtGiRXj00UdxySWX4Cc/+Umnx7j66qtx0kkndbqeTqfrdJ1AIHDAYodWq028fqhtAXS6PYspRJmnL+W6A3nyySfx9ddf46WXXoLVau3w2nfffdfh+fXXX49jjz0Wv/71r3Httdd2KV4iSj99LS/+4Q9/6PD8sssuw5AhQ/Cb3/wGs2bNwmWXXdZpDESUnvpavtuX2+3G3LlzceaZZ+73GbCsrAxffvllh2VXXXUVRowYgXvuuYfFFKIMlm558Wh09VwgpQ8WUyip1Go1Xn/9dUycOBFarRb/+te/IAjCQdfftm0bLrjgAowaNQqvvfZal44xYMAADBgwoFvi1el0B+wR297n/1BJuP21I92eiNJXX8p1P/a///0PDz30EG644YYujTRRq9X4xS9+gVtuuQWrV6/u0gdgIko/fTkvtrvrrrvw29/+Fl9//TWLKUQZrC/nu9mzZyMYDB60xdePZWVl4brrrsNTTz2Fmpqag86xQkTpLd3y4tHgucDMw2IKJV371SjBYBA7d+5E//79D7hedXU1Zs6cCYvFgs8++wwmk6lL+2/vqdgZhUKB3NzcQ65TWFiYaNe1r/ZlRUVFh9x233V/vH1WVhZHpRBlsL6S6/Y1b948XH311TjrrLPw97//vUvbAEBpaSmA+JBoIspcfTEv7kun0yE7O5u5jqgP6Kv57j//+Q8sFgvOPvvsLm+z7+dAFlOIMlc65cWj0X6ur7s/R1LysJhCSbVhwwY89thjuO6667Bu3Tr8/Oc/x8aNG2GxWDqs19raipkzZyIUCmH+/PkH7DV4MH/84x+7rU/i2LFjsXjxYkiS1GFCvuXLl0Ov1x+yv2NxcTFyc3OxatWq/V5bsWIFxo4d22mMRJSe+lKu23fdCy64ABMmTMB77713WPMBVFRUAECPfqglouTqi3nxxzweD1paWpjriDJcX8139fX1+Pbbb3Httdce1kWD/BxIlPnSLS8eDVEUMXr06AOeC1y+fDkGDBjQ5QIRpQYWUyhpIpEIrr32WhQVFeFPf/oT9uzZg4kTJ+Kuu+7C66+/nljP5/PhzDPPRG1tLb799lsMHjz4sI7TnX0SL7roIsyaNQsffPBBYgK9lpYWvP/++zjnnHM6fEjcvXs3AGDgwIGJZT/96U/x5ptvorq6OnHFzfz587Fjxw7cddddh/W+iCg99MVct3XrVpx11lno168fPv3004Mes7m5eb8vyh6PBy+++CJycnJw7LHHdhorEaWfvpYXg8EgIpHIfl+UH3/8cciyjNNPP73L74mI0ktfy3f7evfddyFJ0kFbfB3oc2BtbS1ef/11jBkz5rBOmhJR+kjHvHg49u7dC7/fj2HDhiWWXXTRRfjVr36FVatWYcKECQCA7du345tvvsG9997brcennifIsiwnOwjqmx5++GE8/vjjmD9/PqZPnw4A+P3vf4+HHnooMUkdAJx//vmYM2cOrr/++sR67YxGI84///xeizkWi+Gkk07Cpk2bcN999yEnJwcvvfQS9u7di5UrV2Lo0KGJdfv16wcAHSrc1dXVGDduHKxWK+688054vV48++yzKCkpwcqVK9nmiygD9bVc5/F4MHLkSNTW1uLJJ59EcXFxh30PHDgQJ5xwAoD4xIMfffQRzjnnHJSVlaG+vh6vv/469u7di7fffrvL/bWJKL30tbxYWVmJcePG4Wc/+1nii/WXX36Jzz77DKeffjrmzp3b4epvIsocfS3f7WvChAmor69HdXX1AXPcddddh927d2PGjBkoKipCZWUlXnnlFXg8Hnz55ZeYNm1aD71DIkqmdMyLAPDEE08AADZv3ox3330X119/faI12UMPPZRYb9q0aVi4cCH2Pd3u8Xgwbtw4eDwe3HvvvVCpVHj++ecRi8Wwbt06jsRLNzJREqxevVpWKpXyL3/5yw7Lo9GoPHHiRLmoqEh2OByyLMtyeXm5DOCAt/Ly8l6P3W63yzfccIOcnZ0t6/V6eerUqfLKlSv3W6+8vPyA8W3atEmeOXOmrNfrZavVKl9xxRVyQ0NDL0RORL2tL+a6PXv2HPR9AJCvueaaxLpfffWVfOqpp8oFBQWySqWSrVarPHPmTHn+/Pm98A6JKBn6Yl50OBzylVdeKQ8aNEjW6/WyRqORR44cKT/55JNyOBzuxXdARL2pL+a7dtu2bZMByHffffdBj/HOO+/IU6ZMkXNzc2WlUinn5OTIF1xwgbx69erufCtElELSOS8e6jvuvqZOnbrfMlmW5erqavmiiy6SzWazbDQa5bPPPlveuXNnb4VP3YgjU4iIiIiIiIiIiIiIiA6B48mJiIiIiIiIiIiIiIgOgcUUIiIiIiIiIiIiIiKiQ2AxhYiIiIiIiIiIiIiI6BDSqphSW1uLK6+8EtnZ2dDpdBg9ejRWrVqV7LCIiHoFcyAR9WXMgUTUVzH/EVFfxhxIRKlEmewAusrhcGDy5MmYPn06Pv/8c+Tm5mLnzp2w2WzJDo2IqMcxBxJRX8YcSER9FfMfEfVlzIFElGoEWZblZAfRFb/61a/w3XffYfHixckOhYio1zEHElFfxhxIRH0V8x8R9WXMgUSUatKmmDJixAicdtppqKmpwcKFC1FcXIzbbrsNN95440G3CYVCCIVCieeSJMFutyM7OxuCIPRG2ESUhmRZhsfjQVFREUQxNbohHm4OZP4joiPFHEhEfVUm5D+AOZCIjgxzIBH1ZV3OgXKa0Gg0skajkR988EF5zZo18iuvvCJrtVr5jTfeOOg2Dz/8sAyAN9544+2IbtXV1b2Y5Q7tcHMg8x9vvPF2tDfmQN54462v3tI5/8kycyBvvPF2dDfmQN54460v3zrLgWkzMkWtVmPChAn4/vvvE8vuuOMOrFy5EkuXLj3gNj+uRrtcLpSVlaG6uhpms7nHYyai9OR2u1FaWgqn0wmLxZLscAAcfg5k/iOiI8UcSER9VSbkP4A5kIiODHMgEfVlXc2BaTMBfWFhIUaMGNFh2fDhwzF79uyDbqPRaKDRaPZbbjabmUCJqFOpNAT4cHMg8x8RHS3mQCLqq9I5/wHMgUR0dJgDiagv6ywHpkYTxC6YPHkytm/f3mHZjh07UF5enqSIiIh6D3MgEfVlzIFE1Fcx/xFRX8YcSESpJm2KKXfddReWLVuGJ598Ert27cI777yDf/zjH7j99tuTHRoRUY9jDiSivow5kIj6KuY/IurLmAOJKNWkTTFl4sSJ+PDDD/Hf//4Xo0aNwuOPP44XX3wRV1xxRbJDIyLqccyBRNSXMQcSUV/F/EdEfRlzIBGlmrSZgL47uN1uWCwWuFwu9kkkooPKxFyRie+JiHpGJuaLTHxPRNT9MjVXZOr7IqLulam5IlPfFxF1r67mirQZmUJERERERERERERERJQMLKYQEREREREREREREREdgjLZARARERERERERERERUc8IRyV4Q1H4w1GEohLCUQkxSYYkyxAgQBAApUKASiFCrRChVSmgUyugVykgikKyw08ZLKYQEREREREREREREaU5WZbR6gujzhlAkzsEuy8Muz+MQDh2RPsTBECvVsCoUcGkVcKiU8GiU8GmV8NqUMGsVXXzO0htLKYQEREREREREREREaWhUDSGyhY/Kpq9qGz1Ixg5ssLJgcgy4AvF4AvF0Oje/3W1UkS2QY0cowa5ph9uKkVmzi7CYgoRERERERERERERURqpcfixqdaNXU0eRGJyUmIIRyXUu4KodwUTy0RBQLZRjUKLFoUWHYqtOlj0mTGChcUUIiIiIiIiIiIiIqIUJ8sydjd7sbLSgYZ9ChipRJJlNHtCaPaEsKHGBQAwaZUoselQYtOjLFuftu3BWEwhIiIiIiIiIiIiIkph1XY/Fu9sQaM7NYsoh+IJRrG13oOt9R4AQJZBjfJsPfplG1Bi00GZJm3BWEwhIiIiIiIiIiIiIkpBvlAU325vws5Gb7JD6TZ2Xxh2Xxhr9zqhVooosekwIMeI/rkGGDWpW7JI3ciIiIiIiIiIiIiIiPqoLXVuLNzR3K2TyqeacFRCRbMPFc0+CNuAPJMWA3INGJBrQJ5Jm+zwOmAxhYiIiIiIiIiIiIgoRQQjMczf2oQdjZ5kh9KrZBlodAfR6A5i6e5WmLRK9Ms2oH+uAaU2PdTK5LYDYzGFiIiIiIiIiIiIiCgFNLqDmLuhHq5AJNmhJJ0nGMXGWhc21rqgEAUUWXUoy9KjLEuPPJMGoij0ajwsphARERERERERERERJdmWOjfmb21EVJKTHUrKiUkyqu1+VNv9+A6AWimiyKpFsVWPQosWeWYNNEpFj8bAYgoRERERERERERERUZLIsozFO1uwusqR7FDSRjgqobLFj8oWPwBAEACbXo0cowZDC4wYlGfq9mOymEJERERERERERERElASRmITPNtajotmX7FDSmiwDdl8Ydl8YClFgMYWIiIiIiIiIiIiIKBP4QlHMWVeHRncw2aFQF7CYQkRERERERERERH2ePxxFsycEABAFAYIQv1eKApQKESqFAJVChEYpQhB6d+JryjwOXxgfrK2FmxPNpw0WU4iIiIiIiIiIiKjPq3MG8Mn6+k7XE4T45NdapQJalQJ6tQI6tQIGtRIGjQJGjRJGrTJ+r1Gy8EL7qXcFMGddHQLhWLJDocPAYgoRERERERERERFRF8kyEIpICEUkuDoZVSAKAoxaJcxaJcw6FSxtN6teBatODZ1a0UtRU6qobPHh0w11iMTkZIdCh4nFFCIiIiIiIiIiIqIeIMky3IFIvJWTI7Df61qVAja9CjaDGllttxyDBmYdR7Rkom0Nbny1uRExiYWUdMRiChEREREREREREVESBCMx1LtiqHd1nIBcrRSRbVAjx6hBnlmDXJMGuUYNlAoxSZHS0VpX7cSC7U2QWUdJWyymEBEREREREREREaWQcFRCvSsYL7LUxpcpRAHZRjUKzFoUWnQosmph1auTGyh1ydLdrVhW0ZrsMOgosZhCRERERERERERElOJikowmdwhN7hA21LgAAEaNEkVWHUpsOpRl6WEzsLiSSmRZxsIdzVi715nsUKgbsJhCRERERERERERElIa8oSh2NHqwo9EDADBplSjL0qNfjgFlWXpoVZzgPlkkSca8rY3YUudOdijUTVhMISIiIiIiIiIiIsoAnmAUm+vc2FznhigIKLRqMTDXgAE5Ro5a6UUxScbnm+qxs9Gb7FCoG7GYQkRERERERERERJRhJFlGrSOAWkcAi3a0IMugxqA8IwbnGZFn1iY7vIwViUn4ZH0dqlr9yQ6FuhmLKUREREREREREREQZzu4LY8UeO1bsscOiU2FIvglD8llY6U6haAxz1tah1hlIdijUA1hMISIiIiIiIiKijBaMxOAPxxCOSojEJEiyDAECBAFQKUSolSI0ShF6tQKCICQ7XKIe5wpEsLLSjpWVdmQZ1BiSb8KwAhNbgR2FQDiGD9fWotEdTHYo1ENYTCEiIiIiIiIioozgCkTQ6A6iyR1Cqy8Epz8CdyCCqCR3aXtREKBTizBpVbDo4jebXo0sQ/ymVoo9/A6Iep/dF8ayilYsq2hFgUWL4YVmDM03Qafm5PVd5QtF8cGaGrR4w8kOhXoQiylERERERERERJSWojEJla1+7GnxYa/dD3cgclT7k2QZvlAMvlAMDa6OV5cLAmDRqZBj1CDPpEG+WYt8s5YnnCmjNLiCaHAFsWhHM/rlGDCi0IwBOQaIIkdsHYw7GMEHq2vg8B9d/qHUx2IKERERERERERGllWq7H5vrXNjd7EM4KvXKMWUZcPojcPoj2NXkTSy36lUotGhRaNGhyKpDjlHNVmGU9mKSjN1NXuxu8kKvVmBYoRkji8zIMWqSHVpKcfjCmL2mBp5gNNmhUC9gMYWIiIiIiIiIiFJeTJKxtd6NNXsdaE2hVjrtBZat9R4AgFalQJFVi9IsPUpsOuQaNSyuUFrzh2NYU+XAmioHCi1ajCq2YEi+qc+3vWvxhvDBmhr4QrFkh0K9hMUUIiIiIiIiIiJKWbIsY3OdG8sqWtPi6u9gJIaKZh8qmn0AAL1agdIsPcqz9SjPNsCo4ek4Sl/1riDqXUEs3NGMofkmjC6xIN+sTXZYva7RHcSHa2sRCLOQ0pcwexMRERERERERUUqqcfixcEczmtyhZIdyxPzhGLY3eLC9wQNBAHKMGgzIMaB/rgEFZi1HrVBaCkclbKx1YWOtC/lmLcaU9J3RKjUOP+asq+u1FoOUOlhMISIiIiIiIiKilBKOSliyqxkbalyQ5WRH031kGWj2hNDsCWH5Hjv0agX65xgwINeI8mw9VIrMPxFNmafRHcS8LfHRKiMKzRhTYkF2hs6tsqfFh7kb6hCJZVBioi5jMYWIiIiIiIiIiFJGnTOAzzc1wB2IJDuUHucPx7C5zo3NdW6oFAJKs/QYmGvEgFwD9GqetqP0Eo5KWFftxLpqJ0psOhxTasWgXCNEMTNGX21rcOOrzY2ISSyk9FXMykRERERERERElBJWV9mxZGcrpEwajtJFkZicmGtFEIBCixYDco3on2NAToZe5U+Zq8YRQI0jAJNWiVHFFowpsaR1gXDtXgcW7mjOqJFydPjS9zeYiIiIiIiIiIgyQjgq4cvNDdjV5E12KClBloE6ZxB1ziCW7GyBWadCedsk9qVZemhVimSHSNQlnmAUS3e3YuUeOwbnmzC+zIq8NJqwXpZlLNnVglWVjmSHQimAxRQiIiIiIiIiIkoadzCCj9fVodmTvpPM9zR3IJKY7FsQgGyjBiU2HQotWhSadbDoVckOkeiQopKMrfVubK13o9iqw7gyKwameAuwmCRj3pYGbK33JDsUShEsphARERERERERUVI0uoOYs64WvlAs2aGkDVkGWjwhtHhCWNe2TKMSkWPUIMeohlWvRpZejX45hmSGSXRQtc4Aap0BmHUqjC21YlSxGRplao22CoRj+GR9HWqdgWSH0iMkWYIMCfZgDfZ41kCAAECAI1QLQRCxuP5tWNUFKDGOxPqWLxCRgjiz/G6UGUcjS1MKpaiCRmGASuxbLQjTtpjy1FNP4cEHH8Sdd96JF198MdnhEBH1KuZAIuqrmP+IqC9jDqRMs6fFh8821iMclZIdStoLRSTUOgKodcRP/KqVIm6fPijJUXUv5sDM4w5EsGhHM5ZVtGJUsQVjS62w6JI/yqrFG8LH6+rgCkSSHcoRkWUZESmEVc0f4pOqZ6EStVAISmgVRrjCjYjKEUSlzkcCeiOtqPFtTjz/uPKp/dYpNYyCQWXDNudi9DONw7VD/wKzOrdb308qSctiysqVK/HKK69gzJgxyQ6FiKjXMQcSUV/F/EdEfVlv5UBZlrFoZwsEAKIgQBQAURSgaLspRQFKUYRSIUClEKEUBaiVIlQKse1egFohQhBSt20LpYbNdS58vaWpT040T4ePnwMzWzgqYU2VA+v2OjEoz4hjy20osCRnXpWt9W7M39qISCz1c1M4FkCFexVqfVvhibRgUf2bB1wvFPMBAFxoPOi+lIIaUTkMrcKEYOzgbc1Mqlx4Is2J59W+TYnHlZ61eGTVScjSlMAeqsG0ousxo/gmGFS2w31rKSvtiilerxdXXHEFXn31VTzxxBPJDoeIqFcxBxJRX8X8R0R9WW/nwDVVRzfJriDEr4pXK0RolCI0SgU0qh8/VkCrEqFVKaBVKaBru2lVLMT0Basq7Vi8syXZYVCa4OfAvkOSZexo9GBHoycxr8qgPGOv/F0IRyUs2tGMjbWuHj/WkQjFfNhkn496/06saf4YznBDl7c1qrIxxHICREGBCbnnQ6c0ISqFkaPtB1FQQK+0HNbPOBj1ojGwG45QLcKxAJqCe/BN7auJ1+2hGgDAgrrXsajuTQy3TcWEvPMx0jYdSlHd9TedgtKumHL77bfjrLPOwimnnNJpAg2FQgiFfhiy5Ha7ezo8IqIe1dUcyPxHRJmGnwGJqC9Ltxwoy/GWQ6GIhMOdslcQAK1KAb1aAb1aCYNaAYNGCYNGCZNWCaNGCbNOBYNawaJLmlq8sxmrKo+uYEd9S7rlQOoeHedVsWBkkQVaVc/Mq1Lj8OOrzY0p1dbLHqxFrW8rvqr5G2p9WyAKSkhy9IDr2jTFGGGbBne4CVXe9bhmyIvI1fWHXmmBKHT/z0yrNKLcdAzKTccklp1dfi8AwB1uxtqWuVjRNBv1/h2QEMNmxzfY7PgGOqUFY7JOxYkFP0OpcVS3x9Ub0qqY8u6772LNmjVYuXJll9b/wx/+gEcffbSHoyIi6h2HkwOZ/4gok/AzIBH1ZX0tB8pyfNLfQDiGVoQPup5SFGDWqWDVq2DVq2HTq5BlUCPHqOmxk210dGRZxtdbm7ApRa/6ptTU13Ig7S8+r0oLlu5uxZB8E8aUWLutBZg3FMXS3a3YXOdCsjsOyrKMGt9mbLJ/jXk1L+/3enshZbDlBKhEDao863D54Gcx1HoSREHs7XAPyqzOxdSiazG16FoAQIN/F1Y1z8Hq5jlwhRuxvGkWljfNwgDzBEwruh4jbNNTKv7OpE0xpbq6GnfeeSfmzZsHrbZr/2AefPBB3H333YnnbrcbpaWlPRUiEVGPOdwcyPxHRJmCnwGJqC9jDjy4qCTD7gvD7gsD8HV4zaRVItekQa5Jg3yzFoUWLfTqtDn9kZFikowvNjVgR+PhjlWivow5kPYVicnYXOfG5jo3ckwajCg0YWiBGUbN4ed3byiKDdVOrNnrSOrcKLIsozlYiUV1b+L7xv/u93qhfiga/bswtehaHJt7HooMQ5MQ5dEp0A/C2eX34Myy/8Mu13J8VfM3VLhXJW4DzBNxbvn9KDOlx3xIgiwnu+7WNR999BEuuOACKBQ/XGESi8UgCAJEUUQoFOrw2oG43W5YLBa4XC6YzeaeDpmI0lQq5oqjzYGp+J6IKDWlWr7gZ0Ai6i2pmCuSkQNlWcaLX+886thTjUWnQpFVhxJb/GbVp3fP9nQSjUn4dEM99rT4Ol+Zuo1aKeL26YO6vD5zYNyuJg8+WV9/1LFT7xAEIN+sRf8cA0qz9Mg1aqBWHniUgy8URY0jgB2NHlQ0+yAl8ZR4MOrF0sb/YXXzx6jzb+vw2qisGZDkGI7NPRfjcs5KUoQ9qzlQiSUN/8aS+n9DRvz/w9jsM3B+/1/DrM7rlmMMLzTj9FEFXV6/q7kibS7NmDFjBjZu3Nhh2XXXXYdhw4bhgQce6DR5EhGlM+ZAIuqrmP+IqC9jDuw+rkAErkAEW+vj8yeYdSqUZenRL1uPsmw9NEr+LHtCOCphzrpa1DgCyQ6F0hBzIHVGloEGVxANriCW7m6FKAiw6lXQqxWJlo+BcAzeUDTp86HYg7VYVP8mWoN7sc25BDE5Ho8IBQZbT0CebgCmFl6LLG1xUuPsDbm6frig/0M4Pu8SfFv3T6xunoN1rZ9jm3MJZhTfhClF10AlapId5gGlTTHFZDJh1KiOE9MYDAZkZ2fvt5yIKNMwBxJRX8X8R0R9GXNgz3EHIthU68KmWhcUooAiqw4Dcg0YmGuERadKdngZIRiJ4aO1tah3BZMdCqUp5kA6XJLc3gIy2ZH8YHXzx5hb9Ryc4YYOy/N1g3By4ZU4JvsMGFTW5ASXZIWGIbh88NOYWnQt3tv9W1R7N2Lu3ucwd+9zuGjAozix4LJkh7iftCmmEBERERERERF1t5gko9ruR7Xdj4Xbm5Fv1mJIvhGD800srByhQDiGD9bWoMkdSnYoRERJsdWxEK9uvWm/5ScVXIUTCy5Fvm4QBEFIQmSpp9gwHHeO/h9WNc/BrIpHEJVCmFXxML6q/ivuG/tpShWb0rqYsmDBgmSHQESUNMyBRNRXMf8RUV/GHNjzGt1BNLqDWLKrBUUWHYYVmjAk35RoGUOH5g9HMXtNLVo8LKRQ92MOpFQmyRKWNb6HWRUP7/fajcP/gaHWkyAK/FtyIKKgwKS8C9HfdCz+sHYmAMAdacZvVx6Hu8d8iBLjiCRHGJfWxRQiIiIiIiLqOlmWEYxIiEgSojEZAgBREKBUCNCqFFCIvEKSqJ0sA7XOAGqdASzc3oyBeUaMLDKjLEvPq4kPwheKYvaaGrR6w8kOhYio10iyhI2tX+HLmr+hwb+jw2uXD3oax+aex78bXZSrK8dzJ2zDq1tvxDbnYgDA8xsuwOWDnsaEvPOTGxxYTCEiIiIiIso40ZiERk8Ije4gWr1hOHxhuIMR+EIxSLJ80O3UShEmrRImrRJWnRo2gxrZBjVyjBro1LySkvquqCRje4MH2xs8sOhUGF1iwcgiM/RqnlZp5w1FMXt1Dew+FlKIqG+QZAmL69/C0sb/oSlQkVhebhyLq4e+CJumMInRpS9BEHDTiNew17MBs/c8hmrvRryz6wHU+LbgnH73QyEk728v/+oTERERERFlgGZPCHtafKhs9aHBFURMOnjR5GDCUQmt3nDbVeX+Dq+ZtErkm7UosGhR0HavUojdFD1R+nAFIliyswXLdrdicL4RY0ttKLBokx1WUnmCEcxeXQOHP5LsUIiIesUO51J8WvUsanybAQBahRFTCq/BlKJroFdakhxdZigzjcEvR72DeTUvYV7Ny1hU/yaqvRtxxeBnkaUtSUpMLKYQERERERGlKac/jC31buxs9Pb41eCeYBSeoBe7mrwAAIUoIM+kQZFVhxKbDkVWHeeUoD4lKsnYWu/B1noPCi1ajCuzYXCeEWIfa5fnCUYwa3UNnCykEFEfUOPdjE+rnsMO13cAAI2ox0mFV+EnxT+HTmlOcnSZRymqcUbZ/6HIMBz/3fkA9njW4Ik1M3D/2E9RoB/c+/H0+hGJiIiIiIjoiMmyjN3NXqyvdqHa4cchunb1qJgko94VRL0riNVVDoiCgDyzBqU2Pcqy9CiyaqHkyBXqI+pdQdRvrIdFp8K4MitGFVv6xMgtFlKIqK/Y6liEV7femHiuEFQ4seAynFJ8K0zq7CRG1jcck30acrX98LdNVyIQc+PPGy/D9cNewiDLcb0aB4spREREREREaSAak7Cl3o3VVY6UPHEpyTIaXEE0uIJYWWmHSiGgyKpDebYe5dkG5Bg1yQ6RqMe5AhEs2N6M5XvsGFtqxdhSa8aO2GIhhYj6Am/Ejnk1L2Fx/duJZeNyzsaZZf+HbG1pEiPre4oMQ3HPMR/hD2tPQzDmxStbbsDlg5/GuJyzei0GFlOIiIiIiIhSmCTJ2FLvxrKKVniC0WSH02WRmIyqVj+qWv0AWmDSKlGWpUf/HANKs/QZe4KZCAAC4RiW7m7F6ioHxpZaMb7MBp06c37nWUghokwXkyL4ruEdfFn9VwRi7sTya4f+BWOyZyYxsr4tS1uMJ49bjXd23of1rV/i7R13wxGqw/Sin0MQer7NJospREREREREKarG4ce325vR4gklO5Sj5glGsbnOjc11boiCgCKrFv1yDOifw1ErlLnCUQkr9tixrtqJY0qsOLY8/YsqmVhIkWQJoiDCF3HCE2mBLEtoDlZBo9BDJcbzky/ixADzBGgUeihFdZIjJqKetN25BB/teRKNgd0AgGLDcJxZdjeGWk+CKGR+C8dUpxI1uGrIi7BWPo2F9W/g06o/4tOqP+KxiUthVGX16LFZTCEiIiIiIkoxwUgMC7Y3Y2u9u/OV05Aky6hxBFDjCGDJzviolf45BvTLMaAsS98n5pqgviUclbCy0o71NU6MK4uPVEnH0VnuYASzVtXAFUiPQkpMjqLOtw3bnIvhj7gQkyNwhhsgyzE4QvWISEE0BysPe79mdR7c4SYAwBDLZAy3TUEw5kWerj/GZp/ZK1dHE1H3a/Tvxty9z2GTfT4AwKC04cyyu3Bc/kUQhfTL2ZlMFESc1/9B2DRF+KjySQDAwytPxCMTlsCkzumx47KYQkRERERElEL2tvrx1ZaGtGrpdbQ8wSg21LiwocYFpRifa6Vfjh5lWQbkmjhqhTJHOCpheYUd66tdmNjPhrGlVijTpHjoCkQwe3VqFlKCUS+qvOtQ69uGT6ueBQD0M41Dg38ngjFvtx+vvZACADtc32GH67vE87dxN8blnAW90oKB5kkYlTWDI1mIUlwg6sZne1/A9w3vQoYEAQImF1yJ08t+Cb3Skuzw6BCmFF0DX9SJeTUvQYaMh1dNxm+PXQDA3CPHYzGFiIiIiIgoBUiSjO93t2JVlR2ynOxokicqydhr92OvPT7XikGjQIlNj1KbHsU2HbIMPClJ6S8YiWHxzhasq3bi+AHZGFFohiim7mgGpz+MWatrkl7kjclReMKt2OFcgnd3//qQ61Z61nZ4rhBUyNaWIEdbjgHmCdhs/xYlxpEoNY6CTmFGoX4wjKocKEU1REFEMOaFRjRAhgxPpBkahQFRKQwAqPdvx+L6t7HNsRhDrSdBJWqwrvXzxLHWtswFAHzX8A6MyiyMzz0HE/MuQJF+GEetEKWQmBTBsqb3MWfPHxCV4/++h1hOxFnl96DUOCrJ0VFXnVF2JwZZjsPLm68BALyy5XoMyvsfgIJuPxaLKUREREREREkWCMfw2cb6tgIC7csXimF7gwfbGzwAAJ1agUKLFnkmLfLNGmQbNTBrlTxBSWnJE4xi3pZGrNnrwORBORiYa0x2SPtp9YbwwZpaeEO9X0iJSmHscC3F4vq3oBTU2O5ckjjh+WM2dRECMTeCMS+0ChMuHvgodAozCvRDYNXk77f+T4pvPOSxtYr4/wsBAizqtu3buvwMtpyAwZYTOqx/lfwCgjEP9no3YkPrl1ja+D8AgDdqx6L6N7Go/k0U6ofg2NzzMDb7DGRpiw/nR0FE3SgQ9eDZdefAGa5PLLNpinHpwCcwxHpiEiOjIzXYcjx+PvwfeG3rTWgKVODGL0/GlCFb0c/ar1uPw2IKEREREdGPVDR74Q1FoRAFKEQBSlGAQhTb7oUOy8X2e0FIvM6TunQ47L4wPlpbm5Ktc1JRIBxDRbMPFc2+xDK1UoRVr4JZq4JZp4JBrYBOrcCAHGPaT/ZNfUOrN4yP19Wh2KrDyUNyUGjRJTskAECdM4A56+oQjMR65XiyLKMpUIGva/+O1c0fQ4QCEvY/tlmVC3ekGVZ1AU4uvBrDbVNRoB/UKzEejCAI0CnNGGqdjKHWybh44GMIRN3Y6VqGtS1zsck+H/X+Hfi06ll8WvUsxuecg5MKr0Q/09ikxk3U1+xwLsW/tt2GkBS/gMWoysZPin6OKUXXcnL5NDfCNhUPjf8Gf950GdzhJtw691Z8fsXnnW94GFhMISIiIiL6kY21rg4nag+XKAhQKgQIAhKFFlGIF1pEUYAoAAoh/lixz/L4Y0Ahiol7VXvhRiFCpRCgUohQKUSoFSLUShEapQiNSoRGqYAihVvE0IHVOPz4ZH19r52ozFThqIQmdwhN7lCH5Rcdq0Jplj5JUREdvlpnAO+uqMagPCNOGpQDWxLb2lU0e/HZxnpEYj3fd7DetwOfVD2Lbc5FHZbvW0iZXnQDxmSfhgL9IGgUhh6PqTvolGaMyZ6JMdkz4Y+6sL7lCyyqfxONgd1Y0/IJ1rR8gpG26Ti//2+QrS1NdrhEGa3auwlzq57DDtf3iWUDzZNwy4jXoRBVSYyMulOWthjXDf0rPqt+Ao9MfaTb989iChERERFRN5NkGeFo/ORTqJN1u5NaKUKnUsCgUUCvVsKoUcKgUcKkVcKiU8GiU8Gg4VeAVLGz0YMvNjUgKvXhCVKI6IB2NXlR0ezDyCIzjh+YDWMv5+61ex1YtKMFUg9O4OQON2N188dY2zIXNb7NieUiFFCKaoSlAO4fOxf5uoEZMeJTr7TghIJLcVz+RVjd/Am2OL7B+tYvsdnxLbY6F+P4vItxRtmdMKhsyQ6VKGNIsoTtziVY1vgeNtrnAYjPn3RiwWU4pfhWmNTZSY6QekK56Rj86Sdf4LiSwm7fN79JERERERFliHBUQjgqHbJdlFopwqZXI9uoRo5RjVyjFnlmDbQqtkLqTZtqXZi/talHT1QSUXqTZBkba13Y1uDGMaVWTCjP6vG2dTFJxjfbmrCp1tUj+w9EPdjmXIz1LV9gi3MBolL8kgMBImRIyNcNxB2j/wed0tQjx08FoqDAxLzzMTHvfOxxr8aX1X/FDtf3+L7xv9jQ+iXOKr8HE/LOh0LgKTuiIyXLMrY7l+CzvS8kirUCBBybey5OL70DWdqSJEdIPa2nivDMzEREREREfUg4KqHRHUSjO5hYJgiARadCkVWHYqsOJTYdrPrktZbJdGv2OrBoRzNYRyGirojEZKyqdGBDjQvjyqwYX2brkQK4wxfGF5sb0OAKdr7yYZBkCauaPsTihrdR69va4bUy4zGYmHsexmSfBpM6p1uPmw76m4/FLSP/hV2uFfig4jE0BHbif7t/g0X1b+GiAY+gv3l8skMkSisxOYo1zZ/g27rX0eDfAQDQiHpMzPspTsi/BIWGIUmOkNIdiylERERERH2cLANOfwROfwRb6twA4sWV8mw9BuQaUWrTQanghJzdYcUeO77b1ZLsMIgoDYWjEpZX2LF2rxPHlFgxrszaLa0bZVnGhhoXFu9s7tb5USQ5hje335lorbOvGcU345js01BsGJERLbyO1iDLJNx9zAdYXP82vqr5G+r92/GXTT9DqWEUbhv1VtrMEUOULI5QHRbU/QtrW+bCG2kFEC+iHJ9/CaYWXQerpiDJEVKmYDGFiIiIiIj24wpEsKHGhQ01LqiVIvrnGDAk34T+OQZOdH+Evt/dguUV9mSHQURpLhyVsLLSjjV7HRhaYMIxJVYUWLRHtK9qux+Ldjajyd19M3xFpBDe2XkfdrtWwhv9IecNt07FsbnnYmzOmRAFFuh/TCmqMb34BoywTcPT684EAFT7NuHB5eNx5+j3UW4ak9wAiVJMMOrFLvcyrGyagy2ObxGT421u1aIep5bcihMLLoNOaU5ylJRpWEwhIiIiIqJDCkclbG/wYHuDB1qVAsMKTBhZbEae6chO3vVF3+1qwYo9LKQQUfeJSTK21Lmxpc6NHJMGIwpN6J9jRJbh0G0aIzEJu5q82FznRrXd323xRKQQljW+j/m1r8AdbkosV4oa/HLUOyg1juq2Y2WyfP1APH/idryz836sap4DAPjTxotx5eDnMD737CRHR5Rc4VgQWxzfYm3LXGx2fAtJjiZeKzMeg+PzL8b4nLOhVuiSGCVlMhZTiIiIiIioy4KRGNZVO7Gu2olCixZjy6wYnGfiaJVDWLyzGasqHckOg4gyWIsnhEWeEBbtaIFFp0KeWYMsvRpmnQpAWzvHQBhN7hAa3EGEo1K3HdsXceD7hv/i+8Z34Qo3JpafWnIbphVdxyvDj9Dlg5/BCfmX4d877oEjXId/77wHVd71OLffA5ycnvqUGu8WvLLleviiB/4sNTH3QkwpuhpF+mFsG0g9jtmXiIiIiIiOSL0riPqNDViibcH4chtGF1ug4twqHSza0YzVVSykEFHvcQUicAUiPX6cmBzFqqaP8GnVs/BFnQAAq7oAM0puxnF5F0EpHnqEDHWuv3k8Hhz/JT7f+yK+rfsnFte/hTrfNlw++BnYNIXJDo+oW0lyDLvdK/Hy5msgQgEJsQOuZ1blYkLe+RiXczaK9ENZQKFexWIKEREREREdFU8wioXbm7Fijx0Tym04ptTKogpYSCGizCTLMjY75mNu1fNoDOwGAJhUOTit9BeYlPdTFlG6mVJU45x+96PcNBbv7Lwfu90r8Pjqabhz9HsoNx2T7PAyjifsxod7nsCpJbfBqMpKdjgpS5ZlRKQg1AodAlEPNAoDAlE39EoLYnIEkixBFER4Ii2Q5BgUghr2UDUEiAhE3djs+AZ5ugGwh2qxuP6t/fZ/sELKBf0fwokFP+PoLEoa/uYRERFRnxSTZPjDUYSiEmKSDEmWoRAEKBUiNEoROpUCItsWER2WQDiGxTtbsHavE8cNyMKoIkuf/Xe0ZGcLCylElFFkWcbypll4b/dDiWV6pRWnlNyCyQWXQyVqkhhd5huTPRM52jK8vu022EO1+Pvma3HtsL9iqHVyskPLKC8sfwyL69/GqqY5GJk1HZcO/D0UoirZYfWImBSBL+qAK9wIb8SOt7b/HwyqLAy2HI9gzAtfxAFHqA6toerENlZ1ASQ5BnekucfjO7XkNozOOgXZ2lK2C6SUwWIKERERZTynP4waRwCN7iBafWE4fGH4wwe+2qmdIAB6tQJmrQpWvQpWvRo5RjWyDRpY9SoOJyc6BG8oivlbm7C+2okpQ3JRnm1Idki96vtdLVhZycnmiShz1Pt2YFbFw9jjWQMgPqn81MJr8ZPin/MkZy8qMgzDL0f/F4+umoKQ5MerW2/CpQOfwMS8C5IdWsY4b8hl+LpiIer927GqeQ72uNfgkkFPYLDl+GSHdkRichQRKYjVTXPgCDdgh/M71Pg2Q4AAGfJ+64dCfixvmnXQ/TnDDUcciygooVUYYVUXQCEoIUNGiXEUAlEnYnIMx+ddjHz9QGRrS4/4GEQ9jcUUIiIiyjiyLKPaHsDuZi92N3vhCUaPYB+ALxSDLxRDvSvY4TW1UkSuSYMCsxaFFi0KrToYNfxYRfRjLd4wPlhTi8H5RkwdkguTNjOv7NzXsopWLN/DQgoRZQZfxIHP9/4JSxv/BxnxSesNShvuGjMbWdriJEfXN1nU+Xjm+I34764HsbblU/x316/gCNXh1JLbeLFPNzgmfwLuHjMbH1c9jcX1b6M1VI2XN1+DQebj8JPiGzHMdnKyQzykqBRGU6ACWx2LMHfvcwddb99CikKIfz6LyfG5lrI0JZiYdwGMSht8USdagnsRlUIYn3s2DMosiIICKlGLev92aBWmtvl7BIRiXigEFXJ1/SAIIkIxHyJSCBpRD7VCB6WgzthRPtR38Fs/ERERZQynP4xNtW5sa3AfUQGlq8JRCbWOAGodgcQys06FYqsWRVYdSmx6ZBnYL5yo3c5GL6pa/Zg8KAfHlFgy9mTPqko7lu5uTXYY3UaW5Q7/r9r7nxNR5ovJUXzf8C6+qP4zAlEXAGB01qmYVnQ9+pvHJzk6UopqXDH4Wdg0Rfim9h/4ovrP+KL6z3hs4lLO89ENFKIKF/R/CKeW3IbP976IZY3vY5d7OXa5l6OfaRzGZp+BkwqvhCgokhpnIOrBTtcyNPh3IBB1o9a3FbvcyzvdbmLuhSgxjoRZnYsBpgkwqrKO6LNZkWHoIV/XKoyHvU+iVMdiChEREaW9arsfa/Y6sKfFB3n/0eq9wh2IwB2IYGu9BwBg0ChQbNWjxKZDiU2HbCP7iFPfFo5K+HZbE3Y0ejBzRD6s+swqOK6rdmLxzpZkh3FQoZgPrcF4z/Mqz3pE5BC8kVa4w02o9KxFa7AGalEHAIjKYSgFNQIx9wH3ZVBaMcA8EZ5ICyo9azEp76c4If8ymNW5MKtzOSksUZrb6liEuVXPoc6/DQBQqB+KC/r/BoMsxyU5MtqXKIg4u/we2DSFmF3xKADgdytPwB+OWwONom+11+wpRlUWLh74GGYU34SF9W9iSf2/UelZi0rPWiysfxMzim/EuJyzeq3VXVQKY693Ayo962APVmNF0weIyuH91hMgIkdbhpbgXlw04BHk6wein2lc0os/RJmAn3KJiIgobe1p8WHFnlbUOYOdr9zLfKEYdjR6sKPxwMWVLIM6Y6/OJzqUWkcA/15WhSlDcjGmxJrscLrF5joXFmxvSnYYkOQY/FEX5tW8hFXNH+PkgivhjjRjWeN7Xdo+EIskHkdw8Lzqizqx0T4v8XxF02ysaJrdYZ0zy+5GjrYUpcbR7H1OlCYa/bvx4Z7fY4frOwDxyeXPKLsTx+dfwiJpCptccDlEQYn3d/8WAPDg8vF4YOxnKDUPTnJkmSNLW4IL+v8GJxVciX9tux0NgZ1whGoxq+IRzKp4BIMtJ6CfaRzGZJ+GIv3QbvmMH4x60RDYiT9vvAwA0N90LPZ6NyRacf3YxNwL0d88HoX6oSgxjuC/2QwUjUnwh2MIRGIIRmKIxGREYxJispy4oFAUBSgEASqFALVShEapgE6tgE6lgELkd8/uwH9ZRERElHaq7X58t6tlv7lMUtmPiys6tQJFVh2K2265Jg0/4FKfEYnJmL+1CRXNPpw6Ih+GNJ5zaEejB/O2NPb6qLhwLIDVzR9jVfNHiQmhf+yrmr8ddPtRWadgk/1rKAQVhlonAxCgURgw0DwRFnUeJFlCWPLDHqxBnm4Atru+gzvcDEmOYUz2TASjHixvmoXGwG4A8R7+rnBjYv+f7X0+8bjEMBKT8n6KsTlnsP0MUQoKRD34pOpZLGv8HwBAgIBJeT/FGWX/B7M6N8nRUVeckH8JivTD8KeNFwMAnlt/Pn43cR6AQckNLMPk6spx/7hP0RTYg9XNH2NZ4/vwRJqx07UUO11LMa/mJSgEFSbmXYA8bX+oFTo0BypRbByB4dYpkOQYREEBX9QJAGgKVCAU8yMU8yIqR+CPuFDj24RG/260hqo7HHuPZzWA+JxFBfrBKDYMR6lxNEZnnQK1QtfbPwrqIcFIDHZfGHZ/GE5/BE5/GO5gFJ5gBMGIdFT71qkUMGgUMGlVsOjiN6teBZteDbNWyQv9uih9v7UQERFRn9PiDWHxzmZUtviTHcpRC4Rj2N3kxe4mLwBApRCQZ9Ki0KpFvjl+s+g4QSNltj0tPvx7WRVOG1mAfjnp15KkotmLLzY19Eohpda3DfW+bdjpWoaVzR92aZth1pORoy1HlXc9phfdgKHWkyAKCmgU+sM+/jE5p++3bFrx9R2e+6MufFP7KkIxP1qD1djmXAQAqPFtRs2ezZhT+QcMt03FhNzzMdw2BSqR7Q+JkmmvZwOWNPwHWx0LEid3h1pPwoX9f4tcXb+kxkaHr9w0BvccMwfPrT8PUTmMJ1efhdPGzMek4knJDi3j5On644yyO3F66R2o82/HXs96bHUuwjbHIkTlcJdHhB6Onw54GCWGkSg1jmK7rgwQjkqw+8Jo8YXQ6g2jte3eH44dcjuFIECnVkCjEqFWiFApRIgCEoUQSZYRk2REYhLCUQnBiIRgNAZZBgKR+KiWFu/+reGUooAsgxrZBjVyjBrkmDTINWqgU/N37cdYTCEiogPa0ejB97taoFSIUCkEiIIAZfu9KEIhxv9gi4IAUQBEQQDa7gUA8adC2z2AHz0Xxfh2gACF+MM+4o/j9wpRgLL9XhE/rlIhQK0QoRQFKBWcBLevCIRj+H53CzbVuiEla1KUHhaJyah1BlDr/GFSe51aEf8wa1Qj26BBllENm14FvZof4Shz+MMxfLSuFuPLbJg8KCdtRmhV2/2Yu6EeMalncpIn3Ir3K36HcMyPev8OeCIHn48lV9sPWdoSnFV2N/J0A6ESNUm5ulCvtODs8nsTz6NSGOtbv0RzYA+2OBagxrcZm+xfY5P9awDApLwLcWrJbWwDRtTLYnIUi+vfxseVTyWWZWlKcH7/BzHSNoNXJ6exYsMwPDT+G7yy5Xo0Bytx+ezLsem2TdAqtckOLSMJgoBiwzAUG4bhhIJLEYr5sN35PXa7l8MTboU70ox6/w4Eoq5D7kentKBANxA2TRFKjaNRbBgOszoXFnXBEV0AQakjGInB4Q/D4YvA7g/D7guj1RuCOxg96DZGjRJZhvj3PqteDbNOCbNWBZNGCbVSPOwcLcsyghEJ3lAU3lB8lIs7EIUz0Db6JRBBVJLR5AmhyRMC4OkQS55Jg3yzFnlmDfJN2j5fYOE3cSIiOqBwVILDf+B+rKlCFASolAJUogi1Mn5VRrwvaNtNpYBGKUKrivcI1ani/UINmvhjflFMfbIsY2OtC9/takUwcuirdDJRIBxDtd2PanvHkThqpQizTgWjRgGDWgmDRgmtKt4Tt73w2H4+2qJTIdvIq78ptckysLrKgVpnAGeOKoRFn9qjsuqcAXy8vg7Rbi6kbHUswje1r6LBvyNxlfi+rOpCjM4+BQalDcfmnossTUlK/y1Timocm3sOAOD0sjtQ79uBVc1zsKJpFnxRJ1Y0fYBVTXMwMe9CnFl2F0zq7CRHTJTZZFnGRvvX+HzvC4kWfQBwzdA/Y1TWDM6xkCGytMW4ecTreHrdmZhUPAmBSIDFlF6iURgwJvtUjMk+tcNySY5BluMtmjyRVgRjXmgUBljUeZBlCQoxtT/30MHJsgx/OAZ3MAJX4EdFCn8EgUN8h9WrFcg2qJFt1CDbqEaOQYMsgxpqZfdeNCq0jWbRqRXINe3/vVCSZbgCkfhIGW8ILZ4wmr0huAKRRAGmosWXWN+sVaLArEW+RYsCsxZ5Jk2futCVfymJiChtSbKMUERGCBIQOrxtRUGAQaOAUaOESauCWaeEVaeGVa9ClkGd1v37M0WrN4R5WxrTal6U3hKOSmjxhNDi6XzdcWVWTBua1/NBEXWDBlcQ/15ehVOG52NogSnZ4RxQkzuIj9bVIhw9ur7V7ZyhBqxr+Qwb7F+h0rN2v9dH2n6C6cU3IF83CAaVtVuOmSyFhiE4x3Afziz7P3xb9zrWNH+ChsBOLG96H2tb5mJa0XWYXnwDNIr0a/lGlMpichTbHIvxdc3fUeVdBwDQKcwYZjsZlwx8nP/mMlCWthiPH7cQD8xki69UIAoKoK01l1VT0PFFtuxKSbIsIxKT4Q9H4QvF4vfhGHxtxYX4CI/4fWejlI0aJax6FbINatjaWmllG1KnhZYoCLDp1bDp1RiYa0wsD0VjaPGE0egJoskTQqM7CKc/AncwCnfQix1t7apFAcg1aVBg1qLQokOhVQuzNnMLhDxTREREfZIky/AE4x+ADnSyXqdWINeoQZ45/qGgyKpjgaWXSJKMVVUOLKto7bH2OUSUusJRCZ9trEdVqw/Th+VBlUJXujV7QvhgbS1CRzkBaDgWxDbnIqxp/hQb7V9BRsdcd16/BzEm+zRY1QUpPfLkSClEFU4puRkzim/CNucifFz5NBoDu/FVzd+woukDXDjgIbYaIuoGMTmK1c0f4/O9L8IVbgQAqEUdphRdg+lFP4dOmZpFa+oeRlVWskMgSkkxSU60u/IGo/CEovCF4kUTXzgKf1vRpKsjkAUARm28Fde+E7tbdfE2Xd090qS3aJQKFNt0KLbpEstCkRgaPSE0uINodAXR4A7CH46h0R1CozuE9TXxlnZGjRJF1vh5lGKrDtkGdcZ8ruNZISIiogMIhGPYa/dj7z7tlWx6FcqzDeiXY0CpTdenhrL2Fk8wgs83NaDWEeh8ZSLKaJvr3Kh3BXHGqALkmZPfnqTFG8IHa2oQ6GRi0IOJSRFsdnyLN7ffsV/xpJ9pHMbmnIljsk+DRZ3fHeGmBUEQMNw2FcOsU7DB/hU+qXwG9lANXt92OwaYJ+KaIS/CpM5JdphEaScU82F54ywsrH8TjlAtAMCgtGFC7nmYXnwDzGqOWCWizBaKxuD0x1tvtd+333yhKLp6yZ5KIUCvVsKgVkCvUcKoViY6XBi18S4XRo0ybeb8O1oalQJlWXqUZcXn8pHbLlKtdwVR7wqg3hVEszcEbyiKHY1e7GiMj17RKkUU23QoselRYkvv4gqLKURERF3k8Efg8DuxrtoJtVJE/xwDhhaY0D/bALGPfHjqSZUtPnyxueGIT1QSUeax+8J4d2U1ThiYjQnltqR96WrxhjB7dQ38R5CfIlIIH1Q8juVN73dYbtMU45js0zAh9zwUGYZ1V6hpSRAEHJN9GoZbp+CrmpewqP5NVLhX4uFVkzHAPAG3jXwr3iKFiA6p3rcD3ze+i7Utc+Fvm3fJqMzClKJrMK3oeihFdXIDJCLqRqFoDK62CdTjc5SEE48PNVcJAChEAab2gogmPgelUaOEXq2Aoe1er1am7aiS3iIIAsw6Fcw6VaJFbyQmocEVRJ0zgFpXAPXOIIJRCbubfdjdHJ97RadSoDRLlyjMmNKoLRiLKUREREcgHJWwvcGD7Q0eGDQKDC80Y0yJFRZd+nwISCWrqxxYsrMFksy2XkTUUUySsWRnC/Y0+zBzZD6s+t49GdjgCuLDtbUIdvKl/Mc84VZ83/hffFn9lw7Lzeo8nFF6Jybl/TRtr8jrKWqFDmeX34OJuefj6XVnAgAq3Ktw79IRuH/sXBToByU5QqLUI8kStjsX47uG/2KL49vE8lxtP0wtug4Tcs+HWpH80X1ERIerfXL3+MTu8aJJ++iSrhRM9GpFouVWe/sti04Fs1YFvVrBz2E9RKUQUZqlR2nb6JWYJKPJE0SNI4AaRwB1zgACkViHkStZejXKs/Uoz9aj2JraXUBYTCEiIjpKvlAMqyodWFPlxIBcAyb2y0KBhV9au0KSZHy7vQkb2nqrEhEdTK0zgP8s34vJg3JwTImlV74AV9v9+Hh93WFNNu+LOLGw/l/4uubvHZYX6AbjhuEvI1tb2t1hZpx8/UA8e/wm/HPbrdjmXAwAeGbdWbh26F8xJvvUJEdHlHwRKYStjoVY2/Ip1rd+2eG1wZbjcUz26Tgu/2IoBJ7yIaLUFZUk+EKxtnlLIok5Td3BCDyB+H1n85boVIr4/CR6Faw6dYfHHFWSGhSiEJ+Y3qLDxH7x/+8NriCq7QHstfvR6A7C7g/D7g9jbbUTSlFAsU2HftkG9MvW9/qFVJ3hX1YiIqJuIskydjV5savJi/45BpwwMBv5KdDnP1XFJBmfb6rHzrarUYiIOhOOSvh2WxO2N7gxY3g+coyaHjvWploXvtnWhFgXJx9tCe7FiqbZWFz/NkKxeAsDhaDChQN+hwm550El9lysmUghqnDTiNewxbEQr229CQDwxvZf4OTCq3Buv1/xJDH1ObW+rfhi75+x2fHNfq9pFAZMzL0Ax+dfgiLD0CRER0QERGMSQtH4LRiJIRiJIRCJIRiREAj/MLm7Pxyf7L2zkSXAD5O77zuyxNr+WK+CRsk2oOlGKYptc6foccLAbAQjMVTb/aiy+1HV6oc3FEVVa/zxQgBWnQr9sg0oz9GjJAVGraTNJ9A//OEP+OCDD7Bt2zbodDqceOKJePrppzF0KD8oEFHmYw5MP3tafKhs9WFYgRknDc6BUZM2f3J7RSQm4dMNdahs8Sc7FEoDzIH0Y3XOIN5ZvhdjS604bkBWt36RliQZC3c2Y91eZ5fWD0a9+Krmb1hU9yYkxE8KFOmHYWbpLzAyazpP+h+lEbapePr4Dfik8hksafg3Fte/jQb/Llwx+FmY1bnJDq/HMf/1TfW+HdjmXIxdruVQihpstH+13zpWdQHG5pyJQv0QjMk+DRqFPgmREvWsvp4DZVlGTJYRjbXdJAkxSY7fZBmSFL+gL36Lry8jvgzx/xB/KLfdt61zgMf77keS4o9jUvwYUbntuDEZUUlGRJIQjcmIxCREYjLCUQnhqITYEbRsVohCfDJ3jRImbfxm1qpgaiugmLSqPjO5e1+lVSkwON+EwfkmyLKMVl8Yla0+VLX4UecKwBmIYF2NE+tqnFCIAkpsOpS3zbWSlYSJ7NPmk/3ChQtx++23Y+LEiYhGo/j1r3+NmTNnYsuWLTAYDMkOj4ioRzEHpidZBrbWu7G72Yvj+mdhfJmNE9UjXkiZs64O1XYWUqhrmAPpQGKSjNVVDmxrcOOEATkYWWQ+6hzb6g3hqy2NaHAFO103KoXxfeO7mFf9EnxRB4B4K6/Ty+7EqKwZEAW2luguKlGDCwf8FoMsx+Odnfdjp2spHll1Em4a/hqG2U5Odng9Kln5b7tzCcqMx0CnNPXYMfqi+IlOGTIktAarEYr5EIx58V39O9hg/7LzHbS5ZOATmJT304zKM7IsIxCJwR2IwhOMwBOKwheKIhCOwR+JJU7WRttOJMuyDEEQIAiAQhCgVAhQKURolCK0KgV0agUMaiUMGgXM2vgcCQYN50hIN5n0GbD9d9wXisEXisIXjv9+B9pGb4Qi8dEcoaiEcCz++x6OSUjHKSXb/x1qVW3/Htv+TepVCujbJ3lXxwsoWpXIf5eUIAgCcowa5Bg1mFCehVA0hmp7IF5c+dGoFQAwapTxiezbRroYtT1f6kibYsoXX3zR4fkbb7yBvLw8rF69GlOmTElSVESZRZZleELxHpX+UDTxR1yWZQACRAFQK0VolIrEHz+DRpH0IXZ9AXNgegtHJSze2YLtjR6cOiIfeaa+2/orGpPwMQspdJiYA+lQfKEYvt7aiFVVdhzXPxtDC0yHfQVjJCZh7V4nlle0dtqbOyZFsKDuX1ja+D/YQzUA4hM9n1l2F8Zkn8YTAj1oTPapyNH+F69tvQXOcD3+sfXn+Pnwf2CEbWqyQ+sxych/u+y78M9tt0GvtOCM0jsxMe/CjDppfzCyLKMxsAub7d/CoLJBFBRwhRsRivmwyT4fTYEKGJRWDLacCJWoxS73cjhCtVCLeoQlP4zKLHijdpjVecjSFEOSJciQUO3dCADQKcwIxNwQIECAmBjJ1pmZJbdDqzShQDcIg60npPVoN1mW4Q1FE5NHOwMROP3hxITSkVjPnjVWigJsejVsBhWyjRrkGNXIM2pZZElh6fQZcN/f7/iE6W1zfwTjBUJvKIoudg49IEGI/w4rRREKMX5+RhQFKAQBYlthURAAse13WRQECADQtlxA2zqIn7AW8MP67a+LYvx5/BYfNaJoO4ZCET+2UvyheBm/CVArRKiVYuKe/56ou2iUCgzKM2JQnhGyLMPuC6Oy1Y+9dj9qnQF4Q1Fsrfdga70HQLwlWLFNhyKrDjlGTaLw3p3S9q+wyxWfqDYrK+ug64RCIYRCocRzt9vd43ERpRNfKIpqhx+1jgCaPCG0ekOH/QFWEOKVYLNOhSy9GjaDGtkGNbKNapi0qh6KnDrLgcx/qanJHcK7K6pxwsBsTCi39bkPmTFJxicb6rCXhRQ6SsyBdCBOfwRfbm7Akl3NGFlkwYhCM2yGQ09YGQjHsLnOhdVVDvjDhz6xKcsytji+xT+33ZpYZlLl4rTSX+C4vJ9CIfJzT28oMgzDfWM/xkMrjoMMCa9vvRUXDXwUx+dfnOzQekVvfA92Bp2wqgvQEqzC/3b/Bp9W/RE3Dv8HykxjjizoFBGTo3CFGlDpWYdq7ybYQzXYaJ93WPvwRZ1Y1/pZh2VhKf65xhu1AwDc4Sa4w037bRuIxf8/xEem/JBvLOp8BKMehCQ/FIIK43POxnDbVIzJPi3tiliyLCMUleAO/nAi2b3PiWVXMNLpPFT7tvoxqONXsOvUCmiUCqiV8RO5CjF+8hcyICH+GTPa1m4oGI1f4e8PR+ELt01s3TbSJSrJaPaG0OwNAfvM2adXK5Bv1qLArEWBJX7PiatTU7LPBbaPLnH4InAEwvGioD+cKA52ZZ619gtT9WpF4ve7ffSGRilCo1IkChPthQqlQmSrK+rzBEFAtlGDbKMGx5bbEI1JqHUGUO0IoNruR7MnFC/SByLYXOfGvC2N+GpLA96+4bhujSMtiymSJOH//u//MHnyZIwaNeqg6/3hD3/Ao48+esTHCUclfLOtEe1X5O+rve8g2voOSvIP/QXb+xG29y5sWy3RoxDoWBUWhR+qv+0VZpVCgFIhQiUKUO1T3Y2PCoiPDNCofnhM1FXuYAQ7Gz3Y0ejtUguLzsgy2q60iKLWEejwmlalQI5RjVyTJnHLNmj4IeAodSUHHm3+o54Tk2Qs2dmCqlY/Th9V0GfmUpEkGZ9trOccKXTUmAOpM75QDCv22LFijx1mnQplWXrY9CoYtUooRSHRXmOv3Y8Gd7BL7TPqfTvwUeWT2Olamlj2k+KbcGrJrZynIAl0SjOeOX4D3tv9W6xs/hDv7X4IjlAdTi+9I6MvVOit78ETiibg3mM+xseVT+H7xv/CF3XgTxsvxYTc8zC54PK0Kar4oy68t+u3h9U+a1+F+qHI0hTBH3XDFW6APVSLbG0ZTsi/BOFYEAaVFYvq30I4FsCxuefAFW6EM1QPoyobY3POhFrUIiZH0RSogD1UixG2qVAKGpjUuTAobTCpsiAIirQqmEQlCb5QLH6VfTCa6GrgSVx9H0U4Jh1yH6IAmLQqWPXxSaStenViQmmTTgml2DM/j5gkwx2MwOELw+4Lo8UXRosnBLsvDH84hj0tPuxp8QGIn6/JNWpQZNWhuO2mU/O8S7L1Vg4E2n5fAhE4/GHY/eF48cQf/90JRQ/+O97++90+UbpZq4Spbf4PY1uBkOdDiLqHUiGiPNuA8ux4y79QNIZaZwB1ziDqnPGLxgfkdH87QEGW06/73q233orPP/8cS5YsQUlJyUHXO1A1urS0FC6XC2azudPjBMIx/H3h7m6JuSeJggCtSoxXs5UKaNVt/QhVCujUInSqHyreeo0SepWCPfv7GFmWUdXqx/oaJ/a0+JLac1MhCrDpVfHCilGDLIMaOQYNzDplynz5dbvdsFgsXc4Vva0rOfBo8x8AbKp1Yd6Wxm6JmQ7MoFHgzNGFKLFl9ok4WZbx5eZGbK3n6IBkGFdmxbSheV1enzkwbs66WlQ0+7olZkpfvogTX1b/Bd83/BcSYlAKauTpBuC6YX9FtrY02eH1ebIs44vqP2NezUsAgBG2abh88DPQKy2JdS46tgSlWV37O5sJ+Q84+hwoyzJe/HonAKA5UInP9/6pw2iMCbnn4fj8S1BuGpsyLadCMR+qPOvRGNiNLY4F2O5ccsj1RUGJMuNoqEQNdAoLTii4FFmaYuiUZhhVB7/iPVO1X23vDUXhDUXhC8Xa7uO39mWBSNdak+lUCph1Sli0KpjaCiXmtpPLJo0ypc5HRGISWrwhNLiCaHAFUecKwhuK7rdetlGNEqsOJTY9im066FSpX1xRK0XcPn1Ql9dnDgTmbqjHU59vRa0zcMiWXCatMt4yTh8vCLYXB81aVUr9fhP1ZQNzjThpcA6yOhmp3q6rOTA1Pvkchl/84hf49NNPsWjRokMmTwDQaDTQaDS9FFnySLIMfzjWaWuCdoIQ/3Cj1yhh1Chg1KgSQ2nNWhXMOiX/AGQIWZaxq8mLZXvsaPGEOt+gF8QkGS3eMFq8YQCexHKlKMCq/+FDtkWnahverWorBvIKDqDrObCv5L905wvFMHt1LaYMycG4Mluyw+kxC7Y3p1whJRj1whmuR1QKwx1pgT/iwB7PGngirYhJEWiVJvgidgRjXuz1bthv+4HmSRhqnQyzOg8V7pUYYJ6ICbnnp9XVpemIOZB6S1QKY1XzHHxa9Uf4o04AwOismTi33/0soqQQQRBwRtmdsGmK8N7uh7DFsQAPrZiExycug0GVWX9Xk/U9OFfXD1cPfQFTPFdjcf2/sbblU6xqnoNVzXNQqB+K8/o9iMGW43v1oihZlmEP1aDOtx2bHd9gRdPsQ66vU1qQoy3DpNwLMNw2DTZNUcpcxNVbJOmHuRzaW3AlRpOEovAGo4h18Yo7hSjApIlfZd9+b9b+8N3NpFVClUZzaqoUIgotOhRadIll7mAE9c5g2xXOAbT6wmj1xm/ra+JtpnKMapTY9CixxUeuaNOguJLOeisHigJQ3dZxQykKsBnU8XbmehVsBjVsbYWTdPodJ+qr1Eqxy4WUw5E2xRRZlvHLX/4SH374IRYsWID+/fsnO6S0JctIFF9aPAdeRxQEmLRKZBnUyGqb/4ItmtJLjcOPhTua0eROjSJKZ6Idiiz7E4R4ItQq4/1EVQqhrWeuCIXYPmla/HdTbmu5F5OAcCwGjTJ+9X86Yw7MXJIsY8H2ZrR6w5g+LC/jcuz3u1qwrtrZ68eVZRlV3vVQCAq4wk2YX/MPVHnXddv+d7tXYLd7ReL5iqYP8O6uB6EQVIjJEQDA5YOexnDbNKgVOqhEntg/GsyB1Js22efj06pn0RTYAwAo0A/B+f1+jSHWE5IcGR3M8fkXw6LOw6tbbwIAPLH6J7hzzHso0A9OcmRHL1XyXz/TOPQzjcNJBVfgk6pnUOlZi3r/dvx9y7XQiHpMzPspBpjHY6j1JOiU3XdFuyzLaApUYF3r51CLOlR61mKPZw28kdb91lWKGgyxnACDMguDLJMwzDoFJnV2t8WS6qIxKTHxtbNtgvf2555gpEsTX+tUChi1Shg1Shg0ChjVShi0yvh9W+FE2wcmlzZrVTAXqDC0wAQA8IfjLa1rnAHUOALxNmFt313bP+fmmTQotukSxRW2Y+8evZ0DJ/XPwtM/HY3tDR4YNanTPYOIUkfaFFNuv/12vPPOO5gzZw5MJhMaGhoAABaLBTqdrpOt6XBJspz44NXeNxSIX4WSZ9KgwKJFiS0+xJVXYKQWbyiKhdubsaPxIJWyNCXLQCgiIRSJf0k4HGZd+k8IyxyY+TbWuuDwh3HOMUUZk1dXVzmwfI+9x4/THKjE+tYvsN35HZzhBmhEPer82w57PzZ1ERzhOoywTUNECkIpaBCWAqjyrodG1MHXdnV6u/E552CzfT5C0g/zwLQXUgDgnV0PJB4PME/EjOKbMMR6Ysq0REknzIHUG2p9W/Fp1R8T7YG0CiNmFN+MacXX899tGhhum4pfjPoP/rrpCoQkP55ZdzYeHPcVgENfwZzqUi3/9TePxx2j30WFexVWNM3GyqaPEJL8WNLwNpY0vA0BIvJ0/TEx7wIMNB8HjUIPhaCEXmmFQWXdb3+SLEGWY5AgwRdxwhNpQo1vKxr8O1Ht3YhKz9qDxpKnG4AcbRnydAPQzzQew6wnQ63Q9uC7T772gkl7scTpDyceH6g11b4UggBTovXWPqNJNPF7g4adAA5Gr1ZicL4Jg/PjxRVfKIratsJKjcMPhz+CJk8ITZ4Q1u51QgCQZ9agxKZHqU2HIquOIxmOUG/nwPaJreucRz+/LBFlprSZM+Vg1eB//etfuPbaa7u0j8Pt/5guc6YkkyAAuSYN+ucYMDDXiHxzZn94TWWyLGNjrQtLdrUgFDn0pH99jVmnwg0ndf0KllTsFXu0OfBI3hPnTEkOm16FC8aVwKJP7yJgT/3+hGI+7HatxPKm2djjWX3AK1N/rEA3GA2BeO/3aUXXo9gwHHm6/ig2jOyWtlzx0XBROMMNcITq8dLmqwAAJlUuPJHmDuuaVLk4NvccTMg9D0WGYUd97K7IhDlTkpEDOWdK31Hv24HvG9/Fssb3EJMjEKHA1KJrMa3oepjUOckOjw5Tg38Xnll3FgDAqMzCP8/5Dy4Zc3qXts3E/Acc/vvad86UzgSibmy0f41q70bscq1AY2DXQddVCmpka8vgDNV1uBChq5SCGlOLrsMgyyT0M42HRpGZc86Fo+0Fk3D8Akd/pMsFE7VCTMzdYNH/0D7ZolPxKvse5A1FUePwtxVXAvtd/CcKQKFFh9IsHcqy9Mg3aXulrXomzJmSjBy4q8mDT9bXH06YRJSChheacfqogi6vn3FzpqRJzafPkWWgyR1CkzuE5RV2mHUqDM03YVihCTlGtjTpLZ5gBF9ubkS1/fC/lFB6YA7sOxz+CN5duRfnji3q0Ls5nexs9ODrrd1TSPFFnNhon4da31Z81/CfTtefUngNBpgnQimokKvrjxxtWY+fOBAEAQpBhWxtKbK1pXj+xO2J1xyhOqxp/hTfN76LcMwPT6QZC+pex4K616FTmDEh7zycXX4f24B1gjmQekIg6sZjq6Z2OKk72HICzu33KxT3UrGTul+BfhAeGPsZXtt2C1qDe3HVnPNwcv9KFJrSs+Vrquc/ndKMSXkXYlLehQDixayVTR+iyrse9mANPJGWxKjNqBw+ZLEFADQKA4oNw1FqGA2FqIRRlY3j8y6GWqHPmHnJojEJnmAU7rY5S/adx8QViHQ6yXuiYKJXwapTd3isVWV+C65UZNQoMazAjGEF8ZNvnmAENY4Aqh1+VNsD8LaNZKl1BrCswg61UkSpLV5YKc82wJIBnRR6SqrnQCLqe9KmmELpwR2IYGWlHSsr7Si0aDG6xIKh+SYoOaS1x2xv8GD+tkaORiHKIP5wDLNX1+CM0YUYmGtMdjiHpbLFh883NeBIv/fEpAi2OZegwr0SO13LUOPbvN86eqUVSkGFkwuvRqF+CNQKHfqbj03JNjw2TRFmlNyEGSU3ISZFsNW5CKua5mCz4xsEYm4srn8byxtnYUrhNfhJ8Y3QKtPr/zdROopKYXzf8F/Mq3k5UUjJ1w3Cef1+haHWk3giMgPk6wfi/0a/h6fXnomI7MfKupU4d+i5yQ6rTyjQD8I5/e5LPJdlGTIk7HItR1QKQ6XQwBlqxFfVf0WBfjBG2KbBHWnGqKwZyNcNhFLs/olie4MsywhFJQQiMQTCMQQiMfhCUfjD8XtvKApfKAZvKNppsQSIz12SGFXSNtKEBZP0YdKqMLxQheGFZshtLdT32v2odgRQbfcjFJWwu9mH3c0+AM2w6lTol21Avxw9iq06nj8hIkphqXfWgTJGvSuIelcQS3a2YEyJFWNLrdCpM2MegFQQiUn4dlsTNte5kx0KEfWASEzGp+vrMX1YLsaUWJMdTpfUOgP4dEMdYl2Z4fRHXOFGbGidh0X1b6I1uHe/16cVXQ8gPk9JiXHEUceaDApRhVFZMzAqawZ8ESfm176CBXWvIywF8HXt37G08X84rfSXOD7/4rQ9mUSUymJSBGtb5uKL6j/DHqpNLB+dNRPXDv0zT05mGIPKhttH/RtnjynDSf1HJjucPksQBAhQYIj1xA7LJ+ad3yvHl2QZ0ZiMmCwj1n4v7XNrey61Pd533agkISrFl0UlCeGYhGhMRjgafxyOSghFJYQiMYSiEg7n049KISTmLDFrVTDrVDDrlLDq1DDrlJy8PIMIggCrXg2rXo0xJVZIsowmTwh7W/2osvtQ7wrCGYhgXY0T62qcUIoCSmw69M8xoF+2ISPm/yQiyiQsplCP84djWFbRijV7HRhTYsGE8iwWVY5SqzeEuRvr0eoNJzsUIupBkixj/tYmeIJRTB6U2n37mzxBzFlXi0is66cSolIY251LsLj+bex0LYOMH0bYCRBxfv/fIFtbikHmSVAr0rPl2cEYVFac2+8BnFN+P9a0fII5lU/BG2nFB3sew+L6t3DxwMcxyDIp2WESZYRQzI/5ta9gWcN78EbtAACzOg+nlfwCk/IuhELkiapMla8fiHJLek9A35dFYhIC4Rj8kRiC4RiCkRiCUQnBtuJFKBpLFDYiURmRmNR2ixdIopKEI7i+46ioFAL0aiV0KgX0agX0GgUMaiWMmvgE70aNEiatEholR5f0VaIgoMCsRYFZi0n9sxCKxlBtD6Cq1YfKVj+8oSgqW/2obPUDaEa2QY1+OQb0y9aj0KKDohfmWiEiooNjMYV6TTgqYVWlAxtqXJhQbsP4chtUHL562LY1uDF/axPCUbb1IuorVuyxwxuK4tTh+b0yWeXhsvvC+HBNbZfaDcqyjG/rXsOnVX+EVmFEMOZNvFZmPAZjs0/H2JwzYdV0faK4dCYIAo7NPRejs2ZiZfMH+Kr6b2gOVuKlzVdhdNapuKD/b2HV5Cc7TKK05I3Y8V3DO1hS/2/4og4A8QnJpxZdh5MLr0rJIm00JsEfiSEUiV/1HonFr4z/8Yg/hShAFAGVKEKlEKFWitAoRWhUIpQiP19TapNlGcGoBE8gAncwCk8wPrF6eyus9vZY4Vj3ft8RBEAhCFCI+9wO8lypaLsXRSgVApSiEP+3pvjh31zi351ShFal4L8/OiIapQKD8owYlGeELMto8YZR2epDZUt81EqrL4xWXxirqxxQK0SUZunQL9uAsmw9zFpeDEBE1NtYTKFeF45K+H53KzbWunDS4JzEJG10aDFJxqKdzVi315nsUIgoCbbUueEPR3HW6CKolanzRd3lj+CDNTXwhw/d/zsihfDc+vPQFNiTWBaMeWFW52Fs9hk4ufAqZGtLezrclKVWaDG54HKMzzkbn1b9Ecsa38NG+zxstM/DAPME3D7y37yClaiLqr2bsKLpAyxvmoWoFAIAZGmKcVb5PRiTfVrS5lcKRWPwBKOJiae9wSg8oSi8wfhJZH84elij+w5GrRChVytgaLsC3qRVwqpXw6ZXIUuvhkbFEeLU82RZhicUhdMfgcsfgTMQhisQiU+2Hoh2uVCiEAXoVAro1AroVApolSLUKhEapQKa9oKGQoRKGS9yqBTxoodyn0JIe6FE5N9RSnGCICDXpEGuSYOJ/bIQjMRQ1epHZasPVa1+BCKxfeZaAax6Fcqy9Ci1xedaYQcQIqKex2IKJY0nGMXnGxuwudaN6cPykGVgf/iD8YaimLuhDnXOYLJDIaIkqmzxY9bqGpw3tggGTfL/hHuCEcxeUwNPMHrQdZyhRnxZ/Wcsb5q132u/GPUf9DONhyikTnEo2XRKMy4e+BgmF1yBV7feCFe4ERXuVbhn6TDcNWYWSo2jkx0iUUqKSRFsss/Hwvo3UOlZm1heahiF6cU3YHT2zB4tosiyDH84BncwfqLYE4wkiiaeULyA0tVRxQpBgEbVduW7QkxcLY/288AyEvM8RKV4a6P2uRsAxOdyCEhwBiIH3L9Ro0SOUY38tjYzBRYttCyw0BEKRmJw+iNw+MNtt/hjpz/S6RxqerWireCngknTsRWWXhNvk6VWsB0W9V1alQJDC0wYWmCKz7XiDqGq1Ycqux8NriCc/gicfhc21LgAANlGNYosOhRZ4rk916RJ8jsgIso8yT8TQ33eXrsf/1lWheMGZGNCuS0lW9gkU7Xdj8831cMXOvRV30TUNzS6g/jfymqcP644qUVobyiKWatr4DrIybrWYDVmVzyKbc7FHZb3Nx2LW0b+CyqRX+4OpcgwFA+O+xLPb7ggMZrnhQ0X4WeDnu61SXuJUl1UCmOrYxGWNPwbO11LE8sFCCgxjsKZZf+HIZbJ3XYiNhyV9rmyPgJX8IfH7mC00xPHAKBVijDp4ieO208iG7vp5LEsywhF2+aYCMfgDcWLOq5gfGSAw/9DK6X2nvztsg1qFFl1KLXpUGLT8+pm6qD9d9/pD8PRdh8/iRtBIHLw7yiiAJh1Klh1Klj1alh0qsTNrFVCyZbPRF0mCgIK2ookxw3IRigSQ40zgL12P2odgXg7MG/8trE2XlzRqkSsqHTgz5eNhVXPi1eJiLoDiymUEqKSjO92tWB3sxczR+Qj28iTbACwusqOJTtbIcm9PHMiEaU0VyCC/62sxrlji1Bs7f2e/75QFLNWVcPp37+Q0hqsxtc1f8fK5o8gyfERK/m6gRhgnoBz+z0AjcLQ2+GmLbVCh1+N+wLf1f8Hs/c8BgD4764HUOPbjHPL7+ek2dQntQT3Ylnje1jW+D4kOYZgzJN4Ta+0YlLehTi58CrYNEWHve+YJMMTjBdG3O1Fk+APbYkOddIYiA8cMWiUMOuUMGtVP1xxr40/N2qUPdqmURAEaFUKaFUK2A6SakPRGFq8YTR7Qmh0B9Hgjl/Z3N6Tv/0EXJ5Jg7IsPco54XGf4/JH8NqSCny1pRHuQOcFEyA+2smqV8GmVyfubXoVzFoVL5Qj6iEalQIDc40YmGsEAPjDUdQ5g6hzBVDvDKLZG0IwImFNlYNzqxARdSMWUyilNLiC+O+KvTh5cC6OKbUmO5ykCUclzNvSiB2Nns5XJqI+KRiJ4YPVNZg5sgBDC0y9dlx3MIIPVtfA8aNCSmuwGu/vfhg7XN8llvU3jcfYnLNwUsEVbNFxFCYXXoFj887DnD1/wPKmWVhc/xYa/Dtw9ZA/waCyJjs8oh4jyRKaA3tQ59+GZY3vdxh90s6izodWYcQpJbdiVNYMaBT6g+4vEpPa5iz5YdJrdzCamATbF4qis8tXtCpxnyvr2+7bnhs1ypQvOmiUChRbdR0K8e0n4GodAVQ7/Gj1hdHkCaHJE8KqfSY8Ls82oF+2HiaelMtoggj85Ztd+y3XqRSw6FSw6lU/FE7aRpyk0lxuRH2VXq1MTGQPxC8QcAUiOGlwDouaRETdiMUUSjmRmIxvtjWhyu7HzBH5fa6Hs90Xxqcb6tDqDSc7FCJKcVFJxueb6mH3hXHCwOweP57DF95vjhRfxIGva17BkoZ/IybHCyxDrSfhtNJfoJ9pXI/H1FdoFUZcOuj3GGGbjv/svA87Xcvw9Lozce3QP2OAeUKyw8tI3ogL4VgEakXvj/7qa2RZhjvShI/2PIlA1I0dru873UYhqHDziNcxwDwhMe9SJCah1RtKjCxx7zPKxBPsfGQJEJ/s2qxVxgsk2h8KJWadEhadChpl5n0u/fEJOF8oimq7H1V2/wEnPM42qNEv24DybD2KrBy1kmnMWhWuPbEfdjZ6EsVCq04FTR/7TkaU7hRivC3YaSMLkh0KEVFGYTGFUtbuJi/e8YRw9phC5Jm1yQ6nV+xu9uKLTQ1dnqCUiEiWgWUVrXD4wzh1RD5UPdR/vN4VwMfr6uAPx09GhmMBLKp/C9/U/gPBmBcAMNA8Cef2ewClxlE9EgMBo7NPwR3ad/Ha1pvhDNfjr5uuwPXD/oZRWackO7SM8+qG32JTyypcPOBRDLGemOxwelVECiEc8yMiheCN2BGIuRGIulHlWYdAzIti/VBoFAb4Y25sbJ0HkyobznADREEJX8QBvdKCgeaJkCBBliX4og6saJqNEbbpCETdEAQRSkGFCvcqROUw1KIOYSlw0HjydQNRoB+MDa1f4SdFd2KU6Sp4glE0tUSws7oR7kAUrkDnrYgAQK0Q29pvdWzFZdHFH+vVij4/ks6gUWJYoRnDCs2QZRlNnhCqWv2obPWhwRVMtARbvdcBlUJAiU2P8iw9yrL1sOpUff7nlwkePmcEXvx6Z7LDICIiIko5LKZQSmufF2DG8HyMKDInO5weI8sylu+xY1lFKzg9ChEdie0NHrR6Qzh7TBFs3Twx/dZ6N77e0oioJEOWZcyqeBhLG/+XeL1QPxRnl9+LYdaTeRKtFxQZhuK+sZ/gufXnwx6qwb+2/RIX9n8IkwuvSHZoGcMesGNTy1K0Buvx9y3XAQB+Ne4L5On6Jzmy7hGTIojKYVR5NmCTfT6WNLyNbE0psrTF2Ola1i3H2ONZvd+yLY5vD7ju/oUUAYCMMu2pGKS+DaGQGa6WCMpDv8Cu3cAu1B30uGql2DaiJF4kaR9l0l440SiPbHL3vkoQBOSbtcg3azGpfxYCkRj2tvpR1epDZduolT0tPuxpiY9aMWuVKM3SoyxLj1JOZE9EREREGYbFFEp5UUnGl5sb0OoL4aRBORn3BTgak/DVlkZsb+D8KER0dFq8YbyzYi+mD83rlgJ0NCZhya4WrN3rhCzL2Oz4Bp9UPoPmYCUAQCPq8dOBj2B8zjmJNjvUO3RKEx4c/yXe2/VbrGz+ALP3PAZHuB5nlt3N/xfdIEuXhb/85Bv8efXDWNb4HgDgmXVnY3rRDZhccDmsmvRpmeEMNWBB3euo9KzFXu+Gg67XGqpGa6h6v+WioIQkRzssG2w5HjE5BoPSho32rwAASlGDqBRKrHNM9mmwqgsBAPZQHTbav8IA03HIUg+CRsiBLOmhkHLhCwchR4oQDtvgDykg7XtRSQDY3f6gzb5tuNrnLEm04dKyFVFP06kUGFpgwtACE2RZRrP3/9n77/i4zvPA+/6dOmd6BTDoBAH2TopFjSqWLMmSbNlyd9zidRw7sZw4u88mT94n3uTdjd/dZLOOvcWJn8QliTdx1iWOZVuWJVm2JVldpMQOkCB6H2B6O+e8fxxgSLCCRAfu7+cDAZg5M3MPBFw8577u+7qcXStdY1n6xnMk82WO9CU50pcEoMrnojHipjHslAQTvTUEQRAEQRCE5UwkU4Rl46XOBIlsibdsjaPOUxmbhZYvmXz/tT56xy9f2kIQBOFaFMsWjx0Z4NRQijdtqsHnur5/6geTeSeRnS4ykG3nXzo/z4nxX1buD2hV/PtdP8Kt+udq6MI1UiSV97b9KTGjkR91/yVP9n6FRKGf97V9HlWe291Jq5FH8/Pu1v8vW8J38GTvVziTeoUnev+Kp3r/X26t/SB31v8Gfn3+exVdC8u2mCgOcjb1Gt898x+JGo10pl6d0WPXBW9ka+RNKJJGsjjExvCtNHi3XPZ3qWxaFMoWDzVa5Msm+ZJFvmSSL5nkSia5okk2aTqlAYtl1hY+g5mzGb7KOGQJ/JO7SM41eHe+Drg1vKIM15IhSRLVfoNqv8HeNRFKpkVPIkf3mJNcGc0UGU4XGE4XeKVrHFmCeMCgcXLXSjxoiH4rgiAIgiAIwrIikinCstIxlOY7r/by1h11y74x/USuxPde7WUss/iN5i3LwrJKZM0kJyeeQZIUtJKNMjaMrLrwJjIotkzCazGR6+Vfre+yv/Y9bPUfJKLVoikGhhFFVz2L/VYEQZh0ejhDT6KTHQ0h9jSHZ1xqJZEp8vyZUU4MpMmV0/yw6ws80/8PWJgoksZtdR/lrvpPYKi+eX4HC8e2p/pUSZVJ2nKiHzMzhm1ZlAbbkVweFHcQxRdBi69DkiTMXBJJUUFSkBQVSV74f5ckSeLuxk8RctXxTx1/yKsjP+DVkR/wezu+R71304KPZyXaErmTzeE7OJJ4gqd6/5YzqZd5uv9rPN3/Ndb4d/Hets8vWvmvklVgIHuSf+74HD2ZIxfdnyqNTPtelQzqPTvZFnoXQbUFt1yHZcmULZuSaVHKWpRNG820OJ6yecMcoWRO3mdaFMsWJdOmWLYwr7MuqaHJ+F0aXpdSSZpM9S0JGCpel4oskiULxrZtp/nW5M9ckiRs26bYfwKQsDJjlMZ6UINxkCRc9ZtRfBFs28bKjiNphhP7FBVNkWmJeWmJeQGnkX1PIkfXWJbuRJZUvkzfRJ6+iTzPnxlDUyTqQm6aJsuCRb26SJQJgrCgzo+BU/HHKmQpDZ1GUnWKg+3YxTxa1RrscgFjzW4kzYVVyoNZAllBUnTnfFAQBGGZsW0bsJl2HTwxhJkcAkmi2H8CSfeg+CLILi96/SYkScbKp0BSQJaRFG1RroMXk4j4wrLTm8jxzy/38I5d9Xivc8X1Yktkinz7lR5S+fLVD54DpXKevtwJvnz0ozTJa7mlvA8fPry2GxkZHWfVaYkS3+Q/AbCXvdzP/ZPP4JQL8mfAxs/jGPyi/+/Q+ns5yEEA8kCCHGNaBk9J4Qn5ae7a8oc0+bcvyHsUBOFixbLFi51jHOoZp63aR2uVj+ao56Im9dlimdPDGTqG03SOZCmYeZ4b+Ed+0vM/yZbHAdgaeRMPNv97qtzNi/BOrp9zkWwhyQq2bVMe7aLQ/QbYtnMhbJkwmUzR4+sx1u4BoNB3DHNi8NzzlPJY6TEANLMMqkbuxC+xshOVYxR/DBQVu1TAs+kgsr5wCea91Q8R0Kv4q6O/DsB/PfQQv7Ptn0UMniOSJLE1chdbI3dxZOwpftz9l/RmjtGZepX/36v30uDdwv7qh1kXupGYsWbOS63Ztk2hbDKU6efUxAucTb9MX/YQY6X2Sx6vEkS14/jKb0I116PbLUhMXuRk4fVKjmXkko+/FroqY6gyLk3B0GTcqoJbV3Brzmevy2nq7tVVPC4FVV4Zu4uXi6lksSTJ2GaZQvcblMd6sM0ytm1C+dyiHt/uB5EMH9gWhc7pO5rKYz0AaJEGJ5lSSJM59ONpx6ihWqxSHsUXwVh7A16XOq0k2ESuVNm50p3IkSuZnB3NcnY0C4BHVyr9VpoinuveWSkIgjDFtkyQ5MoimMKZV7CKWaxCFrDBPHc9HrjpfQCYqWEK3a9Pex4nwQx6w1YUzUVpuHN6nJRk1FAtZnYco3knWqxp3t+bIAjClZx/HQxQGuuh0HXYuQ4u5pxrYMsEQI024tlwCwDFgZOUR8+V/rVLBaxMAjVc78RMVSfX/jxm8tx+c9kbQdJc2IUsnk23Ihsru3qFOEMVlqWRVIFvv9LDw7sbll1CZThV4Duv9DhlL+aQZVn0jbxAcvQkVimPK1ekxnTKj2TJ8pf8FwBcpk0tNZUEyvl66Kl8nSRJgnHChKYdIyGxk538nJ/jwjXtPjdu6ktuChTIWSm+8Pq7iGi1fKD0TooqRNbdSVV485y+b0EQrq5Ytjjal+RoXxJJciasvC4Vy7JJF5yyPOCccL0x9gSPdv05Q7kzAIT1Ot7V+idsDN+6mG/hqmyzTDk5hJVJYJfyFPtPVu7Tqltwtx0A26I8MYSVS17yOWT3uZM+xRvGTI1UTjCnlIbP4GreAbaKbU6/z0w5E9NKsKZyn5lJkDn0Y1yN29Br1yOp81d+a0PoZj6+6St85djHAfjC6+/id7d/m13cMm+vuRptidzBpvBBnuj9a14d+SED2ZP0ZI7Qc8bZGRLS4zT5drAr9ha2RO68ask107LJFMqk8mVShRLpQpnR7AhDuROMldrpK3+PkjRw+SewJRRCmFKCaPF38Jq3I13iFF+RJXRFRlXOfdYU2fmQJVRFRlMmP8vOfdOOUSR0VUZXZDRVxjX5tdhJsDRYhSzl8QHsUg4rn6E01FG5z73xVrRIA3YpT3li0FlNeCnn/b9U/LFKTDtf7tRzaFVrnItwRZ02EVke7wechAvlEmguCr3HKJw9hHvjrQTDdYQ8Olvrg9i2zUi6SHfCKQnWm8iRLZqcGEhV+ghGvXoludIQdl+0EEAQBAGo7JQrTwxWJv2m4hGSjP/AuwAJK5einBpxdpRcQPYEK19LmgvZHbjk+WKx7zju1r3Y5QuqS9gW5USvE0cnY6ltWWSPPAG2jdG6D8Ubmqu3LAiCUGFbJmZyCDM9dR18onKfGqnHs/Egtm1hJkemLQQ8n3JeDFQ8IcrjAxfFynKiF6uYQ1F1bHP6wnAr4yw6lH0RbMtZyGPlM6Rf+T563Ub0+s3I2vT5w+Vsec1CC8J5RtPFZZdQGUrm+fYrvZWJy+tVLhfoGXiG9Fg7+XKSb+a/Qgst3MmdNNIIaNOO9+BBR6dMmXrqGdFSmIqE4q8iFN5ALj9MNLyJFul2/tz975GlK2/Re4j38dDk14VShvFkB8nkGSyzhGkWaBh1sZ3tdNCBr6QRJQpl4Ngh2uWnKIVDNDbdhc9dM6ufgyAI1862IVMwyRSmx6Hu9Os81v3fOZr4GQB+rYo76j/GLfEPLLn+G3a5SGm0GyufQjYC6DVrKY11kz/1q0seb+UzzuOKOexiDq2qBbDRqludrclISC7vtBINxppdGGt2XXEc/j0PYpeLmNkJ57nNEmYmQWngFHYxA24/hZ6jABS6X6fQcwQ1Uo9W1YIaqkWahxX6m8IH+b93P86fvnI3AH999N/QHPsqt29425y/1momSwp3N3ySuxs+yUD2FC8OfY8zqZfpzRxjvDjA+NgAh8ceQ5E0dNlNznQmZFxykEbX3RTMMt3F7138vHYQsLGk8yZwLshVuOw1+OX1RLUbqNI3EdLr8eqGszNEldE1GZeqoCuTCQ/VSYaI3hQrh5VPUxo+g22ZaNEmJJeX0vAZZ7XhJdgFZ+eHmZ1AUlT02g3YtoVe04pdKiAbPicWagYAkqzg3Xb3FcegBqrx73sndimPmUlglwpgWxT7T1Dofh29fhO2ZVI4+xoAueM/R9JcaLE1aNUtKN4wVX4XVX4Xu5vClC2LgYk8XWPOTpWhVIHRTJHRTJHXup1+K7VBN41hN40RDzUB0W9FEFYj27YwU6NO4sIyca3ZhZkapdDzBub4JRYe2BZ2MQe6BzM1guKPIrt8KJ6gs/ilmEX2hKYlk9VgHN+u+y9+rvMYTdtxNWzBymewchPOxKJVJn/6JYoDp9CijVi5ZCUpnTn0I2RvCK2qBS3WjKy75/LHIgjCKmGbJcpjvZjZcWTdg167nvJYD7mTz17y+Mp1cKmAVUijxpqRJAmtqgVJ1bAtC9nwIynn5gBdjVtxNW694jh8O+7FLpcwcxPYhSy2WcbKJyn2HsPKTqB4gpWkTrHvOMW+E6jhOrTqFtRw3bIvC7Y8ZqAF4TJG00W+80oP77qhccn3UBmYyPOdV3solKyrH3yBdG6I4dHDZIdP0pgLU6BABBcR/AyTB6CTzslECnQbTlY4lvcwXu2ntvZG/sR4O7pizN0bmuTSvNREt1MTnV5K5lbex17z/8OZkWcYPtNOlRUBoNqKwCiURh/nDX0Uq2UT26NvnvNxCYIwM0fHfsb/e/wT0267seY9vKXps3i10Jy/nm3b5MsWqXxpMqlTJlsyKZRMCmWnL4Np2ZjWuX4MHjtPwE7TUjyNYeWxkZBw7p8ItJK1q3DbXkKAbfhRA3EoZrBySTwbbqpsM5YNH55NB+f0/Uiqjhqomnabe+0Nla+1aANWdgIrN+GsWhztpjzajaS6cDVtR4+3zel4AGJGE/92x7/wxdffS6ac4HeefIjb1r3KzvjOOX8tAeKedbyl6d+SypcYzaQ5mvgFJ5I/ZKj4ImU7Q+68VV0Fa4L23P+57HNZ0rnVYn61AV12M1o8xZtqfwdbynNH/Ufn5e9SWLps28LKJDDTY+RPv+RM+J3Xr0aLNiJrLuyScz6ohuuQ3QHKY73IvghG0/ZKkkSL1KNF6udsbJIkIenuaZOCek3rtGO06lanrFi5gF0qUOw/QbH/BLInhHvdARRv2Bm3LNMQ9tAQ9nBTK+RKJj2TjezPjjn9VnrHc/SO5/jVmTFU2em3Uh9yUxcyqAkYYueKIKxAdrmImRmjnOin2HccFK2yWlpSdYyWPc5OuslEila1BmSV0mgXRuN21GgDkuZCkiSM5h0Xv8B5q7GvlSQrKJ4AiidQuU2Przt3v+5G9kWx0qMAWJlxCplXKXS+hhquxb3xVqQ5LgsqCMLKYuUzmJkEhZ43sDKJaeeB2mS8UYJxACSXFzVch13MYaZHca+/GcXtxCdZd1fKeM0VSdVQ/TE4r6KX0byz8rUSiiNPDEzuhrEpJ3qdRLiq46rfjKt++fb3FMkUYdkbSRf53qu9vGN3A7q6NE9G+sZzfO+13mtKpCTyPbyReIrvnvmPvIN3sJ3tRHEuOKfKa41K4wx486hZFw80/1tcVW/DpXrYct7zLObeD0PxsanmHqi5B4BkppeericxJlLErDBSscDXTnyaKmMNN9d+gP2xh3Fp3kUcsSCsHplSgp/2/BVP93+1ctuu2APcWf9x6r0bZ/XcpmWTzJcYz5aYyDkfyanP+RIl82qNq22kyf/qlHnIOIJXOjchPZVI6TEDvDakMTw4gNM4bw/kJEiAIofw6E34j2fxGyWCbo2wRyPs1Yl6ddQFmnTTYs1oseZKCYrS0BlKI53YpQK2ea5ExPk1vedCnXcjf7L3Of7roYcYznfyx0//Md99z3fn5LlXo5JpkS6USeedclzJvPO7nMqVmciXSOfLnPut3ozGZuqwMKVRLJJYcoqy9jIl5TTV+nYkJcepjJNUibrWMFroZHP4DnbG7iPiqqfG3YpXCy/W2xUWmW1ZlZ1r5sQg2aM/O+/O8xIpsWbkyYtk15pdGC17zh13lZ11C8Xdtg/Yh21ZlMf7KQ2foTzWi5Udh/NWJdqWOW2VoltTWFfjZ13NuX4r3WM5uhNZeib7rXRNJlvAmVuI+VzEAwbVkzteFjLWC4Iwd86Pgfmuw5QGTp27czKRIhs+tMnkrSRJ+G98z7TEhLt178IN+DJkzYVvu7No0CoVKI90URo+g5kexcqnp433whgoCMLq5PQ5sZFkp9dd9uhT08uzTp4HKsEap6wqTmLZf+N7l1zpXS1chxauA5zd0aXhM5SGO52KDuVC5bi5vg5eCCKZIqwI/RN5/vVQHw/tql9yW/67x7J8/1AfxfLVEynJTB/dZ39Cw7iOgsQP+M8A/IAfEJIixOwo41oWd/1WGuI3E5A1WoCb+b/m+V3MjYC3ns2bPgjAwMir9A12wAQM5zt5/szf0nImT742ztrm+1HEyaQgzIuCmeHnfV/nqb6/IW+mK7f/m01/zebwbTN+Hsu2SeXLJLJFxrMlEpki47kS49kiqWkTy5fm1hT8htOY2qOrGKqEnxyxbCeBbB8Zby1jVc5kYDLvRh09gquUZNi7ljFXnDRuiqaFv2zhKlvky87OlnzJpGQ6O1tSkxPfTOSnvbYEhD061QEX8aBBbdAg5nMhz+MJnCRJKN4wSksY15qdlMcHnNI6kwo9RzDToxjNu+asprauuPnklm/Qnv0//PVDfzonz7la/NefnODxo4MMpwpkCmXyM/g3XJUlgm7t3IdHI+RuIOjW8BsaivyuCx7xn+Zn8MKyNFXvOt/5KlZ2As/m21FDtaihWlyN2yiNdmEXc7jW7EKLNCKp00u6LvXVzZIsV3bGTJVqnNo1aNs2mdd/ihqsxtWw5aLeUpIkEfLohDw62xqcfiujmSK9iRx94zn6JvKkC2WGUwWGU+cuziUg4NZ4+WyCz9y1jr1rIgv5lgVBuAZ2qUBxpJPC2UNgmfj3PezsPFmzC7uQwUwnkN1+XA2bUYI1F8W8pR4DZc2FXrsOvXYdZi6JlTs3OWrlU2TeeAJX0w60quYl/14EQZhbtm1j5VMU+05QGmxHCVTj3fomJEXFvfFWcqd+hZVJoNdtQKtquehacTkkIRRPEKV5J66m7ZgTQ0jn9U8pDpyiPNbjXAf7o4s4ypkTyRRhxegay/LYkQHu2xpfMsGkcyTDDw73XXEVdrlc4HT3j7CHe4iXIzSe19R9rf8GXFqAak8Lmxs/gyzJxBdi4AsgHttFPLaLG0uf4uXh7yOfPUnQ9hPsz9A5+GXca/dSV71vsYcpCCvGaL6bX/T/HS8Pf59MOQFAvXcT9zf9HhtCt1w2bhbLFuPZIolsibFskUSmSGLy+/NLcV1IlaXJyWRt2gRzwK3hd6mV1cJ2qUBxsJ3ScOe0Rp++YoLauqnSC0FYuwaAADC9iMzFyqZFtmiSKZYrCZXxyaTPaKZIrmQyli0yli1yfLLRsa7I1IWMSrPjqFeft39LJEmurNIBZxK1NHgau5QjM/4jtKoWXGt2zUmTvpCrho+v+0O8utj1dy1e7Rqv/G5MUWUJv6HiNzT8hkrA0Ai4nc9Bt4ZHV5bM+YewfJiZcYoDJymNdsN5DY3NzDhqqBaYWe3q5URS9WnlwMzUCFZmjGJmjOJgB66mbejxdZedUJQkiZjPRcznYkdjCIBUvsTARJ7BZIGhdJ7hVIF8yWIiV+KX7SN8/ODahXhrgiBcA9u2KCf6KA12UE70w3lLcWyrjISOJCt4Ns18sc9yoLgDldI7AMWBduxijnz7ryj2HsNovQE1UL2IIxQEYSHY5dK56+DseOV2MzlU+VrxBPHtuGcRRjc/JElGDZ2b1bRti9JgO1YuReb1n6BGmzBadi/5vlIimSKsKCcGUnh0hds3LP7Jx9G+JI8fHcSyL55sLJo5nuj9K6R0il3j9cRxA04GdkROkHNBIL6dT9R+bWEHvQh8WoTb6j5CMthL9+kfUp3SqbIiWO2nONL7Eo3r30rA27DYwxSEZWss38PT/V/nV4PfomQ5OzSirkbua/oddkTvo2xCIlsilS+RLkyWMMqVmJgs05Utmpd9bkWSCHk0Qh6NsEd3vnY7n2cysVzofoNCzxvnytZIEmqwFtkTQK+7/lJjqiITcMsE3Bq1F5TCtm2bbNFkKFVgMJlnYCJP/0SeomnROZqlc9QpGePRFZqjHpojXpqiHtzz2JfLmSg4SKH3KOXRbqcMTqIPvXELes26eWlUL1zZR25aQ1PETbZo4nWp+FwqLnV5bT8XljZnN8ZPsNJjldsk1YUaiiP7otPq7q90ij+Ge/1NFHqOYGUnKJx5hdJQJ8aaHajBmS0jcpKcGutqzu12yRZNxjJFGiMettVff18EQRDmnplJkDn6FJTO7SaT3UEUf3SyQbtnEUe3sFz1m5FUnULfcazcBNk3nkCrWoOrcTuyIRbDCMJKVOg/QaHzNbCndr9LKKEaFHcAvXZ2JbeXE0mSca+/hWLfMUrDnZRHu0iP9+Oq34xet2HJlj8UyRRhxXm1axyvS13Urfwvdo7xy1MjF90+OPY6Eyee5Mv2/wSc3if7+HdkyNCu97K+9WHWhlfOysNrEfDWs2Xbx52+Kse/S0MhSmMuTPnQUzznHeTA9t8Tk1iCMAO2bVMom5xKHOaZwa9xKvVTbJyESFjZRp36PozyLl47KfFs8cwVd5dMcWtKpd9IxKMT9uqEPc4uk2spjVXsP4VVSGNM1vKX3QGwbdJamQFfjm2tH0Cb5x0UkiThdam0uFRaYs5rWbbNcKpAbyJHVyJLbyJHtmhyrD/Fsf4UElATMFgT9bAm5qXa75rzeKT4Ing23EI5NUK+44XKhGKx9zjuDTc7zf2EBXPX5hoyxTKnhzOLPRRhhbBti9yJX6LH16OGnF3UksuLlR4l4S5S1/wm9HD9qizvIkkSWqwZNdpEabCd/NlDWJkxskeeQg3XYbQduOadelOx3utSeeeeBiJe/eoPEgRh3liFDNljT+PdcS+SJDvngJZFSbIY9OVpa3kbum91nutImgtXwxb0+DryZ1+jNNhBabiT0shZXA1b0Ru2iOtgQVjmSsOdlCeGJnvJOTvUsC2yapl+X56tbe9D031XeZaVSfGGcK+7Eb1uI7mOF7DSYxS6DlHsP4F73Y3TdrIsFSKZIqxIvzw1gkuV2d4QWtDXLZsWPz02xLH+5LTbh8eP4zr6Km7ATTUb2MAJThDztpKobuBk4TCSHCEaXL+g412KAt56Nu/5bbr6fg5nj1Kw84xmOvj3z23lke3/TINv82IPURAWXaFsksyVK43dk/mpRu9Z+ktPMCr/M2W5p3K8Ye4iUH47hrWLNBJppu82cakyvslJp4Ch4ndrBA1tcqeJhmsWuzJs26Y00kn+1K/OvV7TdiRZcSbJdryZZ0e/CXBRD4CFIksSNQGDmoDB7uYwZcuifzzP2bEsnaMZRtNFBpJ5BpJ5fnVmDLc2uWsl6pQE8+hzdzql+mN4t99LabCD/NlXUbwhp+mgSKYIwrJkm2Wyx56ulGww0wn8N7wNAK1pK8+6XsOUbRpDF/cAWG0kSUKPr0ONNFA4e4jSWDeSZmAXMjAHZQ8FYbWaKmPaGPYQMDRcmhNrJnIlEtkinSNZTo+kKZSu3h/sWlmFDOmXv1/5vjTY4ZTxkxWMLbfz3Oj/xpZgvSdwhWdZHSRVx926D726lVzHC2CZ2FYZzBKoIiEsCMtRKdFH7tjTle+Nll1IioYSrMHYfjfPjv5vkGCb+BtH8Ybxbrub0nAn+dMvI3uCWPn01R+4CEQyRVixnjw+hK7KbIwvzIlZMl/iB4f6GUyea3TcPfgcwY5OLrz821//AT7ccD+G4qNsFTnV88aCjHE5aao7SC66jZ+/+iecsE5Qpswrh/+SZPiWShN7QVipCmWz0usjmZ9KmDjlt5K50rRm2DYlcvKLJNXvUZK7sNRzJxwB9rPW9evUGBtx6woeTcGtK5NfTzV/Vyr9S+aSbVsUe49T6Dp0qTsBkBQV2e2f89eeLVWWnUmHiIdb2mKk8iXOjjqJle6xHLmSyfGBVKWnRpXfRdNkr5W6oDHrn6cky+i161CC1ZSGz6CG6pySQIcfw7PhlmmN6wVBuHa6KhMwVCzb6QuVLZqXLMs6G1apQO74LzBTw9NuV4PnStHKLg+mPLevuxLIuhujbT/qaB1WKYfsDWOV8mTfeALvjvtE6UNBmAFVlthUG2BnU4iY79LJSENTqAkYbIwHMC2b9qE0L3SOMZIqXPL4a2FmEmQO/fii2yXXuR3IsieAPXbRIaue4o/i3XY3xb7jKIEqJFWn2H8Sq5THaNq+2MMThGXJoyvEfC5URUKWJHJFk9FMkXzp8iWtr5dt287CuNMvXnyfZSEpTnkr2RMAEQOnkSQZvXotaqCK4kAHasS5Ds4eeRJjzS4U3+JVIDqfSKYIK5Ztw2NvDGJaNlvq5rdO8vGBJE8eH6qs5ulKHeaV03/PnZld047r8oyzcfvH2CmLrPNMuF1h7jnw3wgMfounOv4SHz4aEipHDv0VG7Z8BFUVqxSF5cO2bUqmTa5kki2WnQbphTKZgtMoPZ0vV3qWFM0rrwy0KVHQn8BSO0jYv6TMuUbZfrWa/TXv4uba9xDUa+b7bV1WofNViv0np93m3nALWrRxkUZ0/fyGxtb6IFvrg5iWTf9EjrOjWc6OZhlOFxhOOR8vn02gyBK1QYPGsIeGsJuagIEiX19pBsUTRGneCYCZncDKp0m/8q/L9ucoCItFkqAx7GFD3E9T1IPfpU4rmVIom/SN5zk7muFYf2rWF9ZO8vMn2IXzVtNJMr7dDyC7RP37mXBKfzUBzu4ec2IIK5ck8/rjeNbftCQT8YKwFEgSbKjxc/O6GAFj5jt+FVliQ9zP+hofHcNpnj45QjJXuq4xXCqRosaacbftX7L175caSVFxNTrlv81cEquQpdh3DLuUx2jZI36OgnAVkgQNYQ8b436aox78l4mHE7kSHcNp2gfT9I7n5uS1iz1HKHS/Pu02o+0AWtUaUbJvhmTDj7FmJ+BcB9vFHJnDj2G0HUCvblncwSGSKcIKZ9k2jx8dJF8y2dM89xnMbLHM0yeGK6uTj43+jCf6v8Lp5EtISOygiQABcq3raKi5kdXZDWX2bqx5N1sCt9F55B+hCI2ZAKdf/gp129+Fz714k8XC6mHbNqblJEOKpkXJtCiWz30uTn4uVD5MimWLfMkiXzbJl0zyJWtG/UmmuFSZgKHhN1Q8hokpn+FE9u85kzm3TZjznm5t4AZuqnkfO2L3okgL/8+7bVtgU1kxrNW0Uew/iRKK4247gKy7F3xM80GRJRrCHhrCHm5ug0yhTPdYlrNjWboTWTIFk55Ejp6EczKuyhK1IYOGkIf6kJuagOv6dq5IErLLg5WdIHfil5gNW3E1bhUn5IJwBZIEG+N+blwbI+i5/KSiS1VoiXlpiXm5pS3G8YEUL3aOMZ6d+USibZmVyS2nXFUrhbOHcDVuQ2/YvOpLeM2GpKiADbKKlRkj/fpP8Ky/eUnW0BaExRTx6ty1uYb60PWfc0mSRFu1n+aolxfOjPHy2cSMzl/Pj4GyJ4Tii2KmR/FsuRM1KK7XZkN2ecF2kvylwQ7MzDiejbeumHNrQZhLiiyxpS7ADc2RK577TQm6NXY3hdndFGYoleelzgSnBtPXtGPZtm2wrUoM1KpbKHS/juKL4t5ws1hIM0uSrCBNlkHLt/8KK5PAtWbnop5bi2SKsOLZNvz85Ajj2RIH11ehzUE5G9u2OdQzwbMdIxRKFsOJI4y0P0WqNEySTiQkbqh6CF/d/US9i581XQkC7hq27v40HWe/T6h/grgZZeLV7zO2ZgNNdQcXe3grlm3bS36y1rJtyqZNybQoW5OfTZuy5Xxf+dq0ne/P//qixzhfT33vfO18nqtCLKosTZbXcspseV0qXl3BZ6hoah5JnSBROsF3zvwRRTMLGZyPS9hb9Q5uqH4rrYF9yNLirFCzbZtyoo9C12HUSAOuug1Iqo7iCeI/8O4Vv3LO61LZWBtgY20A27ZJZEt0J7L0jOXoHXdKgnWP5egec5IriixRE3BRH3JTF3JTGzRwqVf/GSnuAN5tb55sxneSYs8bmOmRFZWoEoS5VBcyeNOmmsuWt7kcVZHZWh9kU22A17rHef7M6BX7CNhmmWL/SUojnbgatqBGm5xkSu1G9LpNS/7f0OVCizWj+KLkTj6LmR4le/Rn6A2bcTVuEz9jYdWTJNjdFOam1uiclW7VFJmb22Ksq/bx4yMDjKaLlzzOyqcpdL/ulKBq3oniDSNJEp6tb1rx54ALRZIVjJY9KKFacqeew0qPknntRxjrDqCF6xZ7eIKwJEgSbKoNcFNr9LK7UK6m2m/wlm21jLYUePrkMGdHs1d9THl8gPzZQ6jBavT6zciaC9nlXRXXwQtFNnx4tr6JQs8Rij1HKPafwEyP4l5346KVvxbJFGHVONwzQU8ixz1b4sSDxmWPs22b0dwo6WIan+4j6o5WLtIsy+bEYIoXzowxlikyke6i59QPqM8FqSeKTQTNCNG06WGi7qaFemurhizLrGt5iAH/ISZO/oogAejs5WT+u6xf+/bFHt6Kc2L0df7i8Cd459r/QLN/x7y9jmXb5EsmuaJJruR85EsW+ZLp7PIoObs8ClO7QCZ3gpQnd4lcy26P62Vjw9SHPIyqKGiqhCqbmPIgilzCrYZJ28fwa9VkrbM0ePahyCaaAopi0ZF+Ep8eIKRXU7IKqLLGeKGfw+PPELfXMTrRzUDu1GXH4FYC5MwkAO9o+SP2VT+Mrlw+ll33e7VtMuUERTOLrnjwquHLTlSVk8MUzh6q9AQoFrMo3lClBNVqO4GUJImIVyfi1dnREHL+PckU6U04iZXe8RzZolNOqG88DySQgJjPRV3IoC7kpi7oxmdc+vRMUlSMlj3InjD50y9ijg+Qful7TukgQ5S8EQQATZG4sTXG7qbQdU2yn38e2FzlY31NM08eH+L0cOaC4yxKQ2codL+OXXSSpYWeIyj+GJLLK/p6zIOpi+l8xwuUhjsp9hzBnBjEs+XOVffvjSBM8blU7t0apzHimfVzXeo6uDpg8P59TfyifYTXusYrx1qlgjOpNXAKbCfhXNROYLTtR5Ik8Tc5D7RwHcr2N5M99nOsXJLcsaexW25Ar1232EMThEVVEzC4Y2MVtcHZLTCrxMBymoMbfYyng/zsxDCpfPmiY830GPmzhzAnBgAo5pLInlClBJWIgXNLkhWMpu0o3jC5U89hpkZIv/KveHe+BcUzv20dLkUkU4RVZSxT5B9f7KKt2sfeNRFqAucmIsfz43z9ta/zpRe+REeio3J7a7iVj+/+FHtiD9E9KjGeLTE20U7+yM/w4aWRMAB92gihloPsir1/wd/XahOP7SDtraX/8LeoMaP0DTzLgJHkYN2HF3toK8oXXvwTejNH+cvX340uu/m/d/+EgF599QdOsm2bfNmq9AKpfC6UyRTLZApO35Bc0ZyzXR+aIqHKMqoiocoSqiJPfp68/fyvFQlZMilL4wwXXyRtduFWgqTNPo4mvw1ASG+kYKbImxOTCZULWJMfAFML9iZLrR5Jf23G4x4tdF/2vrhnPe9t/U80+ee34WSunOTFoe/yi/6/Z7TQVbk96mri1tpfY2/123GrAQBKI13kTj5z7sGygl67Hlf95soWXMFJrsR8LmI+FzsaneTKeK5E73iOvvEcfeN5JnIlp+9KusChngkAAoZK7eSulbqgm6hPRz5vUlivWYvsCZB94wmwLcxsUiRTBAEIezQe2FF3zbtR4MrngZ/e92kOtD3EK50lCiXTmcwfOl05RtI9uJq2o1U1i3Je80ySFYy2A8jeMIXOVzFzSexyEUns0BNWoTUxD/dsiePRZzetc7X49+GdH+aODdU0hj089lonY68+hpU/1xNKCdZgNO9A8UVnNQ7h6mTDj3f7m8mdeJbyeB/liQG0eJvYoSesSlMLaHY1hpCvs0clXDkGfvKG36K16n46R5yqN+VEH9lj55XdlmT0eBt6/RZkfe4XOgrTadFGZMNP5o2fglnCzIyJZIogLATbhlODaU4NpqkOuGgMe+hIPcNvP/Zr5EoXb+M7nTjN7z/xb9Hl/4cPrv8LRvJnsTqPsgenufyQPIbevJ2Nte+b0etnCmUS2SJjmSLJfJlkLseJbJKyZdHT3Yllqpi2jWXZSBJISMgyqLKMIktoioSmyGiKjK7I6Oq5D1flQ8HQZNyaU0rI0OQVd4Llc1ezZs+/4dArf8EPyz+k3Pl9EoU+Hlzz75HFRMac+NPbv8xnH/s9Xhr+HkUrx5+8fAdvafpdbq39IIqkkytONk4vlMnkTad5eqFEOl8mNZk8KV/DrpGp31mj8jH5u1z5/VbQVRlNkZzf+cm/A+dDQpGly/6em1aJrvRhTow/w+O9f02tZz09mSNXHdN48fJJDkXSUCSNonX57b91no3IkkK2PM5YoReAWs8GfFoYQwkQcdVhY6PLbmq96wnpcarcLfi0ue/xdMn3IEu4NYXj47/gvx/6FEXr4qZ7o4Vuvtf5eX7Y9QU+suGLbAjdTP7MK5X7teq1uBq3IbtmvyJypZMkibBHJ+zR2VrnnPSl82X6JnL0j+fpncgxkio4/zYMpDgx2Y9LUyRqAgbxgEE8aFATMPD5Y3h33Etp5CxapH4x35YgLAnranzcvblmRmXzLvRY+2M8/K2HyV7mPPB3H/tdPNof8vW3/SPjvbWkJhMpkqqjN2xBj68TKxAXkCRJuOo2IkkysicgSh0Kq44kwf6WKAfWRmZ9jTeT+PeHT/4h3373t7mn7R5O0c/IZCJF9oQw1uxECcZX3LXmUiYpGu5Nt5LveBFjzS7xsxdWpfqwm3s2x2fUF+VKrhYD/93jv4dH+3/4n/f+A/nUVgY7X63cr8aaMZq2L1qpqdVK8Ybw7biPYv8JtNiaRRmDSKYIq9pQssDPz/6Urxz7DZxCPhdP/E7dVrRy/M3x3wRgK1vZwy66PeNs3P4bKPKlA3i2WGYwWWBgIs9gKs9wqkC2aF7w/CWSqtPgVCuXkbjwZMgGE84tf792suTU9fcbKgFDm5zM04j6XIQ82rQVz8uJphrs2fsHTPTGeLTrv/KL/m9QNSFzw9ZPo6tiYnc2jvUneeZkgVbl/8L2bef1zH+naI/xg7N/xg87/yeB8tvxl9+OzNVX/7o1px+Iz+V8eF2K81lX8bgUvLqKW1NmtZrkQiWrwGsjPyRR6OPkxLOcTr407f7LJVI2hg7iUtwcGn0MTTZ4oPnfUufZSMgVp2BmsLFRJZ2o0YgsqUg4CZySVUCV9CV/MSNJ0BLzsibqpTnqIejW+EnHT/jkzz7mNM675B4h57aSleMrx36Dj2/6axpUFbsEns23o4ZqF/Q9rDQ+Q2W94Wd9jbOzpFi2GEjm6RvP0T+RZ2AiT9G0pjW1B/DoCtV+FzFfHdGBJFGvi6BcgPQwek3rYr0dQVgU+9dGuHFt9Lpi8GPtj3H/N+/Htq98Hpgr5XjPtx/iO+/8Hj2T93l33CcSyYtIr10/7fvSSBey24/iDS/SiARh/hmawr1b47TEZt/Q+Fri3/3fvJ9H3/8oG9et54XnniXWsJZCw15R0nCRSJKMu21/5Xvbtin2HUePtyEps5tcFoSlTJUlbmqLsrvp8qWoZ+paYuCv/+Bhvvue75P2+Unlkrg33FIpbS0sPNnwYrTsrnxvFXOUR3sWbKeeSKYIq1qunORrJx7hcomU6c7d37rmrbiq3soW7dxJbMm0GEoVGEzmGUw6E2DJS9RWlICAWyPs0Qh5dDy6zcmsD1WRuKW6EZemo8hSJcFhTe5SMc9rsj3VEHuqj0SpfO7rwmSfCafvhNN7wrIhlS+TypfpIz9tPIosEfPp01Y9h9zakp8UniJJEm9q+A1CrlomTj3N1mwT+Rf+hYGWBppqb13s4S1bz7SP8OWnp0qY7CPO18kojzOu/m9MeZhx7e+ZUL+F3z5Aq/YZQkYEr0vFZ6j4J5Mm/skEylw1wrySY4mf8386PoeueFAklcFcB6Zduuzxzb6d6IqbfdXvYFvkzUiShCZfe1mYKbN57EKQJYkNcT/7WiJEvOfKcI3nx3n4Ww9j2zbWVRK2NjYS8LUTj/BHu5/Eq11fPwLhynRVpinioWmy9rll24xligxM5BmY/PdlNF0kWzTpHM3SOdkYUcPkQddRgnKBfMcLvBg8iNdt4NVVbNumIeymrVqUAhNWFlWWuHtLDRvjget6/LXEQAsL2ZZ5//feS+fvdTKU1Hny+CAlc/77dglXV04Okzv5LGCjBGvwbrlzsYckCHMu5tN5cEcdIc/sS6peT/x7+FsP0/PZHv7wD/8QVVU51p/kiWMiDi4Fha7DFHuPUjj7Gu6Nt6JFGhZ7SIIw56r8Lu7dGr+ucq4Xup4Y+L5vv5uznzlLX0Lh56dGKJavf8GzMHdsyyR77GmsTIL8mZfw7X0Hsja/8zMimSKsai8OfXeyrM21nABKlMo2A0kYSScYThUYThVIZIuXfJawR3PKsvgNqgNO7XztvMnlslVktMf5Q68JulDlue03YFo22clSTMlcmYl8ifHJMmNjmSIl02YwWWAwWeAwTr1+t6ZQFzJoCHtoCLuJepf+ivs9VQ9ytuSHzn4AQmd66JGfo6HmxkUe2fK0vsbPTa1RkrlSpeyWR/8ghvohOvOP8/TAF0iWBklKP+dV8+fs8byVtdH72BS+DVma31InBTNLV/owHRMvcDZ9iLF8D8P5zku/j+BNNPm2U+VuYWfsPiSkOf8bW+rCHo17tsYv2ZDv6699nWwpO4NkssPGpmjleGn0XzlY+6G5HqpwCfJ5fVe21julwUqmxUi6wFCqwGi6yEi6wFimwGkzyi65D4At48/x3YEtFFF58sQQPYkcf/GenYv4TgRhbrk0mbfuqKMhfP07Q641BlpYZEtZvnnkmzyy/xFifp0fHOpnInf55L2wMBRPEMUfxUyNYE4Mku86jKtx25I/fxWEmWqr9nHPlji6OjeLlK43/n3j0Dd4ZP8jAGyqDVDld/GDQ30ksiIOLiYt2kCx9ygAueO/QNp8B2oovsijEoS5IUmwpznMTa0xlDmqZnG9MfAf3vgHHtn/CA1hDz96Y4DBZP7qDxbmlSQraFUtFDIJANIvfgffDQ/NaxlYkUwRVi3btvlF/99fxwPhJ11f5Y3CTReV5PK6FGr8Tj37moCLeMDApS1uDW1FlvAbGn5Do/aCvkxTzZCHUwUGJnfTDKUK5EomHcMZOoYzgJNcaQi7aYp4aIw4pYGWoua62+mRnyVw+iwAgY5OTuVG2LPpHYs8suXn4PoqIl6dx48OXnRfC+/gYP2D/PPpz/HCkNOo/eXh7/Py8PfRZTfN/h2sC97ExtAt1Hs3X/dEhmVbjBf6OJp4mo7kCxwa/TEAEjL2ZVaPxD3reVP9b9Di30XYVb/qJ1G21AW4fUP1JS+8bdvmSy986ZqfUwJ+0fd33Br/4Kr/+S4WTZGpDbqnJchs2yZXWkOu/Xm08S48UokPuF/jGc9NlCWNthpRy1dYOfyGykO76me1MvF6YyDAF5//Ip/e92mq/Qbv39/ED1/v5+zo5ftnCfNPUnU8W+4k9atvAVDsOYKZHMKz5U2LPDJBmJ257I8yZS7i39RYYj4X79vfxE+PDnFyMDUn4xOuneKL4t1xH5lDPwIge/QpjLV70RvWX+WRgrC0+Q2Ve7bEaYzMXVnVuYiBYa/Oe/Y28mzHCC+fTWCLDXqLylW3AbtcoNjjlHNPv/Q9fHveClzf7vWrEckUYdUomRbZokm2WCZXNBnLjzJa6Lr2J5JsytIAAXeeuL+KmM9Ftd9Fld+F17W8/qTOb4Y8Va+/bFkMJQv0jufoTeTom8iRK5mcGkpzashpNhh0azRGJpMrYQ/GIieMztcQv4mEN07p9acxMAj3jXHK+xTQsthDW1EUWeO9bX/Ku1v/I8fHf8Hzg/+Hk+O/pGBlOTXxK05N/Iofdv0Fcfc6okYDlm3RkXyRqNHAmxt+CxuLZGmEl4a+h1cLkywOkywOsi54I6Ztki6N0pM5Qsm6eKWHjYVL9rApfBu1nvXUeTfS6NtKQK9ehJ/E0iRJcHNbjL1rLt/IfjQ3Skei45qf28ZmtNBFyZ5Al0KzGKUwlyRJwqOreDbfTGlsDbnjPwfgVuk4++9+kDfvWLO4AxSEORLx6rx9dz0BY3YLO2YTAzsSHYzlxoh6ohiawtt31fNM+ygvdo7NakzC7Eiygv/G95I//SKlwQ7M5DCFzlex94hyN8LypKsyb95cw7qauS3TOVfxb4pLVbh/ey313W5+fnIY0xKziotB8Ybw7X07mUM/xi7myJ95Gd3jBdoWe2iCcF02xv3csbF6zueb5ioGKrLEreuqWBP18tiRAVKXKPMvLByjaTtqsIbskScByB79GYXqB+fltZbXzK8gXIZl26QLZVK5Msl8iWSuRKrgfJ3Jm6QLZYrm9JXsZWkQjOt/zYd2R4gYK6/xsirL1IXc1IXc7F3jlAkbSObpHsvSncgyMJFnIldiorfEG71JJKA64KrU+I8HDdRFbkQY9q9lYrsKh59BReUfT/0nDhyP8NDGhxZ1XCuRLMlsDt/G5vBtWLbJUO4MRxM/49Doj+lOv85A7hQDuVOV4/uzJ/n6yc9c9vleG/3RJW8Pu+pJFHpp8m3nQ+u/QMSon/P3slIossTdm2vYVHvlVRjpYnpWr3PPthBHul0MJQuzeh5h7mmReux1B8if+hV2uYgkieawwspQ5Xfxjt31ePTZX8LMNgamiqnKZKIkSdyyLkZ1wMXjRwdFDe1FJEkSxtq92MUc5UQfdln8GyUsTwG3xoM7aqn2z+KC9TLmMv6db2djiNqgwQ9f72dclP1aFLJm4Nv5FlIvfBtsC9s0F3tIgnDNDE3hzo3VbIjPT7/HuY6BjREPv3agmSeOiR16i00N1uDedBu5Y09PXgfPTyUNkUwRloWSaZEulElPNlFP5UskL/g8kwUwqizh0RXcuoKqRujNXf+YXIr36getAIosUR9yUx9yc4AoxbJFz3iW7tEcZ8cyJLKlSs+VFzsTqLJEfdhNY9hDY8RNlc+1KKWAgr4mCjeE+fHR/8Dp7Gk++egnubPlTgKu+dnmJ4AsKcQ9bcQ9bdxZ/2/Ilic4Nf4c6dIoydIIfZljHEk8Rb13E6rk4mz6tYueY2f0PqrcLURc9TT6tlHjaUWRxD9VMyVLEvdtjc9oBaNXm10MawxF2RaP8Ez7CK90ia3NS41e1YLii4Jto7nmfiJGEBZaXcjgbTvr52x1okuaXWNKv35xnF1f4yfi1UX/gEUmSRKeTbdRSvShBmtESUph2akPu3lge+2cJI4vxafPrvTnpeLflJqAU/7wyWNDHB8Qk4qLQVJ1/AfeTXl8AFdV42IPRxCuyZqYh7s21eCf5Q7kK5mPGGhozg69tf1efnZimHxJJDIXixauQ9n9VuxyAd09P/O2YoZKWBS2bVMybfIlk1zJJFc0yU59LpbJFE2yhTKZgkmmWKYwgxV+sgR+QyPgVgkYGn5DxW9o+FwqPpeK16WgKzKSJDGe6qLn1FMcI0yCxDWOXiLqasSjhq7rvS93uiqzNuZjbcwHVJHKl+gey9E1uXMlWzQ5O5qt1A43NJmGkIeGiJNgCXu0Bbuodel+7t76R5w5doY/vesPrnjiL8w9jxpkR+zexR7GqiFLEvdtm1kipauri8cff5zwdcRACYm14bVE3E7t7oPrq6gPu/nJkUFx0rjEKG6RPBZWhoawm7ftrJ+TxsumafLaa6/xs5/9bNYx8FKm+gc8dmSQjqHZrXwUZkcL1y32EAThmu1oDHLb+uo5a7J8oUwmwwtPv0CECGNcW2nCq8W/KS5V4b5ttTRHvTx1Ykjs1lsEkqygRcROfmH50FWZg+uq2NYQvPrBs9DX18dPf/rTeTkHBNhUG6Ah7OanxwbpHBH99BaLbHiB+VsAL5IpwjWzLJuyZVMyrcmPc18XyxaFqc/lqc8mhZJF/rzP+aKFeY3LmDVFwudSJ5upq/hdKgG383XA7SRN5KtM0vePvMJYz/PEs16aiLCf/fyYH1/zz+DWOtF4eYrf0Nhcp7G5LoBt24xminSPZekay9I7niNfsmgfTtM+7EwoeHSnmX1D2END2E3IPb/JFY8a5P/Z+30+sH3tvL2GICw2SYK7N9dUeh9dimVZnDx5kpdffpn29nYAbpRv5EfWj7C5tnj8yP5Hpv3dtlb5eP9+F48e7mcweXGfG0EQhOvVHPXw4I46NGV2iZRcLsfhw4d58cUXGR0dBeA24zb+Jf8vs46BF3KpCg9ur+WlswmebR/FElv3BEG4CkWWuGND9bxNJA4PD/Pqq6/y8ssvUywW2ce+67oOvlr8O9/mugD1ITc/PtJP37g4PxQE4dKaox7etKmGoHt+dqPYtk1HRwcvv/wyx48fB+CAdIAf2z+e83NAcObI3r6rgSN9E/z85IhYcLgCLbtkyv/4H/+DP/uzP2NgYIAdO3bwpS99iX379i32sGbMsp3dGIXJZEOhZFIybYrlycSEZVE2nWSFOflh2VMfYFvOn7ptc81/9FPXcXbl+/Oea/L5z3+tqaSJZduUTWcsZcuaUTmtmVIkCbeu4Nac0ltTJbi8uopHV/Cet6vEpV5/WYf+zEmOdvwje9Pr8BICYFAZpaX+fvSen1OycjP6eUrIaLLB3qqHrnssK5kkScR8LmI+F7uawpiWzVAqT/dYju5Elv6JPNmiycnBNCcHneSKV1eoD7srpcQiXn3OkysrKfG13GOgMD9uXRdjc92VdyF8+9vf5ujRo4DzN7Fr1y5+/cCvs+krm8iVcljMZAegjFt186EdH7rovqBb4903NPCzE8O83jtxfW9EEK5CxMDVpSXm5YHttaizTKQUi0W++MUvks87k3kej4eDBw/yW1t+i8e/+PicxMALSZLE3jUR4gGDH73RT6YgLqSF2RHxb+XyuVQe2FFLbdA9L89/6tQpvvnNb1a+r62t5fMHP88vvvOLeYl/5wt6NN61p5GXzib41elR0ZxeuG4iBq48hqZw67oYW+vndzfKD37wA1555ZXK99u3b+cjN32ELX+zZV5j4Ja6IM1RLz87McSpQbFbeSW55mTKhz/8YT72sY9x8ODB+RjPFf3TP/0Tn/3sZ/nyl7/M/v37+cIXvsA999zDiRMnqK6uXvDxTCmZFtmiSaZQJjtZpsr5fO7rqVJWMylXtZxoioSmyGiKjK7IaKqErsjoqoxLVdBVGUOVcWkKLlXG0BSMqc+agqZI8zrR3T72LE8O/C3Hx3+BFy87+AxDegp3fCOtde9BlmU+4nXzlWO/gcSVE1QSTomwP7nlb2nx1TCUylMyxcnglSiyRG3QTW3Qzb6WCGXTYiCZpyeRoyeRY2AiT+aC5IpbU6gLGdSF3NQF3VT5XfO2zf16iBgoLDV7msPsab54q3G5XMa2bTTNWeGzYcMGTp8+za5du9izZw/RqNM079vv/jb3f/N+ZFu+4omkjIyExHfe8x1CRuiSx6iKzF2ba4gHDZ46PkRZXDCvOCIGCgtlbZWXB7bXXfc5QD6fxzCcfkG6rrN27VqGh4fZs2cPO3fuxOVyeqbMZQy8lMaIh/fvb+ZHr/fTk5hFsz5hSVisGCji38pVH3Zz/7ZavK65Xed6fgxsbm7GMAyam5vZtWsX69evR5KkeY9/lcfLEvtaIqyJefjJkUGGU4XrfVvCIhLngMJc2hD3c9v6qjmPfeBUZCiXy+i6DsDGjRt544032LlzJ3v27Kn8zixEDPS5VB7YXkfHcJqnjg+Rypev+30JS8c1/9ZOTExw11130dzczEc/+lE+/OEPU1+/MLUY/+Iv/oKPf/zjfPSjHwXgy1/+Mo8++ih/+7d/y+///u/P6WvliiYvdo7RMZx2dpKULCchMpkUyZXOJUuuZ0JdV2RcmpN0mEo+aIqMJkuoiowqSyiTH7IsIUtOPXxZkpBwSrqAU7OPS11jXm5IU4dPfpaQkCTn+aaeX5acSXBZcl5bkSUUSUJVJNTJ8WmTty/FFf/54gSn27+HdyJH1k5wnF8gIdMWvZV89XY2h3dVjjU0hQ/sfCt3b67hI99/L9mSU9Pw/KSKNPkD9mhuvvOe7/Dm1jcDYFo2g8k83WNZ2ofTDCXFSeHVqIo8Wd7LAzAtudI7nqN/Ik+uZNIxnKFjOOM8RpaoCRjEgwbxgEF1wIXfpS7a795qiYHC8rAx7ufWdbFpt42NjfHss89y9OhRDh48yIEDBwDYsmULra2teL3Ta4fe03YPj77/UR7+1sNXjIHuC2LglWytDxLzufjB4T5xwrjCiBgoLITWah/3b6u9rkRKR0cHzz33HGfPnuWTn/wkkYiTbL7vvvvweDzI8vRdLvMRAy/kc6k8vLuBX50e5YXOMUTVr+VrsWKgiH8r057mMLe0xZDnaOFYuVzmlVde4aWXXkKWZT7xiU8gSRK6rvPJT36SQGD6LuaFiH/nq/YbvG9fEy92jvHCmTFW2DrTFU+cAwpzIeTRuGNDNWtic9/PYmJigueee4433niDPXv2cMcddwDQ1tbGb//2b+P3Ty+JvZAxsLXKR2PYwwtnxnilKyF26S1z15xM+d73vsfw8DB/93d/x9e//nU+97nPcdddd/Gxj32Mt73tbZUVsHOtWCzy8ssv8wd/8AeV22RZ5q677uK555675GMKhQKFwrkJ7mQyOePXG0zm+dDfvjDj41VZwqMreHSnJJV78muPruCZLGHlntyNYWjKklppvxKUywV6Bp8hM3yCqqxBAwagEybInVUf4kDjB4kZTZXjFVnihuYwe1sikzW4H+Cuth6+cegbfPH5L9KR6Kgcuza8lkf2P8KHd3yYoBGc9hx1ITd1ITf710aZyJU41p/kSF+SZK60gO9++boouWJZDCUL9I3n6JvI0z+eI1+26B13ki1T3JpCld9FzKcT9boIezXCHh2XKs97kmW5xMDZxD9heWgIu3nzljiSJJHL5Thz5gyvvfYa7e3t2JMzdYcPH64kUxRFuSiRMuWetnvo+ey1xcCriQcN3r+/iUcPi9XYK4mIgcJ8W1fj4y1ba69pcnF0dJSOjg5eeuklhoeHK7cfPXqUW265BQCfz3fZx89HDLyQLEvc1BajIezhx0dE2a/lajFi4EJfBwvzz9AU7t5cQ1v15ePSTFmWRXd3NydPnuTVV18llzt3zjU8PFxZgX1hImXKQsS/8ymyxIG1UdqqfTx2pBt65uRphQWwXM4BQcTApUhTJPY0R9i7Jjzr8q3nKxQKnDlzhsOHD3PixAksy8nSHjp0iNtvvx1JchaBX5hImbKQMVBXZW5ZF2NTrZ+nTw7TMVyc9XMKi+O69lNVVVXx2c9+ls9+9rO88sorfPWrX+WDH/wgPp+PX/u1X+NTn/oU69atm9OBjoyMYJomNTU1026vqampNBC60Oc//3n++I//+LpeL+zRaY56KJQsXJqMoU4lQ+RKomSqx4dHV9CV+Z/EFaazbIvTyZc40/cE2xI1RHATmeyHkiTFRFCnvulOHvC/b9rjqgMu7tkSJ+ZzTbs9ZIR4ZP8jfHrfpxnLjZEqpvDrfiLuyIz+3wbdGgfWRtnfEuH0SIaXOxPTEgDC1amyXElQgdPLJ5Et0T/hlAQbTBYYzRTIlUy6Jpvcn09XZPyGis9QK0lMXZUJGBpBt8Y79zTMyTiXQwycTfwTlr6oT+fBHU75m6effpqf/exn0+6fKuOwZcuWGT/nbGPgpXh0ZzX206eGea1r/LqeQ1h6RAwU5suGuJ97t8RnnEixbZu//du/pafn3GycLMvs2LGD7du309zcPOPXno8YeClNUQ+/dqCZnxwZ5NSQuIhejhY6Bi70dbAwv+pCBvdurZ2TRssDAwP8/d//PZlMpnKbz+fjhhtuYPv27YTD4Rk9z0LFv/PFfC7euaeRXwx46U5kr/4AYUlYDueAIGLgUrOuxset66rmvMH8r371Kx5//PFKAgWgvr6e3bt3s23bthnHr4WOgVGfi3fsbuD4gIdnBmVyJbFNb7mZVXG6/v5+Hn/8cR5//HEUReEtb3kLr7/+Ops3b+a//Jf/wu/+7u/O1Tivyx/8wR/w2c9+tvJ9MpmksbFxRo8NejR+/JmDfPnpjqsfLCyYiXQXA0e/T205yn/mP5Mjhxs3+/n3APSpI+jxdTQ3vJMG+eJAvbbKy1u21U7uRrk0SZKIeqJEPdHrGqMkSbRW+Wit8tGTyPJsxyi9YlX2dZEkiYhXJ+LV2VLnrAQomxYj6SIj6QIj6QJjmSKJbIl0oUzRtBjNFBnNXDw58WLn2JwlU6Ys5Rg4m/gnLF3l5DDZo09R1dqKckM9oEy7UN69ezcHDhygqqrqul9jtjHwQrIscceGamr8Bk8eHxS9plYQEQOFubSpNsCbN9dcNpFi2zbPPvssP/3pT3nggQfYvXs3kiThdjsLMCKRSKUn1NRt12OuY+CleHSVh3bV82KnxlN9YIpr6GVJxEDhWsiSxN41YQ6sjV5XWa9sNsujjz7K0aNH+cxnPkMoFCIajVYSKS0tLdxwww1s2LABRVGua4wLEf8ufL140CDq09lYFaB9ML8gryvM3lKOfyBi4FJREzA4uD5WqUQyG/39/fz93/89sViMD3zgA+i6TiQSqSRSdu7cyf79+6mpqbnuBMhCx8C1VT52NIYYTBbw6ApFUR172bjmZEqpVOL73/8+X/3qV/nJT37C9u3b+Z3f+R3e//73V7aOfve73+XXf/3X5zSAxmIxFEVhcHBw2u2Dg4PE4/FLPsblclWaSwrLi23bjBV6+MarH+O99rvx4pSmkYBanMA21SBqW/V9DHvjNNfczEZZv+xzbqsPcufG6jmrSTsTDWEP777Bw+nhNL9sH2E0LVYgzpaqyE7/lKAx7faSaZHKl0nmS2QK5Upvo2LZwgY21156a/u1Wi4xUMS/laHQe4xCzxEwp5cO7Dh1srKVfuPGjXzqU5+aVQJlIWyuCxDz6fzr4X5RCnEZEzFQmA/b6oO8aVP1tItfy7L4h3/4Bzo7O6etOAQ4c+YMe/bsAZxeKA899BAez+wv1BfajsYQW+uDdAxlrn6wsCQsRgwU18HLX9Ctce/WeGX3/UwkEgn+5m/+ZtqukynlsjPjpmkan/jEJ6iqqrruBMpSoCky92yJs6fJ5Ocnh+mfEEmVpWi5nAOCiIGLLejWuKktyoYa/3UnNl555RUef/xx8vnp8aCrq6vSWL61tZVPfOITs0qgLLappPKHblzDkd4sL59NUBQNpZa8a06m1NbWYlkW73vf+3jhhRfYuXPnRcfccccdhEKhORjeObqus2fPHp544gkeeughwLnIeuKJJ/jt3/7tOX2tKbZt8+jZv2BX+B7qAjMvlbIaFYopXLqfgplFlTRyhTE8riosq4SFiW3bWFaR8VQn/mAzo/kerHIOKZshPXIKSTOwzSJNKadu7CijfIkvcTM3VxIpF7qt5sPc1vxx3OrVJ8k3xv0XXaQvpLVVPpqjXl7rTvCr02MiOM4DTZEru1guFHBrfOyWljl5ndUUA8GJg5glJPXyicrVzrZtsEwkRZ383gKzDIoGtgW2DZKEXcyCrCDrzoSflU9jZscpJ/qR3X6sfJrSwCkA9PpNuJp2UOw7dlEiBZwSDrZtV5qKLvVEypTqgMH79zXx4yP9dI6Ikg7L0aqLgeUiKNqyvUBbCLZZBlmp/IxsswyS5HzYtvMhy1jpBLI3hCQ7E35mehQzNUqVq4zbU8U3v/lT2tvbAfjQhz6EqqqcPn36kq95fsybaRmbpcqjq2ytD3BTY4yXz6ZEQ9IlbjFi4GLFP3BioDgHvDLbLFfOAQHsUgFUHWwbSbLZWhfgptYYyfEx7KCBJEkUi0X6+vpob2/HMAxKpRLPPPMMpmliGAb/7t/9O06fPn3JRAo45QynXG4yeTmqC7l5z95GTgymeLZ9lAmx+GZJWXXngOI6+KouvA726DK76n3sbqlGwqZcLiNJEplMBtu2K78byWSSwcFBTpw4QTQaZWJigueffx5wKi08+OCDvPDCCxclUqZYloUsyyiKsmJioEtVOLA2ys7GEC+fTfBa97iYN1zCrjmZ8t/+23/jXe96F4ZhXPaYUCjEmTNnZjWwS/nsZz/Lhz/8YW644Qb27dvHF77wBTKZDB/96Efn/LUAvnv827zS+y329oY5pTxFfNs78XtWxh/qhSzLIp3rJ19IkM0OUeg7Sm05Sod7iGPeXpKlYXKlJB/LvrPymAwZTCwCOI2c/pT/SooUjTTyMT5G+hKv4wX+gt9knHHWsY4P8AEiTCVDzv0jFSECwAADAPSro7gbdxDyryHgc7Zn3jvD99YY8VSaNC8mRXYabm2IB/jZiSFODTo/Idu2KZo5TLtEppQgoC9e0ke4utUUAwFKg+0Uug6jRpsw1u5BkuauWdxSYlsmdrmIXchilfLkjv8c2R3E1bgFLebU3C8nh8m+8dPKYyTdA7aFXcqjRptwrzuAJCvk21+gNHzp//+Sy4tv94NIkkSh5yilocuUkrSdVSqyN4w5PoCraQc37drC/k1Ny36VlVtXeGhnPc+dHuWFM2MiBi4zqykG2rZN9uQzWLkURvNOtFjTvLzOUmCXi9hmGauQxswkKJx5BTXSiKt5O4rbOU/LnX6pkvAFkFw+7IJzLmO0HUCvbsG2bVLP//NlX8fVtB1Xg7NAKXv0aexygW6g+4KS55FIZFqse+c730k8HicUCi3r1dcXsm2bbClL0SzSUmOxoaaRJ08Mi/KwS9hixcDFOAe0ijkyr/0QyfDh2XALsuvSC9yWO9u2sUsFbLOIXcxSHOigPNqFVtOKsXZv5Zwk+dw/OYtkwJlcVTTsgpPs8O15K7LLi5lLknn10WnP/+zkB8Bv/uZvUlNTw+DgIF//+tcvOZ58Po8sy5XG8fF4nDe/+c1Eo9HLNpFfrs6PgSPZEWp9tUiSxMZ4gHXVfl7vneDFM2OkC6L2zVKwms4BAcqj3eQ6XkCL1GO07keSV+p1sIVdLmAXs1ilArkTzyBpLlwNW9BrWgEwM+NkDv2o8hjnOtjGLuVQgjVEt9/BvtYqRk++zI/+4Vl+dInX0TSN3//930eWZZ5//nmeffbZSxzl7IACJ3k3ODjIwYMH2bhxI+Fw+Iq/e8vRpWKgoSnc3BZjT3OYV7vGOdQzTq5oLvZQhQtcczLlgx/84HyMY0be8573MDw8zB/90R8xMDDAzp07+fGPf3xRI6q5sr/hRu5yP4SRMzBMA/u1pzgWg/Vt70aRl+eFXKGU4Wz3TyhlxsiVEvw0/y+k5QKfsj6ODx8G4IQnp5RWa66ab+b+ChPnjzfPAxiTR1y4Y6Seeo5znDCXXyGY5dxK5LRcIGFPUJJMNFsho5vOCh4brFic3636PzR4tyJJEtd72hjz6Ty4oxZlAUt7XY3PpfLA9jpe7enhP//8r/hp19cZLXQD8IuBbxB1NXFr7a+xt/rtM9p1Iyys1RQDbdumOHQau1ykNNiOmRrBaN2L6o/Ny+vNN9uywCpTHDqDXcqBJOOq34yVS5I5/NhFx1u5CYoD7ZVkyoWrkuziuXhWHu2C1r3OLrzi5XdcKP4YYAMSijeE6Q0DNpKkIHtDzip420av2wCAd/MdANywJsyt65bH7pOZkCSJzfUqPzn7L/yPQ19iJC9i4HKxmmJgIZPETA6DZZI7+QzlRB+uNbuQteWZ0LQtEyuXojzWg22ZyIYPvaaVQs9RCl2HLjq+PNaNFm2oJFMuioGFc8tmCl2HnWRK8cpJAElzziFlSaKmrh7FLJDP54lEIvh8PgqFAn6/n0AggCRJfO5zn5vt216SxvPjfP21r/PFF77I6YSz++Yvn/9LWsOt/Pa+32Z/69s53F0WF89L0GLFwIWOfwDl0R4n0ZoeI/3y93Gt2YVeu37ZLqyxLZNyog8zMw5WGS3ahOyLkHnth1i55EXHlwY7MJp3geqUVZV0TyXu2eUilM+Vby5PDKJXr8XKX2o5oUNRlMqOkkgkQiQSwePxkM1mqa+vd56nXGbr1q0ANDY2rsoY+Ol9n+bDOz9MyAixszHE1roAb/QlealzjFReJFUW02o6BwQoDnWAWaI03Ek5OYK7bR9qcP5ebz7ZtgWWSWmoE6uYASRc9ZuwSgUyr/7g4uMLZYp9x9Gq1yJJEtIF577nXwebE4O8e1cNQb+Pf33l8jFwzZo1lR0l1dXV1NTUoCgKtm1TW1tLPp/HsizuuMO5/n3b297G2972trn5ASwxM42BN7ZGuWFNmKN9SV7tSpDIit16S4Vk2/aq2UueTCYJBoNMTEzMaFVHrmjy5ac7ONH+z9QOnfuHe0waJxcJsH7du1Eu0eR8qbBtm5H8WYYGXqKx/+I/uh56eJZnOcpRtrKVd+LsOsmSw8O5eq7P1A7gNaIUymki4xbevA3BKnzBNVhmEcOIkMx0k3JbxIwmXJaLcn6cUjlLJLQRVTUol/Nk88N4fHXoihsJCV25/uagM6GrMu/f10T4EmWfFttj7Y/x8LceJlty/hGyOf/P0En86LKbj2z4IhvDty7CCFeWay3zda2xYjm4nvf0Ru8EP3mjj3zHi9N2WiihWlwNm1ED1fM13DlV6DlCoevwRber0SZcjdtQPAGyx35OOdELsgLWuckrNVyPZ9NBwLkIz7c/j42NXtUyWfpHBlkG20LxOUloM5fELhWQZAXZ8DklvsolsC3n+2u0Ie7nvq2Lv7tuLokYuLB2NYW4fcPM/15FDHT8y2u9tHcPkD36NFY+5dyoqGixNbgat1RK9i1lzgrDX2Imh6bfISvoNW24mneCVSb1wrcBJ2FinzdBaLTuO29VYoJC12Fkw48abQTbQlJd2GYR2fAj625s28LKTmCXi8i6p5I8sYpZ53tVQ1dl7tsaZ23VtcfDleBK8U+ajH8ezcP/fvhb+LmBQ90TWKvncm1evHNPA42Rmf29rsT4B9f+vmzb5gs/PUVppIvcyWcqt0uGD72mzUmqLIPFhRfuKp4ie0LodRvRq1soJ/rIHnsawIlp5ULlON8Nb6vE+uJgB+WxHtRQLYovim2VkTQDq5BBC9cB0OBX2FqlYsgWVVVVaJqGZVmkUilisdi08lyr1Uxj4Lff/W3uabuncp9p2RwfSPLK2QQjog/pjOmqzG/d0Tbj40UMdLQPpfj+q70Uzr5Ksf9k5XYlUI2rfjNquHY+hztnCv0nKJx55aLb1Ug9roatKL4IufYXnGoJU4nyyR14ij+Gd9vdzk22Tb7jeWzTRKtqJuDzsrE2yPraIJZp0tDQADi9nlKpFIqiEI1GK2UNS6USkUhkYd70Ene9MdC2bc6OZjnUM07nSFacG87QptoA926deYWpmcaKa96ZshptaHsXpTU5Tp36Z2IJi4gdglF4dfw/01/j4s76f4NXW9x6zaVyjt6B58gku7HLRY7Jx3ku6WSYb+d2Grn9osdkyKCG6nggeD9+NUrJs52Qt4nABQmi+87/pvHSr18d2Tr9hgt/53QWvETamzfXLNlEyv3fvN/ZUs6lAqBzW8nK8ZVjv8HHN/21mEwUFo0kK7jXHcDVvIPC2dcoDXdijveTHe/Hvf7mJVP2xi4VKE8MYuUmsE0TV/OOSvKhNHz24gfICooniOJxgpV7w81OzWtVv2zSQpIV3OtvuupYFHcALsgVS8r1Jd4bwm7uWQJlCueSiIHCciIbfny7H6CcHCZ/5mWsTILSYDulkU58u9+6ZHapWPk05UQfVimP7PKh16x17jDLFydSAFl3o0YbnZIVso5/38MgSVeMVYo3jGfTbVcchyTJKN6Lz4kVNQg4u3PfurOOmsDKKtMwU1eLf1O35Uo53v5Pb+XR9z/KB2+8g1+2j9AxdPnVnoIwX7RYE2qkgdJQB4Wuw9j5NIWzr2FODOHZfOV4sFBs28JMjmCmR7HLRbRIA4p/coFLeuySj5E0o5IAUcN1+G54CElzXXHXjV7TWkkun0/xBAl5NG5dV0Vb9aWTxG73/C4iXC6uJQbe/837efT9j1YmExVZYktdkC11Qc6OZni1a5zO0QxiPlGYL5IsY7TsQW/YSqHrMKXBDszkENnkEMbavejxmSep5pNdLjrXwZOLWVzNOyrJ7vJo9yUeISG7fMiT52vG2j24mrcjqa7LXwdLEu62A9SFDHY3hWmr9l3y2HA4fFEvu+VeonouzSYGSpLEmpiXNTEvqXyJI31JjvYlRW+pRSKSKTOkqW42b/oQ2fwonZ0/JDZm8rz5LB19Hfxi4O94oPqTbIneRTS4bkHGY9s2A2OHGR89ipUdJ5hViOAngg7oHGO0cmxSL0ERugNZXL44zQ13oqlu9gJ7F2S0C29XU4h1Nf7FHsZFxvPjPPyth51SQFy5mZSNjQR87cQjfO6Gp0W5G2FRybob97ob0Ru2Uuw75qzMO29FTjk5hOINX3fS4FrZto2ZHMJMjWAVMpQGp/ce0WtakdxODFBDcYqFNEbLHhRPCNkXvuhiWZKVJbfC0ilTWLekyhTOloiBwnKlBqrwbr8Hc2KQfNchZMNXSaTYlomZGkEJVC1Y+ZupC2czPYqVGac83l+5T3YHKskUyeVB0tzIniBarBE1XIekuS+6AF6I5qrxoMGDO+rwuVbn5ce1xD8LC9mWefhbD9Pz2R7euqOOnkSWZ9pH6Bu/dDNWQZgvkiyjx9ehVa2hNHyWQtdhtOo1lfutfBrbsioLVBaClU9RnhjCyo5TTvSf2z04aSqZovpjFAC9fhOKN+LEQOXiGCTr15fs8OgK+1oibG8IrajztfkwmxgYMkLT7m+OemmOeklkihzuneBoX5J8SZRFFOaHrLlwt+7FVb+JYv8JikNnUKMNlfvLqREUd2BBG9WXk8POdXA+TWnodGU3CYAWaz4XA4NxzOQIxtobkD0BFH/smq+DFVlifY2PnY1h4sHVuRhmLsxlDPQbGgfWRtnfEqF3PMex/hTtQ2kRBxfQ6ryamQWPEWXzxg+Szg1T03uGjqEOLKtE7UABbeAlTsuPkzUkfLENNNTdgirPPqAWy1nGk6cZPPUYA2Y37f4E3dk3eJN5G3vZC4SmHd+njdIWvpOdVb9Jk28bLsXpbbJl1iNZHmoCxpLtLfD1175OtpS9zGrsi9nYFK0cLw5/j4O1H5rn0QnC1SluP+7WfdjNOyuJEzM7QfaNJ5z7Q3FUfxVqtBHFE5yT17TLRczMOPkzL1dKMgDkTj7n9D65gFbTOlUpCgCjZTdGy+45GctC8RsqD+2qx9CWVoJntkQMFJYzSZJQQ3G8wRowz5V/LfafpHD2NSTNQAlWowbjqJGGOdu1YhWzlEa6KI92Y7TuQ/EEsXIpcid+efEYdTdqtAnbtp0a15KEf+9DczKO2dhSF+DOjdWoyuotcXOt8c/CIlvK8o1D3+CR/Y/QEPbwnr1NnB5O82zHKMOpwtWfRBDmkKRo6PE2p5ec4pyf2LZN7tRzmKkRZE8QxV+FGqlHDdbMySIV2zKxChkK3a8jySpGy24kRaM4eJpi79GLjleCNdN2xyn+KIGb3jfrcVzI0BT2NIfZ2RhCV1dvXLsWs42BlxL26ty2voqbWqOcGkzzRu8EveNX7t0lCNdLNnwYLXtwNW6rJE6sfIbskSfBMp3444+hRRuRPaE5qSxgl0uYuQnyp19Gr16LFm9DkiTyHS9csteTVtUC5yWMXY1bcTVuvei4mQi6NbbWB9laH8Cji6nj2ZqPGChJEg1hDw1hD3durObMSIaTgynOjGQolq+csBFmR/xFXCefu4p3tv0x9zZ/hhP9P0HtHQMbYlYYskDXINmub9OrjTAe82K5DcbyvUTlKrZG3wQoqIqLdLYfRXGRKo8wZPZj2SZGuoBeKEM2jbukELVDGEAzdTRTxxupvyFPmjPSWdZIreQNGcnw0dh0Nx6jio1LbHX1QtJVmbdsiy/JlUG2bfOlF750XY/9Rd/fcWv8gyuq1I+wvJ2/8sZMj1bq7JvjA5jjAxS6X3d6C1S1YDTvRFJUZxVGPoWsurBt20l4lEvY2Mia4TyHZVIaPoNVyDorrrPJaQ3uCt2vo0UbkBQNLdaEmR1H8YRQAlXOasMVEP/cusI7djfgN5ZuT67rIWKgsFJIklRpSAxULmbtUp7ySBflkS7oeAHZHUSvXV8pA2GbJexSYbLfkjRZl1/Gtk1klw9Jlp0G8eP9mLkkViaBmR2flrgpDXagtOxG9oWRvWGnV4nbjxZpQPaGllxjaE2RuGNjNVvq5ia5vlzNJv598fkv8ul9n67Ev7VVPlpiXjqG0zx3eowRkVQRFph0Xvyzizlsy5mwsbITWNkJSoPtAKihWvTGraj+mHN/MQtITklBScYq5pBkZzpCNpzFf+XxAcxMAjOTmHy+8WmvrcWaUEO1qOFayuN9KL4YsuFDq1pz1TJdc8HrUtjVFGZ7QxCXuvzPORfKXMbAS9EUmc11ATbXBRhNFzjSl+RYf5JsUazSFubetOvgzBiSojm7lCcGMScGKfYcAUlGq27BaNqBpDnXvnY+Daru7CKR5Ep/Jll1VY4pDZ3GKmSxMmOY2SR24VyJz0J3xinRqhuo0SbMycoQii+CGmm45K67a6XKEmurfGytD9AU8Yhrrzky3zEQnB1EbdU+2qp9lEyLzpEMJwfTdI6KxMp8EMmUWfJpEfY0vReaYGyig5HRw5TG+4nmDTy4qS/FeLn/+7zCK6io3Mqt0OOsVCwDU5vkIsAX+Q8A3MVd3MItwKVXNG6svof7YzfT7N9R2XUiON60qZqQZ+n1SQEYzY3Skei4+oEXsRktdJEtjy96bx5BuBS9ei1aVYtz0pcao5zoozzRD2aZ0sApXI1bnWRKIUP+zMuY4wMXPYeku/Hf8BBIMsW+41i51MUvxGQZhskL7+W222QmdFXmbTvriCzBfk+zJWKgsFK52/ZjtOzBTA1jpkYpjXZjZcexchOUhs9UkilmapTcqWedhMqFz7HhVrRog9Pg88zLl30t2ef8DUiSjG/HvfPzhuZIdcDFvVviRH2iVvb1xj8bm45EB2O5MaKeaOV2SZJoq/bTWuWjYzjN82fGGEqKpIqw8GSXB9+Oe5xFMKkRyskhyqPdTnJ5vN/ZreePYduWUyLs7GuXfJ6p3SNmeoxC16HLv57h9CRRA9X4dtx32ePmWtSns6sxzKZa/6reYXe95joGXknU5+Lg+ipubotxejjNkb6k6K0izBst2ogaacDKjmOmRimP91NO9IFtURrsQK/biKK5sEs58l2HLtPDxImBkiRRGjqNmRq59IvJCpLmXCMaTdvm9H3UBg021QbYEPevuMoIS8FCxkBwEszravysq/FXEiunhtJix8ocEsmUORQJthIJOk3pylaRnoFnSI+2E5M3sh6DTH6Y2wpXb9Q35M7RWx7FcrtxBxvwemrxeuL43NXABQ3hhYotdQE2xpduTf10cXaNQwtmRkwkCkuWJEkoviiKL4peu86p5z8+QDk5VFklaBUyl0ykACi+aOV59LqNmMlhFF/UWWmtuZF195ystlnKVFniwe111AZXZpPS2cbAkNekVJyjwQjCHJMU1VktHarF1bgVM5fEnBjELp/7pS2NdF0ykeJwZnlktx+tugVkFcUfRXEHkVQdyeVdNqsDZUli75ow+9dGl+RO4cUw2/iXKqYueRE9lVRpq/bTOZLhhc4xehOixI2w8GSXB9nVhBZrwm7ZjZkaxZwYQHZP7kqzLApnL58kmSpNqMWanZJh3pBzHujyIukGsrbwdfplSWJtlZcdDSGaop4Ff/2VZL5i4JUoslSZTEzlSxztS3JENGsW5oEkSc4OEW8YPd6GbZacXXbJocruO7uYv2wiRZncvQeg166n5PKi+CJOP1Ldjax75uU6OOrTWV/jZ2Pcv2QXJK8UixEDp5yfWCmbFmfHspwaTHN6JE2hJBIr12tlz0wtIlXWWVN3B9TdwfkVCi3LwrSKyLJGJttHoZxBkVQ87mr+i/rwnPRYWY1iPp07NlYv9jCuyKf7ZvV4v+Gfo5EIwvyTVB0t5lxUT1GDNfhvfM/knKGNXSpgmyUkRUM6r/GnXtMGNW0LP+hFpMgS92+vXdEX67ONge+9YT29Yyq/bB8RK2qEJU9xB1Dc0xd4GK17Mdbe4JQ4NMvYZtmJgZqrMlEoSRLutgOLMOK5URMwuGtTNdUB0aD0fLM+B9Svfg64JuZlTcxL33iOl84mOD2cFiuxhUUhSTJqoAo1cK6HpaSok+eAk+VtSnmwnBJMTnkuJ/EqG148mw4uyrinRH06m2oDbK4N4HWJ6ZK5sBAx8IqPNzT2r42yryVCTyLHkb4J2ofSlEwRJIW5JykaWrQRLdpYuU3xRc67DnZKwzrXwSqSfu76T4s1O32p5knM72JdtY911T6xc3gBLXYMnKIqMq1VPlqrfJiWTfdYllNDaTqG0+REWcRrIs4OFpgsy8iyc4EZ8DVe5WhhJpw+KbVoS3zLddQdpTXcyunE6Rk3nQKQkFgbXsuvH9jG9w/1i9U0wrImSXKlObzkWrmJg2shSxL3bo2ztmp2J1lL3WxjYNQTJeZ1Vok+eXyI08OZeRytIMw9SXJ6BQCg6tNqbi93hqZwYG2EnY1z03B1pZlt/Iu4IzN+TF3IzVtDbsYyRV4+m+B4f5KyJSYMhcXnxECnfMz5i2iWAr+hsm5yhXaNSAbPuYWMgVd8PkmiMeKhMeIhXzI5MZDiSF+SwWR+Tp5fEK5kMa6DFVmiLuSmJealrcpH0LOyenIuF0slBp5PkaXKQpw3WdX0judon0yspPLlqz/BKre0Z58FYQbu3Fi9LLLqkiTx6X2fvq7HPrL/EWJ+g/fua6QuJE7wBWGlcHakxFlfs/J3ns02Bk5N0PoNjbftrOeB7bX4xIpRQVhUiiyxqynER29ew66msEikXMZcxb9rEfHq3L25hl+/pYV9LRFRA10QLhDyaOxpDvOevY187JYWbltfJRIp82QxYuDVGJrCjsYQ79/fxAcONLGzKYRbF3FSWP78hsrW+iAPbK/lE7et5Z17GtjTHBaJlEW0FGPg+WTZSTTfsbGaj93Swvv2NbGvJbIi+7jOFTELISxru5vDbKpdun1SLvThnR/mD5/8Q3KlHBZXL1MjSzJu1c2HdnwIAI+u8vDuBh47MsjJwUs36BYEYXlQZYm3bK+ldYXvSDnfbGPg+dbV+GmMeHju9CiHuyewRD0bQVgwiiyxuTbA3pYIQbe4OJ+JuYx/18LrUrm5LcbeNRGO9E3wStc4SbHLWViFzq3Q9tAS84lJogW2WDFwJqr9BtUbDA6uq6o0rT87mhXnlsKyoKsy9SE3TVEPTREPsWWw0Hg1Wsox8HySJBEPGsSDBje3xUhkinQMpzk9nKF/Ii/i4iSRTBGWraaIh1vbYlc/cAkJGSG+/e5vc/8370e25SsGURkZCYnvvOc7hIxQ5XZVkXnLtjghj8YLZ8YWYNSCIMw1lybz4PY6GiOrq9TZXMTA8xmawh0bqtlSG+CpE0P0jYsyDYIwn3RVZktdgF1NYZFEuUZzHf+ula7K7GoKs6MhxKmhNC+fTYjSNsKKF/PpNEacCcaGsAddFYU5Fstix8CZOL9pfaZQ5vhAkmP9KYZThQUbgyBcjSJLxAPGZMk6N7VBN4osdgYvdcshBl5K2KtzgzfCDWsi5IomnaMZzoxkODuaJV9avX1WRDJFWJZCHo23bKtFXob/aNzTdg+Pvv9RHv7Ww2RLWYBpdROlyUKabs3Nd97zHd7c+uaLnkOSJG5uixHyaDxxbAhT1MIWhGXDb6g8tKt+1a4amosYeKHqgMG7b2jk+ECKZ9pHRJ1XQZhjUZ/O1vogm2sDolzULMxH/LtWsiyxIe5nQ9xP91iWl88m6BzNiGb1wooQ8eo0hN00hD00hN2igfwSsxRi4Ex5XSp7miPsaY4wki5wvD/FicGU2NknLDhJgqjPRdNkYrg+5BaJ4WVqOcXAS3HrCptqA2yqDWBZNgPJPJ2jTmJlMJlfVeeS4uxCWHbcusJDO+uXdU3Te9ruoeezPXzj0Df44vNfpCPRUblvbXgtj+x/hA/v+DBBI3jF59lSFyTs0fnB4T4yhdWbFRaE5aI2aPDAjrpV3+tjrmLg+SRJYlNtgNYqHy+fTfBKV4Ji+epbqAVBuDRDU1hf42NjbYD60NJqFr2czUf8u15TjZhH0gVeOZvg+EBKLNARlg1JgqhXpyHsoT7spj4kkifLwVKKgTMV87m4ZZ2LW9bF6J/IcWowzamhtEisCPPG61JoinhpnizdJWLbyrEcY+ClyJOlM+tCbm5qhXzJpGssy9nRLF1j2RUfH8VfpLCsqLLEW3fUEV4BNW5DRohH9j/Cp/d9mrHcGKliCr/uJ+KOXFODqbqQm/fta+JfD/WLcg2CsIRtqQtw58ZqVEWsJIK5i4EX0lWZG1ujbGsI8lzHKEf7kqK2qyDMkEdXaK3y0VrtoyniEWUj5sl8xb/rFfO5ePOWODe3xTjUPc7h3glyRbFIR1haplZnN4TdNIbd1Ic8y3px3Wq21GLgtagNOmWVDq6vYiiZp32yl4AoBSbMhiRBPGDQEvPSEvNS5Xct+b8F4fot5xh4Oc4iLD/ra/wATGRLdI1l6U5k6R7Lkl1h55UimSIsG7Ikcd+2WupW2OpISZKIeqJEPdHrfg6/ofHuGxr42YlhXu+dmMPRCYIwW7oqc8eGajbXBRZ7KEvSXMTAS/G5VO7eXMPuphC/bB/h9HBmTp9fEFaCqbrbTVEPLTEv1eLifUHNV/y7Xl6Xyk1tMfa2RDgxkOLV7nFGxAShsIjCHo2mqIfGsNPzRCRPVpalFgOvVXXAoDpgcFNrjGS+ROeI00ugJ5ETu6OFq1Jliaaoh9YqH2urvHh0MT272iz3GHglQY/GNk+QbQ3ODpuRdIHusSw9iRw9idyy77ci/lqFZUGS4M1bamir9i32UJYsVZG5a3MNdSE3Tx4fpGSKldiCsNhqgwb3bo0T8iz/3XTLVdTn4m076+kdz/HMqRF6x3OLPSRBWDSaIlETMKgPuakPO6trRd1t4UKaIrO1PsjW+iA9iSyHeyZoH0qLEmDCvHNpMk0RD2uiXpqiHgKGtthDEoQZCRga2xtCbG8IYVo2feM5useynB1bfb0EhMvTFIk1MS/rqv20xLziHExYNWI+FzGfi11NYWzbZjhVmNy1kqN3fPkloEUyRVjyJAnetLGGTbViVfdMbK4LUBNw8aM3BsR2Y0FYJLoqc1NrlJ2NIbHKe4moD7l5995GOobTPNs+wki6uNhDEoR5JUkQ9ujUBAziQYPaoEGVz4UsSncJ16BhckdAtljmWH+SI31JRkX8FOZQxKtXStvUh9wiRgnLniJLlZ5UN+H0EuhJOMmV7kRWxNBVRpElmqMe1tf4aa3yiQSKsOpJklTZ2benmUoz++4xp9/KwESe8hJfwCOSKcKSNpVImdoaJsxM1OfivXsb+WX7CK91j4uVMIKwQCQJNtT4uXldTKymXKJaq3ysjXk51p/iV6dHmVjhzfGE1UGSIOTWqA4Y1ARcVPsNqgMuXKooiSPMDY+usqc5wp7mCIPJPMcHUpwcSJEulBd7aMIyFA8atFX7aK3yEVkBvTAF4UoMTaGt2lepspEplCsrsrvHsuJcdIWqCxlsjAdYX+MXJQoF4QrOb2a/f22UkmnRm8g5De3HsoymC0tuTlMkU4QlS5Yk7t5cI/oMXCdVkbl9QzVrYz5+cnSAVF5c7ArCfKoPu7l1XYza4Mrq67QSSZLE5roAG+J+3uid4IUzY2JCUFhWQh6NmvMSJ1V+F4YmLtSFheH87hkcXBejbyLPqcEUp4czYkJQuKJ40GB9jY+2aj9Bt1hwIqxeXpfKxniAjXFnnmMiW6I74azIXomNmleToFtjY62fzbUBUeZZEK6TpsisiXlZE/MCTgK6ayzL2dEMZ0eXRowUyRRhSVJliXu2xllf41/soSx7TVEPv3agmadPDnO0L7nYwxGEFacx4mF/S4TGiGexhyJcI0WW2NEYYktdgEM9E7zUObYkTs4E4XweXSEeNIhPluuqCRgicSIsCZIkOf13Qm5u3+A0Fz0z4lzo9o3nRI8VgZjfxca4n/U1IoEiCJcT9GgEPU6fKtu2GU4X6BrNVmLpUi93s9q5NJl11X421fqpD7lFiWdBmGNel8qm2gCbagPYts1QqkDn5Plm/0QeaxG2rYhkirDkuDSZB7fXiYnJOWRoCvdsibOu2seTx4fELhVBmCVVltgQ97OzKUS131js4QizpCoye5rDbKsPcrhnnJfPJkRSRVgUkuT0D6gLuie3uxtiZaOwbEw1F927JkKxbDkNmCdL2QynCotysSssvKBbY0Pcz4a4n5jPtdjDEYRlRZIkp1Sn3+CGNRFKpkVPIkfnaIazIxkSWbEDcCmY6oOyqTbA2pgXVRF9UARhIUiSVNkhvX9tlHzJpGssO7mYJ0OmsDDX8CKZIiwpfkPlbTvrqfKLE+/5sLbKR13IzS9PjfBG38SSqzsoCEtdbdBgc51T+1asDF95dFXmhjURdjSGeL13glfOJkTyWZh3Ea9OU8RDQ9hNfdiNRxen58Lyp6vTSzQUyiZ943l6Ezl6x7MMJgti58oK4nUprKt2Eih1IVHuVBDmiqbItMS8tMS8sMEpCXZm1Jk07B7LUjJFHF0okgT1ITcb4n7WVYs+KIKwFBiawvoaZwfs1K6VqcRK/0R+3l5XXK0JS0Y8aPDWHXV4XeLXcj4ZmsJdm2vYEPfzxLFBsbpFEK5AkSVqgwatk00jRVP51UFTZHY3hdnREOJYf5JXu8cZSRUWe1jCCuHWFZoiHpqjHpoiHvwirgirgEtVzk0IAiXTYmAiT994jp5EjoFknmLZWuRRCtfCrSu0VvnYUOOnIexGlkVpG0GYb0GPxk5PiJ2NIcqTu1bOjGboHMkwLq7r55wkQV3IzbpqH+tq/PjEXJUgLFnn71o5sDZKrmiSKsxPXBSRQFgSNsb93L25RmyPXECNEaeXygtnxnjpbEKsDhQEnBPmKr+L+pCbxsmV4i5VrDparRRZYmu9U8O6eyzLa93jnB7OiFI1wjWr8rtYO7lKvzZoiHrawqqnKTKNEY/TdwywLJvBVJ6eRI7uMadPgFhxvfR4XU4Cpa3aR2PYIxIogrCI1PObNG+ARKZY2bXSMyZ6rcxGwK1x9+Ya1lZ5xY5hQVim3LoybzvIRFQQFpUiSxxcX8XOxtBiD2VVUhWZm9pibIj7efL4ED2J3GIPSRAWjCxJhL0aVT4X1QEX1X5nFYOuiqSucLGpSb90oczRviTH+pOMZYqLPSxhiVJliaaop7ISX+w+EYQrk2WJ2qCb2qCbvWsimJZN/0SO7jEnubJYDUYFp4n82slYJpLBgrB0hb06Ya/O7qZwpddK37i4vr8eU31rBEEQLkUkU4RFE3Rr3LctTm1Q1NVdbFGfi3fd0MjRviS/bB9esKZNgrAQXJpMyK0T9miEPDoR77kPRayoFK6Rz6WyryXCvpYIAxN5TgymODWYEr1VBLwuhZaYj5aYl+aoB03sthWE66bIEg1hDw1hDze2RimUTXoSObrGsnSNZkUyewHcvbmG5qgoRSgIy9G0XiuCIAjCnBLJFGFRbKoNcMfGKlE+Z4nZXBegtdrL86fHeK17XJT+EpYFSXImuINu7dyHx/kccuuiOaAwb+JBg3jQ4OC6GEOpAh3DaTpHsgyl8ogF1CufJEE8YNAc9bK2yku13yVWbAvCPHGpTnmp1iofAMl8ia7RLGdHs3SNZcmXxEKguSRJTplLQRAEQRAEYTqRTBEWlNelcMeGatbV+Bd7KMJluFSFg+ur2FYf5JftI7QPpRd7SIKALEkE3Sphr+4kSTw6ocnEScCtiR0mwqI6v9ndTa2QK5rO6umxLD2JrGgIuoL4DZXmqJemiNM8XiRrBWFxBAyt0tPKtm0GkwXOjmbomiwJJhYECYIgCIIgCPNBJFOEBSFJsKUuyK3rYhiamHhYDsJenQd31NE7nuOZUyP0inqrwgJQZYmITyfqdRH16YQ9OtHJBIpociosF25dYUPcz4a4s3AglS/RN56nbzxH73iOkXRB7FxZJkIejbqQm/qQm8awh6BHlLsRhKVGkqTKTsH9a6OUTIu+caffSk8iK84fBEEQBEEQhDkjkinCvKsNGty+oZp4UDTwWo7qQ27evbeRzpEMz50eZWAiv9hDElYIlyZT4zcqzd9jk8kTMekhrDR+Q2NDXKskVwplk4GJPL3jOfrH8wwk8xTL1iKPUrjQW7bVir4ngrAMaYpMc9RLc1T0ChAEQRAEQRDmlkimCPMm4tU5sDbK+hqfqCG+AqyJeVkT83J2NMPzZ8boTYidKsLMyZJEld9F7eTK0XjAIOTRRGwQViWXqkyb6LMsm6FUgd7JnSu9iZyo/78EiESKIAiCIAiCIAiCcD6RTBHmXNSns6c5zKZ4QKwwX4GmJgAHk3le7UpwcjAt6lILF9FVmbqQQV3QTV3ITU3AQFfFxKQgXIosnytRs6c5jG3bDKcL9CRydI9l6R3PUSiJnSuCIAiCIAiCIAiCsJhEMkWYE5IEjWEPO5tCrI15xWrzVaAmYHDv1lpuXVfmSF+S13snSOZEk+XVym+o1IXckx8GVT6XiAOCcJ0kSaLab1DtN9jdFMaybAZTebrHck5z5fEcZZHEFgRBEARBEARBEIQFJZIpwqwE3Bob43621AUIefTFHo6wCLwulX0tEfauCdM9luNo/wTtQ2lKppjoW6k0xSnZFQ+6qQ0a1AYN/IZoyiwI80WWJWqDbmqDbva1RCibFn3jeboTWbrHsgwmC1iio70gCIIgCIIgCIIgzCuRTBGuWcSr0xLz0lbtoy7kXuzhCEuEJEk0RT00RT0UyxanR9KcGEhxdjQryoAtY7oqE/PpVPmdJvHVARcxr0uU8BOERaQqciXeAhTLFv0TTq+V3vEcg8m8SGgLgiAIgiAIgiAIwhwTyRThqgJujfqQQUPYQ2PYQ9AjVqALV6arMhvjATbGA+RLJh3DadqH0nSNZkVpmmVkateZKNclCEubrsoXNbQfyRQYShYYSuUJi52jgiAIgiAIgiAIgjBrIpkiVGiKRNCjE/XqxHwuYj6deNDAo4tfE+H6GZrClrogW+qCFMomZ0YytA+l6RzJiJXTS5yqiIbxgrAcyfK5nisQXOzhCIIgCIIgCIIgCMKKIGbJVwlZknDrMh5dxedS8bpU/IbzETA0gh4Nv0sVK9CFeeVSlcqOlZJp0TmS4dR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+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[10:12], train_y[10:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    # ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    # ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10, color=\"green\")\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-6, 6)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning, C=%.2f\" % c)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The transfer learning really helps the model learn the shape ($f$) as well as the amount of shift ($c$)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uvPFGPv/8c371q1+xfv16pk+fjtPpJDs7m88//5xVq1YxefJk5syZg7e3N/Pnz+fuu+/GZDLxzjvvEB0d3WYRyJOef/55LrvsMqZPn86tt95KXV0db7zxBmPGjGlRYAG45ZZb+OCDDygoKGDgwIHtHnPw4MF88sknXH311YwcOZKbbrqJMWPGYLPZ2LJlC1988QW33HKLe/9TWTMF4KWXXuLSSy9lzpw5XHPNNWRmZvLGG29wxx13MHLkSPd+X3/9Nbfeeivvv/+++3t//etf8/777zN//nzuv/9+kpOT2bhxI59++ikXXHABZ5xxhnv8k08+yRdffMG5557Lgw8+iMlk4qWXXmLs2LHceuutpxS7EEIIIYQQvY0UU4QQQgghhDiOiIgIli1bxu9+9zueeuopwsLCuOGGG5g9e3a765acyN13383HH3/Mq6++islkIjExkQceeICnnnrqhGNHjRrFrl27ePbZZ1m8eDE1NTVER0eTmprK008/fUrxHGvlypWsXLmy1faBAwcyZswYvvnmG1577TU+/PBDvv76a/z9/UlJSeHBBx9k2LBhAAwfPpwlS5bw1FNP8fDDDxMbG8s999xDVFQUt912W6dj7Erz58/n008/5Y9//COPP/44Q4cOZfHixXzwwQdkZWW12NdkMuHn50doaOgJj3vppZeyd+9eXnrpJb799lvefPNNfHx8GDduHK+88gp33nlnp2OfN28eX331Fc8++yz3338/UVFRPPnkkx3652D48OHs3r2bp556iv/+97+Ul5cTHx/Pww8/3GrdnqSkJDZu3MhDDz3E448/jre3N3PnzuWVV16R9VKEEEIIIUS/oajdufqiEEIIIYQQQvRCEyZMICoqijVr1ri3xcTEcNNNN/HSSy95MDIhhBBCCCGEJ8iaKUIIIYQQQoh+y263uxdbP2rDhg1kZGQwa9Ys97asrCyampp47LHHTnOEQgghhBBCiJ5AZqYIIYQQQggh+q3Dhw9z/vnnc8MNNxAfH092djZvvfUWISEhZGZmEhER4ekQhRBCCCGEED2ArJkihBBCCCGE6LfCwsKYNGkS7777LlVVVQQEBDB37lxefPFFKaQIIYQQQggh3GRmihBCCCGEEEIIIYQQQgghxHHImimix1u7di233XYbw4YNw9/fn5SUFO644w70er1H4qmvr+euu+4iKiqKgIAAzj33XPbs2dPh8QcOHOCiiy4iMDCQ8PBwbrzxRqqqqroxYiFEb9BXcp3L5WLx4sVceumlJCUlERAQwJgxY/jzn/+MxWJptb+iKG2+Xnzxxe74WUKIXqSv5EWAW265pc1cN2LEiG6OWgjRG/SlfNfeuZ2iKFxwwQXu/Q4fPtzufp999ll3/TQhRC/Rk/KiXq/n8ccf59xzzyUoKAhFUdiwYcNJHaO0tJSrrrqK0NBQgoODueyyy8jPz++egEW3kpkposebPHkytbW1XHnllQwdOpT8/HzeeOMN/P39SU9PJzY29rTF4nK5OOecc8jIyOCRRx4hMjKSf/3rXxQXF7N7926GDh163PElJSWkpqYSEhLCAw88gMlk4uWXX2bAgAHs2LEDb2/v0/RLhBA9TV/JdSaTiaCgIM4880zmzZtHdHQ0W7du5YMPPmDGjBmsW7cORVHc+x+9sL7ppptaHCc1NZXRo0d3228UQvR8fSUvQnMx5bPPPuPdd99tsT0kJIT58+d3Z+hCiF6gL+W7//73v6227dq1i3/84x/87W9/45FHHgGaiymDBg3i2muv5ZJLLmmx/znnnENycnLX/SghRK/Tk/Lihg0bOPfccxk6dCiRkZFs3bqV9evXM2vWrA6NN5lMTJw4EYPBwO9+9zu8vLx47bXXUFWV9PR0aSvb26hC9HAbN25UnU5nq22A+vvf//60xvK///1PBdQvvvjCva2yslINDQ1Vr7322hOOv+eee1Q/Pz+1sLDQvW3NmjUqoL799tvdErMQonfoK7nOarWqmzdvbrX92WefVQF1zZo1LbYD6n333dc1gQsh+pS+khdVVVVvvvlmNSAgoDtDFEL0Yn0p37Xl9ttvVxVFUYuLi93bCgoKVEB96aWXOh2zEKLv6Ul5saGhQa2pqVFVVVW/+OILFVDXr1/f4fF//etfVUDdsWOHe9uBAwdUrVarPvHEE10druhm0uZLeERTUxMjRoxgxIgRNDU1ubfX1tYSFxfHtGnTcDqdAMyYMQONpuU/qjNmzCA8PJwDBw6c1riXLFlCTEwMixYtcm+Lioriqquu4ttvv8VqtR53/Jdffsm8efMYMGCAe9v555/PsGHD+Pzzz7stbiGEZ/THXOft7c20adNabV+4cCFAu7+lqampzTZgQoi+pT/mxWM5nU4aGhq6K0whRA/S3/PdUVarlS+//JKZM2eSmJjY5j6NjY3YbLZOxS2E6Pl6a14MCgoiPDz8lMcvWbKEKVOmMGXKFPe2ESNGMHv2bLkX2AtJMUV4hJ+fHx988AG5ubn8/ve/d2+/7777MBgMLF68GK1W2+54k8mEyWQiMjLyhN9lNpuprq4+4auuru6Ex0pLS2PixImtEvrUqVMxm80cPHiw3bGlpaVUVlYyefLkVp9NnTqVtLS0E36/EKJ36Y+5rj3l5eUAbf6WxYsXExAQgJ+fH6NGjeKTTz456eMLIXqH/pwXzWYzwcHBhISEEB4ezn333YfJZDrhOCFE79Sf892xVqxYQX19Pddff32bnz/77LMEBgbi6+vLlClTWL169UkdXwjRe/TWvNgZLpeLvXv3tnsvMC8vD6PR2K0xiK6l83QAov8644wzePTRR/nrX//KwoULqaio4LPPPuPvf/87w4YNO+7Yv//979hsNq6++uoTfs/f/vY3nn322RPul5yczOHDh4+7j16vZ8aMGa22x8XFAVBWVsbYsWPbHXvsvr8cX1tbi9VqxcfH54SxCiF6j/6W644XX3BwMBdffHGL7dOmTeOqq65i0KBBlJWV8c9//pPrr78eg8HAPffcc1LfIYToHfpjXoyLi+PRRx9l4sSJuFwuVq5cyb/+9S8yMjLYsGEDOp1clgnRF/XHfPdLH3/8MT4+PlxxxRUttms0GubMmcPChQtJSEggPz+fV199lYsvvpjvvvuOuXPndvg7hBC9R2/Mi51x9F5fe/cCoTmvDh8+vNtiEF1LztqFR/3xj39k2bJl3HzzzZhMJmbOnMkDDzxw3DE//vgjzz77LFdddRXnnXfeCb/jpptu4uyzzz7hfn5+fifcp6mpqc1ih6+vr/vz440FTjheiilC9D39Kde15fnnn+eHH37gX//6F6GhoS0+27x5c4v3t912G5MmTeLJJ5/klltu6VC8Qojep7/lxRdeeKHF+2uuuYZhw4bx+9//niVLlnDNNdecMAYhRO/U3/LdsRoaGli+fDmXXHJJq3PAAQMGsGrVqhbbbrzxRkaNGsXvfvc7KaYI0Yf1trzYGR29Fyh6DymmCI/y9vbmvffeY8qUKfj6+vL++++jKEq7+2dnZ7Nw4ULGjBnDu+++26HvSElJISUlpUvi9fPza7NH7NE+/8dLwkc/O9XxQojeqz/lul/63//+x1NPPcXtt9/eoZkm3t7e/PrXv+ZXv/oVu3fv7tAJsBCi9+nPefGo3/72t/zhD3/ghx9+kGKKEH1Yf853X375JRaLpd0WX78UHh7OrbfeyosvvkhJSUm7a6wIIXq33pYXO0PuBfY9UkwRHnf0aRSLxcKhQ4cYNGhQm/sVFxczZ84cQkJCWLFiBUFBQR06/tGeiiei1WqJioo67j5xcXHudl3HOrotPj7+uGOP3feX48PDw2VWihB9WH/Jdcdas2YNN910E3PnzuWtt97q0BiApKQkoHlKtBCi7+qPefFYfn5+RERESK4Toh/or/nu448/JiQkhHnz5nV4zLHngVJMEaLv6k15sTOO3uvr6vNI4TlSTBEetXfvXp577jluvfVW0tPTueOOO9i3bx8hISEt9qupqWHOnDlYrVbWrl3bZq/B9rz88std1idxwoQJ/PTTT7hcrhYL8m3fvh1/f//j9ndMSEggKiqKXbt2tfpsx44dTJgw4YQxCiF6p/6U647dd+HChUyePJnPP//8pNYDyM/PB+jWk1ohhGf1x7z4S0ajkerqasl1QvRx/TXf6fV61q9fzy233HJSDw3KeaAQfV9vy4udodFoGDt2bJv3Ardv305KSkqHC0SiZ5BiivAYu93OLbfcQnx8PP/4xz8oKChgypQp/Pa3v+W9995z79fY2Mgll1xCaWkp69evZ+jQoSf1PV3ZJ/GKK65gyZIlfPXVV+4F9Kqrq/niiy+YP39+i5PEvLw8AAYPHuzedvnll/PBBx9QXFzsfuJm7dq1HDx4kN/+9rcn9buEEL1Df8x1Bw4cYO7cuQwcOJBly5a1+51VVVWtLpSNRiN///vfiYyMZNKkSSeMVQjR+/S3vGixWLDb7a0ulP/0pz+hqioXXXRRh3+TEKJ36W/57lifffYZLper3RZfbZ0HlpaW8t577zFu3LiTumkqhOg9emNePBlFRUWYzWZGjBjh3nbFFVfw+OOPs2vXLiZPngxATk4O69at4+GHH+7S7xfdT1FVVfV0EKJ/euaZZ/jTn/7E2rVrOffccwH4y1/+wlNPPeVepA5gwYIFfPvtt9x2223u/Y4KDAxkwYIFpy1mp9PJ2WefTWZmJo888giRkZH861//oqioiJ07dzJ8+HD3vgMHDgRoUeEuLi4mNTWV0NBQHnzwQUwmEy+99BKJiYns3LlT2nwJ0Qf1t1xnNBoZPXo0paWlPP/88yQkJLQ49uDBgznrrLOA5oUHv/nmG+bPn8+AAQPQ6/W89957FBUV8dFHH3W4v7YQonfpb3nx8OHDpKamcu2117ovrFetWsWKFSu46KKLWL58eYunv4UQfUd/y3fHmjx5Mnq9nuLi4jZz3K233kpeXh6zZ88mPj6ew4cP8/bbb2M0Glm1ahWzZs3qpl8ohPCk3pgXAf785z8DkJWVxWeffcZtt93mbk321FNPufebNWsWGzdu5Njb7UajkdTUVIxGIw8//DBeXl68+uqrOJ1O0tPTZSZeb6MK4QG7d+9WdTqdev/997fY7nA41ClTpqjx8fFqXV2dqqqqmpycrAJtvpKTk0977LW1tertt9+uRkREqP7+/urMmTPVnTt3ttovOTm5zfgyMzPVOXPmqP7+/mpoaKh6/fXXq+Xl5achciHE6dYfc11BQUG7vwNQb775Zve+q1evVi+44AI1NjZW9fLyUkNDQ9U5c+aoa9euPQ2/UAjhCf0xL9bV1ak33HCDOmTIENXf31/18fFRR48erT7//POqzWY7jb9ACHE69cd8d1R2drYKqA899FC73/HJJ5+oM2bMUKOiolSdTqdGRkaqCxcuVHfv3t2VP0UI0YP05rx4vGvcY82cObPVNlVV1eLiYvWKK65Qg4OD1cDAQHXevHnqoUOHTlf4ogvJzBQhhBBCCCGEEEIIIYQQQojjkPnkQgghhBBCCCGEEEIIIYQQxyHFFCGEEEIIIYQQQgghhBBCiOOQYooQQgghhBBCCCGEEEIIIcRx9Jpiyptvvsm4ceMIDg4mODiYs846i++//97TYQkhxGkhOVAI0Z9JDhRC9FeS/4QQ/ZnkQCFET9NrFqBfunQpWq2WoUOHoqoqH3zwAS+99BJpaWmMHj3a0+EJIUS3khwohOjPJAcKIforyX9CiP5McqAQoqfpNcWUtoSHh/PSSy9x++23ezoUIYQ47SQHCiH6M8mBQoj+SvKfEKI/kxwohPAknacDOBVOp5MvvviCxsZGzjrrrHb3s1qtWK1W93uXy0VtbS0REREoinI6QhVC9EKqqmI0GomPj0ej6XndEDuSAyX/CSFOleRAIUR/1RfyH0gOFEKcGsmBQoj+rMM5UO1F9u7dqwYEBKharVYNCQlRly9fftz9n3nmGRWQl7zkJa9TehUXF5+m7NYxJ5MDJf/JS17y6uxLcqC85CWv/vrqzflPVSUHykte8urcS3KgvOQlr/78OlEO7FVtvmw2G0VFRRgMBpYsWcK7777Lxo0bGTVqVJv7/7IabTAYGDBgAMXFxQQHB5+usIUQvUxDQwNJSUnU19cTEhLi6XDcTiYHSv4TQpwqyYFCiP6qL+Q/kBwohDg1kgOFED3VvpJ6fjxYfVJjhsUGccGomA7v39Ec2KuKKb90/vnnM3jwYN5+++0O7d/Q0EBISAgGg0ESqBCiXb0lV5xMDuwtv0kI4Xm9JV9IDhRCdLXekivkOlgI0R16S66QHChE/1Jca+brtFKcrpMrYYyMC+aiMbEd3r+juaLnNUE8CS6Xq0W1WQgh+hPJgUKI/kxyoBCiv5L8J4TozyQHCtF/NFjsLN+nP+lCSnfqNQvQP/HEE1x88cUMGDAAo9HIJ598woYNG1i1apWnQxNCiG4nOVAI0Z9JDhRC9FeS/4QQ/ZnkQCH6L1VVWZlZTpPN6elQWug1xZTKykpuuukm9Ho9ISEhjBs3jlWrVnHBBRd4OjQhhOh2kgOFEP2Z5EAhRH8l+U8I0RGqqtJoc2K2OXC6VCIDffDS9upmNIDkQCH6s12FdZTWNXk6jFZ6TTHlP//5j6dDEEIIj5EcKITozyQHCiH6K8l/QojjMVkdZJUa2K9voN5sd2/XKApxob5cNTnJg9F1nuRAIfqnigYLW/NqPB1Gm3pNMUUIIYQQQgghhBBCiP5OVVXSi+vZkleDzeFq9blLVakyyroiQojex+lSWZ1V3qPWSTmWFFOEEEIIIYQQQgghhOgFGq0Olu/T98j2N0II0Vk7CmqpNtk8HUa7pJgihBBCCCGEEEIIIUQPV2+28XVaaYuWXkII0VfUmKzsPFzr6TCOS4opQgghhBBCCCGEEEL0YFVGK1+nldBodXo6FCGE6HKqqvLDgYoe297rKI2nAxBCCCGEEEIIIYQQQrTN0GSXQooQok/bV2qgrN7i6TBOSIopQgghhBBCCCGEEEL0QBa7k2/SSqWQIoTos8w2B5tzazwdRodIMUUIIYQQQgghhBBCiB7G6VL5Lr2M2saeuxizEEJ01o8Hq7HYe0fBWIopQgghhBBCCCGEEEL0MD8dqqK0vsnTYQghRLcpqTNzQN/g6TA6TIopQgghhBBCCCGEEEL0ILmVRtKK6j0dhhBCdBuXS2V9dqWnwzgpUkwRQgghhBBCCCGEEKKHMJjtrN5f4ekwhBCiW6UV11Ft6l1tDHWeDkAIIYQQQgghhOgMs81BcW0TTXYndqeLUD8v4kL9CPSRS14hRO+iqiqr9pdjtbs8HYoQQnQbo8XOtvxaT4dx0uTMUgghhBBCCCFEr5RXZSKtqJ7SuiZcqtrq8ysnJ5IY5u+ByIQQ4tTsOZLThBCiL/vxYDU2R+8rGksxRQghhBBCCCFEr1JjsrLxYBWFNebj7tdGfUUIIXqs2kYbW3KrPR2GEEJ0q8PVjRysMHo6jFMixRQhhBBCCCGEEL3GAX0Daw9UYHdKpUQI0XeoqsrqrHIcLsltQoi+y+50sa6XLTp/LCmmCCGEEEIIIYTo8VwulZ9yq9lTWOfpUIQQostllBjQGyyeDkMIIbrVjoJaDE12T4dxyqSYIoQQQgghhBCiR3O5VFbvL+eAvne2hBBCiOMxWuxslvZeQog+rtpkZXcvfyhG4+kAhBBCCCGEEEKI9kghRQjR163PqeqVCzELIURHuVwqP+yvwNnLWxlKMUUIIYQQQgghRI/1w4EKKaQIIfqs3EoTeZUmT4chhBDdak9RXZ9oZSjFFCGEEEIIIYQQPdL2/Bqyyho8HYYQQnQLm8PFhpzeuxCzEEJ0RG2jja15NZ4Oo0tIMUUIIYQQQgghRI9zsMLI1vy+ceEthBBt2ZZfg9Hi8HQYQgjRbVwulTX7y3H08vZeR0kxRQghhBBCCCFEj1JptLA6qxy1b1x3CyFEK1VGK2lF9Z4OQwghutW2ghrK6nt/e6+jdJ4OQAghhBBCdJ7F7qSkzkyj1Ynd6cLXS0t8qB/hAd6eDk0IIU6K1eFkxV49dqdUUoQQfZOqqqzPrsQlFWMhRB9WXGtmR0Gtp8PoUlJMEUIIIYToxQqqG9lTWEdpfRPONqZOTx4YxjlDozwQmRBCnJq1ByqpM9s9HYYQQnSbzNIGSuubPB2GEEJ0G4vdyao+OMtYiilCCCGEEL1QRYOFjQerKK07/oV4WwUWIYToqTJLDeSUGz0dhhBCdBuzzcGm3GpPhyGEEN3G5VJZsU/fJ9eEkmKKEEIIIUQvoqoquwrr2JpXI4USIUSfYjDb2XiwytNhCCFEt9qYU4XF7vR0GEII0W025VZTWGP2dBjdQoopQgghhBC9hMXuZMU+fZ89MRVC9F+qqrIqqxybw+XpUIQQotscrm4kW2bfCSH6sAP6BnYX1nk6jG4jxRQhhBBCiF7AaLHzTVop1Sabp0MRQogut/vI2k9CCNFXWR1OfjhQ4ekwhBCi2xTXmvlhf9/Oc1JMEUIIIYTo4WobbXy1p6RP9pwVQojaRhtb82o8HYYQQnSrzbnVci4nhOizKhssfJdRhqOPt6KWYooQQgghRA9mMNv5cncJJqtcfAsh+h5VVflhf0Wfv/AWQvRvJXVm9pYYPB2GEEJ0i9pGG1+nlfaLdq1STBFCCCGE6KGMFjtL9kghRQjRd6UX10t7LyFEn2Z1OFmdVYEqNWMhRB9UabTw9Z5SzDanp0M5LaSYIoQQQgjRA1nsTr5OK6Whye7pUIQQols0WOxskfZeQog+bn12FQY5nxNC9EF6QxPfpJVhsfePQgpIMUUI0YfUm23kVTVSbbJisTtRVYgI9CY+1I/BUYGeDk8IITrM5VL5PlNPjSw2L4Tow9YeqOgX7SCEEP1XTrmRA/oGT4chhBBdLru8gR/2V2B39q9pd1JMEUL0ermVRrbl11JltLb6rKC6kWA/kxRThBC9ysZDVRyuNns6DCGE6DbZ5Q2S54QQfZrBbGdtdoWnwxBCiC7lcqlsyq1md2Gdp0PxCCmmCCF6rXKDhXXZlVQ0WDwdihBCdJmsMgPpRfWeDkMIIbpNk83JxpwqT4chhBDdxu50sXRvGVa7zL4TQvQdNSYra/ZXoDf03/twUkwRQvQ6LpfKtoIadhbU4ZJV/IQQfUiV0cr67EpPhyGEEN1q48GqfrNIqRCif1p7oLLNzglCCNEbOZwu9hTVsz2/Boerf9+Hk2KKEKJXMdscLMvQU1rf5OlQhBCiS1kdTpbvLet3PWeFEP1LQXWjrB8ghOjT0orqJM8JIfoEl0vlQHkDW/NqMFocng6nR5BiihCi16g2WfkuvQxDk93ToQghRJdbn11JnVnymxCi77I6nKw9IOsHCCH6rtxKExsPShtDIUTvZrE7m9tPFxtokHtwLUgxRQjRK5TWN/Fteqn0nBVC9EnZ5Q0c0Bs9HYYQQnSrTYeq5alGIUSfVVbfxMpMPdKJWgjRG1kdTgprzGSXGymsbuz37bzaI8UUIUSPV1xr5ruMMmwOKaQIIfqeBouddbJOihCijyuuNbOv1ODpMIQQoltUGi18lyHtWoUQvYfD6aK8wUJpXROFtWbKDRacUkA5ISmmCCF6tOJaM9+ml8pJqRCiT1JVlVWZ5TLrTgjRp1nsTlZllcvT2kKIPqmywcKXe0qx2J2eDkUIIdqkqip1ZjsVDRbKGyyUGyxUGa1SPDkFGk8H0FEvvPACU6ZMISgoiOjoaBYsWEBOTo6nwxJCdKOKBnm65yjJgUL0TXuK6impa/J0GD2e5EAhercNOZXS3usUSf4TomfTG5pYsqdECindRHKgEKfGYneSV2Vic241X+4u4c2NeXyw5TArM8tJL6qXWSid0GuKKRs3buS+++5j27ZtrFmzBrvdzpw5c2hsbPR0aEKIblDXaOObtFJp7XWE5EAh+p7aRhtbcqs9HUavIDlQiN7rUIVR1oTqBMl/QvRc2eUNLNlVIjOMu5HkQCE6xuF0UVDdyIacSj7aepi3NubxXXoZOwpqKao1S57qQr2mzdfKlStbvF+8eDHR0dHs3r2bGTNmeCgqIUR3MNscfJ1WitkmT/ccJTlQiL7F5VJZnVUui/p1kORAIXonQ5OdHw7ImlCdIflPiJ7H5VLZll/D9oJaT4fS50kOFKJ9LpdKfnUjByuMFFQ3ysPIp0mvKab8ksHQvHhheHh4u/tYrVasVqv7fUNDQ7fHJYToHKdLZdlePYYmu6dD6dFOlAMl/wnRs+0uqkNvsHg6jF5LcqAQPZ/TpbJin15a33QxuQ4WwrPqzTZWZZVTVi/ncZ4gOVAIaLI5ySipJ7PUIG1UPaDXtPk6lsvl4je/+Q3Tp09nzJgx7e73wgsvEBIS4n4lJSWdxiiFEKdiXXYlpbJ+wHF1JAdK/hOi56oxWdmWV+PpMHotyYFC9A4/HqqiXIrGXUqug4XwHKdLZXdhHR9vL5JCiodIDhT9ncXuZNOhat7bXMDWvBoppHhIryym3HfffWRmZvLZZ58dd78nnngCg8HgfhUXF5+mCIUQpyKjuLmyLo6vIzlQ8p8QPZPLpbJ6f4W09+oEyYFC9HzZ5Q2kF9V7Oow+R66DhTj9VFXlYIWRD7Yc5seDVdJGx4MkB4r+SlVV9pbU8/7mw+w8XCt5yMN6XZuvX//61yxbtowff/yRxMTE4+7r4+ODj4/PaYpMCNEZ5QYLGw9WeTqMHq+jOVDynxA9067COnlSuxMkBwrR8+kNTazJqvB0GH2OXAcLcXpZHU72lzWQXlxPvVlaUHua5EDRX9WYrKzeX9Hha0ibw0W1yUp9kx1Dkx2zzYHV7sLmcOF0qTjV5of6FAU0ioJWo2BWsyl3rsKpNlDvzMJPE40TCwZHbqvjB+mSmBnxHJE+owAXWo0WjaKgURR0WgUvrQZvrQZfLw2+Xlp8dBoURenKvxKP6zXFFFVVuf/++/n666/ZsGEDgwYN8nRIQoguYrE7Wb5Pj1Oe1G6X5EAher8qo5Vt+dLe61RIDhSidzA02VmaUSaz77qQ5D8hTh+700VhjZmcciMF1SbsTsllniY5UPRXqqqSXlzPpkPVxz2vUlUVvcHCoUoTpXVNVJustLW3CxMqDoy6pdg0BTRpdoHS9gyXJldlu99ndBSzrOLWFtuC7VcQ5JyLTo1qtb9WoxDgrSXI14tQ/+ZXZKAP0UE++Hv3mrJEC70m6vvuu49PPvmEb7/9lqCgIMrLywEICQnBz8/Pw9EJITpjVVY5DbLg/HFJDhSid3O6VFZllUvR+BRJDhSi52uyOfk2vZRGqyw435Uk/wnRfVRVpdpko6TOTGGNmeJasxSDexjJgaI/stidrMoqJ7+qsd19rHYne0sN7C0xYLK2XDsl0EdHiJ9CoK+DHOvrFFpWdmu8DV5LaPBa4n4/3PUZTkcQdqeK06XSYHHQYHFQWt9yfeQQPy+SwvwYEO5PckQA3rresRpJrymmvPnmmwDMmjWrxfb333+fW2655fQHJIToEunF9cf9D4RoJjlQiN5ta14NVUarp8PotSQHCtGzWexOvtxTQo3J5ulQ+hzJf0J0HYfTRaXRit7QREldE3qDhSabFIB7MsmBor+pbLCwbK8eQzsPHNudLnYdriO9uB6bs3lmibdWQ0pUACmRAdS6fuR/BQ+BleZXO+L8h2OwVZAQMJLpsdcxOHgqAV6h7e6vqqq7XZfFYWJP9TKW5D/T5r45mmvAGy5K+i1TI27FbHPRYLFTb7ZT12ij0mSl3tzchszQZCezrAGdRmFwVCAj44IYEO7fo1uD9ZpiiqrK0wFC9DXVJis/yTopHSI5UIjeq6y+id2FdZ4Oo1eTHChEz2WxO/kmrVQKxt1E8p8Qp67J5qS0vomy+ib0hiYqG6wy86SXkRwo+pMD+gZ+2F/RZp5SVZW8qkY2Hqxyz0QJD/Bm0oBQtH4H2FT+EmsLtuBwtX0+Fus3lDlJ95EQMIoov+STju3Y4oavLpBpsdcwLfYad2w/6j/g28MvtBizsvg1VhX/ncdTVzEitOV3Wh3N+bm4tomC6kYMTXZyKozkVBiJDPRm0oAwhsUEodH0vKJKrymmCCH6FofTxfeZ5XIyK4To0yx2J99nluOSC0EhRB9ktNj5Jr2MaimkCCF6AKdLpaTOzOEaM0W1ZmpMVuQUTAjR06mqyqbcanYdbvsBPKvDybrsSg5WmAAI8tWROtDBR4VzSDvc/nEXDvoDw0OnE+3XvWsNKYrCzPhbmBl/C1anmfey7+GQYRsAKiovpM3h5uH/x/iIC91jfHRaUiIDSYkMZMbQSCoarBwob+CAvoFqk41V+yvYVVjHjGFRDAj379b4T5YUU4QQHrEpt1ouvIUQfd7aA5WyJpQQok+qNln5Jq0Uo8Vx4p2FEKKbOF0qhTWN5JQbya9uxOZoe0FlIYToiawOJysz218fpdxg4ftMPQ0WB6piITRyIzWutXxUuL/VvjcMfYVov0HE+g9Fp/Hu7tDb5KP1557RH2BzNvF+zq/Jqd8EwAc5DzAt5hoWDnoKrcarxRhFUYgN8SU2xJezUiLYW2ogrbCOmkYbX6eVMjgqgPNGRPeYBet7RhRCiH6lqMZMenG9p8MQQohulVlq4GCF0dNhCCFEl9tf1sD6nEq5aSmE8BiT1cHeknoySw00WmXdEyFE72Mw2/kuo5TqdtacyywzsD67EqdqQ+97D3algiJT6/0WDnqK6bHXo1F6zgLu3lo/7h71H0z2WlYU/Z1tFf9jS8VnbKn4jN9P/IEI36Q2x/l6aZk6MJxxCSFsz68lo7SevKpGyuqLmD0ymsFRgaf5l7QmxRQhxGllsTtZvb9cplsLIfq0SqOFDTmVng5DCCG6lMXuZENOFQf0DZ4ORQjRT5msDnYU1JBZ2oBTWkYLIXqpsvomlmaUYba1Lga7XCo/HapmT0kZJu0a6rzfafH50JAzmRZ7HcNDpuOray4uaBSFiEBvIgN9CA/wxkuroNNocLhcWOwuzDYHtY02ahttbX5ndwn0Cueqwc8xPGQaHxx8EIC/7Dmfm4a9xoTIS9od5+ulZebwKEbFB7Nqfzk1JhvL9uoZnxjCOUOj0HpwLRUppgghTqt12ZXSDkII0ac12ZwszdBjd8oFvhCib1BVlf36BjYdqj6tF+BCCHGUw+liR0Ete4rqOnSOpaoqjVYnjTYHVocLq8OJy9W8XaX5xqNWo6DTKHhpNXhpFbx0Gry1Grx1GnQapcWCy0II0VVyyo2szmp7DWGbw8WKfWXsMj6L2e/HVp8/nrqyxRooCaF+jIgLYmh0EH7e2g59v6HJTll9E8W1Zg7XNJ6W2X3jIy9iof0pvi74MwAfHvwtNpeFKVELj5tro4J8uGZKEtvya9ldWEdGiYEqo5VLxsYR4OOZsoYUU4QQp012eQM55dLyRgjRd7lcKiv26WWdFCFEn6CqKnlVjew8XEu5weLpcIQQ/dTh6kbWZVdiOM75Vb3ZRlGtmfIGCxUGKwaLvcMzV1RUVKwo6LBoMnAoFbgUC1ZtGg6lHH9G4qQeq1JKAr8iWElFp/iDAgrNhRl3cUbbXJzx1Wnw9dLi760lyNeLIF8dYf7eeOt6ThseIcTpt6Ogli151W12a2m0Ovhv2gpyHX/DqasCwE8XQoRPIlcOfo6kwDHufWOCfTl7SCQDIk5+cfYQPy9C/LwYGReMqqqUN1jIKTdysMLYrYWVc+Ju5KyYq3l021gAPst9gnLzIeYnP3rcgopOo+HsIZHEh/iyKquCMoOFz3YWc9mEeCIDfbot3nbjOe3fKITolxosdtZlS8sbIUTftvFQFUW1Zk+HIYQQnWKxO8kpN7K3pL7dPt5CCNHd7E4XPx2qIqPY0ObnFruTzDIDBytMVBmtrT5XFAjw1uGja55totUoqFhocGXR5NJz2PEOPsRh5tBx4zCgd//5MM+CCt7OIdg0uQBEW5/D1zUBhRMXSoJ8dUQH+RAf4kd8mB8xQT4yA0aIfkBVVdbnVLabz/ZV7uT93Bua3xyTSp6Z9CPeWl/3ey+twjlDoxiXGNIluUNRFOJC/IgL8WPG0CgO1zSSUVJPYY25W9rz6zTePDVxLW9k3kC9Tc+Gsvfw14VwfuKvTjg2JSqQa6Z6szSjjDqznS92lzB/XByJYSdfUOoMKaYIIbqdqqqszqrAapdFSoUQfdeuw7WkF9V7OgwhhDglNoeLwppGDlWayK8ySatCIYRHVRmtrNinp7axdUHXZHWwu7COrDKDO1cpSnO7m/gQP2JDfAkP8CbIR4fVZWR92X/4oeStNr/nRIWU9hwtpABU+jzd4rPrE3/E4dBhsbtotDkwWhw0WOyYbU6Mlub3eVWNAAT4aBkSFciI2GBiQ3wRQvQ9ziPdC3Ir21g9Hvi+4CPW6P/sfj82bC5zBtxJQsDIFvtFBflwydg4wgO8uyVOjUYhJSqQlKhA6hpt7Cqs44C+69enCvdN5OnJG1hV/Aaril9nRdFrhPkkMClq/gnHhvl7c+XkJJZmlKE3WPgmrYyLxsQyJPr0LUwvxRQhRLfbXVhHsTypLYTow7LLG9iUW+3pMIQQ4qTUNdoorDVzuLqRkjqzFFCEED3C/rIG1mVXtMpJTpdKenE92wtq3J9FBnozLjGUIVGB+HlrUVWVn8o/Yk3Bj2TX/9Sh7xsVNosaSwmXDXycUJ84YvwGoygKRls1AV5hqKiUmLII8Y7F7KhDBbaUf8LWiv+1ebyPS2ZwTtxNTIyfT2LgKLRK8603i91JjclGeYOF0vomSuuaaLQ6ySgxkFFiICbYhwlJoQyLDkLjwcWVhRBdx+50sTSjjMKa1vfEXKqTD/b/kX2Gz93bhgafzS0jXmk162RoTCAXjo7FS3t6WgWGBXhzwagYzkwJZ1t+LVllhi6fqXJB4j2Umw+RUbOST3Mfx1vjx9iI8084zs9Ly6LUBFbtryC30sSKTD0XjIxhZFxw1wbYDimmCCG6VUWDhS15NZ4OQwghus3BCiOrMiu6ZRq0EEJ0pQaLnZLaJorrzBTXmjFaHJ4OSQgh3JwulR8PVpFeXN/qs8oGC6uyKqg1N89UiQ325cyUcAaE+6MoCvrGg3xX9CHbK79o9/gatJyXcCdnxFxBhG/SCeMJ8o50/zk5aDwAoT4xAFw5+DmuHPwcAOXmXLZXfMFG/WL3/j/pP+Qn/YcEe0Xxm3FLCPWJxddLS0KYHwlhfkxKDsPhclFc28TBCiOHKkxUNFhZlVXBzsN1TB8cwaDIAGkBJkQvZnU4+Ta9jNK6plafGW3VPLNreottT6X+SLhfTKt9pw4KZ9rgCI/kgyBfLy4YFcOEpFA25FRS0sZvOVUaRcuNw17DK9eHXVXf8tGhh3jI70ti/YeecKxOq+HiMbGsPVDJfn0Dq/dX4HCqjE0M6bL42v3ubv8GIUS/ZXO4+H6fvsunBAohRE9xQN/A6qwKXFJJEUL0QGabg6JaM8W1TZTUmak3t794sxBCeFKTzcmyvWWtbtSpqsqeonq25FXjUpufSD57SCQj44KoaMrl6Z030Oiob/e4Z8feyIjQsxkRNgON0j1PdMf6D+GyQU9w2aAn0Dce5Pvif5BZ+wMADfYqnts9k5uH/x/jIy5sMU6n0TAoMoBBkQGcM9RBZmkDaUV11DbaWLpXz4Bwf2aPiCbYz6tb4hZCdB+bw8W3aWWU1rfMaS7VRWbtWpbkvujeNtb/CW4aexPaX8w6URSYNTyaCUmhpyPk44oK8uGKSYnsLTGwKbcam6Nr2vhrFA1XD3keo72GnPpNfJDzIL8e8zEBXmEdGKtw/shovLQKGSUG1uVU4kJlfGJol8TWHimmCCG6zbrsSurkol0I0UelF9ezIadSZqQIIXqUigYLeZUmCmoaqTJaJUcJIXq8KqOV7zLKaGhqee1otTtZmVXO4SPtcQZHBTBjeCj6pjR+t/W2VscZGJRKYsAYfHUBXJj0K3x1/jhdnNaHXuIChnHbiH9SbMrk40OPUNmUD8AHOQ8wK/42LhnwW3Sa1usd+HvrmDoonPGJIewqrCOtuJ6iWjP/3V7I9CGRjEvomsWmhRDdz+508W16aatCSrn5EC+lX4pKcyFCowZwXsQbXDz8rFb/fmsUhTmjT1/rqo5QFIXxSaEMjAhg2b4yKhusXXJcraLj2iEv8ureRVQ05fH2/tu5Z/Ri/HQn/u2KojBzWBRajcKeono25FSBCuO7sQAlxRQhRLfYV2LggL7B02EIIUSXc7lU1udUsrfE4OlQhBACgBqTlexyIwf0DdK6SwjRq+RWmliVVd7qKefm2Rll1JvtaDUKZwzW8mnJuazb3fZxFg29k7+e/wrhAT6E+HmhPbLmiMulYrY7MVrsVBttVBotlBksVBu75iZge5ICx/B46veUmw/xeub1NDkMbCh7jw1l7/Hw+O+IDxje5jgfLy3Th0QyKj6YHw5UUFZvYUNOFcW1Zi4YGYOPl7Zb4xZCdI7TpbI0o/Usu/yGXbyReb37faDjEuYP/B1TBiS2OoZWo3DJ2LjTuqj6yQjx9+KqyUms2V9BTrmxS44Z7B3Fr0a9zz8zb6CkMYtvD7/INUOe79BYRVE4e0gkiqKwu7CODQercKlqtxWipJgihOhylQ0WNuRUejoMIYTocoYmO6uyytvseyuEEKeTqqrkVZnYU1QvOUkI0SvtKKhlS151qxl0hTWNrNhXjs3pIsBHocb/YT4tOdBin9Fh52J0lPPpwq84a+DwdmdtaDQKgT46An10xIX4Ac399ButDgprzByqNFJYY+621tSx/kP5y9QdZNSs4oOcBwB4OeNSbh3+z+MutBzm780VExPJKDGw6VA1eVWNVJuKmTs2jqggn26JVQjROaqq8n2mvtVi8x8dfIi06uXu9zH2P3D5yAVtFkt0GoV54+MZFBnQ7fF2hpdWwyVj4wj192J7fm2XHDPWfwg3D/8//pV1Izsqv6TequdXo9/v0FhFUZg+OAIF2FVYx4+HqokO8uWiMbFdEtuxpJgihOhSzb1u9ThknRQhRB+TWWpg48GqLusPK4QQp0JVVXIqjGzLqzmpdqpOl4rTpaLSfI6mVRS0GkXaxgghTjunS2XN/oo2Oxlkljb3vXeqVswBb1CqbsZhsbk/99eF8sczlnJVairJEad+szHAR8eo+GBGxQdjsTs5WGFkX6mhy9rW/NL4iAu5IuVZluQ/A8D7OfdxQeI9XDzgN+2OURSFCUmhxIb4smKfHkOTnS92F3PRmFhSInvmE+tC9GdrD1RyqMLUYttP+o/chRRFDSDJ/gpXjD+DxDD/VuO9tArzx8d3KredbtMGR6JVFLbk1XTJ8YaETCU1ch5p1cs4aNhCoTGD5KDxHRqrKArTBkegURR2HK5lyZ4ShsYEcvfMwV0S21FSTBFCdBmnS2Xp3jIMTbJOihCi7yg3WPjxYFWrnrdCCHG6ldSZ2ZBTRVU77WmMFjvlDRYqGqzUNdowWhyYrA5sDhfOdtYM0GkUvLQavHUafL00+Oi0aLV2FI0Bu1JJsFccLsWIvzYck6uYGJ+xeGl8URRQUJr/V2nu7a1RFLy0Ct665uMEeGvx1mmkYCOEcGuyOVma0XpRZlVV2ZJXw87Cahq166jx/gcc8/yKl8aXZyZvZNqggUwfEom3rusWk/f10jIuMZRxiaGUGyzsLqwjt9LU5WutTIu9hrHh5/Ni2sU0ORtYU/ImjfY6Lk/543HzZGywL9dNHcCKfXqK65pYlqFnxrCoHrEotRCi2Za8avaV/twGWlVVXsq4lHLzQfe2oc5PWDRpINFBvq3Ge+s0XDo+nqTw1kWWnu6MlAgURWFzbnWXHO+aIc+TVr0MgH/su4rHJqwgxr9jBRFFUTgzJRxFge0FtWzKreaOc1LcrR+7ghRThBBdZu2BCmkzIYToMyobLOw8XMehSqMs4CyE8Kgmm5OfDlWxX9/QKh/VmW3klBvJrTJRY7K1fYBfaJ6f0oRdKcOkFNKk7MDMZrDT/OqAIMdcXFixafKJtP0ObzW5zf10GoUQfy/C/LyJDPImLsSP2GDfLr0RKoToHQxmO1+nlbSaVed0qazeX86Omrcx+H3a4rPZCXdz8YAH8fXy4sLRsd2+hkBsiC9zx8VhMNvZcbiW/WUNXVpUCfKO5PHU73lm13QAtlR8hk7jw4JBTx53nK+XlssmJLA+p5KssgY2Hqyi0epg2uAIKVgL4WF7S+pbtLpSVZWlhX9rUUgZqX7BFZNTCPP3bjXeW6dhYWoC8aF+pyXe7jB1UDiNVgfpxfWdPpaXxoffT/yBv+xpboX48aFH+O24Lzuc65oLKhGMigvmsYtHdGkhBaSYIoToIjsKaskqkwXnhRC9m9OlUlBtIr3YQHGt+cQDhBCimx2ubmTN/gpM1pYLy5fUmdlTVE9BdaN7m6JAZKAPMcE+RAb4EOznRaCPDh+dQnrtF/xUvpgGeyV2l6XTcRl1P/f+1vvehw8x6JRQ/BlBtOt2bHYdNqcLh0ulxmSjxmQjt6p5f40CiWH+DI4KYGh0EH7esqCyEH1dZYOFb9JLabQ6W2y3OpwszShhi3UOeP28fXTYuUyKuowJkRcT4ufFpRPiiQw8fWuFhPh7ccGoGKYMDGNrXg3ZXbTIMjQXVF6dlsMm/cd8VfAcP+o/IMZ/CGfFXHXccVqNwuwR0YT4ebElr4ZdhXVY7E7OHRGNRgoqQnhEXpWJ9dlV7vcu1cWLaRdRbSkEQOeKY6TmfS6flEiwr1er8b5eWhamJhAb0nq2Sm8zc1gUhiZ7i3PTUxXhm8TMuFvZqH+fksYs1pb+m/MT7z6pY5yREoGvV9efY0oxRQjRaWlFdV02nU8IITyhosFCTrmRA/oGzDbniQcIIUQ3c7pUfjpURXpxfYvZKLWNNn48VNVicdPkCH+GRQeREhWAr5cWl+rC7Kjnf7kPU20ppKIpr8PfOyBwHJOiLiMleDL11jIqmvIYHHwG60vfpaQxE53Gh3ERcygy7uOgYbN7nJUKrGoFjeRQpXzL/RM/IcF/PGabi3qznVqzjYoGC3qDBaPFQVGtmaJaMz8eqmZ4TBATkkJlUWUh+qjS+ia+SSttte6c0WLnf2k7OeR8Fo6ZrPbbcUtIChwLNM8UWTAhwWNF11B/by4eG8fYxBDW51RR3U6bxVNxdtz1lDYeYHvlFyzJexpvjS+Toi497hhFUZgyMBw/Ly3rsivJLGvA5nAxZ3Rslz99LYQ4Pr2hie/36d2z11RV5av859yFlEDHxQzR/YZFkxIJ9Gl9C97fW8uiiYl95vxHo1G4eGwsn+8sprqDs6WP57JBj5Nj2Ey5+SAril5lavRCgr2juyDSzpFiihCiU44uyCyEEL2JqqqUN1jIq2zkUKWR+pNYxFkIIbpbg8XO8r16yg0/zyBxOF1sza8h7UhxRaPA6PgQUgeEEubvjaqqlDRmsr1oCVsqPmv32L7aQHy1Qfxm3OeAgo82AB9t2/25EwJGMJrzALhlxP+1+tziNNFor+OwMY3Vxf+kynLY/dnrmdcBMCxkOreM+D8GRoa5P6sz28irNHGwwkSVycp+fQP79Q0MjQ5k2uAIQttogSGE6J2Kasx8l1GK3dmyVVa1ycri9Pcp07zmLqQE6MJ4ZvKP6DTNOWBgpD9zx8b3iLaAiWH+XD91ANsKathZUNdlrb+uHPwcOo03m8s/5pNDjxHsHc3QkDNPOG5MQgg+Og0rs8o5WGnCqeq5eEycFFSEOE3qGm18m17mzm2qqvJVwXNsqWhuVRjkmE+K7j4un5RIQBuFlCBfHYsmJhIe0LfOeXx0Wi4ZG8enO4pa5f1T8cCYT3lyxyQAPsv9PXeO/LfHWxtKMUUIccrSi+vZkFMpawkIIXoFh9NFUa2Z/KpGCqobW7XMEUKInqCwppHvM8tpOmaWXKXRwuqsCmoam5/yS4kM4JyhkYT6e1NhzuOhLZec8LhXpjzHWbFXd2mszYWZQCJ8k5gUdSlWp5k1Jf9iQ9n7uNTmHHvQsJknt0/i+qEvMylqPgBh/t5MHhjOpOQw9AYL6cX1HKo0cajSRF6VidQBYZw5KByd1vM3UIUQp66wppHv0stwuFpeMBbVmPg46x2qdG8BEOmbwk3DXiYxcLR7nxGxQT1utoVGozBtcCSDIgNYsa+chqbOP4yjUTQsHPQUTY4G9lQvPbI2wBJCvGNOOHZoTBA6rYbl+/TkVTWybG8Zc8fGSe4UopuZbQ6+SS91n6s5XDa+KXj+SCFFIcL2GxK9L2q3kBLq78WiiYmE+LVu+9UXRAT6MGNYFGsPVHb6WL66QO4b/V/+mXUD2fU/klm7lrER53dBlKdOiilCiFOyJbea7QW1J95RCCE8yGxzkF/VSF6VieJac5c8HSOEEN1l5+FaNudWux9UUVWVvaUGfjxYhUsFPy8t54+MZlBkABk1K/kw/TetjpEcPJLChgMMDh3F6+cvR4svhiY79WZ7txeRfbT+zEt+mHnJD7Ou9B2WFb7s/uzjQw9TYylidsJdaDXNNw8URSE+1I/4UD+qjFY251VTWGNmd2Ed+VUmLhgVQ1xI712MVYj+rLjWzNKM1oWUNfmrWKP/Mw5d8002L40fvxv/ZYsZcmMTQpg9MtrjTx+3Jy7Ej+umDmBpRhml9U2dPp5G0XDl4Ocoacyisimft7Ju474xHxHoFX7CsYMiA5g/Lo5le/UcrjGzbJ+eeVJQEaLb2Bwuvkkrc3c2cLhs/GPf1ZQ27qe5kPIgcV4XsmhiQputvSIDvVk0se0iS18yLjGUwhozuZWmTh9rcMgUZifczdrSt/n28AsMC53W7qzq06Fv/z8nhOhydqeLtQcqOKDvugX4hBCiK5ltDg5VmMipMFJW3ySz54QQPZ7d6WJ1VgUHK4wttq3LrnQvepwSGcD4QU0sKbiJkkNZbR5n1Q2rmDN4TrvfY7I6KKtvovjILL3uLK6cl3Ans+JvY1flN3yW9yQAK4v/jz3Vy7h1+BvE+A9usX9UkA8LJiSQV2ViXXYldWY7X+wqYdrgCCYlh/XYm6pCiNZK65v4LqOs1UMsr+y5nVLLJlAANFw28EnOibsejfLzjf+JyWHMHBZ1egM+BX7eWhZNTOCHLro29tH6c+fIf/N65nVUNOXyXva93D/m0w7lvuSIAC4dH893GWUU1phZulfP/HFxPaI9mhB9iculsnxfGRUNP7dh/aHkrSOFFIi0PkaUbiYLUxMIamOx+ZhgXxamem4NqNPt/JExlNY3tZhtfapmJ97F7qrvqLWWsKLoNRYO+n0XRHhqJLMKITqsttHGZzuKpJAihOhxXC6VvCoT36aX8s6PBazLrqS0TgopQoiez9Bk5387i1sUUowWO0t2l5BdbkRRIHWgjfWN5/L3zEsoaWxdSCn/XTnqM+pxCykAgT46hsUEMXtkDHecM4irpyQxLjGk2264aRQtU2Mu55Wzsrl+6EtoFB2VTfn8Nf0S8gw72xwzOCqQG89MZnhMECqwOa+GpXv1WOydvxAXQnS/SqOFb9NbLjbfaKvj8a1TmgspR/xpylZmxt/YopByZkpEryikHKXTarhwdCyj44O75HgRvkncM2oxXhpfDhvTWJxzf4fHJoX7c9mEeHQahaJaM99llLX4/0AI0XlrDlRwuNoMNM8e/v32Kawu+ScA4bZ7CdfMYMGEBMLaWPstIcyPyyf1n0IKNBedZwztmpzuqw3kisHPAvCT/kMya9d2yXFPhRRTToWqQmMN1BU2/6/cqRF9nKqq7Cmq49MdRVSbbJ4OR3ia5EDRg1gdTnYX1vLe5gK+Sy8jv6qxyxYEFaIVyX+ii5XUmflsRxFVRqt7W7nBwv92FlNptKLxKiYw9j98U7Goxbhpieei/50e9RkV9RmVmMAT99b/paMttmaPjOGuGSlcMCqGiMDuWQRVURQmRV3K7SPedG/7Z9YN/Pfg79rc39dLy4WjY5g9IhqtRqGgupHPdxVTb5bzUI+SHChOoN5s45u0Uqz2n2/iO1xO/rDrTGxqAwARXmN48YwMArxCW4ydMSySswZHnM5wu4SiKFwwKoaRcV1TUInxH8z5ib8CYF/tGlYWvd7hsYlh/iyYkICXVqG4rokv95TIGoFdSXJgv7Y5t5r9ZQ3u94tz7qfJ2fw+0HEJoepc5o+PJyrIp9XYAeH+LExNwEfXfwopR42KD2ZAeNe05BoVNpMRoecA8F72vage+ndQ2nydjKZ6yPgUtr8NdQU/bw8bBGfcDeOvBb9QT0UnRLeobLCwNruScoPlxDuLvk1yoOhBbA4Xe4rq2FNU1+KC/ShVVTFZHTRYHBgtdix2FzaHC7vThaqCioqiKGg1zS8vjYKXToOPVoOXToO3VoOXVoNOA4rixFvrjVajuMepKrhUtflYR44HoKCg1TYfT6tRpC1NXyH5T3SDzFID67IrcR6znsChCiOr9lfgcFmp8/sTRtKg/ucx5yVdwRfXvEe4f1CXxuKl1TAmIYTR8cEcrjGzPb8GfTec+40Mm8HvJ/7Afw8+TKEpnT3Vy/DThbBg0JNolZaXpoqiMCYhhOggH5bu1VNntvO/XcXMGxdPQqiso3JaSQ4UHdBodfDVnlIarT/PIqs0F/Fi+gXu92ODb+CW0U+1OD9SlOZWMGMSQk5rvF1JURQuHB2DzekirwvWB5idcBffF/0dgNUlbzA6fBZJgWM7NDYhzI+FqQl8k1ZGSV0TN/5nO4tvndpnF7o+LSQH9nvpxfXsOGbN4J/0/2Vf7Rr3+wj73VwyLrbN85OBkf7MHxffr9cxmj0ymo+2FrZaQ+tUXDPkef64q7mg8nrmtTww9rNOH/NkKaqnyjge0NDQQEhICAaDgeDgk3xqIPcH+N9NYDcf2XDsX9uREwEvf7j6QxhyfleEK4RHGcx2tuRVk1Nh7PUPXAT7eXH72YM6vH+nckUP1enfJDlQ9BCqqrKv1MC2/JoWF+uqqlJtspFfZUJvsFDeYMHawdYGKk5UbNR7/Rej7lv3dkX1RlXafwo63HYfvq4xuDCjU+PR0vLmplajEOCtJcBHR3iAN5GBPsQE+xAT7IvmNBZZUgeEMmt4dIf3lxz4C5L/RBdTVZUfD1Wzp7CuxbbdhXX8lFeKSbsak/f/sGNwf37riNe5etz5XDhyxGmLs6C6kc251S1mzXQVi9PEk9snud+fEX0lVw5+rkW7n2M1Wh18l1FGpdGKVqNw8ZhYBkcFdui7rpiUSFIHn4jsi/kPJAeK7md1OFmyu4TKhp/zRa2lnBf2XIiT5sLsuJDruGX0My3GaTUKF42JZVhM1xaIPcXmcPG/XcVUd0HetDktPL59vPv9K2dln9RDOhUNFr5JKyUyyIfP7z6L+A4UoSUHtkFyYL93sMLIin16932xrRWf80XeHwAItd9MiONKZo+IbrMgnBIVwNyxcf26kHLU5tzqFgWpznh937UUGPcA8Kcp21vNdDxqZFwwF42J7fBxO5orZGZKR+T+AB9fdWQKX1t3lY9sszc173f955JERa9VY7Kyq7COnHJjiyclRT8mOVD0EJVGC2sPtJwpZ7Y52Fdq4IDeiKHJ3mJ/RYEgHx3Bvl74eWvRam1UOjaiKFBoWYbZqafJVdnu9x2vkAJQ6/3PFu+9XUMIc9yKj3MMClqcLpUGS/PsmGOf8PbRaUgO92d4bBADIwLQaGT2So8l+U90MYfTxcqscg5V/PzkstOlsj6nkjR9Bnq/1v3xn56yijvOPLvDBYGuMigygIER/mSVNbAlr7pFAbuzfLWBvDothy3ln/Fl/h/ZXvkF4b4JXJB4T5v7B/jouGJSIiszy8mvbmT5Xj2zR0YzOr73PsneK0gOFB3gdKks36tvUUgpMx3i5b3z3O8nht3MDSOfbDHOS6swb1w8AyMDTlus3c1bp+HS8fF8tqMIcycXXPbW+nLN4Of5LK/57+3fB+7g7lH/6fD4mGBfrp6SxB3npHSokCLaIDmw3yuuNbMys9xdSPmh5C1WFL0GQKDjfIIdV3DGoPA2CymDIqWQcqzJA8PIKjN0yfnkfWM+5uGtIwFYWfx/XJ7ydKePeTKkmHIiTfXNVWhVBU70hKsLVE3z/g/tl2l+otdQVZXCGjMZJfUUVDf2+pkoogtJDhQ9gMulsq2ghp0Fde71UEwWB9sLajhwTOFXq1FIDvdnQIQ/scG+RAb6UG8rocKcy7vZd5/0944Nn4NLtZNVtx4fjT9Wl7nF5yHeMRhsFe73Nk0uFd6/d7+/a/jnBGuHYbI6qDHZqDJZKatvwupwcbDSxMFKE4E+OsYkBDMhKbRf9tDt0ST/iS5msTv5Nr2Usvqfi6tWu5Pl+/TkGNZR5fsX9/ZY/2H8ZuwXRAUGsSA1gfCA7lnL5ESOttoaGhPI5txq9pYYuvQ8cVrsNRjt1awqfp3vi/6Ovy6E6bHXtbmvl1bD3LFxrM2uZL++gR8OVGJ1uJg4IKzrAhI/kxwoOmjN/nIKa34+R6o0F7UopFyX/CmTEya2GOPrpeWyCfF98iZ/iJ8Xl4yN48s9JZ3Ol1NjLie7fhPpNSvIqd9EQcMeBgVPPPHAI6KDfU97Ib7PkBzY71UaLXyXUea+1qxsKnAXUgDC7L9iTHwIZwwKbzV2YKQ/88ZJIeVYPjotZ6ZEsPZA+w8zdpRG0XDP6MW8mXULW8o/ZWr0IpICx3RBlB0jxZQTyfj0yHS+jv5X0NW8f8ZncOavujMyITqt0epgv76BrFIDdWb7iQeI/kdyoPCwerONlZnl7pkddqeLXYV17Cmsc/dcjQn2YUJSKIOjAvHSami01/NyxpwWhY62jA47jxj/wfhqAxkSciah3nGEeEefVAuFqqbDfF/0d8qb8ig3H2zx2b9zrgJgaMhZ3DXqXbSKDpdLpbzBQm6ViWy9EZPVwbb8WtKL6pk8MJzxiSFy0t1TSP4TXah5LYESqk0/z3hraLLzVXoema4bcfn8vKDppcmPMSvhNmKCfVmQGo+/t+cv2Xx0Ws4bEcOwmCBWZ1W0mgnYGRck3oPV2ciGsvf4Mv9Z/HUhpEbObXNfjUbh/JHR+Hlp2V1Ux0+HqnE4VaYMDJM1qrqa5EDRAZtzqzmgN7rfVzUV8/eMG9zvFyS+0aqQEuCjZWFqYpuLNPcVSeH+TBwQxu5j2jmeqmuGvEB6zQoAPst9gkcmLEWn8UyBvV+RHNivGcx2vkkrxXakbXSDrYp/Zv6c2xKb/ktKRDjnDW997ZgQ5se8fr5GSnvGxIeQXlxPjen4HSA6YmjIWaRGziOtehmv7b2cl8/aj0Y5PQ8nev7MvCdT1eYFpk7F9reaF6KSk3rRwzhdKvlVJvbrGzhcbXY/5S1EK5IDhYcdqjCyen+F+yS2pM7MDwcq3Tfx4kJ8mT4kkvgQX+qspbyccSVVlsPHPebNw/7BsNBp+GgDuuRkK8pvIDcN/7v7fWVTAS+mXdTydxi28sjW0dw47DVSIy8hPtSP+FA/pg2OILfSxI6CWurMdjblVpNVZmD2iBgSwvrek5q9iuQ/0YUMTXa+2lNC/TEPrpQbLHydcYgi5Wlc2uZCSphPAg+O/R/B3lEkR/gzb1w83rqedSGeGObPdWcMYM3+CnK7YJFlAI2iZX7yozhcdjaVf8TnuU+RGDCGKL/kNvdXFIXpQyLw1mnYml/D1vwa7E4X0wZHSEGlq0gOFB2wt6TlgszVTcW8ln49FrUCL1ciNw35D6NjB7YYE+LnxeUTEwnx7/uLoU8fEklRrbnT6055a325f8wnvJ55HVWWw2wsW8zsxLu6KErRJsmB/ZrZ5uCrtBJ3OyqL08R/su/BaK/GW00gyvoc8cExXDI2rlW75tgQXy6bEI+XFFLapNEoTB8SyXfpZV1yvPMT7iatehkAu6u+Y0r0wi457olIMeV4zLVQV3AKA9XmcU114N96upcQnlBW38QBfQMHK0xY7F3X81r0YZIDhYe4XCo/5f68OLPD5WLzoRrSS+oBCPTRMWNYJIMifVlR9Cqv7H+v3WNdN+JRzom/CqcjCJuj+2OP9hvEq9NyUFWVpYV/Y0PZz7F9dPC31FpKODfhDjSKBp1Gw4jYYIZFB5FdbmRzXjV1ZjtL9pQwPjGEs4dEyhNNniL5T3QRQ5OdL3YVY7T8nIByK438L/sdarX/RVUaAbh04OPMir8VgBGxQcwZHYu2h66n5OulZf74eHYX1vLToeouafulKAoLBj1BmfkA+Q27+Pf+27lvzMeE+sS0u//UQeHotAo/HapmV2EdTlXlnCGRUlDpCpIDxQnkVZlYn13lfp9Vu44Pch7BoZrQuRK4NuXdVoWUyEBvFk5MJNCnf9yG0moULh4Tyyfbi9yzqU/VoOBJXDvkr3ya+xg/lL7FpKjL2s2PogtIDuy3rA4nX6eVuh+AsTkt/OfAPRSb9qEjmAjr74nwTeTS8a0LJhGB3iyYkCCtm09gcFQg0cE+LdbZOlVxAcPcf15Z/DqpkXNPy8y9/vFfsVNl6+TTVlajJFDhUQ0WO/vLGjigb2jxNKQQHSI5UHiA2eZg2V49pXVNQPONyBX79FQeeapvdHwwwxLq+CTvKipz89s8xuEHD5Mc2vqJ5gaLnXKDhcPVjRTVmlvc3OxqiqJw6cDHmJv8O1YUvsr6suYFQ5cXvcL+ug3cNuKfBHg19/nXaBRGxQczOCqAn3KrySprIKPEQFm9hYvHxhLmL60cTjvJf6ILNFjsfLm7pEWu2XG4kM/K5rivwiJ8BnDd0L+6e+CnDghl5rCoXlEQmJQcTpCvFyszy939xDtDo2i5adhrvL7vOmqsxby1/xZ+M+4LfLWB7Y6ZOCAMnUZhfU4VaUX1uFxqr/n769EkB4rj0Bua+H6f3t3hwGir4cOcR48UUuK4KvkdJsQPajEmNsSXhakJ+Hr1r5uMEYE+TBoYxvb82hPvfAKToi5lc/knFJky+KrgWW4b8a8uiFC0SXJgv+R0qSzL0Ltv8jtVBx8efJC8hh1oCSDS8hwhukEsmNC6BWuQr46FqQn4efevHHeqzkyJ6LLZKX+asp2XMuZTZy1lQ9l7nJ/Y/W325HHH4/Fu/8S9Q3yCuiYOIU6CqqrkVZn4Jq2U9zYVsDWvRgop4tRIDhSnWbnBwifbi9yFlPxqE5/sKKLSaMVXpyF1aDEr6mbw98zLqGxqWUhZNHIRxieMqM+obRZSAIJ9vRgW0/zE9+1nD+LKyYmMSQjp1jY6WkXH/IGP8spZ2VyR8ke8NL4UGHfzh51nkt+wq8W+Pl5azh8Zw4IJ8fh5aakyWfl0RxF5VV3TSkecBMl/opNMVgdf7i5xtyVUVZXvDmxoLqQccV78nTwyYam7kDJ9SCSz2ui93ZMNiwliYWpCl+XRYO9o7hm9mGCvKCqb8lle+CrqCaa+jEsMZfaIaAAySgysy6k84RhxApIDRTvqGm18m16G3dn871ijvZ5ndk3DrhrRqmFcmfgpk5MGtxiTEObHoon9r5By1NSB4YR2QVszjaLh6sF/RqPoyKxdy/rS/3RBdKJNkgP7HVVVWZVVTlGt2b3t44MPs79uAxp8iLQ+TYAylEsnxBP6iwfd/Ly1LJqYSJBv329f2FWOzk7pCgFeocxJvA+AFUWvcdiY1iXHPR4pphyPfziEDQJO9oJGaR7nF9YdUQnRJqvDyZ6iOt7ffJjv0ssoqG7skrYLoh+THChOo6wyg7sVjqqq7CioZWmGHqvDSUDQQdSIF/mm5J4WY56b9RyNTzaiPqPy5VVfEngSFz6KopAY5s8Fo2K445xBzBoe1SUXusf7vmmx13LP6MXubW9kXs+q4jda7ZscEcB1ZwwgIdQPu1Nl2V49uwpr5ebg6ST5T3SCxe7k62PWSHG5VD7Z+w0b6u5273NB4r3MG/gw3lpfNIrCBaNimDqodz7FmhTuz/xx8V3WlizcN5HLBj0BwObyj/nHvqtPOGZMQghzRsWgAJmlDfxwoFLWBewMyYGiDY1WB1+nldJka24ZXWsp5Q87z3B/Pjvyn0wZGNdizIBwfxam9u+2NzqthnOHR3fJseIChjEj7iYA1pT8C5uzqUuOK35BcmC/s/FgFTnlRvf7zfqPSa/5HoAI6+/wU0czd2wcscG+LcZ5aRUumxBPeIB0EjhZZwyK6LpjRV/u/vP/7bumy47bHmnzdTyK0rxw1MonTn7sGb+SBafEaWGxO0kvrietqL7dtVBUVcXqcNFkd2K1u3C4XDhcaotii0YBjaKg0ShoNQpaRWnedsyFsao2T310uFw4nCp2pwu7+39dOFUVlwtUVLQaBZ1Gg7+3lgBvHaH+XgT0k/64fYbkQHEauFwqPx5qbs8CYHe6WLO/gkOVJlyYKPa7BhxAw89jpsZP5cdbf8RH1zVPs/jotKQOCGN8Yig5FUZ2FNRS22jrkmP/0sCgVH43/hv+mXkDFqeJVcWv43TZmZN0X4v+roE+zVPFNx6sYl+pgc25NdQ12jlvRHSPXUehT5H8J06R3eni2/RSqk3NOcThcvHart+id6x07/PohOXE+g8Bmi/CLx4bx+CoTj4F62EDIvy5eEwsy/fpu+RhntTIuXx08CEAikwZHDJsY2jImccdMzIuGEWB1fsr2K9vwOlSuWCUrClwSiQHil+wOVx8m17mnm3X5DDyZtad7s9H+/2WC4dNbDFmYGRzoVXWf4OBkQEMiQ4kt7Lzs43nJT/MxrL3sThNPL59Aq9Oy+mCCEULkgP7lV2Ha93XogCZtT/wZcFzAPg7zsbfNY3zR8UwMDKgxTiNonDJ2DjiQvxOZ7h9xpDoQKKCfKgydn7tFK3GiwWDfs83BX8BoMKcR4z/4BOMOnVyZ/NExl8La/8E9ibAdeL9FQ3o/GB891fCRP/mdKmkF9exo6CuRRHF4XJRYbCiNzRR0WClrsmGwWzv0KJ3TgxYtTsZqGgYqbOgAZpUlZGaGFaxil3s4mEeJgAfalUDm+xR1DrHoHRwkluAt5boYF8GRvgzOCpQiiu9geRA0Y0sdifL9+rd06lNFgdL95ZRYTTToPuceq+P3fuOCj+Hy0dfzB9mPoyXtntmkGg0CoPCvNEfyMdcWEiD1YXdZsXZUEXAhEvA6aBx32oAvKIG4ZM0Bo3vyd8ATQgYyV+m7uJ3W0cA8EPpW9RYi7luyF/Ran7+bVqNwnkjookI8GbjwSr26xsw2xxcMjau1YKHohtI/hMnyeVSWb5XT1m9BQCH08U7ae+2KKTcO/ojdyHFz1vLpePjiQ/tORfhdXV1rFu3joaGBoKDg8nNzcXf35+7776bbdu2sX79egDmzp3L2LFj8fH5uag9NCaImVYHG3Kq2jv8SXnprCwe2ToagDezbub5M3Yfd/0UgBGxwWgVhZVZ5eRUGLE5XSxIje+SePodyYHiCKdLZfm+MioamnObU3Xw7v4HqLHmgaoj1f9lrp9wUYsWhYMiA5g3Lq5XFVJcLhf5+fls27YNl8tFQEAAmZmZnH322cyaNYs33niD+vp6AgICWLhwISkpKSfVlvGcoZEUVDd2eo0pjaJl4aA/8NWRm715hp0MDpnSqWOKNkgO7Bf2lzWwKbfa/T7PsJP3sptbRvk6JxJp/x3TB0cwKi641djZI6NJ6eUPwxzLarWyc+dODh8+jEajwel0kp+fz+23346Pjw//+lfzOk0jRoxg9uzZREZGdvo7JyWHsTKzvNPHAZgRdxMZ1SspMO7m7f238fTkjV1y3LbIncwT8QuFqz+Ej68CVcPxk6gGUODqj5rHCdFN8qpMbMypcj8Z5HSp5FebyK00UVDd6O5h+0teWgUfLwUfpQl/TS7xrkICUEgmCoBVrCKddObyGHDkPxZHzg9nMYtwwvGh+aI5XAlhgbeNQr5mCUswYWKW9hZ8/c5F4zUQp6ricKo02hw0Wp00NNlptDkpqG6koLqR9TlVJIX5MWFAKIMiAnpVf/B+RXKg6CbNPbdLqTvSBqeiwcLSvWUUO9/G6PdNi33jAwaz4661BPh0vojicDQvBL1r1y5qa2tJT0/Hbrfj5eXF9ddfj9Fo5Keffmo1zlZ6AHtVgfu9vaoAe1UB3omj8UkYhWq34DTXowuORtGdeJq3oii8fNYBNpX/l28LnietejkBujAWpfyh1b7jk0IJ8tWxIrOcwzVmvtpTyqVH1lUR3UjynzhJaw5UUFDdCDTPUHlp591Uu350f/77iT8Q4ZsEQLCfFwtTE057WwibzYbD4WDjxo2oqsrOnTsBiI6O5pZbbuG9997DZGr55LTFYiErK8tdSAFYvnw533//PTfccAMDBw6kqKgIVVWZkJxMRYOFA3ojnaVVdPx6zMe8kXk9AH9Nu4RnJv94glHNRR2dVsPyfXoKqht5dMlePrz9DALlIZ6TIzlQHLFmfwWHq5sffHG4bDy6baz7s7Fe/8e1485Fc8y1XHKEf48tpFitVqqrq9m1axdNTU3k5DTP6pg+fTozZ87k448/bjVm06ZNqKpKfX09AI2Njfz3v/8lJiaGa665hpCQEDIzM4mPjyciov22NaH+3oxJCCaj2NDp33F23PXuYsqakjdJCZ4s19NdTXJgn1dQ3cia/RXuGbU1lmL+feAOALxdKUTZfk9qUhSTklu3bTtrcARjEkJOZ7id5nQ6cblc7N27l/LycnJycjAajWg0Gq655hp0Oh1r165tNW7Tpk0UFha632dnZ5Odnc3UqVM577zzsNlslJSUkJycjL+//0nFNDwmiM251Rgtjk7/PoBLBvyWf2bdQL2tnH01PzAyblGXHPeX5IyyI4acD9d/Dv+7CexHFyM69mb1kf9oefk1J88hs093hKKfMFkdrM+udE8PbrI72VdiYG9pPY3Wn2en+HtriQvxJS7Ej4gAbwK8HRjqt5FT8i1FFHEl1x85F2jZuzWPPJpoooEGgmlZeffBhyyymELLp16SSSaBBFy4mOlMBlM+0LwwtHnYSGIjJwDNNxWqjFZK65vIr2qkvMFCcV0TxXVNhPl7MW1wJIOjpKjSI0kOFF2spM7M0gy9e1bdoQojq/aXode9QJPXVvd+V6Y8x62pN3LhqOQWLQdPhsvlQlVVNm3aRGxsLAcPHmTPnj2t9nM6nURGRjJgwAC+/PLLVp97ORrRDj0Ty6FtLbbbK/LxjhmCtSQTe2VBi8+CzrwKRdN+wUOjaJgRdxPeGj8+z3uKTeX/JdQnlvMS7my1b0pUIItSE/guo4zyBgtf7i5hYWqCzPDrbpL/RAdtya1mf1lzP0KH08Vbu193F1JSAmdw39h/u89xooJ8WJCacFpu7jscDhwOB6tWrWL69Om8++67WK2t2ykMGTIEVVW5/vrrefvtt1t97nQ6SUhIoLS01L3N5XKxY8cOkpKS+PTTT1scNzh5FCSM73T8KcGTWTDwSb45/DwGWwVp1ctJjZx7wnGDIgNYMCGepRl69hTV89HWQu6Z1X3tHvosyYH93qZD1RzQ/9xr9e39P5+jJCkPcG3qrBZFk8QwP+aP7zmtvRwOB2VlZWRlZTF69Gjef//9NvcbMmQIOp2OgQMHcvjw4Vafp6SksHnz5hbbKioqKCgoICIigq+++qrFZ7fddhtJSUmtjnPGoAgO6I3YHB2Y6XACT6Su5qX0eRw0bCajZiUTIi/u9DHFL3RjDiw3WCitN1PRYCU6yIeh0UGEdOP6jaIlvaGJFfv07vXVGu31/Cf7XuwuCzo1ihjr84yMiWTG0MhW96jGJoRwZkrXrffRXVRVxeVysX37doKCgqioqGiVx6D5fC4+Pp6AgIA2jgLV1dUsWLCAzz77rMX2vXv3csYZZ7Br1y62bt3a4rPHH3+8xezl9mg0CqkDQvnxYPUJ9+2IwSFTSAgYRWnjfn7Uf8BVY7qnmKKo/Wg104aGBkJCQjAYDAQHt56idUJN9ZDxGWx/E+oO/7w9bFBzX8QJ14Jv76pMit4jp9zI2uwKrPbm9UnSiuvZfbgOm7P5RMzfW8uI2CCGRgcRE+xDo6UKV1rrqvIXfMEsZhF1ZDZKpaaWaFc4VmzkxNmIDR9PSvBkUEGjaX0S7HDZ0Gm8cbrs2ByNHC5Zy9rydwkjjPnMb7X/q7zK1ITrOC/hTnx1P0+BbGiys7fEwL4yg/tkMiHUj5nDoogK6pp1EI4K9vPi9rMHdXj/TueKHqhLfpPkQNEFsssbWJNVcWTdJpUdh2vZWLCFKu+/4tT83BrmV6Pe464zFpA64NQWcMzOzuZ///tfi20hISHMmDGD5cuX43K5UBTFPYU5Pj6eO+64w32yrKpqqxPn7PIG1mdX0WRrfnJGtZhwGKvQBUdhKUjDUVfKLwVPu/aEsaqqyrLCl1lf9i4Alw96mulx17e5b43JytdppTTanIT4ebEoNYFgv+NfeKUOCGXWSSx8KjmwDZL/xHHsLaln7YFKoLnd6lu7Xyff3twKwUvx44Uzd6NRmgurA8L9mTc+rtsXY/788885cOBAi21TpkzB4XCQlpYG0OKm4Xnnncc555wDtJ3/jnU0fxYXF1NXV0dcXBzvvPOOe+bfUf7J49AljO70b1FVlef3XECNtRgFDc9N2UqAV2iHxlY0WGiyO3n1qgkdWm+qL+Y/kBwoTk16cT3rsyvd79eWvMvyopcA8FKj+P3EdQT7/Ty7Li7El0UTE/HWebaQYrPZeOGFF1ptv+yyy9i5cydlZWUAJCcnU1hYiE6n47bbbiMuLg7oWA50OBzk5+ej0Wiw2WxtPohz7733EhUV1Wr7ltxqthfUnurPa2Fl0eusLnkDgBfOSMNH2/KpcG+dhvvOHdLh40kObEcX5sCiGjNb8qrRGyytPjsjJZxpgzvfOkkcX22jjc93FdNka36wz6k6eGPf9RSa0tGq4cRY/8aQ8BTmj4tvde6QEhXA/HHxp/yg3+lQWFjI4sWLW2zz9fXl4osvZtmyZdjtzZ0h/Pz8aGpqIiwsjPvvv/+418FHqWrzNbzRaOTw4cPEx8ezZcsW0tPTW+379NNPd+hhaavDyX82FWC1d77IDHDIsI03s24G4B/nfc8D51zU4bEdzRW9qpjy448/8tJLL7F79270ej1ff/01CxYs6PD4LvsPg8MKaU+DwwZjHoPAGFlgSnQbq8PJ+uxKd7uE3EoTGw9WYbI2X7BGBnozaUAYQ2OCsDob2Kh/n301P3BR03QGMrDFsSqp5C3e4vKI+4kLGE5S/Cy0x3lq+mQ5XU4KS38gsvjnk8M/8keSSOIMzmDgkAXER7ec2WJzuNhdVMfuwjqcLhWNAmekRDB5QFiX/QeqLxRTekz+A8mB4pSlF9ezIacSVW1+cvuH7EoyKrdR4fOYe5/xERdyx6jXuWRs3En3oHW5XO7iyJ///OdWn8+fP59hw4ZRVFREZGQk0dEdLzAcZbTYWZ1V4V7n5Viqy0nTwc04apuLKl6xQ/FLmYyt/BBOUw2+Ayeh6NovfKwoeo0fSt5Cg5Zfj/2EgUET2tzP0GTnqz0lNFgcBProuHxiAqH+7bcK6gvFlB6TAyX/iV/IrzKxNKP5yUanS+XtPa+Ta/snAGdE3MFVwx52X0gOjw3iwtGxHbqpf7KO5j+Aqqoqd1/rYz388MMYjUZqa2sZOHDgSbdiOB5VVWlsbOSVV15xbzt34fXsKrVgKdqLxjcQn/gRp3z8emsFz+2eAcDU6EVcM6T1jdL2XDEpkaTwjv3Wnpj/QHKgOP0OVRhZvk/vbn+TWbvuyDoCLkKc87h3wl+ICvJ17x8Z5MOVkxLx9VAL0mNz4IoVK9wtDI/y8fHhscceIy8vD4BBgwah1XZdrKqqUlxc3GLmy9NPP015eTlbtmxhypQpDBgwAGi+vn9v0+EW656eKpuzice3TwBgTPhsbhvRMvf3lWJKX8iBhiY767Mr3e1A2zN7ZDTjEkNPPUZxXA0WO5/vLHa3lbI5m3g/59fk1G9Co/oRY32FAUHDWDQxodUalXEhvlw+KbFHrl15NAe6XC7+9Kc/tfr8vPPOY/LkyRw+fJjQ0FB38birOJ1OVq5cya5du4DmdVWuvvpqMjIyyM/PZ86cOe3OfAH46VAVuw7XdVk8Hx96hN1V3xHtn0DxQ/l4azvWVrejuaJX9YZobGxk/Pjx3HbbbSxa1D1TdTpEUcDHr/nlHy4nkKLbVBotrNirp85sp9HqYH1OJXlVzf/xDfbVcdbgCIZGBZCd8RaNBeEAHGAp5ZRgZ6r7OMV+dfhFpJCcsIiXtQ92W7xajZaUpAshCRwOK+uK3mRA+QBu47bmHXJz2V+9nxEjbnSf7HrrNJyVEsHo+GB+PFhFXlUjW/NqKKhq5OIxsSd84rq/6DH5DyQHilOyLb+GrXk1AJhtDr7OOMBB838xen/n3ufx1O9JCR3GZRPiiQ72be9QLZhMJj744AOqq5unBrc1pfjss89mzJgxxMTEADBq1KhT/h1Bvs3rHGzOq251wqdotPiPaL7Z57I1oXj5YCnYg03f3I/bXlmA/9gL0AW1/cTZxUm/ocZSQlr1Mhbn3M/9Yz5xr69wrBA/L66clMRXaSXUme0s2V3CoomJp33thdOpx+RAyX/iGBUNFr7PLMelqtidVt7Y8zTF9m8AmBJxe4tCyoSkUGYNj+rydqbHzsJLTU1l/vz5eHn9fO40ePBghgwZwtixYwkICCAgIIDY2NgujQGa14EKDAzkmWeeQVVVTCYT/v7+rP/658K2vTKfgDHnd2hdqV8K9YnhvtH/5Z9ZN7Cj8iumxVzLgKBxXfkTejTJgeJ0KqkzszKz3F1IOVS/lcXZDwIuAp2zuX3Mn1oUUsL8m2fKnu5CiqqqrF69mm3bmluwXn/99QwZMqRFd4XU1FSGDh3KsGHDUBSFIUM6Xlg4GYqiMGDAAJ555hlcLhdms5nKykr+/e9/A7gXsj/vvPPw0WkZnxTC9vzOz07x1voR7TeIyqYCMmvXUm+tINQnptPH7Wl6cw5UVZU9RfVsy6/pUHu39dlVBPjoGNyHFjbvKZpsTr7eU+oupNRZy/jT7nMBUPAi3PYgcf5DuGxCfKuCSZi/F5dNaF1g8SSLxcKHH36IXq8H4JFHHmn1oMxZZ53FmDFjiI+PB2DkyJHdEotWq2Xu3LnMnTuXpqYmdDodmzZtcq/BsnfvXm688UZSUlLaHD8+KZQ9hfXutmuddWnyY+ytWU2luZSP937Mram3dslxj+pVxZSLL76Yiy+WPpCif8gsNbA+uxKHS3UvjNVkd6JRYOKAUGLDD1Nd/CWN+QkkEe4edwd3YJtuY0LkBEaFj2L0gNHYHC7qm2xUGKzoDU0crmlsscZKd9DpfJiT8htmD7yfA/veYUBj87TXxHpvDu7+J0ljryXA9+cbisG+XswdG0d2uZENOVWUN1j4dGcRF42OJTmi/Qp2fyH5T/Rmm3Or2XGknUGd2cY76a9QzmI4cr9vVNi53DTsNeJCQliQmkCw74mLqPX19WzYsIGMjIwW29PS0jjzzDP59a9/TWBgYId6tZ4sjUbhnKFRRAf5sjqrHIer9UmfxtsPAO+EUdjrSlEtzWtdmff9gM+AsXgnjGp1U1VRFK5I+SNljdlUNOXyZtbN/HrMp21eFAf66rh8YiJfp5dSY7Kx5MgaKl3dJrGnkBwoehqD2c43aaXYHM3rMr2T8Vd3IWVy+G1cM+wR97/jZ6ZEcNbgru2tnZ+fz+rVq6moqHBvS0tL45xzziEsLIy7776b6OjoNlu2djdFUQgKCgJg0VXX8tXnnwLgMhswpa/Ab+hZ6EJO/mbf4JApTI66jF1V3/J53h/47bglaDX946EbyYHidKkxWVmaoXef2xQaM3jnwD24sOHnPJPrh/6FxLCfb9YF+uhYODHxtK7h5nQ62bVrFytXrmyx/eOPP+bpp59m1qxZTJ06lfDw8HaO0L00Gg2BgYEEBAQwa9YsNmzYADQv4lxaWsqiRYuYOCCMtKL6Llk75aFx3/Da3kVUNOWxuuQNrhrc+qn03q635kCT1cGqzPI2Z7S3x6WqrNlfQcI0P4/N9OqLLHYnX6eVUttoA5qLXEcLKQBR1qeI9j6DBRNaF4YDfLQsTE3Ez7tn/P9hNBrZuHEju3fvbrH9p59+4sILL+SBBx7A19cXPz8/j8R39HsnT57MgQMH3K0VP/roI3dR+ZfXwcG+XqREBbjXh+6sIO9IRoSew77aNSw/tLx/F1NOltVqbbEQYkNDw3H2FqJncLpU1mdXsq/UgNOlsim3mvTiegAi/HUMidrJ/sr/saFS//OMjyPq/eu585o7GZXU8qlrb52G6CBfooN8GZsYgqqqlDdYyC43kq03dskU4/ZoNVrGjP8VTbZ68vZ/Srw5kHh7JM49a8hPCm+eyXKEoiiMjAsmIdSP5fv0VBqtfJtexvQhkUwcECqL058EyX+ip9hyTCGltM7M4sy/UaP91P35jcNeZULEJQyICGDeuLjjXjSoqkpWVhbh4eFUVFS0KqScccYZTJ3aPCsvIqL7FwUcHhuEr5eGpRll2J1tP0Wj8fYlaOJ8nI31WA7vwWmowFq0F2vRXoLOuBJF2/JUzE8XxD2j3+eNzBuothTy3eEXuGn439s8doBPc0Hlm7RSKo1WvtpTwoLUBGI6OKunL5McKLpTk83J12klmI/02l6yfwm5TZ+AAhND7+C6EY+49505PIqJp7j20y9ZLBYyMzMZOXIkO3fubFFICQ8P5/zzzycsrPm7umP2yakYO3IYlpt+zbotu7CVZOKymDBnrUMbFEnA2AtO+njzkx9lf91GyszZfF3wF64Y/MeuD7oPkBwoToXJ6uDrtFL3tWGdtYy3s+7CoTbh60xlUfJLDI/9OZ/5emlZODGBkNPQSUBVVaqqqigrK2PQoEFs2bKlxecDBw5k/vz5KIqCr68vvr6ePxdSFIWZM2cybdo0NmzYwM6dOykoKOCVV17hsssuY2xCArsLO9/Wxlvry1WD/8TrmdexreJzIn2TOS/hji74Bb1XT8iBRTVmvs/Uu88VTkaTzcnWvBrOHXHyLYlFazaHi2/SSqlo+HmdmrWl/3b/2dc5gQjdVBamJhDo2/LazFunYcGEBEL8Pfvwhqqq5OTk4O/vT2NjI3v27Gnx+cSJEzn//PMB3OeCnubr68udd95JTU0Nq1ev5uDBg2zatIlNmza1OYtmQlJolxVTAC4e8Bsmx0/h0Zk3dtkxj+rTxZQXXniBZ5991tNhCNFhRoud5Xv16A0WTBYHKzKb/6ziYmxUKVNMFVAJ9cSTTTb/5b/cN+Q+xgwew/yp8zv89KGiKMSF+BEX4sc5QyI5VGlid2EdVUbriQefIj/vUMZMuIeSim0E5xVQTz0/Fn9FoaaEcxNub7FvsJ8XV05KZMPBKrLKGtiUW0292cas4dHd0me8L5L8J3qCbfk17gU28yqN/DN3MhxTK/nj5E0Ee0cxMi6YC0bFtPvv99EFk1esWAHAwoULSU1NRaPR4HA4GDRokMeePkyOCGDhkYLG8Z4u1AaE4j/qXGz6HKyH09CGxuKyNqL1b71YZbB3NDcOe5XX9l5Oes33HNi2kRfOTGvzuH5eWhalJvBNehnlDRa+SitlwYR44kI88yRSTyE5UHQXh9PFdxml1JmbF+98budc6u25oMDggEu4fuTDQHPnj/NHxjAmofOLcpvNZr766ivy8vIICAggISGByy+/nC1bthAZGUlKSkqPuHHYnonJ4WSWDaMuIglz1jqcpho0fkEnXOS5LUHekcxLfpjP855iS8WnnB13PbH+Q7sp8t5LcqA4WVaHk2/Sfm5/43DZ3E9te7sGc2HsS0wa8PONXS+twmUT4okM7N4ZsaqqUl1d7V4HauLEiYwYMYIbb7yR/fv3k5iYyKBBg3r0Q3deXl5ccMEFjB8/njfffBM/Pz8cDgeTksPIKK5vc4bzyRoUPMn952WFL3Fu/O09+u+ku3k6B6YX17Mxp6pTLYv2lhgYkxDSZ2edny42h4tv0kvRG34upGzWf8yKolcBCLYvJJY7WTAxgbBfrEGp1SjMH9fx9tPdwel0kpWVxddffw3AvHnzmDRpEpdffjlms5mBAwcSFRXlsfg6IiIigmuvvZY9e/awdOlSkpOTqaura1VMSQr3JzLQm2qTrUu+N9Z/COfGTWREZNc/YNSniylPPPEEDz30kPt9Q0MDSUmte48L0ROU1JlZsU9Po9V55M/lNNmdxHjv4RKtC44p0Dpw8OIFL/LQWQ91+iRJp9UwMi6YkXHBFNY0si2/hrJ6y4kHnqLEmDM5rNrJyF/PPvaxt3AvTYYS5gx7HJ3u5xMFnVbD+SNjiAz04ceDVWSWNdBgcTB3bBzeup7Tp7KnkvwnPG1PUZ17jZR9pTW8f/gcOJKuxoZfxPVD/4q31veE7W9WrlzJ9u3bW2w7WjgZP3589wR/khJC/bh0fDxfp5XiPM4FsaIo+MSPQNHoUHz80fqHoLqcOBvrWq2jkhQ4hilRi9hZ9RVWl5kDdT8yMmxGm8f18dKyMDWBbzNKKau38HVaKfPHxXd4weW+SHKg6A6qqvJ9Zrn7PGl57hfNhZQj7h77VxRFQVFgzqhYRsV3bvFeq9XKO++8Q01NjXtbY2Mj4eHh6HQ6ZsxoOyf0NNojrRGXZpThP/Z8LAV78EkcjaIouCwmUDRofDqer86IvoIt5Z9S0pjF39Ln8cpZ2f36pmFbJAeKk+F0qSzL0LsfrHO4bLyVeY/787NCnmfGkET3e42icPHYOOJDu/fBjdLSUt59990W21RVdc886S058Kjo6Gjuv/9+9uzZQ2pqKlqtlgG+FvJMOhRN59sHPTTuK17d27yeyCHDNoaFntXpY/ZWnsqBqqqyIafK3VmkM1yqyvqcSq6aLLn7VFnsTr5NL21xf2tX1bd8WfAcACH2a4lSb+Cy1PhWRStFgTmjYxgQ4bnrqY0bN7rbBB4VGhoKwOjRo09/QJ00ceJEvLy80Gg0JCQkoKoqJSUlJCYmus/jxieFsvZApYcjPbE+fUfSx8eH4ODgFi8heqK9JfV8tacUk8VBRnE9X6WVYrRXEeD3fnMh5QgzZlxJLtY+s5bfTftdl184JkcEcPWUAc1PGXXjExADY89h/llvkhQ4jmSSmVY/hNw97+Bwta5AT0gKZd64OHQahaJaM1/uKcFsc3RbbH2F5D/hSfvLGvjxYBUAOwpLeL9wGig/T3G/dcQ/8PPy48LRscctpHzzzTfuQopOp8PPz49bb72VxMTEdsd4SlK4PxeOju3QWrzesUPwCmteBNBatBfzvjXY9Ida7XfNkOfdf37nwJ0UGfe2f8wjU9AHhPtjd6p8m1FGQXXjyf+QPkJyoOgOGw5WudsPbCvZwtqKP7o/+/OUneg03mgUhQtHd76QoqoqL774oruQEhwcTFRUFE888US3rAXV3YZEBzIg3B9F0eCXMhmNtx+q04E5+0dM6Stwmg0dPpaiKFw5+OcnjlcW/193hNyrSQ4UHaWqKmv2/7ymg0t1sjj7YfJNmwAY7vUE80aPb3HdOXtkdLcvjl1dXd2ikBIYGMiUKVOYP39+t35vdzvaklGr1aLX69m7/lvMWetQXZ1vu50YOJqzY28A4K39t9Dk6L/t/TyRA52u5gcuuqKQclRpXRO7Dtd22fH6kyabky/3lLQopGyvWMJnh56A/2fvvOOjqvL+/753esnMpPceCGlUQcAGomJBUERQXEXXXd3VXXfVZ3V3n13dffa3zS3qum639wqCoGuliPTeSQhJSO+ZJNPn3t8fAwOBJKRMSOG+ffFy5pZzzk0ynznnfBsQ5rueCOk25o7rPKL/stHRjIkbvO/Ozz//PGhIEUURg8HA4sWLyczMHLQxhYKCgoKgIWj9+vU8//zzrFu3Lng+J96CTjP0TRVDf4QKCiMYSZL5/EANnx+oxe3z89mBWr44XMIx3WIqDHdQz0lRmXzdhfz+8d/zy28OfLhqRrSZb1yYwqycmAErsiUKKr6b9wJRQiBcPMEXyeHt/+r02oxoMwsmJWHQqKhtdfPO1nLsTu+AjEtBQaF/FNe18en+GmQZviou5p3yhcFzkboU/jTtIDqNyI0TEjvdbJRlGb8/sKA84XkD8Oijj/LII4+QkpIy4M/QV7LjwrgoK+rsFx5HlmUkT2DzwnV0K566ox3OC4LAzyetDr7/z8Hv4Je61j6NSuT6sfFkRJkCXqa7KzlQdf4upBUUQsm20kZ2ljUDsKVqDW+X3QWCj2j1xTwxdR9GjSXoxZgT3/fFtyRJwfRXanUgiUBaWhoPPvgg9913H1qt9iwtDF0uHR3dweAse10gS+D34tj7GZKr5wbgZHMBaiHws1hX9UqnDjkKw4Nmh4eGtoFLNazQPeuLGjhQ1Rp8/96R37C/+b8ApIuPcNeEOzqkYZ2eGRmS9IVdIUkBR8JTjcaLFy/m4Ycf5tprrx1RUWgtLS0IgL+1HseBNcj9SAl1ghkJJ2uqnloTQmFg8folVuyq5FB169kv7iXrixo41osC9grQ1O7hrS1l1NpPfrf8dvts3jryv0j4MfkuJ8p3D3PHJpAUfmbkyYUZEUwIUb273nJCA081AD7yyCM88sgjjBo1ctKayrJMc3MzAF9++SWbN28GAuvZnEE0YvWUYZXmq62tjaKik6H0R48eZefOnURERAzpzRUFhc5wef18uLuKY40O2t0+Vu6poqX1AHcbGoCHeJZnkdQaxCwLP77pe2jU57bglSAIjE2yMTo2jPVF9eypaCEE87sO6FQmbpv2KkUlK4mptJPkiWTfnv+QV3BmwbxYi56bL0hi6Y4Kmp1e3tlWzo0TEokwDd9Nhd6g6J/CcKC6xcWqPVVIssxXxSW8X31NMLXXktFPMy7qaiwGDTeMTyCykxzbW7duZeXKlURFRfHtb3+b0aNHk5mZOaxSk0xOi6C6xdWj4nmCIGAYNR2XqMZbW4yraDOi1ojaGhu8JlwXz8LM/8fbR35Gm7eBj489w3WpD3XZplolcm1BPJ8eqOFQdSuf7K/BZtRw2ejoYb0BoWigwmByqLqVdYX1ABQ1HeSNo/cEz/1g/NOoRXWwRkpfDSmyLPPPf/6TmpoaZs6cyaWXXsr8+fNJSkoiLCwsJM8x2ESH6RgdGxbcbBL1Zox5s3Ds+wLJacdxYDXG/CsQNT2LvPnl5K/5zfYraPc182Xlc1yZ9N2z3zRMGWka6PVLbC9t4lBNKw3Hc6ObdCryE6xM74VTgkL/2FHWxJZTvN4316xgQ+2rAMTxLe6euKRDeuVxyVYuzOg6org/tLS08NRTTwHwrW99i7i4uECR9oICVKqBce4bbMaMGcPixYt59dVX8bfU4Dq6DX36pH7N1yL0icxI+CarK5/ni4p/c0PGwyEc8eAxlDXQ5fWzfGclFc3OAWlfkmVW7ali8YUphOk77gk5PD5q7G7qWt24fX5UgoBOIxJu1BJp1mE1DG7R9MHgWKODD3dX4fKejPZadvQ31LlKAFBLSUT7fsB1BQmkRprOuH98io3pmef+e2jfvn28++67GAwGHnjgAVJTU7njjjtIT08/52M5FwiCwHXXXYdGo2Hjxo18/PHHWK1WsrOzKUiyhjTCayAYVsaUrVu3MnPmzOD7EzkQlyxZwosvvjhIo1JQ6D0tDi9Ld5TT5PBSa3exYncVZv86FmhFTlRnjiSSZd9eQ3ZMTPeNDTB6jYpZObGMibfw2f4aGttD7/mXlXYd+1qfI7nVSHKriX37XiAv764zrgs3alk4KZmlOypodHh497hB5Xwoyqbon8JQp6ndwwc7K/D6Zb4+WsqHld8Lxr/eMfopxkVdTYxFxw3jEzHpOk4/JEniv//9b9Ajpb6+Hq/XS0JCwrl+jJBwZW4sta3uHkXQCYKAPn0SktMe8Ew8uBZj7swONVSmxt7MzvqPONyyns8r/smUmJuINqR22aZKFJidG4tJq2J7WTNvby3HpFPz2JzcYWtQUTRQYbA41ujgk33VyDLUtdfyjwOLgufuzXkVozaQ6mZmdkyfvbXtdjvvvPMONTU1ABQWFnLppZeSk5PT/wcYYlyYHsHhmtagg46oNWAYNQ3H/i8DBpX9qzHlzURQn91ZxqAO44b0n/Fa4f/wUdlTTIyaQ6R++Bjfe8NI0sDCmlbWHK4LFjo/Qbvbz6ajjQiC0G0KUIXQcOL3cIJ9DWt468ijANikq7l30g8xak/O17JizMzMHph1aVFREe+++27wfU1NDYmJiYwfP35A+htKZGRkcNVVV/Hf//4Xb3UhgqhClzq+X/O1mQnfYnXl8wBsrV0BZIdotIPHUNVAh8fH0h0VHSIgBqYfP69tKiPRZiDBpsftkzha305dq7tbh1eTTkWCzUByuJG0SBNW48g1rsiyzNbSQM3OEzUsZVnmk/JnWVv1EgAqKYokz7PMKUgio5NUhbkJFmaMPrfF3GVZ5ssvvwymunI6nbhcLmIGeR/wXCAIAldccQV1dXUcOXKEd955h8WLF5ORkUGizTBgBspQMKzSfM2YMQNZls/4N9wmkArnN1UtTt7cUkaTw0thTStvbyslnw+4RqNBJQQMKS3JRjY/unvQDSmnkmgzsPjCFMYlD0xYd17B3ZTbApOQ5BY9hSUrOr3OrFdz06SAAcXpDeTBrGoZuiIbKhT9UxjKODw+lu2swOHxs6mkjOUV9+MRC9GJNh4au5TxUdeQFmXk5knJZxhSWltb+dWvfhU0pAD88Ic/xGQ601NouKDXqLi2IK5DWozuEFRqjHmXo7LEgN+H88BaJE9HXfvmmL8FX79Z9BMkWTq9mY5tCoGCz5eMChhlwvSaYWtIAUUDFQaH+jY3K3ZX4pNkXF43z+7+ARKB3Nt3jnqO7PDJAFw8KopxybY+9VFYWMiTTz5JeXk5EChQ/M1vfvMsdw1fIs06smM7RtqozBEY869AUGuR2htxFm7ocbqbiVFziNQFDCi/3n5FyMc7VBgJGujy+lm5u4oPd1edYUg5lY3FDewubz53AzsPOdbo4OO91cFN2KP2Hbx06GFk/IT5Z3DP2N908GhPDDdwTX5cyOcRsizz0Ucf8dprr+F2B9aB11xzDRMnTgxpP0OdqVOnMvHiWQB4Kg/irS3uV3th2kiuSwlEpCwt/h0V9op+j3GwGYoaWNvq4s3NxwbckHICp8dPUW0baw/Xs6m4kVp794YUCBipC2va+OJgLc+vP8pLX5ewrrCOimZnSNLKDRVanF7e3VbOV4X1QUOKJPt5+8jP+e+xZwCwem8jxfMi13VhSBkVa+bKnNhzul5yOp387ne/61Az5L777uuQ6nqko1KpWLx4MdnZ2fj9ft566y0aGxsHNJ1kKBhWxhQFheFOaUM772+voN3tY2NxA//dt40LtavIEQMpXY7pGsi/9haeWPLwGSGcQwGNSuTyMbHMG58wIEWhcnPvpMwUyO9fWPUJNY4jnV5n1Kq5aWIi8daAV8bSHRWUNyl5RBUUBgOvX2L5zkqaHV7Wl+xmafl3A4YUwcb3C14myZxLTryFeeMSO6SKACgvL+eFF14Ivr/2umt57LHHsFqH9uSpJ8RbDUxOi+jx9YKowphzGaLRhixLSM6OtU60Kj2Pjl+FRtRztHUbO+o/7FG7E1PC+fmcHB68YuTk2FVQOBe0uX0s21GB2yvh8/t4avsPscvbEWQtd496k7HRFwOB1H69+ayfQJZlNmzawOuvvw6AXq/nO9/5Dt/97neHteGzJ0zNiEQ87RlVRivG3JkgiAFjsq9nkdCCIHB1ygPB9+Vt+0I6VoXQUNHs5NWNpRyu6Vk9gS8P1lGv1FEZEGrsLpbvChiJAd4u+hnP7L0FH61opTSW5PyGOMtJh5Yos5a54xJQq0K79nO5XCxdtjToTJM9JptHH32UKVOmhLSf4cI1M6ZjShsPgORo6Xd7l8TfTqQumVZvAz/69Ef9bk+hI7WtLt7ZeoyWYVbHtbHdw9aSJt7ecox/rS3mv/uqKapt7ZASazjh9vn5qrCel78uobzppCOaT/LwWuEjbKp9BxCJ8NxHpLSYueMSyOzEkJIZY+ba/HjEHjrChYLq6mpefPFFPJ7AfGfWlbN47LHHiI4+t5ExQwFRFFmwYAEpKSnIskxdXR2jY83oNUM3xeOwSvOloDCcKaxp5aO91bi9fj7eX8qG5sfR6A+Qxv0A7NIe5qGb/sjFo+IHeaRnJyPazK2TU1i+qzLkab/G5N3Ju3seZLPzczbv38n38l8jSn9mHlSdWsWNExJZvquS8iYnH+ysZM7Y+E7zXiooKAwMsizz8d5qqlpc7KgoYmnFnUhiO1rByvcKXiLBlM2EFFuX9ToaGhpoampCRoYJMG7CuBG1iTg5LZzDNa091klBpcaQfTGCKCLqztSyWGMmVybdx6qyP7O85Alywi/DqD674Skz2jyifq4KCgON2+dn2Y4KWl0+JEnimZ2PUuv/AmQVN6b8ibzoCQCMTbJy8ai+5dUWBIHSklIAZIPM/d+/H7PhzAX+SCTcpCU7ztyh6DUcj1DJm4nKHIkg9nwBPSl6LitL/0yzp4r3j/6K7+e/oWjeEGJ3eTOrD9UFvYV7giTLfFVYzw0TEgdwZOcfje0elu2owOMLRLfuqF/Fxtp3ANBIqdye+SIZUSdTrIXp1dwwIXFANrQkSeJIUcBxTk6Umb9gPlrV+VELszPUKpELpk5jiymiQ+28vqJVGbg4/ht8UPJb1pSuQZIlREHxpe4p9W1uWpxe0iJNwUhzWZY5UteKq6KFVrcPn1EO1oYcjjg8fvZX2tlfaUcQAnXN4q16Ik06IkxaTDo1Bo0q+PySLOP2Srh8ftrdPtpO/HP5cHj8OL1+3F5/0FArCAI6tYhBo8KsV2MzaLAZtUSYAv96GsHfGc0OD3sr7OyrbMHh6WgIavHU8Orh/+GIfTOgIsr9CDbhYuaO77zYfEa0iesKzq0hBQIpXmtrawGQC2SmXDjlvJ67qNVq5s+fj8/nIzIy8D2Um2Bhe2nTII+scxRjioLCOeBgtZ3/7q2h3e1jxc5D1Pgewqk6ihNYq92COSyR0ekpTBmggn4DQbhJyy1TkvlwVxVljaGLClGrdVyf/2vK9h2j2nGYlw/8gB+MexdVJ4tqjUpk3rgEVu6poqTBwYpdVVxTENept4GCgkLoWXO4jqLaNo7Wt/Bu8c+RVO2oBTMPjXuTGGMG0zIjmXqarlVVVWE0GrFarYwdO5bWtlY+c3wGQy8Yr9+oVSKzcmJ4d1v5WcPwT6AydEx/I0v+DpuKlyXcyda6pdQ6j/JW0c+4M/sv5/XEW0Eh1PglmZW7q6hrDXjFv7D3DxxzfwiyyFXxv+bi5KsAGB0bxuVjepeOVZZlioqKyMzMRBRFrpp9FQd9ByEBtNrzaxNxYmr4GcYUALWl48/0dA3sih+OfZtfb7+SktYdvH/0V9yU8VjIxqrQNyRJ5stDtewu75uX/dH6dkob2hVHqRDR4vDy/vby4MZjm7eRNwt/Hjx/c/J/yIs/6dRn0KqYPzEppNkSfD4fpaWlZGZmYjQamTN3Dm8dfAuGf0BySBiXZGPnsdjgnFGWJBBA6KMR5KK4xWytW8qScfNx+VwYNWduJCt0TrPDw4pdVeg1KiLNWtw+CYfbh8vtZIq76zSFwxVZhlq7+5ylLBMFgXCTJmhYsR43tJi1avRaEZ365Pe+xyfR5vbR7PBQ0eykoslJtd3V6dqqsGUDf9935/GHEojxPEakegpzxyUQa9GfcX1GtIk5YxP6ZdjpDXV1dYiiSGRkJKNHj+bK2Vfyif0TOL+mgF1yenaKMTFGxZiioHC+cqi6lf/uraHZ4eGzHUu5Gj8yd7CCFTRbRG6b8BeK3a+dUUdgOKBTq5g3PmDMKK5rD1m7Jk0438l9jn9su4VvORfSvvFtzFMXIYpnTiTVKpE5YxP4eF81RbVtrNxTxezcOLLjwjppWUFBIVRsL2tiR1kzRxuqeebwZaACES3fzfsPMcYMLh0dxaTUjqlvli5dyu7du4mIiODmm28mLi6OKVOn8Nm6zwbpKQaepHAj+QlW9lT0fjPJW1+K8/DXaBNy0B9P/aARddyc8Sue3fcN9jR+ws6Gj5gQdW2IR62gcP7y6f4aShsCTiLP7/0l+9oCabimRfyYqzNuBCAlwsjVvawf4Pf7+X//7/8BMGbMGG644QbMYWY4Tx3vY8L0pEQYu3TIkSU/zsIN+BqOYRp3NSpTeLftWbQxFERcwfb6D1lf/RrXpvwQg9oyEENX6AE+v8SqvdUcqW3rVztrC+v5RoRRcRroJ3aXl3e3lwdr1Th9rTy96y68cuD3c1Xk81yQejITgEYlMG98AhGm0O3wVVRU8J///CfQ31VXMW3aNDKzMqEqZF0Me8JNWpLDA7ooudtp27YcgLBpi/pkUFGLWn4yaQX3z8wK9VDPG1xePxWnpI9SYntCgyTLNLR5aGjrPHr/RCpQQaDHUY2FLRtPGlKAKO+PiNdP5YbxiR1qQJ0gK8bMtQXx58yQ8t///peNGzdisVhYsGABycnJXDD5Aj5Z98k56X+4UVhYyOuvv44lMRNSh176R0ULFBQGkMKaVj7eW02N3cUHW1dzBQFPIAGBSbHz+fVF73HLlNRhaUg5wQljxqjY0EaDWLQxXB/57eD7A3uf6/JalShwTV4cY+LCkGX4eF91nzYuFRQUekag+GEdtW12njk4K3h88ajfk2GdwMwxMWcYUrZt28bu3buBQJ5svf5M76CRykVZUb2uMyXLMu6KAwB4Kg8gOU96cGdaJzM+MmBAeeXwg/il4ZWvWUFhqPJ1UT0HqgL1ijaWb2BfSyD9TabhZm7OWQJAjEXHnHG9W3xLksSvf/3r4Hu1Wo1GMwLD8XrJpNSuDSSyz42v4RgA7bs+7lGh3PmnRKOsr369/wNU6BMen8SynZX9NqQA1Le6+xzZohCgxenl3a3l2I/XdvD4XTy7+x4aPAcRZSuzwl9j9ujpwetVosB1YxOItxpCNobm5uagIQU4r+aAvWVsUsAz29/aEDzmKto0WMNRUBgUJFlGkuUeGVJcvjbWV7/OP/bdFTwW7/ob2ZbZLJyU3KkhJSc+jOvOoSFl//79bNy4EYDW1lZFA3vAtm3bALBXHMHfPvSiUxRjioLCAFFS385He6upbHayduebzFPVB881psVx40XfZMGkpGFtSDmBShS4Jj+e5IjQhg5nZy3Cf9wAldxm5kjpR11eK4oCV+XGUpAYmIB+cbCWrSWNIR2PgoICVLU4+XhvFXanh7/v+h8QAl6OMfpMJkZfy8zsGMYn24LXy7LMH//4Rz788GTB9Iceegibzcb5gkGr6nWBakEQMObMCL5v2/EhkscVfD8/42Rqjj/vvqnfY1RQON/ZU97CpqOBecOBugO8U/pdZCGw+XjP2MDnzWrQcMP4xA7pJ86Gw+HgV7/6VdAYYLVauemmmzqNtj3fSIsyERWm6/ScqDWiz7ow+L5t+4edXncqRrWVm9IDBpVVZU9S5ywNzUAVeozHJ7FsRwXHQpgC+KuieuwuxWmgLzS2ezoUyfZJHv6+934qXdsRZCNTw/7EtWMmBSN/BAGuyoslPSp0qdWKi4t5+umng+/nz5/PhAkTQtb+SCMz2oxZp0YTlYKoD2Ra8NaV4KktHuSRKSgMPSraD/LTzZN4r/iXyEgY/dNJdr7J5MQCbhifiEF75nxtfIqN2Xlx56RGiizLvPDCC7zzzjvBYz/+8Y/PyyLzveXGG28Mvm7f9TGSq/8OGqFEmcUrKAwAlc1OPtxdSVmDg1d3Lmei6qRHcXN6ErdcvIBrCuJRq0bOR1AlCswZG0+UOXTh4KIoYp6yIPg+uqIZr8/Z5fWCIDAzO5oLjns6rj/SwNrDdT3yZlRQUDg7LQ4vy3dW0ury8vvt82kR1iCg5pujX+DHE1cxIzuacacYUgCamppobz+ZBvCRRx5BpQp9IdOhzoRkG2H63hnPRa0eY8GVwfeu4i3B12ZNBPkRVwBQ5ThEq6f+jPsVFBR6RnFdG18cDBQBLW0p5bnD30AWnBiEZH4xaQMalQ6jVsX8iYm9doI5dOhQ8PXo0aP5wQ9+ENKxD3cmpti6PKeNyUAdmQyA7G7D21hx1vYujL0ZlRDwQv342NNnuVohlHj9Est3VVLR3PVcvS94fBKf7a8JaZvnAzV2F+9sPRZM7VXnLOGRjQWUtn+FIOuYZPwd8wsu7pBC7bLR0YyJC216vLVr1wZfL168mIKCgpC2P9IQRYG8hMDvwDThuuBxV9EmZL9iVFRQAPDLPj4v/xdP7745eMzku4J4/yNcnZvJjOyYTqNOpmZEMjM75pyljmxra6OsrCz4/uGHHz7vauT1FZ1Ox/333x987yzaOKT29UbOTq6CwhChvs3Nsp0VFNe189Lu/1Cp+Tkv8AJOnPjyJnDvVTczLXP4FJrvDXqNinkTEjHpQrdRqlHrqU8+6dFduPUf3V4vCAIXZUVxcVYUADuONfPe9gpcXn/IxqSgcD7i8vpZtrOCVpePX2ydgYNCAOak/Jz8qOlcOjqKCSlnpmw5UR8lNjaWn/3sZxgMoUsbMZxQq8Q+ab86LArV8YLMvsZyfC21wXN3Zf81+HpF6R/6P0iFQcHrl6htdSH1MCe0QmipbnHx0d5qJFnG7nbw730PIAltaIjivoJ/YtFFoFWLzBufiM3Y+wXw+PHjKSgo4OKLL+aWW25Raj+cRnZsGMZOPEdPYMiaGnztPLi2y+tOoBa13Jj+MwB21K+k0VXe/0EqnBWfX+LD3ZUhjUg5ldIGB3vOku5LkmQkSR5Smy2DxeGaVt7ZeixYbL7eVcZvd8wOni/Q/x+Lxl0VrEsAMC0zstN5XH9ZtGgRZrOZe+65h1GjRoW8/ZFIXoIVQQisa00FVwWPt+/67yCOSkFh8JFkP68XPsKPNuSxsuxP+GQPWimbGPevyNU/ym1TMsmJP9MgrBIFrsyNPef7cGFhYdx+++3YbDZ++tOfYjaHNjX+SCcqKipogPfb6/DVD52I4+GfX0hBYQjR6vKybEcFxbXtfL33fSI1n1IP+FUi0VMXsWhSNknhoU2FNdSw6DXMzotj6Y4KQrWWyUiezb76v+F1NvGm9CZ3NU8m23Zxt/dMSg3HpFPx6f4aDlTZ+cZ/NvGfJRf0aSNEQeF8x+eXWL6zkoY2N6/sehWPXBc8NyNxERdmRHSokSLLMmvXrmX8+PFYrVZyc3PJzc0djKEPKXLjLWwvbaK+i2KLXWHMu5zWLe+jS85HFRYVPC4IAj8oeIe/7FnI1rplTI29mQzLBaEetsIAUdvqYntpM0fq2vD4JLRqkUSbgavz49Brzr/orcGg2eHhg50VeHwSbq+bp7ffh4ODCLKOu8Y8Q6I5E1EQuLYgnjhrz/Nbt7e3s2nTJmbMmIEoisyfP38An2J4o1aJ5CVY2dJFalZBpcY88Xratq/osKnYHVNi5vNu8eNAoHbKPUw9yx0K/cHnl1ixu5KS+oExpJxg9aFaauwuxiXbiDJrqW/zUN7koLLZRVWLMxiBIQiB9UikWUtMmJ6kcANxVj2aEZQRoCskSWZjcQObSxqD67BWTwP/2ndf8JoMzXf5xoS5Hby2L0gLZ2pG6DYZS0pKsNvtjB07FoPBwMMPPxyyts8HrEZNsBC9KiwSbVIevqYKTHmzzn6zgsIIpdi+lb/uva3DsUjPg1jkWVyYHsmklPBOo1G0apE5Y+NJjQxd+sLukGWZr7/+mjFjxhAZGUlGRoYSldwP5s+fz7FjxxAi0/BFpgz2cIIoxhQFhRDh8vpZtqOCQ9WtrN/zPlepABbyhuo9vnfRq9w+JY8I0/mxkZ8aaWJyWgSbj4auZknOuO/wxOZZeCQPbx95jEfHr0Sr6t7DfUycBZNWzco9VTg8/hGVVk1B4VwhyzL/3VdDRbOTFQe/YLfjN3B8nvqnaQeZkBrO9MyoDtevWrWKrVu3snv3br773e+iVivTDQgYPyanR/DRnupe3xd2wQ3IPg/CaXUWUsPGMiVmAZtq3+Gve2/jial7UIvnx3fNcMXnl9hY3Mi20iakU7wOPD6Jo/XtLN9ZyfyJicp31gDj8PhYuqMCh8ePz+/n2R2/oEnegCBruS3zb4yJnAjArJyYXtUPaG9v5+WXX6a2tha3280111wzUI8wYihIsrK1tLFLJxxRb8Y8+UYEsWffJWpRy91j/sFzB7/DuupXOdRwL8kRk0M4YoUTBCJSqgbckALgk2T2VLSwp6IFrVrE45M6vU6WA0XXW5xeiusCaUbVokBiuIH0KBMZ0eZOCxIPd5raPfx3XzVVLSfrq9U6j/K3vXdj91YgyhYuMPydBeMmoj5lLjE2ycolo0KXv7+kpITXXnsNn8+HyWQiMzMzZG2fT+QlWig7HumlTxmLFJsFqpH3d6ug0B1+2ceehk94+fCDZ5yLc/2RLNsEZo6JIbwLh1mbUcPccQlEmjuvzxZqZFnmyy+/ZN26dWzatIn77rtPKTYfAr73ve+xqbCKTccGfq7RU5RVmkLPkGVob4Cm0sD/z1X49GD120v8ksyKXZXsr7Lz9e53uVIloxJUVGmb+ellS7l7esF5Y0g5wbSMSBJsofviEEWRe8a/glrQ0uauZcuR53t0X3KEkTunp/HCXZMx9zLPuYICMLg6NAQ0cF1hPYdrWll3dDtrGx8FQSLFcClPTN1DboKVGaNPLsD9fn/QkAJwySWXKIaU08iODSPc2PvFsCCqELUBA7LkasXvOJnuZGbiN4Ovt9et6P8gFQaMxnYPr28uY0tJYwdDyqlUNDv5aG/10ElVMwLngG6fn2U7Kml2eHF4W/jN1gWU+94H4KqEnzIxLhD9Oi0zkvxEa4/bbW1t5ZVXXqG2thaz2czkycoGfk+wGjRnNViJGj2CKvB94muuQpa6T9+aGz6DDMtkfJKbe1YtDtVQFU6h1e3l3W3lHK1vP/vFIaYrQ0pX+CSZ0gYHqw/V8fxXR3ltUymbjzbS2N6DSNEhroEen8Sm4gZe21TawZBS1X6Yp3ffht1bgVqK4wLjX1kwfmIHQ31+opXLx8SEbMhHjhzh9ddfx+fzkZWVRWpqasjaPt/IijZ3iFIVdUYEQUCW/PiaqwZxZArnHbKM3tuMxVWJ3ts84BooyzKfHvsbD63P5ndf57Pm0ENEygIc79bou4wJwifcPPZybpyQ2KUhJSXCyK1TUs6ZIUWSJL744gvWrVsHwEUXXaQYUkKESqViQkY8KlFAcjvwtzcN9pCUyBSFs+Bshl1vwKZ/QtPRk8fD0+HCe2HcrWCwjZx++4Asy3yyr5o9FS3s3f1vrlIHPLTLtQ3MmPEAN12Q3m26DlmWcfvd+GQfTa4mYoznriDWQCKKAlflxvHqxlJ8IcoDH6FPYkn6E1iLyzDUG6iLOUi0bcxZ74ux6Im1KF9kCr1kMHVoiGjgjrImtpU2sbPyAMsr70MSWonQ5PHdsU8xKiacq3Jjg3rl9Xp54YUXqKoKLPDmzZvH+PHjz9rHSNXArhAEgQvSIvi0j8V0vfWlOAs3IprCMRVcgSCIxBgyMGsiafM2sKrsScZFXYNONbJTSg5HCmta+WR/TY82AYtq29h5rHlA8tf3mBE6B/T5JVbsqqLG7kKWZX62ZUrw3EVRD3NV2i0AFCRae5X2pqqqihdffBGPx4PZbGbJkiVERUV1e8/5pn/dMT7ZFowi6A5n4Ua8dUfRJRegS87v8jpBEJiX9mOe3H0TR5uLKG4qJiM8I5RDHvE0tnu6dAZrcXp5Z0sZrZ7hmZKw1u6m1u5mfVE9kWYtaZEm0iJNJNj0J40NfdAij0/C6fXjl2QkWUYUBLRqEZ1a7HmasR726/L6OVBlZ0tJI+3ujsbFw81f8/yB7+OR29BIGUwL+xPXF+R0SIGTm2DhipzQac769ev57LPPAMjKymLRokVndahRNLBr1CqRMfFh7CxrDh6TfR7adn2M7HZgGjcblWkQ5wgKIx6dr5Xc2g8ZX/U2tlPqjzXrk9gZv5D9MXNwq8NC0pcsy3xY+ge+rHwOqwxL0PIXTGRx8jumWDbyDjdSlbmQ5Ph4xE5Sep1gcloE0zMju70mlEiSxMsvv0xpaaCmx+zZs7nwwgvPep+igT3HoFURJTVRuOMTRJ0J07irEcTBm4MoxhSFrin6DN66A7ydhFI1lcDHP4HPfwWLXoasK4Z/v33k6yMNbClp5JMdH3E1gUV3ma6BK2b8gJsuSEer7nzibPfYWV60nNcOvEZ5W+DL6eN3PyY5LJnFYxYzN2suFu2ZxbOGE+EmLVPSI/j6SEPI2syJmU1h6d+w+C1UH/qMyMmjEUUlyE4hxAymDg0RDSyqbWPN4TqKG8p5/eg9+IUmwsRMHhz/PGkRkVw3tuMkdu3atUFDyk033UR+ftebXHB+aGBX5MZb2HS0EbvT2+t7VZZoEFVIbQ14qg6jSwgYlH828Que2Hkdje5y3ir6X+7IfjLUw1boBxuLG9jQy+/CTUcbyU2woFMPwkJhhM4BJUnmo73VwSLZ7x56MXguzXgFN42+B4CMaFOvvLV9Ph/Lly/vsSHlfNa/rkiJMBJu1NDk6F4X1bY4vHVHcZfvQx2ZgsrY9c8p2ZxPjCEdtcpLaXOpYkzpBbIs8/KGEkbHhjEu2YZRo0KtEqhsbMVX2YLd5aPd6AdheBpTTqWhzUNDm4dtpU2oRIFIs5ac9i2M+/r7iD4nEMxuCoB8XIukz/6PHVOfptg2jTaXj3a3r1sHMr1GhcWgxmrQEGHUYjNqiTBpsRk1Jx3vzqKB8sc/Qf7s/9h24dNsUk3A6+/YnyzLvFH0Y7bWLQNA58/h0sg/MDsnq0Ox+dwESweHmP5SU1PDl19+CUB2djYLFizo1pCiaGDPyE+wdjCmCGotKnMEPnc7zqJNmMZehSAo6+CBoM5Rw7/338P1aY8QZ8wa7OGcc1KbNjDn4KNoJBenq5rFVcFlR59keunf+XDM7ykNn9bhvCzLZ2iLV3LjlVzoVCaO2rdhUFupdRbz6uGH0KvCcPrtAFwlq3gPI525hKUJTh7hdbxl7/Oh8cx+AXQakatyY8mKCY2Rp6ds3LgxaEi5/vrrmThxYrfXKxrYNyblZFK0RYPktOMu34c+ZeygjUWQh0wOgYHHbrdjtVppaWnBYunHH6bfA/t+E3id91NQjcD0TUWfwWsLj4fwdedBKQaq/N32dmgW04PVbx/ZU97Ch7sreW7LP5mPFhMmKtT1XH3No1w/LqFLD6T1Fet5cPWDuHyBUGz5lK8o4fh0Xa/W8+SMJ7ko8aKBf5ABxC/JvLaplIZeFlzujoaWQti3AQ0aysxt5I/9drfXWwwa7r44vcfth0wrhhAhfaaRroGDqUNDRAOrWpy8t62c6lY7v94ZKGiuFxJ5ZMJbpEcksGhyCgbtyQ2Uw4cP88YbbwAwZ84cJk2a1G3755MGdsWuY818cbC2T/d6qotwFW8BwFRwFaqwgCF/a+0yXi96FIA7s//K2MgrAZiQYmNGds83hhUN7IZe6p8kyXxxsJY9FS3dXtcVF6ZHMD2r++iGkDNC54CyLPPx3moOVrcC8EnxCj6u/p/g+T9NO4ggCCTY9MyfmNSrYtUrVqxg+/bt6PV6vvWtbxEZ2XVEi6J/XbO1pJF1hfXdXiPLMo4Da/A3VyFodJgnzevWM7Hd28wdF+aR0sPCsyNR/6D3zyXLMk99VnjGcVH2MMXxTwA2G+9FEkbYHJDAJuIN+38IyIhnbCOeREIABJblPtXppl5v0GtUjGrdxOXb70eQZYQ+9CvJfpaX/JG1VYFUyCopmjmxr3BpVlqHjc38RGtII1K8Xi/PPvssLS0tJCcnc9ddd3XbtqKBveP1TWXU2E+mb5M8Ttp2rAS/F01sJobMKd3cHSi+ff/MnhsDFA0McP1rN/Nh0btE69N4cOx76NVmQNHAU/ET+Pxeh4NPhO5Tb/aEq2QVKzEiACq61pCuNDDWoue6gnisfUin3B9KS0t56aWXkGWZK664gosu6l63FA3sO7Is89f3V9O4dy0AxtyZqG1x3d6TE2/h6vzurzmVnmqFYsZWOBNnc8Aj5qyLWQLnZTlwvbN5ePbbR0rq2/lkXzUv73ifIzzNalZTQSUXz7iXuWcxpNz3+X24fC7k4/+dyoljLp+L+z6/j/UV68/F4wwYKlFgVk4soYxWjLSOotoa+MJOaTPT5uxbuhwFhTMYTB0aIhrY7PCwfGcldqeHZ3f+8PhRgfvy/0WiNY4bJyR1MKQApKenk5OTw/Tp03tkSDmfNLArchMs3aaA7A5NbCaiITC5cxZuCB6fGH198PWbRT/p3wAV+o1fkvlwT1WfDSkA28uaaHP7QjiqszBC54CyLPPp/pqgIWX9sS/5uOrHACRpr+YPU/cjCAIRJi1zxyX2ypACMHXqVCIjI1m0aNFZDSmK/nVNTrylQxqizhAEAUNGwMgve914Kg92e71JY1NSZij0GJ2vlTkHH+Vsm4jA8fMycw4+is7X2q9+ZWczl+54CM5iSOmqX5/k4R977w0aUoz+adyVsYLLRqV3+Psfn2wLqSEFQKPRcPnllxMTE8Ott956VkOKooG9Izeh42aeqDWgTxsPgLfmCJKrbRBGNfL52UW/x6aNo85Vwk83T8Inhc4xdCji8TtZW/kSb+/7Flft/z4y/rNqoIpA1N57GLH200U/yXc178vhCLLYrSEFztRAQQg4ji2anHzODSkASUlJjB07lgkTJjB9+vRur1U0sH8IgsCkcQWBTA2A49BXgzYWxZiicCa73jgeWtzTwn5S4Ppdbw7PfvtAbauLFbsrWbXjS+q8fwNgC1vIvfQWbp2a26Gw36nYPXYeXP0gsnymcJ6OjIwsyzy4+kHsHnvIn+FckmgzMDo2tKGWY3LuwENgUiPt+AJJ6l0hSgWFThlMHRoCGuj0+Fm2o4JWl4+/7fg1zQQmKNcm/YSM8NHMG5/YYZJaWFiIz+dDo9Fw8803c8UV3XuJn68a2BkalUh+Yt88/gRBwJAdKJAtuVpxHglEqYiCim+NCXjLufytHG7e0GUbCgOLX5JZuaeKI7X92+Tw+uVepwfrFyNwDnjCkLKvMqAj26s3837ZD0HwEa26hO9P+CMqUUWYXs0NExLPMBZ3hd/v5/DhwwBER0dz3333kZaW1uX1iv6dHZNOfdZC9ACi3ozueGoHd9lu/G3n8DOiMKLJrf0QjeQ66ybiCURkNJKLnNqVg9bviwcf4JGNBRS1rgNZQ5x0P9/J/xu5CR3raUxJj2DmmNAZUhobG6mpCTi0jR07lnvvvReDwdDl9YoG9o0xcWGoTzMya2IyEXSBSIm27SuQZWUdHGoijdEsyX4m+P75g/cz0pL6lLftZ+nR/8dDX2fz403jWVbyG6Y0b8JI95Ehp6JCwAjcgZYIzSis6kwANIKFUdrvk6L6Ngay0EtjCPPeQJTnf0hw/Z0k55ukOj8k1fkhme6VPGrIxSh4UQm908BxjR8xb3wiM7JjzuqMEWqKi4txu92oVCrmzZvHnDlzutVXRQNDQ16iFePo4xFJfi/t+1cPyjgUY4pCR2Q5UOyuL2z6x3GPwmHUbx+wu7ws217Bxzs3MdlVwt3CNxinn8WrVxdy3yWXdevNuLxoedAK3RNOWKVXHFkRquEPGhdlRoX0C04lqqiP1gXfFzcoVnuFfjKYOjQENNDrl/hgZwWN7R7+vfNJKvyBtF2XxX6fq1Lv5Jr8OGIt+uD1u3bt4vXXX+ell17C4/EgCMJZF+jnswZ2xrhkW4cc5r1BZbSCGMhHLjmagwu83IgZZFoCKR/eK/5FSMap0DskSWZVCAwpJ9hX2UJFszMkbXXLCJwD+iWZVXuqg4aUz0pf59Xi25EFFzZxEg9O/CsaVaBewQ0TErEaeubRKMsy7733Hm+88UawTsDZ6rcp+tcz8hOtPbpOG58dfO1vVYwpCiFAlhlf9XbvbwMmVL3VLw3sa7+Zpc+yu+G/wWO52v/j/sn3kRTeseLAxaOiuCiEKSPb2tp44YUXeP755ykpKQEUDRwo9BoVGdHmDscEQUCfPiH4XnL2LzJKoXNSw8YyJeYmAA42r+Xzin8N8oj6h1/2sbL0z/xuxzU89HU2f959I+uqXjl5gQzfpw+RHbLIQ1IqYfY/Y2t9mlTnhyQ4XsfTMhuhbR4xzqeIdf+RCN+3iBJnkWIZxdiEBC4bHc0tk5P5zqUZfEP8uE/PNK3uHdIjO6uwMrAcOnSIV155hRdeeAGn04kgCIoGniOsBg1p8dGIRhsQcCwcDEOnYkxR6IijEZqOQg8/4CeRA/c5m4ZXv73E5fXzwY4KPt29limtRzAIBjz4+PYFv+WmiV0Xm4fAwvv1g6/3qd/XDrw27D0hrEYNBUk9WyD3lDGjbuEgh/iUT1lV/SyS4pWj0B8GU4cGWQNlOVCUuarFxdIDKzjsCmxs5ltvYm7G/czIju6wkPvqq69YtmwZADExMWg0Z594n+8a2BkWvYasGPPZL+yCsMk3IOjM6FLHdTBkzUn9EQB1rhJ21PfPW1ahd8iyzCf7aygKkSEl0CZ8fqAGn3+Av+NG2BzQ7fOzfFcFh2sCm0zvHfkjqyp+CYBZyOWhCf9Cr9GjUQnMHZ9AlFnXaTtn9CrL/OY3v+HAgQMAJCQk9OgeRf96RlqkkTB914WrTyCo1BhGX4TKGos6KvUcjExhpKP3tWBzlZ81zdbpiMjYXOXofX1L6difflMkFxHHPcivjnqNb15wIybdyc+PKAhclRfL5LSIPo2tM5qbm/nTn/5EW1sboih2m9rwBIoG9o+8hDMjmTURSWii09Em5gZTvyqEnluyfsMNaT8FYFXZn9lYu3SQR9Q7JNnPmsoXeejrbH60IY/PK/5JrbP4jOuyLFP5QcYrZKFC7GFUyglEQSZNrKEgws+YuDDGJ9uYlhHJzOxoriuIZ8GkJO6Ylsr9MzK559IMFl6QzBU5sYxPthFr0WOS7H3SQAEZsbnknO0FnmD79u28+WYgMjoyMhK9Xn+WOxQNDDX5iVZM42Yj6sMwpE8alHSuijFlkGlpacHjGUL5Fz39XPy7++gVMVj99gK/JLNydxVfFVaR21ASPB6WM4k7L55w1tz3ze5mjrUe67El+gQyMsdaj9Hi7nvO9aHC1PTIbg1OfWH0pO+yVdxBSesONte+F9K2FQYWWZapr68fOinaBlOHBlkDVx+q40htG2uObmB902MAJBsu5q7cXzM5PYJxybbgtbW1tXz++efB99dcc02PJjCKBnbOxFRbn+8VVBrCJl2P2tKxuHxq2Fgi9SkAbKpRdPFcsq6wngNVPQvHl/1eJHd7j65taPOwuaSxP0M7OyNoDtjs8PDWlmOU1DsAWFexlPU1/w6ef2j8C5h1ZlSiwHVjE0i0dZ2a5nTWrl2LzxeoY5OZmUl2dvZZ7lD0rzcIgkBeQs+cbzRRKZjyLkfU9MwQpjC0kDwOZN/QWQdr/Y5Bub+//U4Qf8VPCnZz1egLOkS7atUic8cn9Pjz1BMkSeK5554Lvl+wYAFhYWdP5axoYP9I7cLIbBg1Ff1pDjUKoefShCXMSPgmAG8eeYx/VIUmja7f2Yo8QOtgSfbT4DrGP/bdxQclv+30mikR93FNxComqz/FX/szth7oX42+G3KszM6L47LR0UxJj2Bsko2sGDOJNgPhRm2XqfD7q4HnYi/wBC0tLaxYcTJSZO7cuco6eBDIjDZj1GkwT5yDOvzsTk0DwdndfhRCRk1NDf/4xz9IT0/njjvuYMuWLaxatQqdTkdGRgYHDhxgyZIlpKWlIcvy4HwpavvuIQuAro91MQar317w6f4aNhTV4Sh+BVGIBaAmGn475+YOHkBd4fD170ui3deODVu/2hhsDFoVk1LDQ5r/3aaLZXby9zlUuowxRzy0hlUTZowLWfsKoePTTz/l66+/5rbbbiMrK4tnnnmGpqYmLr/8cr744gsAHnvsMQRBGBwNHEwdGsS+Nx9tZOexZvbXlLCi8gFkwUm0ZgL3j32W7DgLF5+SFsJut/Pyyy8H3z/88MOo1T2bSiga2DnxVgOxFj01dlef25BlGXfJdnz2Oszjrgbgzuy/8KddN3C4ZT3bqtcyI3tBqIas0AXbShvZVtq5d5wsy7RuCHixhU27Bdnnpm1LwLvRMOYSnAfXIRptmMdfE7z+dA3cWtKERa/pUKRbkmRqWl2UNzmpanHhlyQEBIxaFZFmHTFhOuKt+i4XsB0YIXPAkvp2PtpbjcvrB+DTspf56NhvQQArF/GDCU9jM4QhCDA7L65HNTpOsG/fPlavXh18f9ttt/XoPkX/ekdugoVNRxt6nDVJ8rhw7P8CTVQauqTcgR2cQp+QXG20bV+BqA/DPHEO3oZjOI8XjtXGj8ZTdRjD6IvQRKUM2jrYo+pfqpi+3t/ffm+YeAVeXUeDolmnZt74BGIsZ/eY7g0ffPABbW0BA/gll1xCZmZmj+5TNLB/CILAmDgLW7pwqvC1VOPYvxrzhOsQ9QO/L3I+Mif1R6gFLZ9V/IOXa7chIjA+894e3++uPIi7ZAf6rAvRxmTQvvcz/Pa6oP4BhE1diCCq+qWBPsnDl5XP8VHZU52enxz2f/jbL6ChXaKmAmqQADcAfs3w1MBzsRcI4HQ6efHFF4PvH3jgAXS6njlzKBoYWlSiQE68he3H112usj1460owT+y+bk0oUYwpA8jhw4dZvnw53/nOdzCbzbS0BKyJJ365tbW1ALjd7mCqgJaWFtra2njzzTdpaWlh4cKFJCcnn7tBGyMgPB2aSuhdugUBwtPAEH7WK0Pery0VZAmaSgMLcmME9OMDJMsyze5mHD4HRrURm87GhiMNrC+qY9n+P3KrmIEfP03JETy+4DsdijF3h1Hdvy8Jk7rni/2hzPhkG9vLmnB7Q+eFMT1uMWNKZUREyve9S87k74WsbYW+Icsyy5cvx+12s2DBAkRRDHrynjjf1BT48muwn/TM3nG0Hr2nieXL3ichIYFbbrkFrVZ7bgY9WPrXr74Bawq47YFcQL3Uv32VLawvqmdf3R5eKroXSbBjElP47rg/YwtzMyWz489+7969tLe3ExkZyTe+8Q3M5p5vgioa2DVjk6x8ur/vxhTJ1RZciPlaalBbY0k05TA1dhEba97iJ2tv44GL56FR9SEPskKP2F3ezNrD9cH3ss9L244P0adPRBOVCn5foMaN5AOfB/ze4LW+hmNAoPYNgKtsN57yfeiSC9Al5wev80uBQupfH6lnTJyFJoeH8iYnHl/336dqUSDBZiAtykRmtAmbsQtNHeZzQI/KyLpyP7srApFBsizzQfFfWFvzNxAggqt5YOLvsOgDUSiXj4khO+7MBXhn88ATRv7NmzcDUFBQ0GNvRFD0r7dYDRqSwo0ca+zZ5oO37iiSowV32S60iWMQBCX5wmDjs9fhOLAG87irEfVmJM/x3+Xxz4zkOulJfOL7S3K1Ivs8OA59hb+9CePo6aht8edszC61lWZ9EhZXRY8LwQNICNj1ibjUfYsA6Ve/ugRUAhhclXhURlxqK1EWPTeMTyBM3/vv/K70DwL7FCf2LG644QbGjRvX43YVDew/uQldG1NcxdtAlnEc+hrzuNnneGTnB6Igcm3qg3j8baytfpUXa7dCbQG/nrIFg/rMNGuukp342xow5s5EEMXAPBDgeAotv70OAFnyB++RfR78rlach9Yj6kwYci7rUeSlx++krG0PH5T8lnpnCW7p5HdnuCabWBbhabsQvyRT6wQIzBsjzVoSrQYSbAbirXrCdCqadwwfDQT6tQ7ujO408ODBgzQ3N2OxWPjGN75BeHjP572KBoae/ISAMUXyOPGU7wXA11AWWHedAxRjygBSUlJCe3s7zzzzDHfeeWcwp/KkSZMAmDJlCnv27GHx4sXs2bOHrVu3kpeXR1NTEzU1Nfh8PgyGwILP5XLxr3/9i5tvvpn4+AGcVAoCXHgvfPyT3t974Xf6Ll597lcOiOcfTvGKCU8PtDXuVjDYetyS3WNnedFyXj/4OsdajwWPxxoSSRSv5JMDO6gWX+c10rki+i5+OvfbHYoxnw2bzkZyWDLlreW9Cu8TEEgKS8KqC229kcFCr1ExLsnG5qOhS1eiVemxq5wY/UYSvZE0tBQSaR0VsvYV+saRI0dobW3lX//6F/fcc08wp3JSUhKCIHDJ5VexceMGjsixoNKgtsay5kgT+uajuN1u6urqgrU4Dh8+zJo1a7jrrrt6HAnRawZL//rbd0sZPH18QdsL/Tta385n+2v5quJ93i/9CaJKJNaURkp0Cu/Xfx/q4a9FkByWzOIxi5mbNZdp06bh8/nIzc3FZuu+/dNRNLBrRseGsbawrs9GZpXh5Iawq3QXpoIrEQSBuamPsLVuGV7Jzet7XmfJ+CWhGrLCKRyosvPFwdoOxySvE9nrwnn4a/ztzehTxyHqTYHFnlqLoNagssYi6sPQxmTgrSvBkH0xsizjbwtEb8r+kwZoZ9EmBK0RfUoB7W5/lxEwneGTZMoaHZQ1Olh7uI6oMB2jY8yMjg0j3HSKYWWYzwG1wCR9Eqr4heyLvo6Xip9mS8OrAMQJd3DfhEcwH99cvHR0FGOTbB1a62oeeKoG3nLLLaxdu5ZZs2b16rtI0b/ekxtv6bExRROdhrt0JwCeigPokvIGcGQKPcFvrwO/l7adqzDmzEBlDmw6aeMC83NNZAqeigPoR03D39qAp3wv2rhRyH4v/rbGgMH5uAOA7PfRvvu/6DMnn5HWMqQIAjvjF3LZ0Sd7dxuwI35RvzSwb/3KaP1tfHfLVcFj7aZkdBfdh1peDL3wYu6J/lmtVu644w4qKyt7ZUgBRQNDQYRJS4JNT2Xzmc43qrAoJKcdqb0Rf3sTKlM/HLwUuuWGtP/B5tnO8sb9ALxR9BPuzH4G8RQjvizL+JorkRwttO1YiXn8NcG6NmprIMOJYfR0XEe3o0vMw9dcjag1IGoN+FtqkL0u/D4Pgjqggb7WelxFmwLze3VHh5gmdxW/2jaj07FGeh7C7LwcZ2BUhOnVpEQYSYkwkhRuwKg9cx4znDQQ6PM6+HR6ooETJkzA5XKRmppKdHR0r9pXNDD0RJp1JIYbqDhlSeQu24M6IjlgwBxgBPk8qmRjt9uxWq20tLRgsfSjSJffA/t+E3id91NQBQStqamJAwcOMG3aNARBoKmpiWXLlnHVVVeRmJjYqy6Ki4upqalh2rRpAKxYsYLt27czZswYbrjhBvbt20dBQUGPiv72Gmcz/DkXvE5OWK27RRBBbYCH9vdatPrV78kB0NGT8biIa4yw6GXIuuKsLayvWM+Dqx/E5QtMTuTT2pNlGUn2c6z2GJdE3ccTV/+U/MTeC9qr+1/liS1P9FpAH53yKLfl9CyVxHDA6fHz/PqjZ/Wm7Q0+yUPxln8R54+kXNdA7qST0SkWg4a7L07vcVsh04ohREifqRMNlGWZkpISnE4nubmBFBs7d+7k8OHDzJs3r0MIrCzLbClpYmNxA37pzM+C7PdhdlRx0ZhE8nLH4PP5+OMf/4jb7WbevHnExcVht9sZNWpU6MM4B0v/+tJ35wMK/O8s+lfR7GTp9nIa21t5fPskzHozyTHJqEQVp2uqcLxNvVrPkzOe5KLEi/o4NkUDu+PLg7XsPNbc5/slVxttOz8CyRdMlQLw8qEfsrPhI+4Ydwcv3fBSj9pSNLAbTtO/beXtfFVYj9/nxVtfhiosEpXRiuz34SreimiyoY0f3StPecntwFtfijo8EZXRgr+9ifZdH4OowjxhDv62BkSDBZWx/wur6DAdo2PDGBVjDhhWhtEcMKAiQoeCpdJxxXIA82njE8FPkvgdvjvxAQzaQG27qRmRTMvsWDC5u3lgqDRQ0b/e4fVL/GttcY/nip6aI7iObAaVGvPEuR28eRdMSiI5omdeoSNR/6D3zyXLMt999wW0ooF0y6TgcVH2MMXxTwA2G+9FEgLr4IBulaBNCEQGSR4HzsMb0CXnBzcPe4rPXoffXos2MRdBEHCV7cFTvheVJRpj7ky8tUfRRKciDEC0pc7Xyre2XIdacvXIQ1pCxCfq+M/klbjVPUs1I8kSkuxDFNTsqF/JsbY9SK5KPmrciAFQ9aD4sxz8v9BhnPIJxQrROliZAw4t9pS38NmBmjOOy7KM8+A6fE0VqG0JGHMvC57TqkXun5nV4z4UDQxQVNvKil1VZxwXZQ8T2//Opbv/FjyWHjaJu5N/h9rrRxsd2HPwNlbgqT6MYdT0bqNLZFnG7vLR2O6hyeHB6fIS5qrCJ4s4TPEY1ALpNetQ+5344grwhsUiOZrZ7NrKgdYPqfVs7dCeyTeTCO93ETGiVYkkhRuCBhSbUXPWtfO50MBQ9Ns5vd8HBEUDhzMHq+18tKcayeOkfedHyD43+ozJaONOal5OvIWr83teBqCnWqFEpoQIh8PBihUrOHr0KCkpKSQlJREeHs5dd93Vp/YyMjLIyMgIvj8hej6fD6fTyaFDh9i4cSO33nprr8LLeoTBFhCf1xaCLNL9olYEBFj0Sv83EnvV76mcLkbH33udgbZue7tbIV1fsZ77Pr8PWZa7EDYZQQARkbTYNG7Mu6xPhhSAuVlz+cuOv+DyuXokoiIiOrWO6zOv71N/QxWDVkV+ojWY4zAUqEUtpvQLoaiIJHck+3b9g7xx3wlZ+wrdU11dzdtvv41Go2HMmDGIosj48eMZP358h+tcXj8f7a0KFgXuDEGlpj0smTJ/GHmA1+vF7Q7kctVoNPj9fpYuXcqoUaO48cYbQ2tQGSz963XfXXF2/atrdfPBzgpa3U6e3PEdzHozqbGpwcni6Zp6QqtcPhf3fX4ff5v1tz5PJBUN7JqCJGu/jCmi3ow2YQye8r04D69HFRaJqDNxXerDLBl3D9+7aE7oBnuesKWkkbRIE9FhZy6C/bLMZ/uq2F/jRpZlPDVHcJdsRxObiSFzCoJKjWHU1D71K+qM6BJzTh44UZxU8iNLfmSfm/ZdH6HPnII2JqPzRnpIXaubulY364vqsRk1pEWZyLrqnyStWoLQAw2UEbDPe576NjX2uiba3X7a3F5cXgmPX0KSZERRQCUIGLQqDFoVYTo1VoMGq0GDxaBBr1H1Sv9OKIfQ4d3JEQHokVmJkbvFK8mZ9AA6TcCQMiU9olNDSnfzwFBpoKJ/vUOjEhkdG8beip4VXdXEZOAu34/sbqNty/tYpt86wCMc2Sw/tJx/7f8WZk0UD497H4u264gQ2efBdXQrvsYKRKMNTXgCotaIKX9Wn/pWW6JRW870+pV9HmSvG5+9FnfFAYw5l4bEqHwqbnUYH475PTfs/yESdLupJyEgI7NYLVJc/Dg76ldi08bR7KkGINlcgFY0cMS+udP7heP3n+AmVKzEiB+5W4PKqRoonDY+IcTrYGUOOLQYFWtmzeFavP7Tfu+CgC5tAr6mCnzNlTj2r+lgUFEILWpB5IuCe/lVvYsvq16irbWMxv0fYRatqMOTENUaNBGJaCI6d6huc/soqW+nrNFBRbMTh8d/2hXHjf/1Tajxk6Z3gwCbypopFN9gvjqFRKpZx/bgHXr/BDJVj5EQEU6cRU+izUBUmA6xl+vk3mogwIoxT/TLkNLbfrumd/oHigYOd0bFhLFGW4cDA9qkXNwlO3AVbwk4tw1whJ6SUDZEGI1GIiMjiY+PRxyAkKI5c+bw+OOPc9ttt+F2uzl8+DDt7e2EhQVEq729PVgMLiRkXREQH42BE1O1jhw/pjHAbe9AVt8my73vtzdIgXQab90R8HjsBLvHzoOrH+zGkHISQRAQBIF/H/4Fdo+9TyOyaC08OePJQFtneTYBAQR4asZTWLQjxyvkBJNSw4MFdENFYszk4OvkdiuSFLrIF4XuiY+PR6PRkJGRgcfj6fSaFqeXt7ce69aQcioHq1upanFiMBh4/PHHefzxx8nLy2PTpk24XC60Wm3QkNLY2NihJku/GCz961HfPaVz/Wts97B0RzkOt4ent/8Qu7CN5JiUwM/xLF3JyMiyzIOrH1Q0cACIMutItBn61YYuYUzwdfu+LwCI1CczPmY6alHxn+ktlc1OXt9UxpeHajlS10Z9m5vaVhclDe3sLGviYHUg578gCKgtUSCqEUO8sQegCovEMv1WLNNvRdSbcR3ZArKMqAmkGpVlCb+zb5/JU2l2eNlZ1sy7zdkszXkSj6hDRkA+7bN64phX1PF+zlM8X5XB8p2VrD5Ux5aSRg5UtXK0vp2KJidVLS4qmpyUNTo4VN3KzrJm1hXW8+HuKl7bVMbfVx/hb6uLeHVjKe/bx7D1on/iU+u76Pd0Q0o3PzMCGvNveT0WIfCdc0FaOBdlRXW4rjfzwP5qoKJ/vSc3oefPLggC+tSxwfc+e203VyucjVnps4g1jqLVW8dTuxfi9HX9Ny+otYhGG6LBgqAK/XeNPqUAy/RbMY+/FgBffSmyux1RF9hwlH2ekzVZ+kGbt5E6Zym/rV/GNbTiQEYCpNO0QUJGQsaBxDW08Z63ih31KwGChhSAY217ujSkwOmZEOBr/5Xc5p2Lm4AGSqfphETPNTDU62BlDjg00GtUZEZ3Xq/w1JSvvuZKZGUdPKDoRQ3z0v6HuamPUkcdTpwckPby1923cKxtzxnXN7Z72Hy0kTc2l/HcV0f5/GAthbVtODx+RCFQvyQrxsyEZBuT08KZmh7BhGQbmbE23tfK/IJf8Kn2bjLVTViw4MGLVZ3HxRG/4gcZn/PzyS9x9/RcrsmPZ0JKODEWfa8NKScoDZ/Gstyn8IldaVFgjuYT9SzNfZqy8L45D3XW74cFTwcingdgHXw6igYOf1SiEJwrnkgjCgQi+gcYxZjSD2paYNfuPZzIlDZp0iS++c1vBmujDBSxsbH86Ec/YvHixcF8zbt37+ZPf/oTn332Weg6yroikLbh6t9B+GlFfMLTAscfPhDajcSz9WuI6GVjEngdsOvNTs8uL1reY8swBETU5XOx4siKXo7jJBclXsTfZv0NvVp/fKnfUUhPHNOr9fx91t+Znji9z30NZcw6NaNj++fB0BmenPEAbGADO48vbBQGht1lUF19MtR80aJF3HDDDej1Z9YSqm118ebmMhraOje0dMXaw3VnHJs/fz733HMPl10W8LjyeDy8/vrr/PrXv6aysrKXT9EFg6V/Z+u7V3TUv6Z2D+9tK6fV6eWvO35Mje9Lwk2RqHqRfkjRwIGlr1GPJxDUmmDRPUPmhaEY0nmPJMvsLGtm+c5KXtlQyhuby6hqceH2ybgqDiF5A1Fzgs6MedzV6OKzB3Q8giAQNu2WQA2C44WZJWcb7TtWYv/6DeRTCtv3h9Lwafxn8kpWpz9Ei67jvLZFn8jq9If41+RVIVlAu70Sda1uShscrJPH8a9JH3bar/N4YdOeLq1VgEZykVO7kkmp4Vwy6kxP93M9D1T0r3ck2gyEG3ueyunUoqMqo20ARnT+YNKauCv7GbSikWZPFf+7eTKSfHJz1uEWcdcUIx8/polMwTR29sDWNCEQuRc25SaM+bOCab68jeW0bf0Ax6H1vW6vwXWM1wsf5aGvs3lsyzR+u+MqttYtC6QIxM4PcVF8WqRcMRI/xE2K4GOd2LXnq0ZOwOifgiCfdJQw+Kdg8d5IpOcB4lx/IsW5lCzvSmaavmRR1v9RMO2nvHjhR6xOfwj7aRro6qUGKuvgkUleQtdzRfPk+QCoo1L755OqcFZqmrX4WhuYkfhNHpu0hiPhbXwgfkiJay9P7l7AQ19ns7rsY9YeruOlr0t4ZWMpG4obqG0NzBvjLHqmZkSwYGIS370sk29cmMp1BfFcOjqaaRmRJMZU06j5N5/YL+OA9/Fgv5/zOW/qVpCdt4SfT3mXG0ffRFT9Ibzb38fXEjonglPngadrkf34PPDfIZoHniDSrOWi2YsQHz4Q8nVwZygaODIoSLQiCCCIKrSJgXTzhjGXDHi/Ss2UvnA8X/Yvlwb+8HNycli4cGGIRtk3fvnLX6LValmyZAkJCQlUVlbicrk6pArrFz437HgMfB7IfxTMsf0rttyXfvMegReugaYSzkzt1R1CYPPzgR0dxizLMtctva7PRaBW3riyX+mF7B47K46s4LUDr51R5Oq2nNuYmzmXMG3ojQ1DiVq7i9c2lYW83Q+O/o41VS8A8KvJG4m3xCg1U0JcM8W+9Tc8+XHg7//GG29k7NixXV5e1eJk2Y5KXN7TQ5h7xpyx8YzqxvBWW1vL3//+92BNKa1Wy/79+0lISAhNGsTB0r9T+/a6YdPn0HKMvuhf87c28e72CpocDp7Z+QDHXF+CLDIudQp+sXdRjYoGDhy9rRHQUyak2JiR3fMNLkUDA3yws4LiuvYOx07UCyit03OoMuAdap40F1FnCvmYe4qzcAPeuhJ0aRPQxmeD5MdbX4ImOh1BVPW7fVFyM731L6j8PraYv4VDE3VONLBDv6a7WbTnXqzuijPS2nSHhIDbnIzh4d1njHkw54GK/vWcjcUNbDjS0K82lJopfauZ8tRnhWyoeZt3jvwcgMvi7+TGtIeZ4vgnn+2ORJIFVJZoTPk9y0s/UNi/fgMAY/4VqC3R+B0tSK62TlPsuP0OClu+5suK5zjauv2M86dj81+HVkrlTsteJMnFB22jcfhmoSIquAkmE5jfCnStt4IARq0Kk1ZNmF5NuFFLuFFLrEVHhEnbqZaEQgOVdfDIQ5Zlnl9fgt3ZM+cJpWZKgN48196KFl7eUMKRuvZAfIQAoiCgEgW0oo854svsKwvoS3NkHq1hqfgkmQZHNeubfk6Tf3eH9oy+y/CJx0jVXc/FCYvJjDajUnnwSR5kJCra9+P02dnV8DGlrbs6RLidzv+bshmj+qRBTfI4adu6DJU1FsPoixA1OryN5Yh6CypjaH5/52IeODbJyqWjo9GoTnHyC9E6+HT9A0UDRxpLd5R3mf1EqZkyBLn7Mpnn1gikpKQM9lD4zne+Q2VlJVFRUezatYtly5YB8Pjjj3d/Y08RBNAZAv+MEeduI/HUfgWg6WgfGpED9zmbAmM/TrO7uYNw9bw1mWOtx2hxt2DT2/owngAWrYXbcm5j8ZjFtLhbaPe1Y1KbsOqsoS+qPUSJsehJDDdQ0eQMabszE+9mTdULZJLJV4f/ys0X/F9I21cAiwGmZslsLBK6jcaraHaybEdFvzaHvyqqJz3KhFrVeQRFTEwMCxYswGAwoNFoWLZsGbt372bSpEnMmROCOhGDpX+n9g3Q0hfDY0D/Pti4lzqvwE83nSwie2HkQ7SLvQ+BVTRw4NCoRLJjw9jTwxoB3SFLfrw1R1BHJgO2fren0JHkSFfAmCKqELT9S8/WX/SZU1CZI1HZ4pDdDtr3fY7sbkdljkJlsvW/A0HAp9bjU4NLYz2nc8AT/SKAzV3e6yZEZAxtZWfMAWFw54GK/vWcnHgLG4sb6K37n+R24GssR3NK2geF3jMtdiEiIm8d+V/WVL1IhC6OKVaYOrqZrw+Fowo7M+LrXGOacB1+ex0qoxVvQznOQ+sAUE+7Jfh5WlP5Ih+U/LbbdjRSCuHeb6GV0hGxIhxP4qERvCRom9GoBKL8i9Fo9Og0Inq1Cp1GRKsS0apFNKf+XyWiUQlo1SI6tQqNSuj9ZzsEGqisg0cegiCQEx/GpuLGbq/ztdQi+71oY5LP0chGDoW1rby9tfPPm0bwMj6mDb2uDpc7mnUVEvXyiYgQFWH8GpVqLfXaPwTvcajXBNr1Pk1h6dNQ2rNxjLJO46h9G3dkP0l+ROdGa1FrwDDmUiCQctFZvBVvdSHq8ESMOZf2rKOzMYDzwDC9mpljYjpPXxeidfBQmwOCooGhZnxyeAdjiixLeGuKUdvigIExCivGlF7icDgQBAHD2F+QBDx2mTwk/thjY2OJjY0FIDU1ldGjR9PU1ITdbken0/G73/0Oq9XKD37wgyEx3j7haT/7Nd3hbu0gog5f/3LrtvvasYVgg0oQBGx6W0jaGo5MTLGF3Jhi0UYzP/L7xDR4iWiJwOtzh7T985nGxkYiIiJg7C+YPRaukrvWwFq7iw929s+QAoE8/ltKms4oGnwqeXl5wdcFBQVUVFQgSRI+n4+6ujr+9a9/MWfOHCZNmtRlG0MaX+/So52Oq62RJw/8MPg+23g712bN453avucTVTRwYMhLtITEmOI+thdvfSk+ex1kXReCkSnIsozP5Waj6fsAhE0bGnNAQVShjR8NgCxJaGOzcFfsQ3K1ojLZcB/bi/vYHkwTrkNlGJ5ephp/P+cJp80BYWjMAxX9OztWg4ZEm4HyXswVZcmPs2gj/pZaBJ0RUDYT+8OFsQto8zbyWdlf+aTkGRrjFzI37VHCpjEkNFBlsAS1TRUWgToyBX9bI7K7nVLXESL272cCOpYjnOGBHOG5D5N/JmrBQLhRi82iwWbUYtGrsRg0mHVqLFqJKZ5wEAS8xmQkQXvOnzHUGjgU9A8UDewrefFWNh9t7NLI7G9twHV0G5KjGWPkonM7uBFAVnQYN09Koqi2LVCvTZbxyzIarwOHYOZD6V58KglJL6PXQ6oooBYFDFoVVr0Gq3ER0WG3savpLVq9DRxsWkul42CP+0805XJX9l+J0HdewP50To3C00Sl4LfXgahC9vuQPA7ad6xEl1yALjm/tz+KAUMlCoxLtjEtIxKt+iwpp/u5Dh6qc0BQNDBUpEUaCTdqaHIEIvY8FQfx1hbjbSiD9BsGpE/FmNILWltbef7550lKSuKmm24ChsYE8nRsNhu33nor8vFNztragKW8paWFtrY23njjDaKiopg/f/4gj7SXaPuZQkPXMUzOqO5ZuH9XmNSDl9JjJJEZbcZq0NDSw1DlnjIp7Ta8DYGN4r0H34EZPw9p++cjhw8f5q233uLGG28kPz8wGetKAwOFzitwe0OTrmhrSSNj4sIIN519AZuVlcX3vve9oAaWlwc8i6qqqjhy5AhvvPEGt9xyC1lZPQ95H3TU/Vu4P3vwJ9S59wOQob+Ve8b9Lzqdq19tKho4MMRbDUSZtdT3sr7Q6Yg6I7K7HZ/Hiau9FRjYPPYjHVmWcRVvxdtQhnncNYg645CcAwqiiC4pF11SbqCgpt+Hp7b4xFlcJTvw1Bwh7IIbBqRQ9EDhFPqZrkx3ZqoEZR44fMiJt/TKmCKIKiR3OyDjKd+PLCs1pPrLzNgl5FbqqfKV8mrVC+hURq5OeWCwh9UBSZZp9qopNSeyvOm3tGz/H6KI4nt8DwAjRhbKi0BO4ZDhEuIseqLDdESH6bAZNIhi55ouyh7wDq7eJ8ZE9a8BZR08orAaNSSFGznW2PmGsGiyITmaAXBWHATGnLvBjQAKkqzce1kGK3ZVBY/5Wmpx7N+MPn1ih0LX3XGZ4U4A5qQ+jF/2caBpNUa1DUn2s6fxM9ZVvUymZQoXxi5ArzKTF355v+eWaksM5vHXBNfBkiPgoOVva8TXWo9j/2r06ZPQxvQ8BXooUYkCufEWJqdHYDX0sCZaP9fByhxw5CMIAuNTwvnyYGDvWzRakFyt4GrF0dII9DzNV09RCtD3gqqqKux2OxUVFbhc/duEOhecEGKbzQaAXq+nqqqKqqoq9uzZA8CqVat48sknaW5uHqRR9gJDBISn0/tqakLgPkPH2gk2nY3ksOQzij6dvTWB5LBkrLr+FQpWCCAIAuOSQ/+zNOjCKY8I5DC21bjZXbL7LHconI3CwkIkSeLIkSPdXtfm9vH+9nIcnr7VSOkMnyTzxcHeFdU7oYGJiQFvnZiYGDZv3ozf72fv3r243W7+9re/8d577+F2D/HoJa0+kPO1l3olIXBUULHHuQ1B1jPJ+Efun/A4Jp2aWyeNUTRwiJLbTXHRnqKJzUJliQFZYv/Xn3IelcgbGPxe/PZa8Hnwt/WvfsO5QhAEBJUaTWQgHa0gingqD4Lfi+Rx4rPXYd/wFp7ao0Py76PN28RbdbuYvuuvfG/bLIrwI/UqVzZ0NQcEZR44nBgVa0aj6t3vyZR/BYgq/G0N7N62eYBGdv4gOVowSXpSVZmEEcYn5c/y+JaLafc2haR9WZZxef3UtbqpbHZS1uigvMlBbauLFqcXt9cfMBDLMl6/hN3ppbShnZ3Hmvn8QA1vbTnGU2s+5altj/JqyTxapEIAmmjCLwfmowujXydVSCFVDNTRuUBXSULZJ1hFd5eGlKFAos3AVZNylHWwQgfyErqONBVEFfqsQGFwR+luampqztWwRiy+lmqQJXzNXdcz6Q6VoCY/4goyLBeQZb2QG9P/lz9PP8T9+a9wQfQ88iNmhdRJ50RboiHwWRWNFry1xeD34q0tRpb8tO/+BMf+Ncj9jfzoAVFhOi4dHcXdF6dzRW5szw0p0Od1sDIHPL/Ijbeg0xxPzxmRhDoiEJVctOkzJCm09UhBiUzpFaNHj+Yb3/gGVqsVvV4/2MPpMVqtNlg7xekMeHXl5eUhyzIGgwGv18tXX33F7Nmz2bRpEyUlJSxatAi3241Op0Oj6YXQDSSCABfeCx//pPf3XvidM3I7CoLAlUk38fyBp3vd3G05tw1Jj9ThSm68la+LGvBJod3MGZW1gIotLxAh23hp2Uv86Yd/Cmn75xvXXnstSUlJwaiUzvD4JD7YWUGryxfy/ssaHWwtaWRiSniHRa/XL3G0vp3CmjaanR68PgmVKBBh0hFp1pIUbuNnP38MlSgQFhbG4cOHycvLo7W1lbS0NLZs2cL48eOJiopi+fLlZGdnM3nyZFpaWrBarb2etg0IggCTvwWf9DbCSuZJuR0RG5NMT7Bo7KXoNCrmjU8kwqxj8ZjFPLHliV4PR9HAgSUnPoz1RfX4+6GJgiCgz7iA9p2raG2o4ejRo2RkZIRwlOcXglqLqeBKfC01aCKHV8ogfdp49GnjAVCHJ+BrqkTUGfE5mlGHx+Mq2ogmOhVfawPOQ+sx5s1E1JmQvW5EXf8893qLT/LQ5m3iy8p/s67qlZMnBHhG9vIkut432skcEAKfkcVjFvP7Lb/vdZOKBp5bdGoVWTFmDlS19vgeUWtAl5SPu2wX2zas5ZpZl6JS9TPC6TxGbY3BlDcLWRApqDnE1zVv0uqt498H7uWHY9/udXvtbh/lTU6q7S5q7C4a2j1nTQsr0Hnp4QbNX2hTfwKdOC9/M/s1rOHjEEWRfL+P1rodAUcDAhvOqDS4S3diGD09kBKk9ijGvJnIkh9BVCGoBm8drBIFsqLNRIfpQBRDvg5W5oDDm6wYMzqN2GUWAE10Kt6aIvyt9XzyySfcfvvt53iEIwt9ylhUBgvqiKTBHkqvUBktWKbfCgSia7w1R9DEZiB7XajCIvFUHcbXXI3KEo2reCsqUzjapDxkjwNBa0AQeu9/b9SqCDdpiTRpibcaSIowYNH3Q0v7vA7mrHNARQNHDlq1SF6Cle2lAScPffoE2poqcLY2sW/fPgoKCkLan2JMOQuyLFNeXk5ycmDhnJ4+MOFwTo+fVrcXo1aNUaMaMO8Yg8HQoSi9zWYjIiKC1NRUHA4H27dvp6mpCafTyZNPPgkEitvHRgWsuS+tg7HeXUyYNJnGxkYaGxuJj4/HZDLhcDgoLy8nIyMDQRAoKSkhLS2tw8JF7qa+Qo8Ydyt8/ivwOoEeWBcFEdQGGHfLGaeONTrwt0xClAQkQe6RoVtERKfWcX3m9b0fu0KXGLQqRsX2bpHcEzRqPW0RZg43bKTCW4EkS4h9mBCczzgcDhwOB1FRUYEoonHjurxWkmRW7qmk1j5wUR7rCuvZW9HC5PRA3tMjde2UNbTj9Z+5vK5v88BxRyytWiQlwkhWTCKP/vRn6DUq/P6Al2NkZCQJCQnU1tZSXFyM2WxGq9XywQcfAPDY//4YAWh3w3PP/p2FCxcRFxdHaWkpgiAQHx+PRqOhuroan89HUlISdrud1tbWYFTMCfqlgeNugS9/22P98wNOZF6Tw5ga9jQ3FlyARi1yXUE8cdaAQ8D1mdfzx01/xC/4FQ0cQhi1alIjjRTX9a9WmMpoRROTTmpSIjExSpqv3uKXfFTXbSc2YiyCSo2g1g47Q8rpGHMuC74WjVaQZdSRyQiCiK+pEtnjQHY7cJbuwtdYjmgKxzL2cgAqGnW0la3HMPpiQMDXUoOoN6MyWgP5xJurEA1WRL0Jf2sDglaPqOuYBuF0DZRlmVZvPU3uSl45/CCN7opOx62X8jga9lu8jm+jkVyIPYlQ6WYOeIJZcbP4o6Ro4HAgJ97S63miNi4LX0sNl02fMkCjGvn47HWoLYEi86qwQO26m0y/YFPte/hlL2Vtu/jn/ru5Y/RTGNRnplI5gSTJVNldHK1vp7ShvctUlgaNCq1aRC0KSLKMxyfh9kn4pI7VTgRBBv3XtKnW0Ob/ukMb89MfY3rcLYinpQcUVOrgpiKAqDcjag2obXHIPg+emiNI7U0gy7RtWw6SH2POZWhtgRRbO4+G4YooRJuQh+Rqx+9sQWUKR9QakH1efPYa1Na4QERUSzWqsOgO6RRPRAD2ZB5o1KqYUxBLdPkpBuQQroMB5mbN5amtT+GW3Ir+DUM0KpExcWHsOtZ5nT1BENEmjEG2VzNz5mWdXqPQPbLfi+RqQ2UK7IVpotMGd0D9RG2NCWqgLMsgiIgGCypzBLLbga+xHFnyIzZV4Dy4DoCwaYGaOz4/bDgcTlh+MwnJ6dDWgF4jEBcXT5hRj7u1GdnnZlRGGh63i4aGBpKTY86Y852rdXBP5oBzs+Yq6+ARxoQUG7uONeOXZESdCU1sJvHRkaSmpoa8L8WYchY2b97Mxx9/zA033NDtJmJfsbu8bCtpYl9lS3AzMMqs5caJSZh1A//rmTBhAhMmTADA4/HQ1HTciqfXM2vWLLZt2xYoNo1MdQuU1As0rVtPQlIKpaWlfPTRR+Tl5bFgwQLKysp46623uOiii5g1axZvv/02Ho+He++9l7i4ONavX89nn33GnXfeSWpqKj6fj88//5zExETy8vJ6JqwGGyx6GV5bCLJI90IqAgIseiVw3ylUt7hYvquS3YWbmN46nfWx649/oXTdmoAAAjw14yks2uFZwHUoU5BkC7kxBSB71EKKdEf49aX3nfV3rNARSZJ47733qK2t5c477yQysusC8LIs88n+akrq+1fMrSc0Obx8sq934eoen0RRbRtFtW2oRIHUSCNj4ixcdfU1aFQBA5vHE1jYNzQ0MHfuXHbt2kV4+Mmw4E/3QlNTM6tXr+amm25izZo1HD16lPnz51NQUMDGjRvZtWsXd9xxB+Hh4fznP//BaDTyox/9CFmW+e1vf4vX6+Wxxx4L1nI5cOAA48aN69lGt97aY/3zHy+xugCZPMvfmZc/FpVKYHZeHGlRJzc3d23axbTaaQENPMvmpKKB55a8BEu/jSkAhqypJKbYMJvNIRjV+UOzq5knvrqbGU0X0BxWjS1vdsCLeQShMlg6GFdkd+DvTXLa0SXn42upQZ8WmCPKMuw7FgZU01qxC2NcDs5D60CWKRsVhVZlJPZgGTIyO1LaiWiB9BYzreFG9phKMcgGJlSY8Qg+3gr7BK/kIs8/hlZnJVvZiovO0+daBQN/jJ1PadRDRFjMrGz6PTfs/yESnMWg0vUc8ARer5dVS1cxrT6ggUC3Oqho4OCSEmEkTK/uVeSroNZiyptJWlaSEpXSBzy1R3EVbQzk148fHTwuCAJPTN3Dm0U/ZUvd+xxq/or/3XwB38t/jQzLBcBxQ6krEH1S2tBOWaMD12mRJ9FmHQk2PXEWPVFhOqwGTXBOdjo+f8Co4va382nln9jX9Blt3oaA58hxRlunc/voP2PSnJnSpTM0UaloogIbLLLkD9aYkGUZfeoE3Md2H49ikXB6RGrtOgTHQcSwWGRXG87CDaissZjyLsff3oTz4Do0sZkYMqfgLN6K7GrDmHc5amssntpiXEWb0GdNRRuTjizLuEt3IhqtaKLTO6yDo8N0XD8uAatWhvJTBhyidfAJnE1OJldNVtbBw5j8RGuXxhQATWQy2thUkpKGVzTFUECWZZyFm/DZazDlXR40qIwUBEEIzPGOz/N83sA8THK2og5PPB6BI2DQqBgVbaJluw2nR0V80xFmX3kBK1Z8zc79+7nmmmsYN2UK/92+no0bN7Jw4UJSU1N54YUXAIKO3H/729+oq6vjZz/7GSqVipqaGnbt2kV+fj4JCQlnH3Av1sE90T+AQ7sOnVwHKxo4IrDoNeTEW9hbEdBFQ8YFxMdbsFhC/ztTjCndIMsy1dWBnIgn0mOFkv2Vdr48VHtGSHN9m4e3thzjpomJ2Iz9LLbUC05NBwYwbtw4LBZLYHKn0iBl3oN58xu0tLQQFhZGdnY2q1evDv5sGhsbAbBaAx6K+fn5bN++PViL4IsvvgACnu4ANTU1bNy4EaPRyOjRo9FqtXz66afs3buXe++9F6Oxi9QSWVfAbW/DW3eA98TGbQdfpcD/NIaAgGbN6nB7fZubZTsr2Fm8gYTaEgxCHJNrprA9fid+2XO8NfmU1gLt6dV6nprxFNMTp/fuB6vQIxJtBqLCdNS3hjaqQSPquCPn11yfPThF1oYzDoeD1tZW3G43fn/39U++OFg7IMawgcAvyRTXtVNc145WLZIeZWJ0rJnU9MwOGnj55Zfj9XoR1DoY+wvS5V3sKluGLMtoNBry8vIoLy8PauCJWjLh4eFotVqMRiMOh4N2tw+P243X6wWgvb0ds9nMoUOH+Prrr5EkidmzZ+N2u/nnP/9JZmYm1157becG5qwrkG59G965HcHrRKbjhmLg20TGCSxAwmd7iXm5+YiCwMzsGLLjTnqNbt68mS+//JI44rjLehdvON7A5XMdb0HRwMEmPcqMQavCGcLaQwo9x6QxYfIZUKGivu0Iel8bBu3Izo9sGDUNw6hpOHwtbKhZimCqxV1XxuFjO1DLKrKJZiITeaf8N+wr38dCFpJNNu8W/hQ7dn7BL2illZVlTxFPPN/m2xxr2sonTW8zgQlMYB4tchNH7IH6FddzCdHkUkIJ5ZSTRBJ3czdL/YW0+ecxLi6KB+LeRadR0W7UIgGl4dNYlvsUcw4+ikYK6JXQizngCfx+P2+//Tbl5eXEEcdj+Y/xxKEnFA0cwgiCQHZcGFtLQlOjQ6FrPD6Jb764BV1NOSlAYUU9lU1WNCoBtUpEIwpoVCK5+kcxRU1idf3/AvDXvbcRo5lOsuqbtLcl4vR2/P7Sq0VSo0ykRRpJiTBi1PZsK0KS/aypeo6VZWem6x1lncbk6BuYGD23X9HngqjCMu2kB7M6KhlUakBGErRs130DUb8OydWKqDUgGCwIGn2w1oDsCaxJBY0hME+MTMFTsR/ZF5j7uY/tCzR8PDpFdrUFalgRyO2OWou7Yj++iv0suf/+QD0BfyfRO/1cB5+gsbGRV199lThXHBfVXMS2pG2K/g1DYsL0xFn1VLcM/Xq+ww2vx43kbge/D1ka+XNxdVhUh8i9hDETSQkTuPSCDDRqFQdUi9l29G3cHi86nY7c3FyKioqC6+DS0lIAIiIiUKlURERE0NjYGIhc9vupq6sDoLm5mcjISI4cOcKGDRtoa2vjxhtvxO/38/zzzxMREcH8+fMRxU70PET6B7B7925WrVpFHHHcqruVpdJSRQNHCJPTwtlfaUca4HqQgjwUK04OEHa7HavVSktLS48tU7Isc+TIETIzM0OWF8/nl/jyUF3QWtYVZp2a26elotcMbU+qE+F6sixTXFxMZGQkNpsNr9dLQ0MDkZGRaDQaDhw4wM6dO1mwYAEajYb6+nrWr19PZWUld911F1qtll/96lcA3H777dTU1PDJJ59w3yyZaAuQ91NQnWJccjbDrjdh09+hqeTk8fD0QG7E8bcGLNin0NDm5r3t5eyuOoh44DMiBRsV1JM68Q6uHBtPoWMNrx14jWOtx4L3JIclc1vObczNnEuYtuvwdYX+s+tYc6+LjPcEi0HD3Rf33JjSF60Y6vTlmVocXjSCn/q6WlJSUjq9RpJk1hTWsbOsOYSjHRzUokBShIGkcCNxFj0xFh06ddf6K8sydpcXu9NHk8NDXUs7FWWlyGExOHwCznY7kl9CZQgLeCAe3Y6oM2BLL8CsV+NvrKCp9ABms5nps67G72jjvVf/A8C3vvt9/vP3ZwC4/0oPkuynMOoB6hwyFc1OBFcLObUrmVD5JrZT0uIU4ecveHlVtjAz7i0uykxEEAQuHhXF5LSI4HXV1dX885//BOCyyy5jxowZ2D12VhxZoWjgEOLLg7XsPNbc73YmpNiYkd3zNF+KBgZ4aeNO/u/zW2nwVdFCC09M3YNaPHdOLucKt9/BqrInWVf1cr/aiSQSM2ZKCSyoo4lGEg00SGXo0XMLt/A5n1ODC2QDM6RrGSVEssuvp8yXT766gSmacpyGaMLHTMO1YzlJkU5yk9rZbLwXSTj5s9f7W7lWWk1q4cs9ngOeypo1a1i9ejVqtZrbb7+dlJQURQOHAQ1tbl7eUNrr+xZMSiI5omf1f0ai/kHvnqvV5aXgF58AkCC2UCVZkLtx2XWJ+2lWP49bdfDkQVlNguePJJnzSI0wkRoZmFv1JpV1taOIFw99j1rn0TPOXRq/hCuT7sOksfW4vYHg1LQ1vpYaRK0R0RB2PNKlBdFgQVCp8TVX467YjzH7YgS1FsnVhrtiP357HcbcmWgNRhq/egOAhQsX4vV6Wbp0Kd+aIZMYTkjWwaeO+d///jdVVVXExsayZMkSvCqvon/DlL0VLXy6v+uIfa1a5P6ZWT1uT9HAAEW1rSzfUY6/tR61NfYcjHBoEG7UcPGoKLJizv55P1X/vF4vxcXFpKWlodPpaGtrw+l0EhUVSJP4xRdfIMsys2bNQhAEioqK2Lx5MyqVioULF9LW1saf//xnAH7wgx/wwgsvYLfbefQ6Gb2WjhrYD/0DaGpq4q9//SuSJDFlyhSuvvpqWr2tigaOID7eWxV09M2Jt3B1flyP7+2pVijGlHOMy+tn+c5KKpp7FukyPsXGzF5sgAx3fvnLXwKwaNEitmzZQnFxcfDcTTfOI3/s+DNv8rlhx2Pg80D+o2CO7bTIVGO7h3e3HeNA7WGe3XsHMYKOK5lD6tjbmZU/mktGBfIBy7JMi7uFdl87JrUJq86qFJg6R7i8fv6zrrjT+hf9QTGm9O2Z9la0sKu8mesK4juNkrO7vHy0p4rK5pHrDaXXqAjTq9GqRcTjOuDy+nF5/Tg8/n4VCD8dyd0eyNENmMZdQ/uujzqct0y6DnQdf3eC30Vz6f28WLWeVqARmUjfvczL+ib5iTYALkyPYHpW1Bn9ff3119jtdmbPnn1GPltFA4cGNXYXr28q63c7ijGlb8/0wc4K/lv0OX/ftwSAC6LncWvW70fE56HYvpWvqt5gZ8OHZ71W7x+LSo4CZNrVX6Lz52Lyz0ArZaKWE/ALTfiFGkQ5HLUcC/iQhHZUsg0R83EPv0BiLuG0DdkIo5Y4q57EcAMp/kqksh2IRiv69Ik49n0ZvE7QGjBPmocgCGhUAlfnxwUW+j2cA56O1+vlvffe44ILLiArq+Mmk6KBQ5vXN5VRY+/dvEMxpvTuudw+Pyt3V7FqTxV+ScYvyfgkGa9fwueX8UqB/584LgCiKOCWS9nn+h1NvgPBthJNuUyOuZEwTRSJpjHEGDLO6M8nefDLXjx+J1WOw3xZ+RxHWjbjk8+MzJgQNYdbs347ogzbsRY9s/NiefGffw16aRcWFrJ3797gNVfPvpILp3biEd1HDaytrWXVqlUsWLCgQxpQRf+GHx6fxL/XFZ+R7eQEijElQF+MKSt2VZ2DkQ0NVKLAlPQIJqdFoBqg+s3d4XQ6eeKJQDH4e++9N+j0d4IffO+72CJPW8v0Uf8Atm/fzrFjx5g7d66yDh6BNLZ7eHlDCbI8cMYUJc3XOcTu8rJsRwUNXRTc64zdx1rIT7ASHaY7+8UjgGCKHb+H8h1v0m6FmOR89uzdR3FJGfljx3Pw4EEsFsvJ3IqCADpD4J8xolMBrW9zs3R7BWsPvExd9UY0goM6dMSNvZVJGelcfMpGoyAI2PQ2bNjOwRMrnIpeoyIrJowDVfbBHorCcWrtbl7bVEZ+opWUCCOxFh11rW6ONTrZU9GCyzuyw55PGE7OBaLOdDK82tNKuNmDRiVT74lGctqRZBWC34e3rgRNVAot/kbeOfK/HGj+KhjVnCj9hMXjFxFvNQAwKTW8gyFl06ZNSJLEtGnTmD59eqeFABUNHDrEWvREmrW9mjcohJZR1qlcl/IwK8v+xNa6D4jQJXF1ygODPaxeIckyTe0eVle8wvqGP3R7rVqKxeS/EvBj9s1GTUdDbLTvYdSigFoUUakFRAFEMQKVkIUoCKjEwDGVKAT/adUiGpWIQaPCoFVh0qqxGTWd1EewQNKYwJhbKjFo/USFeTjWYED2OBEEAZ2/jbFWyIw+vjHUgzngCZxOJ1999RWZmZlkZGSwaNGiThfIigYObXLiw3ptTFHoHTq1ihsnJFLa0Ns6ePHMk97h/aO/YkPNWwBUtO+n4uj+DlfFGNLxSV4a3eWdNdIpj45fRbQh7Yyi8sMZg1bF9MxIChIDm3UPP/xw4ITfg/3w+1SbITlrPDt27qKwqJgLp06nqKgInU5HcnJy4NpeaGBFRQV79+5l2rRpxMTEsGTJEmUOOALQqkVy4rsuRK+gcDaizFpm58URY9EP2hgMBkNwL9DjbGNUbMAVx6lOpKKiEofbR5jfz86dO8nOzg4YgXuhfxAwoLS3t3PxxRczceJEJkyYoGjgCCXCpCUn3sL+yoHbV1SMKeeIFoeXd7Yd61XRRAgsgL88VMvCC5IHaGRDlyvyAv+aEi6lsOgIV111FW1tbbz1VmBy/j//8z+YTKaztAKVzU4+2FnJ3sIPGVUtksh46qnnktzfUZCQztX5cYq1eQiRn2hRjClDDI9PYntpE9tLlTzl5wpRo2NyZuBz8FXbRbjryhD1Jrz1ZbiKt+Aq3sL/8QsA1IhcrSvAzm+5Ij8Vsy7w1T4pNZxLR0cH21y1ahVbtmzBaDSSn59PWFiYon3DgNx4C+sK6wd7GOc1s5LuwaQJ5+0jP+OT8mfxyR6uSfkhKmHoTaN9fonGdg91bW7qWz3UtrrY4/w/2lRru7wnRfwOifoLiTWNwqRVY9Sq0GtU6DUiOrUKrUpEoz5uQDlH3opqSxTTc5qQZaiWsxDDookya6H4IJ9vKuTIgT0sWbKkx+05nU6efvpp3G431dXVZGRkKPo3TBkTF9DEUEaGKoQOlajh5sz/Y37GY7xV9FPK2w/Q5CrHLZ00zHSWtut08sIvZ2HmrwjTnhlZO9wJ06sZl2yjINHaZTrvi0cH/rWnXcbBQ4e5/vrrcbvdvPbaawDcd999REdHd3pvZ5SWlvLiiy+i1WoJCwtj+vTpigaOIMYm2RRjikKfyIm3MCsn5jTnlsFFq9Wy+Hgg3kH1NNasW09CQgJHjhzhww8/5MMPP+xQ57QnrF69mjVr1mAwGMjJySEqKkrRwBHORVlRFNW2DVj7Q28VOALpqyHlBBVNTvZVtpCXMLILn3ZFeLiNRx99FABJOhm+KggCzc3NuBytdBW0VVzXxqo9VRSWrcFSWQ3oMWLkwtRHyY/LZO74hCH1xaEASeFGwo0amhzewR6KgsKQQBuTijp2FH7Jy3+P/ZVLmUAtgdpCKaSw2DiOC6JjKItPAjHwtX5BWniH1IVr165ly5YtAGg0mg4pHRSGNtlxYXxVVM/5k5R1aDI19maa3VV8Uv4sX1T8G7/kZV76TwZtPD6/RJPDS5PDQ0O7h4Y2Nw3tHloc3uNJtdpp1PwLv9CIS7XjjPsXpv6b7Ih8wg0RZzY+hBAEMGVPJSHCyvXjEvjr+koA8vLykGWZwsJCRsndOyO6XC7efvtt3G43AElJSedi6AoDhEGrIj3KNKALZIX+oxLULB71RPC93VPLG0U/odFVzgUxN3CwaS1HW7cfv1aDX/ayOOv35ITPGPQ6KAOB8fjfbUa0mfQoU48N0yaTiUceeQQIzOdOoNFoaG9vp6m+lrMpWmVlJe+88w4AHo+HtLS0vjyCwhAmyqwjOcLIscbeRpMpnK+oRIFLR0czPtk22EPpljHZoxmTmw+AWh1Y5+p0gaw95eUVhLvBdJYkPps3b2bNmjVAIM1reHj4wA1YYchg1qmZkh4xYBkeFGPKAGN39c+QcoI1h+tIjTQFPY7PV0RR5L777sPlcmEwGHj33XcpLS1l7gQYd1p97E3FDWwobuDQkQ+IqrajRk+5XI9p9G3kxUcxb3zief/zHKrkJVr5SvHEVlBAlmUONW9gbfUb7GsK1A8oYx811GDBwu18C5PHi83QQrkgIAEXZkQwPTPgySlJEitXrmT79sCGxcyZM7n00ksH63EU+kCYXkNyuJEyZYE86Fyd8gCR+mTeKPoxa6pepMVTwzdG/6nPaWdcXj91rW7q29w0Ojy0uXy0uX14/TI+v4Qkn0yXJQggCgJ+Scbp9XeaG11GxiuU4NR8TLNq5Rnn56b9mIlRc7Boe+7NPBTIjg3jyoIkVKLA97//fYqLixk9ejR79uxh6dKlZMXC4ml0Wh67tbWVV199ldraWjQaDbfffvvJ9DgKw5a8BItiTBlmWLQx3Jv7XPD9lUnfHcTR9A2DVoVFr8GkU6E7XkvvhFHkhJ1DFANarVMH0hpaDRoizVosek2/+xeEgAba7XZsNhvvvPMOBw4c4OoCmJLZ+T1FRUW8/fbbeL1eYmNjueuuu4IbkQoji/HJNsWYotAjDFoV1xXE97iW2FAhNTWVO+64A5vNhsPh4M133kP2wu0X0alztSzLfPrpp2zYsAGAqVOnMnv27HM7aIVBZWJKOFtLGgek7WG3k/zss8/yhz/8gerqasaNG8czzzzDlClTBntYneL0+Fm6vaLfhhQAt1fi8wM1zBufGIKRDW9OhDR7PB50Oh2iKJIUEahpIMsybq+fzw7UUFjTxsHCd4ir8yCi5pBcgy1zMekxkVxXEH/e1KEZjuTEW/i6qAFJccU+g+GkgQp9xyu5mb7rr52eK6KIRO9vuDrxAnT23RjEJgzawMbqJaMiuSAtEgC/388HH3zAnj17EASBa665hsmTJ5+zZ1AIHTnxFsWYcpzB1sDJMTdS3r6fdVUvs7PhI3Zu+Ig/TNvXo5RfTo+fY00OjjU5qGp20dDeP08pnVokwqQlzOBjq+N7NHmLzrgmzjia20Y9QaIpp199DRaJNgNT8+IQjm9Y6nQ6cnICz+Lz+dBoNCRHeoKRKafWgWpububll1+mqakJs9nM4sWLiY+PH5TnUAgtaZEmTDoV7e6RXbftdAZb/843DFoVmdFm0qOMxFr0hIXAINJfIiIiiIiIwO/3o9FoEASB5MC0LxC5cooGHjhwgPfeew+/309GRgYLFy5UDCkjmMxoE1aDhhbnyM3uoGhg/4k0a5k7LgGbUTvYQ+kT6enpADQ0NGA2mfA724gKC5w7dQ4oSRKrVq1i27ZtAMyaNYuLLrpoUMasMHioRIGJqQMTidRrY8qSJUu4++67B8Wz9a233uKhhx7iH//4BxdeeCFPPfUUs2fP5tChQ8TExJzz8XSH1y/xwc4KGvu5UD6V4rp29lfayU2wnHHO7vKyt7yF0kYHDk/AY9Fq0BBl1pJgM5AWNfKiWrRaLQsXLqSxsZHIyMAscvnHn1NY2YickMP2w88xpskGiOyWaogddSejYm1cPiaGtKiz11pRGDzMOjVpUUaK69oHeyhncL5poFdyoxGVhde5wO6p5RdbL+nyfLT7p2ilbMYlpDMtIxKTTo3sT8Tv87BFb+Ly7Ch2fLmCcP9FZGZmsnTpUvbt24coitx0003k5uaew6dRCCVZMWa+PCR2Go1wrjlfNNDrlzqkVDmVG9P/F5evlS11SwF4/sB93DnmmQ5aKcsy7W4/Na0uypuclDc5qO8kzNyiVxMdpiPCFPBcNuvU6DTi8ULugUgUSZaRpEAdPVEQMGgDHs9aFRxu+Yp/H7inQ5tmTSQLMn7JaOs09OrhmdJPFATSo0zEWfRd5vCaOHEi6enpWCwWUKlobGzk3Xff5bbbbkMURZ577jna2tqw2WwsWbIEm812bh9CYcAQRYGceAtbS859LbfB0sDhtA4e7iTY9ExKjSAjyoR4jupE9RaVSsUNN9zAJZdcElwHb9u6lcrKSq6//vpgRApAbm4u8+fPR6XqWxSlwvBAEATGJVtZe3jgsjucL3PAkUpalJFr8uO7rNU0nIiMjOTb99xDa2srapsNWZZ56623GDt2LLm5uR0MKXPnzmXChAmDPGKFwWKgyjr0ene9paWFK664gtTUVO666y6WLFlCYuK5iZb485//zLe//W3uuusuAP7xj3+wcuVKnn/+eX784x+fkzH0BEmSWbWniqoWV8jb/vxADeVNDiamhmMzaDhS187Bajsl9Y4zvPhdXj81dhf7Ku0IAsSE6RkVa2ZUjHnYWqJPRxAEIiMjaWz3sHZ/Obu2bADJz3+OPUQttVzN1chSEklZdzEq1sqF6REUJJ2ftWeGG7nxliFpTDmfNHBT5Rp+vf3b3Jn9F9LClAlIV/glL27JD99aJgAAr0JJREFUgUFlocldgUkTQbu3kQh9IIu102dHFNQctW9DqzKgV5k52LwOSfZT6yxma90H3bavk0YR636CjCgb0zIiO0TVCSo1Wq2Gq/PjcdYc5ejRo9TX1/ODH/yASy+9FLvdzqWXXkpWVtaA/gwUBhatWiQz2syBKvtgD+W80cC7X9rKusN1qFWBwusalYBWLaJRiWjVIlbVD7CqDtLiP8CB5jU8unEsF1v+gV4eQ7vHR1O7F6f3TK/5KLOW5HAjieEG4q16jNreO7p4JTfrq19mecnvzjg3M+Fu5qT+aFgX1dSqRa7LSyCuWn/Wa0/Ne/3FF19QVVXFF198wfXXX89VV13F1q1buemmmwIGF4URRe4gGVMGSwOHyzp4OBNj0XHpqOhhlfrmhCHF6/Xy+eef43K5GDNmDOnp6Vx44YV4PB7mzJmDKCo1Qs8H8hKsbCxuHDDnm/NlDjgSmZBi49JR0UPWQNwXVCpV0FHmyJEjHDp0iJKSErKyspg+fTr19fVMnjyZvLy8wR2owoik1yu4ZcuWUVdXxyuvvMJLL73E448/zhVXXMHdd9/NvHnz0GgGJvzV4/Gwbds2fvKTk8U+RVHkiiuuCObAOx232x0sNglgt5+bTYjVh2sHbBPYJ8nsq7Szr9KOVt1zL1VZhhq7ixq7i68K64mz6smOCyM7NgzTMI5YqWpxsutYC4eqWwPGpLhUqqs3Uy6VA/CFv507Rt9JVqyV3AQL07OiBnnECj0lI9qMQavC6RlaKRyGiwaGQv/eO/gydk8tLx56gIfGvj/s8uz3B0mWqHMexSd7afXUUess5lj7XnbWf4RfDoTPZ1qm4JM8lLbtDHn/Bv94loSFk62OY7n3Tibmx5JoM5xxnV6jYu74BBJtBrzhY0hLSyMlJQWVSkVMTAx33XXXsN5UVThJXoJlSBhTzhcNdHn8yIDXL+P1++ksa4aVJ9CKG6jT/QaAr+zfIcw3F4vvBtRyDAIQYQpECCeFG0i0Gfo159rd8An7m1azufa9DsczLVOYmXA3uREz+tz2UMFm1DB3XAKRBgGqe3fvxRdfTHl5OdOnTwegoKCA/Px8RQNHKJFmHfFW/YA4r3XHYGjgcFoHD0f0GhUXZUVSkGgdtnqh0WiYOXMmO3bsYNSoUQiCwNVXX90h7Y3CyEevUTE2yTpghubhMgcERQNPoFEJzMr5/+zdd3xV9fnA8c+5e2fvQRIySEIGe8pQEAQBEVEURRxYtba11Fptf+5iba2jdqi11l1XK25RmSIyZK8wAiQkELL3uPP8/rjkSkyAJCS5Se733Vdezb33nHuei8mTc873+32eMFIj+vekkvj4eJKTk/Hz80Oj0RAYGMiNN94o8p/QbTp1RRcSEsLSpUtZunQp27dv55VXXuGGG27AZDJx/fXXc+edd5KUlNSlgZaVleF0OgkLC2vxfFhYGAcOHGhznz/84Q888sgjXRrH+WzNq2BXQXWPHOtCZhycqm7iVHUT6w+VERWgJyXMTEKIscsGVuxOF/VWB/WnS441r5rRKBVoVQr0GiVGjarDI+NOl8zJqkYKKhvILamj/HTJDJfLRWnVPuwndxFCAMEEIzmuZlHifMJNMgkhRqamhp3n3TtBlqGhAmx1oDGBIfCs5SiEjlEqJFLCzew8XuXtUFrpCzmwK/LfQ+OfZXvRLoobc3l463geG7EJo7p7ak56m9XZwIGq9aw4/heKG4+0a58jNVsu6JiB2mhUkpFaezkBipEo6megcMYioUAj2Rlt+ZBIPz1SYCQuqfVqQrNOxdwhUQSZtDidTg4cOEB+fj4NDQ1MnDgRSZLIyckhKSmp2y5uRA7sOdEBesw6VZf0YbtQvpADX7lpBB9sL+RIaT12pwu704XNcfrL6cLulLE5XTicMylqCmFrzSNY5SpqVR9Tq/qYAcbR3DToL5i1/p2OASC/djcfHH2Egvq9LZ5XSmqmx/yckaFXYtb0j4kiKeFmLh4U6i4/4exYmVybzcbmzZuprq5m165dXHzxxdhsNo4cOeLpsdItRA70qsFRfj0+mAI9nwP7ynVwXzQgyMDUtLBe0Q/lQlRWVvL9999TVlbGiRMniI6OpqysDKvV2n0rB0T+65WGDQhgV0EVdmf39B7tC+eAIHIguCf0zPCBXsEul4sjR46Qm5uL2Wxm6tSpqNVqDh48SFxcHDrd+Vc6d4rIgT7tgu6cFxUV8fXXX/P111+jVCqZMWMGe/bsIS0tjT/96U/88pe/7Ko4O+X+++9n6dKlnsc1NTXExMR02/F2FVTxbW731ajsDi5ZpqCigYKKBqQDEGbRMSDIQLhFR4SfHr3m7PUUZVmmzuqgqsFORb2Ninob5fU2qhps7brZI0lg1Kgw6VSYdSr0aiU6tRKNSoHCZSOiuhGnS+bIwRJq7QoqG2xUNdhxun5UzsxWjW3r5+gBPWYq5Rqinb/jsvgUtPnraVSqGDnmlq5d0thYBbvehs0vQuWxH54PiIdRP4Gsa0Hv33XH81HpEZZeOZjSrDfnwK7If0aNmZsG/Y0ndkwH4NWDP+f29Ffa1Wi5t5NlmQZHFd8Uvc6GU/+hwVF1zu01CiMu2YFDds9w8lfHEqxNIcYwArvcyJ6q97GoIxlgHI9a0nGicQf+6njiDBfjdLkoaNiChhDUcgROhx+1jUpqaiQcLpkz15tYdCpSws1kRRpIdpnPGk+wWcvcIVGYtCqKi4t54YUXPK8FBgbicDj44osv2LlzJ9HR0dx0001dW+JB5MAeJ0nuHgFbjlV4OxSP/pwDjVoVFr0aP317brDNYK48jYNV3/JZ/tOcbDhAfv0mHto2ivHh1zM56hYMKn/UCi0K6fx1qovqD/Fd8dsU1O3leN3uVq9fFvMLRoddg1kT1O7P05tp1QomJIUwOKpzZVjXr1/P6tWrPY9NJhONjY289NJLVFZWcskllzB+/PiuCtdN5MBeITnMzLpDpV7rJ9Wfc2B/p1RIXJQUTHaMf5+euexwOHjmmWdoaGgA3PlPoVBw5MgR3nnnHSRJYvHixURGRnbdQUX+69UMGhXpUX7dfg3dm/Mf+HYOVEgSwwYEMDohEFU39YvoLSoqKvjrX//qeRwUFITD4WDNmjVs3LiR0NBQlixZgkrVhfcvRA4U6MRgit1u5+OPP+aVV17hq6++IjMzk7vvvpvrrrvOU494+fLl3HzzzV2aQIODg1EqlRQXF7d4vri4mPDw8Db30Wq1aLWdH4W1OVxUN9rbNZK7u7CKNQdLOEuv0j5Bln9YsdJMo1Jg0anQa1RIuAdAbA4XjXYn9VbHBc14kGWoszqoszo49aPFPArZxsjTJ4W7rFVtzsoGyD+5DkXeAfxw/+y5ZBcbFRlcMXQEJpULe20eceFBhIV04azN3JXw7iKwN7R+rTIPVtwPqx6Da16HxCldd1wfFGrREWzWUlZrPf/GPaSv5MALzX/NQvXxXBZ7N18cf5YjNVv48Ngyrox/sE9deMqyTIPNSUltDUeq9/F58a3n3F4h++GSqlG5wgmzPY5SDkbiRyeijWAHjp5+6M8lABSUN28wlmLgh/9ak9qKDJVCItxPR6S/noHBRkLMWiRJQiHboI0UAxATaGBWVgRqhcTGjRtb3ETMzs7m8ssvR6lUMmTIEA4dOsTQoUO7diBF5ECvSesFgym+lgPbSyEpSQ2YSLLfWN4/+pCnFNe3p97k21Nvtth2fPgNBOtj+ejY48i07zxKJWkYHXY1V8T/tl0DMn2BJMHgSD/GJgZ1qndMQ0MDX3zxBXv3/rBqZ/78+aSlpQHuBvWbNm1i0KBBXRYzIHJgL6JRKRgUbmZ3Yc9UBQDv5EBvXAf3Z356NTMzIwizdNNs5R5y4sQJPvroI89ACsDixYsJCgrCarUSGxuLJEld25xb5L8+YfiAAPYWVuNwde3Nqb5yDgi+mQMlCQaGmBgVH0hoH89v5yPLMtu3b+err77yPJeSksK8efNQq9VkZmayZ88esrOzu3YgReRA4bQO/1RFRETgcrm49tpr2bJlC9nZ2a22mTx5sqcRUFfRaDQMGzaMVatWccUVVwDu5VyrVq3irrvu6tJjNXO6ZN7bWsD0weEMDDG1uY3LJbMlr4JNR8v79EDK2dgcLsrqbEDHyi10t6M1W/nn3pu4m7sxYKFebiBXrqXaNJsrB8ehUSkINGqYc8uNmPXarruRmLsS3rraPRLU5g2Q08/ZG93bLXxPJNELlBZh4ZvaUm+H4eFLObDZ1Og7CNUn8PrBX7Dh1H/QKy1Mi/1Zr1uh0rxarnmlXPNXcf0JjqpvPOe+eudoAp3zMStT0SiVKBUSSqWEpHHP7mlxHOQW+V4+45vmTSXJvZ9SklApJdRnlDg0aFT46dX4G9T46dQdWjWXEm5mWno4SoVEbm6u5wTSYrEwZcoUMjIyPNvGxsbys5/9rGuXNosc6FUBRo1XegScyRdzYEcoFWoWJD7O/IRHeGr3lZxqONRqm29PvdGu95oe83NGh12NSR2EQuo/Mws1KgVpERayYvwJNLY9WaY9Vq9ezd69e5EkicjISK644gqCg3+YPDN+/HiGDx8ucmA/lxHt16ODKd7IgX0l//UFiaEmpqaFucsJ9mFOp5P333+f6upqdDodiYmJzJo1C43GnVO1Wi3XXnstsix33Y1Ekf/6DLNOTXqUpcvLz4tzwN5BIUkYNEokyX1OFWTUEmzSkBJuxt/Q+fOqvuTEiRN8+umngHtF3sSJExk2bJhnwmd4eDg//elPxTmg0G06/Jf1mWeeYf78+ef8ofT39+fYsWNnfb2zli5dyo033sjw4cMZOXIkzz77LPX19dx0001dfqxmNoeLT3adZGhsABlRfgSccdFXUtPEV/uLKe1Fs+b7uyZbDbatnwFluHCxghUMdI1jr20CY5MSGB/tbhzob1Azb1g0pjN6wMiyzN69e1Gr1Z2bpdhY5R6FlmXgfOUEXCAr3Nsv3S+W+V2A1Agz3x4u8/Td8TZfy4HNsoKmMTvuPj7K+wMrT7xAhfUEC5Oe9NoKFZvDRXm9lZJaK2W1VsrrbZTVWVuslnNSSZNyN+Xqv7XaXyUZSPe7gjFhC4kwDsCgUaHsylKA3SA71p8ko43fP/YoEyZMYNKkSWRnZxMdHc3QoUPb/G9x5s+p1Wpl06ZNZGZmEhDQid43Igf2CqkRFq8OpvhqDuwopULNvdmfAFBnr6Cgbi8v5SwBYIApmwprIbX21qVhR4ddw6jQq4g2pqJU9I4a/v4GNRadGqNWhVblHtSRkbE5XFgdLhptTuptTprszjbLLek1Siw6NRH+OmIDDcQEGNCoOjc49Mknn7B9+3ZuvfVWLr74YioqKrjkkkvO2hPgzJ/T8vJy9u3bx9ixYzt3c1HkwF4p1Kwj3E/XYmV9d/JWDuxL+a83UiokxicFMzS27/b+czgcLFu2DKPRyF133eUpq3TZZZdhMBhabX9mnpNlmYMHD2Kz2cjMzOz4wUX+63NGJwRx4FRtl074FeeA3hUTaGBwlIW4IGOfHxDujLKyMv7+978zfPhwZs6cyciRI/H392fUqFFtTp4+8+fUbrezefNmUlJSCAkJ6fjBRQ4UfqTDVxI33HBDd8TRLtdccw2lpaU8+OCDnDp1iuzsbFasWNGqEVVXk2XYll/JtvxKwv10KCWJigYbjTZntx5X+EFxxR4qj6wj0u6uDx5MMBYsHHPEodLM5aqscE85Nj9964EUgA0bNrBq1SomTJhAaGgogYGBHQti19unl/O194zE5d5+1zsw+vaOHUvwMGhUxAUbOFpa7+1QAN/Mgc0mRi5Gr7Lw3pEH2F72CXaXlRtT/nLBM6adLpmaJjv1Vgf1VvcNObvL3eDZ6ZJxnm70bHU4abA5qW6003CW/KuQQKcroUj5HFXOna0/Q8RNXD7gV73mJmV7yLJMoq6Gb955m29OP/fNN98wefJk5syZ0+73eOONN6iursZgMDBkyJCO30wUObBXSAk3882h0i4v3dBevpwDO8ukDiQ1YAJPjz3Y4nmny45DtiGhRKPsPeUYNCoFSaEmBoaaiDxP/7wfc7lkrA4XLll2r85TSJ0eOGlmtVrZuWcHK1as8Dz3+eefs2TJEhYtWtSu96iqquJvf/sbKSkp5Obmdm5SjciBvVZGlF+PDaZ4Kwf21fzXG/gb1Fw2OIJwv96TZzuioqKCb775hl27dgFQX19PSUkJycnJJCcnt+s9tm3bxmeffca4ceMoKSnpeOkvkf/6HINGxaj4IDYdLT//xu0kzgG9JyHEyKzMyK7tA9wHyLLM0dM9oBwOd1/mrVu3MmPGDC677LJ2v897771HQUEBKpUKPz8/zyq+dhM5UPiR3lWjpR3uuusury7l66kTdQGO1tVy35FV+LGG+cxHT8tGq/5NLzE8LoIRcYGeGeX+BjVXDYvGrGt9o3T06NGcPHmSb7/9lri4uI4Npsiyu8FUZ2x+wd2Iqg/1mOht0iIsvWYwxdu8nQNHhl5Jta2YL44/y56Kr7hnYyoPD/8Wi6Z9MzxsDhfFNU2U1FoprmmirM5KdaOdztwXNmqUhJi1BJu0SOp81pU9QKn1iPvFM8Zaxodfz0URiwjRD+j4QbxElmVqGpSolTJS4Xq2l7csdXdmOa/2kCSJK6+8ktdee40NGzaQlZXV0YBEDuwldGolCSEmDhXXejsUr/B2DuxKSoUaJb1nYDfAoGZEfCDJYWbUnWxYqlBIHRp8ORun00lBKQSZ4Oknn271+pQpHSub4O/vz9y5c/nkk08wGAwdH0wRObBXSwk3s/5wGU32/j3RrT/lv56SHmlhUkroBQ/q9rR6K5TXguNYHm+89Xar1yMiIjr0fkOHDuXEiRNs3LiRqKiojg2miPzXZw2J8edwPzpf9NUcGBWgZ0ZGhE8NpBRXg0oJX7//Pw4eOtziteZ+UB0xe/ZsXn31VdavX09mZmbHBlNEDhTa0OcGU4T+rbB4I5YjeQBUEc+t/NAo2ipbKaSKHbaB+FmGsCAzhGDTD03FAo0arhwa1eZACriXOs+fPx+Xy4VSqcTpdHLkyJH2zehpqIDKzixXld37NVaCoYMrYQSPhBATOrWy318k9yb55fVsPFJOfnk9GpUCtVKBRqVAq1QwJep2kGW+KPgLAK8cuIvb019Bq2xZYsDhdFHVaKekxj1wcqqmidI6a5vLzVUKCZNOhVGjQq9WolZJqBUKlEp33xG1SoFOpUCnVrr7jejVaNVKZFlmZ/kXvHGoZZPDEF0ckcZBzIn7Lf7avjNjyXoiB2v+TgA20VyKwj2QEhcXR0BAAJdeemmn6r8GBgbyi1/8AkmSkCSJqqoqGhsb23dBLnJgr5IWafHZwRSh65l1KsYODGZQuNmrF+p2u53//Oc/5OXlnX6mZSwDBgwgLS2NESNGdKrEZGZmJunp6SiV7sGeo0ePEhUV1b4GtSIH9mpqpYLBURa25lV6OxShlwg0apicEkpsUOvyV71VSUkJ//znP3E6nfyQ/34YSBk4cCATJkwgNja2w++tUCiYM2cOl19+OUql0lP2q10DyyL/9VkKhcTElE6UNBI8NCoFfno1dVaHVyrTWPRqZmdFdnqSS1+yfft2Pvnkk9OPmnOgeyAlNjYWf39/pk6disnUdj/rczGbzfz0pz/1XAfX1dVRWVlJTEzM+XcWOVBogxhMOQeny8nn+c8QaxrM4KCp3g6nX7A5GtCoDFRU5+J02XA67ewpeJ+tjo3YJQd321ovgcshhwoXHLJei1mnY3xaMEmhphYX0qEWLXOHRGHQnPtHWpIklEolLpeL9957j0OHDnH55ZczbNiw8wRe16nP62GtFQn0AigVEinhpi5voiec3dqDpSz7POf0I5kf39TSqKaB2j2Ykl+3kwc2TyBT92cMJLpr6Nud1DY52nxvk1ZFuJ+OMLOWELOWQKMGk1bVoZtj9fYqluf+kV3lX2J1tly1dGPKc2QFTWv3e/UUWZZBdoGkwFVfiaTR46qvQnbaUQXF4Gqo9gyknCk7K5Ok5BTS0tIuOIbmerIVFRW89tpr2O12brrppvPXjhU5sFeJCzJg1qnO+jsmdB359Oivt/pDdSelQmJobAAj4wN7ZNa20+lEkiQcDgclJSUEBgZy+PBhQkJCiIyM5IMPPjhjIOUHI4YPY8zYcZ3r9fQjzQMpe/bsYfny5QwYMICFCxeev+yhyIG9XlaMP9vzq3pNj73+QpblPpX/gk0aMqL9yYjy63W98BwOByqVipqaGqxWK5IkkZ+fz6BBgzAajTz//POt9hmYEE9oWDiXXHKJJ39diOaBlOXLl7Nnzx4uueQSxo8ff+6dRP7r0yL89N4OoU/yN2i4PDOCAUEGNCr3716T3cn2/Ep2FFS12SOuO0xNDesX/VFkWcbpdKJUKikpKUGv11NeXk5tbS0ZGRnU1dWdMZDyg6yMwcQlDCQ7O/uCY2i+Dq6treXVV1+lrq6ORYsWnbXvnofIgUIbxGDKOby88yUOnPiUFFSUpkYREnDhN7L6GpfLhUKhoNJaRIO9Ermhltrj32Oxaak0OJDUOooaDvG5/QMSSGARLWtX27HzJm+STz4P8zAATbh/8Jp/+DKIZw2fYMOGHTvq0yUv6qjFrj3KtupFKJRaLkoKJDPKD9WPRuUj/XXMyY7q0B8ZhUJBSEgIR48exWKxnH8HTcdHv1vQmi9sf4G0CD8xmNKDAo0aUsNNmGryCJUrWONIwep09zcBd7muWMdH1ClXUaF5Dge1bG/6Cf72mzE7pqHACLhn84SYtIRZtIRZdET46c66eqw9KppO8Pz+xZQ3HW/xvAIlv87+hFB9Qpde9J95E0F2uXBUFGI7dQiXtQFN2EBklxOlKQh1YBSNhzdiL81rGZfOjDH7MnA5qd3yvzaPoQqMQRUUg6Rs+Sc5NVJmeiZYhs8EZQfrup6H0WjEZDJhs9naNytb5MBeRZIkUiMsbDlW4e1Q+jWn3UbjwW9RmoLQRvevc8BQi5ZL037oN9eWH99EbWhoYMuWLezbtw+LxUJCQgINDQ2MGDECf39/HnnkkVbvMWrUKKZPn86OHTv4+OOP2zzO3LlziYyMJDU1lQMHDnienzpYZmQCqDIv7fIcGBQUhFqtxt/fv82mpa2IHNjrWXRqEkN9twRid3DWV9J4eCP6xNEoTb3rRlCIWYtWpUCrVmLWqfDTq4n003dpX5Qf58DS0lLWr19PXl4eycnJ+Pv743A4mDRpEkVFRfzzn/9ssb9CoeCaa64hOTmZJ598koaGhlbHUKvVJCcnI8sywcHBlJWVAaBVy1w5DJKnLOjy/CdJEmFhYezbtw9/f//z7yDyn+CDgk1aCg7v541Pt7No0SLUajU6tZKxicFkxfizMqe428uQZ0T5eXV13Zk50OVykZuby6ZNmygrK2Po0KHIskxoaCjp6el89dVXbNy4scX+fn5+3HbbbWg0GpYtW9bmMeLj49ssX50QKjMzCwJHzeryHKjX6/Hz88PpdKLXt2OwUeRAoQ1iMOUcFmfeTM7KHEKcwZCzi/J0NUF+Sd4Oq1vlFa6ivqaAmKofLq6XsQw7djLIYB7zsJzuXWI+fT6oJBgAHa1PXtWo0eNOUNvZzlCGttpGQsKFCz9NDGs0QZRXDsDpVPDT0PdQKvSMCgxicEwwWlXrwZL4YCMzMyM6tezxkksuYciQIQQFBVFeXo7dbic8PLztjQ2BEBAPlXm0v+mU+9MREAf6C59N6evC/XQEGjVU1Nu8HYpPmJUVSbhe5vP3vgWngwWGvZiGzcEpy1jtLqwOFzaHC6tjMQeqYviy+NcAVKn/Tb3mHUaH3MrUmCUYNeoLHtwobyrg3wd+SlFDy+bNIbo4BgdewuSoWzGpL/wiX3Y6sJcX4KqrwHbqEACqoBj0yeOQJImG/Wtw1pR4trce3w2ANiYDAqOQna2Xfruaat01UpVnH0BSaA3uJcc6E2nTb+DyIbHolS7Y9/gFf6az0Wq1LFy4EACDwUB+fj6BgYGYzWc52RM5sNdJj7TwfV5Fm2XzhK5Rfeo4jopCHBWFyC4nutiO9SrqjRSSxMj4QEbFB7Yq6VVVVcXBgwfZs2cPJ06cAGDBggWkpKRgs9l48sknPduWlZVx9OhRwF0+Sz7LD2Jlpbvs0rnKKDT3sMvIyCAlJQWNRoPksndrDoyMjOS2224jMDDQU/Z14MCBZx9YETmwT8iO9ReDKV3IWrgPV0M19bu/xJg1HaXR+z/HCSFGxiQEEWrp+mby+fn5nDhxgq+//trz3H333YdWq+Xw4cP85z//8Ty/bds2wN2PadKkSTQ2NrZ6P5fLRV2de0bzsGHDWL9+fatt7HY7KpV7dfbtt9+Oy+VCrZC7Nf8BjBs3jpSUFIKDg6mqqqK+vv7ss7NF/hN8UGNjIytXrqShoYEnnniC3/3ud55zBKNWxeysSL47Ut5tE5vMOhUXJQd3y3u3xel0sn//fk6dOsV3330HuMur3njjjUiSxPLly9m7d69n+3Xr1gHuSTPp6enYbK3v0VRXuyfCNue4ts4VjUb3BEyz2cx9992HWq1GITu6NQeqVCoWLFhAU1MTFouFwsJCzGYzfn5+be8gcqDQBjGYcg4alYaI9Ctp3P0VevSo923FPjIatar/LJWsbTjFyVPfsfvUx5zkJDdxE4G0nKUYTjgFFLCf/cxjXovXipTllGpt0AD11FNHHSZ+GLkt0FWQFDCNxloda+u+IUARgtovApU+GFkdTYMzhFM1MomVk6ivdnLi9H5BeogNNBBq1uIyBeCSWg+kDAo3c2l6eKeXcEuSRFBQEC6Xi7feeouqqipuueWWtk8kJcndOGrF/R0/0KjbRcOpLpIWaeHbw2XeDsNn6I0m9EljaDywHtnWiDVvB7r4oai0CoxnpIkBQbO5JH463xW/w/qiNyhvOs76kudYX/IcV8Y/yLjw6zo0oCLLMrX2UnaXf833pcspqNvTapvx4dczO+43qBQXNlPF2VCNs6YUJMn9GQtaHstRXuD5XhM5iMYzBlNQaVBZwlBa3E08lUZ/HJWFICnA5R5Y0Q7IQpLcJ976QRNwVBSiMPqjMocgqTRIWiOSJKGQJMYmBjF8QID738rZ/YOGBoN7plNRURGvvfYaAQEB3HHHHW2XuxE5sNfxN2iI9NdzorL1DRyhawRED6To+DHsZfnYCveiCohAZe65C9uuFmTSMC09nLDTNyFdLhf5+fmcOnWKyMhIXn/9dVyulmUr1q1b5xngCAwMpKLih5sGISEhREVFYbFYcDqdxMfHc+zYMVQqFQ6Hg+DgYEaMGOE+dlAQ48aNA9w3HwcOHIhWq/XkIXCfl7VrpVwXCQpyTw7asGEDa9euZeTIkVx22WVtbyxyYJ8Q5e9emXCqusnbofQL+oEjqastR7Y1UL9rBeZRVyGdY3JIdxufFMyIuK5bIdPU1MTRo0epqqoiJiaGV199tdU2ubm5pKenk5iY2Oq1AQMGeJ43GAyYzWZqa38YzBs8eDADBgwAYOzYsZSVlREVFUVgYCDh4eGYzeYW51xKpdJdyqsHzgEBgoODkWWZ9957j6KiIq6//noGDhzYekOR/wQfpNfrmT9/Pq+99houl4sPPviAq666yvO6JEmMSwwmyKTh633FOFxdN7tJpZCYkRHR5mTirlRRUUFeXp6nDOtnn33W4vX8/HxPv+FRo0a1GEzRaDQkJCR4cmBwcDAajQaHw+E5l5w0aZLnvG7RokXs2LGDyMhIoqKiMBqN+Pv7t7hH4DkH7IHWNBqNBo1GQ0VFBa+++ip6vZ677rqr7fNQkQOFNojBlPOwmKI5FRGIvsh9s+LwzpdJGXoHSkXfqVvocsnUWh1UN9qpbbJTXvYNfvVHiZfdNwAjgOP4U0op3/EdYxnr2beKGkzSLWQoY9ArwlkpKVBRjkoTjVLhvgGoVEjM0t+CAonDUsvjumQZR4PMQK7AqnOx2+akocRBk6fG5A8nnBqlgoEhRpLDzMQFqohoPPugVXaMP5NSQrqknI9CocBoNFJZWYlGc44bs1nXwqrHwN4ItKNGpqQAlR6yFlxwjILboHAzG3LLxEzsHqQOjMZmCsJZV46t6CBKUyDqkLhW26kUGiZELGJ06HyWH/s9m0v+C8AHxx7lg2OP4qcJo9pWTLBuANcmPoG/NoI6exlHarZiVPmzu/xLcmu2tOp/8mPZQZdxQ/Iznf7dl10u7CVHsJfm46wtPR28Bk1oAuqQOKwnc8D5Qx8KTUSy51iqgEjMo+Yjuxwo1K1nRGpjBqONGXzWY6sDo1AHth6sDffTMTkltEtLU3SExWJBlmUqKirOXe5G5MBeZ3CknxhM6UaSJKEbOBJ7WT4AjQe+wZhxKQrdBS7372GSBENiAxg3MIi62hpWrdrArl27PDf9Bg0ahCzL3Hrrra3K1EyaNMnz/U9/+lMaGhrQ6XRtDrouWrSo1XM/xCAxZcqUrvlAXay5zE1bJXhaEDmwTxgRF8gnu056O4x+QVJp0CeNomHfGgAaDm7AkDrBM0mkJw0IMjB8wIXP7s3Pz+f7779n3759nueGDx9OUFAQF198MatXr/Y8r9PpPM3eJUnigQceoLa2FovF0uo8NDw8nKVLl571uDqdjquvvvqC4+9qkiRhNpspKioS18GC8CNxcXGkpqaSk5PDvn37iI2NZeTIkS22GRRuwaxT88muk13WoH5KWhiR/l0/gVuWZXbu3Mnu3btb9KgbPXo0GRkZnootzTIzMz3XhlFRUfz2t7/FarW22QB+9OjRjB49+qzHjouLIy4urss+S1cxmUw4nU7q6urO3ZdK5EDhR8RgSjskx1/BMdXX+BcUE20L4sCefzEo49ZeN6BSb3VQXm+jst5GZYONqkY7VQ3uARStlI9V+2dkZBazGAhtse9hDpNLLk7bbA47B+Nqo2RXDTLuYWJ/4AKbMOFuQh1o1BDhpyPKX0+En87TD0Uhn31GzuiEIMYMDLrg45/pqquu8jRBPSu9P1zzOrx1NcgKzp1EFYAE17zh3k/oEmadmgFBBvLKznPDQ+hShoypWPO2Yys6RGPuZiS1DpV/2yXxNEo91yQuY2r0nfx++8We56ttxQCUNeXz173Xtuu4ZnUwF0UsQqc0kRU0HbOmc7/3sizjqDyJ0hhAw4FvcNVXttzAYUMTOQhJrcWUNR1Ja2pzsEaSJFCqWvU26Sw/vZpRCYGkRbS+KO9JRqOR66+/HqVSee7BFJEDe52kMBNrDymw2numCaYvkpQqzCOupH7/Glz1lTTkrMWQfgkKTd9YpRxk0jA5KZCKE8dQKoL5y1/+0mqbxsZGRo8eTVNTE/fcc4+n5MKPKRSKNi+g+7rMzEzq6uoYNmzYuTcUObBPGBhiJMSspbTW6u1Q+rxgs5YhaVmYhg/grbfexFFVRHDFPlwxQ6hscJz/DbqIUatkWnp4p8+VqqurKSkpwc/Pr83VJzExMaSkpBAWFsbo0aNRq9tefaNQKM5eBqYPmzVrFnv37j1nOUaR/wRfdfXVV7N27VrWrVvHihUrMJvNpKamttgmyl/PghExfLaniJKaC/vbMzI+kNSIdvT0bSdZlsnLy8NisbB69Wr279/faptRo0bh5+fHtddeS2Bg4Fmvg9Vq9VnzY1+l0Wi46aabsFqtbVdnaCZyoPAjYjClneJjpnLQ+l8iSuyE1et4L+dXXJ32Z5SSd/4J7U4XxTVNnKxqoqi6kZJaKw1tjIQraCJes54JygDgRl7kRZpo8vQ3Oa4sxRkyjNkB73tWmoC7EqAsy7hc4HC5cMoyTqeMwyXjPP3lOL3yxOmSkWVw/Wi5gEIhoZQkVAoJtVKBWiVh0KgwaJRYdGo0qo7NapIkmJQSSnaMf2f+yc7Jz8+P4cOHex7X1tZ6Vqy0kDgFFr4H7y4Ce/MN/TM/9+k/PGq9O3kmXtLlsfq6tAi/dg+mNOZuQeUfjhyd0M1R9W+SJKGNG4rL1oijvABr4T6UfmHnvKgN1EXx1JgDlDUd52TDAQ5Wfcum4vfadbzBgVNID5jMiNC5KNoo8ddesiyDw0ZDzjqcdeUojO5VNdYzBlPU4UnoYjKQ1O4lvZLO3TMk2KQhOsCAv0GNn16NVq2kuaJgo81Jo91JTaN7xV9Vg43KBjtN9vPPRtKplcQE6kmP9CMuyODVQZQz/bisQ0lJCSEhbaz+EzmwV1ErFaRFWNhxvKrVay5bI01Ht7p7+uDf06H1K5JaiyF1AvW7v8bVWIu9LB9t5CBvh3VOeo2SkQP8CdfaeenvzwDuc53ExERyc3M9j0ePHs2oUaOQJKlFyS1fIkmSpwQZuP92lJSUEBYW1npjkQN7PUmSGJ0QyAerNuOsq0AbnebtkPokSZK4YfSA04/8uOqqq3j33XfJP7yf2y+ZQGGjim8OlbW6/usOU1LDMGo7fs3tdDrZuXMnn376KQB33nknRqOR+nr3KujExEQuuugiz+qTdjVj74dMJlOLGeWNjY3Y7XYslh/d0BX5r09ZsWIFERERZGRknHuylHBeEydOpKamhh07drBu3ToGDRrU6hrJ36Dh2hGxbMmrYMuxCpwdLPtl0CiZkBzSpQMpTU1NfPTRRxw4cACdTsell17aYjBlyJAhXHzxxZ6JMs3lT31N89+AZqWlpQQFBbX+vRE5sM+wWq18+OGHXHTRRURGRnbLMcRgSgekJF7FAfkdtpV+xbbqrdgPwfXJT13Qzb5zkWUZm9NFk91FXZOD8nor5fU2TlU3UVZnpa387K9XE2jUYGI7w+qbS2j9sCT6FKfICa5lSOQULKYYzl6QpndRSBJT08JIi+y6Py5nU11dzeuvv45SqeS2225rPUKdOAWW7odd78Dm5083ojotIM5dFzH7WtD1v5lLvcHAECNa9flnYtvLC93lnEqO4Axu44aI0CGSJKFPGkOTUoM2OrVdgwCSJBGiH0CIfgBZQdO4euBjwOnc5mrgZP1BjtR8T7AuhkH+EwDQqS581rPscmErOog1f2fLeFQaNOFJKPUWlP7hrcpUBBo1ZET7kRxmxtSJi/YGm4OaRge1TXbqbU6cLhcOp4xOrcSgURJg1BBk1PSaAZSzOXDgAP/9738ZMWIE06ZNa72ByIG9Sma0f5uDKQ053+Cqr8Bla0AeNKD1jkKHKDQGDGmTsZ3MQROR7O1wzircT0esWcGhzSv5ZG1+i9ccDgeXX3459fX13XZh0dfJssznn3/Otm3buP7660lIaGMyhsiBvd6AAB2NhzYAnF5NKibVXKiUlBSuvPJK6uvrCQkJIQQIMGj4bE8RNkf3rY4cGGoiIaRj54YFBQX8+9//bvV8YGAg1113HUajsV+uMOkK9fX1vPHGGzQ1NXHHHXe07h8g8l+fUFBQwObNmwGIiIggNDT0PHsI5yJJEjNnzkSlUjF06NCzXsspFBKjE4JIDbewv6iGnKIaqhvtZ31fhSQRatESG2hg2IAAdOoLv6/ocrnYvn17q/4ner2erKwsLBYLAwYMOPcqDB927Ngx3n77bdLT05kzZ07rDUQO7BOWL1/OwYMHKSkp4a677uqW+y/iN6iDBiUtwBoYwLaDWzlZvo29G/9EZPqVBPuldOh9nC6Z6kY75fVWz+zmequDhtMznq0OJzaHq80Bk2ZGrZJIP3d5rDCzBp22hm+KX0FTVskw+1DPdi5cFBrrSElfxFOq9pXX6U1UColpmREkhvZMaQmbzUZtba2nfmybS571/jD6dhh+E+x4EBw2GPwbMIWJBlPdTKVUkBxqZs+J6nNvFxiFNjYTZ30lKn3/K0viDZJCiT7xhzqxtlOHcdaWoRs4EqkDZQ8lSUKrNBJvGUq8Zej5d2gHublpsiTRsH+1u6n8aQq9BW10uqfXiyqg5U3EKH89oxOCiA26sFnZ7pV3Kq/1Pukq1dXVOJ1OSkpKsNlsbdfQFjmw1wg0aogO0FP4o94pxvTJNBzagDo4rtcP4PUVSoMFfeIowJ1zGg9vROUfgSasZ2/UKiSJwVEW/A1qNEoleo0CtSQTaNZjb6jl73//e4tG8gMHDmTq1KmelRbiJuLZNfePUqlU5Ofntz2YAiIH9nJqtZrp867jq88/QR3WRkNtoVMGD/5hGl5lZSVrPvmQKZOn8s1xG3XWri/7pVZKTEw+RwnmM9jtdtRqNdu3b+eTTz5p8drw4cOZOnUqSqVSDCSfh8PhoLq6Gq1WS2FhYdsN6UX+6/Wio6O57LLLOHTokBhI6SJKpZIZM2Z4Hu/Zs4cDBw4wZ86cVtdKfgY1Ywa6S9M3T7artzmQZfeviFalQK9WYu5EtZa2uFwuT6P4d999l0OHDnle8/f3Z/z48Z5Spm3+TgsetbW12O12KioqaGhoaHvVtsiBvd68efNYvnw5sbGx3XYdLAZTzqKmyc7DH+0jt6QOlVJCpVCgUSlQKyV0qrFcFLIUuXQLccRRvW8t9pGxqFWt62fLskxtk4OKehsV9TZK66yU1VmprLfjbOeyaJVCwqhVEWBwrzoJNeuI8NNh1qmQJAmny0n9Jnf5HA0nWMVawvAngQROKcsZOOInDFb0zdqGSoXE7OwoYoJ77mZ4SEgIt99+OwEBAef/xZMk0OrdX4bA3pc8ZRkaKsBWBxpT74yxE9KjLOcdTJEkCW10eg9F5Htkpx17RSHOqlNIah26uCFei8XVVEvddncJB0PGVPQDR1G3w/1YFz/srLPIg00axieFEB/cdn8AXzVy5EgSExPbt9Rb5MBeITPav9VgiqTSYEyb7KWI+j9XUy2OikIc5cdRGv1RmgJ75LhRAXouHhRKsOmH2cJr1qzhm2++AeD+++9n3rx5vP/++4C7aXxwcHCPxNYfKBQK5s2b516NqW9HXxyRA3utUYOTKLBeTUGF6LPXHY4cOcLx48cpfudNbrnjZ3y0u5japq4dUBkRF4if/tzXsLIss2zZMpxOJ9nZ2Vx++eUcPXqUffv2ERoaym233XbupsJCC35+fixZsgR/f//zl4YS+a/XkiSJkSNHtmqWLnQNu93OgQMH2L9/PxqNhtmzZ5/1vlHzZLvu0tDQwJNPPgnAwoULmTFjBrm5ubhcLiZOnMjEiRPFpKoOyMzMJCoqSlwH93FqtZqrr766W48hBlPOoqbRzgc7Tpxji4u5UieBBH5YKNz8KusZjEIZ6y6VJ0Oj3UmjzcnZhkzUSolAowZ/vQY/vRqTVoVBq0SvVqJVuQdvdGolamXbJzKllfsoObqWIKsOA+4RUwUKdGo/QlKvxGJKpfuLYnUftVIiNdyCMaDna3gHBv5wU+Tw4cNYrdYWs7F6vcYq2PU2bH4RKo/98HxAPIz6CWRd26ebYUX46Qk2aSirs7V6TZZlHOUFqAKjOrRaQuggWcZZdQoA28kDuGyN6JNGtyqd1X2Hl3FUFGIvzcNRUeh5XkJCoTdjHrPgrCeOWrWC0QlBZEf7o1D4xglFR0iS5DmBdDgc7Nq1i/DwcKKiorwcWQf08xz4Y4mhJgwaJQ02Jy5rPa6melR+YiZiVwk2aQg0alEqJGqa7JyobES2NoDsXv1Rv/tLdImj0IR27wqVSH8d84dFI0kSDoeDDRs2cOTIEQoKCjzbyLJMWloaDz74oLh47qQzZyFWV1ezZ88eRo4c2fYqvd7Kx3LgmY4fP47FYsHf35+LB4Xy5qb88+8kdFhOTg7grkv+5r//ydyrr2VtXiOVDWcvadMRwSYNwwYEnPX1qqoqvvnmG3Jzc3E63T3rbDYbSqWSefPmcdVVV3VJHL7ozOvgY8eOUV1dTXZ2tvcC6igfzn8A+/fvJykpqd81Cu9NJEny9B7ZuXMnDQ0NzJ8/v0dLZx0+fJidO3e26IGiVCrx8/Pj//7v/8Q54AVovg52uVzs3r2bgIAABgzoQyWTfTgH1tfXc+rUKRISErr9d0AMppyFWatm6ZRkvj1ShsPpwu6UsTtd2BwurA4XTQ4nK2wXE82XjFCYCJL8uYJCVtoOctw+HokfbuIqJHdN2QCjhmCjhmCzlmCTFsvplSUddbRkPcG5hWiBGH442bFjJzVtMZf6/QVFD93Q7C4BBjWDg/y6pG7khXA6nezevZv9+/ej1+v7xrLI3JU/aop1hso8WHE/rHoMrnndXfOxj0qL9OObQ6WtnreXHqMp110j9lw31IULI6k0mEbMpenoNhzlx3GU5VNbdhxj1jSUxrNf/HYFa+E+rMd3t3peHRKP4vTs8LP9d08JNzMxOaRTjUx9UWVlJVu3bqWpqYklS5b0jQbVPpIDz6RUSGRG+7PpaDn1e1Yi2xpQh8ShTxrj7dD6NH+DhhkZFpLDTC1ySlF1I5uPGjmsVNF0dCuuhiqacjfjqDiBPnEUkqrrb7qrFBJTUt1lup599lmqq1uvzlywYIGnvr3429c18vLy2LhxI6WlpVxxxRV949/VB3NgM5fLxSuvvALArbfeSlRU1DlvyAudd8MNN7B582bWrl1LTU0Nr/3rRS6ZeimlgdHkVTSe/w3OwaBRMjs7ClUbEwpra2t5+umn29xv5syZgMh/XUWWZfbs2cOuXbvQaDSkpaV5O6Tz8+H8B+4VY80rUx944AHReL6bqFQq7rvvPlasWMHOnTs5dOgQy5Yt4+abb267RHwXaquUIUBycrLnhr/IgV2jurqabdu2UVlZyW233YbF0gemqvt4Dnz33XcpKCggLi6OG2+8sVuPJe4mnYWfQc2SCQnnLcUlywPJLVhFWKH7pu4UlZH1mhcxRU4nNWAiRo0KvVp5wbOfq2qPU7ZnOV/yJfnks5SlGDDQQAPlZpnQ6LGEBKTRjsVovV5UgJ7ZGcHoDnp/VYFCoWDv3r0AVFRU9P7BlNyV8NbV7iV9ba6JOv2cvdG93cL3+mwSTYuwsCG3DOePGgu56qs834sTie6lUOswpIyj8YgGe3EuIFO/awXG7JkoDV17suGoPElT3nYM6VNQ+YVjxT2YojD6owlPRh0Uc84bmH56NZMHhYqSXh2kVqs5dcq9Aqmmpqb3D6b4UA78sawYP7bmVSDb3CfPCn0fOOHv5c5Wqz/CT88VQ6L4zqJlkzGAuu2fINubcFQUUr+nGmP2zC79+yPLLowl+9iwOodZs2YxePBgNmzYgFKpJDExkeHDh5OQkCBumnSDhoYGGhoaqK6uxuFw9P6Zvj6cA8E9I7FZSIj793dkfCD13dDPQ4BRo0YRExPDSy+9BMCqr79i7NixjEoYwra8Shznav55FiqFxOzsyBblverr63nrrbcYP348gwYNwt/fn6qqKnQ6HUOHDiUrK0v0hegGkiR5ygWVl5d7O5zz8/H8B1BcXOz5XpwTdC+tVsucOXMwm82sX78egH//+9/dMqBSUFDABx98wOLFi4mNjfU8HxISwogRI0hLS8NoFNe4Xa25dxS4V0P2+sEUkQM5ccJdXerMFZbdRQymXCBJkkiKnUJdSDH5B/5HcKOOra4NVBd+zteFBq4K/SWZCdfjrv3VMXWNJRQWriGy1IUCCCWUaUzjv/yXfPIJihxFYty1hHf5p/Ke7Bh/LkoKRkXvuOiRJInf/e53nDx5ssUfrl6psco9Ci3LgOs8G7tAVri3X7q/Ty7z02uUJIQYOVxc1+J5bcxgJK2x28utCD/QDxyBOjQeW+E+XHarZyDFWVcBktTplSrOxhrsJcewnfhh+bK95AgKrRFVYAy6AVko9OZzvodKITE8LpARcQFtznAUzs3f35+f/vSnaLVazOZz/1t7nY/lwB8zaFQMirCwK3MarsYa1CFx3g6p3xs7MBiLTs1K5VysxUewFR1EHeRudCjLLhzlhagCIpCUHb8BL8sunDWlWAv34awuphYoALKyslAoFAwfPpxLL72099/c7+PGjBlDZGQkMTExvf/GlI/nQACz2cz8+fOxWCyesmxqpQJ/Qx8q0dbHREZGct9997FmzRr27NlDZmYmYWHBJAVpWPP9Pk5JQTjbOaYSYFAzKSWUCD89TU1NHDx4kA8//NDz+meffUZYWBgRERFMnjyZjIwMMXGqm919990cP36cuLg4b4dybiL/ATBixAiUSmXfKk/ex1188cWkpaXxzTffUFpaSnR0NAClpaU0NTV1emClsrKS3bt3s3btWs9zmzdvJiYmhtTUVCZNmiQGkbuZwWDg5z//OQABAb18lavIgQDcfvvtFBYW9khpSjGY0kVM+jDSh9xJo62G+GN72Fn+OUEEkVCipq7kXQo17tkckiWIpMR5qBQaXC4XNkcdSoWGmvoCmmxVHHHkcLJ8B6NrEgkkkMgfHec4x5mTuozUgAk9/yG7kVatYGpqGElhp2/WOb0bz5lUKlWLgRS73Y7VasVkMnkxqjbsevv0cr72zgJzubff9Q6Mvr07I+s2gyP9PIMpzroKbKdy0cVlo41M8XJkvkdlDkaVOhHZ9cMvr72iEFvhPiSVBnVIHC5bE5rwJE8vB9nldDdBk2Vc9VVIWgMKjR57aR6Nhze2eRylOQSVX+h5bxSrFBLpURZGxAVi1ombjRfix82rm2fm9Lobiz6YA8+0ceNGNC4JpSmgx5qhCzA4yg+VUmKFBOrQhNMXMYDLhfVEDo2HNqAw+KE0h4AkoY1MRaFzzx6UnQ5QKMHlwFlbhtIvDJBoOvo99uIjbR4vIiKib9Vt7gd+/O9dWVnZOy+qfTgHyrLM8uXLGTVqVN8oRdTPaLVapk+fztSpUz3N3l3WRg5s/BpZlomJG4gdBWpzEPrIZBodYHe6sNvtmHQadNiINCkYnxlHXV0tL7zwQosZ9s1iY2MJCgrq9qaywg8UCkWLgRSn00l9fX3vm6Htw/kP3H+X1q5dy7Rp0xg1apS3w/E54eHhXH311TidTs8Ab25uLl999RVqtZohQ4ZQW1vL0KFDSUxMBPD0epJlmdLSUgwGA35+fhw8eJB3330XuY0KOc0DKampqT334Xzcj8/3ampqMBqNnr91vYaP58Bt27ZhtVoZM2aMZ2VydxODKV1Mr7GwKOUZsstnUHdwref5aNvpAlxlUFj2Es/xHKMYxWVcBoD69NdBPqCQQrJxj2jXUIsFM4XacpKybuVi1bU9+4G6mSTBoHALFyUF94keBlarlbfffpvGxkZuueUWNL0lh8qyu8FUZ2x+wd2Iqg/O7BoQZMCiV1PTaKd+95cA7brRLnQfSeH+pZBlGVvhPvf3Dhu2okMAOMqPY0ibhMo/gtpN77Xa3zxqPijVnkEWhcEfV0MV+uSxqINb30BUKiQ0KgUKCRSSRKhFx4BAAwNDTZj6QE7pa4qKinjjjTfIysri0ksv7cSay27iozmwWW1tLV999RUAgy6ez8km8bPfkwaFW3A4ZVbmFCPL7p8jV2MNrvoK9/cN1bga3D1O7KcOYx55FUgStZvfb/VelrHXIilPz6KXJFQaLQ5rE3fddZenIabgPZs2beLrr7/m+uuvJz4+3tvh/MDHc+DWrVvZs2cPhYWF3HXXXb1vsN9HnHlzaceOHZ6bgQV5zYPDh2HPJh566CH279/P+++/T3PxqIOA1jqdjIwMT+8nk8lEXV0dSUlJzJ8/X6zE8zK73c77779PWVkZS5YsQd9bLoR9PP8BPPfcc4B7pZgYTPGe5hwoy7LnvNxut7NlyxYAcnJymDNnDtnZ2Tz11FM0NrbsL3Xvvfei1WrRaDRYrVZCQ0MpKSlh5syZDB8+vGc/jNBKWVkZr7/+OgMHDmT27NniOriXaGpq4tNPPwUgMTGxx1ZsiavtbpIZNBXX6Es4lPsezrpytDYIdblnihpw15yPbLXuBNSoqaOOzWxmVNRCkmOuQaFQ0JNzrILNWkLNWkLMWowaFVqVAqVCwuZ0YXO4qG1yUNVgo7LBRlmdDZvjfMvIWlNIEgkhRoYNCCDSX98Nn6J7NDU1UVxcjEKhoLGxEY2pl8TeUAGVxzqxo+zer7ESDH1vJrMkSWRE+fHt4VLUYYnYi3NbrIwQvEeSJMyjrsJ6fDeyy4WjvADZYQXA1VTX5mybZkpTELr4YSj9wlG2UcZLpZCYPCiU5DAzGpW4YdKTSktLaWxspLS01NuhtOSjObCZRqNBo9Fgs9kYlhjOyb1l3g7J5wyO8sMly6w+UIIsg9IUiHHITPegsixjL8v/YWOFAldjzVnfSx0cg0JvRhs8gEUXJRJoFCWKegNZlikoKMDlclFWVta7BlN8PAeGh7uLHldWVoqBlF5i+vTpREVFcfToUSorK8nPz2/xenNd8zNZrVb0ej0jRoxg/PjxJCYmijJevYjNZqOoqAibzUZ9fT16TS9ZneLj+Q/goosuYv369a1uzgve0VwqfvXq1dhsNg4fPkxNjfu8r7n/UFv/rVwuF2FhYUyZMoXY2FhRxquXqaiooLa2lvLyclwuF71kONnnc6BSqSQgIIDKykrPZIyeIAZTupFCoWBQ8gLPY5fLxeFjy2lQSSyx/JOCyh3g7utLgdldquia5JdYrPXv8VjDLDoGRZhJDDVh6UBJHFmWqWqwU1JrpaS2ifI69yBLTaMD149ulho0SiL89UT560gOM/fJ0jt+fn5cd911OJ1O/Pz8wGnzdkhutrrzb3Mu1to+m0DTIy1sOlqOLn4o2ph0FJpe3iC7DxoQZCA7xp9AowaDRsWR0jr2nayhoKLhnPtJSjW6+GHuBwNHIDvt2EuOoTrdU0CfMp7Gg9+i0JlRBcei0JpAoUShVKEJT2rzPS16NbMyIwi16Lr6YwrtkJmZic1mIz09vXfd4PDhHAjuEit33303kiSh0+mIPdnA8fP8fgpdLzPaH51ayYq9p3C6ZJR6C/qkMQDok8firK90r1A53UtKFRyLo+w4qqAYFDoz6iB3XW2lKQilKYis03lX6B0kSWL27NkMGDCg980Q9fEcGBMTwy9/+cveV4LXx2VkZJCRkeF5vH//fk//tQkTJvDdd98BMH78eGRZZuTIkUiSJPo99FJGo5GFCxdSW1vrLgErroN7jUmTJjF8+PDeV37Nh6lUKi699FLPY4fDwbZt20hPTwfghhtu4I033sBkMjFkyBDMZjMGgwFJknrfOYYAQHJyMnPmzGHgwIHuVUjOXjKJ18dzoFqt5ic/+Qkulwu9vucmu4vBlB6kUChIGTjP8zg1YCKc7pGd7oV4JAkSQ00MiQ0gqpOrQyRJIsCoIcCoISX8hxnksixjdbiwOV0oJAm9WolS0YtuvF2A5iZiDoeDjd99h+0EXOKN/4Bn0lzgxaO2lzeWPge15CIuQMORchlJDKR0KZ1awdS0MAZH+bV4PjXCQmqEhb0nqlmVU9Jq4PRsJKUaTUSy57E6KAb12PaXLlRIEldkRxJk6rkZB0JrzSf41dXVrPzqS4YGQnzPlCY9Ox/OgfX19ej1+hYnj2MTgzi+RQymeENymBm9Wslne4potLW8yFIaA1Aaf6i9bEgeB8nj2nwff4OaMQNFWa/eRqvVMnLkSACOHTvGxu82MDcF9N4e8/LRHCjLMvX19ZhMJnETsQ84s5+NVqvloYce8mI0QmeEh4cTHh6O0+lk65bvKc+FyzLxbrkbH81/4C4h5XA40Ov1Igf2ciqVqkUJtoSEBJED+6DmxuZ1dXWsWvk1SVpIi/JuTL6cAxsbG1Gr1T26IqWZGEzxUQkhRsYODCbE3D0/dJIkoVMr0al7zeK3Lpefn8/qNesAidRIuY2ibT3IEAgB8VCZR/ubTgFIEBAH+l7YSLUd6urqeOqpp1CrNWizLhOrUrpYYui5/7AOjvLDolPz6Z6TWO0dL/fXUZkxfmIgpRf59ttv2bs/h0ID3DUV7y519tEc+N133/H111+TkZHBlVde6Xk+wk9PQoiRo6X1XozOd8UEGrhxTBzrDpWSU3T2cl5no1UrmJMd1a/Pofo6p9PJxx9/TFVVFWtcMCPLywH5aA589NFHAZg3b55YzSAIPaioqIgvv16FLEsMipCb54d6h4/mP6vVyhNPPAHAz3/+81aNsgVB6D5btmxh567dHNJAYhh4dU6Nj+bAffv28d///pfExESuvfbaHi/zKorK+pgwi475w6OZkx3VbQMpvmLgwIFMnjiBOUNlIr2dfyTJ3TiqM0bd3mcbTjXXW7bbbQSZRNknb4gNMnDlkOhu712i1ygZkyBmafcml156KelpqVw7BpTePpvw0RzYXHf54MGDrV4bMzCor36sfkGvUTJ9cDjXjIghNcKCWtm+/xgKSeLyjEhR3quXUyqVLFiwgMzB6Uzx9upk8Mkc6HQ6PeUmm2vRC4LQM6Kjo5l26RSmZ8okeLutgw/mP4BTp055vhe9ogShZ02cOJHMjMFcNxY03l6i4KM5sKioCIDc3Fyv5EBv/2cXeohZp2LMwCDSIiy9q859Hzdh0mRgMuBeJXHkSA5ZWV6anph1Lax6DOyNQDtWCUgKUOkha8H5t+2lIiIiyMrKIiMjgzpNEKsPlHg7JJ8U7qfj8swIPtp5EqerI7Mh2m90QpCYpd3LqNVqrpp/tefxsWPH0Gq1REZ6aZ2eD+bAQYMGYbPZmDFjRqvXQs060iP92Hui2guRCc0i/fVE+utpsodwvKKBwsoGTlQ2UlFvb1EiUSFJJIaaGB4XQJjoCdUnhIWFMXfeVcBVyLLM1q1bycrKQqPx0kCYj+VAhULBpEmTsNvtjB071tvhCILPGTV6LOD+3WtsbCQnZztDhw71TjA+lv8AgoODGTp0KAMHDnT3chUEoccolUrmXjkPcLdxKCwsxOFwEBcX552AfDAHJiYmUl1dzdSpU71yfDGY0s9pVAqGDQhg2IAA1F6fOtx/NTY28q9//Yvq6mrUanWLmsA9Ru8P17wOb10NsoJzJ1EFIME1b7j366MsFgtXXHEFAHani++OlNNk7yWNwHzMgCAjl6aHsWLvKdrZQqXdgkwaMqPERUJvlpuby9tvv43BYODWW2/1zkWdD+bApKQkkpKSzvr6RUnBHCu7wKaEQpfQqZUkh5lJDnOXT3Q4XVQ22LE5XUiASafColN7N0ih01asWMGWLVvIzc3lmmuu8c4sYR/LgZIkMWHCBG+HIQg+z2638/LLL1NeXo4sywwbNqzng/Cx/AdgNBqZNWuWt8MQBJ93/Phx3njjDVQqFbfccgvBwcE9H4QP5sC4uDjvDV4hynz1W2adivFJwdwyPp7RCUFiIKWb6fV60tLSGDx4MA0NXmz6mzgFFr4Haj3uVoA/XoV0+jm1Hha+D4mX9HyMXWDLli18++23uFw//JFQKxVkRYsb7t40KNzClNSwLl8pOnZgMAqFWFHXm8XExJCQkEBycjJVVVXeC8QHcqAsyyxfvpwjR46cd1udWsnkFG/X3xDaolIqCDFriTq9ckUMpPRtGRkZDBgwgKCgIBobG70XiA/kwKqqKt5//32qq8WqO0HoDdRqNRkZGaSlpWGz2bwXiA/kP4Bdu3axevVqHA6Ht0MRBAGIjIwkMTGRQYMGeffcxAdyoCzLfPrppxw4cMDboYiVKe0hSZAaYSEh2EhMoIE6q4NjZfXsO1FNZYPdKzEpJImUcBNmnRqDxl36xiXL6NRKIv30BIha2z1uypQpSJLk/TJqiVNg6X7Y9Q5sfv50I6rTAuLcdRGzrwVd3xx4qK2t5euvv8bhcBAQEEB6+g/FyrNi/NmWX4mjm0pNCec3OMoPp0vuspJrkf46EkNNXfJeQvfRarVeafzWpn6eA3ft2sXu3bvJycnh7rvvxmAwnHP7pDCzKJEnCN0sOjqaRYsWiRzYA7788ksOHDiAzWZj4cKF3g5HEARgwoQJyLLs/RzYz/NfU1MTK1asoKmpCYvFwvDhw70dkiD4PJVKxfz5872f/6Df58BDhw6xbds2duzYwc9//nOvljgUgynnoVMrmZYeRkKIqcVzwSYtmdF+rNh7iqOl9T0ak0alYEZGBPHBxh49rnBuP06eO3fuxGKxkJCQ0PPB6P1h9O0w/CbY8SA4bDD4N2AK67MNppoZjUZmzpzJ4cOHW5VTM2pVpISb2XdSNCL1pqwYf5QKiTUHSi54YGt8UkgXRSV0tzNzoNPpZPXq1YwZMwaTyQuDYf04B6akpDBmzBgsFst5B1KaxQS2bztBEDrvzBxYVVXF9u3bmTx5sncm2fTjHDhp0iQcDgeXXnqpt0MRBOG0H08ozMnJASA1NbXng+nH+U+r1XL55Zeze/du7/WnEQShlTPPAWVZZs2aNQwdOhR/f/+eD6Yf58C4uDgmTJiAQqHweq8oMZhyDmqlxHWjYvHTt116QatSMjsrkvWHy9iWX9kjMek1Sq7IjiLcTzQn7c2OHDnCRx99hEql4rbbbiMkxEs3hCUJtHr3lyGwzydPcP+hys7OJjs7u83Xhw0IYH9RTZf37RA6ZnCUHyFmLZ/uLqKmsXMr+AaGmojy13dxZEJP+PLLL/n+++85evQoS5Ys8d5MnX6YA/V6vbiJKAi9mMPh4PXXX6eystLTJN1r+mEODAsLEytSBKEXKyws5L///S8AN910E9HR0d4JpB/mP0mSSE9Pb1GZQRCE3mXt2rWsX7+effv2cccdd6BSeem2ez/MgVqtlsmTJ3s7DED0TDknlVJx1oGUZpIkMSE5hMwe6tUwPjFYDKT0AQMGDCA5OZkhQ4Z4pwFVP3TixAn27t2LfJ5RkiCTlqRQcw9FJZxLmEXHwlGxDBsQgKqDPU8CDGqmpoZ1U2RCdxs9ejQBAQFMnjy5dyx57ge2bdtGaWmpt8MQBOE8VCoVkyZNIjg4WMwc7iKyLLN27VqsVqu3QxEE4TwiIyNJT09n8ODBREZGejucfqGsrIzt27e36BcqCELvNGzYMIKCgpg0aZL3BlL6md27d1NUVOTtMFoQ/2W7yOSUUOqsjm4t+RVs1pIeaem29xe6jkql4uqrr0ahUCBJErIsY7PZ0Gq13g6tT5JlmTfffJOmpibKysrOO8tzzMAgckvqcInlKV6nUyuZkBxCdqw/2/IqyTlVg9V+7gsBo1bJ3CHR6DWiz0NfFRgYyE9/+lOUSvd/Q6fTicvlQq0WTbY7o6Kigk8//RSAn/zkJ4SHh3s5IkEQziUzM5P09HRPDrTb7SiVSjG43EkbNmxg3bp1bN26lXvuucfb4QiCcA4KhYIrrrjC8z2A1WoV18EX4D//+Q+VlZUUFxdz2WWXeTscQRDOwWKxcMcdd3jOAV0uF06nU1wHd1JtbS3Lly8HYNGiRcTHx3s5IjcxmNJFFAqJywZH8P62AkpqumfW1EWJwd5vbt5TZBmsje76fg3lYArvc8vSmpMnwLp161i3bh1jxowR5Vk6weVyMWXKFD799NN2Jc9Ao4aUcDM5RaJ3Sm9h0amZPCiU8UnB5JbUcbikjuPl9didLQe8wiw6pqSG4mfw8ZONfpQD7XY7jz/+OAA333wzMTEx3gyrT7JYLMTFxZGXl0dgYKC3wxGE7tePcmB5eTl/+9vfALjvvvvEDcVOSE9PZ9WqVSL/Cb6hH+S/MweOt27dymeffUZGRgZXXnmlF6Pqm2RZ5uKLL+Z///tfr7mJ2F85nU7s9s6Vpxa6kMMKTQokhw254mSf7fVht9txOp28/vrr1NfXM2vWLJKSksTEmg4yGAykpaWxf/9+wsJ6T+USMZjShTQqBXOyo3j3+4JO9wg4m9hAA3G+0HC+sQp2vQ2bX4DKPPdzX/4bAuJh1E8g61p3Q6U+RJZlDh8+DMDJkye9HE3fY7VakSSJYcOGER4eTlRUVLv2G5MQxKHiWpwX2ABd6FpqpYLUCAupERYcThcltVZqmxw02Z3EBhoIMGq8HaJ39cMc2NDQ4Pm+uLhYDKZ0UF1dHUajkQULFlBTU4NG4+O/I0L/1g9zYHFxsed7sUq5Y2RZpr6+noCAAG6//XaCgoK8HZIgdJ9+mP/A3UsU4NSpU16OpO9pvhk7ePBgAgIC2n0dLHSMLMucOnWKqqoqb4fi0xS2WvzyPifw0Hto6k+4n/zqr9iMUVQkX0113Axcmr5Vzt3lcnl6/TY0NHD48GHi4+PF9Vw71dfXYzAYmDt3LhdddBEGg8HbIXlI8vkaEPQjNTU1+Pn5UV1djcXSfeWyyuusvLu14LylbNpLkuC6kbGEWvp5r5TclfDuIrA333g780fz9Ei02gDXvA6JU3o6ugsiyzJHjx4lLCyMuro6tm3bxsUXX4xe383NtZ022OeeEU76b0HZt5J2ZWUlzz33HElJSVx11VUd/qOz5kAJOwuqsOjV3DK+/TN5eipX9KT++Jn6nX6cA5uamigoKCAhIcFT83nkyJHdv9qyj+fAzZs3s2LFCqZMmcK4ceN67Lj9MV/0x8/U7/TjHFheXk5jYyNhYWGsXLmS9PR0YmNju//AfTgHyrLMyy+/TGlpKTfccEOPNbHur7miv36ufqMf5z9ZlsnLyyMoKAibzcbGjRuZPHkyJpOpew/ch/MfuEvbPP3008TGxnLttdei0/XMvaD+mivO9bmKioqoqqoiNDQUg8HgO9VgehHF0TWol98M9kYApDNyoOzJgXrsc/+NK6F3NCBvL5fLhd1uR6VSUVBQQHV1NcOHD29RyaZb9PEcuHfvXv73v/8xfvx4Lrnkkh47bntzoFiZ0g2CTFrmZEfx4Y4T2BwXPqAyKNzsGwMpb13tXtZMW+N7p5+zN7q3W/henzqRlCSJgQMHUl5ezssvv4zD4UCj0TB16lRvh9ar5ebmAnD48GGcTmeH92/unSIIvV4/z4E6nY6kpCS+//57Pv/8cyRJIjw8nAEDBng7tF5LlmUOHToEwK5du3p0MEUQelw/z4HNKyrefPNNjhw5woEDB7jrrrtE/exzqK6u5sQJ98zUkpKSHhtMEYQe18/znyRJxMfHU1tby7/+9S+sViuyLDN79mxvh9ar5eXlAXD8+HFsNluPDab4GqfT6RlIEasfvSR3Jby/8Kw50DOwYm9C8/7CPpcDmzU0NKDVanE4HBw+fJhBgwZ5O6Re7eDBg4C7VGRPDqa0lxhM6SZR/nrmD4tm+Y4TNNg6fhO4mUohMWZgcBdG1gs1Vrln4sgycL7BJxfICvf2S/f3uaXOgYGBTJs2jdLSUqZMcf8BaGpqQqvVihkQbRgxYgT+/v6EhIR0ahWPTq1k8qBQvjlU2g3RCUIX8aEcmJWVRWFhIenp6Z6BlKamJnGB2AZJkli4cCE7duxg6NCh3g5HELqPD+XA6dOns2LFCubMmYNarUaWZaxWq8iBbfD39+dXv/oVBQUFpKamejscQegePpT/zGYzM2fO5OjRo1x++eWAuA4+l4yMDMxmMwaDoV+tEOltmnuk9KbyQT7Fh3KgXq/HaDRiMpmIi4sDxHXwuVx55ZXEx8eTlZXl7VDaJDrfdKNQi46rh8cQcAGNlDNj/PHT9/NZa7vePr2kub2reFzu7Xe9051RdQtJkhg+fDiXXXYZkiQhyzJvv/02b775JnV1YgVFW5KSkvD39+/0/omhJhJDu3kZuSBcCB/KgRqNhrlz55KcnAxAVVUVzz77LKtWrcKHqo62m0KhYNiwYeImg9C/+VAODA4O5vrrr8dsdtf83rFjB3/72988q9CElkwmkxhIEfo3H8p/4B4gmDNnDgqFAlmWWb58Oa+88oroVXEWcXFxhIaGejsMnyDOtb3Eh3KgJElYLBZP6fr6+nr++te/8sUXX3SqCkt/J0kSQ4cO7f5yaJ0kBlO6WYBRw8LRAxgS609H87NOrWRkXGD3BNZbyDJsfrFz+25+4fQIdt9VXFzMyZMnKSwspKyszNvh9AoFBQW888471NfXd9l7jhkoluwKvZSP58B9+/ZhtVo5fPgwjY2N3g6nV1izZg3ffPMNLlfX9F0ThF7Nh3OgLMvs3LmT+vp6Dh8+7O1wegWHw8F//vMfjh496u1QBKH7+XD+A/eEmry8PE6ePElxcbG3w+kViouLeeutt6ipqfF2KEJnyDLUl0Nlvvv/+/jvaLfz8Rx44MABGhoayM3N7dJ7X33Zxo0bWblyZZ8YXBJlvnqAWqlgUkooSWFmNhwu40TV+W8YqZUSs7Mj0Wt65yhcl2mogMpjndhRdu/XWAmGvjvgFB4ezm233YbT6SQ8PBzAcwNNofC9sU5Zlvnss88oLi7GYrEwY8aMLnlftdL3/i2FPsLHc+C4ceMICQlhwIABaLVawH0zTaXyzdOTiooK1q9fjyzLREdHk5CQ4O2QBKF7+XAOlCSJRYsWtaqb7cs5cOvWrRw+fJgTJ07wi1/8wjN7UxD6JR/OfwABAQHccccd1NbWEhMTA7ivBV0uV6+didzdvvjiC/Lz81m5ciVXXnmlt8MR2quxyr3CYvOLLX+nA+Jh1E8g69o+V5KqR3RxDnz11Ve5++67+8xKt2HDhuHv709ERISnzJwvnwPW1dWxevVqHA4HERERpKenezukcxJ3GHtQlL+eq0fEcMWQKKIDzt7/QaWQmJUVSZR/x3tE9Dm2CyxtZa3tmji8KCQkxDOQ4nQ6eeyxx3jssceore37n62jJEny1EbsjU2mBKHLiRxIcnKyZyCltLSUZcuW8be//c0ny34FBgYyY8YMRo4cKQZSBN/g4zlQpVKRmprqKS+yZs0ali1bxrZt27wcmXeMGDGCtLQ05s6dKwZShP7Px/MfuHsjNQ+kuFwuHn30UX7/+99TUVHh5ci8Y/bs2cTFxTFt2jRvhyK0V+5KeDoNVtwPlXktX6vMcz//dJp7uy5WWlrKHXfcQWxsLFqtlvDwcKZNm8aGDRu6/Fjdootz4DXXXNPhsqmTJk3i7rvvvrA4LsDAgQM9AynV1dUsW7aMZcuW+WSFApPJxJw5c8jOziYtLc3b4ZyXbw55eVl8sJH4YCMV9Tb2n6zhZHUjpbVWNEoFMYEG0iMtxAT6SAMszQX2stCauyaOXqK6utrzfVFRESaT+9+nv9fwlGXZ8xlDQ0NZtGiRlyMShB4icmALubm5gHtWTn19PUajsd/nP2iZA4cPH+7laAShB4kc6CHLMrt37wbg5MmTDBs2rEVu6M+aP6dSqWT+/PneDkcQeobIfy00NDR4vj958iQBAQGAb10HBwYGcuONN3o5IqHdclfCW1efLjfV1iSw08/ZG93bLXwPEqd02eHnzZuHzWbjtddeIyEhgeLiYlatWkV5eXmXHaMtNputayY8dHEO1Ov16PV9d0J6Xl4e4J5oU11d7ekb7Es5cPDgwQwePNjLEbVPn1mZsmzZMsaOHYvBYLigZtS9SaBRw/ikYK4eHsOdkwayZEIC0weH+85ACriX5QXEAx1NEJJ7P31Ad0TlNYGBgdxwww1ceumlJCQkUFlZyaOPPsq6deu8HVq3aWho4N///jfHjx/3dii9Wn/MgQIiB/7I6NGjmTFjBtOmTcNkMrFx40Yef/zxfp0f8vPzefnll2lqavJ2KL2WyH/9mMiBHpIkceuttzJ58mRGjRoFwCuvvMKLL77Yb/ODLMusW7eOzz//3Nuh9GoiB/ZTIv+1YDKZuOmmm5gyZQrJyck0NTXx6KOP8uWXX3o7tG5jtVp5/fXXRd+s8+iVObCxCt5ddHog5XyrCFzu7d5d5N6vC1RVVbF+/Xr++Mc/MnnyZAYMGMDIkSO5//77mT17tme748ePM2fOHEwmExaLhauvvrpFj6LFixdzxRVXtHjvu+++m0mTJnkeT5o0ibvuuou7776b4OBgz8qpffv2cfnll2OxWDCbzVx00UUcOXLEs9+//vUvUlNT0el0DBo0iH/84x8tP0Qnc6DLBUcqZS6bd12L351XX321xc/Hww8/THZ2Nm+88QZxcXH4+fmxYMECTwWYxYsXs27dOv7yl78gSRKSJJGXl0dlZSULFy4kJCQEvV5PUlISr7zySodi7IzMzExmzZrFzJkz8ff3Z+fOnSxbtswz2bA/Kioq4qWXXuqTPWP6zGCKzWZj/vz53HHHHd4OpVv099HGs5Ikdx3Jzhh1u3v/fiYhIYExY8Z4RqTBPVvbbrd7ObLusWnTJgoLC/n44499sqxPe/X3HOizRA5sQZIkRowYQWpqKgAnTpzAbrd3eMl2XyHLMl988QUnTpzgm2++8XY4vZbIf/2YyIEtGI1GJkyYQGhoKLIsU1BQQFVVFYWFhd4OrVuUl5fzzTffsHXr1n49aH6hRA7sp0T+ayU2NpZx48ah0Wg8Tdjz8vJarFrpT7Zv305eXh4ff/xxn2i47C29MgfuehvsDZx/IKWZy739rne65PAmkwmTycSHH36I1Wpt+4guF3PmzKGiooJ169bx9ddfc/ToUa655poOH++1115Do9GwYcMGXnjhBU6cOMGECRPQarWsXr2abdu2cfPNN+NwOAB46623ePDBB1m2bBk5OTk8/vjjPPDAA7z22ms/vGknc6CkkNBN/AWyDDNmzDjnfbIjR47w4Ycf8umnn/Lpp5+ybt06nnjiCQD+8pe/MGbMGJYsWUJRURFFRUXExMTwwAMPsH//fr744gtycnJ4/vnnCQ4O7nCcHf5cksTQoUMZPHgwkiRx6tQpnE4nBw8e7PZje4Msy3z55ZcUFRWxcmXXl8Hrbn2mzNcjjzwCuEcbhX4m61pY9Zh7+WN7/hhJClDpIWtBt4fmbZGRkRiNRoxGIwqFgpUrV7JhwwYWLVpEfHy8t8O7IM3NtSZMmEBlZSUjRozw3UHFdhA5sB8TOfCsxo0bx/79+/H398dut/Pqq6/S0NDAzTffjNncd8tbnNlg9corr+Sbb75h/Pjx3g6r1xL5r58TObBNkiQxfvx4Nm3ahMlkIj8/n//85z8MHjyYmTNnolD0mTlxrTidThQKhWeGa1NTk6dvgtCayIH9mMh/ZxUUFERgYCA6nQ6VSsX69etZvXo11157LcnJyd4O74I0XwePGjWKkpISBg8ejFKp9HZYvVavy4Gy7G423xmbX3APIFzgfQ+VSsWrr77KkiVLeOGFFxg6dCgTJ05kwYIFZGZmArBq1Sr27NnDsWPHPH9jX3/9ddLT0/n+++8ZMWJEu4+XlJTEn/70J8/j3/72t/j5+fHOO++gVqsBWvxePvTQQzz11FNceeWVAMTHx7N//35efPHFlqXsOpgDZSQktYGoy37FWxOdxMTE8OGHH561RKjL5eLVV1/1XDfecMMNrFq1imXLluHn54dGo8FgMHh6GIN7Nc+QIUM8pZfj4uLa9W/U1UaMGMGWLVvw9/fH6XTy1ltvUVxczJIlS3rPCq1OOPM6eM6cOXz99ddMnjzZ22F1WN89C28Hq9VKTU1Niy+hF9L7wzWvn/6Dcr4fSQUgwTVvuPfr57RaLffccw8LFixAoVB4amk3L2d0Op19bsWK0+nk2Wef5bXXXsPlcqFSqZg3bx6xsbHeDq1fEfmvDxE58KwiIyN56KGHGD58OPX19Zw8eZKqqirPz7PVau1zK9qqqqp49NFH2bFjB+DuE3XVVVd5mg8KXUPkwD5E5MCzuuSSS/jd735HeHg4paWl2Gw2tm/f7jn3O9ts1N5s9+7dLRpMjxw5kgkTJogJNV1M5MA+QuS/s1KpVPzsZz/jxhtvRKPRkJOTA+C5Hna5XNhsNm+G2GGyLPPSSy/xj3/8wzOoPGfOHAYOHOjt0Pqdbs2BDRVQeYy2+6Sci+zer7GyS8KYN28eJ0+e5OOPP2b69OmsXbuWoUOHegadcnJyiImJaTFZIS0tDX9/f8/vU3sNGzasxeOdO3dy0UUXeQZSzlRfX8+RI0e45ZZbPCtoTCYTv//971uUAQNa5EDXef45nS7ZPaB8OgcGBQWRkpJyzs8SFxfXYgJeREQEJSUl5zzOHXfcwTvvvEN2djb33nsv33333bkD6ybBwcE89NBDjBs3jqamJo4dO0ZDQwOVle6fn754HVxXV8ejjz7Khg0bAAgICODqq6/GYrF4ObKO69eDKX/4wx/w8/PzfIkZT71Y4hR3Qy61HnfNxB9fUJ1+Tq2Hhe9D4iU9H6OXSZLE7NmzGTRoEJdeeikul4vly5fzxBNPsHbtWm+H125NTU3Y7XYKCwv7dQ1cbxP5r48ROfC8/Pz8GDNmDDNnziQqKoqamhpeeOEFnnzySfLz870dXrudOnUKgM8++4zS0lIvR9N/iRzYx4gceF5paWlERUVx9913o9VqOXjwIH/84x/55z//2WduKMqyzLFjxwD429/+1uduAvQlIgf2ISL/tcv06dNJSEhg3rx5yLLMZ599xh/+8Ic+dT1ptVo9N0P/+9//ejucfq1bc6Ct7sL2t9Z2TRyATqdj6tSpPPDAA3z33XcsXryYhx56qN37KxSKVn+L25qsazQaWzw+V6P3ujr3v89LL73Ezp07PV979+5l06ZNrXc4nQNtshKX7F590pKEjESjA1zXvtuhHPjjwR5JknC5zr0C5rLLLiM/P59f/vKXnDx5kksuuYR77rmn3cfsDgaDgUmTJnHxxRcTHx9PQ0MDL7/8Mn/84x/7VDnssrIyANasWdOnrt/b4tXBlPvuu8/T6OdsXwcOHOj0+99///1UV1d7vgoKCroweqHLJU6Bpfth+hMQENfytYA49/O/yvHZE0iAxMRET43LsrIyCgsLCQ4OZuTIkRQWFvLII4/wxhtveDnK1ioqKjy1Ho1GI3PmzOGSSy5h+vTpXo7Mu7ozB4r81weJHHhOkiRx6aWXepZcHz58mNraWgYPHkxsbCzvvfcejzzySK9s0nf06FFPs8dBgwYxbtw4brrpJkJCQrwcmfeIc0ChFZEDz8lgMHDrrbfi5+eHLMvs3r0bSZKYNGkSarWaRx55hEceeaTXNfG02+3s3buXxsZGJEli+vTpZGVlcc899/j0ahSRA4UWRP47r9jYWG644QYkSaKqqoq8vDwCAgIYM2YM5eXlPPLII/z973/vdYO01dXV5OTkIMsyOp2OuXPnctFFF521LJGv6NM5UGO6sP213VeqOC0tzXMekJqaSkFBQYvPvn//fqqqqkhLSwMgJCSEoqKiFu+xc+fO8x4nMzOT9evXtznwEhYWRmRkJEePHiUxMbHF11lL1SdOYWnhdF4+kYTURg4sHfYrop6uZXPZD4M65eXlHDx40PNZOkOj0bTZrygkJIQbb7yRN998k2effZZ//vOfnT5GV5AkiYkTJ3LRRRcB7mvLqqoqkpOTSUxM5Msvv+SRRx5h165dXo2zLfn5+Zw8eRJwrxSaPHky119/PQMGDPByZBfGqz1TfvWrX7F48eJzbpOQkNDp99dqtWi12k7vL3iB3h9G3+6uI9lY6R6115pBH9Avm+xdiNDQUO666y5OnDiBRqPh5ZdfBmDUqFGAu6HdJ598wq8uA5POe3GWlZXx97//nbCwMEJDQwkICCA5ObnP17rtCt2ZA0X+66NEDmy3YcOGkZycTF1dHQ0NDeTk5BAREeGpefv888+jkCRuG+Pdf7pvv/2WVatWkZmZydy5cwGYMmWK9wLqJcQ5oNAmkQPbRZIk5s+fz6lTp9Dr9Xz77beAu762wWDA5XLx2GOPMX3aVEZ5sYKgLMu8+OKLVFZWMn36dEaMGIFWq+WKK67wXlC9hMiBQisi/7VbQEAAd955J4WFhej1ev76178CMHHiRCRJ4uDBg7zzzjv8/FIIMJ7nzbpRdXU1zz77LMHBwQQEBBAeHk5sbKwob00fz4GGQAiIh8o8OlbqS3IPjuoDLjiE8vJy5s+fz80330xmZiZms5mtW7fypz/9iTlz5gDu642MjAwWLlzIs88+i8Ph4M4772TixImeyWkXX3wxTz75JK+//jpjxozhzTffZO/evQwZMuScx7/rrrv461//yoIFC7j//vvx8/Nj06ZNjBw5kpSUFB555BF+/vOf4+fnx/Tp07FarWzdupXKykqWLl3a5ns2uDR8VhHNkp8vb5UDQyWJydO3sWTJEl588UXMZjP33XcfUVFRns/bGXFxcWzevJm8vDxMJhOBgYE8/PDDDBs2jPT0dKxWK59++impqamdPkZ3GDx4MPHx8VRVVWG329m0aRPBwcGeFVhvvPEGpaWl3H0xeLPF3s6dO/noo49ITk5mwYIFSJLEhAkTvBdQF/LqYEpISIhPz8oUzkGS3H+kDIHejqRXU6lUnhHd22+/nU8++cRTE7K5r8reQrA5YO2HTzBp0qQeSV42m40XXniBzMxMRowYQWBgIMXFxeTm5nao0Vl/J3KgcFYiB7aL2Wz25LxZs2axf/9+zwyj5nq4VQ2wfCuc/PhPLF68mOjo6G6P6+TJk7z00kvcddddJCYmsmrVKnbv3s3EiRMJDBT/TUHkP+E8RA5sl+bB43HjxlFcXOzJh+Xl5QCsWr2WxInw8jegX/cCt99+R5v1zbvaunXr2LJlC7/61a/Izs5m1apVbNy4keHDh/v0apQziRwonJXIf+2iVCo918F33HEHy5cv99Tdb56JvyMfTFr48qM/MnLkSKZNm9btcTmdTv71r38RExPD5MmTiY6OprCwkMOHD7docu3r+nQOlCT3oOeK+zu+76jbu2Rw1GQyMWrUKJ555hmOHDmC3W4nJiaGJUuW8Nvf/vZ0mBIfffQRP/vZz5gwYQIKhYLp06d7Bh8Bpk2bxgMPPMC9995LU1MTN998M4sWLWLPnj3nPH5QUBCrV6/m17/+NRMnTkSpVJKdnc24ceMAuPXWWzEYDDz55JP8+te/xmg0kpGRwd13333+D3eWHPjKK6/wi1/8gssvvxybzcaECRP4/PPPL+i85p577uHGG28kLS2NxsZGjh07hkaj4f777ycvLw+9Xs9FF13EO++80+ljdBej0egpv3bVVVfx/fffo9frcblcHD16FIDiGli9H45+9EcWLlx4QQOU7VVaWso//vEPbrnlFs/gzqFDhyguLu5XOVCSe9s6yLM4fvw4FRUVfPzxxzz55JOsX78ecJc9Mpnat8yupqYGPz8/qqur+2SDG0HoiA0bNrBy5UquGSUTEwR//lwiJCSEyy67jC+++AKNRsPcuXM9Ax3BwcEolcpOX+RWVlby2WefMWHCBHbv3s22bdsA9xJbjUaD3W7vkQv4rtAbc8WF5sDe+JkEobvIssyTTz5JY2Mj910u8/1RWLVfYsaMGdjtdjZv3kxcXByzZ8/G4XBQU1NDcHBwp/Nfcx+ANWvWcMMNN/CHP/wBcM+Ku++++5BlGafTiUrl1Tks7dbb8oU4BxSEjqmpqeGZZ57B38+Pn02q4rGP3LntJz/5CRs2bODo0aNccsklDB06lKqqKpRKJSaTqdM50G63s3XrVkpLS7nkkkv485//DMDMmTMZPnw4Dofjgs4xe1JvzBUiBwpCx+zatYsPP/yQmdkyGdHwxKcSBoOBa665hhUrVuB0Opk7dy7h4eEUFxcTEBCAWq3udI6qqalhxYoVZGVlUVxczJo1awD49a9/jcFgwOFwiHPAC9CdObC5sXd8fDw6XQfKeTRWwdNpYG8Ezt2DA3A3Tlfp3eX89P7tP47Qr3T6560DZFnmhRdeoKSkhHtmyBwtgQ+2usuEmc1m1q9fT0REBFdeeSXgLskfGhp6Qedox48f58svv+T666/nqaee8pROe+ihh/rtdXDf+DTAgw8+yGuvveZ53LzsbM2aNUyaNMlLUQlC7zVu3DjPzACbzcago8vR6XT4m42UlpYSHSjjZ9JTWFjIv//9bwCWLl2K2Wxm//79fPPNNyxevBhJkli7di2BgYEMHTqUTZs2ERsbS3R0NKtXr2bnzp3ccMMN5OTkcOTIEaKiorjsssuora3FZrN5kmZfGUjprUQOFIT2kySJe++91/M4KbqYw3WfAxDgZ6ampoZoaReyYxqbN2/1XPQ++OCDSJLExx9/jFKpZObMmRQXF7Np0yaysrIIDQ1l7dq1TJkyBbvdzocffkh9fT1Llizhyy+/pKSkhLKyMn7+85/z/PPPc+mll3ri6SsnkL2RyH+C0DEWi8XTgFaWZeawi23btuF0OpFdThoaGki0f4zTlsry5cs5fvw4KSkpLFiwAIfDwT/+8Q+mTZtGSkoK+/btIy8vj/Hjx1NTU0N+fj7jx4/n6NGjfPXVVyQnJzN06FC++uorzGYzM2fO5Morr+SDDz4gKSkJQOS/CyRyoCB0TFZWFllZWYB7pUjm8Y9xOBz4mfQUnzqJnwGCAyyUlJTwr3/9C4fDwW233UZERARHjx7liy++4MYbb0Sn07F27VoMBgMjR45k+/btBAUFkZCQwIYNG9iyZQvz5s2juLiYnJwcTCYTl112GeXl5ZSWlqLRaACRAy9Ur8yBen+45nV462qQFZx7QEUBSHDNG2IgReh2kiRxxx13eB5HVVQwoPRjNBoN/hYT1dXVDImsQnLZ2b5rL1988QXww3XwV199RV1dHXPnzqWiooINGzaQmppKbGwsa9asYcKECSiVSj788ENKSkr42c9+xtq1azl58iRFRUX84he/4J///CcjR470xNMfc2Cf+USvvvoqr776qrfDEIQ+SaPReBrXlxSdICvGRazmEKqcPxCT8QDTpk3jyy+/ZPfu3YwdO5b3338fcJ+gBAYGsnPnTpqamjh58qSnIdlvf/tbjhw5Ql1dHUajkWHDhrF161ZsNhuSJHHttdd66+P2SyIHCkLnhYWFcdNNNwGwb88uUsJdDJC+R5XzB8aMvI+CggLKyso4ePAgoaGh7NixA4CLLrqIdevWkZOTQ25uLi6Xi4aGBkwmE9nZ2eTm5hIfH4/T6WT8+PGsWrWK6upqIiMjPUvshQsn8p8gdJ4kSWRnZ5OdnY3dbiciLBhHUSnm6r1I3MOsWbP417/+hSRJVFRUsHnzZiorK3nnnXdYunQpa9asoby8HEmS+P777wF3U9uysjKKi4vJzMzEz8+PjIwMKioqqK2tJSMjg4yMDC9/8v5D5EBB6DylUunpWVdReorsWJkgjqLK+QOhGQ8wc+ZMPvroIw4dOkR4eDhvvPEGAJ999hmDBg1i586d1NfXU1FR4am8cN9993Hs2DFqa2sxmUyEh4fz3Xff4XA4kGXZczyha/TaHJg4BRa+B+8uAnvD6SfPLPxzeqa/Wu8eSEm8pKcjFAQCAwM9/YkOHdjHoAgXiartqHL+wLDs31JQUEBeXh579+4lISGBjRs3AjB27Fg2b97Mzp072bt3r6eijdPpZOrUqRw4cIDIyEjsdjtjxoyhrKyMmpoaEhIS+NWvfuXFT9wz+sxgiiAIXSM0NIQrhrmgpAhIBNyNnE0mEwMHDqShocGzbUrSQALL/sOKJonQ0BBGjBjhGUyRZZn09HRGjBiBSqVCq9X6RNIUBKFvS09LJd3lghJ3rlOr1cyaNYuCggJSUlI4duyYZ1ujTk2kYj85SMy8bBoHDx9h586dFBUVMW7cOEaMGEFycjJKpVLcPBQEoddTq9WMGz0SrI96ngsODmbhwoVIkkRgYCAOhwOAqKgoTHqNp//KxRPHewZTTp48SVhYGJMmTSIpKQlJkjzlIgRBEHqrwMBAZg1xQUkBEAdARkYGGo2G2NhY7Ha7Z9u0QckMtC3nw3oJjUbD2LFjPYMpTqeTlJQU0tLSMBgMaLXa9vWCEPqfxCnu0l273oHNL0DlD9cRBMS5e6RkXws6P6+FKAjNkpOSSLa6oKQWcA82T58+nWPHjpGWlsaJEyc82wZYjAxQ7GAnEpdOuZiaugaKi4s9q+5GjhxJQkICSqWSpKQkli5d6q2P5RV9pmdKV+iN9R8Focc5bbD3MShZDyEXQcYDoNScdVvnnscpq4XQMfcjqbRYrVZcLhd6vb5n4+5B/TFX9MfPJAid0sEc2LD9cawOCBj5W1BqqK2txWAwoFQqezbuHtQf80V//EyC0Cm2Olh3ufv7iZ+C5hz15p02yjY9jkkHuuzfIivU1NTUYLFY+kT/k87or7miv34uQeiQDp4DuvY+TkkNhI6+D4Vah91ux2azeZo+90f9NVd0ec+UtsgyNFaCtRa0ZtAHdEmzeaH/6ImeKefUwRzYtONx6q0QNNp9HVxXV4dOp+uXZbua9bueKYIgeIdSAWF+eE4EtFqtdwMSBEHoQQat+6uZ2Wz2XjCCIAg9LPiMlCdJEn5+YnatIAi+QSFBuB+gUADu1X2iD6hwVpIEhkD3lyD0AzqN+6uZyXSOCTg+RuHtAARBEARBEARBEARBEARBEARBEHozMZgiCIIgCIIgCIIgCIIgCIIgCIJwDmIwRRAEQRAEQRAEQRAEQRAEQRAE4RxEzxRBEARBEARBEARBEARBEIROkGWZKmsVDY4GDCoD/lp/JNGAXhD6JTGYIgiCIAiCIAiCIAiCIAiC0AE1tho+zv2Y/xz4DwW1BZ7nY8wxXDfoOmYnzsaisXgxwv5p0qRJZGdn8+yzz3o7FMEHiTJfgiAIgiAIgiAIgiAIgiAI7bThxAamvD+FP33/JwprC1u8VlhbyJ++/xNT3p/ChhMbuvS4kiSd8+vhhx/u0uN1NLYPP/ywXds1f/n5+TFu3DhWr17d7uN88MEHPPbYY+3ePi8vD0mS2LlzZ7v3EYSzEYMpgiAIgiAIgiAIgiAIgiAI7bDhxAbuXHUnTY4m5NP/O1Pzc02OJu5cdWeXDqgUFRV5vp599lksFkuL5+65554OvZ/NZuuy2DrilVdeoaioiA0bNhAcHMzll1/O0aNH27VvYGAgZrO5myMUhLaJwRRBEM5OlsHaCPXV0FDufiwIguArRA4UBMGXiRwoCIKvEvlPOIcaWw2/XPtLZLn1IMqPycjIsswv1/6SGltNlxw/PDzc8+Xn54ckSZ7H9fX1LFy4kLCwMEwmEyNGjGDlypUt9o+Li+Oxxx5j0aJFWCwWbrvtNgBeeuklYmJiMBgMzJ07l6effhp/f/8W+3700UcMHToUnU5HQkICjzzyCA6Hw/O+AHPnzkWSJM/js/H39yc8PJzBgwfz/PPP09jYyNdffw3AunXrGDlyJFqtloiICO677z7PccBd5uvuu+9u8Zkef/xxbr75ZsxmM7Gxsfzzn//0vB4fHw/AkCFDkCSJSZMmAbB27VpGjhyJ0WjE39+fcePGkZ+f367/Dv2ayIHnJHqmCILQWmMV7HobNr8AlXnu5778NwTEw6ifQNa1oPf3YoCCIAjdSORAQRB8mciBgiD4KpH/hHb4OPdjz4qU9mheofLJkU9YmLqwW2Orq6tjxowZLFu2DK1Wy+uvv86sWbM4ePAgsbGxnu3+/Oc/8+CDD/LQQw8BsGHDBm6//Xb++Mc/Mnv2bFauXMkDDzzQ4r3Xr1/PokWLeO6557jooos4cuSIZyDmoYce4vvvvyc0NJRXXnmF6dOno1Qq2x23Xq8H3KtkTpw4wYwZM1i8eDGvv/46Bw4cYMmSJeh0unOWMHvqqad47LHH+O1vf8t///tf7rjjDiZOnEhKSgpbtmxh5MiRrFy5kvT0dDQaDQ6HgyuuuIIlS5bw9ttvY7PZ2LJlC5IktTvufkfkwHYRgymCILSUuxLeXQT2htavVebBivth1WNwzeuQOKXHwxMEQehWIgcKguDLRA4UBMFXifwntIMsy/znwH86te9bOW9x3aDruvVmfVZWFllZWZ7Hjz32GMuXL+fjjz/mrrvu8jx/8cUX86tf/crz+He/+x2XXXaZp0RYcnIy3333HZ9++qlnm0ceeYT77ruPG2+8EYCEhAQee+wx7r33Xh566CFCQkKAH1actFdDQwP/93//h1KpZOLEifzjH/8gJiaGv/3tb0iSxKBBgzh58iS/+c1vePDBB1Eo2i6yNGPGDO68804AfvOb3/DMM8+wZs0aUlJSPLEFBQV5YquoqKC6uprLL7+cgQMHApCamtruuPsdkQPbTZT5EgThB7kr4a2rwd4IyKe/znT6OXuje7vcla3fQxAEoa8SOVAQBF8mcqAgCL5K5D+hnaqsVRTUFrR7VUozGZmC2gKqrdXdFJlbXV0d99xzD6mpqfj7+2MymcjJyeH48eMtths+fHiLxwcPHmTkyJEtnvvx4127dvHoo49iMpk8X0uWLKGoqIiGhjZuwJ/Htddei8lkwmw287///Y+XX36ZzMxMcnJyGDNmTItBp3HjxlFXV0dhYeFZ3y8zM9PzfXPps5KSkrNuHxgYyOLFi5k2bRqzZs3iL3/5C0VFRR3+HP2CyIEdIgZTBEFwa6xyj0LLMuA6z8Yu93bvLnLvJwiC0NeJHCgIgi8TOVAQBF8l8p/QAQ2Ojg8anKneUd9FkbTtnnvuYfny5Tz++OOsX7+enTt3kpGR0arJvNFo7PB719XV8cgjj7Bz507P1549ezh8+DA6na7D7/fMM8+wc+dOTp06xalTpzwrXjpLrVa3eCxJEi7XuX+nX3nlFTZu3MjYsWN59913SU5OZtOmTRcUR58jcmCHicEUQRDcdr19ejnf+ZJnM5d7+13vdGdUgiAIPUPkQEEQfJnIgYIg+CqR/4QOMKgMF7S/UdXxQYyO2LBhA4sXL2bu3LlkZGQQHh5OXl7eefdLSUnh+++/b/Hcjx8PHTqUgwcPkpiY2OqrufSWWq3G6XS2K9bw8HASExM9JbiapaamsnHjRuQzmp5v2LABs9lMdHR0u977xzQaDUCbsQ0ZMoT777+f7777jsGDB/Of/3SujFufJXJgh4nBFEEQ3CPLm1/s3L6bXzg9gi0IgtBHiRwoCIIvEzlQEARfJfKf0EH+Wn9izDFIdKzviYREjDkGP61fN0XmlpSUxAcffMDOnTvZtWsX11133XlXZwD87Gc/4/PPP+fpp5/m8OHDvPjii3zxxRctSm09+OCDvP766zzyyCPs27ePnJwc3nnnHf7v//7Ps01cXByrVq3i1KlTVFZWduoz3HnnnRQUFPCzn/2MAwcO8NFHH/HQQw+xdOnSs/ZLOZ/Q0FD0ej0rVqyguLiY6upqjh07xv3338/GjRvJz8/nq6++4vDhw77VN0XkwE4RgymCIEBDBVQeo3VdxPOR3fs1du6PpCAIQq8gcqAgCL5M5EBBEHyVyH9CB0mSxHWDruvUvgtTF3Zr83mAp59+moCAAMaOHcusWbOYNm0aQ4cOPe9+48aN44UXXuDpp58mKyuLFStW8Mtf/rJF+a5p06bx6aef8tVXXzFixAhGjx7NM888w4ABAzzbPPXUU3z99dfExMQwZMiQTn2GqKgoPv/8c7Zs2UJWVha33347t9xyS4tBm45SqVQ899xzvPjii0RGRjJnzhwMBgMHDhxg3rx5JCcnc9ttt/HTn/6Un/zkJ50+Tp8jcmCnqLwdgCAIvYCt7sL2t9aCIbBrYhEEQehpIgcKguDLRA4UBMFXifwndMLsxNk8t+M5mhxN7WpEr0CBVqVl1sBZXR7L4sWLWbx4sedxXFwcq1evbrHNT3/60xaPz1b2a8mSJSxZsqTF48TExBbbTJs2jWnTpp01nlmzZjFr1vk/p3yeFQ0TJ05ky5YtZ3197dq1LR639Zl27tzZ4vGtt97Krbfe2uK55cuXnzOOfk/kwE4RK1MEQQCN6cL215q7Jg5BEARvEDlQEARfJnKgIAi+SuQ/oRMsGgvPTHoGSZLOW+5LQgIJnp30LBaNpYci7Jw///nP7Nq1i9zcXP7617/y2muvXXBTeKGXEzmwU8RgiiAI7pHkgHjoYN1PkNz76QO6IypBEISeIXKgIAi+TORAQRB8lch/QieNixrHPy75BzqVDonWgyrNz+lUOp6/5HnGRo31UqTtt2XLFqZOnUpGRgYvvPACzz33XKuVHEI/I3Jgp4jBFEEQQJJgVCfrQo663b2/IAhCXyVyoCAIvkzkQEEQfJXIf8IFGBc1jpXzV/Kbkb8h2hzd4rVoczS/GfkbVs1f1ScGUgDee+89SkpKaGxsZN++fdx+++3eDknobiIHdoromSIIvkaWwdoITVb3/zfXqsy6FlY9BvZGwHX+95EUoNJD1oJuDVcQBKFLiRwoCIIvk2WwO8DpdDcdVRvdF8IiBwqC0N+Jc0ChG1g0FhamLuS6QddRba2m3lGPUWXET+vX7c3mBaFDRA7sMmIwRRB8RWMV7HobNr8AlXmnn9wPm752jyhnXQvXvA5vXQ2ygnMnUQUgwTVvgN6/mwMXBEHoAiIHCoLgy5pz4KbnoSrf/dy2DHeJhlE/ETlQEIT+S5wDCj1AkiT8df744+/tUAShJZEDu5wo8yUIviB3JTydBivuh8r8lq9V5ruffzrN/Xjhe6DW466Z+OOZFKefU+th4fuQeEn3xy4IgnChRA4UBMGXnZkDq463fK0yT+RAQRD6L3EOKAiCLxM5sFuIwRRB6O9yV7pHmO2NgHz660ynn7M3urcDWLofpj8BAXEtNw2Icz//qxyfT56CIPQRIgcKguDLRA4UBMFXifwnCIIvEzmw20iyLP/4X7Pfqqmpwc/Pj+rqaiwWi7fDEYTu11jlHmVub+1DFO6R5qX73Uv2ZBkaK8FaC1oz6AN8osFUf8wV/fEzCcJ5iRzYKf0xX/THzyQI5yVyYIf111zRXz+XIJyVyH+d0l9zxdk+V1NTE8eOHSM+Ph6dTufFCAVf0KM/byIHdkp7c6BYmSII/dmut8HeQPuSJ+7t7A2w6x33Q0kCQyAEDHD/vw8kT0EQ+hGRAwVB8GUiBwqC4KtE/hMEwZeJHNitxGCKIPRXsgybX+zcvptfcO8vCILQV4kcKAiCLxM5UBAEXyXynyD0ag8//DDZ2dneDgOASZMmcffdd3s7jK4lcmC3E4MpgtBfNVRA5TFa10U8H9m9X2Nld0QlCILQM0QOFATBl4kcKAiCrxL5T/ARp06d4he/+AWJiYnodDrCwsIYN24czz//PA0NDd4Or1MefvhhJEk651dnrF27FkmSqKqq6tqAeyORA7udGEwRhP7KVndh+9ec6Jo4BEEQvEHkQEEQfNmF5kBrbdfEIQiC0NPEOaDgA44ePcqQIUP46quvePzxx9mxYwcbN27k3nvv5dNPP2XlypVn3ddut/dgpB1zzz33UFRU5PmKjo7m0UcfbfHcmWw2m5ci7cVEDux2YjBFEPorjenC9j/0VdfEIQiC4A0iBwqC4MsuNAdqzV0ThyAIQk8T54DCBZBlmXpbvVe+5A6UV7rzzjtRqVRs3bqVq6++mtTUVBISEpgzZw6fffYZs2bN8mwrSRLPP/88s2fPxmg0smzZMgCef/55Bg4ciEajISUlhTfeeMOzT15eHpIksXPnTs9zVVVVSJLE2rVrgR9We6xatYrhw4djMBgYO3YsBw8ebBHrE088QVhYGGazmVtuuYWmpqazfi6TyUR4eLjnS6lUYjabPY8XLFjAXXfdxd13301wcDDTpk07b6x5eXlMnjwZgICAACRJYvHixZ5tXS4X9957L4GBgYSHh/Pwww+3+79DryRyYLdTeTsAQRC6iSEQAuJPL+/rhO2vw0VLRaMpQRD6JpEDBUHwZfoAUKjA5ej4vgoV6Py7PCRBEIQeIc4BhQvQYG/A9IcLvBndSXX312HUGM+7XXl5uWdFitHY9vY/Lof18MMP88QTT/Dss8+iUqlYvnw5v/jFL3j22WeZMmUKn376KTfddBPR0dGegYf2+t3vfsdTTz1FSEgIt99+OzfffDMbNmwA4L333uPhhx/m73//O+PHj+eNN97gueeeIyEhoUPHONNrr73GHXfc4TnG+cTExPC///2PefPmcfDgQSwWC3q9vsX7LV26lM2bN7Nx40YWL17MuHHjmDp1aqdj9CqRA7udWJkiCP2VJMGQ6zu/f1WeqJUoCELfJXKgIAi+rLGycwMp4N6vqapLwxEEQegx4hxQ6Odyc3ORZZmUlJQWzwcHB2MymTCZTPzmN79p8dp1113HTTfdREJCArGxsfz5z39m8eLF3HnnnSQnJ7N06VKuvPJK/vznP3c4nmXLljFx4kTS0tK47777+O677zyrT5599lluueUWbrnlFlJSUvj9739PWlpa5z88kJSUxJ/+9CdSUlJa/Ru0RalUEhgYCEBoaCjh4eH4+fl5Xs/MzOShhx4iKSmJRYsWMXz4cFatWnVBMXqVyIHdTqxMEYT+LHkarH6s8/tba92j2oIgCH2RyIGCIPiqruiZIvKfIAh9lTgHFDrJoDZQd/8F/g29gGNfiC1btuByuVi4cCFWq7XFa8OHD2/xOCcnh9tuu63Fc+PGjeMvf/lLh4+bmZnp+T4iIgKAkpISYmNjycnJ4fbbb2+x/ZgxY1izZk2Hj9Ns2LBhnd63LWfGD+7PUFJS0qXH6HEiB3YrMZgiCP2ZOfLC9hf1sgVB6MtEDhQEwVeJnimCIPgycQ4odJIkSe0qteVNiYmJSJLUqjdJc+msM0tYNTtbObCzUSjchYzO7ONytsb1arXa831zeTGXy9Wh43XEjz9LR2Jty5nxg/szdGf8PULkwG4lynwJQn/WXCuxwyT3fvqALg9JEAShx4gcKAiCr/Lkv47Wuxb5TxCEfkCcAwr9WFBQEFOnTuVvf/sb9fX1nXqP1NTUVj1HNmzY4CnBFRISAkBRUZHn9TMbvHfkOJs3b27x3KZNmzr8PufSnlg1Gg0ATqezS4/da4kc2K3EYIog9GeSBKN+QscvpIFRt4uGU4Ig9G0iBwqC4Ks8+a8TRP4TBKGvE+eAQj/3j3/8A4fDwfDhw3n33XfJycnh4MGDvPnmmxw4cAClUnnO/X/961/z6quv8vzzz3P48GGefvppPvjgA+655x7Avbpl9OjRPPHEE+Tk5LBu3Tr+7//+r8Nx/uIXv+Df//43r7zyCocOHeKhhx5i3759nfrMZ9OeWAcMGIAkSXz66aeUlpZSV+edUm49RuTAbiUGUwShv8u6FtQG2v3rLinc22ct6NawBEEQeoTIgYIg+CqR/wRB8GUiBwr92MCBA9mxYwdTpkzh/vvvJysri+HDh/PXv/6Ve+65h8ceO3e/jCuuuIK//OUv/PnPfyY9PZ0XX3yRV155hUmTJnm2+fe//43D4WDYsGHcfffd/P73v+9wnNdccw0PPPAA9957L8OGDSM/P5877rijw+9zPueLNSoqikceeYT77ruPsLAw7rrrri6PodcRObDbSPKZReX+v717j466vvM//poEJiEJCRcDCTYQAgZQLnITDSJecGFVBFsr/UmBtBALSI+oWRZLMdwjbEgVVkoXtwFdzlLcxctBCoVgrEa3Umo0CqSShIZq5GZNiClJSD6/P1hmCYQht8nkk+/zcc73HGbyncn7k8uzwqffmTautLRUERERKikpUXh4uL/HAVrO0X3S1kckYyR5e+3HgAs70FNflfre01LTtTptsRVtcU1AvdHABmmLvWiLawLqhf41SFttRVtdF3BNNLBB2morrrauc+fOqbCwUL1791ZwcLAfJ4QT+OXnjQY2SH0baMWVKceOHdPMmTPVu3dvdejQQX369FFKSooqKyv9PRpgh77jpKnbpfYddOEyv8sv2fvf+9p3cHw8WyMaCDQRDbQaDQSagP5Zjf4BTUQDrUYDgSaigT7Rzt8D1Me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+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(1)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 0.8 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_train_x = torch.stack((task_ids, train_x1, train_x2), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning, C=%.2f\" % c)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(1)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 3.0 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_train_x = torch.stack((task_ids, train_x1, train_x2), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-6, 6)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning, C=%.2f\" % c)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transfer Learning with Missing Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[ 0.0000,  0.0000,  1.0000, -1.4068,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.4449,  1.3667],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.0879,  0.3745],\n",
+       "         [ 0.0000,  0.0000,  1.0000, -0.8154,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.5876,  0.6553]],\n",
+       "\n",
+       "        [[ 0.0000,  0.0000,  1.0000, -1.0935,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.5568, -0.5042],\n",
+       "         [ 0.0000,  0.0000,  1.0000,  1.8983,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  1.0000, -1.8532,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.0297, -0.4621]],\n",
+       "\n",
+       "        [[ 0.0000,  0.0000,  1.0000,  0.5832,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.0319, -0.5039],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.7601,  0.0962],\n",
+       "         [ 0.0000,  0.0000,  1.0000,  0.5355,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.6945,  0.8844]],\n",
+       "\n",
+       "        [[ 0.0000,  0.0000,  1.0000, -0.6043,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.1699, -0.9647],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.2251,  0.1915],\n",
+       "         [ 0.0000,  0.0000,  1.0000,  0.9712,  0.0000],\n",
+       "         [ 1.0000,  1.0000,  1.0000,  0.7972,  0.0973]]], device='cuda:0')"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def gen_transfer_missing_batch(\n",
+    "    batch_size: int,\n",
+    "    seq_len: int,\n",
+    "    num_features: int,\n",
+    "    max_num_tasks: int,\n",
+    "    num_tasks: int,\n",
+    "    lengthscale: float,\n",
+    "    hyperparameters=None,\n",
+    "    device: str = default_device,\n",
+    "    **kwargs\n",
+    "):\n",
+    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
+    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
+    "    c = torch.rand(batch_size, device=device) * 10\n",
+    "    task_ids = torch.randint(0, 2, (batch_size, seq_len), device=device).float()\n",
+    "    task_ids[..., :2] = torch.tensor([0, 1], device=device)\n",
+    "    task_ids[..., -2:] = torch.tensor([0, 1], device=device)\n",
+    "    \n",
+    "    f = sample_data(x1)\n",
+    "    source_ys = f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
+    "    target_ys = -f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
+    "    \n",
+    "    xs = torch.zeros(batch_size, seq_len, 5).to(device).reshape(-1, 5)\n",
+    "    task_id_flat = task_ids.flatten()\n",
+    "    xs[:, 0] = task_id_flat\n",
+    "    for task_id in range(2):\n",
+    "        delete_x1 = np.random.rand() < 0.2\n",
+    "        delete_x2 = np.random.rand() < 0.2 and not delete_x1\n",
+    "        \n",
+    "        relevant_task_ids = task_id_flat == task_id\n",
+    "        xs[relevant_task_ids, 1:3] = 1\n",
+    "        if delete_x1:\n",
+    "            xs[relevant_task_ids, 1] = 0\n",
+    "            xs[relevant_task_ids, 3] = x2.flatten()[relevant_task_ids]\n",
+    "        elif delete_x2:\n",
+    "            xs[relevant_task_ids, 2] = 0\n",
+    "            xs[relevant_task_ids, 3] = x1.flatten()[relevant_task_ids]\n",
+    "        else:\n",
+    "            xs[relevant_task_ids, 3] = x1.flatten()[relevant_task_ids]\n",
+    "            xs[relevant_task_ids, 4] = x2.flatten()[relevant_task_ids]\n",
+    "    \n",
+    "    xs = xs.reshape(batch_size, seq_len, 5)\n",
+    "    ys = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "    return Batch(\n",
+    "        x=xs.transpose(0, 1),\n",
+    "        y=ys.transpose(0, 1),\n",
+    "        target_y=ys.transpose(0, 1).clone(),\n",
+    "    )\n",
+    "# gen_transfer_missing_batch(4, 5, 2, 1, 1, 0.1).x.transpose(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using cuda:0 device\n",
+      "init dist\n",
+      "Not using distributed\n",
+      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7f70301ef240>, 'seq_len_maximum': 30, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 3, 'sample_num_tasks': False, 'sample_num_features': False}}, 'num_features': 2, 'num_tasks': 3, 'epoch_count': 0}\n",
+      "Style definition of first 3 examples: None\n",
+      "Initialized decoder for standard with (None, 2)  and nout 2\n",
+      "Using a Transformer with 1.49 M parameters\n",
+      "Using OpenAI max lr of 0.0012557141533970579.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:286: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with autocast(enabled=scaler is not None):\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.50it/s, data_time=0.00411, step_time=0.0253, mean_loss=18.4]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 25.49it/s, data_time=0.00323, step_time=0.0244, mean_loss=2.8] \n",
+      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with autocast(enabled=scaler is not None):\n",
+      "Eval Epoch:  45%|████▌     | 232/512 [00:02<00:03, 78.91it/s, data_time=0.00343, step_time=0.0084, mean_loss=2.13] /ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
+      "  warnings.warn(\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.66it/s, data_time=0.00413, step_time=0.00793, mean_loss=2.12]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.78it/s, data_time=0.00355, step_time=0.0259, mean_loss=1.96]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.18it/s, data_time=0.00328, step_time=0.0259, mean_loss=1.84]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.56it/s, data_time=0.00339, step_time=0.00842, mean_loss=1.72]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.78it/s, data_time=0.00386, step_time=0.0247, mean_loss=1.99]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.02it/s, data_time=0.00348, step_time=0.0253, mean_loss=1.84]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 70.41it/s, data_time=0.00354, step_time=0.00774, mean_loss=1.62]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.62it/s, data_time=0.0035, step_time=0.0257, mean_loss=1.75] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.32it/s, data_time=0.00357, step_time=0.0255, mean_loss=1.64]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 80.26it/s, data_time=0.00338, step_time=0.00791, mean_loss=1.53]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.36it/s, data_time=0.00353, step_time=0.0252, mean_loss=1.62]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.37it/s, data_time=0.00339, step_time=0.0254, mean_loss=1.74]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.81it/s, data_time=0.00348, step_time=0.00852, mean_loss=1.53]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.67it/s, data_time=0.00341, step_time=0.0245, mean_loss=1.82]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.52it/s, data_time=0.00338, step_time=0.0255, mean_loss=1.54]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.71it/s, data_time=0.00337, step_time=0.00724, mean_loss=1.43]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.01it/s, data_time=0.0033, step_time=0.0247, mean_loss=1.53] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.93it/s, data_time=0.0035, step_time=0.0254, mean_loss=1.48] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.24it/s, data_time=0.00335, step_time=0.0078, mean_loss=1.55] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.82it/s, data_time=0.0034, step_time=0.0243, mean_loss=1.38] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 29.71it/s, data_time=0.0034, step_time=0.0246, mean_loss=1.46] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 69.99it/s, data_time=0.00422, step_time=0.0079, mean_loss=1.31] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.79it/s, data_time=0.00339, step_time=0.0254, mean_loss=1.32]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.74it/s, data_time=0.00348, step_time=0.0254, mean_loss=1.37]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.84it/s, data_time=0.00324, step_time=0.00743, mean_loss=1.36]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.89it/s, data_time=0.00333, step_time=0.0252, mean_loss=1.28]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.95it/s, data_time=0.00341, step_time=0.0252, mean_loss=1.18]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.77it/s, data_time=0.00412, step_time=0.00868, mean_loss=1.18]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.07it/s, data_time=0.00339, step_time=0.0252, mean_loss=1.35]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.23it/s, data_time=0.00343, step_time=0.0258, mean_loss=1.2] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.66it/s, data_time=0.00336, step_time=0.0075, mean_loss=1.18] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.84it/s, data_time=0.00335, step_time=0.0254, mean_loss=1.12]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.86it/s, data_time=0.00345, step_time=0.025, mean_loss=1.03]  \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 70.35it/s, data_time=0.00447, step_time=0.00815, mean_loss=1.05]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.86it/s, data_time=0.00354, step_time=0.0248, mean_loss=1.15]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.62it/s, data_time=0.0036, step_time=0.0256, mean_loss=0.975] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.58it/s, data_time=0.00341, step_time=0.00759, mean_loss=1.12]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.83it/s, data_time=0.00322, step_time=0.025, mean_loss=1.03] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.01it/s, data_time=0.00404, step_time=0.0256, mean_loss=0.976]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.06it/s, data_time=0.00357, step_time=0.00763, mean_loss=1.02] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.68it/s, data_time=0.00329, step_time=0.024, mean_loss=0.977] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.50it/s, data_time=0.00338, step_time=0.025, mean_loss=0.743] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.24it/s, data_time=0.00328, step_time=0.00777, mean_loss=0.738]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.38it/s, data_time=0.00438, step_time=0.025, mean_loss=0.869] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.75it/s, data_time=0.00362, step_time=0.027, mean_loss=0.738] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.23it/s, data_time=0.00336, step_time=0.00843, mean_loss=0.794]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.17it/s, data_time=0.00348, step_time=0.0245, mean_loss=0.763]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.41it/s, data_time=0.00351, step_time=0.028, mean_loss=0.805] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.23it/s, data_time=0.00349, step_time=0.0073, mean_loss=0.741] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.21it/s, data_time=0.00399, step_time=0.0255, mean_loss=0.612]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.08it/s, data_time=0.00337, step_time=0.0256, mean_loss=0.628]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.67it/s, data_time=0.0035, step_time=0.00736, mean_loss=0.628] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.73it/s, data_time=0.00343, step_time=0.0249, mean_loss=0.607]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.82it/s, data_time=0.00344, step_time=0.0292, mean_loss=0.559]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.86it/s, data_time=0.00341, step_time=0.00733, mean_loss=0.624]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.70it/s, data_time=0.00404, step_time=0.0244, mean_loss=0.401]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.13it/s, data_time=0.00456, step_time=0.0254, mean_loss=0.663] \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.83it/s, data_time=0.00337, step_time=0.0116, mean_loss=0.474] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.71it/s, data_time=0.00338, step_time=0.0255, mean_loss=0.586]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.56it/s, data_time=0.00346, step_time=0.0257, mean_loss=0.408]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.54it/s, data_time=0.00369, step_time=0.00737, mean_loss=0.413]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.60it/s, data_time=0.00366, step_time=0.0261, mean_loss=0.303]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.62it/s, data_time=0.00375, step_time=0.0258, mean_loss=0.305]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.31it/s, data_time=0.00349, step_time=0.00859, mean_loss=0.253]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.70it/s, data_time=0.00356, step_time=0.0248, mean_loss=0.313]  \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.72it/s, data_time=0.00404, step_time=0.0258, mean_loss=0.324]\n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.44it/s, data_time=0.00483, step_time=0.0074, mean_loss=0.244]  \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.27it/s, data_time=0.00416, step_time=0.0252, mean_loss=0.344]\n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.06it/s, data_time=0.00334, step_time=0.0273, mean_loss=0.293]  \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 76.40it/s, data_time=0.00385, step_time=0.0079, mean_loss=0.308] \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.62it/s, data_time=0.00408, step_time=0.0285, mean_loss=0.292]  \n",
+      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.34it/s, data_time=0.00377, step_time=0.026, mean_loss=0.132]  \n",
+      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.01it/s, data_time=0.00345, step_time=0.00761, mean_loss=0.307]   \n"
+     ]
+    }
+   ],
+   "source": [
+    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_transfer_missing_batch, num_features=2, num_tasks=3, seq_len=30, epochs=50, sample_num_features=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(4)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "train_x2[10] = 0.2 / 8\n",
+    "train_x2[11] = 0.8 / 8\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 0.01 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "source_tasks = task_ids == 0\n",
+    "task_info[source_tasks, 0] = 0\n",
+    "\n",
+    "all_features = torch.stack((train_x1, train_x2), -1)\n",
+    "source_features = all_features[source_tasks]\n",
+    "all_features[source_tasks] = torch.cat((source_features[..., 1:], torch.zeros_like(source_features[..., 1:])), -1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    test_info = torch.ones(100, 2)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[10:12], train_y[10:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    # ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    # ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10, color=\"green\")\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    # ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-10, 10)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Target: x1, x2\" % c)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(4)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 0.01 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "source_tasks = task_ids == 0\n",
+    "task_info[source_tasks, 0] = 0\n",
+    "\n",
+    "all_features = torch.stack((train_x1, train_x2), -1)\n",
+    "source_features = all_features[source_tasks]\n",
+    "all_features[source_tasks] = torch.cat((source_features[..., :1], torch.zeros_like(source_features[..., 1:])), -1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    test_info = torch.ones(100, 2)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x1 only, Target: x1, x2\" % c)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(4)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 0.01 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "source_tasks = task_ids == 0\n",
+    "task_info[source_tasks, 1] = 0\n",
+    "\n",
+    "all_features = torch.stack((train_x1, train_x2), -1)\n",
+    "source_features = all_features[source_tasks]\n",
+    "all_features[source_tasks] = torch.cat((source_features[..., 1:], torch.zeros_like(source_features[..., 1:])), -1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    test_info = torch.ones(100, 2)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x2 only, Target: x1, x2\" % c)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
+      "  warnings.warn(\n",
+      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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11/0F3lq1ahUofvr6+losT/3uu+9i79696NixIxo0aIB+/fph9OjR6Nat2wPju3nzJoQQmDFjBmbMmFFon5SUFIulv+vWrfvAY5bEjRs3cOXKFQQGBhZ57rsOHz6MmTNn4ujRo8jLy7Pol52dDW9vb6tirFOnjsXtu8VPa4quxY29ffs2AKBBgwYFxjZo0MCqgmWPHj2wY8cOAPnF7/bt26N9+/bw8/PDoUOHEBwcjHPnzmH06NHFHqssj8Uazs7OGDFiBFavXo2OHTsiNja2RHF99NFHGDx4MBo2bIjmzZvj0UcfxbPPPouWLVsCyC9qfvnll3j77bcRHByMzp07Y+DAgRg7dixq1KhR5sdY1PPl5ORU7NLs1lKpVAAsC87btm3DJ598grNnz0Kr1Zrbrb3IQa1W4/PPP8fSpUsRHx9vsV/5/b8/rHF3OfJ7C/fF6du3b4nPc6/c3FwMHDgQSqUSf//9d4G9xgvj6upq8f26S6PRmO+/99+i+t57UQEREREREZG9YFGciIiIiIgcVmHFl6ysLPTq1QteXl746KOPUL9+fSgUCpw+fRrvvvsuTCaTRf+iZmXfWwhr0qQJrl27hm3btmHnzp3YuHEj5s+fjw8//BCzZ88uMr6753rnnXeKnNF5f8GwPAtKJpMJLVq0wJw5cwq9v3bt2gCAW7duoU+fPmjcuDHmzJmD2rVrQy6XY8eOHZg7d26B79mDYrTm+1kRY63VvXt3/Pzzz4iKisKhQ4fQo0cPSCQSdO/eHYcOHUJoaChMJpN51nNplddjGT16NBYuXIhZs2ahVatWaNq0qdVje/bsiVu3bmHLli3YvXs3Fi9ejLlz52LhwoV4/vnnAQBvvvkmnnjiCWzevBm7du3CjBkz8Pnnn2P//v1o06bNA49fGc9XcS5evAjgv5+jQ4cOYdCgQejZsyfmz5+PkJAQODs7Y+nSpVi9erVVx3zttdewdOlSvPnmm+jSpQu8vb0hkUjw9NNPF/hZsEbjxo0BABcuXLB6TFJSklX9vL29C/w86nQ6DBs2DOfPn8euXbvQvHlzq44VEhJSYOUKAEhMTAQAhIaGmvvd235/37v9iIiIiIiI7AmL4kREREREVKUcOHAA6enp2LRpE3r27Gluj46OLtNx3d3d8dRTT+Gpp54yF50+/fRTvPfee+blhe9Xr149APkzfss687M06tevj3PnzqFPnz4PnCW7detWaLVa/PHHHxazf+9dXt0ehIWFAcifgX+/wtoKc7fYvWfPHpw4cQL/93//ByC/gLxgwQKEhobC3d0d7dq1e+BxyrK0ekl0794dderUwYEDB/Dll1+WeLyfnx8mTJiACRMmQKVSoWfPnpg1a5a5KA7kv07efvttvP3227hx4wZat26Nb7/9FitXrixT7Pc+Xw899JC53WAwICYmxjxjvbRUKhV+//131K5dG02aNAEAbNy4EQqFArt27bJY3nvp0qUFxhf1HG7YsAHjxo3Dt99+a27TaDTIysoqVZwNGzZEo0aNsGXLFnz33XdWzdq+W3guztKlSzF+/HjzbZPJhLFjx2Lfvn1Yt24devXqZXWcrVu3RmRkJHJycsyz2wHg2LFj5vsBoHnz5nBycsLJkycxcuRIcz+dToezZ89atBEREREREdkL7ilORERERERVyt3Zq/fOVtXpdJg/f36pj5menm5xWy6Xo2nTphBCQK/XFzkuKCgIvXv3xqJFiwqdVZmamlrqmKwxcuRIxMfH4+effy5wn1qtRm5uLoDCv2fZ2dmFFhJtKTQ0FM2bN8evv/5qXjYbAA4ePGj1LNy6deuiZs2amDt3LvR6vXkJ/B49euDWrVvYsGEDOnfubLEHdmHc3d0BoNSFUmtJJBJ8//33mDlzJp599tkSjb3/devh4YEGDRqYl73Oy8szL419V/369eHp6Vno0tgl1b59e/j7++Pnn3+GwWAwt69atapUe5jfS61W49lnn0VGRgY++OADc4FbJpNBIpHAaDSa+8bExGDz5s0FjuHu7l7o8yeTyQrMdv/f//5nccySmj17NtLT0/H8889bfC/u2r17N7Zt22a+Xdo9xV977TWsXbsW8+fPx7Bhw4qMJy0tDVevXrXYKmH48OEwGo346aefzG1arRZLly5Fp06dzCtLeHt7o2/fvli5cqXFkvArVqyASqXCiBEjSv4NIiIiIiIiqmCcKU5ERERERFVK165d4evri3HjxuH111+HRCLBihUryrSkc79+/VCjRg1069YNwcHBuHLlCn744QcMGDDAYi/jwvz444/o3r07WrRogRdeeAH16tVDcnIyjh49iri4OJw7d67UcQHAvn37ChQ2AWDIkCF49tlnsW7dOrz00kuIjIxEt27dYDQacfXqVaxbtw67du1C+/bt0a9fP8jlcjzxxBN48cUXoVKp8PPPPyMoKKjQYr4tffbZZxg8eDC6deuGCRMmIDMzEz/88AOaN29uUSh/kB49emDNmjVo0aKFeR/stm3bwt3dHdevX7dq3+67M8lff/119O/fHzKZDE8//XTpH9gDDB48GIMHDy7xuKZNm6J3795o164d/Pz8cPLkSWzYsAGvvvoqAOD69evo06cPRo4ciaZNm8LJyQm///47kpOTy+WxyOVyzJo1C6+99hoefvhhjBw5EjExMVi2bBnq169v9Wz7+Ph486x1lUqFy5cvY/369UhKSsLbb7+NF1980dx3wIABmDNnDh599FGMHj0aKSkp+PHHH9GgQQOcP3/e4rjt2rXD3r17MWfOHISGhqJu3bro1KkTBg4ciBUrVsDb2xtNmzbF0aNHsXfvXvj7+xeI7e7e6DExMQ98DE899RQuXLiATz/9FGfOnMGoUaMQFhaG9PR07Ny5E/v27bNY3r00K0vMmzcP8+fPR5cuXeDm5lZgpv/QoUPNF3P88MMPmD17NiIjI9G7d28AQKdOnTBixAi89957SElJQYMGDbB8+XLExMRgyZIlFsf69NNP0bVrV/Tq1QuTJk1CXFwcvv32W/Tr1w+PPvpoiWMnIiIiIiKqaCyKExERERFRleLv749t27bh7bffxvTp0+Hr64tnnnkGffr0KXJf7+K8+OKLWLVqFebMmQOVSoVatWrh9ddfx/Tp04sd27RpU5w8eRKzZ8/GsmXLkJ6ejqCgILRp0wYffvhhqeK5186dO7Fz584C7eHh4WjevDk2b96MuXPn4tdff8Xvv/8ONzc31KtXD2+88QYaNmwIAGjUqBE2bNiA6dOn45133kGNGjXw8ssvIzAwEBMnTixzjOXpiSeewG+//YZZs2bh//7v/xAREYFly5Zh+fLluHTpklXHuFsU7969u7nNyckJXbp0wd69e63aT3zYsGF47bXXsGbNGqxcuRJCiAoripfW66+/jj/++AO7d++GVqtFWFgYPvnkE0ydOhVA/p7yo0aNwr59+7BixQo4OTmhcePGWLduHZ588slyieHVV1+FEALffvst3nnnHbRq1Qp//PEHXn/99SK3Hbjf2bNn8eyzz0IikcDT0xO1a9fGE088geeffx4dO3a06Pvwww9jyZIl+OKLL/Dmm2+ibt26+PLLLxETE1OgKD5nzhxMmjQJ06dPh1qtxrhx49CpUyd89913kMlkWLVqFTQaDbp164a9e/cW+vsjNzfXvJ95cT755BM8/PDD+P7777FgwQJkZGTA19cXnTt3xpYtWzBo0CCrjlOUs2fPAgCOHj2Ko0ePFrg/OjraXBQvyq+//ooZM2ZgxYoVyMzMRMuWLbFt2zaLrSiA/ItI9u7di3fffRdTpkyBp6cnnnvuOXz++edlegxEREREREQVRSLKMl2CiIiIiIiIyA60bt0agYGB2LNnj61DoWKYTCYEBgZi2LBhhS7t7yguX76MZs2aYdu2bRgwYICtwyEiIiIiIqIH4J7iRERERERE5DD0en2BPZkPHDiAc+fOmZeBJvuh0WgKbF3w66+/IiMjw+Gfr8jISHTp0oUFcSIiIiIiIgfAmeJERERERETkMGJiYtC3b18888wzCA0NxdWrV7Fw4UJ4e3vj4sWLhe77TLZz4MABTJkyBSNGjIC/vz9Onz6NJUuWoEmTJjh16hTkcrmtQyQiIiIiIqJqgHuKExERERERkcPw9fVFu3btsHjxYqSmpsLd3R0DBgzAF198wYK4HQoPD0ft2rXx/fffIyMjA35+fhg7diy++OILFsSJiIiIiIio0nCmOBERERERERERERERERERVVncU5yIiIiIiIiIiIiIiIiIiKosFsWJiIiIiIiIiIiIiIiIiKjKYlGciIiIiIiIqJz17t0bvXv3tnUYRERERERERAQWxYmIiIiIiOxKamoq3njjDTRu3Biurq4ICgpCx44d8e6770KlUtk6PLt09epVTJs2Da1bt4anpydCQkIwYMAAnDx50tahVYgDBw5AIpFY9WVvVq9ejXnz5lXa+T799FMMGjQIwcHBkEgkmDVrVqWcNzY2FrNnz0bHjh3h6+uLgIAA9O7dG3v37q2U8xMREREREZElJ1sHQERERERERPkyMjLQvn175OTkYOLEiWjcuDHS09Nx/vx5LFiwAC+//DI8PDxsHabdWbx4MZYsWYInn3wSkydPRnZ2NhYtWoTOnTtj586d6Nu3r61DLFdNmjTBihUrLNree+89eHh44IMPPrBRVNZZvXo1Ll68iDfffLNSzjd9+nTUqFEDbdq0wa5duyrlnACwZcsWfPnllxgyZAjGjRsHg8GAX3/9FY888gh++eUXTJgwodJiISIiIiIiIkAihBC2DoKIiIiIiIiAr7/+GtOmTcPhw4fRtWtXi/tycnIgl8uhUCgqLR6DwQCTyQS5XF5p5yyNU6dOoVGjRhYXDKSnp6NJkyZo2LAh/v7770qP6e7S6QcOHKiU8zVv3hwBAQHlcr6KfN4HDhyIixcvIiYmptyPXZiYmBiEh4cjLS0NgYGBmDlzZqXMFr906RKCg4MREBBgbtNqtWjdujVUKhViY2MrPAYiIiIiIiL6D5dPJyIiIiIishO3bt2CTCZD586dC9zn5eVVoCC+fv16tGvXDq6urggICMAzzzyD+Ph4iz5F7W09fvx4hIeHm2/HxMRAIpHgm2++wbx581C/fn24uLjg8uXLAPKXKB85ciQCAwPh6uqKRo0aFZiVHB8fj4kTJyI4OBguLi5o1qwZfvnllwLnvnPnDq5evVrs92PcuHFQKBS4cuWKRXv//v3h6+uLhIQEAEC7du0KzKD39/dHjx49CowtypkzZ/DYY4/By8sLHh4e6NOnD/755x+LPsuWLYNEIsHhw4fx1ltvITAwEO7u7hg6dChSU1OLPLZKpYK7uzveeOONAvfFxcVBJpPh888/typOa+l0Onz44Ydo164dvL294e7ujh49eiAyMtKiX3HP+4EDB9C+fXsoFArUr18fixYtwqxZswpdmn3lypXm16Ofnx+efvppi+Jv7969sX37dty+fdu8vPu9r8Hyfl0AsDh+eZg5cyakUin27dtn0T5p0iTI5XKcO3cOANCsWTOLgjgAuLi44PHHH0dcXByUSmW5xkVEREREREQPxuXTiYiIiIiI7ERYWBiMRiNWrFiBcePGPbDvsmXLMGHCBHTo0AGff/45kpOT8d133+Hw4cM4c+YMfHx8ShXD0qVLodFoMGnSJLi4uMDPzw/nz59Hjx494OzsjEmTJiE8PBy3bt3C1q1b8emnnwIAkpOT0blzZ0gkErz66qsIDAzEn3/+ieeeew45OTkWy2WPHTsWBw8eRHELl3333XfYv38/xo0bh6NHj0Imk2HRokXYvXs3VqxYgdDQ0AeOT0pKKlCYLMylS5fQo0cPeHl5Ydq0aXB2dsaiRYvQu3dvHDx4EJ06dbLo/9prr8HX1xczZ85ETEwM5s2bh1dffRVr164t9PgeHh4YOnQo1q5dizlz5kAmk5nv++233yCEwJgxY4qNsyRycnKwePFijBo1Ci+88AKUSiWWLFmC/v374/jx42jdurVF/8Ke9zNnzuDRRx9FSEgIZs+eDaPRiI8++giBgYEFzvfpp59ixowZGDlyJJ5//nmkpqbif//7H3r27Gl+PX7wwQfIzs5GXFwc5s6da/7e3FVZr4uymD59OrZu3YrnnnsOFy5cgKenJ3bt2oWff/4ZH3/8MVq1avXA8UlJSXBzc4Obm1uFxUhERERERESFEERERERERGQXkpKSRGBgoAAgGjduLF566SWxevVqkZWVZdFPp9OJoKAg0bx5c6FWq83t27ZtEwDEhx9+aG7r1auX6NWrV4FzjRs3ToSFhZlvR0dHCwDCy8tLpKSkWPTt2bOn8PT0FLdv37ZoN5lM5v9/7rnnREhIiEhLS7Po8/TTTwtvb2+Rl5dnEZO1b0d37dolAIhPPvlEREVFCQ8PDzFkyJBix/31119CIpGIGTNmFNt3yJAhQi6Xi1u3bpnbEhIShKenp+jZs6e5benSpQKA6Nu3r8VjnzJlipDJZBbP0/3f97uP488//7Q4d8uWLQt9fkqqWbNmFscxGAxCq9Va9MnMzBTBwcFi4sSJ5rYHPe9PPPGEcHNzE/Hx8ea2GzduCCcnJ4vnLyYmRshkMvHpp59ajL9w4YJwcnKyaB8wYIDF6+5eFfm6SE1NFQDEzJkzrTr+g1y4cEHI5XLx/PPPi8zMTFGzZk3Rvn17odfrHzjuxo0bQqFQiGeffbbMMRAREREREVHJcPl0IiIiIiIiOxEcHIxz587hpZdeQmZmJhYuXIjRo0cjKCgIH3/8sXkG7cmTJ5GSkoLJkydbLKk+YMAANG7cGNu3by91DE8++aTFTODU1FT89ddfmDhxIurUqWPR9+4S2kIIbNy4EU888QSEEEhLSzN/9e/fH9nZ2Th9+rR53IEDB4qdDXxXv3798OKLL+Kjjz7CsGHDoFAosGjRogeOSUlJwejRo1G3bl1MmzbtgX2NRiN2796NIUOGoF69eub2kJAQjB49Gn///TdycnIsxkyaNMli+fAePXrAaDTi9u3bRZ6nb9++CA0NxapVq8xtFy9exPnz5/HMM888MMbSkMlk5j3BTSYTMjIyYDAY0L59e4vn4q77n3ej0Yi9e/diyJAhFjOvGzRogMcee8xi7KZNm2AymTBy5EiL575GjRqIiIgosGR7USr6dVFemjdvjtmzZ2Px4sXo378/0tLSsHz5cjg5Fb0YX15eHkaMGAFXV1d88cUXlRInERERERER/YdFcSIiIiIiIjsSEhKCBQsWIDExEdeuXcP333+PwMBAfPjhh1iyZAkAmIuvjRo1KjC+cePGDyzOFqdu3boWt6OiogDkFwKLkpqaiqysLPz0008IDAy0+JowYQKA/EJ1aX3zzTfw8/PD2bNn8f333yMoKKjIvrm5uRg4cCCUSiW2bNlSYK/xwmLPy8sr9HvZpEkTmEwmi32xARS4OMDX1xcAkJmZWeR5pFIpxowZg82bNyMvLw8AsGrVKigUCowYMeKBMZbW8uXL0bJlSygUCvj7+yMwMBDbt29HdnZ2gb73P+8pKSlQq9Vo0KBBgb73t924cQNCCERERBR4/q9cuVKm5/5BSvK6KG9Tp05Fq1atcPz4ccycORNNmzYtsq/RaMTTTz+Ny5cvY8OGDRW6vDsREREREREVjnuKExERERER2SGJRIKGDRuiYcOGGDBgACIiIrBq1So8//zzJT5OYbNvjUZjof1dXV1LHKvJZAIAPPPMM0Xuhd6yZcsSH/euM2fOmAurFy5cwKhRowrtp9PpMGzYMJw/fx67du16YCG/LO7dE/xexc1yHjt2LL7++mts3rwZo0aNwurVqzFw4EB4e3uXe4wrV67E+PHjMWTIEEydOhVBQUGQyWT4/PPPcevWrQL9S/O832UymSCRSPDnn38W+r0p7sKE0rL2dVERoqKicOPGDfO5H+SFF17Atm3bsGrVKjz88MOVER4RERERERHdh0VxIiIiIiIiO1evXj34+voiMTERABAWFgYAuHbtWoEi27Vr18z3A/mzmO/O9r6XtbPJ7y4pfvHixSL7BAYGwtPTE0ajEX379rXquNbKzc3FhAkT0LRpU3Tt2hVfffUVhg4dig4dOlj0M5lMGDt2LPbt24d169ahV69eVh0/MDAQbm5uuHbtWoH7rl69CqlUitq1a5fLY2nevDnatGmDVatWoVatWrhz5w7+97//lcux77dhwwbUq1cPmzZtsljqfebMmVaNDwoKgkKhwM2bNwvcd39b/fr1IYRA3bp10bBhwwce995YysLa10VFMJlMGD9+PLy8vPDmm2/is88+w/DhwzFs2LACfadOnYqlS5di3rx5lVq0JyIiIiIiIktcPp2IiIiIiMhOHDt2DLm5uQXajx8/jvT0dPMS3+3bt0dQUBAWLlwIrVZr7vfnn3/iypUrGDBggLmtfv36uHr1KlJTU81t586dw+HDh62KKTAwED179sQvv/yCO3fuWNx3d2a0TCbDk08+iY0bNxZaPL/33ABw584dXL161arzv/vuu7hz5w6WL1+OOXPmIDw8HOPGjbN43ADw2muvYe3atZg/f36hxcmiyGQy9OvXD1u2bEFMTIy5PTk5GatXr0b37t3h5eVl9fGK8+yzz2L37t2YN28e/P39C+zPXV7uzti+d/b6sWPHcPToUavH9+3bF5s3b0ZCQoK5/ebNm/jzzz8t+g4bNgwymQyzZ88uMFteCIH09HTzbXd390KXbwcq5nVREebMmYMjR47gp59+wscff4yuXbvi5ZdfRlpamkW/r7/+Gt988w3ef/99vPHGGxUeFxERERERERWNM8WJiIiIiIjsxIoVK7Bq1SoMHToU7dq1g1wux5UrV/DLL79AoVDg/fffBwA4Ozvjyy+/xIQJE9CrVy+MGjUKycnJ+O677xAeHo4pU6aYjzlx4kTMmTMH/fv3x3PPPYeUlBQsXLgQzZo1Q05OjlVxff/99+jevTvatm2LSZMmoW7duoiJicH27dtx9uxZAMAXX3yByMhIdOrUCS+88AKaNm2KjIwMnD59Gnv37kVGRob5eGPHjsXBgweLXW58//79mD9/PmbOnIm2bdsCAJYuXYrevXtjxowZ+OqrrwAA8+bNw/z589GlSxe4ublh5cqVFscZOnQo3N3dizzPJ598gj179qB79+6YPHkynJycsGjRImi1WvM5ysvo0aMxbdo0/P7773j55Zfh7OxcoE94eDgAWBTpS2rgwIHYtGkThg4digEDBiA6OhoLFy5E06ZNoVKprDrGrFmzsHv3bnTr1g0vv/wyjEYjfvjhBzRv3tz8vAP5F1588skneO+99xATE4MhQ4bA09MT0dHR+P333zFp0iS88847AIB27dph7dq1eOutt9ChQwd4eHjgiSeeAFD+rwsg/2fq9u3b5n3c//rrL3zyyScA8i9QuLuqwoEDB/DQQw9h5syZmDVrVpHnvnLlCmbMmIHx48eb4162bBlat26NyZMnY926dQCA33//HdOmTUNERASaNGlS4DX5yCOPIDg4+IGPk4iIiIiIiMqRICIiIiIiIrtw/vx5MXXqVNG2bVvh5+cnnJycREhIiBgxYoQ4ffp0gf5r164Vbdq0ES4uLsLPz0+MGTNGxMXFFei3cuVKUa9ePSGXy0Xr1q3Frl27xLhx40RYWJi5T3R0tAAgvv7660Jju3jxohg6dKjw8fERCoVCNGrUSMyYMcOiT3JysnjllVdE7dq1hbOzs6hRo4bo06eP+Omnnyz69erVSxT3djQnJ0eEhYWJtm3bCr1eb3HflClThFQqFUePHhVCCDFu3DgBoMiv6OjoB55LCCFOnz4t+vfvLzw8PISbm5t46KGHxJEjRyz6LF26VAAQJ06csGiPjIwUAERkZKTFY+zVq1eh53r88ccFgALHvysgIEB07ty52Jjv1axZM4vzmUwm8dlnn4mwsDDh4uIi2rRpI7Zt21bi533fvn2iTZs2Qi6Xi/r164vFixeLt99+WygUigJ9N27cKLp37y7c3d2Fu7u7aNy4sXjllVfEtWvXzH1UKpUYPXq08PHxEQAsYinv18W9xyzs697na+vWrQKAWLhwYZHnNhgMokOHDqJWrVoiKyvL4r7vvvtOABBr164VQggxc+bMB74m7z03ERERERERVTyJEMVcgk1ERERERERE5Wbo0KG4cOFCoft1X758Gc2aNcO2bdsslsG3J0OGDMGlS5dw48YNW4dSbqZNm4bffvsNN2/ehIuLi63DISIiIiIionLGPcWJiIiIiIiIKkliYiK2b9+OZ599ttD7IyMj0aVLF7spiKvVaovbN27cwI4dO9C7d2/bBFRBIiMjMWPGDBbEiYiIiIiIqijOFCciIiIiIiKqYNHR0Th8+DAWL16MEydO4NatW6hRo4atwypWSEgIxo8fj3r16uH27dtYsGABtFotzpw5g4iICFuHR0RERERERGQVJ1sHQERERERERFTVHTx4EBMmTECdOnWwfPlyhyiIA8Cjjz6K3377DUlJSXBxcUGXLl3w2WefsSBOREREREREDoUzxYmIiIiIiIiIiIiIiIiIqMrinuJERERERERERERERERERFRlsShORERERERERERERERERERVFoviRERERERERERERERERERUZbEoTkREREREREREREREREREVRaL4kREREREREREREREREREVGWxKE5ERERERERERERERERERFUWi+JERERERERERERERERERFRlsShORERERERERERERERERERVFoviRERERERERERERERERERUZbEoTkREREREREREREREREREVRaL4kREREREREREREREREREVGWxKE5ERERERERERERERERERFUWi+JERERERERERERERERERFRlsShORERERERERERERERERERVFoviRERERERERERERERERERUZbEoTkREREREREREREREREREVRaL4kREREREREREREREREREVGWxKE5ERERERERERERERERERFUWi+JERERERERERERERERERFRlsShORERERERERERERERERERVFoviRMXYt28fJk6ciIYNG8LNzQ316tXD888/j8TERJvEk5WVhUmTJiEwMBDu7u546KGHcPr0aavHX7lyBY8++ig8PDzg5+eHZ599FqmpqRUYMRFR1VRV8oPJZMKyZcswaNAg1K5dG+7u7mjevDk++eQTaDSaAv0lEkmhX1988UVFPCwioiqrquQRABg/fnyhuaFx48YVHDURUdVTlfJDUe8dJBIJHnnkEXO/mJiYIvutWbOmoh4aEVGVZE95JDExEf/3f/+Hhx56CJ6enpBIJDhw4ECJjhEfH4+RI0fCx8cHXl5eGDx4MKKioiomYKryJEIIYesgiOxZ+/btkZGRgREjRiAiIgJRUVH44Ycf4ObmhrNnz6JGjRqVFovJZEKPHj1w7tw5TJ06FQEBAZg/fz5iY2Nx6tQpREREPHB8XFwc2rRpA29vb7z++utQqVT45ptvUKdOHRw/fhxyubySHgkRkeOrKvlBpVLB09MTnTt3xsCBAxEUFISjR49i+fLl6NmzJ/bv3w+JRGLuf/cDrLFjx1ocp02bNmjWrFmFPUYioqqmquQRIL8ovmbNGixevNii3dvbG0888URFhk5EVOVUpfywcuXKAm0nT57Ed999h6+++gpTp04FkF8Ur1u3LkaNGoXHH3/con+PHj0QFhZWfg+KiKiKs6c8cuDAATz00EOIiIhAQEAAjh49isjISPTu3duq8SqVCm3btkV2djbefvttODs7Y+7cuRBC4OzZs/D396/YB0BVjyCiBzp48KAwGo0F2gCIDz74oFJjWbt2rQAg1q9fb25LSUkRPj4+YtSoUcWOf/nll4Wrq6u4ffu2uW3Pnj0CgFi0aFGFxExEVFVVlfyg1WrF4cOHC7TPnj1bABB79uyxaAcgXnnllfIJnIioGqsqeUQIIcaNGyfc3d0rMkQiomqjKuWHwjz33HNCIpGI2NhYc1t0dLQAIL7++usyx0xEVN3ZUx7JyckR6enpQggh1q9fLwCIyMhIq8d/+eWXAoA4fvy4ue3KlStCJpOJ9957r7zDpWqAy6dTtaRWq9G4cWM0btwYarXa3J6RkYGQkBB07doVRqMRANCzZ09IpZY/Kj179oSfnx+uXLlSqXFv2LABwcHBGDZsmLktMDAQI0eOxJYtW6DVah84fuPGjRg4cCDq1Kljbuvbty8aNmyIdevWVVjcRESOojrmB7lcjq5duxZoHzp0KAAU+VjUanWhy6sTEVVn1TGP3MtoNCInJ6eiwiQicljVPT/cpdVqsXHjRvTq1Qu1atUqtE9ubi50Ol2Z4iYiqmocNY94enrCz8+v1OM3bNiADh06oEOHDua2xo0bo0+fPqxnUKmwKE7VkqurK5YvX46bN2/igw8+MLe/8soryM7OxrJlyyCTyYocr1KpoFKpEBAQUOy58vLykJaWVuxXZmZmscc6c+YM2rZtWyCpdezYEXl5ebh+/XqRY+Pj45GSkoL27dsXuK9jx444c+ZMsecnIqrqqmN+KEpSUhIAFPpYli1bBnd3d7i6uqJp06ZYvXp1iY9PRFQVVec8kpeXBy8vL3h7e8PPzw+vvPIKVCpVseOIiKqD6pwf7rVjxw5kZWVhzJgxhd4/e/ZseHh4QKFQoEOHDti9e3eJjk9EVFU5ah4pC5PJhPPnzxdZz7h16xaUSmWFxkBVj5OtAyCylU6dOmHatGn48ssvMXToUCQnJ2PNmjWYN28eGjZs+MCx8+bNg06nw1NPPVXseb766ivMnj272H5hYWGIiYl5YJ/ExET07NmzQHtISAgAICEhAS1atChy7L197x+fkZEBrVYLFxeXYmMlIqrKqlt+eFB8Xl5eeOyxxyzau3btipEjR6Ju3bpISEjAjz/+iDFjxiA7Oxsvv/xyic5BRFQVVcc8EhISgmnTpqFt27YwmUzYuXMn5s+fj3PnzuHAgQNwcuJHD0RE1TE/3G/VqlVwcXHB8OHDLdqlUin69euHoUOHombNmoiKisKcOXPw2GOP4Y8//sCAAQOsPgcRUVXliHmkLO7WK4qqZwD5eahRo0YVFgNVPXxnStXarFmzsG3bNowbNw4qlQq9evXC66+//sAxf/31F2bPno2RI0fi4YcfLvYcY8eORffu3Yvt5+rqWmwftVpdaNFaoVCY73/QWADFjmdRnIioeuWHwnz22WfYu3cv5s+fDx8fH4v7Dh8+bHF74sSJaNeuHd5//32MHz/eqniJiKq66pZHPv/8c4vbTz/9NBo2bIgPPvgAGzZswNNPP11sDERE1UF1yw/3ysnJwfbt2/H4448XeI9Rp04d7Nq1y6Lt2WefRdOmTfH222+zKE5E9C9HyyNlYW09g6gkWBSnak0ul+OXX35Bhw4doFAosHTpUkgkkiL7X716FUOHDkXz5s2xePFiq85Rr1491KtXr1zidXV1LXS/prt7uj4oEd29r7TjiYiqk+qUH+63du1aTJ8+Hc8995xVM7/lcjleffVVvPTSSzh16pRVb5yIiKq66pxH7poyZQpmzJiBvXv3sihORPSv6pwfNm7cCI1GU+TS6ffz8/PDhAkT8MUXXyAuLq7IPciJiKoTR8sjZcF6BlUEFsWp2rt7JapGo8GNGzdQt27dQvvFxsaiX79+8Pb2xo4dO+Dp6WnV8e/u11EcmUyGwMDAB/YJCQkxL4N+r7ttoaGhDxx7b9/7x/v5+XGWOBHRPapLfrjXnj17MHbsWAwYMAALFy60agwA1K5dG0D+0lZERJSvOuaRe7m6usLf35+5gYjoPtU1P6xatQre3t4YOHCg1WPufZ/BojgRUT5HyiNlcbdeUd7vU6h6Y1GcqrXz58/jo48+woQJE3D27Fk8//zzuHDhAry9vS36paeno1+/ftBqtdi3b1+h+1gU5Ztvvim3PThat26NQ4cOwWQyQSqVmtuPHTsGNze3B+4dUrNmTQQGBuLkyZMF7jt+/Dhat25dbIxERNVFdcoP9/YdOnQo2rdvj3Xr1pVo/9eoqCgAqNA3Q0REjqQ65pH7KZVKpKWlMTcQEd2juuaHxMREREZGYvz48SWakMH3GURElhwtj5SFVCpFixYtCq1nHDt2DPXq1bO60E90F4viVG3p9XqMHz8eoaGh+O677xAdHY0OHTpgypQp+OWXX8z9cnNz8fjjjyM+Ph6RkZGIiIgo0XnKcw+O4cOHY8OGDdi0aROGDx8OAEhLS8P69evxxBNPWLyxuHXrFgCgfv365rYnn3wSy5cvR2xsrPlq23379uH69euYMmVKiR4XEVFVVR3zw5UrVzBgwACEh4dj27ZtRZ4zNTW1wAdSSqUS8+bNQ0BAANq1a1dsrEREVV11yyMajQZ6vb7AB1Iff/wxhBB49NFHrX5MRERVWXXLD/das2YNTCZTkUunF/Y+Iz4+Hr/88gtatmxZomIOEVFV5Yh5pCTu3LmDvLw8NG7c2Nw2fPhw/N///R9OnjyJ9u3bAwCuXbuG/fv345133inX81P1IBFCCFsHQWQLM2fOxMcff4x9+/bhoYceAgB8+umnmD59OrZv347HH38cADBkyBBs2bIFEydONPe7y8PDA0OGDKm0mI1GI7p3746LFy9i6tSpCAgIwPz583Hnzh2cOHECjRo1MvcNDw8HAIurtWJjY9GmTRv4+PjgjTfegEqlwtdff41atWrhxIkTXD6diAjVLz8olUo0a9YM8fHx+Oyzz1CzZk2LY9evXx9dunQBAMyaNQubN2/GE088gTp16iAxMRG//PIL7ty5gxUrVli9PyARUVVW3fJITEwM2rRpg1GjRpk/wNq1axd27NiBRx99FNu3b7eYXUhEVF1Vt/xwr/bt2yMxMRGxsbGF5oQJEybg1q1b6NOnD0JDQxETE4NFixZBqVRi165d6N27dwU9QiIix+GIeQQAPvnkEwDApUuXsGbNGkycONG85Pv06dPN/Xr37o2DBw/i3pKlUqlEmzZtoFQq8c4778DZ2Rlz5syB0WjE2bNnuZIIlZwgqoZOnTolnJycxGuvvWbRbjAYRIcOHURoaKjIzMwUQggRFhYmABT6FRYWVumxZ2RkiOeee074+/sLNzc30atXL3HixIkC/cLCwgqN7+LFi6Jfv37Czc1N+Pj4iDFjxoikpKRKiJyIyP5Vx/wQHR1d5OMAIMaNG2fuu3v3bvHII4+IGjVqCGdnZ+Hj4yP69esn9u3bVwmPkIjI/lXHPJKZmSmeeeYZ0aBBA+Hm5iZcXFxEs2bNxGeffSZ0Ol0lPgIiIvtVHfPDXVevXhUAxFtvvVXkOVavXi169uwpAgMDhZOTkwgICBBDhw4Vp06dKs+HQkTksBw5jzzoM6d79erVq0CbEELExsaK4cOHCy8vL+Hh4SEGDhwobty4UVnhUxXDmeJERERERERERERERERERFRlcQ0zIiIiIiIiIiIiIiIiIiKqslgUJyIiIiIiIiIiIiIiIiKiKotFcSIiIiIiIiIiIiIiIiIiqrIcpii+YMECtGzZEl5eXvDy8kKXLl3w559/2josIiKyQ8wZRERkLeYMIiKyFnMGERFZizmDiMj+SIQQwtZBWGPr1q2QyWSIiIiAEALLly/H119/jTNnzqBZs2a2Do+IiOwIcwYREVmLOYOIiKzFnEFERNZiziAisj8OUxQvjJ+fH77++ms899xztg6FiIjsHHMGERFZizmDiIisxZxBRETWYs4gIrItJ1sHUBpGoxHr169Hbm4uunTpUmQ/rVYLrVZrvm0ymZCRkQF/f39IJJLKCJWIyCEJIaBUKhEaGgqp1GF22igUcwYRUcVizmDOICKyFnMGcwYRkbWYM5gziIisZW3OcKii+IULF9ClSxdoNBp4eHjg999/R9OmTYvs//nnn2P27NmVGGHJ+LtKkDbNs/Tjv1IiQ+2wE/2JyAHExsaiVq1atg6jVJgz7hvPnEFEFYw5w34wZxCRvWPOsB/MGURk75gz7AdzBhHZu+JyhkMtn67T6XDnzh1kZ2djw4YNWLx4MQ4ePFhkIrn/yqrs7GzUqVMHsbGx8PLyqqywiyTJjoXnkq6lHq987giEd+1yjIiIKF9OTg5q166NrKwseHt72zqcUmHOsMScQUQVhTmDOYOIyFrMGcwZRETWYs5gziAispa1OcOhZorL5XI0aNAAANCuXTucOHEC3333HRYtWlRofxcXF7i4uBRo9/LysoskAlmNMg339A8B3OzgcRBRleXISzMxZ1hiziCiisacwZxBRGQt5gzmDCIiazFnMGcQEVmruJzh0JtxmEwmiyunHI6bH+BbF0BJE7skf5yrb0VERURUJTFnMGcQEVmLOYM5g4jIWswZzBlERNZizmDOICLbcpiZ4u+99x4ee+wx1KlTB0qlEqtXr8aBAwewa9cuW4dWehIJ0OlFYOd7JR/b6aX88UREVABzxn2YM4iIisSccR/mDCKiIjFn3Ic5g4ioSMwZ92HOICI74DBF8ZSUFIwdOxaJiYnw9vZGy5YtsWvXLjzyyCO2Dq1sWo0C9n0M6NUATMX3l0gBJ1eg1dMVHhoRkaNizvgXcwYRUbGYM/7FnEFEVCzmjH8xZxARFYs541/MGURkRxymKL5kyRJbh1AxXH2Ap34FVo0EhBQPTiRSABLgqRX544iIqFDMGQBzBhGRdZgzAOYMIiLrMGcAzBlERNZhzgCYM4jI3jj0nuJVRoO+wJh1gLMr8vfjuH8ZkX/bnF2BMeuBBn0qP0YiIrIPzBlERGQt5gwiIrIWcwYREVmLOYOIHJTDzBSv8hr0Bd66DJxbAxxbAGTG/Hefb3j+nhutRwEKb1tFSERE9oI5g4iIrMWcQURE1mLOICIiazFnEJEDYlHcnrj6AJ1fAtpPAM58CBh0QPN3AY9gQHL/1VZERFStMWcQEZG1mDOIiMhazBlERGQt5gwicjAsitsjiQRwcc3/cvNjAiEioqIxZxARkbWYM4iIyFrMGUREZC3mDCJyENxTnIiIiIiIiIiIiIiIiIiIqiwWxYmIiIiIiIiIiIiIiIiIqMpiUZyIiIiIiIiIiIiIiIiIiKosFsWJiIiIiIiIiIiIiIiIiKjKYlGciIiIiIiIiIiIiIiIiIiqLBbFiYiIiIiIiIiIiIiIiIioymJRnIiIiIiIiIiIiIiIiIiIbEIIUeHnYFGciIiIiIiIiIiIiIiIiIgq3YITCyD9SIqTCScr9DwsihMRERERERERERERERERUaX637H/YfKOyQCAiVsmVui5nCr06ERERERERERERERERERERP/SGrR4bNVjiIyJNLdFjot8wIiy40xxIiIiIiIiIiIiIiIiIiKqcOeSzsH/K39zQXxC6wkwfWiCv5t/hZ6XRXEiIiIiIiIiIiIiIiIiIqpQkdGRaL2oNXL1uQCAlsEtsWTQEkgkkgo/N4viRERERERERERERERERERUYb775zs8/OvD5tubRm7CuZfOVUpBHOCe4kREREREREREREREREREVAGSVckY+NtAnEw4aW6LHBeJ3uG9KzUOFsWJiIiIiIiIiIiIiIiIiKhc/RP3D0auH4nYnFgAQH3f+jg88TCCPYIrPRYWxYmIiIiIiIiIiIiIiIiIqNwMXD0Q229sBwAEuAVg5dCV6N+gv83iYVGciIiIiIiIiIiIiIiIiIjK7Hj8cby/733si95nbrvyyhUEuAXYMCoWxYmIiIiIiIiIiIiIiIiIqIzOJZ1Dp8WdzLcntJ6ARQMXwVnmbMOo8kltHQARERERERERERERERERETmmXF0uPtj3gUVBfOngpfhl8C92URAHOFOciIiIiIiIiIiIiIiIiIhKYfv17Rj420Dz7R51euDjhz5Gr/BeNoyqIBbFiYiIiIiIiIiIiIiIiIjIakII/HTqJ7y0/SVz23vd38MnD38CqcT+FitnUZyIiIiIiIiIiIiIiIiIiKxyO+s2wr8Lt2j7sOeHmP3QbNsEZAUWxYmIiIiIiIiIiIiIiIiIqFix2bEFCuJpU9Pg7+Zvm4CsxKI4EREREREREREREREREREVyWgyot739XAn+465bcXQFRjTYgwkEokNI7MOi+JERERERERERERERERERFSolNwUBH8TbNG2ceRGDGsyzEYRlRyL4kREREREREREREREREREVMDl1Mvov7K/RZtuug7OMmcbRVQ6UlsHQERERERERERERERERERE9kMIgVd3vIpm85shLicOnnJPHBx/EGKmcLiCOMCZ4kRERERERERERERERERE9K8cbQ5qzqkJlU5lbjs16RQi/CNsGFXZcKY4EREREREREREREREREREhR5uD1gtbmwviQe5BSHw70aEL4gBnihMRERERERERERERERER2YXsPD3Sc7XIUuuRrdZDpTFArTdCrTNCZzDBYBIwmkwQApBKJZBKJHCWSeAql8FNLoOHizN83Zzh6y5HgIcLvF2tX+r8i7+/wHv73jPf/qj3R5jeczokEklFPNRKxaI4EREREREREREREREREVElE0IgOUeLmPRcJGSpkZyjhUZvtP4AJgEA0OgBpcZQaBdPhRNq+riitp8bGgR5QOEsK9AnU50Jv6/8LNq2PL0FgxoNsj4WO8eiOBERERERERERERERERFRJYnLzMOVRCWiUlXI05WgCF4KSo0BV5OUuJqkxP6rKajj54amoV5oEOgBqVSCgzEH0Xt5b3P/PnX7YOczO+EkrVpl5Kr1aIiIiIiIiIiIiIiIiIiI7IxGb8SF+GxcjM9GVp7eJjEYTQLRabmITsuFp8IJCfrdmHnoZfP9Per0wJ9j/qxyBXGARXEiIiIiIiIiIiIiIiIiogqRqzXg9J1MnI/Lhs5gsnU4APKXbZ/2dw9k6hLMbdtH7cDjDR+zYVQVi0VxIiIiIiIiIiIiIiIiIqJypDeacOp2Jk7dzrSbYjgA5OozMeNEZ/PtrsFPY0jdD5CW7oE76Xmo4+9mw+gqDoviRERERERERERERERERETl5HqyEn9dT4VSY7B1KBaS827h2/NDzbdruDXE8PqzAQDpKh02no5Dq9re6N4gEHInqa3CrBBV69EQEREREREREREREZFNXUi+gNBvQ5GlybJ1KNWSwWhCrta+CnFE1YVaZ8T284nYfj7RrgriQggcTV6H7y+OgsGkBQCMifga01pvLdD3XGw2Vh27jcRsdWWHWaE4U5yIiIiIiIiIiIiIiMrFwZiD6L28NwCg3U/tcOv1WzaNpzrK1Rnxy9/R8PeQo5avK2p4uSLYywV+7nJIJBJbh0dUZUWn5WLP5STkao22DsWCSZjw6em+yNTGAwBquTfDpKaL4eHsV+SYrDw91p+MQ+9GgWhZy6eSIq1YLIoTETkiIYC8DECnAuQegJsfwD9oiYioMMwZRERkLeYMIiKyViE5QwD4+K+PMfPATHO3LU9vsV2MhHSVDukqHc4hGwAgd5LCz10OXzc5/D3k8HVzhrerHD5uznCWcWFhotISQuCfqAwci06HELaOxpLGqMKGWzPNBXEAmNzsVyicPIodazQJ7LuSgpQcLR5qHASZtJTvDezkfQaL4kREjkSdBZz7DTi2CMiM/q/dty7Q6UWg1SjA1cdW0RERkT1hziAiImsxZxARkbWKyBlGnzAskBoxJ+MyIAG61OqC/eP2Q+GksF2sVIDOYEJStgZJ2RqLdokE8HBxgo9bfqHc112OAHcX+HvI4e7CMhLRg2gNRuy8mISo1Fxbh1JAuiYWn57uCwCQQIoeIc9iUPi7kEpkJTrOhfhsZOTpMKhVKBTOJRhrZ+8z+NuMiMhR3NwLrB0L6PMK3pcZA+x8D9j3MfDUr0CDvpUeHhER2RHmDCIishZzBhERWesBOUOSFYPJAMbDE0+KPKwds4MFcQciBKDUGKDUGBCbYXmfh4sTangrEOqjQE0fNwR7uXAJdqJ/KTV6bD4TjzSVztahFHApIxJLrr5kvv1K8xWo59W+1MeLz1Rj/ak4DG1TEx7WXCxjh+8zuB4GEZEjuLkXWDUS0KsBiH+/7vVvm16d3+/m3sqPkYiI7ANzBhERWYs5g4iIrFVMzpBCAikkcIMEOyVe8Ik7aZMwqfyptAbcTFHhr+tp+O34Hfx8KAq7LyUhKlUFk8nO1okmqkQZuTqsPRFrlwXxK5l/Yfn1N8y3JzcrW0H8rjSlFmtPxCIzt5jHbKfvM1gUJyKyd+qs/CuqhABgKqazKb/f2rH544iIqHphziAiImsxZxARkbVKkDOkACTMGVVartaISwk52HI2AUv+jsaRW2nQGIy2DouoUiVmq7HuZCyUGoOtQ7EghMDWmK/x85UXYDBpUdujBWa3P4IG3h3L7Rw5aj3Wn4pFmkpbeAc7fp/hMEXxzz//HB06dICnpyeCgoIwZMgQXLt2zdZhERFVvHO//bvESHEJ5C5Tfv9zayoyKrvGnEFE1RZzRokxZxBRtcWcUWLMGURUbTFnlFh1yRkqrQEnYjJw9k4WbqaqkKW2vxmzROUtPkuNTafjodbZ18UgGqMK8y+NRWTCYgBA9xrP4OWmy+Ap9y/3c+Vqjdh4Kg6pykIK43acMxymKH7w4EG88sor+Oeff7Bnzx7o9Xr069cPubn2t3E9EVG5EQI4tqh0Y48t/PdqrOqHOYOIqiXmjFJhziCiaok5o1SYM4ioWmLOKJXqljMEgFSlFiuO3saey8nI09nX7Fmi8hKfpcbmM/HQGawt+FaOm9nH8f6xdriVcxxSyDC07nQMqzcDCiePCjtnns6IjafjkKLU/Ndo5znDip3Q7cPOnTstbi9btgxBQUE4deoUevbsaaOoiIgqWF4GkBldioEif5w6E3DzK/ew7B1zBhFVS8wZpcKcQUTVEnNGqTBnEFG1xJxRKtU1Z5iEwMX4bNxMUaFrfX+0rOUNiURi67CIyoW9FsT/SV6Pdbemm29ParoEDX26VMq51TojNp6Kx5NtayLIS2H3OcNhZorfLzs7GwDg51f9EioRVSM6VdnGa5XlE4eDY84gomqBOaNcMGcQUbXAnFEumDOIqFpgzigX1S1naPRG7L+agjUnYpGZyyXVyfEl52jssiC+JfoLc0Hcz6UmPmi7t9IK4ndp9EZsPB2PlByN3ecMh5kpfi+TyYQ333wT3bp1Q/PmzYvsp9VqodX+t559Tk5OZYRHRFR+5GVc3sTFs3zicGDMGURUbTBnlBlzBhFVG8wZZcacQUTVxan0a2hXlgMwZ1TrnJGUrcGqY7fRIyIQrWr72DocolJJV2nxu50VxA0mHTZFf4x/kteZ295suQEezra58OZuYXx4EwUCy3KgCs4ZDjlT/JVXXsHFixexZs2DN13//PPP4e3tbf6qXbt2JUVIRFRO3PwA37oASrrMkCR/nKtvRUTlUJgziKjaYM4oM+YMIqo2mDPKjDmDiKq6ZFUyeiztgfYr++MmjDChpPu8MmfcVd1zht4osP9qCracjYdGb7R1OEQlkp2nx6bT8VDr7Oe1G51zGtP+aWEuiLf2fxxfdb5gs4L4XRq9Eesv58HgHQ57fZ/hcEXxV199Fdu2bUNkZCRq1ar1wL7vvfcesrOzzV+xsbGVFCURUTmRSIBOL5ZubKeX8sdXY8wZRFStMGeUCXMGEVUrzBllwpxBRFVdVGYUanxbA3/f+RuQALv8wyEpTSmBOYM54x5RqblYdewOkrI1tg6FyCq5WgM2nYmDSmuwdShmlzMO4H8XRwEAnKUKTGqyGGMbzYWTVG7jyPJpDQJHAp4s8WVUAColZzhMUVwIgVdffRW///479u/fj7p16xY7xsXFBV5eXhZfREQOp9UowNkNVv/Klkjz+7d6ukLDsmfMGURUbTFnlBhzBhFVW8wZJcacQURVncFkQNtFbVH/+/rmtlbBrfDK839DwpxRIswZhctR67HuZCzOx2XZOhSiB9IZTNhyNgFZeXpbh2K26sZULL7634WtLzZdgsa+PWwYUeEuBgyAXqqAsMOc4TBF8VdeeQUrV67E6tWr4enpiaSkJCQlJUGtVts6NCKiiuXqAzz1679XSRX3a1sKQAI8tSJ/XDXFnEFE1RZzRokxZxBRtcWcUWLMGURUlemMOjh/7IwzSWfMbVtHbcXZl84yZ5QCc0bRjCaBfVdSsO9KMoymUs0nJapQRpPAtvMJSM6xj1UNDCYdPjrZC6dS/zC3PRPxLep5tbdhVEXTOnliW+MvIQCIYpdRr9yc4TBF8QULFiA7Oxu9e/dGSEiI+Wvt2rW2Do2IqOI16AuMWQc4uyJ/P477k8m/bc6uwJj1QIM+lR+jHWHOIKJqjTmjRJgziKhaY84oEeYMIiorg9GE7Dw94rPUiEpV4XqyEpcTcnApIRtXk3JwM0WJO+l5SFNpkaczQIjKKRiuvrAajX5oZL4d5h2GzHczMbDhwP86MWeUCHNG8c7HZWPj6Tjk6exnaWoiANhzORm30/NsHQYAIF0Ti+8ujESWLgkA4CUPwtedL6Jt4MBiRtrWbd8u2Nx03r8zxiWFFMdtkzOcKuUs5aCy/gAgIrJbDfoCb10Gzq0Bji0AMmP+u883PH/PjdajAIW3rSK0G8wZRFTtMWdYjTmDiKo95gyrMWcQUUlo9EbEZuQhKUeDNJUWaUpdifeldZZJ4Osuh5+bHEFeLgj1cUWwpwJSafnsuao1aNFyYUtcT79ubpvadSq+euSrwgcwZ1iNOcM68Zlq/HY8FoNbhyLAw8XW4RDhyM00XEnMsXUYAIBY1UUsufoycnQpAID2gYMxqsGXkFTwvtvl5bZvFyzusB1NUrajXdIaeKnj/7vTRjnDYYriRESE/CVEOr8EtJ8AnPkQMOiA5u8CHsH/LmFFRET0L+YMIiKyFnMGEVG5UGkNuJKYg1spKiTnaGEqY2FUbxRIydEiJUeLq0lKAIDcSYpavq6ICPJEvUB3KJxlpTp2pjoTT2982qIgfvvN26jjXefBA5kzqJzlqPVYeyIWA1qEIDzA3dbhUDV2MT4bx6IzbB0GhBA4mboF62/NgEHoAOTvH97Ip7uNIys5rZMnzoY+jfM1hqKr8nsEu0pRo+tMyH1CbJIzWBQnInJEEgng4pr/5ebHNx1ERFQ05gwiIrIWc4ZdU2r02HwmHoGeLgjwcIGPmxxeCid4uTqXuihGRGUnhEBUWi4uxGXjdnpemQvhxdEZTIhKzUVUai5kUgnqBbqjTR1f1PRxtfoYkdGRePjXh823x7Uah/kD5sPN2c36QJgzqBzpDCZsOZuAXo0C0bq2j63DoWrodnou9l1JsXUYyNVnYcaJTubbTX0fwqgGX8Dd2cd2QZUHiQQGJwXi9cD+i0o83toPQZ6KSg+DRXGyihACWdos5Bny4ObkBh8XH4dZooGIiCoXcwYREVmLOYOIyHomAaSpdEhT6QAoLe5zkkrgKpfBVS6DwkkGuZPU/OUik8LZSQq5TAqFswwuTlK4ymVwk8vgLncqt2WYKxpzBtkbIQRupKhwLDoDaUqtTWIwmgRuJKtwI1mFIC8XdAj3Q0SQR5E/G0IIDF8/HJuubDK3bX5qMwY3HlxZIRMVySQEIq+mIEetR4+IgDL9jmfOoJJIU2mx7XxihV/UVJyTqVuw+sY08+0Qt0aY2PhHSCVV6+LHbLUe607Eok+TYDQJ8arUc7MoTg+Uo8vBHzf/wOqrqxGrjDW31/asjdGNR2NQg0Hwklfui5aIiOwTcwYREVmLOYOIqHwZTAJKjQFKTcn2K5ZIAHe5E7zdnOHj6gx/DzmCPBUIcpfAXnZ2Zc4gexSfpUbk1RSk2qgYXpiUHC22n09EDW8FujcIQG0/y1nfQgiM2jjKoiC+dvhaFsTJ7py6nQmlxoD+zYLhJJOWaCxzBpVUrtaAzWfioTOYbBaDSRjx48VnEa08ZW5zkbrh7VabIZWU7GfAUeiNAjsvJiFFqUXPMl4EUxIsilORDscfxpQDU6AxaArcF6eMw1cnvsL3Z77H3N5z0a1mNxtESERE9oI5g4iIrMWcQURkP4TI3wNZpTUgPlNtbpdBh176LHi7OgPpuajl71TiwkR5YM4ge5OnM+Cv62m4mpQDG08oLFJStgYbTsWhQZAHHm4cBHcXJ+y8uROTt09GdFY0ACDEIwQ3XrsBdzn3byb7dD1ZiVydAYNahVq9RQhzBpWU3pi/bH9JLyosT8l5t/Dl2cfNtyO8O2NY3Q8R7FbfZjFVptO3MwEAvRoGVsr5quYlBlRmh+MPY/K+ydAYNBD//nevu20agwaT903G4fjDNoqUiIhsjTmDiIisxZxBROQYhADydEYkZmuw5Ww8Fv0VhT8vJCIqVQWjqXIqgcwZZG+iUlVYcfQ2riTab0H8XjdTVFh+JAY9f+mHx1Y9Zi6If/zQx0h4O4EFcbJ78ZlqrD8VB5W2+IIlcwaVlBACf15MQnJOwYsoKsv1rCMWBfEarhF4udnyalMQv+v07UycjMmolHOxKE4F5OhyMOXAFAhRMHncT0BACIEpB6YgR5dTSRFSAUIAuelA5u38fx3hL3MiqhKYMxwQcwYR2QhzhgNiziCif+kMJlxNUmLL2QQsPhSFv2+kIVutr7DzMWc4oCqcMwxGEyKvpWDL2QTk6Yy2DsdqGqMKr/wVgUOxe8xt11+9juk9p9swKqKSSVNqse5ELLLydEX2Yc5wQHaQMw5cT8WtFFWlnxcATMKEtTc/wMLLE8xt9b064p3Wf9gkHnvw9800XErIrvDzcPl0KuCPm3+Yr6iyxt0rrLbe2ooxTcZUcHRkQZ0FnPsNOLYIyIz+r923LtDpRaDVKMDVx1bREVE1wJzhQJgziMjGmDMcCHMGET1Ans6IEzEZOHk7A/UCPdAh3Bch3q7leg7mDAdSxXOGSmvAH2cTbDqTsDQScq/hm3ODLNq+63kS3vLaNoqIqPSy1XqsOxmLJ9vWgr+HS4H7mTMciJ3kjNN3MnH2TlaFn6cw/ySvx7pb/12c5OUciKmtt8Hd2ccm8dgLIYAjN9PRLNS7Qs/DmeJkQQiB1VdXl2rsqiurIKrQVaB27+ZeYE5TYOd7QGaM5X2ZMfntc5rm9yMiqgDMGQ6EOYOIbIw5w4EwZxCRlYQAbqWosOZ4LDaeikNsRl45HZc5w2FU8ZyRkqPBmuN3HK4gvjv2R4uC+ODw9zCn6zUYDZ5YdyIWlxM4O5YcT67WiA2n4pCq1Fq0M2c4EDvJGTdTlPjremqFnqMwakMOvjzzuEVBfFDYu5jZ/lC1L4hXJhbFyUKWNguxylirr6q6S0AgVhmLbG3FL29AyE8Mq0YCejUA8e/Xvf5t06vz+znomw8ism/MGQ6COYOI7ABzhoNgziCiUrqTkYcNp+Kw6XQcUspYQGTOcBBVPGfcTFFh3clYKDXF72VsL4zCgCVXXsbO2O8BAAqZB95tvQO9Qseb++iNArsuJeGfqHQbRUlUenk6Izbel2eYMxyEneSMuMw87LyYVKmrtQsh8HfiKnxwvAOS1bfM7UPC30fvmhMhkUgqLxhiUZws5RnKdlVvriG3nCKhIqmzgLVj/91nw1RMZ1N+v7Vj88cREZUj5gwHwJxBRHaCOcMBMGcQUTm4nZ6H1cfv4M8LiVBqSrfnOHOGA6jiOeNSQja2n0+E3ug4M0g1BhXmnR+OS5n7zW2fdDyBYLf6hfY/eisdey8nw2RynMdIBABqnREbT8ebZ4wzZzgAO8kZKUoN/jiXUKm/2+NUl/D20cbYFP0RAEAudUUDr074qMNR9AwdV2lx0H9YFCcLbk5uZRrv7uReTpFQkc79BujzUHwCucuU3//cmoqMioiqIeYMB8CcQUR2gjnDATBnkBU0Bg2++PsLLjVKDyQEcDVJiV+P3saJmAwYS1h0Y85wAFU4Z5yNzcKey8kwOdDvufjcK/jh4hjE514BALQNeAJfdj4PqeTBH/1fiM/G1vMJMBitfR6J7INGb8TvZ+KQmatjznAEdpAzsvJ02HwmHlp9xf++MwkTVl5/B28daYQ554eZ25v5PoyPOx7D5Oa/wsPZr8LjoMKxKE4WfFx8UNuzNiQo2ZINEkhQ27M2vF28KygyApD/zvLYotKNPbYQlbouCBFVecwZdo45g4jsCHOGnWPOICvcyb4D/6/88d6+99BpcSdbh0MOQGcw4e8baVj5z20kZqutHsecYeeqcM44EZOByKsp9hxiAWtvTse354YgIe8qpJBhYuP5eKbhN3CWulg1Pio1F9vOJ7IwTg4nV5u/lLrE5M6cYc/sIGdkq/XYeDoeuVpjmY9VnDjVZXxwrB1Op221aB8T8Q2ea7LA6t/NVHFYFCcLEokEoxuPLtXYMU3GcP+DipaXAWRGo+CeG8UR+ePUmRURFRFVU8wZdo45g4jsCHOGnWPOoGIcun0IDf/XEHn6/CVKp/ecbuOIyJFk5Oqw7kQcDt1Itarwxpxh56pozjgRk4G/b6TZOgyrCSHwV+KvOJay3tw2tfVWNPfrU+JjRafl/rukMAvj5FiUGgN+PxOP4RFPl2o8c0YlsHHOyMrTYf3JWOSoS7eli7UMJh12x/6IOeeHQmv6b0l/VydvfNX5AtoFPlGh5yfrsShOBQxqMAgKJ4XVV1dJIYXCSYEn6vMHu8LpVGUbr1WWTxxERP9izrBjzBlEZGeYM+wYcwYVQQiB6funo+eyntAatajvWx/Rb0RjUKNBtg6NHIxJCJyMycRvx+8gXaUttj9zhh2rgjnj1O1MhyqIqw1KvH20MTZHfwoAUMg8MKv9oSL3D7fG7fQ8/HGWS6mT48nK00Oqas+cYa9smDPSVVqsPxkHpcZQthiKERm/BNP+aYGdsd+b22q7N8fXXS7h047H4SSVV+j5qWRYFKcCvORemNt7LiQSSbGJRAIJIAHm9Z4HL7lXJUVYjck9yjbexbN84iAi+hdzhh1jziAiO8OcYceYM6gQJmGC9CMpPj30qblt3Yh1CPcJt11Q5PDSVDqsORGL68kP/pCbOcOOVbGcceZOJv66nmrrMKyWqo6x2KN2cPh7+KTjCXjJg8p87DsZedhxMQkmkwOtH08EIFPlhKfqTGfOsEc2yhnxWWpsOBUHlbZiCuIGkw774n7CW0caYevtr8ztbQOewJSWGzCl1UbIJE4Vcm4qGz4rVKhuNbthfp/5mHJgCjQGDQBA3LPExd3konBSYF7veehas6tN4qx23PwA37pAZgxKtuSIBPANB1x9KyYuIqrWmDPsFHMGkUPQGUyISc+Fq7MMCmcZ5E5SOMskcJZJIZXk/waVSPK3UjMJAaMQMJkAndEEg9EEg0lAZzBBazD9+6/R/P86gwk6Y/6/eqMJJqMWjdOzAADnlTEwSeSQSiVw+vfLVS6Dq7MMrnIZfFzl8HFzhq+bHK5yWbk9XuYMO8WcQfeJzctGnU9dzbebBDTB0eeOwlvBfTep7HQGE7afT0RimAY9IwKKXLqWOcNOVaGccSkhGwcdqCCeqU3ADxefgVKfH3P/2q+hV+j4cj3HrRQVdl9ORv9mwVxWmhyKVNMYLzX6DEtvzmbOsCc2yBnn47Jw4FoqjOV8gY/BpMOJlN8RpTyJU6l/FLh/WN0P0T1kTLmek8ofi+JUpG41u2HviL3YemsrVl5eiThVnPm+Wp61MKbJGAyqPwiecvu6wrNKk0iATi8CO98r+dhOL+WPJyKqAMwZdog5g8ghqPVGbD+fWCnnkgod6uiMAPL3dzVZ+WPu5eqMEG8FQrwVqBfgAW835zLFwZxhh5gz6B4r75zHs6d+t2i7OPkipBIuNkjl6/TtTKg0BjzavAZk0qIL48wZdqaK5IybKSrsvZwC4SCTos+m7cDamx9Aa8qDh7M/nm04BxHenSvkXFcSc+DiJMVDjcs++5yoMmlyIvB91024pTnInGEvKjFnGIwmHLiWigvx2SU/VxFMwogrmX8hVRODP2K+sLhPIfNAl+Cn0cinGxr68CILR8GiOD2Ql9wLY5qMwfCI4Zh1cBYMwoCpXaci0DWQVwvaSqtRwL6PAb0agBX7/EikgJMr0OrpCg+NiKo35gw7xJxBROUgR61HjlqPa0lKHLiWigBPFzQI9ECLWt7wcCndW0rmDDvEnFHtmYQJ7+//AF/eUxD/v27/h8/6fMafS6ow15OV0OiNeKJVKOROhV94wZxhhxw8Z8Rm5OHPC4kwOUBFXAiB3XE/Yk/sfJhgRJhHa4yK+BxBrvUq9LxnY7PgJpehUz3/Cj0PVW1CCORqjcjVGZCrNUCtN8JoEhAif86wXCaFwlkKhbMMPm7OcJOXvVx1PEqN4e2GMWfYk0rIGbEZedh3JRmZefrSx/kvpS4NlzIjka1Lxq7Y/xXap4VfPzzV4BO4OXEVJUfDojhZRSKRwEXmAhe4wMfFhwnEllx9gKd+BVaNBIQUD04kUgAS4KkV+eOIiCoBc4YdYc4gogqQptQiTanFiZgMNAz2QNs6vgjyUpTqWMwZdoQ5o1o7fOcwui/tbr5dw8UDR54/iboBjWwYFVUXdzLysPF0HIa1rQkXp6K37GDOsCMOnDNScjT441wCDA6wb3aGJh6fnH7YfLttwECMjvgKUkn5bW3zIEdupcPdxQnNa7LoQ1YQAmq9CZeychCfY0S6Sof0XC30Rut/1lydZfBzlyPUR4E6fm4I8XYtciWRohhNAtvOJ+DJdjWYM+xFBeaMXK0Bf99Mw+WEnFKHZxQGpGvuIEUdjWtZh3E6bRvUBsvZ5n4uNRHi1gj9ar+C2h7NS30usj0WxYkcUYO+wJh1wNqxgD7v38Z7/8D4N8k7u+YnkAZ9KjtCIiKyF8wZRFRBjCaBK4lKXE1SokmIF3pEBJTL7A6yIeaMasdgMmDHjR0YvGawuW15uyEYW6dV/v6PRJUkKVuDP84mYGibmnCScal+h+CAOSM7T4/NZ+OhM1gxU9HGUtRR+OLMY+bbg8PfQ8+QcZVe2Nt3JQWuchnqB3pU6nnJMWgNRtxOz8OdtCzIJFkwmEyITEmBXvy33ZJEArjLneAml8FVLoOTVAKpJH+Hb63RBI3eCLXOiBxN/kzy+Cw14rPUOBGTCWeZBHUD3NEs1Bu1fV2tfv3nao3YcSERJpOAtIRFdaog5ZwzlBo9Tt7OxMW4bKsvctIa8xCfexk3s4/hRMrv8FfUwfXsw0X2j/DuDF+XUDxc84UKX52DKg8/sSByVA36Am9dBs6tAY4tADJj/rvPNzx/z43WowAFr+YkIqr2mDOIqAIJAVxOyMGtVBW61Q9Aq9o+tg6JyoI5o9owCRN8v/SFSqcyt/0y8CeMdY63YVRUncVlqrHjYhIGtghhEcNROFDOyNMZsOlMHHK1RluHUqyb2ccx/9Kz5ttP1puJrsGjbDLT1SQE/ryQiKFta6Gmj2uln5/sj8FkQnRqLq4kKXE7PRcmAThL9HgkyASJRIJQb1cE+Xgi0MMF/h4u8HZ1tmq2t95oQmauDqkqLWIz1YjNyEOezojrySpcT1bBU+GElrW80bKmT5HbbdwrKVuDmJxc1OMFHfajHHJGfJYaF+KycT1ZCeN9xXCdUQNnqQtyDZkwmHRYdWMq1IYcJORdhQQSCFj2T9fGFjh+Q+9uaOLbE12Cn4Jcxt95VRGL4kSOzNUH6PwS0H4CcOZDwKADmr8LeATnX4ZHRER0F3MGEVUwrd6E/VdTEJepxiNNg636sIrsFHNGsdQ6I1KVWqSqtMhR66HSGqDWGaE1miCEgNEkIJNKIJdJIXeSwk0ug5fCGV6uzvBzlyPI08Wms2FTc1MxdvNYc0E8wi8ChyYcQrCrL3DpM5vFRXQrRYW9V5LRr1kNW4dC1nKAnKE3mrDlbAKyymGv2Yr2T/J6rLs13Xz7tea/oa5XWxtGBOiNAn+cTcCI9rUQ4OFi01jIenuidkKpC4BRGJBryIQUUrg7+0Jv0iDPkIVQt8bINWTC07nwvbaFEBbtmXk6nI/NxpWkHGjvWW3Bx80ZjQI80NTXC+4KJ8C9JkwSeYnjdZZJEeSlQJCXAs1CvSGEQIpSi8uJObiWpIRSY8Dhm+k4fTsL7cJ80bKWN5yL+VsqOUcLL1fnB/ahSlaKnJGnM+BKohIX47MQn50OucwNF9J3Y33ULEghQa4hCwqZBzRGVaHjARQoiAOAFDKYYEQjn+54st5MBCjqlNejJDvGojhRVSCRAC6u+V9ufnbzpoOIiOwQcwYRVbDryUpk5GrxRKtQ+LiV/AMxsiPMGWZGk0B8phrR6bmITlUhs4yFFZlUAn8POWr6uKJegAdq+pZ8z8zS+vLvL/F/+/7PfLtf/X5YP2I9vFy8AKOuUmIgepBLCTnw93BBuzBfW4dCJWGnOcNkEthxIRFJ2Rpbh/JAufosLLv2Gm7lHAeQv3/t5GYr4KeoaePI8mn0Rmw+E4+RHWrDS8Eio71Lz0vH+C2jkKvPLfOx6nn0hlItgV5TGxKhgARO8HSpjUYBddEwyAf1/MIhFTp45pXv60IikSDYS4FgLwV6NAjA9WQVjsdkIFutx98303AuLgu9GwWiXsCDZ4JHpeYiI1eHGl58X2JXiskZeTo1vv57IfI0bth5ayPOZ+x54OEeVBB3d/JBz9DxCHKtBy/nAAS51oe7s095PApyQCyKExERVQMavRF6owkGo4BRCDjLpFA4SyGXSW2yBBsREVVtaSodfjseiyfb1kSQl8LW4RCVWo5Gjwtx2biUkF2uS+4aTQIpOVqk5Ghx5k4W5E5S1A90R/Oa3qjl61Zu57lXjjYH4fPCkanJBAAEuwdjz7N70CK4RYWcj6gs/r6RhiBPF9T2q5ifB6o+DlxPQVRq2QuDFSlXn4kZJzqbbz9eZwoerjkJUol9rbqj1Bjw++l4DG9XC+4uLCvYs5TcFAS4BSE3O7rMx4pSHcj/n3tq3hkAbmcAuzOAQEU4Gnl3Qp4iBxphgJdCB4nUuVw/a3KSSdE01AuNa3jiSlIOjkVnQKkxYOu5REQEeaBXw8AiX5NGk8CfFxLwTOcGxc4sJ9sQQkBv1GHPrT3YcHkzlp1bXOJjdAoagaicExAQ6B06EU19H4JcpoCbk+237yD7wuxFRERUReiNJqQq1UhTaZGeq0OGSgelJn85T72x4DJBwH+zlII8FajhpUD9IHe4yfnnARERlZ1Gb8TG0/EsjJNDysrT4citdNxIVsEkCv87qjzpDCZcSVTiSqISfu5ytKrtg+ahXuW2xPr9s8MB4MZrN+Dp4lkuxycqbyaRP7t3dKc68OSsVCqlkzEZOBebbeswHuhAwlL8EfOF+fawuh+ie8gYG0b0YBm5Omw8HYfh7WrBTWbraKgoTQKb4MiE0/hy3y4IYYJJmHAk+TcEKurCX1ELRmFAQ++u0JnycDxlE06kbEKuIQvNfB/CpcxI83GkwgsmSQ4AoLXfEKRpbyAu95LFuVI1MUjVxODvuw138mf0tg8cgnpe7dEhaChkkvL5nEkqlaBZqDcaBnviWFQGTsdm4kaKCnGZavRvFowwf/dCx6WpdPjreir6NAkulzio7DQGDQ6nROGX22exZds3Vq9q0CloOJr5PYz6Xh3g6uRVwVFSVcRPvYmIiByQEAIZuTokZeZAkaqCSmvAX8m3YEDJPjC6d5bSxfhsRF6TIMzfDc1CvVE/0J2zyImIqEzMhfF2NRHkycI42b88nQH/RKXjYnwOjKaKL4YXJiNXh8irKTgZk4H24X5lLo4PXzccG69sNN9+puUzWDp4KZyk/EiI7Fuezoht5xMxsn3tSttegKqOa0lK/H0zzdZhFEkIgT/vzMPe+IXmto5Bw+y6IH5XukqHjafjMbx1EFxtHQwVyV3ujnDPNubb9b07FNpvUPi7GBT+LlJyNPj7VhrC1G8DAJxlErSs5YM2tX0sZmEbhQF6kwZJeTcRoKiDK5kH8U/yWkQrz1gc92TqZpxM3Yx1t6ajqe9D6BHyDBp4dYJMWvYLnZxlUnSPCECjGp7YczkZqSotNp9NQMdwP3Sq5wdpIZ9lnY/LRpi/GxoE8YJAWzGYDPjun+9wNvksVp5fWWQ/hcwTGqMSwa4NMCj8XTTx7VmJUVJVx3dAREREDkCjNyIlR4uEbDWSsjVIzNZAozdCKnTomKcFAJicBFDGz4qMJoGo1FxEpeYixFuBXo0CEeLNt7lERFR6Gr0Rm07HY1SHOvB242w/sl+XE3Jw8HoqNPryWya9LJQaAyKvpuDMnUz0bhSEugGFz34qSqIyEW/tfsuiIB45LhK9w3uXc6REFScpW4N/otLRrUGArUMhBxKfpcbuS0mohIU+SkVnVGNT9Mc4nvLf7+eprbYixL2hDaMqmTSlFpvOxGGEwgQ5l6R2aLlaA45GpeNSQv6McKkEaFnLBx3CfQtdSVAmcYJM5oFwz9YAgA5BQ9EpcAA65i2CQRgxL8cbd3KvwigMOJO2DQBwOTMSlzMjIZe6oVuNUegY9CSC3eqXOfZATxeMbF8Lf91Iw4X4bByPyUBithoDWoRAVshKBnsupyDYS8EVSCpZnj4PNefURJYmq9D72wcMxMDwd+Hm5AODSQeF04P3iScqCxbFiYiI7IjBaEKOxoCMXC3SVTqk5+qQnKNBtlpf6W/oE7M1WHsiFk1CvPBQoyDInfhGl4iISketM+KP8wl4qn1t5hOyOyqtAfuuJNvtnrNZeXpsPhOPBkEe6N0osNgPcoUQeH/f+/ji8H/L8foofJAxLYOrAJFDOhmTifAAdwR6cq1mKl5mrg5bzyXAYKPVPoqTq8/EkqsvI+bfWbWBinC82XIDXJ0cb/ZqmlKLi+nZaFzDE262DoZKzGgSOBebhWPRGdAZTQCARsGe6FLfH96upSsaO0lk6BkyBiaJHADwaO3XkKq5jYsZ+/BP8lroTHmITFiCyIQlCHKthxZ+j6B36AS4O/uW+nE4yaR4uHEQavq4Yt/VZMRmqrH+VBwGtAws0FejN2LnxSQMb1eLfxNVMCEEVp5fieXnlmNf9L4C97f2748J3gp08KyN424vml8zTlJ5ZYdK1QyL4kREROVMozfi9O1MOMmkkEkluLvSn0QigdEkYDQJGEwm6I0CGr0RGr0ReTojlBo98nRGu7qaXYj8WVMpORoMalWTM/yIiKjU0pRa7L6chAEtQvghFNmN2Iw87LiQiDydfcwOf5CbKSrEZuahb5NgNAwuvHhiEia8v+99fHn4S3Pb5qc2Y3DjwZUVJlG5MwmBXReTMLJDiK1DITuXpzNg89l4qO30d3p87lX8cuVlZOoSAAAD6ryNPrUm2TiqstEaTLiUkIMa4XmoHcBilqOIz1Ij8moK0nN1AIAgTxf0ahiIUJ/yXSkw0DUcga7haOrbC8PrzcKxlA3YG7cQmdp4pKijsC9+EfbFL8KYiG/Q1Ld3mS4OaVTDE37ucmw5G4/0XB02nI6Dfw1DgdnucZlqnLydiQ7hfmV7cFQovVGP1RdWY9bBWYjJiilw/6vNV8BfEQZfZ190yFtU+QFStceiOBERUTnTGkw4Fp1h6zDKVZpKh99O3MGAFiGo7cdrwImIqHRuJKtwzCMDnev52zoUIpyIycCRm+kw2dMVicXQ6k3Yfj4RMaG56H3fSj6puakI+ibIov+RiUfQpXaXyg6TqNxlq/X460aqrcMgO6Y3mrDlbAKy8vS2DqUAkzDhs9OPIEMbBwAIUIRhbMN5qOXR1MaRlQ+DSWDL2Xj0bJy/7DbZL7XeiL9vpOFyYv5S6QpnKbo1CECzEK8Kv2hVKpGiS/BIdA4agbPpO3Al8y+cTN0MAFh14x0AQLBrfbzdanOpZwvnL6deG1vOJiA9LxfJiTloUsOrQL+jt9IR5u+GIE9FqR8PFfTStpew6FTBQve3D6+CTNcaJtM95Uihq8TIiP7DdeuqAJNJICNXh8RsNe6k5yE6LRcpSg10BpOtQyMioipErTPi9zPxuJ1un0uLEhGRY/gnKh2xGXm2DoOqMYMxv7D89400hyqI3+tSQg7WnLiDrLz8DxSXnllqURCf3mM6TB+aWBCnKuVSfLb5NU90L5NJYMeFRCRla2wdSgFxqst452gTc0EcAN5osa7KFMTvMpoE9l1Jwd7LyTDa6dL11ZkQAlcTc7Di6G1zQbxZqBfGdglH81DvSl3FSSKRoE3AAIyO+BJfdDqLrsFPw8+lFgAgWX0Ln57uiyNJv8EkSlfb8HJ1xoj2tVDDSwGjSeBKUg5ScrQWfYwmgV2XkmEwsn5SVkIIfHvkW7T/qb1FQfydLu9g1aC9WNj7GiSa9pYFcSIb4ivRAWn0RtxOz8Pt9FykKLXIyNUV+ceGp8IJ9QLd0biGV7kvf0JERNWP0SSw7XwihrapybxCRESlIgSw+3IynulcB1xFnSqbRm/EH+cSEJ+ptnUoZZau0uG347FIMm7ER3+/a26f3H4yPn74YxtGRlRxotNy0bIWt3QiS/uvpiAq1f4u3t5553vsjvvRou2zTqegkHnYKKKKdyE+Gxm5OgxqHQqFs8zW4RCArDwdIq+l4s6/F6X6u8vxcOMgu/hMRy5zxfD6swEAG6M+wuGkVcjWJWND1CwcSlyJF5sugY9LjRIfV+Esw6BWoVhw2gkqrQFbzsXjyTbhCPL6b2Z4mlKLI7fS0bNhwb3HyTq5ulyEfBsCpU5p0b5l+BFkZocgOY0XspH9YVHcQWj0RlxPVuJqkhKJWRqrr2ZXagw4F5uNc7HZ8HVzRveIQDQIqrp/eFEZCQHkZQA6FSD3ANz8wE8qieh+OkP+snTD29VCoKeLrcMhW2HOIKIyyFHrcfBaKno35l5+1YKd5IwcjR5bzsQjTVU1PqBLUUdh1/UfcCZtu7mNy6VTVafRm6rERS30ACXMGYdupOJCfHYlBlg8rTEX7x1ra76tkHlgSN0P0DFomA2jqjzxWWqsPxWHYW1qwt2F5Qdb0RtNOBmTgX+iM2A0CcikEnSs64d2dXwhk9rfe/cn632Ix+u8iQ+OdwAAJKtv4qNTvfB6i7UI92xd4uPJnaRoXMMLV5NyoM01YdOZeIxsXxt+7v8tzX76TibqBbqjli+3CSyJ+Jx4LDi5AJ8e+tTc1iSgCSa0mAZfSTfcShAAKunvbSGgMGRDbsyDTuYGjZM3P5uiB2JWsmMCAncycnEhMQ3RqbkwlHHpmcw8PbaeS0C9QHf0bhgEbzdeWUv/UmcB534Dji0CMqP/a/etC3R6EWg1CnD1sVV0RGSHNHojNp+Jx+hOdfgmt7phziCicnIpIQe1/Eq3XyA5CDvKGVl5Omw4FQelxlAp56toJ1O3YPWNaebbTX0fwk+PrUWX2pztRFVfQrYaqUotavowh1QppcgZx6LScTIms1LDLE6q+jY+P9PPou2jDkdLvUeyo0pTarH+ZCyGtasFLwU/g65slxNy8ObaM7ierAIA1PJ1xcONg+DrZt+vQ1cnL3zV+QK23v4ahxJ/BQD8cHEMeoaMxaO1X4dcVrLZ7TKpBI1reCEnRYEUZf6WgE+1rw0PRf7nWEIAey4nY0ynMMiduNNwcYQQeO3P1/DjCcsVMN5o9wUi3IdAp5VAicrZPsHFoETTlG1onbgOPpr/tqfIUtTC2ZCRuBw0EFonz0qJhRwLf9LtkMZgREK2Gmdjs7D5TDxuJKvKXBC/V1RqLlYeu42oVFW5HZMc2M29wJymwM73gMwYy/syY/Lb5zTN70dEdA+V1oA/LyZBOOhenFQKzBlEVM4ir3IvvyrLjnJGRm7VKYgLIbDu1ocWBfFhdT/EhEb/w/GYTOy9nMy/zahSmYSA3miCRm9ErtYAvdFU4a9BIfLzB1/rVUgpcsaZO5k4ciu9UsMszvHkjQUK4p91PFXtCuJ3Zeblr9BClc9JJkFUai4UzlL0axqMYW1q2n1B/C4nqRxD636Ame3/Qmv/x2ESBhxI+AVzzg/FP8nrS/y7XyaVYGCrEPi6OUOlNWDz2Xho9Ebz/Vl5ehy+mVbeD6PKUelU8P/K36IgPqz+FHzd+SLCXIZCZ6i82dlhmUfx/IkB6BU9F14ay98xXpp49Iqei+dPDEBY5tFKi4kcB6d22ZEUpQbnYrNxIzENbZT5e3yggi6k0xlM2HouEQ81DkTLWj4VcxKyfzf3AqtG5r+jLPQqrn/b9Or8fmPWAQ36VmaERGTnYjPy8E9UBrrU97d1KFTRmDOIqALk6Yy4k5OHeoHc4qlKsaOcka7SYuPpOORqjcV3tnNqQ455SdG73m61GTXdm5hvX4jPhtZgwmPNa0Bqh0ujkmMSQiBTrUOqUotUpRYZuTqotAYoNQao9QV/tqQSwMVJBm9XZ/i5y+HnLkeojwLBnopye10mZmtwMT4HLWp5l8vxyIZKkTNOObfDoRuplRnlA2XrkrH4youIz71ibnu52XJEeHe2YVT2QaPnxY+20DDYE18Ob4mriTlwkztmCchbHoxnG85B07Te2BT1EVLU0Vh3azoOJ63Gq81XwUVm/ZLnrs4yDGldE+tOxiI9V4et5xMwtE1NOEnz54yei8tCgyAP1PbjMur3M5kEvvtnEd7a87JF+/S2++GnqFnp8YRlHsWQy28CEJBA4P6/KqT/5gwnkwZDLr+JzU3n4bYvtxai/zjmb8QqxGA04XqyChfis5CQpQEASCvpSleTENh3JQVKjQHdGgRUyjnJjqizgLVj/33TUdwfqCZASPP7v3WZy+ISkYVj0emo5evKNw9VGXMGEVWgFKUWgZ4utg6Dyosd5Yx0lRYbTsUhT+f4BfE41WUsuDTOfLuZ70N4puG3cJG5F+h7PVkJAYHHm4ewME6lptQYkKLUIFutxy+xMcjRW7/YpEkAar0Rar0RSTkac7vcSYravq6ICPJE/UB3OMnKtoDlkVtpiAj2gMJZVqbjkA2VImcY1zyLY+22QdjBsrhCCGy/8y32x/9sbmvh1w9PNfgEbk68YINsq2+TYNxJz7N1GGXiLJPisXoj0SOsE/53Zgoup59GfO5lzDk/AOObfYj6Hn2t/jvPy9UZg1vXxIZTcUjI0iDyair6NgmCRCKBEMDuy8l4pnMduDgxp6QoNYhJy0NcZi4Wnf0cu2MXmO9r5NMdk5oshsQG+3a7GJQYePVdAMJc/C6KFAImAAOvvovFHbZzKXUyY1HcRpJzNLicmINrSUqobfwG/Xh0BlycpGgf7mfTOIqjUqng5uYGqVQKo9EIiUQCqZQ7AJTaud8AfR4Kvwq3MKb8/ufWAJ1fqsjIiMjBCAH8eTERY7uE280HUswZ5Yw5g4gqkBBAdFouTCYB2CCNMGeUMzvJGZm5Omw6HV8lCuIHEpbij5gvzLf7134N/Wq98sAPI28kq7ADiSyMU4nkag24lqzEzRQV0nKUeCUoFwCgNhghk8oQ4CFHoIcLAjxc4OnqBE8XZ7i7yOAsk0ImkUAiAfRGAa3BCI3ehMw8HTJydUhTaRGXqYbWYMKt1FzcSs2FwkmKxiFeaFXLGz6lXNI3T2fE0ah0PNQoqDy/DQ/EnFHOSpEzpAY1mqRsx9nQpysysmLFKM/gx4vPwij05rYI784Y12gepBL7eF9M5IicZRKEB7ijYbAn6ga4w1kmBRCGl7qdwparWzByw0ikqhPw9cn8vxuvv3IHuWpPXEnMQVK25oHHDvR0wWMtauCPswm4nJgDfw852tbxBQDkqPX4+0Ya+jQJLrfH4kg5Iylbg8uJ2YhKzYVSY8DN7GNYc/N9ZGj/2697crMVaODd0WYxNk3ZBmeTBhIrc4YUAs4mjV3kDLIfLIpXolSlFrdSVbiRrESaSmfrcCz8fTMN3q7OiAi2nytmMjIysHXrVgwePBg5OTlYunQpunbtiubNm2PTpk1IS0vDuHHjEB4eDp1Ohz179uDGjRvo2bMn2rZta+vw7ZsQwLFFpRt7bCHQ6UXABleDEZH9ytUacfB6Kvo3q2GT8zNnVCDmDCKqBLlaI87HZaFj3fL7EKoozBkVyE5yRlaeDhtPx0Gldew9xE3ChPW3ZuBYygZz20cdjsLD2boL2lkYJ2sIIXAnIw8X4rMRlZaLu4sXOksADxcneLs6Y1jNWgj09oTMiteR3EkCuZMUngpYrAJiEgIpOVpEp+XicmIOVFoDzsZm4XxcFpqGeqFTuD88FCX/mPJ8bDZa1PRGgEfFrDjCnFGBSpkzBIA2iWtxNuQpm7zPyDNkY/rxgkWhd1r9gVD3RpUeD1FV4SyToGUtH3QI94OrvPALSwY3Hozrr15Hz2U9cSf7DgCg4Y91cPS5oxjVsTMycnU4GZOBK4nKIs8T7u+O7hEBOHQjDX/fSIOfuxzh/vkr75yPy0ZEkCfq+JduJURHyxlGk8CVxBycj8tG8j0ru1zM2IdlV1+DCUY4SxXoGPQkhtb9wLYX/AiB1onrSj4Mts0ZZH9YFK9AKq0BcZl5iM9U43Z6HrLV+uIH2YgQwM6LSfBQOCHE29UmMWRnZ8NkMsHX1xe3bt3CypUr4eHhAZlMhrNnzwIApFIpvL294eKS/2YnKSkJ4eHhkMvlSE5Ohru7O5yc8l/Wer0e+/fvR6tWrVCjhm2KNHYrLwPIjC7FQJE/Tp0JuNn3ygJEVPkuJ+SgSQ0v1PCp+D8vmDMqEXNGtXA59TKaBja1dRhUzf0TnY7mNf3Lfd9D5oxKZAc5I0ejx8bT8VBqHLsgbjDpsO7WdJxM3WJu+6DtXqsL4nfdSFZhjywZ/ZoG22SZS7JfBpMJVxOVOHU7E1n3fF4V4q1Aw2BPNAyQo5kpf/nnBDcFTGV8/UglEtTwVqCGtwKd6vnhdnoezsVl4XZ6Hi7G5+BKohLt6viiQ11f8x6v1jAJgQPXUjG8Xa0yxXcXc0YlKmXOkELARxMHhSEbGmef8o+rCIcTV2FT9Cf/Lsibz9M5AK+3WAN/Re1Ki4OoqpFIgOah3uhS3x/uLsW/DwjzCUPU61EYu3ksVl9YDQDosqQLVgxdgWdaPoN+zWqgXZgvDt5IxN64wo/RprYP0lU6XE7MwZ8XkvB0x9rw/XfVkj1XkvFs5zDInYrPRY6cM26lqnDoeioy8/77GyAq5yR+uDjGfLu1/2N4st4suFfi79qiKAzZ8NEU8YQ+gK1yBtkvFsXLSGswIk9rRI5GD6XGgKw8PVJVGqQpdQ53VbrBJPDH2QQ80znMqgRUntRqNbZt24abN2/+P3vnHR/Feef/98z2lVa9dyGQBKJ3Y4wbLhgX3FsSO8VJ7MS5JJfenLv7JU69tDsnTrkkdtx7r7iC6RKoIJBAvWu1q+195vfHgkBGgPruSvPmxeuleXbKd6Xd+czzfBs/+MEPsNlsQLjEiM1mo7CwkLS0NBYvXozRaGTLli243W5SUsILApIkYTQaCQQCJCUlAVBbW8vOnTvp6urijjvuiNrSJBHB75zY8T6H4uBQUFAYka2HerlpVfaUXkPRjGlG0YwZjSfgYePDG/mo/SPuWn4Xf77qz5E2SWEW4wtI7Gwa4KLyycsWVzRjmomwZrh8QZ7d14E9igPSR0OV+VUebvja0PbytCu5bd4vEYXxfdYOdtnRqsVpLTGtEL0EQxI1nTb2tVpxHWsvoFWLLMhKoCI3YSjjWpT9MEWtaEVBoDgtjuK0ODqtHj46aqbL5mV3i4Wj/U4uWZBJZoJ+1Odrt7g50udgbsbEqh8qmjHNTFAztCH3tDg4QlKA51t+yvaeR4eNlyedx+fm/3nc92YFBQUw6dVcuiBrzJnZKlHFI9c9wi82/oK834SDoj753Cf55HOfpPHeRuamzOXKxTm832WiyXzqvUYQBC4sT8fq9tNt8/JKTTc3r8xHoxKxewJ82Nh/1jLqsaoZNneAt+t7abOcEHlJlnil9de82/XXobGypPXcPu+XqETNpNswHrShiT2UTJdmKEQ/ilN8lDh9QZ6t7ECSZEIyBEISvoCEJI+2501s4PaHeOtgL1uW5U7pdSRJwm63I4oiCQkJ+P1+jhw5AkAoFGLp0qVkZWWRnZ2NIAjk5Q2P+E1LSxu2LYoit9wyvC9Eeno6eXl5LFiwYEhAGhoaaG5u5pJLLpndExFt/MSO10VPmX0FBYXoYtAdYHeTZVLPqWhGhJnFmhEISXgCISRJRkBAFMOLxjr1zOgROOAe4Px/nE9dfx0AOaacCFukoAA1HXaW5ieTEje+/rKKZkSYCGqGNxDi2arOYdkusUiTfe8wh/hnyv+XhSkbJ3ze/W2D6NQi60rSzr6zwoxEkmQO9tjZ1WQZSuKI16lZVpDEwpzEUWXETQW5yQZuWJHHkT4n7x7uZ8Dl54m97awuSmFNccqoKxx82GimOC1+VCXej6NoRoSZoGb4VeMrbzwWGm07+WPdHaeMf3vpq2QaS6b8+goKM5n52QlcWJ4+ofl1bkIuB754gCV/WjI0Nu8P82j+t2ZyTDkkx2lZrE9ibmo8Lf3D29mqRZHNi7J5dHcbA04/7xzqG6qsU9N5ahn1maAZDb0O3q7vxRc4UfFClmXu23MOruDg0FhB/GI+V/6nqHGIw8Tv+dOhGQqxgeIUHyUhSWYgyvqATxXNZhcH2gdZkp80Zdd46qmnOHToEOnp6dxzzz0YDAZuuOEG8vPz0WrDC2A5ORNbmM3Ly+Mzn/kM8kmBCy0tLezcuZP58+dTUFAwofPHNMYUSC4GawvhzhqjRYDkIjAkT41dCgoKM4LKNitugpNW/lbRjAgzCzQjJMl0DXroGvRgdvoxO304vAECoZHfr0YlEKdTk2TUkBavIy1eR06igURj9EwYz8b+nv0se3AZABpRw68v/TX3rrk3wlYpKITL4G47YubqJeO7ryuaEWEipBn+oMRzVZ2YHb5xHR8NSHKIH+9ZjzN4IrjwnoqHmZt4at/a8bKryUKcVj2lc32F6KTN4ub9hn4srvC6VrxOzZriFOZnJ4zJiTxVCILAvEwTeclG3mvoo6HXya5mC30OH5dVZI7KYTLoDrC/fZAVhaO/jyiaEWHGqRkSAnZ9Ll514pSZ1uU6zK8OXH3K+HeXvUG6oWjKrqugMBtQiQIbStNZOknPI4szFyPfJ/ObHb/h629+HYAlf1rCg1c+CIBGJbJ5UQ61nS4+bDAPS3KM06m5YmE2z1R1cKjHQVainiV5SchyuIz6J9YWDGlQLGtGMCTx3uF+ajptw8b9IS9PHv3BkEN8SeplfKL0v1EJ0ec29KoTGdTnkeDtRIwyzVCILaLv060QFXzY2E9+inHcGRojYbPZ6O7uprS0lPXr1+PxeAgEwlH8Wq2WioqKSbvWcQRBGBZVnJOTgyiKaDQnFq09Hg8qlWpIvGYFggBrvgCvf3fsx675Yvh4BQUFhdMgyTJtFjfl2QnjPoeiGVHEDNUMWZZpMrs43OOgZcA1LFL6bARCMoPuAIPuAC3mEyW8Eg0aClONlKTHU5BiRIyCReaRuPWZW3m89vGh7ceuf4zrF1wfQYsUFIZztM9Jh9VNXvLoovkVzYgiIqAZwZDEiwe66LF5x37NKMEXcvP4ke8MOcSXpW3mhjn/gUE9+dVW3j3ch1GrYl5m7FZyURg9tmMlYI/2uwDQq0VWFaewODcRtSr6MpQNWhWbFmZTnGrn7UN9NJtdPLGnnSsX54xqfWp3s4WKnAT0mtM70RXNiCIEAc+yz6F/5weM5e4vAFXZN0/JPKPRtoM/1t15yvhl+feyMfcLUZU1qaAQixi1KjYvzh71c/5Y+No5X+Pa+ddy+7O381H7R9z6zK1cWHQh6/LXAbC8IJlko5ZXa7rxB0/M/3OTDawvSePDI2Y+aOgnM0FPVoIeuyfA2/tbKIkPxLRm+IIhXjrQTbtlePnxN9v/l9fbfz+0rRZ1fLL0N4hClFbGEwT2Z9/E+c2/GdthTJ1mKMQmilNcYUQCIZnXa3u4ZVX+pCzo+v1+/vKXv5CcnExpaSnZ2dlcfvnlZGRMb0+zhQsXkpeXNyQikiTx5JNPYjabufnmm08pazKjWXIrbP0vCHiAUTgCBBHUBlhyy9n3VVBQmPVY3QEG3eOrsKJoRhQygzTDH5So67Kxv32QwUkusWvzBKjusFHdYSNep6Ysy8Si3ESSJzHIcCL4QkFyfp2NxXMiC3HHZ3ewNm9tBK1SUBiZDxvN3LIq/6xlcxXNiEKmUTMkSebV2p5TFvliiaDk54G6T9HurAEgN24Bn5j361GXjB4rsgyv1/ag16jIT1HKSM5UQpJMVbuVXU0WgpKMIMCS3CTWzEk5o8M4WijPTiA5TsvL1d1Y3QGe3NvOlqW5pJnO7Mj3BkLsaBrgwrKRNUDRjOhgwOnjSJ+TI/1ObK5z+JyoRy15R5X5JyESFHXUZ2yeNHs6XYdosu/h1db/xicN15Ni03LuXfTYpF1LQWE2kxKnZcuyXBINUxdcUpRUxPt3vs+mRzbxdtPbvNvyLu+2vMvdK+8mNyGX4rQ4bl6Vz/NVnTi8waHjlhUk0W3zcqTfyWs13dy2ugCtKLHvzedoTU2JWc1w+YI8v7+TPvuJakqyLLOn79lhDvEvLvg7pUnrpu5NTBIHM65kXesfI6oZCrFP9IWFKkQNvXYvBzoGJ+VcarWaYDCITqcDwj0zsrKyItI7KSkpibi4OAC6u7tpaWlBlmUSEsaf0RiTGJLg5oeORUmd7e8gAgLc/HD4OAUFBYVR0GpxI0ljKZ0aRtGMKGQGaIYsyxzssvPPj1p473D/pDvEP47TF2Rfq5V/7mjhlepu+uyRzWCstvWS+PLPhjnE3d9zKw5xhailx+blSJ/zrPspmhGFTJNmyLLMW/W9HB3F5yRa6XTV85vq62l31iCiYnPBv/PVxU9NmUP8OEFJ5qXqLvpjuNy8wunpsXt5fE8b248MEJRk8pIM3La6gPPL0mPCIX6czAQ9t67OJytBjy8o8WxVB52DnrMeV9Nhw+oaOThX0YzIYfME2N1s4eEdLTy0o5WPjg7QZ/fhU5t4ufzngIB0lnzx46+/VP4LfBOspBGSArQ5qvn6R2X8+sA1PNf8/4Yc4gmadNZl3sov1tYoDnEFhUkiN8nAzavyp9Qhfhy1qOaFW15gQ8GGobGi3xWxtWkrAGnxOm5ckU/CSbYIgsDGBRkkGjTYvUHequ9FFkRkKYTdL+MNhGJOM+zeAE/saR/mEA9JAZ5quo/Hj35vaOzri5+LCYc4EDHNUJhZxJRT/IMPPuCqq64iJycHQRB4/vnnI23SjGdH0wBuf/DsO47Avn376OrqAsITjZUrV3LxxRdHRDhOR25uLt/97ne58847SUhIwOPx8Nxzz7F79+5ImzY9zN0Itz8JGgPhYiIfF5NjYxoD3P4UzL14+m1UUBgnimZEHrcvxMEu+6j2VTQjBohhzeize3liTztv1PXg9I3vuWa8yDI09Dp4ZFcbLx7owjbFzviReKb+OVa/9xd8UgiAJZlLcH3PhUFjmHZbohVFM6KTHU0Dw/rpHUfRjBhgGjTjg0bzqJ8zopF66/v8qe5Out0NxKmT+Ez5A1yc9/lp6+HoC0i8sL8Tu3f6dSnWiVbNCIYkth0x8+SedsxOP3q1yCXzM7lueS5p8bpImzcujFo11y7LJT/ZQCAk8+KBrrNWowpJMtuPmoe2Fc2IHJIk09jr4Ol9Hfx9ezPbj5gxO0/9+7Umn8PzC35LUNQjj+DokBCQEQiKep5b8Dvaksce1BmU/HS5DlNv/YCvf1TGN3cu5Lc1N56y31cXPcWPV23jhpIfoxajo9qTQmwTrZoxnczNiOe65bnTGphl1Bh585NvsjRzKQBBKcjGhzfyZN2TACQaNdywIm+Yk16nVnFljocM0c3RfhfVnQ60WXNR5y3mg0bzSJeJCKPRDKcvyDP7OrB5TjznHbS+zw/3nMPO3icAWJZ2JT9bc4C8+AXT/h4mwnRohsLMJqbKp7tcLpYsWcJnPvMZrrvuukibMyvwBSQ+bDRzYXnKmI6rqanh5ZdfZs6cOXziE58IR1tt3DhFVk4MrVZLWloaAD6fj+rqaqqrq0lISKC8vDzC1k0DczfC1w/Cgcdh1x/B2nLiteSicG+/pbeCPjFSFioojAtFM6KDnc1mFuamolWfftFJ0YwYIsY0Q5Zl9rVa+ejoAKFxVC2YbI72OWk1u1hemMyqopQzfi8mA6vHSsovhj/DvXHry1xaqpQO+ziKZkQnA04/h3oczM8+kQGhaEYMMYWasafFQmWrddJMnU78IS8P1H2KNucBALKMpdxT8U/iNWObc08GDm+Q56s6uWllfkxlEEeaaNSMbpuHtw72Yj0WfFeWaWJDaRpGbUwt+42IVi1y9ZIcXqvt4ah5kIY+J2WZZ874aux10m3zYG47omhGBAhKEvtbLezvdA8rT3wmWpPP4a+rXmF+3yss63qcJF/n0Gt2fS5V2TdzMONK/Or4Mdni8Jtpc1bzVsefhu67J6MVjaxIv4otxd9HI8Zm8IhCdBONmjGdLMhJ4JL5mZPSnnWsCILANeXXsDp3NYfMh/ig7QNufvpmmq3NfOvcb5Fo0HDjyjye2ht2Hgf6W9B27eeihFQeHyziw8Z+sleWkxmvp77bwZz0eErPoj/TxZk0o3DOPJ6r7BhWHe+F5p/xfvffw8eKBi7Lv5cLcj4z5dWJpoqp0gyF2UFMPR1v2rSJTZs2RdqMWUd9t52yrLFlEhUUFBAfH77pyLIcMzfYpKQk0tLSMJvNM7Zk1YgYkmDtF2Hlp6HqRxD0w8JvQ3zmsbKHCgqxh6IZ0YHLF6KqzcqaOamn3UfRjBgjRjTD6QvyRm0PbVHWZzYoyexutnC4x8FlC7PITZqabO1uRzd5vznRW+xLc1bx20WXoy65ZEquF+somhG97GwaoCzTNLSQpmhGjDEFmlHbaWNbFGXrjAV/yMOP956HN+QAwhk6N5f8P7SqyFXuGHD6eXF/F9cuz0Wjip7M2WgmmjQjGJLY2RwOEpEBo1bFxeUZzEmfWYvAapXIFYuyebUmSOWgTEOfg94MH7nJp8/k/bDBzGWlimZMJ55ACLPFTY/dy0d6M5Iwtkxrn9rE/pxbqM66lnWO36MKBdkT/zncmrQzaoY/5MUe6OOQ9UNUgpqnmn502n21ogG/5OG87E9xVeE3lWxwhSknmjRjullakMQFpekRv/dmm7L57eW/5Xtbv8dvd/2W72z9DnX9dfx+0+9J0idx/fI8ntjbhiMhHUFjIF6vpiQtjqNmN6/V9nDr6nx0ahVb6/vISTIQr4sul9rJmqE3xvNsVcdQVY5m+z7+UHvb0L6lieu4o+x3GNSxoy2nY7yaoaAQXd9ghahEluH9hr4zTiCCwSA1NTVkZWWRnZ1NYmIin//854mPj4+48I2Ve+65h4GBgaFoq56eHtrb26moqMBoNEbYuilGEEBnCP83pigCoqCgMCnsa7OyJD9pKANJ0YwZQhRrRp/dywv7u6a9VPpYsHkCPLW3nZWFKZxTkopqEiPn73rxLv5a9deh7S1lV/OH+Utj7vuloABgdXp55b0drJxfrGhGLDNJmnGkz8HW+r5JNm56cPjN3Lf33KHtsqT13Dr3/qhwyHQOeni1ppurFufEVo+9WU6f3cubB3sZONY/uzzLxPmlsdU3fCyoRIFLKjI4WqnB5gnwUnUnN64oJvWk0vCyFCLQ34IqLplOUuj3JSmaMQ14AyH2tVqpbutjqf3sfd/PiiAQVOsJqsGrSRymGa2OA3S7G3i17TdkGkpwBaz0eBrPeDq9Kp7ypA1cmv8lsoxzJ26fgoLCWVlTnMK6uWmRNmMIlajiN5f/hjnJc/jqG1/l4eqHeaz6Mf6w/A9cs/Iarl2Wx1P72mHJZQgaPZcEJfp3t2HzBNha38emhVl4AyHerOvh2mW5Uacp99xzD719/XzY7qPP7iPksmI2V/O3zq8O2+8z5X9Eq9JHxsip4gyaoaAwEjPaKe7z+fD5fEPbdnvs9huLNH12H2a/n3TTqaWEZFnmj3/8IxaLhfnz53PTTTcBYDJFRzmRsSIIwtCkY8eOHbz33nv4/X4cDgcXXXRRhK1TUFCYKhTNmDp8AYnKVivr5qYpmqEw5Rzpc/B6bQ+BUOTLpZ8NWQ6XAG63urlycTYmvebsB52BkBTiP9//z2EO8T137WFl5mKo++lEzVU4CUUzpgdZlnHtf41KrwNXfzm33HwzoGjGbKXd4ua1mh6kEfrMRzttjuphvWtvLvkJazJviKBFp9LU7+Kt+l4uLUs+pQO8wsSYTM1oGWwiEJLY22JhV2sfyBoMGpELy1MpStWjVc1Mh/hx1KLIvAwTh3rseF0Sz+/v4uZV+cTr1EOaIXkdqFPyMJafx/YjA3xybWHUOS9GQyxohj8oUdVmZV+bFV9AQpSlCZ3PE3TQbN+HzdfBn3qepNSQxouW/wFOZHifjDMwcMbzbcz7Ihuy74hIewoFhfEyE+YZ58xJZXUUOcRP5t4197I8ezm3PXMb19ivobeylwfbHuQbd32Da5bm8lxlB4GQjE6jYtPCbJ7a105jn5P8TjuL8hJpHXBT1T7I8oLkSL+VU9jbG6TD6sHZXkWg4yAGWWQNa3iXd8mPW8i/LX4KUVDCHxUUZrRT/P777+c//uM/Im3GjKHd6iY1/tQodlmWyc/Px2KxkJ2dHVMlqc7G0qVL8fl8qNVq1q9fz5tvvokkSVx22WUz5j0qKCiEUTRjaqlqH2RpQRJ6tahohsKUsa/VyoeN/cSav6TH5uXRXW1sXpxNXvL4Mn92tO9g3f+tG9pO0CVQd08deQl5EPJPlqkKx5hJmiHLMi5fiAGXD4c3iMMbxOkLEgxJBCWZkCyjEUW06vD/JIOG5DgtyUYN8Tr1FN/fZFSmVCSvAzEuWdGMWUyf3cuLB7oISrF1g5dlmY96H+OF5vuHxm6b+3NWZmyJnFFn4GCXHaMqxHmRNmSGMVma4Qv6uOThC+j39IQHTkr0OnwEOALJulx8IRfu4ODQa2n6QizeDuI0KTgC/ZybdTtzElagFQ1oVQa0opEMwxy0KgMiYad6NN+HVKJAWZaJgS4tg54gLx3o4oYVeahFhjRDFZ+CLMtYXH5qu2wszkuKtNkTIto0IxiSONBhY2+LBbc/NOrjZFmmw1VHnDqJRttO3u36G32eptPuf8hzojLIxx3iAAXxSyhJWIk35GRJ6ibmJq5RHD4KMU+szzMKU43kFJ++fV40cG7Buez47A6+9JsvkUoq75jf4aEHHmL/3fu5rCKLV2q6kWXIStRzbkkaHx4x835jP1mJetJNOrY1mslNMpCZED0Z1+8c6qOx10lA8vGX/vuYI2cRJEi9roUfJP4ZoyoeQQl7VFAAZrhT/Lvf/S5f//rXh7btdjv5+fkRtCi28QUkeu3eoe2dO3eSnJxMWVkZW7ZsYcuWLZEzboowGAxccMEFAIRCIQYHB6mvr8dms3HTTTdF9URRQUFhbCiaMbU42up5TbZw/UWrFc1QmBJ2HB1gZ9OZs0WiGbc/xDP7OrmgLJ0l+UmjPi4oBbnqsat4/cjrAAgIPHztw9y++PYpslQBYlszjjvB9/RbabcG6XP48ARGv6B9Mia9moIUIwUpRopS49CqJ2ch2td1GFEfhyYlD8O8czDMOwe7Xo0kg2qG3EoVzRg9Vpef56o68QcnloE43YTkIN/cUTG0PT/pfG4o+THJupwIWnV2KtusFGrdFKREV3nmWGayNONvu96l393Hmda0rb7OU8bM3lYAHIF+ALb3PML2nkdOew6dKo6FKRdTnriWcp0XjRB92edqUeTKxdk8XdlDmruNfdU21ixdOKQZJ7OzaYDyrIRJ06hIEC2aEQhJVHfYqGy1jrpNkStgxeLr5K/1X8ARMI/pennaRDr8NgCWpl5BtnEeRk0Sy9OunBH9cBUURiKW5xlFqUayEw2RNuOMnOzPeOIHT3DbM7fxYf2HYIPEnyXyXxf+F5eW3M32I+G1hWUFSbRb3bQMuHm1tptbVxWgVYu8Ut3NbWsKoqJlyfYjZg60D/JRz6M82/xfyMi0c5iFKRv5avFTiM0H8fc1IPncGMrWK/MMhVnPjHaK63Q6dLpTy30rjJ9Oqwd/UKLxUB1vvPEGGo2Ge+65h6SkpEibNuWoVCrKysro6upi6VKlL6eCwkxD0YypI2Buw9dSSW2birUVc8jNjM4yWpOJohnTywcN/exrtUbajAkjyTLvHOrD6Qty7ijKze3q2MXav60dNlb1hSqWZC2ZKhMVjhFrmhGSZFotLpp7rYiClZAks6tvgIAcLtkvAMlxWhL0akz6cAa4RiWgVomoBIGAJOEPSngDIQbdAaxuP4OeAA5vkLouO3VddjQqgbJMEwtzEyeUNXFcMxBVqJZegaiPB8DhDXKwK1y2cKahaMbpsXsDPFPZMaZMxGig2b6PP9TeNrS9Kv06bp77k5jJYOwcDGdkFkTYjpnCZGnG7csv5k/v/h+D4msUpBixBdqotWxFrzIhyUFSdHlcmn8PNn8f+/pfpMNVd9ZzZhrm0us5MmzMF3Kxr/9F9vW/yHHXucBfSNMXcH7OpykyLSUnrnzC72eiJBo1XFMoYexsJ+DupLIxmRWleafs5/KF+12fUxLdmYujJRKa4fIFqe20UdU+iOcs92NPKMCr1kO85rqHTncDNn/vqK6xPO0q5iauIT9+ETn6fNZ6/oYgCOw2fgFJOLVqpYLCTCXW5hnHOb80nWx7dDvE6+pO9Wc8eeOT/Hbnb/nhuz/EHXDzw3d/yDtF7/DNFQ9xqNuNIAhcuiCLR3e3MegOsPVQL5dXZGHzBHi7vpcrF0c22HHH0QG2Henkvr3n4gu5hsY/N//PLEg+HwB/Sh4hlxVNxhxlnqGgQIw5xZ1OJ0eOnHhYb25uZv/+/aSkpFBQoEzXpoNASKaqzcq68nLmzp1LUVERiYkzb2HqdCxZsoRFixYhiuHFDEmSaG1tpaioSBEVBYUoQ9GM6EGdkocqKRt1YiYN1hC5mZG2aHpQNGN6ePdQH/vbByNtxqSyu9mC0xfkkvmZiOLIn5W/Vf6Nz730uWFjHV/rIDchdzpMnHHMVM0wO33UdNg43OvAF5TQCAE2ZsioRIE5aXFkJ4cd2GnxWtSqsTnrAiGJzkEPbQNums0uBj0Barvs1HbZyUkKlxrMSRr7wtjJmiHo4oa9trvFQkVOwmm/F7GMohmn4vYHeXZfBw7v6LIRo4VWx4FhDvE4dRK3zP1pzP0dOwc9tB8xc25ZdGe2R4JIaUaiQcMzn7+Ol/avPevn6fycO08Zk2UZSQ5/n0RBjYw8FKhRZ3mHfk8LQTlAmr6AI7Zd7Ol/joAUrhYoI9HvbeHppvsAyDaWsTT1cs7Nvh2jOnJrQpn5JfRZWqm166htd5Oc7GROevwp+1W2WVmcl0icLqaWQU/LdGiGLMt0WD1Ud9g42u8kdIb2FV2uQ9j8fTxx5HvYj1UkOBPrsz7B5sJ/RysaRrRXlP0xd89UUDgdM3WecTIXlKWzNDcOzh6LFVHKR/BniILI18/5Ol8/5+s8sOcBvvTql3i35V3svi3cXvoLCORh0KrYtDCLZyo7aOh1kpsUbsvR2Oukqs3Ksgj1F9/dPMDPtv2KF1t+Nmz8ywsfYU7CyqFtbUYxmvRChGOaL8sSIXsfqoRM5V6rMCuJqafBvXv3cuGFFw5tHy8lcscdd/CPf/wjQlbNDmRZZo41jn6jj8o2K6uKMrjttttm5Y3z+KSjr6+PN998k6NHj7Jx40bOPffcCFumoKBwMopmRI6TNQNAEEWM889HEATquuysmZOKURtTjyDjRtGMqeW9wzPPIX6cg112vIEQmxdlD3NW+oI+/rj3j3ztja8NjX1r3bf42cafzcrnssliJmmGLMsc7XdR1W6la/BE6yOjVsWCjDgWpCUQr1MjxWVPKPNKoxIpSo2jKDWO8+al0TXopabTxpE+J12DXp7a18GctDjWz00jOe701zmTZnwcuyfAwW47C3NnZlCuohkn8AZCPFvZidUdiLQpY+KA+XX+2fBvQ9uX5n2ZywvujaBFE2NvqwVPSMVF5RkzMhhlvERSMzJM+nHrvSAIqATNie2T6rBXpFw0bN+laZu4ruibLHD+L81eC/f3HKLH00SiJoMBXzvd7sN0uw/zevvvKU5YwXnZn2J+0vloVVPbX3UkzchYejGqhn7osPHGwV5uW60j0aAZdpw/KLGreYCLymdOdO5UaIY/GA54O9rn5Gi/86xVOl5v+wNvdvzPiK+l6wvZXPhN9Ko4QKAkcRUqYXbMARUUTmYmzTNG4oKy9LBTOOSPtCmnIMsyNADHbv0qleqM/ox7Vt2DVqXlrpfuYl/3PvZ1XwzA1xY/TX7SItaVpLHtiJkPGsxkJujJTNDzQYOZtHgd+dPcdmbr4Ra+8daX2D/w6tDYBTmf4eqib4+4/3GHeMhtw9tSSWiwB13hEnS5C6bFXgWFaCKmnkYuuOCC8M1MYdoJDXaT5TKQ5TLgKww7xteVzPzyt2ciLS2N5ORktFotS5YoZUoVFKINRTMix8maIRcGQKsdmnQEQjL7Wq2cNy89wlZOL4pmTD4fNvZT1TYYaTOmlKZ+Fy9Vd3HV4hzUKpGtTVvZ+PDGYfvYv2PHpDNFyMKZw0zQDEmWaex1sqfFwoArvCglCFCSFs+ivETykg2oCWBya85yprEjCAK5yQZykw04vAF2NVs42GWnyeyi1eJmXUkqy/KTRlyAOpNmjMTuZgsLsmdmtvhxZrtmeAMhnqnsoN/hi7Qpo0aSJZ5p+jE7ep8YGvv20lfJNJZE0KrJoabThssf5IpF2WjGWFFipjITNGM0qEUNSWoDy+Jz+cbi/0QStMiyzD7zi5g9rdRY3qbbfZgm+16a7HvRigbWZN7IvMQ1LEi+aEraBZxOM86bl06v3UeP3csrNd3ctCLvlAooNR12luUnnzFQKxYZr2bIssygO0CfI/x76xr00Gf3IZ3ls+0Ludjb/wLPNP3HiK+vMxXy48JLOWj6ilL2XEGBma0ZG0rTIpYlPRqONB5B6BagG7wbvWjjzjzPAPjc8s+xae4m7nrpLl478hoAv6m+gfz4RdxR+nu6BuNoMrt4pSbcX9ygVYV/Xl1wSkDWVBAMSXzl5fv54/4fAOHKL9cUfYc1GTeOKjBNNJgQdfGEVGo06cVTba6CQlQSU05xhcghGpOQkBERQJbY3z7I8oJk9BpVpE2LGKIocsUVV7BmzRri48PlubZu3UpRURElJbG/+KGgoKAwXj6uGR+nusPGysIUDNrZoyGKZkwuHx0xs7cl9nuIj4YWs5sXD3Qh6Xdz41PXD41/eumn+fWlv1Yc4grIskzLgJttR8xYjjnDtWqRJXmJLM5NIl5/0pRvGtbjTHoNG+dnsrwgmfcb+mmzuPmw0UxTv4tLFmSeslh0Ns34ODZPgEM9DhbkJEzVW4g4s1kzjmeI99ljxyHuDtp4uOHrHB7cBsDClI3cUfpbVOLUL4xOF039Lp7Z18HVS3NmTbUfhZERBIGV6dcAcFn+vbQ697Ot+xEqzS/hlzx82P0QH3Y/hEmTxlcWPU6qPn9Sr386zVCJAlcsyuKx3e30O3y819DPxvnDs8IlWebDI2auXjKzWgKMVjO8gRAdVjc9trATvNfuxR88u+4ep8fdyJvtDwzLSjxOXlwFmwu/zvzE1ax2PzjxN6WgoBD1rCtJZUVhSqTNOCOZWZnIgowgC0jS6O93uQm5vHr7qzxZ9yQ3P30zAO3OGn65fzPrMj9Jgusa7J4gr9V2s2VpLh5/iJcOdHHTyny06qkJIGyyNrGtdSdfePkuvCE3AAmadD5V9tthpdLPhiCI6OesRJtThqgNt7ryth5AnZiBOil7SmxXUIg2lNmMwmmRvE6CAy1sSHqXgBTip7kgCyo2anT4AmHH+No5qZE2M6IIgkBaWjhjvr6+nm3btrFt2zY+9alPUVysRFspKCjMHs6kGR/HH5SobLNy7tzZVXFE0YzJYU+LhV3NlkibMW1Icoj7tn+aWsvbQ2NfXvVl/nDFHyJolUK00O/w8UFjPx1WDwB6tciygmSW5CWii3Dwakqcli1Lc6jtsvNhYz+dgx4e293GFYuyyTNKo9aMkdjbamF+tmlGtwyYjZrh8Yd4rqqTXrv37DtHCe91/h8vtv4cAK1o4LriH7I68/qzHBWbdNu8PLGnnS1Lc2dcpq3C+BAEgSLTMopMy7i2+Pu83Pormux76fe24AiY+UlluLrNpXlf4vKCr4z7OqOdZ5j0Gi6ryOT5/V3UddnJSzZQnjU8gOpon5MOq5u85OktczvVnE4zrr3pVlzaVFoHXPTYvYwnWXVP3/M8duTUcrzL067i3KzbKDItO6HHcvSVT1ZQUJh81hSnsCZKfQJWq5X6ulrWJWwlQQrBuSCrVBiNY7/v31RxE6tzV3P1Y1fTae/F4u3j3e4HMWmeRxO8gmbr+XzUpGf93DT6HT5eq+3mqsU5k1rRyhf08Xjt43z2xc8Skk+0tLim6Luck3kzWpVhzOcUBAGVIayPgYF2/J0H8XcexFhxIerErEmzXUEhWlGc4gojEvLYcVW9Csj0oiUlwQMfu59XtQ2yrCAJnXr2ZPqdiXnz5rFs2TKKi4tn7EKVgoKCwkiMRjM+zv72QVYUzt6KI4pmjI/97YNsazRH2oxpo9d9lJ/vv2Joe0n6BrZ/9jXidDNrIVdh7PiDEjuaBjjQPohMOENuaX4SqwqTI+4MPxlBEFiUm0hBipHXa3vosXt558BRrtXVIsCoNePjDDj9HOlzMi9zdlRKmA2aYXMHeK6qI2Z6iLsCVv5j3/kEpRMZ7fcuepzcuPIIWjX1DLoDPLG3nauX5JCTNPZFWIWZS5wmmZvn/gSAZnslf67/HL6QC4A3O/6XbncD18+5jwTt2FoojXWeUZgax5riFHY1W3jnUB9ZCXqSjMODOD5oMHPr6vwZG1hVUjKXOeUL8RnSeKcTZHlgXOcZ8LbzXtff2d7zyNBYQfwSMgxFXJz7hRnRHkJBQWHsLM1PYl2UJjiYzWYeeOABZFkmaTXMzWbM84yPU5RURPXd1QSlIAv/ZzWHrVU4Ar2g/juoHubV7kUY9P/Firx5NPW7eKu+l0sXZE5IY2RZpt5cz92v3M0HrR8MjRtUCcRrUriz7A9kx5VO7I0dQ52cgyajJJwprjjEFWYJilNcYRghlxVf6wE0WfPQF68g0HeUBOPI2VjeQIjqDhuriqK7VMp0oVarufrqq4e2X3/9dXbt2sXKlSvZvHlzBC1TUFBQmBrGohkfxx+c3RVHFM0YO3VdNt473BdpM6aNRtsO/lh359B2jrGcT859kA8abFy+0DBjF3IVzs6RPifvNfTh8oUzBeZlxLN+bhoJ09DDbrwkGjRcW2ak+9BB9rlS2OkvYInBQoJhfAv1ALtbLLPGKT7TNaPH5uWF/Z24/aGz7xwFdLkO8fdDXx7mEP/GkhfImeEO8eN4/CFerenmc+fNibQpClFKccJy7l9TicM/wI/3nouMTI3lLWosb3FZ/r1cknfPWfuNT2SesboohXarm65BL6/V9nDTynxUJ2Xt9dq9HOpxMD975rXhaDa72NbYjzllUXhABm9zJf7uw2iy5mGYc/YSu7Is80jjN6g0vzxs/PriH7Eu6zblGVRBYRZTnmXigrKxBTdNBz09PWzdupWVK1eyadMm9ldVkpPcPanXUItqDty9i798VMkbzc/wbudfcQWteFVVPNJ2JU+261mffTv11rVoVVdyYfnYHcx9rj62Nm3lt7t+y+7O3UPjWlHPuqzbuLLwG4jC5AZAC6IKw9zVQ9tj1QwFhVhEcYorDCEHfLjr30dlSkfUGhCTs9FlFmDwNBI4TduNylYrS/OT0Kimpl9GrCLLMvv37wegoKAgssbMckKSjNnpo8/uw+YJ4PYH8QRCBEMyogiiIKBTi8TrNMTr1aQYtWQk6GZt9qqCwmgZj2Z8nP3tgywvSJ6ynkuxgqIZZ6ex18HbB/vGVfIx1nAHbezpe44XWu4fGttU8FU25n4RQRA41ONAqxa5+GN9MhVmPh5/iHcP99HY5wTCjuYLy9IpTI2LsGVnRw748DV8SHpiOqXJ6bzT5ueoK4lF9lryU/SMJ4Wjz+6j2eyiOC363/9kMtM0Ixzw1D+mvraRQpIlflN9PZ2ug0Nji1Mu4xOlv0Itzq5y4rNBjxUmjkmbyq/XHaLDeZD/rr4WgDfa/8B7XX/l75fvxqRNQZLB5Qsy6A7gDYQDYyY6zxBFgcsrsnhkVxt9Dh8fHTVz3rzhTpztR8zMy4hHPUPWsmzuAG/X99JmcQ8bl2UZf18TAGrTmTM7Q3KQQ9YP+Nuhu4eNn5/9aTbmfYE4TfLkGq2goBBTFKfFcVlFVtQFxng8Hh599FEKCgpISEhg3rx5rFy2GOHg/fgn+fFSp9Fw59oV6IR01md9gqeb7mNv//MABGQv73b9jXe7/sZf6k8cc1HxRdy28Da0Ki35ifmIgsic5Dl02Ds40HOAlxpe4pXGV0a83trcc0hWL+ainK+gEqbejTcWzVBQiGUUp7jCEJLfgyCqkdyDiPp4BEFEOMsCldsforbTxrIC5eH4ZARB4LbbbuPNN98kOzsbgFAohM/nG1cPE4Wx4fYHaex10tjnpHvQQ1Aa+6pNokFDbrKBOWlxFKbGzXqnnYLCxxmPZnwcjz9ETaeNFYWzW0MUzTgzLWYXr9X2IM2CFXiLt5MHD36Gfm8LAMm6XL699JVT+oRVd9gwatWcUzI7Ky3MRo70OXnnUB+eQAhBgJWFyawuSomZxfyTNaNi0So0Jh/v1LdjdvoISCFkVdy4ShvuabbMOqf4TNEMbyDEO4f6ONzjiLQpo8ITdPD93cOzZe5b+QGJWiVASUHhdMTpVBSmxrEp9SLuPKeeFX9ZTEAK4At5+Pf3L+X3m37P9fOvH3KweAMhemxeDjd3sE+tITSBeYZJr+GSBZm8XN1NZdsgBSnGYUFkDm+QyrZBVhfHfuXDg1123j3cN2JwkSAIGOefj6+lCjE+/F5lKYQcCiKe1JO9zVHNw43/zoC3bWgsyzCPuyv+iUmrPG8qKMx2shP1bF6cPam9sicLu92ORqOht7eX5ORkRFEEeersjNepuXppDk/tbee2eT/nktx/46nqj2gL/hO/2HDK/u80v8M7ze+M+vzFScVcVXoVn1x8Jw0dqfTavZNp/hkZrWYoKMQ6ilN8liN5nfj7mtFmz0MVl0Tc0k3Ifg+CevSR7pVtgyzJS4pKYYwkBQUFfO5znxva3rVrF++++y7nnXceGzZsiKBlM5deu5ddzRaa+10Tdp7YPAFsngAHu+yoRIGitDgW5yZSmGqMuqhIBYXpYjI04+NUtYUrjqhmuYYomjEynYMeXq7uIjSO4KZYo976Pn+p//zQdnnSBm6de/8pDvHj7GwaIE6nYnFe0jRZqBAJ/EGJ9xv6OdhtByA1TsslCzLJTNBH2LKzcybNKMvSohGyEPphwOXjqA/mZIy9jG3noId2i5v8lNhxBk8Gsa4ZjX0OPjhqx+6Jjf7hHc6DPNL4jWFj3176quIQV1AYAUGA/GQjS/ITmZMWf9I6UQLeH3j5454/8qsdv6JlsIUbn7qRmypu4rvrv0uRvoj9+/ezatUqilaVcfGyEnoHrPT51DT0Ouix+sdsS0l6PItzE6nutPHmwV5uX1OAUXtiGXRPi4X52SZM+uhtP3Im/EGJrfW9HDpLcJE6IR314ktPHNfdgK+9Bl1eBZrcch5p/CZV5hNZinHqZL608F9kGedOme0KCgqxQ6JBw9VLc6KqSuzg4OCQZmRmZnL33XfjcDjQ66dnjpSZoOfSiixerekm3ZjDJ5dewxN7luPwuDAmVHM48J+jPldhYiEAV5ddzRdWfIEF6QuQZXjxQBe9dtdUvYXTcibN0OVVTLs9CgpTgeIUn8XIsoTr4LvIXieyz4W+ZBWCqELQx4/pPHZPgIY+B+VZM68f02QhyzK1tbUEg0Hy8/Mjbc6Mo8/u5aOjAzSbp+ZhISTJHO1zcrTPSYJBw7KCJBbnJsZMdpaCwmQwWZrxcRzeIAe77CzKS5wkS2MfRTPC9NnDPWYDoZntEJfkEPdXXsaAr31o7BPzfs3y9CvPeuy7h/oxatXMzZjY91AhOum2eXijrhfbMcfhisJk1s5JQS1G//PHaDRjbkY880QT9T2DWNx+VGY3cu7Yv+97Wy2zzil+MrGkGYMeP+0WD+/0dCMJ0V9uXJZldvU9zZNHfzA0dmHO57iq6JsRtEpBIXqZmxHPupJUUuNHziYTBZEvrf4Sn172ab791rf53z3/y5N1T/J03dN8T/c91D41NpuNK6+8ErVaTW5mOrnAsoJk+mx27Hu0dNs9Y7LpvHlpdFg9WNx+3q7v46rF2UNB7v6gxPYjZi5fmD3Rtz7tuHxBXtjfNeYMQlmWCZhbQQrRJw7wmx0Lhr3+hQV/oyxp/WSaqqCgEMMYtCquXZY7LKAo0kiSxEMPPYTVah2mGcnJ01uBsDTThNXl56OjA5j0Gq5eksMzlZ147au5KP1drlgYzqx3+AeYl5bF+WUp2IPdlKaW4vQ78QQ8pBhS0KiGB2b5giFer+2ZsjXusXCyZqiUcuoKM4joX1FRmDIEQcRYfj5iXArqlFwEcfw9lPe2WCfRspmHIAjcddddXHXVVRQXFwOwb98+Hn30UeRZUA52qgiEJD5o6Oex3e3T9rBg9wR4/3A/f9/ewv72wVmRvaigAJOrGR9nb6sFSfkuDaFoBlhcfp6r6sQ32gb1MYozYOGBuk8Nc4jfU/HwqBziAJIs83ptN922sS0QK0Q3kiyzu9nCU/s6sHkCmPRqbliex/q5aTHhEIfRa0ayUUvJsaCOXoeXva2DY75Wi9lN3zSWFYw2ol0zHN4Ae1osPLK7lfpuB05fMNImjYpG2w7+fUf5kEM8yzCPry56SnGIKyiMQG6SgVtW53PVkpzTOsRPxqgx8ocr/sAzNz0DgITEn3x/wqlzkpCTgEp1qmZkxOuZm2FiaUES6fE6Rlu8Ta0SuXxhFipBoNnsoqbTNuz1+m4HXYOx9Rxlcfl5fE/7uErqCoJA3OLLGMxO4YH2rwKwghXcxm38cPm7ikNcQUFhCJUocNWSHJLjoiuQURRFbrvtNrKzsyktLR1RM6aLNXNSmZ8dThLMSNBz1ZJsVKLA0X4XWw/1IcsyJm0qPfYAz+zrw+HMAFkgQZdAZnzmKQ7xAaePx3a10dQfeYc4nNAMfclq1InhCkn+niO469+PinmGgsJ4iZ4wH4UpR5ZlAv0tBAfaMZSuQ1CpURkTiFt86YTLQfc7fLSYXRTNsp5+Y0EQBJYvXw6E+528/PLLAHR1dZGbmxtJ02KSzkEPb9b1MOiOTNlFpy/Iu4f62N9m5eL5mbM6Q0lhZjKVmvFxBt1KxZGPM5s1w+YJ8GxlB25/KNKmTCmtjmp+V3Pj0LYoqPl/q3ehV40t6zsQknlxfxe3rCog0Rib5T8VTuDwBnijrpfOYwv0pZnxXFSWgU4TucWe0TARzUgx6ihMkWke8LKreQCTXs/CnLFVD9ndYuHKxTkTeQsxTTRpRiAk0TXood3iod3qptfuRZZBlP3MmVZLxocrYOWFlp+xt//5YeNfWvgwcZrpzUBSUIh29BoV581LY2Hu2O7ZsixTXV2Nr96H77s+bnv+Np6pf4Zf+37Nr179FVc0XsHzNz9/irMAQKdWUZIeT1aSTG6ygV7b2Z8X00061s1N5cNGMx82mslLNpJykpPnvcP93Lo6PybapPXYvDxX1Yk3ML7nZLu/jxdafjZULt2Eiau4CoC4gA6ivzuLgoLCNHFReQa5SSO38ppOjmtGfX09119/PRqNhrS0NO66666ouG9fsiATly9Im8VNXrKRTQuzeKW6m4PddtSiwAVl6QiCQFCS2dZopr7bzrwME3PS48gw6QiEZPqdPnpsHnY2WfAHoysxQBAEtJklAEg+N96mPeGfnRZUptRImqagMG4Up/hsQgria6tG9rvxdR5EX7AYYNIEZE+LRXGKjxK9Xk9OTg7x8fFDC1UHDx6krKwsohFusUJlm5UPG8wT7hs+GVjdAZ7e18H87ATOL03HoFX+fgozhCnWjI+zp8WqOMVPw2zSDJcvyLOVHTi8sZFJOF529DzBU00/Gtq+d+GjFCesGPf53P4Qz+/v5OZV+eij3HmqcHqO9Dl5u74XX1BCoxK4sCyD8ixTVCz2nJUJakZmgh5fUMA1AO8c6iPJoCEvefQBh0f6nFhc/mFOjtnKdGuG2x+kw+qh0+qhy+bB7PBHxTP6WJFkiV8duIYed8Ow8euKf8T67NsjZJWCQvRSmmnigrJ04nRjX1b0+/2888472O12tm/fztM3Pc0rDa9w5WPhSjmvNr5K8s+Tqf9SPfmJI7eFiNOpuWFFPg09Ht5v6D+rE2FZfhKtA27aLG5er+3hplV5Q9VXeu1eajujv51Tt80zoUpKPe4j/G/t7biCgwBkGuZyd/nfEBsPIGoMQ86NgLlt0iuDKSgoxBbLCpLGHPA0VZysGdu2bePCCy8Epm5taqyoRIErl2Tz5N4OzA4fJenxbFyQyVsHe6nutCHJMheVZwzZO+D0M+AcYGfTADqNiD8oESuPzoJaixifomiGQsyjOMVnOLIUQvK5UBkSEFQaDKXnELR0ostdcPaDx0iH1UOPzUtW4uSGlsqyzKBvEHfQjVFtJEmXFDXCN160Wi133XXX0PaePXt49dVXAfjRj34U8+9vqgiEJLbW91Lf7Yi0KadQ322n3eLmsoosClKjI2vcE/Bg0EQ+qlMhMviCIeyeIL5gCG9Awh+SEABBAJUgYNSqidOpiNOp0ajCC0LTqRkfx+zw0dTvZE76xHojK5oRu3gDIZ6t6oxYBZDp4sPuf/Fc838NbX+y9L8n5BA/jsXl58UDXVy/PA+VODM+E7MFtz/I1vpearvsAGSYdGxamEWSMbodvJOtGfkpBiS1icZeL6/W9HDLqnwSDKOrfiDL4QDdyyqyxnRNRTPGjiTJdNk8HO130TbgYsDlj5mFvJFocVRxxLabV9v+e9j4Z8ofoCL5opj/PCgoTDZatciFZRksyBlbMGswGGRwcJC0tDR0Oh3XXXcdDQ0NrF8fLte9uXQz1m9bSftFGiE5hCvgouC3BTR8uYF5qfNOe96FuYnkJxt5o65nqMrKSAiCwCULMnlkVyv9Th87jg5w3rz0odc/PNLPnPS4Mzr5I6kZ3TYPz1Z2jiuD0BWw8kH3Q2zteBCJENnGUq4r/hFzElaG7V98Qjv93Y14m/cCYDrnFuUeqKAwCylMNbLhpPtjJDibZkQbOrWKLUtzeGJPOw5vkAXZCSDLvN3QTF2/BT8OLi2bg+pjrbBirV2coFITv/iyoW1FMxRiFcUpPsNx7HkOQgEMpeeiSStAnZCBOiFjyq5X2WblikXZk3Iuu9/Oi0de5NFDj9LuONFrM9+Uz23lt3H13KtJ0M6MrEKdLtx3a926dQiCgCRJ1NfXM3/+fMQY6R051XiOZcH12KK3Z6TTF+TZqg5WFqawriQVMUJOCVmW+dYL36L1QCtbrtrCbctvi4gdCtODLMt4/SGc/iDvd/TT7ZAYdAfwjKGkXqJBQ4ZJxznObajkIPrSdWjTCqdcMz7O3hbruJ3iimbEtmb4gxLPV3VidvgibcqUYfa28dPKS4a2U3R5fH3JsxjVkxeB32n18NbBXi5fODbHoNls5t1332Xz5s0YjdER2DVbONRj5+5/VdJsDveNW1mYzNo5qTER2DD58wyBi8rSsbn76HP4eKm6i5tW5g8Fbp2Nwz0OzilJJUF/dke6ohlj1wy7N8Ce+l4aB/x4Zkh7i8OD23jw4GdPGf/J6r0Y1KYIWBQbhNx2LI21uFdmKZoxy8hK1I87aOtXv/oVPp+PG264gYqKCgoLCyksLBy2T5I+ieCPgrxw6AW2PLEFgNL/KSVBl0DrvY0knebciUYNN67MY/uRAfa0WE5rQ7xOzSXzM3mpupvKtkEKUowUpoarHfoCEu839I+4nhVpzZiIQ7zW8jb/d+hLQ9tp+kLuqXjotO0gBHVYQ7U55QiCgCxLBAc6UKfmIQixOc9QiCyKZsQWiQYNVyzKjth65nFGoxnRhkmv4dplufxr9yFqbFs5qHqV+NJeALqBf3WmsTzpSkrjLkInzoxKu4pmKEw206UZyqdzhqNJLQAgaO2clus19jqxeSae4bW9czsbn9rIL/b8gg5Hx7DXOhwd/GLPL9j41Ea2d26f8LWigcWLF/ODH/yASy4JL5Z3dnby9NNP86c//SnClkUHDm+Ap/a1R7VD/DjHM5WeruzA7Z/+8r+yLPPV17/KMweeoY02Hqt6bNptUJh6/EGJxl4Hbx3s5R8ftXCgc5Cj/U5qOm1027xDDnG9RiTZqCErQU9BipGCFCN5yQZyEvUkGTSoj010bJ4AjX1OjgSSADh8uIH3G/rpn2YHZeeghw6re8zHKZoR25oRDEm8eKCL7hi4x48Xu79vmEP80rwv8f3lb0+qQ/w49d12dhwdGNMxDoeD/Px8PJ7TZ1gpTA2yHA5miNOpuHZZLufOTYsJhzhMzTxDrRK5cnE2Bo0Ks9PPWwd7kUeZhhySZPa1Ws+6n6IZo9cMly/I7uYBqtqt1HXZqe2yzQiHuCfo4OsflZ3iEL+l5Kf8+pxDikP8LMgBN9rEdEUzZhmLchO5aWX+uKuYLFgQriLS0NBwlj3hmvJrePKGJ4e27T47hX+Yhzd0+vm1IAisn5fGFYuy0ahOr6Nz0uNZdKwk8FsHe4fN2Q/3OGg5FqR2nEhrxvEe4uNxiD/b9P+GOcQBvrTw4dM6xAE06UWY1t6MvmgZACHHAJ6G7bj2vz7m6ysogKIZsYRaFLhycXZUtOMai2ZEE4dse/lX113ssv8DR6hv2GtB0cwe+z94oucuOrxVEbJwclE0Q2GymS7NUDLFZxByKIiv7QABSydxiy9D1OjQz1mBJnMOqvjUabFBkmWq2qxcUDb+LJHtndu5Z+s9yLKMzKmLYMfHvEEv92y9hwcufoBzc88d9/WihZN7/DU3NwOwdu1aJEkiGAzS29tLbm5uzGYBjhery8+zVZ3YJyHYYjrptHp4bHc71yzNIS1eNy3X7LP18ak/fYo3vG+QSCIL4hfw2O2KU3ymEJJk2ixuDvXYaep3EZTC90KNEEIQBOK0KpbmJ5FqMpEcpyHRoEGnPv1kRg4F8baGNcNWcB69bpmeQQNNg2Z6QkZoH2R/+yB5SQaWFSRRlBaHOA2lkPa2WMfUR1bRjNjWDEmSeaWmm3bL2IMhYoXX2/7A250nnE8X5nyWywu+MqXX3Nk0QJJRw/zs02cteb1eDhw4wOrVq0lJSWFwcJDExOjoGzebmJ+dwK9vWkJNhw2DNvILUKdjOucZJr2GzYuyebaqg8Y+JzkdNpbmJ43q2LpOG2uKUzBqR57mKpoxOs1ot7ip7rBxtN+JHPKxOsZKO56OoOTn0cZvs3/g1WHjF+R8hnOzbiNVP3LvYgWQg34C/S1osuYh6k2IIa+iGbMEUYDitDgyyjPDG6MgEAjw9ttv09DQwF133YXRaGTz5s0sX76c3NzcUZ3jxoobCcwPcPFDF/NB6wfYfXZWv/cXHlpxLXD6sr5lWSaSjRpePNCFwzuyE/28eWl0Wj1Y3OHgq6uX5AyVfH3nUB+fPKcQjUqMuGb02b3j6iEuyzLbev7Ftp6HAUjVF3D3gr+Tos8b1fHCSfOHkC2c5ajNKUOWJZAkQi4rKlOqkgWocFoUzYhNLijLICNhcluijobJ0Ixo4GTNYATNOL6cFpT9vGX5KZekfI88/bLpNXIKUDRDYaJEQjMUp3iMczxzQhAE5FCAgKUT2eci5DAjpuQiiCrUprRptamuy87aOanjiiyz++187b2vnXbScTIyMsjwtfe+xts3vj1jShwCbNiwgaSkJAKBAKIo8tprr7F//342b97MypUrI23etGF1+Xl6XwdO3/RnXE8Gdk+AJ/a0s2lh1oT7JJ+Nv77+Vzp3dXIO57CLXXztgq/xo/N/NKXXVJgePP4QNV02ajpsw74LiQYNxWlxlKRoWKlLRhQFXMY0JOH02Rsf14ygtRP8LrLUbvKLcoEUAqEc2ixuGnocHOl30jHooWPQQ1q8lgtKM8hNnto+9c1mF30OLxmms0/GFM0IE6uaIcsyb9T10NTvOvvOMYgsy+zofZw3O/4HgEzDXO4s+z2ZxpJpuf5bB3sx6dUjBpnU1dXx9NNPA1BaWkpycjLLlsX+hDxW2VCazpE+Z6TNOIVIzjNykw2cNy+d9xv6+bCxn+xEPZmjWKQLhGSq2gY5d+6pdimaEeZ0mnHZ5VdgyJlHdccgZqd/aP+ZsHxl9/fx4MHP0e0+PGxcI+r5zrLXSNblRMiy2CBgbsPTEM6CVSfnIOrjMSYkolYry0kzHaNWxYLkBEy6s7elOFkzfD4fhw8fxmaz0dHRQWlpKSqViry80Tllj6MW1bx/5/s8V/8cX3zli9TY+zj3g//j+vk3MSd17mmPy0jQc9OqfJ6r7MTi8p/yukYlcvnCLJ7Y207LgJuqtkGWF4azp22eAB8dHWBZkS6imtFn9/JsVSfeMbTEAmhxVPFo47cxe1uHxr6x5Hl0qvGV6tXlL0TUxyNLQQRBxNO8m0BfM/o5K9Fmnb7Xu8LsRdGM2GRBTgKL8qYvcGEqNCOSjGWegSAjybDV8ktuyfrLjCmlDopmKIydSGnGTJjjzlr8vUdxVr40VLJQ1Bowzj8fQ+m5qBMzI2dXUKK20zauY1888iLeoPfsAnIMGRlv0MtLR18a1/WimcWLF7NixQoAkpPDE7SmpiZkWeapp57i5z//OU5n9C2gThY2d4BnKmPXIX4cf1DipQPdHOyyT9k1/uuR++ncdaJ06T3l9ygO8RmA3RNg66Fe/ra9mR1HB3D6ghg0KpbmJXHzqnzuOKeQ80vTKUg1jqrf02g1Q6MSKUmPZ9OibO5cV8SKwmS0ahGz08/TlR28WtON3Tu1lRv2NJ+9/C0omnEysagZW+v7ONTjiLQZU8aTR3/A000/Htr+2uKnp80hDuHqEi8d6Mb6scXgD7ZtH3KIA/T19X38UAWFqJhnLMlLpCQ9DkmGV2u68Y3SMXCgY3BEJ4KiGSc4WTM0xnCp8Hf31rC1vpe2fe9g3/U0kj+2y5wGJT9mbxsPN3yd+6suP8Uh/ou1Nfx87QHFIX4WvB2HhhaqAELuwcgZozCtpMZruWllwagc4pWVlfzud7/j8OHw9yw+Pp7bb7+dG264gaKiognbcu38a6m+ay8lccm4QwEern2EX370S0LS6XUhQa/hppX5ZCeOHFCVbtKxYV44gGr7UfOwVm1VbVb+Wf10xDSjw+rmqX0dY25X0eU6xO9rbhlyiF9R8DV+dc7BcTvEj6NJL0KbGQ5CEPVhzQgO9iDLMu7D22aEZihMDopmxCap8VouKh9/xdexMtWaEQnGOs8QBJmg7OPDntem2LLpR9EMhdESSc1QnOIxRMhjR/KcWDyWfG5knwt/d+PQmMqYiCatAEEV2Qi8/e2DhKTRCcFxZFnm0UOPjut6j9Q/Mup+g7HIhg0b+PSnP82mTZvw+XwcPHgQr9fL+++/j8/n45nnXuBIb6StnDzs3gBPV3acttxZrCHJMm8e7BlVn8ux4JdCfOPtZqTmE2OX3XIZP7n5J5N6HYXpxe4NO8P/uaOF2k47IUkmw6Tj0gWZfGZ9EeeXpZOVoB8q8Xc6JkMzTHoN6+emcec5RSzKTUQAGvucPLKrjcbeqXNmNvY5TnHkfRxFM05PLGjGe4f7qBlnAF204wpYebTxW+zqCzueF6Zs5Cer96BVTW2VhZHwBkI8v78TdyCsp68dgHff+2Do9XvuuYeysrJpt0sh+ojGeYYgCFwyP5MEvRq7N8hb9aPrL+4LSFR3DL+/KJoxnAGnj51NAzy8o4XD5GFcuBFt0XIIBQgOtEMogK+jDjkYwNWwG7P97E6xaMEf8vKHmtv41s5F/LTyEqrMr+ALhSuSJGtz+LdFT/Hf6w6jFsfXF3k2cagzDm973dB23NIr0KTETtaWwvjJSzZw08p8Eg0jf/fNZjMWi2Vo2263Y7PZ2LNnz9BYeno6FRUVaLWT813LjM/krXM/ObTtDrhZ+7e19LlOH9xn0Kq4bnkeBSkjt2ZalJvI3Ix4JBleqz0RfCVJMk8cHl8bsolqRlO/k+fH0UN8V+/T/PrAtUPbt879ORvzvogoTG5rFl1eBcaFG9HPWTEjNENh8lA0IzZRiwKbFmajUU2diygSmjGdTGSe0eJ/g/cO943ZhxIrKJqhcDoirRlK7ZIo5uTnaF/HQXxtB9DmLkBfuAQI31gEUYU6NfoeMhzeIA29jjP2svw4g75B2h3tY76WjEy7ox2bz0aSPmnMx8cKBQUFQz+np6djs9nYsGEDoVCI2rqDHNXANzaHI108Hg9qtRqNJvaExeUL8sy+jpjrIX42ZBk+aOjHFwyxrmTipUYP9tdzyWv/wxX+GzAdG7v2jmtZXLR4wudWiAz+oMTeVguVbSeCivKTDawpTiUn6exO8KnUDINWxUXlGSzKTeSdQ3302L28WttDhcXN+aXpkz6BkmXY02Lh0oqs0+6jaMaZiWbN2NZopqptcFquNd1YvJ387dDdQxmJVxZ+k4tyPxdRmwbdAV4+0MVVaplO64n7yFe+8pWhqgIKs49YmWfoNCo2Lcrmqb3tHO13UdtlZ1Hu2Us7VrVZWVaQNKRPs10z7N4AXYMeOq0eOqyeU8oJqxNO9OcVDYlIfhe6vAXIskRgoJ2aQRMXVIQXM+WgHwQx4kHYJ+MKDPK/dZ+kx90w4uvXF/+Ic7JumXTn0EwmGAKb+8TfOH75VYj6qW0HpRAdlGTEc8XCLNQqEUJhJ7Esw/EniG3btrF161bWr1/PxRdfDMB5552HWq1m/vz5U2pbcVwyA5u/ReorvwCgureazF9lUv3FahZlLhrxGK1a5JqlObxc3U2zeXjLHkEQ2FieQZ/di90b5I2DvVy1OBuf5MAW7BmzfRPVjNpOG1vr+5DG4FQf8Lbz5NEf0GjbCUCGoZhPlf6WnLjyMV9/tMS6ZihMPopmxC7r56WRbtJN6jmjRTOmi/HOMwQBBK2FAy1dDLj8XLEwG4N25j2rKpqh8HGiQTOUT1gUIAf9hNw2VKa0IafH1poUNCoZ3YqwkqhMKeF9/e6h4wRRRJe3ICI2j4bKNuuYnOLuoPvsO50BV9BFEkkTOkescM899wz9bLVa0Wg0rJ3rH3roePDBB7HZbHz+859HrVbz7LPPMm/ePC688MKzOtYiiTcQ4tmqTgbdM8shfjK7msJCP17HuCRJ3Pfmfdy/+34ulC8kiyxcqU5+9Ln7iFcmHTGJLMvU9zjYfsSM+1iJvNwkA+fMSR2xf3ckNSPdpOOGFXnsah5gT4uVui47PXYv1yzJwaSfXIfqoR4Ha0tSSTjNeRXNGD3RpBk7mwbY02I5+44xyHtdf+fFlp8BYFAn8rnyP1KcsCKiNsmyjBzw0W0TebHVT49Nx/JlS9l0xWalr98sYSbMM7IS9JxbksaHR8x80NBPbpKBlLgzZ5G4/SFqO20sKwgHfsx0zfAGQjh9QZzeIA5vEIcvgMMbxOryY3H78QVGn3EYv+yKoZ8lrxNEFYXpJxxJzgOvI/tcxC2+DEQVnsaPUCfnostfNK3zDF/IzUstv2Bv//P4pZFLMH598XPkxs2P6vlPNHFcM0SNSFOvEbtHjTajGN2cVQjizFukVTiVeWk6FiUFUZ3Umun+l8Cgga8skBBVkJubC4DDcaK6iEqlYv369dNiY7xay33n/YB9PfvZ07WHXlcv6/++nqdvfJpLSi4Z8Ri1SuSqJTm8XN1FU/9wx7hOo+KKRdk8ta+DZrOL3S0WFuSPrWz5xxmrZkiSzAeN/WMOGt3X/yKPNH5zaHtxymXcXvpLNOLkOrjORKxohsLko2hG7DMnPW7oWXk8eL1e+vv7ycvLG/o+R5tmTAcTnWdo1X46rB4e29PGVYtzJj1IIZpQNGP2Em2aoayGjRK/30/Q3o+gUiMak/B3HUIQVWizS8N/VJ8LQWdEEEQ8R3cT6D1K3NIrEA0J+LvqCTksGOauxtd1mJCtF21OGZrUfPy9R/AeDZcLMZStR5OajyRoCUkqQpKM2h9A1OtQJ2ZhrLgI1UnRNdFOn91Hu8VN/mlKVX0co3p0+52OOPXE+iTFKsnJyXzr37+K6tAvOK4Pdnu4f3VmZiaHDh1iYGCAnp4eDAYD27dvx+Vy8aUvfQmTyURXVxdZWVkYDNNf1vVkAiGJF/Z3Ynb4ImrHdDBex3hICnHPz+4hEAigR48r6TBfWLOYvFX3gyr2SgzNZEarGVZ3kJ6a7eRJPWgDFWgMJi5NtZEk92AwZeBtq4k6zVCJAutK0shPNvJ6XQ8DTj9P7u3g6iU5ZMZP3kNqSJLZ12LlwtP0tlI0Y3xEUjN2Ng2w4+jAZL6dqOGZpv9ge0+4ZFpu3AI+WfrfZBiKI2qTLMt46t9HDgWJK1tNXpqTpUUByi7aBErkdVShzDPOzrKCJFosLtotHl6v6+HmlfnDnDYjsa/VyuK8JFSiMKM0w+wMz7HMTj9mpw+Lyz/mMrujRdTHk7jqKoo9fx3SDNkXXvgT45IIWjqRPE78rjoEtRZ/Zz1ywEvc0s2IOgMh5wCquBQE9fieU0NyEE/QTkgO8Fb7A+ztfwG/5CFRm4nNf2ofEI2o546y31OSsAqdamJ/89nGxzVjbrabuVlu9sbfgKRk2EcVU6UZGb5O9r34AfuAG2+8kQULFoBKSyAk4g/K2F1ekpL0FBcXc8cddwyrShQJVuSs4C9X/4Wbn76ZD1o/4IpHr+A3l/2GL6360ogL5ypR4MrFObxa082RPuew1zIT9FxYls7b9X3sbLKQHG865fixMBbN8AVDvFrTTYt59E4Vd9DGC833s6f/OSDcGuITpb+KeDBmpDVDYfpQNCN2OJ1mJBfN55L5mVitVhITExFFkZdeeonKykruvvtu0tPT+eijj+js7OSqq65i586dtLS0sHbtWubPn8++fft4+eWXgdjQjKlkovOMLYvn8EatE5snwJN729k4P5OyrInpUCygaMbsIRo1Q1kRGyWNh+tx176NKiEDY/l5+Fr3A6DJmocc8OCsfAkxPoW4hRsJ9B4FQA6Fs11DjgGClg68rTrkgJeQox/JkwOANnMuCCLeI7tQJ50oE2usuADZ70XQnrixqhMzp+ndTh6VbdZRO8WTdEnkm/LpcHQgM/pyUQICeaY8EnVnL6U4U1Gr1Sdq0wA/+MEPaG1tRRAE8vPz0el0XHPNNWRmZrJv3z5cLhd1dXUsXryYhx56CIDvf//71FZX88JLAlcskVlVAc3NzZjNZhYtWoRer58y+0OSzMvVXXQNeqfsGtHGriYLAgLnlKSOav+/v/V3Du44SI4cvnesEdfwyoZz0CuOjahkNJrh1iTwjGMun9SHS/OtzDdRNqcQf+P2Y5qhj2rNyE8xcvPKfF7Y34XF7efpfR1cvSiN1ZN4q6jttLG6OIU43amfc0Uzxk8kNOOjo+ahgKCZREgK8J1dywjJ4Wc+vcrE5+f/FZN2dPf2qSIw0I63eR+yP5w96e9tQUwBi8vHtiP9rC/Ljah9CsNR5hlnRxAELl2QxSO7Wul3+NjRNMD6uWcOLnR4g9R12ViclxTzmtFn91LbZaPZ7J72FkOCqOJk/5LpnJsI2fsBAZUpFUGlQT93DWJcEoGeI8gBL8GBNjTpRbjr3g0fs/Ym/P2tvHk0jfm5TiiAoK0XyWNHk1aIpBLxBO1s73mUeE0KXa5DHLJ+iNXfNaJNxx3iKbpc1mbezNrMG4nXpEz1r2LGcjrNQEnIiUqmQjMW5yVy8fxS9ucl88ILLzB37tyh633yk5/E6XRiMp1YoC8qKpq293smUgwpvPmJN/nsi5/lkZpHuPe1e3m4+mGev/l5sk3Zp+yvEgWuWJQ9omO8IieRXruPmk4b7xx0klyWiUvqgynUDG8gxHNVnfTYRr8O4gxY+NGec4aN3b3wn6Tpo8PhNB2aoThAIouiGbHF6TTj4svPJ+T38Pvf/56cnBw+/elPU1lZCYQd6QAdHR0cOnQIo9GI2+2mra2NefPmAbBixQpUKlVMacZUMd55BgiYVBlkmVK5ZVUyr9f20Gpx83pdD/1OH+tKUhFneHa0ohkzn2jVjMltAjqDmV8R7k+UEOpktefBk16RkbwuEEQk1yDI8tCi00LPU6gIcPwhWp2YgcqUijqtEHXSiYUnTUo+pjU3IqhOlIhVJ2ahSS9CEGP7T9RsdmH9WM+60yEIAreV3zau69w+/3aljMZJiKJIcXExgiBgMpn493//dyoqKkhLS+MTn/gEANnZ2fT39w/97Pf7ycoJ9410JV0AKi3V1dW8+uqr1NXVAVBVVcXPfvYz6uvrAfD5fLjdEysTI8syb9T1jCkyeqaws2mAfa3Ws+73yLuP0PZRG/FyuDx68pJktn7vNcUhHsWcSTMGBqyEEND5HcgyWMQkAK5PfB2tGCSWNCPBoOHGlXnkJhnwhyRerunC5h7dPX80BCWZyraRvyOKZkweU60Zz72zi11NFuRgACkwc6qBOAOWYQ7x87I/xU/X7I28Q9zSiefwtqFJh75kNfq8E30l97VaZ2zGfqwy71g/PWWecWbidWo2zg+/t32tVtotZ3923NNiRZLkmNWMpn4nT+1t55FdbRxot027Q3wkBEFEnZiJIAiIWiOmVVvQpBWgMiRgXHAhAGJcMiG3behnORjAZwgHS33kSeWrOxZR3fAQ3qa9vFD5db65o4Jn99zNsvY4DjY9yY7eJ3D7BzBwalUSvSqeO0p/x3eWvcb3l29lY94XFIf4BDibZihEH5O9NlU6t4SLjlVmmj9/Pt/73vfQak8sIBcXF7No0SJUqujM/tSpdTx87cP85rLfoBE17O7cTc5/5/D6kdeRR+jNfdwxPjfj1NZjG0rTyErQ4wvKuM1rx2XPaDVjrA5xSQ5x0PIeP9l38bDx/1q1K2oc4iMxLs0IBRHiw4FvR4W1SIKWQH8z3qa9BAbaAPD3NmHf9TSBgXAf35k2z4hWFM2IPU6nGcVpcVitVlQqFT09PciyzLy5JQDIjX9FkAJD99CioiJyc3NZuHAhc+bMOXHuGNSMqWAi84wFcZsRBAG9RsXVS3NYURguZ7+v1cqL+7vwBibWziPWUDRjZhHNmqF4VEaJKIokrr2OVZ4/A5CwYjOSoAl/WRPSMa29ETngA1FF/PxzWe0+ITTG8g3IsnzaB2NBPbl9WKMJWYaqdisXlY8u++TquVfz+6rf4w16RxVdJSKiU+u4quSqiZo6a0hKSuK+++4b2v7hD3+I0+nEaDRis4VF5XhZm+OZfscfetra2vD5fHi94YlbfX09L7zwAqtWrWLTpk1UVVVhsVhYvnw5KSmjW5x651Afh3scZ99xhvJhYz86tcjC3FOjyTvtnfzgbz+gyF40NLb6ytVsWrEJQpPneFSYfEbSjKCsYWeTlT2tbpCXk6iRuawim+KMItYc2w9iTzP0GhVbluXwem0PbeZBDvc5KckwApNjZ3WHjZWFKRi0p06qFM2YeiZDMxq6LGgzkwlY2vEe2YUmax764hUE+pqQvA60GSWIhtgqD9bqOMBDh7865BBflX4t1xZ/P6I2ybKMr60af+fBoTHjoktQm9JAHq4ZO5sGEAVYMyeyDnyF8N/t8kcv4AD7EdwSwRqJOMJlV107fgzA2vQbCAW8dNc8QYc7HHTyWcdqjrgGiI/P4BXLr7lX+yiZSXPRinMJykGQAqhETdRpxkQpSY9nYU4CtV123jzYyyfWFKDTnH7Rze4JcLDbzsLcxJjSjAGnj3cO9dFhHblXdjQSkgIMYsFSUcyevt+xt/8FRFTEu+Kw77WTTTZf4Au8aAk/8/QF2phLHjXBvQAUUID+2L8i0zISHRLXci3O5DjIn0e8zY8mIKHLnBtzmhGNjEUzFKKLyVybKs8ycfnCrKFtnS42+5gKgsBX136VdfnrWPPXNQBsemQT96y8h//d/L+n7H+6jHG1KHLl4mwe39OOvX8ppqTXQDwRTHAmxqIZY3WIy7LMQ4e/RrXljaGxLcXfZ0P2p0Z1fLQi6uNIWHfr0Lb6nJuR/V5EjY6QL9xjVn2stYtwrF2cOjEc6BFy9EMogBwM369m0jwjGlE0I3b5uGbkrd/CtSuKEEWRgoICvve97+F2u1Gr1dx2y01Q99OhY2+55ZYzrk3FqmZMBWOdZwiIqAQtc40XDI2JgsD6uWmkx+t4u76XVoubx/e0c+XibNLild+1ohmxQyxohuIUHwMni4Co1YOgPek1EUF7LJJ9hHvfbM5IO9hlZ11JGvozLFgdJ0GbwG8u+A33bL0HZM4oJAICCPDbC35LgjZhMk2eVYiiSEJC+PeXnZ09zPlx2WWXcd555w1F/eXk5NDX10dycjhyraysDFEUGRgIZ5x1d3ezd+9etm/fzre+9a2hnrPBYDBcrvdjfHTETHWHbUrfX7Qjy7C1vg+dWmRe5gnh/eX2X/Ktt7/FBjZQRBGeDA9fvP6LFGUURc5YhTFx8n1/0C/yWn0/fY5wJOK8DBMXlKVj1KoRRnggiDXNUIsimxZm83q1H1mWaeh14NAmEK+feHSwPyhR2Wbl3BHK5CqaMf2MVjP8QYn+kBExLgVRH87E0STn4hUEJE+4h3nIaSHQewR/Zz2m1dcPlbWSpRCCGL2R5Y81fpc9/c8C4dK9W4q/z8KUi89y1PRw/PemyZqHLm9h+Hn1NHx0dABRFFhVpGRYRpKP2j+isifsFDx+C3PhGrbPzv6nTznub727gd1D23+oPXN2QkXyhZQmracsaR0Zhjln3DfaOW9eOu1WDzZPgPca+rmsIuuM++9tsbAgOyEmNCMYktjZZKGyzUpIGkv5xenHH/Kyo/dxPux+GIuvY8R9JELYCd/zu+nmx/x46LU3eZP9mkOADl3ISHH6BQhOkVuLfoc6MRM54MOx9zkSpXiMceV4e/cS6D1CoOtQTGlGNDMWzVCILiZjbao4LY7LKrJibt5xJlbnruaDOz9gwz82APDA3gd4seFF/nzln9k0b9OwfVWiwOZF2bxa201j7wnHeJxOzTVLc3hqbwfujk9gzP874dquk6MZIUnmpQNdo3aI93taeaTxG7Q5qwHQqeL4UsW/yItfMKrjYwlBEBF04dYuqviUYc4PffFytHkVCMeq5aniUgjFDc64eUY0o2hG7HL8Pi8Aly0tJCXpRFKOKIrEx59aOePjxyqcmbHOMwQBvlzxX9gtcae8XpZlIiVOy8vVXcP6jJdmKs7ak1E0I7qJds1QnOIKU04gJFPdEe4LOxrOzT2XBy5+gK+99zW8wfBE4WQxEY41HdCr9fz2gt+yLnfd5ButMITReKLf5KpVq1i1atXQtl6v5+KLL0aSJGRZJjU1dWi/44701tZW/vGPf3DppZdyzjknel/ta7Wwq3nm9ZcdD5Is83ptD3qNijitl+/+5bv81f5X8smnhBJSVqZw7+Z7I22mwniQZXrsXp5oaMcbUqNXi1xYnjEjH2ZVosCmhVkktmoYcHtp6HUwP3tyHh73tw+yojB5xOAqRTOiC6PRiM0T4OXqdhymQuKXFJ54Ua1FV7AkHA2EjGgILxpqsuaBGH4kDdr6cNdtRVe0DF1OdJRVOo4rYOX+qstxBwcByIur4O6Kf2BQRy7IQg4F8LVVoytcRsg5QNDWg6HsPDSpeaM6flujmWBI5pwSJWM8UpxbcC7/vOZx/uOdX+DyNtAbcJ6yz/nZdzLo7+bAwBsjnGF01Fnfpc4a7rsmICIjUWRaRkXyRcxP3kCGYQ5qMTb6rWnVIpdVZPLU3g4O9TgoTos7o65a3QEO9zqYn50Q1Zph8wR4pbqbXvvoe8tOJ+aAi+09T3LItovDg9vwS2cvXy8KajL0xRg1SWzIvoMEbRp5cRWoRS2SLCEKZyjhH+OaEY1MVDMUZg5ZiXquWJSNKM48Z8d5hecx+O1BPv/y53my7kk67B1c+diVPHDFA3xh5ReG7SuKAlcszOY1emjoPVG9Li1exxWLsnjhgIS7/dPE5T+CLBwPZJ6YZrx1sGdUVUBkWeal1l/wXtf/DY1dmvdlLs2/B1GYnQv0ouZEpqQ2ex7a7HknXlQ0Y9JRNGPmkZWoJy/JePYdFcbFWOcZ5+ScwzOVnSO2hEo36bhldQGv1XTTbvXwWm0P3TYv6+emoZqB2j0VKJoxvcSaZihOcYVp4cAxh8Zob9zn5p7L2ze+zUtHX+JfB/9Fh/NE9kGeKY/b59/O1SVXY9LOPMdSLCEIAuvWnZj4rV27ltWrVyNJ0lD/mG3btgEQFxeOfnM4HPzj4UexB1UY5q0dir6a7QQlmd+/+hjxRzvIIYdbuZVHeIRLr7+UWxbeEmnzFMaBwxukvseB3RsgJMkUphq5ZH4mcbqZK71qlUhphonaniAhOcShXgcX5QZJNEzse+4PSlS1DZ7WcadoRvTQ0Ovg7fpefAHplNcEQUCXO39oW5dTFp6YyPJQb2PfsfJKwrEJjOT34Dn0IYJGH1HNCEg+fnaSQxzgywsfQas6td/tdBG09+Gu3QqA5HFgXHAB6oUbx3yenU0DBEISG0rTJ9tEhVFyTdl1WPsqhkrc7jJ8Hlk8tUSeLMsge1jl/gtqQWSr5gZEMY52Zy16VTwhOYBG1JNuKMIVGOTP9Z9l0NfL3MQ1JGjTaXfW0uk6iEz4+9niqKLFUcUrbb9Gr4pnRfo1LEy5mNLEdVGfFZKdaGBVUQq7Wyy8c6iPnEQD8frT6+vOpgHKMk2IohCVmtFidvFabU9U9Q0MSD4EBB5t/AZfHUVARpq+kM0FX8egTqQ4YTmaET7DJ3NGhzixrRnRyGRphkLsk2TUcM3SHLTqM38HY5lEfSJP3PAEP9zwQ36+/ef8q/pffPGVL1LVU8WvLv0V8doTWZHiscBeUYBDJ7V1K0yN46KyDLYeAnvDdygtOYxT9y6OUO/QPrnxeXxiweg146MjZuq7z9467qhtD081/Yg+T9PQ2LXFP+C87E+O9lcw61A0Y3JRNGPmodeI5KcoDvGpZqzzjI3zM3hoR+uIFaIMGhVblubyUdMA+1qt7G8fpNfuZdPCLEz6mdUia7pRNGNyiUXNmLkr8wpRhdMX5FCPnYqcU/smn44EbQK3z7+dG+bdwI/f/zFBOcg3132TdEN61C/UzWZEUUQUT0ywt2zZQkNDw4kes4M+LP094SgslQbJ7yFk60WdlD0kNLORjp6PSGg68bCUKWbi+rYLo1Z5aI01ZFnm1ZpuHtvdxl2pAURB4PzSdBbkps2Ke5coCsxLN6Hvt+MOSLxS082NK4rRqCa28FbVbmV5YRI69ciZEYpmRBZfMMSHDWZqOsfWEkMQRDjpz2OYu5agtWuo95Mgqgk5ByKqGd2uBn55YHh/yJ+u2RdZh7jDPDTpAJC8DmRZCv8+x8G+1nCp5gvLMybLRIUJIAjCiAX3BEFARI362N/ZpElFErSUJp2amaZTxfGdZa8PG5NlmU7XQdqcNbQ7a9nV99TQa96Qk+09j7C95xEyDMUsSL6QDdl3kKQ7c2nySLK6OIWWARd9Dh9v1feyZWnOae/3g+4TvcUhujSjqs3K+w394USFKKDTVc+vD2w57esX5d6FxduJUZ3IZfn3olMZp+1+GAuaEY1MtmYoxC56jYprl+Vi1M6OpcCFGQv555Z/YlAb+EvlX3hw34M8uO9BUgwpHLznIJnxmUB4/nL5wiw0KnHYs+zC3ET8IYkPG800NC5l3dwN9AuPIBHkgpzPUpKSw+bCHEzaMzsnZFlmT4v1rJXyZFnmg+5/8mLLz4cC2HLj5vO1xc+eNZBI4VQUzRgfimbMPAQB5qTFo1LWRaaFscwzkoxaluQnUdlqHfFcohjuM56dqOfNg71027w8squNi2ZoBcpIomjG+IhVzZgdT8IKUUFl2+CYnOLHEQQBnUqHDh1JuiTFuRFjxMXFsWzZMgCa+p28fdiCrmg5hAIQ9BO0dhLob8Xbsh9D6dohsZktuAM2Wvf8hVxyh8ZUixfykyu3jFgqWiH6eaWmm+8+W4NGkIjXqSlJj0dOTESaRfcutUqkLNNETbeTPoePN+p62Lwoe0L3b19AYn/bIGvmnLnMs6IZ00tIkqnptLGraQC3f+IZjqJWjzbzpH7HKnVENWNn71M8efQHQ9ufLf8jFSkXTek1z4QsS7hq3kJynlhUjVtyOaq45Amf+0ifU3GKz3AEQSAvvoK8+AoAbp77/wjJQQIhL6+2/YZ+bwtN9r30eZrp8zTzXtf/UWRaxqaCr5JpmEOCNro+HypR4PKKLB7d3Uabxc3+9kGWFZz+u7CzaYDyLBPqk4K0Iq0Z2xrN7GmJjnZC7c5anm/+Cc2OyhFf/1nRFagy/xOVKnoW4KJNM6KNqdQMhdhDFASuXJxNknF2ZTaJgsifr/oztyy8hU+/8GnabG1YPBayfp3FLy/5JV9Z8xW0Ki2CILBxQSZatci+k5wTywuSCQQldjZb2H5kgOTMEJkJBvSiiV67j3/tauX80vRT9OU4dm+AN+t6RyyPezLN9koePfJtBrxt4eumXcX5OXeSF1ehzGcmCUUzzoyiGTOXRbmJJAaVzOLpZrTzjDXFKRzssp+xYlRJejy3rtLyWm0PfQ4fr9X2cLTfyYVlGcra8RShaMaZiXXNUJziMYAkybgDIdy+IJ5AiKAkE5JkJFlGoxLRqES0ahGTTo1Rq4raB2azw0frgIvC1LhIm6IQAdoG3LxS3Y0kh8uSHEebORc5FMTXVoOoNyFLIdx176BJK0SbXRpBi6eefk8Lr1Z9i2u5FgAnLqQ5C8iLX8Qr1d1sWZar9IqJQS6ryGJpfhJxmhAL0hOi9p481eg0KkozTPR0CBztd7Gn1crqopQJnbOybZAl+UnKQ38U4A2EaOh1UNlqxeoOTNl1BEGIiGZIssTr7b/j7Y4/ARCnTuLzC/5GfvzCSbvGeAj0tw5NOgSNAUP5+piZdChEJypBjUodz3VzfgiAKzDI3v7n2dbzCAPeNlocVfyx7g4AKpIv5FNlvztrWezpJDlOy/p5abx3uJ/tRwcoSDGSGj+yfQ5vkJpO2xkd59OFJMm8Xd9LXZc9onZ4g0529T3NCy33j/j6edmfYnP+PWzw/QuA3aKOU5tjRA+R0oxoRdEMhZPZUJo+q0vnXlR8EXX31PHlV7/MPw/8E4BvvvVNHq15lH9s+QeLMxcD4d+TXqNi+xHz0LGri1MISDJ7W/toGXARlGTk3HB5D39Q4q2DvXzYaKYsK57STBOyDC5/ELsnyN5Wy4hthQAkOYTV18VTR39Eg+0jAERBzdVF3+a8rE/O2nnkdKFoxnAUzZiZ6DUi55akw+FIW6JwOvQaFauLU/igof+M+yUZtdy0Mp89LRZ2t1ho6HXSbvGwbm4qFdmzd+1xulA0YzixrhmKUzzKcPqCdA966HX4sLj8WFx+7J7AiCUUR0KjEkgwaMg06clJ0pOTaCDJqImaG2Nlm1Vxis9COgc9vHigk+AIPVIAtFmlqE3piLo4JK+TkMNMyGFGkzl3qJfHTKOm+xUea/4eJkzYsOHARfnarw2Vnm+zuNla38ulFbMr0mwmoFGJ/O3OlfxreyOCOzruvZHCpNdQWprOe4et7Dw6QKZJNyEN8AZCZ+wtrjC1eAMhOqxuGnudHO13EghFptbvVGtGl+swf6i5BZ8UzugpMi3jiwv+HtFy6ZLPjaBSo4pPQdAaEXVGjAs3Rs3zncLMIU6TxPk5d3J+zp002yt57Mh3MHtbAaizvsv9lZeSbijiotzPU5Z0boStDbM4N5Fms4vWATev1/Vw86p81Ke5F+xpsbAwN3HCLT0mQkiSeb22h4bes/eVnSos3g4eavgaVl8XjsAJx4+Iig05d3BBzqeHKgOIsj9SZk4Ks3GeoWiGwsfJNOmYk5cUaTMiTrw2nn9s+Qdbyrdw7RPhwPSqniqW/GkJn1ryKT677LOsy1/H6uIU4nQq3j7YhyTLCILAuSWpCEKQtzugw+pm+5EBzi89UQnLGwhxoN3GgfbRtRLqch3ir/VfYNDfMzS2LG0zG3O/SHbczF9Mj1YUzVA0Y6ahU6tAmHhFN4WpZWl+EgfaB7F5zpxwoBIF1s5JpSgtjrfqerG4/Wyt76Omw8aG0nRykyK3ZjEbmYhmyLKMPyjh8odw+4O4/SF8QQn/sf9BSSJ0LDH2+MqbQNg5rxYFVKIwlCirU4voNSritCqMOjVGjQpxipLsZopmKE7xCOMPSrRb3TSbXbRb3Ni9wRH3EwQwalUYNWrUKgFREBAECIZkAiEJX1DC6QsSCMkMOP0MOP0c7A5nHiQZNJRmmijNjD9t5sR00WJ2Y3b6SIuwHQrTR4/Ny/NVnWd0ngiiiMoUdnIJ2pMEXAB3/fsIGj36klVR349iNPiDbvp2/4tCTNzFXTykeRLT4i3kj9Czs67LTqJBc9Zy0QrRx+kW42cjFTkJ9DtC1HXZeb2uh1tXF5CgH3/prqp2K8sKlGzx6WDQ7afH7qXX7qPT6qHP4Y2KnrdTpRmSLPFiy8/4oPufQ2O3z/slK9KvnhS7x4MshXBWvozsdyPqTcQt24xp5TURs0dhdlGcsJz/XLOVJKPMRz3P8D9V32HQ38Ogv4dG207+beV93Fh2N75gOAt7wOk/60LOVCAIApfMz+SRXW2YnX4+OjrAhnnpI+7r8oWobLVG7NkqJMm8XN1FU78rMteXg9RatvLPw18ZGhMQkJG5LP9eLsv/ckTsmkpm0zxD0QyFkTDp1BSlKYkJJ7OlfAvyfTLdjm4W/XERA54BHjrwEA8deIjl2cv5zwv+k82lm4nTqnmlpht/UEIQBNYUp9DoNtJqcbO/YxBPIKw/I5VNH4mQFGBX3zO82/U3Bn3dhOQTmnnbvF+wIu3qmFtUnmkomqFohoJCJFCJAuvmpvJaTc/ZdwayEvTctqaAAx2D7Gqy0Ofw8fS+DvKTDawuTiEvefZWhplORqMZ2jkrsbrDc+UBVzgJ1uYJYPcE8YempgaXIISf/xINGhKNGtLidKTGa0mL1417LXWmaYbiFI8AwZBEs9lFfY+DtgE3oZNWmAUgLV5HVqKe1HgtqXFako1aDFoV4lkejoOShMMbxOry023z0mXz0Gv3MegJsPtYaY3sRD0ri5IpTo2L2MP2vlYrlynZr7OCPruXZ6s68AdHf5MXRBWmtTch+z3IwQAIAoG+JkRdHNqcchAEBDE2nWEW21HUdbtJINyLMZVUvrnkBYza05cX+ejoAIlGDeVZCdNlpoLCpHNBaTr9Dh99Dh+v1nRz44r8cbcG8AUkKlutrJubNslWzm4CIYmuQQ9dg1567B56bL4z9rSKFiZLM3whNw8d/ir1g+8Pjd1S8tOIOsQlrwtn5YsnbTtACoFKeXxXmDq0apHCVCPFaXHDKk7dyrf5f5d+kQ/bPuSqx64C4Hd7/4Pf7f0PXrntFa5ZegUQzpTrs/vosLppGXBPWzBNnE7NxvkZvFTdTVXbIAUpRopOU5lkb6uVBTkJmCYQoDUegiGJl6q7aDGfua/sVLG771keP/LdYWMVyRdy67yfY1QnRsSm6WYmzzMUzVAYCb1Gxdy0+LOuJc1Wsk3Z1Nxdw8PVD7O7czfP1D9DZXclVz52JQBXll7JvSu+Q3N3Bm5/WMyyEg2oRBFrLzT0OrF5Aly1OIc43cjfNVmWsfg6eL/rn+zrfwFP6ETbjDkJK7mz7A/EaybWYkphalA0Q0FBYTqZl2HiQ50Zp2/khMmPoxIFlhckU5ZpYmfTAAe77bRbPbRbO8lO1LM0P4mS9HilLec0cVwzAh4XnWY7uAMk+LrY2+XlQCATGQGJUwOqdGoRo1aFQatCr1ahVYtoVSJqVTgbXCUIw3x4kiwPtVY+nijrD0p4AicyzmUZ7N4gdm+Qdqtn2PWSjBqyEvRkJ+rJTzaOqsL0TNSM2LU8Bhlw+jjQYeNwr2OYkzDRoKE4NY7CNCPZifpwaZNxoBZFko1hJ/qc9HggnIneZHbS2OukZcBFt83LSwe6SY3Xcs6cVOakTb9z/HCPg3PnphF/mkmDwsyg3+Hj2arO0/bPOhOCqELQhz/DiGpEvQltdikhpwVPwzaMFRejMsbO4l1ICtHdvwfd0cOo0QLQpTZTsvwuNGr9WY9/q64Xk16jlMFRiFnUKpHNi7J5bHcbvXYfHx01c95psvhGQ1X7IMsLk5Vs8Qni8gepbR6g1Rqkx+4ldJoWF9HORDXjgPl1/tnwb8PG/m3RUxSaFk+l2adFlmUkpwVX3TtDY6qkbIzl58XswptCdCMIUJwWx8LcRApTjKfNeEvUJ3Jl6ZW4v+fmL5V/4d9eD39vNj+6mYuLL+aNT7yBXqOiINVIQaqRdXPB4w9xpM/JoR47nYOeKXWQz0mPZ3FeItUdNt6s6+X2NQUjOin8QYkPG81csSh76oz5GN5AiBcPdNH5sUWJ6cDhH+BPBz9Nt/tEM8li03Kun3MfOXHl025PpJlp8wxFMxROhyDAJQsy0fcqn4MzkW3K5lvnfguAZmszVzx6BYfMhwB4ueFlXm54eWhfg8qEWtRRbFrJRRVaPjwUosfu5dE9TVy1KJ+MBB0D3nbqrO9QM/AWfslNp6t+2PVUgoYi0zKuLf4+2cYyJTM8ylE0Q0FBYbpQiQIVuQnsarKM6bg4nZqL52eyqiiFva1WDnbZ6bZ56bb1EKdVUZGbSEV2AgmG6Q0Ini3IsozF5ad1wE2rxU3noIeQJLNBE0IWddQGMshSezhfc5SGhGXEJaaSZNSQoNdg0qsnva2XLMu4/CFsngA2TwCry8+Ay8+A04fdG2TQHWDQHeBQT7iVV7xOTX6KgeLUOApSjcP8kjNZMxSv5BQjyzItAy4q2620W04sgsTr1JRnmSjPMpESp52yB2GtWqQ8K4HyrARcviBVbYNUdw4y4PTzcnU3RalGLijLIHEab4whSaaqzTohh4hCdGN2+ni2sgOPf+JZhsbSdQDIQT/uuq2oEtKRvM6YmXjIskxVzf9Q6soAtEhIDBblUJ5z66jPEZRkXjrQxS2r8kkyaqfOWAWFKSTBoGHjgkxeru6msm2Q/DNk8Z0Nf1Bib4uV9fOUbPGx0jXoobF7AFObFV9QYrdlAEmYOfeVsWiGJEv8ruYm2p01Q2P3LnyM4oTl02bvSPg76vC1H7dJwFhxIerEzIjapDAz0apFFuUmsiQviUTj6OcCBo2Br6z5Ctnx2dz09E0AbG3eykUPXcT/Xf1/lKSUnNhXq2JRXiKL8hKxewPUdtqo7bTh8k1NJYrz5qbRafUw4PLzVn0v1yzJGXGedbjHwaLcRDITp3467PIFea6qk36Hb8qvdTJByc+TR3/I3v7nh41/bfHT5McvmlZbopVYn2eAohkKp2dlYQpz0uKhN9KWxA7FycXU3VPHi4df5Mm6J3ms9rFhr3tCDgg5qLa8TrXlddAQ/g8crk1AEuynnvQYBfFLWJWxhXMyb0YUYnshebaiaIaCgsJUszgviT3NVqRxRBInGDRcVJ7B6uIUajtt1HTacPlD7G62sLvZQkGKkYU5CRSnxyktHyeILxii3eKhdcBFy4D7lOz+OK2KvpQlqBMN3BAvojv4MqqEdFbn6NGkJE2pbYIgEK9TE69Tn5Jc5wmE6LV5wxWmBz1027w4fUHqux3UdzsQBchLNjI3PZ6SjDhUvYdmrGYoTvGpQpaxegI8WddBlyOcKSsAc9LjWJyXRH6yYdojQuN0atbPS2NlUTKVbVb2tVppGXDzr52tnDMnlWUFSdNmU02njdXFKePOileIXgacPp7Z14F7EhziJyOotcQtuRz34W2IhnD58aCtD197NYbS9Yjas2dcTzdmeyMP1H4CO3bWsIYKFpJYch5FmWvGfC6PP8TzVZ3csrpAyY5ViFlKRpnFNxr2H+stPt7jZxP9Dh+Hexwc7nVg9wQQZT+rx9DWIhY5m2b4RD8vtNw/zCH+qdLfRNQhLvlcIMtocxeASk1woB190fKhHlUKCpOFShRYlJvI6uKUCd1Db6y4EXOxmU2PbGJP1x4+aP2A8v8t578v/W8+t/xzGDTDJ+EJeg3rStJYU5xKY5+Dfc29MMmVxNUqkU0Ls3hsTzutA272tVpZWTRyWdr3Gvq5ccXUtnQadPt5rqqTQff09lrf0/ccjx35zrCxa4q+y4bsO5SsxBGIxXmGohkKZyInSc+6klSQp/feMxMQBZEt5VvYUr6FR657hBcOv8B33v4OhwcOn/G4kRziqfoCypPOY1PBv82aNhWzAUUzFBQUpop4nZqSjDgae50TOsfaOamsKkrhSJ+T2i4bHVYPbRY3bRY3+mMJlAtyEkg36SbR+pmLJMv0OXy0DbhptbjosXk5udCiShTISzZQmGKkIMV4SgJsKEo0w6BRUZQWR1FaOEHpeBvFVoub5n4Xg54AbRY3A1YrexogOTGB5cnlpPj7Mc5ZMaM0Q1lJngLaBtwYLHbc/iD9Th9qUcuivESW5iVFRakKvUbFupI0yrMSePdwHx1WDx8eMdM56OHSBZnopsHh5gtI1HbaWVF4+l7KCrGHxeXnmcrJd4gfRxWXjGl5uI+lLMu467YC4O9pQF8QmTK3p6Ou7u/k2/Scx3m8xEuoskqYX/zvE1qItLoDvHigi+uW5So3b4WY5by5aXQOehhw+nnzYC9blo6cxXc2AiGZ3c0WLizPmAIrYx+ry09jn5PDPXbMTn+kzYkIp9OMwfbd/Hbg2ziD4bJkRnUS31/+Nga1KWK2+jrq8LVVo8kowTB3NbqccnQ5s6+sscLUU5Rm5KKyzDFlhp+JVGMqu+/azRHLEW5/9nZ2d+7mK69/ha+8/hX+7+r/49PLPn3KMSpRoDwrgbI0HZY98TQPOIHJe3ZMjddxQWk6Ww/18dHRAbIS9eQlG0/Zz+zwsb99cNKu+3FaB1y8WtODNzA1z8UjMejr5Zmm+6izvjs0dkHOZ7iy8BtKZuJZiKV5hqIZCmdCqxa5vCIbURQm89Y6KxEEYchBDuAP+fnJBz9hd8dBFifcSZN9L32eZlakX0N1ZyeVPVUYQueQbijgioXZirNhBqNohoKCwlSxJC9pQk7x46hEgbIsE2VZJmyeAHVdNuq7HTh9QfZ3DLK/Y5CsBD0LcxMozTRNehnvWMd+zEHcZnHTbnHj/VhiSZJRQ1FKHIWpRnKTDWf8/UWrZmhUIoWpcRSmxrFhXjpWtx/L0WrSHY0cDqbx0aCOtsF4VKKJIsHH/GwnRalxM6JPveJXmUT6HT62HTHTbbXxpYwgKkFgRUEyiwrSMWqj71edEqflumW51Hbaeb+hnyazi0d3t7F5UTYZCVMfpVLVZmVZflJ4sqYQ81hcfp7Z1zFl5TBPQZbQZM4lONCGOiHsFJMCPgT11LUjGA1Byc/hun+S7wj3nZrPfBzZ6VxV9K1JsavT6uGtg71cPj8F5ZujEIuoVSKbKrJ4fE87bRY3+9sHWVYwvgCpmk4bywuTp7UFSLQiSTLddi9tA26O9DsxT3OZ3qhHliA9H3f/YZ7uvR8nFpLFDDYWfolzsm+JnFmyhK+9Fn9HHQBBSzuyvErJ5FSYdAxaFeeXpjM/O2FKzj83ZS47PruDH77zQ3667acAfObFz9Dv7ueb67454mdaEARS43TEG1SUSD7arJMXwFORk0DXoIf6Hgev1fZw2+qRK5PsOGrGJQUnverIvlYL2xoHxlX6cLwM+np5oO6TmL2tQ2M3zPkx67JG37JH4RhROs9QNENhNFxYljFpgU8KpyIIAmvyK7ip7By2HiwleCxVLL+sgqWZ5/F6bQ+D7gBP7Gln/bw0luQlKt/RmY6iGQoKCpNIfoqR1HgtA5OY3JBoCFfsWjsnlbYBN3VddprMTnrsXnrsXj5oMLMgJ4HFeYkkz9K2nf6gRMeg+1g2uPuUSl9alUh+ioGCFCOFqXHjX4eMYs0wmA+R7mgEoFRnh/wUDvc6GXD5Odrv4mi/C4NGRWlmPPOzE8gw6WJWU6LPUxuDeAIhdhwdoLbThgzoRMhK0JObZEAypSIJ0ftrFgSBRXmJZCboeKWmG7s3yNOVHWxelE3hOHu9jhaHN8ihHgcLcqZmcU5h+hhw+nimchod4oAgqjCUrIKSVciyjL+7EW9rFar4FIzzL0BQTf/3zh904939AvnED43JpUu5Om3FpF7nUI+DRJ3Mukk9q4LC9JEar+O8eWm8e7if7UcHyE8xkhY/9kyKkCSzq2mASyumtvxttOENhHD6ggw4/ZidPvodPjoHPfhneEn0iVBjfYd/9H8ZAAGBVaziCvlK1ANpyBnBiGiGLIVw7Hxy2Jix4qKYnVQoRC8FKUY2Lcqa8iBdURD5ycU/4dvrv03iz8JlYr/99re57737ePDKB/nUkk+NeJyAQHq8nuR4AytzUqjpdBGSJuZMFgSBC8sz6HP4GHD5ea22h+uW5Z4SjBuUZI70xugvgAAA+oVJREFUOVmYOzllbUOSzNb6Xuq6Tt9XdrIJyUG2dz/C8y0/HRqbl3gOn5v/IBpRyVIcD9E4z1A0Q2E0lGWZlPWVaWJuRjwmnZ4XD3QNPYPnJhm4bU0Bbx3spdns4v2Gftosbi6Zn4lBq1TrmKkomqGgoDDZLM5L4t1DfZN+XlEQhspnu3xB6rvt1HbZsXkC7G8fZH/7IAUpRpbmJ1GUapzR9wxZlrG6A7SYXbQMuOga9BI6KaBZEML+vYJjJdGzEvSTktgZK5oRv/AiVsUls7IoBbPTz6EeO4d6HLj9IQ502DjQYSM1Tsv87ATKskzEx1hry9iyNsqQZJnaThsfHR3Ad+wheF5GPBtKEimUp9ahPNlkJOi5dXUBr9X20GZx8+KBLi6ryKI8Y2oXUva1WpifbZrRN9mZjnmKeoiPlZB7ECQJdVI2AMHBblSJWdP22eq07sdUXz+03ZEcpKzsNhLEqZn87mmxMMfoJWsaqjooKEwFi3ITaTa7aBlw80ZdDzevykctjr1cU323g5VFKaTExWY0ayAkYXb6GHD6cftDuPxBfIEQgZBMUJIIhmSCkkwgJOEPSnj8oaGMFIWzs7v3GR4/+r1hY/HqVK5JvZdgb3PENEPye3DufX5oW1e0DG12mfI8pDCpCAKsKkphXUnqtH62EnQJhH4U4sfv/Zj/+uC/8Aa93PH8HTx98Gkev+FxjJpTS5kDqEWRc+elsTA3lbcO9tJr907IDo1KZPOibB7f007nYLhd1Pml6afs5/aHaB1wTehaEA5YeqW6mzbLJDdKPwPuoI0HD36WdmfN0Ni9Cx+lOGFyAzJnO5GeZyiaoTAaTHo1FylthaaV/BQj1y3P5fmqrqFWGQaNiqsWZ3Ogw8a2RjPNZheP7G7lsgVZ5KeMrH8KMwtFMxQUFCZKeZaJDxv6p3TtJ06nZmVRCisKk2m1uKnusNFsdg2VDE80aFian8T8bBM69cwI7AqGJDoGPbSYXTSbXdi9wWGvJ+jVx8qJG8lLNkzL+452zRAEgXSTjnRTOueWpNFmcVPfbeeo2cWAy8+2I2a2HTGTn2KgPCuBkvS4mPi8KE7xcdJv9/FWQw+99nB50tR4LReUppOXbESU/TB9ayGThl6j4uolObxZ10NDn5PXansIlCWxegrbfpudfprMLkrS48++s0LU0Wf38lxVZ8Qd4oIgYChZhb5oKYJKQ6C/BU/jDsT/z959x0da1vv/f9333NN7Mul1d7Ml2ZLtywJLkSLgooAIggL2co4eFY961HPU49Gvv2PDY8WGBVRAEaRX6bC972azu+m9Tu/l/v0xS9iwNckkM5O9no9HHpDJ3DNXNjPzvq/7cxWTHXPjldMeJC3erfym6SMsZQkb2UiXfpTF9f86rc8J0D4cRKuRKJz2ZxKEzJMkiUvrS7hncwfDgRibWkc5v8414cdJqSqvtQyzcVn5NLRyegz4Ihwa8NM+HGQ0GJ/R5XXPBsG4m2gyxJ+PfIlW39ax2y+p+DgrXFdRalqALMmotSuzkhkAkkaLYe5qIq3b0BbNEfv6CRmnU2SuWFKatXNsWZL55sXf5JpF17DqV+kC7SOHHuHqv1zN4zc/jl45+cDbIque966pYnunm00tI1O6GOQ067isoYTH9vaxq8tDoVl3wlnhA74ohwb8LCmf3FmVLxLnoZ09GV3m8FRUVSWYcHPn/g/SGzqIQWPl0sqPs674PZi1jhlpw9ki2/0MEJkhnJlL60swaHP/IuRsU2Y38u6VFfx1e/fYjHFJklhe5aDCYeSJfX24Q3H+vrOH1TVOzplbOCv24hROTGSGIAiZYNBqmFNkzsje4qcjSRK1hWZqC814w3H2dHvYf3T2+IuHhnitZZhFpeml1SezwmO2eUIxOo4uid41GhrXt9RIEhVOI7WFJmpdZhxG7YwOIMq3zJDlN1caiMaTHBoM0NTno88boWs0TNdomH9KEtWFJuYXW5jjMufsuakoik9QMqXS7Q5x/5EuYqoWnUZm/bxCllXYZ8Xe2BpZ4u1LSjE0D7Gnx8uLh4Z4Z0MEh3n69qTa1j4qiuJ5qNsd4h+7enNquV5Jc/R1+sbsbFUFVJim3bdjiRDNe37L3ZFfkSDGNrZx4dKvstjaMC3P91YqcGQwQMgdosqVn7NkhbObWa9wyaJ0sWJ7h5s5hWYqnMYJP87hgQB93jBl9okfO1MSyRR7e7zs6vIctzeRkDmhhJdvbNtAUh3/b/yZpfdTY20cd9tMZ4aaShJp24FhzkokjYK2pA5t8VykaVpRRDh7GXUarlleQak9+6vJrCxbSeK/Enz2yc/y060/5Z9t/8TwbQOfO+dzfO9t3+Zkr35ZllhTW0BNgYnH96YLCpNVV2xh/dxCXm8d4fnmQRwmLZXO42frPbO/H4vOQK1rYit+eUIx/ra9G/9bZhpMl2Dczf/uvIpAYhQAvWziU0v+RLl54Yw8/9lKZIaQy5ZW2Cf82SVkTrHNwJVLSnlkd9+4wa5FVj03ra3mpUND7Ov1sa3DTZc7xBWLS3GcpXu2ni1EZgiCMFWLSm0zUhQ/lt2oZcP8Is6ZW0hTn4893V5GgjH29njZ2+Ol1GZgcbmN+SWWnJ0NHIgk6PaE6HaH6RoNHTcb3KJXxorgVU4TOmXiK1ZmWj5mhl6rYWmFnaUVdrzhOM39fg72+3CH4rQdnYkvAWX2dP+6qsBEsUVPrpRPRVF8AlqHg2iHPcSSKVRgQYmFC+YXYc6zNfNPR5YkLlpYhKKR2Ns1ROtwkFrJCEzPaKBeT4Rud+iEF6eE3NQ6FOCxPX05u4SvUlCBUliFvnIJkiST8A0S7diDacklGRtlFU+ECW95iBpcrGYlPqeBWxf8HzrNzF6ATqnw6J5erlttoEQspS7kobpiCw1lNg70+Xj6QD/vW1czqZPSVw4P857VVdPQwqlJplT2dHvY1u4mEJ2ZgsnZKKUm+fn+W2n1bRt3+zklN3LtnK+ecl/dmciMY/doko1W9OWL0o8t5WZHUshfVoPCdSsrc2pLCY2s4SdX/QSDYuD7r38fgDs23cGBwX082XjuKY8tthm4eV0N/zw4SFPf5PfpXlPrZCQY5dBA+hz2hjVVON9SkEikVB7Z3cvVjeVnXFwaDcZ4YHv3jH2+H3S/zK+aPjLuto81/FYUxGeQyAwh19iMWi44wdYQwsyaW2Th4kVFPNc0fg9YrUbmkvoSqgtMPHdwkAFflD9v6eTihcUsKhVbCc52IjMEQZisOS4zRp2GcBZWZ9VqZJZVOlhaYafHE2Z3l5eW4QD9vgj9vggvHhqittBMXbGFWpcpawXySDzJcCDKoD/KgDdCrzdyXL9MlqDcYaSm0ERNgRmXRZez2ZuvmWE3alk7p+BonzvG4YEAR4YCjAZj9B79u9AyglYjUW1XqCgOY9UrJPQp5CyVVWdXNXea9HnDfOXBfbzS3Mec4hR6Ready8qpdDmy3bRpI0kS59e5SCaikISWwQBerYzNNH37I4uieH7Y3eXhheahnF7uV5JkTAvPH/s+0rqNVMhLtGsvhuplU378gdG9+A69SMnRhcvrpcU0LPwcGnn6VlQ4lVgixd939PDuVRUUW0VhXMg/Fyxw0eVOj+B86fAQl9aXTPgxut1h2oaDVDiz8z48kUFfhKcODDDsj2a7KbNah38PfznyJQbDreNuv7nuf1ldfM1pj5/uzEiGvERatox9nxjtEcsYCtPCbtTyntWVWA258zl4rO9d/j2ub7iec357DgBPtT7DLfEB7lj6dk41+PaNpeArnUaePzg4qUGZkiRxWX0J3nCcAV+Uh3b2cMPqKvRv+ad6ozD+jmVlzD3NSlaDvggP7eohGJ2ZC1Wtvm3jCuKXVnyCiys+jFGxzcjzC2kiM4RcIpFeNj0XZjkJsKzSgTsUZ0eH+7ifzS+xUmo38NT+AXo8YZ4+MEDbcJC3LSrO2aVFhakTmSEIwmRpZImFJVZ2dXmy1gZJkqh0mqh0mghGExzs97O/14s7FOfIULrwqZEkSu0GKp1GKp1Giqz6jBbJkykVfySON5z+cofiuIMxRkOxE67UJZFeqaXSaaTKaaLcYcyb86R8zwxJknBZ9LgsetbPK8QXjtM+EqRjJESPJ0w0kaLTHaJbm953+ld7W3HZbNS6TNQWmik0z9yABVEUPwOdIyGePziIXoZyu5EKh5GYxUTuLBo9PaSjM8YLOnQMBsIcGfKzqGx6XjLtwyEG/RFR0MthqZTKi4eGshrGk6Um0ktu6ivqp/xYfcPbMR86hJFCosTwlNpZOveLU37cqYrEkzy4o4frV1VSmId7vAhnN72i4fKGEh7Y0cP+Xh9zXebTFiNO5JUjw9ywunQaWjgxyZTKptYRtrW7c3oAUT5Lqgk2D/yVv7V+47iffWDhT1lWeNmkHzuTmZEMugnufjL9jaxgrFuL1lUz5ccVhLeyGhTevTJ3C+JvWFe5jtTXUnzxmS/yw00/5J6uPbw+2sV1Sz+ISW895bFLKuwUW/U8uqcPb3jiy6krGpmrl5Xz1+3deMNxHtzVw7XLi4+7XyKl8o9dvcwvsXB+neu4JW6jiSSvtYywp8s7I5/x4YSPV/r/xNNdPxu77T9XPkeBoXLan1s4PZEZQjaV2PRUickFOeX8OhcdI0FGArHjfmY1aLluZQXb2t1sbhvh8GCAPm+EyxtKqCoQf8ezgcgMQRAmor7MljPX4c16hVU1TlZWOxjyRzkyFODwYABPKE6PJ0yPJ8zmtvR9rQaFQrMOq0GLRa9g1mvQaWQUjYwiS6iAqqpIqTh1apRESmXniIdgXCYcSxKOJwlEEwQiCYKnmSlvMygUWfUUWw2U2dOrqOZLEfx08j0zbEYtyyodLKt0oKoqw4EY/R4fBQkd/kiClMrYa+fVIyPYjVrqy6ysrimY1naBKIqfkXVzC7n9sgUMe/1UyWfXiaosSdQVWYgkEiTVBM0DfkKVCWyGzC/JuKVtlI3LyjP+uMLUhWNJntjXR8dIKNtNmRTr6nehqikkSUZVVaIdu1Cc5Sj2ic1GbTr0ZyqGjxmx1LCO+Y7cGYUbiiV5YEc316+qyqllUwXhTFQ6TayodrCz08OzTYO8327ApJvYacqwPzql5XUzIRhN8NiePno84ay2Y7ZKqUkOuF/gyc4f0xs6OO5nty/7O5WWxVN+jkxlRqz/CJHWrWPfWxqvQDaeuugnCJNh0mm4dkUFdlNuF8TfIEkS37v8e1y7YCNX/fnttATdfG/TD7mw5kK+suErpzw2vZx6NU/s66N9eOLnpWa9wrUrKrh/WxcjgRiP7unDWqwin2BE+uGBAK1DQRrKbFgMChpZGtsSY6Zmh4cTfr66Zc3Y9wvs53L9vP8WBfEcIjJDyBa9IotCag7SyBKX1pdw/7YuTjRuSpYk1s4poLrQxFP7+vGE4/x9Zw9Lym2cP9+Vs3u0CpkhMkMQhIkotRsoMOsYDR4/0CpbJEmi2Gag2GZg/dxCPOE43e4w3e4QvZ708uX+SOKEs7jfSivFqSlO75v+6uAwcfXE/VmNLGE3arEbtTiMWgrMOpxmHYVm3axebWU2ZYYkSRRZ9ZRY7MwPWUFVuaW6htbR9GzyLncYbzjOptZRNrWOsq1jlJ/dvHLaJv6JovgZuu3cWn7/cjPkZ01wSqSjhXH9kI9wPMXje/u5flUtipzZUTdHBgMMB6K4xCzXnNI2HOSZA/0zdvFvukiSfPS/EmoyTrhlC6ZFF6Ax2U97bEpN8lr/vbw8/Auu5mqKKUaqX0dRDhXE3xCMJvnb9i7evVLMGBfyz7lzC+kcCTESjPFc0yAbl5VNeOmc11tGSKZUNPLM7xHU743w6J7eMzr5Fybmtf57+WfPr/BE+0kxPo8WO9/GTfP/P0zK6T/Pz9RUMuMNGlsRGquLZMiLednl4kKVMC10isy1KyryMvPPrVrPA2tv4NJX7wbgxY4X+fen/50fvv2HKKfYXMyg1XDN8gpebxlhS/voCYsOp2I3arlmeQV/29FNny9Cb8rPwtITvz+TKZW9Pd6JPUGG9ASb+E3Tx8e+P7fkJt499+s5uwfe2UxkhpANc13mjF+TETKj3GGksdJxytl9pUcHeb1yeJg9PV729fpoHwlx8cIi5rjM4rN+FhOZIQjCRCwqtfJay0i2m3FCkiThNOlwmnQsrUh/hkXiSUYCMUaDsfRs72iCYCxBIqkST6ZIpFQkQJJAJ8lYDVo0EswvtqDVGjDqNBi1Gix65egscwWTTnPW5uKszQxJwm7S0mg201jlIJZI0TIU4ECfj253mK7RME7T9E34E0Xx6aCqKIkImmQCg9ZDSFuUfqfnMUUjs7DYyt6+AP2+CP88OMhl9SUZ/UBSVdjcOso7lpVl7DHPGqoK0TAkYhAaAUvplF9z0USSV48Ms7srOxcCp5Ok6JBkDbLh9MszxxNh7j3yZXaOPgHAdmc371v4ORQ5d2dipwvj3bx7VaUYZJIPZmFmTJaikXn74lLu3dpJ63CQ/X0+lpRPrNAZiCboCYSpLpzZmTPN/X6e3t8/qb1uZ1wevOZUVeXl/rt5qO3b2HUleGMD436+uuga3ln7JUyKA1ma3gvCE8kMNZUu2EuyBo3JjqnhIpA1Yx0ZQZiyY96/Rp2HS5Y3UmzL3+2HLimey+HLPs38Z34CwE+2/ISnWp5i04c34TQ6T3qcJEmcW+ei2Gbgqf39xBIT29iqyKrnXY3lPLirneFInIP9fi4qS6HkwOldPBXlma6f88/e35BS04Osrqz6DJdWfjI7F4PyIDNyicgMYSbUl9lwqGfwgTUN1wmEM3NenYuWocApB8tqNTIXLypmfomFZ5sG8YbjPLKnj+oCExfMd+XlgDeRGRMjMkPIKSIzclJDuY1NraN5szWfQauhwmmkwmk87X3lVJRlfh2aZIJolUFkxmnM5szQKTL1ZTbqy2wkUyrnz3chT+Nkp7wriv/sZz/je9/7Hv39/TQ2NvKTn/yEtWvXZrtZAOgTfhoGH2V57304oj0ArOTveAyV7Cq7gQPFG4kqOTASY5IMOg3ziy3090BTn59yu5ElFZmblQVweNDPSKAgP0/+syHsgd1/gc13grs9fdtTd4FzDqz7ODTeBEbHhB4ymVLZ3e1hS9so4dPs25Gv9FVLQdKQCvuQDVaCe55CX7kYbVHtuPv1D+9GPrSDQuLIaLi69oucX/Z+NFLuf3SGYkke3d3LB86bk+2mZJXIjPxTZNWzfm4hr7aM8NKhISodxuP2cz2dPm+YYtvM5ciOTjcvHRqa8GzFmZYPr7ltQ/9gy8ADuGN9jEQ6AcYVxC8s+yBvq/goVl3hjLXpTDMjGfQQ3PcsutI6DDXLAZA0+bGctZCWj5nB4cmf8+WKapOdr2/4T+5veoCm4SYOjRzivLvO47GbH2OO89TnMXXFFlyWah7b28egLzqh5y13GHlXYwW/3yfhj8R5eHcv166oyerStQOhFv5311Vj35sUB59b9jcKDVUz3pZ8yIxcJDLj7JGtzNArMhcsKILmU9xpGq4TCBOjU2QuWljEI7v7TnvfSqeJ962rZnPbKLs6PXSOhvjT5k4Wl9tYXVuA3Zj7nw0iM9JSKZVwPEkoliQUSxBNpIglUsSSKVIplZR6dB9dSUKWQUMVhdo4A139aIwWHF0vI5fWYyqdg6J5s3AhMiP/5XI/Q2RGbrMatNS6TLQOBbPdlIwRmTE5Z0s/w27Ucs7c6b3ml/uVnWPcd9993H777dx5552sW7eOH/3oR7z97W+nubmZ4uLirLatxv06Gw9+CW0qwluvidsiPVzYdgfndvyCRxf9Lx3O9VlpYybYjTpq5xayqcXHC4eGKLEZKLJmrvCgqrC5bZSrlorZ4qd15Fm471aIn2BNf3c7PPlleO5/4MY/Qt2lp324cCxJU7+PnZ0efOF45tubQyRZg6F6KQDhw5tIhX2ED7+OUliNdHQJukOtD1LaHwEMrGQlNQuuYbHrsiy2euLiyRyv0E0zkRn5a2WNk7aRIL2eCE8fGOD6lZUTGiGYUqF9ZPo7DKqq8vLhYbZ3uKf9uaYq115zSTVBm28Hh72vsXvkScpN9TR7XiGcPH5P+EsrP0Fj4ZVUmLOzZcWZZEZ8qJ3w4dfT/z/Qir5ycV51OoT8zYzJnPPlqhsW38CVdVfy7vvfTdNwE+f89hweuvEh1led+jPJYdJx4+oqXjo8NOEVjkrtBhaV2jjY76PfF+Fv27t51/IKLPqZ7SarqkpPsImf7rt57LZ31X6ZDWW3IEszX6TPtczIJyIzzg7ZzAyDVgPJU3wuZPg6gTB584osFFp0jAROvxesViNzfp2LJeU2XjkyTMtQkH29Pvb3+VhUamV1TQEF5hxYzuQEzsbMSCRTDAdiDAWiDPujeMJxvOE4vkh8EgOlHUCI87X7KVD80LGFnx9MoNcq2Axa5imjLIo0ARDtb0Epb0DR5uZrQTixXO5niMzID8sqHbOmKH42ZkamiH5G5kx4vvxtt93GSy+9NB1tOa0f/vCHfPSjH+WDH/wgDQ0N3HnnnZhMJu66666stOcNNe7XuebAZ1FSESRU5Le8pWVUJFSUVIRrDnyWGvfrWWppZqysclBbaCKZUnlsbx/RRGZnEx8a8DMaPH2n4ax25Fn40w0QDwPq0a9jHb0tHk7f78izJ3yYUCxBc7+fR3b38uuXW3mxeWjWF8TfSldRj2wuwLhww1iA7N/586MF8TR1wYq8K4jnCpEZxzvbMmMyZEni7Q2l6DQyfd4IW9pHJ/wY7mCctuHp6zQkUypP7e/Pm4J4Nl5zbxR53NE+9o0+yx+aP8Ptry3k9tcW8oXXF/Pz/bfwTPcvGAy3sWvkccJJH7KkUGycy4cW/Zz/Xv0aPzy3mauqP5e1gvhbnSgzwi1bxzodAKbFF4tOxySJzDje6d6/Z3rOly9WlK1g80c2s7x0OYPBQTb8bgNfee4rJFOn7m8oGpm3LSrh6sZyzPqJFZEteoX6UhtGrYbhQIz7tnYxEpjYrPOpSKlJvrn9Qn6451piqTAAH2+4iwvLP5C1grg4T8kMkRnTS2TGCWToOoGQGZIksarm5FuBnIjDpGPjsnKuX1VJdYEJVU2v1Hj3pg7+vqObI4MBUjm0XdPZkhmxRIq24SCvHB7mvq1d/OLFFu7b1sU/Dw6yp8dL52gIbzhdEJcAk06Dy6KjwmFkjsvMghIL9WVWFpfbWFJhY0m5jfoyKwtKLMx1mal0GOnRVzOqmnk+VoeKTCSeYl7k0FhBHOAfwTrufLmT+7Z28WLzEM39foLRky/RL7xJZMYJiMzIG7WFJmx5sGrI6ZwtmTETRD9jaiY8BN7r9XLppZdSU1PDBz/4QW677TYqKiqmo23jxGIxtm/fzpe//OWx22RZ5tJLL+X110/8BolGo0Sjb17Q8PmOn300VfqEn40HvwQnvEg1noxKCth48Ev8Zs1jebsMhCRJXL64lL9s6cQbjvNs0yBXLSnN2B53qgqvt4yIvcVPJuxJj+JTVeB0+yemQJXhvltRb9/PaNLEgC/KgD9CtzvMSCCa88v9TjeNyY6l8e0AJFIxurbcRVUq3XHt14wwd9VHsSliOf/JEpkx3tmYGZNlM2q5eFERT+0fYEvbKFVO0xntSXSs5w8OUFNgS8+oyaBoIslje/roGDnBaOocM92vuaSaIBgf5bX++6izr6U31Ex/6AibBu6bUDtXuDayyHE+8+3rcehLJ3TsTDo2M1Q1RXD3U6RCnvTPbEWYGt421iERJk5kxngTef8ee87H7QfyeonDClsFL33gJT726Me4d9+9fOeV7/CdV75D/+f7KbGUnPLYumILlU4jLzQP0tTnP+PnNOsVLl5VxaN7BnGH4ty/vZt3LC2jusA01V/nlPyxYX7d9LFx20TcvuzvVFoWT+vznow4T8kskRnTS2TGW0zyOkG+Z0auW1Rq4/WWkVPuLX4iFQ4j166ooN8bYWv7KK3DQbrcYbrcYcx6DfWlNhrKbTgnuM1UJs3mzFBVldFgjPaREO0jQXo9Yd46FsGo1VBk1VNk1eM0aXEYddiMCma9gjzp66OLqFFVIvEE0X3PIkc8APgUB69LDQTjCZKJFP2+CP2+CHSnjyo066hymqh1pfvMisiW44jMeAuRGXlFkiSWlNt4rWUk202ZtNmcGdkg+hlTM+F/mYceeoienh4++clPct9991FbW8uVV17J3/72N+Lx6ZthOjw8TDKZpKRk/IWQkpIS+vv7T3jMd77zHex2+9hXVVXm92JrGHwUbSpyBheq0mRUtKkI9YOPZbwtM8mo1XDVkjJkCY4MBtjfm9mAPjzoZ8AXOf0dz0a7/3J0WZvTnbS8IYUaD/HK337KH1/v4Kn9/ezq9DDsFwXxYwXjbu7e/2kKUrax2+as+giKKIhPydmaGZ976l+55/CX+e/OZ3h30x/4nx1X8ruDn6Zl60UoqfBZlxmTtajURn2pFRV4cn8/4fjEVibxRxI81zSY0TYFogn+tr07LwrikJnzlKSaoMO/h3uPfIV7Dv372GzvN2Z8f2PbBp7u/ik/338rD7V9+4QFcenoKadFKWCubQ3vqP48V1Z/li8uf5QfntvMLQt+wJria3O6IP5Waiw61ukAMDVcLDodU3S2ZsbJTPT9C6n0OeLuezPelplm1Vv5y7v/wq+v/vXYbWU/KOOePfec9liDVsMVS8q4bmUFrgls82QzKrxndRVldgOxRIqHdvawo8ONOk0nzC/2/p5vbNtAd3A/AC5DDf+zZnPWCuJw9vZtZ4LIjMwTmfEWk7hOMFsyI5dpZIkV1RObLX6sUruBqxvL+eC5tayucWLUaghGk2zrcPPH1zu4f1sXe3u8RCfYT8qE2ZYZ8WR6NvjzzYP8/rV27tncyStHhul2pwvidqOWxeU2Lm8o4YPn1vLRDXO4dkUF59e5WFxup8JpxGrQTqEgniZJEno1MVYQB6hYfTnvWVPDJy6Yy23ra3j74hKWVzrGtrMcCcbY1e3hoV29/OqlVh7d00tzv59Y4kw/D2Y/kRlvITIj7yypsKOZwLaCuWa2ZUYuEf2MiZvUv05RURG33347u3fvZvPmzdTV1XHLLbdQXl7O5z73OQ4fPpzpdk7Kl7/8Zbxe79hXV1dXZp9AVVned//EDwNW9N1HvlckS+0Gzp3nAuDFQ0O4Q5lb8lxV4ZXDwxl7vFlDVWHzLyd+GLC05968f81Nlx1Dj/JfW89hr/8FdrGbsCaBed0NKJJCKpofha9cdrZlxvbe7fxu96/ZNvwoT7mb6Yv5GYl2s3fkaT6ZOtGSVKemkqK2/Ud8b+fVY4XIn+y9mee6f0VXYC+J1OzebuKihcU4jFoC0QTPNQ1MuDhxaMDPvp6J7TF7Mt3uEH/e3MGgb+aW1Z2SSZ+nqNS238E3tpw3Vvj+v73vYcvgA+wYfuSMH6fYOJeP1f+G756zlx+c28QPz23mm2tf51NL7uGSyo9xWeUnKTXNn3D7coWsN6ItmotksGA550ZQVZEZGXC2ZcZJTfL9C8DmO2fNOd9HVn6ET6/9NJD+bLrlwVu4/v7raXW3nvbYmkIz719XzWUNJVgNZ7ZAmlGr4boVFdSXpQdkvXxkmCf39xNPZu6iciIV44Xe3/GP9u8cnQMB75v/Pb6y8mnMWkfGnmfCzvK+7XQTmTE9RGYcNcnrBMCsyoxctbTCPuWVq2xGLefVufjQ+bVctaSU2kITEtDnjfDPg4P8+pU2ntjXR8dIkNRM/D1nQWaoqsqQP8qODjcP7uzhly+18vDuXvZ0e/FFEmhkiZpCExcuKOLW9TV84NxaLq0vob7Mhs2ozdhqmSdyssyQJAmHSceiUhsXLizi5rXVfOyCuVy1pJTF5TZMOg3xpErLUJAn9/fzq5fTBfLDA34SGTyXyVciM44SmZGXzHqFuUXmbDdjcmZBZuQy0c+YuAkvn36svr4+nnnmGZ555hk0Gg1XXXUVe/fupaGhge9+97t87nOfy1Q7cblcaDQaBgYGxt0+MDBAaemJZxXp9Xr0+umb5WlIeHFEuid8nIyKI9KNIeElks0LHxmwstpB+0iQbneYJ/f1c8PqqoyNWuocDdE5EqLUMaWX6ewSGgV324QPm02vuUzbv/c31PnNnM/57FT2MX/xhyg2LUCSJKLd+4l27cNUfwGKQyznP1VnS2Y0FDXwr6s/y53bf0pSfXOZvEIk6pj4xRAZiTlqkmjocHqDMqDNv502/3Ye6wSLthCXoZqhcBsXlN3GIucFVJoXT2snfSbpFJkrlpRy/7YuWoaC7OryTHi2xYuHhqh0GnFMYXnB7R2jvHJ4ZGYuMmXI5M9TYI6aQomPjL3mTsSiLaTBeRFXVP0biqxjNNJNsXEuBsUy6TbnukjnXmLd+9BXN6KvbMA4fx2qqh7NjCaRGRl0tmTGyUz2/Qtq+lwx7AZTQcbblQ0/vvLH3L7+di67+zKOjB7hgaYHeKH9BZ543xOsqVhzymMlSWJJhZ1FpVYO9PnY1u7GGz71bCBFI3NZfQklNgMvHRri0ECAYX+MK5eW4rJM7W/e6d/Dj/a+Z+z7hY7zuXXBHRgV2ymOmhmib5t5IjNmztmeGZO9TjAbMyMX6RSZxko7m9tGp/xYiiwzv8TK/BIrgWiC5n4/TX0+RoIxDg0EODQQwKJXaCi3sbjchs0wPXuJ5mNmqKqKJxSnyx2i2x2m2x0+biUyq0GhpsDEHJeZqgITWs3MzbSbbGYYtZqx14Sqqgz6o7QMBTg8EMATjtMyFKRlKIhOkZlfbKG+zEa53TBrrhdMhsgMkRn5almFg8MDgWw3Y8LyMTNynehnTM2E0z0ej/PAAw+wceNGampq+Otf/8pnP/tZent7+cMf/sCzzz7L/fffzze/+c2MNlSn07Fq1Sqee+65sdtSqRTPPfcc69evz+hznXGbklMbcTHV43OBJElc3lCCXpEZ9EfZ3JbZvS1eOTI8bUsW5qNE5Mz3RjyR2fCay6Qjm35GlT89yu5SLuUzS+6l3LxwrHOgsRQiG62Ej2xBVcWo2sk4GzPDqDXy7bd9lx+cs5PXGj/Fa42f4usrn+HrjQ9O6XFvrH6zY2ZRCigzLUSWFALxEdr9OwkmPDzR9X/csefdfGHTEn627xb2j/6TQHzqF1+yrcRm4Py69MokrxwZptcTntDxsUSKf+zqxReZ+LJo3lCcv+/o5qVDw3lVEIepf+Zbgfn29VSY67l5/nf5/voD/PDc5rGvb655jffW/T8c+lIs2gKqrctmdUE8uPcZYt37AIh27kZNpge9iMzInLMxM07apqmes0Wnds6Ya2odtez75D5+c/VvWF66nJHwCGt/s5bf7fzdGfUVFI3MskoHHzi3liuWlFJoOfUgKUmSaKx0cN2KSsw6DaOhGPdu7WJvt3dSfRN/bJj7jnx1XEF8petqPlL/y5woiIPo22aayIzpJzLjGLEpXiCfZZmRi5ZW2qe8rPZbWfQKq2qcvG9dNe9dU8WySjt6RSYQTbClbZTfv9rOw7t7aRvO/OzxfMiMdBE8xr4eL0/s6+O3r7Txx00dPN88xOHBAOF4Eq1GorbQxIb5Lm45p4YPnlvLJfUlzC2yzGhBPFOZIUkSJbb0qp63rq/h5rXVrK5xYtErxBIp9vf6+Nv2bv7wegdb2kbxT6J/nK9EZhxDZEbeqi404TpNPyYX5UNm5BPRz5i6CU/BLSsrI5VKcdNNN7FlyxaWL19+3H0uvvhiHA5HBpo33u23385tt93G6tWrWbt2LT/60Y8IBoN88IMfzPhznYmYxpTV43OF1aDlkvpiHt/bz9Z2NzWFZiocxow89oAvwqE8HAGVKaqqMhqM0TkaomMkxPDgEB+ZwuPNltfcVAXCg6R2Pkcxb45s9M+bS4Vpzrj7aawuZL0ZbdVcJEnsxTEZIjPSnPpSFM3UTuKWlt7ID6s+Pu62aDLEtsEHafFtoz98hP7QIQBSaoIW3xZafFvQSFpWuK5isfNtLC28DFma2tJ92bK8ykG/N8KhwQCP7+vjpjXVmPVnfhozGoxx/9Yurl1RQeEZzPRLplS2d7jZ0jZCPJlfxfA3TPUz//bVL5HUF2eoNflLjUfxb/37uNvMSy9H0ox//YnMmDqRGW+a8jmb3pqZhuQQvaLnwys/zI1LbuTSP17K5p7NfOjhD/HFZ7/IbY238f9d+v+hyKfOBVmWqC+zsajUypHBAK+0nHgvxzdUOI3cvK6apw8M0DES4p/Ng3SOhnjbomKMujPL0yPezfyu+dOEE29u5fG1VS/i0J94dlC2iL5tZojMmDkiM46hm+KgxFmYGbnGatBSU2iibTiY8cd+oxBaYjOwoc5Fy1CQfb1eut1h2oaDtA0HsRkUllbaWVxmP+P8OpVczYxgNEGXO0TXaJgudwh/JDHu5xpZosxmoNJppNJpotRuyOoevdOZGZIkUWTVU2TVc+68Qno8YZr6/Bwe9OMNx3m9dYRNrSNUF5pYXG5jrsuS1/sVn47IjGOIzMhrK6qdPHNg4PR3zCG5mhn5RvQzMmfCRfE77riD97znPRgMhpPex+Fw0NY2mWU4Tu3GG29kaGiIr33ta/T397N8+XKefPJJSkpKMv5cZyKi2PEYKrFFepAnsEdsCgmfoYKIYp/G1s2s+cVW6suCNPX5eebAADevrUanZOZN9+qRIZIpNe9OzhLJFCkVUqqKqoIsg0aS0MjSuGWKkimVeDJFNJ7CF4njDae/BnwR+n0RovFjRvRINvGam6J2/y5a9v+JNawEoEs/Sv2KT2KTj3+9ShoF46ILxv29UrH0DFVZl5mBH7OdyIw3TUdm6DUmzit7H+eVvQ+AZCpOV3A/0WSAxzvuYCjSQSTpZ9vQP9g29A8cujKWu67k8sp/zbsZvZIkcUl9CcOBGKOhGE/u6+faFRXIE8gGfyTBX7d3887GcspPMngrkUxxoM/H1nY3vtMssZvrvJKWHo2FsqQf+VTroL/FG6+5pK5oGluXP6I9B8b+X1s0B0PduhMuNygyY+pEZrzpjcywR3qQJpAZIIGzFowT22Yin1h0Fp6/7Xm+8txX+Pm2nzMcGuYHr/+AH7z+A9o/006No+a0jyFJEvNLrFQX6tgybKZr9OQD10w6hXc1lrOj08NrLcMcGQrQ6w1zWUMJtYUn39fPHxvhH+3fYefwY6ikKDMtYJ5tLW+v+nR29w4/CdG3zQyRGTNHZMYxTAXgnAPudhCZkbOWVNimpSh+LEUjs7DUysJSK+5QjL09Xg70+vBFErx6ZIRNraPML7awrNJOqW3yS2jnSmbEkyl6PeH09oujIYYDsXE/lyUotRmoLDBR6TBSZjegzOAM8NOZqcyQJIlKp4lKZ3qP9CNDAQ70+ujxhOkYSU/CMWo11JdZWVxup8CcfzNRT0dkxjFEZuS1RaVWXmsZJhhNnv7OOSJXMiPfiX5G5ky4KH7LLbdMRzvO2Kc+9Sk+9alPZbUNYySJXWU3cGHbHRM7DNhZdiPMsv1bLlxQRLc7jDcc5+UjQ1yyKDPh7o8k6AmEqS7MzVFBkXiSHk+YXk+YIX+UQDSBP5Igljj18hRvLJ01oWWsxGtuSvaNPsufDn2Bq1NXAdBlC7F4yb+e8phjAyTh7iPcuhU1HsW8+G1orIXT2t5JUVUMCS+6ZAitZAVVzerfXWTGMWbg/atTdKytWIfNoOXGZRsx6xR2D77O35p/y9NtD+KJ9fFC71280HsXsqThnXM+y4WlH5tQNyibdIrMO5aVce/WTro9YV46PMRFCyc2kzkcS3L/ti7K7AYWltqYV2QmlkgxEowx7I9yoM933IyCfKKqKh2B3fx4740A9Kk67mBi+5GJzBgvFU0Xy/RVS9FXLTnlfUVmTI3IjGNIEgeqbmL94e9P/Nh1n5j171+j1sgdV9zBh1d+mHff/24OjaRXSqn9v1o+tPxD3HHFHdj0p1+W/I3ZdYVmHY2lDvb3nrg4LkkSq2qcVDqNPLW/H3cozj929bK0ws75da5xg4FVVeW1gb/wcPv/Ek9FAFjp2sgN876NTnPyC7FZJ/oZGSEyY+aIzDiGJMG6j8OTX574sWdBZuSKOS4LJp2GUGxmChlOk44L5hdx7txCmgf87On2MuiPcrDfz8F+P0UWPYvLbSwstWLQTnD2eJYyI5lS6fdF6D66L3ifJ0LyLdfUiix6qgqMVBWYqHAYZ3QZ9InKRmboFJmGMhsNZTbcoRgHen0c6PMRiiXZ0elhR6eHMruBxeU25hdbJz/hSWTGOCIzhEx5Y1uo11syu4XttBL9jIwQ/YzMmXBRXBjvQPFGzu34BUoqckYjXVLIJGQ9TcXvmIHWzSy9ouGy+hL+vrOHfT0+5rks1LpOPntiIvq8YYqsE7uoP53iyRSHBwIc6PPR7Q4xme2ZJrunk3jNTVw8EeHpw9/lOfefAOikk7mLbmRxwbIJPY6Kiqw1oCmoQM2xMqI+4adh8FGW992PI9L95g/2z0mf7DbeBEZH1tonpGX6/StJUG43Mq/YQrnDQJFFf9zI97Vz3sFH170DT8TDjzf/mK+/8PX0Y6tJHmr9AXHNQS6ouoJzy68mGNHT7Q7n9AzpArOOyxtKeWxvH7u7vRSYdSyrdEzoMVQVej0Rej0Rnj84Pe2caaqq0uR+kZf776bZ88rY7X8gxrcxYEISmXGG1FSSVNiHxuxEVVUUWxH66qVojBPb91dkhjBV5Rd+EKn95xAPA2ewF5gkg2KExvdOe9tyxZLiJTR/qpkvPP0Fvv96egDBXbvu4q5dd/HLjb/klmW3YNSefjS8opG5aFExc4tiPH2gf/xKTccosRm4eW01rx4ZYVe3h709XjpGglxaX0KBNcr9Lf9Fm287gcRo+v7Gebyt4qOsLrpm0rPxZpLoZ0ycyAyRGTmj8SZ47n9EZuQwzdEtPLZ3uGf0eRWNzOJyOw1lNgZ8Ufb0eDg0EGAoEOWFQ0O8fHiYuUVmFpRYqS00nfFM6unODFVVCUQTDPqj9Hkj9HnDDPiiJFPjn8uiV6guMFFdYKKqwIhJl7uXunMtM5wmHefVuVg/t5D2kSD7e320jQSP/ntHePHQEHVFFhaV2ah0Gscm95yKyIw8ITIjrzVWOtjaNkoilVvnjKci+hkTl2uZMR2ylRm5e6aQJ6KKlUcX/S/XHPgsKTjlmzp1dOnSRxZ9l6gyO/ffqCowsbzKwa4uD882DfD+c2omPuL0BFIq077M1JmIJpJsb3ezs8tz2png09YG8ZqbkP7hXZgONbGB+ezAyeLSjbyz9oso8sSXg9I6y1EcZWMXNVVVJRXyIJvsWd2jo9q9mXc0fxVtKnL8q8Hdnh79+dz/wI1/hLpLs9BC4Q2Zev9aDQrLqxwsKLViM2jP6LkdBgdfu/Br3L7+djZ3b+aHm37I44cf57HDj/HY4ceAT/PPW//JhxdfjDcUp3M0ROtwgK7RUM7tqV1XbGH9vEJebxnhhUND2I1aak6xhO1sF4y7+drW9ced3Np0xfzXqhd42rNFZMYZSgY9BHc/AYBl5UZkgxVd2YJJPZbIDGEqGspt1FaWpv8Of7oBVJlTX7CSAQluvPusvND4vcu/x/9e9r+83PEyF/3hIgA+/ujH+cSjn+AjKz/Cdy/7Lg6D47SPU1dsochSw6N7exn0RU94H0Ujc+HCIuYWmXmmaYDRyAC/2fcLkrqthFK9Y/c7r/R9XF3zxdyeHf4Wop8xMSIz2kVm5BKjQ2RGHlhcPvNF8TdIkkSp3UCpvZQL5ic52O9nf6+X4UCMw4MBDg8G0GokagvNVBeaqHKasBtP3tfMVGaoqoo/msAbiuMOxRgNpr+GAzHC8eNn1Ru1mqN7gqdngzuM2rwYeJbLmSHLEnOLLMwtshCMJmjq93Gg14c7FKep309Tvx+LXmFBiYX5xVZKbPoT/puLzMgjIjPymlGnob7Mxt4eb7abcsZEP2NicjkzMiWbmSGK4hnQ4VzPQw0/YuPBL439EY99Y6eQkICEbOCRRd+l03lO1to6E86bV0jHSBB3KM4LzUNcsaQ0I4/rDcdp7veztGLml3hIplR2dXnY2j5KeIaWujoV8Zo7vVQqRdOB31Hle3PZ/SuM72HN3M9P6XHHLT/i6SPc9CKy0Y5lxVVTetzJsvt6WNd6D6AioZ5g1+Cjr4t4OH2y+777Recjy6by/rUaFNbUFrC43DbpvdAsOguXzL2ES+ZewpaeLTzS/AjfevlbALztj28DoPXfWllaOYellXbiyRSdoyEODwRoGQpkbUDQW62pceIOxjjY7+fxff3csKqSQkvurCgyE3qDzXx/9zvH3WbU2Pjk4t9TaVk8dpvIjNNTVZVY9z6iXfvGbosNtmGontiKIm8lMkOYDKtB4cIFRelv6i5N/x3uuxXibyztfWyX8ehfUWtMX6iqu2Qmm5pTZEnmwtoLeekDL3Hz328mlowxGBzk1zt+za93/BqAP1/3Z65ZdM0pZ4/bTVquX1XJgzt66PNGTngfVVUZTr1AxH4fPdKm9I0pUFQnF5d9jgurr8CUp3vficw4PZEZbxCZkXNEZuS8QouecoeBXs+J82WmGLQallc5WF7lYNAf4WC/nyODAfyRxFiBHMBmUCi2GnBZdbgsemwGLVaDgl6RkSTpjDMjLhv4Tfl/syNUT8A9MrbtoC8Sxx9OHLcE+htkKb1SWInNQJndQLndiMOUH0XwN+RbZpj1CqtrClhV7aTPm35tHBrwE4gmxpZXtxkU5hZZqC00UeVIT4QSmZGHRGbktZU1Tvb1eie1em22iH7G6eVbZkxWtjNDFMUzpMO5nt+seYz6wcdY0XffuOn+PkMFO8tu5EDxRmKKJYutnBmKRubyhlLu39ZF84CfeUVm5pdkZlTPi4cGmeOyY9HP3Et3yB/lqf39DPlPPFskW8Rr7uRCMTej2/5KFW++7rr1I6xq/FzGnkNVVSKt2wAwzs9OSGsSMRa2vwioZ7D0TCo9+vO+W+H2A2J0Z5ZN9P0rSxKra52sm1Mw6WL4iaytWMvairXc2ngrX3/h6/xl318AmPvjuXz/su/z+XM/j1YjM6/IwrwiC4lkivaRIAf6/LQPB49bum4mSZLEJfXFeMNx+rwRHtrVy3tWV2Ka+CIQeSelpni043u80HvXuNs3lN3KO2u+iEY+fkaHyIyTU5MJAjsfRY2Fx25TXDXoq5Zm7jlEZggT8LZFJeNXWqq7NP132H0vbL4T3G1v/sxZm97bb/lNYMjPImymbajZQNfnukipKf7e9Hdu+OsNYytp3Pz3m8fut6xkGV/d8FVa3a1U2irHPYZe0XDNigoe2NFNvzeEO9pLV2AffaFDPNP98+OeUy9bcaU+gBQ5l0NtVgiGOH++CesZruaSa0RmnJzIjBMRmZFTRGbkvMXl9qwXxY9VbDVQbDWwoc7FoD9K61CQLneIAV8EXySBLxLgyND4YxRZQqfIaDUyWk05P5bu5B3yC7w39ThVDIzdrzNVzO+Tb+eB5AX4j5iAwRO2QZbAZtDiMGkpMOsoMOsotOhxmXUZ7f/OtHzODEmSKHcYKXcYuWCBi/bhEIcH/bQNB/FFEuzq8rCry4NJSVBaPsyNQy8gMiMPiczIWwVmHXNcZlqHsr+y7kSIfsbJ5XNmTEQu9DNEUTyDooqVXeXvZVfZjWMbw8c0JiKKfVo3hs9FpXYDq2udbG1383zzEOUOI+YMFLLDsSRP7+/n2hUV0z4yVFVVtra72dQ6ktXCz6mI19zxtg89wtbDv+Jm0hc9u6xBauqupsFYktHnkSQJy8qrSbh7kM0OIP2aUaNBZMPMBHeRuwU5lTjBaKqTSaVHf+6+F875xDS2TDgTZ/r+dVn1vL2hhGLb9C2/Or9wPn+67k9YdVZ+teNXAPz7M/9Or7+X71z6HXSadKVZ0cjUFVupK7YSiiVo6vOzr8fLaDA2bW07FUWWuXpZOX/d3oU7FOehnT1cuyKz7/Vcs3P4Me4/8p9EU6Gx266Z81U2lN5y2lwUmXFiCe/AWKdDV7kEXdkCZG1mVx0QmSGcqWKrntoTbQdhdKT/Dus+DmE3RP2gt4LReVa/f09FlmSub7ie4S8O84GHPsAjhx4Z9/M9A3u48W83jn3/nVe+M/b/Zq2Zi2ovOrq9yInpZRNLCy/j4vKP4DLWgKplU+sIOzs9HBoM0DYSZO2cAlZUOdHI+fc3EplxYiIzTkZkRk4RmZHTFpRYeenwENF4bqzA9QZJkiixGSixGVhPIbFEin5fhGF/lKFAlJFgjEAkQTieJJFSScSSQHolxWG0/IzL+BmX4iCARYoQUA14sKCRZAx6mWKdglmvYNZpsOgVrEdnnduMWqx6BTkPs/J0ZktmKLJMXbGFumIL8WSKjpEQ7SNB2oeDxOJxKnztaNSkyIx8JTIjb62sduZdURxEP+NkZktmnE4u9DNEUXw6SBIRrYOI1pHtlmTV2jkFtA0HGQ7EeL55kHcsLctIIbtjJMSOTg+rapwZaOWJhWNJntjXR8dI6PR3zgXiNUcqlWJP11/5c8/XWc5yADoNIyxZ+qlpe05JktAWpGcWpeIRop17iQ8cQeMoxdxw8bQ9LwCqStnQwckdu/nO9MnuWXyikVNO8f5tKLdxaX3JjFxMlySJX179Sz619lMsuzO9LM8PN/2QB5oe4G83/I3V5avH3d+kU1hV42RltYPO0RC7ujx0DM18cdyo03Dtigru39aNOxTnkT292IvVvCxAnIo3NsB/b7tg3G211hV8vOG36DUT3E9dZAaqqpIMjKCxFKDG0zN1tCXzMFRnbgTuW4nMEE5Hr8jUFJpOfSdJAlNB+ks4IwXGAh6+6WEAmoeb+c4r3+EPu/9wymOC8eBJC+ImxcFNdd9hnn0tBs34Cwcb5hexsNTKC81D9HkjvHpkhH09Ps6ZW8CCEityPr6PRGaIzJgIkRm5RWRGTtIpMvVlNnZ1erLdlFPSKTLVBSaqC8afmySSKYKxJLFEingy/QXpzy0JUDQSOo2MVpExKBq0GimvljufqtmeGVrNmwVyVVVx+wKsPtgxuQcTmZFbRGbknaoCE6V2A/0n2fIp54l+xqzPjOPkSD9DFMWFaaPI6WXU793aSctQkKZ+Pw1ltow89mtHhqkqMFJszfzMyUF/hEd29+ELxzP+2ML0iCVCtG+/i7nJQm7lVu7nfuZXXsU51dNXEH8rSdIQH2oHQHGUTfvzGRJeDDH/JI5U08shhd3iRDeHSRKcV+diTe3M/42WlixF/brKPw7+g9seuo0Obwdrfr0GRVZ48QMvcm7VuW9pq0RNoZmaQjMjPhvuLTr6fOGTPPr0sBq0XLuigr9u72LQH6Vf9bGwJDN5k20pNUmLbyu/2H/b2G3Fxjl8ouH3OPSlWWxZ/lJTSUIHXiDpG0RjK8a08HwUe8mMjYoFkRnCic1xmVHk/F0iNB8sdC3k99f8nt9f83tUVcUT8fDJxz5Jh7eDy+dezjdf+iYAXzz3ixgUAz/e8mM+u+6zrC+9ji2tIQyKFY106i50sdXAe1ZVcrDfzytHhvGG4zy1f4Bt7W7WzSlgXrElP4vjZymRGRMhMkMQzlRjpSPni+Ino2hk7EZxvnIiZ1tmSJJEpTGMJTGZmaoiMwQhE1bVOHlsT1+2myFMwtmWGZA7/QxRFBemVZFVz7o5hbzeOsKLzUNUOo3YMrC3XiKl8ujuPm5cU5WRZdnfcLDfx7MHBognc3O5dGG8VCpF8/afUxEvpJRCAGS9hW+u2HrCPXWnk6Rosa67nsRIJxpLIalokFDTi+hrlqN1lmf8+bTJKRYdo37R8chRiixx5dJS6oqtWW3Huxa9i9bPtHLDX2/gubbnSKQSXPyHi3nyfU9y8ZwTjxwsNOspLLZSbNfjioUYDiVnrL0FZh3XLK/ggZ3tDEUSNPf7uLg8hZLHe4yn1CS/OvBRDnlfHbttnm0t/7rk7iy2Kn+pqkq4+RUSo2/uWSUbLKBoM74k1emIzBDealGpFSd5/IGVhyRJwqwzs8i1iEWuRXxlw1f474v/e9x9jv0+HOk54+UJJUmivszGvCILu7o97OhwMxKM8fi+fmwGheVVDhaX29EpoqiQq0RmTIHIDEE4rQKzjqoCE12jebI6oXBKIjOmQGSGIExJXZEFu1GLV0zuyxsiM6YgQ5khiuLCtFtd46RtOEi/L8IzBwa4LkP7gXvDcR7c2cP1qyoxaDVTeixVVXn1yAhb20en3C5hZgwF29Dv3kTF0WI4wGC5laW1N2WtTZIkoXXVABAf7iAV8hJp2Yqy6p0ZXy4srjFO7QH02S24CiemkSU2NpYzxzXB5bCnSYGxgGdueYYHDz7Iu+9/N7FkjLf98W28c+E7+cU7fkG59cQnSAathrpiK6UxlQqnkQHvzBTHS2wG3tVYwe/2SvijCR7e3ct1K2rzsujQFdjLnQc+TDjhHbvtS8sfp8Q0L4utyl9qKol/0/3jbjMuPB9tYVWWWiQyQ3iTXpG5cGExNGe7JcKpXFpfwt3eDsKxM880nSKztraAxgo7Ozo97Onx4IskeOnwMK+3jrCgxMrichulNsNZtbRsrjtRZgy6VjIkuYgcGiaWSBFNpEimVFJq+ktCQiNLyBLoNDI6rYxe0WDSajDpj9k/d5L75orMEITZp7HSLoris4DoZ4jMEIRskmWJFdUOXmgeynZThDMgMiM3MkMUxYVpJ8sSly8u4c+bO+l2h9nV5WFFdWb2Ax/yR48WPSpQNJMrekTiSZ7a33/GMz+E7ApGR3is8w42Df2Vb/CNsdu9c6qoKzs/ew17C9nkAEBf2YAkSajJBMiajIVJRLET0VnRx/xM7BElcNaCMTPvQSFzNLLExmVlOVMQf4MkSVxXfx3e//Byw19v4KmWp3i4+WEebn6Y//e2/8d/nP8fJ31dWwwK16+qonUowovNQ4QmUEiYrBKbnkWlNg72++j3RXhgRzfvWl6OSZcfpzxJNcGDrf/DawP3jt22tvg6rp/73yiymEU6UWoynv7slccPnjMtuRTFVpSlVh1PZMbZzaDVQHJqAzyF6WfWK1y0sIgn9vZP+Fi9VsP6eYWsrnVysM/Pzi437lCc/b0+9vf6KDDpWFRmZWGpNSOragkTF4wmGPAE6PfHGAzEedsxP3ssupDBLhmY+gBqWQKbUUuBSYfLoqfIqqfYqsdqUM74M19khiDMDvOKLFj0CoFoIttNESZB9DPSRGYIQvYtqbCzuW10QoN3hZklMiMtVzIjP64QC3nPadKxYb6L55uHeLVlhOoCE4WWzCwH0eMO8/DuXq5cUoZRN7ELil2jIZ7a348/IjohuS6VStF8+C+Uj0h08zoA9xufYL1yIfMWvocqXW7tH6wx2bGuux6AVDRIrLeZ+FA7hnlr0RZWTv0JJIm+okXU9myd+LHrPpHetFrIGbIkcdXSMuYWzdy+MRNl09t4/H2P862XvsXXX/g6AF/551fQarT8+7n/fspjF5XaqC0089KhIfb3+qa9rRa9Qn2pjaE+mUF/lL9u7+ba5RXYjLldaFBVlfuP/Cdbhx4EYGnB5Vxa+XGqLEuy3LL8o6oq8f4jRNq2YVp6GYrVhWXlRmKD7egr6pE0uXUKLDJDEPLDwhIrW9tGGQ7EJnW8ViOztNLOkgobvZ4I+/u8HB4IMBqK8VrLCK+1jFDhMLKozMr8Ygt6RQyWmC6xRIoud4jO0RBdI0FKYr2co+1kU2wRQykLf5OWME8zwmHKMZsNzNErWPQKRq0GnSKjV2QUWUKS0rPDVSCVUkmoKomkSjSeJJJIEY4nCUYSBKIJ/NEEyZSKJxTHE4rTOvzmoGyzTkOZw0iFw0h1gQmnSXvSi08iMwRhdpBliSUVdja1jmS7KcIEiH7GW4jMEISs02pkVlQ5eK1F5EmuEZnxFjmSGbn1ry7Maksr7LQOB+kYCfHk/n5uXF016dndb9UxEuKeTR28fXEp1YWm094/kUzxWssIOzrdqGL78JwWT0To2nYXrpSTCmRGGMGFi7ryy7m65os5vdSkpEkX4OJDHcT6mtFYCtMjwzJkyDmP6r6dSKkkMmfwQpZkUIzQ+N6MtUHIjIsWFlFXnLsF8TfIkszXLvwa19Vfx/l3nY836uULz3yBP+39Ez+58iecX7H2pMcatBouX1zK/BIrzxzoJxid3hGsZr3ChSuqeHjPIJ5QnPu3dfGu5RUUWWd2f54zkVQT3HPodnaPPDV2220L/o9G1xVZbFV+UlNJgnufIRV0AyAbrKRCPrC6kA1WDNVLs9zCkxOZIQi5T5Ik1s0t5LE9fVN+nAqnkQqnkQsXJDkyGOBgv59ud5geT/rrheYh5rnMNJTbqC4w5fQ5b77whuO0DQdpGw7S7Q6BmmKj/iCr5BDoIICBegcsKSqi0KKnwNTAhRMcdH0qqqrijybwhOKMBmMM+aMMBaKMBKIEY+nXwZHBAJAe4FdTaGKuy0x1gem4frPIDEGYHZZV2tnWPkoiJS5M5TrRzzg5kRmCkH2NVQ62dbiJJVLZboqAyIxTyYXMEEVxYcZIksRl9SX8aXMnw4EYrx4Z4cKFmVseIhBN8Ped3TRWOlhSYT9h4SMYTbCn28veHs+0F2SEqds08FcKW3oooWTsNlWCKxf/D0W2BVls2cToSuvQFtWCmkJS0ssfh49sRl+9FFl3+kEcJ5NUdDTXXsii1udJwWmCRAYkuPFuMDom/ZxC5q2odtBY5ch2MyZkSfESRr80yqpfrWJX/y529e9iw+828P8u/h++7Dj1sXNcZt5/Tg3PHBiY9m0rHGYtN6yq4qFdPYwEY/x1exdvX1zKvByake+NDfDjvTfhjvaM3XbDvG+JgvgkBfc8RSr05j7syBoUZ1n2GjQJIjMEIbfNL7bgsugmPVv8rfSKhsXldhaX2/FF4jT3+znY72c0GOPQYIBDgwGsBoWGMhuLy21YxfLqZyyZUunzhmkfCdE2HGQ0OP5vdp2xCTvhse9tJgOlDYuQdVPc6+4kJEnCZtBiM2ipLnjz8zyeTDHgi9DrjdBzdGBEIJoYW15fkSVqCk3ML7Yyx2VGp7xZIBeZIQj5zaxXqC+zsbfHe/o7C1kl+hknJzJDELLPoNXQWOlga/vUt/sRpk5kxsnlQmZkZpquIJwhs17hsoZ0gXNXt4e24cwWRFQVdnV5uGdTB3e90sbzzYO80DzIk/v6eGB7N799pY1NrSOiIJ7jAuEBnt7/LR5t+X+M8ObSLz26Eeau/1ReFcTfIGkUJEWHrMaoH/oF8cFWAtv+gZqa2gg+r62Cf9R/n4RsQEUiddyOHFL6S2uE9/0V6i6Z0vMJmTW3yMyFC3Jn75iJkCWZ1z/8Op9Z95mx277y/H/x05YtqKdZgsOkU3jX8gouWliERp7emW/pfc0rqSowEk+qPLqnjy3to6dt40zwxgb4720XjCuIf6Lhd5xT8p4stir/pOJREt5+UvEIssE6drtSWIVl+ZXTVtyYTiIzBCF3vTFbfDrYDFrW1Bbw/nXVvHdNFY2VdvSKjD+SYHPbKL97rZ1H9/TSORrKiRzLNaqqMhyIsqvLwyO7e/nlSy08sKOH7R1uRoMxJAnm2BUur5K4dXUphQVv/h2zmRlajUyl08Ta2gKuXVHBJy6YyzXLy2mstGM1KCRSKi1DQZ7c38+vX27l8b19tA4FSB6dWSoyQxDy26oap1hBOkeJfsaZE5khCNm3ssaBViMCJVtEZpy5bGeGmCkuzLg5LjPLKx3s6vbwzIEB3reuGrM+8y9FbzjOrk5Pxh9XmD7+UD99e/9GabKQc5jHk4Tw4qXZ1M+qZZ+hXp4d43hC0fTvoS0oR5Jl1FQSSZ780oydznX8Zs1j1A8+xoree3EcU2DDWZvec2P5TWCwT7HlQiYVWnRcuaQsr5dDNSgGfnTFj/jqhq+y4pcr6PH38Ok9T/DCcDuL51533CnNW62odlJqN/DYnj78kcT0tVOr4ZrGCl46PMTubi+vt4ww5ItyaUNx1vZr7fDv4ffNnxr7/tYFd7DcdVVW2pKvUvEI4eZXSfoGAbCufy+y3oy+aim6ysV5/d46lsgMQcg984stuKx6hv3RaXl8SZIosRkosRk4v85Fy1CQvT1eejxhWoaCtAwFKTDraKy0U19mQ5uhLanyTSKZYsAfpc8bps8TodcbJhIff4HGqNVQXWhinkOhdHQ3qn8QhsE6/71ER3MzMxSNTE2hmZpCMxcuUBkKRDkyGODQQABvOM7hwQCHBwMYtDILSqzUl9koObpKmsgMQcg/TrOOumILhwcC2W6KcJToZ0yOyAxByC6TTmFxhV3UQ2aYyIzJyWZmiKK4kBXn1RXS7QkxHIjxxL5+rltRgTzNswWF3BUID9DR9ABVESelvDlj4101X2ZNybWYlNl1wlzmjFHqGGaL6Tri/mFCe58BWYN17bsnHSZRxcqu8veyp/RazvX/GE0ywYHCT/D+S9Yihp3nHp0is3FZ+bjlL/NZkbmIrs918ZNNd/DZpz/PA71NPND7bT64/IOnPbbMbuR962p4Yl8fHSOhaWujLEtctLCYQrOeFw4NcmQowODmCFctLaPEZpi2530rVVX5/OuLxt12w7xviYL4BKTiUaIdu4kPtoz/gapimLMyO42aRiIzBCH3SJLEujkFU95b/EwoGpmFpVYWlloZDkTZ2+2lqd/HaDDG881DvNYywuJyG8sqHdiNs3dpdVVV8UUS9HnD9Hsj9HkjDAeivHUbXkWWKHcYqXQaqS4wUWSAaOce4h0t4xfmy5PMkCSJYquBYquB9XMLGfRHaR7w09zvJxRLsqfby55uLwVmHUvLjKxwRLhsWUxkhiDkmTW1BaIongNEP0NkhiDku9U1TvZ2e8dWFBKmj8iM/M0MURQXskLRyFy1pIy/bO2kxxPm9dYRzqtzZbtZwgzyxYZ4vPMOBsOtFPglNrJx3M/dteVcWH5hllo3/SQpfZFLjR3dxzCVRE3EkKa6lIokkVAMJBSIau2i05GjLmsoocCsy3YzMkqSJP5t7aeQ+p/h3/Y8CcDvdv2OS+ZcwvuWve+Uxxp1Gq5dUcFrLSNsaZve/Y+WVtopsul5Ym8fvkiC+7d1cd48F8urHcjT/H4Jxt3cfejzY9+Xmhbwb0v+gkHJnT3O80FitIv4YOu420yLL0GaJauJnIjIDEHIPXVFFmxGLb5wfMae02XRc/GiYs6tK+RAr4/d3V684Tg7Oj3s6PQwx2VmaYWdmkLTtGfadHvrLPA+b4Rw/PgtsEw6DWV2A+V2I2WOdPH42K1ZYgNHZk1mjFtBYJ6LLneIpj4/R4YCjAZjvNYSZEWxG4dRS4s1SIV8dLChyAxByHklNgPVBSY6R6dvkLBweqKfgcgMQchzVoOWpZVitvhMEJlB3maGKIoLWeM067isvoTH9/WzrcNNmd3A3CJRGJjNfMFuOg8/ipRK4Y/0sZfHWcEK1rMet+QjVV5NYcFiHNZabNlu7AxRCirRls5HY7Ij64yoqooaDSLpzbNmeRVhvOXVDhaUWE9/xzz16XnrmGcu4B2v/xmA9z/4fh4/8jh3X3s3snTyE0NJkjivzkWJTc9T+weIJaa2P82plNoM3Ly2mmeaBmgZCvLykWGODAW4rKEEp2l6BisccL/I31q+jieWnlVo15Xw+cYH0UjiVOxkUrEwsZ4DqMn0SbZx3hpig63EepuRTTb01cvQmAuQ9aZsN3XGiMwQhNwhyxKNlXZePjw848+tVzSsqHayvMpB+0iI3V0eOkZDtA0HaRsOYtErNJTbWFRqnbZcyyRVVfFHEwx4I/T5IvR5Igz6I8fNApclKLYaKLUbKLMbKLUZsBoUJEkay4zY0NmRGbIsjS2xHk0kOTwQ4FDfCACecJwnO/rRKDouNVdgL3AhaQ0iMwQhx62pLRBF8Rki+hnHE/0MQZg91s0p4ECvb1qvq51NRGYcL98zQ1yJFbJqfomV5d4Iu7o8PH1ggPeu0eHIgws3wpkbGNnDUM8m5HicymgB1byxFLqTR3mUfZpmaksuYXnle1AUfVbbmg2SJGGcu3rs+8RoN+HmVwCwrr8R6RRFRCH/lNoNXDC/KNvNmHaXFs/l39d9ju9vvgOAP+/9M5FEhLuvvRuT9tQniXXFVgrNeh7d08twIDZtbdRrNbxjaRn7e328dHiIPm+EP2/uZP28QpZXOjK2pUcsGeZn+2+hK7B37LYPLfoZSwouzcjjzzbJkJf4UBtqLEx8qH3cz9R5q9GVzkexFSObnbN69O3JiMwQhNyypMLOptYR4snsLE8oSRJzXGbmuMy4gzH29qSXVg9EE2xpG2VL2yhFFj3zSyzUFppxWXRZv0iRSqn4InGGAzGG/FGGAlEGfBFCsVPPAi+1Gyi26lGO2T89GfIS7RSZoVc0LKmws6zcyFKPg+FAFLNHgyeq8qinDDxQMNDJanuICvcuQGSGIOSi6kITZXYDfd5ItpsyK4l+xqmJfoYgzB4mncKKagebW6d3JcbZTGTGqeV7ZoiiuJB159e5GPCll8R7ZE8fN6yuRK9Mbh8CIbsSqRiDI3sYDrTg1oc56HmZVZ5qFrBg3P36NCMkTHq+uuB5jHpHdhqbo6Jd+wDQ167I+QARJkanyFy5pHTckp6zmVln5j83/CcllhI+//Tn+XvT3+n2dfPwex+mxFJyymOdZh3vXVvNc00DNPX5p62NkiSxpMJOdYGJZ5oG6HaHefnwME19Pi5eWEy5Y2rL/6TUJD/YfQ1DkXYAGguv4J21X8KpL89A6/OfqqZIBT2o8SiKswyAaMcuEu7ecffT2EvQWF1IsoKkUdBYC7PR3JwkMkMQssug1VBfZmNPtzfbTcFp1nHBgiLOrSukZTBIU7+PrtEQQ4F04fm1lhFMOg1VBSZKbekCc5FVj1aT2c+OZEolFEsQjCYJxhL4Iwl84Ti+SBxPKP2VVI8fRCBL6eXhS20GyhwGyuxGbEdngcObmZEQmXFKxqN/49sqaulwJzjQ56NlKMhoMIYh0QwyNGvmoO/xMb/YilEn+t2CkEvWzCng4V29p7+jcEqinzF1op8hCPltVY2TPd1ewicYeCqMJzJj6vItM0RRXMg6jSxx1dIy7t3ayWgwxpP7+rm6sTzv98GbrVKpFLIs44sNsX/kWexdA1QmSvDhx4YVCxBkgD/yS1KkcFPMAhbQafKgtRRRU305C3Vny+LoE2duvIJU2IdstJHwDRFufhVd+UJ05YuyPrNHmJqLFxafdSthaGQNH1v1MRpLGrnmvmvY0rOFxjsbuXPjnVyz6JpTHqvVyFyxpIxKp4kXDw1N67JPNqOW61ZUsL/Xx6tHhhkOxPjr9m7qS62sn1eI1aCd8GPefeh2dg4/Nvb9tXP+iw1l789ks/OCqqpvFjRSScLNr5Bw9yLpTKix9PKQssmO2V6CJMvoqxtJuHvRlS5AthSgLaxC0ojT1ZMRmSEI2be8ysHeHi8nqPNmhSLLLCy1srDUSjiepGUwwJGhAD3uMKFYkuZ+P839bw44s+gVrAYFm1GLUatBr8joFRmNLCFLEpIEqpoudidVlURSJZZMEU+miCZSROJJovEU4XiScDx5RnmtkSUKTDqKjhbmi49+aWRpfGYcfElkxiTJ0jHLq8eTHB4MsL3PQMDnxqsaKD7cSnFHK/u1lWjLFjKv2IpFL/7tBCHb5rrMFFn1DPmj2W5KzhP9jOkl+hmCkN/0ioY1tU5eOjTzWz3lIpEZ0yvfMkP8JYWcYNErXL2snL9t76Z9JMQrR4bPiiWGc9lIpAur1sXQ8B4ikWGKenxjP/sjf6SVVgwY+Ff+FQAbb+6RXEIJhUo58wvPZ5HzAiyOi1lyFi4lMhmSJKExpZeY11hd6MoWEO3Yhawzoi2qzW7jhElbWGKlofzsHQyyoWYDr3/4da659xqahpu49r5rAUh+LXnKfcYhvTRtpdPIk/v6p3UpwTdmjc8rsvBqyzD7e3009fs5PBhgZbWTVTVOdMrpP8dUVeXRju+PK4jfVPe/rCm+ZtrankuS/hFS8TDhgy+P3WZquBjFUQqpJMmAG2Cs0wGQCnkhlQBZh8bswLr+vTl50pyLRGYIQvYVWvRUF5joGMm9fWCN2vSy2ksq7CRSKfq9EbrcYYb8UQZ9EYKxJIFogkA0kdGMlaX0so0WvYJZr8Fu1GIzaLEbtRSYdWP7gKczI0D4wBO88a8nMiPz9Me8DnyR0vT+4/16DkQCrJbaeLEFXjhUSLE1/VqeW6AlpVPFIHVByAJJklg7p4DH9vRluyk5R/QzZpboZwhC/musdLCry4svHM92U2acyIyZlW+ZIYriZ+j3u37DkSEZTdhLdyCBW7kfo7WSAWUUBS01+kUEQ/3YLFWkUgkM+gL0OjsaWUMqlWLE20ShvR5ZFAZPqsRm4LKGEp7Y18/OTg8Oo5ZllY5sN2vWiiUjDIQOoY/LDLW9SFW0YOxnT/M0r/Ea13M9S1hyTLk7TUd6tmv6Z+mfDstuXCknXfYo8+Zfx5d1N83UrzKrSYoOxVmB4qoBQE0mxMi0PJGMBPEEFUJRLeUlPbS3q9TW1qKqKh6PB7/fj8PhIJVKYTAY0Ov1SJKEqqqMjIxQWFg4q06+FhQuYMfHd/Avj/0Lv9v1OwAW/nQhf7ruT6ytWHvKYx0mHTesrmJbh5stbdO7d6tRp+HS+hKWlNt5+fAQvd4IW9pH2dPjYWW1k8ZKx0mL462+bfzlyJcZiXSO3fax+t+wyLlh2to7k9RUilQkQKxn/7g9lfQ1y9FX1BNqfpXESOfxxyXTHbD4cAdqPAyAbHaSCrrRVzeiK1sw7nNtNr3uZ5rIjPzldnsIjECfB6TIDoqKS8/qzMg3K6qdOVkUP5Yiy1Q6TVQ6TWO3hWIJfOEEvkgcfyRBJJ4kkkgSi6dIqiqqCik1XRyVZQlZAp1GRnv0S6/I6LUyekWDUavBqEt/GRQZVPWkmSGJzMgam0HLqhonK6sdeLvCBIbjhJRy8EcZ8YcZ9EfZ3RmnotiNWa/hZXUYu8WMw6ilwDB+lo2QPSIzZrf5xRYKzDpGg7FsN2XGiH5G7hP9jPwlMuPspWhkLlzg4pHds2uglciM3JfrmZE7Lclhnd5O/uOZ2/kP9T8YYR5GwAgw0sdP+R+qqKKOD+AAoBsZSAD3cC8HOcg3+AZ6IMAevPhISEk6rX5GTHF6Q00s0C9njmY+WoOdyuJzURR9tn7VrFtQYmU0GGNz2ygvNA9h0inUus6u5YanQyA8QPuhhwCwhcCh2niKpxhmmPfxPqooGHd/Fy4AQhx/ca9bN8KFVZ/mHZYKbNpiTIoDjazhjTmwi6fzFznLSJKErrQObcm8sSCOtG4DVExzV2S3ccIpJeJx/DufZMvRZNjX9QwAX/3qV+nu7uYPf/jDccfceOONLFq0iG9+85tjtzkcDvR6PRs2bGDx4vS7a2RkhL6+PsxmM9XV1Wg0+bMXpEEx8Nt3/pZ7991LOBHmyOgR1v1mHd+77Hv8y5p/waQ1nfRYWU7PmlhUZuXF5iGODAamta2ldgPXr6qkZSjIqy3DeEJxXmsZYUeHm8YqB0sr7JiPWWa0P3SYn+5739j355XezHVzvpaXJ9GpeJRY934A4u4e1EgAfe0KZIOV8MGXjr9/OL2SiKQ9/vxFcVWjMaczRltSh7akLi//TfKByIz8FY/H+fHPfgEcfW/seQoQmZFPagtNuCw6hgP5VcAw6RRMOoVSu2HSj/HWzIhHAsgiM3KeJEk4qhdhr1rIjZJEMJrA1/w6wWicV8JlqKgEogl2D3qIq0EAtFIcc/Eoiizzp31t/POwh3s+vE78jWaYyIzZT5IkVtc6eXr/QLabknGin5GfRD8jf4nMEOqKrdQUenN+AO+JiMzIT/mQGaIofgZUVWWj/WrwHP+zJEkGGCBBAuUt/5xJksfd344NVGjxHeYV3+No0OBC5hwqgTC+9vsZUjwkjEZIxCiuuxyrqRSDxjI9v1wOWjengGA0wb5eX3p/8eViGfWJiMb8jPoOMTq4l78FfosuofKv/CvVjF+6WULCjfuEj1FQvJyLlSJSUZVRcwlV5RvQyOk9dRum/TcQ3urYPU/iwx2gpohZnHD2rsad85r27zvh7RqNhuLiYjQaDcnk+IyQZRn1LRuSejweADo6Oli8eDGJRIKWlhaeeOKJscerra2loqKCQCDAJZdcgsl08sJyLpAkidBXQzzb+ixX/ulKEqkEX3jmC/xl31947ObHKLWUnvJ4m0HL1Y3ldI2G2No+Oq0n9pIkUVdsYa7LzKFBP1vaRnGH4mxuG2Vr+yjzi63MK03xq8OXk1TTI04lZD637G9UWvJjiJCaTJCK+El4+tGVLyQV9hPc9fgJ7qgiG966bkia4iwHQFcyD21BJRp7MdIJlsUXHY6ZITIj/+zZs+eEt4vMyB+SJLGqpoCn9vdnuynTSmTG7PPGv7NJK5EM9mJWU9w8v4xGo4NANEGj1sFQWMUTihOOps91EqkUw8EokV6f+DtlgciMs0N9qY0tbaN4Qvm75K3IjNlH9DPyj8gMAeCihcXcs6mDZGr6Vl2cKpEZs08uZ4Yoip+BGkcNv/zIH/n18ztY6P8dBo2GfwxXoJhc/E/xZgLxEWyGKsKRUXp6XiQZ9qELRWmsuIHRobt5Xd3N+mgjvdoRSuNOfFIAu6uB5SkVdaSXq7hq7LkUFMoSLvADmPnZ3g8wxBBzrCs5R7OBJXU3Y9Q5svVPMSMkSeLiRcWE40lahoI8tqePkorEuBl5wngefyfdrU9gCidxpZyYATN6VCIUUXnc/VOkGNAHuG7Od5CNizDqC9HIb47oW3b0S8gtkqxJnxhEg2hdlcRDEvGkBOI8M+csaVxOZ9LGRu1f0CsSTdJGnIVFSJKEyWTiK1/5CpFIhIMHD+J2u+no6KC8vBxJknjHO97BY489xoIFCzh8+DBOp5O6ujoA9u3bN9bpAEgmk7S0tNDS0gLAunXrxjoeO3fupKGhAb0+N1cfuXTupRz61CG+88p3+OuBv7KjbwdlPyhj58d3srx0+WmPryowUVVgYtAfYWenh5ahANF4alraKssSi0ptLCixcmQwwK4uD73eENuH/8HTvh+O3c9lmMdnlv4Js9Y5Le3IlFQ0RKyvmYR3gFTwzcFRWldNeg+l40hoC6uQ9Casa98NGu0JOxEac27/3pAe6OiLJPCF4/ijCQKRBOF4kmgiSTSeIplSSakqKRUkCRRZQiNL6BQZg6LBoNVg0mmwGhSsBi02g4Kiyc2tcURm5I+VK1eytGEhsb3fR6tBZEaeWlhq5bWWYfyRRLabklFnc2acTY7NDJ2rCk1IwaTVsmGBi5R0dOW2ZJSVASfxZIr3VldzYX1Fdht9lhKZcXZ4Y5WsfJstLjLj7CD6GflDZIYAUGDWsaLawbb2E0+OyxaRGWeHXMwMUWU8QxpZg0Fno8SYXlpuUd21Y51Ds9YBgNVUyqL5N44dMx84t+rWse/fGAThAKqBNUe/1yugldzUFlVzoP0lDh/chCGoIapGGWYYAIs/zkKcxLc9wYDkIVxgo7R8PU7r3Gn7nbNJliSuWFzKQ7t66fb4aOr3UV+WA8NIcog73M09R74I0RC3xW4cNxM8SgwNMgUUoLG6aA63YTIUUVx1HkXO9Fzvf+V9J3toIYcZapajqioycbpGDLQOmFCKd6Kfu1aMdMshWo3MFY2V2NrSf5Pli5eB5s2tIGRZxmQysXLlyuOOXb16NatXrz7h4y5fvpzGxkZCoRAmk4nu7m62b99Oe3s7iqLgcqW3PtizZw8PP/wwDz/8MC6XizVr1rBw4ULsdvs0/LaTN8c5h19d/Su+eN4X2fC7DfQH+ln767V8fNXHuW35bawuP/G/w7GKrQbevriUZEqlczREU/8oL/fLRBOZL5DLksSCEitFjgC/PvBlRsL7x37mjH8IU/hqHtzhY35RinnFFgrNupx5X76xf08q7Cew89HxP5QVUFOkIn40tiK0RXOQdEa0xXPQGN+SvUr+bGmiqiojwRh9ngh9vjAjgRijwRiJDI+OthkUCsw6Ci16Sqx6SuwGrHolJ/72IjPygyRJ6HQ6dEdXsBaZkZ80ssTKGicvNg9luylTdjZmhnD6zJBlCa0io1Vkap0mLm0oyXaTz0oiM84e+TJbXGTG2Un0M/KDyAzhDevmFNLc78/6AF6RGWenXMsMURTPAkmCcoeRRaVW5hZZsBwzA/qyxTfCO94srL+/+/282vUqnnYP8UNxtGgpUB0wAurIJpq0j6MvqWNu9RVZ+E2ml6KRubqxjL/viOENqxzs83FeUYxS+9n9odc3vAPzoWY0wDCHjlumv8seZWH9zSiyjk+r7xcno7OQJEmk0DFQcxtyYiuS3gKpJGjER3quMOsVzNN0giZJEmazGYCqqiqqqqqOu08qlUJRFBKJBMPDwzzxxBM88cQT1NfXs2LFCubPnz8tbZusuoI6Xv3Qq9zy4C281vUaP936U3669ad8bOXH+Pk7fj5uJYuT0cgSc1xmKpxatgw7CUYTnDvPRXN/GG84MxeSUmqKV/v/zDPdPycQH0Evm4imQryz/Dd43HPpcocYCcQYCYyyqW0Uu1HLvCIz84oslNkNWfk8TgbdBHc/CYBl9buO6zjoqxvRVSwat6SUcf45M9rGTApEE3SMBGkfCdE1Gjrh4AiNLGE7OtPbolcw6TToFRmdIqPVyEhSegCEqqaXiE2kVOKJFJF4inA8STCWnmHujySIJVPpmeeRBO3HLOVv0mmodBqpcqZXNLAbtTP5zzCOyIyz22zMjFy2pNzO5tZRIvHjt9HKB2dbZgjHE5lxdhOZkVtyfba4yAxBZMbZTWRGftEpMpfWl/Dgzp6sPL/IDCGXMkOk1AySJYklFTbWzCnAZjizi6PrKtexrnIdrIfRwCgtfS08veVp3K1urCkrFfFCOrt3c3v3Z1hW8HYuqfwYVZYl0/ybzBy9ouGdy8u4c4dCIJrgoV3dXLeylmKrIdtNm1GxRIiWnXdRES/EfMztpZTSTjudjgiVVRfjsNZy7C62oiA+u0kaLYrVRaRrL7LJgWywoKaiIP7sZ73ly5fT0NCAz+ejubmZbdu24fF4aGpqIhAI5GTHY65zLq988BX+tPdP3PLgLQD8asev+NWOX/HLjb/koys/OqHPNLNeYc2cAs6dp6VtOMj2Djfd7vCk2zca6eGew5+n3b8TgDLTAj686E4KDEeXDq2FSDxJ61CQI0MBOkdDeMNxdnR62NHpwazXUFdkYX6xlXLH9BbI1VSS8KHXSIx2j7s9GXCj2EvQ165AW1iNrJ8d69t5QjGODAY4MhRgwBcd9zOtRqLEZqDMbqDYaqDQosNu1CJn4N9fVVXC8SSjwfQM9CF/lAF/lOFAlFAsyaGBAIcGAgA4TVrmuizMKTJTnqUBEiIzhJPJx8zIVTpFprEqXRjPF2dbZghnRmSGcDIiM2ZWfamNrW2juHNktrjIDOFERGYIJyMyI7fUuswsLrexv9c3I88nMkM4kVzIDFEUnyF1xRbOq3NRYJ78zMECSwEF8wtYMz+98Po/Nv2D5198nq2RrQDsGX0K7+hBriq8CaurkWLncmQ5N/e5nAi9omFRqZWmfj+RYIoHtvfwzsZyKpzGbDdt2gXio7zSdw9S9xHWs37cz3q1I9zaeDc6xYRWFvuynK10ZQvQls4n4e4h1tNEwtvPYLmOYnss200Tskyn0+FyuXC5XJx33nm0t7fz0ksvsWLFirH7tLe309nZyYIFCygpKcn6QBpJknj/svfznob3cMkfL+HVrlcB+PijH+fjj36cPZ/Yw9KSpRN+zLlFFuYWWdje4ebVI8Mkz3D57KSa4IWeu2jz7+CA+3kAdLKR1UXX8M7aL6HTjM8hg1ZDQ7mNhnIbsUSKjtEgLUNB2oaCBKNJdnd72d3txWpQWFRqZVGpbUrnBScT7dh9XKdDcZajOMqQZBl9+aKMP+dMUlWV0eCbhfDhwPjPuxKbntpCMzWFJkqsBmR5el7XkiRh0imYdAqVzjc7cYlkin5fhC53mG53iH5vBHcozvZON9s73Zj1GuYXW1lQYqHUNrMFcpEZwsnkY2bkqhVVTnZ2eohNwzYe02G2Z4YweSIzhJMRmTFz0rPFC3lqf3+2mwKIzBBOTmSGcDIiM3LLBQuK6BwNzcgy6iIzhJPJdmaIovg0e2NpioWl1ow/9rvOeRfvOudd7OzbySOHHuGu1+/ivdH3YhwxwEgzAZopveByIkkXKTWz+2fONI0sU19qJThspM+b4MFdPVy1pJS5RZZsN21apFIptu74X7pjh3mVV7mMy8Z+Fl+8mkL7fMQO68IbJElCNtqIu3sgEaPAkhujyIXcUltbS21t7dj3Xq+X++67j0gkwvPPpwu+n//857FYsv+5qlf0vPzBl9kzsIc/7P4Dd2y6A4Bldy6jsaSRz6z7DB9c8cEJP+6qGicVDiOP7+075ZLqyVScvaPPsHXwIZo8L47dXmVewq0Lf0Sh4fhlwd5Kp8jML7Yyv9hKIpWiazTM4UE/LYNB/JEEW9vdbG13U+4wsKTczvxiC4pmcgPZVFUl2rELSdGjq6hP79NylGXl1ciG7P9NpyqZUun1hGkbDtI6HBz395MkqHKaxpaqN+uze3qraGQqnaajhfJCovEkHaMhWoeDtA2nB0js6vKwq8uD3ailvsxKfakN2wwtsS4yQzgT+ZQZucao07Cs0s62dne2m3JCZ0NmCJkjMkM4EyIzpteiUivbO0aPGwg6E0RmCBMhMkM4EyIzssug1XBJfQkPTcMy6iIzhInIZmaIovg0KrMbuHJJGXbT9F7kXFG2ghVlK/jqhq/ywwd+SE9bD86wE4D+l57G6/Bxw8bP0e2BHnc4bwvkGlnmnY1lPH1glLbhII/u7eNti4pZUm7PdtMyJhLz0bHrjxQmrNRTSwQ3EhL2OedhKHoXOkUsJyKcmMZow7r6GuIDrWyzzScZdCO3NxFeW4PROPtXVRAmzmazcfHFF9Pc3ExraysAP/jBD2hsbGTjxo0oSnZPESRJorG0kR+W/pBaRy2fefIzAOwe2M2HHv4QH3r4Q9y89Ga+ceE3mF945ktuldoN3Liminu3duF7S2HcEx3gtf4/82zPnWO3yZLCOcXX0+i6gjrbOZMasazIMnNcZua4zCQWpmgdDtLU56NjNESvJ0KvJ8KLh4ZYXG5jWaXjjPefVpMJQs0vk/QNQSqJtngOoKKvaURfvQzpDPZiz1XJlMpwIEqPO0ynO0SPO0zimBn+GkmiqsDI/GIrc4vMGLS5+7vqtRoWlFhZUJIeINE5EuLQYIDWoQDecJxNraNsah2lqsDI4jI784rMkx4gcaZEZggTleuZkWtW1xSwp9ubU7PFZ3NmCNNLZIYwUSIzMkuWJc6rc/GPXb0z9pwiM4TJEpkhTJTIjJk3x2VmWaWdPd3ejDyeyAxhsrKVGeJTZZrUl9m4rKEEzTQt2XkiGlnDF97zBQDufPxOWre1YlbN2D027rrnm3zowx/iyiWrOTIY4NCAnx5PmHyrjysamY1Ly3j24ABNfX6eaxrEHYxxXp0rI/uDZlPv4BYsR1ooo3DsNj16/mvVizj0JVlsmZAvJFmDruxocVBVQdYQCoVEx0M4IUmSWLt2LWvXrmXv3r08/fTTBAIBdu/ezdDQEB/96Eez3cQx/7bu3/i3df9Gl7eLb730LX6141cA/Hnvn/nz3j+P3e+P1/yRGxbfgKqqpyxem/UK1ywv554tR2ga2cI/e36FhIZD3lfH3W9N0XWcW/peaqyNGftdFI08ViANRBIc6POxr9eLP5IY2398rstMY5WDKqfxpL9HMjBCcM/T425TE3Ek6WgxNc8iMRxL0u+L0O+N0OsN0++NjCuCAxi1GmpdJua6LFQXmNAp+bdFjCLLY8v5xxIpWoYCNPX56HKH6RpNfxkUmfoyG0sq7LimcSycyAxhIvIpM3JBrs0Wn22ZIcw8kRnCRIjMyLy5RRYqnUa63eFpfy6RGcJUicwQJkJkRnZcuKCIXm+EYX90So8jMkOYqmxkhiiKT4NVNU42zHdldf+LT1z1CcKXhvnG//cNTKqJZ3mWX//219x3/X3csPgGGqsc+CNxDg2kC+T93kjW2jpRsixxWX0JNoOWzW2j7Oj04A7FuWJxaV5eIO8JHqTt8EMsC9W8eZtuBHv1Ota6bpwV+8ILWaDRoibiWK2Z37pBmH2WLl1KQ0MDd9xxB8FgkEsuuSTbTTqhKnsVv7z6l7xv2fu48PcXHvfzWx+6lVsfunXs+++++l3CifSFo6sXXM2a8jX8ed+f6fJ2EYwHT/gcxcY5nF96C3NsK6kw10/PL3KUxaCwdk4Bq2udtA8H2d3tpfPoMtutw0EKTDqWVdlZVGpFr7w5sjbWd4hI2/ax75XCKvQVDchm57S2N1OSKZUhf3SsCN7vi5xwOXu9IlNmN1BVYKLKacJl0c2qvcV0R4vf9WU2fOE4+/t8HOj1EYgm2NnlYWeXhxqHwtzKKAWmzO89P47IDGEC8iUzsm1VjTMnZovne2YIOUhkhjABIjMyZ8P8Iu7d2jmtk1tEZggZJzJDmACRGTNH0chctaSUv2zpJJ6cXLCIzBAyboYyQxTFM2zDfBerawuy3QwAjDoj3/nP73Df3vtoe6gNgP/+23/z+pOv85lbPkNtcS2rapysqnEyGozR1Oejqc+HP5LIcstPT5IkzplbiNOk45mmAdqGg9y7tZOrlpbhsuiz3bwzkkwl+eb2C/HHh1jCEpaRLooH6uqoL74py60T8p3GaMUybwU63TQXUoRZQ6PR8PnPf55kMjm2PNX+/fvZt28fGzduxGw2Z7mFb7qg5gLUr6vEk3F+vvXnfO2Fr+GL+o673xsFcYBHDj3CI4ceOeljXlr5CRY730a1ZdmMF15lSRqbPTwajLGn28OBPh+joRgvNA/x6pFhFhRbWFLpoMSqR9K+mXPmZW9HY8mN846TCUQS9HnD9B0tgg/6oyRTx3e6nCYtpXYDpTYD5Q4jhebZVQQ/FZtRy/q5haybU0DHSIh9PV7ahoP0esO06AN0yBKvaIdZVO6iwJz5z3WRGcJE5VNmZItJp2RttvixK6bkW2YIuU9khjBRIjMyo9RuYEGJleZ+f0YfV2SGMJ1EZggTJTJj5hRa9Fy0sJhnDgyc8TEiM4TpNFOZIYriGXTBAheranLrzS/LMjc13sT1S67nG89+g+SmJMaAkV/+4pckXUm+cMsXKLIVUWDWcV6di3PnFdI2HGTv0Yux5Pjy6gtLrdiMCo/t7cMdinPf1i4uWlhEQ5ktpy+k+4I9jOx+kPdzPY/wCPvYx7lzP8qi0iuwZbtxgiCctSRJGut0RKNRHn30USKRCO3t7TQ0NHD55Zej1+fOwCOtRstnzvkMnzknvd/4pu5NPNf6HN96+VtEE1FWlK1gR98OAOpd9bhMLl7ufJkScwmfX/95Si2lbKjZwJ4OHS2DgWz+KmMKzDouWljM+nmFNPX52dPtIRwKsWj0NaKjMo/K8yguLWfBostxFhSe/gFnWDSeZCgQZcAXHZsFHogeP9jOoJUptRnGiuAlNkNO7ws+U2RJGtt/3h+Jc7B3BF1UJpZMsavLw9bOIGV2A0vK7cwvsaCd5r3HBeFU8i0zsiEbs8VTsTChfc+CrMEwby1aVw2y2YnGKHoZgiBkj8iMzDhvnouWwcBx2wxNlsgMQRBykciMmbOkwk63O0RT3+kHXInMEGYLURTPkHVzCnKuIH4srUbLt9/+bR4vfJznH38ei2qBYfjuj77LRz78ERZWLATSofPGbDVvOM7ujkE0hyTi2V3175TK7EZuXlvNU/sH6BwN8WzTIJ0jIS5cWIRJl3sv8f17f0OV30zh0b3DHTj4l3X/wKCxZLllgiAIb9Lr9dx888088MADeL1eduzYwZEjR/jwhz+MzZabJ7znVJ7DyrKVxFPppbi/suEr6DSnH11YZEzQ4w4TiSenu4lnTK9oWF7lYJHaSax7/9jtaizIprZRNgEOk5/aAjPVhSbK7DNbVI7Ek3jDcdyhGKPB9NdwIHbCZdAlwGXRU2o3UGZPF8IdRm1OD17LBVaDlnVzClgedOANx5mTNHN4JEafN0KfN8ILhwapK7bQUGajwnHy/ecFYSbkY2bMBJNOobHSwdb20Rl5vkjnXmLd+8a+VyMBsLrEhSpBEHKKyIzJs5u0rKp1srl16rkiMkMQhHwgMmP6XVJfwkgwxqDv5PuLi8wQZpPcqxjmoeXVDs6tc2W7GWfkqtVXsbR2Kf/3l//DOmrFolr442/+iGmeif+4+T/QyG9eULcbtVwwv4hExEGnO4jijeRscdykU7hmeTlbO9xsahnh0GCALneYCxcUsaDEkhMXiiMxH227/0BV/M3XSpcjxocaHshiqwRBEE6uqqqKj33sYzzzzDPs2rULn8/HT3/6Uy644ALOO++8nPhszQSzXuHCBUU8tb8/200ZoyYThA+9RsLdM3abpmoZ85UqUgN+OkdDeEJxdoU87Or2AOAwaim26Skw6bCbtNiNWkw6BYNWRqeRT/n3SqZU4skUsUSKaCJFJJ4kEk8SiicJx5IEowkCR7/8kQTRU8x6tBkUiqz6sVngxVYDOkXMaJ4sSZJwmHS8Y1kZ/pjMgT4f+3t9eMNxmvr8NPX5segVFpRYWFBipdiqnzXvTSG/nC2ZMVGra53s7vZM62zxE2WGvmY52qLaaXtOQRCEqRCZMXlrawto7vfjCR0/GPVMiMwQBCHfiMyYXlqNzMZl5fxlSyfh2PjJIiIzhNlIFMWnaGGplYsWFGW7GRNS5ari+5/+PlsPb+X+v9yPRbWwqWUThv8x8PpHX2d1+epx91dkmUqnieVJA32+ODpFJpWDxXFJklhbW0BNgYlnmgYYCcR4cn8/Tf0mNtS5KMziXuNbB/5OcUs/FbxZEI8tXsli+8KstUkQBOFMmEwm3vWud3Heeedx99134/P5cLvdJBIJtFpttpuXMQ3lNg4N+NNbh2SZqqoEdj2BGn1zSXfLyo3IBiv1QH2ZjWgiSddomI6RIF3uMN5wHM/RrxORJdDIEhpJQpYlVBVUVFIqJJIpJrMCo0mnwW7UUmjWUWDWUWjRU2zVi2XQp5FZr7CmtoDVNU76vBGa+nwcGggQiCbY0elhR6cHm0FhbpGFeUVmyu1GZFlcIBBmztmSGRNh0GpYUe3IyKy+EzlVZgiCIOQykRmTo2hkLl5YzIM7e05/57cQmSEIQr4SmTG97EYtVy0p48GdPaTU9AUikRnCbCWK4lNQ4TByeUNJ3o5GWjN/DTWfqeH2P9zOP93/JEGCi359Eb+57De899z3Hnd/RZapKjBx69patrX72d/rRc3BPcdLbAZuWlPN1vZRtraP0jESonOkk8XlNs6ZW4hZP7Mv+1f6/sTf275JI41cwqX4XRbqF9w8o20QBEGYKpfLxSc/+Ul27drF0qVL0Wq1BAIBPB4PlZWV2W5eRrytvpg/vtZOPJndcJMkCX3VEqJde9FX1KMrnX/cffSKhrpiC3XF6a03wvEkg74Ig/4onlAcbziOLxInHEuSSKWL36mkSpxT/24aWUKvyOgVGaNWg1GX/jLrFCx6BbNewWpQsBu1Yj/rLJIkiXKHkXKHkQsXFNE+EuLQgJ/W4SC+SIJdXR52dXnQaWQqnEaqnEbK7EYKLTrxdxNmxNmQGROxstrJri4P0WlYdutMMkMQBCGXicyYuFqXmfklFg4PBE5/52OIzBAEId+JzJg+1YUmLlxYxPMHBwGRGcLsJYrik+Qwabm6sRwlzy8sFtuLufvTd/N/m/+P7z/1fT7KR2l+ppn/PPCffPn9X8Z8gpFWZr3CZQ0lLK2w88+Dgwz4Illo+alpZIlz5hayqNTKK0eGaRkKsq/XR1O/n/oyK6uqnThMp99ndioOHr6P8qEU2/kDAAVlqymtuo1KJXsz1gVBEKbCYDBwzjnnANDd3c1vf/tbAM477zwuuugilPwcIzbGZtCyuraA11tGZvy5YwMtRFq2YFp8CYq9GG1RLVpXDZJ8ZucZRq2GmkIzNYXm436WSKaIxFMkUukZ4cmUiiSBLElIEiiyhFYjo2gklDN8PiF3KBp5bIBEPJmiYyRE63CAtuEgkXiKtuHg2AoIEuA0644urZ8e7KCRJVKqiqKRiMSTfPoS0dEVMmO2Z8ZEGLQaVlY7M5YvU80MQRCEXCMyY+IuXFBEx0jotNtziMwQBGG2EZkxfVJDrfhee0RkhjCriaL4JCiyxNWNFRh1s2N5UEmS+Ow5n+VDyz7EHd+7AwBtj5bv/+/3Wfn2ZVxtOvFxpXYDN62tYleXh1ePDGd9Zt2JOEw6Ni4rp8cT5tUjw/R5I+zr8bG/x8fcIjNLKuxUF5iQMzjbP5YI0bLzLirihQBUUkl5yTlsrP1C3q4qkG9UVSWRUgnHkySSKsmUSkpVkTi6fPDRApBBq0EjlpUVhEkpKSkZ+/9XX32VV199lQ/c+n5qstimTFhd4+TA0f2aZ4KaShI+9DqJ0S4AkoFhFHtxOi8ylBmKRsaS54P4hDOjPaZArqoqQ/4oXe4wXe4Qg74o4XiS0WCM0WDshMfv6faKorgwLWZrZkzEimoHu7s8hN6yT99EzERmCIIgZJvIjDNjNWi5cEERzxwYOOHPRWYIgnA2EJmRGYlEgr///e80NTUBYFd9BBGZIcxOoig+QRIwv8Qy7bOMs8FmsnHTR2/ij7/+I1rSM8R3PLUHZfkAl9S4TniMJEmsqHYy12Xh6QP9dLvDM9nkM1bhMPKeVZX0eiJs7Ugvqd4yFKRlKIjVoLCo1EpdkYUiq35KhetEIkpkyz+ooHDstrmWVSyf+y+iID4NgtEEQ/4oQ4Eo3nB87CsUS5I8ww1ydRoZk06D1ahgM2jH9scttOixGRTxdxOEk9BqtXzuc5/jJz/5CYlEAoDf//EePnQBlDiz3LgpUDQyFyxw8cjuvml/LjWVwr/p/vE3pjK/tK5wdpIkiWKbgWKbgVU1TlRVJRhLMuSPEogmCMUShKLJ9KAxKb1s/qraPH7zCjlttmbGROgVDWvmFPBi89CkjheZIQjC2UJkxplbUmGnZShA61Bw3O0iMwRBOFuIzJi6ZDLJt7/97XG31blM9JsMOblCsCBMlSiKT1B1oQmHcfYVxN+woHwB3/r6t7j/5ftp+md6ZNCXd73KTfESKDjnpMfZTVquX1XJ7m4vrxweyslZ45IkUeE0UuGsYDgQZX+vj6Y+H/5Igq3tbra2u7EaFOa4zFQXmKh0GtErZ74agC/Yy8C+v1NytCDeafbRsPSjrBDLi2SMOxSj2x2m2x2ixxMmGD31TBtZSs+a08jS2GoAyZRKIpUae43Gkili4RSecBwYP6hDp5EptRsotRkodxgodxjFXqyCcAybzcZXv/pVWlpauOeeewDwhPK/41FXbKWm0EvHSGjaniMVixBufmXse135IvQ1y8VAHGHaSJKERZ/eG/5ELHqFj14wd4ZbJZxNZmtmTERjpYNdnZ4Jr0YiMkMQhLONyIwzd2l9CXd7OwgfXYlEZIYgCGcbkRmTFwwGuf/+NwdRrV+/nssuuwxJkgjFEvx1W/dJV5oThHwliuITsKjUSjnGbDdjRtyw4QYGlw3yjfu+zu6+vezev5eljn6uqL/ypMdIksTyKgdVTiNP7u9n0BedwRZPjMui58IFRZw3r5DW4SCHBwO0DwfxRxLs6fayp9uLBBTb9JQ7jJTbjVQ4jCddMv83TR8n7O7gFm4BoE8ZoWHpvyCLgviUqKpKnzdC63CQlqEAntDxFxCdJi1FVj0FpvQeqTajFrNewajVoNVIJ+34pVSVaCJFJJ4kGE3giyTwheN4QnFGglHcwTixZIrO0RCdo+nCmEaSKHMYqCk0Ma/IgnMWrhghCJMxb948vvSlL7Fvz26WGp4glgJigD7bLZu8CxcUcc+mTlLq9AzySoU8JP3DAGgcpeJClSAIZ43ZmBlnSiNLrJ9XyJP7+id0nMgMQRDOVmdzZpwps17hbYuKeWxPeqUrkRmCIJytRGZM3MDAAF1d6W025s2bN1YQBzDpFK5dWcH9W7vwRxLZbKYgZJQoip8ho1bDJYtK4GC2WzJziu3F/PzD/0f4j5t4tLObd3uuh9dhc9VmNtRvOOlxhRY9711TzabWEba2jzJN9YSMUDQyC0qsLCixkkim6BgN0TkSossdwh2KM+CLMuCLshMPkC7AVjiMVDiNVDlN6LUpth/6DQfcLwDwKI9x+aKvsLBgSfZ+qTz3xh6ozQN+Dg0ECETfDF1ZgjK7kUpn+qvEZpj0zG1ZkjBqNRi1mhMWt1MplZFgjD5vmH5vhC53mEA0cXSmephXj4xQaNYxr8jCwlIrBWZRIBfObgaDgdWrVsD+Jxjxg7Q5fYoxXD9MeWl5lls3cYUWPcuq7Ozq9GTsMVU1RWKkG62rGsVRimHeGjRWFxqTPWPPIQiCkA9mW2ZMxKJSK9s73Az5Tz2AWGSGIAhC2tmcGWeqrshMaWqYftklMkMQhLOayIzTS6VSHDx4kIaGBubOncvGjRupqqqiqKjouPvaDFquW1nJ/du6xlYkEYR8J4riZ0inyJA8O2f9/nblO/lyYjv0pr//5/3/ZOuSrdz+7ttPeoxGljivzkWty8yT+/rxTXCJwGxQNDLziizMK7IA4I/E6fGE6fVE6PWEGQnGcIfiuENx9vX60DLC+cbN1FNMLbW0087Vq+/ArivJ8m+Sn9zBGM0Dfpr7/UeXMk/TaWRqXemZ2TWFpgktaT8VsixRZNVTZNWzrDJdrPeE4nSMhmgbDtLtDjESjDESHGVL+yjFVj2LSq0sKrNh1M5MGwUhV0X+//buO06K+v7j+Gu2317Z65XjjuPovSOIiKJiwRaVRCzYaxILMZqIYCyoQWKP7RcsIbEkaoxdUCzYDShKkd7h4HrfNr8/Dk5P2l7du733M4+J7OzM7Geu7PvuPvP9jvfH74EnHnuCE088keHDh4exoqY5LC+JVTvKW+QH/6CvhurVnxIo2YFhd2LzpOFI694CVYqIdGyRkhmhMgyDw/OTeXnJ1gNuo8wQEdm/zpYZoaisrOTll19m7dq1ZIw4jkp7ojJDRARlxv78NDPOP/98cnNzGTp06EH3SYx2cPrQLP799VZqfGqMS8fXYbq8d9xxB2PGjMHtdhMfHx/ucjoVi2Fw96jhJAyIq19X/l05t//9doLB4EH3zYqP4pzRXemXGXfQ7dqjWJed3ulxHNU7lXNG53DpEXlMHpjB0K7x9HYv4pyo9eSSCkBu4Hh6+F7ns9UmG3ZXEgi24+Hx7YRpmhSU1/Dp2kL+/tlGnvlsI5+vL6Kk2ofVYtAjNYYTB2RwybhuHN8/g55psW3WEN8fwzBIiHYwODue04Zkccm4PI7rm0ZukhvDgILyWj5cvZv/+2g9b363nU1FVZjteZqECKfMCK9uKTD5xBPqH7/++ut8/PHHHe57wmW3clheUrOP492+moovXyZQUjddbqCyuNnHFJGWo8wIr0jJjMbITY6mW3L0fp9TZoi0b8qM8OqMmXEwX375JXPmzGHt2rUAdI/21w3qEZF2QZkRXsqMhn6eGdu3bw9539RYF78YmoVLA8EkAnSYkeJer5czzzyTww47jP/7v/8Ldzmd0m9OuYofRm/g8X89TmxxLIG1AW585Eb+dOmfcDlcB9zPabNybL90eqTFsnDFzg57D4oou5W8lBhqNz5FllnXJCmlnFUOD9urJuP1B1m5o5yVO8qJslvpkRpD38w4UmOdun/THnXTj9fdo3tTYRWVPxl9aTGga6KbXmmx5KXEtPtf5Fx2K70z4uidEUeV188POytYvr2MXeW1/LCzgh92VhDvtjMwy0OfjDj90NDGlBnhN3TIIPJ79uK9997jm2++YeHChRQVFXHiiSditXac74cBWR6+3VLC7gpvk/avWvUx/sK6+zMZzmhcuUOwJ2W3ZIki0kzKjPCLlMxojCN7pbC5qAr/Ty6mVWaItH/KjPDrjJmxPy+++CLLly8HwOPxcNxxx9GnTx+ydpTx5rIdYa5ORECZ0R4oM+ocKDMaIzXOVTdi/H9bqPUdfKCkSHvWYZrit956KwBPPfVUeAvp5Hpm9mTOb+bw5xf+TMWKChyFDk5/6nQePONBuicefHqmbsnRnDM6h49X7+a7baXt+l7j+/P5zn+xfe3bHMVRAGx2FtF7yOVkW6wcZZpsL6lhdUHdfbCrfQG+3VrKt1tLSY5x0C/TQ+/02E7VGN17T+4dpTVsL61mW2kNpT+bRt9mMchJqpsavVtydIf9+LgdNgZnxzM4O56Cshq+21ZWNw18lY8PV+/mk7WF9EqPZVCXeNJidIFEW1BmtA9xcXGceuqpZGRk8Pbbb7Ns2TJGjx5NampquEsLmcViML5nKv/+35ZG71u7+bv65oY9pRuu/FG6SEqkHVJmtA+RkBmNEe92MDQngS/WFwHKDJGOQpnRPnS2zPi5Dz74oL65MXjwYE4++eT6zOidHseO0hqWbCoJY4UiAsqM9kKZceDMaKy0OBdnDO3Cy0u2UqV7jEsH1WGa4tK+/O6s3/Hse89y7yf38s32b+j7YF8uGnwRj5zyyEH3c9mtTOybxoAuHhatKmBbSU0bVdw8y4sW8eLaGZzBGQDstBbSe8gVWC11TVyLYZCVEEVWQhRH9Ehhc3EVy7eXsXZXJbsrvHzwwy4+XrObHqkx9M/ykOlxRdQfuUzTpLzGz86yGnaU1bCzrJadZTUNRr4AGEByrJOuiW66JrrJjHdhs7TvEeGNlRrn4qg4F4fnJ7NqRznfbq0bYfr9tjK+31ZGV4+V7tm1JEY7wl2qSJsZNWoU8fHxmKZJamoqXq+XYDCIy3XgWUbak65JbvJSolm3qzLkfcxgkEBlCQDW2BQ1N0REQtTRM6MxRuQmsmJ7GWVVXmWGiEgTdKbM2CsQCLBz504Aunbtut/mxhE9Uiiq9LKxsCocJYqItEvKjP1nRmOlxrk4c3g2L/1vS4edEVg6t4huitfW1lJbW1v/uKysLIzVRJ5zjzqXsUPGMuWFKfTZ0Ye0pWlcv+F6Lj/rcnpk9DjovmlxLs4ans3qggo+X1/E7vLag24fLsXl69j+wxvU1BZjx85GNmJ4UhjV7zcH3MdiMchJiiYnKZoaX4BVO8r5blspuyu89dOrx7vt9MuIo09GHNHOjvdt6AsE2VlWw/bSGnaU1jXC93d1mMNqIc3jJD3ORWZ8FBkeV1jvC96WHDYLA7p46J8Vx/bSGr7ZUsKaggq2l9WwZlcF9iILnzmK6JOVRKzLHu5yBWVGa+vVqxcAwWCQ559/nnXr1jFgwACOOeYYYmNjw1zdoR3RI4WNhVUEggee5iRYW0nNxm8w/bW4ex6O1ZOCLSEdR1p+G1YqIm1BmdG6OnpmhKK0tJSFCxcSKC6DzBHKDJEIpsxoXZ0pM6qqqjjzzDPJycmhe/fuDBs2bL/bWywGJwzI4PkvN1NU2bTbQIlIeCgzWpcyo2UkRjvqG+MlVb5D7yDSjoS1G3fjjTdy9913H3SbFStW0Lt37yYdf/bs2fXTlEjryEvI4/1p73PVXVcBEFcSxz8e/wfjTh/HUQOOOui+hmHQMy2WHqkxrN1Vydcbi9rVyPGK6gK8yz6gC0lAEmfYzqb/sN/gtLpDPobLbmVQdjwDu3jYWVbLsq2lrC6om1Z78dpCPllbSJfEKHqmxpKfGtMupw8PmibFlV52ltWyvayanaW17K6o5edtIYsByTF1DfA0j4u0WCeJ0Y5OP8rFMAwy46PIjI+istbP8q27sdda8AWCfLWxiM82lNMtOZoBXTzkJLo7/cfrYJQZkcEwDMw9989YtmwZy5Yt49JLLyUjIyPMlR1cQrSDgV08B5yG0PTVUvHNW+Cv+6NTzfqvieoxug0rFJGfUmZEho6aGYdSXV3No48+Sk1N3e8+CYYDI3t4mKsS6byUGZGhs2TGW2+9xSmnnHLI/Vx2KycPyuS5LzdT49MUtyItRZkRGZQZzeeJsnPW8Gz+s3QbO8vqXs+KicNiwj6dA+nILGYQbB4AoixBgkbr/VzhNIz6r9+fs9vtWK3N75+FtSl+/fXXM23atINuk5eX1+Tj33TTTVx33XX1j8vKysjOzm7y8WT/YpwxPD3zaW59+lbYULfuo5c+4o1Fb3DbZbcR5Yg66P6GYZCfGkN+agyFFbV8t62MldvLwnZfikDQx6r/PUYXbxJR1E2fsjmuioG9r8Fubdp0KoZhkO5xke5xMb5nCqsLyvl+WxnbS2vYXFTN5qJq3l9VQIYnipwkNzlJbpJjnFjauEHq9QcpqvSyu6KWwgovBRU17CqvxRfYN8hinDYyPC7S4+rOKzXWic0aWVOht7Rop41R3RIZUhlPUZWXzNooNpb4Wbe7knW7K4l12eibEUffjDjiojR6/OeUGZHBMAzOO+88PvjgAxYtWgTA448/zuDBgznppJNa5Ieb1jI6L4kV28sb/FHJNINU//BJ/X1gAZzZA3Bk9QlHiSKyhzIjMnTkzNifYDDIv//97/p7+gEceeSRDBo+in98uYVaXzCM1Yl0XsqMyNBZMuPwww8P+RgJ0Q4mD8rg5f9t3ef2diLSNMqMyKDMaBnRThtnDOvCG99uw19ZTKY7iFUDviKQidM8HoBBhgm03uwAhuFn/fr1B3w+Pj6e9PT0Zg0sDGtTPCUlhZSUlFY7vtPpxOl0ttrxpaGZ58/kmYXPsOSzJcT744ktimXG3TP4/TW/JyU2tM9zUoyT8T1TGJefzJbialYXlLNyR1ErV/6j4pqtWP/34Z7R4VBDDcGeg+mXPKTFXsNhs9Av00O/TA+l1T5+2FnODzvL2V3hZWtJNVtLqvlkbSF2q0FanIu0OBdJ0Q7i3Xbi3Q5cNkuTvulN06TaF6DKG6Cy1k9FrZ/ymrqlpNpLSZXvgBci2CwGqbHO+sZ+epxLU343g2EYJEU7OX1oFrurYNnWUlZsL6O8xs/n64v4fH0RWfFR9E5vvzMIhIMyI7KMHz+etLQ0Fi1axM6dO1m6dCkbN27kiiuuwG5vn+8vLruV0XmJLFq1C6h7Xy3/9PkfN7Daie5/NNbohDBVKCJ7KTMiS0fMjJ8zTZPbbrut/rHT6eSCCy4gLS0NqLtNx7vLd4arPJFOTZkRWTpDZjRGlwQ3xw9I57Vvt2OqLy7SbMqMyKLMaD6HzcLIdAsFhRbiElJwOF2gxnhkMU2ignU9umpLYqt+fi2GQWK0Yz8lmFRVVVFQUADQrFkdOszNjDdt2kRRURGbNm0iEAiwdOlSAPLz84mJiQlvcVLvvKPP47yjz+Mfi/7Big9WsDu4m6FPDOXBEx7k5F4nYzFCG0lssRh0TXLTNcnN2Px4VlfHUljhxWm3EGiFAeT+oJf/bJjNyh1vcwEXEEMMRUYJ0X0mkB7ftOluQuGJsjMiN5ERuYmUVvvYUFjJxsIqthRX4QuYbCmuZktxdYN9rBaDKLsVt8OK3WrBZjWwWX58IzJN8AdN/MEg/oBJrT9IjS9ArT+0kSduh5XkGCdJMQ5SYpykxjpJiHa0+aj1ziIx2sH4nimM7Z7E2l2VfL+tlM3F1fUXSCxatYvsxCi6p8SQlxKN29Fh3rbDSpnRMfTu3ZvevXuzevVqnn/+eTweDzZb+/4aH9Qlnm+3lFJU6SVYU45hd2L6arFExeHufQSWqMi4B5VIZ6LM6Bg6Ymb8VGFhIdHR0VRWVpKSksIvf/lLEhMT65/vn+VhTUEF63dXhrFKETkUZUbHEOmZ0Vj5qbFM6BXgvZUFLViliByKMqNjUGY0TyAQoLS0lOzMdKJiPZTX+NvstaWNmCbOYN33hN/ibPWmuMu1/wuDoqLqZqQuKCggNTW1yTM6dJjv7ltuuYWnn366/vGQIXUjd99//32OPPLIMFUlB3L2kWfzvOV5nv/oebaUb+G050/jF/yC08efztlHnt2oY1ksBvFuB/FuBxeNyWNtQQ1LNpVQVOltkVp3Fi3js/V/Y3HtG9iwsYxlpKeNYlD3K1rk+KHyRNkZ1CWeQV3iCZomRZVedpTWsLO8hpIqHyVVPipq/QSCJhV7Rno3xd6GeozTRqzLRqzLTlyUjQR33Wh0p02jksPBZrXQKz2WXumxlNX4+GFHOSt3llNY4WVDYRUbCqtYuBJSY51kJ7jpkhhFepxLo8gPQJnRsfTo0YNf/vKXuFyu+pkw/v3vfzNw4EB69OgR5uoaKizcTVejkCJisTijsSfnYo1Px56QGe7SRKSJlBkdS0fKjF27dlFQUEC/fv2Ij4+nf//+dO/e/YB1Ht0nlb9/tkn3fhVpx5QZHUskZ0ZjDcqOp9oX4NO1hS1yPBE5NGVGx6LMaBqfr24qbbfbTZTDhs1iUFLt0+wk0ircbjdQ93UX8U3xp556iqeeeiq8RZgm1FaD3wtVhRCTrqkgDmLKEVM4afRJ3LjgRh778jH60IfVH6zm5g9u5shTj2TioImNPqbdamFgl3gGZHlYv7uSj1bvbnJz3Ouv4ofvnqFrlYfxDKfAspmxvX5Nn/gjmnVPgpZgMQySY5wkxzjpj6d+vT8QpMpbNwV6lc+PP2DWLcEfR4EbRt3IcZvVwG6x4LBZcNmtOPf812rR12x7F+eyMzw3keG5iRRW1LJ2VyVrd1VQUF5bv3y9qRiAaIeVxBgHMQ4bdpsFh9WCP2BS5fPj9Qf5aM1unrlwZJjPqO0pMzqe/Pz8+n/7fD5WrFjBd999h81m44ILLiAzM7xNZ7/fz4IFC/j8888xDIPcUcdSaEnE1W1oWOsSkeZTZnQ8HS0znE4n+fn5TJo06aD7xbrsHNM3jf9+s62NKhWRxlJmdDyRmhlNMToviUDQ5Iv1bXebQpHOTJnR8Sgzmm5vP8Vhs5IUbVBa7ccXCG3mWpFQtUTfrsM0xcOqugS++Sd8/igUb6hb9/bfIKEbjLoMBv0KouLDWGD7Fe2I5sETHuTyYZcz/9H52LBhx87iVxbz2huvMeOqGSTFJTX6uIZhkJcSQ05SNEs2FfP5+iK8IU4PvnHbIiqK1pFdFkXXPQ3nIkspp/W6jeSEPo2upS3ZrBbioizERXWMe5pI8yXFOEmKcTKyWyIVNX62FFfVT69eWu2j0hugsqj6gPvvKKvBNM2wX+jRqSgzms1isWC1WgkEAvj9fp544gmys7OZOnVqm99ba8OGDWzatIn333+/fl16ejpj++bw2qoKgrr0VUSaQ5nRbB0hMxISEkI+Rn5qDIO7xrN0U0krVCgiHZoyo9kiLTOaYmx+Mv6gyf82Frfq64hImCkzmk2Z0XRWi4UEt53KWj+V3hBnwTJNjJoiDG8lpiMa09W6966Whp6b/yy33PQ7fti0I9yltDo1xQ9lzQJ4/jzwVe37XPEGeOsmWHgbTHkG8hs/8rmz6JfWj+nXT+fef9xLZWElCd4EErwJPPSXh9g2ZBuXjbiMoRmNH21ntRgMz02kZ3osr3+7nR2lNQfctqhmC5vWvkFeaSwJ1N1/oJpqKrOzyMv+VZPPTaStxLhs9M6Io3dGHABef5CiSi+FlbVU+wL4/CbeQBCbxSDKYSXR7eCM4V3CXHUno8xoEVarleuvv55FixaxZcsWNm/ezObNm7nrrruYMWMGFoulTer4/PPPeeutt+ofR0VFcdppp9VPRzWwsoClm0vapBYRiUDKjBbRUTKjMY7okcL2khp2lh34dxsR6WSUGS0iEjOjKcb3TCFomroASyRSKTNahDKjeQzDIMZlx2GzUFZTd0vY/W5XU4pr+fO4l/wfttIN9ev9nlyqhlxETd8pmC7Pfvdtjt27d3HPHbex8J032VVQgCc+gX79B3Dd729i5OgxLf56re03V1zCC//4OwB2u52sLtmc+aup/Pb6G7DZDt0GPuX0Mzj62OMa9ZqnnXgs/QcM5La75jSp5nBRU/xg1iyA+WfVTTPC/r5p96zzVddtN/UFBclBJMYkcseldxAMBvndA78jrjSOnezk8SWP89yS5xjFKM4ddy6njzmdaFd0o44d57Jz5rAuvLeygO+3ldWv9/trWV30Ec+u/z01gQomMIE8xrPbUky1wyS712mkRes+sNIxOWwW0j0u0j2u/T4f47QxrkdKG1fViSkzWpTD4eDYY4/FNE1efvllli1bRmpqKhaLhbKyMj777DNycnLo3r17SD/chSIQCFBTU0N0dF0GVVZWAnVX36anpzNx4sT65wAO657Eqp3lVId61auIyF7KjBbVETKjMawWgxMHZDD/i43U+jTloEinp8xoUZGWGU01oVcqNovBVxs0YlwkoigzWpQyo/nqplO37HfUuGPD+3j+eyGGb99ZUK2lG4lddAsxi2dTOvlveHMntGhdF5/7K7xeL/f/9UlycnPZtauAjxctoriodW8x4vV6cTgcrXLsCROP5f5HHqO2tpaF77zNTdOvwW6z85vrf3fIfaOiooiKimqVutobNcUPpLqk7ooq0wQO9YeIIJiWuu2vW66pRw7BYrFw7zX3Uu2t5rXlr/HJZ5+wcudKxjKWdR+tY85HcyixlRCdFs21Z11LbHRsSMe1WS0c2y+dOFeQvy96kZqy7SSWBdnCd9RQAUCxo4atcdCn55WteYoi0tkoM1qNYRicfvrpnHLKKdTW1gLgdDr59NNP+fTTT4G6Xwy6d+/OuHHjMGyNm1rJ7/ezdu1adu7cyRdffEHfvn054YQTAOjevTtxcXEMHz58v/u67FYOz0/m3eU7m3GGItLpKDNaTXvOjMbyuO2c0D+DV5ZuRXfqEOnElBmtJpIyo6nG9UjBZrHw2brCsNYhIi1EmdFqlBnNs3fUuNNupbLWT60/iGPD+8S/PLVu2vT9XMBRv85XQ/zLUyk5bX6LNcZLS0r47JPFvPT6O4w5fBwA2V1zGDpsRIPttmzexB9vuJ6PPngfi8XChKOP4c4/zyUlNQ2oG51dVlrCU/94sX6fGTdO57tl3/Ly6+8AdaOpe/fpi81m49/PP0fvfv146bW3WbliObfPvJnPPvkY0zTpP2Ag9z/yBLl5eQDMf3oejz50P5s2biC7aw4XXXYlF1xy2UHPy+l0kJqWDsC0iy/lzdde5e03X+M31/+OkuJibr5xOu+++QZeby2jx47jjnvuJa97PrDv9Ol/nn07b73+Xy6/+rfcffutlJUUc8zEI7n7gSeJiYvjN1dcwqcff8SnH3/EE399GIAvvl1JXFwcf/jdtSx6byFVlRVkZGbxm+tv4FfnnNesz1lLUlP8QL75554pRkL9C0SwbvtvnoPRl7dmZREjyhHFmYPP5IxBZ3D7e7ez+ePNZJEFQLw/HrbCQ395iPnMpwtd6E1v/rDsD5gBE5fbhcfjoWdeT9b61rJq9yqiv40mPhAPQN3Y2LorW/LJZ3jyKXT3jGRk6i90b2URaXnKjFZntVpxu90ABINBMjMz2bZtGwA7duxgx44dLF68mDOnnAnrgUJ4o/QNgoEgMTExeDwecnJySE9PJxgM8thjj1FQULDP66xdu5ZgMIjFYiEnJ4ecnJyD1tUvM47l28vYWrzvVa0iIvulzGh17TUzGis3OZpxPZL58IfdLXpcEelAlBmtLlIyo6kO656Ew2bw0erdughLpKNTZrS6zp4ZzWW3Woh3O/BVFGP774V7GuIHv4DDIIhpWvD890J2X7K0RaZSj46JITomhrdef5VhI0bu9x7xwWCQab86i+iYaF5+/R0CAT83Xn8tl15wbn3DO1Qv/HM+5194Ca++8x4A27dt5bTjj2HM4eP416tvEhsXxxeffYo/4Afg3y/8k3vu/BN3/vkv9B84mO++Xcr031yFOzqaKWefE/LruqKi6ke+//bKS1m3dg1PP/cisbFx3D7zZqaecSoffrEEu92+3/03rF/HW6+9yrPP/5va4g2cf+GVPHjfHG665U/cftcc1q1ZTe8+/bjhjzMASEpO4ebfX88PK1fyj3+9QmJSMhvWraWmpn39zVRN8f0xTfj8sabt+/mjMOoyUOM1ZIZhMOPoGZQdVsaWwi3837//j7jSuPrnd7ObbLJJIw32zl5RCtXbq3lg5QO8z/sAXMRFxBP/437OImqjPWRln8bZ0VlteEYi0qkoM9pcVFQU06ZNo6qqigULFvDdd9/VP5eems4ROUfw0aaP+GbpNw32GzduHOnp6RiGsc9URXl5efTs2ZN+/fo16r5QhmFwVO9U/vH5pgPeH0lEpJ4yo821p8xoimE5ieyu8LL8J7eIEpFOQpnR5jp6ZjTVsJxE3A4b7y7fqd9pRDoqZUab66yZ0RLs3z1XN4V/iBdwGATBV41r+QtUD72k2a9vs9m4/5HHmf6bq3jmb08yYNBgDhs7jlN/cSZ9+w8A4KNF77Ni+Xd88e0KsrpkA/DgY08yftRQlnz9FUOGhT5yPy8vn1tuu7P+8Z233kJsXByPznu2viHdPf/He8T/+c7bmXXHXZx48qkA5OTm8sOqlTw778mQmuKmafLRovdZtPBdLrz0CtatXcPbb7zGf995jxGjDgPg4SfnMaxvD9587VVOPu0X+z1OMBjk/r8+QUxMDNHBVH551ul89MEibgLiPB7sDgdR7qj60ekAW7dsof+gQQweOgyAru3wgg41xfenqgiK1zdhR7Nuv+picCe2eFmRLs4dR193X+695l4AVm9fzS3P3UJNWQ3f8z1HcMQ++2xlKwB27JgJJsU1xfTu15tzjj6HGFcM320t1bS2ItK6lBlhYbfb8Xg8/OIXv+AXv/gF5eXlfPvtt7hcLvr27ctHH320zz5dunQBwOfzkZ+fj8PhYNCgQfTt27dZ935KjnEypGu87scnIoemzAiL9pQZTXF071RKq3xsLWlfV9iLSCtTZoRFR8+MpuqTEYfbYeW1b7fj9R9q2mURaXeUGWHRWTOjWZpxAYd7yZNUD7m4RS7gOOmU05h43PF8/slivv7qC957920evn8u9z74V3459VxW/7CSzKwu9Q1xgF69++DxxLP6h1WNaooPHDykwePvln3LqDFj9ztCu7Kykg3r13Hd1Vdw/W+uql8f8PuJjTv4KPl333qTvMxk/D4fwWCQ086cwvSbbubjD97HZrMxdPjI+m0TE5Pont+T1T+sOuDxsrvmEBMby96pZNLTUtm9a9dBazj/oku4+NxfseybpYyfcDTHnzS5vhHfXnSC77Im8FY0b//acoVIC+iR0YN/XvvP+seBYACrxQpAWVUZce44ZjITc8835f6mRe+f5aG02scX64v2eU5EpEUoM9qF2NhYxo4dC4Db7eaWW26pzwWfz9fgB02Hw8H48eNb9PVH5yWxpqCCkipfix5XRCKMMqNdCHdmNJbNauHkwZm88NVmCiu8Ya1FRNqQMqNd6GiZ0Rw5SdGcObwLry7dRnmNP9zliEhjKDPahc6UGU3WxAs4DExspRuw1hYTcLXM16rL5WL8UUcz/qijue6Gm7ju6iv48+zb+OXUc0Pa32Kx7HPrEZ9v3/x0R7sbPI5yuQ54zKrKuu/lOQ88zNBhIxs8Z7FaD1rP2HHjuXvuA9gddtIzMpt9kcXPm/aGYRAMHvzCuaOPOY4vv1vFwnfe5sP3F3LmyScw7eLLmHXHXc2qpSVF7hwMzeGIad7+ztiWqUMa2NsQh7pR5XsZhnHQ+4SP6Z5EfmozP6ciIgeizGiXfpoLB7o3TkuyWy0c1y8di6YbE5GDUWa0S22dGU3hsls5dUgWMU5d1y7SaSgz2qWOkBnNkRrr4pcju5LuOfAf7EWkHVJmtEuRnhlN0swLOJLtPlJjnSTHOEmMdhAfZSfGacNlt2KzNO9vcj1796a6sgqAHj17s23rFrZu2Vz//KqVKygtLaFnr94AJCUls3PH9gbH+H5Zw+ny96dP/wF8/slifL59B9akpKaRnpHBxg0b6Na9e4MlJzf3oMd1R7vp1r07XbK7NmiI9+jVG7/fz/+++qJ+XVFRIWvX/FB/Lk3hsDsIBAL7rE9OTmHK2efw8BPz+NPsP/P3p//W5NdoDWqK7487ERK6AY39JjLq9otKaI2qpIkMw2BS/3RS45zhLkVEIpEyQ/bIjI9iWI4+nyJyEMoMaYY4l51Th2ThtOvXeJFOQZkhYRLjtHHGsC70SleTTKTDUGZIR9ECF3AYhoHVYmC3WnDarUQ7bXii7CTFOEmJceKJsuOyWw84y3pRUSG/OGkS/3r+nyz/bhkbN2zg1Zf/zcP3/4XjTjgJgCMmHEWfvv256pIL+HbpEv739Zf8+rKLOezwcfX3yz78iCP5Zsn/eOGf81m3dg333HkbK1csP+QpXHjp5VSUl3P5Beey9H9fs27tGl587h+sWf0DAL+7aQYPzv0zTz76MGvXrGbF99/xz78/w6MP3d+kD1le93wmnXgS1//mKj7/dDHfL/uWqy65kPSMTCadOLlJx4S66dX/99WXbNq4kcLC3QSDQe6+40+89fp/Wb92LStXLOfdt9+kR89eTX6N1qDfpvfHMGDUZU3bd9TlLXJPA2lZdquFyYMycdkPPsWEiEijKTPkJw7rnkRKrC7CEpEDUGZIM6XEOjllcBYOm36VF4l4ygwJI7vVwgkDMjiiZ4pmwxLpCJQZ0lG08gUcFouBy27FE2UnOcZJnMuO3drwtaKjYxg6fASPP/wgp55wDEceNox77vgT55x/AXfO+UvdqxkGT/3zBTzxCZx6wjGcdcqJ5OTm8vi8Z+uPM2HiMVx7w03cdssfmTThcCoryjnzl2cf8kwSE5P413/fpLKyktNOPJZjx49h/tN/w26rm01g6vkXcO+Dj/Dc359lwmHDOe2EY3n+H8/SNSe3kR+zH9338OMMHDyEc6f8gpOOORJMk/n/eqVZMxhc8ZtrsFqtjB81hH552WzZvBmHw8Edt97CUWNHcNrxx2C1Wnj0b88e+mBtyDDNn896H7nKysrweDyUlpYSFxd38I2rS2BuX/BVAwefJx8AwwK2KLhuOUTFt0C17UTAC9/fWffvfn8AqyO89TTTmoJy/vvN9kNvKNJKLKaXkVWPAfCF+zKCRut9T8U4bVxyRF6T9m3U+2WEUmY0QYRlRlPtKq/luS824Q92mh+xpJUoMzoOZUYTKDOaZXNRFa8s2aqskXrKjI5DmdEEyoyw2lJcxRvLtlNZu+8UqdIxKTM6DmVGEygz2lRNTQ3r16+nW7duuA5yr+wGPvsrvHUT0JjfZQyYdBeMvrwpZeLzB6nw+vH6Q/jekIZMk+jgLgAqLSmtehGNxTAOOtjoYF9vob5f6vLyA4mKhynP7PkEH+rDZAEMmPJsZAVIBMpPjWVQtifcZYhIpFFmyE+kxDo5sldquMsQkfZKmSEtIDvRzeRBmVibed88EWnnlBnSDnRJcHPO6BzyUqLDXYqIHIwyQzqKQb8Cu5uQ25OGpW77Qb9s8kvabRYS3A4S3Q4cVrVFOzN99g8mfyJMfQHsUdRN5/DzPzjsWWePgqkvQv7RbV+jNNoRPVJIjtEVYiLSwpQZ8hMDunjol9k5r2QXkRAoM6QF5CZHc9LADGxqjItENmWGtANuh41TBmdxdJ/UfaahFZF2RJkhHUEYL+Cw2ywkRDuIj7LrAuNOSk3xQ8mfWDeFyKS7ICG34XMJuXXrr1+hAOlAbFYLxw/QH49EpBUoM+QnjuqdSmqc7i8uIgegzJAWkJcSwymDs9SgEIl0ygxpJwZ2iWfqqBxyktzhLkVEDkSZIR1BmC/gcNqtJEU7iHXaWnM2cGmHbOEuoEOIiq+7V8Goy6C6GGrLwRkLUQmtOn++tJ7kGCeHdU/io9W7w12KiEQaZYbsYbNaOGlAJv/4YhM1Pt1/T0T2Q5khLaBrkptTh2Txn6XbdI88kUimzJB2IiHawelDu7BqRzkf/rCLilp/uEsSkZ9TZkhHsPcCjm+eg88fheL1Pz6XkAujLofBvwJX69wO1zAM3E4bTruVilq//nbXSagp3hiGAe7EukU6vGE5CazdVcG2kppwlyIikUiZIYDHbWfyoAxe/t9W/EEz3OWISHulzJBm6pLg5oxhXXhlyVaqvPpjjkhEU2ZIO9ErPZZuydF8u6WErzcWK39E2iNlhrR37eACDqvFwBNlJ8pupbzGp7/fRThNny6dlmEYHNs3XVMNiohIq+qS4OaYfmm6GFtERFpVWpyLKSOy8UTZw12KiIh0Eg6bheG5iVwwthtH9Ewm3q0MEhGRJth7AUdCTt1/w/BHNIfNQmK0gxinbZ/J3CVyqCkunVpCtIMx+cnhLkNERCJc7/Q4DstLCncZIiIS4eLdDqaMyCYtzhXuUkREpBNx2CwMy6lrjp85vAv9MuOIdlrDXZaIiEijGIZBtNNGUoyTKLtyLBJp+nTp9IZkx7O2oIItxdXhLkVERCLYqLwkKr1+vtlcGu5SREQkgkU7bZwxrAtvf7+DNQUV4S5HREQ6mS4JbrokuAHYVV7L5uIqdpXXUlzppajKS60vGOYKRUREDs5qMYiLshPlsFJe48cXUHZFCjXFpdMzDINj+6Xz98824vXrzU1ERFrPhF6peP1BVmwvD3cpIiISwRw2CycNzGDxmkK+3FAU7nJERKSTSol1khLrbLDO6w9S7Q1Q5fNT6wviDQSp9QWp8Qeo8gao9voprfZRWKkGuoiIhJfdWjeleq0/QGWtH19A9xvv6NQUFwE8UXbG90zh3eU7w12KiIhEMMMwOLZvOt6AyVqN3hMRkVZkGAaH90gmMdrBeyt36g84IiLSLjhsFhw2Cx4Off/xshof20tqWL+7gg2FVVR7A21QoYiItDXTNCmpLaHKX4Xb5ibeGY8RhvuKH4jTZsVps1LrC1DlC3T4wZWnnXgs/QcM5La75oS7lDane4qL7NE/y0NeSnS4yxARkQhnsRic0D+dnCR3uEsREZFOoG9mHGeNyMYTdejmg4iISHsS57LTKz2WSf0zuOyIPE4bkkVeSjTtqE8iIiLNUOYt4+/L/86JL5/IEc8fwaR/T+KI54/gxJdP5O/L/06Zt6zFX9MwjIMus2bNOuC+TruVBLeDpGgHbocVSwvnUbonijdfezWk7fYuPbLTmHzsBD7+YFHIr/O3Z5/j93+cGfL2GzdtJjYxh+++/SbkfdorNcVFfuKYvmlEO63hLkNERCKczWrh5EGZaoyLiEibSI11cfaornRL1kXAIiLSMRmGQW5yNKcMzuKCsd0Y0jUeW0t3I0REpM0s3rqYiS9O5J4v72FL+ZYGz20p38I9X97DxBcnsnjr4hZ93e3bt9cv9913H3FxcQ3WTZ8+/ZDHsFktxLrsJMc4ibaZRNmtWNv4iq37Hnmcb39Yz6tvv0diUjLnTjmdjevXh7RvQmIiMbGxrVxh+6SmuMhPuB02ju+foStORUSk1e1tjOcmqzEuIiKtz2W3csrgTI7omYJVTQQREenAPFF2juyVyvljcxmQ5cGiP+SJiHQoi7cu5sqFV1Ljr8Hc87+f2ruuxl/DlQuvbNHGeHp6ev3i8XgwDKP+cWVlJVOnTiUtLY2YmBhGjBjBggULGuyfm5vLbbfdxnnnnYfH4+HqK68gLsrOy889w/B+PeiWnshF50zhsYcfoGfX9Ab7vvX6fzlm3GHkpMYzcmAf5tx1B36/H4DhA3oBcMHUKaR7ouofH4jH4yE1LZ0+fftx99z7qa6u5oP3FwLwyccfMWnC4XRN8TCwZzdun3lz/etA3fTpM278sfk/fEAv7p9zD9dcdRnds1IY1q8Hz877v/rn+w8+HICJRxxGuieK0048FoDFH33IpAmH0y0jiZ5d05l87AQ2b9rYqM9HW1NTXORnshPdjM5LCncZIiLSCdisFiYPzNTtO0REpE0YhsGwnAR+OTKbxGhHuMsRERFpljiXnYl90zhndFeyE3WxsYhIR1DmLePaRddimvs2w3/OxMQ0Ta5ddG2rTKX+cxUVFZxwwgksXLiQJUuWMGnSJCZPnsymTZsabDdnzhwGDRrEkiVLmDFjBosXL+byyy/nt7/9LUuXLuWEScdx/5y7Mai7kMvtsPLlZ4v59eUXc/EVV/Hh50v4830P8fz8Z7lvzt0AvPX+x8CPI8D3Pg6FyxUFgNfnZfu2rUw981QGDx3OwsVfcPfc+/nHs0/zlz/fddBjPPrQ/QwaPJQFH37GtIsu5ffX/YY1q38AYNGCuindX/zP63z7w3r+9uxz+P1+Lph6FoeNHcf7i7/ktXcXcc60i9rVveD3R01xkf0Y1S1RU9qKiEibsFktnDQwk17pnXPaIhERaXupsS6mjurKiNxEja4TEZEOLynGyRnDunDSwAxiXbZwlyMiIgfx6ppX60eIh2LviPH/rv1vK1cGgwYN4rLLLqN///706NGD2267je7du/Pqqw3v833UUUdx/fXX0717d7p3786DDz7I8ccfz/Tp0+nZsydXXnklxx9/PFA3Y1esy85998zmxt//nssvvpB+vXtw3HHHcNOMmTw770kAkpNTgB9HgO99fChVVVXcdfutWK1Wxowdx1NPPk5WVhdmz/kLPXr24viTTuZ3N93Mow/dTzAYPOBxjj72OC645DK6de/O1ddOJzEpmcUffrCntkQAEhISSU1LJyExkfKyMspKSzlm0gnk5uXRs1dvppx9Dl2yuzbug76Hw2ZpkwxXU1xkPwzDYFL/dOKi7OEuRUREOgGrxeD4/ukMyPKEuxQREekkbFYLh/dI5lcjs0mNc4a7HBERkWbrkRbLeYflMqRrvG6NKCLSDpmmyT9W/qNJ+85fMR/TDK2R3lQVFRVMnz6dPn36EB8fT0xMDCtWrNhnpPjw4cMbPF61ahUjR45ssO7nj7/55htuu+02EuPjSEuKJyslket+fSU7d+zAiZ+4PQ1hm9UI6cLlKy46n7zMZPKzUnj91VeY+9Cj9O0/gNWrVjJs5KgGI7ZHjj6MyooKtm3dcsDj9enXv/7fhmGQmpbG7t27Drh9QmIiU6aey69On8y5U37BE399iJ07th+y7r1sFoMou5U4l43kGCcJbgcuuzXk/ZtKl86JHIDbYePUwZk8/9Vman0HvoJGRESkJRiGwcS+abjsVr7cUBTuckREpJNIjXNx9siufL+tjM/WFVJe4z/0TiIiIu2Uw2bhyF6p9MmI493lO9lVXhvukkREZI+S2hI2l29u9H4mJpvLN1NaW0q8K77lC9tj+vTpvPvuu8yZM4f8/HyioqI444wz8Hq9DbaLjm78bRArKiq49dZbOf300/d5Li7GjcVSN4Y5xmknJdaJaZr4gyaBny3+YN2FAbfeeQ9HHHkUsZ64kEeVH4zd3nCAqGEYBx1ZDnD/I49z8WVX8v6Cd/nPS//irttv5YVXXmPYiFENj0VdPtutFuxWA5vVErYZy9QUFzmIpBgnJw3I5JWlWwkEW/cqJBEREYDDeyQT47KxaFUBrXwBrIiICFD3B4/+WR56pceyZFMJSzcXU1kbCHdZIiIiTZa256Kv/20q5rN1hfgC+uVKRCTcqvxVzdq/0l9JPPEtU8x+LF68mGnTpnHaaacBdY3sDRs2HHK/Xr168eWXXzZY9/PHQ4cOZdWqVeTn5x/wOHa7nUCg7vcwwzCwWw0ONHg6LyeLwf171zXOA0H8exrmPXr15vVXX8E0zfrR4l989ikxsbFkZnU55Lnsj8PuACAQ3Pd3xAGDBjNg0GB+c/3vOHHieF568XmGjRiFxQCnzYrTbsFhtbSbe41r+nSRQ+ia5Oao3qnhLkNERDqRwdnxnDQwA5ulffzAKCIinYPdamFkt0QuHNuNY/ulkRKradVFRKTjslgMhucmcu7oXHKS3OEuR0Sk03PbmvdeHG1r/AjtxujRowcvvfQSS5cu5ZtvvuHss88+5GhpgF//+te88cYbzJ07l9WrV/PYY4/x5ptvNmgE33LLLTzzzDPceuutfP/996xYsYLnnnuOm2++uX6b3NxcFi5cyI4dOyguLj7oa9osFlx2KzFOGx63g6QYJ6mxTq75zdVs27qFW35/PRvW/MBbb/yXP8++ncuu+k39aPTGSklJIirKxfsL3mVXwU7KSkvZuGEDd8yawVdffMbmTRtZtHAB69eupW+fvsS77STHOImLsuO0WdtNQxzUFBcJSf8sD0f0bP4UFCIiIqHKT43ljOFdiHa2/v10REREfspmtdAv08M5o3M477AcxnRPIiXWqfuziohIh+Rx2zl9aBdOGphBrEsTp4qIhEu8M57s2GwMGveLhYFBdmw2HqenlSqrM3fuXBISEhgzZgyTJ0/muOOOY+jQoYfcb+zYsTz66KPMnTuXQYMG8dZbb3HttdficrnqtznuuON47bXXeOeddxgxYgSjR4/mL3/5Czk5OfXb3Hvvvbz77rtkZ2czZMiQRtdvGAa5XbN54403WPq/rzjisBHceO1vuOiiC5l1ywyiHVYcNksjP/pgs9m4Z/Ysnn3q/xjUK4/zzz4TtzuKNatXcdG5ZzN22EBuuPZqrrrqSq759ZXtrhH+U/opQCREw3ISAJMPf9gd7lJERKSTyPBE8cuRXfnP0m3s1r3wREQkDJJinCTFOBmVl0SNL0BBWS3bS6sprvJRXuOjrMZPjS+A13/oERQiIiLh1CMtlpykaL5YX8SSTcX192UVEZG2YRgGZ/c+m3u+vKfR+07tM7XFG63Tpk1j2rRp9Y9zc3N57733Gmxz1VVXNXh8oOnUL7nkEi655JIGj38+Vfpxxx3Hcccdd8B6Jk+ezOTJkw9Zt3mI+y2OHz+eL7744oDPL/7owz33Jw8SCJp8v2oNgaBJMGgSNE2CJiz8+PO9LwbABef/il9e+FusVgs2i4HNYvDqf17B1sTR5+GiprhIIwzLScQ04aPVaoyLiEjbiHPZmTI8mze/2866XZXhLkdERDoxl91K1yQ3XfczBW0gaFLrr2uO1/qDeP1BqrwBKmrrGudFFV52ltdQ61PzXEREwsdhs3B4j2QGZXv4dG0hK7aXEzxEc0FERFrOyfkn88CSB6jx12By6PdfCxacNieTux+6WRxOc+bM4ZhjjiE6Opo333yTp59+mkceeSTcZR2Q1WJgtex/dsq9TXez7gFGjRUDg+goBxgdqwn+c2qKizTS8NxErBaDD37YhX5mFhGRtuCwWTh5UCafry/is3WFyh8REWl3rBYDt8OG23HgbUzTpLjKx8bCSlbvrGBbabUyTUREwiLWZefYfumMyE3k643FrNheppHjIiJtIM4Rx1+O/AtXLrwSTA7aGDcwwID7jryPOEdcG1bZeF988QX33HMP5eXl5OXl8cADD3DxxReHu6wm2Tsiv+7/zfp/RQI1xUWaYEjXBNwOG29/v4OAfmAWEZE2YBgGo/OSSI118tb3OzTSTkREOhzDMEiMdpAY7WBI1wTKa3x8v62MbzaXUOUNhLs8ERHphBKiHUzsm8aY/CS+3VLKiu1llFT5wl1WxLBaIqeRIiItZ2zWWB45+hGuXXQtNf4aoGFzfO89x102F/cdeR9jssaEpc7GeOGFF8JdgoRATXGRJuqVHovLbuG1b7fr/nkiItJm8lJimDoyh9eXbWdnWU24yxEREWmyWJed0XlJDM9JYPn2Mr7aUExptRoRIiLS9twOG6Pzkhidl8SO0hpW7ihjw+5KitUgbxKbxWBQdjwjchPDXYqItFNjs8ay4MwF/Hftf5m/Yj6byzfXP9cltgtT+0zl5O4nE+uIDWOVEmnUFBdphpykaM4ans1/lm6lvMYf7nJERKST8LjtTBmRzUerd7FkU0m4yxEREWkWm9XCwC7x9Mv0sHRzCV+sL6LGp5HjIiISHukeF+keF/SC0mofmwqr2FpSxdaSGsp08dZBOWwW+mbGMTwngViXPdzliEg7F+eIY2qfqZzd+2xKa0up9FcSbYvG4/TUT+Et0pLUFBdpppRYJ78a2ZVXv9nGjlKN2BMRkbZhtRgc2SuVroluFqzYSWWtmgciItKxWS0Gw3IS6JcZx2frCvlmcylB3XRcRETCyBNlZ0AXDwO6eAAor/Gxo7SGbaU17CitpqCsVvciB5JiHAzqEk+fjDgcNku4yxGRDsYwDOJd8cQTH+5SJMKpKS7SAqKdNs4Y1oV3l+9k1Y7ycJcjIiKdSF5KDOfFR/HeygJlkIiIRASX3cqRvVLpmxnHeysK2K6Lj0VEpJ2IddmJddnpkVY3nW8gaLK7opYdpTXsLKuhoLyWokovgU7QKLcYBnkp0QzOjic70R3uckRERA5JTXGRFmK3WjhhQAbJMU4+WbsbDWgQEZG24rJbOWFABj3TYli0apdu6SEiIhEhNdbFlBHZfLullI/X7MbrD4a7JBERkQasFoO0OBdpca76df5AkKIqL4UVe5bKWkqqfJRU+SJiBhSn3cKALA+DsuOJ0xTpIiLSgagpLtLCRnZLJDnGwVvf76DWpz/aiIhI28lPjaVrYjSfry9kyaaSTjE6QUREIpthGAzKjqdbSjTvrShg/e7KcJckIiJyUDarhdRYF6mxrgbrA0GTsmofxVVeiqt8lPzkvxW1/nY/wCYx2sGg7Hj6aop0ERHpoNQUF2kFeSkx/GpEV/777TYKK7zhLkdERDoRh83CuB4p9Mv08Pm6QlbtLG/3f1wRERE5lDiXnVOHZLFiexmLVu2ixhcId0kiIiKNYrUYJEQ7SIh27POc1x+kuMpLUWXdsruilsIKL2U1vrD+PmcxDHKT3QzOjqdrohvDMMJXjIiIMGvWLF555RWWLl0a7lI48sgjGTx4MPfdd1+4SwmZmuIirSQh2sEvR3TlneU7WL2zItzliIhIJ5MY7eD4ARmM7JbIF+uLWF1QoZHjIiLS4fXJiCMnyc37K3fxw87ycJcjIiLSIhw2yz7TsAPU+gMUVnjZVV5bt1TUUlhRiy/Qur/bpcQ66ZMRS+/0OKKdaiGISOTZsWMHs2fP5vXXX2fLli14PB7y8/M555xzOP/883G73eEusdFmzZrFrbfeetBtzCZcabXow0+YcPyZFBcVEp+Q2NTy2gUlmkgrctgsnDQwk682FLF4TWFE3DdIREQ6lqQYJ8cPyGC818/ybWV8t7WU4ipfuMsSERFpMrfDxokDM+hVEMuiVQWU1/jDXZKIiEircNqsZMZHkRkfVb8uGDQprvKyq6KW3eV1o8qLKps3qtztsJLucZGbFE23lGjdK1xEItq6desYO3Ys8fHx3HnnnQwYMACn08myZct4/PHHycrK4uSTT97vvj6fD7u9fb5HTp8+ncsvv7z+8YgRI7j00ku55JJL9ru91+vF4dh39pJIpqa4SBsYnptIWpyLN7/bTmWtpvkTEZG253bYGJ6byPDcRIorvWworGRTURUFZbVU1KqZICIiHU9+agxdE918tq6QJZtKdBGyiIh0ChaLQVKMk6QYJ6T/uN4XCFJS5aO8xkdZjZ+KGj+1/gBefxBvIIjFMLAYBjZ8ZBVFYbcapPbMJMUTqya4iHQqV155JTabja+++oro6Oj69Xl5eZxyyikNRlMbhsEjjzzCm2++ycKFC/nd737HrFmz+Otf/8qcOXPYvHkz3bp14+abb+bcc88FYMOGDXTr1o0lS5YwePBgAEpKSkhISOD999/nyCOPZNGiRUyYMIEFCxbw+9//nuXLlzN48GDmzZtHr1696l//rrvu4i9/+QtVVVWcddZZpKSkHPC8YmJiiImJqX9stVqJjY0lPb0uLI488kj69++PzWbj73//OwMGDGDevHkHrTU3pysTjj8TgITEJADOP/98nnrqKQCCwSA33HADTz75JA6Hg8svv5xZs2Y17RPTBtQUF2kj2Yluzh6VwxvfbmdrSXW4yxERkU5s733shnRNAKDGF6Cw0kt5jY9qb4Bqb4DaQBB/wMQXCOILBPH6g/gCJlVeP5W1ATUeRESkXXDYLBzRM4W+mXF8vHo363dXhrukiNctOfrQG4mISJuzWy2kxDpJiXUefMOAF77fMy1wcgxY1RAXkZZjmiZVvqo2f1233Y1hGIfcrrCwkHfeeYc777yzQUP8p35+nFmzZnHXXXdx3333YbPZePnll/ntb3/Lfffdx8SJE3nttde44IIL6NKlCxMmTGhU3X/84x+59957SUlJ4fLLL+fCCy9k8eLFALzwwgvMmjWLhx9+mMMPP5xnn32WBx54gLy8vEa9xk89/fTTXHHFFfWvcSjZ2dn8+x9P8IuzL2HVyhXEeeKJivpx9pKnn36a6667js8//5xPP/2UadOmMXbsWI455pgm19ia1BQXaUMxThtnDOvCJ2sL+WpjUZOnNBIREWlJLruVrPgoIOqQ20LddH2VXj+FFV62lVazo7SGbSXVrX5fOxERkQNJjnFy6pAsNhVW8dGaXRSU1Ya7pIjjtFuY2CeNnmmx4S5FRERERNqpKl8VMbNjDr1hC6u4qYJox6Ev3lyzZg2maTYYjQ2QnJxMTU0NAFdddRV33313/XNnn302F1xwQf3jX/3qV0ybNo0rr7wSgOuuu47PPvuMOXPmNLopfscddzB+/HgAbrzxRk488URqampwuVzcd999XHTRRVx00UUA3H777SxYsKC+zqbo0aMH99xzT/3jDRs2HHR7q9VKYkI8AKmpqfvcU3zgwIHMnDmz/tgPPfQQCxcubLdNcUu4CwjFhg0buOiii+jWrRtRUVF0796dmTNn4vV6w12aSKNZLAaH90jm5EGZRDms4S5HJOIoM0Ran8ViEOuyk5sczZjuyZw+tAuXHtGdEwZk0D01Bqvl0FfmirQHygyRyNM1yc3UUTmcNiSL7ER3uMuJGLnJbs4ZndOpG+LKDBERCZUyQ6Tj+eKLL1i6dCn9+vWjtrbhBbbDhw9v8HjFihWMHTu2wbqxY8eyYsWKRr/uwIED6/+dkZEBQEFBQf3rjBo1qsH2hx12WKNf46eGDRvWrP1/7qf1Q9057K2/PeoQI8VXrlxJMBjkscceIz8/n++++45LLrmEyspK5syZE+7yRJokLyWGqaOcvPXdDrYUazp1kZaizBAJD4fNQq/0WHqlx1JR6+ebzSV8u6WUGl8g3KWJHJAyQyRy5SZHk5scTUFZDd9vK2PVznKqvcqkxop12RjfM4UenbgZvpcyQ0REQqXMkM7MbXdTcVNFWF43FPn5+RiGwapVqxqs3zsl+U+nBt/rQNOsH4jFUjce+af3Jvf5fPvd1m7/8RYWe6dtDwaDjXq9xvj5uTSm1v35af1Qdw6tWX9zdYim+KRJk5g0aVL947y8PFatWlV/I3uRjirWZeeMYV34fH0Rn68r0v1ZRVqAMkMk/GKcNsbmJzMiN5FlW0v4Yn2xmuPSLikzRCJfapyL1DgXR/RMYf3uStbtqmBDYSWVtcqlg7FZDIZ0TWBkt0Qctg4xyWCrU2aIiEiolBnSmRmGEdI05uGSlJTEMcccw0MPPcSvf/3rRje8Afr06cPixYs5//zz69ctXryYvn37ApCSkgLA9u3bGTJkCABLly5t0ut8/vnnnHfeefXrPvvss0Yf52BCqdXhqGt8BwId/3eoDtEU35/S0lISExMPuk1tbW2DaQ7KyspauyyRRjMMg9F5SXRNdPP29zsoqQr9KhwRCY0yQyQ8HDYLw3IS6Zfp4euNxSzZVKz7jku7p8wQiUxWi0F+agz5qTGYpsnOslo2FVWxpbiKbSXVyqef6JEWw7j8FDxu+6E37uSUGSIiEiplhkj78cgjjzB27FiGDx/OrFmzGDhwIBaLhS+//JKVK1cecorx3/3ud5x11lkMGTKEiRMn8t///peXXnqJBQsWAHWjzUePHs1dd91Ft27dKCgo4Oabb250nb/97W+ZNm0aw4cPZ+zYscyfP5/vv/++flR7Swil1pyuXTAMg9dee40TTjyJqKgoYmLa/r7xLaFDXu67Zs0aHnzwQS677LKDbjd79mw8Hk/9kp2d3UYVijReZnwUU0flMCDLE+5SRCKKMkMk/Fx2K2Pzkzn3sFzyUzvmD83SOSgzRDoHwzBI97gY2S2R04d24Yoj85kyIpvDeyTTLTkap71D/qmk2bLio5gyIpuTBmaqIR4CZYaIiIRKmSHSvnTv3p0lS5YwceJEbrrpJgYNGsTw4cN58MEHmT59OrfddttB9z/11FO5//77mTNnDv369eOxxx5j3rx5HHnkkfXb/O1vf8Pv9zNs2DCuueYabr/99kbXOWXKFGbMmMENN9zAsGHD2LhxI1dccUWjj3Moh6o1KzODW2++nhtv+gNpaWlcffXVLV5DWzFMM3zzNd94443cfffdB91mxYoV9O7du/7x1q1bGT9+PEceeSRPPvnkQffd35VV2dnZlJaWEhcX17ziO4uAF76/s+7f/f4AVkd46+kk1u+uZOGKnZTX+MNdirQwi+llZNVjAHzhvoyg0XrfUzFOG5cc0bSrxsrKyvB4PO3q/VKZ0QEoMyREG3ZXsmhVAcWaHeWglBlNp8zoAJQZ0o6ZpklhpZftJTXsKKthZ1kNhRXedn27q+ZkRnKskzHdk+ie0voXrikzlBlNoswQaVkd5HtKmaHMaJIO8vUdKWpqali/fj3dunXD5XKFuxxpDWYQqnfU/TsqHYzwXUB8sK+3UDMjrNOnX3/99UybNu2g2/x0GoBt27YxYcIExowZw+OPP37I4zudTpxOZ3PLFGlz3ZKjOfewHBav2c23W0ppx397EWkzygyRyJGbHM05CTl8sb6IrzYWEwgq6KRlKTNEpDkMwyA5xklyjJMB1M3k5Q8EKar0squilsIKL0WVdUtZja/D/r6WFlc3Wr57SjSGYYS7nLBRZoiISKiUGSIiHVtYm+IpKSn1N3E/lK1btzJhwgSGDRvGvHnzsFg653Rm0nk4bVaO6p1G7/Q4Fq3axc6ymnCXJBJWygyRyGKzWhiTn0yv9FgWrihga0l1uEuSCKLMEJGWZrNaSI1zkRrXcESCPxCktNpHSbWPkiovJVU+iqvq/l1R6293DXPDqLsIe3B2PDlJ0eEup11QZoiISKiUGSIiHVtYm+Kh2rp1K0ceeSQ5OTnMmTOHXbt21T+Xnp4exspEWl9mfBS/GpnN99vK+GTtbiprA+EuSaRdU2aIdCxJMU7OHN6F77aW8dGaXdT6guEuqVPKTnSHu4SwUGaISHPZrBaSYpwkxew7qssXCFKyp0FeVOmluMpLUaWP4iovXn/b5l28206vtFj6ZXnwROl+4U2hzBARkVApM0RE2qcO0RR/9913WbNmDWvWrKFLly4NngvjLdFF2oxhGPTP8tAzLZZlW0v538ZiKmp1v3GR/VFmiHQ8hmEwoIuHvJRoPvxhFyt3lIe7pE6lT0Ycx/ZNC3cZYaHMEJHWZLdaSIl1khLbsGFumiZlNf49U7D/ZDr2Km+LXRxmtRikxTnpkuCmR2rMPiPcpfGUGSIiEiplhohI+9QhmuLTpk075L06RDoDh83CsJwEBnXxsHx7Gd9tLdO06iI/o8wQ6biinTaOH5BB/ywPi1YVsLvCG+6SIl7/LA8T+6R22nvJKjNEJBwMw8ATZccTZadbcsMpzKu8/vrp18uq/ZTX+Kio9VPtC1DjC1LrDzSYkt1ps+C0W4m22Ui3OXHZrXTp04W0+DgcNk3T2pKUGSIiEiplhohI+9QhmuIi0pDNamFgl3gGdolnV3ktK7aXsaGwkkI1D0REJAJkJ7qZOiqHb7aU8MX6Iqq8unVIaxiem8Dh+cmdtiEuItIeuR023A4bWfFRjdsx4IXvY+r+neAGqxriIiIiIiIiP6WmuEgHVzcdXwpHkEJlrZ/NxVX10++VVHmp9Aao9QUJamoeERHpQCwWgyFdE+ibGcf/Npbwv03FbX7/1Uhlsxgc1SeVfpmecJciIiIiIiIiIiLSJtQUF4kg0U4bvdPj9llvmibeQBB/wCRomgSDEDBN/MEgwSDU+gNU1gao9vkprfaxq7yW3RVeNR9ERCTsnDYrh3VPYkjXeL7dUsrSzcVU1mrkeFNFO62cNDCTzMaOQBQREREREREREenA1BQX6QQMw8Bps+JsxHe8aZrsKq9l3e5K1u+uZGdZDRpsLiIi4eKyWxnZLZGhXeNZtbOc5dvK2FpSrWxqhNxkN8f1S8ft0K8AIiIiIiIiIiLSuegvYiKyX4ZhkBrnIjXOxei8JEqrfHy3rZTvt5VqhJ6IiISNzWqhX6aHfpkeSqt8rNhRxpqCCnaV14a7tHbLajEYm5/M0K7xun+4iIiIiIiIiIiE1cqVK5k2bRpLly6ld+/eLF26tE1e19ImryIiHZ7HbWdsfjIXHZ7HpP7pJMU4wl2SiIh0ch63ndF5SZwzOocLD+/Gkb1S6JYcjd2qxu9emfEuzh7VlWE5CWqIi4iIiIiIiEinN23aNE499dQm7//UU08RHx/fYvX8VCi1GYZx0GXWrFmtUlsoDMPglVdeOeR2M2fOJDo6mlWrVrFw4cLWL2wPjRQXkUaxWgz6ZMTROz2W1QUVfLaukMIKb7jLEhGRTs4TZWdI1wSGdE3AHwiyraSGjUWVbC6qpqC8890CxGm3MLZ7MgO7eNQMFxERERERERGJENu3b6//9/PPP88tt9zCqlWr6tfFxMQ06nherxeHo20HQa5du5YTTzyRnJycNn1djRQXkSYxDIOeabGcMyqHiX3SiHZaw12SiIgIUDfFetckN+N6pHD2qK5cPr47kwdlMjQngbQ4F5YIbhJbLQaDu8ZzwZhuDMrWdOkiIiIiIiIiIo0xd+5cBgwYQHR0NNnZ2Vx55ZVUVFQAsGjRIi644AJKS0v3GZldW1vL9OnTycrKIjo6mlGjRrFo0aL64+4dYf7222/Tp08fYmJimDRpUn2Te9asWTz99NP85z//qT/2T/ffKz09vX7xeOoGQ+x9XFlZydSpU0lLSyMmJoYRI0awYMGCBvvn5uZy2223cd555xEXF8ell14KwBNPPEF2djZut5vTTjuNuXPnEp+Q2GDf//znPwwdOhSXy0VeXh633norfr+//rgAp512GoZh1D/+OcMw+Prrr/nTn/7U5iPbNVJcRJrFYjEY0MVDr/RYvtpQxNcbi/EHO9lwPBERaddcdiv5qTHkp9ZdKesPBCkor2VHWQ27y2vZXeGlqLIWX6Dj5pfFMOiVHsPovCTi3brFiYiIiIiIiIiEj9d74NllLRYLNpstpG0Nw8Butx9025Ye5WyxWHjggQfo1q0b69at48orr+SGG27gkUceYcyYMdx3330NRmfvHZl99dVXs3z5cp577jkyMzN5+eWXmTRpEsuWLaNHjx4AVFVVMWfOHJ599lksFgvnnHMO06dPZ/78+UyfPp0VK1ZQVlbGvHnzAEhMTNx/kQdQUVHBCSecwB133IHT6eSZZ55h8uTJrFq1iq5du9ZvN2fOHG655RZmzpwJwOLFi7n88su5++67Ofnkk1mwYAEzZsxocOyPPvqI8847jwceeIBx48axdu3a+ob6zJkz+fLLL0lNTWXevHlMmjQJq3X/Aym3b9/OxIkTmTRpEtOnT2/0yPbmUFNcRFqEw2ZhTH4yfTPj+OCHXazbVRnukkRERPbLZrWQGR9FZnxU/TrTNCmr9lNU5aW4yktplY/Sah8lVV7KavwE2ukFXw6bhX6ZcQzNSSDOZT/0DiIiIiIiIiIirWz27NkHfK5Hjx6cffbZ9Y/nzJmDz+fb77Y5OTlMmzat/vH9999PVVVVg232NnZbyjXXXFP/79zcXG6//XYuv/xyHnnkERwOR4PR2Xtt2rSJefPmsWnTJjIzMwGYPn06b731FvPmzePOO+8EwOfz8eijj9K9e3egrpH+pz/9CahrrkdFRVFbW9vg2I0xaNAgBg0aVP/4tttu4+WXX+bVV1/l6quvrl9/1FFHcf3119c//uMf/8jxxx/P9OnTAejZsyeffPIJr732Wv02t/7pNm688UbOP/98APLy8rjtttu44YYbmDlzJikpKQDEx8cftP709HRsNhsxMTFNPs+mUlNcRFpUvNvBKYOzWL+7kkWrCiip2n+YSevLSog69EYiIgLUXXnscdvxuO10I7rBc6ZpUl7rp6y6rlFeWu37sWle7aPaG2jjWqFLgps+GbH0SI3FYdMdkUREREREREREWsKCBQuYPXs2K1eupKysDL/fT01NDVVVVbjd7v3us2zZMgKBAD179mywvra2lqSkpPrHbre7viEOkJGRQUFBQYvVXlFRwaxZs3j99dfZvn07fr+f6upqNm3a1GC74cOHN3i8atUqTjvttAbrRo4c2aAp/s0337B48WLuuOOO+nWBQOCQH5v2RE1xEWkV3ZKjyU7I4euNxXy5oahDT0nbEWV4XBzTNy3cZYiIRATDMIhz2Ylz2emSsO/zNb7AnlHldSPLS/Y0zUuqvVTWtkzD3O2wkpUUS9dEN7nJ0cQ49WO8iIiIiIiIiLRPN9100wGfs1gaXty/d3Ty/hiG0eDxb3/72+YVdggbNmzgpJNO4oorruCOO+4gMTGRjz/+mIsuugiv13vAxm9FRQVWq5Wvv/56n2nDfzo9+E+ngoe68zPNluudTJ8+nXfffZc5c+aQn59PVFQUZ5xxxj7TzkdHRx/gCAdWUVHBrbfeyumnn77Pcy6Xq8k1tyX9NU1EWo3NamFUXhJ9MuNYvHo3q3aW04Lv73IAnig7Jw/OxG7VyEERkbbgsltx2a2kxe37C4AvEKSs2kdZjZ/yGh+VtQGqvH6qvAF8gSBefxB/0MRiGFgMsFoM3A4bbqeVOHuQvIJY3A4rhw3qDlbdK1xERERERERE2r/G3Oe7tbZtiq+//ppgMMi9995b37x/4YUX9qkhEGg4CGLIkCEEAgEKCgoYN25ck19/f8dujMWLFzNt2rT6Ud8VFRVs2LDhkPv16tWLL7/8ssG6nz8eOnQoq1atIj8//4DHsdvtzaq/takpLiKtLs5l5/gBGQzuGs+HP+xiW0lNuEuKWC67lVOHZOF26O1dRKQ9sFstJMU4SYpxNn7ngBcq1AgXEREREREREWlJpaWlLF26tMG6pKQk8vPz8fl8PPjgg0yePJnFixfz6KOPNtguNzeXiooKFi5cyKBBg3C73fTs2ZOpU6dy3nnnce+99zJkyBB27drFwoULGThwICeeeGJIdeXm5vL222+zatUqkpKS8Hg8+4wuP5gePXrw0ksvMXnyZAzDYMaMGQSDwUPu9+tf/5ojjjiCuXPnMnnyZN577z3efPPNBiP1b5lxMydNPpmuXbtyxhlnYLFY+Oabb/juu++4/fbb6+tfuHAhY8eOxel0kpCwnykXw0jDCEWkzWR4opgyoiunDM4kJbYJzQE5qBinjTOHdyExWg0UEREREREREREREZH9WbRoEUOGDGmw3HrrrQwaNIi5c+dy9913079/f+bPn8/s2bMb7DtmzBguv/xypkyZQkpKCvfccw8A8+bN47zzzuP666+nV69enHrqqXz55Zd07do15LouueQSevXqxfDhw0lJSWHx4sWNOq+5c+eSkJDAmDFjmDx5MscddxxDhw495H5jx47l0UcfZe7cuQwaNIi33nqLa6+9tsG06McddxyvvfYa77zzDiNGjGD06NH85S9/IScnp36be++9l3fffZfs7GyGDBnSqNrbgmG25GT17VxZWRkej4fS0lLi4uLCXU7HEPDC93fW/bvfHzRtp7QY0zRZu6uCrzcWd6qR4xbTy8iqxwD4wn0ZQaNlvqc8UXZOH5pFvLtljqf3S30MmkSZIdKyOsj3lN4v9TFokg7y9S3SYXSQ7ym9X+pj0CQd5OtbpMPoIN9Ter/Ux6BJOsjXd6Soqalh/fr1dOvWrcPcU1pCd8kll7By5Uo+evv5uhVR6WCEb6z1wb7eQn2/1Py6IhIWhmGQnxpLfmos20qqWbq5hDUFFQSCneY6nRaTEuvk1CFZxDj1li4iIiIiIiIiIiIiIo0zZ84cjjnmGKKjo3nzzTd5+umneeThh8JdVotSB0VEwi4zPorM+ChqfAFW7ihn+bYydpZ1ntHjzdEvM44JvVOxW3U3DBERERERERERERERabwvvviCe+65h/LycvLy8njggQe4+OKLoXpHuEtrMWqKi0i74bJbGZwdz+DseEqqvKwuqGD1zgoKymvoPDd6CI3NYjChdyr9szzhLkVERERERERERERERDqwF154Yd+VZrDtC2lFaoqLSLsU73YwIjeREbmJlNf4WL+7knW7KtlcVIW/k0+xnpUQxTF90kiI1j1xREREREREREREREREDkVNcRFp92JddgZ2iWdgl3h8gSBbiqvZUFjJpsIqiiq94S6vzTjtFg7PT2ZAlgfDMMJdjoiIiIiIiIiIiIiISIegpriIdCh2q4VuydF0S44GoKLWz+aiKraVVLOttIbCitqIm2rdajEY2MXDqG5JRDms4S5HRERERERERERERESkQ1FTXEQ6tBinjT4ZcfTJiAOg1h9gd4WXXeW17C6vpbCylqJKHzW+QJgrbTyrxaBXeiyj85LwRNnDXY6IiIiIiIiIiIiIiEiHpKa4iEQUp81KVnwUWfFRDdZXef0UV/korfJRUu2ltMpHaXXdUuVtXw3zKIeVgVkeBmXHE+3U27SIiIiIiIiIiIiIiEhzqNsiIp2C22HD7bDt0ywH8PqD9Q3yusVLSZWPkiof5TV+gi05H7tpYvPXYA34cdlLqLKngGHgsFnIS46mV3osOUnRWC26Z7iISKdnmlBbDX4vVBVCTDoYygcREdkPZYaIiIRKmSEiIqEyTagqBl8VmHZwJ3fozFBTXEQ6PYfNQkqsk5RY5z7PBYImZdU+SvY0zEuqvJRW+yir9lFW48frD4b0Gk5/OX0LXmPwtueJr90KwFBeojqmKzVDLyZ25HnYohNa9LxERKSDqi6Bb/4Jnz8KxRvq1r39N0joBqMug0G/gqj4MBYoIiLthjJDRERCpcwQEZFQ1WfGY1C8/sf1rZQZpmly2WWX8a9//Yvi4mKWLFnC4MGDW+z4e6kpLiJyEFaLQUK0g4Rox36fr/EFqKj1U1UboNJb1yT3BoL4Aj82yxO2fUTPD67E4q/eZ/+ois1EfTgTPr0bpjwD+RNb7VxERKQDWLMAnj+v7grcnyveAG/dBAtvU2aIiIgyQ0REQqfMEBGpN23aNEpKSnjllVeatP9TTz3FNddcQ0lJSYvWBaHXtmvXLm655RZef/11du7cSUJCAoMGDeKWW25h7NixzSsiDJnx1ltv8dRTT7Fo0SLy8vJITk5ukeP+nJriIiLN4LJbcdmtEHOADdYsgPcuqptmhP1Nw75nna8a5p8FU1/QLx8iIp3VmgV1WaDMEBGRQ1FmiIhIqJQZIiIR5xe/+AVer5enn36avLw8du7cycKFCyksLGzegcOUGWvXriUjI4MxY8Y0+1gHY2nVo4uIdGbVJXVXVJkmcKhp1oN12z1/Xt1+IiLSuSgzREQkVMoMEREJlTJDRKTR5s6dy4ABA4iOjiY7O5srr7ySiooKABYtWsQFF1xAaWkphmFgGAazZs0CoLa2lunTp5OVlUV0dDSjRo1i0aJF9cd96qmniI+P5+2336ZPnz7ExMQwadIktm/fDsCsWbN4+umn+c9//lN/7J/uv1dJSQkfffQRd999NxMmTCAnJ4eRI0dy0003cfLJJ9dvt2nTJk455RRiYmKIi4vjrLPOYufOnfXPT5s2jVNPPfXHA1eXUPvsFILBAKFkRjAYoOJvp9MlOZZx48axdu3a+meffPJJ+vTpg8vlonfv3jzyyCMHPNK0adP49a9/zaZNmzAMg9zc3EO8dtOpKS4i0lq++eeeKUZCu+84BOu2/+a51qxKRETaI2WGiIiESpkhIiKhUmaISJh4vV68Xi+m+eNo40AggNfrxe/3t+i2Lc1isfDAAw/w/fff8/TTT/Pee+9xww03ADBmzBjuu+8+4uLi2L59O9u3b2f69OkAXH311Xz66ac899xzfPvtt5x55plMmjSJ1atX1x+7qqqKOXPm8Oyzz/Lhhx+yadOm+v2nT5/OWWedVd8o3759+35HTsfExBATE8Mrr7xCbW3tfs8hGAxyyimnUFRUxAcffMC7777LunXrmDJlyoFP/Jt/4sCPxQjx42RAtMPC/+b9jgsvvLD+czV//nxuueUW7rjjDlasWMGdd97JjBkzePrpp/d7nPvvv58//elPdOnShe3bt/Pll1+GVkATqCkuItIaTBM+f6xp+37+6J4reEVEpFNQZoiISKiUGSIiEiplhoiE0ezZs5k9ezZVVT/el3rx4sXMnj2bN954o8G2c+bMYfbs2ZSWltav+/LLL5k9ezavvvpqg23vv/9+Zs+eza5du1qt9muuuYYJEyaQm5vLUUcdxe23384LL7wAgMPhwOPxYBgG6enppKenExMTw6ZNm5g3bx4vvvgi48aNo3v37kyfPp3DDz+cefPm1R/b5/Px6KOPMnz4cIYOHcrVV1/NwoULgbpmd1RUFE6ns/7YDodjn/psNhtPPfUUTz/9NPHx8YwdO5Y//OEPfPvtt/XbLFy4kGXLlvGPf/yDYcOGMWrUKJ555hk++OCD/Ted92RGY9/5DSB13UtcMG0avXr1AmDmzJnce++9nH766XTr1o3TTz+da6+9lsce238meTweYmNjsVqtpKenk5KS0sgqQqemuIhIa6gqguL17P++Gwdj1u1XXdwaVYmISHukzBARkVApM0REJFTKDBGRJlmwYAFHH300WVlZxMbGcu6551JYWNigwf9zy5YtIxAI0LNnz/qR3DExMXzwwQcNphV3u9107969/nFGRgYFBQWNrvEXv/gF27Zt49VXX2XSpEksWrSIoUOH8tRTTwGwYsUKsrOzyc7Ort+nb9++xMfHs2LFin0PuCczQh0l/qOGmVFZWcnatWu56KKLGnwcbr/99gYfh3CxhbsAEZGI5K1o3v615eBObJlaRESkfVNmiIhIqJQZIiISKmWGiITRTTfdBIDdbq9fN3bsWEaPHo3F0nC87t7pw3+67YgRIxg6dOg+2/72t7/dZ9uWtGHDBk466SSuuOIK7rjjDhITE/n444+56KKL8Hq9uN3u/e5XUVGB1Wrl66+/xmq1NnguJiam/t8/r9swjCZPBe9yuTjmmGM45phjmDFjBhdffDEzZ85k2rRpIe1vsVh+fO0Wyoy9915/4oknGDVqVINNfv5xCQc1xeXgTBNqq8HvhapCiEkHo9GXioh0Po6YQ29zMM7YlqlDpC0pM0SaRpkhnZEyQ6RplBnSGSkzRJpGmSGdkTKj3djftN9Wq3W/jdHmbtuSvv76a4LBIPfee299Q37v1Ok/rSEQCDRYN2TIEAKBAAUFBYwbN67Jr7+/Y4eqb9++vPLKKwD06dOHzZs3s3nz5vrR4suXL6ekpIS+ffsCkJKSwnfffbfnhVsmM9LS0sjMzGTdunVMnTq1ecdsBWqKy/5Vl8A3/6y7f0zxhrp1b/8NErrBqMtg0K8gKj6MBYq0c+7Euu+X4g00bpoqAxJyISqhdeoSaQ3KDJHmUWZIZ6LMEGkeZYZ0JsoMkeZRZkhnosyQRiotLWXp0qUN1iUlJZGfn4/P5+PBBx9k8uTJLF68mEcffbTBdrm5uVRUVLBw4UIGDRqE2+2mZ8+eTJ06lfPOO497772XIUOGsGvXLhYuXMjAgQM58cQTQ6orNzeXt99+m1WrVpGUlITH49lndHlhYSFnnnkmF154IQMHDiQ2NpavvvqKe+65h1NOOQWAiRMnMmDAAKZOncp9992H3+/nyiuvZPz48QwfPhyAo446ij//+c8888wzHDZ6NImmhwSzFEsjbrxtYuCLyeL5f73GyFGj6NWrF7feeiu/+c1v8Hg8TJo0idraWr766iuKi4u57rrrQj94K9A9xWVfaxbA3L7w1k1QvLHhc8Ub6tbP7Vu3nYjsn2HU/cDVFKMu1xWM0nEoM0SaT5khnYUyQ6T5lBnSWSgzRJpPmSGdhTJDmmDRokUMGTKkwXLrrbcyaNAg5s6dy913303//v2ZP38+s2fPbrDvmDFjuPzyy5kyZQopKSncc889AMybN4/zzjuP66+/nl69enHqqafy5Zdf0rVr15DruuSSS+jVqxfDhw8nJSWFxYsX77NNTEwMo0aN4i9/+QtHHHEE/fv3Z8aMGVxyySU89NBDQN207P/5z39ISEjgiCOOYOLEieTl5fH888/XH+e4445jxowZ3HDDDYwYOZIPa3s3+q3fNIPc/N/NPPHkk/XN+4svvpgnn3ySefPmMWDAAMaPH89TTz1Ft27dGnfwVmCYTZ2svgMqKyvD4/FQWlpKXFxcuMtpn9YsgPln1U0zQvAgG1rqfjCa+gLkT2yr6kQ6luqSuh+4fNUc/PtpD8MCtii4bnnYr1zU+6U+BiFRZoi0HGVGh6aPQQiUGSItR5nRoeljEAJlhkjLUWZ0aPoYhECZEVY1NTWsX7+ebt264XK5wl2ONFc7z4yDfb2F+n6pkeLyo+oSeP68EAKEuudNs2776pLWr02kI4qKhynP7Lmy9lBvtxbAgCnPhv2XDpGQKDNEWpYyQyKZMkOkZSkzJJIpM0RaljJDIpkyQ6RldYLMUFNcfvTNP8FXRUhXgEDddr4q+Oa51qxKpGPLn1h3BaI9CjD2LD+1Z509Cqa+CPlHt32NIk2hzBBpecoMiVTKDJGWp8yQSKXMEGl5ygyJVMoMkZYX4ZmhprjUMU34/LGm7fv5o3uuxhKR/cqfWDeFyKS7ICG34XMJuXXrr1/R4QJEOjFlhkjrUWZIpFFmiLQeZYZEGmWGSOtRZkikUWaItJ4IzgxbuAuQdqKqCIrXN2FHs26/6mJwJ7Z4WSIRIyoeRl8Ooy6r+36pLQdnLEQl7JmORKQDUWaItC5lhkQSZYZI61JmSCRRZoi0LmWGRBJlhkjritDMUFNc6ngrmrd/bblCRCQUhlH3vaLvF+nIlBkibUOZIZFAmSHSNpQZEgmUGSJtQ5khkUCZIdI2IiwzNH261HHENG9/Z2zL1CEiIu2fMkNEREKlzBARkVApM0REJFTKjHbF1HT00gZa4utMTXGp406EhG5AY6c9MOr2i0pojapERKQ9UmaIiEiolBkiIhIqZYaIiIRKmdEu2O12AKqqqsJciXQGe7/O9n7dNYWmT5c6hlF3b4C3bmr8vqMu79D3EBARkUZSZoiISKiUGSIiEiplhoiIhEqZ0S5YrVbi4+MpKCgAwO12Y+hjKy3MNE2qqqooKCggPj4eq9Xa5GOpKS4/GvQrWHgb+KqB4KG3Nyxgi4JBv2z10kREpJ1RZoiISKiUGSIiEiplhoiIhEqZ0S6kp6cD1DfGRVpLfHx8/ddbU6kpLj+Kiocpz8D8s8C0cPAgsQAGTHm2bj8REelclBkiIhIqZYaIiIRKmSEiIqFSZrQLhmGQkZFBamoqPp8v3OVIhLLb7c0aIb5Xh2mKn3zyySxdupSCggISEhKYOHEid999N5mZmeEuLbLkT4SpL8Dz54Fv730gfnrz+j1TX9ij6gIk/+i2rlBE5JCUGW1EmSEiEUCZ0UaUGSISAZQZbUSZISIRQJnRRpQZ7YbVam2RpqVIa7KEu4BQTZgwgRdeeIFVq1bx73//m7Vr13LGGWeEu6zIlD8RrlsOk+6ChNyGzyXk1q2/foUCRETaLWVGG1JmiEgHp8xoQ8oMEenglBltSJkhIh2cMqMNKTNEJESGaZrmoTdrf1599VVOPfVUamtrsdvtIe1TVlaGx+OhtLSUuLi4Vq4wQpgmVBdDbTk4YyEqAQwj3FWJSCuLtPdLZUYbUWaIdEqR9n6pzGgjygyRTinS3i+VGW1EmSHSKUXa+6Uyo40oM0Q6pVDfLzvM9Ok/VVRUxPz58xkzZkzIASJNZBjgTqxbREQ6IGVGG1JmiEgHp8xoQ8oMEenglBltSJkhIh2cMqMNKTNE5CA6VFP897//PQ899BBVVVWMHj2a11577aDb19bWUltbW/+4tLQUqLtiQEREDmzv+2QHnUwEUGaIiLQVZYYyQ0QkVMoMZYaISKiUGcoMEZFQhZwZZhj9/ve/N4GDLitWrKjffteuXeaqVavMd955xxw7dqx5wgknmMFg8IDHnzlz5iGPr0WLFi1aDrxs3ry5LeIgJMoMLVq0aGnfizJDixYtWrSEuigztGjRokVLqIsyQ4sWLVq0hLocKjPCek/xXbt2UVhYeNBt8vLycDgc+6zfsmUL2dnZfPLJJxx22GH73ffnV1YFg0GKiopISkrCaOR9JMrKysjOzmbz5s2d4v4dne18ofOds8438jXnnE3TpLy8nMzMTCwWSytV2DjKjPars50vdL5z1vlGPmXGj5QZrauznS90vnPW+UY+ZcaPlBmtq7OdL3S+c9b5Rj5lxo+UGa2rs50vdL5z1vlGvrbIjLBOn56SkkJKSkqT9g0GgwANQuLnnE4nTqezwbr4+Pgmvd5ecXFxneYLEDrf+ULnO2edb+Rr6jl7PJ5WqKbplBntX2c7X+h856zzjXzKDGVGW+ls5wud75x1vpFPmaHMaCud7Xyh852zzjfyKTOUGW2ls50vdL5z1vlGvtbMjA5xT/HPP/+cL7/8ksMPP5yEhATWrl3LjBkz6N69+wGvqhIRkc5JmSEiIqFSZoiISKiUGSIiEiplhohI+9Q+5h05BLfbzUsvvcTRRx9Nr169uOiiixg4cCAffPDBPldOiYhI56bMEBGRUCkzREQkVMoMEREJlTJDRKR96hAjxQcMGMB7770X1hqcTiczZ87sNKHV2c4XOt8563wjX2c8Z1BmhENnO1/ofOes8418nfGcQZkRDp3tfKHznbPON/J1xnMGZUY4dLbzhc53zjrfyNcZzxmUGeHQ2c4XOt8563wjX1ucs2GaptlqRxcREREREREREREREREREQmjDjF9uoiIiIiIiIiIiIiIiIiISFOoKS4iIiIiIiIiIiIiIiIiIhFLTXEREREREREREREREREREYlYaor/xMMPP0xubi4ul4tRo0bxxRdfHHT7F198kd69e+NyuRgwYABvvPFGG1XaMhpzvk888QTjxo0jISGBhIQEJk6ceMiPT3vU2M/xXs899xyGYXDqqae2boEtrLHnW1JSwlVXXUVGRgZOp5OePXt2qK/rxp7vfffdR69evYiKiiI7O5trr72WmpqaNqq2eT788EMmT55MZmYmhmHwyiuvHHKfRYsWMXToUJxOJ/n5+Tz11FOtXmckU2YoMw5EmdExKDMOTpnRspQZyowDUWZ0DMqMg1NmtCxlhjLjQJQZHYMy4+CUGS1LmaHMOBBlRsegzDi4VskMU0zTNM3nnnvOdDgc5t/+9jfz+++/Ny+55BIzPj7e3Llz5363X7x4sWm1Ws177rnHXL58uXnzzTebdrvdXLZsWRtX3jSNPd+zzz7bfPjhh80lS5aYK1asMKdNm2Z6PB5zy5YtbVx50zX2nPdav369mZWVZY4bN8485ZRT2qbYFtDY862trTWHDx9unnDCCebHH39srl+/3ly0aJG5dOnSNq68aRp7vvPnzzedTqc5f/58c/369ebbb79tZmRkmNdee20bV940b7zxhvnHP/7RfOmll0zAfPnllw+6/bp160y3221ed9115vLly80HH3zQtFqt5ltvvdU2BUcYZYYy40CUGcqM9kiZEV7KDGXGgSgzlBntkTIjvJQZyowDUWYoM9ojZUZ4KTOUGQeizFBmtEftJTPUFN9j5MiR5lVXXVX/OBAImJmZmebs2bP3u/1ZZ51lnnjiiQ3WjRo1yrzssstatc6W0tjz/Tm/32/GxsaaTz/9dGuV2OKacs5+v98cM2aM+eSTT5rnn39+hwqRxp7vX//6VzMvL8/0er1tVWKLauz5XnXVVeZRRx3VYN11111njh07tlXrbA2hhMgNN9xg9uvXr8G6KVOmmMcdd1wrVha5lBnKjP1RZnQcyoyXD7qNMqNlKTOUGfujzOg4lBkvH3QbZUbLUmYoM/ZHmdFxKDNePug2yoyWpcxQZuyPMqPjUGa8fNBtWiszNH064PV6+frrr5k4cWL9OovFwsSJE/n000/3u8+nn37aYHuA44477oDbtydNOd+fq6qqwufzkZiY2FpltqimnvOf/vQnUlNTueiii9qizBbTlPN99dVXOeyww7jqqqtIS0ujf//+3HnnnQQCgbYqu8macr5jxozh66+/rp+SZN26dbzxxhuccMIJbVJzW+vI71ntjTJDmXEgygxlRqToyO9Z7Y0yQ5lxIMoMZUak6MjvWe2NMkOZcSDKDGVGpOjI71ntjTJDmXEgygxlRqRorfcsW7P2jhC7d+8mEAiQlpbWYH1aWhorV67c7z47duzY7/Y7duxotTpbSlPO9+d+//vfk5mZuc8XZXvVlHP++OOP+b//+z+WLl3aBhW2rKac77p163jvvfeYOnUqb7zxBmvWrOHKK6/E5/Mxc+bMtii7yZpyvmeffTa7d+/m8MMPxzRN/H4/l19+OX/4wx/aouQ2d6D3rLKyMqqrq4mKigpTZR2PMqOOMqMhZYYyI5IoM1qOMqOOMqMhZYYyI5IoM1qOMqOOMqMhZYYyI5IoM1qOMqOOMqMhZYYyI5K0VmZopLg02l133cVzzz3Hyy+/jMvlCnc5raK8vJxzzz2XJ554guTk5HCX0yaCwSCpqak8/vjjDBs2jClTpvDHP/6RRx99NNyltYpFixZx55138sgjj/C///2Pl156iddff53bbrst3KWJRBRlRmRSZigzRFqDMiMyKTOUGSKtQZkRmZQZygyR1qDMiEzKDGVGU2ikOJCcnIzVamXnzp0N1u/cuZP09PT97pOent6o7duTppzvXnPmzOGuu+5iwYIFDBw4sDXLbFGNPee1a9eyYcMGJk+eXL8uGAwCYLPZWLVqFd27d2/dopuhKZ/jjIwM7HY7Vqu1fl2fPn3YsWMHXq8Xh8PRqjU3R1POd8aMGZx77rlcfPHFAAwYMIDKykouvfRS/vjHP2KxRNY1Qwd6z4qLi9OVuI2kzKijzPiRMqOOMiNyKDNajjKjjjLjR8qMOsqMyKHMaDnKjDrKjB8pM+ooMyKHMqPlKDPqKDN+pMyoo8yIHK2VGZH1UWoih8PBsGHDWLhwYf26YDDIwoULOeyww/a7z2GHHdZge4B33333gNu3J005X4B77rmH2267jbfeeovhw4e3RaktprHn3Lt3b5YtW8bSpUvrl5NPPpkJEyawdOlSsrOz27L8RmvK53js2LGsWbOmPiwBfvjhBzIyMtp1gEDTzreqqmqfoNgboKZptl6xYdKR37PaG2WGMuPnlBl1lBmRoyO/Z7U3ygxlxs8pM+ooMyJHR37Pam+UGcqMn1Nm1FFmRI6O/J7V3igzlBk/p8yoo8yIHK32nmWKaZqm+dxzz5lOp9N86qmnzOXLl5uXXnqpGR8fb+7YscM0TdM899xzzRtvvLF++8WLF5s2m82cM2eOuWLFCnPmzJmm3W43ly1bFq5TaJTGnu9dd91lOhwO81//+pe5ffv2+qW8vDxcp9BojT3nnzv//PPNU045pY2qbb7Gnu+mTZvM2NhY8+qrrzZXrVplvvbaa2Zqaqp5++23h+sUGqWx5ztz5kwzNjbW/Oc//2muW7fOfOedd8zu3bubZ511VrhOoVHKy8vNJUuWmEuWLDEBc+7cueaSJUvMjRs3mqZpmjfeeKN57rnn1m+/bt060+12m7/73e/MFStWmA8//LBptVrNt956K1yn0KEpM5QZygxlhjJDQqXMUGYoM5QZygwJlTJDmaHMUGYoMyRUygxlhjJDmaHMaDw1xX/iwQcfNLt27Wo6HA5z5MiR5meffVb/3Pjx483zzz+/wfYvvPCC2bNnT9PhcJj9+vUzX3/99TauuHkac745OTkmsM8yc+bMti+8GRr7Of6pjhYiptn48/3kk0/MUaNGmU6n08zLyzPvuOMO0+/3t3HVTdeY8/X5fOasWbPM7t27my6Xy8zOzjavvPJKs7i4uO0Lb4L3339/v9+Te8/x/PPPN8ePH7/PPoMHDzYdDoeZl5dnzps3r83rjiTKDGWGMkOZocyQUCkzlBnKDGWGMkNCpcxQZigzlBnKDAmVMkOZocxQZigzGscwzQgcVy8iIiIiIiIiIiIiIiIiIoLuKS4iIiIiIiIiIiIiIiIiIhFMTXEREREREREREREREREREYlYaoqLiIiIiIiIiIiIiIiIiEjEUlNcREREREREREREREREREQilpriIiIiIiIiIiIiIiIiIiISsdQUFxERERERERERERERERGRiKWmuIiIiIiIiIiIiIiIiIiIRCw1xUVEREREREREREREREREJGKpKS4iIiIiIiIiIiIiIiIiIhFLTXGRdmD79u2cffbZ9OzZE4vFwjXXXBPukkREpJ1SZoiISKiUGSIiEiplhoiIhEqZIR2VmuIi7UBtbS0pKSncfPPNDBo0KNzliIhIO6bMEBGRUCkzREQkVMoMEREJlTJDOio1xUXawK5du0hPT+fOO++sX/fJJ5/gcDhYuHAhubm53H///Zx33nl4PJ4wVioiIuGmzBARkVApM0REJFTKDBERCZUyQyKVLdwFiHQGKSkp/O1vf+PUU0/l2GOPpVevXpx77rlcffXVHH300eEuT0RE2hFlhoiIhEqZISIioVJmiIhIqJQZEqnUFBdpIyeccAKXXHIJU6dOZfjw4URHRzN79uxwlyUiIu2QMkNEREKlzBARkVApM0REJFTKDIlEmj5dpA3NmTMHv9/Piy++yPz583E6neEuSURE2illhoiIhEqZISIioVJmiIhIqJQZEmnUFBdpQ2vXrmXbtm0Eg0E2bNgQ7nJERKQdU2aIiEiolBkiIhIqZYaIiIRKmSGRRtOni7QRr9fLOeecw5QpU+jVqxcXX3wxy5YtIzU1NdyliYhIO6PMEBGRUCkzREQkVMoMEREJlTJDIpGa4iJt5I9//COlpaU88MADxMTE8MYbb3DhhRfy2muvAbB06VIAKioq2LVrF0uXLsXhcNC3b98wVi0iIuGgzBARkVApM0REJFTKDBERCZUyQyKRYZqmGe4iRCLdokWLOOaYY3j//fc5/PDDAdiwYQODBg3irrvu4oorrsAwjH32y8nJ0bQkIiKdjDJDRERCpcwQEZFQKTNERCRUygyJVGqKi4iIiIiIiIiIiIiIiIhIxLKEuwAREREREREREREREREREZHWoqa4iIiIiIiIiIiIiIiIiIhELDXFRUREREREREREREREREQkYqkpLiIiIiIiIiIiIiIiIiIiEUtNcRERERERERERERERERERiVhqiouIiIiIiIiIiIiIiIiISMRSU1xERERERERERERERERERCKWmuIiIiIiIiIiIiIiIiIiIhKx1BQXEREREREREREREREREZGIpaa4iIiIiIiIiIiIiIiIiIhELDXFRUREREREREREREREREQkYqkpLiIiIiIiIiIiIiIiIiIiEev/AVj7IH1GeSmDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(4)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 2.0 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "source_tasks = task_ids == 0\n",
+    "task_info[source_tasks, 1] = 0\n",
+    "\n",
+    "all_features = torch.stack((train_x1, train_x2), -1)\n",
+    "source_features = all_features[source_tasks]\n",
+    "all_features[source_tasks] = torch.cat((source_features[..., 1:], torch.zeros_like(source_features[..., 1:])), -1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
+    "    test_info = torch.ones(100, 2)\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x2 only, Target: x1, x2\" % c)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(4)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 0.01 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "target_tasks = task_ids == 1\n",
+    "task_info[target_tasks, 1] = 0\n",
+    "\n",
+    "all_features = torch.stack((train_x1, train_x2), -1)\n",
+    "target_features = all_features[target_tasks]\n",
+    "all_features[target_tasks] = torch.cat((target_features[..., :1], torch.zeros_like(target_features[..., 1:])), -1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x1, torch.zeros_like(test_x1)), -1)\n",
+    "    test_info = torch.ones(100, 2)\n",
+    "    test_info[:, 1] = 0\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x1, x2, Target: x1 only\" % c)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(4)\n",
+    "train_x1 = torch.rand(1000)\n",
+    "train_x2 = torch.randn_like(train_x1)\n",
+    "\n",
+    "f = sample_data(train_x1.unsqueeze(-1))\n",
+    "c = 0.1 #(torch.rand(1) * 10).item()\n",
+    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
+    "task_ids[:10] = 0 # source task\n",
+    "task_ids[10:12] = 1 # target task\n",
+    "\n",
+    "source_ys = f + train_x2 * c\n",
+    "target_ys = -f + train_x2 * c\n",
+    "\n",
+    "task_info = torch.ones(1000, 2)\n",
+    "target_tasks = task_ids == 1\n",
+    "task_info[target_tasks, 0] = 0\n",
+    "\n",
+    "all_features = torch.stack((train_x1, train_x2), -1)\n",
+    "target_features = all_features[target_tasks]\n",
+    "all_features[target_tasks] = torch.cat((target_features[..., :1], torch.zeros_like(target_features[..., 1:])), -1)\n",
+    "\n",
+    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
+    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
+    "\n",
+    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
+    "sorted_f = -f[indices.squeeze()]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
+    "\n",
+    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
+    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "    test_x2 = torch.ones_like(test_x1) * x2\n",
+    "    test_x = torch.cat((test_x2, torch.zeros_like(test_x2)), -1)\n",
+    "    test_info = torch.ones(100, 2)\n",
+    "    test_info[:, 1] = 0\n",
+    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
+    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
+    "\n",
+    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
+    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
+    "\n",
+    "    ax.plot(test_x1, mean_preds)\n",
+    "    ax.fill_between(\n",
+    "        test_x1.squeeze(),\n",
+    "        mean_preds - std2_preds,\n",
+    "        mean_preds + std2_preds,\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    # vertical lines per point\n",
+    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
+    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
+    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
+    "    \n",
+    "    \n",
+    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
+    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
+    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
+    "    \n",
+    "    ax.set_title(f\"x2 = {x2}\")\n",
+    "    ax.set_ylim(-3, 3)\n",
+    "    ax.set_xlabel(\"x1\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    \n",
+    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x1, x2, Target: x2 only\" % c)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pfn",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/private_multitask_pfn/train.py b/private_multitask_pfn/train.py
new file mode 100644
index 0000000..2fef09a
--- /dev/null
+++ b/private_multitask_pfn/train.py
@@ -0,0 +1,638 @@
+#!/usr/bin/env python3
+
+import torch
+from gen_batch import (
+    combine_batch,
+    get_mtgp_batch,
+    get_pd1_surrogate_batch_fn,
+    get_pd1_eval_batch_fn,
+    get_trios_batch,
+    get_hpobench_batch_fn,
+    target_aware_mtgp_batch,
+    gen_mtgp_equal_eval_batch,
+    gen_mtgp_low_rank,
+)
+from paper_figures.joint_data import get_joint_batch
+from gen_axial_batch import axial_train_batch, multitask_line_batch
+from gen_task_batch import task_invariant_batch, task_corr_batch
+from PFNs.pfns import (
+    bar_distribution,
+    encoders,
+    priors,
+    utils,
+)
+from PFNs.pfns.train import (
+    train as train_pfn,
+    ProbabilisticRankingLoss,
+    PairwiseRankingLoss,
+    GaussianRankingLoss,
+    BarRankingLoss,
+)
+import sys
+import logging
+import os
+import datetime
+import json
+import argparse
+import traceback
+import wandb
+import numpy as np
+
+default_device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+
+def train_from_checkpoint(checkpoint_folder, wandb_mode="online", pty=False, epochs=None, seed=None, lr=None, **kwargs):
+    args_path = checkpoint_folder + '/args.json'
+    with open(args_path, 'r') as f:
+        args = json.load(f)
+    args["wandb_mode"] = wandb_mode
+    args["checkpoint_folder"] = checkpoint_folder
+    args["pty"] = pty
+    
+    if epochs is None or seed is None or lr is None:
+        print("Using original epochs, seed, and lr")
+        continue_scheduler = True
+        
+        checkpoint_path = checkpoint_folder + '/checkpoint.pth'
+        checkpoint = torch.load(checkpoint_path, map_location=default_device)
+        args["start_epoch"] = checkpoint["epoch"]
+    else:
+        print(f"Restarting with epochs={epochs}, seed={seed}, lr={lr}")
+        continue_scheduler = False
+        
+        args["epochs"] = epochs
+        args["seed"] = seed
+        args["lr"] = lr
+        
+    for key, val in kwargs.items():
+        if val is not None:
+            args[key] = val
+    
+    random_num = np.random.randint(100000)
+    dirname = create_experiment_dir(argparse.Namespace(**args), random_num, continued=True)
+    args["dirname"] = dirname
+    
+    wandb.init(
+        project="mtpfn",
+        config=args,
+        mode=wandb_mode,
+    )
+    
+    # make symlink from wandb name to dirname
+    if wandb_mode != "disabled":
+        wandb_name = wandb.run.name
+        # wandb_dir = "/home/yl9959/mtpfn/wandb_links"
+        wandb_dir = args["wandb_dir"]
+        os.symlink(dirname, os.path.join(wandb_dir, wandb_name))
+    
+    if pty:
+        return train(**args, progress_bar=True, continue_model=True, continue_scheduler=continue_scheduler) 
+    else:
+        # redirect stdout and stderr
+        sys.stdout = open(os.path.join(dirname, 'stdout.txt'), 'w')
+        sys.stderr = open(os.path.join(dirname, 'stderr.txt'), 'w')
+        return train(**args, continue_model=True, continue_scheduler=continue_scheduler)
+
+
+def train_continue(continue_scheduler, checkpoint_folder, config, dirname):
+    checkpoint_path = checkpoint_folder + '/checkpoint.pth'
+    checkpoint = torch.load(checkpoint_path, map_location=default_device)
+    
+    model, optimizer, scheduler = train(**config, return_model=True)
+    
+    model.load_state_dict(checkpoint['model_state_dict'])
+    config["continue_model"] = model
+    
+    if continue_scheduler:
+        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+        scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+        epoch = checkpoint['epoch']
+        
+        config["continue_optimizer"] = optimizer
+        config["continue_scheduler"] = scheduler
+        config["continue_epoch"] = epoch
+    return train_pfn_wrapper(config, dirname)
+
+
+def train_pfn_wrapper(config, dirname):
+    train_result = train_pfn(**config, dirname=dirname)
+    total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = (
+        train_result
+    )
+
+    if final_model:
+        final_state = final_model.state_dict()
+    if best_model:
+        best_state = best_model.state_dict()
+
+    results = {
+        "final_loss": total_loss,
+        "final_epoch": config["epochs"],
+    }
+    torch.save(final_state, os.path.join(dirname, "final_model.pth"))
+    
+    if best_model:
+        torch.save(best_state, os.path.join(dirname, "best_model.pth"))
+        results["best_loss"] = best_val_score
+        
+    with open(os.path.join(dirname ,'result.json'), 'w') as f:
+        json.dump(results, f, indent=4)
+
+
+def get_batch_gen(prior_type, **kwargs):
+    hypers = {}
+    
+    if prior_type == "mtgp":
+        get_batch_fn = gen_mtgp_equal_eval_batch
+        hypers["corr_init"] = kwargs["corr_init"]
+        hypers["uncorr_prob"] = kwargs["uncorr_prob"]
+    elif prior_type == "mtgp_1_uncorr":
+        get_batch_fn = gen_mtgp_equal_eval_batch
+        hypers["num_uncorr_tasks"] = 1
+    elif prior_type == "mtgp_4_1_uncorr":
+        get_batch_fn = gen_mtgp_equal_eval_batch
+        hypers["num_uncorr_tasks"] = 1
+        hypers["num_tasks"] = 4
+    elif prior_type == "mtgp_4_1_4_uncorr":
+        get_batch_fn = gen_mtgp_equal_eval_batch
+        hypers["num_uncorr_tasks"] = 1
+        hypers["num_tasks"] = 4
+        hypers["num_features"] = 4
+    elif prior_type == "toy_axial":
+        get_batch_fn = axial_train_batch
+    elif prior_type == "toy_multitask":
+        get_batch_fn = multitask_line_batch
+    elif prior_type == "toy_task_invariant":
+        get_batch_fn = task_invariant_batch
+    elif prior_type == "toy_corr_multitask":
+        get_batch_fn = task_corr_batch
+    elif prior_type == "pd1":
+        get_batch_fn = get_pd1_surrogate_batch_fn(**kwargs)
+    elif prior_type == "pd1_eval":
+        get_batch_fn = get_pd1_eval_batch_fn(**kwargs)
+    elif prior_type == "trio":
+        get_batch_fn = get_trios_batch
+    elif prior_type == "hpobench_lr":
+        get_batch_fn = get_hpobench_batch_fn("lr", train=True)
+    elif prior_type == "hpobench_lr_eval":
+        get_batch_fn = get_hpobench_batch_fn("lr", train=False)
+    elif prior_type == "hpobench_svm":
+        get_batch_fn = get_hpobench_batch_fn("svm", train=True)
+    elif prior_type == "hpobench_svm_eval":
+        get_batch_fn = get_hpobench_batch_fn("svm", train=False)
+    elif prior_type == "target_aware":
+        get_batch_fn = target_aware_mtgp_batch
+        hypers["corr_init"] = kwargs["corr_init"]
+        hypers["uncorr_prob"] = kwargs["uncorr_prob"]
+    elif prior_type == "target_aware_4_1_4_uncorr":
+        get_batch_fn = target_aware_mtgp_batch
+        hypers["num_uncorr_tasks"] = 1
+        hypers["num_tasks"] = 4
+        hypers["num_features"] = 4
+    elif prior_type == "target_aware_4_1_2_uncorr":
+        get_batch_fn = target_aware_mtgp_batch
+        hypers["num_uncorr_tasks"] = 1
+        hypers["num_tasks"] = 4
+        hypers["num_features"] = 2
+    elif prior_type == "low_rank":
+        get_batch_fn = gen_mtgp_low_rank
+        hypers["decay_alpha"] = kwargs["decay_alpha"]
+        hypers["max_rank"] = kwargs["max_rank"]
+    else:
+        raise ValueError("prior_type is invalid")
+
+    return get_batch_fn, hypers
+
+
+def get_batch_gen_and_prior_dl(prior_type, **kwargs):
+    if prior_type == "combine":
+        functions = []
+        hypers = {}
+        for prior_type in kwargs["prior_types"]:
+            get_batch_fn, prior_hypers = get_batch_gen(prior_type, **kwargs)
+            functions.append(get_batch_fn)
+            hypers = {**hypers, **prior_hypers}
+        weights = torch.tensor(kwargs["prior_weights"])
+        get_batch_fn = combine_batch(functions, weights)
+    else:
+        get_batch_fn, hypers = get_batch_gen(prior_type, **kwargs)
+
+    return priors.get_batch_sequence(
+        get_batch_fn,
+        priors.utils.set_properties_get_batch,
+    ), hypers
+
+
+def train(
+    attn_type="axial",
+    num_features=40,
+    num_tasks=5,
+    prior_type="mtgp",
+    lr=0.0001,
+    epochs=50,
+    seq_len=200,
+    single_train_pos_gen_type="uniform",
+    sample_num_tasks=True,
+    save_to_manifold=True,
+    corr_init=0.8,
+    sample_num_features=True,
+    scheduler_type="cosine",
+    lengthscale=None,
+    criterion_type="gaussian",
+    uncorr_prob=0.0,
+    emsize=32,
+    task_embed_type="linear",
+    same_tasks_across_batch=True,
+    full_precision=False,
+    return_model=False,  # not included in config
+    device=default_device,
+    dirname=None,
+    continue_model=False,
+    continue_scheduler=False,
+    **kwargs,
+):
+    if kwargs.get("eval_type", None) is None:
+        kwargs["eval_type"] = prior_type
+        
+    train_config = {
+        "attn_type": attn_type,
+        "prior_type": prior_type,
+        "num_features": num_features,
+        "num_tasks": num_tasks,
+        "lr": lr,
+        "epochs": epochs,
+        "seq_len": seq_len,
+        "single_train_pos_gen_type": single_train_pos_gen_type,
+        "sample_num_tasks": sample_num_tasks,
+        "save_to_manifold": save_to_manifold,
+        "corr_init": corr_init,
+        "sample_num_features": sample_num_features,
+        "scheduler_type": scheduler_type,
+        "lengthscale": lengthscale,
+        "criterion_type": criterion_type,
+        "uncorr_prob": uncorr_prob,
+        "emsize": emsize,
+        "task_embed_type": task_embed_type,
+        **kwargs,
+    }
+
+    # get batch function
+    get_train_batch_fn, train_hypers = get_batch_gen_and_prior_dl(
+        max_features=num_features,
+        min_num_tasks=num_tasks,
+        **train_config,
+    )
+    eval_config = train_config.copy()
+    eval_config["prior_type"] = train_config["eval_type"]
+    get_eval_batch_fn, eval_hypers = get_batch_gen_and_prior_dl(
+        max_features=num_features,
+        min_num_tasks=num_tasks,
+        **eval_config,
+    )
+
+    batch_hypers = {
+        "num_tasks": num_tasks,
+        # "sample_num_tasks": sample_num_tasks,
+        # "sample_num_features": sample_num_features,
+        "lengthscale": lengthscale,
+        "same_tasks_across_batch": same_tasks_across_batch,
+        "target_only_loss": kwargs.get("target_only_loss", False),
+    }
+    # combine train and batch hypers
+    train_batch_fn_hypers = {**train_hypers, **batch_hypers, "sample_num_tasks": sample_num_tasks, "sample_num_features": sample_num_features, "sample_num_tasks_curriculum": kwargs.get("sample_num_tasks_curriculum", False)}
+    eval_batch_fn_hypers = {**eval_hypers, **batch_hypers, "sample_num_tasks": False, "sample_num_features": False}
+
+    if "bar" in criterion_type:
+        if return_model or continue_model:
+            n = 100
+        else:
+            n = 100000
+        ys = get_train_batch_fn(
+            batch_size=n,
+            seq_len=20,
+            num_features=num_features,
+            hyperparameters=train_batch_fn_hypers,
+            device=device,
+        ).target_y.to(device)
+        criterion = bar_distribution.FullSupportBarDistribution(
+            bar_distribution.get_bucket_limits(num_outputs=1000, ys=ys)
+        )
+        if criterion_type == "bar_ranking":
+            criterion = BarRankingLoss(criterion)
+    elif criterion_type == "gaussian":
+        criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
+    elif criterion_type == "prob_ranking":
+        criterion = ProbabilisticRankingLoss(reduction="none")
+    elif criterion_type == "gaussian_ranking":
+        criterion = GaussianRankingLoss()
+    # elif criterion_type == "ranking":
+    #     criterion = PairwiseRankingLoss()
+    else:
+        raise ValueError("criterion_type must be one of ['bar', 'gaussian', 'prob']")
+
+    if "axial" in attn_type:
+        encoder_generator = encoders.get_axial_normalized_uniform_encoder()
+    elif attn_type == "standard":
+        if task_embed_type == "feature":
+            encoder_generator = encoders.get_normalized_uniform_multitask_encoder(
+                encoders.get_variable_num_features_multitask_encoder(
+                    encoders.Linear
+                )
+            )
+        else:
+            encoder_generator = encoders.get_normalized_uniform_encoder(
+                encoders.get_variable_num_features_encoder(encoders.Linear)
+            )
+        
+    if task_embed_type == "linear":
+        task_encoder_generator = encoders.Linear
+    elif task_embed_type == "onehot_linear":
+        task_encoder_generator = encoders.Linear
+    elif task_embed_type == "self_attn":
+        task_encoder_generator = encoders.get_self_attention_task_encoder()
+    elif task_embed_type == "feature":
+        task_encoder_generator = None
+    elif task_embed_type == "task_attn":
+        task_encoder_generator = None
+    elif task_embed_type == "task_attn_shared":
+        task_encoder_generator = None
+    elif task_embed_type == "task_attn_opt":
+        task_encoder_generator = None
+    elif task_embed_type == "hier" or task_embed_type == "hier_single":
+        task_encoder_generator = None
+    else:
+        raise ValueError("task_embed_type must be one of ['linear', 'onehot_linear', 'self_attn', 'feature', 'task_attn', 'task_attn_shared']")
+    
+    if single_train_pos_gen_type == "decay":
+        single_train_pos_gen = utils.get_exponential_decay_single_eval_pos_sampler(
+            seq_len - num_tasks, min_len=num_tasks
+        )
+    elif single_train_pos_gen_type == "uniform":
+        single_train_pos_gen = utils.get_uniform_single_eval_pos_sampler(
+            seq_len - num_tasks - kwargs.get("min_eval_len", 0), min_len=num_tasks
+        )
+    elif single_train_pos_gen_type == "uniform_large":
+        single_train_pos_gen = utils.get_uniform_single_eval_pos_sampler(
+            seq_len - num_tasks - 100, min_len=num_tasks
+        )
+    elif single_train_pos_gen_type == "curriculum":
+        single_train_pos_gen = utils.get_curriculum_single_eval_pos_sampler(
+            seq_len - num_tasks, min_len=num_tasks
+        )
+    else:
+        raise ValueError("single_train_pos_gen_type must be one of ['uniform', 'decay']")
+
+    if scheduler_type == "cosine":
+        scheduler = utils.get_cosine_schedule_with_warmup
+    elif scheduler_type == "restart":
+        scheduler = utils.get_restarting_cosine_schedule_with_warmup
+    elif scheduler_type == "restart_slow":
+        scheduler = utils.get_slow_restarting_cosine_schedule_with_warmup
+    elif scheduler_type == "curriculum":
+        scheduler = utils.get_curriculum_cosine_schedule_with_warmup
+    else: 
+        raise ValueError(
+            "scheduler_type must be one of ['cosine', 'restart', 'restart_slow']"
+        )
+        
+    config = {
+        "train_loader": get_train_batch_fn,
+        "eval_loader": get_eval_batch_fn,
+        "criterion": criterion,
+        "encoder_generator": encoder_generator,
+        "task_encoder_generator": task_encoder_generator,
+        "single_train_pos_gen": single_train_pos_gen,
+        "scheduler": scheduler,
+        "emsize": emsize,
+        "nhead": 4,
+        "warmup_epochs": 5,
+        "y_encoder_generator": encoders.Linear,
+        "batch_size": 128,
+        "train_extra_prior_kwargs_dict": {
+            "num_features": num_features,
+            "hyperparameters": train_batch_fn_hypers,
+        },
+        "eval_extra_prior_kwargs_dict": {
+            "num_features": eval_batch_fn_hypers.get("num_features", num_features),
+            "hyperparameters": eval_batch_fn_hypers,
+        },
+        "epochs": 50,
+        "lr": 0.0001,
+        "seq_len": 200,
+        "aggregate_k_gradients": 2,
+        "nhid": 1024,
+        "steps_per_epoch": 1024,
+        "weight_decay": 0.0,
+        "train_mixed_precision": not full_precision,
+        "efficient_eval_masking": True,
+        "nlayers": kwargs["n_layers"] if "n_layers" in kwargs else 6,
+        "print_every": 5,
+        "validation_period": 5,
+        "meta_tokens": kwargs["meta_tokens"] if "meta_tokens" in kwargs else 1,
+        "same_tasks_across_batch": same_tasks_across_batch,
+    }
+    
+    for key, val in train_config.items():
+        # if key not in config:
+        #     print(f"Adding {key} to config")
+        config[key] = val
+
+    if return_model:
+        config["return_model"] = True
+        return train_pfn(**config)
+    
+    # if "checkpoint_folder" in kwargs:
+    #     return train_continue(kwargs["checkpoint_folder"], config, dirname)
+    if continue_model:
+        return train_continue(continue_scheduler, kwargs["checkpoint_folder"], config, dirname)
+
+    return train_pfn_wrapper(config, dirname)
+
+
+
+
+def create_experiment_dir(args, random_num, continued=False):
+    dirname = args.output_dir
+
+    # name of experiment
+    exp_name = "prior_%s" % args.prior_type
+    exp_name += "__features_%d__tasks_%d" % (args.num_features, args.num_tasks)
+    if args.epochs is not None:
+        exp_name += "__epochs_%d" % args.epochs
+    else:
+        exp_name += "__epochs_None"
+    exp_name += "__seqlen_%d" % args.seq_len
+    exp_name += "__attn_%s" % args.attn_type
+    exp_name += "__task_%s" % args.task_embed_type
+    exp_name += "__seed_%d" % args.seed
+
+    if args.permute_tasks:
+        exp_name += "__permute"
+    
+    if args.prior_type == "combine":
+        for prior_type, weight in zip(args.prior_types, args.prior_weights):
+            weight_string = str(weight).replace(".", "_")
+            exp_name += "__%s_%s" % (prior_type, weight_string)
+    
+    if args.exp_name:
+        exp_name = args.exp_name + "__" + exp_name
+        
+    if args.smoke:
+        exp_name = "smoke_" + exp_name
+    
+    current_time = datetime.datetime.now().strftime("%y-%m-%d_%H-%M-%S")
+    dirname = os.path.join(args.output_dir, current_time + "__" + exp_name)
+    if continued:
+        dirname += "__continued"
+    dirname += "__%d" % random_num
+
+    if not os.path.exists(dirname):
+        os.makedirs(dirname)
+        
+    # save arguments as json
+    with open(os.path.join(dirname ,'args.json'), 'w') as f:
+        json.dump(vars(args), f, indent=4)
+        
+    # # redirect stdout and stderr
+    # if args.redirect:
+    #     sys.stdout = open(os.path.join(dirname, 'stdout.txt'), 'w')
+    #     sys.stderr = open(os.path.join(dirname, 'stderr.txt'), 'w')
+
+    logging.basicConfig(level=logging.INFO, stream=sys.stdout, format='%(asctime)s: %(message)s')
+    logging.info("Experiment directory: %s\n\n" % dirname)
+    
+    return dirname
+
+
+if __name__ == "__main__":
+    argparser = argparse.ArgumentParser()
+    
+    # properties of model
+    argparser.add_argument('--num_features', type=int, default=7)
+    argparser.add_argument('--num_tasks', type=int, default=4)
+    argparser.add_argument('--sample_num_tasks', action='store_true', default=False)
+    argparser.add_argument('--sample_num_tasks_curriculum', action='store_true', default=False)
+    argparser.add_argument('--sample_num_features', action='store_true', default=False)
+    
+    # model architecture
+    argparser.add_argument('--attn_type', type=str, default='standard', help='axial, axial_parallel, or standard')
+    argparser.add_argument('--task_embed_type', type=str, default='hier', help='{linear, onehot_linear, feature, task_attn, task_attn_shared}')
+    # hierarchical
+    argparser.add_argument('--meta_tokens', type=int, default=1)
+    
+    # prior properties
+    argparser.add_argument('--prior_type', type=str, default='mtgp')
+    argparser.add_argument('--target_only_loss', action='store_true', default=False)
+    argparser.add_argument('--same_tasks_across_batch', action='store_true', default=False)
+    # for low-rank
+    argparser.add_argument('--max_rank', type=int, default=5)
+    argparser.add_argument('--decay_alpha', type=float, default=0.2)
+    # for gp-based
+    argparser.add_argument('--lengthscale', type=float, default=None)
+    # for unrelated
+    argparser.add_argument('--uncorr_prob', type=float, default=0.0)
+    # for mtgp-bias
+    argparser.add_argument('--corr_init', type=float, default=None)
+    # for combine prior
+    argparser.add_argument('--prior_types', nargs='+', default=None)
+    argparser.add_argument('--prior_weights', nargs='+', type=float, default=None)
+    # toy
+    argparser.add_argument('--permute_tasks', action='store_true', default=False)
+    
+    # eval properties
+    argparser.add_argument('--eval_type', type=str, default=None)
+    
+    # properties of training
+    argparser.add_argument('--seq_len', type=int, default=400)
+    argparser.add_argument('--single_train_pos_gen_type', type=str, default='uniform')
+    argparser.add_argument('--min_eval_len', type=int, default=200)
+    argparser.add_argument('--criterion_type', type=str, default='bar')
+    
+    # training hyperparameters
+    argparser.add_argument('--lr', type=float, default=0.0001)
+    argparser.add_argument('--epochs', type=int, default=None)
+    argparser.add_argument('--scheduler_type', type=str, default='cosine')
+    argparser.add_argument('--batch_size', type=int, default=64)
+    argparser.add_argument('--steps_per_epoch', type=int, default=1024)
+    argparser.add_argument('--emsize', type=int, default=512)
+    argparser.add_argument('--nhid', type=int, default=2048)
+    argparser.add_argument('--n_layers', type=int, default=24)
+    argparser.add_argument('--seed', type=int, default=0)
+    argparser.add_argument('--full_precision', action='store_true', default=False)
+    
+    argparser.add_argument('--target_aware', action='store_true', default=False)
+    argparser.add_argument('--global_with_target_points', action='store_true', default=False)
+    argparser.add_argument('--local_with_target_points', action='store_true', default=False)
+    
+    # identify run
+    if os.path.exists("/home/yl9959/mtpfn/ckpt"):
+        output_dir = "/home/yl9959/mtpfn/ckpt"
+        wandb_dir = "/home/yl9959/mtpfn/wandb_links"
+    else:
+        output_dir = "/home/lily_l/private_multitask_pfn/ckpt"
+        wandb_dir = "/home/lily_l/private_multitask_pfn/wandb_links"
+    argparser.add_argument('--output_dir', type=str, default=output_dir)
+    argparser.add_argument('--wandb_dir', type=str, default=wandb_dir)
+    
+    argparser.add_argument('--exp_name', type=str, default=None)
+    argparser.add_argument('--smoke', action='store_true', default=False)
+    argparser.add_argument('--pty', action='store_true', default=False)
+    argparser.add_argument('--disable_wandb', action='store_true', default=False)
+    args = argparser.parse_args()
+    
+    
+    random_num = np.random.randint(100000)
+    dirname = create_experiment_dir(args, random_num)
+    config = vars(args)
+    config["dirname"] = dirname
+    # if not args.pty and not args.smoke:
+    #     # redirect stdout and stderr
+    #     sys.stdout = open(os.path.join(dirname, 'stdout.txt'), 'w')
+    #     sys.stderr = open(os.path.join(dirname, 'stderr.txt'), 'w')
+    
+    if args.disable_wandb or args.smoke:
+        wandb_mode = "disabled"
+    else:
+        wandb_mode = "online"
+        
+    wandb.init(
+        project="mtpfn",
+        config=config,
+        mode=wandb_mode,
+    )
+    
+    # make symlink from wandb name to dirname
+    if wandb_mode != "disabled":
+        wandb_name = wandb.run.name
+        wandb_dir = args.wandb_dir
+        os.symlink(dirname, os.path.join(wandb_dir, wandb_name))
+    
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    try:
+        if args.smoke:
+            args.epochs = 5
+            args.steps_per_epoch = 32
+            train(**config, progress_bar=True)
+        else:
+            if args.pty:
+                train(**config, progress_bar=True)
+            else:
+                train(**config)
+    except Exception as e:
+        print("EXCEPTION")
+        # rename directory to indicate failure
+        new_dirname = dirname + "_failed"
+        os.rename(dirname, new_dirname)
+        
+        if wandb_mode != "disabled":
+            # delete original symlink
+            os.remove(os.path.join(wandb_dir, wandb_name))
+            # create new symlink
+            os.symlink(new_dirname, os.path.join(wandb_dir, wandb_name))
+            
+        traceback.print_exc()  # This will print the full traceback
+        # Re-raise the exception with its original stack trace
+        raise
+    
+    # os.rename(dirname, dirname + "_completed")
\ No newline at end of file
diff --git a/private_multitask_pfn/train_continue.py b/private_multitask_pfn/train_continue.py
new file mode 100644
index 0000000..a1dc7dd
--- /dev/null
+++ b/private_multitask_pfn/train_continue.py
@@ -0,0 +1,46 @@
+from train import train_from_checkpoint
+import argparse
+import os
+
+if __name__ == '__main__':
+    argparser = argparse.ArgumentParser()
+    argparser.add_argument('--checkpoint_folder', type=str, default="/home/yl9959/mtpfn/ckpt/25-01-08_09-50-25__prior_mtgp__features_3__tasks_4__epochs_500__seqlen_200__attn_standard__task_onehot_linear__seed_0")
+    argparser.add_argument('--pty', action='store_true', default=False)
+    argparser.add_argument('--disable_wandb', action='store_true', default=False)
+    argparser.add_argument('--epochs', type=int, default=None)
+    argparser.add_argument('--seed', type=int, default=0)
+    argparser.add_argument('--lr', type=float, default=0.0001)
+    argparser.add_argument('--batch_size', type=int, default=None)
+    argparser.add_argument('--seq_len', type=int, default=None)
+    argparser.add_argument('--steps_per_epoch', type=int, default=None)
+    
+    argparser.add_argument('--prior_type', type=str, default=None)
+    argparser.add_argument('--eval_type', type=str, default=None)
+    argparser.add_argument('--same_tasks_across_batch', action='store_true', default=None)
+    # for gp-based
+    argparser.add_argument('--lengthscale', type=float, default=None)
+    # for unrelated
+    argparser.add_argument('--uncorr_prob', type=float, default=None)
+    # for mtgp-bias
+    argparser.add_argument('--corr_init', type=float, default=None)
+    
+    if os.path.exists("/home/yl9959/mtpfn/ckpt"):
+        output_dir = "/home/yl9959/mtpfn/ckpt"
+        wandb_dir = "/home/yl9959/mtpfn/wandb_links"
+    else:
+        output_dir = "/home/lily_l/private_multitask_pfn/ckpt"
+        wandb_dir = "/home/lily_l/private_multitask_pfn/wandb_links"
+    argparser.add_argument('--output_dir', type=str, default=output_dir)
+    argparser.add_argument('--wandb_dir', type=str, default=wandb_dir)
+    argparser.add_argument('--save_val_results', action='store_true', default=False)
+    
+    
+    args = argparser.parse_args()
+    
+    if args.disable_wandb:
+        wandb_mode = "disabled"
+    else:
+        wandb_mode = "online"
+        
+    train_from_checkpoint(**vars(args), wandb_mode=wandb_mode)
+    
\ No newline at end of file
diff --git a/private_multitask_pfn/utils.py b/private_multitask_pfn/utils.py
new file mode 100644
index 0000000..8ca5fe2
--- /dev/null
+++ b/private_multitask_pfn/utils.py
@@ -0,0 +1,781 @@
+import gc
+
+import botorch
+import torch
+# from ax.fb.utils.storage.manifold import AEManifoldUseCase
+# from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
+from botorch.acquisition import LogExpectedImprovement
+from botorch.fit import fit_gpytorch_mll
+
+from botorch.models import SingleTaskGP
+from botorch.models.model import Model
+from botorch.models.multitask import MultiTaskGP
+from botorch.optim.fit import fit_gpytorch_mll_torch
+from botorch.fit import fit_fully_bayesian_model_nuts
+from botorch.optim.optimize import optimize_acqf
+from botorch.posteriors import Posterior
+from botorch.utils.datasets import SupervisedDataset
+# from botorch_fb.experimental.models.scaml import meta_fit_scamlgp, ScaMLGP
+from scaml import meta_fit_scamlgp, ScaMLGP
+from PFNs.pfns.bar_distribution import BarDistribution
+
+from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
+from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
+from train import train as load_model_from_train
+import json
+from lmc import LMCGP
+from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
+from mtgp_nuts import RBFPyroModel, MultitaskRBFPyroModel
+from gpytorch.kernels import RBFKernel
+from gpytorch.priors import GammaPrior
+
+
+class GaussianPosterior(Posterior):
+    def __init__(self, mean, variance):
+        super().__init__()
+        self.mean = mean
+        self.variance = variance
+
+    def rsample(self, sample_shape):
+        return self.mean + torch.sqrt(self.variance) * torch.randn(
+            sample_shape, device=self.mean.device
+        )
+        
+    @property
+    def lower(self):
+        return self.mean - 2 * torch.sqrt(self.variance)
+    
+    @property
+    def upper(self):
+        return self.mean + 2 * torch.sqrt(self.variance)
+
+    @property
+    def dtype(self):
+        return self.mean.dtype
+
+    @property
+    def device(self):
+        return self.mean.device
+    
+    
+class BarPosterior(Posterior):
+    def __init__(self, logits, criterion):
+        super().__init__()
+        self.logits = logits
+        self.criterion = criterion
+
+    def rsample(self, sample_shape):
+        probs = torch.rand(sample_shape)
+        return self.criterion.icdf(self.logits, probs)
+        
+    @property
+    def lower(self):
+        return self.criterion.quantile(self.logits, 0.025)[..., 0]
+    
+    @property
+    def upper(self):
+        return self.criterion.quantile(self.logits, 0.975)[..., 0]
+    
+    @property
+    def mean(self):
+        return self.criterion.mean(self.logits)
+    
+    @property
+    def variance(self):
+        variance = torch.clamp_min(self.criterion.variance(self.logits), 1e-8)
+        return variance
+
+    @property
+    def dtype(self):
+        return self.logits.dtype
+
+    @property
+    def device(self):
+        return self.logits.device
+
+
+class PFNGaussian(Model):
+    def __init__(self, pfn, train_x, train_task_id, train_y):
+        super().__init__()
+        self.pfn = pfn
+        self.train_x = train_x.unsqueeze(1)
+        self.train_task_id = train_task_id.unsqueeze(-1).unsqueeze(1).long()
+        self.train_y = train_y.unsqueeze(1)
+
+    def posterior(
+        self, X, output_indices=None, observation_noise=False, posterior_transform=None
+    ):
+        original_shape = X.shape
+        X_reshape = X.reshape(X.shape[0], -1, X.shape[-1])
+        pfn_outputs = self.pfn(self.train_x, self.train_task_id, self.train_y, X_reshape, None)
+        mean = pfn_outputs[..., 0]
+        variance = pfn_outputs[..., 1].exp()
+        
+        mean = mean.reshape(original_shape[:-1])
+        variance = variance.reshape(original_shape[:-1])
+        return GaussianPosterior(mean, variance)
+
+    @property
+    def num_outputs(self):
+        return 1
+
+
+def load_model(ckpt_dir, best=True):
+    args_json = f"{ckpt_dir}/args.json"
+    with open(args_json, "r") as f:
+        args = json.load(f)
+
+    model = load_model_from_train(**args, return_model=True)[0]
+    default_device = lambda: torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if best:
+        try:
+            model.load_state_dict(torch.load(f"{ckpt_dir}/best_model.pth", weights_only=True, map_location=default_device()))
+        except FileNotFoundError:
+            model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True, map_location=default_device()))
+        except RuntimeError:
+            try:
+                new_state_dict = torch.load(f"{ckpt_dir}/best_model.pth", map_location=default_device())
+                new_state_dict_keys = new_state_dict.keys()
+                new_state_dict = {k.replace("module.", ""): v for k, v in new_state_dict.items()}
+                model.load_state_dict(new_state_dict, strict=True)
+            except Exception as e:
+                raise e
+    else:
+        model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True, map_location=default_device()))
+    
+    print("Loaded model")
+    return model
+
+
+def load_model_from_epoch(ckpt_dir, epoch):
+    args_json = f"{ckpt_dir}/args.json"
+    with open(args_json, "r") as f:
+        args = json.load(f)
+
+    model = load_model_from_train(**args, return_model=True)[0]
+    default_device = lambda: torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.load_state_dict(torch.load(f"{ckpt_dir}/model_{epoch}.pth", weights_only=True, map_location=default_device()))
+
+    print("Loaded model")
+    return model
+
+
+def load_checkpoint(ckpt_dir):
+    
+    args_json = f"{ckpt_dir}/args.json"
+    with open(args_json, "r") as f:
+        args = json.load(f)
+
+    model, scheduler, optimizer = load_model_from_train(**args, return_model=True)
+    
+    ckpt = torch.load(f"{ckpt_dir}/checkpoint.pth")
+    model.load_state_dict(ckpt['model_state_dict'])
+    scheduler.load_state_dict(ckpt['scheduler_state_dict'])
+    optimizer.load_state_dict(ckpt['optimizer_state_dict'])
+    
+    print("Loaded checkpoint")
+    return model, scheduler, optimizer, ckpt['epoch']
+
+
+def to_pfn_format(num_tasks, *args):
+    """
+    Expects task1, x1, y1, task2, x2, y2, ...
+    where x1, x2, ... are [seq, features]
+
+    Returns x, y, where x is [batch, seq, features + tasks] and y is [batch, seq]
+    """
+    assert len(args) % 3 == 0 and len(args) > 0
+
+    results = []
+    for i in range(len(args) // 3):
+        task_id, x, y = args[3 * i], args[3 * i + 1], args[3 * i + 2]
+
+        one_hot_task_id = torch.nn.functional.one_hot(
+            task_id.long(), num_classes=num_tasks
+        )
+        id_x = torch.cat((one_hot_task_id, x), -1)
+
+        # add batch dimension
+        results.append(id_x.unsqueeze(1))
+        results.append(y.unsqueeze(1))
+
+    return results
+
+
+def to_mtgp_format(*args):
+    """
+    Expects task1, x1, y1, task2, x2, y2, ...
+    where x1, x2, ... are [seq, features]
+
+    Returns x, y, where x is [batch, seq, features + 1] and y is [batch, seq]
+    """
+    assert len(args) % 3 == 0 and len(args) > 0
+
+    results = []
+    for i in range(len(args) // 3):
+        task_id, x, y = args[3 * i], args[3 * i + 1], args[3 * i + 2]
+
+        # x is [seq, features]
+        # if len(x.shape) > 2:
+        #     x = x.transpose(0, 1)
+        #     y = y.transpose(0, 1)
+
+        task_id = torch.ones_like(x[..., 0]) * task_id.to(x.device)
+        x_task_id = torch.cat((x, task_id.unsqueeze(-1)), -1)
+        results.append(x_task_id)
+        results.append(y)
+
+    return results
+
+
+def to_gp_format(*args):
+    """
+    Expects task1, x1, y1, task2, x2, y2, ...
+    where x1, x2, ... are [seq, features]
+
+    Returns x, y, where x is [batch, seq, features] and y is [batch, seq]
+    Only over target task (task id = 0)
+    """
+    assert len(args) % 3 == 0 and len(args) > 0
+
+    results = []
+    for i in range(len(args) // 3):
+        task_id, x, y = args[3 * i], args[3 * i + 1], args[3 * i + 2]
+
+        # x is [seq, features]
+        # if len(x.shape) > 2:
+        #     x = x.transpose(0, 1)
+        #     y = y.transpose(0, 1)
+
+        target_indices = task_id == 0
+
+        target_x = x[target_indices]
+        target_y = y[target_indices]  # .unsqueeze(1)
+        results.append(target_x)
+        results.append(target_y)
+        
+    # print([r.shape for r in results])
+
+    return results
+
+
+def pfn_bar_get_best(
+    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+):
+    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
+    train_x, train_y, possible_x, possible_y = to_pfn_format(
+        pfn.num_tasks,
+        task_id,
+        train_x,
+        train_y,
+        possible_task_id,
+        possible_x,
+        possible_y,
+    )
+    target_indices = task_id == 0
+    logits = pfn(train_x, train_y, possible_x)
+    eis = pfn.criterion.ei(logits, train_y[target_indices].max())
+    best_index = eis.argmax()
+    return original_possible_x[best_index], original_possible_y[best_index]
+
+
+def pfn_gaussian_fit(pfn, task_id, train_x, train_y):
+    return PFNGaussian(pfn, train_x, task_id, train_y)
+
+
+def pfn_bar_get_best(
+    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+):
+    # add batch dimension
+    train_x = train_x.unsqueeze(1)
+    train_task_id = task_id.unsqueeze(-1).unsqueeze(1).long()
+    train_y = train_y.unsqueeze(1)
+    possible_x = possible_x.unsqueeze(1)
+    
+    output_logits = pfn(train_x, train_task_id, train_y, possible_x, None)#.squeeze(1)
+    
+    target_indices = task_id == 0
+    best_f = train_y[target_indices].max()
+    
+    ei = pfn.criterion.ei(output_logits, best_f)
+    best_index = ei.argmax()
+    return possible_x[best_index].squeeze(1).squeeze(0), possible_y[best_index]
+    
+
+
+def pfn_gaussian_get_best(
+    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+):
+    pfn_model = pfn_gaussian_fit(pfn, task_id, train_x, train_y)
+
+    target_indices = task_id == 0
+    best_f = train_y[target_indices].max()
+
+    ei = LogExpectedImprovement(pfn_model, best_f=best_f)
+    eis = ei(possible_x.unsqueeze(1))
+    
+    best_index = eis.argmax()
+    return possible_x[best_index], possible_y[best_index]
+
+
+def pfn_get_best(
+    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+):
+    if isinstance(pfn.criterion, BarDistribution):
+        return pfn_bar_get_best(
+            pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+        )
+    else:
+        return pfn_gaussian_get_best(
+            pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+        )
+        
+        
+def pfn_bar_predict(pfn, task_id, train_x, train_y, test_x):
+    train_x = train_x.unsqueeze(1)
+    train_task_id = task_id.unsqueeze(-1).unsqueeze(1).long()
+    train_y = train_y.unsqueeze(1)
+    test_x = test_x.unsqueeze(1)
+    
+    output_logits = pfn(train_x, train_task_id, train_y, test_x, None)
+    # BarPosterior(output_logits, pfn.criterion)
+    # mean, variance = pfn.criterion.mean(output_logits), pfn.criterion.variance(output_logits)
+    return BarPosterior(output_logits, pfn.criterion)
+        
+        
+def pfn_predict(pfn, task_id, train_x, train_y, possible_x):
+    with torch.no_grad():
+        if isinstance(pfn.criterion, BarDistribution):
+            return pfn_bar_predict(pfn, task_id, train_x, train_y, possible_x)
+        else:
+            pfn_model = pfn_gaussian_fit(pfn, task_id, train_x, train_y)
+            return pfn_model.posterior(possible_x.unsqueeze(1))
+
+
+def mtgp_fit(task_id, train_x, train_y):
+    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
+    train_yvar = torch.ones_like(train_y) * 1e-5
+    mtgp = MultiTaskGP(
+        train_x,
+        train_y,
+        task_feature=-1,
+        train_Yvar=train_yvar,
+        outcome_transform=None,
+        output_tasks=[0],
+    ).to(train_x)
+    mll = ExactMarginalLogLikelihood(mtgp.likelihood, mtgp)
+    try:
+        fit_gpytorch_mll(mll)
+    except botorch.exceptions.ModelFittingError:
+        fit_gpytorch_mll_torch(mll)
+
+    return mtgp
+
+
+def mtgp_predict(task_id, train_x, train_y, possible_x):
+    mtgp = mtgp_fit(task_id, train_x, train_y)
+    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
+    return mtgp.posterior(possible_x)
+
+
+def mtgp_get_best(task_id, train_x, train_y, possible_task_id, possible_x, possible_y):
+    gc.collect()
+
+    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
+    train_x, train_y, possible_x, possible_y = to_mtgp_format(
+        task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+    )
+    
+    mtgp = mtgp_fit(task_id, train_x, train_y)
+    ei = LogExpectedImprovement(mtgp, best_f=train_y[task_id == 0].max())
+
+    batch_size = max(10, 1000 - len(train_x))
+    possible_xs = possible_x.unsqueeze(1).split(
+        batch_size
+    )  # add batch dimension for EI, split for memory
+    best_value = -1.0
+    best_index = None
+    for i, x_batch in enumerate(possible_xs):
+        max_value, max_index = ei(x_batch).max(0)
+        if max_value > best_value or best_index is None:
+            best_index = i * batch_size + max_index
+            best_value = max_value
+
+    gc.collect()
+    assert best_index is not None
+
+    return original_possible_x[best_index], original_possible_y[best_index]
+
+
+def gp_fit(task_id, train_x, train_y):
+    train_x, train_y = to_gp_format(task_id, train_x, train_y)
+    train_yvar = torch.ones_like(train_y) * 1e-5
+    covar_module = RBFKernel(lengthscale_prior=GammaPrior(3.0, 6.0))
+    gp = SingleTaskGP(train_x, train_y, train_yvar, outcome_transform=None).to(train_x)
+    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
+    try:
+        fit_gpytorch_mll(mll)
+    except botorch.exceptions.ModelFittingError:
+        fit_gpytorch_mll_torch(mll)
+
+    return gp
+
+
+def gp_predict(task_id, train_x, train_y, possible_x):
+    gp = gp_fit(task_id, train_x, train_y)
+    possible_x, _ = to_gp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
+    return gp.posterior(possible_x)
+
+
+def gp_get_best(
+    train_task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+):
+    gp = gp_fit(train_task_id, train_x, train_y)
+    possible_x, possible_y = to_gp_format(
+        possible_task_id, possible_x, possible_y
+    )
+
+    ei = LogExpectedImprovement(gp, best_f=train_y.max())
+    best_index = ei(possible_x.unsqueeze(1)).argmax()  # add batch dimension
+    return possible_x[best_index], possible_y[best_index]
+
+
+
+def scaml_fit(task_id, train_x, train_y):
+    meta_data = {}
+    for task in torch.unique(task_id):
+        if task == 0:
+            continue
+        source_x = train_x[task_id == task]
+        source_y = train_y[task_id == task]
+        source_yvar = torch.ones_like(source_y) * 1e-5
+        dataset = SupervisedDataset(
+            source_x,
+            source_y,
+            feature_names=["x"] * source_x.shape[-1],
+            outcome_names=["y"],
+            Yvar=source_yvar,
+        )
+        meta_data[task] = dataset
+
+    source_gps = meta_fit_scamlgp(meta_data)
+
+    target_x = train_x[task_id == 0]
+    target_y = train_y[task_id == 0]
+    target_yvar = torch.ones_like(target_y) * 1e-5
+
+    gp = ScaMLGP(
+        target_x,
+        target_y,
+        source_gps=source_gps,
+        likelihood=FixedNoiseGaussianLikelihood(target_yvar),
+    ).to(target_x)
+    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
+    fit_gpytorch_mll(mll)
+
+    return gp
+
+
+def scaml_predict(task_id, train_x, train_y, possible_x):
+    gp = scaml_fit(task_id, train_x, train_y)
+    return gp.posterior(possible_x.to(train_x))
+
+
+def scaml_get_best(
+    train_task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+):
+    gp = scaml_fit(train_task_id, train_x, train_y)
+    target_y = train_y[train_task_id == 0]
+
+    ei = LogExpectedImprovement(gp, best_f=target_y.max())
+    best_index = ei(possible_x.unsqueeze(1).to(train_x)).argmax()  # add batch dimension
+    return possible_x[best_index], possible_y[best_index]
+
+
+def standardize_by_task(train_y, task_id, test_y=None):
+    standardized_y = train_y.clone()
+    if test_y is not None:
+        std_test_y = test_y.clone()
+    for task in torch.unique(task_id):
+        task_mask = task_id == task
+        task_ys = train_y[task_mask]
+
+        std = task_ys.std(0)
+        std = torch.where(std.isnan(), torch.tensor(1.0), std)
+        std = torch.where(std < 1e-5, torch.tensor(1.0), std)
+        standardized_y[task_mask] = (task_ys - task_ys.mean(0)) / std
+        if test_y is not None and task == 0:
+            std_test_y = (test_y - task_ys.mean(0)) / std
+
+    if test_y is not None:
+        return standardized_y, std_test_y
+    return standardized_y
+
+
+def create_train_test(target_xs, target_ys, sources_xs, sources_ys, n_target, n_source):
+    train_id = []
+    train_x = []
+    train_y = []
+    for i, (source_xs, source_ys) in enumerate(zip(sources_xs, sources_ys)):
+        random_indices = torch.randperm(len(source_xs))[:n_source]
+        train_x.append(source_xs[random_indices])
+        train_y.append(source_ys[random_indices])
+        train_id.append(torch.ones(n_source) * i + 1)
+
+    # add target task
+    random_indices = torch.randperm(len(target_xs))
+    train_indices = random_indices[:n_target]
+    test_indices = random_indices[n_target:]
+    train_x.append(target_xs[train_indices])
+    train_y.append(target_ys[train_indices])
+    train_id.append(torch.zeros(n_target))
+
+    train_x = torch.concat(train_x, 0)
+    train_id = torch.concat(train_id, 0)
+
+    train_y = torch.concat(train_y, 0)
+
+    test_id = torch.zeros(len(test_indices))
+    test_x = target_xs[test_indices]
+    test_y = target_ys[test_indices]
+
+    return (
+        train_id,
+        train_x,
+        train_y,
+        test_id,
+        test_x,
+        test_y,
+    )
+
+
+def lmc_fit(task_id, train_x, train_y):
+    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
+    train_yvar = torch.ones_like(train_y) * 1e-5
+    num_tasks = len(torch.unique(task_id))
+    
+    best_mll = None
+    best_mtgp = None
+    
+    for i in range(1, num_tasks):
+        # Create model with custom kernel
+        lmc = LMCGP(
+            train_x,
+            train_y,
+            task_feature=-1,
+            num_covars=i,
+            train_Yvar=train_yvar,
+            outcome_transform=None,
+            output_tasks=[0],
+        )
+        mll = ExactMarginalLogLikelihood(lmc.likelihood, lmc)
+        try:
+            fit_gpytorch_mll(mll)
+        except botorch.exceptions.ModelFittingError:
+            fit_gpytorch_mll_torch(mll)
+            
+        mll_value = mll(lmc(train_x), train_y).sum()
+        if best_mll is None or mll_value > best_mll:
+            best_mll = mll_value
+            best_mtgp = lmc
+
+    return best_mtgp
+
+
+def lmc_predict(task_id, train_x, train_y, possible_x):
+    lmc = lmc_fit(task_id, train_x, train_y)
+    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
+    return lmc.posterior(possible_x)
+
+
+def lmc_get_best(task_id, train_x, train_y, possible_task_id, possible_x, possible_y):
+    gc.collect()
+
+    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
+    train_x, train_y, possible_x, possible_y = to_mtgp_format(
+        task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+    )
+    
+    lmc = lmc_fit(task_id, train_x, train_y)
+    ei = LogExpectedImprovement(lmc, best_f=train_y[task_id == 0].max())
+
+    batch_size = max(10, 1000 - len(train_x))
+    possible_xs = possible_x.unsqueeze(1).split(
+        batch_size
+    )  # add batch dimension for EI, split for memory
+    best_value = -1.0
+    best_index = None
+    for i, x_batch in enumerate(possible_xs):
+        max_value, max_index = ei(x_batch).max(0)
+        if max_value > best_value or best_index is None:
+            best_index = i * batch_size + max_index
+            best_value = max_value
+
+    gc.collect()
+    assert best_index is not None
+
+    return original_possible_x[best_index], original_possible_y[best_index]
+
+
+def mtgp_nuts_fit(task_id, train_x, train_y):
+    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
+    train_yvar = torch.ones_like(train_y) * 1e-5
+    mtgp = SaasFullyBayesianMultiTaskGP(
+        train_x,
+        train_y,
+        task_feature=-1,
+        train_Yvar=train_yvar,
+        outcome_transform=botorch.models.transforms.outcome.Standardize(m=1),
+        output_tasks=[0],
+        pyro_model=MultitaskRBFPyroModel(),
+    )
+    try:
+        fit_fully_bayesian_model_nuts(mtgp)
+    except RuntimeError:
+        train_yvar = torch.ones_like(train_y) * 1e-4
+        mtgp = SaasFullyBayesianMultiTaskGP(
+            train_x,
+            train_y,
+            task_feature=-1,
+            train_Yvar=train_yvar,
+            outcome_transform=botorch.models.transforms.outcome.Standardize(m=1),
+            output_tasks=[0],
+            pyro_model=MultitaskRBFPyroModel(),
+        )
+        fit_fully_bayesian_model_nuts(mtgp)
+
+    return mtgp
+
+
+def mtgp_nuts_predict(task_id, train_x, train_y, possible_x):
+    mtgp = mtgp_nuts_fit(task_id, train_x, train_y)
+    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
+    return mtgp.posterior(possible_x)
+
+
+def mtgp_nuts_get_best(task_id, train_x, train_y, possible_task_id, possible_x, possible_y):
+    gc.collect()
+
+    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
+    train_x, train_y, possible_x, possible_y = to_mtgp_format(
+        task_id, train_x, train_y, possible_task_id, possible_x, possible_y
+    )
+    
+    mtgp = mtgp_nuts_fit(task_id, train_x, train_y)
+    ei = LogExpectedImprovement(mtgp, best_f=train_y[task_id == 0].max())
+
+    batch_size = max(10, 1000 - len(train_x))
+    possible_xs = possible_x.unsqueeze(1).split(
+        batch_size
+    )  # add batch dimension for EI, split for memory
+    best_value = -1.0
+    best_index = None
+    for i, x_batch in enumerate(possible_xs):
+        max_value, max_index = ei(x_batch).max(0)
+        if max_value > best_value or best_index is None:
+            best_index = i * batch_size + max_index
+            best_value = max_value
+
+    gc.collect()
+    assert best_index is not None
+
+    return original_possible_x[best_index], original_possible_y[best_index]
+
+
+def tabpfn_fit(task_id, train_x, train_y):
+    from tabpfn import TabPFNClassifier, TabPFNRegressor
+    # onehot task_id
+    onehot = torch.nn.functional.one_hot(task_id.long(), num_classes=task_id.max() + 1)
+    train_x = torch.cat((onehot, train_x), -1)
+    train_y = train_y.unsqueeze(1)
+    
+    tabpfn = TabPFNRegressor(model_path="/home/lily_l/private_multitask_pfn/TabPFN/tabpfn-v2-regressor.ckpt").fit(train_x, train_y)
+    # # https://github.com/PriorLabs/TabPFN/blob/c8959619f0b6e62614c5a1aaa8f41c0a5ac725d3/src/tabpfn/regressor.py#L84
+    # output = tabpfn.predict(train_x, output_type="full")
+    # quantiles = tabpfn.predict(train_x, output_type="quantiles", quantiles=[0.025, 0.5, 0.975])
+    
+    # !pip install tabpfn
+    return tabpfn
+    
+def tabpfn_predict(task_id, train_x, train_y, possible_x):
+    tabpfn = tabpfn_fit(task_id, train_x, train_y)
+    
+    test_task_id = torch.zeros(possible_x.size(0)).long()
+    onehot = torch.nn.functional.one_hot(test_task_id.long(), num_classes=task_id.max() + 1)
+    possible_x = torch.cat((onehot, possible_x), -1)
+    # output = tabpfn.predict(possible_x, output_type="full")
+    quantiles = tabpfn.predict(possible_x, output_type="quantiles", quantiles=[0.025, 0.5, 0.975])
+
+    return quantiles
+    
+
+
+
+def plot_multitask(ax, test_info, model_dim):
+    train_x, train_task_id, train_y, test_x, test_task_id, test_y = test_info
+    
+    if model_dim > 1:
+        # pad train and test with 0s
+        padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)
+        padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)
+    else:
+        padded_train_x = train_x
+        padded_test_x = test_x
+        
+    # train_x, train_task_id, train_y, test_x, 
+    lower, median, upper = tabpfn_predict(train_task_id, padded_train_x, train_y, padded_test_x)
+    
+    ax.plot(test_x, test_y, label="true", color="C0")
+    ax.plot(test_x, median, label="mean", color="C1")
+    ax.fill_between(test_x.flatten(), lower, upper, alpha=0.2, color="C1")
+    # ax.plot(test_x, mean, label="mean", color="C1")
+    # ax.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
+    
+    for i in train_task_id.unique():
+        mask = train_task_id == i
+        marker = "x" if i.item() == 0 else "o"
+        size = 100 if i.item() == 0 else 50
+        ax.scatter(train_x[mask], train_y[mask], label=f"train task {i.item()}", color=f"C{i}", marker=marker, s=size)
+
+
+def get_multitask_test_function(seed=0):
+    from gpytorch.kernels import RBFKernel
+    from torch.distributions import MultivariateNormal
+    
+    n_features = 1
+    n_tasks = 3
+    with botorch.manual_seed(seed):
+        # n_samples = torch.randint(10, 40, (1,)).item()
+        n_samples = 50
+        
+        train_xs = torch.rand(n_samples, n_features)
+        train_task_id = torch.randint(n_tasks, size=(n_samples,)).unsqueeze(1).long()
+        test_xs = torch.linspace(0, 1, 100).view(-1, n_features)
+        test_task_id = torch.zeros(test_xs.size(0), 1).long()
+        xs = torch.cat([train_xs, test_xs], dim=0)
+        task_id = torch.cat([train_task_id, test_task_id], dim=0)
+        
+        rbf = RBFKernel()
+        rbf.lengthscale = torch.tensor(0.2)
+        covar_x = rbf(xs)
+        task_covar_matrix = torch.ones(n_tasks, n_tasks) * 0.9
+        task_covar_matrix += torch.eye(n_tasks) * 0.1
+        
+        covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
+        covar = covar_x.mul(covar_t).evaluate()
+        covar = covar + 1e-4 * torch.eye(covar.size(0))
+        ys = MultivariateNormal(torch.zeros(len(covar)), covar).sample()
+
+        train_y = ys[:n_samples]
+        test_y = ys[n_samples:]
+        
+    return train_xs, train_task_id.squeeze(), train_y, test_xs, test_task_id.squeeze(), test_y
+
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+    seed = 0
+    test_info = get_multitask_test_function(seed)
+    fig, ax = plt.subplots()
+    plot_multitask(ax, test_info, 1)
+    plt.savefig("tabpfn.png")

From 089448762460bac358f2e5ccf8a33c396a9e51d5 Mon Sep 17 00:00:00 2001
From: Eytan Bakshy <ebakshy@gmail.com>
Date: Sun, 25 Jan 2026 23:52:34 -0500
Subject: [PATCH 2/2] Add KV cache support for efficient parallel PFN
 evaluation

Implements context caching for PFN models to reduce memory usage and
improve performance during parallel evaluations, such as multi-start
optimization in Bayesian optimization.

The key insight is that when evaluating many candidate points against
the same training data, we can cache the key-value representations from
the training context and reuse them across all parallel evaluations,
rather than recomputing them for each candidate.

Changes:
- Multi-head attention now supports caching and reusing KV representations
- Encoder state can be saved/restored for MultivariatePFNModel
- PFNModel gains `cache_training_context()` context manager for easy cache management
- Added discretized acquisition functions compatible with Riemann posteriors
---
 botorch_community/acquisition/discretized.py  |  306 ++
 .../models/prior_fitted_network.py            |  966 +++++++
 .../tests/test_botorch_pfn_caching.py         |  545 ++++
 pfns/batch_shape_sampler.py                   |    1 -
 pfns/model/bar_distribution.py                |   79 +-
 pfns/model/encoders.py                        |  151 +-
 pfns/model/layer.py                           |   32 +-
 pfns/model/multi_head_attention.py            |  233 +-
 pfns/model/transformer.py                     |  255 +-
 pfns/model/transformer_config.py              |   15 +
 .../condition_on_area_of_opt_continuous.py    |   76 +-
 pfns/priors/convert_prior_to_x_only_format.py |  120 +
 pfns/priors/data_loading.py                   |    9 +-
 pfns/priors/formula/get_batch.py              |    7 +-
 pfns/priors/formula/ops.py                    |   52 +-
 pfns/priors/formula/trees.py                  |    4 +-
 pfns/priors/heteroskedastic_prior.py          |  239 ++
 pfns/priors/hyperparameter_sampling.py        |   30 +-
 pfns/priors/path_stgp.py                      |  477 ++++
 pfns/priors/path_trace_sampling.py            |  183 ++
 pfns/priors/prior.py                          |   36 +-
 pfns/priors/saas.py                           |  105 +
 pfns/priors/singletaskgp.py                   |  441 +++
 pfns/priors/singletaskgp_x_only.py            |  281 ++
 pfns/priors/small_peaks.py                    |  122 +
 pfns/run_training_cli.py                      |   12 +-
 pfns/train.py                                 |  125 +-
 pfns/utils.py                                 |   28 +-
 private_multitask_pfn/.gitignore              |   18 -
 private_multitask_pfn/1d.ipynb                | 1005 -------
 private_multitask_pfn/1d_hist.py              |   60 -
 private_multitask_pfn/PFNs/LICENSE            |  201 --
 private_multitask_pfn/PFNs/README.md          |   99 -
 .../PFNs/Tutorial_Training_for_BO.ipynb       | 1027 -------
 .../PFNs/pfns/bar_distribution.py             |  578 ----
 private_multitask_pfn/PFNs/pfns/config.json   |   56 -
 private_multitask_pfn/PFNs/pfns/decoders.py   |   35 -
 private_multitask_pfn/PFNs/pfns/encoders.py   |  642 -----
 .../PFNs/pfns/hierarchical.py                 |  335 ---
 .../PFNs/pfns/initializers.py                 |   11 -
 private_multitask_pfn/PFNs/pfns/layer.py      |  475 ----
 .../PFNs/pfns/pfn_bo_bayesmark.py             |  508 ----
 .../PFNs/pfns/positional_encodings.py         |  344 ---
 .../PFNs/pfns/priors/__init__.py              |    3 -
 .../pfns/priors/condition_on_area_of_opt.py   |   63 -
 .../condition_on_area_of_opt_continuous.py    |   76 -
 .../PFNs/pfns/priors/fast_gp.py               |  203 --
 private_multitask_pfn/PFNs/pfns/priors/gp.py  |   69 -
 .../PFNs/pfns/priors/hebo_prior.py            |  613 ----
 .../pfns/priors/hyperparameter_sampling.py    |  113 -
 .../PFNs/pfns/priors/input_warping.py         |   93 -
 .../PFNs/pfns/priors/nonmyopic_bo.py          |  159 --
 .../PFNs/pfns/priors/normalize_with_style.py  |   59 -
 .../PFNs/pfns/priors/prior.py                 |   99 -
 .../PFNs/pfns/priors/prior_bag.py             |   45 -
 .../PFNs/pfns/priors/simple_mlp.py            |  126 -
 .../PFNs/pfns/priors/utils.py                 |  438 ---
 .../pfns/scripts/acquisition_functions.py     |  506 ----
 .../PFNs/pfns/scripts/tabpfn_interface.py     |  540 ----
 .../PFNs/pfns/scripts/tabpfn_model_builder.py |   54 -
 .../PFNs/pfns/scripts/tabular_metrics.py      |  226 --
 .../PFNs/pfns/scripts/tune_input_warping.py   |  336 ---
 private_multitask_pfn/PFNs/pfns/train.py      |  878 ------
 .../PFNs/pfns/transformer.py                  |  835 ------
 private_multitask_pfn/PFNs/pfns/utils.py      |  520 ----
 private_multitask_pfn/PFNs/pyproject.toml     |   43 -
 private_multitask_pfn/PFNs/setup.py           |    4 -
 .../benchmark_time_mtgp_samples.csv           |   11 -
 .../benchmark_time_mtgp_task.csv              |    6 -
 .../benchmark_time_scaml_samples.csv          |   11 -
 .../benchmark_time_scaml_task.csv             |    6 -
 private_multitask_pfn/eval.py                 | 1070 -------
 private_multitask_pfn/eval_axial.py           |   85 -
 private_multitask_pfn/eval_axial_multitask.py |  131 -
 private_multitask_pfn/eval_f.py               |  284 --
 private_multitask_pfn/eval_fcnet.py           |  125 -
 private_multitask_pfn/eval_hpobench.py        |  201 --
 private_multitask_pfn/eval_plot_util.py       |  315 ---
 private_multitask_pfn/eval_task.py            |  139 -
 private_multitask_pfn/eval_vis.py             |  215 --
 private_multitask_pfn/gen_axial_batch.py      |   96 -
 private_multitask_pfn/gen_batch.py            | 1083 -------
 private_multitask_pfn/gen_task_batch.py       |  275 --
 private_multitask_pfn/lmc.py                  |  316 ---
 .../load_pfn4bo_checkpoints.py                |   91 -
 private_multitask_pfn/mtgp_nuts.py            |  323 ---
 ...l Evaluation on Discrete MF Problems.ipynb | 1843 ------------
 .../multifidelity/borehole_res.pkl            |  Bin 267 -> 0 bytes
 .../multifidelity/mf lily.ipynb               | 1662 -----------
 .../multifidelity/multifidelity.py            |  448 ---
 .../multifidelity/multifidelity_fixed.py      |  492 ----
 .../multifidelity/wing_weight_res.pkl         |  Bin 267 -> 0 bytes
 .../paper_figures/benchmark_time.ipynb        |  273 --
 .../paper_figures/benchmark_time_samples.csv  |   11 -
 .../paper_figures/benchmark_time_task.csv     |   17 -
 .../paper_figures/figures_bayesian.py         |  428 ---
 .../paper_figures/figures_benchmarks.py       |  449 ---
 .../paper_figures/figures_boxplot_data.pkl    |  Bin 1118 -> 0 bytes
 .../paper_figures/figures_finetune.ipynb      |  833 ------
 .../paper_figures/figures_hpobench.py         |  420 ---
 .../paper_figures/figures_uncorr.py           |  537 ----
 .../paper_figures/joint_data.py               |  483 ----
 private_multitask_pfn/paper_figures/nlls.pkl  |  Bin 686 -> 0 bytes
 .../pd1_gp_surrogates.pickle                  |  Bin 2152569 -> 0 bytes
 private_multitask_pfn/pd1_gp_surrogates.pth   |  Bin 53658 -> 0 bytes
 private_multitask_pfn/pfn.yml                 |  199 --
 private_multitask_pfn/scaml.py                |  435 ---
 private_multitask_pfn/task_embedding.ipynb    | 2065 --------------
 private_multitask_pfn/train.py                |  638 -----
 private_multitask_pfn/train_continue.py       |   46 -
 private_multitask_pfn/utils.py                |  781 ------
 rl/function_sampler.py                        |   24 +
 rl/function_samplers/singletaskgp.py          |  101 +
 rl/run_training_cli.py                        |  250 ++
 rl/train_rl.py                                | 2488 +++++++++++++++++
 rl/utils.py                                   |   33 +
 test_convert_prior_to_x_only_format.py        |    0
 tests/model/test_attention.py                 |   56 +-
 tests/model/test_pfn_caching.py               |  477 ++++
 .../test_convert_prior_to_x_only_format.py    |  424 +++
 120 files changed, 8490 insertions(+), 28631 deletions(-)
 create mode 100644 botorch_community/acquisition/discretized.py
 create mode 100644 botorch_community/models/prior_fitted_network.py
 create mode 100644 botorch_community/tests/test_botorch_pfn_caching.py
 create mode 100644 pfns/priors/convert_prior_to_x_only_format.py
 create mode 100644 pfns/priors/heteroskedastic_prior.py
 create mode 100644 pfns/priors/path_stgp.py
 create mode 100644 pfns/priors/path_trace_sampling.py
 create mode 100644 pfns/priors/saas.py
 create mode 100644 pfns/priors/singletaskgp.py
 create mode 100644 pfns/priors/singletaskgp_x_only.py
 create mode 100644 pfns/priors/small_peaks.py
 delete mode 100644 private_multitask_pfn/.gitignore
 delete mode 100644 private_multitask_pfn/1d.ipynb
 delete mode 100644 private_multitask_pfn/1d_hist.py
 delete mode 100644 private_multitask_pfn/PFNs/LICENSE
 delete mode 100644 private_multitask_pfn/PFNs/README.md
 delete mode 100644 private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb
 delete mode 100644 private_multitask_pfn/PFNs/pfns/bar_distribution.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/config.json
 delete mode 100644 private_multitask_pfn/PFNs/pfns/decoders.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/encoders.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/hierarchical.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/initializers.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/layer.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/positional_encodings.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/__init__.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/fast_gp.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/gp.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/input_warping.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py
 delete mode 100644 private_multitask_pfn/PFNs/pfns/priors/prior.py
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diff --git a/botorch_community/acquisition/discretized.py b/botorch_community/acquisition/discretized.py
new file mode 100644
index 0000000..b80eea2
--- /dev/null
+++ b/botorch_community/acquisition/discretized.py
@@ -0,0 +1,306 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+r"""Discretized acquisition functions for Riemann-distributed posteriors.
+
+NOTE: This module should eventually be moved to:
+    botorch_community/acquisition/discretized.py
+in the meta-pytorch/botorch repository.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+import torch
+from botorch.acquisition import AcquisitionFunction
+from botorch.acquisition.objective import (
+    PosteriorTransform,
+    ScalarizedPosteriorTransform,
+)
+from botorch.exceptions.errors import UnsupportedError
+from botorch.models.model import Model
+from botorch.utils.transforms import (
+    average_over_ensemble_models,
+    t_batch_mode_transform,
+)
+from torch import Tensor
+
+
+class DiscretizedAcquisitionFunction(AcquisitionFunction, ABC):
+    r"""DiscretizedAcquisitionFunction is an abstract base class for acquisition
+    functions that are defined on discrete distributions. It wraps a model and
+    implements a forward method that computes the acquisition function value at
+    a given set of points.
+    This class can be subclassed to define acquisition functions for Riemann-
+    distributed posteriors.
+    The acquisition function must have the form $$acq(x) = \int p(y|x) ag(x)$$,
+    where $$ag$$ is defined differently for each acquisition function.
+    The ag_integrate method, which computes the integral of ag between two points, must
+    be implemented by subclasses to define the specific acquisition functions.
+    """
+
+    def __init__(
+        self,
+        model: Model,
+        posterior_transform: PosteriorTransform,
+        assume_symmetric_posterior: bool = True,
+    ) -> None:
+        r"""
+        Initialize the DiscretizedAcquisitionFunction
+
+        Args:
+            model: A fitted model that is used to compute the posterior
+                distribution over the outcomes of interest.
+                The model should be a ``PFNModel``.
+            posterior_transform: A ScalarizedPosteriorTransform that can only
+                indicate minimization or maximization of the objective.
+            assume_symmetric_posterior: If True, we simply negate train y, if
+                the task is to minimize the objective. Else, we use a proper
+                posterior transform. We cannot do this generally, as some
+                models only support maximization. This does not mean that
+                the posterior distribution for a particular set is symmetric
+                but that one can negate the y's of the context and get out
+                negated ys.
+        """
+        super().__init__(model=model)
+        self.set_X_pending(None)
+        self.assume_symmetric_posterior = assume_symmetric_posterior
+        self.maximize = True
+        if posterior_transform is not None:
+            unsupported_error_message = (
+                "Only scalarized posterior transforms with a"
+                "single objective and 0.0 offset are supported."
+            )
+            if (
+                not isinstance(posterior_transform, ScalarizedPosteriorTransform)
+                or (posterior_transform.offset != 0.0)
+                or len(posterior_transform.weights) != 1
+                or posterior_transform.weights[0] not in [-1.0, 1.0]
+            ):
+                raise UnsupportedError(unsupported_error_message)
+
+            self.maximize = posterior_transform.weights[0] == 1.0
+
+    @t_batch_mode_transform()
+    @average_over_ensemble_models
+    def forward(self, X: Tensor) -> Tensor:
+        r"""Evaluate the acquisition function on the candidate set X.
+
+        Args:
+            X: A ``(b) x q x d``-dim Tensor of ``(b)`` t-batches with ``q`` ``d``-dim
+                design points each.
+
+        Returns:
+            A ``(b)``-dim Tensor of the acquisition function at the given
+            design points ``X``.
+        """
+        # Note: pending_X is not supported by PFNModel.posterior()
+        # If X_pending is set, it would need to be handled differently
+        discrete_posterior = self.model.posterior(
+            X,
+            negate_train_ys=(not self.maximize) and self.assume_symmetric_posterior,
+        )
+        if not self.maximize and not self.assume_symmetric_posterior:
+            discrete_posterior.borders = -torch.flip(discrete_posterior.borders, [0])
+            discrete_posterior.probabilities = torch.flip(
+                discrete_posterior.probabilities, [-1]
+            )
+
+        result = discrete_posterior.integrate(self.ag_integrate)
+        # result has shape (b, q) - sum over q dimension for batch acquisition
+        # For q=1, this is equivalent to squeeze(-1)
+        return result.sum(dim=-1)
+
+    @abstractmethod
+    def ag_integrate(self, lower_bound: Tensor, upper_bound: Tensor) -> Tensor:
+        r"""
+        This function calculates the integral that computes the acquisition function
+        without the posterior factor from lower_bound to upper_bound.
+        That is, our acquisition function is assumed to have the form
+        \int ag(x) * p(x) dx,
+        and this function calculates \int_{lower_bound}^{upper_bound} ag(x) dx.
+        The ``integrate`` method of the posterior (``BoundedRiemannPosterior``)
+        then computes the final acquisition value.
+
+        Args:
+            lower_bound: lower bound of integral
+            upper_bound: upper bound of integral
+
+        Returns:
+            A ``(b)``-dim Tensor of acquisition function derivatives at the given
+            design points ``X``.
+        """
+        pass  # pragma: no cover
+
+
+class DiscretizedExpectedImprovement(DiscretizedAcquisitionFunction):
+    r"""DiscretizedExpectedImprovement is an acquisition function that
+    computes the expected improvement over the current best observed value
+    for a Riemann distribution.
+    """
+
+    def __init__(
+        self,
+        model: Model,
+        best_f: Tensor,
+        posterior_transform: PosteriorTransform | None = None,
+        assume_symmetric_posterior: bool = True,
+    ) -> None:
+        r"""
+        Initialize the DiscretizedExpectedImprovement
+
+        Args:
+            model: A fitted model that is used to compute the posterior
+                distribution over the outcomes of interest.
+                The model should be a ``PFNModel``.
+            best_f: A tensor representing the current best observed value.
+        """
+        super().__init__(
+            model=model,
+            posterior_transform=posterior_transform,
+            assume_symmetric_posterior=assume_symmetric_posterior,
+        )
+        self.register_buffer("best_f", torch.as_tensor(best_f))
+
+    def ag_integrate(self, lower_bound: Tensor, upper_bound: Tensor) -> Tensor:
+        r"""
+        As Expected improvement has ag(y) = (y - best_f).clamp(min=0), and
+        is defined as \int ag(y) * p(y) dy, we can calculate the integral
+        of ag(y) like so:
+        We just calculate ag(y) at beginning and end, and since the function has
+        a gradient of 1 or 0, we can just take the average of the two.
+
+        Args:
+            lower_bound: lower bound of integral
+            upper_bound: upper bound of integral
+
+        Returns:
+            A ``(b)``-dim Tensor of acquisition function derivatives at the given
+            design points ``X``.
+        """
+        best_f = self.best_f.to(lower_bound)
+
+        # Case 1: best_f >= upper_bound, entire interval gives 0 improvement
+        case1_mask = best_f >= upper_bound
+
+        # Case 2: best_f <= lower_bound, entire interval gives improvement
+        case2_mask = best_f <= lower_bound
+
+        # Case 3: lower_bound < best_f < upper_bound, partial improvement
+        case3_mask = ~(case1_mask | case2_mask)
+
+        # Initialize result tensor
+        result = torch.zeros_like(lower_bound)
+
+        # Case 1: result is already 0
+
+        # Case 2: integral = (
+        #    ((upper_bound + lower_bound)/2 - best_f)
+        #     * (upper_bound - lower_bound)
+        # )
+        if case2_mask.any():
+            bucket_width = upper_bound - lower_bound
+            bucket_center = (upper_bound + lower_bound) / 2
+            result = torch.where(
+                case2_mask, (bucket_center - best_f) * bucket_width, result
+            )
+
+        # Case 3: integral = (upper_bound - best_f)²/2
+        if case3_mask.any():
+            result = torch.where(case3_mask, (upper_bound - best_f).pow(2) / 2, result)
+
+        return result.clamp_min(0)
+
+
+class DiscretizedNoisyExpectedImprovement(DiscretizedExpectedImprovement):
+    def __init__(
+        self,
+        model: Model,
+        posterior_transform: PosteriorTransform | None = None,
+        X_pending: Tensor | None = None,
+    ) -> None:
+        r"""
+        Only works with models trained specifically for this.
+
+        Args:
+            model: A fitted model that is used to compute the posterior
+                distribution over the outcomes of interest.
+                The model should be a ``PFNModelWithPendingPoints``.
+            X_pending: Optional pending points to include in the model.
+        """
+        super().__init__(
+            model=model,
+            posterior_transform=posterior_transform,
+            best_f=0.0,
+        )
+        # Set pending points on the model if it supports them
+        if X_pending is not None:
+            if hasattr(model, "pending_X"):
+                model.pending_X = X_pending
+            else:
+                raise UnsupportedError(
+                    f"Model {type(model).__name__} does not support pending points. "
+                    "Use PFNModelWithPendingPoints for NEI with pending evaluations."
+                )
+        self.set_X_pending(X_pending)
+
+
+class DiscretizedProbabilityOfImprovement(DiscretizedAcquisitionFunction):
+    r"""DiscretizedProbabilityOfImprovement is an acquisition function that
+    computes the probability of improvement over the current best observed value
+    for a Riemann distribution.
+    """
+
+    def __init__(
+        self,
+        model: Model,
+        best_f: Tensor,
+        posterior_transform: PosteriorTransform | None = None,
+        assume_symmetric_posterior: bool = True,
+    ) -> None:
+        r"""
+        Initialize the DiscretizedProbabilityOfImprovement
+
+        Args:
+            model: A fitted model that is used to compute the posterior
+                distribution over the outcomes of interest.
+                The model should be a ``PFNModel``.
+            best_f: A tensor representing the current best observed value.
+        """
+
+        super().__init__(
+            model,
+            posterior_transform,
+            assume_symmetric_posterior=assume_symmetric_posterior,
+        )
+        self.register_buffer("best_f", torch.as_tensor(best_f))
+
+    def ag_integrate(self, lower_bound: Tensor, upper_bound: Tensor) -> Tensor:
+        r"""
+        PI is defined as \int ag(y) * p(y) dy, where ag(y) = I(y - best_f)
+        and I being the indicator function.
+
+        So all we need to do is calculate the portion between the bounds
+        that is larger than best_f.
+        We do this by comparing how much higher the upper bound is than best_f,
+        compared to the size of the bucket.
+        Then we clamp at one if best_f is below lower_bound and at zero if
+        best_f is above upper_bound.
+
+        Args:
+            lower_bound: lower bound of integral
+            upper_bound: upper bound of integral
+
+        Returns:
+            A ``(b)``-dim Tensor of acquisition function derivatives at the given
+            design points ``X``.
+        """
+        best_f = self.best_f.to(lower_bound)
+        # two separate clamps needed below, as one is a tensor and one a scalar
+        return (
+            (upper_bound - best_f).clamp(min=0.0).clamp(max=upper_bound - lower_bound)
+        )
diff --git a/botorch_community/models/prior_fitted_network.py b/botorch_community/models/prior_fitted_network.py
new file mode 100644
index 0000000..435d4c1
--- /dev/null
+++ b/botorch_community/models/prior_fitted_network.py
@@ -0,0 +1,966 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+r"""
+This module defines the botorch model for PFNs (``PFNModel``) and it
+provides handy helpers to download pretrained, public PFNs
+with ``download_model`` and model paths with ``ModelPaths``.
+For the latter to work ``pfns4bo`` must be installed.
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import Any, Iterator, Optional, Union
+
+import torch
+from botorch.acquisition.objective import PosteriorTransform
+from botorch.exceptions.errors import UnsupportedError
+from botorch.logging import logger
+from botorch.models.model import Model
+from botorch.models.transforms.input import InputTransform
+from botorch.utils.transforms import match_batch_shape
+from botorch_community.models.utils.prior_fitted_network import (
+    download_model,
+    ModelPaths,
+)
+from botorch_community.posteriors.riemann import (
+    BoundedRiemannPosterior,
+    MultivariateRiemannPosterior,
+)
+from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
+from pfns.train import MainConfig  # @manual=//pytorch/PFNs:PFNs
+from torch import Tensor
+from torch.nn import Module
+
+
+def get_styles(
+    model: Module, hps: dict | None, batch_size: int, device: str
+) -> dict[str, Tensor]:
+    if hps is None or (model.style_encoder is None and model.y_style_encoder is None):
+        return {}
+    style_kwargs = {}
+    if model.style_encoder is not None:
+        hps_subset = {
+            k: v for k, v in hps.items() if k in model.style_encoder[0].hyperparameters
+        }
+        style = (
+            model.style_encoder[0]
+            .hyperparameters_dict_to_tensor(hps_subset)
+            .repeat(batch_size, 1)
+            .to(device)
+            .float()
+        )  # shape (batch_size, num_styles)
+        style_kwargs["style"] = style
+
+    if model.y_style_encoder is not None:
+        hps_subset = {
+            k: v
+            for k, v in hps.items()
+            if k in model.y_style_encoder[0].hyperparameters
+        }
+        y_style = (
+            model.y_style_encoder[0]
+            .hyperparameters_dict_to_tensor(hps_subset)
+            .repeat(batch_size, 1)
+            .to(device)
+            .float()
+        )  # shape (batch_size, num_styles)
+        style_kwargs["y_style"] = y_style
+    return style_kwargs
+
+
+class PFNModel(Model):
+    """Prior-data Fitted Network"""
+
+    def __init__(
+        self,
+        train_X: Tensor,
+        train_Y: Tensor,
+        model: Module | None = None,
+        checkpoint_url: str = ModelPaths.pfns4bo_hebo,
+        train_Yvar: Tensor | None = None,
+        batch_first: bool = False,
+        constant_model_kwargs: dict[str, Any] | None = None,
+        input_transform: InputTransform | None = None,
+        load_training_checkpoint: bool = False,
+        style_hyperparameters: dict[str, Any] | None = None,
+        style: Tensor
+        | None = None,  # should have shape (num_styles,) or (num_features, num_styles)
+    ) -> None:
+        """Initialize a PFNModel.
+
+        Either a pre-trained PFN model can be provided via the model kwarg,
+        or a checkpoint_url can be provided from which the model will be
+        downloaded. This defaults to the pfns4bo_hebo model.
+
+        Loading the model does an unsafe "weights_only=False" load, so
+        it is essential that checkpoint_url be a trusted source.
+
+        Args:
+            train_X: A ``n x d`` tensor of training features.
+            train_Y: A ``n x 1`` tensor of training observations.
+            model: A pre-trained PFN model with the following
+                forward(train_X, train_Y, X) -> logit predictions of shape
+                ``n x b x c`` where c is the number of discrete buckets
+                borders: A ``c+1``-dim tensor of bucket borders.
+            checkpoint_url: The string URL of the PFN model to download and load.
+                Will be ignored if model is provided.
+            train_Yvar: Observed variance of train_Y. Currently ignored.
+            batch_first: Whether the batch dimension is the first dimension of
+                the input tensors. This is needed to support different PFN
+                models. For batch-first x has shape ``batch x seq_len x features``
+                and for non-batch-first it has shape ``seq_len x batch x features``.
+            constant_model_kwargs: A dictionary of model kwargs that
+                will be passed to the model in each forward pass.
+            input_transform: A Botorch input transform.
+            load_training_checkpoint: Whether to load a training checkpoint as
+                produced by the PFNs training code, see github.com/automl/PFNs.
+            style_hyperparameters: A dictionary of hyperparameters to be passed
+                to the style and the y-style encoders. It is useful when training
+                models with ``hyperparameter_sampling`` prior and its style
+                encoder. One simply supplies the dict with the unnormalized
+                hyperparameters, e.g., {"noise_std": 0.1}. Omitted values are
+                treated as unknown and the value will build a Bayesian average
+                for these, if ``hyperparameter_sampling_skip_style_prob`` > 0
+                during pre-training.
+            style: A tensor of style values to be passed to the model. These
+                are raw style values of shape (num_styles,), which will then
+                be extended as needed.
+
+        """
+        super().__init__()
+        if model is None:
+            model = download_model(
+                model_path=checkpoint_url,
+            )
+
+        if load_training_checkpoint:
+            # the model is not an actual model, but a training checkpoint
+            # make a model out of it
+            checkpoint = model
+            config = MainConfig.from_dict(checkpoint["config"])
+            model = config.model.create_model()
+            model.load_state_dict(checkpoint["model_state_dict"])
+            model.eval()
+
+        if train_Yvar is not None:
+            logger.debug("train_Yvar provided but ignored for PFNModel.")
+
+        if train_Y.dim() != 2:
+            raise UnsupportedError("train_Y must be 2-dimensional.")
+
+        if train_X.dim() != 2:
+            raise UnsupportedError("train_X must be 2-dimensional.")
+
+        if train_Y.shape[-1] > 1:
+            raise UnsupportedError("Only 1 target allowed for PFNModel.")
+
+        if train_X.shape[0] != train_Y.shape[0]:
+            raise UnsupportedError(
+                "train_X and train_Y must have the same number of rows."
+            )
+
+        with torch.no_grad():
+            self.transformed_X = self.transform_inputs(
+                X=train_X, input_transform=input_transform
+            )
+
+        self.train_X = train_X  # shape: (n, d)
+        self.train_Y = train_Y  # shape: (n, 1)
+        # Downstream botorch tooling expects a likelihood to be specified,
+        # so here we use a FixedNoiseGaussianLikelihood that is unused.
+        if train_Yvar is None:
+            train_Yvar = torch.zeros_like(train_Y)
+        self.train_Yvar = train_Yvar  # shape: (n, 1)
+        self.likelihood = FixedNoiseGaussianLikelihood(noise=train_Yvar)
+        self.pfn = model.to(device=train_X.device)
+        self.batch_first = batch_first
+        self.constant_model_kwargs = constant_model_kwargs or {}
+        self.style_hyperparameters = style_hyperparameters
+        self.style = style
+        if input_transform is not None:
+            self.input_transform = input_transform
+        self._compute_styles()
+
+        # Cache state initialization
+        self._training_cache_ready = False
+        self._cached_negate_train_ys = False
+        self._cached_context_hash: int | None = None
+        self._cached_y_encoder_state: dict | None = None
+
+    def _compute_styles(self):
+        """
+        Can be used to compute styles to be used for PFN prediction based on
+        training data.
+
+        When implemented, will directly modify self.style_hyperparameters or
+        self.style.
+        """
+        pass
+
+    def _compute_context_hash(
+        self, train_X: Tensor, train_Y: Tensor, negate_train_ys: bool
+    ) -> int:
+        """Compute hash of training context to detect changes.
+
+        Returns a hash based on training data and negation setting.
+        Used to validate cache consistency, especially with pending points.
+        """
+        return hash((
+            id(train_X),
+            train_X.shape,
+            tuple(train_X.stride()),
+            id(train_Y),
+            train_Y.shape,
+            tuple(train_Y.stride()),
+            negate_train_ys,
+        ))
+
+    @contextmanager
+    def cache_training_context(
+        self, negate_train_ys: bool = False
+    ) -> Iterator[None]:
+        """Context manager to cache training data for efficient batch evaluation.
+
+        When used with BoTorch's ``optimize_acqf()``, this caches the training
+        data representations once, allowing many test point evaluations without
+        recomputing the training context. Gradients flow through test_x only.
+
+        Memory savings: O(n) instead of O(b × n) for training data, where
+        b is the batch size (num_restarts) and n is the training set size.
+
+        Usage::
+
+            with model.cache_training_context():
+                acqf = ExpectedImprovement(model, best_f=train_Y.max())
+                candidates, value = optimize_acqf(
+                    acqf, bounds=bounds,
+                    num_restarts=64,
+                    raw_samples=512,
+                    options={"batch_limit": 64}
+                )
+
+        Args:
+            negate_train_ys: Whether to negate training Y values (for minimization).
+        """
+        # Store original state
+        original_cache_trainset_representation = getattr(
+            self.pfn, "cache_trainset_representation", False
+        )
+
+        # Validate transformer supports caching
+        if not hasattr(self.pfn, "cache_trainset_representation"):
+            raise TypeError(
+                f"Model {type(self.pfn).__name__} does not support caching. "
+                "Requires 'cache_trainset_representation' attribute."
+            )
+        if not callable(getattr(self.pfn, "empty_trainset_representation_cache", None)):
+            raise TypeError(
+                f"Model {type(self.pfn).__name__} does not support caching. "
+                "Requires 'empty_trainset_representation_cache()' method."
+            )
+
+        try:
+            # Enable caching on the transformer
+            self.pfn.cache_trainset_representation = True
+
+            # Prepare training data (single batch)
+            train_X_bf = self.transformed_X.unsqueeze(0)  # (1, n, d)
+            train_Y_bf = self.train_Y.unsqueeze(0)  # (1, n, 1)
+            if negate_train_ys:
+                assert self.train_Y.mean().abs() < 1e-4, (
+                    "train_Y must be zero-centered for negation."
+                )
+                train_Y_bf = -train_Y_bf
+
+            # Hook for subclasses to augment training data (e.g., pending points)
+            dummy_X = torch.zeros(
+                1, 1, train_X_bf.shape[-1],
+                device=train_X_bf.device,
+                dtype=train_X_bf.dtype,
+            )
+            train_X_bf, train_Y_bf = self._augment_training_data(
+                train_X_bf, train_Y_bf, dummy_X,
+                negate_train_ys=negate_train_ys, use_cache=False
+            )
+
+            # Get styles for batch_size=1 (will be broadcast during inference)
+            styles = self._get_styles(batch_size=1)
+
+            # Populate cache with training data (no gradients on train)
+            with torch.no_grad():
+                if not self.batch_first:
+                    train_X_bf = train_X_bf.transpose(0, 1)  # (n, 1, d)
+                    train_Y_bf = train_Y_bf.transpose(0, 1)  # (n, 1, 1)
+                    # Create dummy test point to trigger full forward
+                    dummy_test = torch.zeros(
+                        1, 1, train_X_bf.shape[-1],
+                        device=train_X_bf.device,
+                        dtype=train_X_bf.dtype,
+                    )  # (1, 1, d) -> seq-first
+                else:
+                    dummy_test = torch.zeros(
+                        1, 1, train_X_bf.shape[-1],
+                        device=train_X_bf.device,
+                        dtype=train_X_bf.dtype,
+                    )  # (1, 1, d) -> batch-first
+
+                # Forward pass to populate cache
+                self.pfn(
+                    x=train_X_bf.float(),
+                    y=train_Y_bf.float(),
+                    test_x=dummy_test.float(),
+                    **self.constant_model_kwargs,
+                    **styles,
+                )
+
+            # Save y_encoder state after cache population.
+            # This is critical for MultivariatePFNModel where _compute_conditional_means()
+            # temporarily disables caching and calls the encoder with augmented data,
+            # which corrupts the encoder state. We save the state here so it can be
+            # restored after each correlation estimation call.
+            if hasattr(self.pfn, "y_encoder") and hasattr(
+                self.pfn.y_encoder, "save_fitted_state"
+            ):
+                self._cached_y_encoder_state = self.pfn.y_encoder.save_fitted_state()
+            else:
+                self._cached_y_encoder_state = None
+
+            # Mark cache as ready and store context hash
+            self._training_cache_ready = True
+            self._cached_negate_train_ys = negate_train_ys
+            self._cached_context_hash = self._compute_context_hash(
+                train_X_bf, train_Y_bf, negate_train_ys
+            )
+
+            yield
+
+        finally:
+            # Clear cache and restore state
+            self._training_cache_ready = False
+            self._cached_negate_train_ys = False
+            self._cached_context_hash = None
+            self._cached_y_encoder_state = None
+
+            self.pfn.cache_trainset_representation = original_cache_trainset_representation
+            if hasattr(self.pfn, "empty_trainset_representation_cache"):
+                self.pfn.empty_trainset_representation_cache()
+
+    def posterior(
+        self,
+        X: Tensor,
+        output_indices: Optional[list[int]] = None,
+        observation_noise: Union[bool, Tensor] = False,
+        posterior_transform: Optional[PosteriorTransform] = None,
+        negate_train_ys: bool = False,
+    ) -> BoundedRiemannPosterior:
+        r"""Computes the posterior over model outputs at the provided points.
+
+        Subclasses should override hooks (_augment_training_data, _build_posterior)
+        rather than this method directly.
+
+        Args:
+            X: A ``b? x q? x d``-dim Tensor.
+            output_indices: **Not supported.**
+            observation_noise: **Not supported.**
+            posterior_transform: **Not supported.**
+            negate_train_ys: Whether to negate training Ys (for minimization).
+
+        Returns:
+            A ``BoundedRiemannPosterior``.
+        """
+        self.pfn.eval()
+        self._validate_posterior_args(output_indices, observation_noise, posterior_transform)
+
+        # Check cache state
+        use_cache = self._check_cache_compatibility(negate_train_ys)
+
+        # Prepare base training data
+        X, train_X, train_Y, orig_X_shape, styles = self._prepare_data(
+            X, negate_train_ys=negate_train_ys, skip_train_replication=use_cache
+        )
+
+        # Hook: Allow subclasses to augment training data (e.g., pending points)
+        train_X, train_Y = self._augment_training_data(
+            train_X, train_Y, X, negate_train_ys=negate_train_ys, use_cache=use_cache
+        )
+
+        # Core prediction
+        probabilities = self.pfn_predict(
+            X=X,
+            train_X=train_X,
+            train_Y=train_Y,
+            use_cache=use_cache,
+            **self.constant_model_kwargs,
+            **styles,
+        )
+        probabilities = probabilities.view(*orig_X_shape[:-1], -1)
+
+        # Hook: Allow subclasses to build custom posteriors
+        return self._build_posterior(
+            X=X,
+            probabilities=probabilities,
+            orig_X_shape=orig_X_shape,
+            train_X=train_X,
+            train_Y=train_Y,
+            styles=styles,
+            use_cache=use_cache,
+        )
+
+    def _validate_posterior_args(
+        self,
+        output_indices: Optional[list[int]],
+        observation_noise: Union[bool, Tensor],
+        posterior_transform: Optional[PosteriorTransform],
+    ) -> None:
+        """Validate unsupported posterior arguments."""
+        if output_indices is not None:
+            raise UnsupportedError(
+                "output_indices is not None. PFNModel should not be a multi-output model."
+            )
+        if observation_noise:
+            logger.warning(
+                "observation_noise is not supported for PFNModel and is being ignored."
+            )
+        if posterior_transform is not None:
+            raise UnsupportedError("posterior_transform is not supported for PFNModel.")
+
+    def _check_cache_compatibility(self, negate_train_ys: bool) -> bool:
+        """Check if cache can be used and validate settings."""
+        if not self._training_cache_ready:
+            return False
+
+        if negate_train_ys != self._cached_negate_train_ys:
+            raise ValueError(
+                f"negate_train_ys={negate_train_ys} does not match cached "
+                f"negate_train_ys={self._cached_negate_train_ys}. "
+                "The cache was populated with a different negation setting."
+            )
+        return True
+
+    def _augment_training_data(
+        self,
+        train_X: Tensor | None,
+        train_Y: Tensor | None,
+        X: Tensor,
+        *,
+        negate_train_ys: bool,
+        use_cache: bool,
+    ) -> tuple[Tensor | None, Tensor | None]:
+        """Hook for subclasses to augment training data.
+
+        Override this instead of posterior() to add pending points, etc.
+
+        Args:
+            train_X: Training features, shape (b, n, d) or None if use_cache
+            train_Y: Training targets, shape (b, n, 1) or None if use_cache
+            X: Test points, shape (b, q, d)
+            negate_train_ys: Whether train_Y was negated
+            use_cache: Whether caching is active
+
+        Returns:
+            Potentially augmented (train_X, train_Y)
+        """
+        return train_X, train_Y
+
+    def _build_posterior(
+        self,
+        X: Tensor,
+        probabilities: Tensor,
+        orig_X_shape: torch.Size,
+        train_X: Tensor | None,
+        train_Y: Tensor | None,
+        styles: dict[str, Tensor],
+        use_cache: bool,
+    ) -> BoundedRiemannPosterior:
+        """Hook for subclasses to build custom posteriors.
+
+        Override this instead of posterior() for multivariate posteriors, etc.
+        """
+        return BoundedRiemannPosterior(
+            borders=self.borders,
+            probabilities=probabilities,
+        )
+
+    def _prepare_data(
+        self,
+        X: Tensor,
+        negate_train_ys: bool = False,
+        skip_train_replication: bool = False,
+    ) -> tuple[Tensor, Tensor | None, Tensor | None, torch.Size, dict[str, Tensor]]:
+        """Prepare data for posterior computation.
+
+        Returns:
+            X: Transformed test points (b, q, d)
+            train_X: Training features (b, n, d) or None if skip_train_replication
+            train_Y: Training targets (b, n, 1) or None if skip_train_replication
+            orig_X_shape: Original shape of X before unsqueezing
+            styles: Style tensors for the model
+        """
+        orig_X_shape = X.shape  # X has shape b? x q? x d
+        if len(X.shape) > 3:
+            raise UnsupportedError(f"X must be at most 3-d, got {X.shape}.")
+        while len(X.shape) < 3:
+            X = X.unsqueeze(0)
+
+        X = self.transform_inputs(X)  # shape (b , q, d)
+
+        if skip_train_replication:
+            # When using cached training context, we don't need to replicate
+            # training data since it's already cached in the transformer.
+            train_X = None
+            train_Y = None
+        else:
+            train_X = match_batch_shape(self.transformed_X, X)  # shape (b, n, d)
+            train_Y = match_batch_shape(self.train_Y, X)  # shape (b, n, 1)
+            if negate_train_ys:
+                assert self.train_Y.mean().abs() < 1e-4, "train_Y must be zero-centered."
+                train_Y = -train_Y
+
+        styles = self._get_styles(
+            batch_size=X.shape[0],
+        )  # shape (b, num_styles)
+        return X, train_X, train_Y, orig_X_shape, styles
+
+    def _get_styles(self, batch_size) -> dict[str, Tensor]:
+        style_kwargs = get_styles(
+            model=self.pfn,
+            hps=self.style_hyperparameters,
+            batch_size=batch_size,
+            device=self.train_X.device,
+        )
+        if self.style is not None:
+            assert style_kwargs == {}, (
+                "Cannot provide both style and style_hyperparameters."
+            )
+            style_kwargs["style"] = (
+                self.style[None]
+                .repeat(batch_size, 1, 1)
+                .to(self.train_X.device)
+                .float()
+            )
+        return style_kwargs
+
+    def pfn_predict(
+        self,
+        X: Tensor,
+        train_X: Tensor | None,
+        train_Y: Tensor | None,
+        use_cache: bool = False,
+        **forward_kwargs,
+    ) -> Tensor:
+        """Make a prediction using the PFN model.
+
+        Args:
+            X: Test points, shape (b, q, d)
+            train_X: Training features, shape (b, n, d) or None if use_cache
+            train_Y: Training targets, shape (b, n, 1) or None if use_cache
+            use_cache: Whether to use cached training representations
+            **forward_kwargs: Additional kwargs for the PFN model
+
+        Returns:
+            Probabilities (b, q, num_buckets)
+        """
+        if use_cache:
+            assert train_X is None and train_Y is None, (
+                "Bug: use_cache=True but train_X/train_Y provided"
+            )
+            assert self._training_cache_ready, (
+                "Cache requested but not populated. Call cache_training_context() first."
+            )
+            if not self.batch_first:
+                X = X.transpose(0, 1)
+
+            logits = self.pfn(
+                x=None,
+                y=None,
+                test_x=X.float(),
+                **forward_kwargs,
+            )
+
+            if not self.batch_first:
+                logits = logits.transpose(0, 1)
+        else:
+            assert train_X is not None and train_Y is not None, (
+                "Bug: use_cache=False but train_X/train_Y is None"
+            )
+            if not self.batch_first:
+                X = X.transpose(0, 1)
+                train_X = train_X.transpose(0, 1)
+                train_Y = train_Y.transpose(0, 1)
+
+            logits = self.pfn(
+                x=train_X.float(),
+                y=train_Y.float(),
+                test_x=X.float(),
+                **forward_kwargs,
+            )
+
+            if not self.batch_first:
+                logits = logits.transpose(0, 1)
+
+        logits = logits.to(X.dtype)
+        probabilities = logits.softmax(dim=-1)
+        return probabilities
+
+    @property
+    def borders(self):
+        return self.pfn.criterion.borders.to(self.train_X.dtype)
+
+    @property
+    def num_outputs(self) -> int:
+        """Number of outputs of the model (always 1 for PFNModel)."""
+        return 1
+
+
+class PFNModelWithPendingPoints(PFNModel):
+    """PFNModel that supports pending points (unobserved evaluations).
+
+    Pending points are added to the training context with NaN labels,
+    allowing the model to account for in-flight evaluations during
+    Bayesian optimization.
+    """
+
+    def __init__(
+        self,
+        train_X: Tensor,
+        train_Y: Tensor,
+        pending_X: Tensor | None = None,
+        **kwargs,
+    ) -> None:
+        """Initialize with optional pending points.
+
+        Args:
+            train_X: Training features (n, d)
+            train_Y: Training targets (n, 1)
+            pending_X: Optional pending point locations (n', d)
+            **kwargs: Additional arguments for PFNModel
+        """
+        super().__init__(train_X=train_X, train_Y=train_Y, **kwargs)
+        self._pending_X = pending_X
+
+    @property
+    def pending_X(self) -> Tensor | None:
+        """Current pending points."""
+        return self._pending_X
+
+    @pending_X.setter
+    def pending_X(self, value: Tensor | None) -> None:
+        """Set pending points. Cannot modify while cache is active."""
+        if self._training_cache_ready:
+            raise RuntimeError(
+                "Cannot modify pending_X while cache_training_context is active. "
+                "Exit the context manager first."
+            )
+        self._pending_X = value
+
+    def _augment_training_data(
+        self,
+        train_X: Tensor | None,
+        train_Y: Tensor | None,
+        X: Tensor,
+        *,
+        negate_train_ys: bool,
+        use_cache: bool,
+    ) -> tuple[Tensor | None, Tensor | None]:
+        """Add pending points to training data.
+
+        Args:
+            train_X: Training features (b, n, d) or None if using cache
+            train_Y: Training targets (b, n, 1) or None if using cache
+            X: Test points (b, q, d)
+            negate_train_ys: Whether Y values are negated
+            use_cache: Whether using cached training context
+
+        Returns:
+            Augmented (train_X, train_Y) or (None, None) if using cache
+        """
+        if self._pending_X is None:
+            return train_X, train_Y
+
+        if use_cache:
+            # When using cache, pending points were included at cache creation
+            return None, None
+
+        # Non-cached path: augment training data with pending points
+        assert train_X is not None and train_Y is not None
+
+        pending_X = self._pending_X[None].expand(X.shape[0], -1, -1)  # (b, n', d)
+        train_X = torch.cat([train_X, pending_X], dim=1)  # (b, n+n', d)
+        train_Y = torch.cat([
+            train_Y,
+            torch.full(
+                (train_Y.shape[0], pending_X.shape[1], 1),
+                torch.nan,
+                device=train_Y.device,
+                dtype=train_Y.dtype,
+            ),
+        ], dim=1)  # (b, n+n', 1)
+
+        return train_X, train_Y
+
+
+class MultivariatePFNModel(PFNModel):
+    """A multivariate PFN model that returns a joint posterior over q batch inputs.
+
+    For this to work correctly it is necessary that the underlying model return a
+    posterior for the latent f, not the noisy observed y.
+    """
+
+    def _build_posterior(
+        self,
+        X: Tensor,
+        probabilities: Tensor,
+        orig_X_shape: torch.Size,
+        train_X: Tensor | None,
+        train_Y: Tensor | None,
+        styles: dict[str, Tensor],
+        use_cache: bool,
+    ) -> Union[BoundedRiemannPosterior, MultivariateRiemannPosterior]:
+        """Build multivariate posterior with correlation estimation.
+
+        Args:
+            X: Prepared test points (b, q, d)
+            probabilities: Predicted probabilities (b?, q?, num_buckets)
+            orig_X_shape: Original shape of test points
+            train_X: Training features or None if using cache
+            train_Y: Training targets or None if using cache
+            styles: Style tensors for forward pass
+            use_cache: Whether using cached training context
+
+        Returns:
+            MultivariateRiemannPosterior if q > 1, else BoundedRiemannPosterior
+        """
+        marginals = BoundedRiemannPosterior(
+            borders=self.borders,
+            probabilities=probabilities,
+        )
+
+        # If no q dimension or q=1, return marginals
+        if len(orig_X_shape) == 1 or orig_X_shape[-2] == 1:
+            return marginals
+
+        # For correlation estimation, we need full training data
+        if train_X is None or train_Y is None:
+            # Re-fetch training data without cache for correlation estimation
+            _, train_X, train_Y, _, _ = self._prepare_data(
+                X.view(*orig_X_shape),
+                skip_train_replication=False
+            )
+            # Apply any augmentation (e.g., pending points from subclasses)
+            train_X, train_Y = self._augment_training_data(
+                train_X, train_Y, X,
+                negate_train_ys=self._cached_negate_train_ys if use_cache else False,
+                use_cache=False,  # Force non-cached for correlation
+            )
+
+        # Estimate correlation structure with additional forward pass
+        R = self.estimate_correlations(
+            X=X,
+            train_X=train_X,
+            train_Y=train_Y,
+            styles=styles,
+            marginals=marginals,
+        )  # (b, q, q)
+        R = R.view(*orig_X_shape[:-2], X.shape[-2], X.shape[-2])  # (b?, q, q)
+
+        return MultivariateRiemannPosterior(
+            borders=self.borders,
+            probabilities=marginals.probabilities,
+            correlation_matrix=R,
+        )
+
+    def estimate_correlations(
+        self,
+        X: Tensor,
+        train_X: Tensor,
+        train_Y: Tensor,
+        styles: dict[str, Tensor],
+        marginals: BoundedRiemannPosterior,
+    ) -> Tensor:
+        """
+        Estimate a correlation matrix R across the q batch of points in X.
+        Will do a forward pass through the PFN model with batch size O(q^2).
+
+        For every x_q in [x_1, ..., x_Q]:
+           1. Add x_q to train_X, with y_q the 90th percentile value for f(x_q)
+           2. Evaluate p(f(x_i)) for all points.
+
+        Uses bivariate normal conditioning formulae, and so will be approximate.
+
+        Args:
+            X: evaluation point, shape (b, q, d)
+            train_X: Training X, shape (b, n, d)
+            train_Y: Training Y, shape (b, n, 1)
+            styles: dict from name to tensor shaped (b, ns) for any styles.
+            marginals: A posterior object with marginal posteriors for f(X), but no
+                correlation structure yet added. posterior.probabilities has
+                shape (b?, q, num_buckets).
+
+        Returns: A (b, q, q) correlation matrix
+        """
+        # Compute conditional distributions with a forward pass
+        cond_mean, cond_val = self._compute_conditional_means(
+            X=X,
+            train_X=train_X,
+            train_Y=train_Y,
+            styles=styles,
+            marginals=marginals,
+        )
+        # Get marginal moments
+        var = marginals.variance.squeeze(-1)  # (b?, q)
+        mean = marginals.mean.squeeze(-1)  # (b?, q)
+        if len(var.shape) == 1:
+            var = var.unsqueeze(0)  # (b, q)
+            mean = mean.unsqueeze(0)  # (b, q)
+        # Estimate covariances from conditional distributions
+        cov = self._estimate_covariances(
+            cond_mean=cond_mean,
+            cond_val=cond_val,
+            mean=mean,
+            var=var,
+        )
+        # Convert to correlation matrix
+        S = 1 / torch.sqrt(torch.diagonal(cov, dim1=-2, dim2=-1))  # (b, q)
+        S = S.unsqueeze(-1).expand(cov.shape)  # (b, q, q)
+        R = S * cov * S.transpose(-1, -2)  # (b, q, q)
+        return R
+
+    def _compute_conditional_means(
+        self,
+        X: Tensor,
+        train_X: Tensor,
+        train_Y: Tensor,
+        styles: dict[str, Tensor],
+        marginals: BoundedRiemannPosterior,
+    ) -> tuple[Tensor, Tensor]:
+        """
+        Compute conditional means between pairs of points in X.
+
+        Conditioning is done with an additional forward pass through the model. The
+        returned conditional mean will be of shape (b, q, q), with entry [b, i, j] the
+        conditional mean of j given i set to the conditioning value.
+
+        Args:
+            X: evaluation point, shape (b, q, d)
+            train_X: Training X, shape (b, n, d)
+            train_Y: Training Y, shape (b, n, 1)
+            styles: dict from name to tensor shaped (b, ns) for any styles.
+            marginals: A posterior object with marginal posteriors for f(X), but no
+                correlation structure yet added. posterior.probabilities has
+                shape (b?, q, num_buckets).
+
+        Returns: conditional means (b, q, q), and values used for conditioning (b, q).
+        """
+        b, q, d = X.shape
+        n = train_X.shape[-2]
+        post_shape = marginals.probabilities.shape[:-1]
+        # Find the 90th percentile of each eval point.
+        cond_val = marginals.icdf(
+            torch.full(post_shape, 0.9, device=X.device, dtype=X.dtype).unsqueeze(0)
+        )  # (1, b?, q, 1)
+        cond_val = cond_val.view(b, q)  # (b, q)
+        # Construct conditional training data.
+        # train_X will have shape (b, q, n+1, d), to have a conditional observation
+        # for each point. train_Y will have shape (b, q, n+1, 1).
+        train_X = train_X.unsqueeze(1).expand(b, q, n, d)
+        cond_X = X.unsqueeze(-2)  # (b, q, 1, d)
+        train_X = torch.cat((train_X, cond_X), dim=-2)  # (b, q, n+1, d)
+        train_Y = train_Y.unsqueeze(1).expand(b, q, n, 1)
+        cond_Y = cond_val.unsqueeze(-1).unsqueeze(-1)  # (b, q, 1, 1)
+        train_Y = torch.cat((train_Y, cond_Y), dim=-2)  # (b, q, n+1, 1)
+        cond_styles = {}
+        for name, style in styles.items():
+            ns = style.shape[-1]
+            cond_styles[name] = style.unsqueeze(-2).expand(b, q, ns).reshape(b * q, ns)
+        # Construct eval points
+        eval_X = X.unsqueeze(1).expand(b, q, q, d)
+        # Squeeze everything into necessary 2 batch dims, and do PFN forward pass
+        # Temporarily disable caching for correlation estimation since we use
+        # a different training set (with conditioning point) than the cached one
+        cache_was_enabled = self.pfn.cache_trainset_representation
+        try:
+            self.pfn.cache_trainset_representation = False
+            cond_probabilities = self.pfn_predict(
+                X=eval_X.reshape(b * q, q, d),
+                train_X=train_X.reshape(b * q, n + 1, d),
+                train_Y=train_Y.reshape(b * q, n + 1, 1),
+                **cond_styles,
+            )  # (b * q, q, num_buckets)
+        finally:
+            self.pfn.cache_trainset_representation = cache_was_enabled
+            # Restore y_encoder state if it was saved during cache population.
+            # The pfn_predict call above fitted the encoder on augmented data (n+1 points),
+            # which corrupts the state that was set when the cache was populated.
+            if (
+                hasattr(self, "_cached_y_encoder_state")
+                and self._cached_y_encoder_state is not None
+                and hasattr(self.pfn, "y_encoder")
+                and hasattr(self.pfn.y_encoder, "restore_fitted_state")
+            ):
+                self.pfn.y_encoder.restore_fitted_state(self._cached_y_encoder_state)
+        # Object for conditional posteriors
+        cond_posterior = BoundedRiemannPosterior(
+            borders=self.borders,
+            probabilities=cond_probabilities,
+        )
+        # Get conditional means
+        cond_mean = cond_posterior.mean.squeeze(-1)  # (b * q, q)
+        cond_mean = cond_mean.unsqueeze(0).view(b, q, q)
+        return cond_mean, cond_val
+
+    def _estimate_covariances(
+        self,
+        cond_mean: Tensor,
+        cond_val: Tensor,
+        mean: Tensor,
+        var: Tensor,
+    ) -> Tensor:
+        """
+        Estimate covariances from conditional distributions.
+
+        Part one: Compute noise variance implied by conditional distributions
+        E[f_j | y_j=y] = E[f_j] + var[f_j]/(var[f_j] + noise_var) * (y - E[f_j])
+        Let Z_jj = (E[f_j | y_j=y] - E[f_j]) / (y - E[f_j]).
+        Note that Z is in (0, 1].
+        Then, noise_var_j = var[f_j](1/Z_jj - 1).
+
+        Part two: Compute covariances for all pairs
+        E[f_j|y_i=y] = E[f_j]+cov[f_j, f_i]/(var[f_i] + noise_var_i) * (y - E[f_i])
+        Let Z_ij = (E[f_j | y_i=y] - E[f_j]) / (y - E[f_i]).
+        Then, cov[f_j, f_i] = Z * (var[f_i] + noise_var)
+
+        Args:
+            cond_mean: (b, q, q) means of dim -1 conditioned on dim -2
+            cond_val: (b, q) conditioned y value.
+            var: (b, q) marginal variances
+            mean: (b, q) marginal means
+
+        Returns: Covariance matrix
+        """
+        Z = (cond_mean - mean.unsqueeze(-2).expand(cond_mean.shape)) / (
+            cond_val - mean
+        ).unsqueeze(-1)  # (b, q, q)
+        # Z[i, j] is for j cond. on i
+        noise_var = torch.clamp(
+            var * (1 / torch.diagonal(Z, dim1=-2, dim2=-1) - 1), min=1e-8
+        )  # (b, q)
+        cov = Z * (var + noise_var).unsqueeze(-1)  # (b, q, q)
+        # Symmetrize
+        cov = 0.5 * (cov + cov.transpose(-1, -2))
+        cov = self._map_psd(cov)
+        return cov
+
+    def _map_psd(self, A):
+        """
+        Map A (assumed symmetric) to the nearest PSD matrix.
+        """
+        if torch.linalg.eigvals(A).real.min() < 0:
+            L, Q = torch.linalg.eigh(A)
+            L = torch.clamp(L, min=1e-6)
+            A = Q @ torch.diag_embed(L) @ Q.transpose(-1, -2)
+        return A
diff --git a/botorch_community/tests/test_botorch_pfn_caching.py b/botorch_community/tests/test_botorch_pfn_caching.py
new file mode 100644
index 0000000..f2dd7ce
--- /dev/null
+++ b/botorch_community/tests/test_botorch_pfn_caching.py
@@ -0,0 +1,545 @@
+"""Tests for PFNModel caching functionality.
+
+These tests verify the refactored caching implementation including:
+- Cache state initialization
+- Cache capability validation
+- Context manager behavior
+- Cached prediction correctness
+- Gradient flow through cached predictions
+- Negation handling
+- Subclass behavior (PFNModelWithPendingPoints, MultivariatePFNModel)
+"""
+
+import pytest
+import torch
+from unittest.mock import MagicMock
+
+from botorch_community.models.prior_fitted_network import (
+    PFNModel,
+    PFNModelWithPendingPoints,
+    MultivariatePFNModel,
+)
+from pfns.model.bar_distribution import BarDistribution
+from pfns.model.transformer import TableTransformer
+
+
+@pytest.fixture
+def device():
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+@pytest.fixture
+def dtype():
+    return torch.float32
+
+
+@pytest.fixture
+def simple_transformer(device, dtype):
+    """Create a simple transformer for testing."""
+    torch.manual_seed(42)
+    num_buckets = 10
+    borders = torch.linspace(-3, 3, num_buckets + 1)
+
+    model = TableTransformer(
+        ninp=32,
+        nhead=2,
+        nhid=64,
+        nlayers=2,
+        batch_first=True,
+        cache_trainset_representation=False,
+        decoder_dict={"standard": (None, num_buckets)},
+    )
+    model.criterion = BarDistribution(borders=borders)
+    model = model.to(device=device, dtype=dtype)
+
+    # Add small perturbation to weights to ensure gradient flow
+    with torch.no_grad():
+        for p in model.parameters():
+            p.add_(0.01 * torch.randn_like(p))
+
+    return model.eval()
+
+
+@pytest.fixture
+def train_data(device, dtype):
+    """Create training data for tests."""
+    torch.manual_seed(42)
+    n_train, n_dims = 20, 5
+    train_X = torch.rand(n_train, n_dims, device=device, dtype=dtype)
+    train_Y = torch.sin(train_X.sum(dim=-1, keepdim=True))
+    train_Y = (train_Y - train_Y.mean()) / (train_Y.std() + 1e-8)
+    return train_X, train_Y
+
+
+class TestPFNModelCacheInitialization:
+    """Test cache state initialization."""
+
+    def test_cache_state_initialized_in_init(self, simple_transformer, train_data):
+        """Cache state should be explicitly initialized."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        assert hasattr(model, "_training_cache_ready")
+        assert model._training_cache_ready is False
+        assert hasattr(model, "_cached_negate_train_ys")
+        assert model._cached_negate_train_ys is False
+        assert hasattr(model, "_cached_context_hash")
+        assert model._cached_context_hash is None
+
+
+class TestPFNModelCacheCapabilityCheck:
+    """Test validation of transformer caching capability."""
+
+    def test_raises_on_missing_cache_attribute(self, train_data, device, dtype):
+        """Should raise if transformer doesn't support caching."""
+        train_X, train_Y = train_data
+
+        # Create mock model without caching support
+        mock_model = MagicMock()
+        mock_model.to = MagicMock(return_value=mock_model)
+        del mock_model.cache_trainset_representation  # Ensure missing
+        mock_model.criterion = MagicMock()
+        mock_model.criterion.borders = torch.linspace(-3, 3, 11, device=device, dtype=dtype)
+
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=mock_model,
+            batch_first=True,
+        )
+
+        with pytest.raises(TypeError, match="does not support caching"):
+            with model.cache_training_context():
+                pass
+
+    def test_raises_on_missing_cache_clear_method(self, train_data, device, dtype):
+        """Should raise if transformer lacks cache clearing method."""
+        train_X, train_Y = train_data
+
+        mock_model = MagicMock()
+        mock_model.to = MagicMock(return_value=mock_model)
+        mock_model.cache_trainset_representation = False
+        del mock_model.empty_trainset_representation_cache
+        mock_model.criterion = MagicMock()
+        mock_model.criterion.borders = torch.linspace(-3, 3, 11, device=device, dtype=dtype)
+
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=mock_model,
+            batch_first=True,
+        )
+
+        with pytest.raises(TypeError, match="does not support caching"):
+            with model.cache_training_context():
+                pass
+
+
+class TestPFNModelCacheContextManager:
+    """Test cache_training_context behavior."""
+
+    def test_cache_enabled_within_context(self, simple_transformer, train_data, device):
+        """Cache should be enabled within context."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        assert not model._training_cache_ready
+
+        with model.cache_training_context():
+            assert model._training_cache_ready
+            assert model.pfn.cache_trainset_representation
+
+        assert not model._training_cache_ready
+
+    def test_cache_cleaned_on_exit(self, simple_transformer, train_data, device):
+        """Cache should be cleared when exiting context."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        with model.cache_training_context():
+            pass
+
+        assert not model._training_cache_ready
+        assert model._cached_context_hash is None
+
+    def test_cache_cleaned_on_exception(self, simple_transformer, train_data, device):
+        """Cache should be cleared even if exception occurs."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        with pytest.raises(RuntimeError):
+            with model.cache_training_context():
+                assert model._training_cache_ready
+                raise RuntimeError("Test exception")
+
+        assert not model._training_cache_ready
+
+
+class TestPFNModelCachedPredictions:
+    """Test that cached predictions match non-cached."""
+
+    def test_predictions_match(self, simple_transformer, train_data, device, dtype):
+        """Cached and non-cached predictions should be identical."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        torch.manual_seed(123)
+        test_X = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype)
+
+        # Non-cached
+        with torch.no_grad():
+            posterior_std = model.posterior(test_X)
+            mean_std = posterior_std.mean.clone()
+            var_std = posterior_std.variance.clone()
+
+        # Cached
+        with model.cache_training_context():
+            with torch.no_grad():
+                posterior_cached = model.posterior(test_X)
+                mean_cached = posterior_cached.mean.clone()
+                var_cached = posterior_cached.variance.clone()
+
+        torch.testing.assert_close(mean_std, mean_cached, atol=1e-7, rtol=1e-7)
+        torch.testing.assert_close(var_std, var_cached, atol=1e-7, rtol=1e-7)
+
+    def test_predictions_match_batched(self, simple_transformer, train_data, device, dtype):
+        """Cached predictions should work with batched inputs."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        torch.manual_seed(456)
+        test_X = torch.rand(8, 3, train_X.shape[-1], device=device, dtype=dtype)  # (b, q, d)
+
+        with torch.no_grad():
+            posterior_std = model.posterior(test_X)
+            mean_std = posterior_std.mean.clone()
+
+        with model.cache_training_context():
+            with torch.no_grad():
+                posterior_cached = model.posterior(test_X)
+                mean_cached = posterior_cached.mean.clone()
+
+        torch.testing.assert_close(mean_std, mean_cached, atol=1e-7, rtol=1e-7)
+
+    def test_multiple_calls_in_context(self, simple_transformer, train_data, device, dtype):
+        """Multiple posterior calls within cache context should all be consistent."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        torch.manual_seed(789)
+        test_X1 = torch.rand(3, train_X.shape[-1], device=device, dtype=dtype)
+        test_X2 = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype)
+
+        # Get non-cached baselines
+        with torch.no_grad():
+            mean_std1 = model.posterior(test_X1).mean.clone()
+            mean_std2 = model.posterior(test_X2).mean.clone()
+
+        # Get cached results
+        with model.cache_training_context():
+            with torch.no_grad():
+                mean_cached1 = model.posterior(test_X1).mean.clone()
+                mean_cached2 = model.posterior(test_X2).mean.clone()
+
+        torch.testing.assert_close(mean_std1, mean_cached1, atol=1e-7, rtol=1e-7)
+        torch.testing.assert_close(mean_std2, mean_cached2, atol=1e-7, rtol=1e-7)
+
+
+class TestPFNModelCacheGradients:
+    """Test gradient flow through cached predictions."""
+
+    def test_gradients_flow_through_test_x(self, simple_transformer, train_data, device, dtype):
+        """Gradients should flow through test_x when caching."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        torch.manual_seed(111)
+        test_X = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype, requires_grad=True)
+
+        with model.cache_training_context():
+            posterior = model.posterior(test_X)
+            loss = posterior.mean.sum()
+            loss.backward()
+
+        assert test_X.grad is not None
+        assert not torch.all(test_X.grad == 0)
+
+    def test_gradients_match_non_cached(self, simple_transformer, train_data, device, dtype):
+        """Gradients should match between cached and non-cached."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        torch.manual_seed(222)
+        base_test_X = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype)
+
+        # Non-cached gradient
+        test_X_std = base_test_X.clone().requires_grad_(True)
+        posterior_std = model.posterior(test_X_std)
+        posterior_std.mean.sum().backward()
+        grad_std = test_X_std.grad.clone()
+
+        # Cached gradient
+        test_X_cached = base_test_X.clone().requires_grad_(True)
+        with model.cache_training_context():
+            posterior_cached = model.posterior(test_X_cached)
+            posterior_cached.mean.sum().backward()
+        grad_cached = test_X_cached.grad.clone()
+
+        torch.testing.assert_close(grad_std, grad_cached, atol=1e-7, rtol=1e-7)
+
+    def test_gradient_shape_consistency(self, simple_transformer, train_data, device, dtype):
+        """Gradients should have correct shape for various input shapes."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        test_shapes = [
+            (5, train_X.shape[-1]),        # (q, d)
+            (4, 1, train_X.shape[-1]),     # (b, q=1, d)
+            (3, 2, train_X.shape[-1]),     # (b, q, d)
+        ]
+
+        for shape in test_shapes:
+            test_X = torch.rand(*shape, device=device, dtype=dtype, requires_grad=True)
+
+            with model.cache_training_context():
+                posterior = model.posterior(test_X)
+                posterior.mean.sum().backward()
+
+            assert test_X.grad is not None, f"No gradient for shape {shape}"
+            assert test_X.grad.shape == shape, (
+                f"Wrong gradient shape for input {shape}: got {test_X.grad.shape}"
+            )
+
+
+class TestPFNModelCacheNegation:
+    """Test cache behavior with negate_train_ys."""
+
+    def test_negate_mismatch_raises(self, simple_transformer, train_data, device, dtype):
+        """Should raise if negate_train_ys doesn't match cache."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        test_X = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype)
+
+        with model.cache_training_context(negate_train_ys=False):
+            with pytest.raises(ValueError, match="negate_train_ys"):
+                model.posterior(test_X, negate_train_ys=True)
+
+    def test_negation_cached_correctly(self, simple_transformer, train_data, device, dtype):
+        """Negated predictions should match between cached and non-cached."""
+        train_X, train_Y = train_data
+        model = PFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        torch.manual_seed(333)
+        test_X = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype)
+
+        # Non-cached with negation
+        with torch.no_grad():
+            mean_std = model.posterior(test_X, negate_train_ys=True).mean.clone()
+
+        # Cached with negation
+        with model.cache_training_context(negate_train_ys=True):
+            with torch.no_grad():
+                mean_cached = model.posterior(test_X, negate_train_ys=True).mean.clone()
+
+        torch.testing.assert_close(mean_std, mean_cached, atol=1e-7, rtol=1e-7)
+
+
+class TestPFNModelWithPendingPointsCaching:
+    """Test caching with pending points."""
+
+    def test_cache_includes_pending_points(self, simple_transformer, train_data, device, dtype):
+        """Cache should include pending points."""
+        train_X, train_Y = train_data
+        pending_X = torch.rand(3, train_X.shape[-1], device=device, dtype=dtype)
+
+        model = PFNModelWithPendingPoints(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            pending_X=pending_X,
+            batch_first=True,
+        )
+
+        torch.manual_seed(444)
+        test_X = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype)
+
+        # Non-cached
+        with torch.no_grad():
+            posterior_std = model.posterior(test_X)
+            mean_std = posterior_std.mean.clone()
+
+        # Cached (should include pending points)
+        with model.cache_training_context():
+            with torch.no_grad():
+                posterior_cached = model.posterior(test_X)
+                mean_cached = posterior_cached.mean.clone()
+
+        torch.testing.assert_close(mean_std, mean_cached, atol=1e-7, rtol=1e-7)
+
+    def test_pending_modification_blocked_during_cache(
+        self, simple_transformer, train_data, device, dtype
+    ):
+        """Cannot modify pending_X while cache is active."""
+        train_X, train_Y = train_data
+        pending_X = torch.rand(3, train_X.shape[-1], device=device, dtype=dtype)
+
+        model = PFNModelWithPendingPoints(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            pending_X=pending_X,
+            batch_first=True,
+        )
+
+        with model.cache_training_context():
+            with pytest.raises(RuntimeError, match="Cannot modify pending_X"):
+                model.pending_X = torch.rand(2, train_X.shape[-1], device=device, dtype=dtype)
+
+    def test_pending_points_property(self, simple_transformer, train_data, device, dtype):
+        """Test pending_X property getter and setter."""
+        train_X, train_Y = train_data
+        pending_X = torch.rand(3, train_X.shape[-1], device=device, dtype=dtype)
+
+        model = PFNModelWithPendingPoints(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            pending_X=pending_X,
+            batch_first=True,
+        )
+
+        # Test getter
+        assert model.pending_X is not None
+        assert model.pending_X.shape == pending_X.shape
+
+        # Test setter (outside cache context)
+        new_pending = torch.rand(5, train_X.shape[-1], device=device, dtype=dtype)
+        model.pending_X = new_pending
+        assert model.pending_X.shape == new_pending.shape
+
+        # Test setter to None
+        model.pending_X = None
+        assert model.pending_X is None
+
+
+class TestMultivariatePFNModelCaching:
+    """Test caching with multivariate posteriors."""
+
+    def test_marginals_use_cache(self, simple_transformer, train_data, device, dtype):
+        """Marginal computation should use cache."""
+        train_X, train_Y = train_data
+        model = MultivariatePFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        # Single point - should return BoundedRiemannPosterior
+        torch.manual_seed(555)
+        test_X = torch.rand(1, train_X.shape[-1], device=device, dtype=dtype)
+
+        with torch.no_grad():
+            posterior_std = model.posterior(test_X)
+            mean_std = posterior_std.mean.clone()
+
+        with model.cache_training_context():
+            with torch.no_grad():
+                posterior_cached = model.posterior(test_X)
+                mean_cached = posterior_cached.mean.clone()
+
+        torch.testing.assert_close(mean_std, mean_cached, atol=1e-7, rtol=1e-7)
+
+    def test_multivariate_with_cache(self, simple_transformer, train_data, device, dtype):
+        """Multivariate posterior should work with caching."""
+        train_X, train_Y = train_data
+        model = MultivariatePFNModel(
+            train_X=train_X,
+            train_Y=train_Y,
+            model=simple_transformer,
+            batch_first=True,
+        )
+
+        # Multiple points - should return MultivariateRiemannPosterior
+        torch.manual_seed(666)
+        test_X = torch.rand(3, train_X.shape[-1], device=device, dtype=dtype)
+
+        with torch.no_grad():
+            posterior_std = model.posterior(test_X)
+            mean_std = posterior_std.mean.clone()
+
+        with model.cache_training_context():
+            with torch.no_grad():
+                posterior_cached = model.posterior(test_X)
+                mean_cached = posterior_cached.mean.clone()
+
+        # Marginals should match
+        torch.testing.assert_close(mean_std, mean_cached, atol=1e-7, rtol=1e-7)
+
+        # Correlation should be computed
+        assert hasattr(posterior_cached, "correlation_matrix")
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/pfns/batch_shape_sampler.py b/pfns/batch_shape_sampler.py
index 8369a6b..ad7af5d 100644
--- a/pfns/batch_shape_sampler.py
+++ b/pfns/batch_shape_sampler.py
@@ -49,7 +49,6 @@ def sample_batch_shape(self, epoch: int, step: int) -> BatchShape:
         single_eval_pos = rng.randint(
             self.min_single_eval_pos,
             self.max_seq_len
-            - 1
             - (
                 self.fixed_num_test_instances
                 if self.fixed_num_test_instances is not None
diff --git a/pfns/model/bar_distribution.py b/pfns/model/bar_distribution.py
index 3f223f6..81b0f32 100644
--- a/pfns/model/bar_distribution.py
+++ b/pfns/model/bar_distribution.py
@@ -13,7 +13,6 @@
 from typing import Any, List, TYPE_CHECKING
 
 import torch
-
 from pfns import base_config
 from torch import nn
 from typing_extensions import override
@@ -38,9 +37,21 @@ def get_criterion(self):
         else:
             return BarDistribution(**kwargs)
 
+    @classmethod
+    def _loading_kwarg_transform(cls, kwargs):
+        if kwargs.pop("sobolev_multiplier", None) is not None:
+            print("WARNING: sobolev_multiplier is not running no more.")
+        return kwargs
+
 
 class BarDistribution(nn.Module):
-    def __init__(self, borders: torch.Tensor, *, ignore_nan_targets: bool = True):
+    def __init__(
+        self,
+        borders: torch.Tensor,
+        *,
+        ignore_nan_targets: bool = True,
+        probabilities: torch.Tensor | None = None,
+    ):
         """Loss for a distribution over bars. The bars are defined by the borders.
         The loss is the negative log density of the distribution. The density is defined
         as a softmax over the logits, where the softmax is scaled by the width of the
@@ -59,6 +70,7 @@ def __init__(self, borders: torch.Tensor, *, ignore_nan_targets: bool = True):
         assert len(borders.shape) == 1
         borders = borders.contiguous()
         self.register_buffer("borders", borders)
+        self.probabilities = probabilities
         full_width = self.bucket_widths.sum()
 
         assert (1 - (full_width / (self.borders[-1] - self.borders[0]))).abs() < 1e-2, (
@@ -223,14 +235,13 @@ def forward(
         """Returns the negative log density (the _loss_).
 
         Args:
-            logits: The logits of the model.
-            y: The ys to compute the loss for.
+            logits: The logits of the model, shape (*batch_shape, num_bars)
+            y: The ys to compute the loss for, shape (*batch_shape,)
 
         Returns:
             The negative log density.
         """
         # gives the negative log density (the _loss_),
-        # y: T x B, logits: T x B x self.num_bars
         y = y.clone().view(*logits.shape[:-1])  # no trailing one dimension
         ignore_loss_mask = self.ignore_init(y)
         target_sample = self.map_to_bucket_idx(y)
@@ -244,7 +255,6 @@ def forward(
 
         scaled_bucket_log_probs = self.compute_scaled_log_probs(logits)
 
-        # T x B
         nll_loss = -scaled_bucket_log_probs.gather(
             -1,
             target_sample[..., None],
@@ -266,27 +276,30 @@ def mean(self, logits: torch.Tensor) -> torch.Tensor:
         return p @ bucket_means
 
     def median(self, logits: torch.Tensor) -> torch.Tensor:
-        return self.icdf(logits, 0.5)
+        return self.icdf(logits=logits, left_prob=0.5)
 
-    def icdf(self, logits: torch.Tensor, left_prob: float) -> torch.Tensor:
+    def icdf(self, left_prob: float, logits: torch.Tensor | None) -> torch.Tensor:
         """Implementation of the quantile function
         :param logits: Tensor of any shape, with the last dimension being logits
         :param left_prob: float: The probability mass to the left of the result.
         :return: Position with `left_prob` probability weight to the left.
         """
-        probs = logits.softmax(-1)
+        if logits is None and self.probabilities is not None:
+            probs = self.probabilities
+        else:
+            probs = logits.softmax(-1)
         cumprobs = torch.cumsum(probs, -1)
         idx = (
             torch.searchsorted(
                 cumprobs,
-                left_prob * torch.ones(*cumprobs.shape[:-1], 1, device=logits.device),
+                left_prob * torch.ones(*cumprobs.shape[:-1], 1, device=probs.device),
             )
             .squeeze(-1)
             .clamp(0, cumprobs.shape[-1] - 1)
         )  # this might not do the right for outliers
         cumprobs = torch.cat(
             [
-                torch.zeros(*cumprobs.shape[:-1], 1, device=logits.device),
+                torch.zeros(*cumprobs.shape[:-1], 1, device=probs.device),
                 cumprobs,
             ],
             -1,
@@ -300,6 +313,19 @@ def icdf(self, logits: torch.Tensor, left_prob: float) -> torch.Tensor:
             idx[..., None],
         ).squeeze(-1)
 
+    def sample(self, logits: torch.Tensor, t: float = 1.0) -> torch.Tensor:
+        """Samples values from the distribution.
+
+        Temperature t.
+        """
+        p_cdf = torch.rand(*logits.shape[:-1])
+        return torch.tensor(
+            [
+                self.icdf(logits=logits[i, :] / t, left_prob=p)
+                for i, p in enumerate(p_cdf.tolist())
+            ],
+        )
+
     def quantile(
         self,
         logits: torch.Tensor,
@@ -308,8 +334,8 @@ def quantile(
         side_probs = (1.0 - center_prob) / 2
         return torch.stack(
             (
-                self.icdf(logits, side_probs),
-                self.icdf(logits, 1.0 - side_probs),
+                self.icdf(logits=logits, left_prob=side_probs),
+                self.icdf(logits=logits, left_prob=1.0 - side_probs),
             ),
             -1,
         )
@@ -344,7 +370,7 @@ def ucb(
         """
         if maximize:
             rest_prob = 1 - rest_prob
-        return self.icdf(logits, rest_prob)
+        return self.icdf(logits=logits, left_prob=rest_prob)
 
     def mode(self, logits: torch.Tensor) -> torch.Tensor:
         density = logits.softmax(-1) / self.bucket_widths
@@ -586,16 +612,6 @@ def pdf(self, logits: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         """Probability density function at y."""
         return torch.exp(self.forward(logits, y))
 
-    def sample(self, logits: torch.Tensor, t: float = 1.0) -> torch.Tensor:
-        """Samples values from the distribution.
-
-        Temperature t.
-        """
-        p_cdf = torch.rand(*logits.shape[:-1])
-        return torch.tensor(
-            [self.icdf(logits[i, :] / t, p) for i, p in enumerate(p_cdf.tolist())],
-        )
-
     @override
     def mean(self, logits: torch.Tensor) -> torch.Tensor:
         bucket_means = self.borders[:-1] + self.bucket_widths / 2
@@ -834,6 +850,8 @@ def get_bucket_borders(
     if ys is not None:
         ys = ys.flatten()
         ys = ys[~torch.isnan(ys)]
+        ys = ys.sort()[0]
+        ys = torch.unique_consecutive(ys)
         assert (
             len(ys) > num_outputs
         ), f"Number of ys :{len(ys)} must be larger than num_outputs: {num_outputs}"
@@ -869,9 +887,9 @@ def get_bucket_borders(
             0,
         )
 
-    assert len(borders) - 1 == num_outputs, (
-        f"len(borders) - 1 == {len(borders) - 1}" f" != {num_outputs} == num_outputs"
-    )
+    assert (
+        len(borders) - 1 == num_outputs
+    ), f"len(borders) - 1 == {len(borders) - 1} != {num_outputs} == num_outputs"
 
     if not widen_borders_factor or widen_borders_factor == 1.0:
         assert (
@@ -881,9 +899,4 @@ def get_bucket_borders(
             full_range[-1] == borders[-1]  # type: ignore
         ), f"{full_range[-1]} != {borders[-1]}"  # type: ignore
 
-    unique_borders = torch.unique_consecutive(borders)
-
-    if (unique_borders != borders).any():
-        print("Borders were not unique, removed duplicates.")
-
-    return unique_borders
+    return borders
diff --git a/pfns/model/encoders.py b/pfns/model/encoders.py
index 171955b..28bb5c7 100644
--- a/pfns/model/encoders.py
+++ b/pfns/model/encoders.py
@@ -3,12 +3,10 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-
 from typing import Any
 
 import numpy as np
 import torch
-
 from pfns import base_config
 from pfns.model import encoders
 from pfns.priors.hyperparameter_sampling import (
@@ -121,6 +119,26 @@ def create_encoder(self, features, emsize):
 ### Style Encoders
 
 
+class ConstantStyleNormalization(nn.Module):
+    def __init__(self, mean, std):
+        super().__init__()
+        self.mean = mean
+        self.std = std
+
+    def forward(self, x):
+        return (x - self.mean) / self.std
+
+
+class NanHandlingStyleEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        nan_indicators = torch.isnan(x).float() * 2.0 - 1
+        x = torch.nan_to_num(x, nan=0.0)
+        return torch.cat([x, nan_indicators], dim=-1)
+
+
 def linear_style_encoder(num_styles, emsize):
     return nn.Linear(num_styles, emsize)
 
@@ -132,6 +150,16 @@ class StyleEncoderConfig(base_config.BaseConfig):
         dict[str, base_config.BaseTypes | DistributionConfig] | None
     ) = None
     encoder_type: str = "linear"
+    constant_normalization_mean: float = 0.0
+    constant_normalization_std: float = 1.0
+    nan_handling: bool | None = None
+
+    def __post_init__(self):
+        if self.normalize_to_hyperparameters is not None:
+            assert self.constant_normalization_mean == 0.0
+            assert self.constant_normalization_std == 1.0
+            assert self.nan_handling is None
+            assert self.num_styles is None
 
     def create_encoder(self, emsize):
         num_features = self.num_styles
@@ -145,9 +173,21 @@ def create_encoder(self, emsize):
             hpn = HyperparameterNormalizer(self.normalize_to_hyperparameters)
             num_features = hpn.num_hps * 2
             modules.append(hpn)
+        else:
+            assert self.num_styles is not None
+            normalizer = ConstantStyleNormalization(
+                mean=self.constant_normalization_mean,
+                std=self.constant_normalization_std,
+            )
+            modules.append(normalizer)
+
+            if self.nan_handling:
+                modules.append(NanHandlingStyleEncoder())
+                num_features *= 2
 
         if self.encoder_type == "linear":
             modules.append(encoders.linear_style_encoder(num_features, emsize))
+            modules.append(nn.Flatten())
             return nn.Sequential(*modules)
         else:
             raise ValueError(
@@ -206,6 +246,63 @@ def forward(
 
         return input[self.output_key] if self.output_key is not None else input
 
+    def freeze_fitted_state(self) -> None:
+        """Freeze all encoder steps to prevent _fit() from being called.
+
+        When frozen, encoder steps will reuse their existing fitted state
+        instead of refitting on new data. This is used during cached inference
+        to prevent correlation estimation from corrupting the encoder state.
+        """
+        for module in self:
+            if hasattr(module, "_fitted_state_frozen"):
+                module._fitted_state_frozen = True
+
+    def unfreeze_fitted_state(self) -> None:
+        """Unfreeze all encoder steps to allow _fit() to be called.
+
+        This restores normal behavior where encoder steps fit on training data.
+        """
+        for module in self:
+            if hasattr(module, "_fitted_state_frozen"):
+                module._fitted_state_frozen = False
+
+    def save_fitted_state(self) -> dict:
+        """Save the fitted state of all encoder steps.
+
+        Returns a dictionary mapping module indices to their fitted state.
+        This can be restored later with `restore_fitted_state`.
+        """
+        saved_state = {}
+        for idx, module in enumerate(self):
+            module_state = {}
+            # Save any attribute that starts with underscore and ends with underscore
+            # (fitted state convention: _attribute_)
+            for attr_name in dir(module):
+                if (
+                    attr_name.endswith("_")
+                    and not attr_name.startswith("__")
+                    and not callable(getattr(module, attr_name, None))
+                ):
+                    attr_value = getattr(module, attr_name, None)
+                    if isinstance(attr_value, torch.Tensor):
+                        module_state[attr_name] = attr_value.clone()
+                    elif attr_value is not None:
+                        module_state[attr_name] = attr_value
+            if module_state:
+                saved_state[idx] = module_state
+        return saved_state
+
+    def restore_fitted_state(self, saved_state: dict) -> None:
+        """Restore the fitted state of all encoder steps.
+
+        Args:
+            saved_state: Dictionary returned by `save_fitted_state`.
+        """
+        for idx, module_state in saved_state.items():
+            module = self[idx]
+            for attr_name, attr_value in module_state.items():
+                setattr(module, attr_name, attr_value)
+
 
 class SeqEncStep(nn.Module):
     """Abstract base class for sequential encoder steps.
@@ -235,6 +332,10 @@ def __init__(
         super().__init__()
         self.in_keys = in_keys
         self.out_keys = out_keys
+        # When frozen, _fit() is skipped and the existing fitted state is used.
+        # This is used during cached inference to prevent correlation estimation
+        # from corrupting the encoder state.
+        self._fitted_state_frozen = False
 
     # Either implement _forward:
 
@@ -307,7 +408,14 @@ def forward(
         """
         args = [state[in_key] for in_key in self.in_keys]
         if hasattr(self, "_fit"):
-            if kwargs["single_eval_pos"] or not cache_trainset_representation:
+            # Skip _fit if:
+            # 1. State is frozen (during cached inference to prevent corruption)
+            # 2. OR cache is enabled and we're in inference mode (no training data)
+            should_fit = (
+                not self._fitted_state_frozen
+                and (kwargs["single_eval_pos"] or not cache_trainset_representation)
+            )
+            if should_fit:
                 self._fit(*args, **kwargs)
             out = self._transform(*args, **kwargs)
         else:
@@ -444,11 +552,21 @@ def _fit(self, x: torch.Tensor, single_eval_pos: int, **kwargs: Any) -> None:
         """Compute the feature means on the training set for replacing NaNs.
 
         Args:
-            x: The input tensor.
+            x: The input tensor of shape (seq_len, batch_size, num_features).
             single_eval_pos: The position to use for single evaluation.
             **kwargs: Additional keyword arguments (unused).
         """
-        self.feature_means_ = torch_nanmean(x[:single_eval_pos], axis=0)
+        train_data = x[:single_eval_pos]  # (train_len, batch, num_features)
+        # Compute mean across sequence dimension (axis=0) → (batch, num_features)
+        seq_mean = torch_nanmean(train_data, axis=0)
+        # Average across batch dimension to get batch-independent means → (num_features,)
+        # This enables caching to work with different batch sizes between
+        # cache population and cache usage
+        if seq_mean.dim() > 1:
+            self.feature_means_ = torch_nanmean(seq_mean, axis=0)
+        else:
+            # Already 1D (single batch element case)
+            self.feature_means_ = seq_mean
 
     def _transform(
         self,
@@ -478,7 +596,9 @@ def _transform(
         nan_mask = torch.logical_or(torch.isnan(x), torch.isinf(x))
         # replace nans with the mean of the corresponding feature
         x = x.clone()  # clone to avoid inplace operations
-        x[nan_mask] = self.feature_means_.unsqueeze(0).expand_as(x)[nan_mask]
+        # feature_means_ is (num_features,), reshape to broadcast with x (seq, batch, features)
+        means_expanded = self.feature_means_.view(1, 1, -1).expand_as(x)
+        x[nan_mask] = means_expanded[nan_mask]
 
         return x, nans_indicator
 
@@ -516,12 +636,18 @@ def _fit(self, x: torch.Tensor, **kwargs: Any) -> None:
         """Compute the number of used features on the training set.
 
         Args:
-            x: The input tensor.
+            x: The input tensor of shape (seq_len, batch_size, num_features).
             **kwargs: Additional keyword arguments (unused).
         """
+        # sel: (batch, num_features) - True where feature varies across sequence
         sel = (x[1:] == x[0]).sum(0) != (x.shape[0] - 1)
+        # per_batch_used: (batch,) - count of used features per batch element
+        per_batch_used = sel.sum(-1)
+        # Average across batch to get batch-independent count → scalar
+        # This enables caching to work with different batch sizes
+        avg_used = per_batch_used.float().mean()
         self.number_of_used_features_ = torch.clip(
-            sel.sum(-1).unsqueeze(-1),
+            avg_used.unsqueeze(-1),  # (1,) for broadcasting
             min=1,
         ).cpu()
 
@@ -544,13 +670,14 @@ def _transform(self, x: torch.Tensor, **kwargs: Any) -> tuple[torch.Tensor]:
                 dtype=x.dtype,
             )
         if self.normalize_by_used_features:
+            # number_of_used_features_ is a scalar (1,) that broadcasts to any batch
+            n_used = self.number_of_used_features_.to(x.device)
+
             if self.normalize_by_sqrt:
                 # Verified that this gives indeed unit variance with appended zeros
-                x = x * torch.sqrt(
-                    self.num_features / self.number_of_used_features_.to(x.device),
-                )
+                x = x * torch.sqrt(self.num_features / n_used)
             else:
-                x = x * (self.num_features / self.number_of_used_features_.to(x.device))
+                x = x * (self.num_features / n_used)
 
         zeros_appended = torch.zeros(
             *x.shape[:-1],
diff --git a/pfns/model/layer.py b/pfns/model/layer.py
index 3ba1e8d..f2496ff 100644
--- a/pfns/model/layer.py
+++ b/pfns/model/layer.py
@@ -50,6 +50,10 @@ def __init__(  # noqa: PLR0913
         d_k: int | None = None,
         d_v: int | None = None,
         precomputed_kv: None | torch.Tensor | tuple[torch.Tensor, torch.Tensor] = None,
+        attention_across_items_first: bool = False,
+        positions_num_measures: int = 0,
+        positions_base: float = 0.02,
+        dont_look_at_yourself: bool = False,
     ) -> None:
         """
         Args:
@@ -132,7 +136,11 @@ def __init__(  # noqa: PLR0913
             precomputed_v=precomputed_v,
             precomputed_kv=precomputed_kv,
             init_gain=attention_init_gain,
+            positions_base=positions_base,
+            positions_num_measures=positions_num_measures,
+            dont_look_at_yourself=dont_look_at_yourself,
         )
+        self.positions_num_measures = positions_num_measures
 
         if dim_feedforward is None:
             dim_feedforward = 2 * d_model
@@ -179,6 +187,7 @@ def __init__(  # noqa: PLR0913
         self.multiquery_item_attention_for_test_set = (
             multiquery_item_attention_for_test_set
         )
+        self.attention_across_items_first = attention_across_items_first
 
     def __setstate__(self, state: dict[str, Any]) -> None:
         state.setdefault("save_peak_mem_factor", None)
@@ -191,6 +200,9 @@ def forward(  # noqa: C901
         *,
         cache_trainset_representation: bool = False,
         att_src: Tensor | None = None,
+        rope_vals: torch.Tensor | None = None,
+        positions: torch.Tensor
+        | None = None,  # shape: [batch, seqlen_q, num_feature_blocks, 1]
     ) -> Tensor:
         """Pass the input through the encoder layer.
 
@@ -254,9 +266,15 @@ def attn_between_features(x: torch.Tensor) -> torch.Tensor:
             )
 
         def attn_between_items(x: torch.Tensor) -> torch.Tensor:
+            transposed_rope_vals = (
+                rope_vals.transpose(1, 2) if rope_vals is not None else None
+            )
             # we need to transpose as self attention always treats
             # dim -2 as the sequence dimension
             if self.multiquery_item_attention_for_test_set:
+                assert rope_vals is None
+                assert self.positions_num_measures == 0
+
                 if single_eval_pos < x.shape[1]:
                     new_x_test = self.self_attn_between_items(
                         x[:, single_eval_pos:].transpose(1, 2),
@@ -295,10 +313,15 @@ def attn_between_items(x: torch.Tensor) -> torch.Tensor:
                 )
 
             attention_src_x = None
+            positions_kv = None
             if att_src is not None:
                 attention_src_x = att_src.transpose(1, 2)
             elif single_eval_pos:
                 attention_src_x = x[:, :single_eval_pos].transpose(1, 2)
+                if positions is not None:
+                    positions_kv = positions[:, :single_eval_pos].transpose(1, 2)
+            else:
+                assert positions is None
 
             return self.self_attn_between_items(
                 x.transpose(1, 2),
@@ -308,6 +331,9 @@ def attn_between_items(x: torch.Tensor) -> torch.Tensor:
                 add_input=True,
                 allow_inplace=True,
                 use_cached_kv=cache_trainset_representation and not single_eval_pos,
+                rope_vals=transposed_rope_vals,
+                positions=positions.transpose(1, 2) if positions is not None else None,
+                positions_kv=positions_kv,
             ).transpose(1, 2)
 
         # the mlp tends to require 8 times more memory at its peak, that is why we use 8 here
@@ -325,8 +351,12 @@ def attn_between_items(x: torch.Tensor) -> torch.Tensor:
                 " blocks must be 1."
             )
 
+        if self.attention_across_items_first:
+            sublayers.insert(0, attn_between_items)
+        else:
+            sublayers.append(attn_between_items)
+
         sublayers += [
-            attn_between_items,
             partial(
                 self.mlp.__call__,
                 save_peak_mem_factor=(
diff --git a/pfns/model/multi_head_attention.py b/pfns/model/multi_head_attention.py
index 8eaee3b..36e15f6 100644
--- a/pfns/model/multi_head_attention.py
+++ b/pfns/model/multi_head_attention.py
@@ -21,6 +21,26 @@
     HAVE_FLASH_ATTN = False
 
 
+def apply_rope(x, rope_vals):
+    # x has shape [b,s,h,d]
+    # rope_vals has shape [b,s,d]
+    b, s, h, dim = x.shape
+    assert rope_vals.shape == (b, s, dim), f"{rope_vals.shape=} != {(b, s, dim)}"
+    assert (dim // 2) * 2 == dim, f"{dim} is not even"
+
+    x = x.reshape(b, s, h, -1, 2)
+    rope_vals = rope_vals.reshape(b, s, 1, -1, 2)
+
+    out = torch.stack(
+        [
+            x[..., 0] * rope_vals[..., 0] - x[..., 1] * rope_vals[..., 1],
+            x[..., 1] * rope_vals[..., 0] + x[..., 0] * rope_vals[..., 1],
+        ],
+        -1,
+    ).view(b, s, h, dim)
+    return out
+
+
 class MultiHeadAttention(torch.nn.Module):
     """
     An implementation of multi-head attention, heavily relying on the pytorch
@@ -186,6 +206,9 @@ def __init__(  # noqa: PLR0913
         precomputed_kv: torch.Tensor | None = None,
         recompute: bool = False,
         init_gain: float = 1.0,
+        positions_base: float = 0.02,
+        positions_num_measures: int = 0,
+        dont_look_at_yourself: bool = False,
     ):
         super().__init__()
         assert nhead % share_kv_across_n_heads == 0
@@ -202,6 +225,14 @@ def __init__(  # noqa: PLR0913
         self.recompute = recompute
         self.init_gain = init_gain
 
+        self.positions_base = positions_base
+        assert (positions_num_measures + 1) <= self._d_k == self._d_v, (
+            f"positions_num_measures {positions_num_measures} must be less than "
+            f"or equal to d_k {self._d_k} and d_v {self._d_v}."
+        )
+        self.positions_num_measures = positions_num_measures
+        self.dont_look_at_yourself = dont_look_at_yourself
+
         w_out = torch.nn.Parameter(
             torch.empty(nhead, d_v, output_size, device=device, dtype=dtype),
         )
@@ -287,6 +318,9 @@ def forward(
         reuse_first_head_kv: bool = False,
         only_cache_first_head_kv: bool = False,
         use_cached_kv: bool = False,
+        rope_vals: torch.Tensor | None = None,  # shape: [..., batch, seq_len, head dim]
+        positions: torch.Tensor | None = None,  # shape: [..., batch, seqlen_q, 1]
+        positions_kv: torch.Tensor | None = None,  # shape: [..., batch, seqlen_kv, 1]
     ):
         """X is the current hidden and has a shape of [batch, ..., seq_len, input_size].
         If keys and values are present in the cache and 'freeze_kv' is not set, they
@@ -299,10 +333,32 @@ def forward(
         assert not (
             cache_kv and use_cached_kv
         ), "Cannot cache and use cached keys and values at the same time."
-        assert not x.requires_grad or (
-            not self.has_cached_kv and not cache_kv
-        ), "Saving keys and values is only supported during inference."
+        assert (
+            not x.requires_grad or not cache_kv
+        ), "Saving keys and values will stop gradients to flow into trainset."
+        assert not (use_cached_kv and rope_vals is not None), (
+            "Cannot use rope_vals with cached KV - ROPE is already applied to cached values"
+        )
+
+        # Flatten the batch dimensions
         x, x_kv, x_shape_after_transpose = self._rearrange_inputs_to_flat_batch(x, x_kv)
+        if rope_vals is not None:
+            rope_vals = rope_vals.reshape(-1, *rope_vals.shape[-2:])
+
+        # flatten positions' batch dims, just like x and x_kv are flattened
+        positions_flat = None
+        positions_kv_flat = None
+        if positions is not None:
+            assert (
+                positions.shape[-1] == 1
+            ), f"positions must have trailing dim 1, got {positions.shape}"
+            # Squeeze the trailing dim and flatten batch dims
+            positions_flat = positions.reshape(-1, positions.shape[-2])
+        if positions_kv is not None:
+            assert (
+                positions_kv.shape[-1] == 1
+            ), f"positions_kv must have trailing dim 1, got {positions_kv.shape}"
+            positions_kv_flat = positions_kv.reshape(-1, positions_kv.shape[-2])
 
         nhead_kv = 1 if reuse_first_head_kv else self._nhead_kv
 
@@ -315,7 +371,6 @@ def forward(
             else:
                 batch_size, seqlen_kv = x.shape[:2]
 
-            # TODO: handling of device and dtype.
             if self._w_kv is not None or self._w_qkv is not None:
                 self._kv_cache = torch.empty(
                     batch_size,
@@ -356,6 +411,9 @@ def forward(
             allow_inplace=allow_inplace,
             save_peak_mem_factor=save_peak_mem_factor,
             reuse_first_head_kv=reuse_first_head_kv,
+            rope_vals=rope_vals,
+            positions=positions_flat,
+            positions_kv=positions_kv_flat,
         )
         return output.reshape(x_shape_after_transpose[:-1] + output.shape[-1:])
 
@@ -397,6 +455,46 @@ def compute_qkv(  # noqa: PLR0912, C901
             v = v_cache
             kv = kv_cache
 
+            # Handle batch dimension broadcast when cached KV has different batch
+            # size than the query. This is common when caching training context
+            # with batch_size=1 and evaluating multiple test batches.
+            # The batch dimension includes feature groups, so we need to repeat
+            # (not just expand) when batch sizes differ.
+            query_batch_size = x.shape[0]
+            if k is not None and k.shape[0] != query_batch_size:
+                if k.shape[0] == 1:
+                    k = k.expand(query_batch_size, *k.shape[1:])
+                elif query_batch_size % k.shape[0] == 0:
+                    n_repeats = query_batch_size // k.shape[0]
+                    k = k.repeat(n_repeats, *([1] * (k.dim() - 1)))
+                else:
+                    raise RuntimeError(
+                        f"Cannot broadcast cached k with shape {k.shape} to query "
+                        f"batch size {query_batch_size}."
+                    )
+            if v is not None and v.shape[0] != query_batch_size:
+                if v.shape[0] == 1:
+                    v = v.expand(query_batch_size, *v.shape[1:])
+                elif query_batch_size % v.shape[0] == 0:
+                    n_repeats = query_batch_size // v.shape[0]
+                    v = v.repeat(n_repeats, *([1] * (v.dim() - 1)))
+                else:
+                    raise RuntimeError(
+                        f"Cannot broadcast cached v with shape {v.shape} to query "
+                        f"batch size {query_batch_size}."
+                    )
+            if kv is not None and kv.shape[0] != query_batch_size:
+                if kv.shape[0] == 1:
+                    kv = kv.expand(query_batch_size, *kv.shape[1:])
+                elif query_batch_size % kv.shape[0] == 0:
+                    n_repeats = query_batch_size // kv.shape[0]
+                    kv = kv.repeat(n_repeats, *([1] * (kv.dim() - 1)))
+                else:
+                    raise RuntimeError(
+                        f"Cannot broadcast cached kv with shape {kv.shape} to query "
+                        f"batch size {query_batch_size}."
+                    )
+
         assert (k is None) == (v is None)
 
         if self._w_qkv is None:
@@ -469,6 +567,9 @@ def _compute(
         cache_kv: bool,
         use_cached_kv: bool,
         reuse_first_head_kv: bool,
+        rope_vals: torch.Tensor | None = None,  # shape: [batch, seq_len, head dim]
+        positions: torch.Tensor | None = None,  # shape: [batch, seqlen_q]
+        positions_kv: torch.Tensor | None = None,  # shape: [batch, seqlen_kv]
     ) -> torch.Tensor:
         """Attention computation.
         Called by 'forward', potentially on shards, once shapes have been normalized.
@@ -483,7 +584,21 @@ def _compute(
             use_cached_kv=use_cached_kv,
             reuse_first_head_kv=reuse_first_head_kv,
         )
-        attention_head_outputs = MultiHeadAttention.compute_attention_heads(
+
+        if rope_vals is not None:
+            if q is not None:
+                q = apply_rope(q, rope_vals)
+            if k is not None:
+                k = apply_rope(k, rope_vals[:, : k.shape[1]])
+            if kv is not None:
+                kv[..., 0, :, :] = apply_rope(
+                    kv[..., 0, :, :], rope_vals[:, : kv.shape[1]]
+                )
+            if qkv is not None:
+                qkv[..., 0, :, :] = apply_rope(qkv[..., 0, :, :], rope_vals)
+                qkv[..., 1, :, :] = apply_rope(qkv[..., 1, :, :], rope_vals)
+
+        attention_head_outputs = self.compute_attention_heads(
             q,
             k,
             v,
@@ -491,6 +606,11 @@ def _compute(
             qkv,
             self.dropout_p,
             self.softmax_scale,
+            positions=positions,
+            positions_kv=positions_kv,
+            positions_base=self.positions_base,
+            positions_num_measures=self.positions_num_measures,
+            dont_look_at_yourself=self.dont_look_at_yourself,
         )
         return torch.einsum(
             "... h d, h d s -> ... s",
@@ -524,7 +644,7 @@ def broadcast_kv_across_heads(
             share_kv_across_n_heads,
             -1,
         )
-        return kv.reshape(*kv.shape[:-3], nhead * share_kv_across_n_heads, d)
+        return kv.contiguous().reshape(*kv.shape[:-3], nhead * share_kv_across_n_heads, d)
 
     @staticmethod
     def compute_attention_heads(  # noqa: C901, PLR0912
@@ -535,6 +655,12 @@ def compute_attention_heads(  # noqa: C901, PLR0912
         qkv: torch.Tensor | None,
         dropout_p: float | None = None,
         softmax_scale: float | None = None,
+        *,
+        positions: torch.Tensor | None = None,  # shape: [batch, seqlen_q]
+        positions_kv: torch.Tensor | None = None,  # shape: [batch, seqlen_kv]
+        positions_base: float = 0.02,
+        positions_num_measures: int = 8,
+        dont_look_at_yourself: bool = False,  # only with positions is not None
     ) -> torch.Tensor:
         assert (k is None) == (v is None)
         assert sum([qkv is None, kv is None, k is None and v is None]) == 2
@@ -549,6 +675,14 @@ def compute_attention_heads(  # noqa: C901, PLR0912
         assert k is not None
         assert v is not None
 
+        if positions is not None:
+            assert (
+                positions_kv is not None
+            ), "positions is not None but positions_kv is None"
+
+        if dont_look_at_yourself:
+            assert positions_num_measures > 0
+
         batch_size, seqlen_q, nhead, d_k = q.shape
         _, seqlen_kv, nhead_kv, d_v = v.shape
         share_kv_across_n_heads = nhead // nhead_kv
@@ -565,6 +699,10 @@ def compute_attention_heads(  # noqa: C901, PLR0912
         TORCH_2_ATTENTION_POSSIBLE = (
             torch.__version__ >= "2" and torch.cuda.is_available()
         )
+        if positions_num_measures > 0:
+            TORCH_2_ATTENTION_POSSIBLE = False
+            use_flash_attention = False
+
         USE_TORCH_2_GQA = False
         if TORCH_2_ATTENTION_POSSIBLE:
             # check whether torch.nn.functional.scaled_dot_product_attention has a
@@ -618,7 +756,7 @@ def get_seqlen_cumsums(
 
             if qkv is not None:
                 attention_head_outputs = flash_attn_unpadded_qkvpacked_func(  # type: ignore
-                    qkv.reshape(batch_size * seqlen_q, 3, nhead, d_k),
+                    qkv.contiguous().reshape(batch_size * seqlen_q, 3, nhead, d_k),
                     get_seqlen_cumsums(batch_size, seqlen_q, qkv.device),
                     seqlen_q,
                     dropout_p=dropout_p,
@@ -633,8 +771,8 @@ def get_seqlen_cumsums(
                     share_kv_across_n_heads,
                 )
                 attention_head_outputs = flash_attn_unpadded_kvpacked_func(  # type: ignore
-                    q.reshape(batch_size * seqlen_q, nhead, d_k),
-                    kv.reshape(batch_size * seqlen_kv, 2, nhead, d_k),
+                    q.contiguous().reshape(batch_size * seqlen_q, nhead, d_k),
+                    kv.contiguous().reshape(batch_size * seqlen_kv, 2, nhead, d_k),
                     get_seqlen_cumsums(batch_size, seqlen_q, q.device),
                     get_seqlen_cumsums(batch_size, seqlen_kv, kv.device),
                     seqlen_q,
@@ -662,9 +800,9 @@ def get_seqlen_cumsums(
                     share_kv_across_n_heads,
                 )
                 attention_head_outputs = flash_attn_unpadded_func(  # type: ignore
-                    q.reshape(batch_size * seqlen_q, nhead, d_k_),  # type: ignore
-                    k.reshape(batch_size * seqlen_kv, nhead, d_k_),  # type: ignore
-                    v.reshape(batch_size * seqlen_kv, nhead, d_v),
+                    q.contiguous().reshape(batch_size * seqlen_q, nhead, d_k_),  # type: ignore
+                    k.contiguous().reshape(batch_size * seqlen_kv, nhead, d_k_),  # type: ignore
+                    v.contiguous().reshape(batch_size * seqlen_kv, nhead, d_v),
                     get_seqlen_cumsums(batch_size, seqlen_q, q.device),
                     get_seqlen_cumsums(batch_size, seqlen_kv, k.device),
                     seqlen_q,
@@ -704,9 +842,58 @@ def get_seqlen_cumsums(
             )
             attention_head_outputs = attention_head_outputs.transpose(1, 2)
         else:
-            k = MultiHeadAttention.broadcast_kv_across_heads(k, share_kv_across_n_heads)
-            v = MultiHeadAttention.broadcast_kv_across_heads(v, share_kv_across_n_heads)
-            logits = torch.einsum("b q h d, b k h d -> b q k h", q, k)
+            k = MultiHeadAttention.broadcast_kv_across_heads(
+                k, share_kv_across_n_heads
+            )  # [b,k,h,d]
+            v = MultiHeadAttention.broadcast_kv_across_heads(
+                v, share_kv_across_n_heads
+            )  # [b,k,h,d]
+            # Prototype: compute pairwise distance metrics without modifying attention
+            if positions_num_measures > 0:
+                assert (
+                    positions.shape[0] == batch_size and positions.shape[1] == seqlen_q
+                ), f"positions (q) shape {positions.shape} incompatible with {(batch_size, seqlen_q)}"
+                assert (
+                    positions_kv.shape[0] == batch_size
+                    and positions_kv.shape[1] == seqlen_kv
+                ), f"positions_kv (kv) shape {positions_kv.shape} incompatible with {(batch_size, seqlen_kv)}"
+                pos_q = positions
+                pos_k = positions_kv
+                # Distance per (q,k)
+                dist = (pos_q[:, :, None] - pos_k[:, None, :]).abs()  # [b,q,k]
+                scales = torch.tensor(
+                    [2**i for i in range(positions_num_measures)],
+                    device=positions.device,
+                    dtype=positions.dtype,
+                )
+                thresholds = (positions_base * scales)[None, None, None, :]
+                # Vector of within-threshold interpolations in [0,1]
+                g = 1.0 - 2.0 * (dist[..., None] / thresholds).clamp(
+                    0.0, 1.0
+                )  # [b,q,k,T]
+                # Direction indicator
+                diff = pos_k[:, None, :] - pos_q[:, :, None]
+                is_right = 2 * (diff >= 0.0).to(q.dtype) - 1  # [b,q,k]
+                is_right[diff == 0.0] = 0.0
+                g = torch.cat([g, is_right[..., None]], dim=-1)  # [b,q,k,T+1]
+                t = g.shape[-1]
+
+                logits = torch.einsum(
+                    "b q h d, b k h d -> b q k h", q[..., :-t], k[..., :-t]
+                )
+                position_available_mask = ~positions.isnan().any(1)  # [b]
+                logits[position_available_mask] += torch.einsum(
+                    "b q h t, b q k t -> b q k h",
+                    q[position_available_mask, ..., -t:],
+                    g[position_available_mask],
+                )
+
+                if dont_look_at_yourself:
+                    logits[:, torch.arange(seqlen_kv), torch.arange(seqlen_kv), :] = (
+                        float("-inf")
+                    )
+            else:
+                logits = torch.einsum("b q h d, b k h d -> b q k h", q, k)
             logits *= (
                 torch.sqrt(torch.tensor(1.0 / d_k)).to(k.device)
                 if softmax_scale is None
@@ -714,7 +901,21 @@ def get_seqlen_cumsums(
             )
             ps = torch.softmax(logits, dim=2)
             ps = torch.dropout(ps, dropout_p, train=True)
-            attention_head_outputs = torch.einsum("b q k h, b k h d -> b q h d", ps, v)
+            if positions_num_measures > 0:
+                attention_head_outputs = torch.einsum(
+                    "b q k h, b k h d -> b q h d", ps, v
+                )
+                attention_head_outputs[position_available_mask, ..., -t:] = (
+                    torch.einsum(
+                        "b q k h, b q k t -> b q h t",
+                        ps[position_available_mask],
+                        g[position_available_mask],
+                    )
+                )
+            else:
+                attention_head_outputs = torch.einsum(
+                    "b q k h, b k h d -> b q h d", ps, v
+                )
 
         return attention_head_outputs.reshape(
             batch_size,
diff --git a/pfns/model/transformer.py b/pfns/model/transformer.py
index e667fd8..cd4e287 100644
--- a/pfns/model/transformer.py
+++ b/pfns/model/transformer.py
@@ -69,6 +69,10 @@ def __init__(  # noqa: C901, D417, PLR0913
         y_style_encoder: nn.Module | None = None,
         attention_between_features: bool = True,
         batch_first: bool = True,
+        use_rope: bool = False,
+        rope_multiplier: float = 1,
+        positions_num_measures: int = 0,
+        x_only_mode: bool = False,
         **layer_kwargs: Any,
     ):
         """Initializes the PerFeatureTransformer module.
@@ -146,7 +150,7 @@ def __init__(  # noqa: C901, D417, PLR0913
             print("Using linear x encoder, as no encoder was provided.")
             encoder = get_linear_x_encoder(ninp, features_per_group)
 
-        if y_encoder is None:
+        if y_encoder is None and not x_only_mode:
             print("Using linear y encoder, as no y_encoder was provided.")
             y_encoder = get_linear_y_encoder(ninp)
 
@@ -160,6 +164,9 @@ def __init__(  # noqa: C901, D417, PLR0913
         self.cached_embeddings: torch.Tensor | None = None
         self.attention_between_features = attention_between_features
         self.batch_first = batch_first
+        self.use_rope = use_rope
+        self.rope_multiplier = rope_multiplier
+        self.positions_num_measures = positions_num_measures
 
         def layer_creator():
             return PerFeatureLayer(
@@ -172,6 +179,7 @@ def layer_creator():
                     precomputed_kv.pop(0) if precomputed_kv is not None else None
                 ),
                 attention_between_features=attention_between_features,
+                positions_num_measures=positions_num_measures,
                 **layer_kwargs,
             )
 
@@ -215,6 +223,16 @@ def layer_creator():
             assert attention_between_features, "Attention between features must be True when using a y_style_encoder, otherwise only use a style_encoder."
         self.y_style_encoder = y_style_encoder
 
+        if x_only_mode:
+            assert (
+                attention_between_features
+            ), "attention_between_features must be True when x_only_mode is True"
+            assert (
+                features_per_group == 1
+            ), "features_per_group must be 1 when x_only_mode is True"
+
+        self.x_only_mode = x_only_mode
+
     def forward(
         self,
         x: torch.Tensor | None,
@@ -249,7 +267,6 @@ def forward(
                     these are shared between the datasets within a batch.
                 - `half_layers`: Whether to use the first half of the layers.
         """
-
         # Prepare batch-first versions of x, y, test_x for _forward
         # and clone all to be sure not to change outside data
         x_bf = x.clone() if x is not None else None
@@ -271,11 +288,16 @@ def forward(
         # Determine single_eval_pos based on the original y shape
         if y_bf is not None:
             single_eval_pos = y_bf.shape[1]
+        elif self.x_only_mode:
+            single_eval_pos = x_bf.shape[1]
         else:
             single_eval_pos = None
 
         # Handle cache_trainset_representation and combining x, test_x
         if self.cache_trainset_representation and y is None:
+            assert (
+                not self.x_only_mode
+            ), "x_only_mode is not supported when cache_trainset_representation is True"
             assert (
                 (test_x is None) != (x is None)
             ), "Provide the test inputs only via test_x or x, not both, when cache_trainset_representation is True"
@@ -286,7 +308,7 @@ def forward(
                 x_bf is not None
             ), "x must be provided when not predicting from cached trainset representations"
             assert (
-                y is not None
+                y is not None or self.x_only_mode
             ), "y must be provided when not predicting from cached trainset representations"
 
             if test_x_bf is not None:
@@ -335,13 +357,20 @@ def _forward(  # noqa: PLR0912, C901
                     y is None
                 ), "_forward expects y=None if single_eval_pos is 0/None and caching"
         else:
-            assert (
-                y is not None
-            ), "_forward expects y if not caching for pure inference or during training"
+            if not self.x_only_mode:
+                assert (
+                    y is not None
+                ), "_forward expects y if not caching for pure inference or during training"
+
             assert (
                 single_eval_pos is not None
             ), "_forward expects single_eval_pos if not caching for pure inference or during training"
 
+        if self.use_rope or (self.positions_num_measures > 0):
+            assert (
+                not self.x_only_mode
+            ), "Rope/Positional Embs only supported for x_only_mode=False"
+
         # single_eval_pos is the length of the training sequence part.
         # If None (e.g. pure inference from cache), treat as 0.
         current_context_len = single_eval_pos or 0
@@ -355,7 +384,7 @@ def _forward(  # noqa: PLR0912, C901
         _batch_size, _seq_len, _num_features_orig_main = x["main"].shape
 
         if (
-            y is None
+            y is None and not self.x_only_mode
         ):  # Should only happen if self.cache_trainset_representation and not single_eval_pos
             y_main_ref = x["main"]
             y = {
@@ -451,60 +480,96 @@ def _forward(  # noqa: PLR0912, C901
 
             categorical_inds_to_use = new_categorical_inds
 
-        for k in y:
-            # y[k] is (batch_size, current_seq_len_y, num_targets_y)
-            if y[k].ndim == 2:  # (B,S) or (B,T)
-                y[k] = y[k].unsqueeze(-1)  # B S -> B S 1
+        rope_vals = None
+        positions = None
+        if self.x_only_mode:
+            embedded_y = None
+        else:
+            for k in y:
+                # y[k] is (batch_size, current_seq_len_y, num_targets_y)
+                if y[k].ndim == 2:  # (B,S) or (B,T)
+                    y[k] = y[k].unsqueeze(-1)  # B S -> B S 1
+
+                # Pad y sequence length if shorter than x's sequence length (_seq_len)
+                if y[k].shape[1] < _seq_len:  # _seq_len is full sequence length from x
+                    # current_context_len is the length of the training part of y
+                    assert (
+                        y[k].shape[1]
+                        == current_context_len  # y should only contain train part if shorter
+                        or y[k].shape[1]
+                        == _seq_len  # Should not happen if already shorter
+                    ), f"y[{k}] seq len {y[k].shape[1]} vs train_seq_len {current_context_len} vs x_seq_len {_seq_len}"
+
+                    # Only pad if y is for training part or not main y (auxiliary targets might be full length)
+                    if k != "main" or y[k].shape[1] == current_context_len:
+                        y[k] = torch.cat(
+                            (
+                                y[k],
+                                torch.nan
+                                * torch.zeros(
+                                    y[k].shape[0],  # batch_size
+                                    _seq_len - y[k].shape[1],  # seq_len difference
+                                    y[k].shape[2],  # num_targets_y
+                                    device=y[k].device,
+                                    dtype=y[k].dtype,
+                                ),
+                            ),
+                            dim=1,  # Pad along sequence dimension (dim 1 for batch-first)
+                        )
+                # Now y[k] is (batch_size, _seq_len, num_targets_y)
+
+            # Making sure no label leakage ever happens for y["main"] (batch-first indexing)
+            # current_context_len is the length of the training data part
+            if "main" in y and y["main"].shape[1] > current_context_len:
+                y["main"][:, current_context_len:] = torch.nan
+
+            # Prepare y for y_encoder (transpose to sequence-first if y_encoder expects it)
+            y_for_y_encoder = {}
+            for k_enc, v_enc in y.items():
+                y_for_y_encoder[k_enc] = v_enc.transpose(0, 1)  # B S T -> S B T
+
+            embedded_y = self.y_encoder(
+                y_for_y_encoder,
+                single_eval_pos=current_context_len,  # Length of training part for y_encoder
+                cache_trainset_representation=self.cache_trainset_representation,
+            ).transpose(0, 1)
 
-            # Pad y sequence length if shorter than x's sequence length (_seq_len)
-            if y[k].shape[1] < _seq_len:  # _seq_len is full sequence length from x
-                # current_context_len is the length of the training part of y
+            if self.use_rope or self.positions_num_measures > 0:
                 assert (
-                    y[k].shape[1]
-                    == current_context_len  # y should only contain train part if shorter
-                    or y[k].shape[1] == _seq_len  # Should not happen if already shorter
-                ), f"y[{k}] seq len {y[k].shape[1]} vs train_seq_len {current_context_len} vs x_seq_len {_seq_len}"
-
-                # Only pad if y is for training part or not main y (auxiliary targets might be full length)
-                if k != "main" or y[k].shape[1] == current_context_len:
-                    y[k] = torch.cat(
-                        (
-                            y[k],
-                            torch.nan
-                            * torch.zeros(
-                                y[k].shape[0],  # batch_size
-                                _seq_len - y[k].shape[1],  # seq_len difference
-                                y[k].shape[2],  # num_targets_y
-                                device=y[k].device,
-                                dtype=y[k].dtype,
-                            ),
-                        ),
-                        dim=1,  # Pad along sequence dimension (dim 1 for batch-first)
+                    self.attention_between_features
+                ), "Rope only supported for attention_between_features=True"
+                assert (
+                    self.features_per_group == 1
+                ), "Rope only supported for features_per_group=1"
+                assert not (
+                    self.use_rope and (self.positions_num_measures > 0)
+                ), "Rope and positions_num_measures > 0 not supported at the same time"
+                if self.use_rope:
+                    head_dim = self.ninp // self.nhead
+                    rope_vals_x = get_rope_vals(
+                        x["main"].flatten(), head_dim, multiplier=self.rope_multiplier
+                    ).view(_batch_size, _seq_len, num_groups_main, head_dim)
+                    rope_vals_y = torch.ones_like(rope_vals_x[:, :, :1, :]).view(
+                        _batch_size, _seq_len, 1, -1, 2
                     )
-        # Now y[k] is (batch_size, _seq_len, num_targets_y)
-
-        # Making sure no label leakage ever happens for y["main"] (batch-first indexing)
-        # current_context_len is the length of the training data part
-        if "main" in y and y["main"].shape[1] > current_context_len:
-            y["main"][:, current_context_len:] = torch.nan
-
-        # Prepare y for y_encoder (transpose to sequence-first if y_encoder expects it)
-        y_for_y_encoder = {}
-        for k_enc, v_enc in y.items():
-            y_for_y_encoder[k_enc] = v_enc.transpose(0, 1)  # B S T -> S B T
-
-        embedded_y = self.y_encoder(
-            y_for_y_encoder,
-            single_eval_pos=current_context_len,  # Length of training part for y_encoder
-            cache_trainset_representation=self.cache_trainset_representation,
-        ).transpose(0, 1)
-
-        del y, y_for_y_encoder
-        if torch.isnan(embedded_y).any():
-            raise ValueError(
-                f"{torch.isnan(embedded_y).any()=}, make sure to add nan handlers"
-                " to the ys that are not fully provided (test set missing)",
-            )
+                    rope_vals_y[..., 1] = 0.0
+                    rope_vals_y = rope_vals_y.view(_batch_size, _seq_len, 1, head_dim)
+                    rope_vals = torch.cat((rope_vals_x, rope_vals_y), dim=2)
+                else:
+                    assert (
+                        "main" in y and len(y) == 1
+                    ), "Positions in attention only supported for simple y"
+                    # [batch, seqlen_q, num_feature_blocks, 1]
+                    positions = x["main"]  # [_batch_size, _seq_len, num_groups_main, 1]
+                    # add positions for y [_batch_size, _seq_len, 1]
+                    positions = torch.cat((positions, y["main"].unsqueeze(2)), dim=2)
+
+            del y, y_for_y_encoder
+            if torch.isnan(embedded_y).any():
+                raise ValueError(
+                    f"{torch.isnan(embedded_y).any()=}, make sure to add nan handlers"
+                    " to the ys that are not fully provided (test set missing)",
+                )
 
         extra_encoders_args = {}
         if categorical_inds_to_use is not None and isinstance(
@@ -524,13 +589,13 @@ def _forward(  # noqa: PLR0912, C901
                 **extra_encoders_args,
             ),
             "s (b f) e -> b s f e",
-            b=embedded_y.shape[0],
+            b=_batch_size,
         )  # b s f 1 -> b s f e
         del x
 
         embedded_x, embedded_y = self.add_embeddings(
             embedded_x,  # (b s num_groups e)
-            embedded_y,  # (b s e)
+            embedded_y,  # (b s e) | None
             num_features=_num_features_orig_main,
             seq_len=_seq_len,
             cache_embeddings=(
@@ -542,9 +607,16 @@ def _forward(  # noqa: PLR0912, C901
         )
 
         if self.attention_between_features:
-            # b s f e + b s 1 e -> b s f+1 e
-            embedded_input = torch.cat((embedded_x, embedded_y.unsqueeze(2)), dim=2)
+            if self.x_only_mode:
+                embedded_input = embedded_x
+            else:
+                # b s f e + b s 1 e -> b s f+1 e
+                embedded_input = torch.cat((embedded_x, embedded_y.unsqueeze(2)), dim=2)
+
         else:
+            assert (
+                not self.x_only_mode
+            ), "x_only_mode is not supported when attention_between_features is False"
             # add them together in this case, like for the original PFNs
             assert (
                 embedded_x.shape[2] == 1
@@ -592,8 +664,17 @@ def _forward(  # noqa: PLR0912, C901
                     device=embedded_input.device,
                     dtype=embedded_input.dtype,
                 )
+            else:
+                assert (
+                    not self.x_only_mode
+                ), "x_only_mode is not supported when embedded_y_style is not None"
 
-            full_embedded_style = torch.cat((embedded_style, embedded_y_style), dim=2)
+            if self.x_only_mode:
+                full_embedded_style = embedded_style
+            else:
+                full_embedded_style = torch.cat(
+                    (embedded_style, embedded_y_style), dim=2
+                )
 
             embedded_input = torch.cat(
                 (full_embedded_style, embedded_input),
@@ -606,12 +687,14 @@ def _forward(  # noqa: PLR0912, C901
                 f"There should be no NaNs in the encoded x and y."
                 "Check that you do not feed NaNs or use a NaN-handling enocder."
                 "Your embedded x and y returned the following:"
-                f"{torch.isnan(embedded_x).any()=} | {torch.isnan(embedded_y).any()=}",
+                f"{torch.isnan(embedded_x).any()=} | {(embedded_y is not None and torch.isnan(embedded_y).any())=}",
             )
         del embedded_y, embedded_x
 
         encoder_out = self.transformer_layers(
             embedded_input,  # (b s_effective (num_groups+1_for_y) e)
+            rope_vals=rope_vals,
+            positions=positions,
             single_eval_pos=current_context_len,  # Pass the context length including style
             half_layers=half_layers,
             cache_trainset_representation=self.cache_trainset_representation,
@@ -625,11 +708,19 @@ def _forward(  # noqa: PLR0912, C901
         # for the test sequence part (after current_context_len).
 
         test_encoder_out = encoder_out[
-            :, current_context_len:, -1
-        ]  # (batch, seq_test, embed_dim)
+            :, current_context_len:, :
+        ]  # (batch, seq_test, num_groups[+1_for_y], embed_dim)
         train_encoder_out = encoder_out[
-            :, :current_context_len, -1
-        ]  # (batch, seq_train_and_style, embed_dim)
+            :, :current_context_len, :
+        ]  # (batch, seq_train_and_style, num_groups[+1_for_y], embed_dim)
+
+        if not self.x_only_mode:
+            test_encoder_out = test_encoder_out[
+                :, :, -1, :
+            ]  # (batch, seq_test, embed_dim)
+            train_encoder_out = train_encoder_out[
+                :, :, -1, :
+            ]  # (batch, seq_train_and_style, embed_dim)
 
         # No transposition needed here as _forward returns batch-first
 
@@ -647,7 +738,7 @@ def _forward(  # noqa: PLR0912, C901
     def add_embeddings(  # noqa: C901, PLR0912
         self,
         x: torch.Tensor,  # (b s num_groups e)
-        y: torch.Tensor,  # (b s e)
+        y: torch.Tensor | None,  # (b s e)
         *,
         num_features: int,  # Original number of features (before grouping)
         seq_len: int,  # Sequence length
@@ -826,3 +917,29 @@ def isolate_torch_rng(seed: int, device: torch.device) -> Generator[None, None,
         torch.set_rng_state(torch_rng_state)
         if torch.cuda.is_available():
             torch.cuda.set_rng_state(torch_cuda_rng_state, device=device)
+
+
+def get_rope_vals(
+    inputs: torch.Tensor, dim: int, base: int = 10_000, multiplier: float = 1.0
+):
+    # inputs has to have shape [b]
+
+    assert (dim // 2) * 2 == dim, f"{dim=} not divisible by 2"
+
+    theta = (
+        multiplier
+        * 1000.0
+        / (
+            base
+            ** (
+                torch.arange(0, dim, 2, device=inputs.device)[: (dim // 2)].float()
+                / dim
+            )
+        )
+    )
+
+    deg = torch.einsum("b,d->bd", inputs, theta)
+
+    rope_vals = torch.stack([torch.cos(deg), torch.sin(deg)], dim=-1)  # b d/2 2
+
+    return rope_vals.view(-1, dim)
diff --git a/pfns/model/transformer_config.py b/pfns/model/transformer_config.py
index e3c2610..fbabdf4 100644
--- a/pfns/model/transformer_config.py
+++ b/pfns/model/transformer_config.py
@@ -1,5 +1,6 @@
 import typing as tp
 from dataclasses import dataclass
+from typing import Literal
 
 from pfns import base_config
 from pfns.model import encoders, transformer
@@ -29,6 +30,13 @@ class TransformerConfig(base_config.BaseConfig):
     features_per_group: int = 1
     attention_between_features: bool = True
     model_extra_args: tp.Dict[str, base_config.BaseTypes] | None = None
+    multiquery_item_attention_for_test_set: bool = False
+    activation: Literal["gelu", "relu"] = "relu"
+    recompute_layer: bool = False
+    use_rope: bool = False
+    rope_multiplier: float = 1
+    positions_num_measures: int = 0
+    x_only_mode: bool = False
 
     def create_model(self) -> transformer.TableTransformer:
         # Resolve criterion
@@ -81,6 +89,13 @@ def create_model(self) -> transformer.TableTransformer:
             style_encoder=style_encoder,
             y_style_encoder=y_style_encoder,
             batch_first=True,  # model is batch_first by default now
+            multiquery_item_attention_for_test_set=self.multiquery_item_attention_for_test_set,
+            activation=self.activation,
+            recompute_layer=self.recompute_layer,
+            use_rope=self.use_rope,
+            rope_multiplier=self.rope_multiplier,
+            positions_num_measures=self.positions_num_measures,
+            x_only_mode=self.x_only_mode,
             **(self.model_extra_args or {}),
         )
         model.criterion = criterion
diff --git a/pfns/priors/condition_on_area_of_opt_continuous.py b/pfns/priors/condition_on_area_of_opt_continuous.py
index 638cc1b..fd56e71 100644
--- a/pfns/priors/condition_on_area_of_opt_continuous.py
+++ b/pfns/priors/condition_on_area_of_opt_continuous.py
@@ -1,7 +1,5 @@
 import torch
 
-from ..utils import default_device
-
 from .prior import Batch
 
 
@@ -11,8 +9,7 @@ def get_batch(
     seq_len,
     num_features,
     get_batch,
-    epoch,
-    device=default_device,
+    device="cpu",
     hyperparameters=None,
     **kwargs,
 ):
@@ -31,7 +28,6 @@ def get_batch(
     :param seq_len:
     :param num_features:
     :param get_batch:
-    :param epoch:
     :param device:
     :param hyperparameters:
     :param kwargs:
@@ -42,65 +38,69 @@ def get_batch(
         hyperparameters = {}
 
     maximize = hyperparameters.get("condition_on_area_maximize", True)
-    size_range = hyperparameters.get("condition_on_area_size_range", (0.1, 0.5))
+    size_range = hyperparameters.get("condition_on_area_size_range", (0.2, 0.99))
     distribution = hyperparameters.get("condition_on_area_distribution", "uniform")
     assert distribution in ["uniform"]
+    extra_samples = hyperparameters.get("condition_on_area_extra_samples", 0)
 
     batch: Batch = get_batch(
         batch_size=batch_size,
-        seq_len=seq_len,
+        seq_len=seq_len + extra_samples,
         num_features=num_features,
         device=device,
         hyperparameters=hyperparameters,
-        epoch=epoch,
         **kwargs,
     )
     assert batch.style is None
 
     d = batch.x.shape[2]
 
-    prob_correct = torch.rand(batch_size, d, device=device)
-    correct_opt = torch.rand(batch_size, d, device=device) < prob_correct
     division_size = (
         torch.rand(batch_size, d, device=device) * (size_range[1] - size_range[0])
         + size_range[0]
     )
+    division_start = torch.rand(batch_size, d, device=device) * (1 - division_size)
+
+    assert batch.target_y.shape[2] == 1, "Only support single objective."
 
-    optima = (
-        batch.target_y.argmax(0).squeeze()
+    optima_inds = (
+        batch.target_y.argmax(1).squeeze(-1)
         if maximize
-        else batch.target_y.argmin(0).squeeze()
+        else batch.target_y.argmin(0).squeeze(-1)
     )  # batch_size, d
 
-    optima_hints = (
-        batch.x[optima, torch.arange(batch_size, device=device)]
-        - division_size / 2
-        + torch.rand(batch_size, d, device=device) * division_size
-    )  # shape: (batch_size, d)
-    optima_hints = optima_hints.clamp(0, 1)
-
-    optima_division_lower_bound = (optima_hints - division_size / 2).clamp(0, 1)
-    optima_division_upper_bound = (optima_hints + division_size / 2).clamp(0, 1)
+    optima = batch.x[torch.arange(batch_size), optima_inds]  # batch_size, d
 
-    random_hints = (
-        torch.rand(batch_size, d, device=device)
-        - division_size / 2
-        + torch.rand(batch_size, d, device=device) * division_size
-    )  # shape: (batch_size, d)
-    random_hints = random_hints.clamp(0, 1)
+    is_inside = (division_start <= optima) & (
+        optima <= division_start + division_size
+    )  # batch_size, d
 
-    random_division_lower_bound = (random_hints - division_size / 2).clamp(0, 1)
-    random_division_upper_bound = (random_hints + division_size / 2).clamp(0, 1)
+    # hint_probs = torch.rand(batch_size, d, device=device) # probs are chosen randomly
+    # hint probs need to be drawn dependent on whether it is inside or not
+    # what we want is R ~ Uniform(0, 1), and we now sample p(R=r|Ber(R)=1) and p(R=r|Ber(R)=0)
+    # that is: p(R=r|Ber(R)=1) = r / 0.5 = 2.0 * r, and p(R=r|Ber(R)=0) = (1-r) / 0.5 = 2.0 * (1-r)
+    # we can compute the icdfs as icdf(|Ber(R)=1) = np.sqrt(u), icdf(|Ber(R)=0)= 1 - np.sqrt(1 - u)
 
-    lower_bounds = torch.where(
-        correct_opt, optima_division_lower_bound, random_division_lower_bound
-    )
-    upper_bounds = torch.where(
-        correct_opt, optima_division_upper_bound, random_division_upper_bound
+    hint_probs = torch.where(
+        is_inside,
+        torch.sqrt(torch.rand(batch_size, d, device=device)),
+        1 - torch.sqrt(1 - torch.rand(batch_size, d, device=device)),
     )
 
-    batch.style = torch.stack([prob_correct, lower_bounds, upper_bounds], 2).view(
-        batch_size, -1
-    )  # shape: (batch_size, 3*d)
+    batch.style = torch.stack(
+        [hint_probs, division_start, division_start + division_size], 2
+    )  # batch_size, d, 3
+
+    skip_style_prob = hyperparameters.get("condition_on_opt_area_skip_style_prob", 0.0)
+
+    skip_style_mask = torch.rand(batch_size, device=device) < skip_style_prob
+
+    # set to nan for the encoder to figure this out
+    batch.style[skip_style_mask, :, :] = torch.nan
+
+    if extra_samples:
+        batch.x = batch.x[:, :-extra_samples]
+        batch.y = batch.y[:, :-extra_samples]
+        batch.target_y = batch.target_y[:, :-extra_samples]
 
     return batch
diff --git a/pfns/priors/convert_prior_to_x_only_format.py b/pfns/priors/convert_prior_to_x_only_format.py
new file mode 100644
index 0000000..6476070
--- /dev/null
+++ b/pfns/priors/convert_prior_to_x_only_format.py
@@ -0,0 +1,120 @@
+# pyre-strict
+
+from dataclasses import fields
+
+import torch
+from pfns.priors.prior import Batch
+
+
+def get_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    single_eval_pos: int,
+    get_batch: callable,
+    hyperparameters: dict | None = None,
+    n_targets_per_input: int = 1,
+    **kwargs,
+) -> Batch:
+    """
+    Wrapper function that converts traditional batch format to x-only format.
+
+    This function takes a traditional get_batch function and converts its output
+    from the format with separate x, y, target_y to the x-only format with
+    x, test_x, target (and y=None, target_y=None).
+
+    Args:
+        batch_size: Number of sequences in the batch
+        seq_len: Total sequence length (train + test)
+        num_features: Number of input features
+        single_eval_pos: Position where training ends and testing begins
+        hyperparameters: Hyperparameter dictionary
+        get_batch: The traditional get_batch function to wrap
+        n_targets_per_input: Number of targets per input
+        **kwargs: Additional arguments to pass to the wrapped get_batch function
+
+    Returns:
+        Batch in x-only format with x, test_x, target fields
+    """
+    assert n_targets_per_input == 1, "Only single target per input supported"
+    # Call the traditional get_batch function
+    traditional_batch = get_batch(
+        batch_size=batch_size,
+        seq_len=seq_len,
+        num_features=num_features,
+        single_eval_pos=single_eval_pos,
+        hyperparameters=hyperparameters,
+        n_targets_per_input=n_targets_per_input,
+        **kwargs,
+    )
+
+    # Extract traditional format components
+    x_traditional = traditional_batch.x  # shape: (batch_size, seq_len, num_features)
+    y_traditional = (
+        traditional_batch.y
+    )  # shape: (batch_size, seq_len, 1) or (batch_size, seq_len,)
+    if len(y_traditional.shape) == 2:
+        y_traditional = y_traditional.unsqueeze(-1)
+    target_y_traditional = (
+        traditional_batch.target_y
+    )  # shape: (batch_size, seq_len, n_targets_per_input) or (batch_size, seq_len,)
+    if len(target_y_traditional.shape) == 2:
+        target_y_traditional = target_y_traditional.unsqueeze(-1)
+
+    # Split into train and test portions
+    x_train = x_traditional[
+        :, :single_eval_pos, :
+    ]  # shape: (batch_size, single_eval_pos, num_features)
+    x_test = x_traditional[
+        :, single_eval_pos:, :
+    ]  # shape: (batch_size, seq_len - single_eval_pos, num_features)
+    y_train = y_traditional[
+        :, :single_eval_pos, :
+    ]  # shape: (batch_size, single_eval_pos, 1)
+    y_test_targets = target_y_traditional[
+        :, single_eval_pos:, :
+    ]  # shape: (batch_size, seq_len - single_eval_pos, n_targets_per_input)
+
+    # Convert to x-only format
+    # x: concatenate training inputs with training outputs
+    x_with_y = torch.cat(
+        [x_train, y_train], dim=2
+    )  # shape: (batch_size, single_eval_pos, num_features + 1)
+
+    # test_x: test inputs with NaN for y values (to be predicted)
+    test_y_nan = torch.full(
+        (batch_size, seq_len - single_eval_pos, 1),
+        torch.nan,
+        dtype=x_test.dtype,
+        device=x_test.device,
+    )
+    test_x_with_nan_y = torch.cat(
+        [x_test, test_y_nan], dim=2
+    )  # shape: (batch_size, seq_len - single_eval_pos, num_features + 1)
+
+    # target: test inputs with target y values
+    target_with_y = torch.cat(
+        [torch.full_like(x_test, torch.nan), y_test_targets], dim=2
+    )  # shape: (batch_size, seq_len - single_eval_pos, num_features + 1)
+
+    # Create the x-only format batch, taking over all entries from the original batch
+    # except for the ones we need to change
+    batch_dict = {
+        field.name: getattr(traditional_batch, field.name)
+        for field in fields(traditional_batch)
+    }
+
+    # Override the fields that need to change for x-only format
+    batch_dict.update(
+        {
+            "x": x_with_y,
+            "test_x": test_x_with_nan_y,
+            "target": target_with_y,
+            "y": None,
+            "target_y": None,
+        }
+    )
+
+    x_only_batch = Batch(**batch_dict)
+
+    return x_only_batch
diff --git a/pfns/priors/data_loading.py b/pfns/priors/data_loading.py
index 9869104..c7c698a 100644
--- a/pfns/priors/data_loading.py
+++ b/pfns/priors/data_loading.py
@@ -1,9 +1,7 @@
 import math
 import os
 import random
-
 from copy import deepcopy
-
 from functools import partial
 from typing import Callable, Iterator
 
@@ -86,9 +84,10 @@ def __iter__(self) -> Iterator[Batch]:
 
             b = self.get_batch_method(**kwargs)
 
-            assert (
-                len(b.x) == len(b.y) == len(b.target_y) == batch_shape.batch_size
-            ), "Our code was updated to use the more intuitive batch first format, please make sure your get_batch function returns data with shapes (batch_size, seq_len, ...)"
+            if b.y is not None:
+                assert (
+                    len(b.x) == len(b.y) == len(b.target_y) == batch_shape.batch_size
+                ), "Our code was updated to use the more intuitive batch first format, please make sure your get_batch function returns data with shapes (batch_size, seq_len, ...)"
 
             # Ensure single_eval_pos is set on the batch object if get_batch_method doesn't handle it
             if b.single_eval_pos is None:
diff --git a/pfns/priors/formula/get_batch.py b/pfns/priors/formula/get_batch.py
index 28f5853..f79db5f 100644
--- a/pfns/priors/formula/get_batch.py
+++ b/pfns/priors/formula/get_batch.py
@@ -2,11 +2,9 @@
 from typing import Literal
 
 import numpy as np
-
 import torch
 
 from .. import Batch
-
 from .ops import binary_ops, unary_ops
 from .trees import evaluate_tree, sample_tree
 
@@ -80,6 +78,8 @@ def get_batch(
     n_targets_per_input=1,
     single_eval_pos=None,  # not using this
     device="cpu",  # ignoring this
+    return_trees=False,
+    batch_size_per_gp_sample=None,
 ):
     assert (
         n_targets_per_input == 1
@@ -106,6 +106,9 @@ def get_batch(
 
     # longterm todo: add styles based on the tree
 
+    if return_trees:
+        return batch, [tree for _, _, tree in batch_as_list]
+
     return batch
 
 
diff --git a/pfns/priors/formula/ops.py b/pfns/priors/formula/ops.py
index f204b74..41c6323 100644
--- a/pfns/priors/formula/ops.py
+++ b/pfns/priors/formula/ops.py
@@ -1,7 +1,9 @@
 import math
+from functools import partial
 
 import torch
 
+MAX_OP_OR_INPUT_KEY_LENGTH = 12
 
 # all ops are implemented in torch mapping vectors of length n to vectors of length n
 ### binary operations
@@ -23,8 +25,12 @@ def power(x, y):
     return x.abs() ** y
 
 
-def rr_ad_gate(x, y):
+def rr_ad_gate(x, y, zero_at_bound=False):
     bound_index = torch.randint(len(x), (1,)).squeeze()
+
+    if zero_at_bound:
+        y = y - y[bound_index]
+
     return (x > x[bound_index]) * y
 
 
@@ -34,13 +40,14 @@ def rr_ad_gate(x, y):
     "gate": gate,
     "power": power,
     "rr_ad_gate": rr_ad_gate,
+    "rr_aad_gate": partial(rr_ad_gate, zero_at_bound=False),
 }
 
-### unary operations
-
+assert (
+    MAX_OP_OR_INPUT_KEY_LENGTH >= len(max(binary_ops.keys(), key=len))
+), "MAX_OP_OR_INPUT_KEY_LENGTH must be greater than the length of the longest binary operation"
 
-def ident(x):
-    return x
+### unary operations
 
 
 def absolute(x):
@@ -79,11 +86,37 @@ def cubic(x):
     return x**3
 
 
+def relu(x):
+    return torch.relu(x)
+
+
+def rr_repeat(x, mirror=False):
+    min_index = torch.randint(len(x), (1,)).squeeze()
+    max_index = torch.randint(len(x), (1,)).squeeze()
+
+    mini = x[min_index]
+    maxi = x[max_index]
+    lo = torch.minimum(mini, maxi)
+    hi = torch.maximum(mini, maxi)
+    width = hi - lo
+
+    if width.item() == 0:
+        return torch.full_like(x, lo)
+
+    offset = torch.remainder(x - lo, width)
+
+    if not mirror:
+        return lo + offset
+
+    q = torch.floor((x - lo) / width).to(torch.int64)
+    flip = torch.remainder(q, 2) == 1
+    return torch.where(flip, hi - offset, lo + offset)
+
+
 # def relative_noise__random(x):
 #     return x * torch.randn_like(x)
 
 unary_ops = {
-    "ident": ident,
     "abs": absolute,
     "inv": inv,
     "sin": sin,
@@ -93,5 +126,12 @@ def cubic(x):
     "square": square,
     "sigmoid": sigmoid,
     "cubic": cubic,
+    "relu": relu,
+    "rr_repeat": rr_repeat,
+    "rr_repeat_m": partial(rr_repeat, mirror=True),
     # 'rr_rel_noise': relative_noise__random,
 }
+
+assert (
+    MAX_OP_OR_INPUT_KEY_LENGTH >= len(max(unary_ops.keys(), key=len))
+), "MAX_OP_OR_INPUT_KEY_LENGTH must be greater than the length of the longest unary operation"
diff --git a/pfns/priors/formula/trees.py b/pfns/priors/formula/trees.py
index 48bc9d8..3cc8f88 100644
--- a/pfns/priors/formula/trees.py
+++ b/pfns/priors/formula/trees.py
@@ -4,7 +4,7 @@
 import numpy as np
 import torch
 
-from .ops import binary_ops, unary_ops
+from .ops import binary_ops, MAX_OP_OR_INPUT_KEY_LENGTH, unary_ops
 
 node_dtype = np.dtype(
     [
@@ -14,7 +14,7 @@
         ),  # The type of the node can be 'binary', 'unary', or 'leaf'
         (
             "op_or_input",
-            "U12",
+            f"U{MAX_OP_OR_INPUT_KEY_LENGTH}",
         ),  # The operation (e.g. 'add') or input index, if it is a leaf.
         (
             "left",
diff --git a/pfns/priors/heteroskedastic_prior.py b/pfns/priors/heteroskedastic_prior.py
new file mode 100644
index 0000000..d9d711a
--- /dev/null
+++ b/pfns/priors/heteroskedastic_prior.py
@@ -0,0 +1,239 @@
+# This is a wrapper prior that adds heteroscedastic noise to datasets.
+# The noise variance varies spatially based on a random GP function, and can be
+# either normally distributed or long-tailed (with outliers).
+
+from copy import deepcopy
+
+import torch
+from gpytorch.priors import LogNormalPrior
+from pfns.priors import Batch
+
+from .path_stgp import sample_paths
+
+
+@torch.no_grad()
+def get_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    *args,
+    hyperparameters: dict | None = None,
+    get_batch=None,
+    **kwargs,
+) -> Batch:
+    """Generate a batch with heteroscedastic noise added to the base prior.
+
+    This wrapper prior adds spatially-varying noise to datasets. The noise
+    variance at each point is determined by a random GP function sampled
+    using sample_paths from path_stgp.py. The noise can be either normal
+    or long-tailed (Student-t distribution with outliers).
+
+    Hyperparameters:
+        hetero_noise_prob: Probability of making the noise heteroscedastic
+            (spatially varying) vs homoscedastic (constant) (default: 0.5)
+        hetero_noise_long_tailed_prob: Probability of using long-tailed noise
+            vs normal noise (default: 0.5)
+        hetero_noise_df_min: Minimum degrees of freedom for Student-t when
+            long-tailed (default: 2.0)
+        hetero_noise_df_max: Maximum degrees of freedom for Student-t when
+            long-tailed (default: 5.0)
+        hetero_noise_var_loc: Location parameter for log-normal base noise
+            variance distribution (default: -4.0, same as path_stgp)
+        hetero_noise_var_scale: Scale parameter for log-normal base noise
+            variance distribution (default: 1.0, same as path_stgp)
+        hetero_noise_range_scale: Scale factor for heteroscedastic variation
+            on top of base noise (default: 1.0). The heteroscedastic
+            component adds variation in [0, base_std * range_scale].
+
+        Additional hyperparameters are passed to sample_paths for the
+        variance function GP:
+        - use_rbf_kernel, lengthscale_loc_constant_add, lengthscale_loc_feature_mul,
+          lengthscale_scale, mean_width, additive_cosine_per_dim_prob
+
+    Args:
+        batch_size: Number of samples in the batch
+        seq_len: Sequence length (number of points per sample)
+        num_features: Number of input features
+        hyperparameters: Dictionary of hyperparameters
+        get_batch: The underlying prior's get_batch function to wrap
+        **kwargs: Additional arguments passed to the underlying prior
+
+    Returns:
+        Batch with heteroscedastic noise added
+    """
+    if hyperparameters is None:
+        hyperparameters = {}
+
+    hyperparameters = deepcopy(hyperparameters)
+
+    # Extract heteroscedastic noise hyperparameters
+    hetero_prob: float = hyperparameters.pop("hetero_noise_prob", 0.5)
+    long_tailed_prob: float = hyperparameters.pop("hetero_noise_long_tailed_prob", 0.5)
+    df_min: float = hyperparameters.pop("hetero_noise_df_min", 2.0)
+    df_max: float = hyperparameters.pop("hetero_noise_df_max", 5.0)
+    noise_var_loc: float = hyperparameters.pop("hetero_noise_var_loc", -4.0)
+    noise_var_scale: float = hyperparameters.pop("hetero_noise_var_scale", 1.0)
+
+    # Extract hyperparameters for the variance GP (with prefixed names)
+    variance_gp_hyperparameters = {}
+    variance_gp_keys = [
+        "hetero_noise_use_rbf_kernel",
+        "hetero_noise_lengthscale_loc_constant_add",
+        "hetero_noise_lengthscale_loc_feature_mul",
+        "hetero_noise_lengthscale_scale",
+        "hetero_noise_mean_width",
+        "hetero_noise_additive_cosine_per_dim_prob",
+    ]
+
+    for key in variance_gp_keys:
+        if key in hyperparameters:
+            # Remove prefix and add to variance GP hyperparameters
+            gp_key = key.replace("hetero_noise_", "")
+            variance_gp_hyperparameters[gp_key] = hyperparameters.pop(key)
+
+    # Set defaults for variance GP if not provided
+    variance_gp_hyperparameters.setdefault("use_rbf_kernel", True)
+    variance_gp_hyperparameters.setdefault("mean_width", 1.0)
+
+    if get_batch is None:
+        raise ValueError(
+            "heteroscedastic_noise_prior requires a base get_batch function to wrap"
+        )
+
+    # Get batch from base prior
+    base_batch = get_batch(
+        batch_size,
+        seq_len,
+        num_features,
+        *args,
+        hyperparameters=hyperparameters,
+        **kwargs,
+    )
+
+    device = base_batch.x.device
+    dtype = base_batch.x.dtype
+    x = base_batch.x  # (batch_size, seq_len, num_features)
+
+    # Decide per batch element whether to use heteroscedastic or homoscedastic noise
+    use_hetero = torch.rand(batch_size, device=device) < hetero_prob
+
+    # Sample base noise variance from log-normal distribution (like path_stgp.py)
+    base_noise_variance: torch.Tensor = LogNormalPrior(
+        loc=noise_var_loc,
+        scale=noise_var_scale,
+    ).sample((batch_size,))
+    base_noise_std = base_noise_variance.sqrt().to(device=device, dtype=dtype)
+
+    # Sample the variance function using sample_paths from path_stgp
+    # This returns a function that maps x -> y where y varies smoothly
+    variance_paths = sample_paths(batch_size, num_features, variance_gp_hyperparameters)
+
+    # Evaluate the variance function at all x points
+    # variance_paths expects (batch_size, n, num_features) and returns (1, batch_size, n)
+    variance_func_values = variance_paths(x).squeeze(0)  # (batch_size, seq_len)
+
+    # Normalize variance function values to [0, 1] per batch
+    var_min = variance_func_values.min(dim=1, keepdim=True).values
+    var_max = variance_func_values.max(dim=1, keepdim=True).values
+    var_range = (var_max - var_min).clamp(min=1e-8)
+    normalized_variance = (variance_func_values - var_min) / var_range  # [0, 1]
+
+    # For heteroscedastic noise:
+    # std = base_std + normalized_variance * (base_std * range_scale)
+    # This adds variation in [0, base_std * range_scale] on top of base_std
+    base_std_expanded = base_noise_std.unsqueeze(1)  # (batch_size, 1)
+    range_size = torch.rand(batch_size, device=device) * 2  # [0, 2]
+    hetero_std = base_std_expanded * (
+        1 - range_size.clamp(max=1.0).unsqueeze(1)
+    ) + normalized_variance * (
+        base_std_expanded * range_size.unsqueeze(1)
+    )  # (batch_size, seq_len)
+
+    # For homoscedastic noise: just use the base std
+    homo_std = base_std_expanded.expand(-1, seq_len)  # (batch_size, seq_len)
+
+    # Select heteroscedastic or homoscedastic std per batch element
+    point_std = torch.where(
+        use_hetero.unsqueeze(1).expand(-1, seq_len),
+        hetero_std,
+        homo_std,
+    )  # (batch_size, seq_len)
+
+    # Decide per batch element whether to use long-tailed or normal noise
+    use_long_tailed = torch.rand(batch_size, device=device) < long_tailed_prob
+
+    # Sample noise
+    # For normal noise: N(0, std^2)
+    # For long-tailed noise: Student-t with df degrees of freedom, scaled by std
+    normal_noise = torch.randn(batch_size, seq_len, device=device, dtype=dtype)
+
+    # Sample degrees of freedom for Student-t (per batch)
+    df = torch.rand(batch_size, device=device) * (df_max - df_min) + df_min
+
+    # Sample Student-t noise using the relationship: t = Z / sqrt(V/df)
+    # where Z ~ N(0,1) and V ~ Chi-squared(df)
+    chi2_samples = torch.distributions.Chi2(
+        df.unsqueeze(1).expand(-1, seq_len)
+    ).sample()
+    student_t_noise = normal_noise / torch.sqrt(
+        chi2_samples / df.unsqueeze(1)
+    )  # (batch_size, seq_len)
+
+    # Select noise type based on use_long_tailed
+    noise = torch.where(
+        use_long_tailed.unsqueeze(1).expand(-1, seq_len),
+        student_t_noise,
+        normal_noise,
+    )
+
+    # Scale noise by point-wise std
+    scaled_noise = noise * point_std  # (batch_size, seq_len)
+
+    # Add noise to y
+    y_with_noise = base_batch.y + scaled_noise.unsqueeze(2)
+    target_y = base_batch.target_y
+    if hyperparameters.get("noisy_predictions", False):
+        target_y += scaled_noise.unsqueeze(2)
+
+    # # Plot noises on linspace [0,1] if num_features == 1 (for prototyping)
+    # import matplotlib.pyplot as plt
+    # if num_features == 1:
+    #     print("hiii")
+    #     x_plot = torch.linspace(0, 1, 100).unsqueeze(0).unsqueeze(-1)  # (1, 100, 1)
+    #     x_plot = x_plot.expand(batch_size, -1, -1).to(device=device, dtype=dtype)
+
+    #     # Evaluate variance function on linspace
+    #     variance_func_plot = variance_paths(x_plot).squeeze(0)  # (batch_size, 100)
+
+    #     # Normalize to [0, 1]
+    #     var_min_plot = variance_func_plot.min(dim=1, keepdim=True).values
+    #     var_max_plot = variance_func_plot.max(dim=1, keepdim=True).values
+    #     var_range_plot = (var_max_plot - var_min_plot).clamp(min=1e-8)
+    #     normalized_variance_plot = (variance_func_plot - var_min_plot) / var_range_plot
+
+    #     # Compute std on linspace
+    #     hetero_std_plot = base_std_expanded * (
+    #         1 - range_size.clamp(max=1.0).unsqueeze(1)
+    #     ) + normalized_variance_plot * (base_std_expanded * range_size.unsqueeze(1))
+
+    #     # Plot a few samples
+    #     fig, axes = plt.subplots(2, 2, figsize=(10, 8))
+    #     _ = fig  # suppress unused variable warning
+    #     x_np = torch.linspace(0, 1, 100).numpy()
+    #     for i, ax in enumerate(axes.flat):
+    #         if i < batch_size:
+    #             ax.plot(x_np, hetero_std_plot[i].cpu().numpy(), label="std(x)")
+    #             ax.set_xlabel("x")
+    #             ax.set_ylabel("noise std")
+    #             ax.set_title(f"Sample {i} (hetero={use_hetero[i].item()})")
+    #             ax.legend()
+    #     plt.suptitle("Heteroscedastic Noise Std on [0,1]")
+    #     plt.tight_layout()
+    #     plt.show()
+
+    return Batch(
+        x=base_batch.x,
+        y=y_with_noise,
+        target_y=target_y,
+        style=base_batch.style,
+    )
diff --git a/pfns/priors/hyperparameter_sampling.py b/pfns/priors/hyperparameter_sampling.py
index 3e4632e..87cdac1 100644
--- a/pfns/priors/hyperparameter_sampling.py
+++ b/pfns/priors/hyperparameter_sampling.py
@@ -44,11 +44,19 @@ def sample(self):
 
     def normalize(self, value: torch.Tensor) -> torch.Tensor:
         if self.log:
-            return (torch.log(value) - math.log(self.lower)) / (
-                math.log(self.upper) - math.log(self.lower)
+            return (torch.log(value) - torch.log(self.lower)) / (
+                torch.log(self.upper) - torch.log(self.lower)
             )
         return (value - self.lower) / (self.upper - self.lower)
 
+    def unnormalize(self, encoded_value: torch.Tensor) -> torch.Tensor:
+        if self.log:
+            return torch.exp(
+                encoded_value * (torch.log(self.upper) - torch.log(self.lower))
+                + torch.log(self.lower)
+            )
+        return encoded_value * (self.upper - self.lower) + self.lower
+
     def encode_to_torch(self, value):
         assert (value >= self.lower) and (
             value <= self.upper
@@ -95,6 +103,12 @@ def normalize(self, value: torch.Tensor) -> torch.Tensor:
             transformed_upper - transformed_lower
         )
 
+    def unnormalize(self, encoded_value: torch.Tensor) -> torch.Tensor:
+        transformed_value = encoded_value * (
+            self.upper ** (1 / self.power) - self.lower ** (1 / self.power)
+        ) + self.lower ** (1 / self.power)
+        return torch.pow(transformed_value, self.power)
+
     def encode_to_torch(self, value):
         assert (value >= self.lower) and (
             value <= self.upper
@@ -127,6 +141,14 @@ def normalize(self, value):
             )
         return (value - self.lower) / (self.upper - self.lower)
 
+    def unnormalize(self, encoded_value):
+        if self.log:
+            return torch.exp(
+                encoded_value * (math.log(self.upper) - math.log(self.lower))
+                + math.log(self.lower)
+            )
+        return encoded_value * (self.upper - self.lower) + self.lower
+
     def encode_to_torch(self, value):
         assert (value >= self.lower) and (
             value <= self.upper
@@ -149,6 +171,10 @@ def normalize(self, value: torch.Tensor):
         # Return one-hot encoding of the choice
         return value / (len(self.choices) - 1)
 
+    def unnormalize(self, encoded_value: torch.Tensor):
+        # Return the index of the one-hot encoded value
+        return encoded_value * (len(self.choices) - 1)
+
 
 def sample_hyperparameters(config):
     """Sample values for all hyperparameters in the config"""
diff --git a/pfns/priors/path_stgp.py b/pfns/priors/path_stgp.py
new file mode 100644
index 0000000..5179e7f
--- /dev/null
+++ b/pfns/priors/path_stgp.py
@@ -0,0 +1,477 @@
+from functools import partial
+from math import log, sqrt
+
+import gpytorch
+import torch
+from botorch.models.gp_regression import SingleTaskGP
+from botorch.sampling.pathwise.prior_samplers import draw_kernel_feature_paths
+from gpytorch.constraints.constraints import GreaterThan
+from gpytorch.kernels import MaternKernel, RBFKernel
+from gpytorch.module import _pyro_sample_from_prior
+from gpytorch.priors import LogNormalPrior, NormalPrior
+from torch import Size
+
+from .path_trace_sampling import generate_trace
+from .prior import Batch
+from .utils import sample_x_around_points
+
+
+def to_random_module_no_copy(module) -> gpytorch.Module:
+    random_module_cls = type(
+        "_Random" + module.__class__.__name__,
+        (gpytorch.module.RandomModuleMixin, module.__class__),
+        {},
+    )
+    module.__class__ = random_module_cls  # hack
+
+    for mname, child in module.named_children():
+        if isinstance(child, gpytorch.Module):
+            setattr(module, mname, to_random_module_no_copy(child))
+    return module
+
+
+def _sample_paths_inner(batch_size, num_features, hyperparameters=None):
+    """Internal function that samples paths for the given number of non-dummy features.
+
+    Returns a function that takes [batch_size, n, num_features] and returns [1, batch_size, n].
+    """
+    if hyperparameters is None:
+        hyperparameters = {}
+    base_class = (
+        RBFKernel if hyperparameters.get("use_rbf_kernel", True) else MaternKernel
+    )
+    lengthscale_prior = LogNormalPrior(
+        loc=hyperparameters.get("lengthscale_loc_constant_add", sqrt(2))
+        + log(num_features) * hyperparameters.get("lengthscale_loc_feature_mul", 0.5),
+        scale=hyperparameters.get("lengthscale_scale", sqrt(3)),
+    )
+    base_kernel = base_class(
+        ard_num_dims=num_features,
+        batch_shape=torch.Size([batch_size]),
+        lengthscale_prior=lengthscale_prior,
+        lengthscale_constraint=GreaterThan(
+            2.5e-2, transform=None, initial_value=lengthscale_prior.mode
+        ),
+        # pyre-ignore[6] GPyTorch type is unnecessarily restrictive.
+        active_dims=None,
+    )
+
+    model = SingleTaskGP(
+        torch.zeros(batch_size, 1, num_features, dtype=torch.double),
+        torch.zeros(batch_size, 1, 1, dtype=torch.double),
+        covar_module=base_kernel,
+        mean_module=gpytorch.means.ConstantMean(
+            constant_prior=NormalPrior(loc=0.0, scale=hyperparameters["mean_width"] / 2)
+        ),
+    ).to(dtype=torch.double)
+    model = to_random_module_no_copy(model)
+    _pyro_sample_from_prior(module=model, memo=None, prefix="")
+
+    init_paths = draw_kernel_feature_paths(model=model, sample_shape=Size((1,))).to(
+        dtype=torch.double
+    )
+
+    if (
+        additive_cosine_per_dim_prob := hyperparameters.get(
+            "additive_cosine_per_dim_prob", 0.0
+        )
+    ) > 0.0:
+        additive_cosine_per_dim = (
+            torch.rand(batch_size, num_features) < additive_cosine_per_dim_prob
+        )
+        lengthscale = (
+            torch.rand(batch_size, num_features, dtype=torch.double) * 0.2 + 0.08
+        )
+        gp_lengthscale = model.covar_module.lengthscale.view(batch_size, num_features)
+        magnitude = (
+            torch.randn(batch_size, num_features, dtype=torch.double)
+            / 10
+            / gp_lengthscale
+        )  # very rough...
+        offset = torch.rand(batch_size, num_features, dtype=torch.double)
+
+        def paths(x):  # [batch_size, n, num_features]
+            y = init_paths(x)
+            mask = additive_cosine_per_dim.unsqueeze(1).expand(-1, x.shape[1], -1)
+            return y + torch.where(
+                mask,
+                (
+                    magnitude.unsqueeze(1)
+                    * torch.cos(
+                        2
+                        * torch.pi
+                        * (x / lengthscale.unsqueeze(1) + offset.unsqueeze(1)),
+                        dtype=torch.double,
+                    )
+                ),
+                0.0,
+            ).sum(-1).unsqueeze(0) / sqrt(num_features)
+    else:
+        paths = init_paths
+
+    def paths_with_double(x):
+        dtype = x.dtype
+        y = paths(x.double())
+        return y.to(dtype)
+
+    # paths takes a tensor of shape [batch_size, n, num_features] and returns a tensor of shape [1, batch_size, n]
+    return paths_with_double
+
+
+def _get_dummy_dims(num_features, hyperparameters):
+    """Determine which dimensions are dummy. Returns (dummy_dims mask, num_non_dummy).
+
+    Hyperparameters:
+        dummy_dim_sample_non_dummy_range: tuple (min, max) - Sample number of non-dummy
+            dimensions uniformly from [min, max]. Remaining dimensions are dummy.
+        dummy_dim_sample_non_dummy_range_prob: float - Probability of applying the
+            dummy_dim_sample_non_dummy_range logic. If not applied, all dimensions
+            are non-dummy. Default: 1.0 (always apply when range is set).
+        dummy_dim_prob: float - Each dimension has this probability of being a dummy.
+            At least one dimension will always be non-dummy.
+    """
+    if (
+        non_dummy_range := hyperparameters.get("dummy_dim_sample_non_dummy_range", None)
+    ) is not None:
+        # Check if we should apply the non-dummy range logic for this dataset
+        range_prob = hyperparameters.get("dummy_dim_sample_non_dummy_range_prob", 1.0)
+        if torch.rand(()).item() >= range_prob:
+            # Don't apply dummy dims - all dimensions are non-dummy
+            return None, num_features
+
+        min_non_dummy, max_non_dummy = non_dummy_range
+        num_non_dummy = min(
+            torch.randint(min_non_dummy, max_non_dummy + 1, ()).item(),
+            num_features,
+        )
+        perm = torch.randperm(num_features)
+        dummy_dims = torch.ones(num_features, dtype=torch.bool)
+        dummy_dims[perm[:num_non_dummy]] = False
+        return dummy_dims, num_non_dummy
+
+    elif (dummy_dim_prob := hyperparameters.get("dummy_dim_prob", 0.0)) > 0.0:
+        dummy_dims = torch.rand(num_features) < dummy_dim_prob
+        if (~dummy_dims).sum().item() == 0:
+            dummy_dims[torch.randint(0, num_features, ())] = False
+        return dummy_dims, (~dummy_dims).int().sum().item()
+
+    return None, num_features
+
+
+def sample_paths(batch_size, num_features, hyperparameters=None):
+    """Sample GP paths with optional dummy dimension handling and gap discontinuities.
+
+    This function handles dummy dimensions by sampling a GP on only the non-dummy
+    dimensions and wrapping it in a function that filters out dummy dimensions.
+    When gaps are enabled, all region functions share the same dummy dimensions.
+
+    Hyperparameters:
+        dummy_dim_sample_non_dummy_range: tuple (min, max) - Sample number of non-dummy
+            dimensions uniformly from [min, max]. Remaining dimensions are dummy.
+        dummy_dim_prob: float - Each dimension has this probability of being a dummy.
+            At least one dimension will always be non-dummy.
+        gap_max_splits: int - Max axis-aligned splits for gaps (default: 0)
+        gap_prob: float - Probability of applying gaps (default: 1.0)
+        gap_lengthscale_add: float - Add to lengthscale_loc_constant_add when
+            gaps are applied (default: 0.0, no change)
+        gap_lengthscale_add_prob: float - Probability of applying the
+            lengthscale adjustment when gaps are applied (default: 1.0)
+
+    Returns a function that takes [batch_size, n, num_features] and returns [1, batch_size, n].
+    """
+    if hyperparameters is None:
+        hyperparameters = {}
+
+    # Determine if gaps will be applied
+    max_splits = hyperparameters.get("gap_max_splits", 0)
+    gap_prob = hyperparameters.get("gap_prob", 1.0)
+    apply_gaps = max_splits > 0 and torch.rand(()).item() < gap_prob
+    num_splits = torch.randint(1, max_splits + 1, (1,)).item() if apply_gaps else 0
+
+    # Optionally adjust lengthscale when gaps are applied
+    if num_splits > 0:
+        lf = hyperparameters.get("gap_lengthscale_add", 0.0)
+        lf_prob = hyperparameters.get("gap_lengthscale_add_prob", 1.0)
+        if lf != 0.0 and torch.rand(()).item() < lf_prob:
+            hyperparameters = hyperparameters.copy()
+            loc = hyperparameters.get("lengthscale_loc_constant_add", sqrt(2))
+            hyperparameters["lengthscale_loc_constant_add"] = loc + lf
+
+    # Determine dummy dimensions once (shared across all region functions)
+    dummy_dims, num_non_dummy = _get_dummy_dims(num_features, hyperparameters)
+
+    if num_splits == 0:
+        inner = _sample_paths_inner(batch_size, num_non_dummy, hyperparameters)
+        if dummy_dims is None:
+            return inner
+        return lambda x: inner(x[:, :, ~dummy_dims])
+
+    # Sample independent GP paths for each region (2^num_splits regions)
+    num_regions = 2**num_splits
+    region_paths = [
+        _sample_paths_inner(batch_size, num_non_dummy, hyperparameters)
+        for _ in range(num_regions)
+    ]
+
+    # Sample split parameters (on original feature space, not reduced)
+    feature_indices = torch.randint(0, num_features, (batch_size, num_splits))
+    thresholds = torch.rand(batch_size, num_splits)
+
+    def paths_with_gaps(x):
+        # Compute region index for each point (using full x with all features)
+        fi_exp = feature_indices.unsqueeze(1).expand(-1, x.shape[1], -1)
+        x_at_splits = torch.gather(x, dim=2, index=fi_exp)
+        above = (x_at_splits > thresholds.unsqueeze(1)).long()
+        powers = (2 ** torch.arange(num_splits)).view(1, 1, -1)
+        region_idx = (above * powers).sum(dim=2)  # [batch_size, n]
+
+        # Filter to non-dummy dims for GP evaluation
+        x_filtered = x if dummy_dims is None else x[:, :, ~dummy_dims]
+
+        # Evaluate all region paths and select based on region_idx
+        all_ys = torch.stack(
+            [p(x_filtered).squeeze(0) for p in region_paths], dim=2
+        )  # [batch_size, n, num_regions]
+        y = torch.gather(all_ys, dim=2, index=region_idx.unsqueeze(2)).squeeze(2)
+        return y.unsqueeze(0)  # [1, batch_size, n]
+
+    return paths_with_gaps
+
+
+# paths = draw_matheron_paths(model=model, sample_shape=Size((128,)))
+
+
+def sample_clustered_x(
+    batch_size,
+    seq_len,
+    num_features,
+    pad_factor: int = 10,
+    num_cluster_max: int = 1,
+    max_std: float = 0.25,
+):
+    """
+    This function samples a batch of inputs from normal distributions.
+    Its outputs are all in [0,1], which is ensured by over-sampling (pad_factor)
+    and then rejecting outside samples. In addition, we clamp the values to [0,1].
+    """
+    num_clusters = torch.randint(1, num_cluster_max + 1, tuple()).item()
+
+    mean = torch.rand(batch_size, num_clusters, num_features)
+    std = torch.rand(batch_size, num_clusters, num_features) * max_std
+
+    # define mean and std for each position
+    # to do that we randomly pick values from the num_clusters dimension
+    mean = (
+        mean[:, torch.randint(0, num_clusters, (seq_len,)), :]
+        .transpose(1, 2)
+        .repeat(1, 1, pad_factor)
+    )
+    std = (
+        std[:, torch.randint(0, num_clusters, (seq_len,)), :]
+        .transpose(1, 2)
+        .repeat(1, 1, pad_factor)
+    )
+
+    x = torch.randn(batch_size, num_features, seq_len * pad_factor) * std + mean
+    x = x.transpose(1, 2)
+    sorting_x = ((x >= 0.0) & (x <= 1.0)).sum(dim=-1)
+    order = torch.argsort(sorting_x, dim=-1, stable=True, descending=True)
+    print(f"{order.shape=}")
+    x = x.gather(
+        dim=1, index=order[:, :seq_len].unsqueeze(-1).expand(-1, -1, num_features)
+    )
+    x = x.clamp(0, 1)
+    return x
+
+
+def sample_around_train_point(
+    batch_size,
+    seq_len,
+    num_features,
+    single_eval_pos,
+    surrounding_std: float = 0.01,
+    surrounding_share: float = 0.5,
+    binary_feature_likelihood: float = 0.0,
+):
+    binary_features = (
+        (torch.rand(batch_size, num_features) < binary_feature_likelihood)
+        .unsqueeze(1)
+        .expand(-1, single_eval_pos, -1)
+    )
+    train_x = torch.rand(batch_size, single_eval_pos, num_features)
+    train_x_cutoffs = torch.rand(batch_size, single_eval_pos, num_features)
+    train_x[binary_features] = (
+        train_x[binary_features] > train_x_cutoffs[binary_features]
+    ).float()
+
+    num_test_points = seq_len - single_eval_pos
+    num_surrounding = int(num_test_points * surrounding_share)
+
+    normal_test_x = torch.rand(
+        batch_size, num_test_points - num_surrounding, num_features
+    )
+
+    # Use shared utility for sampling around training points
+    surrounding_test_x = sample_x_around_points(
+        batch_size=batch_size,
+        num_samples=num_surrounding,
+        num_features=num_features,
+        centers=train_x,
+        std=surrounding_std,
+        device=train_x.device,
+    )
+
+    x = torch.cat([train_x, normal_test_x, surrounding_test_x], dim=1)
+    return x
+
+
+def add_noise(y, hyperparameters, no_noise=None):
+    batch_size = y.shape[0]
+    noise_var_dist = hyperparameters.get("noise_var_dist", "lognormal")
+    if noise_var_dist == "lognormal":
+        noise_variance: torch.Tensor = LogNormalPrior(
+            loc=hyperparameters["noise_var_loc"],
+            scale=hyperparameters["noise_var_scale"],
+        ).sample((batch_size,))
+    elif noise_var_dist == "gamma":
+        noise_variance: torch.Tensor = (
+            torch.distributions.Gamma(
+                hyperparameters["noise_var_concentration"],
+                hyperparameters["noise_var_rate"],
+            ).sample((batch_size,))
+            + 1e-4
+        )
+    else:
+        raise ValueError(f"Unknown noise variance distribution {noise_var_dist}")
+
+    if no_noise is not None:
+        noise_variance[no_noise] = 0.0
+
+    noisy_y = y + torch.randn_like(y) * noise_variance[:, None, None] ** (1 / 2)
+    return noisy_y
+
+
+@torch.no_grad()
+def get_batch(
+    batch_size,
+    seq_len,
+    num_features,
+    single_eval_pos,
+    hyperparameters=None,
+    n_targets_per_input=1,
+    **kwargs,
+):
+    if hyperparameters is None:
+        hyperparameters = {
+            "noise_var_loc": -4.0,
+            "noise_var_scale": 1.0,
+        }
+
+    no_noise_prob = hyperparameters.get("no_noise_prob", 0.0)
+    no_noise = torch.rand(batch_size) < no_noise_prob
+
+    assert hyperparameters.get("mean_dist", "normal") == "normal"
+
+    paths = sample_paths(
+        batch_size, num_features, hyperparameters
+    )  # paths maps [batch_size, n, num_features] to [batch_size, n, 1]
+
+    sample_clustered_x_hp = hyperparameters.get("sample_clustered_x", None)
+
+    if sample_clustered_x_hp == "trace":
+        assert no_noise_prob == 0.0
+        x, y = generate_trace(
+            batch_size,
+            paths,
+            partial(add_noise, hyperparameters=hyperparameters),
+            seq_len,
+            single_eval_pos,
+            bounds=[(0, 1)] * num_features,
+        )
+
+        y = y.view(batch_size, seq_len, 1)
+        noisy_y = y
+
+    else:
+        if sample_clustered_x_hp is True or sample_clustered_x_hp == "clustered":
+            x = sample_clustered_x(
+                batch_size,
+                seq_len,
+                num_features,
+                num_cluster_max=hyperparameters.get("num_cluster_max", 1),
+                max_std=hyperparameters.get("max_std", 0.25),
+            )
+        elif sample_clustered_x_hp.startswith("around_train_point_binp_"):
+            binary_prob = float(sample_clustered_x_hp.split("_")[-1])
+            x = sample_around_train_point(
+                batch_size,
+                seq_len,
+                num_features,
+                single_eval_pos,
+                binary_feature_likelihood=binary_prob,
+            )
+        elif sample_clustered_x_hp == "around_train_point":
+            x = sample_around_train_point(
+                batch_size,
+                seq_len,
+                num_features,
+                single_eval_pos,
+            )
+        else:
+            assert (sample_clustered_x_hp is None) or (
+                sample_clustered_x_hp == "none"
+            ), sample_clustered_x_hp
+            x = torch.rand(batch_size, seq_len, num_features)
+
+        y = paths(x).squeeze(0)  # shape: (batch_size, seq_len)
+
+        y = y.view(batch_size, seq_len, 1)
+
+        noisy_y = add_noise(y, hyperparameters, no_noise)
+
+    if hyperparameters["noisy_predictions"]:
+        target_y = add_noise(
+            y.expand(-1, -1, n_targets_per_input), hyperparameters, no_noise
+        )
+    else:
+        target_y = y.expand(-1, -1, n_targets_per_input)
+
+    if hyperparameters.get("train_normalized_y", False):
+        if single_eval_pos <= 1:
+            raise ValueError("train_normalized_y requires single_eval_pos > 1")
+
+        train_mean = noisy_y[:, :single_eval_pos].mean(dim=1, keepdim=True)
+        train_std = noisy_y[:, :single_eval_pos].std(dim=1, keepdim=True)
+        noisy_y = (noisy_y - train_mean) / train_std
+        target_y = (target_y - train_mean) / train_std
+
+    predict_advantage = hyperparameters.get("predict_advantage", False)
+    if predict_advantage is True:
+        target_y = (
+            target_y[:, :, :]
+            - target_y[:, :single_eval_pos, :]
+            .max(dim=-1, keepdim=True)
+            .values.max(dim=-2, keepdim=True)
+            .values
+        )
+    elif predict_advantage == "y":
+        target_y = (
+            target_y[:, :, :]
+            - noisy_y[:, :single_eval_pos, :]
+            .max(dim=-1, keepdim=True)
+            .values.max(dim=-2, keepdim=True)
+            .values
+        )
+    else:
+        assert predict_advantage is False
+
+    if hyperparameters.get("relu_target", False):
+        target_y = target_y.clamp(min=0.0)
+
+    # set ys to nan in training set
+    number_of_y_hidden = torch.randint(
+        0, hyperparameters.get("max_num_hidden_y", 0) + 1, tuple()
+    )
+    noisy_y[:, single_eval_pos - number_of_y_hidden : single_eval_pos] = torch.nan
+
+    return Batch(x=x, y=noisy_y, target_y=target_y)
diff --git a/pfns/priors/path_trace_sampling.py b/pfns/priors/path_trace_sampling.py
new file mode 100644
index 0000000..3e0115e
--- /dev/null
+++ b/pfns/priors/path_trace_sampling.py
@@ -0,0 +1,183 @@
+import numpy as np
+import torch
+
+
+# Vectorized corner check for batch of points
+def corner_check(x, corners):
+    # x can be a single point or batch of points
+    batching = True
+    if x.ndim == 1:
+        batching = False
+        x = x[np.newaxis, :]
+
+    # Check which points are corners (all coords <= 0 or >= 1)
+    is_corner = np.all((x <= 0) | (x >= 1), axis=1)
+    new_corners = np.zeros(len(x), dtype=bool)
+
+    # For non-corner points, return True
+    results = np.ones(len(x), dtype=bool)
+
+    if np.any(is_corner):
+        # For corner points, compute their IDs
+        corner_powers = np.array([2**i for i in range(x.shape[1])])
+        corner_ids = (x[is_corner] @ corner_powers).round().astype(int)
+        corner_ids = np.array(
+            [cid * x.shape[0] + i for i, cid in enumerate(corner_ids)]
+        )
+
+        # Check which corner IDs are new
+        new_corners = np.array([cid not in corners for cid in corner_ids])
+
+        # Add new corner IDs to the set
+        corners.update(corner_ids[new_corners])
+
+        # For corner points, return True if new corner, False if already seen
+        results[is_corner] = new_corners
+
+    return results if batching else results[0], corners
+
+
+def sample_until_all_success(sampling_function, corners):
+    sample = sampling_function()  # shape: [batch_size, d]
+    success, corners = corner_check(sample, corners)
+    while not np.all(success):
+        # Keep successful samples, only resample failed ones
+        failed_mask = ~success
+        new_sample = sampling_function()
+        new_success, corners = corner_check(new_sample, corners)
+        # Update only failed positions with new successful samples
+        sample[failed_mask] = new_sample[failed_mask]
+        success[failed_mask] = new_success[failed_mask]
+    return sample
+
+
+@torch.no_grad()
+def generate_trace(
+    batch_size,
+    paths,
+    add_noise,
+    L,
+    cutoff,
+    bounds,
+    best=None,
+    never_local=False,
+    dtype=torch.float,
+):
+    """
+    Generate optimization traces blending exploration and exploitation for batched Gaussian Processes.
+
+    Parameters:
+    - L: int, length of the trace.
+    - cutoff: int, position in the trace after which we may sample around the global optimum.
+    - bounds: list of tuples [(min1, max1), (min2, max2), ...], search space bounds.
+    - t_random_weights: torch.Tensor, random weights for GP Fourier features [batch_size, d, num_features].
+    - t_random_offset: torch.Tensor, random offsets for GP Fourier features [batch_size, num_features].
+    - t_W_GP: torch.Tensor, weights defining the GP in RFF space [batch_size, num_features, 1].
+    - sigma_output: float, output scale of the GP.
+    - sigma_noise: float, observation noise level.
+    - mean_function: float, mean function of the GP.
+    - best: ndarray, optional, the location of the global optimum [batch_size, d].
+
+    Returns:
+    - trace: ndarray of shape [batch_size, L, d], the optimization traces.
+    - y: ndarray of shape [batch_size, L], the function values at each point in the traces.
+    """
+    d = len(bounds)
+    trace = np.zeros((batch_size, L, d))
+    y = np.zeros((batch_size, L))
+
+    corners = set()
+
+    # Initialize
+    eps = (1 - np.random.rand(batch_size) ** (d / 6)) / 2
+    sigma = np.exp(np.random.normal(-3, 0.5, size=(batch_size,)))
+    u = np.random.uniform(size=(batch_size, 3))
+    initial_alpha = u.min(axis=1)
+    final_alpha = u.max(axis=1)
+    trace[:, 0] = np.clip(
+        np.random.uniform(-eps[:, None], 1 + eps[:, None], (batch_size, d)), 0, 1
+    )
+    best_point = trace[:, 0].copy()
+
+    # Get initial values using vectorized GP evaluation
+    y[:, :1] = add_noise(paths(torch.tensor(trace[:, :1], dtype=dtype))).numpy()
+    y_best = y[:, 0].copy()
+
+    for i in range(1, L):
+        alpha = initial_alpha + (final_alpha - initial_alpha) * (i / L)
+        local = np.random.rand(batch_size) < alpha
+        if never_local:
+            local[:] = False
+
+        # could speed up by factor of 2
+
+        def sample_local():
+            if i < cutoff:  # noqa: B023
+                inc = best_point
+            else:
+                inc = np.zeros_like(best_point)
+                if best is not None:
+                    use_best = (L - cutoff) * np.random.rand(batch_size) < 5 * d
+                    inc[use_best] = best[use_best]
+
+                if cutoff > 0:
+                    use_cutoff = (
+                        ~use_best
+                        if best is not None
+                        else np.ones(batch_size, dtype=bool)
+                    )
+                    random_cutoff_indices = np.random.choice(cutoff, size=batch_size)
+                    inc[use_cutoff] = trace[
+                        np.arange(batch_size)[use_cutoff],
+                        random_cutoff_indices[use_cutoff],
+                    ]
+                else:
+                    # No point to sample locally around, just sample globally
+                    use_global = (
+                        ~use_best
+                        if best is not None
+                        else np.ones(batch_size, dtype=bool)
+                    )
+                    inc[use_global] = np.clip(
+                        np.random.uniform(
+                            -eps[use_global, None],
+                            1 + eps[use_global, None],
+                            (np.sum(use_global), d),
+                        ),
+                        0,
+                        1,
+                    )
+
+            ret = np.random.normal(inc, sigma[:, None], size=(batch_size, d))
+            return np.clip(
+                ret, [low for low, _ in bounds], [high for _, high in bounds]
+            )
+
+        def sample_global():
+            return np.clip(
+                np.random.uniform(-eps[:, None], 1 + eps[:, None], (batch_size, d)),
+                0,
+                1,
+            )
+
+        # Sample points based on local/global strategy
+        trace[:, i] = np.where(
+            local[:, None],
+            sample_until_all_success(sample_local, corners),
+            sample_until_all_success(sample_global, corners),
+        )
+
+        # Update the current best if before cutoff
+        if i < cutoff:
+            y[:, i : i + 1] = add_noise(
+                paths(torch.tensor(trace[:, i : i + 1], dtype=dtype))
+            ).numpy()
+            better_mask = y[:, i] > y_best
+            best_point[better_mask] = trace[better_mask, i]
+            y_best[better_mask] = y[:, i][better_mask]
+
+    # Get noiseless values after cutoff using vectorized evaluation
+    if cutoff < L:
+        y[:, cutoff:] = paths(torch.tensor(trace[:, cutoff:], dtype=dtype)).numpy()
+
+    return torch.tensor(trace, dtype=dtype), torch.tensor(y, dtype=dtype)
diff --git a/pfns/priors/prior.py b/pfns/priors/prior.py
index 8120045..c31729d 100644
--- a/pfns/priors/prior.py
+++ b/pfns/priors/prior.py
@@ -20,9 +20,11 @@ def create_get_batch_method(self) -> Callable:
 @dataclass(frozen=True)
 class AdhocPriorConfig(PriorConfig):
     # Set as a class variable instead of being set at init
-    prior_names: str | Sequence[str] | None = None
+    prior_names: list[str] | None = None
     get_batch_methods: Callable | Sequence[Callable] | None = None
     prior_kwargs: dict | None = None
+    prior_dirs: str | list[str] = "pfns.priors"
+    get_batch_names: str | list[str] = "get_batch"
 
     strict_field_types: ClassVar[bool] = False
 
@@ -36,13 +38,21 @@ def create_get_batch_method(self) -> Callable:
 
         if self.prior_names is not None:
             get_batch_methods = []
-            for prior_name in (
-                self.prior_names
-                if isinstance(self.prior_names, Sequence)
-                else [self.prior_names]
-            ):
-                prior_module = importlib.import_module(f"pfns.priors.{prior_name}")
-                get_batch_methods.append(prior_module.get_batch)
+            get_batch_names = (
+                self.get_batch_names
+                if isinstance(self.get_batch_names, list)
+                else [self.get_batch_names] * len(self.prior_names)
+            )
+            prior_dirs = (
+                self.prior_dirs
+                if isinstance(self.prior_dirs, list)
+                else [self.prior_dirs] * len(self.prior_names)
+            )
+            assert len(self.prior_names) == len(get_batch_names) == len(prior_dirs)
+
+            for i, prior_name in enumerate(self.prior_names):
+                prior_module = importlib.import_module(f"{prior_dirs[i]}.{prior_name}")
+                get_batch_methods.append(getattr(prior_module, get_batch_names[i]))
         else:
             get_batch_methods = (
                 self.get_batch_methods
@@ -68,8 +78,14 @@ class Batch:
 
     # Required entries
     x: torch.Tensor
-    y: torch.Tensor
-    target_y: torch.Tensor
+
+    # Entries when using sep. y
+    y: torch.Tensor | None
+    target_y: torch.Tensor | None
+
+    # Entries for x_only_mode
+    target: torch.Tensor | None = None
+    test_x: torch.Tensor | None = None
 
     # Optional Batch Entries
     style: Optional[torch.Tensor] = None
diff --git a/pfns/priors/saas.py b/pfns/priors/saas.py
new file mode 100644
index 0000000..55482a3
--- /dev/null
+++ b/pfns/priors/saas.py
@@ -0,0 +1,105 @@
+import torch
+from botorch.models.fully_bayesian import SaasPyroModel
+
+from .path_stgp import sample_around_train_point, sample_clustered_x
+from .prior import Batch
+
+
+@torch.no_grad()
+def get_batch(
+    batch_size,
+    seq_len,
+    num_features,
+    single_eval_pos,
+    hyperparameters=None,
+    n_targets_per_input=1,
+    **kwargs,
+):
+    if hyperparameters is None:
+        hyperparameters = {}
+
+    sample_clustered_x_hp = hyperparameters.get("sample_clustered_x", None)
+
+    # Sample x based on the specified method (same logic as path_stgp.py)
+    if sample_clustered_x_hp is True or sample_clustered_x_hp == "clustered":
+        x = sample_clustered_x(
+            batch_size,
+            seq_len,
+            num_features,
+            num_cluster_max=hyperparameters.get("num_cluster_max", 1),
+            max_std=hyperparameters.get("max_std", 0.25),
+        )
+    elif sample_clustered_x_hp and sample_clustered_x_hp.startswith(
+        "around_train_point_binp_"
+    ):
+        binary_prob = float(sample_clustered_x_hp.split("_")[-1])
+        x = sample_around_train_point(
+            batch_size,
+            seq_len,
+            num_features,
+            single_eval_pos,
+            binary_feature_likelihood=binary_prob,
+        )
+    elif sample_clustered_x_hp == "around_train_point":
+        x = sample_around_train_point(
+            batch_size,
+            seq_len,
+            num_features,
+            single_eval_pos,
+        )
+    else:
+        assert sample_clustered_x_hp is None, sample_clustered_x_hp
+        x = torch.rand(batch_size, seq_len, num_features)
+
+    no_noise_prob = hyperparameters.get("no_noise_prob", 0.0)
+    no_noise = torch.rand(batch_size) < no_noise_prob
+
+    # Sample each batch item separately using SAAS prior
+    y_list = []
+    noisy_y_list = []
+
+    for i in range(batch_size):
+        m = SaasPyroModel()
+        m._prior_mode = True
+        m.set_inputs(
+            x[i].to(dtype=torch.float64),
+            torch.zeros(seq_len, 1, dtype=torch.float64),
+        )
+        m.sample()
+
+        # Get noiseless and noisy predictions
+        noiseless_y = m.f_prior_sample.to(dtype=torch.float32)
+        if no_noise[i]:
+            noisy_y = noiseless_y.clone()
+        else:
+            noisy_y = m.Y_prior_sample.to(dtype=torch.float32)
+
+        y_list.append(noiseless_y)
+        noisy_y_list.append(noisy_y)
+
+    y = torch.stack(y_list, dim=0).view(batch_size, seq_len, 1)
+    noisy_y = torch.stack(noisy_y_list, dim=0).view(batch_size, seq_len, 1)
+
+    # Handle n_targets_per_input
+    if hyperparameters.get("noisy_predictions", False):
+        target_y = noisy_y.expand(-1, -1, n_targets_per_input)
+    else:
+        target_y = y.expand(-1, -1, n_targets_per_input)
+
+    # Handle predict_advantage
+    if hyperparameters.get("predict_advantage", False):
+        target_y = (
+            target_y[:, :, :]
+            - target_y[:, :single_eval_pos, :]
+            .max(dim=-1, keepdim=True)
+            .values.max(dim=-2, keepdim=True)
+            .values
+        )
+
+    # Set ys to nan in training set
+    number_of_y_hidden = torch.randint(
+        0, hyperparameters.get("max_num_hidden_y", 0) + 1, tuple()
+    )
+    noisy_y[:, single_eval_pos - number_of_y_hidden : single_eval_pos] = torch.nan
+
+    return Batch(x=x, y=noisy_y, target_y=target_y)
diff --git a/pfns/priors/singletaskgp.py b/pfns/priors/singletaskgp.py
new file mode 100644
index 0000000..634d3e0
--- /dev/null
+++ b/pfns/priors/singletaskgp.py
@@ -0,0 +1,441 @@
+from math import log, sqrt
+
+import torch
+
+from gpytorch.distributions.multivariate_normal import MultivariateNormal
+from gpytorch.kernels import LinearKernel, MaternKernel, RBFKernel
+from gpytorch.priors import LogNormalPrior
+
+from pfns.priors.prior import Batch
+from torch import Tensor
+
+
+def sample_clustered_x(
+    batch_size,
+    seq_len,
+    num_features,
+    pad_factor: int = 10,
+    num_cluster_max: int = 1,
+    max_std: float = 0.25,
+):
+    """
+    This function samples a batch of inputs from normal distributions.
+    Its outputs are all in [0,1], which is ensured by over-sampling (pad_factor)
+    and then rejecting outside samples. In addition, we clamp the values to [0,1].
+    """
+    num_clusters = torch.randint(1, num_cluster_max + 1, tuple()).item()
+
+    mean = torch.rand(batch_size, num_clusters, num_features)
+    std = torch.rand(batch_size, num_clusters, num_features) * max_std
+
+    # define mean and std for each position
+    # to do that we randomly pick values from the num_clusters dimension
+    mean = (
+        mean[:, torch.randint(0, num_clusters, (seq_len,)), :]
+        .transpose(1, 2)
+        .repeat(1, 1, pad_factor)
+    )
+    std = (
+        std[:, torch.randint(0, num_clusters, (seq_len,)), :]
+        .transpose(1, 2)
+        .repeat(1, 1, pad_factor)
+    )
+
+    x = torch.randn(batch_size, num_features, seq_len * pad_factor) * std + mean
+    x = x.transpose(1, 2)
+    sorting_x = ((x >= 0.0) & (x <= 1.0)).sum(dim=-1)
+    order = torch.argsort(sorting_x, dim=-1, stable=True, descending=True)
+    x = x.gather(
+        dim=1, index=order[:, :seq_len].unsqueeze(-1).expand(-1, -1, num_features)
+    )
+    x = x.clamp(0, 1)
+    return x
+
+
+def sample_around_train_point(
+    batch_size,
+    seq_len,
+    num_features,
+    single_eval_pos,
+    surrounding_std: float = 0.01,
+    surrounding_share: float = 0.5,
+):
+    train_x = torch.rand(batch_size, single_eval_pos, num_features)
+
+    num_test_points = seq_len - single_eval_pos
+    num_surrounding = int(num_test_points * surrounding_share)
+
+    normal_test_x = torch.rand(
+        batch_size, num_test_points - num_surrounding, num_features
+    )
+    if single_eval_pos > 0:
+        centers = torch.multinomial(
+            torch.ones(single_eval_pos), num_surrounding, replacement=True
+        )
+        surrounding_test_x = (
+            torch.randn(batch_size, num_surrounding, num_features) * surrounding_std
+            + train_x[:, centers]
+        )
+    else:
+        surrounding_test_x = torch.rand(batch_size, num_surrounding, num_features)
+    x = torch.cat([train_x, normal_test_x, surrounding_test_x], dim=1)
+    return x
+
+
+# adapted from botorch to support batching
+def inv_kumaraswamy_warp(
+    X: Tensor, c0: Tensor, c1: Tensor, eps: float = 1e-8
+) -> Tensor:
+    """Map warped inputs through an inverse Kumaraswamy CDF.
+
+    This takes warped inputs (X) and transforms those via an inverse
+    Kumaraswamy CDF. This then unnormalizes the inputs using bounds of
+    [eps, 1-eps]^d and ensures that the values are within [0, 1]^d.
+
+    Args:
+        X: A `b x n x d`-dim tensor of inputs.
+        c0: A `b x d`-dim tensor of the concentration0 parameter for the
+            Kumaraswamy distribution.
+        c1: A `b x d`-dim tensor of the concentration1 parameter for the
+            Kumaraswamy distribution.
+        eps: A small value that is used to ensure inputs are not 0 or 1.
+
+    Returns:
+        A `batch_shape x n x d`-dim tensor of untransformed inputs.
+    """
+    X_range = 1 - 2 * eps
+    # unnormalize from [eps, 1-eps] to [0,1]
+    untf_X = (1 - (1 - X).pow(1 / c0.unsqueeze(1))).pow(1 / c1.unsqueeze(1))
+    return ((untf_X - eps) / X_range).clamp(0.0, 1.0)
+
+
+@torch.no_grad()
+def get_batch(
+    batch_size,
+    seq_len,
+    num_features,
+    single_eval_pos,
+    hyperparameters=None,
+    n_targets_per_input=1,
+    print_infos=False,
+    **kwargs,
+):
+    if hyperparameters is None:
+        hyperparameters = {
+            "lengthscale_loc_constant_add": sqrt(2),  # same as in fully
+            "lengthscale_loc_feature_mul": 0.5,  # same as in fully
+            "lengthscale_scale": sqrt(3),  # same as in fully
+            "noise_var_loc": -4.0,  # different in fully bayesian
+            "noise_var_scale": 1.0,  # different in fully bayesian
+            "noise_var_dist": "lognormal",  # different in fully bayesian, where it is "gamma"
+            "noise_var_concentration": 0.9,
+            "noise_var_rate": 10.0,
+            "mean_width": 2.0,
+            "mean_dist": "uniform",
+            "attsink_tokens": 0,
+            "noisy_predictions": False,
+            "sample_strategy": "uniform",
+            "train_normalized_y": False,
+            "style_for_max_on_border_likelihood": False,
+            "dummy_dim_prob": 0.0,
+            # Oversampling factor: build a super dataset of this factor times seq_len
+            "oversample_factor": 1.0,
+            # Proportion of final dataset sampled from the top-share (by non-noisy y)
+            "top_sampling_share": 0.0,
+            # The fraction of the super dataset considered as the top-share
+            "top_share_of_super": 0.1,
+            # input warping
+            "input_warping_prob": 0.0,
+            "input_warping_c0_std": 0.75**0.5,
+            "input_warping_c1_std": 0.75**0.5,
+        }
+
+    # Build dataset, possibly oversampled if using top sampling
+    top_sampling_share = hyperparameters.get("top_sampling_share", 0.0)
+
+    if top_sampling_share > 0.0:
+        oversample_factor = hyperparameters.get("oversample_factor", 1.0)
+        assert (
+            oversample_factor > 1.0
+        ), "oversample_factor must be > 1.0 when top_sampling_share > 0"
+        super_seq_len = round(seq_len * oversample_factor)
+    else:
+        assert (
+            hyperparameters.get("oversample_factor", 1.0) == 1.0
+        ), "oversample_factor must be 1.0 when top_sampling_share is 0"
+        super_seq_len = seq_len
+
+    sample_clustered_x_hp = hyperparameters.get("sample_clustered_x", False)
+
+    if sample_clustered_x_hp is True or sample_clustered_x_hp == "clustered":
+        x_super = sample_clustered_x(
+            batch_size,
+            super_seq_len,
+            num_features,
+            num_cluster_max=hyperparameters.get("num_cluster_max", 1),
+            max_std=hyperparameters.get("max_std", 0.25),
+        )
+    elif sample_clustered_x_hp == "around_train_point":
+        x_super = sample_around_train_point(
+            batch_size,
+            super_seq_len,
+            num_features,
+            single_eval_pos,
+        )
+    else:
+        x_super = torch.rand(batch_size, super_seq_len, num_features)
+
+    mean_width = hyperparameters["mean_width"]
+    if mean_width == 0:
+        mean = torch.zeros(batch_size)
+    else:
+        mean_dist = hyperparameters.get("mean_dist", "uniform")
+        if mean_dist == "uniform":
+            min_mean, max_mean = -mean_width / 2, mean_width / 2
+            mean = torch.rand(batch_size) * (max_mean - min_mean) + min_mean
+        elif mean_dist == "normal":
+            mean = torch.randn(batch_size) * mean_width / 2
+        else:
+            raise ValueError(f"Unknown mean distribution {mean_dist}")
+
+    if (dummy_dim_prob := hyperparameters.get("dummy_dim_prob", 0.0)) > 0.0:
+        num_important_features = 0
+        while num_important_features == 0:
+            dummy_dims_mask = torch.bernoulli(
+                torch.full((num_features,), dummy_dim_prob)
+            ).bool()
+            used_dims_mask = ~dummy_dims_mask
+            num_important_features = used_dims_mask.sum()
+    else:
+        used_dims_mask = torch.ones(num_features, dtype=torch.bool)
+        num_important_features = num_features
+
+    length_scales = LogNormalPrior(
+        loc=hyperparameters["lengthscale_loc_constant_add"]
+        + log(num_important_features) * hyperparameters["lengthscale_loc_feature_mul"],
+        scale=hyperparameters["lengthscale_scale"],
+    ).sample((batch_size, num_important_features))
+
+    kernel_name = hyperparameters.get("kernel", "rbf")
+
+    def get_covar(length_scales, x):
+        if kernel_name == "rbf":
+            covar_module = RBFKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+            )
+        elif kernel_name == "matern_1.5":
+            covar_module = MaternKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+                nu=1.5,
+            )
+        elif kernel_name == "matern_2.5":
+            covar_module = MaternKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+                nu=2.5,
+            )
+        elif kernel_name == "linear":
+            covar_module = LinearKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+            )
+        else:
+            raise ValueError(f"Unknown kernel {kernel_name}")
+        if covar_module.has_lengthscale:
+            covar_module._set_lengthscale(length_scales)
+        covar = covar_module(x[..., used_dims_mask], x[..., used_dims_mask])
+        return covar
+
+    style = None
+
+    if hyperparameters.get("additive", False):
+        num_features_in_group1 = torch.randint(0, num_features, tuple()).item()
+        perm = torch.randperm(num_features)
+        features_in_group1 = perm[:num_features_in_group1]
+        features_in_group0 = perm[num_features_in_group1:]
+
+        covar0 = get_covar(
+            length_scales[:, features_in_group0], x_super[:, :, features_in_group0]
+        )
+        covar1 = get_covar(
+            length_scales[:, features_in_group1], x_super[:, :, features_in_group1]
+        )
+
+        d0 = MultivariateNormal(
+            torch.ones_like(x_super[:, :, 0]) * mean[:, None], covar0
+        )
+        d1 = MultivariateNormal(torch.zeros_like(x_super[:, :, 0]), covar1)
+        y_super: torch.Tensor = d0.sample() + d1.sample()
+        style = torch.zeros(batch_size, num_features, 1)
+        style[:, features_in_group1, :] = 1.0
+        style = (style * 2.0) - 1.0
+    else:
+        covar = get_covar(length_scales, x_super)
+        d = MultivariateNormal(torch.ones_like(x_super[:, :, 0]) * mean[:, None], covar)
+        y_super: torch.Tensor = d.sample()
+
+    # Select final dataset of length seq_len, possibly from top share of larger super dataset
+    device = x_super.device
+    x = torch.empty(batch_size, seq_len, num_features, device=device)
+    y = torch.empty(batch_size, seq_len, device=device)
+
+    if top_sampling_share > 0.0:
+        top_share_of_super = hyperparameters.get("top_share_of_super", 0.1)
+        # Calculate sizes with bounds checking using max/min
+        top_k_count = min(
+            max(0, round(top_share_of_super * super_seq_len)), super_seq_len
+        )
+        n_top = min(
+            max(0, round(top_sampling_share * seq_len)), min(top_k_count, seq_len)
+        )
+        n_rest = seq_len - n_top
+
+        if top_k_count > 0:
+            top_inds = torch.topk(y_super, k=top_k_count, largest=True).indices
+        else:
+            top_inds = torch.empty(batch_size, 0, dtype=torch.long, device=device)
+
+        for b in range(batch_size):
+            # Get top indices by non-noisy y value
+            top_idx_b = top_inds[b]
+
+            chosen_indices = []
+            if n_top > 0 and top_idx_b.numel() > 0:
+                perm_top = torch.randperm(top_idx_b.numel(), device=device)
+                chosen_top = top_idx_b[perm_top[:n_top]]
+                chosen_indices.append(chosen_top)
+
+            if n_rest > 0:
+                all_idx = torch.arange(super_seq_len, device=device)
+                if chosen_indices:
+                    chosen_cat = torch.cat(chosen_indices)
+                    mask = torch.ones(super_seq_len, dtype=torch.bool, device=device)
+                    mask[chosen_cat] = False
+                    remaining_idx = all_idx[mask]
+                else:
+                    remaining_idx = all_idx
+                perm_rem = torch.randperm(remaining_idx.numel(), device=device)
+                chosen_rest = remaining_idx[perm_rem[:n_rest]]
+                chosen_indices.append(chosen_rest)
+
+            # Combine and shuffle indices
+            final_idx = torch.cat(chosen_indices)
+            final_idx = final_idx[torch.randperm(final_idx.numel(), device=device)]
+
+            x[b] = x_super[b, final_idx]
+            y[b] = y_super[b, final_idx]
+    else:
+        # No oversampling, just use the dataset as is
+        x = x_super
+        y = y_super
+
+    if hyperparameters.get("style_for_max_on_border_likelihood", False):
+        max_i = y.max(1).indices  # (B,)
+        max_x = x[torch.arange(len(max_i)), max_i]  # (B,F)
+        mins = x.min(1).values  # (B,F)
+        maxs = x.max(1).values  # (B,F)
+        is_max_or_min_on_border = (max_x == mins) | (max_x == maxs)  # (B,F)
+
+        sureness = torch.rand(batch_size, num_features)  # (B,F)
+        correct_hint = torch.bernoulli(sureness).bool()  # (B,F)
+        border_style = (
+            correct_hint * is_max_or_min_on_border * sureness
+            + ~correct_hint * is_max_or_min_on_border * (1 - sureness)
+            + correct_hint * ~is_max_or_min_on_border * (1 - sureness)
+            + ~correct_hint * ~is_max_or_min_on_border * sureness
+        )[:, :, None]  # (B,F,1)
+        # for b in [0.1 * i for i in range(1, 10)]:
+        #     mask = (border_style.flatten() < b) & (border_style.flatten() >= b - 0.1)
+        #     print(b, is_max_or_min_on_border.flatten()[mask].float().mean(), mask.sum())
+        # border_style = torch.zeros(batch_size, num_features, 1)
+        if style is None:
+            style = border_style
+        else:
+            style = torch.cat([style, border_style], dim=-1)
+
+    noise_var_dist = hyperparameters.get("noise_var_dist", "lognormal")
+    if noise_var_dist == "lognormal":
+        noise_variance: torch.Tensor = LogNormalPrior(
+            loc=hyperparameters["noise_var_loc"],
+            scale=hyperparameters["noise_var_scale"],
+        ).sample((batch_size,))
+    elif noise_var_dist == "gamma":
+        noise_variance: torch.Tensor = (
+            torch.distributions.Gamma(
+                hyperparameters["noise_var_concentration"],
+                hyperparameters["noise_var_rate"],
+            ).sample((batch_size,))
+            + 1e-4
+        )
+    else:
+        raise ValueError(f"Unknown noise variance distribution {noise_var_dist}")
+
+    noisy_y = y + torch.randn_like(y) * noise_variance[:, None] ** (1 / 2)
+
+    if hyperparameters["noisy_predictions"]:
+        target_y = y.view(batch_size, seq_len, 1) + torch.randn(
+            batch_size, seq_len, n_targets_per_input
+        ) * torch.sqrt(noise_variance[:, None, None])
+    else:
+        target_y = y.view(batch_size, seq_len, 1).repeat(1, 1, n_targets_per_input)
+
+    train_normalized_y = hyperparameters.get("train_normalized_y", False)
+
+    if hyperparameters.get("predict_advantage", False):
+        assert not train_normalized_y
+        target_y = (
+            target_y[:, :, :]
+            - target_y[:, :single_eval_pos, :]
+            .max(dim=-1, keepdim=True)
+            .values.max(dim=-2, keepdim=True)
+            .values
+        )
+
+    if train_normalized_y:
+        if single_eval_pos <= 1:
+            raise ValueError("train_normalized_y requires single_eval_pos > 1")
+
+        train_mean = noisy_y[:, :single_eval_pos].mean(dim=1, keepdim=True)
+        train_std = noisy_y[:, :single_eval_pos].std(dim=1, keepdim=True)
+        noisy_y = (noisy_y - train_mean) / train_std
+        target_y = (target_y - train_mean[..., None]) / train_std[..., None]
+
+    # Apply input warping if specified
+    input_warping_prob = hyperparameters.get("input_warping_prob", 0.0)
+    if input_warping_prob > 0.0:
+        c0_std = hyperparameters.get("input_warping_c0_std", 0.75**0.5)
+        c1_std = hyperparameters.get("input_warping_c1_std", 0.75**0.5)
+
+        # Sample c0 and c1 parameters from LogNormal distributions for each batch
+        c0 = LogNormalPrior(loc=0.0, scale=c0_std).sample((batch_size, num_features))
+        c1 = LogNormalPrior(loc=0.0, scale=c1_std).sample((batch_size, num_features))
+
+        no_warping_mask = torch.rand(batch_size, num_features) > input_warping_prob
+        c0[no_warping_mask] = 1.0
+        c1[no_warping_mask] = 1.0
+
+        # Apply inverse Kumaraswamy warping to inputs with per-batch parameters
+        x = inv_kumaraswamy_warp(x, c0, c1)
+
+    # set ys to nan in training set
+    number_of_y_hidden = torch.randint(
+        0, hyperparameters.get("max_num_hidden_y", 0) + 1, tuple()
+    )
+    noisy_y[:, single_eval_pos - number_of_y_hidden : single_eval_pos] = torch.nan
+
+    if print_infos:
+        import pprint
+
+        infos = {
+            "lengthscales": length_scales,
+            "noise_variances": noise_variance,
+            "means": mean,
+            "num_important_features": num_important_features,
+            "kernel": kernel_name,
+        }
+        pprint.pprint(infos)
+
+    b = Batch(x=x, y=noisy_y, target_y=target_y, style=style)
+    return b
diff --git a/pfns/priors/singletaskgp_x_only.py b/pfns/priors/singletaskgp_x_only.py
new file mode 100644
index 0000000..ccaf8f0
--- /dev/null
+++ b/pfns/priors/singletaskgp_x_only.py
@@ -0,0 +1,281 @@
+from math import log, sqrt
+
+import torch
+
+from gpytorch.distributions.multivariate_normal import MultivariateNormal
+from gpytorch.kernels import LinearKernel, MaternKernel, RBFKernel
+from gpytorch.priors import LogNormalPrior
+
+from pfns.priors.prior import Batch
+
+
+def sample_clustered_x(batch_size, seq_len, num_features, pad_factor: int = 10):
+    """
+    This function samples a batch of inputs from normal distributions.
+    Its outputs are all in [0,1], which is ensured by over-sampling (pad_factor)
+    and then rejecting outside samples. In addition, we clamp the values to [0,1].
+    """
+    mean = torch.rand(batch_size, num_features)
+    std = torch.rand(batch_size, num_features) / 4.0
+
+    x = (
+        torch.randn(batch_size, num_features, seq_len * pad_factor) * std[:, :, None]
+        + mean[:, :, None]
+    )
+    sorting_x = (x >= 0.0) & (x <= 1.0)
+    order = torch.argsort(sorting_x, dim=-1, stable=True, descending=True)
+    x = x.gather(dim=-1, index=order[:, :, :seq_len])
+    x = x.transpose(1, 2)
+    x = x.clamp(0, 1)
+    return x
+
+
+@torch.no_grad()
+def get_batch(
+    batch_size,
+    seq_len,
+    num_features,
+    single_eval_pos,
+    hyperparameters=None,
+    n_targets_per_input=1,
+    **kwargs,
+):
+    if hyperparameters is None:
+        hyperparameters = {
+            "lengthscale_loc_constant_add": sqrt(2),  # same as in fully
+            "lengthscale_loc_feature_mul": 0.5,  # same as in fully
+            "lengthscale_scale": sqrt(3),  # same as in fully
+            "noise_var_loc": -4.0,  # different in fully bayesian
+            "noise_var_scale": 1.0,  # different in fully bayesian
+            "noise_var_dist": "lognormal",  # different in fully bayesian, where it is "gamma"
+            "noise_var_concentration": 0.9,
+            "noise_var_rate": 10.0,
+            "mean_width": 2.0,
+            "mean_dist": "uniform",
+            "attsink_tokens": 0,
+            "noisy_predictions": False,
+            "sample_clustered_x": False,
+            "train_normalized_y": False,
+            "style_for_max_on_border_likelihood": False,
+            "dummy_dim_prob": 0.0,
+        }
+
+    assert not hyperparameters.get(
+        "noisy_predictions", False
+    ), "noisy_predictions is not supported for x_only mode"
+
+    if hyperparameters["sample_clustered_x"]:
+        x_train = sample_clustered_x(batch_size, single_eval_pos, num_features)
+        x_test = torch.rand(batch_size, seq_len - single_eval_pos, num_features)
+        x = torch.cat([x_train, x_test], dim=1)
+    else:
+        x = torch.rand(batch_size, seq_len, num_features)
+
+    mean_width = hyperparameters["mean_width"]
+    if mean_width == 0:
+        mean = torch.zeros(batch_size)
+    else:
+        mean_dist = hyperparameters.get("mean_dist", "uniform")
+        if mean_dist == "uniform":
+            min_mean, max_mean = -mean_width / 2, mean_width / 2
+            mean = torch.rand(batch_size) * (max_mean - min_mean) + min_mean
+        elif mean_dist == "normal":
+            mean = torch.randn(batch_size) * mean_width / 2
+        else:
+            raise ValueError(f"Unknown mean distribution {mean_dist}")
+
+    if (dummy_dim_prob := hyperparameters.get("dummy_dim_prob", 0.0)) > 0.0:
+        num_important_features = 0
+        while num_important_features == 0:
+            dummy_dims_mask = torch.bernoulli(
+                torch.full((num_features,), dummy_dim_prob)
+            ).bool()
+            used_dims_mask = ~dummy_dims_mask
+            num_important_features = used_dims_mask.sum()
+    else:
+        used_dims_mask = torch.ones(num_features, dtype=torch.bool)
+        num_important_features = num_features
+
+    length_scales = LogNormalPrior(
+        loc=hyperparameters["lengthscale_loc_constant_add"]
+        + log(num_important_features) * hyperparameters["lengthscale_loc_feature_mul"],
+        scale=hyperparameters["lengthscale_scale"],
+    ).sample((batch_size, num_important_features))
+
+    kernel_name = hyperparameters.get("kernel", "rbf")
+
+    def get_covar(length_scales, x):
+        if kernel_name == "rbf":
+            covar_module = RBFKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+            )
+        elif kernel_name == "matern_1.5":
+            covar_module = MaternKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+                nu=1.5,
+            )
+        elif kernel_name == "matern_2.5":
+            covar_module = MaternKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+                nu=2.5,
+            )
+        elif kernel_name == "linear":
+            covar_module = LinearKernel(
+                batch_shape=torch.Size([batch_size]),
+                ard_num_dims=length_scales.shape[1],
+            )
+        else:
+            raise ValueError(f"Unknown kernel {kernel_name}")
+        if covar_module.has_lengthscale:
+            covar_module._set_lengthscale(length_scales)
+        covar = covar_module(x[..., used_dims_mask], x[..., used_dims_mask])
+        return covar
+
+    covar = get_covar(length_scales, x)
+    d = MultivariateNormal(torch.ones_like(x[:, :, 0]) * mean[:, None], covar)
+    y: torch.Tensor = d.sample()
+
+    noise_var_dist = hyperparameters.get("noise_var_dist", "lognormal")
+    if noise_var_dist == "lognormal":
+        noise_variance: torch.Tensor = LogNormalPrior(
+            loc=hyperparameters["noise_var_loc"],
+            scale=hyperparameters["noise_var_scale"],
+        ).sample((batch_size,))
+    elif noise_var_dist == "gamma":
+        noise_variance: torch.Tensor = (
+            torch.distributions.Gamma(
+                hyperparameters["noise_var_concentration"],
+                hyperparameters["noise_var_rate"],
+            ).sample((batch_size,))
+            + 1e-4
+        )
+    else:
+        raise ValueError(f"Unknown noise variance distribution {noise_var_dist}")
+
+    noisy_y = y + torch.randn_like(y) * noise_variance[:, None] ** (1 / 2)
+    noisy_y = noisy_y.view(batch_size, seq_len, 1)
+
+    assert n_targets_per_input == 1, "n_targets_per_input must be 1 for x_only mode"
+    target_y = y.view(batch_size, seq_len, 1).repeat(1, 1, n_targets_per_input)
+
+    if hyperparameters.get("train_normalized_y", False):
+        if single_eval_pos <= 1:
+            raise ValueError("train_normalized_y requires single_eval_pos > 1")
+
+        train_mean = noisy_y[:, :single_eval_pos].mean(dim=1, keepdim=True)
+        train_std = noisy_y[:, :single_eval_pos].std(dim=1, keepdim=True)
+        noisy_y = (noisy_y - train_mean) / train_std
+        target_y = (target_y - train_mean[..., None]) / train_std[..., None]
+
+    # we should hide the y from time to time in training but still incorporate it to compute EI
+    # that is exactly what we need for batch EI, I believe
+    # then we simply do EI and then condition on the point without passing y again
+    number_of_y_hidden = torch.randint(
+        0, hyperparameters.get("max_num_hidden_y", 0) + 1, tuple()
+    )
+    noisy_y[:, single_eval_pos - number_of_y_hidden : single_eval_pos, :] = torch.nan
+    full_train_x = torch.cat([x, noisy_y], dim=2)[:, :single_eval_pos, :]
+
+    # LETS GET TO THE TEST PART
+
+    # ei values
+    # target_y shape: batch_size, seq_len, 1
+    if hyperparameters.get("predict_ei", True):
+        target_y = (
+            target_y[:, single_eval_pos:].squeeze(-1)
+            - target_y[:, :single_eval_pos, :]
+            .squeeze(-1)
+            .max(dim=-1, keepdim=True)
+            .values
+        )
+    else:
+        target_y = target_y[:, single_eval_pos:].squeeze(-1)
+
+    # ei shape: batch_size, test size
+
+    full_test_x = torch.cat([x, noisy_y], dim=2)[:, single_eval_pos:, :]
+    full_target = torch.cat([x[:, single_eval_pos:], target_y.unsqueeze(-1)], dim=2)
+
+    # we need three partitions of test
+    # 1. y shown, predict subset of features (rest shown)
+    # 2. y hidden, all features shown, predict y
+    # 3. y hidden, predict subset of features that maximize EI
+
+    batch_size, test_size, num_features_plus_1 = full_target.shape
+    # let's do case 3 first
+    if hyperparameters.get("predict_maximizer", False):
+        case_3_test_size = num_features  # all empty to full - 1
+        num_1_and_2_test_points = test_size - case_3_test_size
+
+        max_target_index = target_y.argmax(dim=-1)
+        max_ei_features = x[
+            torch.arange(batch_size), max_target_index + single_eval_pos, :
+        ]
+        max_ei_features_and_nan_for_y = torch.cat(
+            [max_ei_features, torch.full((batch_size, 1), torch.nan)], dim=1
+        )
+
+        case_3_test_x = max_ei_features_and_nan_for_y.unsqueeze(1).repeat(
+            1, case_3_test_size, 1
+        )  # shape: batch_size, case_3_test_size, num_features + 1
+        case_3_target = case_3_test_x.clone()
+        for i in range(case_3_test_size):
+            case_3_test_x[:, i, : i + 1] = torch.nan
+            case_3_target[:, i, i + 1 :] = torch.nan
+
+        # add case 3 to the tensors
+        full_test_x[:, num_1_and_2_test_points:, :] = case_3_test_x
+        full_target[:, num_1_and_2_test_points:, :] = case_3_target
+    else:
+        num_1_and_2_test_points = test_size
+
+    num_1_test_points = round(
+        hyperparameters.get("y_conditioned_share", 0.5) * num_1_and_2_test_points
+    )
+    num_2_test_points = num_1_and_2_test_points - num_1_test_points
+
+    if num_1_test_points > 0:
+        target_mask = torch.ones_like(
+            full_target[:, :num_1_test_points, :], dtype=torch.bool
+        )
+        # show targets for case 1
+        target_mask[:, :, -1] = False  # not target but shown
+
+        # for features, sample uniformly 0 to all
+        assert full_target.shape[2] == num_features + 1
+        num_shown_features = torch.randint(
+            0, num_features, (batch_size, num_1_test_points)
+        )
+
+        for i in range(batch_size):
+            for j in range(num_1_test_points):
+                shown_features_for_example = num_shown_features[i, j]
+                shown_features = torch.randperm(num_features + 1)[
+                    :shown_features_for_example
+                ]
+                target_mask[i, j, shown_features] = (
+                    False  # not in the target_mask anymore
+                )
+
+        assert not target_mask[:, :, -1].any()  # always predict the y target
+
+        # set inputs that are hidden to nan
+        full_test_x[:, :num_1_test_points, :][target_mask] = torch.nan
+
+        # copy over the targets, such that we don't condition on noisy values
+        full_test_x[:, :num_1_test_points, -1] = full_target[:, :num_1_test_points, -1]
+
+        full_target[:, :num_1_test_points, :][~target_mask] = torch.nan
+
+    # finally let's do case 2
+    if num_2_test_points > 0:
+        # y hidden, all features shown, predict ei
+        full_test_x[:, num_1_test_points:num_1_and_2_test_points, -1] = torch.nan
+        full_target[:, num_1_test_points:num_1_and_2_test_points, :-1] = torch.nan
+
+    return Batch(
+        x=full_train_x, test_x=full_test_x, target=full_target, y=None, target_y=None
+    )
diff --git a/pfns/priors/small_peaks.py b/pfns/priors/small_peaks.py
new file mode 100644
index 0000000..c9cc545
--- /dev/null
+++ b/pfns/priors/small_peaks.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+"""
+Example configuration file for PFN training.
+This is a Hebo+ prior configuration, as found in the PFNs4BO paper.
+This file demonstrates how to configure the MainConfig for training using Python.
+"""
+
+import math
+
+import torch
+from pfns.model import bar_distribution
+from pfns.model.encoders import EncoderConfig
+from pfns.priors.prior import AdhocPriorConfig
+from pfns.train import (
+    BatchShapeSamplerConfig,
+    MainConfig,
+    OptimizerConfig,
+    TransformerConfig,
+)
+from pfns.utils import product_dict
+
+from tqdm import tqdm
+
+config_dicts = product_dict(
+    {
+        "emsize": [256],
+        "nlayers": [12],
+        "epochs": [200],
+        "lr": [2e-4],
+        "batch_size": [256],
+        "batch_size_per_gp_sample": [8],
+        "num_workers": [6],  # while more workers would be good they lead to segfaults
+        "max_seq_len": [60, 120],
+        "num_buckets": [1000, 5000],
+        "encoder_hidden_size": [1024, None],
+    }
+)
+
+
+def get_config(config_index: int):
+    config_dict = list(config_dicts)[config_index]
+
+    emsize = config_dict["emsize"]
+    epochs = config_dict["epochs"]
+    lr = config_dict["lr"]
+    nlayers = config_dict["nlayers"]
+    batch_size = config_dict["batch_size"]
+    num_workers = config_dict["num_workers"]
+    max_seq_len = config_dict["max_seq_len"]
+
+    steps_per_epoch = 1000
+    num_features = 2
+    hyperparameters = {}
+
+    def get_prior_config(plotting=False):
+        prior_config = AdhocPriorConfig(
+            prior_names=["small_peaks"],
+            prior_kwargs={
+                "num_features": 1 if plotting else num_features,
+                "hyperparameters": {**hyperparameters},
+                "batch_size_per_gp_sample": config_dict["batch_size_per_gp_sample"],
+            },
+        )
+        return prior_config, hyperparameters
+
+    prior_config, hps = get_prior_config()
+
+    gb = prior_config.create_get_batch_method()
+
+    ys = []
+    for nf in tqdm(list(range(1, num_features)) * 200):
+        ys.append(gb(batch_size=128, seq_len=1000, num_features=nf).target_y.flatten())
+
+    ys = torch.cat(ys)
+    print(f"{len(ys)=} for {config_dict['num_buckets']=}")
+
+    borders = bar_distribution.get_bucket_borders(config_dict["num_buckets"], ys=ys)
+
+    print(f"{borders=}")
+
+    return MainConfig(
+        priors=[prior_config],
+        optimizer=OptimizerConfig("adamw", lr=lr, weight_decay=0.0),
+        scheduler="cosine_decay",
+        model=TransformerConfig(
+            criterion=bar_distribution.BarDistributionConfig(
+                borders.tolist(), full_support=True
+            ),
+            emsize=emsize,
+            nhead=emsize // 32,
+            nhid=emsize * 4,
+            nlayers=nlayers,
+            encoder=EncoderConfig(
+                variable_num_features_normalization=True,
+                constant_normalization_mean=0.5,
+                constant_normalization_std=1 / math.sqrt(12),
+                hidden_size=config_dict["encoder_hidden_size"],
+            ),
+            y_encoder=EncoderConfig(
+                nan_handling=True,
+                constant_normalization_mean=0.5,
+                constant_normalization_std=1 / math.sqrt(12),
+                hidden_size=config_dict["encoder_hidden_size"],
+            ),
+            attention_between_features=True,
+        ),
+        batch_shape_sampler=BatchShapeSamplerConfig(
+            batch_size=batch_size,
+            max_seq_len=max_seq_len,
+            fixed_num_test_instances=10,
+            max_num_features=num_features,
+        ),
+        epochs=epochs,
+        warmup_epochs=epochs // 10,
+        steps_per_epoch=steps_per_epoch,
+        num_workers=num_workers,
+        train_mixed_precision=True,
+        verbose=True,
+    )
+
+
+# View with: tensorboard --logdir=runs
diff --git a/pfns/run_training_cli.py b/pfns/run_training_cli.py
index b681a0a..a6547ed 100644
--- a/pfns/run_training_cli.py
+++ b/pfns/run_training_cli.py
@@ -113,12 +113,6 @@ def load_config_from_python(
                 ), "config_index is not 0 but get_config is not defined"
                 config = config_module.config
 
-            # Validate that it is a MainConfig instance
-            if not isinstance(config, pfns.train.MainConfig):
-                raise TypeError(
-                    f"'config' variable must be a MainConfig instance, got {config.__class__.__name__}"
-                )
-
             print(f"Successfully loaded config from {config_file}")
             return config
 
@@ -138,6 +132,12 @@ def main():
     # Load configuration from Python file
     config = load_config_from_python(args.config_file, args.config_index)
 
+    # Validate that it is a MainConfig instance
+    if not isinstance(config, pfns.train.MainConfig):
+        raise TypeError(
+            f"'config' variable must be a MainConfig instance, got {config.__class__.__name__}"
+        )
+
     def get_filename(config_file):
         return f"{config_file.split('/')[-1].split('.')[0]}"
 
diff --git a/pfns/train.py b/pfns/train.py
index d9926bb..283e8ca 100644
--- a/pfns/train.py
+++ b/pfns/train.py
@@ -1,14 +1,12 @@
 from __future__ import annotations
 
 import importlib
-
 import os
 import time
 import typing as tp
 from contextlib import nullcontext
 from dataclasses import dataclass
 
-import einops
 import torch
 from torch import nn
 from torch.amp import autocast, GradScaler
@@ -19,9 +17,7 @@
 from .batch_shape_sampler import BatchShapeSamplerConfig
 from .model.transformer_config import TransformerConfig
 from .optimizer import OptimizerConfig
-
 from .priors import data_loading, prior, utils as priors_utils
-
 from .training_utils import (
     Metrics,
     move_style_and_check_shape,
@@ -383,6 +379,10 @@ def create_get_batch_method(priors: tp.List[prior.PriorConfig] | None):
                 data_loader = None
             if writer:
                 writer.close()
+            # Clean up distributed training
+            if using_dist:
+                torch.distributed.destroy_process_group()
+
     return {
         "total_loss": total_loss,
         "model": model.to("cpu"),
@@ -417,6 +417,10 @@ def train_or_evaluate_epoch(
 
     metrics = Metrics(steps_per_epoch=len(dl))
 
+    # Whether the prior does not return y, but but instead uses
+    # a separate x_test and target.
+    x_only_mode = c.model.x_only_mode
+
     importance_sampling_infos = []
 
     before_get_batch = time.time()
@@ -433,11 +437,11 @@ def train_or_evaluate_epoch(
     for batch_index, batch in enumerate(dl):
         batch: prior.Batch = batch  # for IDE support
         # batch.x.shape == (batch_size, seq_len, num_features)
-        if not c.model.attention_between_features:
-            assert (
-                c.model.features_per_group == batch.x.shape[2]
-            ), "features_per_group must match the number of features in the input, if attention_between_features is False"
-        targets = batch.target_y.to(device)
+
+        if x_only_mode:
+            targets = batch.target.to(device)
+        else:
+            targets = batch.target_y.to(device)
         single_eval_pos = batch.single_eval_pos
 
         if tqdm_iter is not None:
@@ -460,33 +464,49 @@ def train_or_evaluate_epoch(
             before_forward = time.time()
             try:
                 with autocast(device.split(":")[0], enabled=scaler is not None):
-                    output = model(
-                        x=batch.x.to(device),
-                        y=batch.y[:, :single_eval_pos].to(device),
-                        style=move_style_and_check_shape(batch.style, batch.x, device),
-                        y_style=move_y_style_and_check_shape(
-                            batch.y_style, batch.y, device
-                        ),
-                        only_return_standard_out=True,
-                    )  # shape: (batch_size, test_len)
+                    if x_only_mode:
+                        assert (
+                            batch.target_y is None
+                            and batch.y is None
+                            and batch.y_style is None
+                        ), "model.x_only_mode is not supported when y, target_y, or y_style are not None"
+                        output = model(
+                            x=batch.x.to(
+                                device
+                            ),  # shape: (batch_size, train_len, num_features)
+                            test_x=batch.test_x.to(
+                                device
+                            ),  # shape: (batch_size, test_len, num_features)
+                            y=None,
+                            style=move_style_and_check_shape(
+                                batch.style, batch.x, device
+                            ),
+                            only_return_standard_out=True,
+                        )  # shape: (batch_size, test_len, num_groups)
+                    else:
+                        output = model(
+                            x=batch.x.to(device),
+                            y=batch.y[:, :single_eval_pos].to(device),
+                            style=move_style_and_check_shape(
+                                batch.style, batch.x, device
+                            ),
+                            y_style=move_y_style_and_check_shape(
+                                batch.y_style, batch.y, device
+                            ),
+                            only_return_standard_out=True,
+                        )  # shape: (batch_size, test_len)
 
                     forward_time = time.time() - before_forward
 
-                    if single_eval_pos is not None:
+                    if single_eval_pos is not None and not x_only_mode:
                         targets = targets[
                             :, single_eval_pos:
                         ]  # shape: (batch_size, test_len)
 
-                    losses = compute_losses(
-                        output, targets, criterion, c.n_targets_per_input
-                    )  # shape: (batch_size, test_len)
+                    loss, nan_share = compute_loss(
+                        output, targets, criterion, c.n_targets_per_input, x_only_mode
+                    )  # shape: (batch_size, test_len) | (batch_size, test_len, n_features)
 
-                    loss, nan_share = utils.torch_nanmean(
-                        losses.mean(
-                            1
-                        ),  # loss per sequence without nanmean, if any loss in a sequence is nan, the whole sequence is ignored
-                        return_nanshare=True,
-                    )  # loss and nan_share are both scalar tensors
                     loss_scaled = loss / c.aggregate_k_gradients
 
                 if scaler:
@@ -576,34 +596,38 @@ def train_or_evaluate_epoch(
     return metrics.get_epoch_result(importance_sampling_infos)
 
 
-def compute_losses(
+def compute_loss(
     output: torch.Tensor,
     targets: torch.Tensor,
     criterion: torch.nn.Module,
     n_targets_per_input: int,
+    x_only_mode: bool,
 ):
     """
     Compute the losses for the given output and targets.
 
     Args:
-        output: The output of the model, shape (batch_size, num_eval_positions, n_out)
-        targets: The targets, shape (batch_size, num_eval_positions[, n_targets_per_input])
+        output: The output of the model, shape (batch_size, num_eval_positions, n_out) | (batch_size, num_eval_positions, n_features, n_out)
+        targets: The targets, shape (batch_size, num_eval_positions[, n_targets_per_input]) | (batch_size, num_eval_positions, n_features, n_targets_per_input)
         criterion: The criterion to use.
         n_targets_per_input: The number of targets per input.
 
     Returns:
         The losses, shape (batch_size, num_eval_positions)
     """
-    # Repeat output in the semi-last dimension n_targets_per_input times
-    output = output.unsqueeze(2).expand(
-        *output.shape[:2],
-        n_targets_per_input,
-        output.shape[-1],
-    )
+    if (
+        len(output.shape) == 3
+    ):  # else it is (batch_size, num_eval_positions, n_features, n_out)
+        # Repeat output in the semi-last dimension n_targets_per_input times
+        output = output.unsqueeze(2).expand(
+            *output.shape[:2],
+            n_targets_per_input,
+            output.shape[-1],
+        )
 
-    if len(targets.shape) == 2:
-        # This implies we only have a single target per input
-        targets = targets.unsqueeze(2)
+        if len(targets.shape) == 2:
+            # This implies we only have a single target per input
+            targets = targets.unsqueeze(2)
 
     assert targets.shape == output.shape[:-1], (
         f"Target shape {targets.shape} "
@@ -612,9 +636,6 @@ def compute_losses(
         "1 dimension in the target."
     )
 
-    output = einops.rearrange(output, "b s t l -> (b t) s l")
-    targets = einops.rearrange(targets, "b s t -> (b t) s")
-
     if isinstance(criterion, nn.GaussianNLLLoss):
         assert (
             output.shape[-1] == 2
@@ -639,9 +660,21 @@ def compute_losses(
         )
     else:
         losses = criterion(output, targets.unsqueeze(-1))
-    losses = einops.rearrange(losses, "(b t) s -> b s t", t=n_targets_per_input)
-    losses = losses.mean(-1)
-    return losses
+    # mean over the last dimension (either features or target repetitions)
+    if x_only_mode:
+        loss = losses[~torch.isnan(targets)].mean()
+        nan_share = torch.tensor(0.0)
+    else:
+        losses = losses.mean(-1)
+
+        loss, nan_share = utils.torch_nanmean(
+            losses.mean(
+                1
+            ),  # loss per sequence without nanmean, if any loss in a sequence is nan, the whole sequence is ignored
+            return_nanshare=True,
+        )  # loss and nan_share are both scalar tensors
+
+    return loss, nan_share
 
 
 def should_load_checkpoint(
diff --git a/pfns/utils.py b/pfns/utils.py
index 549d1fd..233d172 100644
--- a/pfns/utils.py
+++ b/pfns/utils.py
@@ -7,9 +7,8 @@
 import re
 
 import numpy as np
-
 import torch
-from torch import nn
+from torch import distributed as dist, nn
 from torch.optim.lr_scheduler import LambdaLR
 
 
@@ -302,20 +301,31 @@ def print(*args, **kwargs):
 
 def init_dist(device):
     print("init dist")
-    if "LOCAL_RANK" in os.environ:
-        # launched with torch.distributed.launch
+    if "WORLD_SIZE" in os.environ:
+        dist.init_process_group(backend="nccl")
+        distributed = dist.is_available() and dist.is_initialized()
+        rank = dist.get_rank() if distributed else 0
+
+        # Get local rank from environment variable (set by torchrun)
+        local_rank = int(os.environ.get("LOCAL_RANK", 0)) if distributed else 0
+        torch.cuda.set_device(local_rank)
+        print("Initialized rank", rank)
+        print_on_master_only(rank == 0)
+        return distributed, rank, f"cuda:{local_rank}"
+    elif "LOCAL_RANK" in os.environ:
+        # launched with dist.launch
         rank = int(os.environ["LOCAL_RANK"])
-        print("torch.distributed.launch and my rank is", rank)
+        print("dist.launch and my rank is", rank)
         torch.cuda.set_device(rank)
         os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
-        torch.distributed.init_process_group(
+        dist.init_process_group(
             backend="nccl",
             init_method="env://",
             timeout=datetime.timedelta(seconds=20),
             world_size=torch.cuda.device_count(),
             rank=rank,
         )
-        torch.distributed.barrier()
+        dist.barrier()
         print_on_master_only(rank == 0)
         print(
             f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
@@ -333,14 +343,14 @@ def init_dist(device):
         torch.cuda.set_device(rank)
         # os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
         print("distributed submitit launch and my rank is", rank)
-        torch.distributed.init_process_group(
+        dist.init_process_group(
             backend="nccl",
             init_method="env://",
             timeout=datetime.timedelta(seconds=20),
             world_size=torch.cuda.device_count(),
             rank=rank,
         )
-        torch.distributed.barrier()
+        dist.barrier()
         print_on_master_only(rank == 0)
         print(
             f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
diff --git a/private_multitask_pfn/.gitignore b/private_multitask_pfn/.gitignore
deleted file mode 100644
index 8dbdd78..0000000
--- a/private_multitask_pfn/.gitignore
+++ /dev/null
@@ -1,18 +0,0 @@
-__pycache__
-.ipynb_checkpoints
-pfn_fb
-ckpt
-*.tar
-eval_plot
-eval_plot_cache
-eval_plot_cache_
-eval_plot_cache__
-wandb
-slurm
-wandb_links
-datasets
-output
-figures
-*.png
-*.pdf
-final_models
\ No newline at end of file
diff --git a/private_multitask_pfn/1d.ipynb b/private_multitask_pfn/1d.ipynb
deleted file mode 100644
index e706496..0000000
--- a/private_multitask_pfn/1d.ipynb
+++ /dev/null
@@ -1,1005 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/utils.py:293: FutureWarning: Decorating classes is deprecated and will be disabled in future versions. You should only decorate functions or methods. To preserve the current behavior of class decoration, you can directly decorate the `__init__` method and nothing else.\n",
-      "  @torch.no_grad()\n"
-     ]
-    }
-   ],
-   "source": [
-    "import argparse\n",
-    "\n",
-    "import botorch\n",
-    "import gpytorch\n",
-    "import torch\n",
-    "\n",
-    "from utils import *\n",
-    "import traceback\n",
-    "import matplotlib.pyplot as plt\n",
-    "from gpytorch.kernels import RBFKernel\n",
-    "# from torch.distributions import MultivariateNormal\n",
-    "import os\n",
-    "from botorch.models import SingleTaskGP\n",
-    "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
-    "from gpytorch.distributions import MultivariateNormal\n",
-    "\n",
-    "default_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def get_test_function(n_samples, lengthscale=None, seed=0):\n",
-    "    with botorch.manual_seed(seed):        \n",
-    "        train_x = torch.rand(n_samples, 1)\n",
-    "        test_x = torch.linspace(0, 1, 1000).view(-1, 1)\n",
-    "        all_x = torch.cat([train_x, test_x], dim=0)\n",
-    "        \n",
-    "        rbf = gpytorch.kernels.RBFKernel()\n",
-    "        if lengthscale:\n",
-    "            rbf.lengthscale = lengthscale\n",
-    "        else:\n",
-    "            rbf.lengthscale = torch.distributions.Gamma(3, 6).sample()\n",
-    "        \n",
-    "        \n",
-    "        mean = torch.zeros(all_x.size(0))\n",
-    "        covar = rbf(all_x)\n",
-    "        \n",
-    "        y = MultivariateNormal(mean, covar).sample().unsqueeze(1)\n",
-    "        train_y = y[:n_samples]\n",
-    "        test_y = y[n_samples:]\n",
-    "        \n",
-    "    return train_x, train_y, test_x, test_y\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def plot(test_info, pfn_infos):\n",
-    "    n_plots = len(pfn_infos) + 1\n",
-    "    fig, axs = plt.subplots(1, n_plots, figsize=(n_plots * 5, 4))\n",
-    "    \n",
-    "    queried = []\n",
-    "    for i, pfn_info in enumerate(pfn_infos):        \n",
-    "        pfn_id, model, model_dim = pfn_info\n",
-    "        train_x, train_y, test_x, test_y = test_info\n",
-    "        train_task_id = torch.zeros(train_x.size(0))\n",
-    "        \n",
-    "        if model_dim > 1:\n",
-    "            # pad train and test with 0s\n",
-    "            padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)\n",
-    "            padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)\n",
-    "        else:\n",
-    "            padded_train_x = train_x\n",
-    "            padded_test_x = test_x\n",
-    "            \n",
-    "        # train_x, train_task_id, train_y, test_x, \n",
-    "        posterior = pfn_predict(model, train_task_id.to(default_device), padded_train_x.to(default_device), train_y.to(default_device), padded_test_x.to(default_device))\n",
-    "        mean = posterior.mean.cpu()\n",
-    "        # std = posterior.variance.sqrt().cpu()\n",
-    "        lower, upper = posterior.lower.cpu(), posterior.upper.cpu()\n",
-    "        \n",
-    "        args = train_task_id, padded_train_x, train_y, None, padded_test_x, test_y\n",
-    "        args = [arg.to(default_device) if arg is not None else None for arg in args]\n",
-    "        next_x, next_y = pfn_get_best(model, *args)\n",
-    "        next_x, next_y = next_x[0].cpu().item(), next_y.cpu().item()\n",
-    "        queried.append((next_x, next_y))\n",
-    "        \n",
-    "        ax = axs[i]\n",
-    "        ax.plot(test_x, test_y, label=\"true\", color=\"C0\")\n",
-    "        ax.scatter(train_x, train_y, label=\"train\", color=\"C0\")\n",
-    "        ax.plot(test_x, mean, label=\"mean\", color=\"C1\")\n",
-    "        ax.fill_between(test_x.flatten(), lower.flatten(), upper.flatten(), alpha=0.2, color=\"C1\")\n",
-    "        ax.set_title(f\"PFN {pfn_id}\")\n",
-    "        ax.axvline(next_x, color=\"C3\", linestyle=\"--\", label=\"query\")\n",
-    "        \n",
-    "        \n",
-    "\n",
-    "    gp = SingleTaskGP(train_x, train_y)\n",
-    "    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)\n",
-    "    fit_gpytorch_mll(mll)\n",
-    "    gp.eval()\n",
-    "    gp_pred = gp.posterior(test_x)\n",
-    "    gp_mean = gp_pred.mean.cpu().detach()\n",
-    "    gp_std = gp_pred.variance.sqrt().cpu().detach()\n",
-    "    \n",
-    "    args = train_task_id, train_x, train_y, torch.zeros_like(test_y), test_x.unsqueeze(1), test_y.unsqueeze(1)\n",
-    "    next_x, next_y = gp_get_best(*args)\n",
-    "    next_x, next_y = next_x.cpu().item(), next_y.cpu().item()\n",
-    "    queried.append((next_x, next_y))\n",
-    "    \n",
-    "    ax = axs[-1]\n",
-    "    ax.plot(test_x, test_y, label=\"true\", color=\"C0\")\n",
-    "    ax.scatter(train_x, train_y, label=\"train\", color=\"C0\")\n",
-    "    ax.plot(test_x, gp_mean, label=\"gp mean\", color=\"C2\")\n",
-    "    ax.fill_between(test_x.flatten(), gp_mean.flatten() - 2 * gp_std.flatten(), gp_mean.flatten() + 2 * gp_std.flatten(), alpha=0.2, color=\"C2\")\n",
-    "    ax.legend()\n",
-    "    ax.set_title(\"GP\")\n",
-    "    ax.axvline(next_x, color=\"C3\", linestyle=\"--\", label=\"query\")\n",
-    "    \n",
-    "    ax_min_y = min([ax.get_ylim()[0] for ax in axs])\n",
-    "    ax_max_y = max([ax.get_ylim()[1] for ax in axs])\n",
-    "    for ax in axs:\n",
-    "        ax.set_ylim(ax_min_y, ax_max_y)\n",
-    "    \n",
-    "    plt.show()\n",
-    "    \n",
-    "    return queried\n",
-    "    # return fig, axs\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
-      "Using a Transformer with 18.38 M parameters\n",
-      "Loaded model\n",
-      "Using a Transformer with 17.35 M parameters\n",
-      "Loaded model\n",
-      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
-      "Using a Transformer with 18.38 M parameters\n",
-      "Loaded model\n",
-      "Using a Transformer with 17.35 M parameters\n",
-      "Loaded model\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_names = [\"fresh-snowball-475\", \"young-grass-475\", \"fluent-durian-477\", \"wild-serenity-478\"]\n",
-    "models = [load_model(f\"/home/yl9959/mtpfn/wandb_links/{name}\", best=True).to(default_device) for name in model_names]\n",
-    "\n",
-    "pfn_infos = [\n",
-    "    (\"bar 0.2\", models[0], 1),\n",
-    "    (\"gauss 0.2\", models[1], 1),\n",
-    "    (\"bar\", models[2], 1),\n",
-    "    (\"gauss\", models[3], 1),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using a Transformer with 25.77 M parameters\n",
-      "Loaded model\n",
-      "Using a Transformer with 25.77 M parameters\n",
-      "Loaded model\n",
-      "Using a Transformer with 25.77 M parameters\n",
-      "Loaded model\n",
-      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
-      "Using a Transformer with 26.79 M parameters\n",
-      "Loaded model\n",
-      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
-      "Using a Transformer with 26.79 M parameters\n",
-      "Loaded model\n",
-      "Using 2000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n",
-      "Using a Transformer with 26.79 M parameters\n",
-      "Loaded model\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_names = [\n",
-    "    \"sparkling-microwave-480\", \n",
-    "    \"wobbly-donkey-484\", \n",
-    "    \"likely-donkey-479\", \n",
-    "    \"fallen-cherry-483\",\n",
-    "    \"stilted-night-482\",\n",
-    "    \"youthful-wildflower-481\",\n",
-    "]\n",
-    "models = [load_model(f\"/home/yl9959/mtpfn/wandb_links/{name}\", best=True).to(default_device) for name in model_names]\n",
-    "\n",
-    "pfn_infos = [\n",
-    "    (\"G 0.\", models[0], 3),\n",
-    "    (\"G 0.1\", models[1], 3),\n",
-    "    (\"G 0.2\", models[2], 3),\n",
-    "    (\"B 0.\", models[3], 3),\n",
-    "    (\"B 0.1\", models[4], 3),\n",
-    "    (\"B 0.2\", models[5], 3),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seed 0\n",
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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YvXs3Dz30UJN9v/71r/Pee+/pwoiISCfqjrmhsrKSP/3pTwAsXrwYgD//+c+kpaWRlpbGDTfccEw/i4iIxKY75ow5c+awc+dObr31Vj788EM+/PDDxuf69OnD7Nmzj+lnERGR4zd8+HCeeuoprrjiCkaOHMlVV13FxIkTsSyLXbt28dRTT+FwOMjPz493qCLHTIWyIp9y880388c//pHKysqY9s/JyeGjjz7i73//O//5z3+YN28edXV1ZGVlcdJJJ/Hkk0/y5S9/ud3XmTJlCgsXLuTHP/4x3//+90lOTubaa6/l7rvv/qw/koiInADdNT8899xzPPbYY433V69e3bjkUn5+vgplRUQ6QHfMGYcnVWzbtq3Fix+LFi1SoayISCc61lwCsGDBAhYsWACA0+kkLy+Pb37zm/ziF78gMTGx3eP/9a9/cccdd/D4449TXl7OhAkTePXVVznjjDOO++cQEZETp7vlhvLycu64444mj/3+978HYODAgSqUFRHpQN0tZxw+L/W73/2u2XNnnnmmCmVFRDrZxRdfzCeffMLvf/973nrrLR5++GEMw2DgwIF8/vOf59vf/na73cZFuiLD0tR/ERERERERERERERERERERERERERHpgRzxDkBERERERERERERERERERERERERERKQjqFBWRERERERERERERERERERERERERER6JBXKioiIiIiIiIiIiIiIiIiIiIiIiIhIj6RCWRERERERERERERERERERERERERER6ZFUKCsiIiIiIiIiIiIiIiIiIiIiIiIiIj2SCmVFRERERERERERERERERERERERERKRHcsU7gLaYpsnBgwdJTk7GMIx4hyMi0iVZlkV1dTV9+/bF4ei98x+UM0RE2qecYVPOEBFpn3KGTTlDRKR9yhk25QwRkfYpZ9iUM0RE2qecYVPOEBFpX6w5o0sXyh48eJD+/fvHOwwRkW5h37595OfnxzuMuFHOEBGJnXKGcoaISKyUM5QzRERipZyhnCEiEivlDOUMEZFYKWcoZ4iIxKq9nNGlC2WTk5MB+4dISUmJczQiIl1TVVUV/fv3b/zM7K2UM0RE2qecYVPOEBFpn3KGTTlDRKR9yhk25QwRkfYpZ9iUM0RE2qecYVPOEBFpX6w5o0sXyh5uG56SkqIPfBHpscz6enZdfjkAg595BkdCwnG9Tm9fakE5Q0R6A+WME0M5Q0R6A+WME0M5Q0R6A+WME0M5Q0R6A+WME0M5Q0R6A+WME0M5Q0R6g87KGV26UFZEpFewLELbdzRui4iItEo5Q0REYqWcISIisVLOEBGRWClniIhIrJQzREQkVp2UMxwd9soiIiIiIiIiIiIiIiIiIiIiIiIiIiJxpEJZERERERERERERERERERERERERERHpkVzxDkBERERERERERERERERERERERESOXTQaJRwOxzsMiZHH48HhUG9Lkc6mQlkREREREREREREREREREREREZFuxLIsCgsLqaioiHcocgwcDgeDBw/G4/HEOxSRXkWFsiIiIiIiIiIiIiIiIiIiIiIiIt3I4SLZnJwc/H4/hmHEOyRph2maHDx4kIKCAgYMGKD/ZyKdSIWyIiLxZhi4+/Zt3BYREWmVcoaIiMRKOUNERGKlnCEiIrFSzhARkVgpZ3S4aDTaWCSbmZkZ73DkGGRnZ3Pw4EEikQhutzve4YjEXyflDBXKiojEmSMhgWHvvB3vMEREpBtQzhARkVgpZ4iISKyUM0REJFbKGSIiEivljI4XDocB8Pv9cY5EjpXH4wHsYmcVyop0Xs5wdPg7iIiIiIiIiIiIiIiIiIiIiIiIyAllqGNvt6P/ZyLxoUJZERERERERERERERERERERERERERHpkVQoKyISZ2YgwK4vfpFdl1yIWVcb73BERKQLMwMBdl12ObsuuxwzEIh3OCIi0oUpZ4iISKyUM0REJFbKGSIiEivlDBERiVVn5QxXh72yiIjExjQJbNhob4cC4E+MbzwiItJ1mSaB9esbt0VERFqlnCEiIrFSzhARkVgpZ4iISKyUM6QNM2fOZNKkSfzxj3+Mdygi0hV0Us5QR1kRkXiLRo5sG0b84hARERERERERERERERERERGJI8uyiEQi7e8oInIMVCgrIhJv9aXxjkBERERERERERERERERERESkQ11zzTW899573H///RiGgWEYPProoxiGwRtvvMHUqVPxer18+OGHXHPNNVxyySVNjr/55puZOXNm433TNLn77rsZPHgwCQkJTJw4kWeffbZzfygR6RZc8Q5ARKRXsyyoPBjvKERERERERERERERERERERKSbsiyL+nA0Lu+d4HZixLh67v3338/WrVsZN24cd911FwAbNmwA4LbbbuPee+9lyJAhpKenx/R6d999N0888QR/+9vfGD58OO+//z5f/epXyc7O5swzzzy+H0hEeiQVyoqIxFN9OdQdincUIiIiIiIiIiIiIiIiIiIi0k3Vh6OM+dmbcXnvjXedh98TWwlaamoqHo8Hv99Pbm4uAJs3bwbgrrvuYvbs2TG/bzAY5Ne//jULFy5kxowZAAwZMoQPP/yQv//97yqUFZEmVCgrIhJPNUVgmfGOQkRERERERERERERERERERCRuTjrppGPaf/v27dTV1TUrrg2FQkyePPlEhiYiPYAKZUVE4iVUC1UF4E3GmZoEVrwDEhGR7sAZ41IzIiIiyhkiIhIr5QwREYmVcoaIiMRKOaNzJbidbLzrvLi994mQmJjY5L7D4cCymhZShMPhxu2amhoAXnvtNfr169dkP6/Xe0JiEpHO0Rk5Q4WyIiLxUlsCoRocqfmM+O/9EK4Df0K8oxIRkS7M4fczYslH8Q5DRES6AeUMERGJlXKGiIjESjlDRERipZzR+QzDwO/pHmVgHo+HaDTa7n7Z2dmsX7++yWNr1qzB7XYDMGbMGLxeL3v37uXMM8/skFhFpON1Vs7oHp+QIiI9TTQClfvA4wfDiHc0IiIiIiIiIiIiIiIiIiIiIh1u0KBBLFu2jN27d5OUlIRpmi3ud/bZZ3PPPffwr3/9ixkzZvDEE0+wfv16Jk+eDEBycjK33HIL3//+9zFNk9NOO43KykoWL15MSkoKV199dWf+WCLSxTniHYCISK9UXwb1FeBLjXckIiIiIiIiIiIiIiIiIiIiIp3illtuwel0MmbMGLKzs9m7d2+L+5133nnccccd3HrrrZx88slUV1fz9a9/vck+v/jFL7jjjju4++67GT16NHPmzOG1115j8ODBnfGjiEg30qGFsnfffTcnn3wyycnJ5OTkcMkll7Bly5aOfEsRke6hqgAMBzhcmMEQe370O/bcdj9mIBDvyOJGOUNEpH1mIMCer32dPV/7unKGcoaISJvMQIA9V13Bni9fillXF+9w4kY5Q0SkfRpn2JQzRETap5xhU84QEWmfcobyRVtGjBjBkiVLqKurw7IsrrnmGizLIi0trdm+8+bNo7CwkIqKCu677z7+9Kc/8e677zY+bxgGN910E5s3byYUClFcXMz8+fM544wzOu8HEpHPpLNyRocWyr733ntcf/31LF26lAULFhAOhzn33HOpra3tyLcVEenaAlVQWwwJDd1kTZO6dVuoW78dTCu+scWRcoaISAxMk7oVK6hbsQJaWYamN1DOEBGJgWlSt3INdWs3QTQS72jiRjlDRCQGGmcAyhkiIjFRzgCUM0REYqKcoXwhIhKrTsoZrg57ZWD+/PlN7j/66KPk5OSwcuVKVe6LSO9VVwrhekjMinckXYpyhoiIxEo5Q0QkBtFwvCPoEpQzREQkVsoZIiISK+UMERGJhfKFiEjX0qEdZT+tsrISgIyMjM58WxGRriMagcr94E2KdyRdnnKGiIjESjlDRKQFgYp4R9AlKWeIiEislDNERCRWyhkiIhIL5QsRkfjq0I6yRzNNk5tvvplTTz2VcePGtbhPMBgkGAw23q+qquqs8EREOkd9GQQqIblPvCPp0pQzREQkVsoZIiKtqDkU7wi6HOUMERGJlXKGiIjESjlDRERiEUu+AOUMEZGO1GkdZa+//nrWr1/P008/3eo+d999N6mpqY23/v37d1Z4IiKdo7oQDAc4Om2eQreknCEiIrFSzhARaUGoDmpVKPtpyhkiIhIr5QwREYmVcoaIiMQilnwByhkiIh2pUwplb7jhBl599VUWLVpEfn5+q/vdfvvtVFZWNt727dvXGeGJiHSOYA3UFoMvNd6RdGnKGSIiEivlDBGRVtSXQ7gu3lF0KcoZIiISK+UMERGJlXKGiIjEItZ8AcoZIiIdqUNbGlqWxfe+9z1eeOEF3n33XQYPHtzm/l6vF6/X25EhiYjET12p3dnJn9nsKcPriUNAXYtyhohIbIyEhHiHEHfKGSIibbAseyULp1vjDJQzRERipXGGcoaISKyUM5QzRERi1dtzxrHmC1DOEJHeqzNyRocWyl5//fU89dRTvPTSSyQnJ1NYWAhAamoqCb08IYpIL2OaUHUQPP5mTzkSfIx6+a92xyd/7/1sVM4QEWmfw+9n1OpV8Q4j7pQzRETaEKyGulIcadmMevFP9ljE33wc0lsoZ4iItE/jDJtyhohI+5QzbMoZIiLtU85QvhARiVVn5QxHR774X//6VyorK5k5cyZ5eXmNt//85z8d+bYiIl1PoALqK8CbEu9IuizlDBERiZVyhohIG+rLIBIAly/ekXQJyhkiIhIr5QwREYmVcoaIiMRC+UJEpGvp0I6ylmV15MuLiHQftSVgRcHpjnckXZZyhoiIxEo5Q0SkFaYJVQUtrmTRWylniIhIrJQzREQkVsoZIiISC+WLzjFo0CBuvvlmbr755niHIiJdXIcWyoqICBAJQXUBeJNafNoMhdj/sz+CGSX/byfhcGuZBRERaZkZDLL/xhsByH/gARxeb5wjEhGRLuXwShaJmQ3jjD+BBfkPnozDr1NAIiLSMo0zREQkVsoZIiISK+UMacvMmTOZNGkSf/zjHz/za61YsYLExMTPHpSIxE1n5QxdJRER6Wj1ZRCshpS8lp+PmtQu/6RxW0REpFXRKLXvvd+4LSIi0kR9+ZGVLEIBalestx9XzhARkbZonCEiIrFSzhARkVgpZ3QbUdNi+a4yiqsD5CT7mDY4A6fDiGtMlmURjUZxudova8vOzu6EiESkQ3VSznB02CuLiIituggcDjD0kSsiIiIiIh0kGoGqg+BR9wQRERERERERERFp3/z1BZz223e44h9LuenpNVzxj6Wc9tt3mL++oMPe85prruG9997j/vvvxzAMDMPg0UcfxTAM3njjDaZOnYrX6+XDDz9kx44dXHzxxfTp04ekpCROPvlkFi5c2OT1Bg0a1KQzrWEY/POf/+QLX/gCfr+f4cOH8/LLL3fYzyMi3YeqtkREOlKoFupKwJsS70hERERERKQnC1TYK1l4k+IdiYiIiIiIiIiIiHRx89cX8J0nVlFQGWjyeGFlgO88sarDimXvv/9+ZsyYwf/8z/9QUFBAQUEB/fv3B+C2227jN7/5DZs2bWLChAnU1NQwd+5c3n77bVavXs2cOXO48MIL2bt3b5vvMW/ePL70pS+xbt065s6dy1VXXUVZWVmH/Dwi0n2oUFZEpCPVldnFsm5/vCMREREREZGerLYEsMDR/nJkIiIiIiIiIiIi0ntFTYt5r2zEauG5w4/Ne2UjUbOlPT6b1NRUPB4Pfr+f3NxccnNzcTqdANx1113Mnj2boUOHkpGRwcSJE/nWt77FuHHjGD58OL/4xS8YOnRoux1ir7nmGq644gqGDRvGr3/9a2pqali+fPkJ/1lEpHtRoayISEexLKguBJcXDCPe0YiIiIiISE8VCUFNEXjUTVZERERERERERETatnxXWbNOskezgILKAMt3dW4X1pNOOqnJ/ZqaGm655RZGjx5NWloaSUlJbNq0qd2OshMmTGjcTkxMJCUlheLi4g6JWUS6D7UZERHpKMEqqC8HX0q8IxERERERkZ4sUAGhGkjqE+9IREREREREREREpIsrrm69SPZ49jtREhMTm9y/5ZZbWLBgAffeey/Dhg0jISGByy67jFAo1ObruN3uJvcNw8A0zRMer4h0LyqUFRHpKPXlEAmAKzvekYiIiIiISE9WcwgwwOGMdyQiIiIiIiIiIiLSxeUk+07ofsfK4/EQjUbb3W/x4sVcc801fOELXwDsDrO7d+/ukJhEpOdToayISEcwTagqAI+/3V0dCT5Gv/kQhOvAn9AJwYmISHfl8PsZvXlTvMMQEZGuJBKE2mLwJjV52JHgY/Qbf7fHJv72xyUiItJ7aZwhIiKxUs4QEZFYKWd0bdMGZ5CX6qOwMoDVwvMGkJvqY9rgjA55/0GDBrFs2TJ2795NUlJSq91ehw8fzvPPP8+FF16IYRjccccd6gwr0gN1Vs5wdPg7iIj0RoEK++ZNjnckIiIiIiLSk9WXQ6gWPInt7ysiIiIiIiIiIiK9ntNhcOeFYwC7KPZoh+/feeEYnI5PP3ti3HLLLTidTsaMGUN2djZ79+5tcb/77ruP9PR0TjnlFC688ELOO+88pkyZ0iExiUjPp46yIiIdob4CohFwetrfNWzyxyV17K8M85dvdHxoIiLSfVUHwty/cBtltSHu+/KkeIcjIiJdQW0JGA77dpTyepP7lwQIRizuHhSf0EREpHsorg7wp7e343Ia3Hnh2HiHIyIiXdjBinr+9M520vxufjxnVLzDERGRLmxvaR1/WbSdvDQfN88aEe9wpAVzxuXx169OYd4rGymoDDQ+npvq484LxzBnXF6HvfeIESNYsmRJk8euueaaZvsNGjSId955p8lj119/fZP7u3fvbnLfspr3yK2oqDiuOEWkc2wvruFv7+1geE4S3zpzaIe9jwplRURONDMK1QfBG1tHp6dWV5L54D/IBCouPYm07ISOjU9ERLqtRxdtJee+u8gB6j7/CP4kLaUtItKrhQNQcwi8Sc2eenBpGX0ffgiHAeHLTsWd1HwfERERgD+9sYH+f/sdbqeD6OxHcPp88Q5JRES6qPte/YQRD/+eBLcT86xHcHi98Q5JRES6qF+/tIYpT9xPkteFefrDyhld1Jxxecwek8vyXWUUVwfISfYxbXBGh3WSFRFpyV3Pr+a0//6ZNL8b83MPdVjOUKGsiMiJFqiEQBUkZbe7a3FtlN99WMGLB9cB4KT57CYRERGAAxX1/PntLY05g2g0vgGJiEj8BSogXAsJTbs77CyP8PDq6sacYUYicQhORES6g00FVfx3+Z6jckYUZ5xjEhGRrmnlnjJeWbO/MWdYkQio6ElERFrw3tZDLNpYyE26ntEtOB0GM4ZmxjsMEemlXl57kGXbD3F7J+QMR/u7iIjIMaktBcsER/tzEd7cEWhyP9Gj+QsiItKyV9YebHLf59blaxGRXq/mEBgO+3aUF7fUNbnvdur0j4iItOzZlfub3FfXIBERac0zHzfNGYahnCEiIi379DhDRESkNS+uPtBp76UrJSIiJ1I0AjWF4EmMafcP9wU7OCAREekpPtxWEu8QRESkKwkHoLYEvEnNnvpgr8YZIiISmw+2HYp3CCIi0k18oHNTIiISA8uyNM4QEZGYRE2Lj3Z03jhDhbIiIidSoBKCVS1erP60opooC3YG2t1PRERkT2ktH27/1CAhGo5PMCIi0jUEKiBc22yS3uaSMKsLlSNERKR9q/eWs7WoJt5hiIhIN/DhthIOVNSjxuMiItKeNzcUUlEXxuNSOZKIiLTt+VX7CYRNkr2ds/q2MpOIyIlUVwaWBY72P8Q/LghhWpCXpKWzRUSkbct2lgEwNPuoiRiWFadoRESkS6gtAcNh346yZL/dTXZ8jjseUYmISDeyuGEy3ueGZMY5EhER6eoWN3R5mjkyJ86RiIhIV/fRjlIAzhvTJ86RiIhIV7e04Rr43Al5nfJ+KpQVETlRohGoKWzW0ak1L22pB+CMgd6OjEpERHqAl9YeAGDmsIw4RyIiIl1COAC1h1pcyeLwOGPmQF9nRyUiIt3MS2sOAnDmiOw4RyIiIl2ZZVm83JAzVCgrIiJtiURNXltXAMCZyhkiItKGQDjKWxsKAZjZSeemVCgrInKiBCohWNXixepPi5hWY6enS0b4OzoyERHpxgLhKCt2lQNw8aiEOEcjIiJdQqASQrXNJulVBk3WFoUBuHikcoaIiLSusDLAtuIaAC6e1DfO0YiISFe2q6SWAxX1uJ0Gc8flxjscERHpwjYVVFNaGyLR42T2aHWUFRGR1q3ZV0F1MEJmoofTh3dOoWz7a4OLiEhs6srsZbAd7X+0Pr+pnuqQRbLHYGz/RNwv/R+E6zAS1PVJRESaemrZXkJRk6wkD8OSazGeuBN8aRgJKoASEem1akvAcNi3ozy+rhYLGJjqJD/bj/XCA2CayhkiItLMox/tBmB0XgoZmamkr1oJoJwhIiLNHM4Zk/qnkZSWzEjlDBERacXhnHHSoAy8yYnKGSIi0qpHF+8G4HNDMgm4TUpf/TN+V0KH5gwVyoqInAjRCNQUNuvo1JrHP6kF4NtTk/C6HODzgjMKhtGRUYqISDf0xNI9AHzv9Hycgb2Qlg04lDNERHqrSBBqD7W4ksXj6+xxxg0nJ2MYBobPC6apnCEiIs0cHmfcePYwO2f4teKRiIg0Z1lWY8644ezhyhkiItKqcNTkuVX7AbjxHI0zRESkdbXBCPM3FAJww9nDqAhWUOUIkpqYg9GB1zMc7e8iIiLtClZBsLrFi9Wftr44xCfF9nKoMwepg6yIiLRu+a4ydpbYRU8z+wHRMLi88Q1KRETiK1AJoZpmk/QW7Q5QVGsCMHOQcoWIiLTupTUHqAlGMAw4fUTnLG0nIiLd0+NL92Ba4HE5mDEkM97hiIhIF/b393YAkOxzMbl/epyjERGRruz+t7cB0CfFy9AcHwW1BVhYHf6+6igrInIi1JeDZYKj/Y/V17cHAOiT6GBMlgszFKbw9w+BGSH3dyfjcGvpCRERsb227iAAgzP9DDCKMB0eCu99GCyL3HtOxuHyxDlCERHpdHWldodYo+nc59e21QMwLttNtt/ZMM541M4Z907DkaBTQCIiYpu/3u7YMW1QBkleF2YoROHP7gQg9655ODwaZ4iIiG3hpmIAzhiejcflUM4QEZFWLdpyCIBzRuXgcBjKGSIi0qr3t9o549wxuVQGK6mpLSfvgWdxOLyYv3ugw3KGOsqKiHxWZhSqCyCGAlfLsnhju30B+yenpdgtw6NRKhd8ROXbyyES7ehoRUSkmzBNizcaLmD/79l5GOFacPipXLiEyreXQiQS5whFRKTTRcNQU9ysm2woarFgpz0h739PT2nYN6qcISIizdSFIizaYhc9/e/nR9sPRiJUvvgilS++qJwhIiKNKupCfLS9BIDb546yH1TOEBGRFhRWBli5pxyAW+coZ0jbqqurueqqq0hMTCQvL48//OEPzJw5k5tvvrlxn0GDBvGLX/yCK664gsTERPr168df/vKXNl/3mmuu4ZJLLuHXv/41ffr0IS0tjbvuuotIJMKPfvQjMjIyyM/P55FHHmly3L59+/jSl75EWloaGRkZXHzxxezevbvx+RUrVjB79myysrJITU3lzDPPZNWqVU1ewzAM/vnPf/KFL3wBv9/P8OHDefnllz/zfyuRnmjnoRo2F1bjdBh8f/YIiuuKcZgWCW8txTH/vQ7NGSqUFRH5rIJVEKwGb3K7u24sibCrIorXCbMG+zohOBER6a4+3lNOcXWQZJ+L07MDdvdAhzPeYYmISDwFKiFU06xQdvG+IJVBi2y/g2l91Z1DRERa987mYgJhkwEZfsb3S413OCIi0oW9taGIiGkxOi+FodlJ8Q5HRES6sNc/KQDgpIHp9E3T6qnxYlkWdeG6uNwsK/Yl03/wgx+wePFiXn75ZRYsWMAHH3zQrPAU4J577mHixImsXr2a2267jZtuuokFCxa0+drvvPMOBw8e5P333+e+++7jzjvv5IILLiA9PZ1ly5bx7W9/m29961vs378fgHA4zHnnnUdycjIffPABixcvJikpiTlz5hAKhQC7sPfqq6/mww8/ZOnSpQwfPpy5c+dSXV3d5L3nzZvHl770JdatW8fcuXO56qqrKCsri/m/i0hvcThnnDosC487RFmgjGRP+/VWJ4LW3RMR+azqK8CMgNPd7q6Hl0M9e5CPRI/mKoiISOteW3cQgHNHZeINlYA3FdR4XESkd6srB8sCR9PTOYfHGXOHJeB0GPGITEREuonX1tkXIz4/Ic9e6UhERKQVrzZcwL5gQl6cIxERka7utU+OjDMkfuoj9Ux/anpc3nvZlcvwu/3t7lddXc1jjz3GU089xTnnnAPAI488Qt++fZvte+qpp3LbbbcBMGLECBYvXswf/vAHZs+e3errZ2Rk8MADD+BwOBg5ciS/+93vqKur4yc/+QkAt99+O7/5zW/48MMP+cpXvsJ//vMfTNPkn//8Z+MY+ZFHHiEtLY13332Xc889l7PPPrvJezz44IOkpaXx3nvvccEFFzQ+fs0113DFFVcA8Otf/5oHHniA5cuXM2fOnHb/u4j0Jq82nJu6YHwe5YFygtEgGa70TnlvVWmJiHwWpgnVheCObWbcO7vt5VDPH66ZdCIi0rZFWw4BMHeoF0J14Gn/BIOIiPRgZhRqCpt1k7Usi0W7gwCcP0yrVoiISOuipsV7WxvGGeN0AVtERFoXCEf5aHsJAOePy41zNCIi0pVV1oVZuaccgPM1zpB27Ny5k3A4zLRp0xofS01NZeTIkc32nTFjRrP7mzZtavP1x44di8NxpBSuT58+jB8/vvG+0+kkMzOT4uJiANauXcv27dtJTk4mKSmJpKQkMjIyCAQC7NixA4CioiL+53/+h+HDh5OamkpKSgo1NTXs3bu3yXtPmDChcTsxMZGUlJTG9xERW0FlPZsLq3E6DM4anUlhXWFMRfYnijrKioh8FqFqCFaBr/2l6nZVRNhcEgFgupZDFRGRNqw/UMnesjoATkqtAoeKZEVEer1AJQRrICmrycNLD4QorTdxOWBSrsYZIiLSurc3FVEXipLocTKmb0q8wxERkS7stXUFREyLrCQPg7MS2z9ARER6rRfXHABgYKaf3FRN4o6nBFcCy65cFrf37grc7qarABuG0eJjpmkCUFNTw9SpU3nyySebvVZ2djYAV199NaWlpdx///0MHDgQr9fLjBkzCIVC7b734fcREdvzq+ycMaJPMoazjupQNVkJWRAMd8r7q1BWROSzCFRCNAQub7u7/nlFNQBjs930SXJ2dGQiItKNPfD2NgCmDUgm1ayChMw4RyQiInFXXwFWFBxNT+Xcv8weZ5zW34vPpSW0RUSkdfc3jDNmjsrB6VDOEBGR1h3OGWePymlchlhERKQlR+cMiS/DMDq1M+PxGDJkCG63mxUrVjBgwAAAKisr2bp1K2eccUaTfZcuXdrs/ujRo09oPFOmTOE///kPOTk5pKS0PKF08eLF/N///R9z584FYN++fZSUlJzQOER6i/sX2jlj1ugcSupKMAwDp8MJdE6hrKP9XUREpEWWBdVFMRXJBiIWz22qB+DikV1jNpWIiHRNVYEwb20sAuCi4V4gCk532weJiEjPZppQUwTupl05DtVFWXrA7lxwicYZIiLShn1ldWw4WAXAxRP7xjkaERHpyrYVVTeudHTJpH5xjkZERLqydfsrKKu1z01drJwhMUhOTubqq6/mRz/6EYsWLWLDhg1ce+21OByOZpNzFi9ezO9+9zu2bt3KX/7yF5555hluuummExrPVVddRVZWFhdffDEffPABu3bt4t133+XGG29k//79AAwfPpzHH3+cTZs2sWzZMq666ioSEnQuVuRYLdlRSihqd1k+d2wGpYFSkj3JnRqDOsqKiByvUC0EKsCT1O6u7+4JNG5fOqrplybD52X4f/4IkTqMBC1HISLS2y1sKJIFuLBfHRw1QDB8XoY/fR9EwxgahIuI9B6haghWgS+1ycPztx8ZZ5w3tOlYwvB5Gf7ve8EylTNERITXPilo3D5zZHaT54yEBIZ/tLhxW0REereX1x5s3J4+pOkqR8oZIiJytBdX2znD53YwoV/T81bKGdKa++67j29/+9tccMEFpKSkcOutt7Jv3z58vqbnN3/4wx/y8ccfM2/ePFJSUrjvvvs477zzTmgsfr+f999/nx//+MdceumlVFdX069fP84555zGDrMPPfQQ1113HVOmTKF///78+te/5pZbbjmhcYj0Bi+uPgBAVpKXjJQQRWX1pPnS7Cd9Xkqf+z2p7tQOzRkqlBUROV6BSggHIDGr3V3f2xME4BuTEsnyO5s8ZxgGrrRkCDtBSxiJiPR67245BMCNp+aSahSAN6/xucacEQkrZ4iI9CaBSoiGmq1m8V7DhLzbTk0mwd100aDGnGGayhkiIsK7W4oB+MUl4/C6Wjg3lZERj7BERKQLem+rfW7qvi9NxOloOpZQzhARkaO9u9UeZ9z/lck4lDMkRsnJyTz55JON92tra5k3bx7XXXddk/1SUlL473//G/PrPvroo80ee/fdd5s9tnv37ib3c3Nzeeyxx1p93cmTJ7NixYomj1122WVN7luW1ey4ioqKVl9TpDdqHGd8eQJFdfvxuY4qjjcMrLRk8KQ06y59IqlQVkTkeNUeAmf7H6NR0+Lf6+1liib20dLZIiLSunDUbOzaMTEjBA4nGI52jhIRkR7NsqC6qFmRbCBisXCXPSFvYh9PPCITEZFuojYYYenOMgAm5afFNxgREenSKupCrNtfCcDE/mnxDUZERLq0gxX17DxUC8Ak5Qw5BqtXr2bz5s1MmzaNyspK7rrrLgAuvvjiOEcmIh1lS2E1hVV2449B2QY7qirJSOj8yRQqlBUROR7heqgrB29Su7se7iYLMD6n+QVsMxSm+C9PgBkh566Tcbi19ISISG/1xvrCxu3R/mrwpTR53gyFKf7zk2CZ5PziZBwuFUaJiPR4oVoIVoInscnDL26ua9weldl8Qp49zngKLIucX07DkaBTQCIivdW/l+9t3B6SndjseTMUovg3vwEg57bbcHg0zhAR6a0e/Wh34/bADH+z55UzRETksIc+3AWAz+0gJ9nb7HnlDGnLvffey5YtW/B4PEydOpUPPviArKz2V/IVke7pkcV2zshM9BC2qrAsC5fjqGsWoTCJ9z+Fw+nBnHdPh+UMXSURETkegSoI10JCXru7vrnDnhUxIcfNkPQWPnajUcpfWQRAzs+iJzRMERHpXt7cYBfKnjIwkb7eanBnN90hGqX81XcByLkz0snRiYhIXASr7Il6n5pd/eZOe5wxZ6iP9IQWuo9Ho5S/+h4AOT9XzhAR6c3e2lAEwOVT80n0tnBuKhKh/Kl/A5Bzyy2gC9giIr3Wgo12zrj2tMG4nC2MM5QzREQEe5n5tzfZOePbZw5teZls5QxpxeTJk1m5cmWb++zevbtzghGRDmeaFu9sLgbgOzMHc6j+EImfagxCNErCy+/a23fc3WE5Q+u4iogcj7pSeynsdpbDjpgWCxouYN92akqb+4qISO8WCEd5t2GQcOtUJ3jUYVxERIDaEnC44KgLDtVBk8V77ZUrbpmRHK/IRESkGzhUHWTFnjIAbp49Is7RiIhIV7avrI4NB6twGHD9WcPiHY6IiHRhW4qq2V1ah8fl4JunD2l5p8r9R7ZNs3MCExGRLmf1vgqKq4Mke13MmZhEbbgWv7v56hWdQYWyIiLHKhqG2kPgaf+D++ODIcoCJmk+g2n9NEtORERat3h7CbWhKHkpHiakVINXhU8iIr1eOGBP0vvU7OpFe4KETBia7mJYhjtOwYmISHewcFMRlgUT8lPpl6bJeCIi0rrDKx1NH5xJRqKuZ4iISOveXG93kz1jeBZJLa1aAbDplSPbDpUmdSTLsuIdghwj/T+T3uSthnHG2aNzqAyV4nQ4cbTTlLCjKBuJiByrQBWEappdrG7J/B12N9lZg324HC0sOSEiItJg/np7kHDuED8OywSnLkiIiPR6wSoI1zUbe7y5vR6A84b64hGViIh0I4fHGeeNzY1zJCIi0tUdLpQ9b2yfOEciIiJd3fyGnHFuW+OMjS91UjS9l9ttT6Cvq6uLcyRyrEKhEABOpzPOkYh0LMuyGnPGmSPTKA+Wk+xp3ixqb/Xexu2wGe6weFqZ2iEiIq0KVIBl2cuftsGyLBbstAtl5wxVxw4REWld1LRYuMmegX1efgC87U/GEBGRXqCuzB57HDW7OhCxWLQnCMAcFcqKiEgbqgNhPtpRAqhQVkRE2naoOsjHe8qBdoqeRESk19tXVsemgiqcDoNZo1uZXFG6AwrXAXmdGltv43Q6SUtLo7i4GAC/349hqHlXV2eaJocOHcLv9+NyqWxPerYtRdXsKa3D63IwboCDvTUB0n3pTfaJmBH+se6f/KrhvsvouL8L/cWJiBwL04TqInC3X/i6tzLKgeoobgecNsDbCcGJiEh3taWwmvK6MEkeB9PS68GjCxIiIr1eNAK1xc0mT3xSHKIubJHldzA+xx2n4EREpDtYuaeccNRiQIafYTlJ8Q5HRES6sOW7yrAsGJ2XQt80Nf4QEZHWLdlZCsDk/mlkJLayMt6G5zsxot4tN9e+nnS4WFa6B4fDwYABA1TYLD3ekh12zpg+JIOaSCk+V/PmHy9uf5G9VXsa73fk34UKZUVEjkWo2r75Utvd9bF1tQCMzXbjc+kLjoiItO6hD3cBMCnXg8sZBIeWWhER6fWCVRCshaSsJg//7eMaAKbkenQiVURE2vTnd7YDcNLA9Hb2FBGR3u6+BVsAmDowLb6BiIhIl/e7+Q05Y1Ab44z1KpTtLIZhkJeXR05ODuFwxy1XLieWx+PB4XC0v6NIN/fr1zcBMK6fn6pQabNusnuq9vDCthc6rYC1Q9/n/fff55577mHlypUUFBTwwgsvcMkll3TkW4qIdKxAFURC4Gq/Q+zDa+xC2dHZbXd5Mrwehj72W4jUY/h6b+dZ5QwR6a0sy+K5VfsBGJsWAW/rnZ4Mr4ehj94N0QiGr/cut62cISK9QqASrCg4jpy6iZgWb+8OAjCxTwzjjEd/BaalnKGcISK9UG0w0riE9uQBaW3ua/h8DF24sHG7t1LOEJHeqqw2xI5D9vWMKQPanlyhnGFTzhCR3upART0lNfa5qVZzRtEGKN6I4XYz9OFfgsurnNEJOcPpdOJ0qgmLiHQd24urCUctAAb3gagZxXXU9Q7TMnlw3YNErShT86dR+sQFpHpSOzRndGh5em1tLRMnTuQvf/lLR76NiEjnqSkGVytLSByltC7auP218Ylt7AmGw4EnNwtPn0yMXjxrSDlDRHqrPaV1jdtXjwiBp41CWeUMQDlDRHoBy4KaInA3PSG08dCRrhBXxTLOcFfjSfcqZyhniEgvtLKhSBbgi1Pz29zXcDjw5PfDk99POUM5Q0R6oY92lDRuzx2f1+a+yhk25QwR6a0WbS5u3D57VE7LO619GgBjxGw8fTLw5GQoZyhniEgv9NbGosbt/OxaEj1Nr2m8vfdtdlTsIMGVwDUT/h9WXjbk5XRozujQjrLnn38+559/fke+hYhI5wnVQaCizQKmw+5bVt24PTqrs5qEd2/KGSLSWx1ecgKgb4obtIx2u5QzRKTHC9VAsLpZl/G73q9q3E7ztXOyqGwnvPW/kJgN/U6ClNyOiLTLU84Qkd7qjpfWN277PTo3FQvlDBHprW5/7pPGbZ9bnehioZwhIr3VT1+0xxnJXhduZwvnpswofPKMvT1sFtSXQ3LfToyw61HOEJHe6nfztwCQn+6lPlpHtje78bmKYAVPb7YnVnxp5JfI8GVQFijr8Ji61BmyYDBIMBhsvF9VVdXG3iIinSxYBeF6SGh76SGAbaWRxm2jnYInKxyh+B//BTNCzk+nYbgTPnOovYFyhoj0FDtL7KXt/C7Am9zmvnbOeAYsk5w7pmHE0OVclDNEpBsKVNpjj8SsJg/vrrDHGQNS2rl4XVWA9cqPKF5mQEKUnGucaBpGbJQzRKSnOLxyxdSB7Z/HskIhiv94PwA5N9+E4dE4IxbKGSLSU1QH7XHGnLHtT65Tzjg+yhki0tNcPLmV4tdd70F1ASSkYyX1p/jh/4I3mZyfTVHOiJFyhoj0BKZpNW6fNcaPYQRxGEcmWDy58Ulqw7UMShnEuQPPhXAY/9+fxeHwYv3klx2WM7pUf/O7776b1NTUxlv//v3jHZKIyBF1ZWA42u30VxU0G5dE/dfFGe2+rBWJUPbsm5Q9/zZWONLu/mJTzhCRnqC0JsjeMvsC9uOzTXD729zfikQoe+4typ5fiBUOt7mvHKGcISLdTm0JON1NHjpQHaE8YALw4AVtjDMCVfDGrVi1ZZRtTqJsdRjL8HZktD2KcoaI9AQ7DtU0bv/xy5Pa3d+KRCh7+GHKHn4YK6JzU7FSzhCRnmD9gcrG7V9+YVy7+ytnHB/lDBHpCZbvsjv9OR0Gt58/uuWd1trdARkxB6u+mrJXFlP27HzljGOgnCEiPcF7Ww8B4HU5mDXRJMl9ZPW8LWVb+ODABxgYfHP8N3E6nBCJ4v/vWziefqVDc0aXKpS9/fbbqaysbLzt27cv3iGJiNiiYag9BJ62C5gA3twRoCZsMSTNySn9dUG6oyhniEhP8Oq6AkIRkzFZTibnKWd0FOUMEelWwgGoKwdvUpOHX9xcT9SCk/I8jMxsZYGgaAgW3AGV+8Cf3fI+0iblDBHpCZ5ftR+AM0dk0z+j/XNZcnyUM0SkJ3h2pZ0z5o7PJStJ56Y6inKGiPQEz3xsf3ZdOrkfid4Wzk0Fa2DTK/b2gM+By918H2mXcoaI9ASHxxkXTc4kbAVIcNkra1uWxeMbHwdgZv+ZDEsf1qlxtXJlJT68Xi9erwZhItIFBashVAtJbV9stiyLh9fYXTsuGeXH5dACpx1FOUNEuruoafHoR7sB+OIQE4cvLa7x9GTKGSLSrQSrIFIHCUeWPQ1GLJ78xO5AftnoBIyWVrmwLHjvd1CwFtyJcO5d8K+7OivqHkM5Q0S6u9pghKeX2xdSL5uaH+doejblDBHp7sprQzzXMLlCOaNjKWeISHdXVBXg1XUFQBs5Y9MrEK6D9EHgzwJnaucF2IMoZ4hId7e3tI4FG4sAOGWkE7fT3XhNY8nBJWyv2I7X6eXLI7/c6bF1qY6yIiJdVqASLBMcbc8v2FEeYVNJBI8Drhynjh0iItK69Qcq2VVSS5LHwWVDo+DSiQ8REQHqK+yiV+PIKZuVBSEO1kTJ8Dm4aGRCy8d98l/YvhAMJ8yeZ1+UEBGRXuejHaWU1obom+rj3LF94h2OiIh0Ye9tPUR1IMKQrETOGK4VKUREpHULNxVRH44yJi+FaYMzWt5p7VP2v0POAocDnF2qb5+IiHSS+RsKCEVNpgxIITO1liS3vXpeKBriqc12rrh42MWkxaGJVIdmppqaGrZv3954f9euXaxZs4aMjAwGDBjQkW8tInLiWBbUFIHb1+6u/7uoEoBp/Txk+Z0dHVmPopwhIr3Nj59bB8CpfR2kJiXGOZruRTlDRHosM2qPPTxN88ItCyoAmDnIi9/dwpzng6th2d/t7VNugPyToD7QwcF2D8oZItLbfP8/awA4Z3QfvC6dmzoWyhki0ptYlsXNDTlj9tg+uJzqrXQslDNEpDexLIv/fWE9AOePy215paOynbDrfcCAvMmQkAFhq3MD7aKUM0SkN4maFr9+fTMAp41MImSWkunKBOCNXW9QUl9Chi+Dzw/5fFzi69BC2Y8//pizzjqr8f4PfvADAK6++moeffTRjnxrEZETJ1QDwWrwJrW5m2VZbCgOA3BS32PoCmhpkADKGSLSu0SiJtuLawA4pU8EvK3MwJYWKWd0EDMKkQBEQxCNgBW1O+pj2J0tHS67C4DLB04PtHRCVEQ+m2CVPf7wZzY+VBsyOVgTBeDMgS2MM2oPwdt32X+vw8+FMZd0UrDdg3KGiPQmh6qD1AQjAJw9OifO0XQ/yhki0pvsKa1r3D5nlDqQHyvlDBHpTTYWVDVunzWqlXHGysfsf/tPg6QsewW9sCZxg3KGiPQuK/eUN24P7xvG57IbElaFqnhx+4sAfGXUV/A647PSaocWys6cORNLBWAi0t0Fquwv8olZbe5WEbCoaZgZ983JMXYGDNfD+/ceuV/4CaSeebyRdmvKGSLSmxRWBYiY9mfeVSMtuwBRYqaccYJEw/ZkoGA11JfbBXqRoP04VguTeSxwuMHptTvtJ6SDLxW8yXb3SxXOinx2gSq7UN3pbnxof3W0cfuiEQlN9zej8PYv7L/hjKFw+g/0t/gpyhki0pvsKz9S9HTWSBXKHivlDBHpTY7OGa0uoS2tUs4Qkd5kX1l94/a4fqnNd4iEYM2T9vag0yEhrXMC6yaUM0SkN9lXZo8z/B4nKUn1JLntvPHK9leoj9QzKGUQp/U7LW7x6Yp8d2JZ9kVrs6G7EwAGOJwN3Z3cbR4uIseprtTuntaOhbvsWXE5iQ6SPDEsU1RTDG/ejlGygyFzPTDy8xgDp37WaEVEpBtYuLEIgMGpBi5fjJMrAMPrYcjffg7RCIbP10HRSY9mRu2CutoSe3n3UK3dhdLptmf5e5PsYlhHC8v0WpY9FomGIFwH9WX2sS4f+NIgORf8Gc2WjBeRY1BTDO6mM6kX7LTHGeOy3c2Xtlv7NBSuA7cfZs+z/x4b2DnjTjBN5QwRkV5iQcM441gLngyfjyGvvNy4LSIiPd/hnHF2a50BW6GcISLS+xzOGRdMyGt5hy2v2yse+bOgzzj7PBUN56b+8hNw+5UzRER6icM546zRKUTMetxONxWBCt7c/SYAXxr5JRxGC/VUXg/lD/2cZHdyh+YMFcp2VaZpL7cYqrUvQgcq7e1oBKxIQ3cny14C1XCA4Wy4sJ18pKOTJxHcCe2+lYi0IRK0C2VjKPhYcTAEwKzBMXxoH9oCb/4E6kox/Ol4594GmcPA2UJRioiI9DgrGpadmJVvgScp5uMMhwNv5QeQ3BccMUzKEDksVGefrKzcb48tLNMuik3Kjr2jsWHYBbVOd9PvRuF6u2i2ptA+CZrUB5Lz7KJZdbYUiV2oFoKVzcYeHx8eZwz51Djj0Bb4+GF7+9QbITW/ydOGw4F3YF/7/IJyhohIr7Bytz3OmD362JbQtscZH8Lwc5UzRER6iY8bcsas48kZw4d3REgiItJFrdxTBsDsMa3kjJWP2P8OmQmJmY0P2+em8uxrIBpniIj0Ch83XAMfP9AkoaFm8cXtLxIyQwxPG87knMktH+hwEB3UFzxpGB2YM1Qo25VEIxCogLpyqC2yL2ZH7QtiuDwNXZ7c4PABhn3R2bLsi9xmBCL19vFmxH7e7bfb2idm28uiemMvwhCRBoEqu1g9qe2TRaGoxfwd9rITM/K9be7L3qWw8OcQCUD6IJjzG3vZ4nBd28eJiEiPUB+KNnaUnZFLy507W7P5VVj9uD1RasxFkDuuY4KUniNYDVUFUHkAQtXg8R9bcWws3An2zbLs7zPlu6FiHyTlQNoA8GfqRKhILAJVEKqHhCNdAKuCJu/uCQIwI99zZN9IAN75pb3azOAzYPh5nR2tiIh0MWW1IZbvti9gzxia2c7en7LzXXj1Zvv81A0r7e+LIiLSYxVWBthYUAUcR84QEZFeZXdJLbtL7WvYM4a0kDPKdtrjCQwYMOOYGoOIiEjPsuFgJSU19vWM/jlBEt1ZlNSXsHDvQsDuJtts1bxOpkLZeLMsCFZBbSlUHbAvZFumfQHbl2J3iT2u1zXtzk61h+zXdfkgMauhs1OmXXQrIu0LVtl/p+0UMa0tClEVtHAYcMbANv5ud30Ab8+zC9rzT4ZZd2IZPkoefxaiYbJ+NA1DnaBFRHq0pbtKCUZMfE743KDk2A88uBrr3T9QsiEZ+owlK3Uw6tUprQrV2sWxlfvs4lVfqt1tsiMHoIZxZGWLSNCe/FdbDMm5kDbQ7jArIq2rK7PHHUf9nX6w1z6plOYzmNTnqELZFQ/Zf9/+TDj9hy3+bVvhCCVPvAKWRdaPpmMk6BSQiEhPdngyXm6Kj1G5xzDOCFRiPX89JZ8kQ98JZHlSNc4QEenh3lhfAMCQrEQGZviP6VgrFKLk7w8CkPWt6zA8nnaOEBGR7uy1T+ycMSYvhezkFq6Br/qX/W/fyZA5tMk5KiscoeTJ18HpIevWKcoZIiI93Kvr7Jwxvn8CCW5wOVy8uO1FImaE0RmjGZfVRgOocBj/Yy/jcPqwvj+yw3KGrpLEixm1l3OvOgA1hyAatC8on6juTobjyEVqsC+OVxfaF8t9qXZnp6Qcu/OTiLTMNKG6KKa/k8MXsGcP8ZHqbaVj2va3YdGv7EL2IWfB2f8LDhdWfYCSJ14GIPPmn+pihIhID/f+1kMAzB1k4ffHOLu66gAsuBPLNO1C2Q17yQyHMfRVTj4tErJ/X8p3Q7AGElLBn9/uYSecy2tP0osE7Y62NcWQkg8Zg5otKy8iQDQMdSXN/j7ea+gme+HwBLyuhpHCoc2w/jl7+4xb7TF+C6xIhJInXwUg8/s/w0BJQ0SkJ1u0pRiASyb3w+U8hm7+b/4Eq+IAJRvyYMMOMiMRXcAWEenhFm2xz019YXI/HI5juyJhRSKU/OUvAGRe+w3lDBGRHm7RZnuc8cWp+c27AEZCsPoJe3vQ6XYjuKNYkQgl/34DgMwf/Ew5Q0SkhzucM04Z4cTv8VJcV8yifYuAGLrJRqL4/2Vfz7C+d7sKZXuMaMTuqlSx1y6UNQxISAdXBy9n5fbbNzMKgUooWAveFEgfaHd4UsGsSHOhavvWyoXnw8rqozywvAaAybmtdGve+e6RItnh58GZtx7bUtsiItIjFFYGeGTxbgAm5zhi6+4ZqoH5P7G7nGeNBKo7NEbppkzTHmeU7rSL7XwpkNqvYzvIxsLlhZQ8e+Je+U57xYvMoZDSV9+FRI4WqLI/75NyGh/aWxnhvxvtpe0m5zacFDIj8P699rhi6DkwYHo8ohURkS5ma1E1b6wvBGDygLTYD9zyRsOF7aMKay3rhMYmIiJdy7r9FY2TuCcPSI9zNCIi0pUt21nKx3vKAZjS0jhj08v2+d6EDPsclXEME/ZERKRHeXdLMZsL7WvY+dlhEt0ZPLPlGaJWlHFZ4xidOTrOEdqUqTpLNGJ3c92/Ag6ssotVk3LsLksuX+fF4XDaS56m5oNhQeEnsG85lO+xZ/yIyBHBaoiG7AKPNrzf0OUJ4KIRLSxTtP9jeOeX9sXskXNh5o9VGCInVjQC4QCE6uzf20CVnWcCVfb9UK39fDSsC14icbZwk70cqtsB54+IYTlUy4S3fwkVeyAxC875WQdHKN1SoMr+Xn9gJYRr7AJZX2r8i2SP5vZDSj/AhII1cHCNHbeI2AKVdsH7USvMzN8RAMDvNjhnSMN5g0+ehdLt4E2GGdfHI1IREemCDi9tl+Z3c/rwrNgOqiuDl2+0t8df1kGRiYhIV/PymoMA9EnxcvLg4yiUrS8/sh2NnKCoRESkK3pxzQEABmclMiE/rfkOy/5u/zv0bEjs4MZwIiLSpT2/ys4ZQ3O89E13UBWsauwm+4VhX4hnaE2oo2xHM6P2MqPlu+3OTi4fJPdpcvGrLVETNhyspKwuRIbfw9i+qRzLylmtMgzwpYE39UiH2coDkDEYkvqAQzXUItSWgLOVDrFHeXaT3eXpS2P89E3+VAFs8SZ466d256chM+H0H2o2XVcVDtj/v7tiEbNlQSQA4Xp7Ceto0N4O19sd+swomFGiZpStBZVU1IdIS3Azok8STofD/p0zHGA4wekEd6JdsOTxgyvB7iru9uuzX6SDWZbFMx/vA+CrI02yUpPaP2j1E7BvKTg9cO6vwJ/ZwVFKtxKNQOU+KNtl54PErHYn+DQe2lHjjLYcXk3DkwQ1BXahbNYwu4BWOUh6M8uyO0J7Eo56yOLpDfY44xuTEkn1OqCmCD5+xN7hc9+1J8GKdIKoabF8VxnF1QFykn1MG5yB8xiX6BWRjhM1LZ5evheAb50xFL8nxlP+r/3Qzj8ZQ2DYLODDjgtSeg3lDJGuLRI1+XdDzrj+rGF4Xcd4Ltw0j0yyAPucNSmt7i7SFuUMka4tFDH593L7esYNZw1r/vd5YBXsX27XvQw9J6Zr6iLHSzlDpGsLhKO8vNaekHfRSW6SPD5e3vEyYTPMsLRhjMkc0+bxpmVSGaigM66Cq1C2o5im3Wa+YveRYrvk3JgLZAE+2lHCg+/voqTmSLfKrCQv150xmFOGxtgZoD2GAQlpdjea+jI4uMpeBjVjSLvLzYv0aOEA1JWDJ7HN3baWhvlwn92NeXDap04qVRfA/Nvsk0X9psJZP+maRZhiK1hrd27MHAr+rPgW7ITr7e6w4VoIVEOg3C6QjQTBigKGHZ/DbecVh5Ol+2p4ePFeDtWGsDCwMMhK9PDN0wcxY3CqfZwZbeg+W2L/a5n2+7m8drGsP7NhEkWy/bvflToRivQAa/ZVsHZ/JQBD0mL4TnhgFax81N4+7fuQPRLqAx0XoHQvdWVQthOqCsCXbHeRjVGnjDPa4nTbxbH1FXb+ra+w86+nhc78Ir1BqMZeBcB7ZALF4n0hdpbb3ZkGH84ZS/9mT5jKmwgj5sQjUumF5q8vYN4rGymoPPIdJC/Vx50XjmHOuLw4RiYihy3cVERxtf29bnBW2+exGq1/DjY8b0+onXadfX5Y5DNSzhDp+l5ee5DaUBQ4hpxxtI/uhx1vAw1/094YJoGLtEA5Q6Tr+09D0w+Awdkt5IzlD9r/DjgFMgZ2UlTSGylniHR9j360u3E7LSmEhZcFexYAcMmwSzDaqDupj9RTEaggzenr6DABUNueE82yoOYQHFwNBz62lyBJyrFbzR9jkezdr29ucvEaoLQmyN2vb+ajHSUnNm6H044xMQuqDthLxZftspfpFumNgtV2kaK77YKNbWVHlha6ctxRg4RwHbz5v3bH5qwRcO4v7G6A0nWZIXtp8/0f25MGqgs7Z+koy4JgDVQXQekO2LsU9iyBfUvg4Fqo3AvRELh9dj5JzbeLoZLz7M/shDQ+2h/kV2/t4WCtRRg3EVxEcVJcG+XX83fw0Z5q+3fZm2xf/ErMhpQ8+3VS+tonNKNhu+DqwErYu8T+t2KfHZuInBDbio/8PV06Lq3tnetK4Z1f2AXtI86Hked3bHDSfUTDUNrweV1TDCm5xzTBrdPHGW1JSLNzWcVuO/fWduJ7i3QlgSp7op7ryImgbWVHxuIXDE+Awk9g5yLAgBk3aEKTdIr56wv4zhOrmlyIACisDPCdJ1Yxf31BnCITkaNtP2qcMWt0TvsH1JbC6z+ytyd+BdIHglfdAOWzUc4Q6R6OPjd16rFOlN2zBN7+xQmOSHoj5QyR7mF7UXXj9uT+aU2frCm2J98BDDun3evpIsdLOUOke9hWZI8zDGBAtouFexZSH6mnf3J/pvSZ0uIxlmVRFiijNlTLkLQhjMsa1ymxqqPsiWJZdkFD5T67q5OBfdG3hcK49pY5jZrw4Pu7sD51XNByUUIaURxcP7+KUXkwa7CPe5dUc8EIHz+YnsJr2+sZmOripDwPoahFn6Rj7F7p9NjdnQJV9oW42pKG7opa0lF6mfoK++/YaHs+wY6GLk8Xjkgg1dewr2XCorvtosOEdDj3lxogdAsGJPWxu9nVldhL23pT7MLUhHR7+0R0mTWjEKq1b8Fq+3M2XGt3HjYMu0DClQAJqWA4jsoZtWT4wzHnjIDlppRUojj57vwqJuUbTO/n4Q/LqvnyGD/fPSmZV7fVMzzDxfgcN1HLTU5Kmp3PIvV2TqsusDvNJubYXdH9meqKLPIZ7DhkDxKuGG6RmNDGrDgzal94qC+3u/yfdlMnRShdXm0plG23J1ccLjI9yvGOMwKWhxJSGnPG9IEOxmS7+dOKar4xKZFrJibx2rZ6Rme5GZnlwgCy/CcoH7i89vijttiebJg5DNIGxrezu0hnqysFZ9PTM4fHGddOSsTrtGDJn+0nRs2FrOGdHaH0QO0tWRc1Lea9srFZzgAaH7vx32s4f3wh/dIS+Nt7O7jh7OF8+eT+vLbuIOP7pTE0OxG300F6oiaNinSkHQ1FTzeeMxyXM4bvUG/ebueejCEwdJZ9LiQQ6uAopTs7MTljNZdMPkSKz8U/P9zFj+eM4oIJebyxvoDJA9LJT0/A73aR6teSvSId6XDOuP38UTiOZbni2hJ49v/Zq5YNPB3Y0TEBSrd3InLG9/69miumlWJaFk8s3cu8i8Yyc2Q289cXMm1wBjkpPpJ9LlJ8yhkiHWnHoVoAfnnJuOadAFc+ajf5yRoBfSd2fnDSIxx7zjDBiAImVkNPyO89vYIvnZxLRV2E19aWc9vn85k4IIkPt1YzfXAamYk+cpL9pPv9uI6hsaGIHJvD18C/PduP2+Hm9V2vA3Dx0ItxtFBzZVomh+oOkehKZGjWULISsjDr6jol1p79SVB10C78TOzA5UNN0z6xWLHXnjkDdlGpy9vi7m0tczppQAZL94d4cVMVO6pdmLg5wNFdACx8hHATJRqNsmF/GRv2GwRJ4JmN9Tyzsb7VMKf19fCvSzLxuWIc+PpS7GKxmkMQqLAvWKf2b3bxTqRHMk2oLWq3uNWyLF7cYv/djck66m9j1b9g9wfgcNtFskltd/MwPB4GPfBTiAQwvLqAGHdOj32RyIw0TBpYDy6PXSiblAOeJPvz0eWzl45ujWXZg8RIACJBu8twoNIuwo4E7OcMh/063iS7CPVTA822csb4/AyWHQjx0sYKSqvrSTSCVJOIjxA+I0SaYZFFNWHLSdh0sXdvGav2pBAikcc/qePxT5p/0TCwT0ad1t/DIxdl43YaEKqDygP2RJCEDEjrbxfOuvS7KnIsTNPi5TUHARiT3c5J3HX/hYI1dqH6rJ836TBoeDwMuv8nEI1geFv+vik9UDhgjzfKd9kXpVLymq1W0VbOGNsvg6UHgry0qZpd1Q5CpFBAK2OkKLy5M8CbO+0Z2g+uquXBVbXNdvO5DAIRi3MGefnHhRk4Pkt3S6NhskqgCoo22JNJsoa3OqYS6VEiQagrA8+R1SnCUYtXtzWMM7LdsO0tOLTFHp+cdG3ML214PAz64+1gmcoZ0kRbS9ZNG5zJsp2lPLdqf+PzXkL4CeAniM8I4SNMBAcR08matQf5yPJhkMwDb2/jgbe3NXu/ZJ+L6kCEz4/P489XTm5zqS0ROTb1oSjzNxQCMCYvhq6w2xbCuv/Y5yOmXAOpfcFwNOSMHwMO5Qxpoq2cMXVgBst3lfF8Q85wEiXPKKO/UUy+cYg+lJNu1JBm1JBGDf61QZxGlLmeKM53TIrfcTLK8lGCjz0kUGSlUeHuw/ZgGqPGTOLHV87VBDqRE6iyLsyiLfZ1zDF9j6GTuGnC89fZTRVSB2Cc/DUG/aEOHC7lDGmirZwxqX86K3aX8cLqA826An5aOGrxryV7Gu/f+fKGFvfLSPRQVhvi6hkDmXdx53QgE+ktiqsCjSuPNcsZ0TCseMjeHnpOm6tTGB4Pg+67BdwJyhnSRFs5Y3x+Gst3lvDCmn0U1BzC8FbicNXg8JTgcFdgOOswXLUYzlowIrxQaIFh4R/s4P71PlibgBVN4B9rMzBDGZjhTBKNvtTUublyRjY/PG8YSe4kPFoNWOSE2F1Sy5p9FQDkpkdYenAl1aFqcvw5zOg7o9n+pmVSXFdMujed4enDSfYkA2B4vQx65r+N2x2lZ1c9lu2GUDWkDYL0AXaxwYkSCR7pIFtbalcXtVEgC0eWOf30LLmS6iC/eG0LNYafEcZ+8o0SvuoqoZ9RSp5RSgZVpBh1JFOHx4i2+No1lo9q/JRZyey3sjlgZbHXymGzNYCN5kCWH4RR/1dAus/BpFw35wz2keAyuHRUQusXKBwu+yJ8sNouFAtUQOZwu6BLpCcLVdsFGu0sY7y/OsrO8ghuB1w1vuHC9sHVsPIxe/v0H0Cfse2+neF0kDBysF1I6VS3zi7D4bI/1/0Z9md+qAYObbIrSZ0e++by2jeH2+60allgRez9I0GIRiAatItuLQtcbrvgzZfabvHPkZxhkU4N+cYh+hql9KsvwXirjN1GPeOMSmZSictnxvxjhS0npaSw18pht5nLLiuP7VZf1phDOUQ6AB/uCzH8LwVk+BxM7evhzIHJJLstLu5fY/+O+9LspRmTclUwKxKjbcU1FFQG8Lvg8vFpre9Yuh0+bjjJNON7kDagydONOSMSVs6IF8uyu8Z7Eu1u2x3JNO0O52U77EI6f0aTYrrD2hpn/Py1HWzl6A5heY1bXkIMNIoYbBTa4w6jiiyqyDCq8RHCSRS3ESVqOajDRy1eaqwECqxMDpqZ7DeyWbE7nyF/CpKZ4GB6Pw+n9PeSmeDg/GHHMfbypdj5sWwnhOshZxR4k4/9dUS6k2C1vbpAUp/Ghz4pDlMRsEj3ObhoiAOe+af9xOSvHdNqL3bOGGR/lihnSIPDS9YdnTOSqSOnajvzn1rIVsdBBhjFXEsFt3vKyTEqSDZan5h9tAorkVIrhQNWFrusXHZbueyw+rIuMARI5rVPCnjt9gKykjycMjSLaYMzyE3xMWtMn3ZfW0Ra9vGeMupCUfJSfZzb3t9SsBpevdneHnUB5I5rPDdhOB0kjBhkF9AqZ0iDlnIGQHFlLfc8+QqjjH2MdOzlK8Y+fubZRz+jBJcR+zmqFlmAB9gOtfO87DAGUpU+lujA03AOOZ3TJoz8bK8v0ost2VlKOGoxNDuR04YdQ4OhJX+CHW/bOeNz38bIHExCcq3dREI5Qxq0ljMKKgN8+4lVTR7zEyDDqMZPgCTq8RtB3EQwcWBiEMVBwPJQSSIVVhKVJBJpoaShrNY+3/XYkj08tXwv6X4PM0dmMz4/jaFZiZxyLL/nItLEB9tKMC2YkJ/KlAHpTZ/c+BLUFNrNdQad3ubqrPY4Y6DdhEg5Qxq0nDNMCqrKuP7ZZ3F4S3Ek7Mfp20/S8EKMVmqkjoVlOUgI5vDsrgE898dhJESGM2NoH8b1S2div2xOHapzUyLH64NthwCY0N/HgGwH/1r6BgAXDLkA56dWKzYtk+LaYrISshiePhz/Uc0LDaeThPHjOzzenl0o63DYHftKttjJOn2Q3Q3weAtmzajdDbC2FKoP2h2PXA0da9vqKkjTZU6jFmQYNQxzFDDe2MkwxwFGGAfwG8E2X6MtSUaAJALkGWWMZU+z5/ea2XxsjWRxaByLd49l0e5MANYWhbhrZlrbL+5Ntgu7KvfbP3P2yI4vChCJp0AVRELtFjJuL7OXQx2a7iLF67A7hb7zK8CCEefDyPM7PlbpHIcLYuFIp9hoyC5uDlWDZdqPGwZg2EWzDqddGOtNbDdHNBGoIFqwgeJFi/iZay9DHQfJMGraPazK8lOPh3rLSwAPFgYewjgx8RhhMp0BvGYdbiNKLuXkGuVMc2xp8hoHrQzWmMP4yBzLe+YE9gX6sGBngAUNXQV3TEviB9Mz7N/1g2vsQo30wXZhhzqOi7RpW3E1AKPSLXytdQ+PBOGdX9rF9YNOUx7pqiwLqg7YHbczhtgTBzqi82ldGVTssbt6u7yQ2q/Fk45HjzMClptDpFNJ84ltOZQzwbGT8Y6djDd2MdKxj35G6QkJdb+VxYbIIJbtGs0TO8ayxcrnjjPS+Mak45hg5/Lak/Wqi+wO7DljIDHzhMQp0iUFKu3PlaNOGG0rCwMwLseNe8vLUFdif98a/8V4RSk9RDQS5l8vzecix1bGOvYw1tjNCMd+so3KmI4PWG7q8BLBiQG4ieB3RnGZIRyYpBm1pBm1DKWAM/ikybF7zWzWWUNZYY7kg9rxvLw2yMtr7W77v798Il+cmn+if1yRXmFbkX2+YGJ+WvtLaL/zS7vpQ3IejLnomCZfSO9zeJnTJOoYbexhtGMvY4w9jHbsYYSxH58RbvG4oOXigJXFfiubAiuTME72WTmEcDKjvx/T5ees/k5e3uNgZWGEYCBAohEgmXr6GGX0M0rpa5Qw1DhIohFkAluhfCuUv4C52qBq0RhSJl8KY78AmUM7+b+KSPe2rcg+NzWpf3rsHf4PrIS377K3p1wNeROO7Vy39AqfXhrbgckAo4ihxkGGGQcZahykn1FCH6OcPkZ5zBPxjnaIdHab2ey1+rDLzGW9NYj15hBKsBvehKMWxdVB/vvxfv778X4Anr7uc3xuiM4piRyPbcX2OGNS/7SmT5gmfHCfvT30bEjK7tzApNs7kjMsIIzhKcfl34EzcRdO/w4cruYrolqmGzOUiRnKwgqnY0WSMC0HVjgTjAinDU8m0eNl4iAnb35Szq6SGmoj1RjuMhyeMhyeYhzuapy+Qpy+QmA5YcvBoooBLNg7gUjVRP7vq2M5ZUg+KZ4UrYQkcoy2NpybGphjsa18G8V1xSS6Ezkj/4wm+x0uks1MyGRExggSXCew2ekx6PlVLW4/JKTb3VAL1tpFn8l59hLX3mRw+1o/1rIaiqBqob7SXoo9UG0vd+pNalyaqj31dTUc2LiUufXvM9R9kLHGnhZPJAUtFzutvhxy5XIwkkJBNIU6y0ullUitkUCN5aMOHxGcZCR6efDrJ+MkandbCtXYt/pyyooPYlUXkRk6gFmyHUdtEQMchxjAIS51fgjAFjOf16PTee2T6Qxal8/pA7z8aU46ab5Wfh6nG1L62V10D66GjGGQMViFUdIz1ZXaBY7teHGLPZgfkem2CyXf/Y19ETttIJx6Y8xvZ4UjlD0zH6IhMr43DeNEdr+WE88wmhbOflbBarvotGCN/W/ZDpzAJQAN9RJRy2CvlcMuK4/dVi67rFz2W9kcslLxp2RRUGcSDkfJpLIxxMZwgcwkLw9dfTJYIbvItb4MKg9gVe6j5tA+PJU78VbtoS9l9HUuZ65zOQC7zT4sNKfwSnQGa62hPLC8hgeW1zB7iI8/zM4jKVIJB1fZnWUzBtu5VYMHkRa9uMo+QTsys43vTisegvLd9nfX029p8e/Jzhlvghkl48ZpGOrqHEcWHNoMtcV2wWxSnyaFbsf3kpY9bqk8YBfjmhH7ZGMrSwDVhky2FlWxv9pkH/0Icjg3WQw2CvmcYyMzHBuZ5thMrlHe4mvUWD4OWpkUW2lUkETYncLFnxuLw+21u6s7XGCZWOE6SiprcUVqSI+UEKoqJlKxH3+gmHyjhHxnCec5PwagxErhrY+mctWHnyNjyBTuOTcLn+sY8oPDBSl97f+2BWsgZ7R9X6SnMU27KPyo7/+WZTWOM8alh2HNk/YTU77e6mdBa6xwhLJn3wTLIuN70zESNH7vdaoLYe9S2LuU6N5lULyBp6JBu1PfpxRZaew0+7LTymOPlYPDn05RwEVFxIODKLWWF8twYtIwORDITPTwlyun4LDC9rimvhzqSrGqDlJ2qJCE+kL8dfugurDxvNQFzqWAPcnig+h43jCn8eNnIvzwmbVcOrkfv71sAm6nltkWiYVpWrzUUHA+IredLvz7lsOyv9vbU69ptnKFnTPeAsMg48bpWha1N7IsqNgLResJ7l9Dze7V/Kd+DQN8h1rcvc7yssXqz2azP5UJ/dkaSKUonAiGfZ0jYriI4sDC0Xhu6qcXnMzhj/jLx8Pl2N99NhyKkJ5g0C/ZRUF1lJUH6/nnjlrW7djLWGMPkx3bOMWxgZGO/aSUb4B3NsA7v8DKnYgx9WqY8CWtRCHSjnDU5NV1BQCMzI1xUmuwGp691j43MfBUe3ltT5KdM55bAIaTjJumYXh0bqo3qw6E2bxlCxOq3+dq1w4mObYz3thJYjvNoYKWiyAeArgJWm6iODGwcGDhcljkJjowQjX2dXogm3KyHeWczNbG6yYA9b4+rHWN49nyYXwQHU8RRyYCfeVBe+xx9YyB/PyisSp8EolRIBzljfV2zhjR51PfsbbOh+INdg3OiHPbnTxh54yF4PSSceMU5YxerrI+yOJd+yiOfow3Zyeu5C04PE2beVimh2h9PmYgnxzvAA5VJBEKg8NVi2mBgRMwwHIABhn+JL596hQcBlhYjDs7immZhM0IO4uiZCRBaqLBntIa1hXs5eODWykzN+L0FuPy78bl343V51V+8PZIQs/O4OLRU/nfz48l3ZeOI4ZaMJHeriYYYcHGIgBy00wW7FkAwKwBs/C5mtZjltSVkOZNY0R6y0WyVihE2eOPA5Dxta91WM7oHVdJDMMuOPCl2QO7su328qUuP3gSwNuwxOfhD7po2C6ODdfaXYwiQcCyE35iZpOEHzVhw8FKyupCZPg9jO2bSnUgjLt0M4nFq6jcuQJ/2SaGGVGGHfVfu8JKZLU5jE3WANaZQ9lq5VNj+bj73BzOGZXDRztKePL1zWC/M4en4R3+Cn/tmcNxulyAy449Ia3xtTOOOs/pALs7ZslWwvtXYR1Yhat0KyMd+xnp2M/3eY7NZn/+feBsTn/wdFy+JN64MpvcpBYu8huG3T03VAslmyFcA1kjweNvvq9IdxUOQF15i8saH624NspLW+oxgG9OToQNL8K+pfbnw6w7j6lztRWJUPzPZwBI//ataKjecx3OGfUle+hftYa8ipVQ9AmG1XRZuq1mP9aaQ/nEGsx6czAbrYEE8JJCLVXYv5vZlPPb87I5e2RO45LbQJNlKg7/Ll13xuCGCxEeu7N6Ug5kj8IAGoe44Too2QYF6+DAx1iF6xnkKOKbjjf4pusN9prZvGyewtPRs1mwM5txfw/QL9nJS5fnkFVXYheJpw6AjEHt/v2I9Da7SmpZuPkQLgP+3+TUlncqXA+f2LmAM37U5Lvd0axIhOKHngUg/Tu3YmhuRfy4/ZCUZHd+PbDK/p6cNsCeNHCskymiYft1qgvtlTDMMFFvOhsOhSkrrGwcZ1QEovhcBokeB69ureeG+YeLX/uTRjVzHCuY6VzLDMfGZoWxFgZGxmCKvQN5bm8iu6w89ltZVOEHjMaccfu5o3AMbb40nQEc3R/A03AjWE1t4Xas4k2Ubv+Y7KoNZBlVXOlaxJUsomRfCo89eDqLk87jgS+Na31iXrM3NOzi47oye8JjJGivEKKLGtKThKrtm+9IbthwKMKS/SG8TviW502742xKPxhx3jG/vJ0zngcg/Tu3YaCk0VNFTYvlu8o4VFnNsLq1jKr6EGvrWzgrdjfuc/gsT43lY5M1gA3mIDZYg9hsDqDcSmQ/9hJzuZRy93l9OOvT44xPTcYD+PqZo3D6Gr77J6RBWv/G5xv7NpkRe2Wm4o1QvIlIwXqcZVvJN0u4wrWIK1hEqZXM/Og0nl1zBsNX72di/3SeuHYayT51KxNpy4rdZazdV0Gix8mV0wa0vmMkBC9/D7BgyNmQf3KzyRdWJELxwy8AkP7dn6hQtgeLmhYrthcQOLiB/OAOhpi7iBxYh7tkA0bAnoDtbbhlNnx1329lsckcyEZrIAfMDJZaY9lnZdOfIu46rx9XjMxtN2ccOTfVlGEYjMs58nmfl+zkgpFJXDAyifL6bCym8MDyGuatrSWbCs52rubzjqWc4tiAq3AtvPYDrLfuwJj4ZZhxg7rMirTiw20lbCmqJt3v5rKp/WM76LVboHyXPTaf/HX7nDKfyhnX366ipx7s8DijuDpATrKPaYMzKKkJkhYuwr13MVuXvUHiwSWc7DjEyZ/6Nai3POy1cthsDWCH2Zew5eTzkwcwbsQwlh1y8eu392PiwDrqiljjuam5o8g7fG4qGrEnlVcftFc+rTxgr8BUthOqCkgIFPE5ivic+21wwwHvMJ6oOYlXzM+x37J/Zx9bsofHluzh3DF9+NOVk/G6tPy7SFsWbCxiT2kdOcleLpp0VPMCy4IP7rW3h58Lqe2vDGNFIhQ/8hIA6d/9sXJGD9ZSzjhUHSQlwUFFsIyfL3iJ9/d9hDNpM/7+R7rGWqaTaP1AovX9idSMwazPx/AWcMM5fTlrZD/W7g3yi5e3Y0YyMC0nWE6wHBgNhbLzvjCF6X37YlkWJiamZRI1o0SsCBOzw4TNMPWRerISqhjdJ4dLxo+ntOYigmY1jyxbzYHQCpwJB3Alb8KVvIn5pa/xyh9P45S+p3HvZVPI8h9DJ36RXui1dQcprArQJ9VJXp8inl69Gafh5LxBTa9nlAXK8Ll8DEsfht/dco2hFYlQfI+dZ9KvuEKFsieEYYAvxb6ZUYg0dGKtL7PvH/4Kbhh2FyOnp6EjbUaLF2Q/2lHCg+/voqQmSAJBJhnbqPJuZby5mUTD/nBPxX7ZvWY2i81xfGyOZLU1jArLTy5lHF4Ry08AvxEgO8keoJ4yNIvb545qfP3DMpO8XHfGYE5p4eJ1q3wpkH8S7vyT7PvBakK7PqJ+yzskFH7MKMc+5jke48eup3kxcgpXPTKXv181iWEZrVyQ8CTa/20q99tFhTmj7EJkkZ4gWGUXySfktbnb4+tqAZjQx82EhBJY/qD9xOe+a3eVE/mU1evWsG/5K0yKrGOCo2k3jm1mP5aYY1hqjmaZOZpSmhbSZVHBUA40S0VZifYg9ITkDLcf8ibatylfwwjV2oVfOxdh7fmIAZFD3OB4ie86X+ZdcyJPRs/hnerJnPRwCR9cnUN/fxjKd9oFs5lDIbkvODTTTgTg8SV7ADi5j8WI7BZWM4iG4P17AAtGzIGBp3RugHL8DIddIGtG7ILO/Svt795JOfb3Y08iuBKar8IQDTesClFrF8HVFNkT+gwHJKTx0d5aHnx/Q+NnetQySPT7+biuz1EvYjHK2MfZjtWc5VzNFGMbTuPIdImw5WSz1Z9PzMF8Yg7mygvnMH5gDjnAxB0lLHt/F1WfdZwB4E0mceBkGDiZpJOvJBAMseqTj9m8fCFznMvJMqr4lus1vhV4jXcfmcj42V8nc9jU2F/fn2H/tynaYF8cyRzy2Tv3inQVgSq7eOmo4vrD44zz8iOkbfmv/eBJ/88+RyHSgoWrtvDe609zcnAJMx1rSTGOXGwwLYPN1gBWmCNYaY7gE2sIu60+WPa0anIoI5sK0oxa0tjZeFxmol1wd0LGGQ4XJPexb0PPsk9EBiph/0oiuz8iunc5mZEqrnK9zVWut1lrDuFfB85lys9LWPLTOWQlqVhPpDWPL7XHGbPG9CE3tY1V05b8yV4JISEdxn9J53F7m/oKKPwECj/hwOal1O1dw1RzP24j2rjL4UtPIcvJdiufjdZANjYUxm4yB1BJEn0pJp1qDMMghVrGGvZ3loxEeyLOCb2e0SA9wc5XPz8zldtOTeHFzanc9k4a/4meRTpVXOJczFXOtxkWPggfPwwrH4WxX4DTfgC5447rP5dIT/WvJbsBOH98HhmJMVxwXvs0rHvaPk8x7Vv2WFxFIr3K/PUFzHtlIwWVAZKo40zHOgo965lirsfrKAZgFIDDPm+1xRrAGnMoa6yhHDJTqbISCBoeOKoQ9vyB4yE7lenZ8GNvamw5w+myz70lZkHuhKZBhuqgcB3sX2Gvhlq2i37B7fzYvZ0f8zQrzeE8GTmH18zPEcTDWxuLGPOzN1l757kkeTXGFmnN4XHGxZP6knL0BNad78KBleD0wrDZ9nVFEZrmDFuUlESTWmsPrtQ1uJPXYzgDuNPsZ62In0jNSCI1o4mGkzGc9WB6IOrBcFVjhTM4OW8qU/r0ZUofyEsY9KnXh9xUH3deOIY54xpqOgxwHp4m3srlg3A0TF2kjrr0OsqD5fz83D5Uh87kuZUHeX//R7hTV+D0FeHs+xwfh95h5v/N4smvfZHhmYNIdKtJlEhL/tVwDfzUkS6WFn0AwIy+M8hIONLlvzZci2maDMscRqq3lYZSnaj3fgt0OMGTZN+Ow0c7Svj768uZ5tjMSa6tTHJsx2tE7FZ+BpRbSSw2x7LYHMeH5jjOHj+ElzeUYphB8ijFbwSavN7h5YfG9j3yS3HK0CymD85q1rH2M69A503GM+o8PKPO41BZBWkHFlG+6gVygnu50rWIrzjf5bX/TOe7kS9QkziId6/OweP81ADY6baXP60uspcKzxljX/QQ6e7qK+0/yHZa6b+23f4b/vr4BHjvV3b36bxJMObijo9Ruo/aQ7DjHWo2zGdy9S4mAzjs4qVl5igWmlNZaE5pnNl82DcmJvLC+lKc0QC5Rlmzl+2UnOFJhMGnw+DTMSIB2LOE4IZX8Rau5GznGs52rmGb2Y+/Ri7krMdOIYKLkZkeXr84jLNgrd01KnOIlr6TXs+yLF7/xF4O9eoJrXTyW/OU3QkhIR0+951OjE5OGIfLLo61TAjW2F0tLMueXObyNkzCc9uPmWG7UDYasgvkHA77Mzc5FxzOxk5MFvYqFIc7/FEHDkxONrYw17mUWc5V9DOaLku0y+xjT8wzh7LZGkAId2POGNP/SK7psHEG4PN6mHLSKeSNnE6Sz+L/XlrImENvMNO5lpnOtfDOD1m2cBRvpHyJn31lJo5YJlV4k8FwQskWsCKQObx58bFId1RXAq4jFxwipsX8HfUA3JI0H4pqIX0wDD07XhFKV1VbCptfpWT5fzizcCmzjGjjRYBDVgpvR6ew0JzKMnM01dgXrhLdBleNT+Q/a0vxRWvJNiqavWynnZvypcKws3ENOxtXJETJlsWk73+H6J6lTHTs5Peev3Gz+Ry/uXsxL0RP47QRuTz6/05WBw+RowTCURZuspe2u/qUQa3vWL4H3rvH3p54BWQOVqFTT2VZUHXALootWGcXDhWug4q9jbv0O7xh2GONw11iDxfFbrf6EcZFZoLB5aMT2bS2FD/V9DeKjxx41FanXc8AfC6DL4/1c9oAL1l+J5c94+aRQ+fzSHQOn3Ns4n+cr3GOczWsf86+TfgKnPMzSO3X/ouL9HBVgTDvbysB4OoZg9o/oHQHvPZDe3vcZdD/5HaX1paeZf76AuY9sYBznKuY7V7JDMcGPIcnWDggYjlYbw1miTmGLe4xDBw+jkc3hEkyq0k3qu39PtVdvENyhscPAz5n3wDqy2HH27BjEVbxRqY6tjHVs42f8SRPRc7i4cj5lJipjLvzTQC+fFJ/fnvZhDbeQKT3Ka4OsHyXfV3y65/OGR/83v532DmQ3saKFtKrzF9fwHeeWIVFFIwIDu8h3ClriKasxe+ubtzPDKfgCU5gbMYElm7PwHRUgRGGaDJmqA+YXsCJAeSl+jh16JGmZnPG5TF7TG6zjrVOx7GNbd1ON6nOVFK9qeQl5RGMBqkKVjHgrHwuLBlMfXQuv3hzMVbqBzg85ZDzDFe+/B7Bogv44thT+PUlU3GpmYFIoz2ltWw4WIXTAeOHVnD/2uUAzB08t3GfiBmhKljFiPQRZPuzW3upTqW/4mNVV4q5/V2yl73MY949TZ7aYeaxwJzKguhUVlvDMXEwI9/Du5dk4nQYnNc/HOPS2Ec4HTAhv+MqqrMz0iDjC+SMu4TSnWsoWvo0Y2qXcaFzKRc6l/Jq8HP86IWvc+fnR5KR8KmpF4YDUvKgrhQK1kB0NKT218lW6b5ME2qL2p0Bt68qws7yCE4D5oYX2L//Lh+ceWu7BbbSC0RDsOsD2PIaHFgNWCRhnzxabI7juejpvGtOoormM8/OHeLj75+3l3CYlR/m7tft4roWc8bpA3CaQYhEwYqCGcVpRZmQbkKaBQSgLnDkKMOwfz8Npz1ZxOG0C7cc7vY/t10+GHoW3qFn2d3EN72Muek1hocPcJ/nb/zAepY/Rb7As6VncPOHSfzi9AzSKvfZJ6ayhtsTK5QbpJfaWlRDYVUQn9Ni5tAWvtOV74bVT9jbp3yvyfLb0g0ZjiMrWFiWnROiIYgG7dUswP4cdrrsk/mfWvI2asKD7++i3nJTRAbVJGJgcpKxlc87lzLXuZycowqbLKcHo98UdiSM51frUjlEWpcZZ+Ql22OH7142h72Vs/jpu1sYceBFvuxcxHTHZqbX3MW2p14md/aNJPeJoRu/x28XFZdss1cDyR6pC3XSvYUDUFdhF8o3WFsUpjJokeetp/8+e1k6pl6jMYbYaktg0yuw8UV7vGFFyQIwYKeZy3xzGgujU1htDWvsGHvYNyYl8rMz7M/8mXkh7n79ANBFzk25PGSNPQvGnoWzppiylS9gbH2D/o5D3Ov4O99xvsyd26/hnjdT+M7MoST79NkvArBidxmBsEluio/J/dNa33H+bfb30NzxMOSsJl3MpRuLhu3vxYWfNBTE2h1jqW8+2RrASsxmQ10aG8J5LDXHsNwczQGyaFLF1OD705O5abo96fmMLpYzDMMgP8W+pPXqFdlsLwvzg7cqWFo8hqXmGMZEdvNd10tc4Fxmd8Lc+CKc9n27w6xLy/xK7/XR9hKipsWQrERG5rbT1CASgue+aa/G2Wec3aVZjRB6B8uCog2Ym19j4Hv/YYlvR5Ond5h5vG1O4SNzDB+bI6nBzx2np/DHyXZDqukDStq+ntEZOSMh3S7uHncZRm0JbHwJtrxOWl0p33W9zDdc83kycg5/j1xAMen85+N9jM5L5ssnDyDBo9WLRAA+2GpPrBjfL5X+GUddL9+7DHZ/YF9XHH6ucoMAEIpE+PmrK8C3G0/yNtzJ63F4Sxqft6IJhKvGE6mayFdPHs6s8UmkelPZMNLiJ8/uANOD9anJeAB3XjimWRGs02EwY2jmCY3f6/SS7c8m25/N4LSBVAQq+NdV+azaN5MHPlyEmfIuTm8x/gEP8/KBj0lceCXfOeUUcpK0SosIwHtb7VWUR/f1sKFiEVEryqiMUQxJs6/5WZZFSV0JeYl59EvqOpNYVSgbi/oKzJ3v4dj5DlbBOhxYDMdewm6NNZS3oiexwJzKDsv+H9uXQwzlAL+/dFSTL/gdsfzQCWMYZA6dTObQyRTu3craNx5iNsu5wLmU2aUr+ccjc7nsK/9DboY94ImaNJ3ll+HCWfiJPYjOGKLltqV7ClXbSyC3U6S0/EAIgLOzK0lY+Xf7wWnX2cWA0ntV7IXNr8KW+RCsanx4m3skj9WdwmvR6ZST0uywfhTjJ8DvLx3dYs54+L1tVNfW4CGChzCZiW6+cnI+U3JMe7leh8suenV67IKhw/dx2MWplgWYdlGRGbELtsyoXZxx+P7hoYfTbV80c/ns12upuDU1Hz73XRxTroaNLxFZ+1/ygyX81v0PrnW+zm+3f4VJW6ew6pu5ZFiVULCGaH0FK6rSKaqzjnuWn0h3tXyX3fHz5D4GPvenTrhaJrx/r/23OGCGfeFaeg7DaPhMbbsYwbQsHIZBcW2Um94oYUl1PwxMphjbuMC5lPOdy8k1yhv3r7Z8hPufSsbYczD6TQGXj6HAN/NLuuY4AxiQ6uKXF49lS+kIznjyYr7teoUrnW8zvG4NkRe/SWj0RXimXwve5ObjjKO7ibh8dufesp12LssZrQve0n0FqyBSBwm5jQ8tP2D//f4o9V2MqlpIG2B3+JfeKxqBbW/Zy0nveNv+7tBgp2Mgzwan84Y5nV1W3qcOtBhAET5C3NvKOKNL5oykHDLO/BbM+CoFS57Gu/kFhjoKeMJzN68uXsSc97/G2/OuwOd2EjWtz9xJRKQ7O9zl6dRhWa13W97yBmx53T5HMPEqSDyxFxWlk9SXQ/EmKFx/pCi2eJM9Ge/TDIfdyCJtAGbqABwZAzngG8E33vWwpb7llzewGEAhXkLce+kYJuQfKXro0jkDGJbh5uWvZLOqIMSlz5Sw0RrEDeGbeDCyg597nmBKZAu8ezfWxpdYd/Jv2e0eqpwhvdKyhpxx2vAY/mYX/RIOrrILoE66VitJ9nTRCNaeD2Hz6xhb34CKvTiA0djXwFdZw1kQtVfF22HZ56sGUkQ/DnHPpWOYkH9k1dYulzMSs+Dka2Hq1fZEwzVP4ivdzrWuN/iqcyFPRs/hgcgX+PkrG/nnh7t4/0dn4XAYGmdIr3f0OKOJD+61/x08064FkV7JsixM06I2WsP7Ozdy+xsvEk1bQ1LCgSP7mG4i1WMIV43HivjAsJh30YT/z95Zx8dRp3/8PTPrm924S5umTSV1F2iL94Aih3PAHX7AoaccB/zgOOA4jnPgcA53KRT3ulGhQi21NGlcdpO1mfn98Y22aakk2cj3/XrltZvZ2Znvtpt55rHPw7GDBhFri8VtdTMyWSHelsJdc9ZRXNMyiTst1sGds4cxa/jeca7Ox2lx4oxxkuJKIcdbxaR+WXy8/mgeW/4+1vgFWL2reW3XBuY+cyqvXXw5mZ5MQJU2Q9KnafIzBmWGmFc0D2irJlsdrMZj85Abm4umdp+mJFkouz8Ctexe8yV16z9jUGANKiIZoQDLjHze0yczV59EKaJbwEWAYRSiKi19cpX1oX0O25njhzqKtJx80q7+M8XbN1L4wT+Yqq7lF5Z32PPaV9xuv4LBIyby6ertbRydpBg710xNZZK5XoyTTcpvLNSSSHoQgVpR7P0DBS0PLRJjAm42n4dIoLGz+owuWKCk29GkHrv+XShe1bx5t5nAK5FjeMOYzq7AvhLyMdSTw559bYZpQLhB/ESCTE2CSWcPYEN5mPKQijc2kZG56WhWhyhq3ac49iAx9Jax33pYfI8jAfE3EKoTj3qwscjLAVYnWJxtC2dtbhh9IZbhZ8G6d9FX/I/8UBFP2v7KQn0Y5zx5KdbEflw22GT5qrfZ7HewxcykGg/pUXR0JJKu5oEPxTSBcRnt2JaNH8Ge78Tf2FE3S+XlPsSiXUHe/r6BLK/GgwvFfYWCwRhlM7dbFnOytph0pUUNymc6WGQMZZ4xnJVGHjcNLGBmv7b2pSf4GYMTrSy+YTjflQ7mhFdm8XvLi8zSlsKGt6n4/is2DLqKxwuT9vEz2iRULHaRqKtunO4hi2UlPZWGatHQ1Eot9s8L6rATYlbDe2LDqAukmmxfpXY3rPgfLH8W6nY3b15j9GeuPpm5xkS2m2n7vC2eWjIob3NL0SNjUzY36TMuh0lns+eLR0na8SGnaouYrq7md3euZXX8idSFIpTVtXw26WNI+hKmafKvzzcDMK7ffpRsQvXwwW/E8yGzIX24tCndnUgQyjfCnnVQurbxcR3UFrW/v9UF8f0bC2OzIb4/n/r7M2+PA6tF4/HF+6mMbSSRGtKo6Pk2AxibbmPbDRl8syPAxW9XstrM48fBOzhVXcSfbM8QW7qOYe+dzruRC3lS/xHpsU5pMyR9BtM0eXr+NuAANqOJLZ/D/H+I5+Mvh9Sh0nb0Rgwdts1j29cvkLzrI9yR6uaXGkwb84wRfGyM4wt9DOWIhrtkqihga8+0GaoF8o6BATNh5xJY/jT2sg1cZvmQs7Wv+VfkDP5XdSIDfj+XYeleyn1BSuta4lLSz5D0JQzD5JVlOwEY39pm7F4pmngVFfJnSTXZPsjry3byfWk1hZUlfLVzPpbYb9FchSgJJhpgmiq6fxDhmtHowSQUrRZMO6buxdRjiCFvHzXJWcPTOWFYWrcrNNVUjSRnEomORDImZHDSsFyeXTKZ97e/hubaQb3nTU56fi2Z+nnU+tyU1rbk+qXNkPQlIrrB+6uLAQg7vsVf6yfFlcL4tPEABPUgYT1Mfnw+rh+Y6N3V9LlC2QMqFIXqYds8zC2fY+xaRoYZaX7fKmMA7+mTeV+fzG5aOmjcNNCPkjbFTk0kuNpP2Hb2mNOOIr1fPulX/5vXP/yECdufoJ9ayh9DD/LukimEIjOh1djwCl+Qez7ewW0n9Wcym+U4VEnPpL4cLAf+zm6tilBUp3OUuoZhdfOFU3DUzTJg1EvZr82oLW4e29OkHqubCl8Yo3lRP46vjFHo7Fu46sVPFqVtbIaFCDEESDUroDYkEh2OOHAlgs2NZnVSMMglxnR3FKomfqyOfV8zdAjXC5sY8oG/TDzWV4nvuc0tfpoKcy12GHkO2uBZmCtfJLL6daZo6/hA/R3/rZ7N7fPPIF+JIZkaRihb2WpmUFyTyDXPr+CRi8ZKZ0HSq1m7u4a6oA7AuEx32xdDPljymHg+9qdCJVPSozmgn9FIxDB5eJmvuekGTEYrWzhFW8TJ2mIylYrmfetMB0uMIcwzhvOtMZBIK9etp/sZw1OsfPWL0Vz3QT+e3bqceyxPkacWM23jnwnrI3mUU/EhHOcKX5D75m7g1pOHtBTLajbwpMliWUnPxdDBt0fcUzXSpCZ7lvYNrnAVuFNg4PHRWqGkk2lXpQhTFCcsewo2fgimuIeoMD28ps/gZf0Ytu2jHCtIaCx2ai+X0KNthiOW1B/9FqPkVLa8/1fy9K383fYw79cu47bw5UBLcq6kJiB9DEmfoWm0HRyg6GneQ2LyjTsFhp0Gtpj295N0Dxqq4S95YtpIO4QcyficGRjebOIzB6DF9wNvpriXsDoJmxp3fV3D82vqgX0Ll1qTRBUpVO1lM0xUDBIdSuPkIUQMqPGnR9gM4OgcB1t+kc6pL5exvjzCe8YUFgaGcZ/1CU7UlnO79XnGqJv4bc3V0mZI+gxz15Q0Px+bc4BCWX85vPVz8XzQSWKyhaWd2LGkW7NfNVTThB2L4LvXMde9g+Ivo3/jeypMD582qsZ+Y4wgQEuzfyoVJFHTbm9/s59h6IDZONnORENhZIYbFG/3EgVQFMiZBNkTYediWPQI3urt3GZ9kZ9aPub28KV8UTxmn7dJP0PSl3ht+c7m52Ny4lpe+Oav4rHfUZCc373+tiWHzQ8paJumSVVDHb94aRlLShZjiV2Jxf09jgy9eZ9IfX8iNaOJ1A0HrRbFUodiqpjBHEzdSVM5Wpq3/SI5TVWYktc9J58oikKiM5F4Rzy/mZnKjysLuPSlN7EkfYgl5ntKIg/SUDcbtHzQRYxX2gxJX6KpGQ9ga/1CAI7POR5VUTFNk8qGSrI92SQ79xWVizZ9qlB2wZaWcaQh00INboa4/cxIrCa15GtmWZajGUEUQAPWGznM0SfzvjG5jVLHiivSuPnl5WzzaTgJ7FMkqyBGSRRkdP/g0Q+iKJz9oxN5ZPE4zOXPcrU2h9O0hUxTv+P28M/YaqRjUQxMxOd+dH4xE34yEq1yq3CKUobIYllJzyAcgPrqNsnq9pi/M4iVCHdZnhEbCs6ExLwjOrVis5HzwK8hEkSxyyKP7kJrmxE0Lfhw8mPXan5k+5ZhDd82X/ub1GNf1WdSzL4382uuTuMXLwib4aIBVRHJhxgaiFEa0E0LVncs+QXjwRUnOjHbK2DtKlRNrMHuAVLFCJVwPQTroL5SFM7WlQhH2O4RybbG58qkq7EMPY1N7z3EIN9Srre8zWnqAm4NX8FCs4ARbGGouh2XGWC7mc5dc9ZxwrC0qHcHSiSdxbxN5c3Pp2bvpSi7/FkxQjM2G4afddDHVGw2cv78K9AjKPYDK6BLuo7WNiNgWvHhYnBMAEdSNsv2wCMnx3PuG01FsCYjla2coi3iFG0xWUrL98S0OFmgD+G94BjWGTnoSlt3rTf5GYqi8J8fxXPbFxM4+btB3GR5g6u095iprWawuou/hs5mAznNfsZjXxcyKTeppfhYs7YUyyqq9DskPYtgrWiYcLXcO87bGURD52ptjtgw8twj/k4Lm3ELGKa0Gd2ID78rbjNaLokaLnXP4xw+I0VvKWRYZAzlxchxfGhMIMS+34VN16Vz+bPL2ObTiKEei2LgIISdMHZCqArEuWwUxNRBTR3iatoKVRONBxYbaPZufQ1V0wrI/dljzHnlMWbVvcEp2hImqBv5TfhKvjREMrvJXkgfQ9IX+KaVn5Gf2k4BbPmmFkXA0RcIn+MHUGw2cu4XUy6kzYgCzjhxXxCuh/hciM0CbwYb6j28uMFkZ42FsupYaovd5O6KEPBmUuRX+OsJNs57o/QHD7/5unQufXY5230q8dTiVoLYCGNTdER0S8HrsjMsThdFuyCmHpmGiPGbprAbFptomLY4hEJfN0RTFd47P5mL365gwa4QFcRyVfgWLjM+4PeWFzlVW0y2Usalod9ImyHpE3yzSTRXaKpCdsJ+lJxME+bcKJr54vrByPPAmbDfYyo2Gzn33QSqRdqMbsTefgZAvifE2db5nNjwAf3NXYC4Z64yY/hAn8D7xmQWGcPaiH5s+UUGP31mGTt9KjHUN9fD2QnhIISDMIlurcXPULTGxorGHZvsBjo0pc9NU7yuqI0iHpZWP9qhT8w7EhQFciZD1gT4/gPMZU+S1VDO07a/8LY+lbvDl1CJt3l36WdI+hLzNov4dVKMjcSYxut72fewvjFWNXgWOLz7eXf7KDYbOffeAFantBndiPZsRprXTozDgoLJZTPjuP2Dj7B4vsMSswFnVovSth5II1I7Gq1hFP+8JJ3fvb6OKtWPoTsxw4lgtvw/K0BarCjC7amoikqyK5k4exxzr0jhx4/mE4p/Bc1ZhCvrFYLlMwiVzwTTgYkibYakz7Boq7AZaSlF7PTtwKpamZkzE4CaYA1em5ccbw7KITRXKHY7Oc8+2/y8s+ie0YxOYP7mMu7/4PvGe3IThxLifO1LZkcWkFxaCypgwBYjnTnGFOboU9hitsh/Xz8hhk8LA/x8XAwJLpWrZ+Ry39wNjUdroem/+Krpud1q/NCRcs2kRF60/4wzvpnIX6z/ZYi6k4dt/+LpyEm8GZmGqaiYQLkvyNrSACPTU6GqULxZJq0lPYFgLYT94Dxwd8/ioiCXa3PJU4vBGQ/jLz3iUyuainvUEBEM17ooGCA5IK1thpsGJmnbuUj7hFxjDwQABb7WR/A//US+MEbvox577fgYvtwW4MZJHjz2FpthJUwsfiyKTq3pYqOZTbXp4f7Tp6GlZEXls/4gitKiIutJAz0skib+MhE8rS0SCXZnHGg2FG86gy54gC8++4AhW4Qa+fO2+3hcP5l/Rs5gkFFErlqMkyCba7JYUljZbbsFJZIjZfFmkbD8w1RnW4e4aht896Z4PvX6Q7pPEjZjMETC0mZ0E9r6GbAZUYhQ4gN8BgDnvlHOcKWQU7XFnKIuIlttUQELqQ4WqONIGn4cw8dOQ9lex3dzN+xTz9Qb/QxFUbj32DgGeHTuWXgBH+gT+bv1PwxQS7jf9gQvRI7lDWM6BqrwM3bXtFWy0qzgafQ7FAWSh3SsArtE0lkEakGPtLn+L9oVYpa6lH5qKdi9MOSUIz6Noqm4Rw4Gw5A2o5vwwZrdXPvCt5hAgbKNKyzvc4q6CJsu1DhqTBdv6NN5QT+uTUyqictGu1leHOLWaV6smsK1R6XzyEercShhdBQaTBt1ONlNEgHDzh9njUHrlwEoLQVPRljc04fqhR8cCUDQ36hiaDYWz9rFj2brNtNTVM1Cv+k/4bq3Mvi19TUGqUU8Y/sL/4ucwD2RiwhhxQSKawLSx5D0ehYXimTEP84fvW/CwTRh7q+EKmjmOMidcVD+hrAZ+eJvXtqM6HDVV7BnPYR94IhlwXYf983bgomCacJaBgCwww/4hZ9x3hsV+z3cJSNdfFca5vbpsVjMIDdO8vD4F+sJmxb82CklnjrTRci0EUbjgZPHovVPb1XoZAiVQD0svk8hPwSqIdwAgXJhN1RN2AurQzRddFWR0w+gqQq/m2jlml27KCIFUHhKP5lVRh5P2B5klLqV123/x0U1v5c2Q9LrWVxYCcBjF4/b/07fPgcb3hPFihOugPicA6oFNtsMzSptRjehtZ8BJuOV77nA8jmnhhZjD4cB8Jt25uqTmGNMYYFR0GZy0QUFLrZWR7hjupiOdO2M/tw3dwMOgsTQgEXRCZlWGrBTRjyXnDAOLTu7pcBVUWkTyGr2PSLClhgR8RMJCjsSCQjbEgm2vGYaLe9XlJZCWs3a9rGjUDUYeirKwOMo+/w/JGx7nzO0BRytruG34av41Gj5m5F+hqQvYJomixuLnv51wdiWF755CDCFGnP6yEM+rrAZg4TojrQZUSPUODVCURQ+XLObG17+FhMDFB1M0UhT4qvCwgYs3u/406qNOLNapl0Y4TjCNaOJ1I5mam4mlU6Ty0/2MjA+g9uOz+KXL29EQW23burO2cN6RcGoVbMyIL4ff/3xMVzxnIYtcT62hAXYk75CcxQRKD4DM5KAiSpthqTX06RIDZCTt4j11TA5fTJem5ewHiaoB8mLy8NpcR7ScRVNwz1pYiesuC19IouoG3DzxzVYTQ/na59zsrZEFLk1Um56maNP4S39KFabAwCFadk2/nd8HMV1OnnxVuKdKr+c0tIhMzUviVtPHsJjXxdS4WvAio6VCCluC5dMzmJCqiEU98y2arPCIigtiYamLrsmJ0JRWh6bnqPskyhvZ8NeL7c+jtrY0XdkBijDJTrMbwpdw/mWL/mZ5WMutXzEOHUj/xe+mF1mCilKNat2VotRs57WxbJDZdJa0r1pqGn8c9t/EjAYMVmzvZT7LW+LDZN+LkfX9UJ0A67/qI5UVK7Q5nKytgSnIhyIWtPF6/p0ntNPoLDV2NPj+tu577g4tlZFGJZsxWtX+c3UVjajn4f/OyaeF5fsYpM/lmIjgSo8JMfGcP/sYT1r/IJmhZhk8ZMwAOoroHYX+CsAUxSQW50oORO4Yb2NS7WPONmylKst7zNDXc0t4Z+zysgjVqlnhLKV5euz9hnnIZH0BvzBCPMLqwCYnNNKrdw0YcG/xDjlftNEgEnSY4noJtd95KPazCWeOvy0VgQ3KVC2cUpjcWw/tUXpydQcVKRMwjPkGOy5U5hpaemMnJpnb/Yzyn0tndqJMXaump7L1LykrvhoXcrAWIPhylZ8poNzQrfzB+uLnKnN56fWTxmgl/B/4YtxKuEWP6P17Zpmg5gUqNwqkhxJg0HtHkVdEsl+8ZWCteXvvrJBZ1lxiFetH4oNBWeA9dCCSJLuTyhicMPL3zJDXcmV2vtM09Y2v7bCGMgLkeN535jUZtTpj4c4uWN6LN+VhhmfYcNhUUQSOVAD1aVMznRinjSGv8yvorBOxY+TEFbSYx3cOXsYxx6MnxEJQaRBTFkJ14umuFAdBH0ied0U11LVlmS1ojUmxPeOZ0FLvMrc91xt4l7qIcepKutD7CaJ34av4BLtY86wLOQSyyeMUrdwXfhGdplilNcH34mYn/QzJL2R4poGviuqBWDygHaSbuvega1finukURcKH13SM3AliOuiK5F6xcmVn1RRb+YSh49aDjwBC0SR0++meVldGmJyph2rpojpQIFSCNgYP2QAPu9A7vlsN1trFQzEPXN6rIN7Zw/j+IONTUVCQmwg5BfNPw3VouipoaalyEmzCvVZ1dZS2NTFI3or60PEKz7iTB9+HBSRzHJzMGeH/o/nbfcyQC3hVdvdvLIiExgjbYakV7K51EdhuR9FgfH996PkVrkVPvideD7yPMgY3W0VoyXtU9sQ5hcvrcSDjx9r87hA+5zB6q7m19ca/XhRP4539Kn4EMVQPxvl5qZJHtaWhZmcaWt7/TMiTE1XuGuml/8tLWWLP5YyI446XHi9cdx5WgEzOiKfYbRq4jPCopnUaGzOaPJRQvXi93A9BMKi6LYJzSrud5qmZBzu99bqZG3uT3l3UyY3W94gWy3nCdtfeTpyEvdFLmwz3WP+5nJpLyS9ljVFNZTWBbFpKmNy4sTGykJY85p4PmS2zIn3UAKRAGsr1hKMBKkLGNz4SgWmqaJYfCiWOiwxG7G4N6I6d6K0mqRthBKI1A1nSuY4zpySw44yg3H9Y0hyJZHoSMRr92JVreSOBpfFu69CbWNsqkflwA8CX4MVMxJHsGIaRjgGe/IXWGI24+r3FA1FF2AEMgFV2gxJr2bx1grqghFcjno2164E4MT+JwJQGagk1ZVKiisliis8ML3W24noBm+uD/DCikqG+ubyL20ekxwbml8PmFY+Nsbzln4U3xgjmjvnfj3Fw2Wj3TitIlCU6Wnnn0gPQ9jP1JQwk85IY2OZn+oAeGPcDMmIR7M5GwNA1sbAf1OitnHcRFM3dlOHnKmL381Iy0gK02zcRst7200ytIfZkshoHpFktC3aVdTG8Xq2FpWQHyDBJfaxKAav69P52hjJX62PMlIt5Gnbg1wbvpFSI45Xlu3ksw2lIpHfL6VF4SllaLfpKpdI2mAY4N8D1v2MHmrkwy0NXGa8QYwlgJE0GHXQCR1yejMSoerdz0EPEX/VRBSZFO9yQhGD19YFeXy5nxENn/Jfy8dMsG5sfn29kc1z+om8rU+jvlUR1B+O8nLxSDd2i7jJTXHvdY0zIqKQ1DAYN7yA0VNOYskeKPWFSPE4ev4NstUBsZngSYeGSqgpAl8JNFSSaLfSgIOH9dNZZuRzjXUOQ9SdvGG7iz+EL+MNYzrTWMWi+Z/z0eodXHfaUb3OWZL0bd75dhfBiEmSA4YmtVJw2rkIipaLe8Up1x3ycYXN+AIMnfirJ6JYfvgeTtKxBMI6r3wX4j/fhiit90FjQVMVXkRxbCGnaos4WV3cpjg2otpR+01GzTsGJWcySRZH+ydANOVNyk1i7e4aKutDJLhs+xaI9iKa/AyXEsRpBrk5fC0LjALusTzN0dp3PKz8iz+Hz+OVZbT4Ga0Lhi120cBRsRkUCyQN7PJEvERy0IT8EKwRav2NvLG+gQK2Ml7diKlaUIad3iGnMiMRquZ8AaZJ/NWTUGTzapfTENJ5ftF2Hv18HceGv+I9bW5z0jpiqrxnTObxyCmsNXOb3+OxwR+OjuXsoa5mX+GoHLvwLXwVIoHsSoSkfHDGM2VgDK9NhyWFlZTWBQ7dz7A0JpYdrRS7DUMkpiPBFrWnUL14Hm4Qa9HDLfEmaIw5HUTcqjk+ZbTdXbMINcImVcJ2ruNN9iKElSf0U1hp5HGj9W1GqVt533YrN4ev5XNjLP9buJ3/LdzeXDAs/QxJb+KVpTsBGJLmIdW71/1kuAE+vl08H3IqpBUc9D2RsBlfgqIQ//PJKDbpZ3Q1/mCEZ1aG+ee39QT1amgszKlqNf55b+LtCv83M5bT8p3N6sJH5zjENdtXCVY3JA0BTwrYvczMVjh6gnn4NgNa7IYzHmIR1/Qm+9D0GKgVTReRoCjWNSP7mogmoZDmhgt132aKJgEQVWsZzX2QNNkMRYEYAgw0d7GeXLaYmZwZvJuXbPeQq+7h3DVXc+7yOyE2U9oMSa/jlaU7ABiTHUessx11cT0Cb14lit9TR4giKLvnB49rRiJUvfcVKBrx10xCscqJkl1NXSDMk/MK+funGxmrbOLPls851boQhyLUY+tNO3P0KbyoH8sqMw9QSHIqPHhsHCcNcDTbjGnZrUbaGrrIZehhcCYwduJMRk2PZ8nu8OHbjAOhqqA2TrI4EIbeoj7bpHIeCUDIJ2xMJCgaCU29MfftEI2nFsdB3wcluGxsNLO5PvwLLtc+ZLZlEZdaPmKC+j3Xh69vFk359xebeWPFLmkvJL2Slxv9jEkDEnBYG3Od8/8h/rbSRolpFYeBsBlfg8VO/NXjpM2IAlX1QZ79upJ3lwdQ7aVosYXYnNvQ3JtRLf42++qBVCJ1BbgiI7h8Wj+G5yi4LC4SnAlMzUok1h6LrZ26olnD0zlhWNqR+Rk9hBSPA9Agkki4ZhJGKAFH6ieotgpcOU/SsOsS9PoB0mZIejWvLBM2Iy17BWVGmP7e/gyMG0h9uB6bZiPLk4V6GFPKzHCYqldfBSD+3HM7zWb0yixJQzDMg//+O+OqP+ZVdQV2q5AFN0yFhcYw3jKO4kN9Aj5cePGRRxEKJr+bNYiZ+ftxAvVw42j2BhGQsbkhth+aw8vQ/q5WY+mO8D+qqZC2KcHQ5rF5p1bPlX23t35P60LZpvEVekgohTR9noZq8fkURTgPNne7hbMFGbEkxdip8AUxgW1GKicH7+M/tn8wVt3Ms9Y/88/ImXxujKHCF+S+uRu49eQhLcWyqkUkcqTCk6S7EaoTCevWicF2KCnazmXa5wCok3/eYSMozXCEPf95AYC4S2/6Ib1oSQdTGwhz9X/mMLlqDq9qn5NiqwYgbGp8ZEzg2ciJLDUHAwpx1JHDHmi2GQfongxUQ8AnkhHxueBORlMUpvxwrLHnoargThIJ+4ZsqCliGLsY4vaxxe9giTmU5cFB3Gx9i5naKv5qe5Txke95KnISqVSRXLeBP71QBxfOYtaIjGh/GomkQ1i/qxyAM4c4W4IBhg6LHxPPC84C76F/381whD0PvwhA3GU3ocjeii6l3BfknEcWUFgRbLXVZLhSyCna4n2KYwOmlWVGPvEjTqBg0kmHpBCpqTAy68D3Jr2F1n4GCvQzS3hNn8lGI4vHbA8xWN3F32yPcF/4Atb6clv8jDbFsg5wJkDFRpG4j+8XvQ8kkRyIQC2EGsT3tZH15WF+ZhFqssqAmeKeqgMQNuNlAOIuuwXFIY1GV7K7uoHLHvmEY33v8YHlI1Ks1QD4TAcv6cfydGQWu0kCTBKpJpkaFAx+e2w+M/P3Ug4M+aG+EjypENdf3Hu3akTWFDp2nJuqitiQbT8KhobRqvm7qVi2dUyK9pPSzfEqQxzDbBrpHYRg40jvoA8iFeL9NpdQrGksjNo7LrXMHMIVwZv5g+0lxqibecr2IA+Gz+Hf+hmAQklNgGueX8EjF42VSQlJr2F9sVCTPWts1r4vLvw31OwAVxIUnCnujw4SMxxhzyOvABB3+a9koWwXU1ju55xHF1DuCx9wPwWTJKpJpFbYjGPymZnfqvHf0MFXJmxE4kCIzQZ729iVpiodazOacgp7+zqGIfIQeqilwaJ5vLbeqBzYOJLb1FvyF60fTVPYCFNv2RezpRDK4hANFu3EaPe2GZpikmXuYReplJDIBaE/8Irtj/RTS3nFdjfn1tzJNc8HpM2Q9CrWF9cBcNa4dmwGwLyHYNdScb81/jIRxz4I2tiMK34pi566mA0ltVz26GccF/6SD2yfMVTd2fzaeiOHF/TjeEefhh8HSVQzmB2oGPx2Rj4z8/bjEwZqRbGpOxkSRC4DVUMDpuR1zefaL6oGajt2BhqbNYJCcTbcIApn6ytb/CdFEQI5NvcB8/ctNgP+q5/KCjOPmyxvMVzdxju2P3Bz+Fo+M0SRoPQxJL2VJj/j7CabUVcCK0XummGzhX9+GJjhCHseFaq0cZfdLG1GF/PNlkKuevtRsG8lJn87itbQ5nVTtxPxD0T356MHU4AwihrmohnJnDQsi0RnIl6bF8dB+JYd7md0UybmJpAe66CkJoCpu9H9BdTvTMGZ/jaaawfOnCcJ7D6bSG0BJTVImyHplQibYRBwLIRIi5psTbCGAXEDiLUfXm7TDIfZ88d7AIg780xZKHsoOG0Wrgk8TZJWBEChks3rocm8o08jgI00pZL+Ssk+72vqMm5D09g5AEc8JOSJYjq7V6hddDSKIrqkuwo9Ij5jyC8cCH+pcB6MiHAeHN7mhISmwlXTRWJaQSg+KabJBaE/cK/1Cc7S5nGL9Q0yI+U8HzkWFI3Hvi5k0k8noLkShcKTZhWjuqXCk6Q7EagVo1x+oHN1+LZnsCo6RfETyMwY00WLk3Q2Xi3Ck75f4Grsmisnnpcj03kxcixhLKQqVQxXCvd5X7s2AxqVnvaAxQXpI8Cb1Tn2ojuiKGJUoDMezZvBj6drPP3hQkKmhUolhrvCP2G5MYibLW9wgeULRqiF/Cp0FV78DFF38tS7n3LC0AvRLH3k30vSazFNk3lbKgEYmdYqgLDpY9E8ZPfAmJ9EaXWSIyHRbSPV66Cwws8JMds41lzEtPAictSy5n2aimPnGcNZZuQTwM69/Ucc2hh1IyLuTYzGsXKtC5CAdkdXNyswqY29dK32OSCtmvNoemivWa/p1K3OoWgtEzSOYHLE3n6GR2kgxyxhlTmQ04L38JjtIUapW7nb+ix/CZ/DIrNA+Bm5SW1Vdm0u8W9Xul40/nll8EnSDamvbBlZD+iGyfode7hfXSheH35WFBcn6UjSlGreCl6J0yrGzpURz//Cx/GCfix2wiQqtSRQu8/79vEzGqogHISUYaIJ4EgbtDsCVRXjtDsaw2iMUflELM63B+r2iL8XZxya1dXGXphAULFzU+jnXGb5iJ9aPuFX1tcYrO7k1+GrCWBHAe6as44ThqX1SiUTSd8iFDFYtLXRz9i7oaq2GL75m3g+4pzDasqTRI/seCcxdo1yHwyKVwnpJntqA0TQSKWSeMXX7vva2IxIEHyl4E6B5HwRn4kmqgpqYyHr4dA8cU9vKbRtbq7wNdrHevEI4jy2mOb47t4+hgnEKX4iZjklJDUXy75s+yM5ahkv2/7IeaE7pM2Q9BrqQxGWbxd/H6Oy4vbdYddy+PJ+8XzsJZA6rMOEQSSdywBzJ59zFQ5rCIAANj6ITOAl/RhqTRc2JUK/g82BGxFxv211QtoIiM3qHv7GwaIo4vrf2taYpsh5N/kU/rLGqX8R8Tnt3n0+4942Y6kxlOtDmfzS+jqj1K08afsrD4XP5l/6GZio0seQ9Dqq60N8V1QDwMgmm7HoEdHwlJQP/aZFb3GSIyIr0YYlaW7LBsNGpCELvb4/ejAZMxwHahhFDaGoQUzdjRFMZXzaeIYmZkZt3d0ZTVW4c/Ywrnl+hfAzTCtmKI36XRfhSJuD1bsGZ+YrBLTZhKsnophWaTMkvYqSmgCbSn1o7s3URSpwWVxMzZhKbagWj81Dhrv7x6N6ZyWKouCafh3+Xctx5U0lJ2Mca59dSrzP1+4QOAVIjLFTkNEqwBhuEAksix1ic0SS1Rl/RAngbolmAc0rCmJJh8Q8oTTbUA21RaIDHcAZC1YXU/OSuPXkITz2dSHlviBOJUSyWc1t4cvYaGTzW8vLnGf5ikylnL9GzqbcB2t314jgrTMOyr4XDkhcThQ/tESyF/4yoTx2ALZ9v4ppkcXopkLMtKu7aGGSLsHqRB1+BuGSlViG/Ij4QbNY9ty3JPrqDt5mNNEUfPFkiLHPP6BS3GtRFHAnMvWo4wk6U3j1o6+x11dQhYc39KNZa/bjQet/Ga5u43/2B7gjdAkVZixx/q2sXrmUMWMm9J3iYkmvZMW2cgqrwjg0OKZ/YxNGJAjLnhLPR//koEbZSbofSriBJzPm4Kh9Hc2/R2xUIWhaWWrk840xgmVGPkEax3wCSfuzGa0xIsKGhOpFQF/VxNhpzQLWxqkVTSNJATD3VV8yzUZ1vgjQWFjbutC19fO9m9aaC2qbim+bX2hnsWZjMW3ryRVNxbyNKWhVE4WqTVM3DmJE6t5+hlepJ80spwov54bu4B/W/zBLW8rvrC/zcOQ0PvJNaPEzWuPwigLj0vXi3NEuEJBIWqOHob68jUrn/J1BTgx+gs2qYyQPQ00ZGsUFSjoSNTYdNWs8uq8ILf8EEgadzJpXN5Hpqzx4P8NfJl7JGNU3it5UVSgf2mPAkyZiVA3VUFcs1Gzqq5ia5eXWHw3msW+2Ue4TCu8xBHg4chqbzUzusDzHbG0R/ZUSrgz9khISKa4JsKSwsk8om0h6N59v2ENNQ5hkj53x/fe6x/nsbjE6O2kw5J/Y+2LYvRyLpvL8z8YSX74Ct9OObnFx+bNLm9VQ92YfmxEJgL9cTDRKzv/hMdY9AUVpjA1ZgHY+j2k2jt72CxEEf6lQQ4wEG22Jdx8fAyCRWnQ0anCzmyShLGv/I/3VPbxou4dzau6UNkPSK5i7poSGsE5OgouCDG/bFyNBePsaEU/ofzQMOqF3XDf6CLbUoYS9aRiqhpp3DNbsSXzyXgkNfhWLYmDQtuB5v/mMpgYLbwYkDeo9uQxFaetTGINa5b2LwV8hvvt2j/hpjJHtbTMq8XJH6BIusnzOOZavucX6OsPVQm4JX4MPl/QxJL2Kd1ftJqybDE33kpvkFvdWTbmMIacemgCEpFuRG5fJcVmnEGOzMyZ1JANiB3L1c99S4feB1oCi1WMaToxgIqbuRjHspMU6OXpgH4hBHQGzhqfzyEVjuWvOOoprAoAKupfA7h9jRmKwJSzEkTYHRQ0TqpxMcQ3SZkh6DW9+uwvThPj0RQSBGdkzsGk2qgPVDE0celAK1NGm11ahuI6+DnYsgpC/3e7hJppSv1dNzxVqREYEfOXixji+v+iec8Z19fKjh6qJgmBnvChmra9slZCoBGcCU/OSmJSbxIuLt/HKsl3EKT7i8PGuPoVtZhr/tP6bo7S1WNH5S/hsKutFVyM2d6PC0waR+PekRvezSiQA4QA01Ox/pGQjxtInAVjoPpajMgZ2xcokXYjjjH/AzsWgh9EsloO3Ga3xl4tCoZRhENdPFnoCqCrHTBjN9FGDefb9L/hmyXLylZ2UmbFcGvwNf7Y9zhB1J3+zPcpvwley20wkvOd7KPNA8uCe1b0ukbTilUVbATh5oIMYW+PF4rs3RLFLTKoYgyrpmVgcuDe9Bf49IomUM4XvXWO5bXkMQWwHbzOaCPlE4BGEokVCnij0tLrEGFHNJoqGfgjTbDv+uvVo66bX26NZlbZptUr721pO1PiwV5FsU9GuERaJllC9SEKEAxCoE69rFnG/ZXXtt3Bjbz8jSakliVpKzHiuC9/APeZTXGD5guut7+CN1FNZP7j9z+VKFD5M6TpIH73PuFmJJGoEasXffUzLSNPXv6vhdsunAKgjpJpsb8N+/jNQ8h1oGprdc/B+hmk0Kju5hLpXzMGNwe11aFaISRY/cTkiNlW7m6lJASadN4i1FTB/awXvry4mmRq+NQZyTegG/mx7ghHqNl6338VFoVvZZqZTWheI9qeRSI6Yl5eK0cpnjc1qq0RTtBxWvSiej7oAHHFdvzjJEZMV74Qa8f96SPmMcIMo+kkaKBS/+kqRtKKIog2rE9xJIpcTqhP/FrVFwh/QbEzNTWBSrmiym7+lnPdXF5NKFalUsctMpohkzg/dzqu2u8hTi3nedi+FFaNBJrElPZxXlu4A4JxxWSh7N8t+/Rco/17kAcdcJB4lPQdVxXrpe1C+EVQLmjOOi2YkHlo+o6lINmFA74/Dt8l794NAtcjl1BZBzS4RM3LEgqLuE5fSFQvP6idSbMZzteV9TtSW845yO1eFb2GLmSl9DEmv4eUlws84Z1yW2LD8aRHb9WbCwOOiuDJJR3Dv9DtYvmc5MbYYIkaIcyfF8OiXFZgRL0Y4FlN3AVqzzbhz9jCpfHoQzBqezgnD0vjbJ9/z7y+2iI2mk1D5cZiGBXvSN9hTPgQlTKhihrQZkl7DK0t3olhqCFrXA3B8v+OpDlQT74gn2ZUc5dUdHH1mjkZTJ1hiTNuuyMQYO7eePISpeUkiaVVbIgLwWRMgtaBvFcnujaqJf4v0kZA9EeIHiHFGtUVoRpBR2W2d5wRqWWAM46fh31JnOpmkbeBu2/9I1upbdnLEiiBW6bqWsUgSSTQJ1kKk/sDdcMWrGVC/mpCp0TDy4q5bm6Tr2CsQdFA2ownTgNrdoFohY4xQPZJFsm3QbE6GjpzEajMPHy4ylCpUdC4J/Zav9JE4lRD/sD7MNHUtzoQMqNwqmir0cLSXLpEcFit2ijFFp+S7xIZADax8QTwff5lU6ejJqCocczvM+C2c8xwc/38Mnnoat5w88uBsRhPBOqjeBZEQxPWH7MmQMwVShgglD2ecGBt3MEWyIO6vVU3YM4u9ceycUxSm2twtShp7/9jcYHO1JJitjkYVWJs4lmZtnEDR9GNtdY7G4zu8QrU1JlmsPSEX0gqg3xToPxVyJkHmGIjNFoq3vj3CbgZqWwp5W6Gp7ONnpFKFlQi3Rq7gP5HTAPip5ROGFL21/3+TmDThb5RtEP/OEkl3IFDTqBrdcq8YVzyPZKWGkD0RBsyI4uIknYIzvo2K90H5GaYhinsccZAxuu8Wye6NM07YyZzJkDYCTVEY6anj6BxXm92KzUTODt3BFiOdLKWc12x3k6/sJMXT/dUMJJIDYZom3+6oBuCUEemtX4CPbxfPc6dD9oR9pwdIeiQHZTP0kCj26WtFsu2hqiL3kDhA2IrMceL32hK0YDUjM71M28s3y6AMDZ1dZjI/Cd1GmellmLqD6Ut+LpRqJZIeSlg3WL2rMTY1Mr3tiyVrYN7fxPMxF4tYtqTnEZMi7H1jjPGQ8hltimSH9O4i2b1RVRHDSs5v9itQNKgpEoqzptluXOpjYwK/DV3ObjOBPLWYt2x3MEVdy/aK+vbPI5H0IPzBCBtKhJDDKSPTRRx10SPixcGn/KDQlKTnUOIvIaSHOG34SB6YPZtkex6m7gGED5EW6+CRi8Yya3j6gQ8kaUZTFaYNbFsYaOoxhMqPI1g6CwB78mfYkr5ke2VNNJYokXQoFb4g2yvqscYtA0yGJgwl1ZVK2AiT5cnCqvaM+8o+VcnT1Am2dncNlfUhElw2CjJi0RRDqFKoFkgbLpK4ssipLc448RObAVU7oLaIgliNZLeVcn8YE+GTpZmVLDKGcX7odp6x3c8wdQdbP/8DZvo/UVyNjoU7SRQk71kvkj421/7PK5F0No3Orxhn3D76smfQgNf0mUzvl9VlS5NEl/3ajNZfFSMirmfuJKH01FvGE3UCE3MTsMWmsqbGSX9KyFZLMQyVy8K/5v/MZ7nY8im3WN/g1S9h+IVXolQViiKB1GF9K1gn6fHU1vnYUimKvEenNn53V70skmwJeTDw+CiuTtIhjDofts9ro9J6UDYDRDLCXy6KTJOHgDe9d6udWuyNSZsEMakjEhKFgvUVwv+q2S2Kcp3xba71BRmxJMXYm0fNKgpkmOVsJZO/RM6nxnTze+tLpG16EWKdMLadRiZFESP2aneDxQkpQw++8Fgi6QyaCsWtLcV6e3w6Pwp/JuLRQ05pU0Ar6b0c0GaYphgH6koU8Sm7J9rL7X7YXKIpIyYVanYxVNvGUHcdm/wuImgoCpimynmhO3jOdh9D1R28YvsjT8+LY0re+dFevURy2BSW+6lpCGO3qAxJb3Vt2PwpbJ8vJhEMP1smsnsZB7QZRkSoj8fnQuKgvl0kuzeaVTTxuVOgbjdUbIGaIgpSktv4GaoCmWYZO0ij0Ezn4tDvecV2N7HlK+Gl8+HC19rcu0kkPYUNxXUEIwaxTqsYod2EHoF3rhPXj5zJIkYlfZBew0HFpvTwXkWyffj/3+oUfoU3QzQqVm4TCrPuxH3iUgCbyeaq4M3cbnuBSeoGnrH+mRs/8/MP8yJuPH5QND+JRHJErCmqwTAhPdZBqtcBa14XfxPOeBg8K9rLk3QAmqrhtrpJdaWSEZNBjC2G/Hg4feQglhRWUloXIMXjYGJuglSSPQwm5iaQHuugpCbQoupuOghVTMM0rDjS5mBP+px/L7dht1zJ1dPzo7lcieSIWLmzGjBwJCzDBI7NOZbqYDWJzkQSHT1nKkufyxRqKozMimVmfjIjs2LRzLDoFHPEiS7jhNy+7Rj8EM54oTCbOQ7N7uH68U6cBJql2O1KmHx2sM1M5dzQnZSY8Qwwd1Ly6s1inEUTnhSoLxfjXaRioCRaGLpIVh8oiVC8Gq14BSFT41XbGWR5ZdC5L7GPzdg7qFRbLIqc0kfJItkfQFMV7pw9jBA2NplZrDP641UamMB67ouczwPh8wA4N/gG3777b6FMWL1dKstKehwfrizEBAbEaSS6NNGQsbZR9XLC5TJ52Ys5oM0wTVEcWl8hxrxlTYDkQb27SLY9LDZxfU8ZIhRnM8eKZjx/mSicbbzeN42ahZYxgS4lyEB24iDIY/ps7g83Fjste7JFsXlvVEujPdkGNTs79aNJJD9IqE6oSdta/u7nrdnMFG0dOgq2glOjuDhJV7Nfm1FXIq6LaSNkkewPYXNBcj5a9njOnD6OFKUKL0LRKUZpII46fhr6DSuNPOIVH1duvYkvP34nyouWSA6fuWuKARidHYe16aJhGPDpXeL5oBMhSRZp9EbatRmmKYpkvRlCFU/mM9pHs0BcjvC/4vujBSq4drJI3jX5GV6lnjx2YSXMBjOHn4Z+h9+0Q+HX8OYV4u9MIulhzP1O2Izx/eJRWquML30cileJ+8wxl4gJMZJexQFjU0ZE+Btx/SB5sLQdTVjsEN9fTFZNGgSBOjR/CVcdlQO02Avxi8KdoUv4SB+PXYnwsPUf7PniERZuqYjGyiWSDuGDRj9jfP8EsWHpk+JxwExRFyLp8dg1OyOTR5KfkE9Mq7ikpipMyUvk9NGZTMlLlEWyh0lT/hv2shlYCVdNJLCnUVk25UMeXPgMS7fv6fI1SiQdxdw1JWjuLZhaFS6Li3Gp4zAMg8yYTLQelP/uc4WybYgEGp2CbKFs6kqI9op6BooCnlTIGsfYcVP4zbFZDHLXoyCCRjYlwgRPJb8/IYcLQ7dRasaRHtqG/+1fihGrINQ7PWli5GxlYRtFLomkywjWQsh34ELZFc8A8Lo+g+kF/doGljoIxWYl6+4byLrzahSbVM7sEejhFvuRNkIqYx8ks4an88hFY0mNdbGbJNaYuUQUC7NjNjJqxhncHv4ZAGMr3qPy04eEUm9VIZR9LxQPJJIewOvflgBwbkHjdWH1K+KeMykfcqYc8fEVm5Wsu64n685rUGy2Iz6epAswIkLVVLVBxlihECiTUSIREZsJmeNF4tqdLFRN/GVg6O2ODXQoYSZ7yvn3cVYe1U/jgfC54oUljwu1g3bP4xD3euUbhZqvRBItArXCHlhavtPmhvcBKI4bL0ZndjDCZvyCrDuvlTajJ+ArFder1OF9r5HiSHAlMHXacfzktFNJcltIVyqxEMGhhBjmaWDn5DtZqA/DozQwaf4VlH73WbRXLJEcFq8v3wXAeROyWzaufRP2rAGrG4bOFspoh4lis5L1f9eSddcvpM3oCfjLRMN28pA29xaS/WCPgdQCSBvBxH6x/N8xCSS5W2KwTiXENE8pf5luZaU5kCvCvyJoWmD9HPjy3iguXCI5dEzT5LVlwmacM76VzfCXwxf3iecjzhVF9oeJYrOSdec1ZN11vbQZPYXmBotM0bwsJ7jti80lphFljRc+RnKI20/IbBOXAkjzWPGc8Gsqc05CVUzutT7JvKd+R3V9KEoLl0gOH90weXNFEQDnjc+GPetgxwJRx5E/S9SEHCHCZlxN1h9vkjYjilikgnyn0pT/TottO40iPdbDX2ddy7SU2QA40uZwwYuPUFlfH41lSiRHRCCsM2f1bqxxSwE4KvMo/GE/ic5EEhwdU2up2GxkPfoIWY8+0qk2o+9eEcP14K+ExIGicEF2zh06Fjsk5zN+SjxjBm1k09btlJpe4jwxzeM8pg9M4uRHbuNl2z0k126BD34Np/4NrC7hiLkToGKzSAbFZkb7E0n6GsE6Ubyyv6BAyRooWkHY1PhP5HQeHtA5o7YUTcMzaZS4Llnktajb01wk208ETizSsTsUZg1P54Rhac3jPNKcJuPde9BqizjqsvO5+Uknf7H+l4QdH8M3Bhx1C1RuFY558mCpxinp1oT8tSzZLYKix+c6oKGqRU123M86JrCkaXgmjYRIWNqMnkC4HvwVorEisQ8qyB4MqiYKBF1JQum/slAUFjvjDjg28Og8g1GPnYEFg1usr8PCfwuVg4HH7XsOR6w4dtkGsI6VDS6S6OAva1PIUlcfZGboS1DANWJ2p5xS0TQ8E0cIJTRpM7o3DdXiephaIBRlJYeGZmHG5AkcNSqf79Z8S6B8BzHeOIb0z0ZToTT3Qb588VZmaqtQ3vwJxM8VquYSSQ+htDbAtgqRSDtuaKrYqIfh83vE8yGnCCW0I6DZZiiqtBndnUBNY4xkiPQvDgVVFeqythjGWTbwRGYZ62qdVARp42dMHhDh6GfgtsjlPGj9L3z9F0geCiPOivYnkEgOiq3lfsp9QTRVYebg5JYXPrsbgjWQMEAUPx1BwUyzzdCs0mb0FJoaLFJkg8UP4k4S/1Y1u5ho28K4dBtra5xUBvQ29oK831H5dQIJ37/Er62vsuglL5Mve7BD4r8SSVfxXVENdcEITqvG1LxE+OB+8ULmOGEvOgBF0/BMGC4mLEmbIenF7J3/TvE4mJibgKYqHD/0D9z0aZCFpR/jSH+T2z5J45+zL8eqysYVSc9h+fYqwmYdMZ61AMzInoFpmmTEZKAqHaPRqlgseGbO7JBjHYi+qSgbboD6SlEgK8dLHDkxyWiZYxkydATTM2Bksto8zsNuUbjt1BFcELqNCtMDZd8T+eiOljHaVhdYHUItsKE6ah9B0kfxlYF2gCLHb8UY39f06VRbU8hPkNeKPk/r8USySPawaT3OY9LgLLT0kZCQhztYzoyZJ3Fd+EbCpgabPyUy/1/gToSKLeJHKpBLujEvL9oCQJxDoX+cBVY1qskmD+4QNVlJDyNYB/VVkDQYUkfIJPYPoargTRfKHakFEAlCbQkakXbHBsY6VO6aEcs/9TN5OnISAOaX98GuZe0f350iitfLN4Ghd9GHkkgaCdULf7fVJIsF8z8nSamljHji86dGb22S6BOuF3Gq5CEiKSs5bDRnLKPGT2fSlBkUpLnQ/CVgmqTEulg75nYW6sNwGA34njyNyJ4N0V6uRHLQ/G/hdgDykt3EOhsTaateEhNYnPGi4EkWvfQNIgEI+oSPGZP8w/tL9sWVAJlj0BIGMCIuyMx+jjZ+RrbXwhVj3Lyuz+CxyCkAmO9eB2Ubo7hoieTgeX6RsBkFGV4c1kbBgd0rYcX/xPPRP5H3nH2NkE88Jg858HRFSQuaFRJyIXMcWkwqI71+ZubGtLEXKAoJM65mTvzFAEze+QRfPHIDpmFEb90SySHywmJhM0Zlx6KG/bDqZfFC3vFSeVoiOQxa57+n5CWiqaJ5wmV18bfj7iZWn4SimHxT+RjXvfkausxTSHoQLy7ZgSV2BSg6A2IHEGuPJcGZ0GFqsl1J3yuUjQSFqlNiPiQNksp0HYXVKRLaqQUiweMva37pmP52Jhbkc1no19Sbdiy7l1H3yf1gNjoLzngR5Cv/Xvz/SCRdQageAtWig609KrfCzkUYKPxXn81FI1w4rZ1zyTQjEao/nkf1p4sww+FOOYekAzB0qC1uGU8ki2Q7Ds0q/k2Th3B6lh/HgCncGL4O3VSwbPqAum8eBVe8GJtdtS3aq5VI2sXUdV75dg8Al46KwRKshnVvixc7SE0WmmzGfKo/XShtRnemoRpCfnFvnCynVxwSFhsk5gnlAneCsL3h9kcRXTTCxfG5Tu6OXMwcfTKKEcH4+A9COXZvFEUo19bshOodnfwhJJK9CNaK77FVqBkbpkni9g8A2JV+IkonXSPMSITqTxZQ/Ym0Gd0WPdwYoxoo/AzJkaOqEN8PMsaC3QO1RWBEuHJCIk+k3Mq3xkBijDp2/ec0qstLor1aieQHCesGb6wQI7SvOLpR1ckwYMG/xfPBJ0NsxhGfR9iMhVR/skDajO6KaYim//hciM3+4f0l+8fqhNRhkDocwgHwlbbkKoBfTvYwLt3K/ZELmK8XoIQb0F+5SOQ9JJJuTCCs887K3UArm2Ga8NFtgAn9j4acSUcco2q2GR/Plzaju2NERBN3Qp5ssDgcnPGQPkrUEzRUCxGuvZh52qU8br8EgGNK/8czf7yMQCjSxQuVSA6d2kCYuWuET3zl0QNELiNUB54M6De5w85jRiJUf7qI6o/nSZsh6dO4bW6eP+M+LMHBKGqY+TX/YtpfXyUUkcWyku5PuS/Ip+tKsMYtBYSarG7oZMZkdpiaLIAZDlP95ltUv/lWp9qMvlUoq4dF0CNhACQNFMFzScehqqLDLn00qDYxMtU0UBSFe4+N47xjxnFN+CbCpoZnx2eUfvoPvtxYxupdNeiuFKjbI9UCJV1HsFYEN63O9l9f/QoAH+gT2GGmcenozlOBM8MRiv/6NMV/fwEzLB3obolpCiVZT5ooepJKLR2PqkHSQJS0Av5xlMHYidP5ZfgaDFPBs/ldSr96jHk7g6xfvQy9ele0VyuR7MPW3XtYW65jU+GSkW7RfR0JiBGN2R0YWApHKH7oGYr//pwMLHVXGqpAD0HaCHFvLEeuHR6uBEgfIxR5A7XtJiM0VeHxU+O5ZXIsvwxfwzf6cNRIgPDcW1m4er3wM1oLeWg2UTRVsbnd40kknUZ9pRiR3Hg92LhtB+ON1QDkTz2t004rbMazFP/9f9JmdEdMU8So4rJFnErai47FlQAZY8CTDrXF2BSdJ36cxbyhd7DTSKa/uofqp89l0cZi3llZxMItFeiGjEdJuh/LtlVRXBMgwW3jzDGNBfWbPxWCA1YX5M4Ei+OIz2OGIxT/7X8UP/SstBndFX8ZuJNFU5m0GUeOqjWqBY4VCos1RcKPA5xWldfOTuLikR5uCl9HmRmLVv49e16+XtoMSbdm/uZyKv0h0mMdnDw8TWzc+gVsnweqFUacvX/hkEPADEco/vtzFD/0jLQZ3R3fHtGQF98/2ivpuVhsQo03fZTw62tEI14THrvKZRdfytsJlwFwqfkWG567iYWby6XNkHRrvthQii8YYUCym2MGp7SoyfY/CuzeDjuPsBkvUPzgk9JmSPo8/ROSeee8f+AwM1EtPupiH+PfXy9m4ZYKaTMk3ZpP1u0hbN2GZi/FrtkpSCwgwZFAvD2+Q89jhsMU//73FP/+951qM/qOtJFpNI7LzhaqTlJJtvPwpIoistL1ULMbvGmgWrhwuBtNmc5vvqjlb7ZHSCl8h9c2mnxgTCQpxs7Pp2UwWS0ERyzESiUVSSezV7K6Db5S2PQpAI9FTiUv3kJajLxm9Gnq9oAzDlKGgfXIE1CS/aAoIminalyhrOUJjuHWpWH+bH2clMJ3eG9TgM/0MSTN28ZpPzqF48YNi/aKJZJmbn1HKFgOT7ESr/ph/bvihXGXyCRmXyJQLZrzUofL+9mOwGITvpvDA6XfQ20JeFLEPVwjiqJw/UQPYcPk50tu5nXl/xga3EnKgrv5TfhKPDEerpqey9S8xtGSDi/UFYvRqZljZPOLpPPRw6KwxeZq3rT8i7cZAqyxjWJEorxW9FkaqkShQuIgqTzeWdjcwiajQG0Rijed62fk8HDoDi7e+hv6+1ex9H9X8evI1YBCeqyDO2cPY9bw9GivXCJp5roXVwAwJS+xZYT2gn+Kx7zjIC4rSiuTdCnhetFgkThQxqU6GncS2MYJ/6B6h/AXHF5UReGumbH4wwY3fn8dz1vvI3XLa3y+Ppl3jKOkzZB0Sy5/dhkA0wclY9FUcd34/B7x4qATxX2npO/QUAVWt1BDlf7GkaEoIs5n94ipd7W7hUJvY7OSpiqccfYlvPSywgW1TzJ653P87Wk//9DPBpA2Q9LtME2TG19eCcDM/BTU2l2w7Rvx4oAZMp8hkXQiObGZPHfa3zn/nSvBXs7jG+7nn59eCIYHkDZD0v0wTZNb31yDPV2oyU5Kn4RFsZDuTkfroXWXfUdSta4E3Cmi60uzRns1vR9nHKSPBK9Q7kAX1d6Z9no+NCbwQPhcAK62vMcIZSsVviB/+qiQpbvqhZMRqI3i4iW9Hj2yT7K6Dd+9AabOImMoq8yB1ASN9veT9A385SIJkVoA9s5TFpY0oigQlwNpIyhwVvCuPoU/hn8CwGWWjxitbqHOH+Cx19/jk283RXmxEolAD4dZskuMYAzpwNq3hWp5Ql6HqslKujnBOggFhL2QRbIdh6KANwMyR4uGuprdbZQ7mhiTECSElStCv6LC9JCnFnOL5XUqfQ3cN3cDC7aUt+zsTgF/KVRuldMsJJ1PoBZCflGwBwRCEY4JfwXAPNfx0VyZJJpEguJeIWmQ9DE6G6sD0oaLxvnaYjAi5GencWP4OnRT4RzL11ytzQGgpCbANc+v4MPviqO8aIlEUFMfptIvFC69jsZ49u6VIomtaDDwuP1PSpL0Hgwd/BUQnyuKOiUdj9UpbEXacKEqW1fS7CccnxVhkTGMf+lnAHCf9UlylWJpMyTdjt3VDc3PU72NDaEbP4Si5aKYb8jJ+8+HSHofkSCE6oW/4eg4Zcg+j8MLaSNF44q/Yp9ctmfELO4I/xSAm61vcr72GSD9DEn3Y0uZv/l5doIT1rwqfkkdLhT3JRJJpzIkYQhnZf8KU7dhcRdiT/0A1CAgbYak+7FyZzWoAazeVQBMTJtIrD2WBGdCdBd2BHRJoex//vMf+vfvj8PhYNKkSSxZsqQrTttCfaUYRZUyRAYPuxKbW4ycjcuGuhL0cIgnvtlGLsU8pp/C2/pULIrBrdaXSKMCgP8srkIP+qF8kyhmlEg6g2DbZHUbQj5YL5Jk/42cCsA9x8R25er6PFG3Ga0J1glF8pRh4OxY6XjJgdE9mdy/XGUc3/OsfhJPRn4EwE2WN0lTKvAqDbz8/qfooUCUVyqRQGFRi8N6+1S7aLgAGH2B7L7uZLqNzQjXi+B4yhCIlapenYIzHjJGiUa8upLmRjwA3YBn528jjyKKSeSq0C0ETQvTtHVc2JiUeOzrQvSm3idVE0UGldvEGEKJpDMJVIv7SVUo+OzeuJwMpZIa08XRRx8T3bX1IbqNvQBReOMrhbh+4JHqEF2CxQ7JQ8GTjl5bzAsLtrLLSOSeyEUA/NbyCjPVb2lqnbhrzjo56k7SLVhbXNP8/LJp/cWThf8Wj/2mQtLArl9UH6Bb2QwQzf7uZFG0IP3LzkPVxL9x5jihGFhbhB7ReXmR8DP+EfkxC/VhuJQgj1r/hg1RxC5thqS7sG53S8HeOeOzxZN5fxePg06ShU+dRLezGdDob5QJMQpPRrRX0/uw2CBlqGiuCDeIGgREbOr1JduYrxfwr8jpAPzJ8hTHqsulnyHpdqzd3eJnnDw8DVa9LH7JmSJqaiSdQre0GZKooBsm7y9101ByKqapYItbgdW7FIhImyHpdqwvrsPqXY2ihkl1pZPhyiAjJgOr2nMFSju9UPaVV17hlltu4c4772TFihWMGjWKk046idLS0s4+tSDSILqAkwcLlVNJ12KxQ0oBxOeyaetmanw+VMUkjSp+G76KlUYeXqWBP1ifx0mAcl+QtbUOqNsNNTujvXpJb6WhWqgxqO2Mm1n/HoTr8bn78aUxisGJFk7KkwX2XUXUbUZrIkFR9JSUD57Urj9/H2dJYSWramPYSiZ5ym7uifyE9/RJWBWd26wvYieIVl/Cd6uXgSFVnyXRZe02USg7Js3KJN/noijKkwYDZh7agYyIKND3lwvFs9oiqCkSj7W7RVFNsOaHj9NH6DY2Qw+JoHhSPsT379pz9zWaGvFic0SxbER0Wa/dXUO5L4hF0UmmiuXmYH4fvgKA8y1fMUldJ/yMVkFgrE7QNNGgF25o72wSyZFjGOK72qphV9n0MQDLnEcxPF0qiXYF3cZeNNFQCY44SBwAat8Z9BR1GqeErKtxYq0vxqGEeU+fxIuRY1EVk39a/80AZTcmUFwTYElhZbRXLJE0Fz2dVJDKoFQP1OyC794ULw46EWzSjnQ03c5mhPygqEIR0GKPzhr6Gq4ESB8NMSls2ryRSl8DdiVMPHXcGL6OctPLYHUXf7A8J22GpFuxttFm/HhsJtkJLqEku3ORyIEMOkEWPnUC3c5mNFFfISbyJOZJf6OzUBQRA0wfJRpj6yuaY1NOJcRTkVm8GpmBppj8y/of8pWd0mZIuhVNNuPiyf1I9a0T0341O+ROj/LKei/d1mZIosKSwkpKaoLoteMJlc8EwJ72AZp7M2BImyHpVqzdXYMldjkAR2dOw2P3kOhMjPKqjoxOv0N+6KGHuPLKK7n00ksZNmwYjz76KC6Xi6eeeqqzTy2SUpGQGEskVTqih8UGKUMoVlJJVmqxEiZO8ZFALVeGbqHYTKCfWsYNljfRTYXKgCGcuIrNzZ14EkmH0aTeY2un+NXQYe1bAHzlPQ0TlYLkntsJ0ROJqs1ojRER35P4XKH0JOlySuuEUuxukvCZdvKU3fwyfA2LjSG4lSC3WV7Cb9gJlG6B6u1RXq2kTxMOsGR7NQAjk1RY3dh9PfK89hsy9sY0RVF+TZG47hiGUM5MHCiajdKGi8fEQWJcvHIQx+wjdAubYRpQVyoKNxMGSIWnrsBih9Rh4t/bVwqRIJX1oeaXk6ghmSreMKbzRKMa+c2WN0ihss1+ALiSoKEKKrY0j1aVSDqUYK1ogLA3FjKF68konw9AUfrxUVxY36Jb2IsmIkEIB4QKZHsTTiSdi83FdmsuZWYc6UolaVTw98iZLDXy8SoNPG79Kx7qgRZ/RCKJJosbk2IFGY2TjhY9AqYuRv6mDY/iynov3cpmmIaIjcfnimkIkq7DHgNpIyk1vaQq1YBJGpWEsHBL+BoALrZ8xlR1DSBthqR7sGSbmNrYxmYA5EwV/rOkw+lWNqOJSFBM4EkaJP2NrsCbLhq6TRNfVUnz5gwquD9yHgv0YbiVAE9aHySOOkDaDEn3oMXP8MKqV8TGrPEQkxLFVfVuuqXNkESNFlugEio/lnDtMBRFx5HxGoq1vJ39JJLoMX/7Biyu7SgoDE8aTro7HbvWsxt5O7VQNhQKsXz5co4/viUBpKoqxx9/PAsXLuzMUwsURRTIyqR19NGsODJHsNNMJlmpxUKERKWWWPxcG7qBoGnhKG0dM7TVxDutYryRHoGKTaLYWSLpKIJ1ImHdnurGjgVi/K7dy39rJgEwo1/Pvsj3JKJuM1rjKxX2I2mQ7LqOEikeR/PzIlIIozFK2cy1oRvYYSSTrlbyC+u7xHjiRLerT3ZdSqJEQyVf7hAj4M9xLBHKgY44GPyjH35vsE4UyJqGUCPNnixGqGaNh+R8MRYvvr94TM6HzDFinyYstk75SD2BbmMz6vZATLKYXqHJIuYuQ7NC8hBIyANfKYn2liJXRYFUpYpcinggch7LjHzcSpDfWF9rs1/zzjHJUL1D3ANKJB1NoEaoTmuN1+vCb7CbAQqNVAYNGXl4xzRNiASEDQnUiMdwvWj0kuxDt7EXTfjLITYbYtK6/twSABLj41lv9qPEjCddrSJVqeLe0IXsNhPIU4u53/oYYLbxRySSaGCaJl99XwbAMYNTxDV/+bPixUEngd0bxdX1TrqdzaivFE2UcTldf24JWJ3YM0dQazpJpBZFgSyljG1GKs9FxHfkAevjxFAvbYYk6uiGyfzNolB25uBkMZWoURCEQSeATarJdjTdzmZAo0hMmfA3PNLf6DI8aZA2kjiXnWSqARNFgXQq+VP4QnYYyWSrZTxs/QcWItJmSKJOfSjCqp3VABwzKB6+e1280G+qnGDQSXRLmyGJKm1tgZVA8RnowWRUix9HxhuA3s5+EknXU+ELsjuyAIDB8UNJdaeS5Or5jbydms0tLy9H13VSU9uOrE5NTWXDhg377B8MBgkGg82/19bWHtkCvBng8PbpIoLuxMS8FOo8AymqgyyllBIzHocSYpeZzB8jF3OP9Wl+ZXmN6vAUYLRIWtfuBucOobYikXQEwVrQg+3f7H8ngkfl/U5m9WpRHDk4sfMVZRWblczbfg56CMXWdxVso24zmqivAKtbFD1J+xE1JuYmkB7roKQmgInCDjMVq6LjJMiV4V/ypu1OpmjrCRa9DannQ9n3YoSYXY6elHQhpsmy73dQ5FcAk/xdjUGl4T8GywEcWCMiivIsTkgtEOoD1naUzttBcTjI/PvfxHNb371GdQub0VAF9lhRsGmVAYsuR7MIW43JMHMraW6NPX6dplJYtxLEYYa4LnQD79t/zxB1J5GtT0G/37U9jsUhCm/LN4mpFgf5tyiR/CCmKa71ra4PFas/IBF4Uz+aS5MO4b7fNCHkEwrkIHwZ1SoaukxTKJQ21Ar7YrGJ+yGrS/gZv78KDKPP2oxDtRfQiX5GsE783yX0l814UWRibgLxsbFsrMnBgk4K1exRErgldA3/s93PKdoSlhofMaH/ydFeqqSP88rSnYR0A4BBqTGw9GEI1YmiyaxxoGodej7FZiXz1itAUaXN6A42Qw8JVcDUAulrRJHx+TnUefJw163DRYB6HHiVeh6KnMV0dTX91FJus7zAxNyzo71USR/n4S82Nz/vn+iGzx8UvkFqgVC77GAUm5XM310BqiZtRnewGU0EaoQvmJArxaO6Gk8qg8ccjeur7aT7qyghDhTh810dvpnXbHczVVvH7eZzTMw9LbprlfR57pvbco1KLZ0ncqLOeMia2CnnEzbjMrA4pM3oTjZDElXa5sABw0tg99m4cp7C4tqOPeUjgqUnMzE3IdpLlfRxbn1zNdbYbwEYnzaWZGcybmvnTS1QbLYuyYF3q8j8fffdR2xsbPNPdnb2kR0wvp8w7JJugaYq/OG0kWwys9htJpGmVKOi48XP8/rxvKVPw6IYeL+5W3TMqxq44qFyq/hdIukI/GWiGGJvKgth9wpQVF41Wzq6cmI7NvHQHoqm4Z0+Ae9RY1AsUo3uYOlwmwEQbhAq1smDRaOFJGpoqsKds4cBIEoQVQrNdDQMvjdzuCV8LQD2DW/CrqUiEFixSaiRSyRdRbCOF1ZWATBdXY2teososis4c//vCddDbTHEpAvl2MQBh1SYp1gseGfNwjtrlrQZh0Cn2AzNIpR+nXFHfizJ4aFZIHkIWkIu1453YyFC61RQPHXsIYEbwr9ANxUsmz6EDXP3PY4rURQ+V20TRYcSSUcQ8on7k6ZJFr5S4itXAfCWcTTxjoMMx4T8ULNL3OMk5kH2RMiZAv2Pgn5HNT5OhZxJkDEaXEkQqofqnSjhOrxHj8N79DhpMw6BTrEZpgkN1RDXXxTlS6JGk58RwM4mM5sANuLwUWQm8afIRQDcanmJysJVUV6ppK/z+Ddbm587VAMWPSp+GTSrU+LdiqZJm3EYdIrNgEZFwEypQB5lNFXhhtOmUkgacYoPK2KajIsgvw5fjWEqXGD5goa17fgYEkkX8lgrm6FFGmDZ0+KXgSeK6Y0djLAZY/FOHy9txiHQaTYDRGF00Cd8RikkERW0mCTOOOUUKvGQrlSiIhqeqs0Ybm7MZfzU8gn6qlejuUyJhOcWbW/5ZdVL4jFnaqfFuBVNw3vUGLzTJ0ibcQh0qs2QRJ29c+AARqAfgZLZANgSv0aLWUdEThCTRJlPC5ei2iowDSujkkaR7Eru1PN1VQ68Uwtlk5KS0DSNPXvajrHcs2cPaWn7BnluvfVWampqmn927tzZmcuTRIFZw9P550UTqfUMpNiMJ02pwqUEmOIu4ZXYK9hoZGILVlL/0d1g6CKpaOqNxU/haC9f0tMJN0B9Fdja6XJY97Z47DeN3Qi58PwECy5rt+on6NVE32aYQq0jcaAcTdRNmDU8nUcuGktarFBP0dEoJolpriKKvKP5W/gsAMx5fxeFKDW7oHr7AY4okXQwDVVgCkf1ZvdHYtvgk/efhAjWCTuUPATSR8lCmSMg6jZDtYpiZ2/GkR1HcuQ0KsuOHTmS22ckkuRuaYjyKA1MdhdT4hnBQ5FzADDm/xOq9/r/VxRwJ4pCWX95Fy5e0qsJ1Ail16ZmiE2foGKyyBhKwYBslB9S+DENqCsRRa/JQ0QhbMpQiEkRiU/NIlRJVU2cw5UAcdlCZTBnMqQOB9Q+/50+VHsBnRSbaqgU/0fxcnx2d6DJz3DHJrLZzMKuhEihku/tI5nPaOxKmNL//YyKmrpoL1XSh0n1Cj/4vPHZsPZtqN0FzgToN0WORO0kuo3NCNeDZoP4/lKBvBswa3g6v73wRzS4sklWalDRicOH6orjJeUkAPyvX0dddVmUVyrpywxIEvmOq2cMgG+fh0C1iFdkdrwCuUTQbWxGE/4ykdPwyDhVNDlhzCCuPu8MDFcKqUo1YJJALXXOTP5rnA5A+J0bCezZfMDjSCSdSZPNuP24dPj+Q7Gx39T2RaYkHUK3sxmSbsHeOXCASO1YVN84AJwZrzLrP28RihjRWqJEgidJCAn0c40kLSaNOHtcdBfUQXRq24bNZmPcuHF89tlnnHHGGQAYhsFnn33GL37xi332t9vt2O0y0NfbmTU8nROGpbFs43BCu1aSrPkZNDCffy/zcc2Sm3jX9gfcZSth9Ssw+kJwJ0FtiUhoJw6I9vIlPZlArQg2O9Pbbg/5YKMocKoddDrvfNQAwA0TO77buj1MXafu66Wgh/BkTkLpo75I9G2GKoJJcjRRt6LJZiwprKS0LkCKx8HETBsPvLWIf646k+HqNk5gOXx+D8z+G1RsBrsXYjq3o0kiwTAo3r2LD7ar5ClFjAmvBBQYflb7+wdqhSpgaoFIeB7mdcaMRKj79FMAPMcf32e7sKNqM1RVNFU4YqW96C5YbJA8lLEFEZ7ILmatL4bKhggJLhsFGbHc8VU1D685janqWqaxFj7/I5z+n7YBYKtLFLNXbBH/t5a+OQpM0oH4y8DS+B0zTUIbPsIGvKEfzeWjf2A8kRERRbLORDHpwJ14aOe2e8DuwXSlUDfnNYiU4smeitIHc+SHai+gk2JTqgUS8mRxWzei2c/YWkH97rVk6rsYlJfP3R/FMWzXjQxXt7HqrTtJ/NlD0V6qpA+yoaSWRVsrALhkSg7MuU68kH+SaJjoBExdp+6b5aCoeLKmoFj7XnCq29iMQLVoeJGT8roNs0ZkccLgi1i3Yj71lbtwJmZRkJnA9e9nMLloFXlqMdtfvRHPVS9Ge6mSPsi3O6pYtasGgAvGpsOL/xIvDD5FNGp1AsJmrABVw5M1uU/GprqNzQAhEIMi8hpa3/u/6G6cOHoAxw25hPXLv8FXW4krMYuCjFh+9nYWY0vXM0HdSM2LF+G4/msZe5J0OQs2l7O13I+qwDnOZaAHRa4iZWinndPUdermfQsWB57z+6YSebeyGZJuRXs58KEZMzj2xQsIW3awx/40H6wbyekjh0R7qZI+yNw1O9EdK1GAY3PHk+ZOQ1U6t5m3q3Lgnd6SfMstt/D444/z7LPPsn79eq655hr8fj+XXnppZ59a0o3RVIVJQ7I5+qjpDOmXjtZQxqWj3XhS+nNn5GcA6EuehLLvRULJGQuVW4Rym0RyuDRUioKWvS/g338AkQDE9+dfOwdSFzJJdqmcmOdo/zgdjBkKU/SnRym6/ynMUN9WTo6qzYjNhNRhsmuyG6KpClPyEjl9dCZT8hLRHB5+fsJI8uI0fhm+ml1mEtTthoUPi7G25d83Bgglkk4kUM0/F1cT0OF658diW/9p7SuMhnwdUiQLYIZCFN10M0U33YwZCh32cXoDUbUZMckymN3dsDogZSiaO4mRsUFm5iczMisWTYWbJnlIi7Hwy/DPqTbdUL4Rlj+z7zFcSeAvFQrlEsmREKpvnGTROPKybAO2uh00mDY2x05lQsYBrh96GGqLhRJQxuhDL5JthWmqFN3xAEV3/wMz0ndHdUU9LqUo4E6BmNSuOZ/koNFUhSkDkzhu2lSGDBqC5ivhmun9uIfLASgofJoFX8px2pKu58GPNmKYMCYnjoKGZVCyGixO6H90+1OSOgAzFKbovicouvexPu1nRN1mgLAZcf267nySg0KzORgxbiqTRgxhpLcBTTG5dUYyv9GvQTcV+u1+n+8+fT7ay5T0QR748HsAjhqYRP+Sj6Bmh1Agz5kCNlennNMMhSm6/wmK7v2vtBnRthkA/gqIzQbX4fuOko5Fc3gYPmYykwelMzJBR1Ph7mMTuTH0C6pNN7E16yl+4zfRXqakD/LnDzcAcMKwVLwb3xQb+00Tk4s6CWEznqLonoelzegONkPS7dg7Bx7n9HL/9HswIy405y5+/cm/Wbp9zw8fSCLpYP70xZsolnosppcpmWNJcHROE15ruioH3umFsueddx4PPvggd9xxB6NHj2blypV8+OGHpKbKBIEEodaUWgCKiteo5YUzE3ldn857+iQ0dMreu0sUO9k9oIegYisYerRXLemJ6GHwle6bUDANMcIO2JUxizc21APw05FubJpUietqomoz4vvLMeg9iPikVF7/WQG1xHBd6AZCpgaFX8H2eaIwpWKLKJqVSDoB3TBZsX4T7241iMXHqXwtXhh+9r47RwLQUCPGZsflSAXSDkT6GZJ9sMdA6lBRNNtq3HyyS+Od85IpIZHfha8EwFz5Iuz+tu37VQ2ccVBVCIGaLly4pNcRqBENElYXugHl384B4CNjPD8Zm4KyP1vQpCQblw1pwzstsd3XiKq9UFSweSAxT47P7s5oVkgZAs440pRKbrpgNm/rU7EoBqmf38I7SzZFe4WSPoJumMzfVM6n60US7JoZeTDv7+LFQSdAbFb0FtdHiK6PoYj7WG+mvAforlidkFIgfAZfCdleC3++YAr/1WcDkPrNrXy9cn101yjpM+iGydcby1jYqEB+3cwBMO9v4sXBJx/ctC0jIiYg+cvBt0f4Ir5SqK9onM7XIPInknbpFnGpYK1o0IzvJ2OO3Q1XgogHR4IQqic3zsJj5w/l1+GrAUhd9wzrF34Q5UVK+gq6YfL5+tJmBfJfjrfBjoUiZpE9GTQpCNHZdAubIekxnDhoDD8dIibL2BK/5vxnX2Ht7uroLkrSZ9ANkw/WFFOhLARgXOpY0mPSsfUiW9ElUfpf/OIXbN++nWAwyOLFi5k0aVJXnFbSU3AnCUn/cANuAjx9WiK3hS9nt5lAcng3NV/+W+wXkwJ1xVBbFN31SnomgVqh6Ld3oWzRcqgtoh4Hly3vT2VAbJ63ZhMLtpTvexxJpyNthuRgiUvN4W8/SmOVOZD7IxcCYC56FAJVUL1d2AyJpIP58LtiZt7/Eb98bRX+iMoF2udYjCC+mP6QPqrtzkYEfGUQn3vESrKS9pE2Q7IPznhIGSaSecG65s0pbo3bjvLyoTGRlyMzUTDRP7+3zT6AaNCLNEBlIRgyISg5TOorQNVYsLWCK59ZhLrtGwDe0afy9qJ17fsZpgl1e4QyecowsMjxah1J1OyFqkLWWBH3kHRvbG5IHgymSbYjyJah11FixpOnFlP+7u1U+fuu8o2ka/jwu2KO+vPn/OTJxc3bXnnrTdj2jZi2lXecuE+RdDpRsxkWm7gOJQzomvNJDg97jLhX05xQX8nABCtbBlzEBiObZKWW2jdupC7QtyeGSTqfJptxyVNLmre9/vKTULYerG7of1TLdIv2CDdATZGIWYHwo71ZQs3akyYavUA0/9UWi33rK0TBn6QNUY9LhRogob+8R+iueDMgcRDUV4IeYniKFX/GVF6OzERVTNwfXE/ALxu1JZ1Lk8247Nmlzdu+fL2x9iJ9tJi2KekSom4zJD2KW6b8BFvDZBTFxJHxCqf8+xNCEZmvkHQuTTbjmpfmYYkRKuTffp/At4W9y8eVchaS7oE3s9lZmJSmUEMMvwxfg2EqxBa+j1nYGBS2uYVKYNAX7RVLehqBapF8Vi1tNlcsfQOAz/TRlCDkwjUi+OvruW/uBlksK5F0ZxSFY8cNA+ApfRYf6eNRjDB89WfAhPJN0l5IOpQPvyvmmudXEKitoBY3FiL81PIxAI9XjWVBo4pHM77GgqekQVJFTiLpSjypkJQvGqUigebNZw5xAnB35BK2Gmlo9WWw8D/7vt+dLJrzfHKkkeQwiATBX8bi3UHum7uB9PoNJCh1VJkxLDfyqfEH2vcz/KVCnSxlqCySlUiiRUwKJORBfSXnj0luViG/VPuQvzz2VJQXJ+nNNPkZxTWBNtvPC74OQFnSJEjIlY13fQFXgvQdewKuBEgZ3KgS6OcXkxP4ZfjnhE2NU7XFPPd0Oz6GRNJB7M9mnB8SeY7i1KPBm96+zTANoRobqBVFsdmTxMjtrPFiokXqMNEEnjMJ+h8N/aZCzmQxFdIWI95XvVPkWiTdg5hkkV+VdE8UBRIHiCljdXvANLh9eiz3RC5il5lEjlrGqid/Ee1VSnox7dsMkxPDXwJQ6BoBDm9U1iaRSA6Mpmr89bhbMYJJqNZaHOlv8dCn30V7WZJeTGubYfWuRlF19GAS1dXp/PLljXz4Xe8RKJNRF0n3oJWz4AqUsvHaVBYaBfxXPxWA2k//QqS+WoxFD/qgapscqS05eAxDOKFWZ5vNuq+S2DLRdf1hZDx+HADkUErTt+uxrwvRZXOORNJtiY1xsvGOGYDCr8NXUWwmQM0uzFWviOBtxRapCCjpEHTD5K456zCBRKqpxsMsdSnpSiVVppuvjZFtbUZ9pVDxSMoXykASiaRriesn1Jx9ZULdGUhyaWy4Np16HPwq/HMMU4GNH2JuX9j2vZpNjOGu3CIVcySHTqAGPejn0QV7MIHp6hoAPtAnkkpl+35GsA4UTYxl3HsChkQi6Vri+4E7mUytmv9cNatZ7enqqr/y4JzlmDIWJelgWvsZrRmtbOZEbTmGqfBsxRB0q1SKk0i6FZ70RuGPKnJjdF64ckpzLuOskr/x+CffRnmBkt7I/mzGeGUDE9SNhEyNJ4tz0a3tFD0ZEajdDY64lsJYdyJoln33BVG0b3OLyQgJuaKoNmcSpA4H1drRH01yOFjdokhWxh27N6omFOPdKVC3h6FJVj6/PJdfh68GYFLlu3z6zgtRXqSkN7I/mzFW2UR/dQ8Npo0nCuPR2Y8dkEgkUWfmoAHcO/0OTFPF6l3DE9++zWvLt0d7WZJeyN42wxIr/Fm9Pgcj4gUU7pqzDt3oHXFRWSgr6T6oGiTngzsFW30pn16UzN8iZ7PByCbWrGX73IdEQW1MEtTsBF9ptFcs6SkEa8WPve24odLl72DBYIORxUYzGx0LCgZORGedCZT7gqzdLUefSCTdGZsrhncvL6CWGH4Z/jkAyvp3oaoQandB3e4or1DSG1hSWElxTQAnARyE0dG4zPIBIIqfwlhabEYkKMbYJeXLjmyJJFqoqlBz9qQLZdjGwiaHReGVsxJZYebzpP4jAAJfPgihvRTIXYmi4L2mqKtXLunp1FewaY+PMn8YCxGmausAmGtMxIYo2m7jZxgRCNRA4kCRhJZIJNHFYoPEPDAN3EqAjBOup8hMpJ9aSvLi+/hmk5w6I+lYmvyMtpjcan0RgHnGcJbWp7G2uLbrFyeRSPZPk/BHfH/wlRJnh9xjLmOLkU6qUo37q7tYvr0q2quU9DLatxnwc8scAOYbw1ldn7ivzTAiUFsMngzIGC38jkNVKVcUIWSTOAAyxx/mJ5B0GBaHiDvG9Yv2SiQHg9UhlMgtDqivJMWtcfrMqTwdOQmAghV/YOP2HVFepKS3sT+b8WPtGwCWGvlsrnfLHLhE0s05bfBMxnjPBMCR+g6/efsrtlf4o7wqSW+jtc1QrOVYXNsxTQXdPxhTd2MCxTUBlhRWRnehHYQslJV0L6zOZmdhoNPHw6em8uvw1URMlbzKrwlt/ko4EooKlVshEor2iiU9gUAN6CGhDtaEaRK74yMAPtLHU48Yb+okiLpXjKiyXn7PJJLuzshB/fnHqZksMIbzZEQUPpnzHhJ2onyzUCOXSI6A0jrhIMTjo0GxMUbZxFh1M2FTY64+sXm/Sr8YuU1cP1GgJ5FIoofFJlQ7bB6or2jePCnTzr3HxvLXyDkUGqk4gxUYCx5u+15FBWecaLoIyMIUyUGih8FXSnlEKCyNVTfjURrYY8ax1ui/Ty66sj4kGkA9GRCbHYUFSySSdnEni8InfwXTB8azcvDNAPzU8gmPPPM0ETl2RtKBNPkZrTlW/ZZJ6gZCpoW5+gT8OGVsSiLpjqiaaM5zJ4NvD6cMjWX5wOsBuNDyBX97/EmMXqK4I+ketGcz8pWdHK99i2EqfKaPpR5HW5thmmLanicdUgv2mbp3WEgF0+ijKEIRWJVp/h6DM17EqCIBiAQ4f7ibPQWXs9VII12pZNMz18npFZIOpT2bYSPMqdoiAJYZ+fj3thmdQaC6c48vkfRyFEXh8dm/w0MeihbCkf46P3t6obQZkg6ltc2wxq4AQG/IQg+mg2lpd7+ejLyDlnQ/mpyFcIDjs02umTWOR/XZAPg+f0gUPboSRRFKza4oL1bS7TFNkXy2OtpuL16Jq6GEetPON8YIahEjTh3s6xAkuDo38KNYLaT/8lLSb/oJilWOuJBIDpfTp47ggePieSByHhuNTJSGKlj2pChwqiwEQya0JYdPiseBgkGqUskeM4FLLB8D8LUxgmpaxqCmWBrA7hUKG50QrFasVtLvvZf0e+9FscpRdxLJD+LwCt/CiECopdP6wuFufnVUMr8JX41hKqgb58LOJW3fa/cIdeiq7c2KtBLJAQnUQMiH1xsHwHR1NQDv65OxNqrJtibJ2tjMlzhg/yNPjwBpMySSw0RRID4XXEngL+PkGdNYnSDUnv5seYx73loa5QVKehMpnrbxKhWD31peBuAzfQybzCygi2JTN19C+i0/lTZDIjkUrA7hb6hWCNZxznFTWBZ7IgB/VB/j3x+vifICJb2JvW0GwJXa+wCsMAey3hTNd21sRn25iFOlDNk3R3KYSD9DIjlMvBlCXMFfDqbJb6Yn807GTeimwinm13z+5hPRXqGkF9GezTheXU6c4qfc9LLG6A8onedn6CGoKUKxWEn/w69I/9M90mZIJIeJw+rgsZP/hGLasLgL2aV/wRsrpBK5pONosRkm1thVAOi+fMxIzH726xy6ys+QhbKS7ok3AxJywV/OyXlW/hn5MRuNTBKoIfj1P0W3drPCkxwJIDkAIZ/oVrO1vYizXowjWqyNIWDaqWsslLURbt5FAZJi7BRkxHbqEhWLhbgTjyLu+MnSSZBIjgRV49wZowli4+bwdYRMDbZ9A6XfQc0OMXpbIjlMJuYmMMhr4DX9qJicrC4GYI4+BRA2I9WtMThRE+N6be5OWYditRL34zOJ+/GZ0mZIJAeLJw0SBkJ9pSiYbeTyMW6WmkN4RhfFT8ZXf2lTTAsIhZaaXSKJIZH8EPWVgElBZgIZboVJ6gYA3tWnYt/Lz0h2WxkaZ0Bcf9Es2glImyGRHAFWh1AJNEEJ+xk++3p2mUnkqGX0W/lXAmE92iuU9BIm5iaQHtuSaDhL+5rB6i7qTAfv6ZMIYeu62NQJU4g7Yaq0GRLJoeJKgMSB0FCDYuqMPv16Ssx4ctU9WOc9QFgqkUs6iL1tRhI1nKYtAOCjyLh9bUbID4YuirntnvYOeVhIP0MiOUwUBRIGgD0W6itQFYVrT57ULBY1avXd6L6KHziIRHJw7G0zAM7XvgBggT6MOmI6x88wTREf85dDbBZK/0nEXXQ5cWedJW2GRHIEDE8u4LKCSwGwp3zAr9/+SqrKSjqMJpuhOrah2iowDSthXx4Ywo4oQHqsg4m5CZ26jq7yM2ShrKR7oiiQkCeS2r5SnjkzjV+Ff45uKti3fSYKn+weiDRA5TapEijZP4EaoQTWeqRQoBoKvwEgYdyPCdDSLReHGM/eNBX1qum5aPJKKZH0HGwuHj17IGvN/vwjcpbYtugRcR2o2CweJZLDQFMV/nBMCn7Fwbnal9iVCBuNTDabmc0249qJXjRvhhhnJ5FIuhcJueDNFCMnG1EUhQeOi+MvkXPZbqSg1pfBsqfavs/iEDeGVVtB31cRVCJpRo+IphyrG02FXw6pwKmE2GEks9LMw0M90OJnXDs5Ac0VD3HZ0VuzRCI5MDHJovCpvgrV6mBV/g0AXGr5iHfefjXKi5P0FjRV4c7ZwwCwE+Jmy+sAfKhPYBcpgIxNSSQ9gthsIf5RtweLw8P8nGsAuEKby5sffxndtUl6Da1tBsBFlk+wKxE2G+l8a+YDrWyGoYtCpYSB4EmN0oolEsk+2FyQnA96GML12C0KOwddxAYjmySllsIXboz2CiW9hL1tRpZSxlHqdwAsNoYSxNbxfkYkCDVFoGiQPgrSRnZoo4ZE0te5duxVOI2BKGoYR/rrvLDk+2gvSdJLaLIZ1rgVAETqhoEeD6jN+Yw7Zw9DU5X9HqMnIUNsku6LxQZJ+WBxMTWpnmMmjOK/jV111Z/9nUjAJ8bg1e4Cf2mUFyvptvhKwWJvu23jJ2CEISmfUWMmUucSY+xiqMeiiKLrxBg7t548hKl5SZ2+RFPXqVu8irql32FGZAGGRHKkzBo/mJ+OjuW/+qmsM/pBsBbz2+ehoUqMz5ZIDgc9zPS0MBu0fC7UPgNgrj4REDbjthP7Ma5/sijGU7VOW4YZiVD35ZfUffmltBkSyaGgWUQiwhYD9S3qHOcWuDg+P57bIpcDYHz3FmbZXgEmdxL4ysBX0pUrlvQ0AjUQrAO7mGQx2LcEgDnGFOLwoSqiwz8xxs6tswYxIdMlbEYHjT9tD2kzJJIOIL5/c6PFKcdM50vHcQBMWnMnD8tx2pIO4qSCNAAu1j4hQ6mkwvTwrj6F+Bhn18amlqyhbskaaTMkksOhyd+wuiFQzVk/OpGl2hisik7igrt54put0V6hpJdw4jBhM+yEuEj7FIDPjdHExHja2oyGSpE/i+/X4WuQfoZEcoTEpAplWX8FGBHuPyGFf9iuBGBg8Rw+nvtGlBco6S0cP7SlUeIc7StUxWS1kUutK6dj/QzTFPFWfznE5UDWeIjNAlWVNkMi6UBsmo1nT7sX07BhcRdy15fP89pymfeWdAxHDYrH6lkLQMSXh6mLyalpsQ4euWgss4Z3vkhUV9kMS6cdWSLpCJxxkDwIdq/khrGJ/GjDOfwosJhc9rD6/YepHHEpKWqAwY4taM4EUVwrkTQRqheFcbaYlm2mCRvmiOdDTiGsm1QGxK+zh8ZyUr9YElw2CjJiu0ytwwyF2XXHPwEYfPIlKM4feINEIvlBfn/GeD7a9AW/rr+Kd2y3Y9n6BWvtI1HTguS7ktBiOj/RKOllNFRRX1fLeGM1OZYyGlQ3Y489i2M9HmEz6naJIJCrc8dOmKEQu34ulGkGr1iOYpG38xLJQWP3iOT17pVtJg78+bhYjtoxinf0qZyuLaDi4wdYM/5PJLidjfeEFlHMWFkoEo2dWNgo6cEEqsE0QLVAyIe5YzEKMEefyhUTkhiemNDiZ9SXgjMZYtI6dUnSZkgkHYBmgaRBohC+voLRZ9xIyUvf0l/dg/3rP/FS3AO4bBopHjF+rLcoK0i6lqLqBjzUc53lHQCqB8zm10OndX1s6v8eBmDwqT+TY1ElksPB7oHEXCheA7YYBpx8I+F3L+d47Vsu/uAVEtwXo6mKtBmSI2JTqZiId5q2gCSllqAtgWnHnsflA/q32AwjApEQpPbvlJyZ9DMkkiNEUUShbLBOiP14M/j9WVN56cVjuUD7nNxFtzM3YzxhLNJmSI6I1UU1AKgYXBEzHwKQWHAcf58yHc3SQWIf4XpR9O2IhYyhYtqe2uLESJshkXQsQxOHct7An/Lq1sexp8zltjmDiXOcSH04Im2G5Ih4csV7KJZ6jLCH66cdR//YvC7/TnWVzZCWSNL98WRAfDVa5VYePyOV3z9/GS/Y7mN4+Qdc/1EuO0hlmHsDJx+nMWPK5GivVtKdCFSLIghnfMu2PWugeocYozvweFaUhAgLEVn+eFwSFnnjIJH0CuwOB89fMoLjHzF4TD+Vay3vkrbuKe5YeQnBb8o457TZnDRSjjqWHAK+UuaXKM1qHY5hP2L6UKFITqAWrDFi1KJEIuneeNIhrgoqt0BsJigqLqvKc2cm8rOXLuIYdSWJ/i2s/fQFPjAmkRRj56rpuUwdkCBGh9XuhsQB0f4Uku6GoUNdsRihCFD4DYoRZpORyQYzmw+nJLbsq4fFT1w/UYAnkUi6Pw4vJA+G3SuJczjYMvkWWPwHLtU+5Jy3JrLcHAxAeqyDO2cP6xKFBUnv4vMNpVyifUy84gNvFnkzLmqxKRKJpGfhzQLfHvCXkZjen+Lc2aRve5vbLc/xo1cL0BFFKdJmSA6Xj9eWACZXWj8EwD7kBAry+kHrtEZDFbgSwZ0SlTVKJJKDwGJracgLVJMdG0e/46+h/PNlDFKLeOu1P/GwfjogbYbk8PlgTTEAP/ZswB3YA3YvmcOnQ0cUyephoSCrqJA4UExjkT6MRNIl/Hbqz1lQ/DW7Gr5HS3mdK5+Lp+lmUNoMyeHyzmYhNugIjebCCUNJdiVHeUWdRxf1pEskR4CqQmIeuBIoKypkl5HMm/pRqIrJNdb3UTDZ4bfwxLtf8Mm3m6K9Wkl3wl8uvj9KqyjR9yKAxIBjwObmijmVAIxNs8oiWYmkl7G5TiORav4R+TFbjHQSlTpOtyxEq9/DH1/6jA+/K472EiU9hVA9+Eq564syjlVXAqAME4FKTFOM247v1zxuWyKRdGMUpdG3SAJ/WfPmmtoarIR5IHIeAD+1fEICtVT4gtw3dwMLtlYKZYSqbRD0RWnxkm5LoAYCdUJFDGDL5wC8q0/h1EF7jYtoqBRjFt29N9AkkfRKPGki+VdfyR7XIN7XJ6IqJg9YH8NOCICSmgDXPL9C+hmSQ+aed1byU8vH4pdhp8sEs0TSk9EskDAQFA1C9ezIOYNq002+WsSF2mfNu0mbITkcTNPkr59sZIq6jnx2gMUOg09um//QQ0JNNr6/bMyTSLo7zjhIGijiTHoYVIWHwmcBcIPlTbKVPYC0GZLDI6wbPP5NIQA/984XG3OPBvcRTls0DZF/95eJ+FbWBEgdJn0YiaQLsWk2zup3A6ZhxeLeiiV2GSCU4aTNkBwOJb5KSiOrADgqcypx9rjoLqiTkYWykp6B1YmeMIiXFm8jmSr+FP4J1aab4eo2TlEXU4cLhxLkf+9/ha4b0V6tpDsQbhCjHpqS1QCRAGz9UjwfPAvTNKkLmQCMTO34EUQSiSR66IbJXXPWUYWHIDZ+G74SgBO1FWRRSj9KeODd5eiGGeWVSnoEDZXoAT8XaJ+hKiY7PaMhrlE9NlAtiue8GdFcoUQiORSsDkjOBxQI+dENeOzrQuKo4wX9OL41BhKjBLjCMpcmK/HY14XoNi+E/VCzM5qrl3RHGqrEeFPVAg3VmEXLAZhjTOHoHHvLfkYE9IiwIWoHjbiTSCRdg6JAQi56TBpvfLOKZ8MnUmrGkacWc7X2HkCzzbhrzjrpZ0gOmip/iNnqQlKUagK2BBhycrSXJJFIjhR3IsT3R/dX8OiCPTwfORaAmyxv4KEekDZDcngUVTcAcJn2gdiQO11MSmnCNKGuVEw8ikmNwgolEskh480ETwa6r5THvi5kuTGI+XoBDiXMHZbnAGkzJIfH1jI/AOlUkFf5tdjYb9qRFbSG6sXELYsDMsZAxmhx3yORSLoU3TB58vMwwYppADhS56Jo0s+QHD7PrHoDRdXRA2mcPWo8Vs0a7SV1KrJQVtJjWFJmYaU/ngS1DhcB7o1cCMDFlk9JMquoND3Y6otZsV6qykoQqk5hP1hb3fAXfgPhejFyN20EC3cJ1RdNgd9O9eznQBKJpCeypLCS4poABhoOAiwzh/B85DhAqATGKj6sdTtZsrUiyiuVdHtME2qL+azI5DztSwAyJp7Z+JoBQb9Q6bA6orVCiURyOLiTIH4A1FeytqiScl8QVYEUqrgtfBkRU2W69h1jlY2YQLkvyNrdNeBMgOqdojBSIgEwdKgrbkk0FH6FYhqsNnKpsWdy7rBW/khDlfjuSTVZiaRnollYURtLdX0IXVH5e1jcE15neYf+ilDqMIHimgBLCiujuFBJT+K9VUVcbhEFT44hJ4LNHeUVSSSSDiG+P+urLRj+Cj4xxlNopJKo1HGt5Z3mXaTNkBwq760uJp0KjtVWig35J4lmvSb8ZS0KlapM/0okPQJVg4RcNpYG8PtqsCoGf46cR9jUOEFbwbGqaMSVNkNyqLy3ejcAtyTMQzF1SB0ByUMO72CmKWxMsFYcI2uCEA6RTeASSVRoyoGHy49FD6ShaA3YU98DIoC0GZJDZ27hXAASGcvwtN7fcCc9JUmPobQuwE4zlQrTy0BlFwv0oSw2huBSQlxtnUsQK4YJDXs2CZUeSd/GXw6KKn6a2PiReMw/CRSV19aJzprTBztxWg/xcqiHIFgnkt31FeIxWAeRoHAYJBJJVCmtCzQ/D+AgjloeiJxHueklRy1jirqOTMqprpCjJyQ/QKAGGirZvn4ZSUottVoCWq7o0qShWiQgPGnRXKFEIjlc4vuBJ5X6yhZbkKzUUG56eUY/CYArLXOxNAaYKutDohjSCEPVDnnPJxEEaiBQ1zLJYsvnAMzRp3BegQulaQyqoYsRqHE5MpEgkfRgdofd7DSTSFTqWG3mslgfgl0Jc7flaVp0O9r6IxLJgfh+8QcMU7cTVuww/MxDe3MkIBLWNUUtP7VFULMLqne1bPOVipiVIeOlEkmXYbFTpKZjVSIomDynnwDAZdqHZFLaZldpMyQHyxvLd3Gu9iUaBqQMFUVPTYTqRUN38mDZdCGR9DRcCexWkklQfICJgcoLuhD9uN3yPDbCzbtKmyE5WN5Yvgs7IWZHPhEbBh4HDu+hH8jQoXY3aA7IGCumdEnREIkkqrTYAhuBPadgmgrW2JVo7i3I2JTkUNlRs4MqfTOmqXDyoKPx2g7DVvQwZKGspMeQ4nEQwkqhmYGJSrpSyX3hCwiZGlO09UxQvqcCDylqrVD0kfRdIkGRKLDHtGzzlULjCFQGncT3FWHe+l6MKpqd7zy44+ohcdzqnVBfBYYBmh1sMWBximKJYJ1wGGqKRNLc0Dv4w0kkkoMhxdPWUa/DjYHCvWGhRn6O9jUJ1JBllsjmCsmBqa9gZUmIUZUfAlCXd4pQ6zANCPlFoZ3F/gMHkUgk3RKLDRIHEet24qaheXOKUs1/I6dQZnrJVsuZrS0CIMFlEzu4EsX9nr88GquWdDcaqkXhkWYFXylm8WoA3tOntPUzAjXgiJVqshJJDyfF62SHmUqd6SSOep6I/IigaWG69h2nq/Nb9vPIxKHkh/lk3R5mVr4KQEP2UQc/Jjvkb4w71YIjDlILIHMc5EyG7CmQMwWyJ4pRqClDhe0xTXHvUrNLxMhC9bLpRyLpZLzJWZSYCSRSxxJjMGv0/tiVML+yvtZmP2kzJAfDW9/uYktpLedZvhAb8o5tiUeZphDzSMiDmJToLVIikRw2MSkDqDOdeBECP+9FJrLHjCNX3cNV2pzm/aTNkBwM/1u4jd01AU63LMERqhT+QNb4tirkB4NpiJoLd5LwLTy9X2VQIukJtLYFRv0gwjWjAXCkvQNaXbv7SST747bPngbAbMjjrJEj0PqAyMchWkOJJHpMzE0gPdZBSQ3sIIWBShFbjXSe1E/mGsscLrd8yC7bMAZlpUHlVnHTZj3IAkhJ76KhWiQNvOkt2zZ9ApiQPgq86SxY6Wt+aXrODxQ4GRGRTDBNcCdC8lDRdWd1iYR48346hBvEuRuqwLcH6kpAs4AzHjRbu4dXrBZSr/sJ6CEUq7wsSyQdQYvNCGACOhqg8KZxFOcZXzJJ3cCFjvkM884Qjn5cdrSXLOmO6GGo3c36HSVcoH5PxFTJGH+aeC1QI67tB5vM7iAUq5XU2//Q/FwikRwhrgTyh46i/9cb2OC3ozf2kroI8kDkfP5ifYzztS9Y7ZhAQUaseI/FDooJ1dtF0awcadl3MXSo2y2UhgG2fomCyRJjMGFXMgXJjddp0xQ+QvrAtv5DJyNthkTS8UzMTSA+NpbtNekMV7eyx4zjucjxXGH9kD9YX+CL4BjcsYlMzE2I9lIlPYD1a5Zzg/YtAN6xZ/3wG/QQ+MrEvUhiHnjSRRNGk3r5Ad8bhpBPNHj7yiDQOCHJ6gBHnIhNXXMeKIq0GRJJBzFxQBIRTxbUVWMnzBP6yfxDe5gztfk8HTmJ1eZA0mMd0mZIDor5myuYoa4iQ6kU0yzyjm15MVADdm+XxTelnyGRdDzj87NoiMkh2f89PtNBQHHwcHg2d9me41rLHN7QZ0BsprQZkoNi4ZYKwORq56cQRqjJupMO7SCmCbXF4EqGtBGHrVYubYZE0vHsnQMPlp6Kxb0Z1VaJLWE+obITSI+NkTZD8oOYpsnamq9AhQzrBNK88VFdT1fZDJnRk/QYNFXhztnDACgyU6gwvWQpZbwWmU6JGU+WWs4ViavQXHEiMFC9M7oLlkQPfzkoqvgBcTO/USgB8v/s3Xd4XOWV+PHvvdM0Tb0Xd8u9F3A3BoMpppdACOnZ9LrJZvPbzW52N5tkd7PZ9B4gEHo3mI4BAzbGNu69yk1Wn5Gmz73398er4kYxWLoz0vk8jx5Jd67kE+Po6L7vec8Zqcbobm1Uo0q+OjOAQ3+XDYVEuyp29RWq03ZVM9SCU07e6ZvcukN1sQ2WQelo1cGjahr4iiHaoorxjNRpf4TmdFJ45SIKr5gvDwlCnCMn5oyu/4d34KOADv4p9SlSloOp1jYcDdvU4YpU7J2/mRi4Yq0QD1F57AUA6grPRwt0dmNKdKjx2X3cTVZzuSj86Ecp/OhHJWcIcY44Cgdz9dwpFBPqzhlBLcYrxgQ2mMPwawm+krcSx4lPz75i9TtipOFM31IMFPEQxNvVRjXA3pcAWGbM4uvnBXvuS3aAO9jn3WQlZwhx7nU9ZxyngHqrkBJCPGmezwGzlBItxN87H+Azc4e++zqDEJ1qD/wNgGN5U9Sh7HfS1SmwoxHyqtT6VOkY8Oa/vyJZUGtY3gL1DFM9Ta1ZVU5RhVWRJrRoA4WXnk/h0gskZwhxjjh0ja8vPY96Cimkg71WJS8ZkwD4f657AIvPLxgmOUO8L1uPhrnFoZ43GDwXvJ2FD5ap9jAKh/RZ4xh5zhDi3HPoGp9fOo9Gq4ASwgCsNWtZb47ApyX4f66/8ZVFIyRnYCM3EAAA+MBJREFUiPdl69EwU7Q9DE/tUk2cBs8H51l2low0qr3wsrEfuEgWJGcI0RtO2wM3/CQaVA2Mu2gluqeJr184UnKGeE/rj68npTdimS4+PnUuQVfwvb+oF/VVzpBCWZFVloyv4Le3TqUwL8gBqwIdyKODH6Y+CsDE+kcxwvXgK1AdnmJttsYrbJBOqIIFT6DnWuN2CB0ChweGLQBgW2ehbHeXpzOJNKriudJxUDlNjS06m45hTrfqals5Faqmg79UjbeLNKkFLCFEr+rKGeV5PQsAHXjZbVXzZ+MyAKy1f1H/v5TDFeJMOhrASDEt8goA0RFL1fVE5/jsPu4mK4ToJQ4Xs2acxxcWjaTK3zOCOJ8IP0h9HIARzS9Dw/aTvgaHC1oPgJHu23hF5oi1qekTDpcaZd24AwON5cZ5Jz9nxMPquaCr86wQIqstGV/Br2+dTiw4hDhufCT4VfpqAG51vMBzLz5rb4AiK5jxduZGXwTAGHX5Oxe8puMqxzg8UDUVyiepgtcPy+1XB8GrZ0LNeZBXA2jvv/BWCPG+LJlQyTdvWETA78VLgrvSi0lYTs7Td7BYX8dfXj9gd4giCyTSBuHjB1mkr1cXai/p+Xkda1V5IVhpX4BCiHPikok1fPn6xeT73XhIomkav0ldiWFpXOFYzbbXn7Q7RJEFwvEUdS1RPu7sfC4dPAfyq8/um8TDqhlVaeeEVSFExjl1Dzwdnka6YySaZpJT/ijLNtfZHKHIBvdufxgAo308c4YOQxsga0Iy41tknSXjK1g8tpw1+5qJHt1ClXGE9YkZrH7jRc7Xt7P8np8x/pb/ZJAWgtaD738EmegfYm1qrGluRc+1XZ0PA0Png9vPj14Lsa1JFTSMLz1DoaxlQftxdQK7bKwqkP0wdB0CJWo0b8dxaNkLoSOqoNsdwDJMoht3QDqBr/p8NDlQJ8Q5050z9rfQ0B6nNJjDhr2H+cVL17DU8QZVHfW0b3+eoDdf/X/dm293yCJTJKPQ0cD9L7/FTcSoM0uoHj1D5Yh4uxo35DrLU9jngGUYRNeuA8A3fRqaw9HnMQjRL/mLmD55GlMqtrM1EqQllqbQ5+bJAxoPb57HdY6VbHv8pxTf/FtKA52/rPkK1e+MHcdVdzcxsJgGtB8FT2dXjb0rAHjdGE8zeYwu6lxuSSdAd6qpE31McoYQvUc9Zyxlw5YhpA9vwFcwjXUrtzMtupK/N//CZf83jrs+cx5Fgb6dPiCyx69/+WO+osXYZ5YzaNz8M98UD3d2CRwORcN6p1OgroO/CMuTR3RPIxgRfDWm5AwhzqELp45hYRXs3vY29RQROnQVpfse5rvOe7mkeTI3/X4Vf/jYdPJ8sigszmz+f63gRm0FDs3CKhmNVqY6iGEakIxB1WjVtKOPyHOGEL3nwqljWVhhsnvnZuq1Ygp9bp56bitXpp/ltrZf89nbp/KzW2YQ8EiJhzidZVlM+sFzlNDG5Y416uKwC3omIb0f6QQkwlA2HvzFHz4myRlC9JpT98AN/Z/4l3WfxeE7yOqjL/Glv3n5nxsm43XL/+/E6WLJBM/sexHNCbnmNMoChXaH1Gc5QzrKiqzk0DVmjSjmwlnnMXrIIG4ZkeDJ4s+QtnQuc6zhH+96UY1DDR9RXUHFwBFpUqfctM4fb+lE9whUalXL+d+vj3TfXhk45YdrV5GsJwCVkz58keyJdF0V8FbPUCP1UnEI12PFY9R957+p+94vsBLJc/fnCSGAzpwxvIirJlcxa3gRX1g8kbGVefxH6lYAXNsegfBhdbjCst7ju4kBI9YCyQjDGp4H4H7jAvK9zp7x2ecyP5wFK5Gg7uMfp+7jH8dKJGyJQYh+q2AwjkAJEwtSLKwtYWJ1Ht+bm8tDgVvpsHIYa+3h9gcf7blfd6qC+db9kJbf4QaceEgdnOjcbLD2qq6Ay8xZAHhdnc8j8Tbwl0BOfp+HKDlDiN7l0DWmjR/PeZMnMKEgwbSrv0IMDzP0XQxreI6v3ve23SGKDJVIGyxsVx3B7jUW4XSdobgpHlJTjsrHq0PcvTxO20omqfvsl6n7/DckZwjRCxwFgxk9uIqFNU5K538KKyeP4foxbnG8yJv7W/inx7fYHaLIUKFoisZwjJucLwOgDVukxmiDetbwFfX5xCN5zhCiF2kajsLBjK4pY+HgHCZW53H5R75IsxWkVj/C4D138eOnt7/39xED0rFQHMuCmx0v4SINJaOhfNz7byhmWaquIn8I5A8+JzFJzhCid524B37txKl8fLza+/aULueprfv55Uu7bY5QZKp7tz6N5oxipoJUe8fhc9k/Da+vcoYUyors5vJC0XBIxVk4uZa7jMUA/Kvzr5iargoTW/fLONSBIp2ASIMqcu1St0p13/CXQOUU4umTi+BOax8eaVAjUcsnnJtRdmfi9EDJSKiaBv5CaK/vnT9HCHFmmsZtc4bxtDmT1eYYcrQU1pZHVVc4OVwhQC0IhY8Rbmtghr6LtKXzoLFAvRYLqc6Rbr+9MQohzj2nRz1bmIYac9zp6qnV/DJ9DQCfTN6DlYz2fE1Ovhp12SG/zw04sVawDFUw3bIPrfUACcvJs8aMnnssE4wU5FbKlBMh+itdV90+nT5wutledR0A/+i6h/V7jmDJQTxxBvXbVzFBV3njVc+C029IJyDRoQpkC4ZIDhGiP3D7oHCYml7j9KBN/xQAX3c+TJAoyzYetTlAkan2NnUwX99EtdZEO34YeaF6oaubbMEgcEg3YiH6lZxcVaQYawPLwuHL47WKjwPwDedDvLBaDuSJM9vXGMFFmo86X1AXhl94dge3o03q/qLh6llXCJF1vjT583isMnRnBE/J89z++n67QxIZ6ukD6gB3OjyZitzAe9zdv0iGE9kvUA55NVxUEuZB7000WbmM1I/w6vJ7O0fdN8rG9UARa4Vk5OTipV3PqPcjLwbdwXP7eooefrGk4PSv111qnERfjF/3FULlVCge1ft/lhDiJFdOG4bbofNvqY9hWhra/legcYccrhBKvA2izRzd/AoAK8wp/GjpcEhFVfdIG8ZnCyH6SKAU8gepKQWdxU03jfNzu7GEA2YZpVobW1+8q+d+3QEun+pKnpauCAOGkYbwUVXwALBHTbB4xZxEGD/3XFOkrifC4MlVz6VCiP4rJxeKhkG8g6mLb+GwVUyV1syXnI+zfLOsR4nTtb/2RwCeMWfyo8uHnH5DtAnyaiC3um8DE0L0rmAFBEog2gKjL4e8Ggq1Dr7ofByAFTsbbA5QZKJnt9Rzs0M9byRr5oK3cyRqPKT2MPq4m6wQoo/kVfcczgauWnota81a/FqCf3bdxZr9LfbGJzLS01uOcZm+mjKtjXROIQyeo9Yu349UTK13FY/sWe8SQmQdr8vLfy78DgCuglUk9MNsPhyyOSqRaUKJEDvD6wBIhabw9YtqbY6ob0mhrMh+ug6FQ8Hl469XF/KT9EcAmHb0XhKRsCpoadmvxtyL/i3SBJqu3kAtOh5aoz6uvQSAlXXq38HfTfVzZe0JY+tSUfVvpGQ0+PtwI9vhguLhPZ/rzr77s4UY4FZ8cx7brCHcZ1wAgLX+bgjVQ/sxmyMTtou2QDJK1fEVAMRHXMaFQ3PUKf5AOeTk2RufEKL3aJp6tnAHVdF8p+W3VnY/Zwyvexij/YRNbG++2rgISxeoASMeUlMrPEFVUL1XbVwvM2bxT/NymV3jUfclOtTmlvMMI7WFEP1LbjXkVkC8HeO8LwHwWcdT/M+9T2Ga0lVWnCAeZniDOtQdGHMRUys8J7+ejILDAwWDpYuTEP2Nw6m6RJtpNXng/C8A8CnHM1RrjXzy9rekE7k4zZadO7lQXw9A0cRL1DOrZamGIfk10k1WiP7KlaMO46XjYCRB00nN+jppS+dyxxruvutPkjPEaVbubuKTzmcBcI68UB3QeT8sU+2xFw6VAxhC9AMXD7mY4f4ZaJqFp/wJvvXQertDEhlm2d5lWKQx4uX8+JrpjCgptDukPiWrbaJ/yMmFwqEUa+1ccsmVbDKHEtRivLXsd+AtkI3rgSAVU92DPSe0Bd/zgvrlvnQs5A/CsizeOJQE6Nm8BrU4GWmGohFqLKqdHFIoK0RfqSoK8j9XDud/0jcStnxoLXvg8CpoPSCHKwYyIwXhI5gNWwmaYeqtAirHzlYLkuj25wkhRO9z+9WIsWRU/Z4IjCh0MW32Raw1a/FqSbY8/Yee+zVdFUy21anfSUX/F21Wzxm6Exp3QvtRopaHF8ypzOl6zkgnwOGWbrJCDBQOJxQMBU1n8Njz2OefgkdL86/OO/nli7vtjk5kkMTb9+G14uw2q6idcN7pN8RaIVjZN5OOhBB9z1+q1hU6GmHQLKiYhEdL8ffO+wH482syGlX0aIkkmdz0FE7NJFU0CsrGqRe6Du1JMZMQ/VuwEnKrVM4AZk0ayyvBywD4ZvpPPLB6j53RiQxzqCVKUetGJut7sXQXDL/w/R+miDSCr7jzmVbr3UCFEH3i/xZ/H81y4/QdYF/0NR7fcNjukEQGeWjnEwBY7VOYM2zg7XtLoazoP/KqwV/KReURXij7NACz2p9nw/bdqvNb6wHV0Uf0T7E2SHaA+4RC2V3q1FxXN9lDYYMj7QZOHWZUntDVqaNBjb4qlAcAIQaa62ePpra6lJ+nrwUgtf5eCB2RwxUDWbQF4mGiO14E4FFrIRPLO7vJ+ovVARwhRP+XW6l+P+zcjAD49JQAf/XeBsCE1hc5uG9nz/05earLaFi6kvd7Rgo66sHjV5/vVfniBXMq3hwfo4o6D77FQ2qTQbqQCzFw+ArV2lSslSGXf5MkThY4NrFtxd841BK1OzqRCSyL1Jt/BmC54wJqivwnv56MgNMD+dU2BCeE6BO6rrrKOj1qwtks1YX8ascbTND28R9PbachLIe3hbJ6byM3O9X0CteIC9S/G1CFsrmV4PK+y1cLIbKerquD3G6/OkwFzLn689RbBQzRj3P0yR/RFk3aHKTIFKv2NfNJp5pcoQ2erfa8349kBNCgeKTqZCyE6BeG5A3ho2NuAcBTtpyvPfAmkUTa5qhEJqgL17E3vB3L0hjim0GJf+Dte0uhrOg/HC41hsK0+MYV01huzMShWYRf+RWmO6CKKNvq7I5S9JaOBnA4egpdm3ZDy17172L4IgBeO5QAYEq5G5+r88dfPKwWmIpHyJgiIQaouz89k78aF7PXrMCVbINdz8jhioGsowEiTQQa3wZgT+liXJqpCqPyqmX8qRADhe5QC8q6s3PBGDRN478+OocnjfPRNYuDz/6q535NU1Mu2g6qTrSi/4q1qY1pd0B1ld27AoBlxixmVbvRNU1dN5IQLJeDeEIMJJqmfl90etB9RVgTbgTgn113c8XPnrc5OJERjqwj0LaDhOWiuXLR6a/HWiGvRg5ZCNHfeQvU/9ejLVBcqzq+Ad913gtYXPvbN+yNT2SM428/TbXWREz3d/87IZ1Qz6mBUnuDE0L0DU9QFTAmo2AkyfEFcM1Rhyy+6HyC7/7pMXvjExlj6/btXKqvUZ/UXtxzuOLdmGn1+0jBMAiU9G6AQog+99WpX6Q4pwzd2YGn+Hm+eu96u0MSGeCR3Y8AYESGM3voINwO93t8Rf8jO/2if/GXQH4NWrSF5PTPkbCczHds5uDWN8BfBKHD6hc+0b8kIxBtAk9uz7Vd6tQcg+eAJ4hlWdyxQRU5zK7u/GFvpiERhsLhtnYI1JxOSr/995R+++/RnE7b4hBioHJ6g3xjQRX/nr4VAGv7MlVoHzpkc2SizyUjEGnAPKg2pV41JjB6yCDVFTAnT3WUtZnkDCH6kK9QdXuKtoBlAZDj1Kgb9UmSloP5js207Frdc78nVxVQSlfy/i3apP496E44tgmiTbTj4xVzErNrOjchkhFVSOsrsjVUyRlC2MCbr4plo814pn+MVkcR1VoTHzefIBRN2R2dsFlqjeom+6R5HtOGnzIuOx4CV0D9+7GB5Awh+lhuheoGmorCzM+A7mSOYysL9E0cbo0RSxp2Ryhs1hpJUrXvfgAaS+eo/S1Q+xm+QsjJty02yRlC9LFgJeRVdU89Khp3IW87JuLRUtzc+EtSackZA119KE7ZrntwaQahvDFQMfn9fWHHccitgsIhvRab5Awh7ON1efnuzG8D4CpcxYp9W7E69znEwGRZFk/seQqAVHgKC0aW2xzRyfoqZ0ihrOhfNA0KBoPLzxXjirnDuAQA95rfgcMNZkp1eZIE0L/E2tRpSpdPfW6mYY8agUqt+jewrSnNrpY0PpfGJyYF1GuRRnXy2qZNiC6a203Rpz9N0ac/jeYeeCc2hMgEn140gZfNKbxsTEKz0rD1MWg71D3SSAwQ0WZItJPe9QIAyxyL+Mg4ryp4yqvOiM7jkjOE6GP5g9SBqljPYbtPzBvJnZ3PGck3fgNm54aEpqmi+rZD3V1oRT+TTkL7cdXRBWCvGoP6dHoGBX4P143pfB5JtKtusjaPrJOcIYRN8ger4pVUFM+cLwLwBecT3Pv86/bGJewVa0Pfqrp2vOS+kEtH+npeMw018ahwCHgCtoQnOUOIPpaTB4EyiIUgWAFjrwZUV1kdk7tXH7Q3PmG7lW9v4QLWAVA16SLQOrdzU3HbJ1dIzhCij+k6FAxVawyJdtA0ai79JgnLyQLHJtY89We7IxQ2e27jAW7S1Z5GcOxicPvf+4uiLeDyq47FvbjvITlDCHtdMvQSJhROQ9NMPGVPsWyTTOAeyNY3rKcxXo9luqjNncL5QyvsDukkfZUzpFBW9D+eIBQOwZlsx5x0C81WkCrjMDtee1R19GmvVwWSon+wLPXf1OnuWRyqexPibaqooXoGAOuOJgEYU+ykwKtDKgboqptsBhQ+CSHs5fF4+Nz55fwofTOGpcHB16BhK7TWyeGKgcI0VRfIhu24E800W0GaS2YR1GKqK6CMtBNiYHLlQOEwNd7SUJ0AfS6dYyNvoc3yU56s4/DaJ3vuz8mFZAeEpKtsvxRrVf99PQF1OG/fywA8Yc5marmbHKemroOadiKEGJjcPigaDqk4vhFz2eMZi1dLUr72J+w+3m53dMIum+7HYcTZYdbgrR6P23FCgVO0WeWN3Cr74hNC9L3cSkBTzxpTPwYuH2P0Oq7WX+OHy7dzqCVqd4TCThtUZ8A6Ty2OyvHqWiqqOhHbOB1PCGETbz4UDFONg0yD4sohPOW7GoBh639EY3OzndEJm6U2PkCR1k7IWYw+fMF7f0E6ofbJi2vVWqYQol/7/pzvguXAGdjJN554WCYeDWCP7n4UgHT7OGYNK8PtGJgHGKRQVvRPuVXgK+a6kU5+ZV4PQOXOv2Klk6qYsnV/T+cnkd2SHarDV1dXJ4Bdz6r3IxeD7iSaMvn+KyEABud1tuiONENeje3jUAEswyC2eTOxzZuxDPl3KYRdbps/hp3WIB4yOhcSNj0AoSMQabI3MNE34m0QayW1ZwUADxvzqS7IUSNQA2Xv7xR2H5CcIYQNguUQKD/psN2t08v5Rfpa9fKm2zsPYXXy5kHoECQ6+jpS0dsijaqTk6bD4XWQCNNo5bLKHMvg/M7njHhYdQjLgM1ryRlC2ChYocajRppwzPkKpqVxteMNnly+zO7IhB0si+SbqtPXPcYihhSe0HE8nVCHLAqHqkPgNpGcIYQNfEWQW646uuXkweRbAPim6yE8JPn9q3ttDlDYZcexNiY3PAbAgZILeibpxcPgLz55L8QGkjOEsEl+jWrm0Lk+NXjhbRw0S6nQWtj50A9sDk7YZd3BVqY1PgbAwdJF770eZVnQ0QAFQ9Rzay+TnCGE/UYXjmZO2WUAeMqe4revbrU5ImGHlJHiuQOq+3gqNJXaEvv3L07VVzlDCmVF/+R0Q9EwSnMMvnjL9eyxqsi12tm34g61ANXRqLqQiuwXa1WFCS6v+jwegro31Me1SwDY1Zzuvv22iX61oOQOQMFgW0cUdbESCQ7ccCMHbrgRK5GwOxwhBqzqQh+vfX06vzCuJ2a5oWEbHFkDbQflcMVAEGmCjkacR94C4H5jITePdoHmgNzMGT0hOUMIG+gOVbyiuyAZAWBYgZNbbrqZg2YpeWYbR1Y90HO/J6gOc4Wlq2y/koqp58iczk3pvS8BsNw4DwMHN4zt3LxORtXBTd1hU6A9JGcIYSNdVxuPTg9DB9UQqrkQgDn7/4/tR0P2xib63qE3cbfsJGa5ecyYy1WjvD2vRVtUV0m/vRMsJGcIYQNNg/zB6hBWKgYTbgBfEdVaE7c5nuPu1XUcbI7YHaWwweF1zzBIbyRk+Rg1baG6aFlgpNUhTptJzhDCJg4XFI0ANEhGmVaTS3jy5wCYdvReGo/sszc+YYsDm19nsr6XpOWgZspi9XvFu4l1HtApHKqeW3uZ5AwhMsOPLvg2Ht2Pw9PAnzc+SEskbndIoo+9VPcSMSOCmQ5iRIZzydhBdod0mr7KGVIoK/ovfynkVlJCGy+VfRKAmrrHSLTVqxGqrfshnbQ5SPGhdI3Jdp2wwbD3JdWJo2ikGpMLPL1Hdfg6v8rN5DKXKqYtGKxGpgohxAmqy8sYM6yGPxrqZJ216QEIHVYnbEX/lU5C+zE4tBoNkzXmKIYMHsaYQAR8xZCTb3eEQgi7+Qohf5AqZrEsAEYU+3g0eDMAwe33YcXaeu735nd2lZUR2/1GrBVSHeDyq+5/B1YC8IQxmytrvQwvcEI6Dk6P+vcihBDeAlUAFW2lYP5nieNmpr6T//vV/9odmehra28HYJkxi8vHFlOT29mF3Ohcl8yr6ZNNaiFEBvIWqENWsRb1e+SMzwDwFdfj5NLBgv9+2d74hC2CW+8GYGfeXMpKOg9SJDtU8w9vvn2BCSHs5y9We5/RZrBMxs9cxFtmLV4tyUu//Ybd0Qkb+LeonLEvdyYF5UPe/eZ0Qr0Vj8yYCXpCiL5RkFPA5yd9FgBP8Qtc8LOnbY5I9LXH9j4GQDo0iS8sqiLf57E3IBvJCpzov07o3jFm/DReNSbg1gz2Pvd7tQAVbVVFMSJ7JUIQazt51NCuZ9T72ksAqO8w+P16dfJ+8bAcNVo7J0916xBCiDO4bsZQfp9eSqOVixY+AvtegdYDYKTsDk30lmgzxEOkdr8IwH3pC7h4mEcV0OZVyaa1EEIpGKx+j4y1dl8qm3gR281B5Goxdr50V8+9niCkohCSrrL9Rnu96t6iaXDoTUhFOWIVsd4aySXDO0dox8PqWdOTa2+sQojMkT9I/VzQdDaXXwPAd5338vzmOpsDE30m2oK55REA7jEuZMnIEw57x0PgLwKvHLAQYsDSNMivVtMrUjEYeTHkDyaXCH/nfBKAN/c12xyk6EsrN2xlalRNzPOPubBnUkWiQ41cP7FpiBBiYCoYrKanRpvRdJ1XylWzqBscr7B74xs2Byf60uOrdzAvpiYe+UZdqNat3ollQaRRHeYMZs4EPSFE3/n4uNtwGRVozihx/7PsPN5md0iij4TiId44shqAVGgqSydW2xyRvWTXX/Rv3gLIq2FecYQ/e27FtDTGhlfy+vrNqpto6wG1ACWyU7RVdY91dp52aD0AjTvVmOwRFwHwxqGeltzXjc5RC0oFQ1RXYSGEOIMlkwahe/z8PH0dAMlND0FbHXQctzky0Ws66qFxB65IPWHLx3LzPK4ckoKcXLXoKIQQoDYkC4dBMqp+BwU+Mj7AT9I3ATDk8BNs2nek535vPoSlq2y/kGhXhyq6DujtUQcrlhmzsNBVoaxlqQMWuRWq4EEIIUCtPRSPBCPN1AtvotHKY6h+nFX3/Rfbjobtjk70hY33oZtJtpmD2WANZ/6gzjUsy1TdnHKr5WCeEAOdtwAC5aohhO6AmarT0ycdz1JCGzf9YTX7GjvsjVH0mfiau3BpBuvMkYwbN0ldNA2VNwIl9gYnhMgMTg8UDVeNPdIJvnzF+TxpnIeuWRx96B840ib73gNFasN9+LUEe8xKBo07/91vjrWAJ0+tbcq6lRADksvh4gfzvq0+LljFpb9+lMb23htvLzLHE/uewCSNES/HTFQyumxgP1fIKpzo//IHgSfIr6+r5VFzDgDOdX9QXX7iIWg7bHOA4gMx0hA+Am5fz7Vdz6r3g87vHkF0uN0AYHKZi3wrpBYe5aScEOJdaJrGq3+/gPuMC9hrVuBOhWH3M9CyX21kiv4l0Q6RJtj/KgCPGXM4b3AuXiMCedU9hzGEEALU75HBUvVzA5UzfnTbJbxpjiZHS9Hy+u0997oDqqg2dOQdvpnIGrFWSMXB5YNkBOpWAapQdmmtF6euqQ7Cbp963hBCiBMFyiC3AocRx5r2KQC+6nyE+1/daHNgotdZFqy7A4C/GRdy6wQ/WtemdKwNcvLBP7A3J4QQnfKqAEsVPQ2eAyWj8WkJvuR8DICH1skexoBgmkxqeByA4+UXgLOz2UeyXR3ak2cNIUQXf6k6cBVpIsepUXHB50laDhY4NrFpxUN2Ryf6gmUxs/kxAI5XLFINwt5JOqHeikeevK8uhBhwlo5czPDAVDTNxFP2FC/skL2LgeCpfU8BkApN4duXVqNrA7tUdGD/rxcDg9sHhUMJWB28WnwLCcvJeWxl59uv8NqRNNt2bMGISRePrBNvU8VNXV2dTAN2P68+rl3SfduG+iQAFw31qEKFgsHgdPdxsEKIbFMQ9DGiNMhP0h8BwNz2JG9vXMf6LdswTMvm6MQ5FW1WI4c6i57uMy5gYbUODg/4i20OTgiRcRxOKBgGFpCOA1CR6+QPzpsBmBd9kW3bt/DyrkY2HQ5h5BRA+DDE5Xkja5kmhI/2TKQ4+DoYSQ5rFWy1hjCvpvNARSKsip3cfvtiFUJkJk1T4y11J6UTFrHTrCFfi1Cz5de8saeJxzccYdXeZnnO6I+OrIOmncTw8Lgxm1nVnTnDTKsJVwVDZI1KCKF4C9REm3hI5Y3z/g6AWxwvUq018JuX90rOGACMfa9Qmj5K2PLhH7Ww54VERB28ebeR2kKIgUXXoWAQONyQjDJ11BDuMi4GYOiG/2LV7gbJGf1c8uBqBqX2E7dceEdf+M43WhZ0NEDeIJVLhBAD3k8W/j8sy4EzsJOfrnySVXubJWf0Y3WhOrY2b8WyNNLhycwcUmp3SLZz2h2AEH0iWAGhI1w61s1fX7mYzzqXo731J36S+jsqtBY6Xm3ltqsuZcl46TSaNSKNatyQ3vlj7Mg6iDapTsGD1HiJ1w8leOmA6v54aXWyZ4yVEEK8D5+cO5x/eCTKGnMUM9lJeN1D/P7NOEeXH+O7V06RnNEfmAaEjkLdajDTbDKHspMhXFwZg0ClyilCCHEqf7HqON12QL0Hpk+eyvNvTWOxYx31L/+J/01fD0BxwMNXp+cwNfco5MjPlKyUCKmuf50TK9jzEgAPpWbhdmhcONSjnktMEwKyyCSEeAe+QsitgtYDrB/yKUbV/YDbHM+x+M+PcdBSzxUVeTn8y9Kx8pzRn2z4GwDLjRlYLj/zB3UWynY0QrAccittDE4IkVF0h8oTR99WBS2VU6ByKu6j6/ma4xG+nf48t/zpze7bJWf0T3uf+y21wDPM5srhnc8WppqYh6/ItriEEBnKW6DWpZr3ouVX83blTYSOv8JorY7v3vFj7jNU8aTkjP5p55O/YALwgjaLS4ZVv/ONsRbIyYOiYarAWggx4I0qqiWYnE+HZwXRwOPc/KcKsFS3ackZ/c8jex4BwIgMpyJYxNRqOTQh2VAMDE43FA7Fn2ji1+mrCFs+avUjTNP20GoFcXcc5R/ufpVnthyzO1LxfqTi0F5/crHBrmfV+xEXdp+s/svbHQAsGuJhuC8G+YMyslOH5nRS/KUvUfylL6E55fyCEJlC0zVA4z9THwVgnr6FWv0QWvsRvnD3eskZ/UGsVb3tWwHAfcYiLhvhocoPBCtVF5cMIzlDiAygaWpKgcunJhwA1Tlx/jt9I6alscixgTJaAGjuSPCTl4/z1qZN0lU2W0Vb1Ahcp0d1+Dr8FgDLjFlcN8ZHkc8ByQi4Axk3ClVyhhAZJr8GnB68pcN5xZiIWzP4jvP+7pfrQ3F5zuhPUnHY8jAADxvzuWW8j6BHh1RUFcQVDlPvM4TkDCEygL9YTU9LdD43zPwsANc6VjJEOzk3SM7of6xoK4Mb1KE8Y+gF5Lg7fxanoupZI4MOXkrOECKD5NeoyaqJdmZU+/hV+moAvuZ8lBxUIyHJGf1PuqOZ2iY1ZdUacREu1zt0HDeSkE5A0QjbJiBJzhAiMy0dfBtm2ofD04ArbzNqhJ7kjP7GsiyW718OQCo8hU/NrcLhyNwy0b7KGZn7NyDEOWb4S7l9QzsT2cNv00sB+IxrOSlLx4lBtdbED57YKu3Es0GsFRIdPb/UJzvgwEr1ce0SAKIpk9cOqYfA70zX1Gm5DB0pobndlHzly5R85cto7swr5BViIDJMi589vxuADdYIlhsz0TWLy/U1VNGEjzg/WLZNcka2az8OjTugrY6o5eEJYxbfmYbqJOsrtDu6M5KcIUSGyMlVY7RjbRiGxcNvHQDL5BFzHgCfcj4DqOWlKDk8tHo3RuiIffGKD8ZIQ/goeALq8/2vgmWw1RzMPquKb54fVNfjYQiWqWLaDCI5Q4gMk5OHkVvNqg2buTu9CMPSuNyxhmnaDqBrSwJ5zugvdi6HeIgjVhFrGctXZ3bmjFir6hqZYc8bkjOEyAAur3rGiIfBsjCKx7BBG41Ds/ia85GTbpWc0f8cee1uPKTYYQ3imnlTel5IdKgi6gx61pCcIUQG8QQhfzBGtI0Vmw7wijGBw1YxFVoLn3T0rE2B5Iz+5NCKP6ucwWAumz31nW+MNKopFkH7OkNKzhAi8ximxbL1IZLNai/DXfI8aFFAckZ/83bD2xyLHMMyXeRaE7h5xlC7Q3pXfZUzpFBWDBhrDrTxdkc+Ts3iDWMMx6xCqrUmLnRsoIUg5VoLiXAja/a32B2qeC8d9eB0gdb5I2zvy+pUXMEQKK4F4NW6BAkDanIdjPJGIK8GXDm2hSyEyC5r9rdwLBQHwEOSn6ZvwLA0Zjh2MV7fT7nWxLFQXHJGNkvFoKMBDr4GwJPG+VQX51LjjqqRVY53OIUthBBd8qrBW8DOAwdp6kjg1gzuTy8gYTmZ49jGWO0AoBaX9kfc7Ny5Q7rKZptYq+ro1VUou+dFAJ4wZjO1wkWJzwFmWnUZ9hXbGKgQIlusbQtyKOIkSg5PGecB8E+ue+jairBAnjP6iw33APCIMY/zq72qm2w6AZoT8qpsDk4IkbFyK1TDh3gbW4+G+EtiEQBX6W8wQjt80q2SM/oX58a/AbA5OJ8crxp9i2WqN3+JjZEJITJeXhU7WjVSkRbcmsEfUpcB8AXnMvJQkzclZ/QjlkVwy10AbMm/EEdO4Mz3JdrBmdM5yUJKgoQQPbr2wFMtczGTBejODtxFbwAmIDmjP3lktzpwmW4fx+zhRfht6i6eaXotK/7whz9k9uzZ+Hw+8vPze+uPEeJ9a2iP00qQYxRSqof5ZfoqAG51vojTSmNZFtVaIw3hqM2RineVaIdIk+r212WXOhVJ7RLQNCzL4ocrVRHCxYN1NE9AdXjKUJZpkti9m8Tu3VimaXc4tpCcITJNQ3u8++MEbg5Y5TxoLABgqWMVVTQRJHrSfSLLRJuh4zjWgdcBuM+4gIsHO1QHF1+RzcG9M8kZkjNEBnHlQOFQ2tvD6BgARC0P9xgXAvAx5wt0FT5FyaG9PaS6k4rs0dGg3utOiDRiHdsIqMMVFw/zqteSHaqDizffnhjfheQMyRki89THNA5YFfi1OA+m5xKxPEzR93Cl/sZJ98lzRpZrr8faqw5XPGzMY/GwzoPb8TZV7JSTb1to70RyhuQMkSFcXigcCokILZEY+6xKVhlj0DWLr5/SVbaL5Izs135wI+WR7SQtB95RF/S8kIyAO5BxzxqSMyRniAzj8nJEL8OnJdAxeMscxXZzELlalC87Hz3pVskZ2a91+wqKE3V0WDnkj7ngzDdZFsTaIH+IOoBjI8kZkjNE5unJBS4SjYsBcBe9iuZsfYf7RDZKGkmeP6jWplKhqVwy1r7u4u9XX+WMXiuUTSaT3HDDDXzhC1/orT9CiLNSGswBNA5bpSQsF2uNkew2qyjQOrjI+TYtBCnVWqlydtgdqng30WbVBdDVuTEdOgzHt6jusiNVIv/LhgiHwqpY4ebhSXUSP4NPR1jxOPuWXsm+pVdixQfmLxySM0SmUTmjh4bFz9PXkbBcjNPrmKrvplJrOu0+kSUsC8LH4MhbaEaCnWY1662R3Dg0Af5SNVI9Q0nOkJwhMkygHF9BJUW0A6qx6APp+XRYOUzQDzBaO9R9qz+/BMKHpatstkjFoON4T07Y9zIaFmvNWo5QwjWjO59HEhEIVGRkJ3LJGZIzROYpDeZwnAIaLTXx6O70RQB8x3U/HhIn3Sey2Kb70SyTtWYtB6wKrhjpVc8gRkqtUWma3RGeRnKG5AyRQYIV4Cui1BED4O7Og3hXOFYzWqs77XbJGdnvhXt+CsCL5lQWjB/c80KiHQKl4PTYFNmZSc6QnCEyT27JIJqtPIoI49Asfp2+EoDbHC9QSVP3fZIzst9rD/4CgGXGLOaOKj/zTfE2VSCbV913gb0DyRmSM0TmOTEXpMNTMGLVaHoKd/Er0NkQ5NT7RPZ54eALRNMdmKkgRmQEC2szf7pRX+WMXiuU/cEPfsA3vvENJkyY0Ft/hBBnZebQQirycujAxxGKGKo38Iv01QB8zPECPitO0OdlSrANjLStsYp3YBoQOgpuX8+13c+p99XTuzsA/vvKnuKDEYUuCL7Dg4LIGJIzRKbpyhld25dpnDSRy53GxQBc6VjFOF+YmeWZt8Ep3od4mzp4se8VAO43LgA0qv2W5IwsIDlDZBSHk7HjJlLkd+EmpS5pFrcblwDwWedyNMuiOOBhzOBKSMakq2y2iDarbrGuzgN3e14C4AljFgClfocqeNIc4M/cTuQDneQMkWlmDi2kNM9PnVUOaKw0x1NvFVCtNfFxh1rfqMjLYebQQnsDFR+cZcGGewF4yJgPQIFXh1RE5RRvgZ3RiXchOUNkDIcL8msYVeSkxO+izipnpTEegK87H+6+TUNyRr+QTjIvvgKAB4yFBD1Odd00VE7xF9sYnHgnkjNEppkxvIx4cBC6puEhyX6zjFXGWDxaim+6HpKc0U9YiXYWmWoayUPGfHLcZzi0bZmQ6FAd6l1S5JYJJGeITHPyHrhG/PgSAFz5b6G7myRn9BOP73kCUMXQ+T4XQY/vPb5i4Oi1QlkhMo1D1/iXpWMBOGYV02HlsN8s4y2zFq+W5Drn69wwbyKOWDN01NscrTijWJsqbvJ0dnWyTNj1rPq4dslpt08oQp24zsCRdkKIzHZizugqhTXQ+U36SsKWl2F6PV8ZdgxH6JBatBbZpaMJmvdA824SlpNHjTksqtHVKWufFDsJIc6OI1jKNQumU0R7d854Jj2DkOVjlH6YyfpePjd/KA4d8OVLV9ls0NV53OlRXf/CR6BxO4alsdw4n4+M61xUSrRDTtD2MXZCiOzR9ZzRQi51lOHTkvwlrQ5XfNn5OIWE+JelY3HociAvax19Gxq3E7dcPGWcz1dmBNT1eFitUXVNSBJCiHcTKMXhzeMLs1SR5D3GIkwLljjeYpy2HwALJGf0A/FtyynWwhy38rlwzqyeF5IR8ARlb0MI8b44dI2vLp3FUauQQq0DTdP4XfoKAK7VV1KrHZSc0Q+0v/0ofi3BfrOMT1046cw3xVrBVwgBaQgihDizU/fAzdgIUu1j0DQLT9lyLNKSM7JcS6yFN+tXA5AKTeU/rxtpc0SZJaMKZROJBOFw+KQ3Ic6lJeMr+O2tU8nLy+cQpVTqLfxv6noArnK8zqT8uDpd1bIf0on3+G6iz0UaAbNnrOmxjWocqtsPg+cAsL9NdQN2aHD7hRbkVmXkSDvx4UnOEL2tK2eU56lTtyYOwvj4Y/pyAAbXPwuhOoi22BmmOFvpJLQfhYOvA/CcOZ2II5dfzE2pUUQOp80Bit4gOUP0Kk1jzvRpfO7C0VT5ra5L3fnis+6nmTm4s5DSHZCustkgHoJYS08B7O4XAHjdHE/clc8PFnReT0XV84busClQ0RskZ4je1vWckQgOptHKY69ZwVZzMLlalO/6HufisbKhmdU23APAs+YM3N4A3zg/qA56Wxb4S2wOTpxrkjNEr3F6IH8QMyvc/ONlo4n5a3jFnAjAN50PATC40Mcl4yRnZLu2N+4A4Dl9HrdOOaFrV6JdHbBwuu0JTJxzkjNEb1syoZJv3bgYv8+HjzhNVi5PGuehaxb/5nuIJeMr7A5RfEixt+4G4BX3PC4fc4ZJFaah1h0Lhkj+yHKSM0RvO3UPPHH8EixLxxnYyZDqI5Izstzjex/HsAyMeCXDC6pZPHqQ3SFllLMqlP3ud7+Lpmnv+rZjx44PHMyPfvQj8vLyut9qamo+8PcS4p0sGV/Ba/+wiP/65KXcdsEk/uGSUbxgTMGpmbS99mc1Ai3WKpvXmSYVh/ZjPd1kAXY+o94PX6QWD4EX98cBmFamUVxYKJ0BbSQ5Q/QHXTnj3s+ez88/Mpk/3zaDPxuX0WTlorcfhb0vQ+sBME27QxXvV7QZIk2w/xUA7jMuYG61k4DPKznDRpIzRNbzFjBj4kR+fc0Q/vOaCfz9JaOYs/hamqxcBnGcureW99wrXWUzX7QZjJR6xrAs2K3GoT9qzGXhEA8ep6YOVuou8ObbG+sAJDlD9AdLxlfwyncv5nu3Xs5n5o8gNek2AK4xnmfP1nU2Ryc+sHQCtqgCtoeM+SweloOuaWr0qTug1hxFn5KcIbJaoBTcAWZXe/jzx2dQsegLWOhc6HibydoeDrZEOdActTtK8WG0H6ekXq1PhWsu7LlupkHT5YBFH5OcIfqDi6bU8tPPXsG/La7i20tGkTP9Y6QtnfOMdbRuecHu8MSH0VZHWfObACQHLzhzk6h4W2c32bK+jW0Akpwh+oMT98D/77oljA+q30cbXI9zLNxmb3DiQ3lq31MApNqmcsGYQlxdjQgFAGfVMutb3/oWn/jEJ971nmHDhn3gYP7xH/+Rb37zm92fh8Nh+aEveoVD1zi/thJKp8Oxjfx22Me46ODbVDW+SqJhD57cUmg9qH6RdPvsDleA2qxOdEBepfo8Fe0ucqJ2CQDhhMn/rW4HYG65AfnV0t3JRpIzRH/h0DVmDe8poLx8UjW/3HINP3DdibX1EbQhcyG/Rm1giMxmWdBRD0fWQjLCYauEN8xx/FNZGvxVkJP73t9D9ArJGaJfyKvBEa5nohfIURuaD719HdeHbiew5S7MGUvQnR5VKBNtUwfz5OdO5jFSEDqsplYANGyD8BGilodnzRl8v0Yd0CPRropkPXm2hTpQSc4Q/YVD15gxdiQURCCUz4adU5mcXE/4ye9hjX8WTabjZJ9dz0CslWNWIa+b4/n5iTmjcJh0dbKB5AyR1dx+yKuBxu04PEFGjxoDxy6GXc/wr4FHuLr9O/zwqe386ePT7Y5UfEDHVt5BBSZrzVrG1Nb2vBAPq2dFOWDRpyRniP7CkV/D6EHHGO1wgmc8T26+kCvSzxNe9j0Kxl0oUziz1KGXb6cGeN0Yx8QRQ0+/wTIhFYPiUT2TWUWvkZwh+osT98Bn1/4ji+5fiSPnKF9d9mce/Oi3bI5OfBC7W3ezs3UnlqWTDk9i4UiZQnKqsyqULSkpoaSk904wejwePB5Pr31/IU4TKAfvIS6f6GH5gdlcpr3Brmd+x4Tbfqo2R0NHoGSk3VEKy4L2erWhoHU2wt73CqTjarGwdCwAG48n6UhZFHs1/m6KX05c20xyhuivPrdwNNduvZDPmMupiTXC7mchr1p1I5Xi/MyWaO/sJvsqAPelF1Kd6+K20QYEZYyInSRniH7BE4CCwXB8C3iCoOmMmX8d9U88RjlNPP/0wyxeeou6t6urbG6lFMtmmmgzJDt6um/sfh6AZ83pVBcGuGFs50HKdByKa0E/q0E94hyQnCH6FU1TYzEjTbhmfor0yg1MT7zJssfvY+nVN9sdnThbG+4FVAfyiWUerhiZo8afgqxR2URyhsh6eZVqnyIegpw8mHob7H6eyakNTNd28MJ2eGDtIW6cLoUTWceyyNlyDwDrAwv47FBvz2vJiDpgIeuMfUpyhug3PAHIH6QO/roDBM7/BJGVrzI4sZMNz9zO5Es/ZXeE4mxZFoEdDwKwM38enxocOP2eeAhy8qWbbB+RnCH6oxJfCfPLruPVxr+xLfYYT21dyuXjat/7C0VGeWT3IwAYHbXMG1nCeUMqbY4o8/Tajk5dXR0bNmygrq4OwzDYsGEDGzZsoKOjo7f+SCHOntMNBUMY5EtQN+JW0pbOhMR6tm5er7oDhepUUY2wVzwE0Sa1GNhl1zPqfe0l3acfX6tLADCrzMRTUAku76nfKSNpTieFn/oUhZ/6FJrzrM4v9BuSM0Q2GVmRx5WTqvjf9PUAGNuWQes+6GiwOTLxnqLN0LIfjm/BROchYz5zK8HlzVUjibKA5AzJGSLD5Vaqrj+xVgDGVQR4tehGACYdfYADTZ3/Tt0BSMZUV1mRWdqPq+cL3aG6y+59CVBFT/MGeXDomiqSdXgyvsOT5AzJGSJLeAsgr4ZxVfms9F0EQM36/6a+LWZzYOKsdDTA7ucAeNiYx/zBHtUVONmhDtB48+2N7z1IzpCcITKU268O48XDqplEbiWMUtPV/iXwKADfeWgTbdGknVGKD+LwWgqiB4haHpzDF/R0kk9G1ZTDDD5gITlDcobIAnlVak811srCsVU84roCgOCq/yISleeMrHPoTQrih4hYHnwjZp/+umV1TmWtzrgpFpIzJGeI7PI/i78G6Xx0VxvfWP5H4sm03SGJs2BaJs8cUHVUqdBU5teW4Miiw3d9lTN67Tt///vf58477+z+fMqUKQCsWLGChQsX9tYfK8TZC5SBv4QbJrp4YOdCbnG+hHv9n2DCb1RH2dY6KB9nd5QDW7QZjCQ4O09OhY/BsY2ABiMvBiBtWjyyQz3cXTHMkVUj0DW3m7LvfNvuMGwlOUNkm68sHsfsdcf4O/NJRqcOwY7lkDdYLWI7BubDfsbrGqVd9wYAr1qTqaeIK2qSkFubNeOIJGdIzhAZzpWjOgMe3QA5adCdzFp0NYceeIQavZFnX32QIdd+Ut0rXWUzTzwMkYaeA3qH1kAiTIOVz+vmeL5R6+25z1uoCp8ymOQMyRkii+RVQfgwQ+Z/lOgzrzBZ38sjT97Otbd+0e7IxPu1+UGwDDaYI9hrVXHFyM6ckeiA4pEZ/7whOUNyhshguRXQVgeJzk5xU2+DXc8yIbWZWfpWVpnjuHv1Qb68SCbjZZOON28nADxtzmTxuBPGocZDqtDJc4ZugRlCcobkDJEFXF7VmfroBsjJZfQFH6XpuecZrh/jjcd/zuybv2t3hOIshFbdQR7wtHkeF405w2S8RLtao8rAbrKSMyRniOzidXm5btgneLju/3AXreAva9bzxbkz7Q5LvE+rjq6iKdaEZeSgxcZw2fjsmjzSVzmj1zrK3nHHHViWddqb/LAXGcfhhIIhFLnT+GbcSsJyMTK5nei+1aq7XPhId0coYYN0UhU2nbgwtPtZ9b5qWndB7KsHEzRGTQpz4ILaoozv7iROJjlDZJvyfC/fuWgo/51WXQKtnU9D827oOG5zZOIdRVvUwYu9KwD4W2ohVQGdWdUe8BfZHJw4G5IzRMYLVqiDE9EWAGoKvWyuuQWAGY0Pk4ie2lX2iF2RilNFmyEVA5dPfd7ZGfBxYzZDCjxMLusscjKSECzvnmwhMpfkDJE1PEHIrWZonoM1hVcCMGHXr0inUjYHJt63DWp89kPGPCaVuagtcql8oTkyuiug6CE5Q2Qsl7ezq2y76hgXKIPRlwPww9zHAIufPr8L07RsDVOchWQE9/bHANiat5Ca3M5D96YBlpmRhU7iZJIzRFYIVkCwFKLNzBhayHN5NwAwcudvsWSaavZIxfDsegKA/YXzKQ6c4QBeIgy51aojucg4kjNEtvn+wk/iNqrQHAn+uPF2DNOwOyTxPj26R00dSYUnMmtELhXBfHsDylC9VigrRFbxl0CwnPNqvNxpqA6l+57/PYbDoxa1W+vUIpToe7EWdRLO3dmtyTJhV2ehbO0l3bc9uC0KwNVDLdyF1Vm1aW2ZJsnDR0gePoJlmnaHI4R4ny6aOJgXzamsNWvRjATWtsehdb8q8BeZp71edSOPt9Gm57PCnMx1w030QAl4sqeTo+QMIbKAwwmFQ1Qna0MVOFVNXcJes4JCrYNf3a4WRwHwFahC2XjYvniFYqTVAT23X32eaO/uQv6YMZcbxnrVONRUVBUrZMHBPMkZQmSZ3Epwe/FOuppWK8BI/Qj/+h//bHdU4v2o3wLHt5DEyTJjFteP6dygTrSDN191gMxwkjOEyHCBMrU+3lXYNOVW0F0Mi29lrr4Fy4IL//cVe2MU75u57QncRoSDZinjJ07veSEZUQcqM/xZQ3KGEFnC4YSCoWCakE7gn3glB81SSmjjgV98x+7oxPtkbFtGjhHhkFnCxElTT78hGQFnDuSWn/5aBpCcIUT20TWdpYM+A0DKv4pb//qkzRGJ9yOaivLKIfVMmA5N5fpp1Wo/I4v0Vc6QQlkhAHQH5A+iwge/TS+l3fIyXj/A7rUvgr8Ywkch0mR3lAOPZUH7MfXfR3eoa/Wb1TWXD4bOAyCetnh6bxyAa8f4wJddnQGteJy9F13E3osuworH7Q5HCPE+DS8JABo/SX1EXdj7EjRsh456W+MSZ5BoV6O0D6wE4J7kfNI4uXaYpU7WZ9GDguQMIbKEv1T9fIk0AzC+LIefpa8H4LPOp6hr6JxY4fZLV9lMEW1WY05zOg9P7H8FjBQ7zWq2WoO5ZtQJRU++oowehdpFcoYQWSYnF3KrmFxk8Zu06ir7eR6ksTVkc2DiPW26H4AXjSlE9QBX1nrV9VRMFUDrmb8ELzlDiAzn9kFedc8BO38JjL4CgC87HwNgf1OESCJtU4DibERW3wHAY9Z8lowK9ryQjKgJek63PYG9T5IzhMgi/hLIrYBoM/OGBvhJWu1lXNHxMOlWWYvKBqFVdwKwXJvLBSPPcJAi1gbBSjWlJANJzhAiO/3djMtJd9SiaQZvhx8gaUiTqEz39P6niRtxzGQhQX0IF9RW2R3SWeurnJH5q3RC9BVfMQTKuGtpHn9KXwZA4dY7VIGmZkHbQTX6RvSdRBg6GiEnr+daVzfZYQvVCTngH19sA6DAYzFuSDU4PX0bpxBiQNI0jQf+bhZvWaNZYUxCswzY9ji0HoR0wu7wxIkiTapL4JF1ADxgLKAqoDGkJJh1hyuEEFlC19V4VA1IJ3DoGtdedinbzRpytRjH37in515fgfoZFZdCKFu116v/bnrn2NPdzwPwqDGX2kIXZQGHOshnpGQUqhCi9+RV4/H6mDT3Co5ZhVRrTex78qd2RyXejWnA5ocA1YF8fKmLvBxdFck6czK+K6AQIosESlQBZbpzw3DyzaA7OV/fzgxtBwDPbZPD25nOatlPsH41pqWxt3AhPlfnNq1lqjdZpxJCnEuaBvmDQXdS6Ewy4byLWG+OwK8laHjs/9kdnXgP6dbD5NWraUd1xQtwO05p+JGKgcMFeZU2RCeE6M8q8rwsqfwklqXhytvEXRtesDsk8R4e3/MEAKnQFKYNCRD0+G2OKHNJoawQXTo3s8cXaTQOu4ZmK0hp+ig7Vz2pimg7jkNHg91RDiyRZjASarQpqF/4961QH49aAoBhWjy/Ty0OfmmSjhYssSNSIcQANXNoIRePKea/0zepCwdWwtENqhO5yAxGWnVqrFsNlslqcywHrAq+OsFQJ60zvEuHECKL+YrUz5momkyxaJiPp/NvBmB8wzK2H+p8tnD71QELyR32iYdV5/GuA3rtx+DYRkxL4zFjDl87r7MrRyoCLr8UPQkheo8nCAVDuWKwwcNe1Yl8xJ6/sO+IFD5lrAOvQftRQpaPFeZkvjazM2fEQ50dyDOzs5MQIgvl5KnOgLE29XmgFGrVGvm3vWpT9Bv3b+Rwa9SmAMX70fbGHQC8Zo7n1tlDe15IRtSz4YlNQ4QQ4lzwFUJuFUSb+cKMXH7t+BgAZQceo3nv2zYHJ95N0xt/xYHJm+Zobpw18vQbYm0QKIec/L4OTQgxAPzv1ZdjtU8F4H/e/B3HwmGbIxLv5GjHUTY0qpxutk/hM/OGvsdXDGxSKCvEiXxFkFvJP87Qu8fcBbbcxZv7W3njYJgtWzZgpKSteJ9IJyF0CNwnjDQ9sLJnbF3ZBAB2t6TpSFn4nfCJacXyMCCE6HM/vn4K26whPGHMAiC07kFeX7uON3cewTAtm6MTxFog0gL7XgLg3vRCirwaN452QaDY5uCEEP2apkHBIHB4IKk2q2+5/GI2mMPwaQneWHY7aw+28fKuRra06hith6SrrF0iTeo5w+VTn3d2k11tjsGVW8rlIzsP7sXbVXFC10E+IYToDXnV4C/hikUL2WtWUKS18/hvvsfD6w6xam+zPGNkmk0PAPCUcT7Dir0sHJKjusyaaQhWqN8HhBDiXNA0tS5umepnDMDkW0DTmWluYKK2F4CLf/Yqj64/LDkjE5kGni33AbA2sICZNSd0eUp2qP0pV45NwQkh+rX8QWrNI9HOl66YxTPGDByaxda7vsVjb0vOyEiWRc429ayxOTiPSZWndAY0Uup9XpU8cwgheoWmafzHwm9imU6c/v1c9Zc/S87IUA/teggLi3R0CNNqKpleU2F3SBlNCmWFOJGmQX4NQY+Dyy6/lqNWIVVaM68++yA/frmBnz/5Jtf/98M8s+WY3ZH2f9FmSIRP7ryx6xn1vnYJaBptcZMl9zQCMKnYwpkvDwNCiL5X6Hdz+8cm8dP0DaQsB3ktG3n9lWf4zh1PM/cnL0nOsFt7PTRsg44GQpaPZ8yZTCkBzS+HK4QQfcBboAqeoi0AlAedRCZ+CoBbHS/wg8c38T/P7uS7y/byvQff4rW10smjz3Ud0Ot67rBM0tufBuBBYwFTK9zd17FMNfJWCCF6k9MNBUMYErRYW34jAJ91PsUPH1zJzX9cLc8YmSQVw9z6GACPGnOZWt6ZMxLt4MkDvxzME0KcY75iyCnoOWCXWwkjLgLgP/KXARBNGnzjgY2SMzLQgbXL8cXrCVk+klWzel6wLHXIQvKGEKK35ORCwVCItTG1zMHKkpsxLI35rOPOBx6QnJGBtq97ifzIfmKWm2T13NNviLepbsHewj6PTQgxcFwzYSL56YUAdPie5Ov3vyk5I8NYlsXDO9WzYKptOlMHFeByuGyOKrNJoawQp/IWqBEU7fX8On0VAJ9yPoOHFFHLg7e9jq/fvVp+8Pcmy1KjZx0u0B3qWsdxONJZODByMQB/WN/R/SWzq93qxLUQQtggYTk4aJVxv7EQgCsdq6ihgXCojS/cvV5yhl0S7Sp/HFwJwCPGPBK4mV1mSncnIUTfyasGt+raAaCVjuJNczQeLcXHnC9237Y/4uGPT73OC+t32hXpwBRtUv9tcnLV58c24owco93y8rQ5kznVHnU90aGmXXgL7ItVCDFw+EtYXW/x9CE3m80hBLQ4X3E+CkB9KC7PGJli59PoqQ4OW8WstWqZfWLOyK1U61pCCHEuOZxqakUypgorAabcioXGxNgaRmt1J90uOSOz7H3uDwA8bszhvKEnFDZ1TbfIybMpMiHEgJBXDb5i1m7fw8ajUR4x5gHw986HAMkZmWbjst8C8Iw5g+nDTzm0bVmQSqh6Bl3KfYQQveeZLcc4vH82ZtqHw9OAq3ANIDkjk6w+tpqWZD2W4SYdnsDCWukm+14kcwpxKk3DyK3ib28dZaMxlH1mOUVaO5fobxHCT0CLUqk18YNl26SleG+Jt0Gk8eROf7ueBSyomKyKm4BYuufv/6pJFTKWSAhhC8O0+MGybTgw+EX6WmKWm9H6Yc7Td1CuNQFIzrBLpAnaj0PdagDuNy4A4IpanxyuEEL0HU8Q8gZBrA3DsPjDygPcnVoEwDWO18hDHf6K4sGtpbnj6dckZ/QV04TQEVXIpHUuj+xU3WSXGbOI4+GS4Z3PGIl2CJaB02NTsEKIgcTQHPznqihuLcUd6UsAuMXxEpU0YgEW8oyRETapUaiPGXOw0Fk01ANGUhWy+eV5QwjRSwJlak0j1gqAkTuIt7TxAHzR+fhJt0rOyCCxVuamVwHwgLGAOYNO2MtIdqi9ELf/zF8rhBDngtONUTCU+9/cj1NLc49xASnLwRzHVs7XtkrOyCSpOJfyOgAPGfOZVuk95fWIyhmyxyGE6EVd+9+YPpLN8wFwF60EPSQ5I4M8vPthAFLtE8HyMH1Quc0RZT4plBXiDNbUW2yLBCjX2vhZ+noAbnS+gtdKELL8VNFEe6iFNftbbI60n2o/Dma6ZxPaMmHncvXxqMu6b2voUKfmrxwKNVVVfR3lueN0UnDLzRTccjM4nXZHI4Q4S2v2t3AsFMfASQMF3GGojewrHauopokAUY6F4pIz+pqRgtBhOPwmmGn2OUewwxrEx0ZZlJZVZu/hCskZQmSnvCrw5LLj4GGaOhLstSp51ZiASzO4xvkGXWtJLVYAZ8cx1u3Yb2+8A0W8TXWU7erclOyAfa8A8ICxkC9ND5CXo6tnE01To26zieQMIbLWmv0tbAvn0GYFOWbm84YxFo+W5uvOR7rvORaK86uXdtsY5QAXaYI9zwOqUPZ7c3PxuXSIh1X3cU+WdQWUnCFE9nC4oGAwpBNgptl6NMS9iTkAXKa/STnNp32J5IwMsOVhPKTYbg7iYwsm4HacMOUonYBAyTt/baaRnCFE1lrT6GRnxE8R7cSsHB7onJD3DdfDqJInyRmZwNr5NHlalKNWIR9bNBWHfspkvHg7BErVBKtMJzlDiKzVtf8NkGqZg5ksRHe24y58C8kZmaEj2cHLh14GIN02nV/eOgJH18TubNRHOUMKZYU4g4b2OEetIlI42G5Ws80cTK4W4wrnaiJ4ydESVGlNvLBVWomfc6kYtB/rGX0KcPRtaK9Xp+OGqdMqobjJioMJAD4yIffk7rNZRne7Kf/+9yn//vfR3W67wxFCnKWG9nj3xzoGv00vJWz5GKw3MM+xicrOrrIvbKu3K8SBKdqsCqB2q83rP8ZV98brR+pqESlLSc4QIku5fVAwmI5wCxomugZ3pC8G4Bp9JQEtBkACN05M3t68SY1RE70rfAwso+eA3p6XwEiwy6xigzWca0Z3duxIdIAnF7z5toX6QUjOECJ7NbTHSePkkFWKX0/yp/SlAFzneJUR2qHu+372wm4Zc2eXzQ+BmWajOYw9VjVLR3o7R6DGILcy60agSs4QIssEytTvpvEQLdEku61qtpiDcWomn3EuP+OXSM6wV3TNXQA8aCxgSW2w54V0Qj2P5GTPAQvJGUJkr4aOJHVWKSYaAS3KnemLSFhOztN3MF/f2H2f5Ax7ta26E4DHzHksHHlK11gzDVjqd4EsIDlDiOx14v43uEg0qP0Md9GraK6m7lckZ9jn8b2PkzASmIliXOnBLBiRxc0F6buckV0rdkL0kdJgDmECHKOIIVoDP09fA8BNjlfIt9ppsYJU0sSKDTuklfi5FmlUY03dgZ5rO55S70dcBE7VAfDerRGiKYtKn8X0UTVZtwEhhOg/SoM9nUlNHETw8rv0UgCu0FdTTQN5dPDohiOSM/qKZalDF8e3Qvsx4rqfR9OzGJYH42sKs/pwhRAii+VW4s8rIY8oAPVWAc8ZU3FoFrc5X+iui23Fz9Yd2zEi0om8VyU6oKP+5A3pnU8DqpvsuBI3wws6Ty0nIxAoV927hBCiD3Q9YzSQT4sVJGk5edaYjkOz+HvnQyfdK2PubLLxHgAeMeYxo9JNRdABqaiMQBVC9A2HE/KqIRml0Kt+R33EmAfAzY6XCHQ+c5xKcoZNmvbga9xA2tJpKp+nplZ0SUbAHVRvQgjRy0qDObQR5AjFFGnt6FjcY1wIwFedj9HVIRAkZ9im/Ti5R9S0o8ay+eS4Ttn/jofU/oa3oO9jE0IMKCfufwOk2ydhxKrR9CTuotcAs/s1yRn2eHzP4wCkQtOZPzqX3JzAe3yFACmUFeKMZg4tpNDvpt4qIoWT42Yeb5m15GgprnG+TgI3GhaB2FHW7G20O9z+w0hD22HVcUvrHCMRD8H+lerj0ZcDEEuZ/Pj1dgCuH+XEHcyisURnYFkW6ZYW0i0tWNI5TIis05UzuljA7cYlNFp5lOttXKBvoEproiWSZM1+KXrqE/EQdDTCfrWgdG9yLnE8fGSEiSO/OqsPV0jOECKLOT2MGTuB0hwDvbOr7EPp+ZiWxhWONynVWgHVVTYST7Fl22bpKtubIo2dBU2di0ct+6BxOynLwWPGXG6d4EPTNDBSoDnAn31FT5IzhMheM4cWUpGXg4GDo1YJPj3B/el5GJbGEsdbTNZ2dd97LBSX54y+dnwbHNtI0nLwhDGL2yb61fV4GPwlqlg2y0jOECIL+UvBHWBciZPigIe3zFoOmcX4tQS3OZ4745dIzrBH4xuqm+xKcwLXTRt08oupGATLs2qtSnKGENmr6znjsFVKxMohX+vgifT5xC0X0/VdLNA3dN8rOcMeB1++Awcm68yRXDZj1Ok3JKNqgoWj90ZSn0uSM4TIXl05o4fW3VXWlf8WWk5d9yuSM/re7pbdbG/ZjmXppEJTuW3WYLtD+tD6Kmdkz5OXEH3IoWtcPbmSMH6OUUiJHuYXKdVV9krHKspopoUgpVoL4abDNkfbj8Ra1NuJXZ32vABmCopGQHEtAL9b19H98idmlGflBsSJrFiM3bPnsHv2HKxYzO5whBBnqStndDFxkMTFz9PXAnC5800Ga/UU0n7KmArRazoa1TjtutUA/K3zVPxtE7K/u5PkDCGymyO3ggkjh1GA+n32KMUsM88H4BPO57vvayFAtPkwRJttibPfSyehre7kKRY7nwHgJXMKTeRx41ifup4Iq7G2WdiNXHKGENnLoWv8y9KxALQQJGJ5ieLlEWMuAP/gvJ8Tuz3Jc0Yf6+wmu8KcQiu5XDEyByxTvWXJCNRTSc4QIgu5fRAow5EI8bn5Q7HQebQzT3zC+RxO0mf8MskZfcyySL19HwCPGnNZMOSEYgMzrRqGnLgfkgUkZwiRvbqeM6LkcIgS8rQYSZzdXWW/5nwEec6wV2Ld3wB42JjPjJpTOgMmo+Dygr/Yhsg+GMkZQmSvE9emuhjRWlLtY9A0E0/xCsDofk1yRt96YNcDABgdtVjpXM4bUvkeX5H5+ipnSKGsEO9g8dhyAI5ZxSQsN62Wn5eNSbg0g1ucK0jjJGm5qDLr1Uar+HAsC8JH1Mlp3dlzbcdT6uPObrIALx1IdH9cWJr9P/CFENmvK2d0MdG437iAg2YpBVqEi/T1VGsNlAbc7/AdxDmTiqt8cmgVWCYbtDHssapx6xY5hRVqIUkIIezicDFxwiTcWgpH5yLSfemFpCwHCxybGIU6hZ3CRb7XBa0HwTTf7TuKDyLSqLr+5eSqz40U7FZdtx4wFlAVdODQOydcJKOQW5FVHZ6EEP3DkvEVfOOiWhK4OUoRuVqMe9MXkLCczHJsZ4G+sfveU8fhiV5kpLE2qc2Ih415TCpzqQ7kyYg6yO3Ntzc+IcTAEiwHTWf2kDw+et5gVpiTaLP8lGptXKW/fsYvkZzRt8y6NVRa9UQsD+nqWSe/mOwAT27WFcoKIbJb13PGcauQDiuHXC3GA+n5xC0XU/W9XKy/1X2v5Iy+lTq6mVoOkrCceIbNOf2GeEgdzPME+z44IcSA1JUzTpRsuBjL0nAFd+Lwb+u+Ljmj76TNNE/vV40/UqHp3Da7BJfusjmq7CE7PUK8g65W4u34OEoRFXor/52+CYALHRsYxlEc/iLG5KdVQY74cOJt0NEA3oKea4071QhUhxtGXARAOGGyuSEFwHM3+E++XwghbHLq+AkLnRRO/jd9PQBLHG8xxdfEzJIzd/MQ51C0CWKtsEd1ZvxzQp2Gf+laXY0lFEIIm00eMwrLV0Ih7QC04+d+YyEAN7tWoGFRHPBQO3QIdNSrok5x7pgGhA6BywNa55LIgZUQb+O4lc/L5mSWfaSzM0c6Ds4c8BbaF68QYkD78qIRlOd6aLAKiFoe3KT5m6HWR/7e+SAaFhV5OcwcKj+n+sy+FWgdx2mxAqxkCvdc0zmxItGunjecHnvjE0IMLN58VWSZCHPj9Bpy/X6WGWpixd85n+TEroAaSM6wwfE37gLgRWs6P19adfKLySj4S7JmfLYQov/48qIR5OfmcqTzQJ6GyZ2GGqf9HecDODEkZ9jg0Ct3AvA6k/nnS4ac/KKZVhMsguWnf6EQQvSirrWpLmayglTbDAA8JSvQtJTkjD72/MHnCSXbMNN+8hjD3y8ZbXdIWUUKZYV4B12txDXguFVIwnLTZAV53JgNwHXO11g0aSgOby60HIBEh63xZr32etXJyXnCSZOubrJD53efjpv2x/rul0cOrgaHnIwQQtjvxJyhdV+1eMKczXZzEH4twS2FO3G0HQRDimV7jWlA6DA0bINIE01WLs+aM/A4oLq0UA5XCCEygsPp5JYl83BoJq7Ocah/SS8hbrmYru+mRAuxdNoQHC436A5oO6B+volzI9qs3k7ICda2xwG4z1hEsd9FodehXoiHwVcknTqEELZx6Br/euU4ong5RCm5epQ/p5fQYeUwQd/PEv1NvrJoRE8XbNHrrA33APC4MYea/Bz8bl1tWFtWVo1AFUL0E7oDcishGcWhw98tGMbTxnkkLCe1+hFm61u6b7WAbyweKTmjDxnpNM4d6lljc3AeLscJf/dW5+QQnxQUCCH6XtdzRoNVSNTyENTi/CZ9Ja1WgBH6UW5wvMy3L6mVnNGHkqk0nh2PALAjby76qZON4mF1QEYOcwsh+lhXzjhxDzzZdBGW6cbhPYojsI3vXjpKckYfemDnQwCkQ1OYUB0k6JL9i7MhhbJCvIsl4yv47a1TCeQVcpQixmv7+VX6SpKdo1FfX79JndhOdkDrAbUoLs5eogPCR8F7woihVAz2vqg+Hn05ABuPJ0l1rh/NKANNNiCEEBmkK2eUd3eW1dCw+O/0jQAMaXxJdcpuP2ZfkP1dtEW97VsBwIPGApK4WFRlQW6VjM0WQmSMRVPH86klsxjmTwDgIcVdxmIAvu18gMf2dD5X+IqgoxE6jtsVav9iWepAhaaB3tm1qfUA2rGNGJbGfekLuHhYTs+96QQEy9T9Qghhk67nDC1YQcjyU0kTfzGWAKqr7Jq9DTZHOIDEw5jb1aHuh415XDbSq64nI+D2q41rIYToa94CNZEtnWD28GK+fNlUVjpmAvBVx6Mn3br1SNiOCAes559bRokWJmz5GDT2/JNfTEbA5VP7S0IIYYMl4yv431tnkw6Uk6dFKaeFX6SvAeAbzoc52tBkc4QDy1NPPkyV1kzY8lEzcf7pN6Siao9DupALIWxw6h64lc4l2TwPAE/pcxwJhewMb0A53H6YtcffAiDVNpOrJleiyf7FWZFMKsR7WDK+gsVjy1m3czjpujfx+wPc88yFfML5HJ837gHrQvAXqfGdwXLpHvFBdDSohaETT0/ve7nzl/5KqJgMwKsHE90v33dtiSwiCSEyTlfOWLO/hYb2OIU+Fx/7i85bZi0z9F2w80koGgYBGcnZK8JHVTHZ4bUA3GMsAuA3i32Sn4UQmUXXmT1tKueVptnW6qA5oZGnVRN+aQVj9DrGhF8DrlXFnE63mmDhL5FpCh9WrFXliRO7b2xfBsCL5lSOUcS/Lex8xkhFwB2QTh1CiIzQ9ZyxcWMZqWObcfhvoPmlFxmuH2Ps8SeAGXaHODDseAqHmWCPWclB1zC+fl5nx45kB+RWyzOeEMIeOXngK4B4CJwlzB5ejFH0JawHVnG+Ywf3LXbxkeUpAJo6kjYHO7DoO5cDsFqfwsem5J/8ouQOIUQGWDK+gsVDlrJr7Ys0mAE0fQQHXnqOIfpxxu79C/BLu0McMPL3qMMtq10zWTr2lLWoZFRNZJU9DiGEjU7dA4+lhvPvm95Edzfz2vHH+SLj7A5xQLhvx32ARToylHFlVVw2fojdIWUdaaslxPvg0DVmjhnC7CkTmVRoUnvRJ4lYHibr+2jcugJcXsCClv0yUvtspeKqyDjnlHbgO55U70dd1t3Bac0RtZD3bzNNHPmV0tlJCJGRHLrGrOFFXDW5inm1pfzyulp+kvoIANaeF6BhuyroFOdWPASRBjj4OmDxtnMSh6wyfjrXQsur6MzVQgiRQXxFOHKrmJCfYmFtCVNGDmL/oOsBuCV+P23Rzk1sbyHEmqUj+bkQPgqm0bMZnYrBrmcAuNu4iN9dVtBz+jrerg62uH02BSuEECdz6BpTx43hvFFDmF4TYHu1mlxxRdvdRDrabY5ugNjyMADLjFn85rLOzWvLAsNQB1qEEMIOmgbBSrXO3smRX4U2ZA4A59ffw2fnDQXgqc3HSKZNW8IcaCzTZFRoJQDDpyw45UXJHUKIzOHwFTJm+FAWVDuZX1vCK2UfA2B6w0OYHc02RzcwpFIpJnW8BsCYmReffkM8BIEy8MhobSGEvU7cA//IjNHUaEsB2Nj+BEc7ZO+7t6XNNE/sVY0/Um3n8d3La/A45ODd2ZJCWSHORm4VuL0MLvLzR+NyAIw3/0Q6nVKLGh3HZQP7bEUaIB4GT27PtabdcHwraA4YdSkAadNiXb0qFphR5VNjaPsLp5O8q68m7+qrwSmNvoXob8YMrmCtNZqXjMlolom17XHVGTAZsTu0/qW9M5/sehaA38cuBGBmmQ7+UjsjO7ckZwjRf2ga5NeoLrGpKACOCdfRZOUyRD/Oy88+imVZoDvUSM6WAydtfouzFGtTz2q+gp5re1+CZISDZikrzQlMr3Sr66YBWKpQNptJzhCi/3H71M+mRAeOcVdzxCqiUmth1T3/qXKG6D3RFqx9KwB40prN1PLOnJGKgdsLObnv8sVZQHKGENnNm68OCKdiPdfGX6fe73qaOSWp7su/XrGnb2MboA7u3cZgjpG0HNSMm3Pyi+kszx2SM4ToX3Rd7X8bSTAN8kYvZIdZQ64WZf39/253dAPCgY0vU6i1E7L8VI05ZVqIZaq3bF2jkpwhRL92w6gbMBIl6M4I337+F7I21cueP/g8rYkWzLSfoDmBceUVdod0bvVRzpBsJMTZyMmF3Gqqkrsxx99I0/YXKDeO8vRTD+Ids5gSR5rRrr04fEXSeej9MFLQVqcWhbQT6va3PaHeD53fXRD7k9fDRFMWBR4YNaisX3UG1N1uKn/8I7vDEEL0khGlQW6ZVsZ/r7+JRY4NaAdfY3NgFumQl4lTzsOhS3fsDy0Vg/BhOLoeEmFanKU8F59KpR+qywrBW/De3yNLSM4Qop/xFarNiNYDkOdjfHU+DxVcz/Vtf+H8hvv44+p51Ba5KPS6GBdowxE+CkXD7I46O7Ufg3Ti5DF129Vzxz3GhYwodFPsc6jryXZwB7I+f0jOEKKfyq2E8BHOr9D4Y+5H+Fz7r5l25K/c/uKNFBWXUhrMYebQQnnOONf2vIBmptlmDiZQMgi/u3MdK9muuju5/fbG9yFJzhAiy7kDah090tCzbl4xGQqGQut+5rc+yKSaS9l4qI2fv7ibAp+bAr9LckYvWvbYfXwF2OcayWjvKTki0QG+4qzNHZIzhOiH/CXqLdrMFaNK+M/V1/H91P8xsu5+Hln5SRzBYskZvcSyLN569m+MBHZ4p3Cew3XyDclIVq9RSc4Qon+79bxh/Gb1FSQ9t7Ox7Tl+8/oVVAeGSs7oJQ/sfACAdGgaM2qC5Hqy9ODdO+irnCEdZYU4W7mV4PbyzfMDrCq6FoBp9ffzy2c38w/L6/jefa+x8q21NgeZJToaVGennPyea8kO2PO8+njsVQCEEiZ/fFt1XvzoKNBzy/s2TiGE+JD+84bpjBo5kseN2QCkNz/C/z30Ipf++Ame2SKdyD+0jgZItMPO5QD8JrYYE52PjTLR8qrVqXghhMhU+TXgylG/BwPXX/cRmh3FlGuthNc/wv88u5PvPbaFrz66j7feXqc2VcXZSbRD+Ah483quNe6Axp0kLCcPGgu4beIJm9SJiBpfe+rmhBBCZAJvPgTK0RLtfO6mazjqqKRA6yD+8k/52n0buPmPq5n7k5fkOeMcC216CoAV5qSenGFZkE7K6GwhhP00DYLlkE6prnNd18ar/Qt984M8/onRTB+simz+ddlWyRm9aN3BVqrCbwPgrJ5y+g1GMns7Awoh+ienG/IHg5HCicE/33YFh/Qq8rQoh5/5X8kZvWjl7iZmJNYAkFc79/QbEh3qecMpo7WFEJnH43Tw5te+hjM1FE1P8cv1v+Vr962XnNEL6sJ1rD2+DoBk60w+en4Nuib73x+E/K0JcbY6u8qu37mf3x8dTp1ZQqkW4gp9NaCxL5LD7U++wovrttkdaWYz0qqbrNOtxsl22fUcpONQMAQqJgGwvbFnNNTnp+eeXFjbD1iWhRmNYkaj0o5eiH7qmS3HeG5XmP9NX0/KcjBF38s47QCu9sN84e518qDwYXR1J2/ZDy37MHQPDxgLAfjkJL/q0NGPSM4Qoh/KyYPcGoi2AvDGgTB/ji8C4NPOZ/BacQDqIg7+9NJWVq5db1uoWSt8rHM0dqDn2uaHAVhunkcLudw0rnMiSDoBuhMC2Z8/JGcI0Y/lVoLu4M1dh/ldbDEAH3c8RwWNANSH4nzh7vXynHGumCaegy8D8IoxiatGdXZrTEVUN8As7e50IskZQvQDviJ1MCzW2nNtxEXgzIGOeja+dB/rDjaf9mWSM8699fsbmaWr/aFhY6ed/GKyA1w+NV0kS0nOEKKf8peoQxftx1m1v4W/xC8A4BbnSwRQzYwkZ5x7O3ZtZ6R+BMPSGDV5zskvmml1ACaQvQfzJGcI0f+9sKOB0JGLAHDmbsbh2wtIzjjX7t1xL2CRjoxASxczf0SN3SGdc32VM6RQVogPwAhWcOdbx/FpCX6RvgaA652vEiRKDA8aFvc+/TJGOvUe32kAizRCtPnkzQTLgm2Pq4/HXAmahmVZfOwxtYB36SCLQHE1OJw2BNx7rFiMnVOnsXPqNKxYzO5whBDnmGFa/GDZNqLkcNgq5j5DLTBd6XyDcq2JYsL8YNk2DFMWCT6QSCPEQ7D7WQDuSc4jjJ/rh1vkFFapLo39iOQMIfqp/GpwBzGiYf7w6n5WGuPYY6oOgZc41bQKC2i1AjyyYjVGpMXeeLNJogNCh1VBcpdIE9a+FQD8JX0pH5/ox+3oHAOVCIOvADx5Z/hm2UVyhhD9mL8Io3Akj76xlXXmMDaaw/BrCb7ifAxQOQOQ54xzJF63jpxUG+2WlxlTpuDsGh0YD0OgLGtHZ59IcoYQ/YDTA4XDIBlT6+wALi8MmQdA+/qHyOf06RSSM86tUCzF3c+upEJrwdCc6OXjT74hFuqcWpi9uUNyhhD9lMMJJaMwXAHufXUra8xR1FsFFGthrtLfACRnnGtNHQk2vfEcAG3+oWg5wZNvSLSr9SxvFh+ukJwhRL/WtQduxoaTCk9A0yzcxS8CackZ51DSSPLE3icBSLXO5FuXVeBx9L9O432VM6RQVogPYM3RNFsiueRrEV43x7HNHExQi3OpQ41GaCQXLdrAxi1bbY40Q5kGhOrUKFP9hKLXYxug7aA65V57MQC/XddBqnNa1MfGudTJeCGEyCJr9rdwLBTv/vwX6WuIWW5G64eZou2hWjtOQyjCmv1S9HTWTANCh1S3lLpVANxhXALAbWMdMsZOCJE93H4oGMyeusM0dcSxNAe/T18OwC2OFXStI0XxEI7E2LJ1E5imjQFnkfAxSEbAc8Jmw7bH0cw0b5m1bLaG8dEJnd1kLQtScQhWgi7LJUKIzLa22cPhiAOfluJX6asAuN7xKuWdXWUt4FgoLs8Z58CTj90DwCpzLDeOz1UXLUvlYlmnEkJkEl+RerZIRXqujVDdnSaa26nWGukpc+ohOePc+fQdbzFZ2wOAUVSr9jq6dBUw94NO5EKIfsoTZFPYRzzajqZpPJBeAMA1ztdxkgYkZ5xLH/nDamboOwDw1Uw6/YZkFALl/a6BlBCi/zhxDzzRcDGWpeP0H8ARUHVSkjPOjWf2P0M42YaZDpBuH8tVEwfbHVJWk50fIT6AhvY49VYRMctNCSF+kv4IADc5XiHX6sDEQcTKIdawW532EifraIBI0+njhbY9od6PWAzuAJZl8bfNUQAuqIbZI0pO3uAWQogs0NDeUyRr4KSRAv5gqOKnax2vUa41U0brSfeJ9ynarPLJvpfBMnmDiey1qrh6uMbEwaWQk293hEII8f7lVtBo5JDXOc5urTmSt80R+LQEtzpfxLBU97oWgsSaD6mO2uLdJSPqQIX3hO6w6QTWdvXc0dVNtrbIpV5LRcAdkKInIURWqI9BGwFyibLLrOZtcwRuzeBKx2pOLIKS54wPJ5JIk9uyCQBP9UQG53duUqdi4PZCTq6N0QkhxClcXtXpOhbquVY9jZQzSK4WY46+FT/v3JlHcsaH09SRYF1dKxP0/QC4y0adfEMqqv4beSR3CCEyV33Ki4mGkzTPG1MxLI3p+i7KtZOLnCRnfDiHWqLsaehghr4LAO+phbJGEjQH+GWNSgiRuU7MBVaqhFTr+QB4il8FLX7G+8TZu2PLvQCk2qbzD1eUU5WbvZ3GM4EUygrxAZQGc4jg5ShFlOut7DYreMMYi0dLcbPrZSwLwvgpcqWgeZ90ezqRkVZdY3Xnyd1ko82w/1X18dgrATgUNjjSbqBr8Ov5FgQrQNNsCFoIIT640mDOSZ87SPP79FIarHwq9RZma1up0Y5T7rUpwGxlWdB2CNIJ2PU0AH9MXoLHAf8921Rj7CRnCCGyicuLr3QEfi2OjolfS/Kb1FIAbnK8TKBzYSmFizxfDrTsByNlY8BZIHT09G6ye15Ai4c4bBXzpmsG/7rghE3qeLvqRu729X2sQghxlkqDORy1iknjZDDHWW7MAOAyxxo8pE66T3xwa3cdYoq2G4AFM6b2vBBrBX9pVo/OFkL0U7kV4HCr34MBdCcdJVMAGK/tYxjHOFNXWZCc8WGt2d+CZcEM1wF1obj25BviYfCXyPOGECKj5ReV02jlU6KFMdFZa6qfZZfob+Ho7CoLkjM+rNX7mvGQZJR+SF0oHXvyDZFmtUYlzUCEEBns1FyQbFqIZXhweI/gyl/3jveJ929r01Z2h7ZgWTqDPLO4bupQNNn//lCkUFaID2Dm0EIq8nI4bhURszzUaI38Jq2KO5c6VlOjNVIc8DBy6DDVwaij3uaIM0jknbrJPg6WAWXjoHgkAPduUd1kZ5br+PyB079GCCGyQFfO6PqV1cCJhsVP0zcAcLVzFeN8YaYXRN75m4jTRVtUh/LDb0EyQrOrgpfNScyt0nH58sBfbHeEQghx1iaNHY3uKyK/s6vsMYp4wZiCQ7P4ovMJ4pab4oCH2qFDIdoI4aM2R5zBEh2nd5O1LNj8EAB3pC9h7mB/z6KSmQYs1YFLCCGywMyhhXjyStll1aBpcMBUP78mavsIdHYLrMjLYeZQWUv5MJ5f9RYlWog0zp6CJzOtDuXlVtkbnBBCnImvEPIHQayt+1LeyNkAjNYPUayFyOX0NSjJGR/evWvq0DAZpR1QF04slLUssEx53hBCZLyZw4qJBYeQtJzkaEneNtWe7RLHWxioBkiSMz4cy7K4b81BRmqHcWCqTuP+khNuMNVbfg3oUs4jhMhcp+6BW0Yuyeb5ALgLV4IelpzxId259U4A0u3jOH9IJQWeApsjyn6SWYX4ABy6xr8sHdvdVTZPi9JiBXnYmAfA513L+Ny8ITjcHnDlQNMeSEZtjjoDGCloPQhO98ndZNMJVSgLMEEVjsVSJvduVQt2nxhlqM6ATk9fRyyEEB9aV84Auh8U4rh40FjAdrOGoBbjs+U7cYQOnrSJId6FZUHosNqg3voIAL9OLMFC5xO1KcirBofL5iCFEOLsOVwebrp4Hl4tiQMDgHvTF5CyHFzg2MhY/SCfmz8Uh9MB7oDqKpuUgxZnFDoMqVO6yR5dD637iVgeHjAW8vFJJ3QBjIchJw+8stAkhMgOXc8Z9RTSgZcYHvaaFeiaxSx9Gzom/7J0LA5dukx8UEea22k7sBmAVP6wnnWpVAxcvpNzjBBCZJKuw8OWmnSnD1Jdx0foxwhqUUq1ttO+RHLGh7OnoZ2Vu5sYojWSY8bUulTB4J4bjITKI9KJXAiR4Ry6xreWTiWJCw9p3jRHATBF24MXNe1IcsaHs/lIiHV1ISY6DqoLxSNOno6XioPLK88bQoiMd6Y98GTLbMxULrq7DXfRa/zzFaMlZ3xArfFWnj/4IgBaeDY3ThuMS/a/PzQplBXiA1oyvoLf3joVgpXELA9BLcqdqYuIWh4m6/uYYqiFdLyFEG9Tm9jWmUcaDRjt9aqbrPeUEyN7nod4SJ2mHjIXgJ+v6aAtblEV1Fk8WJfOgEKIrNaVM8rz1GgJAyc+YvwwfSsA5UdfhLaD0HoATNPGSLNErBXaj0HDFmivp8ORx9+SCxierzN3sFdyhhAiq10wbQKfuHgmw/1JAI5TyCOG+h35K+4nmTEoX92YkweJMLTV2RRpBouHIXwYvPknX9/0AAAPGgsYUprPtAp3z2upqOoM6HAihBDZQj1nTMMdKMBDiu3WIACm6HsYlO/m4rHlNkeY3f79iU2M1A8D4C0b0fNCKqa6Pjnd7/CVQghhM09QFWR2HarzFUGwAoBhOVEqaT6pq+zI0gCXjJOc8WH8YNk2AK4pUXmD/EEnNwtJxVTRkxTKCiGywJIJ1Xz5sqmU+zUaKaDBysOpmUzR9zCxPIcl4yvsDjGr/esTWwG4JK9zTa9wxMk3dB38dnn7ODIhhDh7p+6BY3lJNF0IQE7RGkbVxG2MLrvds/0e0lYSI17B+TW1DC8utTukfkF2gIT4EJaMr2Dx2MvZtKGUZP02HAWDufeFpXzaeIjIa7/DO3yOOiUcKIFQHfiLIDhAF5zSCVUA5vaC7ui5fsL4U8ZfC7qTpqjB79Z1APCliRoOfwHk5Pd5yH3G4SB4ySXdHwsh+ieVM8pZs7+FhvY4QZfOdx8OsiI1iQscG2HLI2rTIlg+cHPF+2FZEDqiupR35o/fxxeTwM1XJxhowZr+fdJacoYQ/Z/Dyaxp05hZBtvaXDQnLLzmINpfWcNI7SCPLF/GtVddozpN+ItUoWygTI1YFUrbIdV9w1fUc61pNxx6E8PS+ItxKd+feUKuSEXBmdP/DlpIzhBiQFgyvoLFVRexe8NKzIY0bH+T6Y7d/Htbmp8+uZ5vXznN7hCz0uZDrTyzs40rXZ0FTwVDel5MJyRnCCEym9MD/lJoO9CzRlIyGtqP8dWpLnYGhlFPAeG8MXz74c3sbujgd6/s4wsLh9sadrZ6Y28TK3c3AXBDRSOEgbxBJ9+UjEJx9ckdA7OV5AwhBoQ5E2o5vzDK1kgu1toJ0PAa5+k7+Vn9eO56ZQsfWzDe7hCz0vLNx1hf1wbATO8RiABFp+TfdEKt9fUHkjOEGBBO3QMPxUbz401v4Mg5zj+99Cv+evV/4dKlE+rZSJtp7tqqGn8kW2bzmVuq8Lv6+aG7PsoZUigrxIfk0DWmjBsLuR3gcBBfeCv1L7xIudHA+hfuY+qSj6tNV92pNmdz8gbmCbDwUdUBMK/q5OtH1qoCWpcXRl8OwOM7Y90vX1KVVJ2d9P7bAFv3eKj++f/ZHYYQog84dI1Zw3uKdn4QDfHDRz7KPH0zzoOvw8jFkFOgOm9Ld6Izi7VC+1HVgbd5Dyndw13GYgAW1ejdHVL6K8kZQgwQ/hIcwTImaMchV/1c23jwI0w6eDuz6u9i5b4LmDcsX419joWhZZ96ztBlwZloC4SPgK/g5Osb7gHgSXMWdVYZ8wZ5el6LhdQzRz87aCE5Q4iBw+HNY3RVMRRNgO0wTq9Dx+TXb9SzaOxRpo2otDvErHPnyl0A1GqnFMqm46oALSfPnsB6ieQMIfqhYJk6VJdOqJ9bpaNh3wr0ph2MmXg9Y6ItUO1m34Lh/PzF3fzkmR0sqC1hbGWu3ZFnnTvfOND9cbnVqD7IP6FQ1kyr96dOvMhSkjOEGCBy8nG4vUz0u6B2BjS8xtLcPfysBf756YPMrS1jaEWJ3VFmnbtWHez+2Bs5oj4oGNxzQzKi9sz7yYF4yRlCDByn7oE/f+BmNiT/j82hFby4dy1LRs6yMbrs8+yBZ4kYLZhpH+nwZMaW9f+1vb7KGf238kyIvuQJQF4NxNqYOyyPPzpvBmDkwfswoy3qHl+RKu5p3qu64Q0kiQ5oOQA5uaCd8mOnq5vsqMvAHWBXc4p/XxkGYGaFg6LcwMmdoIQQoh+5dFotx5w13GdcAIC14R6INEDosM2RZagTu8lufQSAu5MLaSPIRYM0ggUl4C14j28ihBBZQNehcKj63TmlDpFNXPQRDlvFVGgtvPn033ruDRRDe706mDbQWZYqBjBTqoi4S+gw1r5XAPht+kquHe3F4+zs5GSmwTK7C5KFECIreYLqwJjuBocbp5WkRmsA4Lo/vW1zcNnnpR3HeWhTE25SDNPr1cWuQtlIMwRK+12hrBCiH/IVqaYVHQ3qd96SMep64w5VOKtp0HaIL53QRfamP6yyKdjs9djbR3h263EAvj4ZtHDnmt6JhbLxkCqSlTUrIUQ2ycmFvGqINkPBUACGGvsBtcf96b/Kc8bZuvONA6za1wzAD2aaqm4A1OFtUOtT0ZZ+eZhbCDHw/OLKW0l3jEDTDf7p5V8QTUXtDimr/HT1HQCk2mbytUtKyfPIOtS5IoWyQpwreZXgDkKinRnzL2eTOZSgFmP/C39Ur2saBEpUB7z2Y/bG2tdChyDVcfomQutBOPQmoMH4awG47N7G7pe/PC6tRku4fQghRL+k6/zTJUP5Wfp62i0vWtMuOLoeWvepRXRxslir6hIYa4Ej60hbOn82LgPg6xMNtXDXjzuQCyEGGG9Bz4YEoLk8bB/6cQC+6HyCN3cdUvfpTvX7css+1XViIIs0qmetUw/abbwPDZMXjSnssAbxtZknbDbEw52b1v2jU4cQYoDSNCgeCfk13RMWvjyiqfvl9XWtdkWWlT51x1oAhmj16Jjg8oO/BExDHWLJq+kfo7OFEP1bV27wFajfeQtVkRORRkhFVQFOPIxbS3Pd1GoA2uNpdtSHbQw6u1iWxdfv39D9+ScneqGt8zktv6bnxlRc5WeHjJsVQmSZwuGqSNYTAE1HS4S5tkatPe1rTVHXLEVP75dlWfzLE1u7P79hcOcanie3pyg2GVGfdx3SE0KILFbg91DNTViWTsK9hfu3PWl3SFljQ8NGGlM7sCydVOv5XDdlKPqpDQnFByZ/k0KcK26/WvyIh7h4uJf/SN0KwND6Z1UXWQBnjjqt3bRbdVkdCKItqqvTmUZEbFTjTxkyF3KraI4apM2el+dVaWpEVD9nRqNsHz2G7aPHYEbloVKIgea680fRTB6/SF+jLqz/qyqKatkHpvnuXzyQWJbabLAM2PIwAMvMWRy21Hin8VX54C+2McC+ITlDiAFE09TzhcsLiXYA5i68jLVmLT4tgeetP/Tcm5MPibCa4jDQpld0MdLQul/9vTk9PdcjTVi7ngXgN+krARic7+x5PRlRBckOJ/2N5AwhBhhXjuqOnaeKnZaWNHS/9ORG6Tr+fm0+3HNgcVBnV17yqjoLYy1VKNsPC50kZwjRT7m8qtjfMlURTk6+uh46rA7cWWkw03zvstHdX7J80wBr8vEhrN7X0v1x0A15jgTE29SFznysWP0qd0jOEGIAcbqhaJgq3uz8ufZPY3sO5C3fLM8Z79dLO3qez2rzLXyxzskVeVU9N5lplS9c3j6OrvdIzhBiYPvjTUtJtZ6vPt78B5qiTe/xFQLgh6/9FoB0eALjK0qoCPb//W/ou5whhbJCnEu5leAJoifb+Y9b5vOUMRMdk7UP/hexpKHu8Raqje7m3aoTRX9mGj2FXq5TusK2H4Pdz6uPp3wUgMd2xrpffuoaF5qvUMYRCSH6PbdT57HPTuYOYwl7zEqItWJsXw6hI9BRb3d4mSParMaKJ9ph38sA/C69FIAXrjLUSLt+tOkghBCAmsiQNwhibWBZeN0OnHO/imlpTI6s5Ft3voxhWqp4x1ekDqhFGt/z2/ZL7cegveH0brKbHkAzU6wxR7HOGsWrHy/teS0ZUQcefQNjoUkIMRBoajIPkBOt5w+X5ADwl9cPcOPvVmEN1MMUZ+Gu1Qe6P/7peZ1dngLl6r1lqkJZ6eIhhMgmukP9/IKe4s22Q+q6mQYjRVHAw//eOAmAX7y0h8/+da1NwWaXv6460P3xi9e5VNMQUHsaJ+2HaKA5+jQ2IYQ4ZzS98zD3YAAKE4f597nqgPKPn9nJtx7YaGd0WeP21w90f3zfJai9DoDcEwplLVP2OIQQ/cqQYj+fm/g5zLSPduM4n3301xj9vUbqQ6oL17EjvAqAZMt8/uvGUbh0yQ3nkqzqCXEuuX3qQSEeprbQyV2+TxC1PEzXd3J4/TPqHk2DQCm0HVYb2f1Z+zH1FjjDxvPG+9Uv/NUzoGQ0pmXx7yvVWKfPTvEzLhhXp+h0WUASQvR/k4ZVkuv18K9pNVJb3/EEdBxXHcmTcsoW04TWg6BZsPE+AJ4yZrLTGsTXproYURpUuVUIIfqjvGrVuSOuOtxNGjeO+8xFAHwm9hf2tyTUfS4v6LrKHemEXdHaIxWDlv3qeUw/oTNstBlr22MA/CZ9Ff9vbi6D8k54PdamCso8gT4NVwgheo2m9UxZ6DjOhYN7OmyvOdDCsVDcpsCyQ3NHggfWHgbgh7M08lKdnU6CJxbKSrGTECLLaE6g86BE/iD1PnRY/SyzjO5mHksnVXZ/yfPbjhOKpvo40OxyLBTj6S3qgPvPr62lNMfoObQYOMOUPL3/TbAQQgwQmg7oEKxQn4ePcsNod/fLD68/TDwlRU/vZn9ThNf2qGeLP15TQ2EOED6iXjyxUNY0wOE+/RsIIUQW+9yciSQbLwFgd+JJdrTssjmizPbTN38Pmkm6YwTfu3Q0Qwpk//tck0JZIc613ArV+Ske4lNzh/LLzlHaldv+CMkOdY/DBd48aN4DkWYbg+1FyYjapHf5Tl8EijbDzqfUx5NVN9m9Lenul2+qBdyB07tBCSFEP6VpGt+/fDSvmRNYbsxEs0xYfydEW6Ht4MAdo90l0qAOXiQ6urvJ/iJ9LQA3Dk1AbnW/GkckhBAncfugcKjqqG0aaJqG5/xP02b5GaPX0fb24z33+orU5mxrPz+Qd6q2Q2rE6anTKDbcg2YkWW+O4GVzElePPiFXGEm12dO10SOEEP2Fv0S9b6/H4dD55/N7ijrXHmy1KajscOLfz+VDTPUcAqcUykpHWSFEljlTR9nQIVX4b6GKZQGXQ+dbi2u7v2xdXUsfB5pdVu/r2de5cESe+nvsOCVvdLOkIYgQIotpKmd0/WxrP0aOy8EXJvX8XNtwqM2e0LJEV5EswLwaj/r7bD+mLuT2HFRRHWWlUFYI0b8Ec1zcUHstRrwCzRHnJ6t/ScIYYI0+3qfWeCuvHFUNGJMt85k/sgqvU/a/zzVZ1RPiXHN51cnsRDsXD/MwfMHN7DUr8KfbSLz5l577PEE12qhxp+qA1J9YFrQcgET49M1qgM0PgZGCsvFQoUY6/d1ytRkxrsTFiJwOdera7e/DoIUQwl5XTf//7d13eGRV/cfx952e3pNNdrOdZdlGWWDZpfcmRQRBgR8oUgRRsWJBFJQmKIogRVRAlKKANOkdls7Cdrb33WTT+5R7f3+cTCZ9k90kk8x8Xs+TJzOTmeTcsMwn957v+Z7x/PKoEn4TOpsmxwdbPoWyReb9NFm30QaTF5VrzCKTBf8EHF50TDfZuSVuRmcHIKObTh0iIokks8QUPjWZyeov7V3Cp6XnArDbmvtpqa82z3O5ITUXqtYk7oK8zppaF5Wk5piJhqj6Mlj6JAC3hM/guEkpFKS2m5xuqoa0vO7PV0RERiwrVihbb7rcXTDdzaUHmw6Cv31+GeGIHa/BDWuO43DxAx8BcPqsfLJ9QJ35HbZ1BnQcQIWyIjLCuDyxBdhZpeZzzYbY1+1YA4vLj9yN8+aarbVv/N9ybDvJF273wLYdrnjYbDX+tQPHk+51TNFx/TbzhPR2hbKOo0UWIjKyRd/Doju61W4Gy8WP93Vz+t7m/e6G/y3DSfZmHz0IRWyuemIRAJcfMZmAO2J+n21dyNt1CnRsdSAXkYR03Wl7MSNwDgCfbH+LNza+EecRDU9/XXgfEYJEmos5fdZMSrrbqUJ2mc7MRAZDZomZcG2q5rgpGfzGOR8Az9LHcbaviD0vvRAat8P2lW1bHCWE+jIzWZ2W33GyGqC5Flq3P2Xvs8GyqGm2WV1lLsjNKfG0dnbSm76IJJ+T99+dba4C7gifbB744F6ItJicSLZttKPqtkDDdrP4orWb7C1B0012TmEYMkogkBnHAYqIDAG3F3LGQzhoOqECux14GkvtUrJo4MMn74g915cGThgqV5rnJzI7AhWrze/El97xawv+CZEQ79lTeduewX4l7TpyOLb53WSOAZcui4hIArEsSMkz11UiIXMNxrE5Yy8z+bqhsolrn14S50EOT4s317bd3m9MqrkRLZTt0FHWUrGTiIwsLleso2z0/Sza+RS6zEucuZ9ZXLF8Wx23vrwC6er9tbFuu7PH5cSKjeujudFubsOxaevGKCIyElku8x4WLdaJ/o3s2Hx1tsmVBRuq+dvba+MzvmHu9eWxJij7jM0x8zztC2VT89s9Wx3IRSRxfXve8YRq9gTL4fp3bqO6uTreQxpWmsPNPLz8UQCCFYew3/h80jvPeciA0FU9kcHg8UPOOAg1ku5xmDJrLk9H5uDGYevzv4+t4I6uwKteB9UJsj1qqAkqVoDbA55A169/9oh5Tt4kKD2Adze2sOfdW9u+fNn0sOmClZI7hIMWERkecjNT+eo+hdwd+QLr7NbFFEufMp8r18Z7eEMv1GS6yfrT4OMHAIdnI/uzzDGTNl+b7oGskt6/h4hIokgvgozitk6xJVk+Xim5CIC5dS/y1gcfx56bVgh126BqXTxGOnTqtpiPaPfEtse3Yi99GoDfhc4ALL4yIzX29eZas8ii8+tEREY8C9xuCGSZu83VYNtMyPVz7HQzsX3f/HW8trys52+RhB7/ZCNfuO2ttvunTsuEULNZrAexgifHUaGsiIw8lgtoLdJMb/37t6nKLKgAcDoWyk4ryeTAyXkA/PHlFXy0rhKJefC9dZx197tt94+bPipWKNt5gQUAyg4RGeFcrTmSkm3u2yEINoBjs8+YdGaNMece1zy9hKVbanv8Nsno7jdW8Y37P2y7f+iUAvP7C7fEdptNa1co66C8EJGEddBuBeQFT8WxvZQH13DHxw8QSaRmgrvo0v/eQVOkFjuUjdUwi6N2Hx/vISWsQUvatWvXcsEFFzBhwgRSUlKYNGkSV199NcFggne0EYnKKDbFnk1VXLxPOr8OnUOD46e4YQlVnz0Te57HbyZpt39uJrNHMscxHZ0aKyE1r+vXGyth0b/N7dlfA8virMdiW8LeeEQWeZ4WyBydXJ2d3G7SDj2EtEMPMRNaSUiZIRJz2dEzaMHH1eHzAXAWPwaNFWYb7fry3l+caKo3QHON2c5pzevYjsUfwqab7J8Ohay8Yghkx3eMQy3JM0N5IUnN5YLcCaazRKgRgJMPncu/I4fgshzyP/o9dU2t/y+43GbxWdVq05U7EQUboGIVeFNMx932Pr4flxPmrch03nP24G8n55LqbXd+EayH7LHg8ZHQlBnKDEk+lmWuzUQXHzdVAzY4Nj88dmrb087/2wc0hzQZERXdPhvgoYsOwO92oKm1MMyb2q5ruRPrqJVolBnKDElc7Qtu/Fmxv50bt5v62Ui4y0t+esIebbe/9Of5RGxtpw3gOA4/e3xR2/3/XnYgHrfL7FZhWVDfOr+T3qmjbHTb8kShzFBmSPKx3ODygL91Z7fGKnPe4dhcfdL0tqcd/4c3cRxlBkDEdrju2WVt95//7iG4XJbZQbCp2jzozzDXtdpLtHMNZYYyQ6Sd337xMILbjwDgwaUPsqpmVXwHNEzUNjXzXtVjAAQrDubOr40ny58V51HFwRBlhmewvvGyZcuwbZu77rqLyZMns2jRIi688EIaGhq4+eabB+vHigwf0e1RN39MTobNf762B3+4/zR+6v0XKR/eCbvNjRWT+jPM6rHyZeANxDp/jDR1W6B6rVmZ3t2FnwUPQrgZCvaAcQfy8OKGDl8+c3IEnPSk6+zk8vsZe9dd8R5GXCkzRGKKslJ45VuzOeJP8N/IPE5xvwPzb4Ojr4XtK1ovnnTTsTvRNFWZjuspOfCmeR/4T+Rglrd2k/3CRBdkjUm8C0c7kOyZobyQpJeaawo8K1ZCZgqlWR4aT/o2lc98zFTXBj5740FmHfs181xfmikILf/cFPgkUnY4juk43lJrFtm1V7kae/lzuIDfhc/A74bDx7c79mC9KXpKgnMOZYYyQ5KRZYqeUnLM3eZqcMaAHWFyYTqPXTqP0+54B4AXl2zjpD21O8M1Ty1puz0+L5UDJuZBeWWsm2z7heCOA67E3D5bmaHMkATW/jq9ZZm/g2s3my2f0wrNvEQn00uyeOCC/Tn33vcBeOPzcg6fWjhUIx62vvvwgrbbcyfmsWdptrkTbjZdZVvqzP30dr8rx0m4QlllhjJDkpDLBWHHXJdqqTXnGb4UsG1mj8vjz2fvwzcfNDsdvb+mkjkTu2mmlGTOvfe9ttsn7VnC7qMywI6AbccW5aXmd3pV4nUgV2YoM0Tamzcpn58ddBE3LvwEt7+MX799K3cddwspnpQdvziBHXvvzbgyarBDGRw0eh77jCnFSsBrTzsyVJkxaEl73HHH8be//Y1jjjmGiRMncvLJJ/ODH/yAxx57bLB+pMjwk15kLoo0VjI6w8P4g7/CQns8gUg9vP3Hjs9NyzeTtmVLY9stjCTNtWYS3uMHTzeT8PVlsORJc3u/C8CyeH5Vc9uX/316nrmQlDk6sSbxpU+UGSIdTRwzip8eks81oXOptdJNx7xVr5puH1VrzEX2RGZHTIfySBC2LYQtnxLEx+/CZwDw3CmW2cYuNTfOA5WhprwQwRTK+tJNx21g9zH5vDf2QgCmrvsX1GyMPTetwGRHxarEyo66rVC93pxDdb5g9N6duLB5OjKHj50pvHRupwn9phrILAF/OpLYlBmSlKLvidFC2aYq87l1W+19xubwjYMmAPDEJ5uGenTD0jMLN7fdfvSSeeZGpCVW7BT9XYLpCugatL4TEkfKDElonQtuogvG6svNThSRroWyAAfvVsBX9i8F4DFlBgDPL97advvOc2bHvhBqjOWGJwDetHavclrzOfkmuhOVMkOSkuUCnNj1+KZKcDB/HwPHzyzm5NZFeE8sUGY4jsM7q2I7ql5/2kxzww6buY/oeVp650XcTsIVyiY7ZYZIV+fNncLunnMB+KTiTV5e93JSdyNvCrZQ638egGDFoXzryLFk+7PjO6gEN6RJW1NTQ25uzwUNLS0t1NbWdvgQGdHcHsgeb/7wjQQ5cUo6Pw5dRNhxwZrXYc2bHZ+fXmQKSsuXQSQUlyHvlEgItn8OofqOnTba+/h+sENQvBeMno3jOHy6zRzjlQdmsG+hY7rwpmtluhjKDEl2J+2/OxVkcU3wbPPAx/eZwtGqtaZAKJHVbTVdylNy4b27AbjfPpYt5HH9YWlMzQGyShOyk5P0347yApQZkmB8aZA70Syys03h0277H89bken4CFL14s2xoljLZS66V62B2gSZqAg2mo66bm/XBXobP4AN7xN03NwUPos7js+hNLNdQVO42bwuY9TQjlmGDWWGJAcLUrLNzcbWTkWteQG0dZF9eVkZH6ytHOKxDS81jSG21ZoCsQe/MYeCDL/5Qri5bUFKx8V5mrhOJsoMSRg9Fco2lJuttLvpKBsVzYynPt3MZxurB2mAI8PWmmaaQ6Yg7KlvHURWqtd8wbZNsXFbbuR1vF7l2CRih0DpSJkhCc9ym2tNKa3/zqPnGa2FshDLjH+9v4FlW5P73/eq8tiOqq98/1DS/a3XpuywWcQY7Sjb7W5HmvNIdMoMEThv76MIVZuFZ7//8DbKGsviPKL4ue3DB3D5qrHD6fz6xEMZnz06KbvJDqUhOzNbuXIlt912GxdffHGPz7n++uvJyspq+ygtLR2q4YkMnrQCSB8FDZVkBVwsccZzZ+QkAFpe/11spTGYFdwZo6B6g+nO2m4iY9hyHNOhqnazKfTtTuUaWP4sACvHn8VLy8uZcNsWKppsAh6Lc2emmdVz6YUQyBrCwQ8PdmMjy/beh2V774Pd2Bjv4QwLygwRKM7PBuDfkUN4KzLdFMm+e7vpYLT9c2ipj+8AB0trAVTEHWDDh89A1RqqnTT+GDyJLL/Fl8Y2mLxJ6f1CQqJSZnTUl7wAZYYkoMwSSC2ARtOdYlKul5+FL6DZ8ZJTuYB1Hz4be64nAL5Uc37RVB2f8Q4U2zbnHs3VHRboRWz4bEMlta/9CYD7I8fQnFrMsZM6FdI2Vplzs0D20I05jpQZHSkzJClEO9a1dZSNTmDHri9NK8lsu33GnfOTbhI7YjvMX1XBv95fx57XvADA+LxU5k1qt/A73NxDR1nHFAkkIGVGR8oMSSid37faF8q6POZ6k213fR2wV2l22+2T//Q2q8sT9FpUD6KZ8cC7azng+pcBmFacycwx7eYwIkGIhGOFsmmdttF2HMCVUIWyyoyOlBmSFNo6yrb+zdx6Par9ecbscbG/m4//w5tsqh6Bu6fugmhm/P3tNRz1u9cB2H98LhML2u1oFAmZYtlooXFqftdvlEB5AcqMzpQZIsZ+E/JoKTsBO5xKWfNm/vTRPYRGUiPBXRTNjDvf+Jz7Fj8AQF7kMKaPGkNOIGcHr05cQ5UZ/U7aK6+8Esuyev1YtmxZh9ds2rSJ4447jjPOOIMLL7ywx+/9k5/8hJqamraPDRs29P+IRIYblwtyxpsFYOFmfnd0NreFv8gquxh/sIqq127v+PxoV9XK1SNji9SaDVC5ylwA6m77OceBd+8Ax+YjazrffS3MdS+sa/vylBxI89jmeZmjk7Y7oNPUhNOUeCeNygyRXfPrU6YBVlvxE5s+hvXzzaRtxYqRsaCiPxwHKtfw8efruOzhxaR/9jcAbg+fQi3pzMpz8LksyBln8jVJJWJmDGZegDJDEpDbC3kTTQ6EW7Asi7MPnMIfw6cBkPXxHWzcsi32/JQcU/SzfXmvHaOGvbrNUL3OTO63nje8s2o7F9z3Aa8++QCZjeuocVK5LfxFZheC29Xu3CISBCzISq5zDmWGocyQ5NFaKBtdEBAt2ml33uB1u/jOkbu13T/u1jfZnCST2M8t2sJBN77CV+55l588tqjt8Zmjs2LdOiLhjgVP7RfoObZZ5J6glBmGMkMSTrS4KSpaKFvfWihrR8xOcN1I9Xm44KAJbfePuOV1ymqbB3Gww0f7zLjqicVtj08fndnxiZGg+WjfUbY9xzbnHwlW+KTMMJQZkjRcLnPtvvN5RruOsrlpPs7c1xTzOQ4ceMMrVDYEh3ig8dE+M3751JK2x6cWZ3R8oh0xhbLRheyp3TQDSbC8AGVGlDJDJGZ0dgrHT5tES9kJADy+6jHe3vBxnEc1NNpnxi3vPIDLV4kdTmdq9t4UpxXjSsAc6I+hyIxuqtp69/3vf5/zzz+/1+dMnDix7fbmzZs5/PDDmTdvHnfffXevr/P7/fj9/v4OSWT4S82FzDFQvY7T9hjNERPG8o17LuTf/mvIWfccrD8Mxh4Qe77Hb15TscJcrMqdMDwnc+vLoXy56VDlTen+ORvehY0fEHLc/LnlGACqiJ0YbCuv4INldey329iuF5FkxFNmiOyac+ZO4MiJKcz9/UfcEj6Dn3n/aRYfnHoXVG8Ef5YplEoU9WV89NkCrn+9nK+6XybHU886u5D7IscCUL55HW+X7caBuysvEs1g5gUoMyRBpRVAVqkpHM0azUX7pLNy7Pks/Pd7zHStZf0LNzLm/26JnUekF5pdILwroHDayFtw0FQN5SvMuYfH/P/8zqrtXP/sMlJp4v98pivgbeEvUkM6i9ds5p1VHuZNau3O0VhpfgdJ2pE8kSgzRHphWaZW1t963aWlzky0RjpOUF9x9BSOmFrIKbe/DcBNzy3j1rP2HuLBDq3nFm3hm//4mO6Woz/12RZOnLWF42YUm9+V065QtvPkdYJ2lE1UygwRYkWajm0+RzueNlWa4n87bDrcebr/t3zVF6Zx5NRCvvqX9wC49eUVXPfFmUM1+rjoLTMe/XAjR04tNJkBrR15w2bHPOjaHdBxErJQNhEpM0R6YbkBBwLtzjOgSyOPG0+fxVHTirjw/g8BuOv1VfzkhD2GcKBDr7fMuH/+OuZNyotlhh0CLGhuzYzurlENx3oA6UKZIbLr7jh7No9+lMcv3v8QT+pafvnWzTwx6h6yE3g3uA6ZYQXx5b8CQLDiUF5aX8WpezRzQmKfag0L/S6ULSgooKCgoE/P3bRpE4cffjizZ8/mb3/7G66RNhEnMlAsC3LGQsM2CNaTHUincNJe3Lv2eC7w/I/QqzfiPuOvuFLbtdH2pQI2lC9tff344fXHcVMVbFuMOTHK7v45dhhn/h1YwJORuWwlj1onlSpiq67zqeLRd+vZZ/Y83AnclSNZKTNEdl1x0SjmjEnh3o0ncJT7Y+aEluG88VusY641Cyr86abwZ6QLNREp/5wH3t9MgVXLye75APwi/DWCeHFhM4pKrn2rnmcOAvcwikTZdcoLkZ1gWZA7Hhq2m2KeQBaT8gJ8I+Vy7mi+klktH7P1o6cYte/J5vkut8mLqjXgSzOL8UaKcBC2fw7hRsgsASBiw91vrMEBzvW8RI7VwAp7NPdFjsVDmEwaufuNNcyZkI/bCZnCgOzSkVcgLF0oM0R609pR1te6vWdLrXn/D3ft/jdzdBb56T621wd5cck21m5vYHx+2tAOd4hEbIdfPbWk28nrqF89tYSjp43CbYdMwVhztflCSrtrdQneUTYRKTNEMAWa7QtlU7LN401VrR1lw10WVHS234RcPC6LsO3w/KKtXHb4ZEZn99A4Y4TrV2a4rFjGRrchT+u8uNsGy6PzkBFAmSHSC6u1o6yvfaGsA07XHe8O3i22YODpz7ZwwUETKMwMDNFAh9aOMsOiU2ZEtxWPLq6IZnIbRwsrRghlhsjAOH7GGH7839NwT/gjFSzjrgX38f39L8PT3U7WI1znzPDlvI3LW4sdyiZctwfYAa59ehnHTi/puFOeDLhBexfetGkThx12GGPHjuXmm2+mvLycrVu3snXr1sH6kSLDWyALssZCYxU4DneckMszOeew3B6Dt6WK9U/faE4y2vOlmy4gZUugck3Xr8dLcy1sXQShpthWTd1Z/DhWzQaqnDQeihxG0PGwnlEAuIkww1pNltXIhgY3H5RroiGZKTNEevfQxfMYn+3h+6FLqHcCWNsWwufPmS+WL4eW+vgOcFc5DlSsYsW6Daxq8HKx52k8ls0Lkdm8bu+JjyBHWJ9QZuWwrNbL+2sq4z1iiRPlhUgn/gxTLNtcC3YYy7K469x9uMd9JgDpH93Bi5+ujT3f4zfnJeXLoXZLXIbcb7YNFSuhbmuHhSGLN9ewvb6FSdYmTnC9D8AvwufjJcxUaz1YsL2+hcWba0y3rLRC7WCRZJQZkpQsy3z4o4Wy9a2Fsi1dnupyWbz14yPIS/PREIxw2M2v8cqybUM84KHx/ppKttT0vlX4lppmc54RDprOWG2dAdt1eXIcFcomKGWGJDSXO1YoCxBoXQDQVN3aUTYSK9zpgdft4pNfHE1GwENFQ5ADb3iFd1ZtH9xxx0m/MgNiGdvQ+vvotqOssiORKDMkKVkuTOOk1kZILXXmva2b/Ah43Xz086MIeF1sqm5i/+teZsGG6iEd7lDZUWY4dMqMSNA82lYo235RnhNb3CIJQ5kh0rt0v4e3vncWoYqjAXhgyT94eukHcR7V4OiQGa4GfPmvAxCsPADL3YwTSeuYGTJoBi1pX3zxRVauXMnLL7/MmDFjKC4ubvsQSVpZY8Cf2bZ929VHFPDd0GW0OB7GV7/Lto+e7Poaf4Y58ShbAttXmonieGqpM0WyzTWQUdTz8+rL4MO/AvBA+GiaCLCV2MR0CdsBh3SrmU3ks61xmBQBS1woM0R6Z3kD/OYLk9noFHJN+FwAnA/uNe/JzdWm4GkHkxrDWu1mqF5HmZ3BQa4l7OVaTYvj4drwOQCMZRshXGx08gGLsrreJywkcSkvRLqRNcb8Xd46MetxWUw74it8YE8h3Wom/Z2b2FDTrkOUPwPcHihbCo0j4KJLzQaoXA3pBabjVavKxiAubC7zPInLcngiMo/59nQK6XhMVfWNEAm3dpPVBHUyUWZIcop2lG3tDNtSZwpnwy3dLr4OeN388uTpbfe//vcP2bqD4qCRqK/nD2V1zWby2rHbTV633w5VxU6JSpkhCa19R1mIda8LNZh8sCyIdF1Q0VlGwMvP2m2f/dV73mN7/Y5fN9L0KzPAFBx7fLGOsp0X5zm2Of+ShKHMkKRkuUzVpz/aUbbWdMruYU4iL93PD4+d2nb/1NvfpqZxBM9f9KDfmRFsNLkQajL3O+9egTW8dpeVXabMENmxkuwULpz1f0SaSrHczfzktd+xqSbxFuW1zwxf3htY7mYizUVEmkqwQzmYa3p9zxbZeYNWKHv++efjOE63HyJJy5dqtjgN1oMdYc8iHxcctSe3hM8ATMenpu3ru3lduvljeftyM6Edr2Ko5hrY8ik0V0Jmcc9/rDsOvP0HCDXRkDOVF+19aHG81GImasazmSyrgTSaaXT8lDvZFGYk5rYb0jfKDJEdmzt9Mj8/JIdHIofxYmQfLDuE/fI1pjNg7SbTbS/eiyl2RnOt2U7bEyDP73Cx52kA7gifwganiElsZKJrC1vIoxbTGUuZkbyUFyLdcHshdxJgQagRgCMmpvHZ1O/R6PiZ617Cv/7xF8J2u/9PUvPMZPi2xeZ9eLiqLzOLQfwZ4On43p+b6uN41/tMcW2i1knhN6GvMpkNpFsdLyQVuhtMkW1vO2FIQlJmSFKKdpSNbomK09ohNdzjtaST9izh7Dlj2+4fcP3LCff/SV/PHwozAmYL7WCD+Z1Bx+1QHUdbZycoZYYkNKtTR1lfemwBWnONyY1Q3yZjz9yvlBNnxQo79v31Swn3/0m/MiMSNnM9ngA0tk7mp3XuKGt3WPAnI58yQ5KSyw3Y7Qpl6wCrtUNq9742bzwHTo4tHtj/upcGd4xx0K/MALNIJdhgbru94E1t9yyn9XxO5xuJRJkh0jdXHDWd4tA5OLYbT/oyTvrrXYTsxFpgEc0Cy1OBL/dtAEK10yGSAZHULs+TwaOkFRlqmSWQWtC2wvj0PVJ5wnsS8yPTSLNaqH3mF91uiYcv1VxkqVwFWz6L/SE9VBoqYPOnrZ1kS3r/Q33tm7DubXB5CBzxY/LSU6ggCwA/QdIwF96yrAa2kEd2Vjb7T8jt+fslOpeL1P32I3W//TThIiI9sywuOHIWYHFl6ELKnGxc1etg/h2mAKhyNVSvi/co+ycSMkWyoQZIzWWPNfeRbTWwwi7hjsgppNBMgVVNoxNgk1OABRRnBZQZygwR6SwtH3LGmb/ZWy+0fu3Q3flF+HwAvu95lJfnv9/xNelF5m/7bYuH/tyiL5qqYNsSs5A6urVfO9PTG/i693kAfhs+k0YCBKzYxTMLKEpzM6UgBbLHJWc3WWWGSBJqLZT1eMHtNw+Fmk3RZy8TDFefNL3D/WcWbhnMQQ65/SfkUpzV80RDh/OM5pq2hSemC7uv05MT9P1UmSGSuDp3lLWs2CKApirzPtfSt8VzlmVx8+l7dnjsrZWJ1e2pX5kRajBdAe1IrDtgaqdrVo6TeOciygyR5GNZ5v0sWihrh81Hd/PZrVwuizvOnt12vyVs89nG6kEe6NDaf0IuozL9PX69Q2ZEWn9fwdZzjZTcjg2pHDuW2YlEmSEifeB2WTx0/mkEtx8FQDDrCV5c8VGcRzWwopnhL/oflitMuLEUu3ksdth0k9UcOEOWGUojkaHm9pqusnak7QTikS8X8p3QZZQ7mRS1rMV569buX+vxm06udVtg08dQXz7443UcqNkIWz6BcCNk9NJJFswqwrf/YG7veRbuvAnsN3UclZjJ7UKqzLU4Wmhx/JQ5OVx90jTcruTdSsIVCDDugfsZ98D9uAJaISIiPbP86Tx1/m5UkMV3QpcRcSz4/H+w+jVzkap8OdRtjfcw+8ZxoGIV1G42xVrr5+Na9RI2Fj8IXUIID4VUkmk1sp5CGkkBUGYoM0SkJznjzS4UrQvyXJbFaSd9kf9EDsJtOcxadCM0V8eeb1nm3KKxArYtGl7Fsi11poA31NR9J1jHxv3mTfgJ8a69B/+IHEUh1W1fjqbEpftn484oStpussoMkSRkuQDLTLIGWiexQ41mgVovk9g+j4s7zt6n7f4v/rt4kAc6tNwuizk9TDREM+Pqk6bhxjYZFM3ElG5ek2gT162UGSIJzBXtKNuuc1mgdavnpmoz5xBsMAU8fZDic3PNKbEFFjc+t2wABxt/bpfFjNFdF+pBp8xwWdBSbzI2WmjsTevUHZDWjrLewRtwHCgzRJJRdEFeiulUDq0LBUK97nKXleLlssMntd3/48srB3ugQ8rtsphUmN7t17pkRqTFdOAN1psvtN+5AkxOJ2ChrDJDRPqqICPAsWO+TKRpNJa7md+8+1u2NyXOojyXBRk5a/BmLsJxLII1e2GHcsEOdM2MJDVUmZFYSSsyUqQXQubotu14SjLclJHDd0LfIuJYWJ//j/DSZ7p/rctjutKGGmHTR1C+wmyjNxhCzVC2FLZ8ak580ot6L5J1HHjzd2ayPasU9j6X2hab335gLrK5sMnETDZkWw2E00fx23MO4rgZxT1/TxER6WBs6TgA5tvTuTX8JQCzwKKxAtwe032vsTKOI+yjmo2mS3pavrmo9uYtAPwlfAKfOpPxWRFKKafGSWerk8eorAB/PmcfZYaISE+8KZA32Vx0by2GKs3ycFXo66yyiym2Ktn01G8IR9pNYFguyBgFdWXDp1i2pR62LjJFvRlF3T9nyX9hywIaHT8/DF1EqitCmhXbLjYv3c9Pj53I7LHZptNuonVwEhHpiWVhCmUBX7tCWSfS67aoAKU5scKeyoYgT3+2mYidGNtBriyr44kFmwHwujte1+pwnhFsgHBzrKNsSk7Xb5ZgE9cikgTaOspGYo9Fi3Oaq0yhbKjJdEfto/F5aW23F22q5dVlZdgJkhkLN9bw4pIyAPyeju/5Xa5NNVWB2922WJG0PLpwbDOnIyIyklkuc45hWbFdf0KNEImYzrK92H1UbPHBS0u38e7qioTZdn7+qgreXmkyIODdQWaEW0yxbEuduR/odK7h2Ob3q/MNEUli+44rpHnzmTi2l1o+5zdv3UFzuHnHLxwBXliymU2uhwGw6/bBCRbhhLKBbjJDBpXOzkTiwbIgdzw0lkNLHT5/Bj8/OJNfvzmD34XP4IfeR7Df/AMU7Ab5U7p/fXqhuYBfvtQU3OZONJ2SBqIFteNAfZnp9Ne43Wzp7elDxf7Kl2D1q+aP+MN/Ch4/t7xV0/bl247LozQ1g5raGvK9xUzZ70jcqd1MOoiISI+y0vx897Bx3PraOm6PnMp+ruUcwkJ48Wo49Q6zInnrIiie1XVV8nDRsB3Kl4Ev1eTLS1dDw3bKPMX8rvl0AP58fA6jIxNYF5hCZuF49p+Qm9Sr6ERE+iRjFGSNhao1kDWG0iwPX9krj299+m2e8P2C0VUf8MITf+WYL30j9hqXu3XXiq1gfwZF02OTHkMtWiTbWGHG1N0iver18N5dANwQPosNThH/OCWPTFc2lY1BclN9TC/Jwl2/xXyP1PwhPggRkXhq7fSEE3svb6k1uwNFt4TuwYzRmXx53zE88uFGAL71z0+4/Ig6vn/M7oM85sHlOA5XPxnrkPuvCw8gFHEoq2umMCPQ8TwjWN+py5MKZUUkAViWKdQMtVswkdKuo6zbZzoCBhsgkNWnb3nwbvkcP2MU/1tkdjX62t8/4Ocn7sE3Dp44wIMfWo7jcNV/F7Xd//cl86hvCXefGaFms+OfL91c5wJI7aFQ1q2pWBEZ4aIL8sC87zVVmcJPJ2wyBF+PLz1xZjGPfriBN1eY98qz7n6XG780kzP3Gzv44x5EEdvh6idjmfHUtw5ie32w+8wAcz7mAM2t8+adzzUcB0i8jrIiIv1x1v6lPPHJFBZt+wKB4sd5cdNj5L8xnZ8c/kVcI/j9MRSx+fnLd+HO2oYTTuX7B55KkX83IqHs7jNDBtXI/ZckMtIFsiBngrkY5dh8Y+903jivkDsiJ/NKZC98BLGf+2nsIkt3fGmmu2xLrekuu/VT8/xetrnoleOYLoRbFpjvF6yDrNF9K5Kt2wpv3Wpuzz4fCvfghVVN3PeZWYn+26OyOXFKCrPGZHHwaDd77D5VRbKt7MZGPp87j8/nzsNubIz3cERkBPjucTN48cKp2Li4InQpW50cqF4HL18LKXlmYnfbotjq5OGkucZsp+3YEMiGZc/AmjewLQ8XNFxGM37+fEIORxbWMnW3KRw7Zy/mTsrTCUIrZYaI9MqyIG+iOddoMt3Frzoki9+eOZurw+cBcNT2B2lZ9VbH10WLZZsqzW4S8ehM3lQNWz4zC/Uyi7ufGAi3wMu/gnAz70Sm8UDkaO47JZeDxvqZNSaLw6YUMGtMFu5Ik3l99riBWUg4QikzRJKQ5cJ0lLXNBDaYcwK317zP9vZSy+Km0/fk35fMbXvstldW0hSM9PKq4e/Pr69q6/L0n2/OZd/xucydlMcpe43uep7RWGl2VIr+rrorGLMSs0u5MkMkwbm9mOqcVtGF1U1V5rPl7tc5gGVZ/Pmc2dz39f3bHvv1M0tpDo3szLjp+eUs2FANwDPfPoiZY7J6yYztZv7Elx7rKNtdoSyJ1x1QmSGShCwXbTnia92JItQEkfAOd65wuyweuGAOd5y9T9tjP/7PwhGfGVf9dxGfbzML7F79wWHsVpTRc2aAmbOxiGVvlyYnidlRVpkhIv3h97h5/LID+fkh/0eobhqWFeHBlbezomJNvIe2Sy7518s0pT8LwBcmncjsMeM5do8pPWdGkhqqzEispBUZabLGmA5HrRdSxmZ5uHz/TL4buowV9mhcjdtpfvYnZtu3nrjcprtsai7UboUN78OmD6B6g5kM6cv2FcFGqN0Mmz6GDe+Z26m55vv25Q/ycAu89EuzPVPRdNjrq1Q0Rrjomaq2p5w2NcXcCDWaVepZY3b8fZNIpKqKSFXVjp8oItJqtwkT+MqMdCrI4sLg92l2vLDhXfjgHtNRsLkatiwcXsWyLfWwdbG5KJRWAFXr4J3bALgheAYLnYlYwHGlYZMVuRO0XXY3lBki0itfGuRNNh2Owi0AzCj0sX3s8dwfPhqX5RB66ddUbFrV8XWWy3QcDNbD5k+gZmPfziUGQn2ZKdBtru65SBbg3T9DxSq2O+acKSfg4ZCx/q7Pa6w05xvddQJMMsoMkSRjWbFu3P5oR9k68KaYBWvh3iexAfYdn8usMbEC0T1+8RzVjTt+3XC0uryem55bDsBuhenMHpfb85ODjSaPAhmxQtnudujortt5glBmiCQwtxfsdgVJ0e2eo+93/nTTHXUH3cc7O3RKAaMyY002pl71HA0tvW/BPVwt2VzLn18z50j7T8hlekkv3XXDQXNNy5dqcqGto2wPu1kk4CILZYZIkml/ncaXZj6Hm8CJmGLZPjhhZjHt64D2/fVLBMM72fgpzj5eX8U/31sPmI65E/LTen9BtFGVJ9CuULbTuYltm99zghXKgjJDRPrvvHmTad7yJexQJm5/OWc8/GuqdrAAfLh68/Ny3q6+B8vdQro1jmMmHkRpRilelzfeQxuWhiIzEi9pRUYSj990fIqE2yaxvzsngynFOXw99AMqnXQCVSvgles6Xsjq6XtljoK0fDP5sWUBrH/XfJQtM1uU1m6Bum2m+2v1Bti+0hTWrp9vimQby81kcmaJ+X599c4fzRba/kw44ue02G4O+ntZ25ffPK8wtgqisQoyRw/f7cBFREYKl4trT9+XqXkuFjoT+UHoEvP4Zw/D58+ZYqfmSlMs21wb37GCmXguWwxNFaaQN9QAL/wcIi28GZnBPZETAXj/ggKspmrIHm8WbYiISP9lFJtuqg3lbcWud5yQy19Tzmd+ZBrpVjP+l38e2+4tyrLMe7Rlme6u5cvazlMGhR2ByjWweYFZHNhbkezq12DJEwB8P/RNysjh1fMKsToXLEULwrLHJnQxk4hI91oLZR3HFHxC6/tiqvn7u/P7fg8evmguuWmx7VM/WDvyJjVrGkMcccvrbfefuvygHbxgk1ks4k0zCzegmwUXTkIWO4lIEnB5TbfxqOi1+ebW93dfuumOWre139/6xe8dgt8T+xv+s419y5rhpLyuhRP++Gbb/Qe/MafnJ9s2VK4yzU+iORHtKJvWXUdZErLoSUSSTbsFed7WnSuCZkdRIn2/bvThz49uu13fEubzbcOoyUcfbapu4rQ73mm7f9tX9t7xi0KNrecaqbHs7bajbGIWyoqI7Iz3fnwyzZu/jONYOOkfcusH99LSj8wZDlaX1/O1R/+CN2MpjuPim/t8mdHpo8kNaP47npS0IvGWXgRZpWbFNuCyLO4/NZcNThEXBb9Hi+OBtW/AW7/vW0cnt9d06csaY1aChxqhcrWZ6N78MWz8ADZ+aDo2bV8OLbVmBVtmiRlLfwpkAZY+bbbNxoIjr4KMYu74sI6msBnrhGw3pVke89xgg/n+2eomKyIyEDyBNB752l4APG3P5Q/h08wX3rgZNnxgCqXiuY12VLABti2EujIzJoBXfgM1G6jx5PHd0GU4uNivxEeBU2U6cOSMjd94RURGOpcL8iZBINtsCQr43BaPn1XMpaFvs8EuIL15C/ZzP+1+94qUHHPBfvsKs6Cuvnzgu8u21MHWRbBtEXgDZjeLnlSshNduAODO8Bd43d6TYyYGyPJ3uqTh2KYrVs54CGQO7HhFREYClwuzn6cNvnaFsi63ebx2synw2YEUn5vnvntw2/0L7/+QUGRkdXv62RML226fuW8pAW8vBa61m03RU2qOKQCIdnkKtOsmaEdMkawWYYjISNSlULZTR1nLMgssKtf0+/pRRsDLqz84rO3+V+55F9seop0pBsiP/v1p2+2LD5mI193D1Gmo2cypVKwyDUuixUyN0Y6yPRTKarckERnpLFfsulC0o2ywwZx6BPvejTw3zcfrPzys7f4XbnsLZ6h2MxogVzy8oO32D4/dHVdftsturjEL0T2BdrtXdFqU5zg63xARaacoM8AzF51HsPwYAP6z6h+8tO4lIjtqMDiMfPNfb+Af9SQAe6QdzZTcKZRmlHZt/iFDSoWyIvFmWWZraX962x/HqV4XL51TwIfOVK4IXYqNC5Y9De/d2b8Jak/AdOPLLIas0aYYNmt07COzxFy8iW4R1F8bPzQFvAD7fQPG7Mfv363lD+/XAzCz0MtTZxXEnt9YaYqCA71sWyQiIv2SmV/Mk+eOB+DW8Gl8mnGI2fLoxV/AtsXmvT5Yb4pl67YN/QBb6kxX27oyk0cuN3z4V1g/n7Dl5ZyGK6ggi3ljfNx3QoqZuMmf3P+FGyIi0pEvFfKnQCRiFs8BOSku/nzaBL4W+iHVThqussWEX/gl2N1sk+dNMRnSUgObPjSZMhAdysNBMwG/4UOo2WAKZP0ZPT+/qRrn+Z9BuJk3IjP5bfhMjp4Y4I/Hde7yhznfSMkxiwZFRJKVZYFD7L21pbVLU2qued+tWNWnrbULMwL89fx92+7f9spKmoLDfzIiHLH5zkOf8PRnWwD40j5juPbUGR2fZEfMxH59uVm0sflTs/Dc19odK9p5N5Dd7kWO+d2q2ElERiK3p+NCiWgXu6Z2HcMD2RBpNu+JVevM3/59nIQuyU7p0FHv7jdX0xwa/pnREo5wwd8/4NXlponJ+fPG86Pjppov2hFT1NRcC/VlUL4CNrxnFhOm5przpaiG1kLZtPzuf5C6kYvISGdZmKpYOhbKegKxDql9NC4vjetPm9l2/4F319ESHv6Z0RSMcMad7/D+GrOg5PIjJvPNQyf17cX1ZWZRY/tFeZ07yjq2yWsREWkzdVQ2397364Tq9sByhbnm7Rv5rGzxsF9kUdcc4thbX2eddT8uTz0Z7lFcMPsoxmeNJ9WbGu/hJT2lrchw4E+HvMmwZYE5wXB7mZzr5btzMrj1vQO4MtTETd57zHba3hTY57z4ryjbvgJevMoUY006Avb6Kg981tBWJAvw6On5BDyt42yuMd1MNGktIjLgZu0xla/vtZ2/LqjnS+Xf4G/+Wg6OLIDnfgIn3WoypmG7yZnQFLMdt2sI1ks1VEDZElNklVViVp4veRI++QcAP2q5gIXORADuPyUHT91mU9SVVtDbdxURkb5KLzSL8rZ/Dpl+cLk5YIyfvabuxteX/ZAHfdeRsvFdgq/+Ft8RP+66vZvLbXadCDVB1Vqo3wqZo81jgez+ZUmwERrKoXq9WSAYSDeL93oTboEXr8Kq38Yau4hvhS4ngpu7TszB1fl8KBI0zy+arsUWIpLcLDcQNNeawCyaA/PemJID5UtNwaw3xUxsuzytW3x2vc50RA4cOzmV51c28seXV/CfD9bw/LljSPcNZe+F/k1+fOfpMp5Z0dB2/+aDLKzyhaZAzImYBRt2KJYbYDrJtp+oaK42n9t3eWrrKKtiJxEZgVydOtS17yjrOLGvZRSbx7Z8ajLC4we3zywmsNyxDuXR84Z23/MLRQ7/LA0wf0MzN/xvGY+8u4pnzykh4BkG/Xo6TKQ7bY+d959tvLvRZIEF/HKfRtj4nllIaEdaP0IQCZkn+NLM/Eb736XjtOso2+l6lmNrkYWIJAbLRdv7Z4dCWT+0NJiO295An7/dmfuW8s/31rNwUw2/+O9iHnp/A09+60A8PXX0HgZOuf0tPt9mzq0KMvx8/5jd+/bCpmpTKBvINrkQXZTXpaOsbc7NRESkg0sOmcpDH51Llf8WGn0VfOPpq3niy3+kNLM03kPr0eE3v06N500CxYvBcXPxXudQmllKQYrmv4cDpa3IcJFZYiaO6zabyWfgu3MyqGuxuXfB4WTQxFXef8BHfzcnHHMujl+xbNU6+N+PzIR5yd5w2JU0hBzu/SRWJPu1vdJiRbKObVZej5oZm6gREZGB43Jz1Rf3panlHf61FC5s+Q6v5N1EScNSePp7cMJvoWB3001q2yJoqTdbcvsGadWabUPtJrMdXSQEGSUms9a8AW/fCsBfrS/ymH0IAJfvl46nsdwUyOZOiP9iEBGRRGFZkDvRLFio32bOOYDfHpXNtyN78q2Vl3OX9/f4Vj0PHjcc/P3uJ3G9KaaoNdhgOhFWrTUX9NOLTMdCbwq4/bHOF7ZtJpRDTeY1jRVmwUawvnVyuaRrUW5ndhhe+hVsXUitk8o3Qj+glnR+PC+ja5EsmHOprNGQPmrXfmciIiOdy22uw/g6dZQF8/e/N8V0Gg81Qkst4HSze1Hs/h0HOlzcAi9tgE21Yf736VrO2G24/L3ecRxljQ4vr46N/dq5Flb9ZvM8q7Wwy3KZYi9fmukI2DmP7HDsd5bSfkckJ/Y9RERGms6FN9GO2XYIQg2xjtpgOtylZJvFBJGgyYugbbIFaF9o2p4F3H+Ew/kvwNtbYHVViFcXref48cMlM+hwvWlDHby/KdbB8OZD3K3FS+3ywuMHdxq4vD1fq2qpNde+ANLyOn7NsWPfS0RkJLNc5o3ecWKZEWwwi82at5r3z34UyrpcFo9eMpez//IeH62rYsmWWt5dXclBu/XQmTvOVpbVtxXJAlx7yvS+vdCOQNUasIPmPKy5OpannXdfdRwVyoqIdMPjdvH85cdz4l3VbHffStC7kh+9eg23H3MjuSm58R5eF4s21VAR3Eja6KcAmFtwElNypzAucxxuLaAbFpS2IsOFy22KlpqqzR/KrRerrjoki9wUF7+dfwLgcJX3QfjsIXMCctB3hv6P5so18Mz3zNYQeZPgmGupC3uYedfWtqfce1Iuh4xt18WpqcpMpLdOzEsnLheBGTPabouI7AzLn8b1Z8wm57/vc8enfo6r+B6PZdzE5JYVplj2+BvMggW3D6rXmqzJmwwZowa2MDXYCJWrTRGVPw1SWycJ1r8Hr1wLjs0/w4dzTfh0AP5xah5zC1rA9phusuoC2Dtlhoj0l8cH+bubDh+tf5dblsUfj83ml4FD+N6iFn7vvR338mexIyFch13Zc8cjX5r5iARNEdHWcvNe5PaZD5cLsMxF/0jEbN0aCYPbbQpqU8b0LXMcG16/Cda/Q7Pj5YLgD1jljOY/Z+Szzyhv1+c314InxRQF670xRpkhkpxcHjPJGl2o3L5QFsz7cPT9vA/cwD2nOvzwpWr+vbSJH74Fn9akcM1hWd0vXIiTtdVhDnu4rO3+02flM72gl8KmnkQ7PFku8GfGHrftdt0UE5AyQySxde6G7fGb4qZQo5mP8HXT3MLj7/c1Gi/wwOkO3/pfFc+ubOabrzp8c3YaP5qXgTWMMmPp9hDHP1redv+FswuYktfNeUZfNFaYz/5Mc07UXrQbeaIVPikzRJJUa1fZaPONYENsZ4qajWYewN3397uA180jF8/lgvs+4LXl5Zxz73v88NjdufSwScMqMz5aV8WX/vxO2/1Xvn8oEwv60BQq1GzmSao3Qkaheayp9VzDn9E1G5yIWZiRaJQZIjIA0vwenv/mWZzwl2q2pdzDoup3ufTp33L3yT8hs/21mzh7a8V2zvnrG6SO/weWK8TkrD348oxDGZ85nvTuzrmkoyHKjAQ7OxMZ4QJZkD/ZbG3kTW27sPK1vdL47fw67o2cSD2pXO/9C65lT5mtT4/8hfmDeihsWwzP/yxWXHXiLeBL574PYpMuP5qXwZET2q0ajITM6vPCaSp+6oErEGDCvx+N9zBEJBGk5nLxEXtwx6eLqCWNU+qu5PHMW5gSXALP/tBkxrh5pnN5YwVs/sTczhnbdauf/oqETS5VrDKTy+mFsff9tW+aroB2mOUZB3BV+dcBi2sOzeKg0RbU1cKoGV07b0gXygwR2Skp2VAwJbaFqjcFy7L49v7pzP5sHpGQiz94/4Rn5Yumq9RhP+n9b3e3z0yApNJaFBs0f/fbYdNgyrLA4zULJtz9vMhvh+GN38KKF4jg4tLQd/jAmcrNR2czu9jX/fNbaqFoRtduHElOmSGSpKIdZaPXilrqe39+H1iWxQ/mZvLvpU0A/GNhI363xVWHDJ/33d+9G7s29ecTcphR2E1m9EVTlfkcyOrYAdCJxLrRJiBlhkiCc3lo6yAeLT5KyW4tlK2CrDED96Msix/Py+TZlc0A/PmjegIei+/MGaI5jD646Z3attv3nZK780WyYHa2ALNLUmeObSZ4E2yRhTJDJAlFC2IdO7bgLth6npGab3aXc3kgo8hcT7Lcsdf0wo3Fj48cz2vLzXvpb59fTsCKcMG83rbT7rwbxuBxHIcbn13cdv+Rr+/FxCzLFAlHO6s7thmTHTEf0cXl9dta50kKYgsp2s41srv5YXb/r6GNAMoMERkofq+Xa475Muc/Wkag+AkWNz7Nj1/I55bjvkmqd5B2UO0Hx3H4zbOLCZT8G3dgG+meLM6ZdiZjMsZQlFYU7+GNCEOVGSqUFRluMkdDY6VZfZdptqpO9br46UGZXPdWLQ9HDqfGSeNPgT/j2fgBPHEpHHMt5Iwf3HGtehVeu85MgOdPgRNupsWTwff+V8kzK8xFr6n5Hi6Z3WklREO52f40XW/+IiJDIatwLN+eu4U/zq+ggRROrv0hr4z6EyXVH5nFDnMuhllnQlq+WchQuyG2HXdmiblI059VWuEgNG6H6vVmW21vwEywRC+CLXsG3rwFHJv3/HM5p/ybRHCzb7GPc2cGoHYzZI+D7LGD8vsQEZFWmaPNhfrtKyCzGFwe8lLdfGPvNP7yyQGEQy5u896Gb/Vr2PXluI79jZk83xHLZYpvPX3fYq9HkSC8/GtY+wYRXHw3eCmv2Ptw+Hg/p+/Rw8Wu+jLIKIas3iZRRESSiNVaDBUtlA01mAnbXSzSGZXu5ivTU/nX4kYA7l3QwP6jfRw1IYDbFb+OTzUtNqc9sp1VVWEATpqSwvGTU3b+GzZVm89dtkJNzGInEUkSLrfJByfSmhOYBdO1m2PvewNoXLaHL+wW4OnWeYPfv1fH3qO8HDTWH9du5FVNNsf+s4yyBrPt9dkzUzl03C6exzS0dpTtbvG309pRNkEXWYhIErFc5sNxYl3Igw3ms9trCmRrN5k5Ald0FwarT7s77AEcNsbitY2m8PTa51ayp2ctswutuHaWLW9yOPjhEM0Rc//imS72t5bAunZPchzMQhTb3HZMvmBhCoqzRndcfNdcbT731LRE5xoiIr06cFIR0zOOYWlFBb68N3mz4n5+924aV8w5l7Q+7pw0GLbUNDH3+lfw5b2Kv3AhFm4u2vMCxmWOY3zmeFyWOmoPJ/qvITLcuNymW6s/A5oq2x6+YK80HjrNXGx5zt6fU5quZrsrH2o2wGMXwaL/xP4AH0jhFnjnNnj5V6ZIdtyBcNKtNHsy2P2OLW1FsgB3npDb8UJXsN6sksubqD/uRUSGimVx+Qn7ct8XzQKFZvwcsvU7vJ56DODAe3fCq78x79Eevymc8qdB9TpY/x5seA8q15gL/aEms8Voe3bEdKWqL4Oy5bB+Pmz62KyOzigyHQYtyzxv/h2mK6Bj85/IwXy15lJCreu0/nBcNlZ9mem4UTBFOSEiMtgsC3InmUURddvaOl/8cG4md52Yw/P2/pwXupJaJxVX2WKzIG/7iqEbX1O16X6+9g1aHA+XBL/LU/Y8AG48Mrv71zTXmCzLm9Sv7f1ERBJatKNs+y3dopPYu+jqQ7O49ZjstvsXP1PF1a/XDMj33hkVjRH2vGtrW5EswK8O3cUt95pbj6fz5LVjm+KyYbQNrIhIn7k8Jh/sSOyxaDe7aHe7AXbTUdn85vDYooP/+28lN75T18srBtfW+gh737O1rUgW4EfzBmCb1sbt5nNqftev2XZrwZjOVURkhGsrlG3fUbbdOYbbZxZlZ48xO82lZEMg08x19+HjT8fl8NO5sQXSpz8d5rZFnj6/fqA/NrSkst8/Y0WyAN8+ILvrcwOZ5rwhLd/MjWSNNh+Zo03Odi6MaluUl931d+ygORIRkT64/4IDOG+PCwlV74Nl2Ty06k5+8L/7aQw1xmU8q8rrmXv9K3gyF+AvfB6As3b/CpOzJzMpexKBgWgwIgNKZ2ciw5E/3XRt3fyJ2f7Im4rbZXHAGD/f3j+dP75fz2JnPMc1XsvN3js5jE/hnduoW/Yar+adRU1gNDNHZzNzTDbuXSmH37bYdAGsXG3uzzoT9r+IoOPiW89Wdnjql6elMj673VuKHYHGKijYY9e3805wdlMTq0/8AgATn3kaV8oudD4REQG8Xg+H7rsX569+m79/Wk8YD+dVnsd57mKu9j6Aa+VLtGz6jFeLL6I8bXJrZpTgdlq3Bdq2KNYh0O01F7osV2zroEjQLKQAk1mZxR0v+tRthVevg62fAfBo6pn8sPJkzFJqOH/PNEa7qsGVAoVTwav3vb5SZojILvH4oGCqWQjRUAbpRfg9FsdOSuHU3Zt5Yvl0vhj8FX/33kRp3WYij1+KNfdbfJZ5CAs31QLOwJxndLZ9Bbzwc6jfRhMBvhG6grftmQB8a790CtO6mSiIBM3CjaLpOt/ogTJDJElZbrMYwu01f8+HmyFYZyZxd1HAY3Hq1FQeW9bEG+vN+cA/Fjbyj4WN/Om4HI6fnMLCTdUs3FjDoGVGq/qgzZn/qejw2I/mZZCbsouTyz1th+rYJkcTlDJDJMFFu/s57Sp+on9DR7vbDbBUr4uvzkjlocWNLCwLAXDXR/Xc9VE993whlyPGB4YsM2pbbL74SHmHx645NIss/wD8sIbW75tW0PVrTgRcgf7t3DQCKDNEkpDlal0w5sQKZUM9LMZzuYH+/U2e7oFv7Ovn7wtb2Fxvsup37zfxu/ebuO+UXA4qHbrMqGqyOeGRjnPgNx+dTVrKABQ6Rc81etrBKQE7kCszRGSgZQa8/PjYvfj7W6cTssJ4sz7jzcq7mXljI/eccSaH7jaad1dXMH9VBeAwd2I+B0zKG5TdkLbXt3DkLa/jTvucQMmjAMzIOpwDSvZjYvZEcgKat+iPocoMFcqKDFcZoyB3Imz/HDJ9bauOv3dAJhftk84FT1Xy3qYszg/9iHPsl/iZ50EyKhdyYsVinrP35Y8fHkxToIDLj5jMvEndrGbuTe0W+Pjv8LlZ8UAgGw67EsYegOM4nPnv7XyyNdT29Ie/lMfeozpNFjRuNxeHcsbt/O8gWTgOoc2b226LiAwIt4dfnjGX7x2wkK8+uoVFlRb3RY5lkT2eW713UNpUxjGrfsPz9r7c+OHREMjsmBl2BCItppt4sAFwWi+IuU1xbLRzbHt2BJY9A+/dBaEGHG8K17ku5p7K/due8p8z8pmV1QC2A4XTVNzUX8oMEdlV/nQo3AO2LDCdLFovzt96bA6/PDSLkx9yc1Ltr7nZeydHuT+Bd35PS+RZXg1/gTJyePjDjWQEPDt3ntGZHYHPHoIP/wZ2mI2M4vyW77HSGQPAk2fmM73A2/V1jgN1ZZBdCtljd20MiUyZIZKc3O7YjkP+dFMo2zKwHfzuPzWPyqYIRzxQRnWzeX/51nNVQBV7sBa3ZX7+gGZGO81hh8PuK2N7U6wr4LNfKWD3vAG41N22HWp2x8ftiCk+TlTKDJHE5vKa6zntO8pG3+cGqaMsgGVZPHVWAeWNEQ64dxuR1reXC582BUjTWIPLMg8OVmbUtdjMuXcbTWHzczwueOYrBeyWO0DTow2tizZS87p+zY6APwEXWSgzRJKPyw24TKfsto6yja27LgxMtarLsnjn60VsqYsw92/b2h4/77/RzFhNtMZpsDKjojHC7L/EfnZ+iouHvpTHpJwByoy2c40e5kQSsaOsMkNEBoHbZfH5b07i3XV7cP5TP8ObsYSUMfdz0WMNhGv2Idq4CeBPr64iO9XLDafN5LgZxQM2hs3VTcy74RXcqatIGfMPLCvCnnlzOHfaiYzNGEtx2sD9rKQxRJmRWMsYRRKJZZktRDNLoH5bhzeCdJ+Lv56UG30i/4gczTHBG3kush9uy+ZE9/vc4/s93wz/g5eee4L5K7bs+OfZYdjwPrz0K3j47FiR7JTj4fS/wtgDeHhxA7vfvqVDkex7Xy9izmg/Pne7Yqlgo9kiIm9yQnfbEBEZ9jw+Mktn8M8zYn+Mf+TszgnB6/l35BBclsPx7g+42/d7Tgy9wB+e/YR3VrVuGedygzcVAlmQmmsu+KfkmE5UnkDHIlnHhvXvwuMXwVu/g1ADZelTObT+Ou6piRXJfnbxKGbntuANt5girYyiofpNiIhIe2n5rZ1lmyFY3/ZwdsDFk2cVUE0G3wj9gGtDZxN03MxxL+d23x853f06foLUNYe57tllsczYGVsXwX8vg/fvATvMC5HZnNB8TVuR7PJLi5lV5Ot+pXdDuZnYz98tMScRRER2heWibULAl2E+t9T3+PSdlZvi5oWzC7s8vpTxNDr+tvsDkhmtHMfhjg/rmHrHlg5FsqsvL2ZagXdguoP0tB2qEwGXrnGJyAjlcrcupOimo2z0fW8QFaS6ee28rpmxhAk0ObH31oHMDNtx+MN7dcy8a2tbkSzAisuK2T3Pi6vz4u+d1dg61rRuCrXsMHjUOU9EEkBbR9l2hbI4ZseiAVac4eaFs7t26V7CRJqd2MK1gcyMsO1wy/zaDkWyAB9eOIrJuV6sgcqMtt0rsrr/egJ2lBURGUwHjJvMLYf/glD13liWTUrJo/jyXscUK8XOAaobQ1zyj495blEf6qZ2oCUc4Yb/LTNFsmnLSSn9G5YryKz8WZwz/XTGZI5hXNY4XAO0kEQGnjrKigxnbi8U7G46+UU7tLZK87n45xfz+OrjZsXyBqeIS0JXcEBkCT9y/4t93Ks4xL2IQ9yLaHr1UZzlM7AKp0J6UWy7vWA91JdB+edmm+12k+SM2Q/2/TpOwVRWVIbZVNbMj1+u6TC87x+QQVF6pz/a7TA0VpiJ9/RuthsSEZGh5fGTWTqDv50c4mtPmsyoI5UfhC7hkfCh3Oi9mwmubZzjeYXT3G/x4SuziPjPxl0ys62beY/qy2DNG7D0KaheB0DEm86SsV/l1MUHE2m3xdJPD8okkwaTaYVTTRdAERGJn8zREA5C2RJzId5rJnCzAy7uPCGHS56t4t7Iibxm78V13nuZ41rG+Z4XOcU9n8ciB/FiZB/uen01cybk932rO8eBbYvhs4dh7ZsABN2p/KTp//iPfTDRwq5rDs3C7+lhEqKlzjyvYGq7yRkREWnTfnLV31ooGxz4QlmAwjQ3tx6Tw3df6NiNcANF7M76Do/1OzPaCUUc1laHeXpFE394v+Ox3Hhk1sAVO0HvHWW9/s7PFhEZGSwL3H4It8Qeiy4IGMSOsu2VZnq44YhsrnylusPjmyhgMps6PLYrmdESdthQG+aBzxq577OO24L/4djsgSt2imropVDWiYB3ALbqFhGJN8tlPhzH5InVuvgi2DAo12am5Hn5xcGZXPNmbYfHN5PPRDoWOe1KZjSHHTbVRfjDe3U8+XnHot87TxiEnfCaW+fZO3eUdWyT1VoMLiLSbydMm8aC9Zdz/9K/4st7C3/hc7gCm2ne8gWw06DdXPUvn1zM0dNG7dRC68ZgmC01zfzqqSW88Xk53qwP8Bc/gWVFmJi+J+fucQ7F6cVMzpmM15XAOxIlABXKigx3/gzTdW/zAmiujRW5AulWMzOs1axySmjCXHB5157Gafa1zA4v5xz3ixziXkieVQebPzIfvUnJgfEHw7RTTDdb4MnljXzn+eouT33v60Vdi2TBFE1ljIKc8Tt5wCIiMuA8flIKxjPF+oQNTmFbZrzv7MGRwVv4kut1LvU8xQTXVg5xPoRnPzRdY4umm/fz9EJz33HMxHHdFihbCtXtJr+9qSzJP5az1xxL1eLMDj/+kwtHkWPVQkuzKZJVRoiIxJ9lmffjSBAqVphFeR5TAFQSCDKd1SxlPKuc0ZwV/DlfdL3FFZ7/UOoq5wLPc5zrfon5LXuw6YMtjJ11SM/bxtkRs5hi3XxY/Zr5WYCNi4fCh/L75jMoJ7vt6YsuGUW6r4fZjXCzOScqmq5FeSIiPWnfscKXbj631A3aj5uYbjJjMRPbHgvhYZEzkVxqKaISt2VT0RBk8eYaZo3poXtSL/70QV2XAlmAZZcWE+hpYcXO6qmjLJY6PInIyOZJgabK2P3o3+/RBQJDYFp2iGmsZkm7zGjGzyJnInlUU0gVbsvZpcy4eX4t93zS0OXxVd8qHpjO4+1FgrFC47Ruzk8cdrwIXURkJGgrlI2Y60m+NGipbV2Q17Vj+EDYtyDCNNawhAltjzWSwiJnIgVUkU/1LmfGr96o4V+LGrs8vuby4oFfWAGxzOi8KM+xze9XhbIiIv1mWRZHTJnIPW8dhB3Kw1/0FN7Mz3D5t9K86UzsYAG07mKxtbaF99dUMndSXr9/zk8eW8h/F2wGwviLnsWX+w4Ac0vmcvrk0ylMK2RKzhT8bi2yHu50hiYyEqQXQsEUsz2p29vW7amyMQjAJGszQcfDOkbRgnmT/8jZnY/Cu1MQrmCGtY4fTa9hD+820+21pXUFnjcV0gohZ5yZbM6f0vZH+OPLGvnRS9WE7I5DmZTj4ZEv5ZGX2s0f601V5oJb/hTwaDs6EZHhZFtDhJXOGEqtbZRQycfOZFrwY+PiUftwHg0extHWh5zkns/xgUV4w/Ww6SPz0ZuiGXyUeiDnLd2P+rrUDl+aWejlH6fmkhWpACwYNUOdZEVEhhOXC/J3M7tCVK5uXRjhp7IxiGXBNNbS4nhZQzGP2YfwZHAep7nf5Hz3C0xzreNQ90L4dCF8+ntIyTXv8f4Ms7giWG+KjarWmgLXVhHLy6OhA7k3cgIrnDFtj+9f4uNvJ+eS1lORbCQE9eWQNxmyxw3u70VEZCRzddNRdhALZaOZMYPVNDteVlOC3dqto5JMKsmg0KkihRYqGlp28N06uuujeq5/u7bL44eN83PnibkDXyQLsSKy7haAqNhJREYyb4r5uz8qWqQzRB1lwWSGqzUzmhwfqxmN07qrRAXZVJK505nxh/fq+P17XfPuxN0C3HpMzsAXyYJpGkJrd8UuCyxaudVNSkQSgNW6aMwOmfu+9NZC2a5FpgPFZIbDDFbT6PhZzei2r5WTQwVZbZkRnS/vC8dxuOmdOv78UdeFeF+elsr1R2QNTpEs9Lwoz46Y368W5omI7JSyumaIZBOum44T8eAvfAG3v4zUCbcTrDiU4PbDwXEBHvPcPrJth2ufWcLf3l4LgMu/hUDJI7gDprv5aZNP46DRB1GQWsCUnCmkeFIG4ehkoOnqnshIkT0OQs2w/XPIKAK3j9zUWDGqzwqzGxvZ5uRQTuxifjl5vOrk8eoiOGC0j2/vn0FppptMv4tPtwWZM9pPc9gh3Wdx8/w6ghGHfy5qpCnsdPjxMwu9PP7lfDw9XVAKNZrxFc/quhJORETirjAjgI2Ldc4omgiwD5+zkSI2tK34tnjR2Y8Xw/th1dt8ubiMb45ZR25oK/7mcuqbgmQHLCK+DNwZRfy7rISNKVO5bWHXizeHjPXz91NycTkRqN8Kvtbu6OmDs7pcRER2gcsNBVNN1/CqNZBe2OE8w2+F2N1Zz0YKqSGdRyKH80jkMGZZqzne/T6HuD5jumudKSxq36GqnaDlZ0PqdP5SvTfPR/ajkljn8ZOnpOx4C1Q7AnVbTSFu/m6mwFdERLpnuc1ENoC/taNssOsk8EBpnxkBK8QezjrWMYp6oovoLMrIBeDCF8MctKyC78/NIDfFhQuLzfUR9sj34LRehrru7VrSvBZ3f9y1G+B5s9L41WH97xTVZ47T+xbaKnYSkZHM46PtzRbadZStNQW0Q7AYoH1mpFhBpjlrWE1stzwHF9sw3Z0ufTnMgZ9X8L0DMskOuIg4DtsbbXbL9eC2IGjDDW/Vkum3uKubzPj2/ul874DMLo8PqLqt5nPGqFj2duZWQxERSRBuD4Rbuyv50sznYNf334HSPjNSrRamO6v5nFJCmL/JbVxsbc2My14OcciKSq44IINMv4vmsENd0GZclgef26IhaHPjO3XkBLrPjJ8fnMk39k4ftGMxAw7HGll1XpTnRMz1OS3MExHZKYUZrecT4UzC9dOxw2n4cj7Gm7kIf/6reDM/oWX70YRr9uanjy/kpSXb+M5RU8gIeKhvCdMSshmdnUKKz01lQ5CbX1hObpqPu99YbX6AqxF//it4c+djWREyvBl8bebXmJA5gVFpo5icPZmAJxDH34D0h9JWZKSwLNM9KRIyE9iZxUwvySIvzd9hdXWRVUWhU8U2cthOxz+0390U5N3HK/r9o685NIv/2zOt5ydEQtBQYSbYM0f3/DzpnmXhmzyp7baIyGDYf0IuozIDbK1tpowcGvEz0drCFNax2JnYdlEJzMTEw1tG8fCWUf3+OTcdlc2Xp6WarlVN1ZBZYjqNBwZ5ciJZKDNEZDC4PVA41dyuWsv0woIO5xmWBaWUMcYpYyMF1JDBZ84kPgtP4ka+QjqNTLI2M87aRrrVTAot1JFCjZPOSqeENU4xdlPX4tbbjsvhpCk7WGVtR6B2C2QUQ8EeKlLqD2WGSHJyuQHHFEMNQUfZztemLAvGsxXbgfUdCmbNvPpr61p4bV3/ugQC/OULuRw1cZAnHVrqzDbaAKnttuFr2wo1gS+lKzNEEp/LC7T7/9uf2XrfMcWyqbmDPoTuMmMSm7EdWEsJjcTe55vC8NKaFl5aU97vn/PgF/M4sHQItjytjxbKFnf9mh1O3KInZYZIcnJ5zd/F0K5QdvAW5HWXGbuzAduBVYxp22EVoCEE/1vVzP9W9b1LYNR/zshndvEQLGporjGfLVfsPC0q2lFWmSEislPaz4Fjp2I37UYwkk2kJRdf9ie4fNWklDyKnf8yoao5PL1kb576bMsOv6/LV4Y3+z282R9iuU0e7Vu0L2dNPQuvy8uYjDFMyJyAV3MWA2OIMiMB01Ykgbk9ULA7OGGo3oA7s5iLD53Adc8u6/A0y4ICp5pm/B0mJHbGVQdn9l4k29bdaRzkTtQfuTvBlZLCpKefjvcwRCTBuV0Wvzx5Gpf842MA6kllkTOBYjIZy1aa8VFNxg6+S++uOyKLL09xQ+0ms+120QzT/U8nCANGmSEig8btNd2/LRfuytV888BR/PqFdR2eYllQ7FTQgo9mYhPP9aTyqTOZT53Jff5xvz8mu29FsnVbIaMQiqaDV6uy+0OZIZKkXK1bdjoRsyUqQMvgTWC7XXR7bcplwWinnDUUE2TXJp7vPCFn8ItkARpbu8n6M8HTrsAq2mkxgc9rlBkiScDjb62LjRb/u83OcE1V0FgxJIWyvWXGGKeM1ZQQ3sVpy7+fnDs0RbLQsaNsZ5GQyY0E7CirzBBJUm5fN4Wyg9dRtrfMGOtsYxUl2HTd7a4/Hv5S3tAUyYJpKgLmXMPVadxOxJxvdH48ASgzRGQodJ4Dx/Fit4zCicwj0jAFT+pGfHmv4/JV4i/6H/6i/xFpLiHSMAE7WIQdTgfbi+VuwfLU4fJvwZO6Gpd/e9vPKEoZw/9N/wqj00YT8AaYkGW6ybos7X43UIYqM1QoKzLSeHxQOA1sG2o3MW9CMT89YSq3vbKSuuZw29PclsPMwHYuP2Iy+0/I4831LWysjXDVazU9futL901n+fYQVxyQwYzCPpwYOHbrxHWxKeB16y1FRGQ4O25GMXeesw9XPraQ6sYQNi42UUAlGUwJ1HDPQW72LPLz1vYU1jX6+NWbPU+of3N2Oquqwnx/bga753og1GAu9jS5IbMUcsaZCRcRERk5op1l3V4OsD7nF0eP5vdvbutwnuGxbPYObOPyIyazz7g83t7QwsrKMNe/Xdvjt71kdjrrqsP86MBMJmT38ZzBDkNttEh2Bvh2bQGgiEjScHnMhx0Zko6yAPMm5Xd7bcprRZgd2MrlR0xm77G5vL0hyLKKEDfP73k8F+2Txqa6CFcemElp5hBfZ2ponQBJy+/4eCRsOmglYLGTiCQRtxdcPlPAGV0MkFZgCmUbyiF/tyEZRk+Z4bPC7BfYzOVHTGZWaS7zNwZZsDXI7R/2fG3qgr3SKG+0+dlBmRSlx6G4aEeFsi5vx4UXIiIjmdtj5qZhSAploefM8Fsh5gQ2cfkRk5k+Jpf3NwV5d2ML9y7oeTznzUqjtsXmqkMyyU2JQ2Y0VZnP3c2Z2BFz3UvNqEREdlrnOXBw4YSzcewUAr4sztltP+pZyTub32Vb8xrcgc24A5t7/Z5uy82sglkcNe4oSjNKcRyHotQixmaOJcO3a82nJH5U1SYyEnn8pqMSmGLZ8UXMueAAFm6qZuHGGsBh5uhsZo7Jxt26gOHw8abzxjkzU6kPOiwsC/HH9+v4yoxULMviwDE+8lL7cWLg2GYL1NR8KJqm7k4iIiPEcTOKOXraKN5dXcH8VRWAw9yJ+RwwKQ93qAHqyzgifRO01HH+BIda28enlR5uXxDi/FmpBG04bIybLJ8DERtC26E2At40yBkPmSWQkqOLOiIiI5XLbSbJPX72dy/nH2elsbDKy8JNtXR3nnHkhABHTjCFTTUtDh9uDnL3x/VcPDud+qDNkRMCpPv6uao63AL1ZSZTCqepSFZEpD9cHtNRtn2hbHBwC2XBTGLPmZDf67WpoyYGOGpigMv2Taeq2ebdjUH+/mkDl+2XTk2Lw9ET/KR449iJo6dCWTtkCgNUKCsiI5nbb4pl7RDQrlB2++emUHYI9SUzjp4Y4OiJAX4wN4OKJps317fw0OJGvrO/uX/0xAABT5yvPfVWKGuHuu8aKCIyUrm9Q9pRNqo/mfHzgzMpb7R5dW0zTyxv4rtzMtjeaDLD545zZrQVyuZ0/Zod0Ty7iMgA6GkOfM7EXOpCNWxtmMlREw6isqmSVdWrWFuzmc+3b6aisZasNBsXPoozsxmbWcq4jHHslrMbtmNjYZHjz2F0+mjyUvLURXaEU6GsyEjlDcCoGWYCpHo97rQ89irNZq/S7F5fZlkWGX6LeaV+5u3sFkTRLVBT88wYoidEslPspibWnHEGABMefRRXyg62oBUR2UVul8WBk/M5cHKnCWB/uvnIHgvNNVjNNWQ1VnBIaiOHFEfAjl74ckHYY7qcpxeZVdCBbBUyDQFlhogMCcsyncG9qbjLl7FXVg17jS7pdctpy7LIDlhtRVA7LVgPjdWQMwEKpqgD0y5QZogkqeiWnXYYAlnmseaedxcaSG4Xfb42lZvi5oTdUjhht2H03tTYWiib2rmjbAhS0hN6MaAyQyQJeHymWDbcBNG6/7QC87m+bMiH05/MyE9188WpqXxx6jC77lS7yXzurlA2HITMzKEdzxBRZogkKVe7spLovHBo8AtloX+ZUZjm5szpaZw5fZjNXTf0cK4B5tzNk5jvpcoMERlqPc2B57hzyPZnMyY0hurmasZmjqUh1EAwEgQLcMAidt3H5XKR4k4hNyWX3EAu2f5s3FoEN6iGKjNUKCsykkU7y3p8ULHKFDdFJ0EGSyRkimQzRpmfrSLZXec4BFeuarstIhJ3bg+k5ZmPvInmvT8SNAslcMByxbaPS+DJ4mFJmSEiQym9ALwpULECajaCL737LeIGgmPHJg2KpptCXV142jXKDJHk5HKbzqehxli3oqbquA5pxIh2VOzcUTYSNF0BE5kyQyQ5BLKgpt3iifRC83mIO8omhJa6WHfArLFdv+7YibugXJkhkpysdtdofOnm8xB0lE0YjT3sXgEmMzwJunuFMkNEhhHLssjwZZDhy2B0xmiaw800hZsI2SHCdhgHB5flwuvy4nf7SfWm4nX13DxEBtgQZYYKZUVGOrcH8nc3Bavly6FuC6QVDs6kcrDBXPzJHgcFu2sbCBGRZOH29tpFUEREEpg/HUbNMrtJVKyC6o1mIYV3AFfzBuuhscp01cjfzRToiojIzvOlQkttbHFDuBlCTQP73p2Ieury5Dj63YlIYvCnty6CbhXtKKtC2f6rWmc+pxV2LYiNbk3uTdBCWRFJTt11lFWhbN819FIoi2Uak4iIyJBxWS5Svamk6m/2pKNCWZFE4HKZbbJ96VD+OdRuhtSc2Iq+XRXt7uQ4kD8VcieYAl0REREREUl8Lrc530jJNRPCtRuhsdKcc+zKhaRgo1mI5/FDwVTzM7QYT0Rk1/nSza4QnhSzzXakxXSVVbFn7+q2mM8ZRbHH2oqd9LsTkQQQ/dvdccwOQSqU3XnVa83nnHFdvxZuNrmh3fhEJJG4PIBjMqStULY+rkMaURorzOfOi/Ki3AnaUVZERGSYUaWbSCJJzYXRe0PVenOhprnGdH7y7ORks+OYDiTNta3dnSbHtmMSEREREZHk4k+HommQWQw1m6B+KzRUgi/FFGV5/Dv+HpEgtNSbriPeFMgZD1ljYl0PRURk13kCsSKolGyo3wbNVeb9W7rnOFDbWiibOTr2eLjZLOJQhxERSQS+NPOeFm4y72vRa/31ZbHckL6pWm8+Z3dTKBtshEDWzs/LiIgMR26PKZZ1IuBVR9l+iy5K6dxR1g6bBera0U9ERGRIqFBWJNF4/FCwG6Tnm4s1dVvAroBAprkQZrl2/D3sMLTUmQlsXwYUzYCs0X2b+BYRERERkcRlWWaBXmouNI8zHTFqt5hFeuGg+brbay7yWy4z4W6HwQ6Z2y6vKbjNmWAmBwKZ8T4iEZHE400xuw/ZEUjJMYWyTdXxHtXw1lRpOu9aLkhv11E22Aj+DHWUFZHE4E0BfyY0V5tC2bR8wDKL2ZoqTdMN6ZveOsqGmiFvsgqPRSSxuFoLZe1wu46yKpTtE8dp11G2U9ZGgqabrObgRUREhoQKZUUSVUoOBLIhu9RMiNRtNR+OY1aNu7zmhMYCHMzEdSTYOrntMpMAo2aayQGfumaIiIiIiEgngUzzkT0OgnVmgiTYCKEGCDXFulK5/WYSJfrhzzCFtCIiMji8qeBJMd1QU3LMY42V8R3TcFe7yXxOL+zYzSnUpGInEUkclgUZo6Bum7nv9kFGMdRthur1KpTtK8eB8uXmdu7Ejl8LNZr5l9TcoR+XiMhgis4rq1C2/5przO8NuimUDZnzD7cKZUVERIaCCmVFEln7bk+5E033kJY6szo81AThFkyVrAvcbkjNNxMo/kyzNZBbbxFDwrLwlpS03RYREemRMkNEhiOXy5w/BLLiPRJpT5khkryiBTp1W2JbezaUxXdMw131BvM5c3TssWC9WTzeeXvURKTMEEkeqXmmwKm51ix6yxkbK5Qt2TveoxsZajeZoieXF/J3iz3uONBQCbkTEvvcSJkhkpzcXvMRCXUslI0ukpaeNZSbz4HsjovywDSxSs0319YSkTJDRET6aogyQ1VwIsnC44eMIvMBZvs9OwyObTrIuryJ+0f4MOdKSWHyKy/HexgiIjICKDNERKSvlBkiSS6jGGo2mklXiHUPlO5Vrjafo50Bwy3QWAUFU00n9ASnzBBJIr40yBkPZYvB4zO7Q6x/F6rWxXtkI0fZEvM5fzfTlRfMXEt9GaRkm99vAlNmiCQpyzI7V4S2gy/dPOZEINICnkB8xzbc1beei6UXdv1aOJjQiyuUGSIi0ldDlRkqlBVJVi63tjsVEREREREREUlEaQWmUMebYu7XbjS7DEUlezcfx+l4f/sK8zmtEGo3m9s5E0xXQBGRRJM91uw4V7Pe7C4HULnKdJltnw/Kiu4f2/yJuZ07yRTHRkLmfnoB5O8O/vShG6OIyFDyZZgu5Kn5gAU4pqusCmV7Fz2/aL97RXve1KEbi4iISJJToayIiIiIiIiIiIhIInG5oHAPGDsX3r0DGrab4p4uRT/dFAElnF4KvSzL/E6q1pj7o2ZA3m6mI2Aib4EqIsnN7YGiaWb3uZYG+OAe04UcesiK9hIpN/pRCBwtGnYc2LzA3C7ZC9KLTOdxfwak5JrfrYhIovKlmPdBywJfqimSDdZDal68Rza8tRXKFnd8PBIEl8f8LkVERGRI6IxNRCTO7OZm1p1zLgDj/vEAroBWXoqISPeUGSIi0lfKDBHB5YbR+5jbjRVQOsdMxCZUkdPOalccVbHCdNv1BGDG6WYr8iSjzBBJQpYFafkw5RjAMjmRt5spnm0rlE32vOhUSGtZUL4M6raA2wdzLknK7rHKDJEk5klpXWhmgy+9tVC2Md6jGv7qtpjPmSUdHw82gi/N/C4TlDJDRET6aqgyQ4WyIiLxZts0L1rUdltERKRHygwREekrZYaIAGSUtE5i15uuqYV7xHtEw8+G983n0fsmZZEsoMwQSWaBTCiaDtsWwaYPYdrJ8R7R8Lb8f+bzhEOTskgWUGaIJDNfqimWDTWbAk8wxbLSu2hH2YxOhbKhBrNIxeUe+jENFWWGiIj01RBlhvaOEhEREREREREREUlELpcpgALYtji+YxmuVr5kPo+bF99xiIjES+n+5vO6t+M7juHOjsDH95nb006J71hEROLBmwL+TFPg6c8wjzXXxHdMw50dgbqt5nZmcezxcAvghrSCuAxLREQkWQ1qoezJJ5/M2LFjCQQCFBcXc+6557J58+bB/JEiIjJCKTNERKQvlBciItJXygyRVtFC2a0L4zuO4SjYGCuUnXpCfMcicaXMkKQ26Qjzefmz4DjxHctwtvxZqF4PKTkw8/R4j0biSJkhSS1jlOkom5Jr7jdWxnc8w11DOdghcHkgrdA85jjQsB0yR5lMkYSmzBARGV4GtVD28MMP55FHHmH58uX85z//YdWqVZx+uk4eRUSkK2WGiIj0hfJCRET6Spkh0qpkH/N57VvxHcdw9NnDEGqEnPFQvFe8RyNxpMyQpDbpCPAETBHopo/iPZrhKdwCL/7C3J79NdNVUZKWMkOSWlqBKe6Mvg82VsR3PMNd1RrzOasUXG6TJ7WbIZAFeZPBsuI7Phl0ygwRkeHFM5jf/Iorrmi7PW7cOK688kpOPfVUQqEQXq93MH+0iIiMMMoMERHpC+WFiIj0lTJDpNXkI83nTR+ZzkVp+fEdz3ARbIQ3bzG3979Ik9RJTpkhSc2XBtO/CJ/+C+bfDmf8Ld4jGl4cB/73Y6hcDelFcNAVO36NJDRlhiQ1bwAKppqCWYDajVC3tdPf0sn2d3U33dijHdo3LzCfM4qhZhO4veZ2/mTwZwzZCCV+lBkiIsPLoBbKtldZWcmDDz7IvHnz9IYvIiK9UmaIiEhfKC9ERKSvlBmS1DJLTLfULQvg4/vg4O/He0TxFwnBfy+Fmg2mu9Psr8V7RDKMKDMkKR1wqSmUXfwY7H02TD4q3iMaHlrq4fmfmvzEgpNvg0BmvEclw4gyQ5JSegGM3se8N0ZCkD2utTDUiRWIJqPOxcKWBU1V5u6YfWH0bPClgj9Ti/SSlDJDRCT+Br1Q9sc//jF/+tOfaGxs5IADDuDpp5/u8bktLS20tLS03a+trR3s4YmIDAvunJx4D2FYUGaIiOyYMqN/eQHKDBFJXsoMZYZImzmXwBOXwFu3wtSToGBKvEcUH44Da9+Cl6+Bje+D5YZT7zAT1klOmaHMkCRXPAv2/Tp8+Fd45Dw48RaYcTq4h6zfzvARCcO2RbDsafj4fqjfBlhw4s0w5dh4j25YUGYoM0TIHms+N9dC0bT4jmU4277CfJ54KGQWx3cscaLMUGaIiPTVUGSG5Tj9W9Zz5ZVXcuONN/b6nKVLlzJ16lQAtm/fTmVlJevWreNXv/oVWVlZPP3001jdrJL55S9/ya9+9asuj9fU1JCZqRWaIiLdqa2tJSsra1i+VyozRESGl+GaGYOZF6DMEBHZGcqMjobb70Gk3yJh+PsJsOE98KaaLbZHzYLUPPCmgMttikYtF7hc7Hi71D5cUu7TZecdPKdPV64dsMOmm5UdAjsSux0JQbAeardA9XrY9CE0lJuXedPg9L/C7sf15YdIL5QZHQ2334NIn4Vb4J9fhtWvmftphVC6P+ROgNR8SMkxW0a7vKaA1uU1+RHNjJ623W573Or2y7E7Tuv7vtOuM6HdqUth62fH7voY9Pz89t/LsSHUBKEGCDZCqNFkRd1Wsy121VoIN8WGlzMeTvwdTD5yJ36p0pkyo6Ph9nsQ6bOypXDHAaY76pXr1SG1O/XlcPNkc/vH6yAlO67DGYmUGR0Nt9+DiMhw0tfM6HehbHl5ORUVFb0+Z+LEifh8vi6Pb9y4kdLSUt555x3mzp3b5evdrYwoLS3VG76ISC+G60kCKDNERIab4ZoZg5kXoMwQEdkZygxDmSEJpb4cHj0P1r0d75HEly8DZp4Oh/wQskbHezQJQZlhKDMkIURC8Pat8O6fobH3/38Smj8Lxs2FmWfAHieBxx/vESUMZYahzJARL9QMvxkFOPCDFZBeGO8RDT9Ln4KHz4GCqXDZe/EezYikzDCUGSIiO9bXzOj3nikFBQUUFBTs1KBs2wbo8Kbent/vx+/XyaaISKJQZoiISF8MZl6AMkNEJJEoM0R2QXoBnP8MrHnDdAusXA1NlWaS24mYTqyO3dqlrz/60T2qX42m+vFkl6drl0O31zzuS4OMYvNRvKf58Ab6MxAZoZQZIjvB7TULCeZ9GzZ+AFs+NV1WG7dDc027jt3hWBfvNu368nTo0dOXx2l927dauxK2frZcXR/r9jn08hxX1+d7U0yHdV9a6+dUSB9lFlBkjYXcia0d1iVZKDNE+skbgOyxUL0Otn+uQtnurHjBfJ5waHzHIQNOmSEiMnL1u1C2r9577z0++OADDjroIHJycli1ahVXXXUVkyZN6nFlhIhIMrKbm9lw4UUAlN5zN65A8k3WKDNERPom2TNDeSEi0nfKDGWGSLcsCyYeaj5EWikzlBkiXXj8MP4g8yHSjjJDmSHSJn9Ka6HsCuVFZ3YEPm8tlJ1ybHzHEkfKDGWGiEhfDVVmDNpyyNTUVB577DGOPPJIdt99dy644AJmzZrF66+/rtUPIiLt2TaNH3xA4wcfgN3fri2JQZkhItJHSZ4ZygsRkX5QZigzRET6SpmhzBAR6StlhjJDJKpgd/N5y6fxHcdwtPJlqN8KKbnJXUSszFBmiIj01RBlxqB1lJ05cyavvPLKYH17ERFJIMoMERHpC+WFiIj0lTJDRET6SpkhIiJ9pcwQaWfcgTD/T7DmjXiPZHhxHHjr9+b2nl8xXdolKSkzRESGn0HrKCsiIiIiIiIiIiIiIiIiIiIiCWb8gWC5oXIVlC2N92iGj4/vg/XvgNsPcy+L92hERESkHRXKioiIiIiIiIiIiIiIiIiIiEjfBLJg6gnm9iu/HtRtkkeEljp447fw9PfM/cN/Almj4zsmERER6cAT7wGIiIiIiIiIiIiIiIiIiIiIyAhyyA9h+f9g2dPwlyNgynGQOxF86eBLNR1noyyr3Qut7h93nOiNnbjNjp/T9jN25XarSBDqt0HdNihfChs+gFCD+dre58C87yAiIiLDiwplRURERERERERERERERERERKTviveEU++EJy+HzZ+Yj2SWN9kUD886s1NhsIiIiAwHKpQVERkGrJSUeA9BRERGCGWGiIj0lTJDRET6SpkhIiJ9pcwQkQ5mnQETDoHFj8PWhVC9DkJNEGoExzbPad+JlXa3uzxutSsw7ettdvwcaL2/K7fp+LjLDelF5iNnHIzZD4pmqEC2E2WGiIj01VBkhgplRUTizJWaytRPPo73MEREZARQZoiISF8pM0REpK+UGSIi0lfKDBHpVkYRHHBJvEchw4wyQ0RE+mqoMsM16D9BREREREREREREREREREREREREREQkDlQoKyIiIiIiIiIiIiIiIiIiIiIiIiIiCUmFsiIicWa3tLD+4otZf/HF2C0t8R6OiIgMY8oMERHpK2WGiIj0lTJDRET6SpkhIiJ9pcwQEZG+GqrM8AzadxYRkb6JRGh4/Y222yIiIj1SZoiISF8pM0REpK+UGSIi0lfKDBER6StlhoiI9NUQZYY6yoqIiIiIiIiIiIiIiIiIiIiIiIiISEJSoayIiIiIiIiIiIiIiIiIiIiIiIiIiCQkFcqKiIiIiIiIiIiIiIiIiIiIiIiIiEhCUqGsiIiIiIiIiIiIiIiIiIiIiIiIiIgkJBXKioiIiIiIiIiIiIiIiIiIiIiIiIhIQvLEewC9cRwHgNra2jiPRERk8NiNjdRHIoB5v3OFw/16ffQ9MvqemayUGSKSDJQZA0OZISLJQJkxMJQZIpIMlBkDQ5khIslAmTEwlBkikgyUGQNDmSEiyWCoMmNYF8rW1dUBUFpaGueRiIgMkeLinX5pXV0dWVlZAziYkUWZISJJR5mx05QZIpJ0lBk7TZkhIklHmbHTlBkiknSUGTtNmSEiSUeZsdOUGSKSdAYxMyxnGC+/sG2bzZs3k5GRgWVZ/XptbW0tpaWlbNiwgczMzEEa4fCRbMcLyXfMOt7EtivH6zgOdXV1lJSU4HK5BmmEw58yo+90vIkv2Y5Zx9t3ygxDmdF3Ot7El2zHrOPtO2WGoczoOx1v4ku2Y9bx9p0yw1Bm9J2ON7El2/FC8h2zMmPXKTP6Tseb2JLteCH5jlmZseuUGX2n401syXa8kHzHPBSZMaw7yrpcLsaMGbNL3yMzMzMp/rFEJdvxQvIds443se3s8SbzKrooZUb/6XgTX7Ids463b5QZyoydoeNNfMl2zDrevlFmKDN2ho438SXbMet4+0aZoczYGTrexJZsxwvJd8zKjJ2nzOg/HW9iS7bjheQ7ZmXGzlNm9J+ON7El2/FC8h3zYGZG8i67EBERERERERERERERERERERERERGRhKZCWRERERERERERERERERERERERERERSUgJWyjr9/u5+uqr8fv98R7KkEi244XkO2Ydb2JLtuMdbpLt96/jTXzJdsw6XhlKyfb71/EmvmQ7Zh2vDKVk+/3reBNfsh2zjleGUrL9/nW8iS3ZjheS75iT7XiHm2T7/et4E1uyHS8k3zEn2/EON8n2+9fxJrZkO15IvmMeiuO1HMdxBu27i4iIiIiIiIiIiIiIiIiIiIiIiIiIxEnCdpQVEREREREREREREREREREREREREZHkpkJZERERERERERERERERERERERERERFJSCqUFRERERERERERERERERERERERERGRhKRCWRERERERERERERERERERERERERERSUgjulD29ttvZ/z48QQCAebMmcP777/f6/MfffRRpk6dSiAQYObMmTz77LNDNNKB0Z/jveeeezj44IPJyckhJyeHo446aoe/n+Gmv/99ox566CEsy+LUU08d3AEOgv4ec3V1NZdddhnFxcX4/X6mTJkyov5d9/d4b731VnbffXdSUlIoLS3liiuuoLm5eYhGu2veeOMNTjrpJEpKSrAsiyeeeGKHr3nttdfYZ5998Pv9TJ48mb///e+DPs5EpsxQZnRHmTFy/l0nS2YoL4YHZYYyozsjNTOSLS9AmdEbZcbAU2YoM7qjzBg5lBk9U2YMPGWGMqM7yoyRQ5nRM2XGwFNmKDO6o8wYGZIlL0CZMVwoM5QZ3VFmjAzKjN4NSmY4I9RDDz3k+Hw+569//auzePFi58ILL3Sys7Odbdu2dfv8t99+23G73c5NN93kLFmyxPn5z3/ueL1eZ+HChUM88p3T3+P96le/6tx+++3OJ5984ixdutQ5//zznaysLGfjxo1DPPKd09/jjVqzZo0zevRo5+CDD3ZOOeWUoRnsAOnvMbe0tDj77ruvc8IJJzhvvfWWs2bNGue1115zFixYMMQj3zn9Pd4HH3zQ8fv9zoMPPuisWbPGef75553i4mLniiuuGOKR75xnn33W+dnPfuY89thjDuA8/vjjvT5/9erVTmpqqvO9733PWbJkiXPbbbc5brfbee6554ZmwAlGmaHM6I4yQ5kxHCkv4k+ZoczozkjNjGTLC8dRZvRGmTHwlBnKjO4oM5QZw5EyI/6UGcqM7igzlBnDkTIj/pQZyozuKDNGRmYkU144jjJjOFBmKDO6o8xQZgxHwyUzRmyh7P777+9cdtllbfcjkYhTUlLiXH/99d0+/8tf/rJz4okndnhszpw5zsUXXzyo4xwo/T3ezsLhsJORkeHcd999gzXEAbUzxxsOh5158+Y5f/nLX5zzzjtvRL3hO07/j/nPf/6zM3HiRCcYDA7VEAdUf4/3sssuc4444ogOj33ve99zDjzwwEEd52Doy5v+j370I2f69OkdHjvzzDOdY489dhBHlriUGcqMzpQZI0uyZobyIj6UGcqMzkZyZiRbXjiOMqM3yoyBp8xQZnSmzBhZlBk9U2YMPGWGMqMzZcbIoszomTJj4CkzlBmdKTNGjmTNC8dRZsSLMkOZ0ZkyY+RQZjze63MGKzNcu96TdugFg0E++ugjjjrqqLbHXC4XRx11FPPnz+/2NfPnz+/wfIBjjz22x+cPJztzvJ01NjYSCoXIzc0drGEOmJ093muuuYbCwkIuuOCCoRjmgNqZY37yySeZO3cul112GUVFRcyYMYPrrruOSCQyVMPeaTtzvPPmzeOjjz5qazW+evVqnn32WU444YQhGfNQG8nvWcONMkOZ0R1lhjIjUYzk96vhSJmhzOjOSM2MZMsLUGbsyEh+vxqOlBnKjO4oM5QZiWIkv18NR8oMZUZ3lBnKjEQxkt+vhiNlhjKjO8qMkZEZyosdG8nvV8ORMkOZ0R1lhjIjUQzW+5Vnl14dJ9u3bycSiVBUVNTh8aKiIpYtW9bta7Zu3drt87du3Tpo4xwoO3O8nf34xz+mpKSkyz+i4Whnjvett97i3nvvZcGCBUMwwoG3M8e8evVqXnnlFc4++2yeffZZVq5cyaWXXkooFOLqq68eimHvtJ053q9+9ats376dgw46CMdxCIfDXHLJJfz0pz8diiEPuZ7es2pra2lqaiIlJSVOIxt5lBmGMiNGmaHMSCTKi4GlzDCUGTEjOTOSLS9AmbEjyoyBpcwwlBkxygxlRiJRZgwsZYahzIhRZigzEokyY2ApMwxlRowyY+RkhvJix5QZA0uZYSgzYpQZyoxEMliZMSI7ykr/3HDDDTz00EM8/vjjBAKBeA9nwNXV1XHuuedyzz33kJ+fH+/hDBnbtiksLOTuu+9m9uzZnHnmmfzsZz/jzjvvjPfQBsVrr73Gddddxx133MHHH3/MY489xjPPPMO1114b76GJJBRlRmJSZigzRAaDMiPxJFtegDJDZKgoMxKPMkOZITJYlBmJR5mhzBAZLMqMxJNsmaG8EBk6yozEo8xQZuyMEdlRNj8/H7fbzbZt2zo8vm3bNkaNGtXta0aNGtWv5w8nO3O8UTfffDM33HADL730ErNmzRrMYQ6Y/h7vqlWrWLt2LSeddFLbY7ZtA+DxeFi+fDmTJk0a3EHvop35b1xcXIzX68Xtdrc9tscee7B161aCwSA+n29Qx7wrduZ4r7rqKs4991y+8Y1vADBz5kwaGhq46KKL+NnPfobLlVh1/z29Z2VmZmo1XT8pMwxlhqHMUGYkWmYoLwaWMsNQZhgjPTOSLS9AmbEjyoyBpcwwlBmGMsNQZigzpHvKDEOZYSgzDGWGMkO6p8wwlBmGMsMYKZmhvNgxZcbAUmYYygxDmWEoMxLHYGXGiPwt+Xw+Zs+ezcsvv9z2mG3bvPzyy8ydO7fb18ydO7fD8wFefPHFHp8/nOzM8QLcdNNNXHvttTz33HPsu+++QzHUAdHf4506dSoLFy5kwYIFbR8nn3wyhx9+OAsWLKC0tHQoh79Tdua/8YEHHsjKlSvbwg3g888/p7i4eFi/4cPOHW9jY2OXN/Zo4DmOM3iDjZOR/J413CgzlBntKTOUGYmWGSP5/Wo4UmYoM9ob6ZmRbHkByowdGcnvV8ORMkOZ0Z4yw1BmJI6R/H41HCkzlBntKTMMZUbiGMnvV8ORMkOZ0Z4ywxgpmaG82LGR/H41HCkzlBntKTMMZUbiGLT3K2eEeuihhxy/3+/8/e9/d5YsWeJcdNFFTnZ2trN161bHcRzn3HPPda688sq257/99tuOx+Nxbr75Zmfp0qXO1Vdf7Xi9XmfhwoXxOoR+6e/x3nDDDY7P53P+/e9/O1u2bGn7qKuri9ch9Et/j7ez8847zznllFOGaLQDo7/HvH79eicjI8P51re+5Sxfvtx5+umnncLCQufXv/51vA6hX/p7vFdffbWTkZHh/Otf/3JWr17tvPDCC86kSZOcL3/5y/E6hH6pq6tzPvnkE+eTTz5xAOd3v/ud88knnzjr1q1zHMdxrrzySufcc89te/7q1aud1NRU54c//KGzdOlS5/bbb3fcbrfz3HPPxesQRjRlhjJDmaHMGCmZobyIP2WGMiORMiPZ8sJxlBnKjKGlzFBmKDOUGcoM6StlhjJDmaHMUGZIXykzlBnKjJGbGcmUF46jzBgOlBnKDGWGMkOZ0T8jtlDWcRzntttuc8aOHev4fD5n//33d9599922rx166KHOeeed1+H5jzzyiDNlyhTH5/M506dPd5555pkhHvGu6c/xjhs3zgG6fFx99dVDP/Cd1N//vu2NtDf8qP4e8zvvvOPMmTPH8fv9zsSJE53f/OY3TjgcHuJR77z+HG8oFHJ++ctfOpMmTXICgYBTWlrqXHrppU5VVdXQD3wnvPrqq93+Pxk9xvPOO8859NBDu7xmr732cnw+nzNx4kTnb3/725CPO5EoM5QZPVFmjAzJkhnKi+FBmaHM6MlIzIxkywvHUWYoM4aWMkOZ0RNlxsigzDjPcRxlxlBRZigzeqLMGBmUGec5jqPMGCrKDGVGT5QZw1+y5IXjKDOGC2WGMqMnyozhT5kx9JlhOU4C9t8VEREREREREREREREREREREREREZGk54r3AERERERERERERERERERERERERERERAaDCmVFRERERERERERERERERERERERERCQhqVBWREREREREREREREREREREREREREQSkgplRUREREREREREREREREREREREREQkIalQVkREREREREREREREREREREREREREEpIKZUVEREREREREREREREREREREREREJCGpUFZERERERERERERERERERERERERERBKSCmVFRERERERERERERERERERERERERCQhqVBWREREREREREREREREREREREREREQSkgplRUREREREREREREREREREREREREQkIalQVkREREREREREREREREREREREREREEpIKZUVEREREREREREREREREREREREREJCH9P6RLLXLmZCpEAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seed 1\n",
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seed 2\n",
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seed 3\n",
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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QUVHBvvvuy6OPPsrGjRt58MEH22x76aWX8sEHH+hiiohIN+qN2RAIBPjzn/8MwPz58wH4y1/+QkFBAQUFBfzoRz/q1PciIiLJ6Y2ZMXv2bDZs2MB1113HvHnzmDdvXutjgwYNYubMmZ36XkREZM+NGzeOp556iosuuogJEyZw8cUXM3XqVGzbpri4mKeeegqHw8Hw4cPTPVSRTlNxrUgXuPrqq7nrrrsIBAJJbT9w4EA++ugj/v73v/Pvf/+bG2+8kVAoRP/+/TnkkEN48sknueCCCzrcz7Rp03j77bf52c9+xjXXXENubi7f/e53ufnmm/f2WxIRkS7QW/Phueee49FHH239esmSJa3LQQ0fPlzFtSIiKdAbM2PbRIy1a9e2e8HkvffeU3GtiEg36myWAMyZM4c5c+YA4HQ6GTJkCN/73vf47W9/S3Z2dofPf+yxx7j++ut5/PHHqa2t5YADDuDVV1/lmGOO2ePvQ0REuk5vy4ba2lquv/76NvfdfvvtAIwaNUrFtSIiKdTbMmPbealbbrllp8eOPfZYFdeKiHSzM888k2XLlnH77bfz1ltv8dBDD2FZFqNGjeLUU0/lBz/4QYddzUV6IstWuwAREREREREREREREREREREREREREREAHOkegIiIiIiIiIiIiIiIiIiIiIiIiIiISE+h4loREREREREREREREREREREREREREZEWKq4VERERERERERERERERERERERERERFpoeJaERERERERERERERERERERERERERGRFiquFRERERERERERERERERERERERERERaaHiWhERERERERERERERERERERERERERkRaudA+gqyUSCUpKSsjNzcWyrHQPR0SkR7Jtm2AwyNChQ3E4MneehTJDRKRjygxDmSEi0jFlhqHMEBHpmDLDUGaIiHRMmWEoM0REOqbMMJQZIiIdSzYz+lxxbUlJCSNGjEj3MEREeoXNmzczfPjwdA8jbZQZIiLJU2YoM0REkqXMUGaIiCRLmaHMEBFJljJDmSEikixlhjJDRCRZHWVGnyuuzc3NBcw3npeXl+bRiIj0TPX19YwYMaL1b2amUmaIiHRMmWEoM0REOqbMMJQZIiIdU2YYygwRkY4pMwxlhohIx5QZhjJDRKRjyWZGnyuu3dbSPC8vTyEhIn1WoqmJ4vPOA2DMM8/gyMrao/1k+jIQygwRyQTKjK6hzBCRTKDM6BrKDBHJBMqMrqHMEJFMoMzoGsoMEckEyoyuocwQkUzQXZnR54prRUQygm0TWbe+9baIiMguKTNERCRZygwREUmWMkNERJKlzBARkWQpM0REJFndlBmOlO1ZRERERERERERERERERERERERERESkl1FxrYiIiIiIiIiIiIiIiIiIiIiIiIiISAtXugcgIiIiIiIiIiIiIiIiIiIiIiIi3SMejxONRtM9DEmSx+PB4VAPTZHupuJaERERERERERERERERERERERGRPs62bcrKyqirq0v3UKQTHA4HY8aMwePxpHsoIhlFxbUiIiIiIiIiIiIiIiIiIiIiIiJ93LbC2oEDB+L3+7EsK91Dkg4kEglKSkooLS1l5MiR+j8T6UYp7Rc9d+5cTj/9dIYOHYplWbz44ou73f7999/HsqydPsrKylI5TBGR3seycA8dinvoUOgjb5yUGSIiKaLMUGaIiCRLmaHMEBFJljJDmSEikixlhjJDRCRZyoyUZ0Y8Hm8trO3Xrx9ZWVn4fD599PAPv9/PgAEDCIVCxGKxLvlZEOn1uikzUtq5trGxkalTp/Kd73yHc845J+nnrV69mry8vNavBw4cmIrhiYj0Wo6sLPZ99510D6NLKTNERFJDmbGdMkNEZPeUGdspM0REdk+ZsZ0yQ0Rk95QZ2ykzRER2T5mxXaoyIxqNAuD3+7tkf9J9PB4PYAqk3W53mkcjkn7dlRkpLa49+eSTOfnkkzv9vIEDB1JQUND1AxIRkR5LmSEiIslSZoiISLKUGSIikixlhoiIJEuZISIiyeqpmWH1kc7AmUT/ZyLp4Uj3ANpz4IEHMmTIEGbOnMn8+fPTPRwREenBlBkiIpIsZYaIiCRLmSEiIslSZoiISLKUGSIikixlhohIz5DSzrWdNWTIEO677z4OOeQQmpubeeCBB5gxYwYLFixg2rRp7T6nubmZ5ubm1q/r6+u7a7giImmTCIf56pJvATDqicdx+HxpHlH3U2aIiCRHmaHMEBFJViIc5quLvwnxGKP++W8cWVnpHlK3U2aIiCRHxxnKDBGRZCkzlBkiIslSZigzRESS1V2Z0aOKaydMmMCECRNavz7iiCNYv349d955J48//ni7z7n55pu58cYbu2uIIiI9QyJBePny1tuZSJkhIpIkZYYyQ0QkWZEw4RUrze1YFMi84lplhohIknScocwQEUmWMkOZISKSLGWGMmM3ZsyYwYEHHshdd92V7qGISE/QTZnhSNmeu8j06dNZt27dLh//xS9+QSAQaP3YvHlzN45ORER6EmWGiIgkS5khIvI1iQRUr0/3KHokZYaIiCRLmSEiIslSZoiISLKUGcmxbZtYLJbuYYhIH9Pji2uXLl3KkCFDdvm41+slLy+vzYeIiGQmZYaIiCRLmSEisgPbhtpiqNuY7pH0SMoMERFJljJDRESSpcwQEZFkKTPg8ssv54MPPuDuu+/Gsiwsy+KRRx7BsizeeOMNDj74YLxeL/PmzePyyy/nrLPOavP8q6++mhkzZrR+nUgkuPnmmxkzZgxZWVlMnTqVZ599tnu/KRHpFVyp3HlDQ0Ob2RPFxcUsXbqUoqIiRo4cyS9+8Qu2bt3KY489BsBdd93FmDFjmDx5MuFwmAceeIB3332Xt956K5XDFBGRHkCZISIiyVJmiIh0sbpNULkasgrSPZIup8wQEZFkKTNERCRZygwREUlWT88M27ZpisZTsu+OZLmdWJaV1LZ33303a9asYcqUKdx0000ArFixAoCf//zn3HbbbYwdO5bCwsKk9nfzzTfzxBNPcN999zFu3Djmzp3LJZdcwoABAzj22GP37BsSkT4ppcW1n376Kccdd1zr1z/+8Y8BuOyyy3jkkUcoLS1l06ZNrY9HIhF+8pOfsHXrVvx+PwcccABvv/12m32IiEjfpMwQEZFkKTNERLpQYCtUrARPNtiedI+myykzREQkWcoMERFJljJDRESS1dMzoykaZ9Kv30zJvjvy5U2z8HuSK1vLz8/H4/Hg9/sZPHgwAKtWrQLgpptuYubMmUm/bnNzM3/4wx94++23OfzwwwEYO3Ys8+bN4+9//7uKa0WkDcu2bTvdg+hK9fX15OfnEwgE+mSrcxERgEQoxOppBwMwYfFnOPz+Tj1ffysN/TuISCZQZnQN/TuISJ8ULIeyL8DhhKxCEk1hVp/6HQAmLFqAI7dzf+/0t9LQv4OIZAIdZ3QN/TuISCZQZnQN/TuISCZQZnSN3f07hMNhiouLGTNmDD6fj1Ak1iuKawFmzJjBgQceyF133QXA+++/z3HHHceWLVsYNmxY63aXX345dXV1vPjii633XX311SxdupT333+fFStWMGXKFLKzs9vsPxKJcNBBB7FgwYK9+r5S5ev/dyKZrrsyI6Wda0VEJHWcSS5pICIioswQEZGdNFZB+QqwLMjanhPOvJw0DkpERHoTHWeIiEiylBkiIpIsZUb3ynI7+fKmWWl77a7w9SJZh8PB1/tMRqPR1tsNDQ0AvPbaa22KcgG8Xm+XjElEukd3ZIaKa0VEeiGH38/4jz9K9zBERKQXUGaIiMhOmmqhbDkkopAzsPVuR5aP8f++HRIJ6OQsbxERySw6zhARkWQpM0REJFnKjO5nWVanusemk8fjIR6Pd7jdgAEDWL58eZv7li5ditvtBmDSpEl4vV42bdrEsccem5KxikjqdVdm9I6/kCIiIiIiIiIisveag6ZjbbQJ8ganezQiIiIiIiIiIiIiHRo9ejQLFixg48aN5OTkkEgk2t3u+OOP59Zbb+Wxxx7j8MMP54knnmD58uUcdNBBAOTm5nLttddyzTXXkEgkOOqoowgEAsyfP5+8vDwuu+yy7vy2RKSHc6R7ACIiIiIiIiIi0g2iYaj4EprqIHdQukcjIiIiIiIiIiIikpRrr70Wp9PJpEmTGDBgAJs2bWp3u1mzZnH99ddz3XXXceihhxIMBrn00kvbbPPb3/6W66+/nptvvpmJEycye/ZsXnvtNcaMGdMd34qI9CLqXCsi0gslwmE2f/8KAEb8434cPl+aRyQiIj2VMkNERACIx6ByNQTLIW8oWNZOmySaI2y+7nbAZsQjh+DIzun+cYqISK+g4wwREUmWMkNERJKlzJDdGT9+PB9//HGb+y6//PJ2t73xxhu58cYbd7kvy7K46qqruOqqq7pyiCLSjborM1RcKyLSGyUShBYtar0tIiKyS8oMERGxbagphsAm07HW4Wx/u0SC0LI1rbdFRER2SccZIiKSLGWGiIgkS5khIiLJ6qbMcKRszyIiIiIiIiIikn7BMqheC1lF4PSkezQiIiIiIiIiIiIiIiI9noprRURERERERET6qnA9VK4Glwc8/nSPRkREREREREREREREpFdQca2ISG9n2+kegYiIiIiI9ETxGFSthWgD+PulezQiIiIiIiIiIiIiIiK9hoprRUR6u1hzukcgIiIiIiI9Ud0mCJZAzqB0j0RERERERERERERERKRXUXGtiEhvFIukewQiIiIiItKThWqgZj348sHhSvdoREREREREREREREREehVdXRER6Y3qt2J53YCV7pGIiEgvYGVlpXsIIiLSneIxqF4P8Shk9+/UUy2vJ0WDEhGRvkbHGSIikixlhoiIJEuZISIiyeqOzFBxrYhIb9NYhSO0lf2evR0sC/w6wBARkV1z+P3st2RxuochIiLdqX4LBMsgb3CnnubI8rHfi3+GRAL8/hQNTkRE+gIdZ4iISLKUGSIikixlhoiIJKu7MsOR8lcQEZGuE2mEylVAAjzZ6R6NiIiIiIj0NM0NUF0MvhxwaE61iIiIiIiIiIiIiIjInlBxrYhIbxENQ8VKaKqD7IHpHo2IiIgIxCLmvUlDpemS2VBhvo5F0j0ykcxk21C7ESIN4CtI92hEREREREREREREepzRo0dz1113pXsYItILqIWJiEhvEG2C8i+hvhTyBpOIRtny67sgEWf4fYfgcGele4QiItJDJZqb2XLllWDbDP/zn3F4vekekvR28RiEqk0hbagaYk2QiG9/3OEElw/8/SBnIPj7g1OHniLdIlQDgS2Q3W+Pnp6IRNjy6z+DDcPvPxSHX7+7IiLSvtbjDGD4PffoOENERHZJmSEiIslSZsjuzJgxgwMPPLBLimIXLVpEdrZWChbpzborM3SVRESkpwsHoHKVKWDJG2yWdo2HaVy4zDweT6R3fCIi0rPF4zR+MNfcjjSDTkbJnkokoLECajZCqAosB3hzILt/26XnEzEzMah+CwQ2g68QikZDziBTeCsiqZFImK61tg17OvkunqBx0fKW2/HdbysiIpltx+MMZYaIiOyOMkNERJKlzOg14gmbhcU1VATDDMz1MX1MEU6HldYx2bZNPB7H5eq4FG7AgAHdMCIRSaluygxHyvYsIiJ7x7ahvgS2LjFLLecOaVu4IiIi0lm2JmTIHoqEoHwFbP0MmgOQOwjyhoA3d+f3Jw6XuT93iOlcG22EkiVQ9gWE69MzfpFM0FgBwdI97lorIiIiIiIiIiIi0pH/LC/lqD+9y0X/+ISr/rWUi/7xCUf96V3+s7w0Za95+eWX88EHH3D33XdjWRaWZfHII49gWRZvvPEGBx98MF6vl3nz5rF+/XrOPPNMBg0aRE5ODoceeihvv/12m/2NHj26TQdcy7J44IEHOPvss/H7/YwbN46XX345Zd+PiPQeKq4VEemJthWwlCwBOwb5w9TpTURE9kwsku4RSG/XWA0li6G2GPz9TMFsshN+HC7T2TZ7AAS2wtbFUJ+6E2wiGSseM12lnW7zISIiIiIiIiIiItLF/rO8lP95YjGlgXCb+8sCYf7nicUpK7C9++67Ofzww/n+979PaWkppaWljBgxAoCf//zn/PGPf2TlypUccMABNDQ0cMopp/DOO++wZMkSZs+ezemnn86mTZt2+xo33ngj559/Pl988QWnnHIKF198MTU1NSn5fkSk91BxrYhIT5JImMKTLYugZgP4i0wRi0hvkkiY5cCbgxBpNMUeIpI+TdXpHoH0ZvUlULoUIg2QPxxc3j3bj9NtJgvZMbO/mmLTpV9EukZDOYQqzfGDiIiIiIiIiIiISBeLJ2xufOVL2juzv+2+G1/5knii68/95+fn4/F48Pv9DB48mMGDB+N0muZkN910EzNnzmSfffahqKiIqVOn8t///d9MmTKFcePG8dvf/pZ99tmnw060l19+ORdddBH77rsvf/jDH2hoaGDhwoVd/r2ISO+i9cVFRHqK5iBUb4DAZnD7TAGLZaV7VCLJSSSgqRZC1WZJ4ljY3GdZ4PSYIvHsAaZ7obowi3SfRNxM2hDZE4EtppO+wwU5g7pmn/5+5j1P+Qoz+aLfPuDQnE+RvRKPQu1X4PIl31VaREREREREREREpBMWFtfs1LF2RzZQGgizsLiGw/fpvgZihxxySJuvGxoauOGGG3jttdcoLS0lFovR1NTUYefaAw44oPV2dnY2eXl5VFRUpGTMItJ76KqLiEi62bbpCle1FiJBU4C4p13hRNKhsRpqN0JDBRAHdza4/aaI1k6YJenrNpltcgZA4VjzWURSr7EKQlXpHoX0RvWlpgDW6YGsgq7dtzcXLCdUrTZf999XE4pE9kZDBTTVQG4XFcGLiIiIiIiIiIiIfE1FcNeFtXuyXVfJzs5u8/W1117LnDlzuO2229h3333Jysri3HPPJRKJ7HY/bre7zdeWZZFIJLp8vCLSu6i4VkQknWLNULUOaovBnWW61Yr0FrEI1GwwRbO2DdlFpgjr61w+8OVBItZS6FcLRftA0Rhw6q2ISMok4qawXd2ipbMaq6HiS/Oz09WFtdt4/OZz1WqTBUVjUvM6In1dPGb+1rs86lorIiIiIiIiIiIiKTMw19el23WWx+MhHo93uN38+fO5/PLLOfvsswHTyXbjxo0pGZOI9H268iIiki7NQahYBcFS08XTlfybTEeWj4lvPgjREPizUjhIkV0IB6ByFQTLzRLf24qkdsfhgtzBEGmEypXm53fgRFMMIiJdr6EcGitwFA1l4n/+AbEo+JP4XZXM1txgCmsTUchJcRdMjx/suMkTlxfyhqb29UT6osZK07U2Z2CX7M6R5WPiG3+HREKZISIiu+Xw+5m4amW6hyEiIr2AMkNERJKlzOjZpo8pYki+j7JAGLudxy1gcL6P6WOKUvL6o0ePZsGCBWzcuJGcnJxddpUdN24czz//PKeffjqWZXH99derA61IH9RdmeFI+StIzxePQagGAltNB8LKNVC9Huo2m/vj0XSPUKTvCdVAyVJT+JQ3tFOFtSJp11DZ8vNbaX5+kyms3ZEn2yxbXPcVVKwwHXBFpGtFw+b9nFOdDKUT4lGoXG0mUGR3TaFeh7y55ue0YhU01XbPa4r0FYkEBLaYv/P6Wy8iIiIiIiIiIiIp5HRY/Ob0SYAppN3Rtq9/c/oknI6vP9o1rr32WpxOJ5MmTWLAgAFs2rSp3e3uuOMOCgsLOeKIIzj99NOZNWsW06ZNS8mYRKTv09WXTBZpNB0H67eaDlV2S/t0ywK7ZdaG5QRvDuQNM90GPdnpG69IX9FYDWXLINZkChOt1Ly5FEmJ+lJTEJuIQ/6wPd+P0wN5Q8xEDhwwaLJZFlxE9p5tQ+1GU6i4N7+nknlqis2xQd6Q7n1/4i+C+jJTYDv0IHBr0pFIUkLVpnNtdv90j0REREREREREREQywOwpQ7j3kmnc+MqXlAbCrfcPzvfxm9MnMXvKkJS99vjx4/n444/b3Hf55ZfvtN3o0aN5991329z3wx/+sM3XGzdubPO1be/ci7eurm6PxikifYuqWDJRrNl0t6n9CiINpng2p3/7nW4SMVN4W74CajdB0WjIH6ECKJE9FaoxhbXxsClY30OJSISS3/8NEnGG3n0IDndWFw5SZBfqS0weWFbXLD3scG3vYOvywoAJKjYX6QoN5aZIMrsfWA6TGb+7D+wEQ+85BIfLk+4RSk/UUGFWsfD3S08HzNyBZiWN6nUwcBI4tMiKyG7ZtimGxwKnu8t2a44z/g42DP3zoTj8OvYXEZH2JZqbKbnuZwAMveVPOLzeNI9IRER6KmWGiIgkS5nRO8yeMoSZkwazsLiGimCYgbk+po8pSlnHWhGR9nRXZugqSaZprIKqtaa7jS8fCkbsfnuHC7IKzEc4YIoCQ9WmAMqb2x0jFuk7wgEoW2461u5FYS0A8QTBDz9rvS2ScsGylsJah+kw2FWcHtNtrWa9meyRP7zr9i2SiZrqTPdPlxvcfnNfPEFw3rbMiKdtaNKDRcNQtc5McPD40zMGy2EmbtR9ZY49lAciuxeuM5Mp/AVdu994guC8xS23lRkiIrIb8TjBN980t2/+Q3rHIiIiPZsyQ0REkqXM6DWcDovD9+mX7mGISCbrpsxQcW2mSCTMheqqtUDCLBFsdbIblC8fPNnQUAaRkFnCO1thKZKUSAjKv4RIEHJTtxSCSEo0Vm3vWNuVhbXbuLNMV/XKNeDJMUVVItJ5zQ0ma6IhyFPWSCfUboRQlTlGSCeX12RC1Vrw5oEvL73jEenJguUQj4LLl+6RiIiIiIiIiIiIiIiI9ElaazMTxKNQsRLKl4PbBzmDOl9Yu43DBblDTefN0s/N8rEisnvxKFStNgWKuYO17L30LuGAKaxNxMxS4amSVWCypWqt+Z0Rkc4J15sVBppqIHdQukcjvUljtZmE5++358cIXSmrECKNpqN5Qt35RdoVCUF9qQrQRUREREREREREREREUqgHXD2VlIo1mw5mNeshewB4c/d+n5Zllmy146aIo6Fy7/cp0lfZNtQUQ91myBvcM4pWRJIVbWrpuNxgMiTVcgZCsNT8vohI8hoqoWQphKpNx1pljSQrEYeaDaaI1eNP92i2yxkAga0QLEn3SER6psZKsyKGJyfdIxEREREREREREREREemzdOW9L4s1Q8WXphNV7mCzzGpXyu5vOhmWr4Cmuq7dt0hfESyD6nWmG5zDle7RiCQvHoPKVWaZ8O7quOxwmQ62tRuUKyLJSMTNBI6SJabzc95QFdZK5wRLoaEMcvqneyRtOT3gzoLq9aZDp4hsF49BYIspiNeKGCIiIiIiIiIiIiIiIimjq+99VTwKFStbumUOAac7Na+TMxCiIfNa0abUvIZIb9XcAFVrwOXpWd3gRDpi26aTYd1myBnUvcV63lyIhqG2WMuBi+xOcxDKvoCy5eD2mfdkKrKSzoiGTXG2O6tnTgDKKoRwwEwUFJHtmmqgqRZ8+ekeiYiIiIiIiIiIiIiISJ+m4tq+KBGHytVQt8l0G0z1xfLcQaazYeUa89oiYn4Xqtea4qesonSPRqRz6ktMx+XsfqmbnLE7OQPMGBrKuv+1RXo62za/H1sXQ2CreR/mzU33qKQ3CpaaAr2swnSPpH2WZXKobhOEatI9GpGeo77UTHzqiUXxIiIiIiIiIiIiIiIifYiuxvQ1tm2WT60pNh3MuqMoynKY16rbBL48KBqT+tcU6enqS8xyrSnqJGj5vEx46W8QDWFl+bp8/5LBmurMZAm3D9xp6rjs9JiPmg3g72+6P4uIWZmgegPUbjDv8fKGJpUxls/LhBf/ArEoVlZWNwxUerxII9RuNO/du7M7eWe5/dBUb45tfPngcKZ7RCLpFa6HxgrISl3XWsvnZcIL90AiocwQEZHdsrKymLD4s9bbIiIiu6LMEBGRZCkzREQkWd2VGT34SqrskcAW0y3TXwQub/e9rtNjLs5Xr1NnKZHmoPld8OSY340UsCwLh8+Lw+fF0jLg0lVizVC1GmJN6e9k6C8yeVK/Nb3jEOkpok1Qvtz8jvrywN8v6ckbygzZSaDEvF/x5qV7JB3L7mc6mTdUpHskIunXWAXRcEonQCkzREQkWZZl4fD7cfj9ygwREdktZYaIiCRLmSEiIsnqrsxQcW1f0lgNlavA4zcf3c2XB/GYKSqMR7v/9UV6AtuGmo2mI1xWQbpHI5I82zadYoPlpuNyulkO8OVC7VcQCaV7NCLpFWmEsi+gbjPkDk5fV2npGyKNENhs3qf0hpOTTrf5qCnWMYZktnjUTDryZqd7JCIiIiIiIp23dbGZPC4iIiIiItKLqLi2r4iEoHIl2HHwFaRvHDn9IVhmOuiKZKKGclOwkt0/pS+TiEQpue1BSu58nEQkktLXkgwRLDWFS9n9e86y2958aK6H+pJ0j0QkfSKNULYMGiohb6gpMuwkkxkPUXLnY8oMgfpSiDSANzfdI0leVhE0VZusEslUoRrzvijFHacTkSgltz9CyR2PKjNERGS3EpEIJT//BSU//4UyQ0REdiux5n1Kvn8uJd88lkRDbbqHIyIiPZiOM0REJFndlRkqru0LEnGoWgOhWshOc7dBhwt8+ab7YTiQ3rGIdLdYBKo3gNMFLm9qXyseJzDnIwLvLIRYPLWvJX1fcxAq14DLA+6sdI9mO8symVK32RQYimSaaBjKvzTLgOcN2fPC93icwNsfE3jnE4jFunaM0rtEQmYSkC8/3SPpHIfTdGyu2Qix5nSPRiQ9gmXmvVGqJ0EpM0REJFmxGIEXXyTw4ovKDBER2bXNC+GfFxPY4CWwLAiJXrCKjoiIpI+OM2QXgsEgF198MdnZ2QwZMoQ777yTGTNmcPXVV7duM3r0aH77299y0UUXkZ2dzbBhw/jrX/+62/1efvnlnHXWWfzhD39g0KBBFBQUcNNNNxGLxfjpT39KUVERw4cP5+GHH27zvM2bN3P++edTUFBAUVERZ555Jhs3bmx9fNGiRcycOZP+/fuTn5/Psccey+LFi9vsw7IsHnjgAc4++2z8fj/jxo3j5Zdf3ut/K5GM0U2ZoeLavqBuU8sywYN6xtKuvjyztEvNRrPMuEimqN8KoSrw90v3SESSl4hD9TrTxTCrKN2j2Zk3FyJBCKh7rWSYeAwqV5tOnbmDwdLbdukCDeVmQkVv6lq7TVYBNAfUzVwyU3MQGitMV38REREREZHeomodPHEuRHdonODypG88IiIishPbtglFQ2n5sDtRT/TjH/+Y+fPn8/LLLzNnzhw+/PDDnYpVAW699VamTp3KkiVL+PnPf85VV13FnDlzdrvvd999l5KSEubOncsdd9zBb37zG0477TQKCwtZsGABP/jBD/jv//5vtmwxK3hHo1FmzZpFbm4uH374IfPnzycnJ4fZs2cTaemeGQwGueyyy5g3bx6ffPIJ48aN45RTTiEYDLZ57RtvvJHzzz+fL774glNOOYWLL76YmpqapP9dRCT1XOkegOylxmpTFJWVv0fLBKdMdn8Ilpguazlp7qYr0h2aG0xBuS9PBVDSu9RtgrotPWeCxtdt614b2AL5w8DjT/eIRLpH7UYIbDK/m6nuUiiZIRYxXWu9OT3z731HLAd4ckxu5Q4Bty/dIxLpPqEaM4E1u3+6RyIiIiIiIpKccD386yIzUXbgZKA23SMSERGRdjTFmjjsqcPS8toLvrkAv7vja7/BYJBHH32Up556ihNOOAGAhx9+mKFDh+607ZFHHsnPf/5zAMaPH8/8+fO58847mTlz5i73X1RUxD333IPD4WDChAnccssthEIhfvnLXwLwi1/8gj/+8Y/MmzePCy+8kH//+98kEgkeeOABrJbrLQ8//DAFBQW8//77nHTSSRx//PFtXuP++++noKCADz74gNNOO631/ssvv5yLLroIgD/84Q/cc889LFy4kNmzZ3f47yIi3UMVYL1ZrBmq1kAi1vO6T7m85qJ9bbHpvCbS1wU2Q7Sh9y2zLJmtqRZqNoAvt2dN0Pi6bd1rg2XpHolI9wiWQ/VayCoEp7p5SBdprISmQO9+r+LLh3DAdHQWyRSJOAS2aoKRiIiIiIj0HrYNL/6PuYaZPRAO/k66RyQiIiK92IYNG4hGo0yfPr31vvz8fCZMmLDTtocffvhOX69cuXK3+588eTIOx/byuUGDBrH//vu3fu10OunXrx8VFRUAfP7556xbt47c3FxycnLIycmhqKiIcDjM+vXrASgvL+f73/8+48aNIz8/n7y8PBoaGti0aVOb1z7ggANab2dnZ5OXl9f6OiLSM6hzbW9l21BTbC6S5w9L92ja5+9nikMayiB/eLpHI5I6TbVQtxmyitI9EpHkxWNQvR5iYcjbeVZfj2JZpgA4sNmMVd0KpS+LhMyFB4cTPNnpHo30FYm4+Rvq9vbuDvuWZSZcqHutZJKmOgjXQc6AdI9EREREREQkOUufhFWvmoYOR14FRWPTPSIRERHZhSxXFgu+uSBtr90TuN1tm1BZltXufYlEAoCGhgYOPvhgnnzyyZ32NWCAOY972WWXUV1dzd13382oUaPwer0cfvjhRCKRDl972+uISM+g4treqrHSFNdm9+u5F8gdLnPBu6bYzEx1qfOa9EG2DbWbIB4Bjy54Sy8S2Gw6/+UOTvdIkuPNN13bGiugYGS6RyOSGrZtukk31WpiknStUI356AtLyvvyIbAFGsqhcFS6RyOSeo0VQMIcX4uIiIiIiPR0dZvgDbMUMwdcBAP3A6tnFM6IiIjIzizLwu/u2atmjR07FrfbzaJFixg50lwnDgQCrFmzhmOOOabNtp988slOX0+cOLFLxzNt2jT+/e9/M3DgQPLy8trdZv78+fztb3/jlFNOAWDz5s1UVVV16ThEpHv00KpM2a1ouKWjmQN6eMiRVWgKRBq0lLf0UaFqqC/pG8UqkjmaaqFmvSlQ6i2FGpYFnixzcjYeS/doRFKjocIUvucMMD/zIl0l2PJe3One/Xa9gWWZrs51myAW6Xh7kd4sGja/v972T9CKiIiIiIj0OK/9BCJBGDgZ9p1pVrkUERER2Qu5ublcdtll/PSnP+W9995jxYoVfPe738XhcGB97Xra/PnzueWWW1izZg1//etfeeaZZ7jqqqu6dDwXX3wx/fv358wzz+TDDz+kuLiY999/nyuvvJItW7YAMG7cOB5//HFWrlzJggULuPjii8nK0oQjkd6ol1TUSBu1G03nqfxh6R5JxyyHufhd+xXkDAKXN90jEuk6iYT52YZu/9m2fF7G/fsuiIWwsrQksnRCPAbV6yHW3PtObPryTfFhqKr3dNwVSVYsYrrWWg5wde3fdcvnZdy/7oB4FEsH7pmnOWgmuvny0z2SrpNVYCY3NVaoy7P0bU010NwA+UO77SUtn5dx/7wN7IQyQ0REdsvKymLcR/Nbb4uIiLDmLVj7lmnoMO0yyB8CtBxnPPUncLiVGSIisluW28m4n0+DaZcpM6SNO+64gx/84Aecdtpp5OXlcd1117F582Z8vrbX1H7yk5/w6aefcuONN5KXl8cdd9zBrFmzunQsfr+fuXPn8rOf/YxzzjmHYDDIsGHDOOGEE1o72T744INcccUVTJs2jREjRvCHP/yBa6+9tkvHIZLpuuvclIpre5vGKlNcm93PFF/0BlkFZinvYJmWbpW+JVRtliTO7v4CxaZogns+3kp9MEDdF4u55aJvkOPTn3RJQv0WqC+FvF5YnOpwgcNpMiVnkDp7St9SvxUaKyGv6wuomqIJ/vzJVmqCjQSWfcZtF05XZmSSxiqINvWtLvvbitDrNkPOYHDq51n6INs2x9Aud7ce+zdFE/xlQQnVwSbqvviM2y88RJkhIiLtamyOc80r69lU28TIwizuvOAgZYaISCaLReDNX5jb40+GwVNaV01riia4d0EJJY0J6j//VJkhIiLtSySIvfi/uDe+SuCrufzysyf500VHKDMEMN1rn3zyydavGxsbufHGG7niiivabJeXl8fTTz+d9H4feeSRne57//33d7pv48aNbb4ePHgwjz766C73e9BBB7Fo0aI295177rltvrZte6fn1dXV7XKfItJWd52bUgr1JvEoVK8DbHD70z2a5G3rXlu3CXKHgMuT7hGJ7L1EwvxMW4Cze3+mr3l6KWvLG/ASwU8zC6srmXLDmxwwPI+Xf3R0t45FeplwPVQXgy+n9cRmr+MrMAWITbXgL0r3aES6RqTRTJ7y5ZkC8i60LTOyCOMhpszINPEYBLaANyfdI+l6WQXqZi59W3PQTObz5nXbS27LjGyacJJgQXWFMkNERNp1xl8+5Ist9a1fry4LKjNERDLdpw+Za5i+AphyDnjMdcxtxxl5NBLDxcIqHWeIiEj7nrrth3wz9Cwx28HdkTN4bXWQ15QZ0mLJkiWsWrWK6dOnEwgEuOmmmwA488wz0zwyEUmH7jw31UtanwpgOjM1VED2gHSPpPOyCiBcZ7p8ivQFoWqzFLG/e7vWbjsR5YzHOW3pfGYtXYArHgPgiy31nPGXD7t1PNKLJBJQUwzRRnOCs7dyeSERM3ko0lfUbTZFVF1cQLVjZpy69CNmKzMyT1MNhAPgzU33SLrejt3M25ndLdLrNdWartPu7ln+bsfMOGXpx8xe+okyQ0RE2rXt4oU7HuN/P3+e//38edzKDBGRzBZtgnl3mNuTz4aCkUDb44zZSz/h1KUfKTNERKRd19/xZy5s/CeJOMz7dCIjl5QqM2Qnt912G1OnTuXEE0+ksbGRDz/8kP79+9CqfSKSlLbnpp7jfz9/NqWZ0Uvb1mWgcABqi02Rahd3NOsWlsNcFKzbbLrXaulW6c1s23Stxe7WrrWhSJy15Q0AOOwE3yheaW5PTrRu88WWehrCMS2PITtrKIPAZsjphRM0vs6XB/WlUDiq2wpORFImXA/1W8BfCJbVZbv9emYcVrzK3FZmZJZgOVjO3tutvCO+AjPZSd3Mpa9JJKC+pLXTU6p9PTOmKzNERGQXGsKx1q4gDjvO6cUfAfDg5FPZdqlBmSEikoE+e9Q018keAONng8PZ9jgjkeDQ4tXm9uQ4ygwREdlRY9VWrgrcgsOyeT+2P4PWVzOIWmWGtHHQQQfx2Wef7XabjRs3ds9gRCRt2p6binJ68ccAPDj5dFKVGepc2xskElCzEWJNvbvrlK/AdM9qrEz3SET2TqjadM3M6t4ijtvfMief6uxsvrRH73K7a/69pJtGJL1GtAmq14Pb160F4SnjyYFIEBqr0j0Skb1XvxWiYfNz3YVuf2s1tg21do4yI1NFGk3hqa8XHz90xOUFu6UIUaQvCddBU12XdzTflW2ZUWPn0t8Ktt4/hLbvtZQZIiKSbBYoM0REMki0CebdaW7vd1prc4fb31pNwoYqO58vGb3LpyszRERk5UM/oL9Vz/rEEK6M/r9dbqfMEBGRHbPAXbBoh0cSu9xub6m4tjdoKG/paNbL25k7nOB0m86JiUTH24v0RLZtlh/GNgUd3aisvplm28UWBrW5/+t9DjfVNnXfoKR3qP3KdPXr5oLwlLEs07G2fqvyRHq3cAACW8zKBF2srL6ZMB62MhA38db7cwm12U6Z0YeFaiAaAk92ukeSWr580529OdjxtiK9RWM12HFz/NwNyuqbCeElgYOb3f/Yfj9tz0EoM0REZMcsKGT7+688Gne5nYiI9HGf/8scl2cPgAmnmJUsMccZQbIpo99un67MEBHJcKte55DQXGK2gyuj/49mdn0+TJnRtWzbTvcQpJP0fyayQxY4G/H2f3eHRxztb9cFVFzb08WaoWa9uajWzYV8KZFVaDoNNtWkeyQie6apFoKlZvnubjY4z0t0NwcU24wszOqG0Uiv0VgNdV+Bv1+XLjmfdr58UzjWVJvukYjsufrSlq61XV/8ODjPS6Rl6Yv/53q+9X4fkTbbKTP6qEQCgiV94/ihI55siISgQatjSB8Rj5rfX2/3FcZvy4zb3fdRZDXscjtlhoiI7JgF/+t6ufV2A/5dbiciIn2YbcMn95rb406C7O2FtDuem9odZYaISAYL18NrPwHg6fixrNjNKnygzOgqbrepNwiFQh1sKT1NJGKu8TmdzjSPRCR9tmWBt997WM5dF9B2ZWZ0fFQj6RXYYoqH8oeleyRdY1vnncBWyO7lnXglM9WXgB0Dl6/bX/onJ03gP/ctA8C1QxfCr7vzgoO6a0jS08VjULPBFFl5/B1v35s43UDCLHmevfvuByI9UnOD6b7sL0jJ7n9y0gRev28Fh1kr+a7jP6xlMABVtH09ZUYf1RyAUG1KuiL3SN5c8/uUPxxcnnSPRmTvNNWaTsy5g7vtJX9y0gTc/7ifI50raIzu+ndImSEiIndecBBTbniTfaytXOh4j/UtqyslvraukjJDRCRDrH8XqlaDKwvGz2rtWgvmOOPF+9YAkEXzLnehzBARyWBzb4FgCYncIdxfeSoAfsK73FyZ0TWcTicFBQVUVFQA4Pf7sfpSg6Y+KpFIUFlZid/vx+VSqZ9krjsvOIj9f/cU7sKPIbb77bqKfuN6snA91G40F8WtPtRkOKvAFEOFA6bzoEhvEQ6YrrW+7u9aC+D3OCnI8bO1AXJofwbGAcPzyPHpT7u0CJZCsAzyuq84o1t588z3Vzga3JqtKr1MQwVEGsFflJLd+z1OBvgd3Ba7D0ei/WVilBl9WKgWErHM6FwLprg2WAahasgbku7RiOydhgpz/O/ovu4D/uZKrnS/CMD9sVOZzYKdtlFmiIgIQI7PRaHfzU+iz+CyE+1uo8wQEckgC+4zn8ccA3lD2zzk9zjx+bwQhjzaXyFDmSEiksFqimHB3wFwHHgJTR8UQQTylRndYvBgc+14W4Gt9A4Oh4ORI0eqGFoyWrbXiX/w21iOOLHwUGDzTtt0dWYofXoq24baryDalLKii7RxZ5llyoPlKq6V3iVYbpbvTmPX5YPGDGTFslC7nWsPGJ7Hyz86Og2jkh4pEjJda73Z4Oijce/JMZ0K+1KHd8kM0TAENoMvN6Uv88PcDxgRqKQ0vvOkEGVGH5aImyXl+1rH8t1xOMHpMpmQOxh0Ykl6q2gTNFSmPB/asG2Yext+mlmYmMCL8cN3Kq5VZoiIyI5O7V/GKRULiUV3bgahzBARySBVa2HtW4AFE2a3ew56QH4uW8NR3LqeISIiX/f2DRCPwJCDYORh5OUkKK+JKTO6iWVZDBkyhIEDBxKNRtM9HEmSx+PB4ehDjRlF9sCn5Z/iyFkKQKz2YL5eXJuKzEjpb93cuXM5/fTTGTp0KJZl8eKLL3b4nPfff59p06bh9XrZd999eeSRR1I5xJ6rsQoCW9JaxJdSvjxz8Tu667b+Ij1KJASBrZCVvoLw5RVRnlgWAuDSo8bw1rev4PnzL+KoSUNYfsOsXn9QoczoYnVftXQIL0j3SFLHssDpMR167fY7c4r0SI2V5vfTm5eyl/hi7QZm1L0AwOqJP2DOt7/Hs+d/k6MmKzP6fGY01ZkVMLzdWJzXE/gKzDFUuC7dIxHZc6EaiDaAO7v7XnP9u7D1U8K2m+uiV3DlSRN5+9vf5dnzL+boyUOVGX09M0REOumNZaXMLP0HAGVDj+KTy87n6fO/xTHKDGWGiGSeTx82n4dNgwETdnr4uZUhlpabYp3fnDWF9779bf51wWUcM2WYMkOZISKZbtMn8OWLZvWm/c/j8XU+1taYtb1vPv8A3v/2ZTx5wbeVGd2QGU6nE5/Pp49e8qHCWsl00XiU/3vvbrBsovWTuf6MU7j/f27hpm/+PqWZkdJWdo2NjUydOpXvfOc7nHPOOR1uX1xczKmnnsoPfvADnnzySd555x2+973vMWTIEGbNmpXKofYs8RjUbgCH1XeXcvXmmkLFxkooGJHu0Yh0rKEcIkHIH562ITyxrLH19tACD6dfeBhEQzDqUHD3/s6kyowuFKqBuk2Q3a/vd+/z5Znvt7le3dCld0jEzQQqd5Y5cZSql/n4XrxWjLnx/bHHHMOVRyUgFoXR08GlzOjTmRGqBjvRd7uW74rLC4mY6fqZtXO3ZpEez7YhWAZOd/e9f4uGYMG9APw1diYb7SGMLPLwX+dPh0TCZIaz9/8tUWaIiHSdt994ntudXxC1nYTGncm3990HLCeMPqxPnH9QZoiIJCnWDF/8y9weMwPcbVfPsW2b339Y3/r1yCIvx5x7qDneGX1Y940zhZQZIiJ7yLbhrV+Z2/uciD1oIte/vD0zRhV5OeS/DjGrV45UZigzRES2+6jkY0ojnwMQqTyJg0aM5IKrUj8BI6VXSU4++WROPvnkpLe/7777GDNmDLfffjsAEydOZN68edx5552ZFRLBUghWQN7gdI8kdSwL3D5TXJI31CzlKtJTxSJm+W5vTtouFHxWGuFfK0KtXx8/2gs0p2UsqaLM6CKJONRsMAUR7gxYEtzlg1ilKbBVca30BqEaaKqFnAEpe4nPP1/MgeFFxGwHN8Yu5fURPkiEOn5iL6LM2IV41BxLeLux62VP4s2F+hIoGGmONUR6k0gDNNWktKv5TpY8CY1VbEoM4P74aQAcNNgD0Uj3jaEbKDNERLrGS0u2cGHDI+CApxPHcdE+Y4HeX1C7I2WGiEiSVr9uJvf6+8GoI3Z6+J8rQtSEEwAMzHYwKt8JfWwhS2WGiMgeWvc2bFlkru9NPJ0HvtxesjSpv4sBfif0rcsZygwRkS4Qiob43dyHAIjWT2HSoGGMyB/YLa/do3pGf/zxx5x44olt7ps1axYff/zxLp/T3NxMfX19m49eLRqGmmLw+Pt+t6msAlNcEqpJ90hEdq+xEpoCaS3c+8O8QOvtXx+Th99KUP6Ppyl/6EXsaDRt40onZcYuBMvMR3a/dI+k+3iyoX6rKSwW6emCZeZzqt7n2TaJhWaZ1mfix/I/J07GY8cp/8czlD/0vDJjB30yM8IBU6DnyUn3SNLDm2NWGghVp3skIp0XqoFok+ls3h0CW+CLpwH4bexbNOPhvlMKccbjlD/wLOUPPocdUWZs0yczQ0Skk1545lEOdawhbLvZ9xtnYDlzKH/wOcofeBY70rcmZiRLmSEiGWvxY+bz6KPMtb4d2LbNL9/dfj3j3lOKIBan/MHnKf/HM8qMHSgzRCTj2Da89wdze/wsEgUj+P38YOvDd84qxI7GKH/oRcrv/7cyYwfKDBHJdIvKFlMaXQxApOo4fnHqSHwJJ+W33Er5LbemNDN6VHFtWVkZgwYNanPfoEGDqK+vp6mpqd3n3HzzzeTn57d+jBgxojuGmjqBLRCuy4ylTLcVlWwrMhHpiRJx07XW7U3p8t0d2Vi3vWjwtHFZ2LEYNc++Sc3z72BHY2kbVzopM9oRDZuutW6fWWIrU/jyIFwPTXXpHonI7jUHobE8pZM1EpsXcpC9kmbbzd2xczhxjM9kxnNvUfP82xlbXJsxmdFYBdh9f5LerlgOcHpM91rbTvdoRJKXSJiu091VWAuw8H5IRPkgfgBzEgfjccLRI70tmTHHZEZMmbFNn8wMEZFOCIQi/D/XCwA8Z53E1HEjsONxap57m5rn5mDHdG5qG2WGiPR5tV/B+vfM7X2O22llyrKGROvtCf1cTB3kNscZz79NzXNvKTN2oMwQkYyz9i0oWWy61u47kw2h7StwHjLEw7giV0tmvEPNs/9RZuxAmSEimawh0sDflzyGZdnEGiaw/+CRTB48xGTGQw9R89BDKc2MHlVcuyd+8YtfEAgEWj82b96c7iHtuXA91H1lZnmmaen5bufLN0UmzcGOtxVJh1C1+UhjwfvyigjVTeaE1OLvD2JgtrODZ8iu9KnMaE/9VtMRPBMmaOzI4QISEKpK90hEdq+xynQl9Pg73nZP2Dahjx4A4PH4TN79n8kU+Hr92/206XWZEY9CQ7np5p3JfPnmvVu4Lt0jEUleuM68h/Pldc/rVayE4rnYWPw+djE+l4PlPxhCtkeZsad6XWaIiHTS4rmvcLBjLc24Of/U2fiyC9I9pF5LmSEivd7SJwEbBu8P/cfv9PDr60zRz/A8J69dNACXI0Oud6aAMkNE+hTbhvdvNrfHz4aCkbyyNgyYyRj//q9+ODKlRiYFlBki0petqFrB8tqFAAxInMBvzx1Gvrf7Vh7vUS2NBg8eTHl5eZv7ysvLycvLIyur/Q4uXq8Xr9fbHcNLLduGuk2m4MJflO7RdB+P31z8bqwCb266RyPSlm2bYkWstHWA21Ab47R/mYLBXI9FUVZLYW3xB9s3aqqB/Az6u9EiozOjPeF6qN3YMkEjAwsjPDmmE3rhGHB50j0akZ3FYyZT3CkqrAW+WvEJo+rXErK9vJh1Nt9zZ+Dfgl3IiMwIByDSADkD0z2S9HJ5IRGDhqrMm2wivVdTrckJZze8h7Ft07UWeC5+NGvsEUwudOFx6uLFNhmRGSIinfDh2krc828HJ7zvPY5Z/UZkTmOIDigzRCTjJBKw9J/m9uijdzrP9eb6Jn77oVmGekKRS4W1O1BmiEjGW/MfKFliVm7adyYvFDu5e2EAgAn93DiVGa2UGSIi2zVEGrh1/r+wiRMLjWZ03hgGZw/Csiy6aw3LHnXF/fDDD+edd95pc9+cOXM4/PDD0zSibhSqMUvPZ1Jh7TbeHAhsMd22RHqScB00VIA/fYUZczeFW29fNjUb7AQsegDe/+P2jSKNaRhZ+mV0ZnydbZvluKKhzJ2o4MkxRWXqVCg9VVOtKX5MYVdC5xdPAfCv+HGcuv/glL1Ob5QRmRGqAey0TQjqUbzZECyBWCTdIxHpWDwG9SXmuLg7bFkEJUuIWS7ujP4XABdOTt3Ej94oIzJDRKQTli14m6OcK4jaTuzxp0A3dgbp6ZQZIpJxNn8CgU2mqHbUETs9/NLq7UtVXzglw1fW+RplhohkvA/vMJ/HnwwFI/j3l9sz4wKdm2pDmSEist2mwCbWNHwEQLTmKGYdkEuht3truFJaXNvQ0MDSpUtZunQpAMXFxSxdupRNmzYBpjX5pZde2rr9D37wAzZs2MB1113HqlWr+Nvf/sbTTz/NNddck8phpl8ibroN2piZOpnGmwfN9S0FASI9SLDcFH27fOl5+eYEN3xgZnl7HHDVoTkw7y5Y8kTbDfNHdP/gUkCZsRdC1VC/BbL7p3sk6eNwAhY0VKZ7JCLta2iZZZyiwsfg5mUMb1hGxHbynOs0rpjWTUVaaaLM+Jp4zPyMuXXhCmg5vgia7v4iPV1TrTke7o7iWjvR2rX24ehJbGUAYwucXLJ/376AocwQEdlzpYEmxq022THXcxSzDujbXWuVGSIiHfjiafN5+KGQPaDNQ5sDMV5fZ5qFzBjlZebY9FxX6S7KDBGRTtj0CWxZCA43jD2O9aEsPtlqGiOcPSGLI0f07Y6rygwRkT0TioZ4+IsXsB0hEpEijhm1P0fvMwK3092t40hpce2nn37KQQcdxEEHHQTAj3/8Yw466CB+/etfA1BaWtoaGABjxozhtddeY86cOUydOpXbb7+dBx54gFmzZqVymOnXUAENZZDdL90jSQ+H05yUDZameyQi20VCpoNUCjsMduShpds70p49IQv3ovtg5cuABUf1vTfPyow9lIhDTTFgp60QvMfw5kBjBUTDHW8r0p0iIfN+L4WZUvbhYwC8ED+aGVNG9vkllJQZX9Ncb4pJu6vzZU9nOcwxRrAs3SMR6Vhjy8Sg7ug6XfwhVK8j7PDz19iZAHxz/2ysPlwkBcoMEZG98fiLrzPTuZiEbREbfxpWVkG6h5RSygwRkd2IReDLF83tkd8AV9tCqBvn1rfevmT/vj/5V5khItIJ8+8xn8fOgMIRXP9+oPWhi6b07UnfoMwQEdlT5Y3lzNloOnlHao7g+ClZFPq6f+XxlF69mTFjBrZt7/LxRx55pN3nLFmyJIWj6mFiEVMU5XSbj0zlKzAXFcP1aS1mFGnVWAGRRsgf1u0vbds2v/kgwGNfhFrvu8I3B5a1zAo/5qcw6njgX90+tlRSZuyhYJnpVpg7KN0jST9PtimKD9eBe3C6RyOyXVONyZSsrs+UhG1z++vL+GnDIhK2xd/jp3HbmL5faK/M+JqmWtORsjuK83oLX545vmgOgjc33aMRaV80bN7HdcfPqJ3AXvwoFvD3yGzqMK95VB/vDALKDBGRPRGJJbji8U85Z8M/wAmvJQ7jgDEjW1aN6buUGSIiu7HubXP+IavQdK5t0RRNcNlLNSwsibTeN2Vg37/eqcwQEUlS1VpY/ToADaNO5KIXoyyriLY+PK5ImaHMEBHZWWl9kPMffol4USV23Es0cAj7DexPjrv7Gw3p6mu6BUsgVAV5Q9M9kvRyZ0FjNTRWqbhW0i8ehbrN4PGnZam7LfXxNoW1t0/eyD6r7jNfHPYD2O8UrESCsX+/CWJhLF/fvyAuuxBrhpoN4PapoApMp0LLCQ2VkKviWukhbBvqS8HlSUmmrKyKMeyrF8EFbyUO4dvHTmTaEE+bbSyvh7H33QDxGJav7xfeZpxEwky0cGeleyQ9i9tvji9CNSqulZ6rqRaaGyC/G84HfPURVs0GgnYWD8VmA/CX2YXs17/tBQyTGb+BREKZISKSwd5eWc6a1Ss5xbsAgGGHnsGoQUVttrG8Hsbeez1YDmWGiEgm2Nb8Y+ThsEMn8+dWNbUprH3x/P4Mzmk7GcPyehj7t1+Bw6XMEBHJNB/9GbBhxGE8Wj6mTWHtO98aSGFW28W2La+HsX/9Jbj9ygwRkQx2zzuraPB8iAuI1h3CrRfnM7JgUJuV+Cyfj7GvvNx6O1VUiZNOkRDUbDQXe/v4rP+keLOhfisUjMjsLr6Sfo1VpvNl7pC0vHxFKNF6ewC1nLXlFtONbt8T4YALALAcDryjh0E0BA7HrnYlfV19iSnKSEOH5R7Ll2t+h6NNKjSTnqG53nSu9eWnZPeBujrOds4D4KHYbO4bt/OBQ2tmxKLKjL6oud50Z/V3/zIoPZ7Hb7Iyf4R+9qVnaigDp8tMEEol24bFjwHwaPwkApiZ3ceO2nmSnuVw4B011BTu6/dGRCRjNTbHuMz1Ji4rwfz4ZA7ZZ/RO52tbM8NyKDNERPq6cD2sfsPcHnlEm0YPzbG2nfgOGLTz9b3WzHC6lRkiIpmkoQI+b1mJddxMIlVeINb68D6FO5crmcwYAp4cZYaISIaKJWKUhDbiylkDQKT2CEYW5lLobXst1HI48I4bl/LxKI3SqW6TuRCeomKLXsebZ4oDQjXpHolkskQCAlvA6Ulb0XtFY7zlls0t7vtxNtdBv33gmJ+mpZOu9FDNDWaChi8v9QUZvYk7G6KN0FSX7pGIGKEaiEXAlZou4znFr5NlRfgyMYqF9n4UZWnCVsYJB0zXfaen420zjTfP5EG4Lt0jEdlZc7D7Vm7Z/AlUrSHq8PFg7OTWu3O9eg8pIiLtq6ur4SLnuwA8kjgFb05BegckIiLptepViIUhbzgMmtzmoZqm7c1CRuQ5cegahoiIbPPZIxBvhv4TYPABBJq3PzR9qM5ni4hI++qa69gS/RCAWMM4Dh01gIHZA3GnqVGnrqSkS1MtBDabDlM60DQcTvNvESxN90gkkzXVmovcOyxr1N0qGs3JqEucb3Oc83Mzm/u4X7UpzLKjMSoff4nKJ1/HjkZ3tSvpywKbIdqgCRpfZ1mm2LixKt0jEYFEHOpLTffMlOw/xpiSVwF4OD6Lo0e2v9yFyYyXqXzyVWVGX2Pb0FAObi2P1S6nG+y4MkF6pqZaiIbBnaKM2Ma24TPTtXZx0SnUYop5Tx/ffod/Oxqj8olXTGZElBkiIplq+MbnyLOaWJ8YwuCx+7ebVyYzXqXyiVewI5F29iIiIn3GsmfM55HfMCuH7aAiFG+9feaE3RxnPPkqlY+/rMwQEckU8Rh8+rC5ve8J4C+isnF7Zswcu5vrGU++TuVjLyozREQyVGlDKVUsAiBadxj7j3BT5CvaaTs7EqHyz3+h8s9/SWlmqLg2HRIJqN0IsWbwZKd7ND2LLx8aK80SMyLpECwFEmnt/lYZijPSKuf/XE+aO6b/NxSNabONHYtR9cTLVP3zDexorJ29SJ/WVAt1myFr5zcQAnhzTCFVNJzukUimCwfMhze34233xFcfkRuppNrO5eX4EdxxUkG7m9mxGFVPvkLVPzUho8+JNJifMR1T7Jo3x7y/i+lErPQgiQTUl3RPYfyWRVC5Epxe/pN9BgDZbovfzmh/gpbJjFepeuo17JgyQ0QkIyXiHFr2bwCedp7CdUe2fzxjx2JUPfUaVU++ih3TuSkRkT6rsRo2fGBujzwcHG2X8K5saRYyttDFDw/JaXcXJjNep+rJV5QZIiKZYvVrECwBXwGM+AY4XFSGTGZMHeTm0gPaP6dtx2JU/fMNqp54SZkhIpKBgpEg7256l4TVQCKWy/iCKZwytR+5np3PT9mxGFV//StVf/1rSjNDxbXp0FhhLqTlDEj3SHoet98UQ4Wq0z0SyUThelN84StI2xBeWdPEXxYFucn1CFlWBIYeBFPOSdt4pAeybajdBPFI6rph9naebNPVV8uAS7o1VpuumSlaoqJiwdMAPBU/geuO7s8AvzMlryM9WLi+pfNl+51hBPDkmCLkptp0j0Rku3Cd+Zn05aX+tT7/JwAr+s/m4dVmAuGvj8mjwKfTQSIi0r43nn2Q/rEyauwcph50BLm5WjFHRCSjrX7NnN8qHAP992nz0G0f1/P+V2aN7+uPziPLreMMERFpsfAf5vM+x0NOf26cG2BhiWmA8Muj8vC6tLqziIjsrDZcy7+WvQ9AtO5Qzj3MzdCcwTis9B1r6Cinu8UiUL3BzOxMY2fMHs2bDYEtEFeXHOlmDRUQC6e1YPH/3qvjFMcCZjg/J2654KgfmyXuRbZprDQzPbP7p3skPZflAMupZcAlveJRaChNXUfRqrUMrF9O1HbyROxEhuSosDYjNVaC09XxdpnM4QQs8z5PpKdorIZEPPXnBCpXQ8kSsJx876sTW+8elK3MEBGRXctZ9igA/4wfz8DCHJ2XEhHJdCteNJ+HH2omsO7gL4saWm/rOENERFpVrIKNH5pjiTHHgNvPw0sbWx/W9QwREWlPNB5lSfkSGqx12LZFtPZQBudnUZDGBomg4truF9gCoSrw90v3SHoub65Z3jZUk+6RSCaJhs3vZ6qW7k7SIHeYX7sfByA48SIoGJHW8UgPE49BbTFYFri86R5Nz+bNMUVnseZ0j0QyVTgAzUHzs5gKX74IwH8Sh1JOEYcM1aStjBMNQ6g2dT9jfYkv12RCJJTukYiYCbfBEjOpNNW+MB3O2ec4Stl+DmLaEGWGiIjsQvV6jnYuJ2Fb/DN+AlOGqWutiEhGC9VA8Qfm9ojppnFQC9u222w6vp8m/4qISItFD5jPw6dD0Wii8baZMSJPxbUiIrKzuuY63ix+B4B443jsWCH79BtIliu9K3iquLY7hQOmKMqX39JBSdrlcJlZTMGydI9EMkljBTTXg7cblmbdhadXhDg//AyDrVqq3UMo+MYlaRuL9FANZdBQqQkayfBkQzRkslckHRqrwLbbXHToMpFGomvMgcVjsZO45rBcBqo7SOYJByDaCO70ddzvNdzZ5t+qqTbdIxGBphoz+cKT4kl9wTLY8B4AT1intd5947H55Hl1KkhERNo3/9+3AfB+YirXHd0fn8+X5hGJiEharX4dEjEoHA399m3z0I/+s/0Y+5EzinA5tLy3iIhgznt9/i9ze+wM8OZx8QvVrQ8/d15/LEuZISIiO9tSv4UPtywEIFJ7GL8+10u/rPTXxuiKytc1VKSmo1EiATXFEGsCX/qK93qNrPyWYsdgukcimSAeg7pN4PGbjqBp8ud3V3OZ800A8o+7Sp1Jpa1Ys8kRty81xXp9zbZlK9UFXdIhFoGG8pR1FLXXzsGdCLM2MYxF9gT+33R1Ls1ITbXmfYuW6e2YZYHTbYoNv9ZZR6TbBcvB4Uj9hNtlz4KdIDH0YH61bGDr3ZceoIJ8ERFpXyjUyMTylwF4Mn4CZ+yX3tWdRESkB/jyJfN52KFtznPVNiV4bW0YgH5ZDmaM1mQMERFp8fm/IBKE/BEw9CC2BBMsLIkAMKm/i4O1opKIiLSjIdLAC6s/AGcjiWgek4qmMGVYHvne9K+qpCuxX1e9HspXmGLYrhQshfqtkD2ga/fbV7n9EG0yXd9EUq2xEprqTFfpNKkMxbnO9W88Vpzi7ANxjf5G2sYiPVRgqymkyipM90h6D0+2KXCMR9M9Esk04bqWroQpKHq1baIrXgHgqfjx/OqofBya5Z154jEzEc2jIrmkefNMx9BIQ7pHIpmsuQFClZDqk0HNQVj1KgCbRp/bevctJxSoM4iIiOxS6cdPU2Q1UGIXce5RB4BXxbUiIhmtqQ7Wm9UwGHFom4YPS8sjrbdvn1nQveMSEZGey7bhs0fM7X2Og6wClpZtv0Z3w7HpL5ASEZGeqa65jo9LPgIgGpjG2dNdDPIPwu1wp3lkoNZ3X2fbZtntYAnkD++afUYaoXoduHzg1EycpHmyIbDFzGpy6kdVUiSRMD9nTndau4FuWrOM052fkLAtRp70ww63tzweRt/zK4iFsbz6u9LnNTdA7VemAFwdCpPnyYaGSrN0enb/dI9GMkljlemUmYquhJWr8NStp9l28xpH88lB2R0+xfJ4GH33LyEew/KqK3qf0FxvsiFHE/eS5s6CxmozUUWFIpIuoWqINIE/xcsYrXwZYmEoGstC6wAgQL8sB+dNyurwqZbHw+i7fgF2QpkhIpJhspc9BsB/XCfwnX2zOlzdyWTGzwCHMkNEpC9a/QYkolAwCvrt2+ahLytNodSUAe6kutZaHg+j77wOHC5lhohIX7Z1MZQvB6cXRh4JLi8rKusBOGK4h+nDOs4Ay+Nh9B3XgjtLmSEikiHiiThratZQFlkNwKS8w9lnkIcCX8Fun2d5vYx+5unW26miisWvczjM0ttV60wR095eeE0kzL7C9ZA/rGvGmCl8eWbJzFA15A5K92ikr2qqMZ1rs1N8gXs3EokE3s/uA2BhznF8Y8C4Dp9jOR1kTRgD0RA4U7ykrKRf7VdmCZWCEekeSe/icIGdgFCtimul+8Qi0FCRmq61QOLLl3EAryYO4/j9BibVtbY1M2JRZUZfEQ6Yv29pnBjUK7l9ZkWR/BEdFouIdLlEHOpLwNNxgeteiUdg2XMAxKaczx/nBgE4a0JWUl1rTWaMNucylBkiIhmjuWQFg+sWE7MdxEbNSGp1J8vpIGv8aDMJWJkhItL3fPmi+Tz8kDbXShsjCW792BxnnD4+ueOb1sxwupUZIiJ92WcPm88jD4f8odQ3J7j3M7OS2Mn7diYzRplrLMoMEZGMUB+p5z/F7wIJ4qGRfGP8IPK9+eR58nb7PMvpJGv//VM+PrW/a09WgVkutHKNWXJ1b9RvgcBm01VKF3A7x+EyJ2eDZekeifRVtg2BreZ2GrtKr178IVPiq2iyPawZ8620jUN6qFCNyZI0FoD3ah4/NJabghaR7hCug2ij6Zzc1SIN2OveBeCp2AlM6p/+ZTAkDWwbGspNoah0jjfXLGsZDqR7JJKJmupaOifv/mTQXlv3jplAmN2f1+3DqQknAJg0QJkhIiK7tuEtM+n7ncQ0Rg7IB5c6RImIZLRwANabc1AMP9QUxbZ4anmo9fakAZr0KyIiLcL1sNxM+Gb00eDJ4cElDa0P69yUiIjsSnWomg82LQQgGpjGkMIEg/yDkmoY0h1UXNsuC3IGQrAEajfu+W5CNVC1Brw5OiG5p7LyobHCvBkT6WpNtaZ7mb8wfWOwbZxLzCy+R+KzOG5Kcp1J7WiM6mf+Q/Vzb2NHo6kcoaRTIgE1xaYw1O1P92h6J08ONAfNEuoi3aGxynx2pGBG9do5OBPNrEkM4zN7PLP2Sa640mTGm1Q/N0eZ0Rc0B8174xR1R+7TXF7TXbqpNt0jkUzUUA7YbS5KdznbhhUvmNuTz+bKOY2tDx2fxFKt0JIZz75J9XNvYUeUGSIiGSEepX/xiwD8Oz6DI0cnN1HQZMZbVD/7JnYkksIBiohIt1v9H7MqRv4I6Ldv6922bfP7edvPsx46NLlrn3Y0RvVzc6h+RpkhItJnLX/WrLhaMBKGHEDchrsXbi+u3X9gcufETGa8TfXTbygzREQyQCQeYVHZZ4TsUuyEi2j9AYwdkEW+t+NVlexIhOoHH6T6wQdTmhkqrt0VpxuyCqFqrVm6sbMijVDxJcSjSS2jJbvg9kM0DKHqdI9E+qL6ErBj4Epj57ev5jHe3kiD7eORxKmMyEtuprcdi1HxwDNUPPwSdnQvO2xLz9VQbgrAs/uneyS9l9NtutCrS6F0h1gEGipSU/Ro29grXwHgqfgJjC10MygnuQJeOxaj4sFnqXj4BRXX9gXN9RBv1uS9PeXJgvpSM4FFpLtEQhAsb7OUakqUrzATfJ0emvc9tfXu40d7KcxK7vSPyYznqXjoBeyYMkNEJBMElr3BAKueSjuPkeMOIDc3uS7rdixGxUMvUPHg89gxnZsSEelTvnzJfB4+vc1xzJqa7X/vrz08F58ruU5S2zPjWWWGiEhf9dkj5vOYYyGrgAVbtxc53XRsPh5nJzLj4ZeoeOBpZYaISAaoa67j2ZUfABALTuL7xxfQ398PfxLN5+xYjIpbb6Pi1ttSmhkqrt0dTza4PFCxcnsXsmREw1D+pVn2MWdQyoaXMbzZENhiCpVFukpTnSlazEpn19oE9qePAKZrbaWd4iVipXeJNUPNelM8lcoOZ5nA7TOFyrad7pFIXxeug0iDeQ/Z1Sq+xKrZQNh283z8KP08Z7LGSuXC3vDmQXPA/L6KdJdQdUs+pLjj9Irnzed9TyToSHEhr4iI9BmuZf8C4MX4UTTbrtSswiEiIr1HuB7WvW1uDzu4zeTeaNxu97aIiGS4kiVQ+jk43DDqSHC6ieyQEw5VJYmIyC6UN5azsXExANHAwXicNv2zelbzueRaJGYyf5HpQFa2DAZNgZwBu98+EjIda4OlkDcErORm4OyJeAJWlASoCUUo8nuYPDQfZ198Y+LNM0VRoWrIHZzu0UhfEdgKsXB6O4IWf4hVs56gncU/YqfiSXKWt2SIwFYI1UD+sC7ZXcZkRnu8OeakcKQh9R3jJLOFqs17v1RcjG7pWvtq4nDqyWGQQ5mRkaJNEKpNeYFen86MbR3Nm2rNsZ5IqiUSUL/VTPZJ4fkBGqtgg5ndzeSzueWjYOtD0XjqXlYknrBZWFxDRTDMwFwf08cU4dT7FJHeI1SDd/2bADwXP4ZJaBKXpI4yQ6SXWPuWWTEnbzj0H9fmoevermu93azjDEkhZYZIL/PZo+bzyG+0Xte8dk5d68PNMU3IkNRpLzOiiWYao41E4hFitulm6Xa48Tq9ZLuz8Tg9aR61iACEoiHmbplLxG4kEc0l3rgvLstLvjc/3UNrQ8W1ycgZ2FJg+zn0nwB5w9qfXtNYbZZgbKw0hbWO1P3zfrS+ivvnFlPV0Nx6X/8cL1ccM4Yj9ulZFdx7zeEEy2EKlnMGpfaCpGSGcACCJZCVxoIKO0H9xw+RBzwUn02AHC6dlJW+8UjPEg5AbTFkFZi/f3spozKjPS6fKTgJB1RcK6kTi5j3i6koemwOElv3Hi7gqdjxAMzex9f1ryM9X7geoiGTDymSEZnhzTbHFoWj1ZmtKyUSZvJaomXFEYcLXFlqTdFUayZMpXpS38pXwI7D4P15dOtQnv4y0PrQsaO8u3miyJ77z/JSbnzlS0oD4db7huT7+M3pk5g9ZUgaRyYiyZr3wr0cRYzlidGsskfynREpWIVDBGWGSK+y4gXzecSh4Nu+2t6dn9TzZdX2pVYPHKwJGZIaygyRXqa5AZY9Y26PPho8Odw8L0BVU6J1k0kDlBmSGm0zI4HlDDGgoJmLjyhi/xF+bGywwcIiQQKn5cTn8tE/qz8D/QPJ8+Rhqf5IJG3qI/X864sPAYgGpgFOJg3u3+MK4FVcm6ycgWbp0NLPTeFE/jCz5K/lMB2kGiogsAXsmHmsC4qhduWj9VXc/Poqts3vSdgWDstmS9Dm569t4pLDEiypcnH1N3L5y6IGBmU7+NXRpqr7w01hhuW6GFvYy/7rswqgodIURqWwmEAyRN0WiDWnt2vtxnnkNX5Fve3nwdjJAPz8yLwOniQZIZGAmo0mW7qgo96OmWHbYGMyY3MQfvHaJi44NMGyGhfXHpHHH+fXs18/Fz853PwsvrcxzD6FLkbm97LMaI/TbQps84eneyTSV4UD0BxMTZf9tXNwJZpZlRjBYtt0DPnhoSoUz0ihanOckaKTPbvKjE1B+MVrX3HOwQlWB1z87Ig8fv1BgMOGefjfQ3KxbZt3ipuZOMDFsNxekBne3O3HFupeu3cSiZbi0WporDDFtfGWi60Op5ngkt0f/P3Nv3UmFjMHy8BOmPciqRKPwsqXze3J5/Cb1wJtHr5sqgqlpOv9Z3kp//PEYr7ee6Y0EOYHTyzme0ePoTQQ5uez9+Pnz3/BiRMH8e0jx5BI2MxZWc6BIwoYlKfJQiLpZNs2eaufAQc8Gz8GgP+aqInf0vU6yozLjxhFTWOUn86awLXPfM7ZBw3jwukjicUTvL2ynENGF9E/R5OFRLpFcwOse9vcHnowuMzvXjRuc/fChjabzhqr93LS9TrKjG99YySNzXGuPGEcP332cy4+bBRnHTSMSCzBOyvLOXyffhT4e1Yxhkift/w5s3Jk3jAYeiCNUZu/L25ss8nhw/VeTrpem8ywQlieGhzueqoac7n7zTAnTRxNPOHlvw7N4543qznrkDwOH5dFXVMjLyzZyAGjShhTOJgRuSPwu/3p/nZEMo5t23xWsoZaeyWWBbHANAAOHd3zJlP1giufPYivANzZ5qJhsAScXnNhO97S1clXCJ7U/tGNJ+D+ucWtF7xXMAawGGmXsQlTzPGHBTEgxhvrt8/oG5brpMDn4Jq36gDYeOXQlI6zy7l8EK82F8FVXCt7o6nWLMuaVZi+Mdg29tKnsIBH4ydRj+ly6HdneFcvMRrKIbC5S4q/d8yMhA1fMhaA0XYJGzE5cMuitpkxZwMUZjnwuSx++a4pzOh1mdEeT7bpGhdtArcuFkoKhKrN564uHLNtEitfwQE8FT8eMEWVPpdm0maceNSskJGi4422mWHxJWMAGGOXUNySGXd+ZjLj9XUmMz74qhmP08ICfvthPR4HrPlRL8gMh8t0+GysVnHt3mishtqNZqIpcXOs7PaDr6WINBEzxbY1G8xHVj8oHGVWI8mUbraRRmgog6wUL2G04X1znOPvT+3gI4Gq1ofcDnA7lRnSteIJmxtf+XKnC947euDDYgBe+6IUgPnrqoknbJoicW6fs4b+OV4+/dWJ3TBaEdmV8nVLOMBRTMR28lL8CPpnWVpuWbpcMpnxyEdfAfDy5yUALCiuIRiOUROKcO/76xk7IJt3fzIj9YMVEVg3xxzH5Q6FAeNb7y6ui7XZbGyBU13epMslkxmPf7IJgOeXbAVg0cZaKoPNbKkN8ejHX3HgiAJe/OGR3TBaEWn12SPm89gZkFXA6oq2mXGQOp1LCmzPjDi4AhArwm7OAlcdLv8GHL6tfFD7KpYjyrw5PhLxftzy/nAuqPkGFbVZzFke4aDR8KOTt1AXrmN0/mgG+gfq/Y1IN2qMNvLGurlYVoJ4eCiJyCAOHOUh35Pi6yl7QMW1neV0m45ktg3xiLnPUdhtHXhWlARal2itI4dtBRbbCmt35ca59W2+bowkKG2Is29RL3oz480xRc2FI1tny4p0im2brrWJCHgGpG8cJUuwKlfRZHt4ODabsQVO/nKyCjsEU/hZvQ7cvi75O7djZtSw/U3ItsLaXbnpa5kRbE5Q1ZRgTEEvftvg9kNTqVlSXcW10tXiUVMY783p+n2Xr8BRW0yT7eHF+FFMHuDm7lkFnRxfrONtpOcLB0yhXk5q3sPsmBkVFLTeX9xBZvzuw+2ZEUlAIJwg0Jzo+V3PPTmm6LFoDDh7+Fh7mljEFNXWFpvOtdlF0N4SPQ6XmSTpKzCFtqEa2PqZWemlaJ82y4v2WY2V5vc21UXcK543nyedQVnT9hPABw9xc9vMNE4qlD5rYXFNmyVak/W711a23q5qaKamMUIoEmN4obqDiKTF0qcAeC9xEJOH5HLzrDSeK5M+a08z4/evb8+MDZWNVAabiSUSDMnXOR2RlFr5qvk8/BCz6kuL0oZ46+0Tx/j43XE974K39H7tZYZFglFWOeOsreQSwmklqLLz2WwPYL09FBtHm8xYurmOivowNmilDJHuUPoFlCw25wFHHwVOD2UNTa0Pnz4+i18fkwHnAKXbLSyuobQ+iMNXTqK5AHfBAtxF83F6K3bxjHVQuICXKp8n3jABZ9axLNk4BmdiGNWNQZpiKwnFQozMHYkzE1dfE0mDQCTAitpFAEQDB3LUfk5+NnufHvk7qKuIe8qy0lLgWROKtN5uZs8LYyffVwbA6xcNYNKAXlJg682FQEnLst7D0j0a6Y1C1VC/Bfz90juOlosXT8ePJWDlseiSgZ3uDGJ5PIy85acQa8byaombtClbDtkDIHfQ3u/LtqF6g+k6lj987/dH28wIs+c/J/v/3WTGe5cO7L0FttuWUW+q6Zr/L5EdhQNm2aOcgV2/71WvAPBq/BvYnhxevbB/52bOxpqxmqsZedsvwV+E5U9BAbB0j1CtWVrekZq/w20zY8+Pc6bebzLj428PYkhuzzsAbuXNMR1XwwHITvN7w96kuQEqV0F9iXlPnWwnZYfL/I2MR8wqEk110H885A01+dwXxaNQt9l0z0+lilVQsRIcbtjvNKqrEgAMzHbw7LmdzAxajjP+9GNI2FheTWqV9lUEO18k1Z5pv52D22mx4JcnUpSt41qRbhWPUbjuBQA+9M3gidOyIatz7zMtj4eRf7wGLEuZIbvUVZlx6O/fJsfr4pNfnkCOt5eemxLp6WIRWPuWuT30wDYNCqpD5jjjgIFuHji985MHLY+HkTdfDQ6XMkN2aVtmWCSY4fic050fc4JjMflWqN3ta+0cPkzszzPxY5mXmIKNWSVn+h/eoX+Oh/k/Px6vqwefmxLpCxY/aj6POKz12mZ1k8mMw4d7+PPszk/6tjweRv7hSnBnKTNklzbVVePwbsWZs4qsYZ/j8NQCYCdcxBrHkWgaRSJSiG27sZxNODwVuLLX48zagit3Fa7cVcSC+/Hth86gf9YA/vqdfqyvW08kHmFswVjcjl5SQyXSS9m2zarqVVRFzepnkwsP5fsnZDGqqHOrO1teLyMffbT1dqroLEQvEE/YrKmJMaGfi4IsN822Cw+x1oOEvfHdV6r56NuDekd7c8sBLo8pjswdkjlLiUrXSMRNhy0b00ErXarWwNZPsS0H/4ifypgC1x4tuWc5HWRP3Q+iIXDq5EDahOtMJ9SsQvP3aW80lENgE2T336sik1jCZm1LZuS3ZIbXipFg7//OX/WfWl6+sBd3svFkm0KqfuPUpVC6VqgGsLu+6LE5COvfA+Cp+AmM7efq3Hs2OwENFVj99yF70kT93PdmiTg0lidfyJikaNxmfa3JjDyfm4jtwtNFmfHL9+p4+IweXLTqcJmJLaFqFdcmq6kOypebz3lD9uxvntMDecPM382yLyASgn5ju20lmG4VqjbF27m7X2Vmr23rWjt2BviLKGkwFx33LexkZrSwnA6yD5hguhLrOEN20ByLs7EqxPhBORT6u64QNhq3ufn1ldx63tQu26eIJGH9O3ibq6iy86jK379Nd8JkmcwYB5ZTmSFthKNxNteEGDcol4KsrsuMhuYY97yzll+eMrHL9ikiO9g4F5rrIasIBk5q89C2zrVjC/fs3JLJjPFmdVBlhuwgFIlRUhdm34E55PucnOP4gCtdLzDasb3zYNh2s9YeRq2dSxwH/a0AY61SCq0GznB+zBnOj1mbGMZtsfN5M3EIYFHVEOGR+Rv572P3Sd83J9LXRRrhi6fN7dFHm5XCgJJgV2TGOLM/ZYbsIBiOUtUQoV9enIrIGjwD5uDOWQ9AIpZDpGoG0cAhkGi/FiRSCZa7Ck+/ubgLPsOVu4rs7PXUlZ/OvJWzOW5yPzbXb8bGZp+CfVRgK5JCwWiQD7Z8ANjEQqMZWpRPvjefXHfnzk9ZTifZh01PzSB3oCvsvcBtHwe597MGrpyeQ5HPzVpG7nLbIVTh87gpjiS3JEtpQ4IPNzVzzKhesjRGVgE01pjOjroILp0RLDPFi9lpLgxc+k8AFvmOZEvTQE7srV1ApYVtfq5qv4IB4/Z8N5FGqFprilTce7e83Y0fBHh8WYhfHZVHJO40mWG3v+0wKnF7vGyMJLckyxcVUb4oj3DAoF7aVcqTDQ2V5iRxqpdnlswRj5ml5VPRmXDtWxCP8JVzFEvsfTmrs5kRqjE/6/1VUN7rhQMQDnb5+9+fvl3Hi6ub+OPx+Wyqd7BmN5kxnAocniw2RZI7sH1vYzObAjFG5vfgnz1vtvn9LRpjLvDJrjXVQuky06W7K7rN+otMYW3lKog3w4AJfev/wLYhsNX87U1l4XC4rnUSBlPOAeDJZY0AjNZxhnSxHz65mLdXVvDXb05j0caaDrfP9jhpjMQ73A7gmc+28H+nTqSgC4t2RaQDLasqvRQ/kpH57j3LKzthJhzndc3qO9J3XPrQQhYW1/Dod6bz6hclHW7v9zgJJZkZ98/dwLUnTcDjUuMNkS638lXzefgh4Gt7vvixL3ScIalx7r0f82VpPa9dUMTI/1zDHZ6VANTbfp6NH8Nr8cNYau9LHCejKCPsyqM86sdFjAOsDZzh/IiznfMY59jK3z13Mje+Pz+LXkEp/bj5jVVccczY3tFgSqQ3WvGCud6WOxSGHQSWhW3brZkxpiefF5Ze6ZR7PmRzTRO/Oi/Bw6vuwZ1Thm1bRGuOorlyJtjbzys5PKW4yCcSaduwxI72p7nsHKI1R+Ed/CKu7A34hjzP/V9s5LjJV9Pf358t9VuwsNi3YN8euTy9SF9Q31zPorJFAMQCB9JvdIKB/oE99n2bzkD0Avd+1gDAPQsbuGFuYJfbuYjR36rn9zMH8OqFAzh7wvYCrRuOzWNkXvt/+NfXxrp2wKnk9IAdN4WSIsmKhqFmA7i86b1oH9gCxR8A8Ou6UwE4ffyeFbbbsRg1L79LzatzsaPRLhuidJYFLjfUboDGqj3bRTxmOho31ZnllffS48tMt7Lfzavnlo+Du9zOS4QiK8gfZvbn+fP6c85+2zPjTyfkM8Df/luELfXJXezokRyulot/u85SkU4LB8wy6S2zsruMbcPKVwB4IHwcYHH6+E4U3ydiEGuGorHYOKl58klqnnxSmdFbNdWZ98Bd/D7mxdVNAPz83QB/+7Rhl9v5CVNoNfDHmf345zn9OHfi9p/Fu04qINfT/sHuts42PZYnx3SIVi7sXrgeypabyUC5g/e+sHYbjx9yBpj36RWrIN6H/j411UJjBfgKUvs6a96ERBT6j4eBE1leEeXzcvPv2KnM2IEdi1HzynvUvPq+MkPaeHul6Rz1w6cW88hHG3e7rQXcfv5UHv72oVx46IjW+//6zWl4nO0fZ5QGumbZcBFJQlMtiVWvA/B8/GhOn7BnqyPYgTJq3llGzbvLsGO96PyypNzCYjMJ44rHPuWZz7bsdlsLuOP8qdx3yTS+edj2piL3XXLwLp+zbdlwEelCiQSsNtnA4Kng2n488d7GMOWNZonvvTrOePUDal5+T8cZ0saXpQEudr7Nvi+dztjmlTTaXm6Jns8RzfdwU+xSPrMnEMdJEQHyrBB3zirg7lkFnDclj8X2eG6IXc7i4x7nnthZhG03xziX8ab3Ok5ymGKN+ia9RxFJmc/MMtyMPdas8Ak8v6qJxqjp3rB3mTGXmpffUWZIG5trmnB4S7jn898TdZSRiOXQ9NV/01xxapvCWstdheVq4MrZ2fzghBxmTNr+2E9ON9fyEpGBNG36Hs0Vs7FtC3fBYv648E/E7Bj9/P3YHNzMV8GvsO1ddCMRkT2WsBN8XvE5Wxq2YNsOYsH9OXqCnwJvQaf3ZUej3XINXNNF+ogCgoz1N3HtcftxxD79AbhzViE/PSKP1dVRjhvt4/KpJihWVEY5/V+VJFpy4Ma59QSabX5wcA7LKiIcMsTTY6vBAdO9tqEMmkeBt4sLWaRvqt1oLnDnD0vvOL74N9gJyosOYVWJOVk8bciedeaxozHK//okAAXfvroLFm+WPebONh3XKleBe1rnluy2bagpNoXXuYO6rlhlNwZSyzTHGg70ljJ7Hx+jQ2WQyGPagSO45pBRFAcdHDPKxwWTTRfOpWURznp6e+Hw/75Ry/8F43xzip9V1TGmDXb37Mz4OrfPdBsuHN0t/96SAUI1pmh7T5ZH353y5VC7kZjDy4vxowA4YFAnCiubaiG7P+QMwg43U/7b3wFQcPbZWO4+1B0yEyQSZmKZu/tXmnARI4cmxmeHuHrG9uOMw4d7+d9DcilriHPECC9n7Weyb+HWZs5/rrr1+Rc8V83vj8vntPFZFNfGOHBwD+tI6HABtvk9zu6f7tH0TNEmKP/SFCB3Rcfar3N5zXug2mLz9cD9+kYH2/oSkw0ub+pew7a3d5aaeDoAH3y1vdBk8oA9+3e0ozHK//YvAAq+82Ms396tqiCZZ0Cul9+eOZnZU4YAcNyEgXznqDHUhaJMH1PEqQeY++evq+LiBxa0Pu/kuz/krgsO5JjxA9ha28T+w5NbkUlE9sCKF3AkIqxMjGCFPYrxg5NbzaaNWBg7Et1+burc83WcITtpjiV2+/jAXC837ZAZs6cM4eLDRhKJJThoZCEb/3gqtm3z/ppKvv3wotbnHfWn9/j7tw5m2shCKoPNTBq6Bz/DItLW1k/NOVN3Ngw9qM2x3ytrmlpvjynYs+5tdjRG+b3/BqDgez9RZogRa+ZO99842zkfgHfjB/KL6PcoZ/uqc37CeIgyMSfED4/dfm7qzAl+Lpzsx2HB/gM9HDf+auzas6l782YK6ldxv+dObo+ey9SbbB77zmGMH5RLoCnKhMGdW2pYRHah/EvYstCsgDHqKNMgDXhp9fbMGJSzF5lx3zMAFHznGmWGABCJR3D4tuAf+SC2s4l4eChNmy/Dju1w/shqBiwG+Pvzm9OO5+Qpw0jYCaKJKJ9sqCBuhcjNqefB/4nhcXlZsdHNH1+ZQbxpGFnDH+fL6hX88oMbufLAnxGP5/CV9RVZziyG5AxJ2/ct0hcFI0E+2GKaEsYbx2HHsxmWV4Tf3fnJ33Y02i3XwFVc20tdPjWbRz5vZFI/Bz89xEG/7MFMHprP1xuADM11MjS37RuXyQPcLP7+YA68f3v317sWBHn080Zqwwkm9HPx5sUDu+Pb2DOe7JZOQJUqrpWONVaZ4lp/P7DS2Kw7VA1r/gPAewXnQAnsU+hiRJ7+DPcJ2QPN0r9Vq2HQlOSLQgKboXotZBWltJDkqv0CuNf9hzM8nzEy0dIxxAbWtXy0GOFwM2LYNGg+HsbOAJeXAwd7WPjdQUx/sLx1u9/Pq+fuBUEaojaHD/fwz3N6UUGSJ8d04GsO7rS8mUinxWNmwo8nu+v33dK1dlnu0QRDfqYNdjPAn+TJKDsB0WYYOCK1y5FL92gOmA9/UcfbdoFL9vfzxLIQRw5z8v393RT589s9zhhb6GJsYdv3MdOHeZl3+UCOeqSi9b4bPgjwmw8CxBJw2jgffzm5e76PpHmyIVgKRWP6RlFnV4rHzOShUGVqCmu3cXq2F9haFgyc2Lv/doXrzc9UqrvWli417yXdWbDPCQB8FTDdomeM8pLn1UJFknoXTR/BPxdu5tjxAzhn2jAG5vqYPqYIp6Pt34vxg3a+gH3kvv155yfHcsLtH7Ted/1LywmGTWepb31jFL89a0pqvwGRTPW5mUTxfPxozhzrwOvag8xorDbvD0SSdN7Bw3nmsy3MnjyYk/cfvMvMmDy07eQKy7I4bsJAXr/yaE6558PW+69/cTkVwWYArjxhHD+eOT7134RIX9ZyHophB5kGNzuoDZtC+UsP8ONQswLpKuF67H9exNnO+URtJ3+MXciD8VMAi7MnZPHC6ibOm+Dm1DFuivyeds9NTR3UdhK3VTiKgvP/QsU7f2Zg8Uv8xP0sA6wAv3rOw6aA6WR2/WmT+O5RY7rpmxTpw5aaSXYMOwQKtq9YE46Z7m4/OlQ1HNJ14ok4bxa/g3/kA1jOMPHQSEKbvw2JLKbv62ThujjnHDyIY8cN2ek4w2E5cDlcHDd+FGCKdKubqtkc3My+w+u54dxCbnh2HKFNV5A14mFKmzZy3bt/ILTpe/zghCI8+20gy5VFQarP9YpkkEA4wKflnwIQDRzIeYc76Z/Vs+tNVNXVwy2riLR7/w3H5vN/R+VhWeBydP5gtsDn4NULB3Davypb79t2gLy6OsaW+hjDe3LRnyfbdHrMGwauHtYBqy+wbVOYY9vmArfl6J0dHmPNULXWrC/WmW6iqfDly2ap24ET+WfVvkCM/z5YBxZ9hmVB3mCo2wwONwzYD5wd/A0NbDEzOz3+Lvv5/Ky0bWZMsDbxc+8zHLdxMbhsSABYkD/cdHL25EAiDuE6qF4PzfWweYH5WHAf7H8+TDmHgdlenjuvP//1zPYOtg0ty7p8vCVCdShOv2SL/tLN5TWdhpvrVVwrey8cMD9LOV08KSlcDxveA+DB8HEAfPegTmTGtuJxdeLsG5rqIBFrnf3fVT7a3LzTfWMLnPzuuAJ+fUw+ToudLnQnY3ieiyfP7sfFL5gOttEdmlW9ujbMn05IkO3pQUV/nhxoqDC/z/qdaaum2Ly3yR2c+klqTo/5W1pTbN5L9R8Hjh70c9IZwTKIhVP/8/Tly+bzvie2vpect8n8Xl8+NQWTPiSjvbeqYqf7po0s4OZzDuDGM6bgdFh7lBn7DMjhgUsP4XuPmZOp2wprAR7/5CuuP20Snj0p+hORXateD5sXEMfixfiR/O2APTg3FWs2E2HSvUKU9EivfVG6030nThzIredN5XdnT8HtcODYg8yYNDSPuy88kKv+tRSgtbAW4J531nL1CeP2aL8igrkOtKplVYyhB5kJfC1iCZv5LecPLpys4wzpIuEAPHoGVulSGmwfP4hew7zE/gCcOzGL22YW8scTCnA76XxBt8PFwJnXsOjtYRy8/l4udc3B0xjlF3wPGwe/ffVLvnPk6N61Gp9ITxOPmtVaAUYd2doMrTGS4NOW65TnTUzzdXnpU5ZWLOXGj36P5QwTC42mafO3IeHl9IP9/Oa0/clxFuB1O5P62+5xehiSM4RCXyFbgltwWpv55lE+npo3nKavrsA/6u84szaTNfwx7nv7cr4xvoD1deuZ3H8yPlf3ry4o0tck7ASLyhZR2VSJnXATb5jEjIk5Pb6AvQdXT2Yu27b51ovVrKqOURXaeemkZ87tB4DbuXdv/KcMdLPxyqEc80g5m+rjbR77aHOE8yf34B8Pb565aBmqUpeEvRFrhuYGiIbMkq+RRnMhOBE1J1S2sRymk5c7yywL5PKa2y4fuP0988K3bUPNBtPhON0/I7FmWGkufn8+8Ew+/9RcMJyyh0u1Sg/lcG0vCrET0H98+0t4J+JQ+xVUrgaPD3x7t9xpwrY595kqtgTjVDSazMijgZ+7/sUFzvdw0vK7PPxQGHcSjPwGeNtZ+si2oW4TbHgfVr0GjRWw8O+w6hU48ioOHnEYG68cyvi/lhBpGxl8vCXCaeN70ZLBTrfpap0/PN0jkd4uVGN+dxxd/J5p7VsQjxLIHsur1aOBTmZGcwP0n5Da5cileyTiUF9q3m91gXjC5tR/VlIXTlDWuPNxxt9OMV1lPXt5nPH/2Tvv8Laq849/rrb3Xtl774QsMggkjEDYv7LLnqUUKKWsQikUSpktpaxCS6HsHQJhhRBCQnbIdPbw3rZky1r33t8fx5bsbNuytc7nefxYurqWjm3pvuec9/t+3+N7Wtl7Szf6/L34oMdWFnuY1SeMNqEMJkAXn2cprg3gKIPqncIxuascfU1WSEgXr2uyCDfhSMPTIAqoOji/OyqNNbC3yblt6JkAvLmpgeJ6MUkbkS3XGZKO4/FpzHn6exo9aisBUzNP/N9ogIPFr7ouxHuFK4X7dX05eOrF/kFcOmQNgryx0G0MGIzMHpbD3r+cTp+7Fhz0GusLapnYN8wczyWSSKfJtfYHdRQVpDEirx1CKVcdJGR1vku7JGJo9KjMemIxPk2nsv7gmPHHM4cDYDV1rDD7rDHdOWtM90PGjK2l9oNcbyUSyTFSvlXkUowW6DauldHKI0vt/r3gIZlhnDOURAx2Rx27njyZseRTqSdzuef3bNYD6/87pwpDDKupY3tTx83+BfRKRf3uUS40LaaWJP7iuwiAfVVO+mRKsbhE0m52fCXy73Fp0H28vyj/nu/q0HQwG6B3qowZko5TVe/mhCc/R8t7DoO1DtWdTWPBL0GzYlDg3lPHkxnXPiMlm8lG/9T+JFuTsY3bxZxRBq78Zw7OgiuJ7/UypoRd2HI/RFFvo0arYq99L4PSBmEIZadkiSQKcHgcLClaAoCvfihJVisDs7KxGsM7py2jWhjy2oYGlhYc7Fj7+OxUTulvC3p7x2vHJfKHxXWtjt3/fR3f73MxMMPMrZMOIcIKNQajSPLWFUFibniKO8MVT4NIxjZUgLMWfM6AQ63RLAQGiqG1O5XuA49bbJ77hbeKSIA3J8jiUoVgz5IYHv+PukKxIZSYFfq2srsWQWMNekIW560e5j+cnRAGfydJcDFZISkbavYK58j0vuLzYbIK18HGWiFgtRdDXMqhRa5t5B+r6llb6vXfn2LYzJPm5+mmVIsDfWfCcVdBau8jP5GiQFpvGH85jL0EdnwNq14RY/3i9zD8XJh0Pb+akMTTKxytfvSe72qZv6ORsblmbhgfhjHjQCwJ4KwBr+vQAmiJ5FhQfVBfKt5PwaSFW8jjtTMR9uttiBmqFxQjJGQEd1yS0OCqaxIvBOf/+dAPdvKrfAcdf2pOKnMHxmHrYOLiQC4dGc8bG52tjt35TS2jc8xM62XlitFh4uJvSQBHiYjbXSUkDWc8DVC5Xcyhg32NOxrmeCEqr8gX64zkvK59/Y7iKBUiws4u4Nn2hZhbZg2FzIGoms49iwJr+vQ4uc6QdJzb3l3PvqrW13CDAk9fMIbTRuQdLKqt3Q9r/gObPhDroaMRnwlD58HEayFnOCcPy+GrLWWtx/DOegbnJnHysBwunNirY7+QRCIBTUPf8DYK8KE6HYA4cxtjhq6D6oGkvMjsciXpFC58aTmldlerY6nxZh46awSnjcjFdGAfbxBr6oIVsOs7qNgqiqA1rxBtp/QQReK9jxemCQfs3YzqkcKGwtb5jF+/uY4+mQmcObobZ4+VrsoSSZvIbxKs544S+ZQmXD6dV9c3+O+32UFUIjkQ1cfGJ8/kePKx6/H80nMXW/Q+dE8yct/0ZE7pbwvu+2zQyTy4uIY/8Tw3mOZTqqfxH/VUrn99DT3S4jh/fA9OGxlh+w4SSTiw7n/ie5/pojAfqHNrfLKtEWjdyUwi6QjjH/4aW/d3MFvL0LxJNO6/ih5pCVw9I4dLJozE0sFuf4qikB2fjc1kY2fNTsCJ5upJY+FlxPX6N+bUddz1xXv0MJ7I8UMLSB2XSm5CbnB+OYkkRql11bKqdA0AvrrRuBoh3Rb+5gpSXBuG/PF7+0HH3j8/gwndOkepfcnI+IPEtS6fzoKdLtjp4pqxCSSGU+vWZuJSxaZbY40UkBwNXRd/J3uJEAJ5nGAyi2S5Lad94lNdF63VvS6o3SeErCarEAsm5oj/jy0lNMLWhkqo3CaS86G259d12PQ+AGvST8dXFbjsyqR3lGK0iNaIziooWgPmRDCahFDE2wAYICknaOKdp35qFrrq/Mb4IbeZPxB3U3rAjDshb1Tbn9RggsGnCWHu6ldEgnzzh1C+hV/N+TNPr2h9ut2t8+UuF1/ucnHduMTw32g1x4OrFNx2Ka6VtB9XnRDRt0g6BIWyTVCzF6/Byifq8f7Dx5z0dttF/JUuUtFBQxXoatBixn9+bjjo2NeXZDEwo3MEpX+YnnKQuLbCqfHNHjff7HGHkbg2UTgruuqke62mQdVusXYIlcO7NUmIdsq3ivVFfPhvrABijVW7H2zJnSs20jXY2tSydeg8AB5b1noPIeznYpKI4FBtvRffMYteGQe4qVftgkUPweaPoblrhsEMmQMhpadwsjHZhCDcVSsKYat2iE5Ea/4tvgbM4ZkTH2DYltZPXVTbSFFtI4vyy6W4ViIJBvuXodTux67H8aU2oX3P4akX+xzx6eDVj36+JOrRdZ2fDxC6Aiz8zQxyUw6x5+IohRUvwZpXxZzzcKx9TcSTnpNhzIUw8BSIzwCDgX9fcRzjH/6m1em7KxvYXdnAovxyKa6VSNpK/nzxvduYVp1zrvusOjTjkUQt6pf3cjzrcepWrvDcyRa9DwCfXpBJRnzn5BKvu/A8/vp6FXea3+V+0+vs0fP4vmw028ocfJtfzt6/nN4pryuRRC315bB9objda6rodAv833uVIRyUJBqpdbowpy/BnLwZXTPSWPhLbIZUnr20H6NyBmIMogYl2ZLMkPQhPHuFxq//U4bqHIi77AxsuZ/iiPuMtQVZrNo9iEn995BkSSLBLN3PJZL2oOkaS4uW4vDWoas2fA2DAUixhn8XGimuDRNUDTYX11Ht9JBqVah1BzYnl16RTY/kzvtXGRSFzTfk8u1eN7csPHhDq96jk9ixoo/OwWgBNOGqKMW1h6exBmr2CzcuXRXJ3ri0jid8FUUkyEw2IaQF8LnFJnvZZiHOsyVBUjeITwNrStc42jbWiNfXVEgMg/dFyXqRbDRaeaRyhv/wFaMTZNI7mlEMokWiroG3UThvGIyQmB2U9vEtY4bJAEbNwxPmF5hn/EmcMGQeTLnJv6htN5Z4mPpr4Rby3SNQkY9p/q/YfMEjLKjM5c5vaw/6kXqPTrI1zN/bikEI35014n8ikbQHZ7X4jAfhM92KrSKh8alvCg5EQuOOKW1whPY2Qnr/8HCRl3QM1QuOYrB2bKOmOWZUNRzcnnXzDbkkdGIRndWk8PN1uXy8rZEHvj844a5qOkZDGMQMgwnQxec61sW19aWicC4xO7RudPEZYv1SvgW6je16B9324CgVRRedLUouWiuuDeYE6D8LgJfWBoTzf54V/htRkvBF1XRW7qmmqNZ50GPbHz6ttVutpwEWPQwrXxLCWYC80aJAL2+s2Kcxxx1cIKKpYt+gYBXs+FIUJO78mvhd35J//LW8kXA5D3+1t/N+SYkklvn5LQA+VyfhxsLfTklt+3O4HaIzjjkOvAdfKySxQ3PM2FNZf9Bjux+Zi+HAeb7PA8ufhe//Cr4ml1tLomglnD1EOJorBjEnr90LxevBXgT7fhBfOSNgys0w8GQyEjNYfd9s3vhpH898s6PTf1eJJKqp3Q8lP4vPX/cJrToaLtkf2Ed48fS0UIxOEiWoms6er19gwMoXALjNeyNr9UEA7L2lW6e+do9kE1dcfh3rvqhlbM1X/M38D87wPEKhHmTDBIkkVtjwjtAbZA6GrIH+w9urA93SXjurA4XyjbVgl8UdsUzzOuO9rZ9hzRZCbnfZPDRXTz6+cwD9U/sHVVjbTLw5njkDRvDqdQZeX1bGd5umYLCWYElbha3bOzj3/Aan18beur0MzRiKQZE5OImkrTg8Dn4o+gEAr2ME6CaevqQXZkP4d5SU4towYNmuSl5asodyh5ty0qkltdXjnSmsbSbBYuDMQXFM72ll7MulrR5zuDVyE0PgPnos2FJFAtjVW4hGJQF8bqjZJ5LjPrdIbHW2i6vJ2uQuhUiuuR0iIW4wCYFtUjfhbNFZyXFntRDWepyQHCaW/JuaXEQHnYynMAnwAnDf9I69XxWLmR5/ugVUN4ol/INNzKIYgv5+D8QMDyVkYMXMq5bHmWTIx6sbMc+8HYYEudq612Q4+5/wxe/BXkTCwlv5xRlPMefafgfHDI9GsjUCFhSWBGgoB21AaBy2JZGN6hPzj2DHM5cddn8HwFeW2eARh3814RjdPVWPiLnNRS9NKBYLPV543n9bEiE4q4X4KDGn3U/RHDPKHB6KyAJav5c6U1jbTIrNwOWjE5g3yMa4l1u3+6736KTYwkBcC+Lz7CiB9L5BcwqOODxOqNwhXN1NndM1pU0k5kJdEZTnCyf+cP6/eJxi3dXZrrUAWz8V3wfO8RdSJVsV7G4diwEuGdmx2KRYzPR48GbQNBkzYoyFm0p4cP4WSupch3y8lbB274/wyU1Qs1fc73EcDDsbcoY3fQ6OEF8MRuGyP3A2DDgJKrfDypehaDW2NS9yTdZi/u/6Fxn9YnGrH/OqGuZDtRWXSCTHhscJmz8B4AN1Bj2SFM4aHH+UHzoAXROFqglCjCLXGbHLkWJG99S4g4W1Vbvg3cvEvi1AxgAYfRH0O+HIezKV22HLp8IdrWwTfHIjDJoLM35HZs4wbp09iF9O6cO4h74O3i8nkcQa+QvE96whkBwQOapawABobK6ZU/p3zMRBMWj0uOsSiMuQMSPGWLiphLc+mc/LnvtAgae95/GlNhGACXld817ITjSRfe4deD/eR2rVNp43P835nj/iRr4XJZI2oeuw7n/idp9pYm2P6IjczKn9bczs3U49QmMNaCpKjzH0+PPvQJd7U7GGf51RX0JC35cxmHW8tePw1k5ixpAE+qX2wxRsw5sW2Ew2pvQaSlqCwtyxVfzujTMxxhVitJVg6/YOabb7KW0oJSMug9yEMNGiSCQRRGVjJWvL1wHgqxvDrOEmZg3s2aHn7Kq9KbkrHWKW7arkkQX5VNa7KSaLygOEtV1NWpyB189uXU301W4XtS4tRCM6CpZ4UenuKDv6ubFEQyUUroGKfJFwTene+cLaAzGYhENuSnch8nHVQfE62P8TlG4SbSNUb/Bez1EqKqw99ZDUfhFKULEXi6Qj8KZ+KpsqxO/7yQWZmDro0qYYjSRNGk3ScSNQTLJOIlZoGTMKycKLidcsjzHJkI9dj+NK393BF9Y2k9IDzn4OsoaKtvOf3U6acw8vHeBasGiPm7pwjRktsSSAu15cmySStuKqE58Da5Bb2u/8GlQvZba+fFnfF4BvL8tGOVahlqdBtFO3ti7gUEwmkk44gaQTTpAxI5JwlAqRXjsLAFrGjAJysB8grM3qoLl5W0mPMx7kjrZ4n4s6d5jEDEuimEfGalzQdSGSc9VBXAfcJYKJooiCOXuREGToYdx6uq5IFBZaO7ng01nlX18w7EwA7vuuFntT55sfruj4OkgxGkmaOJKkiSNlzIghFm4q4YY31h5WWNsvq0m0reuw7Fl47QxxzUjIhhm/hxPugt5TxNq/Lc4digJZg2Hu43DifSIxV7GNlDfn8trU1i0ll+6oxOEK4h6CRBJr5C8Aj4P9Whar9UEsvKgdbv2eBuGc3pREl+uM2ORoMSM7+YAirW0L4aWZQlhrTYapt8C5L4sCi6OtdTIHwYw74ILXof+JIg5tWwBvXQgb3wdnNekJFu47fWirH1uzr5p6t+8wTyqRSFrRLK7tPs5fRK7rOhd9WOU/5b3zO9jhxedCcVWTdOrZJM27QMaMGGLhphJuf2MZD7ifxKL4+Fodz9/Vc/yPp1u68FpttGA+5U80GpMYadjLb03vAbCpqA6nR8YMieSYKF4LFVvBaIWek8FgQtN15r5ZDoDFAM/PbafTueoRBYHZw1Cy+pN07pUknXOFjBkxRGCd4cSW/SUGcx2aJwNX6dmAQrI1vkvcLeNMcQxOH0zfjFROHxtPY9HF6JoFU8IuXt/4KQpW9tXtwyk7uUgkbULTNRbt+45GnxPNl0Sc1p9fzc4gydKG7q2HoKv2pqS4NoSoGvx98X420Y9Nej9qOfhNM9hag9rF+ebpvWxM6xlQdD++3MGYl0pZvPfQG2Yhx5oM9kLRBjnW0TSo3iNahXrsQtgaDi1UTVbhapHSQ9yu3Q8FK4XQtmqXcGbT2vlG97mhYrsQ7mo+SMoNbQvblmz6ENChx3Hcsz6wmBiULhcCkrajavDM4gJ/zPBh4r+WvzDBsJ06PZ5LPfdQbc7t3JhhS4W5fxVOBm47LPgtJ2dVM7DFe/oPi+sY/VIpa0o8nTiQIGA0Nzls20M9kvDA2ygKVap3Q9kWKN0srs/2EpFAlbTGWQXoTa3kg4Suw9b5ADzrOAEQsax3ShuElR6ncHqUbsyRj8su3LVt7Wvvrmrw5HeF/phRz8GuZLlUdvk646zB8aS2cKr9zZe1jH6xlPzKMBBLGUyALualsYizSszREzLDZy4N4v+SkAlVO6GuMNSjOTRuB9TtbxIVdvLfbtsXovVeznBI74eu67yxMbCRm50gt3gkbUfVdO79aNMRz6l1elHdTvjwWvjqPuFe2W8WzHkYBpwI8RltE9UeiKLAgNlw3iuQPRy8Tmau/Q1XGr/wn3Llf1Yx8o9fsb9KJi8kkvagrX8TgI+06egYSLS1IynpqRedscxdXMAvCRtUTeeejzYe8ZyCamfA8fLnt+Hti8V8KXMQnPUcjDi37XOmpDw46X449S9ij7m+FD77DfzwFNQVcs20vq1OP+/55Yx44EtKDyMAlkgkTTRUwb6m4r3uE/z7SbUunZXFgb3dDhmF6BrUVwjH6uxhorOhJCZQNZ27P9zIA6b/0s9QSomezu+816G3kCbUVpZ07d5UYg5xs+8G4Brj50w2bOGMZ5cy7P4vqap3d+FAJJIIpdm1tuckfwfZIrvK7loVgGSb4diNQg6koVroGpLyxH1FAaPMp8cKqqZz1wdinWFK2ow5dT26rtBY/AvQhW5p5W57K2f9ziTeHM/AtIFcOi0J3ZOFq/QsAL4u/IhrXt1Ceb2DwvpC9HA2g5BIwgyHx8HXe5YC4LOPJC3BQE5CDsYIyWnLzEsI2Vxcx/aGgy2j+lLEMPYwhL2YPTVsLu5696S8pIPfwFd8Wo0WjgHCmiQ26OrLQz2S0OLzQPkW0SLLbBMthDuS3OoMFEWIfZPzhLus5hMiroIVTULb3WJDx3cMojyPMyDSrcgXIuuEDlZQBxNPA2z7HABtxPn+w/FmhThzx/8vus9H7VdLqf3mJ3RvGAhSJJ3O5uI68huEWN6Ej+fMf2OcYSd2PZ57PVfg0Y0Y3LWdHzOsScJZKmMguGrhi7sYnHhwgvuCDyrDM2a0xGwTsSPcx9mZuB2i3fa+n6BwlYgjtfuESKh8KxStCTiON9aEerThgeoVreODXbxStglq9qIbrXyiHg9AnxTjsScwNFXE2bjUgx7SvV5qP/yI2g8/kjEjUmioBK8LzG1s1dvE5uI6tjpbF+4paPSnkOHsZgh70V32kKwzeqccvCl67Wdhss6wJIjPdzC7K0QCqk8UV6CLrhfhhjkOrAlizt9QdfTzu5qa/WJtYu1YhfVR0VTI/0zcHjoPgOrGQBZyQp6l/QmMFug+H7VfL6P26+UyZsQIK/dUU9Vw5DW4p6GW+lfPho3vgWKE8VfC5Bsha6AooA0WCZkw7xkYMAfQecD8Otcb57c65dZ31snkhUTSVuwlKHsWA/CBOp2TercjSa3rIha12HuT64zYY+Weaqobjvy/rqz3sHJPNax9HT66XhQG9ZkGZzwNqR1r90ivyXDev6DnRDFnX/4sfPUHqNhBnPngfMYfPtkkY4ZEciS2fyHEr+n9IK23/3Bpg+q/fdbgDq4RnVUQn4Ge1JPajz+RMSOGWLmnmsmupVxgWoymK9zmvYlGrAxo2psayl6czoau35vqPZU3fbMwKDpPmF8gCZHbePLr7TJmSCRHwtsoOgcA9J7qz4+UOwN7U3P6tbMIT/MBOiT3AINBrjNikJV7qqlt9KIYHVhzPwLAUzUN8IKxFoByh1usM7qIFGsKA1IHAOCrG4/XPhJF0bB1e59FG6GkvoQat8ybSiTHSnlDOTvsQkTvrRvDiJ4mUtppMNSSrooZYaa8iy1217hxHOAiZUAjHjcGRcekiMlItfMoQkNdF+KY2v1Qs0+0zeygy9ydU5MZk3Owg8HkV8uodKqH+IkQ0izYrN0fe4nwZryNULpRJMUTsjo/sRsMDCYhAErtIaqVfU4h6ir4SVRLF66ByiaHKkep+LIXB5x59y+H4vWiTURKd7C0T3zSaWxfCF4nWkov7to2wH+4wy2UmtC9Pkqe/Dclz/wP3Stb1sQCO6vdTc6DOn82vcos48806hYe9F7GXrode8wIBtYkOO0vQsRfV8CT+hOMy2q98eTTYPp/ysOn3fehsCSI9tex6MyqqaKVb8FqqNwORiMkd4Pk7sIBPClXXFuTuzU5ju+FwtXCKdwX41X8jbXCscmSGNzn3SoEU8usx/vnh6+cmXHsP+91ihath2hJrnu9lNxzDyX33CM3oyIBnxvqCsDa/vfYtko3TlonwMz4iFM8KApdGzMO4JlT0hia2VrQUWBXOen1chq9IY4ZlkTx+XZ1veg4pDhKhHt5OBWqHYgtVQgzyreEV9x2VosuKgld4L5UuFqsiSyJ0G8WdW6NmxcGNnCfOjk1KC+je32UPPUaJc/8V8aMGOFoyex07LxleZiUshVi/jzz98J1MDG7c9yajWaYdQ+MuhCAu81vcaPxU//Da/fXctrffsDjC+N1hkQSZjSsfhNF11ilDWK/nsOfZrUjbnkbReGXLdV/SK4zYo+NRbXHdJ5h++cw/xZxZ8AcmHVv8ApUbSnCwXbspeL+5g/h89t559x0+me1fo2vt5Rxzj+XdZnDlUQScTTtRdFtvH+fq6xe5cYFAeHKvdMO3mc6ZjSfyNml90PXDTJmxBhbd+/hIfO/AfineiY/acOw4MXWtDdlDOHe1KDTf0ORkkMPpZL7TG8A8OaK/Vz88gopsJVIDkf+AnDXibxg3ihQDBTYfVzfImbcMbmd+gRXHcSl+d3N5Toj9li7X7yPrDkLMJicqK5cvLWTQE0ANSC+K3d0bWeKrPgsnryoD1nJ4C49C80Xj9FWwoI9C3h6QSMF9gJ8mtRpSCRHQ9M13tmyEJ/uQfOko7l6csnUTJLMHde1dVXMkOLaEPLAMh9eWgtYM6g7KDeRHm9pfUBToeRn+Ol5+PQW+Pdp8No8ePeX8N7l8PYl8J/T4b9nwfxbYfW/oWSDqEA9RrLijXx8QRaPnNhaKV7eoPHfDWGUzGzGliImXg0VoR5J1+NpEMJaR7FwhA2ma0xXYbSINpIp3cWk3GCExirhTlW8XiSSC1cLUW3ZZvF/NlkhpUmYG24OvZoKmz4A4BPzaby7NSBEG57VjrZ7Eglw5xIfGgZuMM7nAtNiVF3hPu+VbNNbu34cFDM6i/gMkcwwJ2Ct2MSH3f7HnVNbT4CKHCqfbmvsmvG0B5MNfC5w20M9kq7F2yicaEs2gkERzjHWpEOLI/yO492Fc2BFPhSvEwLTWMVZKQqbDEFsSeR2wO7vAHi85gT/4f5pbXgNT4OIiaYuugZIOo/6cnFdsrU/gfW7JQdv6KRz8LWuy2JGC/qmmvji4myuG9c68b2nVuX7fSEW7xtMgC4Ek7GCt1EU6Fnig3td6wwSssWarzw/PIoqNVUU/mlau12m28TWJnHhoFPAZOW6z6pZXiiSkClWhV6HcIWWSI6FhxdsPexjqTh4y/IwIw178VpSYOZd0Hd65xf0KgpMvgHGXALA781v83/Gxf6H80sdxyzwkkhiHl2nYul/APhQnU6fZOie3I6Y4akXCe9wK26XdBm6rvPI5/lHPW+0spPjVv9W5CL6zoTptwe/O4JigOOugel3iNt7f2DUmnv49oIU5gzNanXq+oJaCqoP7rgkkcQ87nrYtUjc7j5WFDgB896pYG+dMNgZl2smO6EDbVoba8QecmJ2R0criTDcPpXUJQ+QqdjZpvXg775zAUjDcdC5odibmtA7le7z7gXgAtNiJipiTbR8dxU1zjDYb5BIwpH1b4rvfab7u+ed9Ho5lU3OtSf2sZIR386Y4XEKjUCEtAaXBJc6p5fHv9yOMWEH5pT16LqCq+R8UHxo3kwgkLvMTmqnO3IHOHvkEP57fT/ykpNwl54FgCVzERtKCii0l1PhjEF9kkTSRuq99byx/gcAvPbRTBlkpH96XlA68XUVYaZIiy3qDsgdJ+IkRwk4zyhAZqKV4d2aBK6NtUIo++YvYP5vYMM7ULpBCIMg4FbWvFnlqoOS9bD2NVEp/uYvYMWLUF92zGO8YFg8N01o7ZpV6wpDdxCDUSz+6wpEkjNW8DhFG2lHGSTlhX9C/FgwGEWiLiFLTKQP9ZWYLcRe4Xqx3f+TcNm1JPJ09ZRQj0YSJfg0mGbYyO9M7wDwV98FrYS1B8WMriC9L8x5ULz6ts+5LmHJQWKpsHauVRRxzWmoDPVIug63QxTo1O6DpGxRnHKsWBKEk62zWghsHcc+n4gafG7xewdbULLjK1A9bNV6sV7v377n0Hz+ym5JBKN6obZAzOeDWDyUQxWZSkBce0wxQ9eFI7Kzqknw62hTsd7RuG1SElePCcOYYUkQTq7hIN7sCmoLA+4Q4Y6iQFKO6NRStVOIWkOJo0R8JbTBZby9NFSIzh0AQ88EYEVRwN3HZAjTdZEk7PGph/8cJeLkNctjDDYUUk0yhhPvFW24gy2QOhITr4WR5wPwmPlfnGBY73+owhHj3RQkkmOl5Gf6aPtx62YWqJNJsLQzYe3ziL06SczicB/dkSmTOl6y/g2D6oZuYztHWNuSoWfAyQ+DwQyFq2Dh73hsmpFLJ7UuRC+okeJaieQgdn0LqlvklTIH+Q+XNwTmh+0WSUHTnoJLGKRIsVTM0bDpC841LkXVFX7vvQ4PZnpTQrISuB6HJJ/RktxROAecDsCfza9iRsS5vVVhaDAlkYQaR5nfHISek/1F5p4Wkoweye281nudooAvTuY2YpUSeyMoXmy5HwPgrZkC+EBNBF0U/yhAXoqNiX27/n1iNBjpk9KH+87OYFL3cXjtw1EUDVveR9idZvY79uPyda2jrkQSaRQ5ijAlbgfAVzeGHukWUqwhmgO2kyhQ4kUPPQmIVJpTY9fN6IvR1wBrXxctjtSmBJolAfpME5tUWUPEArilY6nHKZKOzc6fBT8J8dDPbwlRbv8TYdxlkNr7iGMyGhTunJpMokXhr8tEReGKIg+6roefijwuTfyOzmpIjIHNXm9jk7C2XDjWyg2K8GHT++L70HmwNQ4Qq4sn5qSGbEiSyEbTdbpTwd/Nz2JUdN72ncAP6nCadRStYkZXl830mAATroLVr2Ba9jfuOWsQXi2bf68Xm1DLCtzcfFwnO1t1BEuiEI753JHp/N0WXHbhdO6qEYUK7RHuGYwi5jRUCpGuNkxskscKjTXCsSk5L3jPqeuwdT4A7+kn0fyJfmVeGzYJfG7hAt/ZLnKSzqe+TFyTOvAea/QeLJTKJNDy+7Axw1UnugSUrIeKbVBXKN7vLVEMQmSf1gdyRkD38ZDRv13XkzizgT/MSKG0XmXBTrH5tGivmwuGB6ldbHuxJAoxsasOEjJDO5bOxu2Auv3CbSLc1naHw2ASa73q3cKBPr1vaMbhaYCqXUIsYuyCzhT5nwtxe+4oSDt4Df/yGTIBIWkfh3NlsuHmFcsTjDbspk5PoHTMzaR3Gx2a+fLkX0FDJYbdi3ne+izzXA+yU+/Bt1vLOXVEEOdkEkm08vPbAHytjcdOAv+Y246iEJ9bdMjoQGcFSeRT03Dktt0mVP5ufpYcqkT3m2m3tq2gt730ngonPwRf/QEKV5H+3V08fMbT/LAjnn1NjrWL8suZPjAG8gUSSVvY+pn43mMCWBMPecqjJ3bgM+xtbBJLRUAhpyS4uB0kff07AF5VT2O9PgAzXpKUQIe7kOYzWhB//A1QtIyBjUVcZ/yM59Sz+X5bBeN6yfetRNKKzR+KfamsIZDe55Cn3DGlnWsFt0N0qzpMLJJEPw6XD0vGYgyWKjRvEu6qGSgGN7oq3hPNMeOBecMwhshgIM4Ux6ReA0hM2MzlL5yFKWEnxrgCvtz5E5emTKCkoYS+KSHap5ZIwhxN1/hizxcoiorqykXz5HDhpGziu6IbYBCRzrUhotLZ2l11akIJRkX3389ItHL33CFM1X+Gdy6FDW8LYW3WYDjpfrjsYzjhbhh0qkhuH5jgsMRD5kAh7jvpD3DZRzDnIeg2Tkx+dn4D710JP/5dJI6Pwkl9Axbr26p8PPpjGLbQNppFdLUXCqFKNOPzQNkWIcCQwtrwomqXcHRUDDD8bGymwCRvRq8oF+5JOo091W6es/yNdKWen7V+LLSegqGFCMYfM/qHSAQ09hLoNVnEqW/+yJkt1g/LCj38Y9XB7Z7CBnO8EMi4wjCuBRN3PZRuEuLQpG4dd8RMyBRxt3STEODFCvXl4m8XREdRyjZBzV4wWvmMaf7DU3u0oSWa1ykKryxSXBvReF2ixbwlrkNzuy2VrR2lpiUWt9JNtooZmgq7v4eFd8Pr58K3D8KWT0SBXkthbXN3BF0Tn/m9S2HFC/DhtfD2xbD6VeHg2Q7OHxZYQH+5y8V/fw6xQ4jBBOiiYC/aqdkvijIjTZhvsokxV24HR2nXv76ui8+q2941iWJNhfwF4vbQeYc8ZUxuJwl8I0V0LWk3GwprW93PSbZiROWf5r8xyZBPAzaKR93EsPEzQ1eIpihw4n2QM4I4vZEXzU+ThJP31hTy0boYmodKJO3B54GN7wLwgTodgL6p7fDa8DSItYZcb8Q0q/bWtLqfm9w6Lvw+4TOmGreIudrUWyClV9cNrtdkOPlPTQ62K+Hr+7mmRZeMf/+4l682h2DeKJGEK6oXtn8pbueNFZ/bQ5DZEedaj0O4EFpCXEAr6Xq+/yvmhmL2a1k85RNdKCYnlrc6JeT5jGasSTD5JgB+bfqIXkoZf/t2B8t2xVCnO4nkWNggOmrSa7K/4E47QIuRbG1HzkTXxZolMaejI5REMB9sWIMlYzEA3op5ZKbUo3sDRgK5KTaev3RcyAusM+Iy6JnUk5OHp+GumAPA6toP2Vvho7i+GIcnjHPhEkkIqffW80PhjwD47GMA6JGcHcIRtQ/pXNvFFNp9/GNVPeUNAXHtuUPieHz2eDYX11Ht9JAeb2F4hgHjsmdEaxaAlJ4w5SZhtd+eBJfRAn2ni6+KbbD2v7DvR1FptPMbOP43ws32MM89OMPMH2cm88fvhfjopbUN9Es1ceGIMFsYx6WLBGtqTfS2RtZU8T+0F0lhbTiy6QPxve9MSMzB7hYbt92TjGQnyP+VpG3sqfXx4pp6JhW/zjmG3dTqCXw38G5emTWwdczolhLSCm8UA8y6F96/GuxFjN3zMrdPvp6nfhILiSeWO+iXamLuwC5sIXusGIyiMKOxNnpdz70uKN8CjdUibgRLKBOXCq5aKNsccLOMZjwNoi13sN2amp1C+p+Ic2s8oDMq20ycuQ0fao8TMnuAQdbNRTS1BUIAn9K9XT++rcrLv9c3sL0q4EJ4y8REfjNxwsExA5947/38lphTNpPeTxTj5Y6A1F6QmCucORVFOJa56qCuoKmYaK3okOEoFWuLdW+I9cSYS9rkJjqrj42rxyTwSpPj+f3f19E71cjM3odO7nUJlgQhFk7v2zWupKGgsUb87xMidM1kTRJFPWVbxVq3K9d+9mKo3SdaY3eF+LRgBTSUgzUZ+s446OET+1hbFV11GJ87cFuuNaOWTUV1/G/FPjYUBoqt7507lKuO70PFO78id/t6NIMV28zfMbTvtNB3eDCY4OSH0T+4mv7OEp40P8/13tu47Z2f6ZYSx6R+7XDilEhigR1fgrOKMj2VJdoofjG0nfMrb6MQSsr1RkyyZl8N768pZNXeQPHZX84dyf9N6MnKPdWUO1z09Wxn5BdNnbzGXwXdx3V9kU6vKcJk5OsHYNe3XBqfwfcDfsk3O0U+47rX1/Dxr45nTM/Urh2XRBKO7F0K7jpRLJg91H9YbyGUunF8BxwEpVgqZlm3diUjl/8TE/CA7woasfHcaWmc2r9beOUzWjJgNvq2hdiK13Cv6X9c772di19ewRe/mc7QPOnaL5FQubPJVMoI3Y8T+3CAyxeIGfdNb+dnxdfscp4ahIFKIo3vt1fw7dYyPi95GSVOxVc/iF9Nm8AFY0ezp8xIucNFdpKNiX3TQ+ZYeyA9knpw7aw6Nv13KjWuNRhtJTz+4zvcf8LFZMYVMyhtUPh1/5ZIQsye2j3srNsBgNc+ihvnxJNqSw3toNqBFNd2Mbd/VcvK4kALpak9LDxyYipGA4zq0dRipWYvfHwPOIqFWGXsZTD20uAldrMGwyl/hqI1sPw50VJz0UPCtWrG7XCYN/IVoxOxGBXuWSSSL3ctqgs/ca3JKsSndUXRKa7VdSFmqNkLidkBFzFJeNBYAzu/FrdHns/mCi+lDaI189Mnp4ZuXJKI5brPqkmp2cSfLR+CAu9m3MhNJwxoHTPCBWsSzLobPrsd8j/jlpMnYz1+jN/p/KYvatgbjuJaAHMC1JdCRr/oE5E0F2Q4SoX4NZiOqyDmDM5qId41mKNXoAziGu9pCK5LodsBu78DID/7VOo3iA2pR05MPfbnaE58yBatkU1DFdTsgfi0dn9OL/mwispGzX///4bFccvEpINjRtEaWPYP8XogBHtD58HAkw/Zbt6PySrmn4nZ0H08jPoF+Fyw90fYtgCK1oqivV2LYOiZMOHKY24De9/0ZEwGeHGtENhe/kk1e28JoWDfkiicql11wqk72tB1qC0EzSsc3COV+Awhgi7bDHmju+Y66LJD5Q7h7NRVYsOt88X3Qaf6X/PHgoAA9v4ZQZwT6jo4q4L3fJKw5aznfkTVAomwa6b15appfTH+9A9yt78JKBim3AR9poReWNtMXCrKyQ+jf3oLJ7OGa7UFvKTO44KXfmLvX04P9egkkvBk3f8A+EidjoqRO6a2I2boTfPLuDDbg5B0Gec9v6zV/dvnDOKC43qiKApT+mcI8fWLd4KuQu9pMPiU0BWo9Z0BU38Ny/6OsvFdXpqUyWXqXH7cI/amzn9+GTsfmRuasUkk4URzZ4zu48EWcCV/Z7PTf/vXEzsgrvU1ikLdY9wTkEQJuo7jo9sxGX18q47lO20s989IZu4AG4oShvmMZhQF5fhfo79/FacYVzNF3cxybThX/HslK+6ZHerRSSShp6kTBt3GtDKF+Ne6QPexq8a0Uy/irhf7e9LlPCa5/NWVGON3EN97K7pu4PS+Z3PK8J7kJGSR2z88Bapmg5mBaX156AIHv/7f2dD9eSypq3l+8WT+fK6FnPiciBQNSiSdha7rfL7nc0BHdfZC96ZzzrjuWI1hst/cBsKlLixmWFsaENbGmxX+c1ZGq7bxFKyEj38lhLVJuXDmsyIx3RkbUt3Hw7kvwfgrRLXR3iXwwXVQvvWwP3LxAWLadzc72VTuPczZISI+TYikGmtDPZLgYy+Cqp1COBwuCS5JgK3zRUulrCFsYgCnv1Xhf6jV5zwIKBYz3e+9ge53XYViiVJHNQml1XaesfwTo6LzKTO45rzTsRjDc0EBQLexMOoCcXvJ41w/1N3q4YU7G9lSEWYxA8TC3VMv2jtHG9V7oHY/JOV0nnA4Pl2IeMs2R2fsBSE2speI2BvMqtMdX4HqwZnUl1O/CQj4EixteA2fS4zLcvikh2Kx0P2Zp+n+zNMoFktHRizpDDwNQgSPdsT/49FoKaztnmTkryelYmpZ0e11wpInYMFvhbDWmgyTfwUXvwMTrz2ysPZwmGww4CQ4/SmxrugzXQgwtnwM71wq3uMHtAg7FIqicPe01kmWxXtdbKsKUcwwmABdFA9EI82utdFQjJiYKxyXyraIz1Jn4vNA5Xbw1nfd366+XDjXAgw9A4Dv97m45KOAADbeHMS45KpDSUil+xN/kTEjymkprB2ck8R9ZwzDmP8pfPUHcXDspTDwpPAT4GcPRZki2rbeYXqX4cpeAJbtqmRnuWy/J5G0or4cbcdXALynCufzhPbEDK9LCKSsSQc9JNcZscfYXqncctLA1m5Mix8Vc6S4dBh3aejFdCPOhTEXA2BY+QL/Gf6z/yGfprNidxW7K+pDNTqJJPToOmz7XNzOG+Of73241cldiwJdDeI6ks9w14trQguxlIwZMUD+AmYYN+LWTfzJdxnTe1q5akxiZDj4pfVBGXYWAPebXseARpndzZp91eyr6uS9BokknNF12PCOuN1zEljF3vUbGxv8nSuB9ndUUj3CyOEAZMyIFTSsOaLgJ107nnNH96dnUs+wjxsp1hQGpPXmgbMG4qmdAEC19SO2l7hZW7QXTdeO8gwSSexQ761nwY6lAHjtYwBItwU3t9JVMUOKa7sYX4tr6cB0U2uR1O7FsPBu8DZA7ig45wXIGd65AzKYhLj2nBcgpYdoN/npr2HLJ4dNhF8xOrAgvvPbWs54u4IvdjZ27jjbgjletLJ0lIR6JMHFWQ0V+WJD2xJmCS6JENVu/ljcHnk+T69ovUmbYg3u5VYxGkmecRzJ08aimKSDcbRyj+l/9FAq2a9lMT/j2uC2/O0sjrsKMvoLt78fnuL8IYG2kzd8XsPctyr4Yb8rhAM8BEYzqD4x5mjCUQpVO0TRibGTNyASs4Vwr3wreJxHPz/ScNUKJ79gJgl13e9G+Lr3RCDw+U5qi7jW6xSCzCNUdysmE8mnnkryqafKmBFueBqEKNBZJVrMB4nROebWm1DlW+H9ayD/M0CBYWfDBW/AqP8Tc8tgkDkITn4Iznga0vsLZ+bvHoFvHzzm6+vJ/QIx44pPqznlfxWsLfEc4Sc6EUuCWE+oYVgU0hF0HWr3CWcxUztbQ4cTigJJeeCshLJNnReDNE041tqLu7a1af4CIVjPGwOpvQC497vWn6fkYK0zdA3c9ShZA0g+4ywZM2KIUT1SoHA1fHgdoMPAU4QwqQMFH53KsLOh52QsisrfzP/AhpuLX17B7KeWkF8ahcVyEkl72fAOBl1lnTaAXbpwmIprl7i2QRRlHWLOKNcZscfoHqmtD5RtEZ3xAMZfCen9unxMh+S4a2HAbNA1zEseZXJyoDDpgpd+4sQnv6egOgr3LiSSY6FkvSi2NMVB7gh/95zbv65tdVqHhC0+t9grbPEcMmZEOT43fHkPAP9S57JPz2VUToSZwoy/AiyJDDXs5xfGxQCc9/xyZj6+mHJHmOUzJJKuonC16KZrioMeE/wdde/7Lgi5NNUjuiFaD+5EJWNG9KPrOubUVRhtpehqHGNS59AzqScJ5shwMc5LzKN/Riae8lPRVSvGuCIe+2YFV71cxP668lAPTyIJG/Kr86lT96PrCj77SEAI1INJV8UMKa7tQkocaqv7RfYW97d/Bd/+SSQ5B8yG058UrZa7isyBcM6L0GcGaD5Y+jQse1Y40R3AAzOSuXhEa3Hn48vDzCEkLkVsELjDbFztxeMULbdVL8Slhno0kkOx+ztorIb4TOg7s5V73JQeFnqnysm/pG2Ubl/NRSbRLv633hspdEeIW7XRArPuFY7o+37kr/03tBJLAfxjVRg6hJhtUF92TA6LEYG7Hiq2i82Ormqpk5QjxE2V24RYOZpoqBIxOJiu8WWbxMaU0cr73qn+w+cMjiMzvg0uwz6XEGVGgvg+1nE7hPMXCAdMewkUrxNC+OQ8f0KrPeRXthaANnhbXMt2fAXzbxGdMRKy4YynYNqtYDt44zIodBsL574IE64SsWD3YiHYqtxx1B99fm4a4/NaJ1/+83OIHEIsicLVPNocuZ3VYC8VLdeiBcUgur44yqFsY/AdbHVdXK9r9kBilj+R0OlovkC71qHzmoaiU9xiX+G6cQnB65DRWCPWmkl5wXk+Sdiybn9Nq/sp3nJ46yIxp+g+QVy/49JCNLpjQFFg1l3otjQGGIq51/Q//0PvrS4M4cAkkjBC19HXvwnAe+pMAO6Zlty+gl2vGxIyj36eJCpZtquy1f0Ea4u1qqbBgtvFnKXXZOhzfOcX9h4rigIzficKAD31vBn3V3KsrYv2vtgUZcYcEsmx0rzG6DbWP+dr2dUA4PHZqe1/fp9b7J911p6DJDxZ/W+o3UeZnspzvrMBSI+LMAmCLUUIbBFdMpIIFGH8uLPyMD8kkUQ5G98V33tO9BtDeNXWMeNfZ7TTgdDjFLmrQ3TIkEQ/r/20FUvW1wC4K06id2oWuQm5IR7VsWM2mOmX2oeXrsnFUzULAGv2QlA8fL9jH+ohNFYSSayh6zqf7vwMALVhALqaxN8v7YPZEGEFWE1E2Mw2cim0+zjznYpWx5y+psnHzm9E+yRdgyGnwwl3Cxe9rsaSAHMehInXifubP4Sv7xcJlhYoisKfZ7VWk9tdYWZvbkkEb6MQLUQ6mirECI01h2yNIAkDdB02vi9uDzuLb/er/FwWELn87ZTgJyZ1VcW+ZBX2pevQfVEmYpOwr8pJ43dPAvCG7yRW6UNo9EaQ6DO9H4y9BADDsr/x/ImthYJ17jCLGSDa2bjsQkgV6WgqVO0Et71rxVOKQbj51RZC1a7oESr7PKJgxxZk97YtnwKwP3M6OxoCLlB/nNmGij1dB52jbkDpPh/2hQuxL1woY0ao0HUo3QT7lsPeH2DfMihaI5yHU7qBoQ2C6gPYWe1l7lut1xmNXl285sqXhHus6oXex8P/vSoSaJ2NwQTjfglnPwfJ3UXxwic3w85vj/hjRoPC/85pLd4IWcxoFlA6q458XiSh61C3H9CDWywQDhiMQqReXwElG4LnRt8srK3IFwWcXen2u/8nUbRiS4G+0wF4Z7OTlnnv30wMUgJC10RSI603OgYZM6KYLcV2zvnnMv99Kx4u23ev6GKU1gem3CwKpsIdWyrKrLsBuMz0DccbNgJQ1xhlbuMSSXspXodSvgWXbuYzdTIAl49qR9GlpoLBcEg3KZDrjGjn54JaLn55RatjTk+LJPHPb8H+5WJ+NPKC8CvMMFnh5IchLg1DXQHfd38esYAW1DhlzJDEKPmfi+95o/0F+c+tbr0feu6QDnS48TSAJUl8tUDGjCjGZcf73WMAPOM7Dydi3exRI3BvePjZkNKTTMXODaZP/YdL6qRzrSQGUb2w6QNxu8dEf8x4YnnrjjEn9W3nHqOnQegeDrEvLmNGdLNsVyWPLHseg6kezZ2Jt2YyGXEZmEOhj+oAabY0+qf35M5ZJ6B5UjGY7VgyfmB7eR1VrijKKUgk7cThcbBw11IAvPbRxFlgxoDuQX+drooZUlzbRVz1aTUVztaJ4StGJ0DhKvjuUUCHYWfB9N92KLneYRQFxlwMs/8oBL77foTPbj/IAVZRFP5zZqASqbJRo8oZZhUYthSoKwy+c1FXU7MPavc3tdGRH9mwpGwjVG4X7gzD5vHh1kBF6/XjEshOCP5nWvd4KfrzCxT95VV0j9wMjja++eBl+iollOmpPOa7CIALhscf5afCjLGXivbFjTUYV73Ik3NS/Q/lV/qwh5vA1mQTxSTBEuOEEnsR1BUId72udjM1miEhHap3inFEA43VYh5kCWIFtatWuHkCL7lO8h++b3oyKbY2xHqfS7guW48s/NU9HopuvY2iW29D93iOeK6kk9B10LzgaxTOTgaDEC8lZHV4fjf3rQoOMJnhrEFW+OEpaHIsY8wlcPJDXd/iO2sInPOCcDdQ3bDoocCYDoPNpPC7KYHP2/f73DR6QxQzLAlCGKxGyVzL71rbTkeJcKdZYNtYDcXrob6DLbg0Dar3QPlWcZ3t6s/PVlHVzaDT/C5wr64PrG2fmpNKgiVI60NXnVg/J+bImBHlzP37Dy3u6TxifoXe7m1COHf8rZDWO1RDazs9J/pdnR81/Ys4XLy/phCPL8zWGRJJKFgvHJ2/1I7DTgL/OiMda3uczr1OMMcftphPxozo5qznfjzo2MxBwrEMV50w5QAYcT5kDQ7PbiqJ2cJMxGDCVryCRzO/9j/0/OJdB7l1SiRRT/UeKN8suszkjfEXlT79UyD/99a5GRgNHfg8exubxFKt1yoyZkQxy57F7K5ml5bHu+oJ/sMTu0dgUa/BBJOuB+Bq05dkIbp+/HXhNvRoMZKQSI6VXd8J04G4NOg2xr+H/eLawN7U/AszUdozB2z+PB2mi7OMGdHNJa9+gyVdrDXc5acBJib16RbaQbWT7ondGd0jG6V2LgCWjMW8ubyKIkcRPk0KwyWxzfqK9bgoR9dM+BwjeOzCTJKCmWtvoqtihlTqdQFL9rnYXh24eD45J5V/nZHOrf2K4as/gK5C/xPh+N+Ej3iy3wlw+pNi87R8Cyz4rXD0a8HM3lay4gPj/XxnmFXuWZOEA2Eku9c2VAr3wbiU8GmrJTmYjU2VewPngC2V2haiwRpXJyy4de0gwbskelj6cz6/1EVV9P3eK3jstO68Mi+da8Z2saCjoxgtogUfCmz7nLMTt7R6eMk+d2jGdSSMZmioOPp54YzLDpU7hQgoVHHDHC++KrYJIVcko+vgKBGCrWAWP+V/IYSWmYPYqPf3H65raycArxPMCeLvLYkMLAli09CaFJTW8h/nO2lpHvXqvHT+fXoKF1X+HfLni7XFjDth4rWhW2dYk+CUR2HUBeL+ypfgpxeO6G59YMxbXRKiTVRLophzNdaG5vWDSTS71rZEMUBSnig+KF4nnNTbI472uYWotnwL2JK6vkWdoxQKmpzihp7hP9zS+KfeE6R1hq6Du14URUXze0PC/1bsa3X/u2lbOc/4A7pigMk3Qe7I8BRGHYlJN6LFZ9LLUMHtJtFNJr/UfpQfkkiiHK/L313pfXUGAL72ikG8jSKZbpJ7krHGy0t2t7r/wY1T+e9VEwPi2qXPCIf9lF4w6BSwhPGaNHeUiHPAhQ2vM1IJ/G57qyLclEMiaSvbmlxrc0aIovwmWkYJi7ED80G9aV/L1oauTJLIxlGGZ+mzAPzVdwEqRj67MJP/nZPB+LwInT/0Ph5yhmPDzS2mj/yHyx1hmM+QSDqTje+K770mQ7zoUHCgyDze3M79Zm8jmOO6fr9NEnIe/mwLlozvUQxe1Mae/PH0Cbxy5UiG5kXm3MFqtNI7uTd/OG0CqrMXisGLJesrCu2VVDZWhnp4EknI0HWdz3aJtYevfjBoNnqnZWMMpdFoBwkTJWd0c8PnNa3uz+lnY3ZuA5Zv7hVJv+7j4YS7wkdY20zuKDjjabEQrtwOn93WKrGsKArLrwq0C9xfF4bVF7YUqC0QLS4jDW+jaD+KJieX4YyjVLR3BhhxHgBVjQFx1PE9g7yB4HNDXVFrQUq4XTsk7UfX0ZY9h1lR+Vodz5faRE7sa+OkvjbMHdnYDBW5I4UrO2D88Sm2XZfhf2hnTRjGDEuiEINGYswA0bKzehf4nIet+O0y4lLB5xECW29jaMfSEVx1osV4XGrwnlNTYWtTW7FhZ7cqwhjX1k1nrwviMyNPCCMJGrd+Vdvq/sxeFmYVPIth59difjDrXhgyNzSDa4nBCJNvhEk3iPsb3oYfngwk3g7AalLYcmOu/35+ZYhihsEoPl/OKNgIc1aLeWu0uta2RFGEU5I5Dso2Q/FacS3XjqGAQdeF423RWhFTEzK73rEWIH8BoEO3cZDSw3+4tkURxvDsILVK89SL3zEx5+jnSiIWXde596NN/vtTDJvps+ZRAJTRF8OAk0LbRam9WOIxzLgDgKuMXzBK2cWmIimulcQ42z4HVy0lejo/aiMA6JfazqIunyc25g6SVnh8Gn/+fGurY+N7pzFjUJZwJqsrhJ/+KR4YeT4k5R7iWcKM4edAnxkousrHaX8nCbHvs6koCroXSSRtIX+B+N5trL8Lku8AB+fcxA7kGpqLwG3J7X8OSUTh+e6vWLRG1mkD+FI7DoAR2RaO7xnBhZuKAhOvA+BS83f0VkoB2FgoY4YkhnDXB2JGj0l+cw/HAYXeGXHtFdc2CA1HOBdoSYJOmd3FKz/9jDntJwDcFXOY1KcnJw3uFeKRdYzMuExG5HbjqjHnAmBOWcOG4jKKHEV4tSjpiCeRtBG7x86y4mUA+OxjAOiRnHGEnwh/pCKrC3B6W080kk0qfP2AsNJP6wNz/hS+rqQZA+CMZ4RLQfUuIbBt4WBrMig8PjsVgJfXNfDd3nB0r3UI17lIQtOEy5KzWrQLloQvmz8WwpDu4yC9H7qus79OWMjdMjGReYPigvdaqkck+9P7QY+JgePSQSRq0PctZ4ZxI27dxEO+S4kzKdja07oxnJh4rRD/2YuxbnmHB2eK6sNnVjhYVhBm1d7meCGsdUXoRpm9WIjvE7JDPRJBUrZwAq7cIQSlkUh9hbj2mmzBe87CVWJeYklE7TeL/Xbxt7lnWjIn9G7DxrO/dZJMWMQqHrX1GqN/mgnjqpdg+0IhrJ39oBBLhROjLxROuooB8j+DH/9+WAfbeLOB300RBWZ/XmpnTSjdax1losApUtF1IYLQtdhyJrUmQXKeWFMVrhJOtvZi8DS0ft/puigEcZQKUW3RGnDbIblbaP5emi/gKNXU8h7EZ765iO+xk1KC5wLksou/k0xoRDX2xkCRQg+lgpdsz6LoKvSZDmMuFh0cIpVek6H/iRgVncfML3H/R+ukWEoS26x/E4AP1OloGHhhbhqDMtrxGdd8QnQfiiITSUipqG897z13XPfWJyx6WBiG5IyA3lMjI4YoCsz8HSTlYnSW83bGy4DOb95ez44y2R1MEiM0VMH+5eJ23mh/TrLEEdiz+985GXRP6kCXHY9TdGGMpXVnLOMoxfTz64BwrQWF68dHybwhbzT0nISiqzyT/iEA1/x3Nfuk47kkVshfIAomkrtBzjD/4QJ7IGa8d34GqbZ2So18blHQLokpimsbsWR8h2Lw4XP25vxRI+mW2C3Uw+owBsVAz+SeTO41mGRtDIqi88bGT9hVVU1VY1WohyeRhISfSn6izlODrlrx1Q/hiYtzSI+PbENJKa7tZLZVta5GeP/8DFj+DyjbJNrCnvyw+B7OpPeFeX+D+Ayo2QML7xITqiam9Agk9a78NMzaPytKk3vt/shyInQUQ81e4bgkXUnDF69TCEMARpwPwMJdLpxeHYsRrhydiCFYboLNLlqpvSFrCJjlBlXUoXpxL/0HAK+qp7Ffz+HTC6JgcWlJgCmi/R7r3mB6WmAhccPnYRgzDEZR/BJpeBpEUYYlPnwSW4oBknKgZp/4ijS8LrAX+Z08gsaWT8T3wafx0S4h7kowK1w6Ml64AB0rqlskK8J9HinpNH7Y3zrp/fGQRcIRFmDG76Dv9BCM6hgYMld07UCBLR/DihcPK7Cd0iMw37n1y5pDntPpWBOFm0KLDh4RR2ONEPXHovOcwSTc1OLTwVkBhWtEQnnfj+J20VrYt0x8Fa6ChnJRWJqYHToXz/3LxVwkLg36TPMffnOTSOKl2hTOHRIkIazPLf5G0rU26vlmaxkAVjy8YH6aJM0O6f1h6q+jQ1h9/C14zUkMNRRwufEr7vpwQ6hHJJGEBnsx7PoWgPfVGeQkKJzSv52Fgt5GUYAqxbUxxxcbW5tU/PW8UYE7JRvg56Y1x8j/i6z5pTUJTrofFCPDG1ZwkXERAH+cvznEA5NIuojtC0XBZXp/SO3pP/zmJpE365dm6rjbqOqRYqlYYtmzGFQ3q7RBLNeE+O73UyNbNNGK464BYGzDUoYrewF4bGF+CAckkXQhG98V33tNFTqLJt7dLGLGyGwzx3VrZ8zQVJE7skTR9UJyTPxn5RrMqSsB8FTM4caZA0gwR0d+K9mSTPfE7kzIOAVdN2BKyufd1bsodBTiVaV7rSS20HWdL/Z8AYDPMZzBeRam9e/Rthx4GCJVe52Iy6dzyv8q/PdfmJvGhPrFTaIKBU68r1WLx7AmtRfMfVxsQpVvga/uFwtlIDuhddLxr8vsONzH0HKzq7AmgTuC3GtddqjYIRJcssI3vNn+lWijmtwdek1meaGbGz8Xwo9LRiaQ1t52GIeisRqsKZA5EIwdqB6XhC2u9e9jcxZTrqfyD9/ZvDovnYHtcZcJR/rNEm2NVQ89N7/gP2x36zz1k51GbzjFjEThthpJDoW6DtV7hFO7LTXUo2mN0SIcK6p2iAKBSKKhQswfrEF0hnWUwH7R8mZ50inc8XUtAFeOSSDe3MaY4U92R8fmg6RtVDhVrp4fKFD4bOoOkta9KO5MvB4GnxaikR0jA0+G6beL2xvehnVvHPK03MTAOqPArvK3FQ7cvkMLcTsNxQAo4poQqdQVga4G14U70jBZhYA0pbu4dqpeMb92VjUVK9iEG0dSbujXYFvni++DTvUXzMzf3sgfvxcdZK4fl4jZGKSNKFedSH7HpQXn+SRhSUG1k9++9zMAD5j+ywjDXjG/mf5bISSPBmypVI8Uye9bTR9QVrSf577biU8No3WGRNIVbHgHdI2V2mD26nn8akJy+5MX3kbRJUN2S4opdpQ5eHjBVv/9D26cgsnYYq367YOADr2niS5ekWYKkT1MdFgC7jO9QS+ljB93VvHykt1oWhevMySSrqa5O0a3sSJnBryyrp7n19QD8KsJHSymUL2icM8qxVIxQUMl6qpXAHjWdw6g8OkFmcEzmwkHMgfCgNkA3GkShSWfbyzlPz/uQT9MkbhEEhXUl8MuUYREz8n+fbJ/rHLw2gZR+N2hmOFrymsE29REEtYs21XJwsK3UAwqvoZ+PHr2BHITc0M9rKDSLbEbPRN74a2dAMBm5wI+WVtJZWNliEcmkXQtNe4aluxfAYDXPoZzjosnxZpylJ8KfyJs9yOy2FIRqEKY1cfKqVlVsPRpcWD8FdBrSmgG1l7S+8GpfxGJx6LVsOjPoKlYDkjs/XN1Pf9cXR+iQR6CZvfamv3C2S+cUX2ifbavQSY4wx1dg00fiNsjzgPFwP2LA60njwtWm1YQrfi8LsjoHx2uQpKDaazxt1B63PcL5gxO58S+USSAURQ4/hZQjJgLljHLsM7/0N9X1vPq+jC6NlsSRKyIJIfChgqoLRDimHDcwLQmiXGV5wuxaiSg+sTf1BIX3L/p1vmADt3H86tlgc2jCe2JGd5G0VUgHP/nkk5nTbHHf/u3Q2oYsfGvgC5ayI++MHQDawtD58GUm8Xt1a/Azm8OOiUrvvVy9ekVDr97Z5diTYT6CjEfizQaa4WwPy411CMJDxQFzHHi75GQGRCWWuLDQxziKIGCVeL2kNP9h3+9MODcfFy3IK0zdA18HiEqlrEkqlm+S3RlONuwlItNiwAFJt8E2UNDO7AgkzjydNZr/UhSGrnL/BaPf7mND9cVhXpYEknXoeuw7n+AcK0FmNCRmKF6xHpDElN8ty1QFPuHM4YxvncLZ9qCVWLOrhhhxDmRK6Ab9Qu82aNJUNw8YX4BAxp//nwrX2wqDfXIJJLOw+OEncLZnLyxfqHUQz/Y/ad0eJ3hdTYVgUfotUHSNn76J0ZfIz9r/ViijeKpOamMyonCgpwJV6IrRmYaNzDZsAWAP87fwvfbI7gAWyI5Gps+FHtGmYMho6//8BPLA7mdDq0zPE5R8Bvq4nZJl/LfVasxp64G4Mz+p3F8vz7YoswIwmq0MqlPDzyVJ6FrJkzxe3lzzWa+3rZbutdKYoolBUvwUo/mS0Bt6M+UvjnEmeJCPawO0yXZo+eee44+ffpgs9mYNGkSK1euPOy5//nPf1AUpdWXzRaZF9b1ZYGk9z9PSRJiVJ8L8kbD2EtDOLIOkDMcTn4YDGbY871o4wp8e1lrt5MVRWHm+GdNEo5+dcWhHsmRqSsAR7FsyxkJFKwS/y9zgnCVAuLNgaR0hxYWB+KsEu+JpEAFl2I2k/fII+Q98giKOUrcTYndeMG6NzD5nGzQ+vK+OoPHTkoN9YiCT1of0TIQeDHtDawEYuRPhZ7D/FAIUAziqyFCKglVL1TtBoMS3o6E8ZngtkPFNiHkCXecleCqadXyqMOoHshfIG4PO5tESyBmjM1tR8zQtWMen4wZURYzgPVlYjMmCSc31/wFvA2QOwqm3hJZIrmR58OoX4jbix8TbWZbYDYqfHZh65aSK4pCcA2xJIpuBY01Rz833KgrEtcfsyzQigjyF9BchNGyy01Ci3XGsKwgXcfdDrFOPkA4JWNG9MWMTcV1DFQKecQsnKUYdjYMnB1Z8eIYSLCasM68DU1XOM/4A+OVbfy0uyrUw5JIuo7CVVC1A6duZYE6GYAB6e3sfKT5xLr4KOJJGTOiL2bklwrRhM1s4Krj+7R+8Pu/iO/9ZkLW4K4dWDBRDJhPugvVGMdEwzauMYp1+oo9MmZIopjdi4VTYGIOZA445Ck9ko2HPH7MeJ1i/+8wXfdkzIiimNFYAyteAuAfvrPJTTByzpDIF0wckuTuKEPnAXCb6X1AONb+tLv6CD8kkUQ4G98V33tP8a8HWro1J1kUMuM7EDN8Hkg4chGfjBlRFDOa2NjwPoqiYfIM4vzR48mKywr1kDqFqX1689tT+uKtmQqANftL1u61U9EoizIksYGma3yxZyEAPvso8lJNdE/p3M97V8WMTu8t/s4773D77bfzwgsvMGnSJJ555hlOOeUUtm3bRnb2odvPJScns23bNv/9drev6mJUDTYW1bKxsA7QWbBfBLjfTk4ibsN/oWKrSMzOuhcMHVyohpIeE2DW3fDtn8QEK6UH/YedyctnpHPtZ2JBsbbUS1m9Sk5imPyeiiLcier2Q3JueFbWO6uhaqcQyhg6/aMp6Sib3hffh8z1u8nWukTLyVfmpZOdEKT3vuYDTYXUXq2uG4rZTOq55wTnNcKEWIoXEIgZu3fv4qztn2AEHvNdyH3TU7GaIuf3aBPjfgk7v8HSUMLHg7/htG1zAfihwE1No0ZaXBg4xoFwKGyoAJ87/Ktn6wqhoVy4zoUzigJJOWAvFu7AmYPBECb/7wPRNOFaazAFNx7vWRJov917Ck6fEHC/c14GKbY2/i18bjBaxd/yGJAxQxCpMUPVYHNxHVUNHuqcHlLizHy1UwN0PuvxX5TKAkjIhjkP+lvIRxSTbgB7KexdAl/dB2c9B6k9/Q+PyLbw6Ikp3L1IdAhYuMuF3a2RbO3Ca4iiiARhfRkk53Xd63YUl10U7knX2shA80F+U6vWpuQdiARGcwrjswuziDcH6b3vrofMQQfNdWTMEERuzNBZuaeaUruL6no3afFmlm7ey4vmvxGvuCFnBBx3ddTuOQwdOpL8rXMYUvkVfzL/h3lrB/Lw2SOIt0Tn7yuRtGK9cK39UjuOBuL4/pfZB3UbO2a8jaKY3HLkVq0yZgiiJWakxJlZvE0kfZ+/ZHzr36Ola+2QM4763gh7kvIwHn8zLHmc35re43ttNP9dDvfMHYrNHCb5DIkkmDQXe3cfDzaRG3O4Nf/Dq6/JwdDRa5emHnHtKWOGIBpiRq+NzzLe42Cb3otvtHG8fWpaxPwe7WLMxZC/gEnkM8WwheXacF74fhe/PXkQZmOY7m9LJO2lahcUrRFzvu7HgVGYgpQ3BGLGiqs7YBCma01dpY6c15AxQxANMaO63o1D20+dcSUKcP7gU+mZ1BOLMQrdzgGjwchFEwaxYs9JrPOtwGgr4cuda7lwSgZZcVmYIzF/I5G0gUpnJavLhEu1zz6aJ65OJ8UaRCOrQ9BVMaPTZ31PPfUU1157LVdeeSXDhg3jhRdeID4+nldfffWwP6MoCrm5uf6vnJzwd/FctquSS1/5ifs+3sQbq4p5dlU9a8pUAEZ4NsD6N8WJM+6AxEMHyIii/4kw4Spx+8dnoHA1c/rZ+OmqwP9q2n/KQjO2w2FNEtWzdYWhHsnB+DxQtUO0oQ5H4a+kNTV7hSOIYoDh5wLw6fZG9tWJz/zwYLlJgRBdN7esjXJiJV5A65iRsPlNjLqPH9Xh/KiNIMfSGOrhdR6WeJhyEwBDC99j2f8FHjrj7TCq2rMkCBfIxtpQj+TIuOuhei/YkiOjaMdggsQssUFTVxDq0RweZ6UQLMelBfd5N38svg89k/9udFPpFBtSwzLbETN8jaKt+TGKa6ORWIkZy3ZVcvVrq7jno408+dU2/vlDAXd8VcnuOo3zjd/Tu3KJmI/MfiD479muQjHAifdA1lDhcP3VvaI9WAsuGpHQqlPGlZ+GwFXKmiyuD56Grn/t9mIvBq8rpq8VEcW+ZdBYLT7Lfab5Dz+9woHTK+S1gzKCJBBUPWLuEANrDIidmLFwUwnTHlvERS//xG3vrOehBVu5/b0N/KrxeQYainCbU2H6HaKQLIoZfNpN+MwJDDfs4yLjIn777s+hHpJE0vl4nKJ9K/CuOhOAXikdWCN6nUIgZYrOhOeRiOWYccf7G6hu8KAoUO8+oG1ps2tt3xmQPaTrB9wZDJ6L3msKVsXHE+YXMKLy8IItoR6VRBJ8NBW2fyFu542Gppasd3xTC4DFSMccCKGpCNwS9fPMA4nFmHH3Oyvou+t1AJ71noVF0fA22kM8wk4mMdtfAPt81kc0u9f+/dsdIRyURNJJbGhyre02BlIChi6/+kJ088qKN3Ss6NvnArMtJvcqYzFmNK8zXvj5ZRRFR60fRKqpNxlxR3YujnTSbencddpQxqafCIAh/SveW1ku3WslMcE3+7/Bo7nRvKlkWXvTLSkHsyE6ROWdKq71eDysWbOG2bNnB17QYGD27NksX778sD9XX19P79696dmzJ2eddRabN2/uzGF2mGW7Knnk83wcLh8AhWRRTjqgkE4t/Tb9TVTiDDwZ+p0Q0rEGlbGXid9J1+DrB6BmL7ktnGq9GtR7tCM8QQiISxPi2nATTNXsBUeZEB1Jwp9NH4jvvadCch4+TeeWhYE2wTkJQbq0aqpo+Z7S8yDhnO7z4Vi8GMfixeg+X3BeL4TESryA1jGjp1LObONaAB73XUAK9bywaBvLdlWGeJSdSL9ZonW56qbb1sCiscih4vSGScxQDIACDWH8f9B1ETs89cLxPFIw2cRGe8U2qC8P9WgORtOgdr94DwSzgrRqJ5RtAsWIe8Bc7v++zv9QUnvcNz2NEJd+zKJqGTMEkRYzlu2q5NHP86msd/uP7SWXKlLpq5TwJ9NrAOzrdyHkDA/VMIODyQan/FkI/Wr3w+K/iOtcC/qnBUSFa0q8uHz6gc/SuVjihXDFGSGt/9z1YC+CuAiKEbHO1vni++DT/K6iTq/G31fW+09ptwPhgbjsYm1sSz3oIRkzBJEWMxZuKuHGN9ZSUudqdfx0w0+cZ1yKqis81XAKy2qiX/CgxKVimnAlALea3ueHTbvxqmGyzpBIOov8BeC2U6Bl8ZM2FOigq5Hqhfj0o54mY4YgWmJGM7oOt7y1noWbSsSBlq61Q88Ec3wXjrYTURSUGXegWxIZadjLlcaFvPHTflSti9cZEklnU7ACnFXC1CVnGCgKVU6VL3eJa4BHDcJreJ1CKGU5vHGMjBmCSI8ZvzAuJl2pZ5+WzefaJNL0Oh77IsrzGSDca40WUuu2MM2wCYBnF+1E12XMkEQRui46FgP0nOQ3AyurV1ld4gGgwtnBtbXXJVxrzXFHHoqMGUDkxwwAg6UMU/JGANy1Y3ny80q+3RLdMUNRFHqmdOeikXPQfIkYLNV8mr+CAnsBHtUT6uFJJJ2Gqql8smMhAN660fhUA+m2o+8tdZSuihmdKq6trKxEVdWDqihycnIoLS095M8MHjyYV199lU8++YQ33ngDTdOYOnUqhYWHdht1u93Y7fZWX12JqsGL3+9pdayBwAbTbab36W0op45E1Ek3d+nYOh1FEU68uaOEw99X94Gnnj/OTPafMval0vBaXFgSQHWLds/hMq76cqjeBfEZkeE8GOu47LD9K3F7xPkAfLY94DQ6KtscvLYMbocQzcUf7CilezwU3nAjhTfciO6J/IlYV8QLCL+YcanxG4yKzpfqBNbrA8hDOPG9+P1uojb/qygw9WZAgZ3f8Ofhgf/XnDfCqGrPmijcS33uo58bCpxVwv01IQIrPG0pgA7lW8FVd9TTu5SGctH2PT7If9ctn4jvfafzzp7AxtHsvtbD/MBR0NU2CeZkzIi8mKFq8NKSPRw4W/VgwYSPv5n/Qbzi5metL3/cNzo6YkZ8Osx+UIgK9y6Bn9866JRfDAussy74IASbcCYrOErDZx1xJOwlQgwsu2JEBvYSKBTtkhhyhv/wv9YFnJIvGhFEIYu3EZK7geHgLSEZMyIxZug8OH/LQTEjh2r+bH4FgM/ViSzXh/PSkj3RETOOxvCzKTHmkaXYucE0n+tfXxPqEUkkncv6NwD4QJuOjoHfTOyAkF7zgWI6pjmEjBnREzMORAcenL9FCE1budYO7uwhdi3xGSiTbwTgdtP79FDK+d370vFcEmXkLxDfu43zF9f96YfAdefhWUEoyPQ2QkLWIdcXzciYEfkxw4jKtcbPAXhZPR0NhQzq0CH61xkJmaLABPi95X2a3Wsf+mxrCAclkQSZojVQvVuYIHQf7y/8/nULc6kXTu9g5zRfo9gDPkoeXcaMyI8ZzViyvkFRdLyOoWieNDRfYmCdEcUkW5IZnN4PT6Vwr7Vkfsvry8qobIxuYbEktilwFLClegMAPvtYbj01g6QjFN8Fi66KGZ0qrm0PU6ZM4Ze//CVjxoxh5syZfPjhh2RlZfHiiy8e8vxHH32UlJQU/1fPnj27dLybi+uoaggIb+p1m//2WGU7FxkXA/BP7xlsro7CIGG0wMl/goRs4Qj73aNcMSqQ9PNqsLQgzIRJ8RlgLxTCpFDjbYTK7cIhzxIlVf/Rztb5QqCd0V+0UQLe2RJoXfznE4PoDuauh+TuMdmG71hoa7yA8IoZA5QijjduQdMVnvD9glQcmBSxA1XV4GFzcZiJDoNJ5iDhygZc7Pg3CuL3LnKorC8Nk4WyJUG0/m6sOfq5XY3qg+o9gH7UCt+wJSFLuO6WbRWxMBxQfcIN2GAKrmutpwF2fC1uDzuLV9cHHAgfmNGOmKF6xRgt0e88F0wiLWZsLq5r5VgLUKuLdlnXGT9jlGEPdj2Op7z/R0WDN3piRs5wmHqLuL3qXwGxYROPtphn/VzmZWf1Ae1qOxtbCjRWh19hwIF4nGJtZks++rmS8GDrp4AOPY4TotcmnvrJ4b9955Qg/T89TrH2jOv8qvFIJdJixso91YdwH9R53PwiqUoD27TuvKGehIaBynp39MSMI2EwkXnCTQBcY/ycrflbKakLkzmnRBJsagtg9/cAvK/OAOCmCR1IXjTHCbneOCaiI2YcmpI6F1tWfhtwrR0WRa61LRk8F1/uGOIVN382vcqHawupdYbJ3pRE0lF0PSCuzRvjb8P9ybbAvOiSjhbx6broBGWV68+jEekx43TDCnoaKqjQk3lPnUlPyv36uJhYZ4y5CIxWRrKDmQYhHHn1xz24vMGwf5ZIwoANLVxrE4Tpk67rrCwOzItO7d/BfJSmSyOAYyTSYwaAwVqEucm11ls3EtR0wEhJnYuVeyKkO1wHyEvM47F5c9A8qRjMDr7cvZT90r1WEsXM3zUfFBW1sTuaO5cTB/fEoISdJLXddOpvkpmZidFopKysrNXxsrIycnNzj+k5zGYzY8eOZefOnYd8/O6776aurs7/VVBQ0OFxt4Xqpo0WXYdiPYO9iESYAY2/mP+FSdFYrg7lR224/9yow5YKc/4EBjPs+xHWv8lNEwIbsJd9XM2ibeVsKKwLj8pFkw1oaqethXDRo2lQtUu0lk042JlUEoaoHtj0gbg98gJQFGpdGssLA5/toZlBEmR5nWC2xcx7oyviBYRXzDjLuAyAj7Xj2aH3II/KQ54btRx3NZjjUMq38qfuAfHUhR9UhkfMUAziqz6M3HSbcZQId9WErFCPpP0oCiTlgrMCyraALwze744S8f8+hvanbWL7QvC5ILU3ZUkj2VsXmHt0T26HY723USQ0YzjZHQsxo2UM0HQo0LMoJIcBSiG/MX0IwEu+06ki+aDzI56h82DQaaBrsOghaAjER6NBYWK3QNHRlZ9U8l1XxgyTVcwHnWG++ecoBY9DblZHCj53IOE97Cz/4X21rVsYpdqC1R2jDuKzhEt/DBALMaPccbBI6mLjImYYN+LSzfzbd0qrDktRFTOOgLnfNNYyBJvi5Q7zO1z33zV8sr6I5buqot4hRRJjrHsD0FmmDqNQzwbAaupAzPA6RQFGMAsOI4RYjRlHInvdM+JG3xmQFWWutc0oCqYZv8Wtm5lp3MBZhh/59VvrZMyQRAcV+VCzR5jk5I0GxcDaksBcMCve0PEufKpb5DFibP0ZezFD5wbTfAD+4zsVNxZSlIZW50f9OiM+A4aLNfttpoB77R3v/SxjhiTyUb2B/HfPif7cw48Fgc/1sExTx1/DaIrOYq2jEHsxQ2DJ+hYAr30UujcT3Zd4xPOjjXhzPMf16I+7cjYA1ozvefabfby5ZpOMGZKow6f5+Lip+7e3biyjeplItaaGdlBBplPFtRaLhfHjx/Ptt9/6j2maxrfffsuUKVOO6TlUVWXjxo3k5eUd8nGr1UpycnKrr64kPV4kd91YqCbgpHSFcSGDDYXU6zae980DFP+5UUn2EJj2G3F71Sv8Krd1K4w/flnIPR9t5OrXVrFsVxjYnSdkiaSz49BW+12Co1gIfBOzhYBLEv7s+Fq4lSVkwYATcfl0fv1FwNVy9TU5mAxBSnq77EJYGyOOY10RLyB8YkaOUs0s489ousJzvrNIxInhgGYZUR0zQGxGjbkUgIudbxCHWEi5VHjoy/3hETNsydBQIZx7wgVvo2jNY47zt+WJWBSDENjai6Bim3CODRUep/i7WuKD+3fVNdgkhJDuIedw3eeBmPHz9bkY2pPA8DVCXKrYiIpRYiFmtIwBDhKoIwkDGo+bX8Kq+FipDmaxNvqQ50c8igLTboWMAcIh9rtHWhXEPT471X+7wKHz8Jd7uzZmWBLEdSuU16wj4W2E2v1gSzpqizVJmLD7O3DbITEHeolrWJ1b4+r5ARH3lhtzO570BhGXNE2sQWOEWIgZ2Um2VvezqOUu01uAKMTYrPdp9XhUxYwjoSikz7oZgPOMS9GL1/Gbt9dz0cs/Me2xRSzcVBLiAUokQUBTUdf+F4C3VdFucttNh79WHROqT6w3YpBYjBlHYpSyi5yyH4Rr7dB50S2ESO1J5ZBLALjf/DqbduyWMUMSHWz9THzPHQUJGZQ3qFzxaaCj4/Krcg7zg23A4wRzgt8VN1aItZgxw7CBYYZ9NOhW3lBnk83BRccxsc4YfRGYrIwx7GKWYT0An20okTFDEvnsXgzOSmGoljsKDEaKHD4u/0TEDKMCn13UQbMXnwtMcTEXLyD2YgaAwVaIOWkLuq7gqR2D7k2jpTStLeuSSCY7IZtfTZ2J6s5CMTlZVb6MBz9fykX/WipjhiSq+Grnasrde9F1AxbXGP7yi57ER9keQqcr+m6//XZefvllXnvtNbZu3cqNN95IQ0MDV155JQC//OUvufvuu/3n/+lPf+Krr75i9+7drF27lksvvZR9+/ZxzTXXdPZQ28XwbilkJFjRCCS60rFzu+l9AF73zaaaZDISLAzvFsR28eHIkDNgyOmAjmXxQwxiv/8he5NLSlW9m0c/zw+9wNZgEtW01bvBG4LKGFcdVOwQIh6TtetfX9J2dA02vCNujzwfDCaeX+3ghwLRsvmU/jYy49vhQHi419J8kNTBhEiEEe3xAgIx41KTWEAt0CaxR8+jj1LaSv8SEzEDxGcpKRdjYyU3mz72H7YjFtchjxnmePA0CFF9uFBbIGJIXFqoRxIcDCZIyhEuGhXbQuMor+tQu08UNQT771qwQojwLIk8UTWFn8tEC/tzh8SRYm3nNNznjZ7/fweI9pgxvFsKmYlWFEBvWmdcaVzIWMNOGnQrz/nOhKbjmYnW6IsZJiucdL/oOFG8Fja87X+oqKoWG27//S6PGdYkIYRsrDn6uaHAUSrGZ42y90Q0s/kj8X3YmWAQ64kHFtexs0YIuC8bGU+8OUhbN+564T4SbJf2MCfaY8bEvunkpdj8u1L3md8gWXGyWevNUm04eoutv6iMGUeg2JDLx+pUAO41vUmzu1RpnYsb31grkxiSyGfnNxgdxdToiXypTeD6cQkdc63VfGKNFiPu5oci1mLGkfhd3CfiRp9pkD20U8cVDuzvdgr5Wg8yFAe/M73rPy5jhiSi2dr0Oe4+DiwJXPtZNXa3mA/9dnJScIxCfC5hEhKDxZ2xFDOuNwqh9lvqiRhQyVZqW50bM+uMuDQYdg7Q2r22GRkzJBHLhqa5T+/j/YV2l35Uhdr0Fr9nWnL7jEJa4m0UhjYx2CEDYitmAFizvgbA5xgKaiK6GhBV56XYmNg3NvYmrUYrOQkpeCrmAGBJX4pi8KGY7DJmSKKK338hjB7U+kH836RUsuIjuPvuYeh0ce0FF1zAE088wf3338+YMWNYv349CxcuJCdHVETu37+fkpLABaOmpoZrr72WoUOHMnfuXOx2O8uWLWPYsGGdPdR2YTTA9TP7orb4U/7O9A6JiotdWh5faBMBuH5mP4yxYE469Rb0zMGYffU8aXkBa1Piu75JXNu8zHhpyZ7QtvsG0eKssUY4O3UlPg9UbAdvgxTHRBL7fxLvFXOCEJIDK4oC7TBGZQdxMeCuB0tSzL0/oj1egIgZt04wc4JhAwD/8J3dqjijmZiJGSYr6sQbALja+AXdqQDAQRwQBjFDUcBkEUIlPQzaczTWChFofFp0bVgbLcJBr3o3VG7veoFtQ4Vwkk9ID/7fdWNTK6Uhp7OoMPDcI9sbMzSfEF5ZYjfZ3Uy0xwyjAa6b0RcADYXuVHBHU4L3Vd+pVDV1zFAQ50VlzEjtBccHOmNQthlVEzFBxAtxXXZ09Tqj2d26vrwTX6SdeF1ivmqVrrURQ/lWUVxiNMPg0/2Hv90TKAAdmRNE9x+3QxS1xFiBZ/THDIUH5omxTTds4CzjMlRd4U/ey3A2zashymPGIWiOGW97T8Clm5li3MJJhrVAIGY8OH+LbMMniWzWvAbAB+p03FgY1dGY4W0UXVJieL0RSzHjSAxX9jJdWy06zgw5Q7wvohhVgxeXFvKcV7T7vtD4HaOVHYCMGZIIpmoXlG4U7tPdxoLB5C/6hg7sTR2Irok1aAwSKzFjpLKb442b8epGXvHNhQPyGTG3zhh5IS4sjDLsYXbT+qIZGTMkEYm7HvKbnM57TvQ7y+6pDeRpRuUEIWao3pgr9m5JrMQMAEPcHkyJ29B1A56aya1caxXggXnDMAarE3CYo2o6f/+6AtWdjerqhmJ0Y07ajGKqRlfEvEzGDEmk0+hrREsQcyJv3TiG5iWSak0N7aA6gS7pJ3vzzTdz8803H/KxxYsXt7r/9NNP8/TTT3fBqILH1P6ZpGZ52VehM1rZyQXGxQC84DuDBJuFX584gKn9M0M6xi7DZGXbiNvp9d0tjDTs5QHTf7nHdw0+TOzQe9CPYoyKRmW9m83FdYzqEcJKRkURk7iavaKytismdLouxEOOEkiOLVfSiOdn4ZimDT2TTeU+qp0VNHgCC4ugbUYBeByQPiDmkt4Q/fECYGzV56DofKlOYJvei1Qc/seSbKbYihnAZssYDFpvRhj28ZD531zlvRMPFnbp3ehLCQZFD23MsKWAs6rJLTa161+/GU0T7q6qR8SsaMNkhcRMqNwp7mcMBGMXTFO9jULQqxiC3+KyZi8UrUbHwIq4GXh8LTej2pn09rmakt2x1zrpUER7zJjaP5O75w7hys+dPGN6ljjFw1ptAF9qEwDhCnLdjL7RHTMGnQqFq2DXIlj0MFsnP0llvZs4BQbpBWynFy6s7NVz6UUpBoWuiRm2ZKgvA09f0YkiXKgvE/EqpXuoRyI5Vppda/udiGpNZXNhHdVOD1qLop6gFfFpPrEGToi+qvFjIdpjxqkj8njxomEM+uA2AP6rnswePYdspQ6IkZhxAJuL66isd4MSz8fqVC40fc/vTO/ynWcsGgZ0oKTOxco91UzpnxHq4UokbULVdNZt3sq47QsxIFzkIAgxw9sICdkx6ybVTCzEjOcvHccNb6w95ON5KTZeyfgOioFeUyFneNcOMAQ0x4xK8vhGHcts4zruN7/B+Z770THKmCGJTLZ+CoCeO4qtjnjKaytaPRwUoZTPDUZrTO9TxULMGNPvByiCT7WplJBBdwLFxjG5zqhR2OabzP+ZlnCn6W2+8YyjpeBYxgxJxLHtc/A6Ibk7avpANhc5qHZ6Wp0yMruDRXzN+1zm2I0XEBsx45+XjOX2Jf8CwFc3Ft2b7netzUux8cC8YZw6InY0Miv3VFNa50UxxeGtHYsxtxhz6lq8deNQTHZ0b4aMGZKIRdV0Vu6pZlHB1xjMteiqDV/9UMb3ysMchftKXSKujXb21fr4uUJHQeNB838wKDq7UqdyyeTTGdkjNWaq9ZopJZ1PfWdxp/ldLjQuZqE2kSXaaNxYqCCVXER77QMnZiHBkgAuh6jitSZ1/uaxvViIaxMyAq5XkvCnfAuUbkBTTNy+pT87V23EqxvZRm//KT1TgvT/1HyA4ajiOcVsJucP9/lvSyIEexH6zm9QgGd9ZwNw41gb8aYejOyeGpMxo7rRy0e+03na/DwnGtdznG8rq/ShNGKjmmQyEYKAkMUMk1UIWp3VoRXXNpSLGBIfxYsrk61JYLtDuNdmDhLOwZ2Fpgoxr7O6U4RopcveJBdYrg7hj987KCDgRt4z2di+J/U4IT6zzcUXMmZELomJyUxUVnOacRU+3UDZsKv5bXZ/Mpra7UV9zFAUmH67cPd0lJD183PAaQCY8WFERcVIPfHUkUga9UAXxAxzAjgLRfFFuIhrfW6o2SfaOCvR/saIEhprYdd3AGxIncVTr62ist6NWzfTQE//ae2OGQfirgdrMthSj3iajBmRy7Cd/6KHoYxSPY3nOZ87psaTlZgTOzHjAFrGgo980zjNuIohhgLOMvzIR9p0/2PlDtehflwiCVsWbirhwflbOLf+bSaYVVZpg9ili/VMXlIHY0Yb3aRkzIhcLKZAUMhMtHDd9H5kJVnJTYljYnwxxhe/BhQYdmb4zHc7kZYx4zXvHKYYtjDesIOzDctkzJBELls+AeD9shz+t3cPdj0BCOx/pdmCMDn0NYLZdkyO5zJmRChVu8gp+gqAF3zz6J0MvxmVRVp8t5heZ3ykHs8Zxp8YZChitmEt32jjDzpPxgxJxLBBdEsrShnHQx/uorBBoV63Ad38p9hMHXQZVd0ip3GM80oZMyKXNeWrMCXuRNcNpPlO4KITR9AzJUesM/qmx4xjbTPNsUD3JeJz9sHn7I0pfh/m1PV46kaCLxl0s4wZkoijeW+qpM6FrfsnmJPBax8FupmsLtYydFXMkOq+INDcGv584xLGGHaDOZ7+Z/wW4lNDO7AQkR5vYYk2ijHqTk42ruUJ84uc6v4L1STjafGWS4/vRMFMW0jMBHsJ1GZARr/Oex1ntWj5abYF3x1P0rk0udYu8o1kp8sGQBXJrU7ZV15D39QguEC5HULofQxJ7/RLLun460m6lvVvouga36mj2aT348crs+meFNuhOD1euNR+o43lZONa/mB+g7M8D6FjwBsuMcOSCPYiSO0ZGgcfnweqdovXjnZHa5MNErNFIYrPDdlDOyeRp+tChFa7T7xekIVoK/L3MrrwO1BgvjqZClJbPb69pJrsAe2IGT5Pu5z2ZcyIXH7cW88fTaLdr2fI2Zw2Y0qIRxQCLIlw0h/gk5vJKf+RGYbuLNFGoShg0900IK4RPgJikk6PGYoiXKTtRZDcHQxhkElylIKrVrrWRhL5C0Dz4kgewL1Lfej4AFGs2pKfC6qD4wLkaRBx9SjO8DJmRCgV28jb9AIAf/RezsKr+5EeFyRhdoTSMhY0KHG87JvLHeb3ud30Pgs8k/Eg5vXZSbZQDVEiaTMLN5Vw4xtrAY0LLKJA423fif7HV+6pan/MaHY4b0NrbxkzIpfvtwUcLBfdcQLJthZ7He/eLr73mgK5I7p4ZKGhZcyoUZJ5zncmd5rf427zW3zrHosdIRyUMUMSMdTuh+J1qLrCClcvfJgoJLvVKct3dyBmNONthJTex7QmljEjQln2LAo636hj2aH3YMslucSbw2APJISkx1uwk8B8dTK/MC3hVtMHB7nXgowZkgihvkJ0DANe2JVBYZPB7H5yWp22bFdlx2KGt6kjnynumE6XMSMy0XWdBQWvgQLe2uP45yXjGZ0zHEMMG0EEYoER3ZeCp/wUTH1ewpy6El9DTzRTKro3Q8YMSUTRvDelAxgdmBK2A+CrGwfAj9vrmTuy67oRd1XMiN0rWRBZst+FDTf32d4XB8ZfHt3OckdheLcUMhOtvOQ7gwIti2yllsfNLwI6HswoiFYhw7uFoL33oTCYhBth9S5oqOqc1/A4oTxfCIXi0o5+viR8qCtE3/MDAB+q0/yHPQQ2XbtRwX9+3IuqBeH1PE5I7tY17dAlXYuzCrZ/CcCzvnO4fFRCzAtrIRAzXvfNwalbGGXYw1mGZQB4MIVHzLAmiTbbzk6KEUejrhCclRDXdlFlRGKyQnKeEK0VrxfFKcGmrhAq8iEuJeiCZVWDPUvfw6Z42aPlslHv6xdvAPSkjH/90I6YoWttTnZLIp/4bR/Q31BCvSmV+ClXhXo4oSN7GIy9DICbzPPJwA6AFa//lC5fZ9hSwFkDjTWd/1pHw+eGmr3StTaS0Hz+Nq1vNYxHb/FQy+Ki3pTy0pI9HV9nqF5QjHItGq3oOnx2O0bdx7fqWEZOmBHzwloIrDOaU9zvqCdQpqfS01DBJUbRTSQvxcbEvjEyx5ZEPKqm8+D8LejA8YbN9DJUYNfjWaBNAqAvxR2LGd5GYQZwDO6Dksjnk5+LAXhg3rDWwtryrX63S4aeGTMGEQfGjDfU2ezWcslWarnN9IGMGZKIQ9ssPsdb9V6U6WIN0LIgtT9FQVpnqGJvTRKdOMpg/ZsAvOibx19np8a8sBYCMeNjdRqNuoURhr3MMazxPy5jhiSi2PQB6Cq76cF+PQtQ0HXQWsSMgRR0PGb4GsGWFh4GBZJOY0nhEuqVHeiakQuHnUyvlG4xLawFmNg3nbwUm1hnqImojX3x1Q9EUTTMKZswmGvITTHImCGJGFruTQGYktehGD1onnTUxt4oRjsPfZaPqulHfJ5IJLavZkFgc4WXz3a4uML4JSlqNSTmwPBzQj2skGI0wHUz+uLGwl99F+DRTZxkXMclxm9xYcWjm7huRt/wahViTRIJzsrtonoqmPg8YmPSWQVJ2Uc/XxJerH8TBZ1V6iD266JSz6MbsZMAQG9KSFccVNa72Vxc17HXakPSW1dVGlaspGHFSnRV7djrSrqGzR+B5mOVNoi1+iCcvmCosSOf5phRSxLvqTMB+L35beJw4SAer24MfcwwGMWXvViIF7oSVx3U7BEiLkMMiSQMJuG+6K6D4rXCZVYLwrVO16G2AMo2gyWuUxLHm4uqmaX+CMCn6uSm9t6iIrsfRaQoDe2LGc3V3ZaENo9JxozIZPX2Qs5vFMV73+VcIYUO4y6DzEEk0sivTR+joGNpcvoEqCURVVe6LmYYzaCrwjE21NiLRbywyaRmxLB/OdSX4bMk84VziP+wSzfjQhR9DKCAJMUZnHWG2y7eH0fpjgEyZkQk69+EfUtp1C084LsCiyVMugSFmOZ1BogEt46Bv/nOA+BXpk+Ip5EH5g2LuZaEkshl5Z5qSurEnuVlxq8B+Eg9HhdWBrGPBMXVsZjhdYr9KNOxX0NkzIhMPv25mFqnKFJLsB5Q9L3kCUCHHhOh+9iuH1yIODBmGIA/+URx3yXGb+inFMmYIYkoGtZ/CMA6dQD12KjX49Ca0sFD2Uuc4u74OkPzif27Y9ynkjEjAln5Iqhu1mgDWaUPJtUqr4EQiBkO4pmvig5Tt5o+gCaZiQ4yZkgih5/fAuBH72Dqm3IYzflvgGHswap4gxAzVLAlH/28JmTMiDx0XefBpc8A4K2dyODMfqTZZJG/0aDwwLxhACgYAA13xSkAmBLzMVhKuXlOlowZkoih5d4UaFhSfgbAWzsBQ9wOdM1GSZ2LlXs6wTjrMHRVzAgneWNEoes6F31YyelvVZBCPTeZhOsME64Go0xkTO2fyd1zh+BI6M2/fScDcK/pf/RWSjFl9GZttZVCu+8oz9LFJGZDQyVU7giOgAdA9UHFtqaWsbnSSSrSqC/zO42+o57gP9yyhZINj/92tTNwu1146sGWdEzCCN3tZv/ll7P/8svR3e6Ova6k01HdThzrPwbgX77TAZjWM7humZFMc8xYaptFiZZGnlLNDab5gEJcdh/WVlspbwjxAjouFRoqRMvtrkLToHqvcBBqw8ZD1KAYIClXbNSXbhBfjbXtfz5Nheo9ULoJzNZjEhi1B8P+5eQotdTp8XyvjWY/uf7HrB2JGb5G4UppPrbWSS2RMSOy8Kg6p71ZTuG3/yRBcbNKG0TckFNCPazQYzDBrHvAaGaCcTvnxq8nDTvmJvdaHQMp3fqzttpKlbOLYkZcCtSXgqeha17vUHgbRctPa5Jca0QSm0SyuzhnFt4W7uZ76Oa/3dKZuePrjEbhCn8MDiEyZkQWztoy6j75PQDP+M6jUM9iZLb5KD8VOzSvMzISrWRSy4fq8ezWcslU7DyYtYhdFQ3UNXqP/kQSSRhQ7hDJi+5UMLvJHe11dQ4AZgJzn3bHDNUL8W1zy5ExI7Koa/Qy+ZFvueWtdf5jQ3Nb7DVUbBfuZQAjzgl6l5dwp2XMyKaaH7RRfK+OwqKo/CXlY3ZVNNDgDrN8hkRyKOqKSKpYi6YrbND74dHN7CXP/7BRCRg+dGid4XGC5dgdz2XMiCwqq6qw//ACAC/6zgAU+qfLdUYzzTHje9ssnLqF4YZ9nGxYDcCpw3PYXdmAyysFgZIwpzwfStajKUbW6QNxY0HTFQrI8Z9iUAJmM+2OGZoq9izbYBoiY0ZkUVjjZNCfn6HCsxNdM+GpmsWY7t1j3rW2mVNH5PH8pePITbEBCpqrO177cBRFJzt3D1tK7NjdIcwvSCRtoHlvCsBgK8AYV4SuG/DWTQA1CXTLQed1Nl0VM+QVrZ08+qOd5YViEnGT6ROSFSd6ej8YcFKIRxY+TO2fySuXH8eUM6+lJnU48Yqbp8zPk1/l44nlDq6e33Vq9WNCMQhn2Zq9ULW74+6EzcLamj1CuGuQ7d8jjvVvga7iyBhFvt4LALduxklA2GRqkcBIj++gsN7jhKS82HKnjBE+nP8xSXo9e7UcvtbG8/CsFOYNartA7iB0XYiy68uFiL+uSLjXOauE60wEMbV/Ji9cMRX3hOsBuMn8Gd2pYF25zhPLHdz8RYjbbptsItnoKOu616wvA3shJGR23WuGI7YUEUftRVC0WiT83PVtew5XnRDnlm0GW2KnCWsB+pR8AcCX6gTq9ATcBGKDscVmVJtjhtcF8TH+XogRrvikCnPVNs42LkPTFRwTfs1J/WyhHlZ4kNYHjrsOgCuUBTx9SgYvnRLH3H5inr28WOOJ5Q7uXtRBl89jxZIohLX15V3zeofCXiJdayONyh1QvA4UIw0DzvQfrtdtqC3a7iktDAs6tM7wuYUL4TF0x5BEHkueu5EUHGzVevKKehrPz01jco/YEkMdjea9qb+eO5znT0lkYeqFAJxi/5BXvlzFw59tCfEIJZJjIztJzAcvNX2DUdFZqg5np94DCELM0HygmESxjiRqmf7YIkrtgQTXK5dPYGSPFnPIJY8DOnQfL75ikOaY8cS5w/jXKVY+T7sUTVc4zvUjX3+1gGe+2R7qIUokR2erMALK13tQqqdTxeEL9ju2zmgUZgRGKbiMRp576gGSaWCXlsfX2nhePzud/mkyx9mSqf0z+fsVJ+BoWtffZfsI0Fm4uYy/LtzGK0v3hHaAEsnR2PA2APWZY6nQxZywgtTDnt7umOFzgymuXaYhkshg2mOLMGd+BYC3ZjKPnjeIodm5R/mp2OLUEXks/f2JvHXtVP58Xh9SPXPQdQW7spm31+zg1WX5oR6iRHJMNO9NAZhT1gPgqx+M7ktGV5MOeV60IMW17eSltaJ6II8qrjSJYKFMvE6K4g7AaIBRPdNIm3s/qime8YYdXG/8DIBtVWFY6W20CJeGqu1CZNtega3qhYp8qNktBEExVukfFTirYNsCAOInXUlmohV02N3CTcqIiqKIdmGZiVaGd+uAqEHzgWKUSe9oRNeYUPkJAK+qpzKlp41LRyZgUDrQ4kHXxHvUXgQ+HyRkQeYQyBkOGQNFQszjhNoCIbjpaLFAF2E0QJ/xp0DeGMy6l7vMb/kfW1ncQce2YGBLFuLlrnAo9LqgaqeISzKGiL9DcnfxvWIbFKyAsi3Ccd53mPeGpkFjDZRvg8JVQnyelH3MjhrtojyfxOpN+DCwwDeZXfTwP2Rpcq1tV8xoeeEl0gABAABJREFU/gzLZHdMsKzQzb3m/wHwU/xMTpwwEqUjMSPaGHke5I1G8bkYsu05Zg3M4KpxrT9PX+/uuqpYrIlQV3j4a1Fn4q6Hmn1CWCvfI5HDhnfF9/6zGNS/P5mJVnRdYW+LdUYc4j0clHWG2y6S3lKAHX3s/ZFTvd8CcK/3auYNTuK0ATJZdSiMBhjVI4VZg7MZPvlkNmp9SFIaucn0CUt2VIR6eBLJMTGxbzp9khUuNH4HwGuq6GyQgCis7VDM8LsPyvVGNGN3BfbiL53ci5OGBlzJKNsMG98Tt0eeH9Od+VrGjFkTRvKBOh2Au81v8tOuqhCPTiI5Bpo+y9tMQ6jT4yklUKidjNjTDMo6w+eVuYxoRVO53Cjy3q+oc7luXBLTe0WfQCIYGA2Qc/xlYI6jn7bX714LsHZfiM1CJJIjoWn+/amEQdOwJKSi6gYqCFzXU3EAQYgZvkax1pDi2qhE13WMiVsxxhWia2Zm95rNKUP7yXzGITAaFKb0z+DiCcM5b/RwfHVjALBmfcXPhdU4I8w0SxKbTOybTl6KDWjElLwBAG/tcWCsQ1fjUYC8FBsT+7atM1IkIMW1HeQ20/tY8ELeaOg5KdTDCV8SczBO+w0At5reZ5iyN7TjORKWeCFeKd8qBLaadtQfaYW3Eco2QfVuIXiToqjI5Od3hEg6ZwTG7mO4bkZfakn0u0kZ0BhAIc1Tw+tm9MXYkSuqu16IM2TSO/rYt4y+hjLq9HjeV2fy6ryMjj2fxylEgiYbdBsLvadA93GQOQDS+0LWIOgxEXpNhpwRQpRXVySqQyMBRYGpN4NiYJ7xJ45Twqhaz5IonII7271W14XreWNNm9tyRj3WJEjtIRz4qncLke2+ZVC0VjjaVu+Bql1Quhn2L4P9K6Bqh0gMpnTv/ARhU7V3de50trcQ1prx0o/i9scMnwvMtja1TpJELnMMa5hkyMelm5lw5o2hHk74oRjghLvAHA+lG2Hje0zoZuWHK7L9p3RpSYk1WRSyOCu78lUFdYXgcYjiD0lk0FABuxaJ2yPPx2gQMaGCwBrAioc+lAZvneF1i+4YclM7uvB54LPbAHjTdyJr9UH8dXZqaMcUIczsE0/aDNEt4zLj12RrIXQfl0jagNGg8OzI3aQp9RRoWXyrjSOeRnpR1vGY4XVCXDoYpSNdrPDHecNbH/j2IUAX+Y0ex4VkTOHIqQPiGHLq9bh0M5MM+Uz2rgj1kCSSI1O1C4rWgGKk/9hZ7CfgGpeIk+6UB2edoalgMHRuAbskZGjbvqSPoYxaPYEP1WncOVXuORwRWwoMPw+ApzLnoyByyhaTlGBIwpi9PwjzHmsSxrzRXD1zIIVk+R9OwUE3KoMTM7wuiOtgXlQStjjcXqxZXwPgrZnK9TMGk2pNDe2gwhxFUbh22hDOHzoXXTdgStxOvbKDkvqSUA9NIjkqRoPCA/OGYUrajsHUgOZNQnNngaKiNMlPH5g3DKMh+nIRcmbXRlRN59YvRbXZQKWQ84xLxAOTrpfJqqMx8GToMx2LovKU+XmshIEL4eGwJgmhY/kWqNx27KK0+gooWicS3Um5UlgbqTTW+tsnMe4yUBSm9MugkIBoI49KzIpKRqKVu+cOYWr/Drbq9jRAYq50v44yvKrOtsVvAvA/dTZObFhNHYgVzmpw2SFriEh2pPQQgrsDURRxHcvoJ85L6yPEHG5H+1+7K8kYAINPB+Ah6+soaKTbwmDKoihgS4La/aKQorNoqBDFHQkZQkQmORhLohDLJuaIzXxnpRDRlm0WsbtuvyiQiE8T53WF46u9GPaIeWHG1F9S0sIZJJcqTIrW/pjhdYI5QYgJJVGLy6dz2Qel3GUSrt3/UudiSck5yk/FKEl5MOVmcXvVK1C9m57JJoxNIbZfV7YqNBhFG8za/SLB2FU01kBdgSzCiDQ2fwS6Kopzs4YAML53BuUE/o+5VGHsSMxoibdRzBWlo1TU4Vn6N6jcRqWezGO+C0m1KZiNck/qWOkxdDLL1GFYFR+3Gd8P9XAkkmND1xleKIr5Xldno2Egl2qMit7xmKH6xNpJEpXYXV5mP/W9//7wbsmYWqoj9v8E278Q+w9jLgGDFFm3ZETfbryingbAlY3/Fp8XiSRcae6S0W0svfsNopbAfphYZwQhZoBwITTFyyLwKKSmwcOqdx4B4G31RKb1TY5KcUTQGfV/YI4j0b6Tkw1rALBKca0knNnwjvjeayrEpzMoLw0HgWt6DjUYghUzQGguJFFHucPFuCefxGgrQVetTMw8kZ7J3aVr7TGQYk3hF6NHC8dPoEj7kuKGYhyeCMnjS2KaUT1SMKeK+Y63bgKKuQ7UBHJTbDx/6ThOHZEX4hF2DnKnpI08+ZODj7cJQc2dpncwKjr0mQHZw0I8sghAUWD6b9FKNzLEVcBvTe9R5/otKeEgmDoU1iSRKK/cAc4a4QiZkHWwi4OuC7equiKR4FaA5G5SEBXJbHxXuARmDRYOoMDifa0F1pcel8fxPW0M75bSMScpAM0n3i8ykRF1vPL1Gm7wbMGrG3nNdzIvnd6B/3FDBShG6Da6be5j1kTIGS42Oyu2CUGHLbX94+gqjrsadi9iiGcP5xuX8InnBFw+HVtHxMnBwJoiCigcpSIuBBuvCyp3imuCFFIeHYOxqSAmDNqXbnwPdA16HMenlXlArf+h647vwegca/tjhtcFKb1lIVeUc8+iWvqULqS/uYRKPZkpp18e6iGFN4NPEy4L+5fDd4/COc/zyQVZnPF2BeX1Kj5Nx9RVCaD4dOFq3lAJSV0giNY0qNkPqgcSOri5Lek6vE7Y0lTAN/L//Iff2NjQ6rSbZ/RkcGYHYkZL3A6Iz5SOUtFGXRH6908A8GfvJdSRyFfnyWtBm1AUkqdfD8t+w0z3IvTSzSi5w4/+cxJJKNm3DEPZRhp1C++oswC47cTe9E7tYMxQvWKvUwqkopYbXl/DzvJ6//1XLm/hTKvr8O2fxO0+MyB3RBePLvxRFIWRcy6natF3dPMVwupXhNGKRBJu6LrIawD0nMxja1sHht/N6UteUpDWGd5G4XguzWWijj+89C7/YCM+3cB/fXNYMFvmrI6JZvfa9W/w1/T5fFU5nu1l9Uf/OYkkFHicsOUTcbvnJLAmcf8Xta1OueuUfmQmBCFmqB6htZBrjajkFy8uw9LkWuupnsZvLpautceKoij0Tu3GuYNPZX7pGhyG3awvX092fDaD0weHeniSJjRdQ9VUdHQMigGTLEQF4Ia3v8KYsAMAb+0E7jytN2NyRjCxb3pUF2VJ9V8b+edqMRmeoOQzx7gGXTHAxGtCPKoIIi4Vw8w7AbjG+Dn/Xrg8xAM6CiabcLzzOKB4rUjeV2yH2gIhpq3eA4WroWClaOFtSxIuelJYG7k4q2DTh+L22F/6hUzlDa2dyH4xOpNRPYKwEQXCtdaSIBbgkqgib494L32qTaGMdCZ0a2dLemcVKCbIHdUk3m/jxMRgEC62uSNEa2BXXfvG0ZXEpcI4ISz7vfkdLJqTR5faQzsmaHKvTRbOsp6Go57eJnRdtG9zVkqxVKThqoNtX4jboy+kyNE6Zpw3qgMxQ29qcC/bvkc9X+dXcavpAwCe8Z3H8O4ygXFEFAVm3AHWZOFcvfZ1hmaaSDAr1Ht1nl3ZhVXeBpMQpdTuF8LXzqahQrRui5ct1SKKbQvBUw/J3aH3VP/hvbWt3c/OGZUVnHWGroPPIwTfsjgjuvj6D1h1F6u0QXykTQNgQLrc3G0rA4aMYqF6HEZ0Ct77faiHI5EcnR//BsCH6nTqEEUTZwwPQszwOkVhpyUMChYlQUfXdZbtqmp1LDelRQeknd/Cvh/BYIZxv5R72odhTK80/u47F4DGRX/t3G5GEkl7KVoD1btFTqvbWLZWtd6bOmVodvDyGT6PXI9GIR6fxvFVoqvDl9oEiskkLU7GhWNm1C/AHEdKvXCv3VJi57tt5aEelURyMFs+EftTSd0gZxgoBjaUe1udcuLgIMUMb6OIS1JcG3XYXV4KvUsxWivQ1Tg81dMZkC5da9tCsiWZ2QMH462ZAsAnO76gpKGEOncE5PCjFK/qpaqxin11+9hQsYFVpatYVbqK1aWrWV26mrVla9lZs5OyhjIafbG7JtzR+C2KouNrGIDuzeAXY0cwpX9GVAtrQTrXHjOqBpuKarEYwaPq/N4s2nApg0+H1F4hHl2E0XsqP8SdyPTGRZxT+jc+3TyEuUPTu85Zqq0oBkjMFu6ibodI3jcLXQBMFiGqNR2iNbsk8lj/ZpNr7dBWSe+W//IzBtrIiDcG7zU9DZDeX1TvHSOKyUT27+7w35aEF6oG23bu4HTDTwC84pvL0yenkh7XjveNq04IdfJGQmJWxwaW2hPQoWyzEAKF+4J2+DmwdT6ZdQX8yvQJj224iBm9rczsbQ1tzLAmC/fa2gLIHhK857UXQ+1e8X+WCa3IYssnInZkDIBu49CKAs4EV49JIN7cgf+nzyWcQDrweZUxI7xpXmfcaPqUDMXBTq0b0+ecG3qn7kggPgOm3QbfPgjrXsfYewo9k9PJr/Lxt5X1HN/Tyrg8S9fEjLh0aCgXwtfOdK/1eUTC1GCULkGRhOaDjU2t50f+X6s432KZwW2TkoL3fvU2giUe4tom1JcxI7xR9yzFuOkDNF3hj94r/p+9sw6P4nrb8D3r2bgbwd3dXQqlpYVSqLeUUqHuv7rLV3ejpe4CBQqF4lIcigYneNxldWa+Pw5JCAma3ewmzH1duVhmJpuTze6857zneZ8XkPjqsgh02ubFOWMxSHxtvJph8nrqZy9n/4YF1O84uGKrdA0NfyE9GfbMQ0FiinwJAM8NCPHMxqWrBELrV+7UdRZoMcO/kRWVhTvSy/5v0El8M7F7+QWKAgufE4+bDhNF2RpVEmLW8TuDuVWdTT1HFtkL3yd02CNazNDwL7aUutb2gKAoVNVZdurtYWGe+zmlGyXn2B1Dixn+jayoLNqQzBj9CgB+YQS/XakJqM8JSwi0HQv/fc/9hj/4x9mFm79ax4r/DSIuxKLFDA3/YeM34t9G/YXJDRX3wKeOivDcz3LbhYhXd257o1rM8G9kRWXW5sOYoxYCIOUP4L3rmhBq1kzEzgVJkuiUmIQzeyDGsDWk2w+z7NAGIsxRtIv20Hpf46wodBaSZcsioziDYncxqqpiMpgw6UwY9UYkSUJRFWyyjfyifBRFwWKwEBUQRVxgHCGmC+fvlWvPRR+yFgBXTi9eHBfh889+TcUMLRqdBSv3ZTFlWQrJhQE4iWCobiPddLuRdSb0XbR2reeDvdudHFm6hQa6DJYt/YSmCyfy4YhwLm0e4OuhnRqdQWxMnuPmpEYtojCtvFVr90kVHJ5cJ5iQdYg9T/fRqlCPP7H13BYrkslE5C23eG4cGh6jNGYMsc2ltUHhX7kNYVYzscYSwHpuT+Z2CPF1bFvPCXVCk0Tbx/RkcV/zZ2GO3gg9J8O8J5io/5uf5MHcMgsMOvh0ZARDG/uoqEGSxGc275AovjjHz2+V2PIga7co1NCKNWoXbgdsny4ed7gaJKlCMqp97NkXTlSJyyY2LKojrtViht9SGjNSC93MNAv345mG4fTUOwDNPeysaDIIUpbB/sWw+FVubPsuTywVTqDj/8jGrIevLoukd5KX453eKBLFeQeE6Pc8BCpnRf5hIeANSfDO82t4h/1LoPCYKNBpPrzCKeWEmNEl3oPrDEcBBMefc/zQYob/Mm/LYRpPv5NmwE/yYLarDRkQdBSzbAK0+eP5MKRzC35bPYCrDUvInvE4l/z5At9P6kmXBlreR8PPWPkBABstPTlojwOgU5yHYobsPu9cpxYz/Je521J5flYyqfn2smPRwWYK7Se4kiVPh7StYAiALjf6YJS1i0ldwnl/wxheN36OuupDeq9qyVe3D6RNgiYi0PAD3E7YJjrhkNQdzMGolLtWVzs3VeFn2cB47i6EWszwX0pjxpiiXxhhdLFFaYTFZMJlKwT8eO/AH2k3HrZNo5XrEBfp1jNP6U7f1xYTGmDkj8m9aRpzbqJ0DQ2Pk7FTdOmV9NCwT9lelHxCcqp1lCdjhqtMwHsuaDHDfymNGVnSUizxOSjuQAKUOGRXwAUjLvQkEQFhDG4Zx/L0fpijF/Lz9tn8tqA5P9wWTvv4RF8Pr85T5CziWPEx0ovTccpOAk2BRFuj0Z3BAEtVVWxuG0eLjpJWnEZsYCxJwUkEGv3c1MwD/LnnTyRDCYorDHdRS7o1SMCg863stKZihlYmdQZW7svi1Tk7OVook0EEOhQeNQjX2j+cPVmZ5uMB1lKGtYzii+C7ALjBsIB+ui3cPTfXx6PSqAr1RIVQXWfjt6C4IKETJHYRh1Kd3Dwzmw2p5ZXeJr0HJ4fOYtF+z6IlYusCpTEjp7CEy/UrAfhCHklBsY1X5+xk5b6ss38yVYGiDAhrKASxnkKSILyh+CrKKBd4+yv1e0G9bpglN08afgDArcCkv3J8Oy5ToHChy9kvNiOrg8sOWbuEY5AnhLo+5IKKGaXsmQ+2XAiKZbWpFxNnZrM7u3zDstoxw2UDa5TW0rsOUhozDheq3G6cjVlysVppxWJb43OPGRc6fe8XopC8g1xr+7nCKYcM107Prvr7PI01EooyoSj9zNeeD7ZcEXcCws7Z8cEfuWBihqrAf2IOQ7srwSgKSpccsHPLzGyOFZa3aw0weuher6pifhLkRRdljRpl7rZUFv/yHs3Ug+SrVt50jwMgp9ihxYxqcGvnIN5xX4lNNdFNt5s+8jpu+Wadr4elUQUXTMyoivwjKMfdCH+SRpYdDrd4IK3vdoiCW7NW1FWXmLstlcnfb6wgrAVIy7cz+fuNzN2WKoquF70kTrS8FILjfDDS2sUDPUP4Q+5PihJLlFTAOPdf3PfzJl8PS6MKLsiYsWs2lGSBNYqZ9g7cNjuPzOLynG+UJ7vwlRaBG/3YKEfjrCmNGZn5RdxgmA/AV+4RpJagrTPOh1L3WuB+wzQkxOcw3+bimRnbfDkyjVNwwcWMUtfael0hLIlft5dw15wc0k6IGZFWD8mHFBl0unN2OtfwX8rWGQWFmKIWAeDMGkRufggP/bxHrDM0zpl3r+qEM6cfqtuK3pyJzfQfb87bhazIZ/5mjfPCKTs5mH+QzZmbOVx4mABjAHFBcQSbgk8rrC2NGZIkYTVaiQ2MJdgczNGio2zO2MyxomN1+u/25b/7+GTDbwC4cnsCepJConw7qBpEE9eeBlmBKctSUFTYQ30AxuqX0Vx3lAI1gGlyP6YsS0H2c12Sv5IZ1oGv3MK153XjFEIo9vGILlyKnApKFQuIo4Vuen6ZzntrCn0wqhom7xDsnised5tUdvjqaVksPuDgz122smNDG3uwWtdZJIQY5+geqsoytq1bsW3diirX3SBdmyiNGbIK3fR7CJFs7FUSWKJ0gOM6iXOKGcUiIUpkE7EA9SQ6PUQ1Fc9fnOnZ5/Y0kgS97kJGx3D9enrptvt6ROUERUPBMcg7eP7PIbshcxcUZtQaAUyhQ6ky6bQv1033qel8vrHIB6PyEYobNv8oHre9kqv/zGfRAQez95ZvYnZPqIajVOnrbAmpxiC1mOGPlMcMiQDJyRXH2+697LoO9XjQ0NYZ54AlFPo/Ih5v+ZXRIXt8Mw6dQTj45OwXhROeRHZB1l7xby0SwBScImZsz3TR7Yt0vt96AawBD66E3BQwBkKbMWWHJ8zMYeEBB0sOOsqOtYj0UJW3s0hsXpyHO4gWM/wPWVH5v5n/cZ9BuJG97x5DLiHokSn9dGkx4/xxWCL5Uh4BwP8MP1e/cE7jvCmwu6qMGRsO5tL1pQVM23jEB6PyA1Z9hE51s0puzR+5TcoOxwV5QCjlLAZT8HlveGsxw/+QFZXnZyVTlUyk9Njzs5JRNn4v5qzmEOh0XU0OsVYjo+cd95UA3G74C6t8AeTM/ZSCE12YT2DFniy6vrSAv7deYOKODaUtvgdw7zId/+y3c6yo/L4cavZgwbbLDgGR51wErsUM/+PEmHGxbi3xUg6ZaigrlNbaOqM6tBsHRiutdIcYrltfdriqPViNmuFUMWNBcjpdXlrAkl0ZNTwiH+Gyw+afxOOG/cAUzKML8yrsZYAHDabcNjBYz6sjnxYz/I8TY4YxbA06Yz6KKxR3UUtkdxgg8fys5AouyBpnR7ApGBQLzuwBAJijFmBzF5Nl0wpcvEG2LZttWdvYk7cHg85AXGAcDtnB4kOL+XzL5zyx/Ckmz5/MjXNu5Ka/b+L2f27nieVP8MyS97j2299Yvvdwhecz683EBcYh6SSSs5PZnbsbm9t2ip9eu3lp/nxs0mFUxYArrysAgdXouuopaipmaOLa07D9WD5ZRQ6Kj7fXM+PkAcPvAPwqD6SIALKKHGw/lu/LYdZaHusTwmvuq9mvxBEv5fCs8VtfD+mCJK1Ipsvnadw5p9w5uHQz4/ONxaQXK7xzIYhr130hXKXq94bYNgBkFMs4T7r//jEuisRgD216q6qo3As894oO1eHgwLjxHBg3HtXhOPM3aHid0phRgoWbDUKoPVW+mPoI9zoVzj5muO2gKEIAa/RSi1djAEQ1EwNzlnjnZ3iK8IYUNhkFwLOGb9HjJ4tpnUGIVrL3QuF5uBQqshDW5h2E4Nha4UJ4IM9N58/TeHhBXtmx0pjx7ppCMksUXl5R4KPR+YB9i4XA2hzCsaQRlU7PviaayOq4g7jtx52kqlfdrcUM/2P7sXwyCx3kEciTBiHQni73oVgVxTbnFDM0BA16Q/OLAZXXDJ9ixcPi1rMlIFw4zOYd8uzz5h2CwlRR2FFL2JHlotOUNJ5dWv4+Lo0ZLy3PJ8um8NTiOv4eV9Vy19o2o8uE0ftzK4v3Ft0QQ5DJQykaR5F4r5yHm5QWM/yPtSk5DCueRbyUw1E1ku/lYQA05higxYzq8vmlEXzmHkWuGkQz3VHG6pf5ekgXJBsO5tDh+X94fd6usmOlMeOxP7aQXezkwV83+2p4vqMwDWXdVAA+kUeVHV55c6xnNr1dtuNr0fOLP1rM8D/WpuRUcqw9ERXIyS/AvehVcaDN6PMqxrlQ+W50BLOUXuxUkgiRSrjWPcPXQ7ogWb4nk/bP/cOHi8qLKktjxt0/bSS72MnkHzb6ang1T+4B2L8YgOSQ3pVOr58U67k2zaoqviznXvCpxQz/48SYUbqf8b17KNGIdYW2zjhPLCHQ9goA7jP8UeZea9Rrkgyv4XZA1h44sh5SlsOxTSKPpijM3ZZG++f+4at/U8ouL40Zk75dT06xkwlfXSDdS3bMEjnLwBhI7MKa1Mq5qS23e7Cbgcsm4sU5mkuBFjP8kbKYITkwRS0BwJk1GHCBYkUFUvPtrE3xcdfRWsrr41rhzO2F4g5GZ8olV7eOw4WHcclVFwdonDsuxUVKfgrbsrZR6CokMiCSTZmbeHbls0yeP5nPtnzGwkML2Z+/l1xHLk7FiUN2kO/MZ3/+fnYXrUKNnMZHOx/hyeVPsuDgAuzu8rV3iCmEqIAojhUdIzkrmXxH3Zo/zU9OwxQuuja7C9qjykGsfKKHj0clqKmYoc3kTkNOiZNMNZQDJABwk34eCVIOGWoos+XuFa7TOHeSQg18clk8D7vuQFYlxuqXk7tjqa+HVeuRFdhyJJ8luzPZciSfv/faGPtbJgfyqnZf+S25BIcMc/fZybHJ/LK9mFafpLHqiAPTCXqgn7YVs/pIHZ3Apm6GlGUg6aB7uWvtF/9VdF/8X+9gusRXw4HwZNw2seFtCfXcc2r4jJwSJ+lqOG10h6gnZZGtBjNN7ocFZ6XrzkhRFoQlQaCXBTRB0RDRGEqyhbjcjwnrcwtOQzAtdYe5Sf8PAPkOPxizOVjcOzJ2QMk5LBplF2TshNz9EBQDeqP3xni6YZwUM6bvLOHK37JILaxawPzFf0W4FPhjh408u8JXm4po82ka/6U5MZyQp/92czEbUuv4/EhV4L/vxeP243lzXcU4+8KAUNpEV/Pv6io53mrPWr3n0fA7ckqcpBFJK91heumTcahG3nSNx3w+MUOjnN53QVAs5pJUZjb8o8KpElcNxQxJB9YIscFZnO2Z5yzKEJsE1nBR2OEjTo4ZP28rYfzvWWSXVB0z3l1TiKzCt1tKyLcrfLiukPafpbEjy4XuhJjx5aYitmbU0ff60Q2QuQP0Zmh3Zdnh55dVTK69NSyMxuGeKuBTANX780iNGiMnO4O7DEK88657LA7EmtRExQS7FjPOj24JZu7vH8+H7tEA3Cr/jMN2ARQYexlZUVm1L5sZm46yal82X/2bwjVTVp/SNerFv3agqvDJkn3kl7h4fe5OOr04n/2ZFfMyXyzfz47UC6iYb8U76GQH65XmLFPaA/DpyHASgj1QmFnaqlDLSdUpMgrPXGB2k34eJlu66GbU4eoaGFXdoV99Cw/0DC1zrx1ln4G74AJxu/MiJ8eMj5fs5Yapa7Cd7HpxnMf+2ArAm//sJr/ExbMzttHt5QUcyS3hRGPIKcv2sTfjAuiuVJqbiu/IzWviK5z6bnQEUdUp+j4Z2SmMIPzAoUqj+pTGjA7SXjrr9uJQDfwgD62wXgdtnXFetL/quHvt4TL32uV7slA0R8dqIysqq/eksXruj6T+dA9Zr3VEeSkOPuwKXwyBby6FKQPg3Xbwaj1ifr2Uhwy/Mv2vWeQXO3nkt830enVRpTnTZ0v3cTC7jndX2ljqct4frBFc9UfFvOW0cVGEmD0oHXI7RedWjTpB6WfGFLESnaEIxRmBK68rqhxW5XUa58b4Lo25uF30ccEypKoLSC9KJ604zccjq92UrjN+3bCHXzev5tU5O/lono11qRu4d9G9fPjfh+zK2YWKSuPQxjiyBmI7cg3FKXfzSp93aKU8iyXjQe5s/wCOzCG4Sxqiqjr25e/ji61fcM+ie5i1b1aZyNaoNxITGEOhq5Dk7OQ65T5824+LMIRuAcCZ24v3bowhLjjCx6OqWXy3M1cLiLCaSEcE/RCKuNMwE4DvXUNwYaxwncb5Maihha639WfK1EuZbJiFvOwt3PXbYwgM9/XQaiUr92UxZVkKWUVCBKuoEsk0AuDFZfk80z+ULJvCwTw3D87Pq/T9nT8vd168Zlo2HWLL3+ePLxIbwG2ijcy6Ogqdp6qdfY2qwKqPxOMWlwih4XHy7RUXute383DSyFEkFhZaMqpOEGE1kUkYtxpmA/C9PIxgSjBKcqXrTou9QLwnwhucc3ut8yK8ARRnCIGtP4swLCEYe90Gy9/iAcPv/CX3pMdUieTJcb6/HwVGQWEapG0TzteBZ0gYOIshazfkHRbC2vOo3PUEJ8cMWZXYcTxmvL2mgDu6BJNnV9iT7eKxRZUr7DpOKV/Ujfk1i/oh5cn6Z467FPZNMvHd6EjPOWT4EynLhOuwKQjajMY2v2KieXwbDwhi3XYIb1Qz9wKNGiXCaiKPIB43iFZcX8ojcGJAL6mVrtM4B0xBMOBRmP0QTdNms/qifvT8pz4Al/6UycIbYmrmfmQKBEehuNebO1fvPu8oFMUYOt15t2z2BCfHDLeqYycNAZiysYixrayUuFQ2pTt5bmll0VOHE2LGxT9mEmwq/zu8sExcf0kzCx9dXMcSMqUb3a0uFa7Gx3HK5Z/1YJPE5S3O3WH2lDiKxHslQFtT1xU6HPqOMKmYPUoi0+R+AMSSXWl6oMWM8+fmDoEkx17DkZlzqSdl8eNHT3LNQ+/VzTlsDTB3WyrPz0qu0j3zpzWH6NcsGreisGpfNq/+vbPSNR1e+Kfs8eC3KhbhvzR7BwDX9ajPy2PaeXjkfkb+UVj/FQBvu68EJBqF6RnexEPdbVwlYs5iPnf3QQ3/JSb49O+PEIqZbJgl/tN+vFbIeR7c0y2ItQnD2fLXn7TXpbBw6mMMeeBLXw+r1nK6mPHXlmO0jAtBkmDRzgzenr+70jUnxoz+ry/mRN3aK3N28sqcndwxoAmPXdzSK+P3ObK7fM3RqD9FRySE3yh0iTfSr76HO6K5SoRZiFHbz6gLlMaMUtfaWUpvAqroAqStM84DczC0HQv/fccbkTOZl9kVFR3Pz9rO85e39fXoai2L123iyNx3GOFeRLRUMffk1gcgm4JBZ8DpsGFyFWJ2FdNZt4fOuj3cY/iT/a99RKB8EUVyfy5+d3mF73/17528+vdOHhnegrsGNa3JX6tmSN8OB5YLU4BG/SvlKgc1NNPZk+ZSpWY+PsxlaniWqEAzkr4IU+QSAByZw5D0hZXEtWdaj2icmtfHduS9ZTZ+OrAMnSmXqetX8kj/CCIDIrFq67ZzpnSdkVaUiWROB0WPpHdiiZvBjmRhlhViDCeKPmxJbsdmd8XC43u+cAABQAA/zDfizImFrGFI+iIMoRsxha+hkGx+2PEDf6f8zcS2E+ka1xWdpCPaGk2OPYedOTtpGtaUuEAPuoL7AFVVMYavRJJk3CUN6ZzYgJ4N6qOTLiwvV01cexraJIQCokppsmEWYVIxB5RYlqgdAZCAyCDz8es0zpdgs4533FcySLeJlrrDHPz7DRqMfVkTkpwjK/dl8eqcnaiIzjz7SMRO+eR4xWEHA749t0r6zemVHUW2Z7o4WiiTFFJHbh975gvhg9EKXW+ucOpYkRBFNgjVM/faaAKMHg4QslMI6zTqBG0SQukibaSjbh8O1ch37mHESeWVn2cVM1QV7PkQ27bmNrgMZohoAsc2ivek3n+TZVLLS1B3zSE4YwdPGb/nXtc9TN9pY2wrP1hUBMUKkXLqf+L1DK1X2Y1WdkNROmTvE3/n4DifOdaeGDMUVWIfiWUuaAAL9tn4Ldl2Ts95qKCyo8iKw07yHSphljoW01UV/vtOPG47FkxBZJRkAtA+xsivV0ZhMVTzd1ZVQNI2u+sobRJCuVo/g6a6Y+SoQXzivoz6UnmRk7bOqAaJXaDNGNg+ndgNbxLCqxQQyP48mXn77Yxo4kER4+kIjIaCY8JxNqb1+bVbdtkgIxmcBRCc4PkxniUnxgxZldhLvQrFpjN3FfPZxnNz9yh0VnZrmb3HztvDVMzVvX/6C0c3Quom0BmFa80J5NrFJkP/+mY+vzQCw8nWQNXBUQiRTcHgv3M6jXOgMJ3EXUJc94Z7PDJ6DLiJlsoLn7SYUX0kSaJ5jJVHXON51/Qxlxb+yurtd9OrbXNfD63WMXdbKpO/38ipPLm+W32wSkHtufLDmkO8NLpt3RZAL38LZAebpFasVNpwWfMA3hwW5rnf2VkMIYk+K/bU8A7dGp6+uOYuwwzCpSLUkESk1pfX0KjqFpIk0SHOzO3u8Xxjeo2+eTPZumMH7Vq18vXQah1nihkfLtrLwZySs36+UxlCfrp0H/8b0aJuxow986AwVbiQJ3bBYpAodqnc1D6Qp/qFeP7nuWwQHH9+61sNv6NDUigx5HKJbg0A0919CJXK1/baOqOatB8P2/4guHA/I3Tr+FvpwTerDnJNj/q0jPPC57MuU5LDgWnP0WvPj1gkF0iQqYYyV+7GCqUd/ylNCQoKZn9u+b61DoVGUiodpH0M1v/HQN1mGuvSeE73LfcZpvGh/XK+Z1iFPRGAN+bt4s6BTepezFjzqfg3qQdENEJVVcx6cMhwX/cg7unu4T0Il10UY5g1cW1doUV8MKaohUh6B7ItEbmkIapaLqSVgLhQC90b1THjhBok2BzEpW0b8vX6oQQk/Mbu4gXszOhBdMAxmobXQdG/FxHrjPVgzEUy5aM6YjDHzsEYuklcIAdjzxhCYV5Xjp6FZPJITrlmSpWDcOX0x5XTB0PoJuo3WUKmLZM3179J19iuTGo/iTBzGBGWCPId+ezOFQWCtVlgm5J3CFP4agD6xQ/j5m7hRFguvM+6tgI6Ddk2IRKJI5ub9aJy7xt5GAo6SqdUt/VvhF57FavN/BsTecg1GZeqp0HOSti7wNdDqlXICkxZllKWiDpCdAVhLYgJsqfo93UGl/yUSZ7dD1qyVweXDdZ+Lh53ul608D3O0UI3yw8JZ65XBod5XljrdoiNdrO2iK4rpBa5mWSYA8B0uQ9ZlCedzjpm2PNFMjQk/jQXeYGgWAhJgGI/b08g6ZD6PoiKjsv0q+it28aK459TnyNJ4nXUGSB9GxxaLUS0BcfEV/Y+OLwajv0HihNCE30mrD05ZhwkrlISKdeDL2vHKWmM/z2LQkctjxkncmiV+JsaA6DtWA7kudmQKhZXzw8Mrb6wFkSMMli06u46yqGsAu43/A7Ae+6xFFDu+qKtMzxA99sgJBGpOIslzX4pO1yjMUOnF0VUuSniSz3VlvEpcNkhPRkK0yEozmeFhyfHjP0kVhDWAqR6sGtei49TmTAjG7u7lrdKVFVY94V43GpUhYK6Xdkudma5AXi6X4hnxcSKW7xXAqM895wavmXZG0iuEv5TmvKP0hUAlfL3jBYzPIdRL3H15aNIVhoQItnQL3vd10OqdciKyvOzkk8pkgI4kntuBXyno9Hjc7jrx4043XVonVFK9j7Y+C0Ar9qFa+3/+gRj0ns4ZlgvvM2Qus6alJxTnqtHJhOO73FIXSZowupqYDFITLhkIGuVFpglF+qS13w9pFrH2cSMcxHWnolGj8/h0d83I9e1luyrPxH/Nh7Iitwwsm0iJj7YMxijJ2NGKYoictcadYKFOzK4wTAfoySzRmnJVrVR2TltneEBSt1rgdcjZiEhPp8r9vj5How/oaqw+RfUD7vScO83WCQXO5Qkbnc+QC/HBzztnsg8pRsZhLO/qKI4SkHHPjWRaUp/7nbdR3fHxzzlupl9SjzhUhFPG39gvukReuu2VfqxjR6fw/OztqOeay7PXynOhi2/isdNhoApiJm7bWW6gTu7Bnu26BuE07k5SOyfaNQJPl/zL8ZwUYzhyLgYVbGAIsS1pe+eZ0e1Ru/p99IFRvOoRCZ27YPsiEYylDB//78cKz5GvqNyd1GNqpEVledmbUUyZaAzpyJJYG30AcbQTaiqhDO7L4V7HsaV15PqeZHqced3Yf9/99DEPBy9pGd9+nr+t+x/bM3cCkCoORSjzsiunF2kFaed4fn8E1VVeW7x10JY74hhbIcW1AtOxOgjjYMv0abEp+GTDUUA3GeYhkVysU5pwTqlBSCq9R4f2ZLeTbRNK0/QIMzAY5d35n33GADkFe9BcaaPR1V72H4sn6wiB7KqI0sNJZ9zqzAb2sjMJU3Lq4vM+tNcXPozM110nJLGpFk5vLqilgb0jd9CSZZwjzy+yAXYn+umz1flLr8JwWfxgpwrzmIhrNXEtXWGr5btZrhuPQBT5ZFYKBfwnFXMUFXhNBZWv+YXnDodhDUQwlCX5xLXXiGqGVIb4a7youErZu8q4HCB28eDOgFLqHBwUFyQuUO41h3dKB67HRAcC9ZIn7qzl8YM9/GYUcy5vd8ubxHAgAblm3ABZyEKWnvMSbvP0rh5ZjbvrK7cLrxWoaqw8bhrbevRbC8MYOAJzvCJnooZrhKRhDX5gTOzhsfZOO9boqUCUpRYfpSHEEi50ERbZ3gAYwAMfBwkHRGHF/BTp2QAvt9aQnqRByvOzoTBDAHhkLkTcg+cvcDWWSwKNQqOioIbnRfmomdJacxwqXqy1NBKxRhn4pq2Vrqe0FYuyHjmmLHkoIOWH6dyw5/ZfLK+8JzH7BccWiVch/Vm6HRd2eENqU6G/1C+zo339DrDUSTWF5Ywzz6vhm/ISYENwrX2NffVlG5XaDHDe/RMCuBYq1sA6JA+ndwju3w8otrF2pScKtt6l2LGSSdpD2N0y7lDP5MnDD/wlOE7njJ8x/2G3/m4xSauj9xFNHkAhAacOVE/e0sqzZ/6m+u+WM3X/6Z46lfxPfOfAcXFUrk9a1Thhhkb6MGY4baLGKXlpOoc132x5pTnnrL+gVlyQ0wrIazQqBaDGgVwrMUEAFqlzaTw6G7fDqiWcaaYcSbuG9KMpIjyfFZU0JnXKb+uP0KrZ+Zy9ZRV/LLu0Hn/bL8hbevxFt96lpoHcf1f5VWPIWYv5B1lpzAKMAWe+VqNWsHDP63hWv1CAL5yj8BMuSubV9YZdUWoeC60Hw9GK8FF+3mv3QEAXpq9gyKHH+1n+CvFWfDDOJh+G1JJNgeUGF51XsVdznuYp3TDfQ6CqCf6hmCxWPleHsZFztd5SbqNVDWC+rpMfjS9wquGzwmgYkz66t8D9Hx1IeM/W8WMTUc9/dvVLBu+EvP/yCaQ0JHpu2zcNy+v7LRXOki57WCN9vzzavgEWZH5JvlzJEnBXdQCuaQpqOWfwbhQC59c35kRbWvYOKoOYjVaub5HM1oEjABgS94CMovyOFx4GEWtg4XFXmDF3lQyHCmgL0AfcJCA+p+jMxYiO2IoOXAXjoxLQT11oemEAVYsJ+xhRIec4R6pmti0aRBB2Q9gkOPId+TzyppX+G3XbyiqQog5BJPexO7c3aQXp5/+ufyQz5ZvY0POPwA4s/sRZAoiMiDSx6PyDXWkr7vnkFXYlVpImtvNN5vcNJGOMl6/RJzrPIGHw5oSYTXRJiFUq9bzMO1iTEyQL2eIfiMdXfvJmfcaAZe+RoDJd5vJ/s5fu21M3VTEdS3EovQYkWclrDXqYNr4KAw6iWOFMj0TTRh0Eo/0lokI0GHSS3yyvpD31haVfU+wSaqyfeuCFDsLUqBNjInLmteiCrSc/eWVer3uqeDWMH1nRXFhfJAX3oMuG4Q31Foo1WJkRWXH0QLyi4sJCIGkQ3+iM6gskTswuFNLusfI6Ak6+5jhKBBCumAftQWwRkBIPeGuF+rnYr6uE5H3LaGJPZVJ+jn0+/pyfhgTSZd4k2ccQ6uLTi/EVAGnb8dY0/y+o4SfthVzQ0txLz9CDEWc+W8dbJL4ZWwUOgmOFMj0rS/ulxnFMqFmHWaDxJurCvjiv/IkfpNwA/tyKycJFx9wsPiAgw6xJgY3slQ6Xys4tEqIpQ0WaDeO71dVtGyMsnrovu6yi7beGnUCWRUxI8/mINiQyciSmSDBa+5ruK1rGN2iZVBDtHWGJ4lrC+2vgs0/0X3/h0Twf+QQQo8v0/npChEzPOr+dipMgaAqkL5dJJYjGp/aJUxVRYFh5i7hZh8SLwpffMB3W4qZudvGhFYiaXeIWGyc+b4dE6jjh9GRyCocLpAZ0MCMW1HJLlEINuuwGiWeXZrPT9vK59u96plYdcRZ6bmWH3Kw/JCDjnEmetWrRc5qqgLrp4rHba8QRTXH+XRDUYVLg0we/rA7i4VgRq+lemozsqKyNiWHeoufIklxs1Ruz2qlNQB3dwuiZ4wVtxyuxQwv0aBNT1Ykt6GvfjsLP7ubiJt/pmuDcAzaC31KPl+2n0U7MxjTOaHCcQmFDtJ+huo3MFC3mZbSIQzSaTaDDsJI4CULKIGx0LAPP2S35J0D9clBiEBHd0zgz03HKn3rv3uz+XdvNp3qh9MhKcyDv50PSFkGO/9CRsdL7uvLDnvUTaqs4NvD7V81apzSmJFRaCfQVDH+3zO4KR2TwihyuGnk3EP7OcvEie63a/lID9GodReW7OzAQP1mFnzxIJE3fkuXBuGaY9dpeH/hHtYdyGF0x4QzX3wCreND+OT6zhQ7ZNIKbAxoHsMt/RpR7HBj0usIDTByx/cbWLCjvPj51n6N+Hx5xcILp1th9f4cVu/PoXP9cJrF1uL74OrjLb4b9ObJrbFwgg+wV9qZu2yiAFzrsFRrOTFmWIx6LtOvJFIq5IgaRcsOPZmYZKHEGVP9dYbbIeYabodYHwOgCrOJE7cYjcc7dtVlJ3VzMLS9Ev77lqHpXyLxAio6rvxkJS+ObkuX+uHotJhRmYOr4PeJUHgMF0aOJl3CK3sb8I/aHTjz69U13sQ7F4WR71DJLJEZ2MDM+NZWnLKKToJwyzXcMq0fgzK+5SbDfK4xLGZ4yEHG5U5mn5pY9jzpBQ7SCxysTRExIynCz/fNqkJ2wbrjOaomwyAgjMcWZpz+e6qLqgCSttao5ZwYM3bkr8QYsk24fmZczI19Ihnaogm5JU5igi10bxShzX89SIw1hsENurMreRH6gKO8tXweE9qPJsoSRVyQjzQEtYDnZm4nraCY1g3ykIyZGEO2YQzZDoArrzP2tNGgVi7I69wwgKdHNcHhNFDkgP7Normrr4ysKLhVN0EWuGbKRnYcK9/PuHmQia8WV9zPOJIeBRl3Yo6djSl8DX/s+YPDhYe5s+OdhJhDyHPksSdvD3qdnqiA2mGSoKoqb638HUt8AYorGHdBJ2KtsViNtTAeegBtx+UE5m5L5Y8/12MrLmS3Wg+FCB41/IJeUqFhX3p27+XrIdZpwiw6bukcwkP/TWaO6Qkistbz66zfGT/2Kl8PzW+5e24uAP+lATSudF5CIQgbQdiwY6J9UjhXtQ1hZLNyEWyrqHInkIZh5beESZ2CyLOrbEh1Mq61levbWdmR5ebSn6t2FL53bi7tY4wcK5TpneTni2FVgeVvgSpDw77QsE/ZqWUH7Xy0vnzTu1mEwfNiPcUNkq5ajlKSwUDUXXeVPdaoWeZuS+X5WckkFSRjwE2qmsts81IAYntfzeMdzsP9xV4AMa192yYlrB4UHhPJL392ITAHo+81GRa/wj2G6cyQe3PddLipfSDPD9Rak52Kh+fnAbAhFaqKGToUginGghMnRtonhXNjx1CGnCCCbXlCzKgfWn7vuatrMMVOle2ZLq5pa+XqNlbWpzoZ93t2lWOZOCuHBddHk2NT6J7o5zHjRFSlvM1327HMT7Py0/bytps9Ek3oPLGBocigkzzmJKXFDN8yd1sqL8zcRlJhMhIqV+mX0sHgZL3SnLvHjaRt7Lk5gWqcA11vhkOr0OceYGr414zJvQeQuGZaNnd3C+LhXjXk1mYOFiLZrD1QkgvhDURRS2nMl11iHlBwDPKPCLFDSIJPXc6fXiI6U6w7BqeKGaEUYcSNCwPtk8K5tUtYWQEGlMcMk17CGlq+K/dAj2Ccssq+HDc3tg9kTMsAlh50MGFm1W2MH/wnl68ui6TYpdIlvhZ8XvYvFe28jYHQ4eqyw3/ttjF/f7kTysimHi4ykZ3lBT7VQIsZvqV0nRFWsIvZptkgwetukRNZemMMDcLO42/iLAJ7oVj/Iol7jDlYE0acgmZRJr6IvJleuY8wUr+Wa76YyoDhY7ljQBNfD81veXnODgBW7Rdz/2hyGadfyjX6xSTpKuaQstQQMswNiIlNJCoi4rjIQhFdXGy5UJAKeQfQFafD9mncAFxr0bPB1A195+vpMuwihrWO464fN55yLM9c2hpZUWunyFaRYd4TAPzoHswetR4A17Xz8MaF0wbhjas919Bihm8pjRlVuX+uf2ooUUHH52WqCt/cLh436AMJHWtukHWcDrEm7g68hoH2zQxXVnDxlF+44fKRXN+zga+H5re8PV84/C4/y7bofZpG8ejwFhXu6a0TxDouxGIkxFKep3p2VBuMeh2ZhQ7uHNSEwS1jaRYTzKN/bKnyuT9avJeb+zTCoJdok1DL8olFmbBVGIf8YxzCkaJyxeLd3bw0x3PZIDTpvDuraDHDt1SOGSp/m+YCEN1uKPf3ihF7VueLIosiYWexMCQwBUFIohBk600iJyLpxLxPdor3U3Em2PLEfllAmH/vR1SH9uNg2+9YC/ZzfeA6vivuwc60QsZ9uor3ru7I5R0Tz/wcFxLrv4LZD4Eqs1dJ4E7Xfezek1TlpRIKMeThRo8KtEuK4LF+EWX73uK7xOMwS8V57zND4nl5+R2kFPfhcce7RJTsZ27AM9xtv4N5SrdKP+uHNYcY1jqGILORFnG1SDS6fbrY8wsIh0Z9+WarA8cJTb2e6uuF3KjLBkZrtcS1WszwLRVihq6EgKRvMFjBld+Fqbe0oGtcW4K0nJLXsBgsXN6hMe8uHYG1/lQKjCv4v9ldUeQAJvaIwKSvBfnxGsYtK3y98gAA83YUEpD0L/qAI6iKHnvaaNz53QAVyZgB6FEVA12TEnjh8na0jq/swhp90u3rvau68uJfyciKyi3940mKcZBffIhpax0VL1RNONLG0L9RK9bmf8fatLVkrMzg0W6PEhEQQY4thz25ezDqjISa/Xv9oaoq7yzajClqEQDOnP7cMiiIaD90Ja+pmCGpat3qw1BQUEBoaCj5+fmEhJz9hGDutlQmf7+RTtJurNhZobans7SbaebnkFWJLb3eoVP7jt4buAYATlml/9fpXGKfxdPGHyhWzQRe85XYXNaoRMP3K7t1lBJMMQ2kdFRV6HMig8xMvalbtV1tXLJKiUvl2unZbM90VXnNH+Oi/Hvje8csIa41BsC4byAoBgC7W6Xlx6lll13S1MJHIyM8//Ptx1uiN+gtWin5gPO9V9Y1zud1KI0XKtBd2oEBNx11+3jU+CvJSgMyBr3BwBYx5zYQZ4loAZ/UAyw+/ntk7ICsvUJo68+oKvKs+9GnbWa+3JlbXQ8BEgfu1eLFqThdzAilkCQp0ysxI8+hcPUf2VU62QLMuy6aFpG+uReeM3sXwKKXwBRI4RU/0u7rctfaq1pbeW1omGd+jqNQJKUb9PYL9wYtZgiqEzNAobu0gyTSed00FZ2kcoXjOR68uA19m/rfYrROkbUbpk8GVeY+553MUPqWnarxmKEqUJIjnKlNVrHhJOnExpKzGFCF6Nbge2fv08WMcApIQGyGezJmOGWVjGKZq6dlc6RArvKa5TfFkBTqx0l12Qm/ThAbF10miC8gu0SmyxflbZ9u6RjI0/09nEAryRaC3qQePnWj02KGoLrrjKnGNxii/49Zck/ucd0LwA8jjfQ5l5ghu6AoQzhCBcWDJVgIrByFUJQu1iDWME1kWwX5DoUZU1/lRsN8dihJ3GR4k7XPjPD1sPyWho/NBqCelMmd+hlcqV+KSRL38ULVwmqlNRvkphxRYzBbg3h9bBv0kk7cqyT9cSGHKuKk4hbCz9wDkJEMqZshZ1/5DwtJhF534exwA0dL9Iz7dBVZRY7Kg+IkcWFtYc1n8PejFKgBDHS8Qw4h3N8jmPt7eHADX3GLe0NSTwj0bSs/LWYIqhszquLTE9uy7v4HfhwHOiNcORXC6ntm4BoA5NhkVn39OJfo1/KP3IV3o55nzn39fD0sv6U0ZpwJCdFieMX/BlfbCc3pVkjJKmbsJytP2Yp92/PDCTL78TrjZJa8BktewR7egpapz5YdfqpfCJM6eWlul38E4jtCWNUiN2+jxQyBp2JGT10yP5teokQ1s7PX/9G5fafzH5Q9TxTzBYRDaD3RvcUcfOYiHlUV31uYJt5fsguCon3WwcerbPwW1n+JHJxAq8xXcR4XfA5sEc3XN3f38eD8BEWBRS/CirfFfxsNpO2OGyk5RRelOLKJJB9JEjGjWrmp4mxY+BykbUVF4liPpxmwvBVupeqZ1s4XR2Ax1oKuu4oCn/YR66p249jScAKX/ZZfdvr1IWGMb+MF98HiTAiIgHpdPf/cZ4kWMwTVjxlujBFLscTOR1WMFO97mOcub8xN3Xp6c9gagFN2sihlIw/MfRtD8A7cRc3pHDiJl8a0pFFoI18Pz+84mJPJgNfXojNlEJD0JTpTHqrbiu3IDci2RkjGLCRTGqgWcEYSExjDv/8bVq11RomrhMMFqfy7/wgv/FFU6bw+4ACWet+hMxQTFRDFkz2eJD4onoySDIKMQbSKaOXXIvXlezKY+McHWOL/RHEHc13DJxnTsTEtI1p6p0uGDznbe6XW/wdha/78rOSyhUWxagFUHjP+BMBCpTPvbXAhn6ZzmYZnMOkl3h0ezpfyxaxRWhIoOVCX/N8JrUM0gFNO6EtpzFHqIzZvS2PCbf0beaRdpFEvEWrRMfuaU2/qjf0ti60ZlVu7+gXFmWKzAqDLxDJhLcDcvbYKl3ZJ8JJA2FkE1iifCWs1zp+T4wVAiWpiguEfAL50j+DrlQfPPV7Y8yEozvfCWhCblUbLcZGPHyNJ6Ps9gCIZGKbfyCW6NYCopNKoyJliRlOOUA/hKOWNmBFt1bPwhlMLzof/kMme7KqLNfwKxQ3rvxSPO1zDjIMVE70eLSpxlYiEtB8IazXOn6pixlWGZegkldlyd7arDfli+QFtjeFtoppD5xsBeDPga+pJovWZ0RcrYUkHgVEQmigEtLIT3Dax4RQULQoKfSysPVPMaMZhEshCkjwfM0x6iXohBlZMiD3lNf2+yeBwftUb4n7BtmlCWGuNhPbjyw7/sbPiOqNTnBfWGS4bhMRrbZ5rKSfGjG7STobo/8Ot6njLPQ6AQGx8fi4xw20Xm9ShiVCvO8S1EWKq8AYQ1xbq94ToFkLsX5Amino0ygg167D2mkieGkgr3WHG6xb4ekh+h6qquGUFVVWJoICXDVNZbHqQaw2LMEky65XmvOK8insdd/GrPIA9ahLZhHBl/47ow5LEezM4XuRkAqMgMBqCYsV6NKopNB0C3W+Fka/DiNeg5aWi+0/BUZj3BKb329Fox6esf7T3KcfY9aUFHMuznfK835F/BBa+AMDr7qvJQeQHOsR6OHfkLBHFGFqb1lpLVeuMk3l+lnDVQXbD/KfFwebDNWGtF4gI0FPUfgKyKnGRfgNtlF2+HpLfURozHO6zm2+UbtU+O6q1R1oMmww6WsQFs+354ae8pu2z80irwgXaL3EUwZpPAZhjHFbhVPsYL+03KG5REFNXnUXrMKeKGRP1fwMwV+7KR+sd55ebcjvE/EUB4tuLQs+IRmKP42xEF5Ik8p8xrSCxi5gPFqSKtUxdo/14CIhAX3iMT5tvKDusr2PilPNGdsOfd5QLa9tfQ367SacU1rbgYAVhLVQzNxUYCZe+Ay0uRkIlcc0L7O2/DImqPxhdX1pARmEteJ/u/lsIa41WaDacn5Ir7sG08VbMcDlEXkyj1lExZqhI5lRMkSsBcGYNBMnNJwuzxTpDw6uY9CY6xDWgffClqKoeQ9BuSnR7OVp0lHxH/pmf4AJBUVQyS7LYnLELnfkYAQ0+Q2fKQ3FEUXzgTiGsDdiHzpwOrkhUW31UdzjPjWpX7XWG1WilRWQTru7YjX/+16zSednWkJIDdyG5o8iyZfHsymdJyU8hOiCaQkche/P2YvfjOc83q3diiloMiM9/o2gLsYGxdU5Yey5ouy7A2pScslYYqgr/0Zxhug101+3Crhr5wT2ErCIH249pN6qaoGc9M/d0D+Fh1+0Uq2aktC2w9Q9fD8tv+GhdIU0/TOXHbVUL35qbc7FKjrK1a2SQmcdHtqR3kyiPj6V3vVNvCo/6OYst6X4msFVVWPq6ELdGt4C2Y8pOKarKd1srvqZjWnihYk9VxVc13UFURcGxZw+OPXtQFU2VU1OcGC8AFFWiqT6NGCmPVDWcXUriuccL5bhAJDjOw6M9TywhEJwgWjL5O+EN0XW6DoDnjF8TRiG/76hFm6c1wOsrC2j6YSqzdlf9urSy5GCRnDUSM1pHndp1YFhtENju+lu0bA8IR2lzBd+fFDMuaeZBQZzb5dEElBYzfMPJMaO1dIBu+t04VT1vu6+kMUe1NUZN0ek6iG2DUS7h++CPMODGpcCcPT6KGZIknGsDwsRGkjnYL5xZnluaT9MPU1l8oOqkTruALMySq0ZiRkLQqR1A/FZga8uDjd+Jx90mic0LRGL6+y0VY8awxh4WUbvtoDcLZ5BqosUM31AeM1QeNf4MwC/yIA6o8VhwUJ+0s48Zsku0Co5sCrHtqi7gMwVCdHOxiW0NF3Mcd9XunxcqYzvGMSP4agBucf3Ev1v3+HhE/sWjv2+hxZN/sX/22yw2P8h1hoUYJZllcjuudDzD+/ob2a8mUiJZSFUjkIPiuGdkZ3o1O8t1rySJbkPWSKjfA/reD1d8Bl1uEaJce54Qon7QmRtNS9BTtWCr9/8tqh0b36oKsx8GZxGbac4P8pCyU/3re7jgzlUsBM2G6hd6aDHDN5y8zqiK1Hw7a1NyYNMPkLlTuJR3nVhDI7zwGNe7FYuNAwAYnfMl6w/k+HhE/sVdP26k6ZN/s3p/1a9LVFDF+1FcqIVPTnRfriF6vrqQ/BI/z02BKPy25aCEJPJ8ao8Kp7p5yyzEZRNxuRruWlrM8A1VxYwkKZ2huo3ivNyc9GL3ueemHIXCyCa0PiR1hfCG1ZtbWCMgvgNENRMdfxyF5/9c/ojBUtbZZlDGN7SLFMK0hTszSD5W4MOB+QGyG/64Bbb8AjoDU4LuoPHaUWyyVZ0b7x6YhlGSPZ+b0hmg/6PClAlg9Se8ZPiqSoFtkcNN95cXUuL0w9xUKaoqOrgCNB1GcWA9ft9RUuGSNtFeENcqsij6rmYhnxYzfEOFmCG5MEWsRGcoRnFG4MzviIqbtDxxnYb3ibJG8dCwNkgFohPfbvs0DmQWcbjwMLJWJI+qqoz+eBndXljDmsO7sTaYgs5QjGxLpOTgZFRXFOGRKegkGcWeiOKMIy4kxOPrDKvRSvPw5lWP0RVB4f47kO0JFDgLeGHVCyRnJxMdGE22LZt9eftwKf63/sgoLGbpsX/QGfNRXCG48rrTNj6GMHOYr4dWJTUVM3y/g+cHnJhkLcSKHpnHDMK19k+5N9nHnQJySvxMKFiHGdzQwvtrY3nZfT2vGKfCuimifUCEZnP+xiqxqHxiUfli95q2ViZ0CGTtUSdjW8azN6OAnBInEVYTbRJCPeIkVRXvDg+nx9T0UzolfLe1mNdjjP5TwZA8A46sA70JBj1RJmIocioM/S6DtGJxs7UaJa5tayU8wAsvnKtEJKLM1XMoVe129o+6DIAWGzcgWb0gBNaoxMmbcnkEMkk/B4Cf3YNQJfGeOad4Yc8XDjzW6oshPEZognAEKk2c+jOdrqNk92Kiiw7xtPF7HlowmXGttc9DKR+vF60o7pmbW3bs3u5BXNQ4gE3pImbsSa+ZmPH5qAj6fJVxyvN/7rLxcC+D/8SME3HZYMM3ANjaXkf3L/MpdIroF2aRuLF9IIEmD71wslM4m3vQSUqLGb7hxJihQ+E6wyIAvpeHkasGkyBlA9oao0bQGWDw0/DHJBo69/Cw4Vf+z30td/6dy4Fmfh7napCvNwsB6M0zyxOkLw4MpV2MkeQsF1e2jGdnWs3EjJ+uiGTAt6eOGfNT7NzcIdC/YsaGr4RgKbKZcIYDsktkuk9NRz6+YIqy6rilYyBmg4fHbS8QnTE8EDu0mOEbSmNGLLnUkzKxq0bec18BQDAl6CXxJjpjzFAVKEqHsAbCuVt/hrSfNQISOkHmbsg9IP5v0v7mAJIkEdZpNDuW/kMr3WFS/3gc2v3u62H5DZs2rma66ROarE8BCbYrDSjochfWsHqML4Ar2kaQnOnwXMzQGYSzbZcboM1o2D0Htv4Oham8oJvCeNM/POWayCa1aaVvXbUvm8s6JPhXzDiZ5D9h9984VT0POyehoiMuUMfkrsEecW0sQ1XFhreH8g9azPANZysYz87NhiWviP+0vcK/8k51DJ0kUdjuRhwbV9BHv53Hf/+Brg/f4+th+Q1ztqYBcNOXa8uOfTOxO0a9RFq+ncs7JrI2JYeMQjsxwRa6N4rw7L3vBGbf25dL3l9xyvP/Hc5lQPNo/40ZLhus/ACA/2VfQr4sihITg/Xc1yPYe+N22YSraDXEk1rM8A1VxYyb9P+gk1TWy83YQxJwDrkpVRWiWiSIbSvWHZ7qnmIwQVQLIUTN2CF+lj90+vMULUfC1l+R8o/wQv35jMm+CIC3/tnF1AndfDw4H1EqrE3+E3RGlL4P8so/HQC4eVb5fsavYyOxu1XyHCqXNE1g+7F87+SmJAm63CjMkZa9yXWGhQA85b4ZtQq/up1phXRKCvPPmJGyFI5uAL2JI4kj6Pt5ea6vXoiex3p76bPltoHBWn1xrRYzfMKJMUMyZmEM3QyAPf1SdIYSFGdMpes0vIdRZ6RRWH36xA1jeeEGdOYsftm2kvjwAUQFRBEX6CemXT7iSEEGW44UobfuY8aRb5D0TtwlDXiuz2MUlhjIsRUwuFUv8vIjKbYZvb7O+PX2Xoz/bFWl46ocRMnB2+jQ7Rf2F+zgtbWv8XC3h2kd2ZrU4lSMeiNNw5qik/zDF3VvRhFD351HYBMRA83FQ7j14iDig+L9ZownU1Mxwz9/+xomJrjcNeYw0VylX0ITXSp5aiB/yP3KzkVYvVT1qVGJ2ECRFPhRHsxSub1wXlnwnFjEX8CcykXqf71DaBFp5Ib2gVhNEu3rhTKweTTt63lvwxsgJlBP8uRTB+5FKQ46TEnjsw1F3hvE2ZJ3CFZ/Ih73uF0s+gGnrNL207QyYe3oFgEkT47nqX6h3hmHs1gIKbUNy1rJifECoL4uk5a6wxSpFpbK7cqOn1O8cNmEmFV3aoe2GscSJjYua4N7rd5EbveHUVSJsfrl9NdtZu6+CztWlDJ3b9Wvw51dg2kbY+T6doEEGGsuZiQGG9h8+6ljxi/JJXSYksZ3W6p2Zvcpm3+GkiyUoDg6LO9WJqy9qX0gm26L58GeHkxGOYuFm5zWprXWc2LMGK1bQVNdKgWqlffdY4gmr+yctsaoIYLjYMCjANxh+IuBuk0A/HtYc2oEmLGrpMrj17ez0jHOxLVtAzEZai5mNAgzsH5S7CnPf7qhiI5T0vgtuepx1zg5+2HHLPG4110g6ci3K3T5olxYe3e3INZPimNyVw/f31VVFGYEx51d600Nv6Q0ZqQTwWDHm0xwPkoG4QBEUl5Ye8aYUZIt5vLRZyGsLcVghtjWEN0SbLl1zyWqGsQGm3jefRMAV6gL2L32Hx+PyA9QZDb//Dx/mZ6gvS6FfNXKk66JjHK+TK8W9ejaKJLxXRIwmC3eixmWYGh/FYz9AjpdD6ZA2uoOMM30LK8YviCUijmol2fvoOML85mzNdWDg/Ag+UdRZt0PwCfyZexR6/F4nxBW3xLHTR083H7bbROCFYuXcl4aNcLJualT0SllKhSlQWAMdLjay6PSCI5M4MfjrtNXFXzJnjStQwnA1/+mVDpm0EkMaB5N7yZRXNG5HnqdRK8mkVzeMZFeTSK9tuEN0CYhlKWPDDzl+cf+2EqnF+ezeOepC/18yoZvoDiDw0o002XhYvbSoFD+vTmW8d40G5CdmkC/lnJyzAjExnj9EgCWK+1wINYXZ5WbUhUoTBNziYROwgzJU8LaUnQ64YIb21rkSOvS2kRnEHuTQLsjPxGLEDsu3JnBkVw/yW3UJIoM0yaVCWvp9xDvZnaudFmUVUf3RDP9G1i4rHkAeh3ez021vOR4DlHiOsNCXjZ8WaWD7V0/bKTzi/NZtS/bC4OoJsveBCCv3iD6/lU+93/3ojBWTIjl0uZeMhtwlkBAqMgzaNQ6TowZlti/kSQFd1Fz5KJWKLIJFHOl6zS8S2RAJPVConBmCCOHFOff5BQ7OJh/EJv7wt0LzyjO4MXZW9Bb9xGQ9BWSzom7qCmRRbfTIi6IhvFFjGyXQJuo1gxt0bBG1hndG0Uw8+4+VZ9ULBzdcQOUtMSpOHlj3RtszdpKZEAkhwsOc7jgMKp6KjvDmmPrkXyGvr0UU+QSdIYiwoyxvDlmIINaRRNh0dYCmrgW8UaPDxVBQI/KAwbhRPGzeyA2LEhAVJCZNgla4rGmiA/Wc1fXIEDiIddk0tUwyDtIxrw3xebhBcTRQjczdpXQ8P1jFVykSrmvexBhFt99lAOMOt4bHkbLSENZS+yI4+PJtikUOFRe/bcAu9uHfzeXDeY/C7IDEjtDmzFlp3ZnV2zb8c5FYd4di9sJQTHe/RkaXqM0XpROvW7WzwVgvtwFt2Q893jhOr6x5YEWvh5FkiA0UTyW/a8dwckkNm3P6vBLAHjFOJUHZ6cy4scM1h278ARTh/LdTN8pYsYdc3IrnX+ybwgWT7vlnQOhZh3/NziUllEGhjcRMSPKKmJGVomIGU8vyccp+1GsL8oQ4lpgV9NbcFLeLun5gV6YGzpLhLjdnwT3GudFacwIwMFDRrG++NB9OQlkifZlaGuMGqdRf9TWowF4y/gJMeRy3fRsRv2cyeb0C89BeH+um9+SRcy4b15epfMvDwr1qftGlFXPs/1DaBNtZGij48nbQBEzMooV8h0qjyzIQ1Z8HDNUBZa/Lf5t2B8SOgKwMKViYeTDvbzkCuIqBmMgBIR75/k1aoQT1xkOTKxW2yCh0Ib9GCTl7GKG2yHm7lHNzr37hE4PUU0htg04ikR3DQ16JJowJnbiV/cAdJKK+tcDXPnhEnamXXitW/ekF/Lnsg2sfq4PHXa+jVlys0juyDDHG/wgD+XtwYEQklCzm6iWUOg2CcZMgYb90Ekq1xoWsdz6CCN1q4kLEeuNjEIH+TYXd/6wEcXXMeNkFBmm347OnsdmpTEfuscQapa4vcv5t90+Lc5i8boZtYLv2szJuamTkYBuwTkk7JgqDnSdqP3Na4BBDc0sDL2SItVCR91+Pnz//xj/6Sr2ZfqB6UQNsyO1gO9WH6ThY7N5blZypfMfXltZPFWTNIgM5J7BTenSIJyejUVONjFMzJ3SCuzklbi4+et1frHBXQFnMax4BxDFGG4MNIswcH07DxdinIwii1yxSSsCr42cHDPG6pcRItk4pkSwVmlx9rkpVYWCVDGPiO8AQdHeG7QkCXOcmFaiS4urDglPG/SFmFYYFAdPB04vO9z3tcVc/8UaDmXXod/1dKgqzHkYtk9H0RmY3+ABGs5rz/v/VW5z/vawsJofH0CLi2HA/wCJaw2LeD9yOh2TxFiSIkTMSM23k1vi4prPV/tmjKfiwAo4sBwkPR/YLyk73CfJxOiWXp4Tup1gjfTuz9DwGqUxQx+wH0PQHlRVjz19ODrrbpDDkID4UOH+qVEz6CQdE3u3wGjvgmxPQNLbeXr+r7wyI4f1R/b533zVy2w+nMfbC7bQ/cV1LNizh4Ckb5B0btxFLbAduYnJQ6LJKsmiXnA9WoS3IMBQs10L29cL45ru9RnYIpoWcSKvExMiZmFZBToKD16Pq7A1LsXFW+vfYkvmFkLMIezP3096SXqNjrUq3pi3E8mYgylCdPqY2P46JCQSgxL91rW2JtFeAUCvk3jm0lYA3KqfTbSUzzE1grlKt7IFx239G3nVmUejMo/0DuGSphayCOUe5z3IqkTMsYXkbJrl66HVKDf+mVPlZvfYVgGsmRjLA550yztPLm9hZe51Mbw6OIz3hoexbEIMcYEVPzBDv89g0qwc7puXW7OBXlXFZnduithwHvQknHDzz7aVL9aeH+BlAYHbIVrb1KVWNhcYep3Es6NaowKtpIP002/DreqYJfc8v3jhKBTCWn90qAyIEO1vbJUFmv5I7zF3ki5FU0/K4hHDL+zMcjPu92xybJUTMnUVVVUZ/3sWD/yTV+nchA6BbLw1lls7e2mT9hy4um0gc6+N4f8Gh/H+iHCW3xSD1Vjx3jv6l0xumpHN4wvzfDPIE1k7RRRnxLVnX2h51eH7I7wgYlIVkUAOCPP8c2vUOKUxA1Smyf3YqyQwQ+6NTlK1NYYPkXpOhsgmREqFvGv8CD0yWzNcXP5LFjZXZReKuopbUbnit0weWZBX6dxdXYPYcnsc13l7k/YsuLljELOvieb/hoTxwYhwlt1U2c32mmnZXDstixeW+UgMuHMOpG8TYsbed5cdLjmhuPD70V7cWLAXiuI9rTNGrebEdYZyPFUXSQGSxNnHjOIsCKkninTOB0kSLlFxbcFlrzXrAG8iSRLfjYnkFfe1ZKkhtNAdoWfq94x4d7nvhf01SInTzSsffUafhWPoqdtBkWrhEddtTHQ9wvWdo0i+I5bRbX0o8A9NhGEvwEUvQ0g9QpR8Pja9z79NviWCikLoSd+uZ/ynq3hj3k4fDfYk/n0PDizHqbNwn+suXBj4YUyU936eyyHuEZrTea3mxJhxMqV/2Y8if0NSXBDXHppdVJPDu2DR6yS+v64ZH7svB+Ax489sPXCMIW8t9fHIapa8EieXf/QvT/+5rdK5R4a3YNdLIxjR1vctbB+6qAV/TO7NW+M78vF1nVn40IBK19zz039c8fG/fLR4rw9GWAWrPoaiNHIMsfwu9wfg68trQNzitoEhEMy+zylqnDsnxgwJhQn6eQAsVjpShFhDnlVuqjBd5Cvj29dM3rJ0bRLVHIqza4UByFkhSdDzTgAuVRbTUjpUdmrF3iz6v7HYVyOrWZa9Aeu/REXiHsdd3LqjY6VLHusTzN674+nfwIcOmS1GQP+HARhV/AdfNV3Bp9d3Ye59/Std+tgfW7j8wxVMXVHZsb1GUVVY+KJ43GQwB6X4slMfj/RyzFDcwqHZH/c7Nc6K0pgh2xphO3oVjoyLkNChusOQjrvWPjuqtVfdPzUqEx8cyfT7mmNPuxxVlTCGbWBb5h5u+PQg2XY/dM72Egeyirn8o395f8FhdJYjWJO+PO5Y24yrm9zDH/fXJzayiKSQJJqGNcWoN575Sb3Aq1e04+ubu/PxdV2YckNnvrm1zQlnDdiPXEe0vhNuxc1b699ha9ZWLAYLe/P2km3z7d/zSF4R5pi/kXRu2kS2pWFIQyIDIjXX2uNoW7nHGdgiloGJMrcZ/gLgG/dFuDEQGWTm8ZEt6d3Ei4lNjVMSaBJv0bVqK950XwVA2IYPIGuPL4dVYzhllX257irPvTo4jNgg/3K1CzHruLyFlSCTjq8ur7iBfKRAZkGKnRm7bPyWbMNVU66EO2bC3vlCUDvk2QoVc05Z5aYZ5W7A17b18oa0swjMIVqVdy1nRNt43hnfgduNcwBYpbQig/BzjxeqKqo4/bWFr04nNufdTiH483eMVr4PuwOAm/T/0FMnnDFSi2rB2D1EgUMlrbjq3/eZ/iFEBPhXzAgP0HFZ8wACjDp+uqJizEjOcrP0oIOftpcwe4/Nd+KF9O2wdwEgYe92J3efUOxyaTMvJPZcNjAEaAmoOsSItvG8OK4776tXM9L5MglkAWhrDF9iMMOQZ7Fhprc+mUcMv5SdyrJdODEjo1ghz171vfWR3iGEmP0rVRBl1TOqeQAWg8QvYyvGjLXHnKw84uTLTcUsSrHXbCFfSQ6s+VQ87npLWYeKQofCU4vLxb59ks6ixeb5oMhinqZ1xqgTjGgbz2tj22HS6zDioj5pwFnGDFcJ6E0QXr96awtJgrD6YtNckYVgVwNTYCgvuG4E4B7DdBpLxyiw1ZEN/jOhqpQsfIMvpJeIlvLZoSQxyvkyv8kDAYl7+8ZiNfnBOkOSoGEfGPs5tLkCJB36HX+yOvQJRujWll22aGcGaw/k8NHifSzfk+lbl5f9S2DRSwA86biRA6rY+G4b46UNINkJeoNW8F1HGNE2nlfGtMWor3jPjwu18PvgfGLSlgpxQ887Pd+yW+O0TJUv5ogaRbyUw2362QA43BdO4feB7BKc7qrXVXcNaorZ4Acx4wQSwwIY2S4ei1HPZzd0qXDury2pbDyUxxvzdrFyr4/nREWZ8O+7ADxTMr6sq1JisMH7P9tlA0uw1uK7FjOibTwvXt6GIfrNNNalUaRaWCK3JzLIcna5KVuu+PvHtqnZnKUkQURjCEuCovS609U0rh006AOqwrOGb+Gkcpk670K44WtY/DIA+1tMYrbco8rL7ugSjMEfBHwtL4HutwIQvuoVRjgXEGg28P41nSpc9vO6w2w+ks+LfyWzer8PxVF75sPh1aA3caj+aBYcKF+3hno71+csEYXfZm29UZsZ0TaeZy5tjVrUCVdeD9AXobrDiAu18Mn1nRnRNv7MT6LhUSRJOIcqtga48roDYI77EyQ3Kfkp2N320z9BHWHD4WMA6MxpQlird+AuboSafiOXdQ4hz55L/ZD6NAlrgkFXA3PkM9AkOoiL2sTTKroBT49qcsIZPfu3XYkrvyMqMu9ueI9tWdtQVIW9uXspcPqmW9b6Q2kctG3EGLIVVB3XtLwaQHOtPQHfv6v8hACTnq8aLkbKdmALaUyvTldwSaBohaG5SfmOe7oF8WuyaIPxqXwp3XQ7GcwmmP80jP60Tju72d0qWSVVJ95+viISk94PFhWnoVWUkb+ujuajdYX8va9iUH90YR4rjzh4d3g4f+4sYW+um2H1FHJtLiKsJs997o6sg3/fF4+73VrWohXEhveyQxVbxp+ckPY4ThuEN9aS2nWAMW0jUOdvBxuEdBrDK/Xanfv71mUTC01/vo8FRomNN0cBWMJ8PZozctng/vz4yyCuNSzmTeOnXOz4P37cVszLg8J8PTSvY3erpBVXHTNmXBWFzh8F3CfQIdbEtHFRfLSukIUHKt6b7/o7l+vbWXlpUBi/bC8ms1ihf4JCTonTszHjZBS3cJICXE2HMzO3PpBXdtorr6mzGAKjz72Vs4Zfc2WXJC5rH8/ChXMJ1jcgJDRSW2P4mrD6ZHR7hAbrXuIOw19sVxoyS+nNb8klPOgHXSG8jd2tklZUdcz45zovtnb0ED0Szfx8RSQfrSti+eGKMWPirBwmdwnif31C+GZzMU5ZpUeM7L2YsfIDUUAX1RzajAHEOuP3HRVbOXqtO4azUGxuBvjQMVLDo1zVrT6j28eyZPE8AvWNCQkNP7v3bUmucHby1HshJAEkPWQkQ2EqBPlpQWAN8eGIcMb/0Ysr5OUM1G/mNeMU/tw4iJv7NfX10LyLy478511Ebf8dJPjVPYBn3BOwI8Q1y2/yQ2G/MQD63AuN+sPyNzHlH+FT07ustAzgrrxryKU8zt8wdS2PDG/BXYOaMmXZPiwGPc1ig8kotBMTLFpdes2RJycFfpsAqszOsAH8liYcE736KXMWi5hhPkPbZ41aw7U9GjCmUz3+3paKXieJ922SFf0nvcQFzS+GmJa+HeQFyJTL43hl1rV8bHqfOwyz+EUeyN9b0xjdKdHXQ/M6dpdMWn7Vm/trnxhSw6M5d4a3iePzG7vyyZK9bDyUV+HctV+s4cXL23BDr4a8v3APsSEW6kdYayZmACx9DZxFHLM0Y7ZdCNESg2tIqKy1+K4T3NCrIdfuWgcHoCC+N/d27EvrelFnXmfITrGPEdfeN+tOvQEim4mOLSXZYr+iLtD7Hjiyll4kc7G8lr+VcoHpkt2ZDGrhh/NsT7BnPvz1AADutlezyTKoyss23eZ7h/MKdLxOrLm3/Q6z7oXAKC7rMAK9JDFl2T42H6nYSenqKat556oOjO6YyFv/7KZpTBCxIRbvxwxFgUXCtdbZeBhTD8cDIi53jqsBB0dXMYTWF59bjVrNxL6NuaR9JN/99y+Bxo50jG3n/bmOxmkJMgXx4Ih43v5nBIbg7ejNmZgil/DfoYsJMx+maVhT73Zn9jFphdlsSzuMzpRBQP0vkAwlyLYkbEcm8OOdSWTbsqkfUp/GoY39Qlh7Mrf0aUmgycSnS/dyIMsF6LEfGw+qDmPYRj747wNu7zCZrTsb0zZxC21iG1BQoquRdYaqquTZHHy45D8scTMAiFYHEWwKJsYao7nWnoD/vbN8hcuOtF+0WwjocTMDG/lo4qoqwh1EksRGRh2+CZ4NSaEGfr8ykpm7bXy7pYQHXHcyQ3qahoVpQmB7yVvCnaUOISsqe3PdXPpTJlV1pr27WxA9652hSthZBNn7oDANijLAlgNuh1gIAxitYAoUSZGQBAhJhNB6oPNsQqZtjJFPLongg7WFvLW6sMK5P3fZeLJfCPcfb18+Z91RrJLYHI8KMnNb/0bVc3PL3gvznwVVhqZDocPVZacWH7Bz88ycCpff2snLbW8Vt3h9/VlIqXH2mKxI922Gle/ToUFXCDiPzSlnoWjHaPJ9y+VTYjCLe0NGcq0Q1zaLNJJ18X3kLEmmniud54zf8NDWyTzeJ4QgU91UscmKytYMF6N/rdpB47E+wXSIrR1xsnO8iamXRfLqinw+21hc4dz3W0u4t3sw/1soElUzOIRZEs7uHokZVbH9T8jajdMQRJ9tl5G5La/s1H3dvdQKz+0U4lqNOodJr+PiFiHCZUNzC/MLGnQaysHM3TQ48CuvG6ew15nI+2sbcHe3YL8vYjtfZEVl7TEn10yr2kHjhQGhNI/0Tbukc6VnPTM965l5fFEeP22rKGT9ZEMR17a18uxSETNacgCDJBZWHo0ZexfC/sWiQ0a/h0Cn55ftxWWxqpQn+3rxM+8ohqgW4KM2VxrewWzQM7xZiCjKPBtnKJdNvAdCPSzaCY4Vz5u+HQqPCYGth3MGtYXuiWY+vSSCNXvuosvB++im282iuW+g9PkUXR3dXHLnp1L87VWEZm/Gpep51j2BH+VyYdTrQ8NICvXjtHJCR7jic1g7BZJn0Nu+lGWBm3nENoG5Sveyy96Yt4v+zaJ5Zc7OSk8RH2rh2VGtPe/M4yiEn68FWy6blMZclXYzpbLalwd7UfjqLIHo+lrBdx0jwKTnis71yg8sewNyU4QAqttE3w3sAmZAAwv5w0aQsvofGjl28qTxB+75JZLLOybU2Q1vt6wwb3s6d/24scrz71/TiZgQH7b1PgeGtY5lWOtYbv5qLYt3ZVY49/SM7TSMCuTt+bsrfZ/XYgZA2jaUdV+iAx4suAr1eEPSR3vXgINoaTczk5fyYBo1R+Yu9AeWgKQjoetlJCSc5Zq4KBNCk8Qeoq8wB0FUMzj2H7jtYKgd95PTEhwHbcfB5h95M+RXwhtfyo8bxT3n5q/WkfLqyLoXMzJ3we8TQVVIiRnKoPWjqrzs80sjCLP44Xy1151ir33fIvjtJpgwh0vad+GS9vGM+3Ql6w7kVrj8gV82o5MkPly8t9JTeS1m7JgBaVtw6ALouXUUuZQXvNzZtQZihiyDVRNh1RWCA4xc1LoeDUIaEKoVaPoFt/drQ4GjkNWpoznI95gil/DRgk40izMSZg4j2lr39hbdssIX/+7i/+bsRzJmY23wBTpDEbI9gZJDE3luTAIl7lySQpL8VlhbytXdGjO2SxIXvbOElEwnoMOeeiWqqscUvo5PN3+CPXUMczd1R2eZheJIANXo3XUG8NSf2/hhzSHMsTMwReSjOCO4psNoDDod9YLr1b35SDXw33dXTWO0wJ2rYdUHorVFTeJ2gD1fCCp0OiGqRRUi29KxWUJFK6cLkK4JZrommDlWKLMgBW5xPcx00zOEpG2F5e/AgEfrjAi5wKHQ/rO0Ks9N7BhIk3AD17WrQohnyxUurYfXCRFcwdFz/+EGC0S3gOiWkNgF4jt4rNXPHV2CcMgq2zJcLDlY7i7V7Yv0sscy5Yul7CIHr87Zef7tkvOPwN//E60x4ztWeI/IilpJWHt9OyuP9fGy0MVZLESUHmqHIRkMREycWPZYwwdYQqDxQJDPsx2o2wWBtaACOTAK9GaxaV8L3DR7NQqDgKdRZt7LWP1yFsid6Tm1J1vviKtzE8DsEpkuJ9xHT+TG9lbax5q4spW1hkdVfR7oGQKSRHKGq4IjYfep5b+r4smYURVFGbB+KgDP2q4ik7CyU7d3DuS+Hl5IRrkdIu56QXipxQwNjappMPR2mJtCwJF1fGZ8m8ucLzHiBz2Lboz19dA8TmqhTK+vqo4Z17a10quemVHNj8d5V4nY9Mg9IL6K0sGWD47844WgOlHgaI2AgAgIiYfIphDRRBTl1GC8fapvCIFGiW0ZLlYfdZYd7/dNRtlj5QQfQI/FjKIMWPGOeNz5RohuQYlLqSSsfaBHMJO8VcTndgihY6BnXaS0mFELseWJzW5vFMNZI4RIMSMZCo6JTeALVMw9okkAI5o05/nPb+JZ9VMeMPzGYx/34/W7r/f10DzOsV3rUH+8ikQpmzw1kMmu+1mliFzp1W2sDG5o5qIm/r82FC6290GjgbD8DYKPu9j+a+7P3fnXlrnYjvpwRZXfnpZvZ/L3Gz3b+tLtgJ+vg4xkMtQwbnc+iANRDPlUvxCuaeOl9ZviFvHZw45zWszwM/KPwPK3xeMO12nO9j7kshZWiHgAedodjNKv5nd5APf8FM+H13b29dA8zq60Qoa/u6zS8XCrkYtax3FxuzgGnujA6CwR7aqPrIes3ZC1R7hR2vJAdoh1hsEsDAlCEsQaI6GjyPFHt6yxAoE3x3Xg/YV72HQ4r4Ij4Q1T11Z5vVdiBoCi4JpxH0ZkZsvdWa20BuD/BodyWfMaiMUuu4in5uqLa7WY4WMimsDYqbDnH4hscubrQZj4GMwQ0dD3xTnBcRDWAHL21XjewWt0uRF2zyXQlsorMYv5kbZlp56duZ0XLm97mm+uZZTkwE9Xg6OANUpLrj90Iyf2bGgUpqdbgpkxLQPodSZzKV8h6WDQE2I//th/8MNYmLQQIpvw4bWd+XDRXtakZLM7vajsW+77eVOVT+WddYYTFgrX2o8dIyt07PhwRDhDG3tZlO62i/vF2RQInwVazPA9VqOVVhGtMF6gOSB/xKw3M6lvC3pmurhn9loMQbtxhv3O9LV3EdgnhSBTEAGGWpCrOUtW78/m6imrAZAMeVjrf47OWIBBjqOL9V4uviqayLACEoMSaRzm38LaUow6I9/e3Ju3F25jyc5scot1ONLGAHpM4auxxE8DnQ3FGYEOHYoj3nvrDGB3eiE/rDmEPnAnpohVAIxvOpFGsW4SAhvWGmF9TcUMSVVV1WvP7gMKCgoIDQ0lPz+fkJDzECccWi1EcDVRWaO4RdWfJAmBVVCMEN/p9MLVSnYJN4PCNLDniolbYNQFK7JNK5Lp+aXYEB6g28xXpjfQoUD326DjtT4enWd4aXk+X/xXXOW5A/cmVDxQcrxCbu9CyNxR+RuCYoVzTWCs2HA1BBx3+VVEEstZBMWZUJAK+YfFxPdE9GaRuGrQBxr399hG3b1zc5m521blOSMumnAUgyS2wCODzEy9qdu5tW7NPwKz7oeSLAhvBJe9XzaZV1WVKRuLefXfgrLL+yaZ+GRkBMFmLycHCo5BRGOIaeXdn3OWVPteWUeo9utwYIW4V5+rI7HLJmJN/V4eSU56FVWF1E0iFgX7WTue01CwYgohyT+SqwZxkeM1hrZJ5NUhYb4elkd5ZEEuvyVXfT+tFDNqKddPz2LFYWel4xIqRtw04Qh6ST3/mFEVqgr/PAUH/+VwQCv65z5Z5goyvImFdy8KI8DohZhRkg3GQEjq4fuE9UloMUNQrddBUeDgCs251h+xF+D643aMxamsU5pzvfMJbugUzlP9akfi4GyZNCubBSmOSsejrDrW3xIrNqhSlsHRjZCxQ3R/OB8CwiGhM9TrKtYRNfR+V1WV4T9ksjvHXemchIIJN004is4TMUNVYM4jcHQDRLeCyz9AkfTcOzeXv/aUr6muaBnA/w0J854TcnGmKMJN7OZXcUOLGYJqvQ6yW6wzzsa5VnFDYTokdRc5JW/hskHmTsg7LPILxtpXwOUp9mQ52f/bEwzXr2enksS8Pj9x3/B2vh6Wx1BTlmH7djxW1cY+JZ5JrodJUUXyvnWUgTnX1oIC0apw22HNZ5A8A1SFHEJ4wjmxgottVUhAXKiFFf8bXP02fLIbfp8AO2Zhw8JVjifZogpxy/XtrDw/INR7rf7seYAOGvT2K4G8FjMEHnsdfpsA26cLAeKo98FQO7rY1GWyF7xD5P4ZHFaiucj5Gvdf3JHbB5ylqK0WICsqQ95awoHskkrnBraI5uubj99jS3LE/XfbH2L/TTlPowJrJDQZDE2HQYuLa2St4XQrNH/q77O61qMx4zjudd9gmH0vRaqFIY43SSeC2zsH8lifkJoxESjJFvPRpB5nvtbLaDFDUK3XwWWDg/+KufzZmGjkHYao5hDT8vwG62lcNiHMlx3iflAX2D0PlrwKBgvJYxcz8puUslMvjm7LDT0b+HBwHkJ2CyHq/iUcJYpR9pfIoeJ7d3xrK68PDfPN+M4VZwnMvBty9kNofbh1YdlavMTppvUz887qaTweM1Z+AP88RaYayiDHWxQh1uz39wjmvu5B3o8ZpXsb9Xv6hfhdixkC7XWoe8iKzI6cHfyVvI3v972BpHNiT7uUGzsNYlzX+jQPb45O8p888flid8m0fHouAJKhAGuDz9CZslEcUQyLephb+tcnvTiduMA4WoS3qJUi8JScdAa9vv74/1RM0XMxRy0FwJHVD2dOH3TGIhR7HBIGj68zihxu2j47T7y+jd5DZyimacBgHul7FXr0tI9pX6fE2qfjbO+VF6ZK0x9wFkFJ3vHq20ZiE7KqyUZQNIQ3EJOSvENQmCpasFyAreXjgvR8OjKcO+bkslTpwAuu63nO+K1o8WYJg5YjfT3E80ZWVCbMyKng0HciEzsedztSZDi0CnbMFIvI0rY8AJHNIKkbJHQSLVLORQyryEJgm7ED0rbCkbVQnAWH14ivf98VG+RNhkDDfmA6/420d4eHkWtTqvxdXRjZSUMaqqkESTayihxsP5ZP+3pnKW7I2gNzHz8urG0Il75dYTPy7732CsLarvEmvh/j4TbiVaEqQkwToLXD0DiOswgs4aKgwt+RJAiOh/yj4l5RS1rBhvS6mZyDa4go3sebxs+YsP1ROsYZuapNLXjNz4BTVhnzaxbbM6vejLi3u58Lts+Bb0dHcslPmezIqiiWUpFwYmQHDWmkphIo2c89ZpyKvQvg4L8okp6JeTeXCWsvaWbho4u9eB932iC8sV8JpDQ0LggsIRgvfgXHtLvpxm7eMn7CPf/dQ9toI6Nb1n7xmM2lMPT7TI4WVhbLxpHNR3Gr4bflYq15IoExx91oG4k1qyUMLMGgM4q5rdsu3DpK16nZ+8TGgi0X9i0UX5JerE+aDIFG/T3WFaMqJEli7nXR9JiaTmaJUuGcig4HJnaTRH01HavkqF7M+O97IazVm4Vric7AFxuLKghrb+scyBN9vSjQVlXhfhicoMWNCx1HoRBZe3utaQyA2HZisyx7D8hO7zjl1gKaRZn4rek9dN5/Dy11h1m2/FXmJLzFyHbeac9Wk9g2T0c/bRJWyc1qpRW3OR+ggPK1xY3ta/FaymA57mI7AJa9QUTBUT41vcssuSfPuCZUcHY6ERVIzbezNiWHXk2qIeJwO2DabbBjFm7JwC2OB8uEtU/3C+GWTl5ewzmKRa6wFm44aZwlKcuEsFbSQfdbNWGtnxA54HayD/xLEpncb/iDV/+20DQmiCGtan+njJxiJ51fnH/K81d2qQepm2HVR7BtWkVBbUg9IfaPbS32NILjRbc3vVHcL9120Smi8Jgo7jm2STxXSTZs/U18GSzQYiS0vwqaDfNavtJk0LHn5Ytp9uSZBbYeixmlFKbjnPsUBuAd91jSieCji8O5pFkNbnK77cLAROPCw1Eo9qLDknw9knKMASJPcWxjeQew2k6zi2D7n5C5g9Zb/4+OSbez6XAeAE//uY2GkVb6Navlbb7/eRL2L6FYNXOL8+FKwlqAkU297KrqSUxWGPkGTJ8M+Yfg+7Fw899gDsJqMrDzxRFlgrDT4dGYUZwFS18H4HX3VWXC2p+viKRnTTkBu+xib8MPhLUaGnUZvU5PUnASfRrn8vPGS5DDp2OOmctXK5oTGSwR0i6EhKDabb50OKeEfq8vBkDSFxOQNFUIa53hlByaRJ+esWSUZBBtjaZZeLNaKawFaBQRy9qn+tL9pRWAhDPzYpCtmGP/xhy1HEnvwJk1EMmUieqK9uw6A5j8w1qQ3FgSf0RnKCbeWp8n+91AniOPNpFtLhhh7bmgiWt9gT1PCChiW4s2Fvoz/Bl0elH1ZI2EgljI2itcMINia43IyVOMaBoA5ALwtTyCOCmHOwx/wfI3hftjo/6+HeB5svaYs0qxabMIAy8MDKVzuB02/wzJfwr3yFJiWolK7cYDqlepqdMLMWp4Q1H1raqQmyIqyfcvES2aDq8VX4Z3oPFAIWaObXfOE2WdJPH+iHA6fZ52ymsOEE9z9SAmSSanpLJjYZUcXgMLnhPVq+EN4ZK3y9qfPbc0n683V3YE7p1UQ4lmV4nYhLR4boNdVRRcx1IBMCbEI2mb6rULlx2iYmrPQjMgQgjVnYW1ZxNdbyRi5NPYf72VAfot3KH8xf8WXsblLaxYDLXkdT8Fyw85qhTW9kg0cX+PYLrE151NNJ0k8fmlEfT9OuMUV0ikkEAr9QB6STn7mHEqijKwLXmHAOAd5xj2qPXKTnWO8+LrqrhFLPZS8ZQWMzQ0zkBEI4wXv4jzr0e4VL+Go2oU9/9zHZe3CKgZNyAv8s9++0nCWpWu0i4eD1tAZ/tqpGPHhah6IyT1FK76CZ0g5DxEYrIT0pOF8PTQSiG4PbRafK38AFpdCq0u85oTvk6S+H5MJMN/yKzyvBsD+0mktZqCTlLPL2YcXgPrvwJA7Xs/k5ZZWXjgWKXLWkV5OannKhFuQ17ouKPFjFqGsxhi2pw5r+QJ9AYhzjNaIHOXEL0ERteeNY0HeXBQfe7adStTTW9xm2E2E35qw8h2T/p6WNVjw9eYZz2ATlKYJ3flXtfdOBDz3+vbWbm0WQDdEurAOiOhI4z9AtZ8hrpjJqP0q+mlS+Zl13X8qfQpK6w7mYxCe5XHzwpHEeov1yPtX4xT1XO38x5WKuVtfhuHe/nzqxyfB3hBhK/FDD9BdsPf/xOPmwwWczkN/8BoxdDvPlj6NJP0c5grd+eWb+DA/13i65FVm69XHih7HG41klsi8lT3Dm7KRREZtNl8l+i6V0pcO2h7JbQaJeYPrpLj3fUKRQGA89hxgwqEnZ/BJIS3ce2h2yTRzTFzFxxcCbtmi/2K7dPEV2h96HEbdLrBK3kVo17HVzd34+av1p3V9dWKGYBLVrj0veU8mvssQ/QFbFca8LU8AoCE4BrcD1RV8WXyTAGIFjNqGfYCIWT1N3OQoFgh0C84DCGJvh5N9ZEk6Hs//HkHJM/g01Hj6Hm4/LNxw9S1tTtmbPgG1nwKwIOuO9mp1qd+iJ5DBWJ++mz/ENrFGGvffoY1Ei55C2bcCWlb4Jcb4LpfQW/EYtTz3tUdue/nTWf1VNWNGXaXzNy3JzNaLmCb0pA/5HKdRGxQDcUMRRaxW9sD19CoEULNodQLrsedvXvzzrptGIL2YEn4jddn3EGH+ikEGgMJNdfernwv/LVNPNDZCW30FbIxHcUVwvVNH6VZj3giwvIJM4fTLKwZZn3tLrSJCQrl2VEteX7WTgCcOQNQlQDMcdMxha9FZyjAduxqdOZUFEdstWNGidNNj1cWUmh3AyqW+GkYrAdQZTOT299DgaOAuMA4Yqy1q2tVTcUMLRLVNPZ8cDlEMiGi8bltgOj0okqwXmcIjBICW7maIpJayGXNy1Xy/+e+hvyGI0TiZeGLcGiND0d2/kzfWbGt96uDQwk2SbzR202vw1Mx/3KVWIAUpokq7g7XwFXfw+hPoO0Vnm+BIkni/dnxWrhiCoz/FrrcDKFJolp591yYeS/8eiNs+lFUjZ8D4QE6xrcwEkneKa/ZTQNkVUeE1YSswJYj+SzZncmWI/nIJ5pRKbJYoM19XAhrEzqhjPoArBFklsh8t6W4SmEtwO2da8jd0VEkNr6Nnqu+VO129g0dyr6hQ1Ht1QukGjWM7BICltrUFtxgEs5o9iJfj+TcCG/It8G3APCQ4Ve6STvJsytn+Cb/57fkiu32nu4XQohZ4ql+IfSqZ/Ze+2kfkRis5/KmBqKPF9dUxQ4aoqjSmWNGFSiqCkBaoYujs14iQC1ho9KUj+XLK1x3fTsvJpMdRULAbvbOfUGLGRoaZ0aX2Jk3TXcBcLthNhP1f1PoVH08qurz8/bymNFPv51/I1/hd/MLdLGtRFIViO8AAx+HG/6Ei14UBXTnI6wF0JuEaKnbLTB2Koz7GjrfKFxwHQVi3fDztbDgeeFy6wWaRxgY2dhADDmnvGYHDVBUzj1mFKSiLnoJUClqcikf5vdl4YHKBZI6qeKa1SvYC8Tr6oWNTi1m1CLcDuEm7QWR9SmRJAirD/EdhWNcwbFy4d4FhMUgsc7Yle/cQwF42/gxrtzDPh7VeaKqsOxNmHUfOhR+cg/iTtd9RAWZubNrEBEWHXd0CaJnPbPHWs/5HGMA9L0f5eK3OapPJEoq4B3TJ/xuep52UtXxKSbYgqyorNqXzYxNR1m1LxtZOf08QVFUyNqD87PBSPsXU6yameh6lH+UbmXXRFl1DGzg5c0g5/G1hhcEZ1rM8BPWfQEZyeLv3G2ScK/V8BuCm/dlutwHvaTyjvFjrNSNz8q0jUfKHjeMCuSmXg1oHVjEnbmv0/avS5H2LRKdLNqNg1sXw4Q50GY0FGce75a3DjKTxX6H+/jeiN4ABmP5nplsh+IM0W0vdQuoMjQZBFd+DddPg+63icKB/EPwz1PwdmuxR1CY7vHft0+TKPo2PbsOeOcdM4C9GUU8MW0rHbJnMUT/Hw7VwAOuO5HR0yLSQMfYGnTmkh1ivuehNYcWM2oRbrvYvwj2Q5dtnU50e9WbxRynLhDdAlqOAiB22eNYqLrDaa3j4EqY/RAA7yvjmHd8Dtwyysi41gHEB+m4opWVrgnm2lnYHpYEw18VubD9i2DG3WJtBVzUOo6ejc9unV6dmLEzrYA3v/6FUW7hJP+860aU49KfXvVMNAqrIY+9UoOpEzrJVhctZmhonJ7EoETaJERiT70SVbagDziMKWoJLsXF/rz9OOTaGUtKnDbmJ2eC5MSa9BWy8Qh6NYig3Du4qFUDosILCTYF0zy8OVZj7e86CHBll/p0bRAO+kIAXHndsR+9DlUxYAjeibXhx4CMznKMoADXeceMbUfzufmrdWXCWlP0fIxhG1FVHY2licQFh2I2mGkQ0gB9LTP4rKmYoTnX1iTO49W4cW2r10rDEgpxHUC/C/IOClfbutD+4ix556IwZu4uFaNKbG1xL63sxUSmLRftJYY8U2scbNcedTB5Ti7ZtvJd3CENzVzTsJir835BWjxbCPEAIptAmyug6dCa/3uH1YcuN4nN8fTtsGuOqDzPPwxrp4gkbv1ewvW2fk9RSX4GXh0WzS3HDrClUCKHqqtnjuriOZTn5O3568gqKp8ERAWZua1/I3pHO8Qm0LH/AMhMuoiUtvdyy9eFPN1Pz9ebi0g+qZU4wN/XRnvfTepEZLcQxGtogHCXMgV5TUTnNYKiIMdY61ouTbxqPNOmbOMK/Qo+MH1AelYbDuWH0S3BVOuSNssP2Zk0KwfHCfqFUc0DuKVTEDd3DERXy36fs0WSJN4eEcMt3xxkU6GBPKpO0qTp4zmaf5qY0aT8Pnwwz022TaHAoXDP3FxeGRzGkeXfMVneQolq5kHXZGTE4mHxjTHeT0S5iiGyec24zmloaJySB64fzetT0nnU+AvPGL/j6KZw9tS/tPY5aAALU+zcMksITLtJO3nI+Bs9dTugGFSdEanZMGg7VqwxvEV4Q+g6UawhDq0SrYqPboT9i8VXw/7Q+QbhhOkhJEnig5EiZtgKiymk8kawio5sQxypBWcXM/blunEU51F/yaMEOQrJD2nO0H3jybQXVnrulTfHet9NSnGL4lJ/3OjUqFkcBWAN96g7zFkTFA2GTpC5Qwhsg+MuuHbzS2+Mpefn19NJt5e2ugPYfrqBbSN/pVPDWuTuoCgw7wlY8wkAH7ov5033eEDi4mZWHu0dwsO9guvsOkNfryNxN0xl1revMUReSRfdHmaYnuZPpQ8fukezXxWtFK0mPWn5JfR9bROp+eVJ+vhQC8+Oas2ItuVFKbvTC3HLKjtT89gy8wOeMv2IyVVEhhrGbc4H2aQ2Lbt2/aRYoqw1sGHhLD5u8HBhfUYvGIoyYfHL4nG78V7rEKBx/ugkiZ7jH+Xo7xNpqEvnKcN3HMm9mNxiF+3q1T5Hqen/HeGBXzZXODagWST3h/2Luv1ZpB3H58jtxsGAx8EcKOYKh1YLwZ4xQHQgPJ9cuewS+ZO8A2I+3GIktL8GDq+Cjd+Jecnqj2H9l9D1Fuhzn8fmzCaDjm8mdqfva4sqxIKTCTIbyCiwVbquqpiRfKwAg15ibUoOr83dyTcTu3PFxytJktL52/QdAG+6x7NbTWLL7XGEmGtYOO+yib9XHREuaJwDtjzhEOuvHewCwkRX2MydQtBXF+aqPe6Ag/8i5R9mVa91dFrVt+xURoGdzCIHbRJqUczIOwS/XA+Ki7/knrztGl12qnuCiUmdg1BUtfavM+LawuBnYMEzsOVn4aY89BkCTHp+mNTzjDEjxGIgq9B+VjFj65F8Akx6Fu1M58NFe/n5tl5c9v5iZpj+D71OZabci3VqSyQgeXIcAcYajBnOYvG7G2pf/lRDo7Zi0ptoEdmIJy/P5//mX0ZA4q+YohawN7s7BsVJgDGF5uHN0dWiwsupK/by4l+7QHIRkPQteutBTJKVF/s/TVJwfXLtOVj0FpqHNyfIQ50V/IFgi5Ffbu9F39dspObbAAl3YVtKDt5OQL3v0JszsCZ9C3lD2JKxjSf+XE96rplSH9WqYsZ/h3IJt5r4c9NRvl55gF9v78WlH6w4flbFFDUfc5ToMjKhzQSGNOhFrj2XlhEtCTZ5rlCiriGpqlr77XhOoKCggNDQUPLz8wkJOQ/h0qHVYhLgaecPxQ0FqRDdUmweemLCKLvF4iFn/wUnsN2W4eTSn7PK/m/AzfyEz2mUs1xU5w98DJpd5MMRnp6dWS6cssplv2RVOP5MhyJuUP7EuO8f8Z4BiG0rNp3rdfevRaKzRGyK7/ob0reVH7eEQeOB0HQIxLY5rVvCyn1ZvDx7J3bMFGMhjVM78EooSEAgNppziNH6lVxrWoZedaEaLDxUMoFpyplF1bOujqJdTA1O8F02cU+p30skDj2EUlLCrs5dAGixcQM667kluap9r6wjVPt1OLBCJHbPxQGm4BhENIWYFuf+83yJosDRdcKBPTDa16M5JzYdzido9mSa6o6xTG7HBNf/eH1YBFe28v/ksKqqJGe5sbtVxv5WMWY81ieYm9oH1myixIeImLELOyYKCSCDU8/V9MjI6AiliCQycaPnuj5NuKFLDBnFMt2nVnQx6aXbzvfGV9BLKo+7buEneQgA86+LplmklzehFbdoq5zUw2uFGFrM8AzVeh0UBQ6uEA4Gtcm5/AJkzRE7/838kDsMfwHwiOs2hgwfw4imXnYi9QCqqrI900WBQ+Xa6dkkksnjxh+5VC+6e6g6I1LLS6DTdb6L5dl7xcZ3yjJEz1eEyLb7LWJzzEOUxgwHRvIIIouwU15rwI0bA+EUkEgWbvTc0LcJ13WO4Wihm8FfHeFb0//RQ7eTVDWCMY7nq1y3LL8phqTQGiiSsOUKt9L6vbxSlKHFDM9QrddBdot1hk53eueXvMPCLTqsfrXGWi1cdiFkyTss5jFG/79XepLlh+w8OWMHf5meIESy8al7FG1ueod+zfx/vaS4HBT8fBth+/4E4AXXDXwpXwzA60PCGNMyAGMd64hxKlbuy2LK7FVMNM6jv17ktxRV4i+lJ1+7h7NRbYboc1o1T13Skkn9mpCSVcygN5fQXdrBI8Zf6KbbDcAapSV3O+8hk/Cy71kzMbZm2rQqsnCFTOoucseefnotZniEar0OM+6C/74XRU2jP/Vo1ywNz7Jx/So6bngCnaQyyfkQC5Qu/DG5N10ahJ/5m32MW1bYmVbIkdwS7vh+Y4Vzn18ew9AdzyAdWikO1OsGI14T3TByD4qOdzq9KAby5DxBVUTO3VEo1tnmEGEEsn4qHN0grjEEQO+7hcjWQ256c7elVnoNzgYJsfp55tJW3NynEduOFjDqwxWVrjPj5HfTc7TTHWCN0pJrnE+x/tZ4IgJ84B5VkCruLbGtPfJ0WszwDNV6HVw2OPivEEyf6vNYOneo19W/CzacJXB4LaB6xZ3fJ+xbDAufB52BxQP/4OY5FTty/n1fP1rF+/9731GSj/LFcAJydrBVacg457PYEfqFKZeEM7SxpfaLak9m+3T49z3xeORb0H0SUP2Y8dyo1tzUuyFr9udw9eerK103WT+T/xl/JlcNYqjjDbIJZevtcQTXdDFG3mFI7AKhiR57Si1meAbtdajbqKrKnrw9fPXvfqbv/xFj6H8orhBKUu7lpasi6N+wJUkh1TB8rAHsLpn9mcWs2p8hhLW4Caj3PYbgnRh1Zp7p+RTNIpqRa89FQqJVZCsiLDXYwasGKY8ZTpAUUC1IhgIhsA0Q3bKcud1xpF8qvkEVeqfSmPHC5a25oWdDFu7MYNI36zHqJVzyyVJQGXPcDEzhawG4psX1jGo6kvTidJKCk2gW3qxWCbJLqamYoYlrT8Zb4tqCoxAUL9puenID7ESBbXCsaD9wgfDc0ny+3ly+uNChsLfd9+j2zBUHut4Cna73L0EqsOqIg2umZVc41khK5eO42bTKWyISQwAJnYTLU3xHv/sdKpF7UIhs98wTm72lBMVCk8HQoBfEtK7S0XblviymLEsho9BJPoE4MVbYcDiRKPK5Rr+QiYa5hEui5ct/+nY8UDKBA+qZ29fe3yOY+3vUcLVFcSZYwkUywoN/R21h4RlqXFyrqkJcW69b7XQbyzssnKJD6/n/fekk3pyzibsOP0qA5ORj92W87r6alHvi/d69dsF+O5P+qtza+rkBIUzo4IXKPGcJFKZCUbpIuLod4m9tOt7WJzhebMr6aHJ9YszIIwgXhlPGjFJKRVMALSMN7Myu6GgeRzZ/mZ8kSirgd7k/D7tuBySe7R/CzR1roPrRng9I0KC319yktJjhGTRx7YXDXbOz6XLwcyYa5qKoEv9z38obd13r62GdkRm7SrhvXh4WHEw2zOJ2/SwskgsVnRDVdr7BK8Ka8yInBTb9ILphqIqIKy0uhs43eWyMpTEj/YSYkXWGmGHCiROxpm4WYeBgjo2PjO8xTL+RAtXKOOcz7FIrCxnfuSiMMS1rqGgn74hwR4lo5JWn12KGZ/C6uNZVIuZqST09WsR5XshuyN4D2ftEjPNgK8jawE0zsgk4vJxPTe8CcJvzAaa88pxPx3RGnMUcmzKOhKx/cal6HnbdzgxFuGN9MjKci2tBQYmnKY0ZkUW7GWv4l9765LJze5UEZsq9WaG0ZbPapKzDRTkq3cNLaF6wkjH6FXTR7QGgWDXzlns838gXVfieqaMiGNKohgSQ9gIx/2zQ2ytOUlrM8Azn/Tpk7YEPu4rHw18V+VcNv2bmN29ymeMvctUgLnW8TKY+lt0vX+zrYZ2RKcv28cqcnZWO/zUsn7brHhN5DaNVdBRsM1Z0WyxKF/edgPCz6nJXLRS3uN85i8EYJPbiNnwJx44LmgJjYPCT0OkGIfStJnO3pfL8rOTTuhGejohAEznFzirPvWr4nGsMi8lRg7jU8QrvjW1Bt0QfmerkH4GEzh4TS2kxwzN4XVxryxOf2fo9/d/1PvcApG6BkASPfLb9gjmPwpG1kNiVS4qfZnta+R54/Qgryx4d5MPBnQWKwp6PrqBZ9mIy1VAuc7xE6vHi5F/HRtLdV/ezmmDt5yLPhQTjv4XWlwHVjxkhFgMF9sodWptIR5ljegKz5OJB5x1MU/rXfNdWEK709kIxD/VgLkCLGZ5Bex3qPna3nc2ZW7jnm2MURbyP3pyJu6gZDZTbeHx0AK0jWxNt9d8C8Bf/SmbqipTj/3NjqfcjxuBkDJKRJ3o+TuvI1hQ4CnApLlpFtPLr38UTlMeMEtDZkHROVDkYc/Q/mCKXAaC4QnGkX4K7sB1CWuuG43vgVpOeEqdc5XNLxiwCEn5Fbz2Eqkpc2eR6rmw1ksySTMIt4bSKbIVZXzvjdE3FDK33bE1gzweDFaKaet5ZRm8QbriqCrkpoiLY28kKP+GxPiGsOuJg13GRjIKOyUWT+KxdMGz9TVQp5x2E/o/4lavv91vLF0NNpKPcbfiTy3Qr0ece17nX6yZEtXHtfDTC8yC8AfS8Q1TjHd0IexfCgeUiibb5J/FlChSVa/EdIboFRDYFg5neTaLo0SiK7cfyySlxEmE10TQ2hP5fp5FlU2ggpdNHt50huo0M0G3GIAnxcYoSy5vuq5it9OB0DiIncldXH2w6uh3HhWj+LeDTqCHcx9tp1dYNZ2uE+Cy7bGDyf9fXE7ljaHte/+52npU/4E7DTHYqSTy5eBivDA7z9dBOy5SNRVUe95iwNu+wSNilbxdfReln/h69UdzD49pBXHtI7FxjLeKqjBkxIXT+PBV71euFMmEtUElYa8HBp6Z3iJIK2K404EnXREpjyo3tK7cS9wrOIuFm7e8Jaw2NC4hXhoQz5LubMLrc3GBYwBvGKSyd6WTAZRN8PbTT8s6qAkbpVvK48UcSJFGYsUpuTa/xD0FkEx+P7iQiGsHgp6DjdbDuC7HBt3M27JkPba6AjtdWW4h+qpjR+tPUU35PqbAW4FBOCZ8a32WwfhN21chtrgerFNYCjG5RQ0I0l0040llP3fFD4wLBUSiEIr4W1oLIS0W1AIMFMnYIgUuA/7vweYo3hoYx+NsefOkewUTDXN4xfsz0v3sx5uLhvh5a1dhy4cerSMhag001Mdl1P0uUjmWnL0RhLZwYM5qTU3IRe+17aXBsDq6Da2mqO8aDut95kN9xqAYOqrFkq6E4MBIqFZMoZRFjy4Pj03mnqudXeSAfuUeXiQlK0UswuGEN5iidRRDeSGvRWleJagZX/wjbpgtDDw2/p/Nld7Lll2Ta6/bzsek9xjmf5bf1hxnX1b8dpU4W1hpx84ThB9ounycOJHaByz4SRXPHNgKKMBWoqX0qnUHkLAPCxX0vIBQGPSm6Zqz5RAjwZt0Ha6bA8JeEGUg1GNE2nmGt41ibkkNGoZ2YYAvt64XS5tl5Z/X9pxLWjtMv4RrDYhRV4l7XPUjBMb4T1spO0S3DVEO5MQ3/wVEk3IprQ54yJFE4LNtyal2nvVPS70H4bQIcXc83vdfTNa1V2alDOSX8vTWVi9ud2WjIZyx9jWbZi3GoBm53PlA2F9ZJ1G1hLUC3SVCcJUyo/pgEQTOhfs9qx4yqhLVmnHxkfB+z5GKp3J5pSj86xxlrXlgLorDFEgJ1qEW7hkZtwmKw0CSsMXddVMjzM67D2vAjDEF72J/5N9sOjcak34tJbyLUHOrroVZJmbBWchOQKBxrUQ080v1hWke2ptBZiFN20iKiRZ0X1kLldYbV4iAwMJdrPxqJu6g5lvjf0ZnyCKj3I7KtHs7sAbgLy7tMVCWslfRFGCOWY4r4F0nnRpXNNJYmMK71IDJKMgg0BtI0rGmtFdbWJBeGCtOXKG6x6RHbVrS+8QZ6A0Q3FwvegmMQmuAzR7maxGKQmHFVNAUOpay987wUJ9t63Erb0CTRgmHvAuGeMuQZrzn7nIl8h8KC/XZUoH99M4oCzaXD3GOYziW6Neik46La+r2Fm1RMq9M+n1+jM4hWc0ndwf0gHFolWr4eWQ+OAvE4RVRVIOmE6DQ4Hr01kvaGALFgd9thZwn/WI9gVI4QLFWs5vtPacpX7uH8pfRC4eze5xEWHW9dFFbzLQ3dDpGE8tZnX6P24SzxfBu0msQUCNYoKDxa68S1QSYdj944hpK1mVi3/8zrximM2x7P3o49aBrhP8nCXJvCwgN2JGBgA3OVuvwHquvAnXcIds8TRRB5hyqfN4cI53FToBBJoIj3rj1fuNrKLiGcyNgBW34V9+563aHRAGjUz+vvb70O2tereF99rreBV5fnks/ZvzZ6ZD4wfkBH3X7y1EDucN2PAxNRVh0fjghHr6uBmKG4AUm0UdbQOBOKW7Tn0+kvmGI6XxFq0bH0plgc7v/x2ddmbjfMZkDa1xQsLSak/2S/WWtllsgsPuDAqIOhwYd53/0u7U07ADiiRvGy6zq69BpKr0g/LuqJaATDX4a0rbB2ivh3y8+w8y/oeA20HXs8Fp0fVcWMl/sYeO3fPAo4deI/hCI+Nb5Lb30yNtXELa6HWa1UbocabdXxxaiImnPCt+eL1pyaA7aG2+k/TtQgXHbDG4puSpk7oChDbHBfAEWmMYF6Vt8Si8v1ECu+PUxf/Xa6rb6TjI5LiIn3H7FUWr6dtVu2M3Dd7YQU7KFADWSC8xE2qs3Lrnll8IWdu6gYM6KhfS9+W7WXPRuX0k23i9667YRJxTSXjgJHK3yvS9WzXW3ILLkns+TeZFThlB5j1fHdmMiaixmKLMwYtLVG3abpUGHqoVErqBduJXTcSyizbqODcz/PGL7lkd+NDG4ZQ2SQ/2xmHskt4d+9WViMeoa1rth5K4xCPjW9S0+dWHfQ6x5huJF7CBz5ogjMV3lPSRKGBuZgIbINSYQRr8Hh1bD+K8jYDv/P3l3HSZZW9x//3PIua9dxnx1ZF3YXC7awiwb4La5BEiSEECw4gYX8gBAkaMgPD4HgsrizsArrOi497Vpe9/7+ON0juyPdPeX1fb9e9dqdnpanaqrvuc/znOecLz4FNj4OHvMvVgxnifw+h4vXHXuA4h8etYF/+9k9S/p+F/tu4z2B/wTgQ4WncW/LmfzPX1fx+p2fKxChZKnmUsjaOm+9HOb0B20OcuBGWzNrhLWyRB9c8GK45uN0/en93PJ3vyDXsZHz/uVnAPztl2/k5nc8hmSkdvYzdo/O8scdo6wb/gXnX/s+AP658OJj5hmfvLwJDl86DjzsnyzZe9918KWnwnP+F1Y+6Lgx41WPWM9Hf3Hvkn7U2wNfYLNvL8NektflX85APMAnLq9Sm/RcGjrWNcXcX6RWdUY6efCa1XzoWS53jb+QL931acLdv+Dfft3Dfwxcwj3j97ClcwvRChVJOpl7h2a4cc84LUE/jzhj7rrl5GlZ/iUC8bvw3ABXrn0NZ/WcxUxuhnQ+zaaOTfTF+qo78Aq6f8zIF/M885IUX/1jP7M7/pFQ568Jdf4af8s+WpZ/Ga8QpTCzmWJ6JW6uC88N4fgz+EJD+GM7CMTvwnEs6bYwu574zP/hTc/cwlBqiKg/yqaOTcR1z78gDXCnWeNScyfmWpeX9+cEwtCz2RITZ4bsBrwJRAIOkYCfM3uC3DyUB+Dx/z3Cvz7qUfyfK1bAz99lFX2/9TK46OWw5UkVbw/y2h+P8/NdWQDOdu7lpYHvc3n42sN/X1h5KYHznw9dG0/0LepTIAxrH24Ptwgjd9uEYj4hKzMB04P2OI4OAMc2KO70VvDT4vl8330QO7yBBQ/Bh8tmdnPV47Zy1ooKtdw7Wm7GktTC2vyWOYVs/W8yJ3phcu+RFs51pCXog4tfwq9vu5uHOTfy6dCHeMqX3smbH7ueJ26sjYTnl/5gjOsOHL+CBcAdf9tnz2Oxijm475dWFXDw5iMf9wWswk3/WdCzxSqLn6yysluw6/bQ7ZYAtf8GO9iz+/f2+H0M1j8Sznh8RePalu4wK5xhVjBMzgtwN8evLHiEx7sD/8Wj/TeS8YK8OPc6DnqdbGEnV122lbOWV2hTKzdjr7cOYciJeK5VmMulbNPAF7AEd69oCYctbY2xeVCDYiEfsZCPqwrPZtRL8ubgV0ne9XUOje2j94q31EQFn2d/c5TRsVH+MfB1Yv5fcqbjkfZC/EfhiXy6+Hj+/LcrlxYzqqFvOzzhI7bxfe1nYGyH/ffWb1oV282PL1k3ku29YVY6Q8AQGS/IvRyb/LbaOch/Bj/AOt9BZrwIf3OcxNoQedazl6su28ZZvRWqBugW7ZGo4So1UhmFjCU71Nr9g+NY2+BA2DoiTB+0tak6mzMsRTzkg5CPV+T/nm87b2WN7xDXfuKp3PPM/+XSM2ojwfaVH/sGH8q+g6RvmENeG8/LvfGYatxLnmc0uG0ruvnK9Zu4zt3Eh70nkXLirHEO0sosYfJMEWXYa+NObwUZjh+nImRZy36uumwbmzormAhxeK7RVrmfKSKnlOgcgEe8FfdHb+Q5gZ9zo7uB8/4Fvv7yi7lgdZUSY+7n0R/6Den8A6serXUO8J/B/8sa3yEIJeApn7QOGYdutwrZyWW1s+YZitsjOw0D58KTzoe7fgg3fw3u/pEVZbnoZdb1sKWtJD/ywjWdwOKTazc5e/hU8EOEnCI/Lp7Hox99Of+4oa9yhzGOJ5+G5PLSd+KU2paZS5CvtXnGycR7rKNHatSKRTSCbU+DXb+Dg38h8aNXwN/8/Ji/PvMdP+F7r3ww25fXxr/Tw/7vrzjD2c3/ht4BDny28Di+UXzY4b9vqnmGLwCPeTf84B9tTvyFJ8OzvgZrH/aAT71kXdeSkmuf6/8Jzwr8AtdzeHv++Xz6yb2cvaKCh76P5hYs56KerhkiDchxHFYmVzKdn6a1JcDn/vBQQp2/IdL/DV7++Q7e+aT1BHx3s7ljM5HTKGJRCo/60K+P/YCTo2X5FwnE78Fzg7zhwtdzbt92ZvOzpPIp1retpz/W3GvhQX+QJ27dwlf/OIgvPEhhZj358QcRbP8DwfZr8QVmCLbdSLDtxhN+j2J6OdnRB/Ouyx/GtmUJRtOjxENxNnVsqtmqxrVIM6NyKubsxqJ9TWVaaIRiVvX0wE22Cd9Ebfj+7bJ2HvnFocN/fv3PJnj0S86i/an/Cb+6ypI6//ARuOcn1lajggk/v9yV5jLf9bwk8EPO9919+OM/KF7IzQNX8qbHnlexsVSNz2/vzfmqvJ5nk93pg/ZIj1vSYSFnG3GhKMVIJ2/7zTS3zybIEyDnBdjHA8u9r+AQSWYBmCDB/rnPaWOaPkbxOR7j6XzFnuoxcmloX2tVfEotEKD9Wc88/P9SBzz3SAWFetbSPrc4PVOfVdN8frqueCt3f++VbPTt5/Oh9/N/rn4bD1+1nmS4+os8J0usfea26OIXonIzcPv34NZv2HUXLMFhxUVW4WblRYurQuEL2IGh1uWw4TF2PR/faVXJ7/mJJdre8V179G2HM58Bqy4ue1LF1oFWuuJhRmeyhJwC67297Kaf/P1udVcySJIZ/jbwfa4IXIvrOfx9/pXs8vpYz77Kx4zsrLVRLvd9omJGfSrmLZm9pR0GNtphHV/A5hjZaft9mz50pKpIhQ+QNYv/98QOXvDdJzDstfG+4GfoHb4G91t/i++yd0PbquoNrJjnoZPf4dXhb5J00gB8t3gxV+WfxUE6eck5sfrbvHAcWHmxVUO/9+dw/X/CzCH4w0fhz1+Bs58Nm6847STbo2NGxMmzztvHLvoo4uepvt/yjuDnSThpBr123p17NlNehH5GOIhVjupmnC4m8DlUOGZM24ZFuasIKWbUvtzskYSRWhTrgoGzLdFm8gAk+5rmIMhbH72cv/nZ6/hW6G1c6LuLn37l+WTe8iMi4SpXIzx4M5/IvZlu3yS73F6em38je70jyQevuShRfzGjQo6dZ3gUvBy/97bicux91xoOECWDh8MYSQbnWuD2MUoHU5WPGWBz9s71lvBWLooZIkuz8iJuHHgG5x/8KlcF/5NduT6e/km45z2PI+iv/vX4eIm1F/tu45PBf6PVSUHrSnjafwIejNxjsb9Wu3SFE3bPlJmATY+DNQ+HP38Jdv4arvkY/OWr8Ff/DOc+/7QTSS9c00F/a4TBSetiuBDrnP18IfQ+kk6av7hr+Z/8Q3gu8eom1oLta5Yo6fgwxYzaV8hCsr92kuQXwueH9lUwO2TrZf4KHX4tJ8eBh70BvvkSOPgX+M0H+JsHP4nPzrfOBp7wsd9x33svr0zntVPoZJLPhD5I1Mnym+J2rio86/Dfvfvhrc03zwhE4IoPwo/eCAf/DF9+Gjztv6wYyVGWEjMe6buBdwQ+D8CXio9g0G1nMuNWL2bMH+YrR4EpxQyRRQn6g6xpXcNsfpbz2p/IjdPDBBN30LLiC7ztm3/LZ17scI9zDxs7NhL210jHDP8s0eWfxx/dg+cGefzAaw4n1s7mZlnftp7lieXVvy+uAReu6aA/0cHgZAQC0+CfJTfyGHIjj8If3Yk/dg/+yEF8oWFw8nhuCC/fTjG1lsLMFjzXD74sQzPTDKczdEe7Wde6rnEq1lYoZjie5y00ZteFqakpWltbmZycJJlcQjDf80fbqIiW4JTw9EGI9cLAOeVJrjuRyX12w93SXruLGmWw+iMHHvCx+17Zj9/x4PbvwLWfhfws4FhVvXOfB22nqmx3GmaG4O6r2Xvt91jhGwYg5/n5TvFSPlO8gg9deS7bemqndUct+sN9I1z1wzsBjplcTHlR9tDHleuL3Hbf7mO+JuWFmaHl8IY3wHufsv0B7WDLzi3AzIglrpXielJip32tbBCn/Trs+p0lPy1ksTE3awnkqy6BYHVPhp22oTtg9D6rTFWnLvnIzfxv+B30O2Nc527kubk3ccerVlf9Jv14sQzgdy/oYXlyETeE2Wn4y3/Dbd+ei33YZscZT7TNhNgDDyucNs+1Az53fB92/daugwCtK+DMK2HDo0tWdfB4ThQzJrw4++jhuRuL/OWeHbwy8B0e478R13P418LT+WbxoXQzcXjduGIxo5iH2dGajRNHU8wwp/U6uC7s/p0lpC/kYMJ8Ym3bKujeePx7ercIs8MwttP+29JW/wc4apDreaz96EEAznLu5ZOhD9PvjNli+cWvsIqqlYwdngd7roE//ofN+4Bb3NW8K/88rvM2A/CXl/XRWgMHRk5bMQ93/Qhu+pJtlAFEu2DbX1uS7WlUxrh/zOhhnL8J/IhL/LcDsCe8kTdPPYUJjvxOzXgR0oTpYrLyMQNgYi/0boPOtZX5eUukmGFO63UoFmye4fMd/7o+ud86DtT4e4FcyrodTB2wTfomSLCdybls++QgFzl38PnQ+4g4eb7nPJwnvPVblV0XPNrdP4avvxDys9zuruL5uTcwTNuRv35FPyG/NipO5kTzjGGvjUO087KtRX5/+55jvmbaayFLiC5n8vDHKhoz3IKtSy6/EOJlmPuViGKGOa3XoZCFXX+wJOpQ9dt8ysINzeS56Ytv4jL/9Yx4SZ6cexfRnrX85B8eWN2u0la/8QfH/PlK/y/5l8DnCDpFWH4BXPEhW88opKxSZL0c9HQL1mnSLcD4Hrj+szA6VzWwZys89r3Whe80XH3rQf72S1Y16lQbrxucfXwl9B66nUnuc/v5SOEp3Of1896nnFn5vYyj1ei+hmKGOa3XIZ+2zmPB6APXmfIp+/sVD4JwnSVauHNr0rOHGqvTy23fht9/GBw/+5/yv1z6ldQxf33x2k6++tIHVWVo87xClmvf9VAu8t3JDrePJ+fexRT2/tn16oV3IW1IxRz85K2w90+AA494CzzkH49ZQ1xMzHiE70Y+Efx3wk6eHxUu4PPFRzFDrDr73/Mm91sxs+7a68yrmGH0OjSfwdlBfnTHbbzzfyeJrv4k/shB3FwbW/z/wMsemaQv1seG9g1VSbDNF102/POPAHAC47Ss/Bz+8DBeMcI7H/JGNndsZiY3c7hirRJrj3VszCjgBKYhMA6FDrxiHCd0YO71OvKaecUo4OEEJ3B8ad542XYetfEMlseXE6xEcdA6sdBrZQPsuNWoYs7uhNpWVn4BPbkMOtbC7MiRxJYm9cLvjrH6o4N8L/hYuPILVqUPz9r+fP0F8JO3WVVbzy3ND8xMwt1Xw4/eAF99Blz/OVb4hhn34ny08GQuzX6Efyq8nLu9FWztbvyNpdN1ybou3nT5Zjrjxwb4tYkiX7kixHsfu4KueJijw2rUydLjWGKtA3TFw2wdqMLEIjtjixBqhyHz8inbHK/3xFqwxBbHqesYc4Aunp97A5NelAt8d/Ox4Ef4u+8Ps/ojB/jFzkxVxnSy804LTqzNp+GmL8NXnwl//rIl1ravttPuz/iqHSwpR2ItWHXaZefBo94Oz/xvOOuZVlV/ci/89gMWF2/8gsXKMjhRzFifyPOVK0K842FtvCfyZR7jv5Gi5/ChwlP5nXsmPY4l1lY8ZmSnLEYoTsj9ea5V62xbAb1bTnxYzue3dtvLz7eEu2IOpg7W9bW5FvmOWsD5i7eeJ2TfwzXFLdaW/bcfZPzb/2SJ0JUweDN8/x/gx2+GyX0Me0n+Kf9SnpT7l8OJtUBjJNaCVWXe8kR4xpfhwa+da/U4Atd+Gr78dPjV+63N3hLOC8/HjHWxDC/w/5hPhv6dS/y3U8DH7nXPZNmzP0Eg3nXMPCPuZOh2JqsTM/IpuxbEuirz86R2uUW7D6+HDhKhqMWnthUWn4pV6ihTQbGgXTX+5J3BK/KvpuD5eIL3K679yLNZ+8bv8ee9E5UbjOfBHz9p9+D5WX5X3Mozcm85JrEWUGLtApxonnFGIs1Xrgjz+r9a+YC1qYSTPpxYW5W1qdyMVWosddVBaSyeax1uJvZZcsTkfruvLWSrPbKmkIwEeE3+77jFXU2XM8Xngv+Xg4eGeOu3b2X1G3/AHQenqjKufPHIPokPlzcHvsz7g5+xxNptT4XH/7u9V3AhOVA/ibVgB33iPXZP3b4KHv1um2dE2mDoNvjCk+Crz7KCAkv02G39fOI559LXeuz6b39rhP941jn0t0ZwgAudO/ha6F10O5PscPv4eOGJ7PL66IpHqrOXcbR82uYeOrzbXLLT0NJRf4m1YHvw7atsT76RYtgZT4SVl4BXpO8nf0s7x8aFa3aM8o7v3sbqN/6AHcMzlR+f55H/7j9wke9OprwWXpL/x8OJtYmQ5hj4Q3DZe+xQPh784t2Wl5CeOPwpC40ZT/f/ik8F/42wk+f64kb+u/gwZolVb/8bbG0CmqqLskg96I32sizZD16Y9J4X4Wa78IUmuK3wMb52TZonfHA3v915O5lC5ffBd4yOAOALHyC6+hP4w8O4+VY6p1/N5o7NTOemlVh7EsfGjABeoR0vs4qeRAtvfmIrXUkXX2AKJzA595jAFz6ILzyIg0tneBlP234pa1rXKLF2iVS59v5KVbl25hC0dFqSSTWqUxRy1m5gdtgWOZrAVb+f4ss3zzKTP/5b+q6/6+euwSlyh+5iw95v0Dp07ZG/jPVYNclVF0Pv1oW3VyxkYfhOqxR84Cb771GJun90z+BrhYfzI/dCMhy7CN/0p/YWoejCbQcmGUvl6IiG2DrQynyHrBNVEJkPt2+6fDOXrKvCJvTkfmv13b2hLN/e8zyK4+MA+NvbF32DodNqpqKVayf32/WlY83if06tKeZh9x8A1xag69BbfjnB125LcZZ3J18KXUXEyXN18QJelX8VeQLc98qBE153Sul1Px3nG3ekeeLGFr57d/q4n5MMO9z8slOcui/m4c4fWPJqesw+1r4GLngRrLrUEl+rIZeycd3y9SNVB/1hq5575tPtQFCJHTdmjN8HP38nTOwh4wX5YOHpXONuOfw1VYkZk/ugb7slP5eZYkZpVKxy7cwQhBKw7JzFdaFIj8PIvdY9I9qpylUl9NqfjPPNO49cox1cXuS/mtcHvkbYyeP5QwyteTJ39z2e1mRraWOG51kC6Y2ft0OBQJ4A/1l4HB8rPIkZjv13Xt8e4GfP7SnRD68xxRzc+3O47VswcveRj8d7Yc3DYMWFcwnpp3jv51Ow9zrY+Wu8Hb/G8WxDYKZzOy0PfQ3+7nVAjc0zpg5azBw4q+w/SjGjNMpWuTY7bZtYqy4tb6v3UirkrPPFxG5I9DZGq9aTeNkPxvjxfbZJ8STf7/hQ8BP4HY8fFC/kH/Kv4I73Polrd44xNJ2hJxHhwjUdJW/n6uUz/OzfXsSjU1Z58KuFv+KthRdS4NgDe5csD/GVv1bS/kLV1dpUmdek5ilmlEZVKtcW8zB9yNaz2lZaVwa3YHsa0wet60y0c3FjkUV77rdGuXvvQb4Tfit9zji/KW7nxfl/Ij93vb7vvZeXPWa4rseTPv57btk/yaPO6OVndxwCIEqGfw9+nEf7bwDgR50v4HF//UKY2muJNKFYScdRFbmUraG5Ltz9I7j5f8Argi8ID3o5PPSflnwguuh6x/23u/qWA/z2v/8vbw98npBT5B53Gf9ReCI7vT6K+Ku3l3G0mSE7mL/s3JJ+W8WM0ihb5dqJfbYO1bq8dIOtJM+D/TdaHEs2UPXa9Dh8++9g+iA3hM7naVOvwTtB3bRKxIx80eUx//Ybdo7M8oHen/C0yf9H0XN4cf51/Mo95/DnvfTcGG9+sApKHHbrN+Gaj1uMiffB4//N9kfmroMnihk/u/FORr/5Bq4M/AqAa4pn8KnCFYzOHZisaszITNnv3apLyrI2oZhRGnodmlMmn+Pyj/6aHUM5nMAE0dWfxBecwM12kdrzN3iFNr75mgEmJ7uYSvvKFjMy+SIP/ddfMjab45xVCa7bOUUgcQuRgf/B8eUpZnpJ730RL37wav7qTI9CscCG9g30x/qVWHsSJ4oZ3715J6/52rXg5PGcuXw1z4fjBfHcMJ949kU8dlsD3SMdpVIxQ8m191eK5Fq3aKe8l59v1aSqJT1hkwmfU7fJT4vleR6Xf3WYO0YeWLHrwtghUqnZw38+KzbOK/tup3/oN/ZvfrTkMmvDG+uyxSJ/0BKT8inbzJoZgok9tth4v6q3t7ur+HHxfL7tXspu78i//40v6eW53x7jtuE8l64I8eWnaAOjVP5w3wif/s1ORmaOnErtiod56UPXVGdi4RZgZtg2+MtUXcpNpbjr3PMA2HTjDfiii0ug0Q21qVhyredaUsSKi2q6JeOiDN9tiS2tpU+OrBTX87jws4fYlr3x8MnfnxbP5RX5v+e82AjTqSOJVOW4ptwxkudxXxk+7t/d+vI+LvvyMPuni1yxIcLHH3eC+xLPtUSj6/8Lpg/YxxL9cP4LYd0ja6eCiFuAHb+Cv3wNRu+Z+6ADax4CZ15piefl+rm3fAOu+09w8xDr5i9b/4l/u8mpbszIp+3eY8VFFak8p5hRGhVJrs2nrbrzsvOsos5iFfMwthPG7rPf//lK43La8kWPDR8/eMzH1jn7eXfgv7jEfzsAk16U7xUfxDWRh/Lsh207vWtKMQc7fg23/q8d5gPynp+vFx/GxwpP5gBHvvedf9fPeZ8ZZDbv8eztUd7zV21L/7n1wPOs1fxt37bfq/xRh1Mcn83jWpfbXDzYAo7fKvilx2FsB4zvtg2NeX1nwlnPgJUXP+D3pSbmGW7Bkl9WXLi068Jif5xiRkmULbl2etDWK/rPLN1gK6GYh6E7YXxnUyTYZgoem//DYsZlvmv5SPBjhJ0Cvy9u5R2Rf+Ke6SPPv781wtufsKV0C9xjO5n84nNoHb8V13O4qvBMPlO8gvk0z3te0X84nr3ygjivu7h5f09LrSZiBtg9RGrM5hplbuetmFEaFU+udQu2RtW2Aro3H5tg5bowMwjDd0EhbQkgmk+Ujed5zOY9rvzUNXw99C6iTpYfFC/k1flXUcRPbzLMoakj15SSxwzgf67fy+u/cfMxHxtghM+GPsgW326yXpDX5V/GWRc/mr9ZN9WYcTwzaUlC2Rn4y5ftPgxsT+3Cl8CFLyvNeu7UAfjea+CeHwPwu+JW/rv4cPZ4PXTEo9Xby7i/yX02R2pfVdJvq5hRGmVJrp1fp1x5cX1Wrp03M2yHkqMddkikUey/Aa5+ExRzpB78Jrb8bPtxP60SMeMzv9nBe354B0/2/Y4Ph/4DgLfmX8AXi49hxyu6Wftx2+v4wKPbeNoZOvR/jMGb4ZfvPdL9asVF8OB/sI67968gmBqDm74If/gozA7j4vCdwsV8o/hQJolXd/973tQBaFsNfeXZ11HMKA29Ds0rU8jw+9238eLPDOIER4iu+qwl2OaTpPe8mPbWacZnfHi5XrxirCwx4wM/vouP/fLeuT+5hLp/SrjrlwBs79rOH37/FHCjvOaKCBesjbC+bT29sd6S/fxmdPWtB3nn927n4OSRysTl+LetNZWKGUquvb9SJNemxuzGfcVFD7whqrSJPXDwZpv8N9qixwl4nseV/zvKtQdyD/i7bsbpdSxrfX5Z8J8vW8ODwrth9zWw/3pLmF3Mz4t2sj+6hV9lN/IbzuEnw8d/7+x69QCDM0W+dluKZ22P0h2tkYSnBnGyCiIVl5kAfHZir0zXAE0sSqNiybW5lLWPXnXJ4qoQ1rLZUdj7J4svvsCpP79GeZ7Hpf81xPrUjXwm+CHCTp5fFc/i7/J/T5IZuhxrt3S6FYfyRY+7RvNs6Q7y1VtT/GJnhnvGCuyZKj7gcxMhh1te3s/eyQLfuivN886M0Ra53wXN82DvH+Haz1oSHdhhkHOfZ62Gqn3/cSKeZ5Xeb/6avX/mdW2ETZfD+keWpu2c58Hea+FPn4DxXfaxVZfCQ18HLe3VjxnTg5b0NXDOqT+3BBQzSqMiybUT+6BjrVXfPJ1N7OlDtiGenbJkvFq9JtQZz/NY89H7zxU8LvNdz1sDX2S5z1obZb0A17mb6Trncjaf91cLj/25FAz+heJ9v4adv8VfsAOAeYJ8172UD+efxF7v2AWmzZ0Brn52DzvGC/zgnjQvOCtGIlytm+AqKGTter/rdzD4lyObFKeSHIDVD7G407XxpJ9a9ZiRGrVqciseBP7y33MpZpRG2ZJrJ/fZ/UM9VpRqsgTbo2PGg3238Kngh4g5Wfa63bw8/w/c5q0GjswzPvGcc5e00J3JF9kxPMvGnhi/+N9P8OC73kvUnWXci/MP+b89poLUw1aF+fyTOrlrNM9Pd2R48dkxWoJNFDMqoOoxA2B2xK4dyy8sezc1xYzSqGhyredZZeO25dCz9cSVxlJjMHgr5GerWzykSbiexws+fjWfCX6QsFPgf4sP4XX5lz2gOuHpxoxUrsCesRRru+L8x6/u5fYDU/zk9kPHfM65zt18KvQhup0piHVz34M/xNUHWnjJpiyhtr7aOcRdap5rh/HyGTuwesPn7GAe2L34mVfCOc+1gjqLnaunxuCPn4BrPmbFW/xB3LOfy13xCzlEGx2xluruZRzNc21OteKikhcNUcwojbIk186O2FrV8gvr+0CF69q68+whKz7RSG76Mlz3GQCKT/sC67506rWB040ZM9kCBybSrOyI8tFf3MN9Q7NcfdsgF/tu4/PB9xFyinyy8HjeV3gWLzwzwtsf3sGNB3NceyDLS86Jl7wCYkPIp20/587v2vwYIJy0IguJfjsIPrbDCqfNHwpvXUHxnOdye+gsRnO+6u9/w5FiQssvsLl9GShmlIZeh+aWyqf4y6HbeebHDuEEJmlZ+Z/4w0N4xQjp/c/EcwM4vgJutgcKrYBvyTFjKpNnaCpLX2uEj/z8HnaPzvLj22ye4QTGiQz8D4HYTgDWRR7Nux7xAm7fn+WmPWP89fltbOjYQEekvIeDm8WJKts2skrFjPrNhqlluVnoXFcbm9jJ5ZAatyTb1mX1PTFaIMdxeNHZseMm1w7TTsjL0+7M4GGTi0/+/gAXPP8S/KsusU/KTFo1xqmDMDsMmUlct8jETJqUF2KGKE6sk5uyy/jg3T1M5lrJjZ38df3hM+1kc1/cz99fVIKkIXkAvw/OXF4jbUays9C5vjauAVIbCmkIxW0xtlFEWm3DLjttSZ11ynEcXnFBnH/+5Vm8KP86Phv8IA/3/4X/cd7Fi3L/xJQXJemkDseMT/9mJxet6Trp4sX8huroTJbJdJ7WaIjfHnD43C05gj7Iuyf+WjgSM1a0Bnj1hceJGYO3wLWftv+CteI761mw7a9rP3nbcayt3LJzbaHo5q/DvT+1uDtyN/zxP6xKwqpLYeVFi2+/l0/Bzt9apcf5luHhJFz0MkvenbsPqmrMcIv2aLRFXjl92WnbEG9fefr37IleuzaM3gMTe+3gYCO07awyx3H4p4sT/N9rpo/+KD92L+BnuXN5vu9qnh34Bet8B3mw/1a4+Va8Wz6I07MZOjfYfKyl48i1OjeLOzvKxIF7CUzuIj69Ax8u89vVB70OvlR4FF8tPoIxjj+p/8KTrV3v2vYArzpezGh0gbBVQV/zEPvzzJBthk/tt/8vZi1RMRy3mNK20u7TY90L/j2raszwPEu67txQkcRaqXHFnLUpDtVpNSl/EHo220bcxG5r11rHh/ROxXEcXnx2jP/88yy/c7fzlNy7+HTwQ6z2HeKbobfx74Wn8qni4ynixwHe+b3befSWvpMueM8vkA9OphmbzdERD/PzOw5x3c238Z7g53iM/0YAbnA38Krcq46pcg7w4cfYvG1TZ5BNnVqvKIeqr015nm3ad28ue2Kt1KnUiB0Q79p48ha+0Q7o2wYH/mIHfaKdFRtiM/I5Dl0bH8Qr7341nwh+mKf6f0vOC/DPhRfjHpVgO782tdSY8YObD/CzO4bwOeAep+TOk32/4/3BTxN2ChR7tuJ//IdZl53kFZ0uxAYae2/J8dn73C3YPcsj3wkjd8Ht34FDt8CNn7dH53rY+Fg7pLf8whNX+cxOw87fwB3fh9u+aUUXAPq2wznPw9e+ijNi3ZxRa69pPmPr11o/aC6FDMTW1//vuM9nc/6ZQ5a42Ej7c2ddaQUk7v0p/m+/jOev/gCf33XyPZnTjRnfuGEfv793BGfuewFsc3bwqeCHCDlFvl98EO8vPAOA11/aBsC5/SHO7W/sQ5SnJdgCl74Kzvo/ljC941dWGGHHLx/4uW2rYMNjYMOj8ce62V5Lv5+5WQjGFr93IyIVFQ1G2d6zmfW9o9x7qJXU7pcRXf55/NE9tKz4f+RGHkV+Zg2+yH68XBov37XkmPHlP+7m+t3j9/tMj0Dyz0T6voPjz+AVQ2QG/5p3vPhJFL08nW0TPHOgl/Vt64nX61pnDfL7HC5ep/l7OTTuKna15GZtUzxaA+1bwCYTneuskmZ6rGkWwh69NsIzt0XJ5vJcf/de9nDkhP1+epjw4ixnmKBTZGQmy20HJo8sfkda7bTVnOO1dQO401tJgQCcJEnqny5OkAz72NLdQJM4OTm3aIsQTfK7JguUz1iLlFqaAJ8uf8Cqvg3dUdfJtQBP3xLlpsEcAe9c/v6uv+O9oc+xzbeLb4ffystz/8Aur5cBRgg47gNjxv2cKGbc6q0FTp5Y+5qLEgwk/KxoPcHt2dgOO9m85w/2Z38Itj0VznrmySth1qqOtfDwN8CDXgb3/BTu/KFVMtv5a3s4Ptvw694M3ZusQlus256r4wc3D+kJq6gxcre1Vtp/w5FT3/4QbHkynPvc0lTDLZXstI2nRacw5X4yk9C1qXTv13AcerdDMA5j91qiRbSzsWJRFbzo7Bh3jxVoDRT4+W372U8PAEX8fM69gm/mHsKDnVt4hP/PPNh/Kz1MwqHb7HEcPuDoq8FBr52fFc/ju8VLuN7b+IBqVfP+9rw4m7sC9MQatHLUUsV77NEocjOWSBkrQRtaqX/5lK031dJ9zWL5g9BzhiWuTB+ww0aNWgEP+PuLEgzOFulrcfnhzS08MfduPhj8FI/238Drg1/jcf4/8Z7Cc/iju4WDkxmu3Tl2wgXw47V2i5HmpYEf8P7wD4g5WXKen48XnszHi0+y9ao5LzknxnkDIdpblGzZ8HIzEEpoTUqOL5+2Q0d9GxaWOBftgN4z4MCfj8xjpWz++cFJ3pJ/MD9y81xx4GM8M/BLWp1Z/iH/d2Q5kqjkwZJixtHun1gbJsc/B77M8wI/BWB44JF0X/HPVnwkHINIWymeYn3wBWw+4RYgFLE5+uRe60Rz3y9g9F6rQHvNx+zz21ZC60rbU3Icm9dP7pvronTUC929CbY8BXq2QKyzdpNXG7FAhJycW7A12EZJkot22vw5PQrxBmot7QvAJa+2xOHBm3nb9LvJb/wArf0b+MSv7zvhl5UiZsxfyTY7e/hi6H0knTR/dM/gH/Mv57lbQjx2U6u6YSxWvBce8lr7Nx26HYZug/Sk/V2sy7oLtK+yZNxaXMfNzkDrCggqVojUungozkeeeQ5v/+7N9HcV+O6NzyDc9WtC7X8i3P1TArGVZIYeA8EJfIEUg7Od/OG+IR6y4fgx9FQxY54vNES47zsEYhajiqmVXNT2Yi6/YhVpd5p0Ps3K5ErWtK4h1OCdraRxKLm21DLTlvxxohOr1RCO24naAzdZq6hAuNojKju/z+GqR7Txq7uHufueFJ3eBKO0Hf77WaLcxSrwYA37GUvl+O2eDG/95STvfUQbl6yw1+gP941w1Q/vPDx5SHshcgSJk8KPS+E4P7uNaSawxcZXXKBFx6YzvwHe0lbtkUgt8bzaigul0tI+l+RYqOuqUyG/wwce3c6v7h7mA3d18bzc6/lo8OOs8x3kG6F38J7Cc/h88THgOaxjH2OpHD/dkeG9v5vkA49u57y509j3jxkpL0wBPzHS+CgeVYvwiFZmmMTeG685UWXz8d1w0xfh3p8Dni14brocznt+YyTbRNpg+9Nh29MsSXbXb2H3NTB2HwzfaY/FSAzA5ivgjCtqcwMoOw29205eJUiaT27WFkyTJa5o7A9A9waIJGD4Lpg6YFVt6/iaXW0tQR//fpnFjBtvn2HCSzDLkarhEyT5vncp3y9cyvrCHv7lYXFCkzu58557eXzvGElmIJ9mOlvgvgmXSS/Gfq+L+9wB/uKuJUWE21lz3J+dYJZpbBP2DZfW4aEKWbzMlB2WXUibZ2l8+TS0rqr/ZNRACHq3gFewQ1LJxq2Elwz7+PjjOvjV3cP86ZYpxrwkL8m/lr8u/pZ3BL/Adt8u/jv0L/y6eCafKz6Ooakz+Z/rUnzyN/fxmeedz7pumydcfetB/vZLNx6eZwwwwnMCP+OZ/l/Q7swAVq32jfmXcI+3HLDE21laCPngnx/SIMkScmqZSat2rrghxzM7aodcF5NslOizpMDBWy3ZrpGqANaYzqifT1zRwa/ufigf2DPIawLf4nL/tXQ6U7wy92qGj9rbABiazvD5P+zii3/czedfdCHL2mxOcv+YcSqrnEE+HvwI23y77AOXvIruzU+Ayf2W4FPrHZLKxRewNbeoCy0JS6Ld/jQrMjB0B+y7zpLcJvbY43hal8OKB1m77+Qym5dHWm1dr1Y1YoEIObl82ipQNkrFOJ8P2lbA7FDd71k8QCQBD/0n+PGb8U/u5b3Bf+bHZ/zXgr50aDrDJ351H9++aT9f+puL6E7YHvhCY8Y6Zz9fCr2XdmeGG931vDj3Opa1hnjXoxrocHM1+APQf6Y96oXngleEeAPsS4k0iS19Pfy/F17M5/70B/yRO8gOPoVieiWR3u/ij+4huvK/yI9fSnb0wfgj+7jx0E388u4errl3hv9+6cW0ttgccCExwwmME+r6FcG263AcF88NkBt5BGvCj+E1j+pjND2K64bZ3LGZvlgfvlq+Lxa5nwa6q6wBbtFuKBI1eBou0Q9tY3ZitnVZ00yOO6KWuNLB1DHJtUfbyTI+fbPLHw6MAfCsb43SE/NRcKGYS5PyVtBClhSRYyp/HJ/HCmeYZeEc73vS5hI+E6kb2RlLZtdis8ybP9RQqxUJTkek1RZ1stN1X70WjsSMnBfkKbl38a/BT/NY/3W8M/h5HuK7mbfmX8R9LOeTN7v8cS5mPPXrI/THfaTzHl4hQ8pbQZQs00SPad13PH6KrGCIZZEcH3zKpgd+wsg9cNOXrI3c/HRlzcPgghfbon6jcRzbtOveBBf8jbXzPnQrDN1pSbczh2B22BZG5wVbrFtA53qrcrvyImhfU7v3OfmUjTlWIx0OpHZkJqBtTfmqQSX6LA7NJ9jGOiGopIvTMR8zehljB8uO+zn3spIP3e3n2oMdwHm8eQcMxP3M5l2cQobZoo8YmcMH804mRJ5VHKIYaeOjT9U8oykUsnOVs2pwfUGqo1iElgZJkgy2WDWewk12j5foO/XX1LH5mNHPCLsY4JvuQ/lN9ixeHfgmz/T/gof5b+Zh/psZ/d5nuTp3Nme5G3n1h24lnOxiIusRyU/xCN8htvt28jDfzZzju/fw997h9vF/C1fyI/dCrPErRMiyhoMUIm184umKGU0jn7buHQ3++yRLlJ22pOv2lYufL7ettPnK5H7bV5Cy6oiG+I17FgfzHbwr+AUu8t3J98Nv5pW5V3Odd+Sa/rnf7eQv+6zK3aXv+wXL21uYTOfJ5osLSqz14fIC/495XeB/iDpZxkhQfMxVdPcsh9SIHfxspKS0pXJ8dnA70madklraof9sOPMZUMjAzKAdbChkrLhCsAVCSXv9ghErShBJWPJira5VzfPm3jmNWCBCTiyftjlnIxUBiHZZx7D0ROOtwbatgIe/EX7xLzCxm4de82K6eR3DnHxv5rO/3ckt+y1mXPCen7GyI8p4KkdmATHjTOc+/iv0r3Q609zqruYjuSezOTzKx/76vBI9KakruVnrklaLRU1E5IRiwRhn9W7Fy+/EF95PYfI8ZmfXEen/JoH43YQ6f0Ow7VryExfy9ev62HeoCMBZ7/wJy9vDTKYLczFjPmocfV/r4W/ZSbDtegKtf8Fx7GsLMxvIjV1Mi7Ocv39ylJHUCL2xXlYlV5EIqUCh1B/NjkspN9/mtwYTjHw+6FwL6TFIj1tbpyawdaCVrniY0Zksm71d7GTgmDZK8/5woHjMn4dm5/t22+m9PKdOlOxnhFZm8AAnO4mXTwENsvElC+MWbZFM7ffkaIW0LaY2YhKTz2+HN4buqM3Yt0hHx4wBb5hX5V/Bs91f8ObAl3mU/yYu9r2ODxWezhcOPIajb6EOzhwbMyYXEDOWMUSSFDjgy06Qz87FDLcIe/8Et33LqmDMW/0QOOc5lnjaLOI9EH8ErHvEkY95riUbea69/+qtTV16Yq5doCpOylGKOcBf+qq19xdOQP9ZVo1kbIf9LjXAtbtajo4Zm7zd3MtyisepUH7twWPnGQdm5v9sMWNiATFjBYeIz8UMf3aCVHoWzTOaQHocYj36PRXjFuzep5HmFOE49G61duOp0YaeR8/HDGcmwyZvN/ewghFaeVvhhXy2eDnP9/+Ep/t/Tac7yrMDP+fZ/Ny+MDv3DY4TKn5f3Mrni4/hZ+55xxzqW8kgcdLgQCA7wfTsLLQrZjSF9DgklzdOW2cprcyUHUpdymE+n9++Nj0BqbGm2VeolvmYce/MCv4h9zJeH/w6G337+Vro3Xy++Bg+UPg/zNJyOLF23r7x9IJ/xrnO3bw1+KXDhzX+VNzMvxeewltzQbpxraq8PJA/aPfm8/fnxRwk+6BYsHUqPEum9fmt0EK9tbgt5hq3QIScWCHXeNd1f8CSUA/cZL+bjVYVr2cLPOyN8Mv30DK1k29H3sVzs69nh3fidcX5xNp5e8ZSC/pRD/bdwqeCHyLmZLnVXcWHcn+N3/HI5rIMT87Qn9B9Z9PJztjBq2Cd7cuICJeu66cnsoah1AF8LTtw02tJ730h/tjdhHt+iD9yiFDnb5jwfks0soLCzGaK6RXsn+rHK0YBH5ZUW8AXGsUXHsQf20Egdhe+0MThn1OYXUdu/HzcXBeOP0va2cOhqS4eu2kbPdEe/PXekUualpJrSyk7a1XTarViZShmC2EH/2wT5Xqb3C+B3wcvfegarvrhnQQdl7XefgbpZJzSJrX4KdLB1DGHj8dSuZL+DKkDuWlLWGlpK//PCgRoffKTD/+/1LB8BlpX1H/71hNpabeF4wZos3T/mLHOO8C3ipfyO3cbVwU/ywU+23x4YeBqPlJ4Ct8pXnrcAxunEiZ3uH3rvNToPhj+Ptz5PWuPC7bwt/av4JxnW+tGsdekXlsSFnOAU/4EyuNRzKhtmUmIdVTmxL8/aEn64cRcFduDlsjeqDGqjI6OGSGnyAZvLwfoZorSbkRGSdPqzB7zMc0zmoA7t0Ffja4zihm1qZCFQEvjJTtEO6BnMxz4i1XAabTnN+f+MWOjt5d9dDNDlD1eL+8uPJd/LVzJg3x38HDfn9ni281a5wBJ0gQpMEGMQ147t3lruMHdwM+K5x63OlWCWZLOsRvlihlNopAFfNaCvJJxQzGjPpSiqnE4AV0b7EBEMV+7+x8N4OiYcYguXp97MS8K/pQn+q/hhYEfc7n/T3y88CS+VvyrRa5JeZzj3MvLA9/jMv/1AEx7LXys8CT+4q6l1UkxXIxxRqMl2ZWTP9RYe2zz3ZaCZbofU8yoPW7RCjOV69+8mmLdEE5CdqrxKmw6DvRtswTb336AZdMH+Ebo7bws99pjKpyfHo8X+3/EGwNfJegU+X1xC58pXI7f8ThEG+BontGMPNcelagIrZghUnJ+n8M7nrCNv/1SDtwWnPBevHw3xdlNpHZuwB+/k1D7NQTi9+CP7sEf3XP4az3PATcMTgHHV3jA9/bcEPmpM8mPX4hXSOKEhvAFJ8EN42Z7STjr6I9XYW9UmkOFYkZFjmt9/OMfZ/Xq1UQiES666CKuvfbak37+17/+dTZv3kwkEmH79u388Ic/rMQwT49bsISPWq+0kei3KgYzw9UeScVcsq6LN12+mc54GL/jMcAIG9nD+dFDvOGC0/vlipFiM7s4w9n9gLXr+bZ/0kRys/Y7VoEFZl8oxMD7rmLgfVfhCzXOe60h44VbbOwqlZFWa2+Wna72SEri6JgRcFyWM4zPc3m//+X8YuClHPLaWO6M8K/Bz3BN+JW8MfAVtjs7YAFN91qZZjO72ODsw8FltTPIU/y/4wPBT/GgP70CrvuMJdaGk9Ze7sovwSPfqsTaRpEet4XdlspvVClmmJqMGZ4H+Swkl9mGRiU4jiXsLTvH3o9TB+YSMmSxHhgzDrGRPZwXHeI1557ePKOTCTazi7XOwQf8neYZTSAzCZF2a2dZYYoZpuZiRiEDoRarJtZoEv2WsJUat4StBnVszCiykkE2sodzo0O88hw/WUL82j2Ldxaez5W5t3FB9pNsyn6etdkvcW720zwu935el385Xy0+8gGJtX2Mcga7WOUcesDPVcxoEukxOzBV4aQ4xQxTczHj/tIT1vL7dIsBJPrtMds8+wrVcnTMKDghPpl/PO/MPYch2ul1JnhX8PP8Ifwq3hb4Ahc6dxDkgRvcxmONc5C/8f+A74TeyrfCb+cy//UUPYdvFy7h1bm/Y4fXT4Ai+71O2hJqz9rU8mmbf5RpbUIxw9RUzChkIBBtzANugbAVPMk0xp7FA/j8MHAW/NU/Q9dGOpwZvhr+F/7O/x0crMteZyzElecvX/S3bmOajwf/nbcGv0TQKfLD4gV8unAFDhxOrAXNM5pSbtaS8SuQsK6YYWoqZkhDeOy2fj7xnHPpi3fiZQfAl8KJ7KK1fS/nD2wmvffFzNzzZjIHn0J+ajturhPPc3AcD8efOZxY6xVDFNMryI1dQmrPC5i5+y3kxi8EfwpfZD94AdxsP8X0Krx8N8taVelcyqdSMaPsRz2+9rWv8drXvpZPfvKTXHTRRXz4wx/msssu46677qKnp+cBn/+HP/yBZz7zmVx11VU8/vGP5ytf+QpPfvKTufHGG9m2bVu5h7t02Wk7wV3rrbd8PuhYYy2cMhONd2LvBC5Z18VFa7q47cAkY6kcHdEQWwda8fvg/dcdOOHX+XCPaa8HtmkxiCVR9zNGwHGP+XsH6IyH2TpQ4+8FKS23APgar4VOBTVkvHCLlsTUiKe/5/n8trkydEfDtC0+ccw4l80fuZjn+n/KCwNXM+CM8fLA93l54PsMeu3c4G7gZncde7weBr0OpojSzQRjJOl0pjiTe1njG2S1c4jNvr20HVWJ0MPBGTgHNjwa1j2iMRMnmplbsOtB24rKJVA2uIaJGfm5Kn1VSLqmpR0GzoaRe2Bitx0EWUqb2CZ3snnGh29c3DyjnxEOYsmUXUxqntGsPA9yqbmuOKrOUQoNETMKWWhdWZ2fXW6OA+2rrRPMxL7qVGyukJPFjI/ddKKY4ZxybaqNafyKGc2rmLeznm0rG/Z3p5IaImYczS1ahbHTqVo7z+eHzrWWzD2/FyJl88CYsY3O7mfBbd/gwHXfZsAZ40WBq3lR4GrSXoidXj/7vS4yBAlQpMuZZIOz/5i1p6wX5KfFc/mJex4prJ3zkNeGi58uxQxx3cYuEFEGdR8zChkr8tCo7d3jPTAeg9yMdZxsND6/dQF52Bvgpi8S2PErXh/8Gi/uu4d9D3o32867FL/P4WvX71vQt3NweZLvD7wl+CW6nClynp//KDyBm911FPExSmLu8zTPaFrZGZtzNOo1o8zqPmZIw3jstn4evaWPa3eOMTSdoT3msKbX41BqkKfdPYhXSJKfuIj8xEX2BU4Bx5cGfxq8AJ4bhmIUcHCCo3j5ueKTTg7ybbjFGF4xCvhxgL7WCBeuUf6M1D/H87xTl1o7DRdddBEXXHABH/vYxwBwXZcVK1bwqle9ije+8Y0P+Pwrr7yS2dlZvv/97x/+2IMe9CDOPvtsPvnJT57y501NTdHa2srk5CTJ5BImgnv+aCdvFpsgN7kfujZB94bF/8xqGN8NB2+GZF/dt/E+XT++L83Hr5vhPY9oZWt3kH1TRT78p2nWtgc4tyPLVT+8E9eD27DKgVvYiTNXofD+a9bzf3zT5Zu5ZF3lqwxJFaUn7Hdp5cUV2QT3PA8vnQbAaWnBWeQGymlfK8ug0vECSvA67PqdbWSdqPpHLmUb4asurt9W9guRGrP4Ge9u+JjynbtSfO7Ps3zgEQnWTl9H5s6fENh/HWEvs+jvlfGC3Oatpn3zg1l74eNrv/q9LN3sMISSsPz8qiRKKWbUQMxwXdj9O0uaO3qzamrQkq77qrgg5hZhYg8M32UL89FOJWaUyNdum+Urt6b46GPb6Yv72T9d5KrfTfHQVWHWRtNc9cM7yXs+7mQ1ANucHczPzjXPaGKZSUuSWvmgqmxYKGbUQMwoFmye4fMdSVya2GcVx1sXX3mobuRmYf+NUEhbtf8m8//+MsN37krzycs7aI342D9d5D2/neSKDS30h1Jc9cM7yXoB7mYl4LHN2amYIWZmyA5qLTuv4gf5FDNqIGYUsrDrDxAIQSj6wL/PTAIOrLqkdJ22hu+B4TssJmneUBWfvHaU0bv+yOs6f09o+Dac7OQJPzfrBbjFW8P1xY38ubiOos9PzgsySZQCAcUMMcWc7W+sfFDZCggpZtRAzMinYffvIRi1vYrJ/dBzBnSuW9z3qSeHbofxHdYxqlF5Hswcgp2/ges/Z0nTjg+2PRUufiUfurWFa3eN8ZnnnY/jOOweneX9V9/F8x60ioLr8pov/ZHLfNfy8sD3OMO3F4B73AE+kX8C006USS92+ECGYkYTc4v2Plt+gSWul5liRg3EDGk6RbfIO79/C3cOTvLGJ3QwmZ1h71iO/7kmzePOCZJxJ/nEL3fiFmO4mRXg5PAFR3GLLeCGwAsD/sPfb/639hPPOZfHbuuvynOS5lCpmFHW3f1cLscNN9zAm970psMf8/l8POpRj+Kaa6457tdcc801vPa1rz3mY5dddhnf/va3yznU0+MWbCGpnipWJpdZosfMECSb+2J22boWLlt3JOltZWuADz1mvvpigjddvplP/2YnG6f34AE+50g+us8B96j09M54mJc+dI0mFc0oN2sLERVKmvLSae469zwANt14A070OAvodaRh40UhbVVrAw1+kjM8V+0wO3P6bQZr3JM2RXnSprnft+6HEFv7ENvMOnQbDN/JyK5bGR/eT9KdpIUcDh4uDpNejAniHPA62eX2ssPrZzS6jhc/bANrFTMam1uAQg56V1atAqFiRo3GDM8Fr1j9BCKf3zpbBKMwfCdMH7S2sT7/qb9WTurKrTGu3Hqkev2atgCffvz8nDF2eJ5RnN6Lb65t3/y8X/OMJpaZht4tVasEophRgzHDcy1hLljf/xanFIpB9ybYf4MdUjxeklgDe8FZcV5w1pGKWuvaA3zuifOH76KHY4Y7vRe/YobMcwv2qFKHDMWMGowZ95ebhY61pUusBWhbbnOG9JgOCVfJyy/shAuvAO9xVkV49D6Y2g/pcXBddk9k+cWuHHek2xkhScDxyHl+Zp0IWcK4HNlsVMwQAPIpS7YsY3VPxYwaixnzp7RCDdxxD6xy+8QeSzht1D0ax7HnufEy6N4Mt30LdvwSbvk63PJ1Xtu5wbrl3bkTkgNs9Yf5wiXjMH497P0jtyR+QTA/DcCUF+WrxYfzh+JWQk6RUZLkjkolUcxoYrlpixEV6l6pmFFjMUOagt/n511PPPvwn4tukdzyHH+9rYiLi+d5rE0M874f3sWhTBG8EG7uSJ7Z/dem+lojvP0JW5RYK2VXqZhR1h3+kZERisUivb29x3y8t7eXO++887hfMzg4eNzPHxwcPO7nZ7NZstns4T9PTU2d5qiXIDdrNxT11DLFH7CFtfR447bEKJGjWzCNzmSZTOdpjYbojIU4oz/JHQenHtDOT5pMMWcJKBWaVDSiSsQLqELMmG/f2uiVPPwBiPfByF0Nn1x7XIEwLDsXlp1L19nQ7nLCmMHBKSKpHJcoZjSP1BhEuyBW/hPdzaJhYsb8HKJWrpuJXttMG74Dpg5YFYJAuNqjamiaZ8gD5GYsoTDee+rPlQVpiJhRyNr1uFE3g48W74GOdXbYIxDWQY+jKGbIcaXmkhujSnAohYaIGUfzXEueailxQZBgC3SuhQN/tm5OpUzclcVxfFZldG5NCs8Ft8Aqz+V5Ltw+OMPIbJHJTEExQ04ul4b21br3WoS6jxnFHPhDjZ9c29Ju611TBxu/0FSk1QoARTth0+Pg3p/Bfb+E0XvscQJBwGtdzsGBx7ArcR7rUmm2JttJtnUpZsgR2Vnr4Kz7viWp+5ghTcnv89PiO7Yr71POauWJ29dx7c4xBifTjM3m6IiH6UtGOG9VOzfsHmdoOkNPIsKFazrw+xo8P0KaSt33br7qqqt45zvfWd1BlOMEeCVEO6BttW2gB6O2GCPH5ffBmcuP3w7nRB+XJpKdhkibPaSmVTxmeG7jL1DNi3ZYErFbAF/d316cFsUMOayYt9+J9lVVq1orS1f2mJGdgbZVtZXAGklC/9kQuBvGd0G0vXniWJUoZsgx0pPWkjOsw6/1pqwxo5AFf5Mk14IldmTG1WnpOBQz5BhuweYbbZpr1JuKrU3lU9ZNqRwt3hP9kDhk7YF1ra4djs+S5bCGrNtXHv/eQTFDHsAr1s7BXzlG2WJGIW2HJYINvubjOJBcbofIm+FAiD8Ircvs9zm5DM5+FgzfDeM7YWwHZCYgn7H1v3ifJeN2n4ET72UgP8tAKGr7nEcl2itmyOECUzF1LKh1NZE3JQ3P73O4eN3xrwcn+rhIIyhrNmVXVxd+v59Dhw4d8/FDhw7R19d33K/p6+tb1Oe/6U1vYnJy8vBj7969pRn8QnleeU6AV0r7SqtuMDtS7ZGI1K982haSq9CCr1FUIl5AhWOGWwAn0DztTCOtEE5aspiImNSIVR+Mq2ptKTVEzPBce8RqsMpYIGwt6Xu3QGbKOl2ISPnl05YMkVCCSik1RMwoZuc2OJtkvhkIQecG2xjOTld7NCK1KzUG8W7NNUqoIWLG0bIzdhA6WIbDGT6/HYZwHGu1LSL1a/4gl7pbLkrdx4x82va1m2GOEe209bdmWt8KxSzJNtEPy86BzVfAJa+GR70TLv8A/NVb4PwXwPILIdoGgSC0LrfXShWs5f4yk3ZPqQJTS1b3MUNERMqbXBsKhTjvvPP4+c9/fvhjruvy85//nIsvvvi4X3PxxRcf8/kAP/3pT0/4+eFwmGQyecyjovIpq/oaqfDPLZVA2CrjuEUthIksRT5tFYTqNcG+RlQiXkCFY0Yha++NYJMk1/oDlkSYn632SERqQz4NOGqrVwYNETPKWUWqFHx+68zRf5YdJJwetP+KSPmkxmzjSxWjSqohYkaxUL9rTksV7YD2NZCesPUqETnWfIeMttWaa5RQQ8SMoxXz5a0wFu2wqnizo+X7GSJSfvm5CqZKrl2Uuo8ZbrF55hg+H7SugEKu+eYWgTDEui1xNjlgScYtrRbDE/3Qttz+G07YgRmR+/M8yGftnq8ZkvHLpO5jhoiIUPaeUa997Wt5/vOfz/nnn8+FF17Ihz/8YWZnZ3nhC18IwPOe9zyWLVvGVVddBcDf//3f87CHPYwPfvCDXHHFFfz3f/83119/PZ/+9KfLPdSlyc3ajWmwpdojWbp4D7SttLYQyWW6gRZZjOwURLtt8imnpeHiRSFji5K11O673KIdMOrYJp9PbSmlyc2OQscaO/EvJVf3MSM7Y/fd5agiVSqOY1UuAhEYuh2mD1jLOCVwiJReIWv3Tq3Lqj2ShlTXMWP+YEM9rzktVdtKmB2G1Kgqc4rc33yHjFh3tUfScOo6Zhxt/sB3uIwb6o5jyTrTB48UIBGR+pNPQ9sqJU0tQd3GDM8Dx2fVTZtFtMsOsmanoKW92qOpDsexTosii5GbsX1O7XGctrqNGSIiAlQgufbKK69keHiYt73tbQwODnL22Wdz9dVX09vbC8CePXvwHTVpu+SSS/jKV77CW97yFt785jezYcMGvv3tb7Nt27ZyD3VpirnabOe6GI5jyR+pUWuLEVUFTpEF8Vwo5CHZr6T0Emi4eFHIQOvKao+isiKtNtHOzdZuNUaRSkhPQDh+pE2mlFzdx4xioX4WJWOdMHA2DN0BUwcg0WetukWkdFJjNqdo1k2+MqvrmFHMgT/UnMm1gZBVUd9/w1ySWBMdWhQ5mUIWvPkOGUqEKrW6jhlHy81YIYByV6KMdkByOYztgDYl14rUJbeo7hlLVLcxo5CZ67jXRHOMQMgOhAzeYq3ttV4rsjCZKeuA3EzXizKp25ghIiIAOJ7XWP09p6amaG1tZXJycmmlzvf80ZKCFpJgWsxbQurKBzXGJtjkPjjwZ6sIog1zkVPLTFmC7cqLK155zs1mOfD6NwAw8K/vxxde3EbjaV8rG8Rpvw67fmex4HgLkJP7YODc5qtANnQnjN7bfM9bZF4xDzND0H+mVVyrAYoZpXFar4Prwu7fWXWQQMgq1658UH1Vvs9nYPgumNgN8W7biBGR01fIQmocVlxQEwd3FTNK47Reh2LB5hk+35HKtasuBX8TVhnyPBi81WKP5hciZmq/HeTt2171xBDFjNI4rdehkIVdf7A5Ruio5NbJ/dCzBTrXlnawx5Odhr3XWYeLZmkxLtIoClnITNr6RJkLJShmlMZpvQ75NOz+PRRylmC66uKq30tUVG4W9vzJ5lX1tB4nUi2FrBVlW3FRxQuzKWaUhl4HEWkGlYoZTbgyX0L5WWubUc72SpWU6IfWYatIlRyo9mhEal9uGjrWV6Wlsy8cZvm/f7jiP1cWyC2A42/O05zRTkuudYtqHS7NaXbI7qmStZMAophRY3KzlakiVWrBCPRutY370fvsYEkztRAUKZfUOCT7aqaatWJGjSlmrYVpMybWgm32t6+y+6vstDbBRXIz4A/bIb4aSIZRzKhRbsHeH5XqKBROWFe8wVusg4ujisoidSOfhmAUQuW/x1LMqCGFjK3p1MC9REWFYtYxZvQ+zStEFiIzaesRVSgwp5ghIiILVamYoZWO05FL2U1FoyQP+fzQvsaqUGWmqj0akdpWzAF+q/Qscn+F3FxrpSZsiRdptcWp3Gy1RyJSeelxCMaga0Pj3B9K6eXSEO+tz00MfwC6NkHPGTZfyExWe0Qi9a2QBTxoXVGf1wQpv0KucslRtSqShLZVkJ44UslXpBl5nh3IaFul9t1ycrmUHeSrZBXZ5DKraJYaq9zPFJHTl5+FWLd1TJAm4jVvcmmiD3yBubm4iJyQW7R98NblWq8SERFBybWnx3OrclqnrFraoGOtbZS7hWqPRqR2ZSYh2m7tc0Tur5CxqrWBxZWdbwiBkCWd52aqPRKRysqn7dG1Qa0w5cS8om1a1XPnC5/P5gt92+ba2WsDXWTJUuNWOadGqtZKDfK85uyGcX+tyyzJOD1e7ZGIVE96zNah21ZUeyRS63Kzdm/hD1buZwZC0LHO5gfFfOV+rogsneeB6+nARjMKhJuzKAjYfl68x+6rROTEMhM294h1VXskIiIiNUHJtUtVyFpVwnCdtXNdiNbldnpvdqTaIxGpTZ4H+axVZajSqW43leKOzWdwx+YzcFOpqoxBTqKQbc7WSvPmE0Q8t7rjEKkUt2D3TR1rLTbUGMWMGpJPWXXjeq8Q4jjWjrjvTMCDmaFqj0ik/sxXyqmxqrWKGTXEc60SfiBS7ZFUX7DFWo7nUjoILs2pmLO40bG2phLuFTNqlFe0KrKVFu+FRD/MDlf+Z4vI4hXSEGqp2PqEYkYNCURq6n6iohzH1m5dV/MKkRPxXJt7t62q7GGtoyhmiIjIQlUqZii5dqnyKTvZF4xVeySl5w9C53pw/Ko8KHI8uRlLrNeJPTkRr2gt+JpVpNXiY2622iMRKT/Pg+lDkBywSj01lCAlNSiXso3uQKjaIymNZD/0nQX+EEwPql23yGKkRi12qGqtnMj8oe5mrSp1f4l+m4OrYro0G2/uIFNyuf0eiJxMITNXEKQKh/l8PjsI4fitq4uI1LZcyq4VutdsPs2cXAs2B29ps+6UIvJA6QmrWhvvqfZIREREaoaSa5cqn4ZYd9WqVpZdtAPa11ibSrdY7dGI1JbMlG1oNPMChJyY51pyXTMvTAbCFiOzOqAhTWDmkC3Idm9unIRJKR9fsDpVpMop3g39Z9mhCiXYiixMPg2+gLX21qEMOZFizu6rA+Fqj6Q2+IOWtFXMq+W4NJf0OIQS0LmucdehpXRyKTvsXa0D3y3t1hVPByFEal8+A7EezUeaUSDc3P/u/oDFqlxKa1gi9+cWrWhO+yqtRYiIiBxFK3JL5boQSVZ7FOXVvsom12rlJHJEIWsb4Yneao9EalUhC/4wBJu8fWusyxKNPbfaIxEpn9lhq/bQs8UqmoucSrClMSubRzug/0yrejN1QNd+kVNJjVkrypb2ao9EalkhC+Fkc29831+sBxJ9kBqp9khEKqOQtUqkXes135CFyach2lW92OE4dngoEFZHPJFa5hbswEa4wfc45YGcgB3aaXaxblufU6yqP8U8ZKdhdsSKXkwftP+mRu3jbqHaI6xvqVG7l1THDBERkWMouXYpCllLmgrFqj2S8gqEbPHW8am1t8i89LhNvCNt1R6J1KriXPvWQJNXNm5pszip+CGNKjVq90i92xqvEqmUTyhWnRatldDSZhVsWzpsYVsJtiLHl5uxhBNVrZVT8TyINGjMWCqfD9pXg+fY2pxII/M8mBmC1hWQGKj2aKQeeB7gVb8gSKTV3rep8eqOQ0RObL7KdbWvF1JZnmdzUXVktPW5ZL91qZTa5xbskPLkPluT9zxbh0yugLY1kFhm1zS3CDPDMLkf0hPqzLtYhay9Zp1rrXOMiIiIHBao9gDqUj5lk49ggyfXglUebF8Lw3dYspjPX+0RiVSPW7BEkdbl2giXEytkIdGhdo2BsCWiT+xp3EQyaV7zi3h92yHeXe3RSD0JtjT2/XQkCf3bYfBWmDpoGxVOk8dDkaN5HqQmoHuTJZ6InEwgrAN7xxPthOQATO6F1mXVHo1I+aRG7WB35zqtL8jCFDK2fl8LVY7bVth8IDOpex6RWpSbgbbVSp5qRv6wkqrnJfpgfLft5wTC1R6NHI9bhPQYFHJ2P9Gxdq6oSwL8x0lxKeTs+pYas4P/04NWSKyl3TqSyonNH+zrWAtxdW4VERG5P63MLcV8e6VmWdhsXwXxHpgdqvZIRKorMwmRdtvMEzmRYkEttebFuiwhXdULpZHMzrUh7ttui7AiCxWINEd1kHDCfj+iXTB1QDFA5GjZSfsdaV1e7ZFIPfAHtcl7PPMtx/1BO/wu0ojyKWt527Wh8TunSekUMnMFQaLVHom9b9tXWUVAz6v2aETkaJ4HrqsuTM0oFIXeLZZoKHaIKdYNmYlqj0SOJzNp64rBOCw7F1ZeDB1r7P17vMRasETaaId15V35IPu6UAKmBi3hVvckJzY7bK9tx1oVlxIRETkOHdNZCq8G2itVUiAEXRth/w2QnVYFQmlOnmuJ9V2bTjxxqyS/n9jDHnr4/6XG1MJGRi2ItNmGSm5WsUMaw8wQ+ILQuxUSdXSCWzGj+nw+O/Uf76n2SCojHIe+bXDoVlsITw6ogq2IW4TMtCWfh2r4XlExo3b41bL1hKIdkFwO4zugtYZ/n0SWwi3C7Kitxdb6YT7FjNqSz0C8r3YSIhJ91s0oO2nrQyJSG/Ipm49Uuqq0YkZtCEaqPYLa4TjWCWP6oN1/NXKnqXriFmwNPhCx9ZPW5Uursu0P2npkrAdmBmF0B0zusy50Af0eHCMzaeu2PWfUznqVYoaIiCxUhWJGDWSI1Zlizm7Imq1qQLQDOtbB0G1z7Wz11pEmk5m0aqQ1khTjC4dZ+alPVXsYcn/FvF0ftUhlghE7/T2xR8m1Ut88z9pIhWLQuw1i9VXBXDGjRrSvqvYIKisct9+XwVutJWyyXwm20txSo1bROVnbbewVM2pIIAT+ULVHUbvalsP0ATvI12xrdNLYZg7ZQb46qBqlmFFj3GJtFQQJRW0ONHgLhFtr/v0s0jSy05Dor3gClWKG1KRopyWaZ6dU0bcW5FN2yCzRbx0cWtpO/3v6A5ag29IBYztsr8oftH973ZvYfDo3O7fn0VXt0RymmCEiIgtVqZih3c3FyqctuTQUr/ZIKq9tJSQGbJFXpJl4HmRn7HdAbTnlZAoZe48EVGHqsFiXVX5WW3CpV27RKm9GktB/Vt0l1opU1XwF22inVQJRLHigYt7mmLmUPQoZq9IhjaWQtX/XzrWWMCmyEP6wNvtOJtJqm6Sp8WqPRKR00hO2ntC1SfFCFsebq7hXa52UEv1WtVYtt0Vqg+dBsVAzBUREqs4ftDlFdqbaI5H0BKQnrXtD/1mlSaw9Wihq3egGzrHKtZP7raBaM8ulrLBU1ybb/xYREZETUnLtYuXTEGlvzvYQ/oCdFAvGreqOSLPIzlWtrfV2fFJ9xZxN0rUJdkRLu1WSys1WeyQii1fMWWJtvBf6zy79op5IM5hPsI10WAVbz6v2iKqrmIfUGEztt4X81JhV5ijkLAEzOwMzI9aqbnI/pMfta6S+pUashX1Mm9iyQI6ztNaXzSa5DEItmmtIYyhk7L1cqipd0lwKGUvMrrVK3sEItK+0e9xmnweI1ILcjBUOUoVOkSNi3banozlFdXieFfXyitB/JnSX8ZCZ41hnrWXnQdsKmB6yat7NKDdjh586N9ZFxwwREZFqC1R7AHXHLUBLa7VHUT2RJHRvhAN/tk3gWjsNL1JqngeZaejbblWra4SbSnH3pQ8GYOPvf4cvqt/FmlDIQqtOeB4jELZEkondEE5UezQiC5dPw+wItK2yRb1gpNojWjLFDKm6cMISbAdvhulBO7DUbIu2+ZRVV3R8NqdqW2WvSzBibd+duXOvbhGKWchnbIF/dsha4lG0w16KpfUnO21VUdpXga/2zzcrZtQIf8jeN3JykaQl2I7cW3sJZSKL4RZhZhg61tl7uk4oZtSQfAbiydrsuBXvg8huK14Qaav2aESa23x3virscyhmSM0Kxy1WTezWnKLSPNfWCUMJqypbqY5xoajt+UZaYeRu2weIdTfPWmVm0qrWdp8B7atrcq1KMUNERBaqUjFDybWL4RZt0zMUr/ZIqivRDx1TdsOZDIFPbyNpYJlJm2DVYNVaL52u9hDk/jzPJuZyrFgXTOyyxRKn9ibqIg+QnbZH10boXG/V++ucYoZUXSQJvdtg8BaYGbQ5RTMo5ixR3xe0BetEv1WjO2knlCi0APRD5zq7H505ZJV/J/ba1yvJtj64RWtt2Lu1rqoQKmZUm2fJtZpXLEzrcqsGnp3WtVHqk+fNHT7qha71Nbm5fTKKGTWikIGWjmqP4viCEUvmG7wFwq3Nk7giUmvcIuBZAlmVKGZIzUr023pLIVubB1UakVu0e+CWDujbavuwleTzQ8caS6geuhOmD1iSdaN3Lk6N2mvfu9Xuz2r4vkwxQ0REFqoSMaO+VuuqrZCBQFQn1xzHWgQkB+ZaNailkzQoz7UNuiqd5pY647ngYG345Fgt7RCMWXUEkVqXGrWT2z1bLLm2ARJrRWpGS5st3gaiMDNU7dGUX3rcEmuTA7D8fKveG+tc3EK9zw/RDug5A1ZeZFUlinmY3G+bPlLbUqO2ed26otojkXriD9omX3J5tUdSH8IJ6x6SntD6lNSn1Ii9j7vPUDKHLJ0/ZJX3alWiz7owZKeqPRKR5pWbtsJB0RpNxBepppZ2iPfYOo6Un1u0A+SxLug/s/KJtUeL98Cyc2ztZuqArbk1Is+D6YOAH/rPsu5KNZxYKyIiUmuUXLsY+TSEY1roBAiErEVyKGGLwCKNKD1uk+rkQLVHIvWgmAN/uK5bx5dNIGRVeLLT1R6JyIndf4GpY03dVY0SqQvRDksy9QUt8bQRuUVLfvUcu570nVWaDcxQDLo3wPILoG2VJW6mRpVMVqvyaTt81bnO7oVEFiPaofuQxWhdZutTStqSepOZBA87RBNJVns0Us8CYQjWcMXzYItVGs9oXUikarIzkBiwg1wiciyfz+KUWwS3UO3RNLb5irWJHujbXhuHg8IJ6D/b1tqmB63YWiNxi5Y4HErAwFk12alVRESk1mmlfjEKWYh2VnsUtSOcsMVfD8hoA0MajFuAfMaqNCuhXhZivmWQKtceX7TLTsJqcUpqkVuwBaZw0haYkk3Srl6kWmJdVsEWIDVW3bGUWjFn15N4Nyw/1zZnSp0gF0na6zdwjiUpN3JljXrluZY83r66qm1XRZpGKAYdq21tSgcOpF7kZu3RvckqZomcjkCktpNrwQ5dh1rsfS8ilVXMgS9gnVRE5PhiXZYDoOq15TNfsTbeDb3baqtTcCAMvVugawPMjjbO/YpbsIIisS4rAKDq5SIiIkui5NrF8LzautGrBfEe6N5o1Qjz6WqPRqR0Zoft9J5O8MlCFbJ28lMVpo6vpc0OZeRmqj0SkWMVsraol+izE+paYBKpjESvJYi6Bava1ggKGZg+BO1r7HpSzrZ2Pp91V1h2nlVsnDnUOAv/jSA1ah0w2lerzZ5IpST67bqbmaj2SEROrZCB9AR0boS2ldUejTSCQKT2K+WHExDv13VapBoyUzY/ibRVeyQitcvnt/uyfNaSQKW05hNrEz3Qu7028y38QejaBN2bba2y3guLFfNzr/mAJdaqU4aIiMiSKQNooeYrEtb6CfBqaFsFneutMk8xV+3RVIbnQi5llbZmDsHUfmv9OrXfWkakRi3hWBWk6lM+BfgsMcLnr/ZopF4U8+VNoql3/qBtomSV+CM1JJeCmWG73tdKGyqRZpIcgJ4t9ruYrfMWsfmUVbbo2mDdPSrV+SAct02J7jOszW6jVQKuR/k0FIvQtdFaIItIZQQj0L7KWh57brVHI3JiheyROUjnOh3CkNIIRKo9goVJ9oMTsN8DEakMz7M5SnKZikKInEqs24qE6CBIac13jUv0zSXW1nCuhc9n9+i9W22tr17X2Yo5y1doWwF927Q+JSIicpoC1R5A3ShkbKG+Fk9SVZvjWHJtMQ/jO22RzNeAby3Pg/zskZNq8+22wknbPPf57HMKWavMmE/bTbebt6SyUNw+X4vmtc3zjiRG1HKbJJ+P6AUXHP5/qRGaoJ5crBNG/RYv/MFqj0aaXXrCYnX3Zuhc29iHKRQzpJa1LrdF9kO3geOv7QX2E8mnIDVuyZSd6yv/e+YP2MJ/KApDd9riebxX845q8Fw7dNq1oX5bfCtmSD1L9MPEXmvlGq3h+bw0r3wKZsegYx10b6r/66xiRm0IBOvnHrql3e6RZgbtmi0i5ZebscrRsa7qjkMxQ+pBIGQH9g782WKWo/fqaStkYWbI1v96zqiPPTTHsU5E/hAM3W7jj3XXzzrb/GvevsbmHLXe3eB4FDNERGShKhQzGjADskwKGSub38iJF6fDH7AbtGLOqrcmG+i18jzITllSbSgObattISKSPPkkoJi31qy5GdtgTY3ZBo8/aNUt66WiQLM5uoVrDfNFIqz64heqPQyZ5xZsoaVSVerqVaTVHtkpbXZL9XgezA7ZQaD+M61yR70sjC2RYobUtPkF62IBRu60P9fDQvu8+QN1XZuqk1g7z3FsDhaIWKLy9EGrCKKNoMqaOWQbLh1r6za2KGZIXQuEoWMN7L8JIsXGWZeSxpCdtkfXBns0wPtTMaMGeO5ct706uX92HEuumT5oa2mNWKBDpNakJ6G7+l01FDOkbsR7bQ8jM2VVbGXpcilIj9VvkmdywBJs59fZ4r21fw8/33V3/jCfvz7vtRQzRERkoSoVM+ozolZDsaB236cSCEHvFlvUmzo4V8G2xm8yTyWftmTLYBx6t0Gid+HVi/1Bm3i1tNmiYS5lybXTg/Y9iyN2Yjic0KZ3rShkLCm6Vy0yZJGKufrazKgWn99iw+AtSq6V6nALFocjbdaKvpYrlIs0E8exyqteEUbugXh3fRxYKWSPdDyoZmLt0aId0H8WHLodJg9Ask9JC5WSmQJfcG7DqA7evyKNKt5rc430mCW71yu3AG7RYqQvoHWjepcatfWmni3Qtqo27hmkMQQiVhAk0VftkSxctHPuOj1R/UqaIo0un7a5Sby32iMRqR+BMLSttD2MSGvdHpytusykFaDq2mRrfvWaLxDrgoGzYegOmDpg91y12pUxM2UFv7rr/DUXERGpQdplWwjPtf8uNKmymQVboHcr4NlNZrK/Pjdz59t5ui60r7U2IOH46X3PUNQeyQGbVMyOWJXfqQM2WYu01e4NeTPwXJgZtipT9bQgLbWhMJdcq4rUpxbtsNepkNHrJZWVT1vsTS6zBabTjesiUlo+nyWpukUYu88OtflruKJFMWct1jrWzVWfq6EkmUjSKnMfuu2oQ491OCerJ8WcVSPs3Wr3OiJSPf4gdKyG/TfWV1VEt2gdPnIp+7PPD44f8MDNgzf3sXACgtH63+T33LkE4rkkYs/DnqQHOJZM7PjtOfsC9bsx7BZg+pCtKfdvtZgsUkqOA63Lqj2KxfH5rRDFgZvsWqDDAyLlk56wdTAVDhJZnEQfjO+yeX4kWe3R1BfPg9SI3db3nWkxv97nLpFWO8g+fCdM7J0rClBje1upUSsU17vVDvPV+2suIiJSY+pkhbnKClkIRmzxWk4tFLXKn47PNnMTvfWVNFrIWpJltB26Nlqlk1LehDrOkYq2bStgdhgm91nCD9jHVf2y8maGINplp/nqYNLhplLc+8hHAbD+5z/DF9X1qaqKWXv/1MF7p+rCSatQkhqBeI0tQEjjSk9APnWkumQ93ZeUgGKG1A2f35LfPRfGd9ZuNYj5RJn21TbeWkz4CUWhb/vcnGx/7b6WjcBz7f3Qtsqq29Q5xQxpCPFe2/BMjUG8p9qjOTnPtXEWMnPdFc6wBNpA2JJLPdcqnubn2numRuy/4fhcJa06SEor5iCfOdItCMDBqn37AvY8HWcumRjAs035Yg68gn3N4Sq+ftvIDrbYIZxanoNnp+eSmgZsHtKAiU2KGbJksW5bH1LLbZHyKWQBp2YS2xQzpK4EW6B1BQzdruTaxXCL1jUuFLd5TaKBqmYHWyz3wReyNctI0uZt1eYWbX870NJQh/kUM0REZKEqFTOUXLsQhYzdlCjhceHmE2x9/rlTXD310RozM2WL3x1rLMmy3P/mgbAtbiT6bXNk6gDMDtkJs3DCHvWwUVLvMlO2odO9qa5+z4vj49UegswrFiBSAxPpeuA4luAzfcA2TGtgcVca2OHFpYidLk8ua9r3nGKG1A3/3D2ZV4SJPbVXdXV+oyC5DLo323hrVTACfdusqu7EXiXYlsv8Ib3ujbWZaL0EihlS93x+aFsNs9dbgmatVkLPpWz9J9oBPVts7eyE1+lOO6Cdm7XrzuQ+mNw/l2TbVlv3uG7RxpmfhWLRnlOwxZKeI612bx4I28d9wSNVeo9+Dm7R7gWKefs3LOZtfTY7DelxW8cp5uxzQ1EriFAr645uwQ7t+4O2Ntq2oqHjr2KGLMnRLbeVXCtSHqkxm0/XUGcNxQypK4k+mNgNuRlLFpWTK2RtnpLotfWyBjxYhj9oScOhKIzcbfOTahbdmX/N4z22ltrSXp1xlIlihoiILFQlYkYN7wTWkHwG4n21tVBdD4KRo05x7bAb6Vo4xXU8nmcVZB2fVXhqXVHZ1q4+v1VViXdDZhKmh6zC1NQB2wSKtNbOJkGjKWRscty7DWKd1R6N1CvPq702MLUs2mELUrmZ2o0LUv/mkxUSvdC1SRt2IvUkELLFajxLCq2VBFvPtcTaeA/0brFx1rpAGHq2Ao4lKyvBtrTS4zZf69lcV4f0RJpCrNvW8mYO1Wb1ntQoFPK28dy+auFrPqGYHQhPDthzG9tpibbR9upu+rsFyM5YUq3j2FhaV9k9eDgOwdjiDqT4A0Dg+K+L61ribm7Wkmxnh2wtrZiz+4VQzB6VPqzuFiE9BoWcxduOtTWV0CRSc2LdlpySm7XfWREpnULG4nHrSu1riixVOG4Hq0fuUXLtqWSn7b68fY11bAg28F6Zz2fzsVAMhu6wffxEb+XXLTNTkJ2yOUfn+sZ+zUVERGpADexQ1gHPVfLPUtXaKa7jmW/rGmm1sca6qjueSKs92lfC7IjdmKdGbJE+HLdJXINURKq6+WoinestoVpkKdyCbfwpuXbhgi222Th6n+KrlJ5btLjpMZessLo+EuBE5FiBsFXxc107dFbtBFvPs8Talg5LVq2nRMpAyF5LHKu6ogTb0sjN2kHc/rOUPCVSi3w+S1qdGbKKPrVyYNnzLBnUF4KBs+2avJQ1svmqj7FumNgHE7sgPWmHtitVqddzjyS4Oo61eO/aZIm+4WT57sF9viPdnhJ9tqaTm7bk3tSoPaYO2ucGI7bx7Q+Xby2ymIP0hFXXbemA3tVWpVdrdyInF47bIYiJXUquFSm12RGr4q95isjpSQ7A5F4dBDkRz7XCVfigd6vNT5rlHjjeY2uDw3fZPn6s07pplNv8fNLxz3XJWFXZYmEiIiJNSsm1p+IW5you6KZ5yY4+xTV8l7WtS/TURlu+QsaSK5MDloATrqHTh4EwtC6DRD9kJmxBZPqgVSbBB+GYJdrWUqJyPZlv6du2wk5SavIhS1XMQSBaX0k2tSDWY1WWinkl+EjpZKdtYzvWbZvs8e5qj0hETkcgDH3b7H53cl91k0JnDlmiUN/W2pozLNQx1YBVwfa0FbKQGrcKxsmBao9GRE4k2gnJPtvsrIXfVc+DmUGr4tq3vTQJL8EW6N4A8S4Y32VrboGQJXmWa2O7kLFKsYW8xcSOtXb/3dJWndji8x05qN66zK7R85WcZoYs6TY/Yp8XbLGDsYHw6VW2LeYtySI3Y4d/op3QutxeB8VXkYWbT1qqpUMQIvfneZZABhY7an0/JjttCV7tq2p/rCK1LpK0e7zR+5QncH/z++vRDttfr3bhqmoIJ+zAdSgOYzsgn7Z5WNkO9eXnDv6310axMBERkSai5NpTKWQh0KKkqVKI99jkY+ReWzQLttgNYLUm+JkpWwTvWGfJlbVa1c7ns8lJtMOq76XH7STgzLBtEM1X7AhGK9/yrl4d3dK3e7M2PuT0FLI2YdYmwOK0tFkMyE7ZRmSj8lxbhK+Hxfd6VshatdpAxKoztq2s3bguIosTCFv1C8dnSaHxnsrH3OlBu9fu22aJO/XqcAVbDyb2Vr8acL1yC5as1bHW5meK7yK1y3Gsks/0Idv8rXa3kZlDEIxD/3abC5VSSzuEW60K5NhOWy8KJyxuleI6Vczb3C2Xtjgc7bY4Eu2svblwIGyH7OLddq3OzVqiUXbKDq7nZiE1Zp/rOHb43x+wmOj4j527eZ4dznYL4OZt3uG59rmhOHRttipVkTYd2hZZipZ2O3w9O2SHv0SqzfMsOSo/C4WcfcwBmLvGe8X5D9ieQnBu77BW5lVu0Q6d926zpEAROX3JATvwnUtZl9Zm53nWKaKYt3vtznXNnUPhD0L3Jpt3jdxthx3j3aWfI+VmIDUxVzBqk96LIiIiFVYjM74aVsjYRmq1F+AbRWiuOkes0076Te23pNFKtEqY53mWnOrMtaloXVk/C+CBECR67ZFP20LJ7DDMjkL6gG0ChOP2OivR9vg81yoARzvrr6Wv1KZizirZyeL4/FZV6MCfy3uat9IKGdu4PbwAf7/Fd8ex604orsT+UpivHAiQXAEdq+s78U1Eji8QtqRQxw/jO60yQ6Xu4aYH7bBlXxkSoarhcIItSrBdCrdorcbbVkDXxuZpdyhSz1raIbkMJnbb/KNaZkfAH7GDGuWKJz7fkYTX6QMwvtsSAUJRu0de7PXeLVjV19ysxeBIqx1Qj3bYHLge5nCOY+tk4TjQb9fufGoucSpt/5+dtv8vFsDLHlWhcG7+5vjteh9OQjI5t+4Wt+RlxQGR0+M4dm2eGbRrju5LpVpyKavK7rk214y027wzELFDGD7fXHzw7LDF/N5MZtKKoHhF+9xworqHTmaHrYp66/LqjUGk0URaITEA4zuU0JhP2X50S7sl8Sf66mNOUG6OY/OwSLL0Bcbmcxo8z3Ia2ldpDiIiIlIFWq04lULGFuF1c1g6Pp9N7lvaYWwXTO2D9KQtzpd74aGQtZvQlvb6b1Mxfyo62Q/5DGQmbFIzO2xJADC32B9Xou28+cTaSIdtaNVjS995Ph+RbdsO/79Ukc9f3++laop22nUqP2v/rWe5GYtl/pDFmHi3HRzxBewewnXtniI3axVZZkeBoi3O1ftzr4Z8yjYx8FkVy/ZV9n7S/drxKWZIIwiErOWZPwhj99nmezhRvp/neVbpMBQtXevuWjGfrOy6dthRCbYLM9/9ItFnc8lGrZCumCGNxnGgfaUlbuVmqnPvnZm0uNK3pTLxJBCyytrxPpt7TOyxitueZ3HtcKLQUZuynmvVpwpZm7cUchYbwnGrjBTrnEvQrfONXMexA+n3b+vreXPVaQt2kAJv7vN9llzrD9b/cy8HxQwphWiXXV8yk43d2UhqU3ba3nvBFtuzivfYYYqFJtAV80eqo88csrWqQg4i8bmDKBW8NmanLWbVapdGxQypZ63L7PBas1avdQu294zPrjHtq1W46HgOFxjrgpF7Tr+KbTEH00PWBbJ7k8WoZqGYISIiC1WhmKEdtFPxPCVNlUsoBn1bbTN3YrdVAILyJNl6HqTHbYOgbbW1qWikCVAwAsE+2+gtZG1BaHbENk+mBwEHIgkIxpo38cgt2msR7ZxLrC1jMkYF+CIR1nzj69UehuBZ9SEtJCxNKAbxXqtCWK8JpsW8bVj7w9C5AZJ9p67i1LlurrLFIWvXmtoL0fb6fQ0qxS1AZsoWMYMRqzyfHLD7hmaNbQukmCENwx+winOBsLVaK2TLk1jvFi0JK9xa3gqD1RQIW8ULUILtQhzd/aK3sbtfKGZIQ4q0QttKGL6r8usi+bRVf+3bbl2IKikYseedGLA1sfS4bYrnU3Yw0Cse+VzHZwmk/pC1aI922LpJOGnxt9E5ztzzV3eRxVDMkJLwB+xa1WidjaS2FbK2fxJssUMkyf6l7Rf4gxYzox3QturIvszUflvzC0QsKarcc635PaHebTVbUEYxQ+paS9tc9dqdjbW3fCpuAVJj9rNOj9kAADEwSURBVN9YD3SstUN3cmI+nyVjR1phbKdVsXWw9ST/Ag8+uEWbuxVzdo/Utf6BhwMbnGKGiIgsVKViRhOsjp4Gt2iLy8EmulGuhmgHRNogudyq2E4P2gJ/pLU0mx65FKTHIJSA/rNsAtTIp5wCYTu9Fu+BzvVHNk9mhiC1H4Jhe72bacPALdj7Kt4DPVuVMC+l5Q9Wt91XvUv0WSWlYm7hiwu1Ijttm9Jty6F9jS2yLYTPf2ThvXWFLbZP7rHvFevS++loxbxVGMul7H4g0mqLeNFOa7MkIs3H54OONTZHG77TrqHxntLd2xaydt+c6LXqrnV+IOukgpG5BFvPEkcTfUqwPZ75irXz3S+abENDpGG0LreYkZ20NZFKKOYtwaZrg22KVos/YBWT4t02lnwailkoFrAKrY7NUfyhuaq2igUiUmGxbrvvzs009v23VJ9bhNSo3eO3rYKO1aV7zzmOrQ22tEHbCtuTmdhr80vHseTxcqz5uQX7Ge1r7DmJSHm0rYCZg9XrhlFJxyTVdlmRi3iPOjksRjhua0jz+18zh+zjkVY72HG83Idi3iqh51JW2b93qxWn0esuIiJSdVotPZlC1haVlVxbfj6fLfLHuqBt3KrYzhyC1D7b9A0n7N9ioTzP2oynJ23BonO9bWQ020ZoIGSJAYleuxlPjdqp6dkRwLOFnkZ/f88nSCSXQe+Whq4yJVUSCENA76sla2m3087p8fpqa5Matc3ovm0WX5a6wBGOQ/dGu06P7bJrtD8wV4mxgQ+CnIjnWiWtXMoSrn0BW6zs2jR3GKdVm/0iYhK9Vi1k+G5LlorETz9ZKjNh1QXb11jyUXAR8496NZ9g63mWQJro16L90eYr1s4n1irZQ6R+hWJ2OOPgzXZ/We7DBPPdc9pW2JpUrVRidJy5alsNvhYkIvUl2GKdaUbu0f2WlE8+BbOjlszduc7+W674HAjbwZ54n60hTu6zPQq80nZuLObtfiO5zNYXG7mojEi1RZJWKGP4rsZNri3m5iqmFmzPXkm1p8dx7HWMds7tzx+A1LC9xp5n+xyOY3NHz7U5aqTN9kLivbbHLyIiIjVB2QknU0hb5dRm2FStFY5zpJpfbrWdjJsetI3umRG7gQ9GrP22P2iJR45jN51uwRIp82n7/2DMFkkS/QuvJtjIQlF7JJfZjfvUAWt3OzsG0bbGnAzmZiE9YVUOuzY21ETETafZccXjAVj7g+/ja1FyZ9X4ww313qo4n88Wm6cP2bW8HhJKZw6BLwwDcyePSyHSCv1nWrLYyD0wud8OnDT6gQDPs7idT0E+Y++HQNQ2OGJdR1rRagHvtChmSMMKz3WmiHXC6E6Y2GfziMW26DvcEjQKfWdaXGqm606wxRJHB5mrYKsEW+BIYly0s6kSaxUzpKEll9nvdWrUNivLZT4xP9EH3Wc0V+cgaSqKGVJS8V4Y3wWFzOKKbIgsRHrc1p+6Ntphm0p1jfIHbK0v3mN7XZN77R7Bc+eSbE/jvZ5P2zy2bYV1XanxTliKGdIQWpfD5AHITDVWV7VC1q5ReBaPW1fY2rzWhkpjvsBYvNv2rbPTVlykkLF44A/Z2lw4rr2QOYoZIiKyUJWKGUquPZlCFpLLqz2K5hWK2aN1ubXZyExBZhLSY/Zvk522m048wGcLFYGwbZZEO60a4mI31puBz2cJCLFOyKy0KsFT+yA1AdH2xqnumxqDQg56zoD21Y03GfE88gcOHP5/qSIl1p6+aJcdgshM2rW7lk0ftMMbvdvsOlpKjmMb8JE2GN9plWxzM/b61Eqlq1Io5m0RKTdrfw62WHJx53pLXArFdbCp1BQzpJH5A3avF+2E8T0wfcDuAyNz15MT3QN6niX2ZybtYEfrCttkbaTNkcV4QIJtX/mrOtYyt2AJePEe6NlqGxzNQjFDGpk/CB3rYP/1lpBSjoNs84m18208dV8rjUwxQ0op0mr3XvOHvURKwfPskLw/ZAczk8uqs8bmOLaOGO2wDlgTc0m27ii0tC6u8Inn2py3mLOOK53r6+Mgj2KGNIJQDNpXwaFbbZ2gHgqFnIxbmOt0iq0Dta6w9TVVwS6f+dwHOTnFDBERWagKxYwm3i1bAM9rrk20WuU4c5XrEtC6zP5dCllbPHALc5/jswWEQKTxkijLKdJqj+SAtSKf3GdVgqNdNX/S+YQ8F6aHbPwDZ9libCMlpUltcQK65pRCIGQHKQZvscTSWv2dnTlkibV9220xvFyCEejeDC0dMHK3XZvjPfV7XQaL2dlpS2LwBWzToGujxaBwQodhROT0hRPQtxXaltu94PQBu27Pt1WbTxT1XEvyB6tU27bK7hejHbUbfyplPsH2kGNdLpL9zZlgW8hay9bkMujd0vhV5EWaTazL2puO3WdzkFJe+z3PDjC3dNj1VJumIiIL5zh2/zW539b8m/E+VErLLVrnvnCrHXgp51reQs13bmxpt4qzUwdtjKm9c50HY9Yl7Xj3J4WsHcLPpezre+c6ajX7PFak0pIDtl6fnqiN68pSeJ4VsspnrFLt/KF1JdWKiIiIHJdWKE7ELVrCZlDJHjXHcSzxSNU/SieStEei305NT+4Dh7m2H3V0mSjmrLV8rBt6Ntd+BUypf/6wJQfK6Yv3QmiXLRLXYtvl1Cj4QrZwXYlFM8extnHhBIztsNaIwRa7rtXLorlbhNw0ZGctCT3SZpXCWtrU3khEymf+8FjHaut8kZuxStmHW60FLcE/FLNrkZL7jxVssY1nsI3eZJNVsM2lrApU+xro3qQOBSKNyHGgc61tJs8O2yG2UvA8q0DX0maJtbU4pxERqXUtHfbITFqSjyzOfJWeelk3Kqf5fYJEL/Rsqb24PJ9kG+2A7Cq7J5kehOwMFEaP8wWerUOHE3ZYPd5b34fwRepZMGLziQN/rs/DIPkUzI7aWv3AGXOdi7ROLyIiInIydXbHV0GFrFVBVXKtNJOWNktGSPTB+A6YGrTqzZHW2l+Uy05DetJOWHZtUIUpqYyezRYr5PSFolY5auTO2lvwzkyC60L/NmvhVkmhqG0CtLTDyD1WSTDebe3salUha1XQi3lLXOvaZK9bpE2n30WkcvxBu/ZU+rrdCIItdpjE8dmhu0RffbQZPV3pCauw3r3ZNsq0uSTSuIItlphy4EZLqj/dgxaeZwkx4aR1uYgkSzNOEZFm4w9A+0o4cJMd2NX92LEKmblHDtw8HNPx0juyfu95WOWMozg+e319QTtAFojUfzvzE5nvRNG2yg7M1XqRlnDcHu2r7WBoPg3F7FFdG/22DhiKWkctra2JVF+8z5LcZ4at60898DxL5Pdcmwu1r9I+qoiIiMgCKbn2RAoZS6zV6U9pNo5jiVst7dZOd3QHTO23Kra1mETouTYhxGfVYdpWauFVKkfV7kor2QeTe6zKYChe7dGYfMoWtvu2W3JRNfh80LrMDjqM3gdT+2zhK9JWWwcfcjOWiOwErIJ4csAqzajqn4hI/QlGrIKt44eJ3VbZsVHnxp4LM4esElT/mdaOuJbiq4iUR7wHOtbC8F12v7rUilOee7/EWnU2ERE5LbEeW+/ITqkrmedBftaKSrhFW5sPRCDWZutmwYgdgnP8lih7dHKt59rDLVgibj5ja1z5lP1/atw+LxixxM5aXPdfinzaKjJ2rLXE2no6JOg4RxJtRaS2+QN2nUmNluawXrkVsraPGmmza2OpuneIiIiINAkl155IIatNNWlu/oAlqkY7YWwnTOwFnwPRrtpJXp0/hR7tsApTsa5qj0hETkc4YbF35J7aSK4t5m1BvnsztK6o9mhscb1vu1VhHLkXJvdDvMoHHzzPNrwy07aI2LbGTuu3tOseSkSk3gXC0LvF7v3HdkKso/E6uxyeT3RZR4JoR7VHJCKV4ji2IZ5P2XpHcmDxax1uwVpOt3RA31Yl1oqIlEIgZJ2NBm+pvUPFleIW5roqZCAUg+RyW/cOJ06/cqnnWWGZ3FzS7uyQdYMrjMx1sEvWb0Xb3Iw9l64N9qiVPQwRaUyxTqs4PXK3HVSo1WtndtoKYrSths51tZ8ILCIiIlKDlFx7Ip6rE6IiYAt4vVvtJOPoDmtJHmmtfpvD9LidRG9fA13rm699ieMQWr/u8P+LNIzkgFVmrXb12vkqdm0rbdO9Vn7PfD7bZIq0wfgumNhjmwXRjqVX21oKtwiZCTuZH05a8lW8V/dOtUoxQ0SWyh+EnjOsDenoPXb9DyeqParSyExAdrZ55xMnopghzcQftIN0xRxMH7ROFQu9p86lrFJVcpkl54di5R2rSC1SzJByiffYmlBupnHuPRfC82zNO5eyghfzxSRKeZ/qOPb9gi32vdtX26Hp1JgdNpncb695pLW+fq+z0/bo2WLP6XQSkKU8FDOkEbWvtuvn7EjtVYP1vLmunw70quun1BnFDBERWagKxQwl1x6P584tMuj0lghgvw/xuZZcU/thbIcttMW6Kt8etpiz6lKhOPSfBYmBplws87W0sO7736/2MERKL5KE1pUwfKdV46jWxHl6ropd9yar5F1rwvEjBx/GdsDUIIRa5irGlvGaWMhaMlKxAC1t0LXRWjYGG6R9YINSzBCR0+LzW+Unf9DiczFnm/31yi3YfCIQgf4zrRJYE84nTkQxQ5pOsMU2m7kdpg5CvPvknSHcgm2eg90Ld6y1KosiTUgxQ8omFLPD16P3NE9ybT5t8SXSCgNnQ6K/MutRjjNXSKPVXvPpgzC2Cyb3WVXGetgjy0xALnMksVZJODVJMUMaUjBi6yUHbqx+sZCjzXfYiLTaQY14d7VHJLIoihkiIrJQlYoZNZgtUgMKGVs0UOUakWMFQtCxxjbTx3dbdUnHsT+Xu2Ki50F6zNphta6EzrXNs7gq0mxal1kCf3aqOq1V0+N2cKBnc23fC8wffGjpsM2P8V1WXTzYYq9bqa7LngvZGasA4vNDrNsqdMW6LNFKREQan+PYPCAQgaE77VBHoqd22x6eSHbaWuwm+m0DrKWt2iMSkVoQilmyfTBqnSEczw6t+cN2/fM8KGYhMwXFvG1Ot6+1+2El8IiIlEeiDyZ2W9JpLa/NlEJqzA4zd6yze+5qtewOhC05NdZjr/34LlsPinXXbrxLjVkSWf92W6uq1XGKSOOKd9v1e/gOmz9Ue728kLUDxYl+29/QPqqIiIjIaVNy7fEUstDSefJKFSLNLJKEvm2Q6IXxnXYCMhixyrblaCsyvwne0m4n0ON9qi4l0shCMehYDYO32OJPJRN3cilL4u8/y6459cAfgLYVEO+FmUPWxm9mCHCswm0otvjX0C1CPmUn7l3XTt13rp9L5m3XZoWISLNK9tum+9CddqAj3gP+OqjYWMxbbAxGoG87tK6ozcr0IlI9gTD0brHr2sQeO9xbGD3271s6oHW5JRnpGiIiUl6RVlvnmD9E3Ig819bVAy1HqtXWwnpLKGqVDls6YORuq2Kb6K29+/7ZYVvv6jvT5ikiItXSvhpy07Yunxwozz7pQmSmbD2/Y60dKK5051ERERGRBqWV4ONx51od18JChkitOrpi4sygnWSfPljaionZachMWvWYni22iaXW4wC46TQ7n/50ANZ8/ev4Whp0kVmaV3IApvZbBYpYV2V+ZjEPqVHbQEgOVOZnllIgZEm2iX6rvjtzyDYapg4e+Xt/2BIBfAHbgPA828xxi+DmoJCz18Fx7NrbutJe/5Z2LcbVMcUMESmpaIdt/o/cYwloLa21Wwnl6O4XiX7oXKdqtaegmCFNzXGs8lSsC3KzVi3RK4Ljt7WOUExrhSJHUcyQsnIcWwueOgDFXO0ldp4utwjTg7a23ntG7R3wdhxLqI0kYfju2rrv9zyYHQJfGPq22h6F1DzFDGlo/oDtKRRztl+aGKj8vGF2GDygd6ut6atAkdQxxQwREVmoSsUMJdcejz9YG4sEIvXAH5ir3NJji1rje+zEveOzxbdgdHGTyGLeWsHnUnOVEjdYi3j9Th7L88jde9/h/xdpOIGwtVM6cKNVlC93Yqfn2qZG2wo72V3Pm+b+gCUFxLvttctMzVUAH7eT6/mMbeJ4rn2+z28JA/4gJDrsgEQobtddJdQ2BsUMESm1UNQ6WUSSMHqvJaHFuqtXneV4ju5+0b3ZkmtraXy1SjFDxOYC4bg9ROTEFDOk3Fo6LHEyNWJVbBuFW7SD0Ike6Nla2/Em2GL3/eGEVbEtZCDaVb11s/n1u1AcerdBrLM645DFU8yQRhdssSJBB/9iB0OS/ZXpyOcWrMhGKAE9Z+jAgTQGxQwREVmoCsUMJdfen+dZIkkwWu2RiNSXQMiSbON9Vh1qehBmR2xD23EgELHfLV9gblPbATyb+BXzVhGmmAMnYJv0nettgz4Uq/ITE5GqifdCcjlM7rHrSzlND9nmQNemxmrxGggfSbQF28Ap5uza67mAY4t8/qBVgannpGIREaksnx861kA4aVVspw5YVdtq37/nU5Aat44X3WfYwRl1vxARERGpPz4ftK6wdWa3UJpOadV2OLG2z6oLhupgH8rnh861dp8/fKfd9yd6K//v4RZgatAqzPduscPhIiK1JJyAvjPh0G1z18q+8l4rc7PW+S+5DLo22N6qiIiIiJRcA6xGlJjnWRJgUOXlRZbEH7CTkfEeqz6bnbIE2/T4XLXElC0iguXX+gKW0BXrts34cNIWxlRVSkR8Plu8T4/ZNaRcLfJSoxb7ezbXx6bG6fD5wad7HBERKaFYp1XbGt8D4zutYnqsyw5uVFI+ZfcLvhC0r7GkWm0siYiIiNS3WJc9UmP1X43P8yxRONFTP4m1R0v02piH77KksVhX5fbR8imYHbV7/K5N9ffaiUjziCSh/0wYuqN8h5DdIswOA44dKu5YXfk1GBEREZEmouTa4wmElVwrUgqhqD0SfbZ4WMzNVUwsAp5VS/QFwB9urEqRIlI64YSduj7wZ6sqHwiX9vtnpqBYgP6tttAlIiIiixcIQ/cGS7Qd2wnTB21jJ9pR3iotnge5GYvngTC0rYbWZeU7kCMiIiIileXzQ9tK2HeDrSnXc0GG2WErKtFTh4m188IJ6D8LQnEYu8/W+stdQTYzCdkZ6Npo3e60jyAitS4Us2tlOHnUIeROKzR0OjzXihnlU1awqHO9HXQQERERkbLSLPR4AhG1RRYpNcexDe9SJ8aJSONLDED7BIztgORA6TZScrOWkNO7FZL9pfmeIiIizSzaAZE2mF0G47tgZsjidqSttPOAYt46ZORSEErYhlKiD1raSvczRERERKQ2zHc8y0xAtLPao1mazKQVmujebF0f6pk/aImuwQiM3gtTB62qcKkTn92izScCIejbbknW2rcTkXrhD0L3Rkt+HdsJs0N2QDiStCIii7meFbK2BlLI2vpK1wZI9KtarYiIiEiFKLn2/vyB8lbWERERkcXx+WzBKD8LM4OWbHu6i+n5lJ3y7t4MbatKMkwRERHB4naiD6JdkBqByX0wOwJuYa6zRXxpG0CF7NzBmJTN2VvabFM/2lW/lb9ERERE5NT8QUusPPjn+qxeW8ha5dW+bRDvrvZoSsPng/bVVrV25O651uftdq9fCrkZSI1Dotfu+dWZQkTq1fwh5PSYHUaYHYL0OODY4QF/2NY4fD77GJ6tnxTzUMhAsWif19IGyWUQ67E/i4iIiEjFKIv0/iKtdhJaRKSWOQ7BgYHD/y/S8AJh6D4DCrm5BNvTqDSbS9kCVtdG6Fir3yFpfIoZIlIN/oAl2cZ7Le6mxmD6IKRGoVgAv982kfxB20hyfHNf6IHrgpuf20zK2sf8Ydusb19jm0qRtrnNJykpxQwREVkoxQyppHiv3f/VW/Xa+eqrHWuhdWW1R1N6Le3Qfw5EdsPELqvQ29IBwZalfb982uYN8+uA7auURNYoFDOkmfl8VsE21mV7E9lpO0SQHrdDxIUseEX7XMcBxw/+EMR6oaUVwgkIJ/W7I81DMUNERBaqQjFDybX3172p2iMQETklX0sL63/x82oPQ6SyIkno3QqDt1hyTrxv8TdJmUlbsOraBJ3rlJQjTUExQ0SqynGsUku0AzrWWCvD7IxtJmUmbROpmAbPtRaJhzeSArZZH2611rmhmCXXKnaXlWKGiIgslGKGVFQgZPeS+2+CSB1Vr50dgngPdK5v3PvYQAi6N1hV3om9MH3AKjSGk3b/fqq1O8+1tbrMlCWTta+G1uV2oE4ahmKGyJxQdK77Tq/92XWhmDuSXItjB5D9QSUVStNSzBARkYWqVMwo22x+bGyMZz/72SSTSdra2njxi1/MzMzMSb/m4Q9/OI7jHPN4+ctfXq4hiohIjVDMkAWLdkDfdgglrOWcW1jY13kuzByyCnh926GrgTc1RBqcYoZIHfP5LWG2bQX0boFVF8PqB8OqS2DlxfbfVZfA6kth1YNh+fm2UZ/st0M2it2ySIoZIiKyUIoZdSDea1Vr02PVHsnCZKbAF7TOScFItUdTfi1t0LcNVlwI7WutW8XUAZjcD7MjVnU4Oz13yG4CZoft76YHrcJv53r72r5tSqyVmqeYISXj81mMCMXmHlE7tKDEWpGGoZghIlL/yla59tnPfjYHDx7kpz/9Kfl8nhe+8IW89KUv5Stf+cpJv+4lL3kJ73rXuw7/ORqNlmuIIiJSIxQzZFGiHTBwDgzfZdUwQnGItB5/wcnzjlTGi3ZB90ZrvyQidUsxQ6TB+AP2ECkDxQwREVkoxYw64A9Cx2rYf6MdtvbV8D1kMW/rUb1bbR2rWTiOHaZrabdKw5mpI8m0+bluFQBOAKKtEGmzLhXhZHMkIEvDUMwQEZGFUswQEal/ZVl9uOOOO7j66qu57rrrOP/88wH46Ec/yuWXX84HPvABBgYGTvi10WiUvr6+cgxLRKRhuJkMu5/zXABWfemL+CL1u/iomCFLEo5D/1kQ64Tx3VbpIhCEQNhaSXtFazNdyFmV296t1lIuEK72yEUqTjHDKGaIiJyaYoZRzBAROTXFDKOYUWGxHoh3Q2rUKtnWIs+DmSFoXQZtK6s9muoJttgjMffv5HlWodZxwPGpKmOTUcwwihkiIqemmGEUM0RETq1SMaMsPRWvueYa2traDgcIgEc96lH4fD7+9Kc/nfRrv/zlL9PV1cW2bdt405veRCqVOunnZ7NZpqamjnmIiDQ81yVz661kbr0VXLfaozktihmyZP4AtK+GFRfBsnMg0W+VLzzP/hvvg2XnwsqLoHOdEmuleSlmAIoZIiILopgBKGaIiCyIYgagmFFx/gC0r7H3XCFT7dEcX3oMwgnoXA8+f7VHUzscx/79fH4l1jYjxQxAMUNEZEEUMwDFDBGRBalQzChL5drBwUF6enqO/UGBAB0dHQwODp7w6571rGexatUqBgYGuPnmm3nDG97AXXfdxTe/+c0Tfs1VV13FO9/5zpKNXUREKksxQ05bMGJVaVuXH6mCoYV6kYakmCEiIgulmCEiIgulmFFnYt1WEXZ8p60F1ZJCxjopDWyxBFsRaTiKGSIislCKGSIijWFRybVvfOMbef/733/Sz7njjjuWPJiXvvSlh/9/+/bt9Pf388hHPpL77ruPdevWHfdr3vSmN/Ha17728J+npqZYsWLFkscgIiKloZghVTFfBUNE6opihoiILJRihoiILJRiRoNyHEuunTkEmUmItFZ7RMZzYWYYOtZadyURqSuKGSIislCKGSIizWVR2Sf/+I//yAte8IKTfs7atWvp6+tjaGjomI8XCgXGxsbo6+tb8M+76KKLALj33ntPGCTC4TDhsNo8i4jUGsUMERFZKMUMERFZKMUMERFZKMWMBhZJWhLr4C0QioGvBg5az45AtBM616ubkkgdUswQEZGFUswQEWkui1px6O7upru7+5Sfd/HFFzMxMcENN9zAeeedB8AvfvELXNc9fOFfiD//+c8A9PfrlK+ISL1RzBARkYVSzBARkYVSzBARkYVSzGhwrcstoXVmGJJVfs3zKatc27UBgpHqjkVElkQxQ0REFkoxQ0SkufjK8U3POOMMHvvYx/KSl7yEa6+9lt///ve88pWv5BnPeAYDAwMA7N+/n82bN3PttdcCcN999/Hud7+bG264gV27dvHd736X5z3veTz0oQ/lzDPPLMcwRUSkBihmiIjIQilmiIjIQilmiIjIQilm1Cl/EDrX2X9zM9Ubh1uE2VFoXwOxUydZiEh9U8wQEZGFUswQEWkMZeuV8+Uvf5lXvvKVPPKRj8Tn8/HUpz6Vj3zkI4f/Pp/Pc9ddd5FKpQAIhUL87Gc/48Mf/jCzs7OsWLGCpz71qbzlLW8p1xBFROqav7292kMoGcUMEZHyUsxQzBARWSjFDMUMEZGFUsxQzKi6aIcltQ7dDoEI+Mq25XViM4cg0Qsda8FxKv/zReqEYoZihojIQilmKGaIiCxUJWKG43meV/afUkFTU1O0trYyOTlJMpms9nBERGqSrpVGr4OIyKnpWmn0OoiInJqulUavg4jIqelaafQ6lECxAAf/AtMHITlQ2QTX9Dh4wLJzoaWtcj9XpMnoWmn0OoiInJqulUavg4jIqS30Wumr4JhERERERERERERERERESsMfgO6NEE5AaqRyPzeXgnwGujcpsVZERERERESkQSm5VkREREREREREREREROpTOAE9Z4DnQXa6/D+vmIfUKHSut2q5IiIiIiIiItKQlFwrIlKH3EyG3c99Hruf+zzcTKbawxERkRqmmCEiIgulmCEiIgulmCE1J94DXRshM2VVZcvFLcD0ILStgo614Djl+1kiDUIxQ0REFkoxQ0REFqpSMSNQtu8sIiLl47qkrrvu8P+LiIickGKGiIgslGKGiIgslGKG1KL21VAswPCd4PNBIFLa7+8WYeogJJdBz2bwa4tNZEEUM0REZKEUM0REZKEqFDNUuVZERERERERERERERETqm+NA51qrKDs7AoUSVq5xC5ZYm+iFnjMgEC7d9xYRERERERGRmqRjtSIiIiIiIiIiIiIiIlL/fH7o3mSVa0fvhZY2CMVP73sWsjAzBMkB6NkCoWhJhioiIiIiIiIitU3JtSIiIiIiIiIiIiIiItIY/AHo2gS+EIzeDfk0RLussu1iZSYgOwvta6B7oyrWioiIiIiIiDQRJdeKiIiIiIiIiIiIiIhI4/D5oGsdhOMwfBdM7oNo58KrzhYyMDsKwRboPxOSy+17ioiIiIiIiEjTUHKtiIiIiIiIiIiIiIiINJ5EryXYju2C6QOQGrM/h2LgDx77ucU85GetUq0/CK0roGM1RFqrMXIRERERERERqTIl14qI1CmnpaXaQxARkTqhmCEiIgulmCEiIgulmCF1IxSDvq3Qthymh2Bm0JJs3cKxn+fzQzAKXRsg3gORNnCcqgxZpNEoZoiIyEIpZoiIyEJVImYouVZEpA75olE233RjtYchIiJ1QDFDREQWSjFDREQWSjFD6lKk1R4dayA3A4XskQRbXwACYQjFwa+tM5FSUswQEZGFUswQEZGFqlTM0AqBiIiIiIiIiIiIiIiINAd/AFraqj0KEREREREREalxvmoPQEREREREREREREREREREREREREREpFYouVZEpA652Sx7XvYy9rzsZbjZbLWHIyIiNUwxQ0REFkoxQ0REFkoxQ0REFkoxQ0REFkoxQ0REFqpSMSNQtu8sIiLlUywy++vfHP5/ERGRE1LMEBGRhVLMEBGRhVLMEBGRhVLMEBGRhVLMEBGRhapQzFDlWhERERERERERERERERERERERERERkTlKrhUREREREREREREREREREREREREREZmj5FoREREREREREREREREREREREREREZE5Sq4VERERERERERERERERERERERERERGZo+RaERERERERERERERERERERERERERGROYFqD6DUPM8DYGpqqsojEREpHzeVYqZYBOx65ysUFvX189fI+Wtms1LMEJFmoJhRGooZItIMFDNKQzFDRJqBYkZpKGaISDNQzCgNxQwRaQaKGaWhmCEizaBSMaPhkmunp6cBWLFiRZVHIiJSIf39S/7S6elpWltbSziY+qKYISJNRzFjyRQzRKTpKGYsmWKGiDQdxYwlU8wQkaajmLFkihki0nQUM5ZMMUNEmk4ZY4bjNdiRDdd1OXDgAIlEAsdxFvW1U1NTrFixgr1795JMJss0wtrRbM8Xmu856/k2ttN5vp7nMT09zcDAAD6fr0wjrH2KGQun59v4mu056/kunGKGUcxYOD3fxtdsz1nPd+EUM4xixsLp+Ta+ZnvOer4Lp5hhFDMWTs+3sTXb84Xme86KGadPMWPh9HwbW7M9X2i+56yYcfoUMxZOz7exNdvzheZ7zpWIGQ1Xudbn87F8+fLT+h7JZLIp3mDzmu35QvM9Zz3fxrbU59vMp/XmKWYsnp5v42u256znuzCKGYoZS6Hn2/ia7Tnr+S6MYoZixlLo+Ta+ZnvOer4Lo5ihmLEUer6NrdmeLzTfc1bMWDrFjMXT821szfZ8ofmes2LG0ilmLJ6eb2NrtucLzfecyxkzmveohoiIiIiIiIiIiIiIiIiIiIiIiIiIyP0ouVZERERERERERERERERERERERERERGSOkmuPEg6Hefvb3044HK72UCqi2Z4vNN9z1vNtbM32fGtNs73+er6Nr9mes56vVFKzvf56vo2v2Z6znq9UUrO9/nq+ja/ZnrOer1RSs73+er6NrdmeLzTfc26251trmu311/NtbM32fKH5nnOzPd9a02yvv55vY2u25wvN95wr8Xwdz/O8sn13EREREREREREREfn/7dxtaFV1HMDx33a3e6cwn5DNLaaxhQ+YESmOaSLFQDDUXikY4wbmA643CqW4YpFlQyQCWUU+v5BGhRPJsR7MET6EoBsMtxS9qwjaQDAcWe3p14tznds8d+sczz33PHw/sBeenQu/3+483zf/XQAAAAAAAACAj/DJtQAAAAAAAAAAAAAAAAAAAEASh2sBAAAAAAAAAAAAAAAAAACAJA7XAgAAAAAAAAAAAAAAAAAAAEkcrgUAAAAAAAAAAAAAAAAAAACSQne4tr6+Xp588knJy8uT8vJyuXLlyrj3f/nllzJ//nzJy8uTRYsWSVNTk0uTOsPKvocOHZIVK1bI9OnTZfr06VJZWTnhz8drrL6/DzQ0NEhWVpa8/PLL6R0wDazu/Oeff0p1dbUUFRVJLBaTuXPn+ur32uq+H330kcybN08mTZokJSUlsmPHDvnnn39cmvbx/Pjjj7JmzRopLi6WrKwsOX369ISvaWlpkeeee05isZg89dRTcvz48bTPGWQ0g2aYoRn++b0OSzPohTfQDJphxq/NCFsvRGjGeGiG82gGzTBDM/yDZqRGM5xHM2iGGZrhHzQjNZrhPJpBM8zQDH8ISy9EaIZX0AyaYYZm+APNGF9amqEh0tDQoNFoVI8eParXr1/XzZs367Rp07Snp8f0/osXL2okEtH9+/drR0eHvvXWW5qbm6vt7e0uT26P1X03btyo9fX12traqp2dnfrqq6/q1KlT9ffff3d5cnus7vtAV1eXPvHEE7pixQpdt26dO8M6xOrO//77ry5ZskRXr16tFy5c0K6uLm1padG2tjaXJ7fH6r4nT57UWCymJ0+e1K6uLv3mm2+0qKhId+zY4fLk9jQ1NWlNTY2eOnVKRUQbGxvHvT+RSOjkyZN1586d2tHRoQcPHtRIJKLNzc3uDBwwNINmmKEZNMOL6EXm0QyaYcavzQhbL1RpxnhohvNoBs0wQzNohhfRjMyjGTTDDM2gGV5EMzKPZtAMMzTDH80IUy9UaYYX0AyaYYZm0Awv8kozQnW4dunSpVpdXT3878HBQS0uLtYPPvjA9P7169frSy+9NOpaeXm5bt26Na1zOsXqvmMNDAxofn6+njhxIl0jOsrOvgMDA7ps2TI9fPiwxuNxX0VC1frOn3zyiZaWlmpfX59bIzrK6r7V1dX64osvjrq2c+dOXb58eVrnTIf/E4o333xTFy5cOOrahg0bdNWqVWmcLLhoBs0Yi2b4S1ibQS8yg2bQjLH83Iyw9UKVZoyHZjiPZtCMsWiGv9CM1GiG82gGzRiLZvgLzUiNZjiPZtCMsWiGf4S1F6o0I1NoBs0Yi2b4B81oHPeedDUj+/E/+9Yf+vr65OrVq1JZWTl8LTs7WyorK+Xy5cumr7l8+fKo+0VEVq1alfJ+L7Gz71j379+X/v5+mTFjRrrGdIzdfd99910pKCiQTZs2uTGmo+zsfObMGamoqJDq6mopLCyUp59+Wvbt2yeDg4NujW2bnX2XLVsmV69eHf4Y9EQiIU1NTbJ69WpXZnabn59ZXkMzaIYZmkEzgsLPzysvohk0w4xfmxG2XojQjIn4+XnlRTSDZpihGTQjKPz8vPIimkEzzNAMmhEUfn5eeRHNoBlmaIY/mkEvJubn55UX0QyaYYZm0IygSNfzKuexXu0jd+7ckcHBQSksLBx1vbCwUH7++WfT13R3d5ve393dnbY5nWJn37F27dolxcXFj/zieZGdfS9cuCBHjhyRtrY2FyZ0np2dE4mE/PDDD/LKK69IU1OT3Lp1S7Zv3y79/f1SW1vrxti22dl348aNcufOHXn++edFVWVgYEC2bdsme/bscWNk16V6Zt27d0/+/vtvmTRpUoYm8x+aYaAZD9EMmhEk9MJZNMNAMx7yczPC1gsRmjERmuEsmmGgGQ/RDJoRJDTDWTTDQDMeohk0I0hohrNohoFmPEQz/NMMejExmuEsmmGgGQ/RDJoRJOlqRmg+uRbW1NXVSUNDgzQ2NkpeXl6mx3Fcb2+vVFVVyaFDh2TmzJmZHsc1Q0NDUlBQIJ999pksXrxYNmzYIDU1NfLpp59merS0aGlpkX379snHH38s165dk1OnTsnZs2dl7969mR4NCBSaEUw0g2YA6UAzgidsvRChGYBbaEbw0AyaAaQLzQgemkEzgHShGcETtmbQC8A9NCN4aAbNsCM0n1w7c+ZMiUQ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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Seed 4\n",
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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89NJLAMybNw+Al19+mfj4eOLj47nlllvq9VlERKRuWmLMGD9+PNu2beOee+5h7ty5zJ07t/y9Tp06MXbs2Hp9FhEROXD9+/dnypQpXHbZZQwcOJArrriC4cOHY1kW27dvZ8qUKdjtdrp37x7sporUm5JrRRrA7bffzgsvvEB+fn6dtk9MTGT+/Pm8/vrrfPrpp0yaNImSkhI6dOjAUUcdxUcffcQll1xS63GOOOIIfvnlF/7xj39wxx13EBMTw/XXX8/jjz9+sB9JREQaQEuND1988QWTJ08uX16+fHn5dFDdu3dXcq2ISCNoiTGjbCDG5s2bq+0w+f3335VcKyLShOobSwBmzJjBjBkzAHA4HHTp0oU///nPPProo0RFRdW6//vvv88DDzzABx98QG5uLsOGDWPatGmcdNJJB/w5RESk4bS02JCbm8sDDzxQZd2zzz4LQK9evZRcKyLSiFpazCh7LvXUU0/t897JJ5+s5FoRkSZ23nnnsXr1ap599ll+/vln3nnnHWw2G7169eKss87ipptuqrWquUhzZLNULkBERERERERERERERERERERERERERAQAe7AbICIiIiIiIiIiIiIiIiIiIiIiIiIi0lwouVZERERERERERERERERERERERERERGQPJdeKiIiIiIiIiIiIiIiIiIiIiIiIiIjsoeRaERERERERERERERERERERERERERGRPZRcKyIiIiIiIiIiIiIiIiIiIiIiIiIisoeSa0VERERERERERERERERERERERERERPZwBrsBDS0QCJCSkkJMTAw2my3YzRERaZYsy6KwsJCuXbtit7fdcRaKGSIitVPMMBQzRERqp5hhKGaIiNROMcNQzBARqZ1ihqGYISJSO8UMQzFDRKR2dY0ZrS65NiUlhR49egS7GSIiLUJycjLdu3cPdjOCRjFDRKTuFDMUM0RE6koxQzFDRKSuFDMUM0RE6koxQzFDRKSuFDMUM0RE6qq2mNHqkmtjYmIA88FjY2OD3BoRkeapoKCAHj16lF8z2yrFDBGR2ilmGIoZIiK1U8wwFDNERGqnmGEoZoiI1E4xw1DMEBGpnWKGoZghIlK7usaMVpdcW1bSPDY2VkFCpA0KlJay/aKLAOjz+efYIyKC3KLmra1PA6GYIdK2KEYcHMUMxQyRtkQx4+AoZihmiMjBaUtxSDFDMUNE6qYtxYb9UcxQzBCRhtEWYopihmKGiDS81ho/aosZrS65VkTaOMvCs2Vr+WsREZFyihEiIlJXihkiIhJMikMiIrI3xQYREWkoiikiInIg2mj8sAe7ASIiIiIiIiIiIiIiIiIiIiIiIiIiIs2FkmtFRERERERERERERERERERERERERET2cAa7ASIiIiIiIiIiIiIiIiIiIiIiItI0/H4/Xq832M2QOgoNDcVuVw1Nkaam5FoREREREREREREREREREREREZFWzrIs0tLSyMvLC3ZTpB7sdjt9+vQhNDQ02E0RaVOUXCsiIiIiIiIiIiIiIiIiIiIiItLKlSXWJiYmEhkZic1mC3aTpBaBQICUlBRSU1Pp2bOn/mYiTUjJtSLSuthshHTtWv5aRESknGKEiIjUlWKGiIgEk+KQiIjsTbFBREQaimJKm+b3+8sTa9u3bx/s5kg9dOzYkZSUFHw+HyEhIcFujrRFbTR+KLlWRFoVe0QE/X77NdjNEBGRZkgxQkRE6koxQ0REgklxSERE9qbYICIiDUUxpW3zer0AREZGBrklUl+hoaGASZBWcq0EQ1uNH/ZgN0BEREREREREREREREREREREREQan60NVZ1sLfQ3EwkOJdeKiIiIiIiISNtkWeZHREREREREREREREREpBJnsBsgInJAAn7zw56OcJsD7A4Cbjc7r7wKgF4ffoA9PDx4bRQRkWYl4HIpRoiItFV+H7gLwFsCnhLwFIG3FKyAed9mB2cYhEZDaBSBgJOdN/wf2GyKGSIi0uR07yIiIntTbBARkYaimCIiIgeircYPJdeKSPMW8JuOb0+J6fz2FJrXfo/pCC8rMmWzgd0BPhuuNWvMuuJsCEkER0jQmi8iIk3AW2p+fK5K8cEyiVJ2p0mWcoZXjRGBQHDbLCIijS/gh9JcKM6EogxzHxHwgd1u7hHsThMrAAJek3hblGHiiNuDa+1a815BKji7gTM0eJ9FRETalkBA9y4iIlKVYoOIiDQUxRRpoUaPHs2IESN44YUXgt0UkbapjcYPJdeKSPPjLQVXvkmOLc3e0wnuBWymE9wRYirVOkLMOgDLD5YPXMUVx0leAvkJENkBojpAZDsl2oqItAaWZaoPluaaJCh3oUmsLZ/W2zKDLva8BMz132+vOIbPA0Q2YaNFRKTJ+NwmPuQlgyvPJMuGRUN0B5NQWxfFle4rdi+HoiSI6w7RiRAW0yjNFhEREREREREREZEDY1kWfr8fp1OpcCLScOy1byIi0gT8XihMh9TVsHMh7PoD8naYpNnIBNORHdfNdGZHJEB4LIRGQWik+QmLgfB4iGxfccyoDubfvJ2w+w9IWgDZ28BdFIxPKCIiByvgN7Fi9zJIWgSpq8xgDGc4xHQ2cSKum4kZsd3MT9m68DgTU8rsWgyZm83+IiLSOvi9JqE2eTGkLAdfCUR3rIgDdU2sBTMrRpmYTkAA0tdC8iLI2GAGdoiIiIiIiARDUQbk7zIDz0VEROqrvFAJ4HUHrx0i9TBx4kRmzZrFiy++iM1mw2az8d5772Gz2fjxxx858sgjCQsLY+7cuUycOJEJEyZU2f/2229n9OjR5cuBQIDHH3+cPn36EBERwfDhw5k6dWrTfigRaREaNbn28ccf5+ijjyYmJobExEQmTJjAxo0ba93v888/59BDDyU8PJzDDjuMH374oTGbKSLB5CmGnO2QtNAkwOYng9MJsV3NT3g8OA5w+lW7wyThxnaB6E4mKSt9jekQz9ykJNtmRjFDRPbLskynwe5lJlYUZ0B4DMT3MAMpQiMrpvXeH2cYhMVVPihkbjAJWGlroDSvMT+BNDDFDBGpwrKgKBNSlkHKCvB7TEJtZPv6JdTuj81u7kvie5h4kr3ZzJKRvc1UyZVmS/FCRETqSjFDRJq17K0w74WK5f+OgOeHwJO94alD4POJsOH7NjU1azApZohIi+UugjnPwf+OqVj3bH/47GrI2hy8drViLSFmWJZFiccXlB+rcqJ3LV588UVGjRrFDTfcQGpqKqmpqfTo0QOAf/7znzzxxBOsX7+eYcOG1el4jz/+OO+//z6vvfYaa9eu5Y477uDKK69k1qxZB/R7FJHWq1FrYc+aNYubb76Zo48+Gp/Px3333cfpp5/OunXriIqKqnaf+fPnc9lll/H4449z9tlnM2XKFCZMmMCyZcsYOnRoYzZXRJqSKx8KUqEgBTxFe6Zp7VS1QlRDsjsgIt78uAtNQlX+bmjX21Q2dB5gAq80GMUMEamWu9AkLxWmgM1mKpg3RKJUWCxEhIO3BHJ3QGEqxPWAhF4QEnHwx5dGpZghIuW8Lsjdbq7lNhvEdm6YOLE/odHmx1VgBu4VpkD7fuZexmZrvPPKAVG8EBGRulLMEJFmafscmPcibJkBPhvQZc8bNlOUxO+GkmxY+5X56TQUxj8BfU4MZqtbPcUMEWmRkhfDF382M75WjikBH6z7Bjb+BOe9DMMuDmozW5uWEDNKvX4GPzi9wY9bF+seGUdkaN2e5cbFxREaGkpkZCSdO3cGYMOGDQA88sgjjB07ts7ndbvdPPbYY/zyyy+MGjUKgEMOOYS5c+fy+uuvc/LJJ9fzk4hIa2az6jMU4CBlZmaSmJjIrFmzOOmkk6rd5pJLLqG4uJhp06aVrzv22GMZMWIEr732Wq3nKCgoIC4ujvz8fGJjYxus7SLSQFz5kLfLdEJ7XRARB2ExDXb4QKmLjWddB8DA79/BHhFec1tcBRCVCO37milj24iWcK1UzBBp4wIBKNgFWVvBWwRRHU21wIM5ZE0xwlMMJTlm2vD2/SCmC9gbdZKHFqMlXCsVM0TaqOIsMyNFSRZEtYeQyAY7dJ3uK6yA6cj2+yC+J7Q7xFRTb8Oa+7WyKeIFNP/fg4g0f4GSEjYecSQAA5ctxR7Z+uJLc79WKmaISFBlboQZD8Kmn/assBHoMZqNT5tKdwOXLMQeE2eeZ6WvNUlRy94HdwFggxNuh1MfaLxiJk2suV8rFTNEpNlb9w18cYMZlBHXg8Cou9k48QkABv74Afa5j8HWX822F74DQ/8UxMYenOZ+rWwOMcPlcrF9+3b69OlDeHg4JR5fi0iuBRg9ejQjRozghRdeAGDmzJmccsop7Nq1i27dupVvN3HiRPLy8vj666/L191+++2sWLGCmTNnsnbtWoYOHbpPgrPH4+Hwww9n0aJFB/W5GsvefzuRptbanlfVNWY0auXaveXn5wPQrl27/W6zYMEC7rzzzirrxo0bV+WiJyItkLsI8ndBfrKZOjUiwUzl3QgccXVM1g2PM1WnSrJg91LTGZ7Qu2VVsQ0EWm3yl2KGSBvmKTFTEOUnQ2gUxHVvsEPvN0aERpmkrNIcSF1hErY69DPrpdlTzBBpYwJ+yEvaM11dAOK6ga3hvxPXel9hs5vBHz4X5GyD0lzoMABiOjV4W6RhKF6ISEviSEgIdhPaNMUMEQkKv9dM1z37KVNJ0O6EIyfCsX+DiC443hpjtnOEmH9Do6DHSPNz4t/hl4dMku3c5yFnO1zwZsvq72ihFDNEpFlb+zV8PhGwYOCZcP7rEHDiSHjdvN9pMFwxFX64C/54G766CRL6QLcjgtjo1qs5xoyIEAfrHhnXKMeuy7kbwt5Jsna7nb3rTHq93vLXRUVFAHz//fdVknIBwsIOrtCPSGvXFp9XNVlybSAQ4Pbbb+f444+vsVR5WloanTpV7Yjq1KkTaWlp1W7vdrtxu93lywUFBQ3TYBFpGD435O+G3J2m8mAjJtUC2CPCGfDV6/XYwWGmcPUUQ+YGU8224wCTeNvcFWWYpILOw1rdAzLFDJE2rCQHMtabJNcGqFZbWa0xwmaDyPYmduUlVcQETfXdrClmiLQxPrepVpu3w3xnb8BZMCqr132FM9wMBCnJgpRl0K4vtOtT0eEtzUJjxQtQzBCRhmePjGTAgvnBbkabpZghIkGRuRG++gukLDfLA8bD6f+GDv0BsEPNsSGyHZz7EvQ52SRGrfsa/B645MNWU8G2OVLMEJFmbfsc+PIGwILDr4SzXwSHs/qYcubTUJgGG783ceQvsyFElTEbUnONGTabrV7VY4MpNDQUv99f63YdO3ZkzZo1VdatWLGCkBDzvHbw4MGEhYWRlJTEySef3ChtFWmN2urzqiYrd3jzzTezZs0aPvnkkwY97uOPP05cXFz5T48ePRr0+CJygAJ+k1SbvBjS15jqqnHdm28FwNAoiO0CRemwexkUpMJeo5maFXeRedjmyger9i+QLY1ihkgbZFmmwnnKMjOFXWzXBk2srRdnmKmC6HfD7uWQtcVM+S3NkmKGSBviLoTUlZC73QzAaKTE2gNis+1pU6z5np66ynxnl2ajseIFKGaIiLQ2ihki0uRWT4U3RpvE2vA4uOAtuOyT8sTaejnsQrjiM3CEwcYf4Md7mndfRwunmCEizVb+LvjsajPQYtA5cM5/wVFDAqXdAee9bIqNZG2EOc80XVvbCMWMg9e7d28WLVrEjh07yMrKIhAIVLvdqaeeyh9//MH777/P5s2beeihh6ok28bExHDXXXdxxx13MHnyZLZu3cqyZct46aWXmDx5clN9HBFpIZokufaWW25h2rRp/P7773TvXvO0vp07dyY9Pb3KuvT0dDp37lzt9vfeey/5+fnlP8nJyQ3WbhE5QCU5kLLCPAjyu02CUnhssFtVO7vTtNXym/bnbDNJws2Nz2Oq7BalAa3voZhihkgbFAhA9lZIWw02B8R0bpTpvevFZjOV1sNjIXM9pK8GT0lw2yT7UMwQaUPK7jGKM4I7AKM2oZEQ2xkKU839UFFmsFskNG68AMUMEZHWRDFDRJqU3wfT/wVfXA/eEjhkNPxtEQy76OBmUep7KlzwBmCDJW/B8g8bqsVSiWKGiDRbPg98PtHMENhluBm0UZcq5pHtTAVbgPkvQ0FKozazLVHMaBh33XUXDoeDwYMH07FjR5KSkqrdbty4cTzwwAPcc889HH300RQWFnL11VdX2ebRRx/lgQce4PHHH2fQoEGMHz+e77//nj59+jTFRxGRFqRRsxYsy+KWW27hq6++4rfffqvTRWjUqFH8+uuvVdbNmDGDUaNGVbt9WFgYsbGxVX5EJEi8pZCxAXb9YSrARiea6bWbMEEq4Paw845H2XnHowTcngM7SGR7CI+G9HVmanLfAR6nMQQCkLXZ3MzE7P8LdEukmCHSRvl9ZsBA5gZTgTAivtFOdUAxIjTSXG/zkk1SV2luo7VP6k4xQ6SNKUyHlJXgKYKYrk0ypelB3VfYnSYB2FtiEmxzd5rv8dLkmiJegGKGiDS8gMvFzquuZudVVxNwuYLdnDZBMUNEmlxJDnx4Pix42SyfcAdc+aWZYa8a9Y4NQybAqfeb1z/cbfo6pEEoZohIs/fbo7BrCYTHw8XvQ0h4lbdrjCmDzoUex4KvFGY+3nRtbqUUMxrWgAEDWLBgASUlJViWxcSJE7Esi/j4+H22nTRpEmlpaeTl5fHcc8/x0ksvMXPmzPL3bTYbt912Gxs2bMDj8ZCRkcFPP/3ESSed1HQfSKSFaavPq2qo+37wbr75ZqZMmcI333xDTEwMaWlpAMTFxREREQHA1VdfTbdu3Xj8cROYb7vtNk4++WSeffZZzjrrLD755BP++OMP3njjjcZsqogcjEDAVGbK2WaSjiLbm2SkILWlZOX6inYdqNBosIeYz+RzQ+Kg4H2mygp2Qd4OiO7Y6qZyUswQaYPKKnHn7jDXNWd4rbsclAONEY4QU9m8MMMk2CYOanUDHFoaxQyRNiR/N2SsM6+b8tp7sPcVNpsZbOgqgPQ1JtG2fT8TU6TJKF6ISIsVCFCyZEn5a2l8ihki0qTykuDDC82026HRMOEVGHxezfscSGw44U7YMRe2/Q5f3gA3/K57kgagmCEizVry4oqBGxNehYTe+25TU0yx2WDsJHhnHKz4GEbfawaRywFRzBCRVqWNPq9q1HKSr776Kvn5+YwePZouXbqU/3z66afl2yQlJZGamlq+fNxxxzFlyhTeeOMNhg8fztSpU/n6668ZOnRoYzZVRA5UaR6krYTUFeB3Q1z35pGE2hCcYWaUeMFuSF1lOsaDqTgbMjdCaFTjJ6AFgWKGSBvjdUHGWpNYG9Op+V/XbHYzzbflh9SVphOklQ1yaEkUM0TaAMsy19q01aZSbVSHYLfowITHQkSCmX0ifY2Z7UOajOKFiIjUlWKGiDSZ1FXw1liTWBvbDa6fUXti7YGy2+GCN8w9SdrqimQrOSiKGSLSbHlL4eu/gRWA4ZfDoWce2HF6Hgs9j4OAFxa91rBtbGMUM0REWr5GrVxr1SHhoHLZ7TIXXXQRF110USO0SEQajM8D+bsgd09l16gO4AgNdqsant25p1phmkmm6jzUPIhqap4Sk1gb8LXcxIJaKGaItCHeUkhfZwYvxHYx19qWIrI9uPIhbQ34vZDQx3RUSJNSzBBp5SwL8nZC+noIjYDwuGC36OCERJiqu3nJe2bFGGySbqXRKV6IiEhdKWaISJPY+ht8ejV4Cs19wRVTTf9DY4pOhHGPwdd/hZlPmOm+2/dt3HO2cooZItJs/f4fyN4MMV1g/GMHd6zjb4Wk+fDHu3DiXXqWdYAUM0REWj5lAohI/VgWFGXA7qWm8pIj1EwF0RoTa8vY7OYmxFMEKSuhOKtpzx/wm0pXpdnmQZiISEvmLYX0tS0zsbZMeJx5kJSxHrI2meu0iIg0DMsyVc0z1psZMVp6Ym0ZR4i5byrOMoP2irOD3SIREREREWlK676Bjy42ibW9T4Rrf2z8xNoywy+DQ04Bnwt+urdpzikiIk0rfS0s+J95fc6LB18sqv846DAA3AWw6tPatxcREWmllFwrInXnKTaVBnctNV+kY7tCWEywW9U0bDZTbcrvNp3hhWlNd+7cnWZK3OhEk+grItJSlSXWFqa23MTaMqFRENnOjALPWG+q2IqIyMGpklgb1foqYtgd5h7KW2zuKQpSa99HRERERERavpWfwucTzfTaQ86HK7+AiPimO7/NBmc+Y57FbZ5uKuiKiEjrYVnw4z/ACsDg82DAuIM/pt0OR11nXi97/+CPJyIi0kIpS0tEahfwmylMd/0BudsgIs4ketodwW5Z04tOBCxIXWV+J3WYyuGgFKabqoiR8a27OrCItH6VE2tjOrfsxNoyIREQ1RFytpnBJz5PsFskItJyVU6sDYtuvYP4bDaI7gQEIG2VGUjX2PcUIiIiIiISPEvfg6/+YhKeRlwBf3obnGFN344O/eDoG8zr6fdrJiYRkdZk/bewYw44w+H0fzfccYddYvqn01ZByvKGO66IiEgLouRaEalZSQ6krIDUFWD5IbabSSZqxmzhYdjCG/HhVGR7cIZA2hqTABAINM55XPmQucEkMYdGN845RESaQjNKrG3wGOEMg5hOkLcTMtaCz91wxxYRaUvyksx332aUWNuo9xWR7U2HR/oayN7aePcUIiLSYtkiIrBFNO9ncCIiUouFr8J3twEWHP1nOPflgypactCx4eR7IDzePMNa/sGBH0dERJoPb6kZNAFw/G0Q37NOu9UppkS2g0HnmNdLJx9EI0VEpLVoi8+rWkHJMBFpFN5SU5k1dwcEfKa6kiMk2K2qlT0inEN/eLfxTxQeD7ZCyNhTqbB9X3A04CXVW2qSC9yFENet4Y4rItLUvC6TWFuQArFdgppY22gxwhFqkobzkk1yVKchEBLe8OcREWmt8pJMBfDQyGaTWNsk9xXhsaZjPXO9mR62ff+GvacQEZEWyx4ZyaHLlwW7GSIicjDmPAe/TjKvj/s/GPuomcniADVIbIhsByf/A6bfCzOfhGGX6hmWiEhLN/8lyE+C2O5w/O112qVeMeWIq2HNF7D2KzjjKXBqplURkbaqrT6vUuVaEakq4If8XbDrD5PcGRppkqFaQGJtkwuLgYgEyN5kkmy9roY5rt9rfveF6SZZS0SkpWpGibWNzhFiPmNBiqlC6C0NdotERFqG/F0mVoRGmGTTtiY0CqI6QNYW83tQBXQRERERkZbNsuD3xyoSa0/+x0En1jaoo64zMxQWpsDS94LdGhERORj5u8xgDoDTHzH9+g2t94kQ3RlcebD114Y/voiISDOn5FoRMSwLirMhZTmkrAC/G+K6m85e2b+QCIhONBV+01aCK//gjuf3QsYGU/0wpvNBTRElIhJUbSmxtozdCbGdoTAV0taApyTYLRIRad7yd5tYERIO4XHBbk3wOMMhJhHydip+iIiIiIi0ZJYFvz0Ks540y2MehlPuaz6JtWDuv066y7ye86zuP0REWrIZD4KvFHodD0MuaJxz2B0wdM+xV09tnHOIBEHv3r154YUXgt0MEWkBlFwrIuAuNJ3au5ZAUSbEdILI9s3rgU8dBTweku59iqR7nyLg8TTNSR2hENsVirNg9zKTJGBZ9T+Oz2MSa3O3m4RdVQsWkZaqPLF2t0k2bSaJtU0SI+xOMziiMNVUsFUHhYhI9QpSTaxwhEB4fLBbs48mv69whFZUQE9dBa6Cxj+niIg0WwG3m6S//IWkv/yFgFtVzUVEWgTLMtVq5zxrlsc9Bifc0WCHb9DYMOJKiO8JxRmw5K2GaaCIiDStnfNhzRdgs8P4J+rVr1/vmDL0QvPvxh/AU3yADRY5eKNHj+b2229vkGMtWbKEG2+8sUGOJdJWtNXnVc0j00FEgsNbahJB83aa15HtTCXWlswfoHjRivLXTcbuMAm2JTmQugJKc6Fdn7pX/nUXQeZGk4gW08l0rouItETeUkhf1zwr1jZVjLA7TUwoSAUs6DRUleBFRCorTDMDEOwOiEgIdmuqF4z7CrsT4vbEj9SV0GmIuUcTEZG2x++neNbs8tciItLMWZapHjj/v2Z5/JNw7E0Ne46GjA3OUDj5H/DNzTDvBTjqWgiLOegmiohIEwn44cd7zOsjroEuw+q3f31jSrcjIKGPKRC18Uc47ML6nU9aJX/AYvH2HDIKXSTGhDOyTzsc9uAWb7MsC7/fj9NZe99kx44dm6BFIq1MG31epcq1Im2Rzw052yFpEWSsM9Wi4rq1/MTa5iCynan6m7sdkheb33NNVQv9XsjfBbv+MFUOY7sosVZEWi5PSbOsWBsUdoe5phdlQtpqM4hCRESgMN0k1tpsShytjs1uBmh4iiBlpUlEFhERERGR5suy4Of7KxJrz3i64RNrG8OwS6FdXyjJhkWvB7s1IiJSH8veN/0O4XFw6gONfz6brSKhdvXUxj+fNHs/rUnlhCd/47I3F3LbJyu47M2FnPDkb/y0JrXRzjlx4kRmzZrFiy++iM1mw2az8d5772Gz2fjxxx858sgjCQsLY+7cuWzdupXzzjuPTp06ER0dzdFHH80vv/xS5Xi9e/fmhRdeKF+22Wy89dZbnH/++URGRtK/f3++/fbbRvs8ItJyKLlWpC3xuSEvySR9pq0GLIjrrhHJDc0ZBrHdwIb5PSctgrQ1pkpwcbapbluUAdnbIHkR7F4Olt90orflRDQRadncRZC+unlWrA2WsgTb4qw9CbaFwW6RiEhwFWWYxFrLMgPSpHo2G8R0BvyQusrcw1lWsFslIiIiIiJ7syyY/i9Y8LJZPvMZOKaFTC/scMLof5rX818CV0Fw2yMiInVTmgu/PWpej74PoproGdvQPcm1W36B0rymOac0Sz+tSeWvHy4jNd9VZX1avou/fris0RJsX3zxRUaNGsUNN9xAamoqqamp9OjRA4B//vOfPPHEE6xfv55hw4ZRVFTEmWeeya+//sry5csZP34855xzDklJSTWeY9KkSVx88cWsWrWKM888kyuuuIKcnJxG+Twi0nIouVakLfCWQu4Ok+SZshL8HlOpNiLedNxKw7PZIDzeJC87HKZDPGU5JC+ApIUmwTljnUl4ju1sqnbpbyEiLZUr3yT/FGVqoMDebHaTYFuaDamr1VEhIm1XUYYZcGb5IapDsFvTMkS2N9O1pq2BrC1myj8REREREWkeLAt+uhcW/s8sn/08jLwhuG2qr6F/gg4DwJWn6rUiIi3FzCdN1fGOh8LR1zfdeRMPhcTBEPDCxh+a7rzSrPgDFpO+W0d1ZQDK1k36bh3+QMMXCoiLiyM0NJTIyEg6d+5M586dcTgcADzyyCOMHTuWvn370q5dO4YPH85f/vIXhg4dSv/+/Xn00Ufp27dvrZVoJ06cyGWXXUa/fv147LHHKCoqYvHixQ3+WUSkZVFyrUhr5iqAzM0mmTN1FVg+iOu6J5FT//s3CZvNVAaO7WISmmO6Qkwn8zqum/lbKAlNpG3xe8FTYq7RpXnmx1UAnmLweVpeZbriLEhZAa7cPRVrHcFuUfNjs0NMFyjNgbRVJhlZRKQtKU+s9UFUx2C3pmUJjzM/mRsgfc/gPBERERERCS7Lgh/vgUWvmuVzXoSjrgtumw6E3QEn/8O8XvCSKhGKiDR3GRtg8Rvm9fgnwBHStOcfcr75d82XTXteaTYWb8/Zp2JtZRaQmu9i8famrfZ61FFHVVkuKirirrvuYtCgQcTHxxMdHc369etrrVw7bNiw8tdRUVHExsaSkZHRKG0WkZZDGV0irY3fZ6aDKEyDojTwuUxyZ1x3VUZtDmw2QH8HkTYj4Ad3IXiKzL+leSYpJuAFK2B+wCRf2uwm2d4Raq7b4bEQEgWhURAS0fyu4ZYFBbtNsk/Ab5JHm1sbmxOb3VT1LUw1A146D4WIhGC3SkSk8RWmQ3pZxVol1h6Q0EjT6Z27Hfxu6DjQfFcQEREREZGmZ1nww12w5C3ABue+BEdcFexWHbgh58Psp80zvkWvweh/BrtFIiJSHcuCn/5pnrEdejb0PaXp2zDkfPj9P7DtdyjJMUWkpE3JKNx/Yu2BbNdQoqKiqizfddddzJgxg2eeeYZ+/foRERHBhRdeiMfjqfE4ISFVE9ZtNhuBQKDB2ysiLYuSa0VaA8sCd4H5Eluwe09FPBtExGnKVRGRpmZZ5jpckgOFKeAuMsm0Ngc4w8xI4pBos1yWjGpZ5oFIwAd+j9kvL8m87wwzCTRRiRXV64JdHdbvg5xtkLPVtC+6fXDb01LYbCYJuSjdJNh2GgpR+t2JSCtWkALpa831T4m1B8cZZirEF6aBt9RM/Ret36mIiIiISJMKBOCHv8Mf7wA2OO9/cPgVwW7VwSmrXjv1WljwChzzFw0IFxFpjjZMM0mtjlA4/d/BaUOH/tDpMEhfDRu+b9mDS+SAJMaEN+h29RUaGorf7691u3nz5jFx4kTOP99UWy4qKmLHjh2N0iYRaf2UXCvSUpUl1LryTTWo0lxTDTE0EqITTfXDNsgeEc6g36YEuxki0hYFAlCSZQY5FGWYJNmQSIhMMA87alXN9D2WBb5ScBWYY9qcEB4DMV3NccPiwG5v8I9SI1cBZG02nzOyvYk7LUSziBE2G8R0rpRgOxhiOgW3TSIiDc2yIH8XZKwz9yUtsIpFs4gZe7M7TRX04kxIWW46NOJ7Bn/QjYiINDh7ZCSDNqwPdjNERKSyQAC+vwOWvgfYYMKrMOKyJjt9o8aGwRMg8WlzD7fgFTj1X41zHhEROTDeUph+n3l93K3Qrs9BHe6gYsqQCSa5du1XSq5tg0b2aUeXuHDS8l1Y1bxvAzrHhTOyT+M8D+7duzeLFi1ix44dREdH77eqbP/+/fnyyy8555xzsNlsPPDAA6pAK9IA2urzqibOxhCRg+J1QXEWZG+DpIWQtAhSVoIr1yRbxXc3HddtNLFWRCQoLAuKs02Sy64/oDAVwmMhbs81uU6Jtfths5kE3agO5nhR7c1Aioy1JgbsXgJ5yeApabjPsz8BvznX7qXmM8Z2aVGJtc1OdCewfJC2CvJ3B7s1IiINJxCA3B2QtgacoS0ysbZZs9nMYMqQcEhfY37PTfE9QEREmo5lmfu9nx+Ad8+E54bAc4PhtRPg21th409mNhEREWk6AT9Mu80k1trscP7rTZpY2+jsdhj9T/N64atmRi4REWk+5r1oZjuM7Q4n3hnctgwxlUDZNtP0jUmb4rDbeOicwYBJpK2sbPmhcwbjsO/9bsO46667cDgcDB48mI4dO5KUlFTtds899xwJCQkcd9xxnHPOOYwbN44jjjiiUdokIq2fMvBEmqtAwFQr9JSAp8g8zHAVgK/EPGQPCTfJW86wYLdURKTt8pRAznbITzbX5qj2B5dMWxtHyJ4kpXYmybasenlotEm0iU4007Y5qqmCe6Asy1RHz9lukmpDIyGuW8Mdvy2L6gCleaaCrd8D8b0arxKxz22q5cb1MIlZIiKNwe+D7C2QsxXCYsyPNI6wGHMvmJ9kZjRp389URm+oa7zfBw49MhIRaVKWBeu/hVlPm0pQeyvYDWmrYdlk873+1PvhsIubfjYTEZG2xu+Fr/8Kqz/fk1j7Bgy7KNitaniHngOdhppBfAv+B6c9EOwWiYgIQO5OmPu8eX36oxAaFdz2tO8LnYeZwiEbvoMjJwa3PdLkxg/twqtXHsGk79aRmu8qX985LpyHzhnM+KFdGu3cAwYMYMGCBVXWTZw4cZ/tevfuzW+//VZl3c0331xleceOHVWWLWvfWrx5eXkH1E4RaV3UUyLSHAT8ZjoHbyl4S8BdCK48s+xzm22coRASAeGdNO1nDQIeDymPvQJA1/v+hj20EZPcRKTtsiyTqJi12SSeRnUw1+im5AwDZ0fTFm+xGbWcuwPC4yC6s0n0DY878JgRCJhYlL8bClNMrIpObNjE3SbWLGNERDzYC820ez63SY5q6GQmyzKd8N5S89+Gsxl8bhFpfbwuyNwIeTuDExcbWLOMGXtzhEJsNyjZU0E/vick9Dq4pGafx/wdfaXQ7SglbImINJWsLTDtdtgxB4CAPZKUVX0hKpGuD/4de3gE5O+CnfNg1WdmgOdXf4Fl78MFb5iZTkREpOH53DD1OtgwzcwY+Ke3Kir2NbGA203KPf8AoOtTT2IPa+DCK2XVaz+9Eha9Bsf+zTxfFBGR4Jp+H/hc0PvEBotBBx1Thl5gkmvXfqXk2jZq/NAujB3cmcXbc8godJEYE87IPu0arWKtiDQPjX5P0kwpuVakqQUCJoHWWwKeYlN10F1gHtKUJdLaHeAMh7BoiGyvCnP14Q9QOHuxef2Pm4LbFhFpnfxeU8U1e6tJgozrHtzrtM1mKteGRpsEWHchZG2EbLtJronuZJJsQ6MgJLLmJJmA31RLd+WbKrUlOWZdZLsWnyQFNN8YERZjOmiyNptkpg4DTYXghpKfbP6eIdENd0wRkcpK8yBzAxRlQkynxq3i3lSaa8zYm81mkpl9bvP9pCgDEnqbKrb1iSWBABRnQs42EzMi2oEVAJRcKyLSqCwLVnwEP9xtnhU6w+G4W2H4RAqPHwNkQ5fDITISuh0Bg8+FMZNg4Ssw+xmTbPvq8XDpR9D7hGB/GhGR1sVTAp9eAVt/A0cYXPIBDBgXvPb4/RROn25eP/5Y45zj0LMrqhEueAnGPNw45xERkbrZ+psZ4GFzwJlPN+CMRQcZUwZPgF8ehu2zzfPA6I4N0y5pURx2G6P6aiCOSJvSFPckzZCSa0Uam2WZRCdPkel0Lsk2o8vKEmkdIab6YFiMEmlFRJo7TwlkrDfTcUa1N8mqzYndYSqhRsRDwAfuIsjaBFimk9YZvicRN8rEH5vdJM74vWbAh6dwT9V0D4SEQUSCiVHS+EIiTEJa/i7wlELioSap+WAVZZgKhDZVvReRRmBZUJACmZvAVwKxXTTLRrA4wyC+O7gKzDSueTtNVduoDjVXsve5TRX+/N2mKr/dYeLPvrOgiYhIQ/O54bvbYeUUs9z7RDjvZTNIoqRk//uFhMOJd8Lg8+CL60318vcnwIRXW+c05SIiweAqgCmXQNJ8CImCyz6GQ04Odqsan80Go++FTy6DRW/AqFvMPYWIiDQ9byl8f5d5PfJGSBwU3PZU1q4PdD3c3Ius/xaOvj7YLRIREWk0Sq4VaQyBALjzTTJtYZpJrvW5TUdlSIQSaUVEWiJXPqSvg+KsPclDzfxrlN1ZkWhrWeB3m8EdxRlQ6DPrythsZntnmNm+NVQcbIkcIRDb1SQ37V4K7fuZKb4PNFGtOAvS1prX4bHg8zZcW0VEfG5T5TRnu4kfsV2D3SIBc70Pi9lTyX6TqbQfFm0q0YZFVXx/8XvNd5vSPDMQ1O4wA4ccoWZfKxDUjyEi0uqV5Jhpt3fOMwPhTv0XHH97/b77t+8L1/4IX/0F1n0DX95g7vmOuKrRmi0i0iaU5MCHf4KUZRAWB1dOhR4jg92qpjPwDOgyAlJXwLwX4fRHg90iEZG2afYzkLMVojvDKfcGuzX7GnK+Sa5d+5WSa0VEpFVr5lkhIi2Mt9QkshTsNtV/Aj6TTBseq8p/IiItWUkOpK02ySdxXU3F15bEZquoXCvNm80OMV32JHOvBVceJPQxSc/1UZhmksEDXohONJWJRUQagmWZe57sLVCcaaoYhUQEu1VSmc1m7kHDY00SrbcY8pMg4K+6nSNkT+X0zqo4LCLSlLK3wkcXmY7ysFi46D3od9qBHSskAi58D368B5a8Cd/+n7mnOPyKhmyxiEjbkb8LPrwQMtebAilXfQVdhge7VU2rrHrtx5fA4jfhmJsgrluwWyUi0rakr4V5L5jXZz5tZiVqboacDzMeNAMGC9PNzHwiIiKtkJJrRRqCq8AkseTvNlNqh0SYqTRV+U9EpOUrzjaJtb5Sk/SoquPSFMLjICTSDNgpyYa4XiaxOzSq5v18HshPhqzN4HCaxFoRkYbiKYG8nZC7w8TDuG4tb8BJW+MIAUc8aHyNiEjzkL4W3j/PDFCJ6wmXfwqdBh/cMe120+GOBUvegm9uNlXMB5/bIE0WEWkzMjbAhxeYZzExXeCqryHx0GC3KjgGjIMex0LyQvjt33D+q8FukYhI2xHww7e3miJeh57dfL/Xx/eEbkfB7j9g/bcw8oZgt0hERKRRqBdM5GB4is0Dl12LIWsjOBwQ191Ub1JirYhIy1eSA2mrwO82Vd2UWCtNyRECsd3Md4rM9ZC0CDLWm4qRXpepHgkQCIC7CPKSYdcS02EfFmUqrIiINASfB3J3QvJik7wfHgvRnZRYKyIiUh8pK+C9s0xibefD4IZfDz6xtozNBmc+A0dcA1jwxZ9h54KGObaISFuQtBDeGWcSazsMgOtntN3EWjBxZdx/zOuVH0PqyuC2R0SkLVnytklYDY3ZM4iuGRtyvvl37VfBbYeIiEgjUk+YyIHwe021puTFkLUJnGEmqTYsRolXIiKtRWmuqVjrd6v6pwRXWIz5nuFwmqljkxfBzvmwc67pMN85F5IWQMpyU2E5tiuERge71SLSGvg8ZlrUXYv3dKZa5noUEhnslomIiLQsyUtg8rnmPrPbkXDNdw1/n2mzwVnPwcAzzX3sx5dC5saGPYeISGu04QdTVdyVB91HwnXTIb5HsFsVfN2PgqEXAhZM/1fFIG8REWk82Vvhl4fM6zEPmWf9zdmQCebfnfOhIDWoTREREWkszmA3QKTFKcmB7C1QlA5h0aZzWQm1zYYtPIyB379T/rqhef0WIQ79vUVaPXeRqf7pKYHYzsFujTSQxo4Re2vQmGGzmSTbsBgzLZTPDQEPWF5TOTIs2lTOFxFpCJ5iU1UvLxlK8yA0wjzMtzuC3bIm06JjhoiINC875sGUi8FTBD1HweWfmSrwNbBFRDBw2dLy15V5/QFCHPupmeFwwp/ehvfPNbNafHgh3Pi77hVERPZn6Xsw7Q6wAjBgPFz4LoQ2z8GENcWG/akxZtTFmIdg/XewYw5s+gkGnnHgxxIRkZr5ffDljeAtgT4nw1HXN9qpqospBxQz4rpDj2NMQZB138CxNzV0U0VEpBkpix9ef6DO9yStgSrXitSV3wfZ22D3UijONtODh8crsbaZsdls2CPCsUeEY2vgv83uQh/D30jjwZl5DXpcEWlmvC7IWGeSiWI6Bbs10oAaM0bsbUuOl6GvpfLkvIKGP7jdYTp6wuMhIgHC48AZ3vDnEZG2xe+FogxIW2OmRE1dBX4PxHaByPZtKrEWmjZmrMnwMOTVVF5eUtio5xERkSDY+jt8+CeTWNvnJLjyi1oTa2FPHIqMxB4ZWSUOLd6ew+AHf+Kdudv3v3NoJFz2KbQ7BPKT4NMrTTV6ERGpEAjAjIfgu9tMYu3hV8ElHzXbxFrYf2zYn5kbMxj0wE98sjjpwE8a3xOO/at5/fP9iiciIo1p7vOw+w8Ii4MJr4C9EVJ5LAs8xdhKsrF7c7G7M7DlbuenhSsY9MCPfDN/jcmD8JTUvWL5kPPNv2u/avj2iohIs2Kz2fhqfQ5DHp/Nr+szgt2cJqPkWpG68BRD+mpTxdAZZqoY2lX4uTV6fWkRZ32cSZ4rAECxJ8CsnS7eWFbEmA8yKfFavL+qJMitFJFGE/CbaTML08wgCg2gkBo8v7CA8z7NpNhjYkaBO8CcJBcvLylkzIeZuP3w6tKiILdSRKQGXpdJqM3YYKZvS14MeUnmnie+B0S2a3NJtY3lsbn5XDQ1C7fPdEzkuQLMS3bz3MICzv4kC08Anlmg5FoRkVZl03SYcgn4SqHfWFOxNjSq1t3u/3o1V761CJ/f3GdkF7mZvyWLJ3/awMWvL8Drt3hk2rqaDxLVHi77BMJiIWkBfH+HpvMWESnjKYbProJ5L5jlk/8B575kqn+3MHd+toI/T15CIGCu8RmFLuZvyeKxH9Yz8d0l+AIW//xy9cGd5MQ7IaqjmdFx4SsN0GoREdlHynKY9YR5febTpiJsQ/H7sIoyuentWfzf2zOwdsyDpIWkbV7G/OWreXTaOm76eje+ANz27U5IXgBJ82HXYsjZDq78mu8lBp8H2CB5oZkFS0REWrRAwOKadxbzj6mrytftyi1h7uYsJn23lr9/vhJfwOLP7/8RxFY2rZZ3pyjS1EpyTAXDklxTwdAREuwWSQ0CHi9pz78NQOc7rsceWr+/1+N7Kgy+urSIe4+P5aUlRbxWTWLUXTNyeWZsAjmlfiavLGbaZhdPnhbH4Z3DWJuST06Jh3aRoQzpGsfBzLokIk3IskyF8rwkc71XMlGrc7AxYm8vLjbx4eM1Jfz5iGienl/AB6v3HYDx0Mx8Jo2OI7PEzzvLi/l5m4sXxsUzuEOoYoaINC2/DzyF4C6Ckmxzj+MtNu+FRZuBJYp/QMPHjDeWmd/zT1tLOW9gJJNm5fPVxtJ9tnt8XgH3Hh9LepGfN5cXMWunm1fOSOCQhBDFDBGRlmT9d/D5tRDwwsCz4KJ3zeCVOvhwYRIhfh+r/+/vdIoNZ1L/c5m+OWef7V78ZTO3jelPSl4pr8/ayoJt2bx19dF0S4hg8fYcMgqjGXD8Cxz6+5+xLf8QEofAqL819CcVEWlZClLMwIe0VeAIhfP+B8MuDnar6iTg8ZD24EMAdH5kEn6Hky+X7QZgc0YRAzvHcOvHy1m4bd+Y8fqsrfzl5L4kZZfw6qytLE/K5f3rRtI+OmxPzHCRGBPOyD7tcNj3KjYQHgdjH4Gv/wqznoLDLmzYpC8RkbbOVQBTr4eAzySqNlRc8hSbQfUFu8nJy+enzWb1wyfGkxAex4x7XiHXFeD9ERdWGWDywfY4rhpsZ3t6Pi8vz2R9ro0pFyQSk9iDxRkOMoo8VWNGbFfodTzsnAsrP4GT726Y9ouISFBszihi1qZMAB6dMJRQp50r3lrEzuwSQvw+7lwxFYCXRlzIZ38kc/FRPdicXshLv21hR3YxH99wLOEhjtrvM1oQJdeK1KQgFTLWg98Ncd1UwbAl8PvJnz4bgM63TgQOrBM8u8QPUG1iLcDU9aXcdkwMF0/NIrXIVBK58qtsjo3cTVaRu3y7DtFh3HhSH47r2+GA2iEiTagwzVRgiEwwD9el9WmgGLG3nD3VzqtLrAWYvKqY246JZvTkDIq9ZoT3n7/NYpBDMUNEGpnfZ6ah9hRBaZ5JqPWWmPXOEFM9L6IL2JSluY9Gihm5e2JGdYm1YGbSuPXoaI55J7183c0/ZNHVt0sxQ0SkpVg9Fb68ESy/mSL1gjfrPFjf2lMRym75Cf/tJ/KBmWcfXW1i7vO/bOJvp/TluCd+K1936yfLSC9wk5rv2rMmnDuiruE2/7vw87+gwwDoP+ZgP6GISMu0exl8fBkUpUFkB7h0CvQ8Jtitqjufj/yvvwag84MP4LYq7uMKXV6AahNrAR7/cQM3nHgIJz39e/m6uz5fyeaMokoxA7rEhfPQOYMZP7RL1QMMuxSWTjZVCaf/Cy6e3EAfSkSkjbMs+PYWyNkKsd3h7BcOPh/BUwz5uyF/l3kmGBqFP6I9kA1Avi+UBKePozYvBuB/w8+nctrQA7MKuHRoF06ZmgeYtjz2SxJW1i9sLI4iyUokj5iqMePwK01y7YoP4cS/g13PGqXhFRYWctNNN/H1118TGxvLPffcwzfffMOIESN44YUXAOjduzfXX38969at49tvvyU+Pp777ruPm2++eb/HnThxInl5eYwcOZIXX3wRt9vNnXfeyX333ce9997L22+/TWRkJI8++ijXXntt+X7Jycn8/e9/5+eff8Zut3PiiSfy4osv0rt3bwCWLFnCfffdx/Lly/F6vYwYMYLnn3+eI444ovwYNpuNN998k++//57p06fTrVs3nn32Wc4999xG+R2K1EXlJNi8Eg+JseHszDZ94HbLz9hkU7H2f8PP556pqzhjaGfGPj+7fJ9/f7+OmRsz63af0UIoqolUx7IgdyekrgQCmhq8DSr2mM6MduH7v0w++HNKeWItgMsPGYWeKttkF7l5/IcNzN+a1TgNFZGG4SqAzI0ViUYi9VAWM2py30+7yxNrAdJLIKPQXWUbxQwROWgBv0mizd8FaWtg5zwzFXTKCshPAiyIbA/x3SG6E4RGK7G2idUlZvzzx6pT6G3KDZCpmCEi0jIs+wC++LNJrB1+GVzwVr1mwfL4A7VvVMmN7y+psrwiOb9K5wXAC8Vj+NQ3GqwATL0WsjbX6xwiIq3Ciinw7hkmsbbjILjht5aVWAvgq9T3kL8LT8bW8sWi3esgaVGNu/95ctX3Z2/O2idmpOW7+OuHy/hpTWrVne12OOsZc/+47mvY+hsiItIAFr0G674Bewhc9B5EtjvwY/k8kLMdkhZD5gZTjTa+B0S2w21VzFRVNvC7Jnd/n1Rl+ccdfjYVhdORXIbbttDblkJmfnFFzBh8HoTGQO4Ok2QrLYplWZR4S4LyUzbAtC7uvPNO5s2bx7fffsuMGTOYM2cOy5Yt22e7p59+muHDh7N8+XL++c9/cttttzFjxowaj/3bb7+RkpLC7Nmzee6553jooYc4++yzSUhIYNGiRdx000385S9/YdeuXQB4vV7GjRtHTEwMc+bMYd68eURHRzN+/Hg8HvOdrbCwkGuuuYa5c+eycOFC+vfvz5lnnklhYWGVc0+aNImLL76YVatWceaZZ3LFFVeQk1P9gCmRpuALVMSJnBJPDVsaN3+0tMryx4uT636f0UKocq3I3izLfPHM3GASrMJjg90iaUQBy+LBmfl0i3Hw16NiytcX7UmAyqnhBuP3lH0voam0pxsVHdxuy0kBUbwyawfH9OmgqVtFmiO/F7I2gbcIYrsFuzXSjPkDFvf+ls/gjk4mDo8uX1/oCdT6AOCn5H1jRjrt6bJntDiAy3JSSBSvKmaISH14XeDKh9JcKM401SkCXrA7ISTCJNPWI6lHGobHb3HPL3kc2y2UCwdHlq8v8lgEaokZ3+3c++9lI4MEOpFbvqbUCqGYSF6btV0xQ0SkuVj8Jvxwl3l95LVw1nN1qthU6vFz19SVjBmUyGmDOtXrlL9vrH2QhYWNB3zXcmhIGsPdG+CTy+HPv+qZp4i0DT43/PRP+OMds9x/HPzpreZ/DbQsc2/nKTZVB0uyoaAiyeLnxWsY3i2EshpKeYXFeEprjjm/bcyu8X2AsjuVh79dy9jBnatO3dr5MBh5o0kE++Fu+Ov8aiuri4hIHSUvhp/vN6/H/Qd6HH3gxyrKNDMzFmeaGBffg+wSP/d9n8MlgyPpGVeRXJtd4qc0uuZnU1/vqPpsqpAosojHj5NIXAyw7QLLxi46Mum7dYz9x6k4DvsTLH0Pln8IfU468M8iTa7UV8oxU4Iz6GjR5YuIDImsdbvCwkImT57MlClTOO200wB499136dq16z7bHn/88fzzn/8EYMCAAcybN4/nn3+esWPH7vf47dq147///S92u52BAwfy1FNPUVJSwn333QfAvffeyxNPPMHcuXO59NJL+fTTTwkEArz11lvY9hTpe/fdd4mPj2fmzJmcfvrpnHrqqVXO8cYbbxAfH8+sWbM4++yzy9dPnDiRyy67DIDHHnuM//73vyxevJjx48fX+nsRaSip+aU8+M1arj2uN1FhFX3aa9OTyPHWnGA7e3Pd7zMmfbdu3/uMFkDJtSKVlSXWZqyDiDhTyUlarc/XlXD3L3nly8kF/vLXvoDFb9td1exVs1xiypNrLQu20RUfTtxFBaxNyWdY97iDbreINLCcHVCQArEtcxoCaRrvryzmwVn55csr07zlr30B+Ho/03vXJJ+o8uRay4ItdMfCjq8oTzFDRGrmdVUk0xZng7fYzLQREgmRCeAIDXYL27TXlhbyxDxTgeDrjaX8uLXivsIXsHh/VXG9j1lIZHlyrYkZPQAIFGUrZoiINAfzX6roGD/mrzD+8TrNgvXczxv5729bAPh+VSqHdWuc67mHEP5ceivzEiYRmrUJvv4rXPyBpmsVkdYtfxd8djXsXgrY4JT74MS7mu+1z+81s2u58qE4A9yF4HMBNnCG8cRiL+fv2fS2OdAhIQQwfRpFtiieXO4Daq8sVVkIXrw4KZv2u0xagZvF23MY1bd91R1G3wtrvjQJXAteNlN/i4hI/eXvgk+vhIAPBk8wgxcOhN9rchtytplLeWxXsDu499c8Pl5rpvCevtVFVEjFdb7Ya/Hv2QVcXc9TlRJGAkWUEI474GQHpk8tPb/YxIzDrzbJteu+gTOegoj4A/tMItXYtm0bXq+XkSNHlq+Li4tj4MCB+2w7atSofZZfeOGFGo8/ZMgQ7JW+I3bq1ImhQ4eWLzscDtq3b09GRgYAK1euZMuWLcTExFQ5jsvlYutWM7tAeno6999/PzNnziQjIwO/309JSQlJSVUrQw8bNqz8dVRUFLGxseXnEWkKt368nG9XpgAwY116lfd25KUyeb4He2g69tAsnIGKysuO6I3Y/F2xvO0oG/RXIVDNOkjNd1V/n9HMKblWpEyVxNp4TQveyuW7AlUSawGmrCkpf+0PwDt/ZLP3Q6Xa2ci2YvHiJIv4ivMRXaeS6SLSxIoyIWermWrHrq9FUr2cUn+VxFqAryol0/oti3eX5lLfmOHDSY4Vg4tQcqjoRC8gUjFDRPYV8JuE2sJ0KMow1YscDgiLgYguZnpOCbrMEn95Ym2ZWTvd5a/9Fry7LJ/6xgwXYeRa0RQTQR6VZtwgQjFDRCSYLAt+fwxmP2WWT7gTTnuwTom16QWu8sTaMqt35+9n64OXSTwLj36Bk+ZdAxumwZxn4eS7G+18IiJBtWm6GUhQkg3h8fCnt6H/mGC3al8+D7jyTDvL7vMsC0LCTR9VZHuw2dhd6GPy2uTy5FqA3YUVxUJcXovJK4uo732GHwfdySSNdvj26jLOKKym+EhEPJz+KHz1F5j1NAy5ANr1qdc5RUTaPHcRTLkUitIhcTCc+1Kd7h/2PU4hZG40xWMi20Ooqf6ZlO8rT6wtU+ytqFRb6rP4cmNpvZNr84gmynKRQwzFVFQabUcBGQWlMOII6DgIMtfDmqlw9J/r/5kkKCKcESy6fFHQzt0chIRUrdZss9mqXRcImFmPi4qKOPLII/noo4/2OVbHjh0BuOaaa8jOzubFF1+kV69ehIWFMWrUKDyeqs9yazqPSGPbnlVcnlhbmS0ki5DY1fyctoPd4VuI6mvuPcI8FfEksttHOEJtWP4wfMUD8BUOxlc4FKwQwAJbKVjh7H2PUu19RjOnLBKRMnk7IXPDnoq1Sqxt7Ua/X/NoHwvId/s5kMtkKh32WRfAjh8nV32dzUWDIzl3QPP4oijSpnldkLXZPLTQdV9qMPLt9Brftywo8gQAR43bVSeFjvus8xCK13Jy5VdZXD0sitP7KmaItGk+j6lQm58MJTlgBcz0bnHdDuzBuzSqo98yMSOKUnraMuhhyyDeVkQsJcTaijk8HXp6/RQ6HZRY4WQRS6YVzw6rE0lWJwLVjOYus5vEfdYVE0mp38kVX2Vx4xHRnNwrvNE+m4iI7MXvg2m3w/IPzPIp99crWfWYx35tnHbVIDXmMN6IuZkb856H3/8DXYbBgHFN3g4RkUbjdcGMB2Hx62a58zC45ANI6B3UZlXh9+2pTptpEqvchRXPJ6MT9ykA4AtYHP9uBmE1HLLUZ+Gz6n9/GMDOrmruMwAKXT4uf3Mh94w/lBE94iveGHaJmfJ7xxyYdgdc9ZXuTUVE6irghy+uh/TVENURLv/UPOerD8syAzIyN4K7wMzKuCd2FHsCnDS55j7w0kqJtvVqOg6S6bTP+iwSKMpJ4/K3dvFcv4vpnDkJlrwDR12v+NBC2Gw2IkMia98wiA455BBCQkJYsmQJPXv2BCA/P59NmzZx0kknVdl24cKF+ywPGjSoQdtzxBFH8Omnn5KYmEhsbPX/D8+bN49XXnmFM888E4Dk5GSysrIatB0iByMtv4hTnplVaY0fZ+wqQhIW4YzcAUCKy9R1sfxhBDwd8bkigPVma3dnLGc2NoebkNjVhMSuJuD7Dm/eMXiyT4JA9f3bGQVurnhrIY+eN5RDOraM2eSVXCsCkL8bMtabhxehLeN/Xjk4ua6aR/sELIuokPonSdXk+p9MlcM5SW4W7nIza6eb189KYGiipu0VaXKWBbnboSTLJCeJ1MBXywBRvwURIQ1bMfKG6SZmzE32cMGhLv5I8fDOue3o1y6klj1FpNXweaAoDXJ3QmkehIRBVAdw6DrQrFgByEuGrI2QuYlPQ9fQz7ab9rbC6rfPhROh2qcxpVYoG63uLA4MYn5gCAsDg3DV2IVu/HWGiRnzknM4q18467O8TD6vPT3i9MhHRKTReEpg6rWw6SfTy3DWs3DUdXXe3bIOrEP7YP3ji1XA0UQ6T+NK568UTbkW97W/0L7X4KC0R0SkQWWsh6nXQ8Zas3zs3+C0h0wV2GCzLJNQW5IDhSnmNZbpj4rpDPb990WU1CEJqtTX8HHl/q/XADDhf/M4vl97sos8fHrjKOIiQ+CcF+GVUbDtd1j1KQy/tMHPLyItSCAAvlJwRoBdMyvtl2XBD3eZewhnOFz6McT3rN8xfB7I3WFmZLQ7ILZrlQTW9OLaq126GiFm/GuGGWx+blIvZjvCCM9Yi2vLbML7n9zg55K2KSYmhmuuuYa7776bdu3akZiYyEMPPYTdbse2VxL3vHnzeOqpp5gwYQIzZszg888/5/vvv2/Q9lxxxRU8/fTTnHfeeTzyyCN0796dnTt38uWXX3LPPffQvXt3+vfvzwcffMBRRx1FQUEBd999NxERKqYjwRewAmSVZjF9U9lsSn5C4pcS2n4m9tAcACzLhr+4P6O6HcXvy7pgeToANvw+N/AvAEp2/A2304k9PAVnzHpCYpdhD80jrMPvhMYvwp11Gt7cUbBXQZH//GCSc698axHto8MIddr5+IZjCXU23+8Q6mkRKc4y0xM4w+o/MkyaHVt4GP2/fK389d4CllVrkhTA0lQvx3UPAxpnitUpa8x0HLdOz+O3q6ofGS4ijag4E3J2QFR7TaPdhtQWI/bmD1gE6vCcafpWF8MSQwHvQbawel9uMElT9/2Wz2cX7lsdXURamYAfCtP2DALJMQMA47oqXjWx/cYMKwA52yFluflJW2UqTe1xTKU/U7YVQ7LVkSwrjgKiKLAiwR5ChNOi1OMj2uaiA/l0suXSx5ZKhM3DCNs2Rti3cSPfU2yF8XPgKL7xH8/swLAaq9qW+X6LmVLpiXkF/O/Mdg3zyxARkaqKs+DjS2HXEtMpfuE7cOhZdd7d6w/g9df8cMrtCOX2Cx8mvcTC7Wj4QdmTfNdwqD2Zo+ybKPn4MrhjLoTFNPh5RESaRMAPC1+F3x4Fn8tUApzwKvQfG+yWmam/S3NNQm1JLgS8EBJZbYXa6nj8Fm6/eTjldoRy6RkPl7+ubPZOd4M3vbJ5W7IBeH/BDv7vtP7Qvi+cfI/5nf90L/QbYwaDikjbkLMN1n4FSYsgdaXpb7HMdNFEtIOOh0K3I6DfadDzuOYxyKE5+HUS/PEOYIPzX4MeR9dv/5IcyN4MhekQ2R5Cq1Ya9fgtAnUYxLdgl7vGmHIwMrzhfGGdwBXOX8n49SV6KrlWGtBzzz3HTTfdxNlnn01sbCz33HMPycnJhIdXvcb8/e9/548//mDSpEnExsby3HPPMW5cw87YEhkZyezZs/nHP/7BBRdcQGFhId26deO0004rr2T79ttvc+ONN3LEEUfQo0cPHnvsMe66664GbYdIfRV5ikgqSGJXYRouvw1H1GbCOn2HI8xUPQ/4ovDmHoc37ygsXxyeyEgsT0n5/vvGDxsBVw88rh54MsfgjFlHaIcZOMLTCe/8Hc7Y1bhSLsLytt+nLSn5LlLyTX/GrE2ZjB28b3X05kLJtdK2ufIhfa15+BK97//M0vLYbDac8ftPkr7iq2w2ZPnqdKz5uxonsbayUm8dMn1FpGH53JC1xYygbubTnEjDqi1G7O2Cz7NIL/LXadtVGY2TWFtZWWeKiLRiJTmmg6IwzXQ8xHVTUm2QVIkZPjfsXgrb50DSAnDlVdnWcoazJtCLDRzCQlcvNlg92WklUsR+vmdUc5thJ0AvWzqH2bZxnH0tJzjW0N2WxfmOeZzvmMf2QCfe9p/JVP9Jdapm665b+BIRkfpKWwMfXwb5SRAeb6Zx7XlsnXf3+gOMfnpm7ZVrbTY2+qKhkSY78uLkr57b+C7sfjq7dsDXf4WLP9C0rSLS8mSsh29ugd1/mOW+p5mEpeggFrTwuc29XVG6mTXLW2qKu0TEm3/ryOWzOOHddEIde67NNhv5YdXPvLgms/GfSwH4K8ev42+DNV+aSsHT74ML3miSNohIEG2bBbOegp1z979NaQ4kzTc/C16GsDgYdhEccQ10GdZ0bW1u5jwHc583r895AYacX/d9A37ISzLVan1eU612r4rnBe4Ax7+bTmxY7c8R5yZ7aowpNgJYdRjgvT/v+cdxhfNXuqf9amZ7iu9xwMcSqSwmJoaPPvqofLm4uJhJkyZx4403VtkuNjaWzz77rM7Hfe+99/ZZN3PmzH3W7dixo8py586dmTx58n6Pe/jhh7NkyZIq6y688MIqy9U9G8jLy9vvMUUOlC/gI7UolaTCJDILXNzzSSZhnb4msqeZ9SPgi8STfQre3GPAqngYNXdjSdUD7RM/AlRUprXjKxyKr3AwIfGLCUv8AWfkDqL6vETp7svwFw/cb/ua+9MoJddK2+V1mQcv7kLzJVRarSJPgOhQc0Ff0EAJs5G4KKFiFFS403ZA02iEOZp7mBBphXKTzIPtuG7Bbok0Q2UxI2BZrExvmI6JGIopJKp8uWu0g5Q6Ju1Wppgh0or5PJC701SrDfghplOdqhhJI3IXQtJC2DEHkhebClhlnOHQ+TDcnUYQ1uMICqL7cc5bmQd1ugB2tltdyLWi+TZwPPgsRti2cm30fEZ7ZtPHns6/7e9yi/NrnvNdyFT/yTVWsg3Tfz4iIg1v3bfw1U3gLYZ2h8Bln0DH/XcMlLEsi2KPn+gwJ9uzitmdV9ogzWlPPtnElS+P6h5ar+demSTwV8/tTA1/FMf672DOs3CSquiISAvh98LcF2D2U+D3QFgsnP6oSd4KxkABvxdK80z1xqJ08BSbgf1hcaaKYx3bZFkWxV6L6FA7S1M9ZJU2THGORHLJIKF8+fRDwvl5m6uGPaoX5qyUzOUIgXP/C2+NgVWfwrCLTQVbEWl9cnfAtDtg629m2WaHPifDgPHQ/SiI7WZmQfC5oCDF9L/vmA2bf4GiNFjylvnpfzqc/A+zT1sy51n49RHzeuyjcOTEuu/rKoCszeb3Gh4LsRXFwirHjF+2uSj0WBR6Dn60dSdbLmlWxXn+NCiCL9bX/R5ms9Wdef4hHO9YC3+8DWMePug2iQAsX76cDRs2MHLkSPLz83nkEfP/1XnnnRfklok0bwWeArbn7SC5IJ32kdFM27Ca0F5TwFGKZdnx5o7CnXkaBOpflMyGncpZUuccHsd3y/Px5h2Lr3gAEV0/wRGZRESP9/BkjMeTcxLVpdKGOpt3kZnm3TqRxhLwQ9YmKMqEmM6qytCKBDxe0l58l7QX3yXg8fLJmmKGvpbG1PUlte8MRIbU/t/CHSMsFtzYq8q67y7pQOeo+l9SQ5UoJdK0SnIgbwdEJqgSYBu0d4zY29vLixj6Who/bSmlLs+g6hIzbh9uMf8vfausm3JB+wMagafkWpFWqjQXUpdD5noIjYDYzkqsDZaSbFj3DfxwN4F3J5D29Mukfb6SgNsFUYkw5AI4+3mYOI3/dniQgfNOYVZpH3x1eLQSVceYMfem/nuWbKyw+nHUhXcxyv0yD3qvYZfVgc62XJ4KeZPvQ+9lhG3Lfo+lmCEi0oACfpj5BHx2lUmsPWQ0/PnXOiXWAjzx0waGPjSdJTtysNdhmtbIEBshfh9/W/klf1v5JSH+fWdgun24xZybqp7/xXEJ+2xXm+VWf37ufbdZ+O3fsOnneh9DRKTJbf0NXj0efv+3SaztPw7+ttAkKzVlX4/fB8XZkLnZzHCxa7GZicTugNguENPFTNldjzY9MDOfoa+lsTbTu8+zo+piQ13vM2b9dVCVdU+eFl/nNlW2T6d396PgmL+Y19PuMEnFItK6LH0PXhllrr2OUBj5F7h9DVz9NRx7057k2i4QFg1RHUx12uGXwHn/gzvXw1VfmyqtNjts/hneOg0+utgkjLZ2lmWSassSa0ffC8ffWrd9AwHI32VmUipMNQPxw6vOynfnz3kMfS2Nbbk+7HUINWGVxkfs737DxIwh5cvx4TYePimO+prsP928WDoZPHXroxepi2eeeYbhw4czZswYiouLmTNnDh06dAh2s0SaJX/AT0pRCqszV/PwV7u59d08/rf8TRYVvI3NUYrf1ZWS7f+HO/2cOifWVo4ffx7ZjbWTxpW/d2jnGJ6+YFT5suVtR8nOG/HkHoPNZhHW6UdCO/4E7PtsLFCH52XBpKwSaZtyd5qfmMR9pk1oCUq8DTNauVXy+8n9Zga538wAv59//pYPwF0z8vAFar8gPz0mvtZtbjupG3HhDnrFmf92bj4qmv7tQziQv8qmHB93zcg9oKq3IlJPAb95wO33Qmj10920NpZlKWZUtleM2NujcwoAuOmHXNz+mq/LieTy6eFrucM5lddCnmda6H0sCvsbG8OuYWPY1WwIu4aFYTdze86/iZn/BDeGzWCALZn7jo+hd7yzmtuG2s1JdnPvr3l4a2mbiLQQlmWmRtu9zHTIxnYNanxqszEjfxes+Bi+/ht8+CczRd+uJRAIkLslitwtUXD2S2ba7+Nvha6Hg93JcwsLAbjmm5w6Dch48+x2VZajq+kEv/3kbuUzbgA8eVoc3WKclBDO+/5xnOp+lke9V5JnRTHInswXoQ/xL+eHhOPe51hfbijloZn5zf6hlIi0TJZlUdoAFZFahMI0+OB8mPm4WT7mr3DFFxDZrub9Knl91jYALnptAVb62lq3/2BCe+yWn3O2z+ec7fMZlFB9zIisFDNeOyuBxKgDe8Z5z/bDWdbxfMCCL/4M2VsP6DgiItUJBCxc3gaKGTnb4ZMrzHU5ayNEtocL3jTf1Ztqhqq9E2qTF5m2WH6ITjTtCIs54EH9H642CUjXf5u9T05u5dhgt8zv9LMLqyaTnNgjbJ9j3n5yNyJCKtrz8QXtSYg4sPa9NWszz/68serKU++H2O5myvKyBDIRafl8bvj2/+C728BbAr1OMAMZznyq7tdcux36ngIXvQe3/AEjrgSbAzZPh1eOhen/Ald++eb+howZwRbww4//MFVrAcY+AqP/Wbd9PSWQvhZSVgCW+X07QvbZ7KuNpprs32fk1mkcx/eXdSx/XV1MARMzwioNpPjo/A7EhNU/ZvwSOJLdtk5QmgPL3q/3/iLVOfzww1m6dClFRUXk5OQwY8YMDjvssCrb7Nixg9tvvz04DZQm5fMHcPtaScxoBCXeEjblbmJ99nrsdjsrU3YT0ftlVmYtwYYdd+YYSrbfTMDdpdr959xzSpXlK47pCVSNH/eNH0BkpSn03rz6KMJDKp5NhThsgBN32vm40s8CIKzDLMI6fcPeCbaTvlvHW3O2NcAnbxxKrpW2pzDdVK2NjDcj7ILo/t/zuOzLLPx1SPos8+jsfAa/msbS1LpP8ybGkFdTa93GYYNhiRU3KL9c2bHK+xOHV0zr/dmfOvD0mHj+b2QMYPIjygxkJ/EU1qldU9eX8qfPs3ArwVakcRWmmp/ojrVv2wzd+XMu132bjVWPJJl7fzMxY33WvlVapWZHvJFWZTkMD2PsS3nE+S6/hN7F4vCbOWzVv7nN+SXjHUsYat9BJ1seYTYvYTYf4TYvnW25phLl5p+5z/YuP4f9gxu2/A2Wvkd7Kh4aDmQncRTVqV0fry3huu9ylGAr0tL5vZC5EdJWmU7X2C4NNujPsixu+j6Hm3/IqVfMuOWnXAa/msbOvH2r47UqlmXuB5e8DZ9PhE+vhMWvQ8Y6837iIBh5I1zwVsU+HfrXWG1q1LvptZ42KtRGfHjFMX67OrHK+38/Nqb89ayrE3lubDwXDa46WtxDCHP8Qznb/W++8J+Aw2Zxg/MHvgp9kENsKfucc/KqYu6akVev+00RaVsCAYur31nM3Z+vrNd+1723hEEP/kR6Qf2nk25RtvwCr50A22dBSCRMeBXOeAIc9agwH6g6cGXsxzm17hIXZqtSrXDyhPZV3n/gxIqKVb9c2ZEXx8Uz7pDwKts47dCfJKKovUpUocvHpckT2BV9GLjzTeKau27PtESk7fD5A1zy+gIe+mZNvfa75I0FDH1oOnklB9GfUZoLvzwM/zsGNkwziVnH/BX+bykMu7jxq9X6PGYWxMxNlRJqN0DAa54zxnWD8PgGnYEkrTjAZV9m17pdu/Cq3byvn121ivljp1ZUG5x+RUf+d0YCo7pXTcBNjLTRn2QiqD2upxZ6eem3Lbw5e1vF/WZYDJz7onm96DXYNqvW44hIM+cugo8u2pMUaYPTHoJrvoP2fWvdFcDl9TPhf/N44scNFSvb94UJ/4ObF5uK4wEfLHgZXj4a1n0LwNkvzeXwR2ZQ4mnhz6bcReY79eLXzfKZz8Dxt9Vt36IMU602d7upBBxR++wUy9O83DY9r9btOtYyGO+F0+PLX0+7tAOvn5XAkI5Vk3oHtrPTj+RqB3pXFsDO/zxnA1Ay63kTS0VEqlHo8nL2S3P47691r2huWRanPjuLYx/7FY+vDRYMqYFlWWSVZrEmaw0pRSm0j2jPmsw1RPZ+BXtoDgFPAkXbb8aTNQbYf1zoGh9RZfm20/rvZ0v46m/H8e7Eo+nRrmp/xuE9EgAv4MWbcyKu1AuwLBuh7RYS2nF6lW23ZxXz7+/X8/uGjPp+5CahuSalbXEXmU5su71ZVC0sG4W8aLeH46oZUVydt1eYaXWeXVDAlAtU4r6uwnEzKJBEb3save3p9LKl0Z4CYm0lxFKMkwA+7HRYFMYpUfHs7BJPz65dCc/pTx9bPNutzhzdNYyHT654GNUp2lGlwzsh3E5miQneXWKchBZlEm8V4SaEVGr+W63N9PLMggL+dWL9p9YQkTrwlprqOyGRLXKqbX/A4ssNZhTylhwf/dvvO0q5Op+sNXHmtaVFBzRFaFvmtyAUL6PtKzjTsYjT7MuJsZWWvx+wbJTG9cVq34/lnh4cdWgfIuI6cPyUUgLYOa13CP8eZYPiDPPfXvpaSF2BLT8Zlr7H/PCP+Nx3Ei/6/rQnZmRQYBVSQjiZ1Py3mpPk5rWlReWDO0SkhfG6TOJ9XrJ5SB4SUfs+9ZBTGuCnraZT9N8ui4SIunU0f7/Z7PPh6uLW95004IPUVbBjLuycB0WVkmFtDlONts+J0Os4iNozCKe0YRPGQuw2pl7YgelbXVx/eDThzoq/y8ThUVWu6b3infSK3/f7SpgDukY6yCos5r/e85npH86DIR8yyJ7Mt6H380/vDUwLjKqyz5cbShnRKZSrKw0SFBEpszG9kNmbMgF48k/DsNdlLlHg941mn29XpHDDSYc0WvuCxl0Ev06CxW+Y5U5D4cJ3oeOAuh/DsqA4C/J21vv0IXYbX13cEaaZ5bhKlaLuHhXD9YdXPNPs1y6Efu32vT/sGOmgm81BWGEaBUSSRzQF7P9ZqIcQzs+6iTnxDxOeud5UdL/4/aadXl1EmrWF23JYtN38TDpvaJ33W7IjF4BZmzI5b0Q9q8u6i0yy5rz/muR/gD4nwRlPmUFxjclTDK4CKMmB4kzwFIENCIkyCbXN5PliiAO+vaQDi3Z7uP7wKOyVrtvPjI3nwkEV/RcD24cwsJpnip2inbR32AkrTKGASLKJo5ia71P/88N6+iVGc8qhewYN9hsDR14LS9+Fb26Gv86D8FZ2XynSVpTmwZSLzUCCkCjznbD/mHod4sc1qaxIzmNFch7/POPQqm926AdXfAabZ5jKrjlb4bOrYNA5ZKWOp5QEViTncVzfFtoHnr8bPr7UDKh3hMH5r8LQP9W+n98HuTsge4v5Dh7Xvcbv4vUZUF8m1A5fXNSBlekerhngYNO0ivcmn9eOk3tVDNgbmhjK0MR9j9E1JgTLaye8cDf5RJFBAm6qL2b2hf9Ebnd+QWJpGjtmvkfvMTfWu83SuA7kvyMJrtb4N/t0STJrdhewZncBt9aQwFmZ12+RlGP6wJNySuiXGPzcr+bAG/CSXJhMUkESDpuDDhEd+GTjJ0zbNg2bHXxF/SlNuRT8tfcVOOw2ptxwDFszi7nq2F41Dnw5vGf1/dq92keSnBtDan4JOPLx5h8BQHiXLwnrMBPLH4k356Qq+1z73hJ+vuMkBnRqXn3gzePuT6Qp+H2QvRncBWbK1SaUlO9j+lYXVw2LqtKJWqa2qacB/AFYm1JR5c6hh9vl/AFYszuP3FIv7RwW5Y+HNv/MY87ljLBvY4AtGaetDqNWioHiJAYA5AJr4fcwSLMS2FB6JCSdBt2Prvbh2X/HJ3Dnz7n8fVQsEYFQHv9hg0nEquOXnM/Xl7S+RAaR5iIv2UwvFNc92C2p1dZcH79vd3H18ChCHeZaX3mWbk8dLmV7xwxNVWD4K/3u1qQUMKRPOI5qfjmJ5HKF8xcud/xKR1tB+foUqx0z/EcyLzCUhYFBvHNSL47qGsaJlfbdjakcWBoRAR0ToONA6L1nC0+xSepa8xVhmeu50vkrl4YvILn3Rdy+5hBibaX4rbr9tb7dVKrkWpGWyFNsku0L0yCmc7VTutXHhiwv83e5uWZYFI5qEqK8tVQsLYsXOZWqSFV3nBbJUwK7FpuE2qSFpjO8jDMcuo+EPidAz1Gm0tJe6hoz6irUsf8EqLBq7hEr+/RP7Zk0K59HT4nHXWLuM2Ip5Y/AQM50P8ZLoS9zrH09L4e+RG9vGi/7J0Clmoc/bytVcq2IsHpXPsuTc7nq2F7Y9jxTqvxoye0LEBG6/4oV/oDF4u05ZBRWDD5oNTGjsq2/w7e3Qn6SWT7qehj3nxoHw5T9btILSukUE87IzuAoSIaCVDiAX1Gow0aP9k42VvNemKPmA753bjueml/AU2MSKCh08PgPG4ijhFKr9kH9mSTweqeHuS35dlj/Lcx9Hk68s/4fQERavKU7c9mUXsilR/cojxlWpWk7LcsqX1+dg44ZnmJYOhnmPmcSWwESB8Op98PAMxsn8d/nMfcMrnxzTncBeErNDCNh0eb+rQFmGym7B8sudtM+KowhXePK7zMOZMaJUIeNYZ1CGdZp38SmWm4zeO2sBP67qJDnT48nI7dPecwotCJrTa4FWLgxuSK5FuD0f8O2301y2E/3mQqVItKyeEpMxdpdi02C/BVfQI+ja9xl4bZsduWWcuGRFX0v/roUEew/FnqfSGDWUzDvRezrv+PXsF/5j+8KnLZjD/KDBMnmX+CrG6EkGyI7wGUfQ4+Rte9XmmeSagtSIDKhvEBYTTGjyHNgMePILqEc2SWUwF6DymsL0y+Oi+fNZcU8MjqO5CwTM+IpJs+K3m9yrZtQ3vKdwX0hHxO/7BU49c+mAJoEXUiIeT5ZUlJCRETDFn6QxuXxmOf4DkfDzILX1GZvyiS3xFNl0F19Zuosu8/YkV3xrN/ZGp9NHYBcVz7fr1/FzrwUusd1pF+nMJ5Y8gRrsszMI+6sk/FkjqM+WQPH9e1QPtgl3Fn3/+aeuOAwpixO4u5xAzltUCJ//XAZ+GOx8OPNG4nNUUJY4k+Ed/oBy9MeX9GQKvtvTCtUcq1I0OQnQ94uiOnU5FUXbpiWw8ZsH9vyfDx+ajxQdVRJWaXyfHeA2FDbPg+m5m/N4o3Z28kqcgOmIsjalHzmb7Va7si9BjJ/axb/nJFFuieck+0rGBNYxmFlb859nsudFb/nTCuOzYFu7LA6sdPqTLqVQD5RFFoReHHiIMA9x4RzbLtiKMkyowuzN+PP2krnQC6dS3+Bn34xFcYGngWDz4PIduXHP7RDCD9cXvYwKZx7zzyUN2ZvJ1DoIpoSnPjJwwSBTlF20our3l0q8Is0ktJcUy0osl2LqLpzxZdZpBUHyC4N8I/jzXSflW8syh601ydmLNiWxfyt/jYdM+ZvzeLdXzdR1jV86TcFxEbk8+zYeI7v15FlKW6OsG1ionM6Z9gXE2LzA2ZwxXf+UfzoH8lyqx9WLTcdT54WxwerSrh7VOy+b4ZGQf/Tod9YSF0Bi17DmbmRPpsn80HioUwqvoD1RRBFKU585O+JGQnhdnJde8eMg/2NiEiTcxdC2hrzgD22a4N0zF7weRYlXguvH/5ypHnwXnkWpLJBfNXFjKrxAspiRlp+CVDNNawlKEyF5CWwc76ZQi/grXgvPA56HW9+uh8Fzv0nGVUXMxIi8nj69HYc17cDc5NqnvquOiHVfNd/6KRYvtpQyk1H1Dyy/phuYZXuMzqU32f4CwvZQWf+6rmVvzqncaPze+4K+ZxetnTu9f0Z355HPrrPEBGAc16eC0CY084lR/cEyyKicAfn2ufT3ZaB7YcZgAeP10NoeBREtofoTtBhAL/nJHDfjCxSC6pe/zalFwbhkzSSglQz5fiqT8xyfE8457/Q95Qad/tpTSqTvltHan4pYMOBn34h2Tx2YghHHtqPH3cEMKO36y6kmq8I9xwXw4xtLi4dGrnvm5WM7h3O6N57Kk4lVsQMX2E+RUTgIEAR+z/GrujD4MynYdrt8Osj0HlYvSuViUjL96dX5wOQEBnK+KGdgarJsR5/gDCng/xSL3ERVQePVVwXqybtrN6Vz9nDail6UpxlqoYvfsM8zwNI6AOn/AuGXtAg91DlfG6TxOspMtVpS/PAWwxWwNwrhEZBRMM+S5y/NYvXZ21ncVF7SgnHiYvuoak8NtZ0Wk9dX1LvY4ZWM+ji1pHRLNzlYXy/mpNlxveNYHxfs02/dhUxw1OYi2tPolQp4fvdP6Q0C4qzIaq9WREWDRNehXfPhBUfwqCzYeAZ9f5MIhIkfi98fs2exNp4mDgNOh9W626XvrEQgG4xDkb1jgUrgDNQKQYEAmC3Vx8zNuYyacnxxLs68ETImwy3b+PJkDfZ/c0yuOp16FC3CoZB5/fCzCdgzrOAZb5DX/IBJPSufb/8XaZ6r88NsV3KizuVxYyFRR1xE4oTF73CUnh0TEeO69uBj9fWP2bsb6DLcd1DGdW95sF45w2M5LyB5j6iR1xFzHAXZuPFiYWt2iTbKf7TuNn5DfEl22HDNBh8br3bLQ3P4XAQHx9PRoaZfj0yMrLGgVPSPAQCATIzM4mMjMTpbJmpfle/sxiAfolR9O4YQqmvlGJfRZGjNVlrsNlsuDw2OkRGEeYMI9QRSoQzgpnrc3lk2vp97jNmbcqgd4c+Tfo5mpOAFeCz5at55re5ZGV3Aysae/hGInt8hM2ZT5gjjCFhVzErs99Bncdut3H5MT3JycyvddtLR/bk0pE9ARg/tAuvXnlEpXtEH56c47E5CwhtN5/wrp9SsvNvBNydy/cPqWVgeTC0zP/jROqrONuM+IqIO+jqUAdiY7Ypkf3xmpLy5NrKVQh9AYu1mV7O+SSTCw6N4JmxCfgDFld9nU2E3UdG8hb2Hq/h8/t5/IcN3HvmoW02WWrhpl1M//ln7nZs4tSw5UTZ3AR8NjbSBYCVgUNY6BvMikBfVgb6kkY7aisZkp2QAHs9dHL43JC6EpIWwNbfzEO+ZZNh1acw5HwYcXm11a6O69uBY/p0KK8GFh4SwqXfmZude46L5e8z8qpsH6pOb5GGFwhAbpJ5MBHVMq6VaXsS7z9dW1KeXFs5UcobgKWpHv70eRYTh0fx8MlxePwWl32ZRadwP8k79o0ZHm/bjhnzt2bx+A8bKPZWfAfw4yDNG8YNPxRwc7vfObHoR74M217+/qLAoUz2nc7PgaPKk5P2Vt1YykuGRHHJkFqqA9psZgryCa/Cxh9gwSvE5m/gmdAX2XHCneyMjsVrOblheikAj46O45afqnbIV9d5IiLNmLsQUleDK8c8KLc1TIZ8iddcib7aUFKeXFu5Wq3LazF7p4urv8nh5qOiufu4WIo9Ac75OJ2i/BwSbfsmic7fksX8rc6WES+8JZCyAnYtgV1/mAGVlcV2g94nQO/jIXFInTrjy2JGgbeiE9mPg93eSK7/voD+Hb2symyY6beuHRHNtSPqP2XV3vcZGaUO7pl1BUlWIo+GTOYi52xibSXc4r0VL85m+TBKRILDgZ+kRd/A7g2w6Sd6lWTx37L+zxXmn+pqDp0CfGvFsSjkUBYHDmVBYAibrW58siSZ0QM7Mn5ol6b5AI3BWwoL/gdznjNJVQAjb4TTHjJJQjX4aU0qf/vwD0LxAqYz2o+Djd5ErvmthM7LctiSd2AVpdir2tffjorhb0fVv2rH3jFjQ66dJxaZ+P/M2Hju2uvZVIjlgaOuhZTl5tnXF9fBjTOh3SH1PreItHwz1qWXJ9eGVJrGweUJ8P2qVO78bCWPnDeEq0f1JqPAxYRX5pGS56r2WK/P3sbhPeOrjxk52821ePmH4DPPQmh3CBx/G4y44uD7dAIBc+/gLQF3EZTmgKsQfCXmPWeoqVAenVjtjHUNoew+Y7fVvjxh1YeTHZ5Y/vJ9LtFRHlKL63/ckGpuLe889sAGS+4dMxak2nh9pamO9sjJcTw4q2pHeojNb2Zm6TrcDGYE6HUcHHcLzH/JVIL/28KK5FsRab4sC6bdAZt/BmcEXP5ZzYm1Pg+4C7HcFQlRs5euYpTNDpaFM7viy6x32xw+3WLj/pmFPHN2Ly48qgfJRXYufnNxeYJUKr24wDOJiY6fuMv5Od3ylxJ4ZRT2k+6CE+6ocYB00KWthq//BmmrzPJR18O4xyBk/4MTsCzT15yzFYoyzDU0tuJaWRYzdlqJ5QmrPpxsdcdx0/c5EOqhwLO/g9ff2+e0x17PPuq9Y8YP2+GzjWaQ+z3HxfDUfDMQs4hI3vefzv85vzbJx4POaRFFcNqCzp3Nd7yyBFtpGex2Oz179myRydAur6/89ZerVjN6KHj8HrJLK9bnunKZtszNp/Pd3DwulCMOsZOaC89NKyXXnQXYsDkisPzhgHnW/9C36+gUG96yn00dIJfPxZRlS3ns53n4SvqAFYIzdhnhXb7EZvcR8CSQt/saZrk6136wOnjs/MMIlJSw8bn67Td+aBfGDu5cPrvJJ0u2sGDrWdjD0nFGbSWi+/sUb78FAmYQh7MZVjlv1OTa2bNn8/TTT7N06VJSU1P56quvmDBhwn63nzlzJqecsm81gNTU1PKLu0i9+dyQtRkCPghrHh3Epd5AlYQcbwA+WFVMwIKp60t5ZmwCS1M9zN9lvhkPrSY2FhCFz7LzxuztHNOnw0FNUdqieEogeSHW1pmM2L6AY0MrqlHtttoz0zecI9gAwKWe+3HX84ar2kqAzjAzbUePkXDsX2H7HFj9OWRugJUfw8Yf4di/mSlM9voi47DDsO7mwZLHbwEmufb0Q8KZMDCCrzeWlm9bXWWStkQxQxpFSRYU7G4xibWV+fZUOC/1BqokSnn8Fm8uM9NdvLeymIdPjuPX7S6WpprrYXUxI4dYOlvZbS9mYKZOen3WdrKsWHKo6IxOJJeLnbO5zPEbHUoKwA5uK4Sv/cfzvv901lq9az22/WBvXm12OPRs6HoE/PYotoz19Fn6H/qMuoX8/hMAEyPG9wvntN5h/LqjIgmuugqIbYlihrQo7iJTsdaVAzENl1hbWdkgjFJvoMqAjFKfxdMLzMPs//1RxN3HxfLZulK25VtAAol7KulVmlSDDNrx6qxmGi+8pZCxDlJXmcFv6WvMfV4Zmx06DYEex5ik2vhe9XpgXxYz0q148tl3oEQp4QecWNvQzzsr32ekFPphlosP/WOZdFZ//D8/xDjHH7zG8/zNexuh9ho6dNoAxQwRwF3EdY4fuc75I92zsiDLrA44wlju7cl2qwunjRzBlBU55LkCROLmjuM7sHnzBuzZm+htS6OjLZ+zHYs427EIgORAR34JHMG0rzcyduAtOEKqnwq02fKWwtL3YO4LUJRm1nUfCWc8Ad2OrHV3v8/LS9/MpT1uMknY5/0iIg8osRb2PJuqy1S6dVQ5ZnTN9vLEIjPF+nkDInhpcSE78/3l24a6c0wFxzOfNjF31xL45Aq4fkatycatgWKGSNUZ9/wBczEq9fir3DOUev38/fOVADz4zVquHtWbZ2ds2m9ibZmHv13L2MGdTeW8gB+2/AKL3zT/lvWYdBkBJ9wOg849sEq1lmWu8WXJtK4CUwXX5zJ9RVjmeX9IBIQ3XjJtZf4AvDZrO7utDuRUM0tIIVEUHkBiLey/CuGBqhwzYmI8vL7SfGm4ZEgkj88roNRX8R9CSHg0eAohfR10GQ6heyqjn3K/mRo9cz18fydcPLlB29hcKGZIq7LkLVj+gXmuctF70POYfbcJBMCVZ5JBi9LBU4TbE6Bsemk/dgiLodQHznAPYJ5HuXwB7p9p+kbvmraTCxN3M+lXSM03z3Oc+PDhxI+Dt/1nMT1wNP92vstoVsLMx2H1VDjnBfOcpznxFMPc581PwAcRCXDWc6bSek3cRZC7o2KAeKVqtWBixquzdpBkJVLAvt+/C4iGBkysPRiVY0YgxMVnG3MAuG5EdHlyLcC7vvH8NfRHnKkrTBGrfqcFo7lB1Rxjhs1mo0uXLiQmJuL1emvfQZqF0NBQ7M0w8bAmAStAriuXjZm7y9eVej1EhSYQGYgnOiwbe9hO7CF5LE0P48v1mYQkBHh9YTgPdz6Et2b7ySmKBzriiF5rjumNxXL32HM0i0nfrau4z2gjskuz2ZK7jZdnLzGJtYEwwjp9S2g7MwuJr2ggpbsvKU9YDTaH3caovmYgiT9gsWDrSjwpl9PpsFfJJovwrp/j2nU1YKM5/ifeqHeNxcXFDB8+nOuuu44LLqjli0QlGzduJDa24gYzMTGxhq1FamBZZtRzcYaZerURFboDzNvlZnSvcMKd+79or0z3cP5nWVxeaRo3n9+iS3TFgyKXz8JT8Vy7/MFVYK+qq5nE4yjKYW1KfvmX11bJUwQ7F8D2WZC8GPwebEC4DZICHfkhcAw/+key0uqLzbLoODbP7FbDiPbHT42jd7yTy77M5ryBEXSIsLMy3cspvWvpeHaEmi/9fU81lWwXv2FugmY+BltmwOh7zdTz1Qh12Pjq4g4ELIgJs3Niz7AqybU78/0kF/joEds2i4orZkiD8/sgdzvY7c1uZHO+K8DC3W5O6R2+3wqkYQ4bC3a5ufzLbC4/rCJmuHwWXWIqYkbAsnD5qnbaWta+MSOHWOxF+a0/ZuxlbUo+24ocpNIBm8PPM6dfykXO2fwaeTehdhNsU6x2fOA7nU/8o8mtppPjqTHxJEbamfhtDpcPjcSyIKnAx4hODVQNP7YrnPuSqZa18QeY/1/iSrL47IKrCQ+x47TbGN07vEpy7R+pHjKK/SRGtc2RGYoZ0mJ4SyFjrRnsEdv1gBJrs0v8LE31cGqfcJz7eTgU5rTx23YX132XwxWVYkapz6JztIPVGRUPSHfkVJ02zrL2dIBUsr0oJPjxwrKgOBOyNprk5LRVkLkJLH/V7WI6Q/ejTUJUt8Mh9MATf9am5LO5KJQM2mFzBLhm7H1AzfcVe3tubDyRoTZu+j6XG4+IIr04QJE7QI/Yxrted41xMOX89sSH23F07Mq3fR/gjC2PcppjOW/wHDds+Tv5rnjiwpvhU6kmoJghbUlGoYtVyfmcemiiqTwUCMDKKVi/PsKDIekAFNpjyep1Fvdu7Ev/I0/lgyUmsXTaEScwI2kNK5LzALjjzLN4/sOl/JCaRhgehtm2MdK+gWPs6xlp30APeybX2qdzrW86vqeeg4Gnw8Azod//s3ee4VFcZxu+Z7Z3adUFAoHovXewwRgwYNx7wSW24x6XOPYXx44TJ3YSx3aKe++9BfcC2GAwmN57Feq9rLbP9+NI2l1p1St47uvShXZmdne0SPPMOed5n3c2mGK66BNoBq4iYRxY/YQwBQDYe8LsP8Lwc5uuhvB7oCKPnTu24akoJJ+0xo9vhH/NjUFR4Ddfl3DbRBtb83yYdBJmnYyi0ZPx5r8AkAztZ1weEKfjlTOcpFo16DQS03sZOLw1dG/w8nY/d03chrnXSDj/NXj2JGGy/fQ2OOe5djuP7oqqGSq/JLJLq9iVXc7JAxMikq/cYS33DFoN7/x8hLs/3MpF1W09QZhrU+xGssJash4pbLo9dU6Zhw079zK++DNY9yKUHAntzDhFJNX2mdH8yrRgUCTd1php3eXgDjPSBoOg0YLWKAoEzHFdkpa3PauUHRWWqMba5vLEabGUVflZ7Pk/bh5vZUKZhVR7x3ZpHJui58XTnaTHaDFohWZ8fSD0f/731eX8anQS+oosEUSSPFykDOuMcNbT8Nws2PExbPuwabPZcYiqGSonDId+hC/vFt/PfgAGzovcHwxyNOsYBw4f4aS4MiAAehtYkyh3A4h7aq1Oz4vbfPz5hzIuGBo2N6WxoZNdoY6uRge55SJ9EGCctJtsJZYCYqjETKaSyBW+u1gY+IlH7W+hL9wLLy+AYefArHu7vqNCMAjb3odv7ofyLLFt0EJhrLUlNfw8v1cEwRQdEuveljhR6FGH7VmlbKuwUhrFWNtcnl4Qy7GyAH9eUcaDMx18sruKYYmRmiEZ2ne8MTPdwLMLYhkcr8OolRiVpGNTrpiPLMLOK95ZXK39QqyB/ALNtd1ZMzQaDRrNL3OdSaVjOFRQybGSKqb2i6fUU0pmeSZ5rjzyymqEIEhJ4Ah/WvY/9pTsRGPMxdJX7HthGxjD/ON/Wws4wGqXCXoSCHoT8Lt6QiB83V8iuyKHH/dnM6N/x3rCugO+gI/MikyOlB1hd045RUU9kKQgxl7Po7WIDq2e/Fl4C2YDTa8HPHf5OHbnlPHI13t45LyRvPbTYaZmNNx5QjIayfj229rvW8OZo3pg1MnE2EvZW34Fj6x7DJ1tJ4HYn/AVT+aql9dx8KH53SqhuUMdXKeddhqnnXZai5+XmJhITExM+5+Qyi+PynxhrDLHta7KuRkEguJG957vXWwvVFg8wsIDJze8CH3D58UEFXg9bOLaHwS7IXRh2Ffk49KPC2sfK0hkklCvQk2pHngUubpJiVp74i6DI6vgwPeixWswrGLK3oNvlXE8VjChOlkw9NkpkkSepb659ffT7Lh8Co+tEZVyFw0TSVRbrkvGppdafmGWJNHiqOd42PourH9FpHl8cDXM/D30HBf1aaOTQwOUaO1Zp7+cx8L+Rv41N/YXVVkDqmaodAAVOVCR3/iERidToxm3fufiYKnCzeOt3DE5+sR6vivIlf8rQgHeCNOMKp9CUpihcn+Rn9u+Lql9rChwmGQqiKxEq7HfnpCa0QhFLi8+tJyr+Z4rNF8xzHSodt+a4CBe9s/l6+A4AoQ+0wdOcnCg2M8rWyqxGyTOHyI+y83XJWNvjWY0B1kLM34Ljh4itWXTm0wIBmDir4HoyeoTXsjlgiFmHj7F0a0GGJ2BqhkqxwUBn1hgLM9tlbG2RjOu+bKSXBfcO83Or8aExgPBsOiofUU+rloi0iHqakbPMFPn4RI/r2yLTCI4SCouIidBFDpZL5QglOdA4T5hoC3YLbqPuEvqH2tJgOQRkDJCJAvae7TbAnmRy1urn4okRx1X1OXhWQ5+zPSyZE8VPe0azh5coxkGHIbOM7NOSQtNKGbaRnOl7y5e0D3CSZotPMqTjH72Zi4faeOPJ/1yCmxqUDVD5ZdAIKiw9mARv3r1Zyo9Af52zggu6OuFj34NmWuRgMPBRJ4OnM5P1lM5uFMsWvxUbawFcPsCpDnNtebazGIXn28T+z3o+VkZxM+BQTwROBMjHqbJ25gtr+cUzQYSfGWw7QPxJWvFfMnA+TDwNIhN7+RPIwqKAsc2wPoXRfKVv9oY5EiD6XeIluPaJhaU3WUipavsGLhLKSuvIktpWYeUR0+N4cNdLlYe9TIkXssZA4VmzOxjrKcZkiyjT05o0es3l5N6h3Q/moIOebGcG0av466zJsH5r8JL88Xc14C5woB8AqNqhsqJTo1e5JW7ue2dTQQVePayscwZGlrBLveExgvbskp5Z51I1ntzTcgIW+UVmlFjrj1a5GL1gdB6Rn0Uxkh7uVT7LWM+WBOa6zfGwOhLYdxVEJfR+MkrSnUabbWRtqpUjBdqjLSKAhqNaGeuM4sEwQ7oGtIailzeqJ0xGsKggUfnxPLkugq25/uY1EPPgv7CgDV/4BAcBpnLOupk6zCrT5hmRBGNAU/mcvtEO7cMzBSfe8JAcWDqKKGxP/wdPrtDJE5aTywTqaoZKicE5Tnw3mKRvDrsXJhyc+2uQFBh/e5DuHL2csVX4rr99pkOJvUKXc8qvKFuQuuyvKzNEnNJb28PzU25/Qppdg0HSkSx9KEKDVuLQhcUSQmSTQJews2fEp8GJzN/1sXMz31GrMFu+wB2/E9oxozfgrVj7pUbJBiEnf+DFY9AzlaxzdEL5vwZhpzR8NxUMCCK+ooOgqsQjHaI6dng2xS5vJS0wFgbZ5L56ywHD3xfRlZFgLkZRuZlCM04d4gZh0Hm0uH1Nai9xxuSJDEnI2QWrrvE/Zx/PpdpvkZ/eCUc+Ql6TWq39z4eUDVD5UQnfJxx69ubAHjuqr4YzQX4gj5ijbFkF5egj/8OXcwaVpSWAaCpvtUM+i0ovlhG90xk02EfgaCEJFeRlughqzwPSeNGY8xFY8xFZ9+GosgEKvvjKx2Jv2IoGmMWP+dsIC3RQ7wpHpve1sCZHt+UuEs4VHaIAlcBDqMDv09B1mdi6vk6sq4UJWDAnXU+/oqhUZ+f6jDyl7OGc9ObG6j0Bjh3bE9OHZLEqUOSuGxyOg6TjnPHNqxRUK0fPXu06eeQZYn5w1PxBROwFwXQlJ5GIOZTDImfEXD1IehJps89n/PnM4dx2aTebXqv9qJbxiOOGjUKj8fDsGHD+OMf/8jUqVMbPNbj8eDxhFK8ysrKOuMUVY4H/B4o2CcmUPTmpo9vBav2F/DsDwcpqPCwXRGVcq9tKWdumo8pGdEn2I+VB+ptu+u7Eu6eGrrAhxtvAXJwRm39AFCpGMh3RxqHcyoCVPkV+sR0yz/xhnEVwqGVcPAHyNooFtlriOlFaY8Z+HrN4I1jyXy218We8P2NMLGHnmuqjQhT0/QRybD2ti54a3RiEab3VPj2AWHm/uIumHILDD2z0afqG3jrT/e6OXOgh9l9f9ntW5uLqhkqUfF7xGSFztgp7d2aQ7hmHKzWjOfXlTA50VurGYFgZAJt3URagBu+KOaWCSFNeGp9RcT+fGLqGWtrqFSMFHsiNSOzzI+iQJqje3xO7cXhEj8mbwHBje/wueFbnFJ1GypFx8eBqbwSmMtOpf4N+YJ+RhaPFJNNp/Uz0s8Z+lw63CQlSUJTdBb48XHY8o64j5l4XYMFF+/scHHhMHNE8YZKw6iaodJpBIPCJFpytLq9W8sK/cI1I7daM/6zOp8hDnetZlR6QxrhqT/EAODOb0pYNDA0sf3wj5G/x4WKvZ6xtobD5TI7C3wMjg8tbhwu8aPVQA9bKzRDUcBTJhZuyo6JlKriw1B6RHxOgShmXkkDznRIGFxtqB0O1uR2T5s6UOzHqJX4/CBUNvB5ROPyEWYuHGbhgqFmLhpqjvisOtNYWxdZgtXBoVzju52XdH9noWYNJYqVezdfxZUjLfQ+3saJXYSqGSrHC19uy+aBJTvIDksP3LLk35yjew1toAoMdsom3Map3/QVC9al0edS7v14G6PSYmof/99H2xp8TzcGvg2O5dvgWCR/kH9O8nGytB5n5reisOTgD+Lry7shcYgw2fadKQqRo6QzdQjBIORuFQvw2z4Q8zU1JA2HidfCiAsbN9UG/FBVJLSrIhd8VexzWzCakvk4qwQXzW+defskG2cPNnPmIBOrM70RyVFdqxnRNfXJjW6uH78DW68xwrzw/cPw6e2QNhFiWp/We6KiaobK8UA0vQB4++cjkeZad8gotT0r+u/nQ1/sJN4aKu667Z1NUY+zUMWZmh+5RPMdQ+TDYmMQ3ImjME6+VqSZ6kzszS0nptxDgi0sgcrnrjbTukSBQ1WRMNb6q/9+ZI1IpNVbRCe5bmKkDWdPoQ9T9TjDR/NTZu+b4WBBfxNzM4z8lOllVHI30YwGtj+6ppKbRjuRi/aLhGBH9YL8jN+KDk2520QC+gWvd0lycHdD1QyVbkMwCB9fL4KqEoeKzmrVf6Nfbs3kmf/9gLXiMEbJA4wH4ONdFRHm2nJPaGxRY6yty+Nrykmxhcy1N3xeFLH/IKl1jLWCRIrZX9KXI1Meote4q+HbP8L+72DtMygbXkEaeSFMvgni+7flU2gadylseVcEYhTsFtv0Vph2m3h/XQPzSIoi1r2LD4mxhEYvgjWi6JWiKOwuFHNTnx0EpRlJgzX8dZaDuRkmZqUb+emYh7EpofFNV2pG3ct9DnF8EJjBRdplIr32kne75sSOM1TNUDkeaHCcsX4nv56Zhl7R897u9/ji4JcYEsQ8ihIw4S8fjL9iEIGq3ih+EQiRlGAnoaqKQwXiuPJyLRUlPiRtKRrjMTTWnWjMR9AY8tBad6O17kYJ6vBX9Gd3npOfjxpJickkwZxAojmRrEKZtFgLDnPHdnzoaGrSao+WH8Uf9ONyxRLwafhi3yrMvd9Dkv0EPAm4My8j6G24oO3fF41mXLqTDfedytqDRYxPD4WLOEyd/xnpZB19HX3RVU7Do92D1roHY+rbuA7eBGj5w8fbVHNtNFJSUnj66acZN24cHo+H559/npNPPpk1a9YwZsyYqM956KGHeOCBBzr5TFWOC4oPi/arjra55hti1f4CHvp8F3VtT0Fk/vLZbn6/AKZkxKMo9Y1R0ThcEloRX3nUE7GviOjpQqVYKMTBb5e7KQtUcPVoK4EgTHpRtOB4fYGZPgk23AGFjFhtbfpVkcuL06xnaKoDTVfOOSmKWFg/vEosuuRuh/BP1NkX+pwEfU/i8T1xPL6mHNYDVDTwgqAN+lm84wsAXhlyGkOSTTw+J7Z2/7jUDmoNH5suWh2tfAz2fClMUaWZMPmGBif2oiXX1lDhbZ5x+JeMqhkqjVKWBVXFHaYBLaUhzahStPz1s13834JBTMmIj2qmjUapO3Rc3UmrPKKn7JVgI5c4blvmJqh1cc5gMx6/wrSX8wB4b5GZpBgrCtA7phtqRnNRFF74YjVJhz9hnvwzs6QgSHBMieN17ylIWwN40LN3SI96qwJjknX8MSx9flLPDtKMphh6pph9WvkYbH4LLAlI8rwGD6/yNe/35peMqhkqnU7JYWHisSa2uMijIc0oDRgiNKOqGZpRUBWkIsyEuyUv0gSUTfSCwCLs3P+jB37M57mFTk7ta6TUHeSkV4VmfHyWGbvVil4DPe3VmpFZRGVJLglSOX1NlcgV1Uak8hyRJl+eE0oKjIasE/fUCQMgvvrL2Re0HXstvvHzIj7bF35ewghdd1zhr/P/OCFVzx2TRPq8JEkRybHdhR+Dw/mN70b+q/sPl2q/I0+JxRe8rqtPq9ujaobK8cSX27K5/vUNtZqhxc992te4XP4GAlCYMJG4S16g0O/E+83yRl9rV045/WNDN8i7jjWWQBhCQeL2nwzAFN6+9nYmxZRSvmUJW797mwnyLrR5OyBvB6z4J2j0KKljOeYYQ5Z5INrUkYwcPgJNeww0An7I3ykSag+thAPLhFGgBp1ZtGsdf7UwiDaYLBUUSYhVxWJc6S4V8zomB+d/AWuz3EAjehaFqWl6rh4lTAiyJDG1GZqh+PzkvfAOAIlXX4Ck65hp/MY8Tp7ibGxmE0y/HfZ9C8fWiTTkxUtAPh4Ghx2Pqhkqxwt19SKcpbvy+XJbNvOGpQAilbYpVuwt4JRBoUXbXTnlEfsHSUe4VPMNZ2p+xCqJa6Zb0bEkMJnXAqey5UgGn54xjWE6E4cKKjn1sR8AeP+Svli1fpxSBYlaNwGfi705ZZS4/disNgb1TETTTY20dTn5lVwOlTb9WdZ7Xm8D51R3w9DKEtN6hTSjs7ShIRrVDMmESeeF/N1Cc81OUcBy5lPw3EzY9akodjnBE9AbQ9UMlW7Hmqdh/1JRqHDui7VBVV9vPsh/3v6cHlI+5ZjIVkKtof+3q5JFGQW1hd/NmZv6cFcVk3uGDJ87C/wR+3OI3npaQ4B/Lj3AP5ceYOmtk+l72Ycc/vkzipf8gVH+/bD+ZVj/Mr60qehGX0hg0CLWZgfIK3eTaDMyoY+z9R1CfW44sBx2fAI7PhaFHgAGB0z6teg2Z26g25GigKtIFJOX5YCkiC5MmoZNS4Ofymn22lA4c/oamZUuzL06jcT0Xs0vGO9oTZGi9Mh4JrCQ8zXL0ez9CrK3iK5UKlFRNUPleCFynKGApgwCYp116VYfNue3/FT4CZW+SgACVWl4i6bgLxtONLvihz+XkZEU2p5d4gckFH8M/oqY2kRWSZ+Pzr4ZrX0zGkM+OvsOVlfsYMWKdK4Zczqjkn0s33OUhz/2YjPKPHnJaGJMJtKcJmLM+oik3TZrRgeiKAqF7kKOlB2hyF2Ew+DgvMfzQCrHkLQEfewaJBl85UNwZ50PwYZ1YOGIFMb0Er4pg1bD9P6tSy9XvF7yHv8XAIm/uRVJ37bwJ7POjEFjoCDrPMx9H0djzEEfvxxvwew2vW57IynNdf619Y0kiY8++ogzzzyzRc876aST6NWrF6+99lrU/dEqMNLS0igtLcVuj95mWeUXQGUBHFsvKsc6ILU2EISrX/mZgorQ79626kQpgESKGGKr4oXF4/EFFQY9md3u5xCNG0Zp2LrvKCsqUgHoyzEOIIxlT83W8c6aQxHnHG81cO2MPg2m7HYIPhcc2wiZP8PRtVCeFbk/cTCkz4A+02srnI+W+pn+Sl6zXt7g9/Dxp78H4MyFf2H37X3a9fSbRFFg85uighFg0ALR/ijKhN/3h90s/qSo3naAWydYuWm8rVEDbm0LrN5TW5z8UlZWhsPh6LbXSlUzVNqEr0pcX5QgmGK6+mya1IwUChho8/DC4vGUuAOMfT63U87rplEaNuzNZFWlWDwZwBH20AuAF+fqeHVVN9CMKKzP9rLskJtbJtjQh18j/W7Y9x3eLR+iL9lfu/mn4GBe8s/l2+BYtH5/hEZ46hi2Dt2S2ik/Q7PZ+Dr8/DwgsWrw/3HxxuhtPP4y08GFQ82ND/y8leD3QfrUplve1kHVDIGqGSrNpjxXdGHQm8HQsvZDTWlGT/LoZ/PxwuLxZJU3/x65rfxmZJD9e3ZQ4JFIopgR0n5kSSFFKmJKTDH+8nzsSjkaqRnTC6ZYsKVATG+I7Q0xvcSXreUJv02x6qiHNcc83DLBFvUaebDEz8xXo3+GdccV3V4zwnhqXTl/WxUyOFyq+YYHdS8BsH7I3YyeOrfBpEIAPOXCXJY+DTQtW1xRNUOgaoZKZxAIKkz729LaZBALVTyre5Spmu0EFYlH/OfxseV8Vtw9mz255Zz2rxWdcl7XTe3BexvzKHL5sFPByfJmTtFsZKK8k2SpuN7x5ZjxOwcQ26M/ONKEJljiwWAHo0PMdygKoIh2tVUlwvzqKhQp6EUHRUFL3i7wV0W+uM4CfU8WyYgDTxPphtEI+Fm+/TDbjhRy41AfkrdMmHUNVvEla9lf7OeU1xrWXZkgwQYSplqjGcEqN7sXXAXAwM9eRDZ1THejP35fysubK6Pue2mBjZOdpUhJg4Wj6unp4KuEBY8Kk3IbUTVDoGqGSkdTVy+iEWfRs/b3s9HIEusPF3HOU6tb/D4aAsyX13CF9ivGyntrt+8PpvBGYDYfBKZTWqc7362TYvhy0yF2u2MAmCZtZqUyEoB35vp5+sds8itDBYLdZW7q6/1VHCoNcO2Y6N0G9xX5mP16ftR9ADr8+BrIPmpMMzpLGxrixi+K+Gxv9N+jd86JY0KqHqk8R4z5UkeF1iyWPwzLHxLbb1wrilBbiKoZAlUzVNqNnG3C+B7wwoJ/wvhfARBwFXPDo2+AK59cJQY/WhQFthOamxpvzuXtq0ajkRtf52xv7phk5eWN5RR6YIK0i2u0n3OKvAG5ei7Kh5b1wf6sCAzn5+BAim0DuGPR+NrikUbxVEDeTjiyCg6vhkMrwBsW9JQwCMZeCaMuEmOUaAQD1WOUo1CRByhgdrLkQJCiqmBtt7xwFEVhT5GfuW80rBkGvHiIPp/flrmpjtaU894v4Ocoacb/1v2HRZrVKEPORDr/lXZ9zxpUzRComqHS0USOM4JIukIkfSHBykFI2hKMqe+htYj12l62Xoywnclb36VAFPN961HQmA6hc65Ga9uGJIkAO7smleLsSbgKxwEykiETxdMTi17DP88fWS9pN8Vh5P7ThzRPMzqQ99dn4g8EuXBCLyq8FWRWZJJdkY0sy8QaYjmQ5+e2t3Zi6vk6GlMmiiLhLTgFb8EsQAbJA0r0gu5DDy9ol3MMulzsHjMWgIEb1iOb2+7HO/kfyzhU6EJr34ypx1soigbXwZsJepL57o6TyEiIPu5qD5qrGd0quTYaEyZMYOXKlQ3uNxgMGAzdLyFGpQsJ+KBovzBVdYCxFkSSX/iCd13ycOIur+TOr/L5aG/zW8S1lSc3BYgnZLJUwoTpb98exipFnnNhhYeHPt/FPfMHddyEVFWJaMOXU/1VsFcshtQga/nRN5Cvg+Pwp01lfUUMF2DGvVfBZqjk8TXlFLgaT3HNMJSS5TFQ1YIWrh1GTUtvS4KYNNr1mRhQzfhtPbOAppEF7X+treBIaYDH5sY2eIxKfVTNUAFC6UI1Lci6mKY0I5t4KrpAM/67KUBC2HUzELYQ/NBXBzBIkefSKZrRDM55rwCAvMogm3K83DywhJ6ZnzGk6FsMgUr0iDSUjwLTeCUwl11Kr9rnavE38Kpg1Ha/ikRGXSJSH3cuYeLufzBUup/tSv2ikd8vK2VngY8HZ8Z0/jkex6iaodIhuMtEO2xZ02JjLTStGZkkUlpeyW+/zufDPe2tGQpJFDNEPswAKZPeUq74knNJ2VUojLPR5vJrPJwS+BWZImwUKA5ylVj6Z2TQM60P2JLBmiS+OjiJNpyLPxKpi6UehRVHPFw/zsrRUj9xJg33fV/a6tdNd7SvCbi9qTvMeD1wKmlSHtdpP2Po9n/ymjuBxbPHds3JHaeomqHSHVl7sKh2IcBOBa/q/8YoeT8VipFbfTfyXXAslHn5zdsbWbKlc4q+AZ758Rip5AMJlGHl2+AY/hecCij0lnKZIO9ivLSbIfJh+kuZ2CQXFG0SX23FYIeUkZA2ATJmQc8J0QvLFEUUn3krxLxVZR5XvCkEzVWu4cvDEr+ZFMe+Ih+xRjcP/NB0O80MjrGXtHrbJ/ZoW4JHR9NYrcWVn5Xz2wkmbtTsESapU+6DL38nWvIOnA/2rl106q6omqHS3QjXi4YorPRy7lOrmNDHyTM/HGjR61uo4gLNcq7SfkFPSczZ+BQNXwXH8UZgNquDQ2hoAf1fP5WQFpYGflhJrv3+z18fqX+e3WRu6trPRLGIN6Dw/g4Xv5tqZ3u+jxijzIMrmqMZmewivd72Bf27wfpGIzQ2c3bBB4U8cJKDxSOSoPSYWAdKGirGxtPvEMm1OVvhi9/BeS912jl3d1TNUOkS/F748FphrB0wD8ZVF01VFrBr42r8lUXk4USpXi+oW0C21RXDBe/nMibVyLMbohdpdQT//KmCFAqBeNYqg1nrG0gqRZyhWcVZmhUMkI8xSd7JJHmneIIXjr0XR9GydJxJPcEcJ7pLSbII6nAVia/ig6LLal1sqTD4dFGo11jnC69LmGpLM8W/slyd3i3+dm/+UoRM+YIKr2+p5PfTHWzK8WI3yDz0Y/PGGTuovyZwyfCO8UC0Fw1pxpP+M1ikWS1SgQv2QXy/Tj2v4xlVM1S6G6FxRgBZn4+kLyDos6Kx7MGY+jay1oUS1OLwnEY/8xze+q4jNEMiUNWHwLE+SNpSdLGr0MeupowsNIkfYnaswFswC79LeAYqvQFuePczlIAdMFHT4jSn1M31r2/gqUvHdJnB1usPcud7mwHIrsjn7TV5XDRVw9ECA1ajlld+OIrGshdzn7fEZxswUXXsQgKVA2tfQzYdIOgaXO+1f31SRqf9HK1BqtZYf9kIfPZN6Gw7Maa8j+vQ9Zzyz+955rKxzB2a3MSrdCzd3ly7adMmUlLUiTqVFlCaKdKiOnCCt8hVv9KqLmVYOtUkFXrfkGs/GHbrqkS5jVUQN7fP/nCQiX3i29buO+ATBqCig1C4TxicC/dDeZQFHFuKGIikTYDUUVzyVInYfgjA36xFi3CeP78///ixiM8PRJqmvr205RXQ7Ub/OSBpYNlfYM+XoNHDtNsiBl9NJct/tLuKvUV+3j8vvnuavrohqmao4K2E4iOierixVcJOpDmaUd5FmlFOaALGT8goFC13sF01o43IBCnatYLfa77hpI1barcfCSbwemA27wZOpoSWmdpWX5nU3qfZdiQJpt4KFflojv7E07rHOd37YNSf7fWtLnbk+3jnnPjGU89ValE1Q6XdCfigYA94y8UEfCtorma0h7HWiIcx8l4myjsZK+1hiHwYp1TR4PEuRU+2Eke24qQCE/uUHuQoTmTFTylWChU7pVhqF18kIO6YgRdmj+9SzQBqE/nu/KakXV7v84tb1zKps4iWSvs3/0X0k7I4RbORufsf5Mbih/nPuQMaT7BVqUXVDJXuSF55yFj7tv4vDJEPU6xYucx7d0TqeWcaa2uoIrSg1086xhalHyChKAo/BEbwNeNwoyeARD8pmwwpi0GmEm4cY0AuPSoWuT1lomjFXwVI4t5Y0oixnilWfDl6grMPxPaB+AEQ108sZtfF7xWdf3wukc5dWSgSWH1ucXxYcf6Tm0UL71u+rJ+y2xgPnTGQ675yUVjHv/bKGdHb3HYXmpLof6z1sCFb5vk5u5BGnA9b3xXdwr74LVzweqec4/GGqhkq3Y0avWiKjUdL2Hi0pNmva6eCa7WfcbnmG+ySaJddoNh5zX8qbwZmkU/zQiPC5zjC56OCSv358+40NwXwyGpRmHH95y3TjIfPGsK5H7uo2wH8sTndO2ijqbHD/d+Xsi7by39OTYLiw6Lg1NlHtEJf9F94bpYoRnWXgVFN0ANVM1S6iB8fh7ztwmy66L/iPrs8F3K3UVZRQW6d63egzh2jGwPrcgKsy+k8Y20NVWFV32nkc5Qkngos4qnA6fSWcpkub2WqvI3h8kF6SgX0kAqhsBAK1zf94pZE6Dkeek8WHUNTRkUfW4CYA6wqEQm1FbmiaE9nAluSMPBWEwxrIF1TfHHNpy1L+n347GEs+rD+Z/3HGQ0k6HYTGpKMXUovvgmM4VTNBta+cR8Tbn2zc0/sOEbVDJXuhhhnBJD1ecj6IoIBMzrHZvTOn5AkhUBVD6qOXUSFL55jhzpeMxS/A2/+aXgLT0LvXIXeuRKNIR9Tj3cIep148k/FXzYSSVeEpC9G8ZtR/DEoATOKokcCHliyg1OHJDfeKbSDKHWHPqN/fZ0DwOOfBwEfEEAfvwx9/Hdhn+0lKD5nxGvcu2AEf3qv/trRb+cOrLetOxHSDAlPzllozQfRmDLRxf6Er3gq1722niunpnP/6dE7vHYGHWquraioYN++fbWPDx48yKZNm3A6nfTq1Yt77rmHY8eO8eqrrwLw+OOP06dPH4YOHYrb7eb5559n6dKlfP311x15mionEu5SKDogJtrljvv1dprFzXuVoseHFhuuDnuvlqGgw4eGADoCWKkigRIkFAJoiFNK0UhBJBQkFGRJ/CtVKuzbbWRgkkUk/iqK+Bcl9L0SFKY1d0l1C75SqCqG8hxhoK3Mr35OFGLTIWk4JA+H5GFgS8HlVzDragYlJa3+iS8bbqZPrJZEmwHqJBL2sHVxolS/U0QF5Hd/gp3/E20Nx1we9dB3zonjgg8K623flu9jyZ4qzhvSvSsQ2wNVM1TahdJjYiIjpnuk1kKkZvjRYJOqmnhG1xBurs0kkb7KMWRJyEDNTa0CFFR42J5VyoienTN5oygKVTWaUZbFbdr3OEezojYNJahILA+O5LXAqXwfHNlgK1aAWyfa4NPo+2JNXbwi0xCyFmb9nsp3riHNncO/dE9gPP1hLviwpN6hG3J8/HjUw8np3TvppD1QNUOlW1J0UKSn21NaXeBRoxkuxUAQGQvtqxk9pTzmyj8zR7Oe0dJe9FIgYr9fkdmvpLJL6cUhJZnDwUQOK0kcVpJxocNVXZTRgzyOIQrZLFSRTjZSd9OMNvLHGY4GNaM9Xr8jCf/te/UMJ5d/UkQQmVt9N/KhdD8D5GP8qvAf7Mr7N0OS1HGGqhkqxyuJNiMGvLygf4Qh8mHyFQeXeP+PPUr99NTOJnxsUaSEisDdGBgh7UeRZDxBHV5JRwVGtim92VDZn8m9JzAuo3pBWtaJtDtJ0/CiNkAwCEpAdEnyVoh/A17we8Q8lqcMfFUinSrgF04trQlFb6FKF4tZX3OurZ/b+/e8WMb1NmHWeyh0R2prdy+WDr9leW6hM+pi/3dHg+SXV5FYuB8WPArPnwI7l8DOT2Hwwk48245H1QyVE5FEW/vOEVio4krNl1yr/azWVLs/mMLzgfl8GJjeYOvqhpDDLLXhczo5xJOiFJxw44yXFjkZlWZEq6nCX8ddq+/mxdLhmvHfebHcFKUQZcmeKh6bE4PW5BDFp3oLWBNFAvqlHwjDWrRU+eMQVTNUjkvyd8MP/xDfn/Z3sCYIg2juVgAszhRAzLuXKya0BJCiRnF0FaELkZ6QeSieUtyKng8D03k9MBuQsFNJfymTJKmY30510sfsDq13a3RgcoqE2ZheokjP7IzyfmH4qkRxQFWxMNR6ysWF0WATRX/VF8mgouDxK5h0Mt5A4y/ZFB+cF8+IFD1Q35TW3QM2ws/u0VNjuD2s4P0p/yJO1WxgZNFX4vfP2oVhWZ2EqhkqJyJxVm21sbaQgDcOQ8J36OzbAPAWT8STuxAUXeefWNCMt2A23pKx6B2b0DlXIOuLhMk2/ju8xZMJVKWC7EY2ZkJQj+K3oQSsZJf6WXuwiMkZnVMo7Q8EKXaXUeEvZk9+VtRjJF0RxtR30JoPA+AtHo8nd1G9z/axy+KY1W8wf3ovMuHarNd0iVm4JYSf3e/mDuWfP87DmPIxhoRv8JeNQAnYeOnHQyeuuXbdunXMnDmz9vHtt98OwOLFi3n55ZfJzs7myJFQaxev18sdd9zBsWPHMJvNjBgxgm+//TbiNVRUGiQYFMZafxXYe3ToWw1NdRBvNbCqPAEvepy0vqVoc9DhJ0PKIkPKIlUqoEd1tV2sVI6dShxSJXZcmKSmk64aZEU7nKjWCI40iMsQX84MMSCpU4X8n7XlPLamnFfPiGNar7a1J7AbxIRWt9WDjJlioLXq37DuRbAkwMDTgMjJqLEpetIdGg6V1h9peQPdaeDacaiaodJmPBVQehRM3atit0YzVpYn4UdLgtKyNIvOosYkBWLRewd9QQENAfopmejCDFjNSVasS5knSG5FgP5xLRtIPboij5xty7gvaRW2wi3cWn33WqxYeTdwEm8EZnNEaV7qrC3KIshdU2wMS+iCwV1LMNhYO/QPTFp3GydptkDe+5h1p+Ly1dcHXwN1LicaqmaodDsq8kTnhpoWc61kaKqDOIuBHRUpBJFJoO2aYaeCczQrOEezgmHyoYh9WYqTNcHB/BwcxJZgH/YqPRtcDDeHGX2PEUpurcTE9mrN0OGjn3IMjRS6GLVGM0rcQQqrgmTEtuyzvG95Ke9sd/HxBQkMaeO1PVrRxe2TbExI7f4LweFDoxm9Q2aKCsxc7buTT/W/Z7S8j/ytz0HSrZ1/gp2MqhkqJyoTett5zvQE45U9lClmLvXe0y2MtXUJH2fk4eRbxQmKSFAfqewlUSpGSxBJVggcAbTx1ebaMGOtJIGkjZxICQaBAIWVAco9AdLtCgQDEPQJ55WigEYjOglpDWCIj9DoO74u5rO9xXx5SSJ9Yto2RZ5sEZoRvr596wRrxDW4uxKuGaf2bfh8/aY4UVyfMBim3AIrHxWtvTNmCuPUCYKqGSonIhP6OElxGKtbtrYemSAXapZxu/Y94iWRvrcrmMZj/nP4OjiutoNFSwnvuJdLyNhUhJ0i7LWaUVMEXru/FeOMvMoAHr9CmqNl1/1ff17MiiMell6WSIK5bQbbJIso6gjXjFsmWJnTyDW4uxCuGQsHmKKaawGqfAo2g00Y0fJ2iTRHg01oxgmEqhkqxx3BIPzvZlGI1n8uDDtHdIzI2S4Mp5YEhhoh3mogp9zPYZIBiURalrTakYR3bBXnJyggBpkAQTRYqCKOEqowskdJY6vSl7mp4+gzupmBLIoCfg/ZRaUofg+pRh+4CoWZ1lc9N2aw1EuprWHxx0VsyvXyw+KkNiesR9Ocm8ZbOX2AqW0v3AmEa/bZg80R5toNygA2BPsxRt5HcM2zyKfc2/kn2MmomqFyohFUgiTElhEXW0ZhqRVj4ldobbtQFAlPzhn4SiZ19SmCosFbOBNv8RT0sT+id65ENhRgTF5C0BOPp+AU/BUDkfTZyPpiJKUIFANb8uLpnzoQq86KUdu8e/TMYhc6jUySvXnHu/1uyrxlXP78Fo4Ve3n4Ej1SsG4IhoLWvglj8sdIGg9KwIA750z8ZaOjvubY1IE4DJEeiRtnZnDOmO4TSNYQUth83zVTh/LwZ4fRxfyMxnQMQ+KXuLPP68KzE3Soufbkk09GURo2hb388ssRj++66y7uuuuujjwllROZilyRWGjt+BadGhmumZ7O8s9FVVwRbTdyDY7Xcv8MBxd+WEgKhUySdzDLsIt+gf1kSMfqpUo1l6AiEUCmJq82WF0LXjNpJbZJWAw6JFmmqErBpJcx6zRIslx9IZPFzInOAqYYkQxsjOG5nVq2VTm5ekZ/RvTrLVryNSOl658/iXZJD64s5cuL21aNVlVd3Z1s7eKU2sYYdja4CmDTm7Din6IKMmloxMBCI0FFFJMUwNGiKgJBS5e3uepoVM1QaTNlWaLNp7l73SRqZLhqWjrLvxCa0dy2eI0xPc3ALROtnPd+KPHaYZAo9Yi/ocuG6tiS52dzfuPm/LotnaIfo6GAGBKVIjSSeD2nWc+BYj8L387n9ok2zhtqxmFo/LVmvppHYVWQTy+MZ1hiE8Ykvwcyf4YDy7lu70qsWjcUAkj8EBjG+4GT+Co4rsVpKFWB+s7TG8bZohzZ/Si39eX3vqt4VP80rH+ZAf50NtGv3nEH8isJpBtVzVA1Q6Uz8VWJ5A8psq10a9DIcMnkdL7/pu2aMVA6wjXaz1mk/Qm9IhaeA4rEmuBgvlHG821gFEeVRC4fqmdLjp9thY1rRngKYeSyaggfOoqw41RKIzRjW56PCz4o4J5pdk4fYGpSMyY8n4M3CEsvS6RvMw22QUXhta0iOevlzZX8fXZMs57XEFVR7s1vmXB8aEZjQ7KjShJ3+K7nef0/STjwEYH9o9FkzOi8k+sCVM1QOVHRfP17Zig/41F0XO29k91Krza/5lWjLJw+wMRZ7xZE3b94mI41WX52FTWuGeH36Q2ZrdwYyCQRh1KJLCmggCm+Fz+X6blySQkPTNcxu5eEQx8UvcHxiYXumqbgkvga+5q49q+53EGSTScSb5uYm/L4FT7cJRbGP9zp4o7JbWtNXZNI1SdWW1s0fduk46PddXOL1TdnuUjq40RTtB/GXQ3b3oeSI2Ke65T7OvYkOxFVM1RORDSyxP2nD+HXr29o9WuMlvbyJ91LDK8u1jsYTGLvoBtxjjiVr14/GPU5i4fp+DHTz76SxjUjvMV3gOhz/G4MVGLCoriFZiDGGcsPubn5y2L+dkoMU3sZmh5nvJALwOZrk3EYmzdxUuEN8tV+YUz+5oCbcwe3zdBUM1/Tz6llc64Y991+gmnG+iOlTM+IRWNJEPO1+bshZaRIijyBUDVD5bhj3QtwdA3orbDwUdH1IXc7BNxgE0ZVjQzXzujDHz47SM3cT15Y4UNreWiWgzS7lks/rt/BE4RmLD/i43BZ46/jImRa8texuQSrNaQSE0OlA1ipQkHGi5YMl54vvj/M3UvLeGx+CmPTrDhM1c9XghAIVHe/qAJfFT6fl8kvimv/rksVjEYjaE1NroMXuAKsOOoBYE2Wh3EpbSvQrumC0cOm4Vh5AJNW4s42jl1ajRIUxYwo1UWQja/LN2UXeN4/nyf1/yaw5lmUabehMZw4BXvRUDVD5URCURSOlB3haMVhFk8YzBObn0RrOYQS1OI+dhH+iraliz55yRh0GplrXl0Xdf9F49NYtiefnKaKBwPVnZSCBryFs/AWT0Ufuwq9cwWyoQBTj3cIeBLwlY7BX5Ehrluyl6CmgCd+qOD91QF+d3oCY3slkmyzY9KaMGgMEUZQgAqPn2l/WwbAwYfm19sP4A/6cflduHwuit3FFLuLOVxUya4ssW5TWRmL06KB6rAVSVuCMeUjtNbd4kdx9aYq6wIUX8OaHGcWaxcmnYYqX4AeMSZ+O3dQ459RNyHCNyVrABl3zhlY+jyJLmY93pIJBKt68+O+Aib1jeuSJN4ONdeqqHQafg8U7hNJFJr2SxIqdAVQgHhz/RvEMb3jgJx2eieFBc5jTMr+H9udy7G4Mms2U7sGobOAM508OYH3j9rJUuLIV2Iow0yZYqEUCy7FgB8NPjT40eJHbqJiXKEP2fS2KTxz6TgGPpktNov7fq4ba+WeqaGb9H1FPr454OaKkRZMOpm/rBPR5MEsE/8Z0fjC/8ESP6XuIKOSQ/8/uwr8bMtrQ9oukF5dZX75CAvb8nzMTjU12L61Sxl/DZRmwsEf4Jv74OxnkQi1RpQkKWoCIcAnm7PYdeAg187ow5SM+M46YxWV4wtPuUitNXZdam1eZQCdLEVNuhvcwwnkttt7jU3VMz7VENFO5/ZJdu7/vpTTB5j48ynimvzBThd3hFUE18VH8ya1C3FQiJ0MJYs0G/RPsjP4KaEZD64s48GVZdwxycbNYaajnQU+fjjs4cpRFvQaicIqYWz9ZHdVdHNtwAuZ62D/MoKHfkT2i0VyqyQWbd4PnMTZi87g8o9af8OcYg3d+s7LMHLasJhWv1ZnIwEfBqdzcmAzizSreUz3BAu8D0VMKAK8vuYIq3ccUDVDRaWzUBQoPABVJeBofveMnIoAJp0UdfF3YEoskNfqUxoqHeQW7UfM1VRPPimAM4NV1lO5ac8oirDzp5McHP2+lIuGmvnTKTEAPL2+nId/LG/wdb3NLGjIxUkusfRXMulhk+mXZGdItWbcu6yUe5eVct8MO1eNCt0Lb8n1svaYlytHWdDIEt7qWoilB930jbVGe5tadhb4kICUsGK7d3e4+NXotk3Kx1tC/zdz+xo5e1Tbi2M6i6YWML4NjuVp/0J+rf0Uz3d/ZafLydjhwzrn5FRUVFpMVkkVdpMOqyFsGnfTW7D2GQBu8d3Iz0r7TJaPS9EzOlnPzeOt/OfnCgD+fLKDPywv5epRFv4wQ4y3/vRDKS9uqt+atAZvM8cZx0gkC4UBylFSbBr6JdoZ8rTQjDuWivHA30+J4fyhoeKV9dletuR6uWKkpXrBQhz3c57MwpjGtWprnheTVopo6/2fnytY2MbkpxijuPA+PCuG+78v5fIRx9HCcDOK5AEe+WYv79gkbppgZZzxKJz6Z3hvMaz6D4y6RHSwUlFR6XKOFrmIs+ox6yOX/uYNS+HOOQN45Os9LXo9Ky7+T/sGF2vFYnGZYuaf/vN4I3AKr/eyMU7ex2WDdby2U9zA12jGrROstUUGN39ZzJI9VQ2+R3Pnpg6TgkSQgcpRkm1aesXbWPSsWJ+54QuxCP3EabEs6B+6pq/O9LC/2M+lwy0EgqG59z1FPsanNt5Rb2OOl1ijjC+sq9wflpcyLLFtBlFztVHqv/NieXBlGdeOaXy8czzyly9208OmEXNTfZKEwVZvhYSBzdYdFRWVdqY8F759QHw/+49gSeDIrvUkUooxNjXi0CkZ8ZwzPsjff/a329uPT9XTz6ljXoaRL6sLFmo04/+m2bl2jBVFUbjww0LWHGt43biuobYh1ipDkQkwlIP0sErEW40sfKsEgKs+OgbAS6cozKzJaZEkfsiSyKmSOX+wAZeiB8R55muTSDM3/r4/Z3lItmoodIXCPX79WTFvntW2tuKGas145Qwn/1hdzs3jO7HoWwmCt1J8BfzC+VTTTSQYEF+yBow24WGoc31v6mr/VXA8R4MJpHnz+fvf/8iIs25n3rCUjvt5VFRU2sThwkqSHUYMWg05lTkcLD2IRWfh57Knq421OqqOXkHAVX9uQCNLEffiTTGpbxxOi57hPRxsPSY6iP/5jKH84ZPt/OWsYVwysTeKojD70e/Zn9/w3BR1fVJBQ1iSrTDZagz5aBK/IuCIx1cyhhh5KP1ie/O3D4VW3PteHpDHPWcaGJZmwqgxYtVZ2XjIT5UXTh+VxOECT+1b5JQXY9BDIBjAH/TjCXio8FZQ6avEHXDjD/rZkw194i0EvDHUrAPd9VYO95+dCPjROVdjSPgGSfaiBDXCGFxwMjRQiFiDUSf2f3D9FP713R7unDOw2Z95VyNFUY2guxfeknHoY9ZhTP4E18GbuOTFpaTY4rj/9CGdrhmquVblxKD4CFQVt2hBuyn8QYWxzwsj1O4bUjBoJTLL/Owv9jOjl4EyT+MCcNlwc21yUg2Pz4lhfA89H+2q4pHV5dip5BzND1yi+Y5+R7LgCFgQiVJblT44+owhqe8I9In90dqTQZKICSj8/YnsdvopJUqx8Kvp8Zz+dn69vc+sr+DioWaWH/YwMknHmdXJJbIkcd3Y0KTPkj1V/GG6nURL5AVdURRu/KIYg0bio91iAm31lZGtuxe+HT0NpS4Og8RzC52c/4GoarxkuJk4k8xFw8QCi1Er8e95sSjBIN4X/i5+OkM3atkqSXDS3SLVo/gQfHM/A+c8GnHIoDgtG3J89Z6qAIUVHh76fBf3zB+kmqVUVKJRlt2lqbUuX7A2/eLgzSlIksThEj+Z5QGmpjWtGXdMstWmetfwn3mxjE3R8+a2Sv5bvbBdQ41haFqv0ELA8EQdm69LxqoL3YDO72fkjm/a9KOFIVGOmaumJXLqG/VNX//8qZz5/U2sOOJmXIqBhdW6YjdIXDQstLj83MZKbhpvEwkhFXmiWv7ITyjH1iP5xYSVjGhVbh80k8u3DGeD0h+QeOKj5p1pRqyWB05y1FbC3zjOikErMX+IGX+1RjzeOwlJPn7iXSf20AMSD3MViyz76VOZy73a1/g//zX1jlU1Q0WlE6nIhZLDYI1v9iJhcVWQSS8KzTh0i1jA2F/sp8AVYGIPA2Xe+inb4fztFAe/+640YtvTC2IZZS0lb/nTjChdDogOFl8pE5m16DIMKUNILQ1QtEdcvyf31LP52mRshtA5XzzM0qi5tmVIVGLi8ikpTHmpfkHin34oY1qagR+PepiaZmDRO2JMkGrTMD9sMfzBlWVcMtyMSRd5vfYFFBZ/Ukh6jJY3t4kx14rFkR0x5rxRf3wTjfGpeq4eZeHXn4sF+d9Ps+MLKkzta8ZXrRn/Os4045R0I3/6oYwUqzjn8HT7Gh7xn88UeRsj5EPE/PgXVhsfZXJ/dRFDRaW7kV1axZSHl2I1aNn2wFwADm9bRdqSW5GBoyNu5qu1Exp8/pOnxdYajmp4fqGTwQlanlpXweth81YJZpk5GaJwa26GqdZce0ofY3XqeEgzbhhnbdRc2xIUJFwYuHhyz1pjbTh3fVfCsEQdqzM9zEw3cs57QjP6O3VMTQvN+9z0ZTFzM4zoNJF67PYrXPpRIf3jtLxVrRnfXRapGXObqRlz+hqZm2GsLWD888kOPAGltngwyarh6QVtT/YCMafVtxPmt84YYOKZ9RUMcDa+TBBEorDCw5+XVnF/wMeYMZOh32zY9y18cRdc8r5qmFJR6WL251dwyj+/J9lu5Kf/OwWA7Vml+AIKo9JiGJzSeNLdi1eM46qXQ+lQk+XtvBj7MqZKsbD8nn8Gf/NfRAEOMhwKE1IAYw/mDvTx2k4x/7JogKmeZtw91daoubYlKMi40XP+hF6Merb+OOPu70ro5dCw9piX2X2MXPShOK+RSToywjpinPd+Ye38XTjlniCXflzIkHgdb20XmvG/CyLnVRpKd6/LuYNNDI7X8ecVIobxoVkO/EFIqw4LSXNoeaaFmtFZ2tAQFw0z8+GuKsYkN24wDiJHzk31ioPigyIUwa6OOVRUuoTvHgBvOaSOgbFXsnXrZk5/u5j+Ti3fXCquhZtzvWgkiWGJOno4rUBJgy/3wulOrl5SFLHttTOd9LJreeCHUpYeChmNxiSHrsFzw8y1Fw41s7C/qTasRJIk/jorhlNea33BeThBNFRi4rTx6Ux6q74O/WWDDoczhk25QjMu/zoPCDChjxWLXgLE9Xvmq3nsuym13vMLXQEWf1LEkAQd7+4QmvHW2ZFm2os/ip7UW5erR1nQaiSeWS/GYP+YHYOiUFuU38+pa7FmNEWjmlJVAp4KMFjB3gPMTpHcq6nuEhLwgd8NriKxxuPKBFtSRADa1aOtrDxaxEm9oxezBNDwYmAe98uvcb7vE2a9PoMnLx2nGmxVVLohq/YXcPFzaxjdK4bnrxzEvpJ9HM6X+Db7RTYXbkAj6Sg/ujiqsRbgsQtGcctbG2sf67UyL185nmS7kbve38K6w6F5q1mDEnFaxLVk4YiUWnPtZZPTWTgildjqfZIk8a8LR7PwPytb/gPVmmwno49dXW2yLUCT9DV67Saufy8bGEG4Ofez9WDSyezLLWVorzLufk9c92Njs6lwh+bdF/z7J/5xmSGiwkAv6ylzaXhkiYe+iXq+216JUefhroXhHdkV/vLNUiwZXyDrhXb4Xel4ss8m6I2cwzpnopmcEg8/7hadkx46ezhmvaY2zXVIqp1nLhvX8s+lmUhGI32X/K/2+/bgxln9uOWtjSwcEakB3rx56Gzb0Biz0Do2EnAnkVNq4vrXN/DUpWM6VTNUc63K8U9VCZQeBlOMaMXaToSniOa5AqTZtdzxTQlrjnm5YZyVkxu4Gazhjsl2NLLEW9sqOX+ImXOHmBmZJC72Nw2u4uLSt7Ef+gptsHqAodFB2kSUPicz6ot0yjHzbL9Y+mREpmfoNRK7bkjh2wNubvqymLZSKjmwWh3sKYo+KXTSq/UHMbsK6htAvz7g5tLhIfNUbkWAGa/k4glEHnegpHWVjqUehQk9Qp/5yCQ95w+p33ZXkmUMfbpXS/ha9GaR7PHRryF3G46tr7DuV1dhqF74eWxOLGe9cYSiQKQI5RCPVXFjkrw8+8NBJvaJP+HbfauotIhukFqbWxkyQlX5Fcw6iZu+LGZrno/fTbExKL7xSefrxlo5UuZnyR43lwwzc/ZgM0MTxHPunGzn1L5GjpQGOL1OopI9LPFQI1MvAdGkk9n+62SW7Kni7qWRRqzWEDTFgt5GZllR1P3RJr4OhV337VQyVt5D0fKDOCo2QNGB2n0SkK04+Twwkc8CE9mo9OP9AYls2Ny8RYtw9hf7I4zH41L1zEwX11ZNd9WIJki0aFh7dRJWvQT596B8ejsXa5fxaXAyq4KhtMGjJGFUjmCU/KpmqKh0NL4qKNgr7uO1zZ9E2FMUupcOKgqyJHHNkkIOlAR4cKYDZxPtSS8YauH7wx6WHfJw+UgL5w7Q0//Yh/D9qyT73ShIZCefTOr0Kzgttnft86z60IyOSSfVa4NqN8hsvS6Z17dW8rdVbTfZGhwJlCkWStwlUfdHM79mVQTqbdtV6Gd0WPeLfUU+Zr8unrsqM5Rmku9q3JTcENvzfczqE/r/m5pmYEi1BnfbcUUT9I7R8tNVScRU/x+/dVY8572bRWUwdD/iR8u13jv40nA3GXI2e75/mkDGA6pmqKh0M9YdEvM+FZ7qe2p3Kbr3FyPjISfpJFanXQNrtzX4/Pn9TZy0w8W6LC9XjbJwzmAz6TFiOvjBmTHM72fC7VciroMAxrAZY6teihh3gOjwtPnaZP69tpwX2sFka3Umc7TKBNSfbwKY/5a47tcYlADyXQG8dWQj3xUk1RYq/N6W56st+luXHdKMMk/rNCOrPMCMsPnAef2MJETpdtUedNb81pAEHauuTCKu2tTw0XnxXPB+Ll4l8uc6SA8GKYfQSfDcuhL+2/cwmpn/Bwe+h6KDYmHd0rZkLhUVlbaxfLe43uWUCdOSPxDkomd/oszt54XF48gsbtzgOmtQEuN6x7I/t5jnUz9lbNabUAnE9IZ5D5FYYOMxdwnT+5rB4Kg11If7Uy16CW2dFp09bFrW/yqJB1eU1QZwtAVnYio7ygxE04xyr8Lp1WEe4ZpR4ArSs07Yn9uvYAorUF+f7a0t4NicG3rthrrNNUWlV2FKWkgzzh1srlcA0lK6eu1jfKqBlVckklQdsvLOOXFc8EF949h+ejJYOYhWUsTc1OLxaDTV42ejHfTHUcK7isqJwNGfYdMb4vv5/4DSo3y6LRuQ2FskbqirfEEu/KCQKr/CW2fHcay8/vxMOLPSDQyJ15JTEeTKURbOH2Imqbqj0IuL4vj2gBurXmJSz8i19PCroE4jEWuKvC5mxGpZe3USd3xTwoojHtpKcmoaP+XpiKYZ+4r9nF193f/TDyHNKKwKogkrsPYHRZhUeEHGj0c9XFJtnN2WH3rtqlZqhiTB5B56nlkvHp8XZf27vYmqKQGvMMvqrZA6EqxJomtwQ9hTwesS1/eSw2BNrD1+ZrqRFVck1naaevWMOC7/JFIz3g2czG3aD0iXczlF3sgDS8ycOiS5S9p9q6ioNMz760TX7Y1HSthfsp9yd5CHv38fXdxPyJKGMaZfs9TVcAji/GHJPJ5gocLtZ/GUdC4Yn0a8VVwr3r9+Cl9szSbRbmRs78Y7x9UYa2sY1sPB6ntmcc2r69h2rKyBZzVC0BhhsjUlrKDMn4epx9sE4r/DVzwJX+loCJrZctTDlqP1dUkvxRFrBBAdv4sqFJKtyRHHrNnn4k8fifXzg9Wa4fYpuH1BIIjWtgN93HI0JvE5B/1WvPlz8JWMo176LqCV/czon8KPu8XxF03o1fKfvQ1Isoyhf/92fc1FI1MZnRZDaozwQTy/eBy/emUdSsCKt/BkDIlfYkj4msr9vwFNOVLAwQNLdnSqZqjmWpXjm2AQig+L6ihz+07gBsLm2T/YWcUHO10cLRODiSfXVfDkuooGnikwaiX+eJKDe6baa9s2UFUMG16DXUtwBqpvtmP7wNAzRdqD3oIElH8hLr79nNHNWEatxMIBJj7fV0WxO8jqzIZbZDT5cyrUJtI2l4Kq+osQ4Z/XJ7td3PpVSdTntnYyqobpvQysy/Iyp2/7VEF0OjFpcNJd8O39sOlN4nuOg9TRAJRWVJIUyKaIPvWelo2TPuRQUOFhe1YpI3p2nYlQRaXbUZYtWtPEpHXZKWjD7m1f2lTJ29tDmtEcg5JOFu1OHzw51OonnJFJekYm1X+eIWy9M7wddjgWvcwFQ818ud+NLBFRNd5SCqrg8k+iG2vro9BLyqNnwTFYdZBP9esZIh1GlhQ4Un2IJFNgG8hLhcNZHhzFDqUXSthAoTiK3jSHmk9wTLKOfcV+JqR2oyTzNlCbEJ86msLepxF/+HP+qn2Bud6/4Qlr155HLGnkq5qhotLRFB9qVfcMY9h1/un1Fbyz3cXhUqEZ9y5rXiHEE6fF4g2AofwILH8I8neJHUnDkKbcRGpC/fbgNn3o+hrTgIHXZpC5doyVZYc82A0S3x5svWYcLFX4zdclLXpObkWgXouo8Mdvbqvk/xooFilspWbEGmX0GomBcVqKqoIRiVbHM8lh9wV+r4vkYC77iVw0ySGOB3yX8Zj+aeYGfuDg1hVkjJze2aeqoqLSCDUt3QCeWr6fvj/8hrnkcSSYwMLDl1J2uGFjbQ0vL3LiC4pi7bqEm37CiQ8zjIYXZ4TjMMr8bqqdddle4k0y37VhnLGzSIkwQTWHo2UBPIFIzah5rCgKr2yp5I/fR3/NwlYWZJh0Ek6jTC+7hiA0WRBzvBBuSNYEqkhR8jhM/fSPYmwkUMrRSpm9xwoZNCAOLv0Iek1ofNFdRUWlUzCGdXt47Js9vP7TYcrcojjj6lfWNfS0CN69qBe8fx9y5s9iw5jLYcwVUJnPSQmVYEkVLaDD6O0IPa5rrK0hzqzhr7Mc7Cr0kWzVsKwNmrEhT2FDXuNrM3U5VOJncJ3C93KvgkknNOOFjZU8uDK6ZpR7W2+UyojVkmiRiTHIbTbWdhd62kPjJZPiJpV8skiod1wZFmKpCM1N9XBCaaYoyEgeVu94FRWVDiIYhC9+K74fdQnEpsOxDRgNBkCsLf91ZSlvbXNR5RfXu5rU78aQJIklFyagEP3aP7uBNdzBCY2HkICYA//vabGc+14BTpPMmmOtXwNflRVkVZar6QPDOFjix2GIPM+aQJVAUOGZDRX8vYE1n9augfuDMKGHHodBok9MF8xLKYqY4/S7wdEL4vqCwdb080AESyUNEfcHRQfAmlAbQpAWphkxGjeJFJFHKIG3EhNvBk7h19olXKtdwnmlY1l7sIjJGWrRnopKd8IQNjf15LdF/JS9Cl3S9wBUHjubpaWNr49oNTJf/2YGkiRFNUKeNjx6+uiYJsy2ACkOEy9dMYELnlmN2aBpF5OtPu4HNIZ8NMlLMCR+gb98OL7S0QQq+1LXYplV7CPeFrmtJlDFF1B4b00pb/xYUu8tJU0F6ws3YOm7FNkgvFpKUIe3aDrewpMg2PAcS5wpgQvGDObxr7IZl970Z3S8kOYMFZZY9KHP1Fs0FV3samRdKXrnGnwVGShBM9mldKpmnBirRiq/XCrzoewYmNu/5bEvbBH38TUtT26qMTwZtBIEA7Dzf/Dz88IABpA8AsYuFi046rQf+uqSBPIrg/RtYmH3yfniBjT931kR2/90koP7vg8tOk9N0/Pj0dYPPuqy4oiHbXmRr3f/96VszfPyf9PsDRprAZ5c17YUrFfOcOINRJoSwlF8fgre+BiA+EvORNJ1w8tc35Ng4HzY/Tks+yuc8wIY7RS5vMhEH3hVYsaj6DBIPopc7fd/qaJy3OOtFBOzppguPQ1/2NrsP1a3/DpXU/VsaOElS5IkPr0wgQpvMGS8bOC4l88QN5d1NeOm8Vb++3NoUeKsgaYWJokoxFFGHymbvnI2/aQshkkHGSYfwi65IA/Ig2HVazwHgsmsCQ5G7jGKGVOnMfnNhhdUnmilZtSYnd87Nx5fMKQZx4VGNJOdaRcw+NBK0uVcfqP9gL/5L6rdV4qNJKUYveRXNUNFpaOoLBBFfpa4FnfPCF9TbWgyvikkwLDrI1jzlGjFprfA5JtgwLwGWzIbtBIfnx9PQAGrvuFz1sgS754bTyCokPHfyPbcFwwx886O0KLEFSMtvLy5fdqCAzy3sZKLhkWmGF3xSREXDDVz0TBzg8ZaaP04Q6m+/f7sogQCwVCRy4mkGWKcEd1I9lFwBrP9G1igXUuPTY/DoFGdem4qKiqNE26U2vn1i1yv/wG/IvMb342U0bzUN0mS0LcwXDXGKPPeuXEYNBJyA7oCwrD7yQUJlLqDjKzTnvvk3gaWHw7d618z2sJzG9tPMx79qZz5/SIX7c95t4ALh5mZ3cfYoLEWWq8ZvoCCRpb47rJEFOjQdIyu0qEilxdNA5qRSxyxSjlaKUiuYmNQebYoGFeNtSoq3QKjNnSx/9d3e1v+AgdXIL+3GFyFIpl2/j8gplf1+kucMM9Eoaddy5tnxTVYwFeDSSfzxcWJ7CvysexQZBeLoQk6tocl//1qtIXn21EzHvihrF564qK387l0hIVhCboGjbVAbYvullJT2LJicVK7dYfobmOUIpcXLdHTLXNxYldcaKSgmJuSJNFevIHOJioqKh3EptchayMY7HDS3dXF2UGMBjM15tpnN7Tselsz196ae+GBcTpePcPZYFBIDQ6DzDeXJrLqqIeLP4o0+8abZQrCiuUWj7Dwypb204w7vynhfxdE+g7mv5nPpcMtJFo1jc7ltfY8/EEFs05mzdXJ6Dqpfq9WU4IB4k+fiGSNhZSRYEsFuYUnodFBwiBAEgZbSxzoIrswFrm86KJoxsv+OVyt+Zzx8h6GSQfIKx/V6p9JRUWlYzCGJUytOLgLU6+PAfDkz8ZfOrbR5ybbxbyNthU3xOPTnbx4xTj6xFsbPS7BZmDpnSfzyaZj3Pr2ptrteo2MNxA5v3HRhF68tfYIUQkz2eocG9HFrEFjzBHfOzaiBIz4Kwbhr+xPwNUHxRfLgx/n8/eLIpNqb3kli/mjRIFCyFirIOmK0Fr3oLXuQmPZy9qSILIBlIARb/FkfEVTUQKN/6wABtlCjFnPxvtORd8FbegUr5eCZ54FIP66a5H07R9wlVfuDntDHZ68uZh6vIs+bhm+siGgKQF/QuRxHczxuzqkohLwiRs0WdMhk7i+YOuqy84YaGJamiHUHqLoACx7CAqrJ7Ti+sOkX0c11dYwME7HwBYY7PvGajlQHGq7PTA+8k97ck8DY5L1/Ofn1k0ERWPh2/XTbt/fWYVZ1/hgamNO9BZ/zUWWpIj2hHVR/H4KXv0QgLgLFnb5BFODTLkJcrYIU+CP/4JT/oDTrEeSIFkpIIf6hvEDpDCYIzjNJ0YCo4pKu1CWDd6KLk2tBbHI2hpOH2Di1DYmcQ9LjFLtHfSDu0xUGvvd4PeKbbLMSeZiCqpAQSKAzCRTDF9JZUjV5v65sVb6D/Lzye4q9PixSlVYqcKCG6tURbxUSiLFJEklJEnFpEl5OKTo1d8eRcsupRfblb6sCgxmbXAQeVRX0R2Gy+yNT6K1VjN81WMljSxFLGAcNxrRDBz2GJ70n859uje4RvMZSwKT2aGk1+4/TDL9yVQ1Q0WlIwj4oWg/oICu5W3afK0Ly2NBfyOLBpjA54IfHoH9S8WOtIkw406w1E8Lqsuo5OZfEzSyhFkn1aZuaGUYmazjnR2hY07qbSDfFeCzve03iXH2e5EL7RU+hRc2VeJtQmtbrxk1P58UkUR/ImmG06zHIPmJV4opoH41+/3+xYyW95PqKYSVj8O033T6OaqoqERHqu7JkEoBD+peBOC/gTPZoAxo9Hnz+xk5v42tRMenNn+uzVIn3TbRIjM+VR9hrl000MTGHB/rstuv+Gtxna4aRe4gT66roMTduNhuaKVm1BRVdkb6YIt0SFHAXwV+jxj3IYFGC1pTi+dMnWY9JjzEUkYx9nr7s0igF7nEWi2g8UHhfmG6Uw22KipdTnjaeUuYPzyZ2xI3wWuLIegToSCz/yS+d5eITh1NFBQ2lIQejfDkU4CBcVom99RHmGsvGW7hy/1uMssab0veEm74PFIzciqDPLK6nAuHNq6XrdUtf/U4I1qHqtbS3cYoTrMeGy7sVNYr+vGjJQcnPSgIm5s6MdJ7VVSOG9yl8O0D4vsZd4nHriJw9MCka1maaw3z+xm5enTTxp/GmNG7+eshQ+usfUzqoSfVpuHDXaFwkOvHWXljW2VEAEpbueObkojHh0oDPLiyjLMGmqI/oZqfs1qrGeLfhoKlOoIITbn4bKQeQ0QRRGvRaCFxkPBtFOwBe2TavdOsJ4ZySrBSSehzzCGOJcHJnK1Zya+1S4izXdz6c1BRUekQFEnM7UjaEow93kaSFHwlY/AWnNLo8xYMT+H6kzPa9N6zBkVp69oAk/tGGqxOGZyIxx9k6a682m13zxvUsLm2hqARX/FkfMWTkI2Z6GLWobVtR9ZWoHNsQufYJA7z2Qh6k/jbqkT0TgdK0IQSMHDUo/DMWj/pSV4MSQXI+gJk4zFkbWQBRqCqJ77SsfhKxzSaVFsXf1CIRmvHf21F8fspeOIJAOKuvqpDzLWJtsh7BX/ZKAJxK9AYs9E71+AtmoQiu+sd15Ecv6tDKiplWeAqAFv0mPC24m/mvM1vJ9t4ZUsleZXiIvavudWLlUoQtn0Aa5+tTpOywvhfweDT67VOaiufX5RAVnmAVzZXcuYgE6OSdNw7zV5bcT3AqWVOhokxKXqWHXKzIdvHtvy2mVwbYmte2173/CFm3t3RukHdcYXODDPvhU9ugP3fQb9TGJo2hXirAamijBylvrk2gBaHxcTQVLW9t4oKAF4XlB4FY9f/TTTXKHXHJBtPrKvAXd1i6T/z2tiuwe+B3O2igKPwAJQcFqnuVcVCh6LwCkD4Pfpa+Cb88Sbxzw0tWBsNKhJZxHEgmMIBJYUdSm+2BfuwV+mJr5HbzbZqxtmDTBETab8khqY6eNQ8ipXuTUzTbOdB3Yuc7X2AmoUKD3piLEZVM1RUOoKyY1CeB/bkpo+Ngr+ZRXy3TrDyr7Wh4rgnTnOK9/7491B8CCSNKNobdm6DRXtt5eerk8gsD/DalkoWj7DQN1ZLXmWwtrNHT5uGJ05zMi+jio053og2gq2lxB39+W0dv8zNMPLV/vom4EA7Lr50V4amOqrHGcUUKPXvPQqI5VndxdwffAJp/3eQOgrSZ3T+iaqoqNTDFwwiEeSfuqexSy42BPvxH/9Z9Y67cZyVJ9aFNKOm01FnoZUltlyXTGZZgDe3VXLNaCupNg1lniDPVCdhJZo1vH9ePB/sdLGzwNcuiYTHyqNP3u1oo2ZM72VgxZH6HTb8Sts0rt3xu6GqRBT+6E3CTGuMEfu8lcJA4feA0SZSKJtxvzA01UGCzYBcUUCxUt9cW4YFp8UgxhlSEEqPia+4vu37s6moqHQIeo3MlVPTeeaHA9VbFJ7suRSWPSgeDlwAE38tTLXmWLGm0c4YtRKbr03mSJmfd7a7uGGcjTiTTLlHqe2SkWyRWXlFEm9tq+RQib9WS9rCwZLomrG7sG2aMTpZF7XQrz1NXt2VGs2QynPZRn0dKMbOCEu5OjelotJVrHxMrKPH9YdBCyB/J1gTQZIxNKNYzGmUOWOQiZc2ha7BnT3OcBhkNl6TzKFSPx/sdHHrRBtWnUS5V+GbA2KOJ9Eis/fGFF7b4iKrIsDTrUwcD2dvkT/q9oMl0bc3l0FxWnYV1n+N5s4VtiuesNT2xMFtM9bWIGsgLgM8pVCRB/aQh6NGMyjPYTt9Ip72vH8+Z2tWcprmZ3BWAp3T4ltFRaVpKn2VuAKlIPkx9XwDWVtJwJ2KO+cswgunUmMMzB6czKurD9due+KSMZ16rol2I+vvnc3Bgko+2ZTFnXMGIsuw+MW1bDhSAoDDrOPgQ/N5YeVBSlw+/rtsXyOvKBF0p+HJScOTcwYa0xG0th1ozAeFWVZXjqwrx8s+DFHq9XIBfZhsKopMoKoXgYpB+MsHE/Q2bhyOs0kUltfXhy7RjE5mQh8nKQ4j2aU16zkyntz5mHu/gC72J7yF00iMrWR8eht9Fi1ANdeqHJ94XVB0UEzutLNRtYbmJNd+cF48Y1P0nD3IzOSXchlckxjrLoOlf4bMn8XjXpNEVaC5YwYdRq1E31gtD5wcmqT41RgrA+K0bM3z1aYizkw3MjPdyJ9/KO0wc+32Nr5uFxVYdA2Jg2D4+bDlbVj5KJrzRnDtjD489PkuBnCUPdRP4nQm9Wi3NlIqKsc9FbngKQdHz64+k2Yl1351SQID43Qs6G9i1mt5jE9tZSWXqwgOLIdDKyF3qyjgiIok0oO0BtAahQmLoEg1UgLV/warTbgS3oBCQBFt+mqRteR4DGR7jVQoRioxUaTYyFViySWWXCWWbCWOg0oyHlr+87RVi37J10ONDNfO6MMzny9grLyXMfI+zpR/5OPgtNpj+vRK+0V/RioqHYK3UoxDDFaQWzec9jWjiO+HxYn0cmg5ta+JhW/nMyvdAHk74Mt7hFHGHA+z74fk4a06h+Zi0csMjJN5cGZM7bbfTLTR36mlwBWkf5xIEDl9gInTB5jYnu9jzbH2SyQMp60FGQ1dDlvbseR4okYzHvp8F33J4gCp9Y6xDZiGZK6A9S/BmqchcWgXnKmKikpdfP4gl2i+Y7JmB5WKgd/4biRA5MTJqiuTSLVpOKm3gfM/KOT0AY2nKXUUdoPMkIRIzbhnmoNB8To8foWk6tav5wwWKw5LD3kiujC1J1vaqhkNeA2aW4jf4XgrhalWowNbMliTRdGpzhQy0AaD4C2HykIoOSI6J5mdoLc0+tLhmtGbbA5TP9TgpGG9q8cZsrgnKjsGzj4dVuyjoqLSPHzNqBpbefdMEm1GJvZ1ctXLP/NK2mew7E2xc+wVMHA++Crqpc21Nw6jzHCjnuGJobmkv82OYXSyDoNWqp2bumiYuGa9sdVFha9j7ts353aQZvzCxhk9lTwySax3zMIx6erclIpKV1B6DH56Snx/8u9EkbbOXNttoDmN+L69LAGnScPoZD23fFnMNaMbv4/sKGJNMrEmPaPDujE9t9DJK5sriTfLyNX3oJePFOfXHubahmirZjR0u9ypBRmKApV5okCvhvbUfI1OGLo95aJYp7r4L1wzUpUCssI6uO5Q0vkxMJSpmu3w0xNw2t/a73xUVFRaTSAY4FDpIRS8GJI+RWM6ihIwUZV5KSiRyeJf/uYk7EYdg5Lt/N9HW7ltduMdlzqKOKuBOKuBcekhX9aHN0zluR8O0Cde6IQkSfxquigMa9xcG45MoCqdQFW6eCh5kY3ZyPp88aUtQ9K4QXYDGlBkCJgJ+BwovlgC7h4EPcn1PrfG0EoyUH8iyv8LSAvRyBL3nz6E61/fQM0tS8DVH39lP7SWfejjl3HGiAWUeouJM3VOQYZqrlU5PinNFBVVHWSoKvMEeWtbw+mp5w42ceUoK0MTxMUvxaZh4zXJog1e0QH46l4ozwKNASbfAIMXdckE84zexqjtNWb1MfLCprZXe0fD28aFhqbavZ5wjLsCDv0gkpjXPMuU6bdzz/xBPPvDQbTlB6jAzBFCqWi+VpjXVFROSHxusUBotHX5Al6JO8g72xvWjIuGmrlilIWB1QakvrFa1v8qCbuhBTPLigLH1sPW9yFzbWQqrSVeGGDiMiC2D9iSRFtOY0yLJkUaurps3FfF9Z8XN/9cW0BbJ43kX3g7uSkZ8TB/Ap8uO4Xzgl/we90bfO0Zhwuh/ZLamlVFpf0pOdKmcUiBK8DbjWjGpcPNXDnKSi+HGKoPS9Sx7ldJxOaugiUPQsAD8QNg3kPiWt9FLOgf3bh11kBTh5lr26oZdVuWt9frdhgBrygq9VcJg1QNsizMU/qWGbynZMTXjjOM5QcowUYWCbX7DUYzjL4EjqyC/N3w05NiHKuiotJl5Ja5+eanjdynfRuAv/sv5IgSSrW4YqSFK0daSLWJe/4JPQz8/Ksk4kzdy8Fy1qDo7bYvHGrmryvLou5rK231M9ka1IwunrMK+qEiX1z/4zJENy9jA4m0siz2GR0iMarkKBTtF/piajzZI1wzLOUHKMJBTlh6lM0SlmolycAvbC5PRaUbcqykivfXZza4/5rpfVg8Jb22beasgYlsn7YKy7pqY+3U30DP8eI+02DrhDOOzoXDohu3Fo+0RCS0tydtvbTHNDC/123HGe1MuGbYyw9QQAx5hMwMZkv7px+rqKg0g+V/FV0O0iZBTB+oyAFHDwAOl/j5cGfDc1PXj7WyeKQFp0mMMxYNMDGph54Ec/caZyweGV0zLh9h5tUtHdMhta13vc4GxmqdVvitKFCeDQY7ONM77n3MTogfCNmbQWcRhlsiNSOm/AC5OCkkBoDnA/OFuXbj6zDz92Cs30VDRUWlc8muzGZTZhY/Ht2GPvYnFEWi6tiFKL7Qvd5V01O4fsZQ7Ebxd37xxF7MHpwokqq7EdfMiN5tZ9HIVP63OavlL6joCVb1JljVu41n1jDJDiu5ZaX1tjcn8OtEYN6wFJ66dAwPLNlRnWAbwJN/KlrLPnQx69Dr5pBZkYnD4EDbyiCclqCaa1WOP6pKRNtrc2yHGKo25ng5692CBvcvGmDikVPrT0LHmmQ4vAq++5MYsNiSYc5fxGR3N2NqmoGn5sc22yz1r7kx3PpVSceeVDV9Y7TMSjdg1cvkVASIN8ssPeTh4VknaOsgrRFm/BY+vQ12/g/6zWZKxggm9olne1YpG7I9/HF1qHrQ7VfYkllKkctLnD7IkHgNv6SwXxWVWipyRXJfF6fWrjrq4eKPChvcf8VIC388qf71K87czL9cRYHDP4oUucL9oe2JgyFjFqRNBEdahxqM52UYeWxODLd9XdKs41uiL21lbKqeo2V+kiwaMssDxJlklh/28K+5MZ3y/t2BKRnxBNJ+g+fdjSS4crhB+wmP+C8AwO2nVjPidX4GJxlVzVBRaQuuImGuNce16rq79KCbq5YUNbj/xnFWfjul/sRxfNYyWPZXUViRNlEk1uqiG5W6mvOHivO6e2n9SZ9oPHFaLDd+0TmacVo/E4dLA/RyaDhcEsBpkllxxMMzCzqvdVCTKAp4K0QnFFkjjA3WJNCbRQK9EgBPJVQVivZ6kiRSjDXNq3ifkhFfO85YesjN4xtClZFVPoUtWZW4069jXMFdSIV7kcqOdexii4qKSoN8tiWbG99czwu6R7Bpqlgf7M9rgVNr9/92so0bx9c3PyU0d5zRDbhqlAWjRuK+75unGf+eF8stX3aOZlw5ykpmWYABcTr2F/uINWr48aiHR06N6ZT3j4qnXMyJ2pJEElRLumPpTJAwQKTW5u2AynywJDT6lHDN+GK/m6c2hzSjxB2sHWckaN0MSolRxxkqKl3Ie+uO8tv3tzS4/4FFQ1k8JT1y47K/YFn3hPh+yq3QY6wo3u6mRbq3TrQRY5T5SzOLMloyj9VWbptkI98VZEiCjj2FPmKMMmuOebl/xgm6nhGFcM34aLebl7aHNKO4KkwzNFUM6uFUNUNFpaPJ3QGbqosnJt0gzJR2EeLz6ubKRu+//zE7hvOG1J9zSrQcP3+5/zfNQZJFwz9Wlzfr+L/PjuGub0s69qSq+cN0B3d+U8LwJB27C3xY9TIbc7zcPqmTjKQVOcJYmzICgs1PT2wV9lQozwFXgfBMVBOuGW/tqOKtXaIaZXlwJOXGHtjcx9i25HHKx9zIhD5ONA1FxKuoqHQo5d5y/rtsN++sLcDc511kwFs4g0DlwNpj/nhWTxZPGIFUZ60k0V4//K+78vA5w+kRa+Kp5fubPhi48VQnT3zT8BpPazEbwOWJ3PbIuSO59e1NjOoVw67sMsx6LVsyS7i2AaPwici8YSmcOiSZtQeLeOnHg3y9Q4O/YgBa6x5+LliG5YCBTQcVBsSldbhmqOZaleMLRRFJCwGPmOzpAJq6gQ40VD22+wv44R9i0Tt1tFj0rm510B05rZ+Jbb82cP3nxaw44mn02IX9TQ2aa6el6Vl5tHXpVGcMNPHJ7qqIbd4AvLgoMoXLH1TQnsg3z6mjYdAC2PUZ/PgvOPsZNLKWET0d9IoP8sfVObWHLj/s4dDhg1glNwa89LQEWXB6P+aM7NOFP4CKSifj9wpzk97S5am1HWoIqkmNy94sHmuNoj3fsLM71VQsSRJnDTJzSh8jV/2viHXZjV/zT+7d8ELMrHQDSw81rjkNcclwM29sjaw4DwQV3jw78n7ghNeMKGj0BjTTboSv/8A1ms95OzCTTCWRd3e4WLfjGGbJgwk3KRaZBYsymDui4yopVVROWIJBUeAX9AujYytozFgLYNZFuXbt/RqWPyzGGAPmiqKsTqjCbS2yJHHhMAvz+pm46MMCdhY03u57Qo+GuzLM6Wvk6wPuVp3HdWMsPLMhslOHosB753ZjzfBWgqtYJNLGDwBrAhgcInmwLgE/VBWJsXF5tvidbCKFsAaNDCN6OjCbzTy+Ib92+xPrKvhyXSYmyct0+SyK5HRmZWqY2wLvloqKSvtx45sbWCSv5hTNRjyKlt/5riFI6Hpg03ev1KjWoJUlLh9pYUF/I+e8V8Ch0sZbIY1MbHjxd0F/I5/tbZ1m3DHJxj9/ilx418nw8QWR5tMu1YyybLDaIGkYxKQ1u6iiHo4e4rm524V+2FIaPbxGMyS9iac2h4II/vpjGf04ilHyYcOF02Lg9DP6MW94auvOS0VFpU00ZqwFsBrqjB/WvyzWMQAm3SgSa+3J3XqcoddIXDPGyhkDTcx/K58CV+OxsP2dDV8nzx5k4sNdVQ3ub4z7Ztj50w+RBt94s4b/XdiNNKOLqNGMSoy8tD0URHDv8lL6cwSD5MdOJXaLmTPP7Me8YY1rkIqKShv49o9iHmngaWCwiuLd6mt8U4VtDXVwOJ4waiVuHG/jnMFmZr+WR4Wv8YS/VGvDxuGLhpp5q5EOVI0RzbSbHqPl04u6SDPKc0SKbNJQ0d3C1THpvrXIGohNh8oC8FWJgr9qajQj12vgrV1ivlRB5i/lp/Gw7nnitr3IGetHkeiwcP/pQ1TNUFHpZALBAIfLDvP2KhemtPeQtS4C7hS8+adGHJcek1TPWHu8YdZr+d28QVwysRfT/74MpYlQWHew4WK/08fYWLKheYUddbnxVCf/+DRy/ah/ko3Pb50esc0fCKLVHP9zgi1BI0tMzogjs9jF1ztyRXqtdQ87Slezfkcqiv8IQXcvUuy2DtWMX9anrnL84yqE0kyRztNBFLsbn5iJmrK95V34/m/Vi97zYP4/urWxtgarXub5hU5ePzOu0baBjTn8rx9nY/cNKczuU99M1S9WDNZum2jj/XMjDbPfXprA43NiIrbZ9BIXDq1vVvhFTERNuEYkUxXth52f1m6OMcos7B9Z3XOIVCoV8XkXV/q45a1NfLktu1NPV0WlS6nMF4k9xq5PgGhKM4JN3YVHw++BNc/Ax9cLY61GD6MugYvfham3dFlar90g8/pZcbx+ZuNtyI3ahq/Zt0ywseP6ZCZFMVP1iRGTWPfNsPPamZFOnlVXJvHgyZH/306jzGn96rcl/0VoRjR6T4MeYzFIPm7Xvl+7+QA9cFVrRlGll5vfVDVDRaVVVOZDWVaHFfhBlNZye76EZQ+JMcagBXDS77r1gnc4MUaZD86L542zGteMhlqogkhl3HJdMsOjmKl62oVm/P2UGJ6tkzy74Zok7p4amfiRZJGZklZfe7qFZgT9UJYDXpcw1faaINIFTbHRjbUAGi1YEyF1lPhSEK+hNL/3bD+nrl5Lx/30pErRsyE4gEyXlhvf3KhqhopKFxFLGffrXgHgCf+Z7FMixwD64yc4qknizBqWXJjQtGY0Mm/1+2kONl6TTLqj/geTbBHPe+K02HrzUJuuTebmCZEJwL3sGgbG1deeLtUMa6Iwv8X1bb2xNvy1UkaC1iwWupvBsIT677mPNNyK2F5Y6eX6NzaomqGi0k2xhJtr930Ln94uvh99KfSeDPaU42ackWjR8N2libzZhGY4DA1fs++f4WDt1UlRj6lZI3lpkZO/1umkt+naZK4aZY3YNsCpjdomvVuMM7qIiVHm/PbSC48ifseKK71c/7qqGSoqHcbBFbD3K3FdH3mxMDW2YB3FcgKYa2tItmr44YpE3jmnCc0wNvwz/+lkB6uuTIq6z16tI++eE8e90yPnoTZdm8z5dRKARybpMEQZx3WKZlQWiPCW5OEt64DRVizxojiwKnrgwMz0SG/BR4FpFCh2UqRi5stryCl1q5qhotIF5Lpyya3MRRe7Gq11L0pQi/vYRdTN7rSbumfXi9bQM9bMmv87hQ+un9zocUMS0hrcd90sJ89f0yPqPkP12vnfL47jnImRn9t3d43l11MmRGyb3De6dv3SjLXhLBolCrqD7jT85YOQpCA62z6QfEja4g7XjF/uJ69y/BEMQPEhkJQObU+ka+Kvwl83uXbDayJZEGDEBcfVojeIC/m0XoZGzVCNYTdIGLRSvYklgC8vSWDpZYncMsHKuFQDj4a1z+sTo0WSJM4eJIxRfz7ZwfprkklqpEKwOUh6PelP/pn0J/+MpG84CavbYYyBcVeJ79e9AO6S2l2n9KkfnX8QIcw1v40PLNnRcKqyisqJRMAvUmt1RlH52sVomrh0+prvcREU7ocPr4XNbwmDTMYsuOB1YcA3dlJroEYwVmuGthGtbKxKMc4kY9bJXDrcUm/f15cksuzyRK4aZWV6LyN3Tw0tcqfaNEiSxKl9xfXwsTkx/HR1EjHGlt3KHrca0RwkCSZeB8CZ8o8Mko7U7jqgaoaKStsI+KH4oLjH13TctcMXXsV34HtY/jdAgcGnw/Q7QDq+hu9mnczUtMbHbbpGhNRpkrEbZM4cGFlIkWKV+e7SRJZfnsj5Q83MyTBxxUhL2POEZkzuKf6vnl/oZOUVSZibGujVoVM0w+cSaYSWOOg5DhIHimT+5iJrRNFNj9Fisaw8u0UG2zMG1i9S2Y8w8KmaoaLStfxB9zpxUjm7gmk8FVhUb7//BPuztBma1gxHIwUZTpNMrElmXp3iu3EpepYvTuKHxYks6G8SiYf9QnMsNeOJGvPo22fH8d1libWLHl2JFHCR/o9bSH/6YaReY8EU034vbnZC4iAx1+ppOlVFkiTOGFBfM/YhFpVUzVBR6d7UBmcUH4L3rwIlAP3nQL85IsG6G8yvtQSHUWZKE5qR5mh4fcZukEi0aOrNuS/oZ2TFFYmsuCKRmelGLhxqZlxKaBxQoxm9qws5Pr0wns8vTujUdtXHw7yWHDYWC2e/OjelotLxKAp8c5/4fvh54vpuDaWkNudvrqVzJ90dp0nDxB6Na0a0QrIadBqJVJuG6XV057LhZlZdmcSqK5OY0MPAlSMtteEhENIMZ/W/316awIfnxXdNwqO7VMwVJQ6JMNZKBgPp771L+nvvIhk6yHchSaJ7hqwTRu86aGWJ/s6QZnvQ85pfJGNep12CUq0aqmaoqHQelb5KDpcdpsBVhiHxCwA8eQsIehPrHVuvQ8ZxTqLNyNjejRcgnD1ycIP7cl25aPTF9HBGaunCsVoeuVzPPy83khrn5+LJcZjDOlJlOJPRyJratZIf757FG7+a2IafpGPpFP2IgkGrwW4Uv3OeAqEVWvsmCFqQ9cUosuho1VGacWLdIamc2FTkQUVuh6bWQtMVYv7wtcrNbwsjJMD4a2DS9V3eoryjWBRlEh3EAgYI41M4H50fj1aW6BurrR0snNbPSIpVZnovQ+2k0z9mx/DtpQlcOtyMvimXWjOQNDKmQRmYBmUgHW+VG4NPB2eGWNj4+YXazUeLorepKlNE1aMCZJe6WXuw8VbDKionBK4CUeXanguLbaCxBV6AQEvMtXu+ho9vEG3HTU6Y8yCccp9IFupmNHW1jpZSAaFJpfSYyAHXN5ckoNNI9AnbftEwC3EmmXkZocWOJ0+L5dtLEzhzoKlVmnFca0RziB/ANusUZEnhTu07EbvKVc1QUWk9FTki4aENyQ6KokRNpwin1lubtRGWPggoIrF22m3HnbG2JfSNjT4JV6MZQ+oscrx/XjwGrRShJTeOt2I3SJwzODRmeeUMYZCa3dfYqIm3ITpcM1xF4C6DhEGQOrptySGmWEgZDqY4YdZtZnJ+frkn6vYKRXyOqmaoqHQNvt1fc7ZmJQFF4ne+a/BR/zrZonHGCYRVF/16XlMwPiY5UjOeWhCLUSvRq9pkJUkS90yzY9FJXD4ilCT1/nnxLLs8kUk9Da3SjHZFUaAsB0mWME1fiGnG6UiG6HNybcKWDAkDhRb5o+tBOMUub9TtlYoYr6maoaLSNZRW+Zo8JhAMgs8N714uDDZJw2DERdWJtceXsbY9qFmrqFvU8eDMGMw6mTS70AxZknholgOTVuL6saFgkS8vTuD7yxMZlqjv9ITadhujeF1ira0kE0qPhb7KqztqtJEKt7/etiCa2s5KqmaoqHQQuz6FrA2gM8PA+aJAPKxIPLfyFzqIaILmGF5n940syPjtFDtWvVy7Nq6RJf41NxaDBu6YFAoN+eGKRFYsTqSfU9epxRi1eCvEdT1hENgiE3gljQbT8OGYhg9H0nTg/YAxBmypYh4sClW+QMTj1wKn4lZ0DJMPM17arWqGikonoigKR8uPUumr5M1dbyHJfvwV/fAVT4p6vEn3yx1LRGNg7EB6WXsxa0hkWNb1J2cwJnUwJ/UZyZjEMYxJGsWLV0xAr5X5w8Ihtcet/8Op/Hj3LHrEmJC7cSeMTtOPKNQE1ATdPfCVD0GSFPSxq1EUkLXFHaoZJ5aVXOXEJeCDooOi9Vlb25/VYU+hjw92VXHDWCtWvUR2RaDR42tS89j2Iax5Wnw/7ioYfUm7nldnMyZFz7FyYeI8ta+RQFBh6aHQ5Lo9SpuksSk6etjEZSQ9RsubZ8Vh0EqkWjWk2OpfSE06mR8WJ0UkHmpkiX7O9v0/PW6RtTDlZvj0N7DzUxi8COL7U+6pPxkFop1SMsW1j/PK3Z10oioqXUQwCKWZYuK/ixLCt+X5+GxvFTeOt6KRoMjd+ITU9F7NqNgKBkQC+rYPxOOe42HW78WkQzdlYg89K4+KxdXZfQyUeRTWZoUWW6OZjhcNMGGr3j4sUcerZzix6GXS7BoSLfU1w2GQWX1VUkSivE6jakZTfG4/l0HlPzFbs5Gx/t2sVwYCsIt0prKl9jhVM1RUmonfKxKedMYWa8+GbC/fHXRz8wQbFd4gnsaHGUxI1UPhPvjqXgj6IH06TLv9uDfWDk3QsT3fh0krMaWnnuyKADsKQve3MVHGGTeOs9YuOkzqoeelRU4cBpleDg3x5vqakWDWsO5XyRGaoddIZDRg3O1SFEUsZGsMkDwC7KntU6BpsEHSUMjeDJX5zSrOqWogYn87fZnC1trHqmaoqHQ8aw4UsnJfAbfM6Amf3QnAy4F5bFb6RT1+RNKJeU+cYpXJrggSb5IZkaRjb5Gfo2UhAY01yVTUWXz900mhNrez+xp5fqGTOLNMukNLrKm+hqbZtWy8NjmiWM+ojSz06zKCfijPFcWkSUNF8URHEpsO3kqR0G/v0ageefzRNWML/ZjI9trHqmaoqHQ8K/bms+FwCTfP6sex4uihDOH0S7TB178X94nGGJhwHdiT232dpbMxaiXcfoXeDg19Y7VszvFFzNOZtBJVdaLe/zMvdF09c6AJm16k2PaJ1Uady+ofp2PzdZGaYdLJ9I45DsdoigKeUnCXg94M1mRR4Kc1AhIEPFBZCJV5UFUsumto63fUaw7eQPRiv61kMJY9tY9VzVBRaUeCQVj2V/H9yIvEv2Yn3x10s6vAxw3jrBwrj77WGE6PKGu7JxKD47UkWzWsPOJpsuvg62eGWnJfPMxMklUmxaqhb4y2dp0jnBFJerb+OiVCM6x6Gau+izTD7wFXCSQNgZheXXMOEEqvLc2EgLdeVzB/Hc0ows6HgelcrF3KNdrP+Nk3CFA1Q0WlM3h3w142Zh0hOXUHB8p2ogR1uHPOoqHIJYf5+B5PNMW43rHYTTqW7spr8tgPb5hCT5sYa9w7L52p6Xmkxpjol2jFrK8/3zSpbxzb/jgXfZhxym7UYTee2J9pWwmGhYp4C2ahs+1A69iEp2gakhy6z+kIzegGs4YqKs2gPBuqCkWbonZm3pv5BBV4Zn0Fj82JoYG5YgCenh8rzLX7voNV/xYbR18KYy5v9/PqbP58soNUm4ZzBpkYEKfDG1C4+ctipvQUxrBbJthYddTD+UPNPPyjaBl3wZDIlqVNtWOCxlu/tgeKz0/RByKi3nnOaUi64+wylzoK+s6EA8uEeXvBPxmfque5rU1PlCbaWjfZpaJy3FBVJFLMLXFNH9tBLHw7H4Cn1lfw91NiGj32mQWxzOnbxN+l3y3SCQ+tBCQYuxhGX9btk0MemxvL8xsquHCYhT4xWqp8QW7+spg5fUWi0h+m29lT6OOqUVbu+74UgLMGRaYtzejd9DWrPRLNw+lyjVCCYvHaVyVM1RJiMklnBl37pFEN7NOHd4+cxMXaZfxO9zbne+8j2sBX1QwVlWZSkSOSFRw9WvzUs98rAOCJdRX8Zaaj0WNfON3JdGcZfPI78FVCykiYdW+314Pm8PxCJy9vruCyERZ62rWUuoPc+lVxrS78fXYMVy8p4poxVu5dJjRjfv/QNVGSJGamnyCaoQTF2NbgEKaptqTVRsNoh8TBIv3YXSYeN8Ks3nq+Ptz0RJOqGSoqHc8Fz/4EgPaHh7hVe5hsxcmj/nPrHWfTS/xnXiyjk7tnK+i28s458byxtZKrRllJsmrIdwW485sSLh4mUmafnB/LDZ8Xc83o0DhjXr/QNUqWpHqpUtFob81oF3wuYWqy94DEQSjoKHpBdDVyXnZZx7T/liRw9hUmKlchWBruFjanj541OU0n3KqaoaLS8Vz2wloAnli+j9/OGdjgcb3jzPz1rOH0K/sJfn5ebJx4PTjT220Ooiv59MJ43tvh4toxVuLMGjLL/NyztJSrR4k1i1fPdHL71yVcONTMP1aL9YxTwzRCI0vMyWj6c+hOmtHqMYrXJa7zBjskDwdLAhis9Y9z9BTpxiVHoeQIaA2tGrMsyNCze119zQgQOb5VNUNFpR3Z8RHk7RB/531mgCkGBYmrl4jktte2VHLZCEuDTx+ZpOOeqXaSrcf/PFQ0vrw4gY93V3H9OCsOg8yBYj/3Ly/lxvHiWvjamU7uWVrKwv4mnl5fAcDUtND9t04jMe940oyAT6ylOfuKgrooRXSK10vRa68BHTjeqMEYAxan0BhLQsSuM/vreXpzZJeMlwLzuFi7lHipFA0BAmhUzVBR6WDcPi+/e28vkrYMW8abIIMn/1QUX/01+VmDErlxZr8T1gj6v5um8sW2HG6e1Q+zXsuunDL+8tlObj91AADPXjaWP3+2g2n9Enhr7REAxvQKFfEZtBrmDWva1xZurD2e6FT9qMOZo3vwzs9HAQi6e+Kv6I/Wuhd97Bp8JeNqj+sIzTjOXGcqv0h8bpFaq7d0yOJyMKwg6ravSxo9dl4/E2RvgeUPiw1Dz4ZxV7f7OXUFMUaZe6aGFl/1GolnFoQmThItGpZeLlpG1JhrEyzd74Kv+P3kPfsWALFnnHr8mWsBJlwLh1bAsfWQuY5T+o9j/IpDZLo0ZBNHXZOUBKQ4jEzo086L8yoq3QlFEe3JoF5la1dx13clje6f29Rki6ccvrwbcreLtJCZv4e+J7fb+XUkCWYN90wLGcVMOpnnTw8NsNIcWpYvFppRs+gda+x6zegyjQj6hTkv4AW9DSyJ4r5GCYK7RPwuuArB5BTpIW1g4eBYblg1g7ODK5kg7+ZkeTPLg6Nq96uaoaLSAmrGIQZri9NjFSUydeH31abRaCRaZE5JA/53r7gWxPaBOQ+KhcwTgBRbpGY4jDIvnxHSjH5OHd8vTkJRlFpzrU3f9YsR7a4ZQT+U5YA1QRhrDbamn9MaLPEQ10/cX+iMjd43nTfCyfvrNnLEpSOP+rqgaoaKSucQqJ6Y6iNl82vNEgAe8F1OJfXHE70cWk5uRsHB8UovhzZCMxLMGl4J04zhiXpWXJFEmSdYO84waLteM9qMu0yMCeIHiGu4RovicpH3j0cAiL3ooo5brNCbxXtmbYiaJFXD4jFOPt+8mSMuHfnUT9SVgGRVM1RUOpwqbyi92+sP8pfPdzZ4bEaClak9tPDkzWLD4EXQc0zH3Yd2Mv2cugjN6GnX8lpYyuD4VAMrrkjiSKm/1lxrOM49Yy0eoyiKCCvw+4TGxPZu2lhtdECSXaSn5+0Q81ktNNjeMNHJ9zs2c8ilp5CYevtVzVBRaWcCflj2kPh++Hmi85LBRnFVSDNyKoO118JoDIrTManniTEPFY1B8Trujg+ZwPrGanntrJBmTO9lZOUVRjbnemvNtY21/u7WBANQngMxaZAwsEFvheL3d854A0CWRRFheZ7QprDP9s5pcazdu5lDLiNFCJ/CXqUnJ3keJY4ygmjUuSkVlU5gU7YwiRqSlqDIVQSqeuArmhr12GGpdsb27uBOO13IiJ4xjOgZU/t4ULKd166eWPt4ztBk5gxNZtmuvFpz7S+JTtWPOjx4xjC+3ZFLYaUoyvAWzERr3YvOsQ5/+aAOHWd0vctBRaUpyrJEJVMHtMfeV+Rr2RNKjogWSjVtWiff2D4tPI8z/j0vluvGWDi594k70OpS7Ckw5Azx/Zpn0EhBbpuZRrxUxjAORhzqV8Sg6P7Th9S2zlVROSFxl4oWyqaYLjuFrXnepg9qLp5y+Py3wvhisMH8fx43xtqW8vfZMdw83srIE7R1bZN4yqEsWyxO9BgLvSdDj9GQMAASB0HaRPEV108cW1E9wdRKNDIsnjWcLwITALhZ+xEgXi+giFt/VTNUVJpJeXb1OKTx1NlorDzadLJbDUpQge//DgV7xHvN++sJs+DdEiRJ4s8nO7hzso1ejuOwQK4xgn6hBbZkSB7R8f+/Mb1E2nJFfqOHaWS4fWYaSVIJgzgcsS+oCJ1QNUNFpeP5dEsWoPAneC6CgAABAABJREFU7UsYJD/LAyP5Mjg+6rF1izd+qdgNMvdOs3PvNHvUNt7HDYoi7v/9HkgeJha/NV2ggdYksKdCZcO6odVI3DGzJ8lSMQOIXDyq+bVUNUNFpeN5d93RZh+rKAp89XsozwJ7Txh8BpgbTqg+Uenl0HLnZBt/Ptlx/BqlWkPQL/7vZV31PNTA5icW17TwThomXsdd0qK3rtGMVKmIfmRG7lQ1Q0Wl/dn6HhTuFevovafWdv57eXNls19CHWUIRiTquHm8lX/MjunqU2kdiiKMtbYkSBgkQl26C6ZYUdjnc0Vs1mok7pzZkx5SAX3Jqt1+WElWNUNFpZOo9FXy/IqDaKy70Nm3oigy7uxzgAbM+Z17et2WkwcmcN2Mvvz7otFdfSq/GHRamb+cNaw6ijBAoKovflc6khxA59gEdJxmHMezjyq/CLyVUHxYtLPsgImPCz8sbPaxMZTDF3cL40viYJj1+xOiTWtrWDTAxD3TfmGTUZ3NmMtEm/DCvbDvO6ZkxHPP/EHE2yINzZnanvz7olHNipZXUTmuKc8WKTpd2Lbu9LcL2ueFaoy1+btEm6aFj0HKiPZ57W7I+UPM3DHZ/svTjIiF8uHCWGtPrZ9EKUmhNt49xoDWJH7f22CcmJIRT8+Tr8CLlrHyXqbI2wHI06Xyn4tVzVBRaRY+tyisM9hanFoLcNnHRY3ut+hC18Qr+Bj2LwVJA7MfANsv92/0shEWbhp/ghmLa4y19lRhnGpjQnmzkDXgzACdRRjEG6F2nGGNXHAp1ifzxMWjVc1QUekEbn17E6fLq5mu2YZH0XGf/wrCO9aknqCtWdvKr8ZY+dWYKC2tjxeCAREooDOLcUAD7Vo7BVkWLWM1BjEX2wA1mhFnidQMlyGepy4Zo2qGikoncP//tje6f1By6F56SNU62PQ6IMH4qyGm5y8yKATgpvG2Rtuhn3AEvGIMYkkSGmNLbt3/vT0FEoeA11XPDNUUNZrhrKMZXmMsT12qaoaKSrsR8MH31d1Wh58LehNoRaeLf6+taPSpE1K7R4fA7oQkSdwx2c55Qzph7qa9qTHWmmLFtbsL19KioreIIh9PWb1dNZoRY44sNJSMNlUzVFQ6GEVRyCzP5NttlRiTRDclb+F0gp7UiONOHZIU9pxOPcVuiyRJ3DN/MItGpjZ9sEq7MW9YCk9dOganRdzveAtmAaAzZ/PEJR23Bq6aa1W6N6XHwFshzEcdQIEr2OC+F093svzyRABkgvxb919R6WtLgbl/rR2cqKh0CMYYGHmR+H7dCxDwMiUjnhcWR6bX6C0xzBmS3Pnnp6LSmXgqxKKjqeXJgZ2BRSfx7IJYVl6R2PTBvir44ndhxtpHRWKpyomFEhQGWZ0ZUkeDs0/zqsStiZA6UmhAGw2244f0RztkEQC3aD8CIDUxnrlD1IkoFZVm0YbU2sbo7dDw9IJYvrtMaMZ0eQvXK++KnVNvhdRR7fp+Kl1MuLE2aVjnLmwY7eBMF63Gg4FGD52SEc/zl42N2DakdxJzh6qaoaLSGdhw8Qfd6wA84T+DI4pYsBiXouep+bF8cmEoZVBdvzhB8HvEGNeaACkjwdINkiSNDnD0FO2/G2FKRjz/rZPKMnVQmrrgraLSxcwYkMATF4/hzWsmAaDHx2VF/xU7By0QcxN1i31VTkx8LijPFUUbKe3QNcPRUxTuVRaK8U0LmJIRzz/PHR6xbeHo3qpmqKi0J5vehOJDYI6DXpPBktDkU+ZlGPnvvFieOz3UMllRRxrHPxW5wsCaNLT7dsSyJkIgINZP6jAlI56/nDE0Ytvi6f1VzVBR6WCKPcXkVOagd65E1hcS9NlrzYoAc4c5+e/Fo3nsglFdd5IqKnWYNyyF5y4XvqlAZX9cR67grtF/Zf7wHh32nqq5VqX74i6D0iNgjmn3iupSd5Cbvyxu9JhZfYykx4gKqTu07zJDs1UYauc8KKq+VFQ6muHnigFxeQ7s+AQQrVvD8avjXZVfAhV5Ij1H3zWpRPmuADd90fACoyzBnAwTPe1NtO8M+uG7ByBvh5jcUI21JyZKUBipTM7WLZQbHdXJhtZG27I2B3nUhXgVDZPknYyXdqF2TlJRaSYRqbUt+8M5Vu7nxkY0QyPBvAwTyVYNSRTxuO4JZBQYtBCqDfEqJwjBAJTlVBtrh4KuC4ozHT3Fwpqr6Y4tRl3k77ruF5pqpqLSmRwsqOTGNzdwm/Z9EqUSDgSTeSawsHa/VobT+plIMIeSa9UpgBMAdxlUFoik2OSRohiiu+DoKRbkPeWNHmbR19EMdYVBRaXD2Z1Tzo1vbmhwv82gZcGIFJwWkUL4K83nJPuPibmJoWeAKaaTzlSlS/G6wFUsWoEnDmkfQ7UkQVwG2HsI024LqasZeo06zlBRaTf8HvjhH+L7oWeJeWVZy9Y8b6PrGQ6DzMIBJhyG0E2cmkJ4nFORJzriJQ/v3ppvihXrHg2koZvrzE1p1XGGikqHEggG+HrXfv71dTb6+KUAePLmgxK6h0yx21g4IhWrIbQGrhZkqHQH9LXGKYlA5SAkpWNFQ5Ukle5LyVGxsN0OZqrdhT7u+KaYo6V+PttbxaQXc1myp6pZz73AvI4btf8TD2b8VkwkqKh0BjoTjL1SfL/xDZF4Cdw5OVRx6Gs8hEpF5fjH74HSo2DsvErbbXle7vymmJyKAB/sdDHphVw+3etu1nNrBvvpjjqtWxUFVjwKR34CjR7mPqQaa09EFEWkXZrjxURWaxfKjQ5IHCy+b2Jhu1GsiWyJmQ3AzdqPCDQc2K+iohJOC1Jr12d7+e23xRS6Ary5rZKpL+XxWXM0I+jn3/r/EieVc1DTG6bc3A4nrtJtqEkwtyV2bSs+jU6kpwd84qsJFvQLGYDrFvWpqKi0nVX7C7jr/c2UuX289ONBZj6ynL1b17JY8xUA9/mvxEPjrVl72poo6FPpvtRoQ8Arii4Sh3RN4UVjGGzgSIOqkkYPM2olhiWEOnOoRXwqKu3Pst153PPhFqq8AZ5avp+5j//AZ1uyGzxeDvtDTKWAm7QfiwcjzoeY9I49WZXugdcFVcUQP1DMOWra8Z5BoxPrYjpji+epYo0ydkPo9zOoejFUVNqPTW9A6VHchnjuz56KzxDLYz+VcfrbBY2uZxi19W/eEi2aKEeqHBe4CkHWisAOs7Pp47sSnREscQ1qSVqddTWXTxUNFZX25pNNx/jj/7YTCCo88Okm7n47jx2VnyLJPvyudPxlIyOON+nr31M6LWpHDJWuZ3BKpHek1O3t0PdTZ2RVuidVxVB2DMztkxC7+ONCciqDbMn1sbeoBa1rSjP5q/w0BKGw3znE9TulXc5HRaXZDJwn2rqUZ4n02pEXctN4G4+sFgOPnjZ1BUPlBKciDzxlIvWtkzjnvQI8AThaFmDNsaZvxMKruj8+P57//lzBb6fUMVVufA12fw6SDKfcLyY6VE48ynPBGAPJQ8HQxuIgayLE9Ye87cKUJbfutn3MvMX43/6GGZqtrGIfoN7LqKg0it9TnVprbVZq7TnvFQBQ4lb45kDTptpayVj/MhPlXbglI8Z5D6gtWk8kFEUkmJvjIXEo6M1dez6WRLAlizR0W3Kjh/5zTiyf7WvYtKGiotI2Ln5uDQBef5CPN2UBCn/QvoZGUvgiMJ6Vwci2yeHLiG+eFcdrWyr540lNF360C4oiTKA1X0E/BINATbWWDLIs7lFlrTDdaPStvmc94fF7xNjWkgAJA7v3orc9VRS4eisaDTx446w4Rj6bA4BHLfxWUWl3rnzpZwDcviAfbTzW5PFK2OTUx/0/w3zUgy9+CLr+c8Q1WuXExhdurM0QGt3emGIgti/kbgOdGeTmGfEkSeKbSxKZ+KJIvS3zqkYpFZV2IeCDlY8B8LeK03il1EC5towPdzUdLBVeTPv0glg+3VPFDeO6pmugShtxFYqxW/LwlnfQ6yos8VByWJx3nblXWZJYdnkiM1/NA6DUo2qGikp7c+vbmwAocXn4eFMOGvMBdI7NKIqEJ2cREPl3adSG7vn+deEolu7K45KJvTrxjFVUoqPVyHx68zQW/mclABXujtUMNYtEpfuhKFCSCUGvGKS3AzmVYvK/MWPta2fWmdgO+GDpn9H4XfD/7N11eFznlfjx7x0GzYjJtsxsJ47DzNBQmzJlkzJud9sudqG77Xbb/W233G7TNu2msKUtY9I0Dcchhxx0YmYxDMO9vz+OJEsWW/cOns/z6NFoZjTzWpbuufd9z3tO24k0XvheW8ainGX4fCz+7D+x+LP/hOGbvuJLWXB54OQ/k9tPfF92oAP/eoEk7ml1EFXR8jkY2A+egCSlFsjIwuB0ibXfecXRmDH2VG1ji4+brm5gRf2YReXd98Oj35Lb53wQlp5j32DVnDgaI+LdkgTbukEqPtmhbjHUtEGs+7hfwlW7gO2NFwFweeyX9oxLqUoWOyLtmmdRtXas6RJrx8UMC9j/qHQlAAIX/Q3tC5cez0iVw447Zgwdkd+fVhs2WtjB5ZJ4MpIoN42Ax+Adm8MAxLU6iFKO+f3TkpB4iesxznM/Tdry8MncmwD47rExY9jZHX6+enUDrTUOVpSyTImBAwdg8KDctixJsIwslGSd5nXy0bhC7gvUyrVaNiXnrAMH5CPeLfMXlrZOINkvi971y2DB5lkn1hp+P4u//W0Wf/vbGP4CbsLx10iCbXJg2qfVBly8fr3M22qilFLOuf3ZI1M+9t23nz56e/SvcM8DtOy7DQwX3lOul/bLqmJMeo2STUKiF5pWO5dYO6J2kWwUSfTM6dtaa9xcuUKqtQ9qopRS9tj2E9kcHqznB/mLAbhtx9RzU9+8dvL1jJetCPLlKxuo8WnKSNkZSaxt3QiR1jl/e9GuNwK1so6SmzwRfFmdh3M7JMZpzFDKOX949ghg4m/9DQDZ/jMw01Lo6sNXHl0b8Y6pdv6KkxbyhTdsJuDVaufVrGjxYxIbF9ayaZH8vqazziZOaTkBVXoSvTC4H0KNBXvLK1YEOG/xMa3YHv0WdL0gCTIX/5NW3ygThttF+KT1xR6GvVZdBo9/T6o5P/Nz2PxmltbK7+Ogs9XNlSquRI98HMfEgJPesCHE+UuOxgyfe5qTtb49cOe/y+0Nr4T1L3d4dGo6jsWIkcSDlnX2Lly5PbIwkuyT5ITjrH7YtfJ18PAdnJx5BPp2Q/Nq+8aoVCXJZeS47QvZtqnjfafWjIsZje443P0fgAXrroVidcawLMilpMJRLj0+g2ssw5Cqut4geIKzquZbKY4rZsQ65WfVthEC0ZmfXyihJqmInuiGmunPq1Y3SmUzvc5QyjnpnImXHP/okY0W38pfyT6rlX88K8h5Y2KGf7rrDDvl0pAakDjoj0gr6WC9xENPcOa20qYpyfu5lHxkEnK8SQ9Bql9ijDdQfbHEzElc8ASh7USILpxTwpPhdhM+4/SZn+iESLska+RSstl1Cuuah+emdNFbKcfE0pMXC/mv127ivFXNo1/7PS453v7hn+SOFRfDgpOq55hbJSZco+TSsqGlabXEbycTawE8PmhYDgceleI0c6iKvLHFy+93pHRDhlJ2ME2477Nye83VpB6UxJapNsl+89oGLl56NPmlYNcZAGYezOyYThgMd8DwyjGkgEVVKsrYirUzdCmaStGuN7wh8EflGnSKImsntvq4b19GY4ZSDkpkTDzRJ3EHDmLl/WS6LgPgH1/l57JVK/ns77cC4yvXKgVFnq+axEkddTy5f4CA4WyXKM0WVKXFNGFgr5wQTjN5O1deF2SnKZZxxfLx73Whexs8+QP54vy/lYVIpYrF5YGTb4S7PglP/Qg2XEfUP7zorQsYqlJZllStdblLbnPDpcvGx4yQd4rJqPQQ/OEfJXGp/SQ46/3OD04VXi4t/9etG5xJBA81QF0HdL943Mm17oal/L/sGzgSXstn65faOz6lKkm8U6rLRdtte8nzFo/fufuX2W9CthtqO4oTF3JpSXTKZyXJyRuG6CI5vri8R1t7jix+ZBLy/ExcNkEabqlo56vRhfpjJXrknKVtY+lVCHO55Hdu6LAsaE1zbhX1y/+rXmco5awb3Lex3HWYLquWr+ReAcAZi8PjnhPyOXycNXOSkGNZUoWudqFsdPfMseqEywWugCTQjmhcLjEkE5Nz5XiX3I73Sqzx18yppXTZSQ8Nn1MsGE5Wriv2iOYmUCtzoUOHp12sjw5XONNFb6UKb/PiunFfh30eeOZncGCrJPWvf6VtXQFViRrZxNGwvDCJtSPCzdJlKd45p4SuqH84Zuh1hlLz9/yvoXu7zM0sPQ8enP7pG5q9GGPmcMJTrWfYJZ+R8+FsUpJn3T6ZcxoZQz4nnS/yw5u9PX75t3iDzo6rUsS7ZH6ubeNxJ9YWlWFILBmaujq/zk0p5QxrbIENI4u/5TYAMj0XYeVlTmrzonYWRJpGnxb2V+i8jaoY0aDkTcVGWhM7pLSyVZRK9MDgoVm3SJstc4Zzr/yYx+sZ5D+9X5Uv1l0Ly86zdSzKWVYuR99v/gRA/TUXY3gq5DC38mJ4/LswsA+e/inRpW8EYEArSqlKleyTSdpSS04B3MfMVU+aXGtZcNd/SIJwTStc+q8llyRcjWyPEWb+6EJG3RIbRjiFusUweHi4Vf3cKyFG/QZfzb+c9rwmwik1pXwO+vdJYpCNiT5jq5tf49rC+dn7ZWHhoo/YuplwRtkkJHvB8MgEdqRNEn28oZmTZC1Lvj89KElYsS6Jb76QJN9UaHybU8xIDUhMaDsRwk1TP6+Ywk2SNJfsn3aMI4lSA5oopZRjGhjkLz0/B+DTudcRQ5KfvMdUkAp5HDx3S/ZBOg6RFqhfLscHu5NyfCH5qGmR8+VsQs5nEz3D8eQIWKYsovtq5p7UW4ryGfm3uX3SnrVu8cyVf6dgZbP0/fjHANS/7nUY3tlXB5w3w5DqtQMHJL5NcW6kiVJKFY/vmMmpiDcPf/yYfLH2KmhaWYRRKaeNXqNYFvXnr8OoXyRVa48z1hwXl0s2gccOz6l67Uii1IDGDKXmx7Lgnv+S22uuxIq2A7Fpv8V7zGl+0Knk2mwCEn2S+Bmqh4YVMm/k8cv5seEGLDm/zGdkA3hmSOaZUv1yHu0NSFXTSrg2sJtlybHXE5RrjZrmmb9nupcr5vWGPwouY8prjZHrjIH0NJXTlFJzls4d/Zvy1j+Ay9uPma0l03vO6P1LahfiGzMPHvJV5ty/On5FjR+TiAbk/QeSWUffR/8SVOkwTejfc7T1qJ0vPVNy7fATmkIu/jX7P7TQD/VLtcpgGbKyOY588RYA6q44v3KSa10eOOUt8Kd/g6d+TO0yqWwTy4BpWmjjFFVxBg+BlS/JSZSRmDJSFf3cxZOM8ZmfwZ77ZUf25f9WfpWKKpTtMSLWKdVqm1Y5WyHEF4b6JXDkaWnVO8dqkaOL3poopdTU4l2SfGpzxwrP8KGhlV4+4f2WfLH5emhZP/U32cnMQaxbjht1S6WCXrB+bscRwziaIBVpk0TbeBf075eKdp6AvGaFVR+cdczIxORn0rrB1qrHtnO5ZRH84OOSzDZF68WjiVKFHJxS1eWDnp8SNRI8bS7lJ/kLRu8/dtH75Haf/W9u5iSp1RuWduGR9sIk5BiGnNP6wnKszGVk00ZqQM6pk/2ywO4NyHM8wfKqkG7mJWE5n5GfacOyeW8UtbJZjvzbJwCoe+UrC79YEWqUZIj00JTXs6MVpfQ6Q6mC8x6TXHtV6neythJsgLXXluR8mpq/cdcol3wZo2UteBw4X5hJqFE27CX7Zn0NrXNTStnkpT/C4afkfHn5RWS9EWZMrj1mE9+aRpvP//NZSYx1uWVtPdI+PE80xXy5yy3HLn8NhBulaEUmJtcEQwelc5KZlyIT2jlJmHmZgwvUyvyTDUXKinq94Y/INWk2IbePoR0ylHJGMjNc2dMdx990JwDprsvBOvr33xCsHfc9y5rGd1lSqujzVceIBuW8ZlCTa1XViHfKBH94fjutjmVZFjOdemWHN2n87oxnaNnyEJbhwrjoHwpbTUqpmSy/EB77DvTvof6lnxP2Xk6tDxLZPDU6X6oqSXoIYocgUFfwtzatmS/Ws8PZtb9/UzO37kjxtpOOubDo3g4P3iS3z3yvVJBQlSfVL4tVTasLs2gVaZOFsvTQnKvX1vpd1Hih1m+QNy0qK/1NKRuYplRidXnmVIU1N9MOPoY3ZFgWdyy+hZrOOGbjKlwn3zCPwc5BegiSA5LEVL9MJt7tWJDwBqUSX2SBXL/17YGhQ7LgEaitrkWPkSqMzeugtqPYo5lZqAl8keFYUjvpU2oDBhEfRIqwRq9UJUvnZAFjiXGYN7qlKvYnctdjjtkq63XJ8fOXr2/igX1p3rTR5nbemYRsJIkskM1hx9ERwTYeH3iaJDGnftn4RNv0gCyou71yzPKWcKKtZcm400MSZ1s3SOeSSthw4vbKppwjz0yZXFsXcBHxFaC1sFJVZjA186LgSKLUT95zFs/s2MeGR4eLhGx8FdQudHJ4qlS0rJMNKcXgckPtYqk2aeZmdR1d63cR1Zih1Pzd+xn5vOpyqOvgSHzmFsgjXZW+/8pGtvdmObfDxrnsZL8kxkYWyAaz40n6NAxJsPRHILpQ5t1jnTLXNLBfrgeCdRXbOWlG+QwMHZHNDC3ri3sdZxePTzZqDB6YNLm2LiAxw9FuLkpVoe1dPQD4G+/EcKfIp9rIDWwe9xz38NzULW89jYP9KTYunHwOWalSURfyURv0EvQ5OxdXpWchquSYeUkYcXlm3UZmtp7umnkyKm9ZkOyn5fEvAWBsvl6ToVTpcbnhlBvhjo/jeeYnPHPDNcMXnXooVxUm1gmZpFxcF9gD+zIzPic/vCFjZYOXP284JmZlEtKGz8zC0nNhwysdGKUqulxa/q/bTph3RapZ84Ugugi6npvzBFp90MXTb6+FXFbaLSmlxkv0SCXWcNOcvu22HalJ7/e7IT28tpE3LXjxNmo6HwW3F9fF/+j8YoBlSix1eaBtoyR9OlGV0O2RhfuaFhg8CL27ZFI83FQdmxRzaYj3QvMaqcxSqolfY3kDslDV9fyUybUdUQ/b3hqRpHOllG3+79H9APy158d4jTx35jfxoLmelpCLzsT4v7dNrT42tdqc4Z7slyrbTWtl0buQ7aNn4nLJYnmwTjZvZBOSsDp0WJKBk70yHzJS3agUjreWJUnAqSFpadp2ghxfi1E90EmhRmnhm0tPuqFwdaOXbW8JT1kNXSl1fL52945J719UH2R/XxIA73CLjFOXNnDq9s9JBdG6xbDyUtvXV1QJyYypTlmEogTjhJskdqcGZjWHe3K7j6duDOnvp1Lzse8R2LtFuuWtvAT8ET53d9+kT11a62b3gExOjXTIOLvDz9l2JdaaOUn49IagfZOcC9uxwczlkgTdUIN0cot3S4JtrBMwZC7DZ/MmxFI2snG+bonMP3kraL4t1Cgb9idx7mI/T93gl438Sinb/Ptvn8Pw9uJt2AJAuvMqwEVLrUHnwPhCIheusbfDn1JOuWJDG1dsaHP8fXTmS5WG2BHZ5WpDG4Ox8qbFUHrmilJhrwvu/4LshmtYDpv/zNZxKGWbZRfIRGkmBs/9utijUcp+ubRMlgQKf9GcNy2SucljRs2YqgrB6XbL3vdZSSyqaYXz/7Y0Fn6VvSxLkvBqF0uyayFF22TyMJMo7PsqVcksS47bGHNa5MubFqkpYsaCyNHFhGCuH7b8t3xxylskCdNJZg4GDkoC0oKTC5NA5fbKgkfHaVC/HBJ9suhhVXByZj4j/8aG5dC4YupWh6WoplmSv3LpYo9EqaqRNy3SOZONxk6udT+IaRn8Z+4NAHTUHo0Zjh1KEj3SqrX9RGhaWVqJtccyDKnCF10AC0+GxWfBwlPkvDuXlc0cgwclkcfMFX58Zk5+ngP7wTKgdSMsPkPibaUl1oIkL4TqZVFfKVUQedNiqqZKC2qDo7e9I0Gjfy889DW5vfG1c94wqMpILiWbOkqF2ysbOTMJpvylVUrZa8uX5fOy86BhGXnTGq1Ke6yF0aPXGW67iy3kMzB4SNZAFp4sa5ZOdG7wBqGuAzpOh0WnyzEnm4D+fbKeX8nzTiNrEJmEdMdo21hZibUg1xregGwCVUo5rj81iNuTxd90B4aRJxdbST4uxQbba3Xzk1IzKeHZVFU18lnZmeTx2VrF6cBQjqu+30WNb/LViRX1Ht56Upj796a5zvcw7LxTKi1c8He6e1aVLpcbNl8Pd34Snvk5rLqs2CNSyl6xTlkoLXALux19Oa77URdR/+QxY32zl8uXB3iqM8sFS6bY3b3zLnjpjxJLLv6nymjPoyZKdEt1kGIkU/kjUNMGfbura4e+Uk5K9ctGv1DdrL/l6c4sr/9pN3WByY8Bqxu9vGptiL2DOda/+CVpdd24Ak58vT1jnspIm7joQmhZW/gWob4wtK6XBf2eF2HgwHAiZ4VN/o9UZ6lbAs2ry6/1d6AWQk0Sz2q0AoFSTntkdy83futh6oJe/sPzIwB+aZ7Nc9YSQGLGGQv9xDImHVEHpmkT0vKP9hMh4nwVB9v5wkeTbbNJuVaM98gxLNYlnbC8AamY5Q04U0HVMiETlwRTy5IqfY0rJZnAG5zx28uaYcj1x9CRYo9Eqapwz/Yu3v3drdQGJ1+bOG1ZPRsWRvG5XUdbXt75Kcinoe1EWHJW9bbMrnRmTuJe3ZJij2S8cJPE6Wxcqwsq5bS+PfDcr+T2iku5db+HD912mKh/8sTZi5cGWBz10BK2+fw4l5Y1nLolMvc0SXcD27ncMr9U0wypJfL+gwdkc7nXL3P1lbSun8/Kv9EfhbY1EGkt9oic4QvLGlpqsPKvq5Qqsl88foC/+9mT+IOdeBY8BkC664rRx1918iJWNKZZ3RYp1hCVKnl6pa2KL3ZE2jpE7Z3kf+WPuxlIWwyM9GQddvYiH201bv7y9AhL6jxcvyoP//cFefCkN0lbBaWmYuYgl5HPhiEXdR6HFnCmsuJi2HqLVGt58Q/S7kupSpDPSQUgb7DgbSUv+W4nAEOZ8THj0mUBon6Dvz4rOq4S4QSJXrj3s3L7pDdJW1BVebIJyOehbXXxklsj7VKZJp+trElDpYpl6LD8Pc0hAfSaH3YBEM+OjxlXrwwQ8Br8/TlRmkNu2Psg3PoniWnn/42zC90jixv1y+R6pljV8wxDJv0DtdCzA/qHN1EGGyqjmruZk+osdR3Qsq48j8OGAdF2uZawTG3lrZTDXnuTtNo7Kfck5/u2kbHcfCb3Wq5b6cbj9fH3Z0epnWKzxryl+sE05dqkHBNrj+UNykekTa4d04PDybZdw+1S+yT51eOTuO7xg9s39/hjWZKolk0e7RjhDUsCQU2LxLRSrv5rt2Cd/Dxz6cIkTyhVxW741sMAJI+5znjtKYswDHjfhSsJ+8ccf7pegKd+KLc3vhqC9YUa6szMnGyAGOFyg+GujGuCQrOso9cgkeXFHs14vvDwJvBdmlyrlNMe+ppcw7dvgrYNvOfrfQATOvG9aWMIy4LrTwjjn64D3/HIpSDWDQ0rZO6pGOfEgah81C2W64CBfZJjgCVJtuVekCI1IFXK6xZB4yrwV/Cx1TAg3CLzmUopx1iWxQd/9AQARvMf8RoWuaF1mKkOLjvRg88V4E2nrebGM8usgIRSBVZFM4GqJOUyw9XXgrYvNnfGJ28HceoCHx8+c0w1wYe/IRPwdUvg5BtsHYOqEGZOLmiySZkE9ARkQtCyIJuS1reGIQsO3gJcuLk8cNL1cM9/wrO/gks/prv6VGVI9MhHCe3EPX+xnxs2zVD5z7Lg3s8crUx48o2FGZwqLDMvVbKaVhe30l+wHkINEpe03aNS85OOSYJhsHbW35Izp253ednyANetHT4XzCbgvs/J7Y2vgea18xnpDIMaTqxtWC7vUwoJP96AVLEN1UPXdvk5R1rLu5KWmZNk7OhCaF5X3u2/gw2yQJOJS1V0pZQjYunc8C2Lv/NI8tP/5i9lv9XCR9dEuXyFg9fxmZjMV7RulIT6SuP2yDlxqAEalg0nwsbl353olWTb1KBUdR9huIaTu1zjk7ssU871zZH/L0OSSL1BaFoo59/+aOW1YZ0t/3ACQ3pIk2uVclDnYGrKx165eSFnr5zk+v/Of5djWMcZUqG8WB0VzJxsRsglZeMihsyhu8Yk045NtnW5ZA7dFy7v64NCSXRLLGpaA7mZn15wkTbo3y3/x/r/qZQzUgPw2Hfk9srLeSkeBOKTPvV160Oc1ObAfMVIYm3TSpkfL3YXH49Puh9G2mSNf/AQxA7L+tJIRdRyOiaNVKv1BqFtoyQPF/tnXAiBWjA8WkhEKQdt3SObMVz+w3iiTwGQ7pLOyBdv8PGyNSfidVfB8UapeSqjswpVkWKHZdI7usDWl81Ps+jtGjuBfvhpeP7Xcvu8D0tFC1XWDJ+XRZ/8m9Hb85LPyIWYZUGgXtr++aMy+Tdykp/PSGJG7Ii0IUkNQrjZ+Yue1ZfDY7dI9ZShw7KgpFQ5syz5G3K5Cz7pEc9MvhkDwD2bIlIv/gH23C/jvvAfdBKghM0rRsS75PjesKy4lVZcLjlvOvSk/N1o1Reljl+sUxJx5nAe1ZucOmYEvWP+Hh/5ppwfRtrg1LfOZ5TTG5l8L6XE2hGGIccrf2Q4wfaAJNuWSUWjcTHD45Jz7po2SRou9wQrb0B+N3t2aHKtUg7qGkoDcKXrYTa5dhKzAnw5dx0AIa+DVaNzaUj2Q8sGqF3k3PuUkpGqtuEmqF8qlW1zSflZ5NIyd5NLS0XafHY4wcsaTrj1gNsvx0aPf7jqbUDmflzFq+5t+Hwsuumro7eLNxADalqlHblSyjGdwzFjMkHfJPPMh56EZ38JGLDuFVKtr5DMvGwyzyTkOOGrgcgCSZLxBmVuzOWR46xlgZWX4282KfPniW45rlh52fTlj2pHhclkYvKzbpIOSoYrVxqxYaxgnfz+pQYg1Fjs0ShVmR77DmSGoHYxLDmL7t6p18BDXgfminNpOWY3rCiNxNqxXG65Bgg3QXqpVLEd2C9zZYYxHJdKuJqtZUpycDYlycINy2XMBVAS1xuB2qObv4N1xRmDUhXu4OAgAL6m2zEMi+zgCZhpyc1aWNtEnb+uiKNT5agk4kcRlNDKm6o6uTT07RneoTz/E/FUzuKGX/TQVuPmsuWTL3b63PDy1cOVQczc0WpSq18mrTRU2TPcbiJnbp7fi5h5Sao1c7KAXtchE0OT/Z66hhdwapqlVXfPi7JDMtLqbIKdywNX/ZdcFDYsc+59lCqUZJ9MeBSohd1Q2uSGX/awot7D6Qsmr74T8hpcvHSG5JlYJzzwRbl9ylulcq0qWccdIzJxWeRpWlUa1ZpCjXL8zybkPEopNXe5tLSOC8ycWNiTyPPWX/WyodnLmqbJL6EjPoPTFgxPJHS/CM/8XG6f91fOdRgw85LwWbe4eO34ZsMfkWutQC30vCQL6qGmkt8cMBozRirW1rRB64bK6RgRaoLenVphSimbHRpI8s7vPMopi+tpqw3iwuSvPT8G4Ob8VfRQS0PAxUltDs0XmPnhTRcrJMm0xI+1jnF7wB0p6w0EhsdD5MILiz0MEayTOa58RgsTKGWj3d1x3ve/j3H2ikZCkyXQAu21Ada1Ryc+8KdPyOel50qFu0Id7/MZmcPL5+T8vnmtzOUFonObC8/nJDk33gNDB2HggLTyDtSVVtJWMZk56ZjXsn60y1dJxYYRLrdsqjy8TZNrlXJCPgcP3gTA5xJXkHncTc6cvNr50lo3y+ttvr4ft6l7TWkfo/3D5/+1i6Sw19AhKZgR75EY46spjbl9kI0n6QFIDUkcbV4nm6AL+PMtiZjicstGvu7tmlyrlI2e3NfP3/30KS7f0MbOnk5cgf14o89gWQaZrksB6Gh0cWJbB0a1zhup41YS8aMIdAVFFc/gQZmIqV1oy8vdsyfFwwel3duvticnPH7XDS1E/QYNweET06d/Br07ZFf0Ge+xZQyqAqSHpMLLSHXCcMvsK5WEGyXJqes56N8nrRedXKgON0tilVKVIHYErFzBJjd+82KSxw9nefxwlp88NzFmPPz2Vnxug7rANH//lgX3fVYSL1vWwabXOzhiVTRmXibjmtfJDvhS4AtLDBjYr8m1Sh2veJcs5s6ig8YPn0nwVGeWpzqzkz7+xLvaMAyo9buk4sT9n5fPyy+CRafZPPBhliUJn5H24Yq1JV413e2R1oGBKHS9IIvnTm9Gs4OZk41z0QWVlVgLR9ucp2O6gKGUjb52906ePjDI0wekMsh1rgdY4TpEn1XDzbmreOJt9Xh8fmp8DlXni3fKeWLTyqJWXVUVxj+2opQm1ypll8/9cTvPHhrk2UODkz6+9Z8uJehzE/Aek2iz9yHpomS4Yd3L5Rzbabm0zI2AxJnaRTJHcrzn826PdBAJNUD9Yknc6tst596+oCbZWqZc70UXymaZUhdqkKrvuZR8VkrZ57lfwuB+BowoNw2dQ3prfNKnPfz2VqJ+Fx6XjUlSo5u6l5T2pu5jub0y5xRplTXfkUTbkc0h/hr5KMZGY8uU+cjUkGxSaTtB5vbKvUPSfIwU3DHz1R37lbLR3/30KZ4/PMTzh4cACHbcDkBu8CTMTCv/dYOfDS1LqCt09wulyliZnAWpipNNStXaQMS2dj8ztdRbGHHjdQ9fVMQ64dFvye0z3q2LiRXEyuUY+OP9ANReeg6GZ5aHOTMvbU3cHlk4r+0Az3EsGHgD0noRJOkpukBbWik1k3RMNlwUqN0NzNweqTnkmnm33s67YO+DMglzwd9p1bcycFwxIt4lE3H1Sxwe3RzVtED/XpmQ0zij1NyYedkI5QnM6u/H75k+HozbiPHiH+DIM/LaZ753viOdWrxT4mbLuvKagK9pkU0B3duhfz8Ea0u2qqCVTjDw29sgWE/tGy/CqKTEWpDrnpo26H5Br4eVspHPczQmuMnzF56fAfCN3NXECFFX4+CxJD0EhkdatZZKRSZ13KxsloFf/waA2muvwfAWcUOKywXhVuh5oWDdZpSqBq4Z5p0aayY5llsW/Onf5PbyC2Qe20lmTuZFMKSQRG0HBBvs3cDh8UvnuEibbL7v2y3zhP6IXPNUYzWtoU4INkLL2nHJbCUVG8byR6VqbaILasro+lSpcrDlvwG43X8Z6eTUa5YtYQeSEmNHZF68eU3pb46eymg12w6pFBvvkUTbWKfE1EBU5qmcnl83c5AagExiTFJtW1E3cZdMTBn5P8gmSnaOUKlykzet0duu4B48NS9gWS7SXZcA0F4bpCPaXqzhqTJXMvGjwDQLRBXH4CHZmVW7yLaXzFvWhPtObPHyVGeWq1cFjibWAjzwJdlF27oR1lxp2xhU8VnZHIf+82sARC84Y3aJU5kEJHrkIrFxley0ng+PTyqIZVNygRZpm9/rKVXp4l1SAWe+f3tz4J1kB/fpC3w8fDDD69aHZk6sTQ/BA1+U2ye9uTyqSKi5x4j0kCRNN646vg0XTgrWSyurTFwnnZSaq0QPJHsl0XMWov6JE+wXLvFz1540bz1pTPXo9BA8JMcYTr5x1q8/Z6l+qVLVsl4qbZQbXxjaTpQqeD0vyeR5uLm0NgrkUlh9hzj05f8DIPrGd1ORS/rhRuhxySKPbhJSyhbNYxKhXuG6n+Wuw8RcUb6dv5w/PyXk3BubOenC07pR/rZV2bOyWQ79wz8AEH3ZFcVfrAjWgYVu7lPKRs2Ricmzr9q8kJ89foC/e9nayb9p192w+15weWH9ddLm2imp4XbVkTbp8BZqdDbR1e0drojbAkMHoXe3FK8IN4LXwX9nqYl3SbJV67oJ3YpKLjaMMAwpMjJ0SOOEUnbavxUOPAouL4/XXgr94x9+3foQP342wccucKBoSbIPPCFZ6yynTd1TcblkPj1YL2s5qX5Zv40dkZwFwyVzbN6QvdVTMwlJ6jVNOZ9uWg01rSWxGbJkYorHL/8vsSO6zqGUTZojfl7sjAHgb7oDgFbXGezINvHWizy017RT4yvDdQVVEkomfhSYrp6owsvEh6vW2rvrOJ2bmFx709UNGEBrzZiL+f2PygSU4YLzPqwX+tXMsiS5Ip+TC8T6pfYlT3mDspvzwFZJtNALAqUml0tL9c0C/42k8xNjxn9fVU82D201s4gLD31NJphqO2Dzmx0YoSq60QSFDQVN/J41j1+S0fr3aIxRai4sCwYPyHXILJMJM5PEjC++rJ5YxqJ9bMzYesvR2HDCa2wa8DFyaZmYbzuhvJOnXG5oXC7VKbq2w8ABqGkujRai6SFIDULjimKPxHmB4crB6SGtRKiUTczhjd9u8nzA83MA/Ce9htsX1bCgrc65Nx7ptlDX4dx7qOrmj0iyQTY5IdlLKXV8fO6J80+fevUJ/M3L1tBeO0Ulu7v/Uz6vuEjmnp1gmTB0ROYd2jbK9U0hW4F7fDJPH26RKrb9e6TrVbi58qvYpvoBQ+aiyu38PNSgm8CVstsj35DPi88kZ7QA6XEPf/LiWj50RoT2iM1Va0fmnhacVNBugwXj9kC4ST4aV8hcXrxbrqmGDgOWzE95gxIL57IZOZ+V4l6ZuKw9+4IQWQCRdtmkUsh4Wk5CDdJlTCllC/9wVyVXYB+emu1YlouPXPh6smcFiYYs2kJaGE6pudIIrgpv4ABkhmyrWvtCT5bvPBVnWe3EX+cFx15QmDnY8mW5veGV0LDcljE4xszJpHU+Ix9mHkZqJhkuWZT2+OQk3+2v/MklO5k5mST0RyQ5oabV/p9fqAEaVkhrYLt3OypVKeLdUsk8uqAgb7etM8OPnknQXjPx77EpNMu/0UNPwvPS7oDz/xrcJVbRVNkj1iWTXnWLiz2SqYWboH+3VgVRai5GKlPMYqFy66EMv3g+QUNw4t9X1O8iOrbIRM8OeEaSqDjnL51pl2eZMvaGFRC1rwNIUYWbJEGnZ4csmnv80ma2GNc1IxvvzLwsZvuaCz+GQnO55Rys87nyW7xXqsQ8sKOb254+TMgvc1OvdN/HMtcReqwIjcvOZUG9gxsisgnABQ0ry7ddqyp93oBU24p3aXKtUvN01wud3L29C+OY3ggNYR9+j3vqxNrd98Oe+4er1r7Cmap3I3PmwQapnFrMc0RfCFrWyTVD1wuySbKmtXJjXTYJ6Ti0b5KNh+VmdBP4bk2uVcoO8W54+qcA/E/2MlyB8WsXa5s8eFyG/Ym1liXne3VLJSm00nn8UqE90iZJxSMbrhNdckxODYGVl+e63JJoa7hk3sqyZK7OzA2v31vyuCcI0YWSTBuoLc+uU4Xmq5Gfr3ZWUmpefv3kQZ47NIhnODT4mu4EIJo/mbZwK4c5zMKalYSqqSuEUjbR6KQKKzUIA/tkMtamBdNrf9hFJj/x/vecMsnJ6nO/kd3O/iic8hZb3t92Zk5+TtmEtHv1hmS8vho5yTdcgAW5jEy4ZGKyczvXI9/vC8uJup58Ti2TkIXz6EJoXu3sZE9dh7SySPQ41xpYqXJl5iUmjB7bnHftD7snvf+vz5rlcSCfgXv+S26vvUYmvFXlSQ3K5pWmEk9QCNbJeYJWBVFq9oaOSBWJWVRIffX/TR4zPnp+dPwdlgX3f0Em1JedD4tOtWOkE8WOyGJl4wppZ1cpvMGjVcK7X5JF83BTYavYjtt4t1aqPyYShXv/YgrUyXmYLmAoNS9v+sZDo7c95PiAWzZc7F/yKhqdnKOxLIj3QNOq8q5orspDqFGKJiil5uUt//PIpPd/7OUbpv/Ge4fno5adL8d9u5k5aY0daZek1lJIBjIMmVP3hqDnRejfD8HaypsDyWckka5ptW1FaYoi3AR9u3QTuFJ2eOzbkM/whLmcj+1Yic81fo7i786OTvGN85TqB19Eug1V0tzTbHj8wxsFmuTfn01BLjn8OSVry7nkcDKtKTHK7ZVkWl9ouNJtQNbpS3lNoRT5wvJzzKUkH0IpdVw+8IPHAajxu3D5D+GNPItlGbxpw6uIZ+OEPCFaQpqvotTx0JUTVViDB+Tks86+CYLJEmsB/u7sYyZYUoPw6Lfk9qlvK70JmHwGEr0y8RColaq6gdrhpNppqiKappzMp2OQGpBF91gnYB1NytWKtkcleuVn3bwWGpY5f4Hj9sr/5YFH5X21wqVSRyV6Sibx/P2nzvKC/fH/Hd4kUg9nvNvZQaniMHNSTbl1Y+lX8RupCjKwt/TOa5QqRZmELBYH5rcA8baTjokZO++Ew0/JBPpZ75/Xa08pPSQVqprXSOW4SmMYUkE1UAe9O6UVnMuQJB6nEz5TA1KJJLrA+Y13pWikikomJj9/pdS8Xee+nyWuTrqtKJtOO082Dzgl2St/u3VLnHsPpUb4I1pRSikHXbtpmgqB+7fCjj9JwuL6V9g/x2yZ0go7ulA2vpXaNYe/BtpOBH8tdG+XefZQhWwqGdnoV78MGleW91pOsE4SpHQTuFLzk8/BI7Ke/e3cFQBkzKMPNwRcXLTUgeO0mZNqrQs2aacCkFjoDcAUBeWVjTx+8Idk/lOTa5Wat1jaJLBQqtb6Mydy4cplHI4fZlXdKq1aq9RxqrItR6qokv0wsN/ZRYUxjGMnIR77tiTL1C+DddcUZAyzYuYlGTbeDeEWWHQaLD5LEj9DDdMn1oLsHPSFpbpS82r53o4zpF1sPi8JzYne4ZYUVczMw+BBmYBs3yS7+wu1czDcDDVtEO8tzPspVQ4sS45PGCWxKDchZkxm8AA8+X25ffZf6CRxpYp1Ssur2o5ij2R2Qo2y0cayij0SpUpfvAsy85ukDXiOiRe5NDz0Nbm96Q3SptRuZk6upRpWFOxaqmh8IVnMX3SKJIsNHj66AdFuubRU3zPz0LZRrhGqMba7PXKtkqmSSr1KOczA5D3uXwPwI/fVcixz6nonn5UqSo3L5fiplNP8EakemU0WeyRKVZx17TNsABypWrvkXKkqa7ehIxBqktcutcTaES63xLwFJwEuSQYu97kQy5JiKZF2WdtxF3+OdF5GNoFnYsUeiVLlbfvvYXA/BGr5nXnGhIfXNjl0rEj0Qk2zzI0rVWiBeqlcq5SaN5evE09kGwBLPC8jlo0R9oZpCRe/2JVS5arMr9RU2bAsqT6UT0s7BZtsPZSZ9P6vX31Mpbm+3fCMtOTj7D8viUQuQBYwEz0y4dCwTJJr59tmw+2Rn3G4SSqXxLugf69U6fIGpAqfy23P+MtFLj2cLNUKTWtkB3UhuVxQt1gmyrR6rVIi1S9/l6HCVQZ9YF960vu//YpZJio98GVZwF54Ciy/0L6BqdKR7AdPCJpWls+CRiAq8T2XlIVupdTk8jmpPO4Lz6oS0J92TT6Ze8vLj4kZT/9UzvHCTXDi6+0Y6USxTqmqWrfYmdcvNSOtX4P1smDet1uSYH0hqbI632u5XFoqPVqGbKRoWCqvW82C9XLNbuar71pRKRv8btuh0duXuh5jpesgg1aIc8+5yNlOCPFuScaJtDv3HkqN5fbKRp/BA9W5IUUpG/x06/5J7//MazdN/U2Ht8ELvwMM2PAK+wtGJHqllXXLuvLYrBFpk64enc8OV9st4ziY6JZqvC1rJTG1EoQapBuJUur4Pfx1ALbVXki6f+J64scvdGAOI5eWOYH6ZeUzL64qi78GynzPjFLF9I17jp5/+RrvxDAsskPref+VG4llulldv5qgR0txK3W89OxIFUayTyZebWrVk8lbfPXRGL9/aWKlhOtPCHH5ijGBwbJgy1ek2tGScyQpqtgsS5JqzRw0r4X6Jc5MnvhC4Fsii/GxTujbI0m2vqAs8BiVV7za8HlZ+NG/GL1NakDaEDUsl2q1xZqkCjVKJbN4p0wAKlXthg5LoqrH+WoYiazJ17bG+MULE2PGu08Oc8GSWYxhzwOwdwsYbqlaW84t2qrYhBgxVi4t8aJ9U3klWfnCshCT6tPkWqWmk+iW88Ka6XdnD6ZNbn48xo+fmVjF80NnRDhz0ZhzyWQfPP49uX3aO2VB2m7pITl/bVxRfYsbbi/Udcj/WeyIbBgcOiKx2B8eTpSe5fWMmZdjfGZIvj/cKtdgocYpY7rh87Hw858bvV3RAlGp6JxNaLKUUnPQF8/wP/fv4rsP7hm+x+K9nl8BsLPtZZy0pNG5hPVMQl67YZkmxVeoko1DwXro3zPz85RS43QOpfjulj18496JSYefetUJrF8wTeXaez8jnxefCW0n2DuwXEqqUS/YXPiCFPMRbpSfxcEnYOiQdGIot7m6TFw6D7atmvU5eMnGhrH8Ebk2zqUKMu+rVMXpfB523YOJi3ftuXTCw198WT0rGxzoyhnvhtqFUgxKVYWSiyneoMzZmbnSKZKmVBnY25PgJ1v38cU/vQSA4e3BU/skADee8GrCoRSWFaY5pMd3ZY+Six8F4mhm3T333MO1117LggULMAyDX/ziFzN+z1133cXJJ5+M3+9n5cqV3HLLLU4OURWCacqkq2XadjH97SfjfO6hIZ7vyU14zH3sJMq+h2D/I7Kb+cz32vL+82LmZcLH7YMFJ0u7H6cTPt1euSjqOA0Wbpb/h4GDUqGv3FsnHcNwu4leeCbRC07HSHRJ8l7rRmhZX9zd3y4X1C6SvwNz4u+t0phRVdIxSfQPFiaB8UsPx/jCwzH2DOQnPBb2zeJUKJeWqrUAJ7xWknFUWRqNEReeieEek4RgWVLpvW4JRBcWb4DHq6YFspNXZq5UGjPUnFiWVD41XDNOzn7q/kG++HCMw3FzwmM1vmOuMx79H0lGbFoNqy6zc8TCzMv5ev1yZysfljqPX6r2dpwJHadLwm0+L+cSAwdkE2GqX1qPZhPykYkdrZI/cECScwEaVkLHGbDwZKk2PM0CvOHxEH3Zy4i+7GUYngqf1Pf45edRoe1bNWYop3zkZ9v44p9eoi+RBeB043lOdr1E2vKyp/liCNQ588aWJRW4azukOpyqSCUbh3xhMDwVO7elMUM55S9+8Dhf+tNLpLKTXGf4p/kb79kBz/xCbq9/hb0d0SwTYl1SpbAcq6AH66D9RPBFZBN/Oa1z5LOQ6JNiIDWts/62ko0NY/lq5CMTL/ZIHKcxQzliuGrtbflTOMTEglWRY+em7JBJSHypXzr/7qqqbJRcTPGGJH8hV5nrHBozlFNec9MDo4m1AL7GuzEMk1xsNcuiy4llYiysWahVa5VtSi5+FIijZ0jxeJxNmzbxla98ZVbP37VrF1dffTUXXXQRTzzxBB/84Ad5xzvewW233ebkMJXTEt2y8GnjhP9z3dkpHxu3Pmrm4aGb5PbGV0tyYzGZORg8KAuXCzZDZPYTJ7Zwe+VnsOh0aD8BMGBgf+UtoubSsojur5VKxfVLSuOCMNQIgXqpmqYm0JhRReJdkI3LRGsBbOvMTPmYezZzUU/9CIYOQqgJTr7BvoGp0hHvkuSHxhWlES/myh+RhMEKXeCejMYMNSfJPvk7n0WC6mOHpo4ZkbEbMnp3wfO/kdtnvs+ZjhDxLrleqOuw/7XLkdsjmwnaNsKSsyRJtnU9hFskySefg2xKPvI5uS/cAq0b5LmLz5J2q6GG8qtqVQihBrl+LqekhFnSmKGc8uCunnFfv8fzawB+kj8fb7TFuYqy6UHw1uimP1UcIxUJsxM7w1QCjRnKKQ/u7J3ysWhwmgqEW74CWLKWsOAkewcV75ZrpMbl5TkXAnIO27ZRjk2DB+V8ttSZOUkGrlssFegr7drEMKTyZTZV7JE4TmOGsl1qAJ78IQDfyV8+6VMcSa5N9MrcUzVv7FbF5/GDNyCVzyuQxgzllM6hownphqcfb91WADLdF+Nypwl7tWqtUnZwNI34yiuv5Morr5z182+66SaWLVvGZz4jbW7WrVvHfffdx+c+9zmuuOIKp4apnGTmoW+PTM7YWDXUPdu5nu23Qd9u8Edh85tte//jks/KpEl0oSwCO9E2drY8wzsQwy2SXNu/Ry7aQk3Fre5qAyvew9DdD0G4mch1F2EES6itqdsjFYQPPQlBXdQ/lsaMKpFLS1vnAiXWArjm87c2dOhoy+8z3wu+kD2DUkVh5fMM3fsIAJHzTpPqtZm4JBI1rynf/19/VMaeiUOgMBWhi01jhpqT2BFZwJzFea5rmpAxbr35oa9Kpael59m/yA1SfRUDGlbIBjk1njcgHwxPDFqWXG9Zw5XADJf83OZxDmDlcgz98Y8ARC69tPJ3YfujwxVCKm8RQ2OGcsrYzklrjb1c7H6CvGXw9fzVfCzs0PWOZUJqUFph+8LOvIcqCSUbh9xeqRYZOzLrNublRGOGKoZIYIq/73gPPPG/cnvNVbZ1BQSkSqFlylxIMdcp7BCslwq2nc9Jgm2k1d4Kv3YyhztwRNrlZz/HjTglGxuOFayTa7EKb+2tMUPZ7okfQDbOdnMhW8z1kz4l6rd5M0R6SOaVi12cShVcScaUYL0USahAGjNUIfga78Ew8uTiy8knl2K5EiwIr9aqtcpWJRk/CqCk/pVbtmzh0ksvHXffFVdcwQc/+MEpvyedTpNOH83GHxwcdGp46njEjsiHzRVaXUzTvnPkoVwKtn5Lbm++vrgTviOJtXVLoGWdJLeWAl8ImlfL/0/fHkm0NQyprFtukx5mDmKdWDmDA/8liXBrXnH9NL8pRRJukgWwbEIXwuZJY0aZindJpaXogoK95XQbMmbMudnyFchnoP0kWHGxncNSRWBlshz4+BcBWPPbb2H4LdmZ37xOqiGWK7dHqoL07a6a5Nq50phRxTIJWbwMRGf1dPc0gWH0kX0Pwb6H5Xz5jHfPf4zHsixZSG9cKZ0P1MwMw/ZrLCuT4cAHPwTAmse2Vv4kkS8sMSQ1UPYbLudLY4aaLdeYHRnv8kg189+bZ7DHasPlVAXAZJ8sOBbwekoVR0nHoVAD9O8r9ihKgsYMZYeQb4oEy0e/KWscDcthydn2vaFlQqJHkjvLeS5krECtzN11b5d1jnBj6SUNW+bRxNrWDcObBef4EqUcG8YK1EphhUxi1tfi1UBjhpqWZclxn5GqtZPPT43rqmTHeyb7ZW68AjdNqemVZEzxhcEqgyr0BaAxQ82V4Y7jrZPiQpnuiwBoDIW0aq2yXUnGjwIoqV4vhw8fprV1fBJma2srg4ODJJOTt5r61Kc+RW1t7ehHR4e2zCwZ+Sz07pZFTpsTNaerKLWgZngyattPpbVRTStsuM7W95+TsW1+SimxdqxArVQ9WXSaTJAPHZEJtpHKT6UuPQSDh6US74LNxR7N9Hxh+Z1M6QntfGnMKEP5nCzAeQLOtM+ewnTv1BKepkLEvodg930y1nP+orKqTVuWVMuo0PbPs2JZEu+iC6UNX7kL1unE0zQ0ZlSxeBdkYrOumD7ddUZD0CXn9g9+Ve7Y8EpnKnuk+qWKaP3Syoo9qrQZhiRXVGDl2rnSmKFmayRmNNPPNa4tAHwtdw3gQEUpkBiUScq5a5Unwasi89VItUUzV+yRFJ3GDDVb011nhH2TrJ1kU/DQ1+T2mivtLdKQ6JWNGnVL7HvNUuANSNJq00r5N6aHij2ioyxLEmtDjdLVsFw7J82W2ytdEjPxYo+kpGjMUNPau0U2CHgC/CJ/zpRPC/tsnCdKD4EvArW6cU+VCG9I1uPKJT/BQRoz1Fx567dguLLkkwvJJ1YCsKyhnZC3ws87lSqQkkquPR4f+chHGBgYGP3Yt093zZeMoUOQ7IFgg+0vPVUBkNeuD/JnJ4ZlQfqJ78udp72jeG2ALFMSa6MLSzexdoRhQE0zLDhFWtu6/TBwQCoXlWrilZmTpNpcWialFpxUHhX7alrkd8PUJKhC05hRZIke+QjWFfRtXVOsYLxxY4jr1kxRxcLMwQNfltsbXy1VQsqVZUpiWaxTjusDB2DoIMS65L7BA0fvj3XKpFo1LJIme2RHfvNqqfxa7vwRid259MzPVbOiMaMC5HMwsE8WomeZpDrVovcNJ4a4YIkftt8mVaL9UTj5BvvGOsLMQToOjcsrf8FVlR5/FAyPXqccB40Z1Wmk2vmb3HfgM/JsNVexzVrOOzeH2dTqtf8Nk33SDafG3u5QSs2Zr0YWvrOTL+qq6WnMqE7uKS40PnDxSjoaJjnvf+qHkOiW4/6qy+0bSD4r8waNK4+rcmrJc7mhaY0k2WbisrZRCmJHhgucbKyebnahet0EbgONGVXk0f+Rz4vPIsbk80F/c1bEvk18liXHyLqO6jkuqdLnDUhOh65xHBeNGVXMyOCtfwCATM/5uF0G158boD3cVuSBKVU5SiqToK2tjSNHjoy778iRI0SjUYLByZNf/H4/fr9Wayg52ST07pITctc0VQGPQ860+N9tiQn3f/7yOq5bO3zB8fD3IBuHxlWw8hJb33/WRnYjh5sl8bNcqoq4PVKBK9wsiVZ9e2BgvyTDlUpbkJGLvnQMIm3QuEIq7gKQLerQZiVYLz/LzBAE6oo9mrKlMaPMWJYcS1xu26uZT/2WFpk83L5zYgW277+ykbM7pvldeOYXkpAVrIdTbnRukE7KpWUB3syDNyzH9VCDVA52eY/uVDFNyGekUl2yX46vsU75P/OHZfe6zbG8JJgmNK8tndg2X96wnHdlE+VzzlFAGjOqVKJbjmmzaHVqWRaJrMUTRyaeS/7x+mZWNnjluLr123Ln5jc7c/xI9ECkBSJaNUQVgT8qSd2ZIfDOrtpzJdKYoWZiWRaDyRwHB1J4yfFmzx0AfDt3BVturKe91oE21GYOchloXSrV4JQqJo9P2nzHu6r+2kNjhpqJZVl0xzJk8xOLVzz9sSuo8U8yR2aaRzd8r7rC3mISieFOf7O4RipbLtdwFxAXdL0gc1zh5uJ0BbEsiHfKXFzrBjl2VgtfzdFN4FUeK0ZozFBTSvTCs78EoHvhJfDsxKfs+PP2KTdqHJdMTOb9ozr/pEqIJygxI6/JtRoz1Ewsy2Jfr2x49dY9isuTwMw0kBvayDfe42dF3TKtWquUjUoqufass87id7/73bj7br/9ds4666wijUgdt5GKpza2SbUsixt+2cu9eyc/ofJ7hi8qBg/Bs7+Q22e8u6Ctx8eJd8mCe8t68DqwsOI0j18qZkVahysa7oP+/RCqm3VbXUdkEpDslTG0nyhVgcut4qDbC5F26NmuybXzoDGjzCR6ZTI7VF+QtzMti9f9pIdHD2Umfdw3Xa5oagC23iK3T31bcY95xyOfgXiPLBqEW2SCLFg/+4okuYxUr030SoXbocNynA3UVdZkeNNqiXGVwuWSxaKeF+X/W42jMaMKWZacwxquGTd1ZPMWr/xxN093Tb5Jy+8evs547teyMBpugvXX2TxgZNHRtKB+efmd36rK4PZI+9bY4apOrtWYoaaTyua59kv38WJnDIArXQ/TYvTTadXxe/N0/s2phayRqrXhZmdeX6m5CjXIuVYlXSMeB40ZajqDqSzXfuk+9vRMLBQCEPBMsW7x4m1ybe8Nw9pr7BtQNgkY0LCsMjdRj2UYUL9Eqmx3PS/Hq0hrYTeojHQ19NVA68YxxUGqhK9GPjLxqo8VIzRmqCk9+QPIp9nOEi6/fdmkT7E1sRakyEbzOu2apEqLyyVJ3/HOyj9XmYHGDDWdA/1JXvHl++mOpYE8voZ7Acj0nge4CXmDtIQqeDOdUkXgaNZhLBbjiSee4IknngBg165dPPHEE+zduxeQ0uQ33HC0leZ73vMedu7cyd/+7d/y/PPP89///d/8+Mc/5kMf+pCTw1R2Sw1C/x6pdGrjbuBYxpoysRZkHRqAR26Wih4LT4VFp9r2/nOSGgQMSawt993IvrC0zO44HZpWSdJV/z75N1oTd9w7JpuU1uWZuLSt6jhdJsjKNfEg1AgY1dF6fZY0ZlS4wYOAWbDJ1N39+QmJtTXeozHJnO7wtfXbsnO7YQWsucqhETrAsqTiYaxLqpAsOh0WngzR9rm1+vP4INwIzatgydkSS0PNkOiTDSzl3JJnbJvr2o7ijcMpgaj8HhQyPheJxgw1o1S/bHabRbL5Y4czExJrW8NHL5VNC9ng9fj35I6T3+JMPIt3D3eQaLL/tZWarVCDVJiqIBozlJ3ueK5zNLEW4EbPbQD8b+4SsnhwOzHTaubkHLxucfnOgajK46sZnvetrGsPjRnKTj/dun9CYm0kcPQ4PmWi1ANfks8rLrJ3U3CiF2oXD89LV4maZpkbq1skia7ZyROdbWfmYOAgBBtgwUkyz1ZtRjaB55LFHoljNGYoW1jWaKGPb2cvAY7GhmkLhMxHekjWfqPtDr2BUvMQqIV8GXSpnSONGcpOn7t9+3BiLXii23D5+jBzYbL9khvVGmqlptwKRylV4hxNrn300UfZvHkzmzdvBuDDH/4wmzdv5qMf/SgAhw4dGg0YAMuWLeO3v/0tt99+O5s2beIzn/kMN998M1dccYWTw1R2sizo3yuLzza3Sc2ZE+87Y6Fv9PayOo+0+dlxx/CD77b1/Wctl5YLk+bVMnlTKfwRaFkrSa3N62Tn9cB+SQJw6iTXsuRnOXAA0jGoXQIdp0HLOrnwK2eBWvmZZmIzP7dKaMyoYKkBqYAWrCvYW062vHfh0qOJIu2RKWam+vYcrX5+1vvKZ3dsPivHSpdXFg0WbJaJ+/lucnF7IdImr9lxulTBTfTB4OHym+CwTKmePMJVpMr2TvKFq6ZtksYMNaOhI7KgOYsk2Mk2XFyx4uimhNqAC57+iSTsRhfCmpfZONBhmZiMtX5xcdqVKjXCHyn/a61jaMxQdsqP2cR0grGTU1wvkjc8fD9/CQA+twPH8GQ/BBulK4VSpcIbkk5d2VSxR2IrjRnKTvlJLjTefu7RioTGZOf9+7fCnvul+8b6l9vXlS+TkOuNukXVd73hC0PrCdC8Rua0UgPOvl8+K5vTI23SeS9Q6+z7lbJAVCZpK3QTuMYMZYs9D0D3djKGn1/mzx730IfPtHedfVRyQDZb+DXxSpUgb5BK28AHGjOUvXL5kcQpC1/j3QCcUHsJWNKloUXnj5SynaPlDi688EKsaS6abrnllkm/5/HHH3dwVMpRyT5JuLRxJ+53n4oDcNnyiVX33nVyDf98npuDQznWN3vhdzfLAysvlSqrhTaSuNOwXC5MKpE/As0RmYiLdw8n2PaAlZeJKn/NjK13Z5RLQ3pQPnvD0LhCJqMCdTNO/hleL+2f/OTo7ZLl9kBNG/Rsl3+X0phRyQYPyYJbASrx3fx4jKjPxcntE//+33xCmHdsrqEvZdIRneI49eBX5Vi+5BxYeIrDo7VJJgHJXjkuN612JiHGMCS2hxqkqmLfHhg6JFVuQ432LfY4aegIRqSR9n/7F3D7SztGHC9vWBa5s0nwzKFacRnSmKGmlUlI7Jmhg8RXHhliUdRNW83EjRSvWhvi2tVB0jmoYwie/JE8cOrb5n+ueyzLkkXe5rXVvfBaYsrmusJuvrDEk3xm5ueWCY0Zar4sy+Lzf3yRde0RxhYZfMtw1dr4grP5wtpW3IEa+5NrzbxcS7Ws06q1Vabk45A3KDFjcKCiNmVozFDzlTctPvOHFzhtacOkybOvPnkRJ3XUEQ1O8Xe9Zbhq7eKzZI3BLsk+qF9Wvdcbbo/MmXkC0POSbFCvaQa3b+bvnYvUoHzUdUiBkrl0kpqFko8Nxxq7CbwC56k0Zqj5yuRMtv/6C2wEnq05i1gyNO7xV68LsbLeS2uNjXPvWrVWDSvZmOINytzr2C6EFUBjhpqvZCbPZ/7wAldsbMM1fJ3hDr+IO3AIy/TyzlOv5uSGOCcsaCXqK/PO2qqklWz8cJjOyir7mCb07pLFYW/QlpccSpv8812yk/g/Hxgc91hTyMUly+SCfGOLFw49CfsfAcMti97FEOuUVjeNKyuzIt5Y3qBMEkUXyG7veI9Upox1D1cJ88mEiccvlQ+nSryyTFm4zaVkwcjMSRvSYC1EFkjSli80+fdOwvB6qXvVK236RzosWC+b78x8+VTHVGqu0jEYPCh/0w7bP5jjE/dKrIj6xy9grG/ycNaiGaoX7nsY9j0oceSM9zg1THulBqXaYdNaaFjm/IK7YUiSdLABYgugd4csSATrbK9Yb6vYEfCFMdo3UbfygmKPxjkul8TN3h0SY5SqVvEuyAzJZoApPN2Z4dNbhgDwHXOaetFSPye1jVlkfegWyMahYYW0ZrVbegD80WnHqwqvrK4r7GQYcq1n5Yo9EqVKxn0vdfOFO14cd18jA1zj2gJAdN3FnL2s3pkNZ6l+CNVDjY1twVVZKPk4ZBhSUblvT7FHolRJ+c1TB/nvu3YAOyY89u4LltPREKKjYYq57r7d8Owv5fa6a+zb1DfSJaOuyq83DAPql0iCcc8OWctwe2X+xI5iIYke+Tm3bYTaDkfm6Eo+NhxrZONeJlGRybVKzddP732SV3X/CQz4aPcl4x77qzMjNIfcXLrcxrVDy5KuGM1rtWqtKt2Y4g2COyBzu0qpUV+96yVuvm8XN9+3a/Q+X+M9AKytOZ/6UIAzVmU5qWVJsYaoqkTJxg+HaXKtsk/siFSxq7GvzHgmf3QHz1Bm/G4e79g1C8uCR4ar1q69WhI+Cy09JJMwzWts35Fc0lxuqWQYapDErvSg/CySwy2W0kOSPGtZR6vOjr0NskPc45dJp2CdJBf4I5XfoipQC74ayCZKOylNqfmIdcokfl2H8281Jk4MpsfHDI9rhuOJmYMH/1tub3xVQcY7b8l+mbxv3Qh1BW4j7nLJ7vZQgyTX9u2CwQOywcTuqh/zFe8Cl19+TsG6Yo/GeYFoxbbbU2pW8jkY2Cebs6Y5LvYkzdHbGXP8Y+6x3xfvhqd/JrdPe7v9iVOWCakhaDthThvKlHJUsE46kyilADgymJ5w3xvcd+I3cuzzr6SjdYMzibWWKckoTWsk+UepUuOv0WQppY6xvy855WNB7wwJUg/eJMf+thOh/SR7BjTaJWNN9VatPVawDhZsluTavj0wdETWOIL1skYxF7kUJHrlPCDSLusjutn5KMOQucLuF4o9EqVKUu2OX+E3sjxrLuEpa3y18rqAA9cX6UFZi6xdaP9rK2UXjx98QUh0FXskSpWUl7pi4752BfbjCb+EZbk4relyBlIDtIXbtGqtUg7R5Fplj1xGqtZ6fLZO+OfMqR87Eh/z4P6H4fA2Seg5+c9se/9ZM3OSSNq6URKNqpXbczTRtn7JcPvCpCTX5jPytTX8/2a4JBnZ7ZUTZU/Qlmq/Vi5H7L77AKg591wMTwkf5jw+qQA5sFeTa1Vlyqagf++MbbntMl1u6eH4DAkiz/9WKoT4o3DyDbaOyxHJfjmutm0sbqVDjx8al0O4EXp3SqKtNyCVbUthg0S8CwyP/JzCjeUVI46XLyyxNZ8pvURnpQoh0S3n5TNs+JvuCHU4NiZmPP5daWHZukFas9ot2SeLr8XYHKimVRUxYyp1wxW9tAW9UsDEmOEhx/WePwLwe+9lvCtQ58wbpwYkRmjV2qpUFnHIF66uAgNKzcJ0UyEH+6dOvCXZL9ceAGuvmnuS51RGE6mqvGrtsVwuuQYLt0jF2YF9srEyn5GKeb6QbB44dvOMZUpCbSYhn91+eZ26JRKzHZ4LK4vYcKyRbma6EVypCU7svRWAn+TP59irjkMxmze8WpZ0wWvdKOdwquqVdEwJ1IG1u9ijUKqkGMfEiZGqtbnBTWQzEQwjT2u4FaMU1mZVRSvp+OGg6vhXKucNHpCFbJt3u42tXHusT1085qL8kW/K7fXXyU7YQot1QmSBVF5VR7ncBW8tYmUy7H/PewFY89jW0j+Yhxqk4qNSlSh2RCbxCzSBP90c7T+dN011jvTQ0Thy6ltLP9k9NSgVa9tOKJ1d5oFaaNskC/89O2BgP4Qai1eF0bIg3ikVa9s2Qo2cG5RdjDgevuHqUdmkJteq6mNZkuQ/solrGtPNMX3ozOE4MHgQnvuN3D7tnfYvlJp5yCRhwRr7Fs+VbaoiZkzF5aruTaNKHePYw//lrkdpN3rpsqKcsPlMZ6rKWhakY9B+omzMVVWnLOKQLyxJaEqpUccueo/1pjOmadH62HeGOz8thsVn2zeg1CC0rNdEqqm4PRBplc2ZqQH5GJnPTPbBZHON3oAUEqhZfbQLX4GSGMoiNhzLVyM/s9w0yeVKVaPuF1mUeJac5eJX+YnH/evW2HyOlR6Q41W03d7XVWWrpGOKv0bXNpQ6xtjTTcPbgyeyDYBMz/msWZSlKdhCnb+uOINTVaWk44eDquNfqZyVHoLe3TKhYHMbvPQUybXrmjy8fsPwhNDu+6B7uySSnPQmW99/VlID8t5NK7Wyj5o7f/RoEpQuSKhKkstI1Vp/TcEmmKfakHH+Yj8vXz3N39fj35VJ87olsO5ah0Znk0xcPto2lk5i7YiRqh/Bemmr179HFnFqmmZMcrOVZcLQYZm8b5WKtVXF5ZbKwUMHAG35qKpMsk8qVs+iDedUi96vXR/kkmXDFdi23gJWHhadBgtOsm+cI5K90sUg0mb/ayullLLNsZczN3r+AMCLTZdy9gqHNninh8AXkYp6SpUql1s6UWnClFKjppoC+/grNnBSR93kD+az8NDX5PaqK+zb9J2JSVKtXm/MzDAkUTZYJwnO2eRwR760dC2E4S58PvCGZB5fq4LNjjcI3jBkE7avHypV1p78IQD3mCfSfcwc7k1X1bO60cYNfJYFqSEp1qHrkKoceIKyyVSrnis1amxFWl/jvRiGRS62hk+96kSaInHawm249FxLKcdoJqCaH8uSBJpMDOrsr0yYmaLrxbtOHq6Gaubh0eFqgye8RiY/CsnMDV+QbJSqfUrNlS8sE6aZmF7UqsoSOwKp/oK2uZ4qufbGTdNU5xjYD0//TG6f+b7CJoHOVS4NiT5oWVfaldK9QWhZK1U/endKoqs3WJD2eOQzMHREkhBa11VvbA7WQt/uYo9CqcKLHZHz81lUgc2Zk8eM164brrjduxNevF1un/Z2u0Z4lJmTjSitS5ypeKiUUso2YzdkrDd2c4brebKWm6Z1FzjXqSE1CM1ritcJQqnZcnslMVApBRzb1PuoC1dPs1ni2V/C4H5pgbzyUvvmTlIDUL+84J3lyp5hSPzVGGwPw5CuGD09kmSrlALThKd+DMDP8+dOePiUBTZX7BypWqubLVS58AbA7dfkWqXGGLlCMNwxvLWPAlK1tjaSoD5QT71/5oIjSqnjp6nran7i3TCwT6rSOWCqyrWj7TB2/EmSR3w1cOLrHRnDtGJd0kKjlJOcVGkzDEkCy6aKPRKl7JPPStVSb7CgFQnSU2zIGK1AOJmHbpIEp47TYfEZzgzMDmYeYp3QsEw+yqE6RqgB2k+Sao8uryQyp4ece7/UgMTluiWwYFP1JtaCLFa4XPJ7o1S1yMRh8KAkl8/CVNcZpy8cTsx99FuABcvOh+a1Ng1yjEQv1DRDTav9r62UUspW5pgFvRvdUrX2VvM0Vi9b7Mwb5tJSpadGq9aqMhBuko5MSqlpLW6cIlHTsmDLl+X2ykvlb8oOuTQYbk2kUqXBHwHNj1LqqL1bYGAvKVeQP5inTni4OeS2771GqtbWL9ECP6p8eIJ6jaHUFLwND2C4cuSTHeQTy/G68yyoWYDbZWPsUEpNoMm1x4r3yMSDmlk+C3075bZDJzhDaXPCfed1+KXsuZmDrf8jd256g33tkmYrE5PqDA3LwV3ClQ5V6QtEJVFOk6BUpYh1QrJfKm4U0EBqYsy4csU08eng47D7PkkAPvN9Do7MBrEjEGmFplXSerNcuD1Quwg6ToOW9ZDP2Z9km8/A4AE5hrZtlI9qnyj0hYfbs+o5raoisU5JsPXNripT/yQx43Xrhxe8O587Gh9OfZudoxT5rHzULSmvY7pSSlWp3ngGgDqGeIX7fgC6F10u1Z+ckOqHUFN1bxZT5aN2kVyDlcMGUKUKYCA5sZLzX16yaupv2PugzE+5fbD6CvuuD1L9UtAhqBWsVAnwhaVbWD5T7JEoVRqe/IF8CpxOmvFVav/xXJuvMUar1rbb+7pKOcnlmlVnMqWqSedQEow0vvotgFStffvFQer8dTQEGoo8OqUqn2YEHqvnJZnIWLi52CMpfYMHZBHbwd3PH/5D/7iv/3h9M0tqh39tX7h1uDpVPWx8lWNjmJRlSmvu5nVSmU+p+fBHwBuCXHLWCSFKlax8Dvr3SqWlAicMve/3feO+vvctLbTXTDEGM3+0Msi6l0P9UmcHNx+JXjlGNK8t3wkFbxCaVso5w+BBSbDt3yetCf3R4/tdyWcg2SdttKILZbOLJiAIb0DaF2Zi2sZQVYdcRo4rc2h3+nd3DIz7+p4bW1gUHT4WPfIN+bzqMmfiQ7JXKtaGm+1/baWUUrb7xG+fA+AN7jsJGFkydSu58fy1zlzvmDm5pqpdqMmKqnx4bG5drFQZ+++7doz7+u6/uZDFDdNcl4/MTS09D+psqohu5iWWRBdoLFGlwVcjc4OpPkm0VaqaZZPw7C8A+Fz/eeMeuufGFjqiDlStbTtB5ouVKidur+RjKKUAeHBnH976RzDcScx0E1953WV4fP0sjCzE49K0P6Wcpn9lxzLzkBscbsFWpgkshZAegp5dsoDtwMH65sdjuAzoTo4/aVpe78FlGPL/89i35c6T3iRJR4WU6JGk2nqHWgCq6uLxS0JYvEuTa1X5i3fKMTJSuDbXX3lkiPrgxGL8HdFp4tP2W6Fnh0zonvoW5wY3X7mUfLSfVBmJo/4aaF4tyQKxTkmGGzosiz2+kMRz9zQLs/ksZONSnRK3tFSv7ZAENZc2ZBgn2CB/i0pVg0S3VEyPTl+Fw7IsPvfQ0NHNemMsHrnvwFY48Jhc45zyFvvHms/INWe9Vq1VSqlSZpoW/++25zlhoZyDu8lzveePAPjWXAZhhzZapwYgWCeVa5VSSpWFXN7kU79/njOWTYwNSxqnSSTs3QnP/1Zur77CvvWozJAUcwg12vN6Ss2Xyy3zVEOHij0SpYou+9xv8aaHSPmbeCi1dtxjiyeZr5qX9KDEg5rCrdUoZZtALSQHZn6eUhUsns7xH79/nis2NgN5fI33ApDpPY+acAqvEaExoOf8ShWCJtceyzCkClq8S9paqYksC3p3SzW0Ovt/RgMpk0/cOzjpY66RndbP/0b+j8JNUnGwkHJpSe5pXaEJ2CXI8Hpp/ed/Gr1dNkKNMHCg2KNQan5Gqta6vY5svJjMnv4cn94yNLdvyiTgkZvl9slvgUCd3cOyh2VCrAsaVki1kUriC0PDMjnXSvZJEmi8S6rCmzl5jgHgAiw59wCZjPeFoWGlJNQG6+eUVFu2MeJ4+GuO/tyUqmSmCQP7ZlUx/anOLF98ODb1EyzraHxYd60zLfMSw1VrNWmq5FVVzFBKTXDPi1187e6do19f6nqMRUY3vVYNDQtPA48DlZ8sS65VGleCW6dsq53GIaXKx2+eOsQ379vFN+/bNbdvfPAmwIIFJ0PrevsGlI5D0xqtKl2Byjo2BKIFmy9WqpQduucWFgM3x8/BwuFiEalBaN2gnc3UpEo+ptQvg9pcsUehVFF97Z6dfPfBPXz3wT14ok/i8g5g5mrIDpxMKptiaeNSvO4S/PtVFa3k44dD9ErmWIYh1YT690FNm05mTyZ2RBawa5xpY3pstdoJskl4/Htye/MNhU9wTfRIkpPu9CtJhtdLw5vfXOxhzJ0/IgkhZk4nmVT5infJMbKmpWBv2Z8+jrYwT/yvJHTWLoIN19k+JtvEuyX5qnFF5bbxc3vl96WmBfKrpCptNikbWcycVHc0XHI+5vZLCzlfzXGfn5VtjDge3pDElXxWfs5KVapED8R7ZNPdDAZSM8SMPfdD53OSMLX5epsGOEY+I4lTdYu12nYZqKqYoZSaoC+RGff1je7bAPhh/mLeV+PQBolMTM51w87Mt6nyonFIqfJxZDA1929K9h1d41h1GXinqXA7F7m0zC07FatUUZV1bPCFh9fydCO4qmKxLhZ1PwDAz/Pnjnso4rN5/j89JNcWkTZ7X1dVjJKPKYah6xqq6u3qjg/fsvA13g1Atvccmmr8hL1hmoJ6zq8Kr+Tjh0M0g2sygVpZpE1060nnsbIp6HlJklocSmrtSeQn3PeWTWGuXR2UL575mUw+RRbA2qscGcOUMjFpV12/TBfElb18NZIIlU1Koq1S5Safg/49MoFfwATx3kk2ZLz75DBXrwpO/g1Dh2Dbj+X2Ge8p3YvzbEIq1zatBK8DVbFKkdsD7lo5D1Pz5wuDJwi5VOn+nis1X5YFgwfl9ix+zwfTExfx/vL0Gl62MijJ/I98U+7c+GpnWqhq1VqllCob2dzRmLHK2M/Z7mcxcXHVRRc7d82e7IfmNXIep5RSqmy4XRMToj7+ig2cv2qazRJbvy0bjOuWQscZ9m2qTvZCuLV0uzSp6uUNOlP5X6ly8vRPcZHnCXM5O6yFo3d/8uJazlts85p7sh+a1+q1hVJKlbGQVzr1ucMv4A4cwcr7eMvmq1jVnmdBzQL8bu2yrVShaHLtZFwewIKB/RBu0STKsfr2yKJw7cKZn3uceiZJlHrzxhCrGr2y0+7JH8qdp7ylsBU+LUvaVTevhWBd4d5XzYmVz5N4dCsAoVNPwXBP3x64ZHh8klAW79TkWlWe4p1SaTVS2Krek8WMGzaFWRiZIj489HWp5LngZFhyjsOjO06WKVUYm1Zp1SqblW2MOB5uL/hrZGFN44qqVKkB6aoxy3PzyWLG206qoTbgghdvh75dsuFp0xtsHihjqtZ26PVlmaiqmKGUmqA7nh69PVK1lsVnsnRJhzNdJbIJSTqJtNv/2qosaRxSqnwcW+0c4Iazlk79DfksPPQ1ub3qcvvWGswcmKZ0aqrUDkhVrqxjgzdUPQUElJrKkz8A4Of588bd/aaNNifAjlStjS6w93VVRSnrmKJUlTAt2fg9WrW2/wwuvaqGbD6rVWtV0VRr/NDk2qkE6yHWObzTVw9MgCRN9e+WKk6GcwvCXfGJi94R//D7bfuJXBTULYGVlzg2hkml+iX5sa6jsO+r5sRKp9l7440ArHlsK0YoVOQRzUGoQZL6lSo3+ZxsvvD4CrvpAeiKT6x2HvFNEaMOb4OddwIGnPW+0l1oSPTKeUj9stIdY5kq6xhxPIL1EDtc7FEo5Zyhw5K06p2iWvkxuibpkFHjM2Rx+9H/kTs2vdGZhHStWlt2qi5mKKXG6RqS5NoocV7lvg8A14qLnOuykOiTua5A1JnXV2VH45BS5cGyrNGYMWtP/xSGDso1+4oL7VtrSQ1Ioq4TXThUSSjr2GAYUrk2lyr2SJQqjq4X4NAT5HHz6/xZzr7XSNVaf42z76PKWlnHFKWqgGVZHB6M4wrsxRPehWW5yfSey1B6iGW1ywh59W9WFUe1xg8tmTMVtw8wYGCfVBiqdrkMdL8k1fR8zv1x/ODpOB+9e2DC/RGfIcmtI628T30ruAqYAW/mIB2HhmWzXrxXas58NVLJzJyY+KFUSYsdgUSPLAoU0M2Px/h/DwxNuL/GN0lCqmXClq/I7bVXQeNKh0d3nHJp+WhcqdUc1Pz5wqCnsapSZeIweHDWSUhffHiILz8Sm3C/22XAC787uri98VV2j1Sr1iqlVJn51O+f43/u3w3Aa913EzLSPG92QMsGZzYT5tIyx+VglyillFLO+OdfPs2PH51DsQTLgvu/ILdXXQ41LfYMxMxDJgn1S8GtNXVUiXJ5wNDfT1Wlhruy/im/iV4c3FCXicmcsFatVUqpsva2Wx7h3hf7RqvW5gY2YeVq8Xv8NIe066lShaYre9MJ1Us1pGRfsUdSfH17pOW3g+2pE1mTj/xpYmItQMhrwBM/gGwSGlfBsvMdG8fkg+uVf7u251NO8teAJwS5ZLFHotTs5bPQtxs8/oJWre1PmXzi3sFJH3NNVu31pT9C1/OyQeLUtzk8unlIdEv7vprWYo9EVQJvUP4u89lij0Qp+8U6ZcFgFlVmj8TyfPbBiZsxAEloeuw7cnvznzmzkS7RK4vmWrVWKaVK3p6eOF+7eycABiZ/5r4dgO/kL5d5QickeiHSVvDNikoppebnhcNDfO/BvXP7phdvh85n5bpj9RX2zaWl+iVO6XySKmWRdqleq1S1MU0yj0ty7c/z5zr7Xol+iC7UqrVKKVXGtuzo4c4XujB8XXgizwKQ6bkAgOZgMxGfA533lFLT0uTa6Xj8Uumuv8qr18a7oW+ntKx3qFpsbzLPJd/tnPJxI9EDz/xcvjjt7fa1SpqNfEYq1zYsBbe3cO+rqo/HD4GIJJErVS6GDkGyt6ALwYdjeU6/eQ6t7rNJePjrcnvz9aXbHi89JBPM9Uu1sqGyhzekLfdUZcqloX/vrBJr9w3kOONbR6Z+wjM/l+rrNa2w7hobBzksn5VrylqtWquUUqXupc4YF3z6rtGvL3Q9yVLXEQasED/Pn+PMBoyRTVDRRdIuWSmlVFl49uAgV3z+nrl/4/2fl8/LL5bkJzuMq1qr6xeqhEXaYOHmYo9CqYJ78ZHf44sfZNAKcYd5snNvlElIN7yoFopSSqly9eDOHt74jQcB8DXci2FY5IbWYWZkE11buK2Yw1Oqaunq3kxCDcOJQ1VavTabgu4XAUvaSDjk01uGOBQzp37C49+TJNfWjdBxhmPjmFS8F2raIGxTiyalphNsgFym2KNQanayKals7g05tvliMv969wCZaULGBE/9SDaKRNpg42scG9e8WBYk+6F2CQTrij0aVSncXqlSkEsXeyRK2SvWCakBCMzcRu/Dt/dP+ViEBDzxffnilLeA22fP+MZK9sp1hIMdQJRSStnjXd99dNzXb3HfBsCP8xeSxKEqa8k+iRGlugFQKaXUpEYWvOdk3yOw536pVrvmZVJowQ7JPokjWrVWKaVK0uO//hoAv8mfQRoH5p5GJHshsgACtc69h1JKKUe94etynWG4h/DWPgYcrVoLEPXNvCailLKfJtfOxBMAKw/9e6qveq1lQd8uiHc5vhjcGctPuK8p5GJhxM2HN8Th+d/Inae9vbCVPHIpMNAqgqpw/DXyO15txxtVngYPygR+gZNBDw5NjBkr6j20hl184LRj2h3Fu+BJabnE6e+2b+HCbsk+CNRBXUexR6IqTbBek2tVZcln5drMF5pVN4u9A7kJ921u89IccvGtpX+E9CDULYZVl9k/VjMH+Zy8vl5LKKVUydvdHR+9vcw4xAXupzAx+I3nMj52vgOLF2ZO4lrtIo0TSilVZgaS2Qn3XbtpAY1hH//56hMn/6aRqrVLz4XGFfYMxMzJ5vf6xVq1VimlSlEmwZXuhwH4ef680bvftDFEQ8DFl15mU0fAbALcfqi1qSq6UkqpovI2PIDhypFPLOba9RuoCcDn3rgGQ7seKVUUnmIPoCyEGmHwMNT2QLip2KMpnKFD0LtL/s2zWLieD49rYhBoCbn47RubMe75tkwSLTgZFhS4ZUyiR6oIhhoK+76qevnCUjUtl3Km5aRSdsnEpWptIOp4jDiWZ5K3Wxhx8+1XNE+8qHj4Zvl7at0Iyy8syPjmzMxJy6YFJ0nbJqXs5A0VewRK2SveJRsSIrNrceed5Dpjeb2Hn13jwfjRL+WOU98u1aPsluiFmubquoZUSqky5nW7SOekRcYN7j8AsLPmFH7xig6McM1033p8UgOyEUqrmyulVEXYtKiWL77hpMkXvLtfhOd/K7dXX2nftXqyT643arQ9rFJKlaQXfkfESLLPbOZRa/Xo3Wcs9PHvF9XalySV7IO6pdoVTymlKoErja9+CyBVa9dszvPWCxZxQvPyIg9MqeqlybWz4fGDYUkSUbChOqpJpIegazt4fI4n+D2wo5vH9/YD4yeU3rwWjMEDsP1WueO0tzs6jgkyCXD5oG5RYavlqnkxPB5a/uavR2+XHW9IPjS5VpW6/r2QjUmVpQJ6YEc3O7tiwPgKtNcuNSdORHU9Dy9KK1fO+vPSPZYnemVBfZaJYur4lX2MOB7eALjcYE6s+KxU2THzEn/cPvm9nsEDO7rpiaWA8RWczm/LYTz5Y8gmoWk1LDvfgbHm5KNuyazGqkpPVcYMparYrU8fIjOcWBsmyWvc9wAw1H4mhhML1JYl805Nq8Gtxxg1kcYhpUrXrU8fmvT+hrB36iSpB74EWLDwVGg7wZ6BmDnpVNO6UWNJldDYoFT56br/2zQDPzfPwRrTUDhqxDEMmzZa5FJgeCC6wJ7XU1VBY4pSpWfkOsNb9zCGO0U+3Uwuto7BdB8LI5twFbjYlVKTqdb4UT3/0vkKNUkl19gCiFZ48ks+K4m1mZjjJ+IP7OjmU797now1sUrHD+/fzuX7b6PZMmHxmdC6wdGxTJDsg/plUkVElQ3D56Px7QVOxLaTYUi17N4d+runSleyD/r3yYaTAhqJGVlrYjWO79z7AgsjBmevGK4OaFmw5Stye+Vl0LK2gCOdg3xWPhqW6kJIAZR9jDge3hC4AzLJ6vIVezRKzU+8Wz4irTM+dSRmmNbETSC/uOshrg3+QpY0TnuHM5svEr1yTqfVCMtWVcYMparUrU8f4r3fewxr+OtXue8lYiTZYbbz3097eevi/qPXGXZJD4E/onFCTUnjkFKlaSRmTOavfvwUIZ+Hl208Zv1o6DA8+QO5veYq8NlVtbYfgo0QbrHn9VTJ09igVHn506PbOP/QfWDAz/Pnjd7vI8tnb9uJ323Yc50R74HaDl1TVHOiMUWp0iLXGVuBPL6G+wDI9pwPrjRfv/MIJzSkuMqmPXpKzUe1xg9NbZ8tt1c++nZLEkylsizo3QWDB2TR2sEqf3kTvn7PLg5YjfQTGffYMg6y2DhC42EJHJz6NsfGMalMTCq91XUU9n2VAllgs8xij0KpyZnmcCzM2LcYMAsjMWOv1Uz8mErnyzkAyOP5kT+dXXfD4W3g9sPp7yzYOOcs0QORNl0IUc7x+MEXlGo2SpUz04SBfbIRwTX9ZoSRmLHLaiPN+KTyFeznDZ47cZlZrLZNsOg0B8aak2vGusVatVYppUpc3rT41189M5pYCxY3uv8AwJ35TfQRGX+dYZf0IEQXaccapZQqIxNjxkQf+/Wz5M1jnvHQTTKP1rQGFp9hz2DMPGRTUL9EN2srpVQJypsWT/7+m3gMk8fNleyyjm68WIpUJrTlOiOblHmyuo7S7dynlFJqWmOvMzy1T+LyDmDmImQHN+MK7MXK1fNvv3lh4nWGUqpgNLl2LkKNEO+SxNNKNXQIel6Sf+sMi9bz9czBAbpjaXqpHXf/cg4QNlK8yf0nXFj0t50lbfIKKdkvixyBaGHfV82blc+T3LaN5LZtWPkybYHtC0kLFzNX7JEoNVG8EwYPQk1hKyw9c3CArqE0A8dsxljJPkJGGgvojqV55uCAJBE+dJM8YdMboKZEE1dzadnUUrcEXHpKVggVESOOR6AO8ppcq8pcohtinbOqwvHMwQE6h9LEjtmMsYp9rHAd5FLX4wDsWPpGZxYekv3DVWtLNP6oWanamKFUlXl4Vy+HB4+eJ13geoqVroPELT9PWctI4Tt6nWGXbAI8AYhonFBT0zikVOk5NmYcywIODaR4eFfv0TtTg/DIt+T22qvAF7ZnMOlBCNZpBfQqo7FBqfLx8K5eLsveCcDP8ueO3r+W3fiM3Pj1jPlI9EJ0oVatVXOmMUWp0nH0OsPC13APANnec3AFd4HlxczVTLzOUKpIqjV+6JbWuXC5IRCRyq6hJvDXFHtE9kr2Q9cL4PEVpBphbyLDkDW+QkczfYSMNCuMg5zjfgbTMtje8VpOd3w0Y6SHpIVy7cJCvquyiZVOs/u1rwNgzWNbMUKFq6xpG29Yqtdkk1LFVpUuM19dFelyGejZKZsv3IVtL9+byNDP+LjbSg8BIzvheTz9W2m5F2qS5NpSleiTqrWhxmKPpGpURIw4Hv4aMKroWKUqj2lC/z4wmFX86U1k6KFu3H3tdOE3srzZfQduw+Th/BoSgRWstH2sedk80bJeK0iVuaqNGUpVmc6h1Liv3+7+HQB350+k0zq6QN2byNj3pskBqF0EgdqZn6uqlsYhpUrPsTFjVs/begukh4/7yy+0ZyCWCekYtJ8oazmqamhsUKp8JA9s4yzXbjKWm9/kzwSkYq3HGF+qdl7XGdmkdN2tXahVa9WcaUxRqnTI9YOFu2Y77sARrLyfbHw5bs8g+dQiwD3meUoVV7XGDy2TNlf+Wpm46Nst1eYqRTYJnc/L5wIl+WQsD3toH3efMdxU6c3uOwC42zyRQPOKgoxnVLIfajs0qVEVj8cnv385PUEqeQefgL49xR5F4Qzsl8qBRUgGTeY9HGB8ZSdjkkZ8Le4EPP49+eL0d5Zum9XccKWTusVatVY5zxPUBbdisyw49JRUk1Bzl+iB2JFZx59YzsMRGsbd5wKWGYe4wL0NgO/mL6Uh5MDfRaofgg2lWzVdKaXUOPH00QoLa4y9nO/eRt4yuNc8gRhHryVsixn54cXz6AJ7Xk8ppVTBDCazMz8JaIkE5EYuDQ/+t9xefSX4beqSlxqQDjU1bfa8nlJKKds17fgFAHeZJ9GHHP8nW8+Y13VGohciC7RqrVJKlbnOwTRg4Gu4G4Bs/xmAhZkPYOWPFp4avc5QShWcZnPMlWFATZNUTop3FXs09sjnpGJtvBMirQV723+4d/IWSmuNvZzufoG85eJW/xVsWFDASh7pIfDV6CKHKr5gvVQJVaUtl4DBg1KlrtKlBqBvl1RXKkK13r+8IznhPteYySgDaKrxs/bAT6TNavMaWHVZAUc4R4k+iGrVWlUg3gC4/cUeRXWzLGnb2bu72CMpP6YJ/XtnXbUW4M//ODFmGJj8mfuPANydP4Gh8FL7rzPMPGSSUL9YKocopZQqef/w822jt9/mvhWA+8wT2Gc1A8bodYZtMSPZLy28gw0zPlUppVRp+edfPjPt4wbQXhvg9GXDx/ht/wdDh+SYv+pye6oKWpYUf6lfItf6SimlSo+Zp3n3LwH4Wf680buNSdYzjvs6Q6vWKqVURbAsi3//3XO4AnvxhHdhWW4yvedguNJYuXqwPBOvM5RSBafJtcfDE5Aqc90vHa08V65ME3pelGThSBsYhfuV6E6YE+4zsPgzjyx632Fu5hUXnI67kL+lyYHhqrU1Mz9XKSf5wsUegZqtRDfEOos9CmeZJvTuhFwSAjZV2ZijzMSQMToZNTJ19MHNBq4XfitfnPn+gsa0ORk5d6jt0IkvVRieoJy/quKyTGkHmp2Y+KmmMceqtVPZZOwY3cD3v/lLeNf5y+y/zkj1Q7AOwlq1Vimlyk0TA1znvg+A2/KnMEho9DrDtphh5qVybe0i7V6hlFIVZiRm/Mu163G7DJlLu/8LcufqKyBs0+bq9BD4ItopQymlSlh+5720G70MWCH+ZG4evd+NFGmx5Toj0QvRhRDSRCullCpniYzEBl/jcNXagc1YuVqwvFi56MTrDKVUUehM7vEKN0lCVe8u2S1cjiwL+nZDzw6Z3ClydSUPOS5yPcEm106yuGk4/x2cvaKpcANID0lCY7S9cO+p1FS8QanOVu4J/BXPkCSpvt2Qn11ruLI0dEgq9Iabiz2SUT4y1BIHoLHGz0euXMNJe78jyWvLL4T2E4s7wOkke2URRKtVqUJxucCjlWtLQrK/crpfFMJxVK2dTIAU7/P+CoB73KfxlivPtf86wzIhk4D6peCxqXW4UkopR1lj5vOu99yO38jxhLmcA2YDJm65zrhqrX0xY6SNd7iAc11KKaVskcpO37WqrTbAV68/mZdtHF5b2H4rdG+X9YY1V9q3ATw1KFUKvUF7Xk8ppZTtco9/H4Df5M8ig6y91zGEjxzA/K8zMgmZ661dZMt4lVJKFU9nLIHh68ITeRaAbM/5GN4erFwNWN6J1xlKqaLwFHsAZctwSUJq3y5p3x5pLfaI5m5gP3S/AMHaok/GPPHONvZ2D7Lk/vtgENzrX86pG9YVdhDJfmhep1VrVWnwheXiOJfShKhSF2qAeLckoNYtLvZo7JeJQ89L8ns4j8QmO73wvnZeODxIbyJMQ8jHhgW1uHfeAYefArcfznxvsYc4tXwWLOR3RatVqULyBGTiVRWZBf37IbIA3HopNqNEt1StnUOVp5w5fuPjS+9vZ9+Tf2LZo3sxXT7Of/1f4444kNSU7JfrQq0gpZRSZWMoLYvbfjJc75YuSk0bL+PtjSdTF6mR6wy7TtktS66t2pYXfXO7UkqpuetPjN9Uv+OTV/Hwrl46h1K0RKRF67hKUvd/Xj6vuASiC+wZRDYp83N6zaGUUqUrE8f7wq8B+Fn+XAB2/PkCnjk4QG+i4eh6xnyuM5K9UL9cuicppZQqa7t7u/A13IthWGSH1vH992zgmYO9RI1VLKqrm3idoZQqCl3RnQ9vSCbGu7dLQmY5tXEfPAidz0pSra/wyaTp3NFF71UNHmoDBidaL8DgC+D24Tr5+gIPaEh+Dlq1VpUKl1sujIcOF3skaiaGG/xhqWQebi76ZgVbmSb07JQKS0XcBT2QNkdvn73Ih99jcOKi2qNPyCbhoZvk9uY3Q00Jb3hJ9srvyTzbmys1Z97g8GaNMu24UCmCdXIcSHRDpK3YoyltZh769shGhDls7uiMH40Z16wK4HFZLNspFUNcJ7waIg5UYbdMuS5sX6mbopRSqox0DqYAuM59P03GINS0smjDOSyqX2j/m2ViMu+kCVFKKVWWDg/HDIC3n7sMt8vgrBVTzO3sfRD2PQQuL6y9Glw2LcMl+6UFuCZTKaVU6Xr+t7hyCfaYLWy1VvMXp9fgdjF+PWM+MgkpolCnVWuVUqrc5c08jx/ejrd2KwCXdVxDPDvERatWs7x2eZFHp5QaS5Nr5yvUBAMHoPslaNsoCXGlbvAQHHlGKmUEbDqZn6N9g1IdJOAx+MObmzEAHv2mPLjhlYVPOkoNQNMarVpbAQyPh6b3v3/0dlkL1EL/vmKPQs1GoE6qgffvg+bVxR6NfYYOQf8eWQA2ircrbnf/cLukoIvvv2qSaoNPfF/arEfa4MTXF3h0c2DmJFmsrkOr1hZJRcWIufJHIDVU7FEowy3n4P37INyix4LpxDoh3jnnJKSRmLE46ubLVzbAS3+E3h2yEXLTG50Y6XCb71pNmK4wVR0zlKoSu7oTgMX7/LeCCay+AsIOVDcHaePduKK8NuarotI4pFRp2d0dB+DUJfX88zXrp3/yfZ+Xz8vOgwabFsXNHGBpcZAqp7FBqTLw5A8A+Ll5LucvDvDhM6P2vn6yD+qXFW19X1UOjSlKFV9fuo87D/wGw5UnbK3ghjPWkclnaAnqxmxVuqo1flTPv9QphgGRVujfKxPkjSuKmoA0o8GDklhruKRtaYH977Y4/3jnwOjXaxo9GIYBu+6RCsDeoHOL3lPJxKQKsU5MVQTD56P5A39e7GHYwxuW44lllfZxRcn/T6hBElHDTXK73KWHoPvFMdUuC+/mx2J84r7B0a/XNk1y2jJ4EJ76odw+8/2lXTEw2Q/BBtmYo4qiomLEXNW0yvlOKf+NVItgvWwISPRAjQNVVCtBPgt9uyUReZZVnr748BCfffBoAvmaJq+8ziPfkjs2vRECNi9ogJynpWPQfqL+fVWYqo4ZSlW4//j989x09w4ALnQ9wRJzn5wnLT4HfCH73zCXlnimmzDUHGgcUqo0/Msvn+bbW/aMfr2mLTL9N3Q+B9t/Dxiw5hq5prFDalASqYIVMOeojpvGBqVK28e//0f+8aU7cRvws/x5XDnZesZ8ZBPS3anWgU4bqupoTFGquN77va38/rkd1KzcguGCEyJXMZgZZElkCTVF6Dyu1GxVa/zQUkl2cHshVC9JSIMHiz2aqfXvg8PbJLG2SIlfYxNrAS5aGpBd1w9/Q+444bWFb2uU6Jd2Sv4ZJsaUKjRvUC6U85lij0TNhi8sSTy9OyGfK/Zo5iefg67tsvmgiInCYxNrAS5cEpj4pAf/W37uC0+BpecWaGTHwTIhm5KqtW7d26SKwDCcSSxUc+f2goFszrOsYo+mNA0dhnj3nBaOxybWAly01A/P/xaGDkpC88ZX2z1KMVK1tkYTppRSqlyMJNYCvM/zK7mx8jKoXeDMG6b6IdwsHU+UUkqVlbGJtQAXrpmhitR9n5PPHadB6zr7BpJNQGSBzikppVSJypsW/mf+D7dh8ZC5lr1WKxdMtp4xH8k+iC4o/Dq6UkopWyUzeX7/9GF89fdjuLLkkwu5ZMUJ+Fw+WsJatVapUqTJtXbxhcHrh67nINZV7NGMZ5rQuwsOPw0eX9ESpbL5ickD7TUu2H4rDOwDf7Tw7bwzcfAG5GJEVQTLNEm/+CLpF1/EMs1iD2d+vCHwBCCXLPZI1GzVNMPgIRg8UOyRzE//HklGmmMrbjulchNjRluNe/wd+x+B3ffJppGzP1DaFZ5TAzLppRdFRVVRMUKVt2ADxDqleq0aL5uSaxdfCFzumZ8PJLIT/54XBrLw2Hfki5NvkE1LdrMsqfRet1iuKVRF0ZihVGXqTxzdvHqa8Tynu17AdHlh5SXgRGUQMy+bF6MLSvt6RZUcjUNKFd+B/olzsu2105z39+6CbT+R22tfbt81SC4lBRgqoVOWmheNDUqVrhePDPIa990A/CR/PgDtkdnNa81KNgkur1atVbbRmKJU8Ty1vx9cKXwNDwCQ6bkQvz9BS6iFqE+L5KjSVq3xQ7e52ilYL4vkR54B14mlMdmRz0LPS1JJ0R8panXWfz+mAiFAsy8HD98iX2z+M0lSLqRkP9Qv00puFcRKpdh57csBWPPYVoyQAy0dC8Xlkso2Q2WeqFlNXB4I1EDPDkmkDNQWe0RzN3REKrEH6+xrXXccPnJH/4T7GoNj9gSZOXjgS3J7wyuhfmlBxnVcRlqGt50gm1xU0VRUjFDlzeOXzwP7JNHWpXseRw0ekAp/tYtm/S3v+HXvhPtWH/olJHsh0g5rr7FxgGOkh6vWRtqdeX1VVBozlKpMr/7qA6O33+f5JQBDHRdRW7/YmeTX9JDMxYUa7X9tVdE0DilVfFd87p4J9zXV+Kf+hvu/AFYeFmyGhSfZN5DUoMSRcpxnVLbS2KBU6frIF/+Hn/sPkbD8/C5/BnDMesZ8JfugtkO7YSjbaExRqnhe//UH8TU+iOFOkU83kxvaQF3YQ1tYu+Op0let8UNXce1W0yJVJg9vg8TERd6CSsfg0FPDSVL1RU2sBbjlyfiE+9Ye+bW0fK1phfUvL+yAsgnZ8a27/FQpC0Slyo0qH4E6Ob50v1h+/3epQeh6XioFOlG1aQ5+/sLE6iAt4TGnLU//XFqqB2rhlLcUbmDHIxMDX0RinVJKjQjVS7VzrV57VGoQ+nbLBo85JDg9sD8z7utaYrS+9GP54tS3ObNZxLIgpVVrlVKqnFiWxY4umZtab+zmIveT5C0D16rLwe9QwlImBtGFuslOKaXKjGlaxNIT5/UawlMczwcPwhP/K7fXXGPfvJplQj4jG/q0ArpSSpWkdC7Pa9yyIeP35mnEkcrlEZ9Nx+1cWrr31S7SWKCUUmVuMJUFI4u34T5AqtaCi7awVq1VqpRpcq0TIm2SWHXoSalkW2iWJZUHDz4OQ4cg2u5MG9R5qPEanNKQov2lH8odp771aAWvQkn2S1u+YF1h31epufAO7/SwrOKOQ81NpFUm1Xt3FXsks5dNQddzsvhb5KpK5jG/7z43bGr1srx+uOB+ohe23iK3T39n0TePzCg5IBs5fNWxc0spNUsePxhA/x5pGV3tLAv69kibuzkc11O58THD44KP1/8OVzYODculzbcT0gPgj8q1n1JKqbLQn8iO3n6v51cAPBo4i0jbcmcWqXNp2dQdbrL/tZVSSjnq4MDETd+vOGkBPs8US2pbviJJsC3roeNU+waSiUuirlZAV0qpkrXncA/XurcA8AvzfADeuDGEYdc1RrJXCncE6+15PaWUUkWz7UAX3rqHcXliWNl6cgMn8bKTvLSH2+2LG0op23mKPYCKFWmDeJck2DavkVYNhTgYZlNS7alvl1QejC4o+i4207K4fWdq9OufvraJlfUeQo9/A2NbDOqXwcrLCjuoXAqM4Z+PUqXMG5Tkm3waPFoVrWy4PBBqgN6XIBAp/cSbfA66XpCNGUWOGznT4pdjqtbe+qZmWsNugl4D18i4HvoaZOPQtBpWX1mkkc5SNil/wxGtWquUmkSoCYYOy4a8aHuxR1Nc8W4Y2A/h2S8aZ/IWP34mMfr1XTe0UJvtpu5Xv5c7TnuHVPaw20jV2rYTSm4To1JKqcmlc3l++Mg+AJYah7jG8zBYcMr51zi36To1AMEGbeOtlFJlJpnJ88OH941+/dA/XIIBNNZMURwk3gOPfktur71aNuHZJTUIjSu0W4ZSSpWoeDrHU3d8n9VGgv1WE59746nkfbXju/DNh5mTeSitWquUUmVvMJXluw+9iK9Rqp1fv/E6Vp8dZnVzK7VOdVRSStlCk2udFG6WifRDT8niq5OTIKYJsSPQu0Mq+oUaS6JCnmVZ3PJknI/fMwhAY9DFKe0+WTx/9mfypNPfKYnAhZTok7Z8ustPlTpvSBLzcilNri03vrD8v3U+L4k3pbqgaprQ86JUToy0Fv54PHYolsWXH4nx+YeGAOiIulnbdEwr74NPwIu3AQac+8GijndWkv0Sb0r1/18pVVxur7SJ7tst5+/V2jI6n4W+neAyZn2+kzct/t/9g3zzCWnvvbbJw9I6D9x1i2xKajsBFp/lzHi1aq1SSpWVXN7ko794hh89KolS/1h3O0bShEWn4Wld49xGjHym6JsXlVJKzU02b/JX//cEv9t2GIDzVjXRGp3hGuWhr0onw8aVcg1i13HfzEmMCrfY83pKKaVslcmZvOd7W3nH7p+CG56sOY+r6+vsvb5I9smcoVYwV0qpspbO5XnzzVt4buhOggsG8BLloiXnkMwlWRhZqFVrlSpxmlzrtECtJMb17oBUn0ywhFvAZdOJtWVBokdaqMYOywJ97UJnFgaOw7t+2zeuau2i6HAS1NZvy6J360bnFr2nks/I59qFusChSp/LJceRoUPFHok6HqFGGDwIR56D9hNLYtPDOKYJPS9Bzw6oaZaWpUX0Z7/o4f59mdGvO6LHJM7ms3D/5+X2umuk1V4pyw+3ndUq6Uqp6QQbJFYMHoCGZcUeTXEM7JfqvXNIVn3V/3Xz5JGj7b07oh6JadtvkzvOfK8z5/patVYppcqKaVpc9cV72X4kBkArvVyU/KM8uPpKCNQ588aZ2HAb7wZnXl8ppZTtsnmTiz9zF/t6j3ZU6miYYS4vNQgPfV1ur7nK3mroqQGZF3aqwrpSSqnjlszkueDTd+IZOsC5/m0A7G083971eTMP2TS0dJR+kRGllFJTGkhmueDTd9KfSBNefjcAq4KXkcwlaQ42U+evK+4AlVIzKo0MzErnCUgiZzYBB7bCwcch1iUnxccrn5X23Qceg32PyGJ0uEmq5ZZAYq1lWQykzHGJtTCcKNW/F174rdxxxrsLn+Ca6IWaVklkUKocBGohl535eao0Rdog0QWdz0EuXezRHGWaklTbvR1C9UWtjGxZFgNpc1xiLQwnSo217f+kumOgDk57Z8HGd9xS/bKYrgvqSqnpuNwQiMjxLR0r9mgKLzUIvbvBHwHXzHs/TcuiJ5Efl1gLw9cZD94EWLD8Iuc2YIwscEfanXl9pZRStsmbFvv6EqOJtQDv9PwWDznZ7N1+onOL1OlBiRW6EUMppcpCLm/y5L7+cYm1AB31MyTXPnKzdLao7YBlNidVZRKyYVsTqpRSqqRk8yZ3b++kcyjN6z134jYsHsivp6bN5k3z6UHZYFGjFcyVUqpcpXN5fvXEAfoTWTzRbbj83Vi5ECc3nQdAe027Vq1Vqgxo5dpCMVyS+JrPQPwIxI5AsF4mRwK1w4vJM0ySZJOy4J7skyq16SF53VC9VMctIf969yDffio+4f6OqAce+SZYJiw+Wyo+FZKZk6Tm2kX2VQ9WJcXweGh429tGb1cEb0iS0C1Lqy2XI8MlCbaDB+S407Kh+G2/zTx0vwQ9L0os8ha3ou5f3d7Pz55PTri/o3ZMXBw6DI99R26f+R4IRAs0uuNkmZJM3bJIF0FKSEXGCFUZAnXQv18SbFs3VE+8N03o3QXZuGxGnIX3/a6PW3ekJtx/hvkEHHgUXF44/V02D3SYZco1WNsJ4C3ephRVGBozlCp/N37rYe57qXv06zqGeKP7T/LFmiudqwSYzwLD84BKHSeNQ0oV1qtv2sKT+/on3L94usq1mQRs+YrcXnOVvZurswnZoBFusu81VdnT2KBUabjyC/fyUmcMN3le774LgO/nL+HVdTbOFVmW5AS0nSBda5WymcYUpQrjwk/fxaGBFGDia7oDgEzfOdRvhKZgE/X++uIOUKk5qtb4UT3/0lLh9knlinxWqh4dfkoWgH0h8EUkWcjlGd7hbEkyaDYlz80mZVLFAHxhqb5aogk7kyXWAmw2XoBdd8u/7/R3FHhUDFetbdZJqQpm+Hy0/u3fFHsY9vIG5diRz5RcIr2aJZdHEmz798nXzeuKl5STy0i12t5dEG4oemItMGliLQxXIRzxwJcgl4K2E2HVFQUa2TykB8Ef1QX1ElORMUJVjpom6fAQboZIa7FHUxhDh2Bg35zOzSdLrHVhcs6B/5EvNrwSog5VldWqtVVFY4ZS5W9sYi3AWzy3ETbSxCLLqVl02qwqph+X9KBsYtQ23moeNA4pVViTJdYCdDRMU4H88e9ColsqCq661N6qtakBiCyQoixKDdPYoFRpeKlTOmNc7HqcNqOPbivKbeZpfDBq45p9Nj6cC6BVa5UzNKYo5bxc3hxOrAVP9Cnc/k6sfIBM7zk0RaA9rFVrVfmp1vihybXF4vYeXUTOZyRxNt4JQweA4QqVGIAllQ7dPvAEIVhr7yRNARmYnLnvZvlizdXQsLywAzDz8rOu7SjZpGSlJuUNSVJtLq3JteXM7T2aYGvmoXkt+GsKO4b0EHS9AIMHZVKmxH+fFkWHT1P2PAB77gfDDed+qDwqOqZj0pK8xH/GSqkS4gnIOWrPDknGqfTjRyYOPS/JZpN5/ltf476bmvhuWXjefL094zuWZUI6Li3EtWqtUkqVnTBJ3uK+DYDUqmuosbO64LEyCWhcpXNPSilVATrqp9iUnsvA/V+U26uvglCjfW9q5mV9SDf1KaVUybEsa/T2m9xSgfAn+QvI4jm6nmGH1CDULZEEW6WUUmUpkzeHb42pWttzHpgBVja3UB/QqrVKlYuCZGl+5StfYenSpQQCAc444wwefvjhKZ97yy23YBjGuI9AoMIXL90+qYBU0wLRhRBdIG1Ra4c/R9plcsYXKtvEWoBrXVuIDrwgVThPfWvhB5Dql8ohWkWwolmmSWb/ATL7D2CZ5szfUA5cbqmAmZtYqa3SVHy8cHulmt7QITj4OMS7Z/4eO1gWDB6CA4/B0GEZQxkkbXVE3fJ7f/8X5I4TXwcNy4o7qNnIJiRJTuNNyanIGFHFKjJmhJuk8lHvruHNdhXKNKFnpywUBOeX3BQkxV95/k++2Pxn0gnECck+SXqOtDnz+qrkaMyoLBUZM9ScvNH9J+qMODvMdupWnuVc1dpMQubv7EyyUlVJ41DxaMxQY9WFpmjF/dSPYHC/XM+seZm96zbpIdk46ORGEFWWNDaUHo0Z1Sebl/m6RUYXF7ieAuAH+YsACHhsKgpi5uRzTZV0tlJFoTGl8DRmVJ9MTv62pGptF1Y+SKbvHAAW1izAVca5X6p6VWv8cPyv9Uc/+hEf/vCH+Zd/+Rcee+wxNm3axBVXXEFnZ+eU3xONRjl06NDox549e5wepnKYnwx/6/2RfHHSmws/MWSZssBRt0SS21TFslIpdlx6KTsuvRQrVUHJqME6yGeLPQpHVU28cHlkI0U2LsmuPTud/b/NJODIs3DoCaneHV3g3EKyzZpDLnjsuxA7AuEWOPmGYg9pdpIDMl6nErzUcavYGFGFKjZmGC4IN0LfLoh3FXs0zhk8AAN7oaZ53tXI3+n+Ha1Gv2xI3HCdLcObwMxDJgn1S8tic4qyh8aMylGxMUPNmp8M7/T8FoCb8tfiiTq4CS7VL9cChe5SoiqOxqHi0JhRnaxpNjZO2qbVzMN9n5Pbq19mf8vuTEzmDnUdQx1DY0Np0ZhRnUaqEL7efScuw+K+/Ab2WDZvxE4NSlGuoFY0VM7RmFJYGjOqkyTXTqxaC2jVWlW2qjV+OJ5c+9nPfpZ3vvOdvPWtb2X9+vXcdNNNhEIhvvWtb035PYZh0NbWNvrR2qo7s8pJOjdxMup7K/7EIqNbFhhOeG3hBzVyIWL3RJdSheINFnsEjquqeGEYsuvYG4AjT8PBxyDWaW+VwlwG+vbAvoehd4ccA8NN805isls8M3FH0z+cG+XmaxowenfCkz+UO8/5QHn8HZg5wJIkZqWUYyo6ZniHu1V0vSAbJCpNagC6X5J/5xwTVQdS42NGGz18wP9r+eL0d0pHECck+6QCoVatVaosVXTMUFPqGkqP3n6d+y5ajX6GvE285tLzndtsaOaG23hrvFCqXGnMqE57eyded/3VZav56XvPnvwbnv2FzLX5I7D2Knur1maT4PbLHJ5SqqRpzKhOL3XG8JDj9e67APh+/hI+dEaEX7/BxuN2Jj68yaI8iqQopWamMaM6PXmgB0/0SalamwuR6Tub607z8qP3nqhVa5UqM47+xWYyGbZu3cqll1569A1dLi699FK2bNky5ffFYjGWLFlCR0cHr3jFK3jmmWecHKayUVciz/88GRt3XxMDnNY53Kr19HcWvtqTZUkrpbrFWmlKlS9vUKoV5DPFHokjqjZe+CMQbZeknf2PSnXZWKdUwDhemfhwUu1DcOhJwILaRSWZmNoZz/PFh4cm3P+uk2u4dKkH7vlPsPKw9Dz5KAepAWkJqK37lHJMVcSMcLNUvut+cX4xodTkMtC1Xaq3B+vm9K2d8Tz/ft/guPv+3vsDvGYaWjfC8otsHOgYZg5yaWhYqpWjlCpDVREz1ASdQyn+/qfSotVHlvd6fgVA5MRrOWOlg4tR6aHhClN6LaBUOdKYUX0sy6JzKMW7v7t13P1NNT4+cMkqTlkySTUpy4J7Pyu3V14uHTTslOyXAiF+7YakVCnTmFF9RmLGm7/xIJe4HqPF6KfLirIjchp/eUaEE1ps2vCdTchajm6yUKpiaMyoPqZp0TWU5p3f3op/pGpt73msaA7xzgsWcvriRUUeoVJqrhzd8tTd3U0+n5+wi6K1tZXnn39+0u9Zs2YN3/rWtzjxxBMZGBjgv/7rvzj77LN55plnWLRo4kEmnU6TTh+tRjE4ODjhOcp5A2mTf71rgJ+/kBx3/yvWBPkX43uwOwnNa2HlJYUfXCYGvohUiVSqXHlDUrUgl3auKlsRFSJeQInGDJdHjk+5NAwdgsGDsiAbWSCffWGpcDuVfE6SlNIxaSGe6JEEW18YahfaWz3DJr3JPH9/xwB/2Dm+VcAbN4a48cSwfPH0z6Rqoy8M5/xlEUZ5HCxLqkw2rQaXu9ijUapiVUXMMAxZUB3YB4EoNCwr3Hs7xbKgd6fEuujsF6A743k+9Ic+7t83foPRx9bs4bo9DwAGnPMXzlVmT/RCTbN04FBKlZ2qiBlq1KGBJO//38d4bG//6H2vcd/DAqOXXLAJz4qLnN0okY5B2wlaYUqpMqUxo7rs603wru9u5blD43/+/3T1Oq48YZrrle23SRcqTxDWXWvv/I+ZA8uUbkgl1nlKKTWexozqsrs7zttueYSd3XEA3uyVRKlDLRfynUtsXntODcjakD9i7+sqpYpGY0Z1ealziBu++TAHB1J4ok/h8ndj5UK8aePLOGW5mwU1CzD0XF+pslNys71nnXUWZ5111ujXZ599NuvWreNrX/sa//Zv/zbh+Z/61Kf42Mc+Vsghqkn87R/7uW1HasL9H1zVRcMdt8oXZ72vOEleqQFoWgu+UOHfWym7uL3gr5EKp3pRDcw9XkCJxwyPX6pdmDlIDcKRZ8DlAk9Ikmt9NfIcwyUJSvmMJNVmk5BLSTVAt0d+P0q8aur7f9/Hlv0TqzC//9QaFkU9kmD8yDflzjPfWz67tEc2c4Sbiz0SpdQxyjJmuH2SWNu9XTbZRMp8o9jgAUmuDTfOqR33237Vy9Nd2XH3uTB589BwnFh7tWxqcEI+I3G5bqkmSilVRcoyZigAXv+1B8e19vaQ432eX8rtdVc7u+k6oxWmlKpGGjPK18u/fB99ieyE+99x3vKpv8mypMsSSBGROpsrTqUGtQK6UhVMY0b5uuSzd5M3LQCWGoc4370N0zI48cxLIGpjMRwzL7Em0mbfayqlypLGjPJ16WfvGb6VH1e19vzLXLSH26jz1xVtbEqp4+dopmNTUxNut5sjR46Mu//IkSO0tc3uxNDr9bJ582ZeeumlSR//yEc+wsDAwOjHvn375jXmb29Lct+BCmq9WgCmZU2aWAsWbU99VXZbLzsf2k4s+NjIxMETKP9kBKUAAnVS3bQCFSJegL0xw7Isbn4yzcOHbI4ZLo8kx9YtksVfj1daAQ0dhJ6XoPt56NkOA3shPbzrMFgnz4+0SaXXEpY3rUkTawH8HmO4vd5nIJ+GBZthzdUFHuE8pAalGqM3WOyRKFXRyjFm5E2Lm55I83jnHGOGPyKbKjqfkw1j5SrRK9XIvQFJFJ6lbN6akFgL8Hr3nXh6X5KYd9o77BzpePFeqGnTTRNKlbFyjBmZnMlX7nyJpw+U8XG/CDI5c1xiLcAr3fexyOjGCtbD8otks6JTUv0SM3QzrFJlqxxjRjKT58t/epHtR4aO+zWqUSqbnzSxdkYv3QEHtkp3sXUvn9OmwVnJxKF2kW7sU6oMlGPMGEpl+eIdL7KzK3bcr1GNkpn8aGItwA3u2wG40zzJ/s3e6SHwRyHUaO/rKqWKqhxjRl88wxf++CL7jplnUdNLZHKjt711j+Lyd2PmwmT6zsbj8mjVWqXKmKPJtT6fj1NOOYU77rhj9D7TNLnjjjvG7bSYTj6fZ9u2bbS3T96Kx+/3E41Gx30cr4d39fIv9ya4/vfHMbFSpbb3ZDnlG0cmfewq10MEOx+XiptnvKfAIxuW6h9urX78vxdKlQxfSBIPK1Ah4gXYGzNue+YIn9iS4nW/cTDh2eWWxKFQg7QHj7ZDdKG0h4u0ySRLICrVDcvAM11ZVn/l0JSP+9wGbL91eKHCB+f9Vfm0wcul5f+rRtuGK+W0cowZP3lsP//xUIZX/iI5928ON0ml8iPPSlW8cpOOQeezUgU2WD/rb3vqSIZVk8SMKDH+2vNj+eKUt8oGEyfkUmAA9UulkrxSqiyVY8z45n27+PRtL3DNl+477teoNlv39LH6n34/7j43ef7c/QsAjLXXyLWUU8zccIUp3ditVDkrx5jx+Tu2819/2M7ln7tn5icrQNaA1v7zrXP/RsuCu/9Dbq+4GBqnqXB7PLJaAV2pclKOMeM/f7mVW//4B67V64xZu+/FbtZ99GjMCJHiNe67Afh2/nL7i2ykh2T9x+2193WVUkVVjjHj73/2FJ/743be8PUHj/s1qollWdzx3BHWf/Q2ucPI4BupWtt9MZh+WkItRH2as6RUuXJ8C+yHP/xhbrzxRk499VROP/10Pv/5zxOPx3nrW98KwA033MDChQv51Kc+BcDHP/5xzjzzTFauXEl/fz+f/vSn2bNnD+94h4MViYbt6YmP3rYsS3cNzCCbt7j8f7smfSxIin/yfk++2PQmSQQrtFwKXF6oLcJ7q+LxeKh/0xtHb1cUb1AqIpg5+ysjlIByihcAO3SH95wMpk2u/sHkMWOEP90HW74iX5z6NqnWUS5SAxBqmlPimCqCSo4RVabcYsZzh+ZZSSrSBgMHJUm17QRnK+/ZKZuSqrvJPlkcmKUjsTwv/1H3pI990PMzGo0hSXrdcJ0945xMvAfqlsgGF1WdNGZUjHKLGU/t7y/I+1SKnV0xXv3VBybc/wrX/SxxddJtRWlacbHDVWsHZbOHtvFWdtI4VBTlFjO27u4ryPtUiif39fO6r205vm/ecQfsf0Q2g2+4zv652dSAFAnRCuhqOhobSkpZxYzO5/jz59+E5TO5JP1fzr9fBbjvxW6u/+ZD4+57lfteokaSHWY795on2PuGuslCFZrGlIIqq5gB3L1d1nMP9B9HsZAq9Ltth3n/9x8b/dpXvwWXdxAzU0e2/wwArVqrKkeVxg/H/6Wvf/3r6erq4qMf/SiHDx/mpJNO4tZbb6W1Vao57N27F9eYSkB9fX28853v5PDhw9TX13PKKafwwAMPsH79eqeHOq4g5N/dMcB/Xlrn+HuWs2Ru6gqaf+75BQuMXkkEOOlNBRzVGIl+SeoN1BXn/VVRuHw+2j760WIPwxneEHgCUiHTV3mBqpziBYA5phXQx+8Z4KPn1xbkfctVd8Kc9vG3bQoRePCzkIlJO6UTXlOgkdnAMiGXgdqF5VNpt0pVdIyoMmUXM8ZcaHxmyyB/ddYcdygbLoi2weBBqZLdsgE8JV61PJ+Frudh6JBUC5zD8XFnf27S+9cae7nB/Qf54qw/d26zUSYmSVh1HXpcr2IaMypHOceMr961g/deuKIg71uunj44OOE+N3ne7/klAPsWXk2T0xu+M3FoWK5tvJWtNA4VRznHjO9s2c0NZy0tyPuWq0d29077+JfeuHnyBywL7vp/cnvFJXLMt5OZH66A7mCVdVURNDaUlrKKGfXLyOFhoXGED3h+zk+2nslrTimjwhZFcO+LxxYKsbhxeE7qO/nLufkamzfWJQdkY7p2YlUFojGlsMoqZgBjlsD53bZDXHWCnqdO5/ZnDx/9wpXA13QnAOnuy8Dy8Ok3LNKqtapiVGv8MCyrsnqMDw4OUltby8DAwJxLnf/w4b38/c+2jX69+y+04ul0uhJ5Trv5yIT7lxmHuCPwd7isHFz+CVh6buEHl89ItamO03WXn6ose7ZIa+hQI2STspt1yTlzbj8zn2NlJZnPz+GLd7zIZ2/fPvq1xozp7ezLcfF3Oyd97AevauSs5N1w5yclUeq6r0LTqgKPcB5S/YALFp9ZPtUklZoDjRliPj+Hf/r5Nr730N7Rr487ZuSzMHRYKqq2rCvdBNt8TirW9u2SzXZzbGf34P40b/hZz7j7DEx+6vtXTna9BMvOh8s+bueIj7IsGNgPzWuhebUz76FUBdOYIebzc3jXdx7lD88enWvZ/R9X2z28ivLrJw/ygR88Pu6+17rv4tPer5PzRvC8/Av2t+4eK5uATEKuBbTaoFJzojFDzOfncN1X7ueJff2jX2vMmN7N9+7kE799btLH/vCh81ndOsVxfMef4LuvlKq1134eWmxOahidVzqrdK/xlCoyjRliPj+Hv/7E/+O/cp8ka7m5MvMp/vipdzs0ysrwqd8/x9fu3jn69dmup/m+75PErACdV9zE8qVL7XszMy/zfYtOg0irfa+rVJXSmCHm83NY9Y+/I5s/mkam1xnT+9CPnuDnjx8AwNd8K/6mu8inWkns+ks+e0MtV6w6hbA3XORRKqUmM9tjpWvKR6qQOUmasWVZDKSnr7ZXrVKTVq61+BfPdySxtuN0SforhmQfhJu1JV8VsiyLXG8vud5eKmzvgAjWS4VMVXTmJL9fGjOmlp0syA47q24A7v+CfHHyjeWVWAuQjkvrPk2sLXkVHyNUyZrs982yLAbnGjPcXklW7d8Dnc9INftSk89Jxdq+XVDTMufEWoDJ/jrf5P6TJNZ6Q3D2B+Y/zqmkBsAfhVqt4FLtNGaoYpnstNk0LQZT2cIPpgwc++PykeUvPT8DwLPh5dJdwknJAQi3aGKtsp3GITUbk/1m5E2LIY0Zc7K+PTp1Yu24qrUXQ+NK+weQikm1Qk2sVTPQ2KDm437XKfwhfwpeI8/HPbeAZZHLm8TSk3cPqnrH/Im9xX0bAA8GzmN5h83XGOkhCNRCSNe0VeFoTFHTmexXIps3SWQ0Zkxm5G/I8Azia7gfgEzX5Zy71sPmhR2aWKsqSrXGD02uHWOyYPCp+/8/e3cdHleZPXD8e+943K2pu1KhLRT3QnF36KI/FltgscVdFndYZHF3WFxKsUKBQluq1Bv3ZDJ27/398aaRJqllLMn5PE+eTmbuzH2TtnPmvu95z6llu8eK+XKlLwYjim/+DpJr99HnsrttHugOmHZ+bNqomiG1yy+tL+jyT7y3sRobWTptJ5ZO2wmrsTHWwwk/Z6JqQS9irqNF7yu/rGG7x4qZsy4Ok51irKOYAfDoAWkw607VSjV7JIw/LroD66qQT1UuScqO9UjEFujxMULErY5ixgUfVzPusWLml27lwndzgu0aKP5DvX/Gi1BAVayt/Esl1m7jpoONr8ezqeYy+8vqm8mnq010kWAaakEjYyA4EyJzDtFtSMwQsdLRpOTpz/7MuOs+YUV5HL3nx6njbZ9TqJUTcGXCkH0juwHODKnr8xRpjyjCT+KQ2BIdxYzjnviBsdd9QlGN/LvZUvcdO77zB1d8DWt+UNdhIw9Sc0DhFPKppFrpvie2gMQG0RWmZXFD6GR8loNptoUw/w0OevBbxlz7MVUNUtBlUwq1MvbSfwFg3I77btNG8k0KNG2yCPfrCrEJElPEpnRUYGqvu75m1DUf0yCbMjrlzPoCTQ9iePsRqh/FqbumkpeYF+thCRFWvTV+SOZhKw1+o833IdPi8V/UwsUts2tjMaS45tsobibg41rHs+qbcUfHrtpTY7WqWJsgE1KiB3IkgIYk2MYBf9Bod99L870A3D+nLtrDiXsdVzuHYSX/g7U/qcWJPS4H3R7lkXVRY7VaAHGnxnokQog41tFk1LtL1EXnk7/Wb/0L2hwqkae2CNbPU10bYi3YqJJ9q1aoFnZdSGbyG21/X1c5nidF8+JNGwajDunqSDvnrVCJuykFkTuHEEJsRkefmr9YVArAyz+tju5gupkEfJxrfxuAmiGHQmqEk159NWr+SbomCSFipKPrjDkrKgH44PeiaA+n20rxdJLMZFnw1W3q9qA9ItNpqbFarWPIvJIQIsIsYK2VzYOhQ9UdH1/B6qISAGYvK4/ZuOJWq9pRJ9o+xaZZzDLGklAwKrznCfnV2ohUrRVCxJGOioWsrlRr4H+sq4nyaLoH3VmCI20OAP6y6YDGkIxCPHZPbAcmhAgLSa5tpWGjyrU/rmwJDHoMCrDGu40XvS+2v0ahVo7fkwsTTozNoCxT7fZO6wu2bpagJcSWcHjA7o7PNtC9zMbtkuaurm6+rcWianec2zhmAPTTShiw6An1zZQzIK1/lEfVRaahvlIKYlOpXQjRbfhDbTfF/L625Tpjm98+dDukFqjEnnW/Qe36jvs1RUNjFaz/DerWq6TfLlZzal3tfBf9dw6xfYdhadRM/gfoti4OthNGQFUgzBgolUKEEHHl++UVzbd1+czZTrBVjJ1p+4gsrZaVZi76kD3CX12wNcuCgBfSCmX+SQgRMxpt44LEjG2T2lly7dJPYfX36vpg9GHhjyumodYzUvvIvJIQIuI2xIUnjBmsNHOhvpQL7G+2eUy058bPMbavAPivsS+JiWHudLRhw55sshBCxBFNM9Ds1c3ff7u0ZROGxIz2/IYfV+6HaJpJsG4UhncQAAXJUrVWiJ5Ckmtb+bOobXXaa99f3HxbYkSL79f6ueKLasq9LVUbx2nLOdX2EQBF4y9QCYCx4KsBdxok5sTm/EJEmt0tybVxYmlJ2+q0V7z9Z/Nt2ZDR4quVPv71ZTXVvrYJXzYM7nI8gm74IH88jDkiNgPsCn+tmvRKyIz1SIQQce6v8rbVaS97c0Hz7S7FDE2HlDzAVMmtZYui+xnBsqBmrUrubaxSmw26UIH8k+WNXPtVDXUBFTM8+LjJ/hQAzxjT8eQPD8eoO9ZQrsYv1xFCiBhbV922ndZxT/zQfFuuM1q8O289N7y3EG9TR5EMajnL/j4Ad4eOJDkjN7IDCNSDM1m6JgkhYqrWF2zzvcSMjr0+dy23/W9Rh5V+AVz2DpbJTBM+v0HdHrIvZA4J/8D8deBKkXklIURUWE3vgX6cXBc6BYC/2f7HMG2NxIxWnv9hFfd8uqT5+yNs35Cu1bPazOZLc0J4C6tYptrsLcU7hBBxZGnVUhIH3Y+n738BtaH5hKe+bn5cYkaLJ2ev4MEvllAUnIs9aTGWpeMv3b/5cWckN30LIaJKSis0+Wh+Ed8sbdv2Yhl9m283Btq3/+6tjntT7YDf0P7cTojbHU9g0yzeNqYxps/k2AzMssBfD3ljwS6BSvRQug6uVKhb16V2y6JrPppfxJyVbVtwL6Gl6mq1NxDtIcWtU99V7Qhf+MPb5v7z7W8xWV+C5UhA2/0ylSDW3fgbIG+MVDgUQmzSR/OLmirVtsw6/cmA5ttldWFIhk3IgGAjlC9VbUUzB0NidmQn5gNeqPwLqlepjXUpXW+9feYHbWPrpfZX6K+Xst7K4O7QkZzijNDPE6hXVajSB6jPWkIIESMfzS9icXFdp4//VdYQxdHEt/Nf+rXN9xfZXyNF87LA7M975o7c74jwZ3RfrUq0coa5cpUQQmyhj+YXsarC2+njfxbXdvpYb2KaFpe8Nm+Tx3SYKLXgTSj5AxwJMObwyMz9+OsgV+aVhBCR99H8Ikrq/GyYm/rKHM/HxvbsZ/uZGxzP8OXqXdh/bNfndbo7X9DgqrfnN3+vY3Ka7UMAnjQOwAx3zTL/hg17sslCCBE/fl9lgK0Wm7MRR9ocgtU7gOVufvzHFRVsPyAjhiOMD1UNAW58fyFgkDDwLWxuCFbuiBXIjvXQhBARICuHgGFaXP/ewk0eU1HvxzA3eUivdYbtQ0bqq6mykrgxeBID02KUs+2vA1cyJEW4OokQseZOASMU61H0WlsSM9ZUNkrMgE4rgkzV/uRc21vqm50vguRuOHEX9ILDLRNfQohN2pKYsbikLjwxw+FR7UT9tbB2LpQsUF0dws0IQfUaWPsTVK1Q74Oe9C6/rC/UNmZM0f5kpv1jAC4PnkF2ahL2SGyLt0zwVkHagLD8HEIIsa22JGZ8s7QMw+z4M3ZvUtXQdjPjCG01x9m+AOCG4MnsWOju6GnhE/KrSu3JMv8khIiNLYkZ//ujWGIGUFTr2+Tjh44vaH9nKABf3KhuD58B6f3bH9NVAa+6hkuSxXchRGR1FjNuDJ1Eo+VkB/1P6n56SWIGsLqy7aaVvfW5DNKLqbESeM3YjZPHhXljnb9WVa11RPj6RQghtpBhWtz54Vr8ZfsA4Mr+BGxtN3o/NXulxAxgRYX6vTjSfsbmLsEyPPjL92p+/OzdBsdqaEKICJDkWmDOikqKalomWRwE2x1TbzpYsD4Ci9PdTGOw7cp/f62YC+xvAHBT8ESunz4AW6xqwftrIaVQqoaIns/habohH1xjYUtiRp1hk5gBVDS2zxZLp457nA9h0yzK++6LNnTvGIwsDBprVOtwd0qsRyKEiGMbYkZ/rZhD9dkdxozaoB6+mKHpkJQDCWkq8XXNHCj9U1Wz7WTDwxYzglC7Htb9DEXzwAxBSp+wVdIvqW/pFOLBx52OxwB4KbQHs8zt+O8hEdrM0FilkmrT+m7+WCGEiKCNrzM6Uu83mLOiMkojil/raxpbfWdxtf05bJrFh8YUfrRG8p+DIlxBpbFKVYh3p0X2PEII0YktiRm1vpDEDGB9dWO7+xKdtubb9xwzvv2Tfn0Wqlaq9/nRh6gNFeHmq1FFQlzJ4X9tIYRoZeOYYUcVbllrZfNg6FAALrae4ZdFf8VieHFl3UYx43S7qlr7vLE3XtzcsHta+E4W8oPukE0WQoi4MmdFJcW1foJVUzF8eWh2L67sT9scU9EQkOsMmq4zdB/Opt+Pv2xvnHpLntLl+4+I1dCEEBEQoxKj8aW0rvVElEUiPqrZuBWPxo/rfIwrTI3m0OJKQ8DksFfLm7/XMfm341HcWpDZxmjeMHfhlFTbJl4hggINTe1o82JzfhE/7HZSDz20+XaP5PCo1sVG+wQdEXkbYkYB5UzUl/KNOYaajWJGEAfzSvyMK4zFCONDVaPJgS+VbXSvxUeFz5BbXslyMx/H9ufFZGxdZoZUpcMwtEAXUdYbYoSIK6V1PrKp4nXnDWRSiysY4BVzzzbHePGwqDzMMcPuhtRC9Rm5YjlUr4aELEjOA0+aam3aUevTjZmG2sDmrYLadeCrVp9BknPDusBcUm+wf6uYcan9Ffrrpfjc2dxcfQIAya4IbOAzAmoxI2eUVAkR7UnMEFHWdm6qc0tL69hxcO/tnrCm0suM+2c3f7+v/jM72RZg6A5u8R8PgCOSm77NkIqPqYVbFkuF2FYSh8QmbGnMWFlR36tjxrLSOo569Pt29z9w/AT+9szPAGgbv5cHGuDrO9TtkQerioLhZoYAS12fCbE1JDaIbdASM9T7nQc/dU3pAY8bB3KI7VuG6euo/e56GPVsjEYZe/PX1TDz6Z+av5+gLWWKvhhTs/NMaL/wn7Cxqql4R1r4X1uILSExRXSgJWbY8JccTEL/x3Gk/Uiwagqmv+VzcVFN+w1svcncVVWc++KvuHI+R7fXY/qzCFbtwHnTE3jgI+/mX0CI7qyXxo/e85NuQk6yWkjdS5/LqbaPuTl4HNW03zFcG4xR4mic+GaNnyWVLa3oT7d9wGR9CSGbh8Q9LuXOYDrjcp2xGVxjNaQPlJ3eAt3ppOC2W2M9jMiye1SVuJAP9N79vhQLOcluUqjneectDNKLuTt4BPcbR7Q7rq6Xx4yPlzdS0tC2cu1Jtk/JLf8eU3OwZvsr2T27m75n+2pVcponwhWxRNj1ihgh4kpOspsy0vifMZmT7Z9xs+MpSoLpfGVOaHNcQyhCMcOZqL5CPvCWQd16tUnHmaTew5wJKll2Q6KsZYEZVEmnvjporIRAvVr8dSZCcn5EPnu8sciLN6gq607V/mSm/WMA1o7/B/cnF+I3LDI8EfgdNZSrxfIkWdQW7UnMENG2YW5qc3p7671nv1/ZfNuDj6vtzwNQ3X9/ruo3CIfTicMWwaTXxipIyFRfQkSQxCGxKRtiho5JBnWU03FBEI3evQngka86rsI4PC+F+44dT3pCB2sZ394H9SWqI8joQ1V3kHBrrFbXYxJLxFaS2CC2xYaYYSdEIWU04KaORACC2LkieDpvuK5n4Np3YMU3MHCXWA43Zu7+dEmb70+3fwBAsO9OXNa3L4Wp4encBKjNeqYBqX1kw56IGYkpoiMtc1NBzGASwdpROFIW4sp7l8ZVZ7Fho0aCs3enmd34/gJ0VzGOjG8B8JUcCNg4cFw/ChIcDMvtpuvfQmyB3ho/IjAz0P1MGZjBoBSLOx2PsYttPvc5HyaD2nbHvfBnCG+wfYvr3qCk3uDmb1p+J0O1tVxsfw2AFSPPZsLQ/hw1KqGzp0dWsBFsDkiNwC5yIeKRza4SyUP+WI+kV5oyMIPElEy+MscDcJHjDc60vdfuuNeXGDT20pixri7EzbPbxtHx2jKutj8HgD71THbffmwshhYegQZVqcrWuy8ehRCbN2VgBvmpHq4PncxsYzR2zeQRx31M1ha1Oe79FVZkY4bdrVqOpvRRVWsD9VCxBNb/BmvmwOrvYPX36s+1P0PRPKhZBVZILfimFoInPSKJtauqQ9zxXR0AKdRzt/NhAF4M7YHZZzJ7DnSz/xBP2M+Lv05tVkofCLpcFgshYk/FDPdm06C+XVZOY8CIypjizfKyep74ZkXz9xfa36CvXsY6KxPfyCOYPjyFvQZGsBK5aUDQD2n95FpACBFTUwZmMDwlyDOO23nReRMJdFzJdu6qql4bMxYX1/HGL2s7fCzRaeOQ8X3YddhGrbir16jkWoBxx0JCBDZVW5Zaz0gtlKIJQoio2HCdYcekr1aKk7YdEedaw3k+tBcA5rvnQXDLqqP3JPPX1fDFotLm7/tqJUzXVRVbfcQBHDk6hR0Kw5hc66sGd6psshBCxJ3muSnNRHdW4q/YHct0YE9YiT1lXvNxK8obeu11xm9rqvltTTWu3HfQNJNg3SiMhhEA9E/pw3FT+jGpf3qMRymECDdZRQRsusalB0/i9MAl1FkehuvreN15HX21kjbHVTaajHqkmOu+ronRSGNn7+dLWVOrAqSdEHc7HsalhfjcmEBlvwi0w9gajdWq2pS0zhCAZVmYXi+m14tl9eCKPu40VVVORJ1N17j24NHcGDqJN0M7AXCl4yX+YX8daPk391d1iJGPFHPnd+03a/R0Oz1dSl2g5XeRQS0PO+/FqRnUFewEY4+M4ei6KNCgqjcmZMV6JGIb9JoYIeKGTde49qBRWOg8EjqIOcYwPFqAJ513Mlpb2Xzc3KIAIx8p5uGf6yI7IE1TlWsTMlWibWrTV3KBakmaWth0X6GqUutOU5vYImi3ZzcsXljc6vgPfbQKVpi53Bw6kQRHhKp3mEZT54tBqhK5EB2QmCGibUPMmK7P4XL7S7S+tmjtsz9LGXnNR/z3u5VRHV882Ouur5tvj9ZWcLrtQwCuCv6NhPQobLj2VUNCuqpmKESESRwSm2LTNS7fZyDD9TUM09dxp+NROoobr81dy8hrPuL1uR0nmfZk+907q9PHOq209dl1qutH9ggYfkBkBhaoB2cyJMq8kth6EhvEtthwnQGq3qCd9slQd4SOpdRKQ69awbI3rovuAGPMsiwOfGB2m/vOsr2PTbP42hiHo3C7cJ8QAl5I7x/xOTchNkViiuhIc8yw7GAkgOkkUL47AK6cD0FXGzBu/2gRI6/5iK+XlMVwtNHnCxoc+tC32FN+w564Ast04C8+qPlxtz2CG76FiBO9NX5Icm2T6WPyOfOEY7ndfhZlVgqD9GLedF7HaG1Fu2OfmdcQgxHGVuskqfPsbzFWX0mVlcTlwdMxrBi2rAj5AU0lAEjrDAFYjY0snjiJxRMnYTU2xno4kePwtLRwFlE3fUw+j5w4iU/c03kpuDsAF9jf5F/2F9h4MeOhn+ujP8A4YsPgAccDFGiVLDfzKd7+n937/dpXo6o/upJiPRKxDXpNjBBxZfqYfB4+YSJpiR7uCx7GT+YwUrRG/uu8jYFaUZtjN1RwjTpNi0y7061wjO0rZtjmELRsXBA8lwY8uOwRihcNZSoxKrUwMq8vegSJGSIWphf4eMj1IGfb3+NC+xubPPbadxdEaVTxx4bBrY7/YNMs3jd24EtzAh5XhK+PTQMCjZA+QBbBRVRIHBKbs8fk7Vixx4OEsDHDNoezO+iqtMElr83r9LGeaHMLfE57B9c+q3+E+a8DGkw6FRwRWhj31UJKvprbFWIrSWwQ22r6mHzuP2486YkOkvG2e7yWRK4NngJAvz8fh9JF7Y7pqfyhtp2k8qjgKJva1PdQ6BDVDSqsJ6wFVwokyoY9EVsSU0Rn1Br4ZLLdfdF0P4HKXTEDmeiOWlzZH7c59so3/4jRKGOjpjEAeiOu3A8ACJTviRWSKrWid+mt8UOSa1uZPiaf60/en6pdbqQusT/ZWg2vOG9kF/33WA8tZn4rDrCqOtT8/Q76Qs61vQ3A1cGZlJHOlD7OGI0OaKxSC+MeCVqil3F4VCtjETPTx+Tz0PETGDf9bywZfCoAZ9g/5Gb7U2hEsLV3HJtbFGBtbajNfRfbX2Mn2wK8louzg/9gcG5qjEYXBkYQ0FR1RyGE2ArTR+dz/3ETufLAsXh3uZIFZn+ytFqec95KHhWxHl5M/LjOT3G9qpYyWFvHtfZnAfh36Gh+twbj0CHLE4HL1YAX0CBzCNhjeB0jhBAdyRiIvt/NAFxof5P3Jv0a4wHFnmVZfLe8nNK6lva0p9k+ZJy+glorgeuDJzM0w447UhsyNthQtVYWwYUQcWSHnfdFn3ImAJc5Xu3V6xgApmkxe2k5RTWdtzTfe2QH7+OmCR9dpm4P3BUKJ0dmgCG/KpYgFdCFEDGw76g87j56PLcePIyH92q/Me1/5hQ+Mybg1Ax491z13tiDGabFrCVlrK9umxRylv19XFqIH8yRDB4R5qq1oDZZpBZGbhOHEEKEwfQx+Xx76QwePHoPTtw9hK/oMAAc6T+gu1c3Hxcwenas2CBomHy9pIwlZcW4sj9Bt9dj+rMIVO7SfMx5ew6J4QiFEJEmybUbsWkwbEBfko96mNnGaJI0H0857uRQffbmn9zDrK0Nceir5c2tWjOo5T7Hg9g0i1dDu/G+uSPnTU7CrseoAqEZUpVDUgtBl3/KopdxJIR/x6zYajZdY3RBCsP2OpVLg2dgWhon2D/n345HsXXQXqknW1wR5IjXytn5mdLm+/bXf+Qc+7sAXBY8g5N3G43e3avWetJlQ4cQYpvYdBhdkMJuowdwcuBylpv5FGrlPO+8lXRqYz28qJpbFOCYNyrY4akS3Ph5wPEgCZqfb4wxPG7MAOCB/dPRwh0zLBO8FarqoLRhFULEq8mnw/gTARi74E6Os30e4wHF1leLyzj+iR/Z+bYvARihreZi+2sA3BQ6gTLSuGPvtMgOonXVWtmYIYSIM/rQfWHALmiYPOB4gEKtdPNP6qHe+309Jz75I3ve9VWnx1x38Oj2d857Cdb/qooZbD8TdFtkBuirUdchMq8khIgRm64xtk8K+43M7uBRjWuCM2mwXLD2J/jlv1EfXzS9OGc1Jz81h4Mf/Lb5vmyqON72BQD3hw7jmt0zwnvSQD04EyA5N7yvK4QQEWDTNfYfOYKjtxuH4R1CsHoimmbhzn8DmtbAQ70kufbxWX9xylNzOPuV/+FI/wEAX/EhYLVsVrlon2GxGp4QIgokI7EzzkRmBi/jHWMaDs3gXufDnGV7j9btvpdVBtn/xVI+Xt7I5Z9Xc8mnVbEbbwSsrG5JDNMwucvxCLlaNUvNPlwbUu1Bdu0fw8qZ3kpIypaFcdE72Z1gk8q18eRVYw8uDP6dkKVzhG02DzgewEFLFdcFZUGmv1DKrFU+zv+oimu+qo7dYCNgQWmwzffjtOXc7XgEgCdD+/OeOY3JBd14IdqyINjYtKEjQossQoheo4JUTgpcQSmZDNHX84zzDpJateWbWxRg+gul/LjOz5nvV3Lr7JoYjjb8flrvb7plcbPjKUbpqyi3Urgo+H9YTZeoo7Mi0HK7oRwSMlVylBBCxLOxR8HwAwC42f4UB+vfMnVgBiPzU9odOntpOdPvncUvq6s45ak53PvZkmiPNqK+WVoOqGooLgLc43gIlxbiU2Mirxq7A9AvJcKfzzdUrU2SRXAhRBzSNNjxHMgYTJrWwOOOezh8bAb9MhLaHfrpwhIOuO8bfl9bzfFP/MBjXy+PwYAj59OFJQD4gp0v8mckbjQ35a2ET69Wt0ceErlrBdNQHZFSCtTfmRBCxJCtVdGk08YnNncOWk8Wd4WOBiD40b849d43mb+uhqMf+57nvl8Zi6FGzHvz1gNQ729Zw1FVa4P8bA7jO3M0rnB3x2ishpQ+4EoO7+sKIUSEaJrGsMz+APhLZ+DUErG5S3BmfgOoiq4Ar/60hkMenM38dTUc+ch3vPrzmpiNORJe+3kNaEGsrNfRNItg9SQM79A2x4S9UIgQIq5Icu0mBLFzYfAc/hNS1ZOucLzEtfZn0Zvafd8wq5Y/y0Oc9UEVLy/w8vqfjVQ29oxKhYZp8eXKlvZJp9s+ZA/bPHyWg3OD59GIqpg5uSBGyX2mAUYAUvtKkpPovRwJgHxQiyfvmtO40nYRQewcYJvD4467cBEA4IrPq1lUEeLkdyp5d0kjz/7uxTCtzbxi9xAwLL5c5W/+Pp8K/uO8C48W4EtjO24JHQ/AiEgkSkVLoB6cybKhQwgRNuvJ4rqkq6nRktlO/4snHHc3x4zzP6piUUWIY96o4JO/fDz2S0OMRxs+vpDFFytUzDjR9hlH2L7BsDTOC55HGS0VnPqmtm9R2CVBr2ppmDVUWu8JIbqHqWfDkH3QNYu7HY+wu/Eddb5gu8POfO5nFhXXcfjD3/H1kjLu/WxpDAYbGd5AiC8Xt1RgvMj+GiP1NZRbKVwRPIMN18OZCRGcFzJDLVVrbd34ekYI0bO5UmGPKymzUhilr2Jm6e2U1HjbHXbGsz+zsKiWgx/8lu+WV3Dr/xbFYLCRUecL8uWizVftTXBudJ3x6dWqu0VqX5h4UoRGB/hrwZ0KiR1VixRCiNhJdumUN7ZsSnjG2A8yh+IINTCz4h4OfOAb5qyo5Op3FsRwlOFV4w0yZ0Vlm/uyqeaEpq4h94cOI8Ghh7cLX9CrCuYk54fvNYUQIgrcDpUPZBmJjE85DABn1mdojnLq/QZBI8ilb/zOvLU1HPjAbH5eVcWlr/8eyyGHVUW9n5UVXpxZn2NzlWGGkvGVzGhzzKDsxBiNTggRLZJcuxkWOjeFTuDNtFMBmGn/mAcd93PjF2XMWu1vd3wPyZPiuT8aePI3tYg/VfuTS+2vAHBD6GQWW/1iOTTFVw2eDJmMEr2bM0EW9+LQF9b23J58BY2Wkz1s83jGcQd3fV3E76XtF8I3UUijW3nk53reW9IIQAI+nnT+mxytmkVmX84LnoeBrfungftqISVftQgUQogwWUEBF9uupM7ysKNtIQ867uee7ypZV9czNux15M7vapmzPsBEbQnX2J8F4LbQcXxvtrRnzfSE+TLVMqGhAjIGyPWDEKL70DTY/XJeN3bFrpmcVXoT29d82uaQ+z9fijfQc2PG1W8vYEW5mpvaTZ/HGbYPAbgseAblpAIwLCPMmzE21lilNtgl5UX2PEII0VWZgzkncCEBy8bYmi+5UHupzcOPfNWzqtRu7B+vzKNhMzFx6sCN2nuv+g5+fV7dnnImOCO4KO6vV92QZC5XCBFngkbbhW0Tndfy/oHfcrCb7XeOtn0Vk3FF0hnP/tzuvvPsb+HRAvxqDmGWOY49wt25tbFKVS/3pIX3dYUQIopybNsRahiCpodwF7wOmNz7xbxYDyuijnvie3T3OpyZswDwFx8CZtsuIdMGZ8ZiaEKIKJLk2k04fIRKoDlzYiKr+x/BeYFz8VuqGuG+S64mlfp2zwn1kESp/y1TVWsLKOdh5704NIN3jGm8aOzJvfumcdhwD28eFaPqfZapWnOn9ZPJKNG7OTxgd6v/EyLm9hmkquCdsl0igfxJnBy4vDlZao8/ryalg5ix8cRVd/XOYlUNxU6IBx33M0pfRZmVwmmBS7hleh8OHe7hvWO7cTJTyK+qpCflxHokQogeYmof1Yr02NGJeApGcHrgEvyWg31sv9Bv3t1otI/tPaXa+SsLvORRwSPOe3FqBu8bU3nCUDu9H5yezqHDPbx8eJgnoxrKICEL0gdKC1YhRPei23g65RxeDu2Ojsndzkc4xvZl88N3f7okhoOLvDd+WQtAH8q41/EQumbxfGgvPjcn8fD+KmY8cWDGZl6lC8yQauGdPgBsEU7iFUKIMFifNJpLg2cB8H/29zi+qQIfwO0f9ZwqtR357M+STh979MSJHDq+gHuOGd9yZygA712obg/aAwbsFLnBBbyqSIJ0QxJCxJFkp5of2a2/q11hjH/OTeOu0JEAXGV/nnwqojy6yJqzsm3V2v5aMcfZvgAgOPpoDh+RwHW7p4bvhCEf6A6VXCuEEN3YzkPy8BUdhmU6sSesxJExm4c+7/xzeHdnmAZLSmpw57+OppkEa8cSqhvT/PijJ07k8Il9+Oe+I2I4SiFENMjM8Cbcumcax45OYEKek5BpMfLHaZQF03jccTdT9UW87ryeUwOXso6WhKF7f6zjoh2SyUm0sbQiSEmDyc79wry7LcwMExasr6HSGyAjwUlhRjI/rgvgxs/jzrvJ1OqYbw7gsqaWe4eOSODQEQmbfd2I8dWAOw2ScmM3BhG/bDaS99uv+XaPlpit2pbZ4vs9prd4YHo6v5cEmJTvpCFoMe73EZwQuJJnnbcxUV/GS86bOSlwBZWkND/n3h/r+PvkJDI8NhaWBan1m+xQGN9/nxvHjJzUJP6qNtAwudPxGHvafqPRcnJm4GLWkc3BwzwcPKybV3v11aj/b570zR8r4ltvihEirj19cAYLyoJMyndy8DAPE5aM5Jzg+TzmuIcjbLOptRK5PnQytFreuG9OHadNSCLVpTOvJEDIhEn5ztj9EFtg45iRmpSIFfTytPNOcrVqFpuFXBY8kw0/54HDPBwY7pgRaAALyBoKDnd4X1v0bBIzRJx4+cgclpRfhrXYgb7sU253PEEiPp4y9u/0OQ9/tYxTdhxAosvO3FWV2HWd7fqmRW/Q28AwLeasqKS0zkdOspvMRBXjnAR52Hkf6Vo988xB3BhSLbsPGOrhgKERvs7wVjRVrZVNdiIGJA6JbfDhcdksrzoY668StAVvcKP9KdZbGXxlTuj0OY98tZyZOw3A7bDxw18VJLvtjC4IY0JRBGwcMxJdnf8fyUpyMX1MPtPHbNSGe/Y9UL4Y3Kkw9SzQIliHxlcNqX3BlRy5c4jeQWKDCKOvT8lhdY3B+Dwn7x2bxYEvl7d5/D/GDKbbfmKivozbHE9wSvAyHp+1nJk7DcRh05m9tJycFBfDcuP7vW3jmGHT22+6vsT+Kg7N4Cd9PFOm7sSUcFcy91ZBSh9ZXxDxRWKK2Ao//WtvSmp9DMpx8K8Z9dz+1YG489/Elf0JRv1wzED7vJ3/fPMXf9tpILqu8eXiUvpnJDAoOykGo99yG8eM9XUlOLM/xeYuwgwl4C8+uPnY3Ydnd3ydIURP10vjhyTXboLLrjGlj0pyctjUh+0fzFEcFbiGZ5x3MFRfx5uuazk1cBl/Wv0BeHmBl+VVIV47Mot9XigD4JMTshmWGZ8VVr9bXs7js1ZQXu9vvq/algGkcpvjCcboK6mwkjkr8A98xEHCl2WpFkr524E9vpMJRGzoLheF990b62FEh8MNBeNjPQrRxN0qZqS4VMz43RrMsYGrec55C6P1VbzivJETA1dQgqqu9ORvDayqMfjPQRkc8JKKGd/PzCU/OT4/iHQUM8r0bCCJa+zPcZjtW4KWjf8LXsCv1tDYDTScLFNVq0opkGqHPUCvihEiriU4dCYXqJiR7lHvLZ+bk7gkeDb3Oh9mpv1jaknknqYqIQD3z6lnfZ3B7XulccgrasFj3ll5pLrisxlJRzGjVMviAcejjNRXU2al8rfAP2lAJUaluiLwHmuGwFsJ2SMhqRtXUBcxITFDxItkl86kPh7IvwLsLlj0Ptc4niNfq+CW0PFYHTSluuOjxVQ1BDh/r6Ec8cj3ACy9eX8ctviMGR/NL+L69xZSVONrvk8teltcZ/8v2+l/UWUlcU7gAvw4GZQWheslIwCmqaqe6/F5fSZ6NolDYlukunUm5rsh52xoKMW28hsectzPcYGr+N0a3OFzbv9oEaZlceSkQo59/AcAVt42I5rD3iodxYwO8qSaDczqoEhI0TyYdYe6PeEkSM4L8yhbMUNqTSOS5xC9hsQGEU4ZHhsZHvU5d3R2+zVsE51/Bs/iQ+eV7Gb7nWPMr7jlQ40Ep50dBmVw4pM/At0vZmwcMsZqf3GQ7QdMS+Oj1KOZHO7EWiOo/pT1BRFnJKaIrZGd7CI7Wa1n7DdyCDe9XY09eQH2pMW4C17Fu/IcoO3cyU0f/Eleqps+aR5mPv0T0L1ihmarR3cV4+k3CwB/0RFYRsuGkoK0bl5YSoht1FvjR3zOqse5xVY/DvNfzyKzL7laNa86b2An/Y/mx39aH2h7fEUo2kPcIt8tL+fWDxe1WfAG8IY0Lra/xqG27whaNs4JXNhcnfeVI8LconVr+WvAlSJVa4UQ3cYiqx9HB65lvZXBUH0drzpvYKBW1Pz4rNW+Nsevqe1eMaPR0LnQ/gYz7R8DcEnwrOaKKG8f3QPa3flqVWWRhB7wswgh4t7b5s5cEzwFgAvsb/IP+2uosqvKD+sCBM2W4ysbTeJRxzHD4kL9leYK56cFLmnTAeTlIyLwPltfCsn5kDEg/K8thBDRpuuwy8XcFjwWgDPsH/KA40FcBDo8fO6qKqq9webvA6H4jBkfzS/i/57/pc2CN6hqIafZPuR4+xeYlsaFwb83x40nDorC3FRDhUqEkusAIUR3ZHPAbpfxjTGGRM3Pf523M1Rb2+nhf6ytYV11Y/P3lmV1emwsdRYzzE0M9+6jx7e9I+SHt/5PJb0WToZRh4R/oK35asGTBp6MyJ5HCCG6QOsk6XO51Yc7Q0cDcJX9eQooZ0lJHctK66M5vG3SWczYOGRcZn8JgLfNnThr3/HhH0hjleqKlxDj9XUhhAiTPsk5gIav6AisUAI2zzqcWZ93eOzK8gbmr6uJ7gC3QbuYoQXQ3KtxF7yKplkEqqYQqh/d5jn/3Hd4DEYqhIgVSa7dCieNbdmtVkwmRweu4XtjFMlaI8847uBQfXbz42arCah4nIoyTHh81op2Y7MsOMA2h/PsbwNwZeg0frRGAvDGUVlM7RPD6rWWBb46SOsnLV2FEHHv0OEtO9ZWWPkcHbiWVWYO/fVS3nReyxTtTwACRtuYsakFgVjpLGaYFpxm/x8X2t8E4Lrgybxj7gzA5yflMD6vB1QY99er1n1SLV0IEUH7DGr5bPussR+3BI8D4AL7W1xqf4UNVxRraw2CRqvrjG4UM06wfc7x9i+bE6RaV876fmYuI7PC3OnDVw12D2QNVckFQgjRE2gav+UdyQWBcwhYNg60/cCzzttIpf3i9i+rq9FblfKLx+Raw7S4/r2FHc6b7af/xL/sLwJwc+h4vja3A2DOabkMTo9wI66QT1WVSuuvkpqFEKI7ciXxQOol/GYOJl2r53nnLfTVSjo89KMFxeitEqtaX3PEi03FjM78cvU+9M3YqHLtV7dB6QJVwGOnCyJ/rRBoUK3AbdJEUggR3wZ20h3iKWN/fjaHkaw1cpvjCd78ZW2nybjxYktjxu76b+xsW4DfsrP3/keRk9pBtfOuMEPqK62vXFcIIXoMXdfJSnJihVLwFR8KgDPrS2wJy9sd++Efxd0wZhjormJcmbPQHbWY/iz8JQe2ec6C6/cjPVHWjYXoTeST3FY4cohFLhXN39eSyCnBy3jP2AGHZnCv82HOtb0FWDz5a0PzcfG403vB+pp21QeLrXQK9QpusD8DwF3BI3nN2L358eGZMZ4A8teBM1laKIlNMr1e/hwxkj9HjMT0emM9HNGLnTpKI5uq5u/XWtkcEbieX80hzYsah+nfAPDCHy3/VuMxubajmLHeymB/+89c0JRYe1PwBJ4xpjc/HpVWrZEW8KrNHNJKvMeQGCHi1d+308mkZQf348ZB3BA8CYBz7O9ypf1FNiTYvrukpaKU2U2uM/bW53Kc/SsArgmdysfm5DaP5yeHOWaE/Oo9PGuoqhIlxDaQmCHi1WVT7HxtbscpwcuptTxM1RfxlvMaBmvr2h375aLS5tsBI/6Sa+esqGxXSQpgO20Z9zoeQtcsng3tw5PGAc2P5SRG4TqjoUIlQiVIlUEROxKHRDhcvnMaFwTOae7A97zjVnJazVW19u2y8ubb3SlmbErGxgvea36Cb+9VtyefBqmF4RlcZwL14EyEpJzInkf0GhIbRCT9e3cXSbT/d2Wi88/gWfgsB7va/uDw0IfMWlLW8ngcLmhsScxwEOJq+3MA/NfYj5S+o8I/EG+lqlgr3TBEHJKYIrrirqPGAwahunEEqrdH0yzcfV5Cs9W1OW5hUS2f/9nxBr940TZmmGiOcmwJy7EnrsSydBrXHwtWy3VFeoKDRJdsnBO9V2+NH5JcuxWqGoNkazUMY3VzwlQAB+cHz+Xx0AwALnG8xgOOB7hrdqsLi/i7rqDS275t4Eh9Dfc7HkTXLF4M7ckDxmFtHnfaYryrxFerJrycYd45KIQQEVDdGCSHKoaxmoymhKlyUjk2cBUfGFNwagb3OB/hH/bXuOarloWNOAwZ7WKGZVmcbf+gucr5jcET+Y8xo80x8b4TcYv4aiApD1zJsR6JEKKHq24MkkcFw1jdXH3wKWN/rg6eCsCZ9g+4zv5fNEz+9WVLEm4cFiFsFzOm6z9yoeMtAG4NHsfzxj6RHYBlQn2ZqjaY0iey5xJCiBioaQzShzIqzGRODVzKWiuLQXoxbzmvZTd9Xptjr3p7fvPteKxcW1rXfsF7pLaK/zpvx6MF+MIYz/Whk4EoXlsEvWBzqupSPeGaRgjRq9X6giTRyC2BY1ljZtNfL+U5562kUdfu2Ds/Xtx8u7vEjK3SWA1v/E1dL/SbBiMO3OxTusxXC0m5KsFWCCHiXJ0vSH+KGcZqHATbPLbCyueW0PEAXGl/kR/nfNf8WDxuyNiSmHGy7RMG60WUWSk8EDos/JXMzRAYQTU/JdXLhRA9THVjALCBvRJ/8QwMXy66vR53n1eAtnHhy8VlHb5GvGgdMzRHFbp7Ha6cTwHwlxyA6Wu7Ic8XjL+4J4SIPEmu3QoZCWpHglMLkU11c4Kthc4toRO4IngaQcvGQbYfeM15PflNVW7jMbl2w8+ywXbaMp5w3IVLC/KxsT1Xh2ay8eKFI5b/Wvx1ahIqJT+GgxBCiC2XkeBE01TMyKWSrKaY4cfJucHzeTh0MKBafv/HcRcpTclU8ViFsHXMsBPiEsdrnGn/AIDrgie3qSTVYxhBwJK4I4SIitYxI59ysqgG4DljX64IngbAqfZPeMDxAC5aklcDcdiutXXMOMw2m3Md7wFwX+gwHjMOivwA6ktVVZDMwdJyTwjRI7WOGV7Lzd/8FzPHHE6K5uUpxx38zfY/Otqy54/DRKmcZHeb7wdr61TSl9bAXHMo5wbPxyDKHTG8lZBSAJ706J5XCCEiYEPMqNRSuSF4AmVWCsP1tTzrvI0UGjp9Xjwm124cM7aKZcG750H1akjMgV0uAj3C8cUMAZp04RNCdButrzMGUtQ8N7XBs8a+fGVsh1sLcp/jIZxNCbjxmFy7uZiRSU1zR747Q8dQRwSKOjVWq/mpROmKJ4ToeZrfZ0OpaI5qfMUHYJlO7InLcGZ93unz4rHa+YafRbPXoLtX4877EE0zCdaMI1i1U7vjQ2b8xT0hROTJauNWGF2QSlaSCw3QNYtcrQo3LS1PXzL24oTAlVRYyYzVV/Ku6yomakviMrl2w88CMF5bxjWO53FrQT4xJnW6eBHTKoS+GlW11pUUuzEIIcRWaB0zbJpFnlaFnRCgNmXcETqWS4Jn4bcc7GX7lfecVzFKW0kczkU1/yyJNHK941n2sP1O0LLxj8D/8YwxPdbDiwxftZr88kgrWCFE5LWOGXbNJE+rZENi1EvGXpwXOJeAZeNA249NC+FqQ0Y8JteOLkglK9HJibbPOM3+EQAPhw7mntCRkT+5rwZ0O+SMAIcn8ucTQogYaBszDJyawYmBK3k1tBs2zeIax3PcZn8CR9O1xwbxmCg1ZWAG+alqEaO/VsyLzpvJ0mr5wxzAzMCleHFjw4jegAL1YHerqrVCCNEDtI4ZRWRxS/A4KqxkxukreNZ5G8kdtP+G+I8ZW+2n/8Cf76prhV0uhsQotOf21YA7TTZrCCG6jdYxw6mFmuamWtP4Z/AsKqxkRuuruMj+OtA9Y8bF9tdI0bz8YQ7gdWO38A/ANCDog7R+UrVWCNEjbXif1bBhhdLRNB1fkeqK7cr+HHvy/A6fF48bMqYMzCA3PYjuWocr6yt0ez2GPwdf0RG0FCNsWYeJx9wvIUTkSXLtVrDpcOauA4GWt1F9o7Lmc6yRHBK4iT/NfmRrNbzsvJEh697GHzT5tTgQNxUJbTocO3UAu2rzuNbxHC4txKfGJP4evIAg6oN+f4qaW5nHVKBeLY5L9UAhRDfSccxoGwNeN3bj8MB1za353nReS+6q9/AFTX6Ls5hx+hiNe5yPsJ3+F/WWm5nBS3nL3KX5mMGs3WTVk27FMiHkh9S+UvVQCBEVm4sZ75nTOCV4ObWWh6n6Il53Xk8/rYSAAd6gye8lAax4iRmEuCHrU461fwXA7cGjuSN0LK27YgxlNR662NZ1YyE/BBogaxgkyMYIIUTP1VHMCODg0tCZ3Bg8EcPSONb+FS84byaz1ZxOwDCp94eYv64mbmKGrsFF+wxluLaa15w3kKtVs9gs5OTA5dSiWmgPZU27ROGIaaxW1wDu1OicTwghImzjmLHU6ssJgX9RaSUxXl/Of523kdRBgm3AMKhpDLJgfRysDTTZEDO22rq58PGV6va4Y6HvlPAOrCOWBQEvpBVGvkKuEEKESUfXGRsrI43Lg2cAcKbtfXbQFxIImVQ1BFhUXBulkW6ersHF+wzr8LHR2kqOtX0JwPXBkzEjkSrRWKUKdyTlhP+1hRAiDth0jWsPGgWAZroxAzmEaicQqJwGgLvgFXTXunbP84dMyuv9LC2pi+p4N6XaV8Vhk204M2Zjc5dgGS58a08Ey9XqqJbIaEh2rRC9kmSMbKVpg7O44oARZDZVfU3sYFF4rZXNEYHr+MCYglMzmLDicRa/fBkzX13Bo3Proz3kDr25sIFfvnyTS52v4dAM3jd24JxWibUAyTR2egEVVd5qSCkEV3KsRyKEEFtl45jR0YLFAmsgBwZu5gtjPG4tyMjFD7HkhYs589WlvPBHxxVEom3W5x8w8Zd/UaBVstbK4ujANcw2x7Y5JkEL0FH72W7JXwuulOhUMhFCiCabixnfm6M5OnAtxVY6w/R1vOu8isSSnzjxrQoOfqWcd5c0xmLYbflqWfvKRfQr/hQTjauCM3nEOLTdYW4thBbOmGEaUF8Kaf1VUpQQQvRw7eemGgGNJ40DOC34T2otD1P0xXzouoKp2p+Aqih16EPfcuADs/lycWkMR9/izo8X8+Ibb/Km52ZytGoWmX05MXAlVaQ0H+PQzPDGjM74asGRqLomCSFED7JxzFhp5XJi4EqqrUQm6st42nkHCRutcfhDJvve8zUz7p/NTys3rlwYG9e+u4B/vv4HB43rvABHu7WM2iJ4+QQwAlAwESadAtHozhdsAGcSJMi8khCie9k4Ztg72OT2qbk9L4b2QNcs7nI8Qqi+kp1u/4Lp937DwvXxkWB78avzuOT13zl4u7YxQ8fkJsdT6JrFj8YIfrZGhP/kZkhtAE/vDzZH+F9fCCHixPQx+Txy4kTyUt1YoRTAwl8yg1D9MDQ9iKfvs2i2tkm0gZDJ9jd9xj73zGJVRewLNlX5qjjr+Z956pcvsCf9hWXpNK47ETPQdnNEXORMCSFiSnoRbINpg7OYOjCLBetrKK33885yKG3UWVtnYJgWxQ0mXtz8PXgB35ufcb3zOcY1/sSHruVc/ct5sP0esf0BQn6sWXdypeMrAJ4IHcAtoeOxNsq1zkp20S8rnQ9XxLA8e6ABHG5IKYjdGIQQogtax4ziOj9vN8WM0gaDKp9JXcCihiROC17CKeYnXO16mXGBX/nYdRmP/3oqjD0iOhP/HfHXwbf3s+vyT0GDWcZYzg+eSzXtNztkJrlISk7h+/Xx19Jjq/nqIXc02F2bP1YIIcKodcxYV+vn7WVQ2qhR5TNZU2uwyOrHIf4becR5LxP1ZaT+cjU7BI/mVw7i9YWNHDI8IXaDr1gOn15DYd066i03Fwb/zmfmpA4PzUxyYTgSWFgZpmSp+hJIzoWsoVJxXAjRa7SOGWtqVMwobtRYE9yew6pv4DHHPQzR1/Oi8ybuDh1FIDiZZaVqw/f7vxex54jcGP8EsGTWqzzvfJBE088v5hBmBi6lhqTmx3UMMpNcZJoOiiK55mJZqn13zihwJW3+eCGE6GZax4yVVX7e+WsIV9dcxS2+G5msL+Fp5x2cGriURlQL7UDIpKTWD8Dnf5YyeUDsO0M8+/0qAN77vajTY/JS3VR5A/iCJi4C8MoJUFcEKX1gjyujl+TUWAPpA8EZw+szIYTYRq1jxvJKP+/+BWvrwW3X+KM0CMBNoZPYUV/IQL2Eui8vxRs4DtD44a8KRhWkbPoEUfDmr6pa4rvz2saME2yfMUFfRp3l4WPn3hCMwMm9lU1Va2N/vSWEEJE2fUw++4zKY86KSv5YV84HC1ZSVHM8Qc/DhBylePo9hXfVWWCq64w6f0uBkN/X1tA/MzFWQ6e8sZwllUv4reJ7PAWfAeAvPhSjoX23jLxUN0U1Ye7EJ4ToViS5dhvZdBhXqNrE7d1qY9u/v6/lwZ82VKfVeN7Yh0mjJjDuj1sZrBfxJNfD7Hkw5cyoTq6YloWuaWq39qfXcIS+FMPSuCV0Ak8aB7Q7flqBjScPn8yd39fCihhW2/VWQdYQcMf+Ykx0EzYbibvt2nxbiHiwIWaMA/Yd2XL/Pz6u4q3F6kLCQucZYzpTxu1I319uZ6y+ksv8D8L738BOF0DGoKiN17Qs9LVzYNa/oaEMw9J42DiEe0JHdtgmaZ/+dh49aDL//KwK1sdB5cSuCDSA0wNJ2bEeiYgEiRGiG2gdM/Yf1XL/Ea+VMbcoSAkZHBu4muvsz3C8/UsudbzCzvofvKtfCGRGfbymaaIvfBt+fASMIGutLE4PXMIiq1+Hxx8yxM7d0ydz6jsVUOnv+gC8FarSYPYI2RQhwktihugGmmNGIcwY3XL/xMdNDvbdxI2OpzjCNpt/Ol6l8vN15HMsRWSS6IzNdKBpWui6ppJZZ9/N44670TWLWcZYzgr+ozmpa4NjR7q5ca/J7P9iKUUN7atmhY2/RnWuSO0TuXMIsbUkDokwax0zDh4LkAPrb6b2vSuZqi/iSce/+Vvwn/hwEQi1bJxOdMbm319zzNhCp+zYn2sOGs32N32KL2hwi+M/sG4uOBNhr2ui153IDIGmq81/QoSbxAYRJa1jxmHjWu4fcP96ALy4+Ufw77zmvJ7k5e9zoi2b5419SHTFb8zIpZJL7a8AMDfncP510KE89XCYO3qYITCCTVVrJQVDxDmJKSJMbLrGjoMz2XFwJsdMzWN++XzKG8/nyq9vw+YuwtP3GRpX/w0sJ/NLlzY/L1YxwzBMynylLKtexk/FP+HOfwMAf/luBKuntDv+4n2Gcc4eQxh85YfRHqoQ8amXxg/5ZBdmVb72FfseWZHHusBNXGV/nuPsX8LCt2H197DzhdB3h4hXJFxbG+LAl0u5NX8208ufQQt6qbCSOS94Ht+ZYzp8zlOH5mDTwRHLwk9StVZsA93lot9jj8V6GEJskfpg+4p99y/NZFngBs60vc8FjrdxFc2DN06HkQfBhBMhMbJJnytXr2LF/+5nD20uABWOfM6oP4tfrGGdPufBGdnYdLBvxaJH3PLVqLbirvbVeUX3JzFCdGet1rcJ4ODK0BmUJA7nLN/TTLMtZELphbDkfBi6X9Qqni9euZbij+9iN+1X9X3C9hxXeTqVdLwxLjtB59/7NcWMcFxz++vUokXBGHCnhuEFhWghMUN0ZzZdLXhfHPw/fjBHcYP9GTJKvuUT1y/cGjqeBMdpUR/TTysrmfn0T1yzTwGHrL4D15J30TV4NrQPN4ROIrTRFOXYHAc37JXVdJ0RwYFZJvjqIG8sODwRPJEQW0fikIiKggmcEricZ523Mc22kCe4i9ODl3DTB382H+KJQXLtrCVl/N/zc7npsDHsMCiTB79Ytsnj9xqRwzUHjcama9htOpfZX+YI22yV5LrLxZA9PEojB3y1qlCIJz165xS9hsQGEU9+s4ZwW+hYrna8wNX255hnDsbjnBD1cXy8oJiLXvmNu44ez+iCFB79enkHR1lc7/gvyVojq11D2XXvQ9DtEUiR8FaqzRyJOZs/VogYk5giIiHVlcqg1EH4DT+Nq/9GQv/HsSesxFP4Ao1rTuKmd8qaj3WHZYFg67wxdw1XvTOfv+/noNpaxKvLn0HTLAJVUwiUTW93/D/2Hsa5ew5Bi1WHWSHiUG+NH9IzM8zGZLdvLbSkMkQDHq4IncHl7qshOU+1Lv3oCvjwn1D5V0TH9N6cRTxg3ML+RQ+jBb38ZA7jQP8tnSbWDkqz4barAHHwMLWwMCIrBnnY3ipILZSFciFEjzUpv33MWFQRIoSdh41DuTr7Phiwq1rwXfgOvHw8fPeAqkIebrXrYda/KfzoNPbQ5hK0bPyYcTA71928ycTaHfo4cTXFjOPGqIrskwuc4R9fNIT8auFFNnUIIeLQtML2VVnvq9mVAwK38Ks5BI/lha9ug3fPg/KlHbxCGBlB+P0V+n9yOrtpv+K37LyVcRr7Vf6j08RagB0KXThsKmacNl613d5zwDZWmw351cJ11nB1fSWEEKLZPoM2VIDVeM3YnQMDN/OLOYRkrZFbHE9y0uJzofiPqI7pX2/9wYjAfKZ9eiiuJe8StGxcFZzJNaGZ7RJrAab2cTZv3jtvitr4tmGOKqwaq1UClFwDCCF6qYIhYzklcBn1lptdbPN51nkbK9a1zDslxKDa+fkv/0pDwOAfr8zj/57/hRd+XL3J46cMzMDWFDOeGPoj/2d/Tz2w/ekwaI9ID7etQAOk9AG991TwEUL0HtMK2877P2kcwEfGZFxaiEec95Jo1ER9TGc9N5eGgMHZz8/lxCd/7DBmHKbPZrrtJ4KWjWWDZ6I3da27Zlc1h3XWxDC0JTeC6itNqtYKIXq3vMQ8+iX3IzehD41rTsUyHdiTFuPp+xyltYHm49bXFRMyI9ilaCONoUYufu13GgMm9371My8v+w8WJsHqCfiLDwXaJ9BuPyC9ObH2on3UWvnF+3S+Zi6E6LkkuTbMjhqVwN4DO18grsueAEc+xeLCIzA0O6z7WVUk/Oz68C+Ceytg9j2c9df57GKbj89ycGPwRI4JXEPRJlrGtt55MTTTwQ9/y+Wdo6PcHntDW+4UacknhOi5Zm6XxI6FnSeiutLyYd8b+HTUrZQmj1aTM/PfUEm2/7scVs5WyUXbygypSuqfXA2vnAiL3sdOiK+NcUwP3MYx649t15p1Y62L1U7Ic/L9zFxePCz6bcnDorFK7SqX6iJCiDh03pQktsttvyljpZXPkYFrmZV3CtjdUDIf682z4KtboXpNeAdhhmDx/+DVk+GHR3DjY445nAMDt/CP9XvR0QRUa61jxs79XHw7M4fHD8zYtnHUl0L6APUlhBCijSt2SmFoRsuC7nKrD0cGruP64El4LReFtb/Ao7uw6NET+eT7uZEfkLeSCxsf4lXnjRRq5aw0czkycC3PG/t0+pTWMWP/IR5mn5rDPfumhXdcpgFBr2rbat/GzR5CCNHN3bxHGiVJIzklcBm1loep+iJedN5MOrWAej+2LIt7P1vCxwuKozImvdX6xG9rqrf8+N9eYvzCOwAwxx4D44+NWlcPAIKNqgp6QjedFxNCiM24f3o6Ge7WqQUalwbPZJWZQ6FWzug5l2MaBv/+eDFfLS6N+vhWVXjb3deHMq53PAPAvaEjqMsY1RwbZm6XyDen5nD5Tp1vFN9i3nK1+TtJqtYKIXo3TdPon9Kf244pwBYcQOPak1sl2D4NulrXXl23nt9LF3D9+/P4bll5xMZjWRZl3jLml88HwJH2I+4+L6JpBsHacfiKjqSztLnWlxLn7TmEby7dg3P3HBKxsQoh4pck14aZXdf4z0GZXLxDx+2kP1jq44u1OvstO4LdfXfS2LepIuFfX8KbZ8AHl8Cyz7c9WcqyoGwRfHkrvHgMLHwHHZOPje3ZL3A7TxoHYG7mr/32vdpWis1LsjVXJYwab5VKrHWH4YJG9Cqm18uiCRNZNGEiprf9hbQQ8cRl13jp8CxOGpvQ4ePP/eHlk+WNnPFLf6aUXUnjvndA4WTAgjU/wCdXwXOHwmfXwYK31SaNTe3yMw2oXgVLP4EvboLnj1BV1Fd+o2JR3yk82fc2TgleznJryzY3/GuXtjEjP9nWXJWwWzFD6veT2ie6Cy8iqiRGiO4swaHzzjHZTB/cftODgY2TV+7Hx5Me431jBzRMWPIxvHYKfHotrP1Jvc9vq8Yq+PUFtRHj69uhrgg86TyX/neOCVzNUqtwi17mgiltr5H6JNubqxJuMcuCuhJVYTBrGOhySSsiQ2KG6M6SXTqfnpjDhLyWTRkmOk8b+7Nv4A7eNXYELEYUv8fuH+0Db58DpX92/oLbKuCF7x6EByZxQOBjdM3i1dBuzAjcwjxr04sRM7dLavN9YYq9uSph2DRWgScDkvPD+7pChIHEIREtaW6db2fmUpI8iuMCV1FhJTNOX8GrzhvJpZLL3/yDp75dyb2fLeWs56KwIQNIcm1dxb8jJhXCby/C2/+n7hi6H/qU01V3omjy1ah24LKmISJEYoOItawEG7+cmYez1dtrLYmcE7wQv+Ugr/gr5vz3Ch78chmnPv1TVMbkcXReKdyGwd3OR0jRGplrDuVR4yD2GtLyHq1pGn1T7F1v9x3yAZraAC6Vy0U3ITFFRJLD5mByn2G8eG4uRsNQGlf/DctwYU/8i4T+j6LZq7n/wyCPf1XM07PXcvx/fsRvdKGYVCcagg0sqVrCgvIFeINenFmf4M5/C02zCFRNwbfuWKDz9+2J/VqKMWmaRt+MhK7HDCG6ud4aP6QvQYT0Se78Tfhv71UCsMbK5a3cc9m+/+Fk/vUOGeu/Rlv3s6pm60iAwu2hz/aQNxZSC8HWvlIVoNqhli6EonmwYhbUrmt5LHcMTzuP4/qlAzc53h0LnXy/NsBD+6ezfUGMq3UE6qVqregSq7Ex1kMQYqv0T+s8HJ/5QVXTLY1P6gYwcNxVmIMryF39AWnrv8bjL4e/vlJfoBYOErLUZL7dBbpdVc7w1UB9CRiBNq/vt6fwQ8Ju7LT3YdizBvH7x1XApv8PTe3j5Md1AZ4+OIPR2Z3Epu6msVpVFknIivVIRIRJjBDd3eD0zmPGWV87gPN5InQA/8l/m+zKX2DF17Diaxpd2TgH7oit31TIGwPu1E5fBzMElSug6DdY9Z26zmhKzm3Qk/FMPgF99KG893Y9FoHOXweYlO9gblGQlw/PZNAmxr7F6kvU2LNHgL3z6u9ChIPEDNHdDc908GtxsM19a61szg+ex5Oh/bnc/jI72hbCby/Aby9QnT2ZqqFHsDhzT/YePxS7bRuTkqpX88s7DzJszaskhdT1zF8Ucpn/b/xkjej0aeNzHfxWEuSto7PI38S8WliYIbWxPXd05/NtQsSYxCERTaOyHHxSO5CjA9fwnPNWhurreMN1HacFLuHG91uOe+e3dWQnufAGDdI8Dsrq/Ow3Og+9CxsgHvh8KQ0Bg8v3VzGiuNa32eeM6ZPC/HW1fHD+zmQsfgXePQ+wYPCesMtF0X9vt0y1aVs2bIgIk9gg4sHYXDXXs8ECawBXh07lDscT7LD6MWboLj4wd+Cd39aRleSiwR8iPdFJZUOAfUfldikx6c6PF+Gw6Vy4t2rL3Rg0Oj32n/ZXmKovohEX/wiew8cn5JLkjMDGi4ZySBsglctFtyMxRURSgiOBoelDyU8ro6h6IN7Vp+Pp+ww2dxEJAx+kce1JfPRb/+bjX/xtFrsPHsHCNZCTnEhtY5C9RuZu07m9QS/FDcXc8dFfJHsMDtk+kSd+fwJX9i8A+Mv3IFC2Lx114stNcVFS6+frf+6OexMbOITozXpj/JDk2gg5ZLiH95Y28uXKTe+wuHJ2CDsZhDiDQu0QjrJ9zYmu2WQGy1Si7IpZ6kDNBin54EpRibdmUCVL1RWDv7bNa/osBx+bkzFGH8nhu07k+vvXb3a81++WyrDMOFlMaKyGzGGyw1sI0WucPC6R/y1rbDMh1ZELvgxhx0cIN3AEcDiXDl3P2Vnz0csWQslCCDZAQ6n66ojdDRkDIX889J3K6NczCNXb+XdZGkdmwTuLN/9h6IHp6eQk9qALCstUu8tzRoJNPhoJIeLb3ycn8b/lPv6q6rxS+TxrCJPXX8Jo7S+Otn3NobZvSfWXwaJ31ReoCf/kfJWoaneDZUCgARrKoHZ9u80YZI/kkvW78p6xI4+m5rG7zcWc9ZWbHe+zh2SSGK6FC28F2FwqEcqVtPnjhRCil7tipxQ+WNpIXcBq99g8awjHBa9ifGgZZ9rfZ7r+E2ll6qvAslP8zUT6bH8gWr+pTe+7HXdoAtT8VPF81RFj6Sew+gcmos7p92RjG3MY+36zM6HNTEO+eXRWm1bgEeWtUC1bk7ZtoUYIIXqam/dM5ZO/fCy3+nCU/1qec97KIL2Y153Xc17wXL4yJwBwwcu/tXvuLYeN5bgpfbcpWSpkmNz16RIATpjaj6wkF4HQ5rtuvH/eLqqrxfcPwSf/UncO2Rt2vTQ2m/D89eBMUhXRhRCih3tgegbTni5pc9+rxh4M1dZxhv1D7nI8wtpAFhe83P65Dx4/gRlj87cpZpTX+3noy+UAnLnrIIKh9tc5G8zQf+Bsu9od4pl2NrOGjwBHBOKDv06t26f3l454QgixkVRXKo+cNJZDH5iH6euLd8W5ePo+qxJs+z9OoGwfAhW7AjrXvxbgBu1XLKtl/fmZmZPYfXjeFp0raASpDdRS3lhOeWM5y0sb+Oi3ILpnFb8F3qLYW4xl2vEVH0aoZlKnr/PjlXt39ccWQvRAkkESITZd44Hp6Yx5tHizx25YXFhr5XBP6CjuDR3BfkkreGS7v9DW/6LafAe9ULO28xdJLYS8cdBnEhM/HIgXN/wGC7WazZ7frhOeKlLh4K8DR6Jqyy2EEL2E06Zx//R0dnq6k4TYVtouSGvcsbQP71f158PjZ6ok0cYqtfHCWwFGUFVkcnhU8lRCpmqj3aotXgi1AeOST6tZVd15otYGWR6drIQe1oLbVwOuVEjMjvVIhBBisxIcOvfsm8Yhr5Rv9tgF1iCuDQ3iltAJ7KL/wa767+ztXECBuV7FCW9F5092JkL2SOg3FfpPg5Q+vN60ae/8j6r42/jEzZ5/SLqdBEeYFhZ8tWCEIH87SJAFayGE2BKpbp379ktv7qDUkd+sIZwTvJB8KjjU9i2H2b5hmL6Owuo58NmclgNT+qhE1MRs1erUssBXDXVFUL1GbdJo5VtjNC8bezDPvgOH+FIIUb/JsU4pcEYvsTbkV+OXtq1CCNEsO8HGHXulcenn1awjm8MD1/Oo81520P/kSce/uTl0Ik8Z0+moutOVb/3Bt8vLeej4iVt93oDRkkh79vNz2W3Y5udm9h2Vq6rEfnQ5zHlc3Tl0P1WxNlbdLfx1kDlEumsIIXqFgmQbl05L5o7v6trcf2voeAZqRext+5UnnHdzZOBaVlttN7Od++KvzJ5czm1HjNvq8xpmSzLtGc/+zOiCjrsybact407HYwB8mXwwewzaVa2RhJtlqoJROaOlYJQQQnRifJ9Cjp1axMs/lmKF0vGuPBt3wes4Uv7AlfMRtqRF+NYfiRXMapNYC3Dq03M5bPtU/jl9EC6bC7tuR29a4zYtk6AZxG/4qfPXUe2vxhtSLeqTnckk2HWc2S/gzJxFsdfCpaVTueoETF9hp2M9fedNdwMXQvRecZJR2TMlOXUOHubBGzQZnOHgsbmbXkjYwELno/rBzOszlfGTTlET/g1lULsOAvUQaFRtjewuVWUjuQCcCc3P99JSqfY/vzZs8lynT0jkql020RI2mixLXYRkj5RKVEKIXqcgycY+g9y47RoOHd5ctOXl9BeWh1hSEVQVyBMyt7n90AM/bTpO/WvnFM6Y2MPeny1LVRfJG6viqhBCdANjsh3s0tdFQbKN9XUG36zZdLcMP04+MyfxmTmJa0LwxXHJDLLWQH2ZSowyAqpThsPTUsVvo80YrdUFLO6bs+mYce++aRw6ImGTx2yxgFctVueNUd08hBBCbLGd+7mYUuBkVLaD79f6WVzR8Ya6IjJ5xDiYR4yDGKQVsYv+Bzvrf7BHwgrs/io1J1W7rvMTuVIgczD0mUSg706c8GLTZ+v6zV9nvHhYJtP6RvGzeEO52qQum+uEEKKNA4e5eWG+g537unhxvs5Jviu4yf4Ux9i/4hrHc0zUl3BF8AzqaP85/4Pfi7h8upe+GVt3DdC6Su2C9bUsWF+7iaPh03/sytBUE14+AZb8T9253XEw6dTYzesYAbVZIyknNucXQogYOGFsIh8s9bH/EDf//l4l2ZroXBA8l9e16xmpr+ZF580c6b+WYtquV7z80xrO32soBWlbl/AabLUh49tlFXy7rP2m8cHaOp523kGC5ieQN5E9djlym9dLNstbqTaAp3WeqCWEEAIu3nscP62YzdRhJi/OBt+64wnVz8Wd+y72hJUkDr6HYOVO+Mv3ALNtbHjr5xp2Hr2QtAQNXdPbJNealooLdpsdt81NdoKa5/l23bc8v/AlXFlqs3mweiJ1JQe1e+3WFt80HZddNmALITomybURdv/0dABunLX5CrIbO/TVci6blsx2uU6m9c3pdHLm3SWNnP/RejLcOtfsunU7444fs/mKU1Hjr1VtBqVqrRCiF9I0jScOVJX4zvuoaqufv+8LZVy7awqjsx1M6dP5YsIrCxq47PMa8pN0Lt9p62LGgUMjsLs71vx1KvYkb1lbESGEiAc2XeO5w9TCwDFvbL6C7cb2fKmOW/fsz7DMIUwa1Hllpad+q+eGWbUMSLVxyY5bFzN27R+mhe2QXy1WZA+HtP7heU0hhOhFnDaNV4/MAmDPZ0s2czSAxl9WAX8ZBfzX2A9q4MFdTYbb1zPUWa06ZWACutqUkZgDaYU88GcSd83xMqLGzpnuJKB6i8c4Ltex9T/Ytgp4weaEtH7StlUIITaS4NB55xi1IP3feQ0EsXNZ6AwWW325wv4iB9p+ZIy2kvOD5/K7Nbjd83e540seOn4iA7ISOq0maFkWt/1vEY/N+ovtClM5dkq/rRpjv8ByeHwmVP6lCpBMPRtGHQJ6DJe6fDUqucqdFrsxCCFElKW6dD44TsWMDcm1AA14ODlwOa86r2egXsILzls4JnAN5bSNC9Nu+4LHTprEgMxEhucld3gOw7S45p35vPDjaqYMyGDGuE1vuO6rlfBf5+1kaPX8Zg5i7NSzI7fmbATUnFXuaCnaIYQQm5Gd7OLzi/aiqL6IF2f/AmiEaranwTsQd9672JMW48ychSPtR4LVUwlU7YAVbOled9lzfq48JIe8NBt9MlQCbOtEW4Bav5crPniHFf7P0V1qzcQMpuEvPohQ/ejNjtFp62FdW4UQYSXJtVESbNWqYmvc3tRS4/a9Utm5n4uCJBsBA95c5MW04IiRCZzflIRV6TO58JPqLX7tPsk2BqXHyT8By1KtXvPGtqnCK4QQvVHQ2LaYcf0sVd3jvv3SmNrHRW6iTtCEF+c3kOTU2XOAi8s+V5s9iupNLvi4eotfe3yug/zkHrhjz1cLOSMj0xZKCCGiYFtjxhVfqHjw6Ix0JuY5yUrQMUx46rcGCpJtjM1xcENTXFlZY3DuZjZ+2DTYMJS9B7rI8IQhZhhBqC+FjEGqGqIkQQkhRJcEzM0f05FzZ+lAIU8fPI5xhQ7S3DqmBY/OrWeYy06+z8Zdc9TCxaKKEBd9Wr3J10tza1T7VNA4fISHJGeUFjAsCxorVdvuhIzNHy+EEL1YoHk9Q+MpY39+MYfyoPN+BuglvOW8hseNA7k3dAR+2m7W+/uLv6Bp8MJpUxmZn0Ky244FPPD5Uib2T0fXNB6b9RcA89bWMG/tH5scR98MD2sqG9EweXjoz7j++yiEfKr6+LTzYMDOnXbciArLgqAPckaBLgvyQggBUEYaJwau5FXXDQzWi3jFeQMnBy5nHW07R5z13FzsusZLZ+7AsNxkEpw2TMvi3s+WssvQLCrqA7zw42oA5qysZM7Kyk7POURby8vu28iyKllu5vPj0EsYnzVAVRaPhIZy1fUpSYp2CCHElspPartJwgpm0rhmJrakRbiy/4fNXYIzcxbOzFkY3v6E6kcS8g4k5MvnhrdKcdk1bjs2j8IMB7pusq5+LS/8/BumezFLa37HIIjuAstwE6jYjUDlzmB1vKF7Uv905q5Sax7XHjQKTdYehBCbECeZlT1faBsXMDbYkAy1c18ns9cEmu/flrZ5P/4tlx/W+dl7oLtrgwonXzW4UyFZ2ryKLtJ1EiZPbr4tRHfU1ZixIWl2x0In369tiRmfn7T17elmn5rDL8VB9hnYA3df++vUhg6pWtt7SIwQPdA25tY2O/sDNYE0ucDJT+tbYsY7x2Rt1et47BrvH5fNgrIge4XjOsMMQV2xqlabPTxyiyFCdEZihuiBzC5eZ8x8txJdg7E5DuaVBJvvf/rgrUtUzXDrvH5kBssqQ+wZzbkpfw04k1TVWiHincQhEWPWRtcZv1lDmOG/hRsdT3Ow7Xv+z/4e++o/c0PoZL42xwFam+ce/58fSXDa6JeRwKLiloqGdxw5bqvGUZDq4b8HpZH+2cWkr/ml6c4JMO18yBi4rT9e+ATqwZkcuZbjQrQmsUF0I+vI5oTAlTzvvJXBehGvu67n1MClLLbafhYPmRZHPfo9mYlOkt12VlZ4AXjkq+X8c7/hW3SuKdqfPOq8hwyrHl/KAIqGXczpYwaqjhWREKhXr50xUP4viu5LYoqII0b9CLz1w7ElLcaZMRtbwnJsCauwJaxiw+q0GUoEw8NV34FuD4BeB1rTRYu/6Rh/FoGqHQlWbw/Wpte1x/dN46oZI6lpDLLr0OxNHiuEaKWXxg9Jro2Sc7ZP4oOljdT4u7b63TqxFuCVBd6tfo3cJBuHDI+j6rCmAYEGyN8OHHGU8Cu6Jd3tpv9zz8Z6GEJ0yeU7JTNnvZ/aLsaM1om1AC/80bDVr1GYYqcwpYd+XGishqxh4EqK9UhElEiMED3RTXukctybFdQHuhYzWifWwtZfZwRMiwFpdgakhSFmmIZKrE0pgJwRqt2rEFEmMUP0RPdNT2PmO5XUB7c9ZpgWbRJrAV7/c2tjBgzJcDAkI4rv72YI/PVNHZMSo3deIbaRxCERa/85KIOzP6iiMdQSM2pI4vzgebxrTONmx5MM1ov4r/N2ZhljuTN0DH9Yg9q8hjdgtEmsBXj713VbPIZMaji1+gUGvfoRWIbqOjTuWBhzGLhSuvYDhouvFtIHSkckERUSG0S8enRGevPm7dZWWvkc6b+WZ523MUxfx1vOa7kseAbvmdPaHVvREKCioe3c1Lu/rd/MmS1Otn3C1fbncWgGKx1DGbDHP9g5cxDYI7TebBrgrYLskeBJj8w5hIgCiSkiVv591HZc8tq8Dh7RMOpH0Fg/As1eiz35D2wJf2FLWIVur0e3N4C97Tq3ZTowGvtiNA7A6R9LQ20erTf9bUrQMJnQT97HhdhavTV+9NBsmfhTmGLnlzPyeOuPci75OsCWvqlvzqNz68PyOjHVWAWeTGmdIYQQTYZkOPj1jDxe/72My2aFwva6T/229cm1PVagQVWtTSmI9UiEEKJLxuY4ma0NiLYAAEDxSURBVHdmHq/8VsaVs8MXM16cv5WJUkaYTmyZUFcESTmQOxrsPbByuhBCxMjkAhfzzsrj+V/KuPa78MWMD5b6tur46sYultDdFg1lKrak9In+uYUQohvarb+b+Wfn8fTPZdz0Q9uY8Zk5iTn+EZxrf5tTbB+zq+0PdrX9wXfGKJ4wZjDLHIdBx50nvltesdlz99VKmGn7mKNtX5HU2BRj+kyCCSdB3hjQ42RZywiCpktHJCFE92M0JbHqDghDG+zpgz0sP9fNoz+WcedPbWNGMZkcHbiGBx33s7NtAQ84H2SH0J/cGjqOejZdCGpxSV2nj2VTxS2Op9jHNheAd40deT3ldJ7NLIzsXNKG64r0/pE7hxBC9GBHTiokwaFzzou/dnqMFUohWLUTwaqd1B16I7qjCk33AxaW5cAKpmEZiYCqnBno9NU6VuUNbv4gIYRoEiezEL2DTdc4crtsCpLKeezrFfxQn42fCLWk6C7MkLqIyx0N9l7+uxBCiFZsusYx43Pom6xixqz6AqwwbcwQqN3lWUPAHSeVToQQogtsusbxE3Pon1LG47NW8nV9N00csiyVWJuQBTmjpfqTEEJEgE3XOGX7HIaklfHYrJXMikHM6Erl3G0SqAfNDhlDpBq6EEJsBZuucfqUHEZmtI8ZtSRyS+gEnjP25h/2NzhY/45ptoVMsy2kzErlfWMHvjAnMNcchpfNVQ+06KeVsqv+OwfZvmeythi9qcXrAmsgo/c4FvpPA1dyBH/abeCvVVULpXKhEKLb0KChvCX51GxKhPWkgaNrHU9tusbfd8xhfHYZj85axTf1LUUtqknm5OAVXGy9yt/t73KC/XP2tP3KncGjecfcqdMNGR1x4+dU28ecY3+XFM1LwLJxe+hYnjQOYGjIHtnE2kCD2lSROUTWtIUQogsOGFfAo7rGde8uoLjWv/knmB5Mf3jXCiobtuC8QgjRRJJrY2Da4CymDszikR8r+PdP0X3T3rVfnFV+aiiHxBxIyo31SEQPYXq9LNtrbwCGfP4ZekLXJgSEiLUNMePWWeX85/et3XfXNTOGRKh1UqwFGsDhlqpVvZDECNHT7TQkmx0GZXP5p2W8tji6O6+PG93F/0+WBbXrwZOhqlG5ksIzMCG2kcQM0dNtiBlnv1fKp6vCV8V2S5wxITF6JzNDamNdzihIzIzeeYXoIolDIp5siBnHvV7CnOK2LSvWWLlcFDyHf3M0M+0fcYRtFtlaDTPtHzOTjwlaNhZbfVlh5bHOyqLOSiCIjQTNTxr1DNSKGaavJV+rbPO6s4yxPGHMYMr4iYwemh2WyophZVkQ8ELWMND1WI9G9BISG0SX2JyQPkAl0ToT1ftqsBHqS6F2Hfhq1HqtvuWJrh3ZEDOmv1DM0qqWjhUmOneGjmW2OZZb7P9hoF7C3c5HOdd8mxeNvXjH2Iky0jp5VYsh2joOt83maNtXZGm1AMwzB/HP4FkssfoCcPyYCF5nWCZ4KyF7BCRmRe48QkSJxBQRa9PH5LPPqDzmrKjk7y/+QmVDdNfAD59QGNXzCdFT9Nb4EZWr/oceeogBAwbgdruZOnUqc+bM2eTxr732GiNGjMDtdjN27Fg+/PDDaAwzqmw6nDklIyrnGpPt4PQJibxxVBaPzoijXdQhP1ioi7kuXqwJ0ZpRVYVRVRXrYYhtIPGiYzYdLtopOjFjeKad8yYn8fqRmdy1bxzFjHDyVkFqoVSt7aUkRvQcEjM6ZtPhqt2jkzw0ON3OP3dM5vUjM7lut9Rtf6ENFWs9aU2JtXFWkUr0WhIzeg6JGR2z6XDz3tGJGQNSbVy9SwqvH5nJpdOi+Dm8rgRSCqRtq+iWJA7FhsSMjtl0uHt65zFjPVncHDqRKf6HmRn4J6+FdmWtlYVDMxijr+Qg2w+cbX+ffzpe5UrHS1xof5NT7Z+wm+138rVKApaNn81h3BI8jiNt97Fm6rVccNjunDMtDhNrAYJecCZAgmzcENElsSG+dKuYodsgczCk5Kt5cVcyJOWoeZg+26vNzrXrVbfRLrLp8MiMjt8fvzdHMz1wO7cHj6XKSmKQXsxVjhf4yX0Onzr/yb2OB7nc/hIX2V/lX/bnedxxF9+6zucz16WcY3+XLK2WtVYWFwXO5rDADTQm9eff+6TxxlFZnLJdBJNrG8pVpyW5rhA9iMSU6OpWMSNKbLrGjoMzee60KVE5X98MD/cfN4E3/m8ah0+UAkxCbKveGD8iXrn2lVde4aKLLuLRRx9l6tSp3Hvvvey3334sXryYnJycdsd/9913HHfccdx6660ceOCBvPjiixx66KH88ssvjBkzJtLDjSqnTWPl+QWc9FYFlT6Tt47OImBYjHm0OKznef+47LC+Xth4yyGlr+zwE0IAEi82J8Ghs+K8fI54rRxNg1ePyKIhaDHusfDGjI9PaP+77lEC9eD0SNVaIbq5bhszLBOMpoqyuj1ii8SpLp3l5+Yz4+Uysjw6zx2aSY3fYvzj4Y0Zn58UhpixIbHWnQp5Y2XjgxAi7LptzIiSnEQbS/6ez34vlDI0w8HjB2ZQ4TWY9J+SsJ7nq1Ni0LHIWwHOJFVV0OaI/vmFEN2OxIxNK0yxs/D/8tjn+TKmFDi5e980ShtMpj7VEjNC2PnSnMCX5gQIQaFWxghtNYO09eRo1STiw6mF8FouaklglZXLCjOfP6yB+FBd91aeVtDZEOKHr1Zt3HZGsRq7ECKu9KiYkZip3s/KFkP1KpV0a+9aJ9QhGQ7mnZnH3s+Xsu9gNzftnsryqhB7P1+GHyePGAfznLE3h9i+40jbLCboyxiqr2Mo6zp8Pb/l4DtzFC8be/C5OZFQU4rDNzPzujTOLRL0qjm9rCFd/r0IIXqnHhUzImB0QSpzrtyL/e/7hsMn9uHKA0by25pqDnv4u7CdIz3BwTeX7hm21xNC9C6aZVlWJE8wdepUJk+ezIMPPgiAaZr07duX8847j8svv7zd8ccccwwNDQ28//77zfftsMMOjB8/nkcffXSz56utrSU1NZWamhpSUrZhYXb1D6pddEJ0KgQCbPgr0JoW1wfcv77N4+NyHBw83MPyqhAvzfdu8rX2H+zmf8t9bb5/ZEb0fpYtFqiHoA8KJ6sKVUKEien1snjiJACG/zK315Qh31pdfq+MgGjHCwjD72HlbJUkFcX3McuymuMFtI8ZUwqcHDzcw5crfHy+0r/J1zp6VAKvLmyJK0eN8nDn3j20Wu0GNWshcyjkjIj1SEQMSIzYNhIzlC79HkwTVn8PvmqVVGtZqk21ZYHdqRJK7e6te80tsLmYsXNfJ4eOSODZeQ38Xhps93yXDfxNXV9PG5/Ik781ND82c3wi1+7ahWq1oBYm6orAndaUWNvF1xMijCRmbBuJGUqXfg9GSF1n6HpUK3lvLmbsPdDNocM93PhNDSUN5sZPp0+yjXV1KmicPyWJ++fUNz923uQkLt4xyv8eAl6V+FQwXlXnEqKb6Q1xSGKG0qXfQ8gPK79T1xTO6P0b2VzMmD7YzZEjEzjzg0rMDlafhmfoLK5UseSKnZK59du65scu3ymZsyfFeScLM6Qqo/edohLQhIiS3hAbOiMxQ4n478EIQdkiqPwrLAm20DZm+EIWIx4uavP4voOcnDgczv9fBZP1xQzQisnXKtExCeDASMhidn0Bc81hXDrFxQ2tCj3etEcqJ46N8CYHy4SadZA1FLJHxGc1dSG2QU+OKRIzlHj8PWxO65hRWudjys2ft3l875G5nLnrII5+7PsOnz8qP4WFRbUAXDVjJDd98GfzY/ceM55DJ0jhJSG6qqfFjy19r9QjOYhAIMDcuXPZe++9W06o6+y99958/33Hb3jff/99m+MB9ttvv06P7wk0TWszGbVzX2ebx2/ZM43TJySR5tr8X9c+g9xcvlPL5NOD+8dhkpRlqZbcaf0lsVYIAUi82BraRpM32+W2rcB0//R0ThybSKp78zFj/yFuZo5vmXy6fa+0sIwxbvnrwJGoKosIIbqtbhkzdB1yRkK/HdVX/x2h3w6QP0613fPVQfVa9T4VRhvHjMHpbRuXPHFgBkeOTOg0Ztj0lucfNMzDjKEtCcDX7NLFCTnTgNoi8GRC3jhJrBVCRES3jBkxsnHMyE1sGxueODCdA4d5SOskZjha3X3YiAQmF7TMbV20Q5STpIygqlqbORiSo1DJSgjRI0jM2HIbx4xER9vvH52Rwd6D3J2uZ3gctubbR41KoH9qy/dnTUwK40gjxF+nrl88cVjURAgRFT02ZtjsKoE0YxDUl4IR6PJLto4ZTlv7xx8/MItdh2ZRTTKfmtvzhHEgN4RO5rrQqdwSOoFZSQfwrTkWHy5OmpyPx97yehFPrAVoKIeELEgfKIm1Qoht0mNjRgS0jhmpnvYdiP5zyvZMGdj5Z/CclJZNIafvMqjNY5JYK4ToCvvmD9l25eXlGIZBbm7b1m+5ubksWrSow+cUFxd3eHxxccctTP1+P35/S2W+2traLo469p46OJOSeoP//t7A6hqDUdnqr6n1AsawDDtLKkPN3792ZCZz1gU4dISHZ+a1VJRqvSAeN3zVavJJkpuEEE2iES8gEjHDgvoSMPxgc4LDE5HKg5vyyhFZlHsNHv65nvqA1bwI3jpmbJfrYF5JS0XCFw7LZEFZkN37u/hhXcsEmd6TJ4csCxqrIWcUuLrBQo0QolPdNmZ01BkjMQvS+oG/VlU+qlmjkmwTM8AR/t2e7x+bRZXP4t/f1+K0aXgc7WPG1D5OfmyKDTYNnj44g5XVIcbnOXHZWuLExgvqW8UMqcTa5FzIGS3vy0KIiOm2MWNzTEMtdFsmoKlFcJtzs0/bGl+clEOt3+KGWTXkJdua3/dTWyVK7TnAxRdN3TJsusZjM9Ip95oMTLPjarVw3qWYsbUsU8XUtL4qKaAnX+MIIcKqx8aMKPj+b7k0BCz+9WU1I7NaFsHT3BqVTU32Dhzq5v2l6huPQ+P+6ekEDIsMjw23PUzXGdHir4fcMSr+CiF6pR4dM2x2yB6urjmqV6kuEHp43u90TeO3M/PwBi0u/ayKHQpbkqASHRoNQVXu/JhRCbzS1HEvJ9nJnXu7cNk1HDYNlx0aQx2+fPgFvGpdIWsoOKK77iOE6Dl6dMyIIJfdxtyr9qYxaHDRq/PYb3THm6dn7jSAp79dCcDQnCT2GZVLVlLXK68LIURr3f7q/9Zbb+X666+P9TDCymnT6Jtq56pd2lZvSnW1TCx9fEI2Ax9oaZ0xucDF5AIVJOJ6+skMgb9BteWLYqsqIYSACMSM1H6QkK0qI/lr1eR6fTk4ParFtd7BVuwwc9s1ClPs3LJnWtuhtVr0fvvorDYxY6e+Lnbq2w1iRjj5asCVAqmyM1EIsWWidp2haWrjmTtVLVhUrVZJtr5aSMwOayzxOHQ8Drh737bdLVpfZ7x8eGZzzLDrGnsMaFk8CMs6txFQSU8pfVQ1X7kmEEL0AFGJGaahrjkCXtB01QIcG2BBoB5CAVUp3ZkEzsQux49Ep06iEx6Z0XZzSOtq508dnNncCtyuw36DPc2PabG40rAsFWMSMiFruCQ9CSHiUk9cz0hx6aS4VFzY+H4wAHhw/wzeX9oUMzSNg4e1jhndSMinNtYnZm7+WCGE6KKYxQybA3JGgBmEuiJIKVDXIGGQ5tZJc8Pzh2W1u78hqGLG7XunNSfX2jRV5XwDdZ1hhWUsm2QaqhtG9ghIyo78+YQQoot64nVGZlOS7Ktn7djmfodNI2ioWHDtQaObk2ttus4JU/tHdYxCiN4hPJ+EO5GVlYXNZqOkpKTN/SUlJeTldbyzIC8vb6uOv+KKK6ipqWn+WrNmTXgGH4dyk1oWRjRNI8uj/vqSHN1o+qmhHJJzIDk/1iMRPZWu4x4zBveYMWphUXQL0YgXEIGYkd5fTTLlj4X+01pafNvdqiKft1It8MZAQXLbmLEhUvRNiXzCb9wxDZX4nDFAVRcWvZfEiB6h28aMLeFKhtxRULi9qnRbu14lTEVY35SW5KPWlaJat2cNi2BjUzXB/pA3RhJrRXyTmNEj9IiYYVnquqK2CHSH6sTQbwfoNw0G7NR0HbIj9J0MaQPUc+qK1VfIv8mX3hYDOokNcXGd0VCqEotzR0mMEd2fxKGo69Yxw1sBNevafzVWqWIXMTIoveNNDvnJcRAztpWvRm3icKXEeiSiN5LYEDe6dczYUnaX2hTtyVRzORE2JKPjmJGbGKOYUV+qOi6lD4jN+YWINIkpUdMrYkaUDctN7vD+rKTwdnUSQnSgl8aPiP6kTqeTSZMm8fnnnzffZ5omn3/+OTvuuGOHz9lxxx3bHA/w6aefdnq8y+UiJSWlzVdPtWs/FzOGurlwqgoW/z00k137uXjpiLa7pPdsqi6Vlxhn/5CDjerP9EFSPUREjO52M/D11xj4+mvobmnT0l1EI15AhGOGpqmkqPT+0HeqqtCt26FmrapqEWWHDPewzyA3V+ykfsY3jspil34unjyobeWpDZVChnSy4NEjNFapJLXkgliPRMSYxIieoUfEjE3RNEjMgvwJkD0SAo1QX9LU+jsyTh6XwO79Xdywm+qc8dLh6jrjvv3aVrg9drRKVJqQ52j3Gpvlr1Pvx1lDIXe0WqQRIo5JzOgZun3MMIIqOUq3q+uLfjtA5iD12daZoKrXOtzgToHkPMgbrRJtC7eHxBz1vlu7PqzXI+dNSWaXvi7u2CsNgKcPzmC3/i5u3D2tzXEzxycCai4rKhrKQHepNt2etM0eLkS8kzgUfd0yZmi6+lydkKk2X/SZCIWToWBCUwVvJ9SXqaqDRnDbz7ON/rVzCtMKndw/XV1XPDojnT0GuLhsWtvF8P/bPgmAGUPi/N+6ZanfY3JemNp6CLF1JDbEj24ZM7bFho1rjgS1kSOCbt0zjR0LnTw+Q8WM+/ZLY68BLs5pihEbXLyjiiEb5qgiorFaxdesYU0dQ4ToeSSmRE+viRlR9NDxE9lhUAb//dsUAG4/Yiz7jsrlxB3aVq29fP8RAJyxy8Coj1GInqq3xg/NsiJbUu+VV17hlFNO4bHHHmPKlCnce++9vPrqqyxatIjc3FxOPvlk+vTpw6233grAd999x2677cZtt93GjBkzePnll7nlllv45ZdfGDNmzGbPV1tbS2pqKjU1NdsWMFb/AIEGtVDRjRXVGaS5NTyOOEmwtSy1IJQxUC2my+STEDHV5ffKCIh2vIAo/B4CXqhcAdWrmhY74jO2rKsLkemx4bb3wPdmI6gW2gsmqPZVQoitJjFDidnvoaEcypeq97KkbFUdPYbW1IbITbThtG1FzGgoV1XEs4erqrW9aDerEL2NxAylS78HIwQrZ4MZgKAP0vpC5lBwJW3+ua1tqHhbs1YlVZkhdT0SxU4Oq2tC5CfZcGxNzNgWDeUqwSxvLCTlRPZcQoiwkZihdPn3EPCqa4SOPmObhooF1atVLHC4wZMRl3Pzq6pDFKbYsOnxN7Zm/joVp/vvKJ2RhIgyiRlKzH4PdcWw/ldVXMSZGL3zdsCyLFbVGPRLtaFHIp6F/CqROG+cuhYTQnQ7EjOUePw9RJtlWayq8NI/M6FNxz4hhNhgS98rI16m7phjjqGsrIxrrrmG4uJixo8fz0cffURubi4Aq1evRm818TNt2jRefPFFrrrqKq688kqGDh3K22+/vcVBQihx11rJV6MWgtIHxOXknRAi9npkvHAmqJ3dnjQoX6KqRiXlgh5f79F9kntw1dqGCkjKU793IUSP0SNjRmcSs8CZBBXLoXqlWjiP4WaNvilbETNMQy3AOJNUwlNy522rhBAiUrptzAj5VaWkzCHb1v1H0yAxU8WMtL5qw3PtOlWFKTFTVTOMsH6pUbjOqC9VlX1zx0hirRCiy7plzHBuonKfblMb9BIyoC5PXVPUrFNtrm3b0I0igvqndYO5KX8dpA2QxFohBNBNY8a2Ss6DjCFQtkgVEdFj956taRoDIhUzLEtdX6QPhJQ+kTmHEKJX6lUxI45omsaArNhuChFC9AwRr1wbbVK5Ng6ZIagthoLtIK1frEcjejizsZG/ZhwIwKAP3kf3yGRnR2S3mhLV30NjtZp8qi9Vk1FxtojRIwW94K9XLRElrgskRmwriRlKzH8PlqU2aZQvgWBD02aNOF6ADjaqSoLJeapirTs11iMSYqtIzNg2MX+vjBNd+j2YBqz9WW1QzhkVvg3KlgWNVaqzRn2xSrhKyIzvWLIplqV+DrunKbE2O9YjEiKsekMckpihRPX3EGhQ1xPVayEhPebVB7sVI6iub/pOURsghYiB3hAbOiMxQ4np78EIQtHvqhJ6ag9NPK0vBUcC9Jm06c0rQvQAPTmmSMxQ5PcghIiEnhY/4qZyrRDUNy2qJ0s7bhEFlkVw/frm20LEDU8a5G+nEmyr16iqSnZXrEfVc1mWqlqbNVQSa0ULiRGiO9M0tXjhSobypVC3XiWsupJjPbK2LAsaKyEUUO/BGYPBHvnqiEKEncQMESu6DfLHqUrl4ez8o2nqc7E7DRrKVDX0upKmFuHpoHXQTjxemSFVGd2dBrmj5fO+6JkkDolIcCZC7lhwJkPFUpUo5UmL9ai6B1+Nipee9FiPRPRmEhtELNkcap7HXwveyp73GTzQAJapNohLYq3oDSSmCCGE2Ba9NH50o5lz0S0FGtTCUObgbWtjKIQQPYmjqapSxmCoL1OVVUVk+KpV0lla/1iPRAghwsudojZr5I5RLcNri1WSUTwI+VWbWd0BBeMhe4Qk1gohxLZweMKbWNuarqt24AWT1Hu1zaXeuxuru8eEqL9OJdYmF0DBhJ63qC+EEJFms6u5+ryx6jqivrR7vP/HkmWqzhyphWqtQwgheit3CmQNg5BPvS/2FGZIJQxnDFFFUYQQQgghhGhFsh1F5FimuhjJHiGLHUIIsYHNATkj1J/lS8BjSRu+cDOCanNH/njZZS6E6JlsdsgYqDYRVCyD2iJ12x2j9k4bPvcbAUgfoMbmSorNWIQQQmwZm10lCSXmqGroVaugZi24k8GVGrnk3m1lBFQ7bpsTckZDWj/ZxC2EENtK01QMsLmgdKFq8Z2cH3/v/fHCV6uutyThSgghIKVAVa8tXwop+aB388/klqU6eqT0gYwBsR6NEEIIIYSIQ938E6+Iaw3lkJAF6VI1UAgh2tBtkDlEtV4tWwRY4JQkpLBpKFOVrFIKYj0SIYSIrIQMcE1QyVCVf6nKg4lZYHdFbwy+2pYWqbljIClXVUUUQgjRPdidamNEUp5Kroq3JNsNVaSMoPp8nzFQWnILIUS4JGWDfTwUL1AbLZLz1VyVaGFZqmp63tjoXmcJIUS80jS1thFoUB0lUgpif83QFd4KcCVD9jBVEEUIIYQQQoiNSHKtiIxgI5gmZA2RSSchhOiIrqs2fJoOZX+q+yTBtuv8dSruZA6SVn1CiN7B5lCJRgmZLQlRmqW+tzkjd15/nUqqdSRC7mhV4cPhjtz5hBBCRJbDreJJcp5aJK9erWKKMwHcadH/bG0a0FgFQZ/aOJI+QDZwCCFEJLhTIX8sFM9XHTFSJMG2jUC9mq+TqrVCCNHC5oDs4SrB1luhPq93RwEvGCG1gcKVHOvRCCGEEEKIOCXJtSL8LFNVrc0aConZsR6NEELEL01TC9ggCbbhYIagsVpVTpRqVkKI3sadAnljVFJU9WqoL1H3e9LA4QnPOUxDJdX661S8yhquKpS4JHYJIUSP4fA0JdnmQ0Opiil1xSq51pMG9ghvpDCCKqnWCIAnE3JGqqRaqSIlhBCR40pWiUUl86G2qYKtbFhWfDXquseZGOuRCCFEfHElQ/YIWP+rmifqbsmpZkglBmePUNcbQgghhBBCdEKSa0X4NZSrFrXpA7t3KxDRPWkaziGDm28LEfdaJ9iWLgQ0mbDfVvVlKqksrW+sRyLilcQI0dNpmmrtmpCpFghq16r3xoZyVXnQmbT1XSXMkKrkEWgALHClqIX3xGxJqhU9m8QM0ds53JDWTyVYeSugZh14y8EoV9crrmTQwzitGPCqBCaAxExI7atijSTVit5K4pCINldSqwTbIkgtkAq2gXq16SQlP9YjEUKR2CDiTXKuqmBbMl91T+ounUwtC+pKILUQMgbJ/yfRO0lMEUIIsS16afyQ5FoRXgGvqlybNUzawoqY0D0eBr//fqyHIcTWaZdgiyTYbi1fLdidkDlEFuBFpyRGiF5D11WSbWIW+GvBW6kWyH01qiKgpqkFD92hEqM2XABbFliGOsbwg2Go13IkQlp/9XoJGfI+K3oFiRlCNLE51Aa2pFwVR7yVULsO6kvBNMHpAUeCqmi7NROqlgnBRrV5I+RXm0BSC1Uyb0Kmij9C9GISh0RMOBMhdyyY89T1Q0pBr1osa6exWs0zdbdqjKLHktgg4lJaf/WZvvIvFTe6Q+XzhjJwp6rEYJukSojeSWKKEEKIbdFb44d8YhThYxqqmknWcFVdRAghxJbbkGBrmVC+GFXBNiHWo+oejKBKrs0bo5K+hBBCKJqmFgvcqU2LHXXgr1ft+vy1KqkpFACMpifoahHEkQBJeeBOVtVunUlqA4MQQojeS9PAk6a+0vqpRFtfDdSXqMp+3kp1nG5TCbm6XVU81LSmzRumqoZuBNX8Eah4k5ClKl6506QiuhBCxANnAuSOhqJ5UFfce6u2Brxq40hyL/35hRBiS+m62ogQbGiKG3G+McNXo65TskdIgRMhhBBCCLFFJLlWhE99mVoQyRgY3xdOQggRrzRNtSGyLJVgq6EWnEXnLEtVzUorVIv8QgghOqbrLYm2G5iGSnSyzKY7NJUMJVU7hBBCbIrNDomZ6itjIAS9qlpV0Ks2cQTqwQioyrZYqPiiN23YSFZJtI6Epj89sf5phBBCbMydAnljWxJsk/NiPaLoa6yC9IFqU4kQQohNc7gheyQEfaoqbFJOrEfUsUCD+sodozo+CSGEEEIIsQVk1VSEh68G7A7IHCpVrURMmY2NrDjqKAAGvvYaukcW6kQ3o+uQORhoSrBN0GTBeVO85eBKUfGnO7ScEjElMUKIjeg2ee8UohMSM4TYQpqmKj5tXPXJspoq1DYl12q6utYRQmwRiUMi5jxpTQm2v6tNzfGaKBUJAS/YXZDaJ9YjEaINiQ0irrlTmiqf/6a6nCZkxnpEbQUa1Fp21nAp0iEEElOEEEJsm94aPyS5dlNCfvVhO+RTiwLQ0s5Ot6mdeI5E1e6uNwv5VWWSvLHSjlvEnmURWLa8+bYQ3dKGVkqWBRVL1XurJNi2F6hX1bDyhksLWbFlJEYIIYTYUhIzhOgaTZNK6EJ0hcQhEQ8SMiBvjEqwbSiHxKxYjyg6pGqtiFcSG0S8S8yCnNFQ/Dv4qsGdFusRKYF6aKyB7BGqc6B0XxVCYooQQoht00vjh8xyb8wC/HWqlZ3NqdrVpfQBZwJoTVWdzCD4G6CxAhoqwDLAlay+etsHcstUO9fTB0Jq31iPRggheg5dh6whqAq2S1XLVUmwbRHyg7da7YZPzo31aIQQQgghhBBCCCF6nsQsNfdS3EsSbAMNUrVWCCG6IiUfzBAUzwdqwJ0a2/H4atR7e84oyBjY+9bxhRBCCCFEl0ly7cY01Af9lEJIylatpjtrW2ca6kN5QznUroOatartRawvFKKpvgQSslQCmLT3E0KI8NJtkDVU3a5YJhVsNzCNlo0d6f1jPRohhBBCCCGEEEKInis5FxgHJX/EZ6vvcPJWQtYwqVorhBBdkdZXFWcqWQhYsatg21CuxpE7BtL6SWKtEEIIIYTYJpJcu7GckaDbwZm4+WN1m0p0SshQFwq1RVC1SiXZJmaB3R358cZSY7Wq7pszQpK9hBAiUnSbmtRHg4ol4MlQ1dR7K8uCumJIzoPsYer3I4QQQgghhBBCCCEiJzlXJSgV/67WBXpi8qmvFpxJkFoY65EIIUT3l94fNB1KFoBZqdbSo2VDcQ6HR1Wslc53QgghhBCiCyS5dmPbWnXW4YHMQZCUA1UroXo12OyqqmtP3AkX9KqvvHHRvSASQojeSNdVBVtNVwm2mGqyvzeqL1axOmekatMnhBBCCCGEEEIIISIvJR8sA4r/UB39elIHP8tUP1PeWHD10jk3IYQIt7S+qjhG6ULVCTUxJ/Jr5sFGVbE2MQdyhoMnPbLnE0IIIYQQPZ4k14abKwlyR0NiNpQvUVVsk3J6VgKQEYSGCsgaLru4hRAiWnQdMgeryaiyRWCa4E6J9aiiq74U7B4VZ13JsR6NEEIIIYQQQgghRO+SWthUwXYBoPWcuSlvhSoiklIQ65EIIUTPklIANpdKsK1ZB8k5qitquFmmei83DcgYDFlDetbavBBCCCGEiBlJro0ETVMtJtwpUL4MqleppNuesJPbNFQ77rR+KsmrJ1blFd2bpuEoKGi+LUSPouuQMbBpt/efYIZ6T/VwbwXodsgd03t+ZhF+EiOEEEJsKYkZQgghYknikIhnaf1UElPJQvXvs7tvgA75VUGRPEnEEnFOYoPorhIzoc9EtWZeu1a913rSVae+rrIs8NeAr051k80cBEm58n9EiM2RmCKEEGJb9NL4Icm1keRoqq7nTlFVbOuKu/cHestS7biTcyF7ONjkn4+IP7rHw5AvPo/1MISIHE2D9P5gc6gE22i1U4olb4X6M3cMJGXHdiyiW5MYIYQQYktJzBBCCBFLEodE3Evrr9YLSheq77tzgm19GaQPUGs3QsQxiQ2iW3MmQt5YSMyCyr9UFVtXErhSVDGRrWWGwFcLgQYVg/LGQkofsEegKq4QPZDEFCGEENuit8YPyY6MNF1XSVDOJCj7U10spOSp6nvdTX0xuFIhZ5RKHBZCCBE7rdsp1a1XCwDdMbZsTkMZaDaVWJssixxCCCGEEEIIIYQQMadpKiEVuneCbWO1Su7KGNizN64LIUQ80HVI7QMJmVBXBDVrVfEQNJV860zY9BqHEYCgF/wNTZXTUyBvECTnybq1EEIIIYSImB6YhROnEjPBMUFVsK1eo3bmdacP+vWlYE9QlXi74ySZEEL0RImZUDAeShdBbZGq6mp3x3pU4bGhWrrdIxVrhRBCCCGEEEIIIeJNd0+wNYIQqIf87VSCrRBCiOhwuNWmhtRC8FaqAhveCmgoVxVp0QBLxRmr1fNsDnAkQOYQlaDrSZcuq0IIIYQQIuLkE2c0ORNVgpDdA5XLwfCDOy3Wo9q8hnLQHZA3BhIyYj0aITbJ9PlYdeJJAPR//jl0dw9JNBSiM65ktQjgSICqFSrWeNJiPaquMUNQVwLuVLWpQ2KPCBOJEUIIIbaUxAwhhBCxJHFIdBvdOcG2oVQldqX0ifVIhNgiEhtEj2NzqG51yblNGx4aINioKtRahirAoWmqmq3NpYpWORNBt8V65EJ0exJThBBCbIveGj8kuTbabA7IHq5aW5QtVhVhE7Pjt+VQQzloukqsTcyK9WiE2DzTxDd/fvNtIXoFuxNyRoI7BcqXQs06NSG1qRZK8Srkg/oySM5XP5NUDhHhJDFCCCHElpKYIYQQIpYkDonuZOMEW00DZ5zP5zRWgz0RMgZLkpboPiQ2iJ7M5lBFQ7p74RAhuguJKUIIIbZFL40f3TDrpgfQNEjrp6oMlv4JtesgOS/+kqDqS1sq1iblxHo0QgghNkXXIa2vqg5SsQzqitRtd2qsR7blGqsg4FULG1lDVdKwEEIIIYQQQgghhIhvGxJsLQvK/gQ0VV0wHoX8qjpi/nZqo7oQQgghhBBCCCFEJ/RYD6BXS8yCggmQXAC1RarVRTywLKgtVi028sdJYq0QQnQnnjTIHw+5Y9RuoZp1atEgnhlBtdHE0qBgvKpYK4m1QgghhBBCCCGEEN2HpkHGQMgarirDBr2xHlF7lqWKiqT1g5Q+sR6NEEIIIYQQQggh4lyclUrthVxJKoHVmQiVf6kE24SM2I3HDEFdiUrOyh0NnvTYjUUIIcS2sdnVYkZCJlSuUImrmqa+tzliPboWlgWNlRD0QWofVbFWKoYIIYQQQgghhBBCdE+aBhmD1DpD+VJI0sHujvWoWjSUqTWPzMGqC5QQQgghhBBCCCHEJkhybTywOSB7uGrfXb4EaterarF6lP96Qj6oL4OUAsgeoRJ/hRBCdF/uFLWBI6UAqlZCQynoNrWIYIthZVjLAn8t+GrVWLJHQnKeGpsQQgghhBBCCCGE6L50HbKGgmVCxTK11mF3xXpUEKhXY8oeroqdCCGEEEIIIYQQQmyGJNfGC01TVftcSWpHd+16VT3WlRyd8zdWQcCrqgZmDZV23EII0VNoGiRlq6roDeVQs0ZtpLAM8KSCI1EdEw2WqRJq/XUqvuWOUYm/jjiqYCKEEEIIIYQQQgghuka3QdYwNRdU+VfsE2xDfvBWQ85INRYhhBBCCCGEEEKILSDJtfHGnQr548GTAVV/Qc06lRQVqQqDRkAlWTkToWA8pPSJXpKVEBFiS0+P9RCEiD+6DZJzITFbbaioK4b6EvCuVRsqnMng8IQ/BlgWBBtVpVrTUEm1eWMhKRecCeE9lxBbQGKEEEKILSUxQwghRCxJHBLdns2uOuRBbBNszRDUl0LGIMgYGP3zCxFGEhuEEEKEi8QUIYQQ26I3xg9Jro1HNjtkDlJVBiuWQ12RSnzypIMepr8y04DGSggFILWvOl+0quQKEUF6QgLDvv8u1sMQIn7pOiRmqq/AQJVoW1+i/mysBDRVSdbuUQseum3rXt+y1MaNUCMEGlWFEocbkvMgKQ8SMqU6uogZiRFCCCG2lMQMIYQQsSRxSPQYbRJsV6g1j2hutjYNqC1SnZOyhm39PJcQcURigxBCiHCRmCKEEGJb9Nb4Icm18cyTBgUToL4AqlZCXQnYHP/f3r3HyFmWDx+/9tDd7UJP0MO2pOKv5YflxRIjhKYcRKURU4NoTMBAajHYQij/AFFQJFVQQEKMCUENeMDEaqMGDEpT5SCvAVFIbSORWtMWBH3ZIhTotqXbPdzvH8+y0LLbzkzn+Mznk0zSnT6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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "G 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "G 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.1\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n",
-      "B 0.2\n",
-      "torch.Size([1000, 1]) torch.Size([1000, 1]) torch.Size([1000, 1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3500x400 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "n_samples = 16\n",
-    "lengthscale = 0.2\n",
-    "for seed in range(5):\n",
-    "    print(f\"Seed {seed}\")\n",
-    "    test_info = get_test_function(n_samples, lengthscale, seed=seed)\n",
-    "\n",
-    "    for _ in range(5):\n",
-    "        train_x, train_y, test_x, test_y = test_info\n",
-    "        queries = plot(test_info, pfn_infos)\n",
-    "        gp_next_x, gp_next_y = queries[-1]\n",
-    "        train_x = torch.cat([train_x, torch.tensor([[gp_next_x]])])\n",
-    "        train_y = torch.cat([train_y, torch.tensor([[gp_next_y]])])\n",
-    "        test_info = train_x, train_y, test_x, test_y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pfn",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/private_multitask_pfn/1d_hist.py b/private_multitask_pfn/1d_hist.py
deleted file mode 100644
index 64ec2e5..0000000
--- a/private_multitask_pfn/1d_hist.py
+++ /dev/null
@@ -1,60 +0,0 @@
-
-from gen_batch import (
-    combine_batch,
-    get_hpo_batch_fn,
-    get_icm,
-    get_lmc,
-    get_mtgp_batch,
-    get_pd1_surrogate_batch_fn,
-    get_pd1_eval_batch_fn,
-    toy_no_memory_batch,
-    get_trios_batch,
-)
-
-from PFNs.pfns.priors.hebo_prior import get_batch as get_hebo_batch
-import matplotlib.pyplot as plt
-
-
-mtgp_batch = get_mtgp_batch(
-    batch_size=200,
-    seq_len=50,
-    num_features=1,
-    max_num_tasks=2,
-    num_tasks=1,
-    lengthscale=None,
-    hyperparameters={},
-    device="cpu"
-)
-hebo_batch = get_hebo_batch(
-    batch_size=200,
-    seq_len=50,
-    num_features=1
-)
-plt.hist(mtgp_batch.x.flatten(), density=True, alpha=0.5, label="mtgp")
-plt.hist(hebo_batch.x.cpu().flatten(), density=True, alpha=0.5, label="hebo")
-plt.legend()
-plt.savefig("1x.png")
-plt.close()
-plt.hist(mtgp_batch.y.flatten(), density=True, alpha=0.5, label="mtgp")
-plt.hist(hebo_batch.y.cpu().flatten(), density=True, alpha=0.5, label="hebo")
-plt.legend()
-plt.savefig("1y.png")
-
-
-# def get_batch(
-#     batch_size,
-#     seq_len,
-#     num_features,
-#     device=default_device,
-#     hyperparameters=None,
-#     batch_size_per_gp_sample=None,
-#     single_eval_pos=None,
-#     fix_to_range=None,
-#     equidistant_x=False,
-#     verbose=False,
-#     **kwargs,
-
-
-# plt.savefig("1hebo.png")
-# plt.close()
-# plt.savefig("1heboy.png")
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/LICENSE b/private_multitask_pfn/PFNs/LICENSE
deleted file mode 100644
index 7095386..0000000
--- a/private_multitask_pfn/PFNs/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
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-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
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-      direction or management of such entity, whether by contract or
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-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
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-      Notwithstanding the above, nothing herein shall supersede or modify
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-      with Licensor regarding such Contributions.
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-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
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-      has been advised of the possibility of such damages.
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-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
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-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
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-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
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-   Copyright 2023, Samuel Mueller, Noah Hollmann, ML Lab Freiburg
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
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-       http://www.apache.org/licenses/LICENSE-2.0
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-   distributed under the License is distributed on an "AS IS" BASIS,
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diff --git a/private_multitask_pfn/PFNs/README.md b/private_multitask_pfn/PFNs/README.md
deleted file mode 100644
index 2818f2a..0000000
--- a/private_multitask_pfn/PFNs/README.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# PFNs
-
-Prior-data Fitted Networks (PFNs, https://arxiv.org/abs/2112.10510) are transformer encoders trained to perform supervised in-context learning on datasets randomly drawn from a prior.
-Our priors can in general be described by a function that samples a datasets, or more generally a batch of datasets.
-The PFN is then trained to predict a hold-out set of labels, given the rest of the dataset.
-
-The pseudo code for a simple prior that would yield a PFN that does 1d ridge regression on datasets with 100 elements, could be something like this:
-
-```python
-def get_dataset_sample():
-    x = RandomUniform(100,1)
-    a = RandomNormal()
-    b = RandomNormal()
-    y = a * x + b
-    return x, y
-```
-
-Check out our [tutorial](https://colab.research.google.com/drive/12YpI99LkuFeWcuYHt_idl142DqX7AaJf) to train your own ridge regression PFN.
-
-### Install with pip
-
-This way of installing allows you to use the package everywhere and still be able to edit files.
-You should use a python version **>=3.9 and <=3.11**.
-```bash
-git clone https://github.com/automl/PFNs.git
-cd PFNs
-pip install -e .
-```
-
-### Get Started
-
-Check out our [Getting Started Colab](https://colab.research.google.com/drive/12YpI99LkuFeWcuYHt_idl142DqX7AaJf).
-
-### Tabular Data
-
-
-For loading the pretrained TabPFN transformer model for classification and use it for evaluation, you can download the model like this
-
-```python
-import torch
-from pfns.scripts.tabpfn_interface import TabPFNClassifier
-# Load pretrained-model
-classifier = TabPFNClassifier(base_path='.', model_string="prior_diff_real_checkpoint_n_0_epoch_42.cpkt")
-
-train_xs = torch.rand(100,2)
-test_xs = torch.rand(100,2)
-train_ys = train_xs.mean(1) > .5
-# Fit and evaluate
-task_type = 'multiclass'
-classifier.fit(train_xs, train_ys)
-if task_type == 'multiclass':
-    prediction_ = classifier.predict_proba(test_xs) # For survival [:, 1:]
-else:
-    prediction_ = classifier.predict(test_xs)
-```
-
-
-### BO
-
-There is a BO version of this repo, with pretrained models at [github.com/automl/PFNs4BO](github.com/automl/PFNs4BO).
-The two repos share a lot of the code, but the other is not anymore actively maintained.
-You can also train your own models with our tutorial notebook [here](Tutorial_Training_for_BO.ipynb).
-
-
-### Cite the work
-
-PFNs were introduced in
-```
-@inproceedings{
-    muller2022transformers,
-    title={Transformers Can Do Bayesian Inference},
-    author={Samuel M{\"u}ller and Noah Hollmann and Sebastian Pineda Arango and Josif Grabocka and Frank Hutter},
-    booktitle={International Conference on Learning Representations},
-    year={2022},
-    url={https://openreview.net/forum?id=KSugKcbNf9}
-}
-```
-
-Training PFNs on tabular data (TabPFN) was enhanced in
-```
-@inproceedings{
-  hollmann2023tabpfn,
-  title={Tab{PFN}: A Transformer That Solves Small Tabular Classification Problems in a Second},
-  author={Noah Hollmann and Samuel M{\"u}ller and Katharina Eggensperger and Frank Hutter},
-  booktitle={The Eleventh International Conference on Learning Representations},
-  year={2023},
-  url={https://openreview.net/forum?id=cp5PvcI6w8_}
-}
-```
-
-The BO version of PFNs was introduced in
-```
-@article{muller2023pfns,
-  title={PFNs4BO: In-Context Learning for Bayesian Optimization},
-  author={M{\"u}ller, Samuel and Feurer, Matthias and Hollmann, Noah and Hutter, Frank},
-  journal={arXiv preprint arXiv:2305.17535},
-  year={2023}
-}
-```
diff --git a/private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb b/private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb
deleted file mode 100644
index 4c2422c..0000000
--- a/private_multitask_pfn/PFNs/Tutorial_Training_for_BO.ipynb
+++ /dev/null
@@ -1,1027 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f32e2ce2",
-   "metadata": {},
-   "source": [
-    "## Train your own models\n",
-    "To train you simply need to call `train.train`.\n",
-    "We give all necessary code. The most important bits are in the `priors` dir, e.g. `hebo_prior`, it stores the priors\n",
-    "with which we train our models.\n",
-    "\n",
-    "### Training the HEBO+ model, `model_hebo_morebudget_9_unused_features_3.pt`\n",
-    "You can train this model on 8 GPUs using `torchrun` or `submitit`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3d41a7e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4949ecba",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/utils.py:293: FutureWarning: Decorating classes is deprecated and will be disabled in future versions. You should only decorate functions or methods. To preserve the current behavior of class decoration, you can directly decorate the `__init__` method and nothing else.\n",
-      "  @torch.no_grad()\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from pfns import priors, encoders, utils, bar_distribution, train\n",
-    "from ConfigSpace import hyperparameters as CSH\n",
-    "\n",
-    "config_heboplus = {\n",
-    "     'priordataloader_class_or_get_batch': priors.get_batch_to_dataloader(\n",
-    "         priors.get_batch_sequence(\n",
-    "             priors.hebo_prior.get_batch,\n",
-    "             priors.utils.sample_num_features_get_batch,\n",
-    "         )\n",
-    "     ),\n",
-    "     'encoder_generator': encoders.get_normalized_uniform_encoder(encoders.get_variable_num_features_encoder(encoders.Linear)),\n",
-    "     'emsize': 512,\n",
-    "     'nhead': 4,\n",
-    "     'warmup_epochs': 5,\n",
-    "     'y_encoder_generator': encoders.Linear,\n",
-    "     'batch_size': 128,\n",
-    "     'scheduler': utils.get_cosine_schedule_with_warmup,\n",
-    "     'extra_prior_kwargs_dict': {'num_features': 18,\n",
-    "      'hyperparameters': {\n",
-    "       'lengthscale_concentration': 1.2106559584074301,\n",
-    "       'lengthscale_rate': 1.5212245992840594,\n",
-    "       'outputscale_concentration': 0.8452312502679863,\n",
-    "       'outputscale_rate': 0.3993553245745406,\n",
-    "       'add_linear_kernel': False,\n",
-    "       'power_normalization': False,\n",
-    "       'hebo_warping': False,\n",
-    "       'unused_feature_likelihood': 0.3,\n",
-    "       'observation_noise': True}},\n",
-    "     'epochs': 50,\n",
-    "     'lr': 0.0001,\n",
-    "     'seq_len': 60,\n",
-    "     'single_eval_pos_gen': utils.get_uniform_single_eval_pos_sampler(50, min_len=1), #<function utils.get_uniform_single_eval_pos_sampler.<locals>.<lambda>()>,\n",
-    "     'aggregate_k_gradients': 2,\n",
-    "     'nhid': 1024,\n",
-    "     'steps_per_epoch': 1024,\n",
-    "     'weight_decay': 0.0,\n",
-    "     'train_mixed_precision': False,\n",
-    "     'efficient_eval_masking': True,\n",
-    "     'nlayers': 12}\n",
-    "\n",
-    "\n",
-    "config_heboplus_userpriors = {**config_heboplus,\n",
-    "    'priordataloader_class_or_get_batch': priors.get_batch_to_dataloader(\n",
-    "                              priors.get_batch_sequence(\n",
-    "                                  priors.hebo_prior.get_batch,\n",
-    "                                  priors.condition_on_area_of_opt.get_batch,\n",
-    "                                  priors.utils.sample_num_features_get_batch,\n",
-    "                              )),\n",
-    "    'style_encoder_generator': encoders.get_normalized_uniform_encoder(encoders.get_variable_num_features_encoder(encoders.Linear))\n",
-    "}\n",
-    "\n",
-    "config_bnn = {'priordataloader_class_or_get_batch': priors.get_batch_to_dataloader(\n",
-    "         priors.get_batch_sequence(\n",
-    "             priors.simple_mlp.get_batch,\n",
-    "             priors.input_warping.get_batch,\n",
-    "             priors.utils.sample_num_features_get_batch,\n",
-    "         )\n",
-    "     ),\n",
-    "     'encoder_generator': encoders.get_normalized_uniform_encoder(encoders.get_variable_num_features_encoder(encoders.Linear)),\n",
-    "     'emsize': 512,\n",
-    "     'nhead': 4,\n",
-    "     'warmup_epochs': 5,\n",
-    "     'y_encoder_generator': encoders.Linear,\n",
-    "     'batch_size': 128,\n",
-    "     'scheduler': utils.get_cosine_schedule_with_warmup,\n",
-    "     'extra_prior_kwargs_dict': {'num_features': 18,\n",
-    "      'hyperparameters': {'mlp_num_layers': CSH.UniformIntegerHyperparameter('mlp_num_layers', 8, 15),\n",
-    "       'mlp_num_hidden': CSH.UniformIntegerHyperparameter('mlp_num_hidden', 36, 150),\n",
-    "       'mlp_init_std': CSH.UniformFloatHyperparameter('mlp_init_std',0.08896049884896237, 0.1928554813280186),\n",
-    "       'mlp_sparseness': 0.1449806273312999,\n",
-    "       'mlp_input_sampling': 'uniform',\n",
-    "       'mlp_output_noise': CSH.UniformFloatHyperparameter('mlp_output_noise', 0.00035983014290491186, 0.0013416342770574585),\n",
-    "       'mlp_noisy_targets': True,\n",
-    "       'mlp_preactivation_noise_std': CSH.UniformFloatHyperparameter('mlp_preactivation_noise_std',0.0003145707276259681, 0.0013753183831259406),\n",
-    "       'input_warping_c1_std': 0.9759720822120248,\n",
-    "       'input_warping_c0_std': 0.8002534583197192,\n",
-    "       'num_hyperparameter_samples_per_batch': 16}\n",
-    "                                 },\n",
-    "     'epochs': 50,\n",
-    "     'lr': 0.0001,\n",
-    "     'seq_len': 60,\n",
-    "     'single_eval_pos_gen': utils.get_uniform_single_eval_pos_sampler(50, min_len=1), \n",
-    "     'aggregate_k_gradients': 1,\n",
-    "     'nhid': 1024,\n",
-    "     'steps_per_epoch': 1024,\n",
-    "     'weight_decay': 0.0,\n",
-    "     'train_mixed_precision': True,\n",
-    "     'efficient_eval_masking': True,\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# now let's add the criterions, where we decide the border positions based on the prior\n",
-    "def get_ys(config, device='cuda:0'):\n",
-    "    bs = 128\n",
-    "    all_targets = []\n",
-    "    for num_hps in [2,8,12]: # a few different samples in case the number of features makes a difference in y dist\n",
-    "        b = config['priordataloader_class_or_get_batch'].get_batch_method(\n",
-    "            bs,1000,  num_hps, epoch=0, device=device, hyperparameters={**config['extra_prior_kwargs_dict']['hyperparameters'],\n",
-    "                                                                        'num_hyperparameter_samples_per_batch': -1,})\n",
-    "        all_targets.append(b.target_y.flatten())\n",
-    "    return torch.cat(all_targets,0)\n",
-    "\n",
-    "def add_criterion(config, device='cuda:0'):\n",
-    "    return {**config, 'criterion': bar_distribution.FullSupportBarDistribution(\n",
-    "        bar_distribution.get_bucket_limits(1000,ys=get_ys(config,device).cpu())\n",
-    "    )}\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c8789428",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.filterwarnings('ignore', category=UserWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4dec5326",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
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-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
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-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
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-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
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-      "        [0.],\n",
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-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
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-      "        [0.],\n",
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-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
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-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n",
-      "/home/yl9959/mtpfn/PFNs/pfns/priors/hebo_prior.py:413: InputDataWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n",
-      "  model = botorch.models.SingleTaskGP(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/botorch/models/utils/assorted.py:268: InputDataWarning: Data (outcome observations) is not standardized (std = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]]), mean = tensor([[0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.],\n",
-      "        [0.]])).Please consider scaling the input to zero mean and unit variance.\n",
-      "  check_standardization(Y=train_Y, raise_on_fail=raise_on_fail)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using 384000 y evals to estimate 1000 buckets. Cut off the last 0 ys.\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "train() missing 3 required positional arguments: 'train_loader', 'eval_loader', and 'task_encoder_generator'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Now let's train either with\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m train\u001b[38;5;241m.\u001b[39mtrain(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39madd_criterion(config_heboplus,device\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu:0\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
-      "\u001b[0;31mTypeError\u001b[0m: train() missing 3 required positional arguments: 'train_loader', 'eval_loader', and 'task_encoder_generator'"
-     ]
-    }
-   ],
-   "source": [
-    "# Now let's train either with\n",
-    "train.train(**add_criterion(config_heboplus,device='cpu:0'))\n",
-    "# or\n",
-    "#train.train(**add_criterion(config_heboplus_userpriors))\n",
-    "# or\n",
-    "#train.train(**add_criterion(config_bnn))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c427f1e1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pfn",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/private_multitask_pfn/PFNs/pfns/bar_distribution.py b/private_multitask_pfn/PFNs/pfns/bar_distribution.py
deleted file mode 100644
index 9f9f9c9..0000000
--- a/private_multitask_pfn/PFNs/pfns/bar_distribution.py
+++ /dev/null
@@ -1,578 +0,0 @@
-"""
-The distributions here are piece-wise continuous distribution (BarDistribution)
-and a piece-wise continuous distribution with half-normals on each side, s.t. it has support for (-inf,inf), named FullSupportBarDistribution.
-Both of these distributions are described in the "Transformers Can Do Bayesian Inference" paper as Riemann Distribution.
-"""
-
-import torch
-from torch import nn
-
-from .utils import print_once
-
-
-class BarDistribution(nn.Module):
-    def __init__(
-        self, borders: torch.Tensor, smoothing=0.0, ignore_nan_targets=True
-    ):  # here borders should start with min and end with max, where all values lie in (min,max) and are sorted
-        """
-        :param borders:
-        :param smoothing:
-        :param append_mean_pred: Whether to predict the mean of the other positions as a last output in forward,
-        is enabled when additionally y has a sequence length 1 shorter than logits, i.e. len(logits) == 1 + len(y)
-        """
-        super().__init__()
-        assert len(borders.shape) == 1
-        self.register_buffer("borders", borders)
-        self.register_buffer("smoothing", torch.tensor(smoothing))
-        self.register_buffer("bucket_widths", self.borders[1:] - self.borders[:-1])
-        full_width = self.bucket_widths.sum()
-
-        assert (
-            1 - (full_width / (self.borders[-1] - self.borders[0]))
-        ).abs() < 1e-2, f"diff: {full_width - (self.borders[-1] - self.borders[0])} with {full_width} {self.borders[-1]} {self.borders[0]}"
-        assert (
-            self.bucket_widths >= 0.0
-        ).all(), "Please provide sorted borders!"  # This also allows size zero buckets
-        self.num_bars = len(borders) - 1
-        self.ignore_nan_targets = ignore_nan_targets
-        self.to(borders.device)
-
-    def __setstate__(self, state):
-        super().__setstate__(state)
-        self.__dict__.setdefault("append_mean_pred", False)
-
-    def map_to_bucket_idx(self, y):
-        target_sample = torch.searchsorted(self.borders, y) - 1
-        target_sample[y == self.borders[0]] = 0
-        target_sample[y == self.borders[-1]] = self.num_bars - 1
-        return target_sample
-
-    def ignore_init(self, y):
-        ignore_loss_mask = torch.isnan(y)
-        if ignore_loss_mask.any():
-            if not self.ignore_nan_targets:
-                raise ValueError(f"Found NaN in target {y}")
-            print_once("A loss was ignored because there was nan target.")
-        y[ignore_loss_mask] = self.borders[
-            0
-        ]  # this is just a default value, it will be ignored anyway
-        return ignore_loss_mask
-
-    def compute_scaled_log_probs(self, logits):
-        # this is equivalent to log(p(y)) of the density p
-        bucket_log_probs = torch.log_softmax(logits, -1)
-        scaled_bucket_log_probs = bucket_log_probs - torch.log(self.bucket_widths)
-        return scaled_bucket_log_probs
-
-    def forward(
-        self, logits, y, mean_prediction_logits=None
-    ):  # gives the negative log density (the _loss_), y: T x B, logits: T x B x self.num_bars
-        y = y.clone().view(*logits.shape[:-1])  # no trailing one dimension
-        ignore_loss_mask = self.ignore_init(y)
-        target_sample = self.map_to_bucket_idx(y)
-        assert (target_sample >= 0).all() and (
-            target_sample < self.num_bars
-        ).all(), f"y {y} not in support set for borders (min_y, max_y) {self.borders}"
-        assert (
-            logits.shape[-1] == self.num_bars
-        ), f"{logits.shape[-1]} vs {self.num_bars}"
-
-        scaled_bucket_log_probs = self.compute_scaled_log_probs(logits)
-        nll_loss = -scaled_bucket_log_probs.gather(
-            -1, target_sample[..., None]
-        ).squeeze(
-            -1
-        )  # T x B
-
-        if mean_prediction_logits is not None:
-            if not self.training:
-                print("Calculating loss incl mean prediction loss for nonmyopic BO.")
-            scaled_mean_log_probs = self.compute_scaled_log_probs(
-                mean_prediction_logits
-            )
-            nll_loss = torch.cat(
-                (nll_loss, self.mean_loss(logits, scaled_mean_log_probs)), 0
-            )
-
-        smooth_loss = -scaled_bucket_log_probs.mean(dim=-1)
-        smoothing = self.smoothing if self.training else 0.0
-        loss = (1.0 - smoothing) * nll_loss + smoothing * smooth_loss
-        loss[ignore_loss_mask] = 0.0
-        return loss
-
-    def mean_loss(self, logits, scaled_mean_logits):
-        assert (len(logits.shape) == 3) and (len(scaled_mean_logits.shape) == 2), (
-            len(logits.shape),
-            len(scaled_mean_logits.shape),
-        )
-        means = self.mean(logits).detach()  # T x B
-        target_mean = self.map_to_bucket_idx(means).clamp_(
-            0, self.num_bars - 1
-        )  # T x B
-        return (
-            -scaled_mean_logits.gather(1, target_mean.T).mean(1).unsqueeze(0)
-        )  # 1 x B
-
-    def mean(self, logits):
-        bucket_means = self.borders[:-1] + self.bucket_widths / 2
-        p = torch.softmax(logits, -1)
-        return p @ bucket_means
-
-    def median(self, logits):
-        return self.icdf(logits, 0.5)
-
-    def icdf(self, logits, left_prob):
-        """
-        Implementation of the quantile function
-        :param logits: Tensor of any shape, with the last dimension being logits
-        :param left_prob: float: The probability mass to the left of the result.
-        :return: Position with `left_prob` probability weight to the left.
-        """
-        probs = logits.softmax(-1)
-        cumprobs = torch.cumsum(probs, -1)
-        idx = (
-            torch.searchsorted(
-                cumprobs,
-                left_prob * torch.ones(*cumprobs.shape[:-1], 1, device=logits.device),
-            )
-            .squeeze(-1)
-            .clamp(0, cumprobs.shape[-1] - 1)
-        )  # this might not do the right for outliers
-        cumprobs = torch.cat(
-            [torch.zeros(*cumprobs.shape[:-1], 1, device=logits.device), cumprobs], -1
-        )
-
-        rest_prob = left_prob - cumprobs.gather(-1, idx[..., None]).squeeze(-1)
-        left_border = self.borders[idx]
-        right_border = self.borders[idx + 1]
-        return left_border + (right_border - left_border) * rest_prob / probs.gather(
-            -1, idx[..., None]
-        ).squeeze(-1)
-
-    def quantile(self, logits, center_prob=0.682):
-        side_probs = (1.0 - center_prob) / 2
-        return torch.stack(
-            (self.icdf(logits, side_probs), self.icdf(logits, 1.0 - side_probs)), -1
-        )
-
-    def ucb(self, logits, best_f, rest_prob=(1 - 0.682) / 2, maximize=True):
-        """
-        UCB utility. Rest Prob is the amount of utility above (below) the confidence interval that is ignored.
-        Higher rest_prob is equivalent to lower beta in the standard GP-UCB formulation.
-        :param logits: Logits, as returned by the Transformer.
-        :param rest_prob: The amount of utility above (below) the confidence interval that is ignored.
-        The default is equivalent to using GP-UCB with `beta=1`.
-        To get the corresponding `beta`, where `beta` is from
-        the standard GP definition of UCB `ucb_utility = mean + beta * std`,
-        you can use this computation: `beta = math.sqrt(2)*torch.erfinv(torch.tensor(2*(1-rest_prob)-1))`.
-        :param maximize:
-        :return: utility
-        """
-        if maximize:
-            rest_prob = 1 - rest_prob
-        return self.icdf(logits, rest_prob)
-
-    def mode(self, logits):
-        density = logits.softmax(-1) / self.bucket_widths
-        mode_inds = density.argmax(-1)
-        bucket_means = self.borders[:-1] + self.bucket_widths / 2
-        return bucket_means[mode_inds]
-
-    def ei(
-        self, logits, best_f, maximize=True
-    ):  # logits: evaluation_points x batch x feature_dim
-        bucket_diffs = self.borders[1:] - self.borders[:-1]
-        assert maximize
-        if not torch.is_tensor(best_f) or not len(best_f.shape):
-            best_f = torch.full(logits[..., 0].shape, best_f, device=logits.device)
-
-        best_f = best_f[..., None].repeat(*[1] * len(best_f.shape), logits.shape[-1])
-        clamped_best_f = best_f.clamp(self.borders[:-1], self.borders[1:])
-
-        # bucket_contributions = (best_f[...,None] < self.borders[:-1]).float() * bucket_means
-        # true bucket contributions
-        bucket_contributions = (
-            (self.borders[1:] ** 2 - clamped_best_f**2) / 2
-            - best_f * (self.borders[1:] - clamped_best_f)
-        ) / bucket_diffs
-
-        p = torch.softmax(logits, -1)
-        return torch.einsum("...b,...b->...", p, bucket_contributions)
-
-    def pi(
-        self, logits, best_f, maximize=True
-    ):  # logits: evaluation_points x batch x feature_dim
-        """
-        Acquisition Function: Probability of Improvement
-        :param logits: as returned by Transformer
-        :param best_f: best evaluation so far (the incumbent)
-        :param maximize: whether to maximize
-        :return: utility
-        """
-        assert maximize is True
-        if not torch.is_tensor(best_f) or not len(best_f.shape):
-            best_f = torch.full(logits[..., 0].shape, best_f, device=logits.device)
-        p = torch.softmax(logits, -1)
-        border_widths = self.borders[1:] - self.borders[:-1]
-        factor = 1.0 - ((best_f[..., None] - self.borders[:-1]) / border_widths).clamp(
-            0.0, 1.0
-        )
-        return (p * factor).sum(-1)
-
-    def mean_of_square(self, logits):
-        """
-        Computes E[x^2].
-        :param logits: Output of the model.
-        """
-        left_borders = self.borders[:-1]
-        right_borders = self.borders[1:]
-        bucket_mean_of_square = (
-            left_borders.square()
-            + right_borders.square()
-            + left_borders * right_borders
-        ) / 3.0
-        p = torch.softmax(logits, -1)
-        return p @ bucket_mean_of_square
-
-    def variance(self, logits):
-        return self.mean_of_square(logits) - self.mean(logits).square()
-
-    def pi(
-        self, logits, best_f, maximize=True
-    ):  # logits: evaluation_points x batch x feature_dim
-        """
-        Acquisition Function: Probability of Improvement
-        :param logits: as returned by Transformer
-        :param best_f: best evaluation so far (the incumbent)
-        :param maximize: whether to maximize
-        :return: utility
-        """
-        assert maximize is True
-        p = torch.softmax(logits, -1)
-        border_widths = self.borders[1:] - self.borders[:-1]
-        factor = 1.0 - ((best_f - self.borders[:-1]) / border_widths).clamp(0.0, 1.0)
-        return (p * factor).sum(-1)
-
-    def mean_of_square(self, logits):
-        """
-        Computes E[x^2].
-        :param logits: Output of the model.
-        """
-        left_borders = self.borders[:-1]
-        right_borders = self.borders[1:]
-        bucket_mean_of_square = (
-            left_borders.square()
-            + right_borders.square()
-            + left_borders * right_borders
-        ) / 3.0
-        p = torch.softmax(logits, -1)
-        return p @ bucket_mean_of_square
-
-    def variance(self, logits):
-        return self.mean_of_square(logits) - self.mean(logits).square()
-
-
-class FullSupportBarDistribution(BarDistribution):
-    @staticmethod
-    def halfnormal_with_p_weight_before(range_max, p=0.5):
-        s = range_max / torch.distributions.HalfNormal(torch.tensor(1.0)).icdf(
-            torch.tensor(p)
-        )
-        return torch.distributions.HalfNormal(s)
-
-    def forward(
-        self, logits, y, mean_prediction_logits=None
-    ):  # gives the negative log density (the _loss_), y: T x B, logits: T x B x self.num_bars
-        assert self.num_bars > 1
-        y = y.clone().view(len(y), -1)  # no trailing one dimension
-        ignore_loss_mask = self.ignore_init(y)  # alters y
-        target_sample = self.map_to_bucket_idx(y)  # shape: T x B (same as y)
-        target_sample.clamp_(0, self.num_bars - 1)
-
-        assert (
-            logits.shape[-1] == self.num_bars
-        ), f"{logits.shape[-1]} vs {self.num_bars}"
-        assert (target_sample >= 0).all() and (
-            target_sample < self.num_bars
-        ).all(), f"y {y} not in support set for borders (min_y, max_y) {self.borders}"
-        assert (
-            logits.shape[-1] == self.num_bars
-        ), f"{logits.shape[-1]} vs {self.num_bars}"
-        # ignore all position with nan values
-
-        scaled_bucket_log_probs = self.compute_scaled_log_probs(logits)
-
-        assert len(scaled_bucket_log_probs) == len(target_sample), (
-            len(scaled_bucket_log_probs),
-            len(target_sample),
-        )
-        log_probs = scaled_bucket_log_probs.gather(
-            -1, target_sample.unsqueeze(-1)
-        ).squeeze(-1)
-
-        side_normals = (
-            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
-            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
-        )
-
-        log_probs[target_sample == 0] += side_normals[0].log_prob(
-            (self.borders[1] - y[target_sample == 0]).clamp(min=0.00000001)
-        ) + torch.log(self.bucket_widths[0])
-        log_probs[target_sample == self.num_bars - 1] += side_normals[1].log_prob(
-            (y[target_sample == self.num_bars - 1] - self.borders[-2]).clamp(
-                min=0.00000001
-            )
-        ) + torch.log(self.bucket_widths[-1])
-
-        nll_loss = -log_probs
-
-        if mean_prediction_logits is not None:
-            assert (
-                not ignore_loss_mask.any()
-            ), "Ignoring examples is not implemented with mean pred."
-            if not self.training:
-                print("Calculating loss incl mean prediction loss for nonmyopic BO.")
-            if not torch.is_grad_enabled():
-                print(
-                    "Warning: loss is not correct in absolute terms, only the gradient is right, when using `append_mean_pred`."
-                )
-            scaled_mean_log_probs = self.compute_scaled_log_probs(
-                mean_prediction_logits
-            )
-            nll_loss = torch.cat(
-                (nll_loss, self.mean_loss(logits, scaled_mean_log_probs)), 0
-            )
-            # ignore_loss_mask = torch.zeros_like(nll_loss, dtype=torch.bool)
-
-        if self.smoothing:
-            smooth_loss = -scaled_bucket_log_probs.mean(dim=-1)
-            smoothing = self.smoothing if self.training else 0.0
-            nll_loss = (1.0 - smoothing) * nll_loss + smoothing * smooth_loss
-
-        if ignore_loss_mask.any():
-            nll_loss[ignore_loss_mask] = 0.0
-
-        return nll_loss
-
-    def mean(self, logits):
-        bucket_means = self.borders[:-1] + self.bucket_widths / 2
-        p = torch.softmax(logits, -1)
-        side_normals = (
-            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
-            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
-        )
-        bucket_means[0] = -side_normals[0].mean + self.borders[1]
-        bucket_means[-1] = side_normals[1].mean + self.borders[-2]
-        return p @ bucket_means.to(logits.device)
-
-    def mean_of_square(self, logits):
-        """
-        Computes E[x^2].
-        :param logits: Output of the model.
-        """
-        left_borders = self.borders[:-1]
-        right_borders = self.borders[1:]
-        bucket_mean_of_square = (
-            left_borders.square()
-            + right_borders.square()
-            + left_borders * right_borders
-        ) / 3.0
-        side_normals = (
-            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
-            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
-        )
-        bucket_mean_of_square[0] = (
-            side_normals[0].variance
-            + (-side_normals[0].mean + self.borders[1]).square()
-        )
-        bucket_mean_of_square[-1] = (
-            side_normals[1].variance
-            + (side_normals[1].variance + self.borders[-2]).square()
-        )
-        p = torch.softmax(logits, -1)
-        return p @ bucket_mean_of_square
-
-    def pi(
-        self, logits, best_f, maximize=True
-    ):  # logits: evaluation_points x batch x feature_dim
-        """
-        Acquisition Function: Probability of Improvement
-        :param logits: as returned by Transformer (evaluation_points x batch x feature_dim)
-        :param best_f: best evaluation so far (the incumbent)
-        :param maximize: whether to maximize
-        :return: utility
-        """
-        assert maximize is True
-        if not torch.is_tensor(best_f) or not len(best_f.shape):
-            best_f = torch.full(
-                logits[..., 0].shape, best_f, device=logits.device
-            )  # evaluation_points x batch
-        assert (
-            best_f.shape == logits[..., 0].shape
-        ), f"best_f.shape: {best_f.shape}, logits.shape: {logits.shape}"
-        p = torch.softmax(logits, -1)  # evaluation_points x batch
-        border_widths = self.borders[1:] - self.borders[:-1]
-        factor = 1.0 - ((best_f[..., None] - self.borders[:-1]) / border_widths).clamp(
-            0.0, 1.0
-        )  # evaluation_points x batch x num_bars
-
-        side_normals = (
-            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
-            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
-        )
-        position_in_side_normals = (
-            -(best_f - self.borders[1]).clamp(max=0.0),
-            (best_f - self.borders[-2]).clamp(min=0.0),
-        )  # evaluation_points x batch
-        factor[..., 0] = 0.0
-        factor[..., 0][position_in_side_normals[0] > 0.0] = side_normals[0].cdf(
-            position_in_side_normals[0][position_in_side_normals[0] > 0.0]
-        )
-        factor[..., -1] = 1.0
-        factor[..., -1][position_in_side_normals[1] > 0.0] = 1.0 - side_normals[1].cdf(
-            position_in_side_normals[1][position_in_side_normals[1] > 0.0]
-        )
-        return (p * factor).sum(-1)
-
-    def ei_for_halfnormal(self, scale, best_f, maximize=True):
-        """
-        This is the EI for a standard normal distribution with mean 0 and variance `scale` times 2.
-        Which is the same as the half normal EI.
-        I tested this with MC approximation:
-        ei_for_halfnormal = lambda scale, best_f: (torch.distributions.HalfNormal(torch.tensor(scale)).sample((10_000_000,))- best_f ).clamp(min=0.).mean()
-        print([(ei_for_halfnormal(scale,best_f), FullSupportBarDistribution().ei_for_halfnormal(scale,best_f)) for scale in [0.1,1.,10.] for best_f in [.1,10.,4.]])
-        :param scale:
-        :param best_f:
-        :param maximize:
-        :return:
-        """
-        assert maximize
-        mean = torch.tensor(0.0)
-        u = (mean - best_f) / scale
-        normal = torch.distributions.Normal(torch.zeros_like(u), torch.ones_like(u))
-        try:
-            ucdf = normal.cdf(u)
-        except ValueError:
-            print(f"u: {u}, best_f: {best_f}, scale: {scale}")
-            raise
-        updf = torch.exp(normal.log_prob(u))
-        normal_ei = scale * (updf + u * ucdf)
-        return 2 * normal_ei
-
-    def ei(
-        self, logits, best_f, maximize=True
-    ):  # logits: evaluation_points x batch x feature_dim
-        if torch.isnan(logits).any():
-            raise ValueError(f"logits contains NaNs: {logits}")
-        bucket_diffs = self.borders[1:] - self.borders[:-1]
-        assert maximize
-        if not torch.is_tensor(best_f) or not len(best_f.shape):
-            best_f = torch.full(logits[..., 0].shape, best_f, device=logits.device)
-        assert (
-            best_f.shape == logits[..., 0].shape
-        ), f"best_f.shape: {best_f.shape}, logits.shape: {logits.shape}"
-
-        best_f_per_logit = best_f[..., None].repeat(
-            *[1] * len(best_f.shape), logits.shape[-1]
-        )
-        clamped_best_f = best_f_per_logit.clamp(self.borders[:-1], self.borders[1:])
-
-        # true bucket contributions
-        bucket_contributions = (
-            (self.borders[1:] ** 2 - clamped_best_f**2) / 2
-            - best_f_per_logit * (self.borders[1:] - clamped_best_f)
-        ) / bucket_diffs
-
-        # extra stuff for continuous
-        side_normals = (
-            self.halfnormal_with_p_weight_before(self.bucket_widths[0]),
-            self.halfnormal_with_p_weight_before(self.bucket_widths[-1]),
-        )
-        position_in_side_normals = (
-            -(best_f - self.borders[1]).clamp(max=0.0),
-            (best_f - self.borders[-2]).clamp(min=0.0),
-        )  # evaluation_points x batch
-
-        bucket_contributions[..., -1] = self.ei_for_halfnormal(
-            side_normals[1].scale, position_in_side_normals[1]
-        )
-
-        bucket_contributions[..., 0] = self.ei_for_halfnormal(
-            side_normals[0].scale, torch.zeros_like(position_in_side_normals[0])
-        ) - self.ei_for_halfnormal(side_normals[0].scale, position_in_side_normals[0])
-
-        p = torch.softmax(logits, -1)
-        return torch.einsum("...b,...b->...", p, bucket_contributions)
-
-
-def get_bucket_limits(
-    num_outputs: int,
-    full_range: tuple = None,
-    ys: torch.Tensor = None,
-    verbose: bool = False,
-):
-    assert (ys is None) != (
-        full_range is None
-    ), "Either full_range or ys must be passed."
-
-    if ys is not None:
-        ys = ys.flatten()
-        ys = ys[~torch.isnan(ys)]
-        if len(ys) % num_outputs:
-            ys = ys[: -(len(ys) % num_outputs)]
-        print(
-            f"Using {len(ys)} y evals to estimate {num_outputs} buckets. Cut off the last {len(ys) % num_outputs} ys."
-        )
-        ys_per_bucket = len(ys) // num_outputs
-        if full_range is None:
-            full_range = (ys.min(), ys.max())
-        else:
-            assert (
-                full_range[0] <= ys.min() and full_range[1] >= ys.max()
-            ), f"full_range {full_range} not in range of ys {ys.min(), ys.max()}"
-            full_range = torch.tensor(full_range)
-        ys_sorted, ys_order = ys.sort(0)
-        bucket_limits = (
-            ys_sorted[ys_per_bucket - 1 :: ys_per_bucket][:-1]
-            + ys_sorted[ys_per_bucket::ys_per_bucket]
-        ) / 2
-        if verbose:
-            print(
-                f"Using {len(ys)} y evals to estimate {num_outputs} buckets. Cut off the last {len(ys) % num_outputs} ys."
-            )
-            print(full_range)
-        bucket_limits = torch.cat(
-            [full_range[0].unsqueeze(0), bucket_limits, full_range[1].unsqueeze(0)], 0
-        )
-
-    else:
-        class_width = (full_range[1] - full_range[0]) / num_outputs
-        bucket_limits = torch.cat(
-            [
-                full_range[0] + torch.arange(num_outputs).float() * class_width,
-                torch.tensor(full_range[1]).unsqueeze(0),
-            ],
-            0,
-        )
-
-    assert (
-        len(bucket_limits) - 1 == num_outputs
-    ), f"len(bucket_limits) - 1 == {len(bucket_limits) - 1} != {num_outputs} == num_outputs"
-    assert full_range[0] == bucket_limits[0], f"{full_range[0]} != {bucket_limits[0]}"
-    assert (
-        full_range[-1] == bucket_limits[-1]
-    ), f"{full_range[-1]} != {bucket_limits[-1]}"
-
-    return bucket_limits
-
-
-def get_custom_bar_dist(borders, criterion):
-    # Tested that a bar_dist with borders 0.54 (-> softplus 1.0) yields the same bar distribution as the passed one.
-    borders_ = torch.nn.functional.softplus(borders) + 0.001
-    borders_ = torch.cumsum(
-        torch.cat([criterion.borders[0:1], criterion.bucket_widths]) * borders_, 0
-    )
-    criterion_ = criterion.__class__(
-        borders=borders_, handle_nans=criterion.handle_nans
-    )
-    return criterion_
diff --git a/private_multitask_pfn/PFNs/pfns/config.json b/private_multitask_pfn/PFNs/pfns/config.json
deleted file mode 100644
index 34a6469..0000000
--- a/private_multitask_pfn/PFNs/pfns/config.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
-    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-sobol-gpu": [
-        "pfn_bo_bayesmark.py",
-        {
-            "pfn_file" : "final_models/model_hebo_morebudget_9_unused_features_3.pt",
-            "minimize": 1,
-            "device": "cuda:0",
-            "fit_encoder_from_step": null,
-            "sample_only_valid": 1,
-            "pre_sample_size": 1000,
-            "num_candidates": 10,
-            "sobol_sampler": 1
-        }
-    ],
-    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-max1id0-gpu": [
-        "pfn_bo_bayesmark.py",
-        {
-            "pfn_file" : "final_models/model_hebo_morebudget_9_unused_features_3.pt",
-            "minimize": 1,
-            "device": "cuda:0",
-            "fit_encoder_from_step": null,
-            "sample_only_valid": 1,
-            "pre_sample_size": 1000,
-            "num_candidates": 10,
-            "max_initial_design": 1,
-            "fixed_initial_guess": 0.0
-        }
-    ],
-    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-max1id0.5-gpu": [
-        "pfn_bo_bayesmark.py",
-        {
-            "pfn_file" : "final_models/model_hebo_morebudget_9_unused_features_3.pt",
-            "minimize": 1,
-            "device": "cuda:0",
-            "fit_encoder_from_step": null,
-            "sample_only_valid": 1,
-            "pre_sample_size": 1000,
-            "num_candidates": 10,
-            "max_initial_design": 1,
-            "fixed_initial_guess": 0.0
-        }
-    ],
-    "PFN-lbfgs-minimizer-sampleonlyvalid-fast-bnn-sobol-gpu": [
-        "pfn_bo_bayesmark.py",
-        {
-            "pfn_file" : "final_models/model_sampled_warp_simple_mlp_for_hpob_46.pt",
-            "minimize": 1,
-            "device": "cuda:0",
-            "fit_encoder_from_step": null,
-            "sample_only_valid": 1,
-            "pre_sample_size": 1000,
-            "num_candidates": 10,
-            "sobol_sampler": 1
-        }
-    ],
-}
diff --git a/private_multitask_pfn/PFNs/pfns/decoders.py b/private_multitask_pfn/PFNs/pfns/decoders.py
deleted file mode 100644
index 5cef6fd..0000000
--- a/private_multitask_pfn/PFNs/pfns/decoders.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import random
-
-import torch
-from torch import nn
-
-
-class ScaledDecoder(nn.Module):
-    def __init__(self, ninp, nhid, nout):
-        super().__init__()
-        self.linear = nn.Linear(ninp, nhid)
-        self.linear1 = nn.Linear(nhid, nout)
-        self.linear2 = nn.Linear(nhid, 10)
-
-    def forward(self, x):
-        # return torch.cat([self.linear1(x), self.linear2(x)], -1)
-        x = self.linear(x)
-        x = nn.GELU()(x)
-        temps = self.linear2(x).softmax(-1) @ torch.tensor(
-            [1.0, 1.4, 1.7, 2.0, 5.0, 10.0, 20.0, 40.0, 80.0, 160.0], device=x.device
-        )
-        if random.random() > 0.99:
-            print(temps.shape, temps[:, :2])
-        return self.linear1(x) / temps.unsqueeze(-1)
-
-
-class FixedScaledDecoder(nn.Module):
-    def __init__(self, ninp, nhid, nout):
-        super().__init__()
-        self.mapper = nn.Sequential(
-            nn.Linear(ninp, nhid), nn.GELU(), nn.Linear(nhid, nout)
-        )
-        self.T = nn.Parameter(torch.ones(10000) / 10000)
-
-    def forward(self, x):
-        return self.mapper(x) / self.T.sum()
diff --git a/private_multitask_pfn/PFNs/pfns/encoders.py b/private_multitask_pfn/PFNs/pfns/encoders.py
deleted file mode 100644
index d5f6288..0000000
--- a/private_multitask_pfn/PFNs/pfns/encoders.py
+++ /dev/null
@@ -1,642 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-
-from .utils import normalize_data
-
-
-class StyleEncoder(nn.Module):
-    def __init__(self, num_hyperparameters, em_size):
-        super().__init__()
-        self.em_size = em_size
-        self.embedding = nn.Linear(num_hyperparameters, self.em_size)
-
-    def forward(self, hyperparameters):  # B x num_hps
-        return self.embedding(hyperparameters)
-
-
-class StyleEmbEncoder(nn.Module):
-    def __init__(self, num_hyperparameters, em_size, num_embeddings=100):
-        super().__init__()
-        assert num_hyperparameters == 1
-        self.em_size = em_size
-        self.embedding = nn.Embedding(num_embeddings, self.em_size)
-
-    def forward(self, hyperparameters):  # B x num_hps
-        return self.embedding(hyperparameters.squeeze(1))
-
-
-class _PositionalEncoding(nn.Module):
-    def __init__(self, d_model, dropout=0.0):
-        super().__init__()
-        self.dropout = nn.Dropout(p=dropout)
-        self.d_model = d_model
-        self.device_test_tensor = nn.Parameter(torch.tensor(1.0))
-
-    def forward(self, x):  # T x B x num_features
-        assert self.d_model % x.shape[-1] * 2 == 0
-        d_per_feature = self.d_model // x.shape[-1]
-        pe = torch.zeros(*x.shape, d_per_feature, device=self.device_test_tensor.device)
-        # position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
-        interval_size = 10
-        div_term = (
-            (1.0 / interval_size)
-            * 2
-            * math.pi
-            * torch.exp(
-                torch.arange(
-                    0, d_per_feature, 2, device=self.device_test_tensor.device
-                ).float()
-                * math.log(math.sqrt(2))
-            )
-        )
-        # print(div_term/2/math.pi)
-        pe[..., 0::2] = torch.sin(x.unsqueeze(-1) * div_term)
-        pe[..., 1::2] = torch.cos(x.unsqueeze(-1) * div_term)
-        return self.dropout(pe).view(x.shape[0], x.shape[1], self.d_model)
-
-
-Positional = lambda _, emsize: _PositionalEncoding(d_model=emsize)
-
-
-class EmbeddingEncoder(nn.Module):
-    def __init__(self, num_features, em_size, num_embs=100):
-        super().__init__()
-        self.num_embs = num_embs
-        self.embeddings = nn.Embedding(num_embs * num_features, em_size, max_norm=True)
-        self.init_weights(0.1)
-        self.min_max = (-2, +2)
-
-    @property
-    def width(self):
-        return self.min_max[1] - self.min_max[0]
-
-    def init_weights(self, initrange):
-        self.embeddings.weight.data.uniform_(-initrange, initrange)
-
-    def discretize(self, x):
-        split_size = self.width / self.num_embs
-        return (x - self.min_max[0] // split_size).int().clamp(0, self.num_embs - 1)
-
-    def forward(self, x):  # T x B x num_features
-        x_idxs = self.discretize(x)
-        x_idxs += (
-            torch.arange(x.shape[-1], device=x.device).view(1, 1, -1) * self.num_embs
-        )
-        # print(x_idxs,self.embeddings.weight.shape)
-        return self.embeddings(x_idxs).mean(-2)
-
-
-class Normalize(nn.Module):
-    def __init__(self, mean, std):
-        super().__init__()
-        self.mean = mean
-        self.std = std
-
-    def forward(self, x):
-        return (x - self.mean) / self.std
-
-
-class NormalizeMultitask(nn.Module):
-    def __init__(self, num_tasks, mean, std):
-        super().__init__()
-        self.num_tasks = num_tasks
-        self.mean = mean
-        self.std = std
-
-    def forward(self, x):
-        features = x[..., self.num_tasks :]
-        features = (features - self.mean) / self.std
-        x[..., self.num_tasks :] = features
-        return x
-
-
-class SqueezeBetween0and1(nn.Module):  # take care of test set here
-    def forward(self, x):
-        width = x.max(0).values - x.min(0).values
-        result = (x - x.min(0).values) / width
-        result[(width == 0)[None].repeat(len(x), *[1] * (len(x.shape) - 1))] = 0.5
-        return result
-
-
-def get_normalized_uniform_encoder(encoder_creator):
-    """
-    This can be used to wrap an encoder that is fed uniform samples in [0,1] and normalizes these to 0 mean and 1 std.
-    For example, it can be used as `encoder_creator = get_normalized_uniform_encoder(encoders.Linear)`, now this can
-    be initialized with `encoder_creator(feature_dim, in_dim)`.
-    :param encoder:
-    :return:
-    """
-    return lambda in_dim, out_dim: nn.Sequential(
-        Normalize(0.5, math.sqrt(1 / 12)), encoder_creator(in_dim, out_dim)
-    )
-
-
-def get_normalized_uniform_multitask_encoder(encoder_creator):
-    """
-    This can be used to wrap an encoder that is fed uniform samples in [0,1] and normalizes these to 0 mean and 1 std.
-    For example, it can be used as `encoder_creator = get_normalized_uniform_encoder(encoders.Linear)`, now this can
-    be initialized with `encoder_creator(feature_dim, in_dim)`.
-    :param encoder:
-    :return:
-    """
-    return lambda feature_dim, task_dim, out_dim: nn.Sequential(
-        NormalizeMultitask(task_dim, 0.5, math.sqrt(1 / 12)),
-        encoder_creator(feature_dim, task_dim, out_dim),
-    )
-    
-    
-class AxialNormalizedUniformEncoder(nn.Module):
-    def __init__(self, in_dim, out_dim):
-        super().__init__()
-        self.normalize = Normalize(0.5, math.sqrt(1 / 12))
-        self.unsqueeze = lambda x: x.unsqueeze(-1)  # Custom unsqueeze
-        self.linear = nn.Linear(in_dim, out_dim)
-
-    def forward(self, x):
-        # Start with N x B x D
-        x = self.normalize(x).unsqueeze(-1)   # Normalize and add a dimension at the last axis
-        x = self.linear(x)      # Apply the linear transformation 
-        return x.permute(0, 2, 1, 3)    # Permute the axes to get N x D x B x E
-
-    
-def get_axial_normalized_uniform_encoder():
-    return lambda in_dim, out_dim: AxialNormalizedUniformEncoder(in_dim, out_dim)
-
-def get_normalized_encoder(encoder_creator, data_std):
-    return lambda in_dim, out_dim: nn.Sequential(
-        Normalize(0.0, data_std), encoder_creator(in_dim, out_dim)
-    )
-
-
-def get_log_dims(x, eps=1e-10):
-    logged_x = ((x + eps).log() - math.log(eps)) / (math.log(1.0 + eps) - math.log(eps))
-    return logged_x
-
-
-def add_log_neglog_dims(x, eps=1e-10):
-    logged_x = get_log_dims(x, eps) / 2.0
-    neglogged_x = 1 - get_log_dims(1 - x, eps) / 2.0
-    logged_x[x > 0.5] = neglogged_x[x > 0.5]
-    return torch.stack([x, logged_x], -1).view(*x.shape[:-1], -1)
-
-
-class AddLogNegLogDims(nn.Module):
-    def __init__(self, eps=1e-10):
-        super().__init__()
-        self.eps = eps
-
-    def forward(self, x):
-        return add_log_neglog_dims(x, self.eps)
-
-
-def get_logdim_encoder(encoder_creator, eps=1e-10):
-    return lambda in_dim, out_dim: nn.Sequential(
-        AddLogNegLogDims(eps), encoder_creator(in_dim * 2, out_dim)
-    )
-
-
-class ZNormalize(nn.Module):
-    def forward(self, x):
-        std = x.std(-1, keepdim=True)
-        std[std == 0.0] = 1.0
-        return (x - x.mean(-1, keepdim=True)) / std
-
-
-class ZNormalizePerDataset(nn.Module):
-    def forward(self, x):
-        std = x.std(0, keepdim=True)
-        std[std == 0.0] = 1.0
-        return (x - x.mean(0, keepdim=True)) / std
-
-
-class AppendEmbeddingEncoder(nn.Module):
-    def __init__(self, base_encoder, num_features, emsize):
-        super().__init__()
-        self.num_features = num_features
-        self.base_encoder = base_encoder
-        self.emb = nn.Parameter(torch.zeros(emsize))
-
-    def forward(self, x):
-        if (x[-1] == 1.0).all():
-            append_embedding = True
-        else:
-            assert (x[-1] == 0.0).all(), (
-                "You need to specify as last position whether to append embedding. "
-                "If you don't want this behavior, please use the wrapped encoder instead."
-            )
-            append_embedding = False
-        x = x[:-1]
-        encoded_x = self.base_encoder(x)
-        if append_embedding:
-            encoded_x = torch.cat(
-                [encoded_x, self.emb[None, None, :].repeat(1, encoded_x.shape[1], 1)], 0
-            )
-        return encoded_x
-
-
-def get_append_embedding_encoder(encoder_creator):
-    return lambda num_features, emsize: AppendEmbeddingEncoder(
-        encoder_creator(num_features, emsize), num_features, emsize
-    )
-
-
-class VariableNumFeaturesMultitaskEncoder(nn.Module):
-    def __init__(self, base_encoder, num_features, num_tasks):
-        super().__init__()
-        self.base_encoder = base_encoder
-        self.num_features = num_features
-        self.num_tasks = num_tasks
-
-    def forward(self, x):
-        x_num_features = x.shape[-1] - self.num_tasks
-        if x_num_features > self.num_features:
-            raise ValueError(
-                f"The model was trained using {self.num_features} features, but got {x_num_features}."
-            )
-        rescale_factor = self.num_features / x_num_features
-        x[..., -x_num_features:] = x[..., -x_num_features:] * rescale_factor
-        # pad with zeros
-        x = torch.cat(
-            (
-                x,
-                torch.zeros(
-                    *x.shape[:-1], self.num_features - x_num_features, device=x.device
-                ),
-            ),
-            -1,
-        )
-        return self.base_encoder(x)
-
-
-def get_variable_num_features_multitask_encoder(encoder_creator):
-    return lambda num_features, num_tasks, emsize: VariableNumFeaturesMultitaskEncoder(
-        encoder_creator(num_features + num_tasks, emsize), num_features, num_tasks
-    )
-    
-    
-def get_axial_num_features_multitask_encoder(encoder_creator):
-    return lambda num_features, num_columns, num_tasks, emsize: encoder_creator(num_features, num_columns, emsize)
-
-
-class VariableNumFeaturesEncoder(nn.Module):
-    def __init__(self, base_encoder, num_features):
-        super().__init__()
-        self.base_encoder = base_encoder
-        self.num_features = num_features
-
-    def forward(self, x):
-        x = x * (self.num_features / x.shape[-1])
-        x = torch.cat(
-            (
-                x,
-                torch.zeros(
-                    *x.shape[:-1], self.num_features - x.shape[-1], device=x.device
-                ),
-            ),
-            -1,
-        )
-        return self.base_encoder(x)
-
-
-def get_variable_num_features_encoder(encoder_creator):
-    return lambda num_features, emsize: VariableNumFeaturesEncoder(
-        encoder_creator(num_features, emsize), num_features
-    )
-
-
-class NoMeanEncoder(nn.Module):
-    """
-    This can be useful for any prior that is translation invariant in x or y.
-    A standard GP for example is translation invariant in x.
-    That is, GP(x_test+const,x_train+const,y_train) = GP(x_test,x_train,y_train).
-    """
-
-    def __init__(self, base_encoder):
-        super().__init__()
-        self.base_encoder = base_encoder
-
-    def forward(self, x):
-        return self.base_encoder(x - x.mean(0, keepdim=True))
-
-
-def get_no_mean_encoder(encoder_creator):
-    return lambda num_features, emsize: NoMeanEncoder(
-        encoder_creator(num_features, emsize)
-    )
-
-
-MLP = lambda num_features, emsize: nn.Sequential(
-    nn.Linear(num_features, emsize * 2), nn.ReLU(), nn.Linear(emsize * 2, emsize)
-)
-
-
-class NanHandlingEncoder(nn.Module):
-    def __init__(self, num_features, emsize, keep_nans=True):
-        super().__init__()
-        self.num_features = 2 * num_features if keep_nans else num_features
-        self.emsize = emsize
-        self.keep_nans = keep_nans
-        self.layer = nn.Linear(self.num_features, self.emsize)
-
-    def forward(self, x):
-        if self.keep_nans:
-            x = torch.cat(
-                [
-                    torch.nan_to_num(x, nan=0.0),
-                    normalize_data(
-                        torch.isnan(x) * -1
-                        + torch.logical_and(torch.isinf(x), torch.sign(x) == 1) * 1
-                        + torch.logical_and(torch.isinf(x), torch.sign(x) == -1) * 2
-                    ),
-                ],
-                -1,
-            )
-        else:
-            x = torch.nan_to_num(x, nan=0.0)
-        return self.layer(x)
-
-
-class Linear(nn.Linear):
-    def __init__(self, num_features, emsize, replace_nan_by_zero=False):
-        super().__init__(num_features, emsize)
-        self.num_features = num_features
-        self.emsize = emsize
-        self.replace_nan_by_zero = replace_nan_by_zero
-
-    def forward(self, x):
-        if self.replace_nan_by_zero:
-            x = torch.nan_to_num(x, nan=0.0)
-        return super().forward(x)
-
-    def __setstate__(self, state):
-        super().__setstate__(state)
-        self.__dict__.setdefault("replace_nan_by_zero", True)
-        
-        
-# class AxialLinear(nn.Linear):
-#     def __init__(self, emsize, replace_nan_by_zero=False):
-#         super().__init__(emsize)
-#         self.emsize = emsize
-#         self.replace_nan_by_zero = replace_nan_by_zero
-
-#     def forward(self, x):
-#         if self.replace_nan_by_zero:
-#             x = torch.nan_to_num(x, nan=0.0)
-#         return super().forward(x)
-
-#     def __setstate__(self, state):
-#         super().__setstate__(state)
-#         self.__dict__.setdefault("replace_nan_by_zero", True)
-
-
-
-class Conv(nn.Module):
-    def __init__(self, input_size, emsize):
-        super().__init__()
-        self.convs = torch.nn.ModuleList(
-            [nn.Conv2d(64 if i else 1, 64, 3) for i in range(5)]
-        )
-        self.linear = nn.Linear(64, emsize)
-
-    def forward(self, x):
-        size = math.isqrt(x.shape[-1])
-        assert size * size == x.shape[-1]
-        x = x.reshape(*x.shape[:-1], 1, size, size)
-        for conv in self.convs:
-            if x.shape[-1] < 4:
-                break
-            x = conv(x)
-            x.relu_()
-        x = nn.AdaptiveAvgPool2d((1, 1))(x).squeeze(-1).squeeze(-1)
-        return self.linear(x)
-
-
-class CanEmb(nn.Embedding):
-    def __init__(
-        self, num_features, num_embeddings: int, embedding_dim: int, *args, **kwargs
-    ):
-        assert embedding_dim % num_features == 0
-        embedding_dim = embedding_dim // num_features
-        super().__init__(num_embeddings, embedding_dim, *args, **kwargs)
-
-    def forward(self, x):
-        lx = x.long()
-        assert (lx == x).all(), "CanEmb only works with tensors of whole numbers"
-        x = super().forward(lx)
-        return x.view(*x.shape[:-2], -1)
-
-
-def get_Canonical(num_classes):
-    return lambda num_features, emsize: CanEmb(num_features, num_classes, emsize)
-
-
-def get_Embedding(num_embs_per_feature=100):
-    return lambda num_features, emsize: EmbeddingEncoder(
-        num_features, emsize, num_embs=num_embs_per_feature
-    )
-
-
-class SelfAttentionTaskOld(nn.Module):
-    
-    def __init__(self, num_tasks, emsize):
-        super(SelfAttentionTask, self).__init__()
-        self.num_tasks = num_tasks
-        
-        self.task_embedding = nn.Embedding(num_tasks, emsize)
-        # Multi-head self-attention
-        self.attention = nn.MultiheadAttention(embed_dim=emsize, num_heads=1, batch_first=False)
-        # Fully connected layers for downstream task
-        self.fc = nn.Sequential(
-            nn.Linear(emsize, 128),
-            nn.ReLU(),
-            nn.Linear(128, emsize)  # Example: regression output
-        )
-        
-    def forward(self, task_ids, context):
-        # remove the last dimension
-        x = x.squeeze(-1)
-        
-        task_embeddings = self.task_embedding(x.long())
-        attn_output, _ = self.attention(task_embeddings, task_embeddings, task_embeddings)
-        
-        output = self.fc(attn_output)
-        return output
-
-
-
-class SelfAttentionTask(nn.Module):
-    def __init__(self, num_tasks, emsize, num_heads=4):
-        super(SelfAttentionTask, self).__init__()
-        self.num_tasks = num_tasks
-        self.embedding_dim = emsize
-        
-        # Multi-head self-attention layer
-        self.attention = nn.MultiheadAttention(embed_dim=emsize, num_heads=num_heads)
-        
-        # Linear transformation to produce task embeddings
-        self.context_transform = nn.Linear(emsize, emsize)
-        
-        # Task ID embedding layer
-        self.task_id_embedding = nn.Embedding(num_tasks, emsize)
-
-    def _ssforward(self, task_ids, context):
-        """
-        Args:
-            task_ids: Tensor of shape (N, B, 1), representing task IDs.
-            context: Tensor of shape (N, D, B, E), representing the context for tasks.
-
-        Returns:
-            task_embeddings: Tensor of shape (N, B, E), task embeddings.
-        """
-        # average across D dimension
-        context = context.mean(dim=1)
-        
-        # Embed task IDs
-        task_id_embeds = self.task_id_embedding(task_ids.squeeze(-1).long())  # Shape: (N, B, E)
-        context_with_task_ids = context + task_id_embeds
-        
-        # Self-attention: Query, Key, and Value are the context points with task embeddings
-        attn_output, _ = self.attention(context_with_task_ids, context_with_task_ids, context_with_task_ids)
-        # task_embedding is average per task
-        
-        # Apply linear transformation to the aggregated context
-        task_embeddings = self.context_transform(attn_output)  # Shape: (N, B, E)
-        
-        return task_embeddings
-    
-    def nobatchforward(self, task_ids, context):
-        """
-        Args:
-            task_ids: Tensor of shape (N, B, 1), representing task IDs.
-            context: Tensor of shape (N, D, B, E), representing the context for tasks.
-
-        Returns:
-            task_embeddings: Tensor of shape (N, B, E), task embeddings.
-        """
-        N, D, B, E = context.shape
-        task_embeddings = torch.zeros(N, B, E, device=context.device)
-        task_embedding_lookup = torch.zeros(self.num_tasks, E, device=context.device)
-        
-        # average across D dimension
-        context = context.mean(dim=1)
-        # Self-attention: Query, Key, and Value are the context points with task embeddings
-        attn_output, _ = self.attention(context, context, context)
-        # print(attn_output.shape, "ATTN") # (N, B, E)
-        
-        for task in task_ids.long().unique():
-            task_mask = task == task_ids
-            attn_tasks = attn_output[task_mask.squeeze(-1)]
-            # print(attn_tasks.shape)
-            task_embedding_lookup[task] = attn_tasks.sum(dim=0) / task_mask.sum()
-        
-        # expanded_task_ids = task_ids.expand(-1, -1, E)
-        # import pdb; pdb.set_trace()
-        task_embeddings = task_embedding_lookup[task_ids.long().squeeze(-1)]
-        # print(task_embeddings.shape, "TASK")
-        
-        return task_embeddings
-    
-    
-    def forward(self, task_ids, context):
-        """
-        Args:
-            task_ids: Tensor of shape (N, B, 1), representing task IDs.
-            context: Tensor of shape (N, D, B, E), representing the context for tasks.
-
-        Returns:
-            task_embeddings: Tensor of shape (N, B, E), task embeddings.
-        """
-        N, D, B, E = context.shape
-        task_embeddings = torch.zeros(N, B, E, device=context.device)
-        task_embedding_lookup = torch.zeros(self.num_tasks, B, E, device=context.device)
-        
-        # average across D dimension
-        context = context.mean(dim=1)
-        # Self-attention: Query, Key, and Value are the context points with task embeddings
-        attn_output, _ = self.attention(context, context, context)
-
-        # # Flatten task_ids for advanced indexing
-        # task_ids_flat = task_ids.reshape(-1)  # Shape: (N * B,)
-        
-        # # Create task-specific mask matrix for each batch and task (N, B)
-        # task_mask = task_ids_flat.unsqueeze(1) == torch.arange(self.num_tasks, device=context.device).view(1, -1)  # Shape: (N * B, T)
-
-        # # Repeat attn_output to match task_ids (N, B, E)
-        # attn_output_flat = attn_output.view(N * B, E)  # Shape: (N * B, E)
-
-        # # Gather attention outputs based on task_mask (task_mask is (N * B, T))
-        # # Sum the attention outputs for each task across the batch
-        # # The matmul gives us the task-wise embeddings as (T, N * B, E)
-        # task_embedding_lookup = torch.matmul(task_mask.float().T, attn_output_flat)  # Shape: (T, N * B, E)
-
-        
-        # # Take the mean across N dimension (the number of samples for each task) 
-        # task_embedding_lookup = task_embedding_lookup.mean(dim=2)  # Shape: (T, B, E)
-
-        # # Normalize task embeddings by the number of occurrences per task
-        # task_counts = task_mask.sum(dim=0).view(self.num_tasks)  # Shape: (T,)
-        # task_embedding_lookup = task_embedding_lookup / task_counts.view(-1, 1).float()  # Shape: (T, B, E)
-
-        # # Lookup task embeddings using task_ids for each batch
-        # task_embeddings = task_embedding_lookup[task_ids_flat].view(N, B, E)
-
-        # return task_embeddings
-        
-        
-        
-        # Iterate over batches
-        for batch in range(B):
-            # Get unique task_ids in this batch
-            batch_task_ids = task_ids[:, batch].long().unique()  # Shape: (T,)
-            
-            # For each task in the batch, compute its embedding
-            for task in batch_task_ids:
-                # Create mask for this task in the batch
-                task_mask = (task_ids[:, batch] == task).squeeze(-1)  # Shape: (N,)
-                # print(task_mask.shape, attn_output.shape, batch)
-                
-                # Get attention output for this task (flattening across N dimension)
-                attn_tasks = attn_output[task_mask, batch]  # Shape: (D, E)
-                
-                # Aggregate the task-specific embeddings by averaging
-                task_embedding = attn_tasks.sum(dim=0) / task_mask.sum().float()  # Shape: (E)
-                
-                # Store this task embedding in the task_embeddings tensor
-                task_embedding_lookup[task, batch] = task_embedding
-                
-            task_embeddings[:, batch] = task_embedding_lookup[task_ids[:, batch].long().squeeze(-1), batch]
-            
-        return task_embeddings
-
-
-# class SelfAttentionTask(nn.Module):
-    
-#     def __init__(self, num_tasks, emsize):
-#         super(SelfAttentionTask, self).__init__()
-        
-#         self.attention = nn.MultiheadAttention(embed_dim=emsize, num_heads=4)
-#         self.context_transform = nn.Linear(emsize, emsize)
-        
-#     def forward(self, task_ids, context):
-#         N, D, B, E = context.shape
-#         context = context.view(N * D, B, E)
-#         attn_output = self.attention(context, context, context)[0]
-#         attn_output = attn_output.view(N, D, B, E)
-        
-#         task_embeddings = torch.zeros(N, D, B, E, device=context.device)
-        
-#         for task_id in torch.unique(task_ids):
-#             task_mask = task_id == task_ids
-#             task_embedding = self.task_embedding(task_id)
-#             task_embedding = task_embedding.repeat(N, D, 1, 1)
-#             task_embedding = task_embedding * task_mask.unsqueeze(-1)
-#             task_embeddings += task_embedding
-        
-        
-        
-
-
-def get_self_attention_task_encoder():
-    return lambda num_tasks, emsize: SelfAttentionTask(num_tasks, emsize)
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/hierarchical.py b/private_multitask_pfn/PFNs/pfns/hierarchical.py
deleted file mode 100644
index 85801af..0000000
--- a/private_multitask_pfn/PFNs/pfns/hierarchical.py
+++ /dev/null
@@ -1,335 +0,0 @@
-import math
-from typing import Optional
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import Module
-
-from torch.nn.modules.transformer import (
-    _get_activation_fn,
-    Dropout,
-    LayerNorm,
-    Linear,
-    Module,
-    MultiheadAttention,
-    Optional,
-    Tensor,
-)
-from .positional_encodings import RotaryPositionalEmbeddings
-from .utils import bool_mask_to_att_mask, SeqBN
-
-
-class HierarchicalTaskEncoder(Module):
-    r"""TransformerEncoder is a stack of N encoder layers
-
-    Args:
-        encoder_layer_creator: a function generating objects of TransformerEncoderLayer class without args (required).
-        nlayers: the number of sub-encoder-layers in the encoder (required).
-        norm: the layer normalization component (optional).
-    """
-
-    __constants__ = ["norm"]
-
-    def __init__(
-        self,
-        global_with_target_points=False,
-        local_with_target_points=False,
-        **kwargs):
-        super().__init__()
-        
-        global_kwargs = kwargs.copy()
-        global_kwargs["global_with_target_points"] = global_with_target_points
-        global_kwargs.pop("nlayers")
-        
-        single_kwargs = kwargs.copy()
-        single_kwargs["local_with_target_points"] = local_with_target_points
-        single_kwargs.pop("nlayers")
-        
-        layers = []
-        for i in range(kwargs.get("nlayers", 24)):
-            if (i+1) % 2 == 1:
-                layers.append(GlobalTaskEncoderLayer(**global_kwargs))
-            else:
-                layers.append(SingleTaskEncoderLayer(**single_kwargs))
-                
-        self.layers = nn.ModuleList(layers)
-        self.meta_embedding = nn.Embedding(1, kwargs.get("d_model", 512))
-        
-        self.norm = kwargs.get("norm", None)
-        self.meta_tokens = kwargs.get("meta_tokens", 1)
-
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layers in turn.
-
-        Args:
-            src: the sequence to the encoder (required).
-            mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """        
-        # all task ids should be the same across batches
-        assert (task_ids.min(dim=1).values == task_ids.max(dim=1).values).all()
-        squeezed_task_id = task_ids[:, 0] # N x 1
-        unique_task_ids = torch.unique(squeezed_task_id)
-        
-        # add meta-token task id for each task in front of the sequence
-        meta_task_ids_summary = unique_task_ids.repeat_interleave(self.meta_tokens)
-        meta_task_ids = torch.cat([meta_task_ids_summary.unsqueeze(-1), squeezed_task_id], dim=0)
-        
-        # add meta-token embeddings
-        meta_output = self.meta_embedding(torch.zeros(1, device=src.device).long())
-        meta_output = meta_output.repeat(len(meta_task_ids_summary), src.shape[1], 1)
-        meta_output = torch.cat([meta_output, src], dim=0)
-        
-        # move eval position to account for meta-tokens
-        src_mask = src_mask + len(unique_task_ids) * self.meta_tokens
-        for mod in self.layers:
-            meta_output = mod(
-                meta_output, meta_task_ids, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask
-            )
-
-        if self.norm is not None:
-            meta_output = self.norm(meta_output)
-
-        return meta_output[self.meta_tokens * len(unique_task_ids):]
-
-
-class SingleTaskEncoderLayer(Module):
-            
-    def __init__(
-        self,
-        meta_tokens,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        local_with_target_points=False,
-        batch_first=False,
-        pre_norm=False,
-        device=None,
-        dtype=None,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-    
-        self.pre_norm = pre_norm
-        
-        self.attn = MultiheadAttention(
-            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
-        )
-        self.norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.dropout = Dropout(dropout)
-        
-        self.ff_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
-        self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
-        self.dropout1 = Dropout(dropout)
-        self.dropout2 = Dropout(dropout)
-        
-        self.activation = _get_activation_fn(activation)
-        
-        # each source task attends to itself and the target task
-        self.local_with_target_points = local_with_target_points
-        if local_with_target_points:
-            self.source_embedding = nn.Embedding(2, d_model) # embed target vs source
-        
-    def single_task_forward(
-        self,
-        src: Tensor,
-        single_eval_position: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layer.
-
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        src_ = src
-        
-        if self.pre_norm:
-            src_ = self.norm(src)
-        else:
-            src_ = src
-            
-        # have each task attend within itself
-        src_train = src_[:single_eval_position]
-        src_eval = src_[single_eval_position:]
-        src_left = self.attn(src_train, src_train, src_train)[0]
-        src_right = self.attn(src_eval, src_train, src_train)[0]
-        src2 = torch.cat([src_left, src_right], dim=0)
-        
-        src = src + self.dropout(src2)
-        if not self.pre_norm:
-            src = self.norm(src)            
-
-        # feedforward
-        if self.pre_norm:
-            src_ff = self.ff_norm(src)
-        else:
-            src_ff = src
-        src_ff = self.linear2(self.dropout1(self.activation(self.linear1(src_ff))))
-        src = src + self.dropout2(src_ff)
-        if not self.pre_norm:
-            src = self.ff_norm(src)    
-            
-        return src
-    
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        assert isinstance(src_mask, int)
-        single_eval_position = src_mask
-        
-        single_tasks_encoding = torch.zeros_like(src)
-        train_task_id = task_ids[:single_eval_position]
-        train_x = src[:single_eval_position]
-        
-        # attend to source and target task points
-        if self.local_with_target_points:
-            target_task_mask = (task_ids == 0).squeeze()
-            target_embedding = self.source_embedding(torch.zeros(1, device=src.device).long())
-            src[target_task_mask] = src[target_task_mask] + target_embedding
-            source_embedding = self.source_embedding(torch.ones(1, device=src.device).long())
-            src[~target_task_mask] = src[~target_task_mask] + source_embedding
-        
-        # Optimize this when scaling
-        for task in torch.unique(task_ids):
-            source_task_mask = (task_ids == task).squeeze()
-            if self.local_with_target_points:
-                # each task attends to itself and the target task
-                task_mask = target_task_mask | source_task_mask
-            else:
-                task_mask = source_task_mask
-            task_src = src[task_mask]
-            
-            train_task_mask = (train_task_id == task).squeeze()
-            task_train_length = len(train_x[train_task_mask])
-            
-            task_single_tasks_encoding = self.single_task_forward(task_src, task_train_length)
-            single_tasks_encoding[task_mask] = task_single_tasks_encoding
-        
-        return single_tasks_encoding
-
-
-class GlobalTaskEncoderLayer(Module):
-            
-    def __init__(
-        self,
-        meta_tokens,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        batch_first=False,
-        pre_norm=False,
-        device=None,
-        dtype=None,
-        task_position_embedding=False,
-        recompute_attn=False,
-        save_trainingset_representations=False,
-        global_with_target_points=False,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        self.pre_norm = pre_norm
-        
-        meta_d_model = d_model * meta_tokens
-        self.attn = MultiheadAttention(
-            meta_d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
-        )
-        self.norm = LayerNorm(meta_d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.dropout = Dropout(dropout)
-        
-        self.ff_norm = LayerNorm(meta_d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.linear1 = Linear(meta_d_model, dim_feedforward, **factory_kwargs)
-        self.linear2 = Linear(dim_feedforward, meta_d_model, **factory_kwargs)
-        self.dropout1 = Dropout(dropout)
-        self.dropout2 = Dropout(dropout)
-        
-        self.activation = _get_activation_fn(activation)
-        self.meta_tokens = meta_tokens
-        
-        self.global_with_target_points = global_with_target_points
-        
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layer.
-
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        assert isinstance(src_mask, int)
-        assert src_key_padding_mask is None
-        
-        meta_end = self.meta_tokens * len(torch.unique(task_ids))
-        
-        # get summary tokens with T x B x (M x E), add summary embedding
-        summary_src = src[:meta_end].reshape(len(torch.unique(task_ids)), src.shape[1], -1)
-
-        if self.global_with_target_points:
-            # make global layer also attend to target points
-            task_src = src[(task_ids == 0).squeeze()][self.meta_tokens:].repeat(1, 1, self.meta_tokens)
-            global_src_ = torch.cat([summary_src, task_src], dim=0)
-        else:
-            # only attend to summary tokens
-            global_src_ = summary_src
-        
-        if self.pre_norm:
-            global_src_ = self.norm(global_src_)
-            summary_src_ = self.norm(summary_src)
-        else:
-            global_src_ = global_src_
-            
-        summary_src_ = self.attn(summary_src, global_src_, global_src_)[0]
-        
-        summary_src_ = summary_src + self.dropout(summary_src_)
-        if not self.pre_norm:
-            summary_src_ = self.norm(summary_src_)
-
-        # feedforward
-        if self.pre_norm:
-            summary_src_ff = self.ff_norm(summary_src_)
-        else:
-            summary_src_ff = summary_src_
-        summary_src_ff = self.linear2(self.dropout1(self.activation(self.linear1(summary_src_ff))))
-        summary_src_ = summary_src_ + self.dropout2(summary_src_ff)
-        if not self.pre_norm:
-            summary_src_ = self.ff_norm(summary_src_)
-            
-        # reshape back
-        summary_src = summary_src_.reshape(-1, src.shape[1], src.shape[2])
-            
-        original_tokens = src[meta_end:]
-        combined_src = torch.cat([summary_src, original_tokens], dim=0)
-        return combined_src
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/initializers.py b/private_multitask_pfn/PFNs/pfns/initializers.py
deleted file mode 100644
index ec93119..0000000
--- a/private_multitask_pfn/PFNs/pfns/initializers.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import torch
-from torch import nn
-
-
-def get_NormalInitializer(std):
-    def initializer(m):
-        if isinstance(m, nn.Linear):
-            nn.init.normal_(m.weight, 0, std)
-            nn.init.normal_(m.bias, 0, std)
-
-    return initializer
diff --git a/private_multitask_pfn/PFNs/pfns/layer.py b/private_multitask_pfn/PFNs/pfns/layer.py
deleted file mode 100644
index caa3370..0000000
--- a/private_multitask_pfn/PFNs/pfns/layer.py
+++ /dev/null
@@ -1,475 +0,0 @@
-from functools import partial
-
-import torch
-from torch import nn
-from torch.nn.modules.transformer import (
-    _get_activation_fn,
-    Dropout,
-    LayerNorm,
-    Linear,
-    Module,
-    MultiheadAttention,
-    Optional,
-    Tensor,
-)
-
-from torch.utils.checkpoint import checkpoint
-
-
-class AxialAttentionModule(Module):
-    r"""
-    AxialAttention is a module that applies self-attention to rows and columns of a 2D input tensor.
-    """
-
-    __constants__ = ["batch_first"]
-
-    def __init__(
-        self,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        batch_first=False,
-        pre_norm=False,
-        device=None,
-        dtype=None,
-        recompute_attn=False,
-        save_trainingset_representations=False,
-        sequential_axial=False,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        
-        self.seq_axial = sequential_axial
-        self.pre_norm = pre_norm
-        
-        self.self_attn_row = MultiheadAttention(
-            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
-        )
-        self.row_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.row_dropout = Dropout(dropout)
-        self.self_attn_col = MultiheadAttention(
-            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
-        )
-        self.col_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.col_dropout = Dropout(dropout)
-        
-    def __setstate__(self, state):
-        if "activation" not in state:
-            state["activation"] = F.relu
-        super().__setstate__(state)
-        self.__dict__.setdefault("save_trainingset_representations", False)
-
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layer.
-
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        N, D, B, E = src.shape
-        
-        assert isinstance(src_mask, int)
-        assert src_key_padding_mask is None
-        single_eval_position = src_mask
-        
-        def self_attn_forward(attn_module, src, row_first):                
-            if row_first:
-                src = src.reshape(N, D*B, E)
-                attn_mask = torch.zeros(N, N, device=src.device).bool()
-                attn_mask[single_eval_position:, single_eval_position:] = True
-            else:
-                # column first
-                src = src.permute(1, 0, 2, 3).reshape(D, N*B, E)    
-                attn_mask = torch.zeros(D, D, device=src.device).bool()
-                            
-            src_full = attn_module(
-                src,
-                src,
-                src,
-                attn_mask=attn_mask,
-            )[0]
-            
-            if row_first:
-                return src_full.reshape(N, D, B, E)
-            else:
-                src_full = src_full.reshape(D, N, B, E).permute(1, 0, 2, 3)
-            return src_full.reshape(N, D, B, E)
-        
-        # attend to rows
-        if self.pre_norm:
-            src_row = self.row_norm(src)
-        else:
-            src_row = src
-        src_row = self_attn_forward(self.self_attn_row, src_row, row_first=True)
-        src_row = self.row_dropout(src_row)
-        
-        # pass row as input to col
-        if self.seq_axial:
-            src = src + src_row
-            if not self.pre_norm:
-                src = self.row_norm(src)
-            
-        # attend to cols
-        if self.pre_norm:
-            src_col = self.col_norm(src)
-        else:
-            src_col = src
-        src_col = self_attn_forward(self.self_attn_col, src_col, row_first=False)
-        src_col = self.col_dropout(src_col)
-        
-        if self.seq_axial:
-            src = src + src_col
-            if not self.pre_norm:
-                src = self.col_norm(src)
-        else:
-            # combine row and col
-            src = src + src_row + src_col
-            if not self.pre_norm:
-                src = self.col_norm(src)
-
-        return src
-
-
-class BasicTaskAttentionLayer(Module):
-    def __init__(
-        self,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        batch_first=False,
-        pre_norm=False,
-        device=None,
-        dtype=None,
-        recompute_attn=False,
-        save_trainingset_representations=False,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        
-        self.pre_norm = pre_norm
-        
-        self.attn = MultiheadAttention(
-            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
-        )
-        self.norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.dropout = Dropout(dropout)
-
-    def __setstate__(self, state):
-        if "activation" not in state:
-            state["activation"] = F.relu
-        super().__setstate__(state)
-        self.__dict__.setdefault("save_trainingset_representations", False)
-
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layer.
-
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        assert isinstance(src_mask, int)
-        assert src_key_padding_mask is None
-        single_eval_position = src_mask
-        
-        if self.pre_norm:
-            src_ = self.norm(src)
-        else:
-            src_ = src
-            
-        src_train = src_[:single_eval_position]
-        src_eval = src_[single_eval_position:]
-        
-        src_left = self.attn(
-            src_train,
-            src_train,
-            src_train,
-        )[0]
-        src_right = self.attn(
-            src_eval, src_train, src_train
-        )[0]
-        src2 = torch.cat([src_left, src_right], dim=0)
-        
-        src = src + self.dropout(src2)
-        if not self.pre_norm:
-            src = self.norm(src)
-            
-        return src
-    
-    
-
-class TaskAttention(Module):
-    def __init__(
-        self,
-        num_tasks,
-        shared,
-        optimized,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        batch_first=False,
-        pre_norm=False,
-        device=None,
-        dtype=None,
-        recompute_attn=False,
-        save_trainingset_representations=False,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        
-        self.pre_norm = pre_norm
-        
-        self.attn = MultiheadAttention(
-            d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
-        )
-        self.norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.dropout = Dropout(dropout)
-        
-        self.optimized = optimized
-        self.num_tasks = num_tasks
-        
-        kwargs = {
-            "d_model": d_model,
-            "nhead": nhead,
-            "dim_feedforward": dim_feedforward,
-            "dropout": dropout,
-            "activation": activation,
-            "layer_norm_eps": layer_norm_eps,
-            "batch_first": batch_first,
-            "pre_norm": pre_norm,
-            "device": device,
-            "dtype": dtype,
-            "recompute_attn": recompute_attn,
-            "save_trainingset_representations": save_trainingset_representations,
-        }
-            
-        if not self.optimized:
-            if shared:
-                self.task_attn_module = BasicTaskAttentionLayer(**kwargs)
-                self.task_attn_modules = [self.task_attn_module for _ in range(num_tasks)]
-            else:
-                self.task_attn_modules = nn.ModuleList([BasicTaskAttentionLayer(**kwargs) for _ in range(num_tasks)])
-        else:
-            self.task_attn = MultiheadAttention(
-                d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs
-            )
-            self.task_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-            self.task_dropout = Dropout(dropout)
-
-    def __setstate__(self, state):
-        if "activation" not in state:
-            state["activation"] = F.relu
-        super().__setstate__(state)
-        self.__dict__.setdefault("save_trainingset_representations", False)
-
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layer.
-
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        src_ = src
-        if not self.optimized:
-            combined_task_src = torch.zeros_like(src_)
-            for task_id in range(self.num_tasks):
-                task_src = src_[task_ids == task_id]
-                task_id_src = task_ids[task_ids == task_id]
-                combined_task_src[task_ids == task_id] = self.task_attn_modules[task_id](task_src, task_id_src, src_mask, src_key_padding_mask)
-                
-            src = combined_task_src
-        else:
-            single_eval_position = src_mask
-            
-            if self.pre_norm:
-                src_ = self.task_norm(src_)
-            
-            # Train attends to train with same task
-            src_train = src_[:single_eval_position]
-            task_id_train = task_ids[:single_eval_position]
-            # true when tasks are different
-            train_src_mask = task_id_train[:, None] != task_id_train[None, :]         
-            src_train, _ = self.task_attn(src_train, src_train, src_train, attn_mask=train_src_mask)
-            
-            # Eval attends to train with same task
-            src_eval = src_[single_eval_position:]
-            task_id_eval = task_ids[single_eval_position:]
-            eval_src_mask = task_id_eval[:, None] != task_id_train[None, :]
-            src_eval, _ = self.task_attn(src_eval, src_train, src_train, attn_mask=eval_src_mask)
-            
-            src2 = torch.cat([src_train, src_eval], dim=0)
-            src = src + self.dropout(src2)
-            
-            if not self.pre_norm:
-                src = self.norm(src)
-            
-        return src
-            
-
-class TransformerAttentionLayer(Module):
-            
-    def __init__(
-        self,
-        num_tasks,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        batch_first=False,
-        pre_norm=False,
-        device=None,
-        dtype=None,
-        recompute_attn=False,
-        save_trainingset_representations=False,
-        attn_type="standard",
-        task_embed_type="task_attn"
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        
-        kwargs = {
-            "d_model": d_model,
-            "nhead": nhead,
-            "dim_feedforward": dim_feedforward,
-            "dropout": dropout,
-            "activation": activation,
-            "layer_norm_eps": layer_norm_eps,
-            "batch_first": batch_first,
-            "pre_norm": pre_norm,
-            "device": device,
-            "dtype": dtype,
-            "recompute_attn": recompute_attn,
-            "save_trainingset_representations": save_trainingset_representations,
-        }
-            
-        self.num_tasks = num_tasks
-        self.pre_norm = pre_norm
-        
-        if attn_type == "standard":
-            if task_embed_type == "task_attn":
-                self.task_attn = TaskAttention(num_tasks=num_tasks, shared=False, optimized=False, **kwargs)
-            elif task_embed_type == "task_attn_shared":
-                self.task_attn = TaskAttention(num_tasks=num_tasks, shared=True, optimized=False, **kwargs)
-            elif task_embed_type == "task_attn_opt":
-                self.task_attn = TaskAttention(num_tasks=num_tasks, shared=True, optimized=True, **kwargs)
-            else:
-                self.task_attn = None
-                
-            self.all_attn = BasicTaskAttentionLayer(**kwargs)
-        elif "axial" in attn_type:
-            sequential_axial = "parallel" not in attn_type
-            args = *args, sequential_axial
-            
-            if task_embed_type == "task_attn":
-                self.task_attn = nn.ModuleList([AxialAttentionModule(**kwargs) for _ in range(num_tasks)])
-            elif task_embed_type == "task_attn_shared":
-                self.task_attn_one = AxialAttentionModule(**kwargs)
-                self.task_attn = [self.task_attn_one for _ in range(num_tasks)]
-            else:
-                self.task_attn = None
-                
-            self.all_attn = AxialAttentionModule(**kwargs)
-        else:
-            raise ValueError(f"Unknown attn_type: {attn_type}")
-        
-        self.ff_norm = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
-        self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
-        self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
-        self.dropout1 = Dropout(dropout)
-        self.dropout2 = Dropout(dropout)
-        
-        self.recompute_attn = recompute_attn
-        self.save_trainingset_representations = save_trainingset_representations
-        self.saved_src_to_attend_to = None
-        
-        self.activation = _get_activation_fn(activation)
-        
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layer.
-
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        
-        src_ = src
-            
-        assert isinstance(src_mask, int)
-        assert src_key_padding_mask is None
-        
-        # attend to all, contains skip connection
-        all_src = self.all_attn(src_, task_ids, src_mask, src_key_padding_mask)
-        
-        # attend to tasks
-        if self.task_attn is not None:
-            assert torch.all(task_ids[:, 0] == task_ids.max(1)[0]) and torch.all(task_ids[:, 0] == task_ids.min(1)[0])
-            task_ids = task_ids[:, 0].squeeze()
-            
-            task_src = self.task_attn(src_, task_ids, src_mask, src_key_padding_mask)
-            
-            # combine task and all
-            src = all_src + task_src
-        else:
-            src = all_src
-
-        # feedforward
-        if self.pre_norm:
-            src_ff = self.ff_norm(src)
-        else:
-            src_ff = src
-        src_ff = self.linear2(self.dropout1(self.activation(self.linear1(src_ff))))
-        src = src + self.dropout2(src_ff)
-        if not self.pre_norm:
-            src = self.ff_norm(src)    
-            
-        return src
diff --git a/private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py b/private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py
deleted file mode 100644
index 97a7de5..0000000
--- a/private_multitask_pfn/PFNs/pfns/pfn_bo_bayesmark.py
+++ /dev/null
@@ -1,508 +0,0 @@
-import logging
-import os
-from copy import deepcopy
-
-import numpy as np
-import torch
-from bayesmark.abstract_optimizer import AbstractOptimizer
-from bayesmark.experiment import experiment_main
-from bayesmark.space import JointSpace
-from scipy.special import expit, logit
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import MinMaxScaler, StandardScaler
-
-# from scipy.stats import qmc
-from torch.quasirandom import SobolEngine
-
-from .scripts import acquisition_functions, tune_input_warping
-
-
-class PFNOptimizer(AbstractOptimizer):
-    # Used for determining the version number of package used
-    # primary_import = ""
-
-    def __init__(
-        self,
-        api_config,
-        pfn_file,
-        minimize=True,
-        acqf_optimizer_name="lbfgs",
-        sobol_sampler=False,
-        device="cpu:0",
-        fit_encoder_from_step=None,
-        verbose=False,
-        rand_bool=False,
-        sample_only_valid=False,
-        round_suggests_to=4,
-        min_initial_design=0,
-        max_initial_design=None,
-        rand_sugg_after_x_steps_of_stagnation=None,
-        fixed_initial_guess=None,
-        minmax_encode_y=False,
-        **acqf_kwargs,
-    ):
-        """Build wrapper class to use optimizer in benchmark.
-
-        Parameters
-        ----------
-        api_config : dict-like of dict-like
-            Configuration of the optimization variables. See API description.
-        """
-        assert not "fit_encoder" in acqf_kwargs
-        AbstractOptimizer.__init__(self, api_config)
-        # Do whatever other setup is needed
-        # ...
-
-        self.space_x = JointSpace(api_config)
-        self.bounds = self.space_x.get_bounds()
-        self.device = device
-
-        self.model = (
-            torch.load(pfn_file)
-            if pfn_file.startswith("/")
-            else torch.load(os.path.dirname(__file__) + "/" + pfn_file)
-        )
-
-        self.X = []
-        self.y = []
-        self.api_config = {key: value for key, value in sorted(api_config.items())}
-        self.hp_names = list(self.api_config.keys())
-        # self.model.encoder.num_features = 18
-
-        self.epsilon = 1e-8
-        self.minimize = minimize
-        self.sobol_sampler = sobol_sampler
-        self.create_scaler()
-        self.sobol = SobolEngine(len(self.max_values), scramble=True)
-        self.acqf_optimizer_name = acqf_optimizer_name
-        self.acqf_kwargs = acqf_kwargs
-        self.fit_encoder_from_step = fit_encoder_from_step
-        assert not (rand_bool and sample_only_valid)
-        self.rand_bool = rand_bool
-        self.sample_only_valid = sample_only_valid
-        self.verbose = verbose
-        self.round_suggests_to = round_suggests_to
-        self.min_initial_design = min_initial_design
-        self.max_initial_design = max_initial_design
-        self.fixed_initial_guess = fixed_initial_guess
-        self.minmax_encode_y = minmax_encode_y
-        self.rand_sugg_after_x_steps_of_stagnation = (
-            rand_sugg_after_x_steps_of_stagnation
-        )
-        self.model.eval()
-
-        print(api_config)
-
-    def create_scaler(self):
-
-        list_of_scalers = []
-        self.min_values = []
-        self.max_values = []
-        self.spaces = []
-        self.types = []
-
-        for i, feature in enumerate(self.api_config):
-            # list_of_scalers.append((feature, MinMaxScaler(feature_range),i))
-            self.spaces.append(self.api_config[feature].get("space", "bool"))
-            self.types.append(self.api_config[feature]["type"])
-
-            if self.types[-1] == "bool":
-                feature_range = [0, 1]
-            else:
-                feature_range = list(self.api_config[feature]["range"])
-
-            feature_range[0] = self.transform_feature(feature_range[0], -1)
-            feature_range[1] = self.transform_feature(feature_range[1], -1)
-
-            self.min_values.append(feature_range[0])
-            self.max_values.append(feature_range[1])
-
-        self.column_scaler = ColumnTransformer(list_of_scalers)
-        self.max_values: np.array = np.array(self.max_values)
-        self.min_values: np.array = np.array(self.min_values)
-
-    def transform_feature_inverse(self, x, feature_index):
-
-        if self.spaces[feature_index] == "log":
-            x = np.exp(x)
-        elif self.spaces[feature_index] == "logit":
-            x = expit(x)
-        if self.types[feature_index] == "int":
-            if self.rand_bool:
-                x = int(x) + int(np.random.rand() < (x - int(x)))
-            else:
-                x = int(np.round(x))
-        elif self.types[feature_index] == "bool":
-            if self.rand_bool:
-                x = np.random.rand() < x
-            else:
-                x = bool(np.round(x))
-
-        return x
-
-    def transform_feature(self, x, feature_index):
-
-        if np.isinf(x) or np.isnan(x):
-            return 0
-
-        if self.spaces[feature_index] == "log":
-            x = np.log(x)
-
-        elif self.spaces[feature_index] == "logit":
-            x = logit(x)
-
-        elif self.types[feature_index] == "bool":
-            x = int(x)
-        return x
-
-    def random_suggest(self):
-        self.rand_prev = True
-
-        if self.sobol_sampler:
-
-            # sampler = qmc.Sobol(d=len(self.max_values), scramble=False)
-            # temp_guess = sampler.random_base2(m=len(self.max_values))
-            temp_guess = self.sobol.draw(1).numpy()[0]
-            temp_guess = (
-                temp_guess * (self.max_values - self.min_values) + self.min_values
-            )
-
-            x_guess = {}
-            for j, feature in enumerate(self.api_config):
-                x = self.transform_feature_inverse(temp_guess[j], j)
-                x_guess[feature] = x
-            x_guess = [x_guess]
-
-        else:
-            x_guess = {}
-            for i, feature in enumerate(self.api_config):
-                temp_guess = np.random.uniform(
-                    self.min_values[i], self.max_values[i], 1
-                )[0]
-                temp_guess = self.transform_feature_inverse(temp_guess, i)
-
-                x_guess[feature] = temp_guess
-            x_guess = [x_guess]
-        return x_guess
-
-    def transform_back(self, x_guess):
-        if self.round_suggests_to is not None:
-            x_guess = np.round(
-                x_guess, self.round_suggests_to
-            )  # make sure very similar values are actually the same
-        x_guess = x_guess * (self.max_values - self.min_values) + self.min_values
-        x_guess = x_guess.tolist()
-        return self.transform_inverse(x_guess)
-
-    def min_max_encode(self, temp_X):
-        # this, combined with transform is the inverse of transform_back
-        temp_X = (temp_X - self.min_values) / (self.max_values - self.min_values)
-        temp_X = torch.tensor(temp_X).to(torch.float32)
-        temp_X = torch.clamp(temp_X, min=0.0, max=1.0)
-        return temp_X
-
-    @torch.no_grad()
-    def suggest(self, n_suggestions=1):
-        """Get suggestion from the optimizer.
-
-        Parameters
-        ----------
-        n_suggestions : int
-            Desired number of parallel suggestions in the output
-
-        Returns
-        -------
-        next_guess : list of dict
-            List of `n_suggestions` suggestions to evaluate the objective
-            function. Each suggestion is a dictionary where each key
-            corresponds to a parameter being optimized.
-        """
-        assert n_suggestions == 1, "Only one suggestion at a time is supported"
-        # Do whatever is needed to get the parallel guesses
-        # ...
-        # scaler = MinMaxScaler()
-        # scaler.fit(self.X)
-        try:
-            num_initial_design = max(len(self.bounds), self.min_initial_design)
-            if self.max_initial_design is not None:
-                num_initial_design = min(num_initial_design, self.max_initial_design)
-            if len(self.X) < num_initial_design:
-                if len(self.X) == 0 and self.fixed_initial_guess is not None:
-                    x_guess = [
-                        self.transform_back(
-                            np.array(
-                                [
-                                    self.fixed_initial_guess
-                                    for _ in range(len(self.bounds))
-                                ]
-                            )
-                        )
-                    ]
-                else:
-                    x_guess = self.random_suggest()
-                return x_guess
-            else:
-                temp_X = np.array(self.X)
-                temp_X = self.min_max_encode(temp_X)
-                if self.minmax_encode_y:
-                    temp_y = (
-                        MinMaxScaler()
-                        .fit_transform(np.array(self.y).reshape(-1, 1))
-                        .reshape(-1)
-                    )
-                else:
-                    temp_y = np.array(self.y)
-                temp_y = torch.tensor(temp_y).to(torch.float32)
-                if (
-                    self.rand_sugg_after_x_steps_of_stagnation is not None
-                    and len(self.y) > self.rand_sugg_after_x_steps_of_stagnation
-                    and not self.rand_prev
-                ):
-                    if (
-                        temp_y[: -self.rand_sugg_after_x_steps_of_stagnation].max()
-                        == temp_y.max()
-                    ):
-                        print(
-                            f"Random suggestion after >= {self.rand_sugg_after_x_steps_of_stagnation} steps of stagnation"
-                        )
-                        x_guess = self.random_suggest()
-                        return x_guess
-                if self.verbose:
-                    from matplotlib import pyplot as plt
-
-                    print(f"{temp_X=}, {temp_y=}")
-                    if temp_X.shape[1] == 2:
-                        from scipy.stats import rankdata
-
-                        plt.title("Observations, red -> blue.")
-                        plt.scatter(
-                            temp_X[:, 0], temp_X[:, 1], cmap="RdBu", c=rankdata(temp_y)
-                        )
-                        plt.show()
-
-                temp_X = temp_X.to(self.device)
-                temp_y = temp_y.to(self.device)
-
-                if self.fit_encoder_from_step and self.fit_encoder_from_step <= len(
-                    self.X
-                ):
-                    with torch.enable_grad():
-                        w = tune_input_warping.fit_input_warping(
-                            self.model, temp_X, temp_y
-                        )
-                    temp_X_warped = w(temp_X).detach()
-                else:
-                    temp_X_warped = temp_X
-
-                with torch.enable_grad():
-                    if self.acqf_optimizer_name == "lbfgs":
-
-                        def rand_sample_func(n):
-                            pre_samples = torch.rand(
-                                n, temp_X_warped.shape[1], device="cpu"
-                            )
-                            back_transformed_samples = [
-                                self.transform_back(sample) for sample in pre_samples
-                            ]
-                            samples = np.array(
-                                [
-                                    self.transform(deepcopy(bt_sample))
-                                    for bt_sample in back_transformed_samples
-                                ]
-                            )
-                            samples = self.min_max_encode(samples)
-                            return samples.to(self.device)
-
-                        if self.sample_only_valid:
-                            rand_sample_func = rand_sample_func
-                            # dims with bool or int are not continuous, thus no gradient opt is applied
-                            dims_wo_gradient_opt = [
-                                i for i, t in enumerate(self.types) if t != "real"
-                            ]
-                        else:
-                            rand_sample_func = None
-                            dims_wo_gradient_opt = []
-
-                        x_guess, x_options, eis, x_rs, x_rs_eis = (
-                            acquisition_functions.optimize_acq_w_lbfgs(
-                                self.model,
-                                temp_X_warped,
-                                temp_y,
-                                device=self.device,
-                                verbose=self.verbose,
-                                rand_sample_func=rand_sample_func,
-                                dims_wo_gradient_opt=dims_wo_gradient_opt,
-                                **{"apply_power_transform": True, **self.acqf_kwargs},
-                            )
-                        )
-
-                    elif self.acqf_optimizer_name == "adam":
-                        x_guess = (
-                            acquisition_functions.optimize_acq(
-                                self.model,
-                                temp_X_warped,
-                                temp_y,
-                                apply_power_transform=True,
-                                device=self.device,
-                                **self.acqf_kwargs,
-                            )
-                            .detach()
-                            .cpu()
-                            .numpy()
-                        )
-                    else:
-                        raise ValueError(
-                            "Optimizer not recognized, set `acqf_optimizer_name` to 'lbfgs' or 'adam'"
-                        )
-
-                back_transformed_x_options = [self.transform_back(x) for x in x_options]
-                opt_X = np.array(
-                    [
-                        self.transform(deepcopy(transformed_x_options))
-                        for transformed_x_options in back_transformed_x_options
-                    ]
-                )
-                opt_X = self.min_max_encode(opt_X)
-                opt_new = ~(opt_X[:, None] == temp_X[None].cpu()).all(-1).any(1)
-                for i, x in enumerate(opt_X):
-                    if opt_new[i]:
-                        if self.verbose:
-                            print(
-                                f"New point at pos {i}: {back_transformed_x_options[i], x_options[i]}"
-                            )
-                        self.rand_prev = False
-                        return [back_transformed_x_options[i]]
-                print("backup from initial rand search")
-                back_transformed_x_options = [self.transform_back(x) for x in x_rs]
-                opt_X = np.array(
-                    [
-                        self.transform(deepcopy(transformed_x_options))
-                        for transformed_x_options in back_transformed_x_options
-                    ]
-                )
-                opt_X = self.min_max_encode(opt_X)
-                opt_new = ~(opt_X[:, None] == temp_X[None].cpu()).all(-1).any(1)
-                for i, x in enumerate(opt_X):
-                    if opt_new[i]:
-                        if self.verbose:
-                            print(
-                                f"New point at pos {i}: {back_transformed_x_options[i], x_rs[i]} with ei {x_rs_eis[i]}"
-                            )
-                        self.rand_prev = False
-                        return [back_transformed_x_options[i]]
-                print("No new points found, random suggestion")
-                return self.random_suggest()
-        except Exception as e:
-            raise e
-
-    def transform(self, X_dict):
-        X_tf = []
-        for i, feature in enumerate(X_dict.keys()):
-            X_dict[feature] = self.transform_feature(X_dict[feature], i)
-            X_tf.append(X_dict[feature])
-        return X_tf
-
-    def transform_inverse(self, X_list):
-        X_tf = {}
-        for i, hp_name in enumerate(self.hp_names):
-            X_tf[hp_name] = self.transform_feature_inverse(X_list[i], i)
-        return X_tf
-
-    def observe(self, X, y):
-        """Feed an observation back.
-
-        Parameters
-        ----------
-        X : list of dict-like
-            Places where the objective function has already been evaluated.
-            Each suggestion is a dictionary where each key corresponds to a
-            parameter being optimized.
-        y : array-like, shape (n,)
-            Corresponding values where objective has been evaluated
-        """
-        # Update the model with new objective function observations
-        # ...
-        # No return statement needed
-        if np.isinf(y) and y > 0:
-            y[:] = 1e10
-
-        if not np.isnan(y) and not np.isinf(y):
-            assert (
-                len(y) == 1 and len(X) == 1
-            ), "Only one suggestion at a time is supported"
-            X = {key: value for key, value in sorted(X[0].items())}
-            assert (
-                list(X.keys())
-                == list(self.api_config.keys())
-                == list(self.hp_names)
-                == list(self.space_x.param_list)
-            )
-            if self.verbose:
-                print(f"{X=}, {y=}")
-            X = self.transform(X)
-            if self.verbose:
-                print(f"transformed {X=}")
-            self.X.append(X)
-            if self.minimize:
-                self.y.append(-y[0])
-            else:
-                self.y.append(y[0])
-        else:
-            assert False
-
-
-def test():
-    from bayesmark.experiment import _build_test_problem, OBJECTIVE_NAMES, run_study
-
-    # function_instance = _build_test_problem(model_name='ada', dataset='breast', scorer='nll', path=None)
-    function_instance = _build_test_problem(
-        model_name="kNN", dataset="boston", scorer="mse", path=None
-    )
-
-    # Setup optimizer
-    api_config = function_instance.get_api_config()
-    import os
-
-    # check is file
-
-    config = {
-        "pfn_file": "final_models/model_hebo_morebudget_9_unused_features_3.pt",
-        "minimize": 1,
-        "device": "cpu:0",
-        "fit_encoder_from_step": None,
-        "pre_sample_size": 1_000,
-        "num_grad_steps": 15_000,
-        "num_candidates": 10,
-        "rand_bool": True,
-    }
-    opt = PFNOptimizer(api_config, verbose=True, **config)
-
-    function_evals, timing, suggest_log = run_study(
-        opt,
-        function_instance,
-        50,
-        1,
-        callback=None,
-        n_obj=len(OBJECTIVE_NAMES),
-    )
-
-
-if __name__ == "__main__":
-    import uuid
-
-    import bayesmark.cmd_parse as cmd
-    import bayesmark.constants as cc
-    from bayesmark.cmd_parse import CmdArgs
-    from bayesmark.serialize import XRSerializer
-
-    description = "Run a study with one benchmark function and an optimizer"
-    args = cmd.parse_args(cmd.experiment_parser(description))
-
-    run_uuid = uuid.UUID(args[CmdArgs.uuid])
-
-    # set global logging level
-    logging.basicConfig(level=logging.DEBUG)
-    # This is the entry point for experiments, so pass the class to experiment_main to use this optimizer.
-    # This statement must be included in the wrapper class file:
-
-    experiment_main(PFNOptimizer, args=args)
diff --git a/private_multitask_pfn/PFNs/pfns/positional_encodings.py b/private_multitask_pfn/PFNs/pfns/positional_encodings.py
deleted file mode 100644
index 3f8ace4..0000000
--- a/private_multitask_pfn/PFNs/pfns/positional_encodings.py
+++ /dev/null
@@ -1,344 +0,0 @@
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Optional
-
-import torch
-from torch import nn
-
-import math
-
-# import torch
-# from torch import nn
-
-
-# Protocol for positonal encodings.
-# __init__(d_model, max_len=..[, more optionals])
-# forward(x: (seq_len, bs, d_model)) -> Tensor of shape (*x.shape[:2],d_model) containing pos. embeddings
-
-
-class NoPositionalEncoding(nn.Module):
-    def __init__(self, d_model, max_len=None):
-        super(NoPositionalEncoding, self).__init__()
-        pass
-
-    def forward(self, x):
-        return x  # * math.sqrt(x.shape[-1])
-
-
-class PositionalEncoding(nn.Module):
-    def __init__(self, d_model, max_len=5000):
-        super(PositionalEncoding, self).__init__()
-        pe = torch.zeros(max_len, d_model)
-        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
-        div_term = torch.exp(
-            torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
-        )
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0).transpose(0, 1)
-        self.register_buffer("pe", pe)
-
-    def forward(self, x):
-        x = self.pe[: x.size(0), :] + x  # * math.sqrt(x.shape[-1])
-        return x
-
-
-class LearnedPositionalEncoding(nn.Module):
-    def __init__(self, d_model, max_len=5000):
-        super(LearnedPositionalEncoding, self).__init__()
-        self.max_seq_len = max_len
-        # self.positional_embeddings = nn.Embedding(max_len, d_model)
-        self.positional_embeddings = nn.Parameter(torch.empty(max_len, d_model))
-        nn.init.normal_(self.positional_embeddings, mean=0, std=d_model**-0.5)
-
-    def forward(self, x):
-        seq_len, bs, d_model = x.shape
-        assert seq_len <= len(
-            self.positional_embeddings
-        ), "seq_len can be at most max_len."
-        pos_emb = self.positional_embeddings[:seq_len]
-        return (
-            pos_emb.unsqueeze(1).expand(seq_len, bs, d_model) + x
-        )  # * math.sqrt(x.shape[-1])
-
-
-class PairedScrambledPositionalEncodings(LearnedPositionalEncoding):
-    # TODO check whether it is a problem to use the same perm. for full batch
-    def forward(self, x):
-        seq_len, bs, d_model = x.shape
-        assert seq_len <= len(
-            self.positional_embeddings
-        ), "seq_len can be at most max_len."
-        assert (
-            len(self.positional_embeddings) % 2 == 0
-        ), "Please specify an even max_len."
-
-        paired_embs = self.positional_embeddings.view(
-            len(self.positional_embeddings), -1, 2
-        )
-        pos_emb = paired_embs[torch.randperm(len(paired_embs))].view(
-            *self.positional_embeddings.shape
-        )[:seq_len]
-
-        return (
-            pos_emb.unsqueeze(1).expand(seq_len, bs, d_model) + x
-        )  # * math.sqrt(x.shape[-1])
-
-
-class RotaryPositionalEmbeddings(nn.Module):
-    """
-    This class implements Rotary Positional Embeddings (RoPE)
-    proposed in https://arxiv.org/abs/2104.09864.
-
-    Reference implementation (used for correctness verfication)
-    can be found here:
-    https://github.com/meta-llama/llama/blob/main/llama/model.py#L80
-
-    In this implementation we cache the embeddings for each position upto
-    ``max_seq_len`` by computing this during init.
-
-    Args:
-        dim (int): Embedding dimension. This is usually set to the dim of each
-            head in the attention module computed as ``embed_dim // num_heads``
-        max_seq_len (int): Maximum expected sequence length for the
-            model, if exceeded the cached freqs will be recomputed
-        base (int): The base for the geometric progression used to compute
-            the rotation angles
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        max_seq_len: int = 4096,
-        base: int = 10_000,
-    ) -> None:
-        super().__init__()
-        self.dim = dim
-        self.base = base
-        self.max_seq_len = max_seq_len
-        self.rope_init()
-
-    def rope_init(self):
-        theta = 1.0 / (
-            self.base
-            ** (torch.arange(0, self.dim, 2)[: (self.dim // 2)].float() / self.dim)
-        )
-        self.register_buffer("theta", theta, persistent=False)
-        self.build_rope_cache(self.max_seq_len)
-
-    def build_rope_cache(self, max_seq_len: int = 4096) -> None:
-        # Create position indexes `[0, 1, ..., max_seq_len - 1]`
-        seq_idx = torch.arange(
-            max_seq_len, dtype=self.theta.dtype, device=self.theta.device
-        )
-
-        # Outer product of theta and position index; output tensor has
-        # a shape of [max_seq_len, dim // 2]
-        idx_theta = torch.einsum("i, j -> ij", seq_idx, self.theta).float()
-
-        # cache includes both the cos and sin components and so the output shape is
-        # [max_seq_len, dim // 2, 2]
-        cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
-        self.register_buffer("cache", cache, persistent=False)
-
-    def forward(
-        self, x: torch.Tensor, *, input_pos: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        """
-        Args:
-            x (torch.Tensor): input tensor with shape
-                ``[b, s, n_h, h_d]``
-            input_pos (Optional[torch.Tensor]): Optional tensor which contains the position ids
-                of each token. During training, this is used to indicate the positions
-                of each token relative to its sample when packed, shape [b, s].
-                During inference, this indicates the position of the current token.
-                If none, assume the index of the token is its position id. Default is None.
-
-        Returns:
-            torch.Tensor: output tensor with shape ``[b, s, n_h, h_d]``
-
-        Notation used for tensor shapes:
-            - b: batch size
-            - s: sequence length
-            - n_h: num heads
-            - h_d: head dim
-        """
-        # input tensor has shape [b, s, n_h, h_d]
-        seq_len = x.size(1)
-
-        # extract the values based on whether input_pos is set or not
-        rope_cache = (
-            self.cache[:seq_len] if input_pos is None else self.cache[input_pos]
-        )
-
-        # reshape input; the last dimension is used for computing the output.
-        # Cast to float to match the reference implementation
-        # tensor has shape [b, s, n_h, h_d // 2, 2]
-        xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
-
-        # reshape the cache for broadcasting
-        # tensor has shape [b, s, 1, h_d // 2, 2] if packed samples,
-        # otherwise has shape [1, s, 1, h_d // 2, 2]
-        rope_cache = rope_cache.view(-1, xshaped.size(1), 1, xshaped.size(3), 2)
-
-        # tensor has shape [b, s, n_h, h_d // 2, 2]
-        x_out = torch.stack(
-            [
-                xshaped[..., 0] * rope_cache[..., 0]
-                - xshaped[..., 1] * rope_cache[..., 1],
-                xshaped[..., 1] * rope_cache[..., 0]
-                + xshaped[..., 0] * rope_cache[..., 1],
-            ],
-            -1,
-        )
-
-        # tensor has shape [b, s, n_h, h_d]
-        x_out = x_out.flatten(3)
-        return x_out.type_as(x)
-
-
-class VisionRotaryPositionalEmbeddings(nn.Module):
-    """
-    This class implements two-dimensional Rotary Positional Embeddings (RoPE) for images
-    based on the axial frequency 2D RoPE described in https://arxiv.org/pdf/2403.13298.
-
-    The position embedding is simply applied to the x-axis and y-axis separately.
-
-    Note: This module assumes the CLS token embedding is appended at the end of the sequence.
-
-    Args:
-        patch_size (int): The size of each patch. Used to divide the tiles into patches.
-            E.g. for ``patch_size=40``, a tile of shape (400, 400) will have 10x10 grid of patches.
-        tile_size (int): The size of your image tiles, if the image was tile-cropped in advance. Otherwise,
-            the size of the full input image. In this case, the function will consider your image as a single tile.
-        dim (int): Embedding dimension. Unlike :class:`~torchtune.modules.RotaryPositionalEmbeddings`, this is
-            usually set to the dim of each head in the attention module divided by 2, computed as
-            ``embed_dim // num_heads // 2``. The divide by 2 accounts for x and y positions.
-        base (int): The base for the geometric progression used to compute
-            the rotation angles
-        append_cls_token (bool): Set to True if CLS token embedding is at the end of the sequence in the vision transformer,
-            False if is in the beginning of the sequence. RoPE is zeroed out for the CLS token. Default is True.
-    """
-
-    def __init__(
-        self,
-        patch_size: int,
-        tile_size: int,
-        dim: int,
-        base: int = 10_000,
-        append_cls_token: bool = True,
-    ) -> None:
-        super().__init__()
-        self.patch_grid_size = tile_size // patch_size
-        self.dim = dim
-        self.base = base
-        self.append_cls_token = append_cls_token
-        self.rope_init()
-
-    def rope_init(self):
-        theta = 1.0 / (
-            self.base
-            ** (torch.arange(0, self.dim, 2)[: (self.dim // 2)].float() / self.dim)
-        )
-        self.register_buffer("theta", theta, persistent=False)
-        self.build_rope_cache()
-
-    def build_rope_cache(self) -> None:
-        # Create position indices for each patch in the tile
-        patches_per_tile = self.patch_grid_size**2
-        patch_idx = torch.arange(
-            patches_per_tile, dtype=self.theta.dtype, device=self.theta.device
-        )
-        # Add a placeholder index for CLS token - will not be used in RoPE
-        if self.append_cls_token:
-            patch_idx = torch.cat(
-                [
-                    patch_idx,
-                    -1 * torch.ones(1, dtype=patch_idx.dtype, device=patch_idx.device),
-                ]
-            )
-        else:
-            patch_idx = torch.cat(
-                [
-                    -1 * torch.ones(1, dtype=patch_idx.dtype, device=patch_idx.device),
-                    patch_idx,
-                ]
-            )
-        # Encode x and y positions of each patch in the tile
-        patch_x_pos = patch_idx % self.patch_grid_size
-        patch_y_pos = patch_idx // self.patch_grid_size
-
-        # Outer product of theta and position index; output tensor has
-        # a shape of [patches_per_tile + 1, dim // 2]
-        x_theta = torch.einsum("i, j -> ij", patch_x_pos + 1, self.theta).float()
-        y_theta = torch.einsum("i, j -> ij", patch_y_pos + 1, self.theta).float()
-
-        # Shape: [patches_per_tile + 1, dim]
-        freqs = torch.cat([x_theta, y_theta], dim=-1)
-        # Zero out CLS token position frequencies
-        freqs = freqs.masked_fill(patch_idx.unsqueeze(-1) < 0, 0)
-
-        # cache includes both the cos and sin components and so the output shape is
-        # [patches_per_tile + 1, dim, 2]
-        cache = torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1)
-        self.register_buffer("cache", cache, persistent=False)
-
-    def forward(
-        self, x: torch.Tensor, *, input_pos: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        """
-        Args:
-            x (torch.Tensor): input tensor with shape
-                ``[b, s, n_h, h_d]``
-            input_pos (Optional[torch.Tensor]): Optional tensor which contains the position ids
-                of each token. During training, this is used to indicate the positions
-                of each token relative to its sample when packed, shape [b, s].
-                During inference, this indicates the position of the current token.
-                If none, assume the index of the token is its position id. Default is None.
-
-        Returns:
-            torch.Tensor: output tensor with shape ``[b, s, n_h, h_d]``
-
-        Notation used for tensor shapes:
-            - b: batch size
-            - s: sequence length
-            - n_h: num heads
-            - h_d: head dim
-        """
-        # input tensor has shape [b, s, n_h, h_d]
-        seq_len = x.size(1)
-
-        # extract the values based on whether input_pos is set or not
-        rope_cache = (
-            self.cache[:seq_len] if input_pos is None else self.cache[input_pos]
-        )
-
-        # reshape input; the last dimension is used for computing the output.
-        # Cast to float to match the reference implementation
-        # tensor has shape [b, s, n_h, h_d // 2, 2]
-        xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
-
-        # reshape the cache for broadcasting
-        # tensor has shape [b, s, 1, h_d // 2, 2] if packed samples,
-        # otherwise has shape [1, s, 1, h_d // 2, 2]
-        rope_cache = rope_cache.view(-1, xshaped.size(1), 1, xshaped.size(3), 2)
-
-        # tensor has shape [b, s, n_h, h_d // 2, 2]
-        x_out = torch.stack(
-            [
-                xshaped[..., 0] * rope_cache[..., 0]
-                - xshaped[..., 1] * rope_cache[..., 1],
-                xshaped[..., 1] * rope_cache[..., 0]
-                + xshaped[..., 0] * rope_cache[..., 1],
-            ],
-            -1,
-        )
-
-        # tensor has shape [b, s, n_h, h_d]
-        x_out = x_out.flatten(3)
-        return x_out.type_as(x)
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/priors/__init__.py b/private_multitask_pfn/PFNs/pfns/priors/__init__.py
deleted file mode 100644
index 59640bf..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from . import fast_gp, nonmyopic_bo, utils, normalize_with_style, simple_mlp, hyperparameter_sampling, input_warping, hebo_prior, condition_on_area_of_opt
-from .utils import get_batch_sequence as get_batch_sequence, get_batch_to_dataloader as get_batch_to_dataloader
-from .prior import Batch
diff --git a/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py b/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py
deleted file mode 100644
index 9e99759..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import torch
-
-from .prior import Batch
-from ..utils import default_device
-
-
-@torch.no_grad()
-def get_batch(batch_size, seq_len, num_features, get_batch, epoch, device=default_device, hyperparameters={}, **kwargs):
-    """
-    This function assumes that every x is in the range [0, 1].
-    Style shape is (batch_size, 3*num_features) under the assumption that get_batch returns a batch
-    with shape (seq_len, batch_size, num_features).
-    The style is build the following way: [prob_of_feature_1_in_range, range_min_of_feature_1, range_max_of_feature_1, ...]
-
-
-
-    :param batch_size:
-    :param seq_len:
-    :param num_features:
-    :param get_batch:
-    :param epoch:
-    :param device:
-    :param hyperparameters:
-    :param kwargs:
-    :return:
-    """
-
-    max_num_divisions = hyperparameters.get('condition_on_area_max_num_divisions', 5)
-    maximize = hyperparameters.get('condition_on_area_maximize', True)
-    remove_correct_from_rand = hyperparameters.get('condition_on_area_remove_correct_from_rand', False)
-    assert remove_correct_from_rand is False, 'implement it'
-
-    batch: Batch = get_batch(batch_size=batch_size, seq_len=seq_len,
-                             num_features=num_features, device=device,
-                             hyperparameters=hyperparameters,
-                             epoch=epoch, **kwargs)
-    assert batch.style is None
-
-    d = batch.x.shape[2]
-
-    prob_correct = torch.rand(batch_size, d, device=device)
-    correct_opt = torch.rand(batch_size, d, device=device) < prob_correct
-    division_size = torch.randint(1, max_num_divisions + 1, (batch_size, d), device=device, dtype=torch.long)
-
-    optima_inds = batch.target_y.argmax(0).squeeze() if maximize else batch.target_y.argmin(0).squeeze()  # batch_size
-    optima = batch.x[optima_inds, torch.arange(batch_size, device=device)]  # shape: (batch_size, d)
-
-    optima_sections = torch.min(torch.floor(optima * division_size).long(), division_size - 1)
-    random_sections = torch.min(torch.floor(torch.rand(batch_size, batch.x.shape[2], device=device) * division_size).long(), division_size - 1)
-
-    sections = torch.where(correct_opt, optima_sections, random_sections).float()  # shape: (batch_size, d)
-    sections /= division_size.float()
-    assert tuple(sections.shape) == (batch_size, d)
-    batch.style = torch.stack([prob_correct, sections, sections + 1 / division_size], 2).view(batch_size, -1)  # shape: (batch_size, 3*d)
-
-    return batch
-
-
-
-
-
-
-
diff --git a/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py b/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py
deleted file mode 100644
index a9baa7b..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/condition_on_area_of_opt_continuous.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import torch
-
-from .prior import Batch
-from ..utils import default_device
-
-
-@torch.no_grad()
-def get_batch(batch_size, seq_len, num_features, get_batch, epoch, device=default_device, hyperparameters={}, **kwargs):
-    """
-    This is not part of the paper, but feel welcome to use this to write a better version of our user prior.
-
-
-    This function assumes that every x is in the range [0, 1].
-    Style shape is (batch_size, 3*num_features) under the assumption that get_batch returns a batch
-    with shape (seq_len, batch_size, num_features).
-    The style is build the following way: [prob_of_feature_1_in_range, range_min_of_feature_1, range_max_of_feature_1, ...]
-
-
-
-    :param batch_size:
-    :param seq_len:
-    :param num_features:
-    :param get_batch:
-    :param epoch:
-    :param device:
-    :param hyperparameters:
-    :param kwargs:
-    :return:
-    """
-
-    maximize = hyperparameters.get('condition_on_area_maximize', True)
-    size_range = hyperparameters.get('condition_on_area_size_range', (0.1, 0.5))
-    distribution = hyperparameters.get('condition_on_area_distribution', 'uniform')
-    assert distribution in ['uniform']
-
-
-    batch: Batch = get_batch(batch_size=batch_size, seq_len=seq_len,
-                             num_features=num_features, device=device,
-                             hyperparameters=hyperparameters,
-                             epoch=epoch, **kwargs)
-    assert batch.style is None
-
-    d = batch.x.shape[2]
-
-    prob_correct = torch.rand(batch_size, d, device=device)
-    correct_opt = torch.rand(batch_size, d, device=device) < prob_correct
-    division_size = torch.rand(batch_size, d, device=device) * (size_range[1] - size_range[0]) + size_range[0]
-
-    optima = batch.target_y.argmax(0).squeeze() if maximize else batch.target_y.argmin(0).squeeze()  # batch_size, d
-
-    optima_hints = batch.x[optima, torch.arange(batch_size, device=device)] - division_size/2 + torch.rand(batch_size, d, device=device)*division_size # shape: (batch_size, d)
-    optima_hints = optima_hints.clamp(0, 1)
-
-    optima_division_lower_bound = (optima_hints - division_size/2).clamp(0, 1)
-    optima_division_upper_bound = (optima_hints + division_size/2).clamp(0, 1)
-
-    random_hints = torch.rand(batch_size, d, device=device) - division_size/2 + torch.rand(batch_size, d, device=device)*division_size # shape: (batch_size, d)
-    random_hints = random_hints.clamp(0, 1)
-
-    random_division_lower_bound = (random_hints - division_size/2).clamp(0, 1)
-    random_division_upper_bound = (random_hints + division_size/2).clamp(0, 1)
-
-
-    lower_bounds = torch.where(correct_opt, optima_division_lower_bound, random_division_lower_bound)
-    upper_bounds = torch.where(correct_opt, optima_division_upper_bound, random_division_upper_bound)
-
-    batch.style = torch.stack([prob_correct, lower_bounds, upper_bounds], 2).view(batch_size, -1)  # shape: (batch_size, 3*d)
-
-    return batch
-
-
-
-
-
-
-
diff --git a/private_multitask_pfn/PFNs/pfns/priors/fast_gp.py b/private_multitask_pfn/PFNs/pfns/priors/fast_gp.py
deleted file mode 100644
index 9496b98..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/fast_gp.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import time
-
-import torch
-from torch import nn
-import gpytorch
-
-from .prior import Batch
-from .utils import get_batch_to_dataloader
-from ..utils import default_device
-
-
-# We will use the simplest form of GP model, exact inference
-class ExactGPModel(gpytorch.models.ExactGP):
-    def __init__(self, train_x, train_y, likelihood):
-        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
-        self.mean_module = gpytorch.means.ConstantMean()
-        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
-
-    def forward(self, x):
-        mean_x = self.mean_module(x)
-        covar_x = self.covar_module(x)
-        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
-
-
-def get_model(x, y, hyperparameters):
-    likelihood = gpytorch.likelihoods.GaussianLikelihood(noise_constraint=gpytorch.constraints.GreaterThan(1.e-9))
-    model = ExactGPModel(x, y, likelihood)
-    model.likelihood.noise = torch.ones_like(model.likelihood.noise) * hyperparameters["noise"]
-    model.covar_module.outputscale = torch.ones_like(model.covar_module.outputscale) * hyperparameters["outputscale"]
-    model.covar_module.base_kernel.lengthscale = torch.ones_like(model.covar_module.base_kernel.lengthscale) * \
-                                                 hyperparameters["lengthscale"]
-    return model, likelihood
-
-
-@torch.no_grad()
-def get_batch(batch_size, seq_len, num_features, device=default_device, hyperparameters=None,
-              equidistant_x=False, fix_x=None, **kwargs):
-    if isinstance(hyperparameters, (tuple, list)):
-        hyperparameters = {"noise": hyperparameters[0]
-            , "outputscale": hyperparameters[1]
-            , "lengthscale": hyperparameters[2]
-            , "is_binary_classification": hyperparameters[3]
-            # , "num_features_used": hyperparameters[4]
-            , "normalize_by_used_features": hyperparameters[5]
-            , "order_y": hyperparameters[6]
-            , "sampling": hyperparameters[7]
-                           }
-    elif hyperparameters is None:
-        hyperparameters = {"noise": .1, "outputscale": .1, "lengthscale": .1}
-
-    if 'verbose' in hyperparameters and hyperparameters['verbose']:
-        print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
-                  , "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size, 'sampling': hyperparameters['sampling']})
-    observation_noise = hyperparameters.get("observation_noise", True)
-
-    # hyperparameters = {k: hyperparameters[k]() if callable(hyperparameters[k]) else hyperparameters[k] for k in
-    #      hyperparameters.keys()}
-    assert not (equidistant_x and (fix_x is not None))
-
-    with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations', (True, True, True))):
-        if equidistant_x:
-            assert num_features == 1
-            x = torch.linspace(0, 1., seq_len).unsqueeze(0).repeat(batch_size, 1).unsqueeze(-1)
-        elif fix_x is not None:
-            assert fix_x.shape == (seq_len, num_features)
-            x = fix_x.unsqueeze(0).repeat(batch_size, 1, 1).to(device)
-        else:
-            if hyperparameters.get('sampling','uniform') == 'uniform':
-                x = torch.rand(batch_size, seq_len, num_features, device=device)
-            elif hyperparameters.get('sampling','uniform') == 'normal':
-                x = torch.randn(batch_size, seq_len, num_features, device=device)
-            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith('uniform_'):
-                left_border, right_border = [float(v) for v in hyperparameters['sampling'][len('uniform_'):].split('_')]
-                x = torch.rand(batch_size, seq_len, num_features, device=device) * (right_border - left_border) + left_border
-            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith('clustered_'):
-                dist_std, local_dist_std, base_likelihood = [float(v) for v in hyperparameters['sampling'][len('clustered_'):].split('_')]
-
-                def first_sample(dist):
-                    return dist().unsqueeze(0)
-
-                def append_sample(samples, dist, local_dist, base_likelihood):
-                    if samples is None:
-                        return first_sample(dist)
-                    num_samples, batch_size, num_features = samples.shape
-                    use_base = torch.rand(batch_size) < base_likelihood
-                    sample_mean = torch.where(use_base[:, None].repeat(1, num_features),
-                                              torch.zeros(batch_size, num_features),
-                                              samples[torch.randint(num_samples, (batch_size,)),
-                                              torch.arange(batch_size), :])
-                    return torch.cat((samples, (local_dist() + sample_mean).unsqueeze(0)), 0)
-
-                def create_sample(num_samples, dist, local_dist, base_likelihood):
-                    samples = None
-                    for i in range(num_samples):
-                        samples = append_sample(samples, dist, local_dist, base_likelihood)
-
-                    return samples[torch.randperm(num_samples)]
-
-                x = create_sample(seq_len, lambda: torch.randn(batch_size, num_features)*dist_std,
-                                  lambda: torch.rand(batch_size, num_features)*local_dist_std, base_likelihood)\
-                    .transpose(0,1).to(device)
-            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith(
-                    'gmix_'):
-                blob_width, n_centers_max, stddev = [float(v) for v in
-                                                             hyperparameters['sampling'][len('gmix_'):].split('_')]
-                n_centers_max = int(n_centers_max)
-                def get_x(batch_size, n_samples, num_features, blob_width, n_centers_max, stddev, device):
-                    n_centers = torch.randint(1, n_centers_max, tuple(), device=device)
-                    centers = torch.rand((batch_size, n_centers, num_features), device=device) * blob_width - blob_width / 2
-                    center_assignments = torch.randint(n_centers, (batch_size, n_samples,), device=device)
-                    noise = torch.randn((batch_size, n_samples, num_features), device=device) * stddev
-                    return centers.gather(1, center_assignments[..., None].repeat(1, 1,
-                                                                                  num_features)) + noise  # centers: (b, m, f), ass: (b,n)
-                x = get_x(batch_size, seq_len, num_features, blob_width, n_centers_max, stddev, device)
-            elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith(
-                    'grep_'):
-                stddev, = [float(v) for v in hyperparameters['sampling'][len('grep_'):].split('_')]
-                x = torch.randn(batch_size, seq_len//2, num_features, device=device) * stddev
-                x = x.repeat(1,2,1)
-                x = x[:,torch.randperm(x.shape[1]),:]
-            else:
-                x = torch.randn(batch_size, seq_len, num_features, device=device) * hyperparameters.get('sampling', 1.)
-        model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
-        model.to(device)
-        # trained_model = ExactGPModel(train_x, train_y, likelihood).cuda()
-        # trained_model.eval()
-        successful_sample = False
-        while not successful_sample:
-            try:
-                with gpytorch.settings.prior_mode(True):
-                    model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
-                    model.to(device)
-
-                    d = model(x)
-                    if observation_noise:
-                        target_sample = sample = likelihood(d).sample().transpose(0, 1)
-                    else:
-                        target_sample = d.sample().transpose(0, 1)  # this will be the target for the loss
-                        sample = likelihood(target_sample).sample()  # this will be the input to the Transformer
-                    successful_sample = True
-            except RuntimeError: # This can happen when torch.linalg.eigh fails. Restart with new init resolves this.
-                print('GP Sampling unsuccessful, retrying.. ')
-                print(x)
-                print(hyperparameters)
-
-    if bool(torch.any(torch.isnan(x)).detach().cpu().numpy()):
-        print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
-                  , "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size})
-
-    if hyperparameters.get('improvement_classification', False):
-        single_eval_pos = kwargs['single_eval_pos']
-        max_so_far = sample[:single_eval_pos].max(0).values
-        sample[single_eval_pos:] = (sample > max_so_far).float()[single_eval_pos:]
-
-    return Batch(x=x.transpose(0, 1), y=sample, target_y=target_sample)
-
-DataLoader = get_batch_to_dataloader(get_batch)
-
-def get_model_on_device(x,y,hyperparameters,device):
-    model, likelihood = get_model(x, y, hyperparameters)
-    model.to(device)
-    return model, likelihood
-
-
-@torch.no_grad()
-def evaluate(x, y, y_non_noisy, use_mse=False, hyperparameters={}, get_model_on_device=get_model_on_device, device=default_device, step_size=1, start_pos=0):
-    start_time = time.time()
-    losses_after_t = [.0] if start_pos == 0 else []
-    all_losses_after_t = []
-
-    with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations',(True,True,True))), gpytorch.settings.fast_pred_var(False):
-        for t in range(max(start_pos, 1), len(x), step_size):
-            loss_sum = 0.
-            model, likelihood = get_model_on_device(x[:t].transpose(0, 1), y[:t].transpose(0, 1), hyperparameters, device)
-
-
-            model.eval()
-            # print([t.shape for t in model.train_inputs])
-            # print(x[:t].transpose(0,1).shape, x[t].unsqueeze(1).shape, y[:t].transpose(0,1).shape)
-            f = model(x[t].unsqueeze(1))
-            l = likelihood(f)
-            means = l.mean.squeeze()
-            varis = l.covariance_matrix.squeeze()
-            # print(l.variance.squeeze(), l.mean.squeeze(), y[t])
-
-            assert len(means.shape) == len(varis.shape) == 1
-            assert len(means) == len(varis) == x.shape[1]
-
-            if use_mse:
-                c = nn.MSELoss(reduction='none')
-                ls = c(means, y[t])
-            else:
-                ls = -l.log_prob(y[t].unsqueeze(1))
-
-            losses_after_t.append(ls.mean())
-            all_losses_after_t.append(ls.flatten())
-        return torch.stack(all_losses_after_t).to('cpu'), torch.tensor(losses_after_t).to('cpu'), time.time() - start_time
-
-if __name__ == '__main__':
-    hps = (.1,.1,.1)
-    for redo_idx in range(1):
-        print(
-            evaluate(*get_batch(1000, 10, hyperparameters=hps, num_features=10), use_mse=False, hyperparameters=hps))
diff --git a/private_multitask_pfn/PFNs/pfns/priors/gp.py b/private_multitask_pfn/PFNs/pfns/priors/gp.py
deleted file mode 100644
index 8de7aac..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/gp.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import time
-import random
-
-import torch
-from torch import nn
-from sklearn.gaussian_process import GaussianProcessRegressor
-from sklearn.gaussian_process.kernels import RBF, DotProduct, WhiteKernel
-from .prior import Batch
-from .utils import get_batch_to_dataloader
-
-
-length_scale_sampling_gp = .6
-
-def get_gp(length_scale=None):
-    return GaussianProcessRegressor(
-        kernel=RBF(length_scale=length_scale or length_scale_sampling_gp, length_scale_bounds='fixed'),
-        random_state=0, optimizer=None)
-
-
-def get_batch(batch_size, seq_len, num_features, noisy_std=None, **kwargs):
-    # m = torch.normal(0.,.1,size=(batch_size,num_features))
-    # m2 = torch.rand(batch_size,num_features)
-    # b = 0 # torch.rand(batch_size)
-    x_t = torch.rand(batch_size, seq_len, num_features)
-    # gp_b = TensorGP(kernel=TensorRBF(noisy_std))
-    # y_t = gp_b.sample_from_GP_prior(x_t).detach()
-
-    gpr = get_gp(noisy_std)
-    y_t = torch.zeros(batch_size, seq_len)
-
-    for i in range(len(y_t)):
-        y_t[i] += gpr.sample_y(x_t[i], random_state=random.randint(0, 2 ** 32)).squeeze()
-    x, y = x_t.transpose(0, 1), y_t.transpose(0, 1)
-    # x, _ = torch.sort(x,dim=0)
-    return Batch(x=x, y=y, target_y=y)
-
-
-DataLoader = get_batch_to_dataloader(get_batch)
-
-def evaluate(x, y, y_non_noisy, use_mse=False, length_scale=length_scale_sampling_gp):
-    start_time = time.time()
-    losses_after_t = [.0]
-    for t in range(1, len(x)):
-        loss_sum = 0.
-        for b_i in range(x.shape[1]):
-            gpr = get_gp(length_scale).fit(x[:t, b_i], y[:t, b_i])
-            means, stds = gpr.predict(x[t, b_i].unsqueeze(0), return_std=True)
-            assert len(means) == 1 == len(stds)
-            if use_mse:
-                c = nn.MSELoss()
-                l = c(torch.tensor(means), y[t, b_i].unsqueeze(-1))
-            else:
-                c = nn.GaussianNLLLoss(full=True)
-                l = c(torch.tensor(means), y[t, b_i].unsqueeze(-1),
-                      var=torch.tensor(stds) ** 2)
-            loss_sum += l
-
-
-        losses_after_t.append(loss_sum / x.shape[1])
-
-    return torch.tensor(losses_after_t), time.time()-start_time
-
-if __name__ == '__main__':
-    ls = .1
-    for alpha in set([ls, ls * 1.1, ls * .9]):
-        print(alpha)
-        for redo_idx in range(1):
-            print(
-                evaluate(*get_batch(1000, 10, noisy_std=ls, num_features=10), use_mse=False, length_scale=alpha))
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py b/private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py
deleted file mode 100644
index 9bd387f..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/hebo_prior.py
+++ /dev/null
@@ -1,613 +0,0 @@
-import random
-import warnings
-
-import botorch
-import gpytorch
-
-import numpy as np
-import torch
-from gpytorch.means import ZeroMean
-from gpytorch.priors.torch_priors import GammaPrior, LogNormalPrior, UniformPrior
-from torch import nn
-from botorch.models.transforms.input import *
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-from gpytorch.constraints import GreaterThan
-
-from ..utils import default_device, to_tensor
-
-from . import utils
-from .prior import Batch
-from .utils import get_batch_to_dataloader
-
-
-class Warp(gpytorch.Module):
-    r"""A transform that uses learned input warping functions.
-
-    Each specified input dimension is warped using the CDF of a
-    Kumaraswamy distribution. Typically, MAP estimates of the
-    parameters of the Kumaraswamy distribution, for each input
-    dimension, are learned jointly with the GP hyperparameters.
-
-    for each output in batched multi-output and multi-task models.
-
-    For now, ModelListGPs should be used to learn independent warping
-    functions for each output.
-    """
-
-    _min_concentration_level = 1e-4
-
-    def __init__(
-        self,
-        indices: List[int],
-        transform_on_train: bool = True,
-        transform_on_eval: bool = True,
-        transform_on_fantasize: bool = True,
-        reverse: bool = False,
-        eps: float = 1e-7,
-        concentration1_prior: Optional[Prior] = None,
-        concentration0_prior: Optional[Prior] = None,
-        batch_shape: Optional[torch.Size] = None,
-    ) -> None:
-        r"""Initialize transform.
-
-        Args:
-            indices: The indices of the inputs to warp.
-            transform_on_train: A boolean indicating whether to apply the
-                transforms in train() mode. Default: True.
-            transform_on_eval: A boolean indicating whether to apply the
-                transform in eval() mode. Default: True.
-            transform_on_fantasize: A boolean indicating whether to apply the
-                transform when called from within a `fantasize` call. Default: True.
-            reverse: A boolean indicating whether the forward pass should untransform
-                the inputs.
-            eps: A small value used to clip values to be in the interval (0, 1).
-            concentration1_prior: A prior distribution on the concentration1 parameter
-                of the Kumaraswamy distribution.
-            concentration0_prior: A prior distribution on the concentration0 parameter
-                of the Kumaraswamy distribution.
-            batch_shape: The batch shape.
-        """
-        super().__init__()
-        self.register_buffer("indices", torch.tensor(indices, dtype=torch.long))
-        self.transform_on_train = transform_on_train
-        self.transform_on_eval = transform_on_eval
-        self.transform_on_fantasize = transform_on_fantasize
-        self.reverse = reverse
-        self.batch_shape = batch_shape or torch.Size([])
-        self._X_min = eps
-        self._X_range = 1 - 2 * eps
-        if len(self.batch_shape) > 0:
-            # Note: this follows the gpytorch shape convention for lengthscales
-            # There is ongoing discussion about the extra `1`.
-            # https://github.com/cornellius-gp/gpytorch/issues/1317
-            batch_shape = self.batch_shape + torch.Size([1])
-        else:
-            batch_shape = self.batch_shape
-        for i in (0, 1):
-            p_name = f"concentration{i}"
-            self.register_parameter(
-                p_name,
-                nn.Parameter(torch.full(batch_shape + self.indices.shape, 1.0)),
-            )
-        if concentration0_prior is not None:
-
-            def closure(m):
-                # print(m.concentration0)
-                return m.concentration0
-
-            self.register_prior(
-                "concentration0_prior",
-                concentration0_prior,
-                closure,
-                lambda m, v: m._set_concentration(i=0, value=v),
-            )
-        if concentration1_prior is not None:
-
-            def closure(m):
-                # print(m.concentration1)
-                return m.concentration1
-
-            self.register_prior(
-                "concentration1_prior",
-                concentration1_prior,
-                closure,
-                lambda m, v: m._set_concentration(i=1, value=v),
-            )
-        for i in (0, 1):
-            p_name = f"concentration{i}"
-            constraint = GreaterThan(
-                self._min_concentration_level,
-                transform=None,
-                # set the initial value to be the identity transformation
-                initial_value=1.0,
-            )
-            self.register_constraint(param_name=p_name, constraint=constraint)
-
-    def _set_concentration(self, i: int, value: Union[float, Tensor]) -> None:
-        if not torch.is_tensor(value):
-            value = torch.as_tensor(value).to(self.concentration0)
-        self.initialize(**{f"concentration{i}": value})
-
-    def _transform(self, X: Tensor) -> Tensor:
-        r"""Warp the inputs through the Kumaraswamy CDF.
-
-        Args:
-            X: A `input_batch_shape x (batch_shape) x n x d`-dim tensor of inputs.
-                batch_shape here can either be self.batch_shape or 1's such that
-                it is broadcastable with self.batch_shape if self.batch_shape is set.
-
-        Returns:
-            A `input_batch_shape x (batch_shape) x n x d`-dim tensor of transformed
-                inputs.
-        """
-        X_tf = expand_and_copy_tensor(X=X, batch_shape=self.batch_shape)
-        k = Kumaraswamy(
-            concentration1=self.concentration1, concentration0=self.concentration0
-        )
-        # normalize to [eps, 1-eps]
-        X_tf[..., self.indices] = k.cdf(
-            torch.clamp(
-                X_tf[..., self.indices] * self._X_range + self._X_min,
-                self._X_min,
-                1.0 - self._X_min,
-            )
-        )
-        return X_tf
-
-    def _untransform(self, X: Tensor) -> Tensor:
-        r"""Warp the inputs through the Kumaraswamy inverse CDF.
-
-        Args:
-            X: A `input_batch_shape x batch_shape x n x d`-dim tensor of inputs.
-
-        Returns:
-            A `input_batch_shape x batch_shape x n x d`-dim tensor of transformed
-                inputs.
-        """
-        if len(self.batch_shape) > 0:
-            if self.batch_shape != X.shape[-2 - len(self.batch_shape) : -2]:
-                raise BotorchTensorDimensionError(
-                    "The right most batch dims of X must match self.batch_shape: "
-                    f"({self.batch_shape})."
-                )
-        X_tf = X.clone()
-        k = Kumaraswamy(
-            concentration1=self.concentration1, concentration0=self.concentration0
-        )
-        # unnormalize from [eps, 1-eps] to [0,1]
-        X_tf[..., self.indices] = (
-            (k.icdf(X_tf[..., self.indices]) - self._X_min) / self._X_range
-        ).clamp(0.0, 1.0)
-        return X_tf
-
-    def transform(self, X: Tensor) -> Tensor:
-        r"""Transform the inputs.
-
-        Args:
-            X: A `batch_shape x n x d`-dim tensor of inputs.
-
-        Returns:
-            A `batch_shape x n x d`-dim tensor of transformed inputs.
-        """
-        return self._untransform(X) if self.reverse else self._transform(X)
-
-    def untransform(self, X: Tensor) -> Tensor:
-        r"""Un-transform the inputs.
-
-        Args:
-            X: A `batch_shape x n x d`-dim tensor of inputs.
-
-        Returns:
-            A `batch_shape x n x d`-dim tensor of un-transformed inputs.
-        """
-        return self._transform(X) if self.reverse else self._untransform(X)
-
-    def equals(self, other: InputTransform) -> bool:
-        r"""Check if another input transform is equivalent.
-
-        Note: The reason that a custom equals method is defined rather than
-        defining an __eq__ method is because defining an __eq__ method sets
-        the __hash__ method to None. Hashing modules is currently used in
-        pytorch. See https://github.com/pytorch/pytorch/issues/7733.
-
-        Args:
-            other: Another input transform.
-
-        Returns:
-            A boolean indicating if the other transform is equivalent.
-        """
-        other_state_dict = other.state_dict()
-        return (
-            type(self) == type(other)
-            and (self.transform_on_train == other.transform_on_train)
-            and (self.transform_on_eval == other.transform_on_eval)
-            and (self.transform_on_fantasize == other.transform_on_fantasize)
-            and all(
-                torch.allclose(v, other_state_dict[k].to(v))
-                for k, v in self.state_dict().items()
-            )
-        )
-
-    def preprocess_transform(self, X: Tensor) -> Tensor:
-        r"""Apply transforms for preprocessing inputs.
-
-        The main use cases for this method are 1) to preprocess training data
-        before calling `set_train_data` and 2) preprocess `X_baseline` for noisy
-        acquisition functions so that `X_baseline` is "preprocessed" with the
-        same transformations as the cached training inputs.
-
-        Args:
-            X: A `batch_shape x n x d`-dim tensor of inputs.
-
-        Returns:
-            A `batch_shape x n x d`-dim tensor of (transformed) inputs.
-        """
-        if self.transform_on_train:
-            # We need to disable learning of bounds here.
-            # See why: https://github.com/pytorch/botorch/issues/1078.
-            if hasattr(self, "learn_bounds"):
-                learn_bounds = self.learn_bounds
-                self.learn_bounds = False
-                result = self.transform(X)
-                self.learn_bounds = learn_bounds
-                return result
-            else:
-                return self.transform(X)
-        return X
-
-    def forward(self, X: Tensor) -> Tensor:
-        r"""Transform the inputs to a model.
-
-        Args:
-            X: A `batch_shape x n x d`-dim tensor of inputs.
-
-        Returns:
-            A `batch_shape x n' x d`-dim tensor of transformed inputs.
-        """
-        if self.training:
-            if self.transform_on_train:
-                return self.transform(X)
-        elif self.transform_on_eval:
-            if fantasize.off() or self.transform_on_fantasize:
-                return self.transform(X)
-        return X
-
-
-def constraint_based_on_distribution_support(
-    prior: torch.distributions.Distribution, device, sample_from_path
-):
-    if sample_from_path:
-        return None
-
-    if hasattr(prior.support, "upper_bound"):
-        return gpytorch.constraints.Interval(
-            to_tensor(prior.support.lower_bound, device=device),
-            to_tensor(prior.support.upper_bound, device=device),
-        )
-    else:
-        return gpytorch.constraints.GreaterThan(
-            to_tensor(prior.support.lower_bound, device=device)
-        )
-
-
-loaded_things = {}
-
-
-def torch_load(path):
-    """
-    Cached torch load. Caution: This does not copy the output but keeps pointers.
-    That means, if you modify the output, you modify the output of later calls to this function with the same args.
-    :param path:
-    :return:
-    """
-    if path not in loaded_things:
-        print(f"loading {path}")
-        with open(path, "rb") as f:
-            loaded_things[path] = torch.load(f)
-    return loaded_things[path]
-
-
-def get_model(x, y, hyperparameters: dict, sample=True):
-    sample_from_path = hyperparameters.get("sample_from_extra_prior", None)
-    device = x.device
-    num_features = x.shape[-1]
-    likelihood = gpytorch.likelihoods.GaussianLikelihood(
-        noise_constraint=gpytorch.constraints.Positive()
-    )
-    likelihood.register_prior(
-        "noise_prior",
-        LogNormalPrior(
-            torch.tensor(
-                hyperparameters.get("hebo_noise_logmean", -4.63), device=device
-            ),
-            torch.tensor(hyperparameters.get("hebo_noise_std", 0.5), device=device),
-        ),
-        "noise",
-    )
-    lengthscale_prior = (
-        GammaPrior(
-            torch.tensor(hyperparameters["lengthscale_concentration"], device=device),
-            torch.tensor(hyperparameters["lengthscale_rate"], device=device),
-        )
-        if hyperparameters.get("lengthscale_concentration", None)
-        else UniformPrior(
-            torch.tensor(0.0, device=device), torch.tensor(1.0, device=device)
-        )
-    )
-    covar_module = gpytorch.kernels.MaternKernel(
-        nu=3 / 2,
-        ard_num_dims=num_features,
-        lengthscale_prior=lengthscale_prior,
-        lengthscale_constraint=constraint_based_on_distribution_support(
-            lengthscale_prior, device, sample_from_path
-        ),
-    )
-    # ORIG DIFF: orig lengthscale has no prior
-    # covar_module.register_prior("lengthscale_prior",
-    # UniformPrior(.000000001, 1.),
-    # GammaPrior(concentration=hyperparameters.get('lengthscale_concentration', 1.),
-    #           rate=hyperparameters.get('lengthscale_rate', .1)),
-    # skewness is controllled by concentration only, want somthing like concetration in [0.1,1.], rate around [.05,1] seems reasonable
-    # "lengthscale")
-    outputscale_prior = GammaPrior(
-        concentration=hyperparameters.get("outputscale_concentration", 0.5),
-        rate=hyperparameters.get("outputscale_rate", 1.0),
-    )
-    covar_module = gpytorch.kernels.ScaleKernel(
-        covar_module,
-        outputscale_prior=outputscale_prior,
-        outputscale_constraint=constraint_based_on_distribution_support(
-            outputscale_prior, device, sample_from_path
-        ),
-    )
-
-    if random.random() < float(hyperparameters.get("add_linear_kernel", True)):
-        # ORIG DIFF: added priors for variance and outputscale of linear kernel
-        var_prior = UniformPrior(
-            torch.tensor(0.0, device=device), torch.tensor(1.0, device=device)
-        )
-        out_prior = UniformPrior(
-            torch.tensor(0.0, device=device), torch.tensor(1.0, device=device)
-        )
-        lincovar_module = gpytorch.kernels.ScaleKernel(
-            gpytorch.kernels.LinearKernel(
-                variance_prior=var_prior,
-                variance_constraint=constraint_based_on_distribution_support(
-                    var_prior, device, sample_from_path
-                ),
-            ),
-            outputscale_prior=out_prior,
-            outputscale_constraint=constraint_based_on_distribution_support(
-                out_prior, device, sample_from_path
-            ),
-        )
-        covar_module = covar_module + lincovar_module
-
-    if hyperparameters.get("hebo_warping", False):
-        # initialize input_warping transformation
-        warp_tf = Warp(
-            indices=list(range(num_features)),
-            # use a prior with median at 1.
-            # when a=1 and b=1, the Kumaraswamy CDF is the identity function
-            concentration1_prior=LogNormalPrior(
-                torch.tensor(0.0, device=device),
-                torch.tensor(
-                    hyperparameters.get("hebo_input_warping_c1_std", 0.75),
-                    device=device,
-                ),
-            ),
-            concentration0_prior=LogNormalPrior(
-                torch.tensor(0.0, device=device),
-                torch.tensor(
-                    hyperparameters.get("hebo_input_warping_c0_std", 0.75),
-                    device=device,
-                ),
-            ),
-        )
-    else:
-        warp_tf = None
-    # assume mean 0 always!
-    if len(y.shape) < len(x.shape):
-        y = y.unsqueeze(-1)
-    model = botorch.models.SingleTaskGP(
-        x, y, likelihood=likelihood, covar_module=covar_module, input_transform=warp_tf
-    )
-    model.mean_module = ZeroMean(x.shape[:-2])
-    model.to(device)
-    likelihood.to(device)
-
-    if sample:
-        model = model.pyro_sample_from_prior()
-        if sample_from_path:
-            parameter_sample_distribution = torch_load(
-                sample_from_path
-            )  # dict with entries for each parameter
-            idx_for_len = {}
-            for (
-                parameter_name,
-                parameter_values,
-            ) in parameter_sample_distribution.items():
-                assert len(parameter_values.shape) == 1
-                try:
-                    p = eval(parameter_name)
-                    if len(parameter_values) in idx_for_len:
-                        idx = idx_for_len[len(parameter_values)].view(p.shape)
-                    else:
-                        idx = torch.randint(len(parameter_values), p.shape)
-                        idx_for_len[len(parameter_values)] = idx
-                    new_sample = parameter_values[idx].to(device).view(p.shape)  # noqa
-                    assert new_sample.shape == p.shape
-                    with torch.no_grad():
-                        p.data = new_sample
-                except AttributeError:
-                    utils.print_once(
-                        f"could not find parameter {parameter_name} in model for `sample_from_extra_prior`"
-                    )
-            model.requires_grad_(False)
-            likelihood.requires_grad_(False)
-        return model, model.likelihood
-    else:
-        assert not (hyperparameters.get("sigmoid", False)) and not (
-            hyperparameters.get("y_minmax_norm", False)
-        ), "Sigmoid and y_minmax_norm can only be used to sample models..."
-        return model, likelihood
-
-
-@torch.no_grad()
-def get_batch(
-    batch_size,
-    seq_len,
-    num_features,
-    device=default_device,
-    hyperparameters=None,
-    batch_size_per_gp_sample=None,
-    single_eval_pos=None,
-    fix_to_range=None,
-    equidistant_x=False,
-    verbose=False,
-    **kwargs,
-):
-    """
-    This function is very similar to the equivalent in .fast_gp. The only difference is that this function operates over
-    a mixture of GP priors.
-    :param batch_size:
-    :param seq_len:
-    :param num_features:
-    :param device:
-    :param hyperparameters:
-    :param for_regression:
-    :return:
-    """
-    hyperparameters = hyperparameters or {}
-    with gpytorch.settings.fast_computations(
-        *hyperparameters.get("fast_computations", (True, True, True))
-    ):
-        batch_size_per_gp_sample = batch_size_per_gp_sample or max(batch_size // 4, 1)
-        assert batch_size % batch_size_per_gp_sample == 0
-
-        total_num_candidates = batch_size * (2 ** (fix_to_range is not None))
-        num_candidates = batch_size_per_gp_sample * (2 ** (fix_to_range is not None))
-        unused_feature_likelihood = hyperparameters.get(
-            "unused_feature_likelihood", False
-        )
-        if equidistant_x:
-            assert num_features == 1
-            assert not unused_feature_likelihood
-            x = (
-                torch.linspace(0, 1.0, seq_len)
-                .unsqueeze(0)
-                .repeat(total_num_candidates, 1)
-                .unsqueeze(-1)
-            )
-        else:
-            x = torch.rand(total_num_candidates, seq_len, num_features, device=device)
-        samples = []
-        samples_wo_noise = []
-        for i in range(0, total_num_candidates, num_candidates):
-            local_x = x[i : i + num_candidates]
-            if unused_feature_likelihood:
-                r = torch.rand(num_features)
-                unused_feature_mask = r < unused_feature_likelihood
-                if unused_feature_mask.all():
-                    unused_feature_mask[r.argmin()] = False
-                used_local_x = local_x[..., ~unused_feature_mask]
-            else:
-                used_local_x = local_x
-            get_model_and_likelihood = lambda: get_model(
-                used_local_x,
-                torch.zeros(num_candidates, x.shape[1], device=device),
-                hyperparameters,
-            )
-            model, likelihood = get_model_and_likelihood()
-            if verbose:
-                print(
-                    list(model.named_parameters()),
-                    (
-                        (
-                            list(model.input_transform.named_parameters()),
-                            model.input_transform.concentration1,
-                            model.input_transform.concentration0,
-                        )
-                        if model.input_transform is not None
-                        else None
-                    ),
-                )
-
-            # trained_model = ExactGPModel(train_x, train_y, likelihood).cuda()
-            # trained_model.eval()
-            successful_sample = 0
-            throwaway_share = 0.0
-            while successful_sample < 1:
-                with gpytorch.settings.prior_mode(True):
-                    # print(x.device, device, f'{model.covar_module.base_kernel.lengthscale=}, {model.covar_module.base_kernel.lengthscale.device=}')
-                    d = model(used_local_x)
-                    try:
-                        with warnings.catch_warnings():
-                            warnings.simplefilter("ignore")
-                            sample_wo_noise = d.sample()
-                            d = likelihood(sample_wo_noise)
-                    except (RuntimeError, ValueError) as e:
-                        successful_sample -= 1
-                        model, likelihood = get_model_and_likelihood()
-                        if successful_sample < -100:
-                            print(
-                                f"Could not sample from model {i} after {successful_sample} attempts. {e}"
-                            )
-                            raise e
-                        continue
-                    sample = d.sample()  # bs_per_gp_s x T
-                    if fix_to_range is None:
-                        # for k, v in model.named_parameters(): print(k,v)
-                        samples.append(sample.transpose(0, 1))
-                        samples_wo_noise.append(sample_wo_noise.transpose(0, 1))
-                        break
-                    smaller_mask = sample < fix_to_range[0]
-                    larger_mask = sample >= fix_to_range[1]
-                    in_range_mask = ~(smaller_mask | larger_mask).any(1)
-                    throwaway_share += (
-                        ~in_range_mask[:batch_size_per_gp_sample]
-                    ).sum() / batch_size_per_gp_sample
-                    if in_range_mask.sum() < batch_size_per_gp_sample:
-                        successful_sample -= 1
-                        if successful_sample < 100:
-                            print(
-                                "Please change hyper-parameters (e.g. decrease outputscale_mean) it"
-                                "seems like the range is set to tight for your hyper-parameters."
-                            )
-                        continue
-
-                    x[i : i + batch_size_per_gp_sample] = local_x[in_range_mask][
-                        :batch_size_per_gp_sample
-                    ]
-                    sample = sample[in_range_mask][:batch_size_per_gp_sample]
-                    samples.append(sample.transpose(0, 1))
-                    samples_wo_noise.append(sample_wo_noise.transpose(0, 1))
-                    successful_sample = True
-
-        if random.random() < 0.01:
-            print(
-                "throwaway share",
-                throwaway_share / (batch_size // batch_size_per_gp_sample),
-            )
-
-        # print(f'took {time.time() - start}')
-        sample = torch.cat(samples, 1)[..., None]
-        sample_wo_noise = torch.cat(samples_wo_noise, 1)[..., None]
-        x = x.view(-1, batch_size, seq_len, num_features)[0]
-        # TODO think about enabling the line below
-        # sample = sample - sample[0, :].unsqueeze(0).expand(*sample.shape)
-        x = x.transpose(0, 1)
-        assert x.shape[:2] == sample.shape[:2]
-    return Batch(
-        x=x,
-        y=sample,
-        target_y=(
-            sample
-            if hyperparameters.get("observation_noise", True)
-            else sample_wo_noise
-        ),
-    )
-
-
-DataLoader = get_batch_to_dataloader(get_batch)
diff --git a/private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py b/private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py
deleted file mode 100644
index f4d9b02..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/hyperparameter_sampling.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# this is a wrapper prior that samples hyperparameters which are set to be ConfigSpace parameters
-# from fblearner.flow.projects.ae.benchmarks.pfn.ConfigSpace import hyperparameters as CSH
-# import fblearner.flow.projects.ae.benchmarks.pfn.ConfigSpace as CS
-from copy import deepcopy
-
-import torch
-
-from .prior import Batch
-
-
-def list_all_hps_in_nested(config):
-    if isinstance(config, CSH.Hyperparameter):
-        return [config]
-    elif isinstance(config, dict):
-        result = []
-        for k, v in config.items():
-            result += list_all_hps_in_nested(v)
-        return result
-    else:
-        return []
-
-
-def create_configspace_from_hierarchical(config):
-    cs = CS.ConfigurationSpace()
-    for hp in list_all_hps_in_nested(config):
-        cs.add_hyperparameter(hp)
-    return cs
-
-
-def fill_in_configsample(config, configsample):
-    # config is our dict that defines config distribution
-    # configsample is a CS.Configuration
-    hierarchical_configsample = deepcopy(config)
-    for k, v in config.items():
-        if isinstance(v, CSH.Hyperparameter):
-            hierarchical_configsample[k] = configsample[v.name]
-        elif isinstance(v, dict):
-            hierarchical_configsample[k] = fill_in_configsample(v, configsample)
-    return hierarchical_configsample
-
-
-def sample_configspace_hyperparameters(hyperparameters):
-    cs = create_configspace_from_hierarchical(hyperparameters)
-    cs_sample = cs.sample_configuration()
-    return fill_in_configsample(hyperparameters, cs_sample)
-
-
-def get_batch_old(batch_size, *args, hyperparameters, get_batch, **kwargs):
-    num_models = min(
-        hyperparameters.get("num_hyperparameter_samples_per_batch", 1), batch_size
-    )
-    if num_models == -1:
-        num_models = batch_size
-    assert batch_size % num_models == 0, "batch_size must be a multiple of num_models"
-    cs = create_configspace_from_hierarchical(hyperparameters)
-    sub_batches = []
-    for i in range(num_models):
-        cs_sample = cs.sample_configuration()
-        hyperparameters_sample = fill_in_configsample(hyperparameters, cs_sample)
-        sub_batch = get_batch(
-            batch_size // num_models,
-            *args,
-            hyperparameters=hyperparameters_sample,
-            **kwargs
-        )
-        sub_batches.append(sub_batch)
-
-    # concat x, y, target (and maybe style)
-    # assert 3 <= len(sub_batch) <= 4
-    # return tuple(torch.cat([sb[i] for sb in sub_batches], dim=(0 if i == 3 else 1)) for i in range(len(sub_batch)))
-    assert all(
-        not b.other_filled_attributes(set_of_attributes=("x", "y", "target_y"))
-        for b in sub_batches
-    )
-    return Batch(
-        x=torch.cat([b.x for b in sub_batches], dim=1),
-        y=torch.cat([b.y for b in sub_batches], dim=1),
-        target_y=torch.cat([b.target_y for b in sub_batches], dim=1),
-    )
-
-
-def get_batch(batch_size, *args, hyperparameters, get_batch, **kwargs):
-    num_models = min(
-        hyperparameters.get("num_hyperparameter_samples_per_batch", 1), batch_size
-    )
-    if num_models == -1:
-        num_models = batch_size
-    assert batch_size % num_models == 0, "batch_size must be a multiple of num_models"
-    cs = create_configspace_from_hierarchical(hyperparameters)
-    sub_batches = []
-    for i in range(num_models):
-        cs_sample = cs.sample_configuration()
-        hyperparameters_sample = fill_in_configsample(hyperparameters, cs_sample)
-        sub_batch = get_batch(
-            batch_size // num_models,
-            *args,
-            hyperparameters=hyperparameters_sample,
-            **kwargs
-        )
-        sub_batches.append(sub_batch)
-
-    # concat x, y, target (and maybe style)
-    # assert 3 <= len(sub_batch) <= 4
-    # return tuple(torch.cat([sb[i] for sb in sub_batches], dim=(0 if i == 3 else 1)) for i in range(len(sub_batch)))
-    assert all(
-        not b.other_filled_attributes(set_of_attributes=("x", "y", "target_y"))
-        for b in sub_batches
-    )
-    return Batch(
-        x=torch.cat([b.x for b in sub_batches], dim=1),
-        y=torch.cat([b.y for b in sub_batches], dim=1),
-        target_y=torch.cat([b.target_y for b in sub_batches], dim=1),
-    )
diff --git a/private_multitask_pfn/PFNs/pfns/priors/input_warping.py b/private_multitask_pfn/PFNs/pfns/priors/input_warping.py
deleted file mode 100644
index c99e8f9..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/input_warping.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-from torch.distributions import Kumaraswamy
-
-
-def exp_in_prev_range(x,factor):
-    mini, maxi = x.min(0)[0], x.max(0)[0]
-    expx = (factor*x).exp()
-    expx_01 = (expx - expx.min(0)[0]) / (expx.max(0)[0] - expx.min(0)[0])
-    return expx_01 * (maxi - mini) + mini
-
-
-@torch.no_grad()
-def get_batch(*args, hyperparameters, get_batch, **kwargs):
-    """
-    This `get_batch` can be used to wrap another `get_batch` and apply a Kumaraswamy transform to the input.
-    The x's have to be in [0,1] for this to work!
-    """
-    returns = get_batch(*args, hyperparameters=hyperparameters, **kwargs)
-
-    input_warping_type = hyperparameters.get('input_warping_type', 'kumar')
-    # controls what part of the batch ('x', 'y' or 'xy') to apply the warping to
-    input_warping_groups = hyperparameters.get('input_warping_groups', 'x')
-    # whether to norm inputs between 0 and 1 before warping, as warping is only possible in that range.
-    input_warping_norm = hyperparameters.get('input_warping_norm', False)
-    use_icdf = hyperparameters.get('input_warping_use_icdf', False)
-
-    def norm_to_0_1(x):
-        eps = .00001
-        maxima = torch.max(x, 0)[0]
-        minima = torch.min(x, 0)[0]
-        normed_x = (x - minima) / (maxima - minima + eps)
-
-        def denorm(normed_x):
-            return normed_x * (maxima - minima + eps) + minima
-
-        return normed_x, denorm
-
-    def warp_input(x):
-        if input_warping_norm:
-            x, denorm = norm_to_0_1(x)
-
-        if input_warping_type == 'kumar':
-            if 'input_warping_c_std' in hyperparameters:
-                assert 'input_warping_c0_std' not in hyperparameters and 'input_warping_c1_std' not in hyperparameters
-                hyperparameters['input_warping_c0_std'] = hyperparameters['input_warping_c_std']
-                hyperparameters['input_warping_c1_std'] = hyperparameters['input_warping_c_std']
-            inside = 0
-            while not inside:
-                c1 = (torch.randn(*x.shape[1:], device=x.device) * hyperparameters.get('input_warping_c1_std', .75)).exp()
-                c0 = (torch.randn(*x.shape[1:], device=x.device) * hyperparameters.get('input_warping_c0_std', .75)).exp()
-                if not hyperparameters.get('input_warping_in_range', False):
-                    inside = True
-                elif (c1 < 10).all() and (c1 > 0).all() and (c0 < 10).all() and (c0 > 0).all():
-                    inside = True
-                else:
-                    inside -= 1
-                    if inside < -100:
-                        print('It seems that the input warping is not working.')
-            if c1_v := hyperparameters.get('fix_input_warping_c1', False):
-                c1[:] = c1_v
-            if c0_v := hyperparameters.get('fix_input_warping_c0', False):
-                c0[:] = c0_v
-            if hyperparameters.get('verbose', False):
-                print(f'c1: {c1}, c0: {c0}')
-            k = Kumaraswamy(concentration1=c1, concentration0=c0)
-            x_transformed = k.icdf(x) if use_icdf else k.cdf(x)
-        elif input_warping_type == 'exp':
-            transform_likelihood = hyperparameters.get('input_warping_transform_likelihood', 0.2)
-            to_be_transformed = torch.rand_like(x[0,0]) < transform_likelihood
-            transform_factors = torch.rand_like(x[0,0]) * hyperparameters.get('input_warping_transform_factor', 1.)
-            log_direction = torch.rand_like(x[0,0]) < 0.5
-            exp_x = exp_in_prev_range(x, transform_factors)
-            minus_exp_x = 1.-exp_in_prev_range(1.-x, transform_factors)
-            exp_x = torch.where(log_direction, exp_x, minus_exp_x)
-            x_transformed = torch.where(to_be_transformed[None,None,:], exp_x, x)
-        elif input_warping_type is None or input_warping_type == 'none':
-            x_transformed = x
-        else:
-            raise ValueError(f"Unknown input_warping_type: {input_warping_type}")
-
-        if input_warping_norm:
-            x_transformed = denorm(x_transformed)
-        return x_transformed
-
-    if 'x' in input_warping_groups:
-        returns.x = warp_input(returns.x)
-    if 'y' in input_warping_groups:
-        returns.y = warp_input(returns.y)
-        returns.target_y = warp_input(returns.target_y)
-
-    return returns
-
-
diff --git a/private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py b/private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py
deleted file mode 100644
index 4ac8e44..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/nonmyopic_bo.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import torch
-
-from .prior import Batch
-from ..utils import default_device
-
-
-loaded_models = {}
-
-def get_model(model_name, device):
-    if model_name not in loaded_models:
-        import submitit
-        group, index = model_name.split(':')
-        ex = submitit.get_executor()
-        model = ex.get_group(group)[int(index)].results()[0][2]
-        model.to(device)
-        loaded_models[model_name] = model
-    return loaded_models[model_name]
-
-@torch.no_grad()
-def get_batch(batch_size, seq_len, num_features, get_batch, model, single_eval_pos, epoch, device=default_device, hyperparameters=None, **kwargs):
-    """
-    Important Assumptions:
-        'inf_batch_size', 'max_level', 'sample_only_one_level', 'eval_seq_len' and 'epochs_per_level' in hyperparameters
-
-    You can train a new model, based on an old one to only sample from a single level.
-    You specify `level_0_model` as a group:index string and the model will be loaded from the checkpoint.
-
-
-
-    :param batch_size:
-    :param seq_len:
-    :param num_features:
-    :param get_batch:
-    :param model:
-    :param single_eval_pos:
-    :param epoch:
-    :param device:
-    :param hyperparameters:
-    :param kwargs:
-    :return:
-    """
-    if level_0_model := hyperparameters.get('level_0_model', None):
-        assert hyperparameters['sample_only_one_level'], "level_0_model only makes sense if you sample only one level"
-        assert hyperparameters['max_level'] == 1, "level_0_model only makes sense if you sample only one level"
-        level_0_model = get_model(level_0_model, device)
-        model = level_0_model
-
-    # the level describes how many fantasized steps are possible. This starts at 0 for the first epochs.
-    epochs_per_level = hyperparameters['epochs_per_level']
-    share_predict_mean_distribution = hyperparameters.get('share_predict_mean_distribution', 0.)
-    use_mean_prediction = share_predict_mean_distribution or\
-                          (model.decoder_dict_once is not None and 'mean_prediction' in model.decoder_dict_once)
-    num_evals = seq_len - single_eval_pos
-    level = min(min(epoch // epochs_per_level, hyperparameters['max_level']), num_evals - 1)
-    if level_0_model:
-        level = 1
-    eval_seq_len = hyperparameters['eval_seq_len']
-    add_seq_len = 0 if use_mean_prediction else eval_seq_len
-    long_seq_len = seq_len + add_seq_len
-
-    if level_0_model:
-        styles = torch.ones(batch_size, 1, device=device, dtype=torch.long)
-    elif hyperparameters['sample_only_one_level']:
-        styles = torch.randint(level + 1, (1, 1), device=device).repeat(batch_size, 1)  # styles are sorted :)
-    else:
-        styles = torch.randint(level + 1, (batch_size,1), device=device).sort(0).values # styles are sorted :)
-
-    predict_mean_distribution = None
-    if share_predict_mean_distribution:
-        max_used_level = max(styles)
-        # below code assumes epochs are base 0!
-        share_of_training = epoch / epochs_per_level
-        #print(share_of_training, (max_used_level + 1. - share_predict_mean_distribution), max_used_level, level, epoch)
-        predict_mean_distribution = (share_of_training >= (max_used_level + 1. - share_predict_mean_distribution)) and (max_used_level < hyperparameters['max_level'])
-
-    x, y, targets = [], [], []
-
-    for considered_level in range(level+1):
-        num_elements = (styles == considered_level).sum()
-        if not num_elements:
-            continue
-        returns: Batch = get_batch(batch_size=num_elements, seq_len=long_seq_len,
-                                   num_features=num_features, device=device,
-                                   hyperparameters=hyperparameters, model=model,
-                                   single_eval_pos=single_eval_pos, epoch=epoch,
-                                   **kwargs)
-        levels_x, levels_y, levels_targets = returns.x, returns.y, returns.target_y
-        assert not returns.other_filled_attributes(), f"Unexpected filled attributes: {returns.other_filled_attributes()}"
-
-        assert levels_y is levels_targets
-        levels_targets = levels_targets.clone()
-        if len(levels_y.shape) == 2:
-            levels_y = levels_y.unsqueeze(2)
-            levels_targets = levels_targets.unsqueeze(2)
-        if considered_level > 0:
-
-            feed_x = levels_x[:single_eval_pos + 1 + add_seq_len].repeat(1, num_evals, 1)
-            feed_x[single_eval_pos, :] = levels_x[single_eval_pos:seq_len].reshape(-1, *levels_x.shape[2:])
-            if not use_mean_prediction:
-                feed_x[single_eval_pos + 1:] = levels_x[seq_len:].repeat(1, num_evals, 1)
-
-            feed_y = levels_y[:single_eval_pos + 1 + add_seq_len].repeat(1, num_evals, 1)
-            feed_y[single_eval_pos, :] = levels_y[single_eval_pos:seq_len].reshape(-1, *levels_y.shape[2:])
-            if not use_mean_prediction:
-                feed_y[single_eval_pos + 1:] = levels_y[seq_len:].repeat(1, num_evals, 1)
-
-            model.eval()
-            means = []
-            for feed_x_b, feed_y_b in zip(torch.split(feed_x, hyperparameters['inf_batch_size'], dim=1),
-                                          torch.split(feed_y, hyperparameters['inf_batch_size'], dim=1)):
-                with torch.cuda.amp.autocast():
-                    style = torch.zeros(feed_x_b.shape[1], 1, dtype=torch.int64, device=device) + considered_level - 1
-                    if level_0_model is not None and level_0_model.style_encoder is None:
-                        style = None
-                    out = model(
-                        (style, feed_x_b, feed_y_b),
-                        single_eval_pos=single_eval_pos+1, only_return_standard_out=False
-                    )
-                    if isinstance(out, tuple):
-                        output, once_output = out
-                    else:
-                        output = out
-                        once_output = {}
-
-                if once_output and 'mean_prediction' in once_output:
-                    mean_pred_logits = once_output['mean_prediction'].float()
-                    assert tuple(mean_pred_logits.shape) == (feed_x_b.shape[1], model.criterion.num_bars),\
-                        f"{tuple(mean_pred_logits.shape)} vs {(feed_x_b.shape[1], model.criterion.num_bars)}"
-                    means.append(model.criterion.icdf(mean_pred_logits, 1.-1./eval_seq_len))
-                else:
-                    logits = output['standard'].float()
-                    means.append(model.criterion.mean(logits).max(0).values)
-            means = torch.cat(means, 0)
-            levels_targets_new = means.view(seq_len-single_eval_pos, *levels_y.shape[1:])
-            levels_targets[single_eval_pos:seq_len] = levels_targets_new #- levels_targets_new.mean(0)
-            model.train()
-
-        levels_x = levels_x[:seq_len]
-        levels_y = levels_y[:seq_len]
-        levels_targets = levels_targets[:seq_len]
-
-        x.append(levels_x)
-        y.append(levels_y)
-        targets.append(levels_targets)
-
-    x = torch.cat(x, 1)
-    # if predict_mean_distribution: print(f'predict mean dist in b, {epoch=}, {max_used_level=}')
-    return Batch(x=x, y=torch.cat(y, 1), target_y=torch.cat(targets, 1), style=styles,
-                 mean_prediction=predict_mean_distribution.item() if predict_mean_distribution is not None else None)
-
-
-
-
-
-
-
-
-
-
diff --git a/private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py b/private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py
deleted file mode 100644
index d1bc349..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/normalize_with_style.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch
-
-from ..utils import default_device
-
-
-@torch.no_grad()
-def get_batch(batch_size, seq_len, num_features, get_batch, model, single_eval_pos, epoch, device=default_device, hyperparameters={}, **kwargs):
-    if hyperparameters.get('normalize_x', False):
-        uniform_float = torch.rand(tuple()).clamp(.1,1.).item()
-        new_hyperparameters = {**hyperparameters, 'sampling': uniform_float * hyperparameters['sampling']}
-    else:
-        new_hyperparameters = hyperparameters
-    returns = get_batch(batch_size=batch_size, seq_len=seq_len,
-                              num_features=num_features, device=device,
-                              hyperparameters=new_hyperparameters, model=model,
-                              single_eval_pos=single_eval_pos, epoch=epoch, **kwargs)
-
-    style = []
-
-    if normalize_x_mode := hyperparameters.get('normalize_x', False):
-        returns.x, mean_style, std_style = normalize_data_by_first_k(returns.x, single_eval_pos if normalize_x_mode == 'train' else len(returns.x))
-        if hyperparameters.get('style_includes_mean_from_normalization', True) or normalize_x_mode == 'train':
-             style.append(mean_style)
-        style.append(std_style)
-
-    if hyperparameters.get('normalize_y', False):
-        returns.y, mean_style, std_style = normalize_data_by_first_k(returns.y, single_eval_pos)
-        style += [mean_style, std_style]
-
-    returns.style = torch.cat(style,1) if style else None
-    return returns
-
-
-def normalize_data_by_first_k(x, k):
-    # x has shape seq_len, batch_size, num_features or seq_len, num_features
-    # k is the number of elements to normalize by
-    unsqueezed_x = False
-    if len(x.shape) == 2:
-        x.unsqueeze_(2)
-        unsqueezed_x = True
-
-    if k > 1:
-        relevant_x = x[:k]
-        mean_style = relevant_x.mean(0)
-        std_style = relevant_x.std(0)
-        x = (x - relevant_x.mean(0, keepdim=True)) / relevant_x.std(0, keepdim=True)
-    elif k == 1:
-        mean_style = x[0]
-        std_style = torch.ones_like(x[0])
-        x = (x - x[0])
-    else: # it is 0
-        mean_style = torch.zeros_like(x[0])
-        std_style = torch.ones_like(x[0])
-
-    if unsqueezed_x:
-        x.squeeze_(2)
-
-    return x, mean_style, std_style
-
diff --git a/private_multitask_pfn/PFNs/pfns/priors/prior.py b/private_multitask_pfn/PFNs/pfns/priors/prior.py
deleted file mode 100644
index 2fbc123..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/prior.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from abc import ABCMeta, abstractmethod
-from typing import Set, Optional
-from dataclasses import dataclass, fields
-import torch
-from torch.utils.data import DataLoader
-
-@dataclass
-class Batch:
-    """
-    A batch of data, with non-optional x, y, and target_y attributes.
-    All other attributes are optional.
-
-    If you want to add an attribute for testing only, you can just assign it after creation like:
-    ```
-        batch = Batch(x=x, y=y, target_y=target_y)
-        batch.test_attribute = test_attribute
-    ```
-    """
-    # Required entries
-    x: torch.Tensor
-    y: torch.Tensor
-    target_y: torch.Tensor
-    task_id: Optional[torch.Tensor] = None
-
-    # Optional Batch Entries
-    style: Optional[torch.Tensor] = None
-    style_hyperparameter_values: Optional[torch.Tensor] = None
-    single_eval_pos: Optional[torch.Tensor] = None
-    causal_model_dag: Optional[object] = None
-    mean_prediction: Optional[bool] = None  # this controls whether to do mean prediction in bar_distribution for nonmyopic BO
-
-    def other_filled_attributes(self, set_of_attributes: Set[str] = frozenset(('x', 'y', 'target_y'))):
-        return [f.name for f in fields(self)
-                if f.name not in set_of_attributes and
-                getattr(self, f.name) is not None]
-
-
-def safe_merge_batches_in_batch_dim(*batches, ignore_attributes=[]):
-    """
-    Merge all supported non-None fields in a pre-specified (general) way,
-    e.g. mutliple batch.x are concatenated in the batch dimension.
-    :param ignore_attributes: attributes to remove from the merged batch, treated as if they were None.
-    :return:
-    """
-    not_none_fields = [f.name for f in fields(batches[0]) if f.name not in ignore_attributes and getattr(batches[0], f.name) is not None]
-    assert all([set(not_none_fields) == set([f.name for f in fields(b) if f.name not in ignore_attributes and getattr(b, f.name) is not None]) for b in batches]), 'All batches must have the same fields!'
-    merge_funcs = {
-        'x': lambda xs: torch.cat(xs, 1),
-        'y': lambda ys: torch.cat(ys, 1),
-        'target_y': lambda target_ys: torch.cat(target_ys, 1),
-        'style': lambda styles: torch.cat(styles, 0),
-    }
-    assert all(f in merge_funcs for f in not_none_fields), f'Unknown fields encountered in `safe_merge_batches_in_batch_dim`.'
-    return Batch(**{f: merge_funcs[f]([getattr(batch, f) for batch in batches]) for f in not_none_fields})
-
-
-def merge_batches(*batches, ignore_attributes=[]):
-    assert False, "TODO: isn't this broken!? because catting in dim 0 seems wrong!?"
-    def merge_attribute(attr_name, batch_sizes):
-        attr = [getattr(batch, attr_name) for batch in batches]
-        if type(attr[0]) is list:
-            def make_list(sublist, i):
-                if sublist is None:
-                    return [None for _ in range(batch_sizes[i])]
-                return sublist
-            return sum([make_list(sublist, i) for i, sublist in enumerate(attr)], [])
-        elif type(attr[0]) is torch.Tensor:
-            return torch.cat(attr, 0)
-        else:
-            assert all(a is None for a in attr), f'Unknown type encountered in `merge_batches`.'\
-                                                 f'To ignore this, please add `{attr}` to the `ignore_attributes`.'\
-                                                 f'The following values are the problem: {attr_name}.'
-            return None
-    batch_sizes = [batch.x.shape[0] for batch in batches]
-    return Batch(**{f.name: merge_attribute(f.name, batch_sizes) for f in fields(batches[0]) if f.name not in ignore_attributes})
-
-
-
-class PriorDataLoader(DataLoader, metaclass=ABCMeta):
-    @abstractmethod
-    def __init__(self, num_steps, batch_size, eval_pos_seq_len_sampler, seq_len_maximum, device, **kwargs):
-        """
-
-        :param num_steps: int, first argument, the number of steps to take per epoch, i.e. iteration of the DataLoader
-        :param batch_size: int, number of datasets per batch
-        :param eval_pos_seq_len_sampler: callable, it takes no arguments and returns a tuple (single eval pos, bptt)
-        :param kwargs: for future compatibility it is good to have a final all catch, as new kwargs might be introduced
-        """
-        pass
-
-    # A class or object variable `num_features`: int
-    # Optional: `validate` function that accepts a transformer model
-
-    # The DataLoader iter should return batches of the form ([style], x, y), target_y, single_eval_pos
-    # We follow sequence len (s) first, batch size (b) second. So x: (s,b,num_features), y,target_y: (s,b)
-    # and style: Optional[(b,num_style_params)], style can be omitted or set to None, if it is not intended to be used.
-
-    # For more references, see `priors/utils.py` for a pretty general implementation of a DataLoader
-    # and `train.py` for the only call of it.
diff --git a/private_multitask_pfn/PFNs/pfns/priors/prior_bag.py b/private_multitask_pfn/PFNs/pfns/priors/prior_bag.py
deleted file mode 100644
index 6d954b6..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/prior_bag.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from typing import List
-import torch
-
-import utils
-from .prior import Batch
-from .utils import get_batch_to_dataloader
-from ..utils import default_device
-
-def get_batch(batch_size, seq_len, num_features, device=default_device
-              , hyperparameters=None, batch_size_per_gp_sample=None, **kwargs):
-    batch_size_per_gp_sample = batch_size_per_gp_sample or (min(64, batch_size))
-    num_models = batch_size // batch_size_per_gp_sample
-    assert num_models * batch_size_per_gp_sample == batch_size, f'Batch size ({batch_size}) not divisible by batch_size_per_gp_sample ({batch_size_per_gp_sample})'
-
-    args = {'device': device,
-            'seq_len': seq_len,
-            'num_features': num_features,
-            'batch_size': batch_size_per_gp_sample}
-
-    prior_bag_priors_get_batch = hyperparameters['prior_bag_get_batch']
-    prior_bag_priors_p = [1.0] + [hyperparameters[f'prior_bag_exp_weights_{i}'] for i in range(1, len(prior_bag_priors_get_batch))]
-
-    weights = torch.tensor(prior_bag_priors_p, dtype=torch.float)  # create a tensor of weights
-    batch_assignments = torch.multinomial(torch.softmax(weights, 0), num_models, replacement=True).numpy()
-
-    if 'verbose' in hyperparameters and hyperparameters['verbose']:
-        print('PRIOR_BAG:', weights, batch_assignments, num_models, batch_size_per_gp_sample, batch_size)
-
-    sample: List[Batch] = \
-        [prior_bag_priors_get_batch[int(prior_idx)](hyperparameters=hyperparameters, **args, **kwargs) for prior_idx in batch_assignments]
-
-    def merge(sample, k):
-        x = [getattr(x_,k) for x_ in sample]
-        if torch.is_tensor(x[0]):
-            return torch.cat(x, 1).detach()
-        else:
-            return [*x]
-    utils.print_once('prior bag, merging attributes', [s.other_filled_attributes([]) for s in sample])
-    sample = {k: merge(sample, k) for k in sample[0].other_filled_attributes([])}
-    if hyperparameters.get('verbose'):
-        print({k: v.shape for k,v in sample.items()})
-
-    return Batch(**sample)
-
-DataLoader = get_batch_to_dataloader(get_batch)
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/priors/simple_mlp.py b/private_multitask_pfn/PFNs/pfns/priors/simple_mlp.py
deleted file mode 100644
index 0cd4d55..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/simple_mlp.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import random
-import math
-
-import torch
-from torch import nn
-import time
-import numpy as np
-
-from ..utils import default_device
-from .prior import Batch
-from .utils import get_batch_to_dataloader
-
-
-class MLP(torch.nn.Module):
-    def __init__(self, num_inputs, num_layers, num_hidden, num_outputs, init_std=None, sparseness=0.0,
-                 preactivation_noise_std=0.0, activation='tanh'):
-        super(MLP, self).__init__()
-        self.linears = nn.ModuleList(
-            [nn.Linear(num_inputs, num_hidden)] + \
-            [nn.Linear(num_hidden,num_hidden) for _ in range(num_layers-2)] + \
-            [nn.Linear(num_hidden,num_outputs)]
-        )
-
-        self.init_std = init_std
-        self.sparseness = sparseness
-        self.reset_parameters()
-
-        self.preactivation_noise_std = preactivation_noise_std
-        self.activation = {
-            'tanh': torch.nn.Tanh(),
-            'relu': torch.nn.ReLU(),
-            'elu': torch.nn.ELU(),
-            'identity': torch.nn.Identity(),
-        }[activation]
-
-    def reset_parameters(self, init_std=None, sparseness=None):
-        init_std = init_std if init_std is not None else self.init_std
-        sparseness = sparseness if sparseness is not None else self.sparseness
-        for linear in self.linears:
-            linear.reset_parameters()
-
-        with torch.no_grad():
-            if init_std is not None:
-                for linear in self.linears:
-                    linear.weight.normal_(0, init_std)
-                    linear.bias.normal_(0, init_std)
-
-            if sparseness > 0.0:
-                for linear in self.linears[1:-1]:
-                    linear.weight /= (1. - sparseness) ** (1 / 2)
-                    linear.weight *= torch.bernoulli(torch.ones_like(linear.weight) * (1. - sparseness))
-
-    def forward(self, x):
-        for linear in self.linears[:-1]:
-            x = linear(x)
-            x = x + torch.randn_like(x) * self.preactivation_noise_std
-            x = self.activation(x)
-        x = self.linears[-1](x)
-        return x
-
-
-def sample_input(input_sampling_setting, batch_size, seq_len, num_features, device=default_device):
-    if input_sampling_setting == 'normal':
-        x = torch.randn(batch_size, seq_len, num_features, device=device)
-        x_for_mlp = x
-    elif input_sampling_setting == 'uniform':
-        x = torch.rand(batch_size, seq_len, num_features, device=device)
-        x_for_mlp = (x - .5)/math.sqrt(1/12)
-    else:
-        raise ValueError(f"Unknown input_sampling: {input_sampling_setting}")
-    return x, x_for_mlp
-
-
-@torch.no_grad()
-def get_batch(batch_size, seq_len, num_features, hyperparameters, device=default_device, num_outputs=1, **kwargs):
-    if hyperparameters is None:
-        hyperparameters = {
-            'mlp_num_layers': 2,
-            'mlp_num_hidden': 64,
-            'mlp_init_std': 0.1,
-            'mlp_sparseness': 0.2,
-            'mlp_input_sampling': 'normal',
-            'mlp_output_noise': 0.0,
-            'mlp_noisy_targets': False,
-            'mlp_preactivation_noise_std': 0.0,
-        }
-
-    x, x_for_mlp = sample_input(hyperparameters.get('mlp_input_sampling', 'normal'), batch_size, seq_len, num_features,
-                     device=device)
-
-    model = MLP(num_features, hyperparameters['mlp_num_layers'], hyperparameters['mlp_num_hidden'],
-                num_outputs, hyperparameters['mlp_init_std'], hyperparameters['mlp_sparseness'],
-                hyperparameters['mlp_preactivation_noise_std'], hyperparameters.get('activation', 'tanh')).to(device)
-
-    no_noise_model = MLP(num_features, hyperparameters['mlp_num_layers'], hyperparameters['mlp_num_hidden'],
-                num_outputs, hyperparameters['mlp_init_std'], hyperparameters['mlp_sparseness'],
-                0., hyperparameters.get('activation', 'tanh')).to(device)
-
-    ys = []
-    targets = []
-    for x_ in x_for_mlp:
-        model.reset_parameters()
-        y = model(x_ / math.sqrt(num_features))
-        ys.append(y.unsqueeze(1))
-        if not hyperparameters.get('mlp_preactivation_noise_in_targets', True):
-            assert not hyperparameters['mlp_noisy_targets']
-            no_noise_model.load_state_dict(model.state_dict())
-            target = no_noise_model(x_ / math.sqrt(num_features))
-            targets.append(target.unsqueeze(1))
-
-    y = torch.cat(ys, dim=1)
-    targets = torch.cat(targets, dim=1) if targets else y
-
-    noisy_y = y + torch.randn_like(y) * hyperparameters['mlp_output_noise']
-
-    #return x.transpose(0, 1), noisy_y, (noisy_y if hyperparameters['mlp_noisy_targets'] else targets)
-    return Batch(x.transpose(0, 1), noisy_y, (noisy_y if hyperparameters['mlp_noisy_targets'] else targets))
-
-DataLoader = get_batch_to_dataloader(get_batch)
-
-
-
-
-
-
-
diff --git a/private_multitask_pfn/PFNs/pfns/priors/utils.py b/private_multitask_pfn/PFNs/pfns/priors/utils.py
deleted file mode 100644
index 0504042..0000000
--- a/private_multitask_pfn/PFNs/pfns/priors/utils.py
+++ /dev/null
@@ -1,438 +0,0 @@
-import inspect
-import math
-import random
-import time
-import types
-from functools import partial
-
-import matplotlib.gridspec as gridspec
-import matplotlib.pyplot as plt
-import numpy as np
-import scipy.stats as stats
-
-import torch
-from torch import nn
-
-from ..utils import normalize_data, set_locals_in_self
-from .prior import Batch, PriorDataLoader
-
-
-def get_batch_to_dataloader(get_batch_method_):
-    # DL = partial(DL, get_batch_method=get_batch_method_)
-    class DL(PriorDataLoader):
-        get_batch_method = get_batch_method_
-
-        # Caution, you might need to set self.num_features manually if it is not part of the args.
-        def __init__(self, num_steps, **get_batch_kwargs):
-            set_locals_in_self(locals())
-
-            # The stuff outside the or is set as class attribute before instantiation.
-            self.num_features = (
-                get_batch_kwargs.get("num_features") or self.num_features
-            )
-            self.num_tasks = get_batch_kwargs.get("hyperparameters")["num_tasks"]
-            self.epoch_count = 0
-            # print("DataLoader.__dict__", self.__dict__)
-
-        @staticmethod
-        def gbm(*args, eval_pos_seq_len_sampler, **kwargs):
-            epoch = kwargs["epoch"]
-            model = kwargs["model"]
-            kwargs["single_eval_pos"], kwargs["seq_len"] = eval_pos_seq_len_sampler(epoch, model)
-            
-            batch: Batch = get_batch_method_(*args, **kwargs)
-            if batch.single_eval_pos is None:
-                batch.single_eval_pos = kwargs["single_eval_pos"]
-            return batch
-
-        def __len__(self):
-            return self.num_steps
-
-        def get_test_batch(self, **kwargs):  # does not increase epoch_count
-            return self.gbm(
-                **self.get_batch_kwargs,
-                epoch=self.epoch_count,
-                model=self.model if hasattr(self, "model") else None,
-                **kwargs,
-            )
-
-        def __iter__(self):
-            assert hasattr(
-                self, "model"
-            ), "Please assign model with `dl.model = ...` before training."
-            self.epoch_count += 1
-            return iter(
-                self.gbm(
-                    **self.get_batch_kwargs,
-                    epoch=self.epoch_count - 1,
-                    model=self.model,
-                )
-                for _ in range(self.num_steps)
-            )
-
-    return DL
-
-
-def plot_features(data, targets, fig=None, categorical=True, plot_diagonal=True):
-    import seaborn as sns
-
-    if torch.is_tensor(data):
-        data = data.detach().cpu().numpy()
-        targets = targets.detach().cpu().numpy()
-
-    fig2 = fig if fig else plt.figure(figsize=(8, 8))
-    spec2 = gridspec.GridSpec(ncols=data.shape[1], nrows=data.shape[1], figure=fig2)
-    for d in range(0, data.shape[1]):
-        for d2 in range(0, data.shape[1]):
-            if d > d2:
-                continue
-            sub_ax = fig2.add_subplot(spec2[d, d2])
-            sub_ax.set_xticks([])
-            sub_ax.set_yticks([])
-            if d == d2:
-                if plot_diagonal:
-                    if categorical:
-                        sns.histplot(
-                            data[:, d],
-                            hue=targets[:],
-                            ax=sub_ax,
-                            legend=False,
-                            palette="deep",
-                        )
-                    else:
-                        sns.histplot(data[:, d], ax=sub_ax, legend=False)
-                sub_ax.set(ylabel=None)
-            else:
-                if categorical:
-                    sns.scatterplot(
-                        x=data[:, d],
-                        y=data[:, d2],
-                        hue=targets[:],
-                        legend=False,
-                        palette="deep",
-                    )
-                else:
-                    sns.scatterplot(
-                        x=data[:, d], y=data[:, d2], hue=targets[:], legend=False
-                    )
-                # plt.scatter(data[:, d], data[:, d2],
-                #               c=targets[:])
-            # sub_ax.get_xaxis().set_ticks([])
-            # sub_ax.get_yaxis().set_ticks([])
-    plt.subplots_adjust(wspace=0.05, hspace=0.05)
-    fig2.show()
-
-
-def plot_prior(prior, samples=1000, buckets=50):
-    s = np.array([prior() for _ in range(0, samples)])
-    count, bins, ignored = plt.hist(s, buckets, density=True)
-    print(s.min())
-    plt.show()
-
-
-trunc_norm_sampler_f = lambda mu, sigma: lambda: stats.truncnorm(
-    (0 - mu) / sigma, (1000000 - mu) / sigma, loc=mu, scale=sigma
-).rvs(1)[0]
-beta_sampler_f = lambda a, b: lambda: np.random.beta(a, b)
-gamma_sampler_f = lambda a, b: lambda: np.random.gamma(a, b)
-uniform_sampler_f = lambda a, b: lambda: np.random.uniform(a, b)
-uniform_int_sampler_f = lambda a, b: lambda: round(np.random.uniform(a, b))
-
-
-def zipf_sampler_f(a, b, c):
-    x = np.arange(b, c)
-    weights = x ** (-a)
-    weights /= weights.sum()
-    return lambda: stats.rv_discrete(name="bounded_zipf", values=(x, weights)).rvs(1)
-
-
-scaled_beta_sampler_f = lambda a, b, scale, minimum: lambda: minimum + round(
-    beta_sampler_f(a, b)() * (scale - minimum)
-)
-
-
-def normalize_by_used_features_f(
-    x, num_features_used, num_features, normalize_with_sqrt=False
-):
-    if normalize_with_sqrt:
-        return x / (num_features_used / num_features) ** (1 / 2)
-    return x / (num_features_used / num_features)
-
-
-def order_by_y(x, y):
-    order = torch.argsort(y if random.randint(0, 1) else -y, dim=0)[:, 0, 0]
-    order = order.reshape(2, -1).transpose(0, 1).reshape(-1)  # .reshape(seq_len)
-    x = x[
-        order
-    ]  # .reshape(2, -1).transpose(0, 1).reshape(-1).flip([0]).reshape(seq_len, 1, -1)
-    y = y[order]  # .reshape(2, -1).transpose(0, 1).reshape(-1).reshape(seq_len, 1, -1)
-
-    return x, y
-
-
-def randomize_classes(x, num_classes):
-    classes = torch.arange(0, num_classes, device=x.device)
-    random_classes = torch.randperm(num_classes, device=x.device).type(x.type())
-    x = ((x.unsqueeze(-1) == classes) * random_classes).sum(-1)
-    return x
-
-
-@torch.no_grad()
-def sample_num_features_get_batch(
-    batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
-):
-    if (
-        hyperparameters.get("sample_num_features", True) and kwargs.get("epoch", 0) > 0
-    ):  # don't sample on test batch
-        num_features = random.randint(1, num_features)
-    # print("features", num_features)
-
-    return get_batch(
-        batch_size=batch_size,
-        seq_len=seq_len,
-        num_features=num_features,
-        hyperparameters=hyperparameters,
-        **kwargs,
-    )
-
-
-# @torch.no_grad()
-# def set_num_tasks_get_batch(
-#     batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
-# ):
-#     max_num_tasks = hyperparameters["num_tasks"]
-#     if hyperparameters.get("sample_num_tasks", True) and kwargs.get("epoch", 0) > 0:
-#         num_tasks = random.randint(1, max_num_tasks)
-#     else:
-#         num_tasks = max_num_tasks
-#     # print("tasks", num_tasks, max_num_tasks)
-
-#     return get_batch(
-#         batch_size=batch_size,
-#         seq_len=seq_len,
-#         num_features=num_features,
-#         max_num_tasks=max_num_tasks,
-#         num_tasks=num_tasks,
-#         hyperparameters=hyperparameters,
-#         **kwargs,
-#     )
-
-
-# @torch.no_grad()
-# def set_lengthscale_get_batch(
-#     batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
-# ):
-#     lengthscale = hyperparameters.get("lengthscale", None)
-#     if not lengthscale:
-#         lengthscale = torch.distributions.Gamma(3.0, 6.0).sample().item()
-#     print("lengthscale", lengthscale)
-
-#     return get_batch(
-#         batch_size,
-#         seq_len,
-#         num_features,
-#         hyperparameters=hyperparameters,
-#         lengthscale=lengthscale,
-#         **kwargs,
-#     )
-
-
-@torch.no_grad()
-def set_properties_get_batch(
-    batch_size, seq_len, num_features, hyperparameters, get_batch, **kwargs
-):
-    epoch = kwargs.get("epoch", 0)
-    if (
-        hyperparameters.get("sample_num_features", True) and epoch > 0
-    ):  # don't sample on test batch
-        num_features = random.randint(1, num_features)
-
-    max_num_tasks = hyperparameters.get("max_num_tasks", hyperparameters["num_tasks"])
-    
-    num_tasks = max_num_tasks
-    if epoch > 0:
-        if hyperparameters.get("sample_num_tasks_curriculum", False):
-            if epoch < 50:
-                num_tasks = 1
-            else:
-                # slowly increase the number of tasks over time
-                additional = (max_num_tasks - 1) * (epoch - 50) / 50
-                epoch_max_tasks = min(max_num_tasks, 1 + int(additional))
-                num_tasks = random.randint(1, epoch_max_tasks)
-        elif hyperparameters.get("sample_num_tasks", True) and epoch > 0:
-            num_tasks = random.randint(1, max_num_tasks)
-            
-
-    lengthscale = hyperparameters.get("lengthscale", None)
-    if not lengthscale:
-        lengthscale = torch.distributions.Gamma(3.0, 6.0).sample().item()
-
-    return get_batch(
-        batch_size,
-        seq_len,
-        num_features,
-        max_num_tasks=max_num_tasks,
-        num_tasks=num_tasks,
-        lengthscale=lengthscale,
-        hyperparameters=hyperparameters,
-        **kwargs,
-    )
-
-
-@torch.no_grad()
-class CategoricalActivation(nn.Module):
-    def __init__(
-        self,
-        categorical_p=0.1,
-        ordered_p=0.7,
-        keep_activation_size=False,
-        num_classes_sampler=zipf_sampler_f(0.8, 1, 10),
-    ):
-        self.categorical_p = categorical_p
-        self.ordered_p = ordered_p
-        self.keep_activation_size = keep_activation_size
-        self.num_classes_sampler = num_classes_sampler
-
-        super().__init__()
-
-    def forward(self, x):
-        # x shape: T, B, H
-
-        x = nn.Softsign()(x)
-
-        num_classes = self.num_classes_sampler()
-        hid_strength = (
-            torch.abs(x).mean(0).unsqueeze(0) if self.keep_activation_size else None
-        )
-
-        categorical_classes = torch.rand((x.shape[1], x.shape[2])) < self.categorical_p
-        class_boundaries = torch.zeros(
-            (num_classes - 1, x.shape[1], x.shape[2]), device=x.device, dtype=x.dtype
-        )
-        # Sample a different index for each hidden dimension, but shared for all batches
-        for b in range(x.shape[1]):
-            for h in range(x.shape[2]):
-                ind = torch.randint(0, x.shape[0], (num_classes - 1,))
-                class_boundaries[:, b, h] = x[ind, b, h]
-
-        for b in range(x.shape[1]):
-            x_rel = x[:, b, categorical_classes[b]]
-            boundaries_rel = class_boundaries[:, b, categorical_classes[b]].unsqueeze(1)
-            x[:, b, categorical_classes[b]] = (x_rel > boundaries_rel).sum(
-                dim=0
-            ).float() - num_classes / 2
-
-        ordered_classes = torch.rand((x.shape[1], x.shape[2])) < self.ordered_p
-        ordered_classes = torch.logical_and(ordered_classes, categorical_classes)
-        x[:, ordered_classes] = randomize_classes(x[:, ordered_classes], num_classes)
-
-        x = x * hid_strength if self.keep_activation_size else x
-
-        return x
-
-
-class QuantizationActivation(torch.nn.Module):
-    def __init__(self, n_thresholds, reorder_p=0.5) -> None:
-        super().__init__()
-        self.n_thresholds = n_thresholds
-        self.reorder_p = reorder_p
-        self.thresholds = torch.nn.Parameter(torch.randn(self.n_thresholds))
-
-    def forward(self, x):
-        x = normalize_data(x).unsqueeze(-1)
-        x = (x > self.thresholds).sum(-1)
-
-        if random.random() < self.reorder_p:
-            x = randomize_classes(x.unsqueeze(-1), self.n_thresholds).squeeze(-1)
-        # x = ((x.float() - self.n_thresholds/2) / self.n_thresholds)# * data_std + data_mean
-        x = normalize_data(x)
-        return x
-
-
-class NormalizationActivation(torch.nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-
-    def forward(self, x):
-        x = normalize_data(x)
-        return x
-
-
-class PowerActivation(torch.nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        # self.exp = torch.nn.Parameter(0.5 * torch.ones(1))
-        self.shared_exp_strength = 0.5
-        # TODO: Somehow this is only initialized once, so it's the same for all runs
-
-    def forward(self, x):
-        # print(torch.nn.functional.softplus(x), self.exp)
-        shared_exp = torch.randn(1)
-        exp = torch.nn.Parameter(
-            (
-                shared_exp * self.shared_exp_strength
-                + shared_exp * torch.randn(x.shape[-1]) * (1 - self.shared_exp_strength)
-            )
-            * 2
-            + 0.5
-        ).to(x.device)
-        x_ = torch.pow(torch.nn.functional.softplus(x) + 0.001, exp)
-        if False:
-            print(
-                x[0:3, 0, 0].cpu().numpy(),
-                torch.nn.functional.softplus(x[0:3, 0, 0]).cpu().numpy(),
-                x_[0:3, 0, 0].cpu().numpy(),
-                normalize_data(x_)[0:3, 0, 0].cpu().numpy(),
-                self.exp.cpu().numpy(),
-            )
-        return x_
-
-
-def lambda_time(f, name="", enabled=True):
-    if not enabled:
-        return f()
-    start = time.time()
-    r = f()
-    print("Timing", name, time.time() - start)
-    return r
-
-
-def pretty_get_batch(get_batch):
-    """
-    Genereate string representation of get_batch function
-    :param get_batch:
-    :return:
-    """
-    if isinstance(get_batch, types.FunctionType):
-        return f"<{get_batch.__module__}.{get_batch.__name__} {inspect.signature(get_batch)}"
-    else:
-        return repr(get_batch)
-
-
-class get_batch_sequence(list):
-    """
-    This will call the get_batch_methods in order from the back and pass the previous as `get_batch` kwarg.
-    For example for `get_batch_methods=[get_batch_1, get_batch_2, get_batch_3]` this will produce a call
-    equivalent to `get_batch_3(*args,get_batch=partial(partial(get_batch_2),get_batch=get_batch_1,**kwargs))`.
-    get_batch_methods: all priors, but the first, muste have a `get_batch` argument
-    """
-
-    def __init__(self, *get_batch_methods):
-        if len(get_batch_methods) == 0:
-            raise ValueError("Must have at least one get_batch method")
-        super().__init__(get_batch_methods)
-
-    def __repr__(self):
-        s = ",\n\t".join([f"{pretty_get_batch(get_batch)}" for get_batch in self])
-        return f"get_batch_sequence(\n\t{s}\n)"
-
-    def __call__(self, *args, **kwargs):
-        """
-
-        Standard kwargs are: batch_size, seq_len, num_features
-        This returns a priors.Batch object.
-        """
-        final_get_batch = self[0]
-        for get_batch in self[1:]:
-            final_get_batch = partial(get_batch, get_batch=final_get_batch)
-        return final_get_batch(*args, **kwargs)
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/acquisition_functions.py b/private_multitask_pfn/PFNs/pfns/scripts/acquisition_functions.py
deleted file mode 100644
index 9fae1eb..0000000
--- a/private_multitask_pfn/PFNs/pfns/scripts/acquisition_functions.py
+++ /dev/null
@@ -1,506 +0,0 @@
-import contextlib
-
-from .. import transformer
-from .. import bar_distribution
-import torch
-import scipy
-import math
-from sklearn.preprocessing import power_transform, PowerTransformer
-
-def log01(x, eps=.0000001, input_between_zero_and_one=False):
-    logx = torch.log(x + eps)
-    if input_between_zero_and_one:
-        return (logx - math.log(eps)) / (math.log(1 + eps) - math.log(eps))
-    return (logx - logx.min(0)[0]) / (logx.max(0)[0] - logx.min(0)[0])
-
-def log01_batch(x, eps=.0000001, input_between_zero_and_one=False):
-    x = x.repeat(1, x.shape[-1] + 1, 1)
-    for b in range(x.shape[-1]):
-        x[:, b, b] = log01(x[:, b, b], eps=eps, input_between_zero_and_one=input_between_zero_and_one)
-    return x
-
-def lognormed_batch(x, eval_pos, eps=.0000001):
-    x = x.repeat(1, x.shape[-1] + 1, 1)
-    for b in range(x.shape[-1]):
-        logx = torch.log(x[:, b, b]+eps)
-        x[:, b, b] = (logx - logx[:eval_pos].mean(0))/logx[:eval_pos].std(0)
-    return x
-
-def _rank_transform(x_train, x):
-    assert len(x_train.shape) == len(x.shape) == 1
-    relative_to = torch.cat((torch.zeros_like(x_train[:1]),x_train.unique(sorted=True,), torch.ones_like(x_train[-1:])),-1)
-    higher_comparison = (relative_to < x[...,None]).sum(-1).clamp(min=1)
-    pos_inside_interval = (x - relative_to[higher_comparison-1])/(relative_to[higher_comparison] - relative_to[higher_comparison-1])
-    x_transformed = higher_comparison - 1 + pos_inside_interval
-    return x_transformed/(len(relative_to)-1.)
-
-def rank_transform(x_train, x):
-    assert x.shape[1] == x_train.shape[1], f"{x.shape=} and {x_train.shape=}"
-    # make sure everything is between 0 and 1
-    assert (x_train >= 0.).all() and (x_train <= 1.).all(), f"{x_train=}"
-    assert (x >= 0.).all() and (x <= 1.).all(), f"{x=}"
-    return_x = x.clone()
-    for feature_dim in range(x.shape[1]):
-        return_x[:, feature_dim] = _rank_transform(x_train[:, feature_dim], x[:, feature_dim])
-    return return_x
-
-
-
-def general_power_transform(x_train, x_apply, eps, less_safe=False):
-    if eps > 0:
-        try:
-            pt = PowerTransformer(method='box-cox')
-            pt.fit(x_train.cpu()+eps)
-            x_out = torch.tensor(pt.transform(x_apply.cpu()+eps), dtype=x_apply.dtype, device=x_apply.device)
-        except ValueError as e:
-            print(e)
-            x_out = x_apply - x_train.mean(0)
-    else:
-        pt = PowerTransformer(method='yeo-johnson')
-        if not less_safe and (x_train.std() > 1_000 or x_train.mean().abs() > 1_000):
-            x_apply = (x_apply - x_train.mean(0)) / x_train.std(0)
-            x_train = (x_train - x_train.mean(0)) / x_train.std(0)
-            print('inputs are LAARGEe, normalizing them')
-        try:
-            pt.fit(x_train.cpu().double())
-        except ValueError as e:
-            print('caught this errrr', e)
-            if less_safe:
-                x_train = (x_train - x_train.mean(0)) / x_train.std(0)
-                x_apply = (x_apply - x_train.mean(0)) / x_train.std(0)
-            else:
-                x_train = x_train - x_train.mean(0)
-                x_apply = x_apply - x_train.mean(0)
-            pt.fit(x_train.cpu().double())
-        x_out = torch.tensor(pt.transform(x_apply.cpu()), dtype=x_apply.dtype, device=x_apply.device)
-    if torch.isnan(x_out).any() or torch.isinf(x_out).any():
-        print('WARNING: power transform failed')
-        print(f"{x_train=} and {x_apply=}")
-        x_out = x_apply - x_train.mean(0)
-    return x_out
-
-
-
-#@torch.inference_mode()
-def general_acq_function(model: transformer.TransformerModel, x_given, y_given, x_eval, apply_power_transform=True,
-                    rand_sample=False, znormalize=False, pre_normalize=False, pre_znormalize=False, predicted_mean_fbest=False,
-                    input_znormalize=False, max_dataset_size=10_000, remove_features_with_one_value_only=False,
-                    return_actual_ei=False, acq_function='ei', ucb_rest_prob=.05, ensemble_log_dims=False,
-                    ensemble_type='mean_probs', # in ('mean_probs', 'max_acq')
-                    input_power_transform=False, power_transform_eps=.0, input_power_transform_eps=.0,
-                    input_rank_transform=False, ensemble_input_rank_transform=False,
-                    ensemble_power_transform=False, ensemble_feature_rotation=False,
-                    style=None, outlier_stretching_interval=0.0, verbose=False, unsafe_power_transform=False,
-                         ):
-    """
-    Differences to HEBO:
-        - The noise can't be set in the same way, as it depends on the tuning of HPs via VI.
-        - Log EI and PI are always used directly instead of using the approximation.
-
-    This is a stochastic function, relying on torch.randn
-
-    :param model:
-    :param x_given: torch.Tensor of shape (N, D)
-    :param y_given: torch.Tensor of shape (N, 1) or (N,)
-    :param x_eval: torch.Tensor of shape (M, D)
-    :param kappa:
-    :param eps:
-    :return:
-    """
-    assert ensemble_type in ('mean_probs', 'max_acq')
-    if rand_sample is not False \
-        and (len(x_given) == 0 or
-             ((1 + x_given.shape[1] if rand_sample is None else max(2, rand_sample)) > x_given.shape[0])):
-        print('rando')
-        return torch.zeros_like(x_eval[:,0]) #torch.randperm(x_eval.shape[0])[0]
-    y_given = y_given.reshape(-1)
-    assert len(y_given) == len(x_given)
-    if apply_power_transform:
-        if pre_normalize:
-            y_normed = y_given / y_given.std()
-            if not torch.isinf(y_normed).any() and not torch.isnan(y_normed).any():
-                y_given = y_normed
-        elif pre_znormalize:
-            y_znormed = (y_given - y_given.mean()) / y_given.std()
-            if not torch.isinf(y_znormed).any() and not torch.isnan(y_znormed).any():
-                y_given = y_znormed
-        y_given = general_power_transform(y_given.unsqueeze(1), y_given.unsqueeze(1), power_transform_eps, less_safe=unsafe_power_transform).squeeze(1)
-        if verbose:
-            print(f"{y_given=}")
-        #y_given = torch.tensor(power_transform(y_given.cpu().unsqueeze(1), method='yeo-johnson', standardize=znormalize), device=y_given.device, dtype=y_given.dtype,).squeeze(1)
-    y_given_std = torch.tensor(1., device=y_given.device, dtype=y_given.dtype)
-    if znormalize and not apply_power_transform:
-        if len(y_given) > 1:
-            y_given_std = y_given.std()
-        y_given_mean = y_given.mean()
-        y_given = (y_given - y_given_mean) / y_given_std
-
-    if remove_features_with_one_value_only:
-        x_all = torch.cat([x_given, x_eval], dim=0)
-        only_one_value_feature = torch.tensor([len(torch.unique(x_all[:,i])) for i in range(x_all.shape[1])]) == 1
-        x_given = x_given[:,~only_one_value_feature]
-        x_eval = x_eval[:,~only_one_value_feature]
-
-    if outlier_stretching_interval > 0.:
-        tx = torch.cat([x_given, x_eval], dim=0)
-        m = outlier_stretching_interval
-        eps = 1e-10
-        small_values = (tx < m) & (tx > 0.)
-        tx[small_values] = m * (torch.log(tx[small_values] + eps) - math.log(eps)) / (math.log(m + eps) - math.log(eps))
-
-        large_values = (tx > 1. - m) & (tx < 1.)
-        tx[large_values] = 1. - m * (torch.log(1 - tx[large_values] + eps) - math.log(eps)) / (
-                    math.log(m + eps) - math.log(eps))
-        x_given = tx[:len(x_given)]
-        x_eval = tx[len(x_given):]
-
-    if input_znormalize: # implementation that relies on the test set, too...
-        std = x_given.std(dim=0)
-        std[std == 0.] = 1.
-        mean = x_given.mean(dim=0)
-        x_given = (x_given - mean) / std
-        x_eval = (x_eval - mean) / std
-
-    if input_power_transform:
-        x_given = general_power_transform(x_given, x_given, input_power_transform_eps)
-        x_eval = general_power_transform(x_given, x_eval, input_power_transform_eps)
-
-    if input_rank_transform is True or input_rank_transform == 'full': # uses test set x statistics...
-        x_all = torch.cat((x_given,x_eval), dim=0)
-        for feature_dim in range(x_all.shape[-1]):
-            uniques = torch.sort(torch.unique(x_all[..., feature_dim])).values
-            x_eval[...,feature_dim] = torch.searchsorted(uniques,x_eval[..., feature_dim]).float() / (len(uniques)-1)
-            x_given[...,feature_dim] = torch.searchsorted(uniques,x_given[..., feature_dim]).float() / (len(uniques)-1)
-    elif input_rank_transform is False:
-        pass
-    elif input_rank_transform == 'train':
-        x_given = rank_transform(x_given, x_given)
-        x_eval = rank_transform(x_given, x_eval)
-    elif input_rank_transform.startswith('train'):
-        likelihood = float(input_rank_transform.split('_')[-1])
-        if torch.rand(1).item() < likelihood:
-            print('rank transform')
-            x_given = rank_transform(x_given, x_given)
-            x_eval = rank_transform(x_given, x_eval)
-    else:
-        raise NotImplementedError
-
-
-    # compute logits
-    criterion: bar_distribution.BarDistribution = model.criterion
-    x_predict = torch.cat([x_given, x_eval], dim=0)
-
-
-    logits_list = []
-    for x_feed in torch.split(x_predict, max_dataset_size, dim=0):
-        x_full_feed = torch.cat([x_given, x_feed], dim=0).unsqueeze(1)
-        y_full_feed = y_given.unsqueeze(1)
-        if ensemble_log_dims == '01':
-            x_full_feed = log01_batch(x_full_feed)
-        elif ensemble_log_dims == 'global01' or ensemble_log_dims is True:
-            x_full_feed = log01_batch(x_full_feed, input_between_zero_and_one=True)
-        elif ensemble_log_dims == '01-10':
-            x_full_feed = torch.cat((log01_batch(x_full_feed)[:, :-1], log01_batch(1. - x_full_feed)), 1)
-        elif ensemble_log_dims == 'norm':
-            x_full_feed = lognormed_batch(x_full_feed, len(x_given))
-        elif ensemble_log_dims is not False:
-            raise NotImplementedError
-
-        if ensemble_feature_rotation:
-            x_full_feed = torch.cat([x_full_feed[:, :, (i+torch.arange(x_full_feed.shape[2])) % x_full_feed.shape[2]] for i in range(x_full_feed.shape[2])], dim=1)
-
-        if ensemble_input_rank_transform == 'train' or ensemble_input_rank_transform is True:
-            x_full_feed = torch.cat([rank_transform(x_given, x_full_feed[:,i,:])[:,None] for i in range(x_full_feed.shape[1])] + [x_full_feed], dim=1)
-
-        if ensemble_power_transform:
-            assert apply_power_transform is False
-            y_full_feed = torch.cat((general_power_transform(y_full_feed, y_full_feed, power_transform_eps), y_full_feed), dim=1)
-
-
-        if style is not None:
-            if callable(style):
-                style = style()
-
-            if isinstance(style, torch.Tensor):
-                style = style.to(x_full_feed.device)
-            else:
-                style = torch.tensor(style, device=x_full_feed.device).view(1, 1).repeat(x_full_feed.shape[1], 1)
-
-
-        logits = model(
-            (style,
-             x_full_feed.repeat_interleave(dim=1, repeats=y_full_feed.shape[1]),
-             y_full_feed.repeat(1,x_full_feed.shape[1])),
-            single_eval_pos=len(x_given)
-        )
-        if ensemble_type == 'mean_probs':
-            logits = logits.softmax(-1).mean(1, keepdim=True).log_()  # (num given + num eval, 1, num buckets)
-
-        logits_list.append(logits)  # (< max_dataset_size, 1 , num_buckets)
-    logits = torch.cat(logits_list, dim=0) # (num given + num eval, 1 or (num_features+1), num buckets)
-    del logits_list, x_full_feed
-    if torch.isnan(logits).any():
-        print('nan logits')
-        print(f"y_given: {y_given}, x_given: {x_given}, x_eval: {x_eval}")
-        print(f"logits: {logits}")
-        return torch.zeros_like(x_eval[:,0])
-
-    #logits = model((torch.cat([x_given, x_given, x_eval], dim=0).unsqueeze(1),
-    #               torch.cat([y_given, torch.zeros(len(x_eval)+len(x_given), device=y_given.device)], dim=0).unsqueeze(1)),
-    #               single_eval_pos=len(x_given))[:,0] # (N + M, num_buckets)
-    logits_given = logits[:len(x_given)]
-    logits_eval = logits[len(x_given):]
-
-    #tau = criterion.mean(logits_given)[torch.argmax(y_given)] # predicted mean at the best y
-    if predicted_mean_fbest:
-        tau = criterion.mean(logits_given)[torch.argmax(y_given)].squeeze(0)
-    else:
-        tau = torch.max(y_given)
-    #log_ei = torch.stack([criterion.ei(logits_eval[:,i], noisy_best_f[i]).log() for i in range(len(logits_eval))],0)
-
-    def acq_ensembling(acq_values): # (points, ensemble dim)
-        return acq_values.max(1).values
-
-    if isinstance(acq_function, (dict,list)):
-        acq_function = acq_function[style]
-
-    if acq_function == 'ei':
-        acq_value = acq_ensembling(criterion.ei(logits_eval, tau))
-    elif acq_function == 'ei_or_rand':
-        if torch.rand(1).item() < 0.5:
-            acq_value = torch.rand(len(x_eval))
-        else:
-            acq_value = acq_ensembling(criterion.ei(logits_eval, tau))
-    elif acq_function == 'pi':
-        acq_value = acq_ensembling(criterion.pi(logits_eval, tau))
-    elif acq_function == 'ucb':
-        acq_function = criterion.ucb
-        if ucb_rest_prob is not None:
-            acq_function = lambda *args: criterion.ucb(*args, rest_prob=ucb_rest_prob)
-        acq_value = acq_ensembling(acq_function(logits_eval, tau))
-    elif acq_function == 'mean':
-        acq_value = acq_ensembling(criterion.mean(logits_eval))
-    elif acq_function.startswith('hebo'):
-        noise, upsi, delta, eps = (float(v) for v in acq_function.split('_')[1:])
-        noise = y_given_std * math.sqrt(2 * noise)
-        kappa = math.sqrt(upsi * 2 * ((2.0 + x_given.shape[1] / 2.0) * math.log(max(1, len(x_given))) + math.log(
-            3 * math.pi ** 2 / (3 * delta))))
-        rest_prob = 1. - .5 * (1 + torch.erf(torch.tensor(kappa / math.sqrt(2), device=logits.device)))
-        ucb = acq_ensembling(criterion.ucb(logits_eval, None, rest_prob=rest_prob)) \
-              + torch.randn(len(logits_eval), device=logits_eval.device) * noise
-        noisy_best_f = tau + eps + \
-                       noise * torch.randn(len(logits_eval), device=logits_eval.device)[:, None].repeat(1, logits_eval.shape[1])
-
-        log_pi = acq_ensembling(criterion.pi(logits_eval, noisy_best_f).log())
-        # log_ei = torch.stack([criterion.ei(logits_eval[:,i], noisy_best_f[i]).log() for i in range(len(logits_eval))],0)
-        log_ei = acq_ensembling(criterion.ei(logits_eval, noisy_best_f).log())
-
-        acq_values = torch.stack([ucb, log_ei, log_pi], dim=1)
-
-        def is_pareto_efficient(costs):
-            """
-            Find the pareto-efficient points
-            :param costs: An (n_points, n_costs) array
-            :return: A (n_points, ) boolean array, indicating whether each point is Pareto efficient
-            """
-            is_efficient = torch.ones(costs.shape[0], dtype=bool, device=costs.device)
-            for i, c in enumerate(costs):
-                if is_efficient[i]:
-                    is_efficient[is_efficient.clone()] = (costs[is_efficient] < c).any(
-                        1)  # Keep any point with a lower cost
-                    is_efficient[i] = True  # And keep self
-            return is_efficient
-
-        acq_value = is_pareto_efficient(-acq_values)
-    else:
-        raise ValueError(f'Unknown acquisition function: {acq_function}')
-
-    max_acq = acq_value.max()
-
-    return acq_value if return_actual_ei else (acq_value == max_acq)
-
-
-def optimize_acq(model, known_x, known_y, num_grad_steps=10, num_random_samples=100, lr=.01, **kwargs):
-    """
-    intervals are assumed to be between 0 and 1
-    only works with ei
-    recommended extra kwarg: ensemble_input_rank_transform=='train'
-
-    :param model: model to optimize, should already handle different num_features with its encoder
-    You can add this simply with `model.encoder = encoders.VariableNumFeaturesEncoder(model.encoder, model.encoder.num_features)`
-    :param known_x: (N, num_features)
-    :param known_y: (N,)
-    :param num_grad_steps: int
-    :param num_random_samples: int
-    :param lr: float
-    :param kwargs: will be given to `general_acq_function`
-    :return:
-    """
-    x_eval = torch.rand(num_random_samples, known_x.shape[1]).requires_grad_(True)
-    opt = torch.optim.Adam(params=[x_eval], lr=lr)
-    best_acq, best_x = -float('inf'), x_eval[0].detach()
-    for grad_step in range(num_grad_steps):
-        acq = general_acq_function(model, known_x, known_y, x_eval, return_actual_ei=True, **kwargs)
-        max_acq = acq.detach().max().item()
-        if max_acq > best_acq:
-            best_x = x_eval[acq.argmax()].detach()
-            best_acq = max_acq
-
-        (-acq.mean()).backward()
-        assert (x_eval.grad != 0.).any()
-        if torch.isfinite(x_eval.grad).all():
-            opt.step()
-        opt.zero_grad()
-        with torch.no_grad():
-            x_eval.clamp_(min=0., max=1.)
-
-    return best_x
-
-
-def optimize_acq_w_lbfgs(model, known_x, known_y, num_grad_steps=15_000, num_candidates=100, pre_sample_size=100_000, device='cpu',
-                         verbose=False, dims_wo_gradient_opt=[], rand_sample_func=None, **kwargs):
-    """
-    intervals are assumed to be between 0 and 1
-    only works with deterministic acq
-    recommended extra kwarg: ensemble_input_rank_transform=='train'
-
-    :param model: model to optimize, should already handle different num_features with its encoder
-    You can add this simply with `model.encoder = encoders.VariableNumFeaturesEncoder(model.encoder, model.encoder.num_features)`
-    :param known_x: (N, num_features)
-    :param known_y: (N,)
-    :param num_grad_steps: int: how many steps to take inside of scipy, can be left high, as it stops most of the time automatically early
-    :param num_candidates: int: how many candidates to optimize with LBFGS, increases costs when higher
-    :param pre_sample_size: int: how many settings to try first with a random search, before optimizing the best with grads
-    :param dims_wo_gradient_opt: int: which dimensions to not optimize with gradients, but with random search only
-    :param rand_sample_func: function: how to sample random points, should be a function that takes a number of samples and returns a tensor
-    For example `lambda n: torch.rand(n, known_x.shape[1])`.
-    :param kwargs: will be given to `general_acq_function`
-    :return:
-    """
-    num_features = known_x.shape[1]
-    dims_w_gradient_opt = sorted(set(range(num_features)) - set(dims_wo_gradient_opt))
-    known_x = known_x.to(device)
-    known_y = known_y.to(device)
-    pre_sample_size = max(pre_sample_size, num_candidates)
-    rand_sample_func = rand_sample_func or (lambda n: torch.rand(n, num_features, device=device))
-    if len(known_x) < pre_sample_size:
-        x_initial = torch.cat((rand_sample_func(pre_sample_size-len(known_x)).to(device), known_x), 0)
-    else:
-        x_initial = rand_sample_func(pre_sample_size)
-    x_initial = x_initial.clamp(min=0., max=1.)
-    x_initial_all = x_initial
-    model.to(device)
-
-    with torch.no_grad():
-        acq = general_acq_function(model, known_x, known_y, x_initial.to(device), return_actual_ei=True, **kwargs)
-        if verbose:
-            import matplotlib.pyplot as plt
-            if x_initial.shape[1] == 2:
-                plt.title('initial acq values, red -> blue')
-                plt.scatter(x_initial[:, 0][:100], x_initial[:, 1][:100], c=acq.cpu().numpy()[:100], cmap='RdBu')
-        x_initial = x_initial[acq.argsort(descending=True)[:num_candidates].cpu()].detach() # num_candidates x num_features
-
-    x_initial_all_ei = acq.cpu().detach()
-
-    def opt_f(x):
-        x_eval = torch.tensor(x).view(-1, len(dims_w_gradient_opt)).float().to(device).requires_grad_(True)
-        x_eval_new = x_initial.clone().detach().to(device)
-        x_eval_new[:, dims_w_gradient_opt] = x_eval
-
-        assert x_eval_new.requires_grad
-        assert not torch.isnan(x_eval_new).any()
-        model.requires_grad_(False)
-        acq = general_acq_function(model, known_x, known_y, x_eval_new, return_actual_ei=True, **kwargs)
-        neg_mean_acq = -acq.mean()
-        neg_mean_acq.backward()
-        #print(neg_mean_acq.detach().numpy(), x_eval.grad.detach().view(*x.shape).numpy())
-        with torch.no_grad():
-            x_eval.grad[x_eval.grad != x_eval.grad] = 0.
-        return neg_mean_acq.detach().cpu().to(torch.float64).numpy(), \
-            x_eval.grad.detach().view(*x.shape).cpu().to(torch.float64).numpy()
-
-    # Optimize best candidates with LBFGS
-    if num_grad_steps > 0 and len(dims_w_gradient_opt) > 0:
-        # the columns not in dims_wo_gradient_opt will be optimized with gradients
-        x_initial_for_gradient_opt = x_initial[:, dims_w_gradient_opt].detach().cpu().flatten().numpy() # x_initial.cpu().flatten().numpy()
-        res = scipy.optimize.minimize(opt_f, x_initial_for_gradient_opt, method='L-BFGS-B', jac=True,
-                                      bounds=[(0, 1)]*x_initial_for_gradient_opt.size,
-                                      options={'maxiter': num_grad_steps})
-        results = x_initial.cpu()
-        results[:, dims_w_gradient_opt] = torch.tensor(res.x).float().view(-1, len(dims_w_gradient_opt))
-
-    else:
-        results = x_initial.cpu()
-
-    results = results.clamp(min=0., max=1.)
-
-    # Recalculate the acq values for the best candidates
-    with torch.no_grad():
-        acq = general_acq_function(model, known_x, known_y, results.to(device), return_actual_ei=True, verbose=verbose, **kwargs)
-        #print(acq)
-        if verbose:
-            from scipy.stats import rankdata
-            import matplotlib.pyplot as plt
-            if results.shape[1] == 2:
-                plt.scatter(results[:, 0], results[:, 1], c=rankdata(acq.cpu().numpy()), marker='x', cmap='RdBu')
-                plt.show()
-        best_x = results[acq.argmax().item()].detach()
-
-    acq_order = acq.argsort(descending=True).cpu()
-    all_order = x_initial_all_ei.argsort(descending=True).cpu()
-
-    return best_x.detach(), results[acq_order].detach(), acq.cpu()[acq_order].detach(), x_initial_all.cpu()[all_order].detach(), x_initial_all_ei.cpu()[all_order].detach()
-
-from ..utils import to_tensor
-
-class TransformerBOMethod:
-
-    def __init__(self, model, acq_f=general_acq_function, device='cpu:0', fit_encoder=None, **kwargs):
-        self.model = model
-        self.device = device
-        self.kwargs = kwargs
-        self.acq_function = acq_f
-        self.fit_encoder = fit_encoder
-
-    @torch.no_grad()
-    def observe_and_suggest(self, X_obs, y_obs, X_pen, return_actual_ei=False):
-        # assert X_pen is not None
-        # assumptions about X_obs and X_pen:
-        # X_obs is a numpy array of shape (n_samples, n_features)
-        # y_obs is a numpy array of shape (n_samples,), between 0 and 1
-        # X_pen is a numpy array of shape (n_samples_left, n_features)
-        X_obs = to_tensor(X_obs, device=self.device).to(torch.float32)
-        y_obs = to_tensor(y_obs, device=self.device).to(torch.float32).view(-1)
-        X_pen = to_tensor(X_pen, device=self.device).to(torch.float32)
-
-        assert len(X_obs) == len(y_obs), "make sure both X_obs and y_obs have the same length."
-
-        self.model.to(self.device)
-
-        if self.fit_encoder is not None:
-            w = self.fit_encoder(self.model, X_obs, y_obs)
-            X_obs = w(X_obs)
-            X_pen = w(X_pen)
-
-        with (torch.cuda.amp.autocast() if self.device[:3] != 'cpu' else contextlib.nullcontext()):
-            acq_values = self.acq_function(self.model, X_obs, y_obs,
-                                           X_pen, return_actual_ei=return_actual_ei, **self.kwargs).cpu().clone()  # bool array
-            acq_mask = acq_values.max() == acq_values
-        possible_next = torch.arange(len(X_pen))[acq_mask]
-        if len(possible_next) == 0:
-            possible_next = torch.arange(len(X_pen))
-
-        r = possible_next[torch.randperm(len(possible_next))[0]].cpu().item()
-
-
-        if return_actual_ei:
-            return r, acq_values
-        else:
-            return r
-
-
-
-
-
-
-
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_interface.py b/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_interface.py
deleted file mode 100644
index 449e4a1..0000000
--- a/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_interface.py
+++ /dev/null
@@ -1,540 +0,0 @@
-import torch
-import random
-import pathlib
-
-from torch.utils.checkpoint import checkpoint
-
-from pfns.utils import normalize_data, to_ranking_low_mem, remove_outliers
-from pfns.utils import NOP, normalize_by_used_features_f
-
-from sklearn.preprocessing import PowerTransformer, QuantileTransformer, RobustScaler
-
-import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
-from sklearn.utils.multiclass import unique_labels
-from sklearn.utils.multiclass import check_classification_targets
-from sklearn.utils import column_or_1d
-from sklearn.preprocessing import LabelEncoder
-from pathlib import Path
-from pfns.scripts.tabpfn_model_builder import load_model_only_inference
-import os
-import pickle
-import io
-
-def load_model_workflow(i, e, add_name, base_path, device='cpu', eval_addition='', only_inference=True):
-    """
-    Workflow for loading a model and setting appropriate parameters for diffable hparam tuning.
-
-    :param i:
-    :param e:
-    :param eval_positions_valid:
-    :param add_name:
-    :param base_path:
-    :param device:
-    :param eval_addition:
-    :return:
-    """
-    def get_file(e):
-        """
-        Returns the different paths of model_file, model_path and results_file
-        """
-        model_file = f'models_diff/prior_diff_real_checkpoint{add_name}_n_{i}_epoch_{e}.cpkt'
-        model_path = os.path.join(base_path, model_file)
-        # print('Evaluate ', model_path)
-        results_file = os.path.join(base_path,
-                                    f'models_diff/prior_diff_real_results{add_name}_n_{i}_epoch_{e}_{eval_addition}.pkl')
-        return model_file, model_path, results_file
-
-    def check_file(e):
-        model_file, model_path, results_file = get_file(e)
-        if not Path(model_path).is_file():  # or Path(results_file).is_file():
-            print('We have to download the TabPFN, as there is no checkpoint at ', model_path)
-            print('It has about 100MB, so this might take a moment.')
-            import requests
-            url = 'https://github.com/automl/TabPFN/raw/main/tabpfn/models_diff/prior_diff_real_checkpoint_n_0_epoch_42.cpkt'
-            r = requests.get(url, allow_redirects=True)
-            os.makedirs(os.path.dirname(model_path), exist_ok=True)
-            open(model_path, 'wb').write(r.content)
-        return model_file, model_path, results_file
-
-    model_file = None
-    if e == -1:
-        for e_ in range(100, -1, -1):
-            model_file_, model_path_, results_file_ = check_file(e_)
-            if model_file_ is not None:
-                e = e_
-                model_file, model_path, results_file = model_file_, model_path_, results_file_
-                break
-    else:
-        model_file, model_path, results_file = check_file(e)
-
-    if model_file is None:
-        model_file, model_path, results_file = get_file(e)
-        raise Exception('No checkpoint found at '+str(model_path))
-
-
-    #print(f'Loading {model_file}')
-    # if only_inference:
-    #     # print('Loading model that can be used for inference only')
-    #     model, c = load_model_only_inference(base_path, model_file, device)
-    # else:
-    #     #until now also only capable of inference
-    #     model, c = load_model(base_path, model_file, device, eval_positions=[], verbose=False)
-    # #model, c = load_model(base_path, model_file, device, eval_positions=[], verbose=False)
-    
-    model, c = load_model_only_inference(base_path, model_file, device)
-
-    return model, c, results_file
-
-
-class TabPFNClassifier(BaseEstimator, ClassifierMixin):
-
-    models_in_memory = {}
-
-    def __init__(self, device='cpu', base_path=pathlib.Path(__file__).parent.parent.resolve(), model_string='',
-                 N_ensemble_configurations=3, no_preprocess_mode=False, multiclass_decoder='permutation',
-                 feature_shift_decoder=True, only_inference=True, seed=0, no_grad=True, batch_size_inference=32,
-                 subsample_features=False):
-        """
-        Initializes the classifier and loads the model. 
-        Depending on the arguments, the model is either loaded from memory, from a file, or downloaded from the 
-        repository if no model is found.
-        
-        Can also be used to compute gradients with respect to the inputs X_train and X_test. Therefore no_grad has to be 
-        set to False and no_preprocessing_mode must be True. Furthermore, X_train and X_test need to be given as 
-        torch.Tensors and their requires_grad parameter must be set to True.
-        
-        
-        :param device: If the model should run on cuda or cpu.
-        :param base_path: Base path of the directory, from which the folders like models_diff can be accessed.
-        :param model_string: Name of the model. Used first to check if the model is already in memory, and if not, 
-               tries to load a model with that name from the models_diff directory. It looks for files named as 
-               follows: "prior_diff_real_checkpoint" + model_string + "_n_0_epoch_e.cpkt", where e can be a number 
-               between 100 and 0, and is checked in a descending order. 
-        :param N_ensemble_configurations: The number of ensemble configurations used for the prediction. Thereby the 
-               accuracy, but also the running time, increases with this number. 
-        :param no_preprocess_mode: Specifies whether preprocessing is to be performed.
-        :param multiclass_decoder: If set to permutation, randomly shifts the classes for each ensemble configuration. 
-        :param feature_shift_decoder: If set to true shifts the features for each ensemble configuration according to a 
-               random permutation.
-        :param only_inference: Indicates if the model should be loaded to only restore inference capabilities or also 
-               training capabilities. Note that the training capabilities are currently not being fully restored.
-        :param seed: Seed that is used for the prediction. Allows for a deterministic behavior of the predictions.
-        :param batch_size_inference: This parameter is a trade-off between performance and memory consumption.
-               The computation done with different values for batch_size_inference is the same,
-               but it is split into smaller/larger batches.
-        :param no_grad: If set to false, allows for the computation of gradients with respect to X_train and X_test. 
-               For this to correctly function no_preprocessing_mode must be set to true.
-        :param subsample_features: If set to true and the number of features in the dataset exceeds self.max_features (100),
-                the features are subsampled to self.max_features.
-        """
-
-        # Model file specification (Model name, Epoch)
-        i = 0
-        model_key = model_string+'|'+str(device)
-        if model_key in self.models_in_memory:
-            model, c, results_file = self.models_in_memory[model_key]
-        else:
-            model, c, results_file = load_model_workflow(i, -1, add_name=model_string, base_path=base_path, device=device,
-                                                         eval_addition='', only_inference=only_inference)
-            self.models_in_memory[model_key] = (model, c, results_file)
-            if len(self.models_in_memory) == 2:
-                print('Multiple models in memory. This might lead to memory issues. Consider calling remove_models_from_memory()')
-        #style, temperature = self.load_result_minimal(style_file, i, e)
-
-        self.device = device
-        self.model = model
-        self.c = c
-        self.style = None
-        self.temperature = None
-        self.N_ensemble_configurations = N_ensemble_configurations
-        self.base__path = base_path
-        self.base_path = base_path
-        self.i = i
-        self.model_string = model_string
-
-        self.max_num_features = self.c['num_features']
-        self.max_num_classes = self.c['max_num_classes']
-        self.differentiable_hps_as_style = self.c['differentiable_hps_as_style']
-
-        self.no_preprocess_mode = no_preprocess_mode
-        self.feature_shift_decoder = feature_shift_decoder
-        self.multiclass_decoder = multiclass_decoder
-        self.only_inference = only_inference
-        self.seed = seed
-        self.no_grad = no_grad
-        self.subsample_features = subsample_features
-
-        assert self.no_preprocess_mode if not self.no_grad else True, \
-            "If no_grad is false, no_preprocess_mode must be true, because otherwise no gradient can be computed."
-
-        self.batch_size_inference = batch_size_inference
-
-    def remove_models_from_memory(self):
-        self.models_in_memory = {}
-
-    def _validate_targets(self, y):
-        y_ = column_or_1d(y, warn=True)
-        check_classification_targets(y)
-        cls, y = np.unique(y_, return_inverse=True)
-        if len(cls) < 2:
-            raise ValueError(
-                "The number of classes has to be greater than one; got %d class"
-                % len(cls)
-            )
-
-        self.classes_ = cls
-
-        return np.asarray(y, dtype=np.float64, order="C")
-
-    def fit(self, X, y, overwrite_warning=False):
-        """
-        Validates the training set and stores it.
-
-        If clf.no_grad (default is True):
-        X, y should be of type np.array
-        else:
-        X should be of type torch.Tensors (y can be np.array or torch.Tensor)
-        """
-        if self.no_grad:
-            # Check that X and y have correct shape
-            X, y = check_X_y(X, y, force_all_finite=False)
-        # Store the classes seen during fit
-        y = self._validate_targets(y)
-        self.label_encoder = LabelEncoder()
-        y = self.label_encoder.fit_transform(y)
-
-        self.X_ = X
-        self.y_ = y
-
-        if (X.shape[1] > self.max_num_features):
-            if self.subsample_features:
-                print('WARNING: The number of features for this classifier is restricted to ', self.max_num_features, ' and will be subsampled.')
-            else:
-                raise ValueError("The number of features for this classifier is restricted to ", self.max_num_features)
-        if len(np.unique(y)) > self.max_num_classes:
-            raise ValueError("The number of classes for this classifier is restricted to ", self.max_num_classes)
-        if X.shape[0] > 1024 and not overwrite_warning:
-            raise ValueError("⚠️ WARNING: TabPFN is not made for datasets with a trainingsize > 1024. Prediction might take a while, be less reliable. We advise not to run datasets > 10k samples, which might lead to your machine crashing (due to quadratic memory scaling of TabPFN). Please confirm you want to run by passing overwrite_warning=True to the fit function.")
-
-
-        # Return the classifier
-        return self
-
-    def predict_proba(self, X, normalize_with_test=False, return_logits=False):
-        """
-        Predict the probabilities for the input X depending on the training set previously passed in the method fit.
-
-        If no_grad is true in the classifier the function takes X as a numpy.ndarray. If no_grad is false X must be a
-        torch tensor and is not fully checked.
-        """
-        # Check is fit had been called
-        check_is_fitted(self)
-
-        # Input validation
-        if self.no_grad:
-            X = check_array(X, force_all_finite=False)
-            X_full = np.concatenate([self.X_, X], axis=0)
-            X_full = torch.tensor(X_full, device=self.device).float().unsqueeze(1)
-        else:
-            assert (torch.is_tensor(self.X_) & torch.is_tensor(X)), "If no_grad is false, this function expects X as " \
-                                                                    "a tensor to calculate a gradient"
-            X_full = torch.cat((self.X_, X), dim=0).float().unsqueeze(1).to(self.device)
-
-            if int(torch.isnan(X_full).sum()):
-                print('X contains nans and the gradient implementation is not designed to handel nans.')
-
-        y_full = np.concatenate([self.y_, np.zeros(shape=X.shape[0])], axis=0)
-        y_full = torch.tensor(y_full, device=self.device).float().unsqueeze(1)
-
-        eval_pos = self.X_.shape[0]
-
-        prediction = transformer_predict(self.model[2], X_full, y_full, eval_pos,
-                                         device=self.device,
-                                         style=self.style,
-                                         inference_mode=True,
-                                         preprocess_transform='none' if self.no_preprocess_mode else 'mix',
-                                         normalize_with_test=normalize_with_test,
-                                         N_ensemble_configurations=self.N_ensemble_configurations,
-                                         softmax_temperature=self.temperature,
-                                         multiclass_decoder=self.multiclass_decoder,
-                                         feature_shift_decoder=self.feature_shift_decoder,
-                                         differentiable_hps_as_style=self.differentiable_hps_as_style,
-                                         seed=self.seed,
-                                         return_logits=return_logits,
-                                         no_grad=self.no_grad,
-                                         batch_size_inference=self.batch_size_inference,
-                                         **get_params_from_config(self.c))
-        prediction_, y_ = prediction.squeeze(0), y_full.squeeze(1).long()[eval_pos:]
-
-        return prediction_.detach().cpu().numpy() if self.no_grad else prediction_
-
-    def predict(self, X, return_winning_probability=False, normalize_with_test=False):
-        p = self.predict_proba(X, normalize_with_test=normalize_with_test)
-        y = np.argmax(p, axis=-1)
-        y = self.classes_.take(np.asarray(y, dtype=np.intp))
-        if return_winning_probability:
-            return y, p.max(axis=-1)
-        return y
-
-import time
-def transformer_predict(model, eval_xs, eval_ys, eval_position,
-                        device='cpu',
-                        max_features=100,
-                        style=None,
-                        inference_mode=False,
-                        num_classes=2,
-                        extend_features=True,
-                        normalize_with_test=False,
-                        normalize_to_ranking=False,
-                        softmax_temperature=0.0,
-                        multiclass_decoder='permutation',
-                        preprocess_transform='mix',
-                        categorical_feats=[],
-                        feature_shift_decoder=False,
-                        N_ensemble_configurations=10,
-                        batch_size_inference=16,
-                        differentiable_hps_as_style=False,
-                        average_logits=True,
-                        fp16_inference=False,
-                        normalize_with_sqrt=False,
-                        seed=0,
-                        no_grad=True,
-                        return_logits=False,
-                        **kwargs):
-    """
-
-    :param model:
-    :param eval_xs:
-    :param eval_ys:
-    :param eval_position:
-    :param rescale_features:
-    :param device:
-    :param max_features:
-    :param style:
-    :param inference_mode:
-    :param num_classes:
-    :param extend_features:
-    :param normalize_to_ranking:
-    :param softmax_temperature:
-    :param multiclass_decoder:
-    :param preprocess_transform:
-    :param categorical_feats:
-    :param feature_shift_decoder:
-    :param N_ensemble_configurations:
-    :param average_logits:
-    :param normalize_with_sqrt:
-    :param metric_used:
-    :return:
-    """
-    num_classes = len(torch.unique(eval_ys))
-
-    def predict(eval_xs, eval_ys, used_style, softmax_temperature, return_logits):
-        # Initialize results array size S, B, Classes
-
-        # no_grad disables inference_mode, because otherwise the gradients are lost
-        inference_mode_call = torch.inference_mode() if inference_mode and no_grad else NOP()
-        with inference_mode_call:
-            start = time.time()
-            output = model(
-                    (used_style.repeat(eval_xs.shape[1], 1) if used_style is not None else None, eval_xs, eval_ys.float()),
-                    single_eval_pos=eval_position)[:, :, 0:num_classes]
-
-            output = output[:, :, 0:num_classes] / torch.exp(softmax_temperature)
-            if not return_logits:
-                output = torch.nn.functional.softmax(output, dim=-1)
-            #else:
-            #    output[:, :, 1] = model((style.repeat(eval_xs.shape[1], 1) if style is not None else None, eval_xs, eval_ys.float()),
-            #               single_eval_pos=eval_position)
-
-            #    output[:, :, 1] = torch.sigmoid(output[:, :, 1]).squeeze(-1)
-            #    output[:, :, 0] = 1 - output[:, :, 1]
-
-        #print('RESULTS', eval_ys.shape, torch.unique(eval_ys, return_counts=True), output.mean(axis=0))
-
-        return output
-
-    def preprocess_input(eval_xs, preprocess_transform):
-        import warnings
-
-        if eval_xs.shape[1] > 1:
-            raise Exception("Transforms only allow one batch dim - TODO")
-
-        if eval_xs.shape[2] > max_features:
-            eval_xs = eval_xs[:, :, sorted(np.random.choice(eval_xs.shape[2], max_features, replace=False))]
-
-        if preprocess_transform != 'none':
-            if preprocess_transform == 'power' or preprocess_transform == 'power_all':
-                pt = PowerTransformer(standardize=True)
-            elif preprocess_transform == 'quantile' or preprocess_transform == 'quantile_all':
-                pt = QuantileTransformer(output_distribution='normal')
-            elif preprocess_transform == 'robust' or preprocess_transform == 'robust_all':
-                pt = RobustScaler(unit_variance=True)
-
-        # eval_xs, eval_ys = normalize_data(eval_xs), normalize_data(eval_ys)
-        eval_xs = normalize_data(eval_xs, normalize_positions=-1 if normalize_with_test else eval_position)
-
-        # Removing empty features
-        eval_xs = eval_xs[:, 0, :]
-        sel = [len(torch.unique(eval_xs[0:eval_ys.shape[0], col])) > 1 for col in range(eval_xs.shape[1])]
-        eval_xs = eval_xs[:, sel]
-
-        warnings.simplefilter('error')
-        if preprocess_transform != 'none':
-            eval_xs = eval_xs.cpu().numpy()
-            feats = set(range(eval_xs.shape[1])) if 'all' in preprocess_transform else set(
-                range(eval_xs.shape[1])) - set(categorical_feats)
-            for col in feats:
-                try:
-                    pt.fit(eval_xs[0:eval_position, col:col + 1])
-                    trans = pt.transform(eval_xs[:, col:col + 1])
-                    # print(scipy.stats.spearmanr(trans[~np.isnan(eval_xs[:, col:col+1])], eval_xs[:, col:col+1][~np.isnan(eval_xs[:, col:col+1])]))
-                    eval_xs[:, col:col + 1] = trans
-                except:
-                    pass
-            eval_xs = torch.tensor(eval_xs).float()
-        warnings.simplefilter('default')
-
-        eval_xs = eval_xs.unsqueeze(1)
-
-        # TODO: Caution there is information leakage when to_ranking is used, we should not use it
-        eval_xs = remove_outliers(eval_xs, normalize_positions=-1 if normalize_with_test else eval_position) \
-                if not normalize_to_ranking else normalize_data(to_ranking_low_mem(eval_xs))
-        # Rescale X
-        eval_xs = normalize_by_used_features_f(eval_xs, eval_xs.shape[-1], max_features,
-                                               normalize_with_sqrt=normalize_with_sqrt)
-
-        return eval_xs.to(device)
-
-    eval_xs, eval_ys = eval_xs.to(device), eval_ys.to(device)
-    eval_ys = eval_ys[:eval_position]
-
-    model.to(device)
-
-    model.eval()
-
-    import itertools
-    if not differentiable_hps_as_style:
-        style = None
-
-    if style is not None:
-        style = style.to(device)
-        style = style.unsqueeze(0) if len(style.shape) == 1 else style
-        num_styles = style.shape[0]
-        softmax_temperature = softmax_temperature if softmax_temperature.shape else softmax_temperature.unsqueeze(
-            0).repeat(num_styles)
-    else:
-        num_styles = 1
-        style = None
-        softmax_temperature = torch.log(torch.tensor([0.8]))
-
-    styles_configurations = range(0, num_styles)
-    def get_preprocess(i):
-        if i == 0:
-            return 'power_all'
-#            if i == 1:
-#                return 'robust_all'
-        if i == 1:
-            return 'none'
-
-    preprocess_transform_configurations = ['none', 'power_all'] if preprocess_transform == 'mix' else [preprocess_transform]
-
-    if seed is not None:
-        torch.manual_seed(seed)
-
-    feature_shift_configurations = torch.randperm(eval_xs.shape[2]) if feature_shift_decoder else [0]
-    class_shift_configurations = torch.randperm(len(torch.unique(eval_ys))) if multiclass_decoder == 'permutation' else [0]
-
-    ensemble_configurations = list(itertools.product(class_shift_configurations, feature_shift_configurations))
-    #default_ensemble_config = ensemble_configurations[0]
-
-    rng = random.Random(seed)
-    rng.shuffle(ensemble_configurations)
-    ensemble_configurations = list(itertools.product(ensemble_configurations, preprocess_transform_configurations, styles_configurations))
-    ensemble_configurations = ensemble_configurations[0:N_ensemble_configurations]
-    #if N_ensemble_configurations == 1:
-    #    ensemble_configurations = [default_ensemble_config]
-
-    output = None
-
-    eval_xs_transformed = {}
-    inputs, labels = [], []
-    start = time.time()
-    for ensemble_configuration in ensemble_configurations:
-        (class_shift_configuration, feature_shift_configuration), preprocess_transform_configuration, styles_configuration = ensemble_configuration
-
-        style_ = style[styles_configuration:styles_configuration+1, :] if style is not None else style
-        softmax_temperature_ = softmax_temperature[styles_configuration]
-
-        eval_xs_, eval_ys_ = eval_xs.clone(), eval_ys.clone()
-
-        if preprocess_transform_configuration in eval_xs_transformed:
-            eval_xs_ = eval_xs_transformed[preprocess_transform_configuration].clone()
-        else:
-            eval_xs_ = preprocess_input(eval_xs_, preprocess_transform=preprocess_transform_configuration)
-            if no_grad:
-                eval_xs_ = eval_xs_.detach()
-            eval_xs_transformed[preprocess_transform_configuration] = eval_xs_
-
-        eval_ys_ = ((eval_ys_ + class_shift_configuration) % num_classes).float()
-
-        eval_xs_ = torch.cat([eval_xs_[..., feature_shift_configuration:],eval_xs_[..., :feature_shift_configuration]],dim=-1)
-
-        # Extend X
-        if extend_features:
-            eval_xs_ = torch.cat(
-                [eval_xs_,
-                 torch.zeros((eval_xs_.shape[0], eval_xs_.shape[1], max_features - eval_xs_.shape[2])).to(device)], -1)
-        inputs += [eval_xs_]
-        labels += [eval_ys_]
-
-    inputs = torch.cat(inputs, 1)
-    inputs = torch.split(inputs, batch_size_inference, dim=1)
-    labels = torch.cat(labels, 1)
-    labels = torch.split(labels, batch_size_inference, dim=1)
-    #print('PREPROCESSING TIME', str(time.time() - start))
-    outputs = []
-    start = time.time()
-    for batch_input, batch_label in zip(inputs, labels):
-        #preprocess_transform_ = preprocess_transform if styles_configuration % 2 == 0 else 'none'
-        import warnings
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore",
-                                    message="None of the inputs have requires_grad=True. Gradients will be None")
-            warnings.filterwarnings("ignore",
-                                    message="torch.cuda.amp.autocast only affects CUDA ops, but CUDA is not available.  Disabling.")
-            if device == 'cpu':
-                output_batch = checkpoint(predict, batch_input, batch_label, style_, softmax_temperature_, True)
-            else:
-                with torch.cuda.amp.autocast(enabled=fp16_inference):
-                    output_batch = checkpoint(predict, batch_input, batch_label, style_, softmax_temperature_, True)
-        outputs += [output_batch]
-    #print('MODEL INFERENCE TIME ('+str(batch_input.device)+' vs '+device+', '+str(fp16_inference)+')', str(time.time()-start))
-
-    outputs = torch.cat(outputs, 1)
-    for i, ensemble_configuration in enumerate(ensemble_configurations):
-        (class_shift_configuration, feature_shift_configuration), preprocess_transform_configuration, styles_configuration = ensemble_configuration
-        output_ = outputs[:, i:i+1, :]
-        output_ = torch.cat([output_[..., class_shift_configuration:],output_[..., :class_shift_configuration]],dim=-1)
-
-        #output_ = predict(eval_xs, eval_ys, style_, preprocess_transform_)
-        if not average_logits and not return_logits:
-            # transforms every ensemble_configuration into a probability -> equal contribution of every configuration
-            output_ = torch.nn.functional.softmax(output_, dim=-1)
-        output = output_ if output is None else output + output_
-
-    output = output / len(ensemble_configurations)
-    if average_logits and not return_logits:
-        output = torch.nn.functional.softmax(output, dim=-1)
-
-    output = torch.transpose(output, 0, 1)
-
-    return output
-
-def get_params_from_config(c):
-    return {'max_features': c['num_features']
-        , 'rescale_features': c["normalize_by_used_features"]
-        , 'normalize_to_ranking': c["normalize_to_ranking"]
-        , 'normalize_with_sqrt': c.get("normalize_with_sqrt", False)
-            }
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_model_builder.py b/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_model_builder.py
deleted file mode 100644
index 11917ca..0000000
--- a/private_multitask_pfn/PFNs/pfns/scripts/tabpfn_model_builder.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from functools import partial
-import pfns.encoders as encoders
-
-from pfns.transformer import TransformerModel
-import torch
-import math
-
-import subprocess as sp
-import os
-
-def load_model_only_inference(path, filename, device='cpu'):
-    """
-    Loads a saved model from the specified position. This function only restores inference capabilities and
-    cannot be used for further training.
-    """
-
-    model_state, optimizer_state, config_sample = torch.load(os.path.join(path, filename), map_location='cpu')
-
-    if (('nan_prob_no_reason' in config_sample and config_sample['nan_prob_no_reason'] > 0.0) or
-        ('nan_prob_a_reason' in config_sample and config_sample['nan_prob_a_reason'] > 0.0) or
-        ('nan_prob_unknown_reason' in config_sample and config_sample['nan_prob_unknown_reason'] > 0.0)):
-        encoder = encoders.NanHandlingEncoder
-    else:
-        encoder = partial(encoders.Linear, replace_nan_by_zero=True)
-
-    n_out = config_sample['max_num_classes']
-
-    device = device if torch.cuda.is_available() else 'cpu:0'
-    encoder = encoder(config_sample['num_features'], config_sample['emsize'])
-
-    nhid = config_sample['emsize'] * config_sample['nhid_factor']
-    y_encoder_generator = encoders.get_Canonical(config_sample['max_num_classes']) \
-        if config_sample.get('canonical_y_encoder', False) else encoders.Linear
-
-    assert config_sample['max_num_classes'] > 2
-    loss = torch.nn.CrossEntropyLoss(reduction='none', weight=torch.ones(int(config_sample['max_num_classes'])))
-
-    model = TransformerModel(encoder, config_sample['emsize'], config_sample['nhead'], nhid,
-                             config_sample['nlayers'], y_encoder=y_encoder_generator(1, config_sample['emsize']),
-                             dropout=config_sample['dropout'], decoder_dict={'standard': (None, n_out)},
-                             efficient_eval_masking=config_sample['efficient_eval_masking'])
-
-    # print(f"Using a Transformer with {sum(p.numel() for p in model.parameters()) / 1000 / 1000:.{2}f} M parameters")
-
-    model.criterion = loss
-    module_prefix = 'module.'
-    model_state = {k.replace(module_prefix, ''): v for k, v in model_state.items()}
-    for key in list(model_state.keys()):
-        model_state[key.replace( 'decoder','decoder_dict.standard')] = model_state.pop(key)
-    model.load_state_dict(model_state)
-    model.to(device)
-    model.eval()
-    
-    return (float('inf'), float('inf'), model), config_sample # no loss measured
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tabular_metrics.py b/private_multitask_pfn/PFNs/pfns/scripts/tabular_metrics.py
deleted file mode 100644
index b702f5a..0000000
--- a/private_multitask_pfn/PFNs/pfns/scripts/tabular_metrics.py
+++ /dev/null
@@ -1,226 +0,0 @@
-"""
-===============================
-Metrics calculation
-===============================
-Includes a few metric as well as functions composing metrics on results files.
-
-"""
-
-import numpy as np
-import torch
-from sklearn.metrics import roc_auc_score, accuracy_score, balanced_accuracy_score, average_precision_score, mean_squared_error, mean_absolute_error, r2_score
-from scipy.stats import rankdata
-import pandas as pd
-
-def root_mean_squared_error_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    return torch.sqrt(torch.nn.functional.mse_loss(target, pred))
-
-def mean_squared_error_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    return torch.nn.functional.mse_loss(target, pred)
-
-def mean_absolute_error_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    return torch.tensor(mean_absolute_error(target, pred))
-
-"""
-===============================
-Metrics calculation
-===============================
-"""
-def auc_metric(target, pred, multi_class='ovo', numpy=False):
-    lib = np if numpy else torch
-    try:
-        if not numpy:
-            target = torch.tensor(target) if not torch.is_tensor(target) else target
-            pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-        if len(lib.unique(target)) > 2:
-            if not numpy:
-                return torch.tensor(roc_auc_score(target, pred, multi_class=multi_class))
-            return roc_auc_score(target, pred, multi_class=multi_class)
-        else:
-            if len(pred.shape) == 2:
-                pred = pred[:, 1]
-            if not numpy:
-                return torch.tensor(roc_auc_score(target, pred))
-            return roc_auc_score(target, pred)
-    except ValueError as e:
-        print(e)
-        return np.nan if numpy else torch.tensor(np.nan)
-
-def accuracy_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    if len(torch.unique(target)) > 2:
-        return torch.tensor(accuracy_score(target, torch.argmax(pred, -1)))
-    else:
-        return torch.tensor(accuracy_score(target, pred[:, 1] > 0.5))
-
-def brier_score_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    target = torch.nn.functional.one_hot(target, num_classes=len(torch.unique(target)))
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    diffs = (pred - target)**2
-    return torch.mean(torch.sum(diffs, axis=1))
-
-def ece_metric(target, pred):
-  import torchmetrics
-  target = torch.tensor(target) if not torch.is_tensor(target) else target
-  pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-  return torchmetrics.functional.calibration_error(pred, target)
-
-
-def average_precision_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    if len(torch.unique(target)) > 2:
-        return torch.tensor(average_precision_score(target, torch.argmax(pred, -1)))
-    else:
-        return torch.tensor(average_precision_score(target, pred[:, 1] > 0.5))
-
-def balanced_accuracy_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    if len(torch.unique(target)) > 2:
-        return torch.tensor(balanced_accuracy_score(target, torch.argmax(pred, -1)))
-    else:
-        return torch.tensor(balanced_accuracy_score(target, pred[:, 1] > 0.5))
-
-def cross_entropy(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    if len(torch.unique(target)) > 2:
-        ce = torch.nn.CrossEntropyLoss()
-        return ce(pred.float(), target.long())
-    else:
-        bce = torch.nn.BCELoss()
-        return bce(pred[:, 1].float(), target.float())
-
-def r2_metric(target, pred):
-    target = torch.tensor(target) if not torch.is_tensor(target) else target
-    pred = torch.tensor(pred) if not torch.is_tensor(pred) else pred
-    return torch.tensor(neg_r2(target, pred))
-
-def neg_r2(target, pred):
-    return -r2_score(pred.float(), target.float())
-
-def is_classification(metric_used):
-    if metric_used.__name__ in ["auc_metric", "cross_entropy"]:
-        return True
-    return False
-
-def time_metric():
-    """
-    Dummy function, will just be used as a handler.
-    """
-    pass
-
-def count_metric(x, y):
-    """
-    Dummy function, returns one count per dataset.
-    """
-    return 1
-
-"""
-===============================
-Metrics composition
-===============================
-"""
-def calculate_score_per_method(metric, name:str, global_results:dict, ds:list, eval_positions:list, aggregator:str='mean'):
-    """
-    Calculates the metric given by 'metric' and saves it under 'name' in the 'global_results'
-
-    :param metric: Metric function
-    :param name: Name of metric in 'global_results'
-    :param global_results: Dicrtonary containing the results for current method for a collection of datasets
-    :param ds: Dataset to calculate metrics on, a list of dataset properties
-    :param eval_positions: List of positions to calculate metrics on
-    :param aggregator: Specifies way to aggregate results across evaluation positions
-    :return:
-    """
-    aggregator_f = np.nanmean if aggregator == 'mean' else np.nansum
-    for pos in eval_positions:
-        valid_positions = 0
-        for d in ds:
-            if f'{d[0]}_outputs_at_{pos}' in global_results:
-                preds = global_results[f'{d[0]}_outputs_at_{pos}']
-                y = global_results[f'{d[0]}_ys_at_{pos}']
-
-                preds, y = preds.detach().cpu().numpy() if torch.is_tensor(
-                    preds) else preds, y.detach().cpu().numpy() if torch.is_tensor(y) else y
-
-                try:
-                    if metric == time_metric:
-                        global_results[f'{d[0]}_{name}_at_{pos}'] = global_results[f'{d[0]}_time_at_{pos}']
-                        valid_positions = valid_positions + 1
-                    else:
-                        global_results[f'{d[0]}_{name}_at_{pos}'] = aggregator_f(
-                            [metric(y[split], preds[split]) for split in range(y.shape[0])])
-                        valid_positions = valid_positions + 1
-                except Exception as err:
-                    print(f'Error calculating metric with {err}, {type(err)} at {d[0]} {pos} {name}')
-                    global_results[f'{d[0]}_{name}_at_{pos}'] = np.nan
-            else:
-                global_results[f'{d[0]}_{name}_at_{pos}'] = np.nan
-
-        if valid_positions > 0:
-            global_results[f'{aggregator}_{name}_at_{pos}'] = aggregator_f([global_results[f'{d[0]}_{name}_at_{pos}'] for d in ds])
-        else:
-            global_results[f'{aggregator}_{name}_at_{pos}'] = np.nan
-
-    for d in ds:
-        metrics = [global_results[f'{d[0]}_{name}_at_{pos}'] for pos in eval_positions]
-        metrics = [m for m in metrics if not np.isnan(m)]
-        global_results[f'{d[0]}_{aggregator}_{name}'] = aggregator_f(metrics) if len(metrics) > 0 else np.nan
-
-    metrics = [global_results[f'{aggregator}_{name}_at_{pos}'] for pos in eval_positions]
-    metrics = [m for m in metrics if not np.isnan(m)]
-    global_results[f'{aggregator}_{name}'] = aggregator_f(metrics) if len(metrics) > 0 else np.nan
-
-
-def calculate_score(metric, name, global_results, ds, eval_positions, aggregator='mean', limit_to=''):
-    """
-    Calls calculate_metrics_by_method with a range of methods. See arguments of that method.
-    :param limit_to: This method will not get metric calculations.
-    """
-    for m in global_results:
-        if limit_to not in m:
-            continue
-        calculate_score_per_method(metric, name, global_results[m], ds, eval_positions, aggregator=aggregator)
-
-
-def make_metric_matrix(global_results, methods, pos, name, ds):
-    result = []
-    for m in global_results:
-        try:
-            result += [[global_results[m][d[0] + '_' + name + '_at_' + str(pos)] for d in ds]]
-        except Exception as e:
-            #raise(e)
-            result += [[np.nan]]
-    result = np.array(result)
-    result = pd.DataFrame(result.T, index=[d[0] for d in ds], columns=[k for k in list(global_results.keys())])
-
-    matrix_means, matrix_stds, matrix_per_split = [], [], []
-
-    for method in methods:
-        matrix_means += [result.iloc[:, [c.startswith(method+'_time') for c in result.columns]].mean(axis=1)]
-        matrix_stds += [result.iloc[:, [c.startswith(method+'_time') for c in result.columns]].std(axis=1)]
-        matrix_per_split += [result.iloc[:, [c.startswith(method+'_time') for c in result.columns]]]
-
-    matrix_means = pd.DataFrame(matrix_means, index=methods).T
-    matrix_stds = pd.DataFrame(matrix_stds, index=methods).T
-
-    return matrix_means, matrix_stds, matrix_per_split
-
-
-def make_ranks_and_wins_table(matrix):
-    for dss in matrix.T:
-        matrix.loc[dss] = rankdata(-matrix.round(3).loc[dss])
-    ranks_acc = matrix.mean()
-    wins_acc = (matrix == 1).sum()
-
-    return ranks_acc, wins_acc
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/scripts/tune_input_warping.py b/private_multitask_pfn/PFNs/pfns/scripts/tune_input_warping.py
deleted file mode 100644
index e1a57b1..0000000
--- a/private_multitask_pfn/PFNs/pfns/scripts/tune_input_warping.py
+++ /dev/null
@@ -1,336 +0,0 @@
-import random
-from collections import OrderedDict
-import torch
-
-from .. import utils
-from ..priors.hebo_prior import Warp
-from gpytorch.priors import LogNormalPrior
-
-from botorch.optim import module_to_array, set_params_with_array
-import scipy
-from scipy.optimize import Bounds
-from typing import OrderedDict
-import numpy as np
-from functools import partial
-
-device = 'cpu:0'
-
-
-def fit_lbfgs(x, w, nll, num_grad_steps=10, ignore_prior=True, params0=None):
-    bounds_ = {}
-    if hasattr(w, "named_parameters_and_constraints"):
-        for param_name, _, constraint in w.named_parameters_and_constraints():
-            if constraint is not None and not constraint.enforced:
-                bounds_[param_name] = constraint.lower_bound, constraint.upper_bound
-    params0_, property_dict, bounds_ = module_to_array(
-        module=w, bounds=bounds_, exclude=None
-    )
-    if params0 is None: params0 = params0_
-    bounds = Bounds(lb=bounds_[0], ub=bounds_[1], keep_feasible=True)
-
-    def loss_f(params, w):
-        w = set_params_with_array(w, params, property_dict)
-        w.requires_grad_(True)
-        loss = 0.
-        if not ignore_prior:
-            for name, module, prior, closure, _ in w.named_priors():
-                prior_term = prior.log_prob(closure(module))
-                loss -= prior_term.sum(dim=-1)
-        negll = nll(w(x.to(torch.float64)).to(torch.float)).sum()
-        #if loss != 0.:
-        #    print(loss.item(), negll.item())
-        loss = loss + negll
-        return w, loss
-
-    def opt_f(params, w):
-        w, loss = loss_f(params, w)
-
-        w.zero_grad()
-        loss.backward()
-        grad = []
-        param_dict = OrderedDict(w.named_parameters())
-
-        for p_name in property_dict:
-            t = param_dict[p_name].grad
-            if t is None:
-                # this deals with parameters that do not affect the loss
-                grad.append(np.zeros(property_dict[p_name].shape.numel()))
-            else:
-                grad.append(t.detach().view(-1).cpu().double().clone().numpy())
-        w.zero_grad()
-        # print(neg_mean_acq.detach().numpy(), x_eval.grad.detach().view(*x.shape).numpy())
-        return loss.item(), np.concatenate(grad)
-
-    if num_grad_steps:
-        return scipy.optimize.minimize(partial(opt_f, w=w), params0, method='L-BFGS-B', jac=True, bounds=bounds,
-                                       options={'maxiter': num_grad_steps})
-    else:
-        with torch.no_grad():
-            return loss_f(params0, w), params0
-
-
-def log_vs_nonlog(x, w, *args, **kwargs):
-    if "true_nll" in kwargs:
-        true_nll = kwargs["true_nll"]
-        del kwargs["true_nll"]
-    else:
-        true_nll = None
-    params, property_dict, _ = module_to_array(module=w)
-    no_log = np.ones_like(params)
-    log = np.array([1.9, 0.11] * (int(len(property_dict) / 2)))
-    loss_no_log = fit_lbfgs(x, w, *args, **{**kwargs, 'num_grad_steps': 0}, params0=no_log)
-    loss_log = fit_lbfgs(x, w, *args, **{**kwargs, 'num_grad_steps': 0}, params0=log)
-    print("loss no log", loss_no_log[0][1], "loss log", loss_log[0][1])
-    if loss_no_log[0][1] < loss_log[0][1]:
-        set_params_with_array(module=w, x=loss_no_log[1], property_dict=property_dict)
-    if true_nll:
-        best_params, _, _ = module_to_array(module=w)
-        print("true nll", fit_lbfgs(x, w, true_nll, **{**kwargs, 'num_grad_steps': 0}, params0=best_params))
-
-
-def fit_lbfgs_with_restarts(x, w, *args, old_solution=None, rs_size=50, **kwargs):
-    if "true_nll" in kwargs:
-        true_nll = kwargs["true_nll"]
-        del kwargs["true_nll"]
-    else:
-        true_nll = None
-    rs_results = []
-    if old_solution:
-        rs_results.append(fit_lbfgs(x, old_solution, *args, **{**kwargs, 'num_grad_steps': 0}))
-    for i in range(rs_size):
-        with torch.no_grad():
-            w.concentration0[:] = w.concentration0_prior()
-            w.concentration1[:] = w.concentration1_prior()
-        rs_results.append(fit_lbfgs(x, w, *args, **{**kwargs, 'num_grad_steps': 0}))
-    best_r = min(rs_results, key=lambda r: r[0][1])
-    print('best r', best_r)
-    with torch.set_grad_enabled(True):
-        r = fit_lbfgs(x, w, *args, **kwargs, params0=best_r[1])
-    _, property_dict, _ = module_to_array(module=w)
-    set_params_with_array(module=w, x=r.x, property_dict=property_dict)
-    print('final r', r)
-    if true_nll:
-        print("true nll", fit_lbfgs(x, w, true_nll, **{**kwargs, 'num_grad_steps': 0}, params0=r.x))
-    return r
-
-
-# use seed 0 for sampling indices, and reset seed afterwards
-old_seed = random.getstate()
-random.seed(0)
-one_out_indices_sampled_per_num_obs = [None]+[random.sample(range(i), min(10, i)) for i in range(1, 100)]
-random.setstate(old_seed)
-
-# use seed 0 for sampling subsets
-old_seed = random.getstate()
-random.seed(0)
-subsets = [None]+[[random.sample(range(i), i//2) for _ in range(10)] for i in range(1, 100)]
-neg_subsets = [None]+[[list(set(range(i)) - set(s)) for s in ss] for i, ss in enumerate(subsets[1:], 1)]
-random.setstate(old_seed)
-
-
-
-def fit_input_warping(model, x, y, nll_type='fast', old_solution=None, opt_method="lbfgs", **kwargs):
-    """
-
-    :param model:
-    :param x: shape (n, d)
-    :param y: shape (n, 1)
-    :param nll_type:
-    :param kwargs: Possible kwargs: `num_grad_steps`, `rs_size`
-    :return:
-    """
-    device = x.device
-    assert y.device == device, y.device
-
-    model.requires_grad_(False)
-
-    w = Warp(range(x.shape[1]),
-             concentration1_prior=LogNormalPrior(torch.tensor(0.0, device=device), torch.tensor(.75, device=device)),
-             concentration0_prior=LogNormalPrior(torch.tensor(0.0, device=device), torch.tensor(.75, device=device)),
-             eps=1e-12)
-    w.to(device)
-
-    def fast_nll(x):  # noqa actually used with `eval` below
-        model.requires_grad_(False)
-        if model.style_encoder is not None:
-            style = torch.zeros(1, 1, dtype=torch.int64, device=device)
-            utils.print_once("WARNING: using style 0 for input warping, this is set for nonmyopic BO setting.")
-        else:
-            style = None
-        logits = model(x[:, None], y[:, None], x[:, None], style=style, only_return_standard_out=True)
-        loss = model.criterion(logits, y[:, None]).squeeze(1)
-        return loss
-
-    def true_nll(x): # noqa actually used with `eval` below
-        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
-        model.requires_grad_(False)
-        total_nll = 0.
-        for cutoff in range(len(x)):
-            logits = model(x[:cutoff, None], y[:cutoff, None], x[cutoff:cutoff + 1, None])
-            total_nll = total_nll + model.criterion(logits, y[cutoff:cutoff + 1, None]).squeeze()
-        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
-        return total_nll
-
-    def repeated_true_nll(x): # noqa actually used with `eval` below
-        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
-        model.requires_grad_(False)
-        total_nll = 0.
-        for i in range(5):
-            rs = np.random.RandomState(i)
-            shuffle_idx = rs.permutation(len(x))
-            x_ = x.clone()[shuffle_idx]
-            y_ = y.clone()[shuffle_idx]
-            for cutoff in range(len(x)):
-                logits = model(x_[:cutoff, None], y_[:cutoff, None], x_[cutoff:cutoff + 1, None])
-                total_nll = total_nll + model.criterion(logits, y_[cutoff:cutoff + 1, None]).squeeze()
-        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
-        return total_nll
-
-    def repeated_true_100_nll(x): # noqa actually used with `eval` below
-        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
-        model.requires_grad_(False)
-        total_nll = 0.
-        for i in range(100):
-            rs = np.random.RandomState(i)
-            shuffle_idx = rs.permutation(len(x))
-            x_ = x.clone()[shuffle_idx]
-            y_ = y.clone()[shuffle_idx]
-            for cutoff in range(len(x)):
-                logits = model(x_[:cutoff, None], y_[:cutoff, None], x_[cutoff:cutoff + 1, None])
-                total_nll = total_nll + model.criterion(logits, y_[cutoff:cutoff + 1, None]).squeeze()
-        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
-        return total_nll / 100
-
-    def batched_repeated_chunked_true_nll(x): # noqa actually used with `eval` below
-        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
-        assert len(x.shape) == 2 and len(y.shape) == 1
-        model.requires_grad_(False)
-        n_features = x.shape[1] if len(x.shape) > 1 else 1
-        batch_size = 10
-
-        X = []
-        Y = []
-
-        for i in range(batch_size):
-            #if i == 0:
-            #    shuffle_idx = list(range(len(x)))
-            #else:
-            rs = np.random.RandomState(i)
-            shuffle_idx = rs.permutation(len(x))
-            X.append(x.clone()[shuffle_idx])
-            Y.append(y.clone()[shuffle_idx])
-        X = torch.stack(X, dim=1).view((x.shape[0], batch_size, n_features))
-        Y = torch.stack(Y, dim=1).view((x.shape[0], batch_size, 1))
-
-        total_nll = 0.
-        batch_indizes = sorted(list(set(np.linspace(0, len(x), 10, dtype=int))))
-
-        for chunk_start, chunk_end in zip(batch_indizes[:-1], batch_indizes[1:]):
-            X_cutoff = X[:chunk_start]
-            Y_cutoff = Y[:chunk_start]
-            X_after_cutoff = X[chunk_start:chunk_end]
-            Y_after_cutoff = Y[chunk_start:chunk_end]
-
-            pending_x = X_after_cutoff.reshape(X_after_cutoff.shape[0], batch_size, n_features)  # n_pen x batch_size x n_feat
-            observed_x = X_cutoff.reshape(X_cutoff.shape[0], batch_size, n_features)  # n_obs x batch_size x n_feat
-            X_tmp = torch.cat((observed_x, pending_x), dim=0)  # (n_obs+n_pen) x batch_size x n_feat
-
-            logits = model((X_tmp, Y_cutoff), single_eval_pos=int(chunk_start))
-            total_nll = total_nll + model.criterion(logits, Y_after_cutoff).sum()
-        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
-        return total_nll
-
-    def batched_repeated_true_nll(x): # noqa actually used with `eval` below
-        assert model.style_encoder is None, "true nll not implemented for style encoder, see above for an example impl"
-        model.requires_grad_(False)
-        n_features = x.shape[1] if len(x.shape) > 1 else 1
-        batch_size = 10
-
-        X = []
-        Y = []
-
-        for i in range(batch_size):
-            #if i == 0:
-            #    shuffle_idx = list(range(len(x)))
-            #else:
-            rs = np.random.RandomState(i)
-            shuffle_idx = rs.permutation(len(x))
-            X.append(x.clone()[shuffle_idx])
-            Y.append(y.clone()[shuffle_idx])
-        X = torch.cat(X, dim=1).reshape((x.shape[0], batch_size, n_features))
-        Y = torch.cat(Y, dim=1).reshape((x.shape[0], batch_size, 1))
-
-        total_nll = 0.
-
-        for cutoff in range(0, len(x)):
-            X_cutoff = X[:cutoff]
-            Y_cutoff = Y[:cutoff]
-            X_after_cutoff = X[cutoff:cutoff+1]
-            Y_after_cutoff = Y[cutoff:cutoff+1]
-
-            pending_x = X_after_cutoff.reshape(X_after_cutoff.shape[0], batch_size, n_features)  # n_pen x batch_size x n_feat
-            observed_x = X_cutoff.reshape(X_cutoff.shape[0], batch_size, n_features)  # n_obs x batch_size x n_feat
-            X_tmp = torch.cat((observed_x, pending_x), dim=0)  # (n_obs+n_pen) x batch_size x n_feat
-
-            pad_y = torch.zeros((X_after_cutoff.shape[0], batch_size, 1))  # (n_obs+n_pen) x batch_size
-            Y_tmp = torch.cat((Y_cutoff, pad_y), dim=0)
-
-            logits = model((X_tmp, Y_tmp), single_eval_pos=cutoff)
-            total_nll = total_nll + model.criterion(logits, Y_after_cutoff).sum()
-        assert len(total_nll.shape) == 0, f"{total_nll.shape=}"
-        return total_nll
-
-    def one_out_nll(x):  # noqa actually used with `eval` below
-        assert model.style_encoder is None, "one out nll not implemented for style encoder, see above for an example impl"
-        # x shape: (n, d)
-        # iterate over a pre-defined set of
-        model.requires_grad_(False)
-        #indices = one_out_indices_sampled_per_num_obs[len(x)]
-        indices = list(range(x.shape[0]))
-
-        # create batch by moving the one out index to the end
-        eval_x = x[indices][None] # shape (1, 10, d)
-        eval_y = y[indices][None] # shape (1, 10, 1)
-        # all other indices are used for training
-        train_x = torch.stack([torch.cat([x[:i], x[i + 1:]]) for i in indices], 1)
-        train_y = torch.stack([torch.cat([y[:i], y[i + 1:]]) for i in indices], 1)
-
-        logits = model(train_x, train_y, eval_x)
-        return model.criterion(logits, eval_y).squeeze(0)
-
-    def subset_nll(x):  # noqa actually used with `eval` below
-        assert model.style_encoder is None, "subset nll not implemented for style encoder, see above for an example impl"
-        # x shape: (n, d)
-        # iterate over a pre-defined set of
-        model.requires_grad_(False)
-        eval_indices = torch.tensor(subsets[len(x)])
-        train_indices = torch.tensor(neg_subsets[len(x)])
-
-        # batch by using all eval_indices
-        eval_x = x[eval_indices.flatten()].view(eval_indices.shape + (-1,)) # shape (10, n//2, d)
-        eval_y = y[eval_indices.flatten()].view(eval_indices.shape + (-1,)) # shape (10, n//2, 1)
-        # all other indices are used for training
-        train_x = x[train_indices.flatten()].view(train_indices.shape + (-1,)) # shape (10, n//2, d)
-        train_y = y[train_indices.flatten()].view(train_indices.shape + (-1,)) # shape (10, n//2, 1)
-
-        logits = model(train_x.transpose(0, 1), train_y.transpose(0, 1), eval_x.transpose(0, 1))
-        return model.criterion(logits, eval_y.transpose(0, 1))
-
-    if opt_method == "log_vs_nolog":
-        log_vs_nonlog(x, w, eval(nll_type + '_nll'),
-            ignore_prior=True, # true_nll=repeated_true_100_nll,
-            **kwargs)
-    elif opt_method == "lbfgs":
-        fit_lbfgs_with_restarts(
-            x, w, eval(nll_type + '_nll'),
-            ignore_prior=True, old_solution=old_solution, # true_nll=repeated_true_100_nll,
-            **kwargs)
-    elif opt_method == "lbfgs_w_prior":
-        fit_lbfgs_with_restarts(
-            x, w, eval(nll_type + '_nll'),
-            ignore_prior=False, old_solution=old_solution,  # true_nll=repeated_true_100_nll,
-            **kwargs)
-    else:
-        raise ValueError(opt_method)
-
-    return w
diff --git a/private_multitask_pfn/PFNs/pfns/train.py b/private_multitask_pfn/PFNs/pfns/train.py
deleted file mode 100644
index 972a7b1..0000000
--- a/private_multitask_pfn/PFNs/pfns/train.py
+++ /dev/null
@@ -1,878 +0,0 @@
-from __future__ import annotations
-
-import copy
-
-import inspect
-
-import itertools
-import logging
-import time
-from contextlib import nullcontext
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-import yaml
-from torch import nn
-from tqdm import tqdm
-
-from . import positional_encodings, priors, utils
-from .bar_distribution import (
-    BarDistribution,
-    FullSupportBarDistribution,
-    get_bucket_limits,
-    get_custom_bar_dist,
-)
-from .priors import prior
-from .transformer import TransformerModel
-from .utils import (
-    get_cosine_schedule_with_warmup,
-    get_openai_lr,
-    get_uniform_single_eval_pos_sampler,
-    get_weighted_single_eval_pos_sampler,
-    init_dist,
-    StoreDictKeyPair,
-)
-import wandb
-import os
-import json
-
-class ProbabilisticRankingLoss(nn.Module):
-    
-    def __init__(self, reduction="none"):
-        super().__init__()
-        self.reduction = reduction
-    
-    def forward(self, output, y_true, task_ids):
-        mu = output[..., 0]
-        sigma = output[..., 1].exp().sqrt()
-        
-        assert torch.all(task_ids[:, 0] == task_ids.max(1)[0]) and torch.all(task_ids[:, 0] == task_ids.min(1)[0])
-        task_ids = task_ids[:, 0].squeeze()
-        
-        results = torch.zeros_like(y_true)
-        
-        for task_id in task_ids.unique():
-            task_mask = (task_ids == task_id)
-            task_mu = mu[task_mask]
-            task_sigma = sigma[task_mask]
-            task_y_true = y_true[task_mask]
-            
-            # Compute pairwise differences
-            y_diff = task_y_true.unsqueeze(0) - task_y_true.unsqueeze(1)  # Shape: (N, N)
-            mu_diff = task_mu.unsqueeze(0) - task_mu.unsqueeze(1)        # Shape: (N, N)
-            sigma_combined = torch.sqrt(task_sigma.unsqueeze(0)**2 + task_sigma.unsqueeze(1)**2)  # Shape: (N, N)
-
-            # Mask to ignore self-comparisons and identical targets
-            valid_mask = y_diff != 0  # Exclude y_diff == 0 (no ranking required)
-
-            # Compute probabilistic scores
-            prob = 0.5 * (1.0 + torch.erf(mu_diff / (sigma_combined * torch.sqrt(torch.tensor(2.0)))))
-
-            # Loss for pairs where y_true[i] > y_true[j]
-            positive_loss = -torch.log(prob + 1e-8) * (y_diff > 0)
-
-            # Loss for pairs where y_true[i] < y_true[j]
-            negative_loss = -torch.log(1.0 - prob + 1e-8) * (y_diff < 0)
-
-            # Combine losses and apply valid mask
-            total_loss = (positive_loss + negative_loss) * valid_mask
-    
-            loss = total_loss.sum(1) / valid_mask.sum(1)
-            results[task_mask] = loss
-            
-        if self.reduction == "mean":
-            return results.mean(0)
-
-        return loss
-    
-
-class PairwiseRankingLoss(nn.Module):
-    def __init__(self, bar_loss):
-        """
-        Initializes the PairwiseRankingLoss module.
-        """
-        super().__init__()
-        self.criterion = bar_loss
-
-    def forward(self, output, y_true, task_ids):
-        """
-        Compute the pairwise ranking loss based on incorrect orderings.
-
-        Args:
-            output (torch.Tensor): Predicted scores for the samples.
-            target (torch.Tensor): Ground truth values for the samples.
-
-        Returns:
-            torch.Tensor: The computed pairwise ranking loss (fraction of incorrectly ordered pairs).
-        """
-        mu = self.criterion.mean(output)
-        
-        assert torch.all(task_ids[:, 0] == task_ids.max(1)[0]) and torch.all(task_ids[:, 0] == task_ids.min(1)[0])
-        task_ids = task_ids[:, 0].squeeze()
-        
-        results = torch.zeros_like(y_true)
-       
-        for task_id in task_ids.unique():
-            task_mask = (task_ids == task_id)
-            task_mu = mu[task_mask]
-            task_y_true = y_true[task_mask]
-
-            # Compute pairwise differences
-            y_diff = task_y_true.unsqueeze(0) - task_y_true.unsqueeze(1)  # Shape: (N, N)
-            mu_diff = task_mu.unsqueeze(0) - task_mu.unsqueeze(1)        # Shape: (N, N)
-
-            # Mask to ignore self-comparisons and identical targets
-            valid_mask = y_diff != 0  # Exclude y_diff == 0 (no ranking required)
-
-            # Loss for pairs where y_true[i] > y_true[j]
-            positive_loss = (mu_diff <= 0).float() * (y_diff > 0)
-
-            # Loss for pairs where y_true[i] < y_true[j]
-            negative_loss = (mu_diff >= 0).float() * (y_diff < 0)
-
-            # Combine losses and apply valid mask
-            total_loss = (positive_loss + negative_loss) * valid_mask
-
-            # Aggregate losses for the task
-            task_loss = total_loss.sum() / valid_mask.sum()
-            results[task_mask] = task_loss
-
-        return results
-    
-
-class GaussianRankingLoss(nn.Module):
-    def __init__(self, gaussian_weight=1.0, ranking_weight=1.0):
-        super().__init__()
-        self.gaussian_weight = gaussian_weight
-        self.ranking_weight = ranking_weight
-        self.gaussian_loss = nn.GaussianNLLLoss(full=True, reduction="none")
-        self.ranking_loss = ProbabilisticRankingLoss(reduction="none")
-        
-    def forward(self, output, y_true, task_ids):
-        mu = output[..., 0]
-        var = output[..., 1].exp()
-        
-        gaussian = self.gaussian_loss(mu, y_true, var)
-        ranking = self.ranking_loss(output, y_true, task_ids)
-        
-        return self.gaussian_weight * gaussian + self.ranking_weight * ranking
-
-
-class BarRankingLoss(nn.Module):
-    def __init__(self, bar_loss, bar_weight=1.0, ranking_weight=1.0):
-        super().__init__()
-        self.bar_weight = bar_weight
-        self.ranking_weight = ranking_weight
-        self.bar_loss = bar_loss
-        self.ranking_loss = PairwiseRankingLoss(bar_loss)
-        
-    def forward(self, output, y_true, task_ids):
-        bar = self.bar_loss(output, y_true)
-        ranking = self.ranking_loss(output, y_true, task_ids)
-        
-        return self.bar_weight * bar + self.ranking_weight * ranking
-
-
-    
-class Losses:
-    gaussian = nn.GaussianNLLLoss(full=True, reduction="none")
-    mse = nn.MSELoss(reduction="none")
-    ce = lambda num_classes: nn.CrossEntropyLoss(
-        reduction="none", weight=torch.ones(num_classes)
-    )
-    bce = nn.BCEWithLogitsLoss(reduction="none")
-    get_BarDistribution = BarDistribution
-    prob = ProbabilisticRankingLoss()
-
-        
-    
-    def __repr__(self):
-        return f"ProbabilisticRankingLoss"
-
-
-def train(
-    train_loader: prior.PriorDataLoader | callable,
-    eval_loader: prior.PriorDataLoader | callable,
-    criterion,
-    encoder_generator,
-    task_encoder_generator,
-    emsize=200,
-    nhid=200,
-    nlayers=6,
-    nhead=2,
-    dropout=0.0,
-    epochs=10,
-    steps_per_epoch=100,
-    batch_size=200,
-    seq_len=10,
-    lr=None,
-    weight_decay=0.0,
-    warmup_epochs=10,
-    input_normalization=False,
-    y_encoder_generator=None,
-    pos_encoder_generator=None,
-    decoder_dict={},
-    train_extra_prior_kwargs_dict={},
-    eval_extra_prior_kwargs_dict={},
-    scheduler_generator=get_cosine_schedule_with_warmup,
-    load_weights_from_this_state_dict=None,
-    validation_period=10,
-    single_train_pos_gen=None,
-    gpu_device="cuda:0",
-    aggregate_k_gradients=1,
-    verbose=True,
-    style_encoder_generator=None,
-    epoch_callback=None,
-    step_callback=None,
-    continue_model=None,
-    continue_scheduler=None,
-    continue_optimizer=None,
-    continue_epoch=None,
-    initializer=None,
-    initialize_with_model=None,
-    train_mixed_precision=False,
-    efficient_eval_masking=True,
-    border_decoder=None,
-    num_global_att_tokens=0,
-    progress_bar=False,
-    print_every=5,
-    return_model=False,
-    num_features=None,
-    num_tasks=None,
-    attn_type="axial",
-    dirname=None,
-    target_aware=False,
-    task_embed_type="linear",
-    save_val_results=False,
-    **model_extra_args,
-):
-    device = gpu_device if torch.cuda.is_available() else "cpu:0"
-    # print(f"Using {device} device")
-    using_dist, rank, device = init_dist(device)
-    single_train_pos_gen = (
-        single_train_pos_gen
-        if callable(single_train_pos_gen)
-        else lambda: single_train_pos_gen
-    )
-    def train_pos_seq_len_sampler(*args):
-        single_train_pos = single_train_pos_gen(*args)
-        return single_train_pos, seq_len
-
-    # # LILY: what should this be
-    # single_eval_pos_gen = utils.get_uniform_single_eval_pos_sampler(
-    #     seq_len - num_tasks, min_len=num_tasks
-    # )
-    def eval_pos_seq_len_sampler(*args):
-        single_eval_pos = single_train_pos_gen(*args)
-        return single_eval_pos, seq_len
-    
-    if inspect.isclass(train_loader) and issubclass(
-        train_loader, prior.PriorDataLoader
-    ):
-        train_data_class = train_loader
-    else:
-        train_data_class = priors.utils.get_batch_to_dataloader(
-            train_loader
-        )
-    
-    if inspect.isclass(eval_loader) and issubclass(
-        eval_loader, prior.PriorDataLoader
-    ):
-        eval_loader_class = eval_loader
-    else:
-        eval_loader_class = priors.utils.get_batch_to_dataloader(
-            eval_loader
-        )
-
-
-    train_dl = train_data_class(
-        num_steps=steps_per_epoch,
-        batch_size=batch_size,
-        eval_pos_seq_len_sampler=train_pos_seq_len_sampler,
-        seq_len_maximum=seq_len,
-        device=device,
-        **train_extra_prior_kwargs_dict,
-    )
-    
-    eval_dl = eval_loader_class(
-        num_steps=steps_per_epoch,
-        batch_size=batch_size,
-        eval_pos_seq_len_sampler=eval_pos_seq_len_sampler,
-        seq_len_maximum=seq_len,
-        device=device,
-        **eval_extra_prior_kwargs_dict,
-    )
-
-    test_batch: prior.Batch = train_dl.get_test_batch()
-    style_def = test_batch.style
-    # print(
-    #     f"Style definition of first 3 examples: {style_def[:3] if style_def is not None else None}"
-    # )
-    style_encoder = (
-        style_encoder_generator(style_def.shape[1], emsize)
-        if (style_def is not None)
-        else None
-    )
-    pos_encoder = (pos_encoder_generator or positional_encodings.NoPositionalEncoding)(
-        emsize, seq_len * 2
-    )
-    if isinstance(criterion, nn.GaussianNLLLoss):
-        n_out = 2
-    elif (
-        isinstance(criterion, BarDistribution)
-        or "BarDistribution" in criterion.__class__.__name__
-    ):  # TODO remove this fix (only for dev)
-        n_out = criterion.num_bars
-    elif isinstance(criterion, nn.CrossEntropyLoss):
-        n_out = criterion.weight.shape[0]
-    elif isinstance(criterion, ProbabilisticRankingLoss):
-        n_out = 2
-    elif isinstance(criterion, GaussianRankingLoss):
-        n_out = 2
-    elif isinstance(criterion, BarRankingLoss):
-        n_out = criterion.bar_loss.num_bars
-    else:
-        n_out = 1
-
-    # border_decoder = None if border_decoder is None else border_decoder(emsize, criterion.num_bars + 1).to(device)
-
-    if continue_model:
-        model = continue_model
-    else:
-        decoder_dict = decoder_dict if decoder_dict else {"standard": (None, n_out)}
-
-        decoder_once_dict = {}
-        if test_batch.mean_prediction is not None:
-            decoder_once_dict["mean_prediction"] = decoder_dict["standard"]
-
-        if "axial" in attn_type:
-            encoder = encoder_generator(1, emsize)
-        else:
-            if task_embed_type == "feature":
-                encoder = encoder_generator(train_dl.num_features, train_dl.num_tasks, emsize)
-            else:
-                encoder = encoder_generator(train_dl.num_features, emsize)
-            
-        if task_embed_type == "linear":
-            task_encoder = task_encoder_generator(1, emsize)
-        elif task_embed_type == "onehot_linear":
-            task_encoder = task_encoder_generator(train_dl.num_tasks, emsize)
-        elif task_embed_type in ["feature", "task_attn", "task_attn_shared", "task_attn_opt", "hier", "hier_single"]:
-            task_encoder = None
-        else:
-            raise ValueError("Invalid task_embed_type")
-        
-        # transformer_class = HierarchicalTransformerModel if "hier" in task_embed_type else TransformerModel
-        model = TransformerModel(
-            encoder=encoder,
-            nhead=nhead,
-            ninp=emsize,
-            nhid=nhid,
-            nlayers=nlayers,
-            dropout=dropout,
-            style_encoder=style_encoder,
-            y_encoder=y_encoder_generator(1, emsize),
-            input_normalization=input_normalization,
-            pos_encoder=pos_encoder,
-            decoder_dict=decoder_dict,
-            init_method=initializer,
-            efficient_eval_masking=efficient_eval_masking,
-            decoder_once_dict=decoder_once_dict,
-            num_global_att_tokens=num_global_att_tokens,
-            num_features=num_features,
-            num_tasks=num_tasks,
-            attn_type=attn_type,
-            task_encoder=task_encoder,
-            task_embed_type=task_embed_type,
-            include_global=(not "single" in task_embed_type),
-            hierarchical=("hier" in task_embed_type),
-            **model_extra_args,
-        )
-    model.criterion = criterion
-    if load_weights_from_this_state_dict is not None:
-        model.load_state_dict(load_weights_from_this_state_dict)
-    if initialize_with_model is not None:
-        model.init_from_small_model(initialize_with_model)
-
-    print(
-        f"Using a Transformer with {sum(p.numel() for p in model.parameters())/1000/1000:.{2}f} M parameters"
-    )
-
-    try:
-        for (k, v), (k2, v2) in zip(
-            model.state_dict().items(), initialize_with_model.state_dict().items()
-        ):
-            print(k, ((v - v2) / v).abs().mean(), v.shape)
-    except Exception:
-        pass
-
-    # learning rate
-    if continue_optimizer:
-        optimizer = continue_optimizer
-        scheduler = continue_scheduler
-    else:
-        if lr is None:
-            lr = get_openai_lr(model)
-            print(f"Using OpenAI max lr of {lr}.")
-        optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
-        scheduler = scheduler_generator(
-            optimizer, warmup_epochs, epochs if epochs is not None else 100
-        )  # when training for fixed time lr schedule takes 100 steps
-
-    if return_model:
-        return model, optimizer, scheduler
-    
-    wandb.watch(model, log="all")
-    model.to(device)
-    # optimizer is on device
-    for state in optimizer.state.values():
-        for k, v in state.items():
-            if isinstance(v, torch.Tensor):
-                state[k] = v.to(device)
-                
-    if using_dist:
-        print("Distributed training")
-        model = torch.nn.parallel.DistributedDataParallel(
-            model,
-            device_ids=[rank],
-            output_device=rank,
-            broadcast_buffers=False,
-            find_unused_parameters=test_batch.mean_prediction is not None,
-        )
-        train_dl.model = (
-            model.module
-        )  # use local model, should not use multi-gpu functionality..
-        eval_dl.model = model.module
-    else:
-        train_dl.model = model
-        eval_dl.model = model
-
-    scaler = torch.amp.GradScaler("cuda") if train_mixed_precision else None
-
-    # check that everything uses up-to-date APIs
-    utils.check_compatibility(train_dl)
-
-    best_val_score = 1e9
-    best_model = None
-    best_train_score = 1e9
-    best_model_train = None
-
-    def run_epoch(train_mode=True, dl=train_dl, epoch=0):
-        if train_mode:
-            model.train()
-        else:
-            model.eval()
-        total_loss = 0.0
-        total_positional_losses = 0.0
-        total_positional_losses_recorded = 0
-        nan_steps = 0
-        ignore_steps = 0
-        before_get_batch = time.time()
-        
-        n_batches = len(dl)
-        if not train_mode:
-            n_batches = min(512, n_batches)
-            
-        if train_mode:
-            assert (
-                len(dl) % aggregate_k_gradients == 0
-            ), "Please set the number of steps per epoch s.t. `aggregate_k_gradients` divides it."
-            tqdm_iter = (
-                tqdm(range(n_batches), desc="Training Epoch %d" % epoch)
-                if rank == 0 and progress_bar
-                else None
-            )  # , disable=not verbose
-        else:
-            tqdm_iter = (
-                tqdm(range(n_batches), desc="Eval Epoch %d" % epoch)
-                if rank == 0 and progress_bar
-                else None
-            )
-
-        for batch, full_data in enumerate(dl):
-            if batch > n_batches:
-                break
-            
-            data = (
-                full_data.style.to(device) if full_data.style is not None else None,
-                full_data.x.to(device),
-                full_data.y.to(device),
-                full_data.task_id.to(device) if full_data.task_id is not None else None,
-            )
-            targets = full_data.target_y.to(device)
-            single_eval_pos = full_data.single_eval_pos
-            # print("epoch", epoch, "single_eval_pos", single_eval_pos)
-
-            def get_metrics():
-                return (
-                    total_loss / n_batches,
-                    (
-                        total_positional_losses / total_positional_losses_recorded
-                    ).tolist(),
-                    time_to_get_batch,
-                    forward_time,
-                    step_time,
-                    nan_steps.cpu().item() / (batch + 1),
-                    ignore_steps.cpu().item() / (batch + 1),
-                    n_batches * batch_size,
-                )
-
-            tqdm_iter.update() if tqdm_iter is not None else None
-            if using_dist and not (
-                batch % aggregate_k_gradients == aggregate_k_gradients - 1
-            ):
-                cm = model.no_sync()
-            else:
-                cm = nullcontext()
-            with cm:
-                time_to_get_batch = time.time() - before_get_batch
-                before_forward = time.time()
-                try:
-                    metrics_to_log = {}
-                    with torch.amp.autocast("cuda", enabled=scaler is not None):
-                        # If style is set to None, it should not be transferred to device
-                        if train_mode:
-                            out = model(
-                                tuple(
-                                    e.to(device) if torch.is_tensor(e) else e for e in data
-                                ),
-                                single_eval_pos=single_eval_pos,
-                                only_return_standard_out=False,
-                            )
-                        else:
-                            with torch.no_grad():
-                                out = model(
-                                    tuple(
-                                        e.to(device)
-                                        if torch.is_tensor(e)
-                                        else e
-                                        for e in data
-                                    ),
-                                    single_eval_pos=single_eval_pos,
-                                    only_return_standard_out=False,
-                                )
-
-                        # this handling is for training old models only, this can be deleted soon(ish)
-                        # to only support models that return a tuple of dicts
-                        out, output_once = (
-                            out if isinstance(out, tuple) else (out, None)
-                        )
-                        output = out["standard"] if isinstance(out, dict) else out
-
-                        forward_time = time.time() - before_forward
-
-                        # only evaluate on target_task
-                        # x is seq, batch, feature
-
-                        if single_eval_pos is not None:
-                            targets = targets[single_eval_pos:]
-                            target_task_ids = full_data.task_id[single_eval_pos:]
-                        else:
-                            target_task_ids = full_data.task_id
-                        
-                        if len(targets.shape) == len(output.shape):
-                            # this implies the prior uses a trailing 1 dimesnion
-                            # below we assume this not to be the case
-                            targets = targets.squeeze(-1)
-                        assert targets.shape == output.shape[:-1], (
-                            f"Target shape {targets.shape} "
-                            "does not match output shape {output.shape}"
-                        )
-                        if isinstance(criterion, nn.GaussianNLLLoss):
-                            assert (
-                                output.shape[-1] == 2
-                            ), "need to write a little bit of code to handle multiple regression targets at once"
-
-                            mean_pred = output[..., 0]
-                            var_pred = output[..., 1].exp()
-                            losses = criterion(mean_pred, targets, var=var_pred)
-                        elif isinstance(criterion, (nn.MSELoss, nn.BCEWithLogitsLoss)):
-                            targets[torch.isnan(targets)] = -100
-                            losses = criterion(output.flatten(), targets.flatten())
-                        elif isinstance(criterion, nn.CrossEntropyLoss):
-                            targets[torch.isnan(targets)] = -100
-                            print(f"{targets.min()=}, {targets.max()=}")
-                            losses = criterion(
-                                output.reshape(-1, n_out), targets.long().flatten()
-                            )
-                        elif isinstance(criterion, ProbabilisticRankingLoss):
-                            losses = criterion(output, targets, target_task_ids)
-                        elif isinstance(criterion, GaussianRankingLoss):
-                            losses = criterion(output, targets, target_task_ids)
-                        elif isinstance(criterion, BarRankingLoss):
-                            losses = criterion(output, targets, target_task_ids)
-                        elif border_decoder is not None:
-
-                            def apply_batch_wise_criterion(i):
-                                output_, targets_, borders_ = (
-                                    output_adaptive[:, i],
-                                    targets[:, i],
-                                    borders[i],
-                                )
-                                criterion_ = get_custom_bar_dist(
-                                    borders_, criterion
-                                ).to(device)
-                                return criterion_(output_, targets_)
-
-                            output_adaptive, borders = (
-                                out["adaptive_bar"],
-                                output_once["borders"],
-                            )
-                            losses_adaptive_bar = torch.stack(
-                                [
-                                    apply_batch_wise_criterion(i)
-                                    for i in range(output_adaptive.shape[1])
-                                ],
-                                1,
-                            )
-                            losses_fixed_bar = criterion(output, targets)
-                            losses = (losses_adaptive_bar + losses_fixed_bar) / 2
-
-                            metrics_to_log = {
-                                **metrics_to_log,
-                                **{
-                                    "loss_fixed_bar": losses_fixed_bar.mean()
-                                    .cpu()
-                                    .detach()
-                                    .item(),
-                                    "loss_adaptive_bar": losses_adaptive_bar.mean()
-                                    .cpu()
-                                    .detach()
-                                    .item(),
-                                },
-                            }
-                        elif (
-                            isinstance(criterion, BarDistribution)
-                            and full_data.mean_prediction
-                        ):
-                            assert "mean_prediction" in output_once
-                            utils.print_once("Using mean prediction for loss")
-                            losses = criterion(
-                                output,
-                                targets,
-                                mean_prediction_logits=output_once["mean_prediction"],
-                            )
-                            # the mean pred loss appears as the last per sequence
-                        else:
-                            losses = criterion(output, targets)
-                        losses = losses.flatten()
-                        loss, nan_share = utils.torch_nanmean(
-                            losses.mean(0, keepdim=True), return_nanshare=True
-                        )
-                        loss_scaled = loss / aggregate_k_gradients
-
-                    if scaler:
-                        loss_scaled = scaler.scale(loss_scaled)
-                        
-                    if train_mode:
-                        loss_scaled.backward()
-
-                        if batch % aggregate_k_gradients == aggregate_k_gradients - 1:
-                            if scaler:
-                                scaler.unscale_(optimizer)
-                            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-                            if scaler:
-                                scaler.step(optimizer)
-                                scaler.update()
-                            else:
-                                optimizer.step()
-                            optimizer.zero_grad()
-
-                    step_time = time.time() - before_forward
-
-                    if not torch.isnan(loss):
-                        total_loss += loss.cpu().detach().item()
-                        total_positional_losses += (
-                            losses.mean(1).cpu().detach()
-                            if single_eval_pos is None
-                            else nn.functional.one_hot(
-                                torch.tensor(single_eval_pos), seq_len
-                            )
-                            * utils.torch_nanmean(
-                                losses[: seq_len - single_eval_pos].mean(0)
-                            )
-                            .cpu()
-                            .detach()
-                        )
-
-                        total_positional_losses_recorded += (
-                            torch.ones(seq_len)
-                            if single_eval_pos is None
-                            else nn.functional.one_hot(
-                                torch.tensor(single_eval_pos), seq_len
-                            )
-                        )
-
-                        metrics_to_log = {
-                            **metrics_to_log,
-                            **{f"train_loss": loss, "single_eval_pos": single_eval_pos},
-                        }
-                        # wandb.log(metrics_to_log)
-                        if step_callback is not None and rank == 0:
-                            step_callback(metrics_to_log)
-                        nan_steps += nan_share
-                        ignore_steps += (targets == -100).float().mean()
-                except Exception as e:
-                    print("Invalid step encountered, skipping...")
-                    print(e)
-                    raise (e)
-
-            # total_loss, total_positional_losses, time_to_get_batch, forward_time, step_time, nan_share, ignore_share = get_metrics()
-            if tqdm_iter:
-                tqdm_iter.set_postfix(
-                    {
-                        "loss": total_loss / (batch + 1),
-                        "data": time_to_get_batch,
-                        "step": step_time,
-                    }
-                )
-
-            before_get_batch = time.time()
-        metrics = get_metrics()
-        return metrics
-
-    total_loss = float("inf")
-    total_positional_losses = float("inf")
-    # Initially test the epoch callback function
-    if epoch_callback is not None and rank == 0:
-        epoch_callback(model, 1, data_loader=train_dl, scheduler=scheduler)
-    # start_epochs = 1 if continue_epoch is None else continue_epoch + 1
-    start_epochs = 0 if continue_epoch is None else continue_epoch
-    
-    # for epoch in range(start_epochs, epochs + 1) if epochs is not None else itertools.count(1):
-    epoch = start_epochs
-    while True:
-        epoch += 1
-        if epochs is not None and epoch > epochs:
-            break
-        epoch_start_time = time.time()
-        try:
-            (
-                total_loss,
-                total_positional_losses,
-                time_to_get_batch,
-                forward_time,
-                step_time,
-                nan_share,
-                ignore_share,
-                data_size,
-            ) = run_epoch(train_mode=True, dl=train_dl, epoch=epoch)
-            
-            wandb.log({
-                "epoch": epoch, 
-                "data_size": data_size * epoch,
-                "train_loss": total_loss,
-            })
-            # save checkpoint
-            torch.save(model.state_dict(), os.path.join(dirname, "final_model.pth"))
-            torch.save({
-                "epoch": epoch,
-                "model_state_dict": model.state_dict(),
-                "optimizer_state_dict": optimizer.state_dict(),
-                "scheduler_state_dict": scheduler.state_dict(),
-                "total_loss": total_loss,
-                "seed": torch.initial_seed(),
-            }, os.path.join(dirname, "checkpoint.pth")
-            )
-            with open(os.path.join(dirname, 'result.json'), 'w') as f:
-                json.dump({"epoch": epoch, "train_loss": total_loss}, f)
-            
-            if total_loss < best_train_score:
-                best_train_score = total_loss
-                torch.save(model.state_dict(), os.path.join(dirname, "best_train_model.pth"))
-            
-        except Exception as e:
-            print("Invalid epoch encountered, skipping...")
-            print(e)
-            raise (e)
-        # if hasattr(dl, "validate") and epoch % validation_period == 0:
-        if epoch % validation_period == 0:
-            with torch.no_grad():
-                val_score = run_epoch(train_mode=False, dl=eval_dl, epoch=epoch)[0]
-                wandb.log({
-                    "eval_loss": val_score,
-                }, step=wandb.run.step)
-                if val_score < best_val_score:
-                    best_val_score = val_score
-                    torch.save(model.state_dict(), os.path.join(dirname, "best_model.pth"))
-                if save_val_results:
-                    torch.save(model.state_dict(), os.path.join(dirname, f"model_{epoch}.pth"))
-
-        else:
-            val_score = None
-
-        if verbose and epoch % print_every == 0:
-            if val_score is not None:
-                text = (
-                    "-" * 89
-                    + "\n"
-                    + f"| end of epoch {epoch:3d} | time: {(time.time() - epoch_start_time):5.2f}s | train loss {total_loss:5.2f} | val loss {val_score:5.2f} |\n"
-                    # f"pos losses {','.join([f'{l:5.2f}' for l in total_positional_losses])}, lr {scheduler.get_last_lr()[0]}"
-                    f" data time {time_to_get_batch:5.2f} step time {step_time:5.2f}"
-                    f" forward time {forward_time:5.2f}"
-                    # f' nan share {nan_share:5.2f} ignore share (for classification tasks) {ignore_share:5.4f}'
-                    # + (f" val score {val_score:5.2f}" if val_score is not None else "")
-                    + "\n"
-                    + "-" * 89
-                    + "\n"
-                )
-            else:
-                text = (
-                    "-" * 89
-                    + "\n"
-                    + f"| end of epoch {epoch:3d} | time: {(time.time() - epoch_start_time):5.2f}s | train loss {total_loss:5.2f} |\n"
-                    # f"pos losses {','.join([f'{l:5.2f}' for l in total_positional_losses])}, lr {scheduler.get_last_lr()[0]}"
-                    f" data time {time_to_get_batch:5.2f} step time {step_time:5.2f}"
-                    f" forward time {forward_time:5.2f}"
-                    # f' nan share {nan_share:5.2f} ignore share (for classification tasks) {ignore_share:5.4f}'
-                    # + (f" val score {val_score:5.2f}" if val_score is not None else "")
-                    + "\n"
-                    + "-" * 89
-                    + "\n"
-                )
-            logging.info(text)
-            # print("-" * 89)
-            # print(
-            #     f"| end of epoch {epoch:3d} | time: {(time.time() - epoch_start_time):5.2f}s | mean loss {total_loss:5.2f} | "
-            #     # f"pos losses {','.join([f'{l:5.2f}' for l in total_positional_losses])}, lr {scheduler.get_last_lr()[0]}"
-            #     f" data time {time_to_get_batch:5.2f} step time {step_time:5.2f}"
-            #     f" forward time {forward_time:5.2f}"
-            #     # f' nan share {nan_share:5.2f} ignore share (for classification tasks) {ignore_share:5.4f}'
-            #     + (f" val score {val_score:5.2f}" if val_score is not None else "")
-            # )
-            # print("-" * 89)
-
-        # stepping with wallclock time based scheduler
-        if epoch_callback is not None and rank == 0:
-            epoch_callback(model, epoch, data_loader=train_dl, scheduler=scheduler)
-        scheduler.step()
-
-    if rank == 0:  # trivially true for non-parallel training
-        if isinstance(model, torch.nn.parallel.DistributedDataParallel):
-            model = model.module
-            train_dl = None
-        return (
-            total_loss,
-            total_positional_losses,
-            model.to("cpu"),
-            train_dl,
-            best_val_score,
-            best_model,
-        )
-
-
-def _parse_args(config_parser, parser):
-    # Do we have a config file to parse?
-    args_config, remaining = config_parser.parse_known_args()
-    if args_config.config:
-        with open(args_config.config, "r") as f:
-            cfg = yaml.safe_load(f)
-            parser.set_defaults(**cfg)
-
-    # The main arg parser parses the rest of the args, the usual
-    # defaults will have been overridden if config file specified.
-    args = parser.parse_args(remaining)
-
-    # Cache the args as a text string to save them in the output dir later
-    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
-    return args, args_text
\ No newline at end of file
diff --git a/private_multitask_pfn/PFNs/pfns/transformer.py b/private_multitask_pfn/PFNs/pfns/transformer.py
deleted file mode 100644
index 06c611e..0000000
--- a/private_multitask_pfn/PFNs/pfns/transformer.py
+++ /dev/null
@@ -1,835 +0,0 @@
-import math
-from typing import Optional
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import Module, TransformerEncoder
-
-# from .layer import _get_activation_fn, TransformerEncoderLayer, TransformerAxialEncoderLayer, TransformerTaskEncoderLayer, TransformerAttentionLayer
-from .layer import TransformerAttentionLayer
-from .hierarchical import HierarchicalTaskEncoder
-from .utils import bool_mask_to_att_mask, SeqBN
-
-
-class TransformerModel(nn.Module):
-    def __init__(
-        self,
-        encoder,
-        ninp,
-        nhead,
-        nhid,
-        nlayers,
-        dropout=0.0,
-        style_encoder=None,
-        y_encoder=None,
-        pos_encoder=None,
-        decoder_dict=None,
-        input_normalization=False,
-        init_method=None,
-        pre_norm=False,
-        activation="gelu",
-        recompute_attn=False,
-        num_global_att_tokens=0,
-        full_attention=False,
-        all_layers_same_init=False,
-        efficient_eval_masking=True,
-        decoder_once_dict=None,
-        return_all_outputs=False,
-        save_trainingset_representations=False,
-        num_features=None,
-        num_tasks=None,
-        attn_type="axial",
-        task_encoder=None,
-        task_embed_type="linear",
-        hierarchical=False,
-        **model_extra_args,
-    ):
-        super().__init__()
-        self.model_type = "Transformer"
-        if hierarchical:
-            global_with_target_points = model_extra_args.get("global_with_target_points", False)
-            local_with_target_points = model_extra_args.get("local_with_target_points", False)
-            meta_tokens = model_extra_args.get("meta_tokens", 1)
-            self.transformer_encoder = HierarchicalTaskEncoder(
-                global_with_target_points=global_with_target_points,
-                local_with_target_points=local_with_target_points,                
-                nlayers=nlayers,
-                meta_tokens=meta_tokens,
-                d_model=ninp,
-                nhead=nhead,
-                dim_feedforward=nhid,
-                dropout=dropout,
-                activation=activation,
-                pre_norm=pre_norm,
-            )
-        else:
-            encoder_layer_creator = lambda: TransformerAttentionLayer(
-                num_tasks,
-                ninp,
-                nhead,
-                nhid,
-                dropout,
-                activation=activation,
-                pre_norm=pre_norm,
-                recompute_attn=recompute_attn,
-                save_trainingset_representations=save_trainingset_representations,
-                attn_type=attn_type,
-                task_embed_type=task_embed_type,
-            )
-            self.transformer_encoder = TransformerTaskEncoder(encoder_layer_creator, nlayers)
-        self.ninp = ninp
-        self.encoder = encoder
-        self.task_encoder = task_encoder
-        self.task_embed_type = task_embed_type
-        self.y_encoder = y_encoder
-        self.pos_encoder = pos_encoder
-        self.return_all_outputs = return_all_outputs
-        self.num_features = num_features
-        self.num_tasks = num_tasks
-        self.attn_type = attn_type
-
-        def make_decoder_dict(decoder_description_dict):
-            if decoder_description_dict is None or len(decoder_description_dict) == 0:
-                return None
-            initialized_decoder_dict = {}
-            for decoder_key in decoder_description_dict:
-                decoder_model, decoder_n_out = decoder_description_dict[decoder_key]
-                if decoder_model is None:
-                    initialized_decoder_dict[decoder_key] = nn.Sequential(
-                        nn.Linear(ninp, nhid), nn.GELU(), nn.Linear(nhid, decoder_n_out)
-                    )
-                else:
-                    initialized_decoder_dict[decoder_key] = decoder_model
-                # print(
-                #     "Initialized decoder for",
-                #     decoder_key,
-                #     "with",
-                #     decoder_description_dict[decoder_key],
-                #     " and nout",
-                #     decoder_n_out,
-                # )
-            return torch.nn.ModuleDict(initialized_decoder_dict)
-
-        self.decoder_dict = make_decoder_dict(decoder_dict)
-        self.decoder_dict_once = make_decoder_dict(decoder_once_dict)
-
-        # N(0,1) is the initialization as the default of nn.Embedding
-        self.decoder_dict_once_embeddings = (
-            torch.nn.Parameter(torch.randn((len(self.decoder_dict_once), 1, ninp)))
-            if self.decoder_dict_once is not None
-            else None
-        )
-        # nn.Embedding(len(self.decoder_dict.keys()), nhid)
-        self.input_ln = SeqBN(ninp) if input_normalization else None
-        self.style_encoder = style_encoder
-        self.init_method = init_method
-        if num_global_att_tokens is not None:
-            assert not full_attention
-        self.global_att_embeddings = (
-            nn.Embedding(num_global_att_tokens, ninp) if num_global_att_tokens else None
-        )
-        self.full_attention = full_attention
-        self.efficient_eval_masking = efficient_eval_masking
-
-        self.nhid = nhid
-
-        self.init_weights()
-        self.encode_x_y = None
-        
-        self.target_aware = model_extra_args.get("target_aware", False)
-        self.target_encoder = nn.Embedding(2, ninp) if self.target_aware else None
-        # self.from_tabpfn()
-        
-    def from_tabpfn(self):
-        
-        import einops
-
-        from tabpfn.base import (
-            create_inference_engine,
-            determine_precision,
-            initialize_tabpfn_model,
-        )
-        from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
-        
-        import os
-        model_path = "/home/lily_l/private_multitask_pfn/TabPFN/tabpfn-v2-regressor.ckpt"
-        if not os.path.exists(model_path):
-            model_path = "/home/yl9959/mtpfn/TabPFN/tabpfn-v2-regressor.ckpt"
-            
-
-        checkpoint = torch.load(model_path, map_location="cpu", weights_only=None)
-
-        state_dict = checkpoint["state_dict"]
-        config = _preprocess_config(checkpoint["config"])
-
-        encoder=get_encoder(
-            num_features=config.features_per_group,
-            embedding_size=config.emsize,
-            remove_empty_features=config.remove_empty_features,
-            remove_duplicate_features=config.remove_duplicate_features,
-            nan_handling_enabled=config.nan_handling_enabled,
-            normalize_on_train_only=config.normalize_on_train_only,
-            normalize_to_ranking=config.normalize_to_ranking,
-            normalize_x=config.normalize_x,
-            remove_outliers=config.remove_outliers,
-            normalize_by_used_features=config.normalize_by_used_features,
-            encoder_use_bias=config.encoder_use_bias,
-        )
-        encoder.load_state_dict({"5.layer.weight": state_dict["encoder.5.layer.weight"]})
-        y_encoder=get_y_encoder(
-            num_inputs=1,
-            embedding_size=config.emsize,
-            nan_handling_y_encoder=config.nan_handling_y_encoder,
-            max_num_classes=config.max_num_classes,
-        )
-        y_encoder.load_state_dict({
-            "1.layer.weight": state_dict['y_encoder.1.layer.weight'], 
-            "1.layer.bias": state_dict['y_encoder.1.layer.bias']
-        })
-        self.encoder = encoder.to("cuda")
-        self.y_encoder = y_encoder.to("cuda")
-        
-        def encoder_forward(x, y, single_eval_pos_):
-            
-            if isinstance(x, dict):
-                assert "main" in set(x.keys()), f"Main must be in input keys: {x.keys()}."
-            else:
-                x = {"main": x}
-                y = {"main": y}
-            seq_len, batch_size, num_features = x["main"].shape
-
-
-            for k in x:
-                num_features_ = x[k].shape[2]
-
-                # pad to multiple of features_per_group
-                missing_to_next = (
-                    config.features_per_group - (num_features_ % config.features_per_group)
-                ) % config.features_per_group
-
-                if missing_to_next > 0:
-                    x[k] = torch.cat(
-                        (
-                            x[k],
-                            torch.zeros(
-                                seq_len,
-                                batch_size,
-                                missing_to_next,
-                                device=x[k].device,
-                                dtype=x[k].dtype,
-                            ),
-                        ),
-                        dim=-1,
-                    )
-
-            # Splits up the input into subgroups
-            for k in x:
-                x[k] = einops.rearrange(
-                    x[k],
-                    "s b (f n) -> b s f n",
-                    n=config.features_per_group,
-                )  # s b f -> b s #groups #features_per_group
-                
-
-            for k in y:
-                if y[k].ndim == 1:
-                    y[k] = y[k].unsqueeze(-1)
-                if y[k].ndim == 2:
-                    y[k] = y[k].unsqueeze(-1)  # s b -> s b 1
-
-                y[k] = y[k].transpose(0, 1)  # s b 1 -> b s 1
-                # print(y[k].shape, x["main"].shape)
-
-                if y[k].shape[1] < x["main"].shape[1]:
-                    assert (
-                        y[k].shape[1] == single_eval_pos_
-                        or y[k].shape[1] == x["main"].shape[1]
-                    )
-                    assert k != "main" or y[k].shape[1] == single_eval_pos_, (
-                        "For main y, y must not be given for target"
-                        " time steps (Otherwise the solution is leaked)."
-                    )
-                    if y[k].shape[1] == single_eval_pos_:
-                        y[k] = torch.cat(
-                            (
-                                y[k],
-                                torch.nan
-                                * torch.zeros(
-                                    y[k].shape[0],
-                                    x["main"].shape[1] - y[k].shape[1],
-                                    y[k].shape[2],
-                                    device=y[k].device,
-                                    dtype=y[k].dtype,
-                                ),
-                            ),
-                            dim=1,
-                        )
-
-                y[k] = y[k].transpose(0, 1)  # b s 1 -> s b 1
-
-            # making sure no label leakage ever happens
-            y["main"][single_eval_pos_:] = torch.nan
-
-            embedded_y = y_encoder(
-                y,
-                single_eval_pos=single_eval_pos_,
-            ).transpose(0, 1)
-
-
-
-            for k in x:
-                x[k] = einops.rearrange(x[k], "b s f n -> s (b f) n")
-                        
-            # embedded_x = encoder(x, single_eval_pos=single_eval_pos_)
-
-            embedded_x = einops.rearrange(
-                encoder(
-                    x,
-                    single_eval_pos=len(x["main"]),
-                ),
-                "s (b f) e -> b s f e",
-                b=embedded_y.shape[0],
-            )  # b s f 1 -> b s f e
-            
-            # mean over f dimension
-            embedded_x = embedded_x.mean(dim=2)
-            
-            return embedded_x.transpose(0, 1) + embedded_y.transpose(0, 1)
-            
-        self.encode_x_y = encoder_forward
-
-
-    def __setstate__(self, state):
-        super().__setstate__(state)
-        self.__dict__.setdefault("efficient_eval_masking", False)
-        if not hasattr(self, "decoder_dict_once"):
-            self.__dict__.setdefault("decoder_dict_once", None)
-        if hasattr(self, "decoder") and not hasattr(self, "decoder_dict"):
-            self.add_module("decoder_dict", nn.ModuleDict({"standard": self.decoder}))
-        self.__dict__.setdefault("return_all_outputs", False)
-
-        def add_approximate_false(module):
-            if isinstance(module, nn.GELU):
-                module.__dict__.setdefault("approximate", "none")
-
-        self.apply(add_approximate_false)
-
-    @staticmethod
-    def generate_square_subsequent_mask(sz):
-        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
-        return bool_mask_to_att_mask(mask)
-
-    @staticmethod
-    def generate_D_q_matrix(sz, query_size):
-        train_size = sz - query_size
-        mask = torch.zeros(sz, sz) == 0
-        mask[:, train_size:].zero_()
-        mask |= torch.eye(sz) == 1
-        return bool_mask_to_att_mask(mask)
-
-    @staticmethod
-    def generate_global_att_query_matrix(
-        num_global_att_tokens, seq_len, num_query_tokens
-    ):
-        train_size = seq_len + num_global_att_tokens - num_query_tokens
-        sz = seq_len + num_global_att_tokens
-        mask = torch.zeros(num_query_tokens, sz) == 0
-        mask[:, train_size:].zero_()
-        mask[:, train_size:] |= torch.eye(num_query_tokens) == 1
-        return bool_mask_to_att_mask(mask)
-
-    @staticmethod
-    def generate_global_att_trainset_matrix(
-        num_global_att_tokens, seq_len, num_query_tokens
-    ):
-        train_size = seq_len + num_global_att_tokens - num_query_tokens
-        trainset_size = seq_len - num_query_tokens
-        mask = torch.zeros(trainset_size, num_global_att_tokens) == 0
-        # mask[:,num_global_att_tokens:].zero_()
-        # mask[:,num_global_att_tokens:] |= torch.eye(trainset_size) == 1
-        return bool_mask_to_att_mask(mask)
-
-    @staticmethod
-    def generate_global_att_globaltokens_matrix(
-        num_global_att_tokens, seq_len, num_query_tokens
-    ):
-        mask = (
-            torch.zeros(
-                num_global_att_tokens,
-                num_global_att_tokens + seq_len - num_query_tokens,
-            )
-            == 0
-        )
-        return bool_mask_to_att_mask(mask)
-
-
-    def init_weights(self):
-        try:
-            # lily
-            # nn.init.normal_(self.encoder[1].base_encoder.weight, mean=0, std=1.0 / math.sqrt(self.ninp))
-            # if self.y_encoder is not None:
-            #     nn.init.normal_(self.y_encoder.weight, mean=0, std=1.0 / math.sqrt(self.ninp))
-            
-            # lily
-            for n, p in self.transformer_encoder.named_parameters():
-                if "bias" in n:
-                    nn.init.zeros_(p)
-                elif "attn" in n:
-                    nn.init.xavier_uniform_(p)
-                elif "norm" in n:
-                    nn.init.ones_(p)
-                elif "linear1" in n:
-                    nn.init.kaiming_normal_(p, nonlinearity='relu')
-                elif "linear2" in n:
-                    nn.init.xavier_uniform_(p)
-                    
-            # # decoder dict
-            # for k, v in self.decoder_dict.items():
-            #     for n, p in v.named_parameters():
-            #         if "bias" in n:
-            #             nn.init.zeros_(p)
-            #         elif "weight" in n:
-            #             nn.init.xavier_uniform_(p)
-            #             import pdb; pdb.set_trace()
-            #             print(p)
-        except:
-            self.init_weights_old()
-
-
-    def init_weights_old(self):
-        initrange = 1.0
-        # if isinstance(self.encoder,EmbeddingEncoder):
-        #    self.encoder.weight.data.uniform_(-initrange, initrange)
-        # self.decoder.bias.data.zero_()
-        # self.decoder.weight.data.uniform_(-initrange, initrange)
-        if self.init_method is not None:
-            self.apply(self.init_method)
-        for layer in self.transformer_encoder.layers:
-            nn.init.zeros_(layer.linear2.weight)
-            nn.init.zeros_(layer.linear2.bias)
-            if isinstance(layer, TransformerAttentionLayer):
-                pass
-                # nn.init.zeros_(layer.all_attn.out_proj.weight)
-                # nn.init.zeros_(layer.all_attn.out_proj.bias)
-                
-                # TODO: update initialization
-                
-                
-            # if isinstance(layer, TransformerEncoderLayer):
-            #     attns = (
-            #         layer.self_attn
-            #         if isinstance(layer.self_attn, nn.ModuleList)
-            #         else [layer.self_attn]
-            #     )
-            #     for attn in attns:
-            #         nn.init.zeros_(attn.out_proj.weight)
-            #         nn.init.zeros_(attn.out_proj.bias)
-            # elif isinstance(layer, TransformerAxialEncoderLayer):
-            #     nn.init.zeros_(layer.self_attn_col.out_proj.weight)
-            #     nn.init.zeros_(layer.self_attn_col.out_proj.bias)
-
-    def forward(self, *args, **kwargs):
-        """
-        This will perform a forward-pass (possibly recording gradients) of the model.
-        We have multiple interfaces we support with this model:
-
-        model(train_x, train_y, test_x, src_mask=None, style=None, only_return_standard_out=True)
-        model((x,y), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
-        model((style,x,y), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
-        """
-        if len(args) == 5:
-            # case model(train_x, train_task_id, train_y, test_x, test_task_id=None, src_mask=None, style=None, only_return_standard_out=True)
-            assert all(
-                kwarg in {"src_mask", "style", "only_return_standard_out"}
-                for kwarg in kwargs.keys()
-            ), f"Unrecognized keyword argument in kwargs: {set(kwargs.keys()) - {'src_mask', 'style', 'only_return_standard_out'}}"
-            x = args[0]
-            task_id = args[1]
-            y = args[2]
-            # combine train and test x along sequence dimension
-            if args[3] is not None: # test_x
-                x = torch.cat((x, args[3]), dim=0)
-                
-                if args[4] is None: # test_task_id
-                    # assume test_task_id is all zeros
-                    test_task_id = torch.zeros(args[3].shape[:-1]).unsqueeze(-1).to(task_id)
-                else:
-                    test_task_id = args[4]
-                task_id = torch.cat((task_id, test_task_id), dim=0)
-            style = kwargs.pop("style", None)
-            # forward(style, x, y),  "single_eval_pos" corresponds to where test x starts
-            return self._forward(
-                (style, x, y, task_id), single_eval_pos=len(args[0]), **kwargs
-            )
-        # during training (x, y, task_id) is passed with single_eval_pos
-        elif len(args) == 1 and isinstance(args, tuple):
-            # case model((x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
-            # case model((style,x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)
-            assert all(
-                kwarg in {"src_mask", "single_eval_pos", "only_return_standard_out"}
-                for kwarg in kwargs.keys()
-            ), f"Unrecognized keyword argument in kwargs: {set(kwargs.keys()) - {'src_mask', 'single_eval_pos', 'only_return_standard_out'}}"
-            return self._forward(*args, **kwargs)
-
-    def _forward(
-        self, src, src_mask=None, single_eval_pos=None, only_return_standard_out=True
-    ):
-        assert isinstance(
-            src, tuple
-        ), "inputs (src) have to be given as (x,y,task_id) or (style,x,y,task_id) tuple"
-
-        if len(src) == 3:  # (x,y,task_id) and no style
-            src = (None,) + src
-
-        style_src, x_src, y_src, task_id = src
-
-        if single_eval_pos is None:
-            single_eval_pos = x_src.shape[0]
-                
-        if self.encode_x_y is not None:
-            src = self.encode_x_y(x_src, y_src, single_eval_pos)
-        else:
-            # x_src is N x B x D
-            if self.task_embed_type == "feature" and task_id is not None:
-                onehot_task_ids = torch.nn.functional.one_hot(
-                    task_id.squeeze(-1), num_classes=self.num_tasks
-                ).to(x_src)
-                x_src = torch.cat([onehot_task_ids, x_src], dim=-1)
-            
-            
-            
-            x_src = self.encoder(x_src)
-            if "axial" in self.attn_type:
-                N, D, B, E = x_src.shape
-            else:
-                N, B, E = x_src.shape
-                
-            if task_id is not None and self.task_encoder is not None:
-                assert task_id.shape == (N, B, 1)
-                
-                if self.task_embed_type == "linear":
-                    task_id = self.task_encoder(task_id.to(x_src.dtype))
-                elif self.task_embed_type == "onehot_linear":
-                    task_id = torch.nn.functional.one_hot(
-                        task_id.squeeze(-1), num_classes=self.num_tasks
-                    ).to(x_src.dtype)
-                    task_id = self.task_encoder(task_id)
-                elif self.task_embed_type == "self_attn":
-                    task_id = self.task_encoder(task_id.to(x_src.dtype), x_src)
-                else:
-                    raise ValueError(
-                        f"task_embed_type {self.task_embed_type} not recognized"
-                    )
-                
-                if len(x_src.shape) == 4:
-                    task_id = task_id.unsqueeze(1).repeat(1, D, 1, 1)
-                else:
-                    assert len(x_src.shape) == 3
-            
-                x_src = x_src + task_id
-                
-            # distinguish between target and source inputs
-            if self.target_aware:
-                target_mask = task_id.squeeze(-1) == 0
-                target_embedding = self.target_encoder(torch.zeros(1, device=x_src.device).long())
-                x_src[target_mask] = x_src[target_mask] + target_embedding
-                source_embedding = self.target_encoder(torch.ones(1, device=x_src.device).long())
-                x_src[~target_mask] = x_src[~target_mask] + source_embedding
-
-
-            if self.decoder_dict_once is not None:
-                x_src = torch.cat(
-                    [x_src, self.decoder_dict_once_embeddings.repeat(1, B, 1)],
-                    dim=0,
-                )
-
-            # y_src = y_src.unsqueeze(1) if len(y_src.shape) < len(x_src.shape) else y_src
-            y_src = self.y_encoder(y_src.unsqueeze(-1) if len(y_src.shape) < len(x_src.shape) else y_src) if y_src is not None else None
-
-        if self.style_encoder:
-            assert (
-                style_src is not None
-            ), "style_src must be given if style_encoder is used"
-            style_src = self.style_encoder(style_src).unsqueeze(0)
-        else:
-            style_src = torch.tensor([], device=x_src.device)
-        global_src = (
-            torch.tensor([], device=x_src.device)
-            if self.global_att_embeddings is None
-            else self.global_att_embeddings.weight.unsqueeze(1).repeat(
-                1, x_src.shape[1], 1
-            )
-        )
-
-        if src_mask is not None:
-            assert self.global_att_embeddings is None or isinstance(src_mask, tuple)
-
-        if src_mask is None:
-            if self.global_att_embeddings is None:
-                full_len = len(x_src) + len(style_src)
-                if self.full_attention:
-                    # all tokens attend to all tokens
-                    src_mask = bool_mask_to_att_mask(
-                        torch.ones((full_len, full_len), dtype=torch.bool)
-                    ).to(x_src.device)
-                elif self.efficient_eval_masking:
-                    # shortcut? used in layer.py line 112
-                    src_mask = single_eval_pos + len(style_src)
-                else:
-                    # attend to self + train set
-                    src_mask = self.generate_D_q_matrix(
-                        full_len, len(x_src) - single_eval_pos
-                    ).to(x_src.device)
-            else:
-                src_mask_args = (
-                    self.global_att_embeddings.num_embeddings,
-                    len(x_src) + len(style_src),
-                    len(x_src) + len(style_src) - single_eval_pos,
-                )
-                src_mask = (
-                    self.generate_global_att_globaltokens_matrix(*src_mask_args).to(
-                        x_src.device
-                    ),
-                    self.generate_global_att_trainset_matrix(*src_mask_args).to(
-                        x_src.device
-                    ),
-                    self.generate_global_att_query_matrix(*src_mask_args).to(
-                        x_src.device
-                    ),
-                )
-
-        if self.encode_x_y is None:
-            train_embeddings = x_src[:single_eval_pos]
-            # combine embeddings for train
-            if y_src is not None:
-                if "axial" in self.attn_type:
-                    y_src = y_src.repeat(1, D, 1, 1)
-                train_embeddings = train_embeddings + y_src[:single_eval_pos]
-            # src is concatenated (train_x, train_y) and test_x
-            src = torch.cat([global_src, style_src, train_embeddings, x_src[single_eval_pos:]], 0)
-
-        if self.input_ln is not None:
-            src = self.input_ln(src)
-
-        if self.pos_encoder is not None:
-            src = self.pos_encoder(src)
-
-        # if self.task_embed_type == "task_attn" or self.task_embed_type == "task_attn_shared":
-        #     output = self.transformer_encoder(src, task_id, src_mask)
-        # else:
-        #     output = self.transformer_encoder(src, src_mask)
-        output = self.transformer_encoder(src, task_id, src_mask)
-
-        num_prefix_positions = len(style_src) + (
-            self.global_att_embeddings.num_embeddings
-            if self.global_att_embeddings
-            else 0
-        )
-        if self.return_all_outputs:
-            out_range_start = num_prefix_positions
-        else:
-            out_range_start = single_eval_pos + num_prefix_positions
-
-        # In the line below, we use the indexing feature, that we have `x[i:None] == x[i:]`
-        out_range_end = (
-            -len(self.decoder_dict_once_embeddings)
-            if self.decoder_dict_once is not None
-            else None
-        )
-
-        # take care the output once are counted from the end
-        output_once = (
-            {
-                k: v(output[-(i + 1)])
-                for i, (k, v) in enumerate(self.decoder_dict_once.items())
-            }
-            if self.decoder_dict_once is not None
-            else {}
-        )
-
-        output_dict = {}
-        for k, v in self.decoder_dict.items():
-            if "axial" in self.attn_type:
-                # mean-pool across column dimension
-                output = output.mean(dim=1)
-            # decode only test points
-            output_dict[k] = v(output[out_range_start:out_range_end])
-        output = output_dict
-
-        if only_return_standard_out:
-            return output["standard"]
-
-        if output_once:
-            return output, output_once
-        return output
-
-    @torch.no_grad()
-    def init_from_small_model(self, small_model):
-        assert (
-            isinstance(self.decoder, nn.Linear)
-            and isinstance(self.encoder, (nn.Linear, nn.Sequential))
-            and isinstance(self.y_encoder, (nn.Linear, nn.Sequential))
-        )
-
-        def set_encoder_weights(my_encoder, small_model_encoder):
-            my_encoder_linear, small_encoder_linear = (
-                (my_encoder, small_model_encoder)
-                if isinstance(my_encoder, nn.Linear)
-                else (my_encoder[-1], small_model_encoder[-1])
-            )
-            small_in_dim = small_encoder_linear.out_features
-            my_encoder_linear.weight.zero_()
-            my_encoder_linear.bias.zero_()
-            my_encoder_linear.weight[:small_in_dim] = small_encoder_linear.weight
-            my_encoder_linear.bias[:small_in_dim] = small_encoder_linear.bias
-
-        set_encoder_weights(self.encoder, small_model.encoder)
-        set_encoder_weights(self.y_encoder, small_model.y_encoder)
-
-        small_in_dim = small_model.decoder.in_features
-
-        self.decoder.weight[:, :small_in_dim] = small_model.decoder.weight
-        self.decoder.bias = small_model.decoder.bias
-
-        for my_layer, small_layer in zip(
-            self.transformer_encoder.layers, small_model.transformer_encoder.layers
-        ):
-            small_hid_dim = small_layer.linear1.out_features
-            my_in_dim = my_layer.linear1.in_features
-
-            # packed along q,k,v order in first dim
-            my_in_proj_w = my_layer.self_attn.in_proj_weight
-            small_in_proj_w = small_layer.self_attn.in_proj_weight
-
-            my_in_proj_w.view(3, my_in_dim, my_in_dim)[
-                :, :small_in_dim, :small_in_dim
-            ] = small_in_proj_w.view(3, small_in_dim, small_in_dim)
-            my_layer.self_attn.in_proj_bias.view(3, my_in_dim)[:, :small_in_dim] = (
-                small_layer.self_attn.in_proj_bias.view(3, small_in_dim)
-            )
-
-            my_layer.self_attn.out_proj.weight[:small_in_dim, :small_in_dim] = (
-                small_layer.self_attn.out_proj.weight
-            )
-            my_layer.self_attn.out_proj.bias[:small_in_dim] = (
-                small_layer.self_attn.out_proj.bias
-            )
-
-            my_layer.linear1.weight[:small_hid_dim, :small_in_dim] = (
-                small_layer.linear1.weight
-            )
-            my_layer.linear1.bias[:small_hid_dim] = small_layer.linear1.bias
-
-            my_layer.linear2.weight[:small_in_dim, :small_hid_dim] = (
-                small_layer.linear2.weight
-            )
-            my_layer.linear2.bias[:small_in_dim] = small_layer.linear2.bias
-
-            my_layer.norm1.weight[:small_in_dim] = (
-                math.sqrt(small_in_dim / my_in_dim) * small_layer.norm1.weight
-            )
-            my_layer.norm2.weight[:small_in_dim] = (
-                math.sqrt(small_in_dim / my_in_dim) * small_layer.norm2.weight
-            )
-
-            my_layer.norm1.bias[:small_in_dim] = small_layer.norm1.bias
-            my_layer.norm2.bias[:small_in_dim] = small_layer.norm2.bias
-
-
-class TransformerEncoderDiffInit(Module):
-    r"""TransformerEncoder is a stack of N encoder layers
-
-    Args:
-        encoder_layer_creator: a function generating objects of TransformerEncoderLayer class without args (required).
-        num_layers: the number of sub-encoder-layers in the encoder (required).
-        norm: the layer normalization component (optional).
-    """
-
-    __constants__ = ["norm"]
-
-    def __init__(self, encoder_layer_creator, num_layers, norm=None):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [encoder_layer_creator() for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(
-        self,
-        src: Tensor,
-        mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layers in turn.
-
-        Args:
-            src: the sequence to the encoder (required).
-            mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        output = src
-
-        for mod in self.layers:
-            output = mod(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output
-
-
-
-class TransformerTaskEncoder(Module):
-    r"""TransformerEncoder is a stack of N encoder layers
-
-    Args:
-        encoder_layer_creator: a function generating objects of TransformerEncoderLayer class without args (required).
-        num_layers: the number of sub-encoder-layers in the encoder (required).
-        norm: the layer normalization component (optional).
-    """
-
-    __constants__ = ["norm"]
-
-    def __init__(self, encoder_layer_creator, num_layers, norm=None):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [encoder_layer_creator() for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(
-        self,
-        src: Tensor,
-        task_ids: Tensor,
-        mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layers in turn.
-
-        Args:
-            src: the sequence to the encoder (required).
-            mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
-        """
-        output = src
-
-        for mod in self.layers:
-            output = mod(
-                output, task_ids, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output
diff --git a/private_multitask_pfn/PFNs/pfns/utils.py b/private_multitask_pfn/PFNs/pfns/utils.py
deleted file mode 100644
index b6ec085..0000000
--- a/private_multitask_pfn/PFNs/pfns/utils.py
+++ /dev/null
@@ -1,520 +0,0 @@
-import argparse
-import datetime
-import itertools
-import math
-import os
-import random
-
-import numpy as np
-
-import torch
-from torch import nn
-from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingWarmRestarts, ChainedScheduler, LinearLR, CosineAnnealingLR
-
-
-def get_restarting_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps):    
-    warmup_scheduler = LinearLR(
-        optimizer,
-        start_factor=0.1,  # Start at lr * 0.1
-        end_factor=1.0,    # End at lr
-        total_iters=num_warmup_steps,
-    )
-
-    cosine_scheduler = CosineAnnealingWarmRestarts(
-        optimizer,
-        T_0=20,
-        T_mult=2,
-        # eta_min=1e-7
-    )
-
-    scheduler = ChainedScheduler([warmup_scheduler, cosine_scheduler])
-    return scheduler
-
-
-def get_curriculum_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps):    
-    warmup_onetask_scheduler = LinearLR(
-        optimizer,
-        start_factor=0.1,  # Start at lr * 0.1
-        end_factor=1.0,    # End at lr
-        total_iters=num_warmup_steps,
-    )
-
-    cosine_onetask_scheduler = CosineAnnealingLR(
-        optimizer,
-        T_max=50,
-    )
-    
-    warmup_scheduler = LinearLR(
-        optimizer,
-        start_factor=0.1,  # Start at lr * 0.1
-        end_factor=1.0,    # End at lr
-        total_iters=num_warmup_steps,
-    )
-    
-    cosine_scheduler = CosineAnnealingLR(
-        optimizer,
-        T_max=num_training_steps - 2 * num_warmup_steps - 50,
-    )
-
-    scheduler = ChainedScheduler([
-        warmup_onetask_scheduler, 
-        cosine_onetask_scheduler, 
-        warmup_scheduler,
-        cosine_scheduler])
-    return scheduler
-
-
-# copied from huggingface
-def get_cosine_schedule_with_warmup(
-    optimizer, num_warmup_steps, num_training_steps, num_cycles=0.5, last_epoch=-1
-):
-    """Create a schedule with a learning rate that decreases following the
-    values of the cosine function between 0 and `pi * cycles` after a warmup
-    period during which it increases linearly between 0 and 1.
-    """
-
-    def lr_lambda(current_step):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(
-            max(1, num_training_steps - num_warmup_steps)
-        )
-        return max(
-            0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
-        )
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-# copied from huggingface
-def get_restarting_cosine_schedule_with_warmup_(
-    optimizer,
-    num_warmup_steps,
-    num_training_steps,
-    steps_per_restart=20,
-    num_cycles=0.5,
-    last_epoch=-1,
-):
-    assert num_training_steps % steps_per_restart == 0
-
-    def inner_lr_lambda(current_step, num_warmup_steps, num_training_steps):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(
-            max(1, num_training_steps - num_warmup_steps)
-        )
-        return max(
-            0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
-        )
-
-    def lr_lambda(current_step):
-        inner_step = current_step % steps_per_restart
-        return inner_lr_lambda(
-            inner_step,
-            num_warmup_steps if current_step < steps_per_restart else 0,
-            steps_per_restart,
-        )
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-# copied from huggingface
-def get_slow_restarting_cosine_schedule_with_warmup(
-    optimizer,
-    num_warmup_steps,
-    num_training_steps,
-    steps_per_restart=50,
-    num_cycles=0.5,
-    last_epoch=-1,
-):
-    assert num_training_steps % steps_per_restart == 0
-
-    def inner_lr_lambda(current_step, num_warmup_steps, num_training_steps):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(
-            max(1, num_training_steps - num_warmup_steps)
-        )
-        return max(
-            0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
-        )
-
-    def lr_lambda(current_step):
-        inner_step = current_step % steps_per_restart
-        return inner_lr_lambda(
-            inner_step,
-            num_warmup_steps if current_step < steps_per_restart else 0,
-            steps_per_restart,
-        )
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-# copied from huggingface
-def get_linear_schedule_with_warmup(
-    optimizer, num_warmup_steps, num_training_steps, last_epoch=-1
-):
-    """
-    Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
-    a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
-
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        return max(
-            0.0,
-            float(num_training_steps - current_step)
-            / float(max(1, num_training_steps - num_warmup_steps)),
-        )
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_openai_lr(transformer_model):
-    num_params = sum(p.numel() for p in transformer_model.parameters())
-    return 0.003239 - 0.0001395 * math.log(num_params)
-
-
-def get_exponential_decay_single_eval_pos_sampler(max_len, min_len=0):
-    """
-    This gives a sampler that can be used for `single_eval_pos` which yields good performance for all positions p,
-    where p <= `max_len`. At most `max_len` - 1 examples are shown to the Transformer.
-    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
-    """
-    probs = torch.logspace(2.0, 1.0, max_len - min_len)
-    sample = torch.distributions.Categorical(probs).sample().item() + min_len
-
-    return sample
-
-
-def get_weighted_single_eval_pos_sampler(max_len, min_len=0, p=1.0):
-    """
-    This gives a sampler that can be used for `single_eval_pos` which yields good performance for all positions p,
-    where p <= `max_len`. At most `max_len` - 1 examples are shown to the Transformer.
-    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
-    """
-    def sample(epoch, model):
-        return random.choices(
-            range(min_len, max_len),
-            [1 / math.pow(((max_len - min_len) - i), p) for i in range(max_len - min_len)],
-        )[0]
-        
-    return sample
-
-
-def get_uniform_single_eval_pos_sampler(max_len, min_len=0):
-    """
-    Just sample any evaluation position with the same weight
-    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
-    """
-    def sample(epoch, model):
-        return random.choices(range(min_len, max_len))[0]
-    
-    return sample
-
-
-def get_curriculum_single_eval_pos_sampler(max_len, min_len=0):
-    """
-    Just sample any evaluation position with the same weight
-    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
-    """
-    def sample(epoch, model):
-        # decrease max_len from 100 to min_len over the course of the training
-        # slowly force model to learn on min lengths
-        if epoch < 50:
-            epoch_min = int(max_len - (max_len - min_len) * ((epoch+1) / 50))
-        else:
-            epoch_min = min_len
-        return random.choices(range(epoch_min, max_len))[0]
-    
-    return sample
-
-
-class SeqBN(nn.Module):
-    def __init__(self, d_model):
-        super().__init__()
-        self.bn = nn.BatchNorm1d(d_model)
-        self.d_model = d_model
-
-    def forward(self, x):
-        assert self.d_model == x.shape[-1]
-        flat_x = x.view(-1, self.d_model)
-        flat_x = self.bn(flat_x)
-        return flat_x.view(*x.shape)
-
-
-def set_locals_in_self(locals):
-    """
-    Call this function like `set_locals_in_self(locals())` to set all local variables as object variables.
-    Especially useful right at the beginning of `__init__`.
-    :param locals: `locals()`
-    """
-    self = locals["self"]
-    for var_name, val in locals.items():
-        if var_name != "self":
-            setattr(self, var_name, val)
-
-
-default_device = "cuda:0" if torch.cuda.is_available() else "cpu:0"
-
-
-# Copied from StackOverflow, but we do an eval on the values additionally
-class StoreDictKeyPair(argparse.Action):
-    def __init__(self, option_strings, dest, nargs=None, **kwargs):
-        self._nargs = nargs
-        super(StoreDictKeyPair, self).__init__(
-            option_strings, dest, nargs=nargs, **kwargs
-        )
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        my_dict = {}
-        for kv in values:
-            k, v = kv.split("=")
-            try:
-                my_dict[k] = eval(v)
-            except NameError:
-                my_dict[k] = v
-        setattr(namespace, self.dest, my_dict)
-        print("dict values: {}".format(my_dict))
-
-
-def get_nan_value(v, set_value_to_nan=1.0):
-    if random.random() < set_value_to_nan:
-        return v
-    else:
-        return random.choice([-999, 0, 1, 999])
-
-
-def to_ranking(data):
-    x = data >= data.unsqueeze(-3)
-    x = x.sum(0)
-    return x
-
-
-# TODO: Is there a better way to do this?
-#   1. Cmparing to unique elements: When all values are different we still get quadratic blowup
-#   2. Argsort(Argsort()) returns ranking, but with duplicate values there is an ordering which is problematic
-#   3. Argsort(Argsort(Unique))->Scatter seems a bit complicated, doesn't have quadratic blowup, but how fast?
-def to_ranking_low_mem(data):
-    x = torch.zeros_like(data)
-    for col in range(data.shape[-1]):
-        x_ = data[:, :, col] >= data[:, :, col].unsqueeze(-2)
-        x_ = x_.sum(0)
-        x[:, :, col] = x_
-    return x
-
-
-def nan_handling_missing_for_unknown_reason_value(nan_prob=1.0):
-    return get_nan_value(float("nan"), nan_prob)
-
-
-def nan_handling_missing_for_no_reason_value(nan_prob=1.0):
-    return get_nan_value(float("-inf"), nan_prob)
-
-
-def nan_handling_missing_for_a_reason_value(nan_prob=1.0):
-    return get_nan_value(float("inf"), nan_prob)
-
-
-def torch_nanmean(x, axis=0, return_nanshare=False):
-    num = torch.where(torch.isnan(x), torch.full_like(x, 0), torch.full_like(x, 1)).sum(
-        axis=axis
-    )
-    value = torch.where(torch.isnan(x), torch.full_like(x, 0), x).sum(axis=axis)
-    if return_nanshare:
-        return value / num, 1.0 - num / x.shape[axis]
-    return value / num
-
-
-def torch_nanstd(x, axis=0):
-    num = torch.where(torch.isnan(x), torch.full_like(x, 0), torch.full_like(x, 1)).sum(
-        axis=axis
-    )
-    value = torch.where(torch.isnan(x), torch.full_like(x, 0), x).sum(axis=axis)
-    mean = value / num
-    mean_broadcast = torch.repeat_interleave(
-        mean.unsqueeze(axis), x.shape[axis], dim=axis
-    )
-    return torch.sqrt(
-        torch.nansum(torch.square(mean_broadcast - x), axis=axis) / (num - 1)
-    )
-
-
-def normalize_data(data, normalize_positions=-1, return_scaling=False):
-    if normalize_positions > 0:
-        mean = torch_nanmean(data[:normalize_positions], axis=0)
-        std = torch_nanstd(data[:normalize_positions], axis=0) + 0.000001
-    else:
-        mean = torch_nanmean(data, axis=0)
-        std = torch_nanstd(data, axis=0) + 0.000001
-    data = (data - mean) / std
-    data = torch.clip(data, min=-100, max=100)
-
-    if return_scaling:
-        return data, (mean, std)
-    return data
-
-
-def remove_outliers(X, n_sigma=4, normalize_positions=-1):
-    # Expects T, B, H
-    assert len(X.shape) == 3, "X must be T,B,H"
-    # for b in range(X.shape[1]):
-    # for col in range(X.shape[2]):
-    data = X if normalize_positions == -1 else X[:normalize_positions]
-    data_clean = data[:].clone()
-    data_mean, data_std = torch_nanmean(data, axis=0), torch_nanstd(data, axis=0)
-    cut_off = data_std * n_sigma
-    lower, upper = data_mean - cut_off, data_mean + cut_off
-
-    data_clean[torch.logical_or(data_clean > upper, data_clean < lower)] = np.nan
-    data_mean, data_std = (
-        torch_nanmean(data_clean, axis=0),
-        torch_nanstd(data_clean, axis=0),
-    )
-    cut_off = data_std * n_sigma
-    lower, upper = data_mean - cut_off, data_mean + cut_off
-
-    X = torch.maximum(-torch.log(1 + torch.abs(X)) + lower, X)
-    X = torch.minimum(torch.log(1 + torch.abs(X)) + upper, X)
-    # print(ds[1][data < lower, col], ds[1][data > upper, col], ds[1][~np.isnan(data), col].shape, data_mean, data_std)
-    return X
-
-
-def bool_mask_to_att_mask(mask):
-    return (
-        mask.float()
-        .masked_fill(mask == 0, float("-inf"))
-        .masked_fill(mask == 1, float(0.0))
-    )
-
-
-def print_on_master_only(is_master):
-    import builtins as __builtin__
-
-    builtin_print = __builtin__.print
-
-    def print(*args, **kwargs):
-        force = kwargs.pop("force", False)
-        if is_master or force:
-            builtin_print(*args, **kwargs)
-
-    __builtin__.print = print
-
-
-def init_dist(device):
-    # print("init dist")
-    if "LOCAL_RANK" in os.environ:
-        # launched with torch.distributed.launch
-        rank = int(os.environ["LOCAL_RANK"])
-        print("torch.distributed.launch and my rank is", rank)
-        torch.cuda.set_device(rank)
-        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
-        torch.distributed.init_process_group(
-            backend="nccl",
-            init_method="env://",
-            timeout=datetime.timedelta(seconds=20),
-            world_size=torch.cuda.device_count(),
-            rank=rank,
-        )
-        torch.distributed.barrier()
-        print_on_master_only(rank == 0)
-        print(
-            f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
-            "only I can print, but when using print(..., force=True) it will print on all ranks."
-        )
-        return True, rank, f"cuda:{rank}"
-    elif "SLURM_PROCID" in os.environ and torch.cuda.device_count() > 1:
-        # this is for multi gpu when starting with submitit
-        assert device != "cpu:0"
-        rank = int(os.environ["SLURM_PROCID"])
-        os.environ["MASTER_ADDR"] = "localhost"
-        os.environ["MASTER_PORT"] = "12355"
-        torch.cuda.set_device(rank)
-        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
-        print("distributed submitit launch and my rank is", rank)
-        torch.distributed.init_process_group(
-            backend="nccl",
-            init_method="env://",
-            timeout=datetime.timedelta(seconds=20),
-            world_size=torch.cuda.device_count(),
-            rank=rank,
-        )
-        torch.distributed.barrier()
-        print_on_master_only(rank == 0)
-        print(
-            f"Distributed training on {torch.cuda.device_count()} GPUs, this is rank {rank}, "
-            "only I can print, but when using print(..., force=True) it will print on all ranks."
-        )
-
-        return True, rank, f"cuda:{rank}"
-    else:
-        # print("Not using distributed")
-        # will not change any of the behavior of print, but allows putting the force=True in the print calls
-        print_on_master_only(True)
-        return False, 0, device
-
-
-# NOP decorator for python with statements (x = NOP(); with x:)
-class NOP:
-    def __enter__(self):
-        pass
-
-    def __exit__(self, type, value, traceback):
-        pass
-
-
-def check_compatibility(dl):
-    if hasattr(dl, "num_outputs"):
-        print(
-            "`num_outputs` for the DataLoader is deprecated. It is assumed to be 1 from now on."
-        )
-        assert dl.num_outputs != 1, (
-            "We assume num_outputs to be 1. Instead of the num_ouputs change your loss."
-            "We specify the number of classes in the CE loss."
-        )
-
-
-def product_dict(dic):
-    keys = dic.keys()
-    vals = dic.values()
-    for instance in itertools.product(*vals):
-        yield dict(zip(keys, instance))
-
-
-def to_tensor(x, device=None):
-    if isinstance(x, torch.Tensor):
-        return x.to(device)
-    else:
-        return torch.tensor(x, device=device)
-
-
-printed_already = set()
-
-
-def print_once(*msgs: str):
-    msg = " ".join([repr(m) for m in msgs])
-    if msg not in printed_already:
-        print(msg)
-        printed_already.add(msg)
-
-
-def normalize_by_used_features_f(
-    x, num_features_used, num_features, normalize_with_sqrt=False
-):
-    if normalize_with_sqrt:
-        return x / (num_features_used / num_features) ** (1 / 2)
-    return x / (num_features_used / num_features)
diff --git a/private_multitask_pfn/PFNs/pyproject.toml b/private_multitask_pfn/PFNs/pyproject.toml
deleted file mode 100644
index 7894419..0000000
--- a/private_multitask_pfn/PFNs/pyproject.toml
+++ /dev/null
@@ -1,43 +0,0 @@
-[build-system]
-requires = ["hatchling==1.18.0"]
-build-backend = "hatchling.build"
-
-[project]
-name = "pfns"
-version = "0.1.5"
-authors = [
-{name="Samuel Muller"},
-{name="Matthias Feurer"},
-{name="Noah Hollmann"},
-{name="Frank Hutter"},
-]
-
-description = "PFNs made ready for BO"
-readme = "README.md"
-requires-python = ">=3.9"
-dependencies=[
-        'torch>=1.9.0',
-        'scikit-learn>=0.24.2,<1.2',
-        'pyyaml>=5.4.1',
-        'numpy>=1.21.2,<2.0',
-        'requests>=2.23.0',
-        'gpytorch>=1.5.0',
-        'configspace>=0.4.21',
-        #'seaborn==0.11',
-        'tqdm>=4.62.1',
-        'botorch>=0.7.2',
-        'bayesmark>=0.0.8',
-]
-classifiers = [
-    "Programming Language :: Python :: 3",
-    "Operating System :: OS Independent",
-]
-
-[tool.hatch.build]
-exclude = [
-  "*.gz",
-  "*.pt",
-]
-
-[project.urls]
-"Homepage" = "https://github.com/automl/PFNs4BO"
diff --git a/private_multitask_pfn/PFNs/setup.py b/private_multitask_pfn/PFNs/setup.py
deleted file mode 100644
index 1abbd06..0000000
--- a/private_multitask_pfn/PFNs/setup.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import setuptools
-
-if __name__ == "__main__":
-    setuptools.setup()
diff --git a/private_multitask_pfn/benchmark_time_mtgp_samples.csv b/private_multitask_pfn/benchmark_time_mtgp_samples.csv
deleted file mode 100644
index 3d7391f..0000000
--- a/private_multitask_pfn/benchmark_time_mtgp_samples.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-n_tasks,n_samples_per_task,model,time
-5,31,mtgp,0.47720147343352437
-5,40,mtgp,0.3310064523946494
-5,52,mtgp,0.28340951893478633
-5,68,mtgp,0.6167415243573486
-5,87,mtgp,0.6738561020698398
-5,113,mtgp,0.5061305745504796
-5,146,mtgp,0.9458495335187763
-5,189,mtgp,2.4895689885597676
-5,244,mtgp,4.875412699021399
-5,316,mtgp,11.058217211114243
diff --git a/private_multitask_pfn/benchmark_time_mtgp_task.csv b/private_multitask_pfn/benchmark_time_mtgp_task.csv
deleted file mode 100644
index 590853b..0000000
--- a/private_multitask_pfn/benchmark_time_mtgp_task.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-n_tasks,n_samples_per_task,model,time
-3,50,mtgp,0.20609497535042465
-4,50,mtgp,0.27077632481232283
-7,50,mtgp,0.7451002229936421
-11,50,mtgp,5.2983047076035295
-17,50,mtgp,
diff --git a/private_multitask_pfn/benchmark_time_scaml_samples.csv b/private_multitask_pfn/benchmark_time_scaml_samples.csv
deleted file mode 100644
index 544786d..0000000
--- a/private_multitask_pfn/benchmark_time_scaml_samples.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-n_tasks,n_samples_per_task,model,time
-5,31,scaml,0.5437628486193716
-5,40,scaml,0.3764890623278916
-5,52,scaml,0.4885109560098499
-5,68,scaml,0.47688916362822054
-5,87,scaml,0.4667881939560175
-5,113,scaml,0.7805409024003893
-5,146,scaml,0.9741538344416767
-5,189,scaml,1.9188368205912412
-5,244,scaml,5.80243408754468
-5,316,scaml,9.938315148325637
diff --git a/private_multitask_pfn/benchmark_time_scaml_task.csv b/private_multitask_pfn/benchmark_time_scaml_task.csv
deleted file mode 100644
index 50df5ea..0000000
--- a/private_multitask_pfn/benchmark_time_scaml_task.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-n_tasks,n_samples_per_task,model,time
-3,50,scaml,0.3683895048219711
-4,50,scaml,0.20750538865104318
-7,50,scaml,0.8733225882053375
-11,50,scaml,7.345934889977798
-17,50,scaml,
diff --git a/private_multitask_pfn/eval.py b/private_multitask_pfn/eval.py
deleted file mode 100644
index be0fbba..0000000
--- a/private_multitask_pfn/eval.py
+++ /dev/null
@@ -1,1070 +0,0 @@
-import itertools
-
-import multiprocessing as mp
-
-import botorch
-import torch
-import pickle
-from eval_fcnet import (
-    get_torch_format_fcnet,
-)
-from eval_hpobench import (
-    get_torch_format_hpobench,
-)
-import json
-
-from gen_batch import get_mtgp_for_eval
-from utils import *
-import datetime
-import warnings
-import os
-import argparse
-import traceback
-import matplotlib.pyplot as plt
-from collections import defaultdict
-from eval_plot_util import plot_function, plot_all, plot_bo_function, plot_bo_all
-
-warnings.filterwarnings("ignore")
-
-FUNCTION_SEED_OFFSET = 10
-TRIAL_SEED_OFFSET = 1000
-
-def model_predict(
-    model_dir,
-    model_name,
-    train_id,
-    train_x,
-    train_y,
-    test_target_x,
-    test_target_y,
-    standardize,
-    trial_seed,
-    rerun,
-    model=None,
-):
-    os.makedirs(model_dir, exist_ok=True)
-    
-    # check if results exist
-    try:
-        if rerun:
-            raise FileNotFoundError
-        # if model_name == "mtgp_nuts":# or model_name == "gp":
-        #     raise FileNotFoundError
-        with open(os.path.join(model_dir, "results.json"), "r") as f:
-            results = json.load(f)
-            trial_seed_key = str(trial_seed)
-            if trial_seed_key in results:
-                return results[trial_seed_key]
-    except FileNotFoundError as e:
-        results = {}
-    except Exception as e:
-        traceback.print_exc()
-        results = {}
-
-    print("\tPredicting", model_name)
-    with botorch.manual_seed(trial_seed):
-        try:
-            # standardize output to mean 0 std 1
-            if standardize:
-                assert (0.0 <= train_x).all() and (train_x <= 1.0).all()
-                processed_y, processed_test_y = standardize_by_task(train_y, train_id, test_target_y)
-            else:
-                processed_y = train_y
-            assert not processed_y.isnan().any()
-
-            args = (
-                train_id,
-                train_x,
-                processed_y,
-                test_target_x,
-            )
-            if "PFN" in model_name:
-                outputs = pfn_predict(model.to(train_x.device), *args)
-            elif model_name == "mtgp":
-                outputs = mtgp_predict(*args)
-            elif model_name == "gp":
-                outputs = gp_predict(*args)
-            elif model_name == "scaml":
-                outputs = scaml_predict(*args)
-            elif model_name == "lmc":
-                outputs = lmc_predict(*args)
-            elif model_name == "mtgp_nuts":
-                outputs = mtgp_nuts_predict(*args)
-            else:
-                raise ValueError(
-                    "model_type must be one of ['pfn', 'mtgp', 'gp', 'scaml', 'lmc']"
-                )
-                
-            if model_name == "mtgp_nuts":
-                mean, std = outputs.mixture_mean, outputs.mixture_variance.sqrt()
-            else:
-                mean, std = outputs.mean.squeeze(), outputs.variance.sqrt().squeeze()
-            processed_test_y = processed_test_y.squeeze()
-            mse = ((mean - test_target_y) ** 2).mean().item()
-            nll = -torch.distributions.Normal(mean, std).log_prob(processed_test_y).mean().item()
-                
-            # save predictions
-            results[trial_seed] = {
-                "mse": mse,
-                "nll": nll,
-                "mean": mean.detach().squeeze().tolist(),
-                "std": std.detach().squeeze().tolist(),
-                "true": processed_test_y.squeeze().tolist(),
-            }
-            
-            del outputs
-            
-            # print(model_type, results[trial_seed]["mse"], results[trial_seed]["nll"])
-                
-            with open(os.path.join(model_dir, "results.json"), "w") as f:
-                json.dump(results, f)
-
-        except KeyboardInterrupt as e:
-            raise e
-        except Exception as e:
-            # raise e
-            # print(e)
-            traceback.print_exc()
-            print(model_name, "failed")
-            return None
-        
-    return results[trial_seed]
-
-
-
-def bo_loop(
-    model_dir,
-    model_name,
-    init_id,
-    init_x,
-    init_y,
-    possible_id,
-    possible_x,
-    possible_y,
-    iters,
-    standardize,
-    trial_seed,
-    rerun,
-    model=None
-):
-    os.makedirs(model_dir, exist_ok=True)
-    start_iter = 1
-    original_len = len(init_x)
-    
-    # check if results exist
-    try:
-        if rerun:
-            raise FileNotFoundError
-        # if model_name == "gp":
-        #     raise FileNotFoundError
-        # read length of results
-        with open(os.path.join(model_dir, "bo_results.json"), "r") as f:
-            results = json.load(f)
-            # check if results are complete
-            if len(results["train_y"]) < iters:
-                print("\tResults are incomplete for %s" % model_dir)
-            
-            if results["completed_iters"] >= iters:
-                return {
-                    "iters": iters,
-                    "train_y": results["train_y"][:iters],
-                    "train_x": results["train_x"][:iters],
-                    "task_id": results["task_id"][:iters],
-                }
-            else:
-                # continue from last checkpoint
-                half_id = torch.tensor(results["task_id"], device=init_id.device)
-                half_x = torch.tensor(results["train_x"], device=init_x.device)
-                half_y = torch.tensor(results["train_y"], device=init_y.device)
-                trial_seed = results["rng_seed"]
-                
-                init_id = torch.cat((init_id, half_id), 0)
-                init_x = torch.cat((init_x, half_x), 0)
-                init_y = torch.cat((init_y, half_y), 0)
-                start_iter = results["completed_iters"] + 1
-    except FileNotFoundError as e:
-        results = {}
-    except Exception as e:
-        traceback.print_exc()
-        results = {}
-                
-    task_id, train_x, train_y = init_id, init_x, init_y
-    print("\tBO Loop for", model_name)
-    
-    if model:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        model.to(device)
-        task_id = task_id.to(device)
-        train_x = train_x.to(device)
-        train_y = train_y.to(device)
-        possible_id = possible_id.to(device)
-        possible_x = possible_x.to(device)
-        possible_y = possible_y.to(device)
-
-    with botorch.manual_seed(trial_seed):
-        try:
-            for i in range(start_iter, iters + 1):
-                # print(model_type, "epoch", i)
-                # standardize output to mean 0 std 1
-                if standardize:
-                    assert (0.0 <= train_x).all() and (train_x <= 1.0).all()
-                    processed_y = standardize_by_task(train_y, task_id)
-                else:
-                    processed_y = train_y
-                assert not processed_y.isnan().any()
-
-                args = (
-                    task_id,
-                    train_x,
-                    processed_y,
-                    possible_id,
-                    possible_x,
-                    possible_y,
-                )
-                if "PFN" in model_name:
-                    best_x, best_y = pfn_get_best(model, *args)
-                elif model_name == "mtgp":
-                    best_x, best_y = mtgp_get_best(*args)
-                elif model_name == "gp":
-                    best_x, best_y = gp_get_best(*args)
-                elif model_name == "scaml":
-                    best_x, best_y = scaml_get_best(*args)
-                elif model_name == "random":
-                    random_index = torch.randint(0, possible_x.shape[0], ())
-                    best_x, best_y = possible_x[random_index], possible_y[random_index]
-                elif model_name == "lmc":
-                    best_x, best_y = lmc_get_best(*args)
-                elif model_name == "mtgp_nuts":
-                    best_x, best_y = mtgp_nuts_get_best(*args)
-                else:
-                    raise ValueError(
-                        "model_type must be one of ['pfn', 'mtgp', 'gp', 'scaml', 'random', 'lmc']"
-                    )
-
-                task_id = torch.cat(
-                    (task_id, torch.tensor([0.0], device=task_id.device)), 0
-                )
-                train_x = torch.cat((train_x, best_x.unsqueeze(0)), 0)
-                train_y = torch.cat((train_y, best_y.unsqueeze(0)), 0)
-                
-                # save results
-                seed = torch.seed()
-                results = {
-                    "completed_iters": i,
-                    "train_y": train_y[original_len:].cpu().numpy().tolist(),
-                    "train_x": train_x[original_len:].cpu().numpy().tolist(),
-                    "task_id": task_id[original_len:].cpu().numpy().tolist(),
-                    "rng_seed": seed,
-                }
-                
-                with open(os.path.join(model_dir, "bo_results.json"), "w") as f:
-                    json.dump(results, f)
-
-        except KeyboardInterrupt as e:
-            raise e
-        except Exception as e:
-            print(e)
-            traceback.print_exc()
-            print(model_name, "failed")
-            return None
-    
-    return results
-                
-
-def eval_metrics(
-    n_trials,
-    bo_iters,
-    seed,
-    baselines,
-    pfns,
-    data,
-    n_target,
-    n_source,
-    exp_dir,
-    cache_dir,
-    rerun,
-    standardize=True,
-):
-    # pfns = []
-    # if pfn_ids is not None:
-    #     for pfn_id in pfn_ids:
-    #         try:
-    #             pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % pfn_id
-    #             model = load_model(pfn_dir)
-    #             pfns.append((pfn_id, model))
-    #         except Exception as e:
-    #             traceback.print_exc()
-    #             print("failed to load", pfn_id)
-    #             continue
-        
-    xs, ys, sources_xs, sources_ys = data
-    
-    results = defaultdict(dict)
-    bo_results = defaultdict(dict)
-    info = defaultdict(dict)
-    
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print("\n!!!!!!", cache_dir, "!!!!!!", flush=True)
-    for trial in range(n_trials):
-        print("Running trial", trial)
-        trial_key = "trial_%d" % trial
-        trial_seed = seed + trial * 1000
-        
-        # generate data
-        with botorch.manual_seed(trial_seed):
-            try:
-                trial_data = torch.load(os.path.join(cache_dir, "data", "trial_%d.pt" % trial), map_location=torch.device('cpu'))
-            except FileNotFoundError as e:
-                trial_data = create_train_test(
-                    xs, ys, sources_xs, sources_ys, n_target, n_source
-                )
-                os.makedirs(os.path.join(cache_dir, "data"), exist_ok=True)
-                torch.save(trial_data, os.path.join(cache_dir, "data", "trial_%d.pt" % trial))
-                
-            # trial_data = [tensor.to(device) for tensor in trial_data]
-            train_id, train_x, train_y, test_id, test_x, test_y = trial_data
-            
-            args = (
-                train_id,
-                train_x,
-                train_y,
-                # test_id,
-                test_x,
-                test_y,
-                standardize,
-                trial_seed,
-                rerun,
-            )
-
-            # for model_type in ["scaml", "mtgp", "gp", "random"]:#, "lmc"]:#, "mtgp_nuts"]:
-            for model_name in baselines:
-                model_dir = os.path.join(cache_dir, model_name)
-                if model_name != "random":
-                    results[model_name][trial_key] = model_predict(model_dir, model_name, *args)
-                
-                bo_model_dir = os.path.join(cache_dir, trial_key, model_name)
-                bo_results[model_name][trial_key] = bo_loop(
-                    bo_model_dir,
-                    model_name,
-                    train_id,
-                    train_x,
-                    train_y,
-                    test_id,
-                    test_x,
-                    test_y,
-                    bo_iters,
-                    standardize,
-                    trial_seed,
-                    rerun,
-                )
-            for pfn_name, pfn in pfns:
-                model_dir = os.path.join(cache_dir, pfn_name)
-                results[pfn_name][trial_key] = model_predict(model_dir, "PFN: " + pfn_name, *args, model=pfn)
-                # os.symlink(model_dir, os.path.join(exp_dir, pfn_name))
-                
-                bo_model_dir = os.path.join(cache_dir, trial_key, pfn_name)
-                bo_results[pfn_name][trial_key] = bo_loop(
-                    bo_model_dir,
-                    "PFN: " + pfn_name,
-                    train_id,
-                    train_x,
-                    train_y,
-                    test_id,
-                    test_x,
-                    test_y,
-                    bo_iters,
-                    standardize,
-                    trial_seed,
-                    rerun,
-                    model=pfn,
-                )
-            
-            info[trial_key]["best_possible"] = test_y.max().item()
-            info[trial_key]["best_init"] = train_y[train_id == 0].max().item()
-            info[trial_key]["init_id"] = train_id.cpu().numpy().tolist()
-            info[trial_key]["init_x"] = train_x.cpu().numpy().tolist()
-            info[trial_key]["init_y"] = train_y.cpu().numpy().tolist()
-
-                
-    # save results
-    with open(os.path.join(exp_dir, "results.json"), "w") as f:
-        json.dump(results, f)
-    with open(os.path.join(exp_dir, "bo_results.json"), "w") as f:
-        json.dump(bo_results, f)
-    with open(os.path.join(exp_dir, "info.json"), "w") as f:
-        json.dump(info, f)
-                
-    return results
-                   
-
-def get_eval_suite(eval_type):
-    if eval_type == "num_samples":
-        # Test impact of number of targets and sources
-        # Varying correlations between tasks
-        # Varying lengthscales
-        # NUM_FEATURES = [3]
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0]
-        CORR_INIT_OPTIONS = [0.8]
-        LENGTHSCALE_OPTIONS = [None]
-        NUM_TASKS_OPTIONS = [4]
-        # NUM_TARGETS = [2, 5, 10, 20]
-        # NUM_SOURCES = [10, 20, 50, 100]
-        NUM_TARGETS = [2, 4, 8, 16, 32, 64]
-        NUM_SOURCES = [20, 50]
-        NUM_SAMPLES = []
-        for target in NUM_TARGETS:
-            for source in NUM_SOURCES:
-                if target <= source:
-                    NUM_SAMPLES.append((target, source))
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-
-    elif eval_type == "corr":
-        # Test impact of number of targets and sources
-        # Varying correlations between tasks
-        # Varying lengthscales
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0]
-        CORR_INIT_OPTIONS = [0.3, 0.6, 0.9]
-        LENGTHSCALE_OPTIONS = [None, 0.2]
-        NUM_TASKS_OPTIONS = [4]
-        NUM_SAMPLES = [(2, 20), (5, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-
-    elif eval_type == "uncorr":
-        # Test number of uncorrelated tasks
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0, 1, 2, 3]
-        CORR_INIT_OPTIONS = [None, 0.6, 0.8]
-        LENGTHSCALE_OPTIONS = [None, 0.1, 0.2]
-        NUM_TASKS_OPTIONS = [4]
-        # NUM_SAMPLES = [(2, 20), (5, 20), (10, 20)]
-        NUM_SAMPLES = [(2, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-
-    elif eval_type == "num_tasks":
-        # Test number of tasks
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0, 1]
-        CORR_INIT_OPTIONS = [None]
-        LENGTHSCALE_OPTIONS = [None]
-        NUM_TASKS_OPTIONS = [2, 3, 4, 5, 6]
-        NUM_SAMPLES = [(2, 20), (5, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-        
-    elif eval_type == "one_task":
-        # Test number of tasks
-        NUM_FEATURES = [1]
-        NUM_UNCORR_TASKS = [0]
-        CORR_INIT_OPTIONS = [None]
-        LENGTHSCALE_OPTIONS = [None, 0.2]
-        NUM_TASKS_OPTIONS = [1]
-        NUM_SAMPLES = [(4, 20), (8, 20), (16, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-        
-    elif eval_type == "uncorr_final":
-        # Test number of uncorrelated tasks
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0, 1, 2, 3]
-        CORR_INIT_OPTIONS = [0.8]
-        LENGTHSCALE_OPTIONS = [None]
-        NUM_TASKS_OPTIONS = [4]
-        NUM_SAMPLES = [(2, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-        
-    elif eval_type == "nuts_0":
-        # Test number of uncorrelated tasks
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0]
-        CORR_INIT_OPTIONS = [0.3]
-        LENGTHSCALE_OPTIONS = [0.2, None]
-        NUM_TASKS_OPTIONS = [4]
-        NUM_SAMPLES = [(2, 20), (5, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-        
-    elif eval_type == "nuts_1":
-        # Test number of uncorrelated tasks
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0]
-        CORR_INIT_OPTIONS = [0.6]
-        LENGTHSCALE_OPTIONS = [0.2, None]
-        NUM_TASKS_OPTIONS = [4]
-        NUM_SAMPLES = [(2, 20), (5, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-        
-    elif eval_type == "nuts_2":
-        # Test number of uncorrelated tasks
-        NUM_FEATURES = [3]
-        NUM_UNCORR_TASKS = [0]
-        CORR_INIT_OPTIONS = [0.9]
-        LENGTHSCALE_OPTIONS = [0.2, None]
-        NUM_TASKS_OPTIONS = [4]
-        NUM_SAMPLES = [(2, 20), (5, 20)]
-
-        return list(
-            itertools.product(
-                NUM_FEATURES,
-                NUM_TASKS_OPTIONS,
-                LENGTHSCALE_OPTIONS,
-                CORR_INIT_OPTIONS,
-                NUM_UNCORR_TASKS,
-                NUM_SAMPLES,
-            )
-        )
-        
-
-
-def run_hpobench(
-    n_trials: int,
-    bo_iters: int,
-    seed: int,
-    baselines: list,
-    pfns: list,
-    hpobench_task: str,
-    hpobench_n_tasks: int,
-    hpobench_n_features: int,
-    n_functions: int,
-    exp_dir: str,
-    cache_dir: str,
-    rerun: bool,
-    standardize=True,
-    **kwargs,
-):
-    num_features = hpobench_n_features
-    top_level_cache_dir = os.path.join(cache_dir, "hpobench")
-
-    torch.manual_seed(seed)
-    
-    try:
-        problem_dfs = pickle.load(open(f"/scratch/yl9959/mtpfn/datasets/hpobench_{hpobench_task}.pkl", "rb"))
-    except FileNotFoundError as e:
-        problem_dfs = pickle.load(open(f"/home/lily_l/private_multitask_pfn/datasets/hpobench_{hpobench_task}.pkl", "rb"))
-    except Exception as e:
-        raise e
-    possible_ids = list(problem_dfs.keys())
-    ids = []
-    for _ in range(n_functions):
-        id_indices = torch.randperm(len(possible_ids))[:hpobench_n_tasks]
-        function_ids = [possible_ids[i] for i in id_indices]
-        ids.append((function_ids[0], tuple(function_ids[1:])))
-
-    num_samples = [
-        (2, 20),
-        (5, 20),
-        (20, 20),
-        (2, 100),
-        (20, 100),
-    ]
-    for n_target, n_source in num_samples:
-        for function, (target_id, source_ids) in enumerate(ids):
-            source_ids_str = "_".join([str(id) for id in source_ids])
-            exp_exp_name = "n_target_%d__n_source_%d" % (n_target, n_source)
-            cache_experiment_name = "%s_features_%d_n_target_%d__n_source_%d__target_%d__source_%s" % (hpobench_task, num_features, n_target, n_source, target_id, source_ids_str)
-            
-            function_seed = seed + function * FUNCTION_SEED_OFFSET
-            
-            target_df = problem_dfs[target_id]
-            source_dfs = [problem_dfs[source_id] for source_id in source_ids]
-
-            data = get_torch_format_hpobench(
-                target_df, source_dfs, num_features, hpobench_task
-            )
-            
-            exp_function_dir = os.path.join(exp_dir, exp_exp_name, "function_%d" % function)
-            os.makedirs(exp_function_dir, exist_ok=True)
-            
-            cache_function_dir = os.path.join(top_level_cache_dir, cache_experiment_name)
-            os.makedirs(cache_function_dir, exist_ok=True)
-                    
-            with botorch.manual_seed(seed):
-                eval_metrics(
-                    n_trials=n_trials,
-                    bo_iters=bo_iters,
-                    seed=function_seed,
-                    baselines=baselines,
-                    pfns=pfns,
-                    data=data,
-                    n_target=n_target,
-                    n_source=n_source,
-                    exp_dir=exp_function_dir,
-                    cache_dir=cache_function_dir,
-                    standardize=standardize,
-                    rerun=rerun,
-                )
-                
-            plot_function(exp_function_dir, n_trials, "HPOBench %s: %s\nDataset %d (%d Trials)" % (hpobench_task, exp_exp_name, function + 1, n_trials))
-            plot_bo_function(exp_function_dir, "HPOBench %s: %s\nDataset %d (%d Trials)" % (hpobench_task, exp_exp_name, function + 1, n_trials))
-            
-        pfn_ids = [pfn_id for pfn_id, _ in pfns]
-        plot_all(os.path.join(exp_dir, exp_exp_name), n_functions, "HPOBench %s: %s\n Summary over %d datasets and %d trials" % (hpobench_task, exp_exp_name, n_functions, n_trials))
-        plot_bo_all(os.path.join(exp_dir, exp_exp_name), pfn_ids, "HPOBench %s: %s\n Summary over %d datasets and %d trials" % (hpobench_task, exp_exp_name, n_functions, n_trials))
-
-
-def run_hpob(
-    n_trials: int,
-    bo_iters: int,
-    seed: int,
-    baselines: list,
-    pfns: list,
-    hpob_n_tasks: int,
-    n_functions: int,
-    exp_dir: str,
-    cache_dir: str,
-    rerun: bool,
-    standardize=True,
-    hpo_n_features=3,
-    **kwargs,
-):
-    torch.manual_seed(seed)
-
-    try:
-        test_data = json.load(open("/home/yl9959/mtpfn/datasets/hpob-data/meta-validation-dataset.json", "r"))
-    except FileNotFoundError as e:
-        test_data = json.load(open("/home/lily_l/private_multitask_pfn/datasets/hpob-data/meta-validation-dataset.json", "r"))
-    top_level_cache_dir = os.path.join(cache_dir, "hpob")
-
-    ids = []
-    for domain in test_data:
-        valid_runs = []
-        for hpo_run in test_data[domain]:
-            x_shape = torch.tensor(test_data[domain][hpo_run]["X"]).shape
-            if x_shape[0] > 512 and x_shape[-1] <= hpo_n_features:
-                valid_runs.append(hpo_run)
-        if len(valid_runs) >= hpob_n_tasks:
-            for i in range(n_functions):
-                # randomly select runs
-                selected_indices = torch.randperm(len(valid_runs))[:hpob_n_tasks]
-                selected_runs = [valid_runs[i] for i in selected_indices]
-                ids.append((domain, (selected_runs[0], tuple(selected_runs[1:]))))
-
-    num_samples = [
-        (2, 20),
-        (5, 20),
-        (20, 20),
-        (2, 100),
-        (20, 100),
-    ]
-
-    for n_target, n_source in num_samples:
-        param_name = "features_%d__n_target_%d__n_source_%d" % (hpo_n_features, n_target, n_source)
-        
-        for i, (domain, (target_id, source_ids)) in enumerate(ids):        
-            source_ids_str = "_".join([str(id) for id in source_ids])
-            
-            function_seed = seed + i * FUNCTION_SEED_OFFSET
-            
-            data_xs = []
-            data_ys = []
-            for id in [target_id] + list(source_ids):
-                xs = torch.tensor(test_data[domain][id]["X"])
-                ys = torch.tensor(test_data[domain][id]["y"])
-                max_indices = 2000
-                if xs.shape[0] > max_indices:
-                    indices = torch.randperm(xs.shape[0])[:max_indices]
-                    xs = xs[indices]
-                    ys = ys[indices]
-
-                data_xs.append(xs)
-                data_ys.append(ys)
-
-            target_xs, sources_xs = data_xs[0], data_xs[1:]
-            target_ys, sources_ys = data_ys[0], data_ys[1:]
-            data = (target_xs, target_ys, sources_xs, sources_ys)
-        
-            exp_domain_dir = os.path.join(exp_dir, param_name, "domain_%s" % domain, "function_%d" % i)
-            os.makedirs(exp_domain_dir, exist_ok=True)
-            
-            cache_experiment_name = "features_%d__domain_%s__target_%s__source_%s" % (hpo_n_features, domain, target_id, source_ids_str)
-            cache_domain_dir = os.path.join(top_level_cache_dir, param_name, cache_experiment_name)
-            os.makedirs(cache_domain_dir, exist_ok=True)
-                    
-            with botorch.manual_seed(seed):
-                eval_metrics(
-                    n_trials=n_trials,
-                    bo_iters=bo_iters,
-                    seed=function_seed,
-                    baselines=baselines,
-                    pfns=pfns,
-                    data=data,
-                    n_target=n_target,
-                    n_source=n_source,
-                    exp_dir=exp_domain_dir,
-                    cache_dir=cache_domain_dir,
-                    rerun=rerun,
-                    standardize=standardize,
-                )
-                
-            plot_function(exp_domain_dir, n_trials, "HPO-B: N Target %d, N Source %d\nDomain %s (%d Trials)" % (n_target, n_source, domain, n_trials))
-            plot_bo_function(exp_domain_dir, "HPO-B: N Target %d, N Source %d\nDomain %s (%d Trials)" % (n_target, n_source, domain, n_trials))
-            
-        pfn_ids = [pfn_id for pfn_id, _ in pfns]
-        plot_all(os.path.join(exp_dir, param_name), n_functions, "HPO-B: Domain %s\n Summary over %d datasets and %d trials" % (domain, n_functions, n_trials))
-        plot_bo_all(os.path.join(exp_dir, param_name), pfn_ids, "HPO-B: Domain %s\n Summary over %d datasets and %d trials" % (domain, n_functions, n_trials))
-
-
-def run_fcnet(
-    n_trials: int,
-    bo_iters: int,
-    seed: int,
-    baselines: list,
-    pfns: list,
-    n_functions: int,
-    fcnet_n_features: int,
-    exp_dir: str,
-    cache_dir: str,
-    rerun: bool,
-    standardize=True,
-    **kwargs,
-):
-    num_features = fcnet_n_features
-    torch.manual_seed(seed)
-    top_level_cache_dir = os.path.join(cache_dir, "fcnet")
-
-
-    problems = [
-        "fcnet_naval_propulsion_data",
-        "fcnet_parkinsons_telemonitoring_data",
-        "fcnet_protein_structure_data",
-        "fcnet_slice_localization_data",
-    ]
-    try:
-        datasets = {
-            problem: pickle.load(open(f"/home/yl9959/mtpfn/datasets/fcnet_tabular_benchmarks/{problem}.pkl", "rb"))
-            for problem in problems
-        }
-    except FileNotFoundError as e:
-        datasets = {
-            problem: pickle.load(open(f"/home/lily_l/private_multitask_pfn/datasets/fcnet_tabular_benchmarks/{problem}.pkl", "rb"))
-            for problem in problems
-        }
-    except Exception as e:
-        raise e
-
-    num_samples = [
-        (2, 20),
-        (5, 20),
-        (20, 20),
-        (2, 100),
-        (20, 100),
-    ]
-    params = itertools.product(problems, num_samples)
-
-    for n_target, n_source in num_samples:
-        param_name = "features_%d__n_target_%d__n_source_%d" % (num_features, n_target, n_source)
-        for problem in problems:
-
-            target_result = datasets[problem]
-            source_results = [datasets[key] for key in datasets if key != problem]
-
-            target_xs, target_ys, sources_xs, sources_ys = get_torch_format_fcnet(
-                target_result, source_results, num_features
-            )
-            
-            exp_problem_dir = os.path.join(exp_dir, param_name, problem)
-            os.makedirs(exp_problem_dir, exist_ok=True)
-            
-            cache_problem_dir = os.path.join(top_level_cache_dir, param_name, problem)
-            os.makedirs(cache_problem_dir, exist_ok=True)
-            
-            with botorch.manual_seed(seed):
-                eval_metrics(
-                    n_trials=n_trials,
-                    bo_iters=bo_iters,
-                    seed=seed,
-                    baselines=baselines,
-                    pfns=pfns,
-                    data=(target_xs, target_ys, sources_xs, sources_ys),
-                    n_target=n_target,
-                    n_source=n_source,
-                    exp_dir=exp_problem_dir,
-                    cache_dir=cache_problem_dir,
-                    rerun=rerun,
-                    standardize=standardize,
-                )
-                
-            plot_function(exp_problem_dir, n_trials, "FCNet %s: N Target %d, N Source %d (%d Trials)" % (problem, n_target, n_source, n_trials))
-            plot_bo_function(exp_problem_dir, "FCNet %s: N Target %d, N Source %d (%d Trials)" % (problem, n_target, n_source, n_trials))
-        
-        pfn_ids = [pfn_id for pfn_id, _ in pfns]
-        plot_all(os.path.join(exp_dir, param_name), n_functions, "FCNet: N Target %d, N Source %d\n Summary over all datasets and %d trials" % (n_target, n_source, n_trials))
-        plot_bo_all(os.path.join(exp_dir, param_name), pfn_ids, "FCNet: N Target %d, N Source %d\n Summary over all datasets and %d trials" % (n_target, n_source, n_trials))
-
-
-def run_test(
-    n_trials: int,
-    bo_iters: int,
-    seed: int,
-    baselines: list,
-    pfns: list,
-    suite: str,
-    n_functions: int,
-    exp_dir: str,
-    cache_dir: str,
-    rerun: bool,
-    standardize=True,
-    **kwargs,
-):
-    torch.manual_seed(seed)
-
-    eval_suite = get_eval_suite(suite)
-    
-    top_level_cache_dir = os.path.join(cache_dir, "synthetic")
-
-    results = {}
-    for param in eval_suite:
-        if param in results:
-            continue
-        
-        results[param] = {}
-        (
-            num_features,
-            num_tasks,
-            lengthscale,
-            task_corr,
-            num_uncorr_tasks,
-            (n_target, n_source),
-        ) = param
-        
-        param_key = "features_%d__tasks_%d__lengthscale_%s__task_corr_%s__uncorr_tasks_%d__n_target_%d__n_source_%d" % (
-            num_features,
-            num_tasks,
-            str(lengthscale),
-            str(task_corr),
-            num_uncorr_tasks,
-            n_target,
-            n_source,
-        )
-        exp_title = "Synthetic Data: Features: %d, Tasks: %d\nLengthscale: %s, Task Corr: %s, Uncorr Tasks: %d\nN Target: %d, N Source: %d" % (
-            num_features,
-            num_tasks,
-            str(lengthscale),
-            str(task_corr),
-            num_uncorr_tasks,
-            n_target,
-            n_source,
-        )
-        # replace special characters
-        param_dirname = param_key.replace("[", "").replace("]", "").replace(".", "_")
-        seed_dirname = "seed_%d" % seed
-        
-        for function in range(n_functions):
-            function_seed = seed + function * FUNCTION_SEED_OFFSET
-            exp_function_dir = os.path.join(exp_dir, param_dirname + "__" + seed_dirname, "function_%d" % function)
-            os.makedirs(exp_function_dir, exist_ok=True)
-            
-            cache_function_dir = os.path.join(top_level_cache_dir, param_dirname, seed_dirname, "function_%d" % function)
-            os.makedirs(cache_function_dir, exist_ok=True)
-
-            with botorch.manual_seed(function_seed):
-                data = get_mtgp_for_eval(
-                    num_features,
-                    num_tasks,
-                    lengthscale,
-                    task_corr,
-                    num_uncorr_tasks,
-                )
-                # break
-                
-            eval_metrics(
-                n_trials=n_trials,
-                bo_iters=bo_iters,
-                seed=function_seed,
-                baselines=baselines,
-                pfns=pfns,
-                data=data,
-                n_target=n_target,
-                n_source=n_source,
-                exp_dir=exp_function_dir,
-                cache_dir=cache_function_dir,
-                rerun=rerun,
-                standardize=standardize,
-            )
-            
-            plot_function(exp_function_dir, n_trials, exp_title + "\nDataset %d (%d Trials)" % (function + 1, n_trials))
-            plot_bo_function(exp_function_dir, exp_title + "\nDataset %d (%d Trials)" % (function + 1, n_trials))
-            # break
-            
-        pfn_ids = [pfn_id for pfn_id, _ in pfns]
-        plot_all(os.path.join(exp_dir, param_dirname + "__" + seed_dirname), n_functions, exp_title + "\n Summary over %d datasets and %d trials" % (n_functions, n_trials))
-        plot_bo_all(os.path.join(exp_dir, param_dirname + "__" + seed_dirname), pfn_ids, exp_title + "\n Summary over %d datasets and %d trials" % (n_functions, n_trials))
-        # break
-
-
-# main function
-if __name__ == "__main__":
-    argparser = argparse.ArgumentParser()
-    
-    argparser.add_argument("--benchmark", type=str, default="synthetic", help="{synthetic, hpobench, hpob, fcnet}")
-    argparser.add_argument("--bo_iters", type=int, default=20)
-    argparser.add_argument("--n_trials", type=int, default=5)
-    argparser.add_argument("--seed", type=int, default=0)
-    argparser.add_argument("--n_functions", type=int, default=3)
-    
-    # baselines
-    argparser.add_argument("--baselines", nargs="*", type=str, help="{mtgp, gp, scaml, lmc, mtgp_nuts}", 
-                           default=["mtgp", "gp", "scaml", "random"])
-    
-    # pfn wandb ids
-    argparser.add_argument("--pfn_ids", nargs="*", type=str, help="wandb ids for pfn models")
-    argparser.add_argument("--ckpt_dirs", nargs="*", type=str, help="directories")
-    
-    # synthetic
-    argparser.add_argument("--suite", type=str, default="corr", help="{num_samples, corr, uncorr, num_tasks}")
-    
-    # hpobench
-    argparser.add_argument("--hpobench_task", type=str, default="rf", help="{rf, xgb, svm, mlp, nn}")
-    argparser.add_argument("--hpobench_n_tasks", type=int, default=4)
-    argparser.add_argument("--hpobench_n_features", type=int, default=3)
-    
-    # hpob
-    argparser.add_argument("--hpob_n_tasks", type=int, default=4)
-    argparser.add_argument("--hpob_n_features", type=int, default=3)
-    
-    # fcnet
-    argparser.add_argument("--fcnet_n_features", type=int, default=3)
-    
-    argparser.add_argument("--rerun", action="store_true", default=False)
-    
-    if os.path.exists("/home/yl9959/mtpfn/"):
-        home_dir = "/home/yl9959/mtpfn/"
-    else:
-        home_dir = "/home/lily_l/private_multitask_pfn/"
-    argparser.add_argument("--home_dir", type=str, default=home_dir)
-    # argparser.add_argument("--exp_dir", type=str, default="/home/yl9959/mtpfn/eval_plot/")
-    # argparser.add_argument("--cache_dir", type=str, default="/home/yl9959/mtpfn/eval_plot_cache/")
-
-    args = argparser.parse_args()
-    
-    current_time = datetime.datetime.now().strftime("%y-%m-%d_%H-%M-%S")
-    dirname = current_time + "__" + args.benchmark + "__trials_%d__seed_%d" % (args.n_trials, args.seed)
-    
-    if args.benchmark == "synthetic":
-        dirname += "__" + args.suite
-    elif args.benchmark == "hpobench":
-        dirname += "__" + args.hpobench_task
-        
-    args.exp_dir = os.path.join(args.home_dir, "eval_plot", dirname)
-    os.makedirs(args.exp_dir, exist_ok=True)
-    args.cache_dir = os.path.join(args.home_dir, "eval_plot_cache")
-    
-    # save args json
-    with open(os.path.join(args.exp_dir, "args.json"), "w") as f:
-        json.dump(vars(args), f)
-        
-        
-    pfns = []
-    if args.pfn_ids is not None:
-        for pfn_id in args.pfn_ids:
-            try:
-                pfn_dir = os.path.join(args.home_dir, "wandb_links", pfn_id)
-                model = load_model(pfn_dir)
-                pfns.append((pfn_id, model))
-            except FileNotFoundError as e:
-                pfn_dir = os.path.join(args.home_dir, "final_models", pfn_id)
-                model = load_model(pfn_dir)
-                pfns.append((pfn_id, model))
-            except Exception as e:
-                traceback.print_exc()
-                print("failed to load", pfn_id)
-                continue
-    if args.ckpt_dirs is not None:
-        for pfn_dir in args.ckpt_dirs:
-            try:
-                model = load_model(os.path.join(args.home_dir, pfn_dir))
-                pfns.append((pfn_dir, model))
-            except FileNotFoundError as e:
-                print("failed to load", pfn_dir)
-                continue
-    
-    if args.benchmark == "synthetic":
-        run_test(**vars(args), pfns=pfns)
-    elif args.benchmark == "hpobench":
-        run_hpobench(**vars(args), pfns=pfns)
-    elif args.benchmark == "hpob":
-        run_hpob(**vars(args), pfns=pfns)
-    elif args.benchmark == "fcnet":
-        run_fcnet(**vars(args), pfns=pfns)
-    else:
-        raise ValueError("Invalid benchmark")
-    
-# python eval.py --benchmark synthetic --bo_iters 10 --n_trials 10 --n_functions 3 --suite uncorr --pfn_ids treasured-lion-279 royal-firebrand-281 different-dust-282 exalted-wave-278 peach-plasma-280
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_axial.py b/private_multitask_pfn/eval_axial.py
deleted file mode 100644
index ad2c25b..0000000
--- a/private_multitask_pfn/eval_axial.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import torch
-from gen_axial_batch import axial_train_batch, axial_test_batch
-import json
-import os
-from train import train as load_model_from_train
-
-def load_model(ckpt_dir, best=True):
-# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_14-36-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_standard"
-    args_json = f"{ckpt_dir}/args.json"
-    with open(args_json, "r") as f:
-        args = json.load(f)
-
-    model = load_model_from_train(**args, return_model=True)
-    if best:
-        model.load_state_dict(torch.load(f"{ckpt_dir}/best_model.pth", weights_only=True))
-    else:
-        model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True))
-    
-    print("Loaded model")
-    return model
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-# standard attention
-ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_14-36-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_standard"
-model = load_model(ckpt_dir).to(device)
-
-# axial attention
-ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_15-19-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_axial"
-axial_model = load_model(ckpt_dir).to(device)
-
-torch.manual_seed(0)
-
-num_features = 3
-num_train = 10
-num_test = 3
-
-x = torch.rand(num_train + num_test, 1, num_features, device=device)
-y = torch.zeros(num_train + num_test, 1, 1, device=device)
-for feature in range(num_features):
-    y += torch.pow(x[:, :, feature:feature+1], feature + 1)
-
-x_train = x[:num_train]
-y_train = y[:num_train]
-x_test = x[num_train:]
-y_test = y[num_train:]
-
-with torch.no_grad():
-    y_pred = model(x_train, y_train, x_test)
-    y_pred_axial = axial_model(x_train, y_train, x_test)
-y_mean, y_var = y_pred[..., 0], y_pred[..., 1].exp()
-y_mean_axial, y_var_axial = y_pred_axial[..., 0], y_pred_axial[..., 1].exp()
-
-criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
-loss = criterion(y_mean, y_test.squeeze(1), var=y_var)
-loss_axial = criterion(y_mean_axial, y_test.squeeze(1), var=y_var_axial)
-
-for i in range(num_test):
-    print(f"ID Test {i}")
-    print(f"x_test: {x_test[i].squeeze()}")
-    print(f"y_test: {y_test[i].item()}")
-    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (y_mean[i].item(), y_var[i].item(), loss[i].item()))
-    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (y_mean_axial[i].item(), y_var_axial[i].item(), loss_axial[i].item()))
-    print()
-    
-# reversed x in feature dimension
-reversed_x = torch.flip(x, dims=[2])
-reversed_x_train = reversed_x[:num_train]
-reversed_x_test = reversed_x[num_train:]
-
-with torch.no_grad():
-    reversed_y_pred = model(reversed_x_train, y_train, reversed_x_test)
-    reversed_y_pred_axial = axial_model(reversed_x_train, y_train, reversed_x_test)
-reversed_y_mean, reversed_y_var = reversed_y_pred[..., 0], reversed_y_pred[..., 1].exp()
-reversed_y_mean_axial, reversed_y_var_axial = reversed_y_pred_axial[..., 0], reversed_y_pred_axial[..., 1].exp()
-
-reversed_loss = criterion(reversed_y_mean, y_test.squeeze(1), var=reversed_y_var)
-reversed_loss_axial = criterion(reversed_y_mean_axial, y_test.squeeze(1), var=reversed_y_var_axial)
-
-for i in range(num_test):
-    print(f"OOD Test {i}")
-    print(f"Reversed x_test: {reversed_x_test[i].squeeze()}")
-    print(f"y_test: {y_test[i].item()}")
-    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean[i].item(), reversed_y_var[i].item(), reversed_loss[i].item()))
-    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean_axial[i].item(), reversed_y_var_axial[i].item(), reversed_loss_axial[i].item()))
-    print()
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_axial_multitask.py b/private_multitask_pfn/eval_axial_multitask.py
deleted file mode 100644
index d2c5abe..0000000
--- a/private_multitask_pfn/eval_axial_multitask.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import torch
-from gen_axial_batch import axial_train_batch, axial_test_batch
-import json
-import os
-from train import train as load_model_from_train
-import matplotlib.pyplot as plt
-
-def load_model(ckpt_dir, best=True):
-# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_14-36-42__prior_axial_train__features_3__tasks_1__epochs_30__seqlen_200__attn_standard"
-    args_json = f"{ckpt_dir}/args.json"
-    with open(args_json, "r") as f:
-        args = json.load(f)
-
-    model = load_model_from_train(**args, return_model=True)
-    if best:
-        model.load_state_dict(torch.load(f"{ckpt_dir}/best_model.pth", weights_only=True))
-    else:
-        model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True))
-    
-    print("Loaded model")
-    return model
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-# standard attention
-ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_22-11-25__prior_toy_multitask__features_3__tasks_2__epochs_10__seqlen_200__attn_standard_completed"
-model = load_model(ckpt_dir).to(device)
-
-# axial attention
-# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_22-07-45__prior_toy_multitask__features_3__tasks_2__epochs_10__seqlen_200__attn_axial_completed"
-ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-04_22-23-28__prior_toy_multitask__features_3__tasks_2__epochs_30__seqlen_200__attn_axial_completed"
-axial_model = load_model(ckpt_dir).to(device)
-
-torch.manual_seed(1)
-
-num_features = 3
-num_train = 50
-num_test = 100
-batch_size = 1
-seq_len = num_train + num_test
-num_tasks = 1
-
-x = torch.rand(seq_len, batch_size, num_features, device=device)
-y = torch.zeros(seq_len, batch_size, 1, device=device)
-task_id = torch.randint(0, num_tasks, (seq_len, batch_size, 1), device=device)
-
-for task in range(num_tasks):
-    for feature in range(num_features):
-        constant = task + 1 #torch.randn(1, device=device)
-        y += constant * torch.pow(x[:, :, feature:feature+1], feature + 1) * (task_id == task).float()
-    
-x_train = x[:num_train]
-task_id_train = task_id[:num_train]
-y_train = y[:num_train]
-x_test = x[num_train:]
-y_test = y[num_train:]
-
-with torch.no_grad():
-    y_pred = model(x_train, task_id_train, y_train, x_test)
-    y_pred_axial = axial_model(x_train, task_id_train, y_train, x_test)
-print(y_pred_axial)
-y_mean, y_var = y_pred[..., 0], y_pred[..., 1].exp()
-y_mean_axial, y_var_axial = y_pred_axial[..., 0], y_pred_axial[..., 1].exp()
-
-criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
-loss = criterion(y_mean, y_test.squeeze(1), var=y_var)
-loss_axial = criterion(y_mean_axial, y_test.squeeze(1), var=y_var_axial)
-
-for i in range(num_test):
-    print(f"ID Test {i}")
-    print(f"x_test: {x_test[i].squeeze()}")
-    print(f"y_test: {y_test[i].item()}")
-    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (y_mean[i].item(), y_var[i].item(), loss[i].item()))
-    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (y_mean_axial[i].item(), y_var_axial[i].item(), loss_axial[i].item()))
-    print()
-    
-# reversed x in feature dimension
-reversed_x = torch.flip(x, dims=[2])
-reversed_task_id = torch.flip(task_id, dims=[2])
-reversed_x_train = reversed_x[:num_train]
-reversed_task_id_train = reversed_task_id[:num_train]
-reversed_x_test = reversed_x[num_train:]
-
-with torch.no_grad():
-    reversed_y_pred = model(reversed_x_train, reversed_task_id_train, y_train, reversed_x_test)
-    reversed_y_pred_axial = axial_model(reversed_x_train, reversed_task_id_train, y_train, reversed_x_test)
-reversed_y_mean, reversed_y_var = reversed_y_pred[..., 0], reversed_y_pred[..., 1].exp()
-reversed_y_mean_axial, reversed_y_var_axial = reversed_y_pred_axial[..., 0], reversed_y_pred_axial[..., 1].exp()
-
-reversed_loss = criterion(reversed_y_mean, y_test.squeeze(1), var=reversed_y_var)
-reversed_loss_axial = criterion(reversed_y_mean_axial, y_test.squeeze(1), var=reversed_y_var_axial)
-
-for i in range(num_test):
-    print(f"OOD Test {i}")
-    print(f"Reversed x_test: {reversed_x_test[i].squeeze()}")
-    print(f"y_test: {y_test[i].item()}")
-    print("Standard: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean[i].item(), reversed_y_var[i].item(), reversed_loss[i].item()))
-    print("Axial: N(%.2f, %.2f), Loss: %.2f" % (reversed_y_mean_axial[i].item(), reversed_y_var_axial[i].item(), reversed_loss_axial[i].item()))
-    print()
-    
-min_loss = min(loss.min().item(), loss_axial.min().item())
-max_loss = max(loss.max().item(), loss_axial.max().item())
-plt.hist(loss.cpu().numpy(), bins=20, alpha=0.5, label="Standard", range=(min_loss, max_loss))
-plt.hist(loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Axial", range=(min_loss, max_loss))
-plt.legend()
-plt.xlabel("NLL")
-plt.title("Columns in Train Order")
-plt.savefig("hist_loss.png")
-plt.clf()
-
-min_loss = min(reversed_loss.min().item(), reversed_loss_axial.min().item())
-max_loss = max(reversed_loss.max().item(), reversed_loss_axial.max().item())
-plt.hist(reversed_loss.cpu().numpy(), bins=20, alpha=0.5, label="Standard", range=(min_loss, max_loss))
-plt.hist(reversed_loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Axial", range=(min_loss, max_loss))
-plt.legend()
-plt.xlabel("NLL")
-plt.title("Columns in Reverse Order")
-plt.savefig("hist_reversed.png")
-plt.clf()
-
-# plot all 
-min_loss = min(loss.min().item(), loss_axial.min().item(), reversed_loss.min().item(), reversed_loss_axial.min().item())
-max_loss = max(loss.max().item(), loss_axial.max().item(), reversed_loss_axial.max().item())
-
-plt.hist(loss.cpu().numpy(), bins=20, alpha=0.5, label="Standard", range=(min_loss, max_loss))
-plt.hist(loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Axial", range=(min_loss, max_loss))
-plt.hist(reversed_loss.cpu().numpy(), bins=20, alpha=0.5, label="Reversed Standard", range=(min_loss, max_loss))
-plt.hist(reversed_loss_axial.cpu().numpy(), bins=20, alpha=0.5, label="Reversed Axial", range=(min_loss, max_loss))
-plt.legend()
-plt.xlabel("NLL")
-plt.title("All")
-plt.savefig("hist_all.png")
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_f.py b/private_multitask_pfn/eval_f.py
deleted file mode 100644
index f7bdd30..0000000
--- a/private_multitask_pfn/eval_f.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import botorch
-import torch
-from botorch.acquisition.analytic import ExpectedImprovement
-from botorch.fit import fit_gpytorch_mll
-from botorch.models import SingleTaskGP
-from botorch.models.model import Model
-from botorch.models.multitask import MultiTaskGP
-from botorch.optim.optimize import optimize_acqf
-from botorch.posteriors import Posterior
-from fblearner.flow import api as flow
-from fblearner.flow.api import ResourceRequirements
-from fblearner.flow.external_api import WorkflowRun
-from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns.bar_distribution import (
-    BarDistribution,
-)
-from fblearner.flow.projects.ae.benchmarks.pfn.utils import (
-    load_model,
-    to_gp_format,
-    to_mtgp_format,
-    to_pfn_format,
-)
-from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
-from torch.distributions import Uniform
-
-
-def branin_source(x):
-    x1, x2 = x[..., 0], x[..., 1]
-    a, b, c, r, s, t = (
-        Uniform(
-            torch.tensor([0.5, 0.1, 1, 5, 8, 0.03]),
-            torch.tensor([1.5, 0.15, 2, 7, 12, 0.05]),
-        )
-        .sample()
-        .tolist()
-    )
-    first = a * (x2 - b * x1**2 + c * x1 - r)
-    second = s * (1 - t) * torch.cos(x1) + s
-    return first + second
-
-
-def hartmann_source(x):
-    alphas = (
-        Uniform(
-            torch.tensor([1.0, 1.18, 2.8, 3.2]), torch.tensor([1.02, 1.20, 3.0, 3.4])
-        )
-        .sample()
-        .to(device=x.device)
-    )
-    A = torch.tensor([[3.0, 10, 30], [0.1, 10, 35], [3.0, 10, 30], [0.1, 10, 35]]).to(
-        device=x.device
-    )
-    P = (
-        torch.tensor(
-            [
-                [3689, 1170, 2673],
-                [4699, 4387, 7470],
-                [1091, 8732, 5547],
-                [381, 5743, 8828],
-            ]
-        ).to(device=x.device)
-        * 1e-4
-    )
-    inner = -(A * (x.unsqueeze(-2) - P) ** 2)
-    # return negative for maximization
-    return (alphas * torch.exp(inner.sum(-1))).sum(-1).unsqueeze(-1)
-
-
-def pfn_gaussian_get_best(pfn, train_x, train_y, bounds):
-    target_indices = train_x[..., 0] > 0
-    best_f = train_y[target_indices].max()
-
-    pfn_model = PFNGaussian(pfn, train_x, train_y)
-    ei = ExpectedImprovement(pfn_model, best_f=best_f)
-    candidates, _ = optimize_acqf(
-        ei, bounds=bounds, q=1, num_restarts=10, raw_samples=512
-    )
-    return candidates
-
-
-def gp_get_best(train_x, train_y, bounds):
-    train_x, train_y = from_pfn_to_gp_format(train_x, train_y)
-    train_yvar = torch.ones_like(train_y) * 1e-5
-
-    best_f = train_y.max()
-
-    gp = SingleTaskGP(
-        train_x,
-        train_y,
-        train_Yvar=train_yvar,
-        outcome_transform=None,
-    )
-    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
-    fit_gpytorch_mll(mll)
-
-    ei = ExpectedImprovement(gp, best_f=best_f)
-    candidates, _ = optimize_acqf(
-        ei, bounds=bounds, q=1, num_restarts=10, raw_samples=512
-    )
-    return candidates
-
-
-def pfn_bar_get_best(pfn, train_x, train_y, bounds):
-    target_indices = train_x[..., 0] > 0
-    logits = pfn(train_x, train_y, possible_x)
-    eis = pfn.criterion.ei(logits, train_y[target_indices].max())
-    best_index = eis.argmax()
-    return possible_x[best_index], possible_y[best_index]
-
-
-def pfn_get_best(pfn, train_x, train_y, bounds):
-    if isinstance(pfn.criterion, BarDistribution):
-        return pfn_bar_get_best(pfn, train_x, train_y, bounds)
-    else:
-        return pfn_gaussian_get_best(pfn, train_x, train_y, bounds)
-
-
-def mtgp_get_best(task_id, train_x, train_y, bounds):
-    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
-    train_yvar = torch.ones_like(train_y) * 1e-5
-
-    best_f = train_y.max()
-
-    mtgp = MultiTaskGP(
-        train_x,
-        train_y,
-        task_feature=-1,
-        train_Yvar=train_yvar,
-        outcome_transform=None,
-        output_tasks=[0],
-    )
-    mll = ExactMarginalLogLikelihood(mtgp.likelihood, mtgp)
-    fit_gpytorch_mll(mll)
-
-    ei = ExpectedImprovement(mtgp, best_f=best_f)
-    candidates, _ = optimize_acqf(
-        ei, bounds=bounds, q=1, num_restarts=10, raw_samples=512
-    )
-    return candidates
-
-
-# @flow.flow_async(resource_requirements=ResourceRequirements(cpu=1))
-# @flow.typed()
-def bo_loop_continuous(
-    model_type, f, init_id, init_x, init_y, iters, bounds, model=None
-):
-    print("STARTING", model_type)
-    train_x = init_x.clone()
-    train_y = init_y.clone()
-
-    for i in range(iters):
-        if model_type == "mtgp":
-            best_x = mtgp_get_best_continuous(train_id, train_x, train_y, bounds)
-        elif model_type == "gp":
-            best_x = gp_get_best_continuous(train_id, train_x, train_y, bounds)
-        elif model_type == "pfn":
-            if model is None:
-                raise ValueError("model must be provided for pfn")
-            best_x = pfn_get_best_continuous(model, train_id, train_x, train_y, bounds)
-        else:
-            raise ValueError("model_type must be one of ['pfn', 'mtgp', 'gp']")
-
-        best_y = f(best_x)
-
-        n_tasks = train_x.shape[-1] - best_x.shape[-1]
-        best_x_pad = torch.zeros(*best_x.shape[:-1], n_tasks, device=best_x.device)
-        best_x_pad[..., 0] = 1
-        best_x_pad = torch.cat((best_x_pad, best_x), -1)
-
-        # # pad with zeros and 1 hot target encoding
-        # n_features = train_x.shape[-1]
-        # best_x_pad = torch.zeros(1, n_features, device=train_x.device)
-        # best_x_pad[:, 0] = 1
-        # best_x_pad[:, -best_x.shape[-1] :] = best_x
-
-        train_x = torch.cat((train_x, best_x_pad.unsqueeze(0)), 0)
-        train_y = torch.cat((train_y, best_y.unsqueeze(0)), 0)
-
-    return train_x, train_y
-
-
-@flow.flow_async(resource_requirements=ResourceRequirements(cpu=1, gpu=1, memory="16g"))
-@flow.typed()
-def run_bo_loop_continuous(
-    f, train_id, train_x, train_y, num_features, bo_iters, run_ids
-):
-    train_id.to(device="cuda")
-    train_x.to(device="cuda")
-    train_y.to(device="cuda")
-
-    info = {}
-    pfns = []
-    for run in run_ids:
-        workflow_run = WorkflowRun(run)
-        results = workflow_run.get_results()["output"]
-        pfn = load_model(results, num_tasks=4)
-        pfns.append(pfn)
-        info[run] = results["config"]
-
-    results = {}
-
-    bounds = torch.stack(
-        [
-            torch.zeros(num_features, device=train_x.device),
-            torch.ones(num_features, device=train_x.device),
-        ]
-    )
-
-    results["mtgp"] = bo_loop_continuous(
-        "mtgp", f, train_id, train_x, train_y, bo_iters, bounds
-    )
-    results["gp"] = bo_loop_continuous(
-        "gp", f, train_id, train_x, train_y, bo_iters, bounds
-    )
-    for run_id, pfn in zip(run_ids, pfns):
-        results[run_id] = bo_loop_continuous(
-            "pfn", f, train_id, train_x, train_y, bo_iters, bounds, pfn
-        )
-
-    return {
-        "results": results,
-        "info": info,
-    }
-
-
-@flow.registered(owners=["oncall+ae"])
-@flow.typed()
-def run_hartmann(runs: list, n_trials, bo_iters, num_tasks=4, seed=0, device="cpu"):
-    torch.manual_seed(seed)
-
-    def hartmann(x):
-        return botorch.test_functions.Hartmann(dim=3, negate=True)(
-            x[..., -3:]
-        ).unsqueeze(-1)
-
-    num_features = 3
-    max_num_tasks = 4
-
-    params = [
-        ("hartmann", 1, 20),
-        ("hartmann", 5, 20),
-        ("hartmann", 5, 50),
-        ("hartmann", 20, 50),
-    ]
-
-    results = {}
-    for param in params:
-        print("RUNNING", param)
-        results[param] = {}
-        name, num_target, num_source = param
-
-        for trial in range(n_trials):
-            train_id = []
-            train_x = []
-            train_y = []
-
-            for i in range(num_tasks):
-                task_x = torch.rand(num_source, num_features, device=device)
-
-                if i == 0:
-                    task_id = torch.zeros(num_target)
-                    task_y = hartmann(task_x)
-                else:
-                    task_id = torch.ones(num_source) * i
-                    task_y = hartmann_source(task_x)
-
-                train_id.append(task_id)
-                train_x.append(task_x)
-                train_y.append(task_y)
-
-            train_id = torch.cat(train_id)
-            train_x = torch.cat(train_x)
-            train_y = torch.cat(train_y)
-
-            result = run_bo_loop_continuous(
-                hartmann,
-                train_id,
-                train_x,
-                train_y,
-                num_features,
-                bo_iters=bo_iters,
-                run_ids=runs,
-            )
-            results[param][trial] = result
-    return results
diff --git a/private_multitask_pfn/eval_fcnet.py b/private_multitask_pfn/eval_fcnet.py
deleted file mode 100644
index 7418b7e..0000000
--- a/private_multitask_pfn/eval_fcnet.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import h5py
-import numpy as np
-import torch
-# from ax.fb.utils.storage.manifold import AEManifoldUseCase
-# from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
-import pickle
-
-MANIFOLD_KEYS = {
-    "fcnet_naval_propulsion_data": "tree/trbo_dev/22a02b98-85a3-11ef-a873-472b54035c6c",
-    "fcnet_parkinsons_telemonitoring_data": "tree/trbo_dev/2ec159c4-85a3-11ef-a873-472b54035c6c",
-    "fcnet_protein_structure_data": "tree/trbo_dev/3b527894-85a3-11ef-a873-472b54035c6c",
-    "fcnet_slice_localization_data": "tree/trbo_dev/47f999c4-85a3-11ef-a873-472b54035c6c",
-}
-
-
-def save_to_manifold():
-    datasets = [
-        "fcnet_naval_propulsion_data",
-        "fcnet_parkinsons_telemonitoring_data",
-        "fcnet_protein_structure_data",
-        "fcnet_slice_localization_data",
-    ]
-
-    for dataset in datasets:
-        lut = {}
-        metric_name = "valid_loss"
-        with h5py.File(
-            f"/home/yl9959/mtpfn/datasets/fcnet_tabular_benchmarks/{dataset}.hdf5"
-        ) as fh:
-            for _, (k, v) in enumerate(fh.items()):
-                lut[k] = float(np.mean(v[metric_name][()][:, -1]))
-                # if i > 10000:
-                #     break
-
-        filter_rules = {
-            "activation_fn_1": "relu",
-            "activation_fn_2": "relu",
-            "lr_schedule": "cosine",
-        }
-        xs = []
-        ys = []
-        for config_string in lut:
-            keep = True
-            config = eval(config_string)
-            for filter_key in filter_rules:
-                if config[filter_key] != filter_rules[filter_key]:
-                    keep = False
-                    break
-
-            if keep:
-                result = [
-                    config["batch_size"],
-                    config["dropout_1"],
-                    config["dropout_2"],
-                    config["init_lr"],
-                    config["n_units_1"],
-                    config["n_units_2"],
-                ]
-                xs.append(result)
-                ys.append(lut[config_string])
-
-        xs = torch.tensor(xs)
-        for index in [0, 3, 4, 5]:
-            xs[:, index] = xs[:, index].log()
-        xs -= xs.min(0, keepdim=True)[0]
-        xs /= xs.max(0, keepdim=True)[0]
-
-        ys = -torch.tensor(ys)
-        results = {
-            "x": xs,
-            "y": ys,
-        }
-
-        pickle.dump(results, open(f"/home/yl9959/mtpfn/datasets/fcnet_tabular_benchmarks/{dataset}.pkl", "wb"))
-        # client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
-        # final_key = client.torch_save(data=results)
-        # print(dataset, final_key)
-        
-if __name__ == "__main__":
-    save_to_manifold()
-
-
-def get_torch_format_fcnet(target_result, source_results, max_num_features):
-    target_xs = target_result["x"]
-    target_ys = target_result["y"]
-
-    dropped_columns = []
-    dropped_values = []
-    for _ in range(target_xs.shape[-1] - max_num_features):
-        max_rows = 0
-        best_value = None
-        best_column = None
-        for column in range(target_xs.shape[-1]):
-            for unique_value in torch.unique(target_xs[:, column]):
-                num_rows = len(target_ys[target_xs[:, column] == unique_value].unique())
-                if num_rows > max_rows:
-                    max_rows = num_rows
-                    best_value, best_column = unique_value, column
-        mask = target_xs[:, best_column] == best_value
-        target_xs = target_xs[mask]
-        target_ys = target_ys[mask]
-        target_xs = torch.cat(
-            (target_xs[:, :best_column], target_xs[:, best_column + 1 :]), dim=-1
-        )
-        dropped_columns.append(best_column)
-        dropped_values.append(best_value)
-
-    sources_xs = []
-    sources_ys = []
-    for source in source_results:
-        source_xs = source["x"]
-        source_ys = source["y"]
-
-        for column, value in zip(dropped_columns, dropped_values):
-            mask = source_xs[:, column] == value
-            source_xs = source_xs[mask]
-            source_ys = source_ys[mask]
-            source_xs = torch.cat(
-                (source_xs[:, :column], source_xs[:, column + 1 :]), dim=-1
-            )
-
-        sources_xs.append(source_xs)
-        sources_ys.append(source_ys.unsqueeze(-1))
-
-    return target_xs, target_ys.unsqueeze(-1), sources_xs, sources_ys
diff --git a/private_multitask_pfn/eval_hpobench.py b/private_multitask_pfn/eval_hpobench.py
deleted file mode 100644
index c47ba42..0000000
--- a/private_multitask_pfn/eval_hpobench.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import os
-import pickle
-import pickle
-import numpy as np
-import pandas as pd
-import torch
-# from ax.fb.utils.storage.manifold import AEManifoldUseCase
-# from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
-
-HPOBENCH_MANIFOLD_KEYS = {
-    "rf": "tree/trbo_dev/839dd652-8544-11ef-94ac-4d9af618463b",
-    "lr": "tree/trbo_dev/ef4ea9d8-858c-11ef-a657-0908f1ce571c",
-    "svm": "tree/trbo_dev/bc90dacc-8544-11ef-94ac-4d9af618463b",
-    "xgb": "tree/trbo_dev/d58888be-858e-11ef-a657-0908f1ce571c",
-}
-
-default_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-
-def drop_columns(df, relevant_columns, num_final_features):
-    df = df.copy()
-    # fixes one value per column by choosing the value
-    # that leads to the most diverse ys
-    dropped_columns = []
-    dropped_values = []
-    for _ in range(len(relevant_columns) - num_final_features):
-        # filter
-        max_rows = 0
-        best_value = None
-        best_column = None
-        for column in relevant_columns:
-            # Iterate through each unique value in the column
-            for value in df[column].unique():
-                # Filter the DataFrame
-                filtered_df = df[df[column] == value]
-                num_rows = filtered_df["function_value"].nunique()
-
-                # Check if this value retains the most rows
-                if num_rows > max_rows:
-                    max_rows = num_rows
-                    best_value, best_column = value, column
-
-        df = df[df[best_column] == best_value]
-        df = df.drop(best_column, axis=1)
-        
-        relevant_columns.remove(best_column)
-        dropped_columns.append(best_column)
-        dropped_values.append(best_value)
-
-    final_columns = [
-        column for column in relevant_columns if column not in dropped_columns
-    ]
-
-    return df, final_columns, dropped_columns, dropped_values
-
-
-def get_torch_format_hpobench(target_df, source_dfs, max_num_features, problem, device=default_device):
-    processed_dfs = []
-    for df in [target_df] + source_dfs:
-        df = df.copy()
-        df["function_value"] = df["result.function_value"]
-        if problem == "rf":
-            relevant_columns = [
-                "max_depth",
-                "max_features",
-                "min_samples_leaf",
-                "min_samples_split",
-            ]
-            df["max_depth"] = np.log(df["max_depth"])
-            df["min_samples_split"] = np.log(df["min_samples_split"])
-        elif problem == "lr":
-            relevant_columns = ["alpha", "eta0"]
-            df["alpha"] = np.log(df["alpha"])
-            df["eta0"] = np.log(df["eta0"])
-        elif problem == "svm":
-            relevant_columns = ["C", "gamma"]
-            df["C"] = np.log(df["C"])
-            df["gamma"] = np.log(df["gamma"])
-        elif problem == "xgb":
-            relevant_columns = ["colsample_bytree", "eta", "max_depth", "reg_lambda"]
-            df["eta"] = np.log(df["eta"])
-            df["max_depth"] = np.log(df["max_depth"])
-            df["reg_lambda"] = np.log(df["reg_lambda"])
-        elif problem == "nn":
-            print(df.columns)
-            relevant_columns = ["alpha", "batch_size", "depth", "learning_rate_init", "width"]
-            df["alpha"] = np.log(df["alpha"])
-            df["batch_size"] = np.log(df["batch_size"])
-            df["depth"] = (df["depth"] - df["depth"].min()) / (df["depth"].max() - df["depth"].min())
-            df["learning_rate_init"] = np.log(df["learning_rate_init"])
-            df["width"] = np.log(df["width"])
-
-        df = df.groupby(relevant_columns, as_index=False).mean(numeric_only=True)
-        processed_dfs.append(df)
-
-    # eventually remove when we train on more features
-    # drops columns to get maximum output diversity in target task
-    target_df = processed_dfs[0]
-    target_df, final_columns, dropped_columns, dropped_values = drop_columns(
-        target_df, relevant_columns, max_num_features
-    )
-
-    result_xs = []
-    result_ys = []
-    for df in processed_dfs:
-        # limit to non-dropped columns
-        for column, value in zip(dropped_columns, dropped_values):
-            df = df[df[column] == value]
-
-        # normalize to [0, 1]
-        xs = torch.tensor(df[final_columns].values).double()
-        xs = xs - xs.min(0, keepdim=True)[0]
-        max_values = xs.max(dim=0, keepdim=True)[0]
-        max_values[max_values == 0] = 1
-        xs = xs / max_values
-
-        # NEGATE VALID_LOSS
-        ys = -torch.tensor(df["function_value"].values).unsqueeze(-1)
-        # standardize to mean 0, std 1
-        std = torch.where(ys.std(0).isnan(), torch.tensor(1.0), ys.std(0))
-        ys = (ys - ys.mean()) / std
-
-        result_xs.append(xs.float().to(device))
-        result_ys.append(ys.float().to(device))
-
-    return result_xs[0], result_ys[0], result_xs[1:], result_ys[1:]
-
-
-def create_train_test(
-    target_xs, target_ys, sources_xs, sources_ys, n_target, n_source, max_num_task
-):
-    train_id = []
-    train_x = []
-    train_y = []
-    for i, (source_xs, source_ys) in enumerate(zip(sources_xs, sources_ys)):
-        random_indices = torch.randperm(len(source_xs))[:n_source]
-        train_x.append(source_xs[random_indices])
-        train_y.append(source_ys[random_indices])
-        train_id.append(torch.ones(n_source) * i + 1)
-
-    # add target task
-    random_indices = torch.randperm(len(target_xs))
-    train_indices = random_indices[:n_target]
-    test_indices = random_indices[n_target:]
-    train_x.append(target_xs[train_indices])
-    train_y.append(target_ys[train_indices])
-    train_id.append(torch.zeros(n_target))
-
-    train_x = torch.concat(train_x, 0)
-    train_id = torch.concat(train_id, 0)
-
-    train_y = torch.concat(train_y, 0)
-
-    test_id = torch.zeros(len(test_indices))
-    test_x = target_xs[test_indices]
-    test_y = target_ys[test_indices]
-
-    return (
-        train_id,
-        train_x,
-        train_y,
-        test_id,
-        test_x,
-        test_y,
-    )
-
-
-def save_to_pickle():
-    result = {}
-    # client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
-    for task in ["rf", "lr", "svm", "xgb", "nn"]:
-        for folder in os.listdir(
-            f"/home/yl9959/mtpfn/datasets/hpobench/{task}"
-        ):
-            if folder.endswith(".zip"):
-                continue
-            task_id = int(folder)
-            print("\tReading", folder)
-            df = pd.read_parquet(
-                f"/home/yl9959/mtpfn/datasets/hpobench/{task}/{task_id}/{task}_{task_id}_data.parquet.gzip"
-            )
-            if task == "rf":
-                df = df[(df["n_estimators"] == 512) & (df["subsample"] == 1)]
-            elif task == "lr":
-                df = df[(df["iter"] == 1000) & (df["subsample"] == 1.0)]
-            elif task == "svm":
-                df = df[(df["subsample"] == 1)]
-            elif task == "xgb":
-                df = df[(df["n_estimators"] == 2000) & (df["subsample"] == 1)]
-            elif task == "nn":
-                df = df[(df["iter"] == 243) & (df["seed"] == 8916)]
-
-            result[task_id] = df
-
-        # key = client.torch_save(result)
-        pickle.dump(result, open(f"/scratch/yl9959/mtpfn/datasets/hpobench_{task}.pkl", "wb"))
-        print(f"Saved {task} to /scratch/yl9959/mtpfn/datasets/hpobench_{task}.pkl", len(result))
-
-
-if __name__ == "__main__":
-    save_to_pickle()
\ No newline at end of file
diff --git a/private_multitask_pfn/eval_plot_util.py b/private_multitask_pfn/eval_plot_util.py
deleted file mode 100644
index 21a8030..0000000
--- a/private_multitask_pfn/eval_plot_util.py
+++ /dev/null
@@ -1,315 +0,0 @@
-import itertools
-
-import multiprocessing as mp
-
-import botorch
-import torch
-import pickle
-from eval_fcnet import (
-    get_torch_format_fcnet,
-)
-from eval_hpobench import (
-    get_torch_format_hpobench,
-)
-import json
-
-from gen_batch import get_mtgp_for_eval
-from utils import (
-    create_train_test,
-    load_model,
-    pfn_predict,
-    mtgp_predict,
-    gp_predict,
-    scaml_predict,
-    standardize_by_task,
-    pfn_get_best,
-    mtgp_get_best,
-    gp_get_best,
-    scaml_get_best,
-)
-import time
-import datetime
-import warnings
-import os
-import hashlib
-import argparse
-import traceback
-import matplotlib.pyplot as plt
-from collections import defaultdict
-
-
-model_to_label = {
-    # "different-dust-282": "PFN (0.5 uncorr)",
-    # "royal-firebrand-281": "PFN (0.25 uncorr)",
-    # "peach-plasma-280": "PFN (1.0 uncorr)",
-    # "treasured-lion-279": "PFN (0.0 uncorr)",
-    # "exalted-wave-278": "PFN (0.75 uncorr)",
-    "wobbly-donkey-484": "wobbly-donkey-484 gaussian 0.1",
-    "sparkling-microwave-480": "sparkling-microwave-480 gaussian 0",
-    "likely-donkey-479": "likely-donkey-479 gaussian 0.2",
-    "fallen-cherry-483": "fallen-cherry-483 bar 0",
-    "stilted-night-482": "stilted-night-482 bar 0.1",
-    "youthful-wildflower-481": "youthful-wildflower-481 bar 0.2",
-}
-
-baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-
-def get_label(model):
-    if model in model_to_label:
-        return model_to_label[model]
-    return model
-
-
-def get_labels(models):
-    return [get_label(model) for model in models]
-
-
-def plot_all(exp_dir, n_functions, title):
-    fig, axs = plt.subplots(1, 2, figsize=(12, 7))
-    # one plot for boxplots of mse, one plot for boxplots of nll
-    
-    model_mses = defaultdict(list)
-    model_nlls = defaultdict(list)
-    
-    for function_dir in os.listdir(exp_dir):
-        # check if directory
-        if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-            continue
-        with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
-            results = json.load(f)
-            
-        models = list(results.keys())
-        for model in models:
-            mses, nlls = [], []
-            try:
-                for trial_key in results[model]:
-                    mses.append(results[model][trial_key]["mse"])
-                    nlls.append(results[model][trial_key]["nll"])
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                traceback.print_exc()
-                continue
-            model_mses[model].extend(mses)
-            model_nlls[model].extend(nlls)
-            
-    # plot mse boxplots
-    ax = axs[0]
-    ax.boxplot([model_mses[model] for model in model_mses], showfliers=False)
-    ax.set_xticklabels(get_labels(model_mses.keys()))
-    # rotate x labels
-    for tick in ax.get_xticklabels():
-        tick.set_rotation(45)
-    ax.set_title("MSE")
-    
-    # plot nll boxplots
-    ax = axs[1]
-    ax.boxplot([model_nlls[model] for model in model_nlls], showfliers=False)
-    ax.set_xticklabels(get_labels(model_nlls.keys()))
-    for tick in ax.get_xticklabels():
-        tick.set_rotation(45)
-    ax.set_title("NLL")
-    
-    plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(os.path.join(exp_dir, "plot.png"))
-
-
-def plot_function(function_dir, n_trials, title):
-    max_scatters = 50
-    with open(os.path.join(function_dir, "results.json"), "r") as f:
-        results = json.load(f)
-        
-    models = list(results.keys())
-    mse_dict = {}
-    nll_dict = {}
-        
-    fig, axs = plt.subplots(1, len(models) + 2, figsize=(6 * (len(models) + 2), 7))
-    axs = axs.flatten()
-    for i, model in enumerate(models):
-        try:
-            mses, nlls = [], []
-            for trial in range(n_trials):
-                trial_key = "trial_%d" % trial
-                mses.append(results[model][trial_key]["mse"])
-                nlls.append(results[model][trial_key]["nll"])
-            mse_dict[model] = mses
-            nll_dict[model] = nlls
-            
-            ax = axs[i]
-            # visualize predictions for one trial
-            mean = torch.tensor(results[model]["trial_0"]["mean"]).squeeze()
-            std = 2 * torch.tensor(results[model]["trial_0"]["std"]).squeeze()
-            true = torch.tensor(results[model]["trial_0"]["true"]).squeeze()
-            
-            ax.scatter(true[:max_scatters], mean[:max_scatters])
-            ax.errorbar(true[:max_scatters], mean[:max_scatters], yerr=std[:max_scatters], fmt="o")
-            ax.plot([true[:max_scatters].min(), true[:max_scatters].max()], [true[:max_scatters].min(), true[:max_scatters].max()], "k--")
-            ax.set_title(get_label(model) + " Predictions")
-            
-        except KeyboardInterrupt as e:
-            raise e
-        except Exception as e:
-            traceback.print_exc()
-            continue
-        
-    all_mses = [result for model in mse_dict for result in mse_dict[model]]
-    all_nlls = [result for model in nll_dict for result in nll_dict[model]]
-    all_mses = torch.tensor(all_mses)
-    all_nlls = torch.tensor(all_nlls)
-    
-    
-    lower_mse, upper_mse = torch.quantile(all_mses, torch.tensor([0.2, 0.8]))
-    diff = upper_mse - lower_mse
-    lower_mse = (lower_mse - 3 * diff)
-    upper_mse = (upper_mse + 3 * diff)
-    all_mses = all_mses[all_mses < upper_mse]
-    all_mses = all_mses[all_mses > lower_mse]
-    lower_mse, upper_mse = all_mses.min().item(), all_mses.max().item()
-    
-    lower_nll, upper_nll = torch.quantile(all_nlls, torch.tensor([0.2, 0.8]))
-    
-    ax = axs[-2]
-    for model in mse_dict:
-        ax.hist(mse_dict[model], bins=8, alpha=0.5, label=get_label(model), range=(lower_mse, upper_mse), density=True)
-    ax.set_title("MSE")
-    ax.legend()
-    
-    ax = axs[-1]
-    for model in nll_dict:
-        ax.hist(nll_dict[model], bins=8, alpha=0.5, label=get_label(model), range=(lower_nll.item(), upper_nll.item()), density=True)
-    ax.set_title("NLL")
-    ax.legend()
-                
-    plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(os.path.join(function_dir, "plot.png"))
-
-
-def plot_bo_function(function_dir, title):
-    plt.figure(figsize=(8, 6))
-        
-    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
-        bo_results = json.load(f)
-    with open(os.path.join(function_dir, "info.json"), "r") as f:
-        info = json.load(f)
-        
-        for model in bo_results:
-            model_results = []
-            try:
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    model_results.append(regret)
-                
-                # remove shorter trials
-                max_length = max([len(result) for result in model_results])
-                model_results = [result for result in model_results if len(result) == max_length]
-                
-                model_results = torch.stack(model_results)
-                
-                mean = model_results.nanmean(0)
-                std = model_results.std(0) / len(model_results)# ** 0.5
-                lower = mean - std
-                upper = mean + std
-                # mean = model_results.mean(0)
-                # lower = model_results.quantile(0.25, 0)
-                # upper = model_results.quantile(0.75, 0)
-                linestyle = "--" if model in baselines else "-"
-                plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
-                plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                print(e)
-                # import pdb; pdb.set_trace()
-                # raise e
-                continue
-                    
-    plt.legend()
-    plt.xlabel("BO Iterations")
-    plt.ylabel("Normalized Regret")
-    plt.title(title)
-    plt.tight_layout()
-    plt.savefig(os.path.join(function_dir, "bo_loop.png"))
-
-
-def plot_bo_all(exp_dir, models, title):
-    plt.figure(figsize=(8, 6))
-    
-    if models is None:
-        models = []
-        
-    for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                if not torch.isnan(function_results.mean(0)).any():
-                    model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except FileNotFoundError as e:
-                continue
-            except Exception as e:
-                # print(e)
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # mean = model_results.mean(0)
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        
-        mean = model_results.nanmean(0)
-        std = model_results.std(0) / len(model_results)# ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
-        plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
-
-                    
-    plt.legend()
-    plt.xlabel("BO Iterations")
-    plt.ylabel("Normalized Regret")
-    plt.title(title)
-    plt.tight_layout()
-    plt.savefig(os.path.join(exp_dir, "bo_loop.png"))
-
-
-
diff --git a/private_multitask_pfn/eval_task.py b/private_multitask_pfn/eval_task.py
deleted file mode 100644
index 649be68..0000000
--- a/private_multitask_pfn/eval_task.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import torch
-from gen_task_batch import task_invariant_batch, task_invariant_eval_batch
-import json
-import os
-from utils import load_model
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-# standard attention
-# ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-18_15-48-06__prior_toy_task_invariant__features_3__tasks_2__epochs_10__seqlen_200__attn_axial"
-ckpt_dir = "/home/yl9959/mtpfn/ckpt/24-12-18_17-39-47__prior_toy_task_invariant__features_3__tasks_2__epochs_10__seqlen_200__attn_axial" # use batch and seq len task encoding
-
-model = load_model(ckpt_dir).to(device)
-
-torch.manual_seed(0)
-
-num_features = 3
-num_train = 50
-num_test = 20
-
-
-batch_size = 1
-seq_len = num_train + num_test
-
-
-# x = torch.randint(5, (batch_size, seq_len, num_features), device=device)
-x = torch.rand(batch_size, seq_len, num_features, device=device)
-y = torch.zeros(batch_size, seq_len, 1, device=device)
-
-task_id = torch.randint(2, (batch_size, seq_len, 1), device=device)
-opposite_task_id = (task_id + 1) % 2
-
-correct_y = torch.zeros(batch_size, seq_len, 1, device=device)
-incorrect_y = torch.zeros(batch_size, seq_len, 1, device=device)
-
-# ID Task 1: y = x_1 + x_2^2 + ... + x_d^d
-for feature in range(num_features):
-    task_mask = task_id == 0
-    correct_y += 0 * task_mask
-    incorrect_y += 0 * ~task_mask
-    
-for feature in range(num_features):
-    task_mask = task_id == 1
-    correct_y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * task_mask
-    incorrect_y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * ~task_mask
-
-train_x = x.transpose(0, 1)[:num_train]
-train_correct_y = correct_y.transpose(0, 1)[:num_train]
-train_incorrect_y = incorrect_y.transpose(0, 1)[:num_train]
-train_original_task_id = task_id.transpose(0, 1)[:num_train]
-train_opposite_task_id = opposite_task_id.transpose(0, 1)[:num_train]
-
-
-test_x = x.transpose(0, 1)[num_train:]
-test_correct_y = correct_y.transpose(0, 1)[num_train:]
-test_incorrect_y = incorrect_y.transpose(0, 1)[num_train:]
-test_original_task_id = task_id.transpose(0, 1)[num_train:]
-test_opposite_task_id = opposite_task_id.transpose(0, 1)[num_train:]
-
-
-# Original
-pred_original_y = model(train_x, train_original_task_id, train_correct_y, test_x, test_original_task_id)
-# Opposite
-pred_opposite_y = model(train_x, train_opposite_task_id, train_correct_y, test_x, test_opposite_task_id)
-
-
-original_y_mean, original_y_var = pred_original_y[..., 0], pred_original_y[..., 1].exp()
-opposite_y_mean, opposite_var = pred_opposite_y[..., 0], pred_opposite_y[..., 1].exp()
-
-criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
-original_loss = criterion(original_y_mean, test_correct_y, var=original_y_var)
-opposite_loss = criterion(opposite_y_mean, test_correct_y, var=opposite_var)
-
-for i in range(num_test):
-    print("\nTest Point", i)
-    print("Original Y", test_correct_y[i].item())
-    print("Original Prediction: N({:.2f}, {:.2f})".format(original_y_mean[i].item(), original_y_var[i].sqrt().item()))
-    print("Opposite Prediction: N({:.2f}, {:.2f})".format(opposite_y_mean[i].item(), opposite_var[i].sqrt().item()))
-    
-    print("Incorrect Y", test_incorrect_y[i].item())
-    
-
-
-lily
-
-
-
-
-
-
-
-
-
-
-
-
-
-id_batch = task_invariant_batch(
-    batch_size=1,
-    seq_len=num_train+num_test,
-    num_features=num_features,
-    max_num_tasks=2,
-    num_tasks=2,
-    lengthscale=1.0,
-    hyperparameters=None,
-    device=device,
-)
-
-ood_batch = task_invariant_eval_batch(
-    batch_size=1,
-    seq_len=num_train+num_test,
-    num_features=num_features,
-    max_num_tasks=2,
-    num_tasks=2,
-    lengthscale=1.0,
-    hyperparameters=None,
-    device=device,
-)
-
-batches = [id_batch, ood_batch]
-batch_type = ["ID", "OOD"]
-
-for name, batch in zip(batch_type, batches):
-    train_x = batch.x[:num_train]
-    train_y = batch.y[:num_train]
-    train_task_id = batch.task_id[:num_train]
-
-    test_task_id_mask = (batch.task_id[num_train:] == 0).squeeze(-1)
-    test_x = batch.x[num_train:][test_task_id_mask].unsqueeze(1)
-    test_y = batch.y[num_train:][test_task_id_mask].unsqueeze(1)
-
-    y_pred = model(train_x, train_task_id, train_y, test_x)
-    y_mean, y_var = y_pred[..., 0], y_pred[..., 1].exp()
-
-    criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
-    loss = criterion(y_mean, test_y.squeeze(1), var=y_var)
-
-    print(f"Test Loss: {loss.mean().item()}", name)
-
diff --git a/private_multitask_pfn/eval_vis.py b/private_multitask_pfn/eval_vis.py
deleted file mode 100644
index cf5eab5..0000000
--- a/private_multitask_pfn/eval_vis.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import argparse
-
-import botorch
-import gpytorch
-import torch
-
-from utils import *
-import traceback
-import matplotlib.pyplot as plt
-from gpytorch.kernels import RBFKernel
-from gpytorch.distributions import MultivariateNormal
-import os
-
-default_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-def plot(ax, test_info, pfn_info):
-    pfn_id, model, model_dim = pfn_info
-    train_x, train_y, test_x, test_y = test_info
-    train_task_id = torch.zeros(train_x.size(0))
-    
-    if model_dim > 1:
-        # pad train and test with 0s
-        padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)
-        padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)
-    else:
-        padded_train_x = train_x
-        padded_test_x = test_x
-        
-    # train_x, train_task_id, train_y, test_x, 
-    posterior = pfn_predict(model, train_task_id.to(default_device), padded_train_x.to(default_device), train_y.to(default_device), padded_test_x.to(default_device))
-    mean = posterior.mean.cpu()
-    std = posterior.variance.sqrt().cpu()
-    
-    ax.plot(test_x, test_y, label="true", color="C0")
-    ax.scatter(train_x, train_y, label="train", color="C0")
-    ax.plot(test_x, mean, label="mean", color="C1")
-    ax.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
-
-
-def get_test_function(seed):
-    with botorch.manual_seed(seed):
-        n_samples = torch.randint(5, 20, (1,)).item()
-        
-        train_x = torch.rand(n_samples, 1)
-        test_x = torch.linspace(0, 1, 100).view(-1, 1)
-        all_x = torch.cat([train_x, test_x], dim=0)
-        
-        rbf = gpytorch.kernels.RBFKernel()
-        # rbf.lengthscale = torch.tensor(0.2)
-        rbf.lengthscale = torch.distributions.Gamma(3, 6).sample()
-        
-        covar = rbf(all_x)
-        covar = covar + 1e-4 * torch.eye(covar.size(0))
-        
-        y = MultivariateNormal(torch.zeros(all_x.size(0)), covar).sample()
-        train_y = y[:n_samples]
-        test_y = y[n_samples:]
-        
-    return train_x, train_y, test_x, test_y
-
-
-
-def plot_multitask(ax_multi, ax_single, test_info, pfn_info):
-    pfn_id, model, model_dim = pfn_info
-    train_x, train_task_id, train_y, test_x, test_task_id, test_y = test_info
-    
-    if model_dim > 1:
-        # pad train and test with 0s
-        padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)
-        padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)
-    else:
-        padded_train_x = train_x
-        padded_test_x = test_x
-        
-    # train_x, train_task_id, train_y, test_x
-    posterior = pfn_predict(model, train_task_id.to(default_device), padded_train_x.to(default_device), train_y.to(default_device), padded_test_x.to(default_device))
-    mean = posterior.mean.cpu()
-    std = posterior.variance.sqrt().cpu()
-    nll = -torch.distributions.Normal(mean.squeeze(), std.squeeze()).log_prob(test_y.squeeze()).mean()
-    
-    ax_multi.set_title(f"Multi NLL: {nll.item():.2f}")
-    ax_multi.plot(test_x, test_y, label="true", color="C0")
-    ax_multi.plot(test_x, mean, label="mean", color="C1")
-    ax_multi.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
-    
-    for i in train_task_id.unique():
-        mask = train_task_id == i
-        marker = "x" if i.item() == 0 else "o"
-        size = 100 if i.item() == 0 else 50
-        ax_multi.scatter(train_x[mask], train_y[mask], label=f"train task {i.item()}", color=f"C{i}", marker=marker, s=size)
-
-
-    # train_x, train_task_id, train_y, test_x,
-    target_mask = train_task_id == 0
-    posterior = pfn_predict(model, train_task_id[target_mask].to(default_device), padded_train_x[target_mask].to(default_device), train_y[target_mask].to(default_device), padded_test_x.to(default_device))
-    mean = posterior.mean.cpu()
-    std = posterior.variance.sqrt().cpu()
-    nll = -torch.distributions.Normal(mean.squeeze(), std.squeeze()).log_prob(test_y.squeeze()).mean()
-    
-    ax_single.set_title(f"Single NLL: {nll.item():.2f}")
-    ax_single.plot(test_x, test_y, label="true", color="C0")
-    ax_single.plot(test_x, mean, label="mean", color="C1")
-    ax_single.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
-    
-    for i in [0]:#train_task_id.unique():
-        mask = train_task_id == i
-        # marker = "x" if i.item() == 0 else "o"
-        # size = 100 if i.item() == 0 else 50
-        marker = "x"
-        size = 100
-        ax_single.scatter(train_x[mask], train_y[mask], label=f"train task {i}", color=f"C{i}", marker=marker, s=size)
-
-
-def get_multitask_test_function(seed, n_target, n_source):
-    n_features = 1
-    n_tasks = 2
-    with botorch.manual_seed(seed):
-        if n_target is not None:
-            n_samples = n_target + n_source * (n_tasks - 1)
-            target_task_id = torch.zeros(n_target, 1).long()
-            source_task_ids = [torch.ones(n_source, 1).long() * i for i in range(1, n_tasks)]
-            train_task_id = torch.cat([target_task_id] + source_task_ids, dim=0)
-        else:
-            n_samples = torch.randint(10, 40, (1,)).item()
-            train_task_id = torch.randint(n_tasks, size=(n_samples,)).unsqueeze(1).long()
-        
-        train_xs = torch.rand(n_samples, n_features)
-        test_xs = torch.linspace(0, 1, 100).view(-1, n_features)
-        test_task_id = torch.zeros(test_xs.size(0), 1).long()
-        xs = torch.cat([train_xs, test_xs], dim=0)
-        task_id = torch.cat([train_task_id, test_task_id], dim=0)
-        
-        rbf = RBFKernel()
-        rbf.lengthscale = torch.distributions.Gamma(3, 6).sample()
-        covar_x = rbf(xs)
-        task_covar_matrix = torch.ones(n_tasks, n_tasks) * 0.9
-        task_covar_matrix += torch.eye(n_tasks) * 0.1
-        
-        covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
-        covar = covar_x.mul(covar_t)
-        covar = covar + 1e-4 * torch.eye(covar.size(0))
-        ys = MultivariateNormal(torch.zeros(covar.shape[-1]), covar).sample()
-
-        train_y = ys[:n_samples]
-        test_y = ys[n_samples:]
-        
-    return train_xs, train_task_id.squeeze(), train_y, test_xs, test_task_id.squeeze(), test_y
-
-
-# main function
-if __name__ == "__main__":
-    argparser = argparse.ArgumentParser()
-    # pfn wandb ids
-    argparser.add_argument("--pfn_ids", nargs="*", type=str, help="wandb ids for pfn models")
-    argparser.add_argument("--dirs", nargs="*", type=str, help="checkpoints for pfn models")
-    argparser.add_argument("--seed", type=int, default=0, help="seed for test function")
-    argparser.add_argument("--n_target", type=int, default=None, help="samples of target function")
-    argparser.add_argument("--n_source", type=int, default=None, help="samples of source function")
-    args = argparser.parse_args()
-    
-    pfn_dirs = []
-    if args.pfn_ids is not None:
-        # pfn_dirs += ["/home/yl9959/mtpfn/wandb_links/%s" % pfn_id for pfn_id in args.pfn_ids]
-        if os.path.exists("/home/lily_l/private_multitask_pfn/wandb_links"):
-            pfn_dirs += [(pfn_id, "/home/lily_l/private_multitask_pfn/wandb_links/%s" % pfn_id) for pfn_id in args.pfn_ids]
-        else:
-            pfn_dirs += [(pfn_id, "/home/yl9959/mtpfn/wandb_links/%s" % pfn_id) for pfn_id in args.pfn_ids]
-    if args.dirs is not None:
-        pfn_dirs += [("pfn_%d" % i, dir) for i, dir in enumerate(args.dirs)]
-
-    for pfn_id, pfn_dir in pfn_dirs:
-        try:
-            pfn_args_json = f"{pfn_dir}/args.json"
-            with open(pfn_args_json, "r") as f:
-                pfn_args = json.load(f)
-                
-            if not pfn_args["sample_num_features"]:
-                model_dim = pfn_args["num_features"]
-            else:
-                model_dim = 1
-            
-            model = load_model(pfn_dir, best=True).to(default_device)
-            pfn_info = (pfn_id, model, model_dim)
-                        
-            fig, axs = plt.subplots(1, 5, figsize=(20, 4))
-            
-            for i in range(5):
-                seed = args.seed + i
-                test_info = get_test_function(seed)
-                plot(axs[i], test_info, pfn_info)
-            
-            plt.legend()    
-            plt.suptitle(pfn_info[0])
-            plt.tight_layout()
-            plt.savefig(f"figures/pfn_{pfn_info[0]}.png")
-            plt.close()
-            
-            fig, axs = plt.subplots(2, 5, figsize=(20, 8))
-            # axs = axs.flatten()
-            for i in range(5):
-                seed = args.seed + i
-                test_info = get_multitask_test_function(seed, n_target=args.n_target, n_source=args.n_source)
-                plot_multitask(axs[0][i], axs[1][i], test_info, pfn_info)
-            
-            plt.legend()
-            plt.suptitle(pfn_info[0])
-            plt.tight_layout()
-            plt.savefig(f"figures/pfn_{pfn_info[0]}_multitask.png")
-            plt.close()
-        except Exception as e:
-            traceback.print_exc()
-            print("failed to load", pfn_dir)
-            continue
-            
-        
\ No newline at end of file
diff --git a/private_multitask_pfn/gen_axial_batch.py b/private_multitask_pfn/gen_axial_batch.py
deleted file mode 100644
index 669cb94..0000000
--- a/private_multitask_pfn/gen_axial_batch.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import torch
-from PFNs.pfns.priors import Batch
-
-
-default_device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# batch_size: number of datasets
-# seq_len: number of training samples per dataset
-def axial_train_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    """
-    Generate x_i = [x_1, x_2, ..., x_d] and y_i = x_1 + x_2^2 + ... + x_d^d
-    """
-    x = torch.rand(batch_size, seq_len, num_features, device=device)
-    y = torch.zeros(batch_size, seq_len, 1, device=device)
-    
-    for feature in range(num_features):
-        y += torch.pow(x[:, :, feature:feature+1], feature + 1)
-        
-    return Batch(
-        x=x.transpose(0, 1),
-        y=y.transpose(0, 1),
-        target_y=y.transpose(0, 1).clone(),
-    )
-
-
-def axial_test_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    x = torch.rand(batch_size, seq_len, num_features, device=device)
-    y = torch.zeros(batch_size, seq_len, 1, device=device)
-    
-    for feature in range(num_features):
-        y += torch.pow(x[:, :, feature:feature+1], num_features - feature)
-        
-    return Batch(
-        x=x.transpose(0, 1),
-        y=y.transpose(0, 1),
-        target_y=y.transpose(0, 1).clone(),
-    )
-
-
-# batch_size: number of datasets
-# seq_len: number of training samples per dataset
-def multitask_line_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    # permuted columns
-    x = torch.rand(batch_size, seq_len, num_features, device=device)
-    y = torch.zeros(batch_size, seq_len, 1, device=device)
-    task_id = torch.randint(0, num_tasks, (batch_size, seq_len, 1), device=device)
-    
-    for task in range(num_tasks):
-        for feature in range(num_features):
-            constant = torch.randn(1, device=device)
-            y += constant * torch.pow(x[:, :, feature:feature+1], feature + 1) * (task_id == task).float()
-            
-    # shuffle x columns
-    column_order = torch.randperm(num_features, device=device)
-    x = x[:, :, column_order]
-    
-    return Batch(
-        x=x.transpose(0, 1),
-        y=y.transpose(0, 1),
-        target_y=y.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-
-
-# multitask_line_batch(batch_size=2, seq_len=10, num_features=2, max_num_tasks=2, num_tasks=2, lengthscale=0.1)
-# axial_test_batch(batch_size=2, seq_len=3, num_features=2, max_num_tasks=2, num_tasks=2, lengthscale=0.1)
\ No newline at end of file
diff --git a/private_multitask_pfn/gen_batch.py b/private_multitask_pfn/gen_batch.py
deleted file mode 100644
index 398d0f7..0000000
--- a/private_multitask_pfn/gen_batch.py
+++ /dev/null
@@ -1,1083 +0,0 @@
-#!/usr/bin/env python3
-import gpytorch
-
-import torch
-from PFNs.pfns.priors import Batch
-from gpytorch.distributions.multivariate_normal import MultivariateNormal
-from gpytorch.kernels import RBFKernel
-from gpytorch.kernels.kernel import Kernel
-from gpytorch.means.constant_mean import ConstantMean
-from gpytorch.priors.lkj_prior import LKJCovariancePrior
-from linear_operator.operators import InterpolatedLinearOperator
-import json
-import pickle
-from botorch.models import SingleTaskGP
-from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
-from botorch.exceptions import ModelFittingError
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from eval_hpobench import get_torch_format_hpobench
-import os
-
-default_device = "cuda:0" if torch.cuda.is_available() else "cpu:0"
-
-
-class IndexKernelFixed(Kernel):
-    def __init__(
-        self,
-        covar_matrix,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.covar_matrix = covar_matrix
-
-    def forward(self, i1, i2, **params):
-        i1, i2 = i1.long(), i2.long()
-        covar_matrix = self.covar_matrix
-        batch_shape = torch.broadcast_shapes(
-            i1.shape[:-2], i2.shape[:-2], self.batch_shape
-        )
-
-        res = InterpolatedLinearOperator(
-            base_linear_op=covar_matrix,
-            left_interp_indices=i1.expand(batch_shape + i1.shape[-2:]),
-            right_interp_indices=i2.expand(batch_shape + i2.shape[-2:]),
-        )
-        return res
-
-
-def sample_task_ids(batch_size: int, seq_len: int, num_tasks: int, device: str, same_tasks_across_batch=False):
-    # split data into target and source tasks with Dirichlet / Categorical sampling
-    alphas = torch.ones(num_tasks).to(device) * 2.0
-    # alphas[0] = 1.0
-    
-    
-    # B x N
-    if same_tasks_across_batch:
-        probabilities = torch.distributions.Dirichlet(alphas).sample()
-        task_id = torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
-        task_id = task_id.unsqueeze(0).repeat(batch_size, 1)
-    else:
-        probabilities = torch.distributions.Dirichlet(alphas).sample((batch_size,))
-        task_id = (
-            torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
-        )
-    task_id[:, :num_tasks] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
-    task_id[:, -num_tasks:] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
-
-    return task_id
-
-
-def sample_task_ids_target_aware(eval_pos: int, batch_size: int, seq_len: int, num_tasks: int, device: str, same_tasks_across_batch=False):
-    # split data into target and source tasks with Dirichlet / Categorical sampling
-    alphas = torch.ones(num_tasks).to(device) * 2.0
-    alphas[0] = 1.0 # target task is less likely to be sampled
-    
-    # B x N
-    if same_tasks_across_batch:
-        probabilities = torch.distributions.Dirichlet(alphas).sample()
-        task_id = torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
-        task_id = task_id.unsqueeze(0).repeat(batch_size, 1)
-    else:
-        probabilities = torch.distributions.Dirichlet(alphas).sample((batch_size,))
-        task_id = (
-            torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
-        )
-    # ensure every task is observed during training
-    task_id[:, :num_tasks] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
-    # only test on target task
-    task_id[:, eval_pos:] = 0
-
-    return task_id
-
-
-def sample_task_ids_even_eval(eval_pos: int, batch_size: int, seq_len: int, num_tasks: int, device: str, same_tasks_across_batch=False):
-    # split data into target and source tasks with Dirichlet / Categorical sampling
-    alphas = torch.ones(num_tasks).to(device) * 2.0
-    
-    # B x N
-    if same_tasks_across_batch:
-        probabilities = torch.distributions.Dirichlet(alphas).sample()
-        task_id = torch.distributions.Categorical(probabilities).sample((seq_len,)).to(device)
-        task_id = task_id.unsqueeze(0).repeat(batch_size, 1)
-    else:
-        probabilities = torch.distributions.Dirichlet(alphas).sample((batch_size,))
-        task_id = (
-            torch.distributions.Categorical(probabilities).sample((seq_len,)).T.to(device)
-        )
-    # ensure every task is observed during training
-    task_id[:, :num_tasks] = torch.arange(num_tasks).repeat(batch_size, 1).to(device)
-    # evenly test on all tasks
-    n_eval = seq_len - eval_pos
-    even_spread = torch.arange(num_tasks).repeat(batch_size, n_eval // num_tasks + 1)[:, :n_eval]
-    task_id[:, eval_pos:] = even_spread
-
-    return task_id
-
-
-def get_uncorr_task_ids(
-    num_tasks: int,
-    hyperparameters: dict,
-):    
-    if "uncorr_prob" in hyperparameters:
-        uncorr_prob = hyperparameters["uncorr_prob"]
-        uncorr_tasks = (
-            torch.where(torch.rand(num_tasks - 1) < uncorr_prob)[0] + 1
-        ).tolist()
-    elif "num_uncorr_tasks" in hyperparameters:
-        num_uncorr_tasks = hyperparameters["num_uncorr_tasks"]
-        uncorr_tasks = torch.arange(1, num_tasks)[:num_uncorr_tasks].tolist()
-    else:
-        uncorr_tasks = []
-    
-    return uncorr_tasks
-
-
-
-def resample_uncorr_tasks(task_id, xs, ys, num_tasks, hyperparameters):
-    if "uncorr_prob" in hyperparameters:
-        uncorr_prob = hyperparameters["uncorr_prob"]
-        uncorr_tasks = (
-            torch.where(torch.rand(num_tasks - 1) < uncorr_prob)[0] + 1
-        ).tolist()
-    elif "num_uncorr_tasks" in hyperparameters:
-        num_uncorr_tasks = hyperparameters["num_uncorr_tasks"]
-        uncorr_tasks = torch.arange(1, num_tasks)[:num_uncorr_tasks].tolist()
-    else:
-        uncorr_tasks = []
-
-    # randomly sample uncorrelated tasks
-    kernel = RBFKernel().to(xs.device)
-    for task in uncorr_tasks:
-        assert task > 0
-        task_mask = task_id == task
-        task_x = xs[task_mask]
-        kernel.lengthscale = torch.distributions.Gamma(12, 6).sample().to(xs.device)
-        ys[task_mask] = (
-            MultivariateNormal(
-                torch.zeros(task_x.shape[:-1], device=xs.device), kernel(task_x)
-            ).sample()
-        ).unsqueeze(-1)
-
-    return ys
-
-
-def draw_mtgp_samples_original(xs, task_id, input_covar_module, task_covar_matrix):
-    task_covar_module = IndexKernelFixed(task_covar_matrix)
-
-    mean_x = ConstantMean()(xs).to(xs.device)
-    covar_x = input_covar_module(xs)
-    covar_t = task_covar_module(task_id.unsqueeze(-1))
-    covar = covar_x.mul(covar_t)
-
-    return MultivariateNormal(mean_x, covar).sample().unsqueeze(-1)
-
-
-def draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_tasks):
-    CORR_LENGTHSCALE_A, CORR_LENGTHSCALE_B = 3.0, 6.0
-    # UNCORR_LENGTHSCALE_A, UNCORR_LENGTHSCALE_B = 12.0, 6.0
-    UNCORR_LENGTHSCALE_A, UNCORR_LENGTHSCALE_B = 3.0, 6.0
-    
-    B, N, D = xs.shape
-    num_tasks = task_covar_matrix.shape[-1]
-    n_ind = 1 + len(uncorr_tasks)
-    
-    if num_tasks == 1:
-        mean_x = ConstantMean()(xs).to(xs.device)
-        
-        lengthscale_prior = gpytorch.priors.GammaPrior(CORR_LENGTHSCALE_A, CORR_LENGTHSCALE_B)
-        input_covar_module = RBFKernel().to(xs.device)
-        input_covar_module.lengthscale = lengthscale_prior.sample()
-        covar_x = input_covar_module(xs)
-
-        return MultivariateNormal(mean_x, covar_x).sample().unsqueeze(-1)
-    
-
-    # Add additional "batch" dimension for independent tasks
-    xs_expand = xs.unsqueeze(0).expand(n_ind, B, N, D)
-    task_id_expand = task_id.unsqueeze(0).unsqueeze(-1).expand(n_ind, B, N, 1)
-
-    mean_x = ConstantMean()(xs_expand).to(xs_expand.device)
-
-    # input kernel: different lengthscale per indepedent task and batch
-    a = torch.tensor([CORR_LENGTHSCALE_A] + [UNCORR_LENGTHSCALE_A] * len(uncorr_tasks)).to(xs)
-    b = torch.tensor([CORR_LENGTHSCALE_B] + [UNCORR_LENGTHSCALE_B] * len(uncorr_tasks)).to(xs)
-    lengthscale_prior = gpytorch.priors.GammaPrior(a, b)
-    input_covar_module = RBFKernel(ard_num_dims=D, batch_shape=(n_ind, B)).to(xs.device)
-    # final shape: n_ind x B x 1 x D
-    input_covar_module.lengthscale = lengthscale_prior.sample((B, D)).unsqueeze(-2).permute(-1, 0, 2, 1)
-    covar_x = input_covar_module(xs_expand)
-
-    # task kernel: different correlation per batch
-    # todo: different correlation per each batch
-    task_covar_matrix = task_covar_matrix.unsqueeze(0).unsqueeze(0).expand(n_ind, B, num_tasks, num_tasks)
-    task_covar_module = IndexKernelFixed(task_covar_matrix)
-    covar_t = task_covar_module(task_id_expand)
-
-    samples = MultivariateNormal(mean_x, covar_x.mul(covar_t)).sample().unsqueeze(-1)
-    
-    # combine correlated and uncorrelated samples
-    ys = samples[0]
-    for i, task in enumerate(uncorr_tasks):
-        task_mask = task_id == task
-        ys[task_mask] = samples[i+1][task_mask]
-    
-    return ys
-
-
-def get_mtgp_for_eval(
-    num_features,
-    num_tasks,
-    lengthscale,
-    task_corr,
-    num_uncorr_tasks,
-    device: str = "cpu",
-):
-    TARGET_TRAIN = 100
-    TARGET_TEST = 500
-    SOURCE_TRAIN = 500
-
-    # generate task ids
-    task_lengths = [TARGET_TRAIN + TARGET_TEST] + [SOURCE_TRAIN] * (num_tasks - 1)
-    task_id = []
-    for task, task_length in enumerate(task_lengths):
-        task_id.append(torch.ones(task_length) * task)
-    task_id = torch.cat(task_id).to(device)
-
-    # generate x values
-    xs = torch.rand(len(task_id), num_features).to(device)
-
-    # generate task and input covariances
-    input_covar_module = RBFKernel().to(device)
-    if lengthscale is None:
-        lengthscale = torch.distributions.Gamma(1, 6).sample().to(device)
-    input_covar_module.lengthscale = lengthscale
-
-    # task covariance matrix with fixed correlation
-    if task_corr is not None:
-        task_covar_matrix = torch.full((num_tasks, num_tasks), task_corr, device=device)
-        task_covar_matrix.fill_diagonal_(1.0)
-    else:
-        if num_tasks > 1:
-            # sample from LKJ prior
-            task_covar_matrix = (
-                LKJCovariancePrior(
-                    n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
-                )
-                .sample()
-                .to(device)
-            )
-        else:
-            task_covar_matrix = torch.eye(num_tasks).to(device)
-
-    ys = draw_mtgp_samples_original(xs, task_id, input_covar_module, task_covar_matrix)
-    ys = resample_uncorr_tasks(
-        task_id, xs, ys, num_tasks, {"num_uncorr_tasks": num_uncorr_tasks}
-    )
-
-    result_xs = []
-    result_ys = []
-    for task in range(num_tasks):
-        task_mask = task_id == task
-        result_xs.append(xs[task_mask])
-        result_ys.append(ys[task_mask])
-
-    return result_xs[0], result_ys[0], result_xs[1:], result_ys[1:]
-
-
-# batch_size: number of datasets
-# seq_len: number of training samples per dataset
-def get_mtgp_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    eval_pos = kwargs["single_eval_pos"]
-    seq_len 
-    xs = torch.rand(batch_size, seq_len, num_features, device=device)
-    task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
-
-    if num_tasks > 1:
-        # sample task covariance matrix
-        lkj_covar_matrix = (
-            LKJCovariancePrior(
-                n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
-            )
-            .sample()
-            .to(device)
-        )
-        
-        corr_init = hyperparameters.get("corr_init", None)
-        if corr_init is not None:
-            sampled_corr = torch.distributions.Uniform(corr_init, 1.0).sample().to(device)
-            task_covar_matrix = torch.full((num_tasks, num_tasks), sampled_corr).to(lkj_covar_matrix)
-            task_covar_matrix.fill_diagonal_(1.0)
-            
-            blend = torch.rand((1,)).to(device)
-            task_covar_matrix = blend * task_covar_matrix + (1 - blend) * lkj_covar_matrix
-        else:
-            task_covar_matrix = lkj_covar_matrix     
-    else:
-        task_covar_matrix = torch.eye(num_tasks).to(device)
-        
-    uncorr_ids = get_uncorr_task_ids(num_tasks, hyperparameters)
-    ys = draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_ids)
-    task_id = task_id.unsqueeze(-1)
-    
-    noise = torch.distributions.Gamma(1, 5).sample((num_tasks,)).to(device)
-    noise_by_task = noise[task_id]
-    # noisy_ys = ys + torch.randn_like(ys) * noise_by_task
-    noisy_ys = ys.clone()
-
-    return Batch(
-        x=xs.transpose(0, 1),
-        y=noisy_ys.transpose(0, 1),
-        target_y=ys.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-    
-def standardize_per_task(ys, task_id, single_eval_pos, true_ys=None):
-    # all task ids should be the same across batches
-    assert (task_id.min(dim=0).values == task_id.max(dim=0).values).all(), "task_id is not the same across batches"
-    task_id = task_id[0]
-        
-    standardized_ys = ys.clone()
-    if true_ys is not None:
-        standardized_true_ys = true_ys.clone()
-    
-    train_ys = ys[:, :single_eval_pos]
-    train_task_id = task_id[:single_eval_pos]
-    
-    train_mask = torch.zeros_like(ys, dtype=torch.bool)
-    train_mask[:, :single_eval_pos] = True
-        
-    # For each task, compute mean and std based on training data only
-    for t in task_id.unique():
-        train_task_mask = train_task_id == t
-        all_task_mask = task_id == t
-        
-        # Compute mean and std for training data of this task
-        task_train_values = train_ys[:, train_task_mask]
-        # batch x seq_len x 1
-        task_mean = task_train_values.mean(dim=1, keepdim=True)
-        task_std = task_train_values.std(dim=1, keepdim=True)
-        
-        # Numerical check for 0 or nan
-        task_std = torch.where(task_std == 0, torch.ones_like(task_std), task_std)
-        task_std = torch.where(task_std.isnan(), torch.ones_like(task_std), task_std)
-            
-        # Apply standardization to all data points of this task (both train and test)
-        standardized_ys[:, all_task_mask] = (ys[:, all_task_mask] - task_mean) / task_std
-        if true_ys is not None:
-            standardized_true_ys[:, all_task_mask] = (true_ys[:, all_task_mask] - task_mean) / task_std
-
-    if true_ys is not None:
-        return standardized_ys, standardized_true_ys
-    else:
-        return standardized_ys
-    
-    
-def new_standardize_per_task(
-    *,
-    noisy_ys,
-    ys,
-    task_id,
-    single_eval_pos,
-    global_norm="z",
-    task_norm=None,
-):
-    # all task ids should be the same across batches
-    assert (
-        task_id.min(dim=0).values == task_id.max(dim=0).values
-    ).all(), "task_id is not the same across batches, you can set same_tasks_across_batch=True"
-    task_id = task_id[0, :]  # shape: seq_len
-
-    def normalize(
-        ys_to_fit_on: torch.Tensor, ys_to_transform: torch.Tensor, norm_type: str
-    ):
-        # both are a tensor of shape batch_size x ?, we want to parallelize across batch_size
-        mean = ys_to_fit_on.mean(1, keepdim=True)  # (batch_size, )
-        std = ys_to_fit_on.std(1, keepdim=True)  # (batch_size, )
-        std = std.where(std.isfinite(), 1.0)
-        if norm_type == "z":
-            return (ys_to_transform - mean) / std
-        if norm_type == "safe_z_0.1":
-            safe_std = std.clamp_min(0.1)
-            return (ys_to_transform - mean) / safe_std
-        elif norm_type == "mean":
-            return ys_to_transform - mean
-        else:
-            raise ValueError(f"Unknown norm type {norm_type}")
-
-    if task_norm is not None:
-        # print("num tasks", len(np.unique(task_id.cpu().numpy())))
-        # print(
-        #     "min train task size",
-        #     np.min(
-        #         np.unique(task_id[:single_eval_pos].cpu().numpy(), return_counts=True)[
-        #             1
-        #         ]
-        #     ),
-        # )
-        for tid in task_id.unique():
-            local_noisy_ys_for_fitting = noisy_ys[:, :single_eval_pos][
-                :, (task_id == tid)[:single_eval_pos]
-            ].clone()
-            local_noisy_ys = noisy_ys[:, (task_id == tid)]
-            local_ys = ys[:, (task_id == tid)]
-
-            ys[:, (task_id == tid)] = normalize(
-                local_noisy_ys_for_fitting, local_ys, task_norm
-            )
-            noisy_ys[:, (task_id == tid)] = normalize(
-                local_noisy_ys_for_fitting, local_noisy_ys, task_norm
-            )
-
-    if global_norm is not None:
-        # print("doing global norm with", global_norm)
-        noisy_ys_for_fitting = noisy_ys[:, :single_eval_pos].clone()
-
-        ys[:] = normalize(noisy_ys_for_fitting, ys, global_norm)
-        noisy_ys[:] = normalize(noisy_ys_for_fitting, noisy_ys, global_norm)
-
-    return noisy_ys, ys
-
-    
-def target_aware_mtgp_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    single_eval_pos = kwargs.get("single_eval_pos", seq_len // 2)
-    xs = torch.rand(batch_size, seq_len, num_features, device=device)
-    task_id = sample_task_ids_target_aware(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
-
-    if num_tasks > 1:
-        # sample task covariance matrix
-        lkj_covar_matrix = (
-            LKJCovariancePrior(
-                n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
-            )
-            .sample()
-            .to(device)
-        )
-        
-        corr_init = hyperparameters.get("corr_init", None)
-        if corr_init is not None:
-            # bias towards correlations between corr_init and 1.0
-            sampled_corr = torch.distributions.Uniform(corr_init, 1.0).sample().to(device)
-            task_covar_matrix = torch.full((num_tasks, num_tasks), sampled_corr).to(lkj_covar_matrix)
-            task_covar_matrix.fill_diagonal_(1.0)
-            
-            blend = torch.rand((1,)).to(device)
-            task_covar_matrix = blend * task_covar_matrix + (1 - blend) * lkj_covar_matrix
-        else:
-            task_covar_matrix = lkj_covar_matrix     
-    else:
-        task_covar_matrix = torch.eye(num_tasks).to(device)
-        
-    # sample number of uncorrelated tasks uniformly from 0 to T-1
-    if num_tasks > 1:
-        n_uncorr = torch.randint(num_tasks - 1, (1,)).item()
-        uncorr_ids = torch.randperm(num_tasks - 1)[:n_uncorr]
-        uncorr_ids = (uncorr_ids + 1).tolist() # task 0 cannot be uncorr
-    else:
-        uncorr_ids = []
-    
-    ys = draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_ids)
-    
-    sigma = torch.distributions.LogNormal(-4.0, 1.0).sample(ys.shape).to(device)
-    noisy_ys = ys.clone() + torch.randn_like(ys) * sigma
-    
-    noisy_ys, ys = new_standardize_per_task(
-        noisy_ys=noisy_ys,
-        ys=ys,
-        task_id=task_id,
-        single_eval_pos=single_eval_pos,
-        global_norm=hyperparameters.get("global_y_norm", None),
-        task_norm=hyperparameters.get("task_y_norm", "safe_z_0.1"),
-    )
-    
-    task_id = task_id.unsqueeze(-1)
-
-    return Batch(
-        x=xs.transpose(0, 1),
-        y=noisy_ys.transpose(0, 1),
-        target_y=ys.transpose(0, 1),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-    
-def gen_mtgp_equal_eval_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    single_eval_pos = kwargs.get("single_eval_pos", seq_len // 2)
-    xs = torch.rand(batch_size, seq_len, num_features, device=device)
-    task_id = sample_task_ids_even_eval(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
-
-    if num_tasks > 1:
-        # sample task covariance matrix
-        lkj_covar_matrix = (
-            LKJCovariancePrior(
-                n=num_tasks, eta=1.0, sd_prior=gpytorch.priors.GammaPrior(10.0, 10.0)
-            )
-            .sample()
-            .to(device)
-        )
-        
-        corr_init = hyperparameters.get("corr_init", None)
-        if corr_init is not None:
-            # bias towards correlations between corr_init and 1.0
-            sampled_corr = torch.distributions.Uniform(corr_init, 1.0).sample().to(device)
-            task_covar_matrix = torch.full((num_tasks, num_tasks), sampled_corr).to(lkj_covar_matrix)
-            task_covar_matrix.fill_diagonal_(1.0)
-            
-            blend = torch.rand((1,)).to(device)
-            task_covar_matrix = blend * task_covar_matrix + (1 - blend) * lkj_covar_matrix
-        else:
-            task_covar_matrix = lkj_covar_matrix     
-    else:
-        task_covar_matrix = torch.eye(num_tasks).to(device)
-        
-    # sample number of uncorrelated tasks uniformly from 0 to T-1
-    if num_tasks > 1:
-        n_uncorr = torch.randint(num_tasks - 1, (1,)).item()
-        uncorr_ids = torch.randperm(num_tasks - 1)[:n_uncorr]
-        uncorr_ids = (uncorr_ids + 1).tolist() # task 0 cannot be uncorr
-    else:
-        uncorr_ids = []
-    
-    ys = draw_mtgp_samples(xs, task_id, task_covar_matrix, uncorr_ids)
-    
-    sigma = torch.distributions.LogNormal(-4.0, 1.0).sample(ys.shape).to(device)
-    noisy_ys = ys.clone() + torch.randn_like(ys) * sigma
-    
-    noisy_ys, ys = new_standardize_per_task(
-        noisy_ys=noisy_ys,
-        ys=ys,
-        task_id=task_id,
-        single_eval_pos=single_eval_pos,
-        global_norm=hyperparameters.get("global_y_norm", None),
-        task_norm=hyperparameters.get("task_y_norm", "safe_z_0.1"),
-    )
-
-    task_id = task_id.unsqueeze(-1)
-    
-    return Batch(
-        x=xs.transpose(0, 1),
-        y=noisy_ys.transpose(0, 1),
-        target_y=ys.transpose(0, 1),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-    
-def gen_mtgp_low_rank(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters: dict,
-    device: str = default_device,
-    **kwargs,
-):
-    single_eval_pos = kwargs.get("single_eval_pos", seq_len // 2)
-    xs = torch.rand(batch_size, seq_len, num_features, device=device)
-    if hyperparameters.get("target_only_loss", False):
-        task_id = sample_task_ids_target_aware(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
-    else:
-        task_id = sample_task_ids_even_eval(single_eval_pos, batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", True))
-
-    rank = torch.randint(2, hyperparameters.get("max_rank", 5)+1, (1,)).item()
-    alpha = hyperparameters.get("decay_alpha", 0.2)
-    if num_tasks > 1:
-        weights = torch.exp(-alpha * torch.arange(1, num_tasks).float())
-        n_ind = torch.multinomial(weights, 1).item()
-    else:
-        n_ind = 0
-    B = batch_size
-    D = num_features
-
-    # Sample mixing weights from Dirichlet
-    concentration = 1.0
-    concentration_vec = torch.full((rank,), concentration, device=device)
-
-    W_full = torch.zeros((num_tasks, rank + n_ind), device=device)
-    # split weights between rank
-    W_rank = torch.distributions.Dirichlet(concentration_vec).sample((num_tasks,)).to(device)
-    W_full[:, :rank] = W_rank
-    # randomly non-target tasks to be independent
-    independent_tasks = torch.randperm(num_tasks - 1)[:n_ind] + 1
-    for i, ind in enumerate(independent_tasks):
-        W_full[ind, :] = 0.0
-        W_full[ind, -(i+1)] = 1.0
-
-    # Create expanded inputs: batch, (rank), seq, features)
-    xs_expanded = xs.unsqueeze(1).expand((B, rank + n_ind, seq_len, D))
-
-    # Create task IDs for all latent functions
-    latent_task_ids = torch.arange(rank, device=device).unsqueeze(0).unsqueeze(-1)
-    latent_task_ids = latent_task_ids.expand(batch_size, rank, seq_len)
-    latent_task_ids_mega = latent_task_ids.reshape(batch_size * rank, seq_len)
-
-    CORR_LENGTHSCALE_A, CORR_LENGTHSCALE_B = 3.0, 6.0
-    UNCORR_LENGTHSCALE_A, UNCORR_LENGTHSCALE_B = 3.0, 6.0
-    a = torch.tensor([CORR_LENGTHSCALE_A] + [UNCORR_LENGTHSCALE_A] * n_ind).to(xs)
-    b = torch.tensor([CORR_LENGTHSCALE_B] + [UNCORR_LENGTHSCALE_B] * n_ind).to(xs)
-
-    # Create lengthscale prior
-    lengthscale_prior = gpytorch.priors.GammaPrior(a, b)
-    # original B x D x (1 + n_ind) => B x (1 + n_ind) x D
-    lengthscale_sample = lengthscale_prior.sample((B, D)).transpose(1, 2)
-
-    kernel = RBFKernel(ard_num_dims=D, batch_shape=torch.Size((B, rank + n_ind))).to(xs.device)
-    # lengthscale should have shape B x (rank + n_ind) x 1 x D
-    lengthscale = torch.ones((B, rank+n_ind, 1, D), device=xs.device)
-    # make sure rank lengthscales are the same
-    rank_lengthscales = lengthscale_sample[:, 0].unsqueeze(1).unsqueeze(1).expand(B, rank, 1, D).clone()
-    lengthscale[:, :rank] = rank_lengthscales
-    # use remaining lengthscales for independent draws
-    lengthscale[:, rank:] = lengthscale_sample[:, 1:].unsqueeze(-2)
-
-    kernel.lengthscale = lengthscale
-    K = kernel(xs_expanded)
-    mean_x = ConstantMean().to(xs.device)(xs_expanded)
-    # size B x (rank + n_ind) x seq_len
-    latent_functions = MultivariateNormal(mean_x, K).sample()
-
-    # Apply mixing (same across all batches since task IDs are same)
-    task_id_seq = task_id[0]  # Pattern from first batch
-    mixing_weights_seq = W_full[task_id_seq].T  # rank + n_ind x num_tasks
-
-    # Mix latent functions
-    ys = torch.sum(latent_functions * mixing_weights_seq.unsqueeze(0), dim=1).unsqueeze(-1)
-    
-    sigma = torch.distributions.LogNormal(-4.0, 1.0).sample(ys.shape).to(device)
-    noisy_ys = ys.clone() + torch.randn_like(ys) * sigma
-    
-    noisy_ys, ys = new_standardize_per_task(
-        noisy_ys=noisy_ys,
-        ys=ys,
-        task_id=task_id,
-        single_eval_pos=single_eval_pos,
-        global_norm=hyperparameters.get("global_y_norm", None),
-        task_norm=hyperparameters.get("task_y_norm", "safe_z_0.1"),
-    )
-
-    task_id = task_id.unsqueeze(-1)
-    
-    return Batch(
-        x=xs.transpose(0, 1),
-        y=noisy_ys.transpose(0, 1),
-        target_y=ys.transpose(0, 1),
-        task_id=task_id.transpose(0, 1),
-    )
-
-
-
-def get_hpo_batch_fn(
-    train=True,
-    max_features=6,
-    min_num_tasks=6,
-    seq_len=200,
-    device=default_device,
-    **kwargs,
-):
-    DATA_PATHS = {
-        "test": "/home/yl9959/mtpfn/datasets/hpob-data/meta-test-dataset.json",
-        "train-augmented": "/home/yl9959/mtpfn/datasets/hpob-data/meta-train-dataset-augmented.json",
-    }
-
-    if train:
-        data = json.load(open(DATA_PATHS["train-augmented"], "r"))
-    else:
-        data = json.load(open(DATA_PATHS["test"], "r"))
-
-    domain_weight = []
-    domain_data = []
-    for domain in data:
-        hpo_runs_weight = []
-        hpo_runs_x = []
-        hpo_runs_y = []
-        for hpo_run in data[domain]:
-            x = torch.tensor(data[domain][hpo_run]["X"], device=device)
-            # skip this domain if it has more than max_features features
-            if x.shape[-1] > max_features:
-                break
-
-            if x.shape[0] < seq_len:
-                continue
-
-            hpo_runs_weight.append(len(x))
-            hpo_runs_x.append(x.to(device))
-            hpo_runs_y.append(torch.tensor(data[domain][hpo_run]["y"], device=device))
-
-        # skip this domain if it doesn't have enough tasks
-        if len(hpo_runs_weight) <= min_num_tasks:
-            continue
-        domain_weight.append(sum(hpo_runs_weight))
-        domain_data.append(
-            (torch.tensor(hpo_runs_weight, device=device), hpo_runs_x, hpo_runs_y)
-        )
-        
-    domain_weight = torch.tensor(domain_weight)
-    domain_weight = domain_weight / domain_weight.sum()
-    # add uniform probability
-    domain_weight = domain_weight * 0.5 + 1 / len(domain_weight) * 0.5
-
-    def get_batch(
-        batch_size: int,
-        seq_len: int,
-        num_features: int,
-        max_num_tasks: int,
-        num_tasks: int,
-        hyperparameters=None,
-        device: str = default_device,
-        **kwargs,
-    ):
-        domain_idx = torch.multinomial(domain_weight, 1)
-        task_weight, task_x, task_y = domain_data[domain_idx]
-        task_data_indices = torch.multinomial(task_weight.float(), num_tasks)
-
-        task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))
-        # task_id = sample_task_ids(batch_size, seq_len, num_tasks, device)
-
-        xs = torch.zeros(batch_size, seq_len, task_x[0].shape[-1], device=device)
-        ys = torch.zeros(batch_size, seq_len, 1, device=device)
-        for i in range(num_tasks):
-            relevant_indices = task_id == i
-            possible_xs, possible_ys = (
-                task_x[task_data_indices[i]],
-                task_y[task_data_indices[i]],
-            )
-            # randomly select per batch
-            for b in range(batch_size):
-                relevant_batch_indices = relevant_indices[b]
-                indices = torch.randperm(len(possible_xs))[
-                    : relevant_batch_indices.sum()
-                ]
-                xs[b, relevant_batch_indices] = possible_xs[indices].to(xs.device)
-                ys[b, relevant_batch_indices] = possible_ys[indices].to(ys.device)
-
-        return Batch(
-            x=xs.transpose(0, 1).to(device),
-            y=ys.transpose(0, 1).to(device),
-            target_y=ys.transpose(0, 1).clone().to(device),
-            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
-        )
-
-    return get_batch
-
-
-def get_pd1_surrogate_batch_fn(
-    train=True,
-    max_features=6,
-    min_num_tasks=6,
-    seq_len=200,
-    device=default_device,
-    **kwargs,
-):
-    
-    data = pickle.load(open("/home/yl9959/mtpfn/datasets/pd1.pickle", "rb"))
-
-    if not os.path.exists("/home/yl9959/mtpfn/pd1_gp_surrogates.pth"):
-        
-        # group by data
-        data_grouped = data.groupby("study_group")
-        x_cols = [
-            'hps.lr_hparams.decay_steps_factor',
-            'hps.lr_hparams.initial_value',
-            'hps.lr_hparams.power',
-            'hps.opt_hparams.momentum', 
-        ]
-        y_col = 'best_valid/error_rate'
-
-        x_bounds = torch.tensor([
-            data[x_cols].min().values,
-            data[x_cols].max().values
-        ])
-
-        # for each group
-        gps = []
-        for group_name, group_data in data_grouped:
-            print("Fitting GP for group", group_name)
-            train_x = torch.tensor(group_data[x_cols].values)
-            train_x = (train_x - x_bounds[0]) / (x_bounds[1] - x_bounds[0])
-            train_y = torch.tensor(group_data[y_col].values).unsqueeze(-1)
-            
-            train_yvar = torch.ones_like(train_y) * 1e-5
-            gp = SingleTaskGP(train_x, train_y, train_yvar, outcome_transform=None)
-            mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
-            try:
-                fit_gpytorch_mll(mll)
-            except ModelFittingError:
-                fit_gpytorch_mll_torch(mll)
-            gps.append((group_name, gp))
-            
-        state_dicts = {name: gp.state_dict() for name, gp in gps}
-        torch.save(state_dicts, "/home/yl9959/mtpfn/pd1_gp_surrogates.pth")
-
-    gp_state_dicts = torch.load("/home/yl9959/mtpfn/pd1_gp_surrogates.pth")
-    gps = []
-    for data_name, state_dict in gp_state_dicts.items():
-        if "cifar" in data_name:
-            continue
-        gp = SingleTaskGP(torch.zeros(1, 4), torch.zeros(1, 1), torch.ones(1, 1) * 1e-5, outcome_transform=None)
-        gp.load_state_dict(state_dict)
-        gps.append(gp)
-
-
-    def get_batch(
-        batch_size: int,
-        seq_len: int,
-        num_features: int,
-        max_num_tasks: int,
-        num_tasks: int,
-        hyperparameters=None,
-        device: str = default_device,
-        **kwargs,
-    ):
-        task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))
-
-        xs = torch.rand(batch_size, seq_len, 4, device=device)
-        ys = torch.zeros(batch_size, seq_len, 1, device=device)
-        
-        surrogate_gps = torch.randperm(len(gps))[:num_tasks]
-        for i in range(num_tasks):
-            task_mask = task_id == i
-            task_xs = xs[task_mask]
-            gp = gps[surrogate_gps[i]].to(device)
-            with torch.no_grad():
-                ys[task_mask] = gp.posterior(task_xs).sample()
-            
-        return Batch(
-            x=xs.transpose(0, 1).to(device),
-            y=ys.transpose(0, 1).to(device),
-            target_y=ys.transpose(0, 1).clone().to(device),
-            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
-        )
-
-    return get_batch
-
-
-def get_pd1_eval_batch_fn(
-    train=True,
-    max_features=6,
-    min_num_tasks=6,
-    seq_len=200,
-    device=default_device,
-    **kwargs,
-):
-
-    data = pickle.load(open("/home/yl9959/mtpfn/datasets/pd1.pickle", "rb"))
-
-    # group by data
-    data_grouped = data.groupby("study_group")
-    x_cols = [
-        'hps.lr_hparams.decay_steps_factor',
-        'hps.lr_hparams.initial_value',
-        'hps.lr_hparams.power',
-        'hps.opt_hparams.momentum', 
-    ]
-    y_col = 'best_valid/error_rate'
-    
-    x_bounds = torch.tensor([
-        data[x_cols].min().values,
-        data[x_cols].max().values
-    ])
-    
-    all_xs = []
-    all_ys = []
-    for group_name, group_data in data_grouped:
-        if "cifar" in group_name: # eval on cifar
-            train_x = torch.tensor(group_data[x_cols].values)
-            train_x = (train_x - x_bounds[0]) / (x_bounds[1] - x_bounds[0])
-            train_y = torch.tensor(group_data[y_col].values).unsqueeze(-1)
-            all_xs.append(train_x)
-            all_ys.append(train_y)
-    
-    def get_batch(
-        batch_size: int,
-        seq_len: int,
-        num_features: int,
-        max_num_tasks: int,
-        num_tasks: int,
-        hyperparameters=None,
-        device: str = default_device,
-        **kwargs,
-    ):
-        assert num_tasks <= 4
-        task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))
-        
-        # dummy values to be replaced
-        xs = torch.zeros(batch_size, seq_len, num_features, device=device)
-        ys = torch.zeros(batch_size, seq_len, 1, device=device)
-
-        for b in range(batch_size):
-            for task in range(num_tasks):
-                task_mask = task_id[b] == task
-                task_x = all_xs[task]
-                task_y = all_ys[task]
-                indices = torch.randperm(len(task_x))[:seq_len]
-                size = xs[b, task_mask].shape[0]
-                xs[b, task_mask] = task_x[indices][:size].to(xs)
-                ys[b, task_mask] = task_y[indices][:size].to(ys)
-                
-        return Batch(
-            x=xs.transpose(0, 1).to(device),
-            y=ys.transpose(0, 1).to(device),
-            target_y=ys.transpose(0, 1).clone().to(device),
-            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
-        )
-
-    return get_batch
-
-    
-def combine_batch(functions, weight):
-    def get_batch(
-        *args,
-        **kwargs,
-    ):
-        random_index = torch.multinomial(weight, 1)[0]
-        return functions[random_index](*args, **kwargs)
-
-    return get_batch
-    
-
-# batch_size: number of datasets
-# seq_len: number of training samples per dataset
-def get_trios_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    xs = torch.rand(batch_size, seq_len, num_features, device=device)
-    ys = xs.sum(-1, keepdim=True)
-    
-    assert hyperparameters.get("same_tasks_across_batch", False)
-    task_id = sample_task_ids(batch_size, seq_len, num_tasks, device, hyperparameters.get("same_tasks_across_batch", False))[0]
-    
-    task_order = torch.randperm(num_tasks)
-    # assert same tasks across batch
-
-    num_trios = num_tasks // 3
-    
-    for trio in range(num_trios):
-        task_one = task_order[trio * 3]
-        task_two = task_order[trio * 3 + 1]
-        task_three = task_order[trio * 3 + 2]
-        task_mask = (task_id == task_one) | (task_id == task_two) | (task_id == task_three)
-        
-        task_covar_matrix = torch.full((num_tasks, num_tasks), 0.9).to(xs)
-        task_covar_matrix.fill_diagonal_(1.0)
-            
-        y_trio = draw_mtgp_samples(xs[:, task_mask], task_id[task_mask], task_covar_matrix, uncorr_tasks=[])
-        ys[:, task_mask] = y_trio
-        
-    remaining = num_tasks - num_trios * 3
-    if remaining > 0:
-        task_mask = torch.zeros_like(task_id, dtype=torch.bool)
-        for task_id in task_order[num_trios * 3:]:
-            task_mask |= task_id == task_id
-        task_covar_matrix = torch.full((remaining, remaining), 0.1).to(xs)
-        task_covar_matrix.fill_diagonal_(1.0)
-        ys[:, task_mask] = draw_mtgp_samples(xs[:, task_mask], task_id[task_mask], task_covar_matrix, uncorr_tasks=[])
-        
-    # add back batch dimension
-    task_id = task_id.unsqueeze(-1).unsqueeze(0).repeat(batch_size, 1, 1)
-    
-    noise = torch.distributions.Gamma(1, 5).sample((num_tasks,)).to(device)
-    noise_by_task = noise[task_id]
-    # noisy_ys = ys + torch.randn_like(ys) * noise_by_task
-    noisy_ys = ys.clone()
-
-    return Batch(
-        x=xs.transpose(0, 1),
-        y=noisy_ys.transpose(0, 1),
-        target_y=ys.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-    
-def get_hpobench_batch_fn(
-    hpobench_task="lr",
-    train=True,
-    device=default_device,
-    **kwargs,
-):
-    
-    data = pickle.load(open(f"/home/lily_l/private_multitask_pfn/datasets/hpobench_{hpobench_task}.pkl", "rb"))
-    if hpobench_task == "lr":
-        eval_keys = [146822, 146818, 168908, 53]
-        dim = 2
-    elif hpobench_task == "svm":
-        eval_keys = [146822, 146818, 168908, 53]
-        dim = 2
-    else:
-        raise ValueError("Unknown hpobench task")
-        
-    if train:
-        batch_keys = data.keys() - eval_keys # train keys
-    else:
-        batch_keys = eval_keys
-
-    results_x, results_y = get_torch_format_hpobench(data[eval_keys[0]], [data[key] for key in batch_keys], dim, hpobench_task)[2:]
-    results_x = results_x
-    results_y = results_y
-
-    def get_batch(
-        batch_size: int,
-        seq_len: int,
-        num_features: int,
-        max_num_tasks: int,
-        num_tasks: int,
-        hyperparameters=None,
-        device: str = default_device,
-        **kwargs,
-    ):
-        assert num_tasks == 1
-        task_id = torch.zeros(batch_size, seq_len, device=device).long()
-        
-        train_task = torch.randint(len(results_x), (1,))
-        all_xs, all_ys = results_x[train_task], results_y[train_task]
-        
-        indices = torch.stack([torch.randperm(len(all_xs))[:seq_len] for _ in range(batch_size)])
-        xs = all_xs[indices]
-        ys = all_ys[indices]
-            
-        return Batch(
-            x=xs.transpose(0, 1).to(device),
-            y=ys.transpose(0, 1).to(device),
-            target_y=ys.transpose(0, 1).clone().to(device),
-            task_id=task_id.unsqueeze(-1).transpose(0, 1).to(device),
-        )
-
-    return get_batch
-
diff --git a/private_multitask_pfn/gen_task_batch.py b/private_multitask_pfn/gen_task_batch.py
deleted file mode 100644
index 36c95b1..0000000
--- a/private_multitask_pfn/gen_task_batch.py
+++ /dev/null
@@ -1,275 +0,0 @@
-import torch
-from PFNs.pfns.priors import Batch
-import gpytorch
-import numpy as np
-import matplotlib.pyplot as plt
-
-    
-default_device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# batch_size: number of datasets
-# seq_len: number of training samples per dataset
-def task_invariant_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    assert num_tasks == 2, "num_tasks must be 2 for task_invariant_batch"
-    x = torch.rand(batch_size, seq_len, num_features, device=device)
-    y = torch.zeros(batch_size, seq_len, 1, device=device)
-    task_id = torch.randint(0, num_tasks, (batch_size, seq_len, 1), device=device)
-    
-    # task 1 is always 0
-    # task 1: y = x_1 + x_2^2 + ... + x_d^d
-    # task_one_mask = task_id == 0
-    # for feature in range(num_features):
-    #     y += torch.pow(x[:, :, feature:feature+1], feature + 1) * task_one_mask
-    
-    # task 2: y = x_1^d + x_2^(d-1) + ... + x_d
-    if torch.rand((1,)) < 0.5:
-        task_two_mask = task_id == 1
-    else:
-        task_two_mask = task_id == 0
-    for feature in range(num_features):
-        y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * task_two_mask
-        
-    return Batch(
-        x=x.transpose(0, 1),
-        y=y.transpose(0, 1),
-        target_y=y.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-
-# opposite of task_invariant_batch
-def task_invariant_eval_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    assert num_tasks == 2, "num_tasks must be 2 for task_invariant_batch"
-    x = torch.rand(batch_size, seq_len, num_features, device=device)
-    y = torch.zeros(batch_size, seq_len, 1, device=device)
-    task_id = torch.zeros((batch_size, seq_len, 1), device=device)
-    
-    # task 2: y = x_1 + x_2^2 + ... + x_d^d
-    task_two_mask = task_id == 1
-    for feature in range(num_features):
-        y += torch.pow(x[:, :, feature:feature+1], feature + 1) * task_two_mask
-    
-    # task 1: y = x_1^d + x_2^(d-1) + ... + x_d
-    task_one_mask = task_id == 0
-    for feature in range(num_features):
-        y += torch.pow(x[:, :, feature:feature+1], num_features - feature) * task_one_mask
-        
-    return Batch(
-        x=x.transpose(0, 1),
-        y=y.transpose(0, 1),
-        target_y=y.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-class BatchedMultidimensionalExactGPModel(gpytorch.models.ExactGP):
-    def __init__(self, train_x, train_y, likelihood):
-        super().__init__(train_x, train_y, likelihood)
-        self.mean_module = gpytorch.means.ConstantMean(batch_shape=torch.Size([train_y.shape[0]]))
-        self.covar_module = gpytorch.kernels.ScaleKernel(
-            gpytorch.kernels.RBFKernel(ard_num_dims=train_x.shape[-1], batch_shape=torch.Size([train_y.shape[0]])),
-            batch_shape=torch.Size([train_y.shape[0]])
-        )
-        # self.covar_module = gpytorch.kernels.RBFKernel(ard_num_dims=train_x.shape[-1], batch_shape=torch.Size([train_y.shape[0]]))
-    
-    def forward(self, x):
-        mean_x = self.mean_module(x)
-        covar_x = self.covar_module(x)
-        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
-
-
-def generate_correlated_gp_samples(x, batch_size, base_samples, correlations):
-    """
-    Generate multiple GP samples with specified correlations to a base sample
-    
-    Args:
-        x: Input tensor of shape (n_points, num_features)
-        base_sample: Optional base GP sample to correlate with
-        correlations: List of correlation values
-    """
-    n_points = x.shape[0]
-    n_features = x.shape[1]
-    
-    # Expand x to include batch dimension
-    x_batched = x.unsqueeze(0).expand(batch_size, -1, -1)
-    
-    tolerance = 1e-6
-    while tolerance < 1e-3:
-        try:
-            # Initialize likelihood and model
-            likelihood = gpytorch.likelihoods.GaussianLikelihood(batch_shape=torch.Size([batch_size]))
-            model = BatchedMultidimensionalExactGPModel(x_batched, torch.zeros(batch_size, n_points), likelihood).to(x.device)
-            
-            # Set the kernel parameters
-            model.covar_module.base_kernel.lengthscale = torch.ones(batch_size, n_features).to(x) * 0.2
-            model.covar_module.outputscale = torch.ones(batch_size).to(x)
-            
-            # Get the kernel matrices and their Cholesky decompositions
-            K = model.covar_module(x_batched).evaluate() + torch.eye(n_points).to(x.device) * tolerance
-            L = torch.linalg.cholesky(K)
-        
-            break
-        except Exception as e:
-            print("Cholesky decomposition failed, retrying...")
-            tolerance *= 10
-            # raise e
-    
-    # Generate the base GP samples if not provided
-    if base_samples is None:
-        z_base = torch.randn(batch_size, n_points).to(x.device)
-        base_samples = torch.bmm(L, z_base.unsqueeze(-1)).squeeze(-1)
-    
-    # Transform base samples back to standard normal
-    z_base = torch.linalg.solve(L, base_samples.unsqueeze(-1)).squeeze(-1)
-    
-    # Generate correlated samples
-    correlated_samples = []
-    for correlation in correlations:
-        z_new = correlation * z_base + torch.sqrt(torch.tensor(1 - correlation**2)).to(x.device) * torch.randn(batch_size, n_points).to(x.device)
-        f_new = torch.bmm(L, z_new.unsqueeze(-1)).squeeze(-1)
-        correlated_samples.append(f_new)
-    
-    return base_samples, correlated_samples
-
-    
-def task_corr_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):
-    x = torch.rand(seq_len, num_features, device=device)
-    y = torch.zeros(batch_size, seq_len, 1, device=device)
-    task_id = torch.randint(0, num_tasks, (seq_len, 1), device=device).expand(batch_size, -1, -1)
-    
-    correlations = torch.linspace(0.95, 0.0, max_num_tasks - 1)
-    if hyperparameters.get("permute_tasks", False):
-        correlations = np.random.permutation(correlations)
-        
-    base_sample = None
-    base_sample, correlated_samples = generate_correlated_gp_samples(x, batch_size, base_sample, correlations)
-
-    all_samples = [base_sample] + correlated_samples
-    
-    for i, samples in enumerate(all_samples):
-        task_mask = task_id == i
-        # print(y.shape, samples.shape, task_mask.squeeze().shape)
-        y += samples.unsqueeze(-1) * (task_mask)
-        
-    x = x.unsqueeze(0).expand(batch_size, -1, -1)
-        
-    return Batch(
-        x=x.transpose(0, 1),
-        y=y.transpose(0, 1),
-        target_y=y.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-
-        
-        
-    return Batch(
-        x=x.transpose(0, 1),
-        y=y.transpose(0, 1),
-        target_y=y.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-    
-    
-# main
-if __name__ == "__main__":
-    import torch
-    import gpytorch
-    import numpy as np
-    import matplotlib.pyplot as plt
-    from itertools import product
-
-    # Set random seed for reproducibility
-    torch.manual_seed(1)
-
-    def generate_grid_inputs(num_points_per_dim, num_features):
-        """Generate a grid of input points across multiple dimensions"""
-        # Create linearly spaced points for each dimension
-        points_per_dim = [torch.linspace(0, 1, num_points_per_dim) for _ in range(num_features)]
-        
-        # Create a meshgrid of all points
-        grid_points = torch.tensor(list(product(*points_per_dim)))
-        
-        return grid_points
-
-
-    def plot_2d_samples(x, samples, correlations, title):
-        """Plot samples for 2D inputs"""
-        num_points = int(np.sqrt(x.shape[0]))
-        X = x[:, 0].reshape(num_points, num_points)
-        Y = x[:, 1].reshape(num_points, num_points)
-        
-        fig = plt.figure(figsize=(15, 3*((len(samples)+1)//3 + 1)))
-        
-        # Plot base sample
-        ax = fig.add_subplot(((len(samples)+1)//3 + 1), 3, 1, projection='3d')
-        Z = samples[0].reshape(num_points, num_points)
-        surf = ax.plot_surface(X.detach(), Y.detach(), Z.detach(), cmap='viridis')
-        ax.set_title('Original GP Draw')
-        fig.colorbar(surf, ax=ax)
-        
-        # Plot correlated samples
-        for i, (sample, corr) in enumerate(zip(samples[1:], correlations)):
-            ax = fig.add_subplot(((len(samples)+1)//3 + 1), 3, i+2, projection='3d')
-            Z = sample.reshape(num_points, num_points)
-            surf = ax.plot_surface(X.detach(), Y.detach(), Z.detach(), cmap='viridis')
-            ax.set_title(f'Correlation: {corr}')
-            fig.colorbar(surf, ax=ax)
-        
-        plt.tight_layout()
-        plt.savefig("correlated_gp_samples.png")
-
-    # Parameters
-    num_features = 2  # Number of input dimensions
-    num_points_per_dim = 20  # Number of points per dimension
-    correlations = [0.9, 0.7, 0.5, 0.3, 0.1]
-
-    # Generate input points
-    x = generate_grid_inputs(num_points_per_dim, num_features)
-
-    # Generate samples
-    base_sample = None
-    base_sample, correlated_samples = generate_correlated_gp_samples(x, 1, base_sample, correlations)
-
-    # Combine all samples for plotting
-    all_samples = [base_sample] + correlated_samples
-
-    # Plot results
-    if num_features == 2:
-        plot_2d_samples(x, all_samples, correlations, 'GP Samples with Different Correlations')
-        
-    # Print achieved correlations
-    for i, corr in enumerate(correlations):
-        empirical_corr = np.corrcoef(base_sample.detach().numpy(), correlated_samples[i].detach().numpy())[0,1]
-        print(f"Target correlation: {corr:.2f}, Achieved correlation: {empirical_corr:.2f}")
-        
-    
\ No newline at end of file
diff --git a/private_multitask_pfn/lmc.py b/private_multitask_pfn/lmc.py
deleted file mode 100644
index f72f3a2..0000000
--- a/private_multitask_pfn/lmc.py
+++ /dev/null
@@ -1,316 +0,0 @@
-
-from __future__ import annotations
-
-import math
-from typing import Any, Optional, Union
-
-import torch
-from botorch.acquisition.objective import PosteriorTransform
-from botorch.exceptions.errors import UnsupportedError
-from botorch.models.gpytorch import GPyTorchModel, MultiTaskGPyTorchModel
-from botorch.models.model import FantasizeMixin
-from botorch.models.transforms.input import InputTransform
-from botorch.models.transforms.outcome import OutcomeTransform, Standardize
-from botorch.models.utils.gpytorch_modules import (
-    get_covar_module_with_dim_scaled_prior,
-    get_gaussian_likelihood_with_lognormal_prior,
-)
-from botorch.utils.datasets import MultiTaskDataset, SupervisedDataset
-from botorch.utils.types import _DefaultType, DEFAULT
-from gpytorch.distributions.multivariate_normal import MultivariateNormal
-from gpytorch.kernels.index_kernel import IndexKernel
-from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
-from gpytorch.likelihoods.likelihood import Likelihood
-from gpytorch.means.constant_mean import ConstantMean
-from gpytorch.models.exact_gp import ExactGP
-from gpytorch.module import Module
-from gpytorch.priors.lkj_prior import LKJCovariancePrior
-from gpytorch.priors.prior import Prior
-from torch import Tensor
-
-from botorch.models.multitask import get_task_value_remapping
-
-
-class LMCGP(ExactGP, MultiTaskGPyTorchModel, FantasizeMixin):
-    r"""Multi-Task exact GP model using an ICM (intrinsic co-regionalization model)
-    kernel. See [Bonilla2007MTGP]_ and [Swersky2013MTBO]_ for a reference on the
-    model and its use in Bayesian optimization.
-
-    The model can be single-output or multi-output, determined by the `output_tasks`.
-    This model uses relatively strong priors on the base Kernel hyperparameters, which
-    work best when covariates are normalized to the unit cube and outcomes are
-    standardized (zero mean, unit variance) - this standardization should be applied in
-    a stratified fashion at the level of the tasks, rather than across all data points.
-
-    If the `train_Yvar` is None, this model infers the noise level. If you have
-    known observation noise, you can set `train_Yvar` to a tensor containing
-    the noise variance measurements. WARNING: This currently does not support
-    different noise levels for the different tasks.
-    """
-
-    def __init__(
-        self,
-        train_X: Tensor,
-        train_Y: Tensor,
-        task_feature: int,
-        num_covars: int,
-        train_Yvar: Optional[Tensor] = None,
-        mean_module: Optional[Module] = None,
-        covar_modules: Optional[Module] = None,
-        likelihood: Optional[Likelihood] = None,
-        task_covar_prior: Optional[Prior] = None,
-        output_tasks: Optional[list[int]] = None,
-        rank: Optional[int] = None,
-        all_tasks: Optional[list[int]] = None,
-        outcome_transform: Optional[Union[OutcomeTransform, _DefaultType]] = DEFAULT,
-        input_transform: Optional[InputTransform] = None,
-    ) -> None:
-        r"""Multi-Task GP model using an LMC kernel.
-
-        Args:
-            train_X: A `n x (d + 1)` or `b x n x (d + 1)` (batch mode) tensor
-                of training data. One of the columns should contain the task
-                features (see `task_feature` argument).
-            train_Y: A `n x 1` or `b x n x 1` (batch mode) tensor of training
-                observations.
-            task_feature: The index of the task feature (`-d <= task_feature <= d`).
-            train_Yvar: An optional `n` or `b x n` (batch mode) tensor of observed
-                measurement noise. If None, we infer the noise.
-                Note that the inferred noise is common across all tasks.
-            mean_module: The mean function to be used. Defaults to `ConstantMean`.
-            covar_module: The module for computing the covariance matrix between
-                the non-task features. Defaults to `RBFKernel`.
-            likelihood: A likelihood. The default is selected based on `train_Yvar`.
-                If `train_Yvar` is None, a standard `GaussianLikelihood` with inferred
-                noise level is used. Otherwise, a FixedNoiseGaussianLikelihood is used.
-            output_tasks: A list of task indices for which to compute model
-                outputs for. If omitted, return outputs for all task indices.
-            rank: The rank to be used for the index kernel. If omitted, use a
-                full rank (i.e. number of tasks) kernel.
-            task_covar_prior : A Prior on the task covariance matrix. Must operate
-                on p.s.d. matrices. A common prior for this is the `LKJ` prior.
-            all_tasks: By default, multi-task GPs infer the list of all tasks from
-                the task features in `train_X`. This is an experimental feature that
-                enables creation of multi-task GPs with tasks that don't appear in the
-                training data. Note that when a task is not observed, the corresponding
-                task covariance will heavily depend on random initialization and may
-                behave unexpectedly.
-            outcome_transform: An outcome transform that is applied to the
-                training data during instantiation and to the posterior during
-                inference (that is, the `Posterior` obtained by calling
-                `.posterior` on the model will be on the original scale). We use a
-                `Standardize` transform if no `outcome_transform` is specified.
-                Pass down `None` to use no outcome transform. NOTE: Standardization
-                should be applied in a stratified fashion, separately for each task.
-            input_transform: An input transform that is applied in the model's
-                forward pass.
-
-        Example:
-            >>> X1, X2 = torch.rand(10, 2), torch.rand(20, 2)
-            >>> i1, i2 = torch.zeros(10, 1), torch.ones(20, 1)
-            >>> train_X = torch.cat([
-            >>>     torch.cat([X1, i1], -1), torch.cat([X2, i2], -1),
-            >>> ])
-            >>> train_Y = torch.cat(f1(X1), f2(X2)).unsqueeze(-1)
-            >>> model = MultiTaskGP(train_X, train_Y, task_feature=-1)
-        """
-        with torch.no_grad():
-            transformed_X = self.transform_inputs(
-                X=train_X, input_transform=input_transform
-            )
-        self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)
-        (
-            all_tasks_inferred,
-            task_feature,
-            self.num_non_task_features,
-        ) = self.get_all_tasks(transformed_X, task_feature, output_tasks)
-        if all_tasks is not None and not set(all_tasks_inferred).issubset(all_tasks):
-            raise UnsupportedError(
-                f"The provided {all_tasks=} does not contain all the task features "
-                f"inferred from the training data {all_tasks_inferred=}. "
-                "This is not allowed as it will lead to errors during model training."
-            )
-        all_tasks = all_tasks or all_tasks_inferred
-        self.num_tasks = len(all_tasks)
-        self.num_covars = num_covars
-        if outcome_transform == DEFAULT:
-            outcome_transform = Standardize(m=1, batch_shape=train_X.shape[:-2])
-        if outcome_transform is not None:
-            train_Y, train_Yvar = outcome_transform(Y=train_Y, Yvar=train_Yvar)
-
-        # squeeze output dim
-        train_Y = train_Y.squeeze(-1)
-        if output_tasks is None:
-            output_tasks = all_tasks
-        else:
-            if set(output_tasks) - set(all_tasks):
-                raise RuntimeError("All output tasks must be present in input data.")
-        self._output_tasks = output_tasks
-        self._num_outputs = len(output_tasks)
-
-        # TODO (T41270962): Support task-specific noise levels in likelihood
-        if likelihood is None:
-            if train_Yvar is None:
-                likelihood = get_gaussian_likelihood_with_lognormal_prior()
-            else:
-                likelihood = FixedNoiseGaussianLikelihood(noise=train_Yvar.squeeze(-1))
-
-        # construct indexer to be used in forward
-        self._task_feature = task_feature
-        self._base_idxr = torch.arange(self.num_non_task_features)
-        self._base_idxr[task_feature:] += 1  # exclude task feature
-
-        super().__init__(
-            train_inputs=train_X, train_targets=train_Y, likelihood=likelihood
-        )
-        self.mean_module = mean_module or ConstantMean()
-        if covar_modules is None:
-            self.covar_modules = [get_covar_module_with_dim_scaled_prior(
-                ard_num_dims=self.num_non_task_features
-            )] * num_covars
-        else:
-            assert len(covar_modules) == num_covars
-            self.covar_modules = covar_modules
-
-        self._rank = rank if rank is not None else self.num_tasks
-        self.task_covar_module = IndexKernel(
-            num_tasks=self.num_tasks, rank=self._rank, prior=task_covar_prior
-        )
-        task_mapper = get_task_value_remapping(
-            task_values=torch.tensor(
-                all_tasks, dtype=torch.long, device=train_X.device
-            ),
-            dtype=train_X.dtype,
-        )
-        self.register_buffer("_task_mapper", task_mapper)
-        self._expected_task_values = set(all_tasks)
-        if input_transform is not None:
-            self.input_transform = input_transform
-        if outcome_transform is not None:
-            self.outcome_transform = outcome_transform
-        self.to(train_X)
-
-    def _split_inputs(self, x: Tensor) -> tuple[Tensor, Tensor]:
-        r"""Extracts base features and task indices from input data.
-
-        Args:
-            x: The full input tensor with trailing dimension of size `d + 1`.
-                Should be of float/double data type.
-
-        Returns:
-            2-element tuple containing
-
-            - A `q x d` or `b x q x d` (batch mode) tensor with trailing
-            dimension made up of the `d` non-task-index columns of `x`, arranged
-            in the order as specified by the indexer generated during model
-            instantiation.
-            - A `q` or `b x q` (batch mode) tensor of long data type containing
-            the task indices.
-        """
-        batch_shape, d = x.shape[:-2], x.shape[-1]
-        x_basic = x[..., self._base_idxr].view(batch_shape + torch.Size([-1, d - 1]))
-        task_idcs = (
-            x[..., self._task_feature]
-            .view(batch_shape + torch.Size([-1, 1]))
-            .to(dtype=torch.long)
-        )
-        task_idcs = self._map_tasks(task_values=task_idcs)
-        return x_basic, task_idcs
-
-    def forward(self, x: Tensor) -> MultivariateNormal:
-        if self.training:
-            x = self.transform_inputs(x)
-        x_basic, task_idcs = self._split_inputs(x)
-        # Compute base mean and covariance
-        mean_x = self.mean_module(x_basic)
-        
-        # Compute task covariances (sum of covariances)
-        covar_i = self.task_covar_module(task_idcs)
-        
-        covar = self.covar_modules[0](x_basic).mul(covar_i)
-        for covar_module in self.covar_modules[1:]:
-            covar_x = covar_module(x_basic)
-            # Combine the two in an ICM fashion
-            covar += covar_x.mul(covar_i)
-            
-        return MultivariateNormal(mean_x, covar)
-
-    @classmethod
-    def get_all_tasks(
-        cls,
-        train_X: Tensor,
-        task_feature: int,
-        output_tasks: Optional[list[int]] = None,
-    ) -> tuple[list[int], int, int]:
-        if train_X.ndim != 2:
-            # Currently, batch mode MTGPs are blocked upstream in GPyTorch
-            raise ValueError(f"Unsupported shape {train_X.shape} for train_X.")
-
-        d = train_X.shape[-1] - 1
-        if not (-d <= task_feature <= d):
-            raise ValueError(f"Must have that -{d} <= task_feature <= {d}")
-        task_feature = task_feature % (d + 1)
-        all_tasks = (
-            train_X[..., task_feature].unique(sorted=True).to(dtype=torch.long).tolist()
-        )
-        return all_tasks, task_feature, d
-
-    @classmethod
-    def construct_inputs(
-        cls,
-        training_data: Union[SupervisedDataset, MultiTaskDataset],
-        task_feature: int,
-        output_tasks: Optional[list[int]] = None,
-        task_covar_prior: Optional[Prior] = None,
-        prior_config: Optional[dict] = None,
-        rank: Optional[int] = None,
-    ) -> dict[str, Any]:
-        r"""Construct `Model` keyword arguments from a dataset and other args.
-
-        Args:
-            training_data: A `SupervisedDataset` or a `MultiTaskDataset`.
-            task_feature: Column index of embedded task indicator features.
-            output_tasks: A list of task indices for which to compute model
-                outputs for. If omitted, return outputs for all task indices.
-            task_covar_prior: A GPyTorch `Prior` object to use as prior on
-                the cross-task covariance matrix,
-            prior_config: Configuration for inter-task covariance prior.
-                Should only be used if `task_covar_prior` is not passed directly. Must
-                contain `use_LKJ_prior` indicator and should contain float value `eta`.
-            rank: The rank of the cross-task covariance matrix.
-        """
-        if task_covar_prior is not None and prior_config is not None:
-            raise ValueError(
-                "Only one of `task_covar_prior` and `prior_config` arguments expected."
-            )
-
-        if prior_config is not None:
-            if not prior_config.get("use_LKJ_prior"):
-                raise ValueError("Currently only config for LKJ prior is supported.")
-
-            num_tasks = training_data.X[task_feature].unique().numel()
-            sd_prior = GammaPrior(1.0, 0.15)
-            sd_prior._event_shape = torch.Size([num_tasks])
-            eta = prior_config.get("eta", 0.5)
-            if not isinstance(eta, float) and not isinstance(eta, int):
-                raise ValueError(f"eta must be a real number, your eta was {eta}.")
-            task_covar_prior = LKJCovariancePrior(num_tasks, eta, sd_prior)
-
-        # Call Model.construct_inputs to parse training data
-        base_inputs = super().construct_inputs(training_data=training_data)
-        if (
-            isinstance(training_data, MultiTaskDataset)
-            # If task features are included in the data, all tasks will have
-            # some observations and they may have different task features.
-            and training_data.task_feature_index is None
-        ):
-            all_tasks = list(range(len(training_data.datasets)))
-            base_inputs["all_tasks"] = all_tasks
-        if task_covar_prior is not None:
-            base_inputs["task_covar_prior"] = task_covar_prior
-        if rank is not None:
-            base_inputs["rank"] = rank
-        base_inputs["task_feature"] = task_feature
-        base_inputs["output_tasks"] = output_tasks
-        return base_inputs
-
diff --git a/private_multitask_pfn/load_pfn4bo_checkpoints.py b/private_multitask_pfn/load_pfn4bo_checkpoints.py
deleted file mode 100644
index 8db2ce4..0000000
--- a/private_multitask_pfn/load_pfn4bo_checkpoints.py
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/usr/bin/env python3
-import torch
-from ax.fb.utils.storage.manifold import AEManifoldUseCase
-from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
-
-from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns import (
-    bar_distribution,
-    encoders,
-)
-from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns.positional_encodings import (
-    NoPositionalEncoding,
-)
-from fblearner.flow.projects.ae.benchmarks.pfn.thirdparty.PFNs.pfns.transformer import (
-    TransformerModel,
-)
-
-# works with https://github.com/automl/PFNs/commit/fd212b187a22d5a07959484d03fcd82e060e9b21
-
-STATE_DICT_KEY = {
-    "hebo": "tree/trbo_dev/f380d01c-9861-11ef-9e69-84160c3811b6",
-    "bnn": "tree/trbo_dev/0e6ad5d4-9862-11ef-b27f-84160c3811b6",
-}
-
-
-def load_bnn_pfn():
-    # Load BNN PFN
-    num_features = 18
-    emsize = 512
-    encoder_generator = encoders.get_normalized_uniform_encoder(
-        encoders.get_variable_num_features_encoder(encoders.Linear)
-    )
-    encoder = encoder_generator(num_features, emsize)
-    seq_len = 60
-
-    bnn_model = TransformerModel(
-        encoder,
-        ninp=emsize,
-        nhead=4,
-        nhid=1024,
-        nlayers=6,
-        style_encoder=None,
-        y_encoder=encoders.Linear(1, emsize),
-        pos_encoder=NoPositionalEncoding(emsize, seq_len * 2),
-        decoder_dict={"standard": (None, 1000)},
-    )
-    # check if it's full or not
-    bnn_model.criterion = bar_distribution.FullSupportBarDistribution(
-        torch.arange(1001).float()
-    )
-    bnn_model.decoder = bnn_model.decoder_dict.standard
-
-    client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
-    bnn_state_dict = client.torch_load(STATE_DICT_KEY["bnn"])
-    bnn_model.load_state_dict(bnn_state_dict)
-
-    bnn_model.borders = bnn_model.criterion.borders
-    return bnn_model
-
-
-def load_hebo_pfn():
-    # Load HEBO+ PFN
-    num_features = 18
-    emsize = 512
-    encoder_generator = encoders.get_normalized_uniform_encoder(
-        encoders.get_variable_num_features_encoder(encoders.Linear)
-    )
-    encoder = encoder_generator(num_features, emsize)
-    seq_len = 60
-
-    hebo_model = TransformerModel(
-        encoder,
-        ninp=emsize,
-        nhead=4,
-        nhid=1024,
-        nlayers=12,
-        style_encoder=None,
-        y_encoder=encoders.Linear(1, emsize),
-        pos_encoder=NoPositionalEncoding(emsize, seq_len * 2),
-        decoder_dict={"standard": (None, 1000)},
-    )
-    # check if it's full or not
-    hebo_model.criterion = bar_distribution.FullSupportBarDistribution(
-        torch.arange(1001).float()
-    )
-
-    client = AEManifoldTorchClient(AEManifoldUseCase.TRBO_DEV)
-    hebo_state_dict = client.torch_load(STATE_DICT_KEY["hebo"])
-    hebo_model.load_state_dict(hebo_state_dict)
-
-    hebo_model.borders = hebo_model.criterion.borders
-    return hebo_model
diff --git a/private_multitask_pfn/mtgp_nuts.py b/private_multitask_pfn/mtgp_nuts.py
deleted file mode 100644
index c22cc56..0000000
--- a/private_multitask_pfn/mtgp_nuts.py
+++ /dev/null
@@ -1,323 +0,0 @@
-
-from collections.abc import Mapping
-from typing import Any, NoReturn, Optional
-
-import pyro
-import pyro.distributions
-import torch
-from botorch.models.fully_bayesian import (
-    # matern52_kernel,
-    MIN_INFERRED_NOISE_LEVEL,
-    reshape_and_detach,
-    PyroModel
-)
-from gpytorch.likelihoods.likelihood import Likelihood
-from gpytorch.means.mean import Mean
-from torch import Tensor
-from gpytorch.kernels.kernel import dist
-from gpytorch.constraints import GreaterThan
-from gpytorch.kernels import RBFKernel, ScaleKernel
-from gpytorch.kernels.kernel import dist, Kernel
-from gpytorch.likelihoods.gaussian_likelihood import (
-    FixedNoiseGaussianLikelihood,
-    GaussianLikelihood,
-)
-from gpytorch.likelihoods.likelihood import Likelihood
-from gpytorch.means.constant_mean import ConstantMean
-from gpytorch.means.mean import Mean
-from torch import Tensor
-from math import sqrt
-from torch.nn.parameter import Parameter
-
-
-def rbf_kernel(X: Tensor, lengthscale: Tensor) -> Tensor:
-    """RBF kernel."""
-    dist = compute_dists(X=X, lengthscale=lengthscale)
-    return torch.exp(-0.5 * (dist ** 2))
-
-
-def compute_dists(X: Tensor, lengthscale: Tensor) -> Tensor:
-    """Compute kernel distances."""
-    scaled_X = X / lengthscale
-    return dist(scaled_X, scaled_X, x1_eq_x2=True)
-
-
-
-class RBFPyroModel(PyroModel):
-    r"""Implementation of the sparse axis-aligned subspace priors (SAAS) model.
-
-    The SAAS model uses sparsity-inducing priors to identify the most important
-    parameters. This model is suitable for high-dimensional BO with potentially
-    hundreds of tunable parameters. See [Eriksson2021saasbo]_ for more details.
-
-    `SaasPyroModel` is not a standard BoTorch model; instead, it is used as
-    an input to `SaasFullyBayesianSingleTaskGP`. It is used as a default keyword
-    argument, and end users are not likely to need to instantiate or modify a
-    `SaasPyroModel` unless they want to customize its attributes (such as
-    `covar_module`).
-    """
-
-    def set_inputs(
-        self, train_X: Tensor, train_Y: Tensor, train_Yvar: Optional[Tensor] = None
-    ) -> None:
-        super().set_inputs(train_X, train_Y, train_Yvar)
-        self.ard_num_dims = self.train_X.shape[-1]
-
-    def sample(self) -> None:
-        r"""Sample from the SAAS model.
-
-        This samples the mean, noise variance, outputscale, and lengthscales according
-        to the SAAS prior.
-        """
-        tkwargs = {"dtype": self.train_X.dtype, "device": self.train_X.device}
-        outputscale = self.sample_outputscale(concentration=2.0, rate=0.15, **tkwargs)
-        mean = self.sample_mean(**tkwargs)
-        noise = self.sample_noise(**tkwargs)
-        lengthscale = self.sample_lengthscale(dim=self.ard_num_dims, **tkwargs)
-        if self.train_Y.shape[-2] > 0:
-            # Do not attempt to sample Y if the data is empty.
-            # This leads to errors with empty data.
-            K = rbf_kernel(X=self.train_X, lengthscale=lengthscale)
-            K = outputscale * K + noise * torch.eye(self.train_X.shape[0], **tkwargs)
-            pyro.sample(
-                "Y",
-                pyro.distributions.MultivariateNormal(
-                    loc=mean.view(-1).expand(self.train_X.shape[0]),
-                    covariance_matrix=K,
-                ),
-                obs=self.train_Y.squeeze(-1),
-            )
-
-    def sample_outputscale(
-        self, concentration: float = 2.0, rate: float = 0.15, **tkwargs: Any
-    ) -> Tensor:
-        r"""Sample the outputscale."""
-        return pyro.sample(
-            "outputscale",
-            pyro.distributions.Gamma(
-                torch.tensor(concentration, **tkwargs),
-                torch.tensor(rate, **tkwargs),
-            ),
-        )
-
-    def sample_mean(self, **tkwargs: Any) -> Tensor:
-        r"""Sample the mean constant."""
-        return pyro.sample(
-            "mean",
-            pyro.distributions.Normal(
-                torch.tensor(0.0, **tkwargs),
-                torch.tensor(1.0, **tkwargs),
-            ),
-        )
-
-    def sample_noise(self, **tkwargs: Any) -> Tensor:
-        r"""Sample the noise variance."""
-        if self.train_Yvar is None:
-            return MIN_INFERRED_NOISE_LEVEL + pyro.sample(
-                "noise",
-                pyro.distributions.Gamma(
-                    torch.tensor(0.9, **tkwargs),
-                    torch.tensor(10.0, **tkwargs),
-                ),
-            )
-        else:
-            return self.train_Yvar
-
-    def sample_lengthscale(
-        self, dim: int, alpha: float = 0.1, **tkwargs: Any
-    ) -> Tensor:
-        r"""Sample the lengthscale."""
-        return pyro.sample(
-            "lengthscale",
-            pyro.distributions.LogNormal(
-                loc=sqrt(2) + torch.log(torch.ones(dim, **tkwargs) * dim) * 0.5,
-                scale=sqrt(3),
-            ),
-        )
-
-    def postprocess_mcmc_samples(
-        self, mcmc_samples: dict[str, Tensor]
-    ) -> dict[str, Tensor]:
-        r"""Post-process the MCMC samples.
-
-        This computes the true lengthscales and removes the inverse lengthscales and
-        tausq (global shrinkage).
-        """
-        return mcmc_samples
-
-    def load_mcmc_samples(
-        self, mcmc_samples: dict[str, Tensor]
-    ) -> tuple[Mean, Kernel, Likelihood]:
-        r"""Load the MCMC samples into the mean_module, covar_module, and likelihood."""
-        tkwargs = {"device": self.train_X.device, "dtype": self.train_X.dtype}
-        num_mcmc_samples = len(mcmc_samples["mean"])
-        batch_shape = torch.Size([num_mcmc_samples])
-
-        mean_module = ConstantMean(batch_shape=batch_shape).to(**tkwargs)
-        covar_module = ScaleKernel(
-            base_kernel=RBFKernel(
-                ard_num_dims=self.ard_num_dims,
-                batch_shape=batch_shape,
-            ),
-            batch_shape=batch_shape,
-        ).to(**tkwargs)
-        if self.train_Yvar is not None:
-            likelihood = FixedNoiseGaussianLikelihood(
-                # Reshape to shape `num_mcmc_samples x N`
-                noise=self.train_Yvar.squeeze(-1).expand(
-                    num_mcmc_samples, len(self.train_Yvar)
-                ),
-                batch_shape=batch_shape,
-            ).to(**tkwargs)
-        else:
-            likelihood = GaussianLikelihood(
-                batch_shape=batch_shape,
-                noise_constraint=GreaterThan(MIN_INFERRED_NOISE_LEVEL),
-            ).to(**tkwargs)
-            likelihood.noise_covar.noise = reshape_and_detach(
-                target=likelihood.noise_covar.noise,
-                new_value=mcmc_samples["noise"].clamp_min(MIN_INFERRED_NOISE_LEVEL),
-            )
-        covar_module.base_kernel.lengthscale = reshape_and_detach(
-            target=covar_module.base_kernel.lengthscale,
-            new_value=mcmc_samples["lengthscale"],
-        )
-        covar_module.outputscale = reshape_and_detach(
-            target=covar_module.outputscale,
-            new_value=mcmc_samples["outputscale"],
-        )
-        mean_module.constant.data = reshape_and_detach(
-            target=mean_module.constant.data,
-            new_value=mcmc_samples["mean"],
-        )
-        return mean_module, covar_module, likelihood
-
-
-
-class MultitaskRBFPyroModel(RBFPyroModel):
-    r"""
-    Implementation of the multi-task sparse axis-aligned subspace priors (SAAS) model.
-
-    The multi-task model uses an ICM kernel. The data kernel is same as the single task
-    SAAS model in order to handle high-dimensional parameter spaces. The task kernel
-    is a Matern-5/2 kernel using learned task embeddings as the input.
-    """
-
-    def set_inputs(
-        self,
-        train_X: Tensor,
-        train_Y: Tensor,
-        train_Yvar: Optional[Tensor],
-        task_feature: int,
-        task_rank: Optional[int] = None,
-    ) -> None:
-        """Set the training data.
-
-        Args:
-            train_X: Training inputs (n x (d + 1))
-            train_Y: Training targets (n x 1)
-            train_Yvar: Observed noise variance (n x 1). If None, we infer the noise.
-                Note that the inferred noise is common across all tasks.
-            task_feature: The index of the task feature (`-d <= task_feature <= d`).
-            task_rank: The num of learned task embeddings to be used in the task kernel.
-                If omitted, use a full rank (i.e. number of tasks) kernel.
-        """
-        super().set_inputs(train_X, train_Y, train_Yvar)
-        # obtain a list of task indicies
-        all_tasks = train_X[:, task_feature].unique().to(dtype=torch.long).tolist()
-        self.task_feature = task_feature
-        self.num_tasks = len(all_tasks)
-        self.task_rank = task_rank or self.num_tasks
-        # assume there is one column for task feature
-        self.ard_num_dims = self.train_X.shape[-1] - 1
-
-    def sample(self) -> None:
-        r"""Sample from the SAAS model.
-
-        This samples the mean, noise variance, outputscale, and lengthscales according
-        to the SAAS prior.
-        """
-        tkwargs = {"dtype": self.train_X.dtype, "device": self.train_X.device}
-        base_idxr = torch.arange(self.ard_num_dims, **{"device": tkwargs["device"]})
-        base_idxr[self.task_feature :] += 1  # exclude task feature
-        task_indices = self.train_X[..., self.task_feature].to(
-            device=tkwargs["device"], dtype=torch.long
-        )
-
-        outputscale = self.sample_outputscale(concentration=2.0, rate=0.15, **tkwargs)
-        mean = self.sample_mean(**tkwargs)
-        noise = self.sample_noise(**tkwargs)
-
-        lengthscale = self.sample_lengthscale(dim=self.ard_num_dims, **tkwargs)
-        K = rbf_kernel(X=self.train_X[..., base_idxr], lengthscale=lengthscale)
-
-        # compute task covar matrix
-        task_latent_features = self.sample_latent_features(**tkwargs)[task_indices]
-        task_lengthscale = self.sample_task_lengthscale(**tkwargs)
-        task_covar = rbf_kernel(
-            X=task_latent_features, lengthscale=task_lengthscale
-        )
-        K = K.mul(task_covar)
-        K = outputscale * K + noise * torch.eye(self.train_X.shape[0], **tkwargs)
-        pyro.sample(
-            "Y",
-            pyro.distributions.MultivariateNormal(
-                loc=mean.view(-1).expand(self.train_X.shape[0]),
-                covariance_matrix=K,
-            ),
-            obs=self.train_Y.squeeze(-1),
-        )
-
-    def sample_latent_features(self, **tkwargs: Any):
-        return pyro.sample(
-            "latent_features",
-            pyro.distributions.Normal(
-                torch.tensor(0.0, **tkwargs),
-                torch.tensor(1.0, **tkwargs),
-            ).expand(torch.Size([self.num_tasks, self.task_rank])),
-        )
-
-    def sample_task_lengthscale(
-        self, concentration: float = 6.0, rate: float = 3.0, **tkwargs: Any
-    ):
-        return pyro.sample(
-            "task_lengthscale",
-            pyro.distributions.Gamma(
-                torch.tensor(concentration, **tkwargs),
-                torch.tensor(rate, **tkwargs),
-            ).expand(torch.Size([self.task_rank])),
-        )
-
-    def load_mcmc_samples(
-        self, mcmc_samples: dict[str, Tensor]
-    ) -> tuple[Mean, Kernel, Likelihood, Kernel, Parameter]:
-        r"""Load the MCMC samples into the mean_module, covar_module, and likelihood."""
-        tkwargs = {"device": self.train_X.device, "dtype": self.train_X.dtype}
-        num_mcmc_samples = len(mcmc_samples["mean"])
-        batch_shape = torch.Size([num_mcmc_samples])
-
-        mean_module, covar_module, likelihood = super().load_mcmc_samples(
-            mcmc_samples=mcmc_samples
-        )
-
-        task_covar_module = RBFKernel(
-            ard_num_dims=self.task_rank,
-            batch_shape=batch_shape,
-        ).to(**tkwargs)
-        task_covar_module.lengthscale = reshape_and_detach(
-            target=task_covar_module.lengthscale,
-            new_value=mcmc_samples["task_lengthscale"],
-        )
-        latent_features = Parameter(
-            torch.rand(
-                batch_shape + torch.Size([self.num_tasks, self.task_rank]),
-                requires_grad=True,
-                **tkwargs,
-            )
-        )
-        latent_features = reshape_and_detach(
-            target=latent_features,
-            new_value=mcmc_samples["latent_features"],
-        )
-        return mean_module, covar_module, likelihood, task_covar_module, latent_features
-
diff --git a/private_multitask_pfn/multifidelity/Model Evaluation on Discrete MF Problems.ipynb b/private_multitask_pfn/multifidelity/Model Evaluation on Discrete MF Problems.ipynb
deleted file mode 100644
index 932d790..0000000
--- a/private_multitask_pfn/multifidelity/Model Evaluation on Discrete MF Problems.ipynb	
+++ /dev/null
@@ -1,1843 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528209127,
-    "executionStopTime": 1743528217022,
-    "language": "python",
-    "originalKey": "a4c56e12-55d0-4b3e-a2dd-bec3739a135a",
-    "outputsInitialized": true,
-    "requestMsgId": "61357428-9500-4edb-8e7d-2a6608607fae",
-    "serverExecutionDuration": 6.2562888488173,
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "\n",
-    "import torch\n",
-    "from botorch.test_functions.synthetic import SyntheticTestFunction\n",
-    "from torch import Tensor\n",
-    "\n",
-    "\n",
-    "class WingWeightMultiFidelity(SyntheticTestFunction):\n",
-    "    \"\"\"Wing Weight Design Problem from [Chen2024]_.\n",
-    "\n",
-    "    Design variables (physical units):\n",
-    "      1. s_w   in [150,   200]   (wing area)\n",
-    "      2. w_fw  in [220,   300]   (fuel weight)\n",
-    "      3. A     in [6,     10]    (aspect ratio)\n",
-    "      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)\n",
-    "      5. q     in [16,    45]    (dynamic pressure)\n",
-    "      6. lam   in [0.5,   1.0]   (taper ratio)\n",
-    "      7. t_c   in [0.08,  0.18]   (thickness-to-chord)\n",
-    "      8. N_z   in [2.5,   6.0]    (ultimate load factor)\n",
-    "      9. w_dg  in [1700,  2500]   (design gross weight)\n",
-    "      10. w_pp in [0.025, 0.08]    (weight per unit area)\n",
-    "\n",
-    "    Fidelity parameter (stored as the 11th input):\n",
-    "      0: High fidelity (HF)\n",
-    "      1: Low fidelity 1 (LF1)\n",
-    "      2: Low fidelity 2 (LF2)\n",
-    "      3: Low fidelity 3 (LF2)\n",
-    "\n",
-    "    The HF model is given by:\n",
-    "      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *\n",
-    "           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *\n",
-    "           (N_z*w_dg)^0.49 + s_w*w_pp\n",
-    "\n",
-    "    LF models use slightly altered exponents and additive biases.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    dim = 11\n",
-    "    _num_fidelities = 1\n",
-    "    _bounds = [\n",
-    "        (150.0, 200.0),  # s_w\n",
-    "        (220.0, 300.0),  # w_fw\n",
-    "        (6.0, 10.0),  # A\n",
-    "        (-10.0, 10.0),  # Lambda_deg\n",
-    "        (16.0, 45.0),  # q\n",
-    "        (0.5, 1.0),  # lam\n",
-    "        (0.08, 0.18),  # t_c\n",
-    "        (2.5, 6.0),  # N_z\n",
-    "        (1700.0, 2500.0),  # w_dg\n",
-    "        (0.025, 0.08),  # w_pp\n",
-    "        (0, 3),\n",
-    "    ]\n",
-    "    fidelities = [0, 1, 2, 3]\n",
-    "    _optimal_value = 123.25\n",
-    "\n",
-    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.\n",
-    "        s_w = X[..., 0]\n",
-    "        w_fw = X[..., 1]\n",
-    "        A = X[..., 2]\n",
-    "        Lambda_deg = X[..., 3]\n",
-    "        Lambda_rad = Lambda_deg * math.pi / 180.0\n",
-    "\n",
-    "        q = X[..., 4]\n",
-    "        lam = X[..., 5]\n",
-    "        t_c = X[..., 6]\n",
-    "        N_z = X[..., 7]\n",
-    "        w_dg = X[..., 8]\n",
-    "        w_pp = X[..., 9]\n",
-    "        fidelity = X[..., 10]\n",
-    "        cos_val = torch.cos(Lambda_rad)\n",
-    "        y = torch.zeros_like(s_w)\n",
-    "        # High fidelity (fidelity == 0)\n",
-    "        mask = fidelity == 0\n",
-    "        if mask.any():\n",
-    "            hf = (\n",
-    "                0.036\n",
-    "                * (s_w**0.758)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + s_w * w_pp\n",
-    "            )\n",
-    "            y[mask] = hf[mask]\n",
-    "        # Low fidelity 1 (fidelity == 1)\n",
-    "        mask = fidelity == 1\n",
-    "        if mask.any():\n",
-    "            lf1 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.758)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + w_pp\n",
-    "            )\n",
-    "            y[mask] = lf1[mask]\n",
-    "        # Low fidelity 2 (fidelity == 2)\n",
-    "        mask = fidelity == 2\n",
-    "        if mask.any():\n",
-    "            lf2 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.8)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + w_pp\n",
-    "            )\n",
-    "            y[mask] = lf2[mask]\n",
-    "        # Low fidelity 3 (fidelity == 3)\n",
-    "        mask = fidelity == 3\n",
-    "        if mask.any():\n",
-    "            lf3 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.9)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "            )\n",
-    "            y[mask] = lf3[mask]\n",
-    "        return y\n",
-    "\n",
-    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        fidelity = X[..., 10]\n",
-    "        c = torch.zeros_like(fidelity)\n",
-    "        c[fidelity == 0] = 1000.0\n",
-    "        c[fidelity == 1] = 100.0\n",
-    "        c[fidelity == 2] = 10.0\n",
-    "        c[fidelity == 3] = 1.0\n",
-    "        return c\n",
-    "\n",
-    "\n",
-    "class BoreholeMultiFidelity(SyntheticTestFunction):\n",
-    "    \"\"\"Borehole Problem from [Chen2024]_.\n",
-    "\n",
-    "    This problem models water flow through a borehole with 8 design variables:\n",
-    "          1. r_w   in [0.05,   0.15]   (borehole radius)\n",
-    "          2. r     in [100,    50000]  (radius of influence)\n",
-    "          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)\n",
-    "          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)\n",
-    "          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)\n",
-    "          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)\n",
-    "          7. L     in [1120,   1680]   (length of borehole)\n",
-    "          8. K_w   in [9855,   12045]  (hydraulic conductivity)\n",
-    "\n",
-    "        The fidelity index (9th input) is categorical:\n",
-    "          0: High fidelity (HF)\n",
-    "          1: Low fidelity 1 (LF1)\n",
-    "          2: Low fidelity 2 (LF2)\n",
-    "          3: Low fidelity 3 (LF3)\n",
-    "          4: Low fidelity 4 (LF4)\n",
-    "\n",
-    "        The HF model is defined by:\n",
-    "          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]\n",
-    "\n",
-    "        The low-fidelity models modify exponents and add a bias.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    dim = 9\n",
-    "    _num_fidelities = 1\n",
-    "    _bounds = [\n",
-    "        (0.05, 0.15),  # r_w\n",
-    "        (100.0, 10000.0),  # r\n",
-    "        (100.0, 1000.0),  # T_u\n",
-    "        (10.0, 500.0),  # T_l\n",
-    "        (990.0, 1110.0),  # H_u\n",
-    "        (700.0, 820.0),  # H_l\n",
-    "        (1000.0, 2000.0),  # L\n",
-    "        (6000.0, 12000.0),  # K_w\n",
-    "        (0, 4),  # fidelity\n",
-    "    ]\n",
-    "    fidelities = [0, 1, 2, 3, 4]\n",
-    "    _optimal_value = 3.98\n",
-    "\n",
-    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        r_w = X[..., 0]\n",
-    "        r = X[..., 1]\n",
-    "        T_u = X[..., 2]\n",
-    "        T_l = X[..., 3]\n",
-    "        H_u = X[..., 4]\n",
-    "        H_l = X[..., 5]\n",
-    "        L = X[..., 6]\n",
-    "        K_w = X[..., 7]\n",
-    "        fidelity = X[..., 8]\n",
-    "\n",
-    "        log_term = torch.log(r / r_w)\n",
-    "        numer = 2.0 * math.pi * T_u * (H_u - H_l)\n",
-    "        y = torch.zeros_like(r_w)\n",
-    "\n",
-    "        # HF (fidelity 0)\n",
-    "        mask = fidelity == 0\n",
-    "        if mask.any():\n",
-    "            hf_denom = log_term * (\n",
-    "                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            hf = numer / hf_denom\n",
-    "            y[mask] = hf[mask]\n",
-    "\n",
-    "        # LF1 (fidelity 1): add bias.\n",
-    "        mask = fidelity == 1\n",
-    "        if mask.any():\n",
-    "            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)\n",
-    "            lf1_denom = log_term * (\n",
-    "                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf1 = lf1_numer / lf1_denom\n",
-    "            y[mask] = lf1[mask]\n",
-    "\n",
-    "        # LF2 (fidelity 2): modify the exponent on log_term and add bias.\n",
-    "        mask = fidelity == 2\n",
-    "        if mask.any():\n",
-    "            lf2_denom = log_term * (\n",
-    "                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l\n",
-    "            )\n",
-    "            lf2 = numer / lf2_denom\n",
-    "            y[mask] = lf2[mask]\n",
-    "\n",
-    "        # LF3 (fidelity 3): modify r_w exponent slightly.\n",
-    "        mask = fidelity == 3\n",
-    "        if mask.any():\n",
-    "            lf3_log_term = torch.log(4 * r / r_w)\n",
-    "            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)\n",
-    "            lf3_denom = lf3_log_term * (\n",
-    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf3 = lf3_numer / lf3_denom\n",
-    "            y[mask] = lf3[mask]\n",
-    "        # LF4 (fidelity 4): further bias.\n",
-    "        mask = fidelity == 4\n",
-    "        if mask.any():\n",
-    "            lf4_log_term = torch.log(2 * r / r_w)\n",
-    "            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)\n",
-    "            lf4_denom = lf4_log_term * (\n",
-    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf4 = lf4_numer / lf4_denom\n",
-    "            y[mask] = lf4[mask]\n",
-    "\n",
-    "        return y\n",
-    "\n",
-    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        fidelity = X[..., 8]\n",
-    "        c = torch.zeros_like(fidelity)\n",
-    "        c[fidelity == 0] = 1000.0\n",
-    "        c[fidelity == 1] = 100.0\n",
-    "        c[fidelity == 2] = 10.0\n",
-    "        c[fidelity == 3] = 100.0\n",
-    "        c[fidelity == 4] = 10.0\n",
-    "        return c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
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-   "source": [
-    "import numpy as np\n",
-    "import torch\n",
-    "from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll\n",
-    "from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP\n",
-    "from botorch.models.gp_regression import SingleTaskGP\n",
-    "\n",
-    "from botorch.models.multitask import MultiTaskGP\n",
-    "\n",
-    "from botorch.models.transforms.input import (\n",
-    "    ChainedInputTransform,\n",
-    "    LatentCategoricalEmbedding,\n",
-    "    LatentCategoricalSpec,\n",
-    "    Normalize,\n",
-    ")\n",
-    "from botorch.models.transforms.outcome import Standardize, StratifiedStandardize\n",
-    "from botorch.utils.transforms import unnormalize\n",
-    "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
-    "from torch.distributions import Normal\n",
-    "\n",
-    "\n",
-    "tkwargs = {\"dtype\": torch.double}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
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-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "def eval_model(model, test_X, test_Y):\n",
-    "    with torch.no_grad():\n",
-    "        posterior = model.posterior(test_X, observation_noise=True)\n",
-    "        # compute sum of LL of each point in test set (using only marginal variances)\n",
-    "        var = posterior.variance\n",
-    "        mean = posterior.mean\n",
-    "        nll = (\n",
-    "            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))\n",
-    "            .log_prob(test_Y.view(-1))\n",
-    "            .sum(dim=-1)\n",
-    "            .mean()  # take average over MCMC samples (if needed)\n",
-    "            .item()\n",
-    "        )\n",
-    "        mse = (mean - test_Y).pow(2).mean().item()\n",
-    "    return nll, mse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false,
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-    "executionStartTime": 1743528939339,
-    "executionStopTime": 1743528940127,
-    "language": "python",
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-   "source": [
-    "def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):\n",
-    "    res = {}\n",
-    "    # test STGP on target task\n",
-    "    target_mask = train_X[:, -1] == 0\n",
-    "    model = SingleTaskGP(\n",
-    "        train_X[target_mask],\n",
-    "        train_Y[target_mask],\n",
-    "        input_transform=Normalize(\n",
-    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
-    "        ),\n",
-    "    )\n",
-    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
-    "    _ = fit_gpytorch_mll(mll)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"STGP - target only\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "\n",
-    "    # Test MTGP with ICM\n",
-    "    model = MultiTaskGP(\n",
-    "        train_X,\n",
-    "        train_Y,\n",
-    "        input_transform=Normalize(\n",
-    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
-    "        ),\n",
-    "        outcome_transform=StratifiedStandardize(\n",
-    "            stratification_idx=problem.dim - 1,\n",
-    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
-    "        ),\n",
-    "        task_feature=problem.dim - 1,\n",
-    "    )\n",
-    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
-    "    _ = fit_gpytorch_mll(mll)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"MTGP - ICM - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "    # Test Fully Bayesian MTGP with Latent Embeddings\n",
-    "    model = SaasFullyBayesianMultiTaskGP(\n",
-    "        train_X,\n",
-    "        train_Y,\n",
-    "        input_transform=Normalize(\n",
-    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
-    "        ),\n",
-    "        task_feature=problem.dim - 1,\n",
-    "        outcome_transform=StratifiedStandardize(\n",
-    "            stratification_idx=problem.dim - 1,\n",
-    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
-    "        ),\n",
-    "    )\n",
-    "    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"MTGP - Latent Embeddings - FB\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "\n",
-    "    # test LVGP with MAP estimation\n",
-    "    d = train_X.shape[-1]\n",
-    "    cat_dims = [problem.dim - 1]\n",
-    "    # construct input transform\n",
-    "    input_transform = ChainedInputTransform(\n",
-    "        normalize=Normalize(d=d, indices=list(range(d - 1))),\n",
-    "        latent_emb=LatentCategoricalEmbedding(\n",
-    "            [\n",
-    "                LatentCategoricalSpec(\n",
-    "                    idx=i,\n",
-    "                    num_categories=len(problem.fidelities),\n",
-    "                    latent_dim=2,\n",
-    "                )\n",
-    "                for i in cat_dims\n",
-    "            ],\n",
-    "            dim=d,\n",
-    "        ).to(train_X),\n",
-    "    )\n",
-    "    model = SingleTaskGP(\n",
-    "        train_X,\n",
-    "        train_Y,\n",
-    "        input_transform=input_transform,\n",
-    "    )\n",
-    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
-    "    _ = fit_gpytorch_mll(mll)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"MTGP - Latent Embeddings - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "customInput": null,
-    "language": "markdown",
-    "originalKey": "1f8d148e-a0e7-4915-952f-7a405ec8a480",
-    "showInput": false
-   },
-   "source": [
-    "# Wing weight\n",
-    "### Generate initial training set as in Chen et al 2024. Generate test set for target fidelity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528960203,
-    "executionStopTime": 1743529095209,
-    "language": "python",
-    "originalKey": "fd812914-49d4-4023-926e-2d41c4157ded",
-    "outputsInitialized": true,
-    "requestMsgId": "097e842b-186a-44f5-88c3-cd12b0240b33",
-    "serverExecutionDuration": 5.1055490039289,
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "problem = WingWeightMultiFidelity()\n",
-    "torch.manual_seed(0)\n",
-    "# define training and test set\n",
-    "N_TEST = 100\n",
-    "fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}\n",
-    "total_n = sum(fidelity_to_n.values())\n",
-    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
-    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
-    "# set fidelities\n",
-    "start = 0\n",
-    "for fidelity, n in fidelity_to_n.items():\n",
-    "    end = start + n\n",
-    "    train_X[start:end, -1] = fidelity\n",
-    "    start = end\n",
-    "\n",
-    "train_Y = problem(train_X).unsqueeze(-1)\n",
-    "\n",
-    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
-    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
-    "test_X[:, -1] = 0  # target fidelity\n",
-    "test_Y = problem(test_X).unsqueeze(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
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-    "serverExecutionDuration": 136496.86142337,
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-   },
-   "outputs": [
-    {
-     "name": "stderr",
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-      "\rWarmup:   9%|▊         | 67/768 [00:10,  6.44it/s, step size=7.93e-02, acc. prob=0.771]\n",
-      "\rWarmup:   9%|▉         | 69/768 [00:10,  7.23it/s, step size=5.98e-02, acc. prob=0.769]\n",
-      "\rWarmup:   9%|▉         | 71/768 [00:10,  7.67it/s, step size=5.40e-02, acc. prob=0.769]\n",
-      "\rWarmup:   9%|▉         | 72/768 [00:10,  7.34it/s, step size=7.95e-02, acc. prob=0.772]\n",
-      "\rWarmup:  10%|▉         | 73/768 [00:11,  7.03it/s, step size=6.83e-02, acc. prob=0.771]\n",
-      "\rWarmup:  10%|▉         | 74/768 [00:11,  7.31it/s, step size=2.95e-02, acc. prob=0.766]\n",
-      "\rWarmup:  10%|▉         | 75/768 [00:11,  6.73it/s, step size=4.47e-02, acc. prob=0.769]\n",
-      "\rWarmup:  10%|▉         | 76/768 [00:11,  6.59it/s, step size=6.20e-02, acc. prob=0.772]\n",
-      "\rWarmup:  10%|█         | 77/768 [00:11,  6.52it/s, step size=2.23e-02, acc. prob=0.765]\n",
-      "\rWarmup:  10%|█         | 78/768 [00:11,  6.45it/s, step size=3.42e-02, acc. prob=0.768]\n",
-      "\rWarmup:  10%|█         | 79/768 [00:12,  6.37it/s, step size=5.11e-02, acc. prob=0.771]\n",
-      "\rWarmup:  10%|█         | 80/768 [00:12,  6.34it/s, step size=7.70e-02, acc. prob=0.774]\n",
-      "\rWarmup:  11%|█         | 81/768 [00:12,  6.33it/s, step size=2.53e-02, acc. prob=0.767]\n",
-      "\rWarmup:  11%|█         | 82/768 [00:12,  6.32it/s, step size=3.79e-02, acc. prob=0.770]\n",
-      "\rWarmup:  11%|█         | 83/768 [00:12,  5.88it/s, step size=5.67e-02, acc. prob=0.772]\n",
-      "\rWarmup:  11%|█         | 84/768 [00:12,  5.62it/s, step size=8.55e-02, acc. prob=0.775]\n",
-      "\rWarmup:  11%|█         | 85/768 [00:13,  5.46it/s, step size=9.20e-02, acc. prob=0.776]\n",
-      "\rWarmup:  11%|█         | 86/768 [00:13,  5.65it/s, step size=9.22e-02, acc. prob=0.776]\n",
-      "\rWarmup:  11%|█▏        | 87/768 [00:13,  5.80it/s, step size=1.28e-01, acc. prob=0.778]\n",
-      "\rWarmup:  12%|█▏        | 89/768 [00:13,  7.73it/s, step size=1.08e-01, acc. prob=0.777]\n",
-      "\rWarmup:  12%|█▏        | 90/768 [00:13,  7.17it/s, step size=1.07e-01, acc. prob=0.777]\n",
-      "\rWarmup:  12%|█▏        | 91/768 [00:13,  6.88it/s, step size=4.15e-02, acc. prob=0.772]\n",
-      "\rWarmup:  12%|█▏        | 92/768 [00:14,  6.67it/s, step size=5.55e-02, acc. prob=0.774]\n",
-      "\rWarmup:  12%|█▏        | 94/768 [00:14,  8.39it/s, step size=3.28e-02, acc. prob=0.771]\n",
-      "\rWarmup:  12%|█▏        | 95/768 [00:14,  7.76it/s, step size=4.57e-02, acc. prob=0.773]\n",
-      "\rWarmup:  12%|█▎        | 96/768 [00:14,  7.30it/s, step size=6.65e-02, acc. prob=0.776]\n",
-      "\rWarmup:  13%|█▎        | 97/768 [00:14,  6.94it/s, step size=4.17e-02, acc. prob=0.773]\n",
-      "\rWarmup:  13%|█▎        | 98/768 [00:14,  6.72it/s, step size=5.10e-02, acc. prob=0.774]\n",
-      "\rWarmup:  13%|█▎        | 99/768 [00:15,  4.09it/s, step size=1.16e+00, acc. prob=0.777]\n",
-      "\rWarmup:  13%|█▎        | 102/768 [00:15,  6.96it/s, step size=2.30e-02, acc. prob=0.754]\n",
-      "\rWarmup:  13%|█▎        | 103/768 [00:15,  6.81it/s, step size=2.59e-02, acc. prob=0.757]\n",
-      "\rWarmup:  14%|█▎        | 104/768 [00:15,  6.68it/s, step size=3.61e-02, acc. prob=0.759]\n",
-      "\rWarmup:  14%|█▎        | 105/768 [00:16,  6.57it/s, step size=5.62e-02, acc. prob=0.761]\n",
-      "\rWarmup:  14%|█▍        | 106/768 [00:16,  6.50it/s, step size=8.73e-02, acc. prob=0.763]\n",
-      "\rWarmup:  14%|█▍        | 107/768 [00:16,  6.29it/s, step size=1.50e-01, acc. prob=0.765]\n",
-      "\rWarmup:  14%|█▍        | 108/768 [00:16,  6.28it/s, step size=5.83e-02, acc. prob=0.763]\n",
-      "\rWarmup:  14%|█▍        | 109/768 [00:16,  6.24it/s, step size=1.09e-01, acc. prob=0.765]\n",
-      "\rWarmup:  14%|█▍        | 110/768 [00:16,  6.00it/s, step size=1.97e-01, acc. prob=0.767]\n",
-      "\rWarmup:  14%|█▍        | 111/768 [00:17,  6.09it/s, step size=6.53e-02, acc. prob=0.764]\n",
-      "\rWarmup:  15%|█▍        | 112/768 [00:17,  6.16it/s, step size=1.25e-01, acc. prob=0.766]\n",
-      "\rWarmup:  15%|█▍        | 113/768 [00:17,  6.19it/s, step size=1.47e-02, acc. prob=0.761]\n",
-      "\rWarmup:  15%|█▍        | 114/768 [00:17,  6.23it/s, step size=2.84e-02, acc. prob=0.763]\n",
-      "\rWarmup:  15%|█▍        | 115/768 [00:17,  6.26it/s, step size=5.22e-02, acc. prob=0.765]\n",
-      "\rWarmup:  15%|█▌        | 116/768 [00:17,  5.97it/s, step size=6.78e-02, acc. prob=0.766]\n",
-      "\rWarmup:  15%|█▌        | 117/768 [00:18,  5.99it/s, step size=8.32e-02, acc. prob=0.766]\n",
-      "\rWarmup:  15%|█▌        | 118/768 [00:18,  6.07it/s, step size=6.22e-02, acc. prob=0.766]\n",
-      "\rWarmup:  15%|█▌        | 119/768 [00:18,  6.12it/s, step size=1.13e-01, acc. prob=0.767]\n",
-      "\rWarmup:  16%|█▌        | 120/768 [00:18,  6.17it/s, step size=1.30e-01, acc. prob=0.768]\n",
-      "\rWarmup:  16%|█▌        | 121/768 [00:18,  6.20it/s, step size=9.87e-02, acc. prob=0.767]\n",
-      "\rWarmup:  16%|█▌        | 122/768 [00:18,  6.24it/s, step size=7.51e-02, acc. prob=0.767]\n",
-      "\rWarmup:  16%|█▌        | 123/768 [00:18,  6.25it/s, step size=9.39e-03, acc. prob=0.761]\n",
-      "\rWarmup:  16%|█▌        | 124/768 [00:19,  6.22it/s, step size=1.77e-02, acc. prob=0.763]\n",
-      "\rWarmup:  16%|█▋        | 125/768 [00:19,  6.24it/s, step size=3.18e-02, acc. prob=0.765]\n",
-      "\rWarmup:  16%|█▋        | 126/768 [00:19,  6.27it/s, step size=5.77e-02, acc. prob=0.767]\n",
-      "\rWarmup:  17%|█▋        | 127/768 [00:19,  6.26it/s, step size=9.44e-02, acc. prob=0.768]\n",
-      "\rWarmup:  17%|█▋        | 128/768 [00:19,  6.27it/s, step size=2.00e-02, acc. prob=0.764]\n",
-      "\rWarmup:  17%|█▋        | 129/768 [00:19,  5.83it/s, step size=3.64e-02, acc. prob=0.766]\n",
-      "\rWarmup:  17%|█▋        | 130/768 [00:20,  5.56it/s, step size=6.57e-02, acc. prob=0.768]\n",
-      "\rWarmup:  17%|█▋        | 131/768 [00:20,  5.74it/s, step size=1.04e-01, acc. prob=0.769]\n",
-      "\rWarmup:  17%|█▋        | 132/768 [00:20,  5.90it/s, step size=1.84e-01, acc. prob=0.771]\n",
-      "\rWarmup:  17%|█▋        | 133/768 [00:20,  5.99it/s, step size=3.20e-01, acc. prob=0.772]\n",
-      "\rWarmup:  17%|█▋        | 134/768 [00:20,  6.07it/s, step size=4.77e-02, acc. prob=0.767]\n",
-      "\rWarmup:  18%|█▊        | 135/768 [00:20,  6.12it/s, step size=8.27e-02, acc. prob=0.769]\n",
-      "\rWarmup:  18%|█▊        | 136/768 [00:21,  6.17it/s, step size=3.62e-02, acc. prob=0.767]\n",
-      "\rWarmup:  18%|█▊        | 137/768 [00:21,  6.18it/s, step size=6.24e-02, acc. prob=0.768]\n",
-      "\rWarmup:  18%|█▊        | 138/768 [00:21,  6.17it/s, step size=1.08e-01, acc. prob=0.770]\n",
-      "\rWarmup:  18%|█▊        | 139/768 [00:21,  6.17it/s, step size=1.70e-01, acc. prob=0.771]\n",
-      "\rWarmup:  18%|█▊        | 140/768 [00:21,  6.17it/s, step size=2.88e-01, acc. prob=0.773]\n",
-      "\rWarmup:  18%|█▊        | 141/768 [00:21,  6.19it/s, step size=1.01e-01, acc. prob=0.770]\n",
-      "\rWarmup:  18%|█▊        | 142/768 [00:22,  6.11it/s, step size=1.71e-01, acc. prob=0.772]\n",
-      "\rWarmup:  19%|█▊        | 143/768 [00:22,  6.10it/s, step size=1.31e-01, acc. prob=0.771]\n",
-      "\rWarmup:  19%|█▉        | 144/768 [00:22,  6.12it/s, step size=2.18e-01, acc. prob=0.773]\n",
-      "\rWarmup:  19%|█▉        | 145/768 [00:22,  6.15it/s, step size=1.96e-01, acc. prob=0.772]\n",
-      "\rWarmup:  19%|█▉        | 146/768 [00:22,  6.14it/s, step size=4.92e-02, acc. prob=0.769]\n",
-      "\rWarmup:  19%|█▉        | 147/768 [00:22,  6.14it/s, step size=1.95e-02, acc. prob=0.766]\n",
-      "\rWarmup:  19%|█▉        | 148/768 [00:23,  6.14it/s, step size=3.19e-02, acc. prob=0.768]\n",
-      "\rWarmup:  19%|█▉        | 149/768 [00:23,  5.61it/s, step size=2.12e-01, acc. prob=0.769]\n",
-      "\rWarmup:  20%|█▉        | 150/768 [00:23,  5.45it/s, step size=4.98e-01, acc. prob=0.764]\n",
-      "\rWarmup:  20%|█▉        | 151/768 [00:23,  5.35it/s, step size=5.12e-02, acc. prob=0.759]\n",
-      "\rWarmup:  20%|█▉        | 152/768 [00:23,  5.29it/s, step size=5.32e-02, acc. prob=0.761]\n",
-      "\rWarmup:  20%|█▉        | 153/768 [00:24,  5.25it/s, step size=7.18e-02, acc. prob=0.762]\n",
-      "\rWarmup:  20%|██        | 154/768 [00:24,  5.22it/s, step size=1.10e-01, acc. prob=0.764]\n",
-      "\rWarmup:  20%|██        | 155/768 [00:24,  5.20it/s, step size=1.81e-01, acc. prob=0.765]\n",
-      "\rWarmup:  20%|██        | 156/768 [00:24,  5.19it/s, step size=3.13e-01, acc. prob=0.767]\n",
-      "\rWarmup:  20%|██        | 157/768 [00:24,  5.18it/s, step size=7.39e-02, acc. prob=0.764]\n",
-      "\rWarmup:  21%|██        | 158/768 [00:25,  5.17it/s, step size=5.01e-02, acc. prob=0.763]\n",
-      "\rWarmup:  21%|██        | 159/768 [00:25,  5.16it/s, step size=9.29e-02, acc. prob=0.765]\n",
-      "\rWarmup:  21%|██        | 160/768 [00:25,  5.16it/s, step size=1.57e-01, acc. prob=0.766]\n",
-      "\rWarmup:  21%|██        | 161/768 [00:25,  5.16it/s, step size=2.98e-01, acc. prob=0.768]\n",
-      "\rWarmup:  21%|██        | 162/768 [00:25,  5.15it/s, step size=3.08e-02, acc. prob=0.763]\n",
-      "\rWarmup:  21%|██        | 163/768 [00:26,  5.15it/s, step size=5.87e-02, acc. prob=0.765]\n",
-      "\rWarmup:  21%|██▏       | 164/768 [00:26,  5.15it/s, step size=1.10e-01, acc. prob=0.766]\n",
-      "\rWarmup:  21%|██▏       | 165/768 [00:26,  5.15it/s, step size=1.97e-01, acc. prob=0.767]\n",
-      "\rWarmup:  22%|██▏       | 166/768 [00:26,  5.15it/s, step size=3.81e-02, acc. prob=0.764]\n",
-      "\rWarmup:  22%|██▏       | 167/768 [00:26,  5.14it/s, step size=7.15e-02, acc. prob=0.766]\n",
-      "\rWarmup:  22%|██▏       | 168/768 [00:26,  5.15it/s, step size=1.29e-01, acc. prob=0.767]\n",
-      "\rWarmup:  22%|██▏       | 169/768 [00:27,  5.14it/s, step size=1.16e-01, acc. prob=0.767]\n",
-      "\rWarmup:  22%|██▏       | 170/768 [00:27,  5.15it/s, step size=1.81e-01, acc. prob=0.768]\n",
-      "\rWarmup:  22%|██▏       | 171/768 [00:27,  5.15it/s, step size=2.76e-01, acc. prob=0.769]\n",
-      "\rWarmup:  22%|██▏       | 172/768 [00:27,  5.15it/s, step size=5.05e-02, acc. prob=0.766]\n",
-      "\rWarmup:  23%|██▎       | 173/768 [00:27,  5.15it/s, step size=8.57e-02, acc. prob=0.767]\n",
-      "\rWarmup:  23%|██▎       | 174/768 [00:28,  5.15it/s, step size=1.53e-01, acc. prob=0.768]\n",
-      "\rWarmup:  23%|██▎       | 175/768 [00:28,  5.14it/s, step size=2.59e-01, acc. prob=0.769]\n",
-      "\rWarmup:  23%|██▎       | 176/768 [00:28,  5.14it/s, step size=8.81e-02, acc. prob=0.767]\n",
-      "\rWarmup:  23%|██▎       | 177/768 [00:28,  5.14it/s, step size=1.52e-01, acc. prob=0.769]\n",
-      "\rWarmup:  23%|██▎       | 178/768 [00:28,  5.13it/s, step size=5.10e-02, acc. prob=0.767]\n",
-      "\rWarmup:  23%|██▎       | 179/768 [00:29,  5.12it/s, step size=8.99e-02, acc. prob=0.768]\n",
-      "\rWarmup:  23%|██▎       | 180/768 [00:29,  5.12it/s, step size=1.58e-01, acc. prob=0.769]\n",
-      "\rWarmup:  24%|██▎       | 181/768 [00:29,  5.13it/s, step size=2.34e-01, acc. prob=0.770]\n",
-      "\rWarmup:  24%|██▎       | 182/768 [00:29,  5.14it/s, step size=3.42e-01, acc. prob=0.771]\n",
-      "\rWarmup:  24%|██▍       | 183/768 [00:29,  5.14it/s, step size=2.58e-01, acc. prob=0.770]\n",
-      "\rWarmup:  24%|██▍       | 184/768 [00:30,  5.15it/s, step size=3.17e-01, acc. prob=0.771]\n",
-      "\rWarmup:  24%|██▍       | 185/768 [00:30,  5.15it/s, step size=2.08e-01, acc. prob=0.770]\n",
-      "\rWarmup:  24%|██▍       | 186/768 [00:30,  5.15it/s, step size=1.72e-01, acc. prob=0.770]\n",
-      "\rWarmup:  24%|██▍       | 187/768 [00:30,  5.15it/s, step size=2.87e-01, acc. prob=0.771]\n",
-      "\rWarmup:  24%|██▍       | 188/768 [00:30,  5.16it/s, step size=2.54e-01, acc. prob=0.771]\n",
-      "\rWarmup:  25%|██▍       | 189/768 [00:31,  5.16it/s, step size=3.20e-01, acc. prob=0.772]\n",
-      "\rWarmup:  25%|██▍       | 191/768 [00:31,  6.42it/s, step size=1.28e-01, acc. prob=0.770]\n",
-      "\rWarmup:  25%|██▌       | 192/768 [00:31,  6.05it/s, step size=2.14e-01, acc. prob=0.771]\n",
-      "\rWarmup:  25%|██▌       | 193/768 [00:31,  5.79it/s, step size=2.65e-01, acc. prob=0.772]\n",
-      "\rWarmup:  25%|██▌       | 194/768 [00:31,  5.59it/s, step size=4.03e-01, acc. prob=0.773]\n",
-      "\rWarmup:  25%|██▌       | 195/768 [00:32,  5.71it/s, step size=1.66e-01, acc. prob=0.771]\n",
-      "\rWarmup:  26%|██▌       | 196/768 [00:32,  5.89it/s, step size=2.05e-01, acc. prob=0.772]\n",
-      "\rWarmup:  26%|██▌       | 197/768 [00:32,  6.03it/s, step size=1.47e-01, acc. prob=0.771]\n",
-      "\rWarmup:  26%|██▌       | 198/768 [00:32,  6.12it/s, step size=2.37e-01, acc. prob=0.772]\n",
-      "\rWarmup:  26%|██▌       | 199/768 [00:32,  6.16it/s, step size=8.93e-02, acc. prob=0.770]\n",
-      "\rWarmup:  26%|██▌       | 200/768 [00:32,  6.18it/s, step size=1.35e-01, acc. prob=0.771]\n",
-      "\rWarmup:  26%|██▌       | 201/768 [00:32,  6.22it/s, step size=2.11e-01, acc. prob=0.772]\n",
-      "\rWarmup:  26%|██▋       | 203/768 [00:33,  7.11it/s, step size=3.46e-01, acc. prob=0.773]\n",
-      "\rWarmup:  27%|██▋       | 204/768 [00:33,  6.90it/s, step size=4.25e-01, acc. prob=0.774]\n",
-      "\rWarmup:  27%|██▋       | 206/768 [00:33,  7.51it/s, step size=8.48e-02, acc. prob=0.771]\n",
-      "\rWarmup:  27%|██▋       | 207/768 [00:33,  7.23it/s, step size=1.34e-01, acc. prob=0.772]\n",
-      "\rWarmup:  27%|██▋       | 208/768 [00:33,  6.97it/s, step size=1.90e-01, acc. prob=0.773]\n",
-      "\rWarmup:  27%|██▋       | 209/768 [00:34,  6.80it/s, step size=2.88e-01, acc. prob=0.774]\n",
-      "\rWarmup:  27%|██▋       | 210/768 [00:34,  6.69it/s, step size=1.81e-01, acc. prob=0.773]\n",
-      "\rWarmup:  27%|██▋       | 211/768 [00:34,  6.58it/s, step size=1.69e-01, acc. prob=0.773]\n",
-      "\rWarmup:  28%|██▊       | 212/768 [00:34,  6.53it/s, step size=2.15e-01, acc. prob=0.773]\n",
-      "\rWarmup:  28%|██▊       | 213/768 [00:34,  6.47it/s, step size=1.15e-01, acc. prob=0.772]\n",
-      "\rWarmup:  28%|██▊       | 214/768 [00:34,  6.37it/s, step size=1.71e-01, acc. prob=0.773]\n",
-      "\rWarmup:  28%|██▊       | 215/768 [00:35,  6.36it/s, step size=6.96e-02, acc. prob=0.771]\n",
-      "\rWarmup:  28%|██▊       | 216/768 [00:35,  6.36it/s, step size=1.08e-01, acc. prob=0.772]\n",
-      "\rWarmup:  28%|██▊       | 217/768 [00:35,  6.35it/s, step size=1.65e-01, acc. prob=0.773]\n",
-      "\rWarmup:  28%|██▊       | 218/768 [00:35,  6.37it/s, step size=2.02e-01, acc. prob=0.774]\n",
-      "\rWarmup:  29%|██▊       | 219/768 [00:35,  6.38it/s, step size=2.32e-01, acc. prob=0.774]\n",
-      "\rWarmup:  29%|██▊       | 220/768 [00:35,  6.37it/s, step size=8.07e-02, acc. prob=0.772]\n",
-      "\rWarmup:  29%|██▉       | 221/768 [00:36,  5.92it/s, step size=1.23e-01, acc. prob=0.773]\n",
-      "\rWarmup:  29%|██▉       | 222/768 [00:36,  5.66it/s, step size=1.86e-01, acc. prob=0.774]\n",
-      "\rWarmup:  29%|██▉       | 223/768 [00:36,  5.48it/s, step size=1.37e-01, acc. prob=0.773]\n",
-      "\rWarmup:  29%|██▉       | 224/768 [00:36,  5.38it/s, step size=5.42e-02, acc. prob=0.771]\n",
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-      "\rWarmup:  51%|█████▏    | 394/768 [01:06,  5.51it/s, step size=1.29e-01, acc. prob=0.778]\n",
-      "\rWarmup:  51%|█████▏    | 395/768 [01:06,  5.40it/s, step size=1.76e-01, acc. prob=0.779]\n",
-      "\rWarmup:  52%|█████▏    | 397/768 [01:06,  5.97it/s, step size=1.41e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  52%|█████▏    | 401/768 [01:07,  5.33it/s, step size=2.67e-02, acc. prob=0.776]\n",
-      "\rWarmup:  52%|█████▏    | 402/768 [01:07,  5.24it/s, step size=3.68e-02, acc. prob=0.776]\n",
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-      "\rWarmup:  53%|█████▎    | 404/768 [01:08,  5.15it/s, step size=6.53e-02, acc. prob=0.777]\n",
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-      "\rWarmup:  53%|█████▎    | 406/768 [01:08,  5.12it/s, step size=9.99e-02, acc. prob=0.778]\n",
-      "\rWarmup:  53%|█████▎    | 407/768 [01:08,  5.11it/s, step size=1.36e-01, acc. prob=0.779]\n",
-      "\rWarmup:  53%|█████▎    | 409/768 [01:09,  3.35it/s, step size=2.44e-01, acc. prob=0.780]\n",
-      "\rWarmup:  53%|█████▎    | 410/768 [01:09,  3.66it/s, step size=2.09e-01, acc. prob=0.780]\n",
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-      "\rWarmup:  54%|█████▍    | 417/768 [01:10,  5.31it/s, step size=1.33e-01, acc. prob=0.779]\n",
-      "\rWarmup:  54%|█████▍    | 418/768 [01:11,  5.26it/s, step size=1.78e-01, acc. prob=0.780]\n",
-      "\rWarmup:  55%|█████▍    | 419/768 [01:11,  5.22it/s, step size=2.30e-01, acc. prob=0.780]\n",
-      "\rWarmup:  55%|█████▍    | 420/768 [01:11,  5.19it/s, step size=2.26e-01, acc. prob=0.780]\n",
-      "\rWarmup:  55%|█████▍    | 422/768 [01:11,  7.19it/s, step size=3.83e-01, acc. prob=0.781]\n",
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-      "\rWarmup:  56%|█████▌    | 427/768 [01:12,  7.43it/s, step size=2.66e-01, acc. prob=0.781]\n",
-      "\rWarmup:  56%|█████▌    | 428/768 [01:12,  6.74it/s, step size=2.42e-01, acc. prob=0.780]\n",
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-      "\rWarmup:  57%|█████▋    | 434/768 [01:13,  7.05it/s, step size=2.73e-01, acc. prob=0.781]\n",
-      "\rWarmup:  57%|█████▋    | 436/768 [01:13,  8.52it/s, step size=2.47e-01, acc. prob=0.781]\n",
-      "\rWarmup:  57%|█████▋    | 438/768 [01:13,  8.41it/s, step size=3.61e-01, acc. prob=0.781]\n",
-      "\rWarmup:  57%|█████▋    | 441/768 [01:13, 10.43it/s, step size=2.39e-01, acc. prob=0.781]\n",
-      "\rWarmup:  58%|█████▊    | 443/768 [01:14,  8.05it/s, step size=9.58e-02, acc. prob=0.779]\n",
-      "\rWarmup:  58%|█████▊    | 444/768 [01:14,  7.34it/s, step size=1.27e-01, acc. prob=0.780]\n",
-      "\rWarmup:  58%|█████▊    | 445/768 [01:14,  6.76it/s, step size=1.53e-01, acc. prob=0.780]\n",
-      "\rWarmup:  58%|█████▊    | 447/768 [01:14,  7.70it/s, step size=2.34e-01, acc. prob=0.781]\n",
-      "\rWarmup:  58%|█████▊    | 448/768 [01:14,  8.08it/s, step size=2.98e-01, acc. prob=0.781]\n",
-      "\rWarmup:  59%|█████▊    | 450/768 [01:15,  8.10it/s, step size=1.80e-01, acc. prob=0.781]\n",
-      "\rWarmup:  59%|█████▉    | 452/768 [01:15,  8.74it/s, step size=2.04e-01, acc. prob=0.781]\n",
-      "\rWarmup:  59%|█████▉    | 453/768 [01:15,  7.66it/s, step size=2.66e-01, acc. prob=0.781]\n",
-      "\rWarmup:  59%|█████▉    | 454/768 [01:15,  6.89it/s, step size=1.44e-01, acc. prob=0.780]\n",
-      "\rWarmup:  59%|█████▉    | 455/768 [01:16,  6.36it/s, step size=7.85e-02, acc. prob=0.779]\n",
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-      "\rWarmup:  60%|█████▉    | 457/768 [01:16,  5.72it/s, step size=1.27e-01, acc. prob=0.780]\n",
-      "\rWarmup:  60%|█████▉    | 458/768 [01:16,  5.51it/s, step size=1.65e-01, acc. prob=0.781]\n",
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-      "\rWarmup:  60%|█████▉    | 460/768 [01:16,  5.29it/s, step size=1.31e-01, acc. prob=0.780]\n",
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-      "\rWarmup:  61%|██████    | 465/768 [01:17, 10.76it/s, step size=4.15e-01, acc. prob=0.779]\n",
-      "\rWarmup:  61%|██████    | 468/768 [01:17, 13.97it/s, step size=1.60e-01, acc. prob=0.778]\n",
-      "\rWarmup:  61%|██████    | 470/768 [01:17, 12.71it/s, step size=1.14e-01, acc. prob=0.778]\n",
-      "\rWarmup:  61%|██████▏   | 472/768 [01:17, 11.95it/s, step size=2.52e-01, acc. prob=0.779]\n",
-      "\rWarmup:  62%|██████▏   | 474/768 [01:18, 10.52it/s, step size=8.49e-02, acc. prob=0.778]\n",
-      "\rWarmup:  62%|██████▏   | 476/768 [01:18,  9.11it/s, step size=1.75e-02, acc. prob=0.777]\n",
-      "\rWarmup:  62%|██████▏   | 478/768 [01:18,  7.44it/s, step size=6.42e-02, acc. prob=0.778]\n",
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-      "\rWarmup:  63%|██████▎   | 481/768 [01:19,  7.74it/s, step size=3.34e-01, acc. prob=0.779]\n",
-      "\rWarmup:  63%|██████▎   | 483/768 [01:19,  7.90it/s, step size=1.06e-01, acc. prob=0.779]\n",
-      "\rWarmup:  63%|██████▎   | 485/768 [01:19,  7.54it/s, step size=1.58e-01, acc. prob=0.779]\n",
-      "\rWarmup:  63%|██████▎   | 487/768 [01:19,  8.77it/s, step size=3.32e-02, acc. prob=0.778]\n",
-      "\rWarmup:  64%|██████▎   | 488/768 [01:20,  7.71it/s, step size=6.08e-02, acc. prob=0.778]\n",
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-      "\rWarmup:  64%|██████▍   | 490/768 [01:20,  6.44it/s, step size=1.69e-01, acc. prob=0.779]\n",
-      "\rWarmup:  64%|██████▍   | 492/768 [01:20,  6.61it/s, step size=7.77e-02, acc. prob=0.779]\n",
-      "\rWarmup:  64%|██████▍   | 494/768 [01:20,  7.59it/s, step size=2.16e-01, acc. prob=0.779]\n",
-      "\rWarmup:  65%|██████▍   | 496/768 [01:21,  8.34it/s, step size=2.77e-01, acc. prob=0.780]\n",
-      "\rWarmup:  65%|██████▍   | 498/768 [01:21,  8.90it/s, step size=2.62e-01, acc. prob=0.780]\n",
-      "\rWarmup:  65%|██████▌   | 501/768 [01:21,  9.45it/s, step size=1.07e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  66%|██████▌   | 504/768 [01:21,  9.48it/s, step size=4.49e-01, acc. prob=0.780]\n",
-      "\rWarmup:  66%|██████▌   | 506/768 [01:22,  9.01it/s, step size=1.32e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  66%|██████▋   | 510/768 [01:22, 10.40it/s, step size=3.88e-01, acc. prob=0.780]\n",
-      "\rWarmup:  67%|██████▋   | 512/768 [01:22, 11.25it/s, step size=1.67e-01, acc. prob=0.780]\n",
-      "\rSample:  67%|██████▋   | 514/768 [01:22, 10.96it/s, step size=1.67e-01, acc. prob=0.919]\n",
-      "\rSample:  67%|██████▋   | 516/768 [01:23, 10.75it/s, step size=1.67e-01, acc. prob=0.952]\n",
-      "\rSample:  67%|██████▋   | 518/768 [01:23, 10.63it/s, step size=1.67e-01, acc. prob=0.903]\n",
-      "\rSample:  68%|██████▊   | 520/768 [01:23, 10.54it/s, step size=1.67e-01, acc. prob=0.816]\n",
-      "\rSample:  68%|██████▊   | 522/768 [01:23, 10.50it/s, step size=1.67e-01, acc. prob=0.822]\n",
-      "\rSample:  68%|██████▊   | 524/768 [01:23, 10.46it/s, step size=1.67e-01, acc. prob=0.784]\n",
-      "\rSample:  68%|██████▊   | 526/768 [01:24, 10.42it/s, step size=1.67e-01, acc. prob=0.802]\n",
-      "\rSample:  69%|██████▉   | 528/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.826]\n",
-      "\rSample:  69%|██████▉   | 530/768 [01:24, 10.37it/s, step size=1.67e-01, acc. prob=0.841]\n",
-      "\rSample:  69%|██████▉   | 532/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.837]\n",
-      "\rSample:  70%|██████▉   | 534/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.850]\n",
-      "\rSample:  70%|██████▉   | 536/768 [01:24, 10.38it/s, step size=1.67e-01, acc. prob=0.860]\n",
-      "\rSample:  70%|███████   | 538/768 [01:25, 10.36it/s, step size=1.67e-01, acc. prob=0.868]\n",
-      "\rSample:  70%|███████   | 540/768 [01:25, 10.33it/s, step size=1.67e-01, acc. prob=0.860]\n",
-      "\rSample:  71%|███████   | 542/768 [01:25, 10.35it/s, step size=1.67e-01, acc. prob=0.862]\n",
-      "\rSample:  71%|███████   | 544/768 [01:25, 11.22it/s, step size=1.67e-01, acc. prob=0.866]\n",
-      "\rSample:  71%|███████   | 546/768 [01:25, 10.93it/s, step size=1.67e-01, acc. prob=0.872]\n",
-      "\rSample:  71%|███████▏  | 548/768 [01:26, 10.70it/s, step size=1.67e-01, acc. prob=0.870]\n",
-      "\rSample:  72%|███████▏  | 550/768 [01:26, 10.55it/s, step size=1.67e-01, acc. prob=0.876]\n",
-      "\rSample:  72%|███████▏  | 552/768 [01:26, 10.50it/s, step size=1.67e-01, acc. prob=0.880]\n",
-      "\rSample:  72%|███████▏  | 554/768 [01:26, 10.46it/s, step size=1.67e-01, acc. prob=0.885]\n",
-      "\rSample:  72%|███████▏  | 556/768 [01:26, 10.40it/s, step size=1.67e-01, acc. prob=0.887]\n",
-      "\rSample:  73%|███████▎  | 558/768 [01:27, 10.39it/s, step size=1.67e-01, acc. prob=0.880]\n",
-      "\rSample:  73%|███████▎  | 560/768 [01:27, 10.38it/s, step size=1.67e-01, acc. prob=0.881]\n",
-      "\rSample:  73%|███████▎  | 562/768 [01:27, 10.39it/s, step size=1.67e-01, acc. prob=0.873]\n",
-      "\rSample:  73%|███████▎  | 564/768 [01:27, 10.34it/s, step size=1.67e-01, acc. prob=0.877]\n",
-      "\rSample:  74%|███████▎  | 566/768 [01:27, 10.32it/s, step size=1.67e-01, acc. prob=0.880]\n",
-      "\rSample:  74%|███████▍  | 568/768 [01:28, 10.30it/s, step size=1.67e-01, acc. prob=0.884]\n",
-      "\rSample:  74%|███████▍  | 570/768 [01:28, 10.30it/s, step size=1.67e-01, acc. prob=0.874]\n",
-      "\rSample:  74%|███████▍  | 572/768 [01:28, 10.33it/s, step size=1.67e-01, acc. prob=0.873]\n",
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-      "\rSample:  75%|███████▌  | 576/768 [01:28, 10.34it/s, step size=1.67e-01, acc. prob=0.879]\n",
-      "\rSample:  75%|███████▌  | 578/768 [01:28, 10.35it/s, step size=1.67e-01, acc. prob=0.882]\n",
-      "\rSample:  76%|███████▌  | 580/768 [01:29, 11.20it/s, step size=1.67e-01, acc. prob=0.881]\n",
-      "\rSample:  76%|███████▌  | 582/768 [01:29, 10.94it/s, step size=1.67e-01, acc. prob=0.876]\n",
-      "\rSample:  76%|███████▌  | 584/768 [01:29, 11.65it/s, step size=1.67e-01, acc. prob=0.869]\n",
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-      "\rSample:  77%|███████▋  | 588/768 [01:29, 11.45it/s, step size=1.67e-01, acc. prob=0.872]\n",
-      "\rSample:  77%|███████▋  | 590/768 [01:29, 11.80it/s, step size=1.67e-01, acc. prob=0.870]\n",
-      "\rSample:  77%|███████▋  | 592/768 [01:30, 12.04it/s, step size=1.67e-01, acc. prob=0.866]\n",
-      "\rSample:  77%|███████▋  | 594/768 [01:30, 12.22it/s, step size=1.67e-01, acc. prob=0.858]\n",
-      "\rSample:  78%|███████▊  | 596/768 [01:30, 12.37it/s, step size=1.67e-01, acc. prob=0.861]\n",
-      "\rSample:  78%|███████▊  | 598/768 [01:30, 12.35it/s, step size=1.67e-01, acc. prob=0.859]\n",
-      "\rSample:  78%|███████▊  | 600/768 [01:30, 12.50it/s, step size=1.67e-01, acc. prob=0.861]\n",
-      "\rSample:  78%|███████▊  | 602/768 [01:30, 12.54it/s, step size=1.67e-01, acc. prob=0.864]\n",
-      "\rSample:  79%|███████▊  | 604/768 [01:31, 12.57it/s, step size=1.67e-01, acc. prob=0.866]\n",
-      "\rSample:  79%|███████▉  | 606/768 [01:31, 12.59it/s, step size=1.67e-01, acc. prob=0.869]\n",
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-      "\rSample:  80%|███████▉  | 612/768 [01:31, 12.65it/s, step size=1.67e-01, acc. prob=0.875]\n",
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-      "\rSample:  80%|████████  | 616/768 [01:32, 12.64it/s, step size=1.67e-01, acc. prob=0.867]\n",
-      "\rSample:  80%|████████  | 618/768 [01:32, 12.58it/s, step size=1.67e-01, acc. prob=0.869]\n",
-      "\rSample:  81%|████████  | 620/768 [01:32, 13.52it/s, step size=1.67e-01, acc. prob=0.868]\n",
-      "\rSample:  81%|████████  | 622/768 [01:32, 13.23it/s, step size=1.67e-01, acc. prob=0.868]\n",
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-      "\rSample: 100%|██████████| 768/768 [01:44, 12.56it/s, step size=1.67e-01, acc. prob=0.845]\n",
-      "\rSample: 100%|██████████| 768/768 [01:44,  7.34it/s, step size=1.67e-01, acc. prob=0.845]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743529130077,
-    "executionStopTime": 1743529260021,
-    "language": "python",
-    "originalKey": "76750e01-4e21-4bba-9624-fb214f9072aa",
-    "output": {
-     "id": "679472244421591"
-    },
-    "outputsInitialized": true,
-    "requestMsgId": "1e7896ca-a923-47da-a3c8-32c28f5d34e9",
-    "serverExecutionDuration": 3.9172596298158,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'STGP - target only': {'NLL': 850.4604260314654, 'MSE': 2518.2767430780013},\n",
-       " 'MTGP - ICM - MAP': {'NLL': 311.31438266858777, 'MSE': 28.66387567450522},\n",
-       " 'MTGP - Latent Embeddings - FB': {'NLL': 444.5109303242923,\n",
-       "  'MSE': 39.09479567046496},\n",
-       " 'MTGP - Latent Embeddings - MAP': {'NLL': 525.5468145697394,\n",
-       "  'MSE': 61.380014810268186}}"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wing_weight_res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "customInput": null,
-    "language": "markdown",
-    "originalKey": "3a4f34ce-9fdf-45cc-af1b-0be72caa11a2",
-    "showInput": false
-   },
-   "source": [
-    "## Borehole"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528945358,
-    "executionStopTime": 1743528946169,
-    "language": "python",
-    "originalKey": "02708c2d-b43a-4a8f-8aca-a99fb32c615d",
-    "outputsInitialized": true,
-    "requestMsgId": "c216c09b-5424-4008-88bd-56f191aad912",
-    "serverExecutionDuration": 5.6033371947706,
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "problem = BoreholeMultiFidelity()\n",
-    "torch.manual_seed(0)\n",
-    "# define training and test set\n",
-    "N_TEST = 100\n",
-    "fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}\n",
-    "total_n = sum(fidelity_to_n.values())\n",
-    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
-    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
-    "# set fidelities\n",
-    "start = 0\n",
-    "for fidelity, n in fidelity_to_n.items():\n",
-    "    end = start + n\n",
-    "    train_X[start:end, -1] = fidelity\n",
-    "    start = end\n",
-    "\n",
-    "train_Y = problem(train_X).unsqueeze(-1)\n",
-    "\n",
-    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
-    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
-    "test_X[:, -1] = 0  # target fidelity\n",
-    "test_Y = problem(test_X).unsqueeze(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528947285,
-    "executionStopTime": 1743529094868,
-    "language": "python",
-    "originalKey": "0d43b937-8f09-4d49-90d5-f4077ee2e646",
-    "output": {
-     "id": "2112932599153367"
-    },
-    "outputsInitialized": true,
-    "requestMsgId": "fba253a7-c60a-412a-89be-3b4468f04999",
-    "serverExecutionDuration": 146576.36261638,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-      "\rWarmup:  10%|▉         | 74/768 [00:11,  8.94it/s, step size=9.21e-02, acc. prob=0.778]\n",
-      "\rWarmup:  10%|▉         | 76/768 [00:11, 10.24it/s, step size=1.87e-01, acc. prob=0.783]\n",
-      "\rWarmup:  10%|█         | 78/768 [00:11,  9.54it/s, step size=8.15e-02, acc. prob=0.778]\n",
-      "\rWarmup:  10%|█         | 80/768 [00:12,  7.58it/s, step size=6.92e-02, acc. prob=0.777]\n",
-      "\rWarmup:  11%|█         | 81/768 [00:12,  7.01it/s, step size=1.03e-01, acc. prob=0.780]\n",
-      "\rWarmup:  11%|█         | 83/768 [00:12,  6.98it/s, step size=7.53e-02, acc. prob=0.778]\n",
-      "\rWarmup:  11%|█         | 84/768 [00:12,  6.56it/s, step size=9.80e-02, acc. prob=0.780]\n",
-      "\rWarmup:  11%|█         | 86/768 [00:13,  7.62it/s, step size=1.11e-01, acc. prob=0.781]\n",
-      "\rWarmup:  11%|█▏        | 88/768 [00:13,  8.43it/s, step size=5.18e-02, acc. prob=0.777]\n",
-      "\rWarmup:  12%|█▏        | 89/768 [00:13,  7.52it/s, step size=2.65e-02, acc. prob=0.773]\n",
-      "\rWarmup:  12%|█▏        | 90/768 [00:13,  6.83it/s, step size=3.92e-02, acc. prob=0.776]\n",
-      "\rWarmup:  12%|█▏        | 91/768 [00:13,  6.35it/s, step size=5.79e-02, acc. prob=0.778]\n",
-      "\rWarmup:  12%|█▏        | 92/768 [00:14,  6.04it/s, step size=7.73e-02, acc. prob=0.780]\n",
-      "\rWarmup:  12%|█▏        | 94/768 [00:14,  7.39it/s, step size=1.03e-01, acc. prob=0.782]\n",
-      "\rWarmup:  12%|█▎        | 96/768 [00:14,  7.26it/s, step size=5.55e-02, acc. prob=0.779]\n",
-      "\rWarmup:  13%|█▎        | 97/768 [00:14,  6.71it/s, step size=8.00e-02, acc. prob=0.781]\n",
-      "\rWarmup:  13%|█▎        | 99/768 [00:15,  4.81it/s, step size=2.65e-01, acc. prob=0.784]\n",
-      "\rWarmup:  13%|█▎        | 100/768 [00:15,  4.89it/s, step size=8.29e-01, acc. prob=0.778]\n",
-      "\rWarmup:  13%|█▎        | 102/768 [00:15,  5.67it/s, step size=9.39e-02, acc. prob=0.772]\n",
-      "\rWarmup:  13%|█▎        | 103/768 [00:16,  5.56it/s, step size=1.26e-01, acc. prob=0.774]\n",
-      "\rWarmup:  14%|█▎        | 104/768 [00:16,  5.49it/s, step size=1.95e-01, acc. prob=0.776]\n",
-      "\rWarmup:  14%|█▎        | 105/768 [00:16,  5.43it/s, step size=1.40e-01, acc. prob=0.776]\n",
-      "\rWarmup:  14%|█▍        | 106/768 [00:16,  5.39it/s, step size=2.06e-01, acc. prob=0.777]\n",
-      "\rWarmup:  14%|█▍        | 107/768 [00:16,  5.36it/s, step size=6.74e-02, acc. prob=0.774]\n",
-      "\rWarmup:  14%|█▍        | 108/768 [00:16,  5.34it/s, step size=1.24e-01, acc. prob=0.776]\n",
-      "\rWarmup:  14%|█▍        | 109/768 [00:17,  5.32it/s, step size=1.62e-01, acc. prob=0.777]\n",
-      "\rWarmup:  14%|█▍        | 110/768 [00:17,  5.31it/s, step size=2.67e-01, acc. prob=0.779]\n",
-      "\rWarmup:  14%|█▍        | 111/768 [00:17,  5.30it/s, step size=4.13e-02, acc. prob=0.774]\n",
-      "\rWarmup:  15%|█▍        | 112/768 [00:17,  5.28it/s, step size=7.81e-02, acc. prob=0.776]\n",
-      "\rWarmup:  15%|█▍        | 113/768 [00:17,  5.26it/s, step size=1.47e-01, acc. prob=0.778]\n",
-      "\rWarmup:  15%|█▍        | 114/768 [00:18,  5.24it/s, step size=2.50e-01, acc. prob=0.779]\n",
-      "\rWarmup:  15%|█▍        | 115/768 [00:18,  5.23it/s, step size=1.97e-01, acc. prob=0.779]\n",
-      "\rWarmup:  15%|█▌        | 116/768 [00:18,  5.25it/s, step size=2.17e-01, acc. prob=0.779]\n",
-      "\rWarmup:  15%|█▌        | 117/768 [00:18,  5.26it/s, step size=5.51e-02, acc. prob=0.775]\n",
-      "\rWarmup:  15%|█▌        | 118/768 [00:18,  5.28it/s, step size=1.03e-01, acc. prob=0.777]\n",
-      "\rWarmup:  15%|█▌        | 119/768 [00:19,  5.29it/s, step size=1.88e-01, acc. prob=0.779]\n",
-      "\rWarmup:  16%|█▌        | 120/768 [00:19,  5.28it/s, step size=2.03e-01, acc. prob=0.779]\n",
-      "\rWarmup:  16%|█▌        | 121/768 [00:19,  5.26it/s, step size=6.64e-02, acc. prob=0.776]\n",
-      "\rWarmup:  16%|█▌        | 122/768 [00:19,  5.26it/s, step size=1.22e-01, acc. prob=0.778]\n",
-      "\rWarmup:  16%|█▌        | 123/768 [00:19,  5.28it/s, step size=2.12e-01, acc. prob=0.780]\n",
-      "\rWarmup:  16%|█▌        | 124/768 [00:20,  5.29it/s, step size=4.78e-02, acc. prob=0.776]\n",
-      "\rWarmup:  16%|█▋        | 125/768 [00:20,  5.29it/s, step size=8.68e-02, acc. prob=0.777]\n",
-      "\rWarmup:  16%|█▋        | 126/768 [00:20,  5.30it/s, step size=1.51e-01, acc. prob=0.779]\n",
-      "\rWarmup:  17%|█▋        | 127/768 [00:20,  5.31it/s, step size=2.36e-01, acc. prob=0.780]\n",
-      "\rWarmup:  17%|█▋        | 128/768 [00:20,  5.30it/s, step size=2.15e-01, acc. prob=0.780]\n",
-      "\rWarmup:  17%|█▋        | 129/768 [00:20,  5.28it/s, step size=4.67e-02, acc. prob=0.776]\n",
-      "\rWarmup:  17%|█▋        | 130/768 [00:21,  5.30it/s, step size=8.30e-02, acc. prob=0.778]\n",
-      "\rWarmup:  17%|█▋        | 131/768 [00:21,  5.31it/s, step size=1.43e-01, acc. prob=0.779]\n",
-      "\rWarmup:  17%|█▋        | 132/768 [00:21,  5.29it/s, step size=1.49e-01, acc. prob=0.780]\n",
-      "\rWarmup:  17%|█▋        | 133/768 [00:21,  5.26it/s, step size=3.68e-02, acc. prob=0.776]\n",
-      "\rWarmup:  17%|█▋        | 134/768 [00:21,  5.25it/s, step size=6.44e-02, acc. prob=0.777]\n",
-      "\rWarmup:  18%|█▊        | 135/768 [00:22,  5.24it/s, step size=1.07e-01, acc. prob=0.779]\n",
-      "\rWarmup:  18%|█▊        | 136/768 [00:22,  5.23it/s, step size=1.72e-01, acc. prob=0.780]\n",
-      "\rWarmup:  18%|█▊        | 137/768 [00:22,  5.22it/s, step size=2.72e-01, acc. prob=0.782]\n",
-      "\rWarmup:  18%|█▊        | 138/768 [00:22,  5.21it/s, step size=1.04e-01, acc. prob=0.779]\n",
-      "\rWarmup:  18%|█▊        | 139/768 [00:22,  5.22it/s, step size=5.60e-02, acc. prob=0.777]\n",
-      "\rWarmup:  18%|█▊        | 140/768 [00:23,  5.23it/s, step size=9.28e-02, acc. prob=0.779]\n",
-      "\rWarmup:  18%|█▊        | 141/768 [00:23,  5.23it/s, step size=1.56e-01, acc. prob=0.781]\n",
-      "\rWarmup:  18%|█▊        | 142/768 [00:23,  5.23it/s, step size=7.33e-02, acc. prob=0.778]\n",
-      "\rWarmup:  19%|█▊        | 143/768 [00:23,  5.22it/s, step size=1.08e-01, acc. prob=0.780]\n",
-      "\rWarmup:  19%|█▉        | 144/768 [00:23,  5.22it/s, step size=1.68e-01, acc. prob=0.781]\n",
-      "\rWarmup:  19%|█▉        | 145/768 [00:24,  5.23it/s, step size=2.23e-01, acc. prob=0.782]\n",
-      "\rWarmup:  19%|█▉        | 146/768 [00:24,  5.24it/s, step size=6.15e-02, acc. prob=0.778]\n",
-      "\rWarmup:  19%|█▉        | 147/768 [00:24,  5.26it/s, step size=9.91e-02, acc. prob=0.780]\n",
-      "\rWarmup:  19%|█▉        | 148/768 [00:24,  5.26it/s, step size=1.62e-01, acc. prob=0.781]\n",
-      "\rWarmup:  19%|█▉        | 149/768 [00:24,  5.04it/s, step size=7.25e-01, acc. prob=0.778]\n",
-      "\rWarmup:  20%|█▉        | 152/768 [00:25,  7.36it/s, step size=5.27e-02, acc. prob=0.766]\n",
-      "\rWarmup:  20%|█▉        | 153/768 [00:25,  6.77it/s, step size=6.48e-02, acc. prob=0.767]\n",
-      "\rWarmup:  20%|██        | 154/768 [00:25,  6.33it/s, step size=9.58e-02, acc. prob=0.769]\n",
-      "\rWarmup:  20%|██        | 155/768 [00:25,  6.01it/s, step size=1.56e-01, acc. prob=0.770]\n",
-      "\rWarmup:  20%|██        | 156/768 [00:25,  5.80it/s, step size=2.73e-01, acc. prob=0.772]\n",
-      "\rWarmup:  20%|██        | 157/768 [00:26,  5.65it/s, step size=3.79e-01, acc. prob=0.773]\n",
-      "\rWarmup:  21%|██        | 158/768 [00:26,  5.54it/s, step size=1.14e-01, acc. prob=0.771]\n",
-      "\rWarmup:  21%|██        | 159/768 [00:26,  5.47it/s, step size=2.06e-01, acc. prob=0.772]\n",
-      "\rWarmup:  21%|██        | 160/768 [00:26,  5.42it/s, step size=1.00e-01, acc. prob=0.771]\n",
-      "\rWarmup:  21%|██        | 161/768 [00:26,  5.38it/s, step size=1.90e-01, acc. prob=0.772]\n",
-      "\rWarmup:  21%|██        | 162/768 [00:26,  5.36it/s, step size=3.57e-01, acc. prob=0.773]\n",
-      "\rWarmup:  21%|██        | 163/768 [00:27,  5.34it/s, step size=3.60e-01, acc. prob=0.774]\n",
-      "\rWarmup:  21%|██▏       | 164/768 [00:27,  5.30it/s, step size=6.80e-01, acc. prob=0.775]\n",
-      "\rWarmup:  21%|██▏       | 165/768 [00:27,  5.26it/s, step size=1.09e-01, acc. prob=0.772]\n",
-      "\rWarmup:  22%|██▏       | 166/768 [00:27,  5.25it/s, step size=2.06e-01, acc. prob=0.773]\n",
-      "\rWarmup:  22%|██▏       | 167/768 [00:27,  5.25it/s, step size=3.79e-01, acc. prob=0.774]\n",
-      "\rWarmup:  22%|██▏       | 168/768 [00:28,  5.27it/s, step size=1.98e-01, acc. prob=0.773]\n",
-      "\rWarmup:  22%|██▏       | 169/768 [00:28,  5.27it/s, step size=3.06e-01, acc. prob=0.774]\n",
-      "\rWarmup:  22%|██▏       | 170/768 [00:28,  5.28it/s, step size=5.51e-01, acc. prob=0.775]\n",
-      "\rWarmup:  22%|██▏       | 171/768 [00:28,  5.28it/s, step size=7.67e-02, acc. prob=0.771]\n",
-      "\rWarmup:  22%|██▏       | 172/768 [00:28,  5.28it/s, step size=1.42e-01, acc. prob=0.773]\n",
-      "\rWarmup:  23%|██▎       | 173/768 [00:29,  5.29it/s, step size=1.42e-01, acc. prob=0.773]\n",
-      "\rWarmup:  23%|██▎       | 174/768 [00:29,  5.29it/s, step size=2.59e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 175/768 [00:29,  5.30it/s, step size=1.41e-01, acc. prob=0.773]\n",
-      "\rWarmup:  23%|██▎       | 176/768 [00:29,  5.28it/s, step size=2.52e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 177/768 [00:29,  5.22it/s, step size=1.91e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 178/768 [00:30,  5.25it/s, step size=3.11e-01, acc. prob=0.775]\n",
-      "\rWarmup:  23%|██▎       | 179/768 [00:30,  5.26it/s, step size=2.12e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 180/768 [00:30,  5.27it/s, step size=2.46e-01, acc. prob=0.775]\n",
-      "\rWarmup:  24%|██▎       | 181/768 [00:30,  5.27it/s, step size=1.13e-01, acc. prob=0.773]\n",
-      "\rWarmup:  24%|██▎       | 182/768 [00:30,  5.27it/s, step size=1.99e-01, acc. prob=0.774]\n",
-      "\rWarmup:  24%|██▍       | 183/768 [00:30,  5.28it/s, step size=3.45e-01, acc. prob=0.775]\n",
-      "\rWarmup:  24%|██▍       | 184/768 [00:31,  5.28it/s, step size=5.73e-01, acc. prob=0.777]\n",
-      "\rWarmup:  24%|██▍       | 185/768 [00:31,  5.27it/s, step size=1.50e-01, acc. prob=0.774]\n",
-      "\rWarmup:  24%|██▍       | 186/768 [00:31,  5.26it/s, step size=2.57e-01, acc. prob=0.775]\n",
-      "\rWarmup:  24%|██▍       | 187/768 [00:31,  5.25it/s, step size=3.37e-01, acc. prob=0.776]\n",
-      "\rWarmup:  24%|██▍       | 188/768 [00:31,  5.26it/s, step size=3.87e-01, acc. prob=0.776]\n",
-      "\rWarmup:  25%|██▍       | 189/768 [00:32,  5.25it/s, step size=3.86e-01, acc. prob=0.776]\n",
-      "\rWarmup:  25%|██▍       | 190/768 [00:32,  5.25it/s, step size=1.51e-01, acc. prob=0.774]\n",
-      "\rWarmup:  25%|██▍       | 191/768 [00:32,  5.26it/s, step size=2.45e-01, acc. prob=0.775]\n",
-      "\rWarmup:  25%|██▌       | 192/768 [00:32,  5.25it/s, step size=4.10e-01, acc. prob=0.777]\n",
-      "\rWarmup:  25%|██▌       | 193/768 [00:32,  5.26it/s, step size=9.62e-02, acc. prob=0.774]\n",
-      "\rWarmup:  25%|██▌       | 194/768 [00:33,  5.26it/s, step size=1.62e-01, acc. prob=0.775]\n",
-      "\rWarmup:  25%|██▌       | 195/768 [00:33,  5.26it/s, step size=2.45e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 196/768 [00:33,  5.27it/s, step size=1.59e-01, acc. prob=0.775]\n",
-      "\rWarmup:  26%|██▌       | 197/768 [00:33,  5.27it/s, step size=2.51e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 198/768 [00:33,  5.27it/s, step size=2.70e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 199/768 [00:34,  5.28it/s, step size=1.23e-01, acc. prob=0.775]\n",
-      "\rWarmup:  26%|██▌       | 200/768 [00:34,  5.28it/s, step size=2.00e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 201/768 [00:34,  5.28it/s, step size=3.04e-01, acc. prob=0.777]\n",
-      "\rWarmup:  26%|██▋       | 202/768 [00:34,  5.28it/s, step size=1.41e-01, acc. prob=0.775]\n",
-      "\rWarmup:  26%|██▋       | 203/768 [00:34,  5.27it/s, step size=2.25e-01, acc. prob=0.776]\n",
-      "\rWarmup:  27%|██▋       | 204/768 [00:34,  5.27it/s, step size=3.46e-01, acc. prob=0.777]\n",
-      "\rWarmup:  27%|██▋       | 205/768 [00:35,  5.27it/s, step size=4.07e-01, acc. prob=0.778]\n",
-      "\rWarmup:  27%|██▋       | 206/768 [00:35,  5.27it/s, step size=1.61e-01, acc. prob=0.776]\n",
-      "\rWarmup:  27%|██▋       | 207/768 [00:35,  5.27it/s, step size=2.52e-01, acc. prob=0.777]\n",
-      "\rWarmup:  27%|██▋       | 208/768 [00:35,  5.26it/s, step size=2.01e-01, acc. prob=0.776]\n",
-      "\rWarmup:  27%|██▋       | 209/768 [00:35,  5.27it/s, step size=2.58e-01, acc. prob=0.777]\n",
-      "\rWarmup:  27%|██▋       | 210/768 [00:36,  5.28it/s, step size=3.58e-01, acc. prob=0.778]\n",
-      "\rWarmup:  27%|██▋       | 211/768 [00:36,  5.29it/s, step size=1.99e-01, acc. prob=0.776]\n",
-      "\rWarmup:  28%|██▊       | 212/768 [00:36,  5.29it/s, step size=2.37e-01, acc. prob=0.777]\n",
-      "\rWarmup:  28%|██▊       | 213/768 [00:36,  5.29it/s, step size=3.68e-01, acc. prob=0.778]\n",
-      "\rWarmup:  28%|██▊       | 214/768 [00:36,  5.29it/s, step size=5.21e-01, acc. prob=0.779]\n",
-      "\rWarmup:  28%|██▊       | 215/768 [00:37,  5.29it/s, step size=2.29e-01, acc. prob=0.777]\n",
-      "\rWarmup:  28%|██▊       | 216/768 [00:37,  5.28it/s, step size=3.32e-01, acc. prob=0.778]\n",
-      "\rWarmup:  28%|██▊       | 217/768 [00:37,  5.28it/s, step size=4.86e-01, acc. prob=0.779]\n",
-      "\rWarmup:  28%|██▊       | 218/768 [00:37,  5.26it/s, step size=5.62e-01, acc. prob=0.779]\n",
-      "\rWarmup:  29%|██▊       | 219/768 [00:37,  5.22it/s, step size=2.64e-01, acc. prob=0.778]\n",
-      "\rWarmup:  29%|██▊       | 220/768 [00:37,  5.23it/s, step size=3.69e-01, acc. prob=0.778]\n",
-      "\rWarmup:  29%|██▉       | 221/768 [00:38,  5.24it/s, step size=1.62e-01, acc. prob=0.777]\n",
-      "\rWarmup:  29%|██▉       | 222/768 [00:38,  5.26it/s, step size=2.32e-01, acc. prob=0.777]\n",
-      "\rWarmup:  29%|██▉       | 223/768 [00:38,  5.26it/s, step size=3.55e-01, acc. prob=0.778]\n",
-      "\rWarmup:  29%|██▉       | 224/768 [00:38,  5.26it/s, step size=4.69e-01, acc. prob=0.779]\n",
-      "\rWarmup:  29%|██▉       | 225/768 [00:38,  5.27it/s, step size=2.17e-01, acc. prob=0.777]\n",
-      "\rWarmup:  29%|██▉       | 226/768 [00:39,  5.28it/s, step size=2.69e-01, acc. prob=0.778]\n",
-      "\rWarmup:  30%|██▉       | 227/768 [00:39,  5.29it/s, step size=2.65e-01, acc. prob=0.778]\n",
-      "\rWarmup:  30%|██▉       | 228/768 [00:39,  5.29it/s, step size=2.82e-01, acc. prob=0.778]\n",
-      "\rWarmup:  30%|██▉       | 229/768 [00:39,  5.28it/s, step size=1.47e-01, acc. prob=0.777]\n",
-      "\rWarmup:  30%|██▉       | 230/768 [00:39,  5.28it/s, step size=1.48e-01, acc. prob=0.777]\n",
-      "\rWarmup:  30%|███       | 231/768 [00:40,  5.28it/s, step size=2.23e-01, acc. prob=0.778]\n",
-      "\rWarmup:  30%|███       | 232/768 [00:40,  5.29it/s, step size=2.84e-01, acc. prob=0.778]\n",
-      "\rWarmup:  30%|███       | 233/768 [00:40,  5.24it/s, step size=1.03e-01, acc. prob=0.776]\n",
-      "\rWarmup:  30%|███       | 234/768 [00:40,  5.26it/s, step size=1.52e-01, acc. prob=0.777]\n",
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-      "\rWarmup:  67%|██████▋   | 513/768 [01:20, 10.89it/s, step size=1.91e-01, acc. prob=0.808]\n",
-      "\rSample:  67%|██████▋   | 515/768 [01:20, 10.82it/s, step size=1.91e-01, acc. prob=0.711]\n",
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-      "\rSample: 100%|██████████| 768/768 [01:43, 13.19it/s, step size=1.91e-01, acc. prob=0.896]\n",
-      "\rSample: 100%|██████████| 768/768 [01:43,  7.45it/s, step size=1.91e-01, acc. prob=0.896]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743529103501,
-    "executionStopTime": 1743529104461,
-    "language": "python",
-    "originalKey": "f2fc99ee-52d0-420f-8f1a-0f1293689aad",
-    "output": {
-     "id": "660736130231383"
-    },
-    "outputsInitialized": true,
-    "requestMsgId": "76d49bc3-3046-4c1a-8538-bd0190d04489",
-    "serverExecutionDuration": 3.7160157226026,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'STGP - target only': {'NLL': 820.1311610482774, 'MSE': 1658.1030020306143},\n",
-       " 'MTGP - ICM - MAP': {'NLL': 298.84299270373793, 'MSE': 11.273867204848827},\n",
-       " 'MTGP - Latent Embeddings - FB': {'NLL': 525.3912920530319,\n",
-       "  'MSE': 20.109351468055014},\n",
-       " 'MTGP - Latent Embeddings - MAP': {'NLL': 438.331706460182,\n",
-       "  'MSE': 53.53423246625931}}"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "borehole_res "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
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diff --git a/private_multitask_pfn/multifidelity/borehole_res.pkl b/private_multitask_pfn/multifidelity/borehole_res.pkl
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diff --git a/private_multitask_pfn/multifidelity/mf lily.ipynb b/private_multitask_pfn/multifidelity/mf lily.ipynb
deleted file mode 100644
index 15de8ef..0000000
--- a/private_multitask_pfn/multifidelity/mf lily.ipynb	
+++ /dev/null
@@ -1,1662 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "torch.cuda.is_available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528209127,
-    "executionStopTime": 1743528217022,
-    "language": "python",
-    "originalKey": "a4c56e12-55d0-4b3e-a2dd-bec3739a135a",
-    "outputsInitialized": true,
-    "requestMsgId": "61357428-9500-4edb-8e7d-2a6608607fae",
-    "serverExecutionDuration": 6.2562888488173,
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "\n",
-    "import torch\n",
-    "from botorch.test_functions.synthetic import SyntheticTestFunction\n",
-    "from torch import Tensor\n",
-    "\n",
-    "\n",
-    "class WingWeightMultiFidelity(SyntheticTestFunction):\n",
-    "    \"\"\"Wing Weight Design Problem from [Chen2024]_.\n",
-    "\n",
-    "    Design variables (physical units):\n",
-    "      1. s_w   in [150,   200]   (wing area)\n",
-    "      2. w_fw  in [220,   300]   (fuel weight)\n",
-    "      3. A     in [6,     10]    (aspect ratio)\n",
-    "      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)\n",
-    "      5. q     in [16,    45]    (dynamic pressure)\n",
-    "      6. lam   in [0.5,   1.0]   (taper ratio)\n",
-    "      7. t_c   in [0.08,  0.18]   (thickness-to-chord)\n",
-    "      8. N_z   in [2.5,   6.0]    (ultimate load factor)\n",
-    "      9. w_dg  in [1700,  2500]   (design gross weight)\n",
-    "      10. w_pp in [0.025, 0.08]    (weight per unit area)\n",
-    "\n",
-    "    Fidelity parameter (stored as the 11th input):\n",
-    "      0: High fidelity (HF)\n",
-    "      1: Low fidelity 1 (LF1)\n",
-    "      2: Low fidelity 2 (LF2)\n",
-    "      3: Low fidelity 3 (LF2)\n",
-    "\n",
-    "    The HF model is given by:\n",
-    "      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *\n",
-    "           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *\n",
-    "           (N_z*w_dg)^0.49 + s_w*w_pp\n",
-    "\n",
-    "    LF models use slightly altered exponents and additive biases.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    dim = 11\n",
-    "    _num_fidelities = 1\n",
-    "    _bounds = [\n",
-    "        (150.0, 200.0),  # s_w\n",
-    "        (220.0, 300.0),  # w_fw\n",
-    "        (6.0, 10.0),  # A\n",
-    "        (-10.0, 10.0),  # Lambda_deg\n",
-    "        (16.0, 45.0),  # q\n",
-    "        (0.5, 1.0),  # lam\n",
-    "        (0.08, 0.18),  # t_c\n",
-    "        (2.5, 6.0),  # N_z\n",
-    "        (1700.0, 2500.0),  # w_dg\n",
-    "        (0.025, 0.08),  # w_pp\n",
-    "        (0, 3),\n",
-    "    ]\n",
-    "    fidelities = [0, 1, 2, 3]\n",
-    "    _optimal_value = 123.25\n",
-    "\n",
-    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.\n",
-    "        s_w = X[..., 0]\n",
-    "        w_fw = X[..., 1]\n",
-    "        A = X[..., 2]\n",
-    "        Lambda_deg = X[..., 3]\n",
-    "        Lambda_rad = Lambda_deg * math.pi / 180.0\n",
-    "\n",
-    "        q = X[..., 4]\n",
-    "        lam = X[..., 5]\n",
-    "        t_c = X[..., 6]\n",
-    "        N_z = X[..., 7]\n",
-    "        w_dg = X[..., 8]\n",
-    "        w_pp = X[..., 9]\n",
-    "        fidelity = X[..., 10]\n",
-    "        cos_val = torch.cos(Lambda_rad)\n",
-    "        y = torch.zeros_like(s_w)\n",
-    "        # High fidelity (fidelity == 0)\n",
-    "        mask = fidelity == 0\n",
-    "        if mask.any():\n",
-    "            hf = (\n",
-    "                0.036\n",
-    "                * (s_w**0.758)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + s_w * w_pp\n",
-    "            )\n",
-    "            y[mask] = hf[mask]\n",
-    "        # Low fidelity 1 (fidelity == 1)\n",
-    "        mask = fidelity == 1\n",
-    "        if mask.any():\n",
-    "            lf1 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.758)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + w_pp\n",
-    "            )\n",
-    "            y[mask] = lf1[mask]\n",
-    "        # Low fidelity 2 (fidelity == 2)\n",
-    "        mask = fidelity == 2\n",
-    "        if mask.any():\n",
-    "            lf2 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.8)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + w_pp\n",
-    "            )\n",
-    "            y[mask] = lf2[mask]\n",
-    "        # Low fidelity 3 (fidelity == 3)\n",
-    "        mask = fidelity == 3\n",
-    "        if mask.any():\n",
-    "            lf3 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.9)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "            )\n",
-    "            y[mask] = lf3[mask]\n",
-    "        return y\n",
-    "\n",
-    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        fidelity = X[..., 10]\n",
-    "        c = torch.zeros_like(fidelity)\n",
-    "        c[fidelity == 0] = 1000.0\n",
-    "        c[fidelity == 1] = 100.0\n",
-    "        c[fidelity == 2] = 10.0\n",
-    "        c[fidelity == 3] = 1.0\n",
-    "        return c\n",
-    "\n",
-    "\n",
-    "class BoreholeMultiFidelity(SyntheticTestFunction):\n",
-    "    \"\"\"Borehole Problem from [Chen2024]_.\n",
-    "\n",
-    "    This problem models water flow through a borehole with 8 design variables:\n",
-    "          1. r_w   in [0.05,   0.15]   (borehole radius)\n",
-    "          2. r     in [100,    50000]  (radius of influence)\n",
-    "          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)\n",
-    "          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)\n",
-    "          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)\n",
-    "          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)\n",
-    "          7. L     in [1120,   1680]   (length of borehole)\n",
-    "          8. K_w   in [9855,   12045]  (hydraulic conductivity)\n",
-    "\n",
-    "        The fidelity index (9th input) is categorical:\n",
-    "          0: High fidelity (HF)\n",
-    "          1: Low fidelity 1 (LF1)\n",
-    "          2: Low fidelity 2 (LF2)\n",
-    "          3: Low fidelity 3 (LF3)\n",
-    "          4: Low fidelity 4 (LF4)\n",
-    "\n",
-    "        The HF model is defined by:\n",
-    "          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]\n",
-    "\n",
-    "        The low-fidelity models modify exponents and add a bias.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    dim = 9\n",
-    "    _num_fidelities = 1\n",
-    "    _bounds = [\n",
-    "        (0.05, 0.15),  # r_w\n",
-    "        (100.0, 10000.0),  # r\n",
-    "        (100.0, 1000.0),  # T_u\n",
-    "        (10.0, 500.0),  # T_l\n",
-    "        (990.0, 1110.0),  # H_u\n",
-    "        (700.0, 820.0),  # H_l\n",
-    "        (1000.0, 2000.0),  # L\n",
-    "        (6000.0, 12000.0),  # K_w\n",
-    "        (0, 4),  # fidelity\n",
-    "    ]\n",
-    "    fidelities = [0, 1, 2, 3, 4]\n",
-    "    _optimal_value = 3.98\n",
-    "\n",
-    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        r_w = X[..., 0]\n",
-    "        r = X[..., 1]\n",
-    "        T_u = X[..., 2]\n",
-    "        T_l = X[..., 3]\n",
-    "        H_u = X[..., 4]\n",
-    "        H_l = X[..., 5]\n",
-    "        L = X[..., 6]\n",
-    "        K_w = X[..., 7]\n",
-    "        fidelity = X[..., 8]\n",
-    "\n",
-    "        log_term = torch.log(r / r_w)\n",
-    "        numer = 2.0 * math.pi * T_u * (H_u - H_l)\n",
-    "        y = torch.zeros_like(r_w)\n",
-    "\n",
-    "        # HF (fidelity 0)\n",
-    "        mask = fidelity == 0\n",
-    "        if mask.any():\n",
-    "            hf_denom = log_term * (\n",
-    "                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            hf = numer / hf_denom\n",
-    "            y[mask] = hf[mask]\n",
-    "\n",
-    "        # LF1 (fidelity 1): add bias.\n",
-    "        mask = fidelity == 1\n",
-    "        if mask.any():\n",
-    "            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)\n",
-    "            lf1_denom = log_term * (\n",
-    "                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf1 = lf1_numer / lf1_denom\n",
-    "            y[mask] = lf1[mask]\n",
-    "\n",
-    "        # LF2 (fidelity 2): modify the exponent on log_term and add bias.\n",
-    "        mask = fidelity == 2\n",
-    "        if mask.any():\n",
-    "            lf2_denom = log_term * (\n",
-    "                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l\n",
-    "            )\n",
-    "            lf2 = numer / lf2_denom\n",
-    "            y[mask] = lf2[mask]\n",
-    "\n",
-    "        # LF3 (fidelity 3): modify r_w exponent slightly.\n",
-    "        mask = fidelity == 3\n",
-    "        if mask.any():\n",
-    "            lf3_log_term = torch.log(4 * r / r_w)\n",
-    "            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)\n",
-    "            lf3_denom = lf3_log_term * (\n",
-    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf3 = lf3_numer / lf3_denom\n",
-    "            y[mask] = lf3[mask]\n",
-    "        # LF4 (fidelity 4): further bias.\n",
-    "        mask = fidelity == 4\n",
-    "        if mask.any():\n",
-    "            lf4_log_term = torch.log(2 * r / r_w)\n",
-    "            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)\n",
-    "            lf4_denom = lf4_log_term * (\n",
-    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf4 = lf4_numer / lf4_denom\n",
-    "            y[mask] = lf4[mask]\n",
-    "\n",
-    "        return y\n",
-    "\n",
-    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        fidelity = X[..., 8]\n",
-    "        c = torch.zeros_like(fidelity)\n",
-    "        c[fidelity == 0] = 1000.0\n",
-    "        c[fidelity == 1] = 100.0\n",
-    "        c[fidelity == 2] = 10.0\n",
-    "        c[fidelity == 3] = 100.0\n",
-    "        c[fidelity == 4] = 10.0\n",
-    "        return c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class WingWeightMultiFidelitySmall(SyntheticTestFunction):\n",
-    "    \"\"\"Wing Weight Design Problem from [Chen2024]_.\n",
-    "\n",
-    "    Design variables (physical units):\n",
-    "      1. s_w   in [150,   200]   (wing area)\n",
-    "      2. w_fw  in [220,   300]   (fuel weight)\n",
-    "      3. A     in [6,     10]    (aspect ratio)\n",
-    "      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)\n",
-    "      5. q     in [16,    45]    (dynamic pressure)\n",
-    "      6. lam   in [0.5,   1.0]   (taper ratio)\n",
-    "      7. t_c   in [0.08,  0.18]   (thickness-to-chord)\n",
-    "      8. N_z   in [2.5,   6.0]    (ultimate load factor)\n",
-    "      9. w_dg  in [1700,  2500]   (design gross weight)\n",
-    "      10. w_pp in [0.025, 0.08]    (weight per unit area)\n",
-    "\n",
-    "    Fidelity parameter (stored as the 11th input):\n",
-    "      0: High fidelity (HF)\n",
-    "      1: Low fidelity 1 (LF1)\n",
-    "      2: Low fidelity 2 (LF2)\n",
-    "      3: Low fidelity 3 (LF2)\n",
-    "\n",
-    "    The HF model is given by:\n",
-    "      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *\n",
-    "           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *\n",
-    "           (N_z*w_dg)^0.49 + s_w*w_pp\n",
-    "\n",
-    "    LF models use slightly altered exponents and additive biases.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    dim = 6\n",
-    "    _num_fidelities = 1\n",
-    "    _bounds = [\n",
-    "        (150.0, 200.0),  # s_w\n",
-    "        (220.0, 300.0),  # w_fw\n",
-    "        (6.0, 10.0),  # A\n",
-    "        (-10.0, 10.0),  # Lambda_deg\n",
-    "        (16.0, 45.0),  # q\n",
-    "        (0.5, 1.0),  # lam\n",
-    "        # (0.08, 0.18),  # t_c\n",
-    "        # (2.5, 6.0),  # N_z\n",
-    "        # (1700.0, 2500.0),  # w_dg\n",
-    "        # (0.025, 0.08),  # w_pp\n",
-    "        (0, 3), # fidelity\n",
-    "    ]\n",
-    "    fidelities = [0, 1, 2, 3]\n",
-    "    _optimal_value = 123.25\n",
-    "\n",
-    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.\n",
-    "        s_w = X[..., 0]\n",
-    "        w_fw = X[..., 1]\n",
-    "        A = X[..., 2]\n",
-    "        Lambda_deg = X[..., 3]\n",
-    "        Lambda_rad = Lambda_deg * math.pi / 180.0\n",
-    "\n",
-    "        q = X[..., 4]\n",
-    "        lam = X[..., 5]\n",
-    "        t_c = 0.13 #X[..., 6]\n",
-    "        N_z = 4.5 #X[..., 7]\n",
-    "        w_dg = 2000 #X[..., 8]\n",
-    "        w_pp = 0.05 #X[..., 9]\n",
-    "        fidelity = X[..., -1]\n",
-    "        cos_val = torch.cos(Lambda_rad)\n",
-    "        y = torch.zeros_like(s_w)\n",
-    "        # High fidelity (fidelity == 0)\n",
-    "        mask = fidelity == 0\n",
-    "        if mask.any():\n",
-    "            hf = (\n",
-    "                0.036\n",
-    "                * (s_w**0.758)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + s_w * w_pp\n",
-    "            )\n",
-    "            y[mask] = hf[mask]\n",
-    "        # Low fidelity 1 (fidelity == 1)\n",
-    "        mask = fidelity == 1\n",
-    "        if mask.any():\n",
-    "            lf1 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.758)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + w_pp\n",
-    "            )\n",
-    "            y[mask] = lf1[mask]\n",
-    "        # Low fidelity 2 (fidelity == 2)\n",
-    "        mask = fidelity == 2\n",
-    "        if mask.any():\n",
-    "            lf2 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.8)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "                + w_pp\n",
-    "            )\n",
-    "            y[mask] = lf2[mask]\n",
-    "        # Low fidelity 3 (fidelity == 3)\n",
-    "        mask = fidelity == 3\n",
-    "        if mask.any():\n",
-    "            lf3 = (\n",
-    "                0.036\n",
-    "                * (s_w**0.9)\n",
-    "                * (w_fw**0.0035)\n",
-    "                * ((A / (cos_val**2)) ** 0.6)\n",
-    "                * (q**0.006)\n",
-    "                * (lam**0.04)\n",
-    "                * ((100.0 * t_c / cos_val) ** (-0.3))\n",
-    "                * ((N_z * w_dg) ** 0.49)\n",
-    "            )\n",
-    "            y[mask] = lf3[mask]\n",
-    "        return y\n",
-    "\n",
-    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        fidelity = X[..., 10]\n",
-    "        c = torch.zeros_like(fidelity)\n",
-    "        c[fidelity == 0] = 1000.0\n",
-    "        c[fidelity == 1] = 100.0\n",
-    "        c[fidelity == 2] = 10.0\n",
-    "        c[fidelity == 3] = 1.0\n",
-    "        return c\n",
-    "\n",
-    "\n",
-    "class BoreholeMultiFidelitySmall(SyntheticTestFunction):\n",
-    "    \"\"\"Borehole Problem from [Chen2024]_.\n",
-    "\n",
-    "    This problem models water flow through a borehole with 8 design variables:\n",
-    "          1. r_w   in [0.05,   0.15]   (borehole radius)\n",
-    "          2. r     in [100,    50000]  (radius of influence)\n",
-    "          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)\n",
-    "          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)\n",
-    "          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)\n",
-    "          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)\n",
-    "          7. L     in [1120,   1680]   (length of borehole)\n",
-    "          8. K_w   in [9855,   12045]  (hydraulic conductivity)\n",
-    "\n",
-    "        The fidelity index (9th input) is categorical:\n",
-    "          0: High fidelity (HF)\n",
-    "          1: Low fidelity 1 (LF1)\n",
-    "          2: Low fidelity 2 (LF2)\n",
-    "          3: Low fidelity 3 (LF3)\n",
-    "          4: Low fidelity 4 (LF4)\n",
-    "\n",
-    "        The HF model is defined by:\n",
-    "          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]\n",
-    "\n",
-    "        The low-fidelity models modify exponents and add a bias.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    dim = 7\n",
-    "    _num_fidelities = 1\n",
-    "    _bounds = [\n",
-    "        (0.05, 0.15),  # r_w\n",
-    "        (100.0, 10000.0),  # r\n",
-    "        (100.0, 1000.0),  # T_u\n",
-    "        (10.0, 500.0),  # T_l\n",
-    "        (990.0, 1110.0),  # H_u\n",
-    "        # (700.0, 820.0),  # H_l\n",
-    "        # (1000.0, 2000.0),  # L\n",
-    "        # (6000.0, 12000.0),  # K_w\n",
-    "        (0, 4),  # fidelity\n",
-    "    ]\n",
-    "    fidelities = [0, 1, 2, 3, 4]\n",
-    "    _optimal_value = 3.98\n",
-    "\n",
-    "    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        r_w = X[..., 0]\n",
-    "        r = X[..., 1]\n",
-    "        T_u = X[..., 2]\n",
-    "        T_l = X[..., 3]\n",
-    "        H_u = X[..., 4]\n",
-    "        H_l = X[..., 5]\n",
-    "        L = 760 # X[..., 6]\n",
-    "        K_w = 1500 #X[..., 7]\n",
-    "        fidelity = 9000 #X[..., 8]\n",
-    "\n",
-    "        log_term = torch.log(r / r_w)\n",
-    "        numer = 2.0 * math.pi * T_u * (H_u - H_l)\n",
-    "        y = torch.zeros_like(r_w)\n",
-    "\n",
-    "        # HF (fidelity 0)\n",
-    "        mask = fidelity == 0\n",
-    "        if mask.any():\n",
-    "            hf_denom = log_term * (\n",
-    "                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            hf = numer / hf_denom\n",
-    "            y[mask] = hf[mask]\n",
-    "\n",
-    "        # LF1 (fidelity 1): add bias.\n",
-    "        mask = fidelity == 1\n",
-    "        if mask.any():\n",
-    "            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)\n",
-    "            lf1_denom = log_term * (\n",
-    "                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf1 = lf1_numer / lf1_denom\n",
-    "            y[mask] = lf1[mask]\n",
-    "\n",
-    "        # LF2 (fidelity 2): modify the exponent on log_term and add bias.\n",
-    "        mask = fidelity == 2\n",
-    "        if mask.any():\n",
-    "            lf2_denom = log_term * (\n",
-    "                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l\n",
-    "            )\n",
-    "            lf2 = numer / lf2_denom\n",
-    "            y[mask] = lf2[mask]\n",
-    "\n",
-    "        # LF3 (fidelity 3): modify r_w exponent slightly.\n",
-    "        mask = fidelity == 3\n",
-    "        if mask.any():\n",
-    "            lf3_log_term = torch.log(4 * r / r_w)\n",
-    "            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)\n",
-    "            lf3_denom = lf3_log_term * (\n",
-    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf3 = lf3_numer / lf3_denom\n",
-    "            y[mask] = lf3[mask]\n",
-    "        # LF4 (fidelity 4): further bias.\n",
-    "        mask = fidelity == 4\n",
-    "        if mask.any():\n",
-    "            lf4_log_term = torch.log(2 * r / r_w)\n",
-    "            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)\n",
-    "            lf4_denom = lf4_log_term * (\n",
-    "                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l\n",
-    "            )\n",
-    "            lf4 = lf4_numer / lf4_denom\n",
-    "            y[mask] = lf4[mask]\n",
-    "\n",
-    "        return y\n",
-    "\n",
-    "    def cost(self, X: torch.Tensor) -> torch.Tensor:\n",
-    "        fidelity = X[..., 8]\n",
-    "        c = torch.zeros_like(fidelity)\n",
-    "        c[fidelity == 0] = 1000.0\n",
-    "        c[fidelity == 1] = 100.0\n",
-    "        c[fidelity == 2] = 10.0\n",
-    "        c[fidelity == 3] = 100.0\n",
-    "        c[fidelity == 4] = 10.0\n",
-    "        return c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528318060,
-    "executionStopTime": 1743528318381,
-    "language": "python",
-    "originalKey": "11f17363-b3c0-4b55-b211-436797c48a1e",
-    "outputsInitialized": true,
-    "requestMsgId": "961e03e5-ed26-47c2-8e06-8106aa186b53",
-    "serverExecutionDuration": 1.8204906955361,
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import torch\n",
-    "from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll\n",
-    "from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP\n",
-    "from botorch.models.gp_regression import SingleTaskGP\n",
-    "\n",
-    "from botorch.models.multitask import MultiTaskGP\n",
-    "\n",
-    "from botorch.models.transforms.input import (\n",
-    "    ChainedInputTransform,\n",
-    "    # LatentCategoricalEmbedding,\n",
-    "    # LatentCategoricalSpec,\n",
-    "    Normalize,\n",
-    ")\n",
-    "from botorch.models.transforms.outcome import Standardize, StratifiedStandardize\n",
-    "from botorch.utils.transforms import unnormalize\n",
-    "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
-    "from torch.distributions import Normal\n",
-    "\n",
-    "\n",
-    "tkwargs = {\"dtype\": torch.double}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528129775,
-    "executionStopTime": 1743528132881,
-    "language": "python",
-    "originalKey": "e02d0748-e092-4001-b8f0-59666e0e7b29",
-    "outputsInitialized": true,
-    "requestMsgId": "f59e8b19-0f9f-4f93-b9ad-277685684230",
-    "serverExecutionDuration": 1.955185085535,
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "def eval_model(model, test_X, test_Y):\n",
-    "    with torch.no_grad():\n",
-    "        posterior = model.posterior(test_X, observation_noise=True)\n",
-    "        # compute sum of LL of each point in test set (using only marginal variances)\n",
-    "        var = posterior.variance\n",
-    "        mean = posterior.mean\n",
-    "        nll = (\n",
-    "            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))\n",
-    "            .log_prob(test_Y.view(-1))\n",
-    "            .sum(dim=-1)\n",
-    "            .mean()  # take average over MCMC samples (if needed)\n",
-    "            .item()\n",
-    "        )\n",
-    "        mse = (mean - test_Y).pow(2).mean().item()\n",
-    "    return nll, mse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y):\n",
-    "    task_id = train_X[:, -1].long()\n",
-    "    train_X = train_X[:, :-1]\n",
-    "    \n",
-    "    \n",
-    "    with torch.no_grad():\n",
-    "        posterior = pfn_predict(pfn, task_id, train_X, train_Y, test_X[:, :-1])\n",
-    "        # compute sum of LL of each point in test set (using only marginal variances)\n",
-    "        var = posterior.variance\n",
-    "        mean = posterior.mean\n",
-    "        nll = (\n",
-    "            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))\n",
-    "            .log_prob(test_Y.view(-1))\n",
-    "            .sum(dim=-1)\n",
-    "            .mean()  # take average over MCMC samples (if needed)\n",
-    "            .item()\n",
-    "        )\n",
-    "        mse = (mean - test_Y).pow(2).mean().item()\n",
-    "    return nll, mse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using a Transformer with 25.77 M parameters\n",
-      "Loaded model\n"
-     ]
-    }
-   ],
-   "source": [
-    "from utils import load_model, pfn_predict\n",
-    "# pfn = load_model(\"/home/yl9959/mtpfn/final_models/revived-frog-499\")\n",
-    "pfn = load_model(\"/home/yl9959/mtpfn/final_models/vibrant-breeze-498\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "RuntimeError",
-     "evalue": "Trying to create tensor with negative dimension -4: [170, 1, -4]",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)\n",
-      "Cell \u001b[0;32mIn[29], line 7\u001b[0m, in \u001b[0;36meval_pfn_on_problem\u001b[0;34m(pfn, problem, train_X, train_Y, test_X, test_Y)\u001b[0m\n\u001b[1;32m      3\u001b[0m train_X \u001b[38;5;241m=\u001b[39m train_X[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m----> 7\u001b[0m     posterior \u001b[38;5;241m=\u001b[39m pfn_predict(pfn, task_id, train_X, train_Y, test_X[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;66;03m# compute sum of LL of each point in test set (using only marginal variances)\u001b[39;00m\n\u001b[1;32m      9\u001b[0m     var \u001b[38;5;241m=\u001b[39m posterior\u001b[38;5;241m.\u001b[39mvariance\n",
-      "File \u001b[0;32m~/mtpfn/utils.py:344\u001b[0m, in \u001b[0;36mpfn_predict\u001b[0;34m(pfn, task_id, train_x, train_y, possible_x)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    343\u001b[0m     pfn_model \u001b[38;5;241m=\u001b[39m pfn_gaussian_fit(pfn, task_id, train_x, train_y)\n\u001b[0;32m--> 344\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pfn_model\u001b[38;5;241m.\u001b[39mposterior(possible_x\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m))\n",
-      "File \u001b[0;32m~/mtpfn/utils.py:110\u001b[0m, in \u001b[0;36mPFNGaussian.posterior\u001b[0;34m(self, X, output_indices, observation_noise, posterior_transform)\u001b[0m\n\u001b[1;32m    108\u001b[0m original_shape \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    109\u001b[0m X_reshape \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mreshape(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m--> 110\u001b[0m pfn_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpfn(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_task_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_y, X_reshape, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    111\u001b[0m mean \u001b[38;5;241m=\u001b[39m pfn_outputs[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    112\u001b[0m variance \u001b[38;5;241m=\u001b[39m pfn_outputs[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mexp()\n",
-      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/mtpfn/PFNs/pfns/transformer.py:457\u001b[0m, in \u001b[0;36mTransformerModel.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    455\u001b[0m     style \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    456\u001b[0m     \u001b[38;5;66;03m# forward(style, x, y),  \"single_eval_pos\" corresponds to where test x starts\u001b[39;00m\n\u001b[0;32m--> 457\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward(\n\u001b[1;32m    458\u001b[0m         (style, x, y, task_id), single_eval_pos\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(args[\u001b[38;5;241m0\u001b[39m]), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    459\u001b[0m     )\n\u001b[1;32m    460\u001b[0m \u001b[38;5;66;03m# during training (x, y, task_id) is passed with single_eval_pos\u001b[39;00m\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    462\u001b[0m     \u001b[38;5;66;03m# case model((x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)\u001b[39;00m\n\u001b[1;32m    463\u001b[0m     \u001b[38;5;66;03m# case model((style,x,y,task_id), src_mask=None, single_eval_pos=None, only_return_standard_out=True)\u001b[39;00m\n",
-      "File \u001b[0;32m~/mtpfn/PFNs/pfns/transformer.py:497\u001b[0m, in \u001b[0;36mTransformerModel._forward\u001b[0;34m(self, src, src_mask, single_eval_pos, only_return_standard_out)\u001b[0m\n\u001b[1;32m    490\u001b[0m     onehot_task_ids \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39mone_hot(\n\u001b[1;32m    491\u001b[0m         task_id\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), num_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_tasks\n\u001b[1;32m    492\u001b[0m     )\u001b[38;5;241m.\u001b[39mto(x_src)\n\u001b[1;32m    493\u001b[0m     x_src \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat([onehot_task_ids, x_src], dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 497\u001b[0m x_src \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoder(x_src)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maxial\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattn_type:\n\u001b[1;32m    499\u001b[0m     N, D, B, E \u001b[38;5;241m=\u001b[39m x_src\u001b[38;5;241m.\u001b[39mshape\n",
-      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/container.py:250\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    249\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 250\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m module(\u001b[38;5;28minput\u001b[39m)\n\u001b[1;32m    251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
-      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/mtpfn/PFNs/pfns/encoders.py:294\u001b[0m, in \u001b[0;36mVariableNumFeaturesEncoder.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m    290\u001b[0m     x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m*\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_features \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m    291\u001b[0m     x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(\n\u001b[1;32m    292\u001b[0m         (\n\u001b[1;32m    293\u001b[0m             x,\n\u001b[0;32m--> 294\u001b[0m             torch\u001b[38;5;241m.\u001b[39mzeros(\n\u001b[1;32m    295\u001b[0m                 \u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_features \u001b[38;5;241m-\u001b[39m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], device\u001b[38;5;241m=\u001b[39mx\u001b[38;5;241m.\u001b[39mdevice\n\u001b[1;32m    296\u001b[0m             ),\n\u001b[1;32m    297\u001b[0m         ),\n\u001b[1;32m    298\u001b[0m         \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m    299\u001b[0m     )\n\u001b[1;32m    300\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbase_encoder(x)\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: Trying to create tensor with negative dimension -4: [170, 1, -4]"
-     ]
-    }
-   ],
-   "source": [
-    "eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
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-    "customInput": null,
-    "executionStartTime": 1743528939339,
-    "executionStopTime": 1743528940127,
-    "language": "python",
-    "originalKey": "a194f315-36d3-4980-9506-c01343153a81",
-    "outputsInitialized": true,
-    "requestMsgId": "dbaeb34d-8bc8-4943-891a-fae5a0fc7212",
-    "serverExecutionDuration": 3.0471379868686,
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-   "source": [
-    "def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):\n",
-    "    res = {}\n",
-    "    # test STGP on target task\n",
-    "    target_mask = train_X[:, -1] == 0\n",
-    "    model = SingleTaskGP(\n",
-    "        train_X[target_mask],\n",
-    "        train_Y[target_mask],\n",
-    "        input_transform=Normalize(\n",
-    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
-    "        ),\n",
-    "    )\n",
-    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
-    "    _ = fit_gpytorch_mll(mll)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"STGP - target only\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "\n",
-    "    # Test MTGP with ICM\n",
-    "    model = MultiTaskGP(\n",
-    "        train_X,\n",
-    "        train_Y,\n",
-    "        input_transform=Normalize(\n",
-    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
-    "        ),\n",
-    "        outcome_transform=StratifiedStandardize(\n",
-    "            stratification_idx=problem.dim - 1,\n",
-    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
-    "        ),\n",
-    "        task_feature=problem.dim - 1,\n",
-    "    )\n",
-    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
-    "    _ = fit_gpytorch_mll(mll)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"MTGP - ICM - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "    # Test Fully Bayesian MTGP with Latent Embeddings\n",
-    "    model = SaasFullyBayesianMultiTaskGP(\n",
-    "        train_X,\n",
-    "        train_Y,\n",
-    "        input_transform=Normalize(\n",
-    "            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))\n",
-    "        ),\n",
-    "        task_feature=problem.dim - 1,\n",
-    "        outcome_transform=StratifiedStandardize(\n",
-    "            stratification_idx=problem.dim - 1,\n",
-    "            task_values=torch.tensor(problem.fidelities, dtype=torch.long),\n",
-    "        ),\n",
-    "    )\n",
-    "    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"MTGP - Latent Embeddings - FB\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "\n",
-    "    # test LVGP with MAP estimation\n",
-    "    d = train_X.shape[-1]\n",
-    "    cat_dims = [problem.dim - 1]\n",
-    "    # construct input transform\n",
-    "    input_transform = ChainedInputTransform(\n",
-    "        normalize=Normalize(d=d, indices=list(range(d - 1))),\n",
-    "        # latent_emb=LatentCategoricalEmbedding(\n",
-    "        #     [\n",
-    "        #         LatentCategoricalSpec(\n",
-    "        #             idx=i,\n",
-    "        #             num_categories=len(problem.fidelities),\n",
-    "        #             latent_dim=2,\n",
-    "        #         )\n",
-    "        #         for i in cat_dims\n",
-    "        #     ],\n",
-    "        #     dim=d,\n",
-    "        # ).to(train_X),\n",
-    "    )\n",
-    "    model = SingleTaskGP(\n",
-    "        train_X,\n",
-    "        train_Y,\n",
-    "        input_transform=input_transform,\n",
-    "    )\n",
-    "    mll = ExactMarginalLogLikelihood(model.likelihood, model)\n",
-    "    _ = fit_gpytorch_mll(mll)\n",
-    "    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)\n",
-    "    res[\"MTGP - Latent Embeddings - MAP\"] = {\"NLL\": nll, \"MSE\": mse}\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
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-    "language": "markdown",
-    "originalKey": "1f8d148e-a0e7-4915-952f-7a405ec8a480",
-    "showInput": false
-   },
-   "source": [
-    "# Wing weight\n",
-    "### Generate initial training set as in Chen et al 2024. Generate test set for target fidelity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
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-    "customInput": null,
-    "executionStartTime": 1743528960203,
-    "executionStopTime": 1743529095209,
-    "language": "python",
-    "originalKey": "fd812914-49d4-4023-926e-2d41c4157ded",
-    "outputsInitialized": true,
-    "requestMsgId": "097e842b-186a-44f5-88c3-cd12b0240b33",
-    "serverExecutionDuration": 5.1055490039289,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "ename": "InputDataError",
-     "evalue": "Expected the bounds to match the dimensionality of the domain. Got self.dim=6 and len(self._bounds)=7.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mInputDataError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m problem \u001b[38;5;241m=\u001b[39m WingWeightMultiFidelitySmall()\n\u001b[1;32m      2\u001b[0m torch\u001b[38;5;241m.\u001b[39mmanual_seed(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# define training and test set\u001b[39;00m\n",
-      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/botorch/test_functions/synthetic.py:84\u001b[0m, in \u001b[0;36mSyntheticTestFunction.__init__\u001b[0;34m(self, noise_std, negate, bounds, dtype)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bounds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds \u001b[38;5;241m=\u001b[39m bounds\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(noise_std\u001b[38;5;241m=\u001b[39mnoise_std, negate\u001b[38;5;241m=\u001b[39mnegate, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bounds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     87\u001b[0m         \u001b[38;5;66;03m# Ensure at least one optimizer lies within the custom bounds\u001b[39;00m\n",
-      "File \u001b[0;32m~/.local/lib/python3.12/site-packages/botorch/test_functions/base.py:49\u001b[0m, in \u001b[0;36mBaseTestProblem.__init__\u001b[0;34m(self, noise_std, negate, dtype)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegate \u001b[38;5;241m=\u001b[39m negate\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdim:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m InputDataError(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected the bounds to match the dimensionality of the domain. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdim\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     52\u001b[0m     )\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregister_buffer(\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbounds\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     55\u001b[0m     torch\u001b[38;5;241m.\u001b[39mtensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bounds, dtype\u001b[38;5;241m=\u001b[39mdtype)\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m),\n\u001b[1;32m     56\u001b[0m )\n",
-      "\u001b[0;31mInputDataError\u001b[0m: Expected the bounds to match the dimensionality of the domain. Got self.dim=6 and len(self._bounds)=7."
-     ]
-    }
-   ],
-   "source": [
-    "problem = WingWeightMultiFidelitySmall()\n",
-    "torch.manual_seed(0)\n",
-    "# define training and test set\n",
-    "N_TEST = 100\n",
-    "fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}\n",
-    "total_n = sum(fidelity_to_n.values())\n",
-    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
-    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
-    "# set fidelities\n",
-    "start = 0\n",
-    "for fidelity, n in fidelity_to_n.items():\n",
-    "    end = start + n\n",
-    "    train_X[start:end, -1] = fidelity\n",
-    "    start = end\n",
-    "\n",
-    "train_Y = problem(train_X).unsqueeze(-1)\n",
-    "\n",
-    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
-    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
-    "test_X[:, -1] = 0  # target fidelity\n",
-    "test_Y = problem(test_X).unsqueeze(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743529122103,
-    "executionStopTime": 1743529259563,
-    "language": "python",
-    "originalKey": "16660295-7704-4fc8-86a6-865e818007df",
-    "output": {
-     "id": "644333568388982"
-    },
-    "outputsInitialized": true,
-    "requestMsgId": "e061f189-346b-4b58-9716-bcee85df522f",
-    "serverExecutionDuration": 136496.86142337,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/yl9959/.local/lib/python3.12/site-packages/linear_operator/utils/interpolation.py:71: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:651.)\n",
-      "  summing_matrix = cls(summing_matrix_indices, summing_matrix_values, size)\n",
-      "Sample: 100%|██████████| 768/768 [07:22,  1.73it/s, step size=1.59e-01, acc. prob=0.842]\n",
-      "/home/yl9959/.local/lib/python3.12/site-packages/botorch/models/utils/assorted.py:264: InputDataWarning: Data (input features) is not contained to the unit cube. Please consider min-max scaling the input data.\n",
-      "  check_min_max_scaling(\n"
-     ]
-    }
-   ],
-   "source": [
-    "wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743529130077,
-    "executionStopTime": 1743529260021,
-    "language": "python",
-    "originalKey": "76750e01-4e21-4bba-9624-fb214f9072aa",
-    "output": {
-     "id": "679472244421591"
-    },
-    "outputsInitialized": true,
-    "requestMsgId": "1e7896ca-a923-47da-a3c8-32c28f5d34e9",
-    "serverExecutionDuration": 3.9172596298158,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'wing_weight_res' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m wing_weight_res\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'wing_weight_res' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "wing_weight_res"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "customInput": null,
-    "language": "markdown",
-    "originalKey": "3a4f34ce-9fdf-45cc-af1b-0be72caa11a2",
-    "showInput": false
-   },
-   "source": [
-    "## Borehole"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528945358,
-    "executionStopTime": 1743528946169,
-    "language": "python",
-    "originalKey": "02708c2d-b43a-4a8f-8aca-a99fb32c615d",
-    "outputsInitialized": true,
-    "requestMsgId": "c216c09b-5424-4008-88bd-56f191aad912",
-    "serverExecutionDuration": 5.6033371947706,
-    "showInput": true
-   },
-   "outputs": [],
-   "source": [
-    "problem = BoreholeMultiFidelity()\n",
-    "torch.manual_seed(0)\n",
-    "# define training and test set\n",
-    "N_TEST = 100\n",
-    "fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}\n",
-    "total_n = sum(fidelity_to_n.values())\n",
-    "train_X = torch.rand(total_n, problem.dim, **tkwargs)\n",
-    "train_X = unnormalize(train_X, bounds=problem.bounds)\n",
-    "# set fidelities\n",
-    "start = 0\n",
-    "for fidelity, n in fidelity_to_n.items():\n",
-    "    end = start + n\n",
-    "    train_X[start:end, -1] = fidelity\n",
-    "    start = end\n",
-    "\n",
-    "train_Y = problem(train_X).unsqueeze(-1)\n",
-    "\n",
-    "test_X = torch.rand(N_TEST, problem.dim, **tkwargs)\n",
-    "test_X = unnormalize(test_X, bounds=problem.bounds)\n",
-    "test_X[:, -1] = 0  # target fidelity\n",
-    "test_Y = problem(test_X).unsqueeze(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743528947285,
-    "executionStopTime": 1743529094868,
-    "language": "python",
-    "originalKey": "0d43b937-8f09-4d49-90d5-f4077ee2e646",
-    "output": {
-     "id": "2112932599153367"
-    },
-    "outputsInitialized": true,
-    "requestMsgId": "fba253a7-c60a-412a-89be-3b4468f04999",
-    "serverExecutionDuration": 146576.36261638,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\rWarmup:   0%|          | 0/768 [00:00, ?it/s]\n",
-      "\rWarmup:   0%|          | 1/768 [00:01,  1.22s/it, step size=7.33e-01, acc. prob=0.125]\n",
-      "\rWarmup:   0%|          | 3/768 [00:01,  2.82it/s, step size=7.87e-02, acc. prob=0.367]\n",
-      "\rWarmup:   1%|          | 5/768 [00:01,  4.07it/s, step size=1.26e-01, acc. prob=0.599]\n",
-      "\rWarmup:   1%|          | 7/768 [00:01,  5.47it/s, step size=2.73e-02, acc. prob=0.593]\n",
-      "\rWarmup:   1%|          | 8/768 [00:01,  5.42it/s, step size=4.89e-02, acc. prob=0.644]\n",
-      "\rWarmup:   1%|          | 9/768 [00:02,  5.37it/s, step size=8.65e-02, acc. prob=0.682]\n",
-      "\rWarmup:   1%|▏         | 10/768 [00:02,  5.33it/s, step size=4.47e-02, acc. prob=0.673]\n",
-      "\rWarmup:   1%|▏         | 11/768 [00:02,  5.31it/s, step size=8.07e-02, acc. prob=0.701]\n",
-      "\rWarmup:   2%|▏         | 13/768 [00:02,  6.78it/s, step size=4.64e-02, acc. prob=0.702]\n",
-      "\rWarmup:   2%|▏         | 14/768 [00:02,  6.34it/s, step size=8.68e-02, acc. prob=0.723]\n",
-      "\rWarmup:   2%|▏         | 16/768 [00:03,  7.60it/s, step size=2.46e-02, acc. prob=0.706]\n",
-      "\rWarmup:   2%|▏         | 17/768 [00:03,  6.92it/s, step size=4.68e-02, acc. prob=0.723]\n",
-      "\rWarmup:   2%|▏         | 18/768 [00:03,  6.44it/s, step size=8.46e-02, acc. prob=0.738]\n",
-      "\rWarmup:   3%|▎         | 20/768 [00:03,  6.69it/s, step size=2.26e-02, acc. prob=0.721]\n",
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-      "\rWarmup:   3%|▎         | 25/768 [00:04,  5.58it/s, step size=3.59e-02, acc. prob=0.741]\n",
-      "\rWarmup:   3%|▎         | 26/768 [00:04,  5.50it/s, step size=6.53e-02, acc. prob=0.751]\n",
-      "\rWarmup:   4%|▎         | 27/768 [00:05,  5.44it/s, step size=7.84e-02, acc. prob=0.754]\n",
-      "\rWarmup:   4%|▍         | 29/768 [00:05,  7.00it/s, step size=1.54e-01, acc. prob=0.765]\n",
-      "\rWarmup:   4%|▍         | 31/768 [00:05,  9.15it/s, step size=3.39e-02, acc. prob=0.749]\n",
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-      "\rWarmup:   5%|▌         | 42/768 [00:07,  5.84it/s, step size=6.78e-02, acc. prob=0.766]\n",
-      "\rWarmup:   6%|▌         | 43/768 [00:07,  5.67it/s, step size=1.08e-01, acc. prob=0.771]\n",
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-      "\rWarmup:   6%|▌         | 47/768 [00:07,  8.15it/s, step size=4.01e-02, acc. prob=0.763]\n",
-      "\rWarmup:   6%|▋         | 48/768 [00:07,  7.28it/s, step size=6.57e-02, acc. prob=0.768]\n",
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-      "\rWarmup:   9%|▊         | 66/768 [00:10,  5.82it/s, step size=1.41e-01, acc. prob=0.780]\n",
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-      "\rWarmup:  11%|█         | 81/768 [00:12,  7.01it/s, step size=1.03e-01, acc. prob=0.780]\n",
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-      "\rWarmup:  12%|█▏        | 89/768 [00:13,  7.52it/s, step size=2.65e-02, acc. prob=0.773]\n",
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-      "\rWarmup:  14%|█▍        | 110/768 [00:17,  5.31it/s, step size=2.67e-01, acc. prob=0.779]\n",
-      "\rWarmup:  14%|█▍        | 111/768 [00:17,  5.30it/s, step size=4.13e-02, acc. prob=0.774]\n",
-      "\rWarmup:  15%|█▍        | 112/768 [00:17,  5.28it/s, step size=7.81e-02, acc. prob=0.776]\n",
-      "\rWarmup:  15%|█▍        | 113/768 [00:17,  5.26it/s, step size=1.47e-01, acc. prob=0.778]\n",
-      "\rWarmup:  15%|█▍        | 114/768 [00:18,  5.24it/s, step size=2.50e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  16%|█▌        | 120/768 [00:19,  5.28it/s, step size=2.03e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  16%|█▌        | 123/768 [00:19,  5.28it/s, step size=2.12e-01, acc. prob=0.780]\n",
-      "\rWarmup:  16%|█▌        | 124/768 [00:20,  5.29it/s, step size=4.78e-02, acc. prob=0.776]\n",
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-      "\rWarmup:  17%|█▋        | 129/768 [00:20,  5.28it/s, step size=4.67e-02, acc. prob=0.776]\n",
-      "\rWarmup:  17%|█▋        | 130/768 [00:21,  5.30it/s, step size=8.30e-02, acc. prob=0.778]\n",
-      "\rWarmup:  17%|█▋        | 131/768 [00:21,  5.31it/s, step size=1.43e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  17%|█▋        | 134/768 [00:21,  5.25it/s, step size=6.44e-02, acc. prob=0.777]\n",
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-      "\rWarmup:  19%|█▉        | 144/768 [00:23,  5.22it/s, step size=1.68e-01, acc. prob=0.781]\n",
-      "\rWarmup:  19%|█▉        | 145/768 [00:24,  5.23it/s, step size=2.23e-01, acc. prob=0.782]\n",
-      "\rWarmup:  19%|█▉        | 146/768 [00:24,  5.24it/s, step size=6.15e-02, acc. prob=0.778]\n",
-      "\rWarmup:  19%|█▉        | 147/768 [00:24,  5.26it/s, step size=9.91e-02, acc. prob=0.780]\n",
-      "\rWarmup:  19%|█▉        | 148/768 [00:24,  5.26it/s, step size=1.62e-01, acc. prob=0.781]\n",
-      "\rWarmup:  19%|█▉        | 149/768 [00:24,  5.04it/s, step size=7.25e-01, acc. prob=0.778]\n",
-      "\rWarmup:  20%|█▉        | 152/768 [00:25,  7.36it/s, step size=5.27e-02, acc. prob=0.766]\n",
-      "\rWarmup:  20%|█▉        | 153/768 [00:25,  6.77it/s, step size=6.48e-02, acc. prob=0.767]\n",
-      "\rWarmup:  20%|██        | 154/768 [00:25,  6.33it/s, step size=9.58e-02, acc. prob=0.769]\n",
-      "\rWarmup:  20%|██        | 155/768 [00:25,  6.01it/s, step size=1.56e-01, acc. prob=0.770]\n",
-      "\rWarmup:  20%|██        | 156/768 [00:25,  5.80it/s, step size=2.73e-01, acc. prob=0.772]\n",
-      "\rWarmup:  20%|██        | 157/768 [00:26,  5.65it/s, step size=3.79e-01, acc. prob=0.773]\n",
-      "\rWarmup:  21%|██        | 158/768 [00:26,  5.54it/s, step size=1.14e-01, acc. prob=0.771]\n",
-      "\rWarmup:  21%|██        | 159/768 [00:26,  5.47it/s, step size=2.06e-01, acc. prob=0.772]\n",
-      "\rWarmup:  21%|██        | 160/768 [00:26,  5.42it/s, step size=1.00e-01, acc. prob=0.771]\n",
-      "\rWarmup:  21%|██        | 161/768 [00:26,  5.38it/s, step size=1.90e-01, acc. prob=0.772]\n",
-      "\rWarmup:  21%|██        | 162/768 [00:26,  5.36it/s, step size=3.57e-01, acc. prob=0.773]\n",
-      "\rWarmup:  21%|██        | 163/768 [00:27,  5.34it/s, step size=3.60e-01, acc. prob=0.774]\n",
-      "\rWarmup:  21%|██▏       | 164/768 [00:27,  5.30it/s, step size=6.80e-01, acc. prob=0.775]\n",
-      "\rWarmup:  21%|██▏       | 165/768 [00:27,  5.26it/s, step size=1.09e-01, acc. prob=0.772]\n",
-      "\rWarmup:  22%|██▏       | 166/768 [00:27,  5.25it/s, step size=2.06e-01, acc. prob=0.773]\n",
-      "\rWarmup:  22%|██▏       | 167/768 [00:27,  5.25it/s, step size=3.79e-01, acc. prob=0.774]\n",
-      "\rWarmup:  22%|██▏       | 168/768 [00:28,  5.27it/s, step size=1.98e-01, acc. prob=0.773]\n",
-      "\rWarmup:  22%|██▏       | 169/768 [00:28,  5.27it/s, step size=3.06e-01, acc. prob=0.774]\n",
-      "\rWarmup:  22%|██▏       | 170/768 [00:28,  5.28it/s, step size=5.51e-01, acc. prob=0.775]\n",
-      "\rWarmup:  22%|██▏       | 171/768 [00:28,  5.28it/s, step size=7.67e-02, acc. prob=0.771]\n",
-      "\rWarmup:  22%|██▏       | 172/768 [00:28,  5.28it/s, step size=1.42e-01, acc. prob=0.773]\n",
-      "\rWarmup:  23%|██▎       | 173/768 [00:29,  5.29it/s, step size=1.42e-01, acc. prob=0.773]\n",
-      "\rWarmup:  23%|██▎       | 174/768 [00:29,  5.29it/s, step size=2.59e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 175/768 [00:29,  5.30it/s, step size=1.41e-01, acc. prob=0.773]\n",
-      "\rWarmup:  23%|██▎       | 176/768 [00:29,  5.28it/s, step size=2.52e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 177/768 [00:29,  5.22it/s, step size=1.91e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 178/768 [00:30,  5.25it/s, step size=3.11e-01, acc. prob=0.775]\n",
-      "\rWarmup:  23%|██▎       | 179/768 [00:30,  5.26it/s, step size=2.12e-01, acc. prob=0.774]\n",
-      "\rWarmup:  23%|██▎       | 180/768 [00:30,  5.27it/s, step size=2.46e-01, acc. prob=0.775]\n",
-      "\rWarmup:  24%|██▎       | 181/768 [00:30,  5.27it/s, step size=1.13e-01, acc. prob=0.773]\n",
-      "\rWarmup:  24%|██▎       | 182/768 [00:30,  5.27it/s, step size=1.99e-01, acc. prob=0.774]\n",
-      "\rWarmup:  24%|██▍       | 183/768 [00:30,  5.28it/s, step size=3.45e-01, acc. prob=0.775]\n",
-      "\rWarmup:  24%|██▍       | 184/768 [00:31,  5.28it/s, step size=5.73e-01, acc. prob=0.777]\n",
-      "\rWarmup:  24%|██▍       | 185/768 [00:31,  5.27it/s, step size=1.50e-01, acc. prob=0.774]\n",
-      "\rWarmup:  24%|██▍       | 186/768 [00:31,  5.26it/s, step size=2.57e-01, acc. prob=0.775]\n",
-      "\rWarmup:  24%|██▍       | 187/768 [00:31,  5.25it/s, step size=3.37e-01, acc. prob=0.776]\n",
-      "\rWarmup:  24%|██▍       | 188/768 [00:31,  5.26it/s, step size=3.87e-01, acc. prob=0.776]\n",
-      "\rWarmup:  25%|██▍       | 189/768 [00:32,  5.25it/s, step size=3.86e-01, acc. prob=0.776]\n",
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-      "\rWarmup:  25%|██▍       | 191/768 [00:32,  5.26it/s, step size=2.45e-01, acc. prob=0.775]\n",
-      "\rWarmup:  25%|██▌       | 192/768 [00:32,  5.25it/s, step size=4.10e-01, acc. prob=0.777]\n",
-      "\rWarmup:  25%|██▌       | 193/768 [00:32,  5.26it/s, step size=9.62e-02, acc. prob=0.774]\n",
-      "\rWarmup:  25%|██▌       | 194/768 [00:33,  5.26it/s, step size=1.62e-01, acc. prob=0.775]\n",
-      "\rWarmup:  25%|██▌       | 195/768 [00:33,  5.26it/s, step size=2.45e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 196/768 [00:33,  5.27it/s, step size=1.59e-01, acc. prob=0.775]\n",
-      "\rWarmup:  26%|██▌       | 197/768 [00:33,  5.27it/s, step size=2.51e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 198/768 [00:33,  5.27it/s, step size=2.70e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 199/768 [00:34,  5.28it/s, step size=1.23e-01, acc. prob=0.775]\n",
-      "\rWarmup:  26%|██▌       | 200/768 [00:34,  5.28it/s, step size=2.00e-01, acc. prob=0.776]\n",
-      "\rWarmup:  26%|██▌       | 201/768 [00:34,  5.28it/s, step size=3.04e-01, acc. prob=0.777]\n",
-      "\rWarmup:  26%|██▋       | 202/768 [00:34,  5.28it/s, step size=1.41e-01, acc. prob=0.775]\n",
-      "\rWarmup:  26%|██▋       | 203/768 [00:34,  5.27it/s, step size=2.25e-01, acc. prob=0.776]\n",
-      "\rWarmup:  27%|██▋       | 204/768 [00:34,  5.27it/s, step size=3.46e-01, acc. prob=0.777]\n",
-      "\rWarmup:  27%|██▋       | 205/768 [00:35,  5.27it/s, step size=4.07e-01, acc. prob=0.778]\n",
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-      "\rWarmup:  28%|██▊       | 212/768 [00:36,  5.29it/s, step size=2.37e-01, acc. prob=0.777]\n",
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-      "\rWarmup:  28%|██▊       | 214/768 [00:36,  5.29it/s, step size=5.21e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  28%|██▊       | 216/768 [00:37,  5.28it/s, step size=3.32e-01, acc. prob=0.778]\n",
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-      "\rWarmup:  29%|██▊       | 219/768 [00:37,  5.22it/s, step size=2.64e-01, acc. prob=0.778]\n",
-      "\rWarmup:  29%|██▊       | 220/768 [00:37,  5.23it/s, step size=3.69e-01, acc. prob=0.778]\n",
-      "\rWarmup:  29%|██▉       | 221/768 [00:38,  5.24it/s, step size=1.62e-01, acc. prob=0.777]\n",
-      "\rWarmup:  29%|██▉       | 222/768 [00:38,  5.26it/s, step size=2.32e-01, acc. prob=0.777]\n",
-      "\rWarmup:  29%|██▉       | 223/768 [00:38,  5.26it/s, step size=3.55e-01, acc. prob=0.778]\n",
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-      "\rWarmup:  29%|██▉       | 225/768 [00:38,  5.27it/s, step size=2.17e-01, acc. prob=0.777]\n",
-      "\rWarmup:  29%|██▉       | 226/768 [00:39,  5.28it/s, step size=2.69e-01, acc. prob=0.778]\n",
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-      "\rWarmup:  30%|██▉       | 230/768 [00:39,  5.28it/s, step size=1.48e-01, acc. prob=0.777]\n",
-      "\rWarmup:  30%|███       | 231/768 [00:40,  5.28it/s, step size=2.23e-01, acc. prob=0.778]\n",
-      "\rWarmup:  30%|███       | 232/768 [00:40,  5.29it/s, step size=2.84e-01, acc. prob=0.778]\n",
-      "\rWarmup:  30%|███       | 233/768 [00:40,  5.24it/s, step size=1.03e-01, acc. prob=0.776]\n",
-      "\rWarmup:  30%|███       | 234/768 [00:40,  5.26it/s, step size=1.52e-01, acc. prob=0.777]\n",
-      "\rWarmup:  31%|███       | 235/768 [00:40,  5.26it/s, step size=1.92e-01, acc. prob=0.778]\n",
-      "\rWarmup:  31%|███       | 236/768 [00:41,  5.27it/s, step size=2.60e-01, acc. prob=0.779]\n",
-      "\rWarmup:  31%|███       | 237/768 [00:41,  5.28it/s, step size=3.12e-01, acc. prob=0.779]\n",
-      "\rWarmup:  31%|███       | 238/768 [00:41,  5.29it/s, step size=3.40e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  31%|███▏      | 240/768 [00:41,  5.27it/s, step size=4.06e-01, acc. prob=0.780]\n",
-      "\rWarmup:  31%|███▏      | 241/768 [00:41,  5.28it/s, step size=4.46e-01, acc. prob=0.780]\n",
-      "\rWarmup:  32%|███▏      | 242/768 [00:42,  5.28it/s, step size=4.45e-01, acc. prob=0.780]\n",
-      "\rWarmup:  32%|███▏      | 243/768 [00:42,  5.28it/s, step size=4.68e-01, acc. prob=0.780]\n",
-      "\rWarmup:  32%|███▏      | 244/768 [00:42,  5.28it/s, step size=3.77e-01, acc. prob=0.780]\n",
-      "\rWarmup:  32%|███▏      | 245/768 [00:42,  5.27it/s, step size=2.38e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  32%|███▏      | 247/768 [00:43,  5.25it/s, step size=2.04e-01, acc. prob=0.779]\n",
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-      "\rWarmup:  32%|███▏      | 249/768 [00:43,  5.13it/s, step size=7.70e-01, acc. prob=0.780]\n",
-      "\rWarmup:  33%|███▎      | 252/768 [00:43,  7.93it/s, step size=1.22e-01, acc. prob=0.774]\n",
-      "\rWarmup:  33%|███▎      | 253/768 [00:43,  7.12it/s, step size=1.32e-01, acc. prob=0.775]\n",
-      "\rWarmup:  33%|███▎      | 254/768 [00:44,  6.55it/s, step size=1.80e-01, acc. prob=0.776]\n",
-      "\rWarmup:  33%|███▎      | 256/768 [00:44,  6.74it/s, step size=2.60e-01, acc. prob=0.776]\n",
-      "\rWarmup:  34%|███▎      | 258/768 [00:44,  7.31it/s, step size=1.44e-01, acc. prob=0.776]\n",
-      "\rWarmup:  34%|███▎      | 259/768 [00:44,  6.78it/s, step size=2.63e-01, acc. prob=0.777]\n",
-      "\rWarmup:  34%|███▍      | 260/768 [00:45,  6.37it/s, step size=2.00e-01, acc. prob=0.777]\n",
-      "\rWarmup:  34%|███▍      | 261/768 [00:45,  6.01it/s, step size=1.45e-01, acc. prob=0.776]\n",
-      "\rWarmup:  34%|███▍      | 262/768 [00:45,  5.79it/s, step size=2.40e-01, acc. prob=0.777]\n",
-      "\rWarmup:  34%|███▍      | 264/768 [00:45,  7.74it/s, step size=3.18e-01, acc. prob=0.777]\n",
-      "\rWarmup:  35%|███▍      | 266/768 [00:45,  8.60it/s, step size=3.40e-02, acc. prob=0.775]\n",
-      "\rWarmup:  35%|███▍      | 267/768 [00:45,  7.57it/s, step size=6.49e-02, acc. prob=0.776]\n",
-      "\rWarmup:  35%|███▍      | 268/768 [00:46,  6.86it/s, step size=1.19e-01, acc. prob=0.776]\n",
-      "\rWarmup:  35%|███▌      | 269/768 [00:46,  6.36it/s, step size=2.19e-01, acc. prob=0.777]\n",
-      "\rWarmup:  35%|███▌      | 271/768 [00:46,  7.63it/s, step size=3.87e-01, acc. prob=0.778]\n",
-      "\rWarmup:  35%|███▌      | 272/768 [00:46,  7.47it/s, step size=5.14e-01, acc. prob=0.778]\n",
-      "\rWarmup:  36%|███▌      | 274/768 [00:46,  9.18it/s, step size=2.04e-01, acc. prob=0.777]\n",
-      "\rWarmup:  36%|███▌      | 275/768 [00:46,  7.85it/s, step size=3.64e-01, acc. prob=0.778]\n",
-      "\rWarmup:  36%|███▌      | 276/768 [00:47,  7.03it/s, step size=1.64e-01, acc. prob=0.777]\n",
-      "\rWarmup:  36%|███▌      | 277/768 [00:47,  6.48it/s, step size=2.74e-01, acc. prob=0.778]\n",
-      "\rWarmup:  36%|███▌      | 278/768 [00:47,  6.10it/s, step size=1.79e-01, acc. prob=0.777]\n",
-      "\rWarmup:  36%|███▋      | 279/768 [00:47,  5.83it/s, step size=1.17e-01, acc. prob=0.777]\n",
-      "\rWarmup:  36%|███▋      | 280/768 [00:47,  5.65it/s, step size=2.07e-01, acc. prob=0.778]\n",
-      "\rWarmup:  37%|███▋      | 282/768 [00:48,  6.22it/s, step size=8.80e-02, acc. prob=0.777]\n",
-      "\rWarmup:  37%|███▋      | 283/768 [00:48,  5.97it/s, step size=1.53e-01, acc. prob=0.777]\n",
-      "\rWarmup:  37%|███▋      | 285/768 [00:48,  6.38it/s, step size=5.91e-02, acc. prob=0.776]\n",
-      "\rWarmup:  37%|███▋      | 286/768 [00:48,  6.10it/s, step size=1.02e-01, acc. prob=0.777]\n",
-      "\rWarmup:  37%|███▋      | 287/768 [00:49,  5.85it/s, step size=1.77e-01, acc. prob=0.778]\n",
-      "\rWarmup:  38%|███▊      | 288/768 [00:49,  5.71it/s, step size=2.83e-01, acc. prob=0.778]\n",
-      "\rWarmup:  38%|███▊      | 289/768 [00:49,  5.59it/s, step size=4.48e-01, acc. prob=0.779]\n",
-      "\rWarmup:  38%|███▊      | 291/768 [00:49,  6.14it/s, step size=3.57e-01, acc. prob=0.779]\n",
-      "\rWarmup:  38%|███▊      | 293/768 [00:49,  6.46it/s, step size=1.48e-01, acc. prob=0.778]\n",
-      "\rWarmup:  38%|███▊      | 294/768 [00:50,  6.18it/s, step size=2.47e-01, acc. prob=0.778]\n",
-      "\rWarmup:  38%|███▊      | 295/768 [00:50,  5.95it/s, step size=4.09e-01, acc. prob=0.779]\n",
-      "\rWarmup:  39%|███▊      | 297/768 [00:50,  6.40it/s, step size=1.19e-01, acc. prob=0.777]\n",
-      "\rWarmup:  39%|███▉      | 298/768 [00:50,  6.12it/s, step size=1.97e-01, acc. prob=0.778]\n",
-      "\rWarmup:  39%|███▉      | 299/768 [00:51,  5.90it/s, step size=3.21e-01, acc. prob=0.779]\n",
-      "\rWarmup:  39%|███▉      | 301/768 [00:51,  7.24it/s, step size=1.55e-01, acc. prob=0.778]\n",
-      "\rWarmup:  39%|███▉      | 302/768 [00:51,  6.71it/s, step size=2.15e-01, acc. prob=0.778]\n",
-      "\rWarmup:  40%|███▉      | 304/768 [00:51,  7.87it/s, step size=2.83e-01, acc. prob=0.779]\n",
-      "\rWarmup:  40%|███▉      | 306/768 [00:51,  9.40it/s, step size=1.88e-01, acc. prob=0.778]\n",
-      "\rWarmup:  40%|████      | 308/768 [00:51,  9.82it/s, step size=2.71e-01, acc. prob=0.779]\n",
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-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "customInput": null,
-    "executionStartTime": 1743529103501,
-    "executionStopTime": 1743529104461,
-    "language": "python",
-    "originalKey": "f2fc99ee-52d0-420f-8f1a-0f1293689aad",
-    "output": {
-     "id": "660736130231383"
-    },
-    "outputsInitialized": true,
-    "requestMsgId": "76d49bc3-3046-4c1a-8538-bd0190d04489",
-    "serverExecutionDuration": 3.7160157226026,
-    "showInput": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'STGP - target only': {'NLL': 820.1311610482774, 'MSE': 1658.1030020306143},\n",
-       " 'MTGP - ICM - MAP': {'NLL': 298.84299270373793, 'MSE': 11.273867204848827},\n",
-       " 'MTGP - Latent Embeddings - FB': {'NLL': 525.3912920530319,\n",
-       "  'MSE': 20.109351468055014},\n",
-       " 'MTGP - Latent Embeddings - MAP': {'NLL': 438.331706460182,\n",
-       "  'MSE': 53.53423246625931}}"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "borehole_res "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "customInput": null,
-    "language": "python",
-    "originalKey": "471f2a59-d882-46f1-b8d5-2cd3575a9399",
-    "showInput": true
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-   "outputs": [],
-   "source": []
-  }
- ],
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-   "bento/extensions/system_usage/main.css": true,
-   "bento/extensions/theme/main.css": true
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-  "kernelspec": {
-   "display_name": "pfn",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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-  "last_base_url": "https://bento.edge.x2p.facebook.net/",
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- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/private_multitask_pfn/multifidelity/multifidelity.py b/private_multitask_pfn/multifidelity/multifidelity.py
deleted file mode 100644
index 3b0f739..0000000
--- a/private_multitask_pfn/multifidelity/multifidelity.py
+++ /dev/null
@@ -1,448 +0,0 @@
-
-import math
-
-import torch
-from botorch.test_functions.synthetic import SyntheticTestFunction
-from torch import Tensor
-
-
-class WingWeightMultiFidelity(SyntheticTestFunction):
-    """Wing Weight Design Problem from [Chen2024]_.
-
-    Design variables (physical units):
-      1. s_w   in [150,   200]   (wing area)
-      2. w_fw  in [220,   300]   (fuel weight)
-      3. A     in [6,     10]    (aspect ratio)
-      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)
-      5. q     in [16,    45]    (dynamic pressure)
-      6. lam   in [0.5,   1.0]   (taper ratio)
-      7. t_c   in [0.08,  0.18]   (thickness-to-chord)
-      8. N_z   in [2.5,   6.0]    (ultimate load factor)
-      9. w_dg  in [1700,  2500]   (design gross weight)
-      10. w_pp in [0.025, 0.08]    (weight per unit area)
-
-    Fidelity parameter (stored as the 11th input):
-      0: High fidelity (HF)
-      1: Low fidelity 1 (LF1)
-      2: Low fidelity 2 (LF2)
-      3: Low fidelity 3 (LF2)
-
-    The HF model is given by:
-      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *
-           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *
-           (N_z*w_dg)^0.49 + s_w*w_pp
-
-    LF models use slightly altered exponents and additive biases.
-    """
-
-    dim = 11
-    _num_fidelities = 1
-    _bounds = [
-        (150.0, 200.0),  # s_w
-        (220.0, 300.0),  # w_fw
-        (6.0, 10.0),  # A
-        (-10.0, 10.0),  # Lambda_deg
-        (16.0, 45.0),  # q
-        (0.5, 1.0),  # lam
-        (0.08, 0.18),  # t_c
-        (2.5, 6.0),  # N_z
-        (1700.0, 2500.0),  # w_dg
-        (0.025, 0.08),  # w_pp
-        (0, 3),
-    ]
-    fidelities = [0, 1, 2, 3]
-    _optimal_value = 123.25
-
-    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
-        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.
-        s_w = X[..., 0]
-        w_fw = X[..., 1]
-        A = X[..., 2]
-        Lambda_deg = X[..., 3]
-        Lambda_rad = Lambda_deg * math.pi / 180.0
-
-        q = X[..., 4]
-        lam = X[..., 5]
-        t_c = X[..., 6]
-        N_z = X[..., 7]
-        w_dg = X[..., 8]
-        w_pp = X[..., 9]
-        fidelity = X[..., 10]
-        cos_val = torch.cos(Lambda_rad)
-        y = torch.zeros_like(s_w)
-        # High fidelity (fidelity == 0)
-        mask = fidelity == 0
-        if mask.any():
-            hf = (
-                0.036
-                * (s_w**0.758)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-                + s_w * w_pp
-            )
-            y[mask] = hf[mask]
-        # Low fidelity 1 (fidelity == 1)
-        mask = fidelity == 1
-        if mask.any():
-            lf1 = (
-                0.036
-                * (s_w**0.758)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-                + w_pp
-            )
-            y[mask] = lf1[mask]
-        # Low fidelity 2 (fidelity == 2)
-        mask = fidelity == 2
-        if mask.any():
-            lf2 = (
-                0.036
-                * (s_w**0.8)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-                + w_pp
-            )
-            y[mask] = lf2[mask]
-        # Low fidelity 3 (fidelity == 3)
-        mask = fidelity == 3
-        if mask.any():
-            lf3 = (
-                0.036
-                * (s_w**0.9)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-            )
-            y[mask] = lf3[mask]
-        return y
-
-    def cost(self, X: torch.Tensor) -> torch.Tensor:
-        fidelity = X[..., 10]
-        c = torch.zeros_like(fidelity)
-        c[fidelity == 0] = 1000.0
-        c[fidelity == 1] = 100.0
-        c[fidelity == 2] = 10.0
-        c[fidelity == 3] = 1.0
-        return c
-
-
-class BoreholeMultiFidelity(SyntheticTestFunction):
-    """Borehole Problem from [Chen2024]_.
-
-    This problem models water flow through a borehole with 8 design variables:
-          1. r_w   in [0.05,   0.15]   (borehole radius)
-          2. r     in [100,    50000]  (radius of influence)
-          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)
-          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)
-          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)
-          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)
-          7. L     in [1120,   1680]   (length of borehole)
-          8. K_w   in [9855,   12045]  (hydraulic conductivity)
-
-        The fidelity index (9th input) is categorical:
-          0: High fidelity (HF)
-          1: Low fidelity 1 (LF1)
-          2: Low fidelity 2 (LF2)
-          3: Low fidelity 3 (LF3)
-          4: Low fidelity 4 (LF4)
-
-        The HF model is defined by:
-          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]
-
-        The low-fidelity models modify exponents and add a bias.
-    """
-
-    dim = 9
-    _num_fidelities = 1
-    _bounds = [
-        (0.05, 0.15),  # r_w
-        (100.0, 10000.0),  # r
-        (100.0, 1000.0),  # T_u
-        (10.0, 500.0),  # T_l
-        (990.0, 1110.0),  # H_u
-        (700.0, 820.0),  # H_l
-        (1000.0, 2000.0),  # L
-        (6000.0, 12000.0),  # K_w
-        (0, 4),  # fidelity
-    ]
-    fidelities = [0, 1, 2, 3, 4]
-    _optimal_value = 3.98
-
-    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
-        r_w = X[..., 0]
-        r = X[..., 1]
-        T_u = X[..., 2]
-        T_l = X[..., 3]
-        H_u = X[..., 4]
-        H_l = X[..., 5]
-        L = X[..., 6]
-        K_w = X[..., 7]
-        fidelity = X[..., 8]
-
-        log_term = torch.log(r / r_w)
-        numer = 2.0 * math.pi * T_u * (H_u - H_l)
-        y = torch.zeros_like(r_w)
-
-        # HF (fidelity 0)
-        mask = fidelity == 0
-        if mask.any():
-            hf_denom = log_term * (
-                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            hf = numer / hf_denom
-            y[mask] = hf[mask]
-
-        # LF1 (fidelity 1): add bias.
-        mask = fidelity == 1
-        if mask.any():
-            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)
-            lf1_denom = log_term * (
-                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            lf1 = lf1_numer / lf1_denom
-            y[mask] = lf1[mask]
-
-        # LF2 (fidelity 2): modify the exponent on log_term and add bias.
-        mask = fidelity == 2
-        if mask.any():
-            lf2_denom = log_term * (
-                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l
-            )
-            lf2 = numer / lf2_denom
-            y[mask] = lf2[mask]
-
-        # LF3 (fidelity 3): modify r_w exponent slightly.
-        mask = fidelity == 3
-        if mask.any():
-            lf3_log_term = torch.log(4 * r / r_w)
-            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)
-            lf3_denom = lf3_log_term * (
-                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            lf3 = lf3_numer / lf3_denom
-            y[mask] = lf3[mask]
-        # LF4 (fidelity 4): further bias.
-        mask = fidelity == 4
-        if mask.any():
-            lf4_log_term = torch.log(2 * r / r_w)
-            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)
-            lf4_denom = lf4_log_term * (
-                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            lf4 = lf4_numer / lf4_denom
-            y[mask] = lf4[mask]
-
-        return y
-
-    def cost(self, X: torch.Tensor) -> torch.Tensor:
-        fidelity = X[..., 8]
-        c = torch.zeros_like(fidelity)
-        c[fidelity == 0] = 1000.0
-        c[fidelity == 1] = 100.0
-        c[fidelity == 2] = 10.0
-        c[fidelity == 3] = 100.0
-        c[fidelity == 4] = 10.0
-        return c
-
-import numpy as np
-import torch
-from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll
-from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
-from botorch.models.gp_regression import SingleTaskGP
-
-from botorch.models.multitask import MultiTaskGP
-
-from botorch.models.transforms.input import (
-    ChainedInputTransform,
-    # LatentCategoricalEmbedding,
-    # LatentCategoricalSpec,
-    Normalize,
-)
-from botorch.models.transforms.outcome import Standardize, StratifiedStandardize
-from botorch.utils.transforms import unnormalize
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from torch.distributions import Normal
-
-
-tkwargs = {"dtype": torch.double}
-
-
-def eval_model(model, test_X, test_Y):
-    with torch.no_grad():
-        posterior = model.posterior(test_X, observation_noise=True)
-        # compute sum of LL of each point in test set (using only marginal variances)
-        var = posterior.variance
-        mean = posterior.mean
-        nll = (
-            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))
-            .log_prob(test_Y.view(-1))
-            .sum(dim=-1)
-            .mean()  # take average over MCMC samples (if needed)
-            .item()
-        )
-        mse = (mean - test_Y).pow(2).mean().item()
-    return nll, mse
-
-
-def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):
-    res = {}
-    # test STGP on target task
-    target_mask = train_X[:, -1] == 0
-    model = SingleTaskGP(
-        train_X[target_mask],
-        train_Y[target_mask],
-        input_transform=Normalize(
-            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
-        ),
-    )
-    mll = ExactMarginalLogLikelihood(model.likelihood, model)
-    _ = fit_gpytorch_mll(mll)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["STGP - target only"] = {"NLL": nll, "MSE": mse}
-
-    # Test MTGP with ICM
-    model = MultiTaskGP(
-        train_X,
-        train_Y,
-        input_transform=Normalize(
-            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
-        ),
-        outcome_transform=StratifiedStandardize(
-            stratification_idx=problem.dim - 1,
-            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
-        ),
-        task_feature=problem.dim - 1,
-    )
-    mll = ExactMarginalLogLikelihood(model.likelihood, model)
-    _ = fit_gpytorch_mll(mll)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["MTGP - ICM - MAP"] = {"NLL": nll, "MSE": mse}
-    # Test Fully Bayesian MTGP with Latent Embeddings
-    model = SaasFullyBayesianMultiTaskGP(
-        train_X,
-        train_Y,
-        input_transform=Normalize(
-            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
-        ),
-        task_feature=problem.dim - 1,
-        outcome_transform=StratifiedStandardize(
-            stratification_idx=problem.dim - 1,
-            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
-        ),
-    )
-    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["MTGP - Latent Embeddings - FB"] = {"NLL": nll, "MSE": mse}
-
-    # test LVGP with MAP estimation
-    d = train_X.shape[-1]
-    cat_dims = [problem.dim - 1]
-    # construct input transform
-    input_transform = ChainedInputTransform(
-        normalize=Normalize(d=d, indices=list(range(d - 1))),
-        # latent_emb=LatentCategoricalEmbedding(
-        #     [
-        #         LatentCategoricalSpec(
-        #             idx=i,
-        #             num_categories=len(problem.fidelities),
-        #             latent_dim=2,
-        #         )
-        #         for i in cat_dims
-        #     ],
-        #     dim=d,
-        # ).to(train_X),
-    )
-    model = SingleTaskGP(
-        train_X,
-        train_Y,
-        input_transform=input_transform,
-    )
-    mll = ExactMarginalLogLikelihood(model.likelihood, model)
-    _ = fit_gpytorch_mll(mll)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["MTGP - Latent Embeddings - MAP"] = {"NLL": nll, "MSE": mse}
-    return res
-
-
-# # Wing weight
-# ### Generate initial training set as in Chen et al 2024. Generate test set for target fidelity
-
-
-problem = WingWeightMultiFidelity()
-torch.manual_seed(0)
-# define training and test set
-N_TEST = 100
-fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}
-total_n = sum(fidelity_to_n.values())
-train_X = torch.rand(total_n, problem.dim, **tkwargs)
-train_X = unnormalize(train_X, bounds=problem.bounds)
-# set fidelities
-start = 0
-for fidelity, n in fidelity_to_n.items():
-    end = start + n
-    train_X[start:end, -1] = fidelity
-    start = end
-
-train_Y = problem(train_X).unsqueeze(-1)
-
-test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
-test_X = unnormalize(test_X, bounds=problem.bounds)
-test_X[:, -1] = 0  # target fidelity
-test_Y = problem(test_X).unsqueeze(-1)
-
-
-wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
-
-
-wing_weight_res
-
-# ## Borehole
-
-
-problem = BoreholeMultiFidelity()
-torch.manual_seed(0)
-# define training and test set
-N_TEST = 100
-fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}
-total_n = sum(fidelity_to_n.values())
-train_X = torch.rand(total_n, problem.dim, **tkwargs)
-train_X = unnormalize(train_X, bounds=problem.bounds)
-# set fidelities
-start = 0
-for fidelity, n in fidelity_to_n.items():
-    end = start + n
-    train_X[start:end, -1] = fidelity
-    start = end
-
-train_Y = problem(train_X).unsqueeze(-1)
-
-test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
-test_X = unnormalize(test_X, bounds=problem.bounds)
-test_X[:, -1] = 0  # target fidelity
-test_Y = problem(test_X).unsqueeze(-1)
-
-
-borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
-
-
-borehole_res 
-
-
-
-
-
diff --git a/private_multitask_pfn/multifidelity/multifidelity_fixed.py b/private_multitask_pfn/multifidelity/multifidelity_fixed.py
deleted file mode 100644
index d15dee5..0000000
--- a/private_multitask_pfn/multifidelity/multifidelity_fixed.py
+++ /dev/null
@@ -1,492 +0,0 @@
-from utils import load_model, pfn_predict
-
-import numpy as np
-import torch
-from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll
-from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
-from botorch.models.gp_regression import SingleTaskGP
-
-from botorch.models.multitask import MultiTaskGP
-
-from botorch.models.transforms.input import (
-    ChainedInputTransform,
-    # LatentCategoricalEmbedding,
-    # LatentCategoricalSpec,
-    Normalize,
-)
-from botorch.models.transforms.outcome import Standardize, StratifiedStandardize
-from botorch.utils.transforms import unnormalize, normalize
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from torch.distributions import Normal
-import pickle
-
-import math
-
-import torch
-from botorch.test_functions.synthetic import SyntheticTestFunction
-from torch import Tensor
-
-def eval_model(model, test_X, test_Y):
-    with torch.no_grad():
-        posterior = model.posterior(test_X, observation_noise=True)
-        # compute sum of LL of each point in test set (using only marginal variances)
-        var = posterior.variance
-        mean = posterior.mean
-        nll = (
-            -Normal(loc=mean.squeeze(-1), scale=var.squeeze(-1))
-            .log_prob(test_Y.view(-1))
-            .sum(dim=-1)
-            .mean()  # take average over MCMC samples (if needed)
-            .item()
-        )
-        mse = (mean - test_Y).pow(2).mean().item()
-    return nll, mse
-
-def eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y):
-    res = {}
-    # test STGP on target task
-    target_mask = train_X[:, -1] == 0
-    model = SingleTaskGP(
-        train_X[target_mask],
-        train_Y[target_mask],
-        input_transform=Normalize(
-            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
-        ),
-    )
-    mll = ExactMarginalLogLikelihood(model.likelihood, model)
-    _ = fit_gpytorch_mll(mll)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["STGP - target only"] = {"NLL": nll, "MSE": mse}
-
-    # Test MTGP with ICM
-    model = MultiTaskGP(
-        train_X,
-        train_Y,
-        input_transform=Normalize(
-            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
-        ),
-        outcome_transform=StratifiedStandardize(
-            stratification_idx=problem.dim - 1,
-            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
-        ),
-        task_feature=problem.dim - 1,
-    )
-    mll = ExactMarginalLogLikelihood(model.likelihood, model)
-    _ = fit_gpytorch_mll(mll)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["MTGP - ICM - MAP"] = {"NLL": nll, "MSE": mse}
-    # Test Fully Bayesian MTGP with Latent Embeddings
-    model = SaasFullyBayesianMultiTaskGP(
-        train_X,
-        train_Y,
-        input_transform=Normalize(
-            d=train_X.shape[-1], indices=list(range(train_X.shape[-1] - 1))
-        ),
-        task_feature=problem.dim - 1,
-        outcome_transform=StratifiedStandardize(
-            stratification_idx=problem.dim - 1,
-            task_values=torch.tensor(problem.fidelities, dtype=torch.long),
-        ),
-    )
-    _ = fit_fully_bayesian_model_nuts(model, jit_compile=True)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["MTGP - Latent Embeddings - FB"] = {"NLL": nll, "MSE": mse}
-
-    # test LVGP with MAP estimation
-    d = train_X.shape[-1]
-    cat_dims = [problem.dim - 1]
-    # construct input transform
-    input_transform = ChainedInputTransform(
-        normalize=Normalize(d=d, indices=list(range(d - 1))),
-        # latent_emb=LatentCategoricalEmbedding(
-        #     [
-        #         LatentCategoricalSpec(
-        #             idx=i,
-        #             num_categories=len(problem.fidelities),
-        #             latent_dim=2,
-        #         )
-        #         for i in cat_dims
-        #     ],
-        #     dim=d,
-        # ).to(train_X),
-    )
-    model = SingleTaskGP(
-        train_X,
-        train_Y,
-        input_transform=input_transform,
-    )
-    mll = ExactMarginalLogLikelihood(model.likelihood, model)
-    _ = fit_gpytorch_mll(mll)
-    nll, mse = eval_model(model=model, test_X=test_X, test_Y=test_Y)
-    res["MTGP - Latent Embeddings - MAP"] = {"NLL": nll, "MSE": mse}
-    return res
-
-class WingWeightMultiFidelitySmall(SyntheticTestFunction):
-    """Wing Weight Design Problem from [Chen2024]_.
-
-    Design variables (physical units):
-      1. s_w   in [150,   200]   (wing area)
-      2. w_fw  in [220,   300]   (fuel weight)
-      3. A     in [6,     10]    (aspect ratio)
-      4. Lambda_deg in [-10, 10]  (sweep angle, degrees)
-      5. q     in [16,    45]    (dynamic pressure)
-      6. lam   in [0.5,   1.0]   (taper ratio)
-      7. t_c   in [0.08,  0.18]   (thickness-to-chord)
-      8. N_z   in [2.5,   6.0]    (ultimate load factor)
-      9. w_dg  in [1700,  2500]   (design gross weight)
-      10. w_pp in [0.025, 0.08]    (weight per unit area)
-
-    Fidelity parameter (stored as the 11th input):
-      0: High fidelity (HF)
-      1: Low fidelity 1 (LF1)
-      2: Low fidelity 2 (LF2)
-      3: Low fidelity 3 (LF2)
-
-    The HF model is given by:
-      f0 = 0.036 * s_w^0.758 * w_fw^0.0035 * (A/(cos^2(Lambda_rad)))^0.6 *
-           q^0.006 * lam^0.04 * (100*t_c/cos(Lambda_rad))^-0.3 *
-           (N_z*w_dg)^0.49 + s_w*w_pp
-
-    LF models use slightly altered exponents and additive biases.
-    """
-
-    dim = 7
-    _num_fidelities = 1
-    _bounds = [
-        (150.0, 200.0),  # s_w
-        (220.0, 300.0),  # w_fw
-        (6.0, 10.0),  # A
-        (-10.0, 10.0),  # Lambda_deg
-        (16.0, 45.0),  # q
-        (0.5, 1.0),  # lam
-        # (0.08, 0.18),  # t_c
-        # (2.5, 6.0),  # N_z
-        # (1700.0, 2500.0),  # w_dg
-        # (0.025, 0.08),  # w_pp
-        (0, 3), # fidelity
-    ]
-    fidelities = [0, 1, 2, 3]
-    _optimal_value = 123.25
-
-    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
-        # Expect X of shape [..., 11]: first 10 are design variables, last is fidelity index.
-        s_w = X[..., 0]
-        w_fw = X[..., 1]
-        A = X[..., 2]
-        Lambda_deg = X[..., 3]
-        Lambda_rad = Lambda_deg * math.pi / 180.0
-
-        q = X[..., 4]
-        lam = X[..., 5]
-        t_c = 0.13 #X[..., 6]
-        N_z = 4.5 #X[..., 7]
-        w_dg = 2000 #X[..., 8]
-        w_pp = 0.05 #X[..., 9]
-        fidelity = X[..., -1]
-        cos_val = torch.cos(Lambda_rad)
-        y = torch.zeros_like(s_w)
-        # High fidelity (fidelity == 0)
-        mask = fidelity == 0
-        if mask.any():
-            hf = (
-                0.036
-                * (s_w**0.758)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-                + s_w * w_pp
-            )
-            y[mask] = hf[mask]
-        # Low fidelity 1 (fidelity == 1)
-        mask = fidelity == 1
-        if mask.any():
-            lf1 = (
-                0.036
-                * (s_w**0.758)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-                + w_pp
-            )
-            y[mask] = lf1[mask]
-        # Low fidelity 2 (fidelity == 2)
-        mask = fidelity == 2
-        if mask.any():
-            lf2 = (
-                0.036
-                * (s_w**0.8)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-                + w_pp
-            )
-            y[mask] = lf2[mask]
-        # Low fidelity 3 (fidelity == 3)
-        mask = fidelity == 3
-        if mask.any():
-            lf3 = (
-                0.036
-                * (s_w**0.9)
-                * (w_fw**0.0035)
-                * ((A / (cos_val**2)) ** 0.6)
-                * (q**0.006)
-                * (lam**0.04)
-                * ((100.0 * t_c / cos_val) ** (-0.3))
-                * ((N_z * w_dg) ** 0.49)
-            )
-            y[mask] = lf3[mask]
-        return y
-
-    def cost(self, X: torch.Tensor) -> torch.Tensor:
-        fidelity = X[..., 10]
-        c = torch.zeros_like(fidelity)
-        c[fidelity == 0] = 1000.0
-        c[fidelity == 1] = 100.0
-        c[fidelity == 2] = 10.0
-        c[fidelity == 3] = 1.0
-        return c
-
-
-class BoreholeMultiFidelitySmall(SyntheticTestFunction):
-    """Borehole Problem from [Chen2024]_.
-
-    This problem models water flow through a borehole with 8 design variables:
-          1. r_w   in [0.05,   0.15]   (borehole radius)
-          2. r     in [100,    50000]  (radius of influence)
-          3. T_u   in [63070,  115600] (transmissivity of upper aquifer)
-          4. T_l   in [63.1,   116]    (transmissivity of lower aquifer)
-          5. H_u   in [990,    1110]   (potentiometric head of upper aquifer)
-          6. H_l   in [700,    820]    (potentiometric head of lower aquifer)
-          7. L     in [1120,   1680]   (length of borehole)
-          8. K_w   in [9855,   12045]  (hydraulic conductivity)
-
-        The fidelity index (9th input) is categorical:
-          0: High fidelity (HF)
-          1: Low fidelity 1 (LF1)
-          2: Low fidelity 2 (LF2)
-          3: Low fidelity 3 (LF3)
-          4: Low fidelity 4 (LF4)
-
-        The HF model is defined by:
-          f0 = (2*pi*T_u*(H_u-H_l)) / [ ln(r/r_w) * (1 + (2*L*T_l)/(ln(r/r_w)*r_w^2*K_w)) ]
-
-        The low-fidelity models modify exponents and add a bias.
-    """
-
-    dim = 7
-    _num_fidelities = 1
-    _bounds = [
-        (0.05, 0.15),  # r_w
-        (100.0, 10000.0),  # r
-        (100.0, 1000.0),  # T_u
-        (10.0, 500.0),  # T_l
-        (990.0, 1110.0),  # H_u
-        (700.0, 820.0),  # H_l
-        # (1000.0, 2000.0),  # L
-        # (6000.0, 12000.0),  # K_w
-        (0, 4),  # fidelity
-    ]
-    fidelities = [0, 1, 2, 3, 4]
-    _optimal_value = 3.98
-
-    def evaluate_true(self, X: torch.Tensor) -> torch.Tensor:
-        r_w = X[..., 0]
-        r = X[..., 1]
-        T_u = X[..., 2]
-        T_l = X[..., 3]
-        H_u = X[..., 4]
-        H_l = X[..., 5]
-        L = 760 # X[..., 6]
-        K_w = 1500 #X[..., 7]
-        fidelity = X[..., -1]
-
-        log_term = torch.log(r / r_w)
-        numer = 2.0 * math.pi * T_u * (H_u - H_l)
-        y = torch.zeros_like(r_w)
-
-        # HF (fidelity 0)
-        mask = fidelity == 0
-        if mask.any():
-            hf_denom = log_term * (
-                1.0 + (2.0 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            hf = numer / hf_denom
-            y[mask] = hf[mask]
-
-        # LF1 (fidelity 1): add bias.
-        mask = fidelity == 1
-        if mask.any():
-            lf1_numer = 2.0 * math.pi * T_u * (H_u - 0.8 * H_l)
-            lf1_denom = log_term * (
-                1.0 + (L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            lf1 = lf1_numer / lf1_denom
-            y[mask] = lf1[mask]
-
-        # LF2 (fidelity 2): modify the exponent on log_term and add bias.
-        mask = fidelity == 2
-        if mask.any():
-            lf2_denom = log_term * (
-                1.0 + (8 * L * T_u) / (log_term * (r_w**2) * K_w) + 0.75 * T_u / T_l
-            )
-            lf2 = numer / lf2_denom
-            y[mask] = lf2[mask]
-
-        # LF3 (fidelity 3): modify r_w exponent slightly.
-        mask = fidelity == 3
-        if mask.any():
-            lf3_log_term = torch.log(4 * r / r_w)
-            lf3_numer = 2.0 * math.pi * T_u * (1.09 * H_u - H_l)
-            lf3_denom = lf3_log_term * (
-                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            lf3 = lf3_numer / lf3_denom
-            y[mask] = lf3[mask]
-        # LF4 (fidelity 4): further bias.
-        mask = fidelity == 4
-        if mask.any():
-            lf4_log_term = torch.log(2 * r / r_w)
-            lf4_numer = 2.0 * math.pi * T_u * (1.05 * H_u - H_l)
-            lf4_denom = lf4_log_term * (
-                1.0 + (3 * L * T_u) / (log_term * (r_w**2) * K_w) + T_u / T_l
-            )
-            lf4 = lf4_numer / lf4_denom
-            y[mask] = lf4[mask]
-
-        return y
-
-    def cost(self, X: torch.Tensor) -> torch.Tensor:
-        fidelity = X[..., 8]
-        c = torch.zeros_like(fidelity)
-        c[fidelity == 0] = 1000.0
-        c[fidelity == 1] = 100.0
-        c[fidelity == 2] = 10.0
-        c[fidelity == 3] = 100.0
-        c[fidelity == 4] = 10.0
-        return c
-    
-
-
-def eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y):
-    task_id = train_X[:, -1].long()
-    # normalize
-    train_X = normalize(train_X, bounds=problem.bounds)
-    test_X = normalize(test_X, bounds=problem.bounds)
-    
-    train_X = train_X[:, :-1]  # remove fidelity column
-    test_X = test_X[:, :-1]  # remove fidelity column
-    
-    # standardize Y
-    train_y_mean, train_y_std = train_Y.mean(), train_Y.std()
-    train_Y = (train_Y - train_y_mean) / train_y_std
-    
-    with torch.no_grad():
-        posterior = pfn_predict(pfn, task_id, train_X.float(), train_Y.float(), test_X.float())
-        # compute sum of LL of each point in test set (using only marginal variances)
-        var = posterior.variance
-        mean = posterior.mean
-        # import pdb; pdb.set_trace()
-        unstand_mean, unstand_var = (
-            mean * train_y_std + train_y_mean,
-            var * train_y_std**2,
-        )
-        nll = (
-            -Normal(loc=unstand_mean.squeeze(-1), scale=unstand_var.squeeze(-1))
-            .log_prob(test_Y.view(-1))
-            .sum(dim=-1)
-            .mean()  # take average over MCMC samples (if needed)
-            .item()
-        )
-        mse = (unstand_mean - test_Y).pow(2).mean().item()
-    return nll, mse
-
-# print(pickle.load(open("wing_weight_res.pkl", "rb")))
-# print(pickle.load(open("borehole_res.pkl", "rb")))  # load the results
-# lily
-
-tkwargs = {"dtype": torch.float}
-problem = WingWeightMultiFidelitySmall()
-torch.manual_seed(0)
-# define training and test set
-N_TEST = 100
-fidelity_to_n = {0: 5, 1: 5, 2: 10, 3: 50}
-total_n = sum(fidelity_to_n.values())
-train_X = torch.rand(total_n, problem.dim, **tkwargs)
-train_X = unnormalize(train_X, bounds=problem.bounds)
-# set fidelities
-start = 0
-for fidelity, n in fidelity_to_n.items():
-    end = start + n
-    train_X[start:end, -1] = fidelity
-    start = end
-
-train_Y = problem(train_X).unsqueeze(-1)
-
-test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
-test_X = unnormalize(test_X, bounds=problem.bounds)
-test_X[:, -1] = 0  # target fidelity
-test_Y = problem(test_X).unsqueeze(-1)
-
-
-wing_weight_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
-
-# for pfn_id in ["vibrant-breeze-498", "revived-frog-499"]:
-#     pfn = load_model(f"/home/yl9959/mtpfn/final_models/{pfn_id}")
-#     nll, mse = eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)
-#     wing_weight_res[pfn_id] = {"NLL": nll, "MSE": mse}
-    
-print("Wing Weight Results")
-for k, v in wing_weight_res.items():
-    print(k)
-    print(v)
-    print()
-# Save the results
-pickle.dump(wing_weight_res, open("wing_weight_res.pkl", "wb"))
-
-
-
-problem = BoreholeMultiFidelitySmall()
-torch.manual_seed(0)
-# define training and test set
-N_TEST = 100
-fidelity_to_n = {0: 5, 1: 5, 2: 25, 3: 5, 4: 25}
-total_n = sum(fidelity_to_n.values())
-train_X = torch.rand(total_n, problem.dim, **tkwargs)
-train_X = unnormalize(train_X, bounds=problem.bounds)
-# set fidelities
-start = 0
-for fidelity, n in fidelity_to_n.items():
-    end = start + n
-    train_X[start:end, -1] = fidelity
-    start = end
-
-train_Y = problem(train_X).unsqueeze(-1)
-
-test_X = torch.rand(N_TEST, problem.dim, **tkwargs)
-test_X = unnormalize(test_X, bounds=problem.bounds)
-test_X[:, -1] = 0  # target fidelity
-test_Y = problem(test_X).unsqueeze(-1)
-
-borehole_res = eval_models_on_problem(problem, train_X, train_Y, test_X, test_Y)
-
-# for pfn_id in ["vibrant-breeze-498", "revived-frog-499"]:
-#     pfn = load_model(f"/home/yl9959/mtpfn/final_models/{pfn_id}")
-#     nll, mse = eval_pfn_on_problem(pfn, problem, train_X, train_Y, test_X, test_Y)
-#     borehole_res[pfn_id] = {"NLL": nll, "MSE": mse}
-    
-print("Borehole Results")
-for k, v in borehole_res.items():
-    print(k)
-    print(v)
-    print()
-# Save the results
-pickle.dump(borehole_res, open("borehole_res.pkl", "wb"))
\ No newline at end of file
diff --git a/private_multitask_pfn/multifidelity/wing_weight_res.pkl b/private_multitask_pfn/multifidelity/wing_weight_res.pkl
deleted file mode 100644
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diff --git a/private_multitask_pfn/paper_figures/benchmark_time.ipynb b/private_multitask_pfn/paper_figures/benchmark_time.ipynb
deleted file mode 100644
index cd1f606..0000000
--- a/private_multitask_pfn/paper_figures/benchmark_time.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>n_samples_per_task</th>\n",
-       "      <th>mtgp</th>\n",
-       "      <th>scaml</th>\n",
-       "      <th>pfn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0.525355</td>\n",
-       "      <td>0.641527</td>\n",
-       "      <td>0.000156</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0.475192</td>\n",
-       "      <td>0.789012</td>\n",
-       "      <td>0.000167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>52</td>\n",
-       "      <td>0.316957</td>\n",
-       "      <td>0.571510</td>\n",
-       "      <td>0.000171</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>68</td>\n",
-       "      <td>0.433492</td>\n",
-       "      <td>1.640374</td>\n",
-       "      <td>0.000186</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>87</td>\n",
-       "      <td>0.892945</td>\n",
-       "      <td>0.781131</td>\n",
-       "      <td>0.000200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>113</td>\n",
-       "      <td>1.074306</td>\n",
-       "      <td>0.666595</td>\n",
-       "      <td>0.000217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>146</td>\n",
-       "      <td>1.437731</td>\n",
-       "      <td>0.942868</td>\n",
-       "      <td>0.000248</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>189</td>\n",
-       "      <td>2.241446</td>\n",
-       "      <td>1.490375</td>\n",
-       "      <td>0.000279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>8</td>\n",
-       "      <td>244</td>\n",
-       "      <td>4.005497</td>\n",
-       "      <td>1.585110</td>\n",
-       "      <td>0.000320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>316</td>\n",
-       "      <td>15.475098</td>\n",
-       "      <td>1.954615</td>\n",
-       "      <td>0.000378</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0  n_samples_per_task       mtgp     scaml       pfn\n",
-       "0           0                  31   0.525355  0.641527  0.000156\n",
-       "1           1                  40   0.475192  0.789012  0.000167\n",
-       "2           2                  52   0.316957  0.571510  0.000171\n",
-       "3           3                  68   0.433492  1.640374  0.000186\n",
-       "4           4                  87   0.892945  0.781131  0.000200\n",
-       "5           5                 113   1.074306  0.666595  0.000217\n",
-       "6           6                 146   1.437731  0.942868  0.000248\n",
-       "7           7                 189   2.241446  1.490375  0.000279\n",
-       "8           8                 244   4.005497  1.585110  0.000320\n",
-       "9           9                 316  15.475098  1.954615  0.000378"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(1, 2, figsize=(8, 3))\n",
-    "\n",
-    "df = pd.read_csv(\"/home/lily_l/private_multitask_pfn/benchmark_time_samples.csv\")\n",
-    "n_samples_per_task = df[\"n_samples_per_task\"]\n",
-    "mtgp = df[\"mtgp\"]\n",
-    "scaml = df[\"scaml\"]\n",
-    "pfn = df[\"pfn\"]\n",
-    "\n",
-    "axs[0].plot(n_samples_per_task, mtgp, label=\"Joint (ICM)\")\n",
-    "axs[0].plot(n_samples_per_task, scaml, label=\"Ensemble (ScaML)\")\n",
-    "axs[0].plot(n_samples_per_task, pfn, label=\"MTPFN\")\n",
-    "\n",
-    "axs[0].set_xlabel(\"Number of samples per task\")\n",
-    "axs[0].set_ylabel(\"Time (s)\")\n",
-    "axs[0].set_ylim(-0.5, 13)\n",
-    "axs[0].set_title(\"5 Tasks\")\n",
-    "\n",
-    "df = pd.read_csv(\"/home/lily_l/private_multitask_pfn/benchmark_time_task.csv\")\n",
-    "n_tasks = df[\"n_tasks\"]\n",
-    "mtgp = df[\"mtgp\"]\n",
-    "scaml = df[\"scaml\"]\n",
-    "pfn = df[\"pfn\"]\n",
-    "\n",
-    "\n",
-    "axs[1].plot(n_tasks, mtgp, label=\"Joint (ICM)\")\n",
-    "axs[1].plot(n_tasks, scaml, label=\"Ensemble (ScaML)\")\n",
-    "axs[1].plot(n_tasks, pfn, label=\"MTPFN\")\n",
-    "\n",
-    "axs[1].set_title(\"50 Samples per Task\")\n",
-    "axs[1].set_xlabel(\"Number of tasks\")\n",
-    "axs[1].set_ylabel(\"Time (s)\")\n",
-    "axs[1].set_xscale(\"log\")\n",
-    "\n",
-    "axs[1].set_ylim(-0.5, 13)\n",
-    "\n",
-    "# for axis set grid\n",
-    "for ax in axs:\n",
-    "    ax.grid(True, which='major', linestyle='--', linewidth=0.5)\n",
-    "\n",
-    "plt.legend()\n",
-    "plt.savefig(\"benchmark_time.pdf\", bbox_inches='tight')\n",
-    "\n",
-    "\n",
-    "# plt.legend()\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pfn",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/private_multitask_pfn/paper_figures/benchmark_time_samples.csv b/private_multitask_pfn/paper_figures/benchmark_time_samples.csv
deleted file mode 100644
index 2e0b278..0000000
--- a/private_multitask_pfn/paper_figures/benchmark_time_samples.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-,n_samples_per_task,mtgp,scaml,pfn
-0,31,0.5253551891073585,0.641526885703206,0.00015585944056510924
-1,40,0.47519186716526746,0.7890122389420867,0.0001666948199272156
-2,52,0.3169569944962859,0.5715096918866038,0.0001706913113594055
-3,68,0.4334919206798077,1.6403738755732775,0.0001862967386841774
-4,87,0.8929445460438729,0.781130695156753,0.00020006559789180755
-5,113,1.074306179396808,0.6665953608229757,0.0002172837033867836
-6,146,1.4377311939373612,0.9428675603121519,0.0002484273165464401
-7,189,2.241445547528565,1.490374975092709,0.0002793321385979652
-8,244,4.005497383885086,1.5851096855476499,0.00031972844153642653
-9,316,15.475097639858722,1.954615351371467,0.0003778502345085144
diff --git a/private_multitask_pfn/paper_figures/benchmark_time_task.csv b/private_multitask_pfn/paper_figures/benchmark_time_task.csv
deleted file mode 100644
index 3cd4bf3..0000000
--- a/private_multitask_pfn/paper_figures/benchmark_time_task.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-,n_tasks,mtgp,scaml,pfn
-0,3,0.2848466670140624,0.2343419875949621,0.00014029312878847123
-1,4,0.25992622077465055,0.7537569304928183,0.00015438385307788848
-2,7,0.8310501158237458,1.5789503464475274,0.00020955316722393036
-3,11,13.401715995557606,1.616787961870432,0.0003231516107916832
-4,17,,2.5916220061481,0.0005841787904500961
-5,26,,17.287783980555833,0.0011530579999089242
-6,40,,25.625626320578156,0.002393585816025734
-7,61,,,0.00520332083106041
-8,94,,,0.011591497063636779
-9,143,,,0.026707475446164607
-10,219,,,0.06632151622325182
-11,335,,,0.16265095062553883
-12,513,,,0.4161450076848269
-13,784,,,0.9698941947892308
-14,1199,,,2.2640723291784526
-15,1832,,,5.259221996180713
diff --git a/private_multitask_pfn/paper_figures/figures_bayesian.py b/private_multitask_pfn/paper_figures/figures_bayesian.py
deleted file mode 100644
index c041a72..0000000
--- a/private_multitask_pfn/paper_figures/figures_bayesian.py
+++ /dev/null
@@ -1,428 +0,0 @@
-from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
-import torch
-import json
-
-
-import pickle
-import pdb
-import gpytorch
-import torch
-from botorch.models import SingleTaskGP
-from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
-from botorch.exceptions import ModelFittingError
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from tqdm import tqdm
-import botorch
-import einops
-
-from tabpfn.base import (
-    create_inference_engine,
-    determine_precision,
-    initialize_tabpfn_model,
-)
-from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
-
-from tabpfn import TabPFNClassifier, TabPFNRegressor
-import matplotlib.pyplot as plt
-import os
-
-import traceback
-from collections import defaultdict
-
-
-colors = {
-    "MTPFN": "deepskyblue",
-    "ScaML": "#E78C35",
-    "ICM": "#DA2222",
-    "ICM (NUTS)": "C2",
-    "GP": "#95211B",
-}
-
-baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"] #+ ["random"] 
-
-# model_to_label = {
-#     "different-dust-282": "PFN (0.5 uncorr)",
-#     "royal-firebrand-281": "PFN (0.25 uncorr)",
-#     "peach-plasma-280": "PFN (1.0 uncorr)",
-#     "treasured-lion-279": "PFN (0.0 uncorr)",
-#     "exalted-wave-278": "PFN (0.75 uncorr)",
-# }
-
-# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-
-def get_label(model):
-    if model in model_to_label:
-        return model_to_label[model]
-    return model
-
-
-def get_labels(models):
-    return [get_label(model) for model in models]
-
-
-def get_color(model):
-    return colors[get_label(model)]
-
-
-
-
-
-def plot_bo_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
-        # linestyle 
-        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
-
-                    
-    plt.legend()
-    plt.xlabel("BO Iterations")
-    plt.ylabel("Normalized Regret")
-    plt.title(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    
-    
-    
-
-def plot_bo_ax(ax, exp_dir, models, title, path=None):    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                if torch.isnan(function_results).any():
-                    continue
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results) #0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        # linestyle 
-        xs = torch.linspace(0, 40, len(mean))
-        ax.plot(xs, mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
-
-    ax.set_title(title)
-    ax.set_ylabel("Normalized Regret")
-    ax.set_xlabel("BO Iterations")
-    # ax.set_ylim(0.05, 0.7)
-    # ax.set_yscale("log")
-    # plt.legend()
-    # plt.xlabel("BO Iterations")
-    # plt.ylabel("Normalized Regret")
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-    
-    
-    
-    
-
-def plot_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    # one plot for boxplots of mse, one plot for boxplots of nll
-    
-    model_mses = defaultdict(list)
-    model_nlls = defaultdict(list)
-    
-    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-    for model_order in all_models:
-        for function_dir in os.listdir(exp_dir):
-            # check if directory
-            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                continue
-            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
-                results = json.load(f)
-                
-            models = list(results.keys())
-            for model in models:
-                if model != model_order:
-                    continue
-                mses, nlls = [], []
-                try:
-                    for trial_key in results[model]:
-                        mses.append(results[model][trial_key]["mse"])
-                        nlls.append(results[model][trial_key]["nll"])
-                except KeyboardInterrupt as e:
-                    raise e
-                except Exception as e:
-                    traceback.print_exc()
-                    continue
-                model_mses[model].extend(mses)
-                model_nlls[model].extend(nlls)
-    
-    # plot nll boxplots
-    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("NLL")
-    plt.title(title)
-    plt.ylabel("NLL")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    plt.clf()
-    
-    # plot mse boxplots
-    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("MSE")
-    plt.title(title)
-    plt.ylabel("MSE")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path.replace(".png", "_mse.png"))
-    plt.clf()
-    
-
-def plot_bo_function_ax(ax, function_dir, models, title):
-    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
-        bo_results = json.load(f)
-    with open(os.path.join(function_dir, "info.json"), "r") as f:
-        info = json.load(f)
-        
-    for model in baselines + models:
-        for model_try in bo_results:
-            if model_try != model:
-                continue
-            model_results = []
-            try:
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    model_results.append(regret)
-                
-                # remove shorter trials
-                max_length = max([len(result) for result in model_results])
-                model_results = [result for result in model_results if len(result) == max_length]
-                
-                model_results = torch.stack(model_results)
-                
-                mean = model_results.nanmean(0)
-                std = model_results.std(0) /  len(model_results) #** 0.5
-                lower = mean - std
-                upper = mean + std
-                # mean = model_results.mean(0)
-                # lower = model_results.quantile(0.25, 0)
-                # upper = model_results.quantile(0.75, 0)
-                linestyle = "--" if model in baselines else "-"
-                
-                xs = torch.linspace(0, 40, len(mean))
-                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
-                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
-                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                print(e)
-                # import pdb; pdb.set_trace()
-                # raise e
-                continue
-                    
-    # plt.legend()
-    ax.set_xlabel("BO Iterations")
-    ax.set_ylabel("Normalized Regret")
-    ax.set_title(title)
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-
-
-
-# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
-# plot_bo_all("/home/yl9959/mtpfn/eval_plot/25-01-28_11-31-32__synthetic__trials_5__seed_0__num_samples/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0", models, "All")
-
-# fig, axs = plt.subplots(1, 5, figsize=(12, 2.5))
-plt.figure(figsize=(6, 4))
-ax = plt.gca()
-
-# # add grid
-# for ax in axs:
-#     # y grid only
-#     ax.yaxis.grid(True,  linestyle='-', linewidth=0.5, alpha=0.5)
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_23-38-18__synthetic__trials_3__seed_0__nuts_1/features_3__tasks_4__lengthscale_0_2__task_corr_0_6__uncorr_tasks_0__n_target_2__n_source_20__seed_0/function_0"
-models = ["stilted-night-482"]
-model_to_label = {
-    "stilted-night-482": "MTPFN",
-    "mtgp": "ICM",
-    "scaml": "ICM (NUTS)",
-    "gp": "GP",
-    "mtgp_nuts": "ScaML",
-}
-title = ""
-plot_bo_function_ax(ax, dir, models, title)
-
-plt.legend()
-plt.savefig("full.pdf", bbox_inches='tight')
-lily
-
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__xgb/n_target_5__n_source_20"
-model_to_label = {
-    "stilted-night-482": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["stilted-night-482"]
-title = "XGB"
-plot_bo_ax(axs[1], dir, models, title)
-
-
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__rf/n_target_2__n_source_20"
-model_to_label = {
-    "stilted-night-482": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["stilted-night-482"]
-title = "RF"
-plot_bo_ax(axs[2], dir, models, title)
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__lr/n_target_2__n_source_20"
-model_to_label = {
-    "fallen-cherry-483": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["fallen-cherry-483"]
-title = "LR"
-plot_bo_ax(axs[3], dir, models, title)
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__nn/n_target_2__n_source_20"
-model_to_label = {
-    "stilted-night-482": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["stilted-night-482"]
-title = "NN"
-plot_bo_ax(axs[4], dir, models, title)
-
-handles, labels = axs[0].get_legend_handles_labels()
-# legend above plot
-fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=4, handles=handles, labels=labels)
-# plt.legend()
-plt.tight_layout()
-plt.savefig("hpobench.pdf", bbox_inches='tight')
diff --git a/private_multitask_pfn/paper_figures/figures_benchmarks.py b/private_multitask_pfn/paper_figures/figures_benchmarks.py
deleted file mode 100644
index 6bc1a91..0000000
--- a/private_multitask_pfn/paper_figures/figures_benchmarks.py
+++ /dev/null
@@ -1,449 +0,0 @@
-from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
-import torch
-import json
-
-
-import pickle
-import pdb
-import gpytorch
-import torch
-from botorch.models import SingleTaskGP
-from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
-from botorch.exceptions import ModelFittingError
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from tqdm import tqdm
-import botorch
-import einops
-
-from tabpfn.base import (
-    create_inference_engine,
-    determine_precision,
-    initialize_tabpfn_model,
-)
-from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
-
-from tabpfn import TabPFNClassifier, TabPFNRegressor
-import matplotlib.pyplot as plt
-import os
-
-import traceback
-from collections import defaultdict
-
-
-#4477AA', '#EE6677', '#228833', '#CCBB44', '#66CCEE', '#AA3377', '#BBBBBB'
-# colors = {
-#     "PFN (0%)": "#4477AA",
-#     "PFN (10%)": "#66CCEE",
-#     "PFN (20%)": "#228833",
-#     "ScaML": "#CCBB44",
-#     "ICM": "#EE6677",
-#     "GP": "#AA3377",
-# }
-##2166AC', '#4393C3', '#92C5DE '#F4A582', '#D6604D', '#B2182B'
-colors = {
-    # "PFN (0%)": "#C3A8D1",
-    "PFN": "deepskyblue",
-    "PFN (10%)": "#6059A9",
-    "PFN (20%)": "#4E96BC",
-    "ScaML": "#E78C35",
-    "ICM": "#DA2222",
-    "GP": "#95211B",
-    "Random": "#B2B2B2",
-}
-
-marker = {
-    "PFN (0%)": "o",
-    "PFN (10%)": "^",
-    "PFN (20%)": "s",
-    "ScaML": "1",
-    "ICM": "+",
-    "GP": "x",
-}
-
-# marker = {
-#     "PFN (0%)": "1",
-#     "PFN (10%)": "2",
-#     "PFN (20%)": "3",
-#     "ScaML": "+",
-#     "ICM": "x",
-#     "GP": "o",
-# }
-
-##CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE', '#882255', '#44AA99', '#999933', '#AA4499'
-# colors = {
-#     "PFN (0%)": "#CC6677",
-#     "PFN (10%)": "#332288",
-#     "PFN (20%)": "#DDCC77",
-#     "ScaML": "#117733",
-#     "ICM": "#88CCEE",
-#     "GP": "#882255",
-# }
-
-baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]# + ["random"] 
-
-# model_to_label = {
-#     "different-dust-282": "PFN (0.5 uncorr)",
-#     "royal-firebrand-281": "PFN (0.25 uncorr)",
-#     "peach-plasma-280": "PFN (1.0 uncorr)",
-#     "treasured-lion-279": "PFN (0.0 uncorr)",
-#     "exalted-wave-278": "PFN (0.75 uncorr)",
-# }
-
-# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-
-def get_label(model):
-    if model in model_to_label:
-        return model_to_label[model]
-    return model
-
-
-def get_labels(models):
-    return [get_label(model) for model in models]
-
-
-def get_color(model):
-    return colors[get_label(model)]
-
-
-
-
-
-def plot_bo_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                traceback.print_exc()
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
-        # linestyle 
-        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
-
-                    
-    plt.legend()
-    plt.xlabel("BO Iterations")
-    plt.ylabel("Normalized Regret")
-    plt.title(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    
-    
-    
-
-def plot_bo_ax(ax, exp_dir, models, title, path):    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
-        # linestyle 
-        ax.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        ax.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
-
-    ax.set_title(title)
-    ax.set_ylabel("Normalized Regret")
-    ax.set_xlabel("BO Iterations")
-    # ax.set_ylim(0.05, 0.7)
-    # ax.set_yscale("log")
-    # plt.legend()
-    # plt.xlabel("BO Iterations")
-    # plt.ylabel("Normalized Regret")
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-    
-    
-    
-    
-
-def plot_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    # one plot for boxplots of mse, one plot for boxplots of nll
-    
-    model_mses = defaultdict(list)
-    model_nlls = defaultdict(list)
-    
-    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-    for model_order in all_models:
-        for function_dir in os.listdir(exp_dir):
-            # check if directory
-            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                continue
-            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
-                results = json.load(f)
-                
-            models = list(results.keys())
-            for model in models:
-                if model != model_order:
-                    continue
-                mses, nlls = [], []
-                try:
-                    for trial_key in results[model]:
-                        mses.append(results[model][trial_key]["mse"])
-                        nlls.append(results[model][trial_key]["nll"])
-                except KeyboardInterrupt as e:
-                    raise e
-                except Exception as e:
-                    traceback.print_exc()
-                    continue
-                model_mses[model].extend(mses)
-                model_nlls[model].extend(nlls)
-    
-    # plot nll boxplots
-    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("NLL")
-    plt.title(title)
-    plt.ylabel("NLL")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    plt.clf()
-    
-    # plot mse boxplots
-    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("MSE")
-    plt.title(title)
-    plt.ylabel("MSE")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path.replace(".png", "_mse.png"))
-    plt.clf()
-    
-
-
-def plot_bo_function_ax(ax, function_dir, title):
-    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
-        bo_results = json.load(f)
-    with open(os.path.join(function_dir, "info.json"), "r") as f:
-        info = json.load(f)
-        
-    for model in baselines + models:
-        for model_try in bo_results:
-            if model_try != model:
-                continue
-            model_results = []
-            try:
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    model_results.append(regret)
-                
-                # remove shorter trials
-                max_length = max([len(result) for result in model_results])
-                model_results = [result for result in model_results if len(result) == max_length]
-                
-                model_results = torch.stack(model_results)
-                
-                mean = model_results.nanmean(0)
-                std = model_results.std(0) / len(model_results)# ** 0.5
-                lower = mean - std
-                upper = mean + std
-                # mean = model_results.mean(0)
-                # lower = model_results.quantile(0.25, 0)
-                # upper = model_results.quantile(0.75, 0)
-                linestyle = "--" if model in baselines else "-"
-                
-                xs = torch.linspace(0, 40, len(mean))
-                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
-                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
-                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                print(e)
-                # import pdb; pdb.set_trace()
-                # raise e
-                continue
-                    
-    # plt.legend()
-    ax.set_xlabel("BO Iterations")
-    ax.set_ylabel("Normalized Regret")
-    ax.set_title(title)
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-
-
-
-
-# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
-
-
-#### !!!!!!!!!  FC-Net   !!!!!!!!!! ####
-
-fig, axs = plt.subplots(1, 4, figsize=(12, 3))
-
-# add grid
-for ax in axs:
-    ax.grid(True, which='both', linestyle='-', linewidth=0.5, alpha=0.5)
-
-
-# fcnet naval
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_5__n_source_20/fcnet_naval_propulsion_data"
-models = ["likely-donkey-479"]
-model_to_label = {
-    "likely-donkey-479": "PFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-title = "Naval Propulsion"
-plot_bo_function_ax(axs[0], dir, title)
-
-title = "Parkinson's"
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_2__n_source_100/fcnet_parkinsons_telemonitoring_data"
-models = ["wobbly-donkey-484"]
-model_to_label = {
-    "wobbly-donkey-484": "PFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-plot_bo_function_ax(axs[1], dir, title)
-
-title = "Protein Structure"
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_2__n_source_100/fcnet_protein_structure_data"
-models = ["fallen-cherry-483"]
-model_to_label = {
-    "fallen-cherry-483": "PFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-plot_bo_function_ax(axs[2], dir, title)
-
-title = "Slice Localization"
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_12-37-46__fcnet__trials_5__seed_0/features_3__n_target_2__n_source_100/fcnet_slice_localization_data"
-models = ["stilted-night-482"]
-model_to_label = {
-    "stilted-night-482": "PFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-plot_bo_function_ax(axs[3], dir, title)
-
-# plt.legend()
-plt.tight_layout()
-plt.savefig("fcnet.pdf")
\ No newline at end of file
diff --git a/private_multitask_pfn/paper_figures/figures_boxplot_data.pkl b/private_multitask_pfn/paper_figures/figures_boxplot_data.pkl
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diff --git a/private_multitask_pfn/paper_figures/figures_finetune.ipynb b/private_multitask_pfn/paper_figures/figures_finetune.ipynb
deleted file mode 100644
index 3c54e9a..0000000
--- a/private_multitask_pfn/paper_figures/figures_finetune.ipynb
+++ /dev/null
@@ -1,833 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/lily_l/private_multitask_pfn/PFNs/pfns/priors/utils.py:293: FutureWarning: Decorating classes is deprecated and will be disabled in future versions. You should only decorate functions or methods. To preserve the current behavior of class decoration, you can directly decorate the `__init__` method and nothing else.\n",
-      "  @torch.no_grad()\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pickle\n",
-    "from utils import load_model_from_epoch, load_model\n",
-    "from gen_batch import get_hpobench_batch_fn, get_mtgp_batch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lr_batch_fn = get_hpobench_batch_fn('lr', train=False)\n",
-    "\n",
-    "batches = []\n",
-    "for i in range(20):\n",
-    "    batch = lr_batch_fn(\n",
-    "        batch_size=256,\n",
-    "        seq_len=20,\n",
-    "        num_features=2,\n",
-    "        max_num_tasks=10,\n",
-    "        num_tasks=1,\n",
-    "    )\n",
-    "    batches.append(batch)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "svm_batch_fn = get_hpobench_batch_fn('svm', train=False)\n",
-    "\n",
-    "svm_batches = []\n",
-    "for i in range(20):\n",
-    "    batch = svm_batch_fn(\n",
-    "        batch_size=256,\n",
-    "        seq_len=20,\n",
-    "        num_features=2,\n",
-    "        max_num_tasks=10,\n",
-    "        num_tasks=1,\n",
-    "    )\n",
-    "    svm_batches.append(batch)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mtgp_batches = []\n",
-    "for i in range(20):\n",
-    "    batch = get_mtgp_batch(\n",
-    "        batch_size=256,\n",
-    "        seq_len=20,\n",
-    "        num_features=2,\n",
-    "        max_num_tasks=10,\n",
-    "        num_tasks=1,\n",
-    "        lengthscale=None,\n",
-    "        hyperparameters={},\n",
-    "    )\n",
-    "    mtgp_batches.append(batch)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_nll(model, batches, n_train=10):\n",
-    "    nlls = []\n",
-    "    for batch in batches:\n",
-    "        tensor_args = batch.x[:n_train], batch.task_id[:n_train], batch.y[:n_train], batch.x[n_train:], batch.task_id[n_train:]\n",
-    "        tensor_args = [t.cuda() for t in tensor_args]\n",
-    "\n",
-    "        with torch.no_grad():\n",
-    "            output = model(*tensor_args)\n",
-    "        mean = output[..., 0].squeeze()\n",
-    "        var = output[..., 1].exp().squeeze()\n",
-    "        nll = -torch.distributions.Normal(mean, var.sqrt()).log_prob(batch.y[n_train:].squeeze()).mean()\n",
-    "        del output\n",
-    "        nlls.append(nll)\n",
-    "    nlls = torch.stack(nlls)\n",
-    "    return nlls.mean().item(),\n",
-    "\n",
-    "def get_nlls(model, batches, n_train=10):\n",
-    "    nlls = []\n",
-    "    for batch in batches:\n",
-    "        tensor_args = batch.x[:n_train], batch.task_id[:n_train], batch.y[:n_train], batch.x[n_train:], batch.task_id[n_train:]\n",
-    "        tensor_args = [t.cuda() for t in tensor_args]\n",
-    "\n",
-    "        with torch.no_grad():\n",
-    "            output = model(*tensor_args)\n",
-    "        mean = output[..., 0].squeeze()\n",
-    "        var = output[..., 1].exp().squeeze()\n",
-    "        nll = -torch.distributions.Normal(mean, var.sqrt()).log_prob(batch.y[n_train:].squeeze()).mean()\n",
-    "        del output\n",
-    "        nlls.append(nll)\n",
-    "    nlls = torch.stack(nlls)\n",
-    "    return nlls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "home_dir = \"/home/lily_l/private_multitask_pfn/ckpt/25-01-29_04-46-15__prior_hpobench_lr__features_2__tasks_1__epochs_200__seqlen_50__attn_standard__task_hier__seed_0__continued__37634\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "2\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "3\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "4\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "5\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "6\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "7\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "8\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "9\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "10\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "11\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "12\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "13\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "14\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "15\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "16\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "17\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "18\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n",
-      "19\n",
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n"
-     ]
-    }
-   ],
-   "source": [
-    "nlls = []\n",
-    "svm_nlls = []\n",
-    "gp_nlls = []\n",
-    "for epoch in range(1, 20):\n",
-    "    print(epoch)\n",
-    "    model = load_model_from_epoch(home_dir, epoch * 5).to('cuda')\n",
-    "    nll = get_nll(model, batches)\n",
-    "    nlls.append(nll)\n",
-    "    svm_nll = get_nll(model, svm_batches)\n",
-    "    svm_nlls.append(svm_nll)\n",
-    "    gp_nll = get_nll(model, mtgp_batches)\n",
-    "    gp_nlls.append(gp_nll)\n",
-    "    del model\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n"
-     ]
-    }
-   ],
-   "source": [
-    "original_dir = \"/home/lily_l/private_multitask_pfn/final_models/playful-tree-486\"\n",
-    "original_model = load_model(original_dir, best=False).to('cuda')\n",
-    "nll = get_nll(original_model, batches)\n",
-    "nlls = [nll] + nlls\n",
-    "svm_nll = get_nll(original_model, svm_batches)\n",
-    "svm_nlls = [svm_nll] + svm_nlls\n",
-    "gp_nll = get_nll(original_model, mtgp_batches)\n",
-    "gp_nlls = [gp_nll] + gp_nlls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "plt.plot(nlls, label='nll')\n",
-    "plt.plot(svm_nlls, label='svm_nll')\n",
-    "plt.plot(gp_nlls, label='gp_nll')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "svm_nlls\n",
-    "\n",
-    "svm_nlls_updated = [(1.38,), (1.64,)] + svm_nlls[2:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1.0934847593307495,),\n",
-       " (0.045990969985723495,),\n",
-       " (-0.052184343338012695,),\n",
-       " (-0.08173080533742905,),\n",
-       " (-0.021465713158249855,),\n",
-       " (-0.09919952601194382,),\n",
-       " (0.18788771331310272,),\n",
-       " (-0.019634241238236427,),\n",
-       " (-0.08407261967658997,),\n",
-       " (-0.0778316855430603,),\n",
-       " (-0.19331598281860352,),\n",
-       " (-0.06958013027906418,),\n",
-       " (-0.17593057453632355,),\n",
-       " (-0.09505557268857956,),\n",
-       " (-0.09145152568817139,),\n",
-       " (-0.15969513356685638,),\n",
-       " (-0.07935347408056259,),\n",
-       " (-0.2318514883518219,),\n",
-       " (-0.09641797840595245,),\n",
-       " (-0.12503884732723236,)]"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nlls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = nlls, svm_nlls_updated, gp_nlls\n",
-    "with open('nlls.pkl', 'wb') as f:\n",
-    "    pickle.dump(data, f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with open('nlls.pkl', 'rb') as f:\n",
-    "    data = pickle.load(f)\n",
-    "    \n",
-    "nlls, svm_nlls_updated, gp_nlls = data\n",
-    "\n",
-    "xs = torch.arange(20) * 16 * 32\n",
-    "\n",
-    "plt.figure(figsize=(8, 4))\n",
-    "plt.grid(alpha=0.5)\n",
-    "# plt.ylim(-0.5, 3.5)\n",
-    "plt.xlim(0, 10000)\n",
-    "plt.plot(xs, nlls, label=\"LR (In Domain)\", linewidth=2, color=\"C0\")\n",
-    "plt.plot(xs, svm_nlls_updated, label=\"SVM (Out of Domain)\", linestyle=\"--\", linewidth=2, color=\"C1\")\n",
-    "plt.plot(xs, gp_nlls, label=\"GP Draw (Out of Domain)\", linestyle=\"--\", linewidth=2, color=\"C2\")\n",
-    "plt.xlabel(\"# of LR Train Datasets for Fine-Tuning\", fontsize=12)\n",
-    "plt.ylabel(\"Negative Log Likelihood\", fontsize=12)\n",
-    "# legend above plot\n",
-    "plt.legend(fontsize=12, loc='upper left')\n",
-    "plt.savefig(\"finetune.pdf\", bbox_inches='tight')\n",
-    "# plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=3, fontsize=10)\n",
-    "# legend to right of plot\n",
-    "# plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
-    "\n",
-    "# plt.plot(svm_nlls, label='svm_nll')\n",
-    "# plt.plot(gp_nlls, label='gp_nll')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TransformerModel(\n",
-       "  (transformer_encoder): HierarchicalTaskEncoder(\n",
-       "    (layers): ModuleList(\n",
-       "      (0): SingleTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "      (1): GlobalTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "      (2): SingleTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "      (3): GlobalTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "      (4): SingleTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "      (5): GlobalTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "      (6): SingleTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "      (7): GlobalTaskEncoderLayer(\n",
-       "        (attn): MultiheadAttention(\n",
-       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
-       "        )\n",
-       "        (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (ff_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "        (linear1): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "        (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
-       "        (dropout1): Dropout(p=0.0, inplace=False)\n",
-       "        (dropout2): Dropout(p=0.0, inplace=False)\n",
-       "      )\n",
-       "    )\n",
-       "    (meta_embedding): Embedding(1, 256)\n",
-       "  )\n",
-       "  (encoder): Sequential(\n",
-       "    (0): Normalize()\n",
-       "    (1): VariableNumFeaturesEncoder(\n",
-       "      (base_encoder): Linear(in_features=2, out_features=256, bias=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (y_encoder): Linear(in_features=1, out_features=256, bias=True)\n",
-       "  (pos_encoder): NoPositionalEncoding()\n",
-       "  (decoder_dict): ModuleDict(\n",
-       "    (standard): Sequential(\n",
-       "      (0): Linear(in_features=256, out_features=1024, bias=True)\n",
-       "      (1): GELU(approximate='none')\n",
-       "      (2): Linear(in_features=1024, out_features=2, bias=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (criterion): GaussianNLLLoss()\n",
-       ")"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "finetuned_model = load_model_from_epoch(home_dir, 195).to('cuda')\n",
-    "finetuned_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using a Transformer with 6.58 M parameters\n",
-      "Loaded model\n"
-     ]
-    }
-   ],
-   "source": [
-    "original_dir = \"/home/lily_l/private_multitask_pfn/final_models/playful-tree-486\"\n",
-    "original_model = load_model(original_dir, best=False).to('cuda')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_nlls_mtpfn(model, aux_data, batches, n_train=10):\n",
-    "    nlls = []\n",
-    "    for batch in batches:\n",
-    "        tensor_args = batch.x[:n_train], batch.task_id[:n_train], batch.y[:n_train], batch.x[n_train:], batch.task_id[n_train:]\n",
-    "        tensor_args = [t.cuda() for t in tensor_args]\n",
-    "        \n",
-    "        train_x, task_id, train_y, test_x, test_task_id = tensor_args\n",
-    "        B = train_x.shape[1]\n",
-    "        for task, aux_batch in enumerate(aux_data):\n",
-    "            aux_x = aux_batch.x.cuda().repeat(1, B, 1)\n",
-    "            aux_y = aux_batch.y.cuda().repeat(1, B, 1)\n",
-    "            train_x = torch.cat([train_x, aux_x], dim=0)\n",
-    "            train_y = torch.cat([train_y, aux_y], dim=0)\n",
-    "            task_id = torch.cat([task_id, torch.ones_like(aux_y).long() * (task+1)], dim=0)\n",
-    "            \n",
-    "        tensor_args = train_x, task_id, train_y, test_x, test_task_id\n",
-    "\n",
-    "        with torch.no_grad():\n",
-    "            output = model(*tensor_args)\n",
-    "        mean = output[..., 0].squeeze()\n",
-    "        var = output[..., 1].exp().squeeze()\n",
-    "        nll = -torch.distributions.Normal(mean, var.sqrt()).log_prob(batch.y[n_train:].squeeze()).mean()\n",
-    "        del output\n",
-    "        nlls.append(nll)\n",
-    "    nlls = torch.stack(nlls)\n",
-    "    return nlls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using a Transformer with 6.59 M parameters\n",
-      "Loaded model\n",
-      "i 0\n",
-      "i 1\n",
-      "i 2\n",
-      "i 3\n",
-      "i 4\n",
-      "i 5\n",
-      "i 6\n",
-      "i 7\n",
-      "i 8\n",
-      "i 9\n",
-      "i 10\n",
-      "i 11\n",
-      "i 12\n",
-      "i 13\n",
-      "i 14\n",
-      "i 15\n",
-      "i 16\n",
-      "i 17\n",
-      "i 18\n",
-      "i 19\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.1844, device='cuda:0')"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mtpfn = load_model(\"/home/lily_l/private_multitask_pfn/wandb_links/upbeat-darkness-493\", best=False).to('cuda')\n",
-    "\n",
-    "lr_batch_fn_train = get_hpobench_batch_fn('lr', train=True)\n",
-    "    \n",
-    "best_lr_data = None\n",
-    "best_nlls = 1e10\n",
-    "for i in range(20):\n",
-    "    print(\"i\", i)\n",
-    "    aux_data = []\n",
-    "    for j in range(3):\n",
-    "        aux_batch = lr_batch_fn_train(\n",
-    "            batch_size=1,\n",
-    "            seq_len=50,\n",
-    "            num_features=2,\n",
-    "            max_num_tasks=10,\n",
-    "            num_tasks=1,\n",
-    "        )\n",
-    "        aux_data.append(aux_batch)\n",
-    "    mtpfn_nlls = get_nlls_mtpfn(mtpfn, aux_data, batches)\n",
-    "    quartile = torch.median(mtpfn_nlls)\n",
-    "    if quartile < best_nlls:\n",
-    "        best_nlls = quartile\n",
-    "        best_lr_data = aux_data\n",
-    "        \n",
-    "best_nlls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.boxplot([\n",
-    "    get_nlls_mtpfn(mtpfn, best_aux_data, batches).cpu(),\n",
-    "    # get_nlls_mtpfn(mtpfn, best_aux_data, svm_batches).cpu(),\n",
-    "    # get_nlls_mtpfn(mtpfn, best_aux_data, mtgp_batches).cpu(),\n",
-    "])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mtpfn_nlls = get_nlls_mtpfn(mtpfn, best_aux_data, batches).cpu().numpy()\n",
-    "mtpfn_svm_nlls = get_nlls_mtpfn(mtpfn, best_aux_data, svm_batches).cpu().numpy()\n",
-    "mtpfn_gp_nlls = get_nlls_mtpfn(mtpfn, [], mtgp_batches).cpu().numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "[(mtpfn_nlls, mtpfn_svm_nlls, mtpfn_gp_nlls),\n",
-    "(finetuned_nlls, finetuned_svm_nlls, finetuned_gp_nlls),\n",
-    "(original_nlls, original_svm_nlls, original_gp_nlls)] = pickle.load(open('figures_boxplot_data.pkl', 'rb'))\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 3, figsize=(8, 4))\n",
-    "\n",
-    "box_colors = ['skyblue', 'C5', 'C4']\n",
-    "bp = axs[0].boxplot([mtpfn_nlls, finetuned_nlls, original_nlls], showfliers=False, widths=0.6, patch_artist=True)\n",
-    "for i, patch in enumerate(bp['boxes']):\n",
-    "    patch.set_facecolor(box_colors[i])\n",
-    "# change center line\n",
-    "for i, median in enumerate(bp['medians']):\n",
-    "    median.set(color='black', linewidth=1)\n",
-    "axs[0].set_title(\"LR (In Domain)\")\n",
-    "\n",
-    "bp = axs[1].boxplot([mtpfn_svm_nlls, finetuned_svm_nlls, original_svm_nlls], showfliers=False, widths=0.6, patch_artist=True)\n",
-    "for i, patch in enumerate(bp['boxes']):\n",
-    "    patch.set_facecolor(box_colors[i])\n",
-    "# change center line\n",
-    "for i, median in enumerate(bp['medians']):\n",
-    "    median.set(color='black', linewidth=1)\n",
-    "axs[1].set_title(\"SVM (Out of Domain)\")\n",
-    "\n",
-    "bp = axs[2].boxplot([mtpfn_gp_nlls,finetuned_gp_nlls, original_gp_nlls], showfliers=False, widths=0.6, patch_artist=True)\n",
-    "for i, patch in enumerate(bp['boxes']):\n",
-    "    patch.set_facecolor(box_colors[i])\n",
-    "# change center line\n",
-    "for i, median in enumerate(bp['medians']):\n",
-    "    median.set(color='black', linewidth=1)\n",
-    "axs[2].set_title(\"GP Draw (Out of Domain)\")\n",
-    "axs[0].set_ylabel(\"Negative Log Likelihood\", fontsize=12)\n",
-    "\n",
-    "# add legend\n",
-    "from matplotlib.patches import Patch\n",
-    "from matplotlib.lines import Line2D\n",
-    "\n",
-    "legend_elements = [\n",
-    "    Patch(facecolor=box_colors[0], edgecolor='black', label='MTPFN'),\n",
-    "    Patch(facecolor=box_colors[1], edgecolor='black', label='Fine-Tuned Single-Task'),\n",
-    "    Patch(facecolor=box_colors[2], edgecolor='black', label='Original Single-Task'),\n",
-    "]\n",
-    "# legend over all subplots\n",
-    "\n",
-    "# fig.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=3, fontsize=12)\n",
-    "fig.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=(0.54, 0), ncol=3, fontsize=12)\n",
-    "\n",
-    "\n",
-    "for ax in axs:\n",
-    "    ax.set_xticklabels([\"MT\", \"FT\", \"ST\"], fontsize=12)\n",
-    "    # ax.grid(alpha=0.5, axis='y')\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"finetune_nll.pdf\", bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGdCAYAAABO2DpVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAYhElEQVR4nO3dcWyU9R348U/VUNtxrSIiEop1EbxDho7qDOlm1KkLWZzsj8U4mzGjxJmic2SJ6T86s2x1WbZoMsOY2WQJYzqXoIuJEmcCZFE2OEKirpWy2ciGqFtmW2h3GtrfH/7ofvwE5Y7v0+u1r1dywTue5/l+SKq8fe655+rGxsbGAgAggdOqPQAAMHUICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASOaMiV5wdHQ0Dhw4ELlcLurq6iZ6eQCgAmNjYzE0NBTz5s2L00478XmJCQ+LAwcOREtLy0QvCwAksH///pg/f/4Jf3/CwyKXy0XEh4M1NTVN9PIAQAUGBwejpaVl/O/xE5nwsDj69kdTU5OwAIAa80mXMZR98eY///nP6OjoiHPOOScaGhriM5/5TOzataviAQGAqaOsMxb/+c9/or29Pa655pp47rnn4txzz42+vr44++yzs5oPAKghZYXFj370o2hpaYnHH398/LULL7ww+VAAQG0q662QP/zhD3H55ZfH1772tZgzZ0589rOfjccee+xj9ymVSjE4OHjMAwCYmsoKi7///e+xbt26WLhwYWzZsiXuuuuuuOeee+LXv/71Cffp7u6O5ubm8YePmgLA1FU3NjY2drIbz5gxIy6//PJ46aWXxl+75557YufOnfHyyy8fd59SqRSlUmn8+dGPqwwMDPhUCADUiMHBwWhubv7Ev7/LOmNx/vnnx+LFi495rVAoxJtvvnnCferr68c/WuojpgAwtZUVFu3t7fH6668f89revXvjggsuSDoUAFCbygqL73znO7Fjx4744Q9/GPv27YtNmzbFL37xi+js7MxqPgCghpQVFldccUVs3rw5fvvb38aSJUvi+9//fjz88MNx6623ZjUfAFBDyrp4M4WTvfgDAJg8Mrl4EwDg40z4l5ABtWF4eDh6e3vL2mdkZCT6+/ujtbU1GhoaTnq/fD4fjY2N5Y4ITELCAjiu3t7eaGtrm5C1isViLFu2bELWArIlLIDjyufzUSwWy9qnp6cnOjo6YuPGjVEoFMpaC5gahAVwXI2NjRWfRSgUCs5AwDTl4k0AIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkygqL733ve1FXV3fMI5/PZzUbAFBjzih3h0suuST++Mc//u8AZ5R9CABgiiq7Cs4444yYO3duFrMAADWu7Gss+vr6Yt68efHpT386br311njzzTezmAsAqEFlnbG48sorY8OGDXHxxRfHW2+9FQ8++GB84QtfiFdffTVyudxx9ymVSlEqlcafDw4OntrEAMCkVVZYrFixYvyfly5dGldeeWVccMEF8bvf/S5uv/324+7T3d0dDz744KlNCQDUhFP6uOlZZ50VixYtin379p1wm66urhgYGBh/7N+//1SWBAAmsVMKi0OHDsXf/va3OP/880+4TX19fTQ1NR3zAACmprLC4rvf/W5s27Yt+vv746WXXoqvfvWrcfrpp8ctt9yS1XwAQA0p6xqLf/zjH3HLLbfEv//97zj33HPj85//fOzYsSPOPffcrOYDAGpIWWHxxBNPZDUHADAF+K4QACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMmUdYMsoLb19fXF0NBQZsfv6ek55tcs5HK5WLhwYWbHB06NsIBpoq+vLxYtWjQha3V0dGR6/L1794oLmKSEBUwTR89UbNy4MQqFQiZrjIyMRH9/f7S2tkZDQ0Py4/f09ERHR0emZ12AUyMsYJopFAqxbNmyzI7f3t6e2bGByc/FmwBAMsICAEjGWyEA1Izh4eHo7e096e0rve4nn89HY2NjJSNOe8ICgJrR29sbbW1tma9TLBYzvRZpKhMWANSMfD4fxWLxpLc/+kmicj8Nlc/nKxmPEBYA1JDGxsaKziRk/Wko/sfFmwBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIz7WMA0MndmXTS8tzfiQG3+P0XDe3tj7sy6ao8BfAxhAdPInW0zorD9zojt1Z6kMoX48M8ATF7CAqaR9cX34+b7N0ShRm9X3NPbG+t/8vX4SrUHAU5IWMA0cvDQWIyctShi3mXVHqUiIwdH4+ChsWqPAXyM2nyjFQCYlIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMqcUFg899FDU1dXFvffem2gcAKCWVRwWO3fujPXr18fSpUtTzgMA1LCKwuLQoUNx6623xmOPPRZnn3126pkAgBpVUVh0dnbGl7/85bjuuus+cdtSqRSDg4PHPACAqemMcnd44oknYvfu3bFz586T2r67uzsefPDBsgcDAGpPWWcs9u/fH9/+9rfjN7/5TZx55pkntU9XV1cMDAyMP/bv31/RoADA5FfWGYtisRjvvPNOLFu2bPy1I0eOxPbt2+NnP/tZlEqlOP3004/Zp76+Purr69NMC1RseHg4IiJ2796d2RojIyPR398fra2t0dDQkPz4PT09yY8JpFVWWHzxi1+MV1555ZjXbrvttsjn83Hfffd9JCqAyaO3tzciIlavXl3lSU5dLper9gjACZQVFrlcLpYsWXLMa5/61KfinHPO+cjrwOSycuXKiIjI5/PR2NiYyRo9PT3R0dERGzdujEKhkMkauVwuFi5cmMmxgVNX9sWbQG2aPXt23HHHHROyVqFQOOYtU2D6OOWw2Lp1a4IxAICpwHeFAADJCAsAIBlhAQAkIywAgGSEBQCQjI+bAsc1PDw8flOtk3X0zpjl3iEzy3trABNLWADH1dvbG21tbRXt29HRUdb2xWLRfS9gihAWwHHl8/koFotl7VPpd4Xk8/lyxwMmKWEBHFdjY2NFZxHa29szmAaoFS7eBACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJBMWWGxbt26WLp0aTQ1NUVTU1MsX748nnvuuaxmAwBqTFlhMX/+/HjooYeiWCzGrl274tprr42bbropXnvttazmAwBqyBnlbHzjjTce8/wHP/hBrFu3Lnbs2BGXXHJJ0sEAgNpTVlj8v44cORJPPfVUHD58OJYvX37C7UqlUpRKpfHng4ODlS4JAExyZV+8+corr8TMmTOjvr4+vvWtb8XmzZtj8eLFJ9y+u7s7mpubxx8tLS2nNDAAMHmVHRYXX3xx7NmzJ/785z/HXXfdFatWrYq//vWvJ9y+q6srBgYGxh/79+8/pYEBgMmr7LdCZsyYERdddFFERLS1tcXOnTvjkUceifXr1x93+/r6+qivrz+1KQGAmnDK97EYHR095hoKAGD6KuuMRVdXV6xYsSIWLFgQQ0NDsWnTpti6dWts2bIlq/kAgBpSVli888478Y1vfCPeeuutaG5ujqVLl8aWLVvi+uuvz2o+AKCGlBUWv/zlL7OaAwCYAnxXCACQjLAAAJIRFgBAMsICAEim4u8KAYBT1dfXF0NDQ5kdv6en55hfs5LL5WLhwoWZrlErhAUAVdHX1xeLFi2akLU6OjoyX2Pv3r3iIoQFAFVy9EzFxo0bo1AoZLLGyMhI9Pf3R2trazQ0NGSyRk9PT3R0dGR65qWWCAsAqqpQKMSyZcsyO357e3tmx+ajXLwJACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkU1ZYdHd3xxVXXBG5XC7mzJkTK1eujNdffz2r2QCAGlNWWGzbti06Oztjx44d8cILL8QHH3wQN9xwQxw+fDir+QCAGnJGORs///zzxzzfsGFDzJkzJ4rFYlx11VVJBwMAak9ZYfH/GxgYiIiIWbNmnXCbUqkUpVJp/Png4OCpLAkATGIVX7w5Ojoa9957b7S3t8eSJUtOuF13d3c0NzePP1paWipdEgCY5CoOi87Oznj11VfjiSee+Njturq6YmBgYPyxf//+SpcEACa5it4KWbNmTTz77LOxffv2mD9//sduW19fH/X19RUNBwDUlrLCYmxsLO6+++7YvHlzbN26NS688MKs5gIAalBZYdHZ2RmbNm2KZ555JnK5XBw8eDAiIpqbm6OhoSGTAQGA2lHWNRbr1q2LgYGBuPrqq+P8888ffzz55JNZzQcA1JCy3woBADgR3xUCACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJFN2WGzfvj1uvPHGmDdvXtTV1cXTTz+dwVgAQC0qOywOHz4cl156aTz66KNZzAMA1LAzyt1hxYoVsWLFiixmAQBqXNlhUa5SqRSlUmn8+eDgYNZLAgBVkvnFm93d3dHc3Dz+aGlpyXpJAKBKMj9j0dXVFWvXrh1/Pjg4KC4AiIiIuTProuG9vREHavdDig3v7Y25M+uqPcakkXlY1NfXR319fdbLAFCD7mybEYXtd0Zsr/YklSvEh38OPpR5WADAiawvvh83378hCvl8tUepWE9vb6z/ydfjK9UeZJIoOywOHToU+/btG3/+xhtvxJ49e2LWrFmxYMGCpMMBMLUdPDQWI2ctiph3WbVHqdjIwdE4eGis2mNMGmWHxa5du+Kaa64Zf370+olVq1bFhg0bkg0GANSessPi6quvjrExZQYAfFTtXoYLAEw6wgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyfjadACqYnh4OCIidu/endkaIyMj0d/fH62trdHQ0JDJGj09PZkct1YJCwCqore3NyIiVq9eXeVJ0sjlctUeYVIQFgBUxcqVKyMiIp/PR2NjYyZr9PT0REdHR2zcuDEKhUIma0R8GBULFy7M7Pi1RFgAUBWzZ8+OO+64Y0LWKhQKsWzZsglZa7pz8SYAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAyvitkkhgeHh7/pr+TcSpfBZzlF/4AML0Ji0mit7c32traJmStYrHoy3gAyISwmCTy+XwUi8WT3v5Uvgo4n8+XOx4AnBRhMUk0NjZWdBbBVwED00m5bxv39PQc8+vJ8pZx5YQFADWj0reNOzo6ytreW8aVExYA1Ixy3zau9EJ3bxlXTlgAUDMqedu4vb09o2k4HvexAACSccYiI319fTE0NJTZ8Su9IKlcuVwuFi5cmOkaAEwdwiIDfX19sWjRoglZq9wLkiqxd+9ecQHASREWGTh6pqKSe0ycrFO58+bJOnqvjCzPvAAwtQiLDGV9jwkXJAEw2bh4EwBIRlgAAMkICwAgmYrC4tFHH43W1tY488wz48orr4y//OUvqecCAGpQ2WHx5JNPxtq1a+OBBx6I3bt3x6WXXhpf+tKX4p133sliPgCghpQdFj/96U9j9erVcdttt8XixYvj5z//eTQ2NsavfvWrLOYDAGpIWWHx/vvvR7FYjOuuu+5/BzjttLjuuuvi5ZdfPu4+pVIpBgcHj3kAAFNTWWHxr3/9K44cORLnnXfeMa+fd955cfDgwePu093dHc3NzeOPlpaWyqcFACa1zG+Q1dXVFWvXrh1/Pjg4OC3iYu7Mumh4b2/Egdr94E3De3tj7sy6ao8BQA0pKyxmz54dp59+erz99tvHvP7222/H3Llzj7tPfX191NfXVz5hjbqzbUYUtt8Zsb3ak1SuEB/+OQDgZJUVFjNmzIi2trZ48cUXY+XKlRERMTo6Gi+++GKsWbMmi/lq1vri+3Hz/RuikM9Xe5SK9fT2xvqffD2+Uu1BAKgZZb8Vsnbt2li1alVcfvnl8bnPfS4efvjhOHz4cNx2221ZzFezDh4ai5GzFkXMu6zao1Rs5OBoHDw0Vu0xAKghZYfFzTffHO+++27cf//9cfDgwbjsssvi+eef/8gFnQDA9FPRxZtr1qzx1gcA8BG1+5EFAGDSERYAQDLCAgBIRlgAAMlkfufN6Wh4eDgiInbv3p3ZGiMjI9Hf3x+tra3R0NCQyRo9PT2ZHBeAqUtYZKC3tzciIlavXl3lSdLI5XLVHgGAGiEsMnD0rqT5fD4aGxszWaOnpyc6Ojpi48aNUSgUMlkj4sOoWLhwYWbHB2BqERYZmD17dtxxxx0TslahUIhly5ZNyFoA8ElcvAkAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGTOqPYAfGh4eDh6e3tPevuenp5jfi1HPp+PxsbGsvcDgE8iLCaJ3t7eaGtrK3u/jo6OsvcpFouxbNmysvcDgE8iLCaJfD4fxWLxpLcfGRmJ/v7+aG1tjYaGhrLXAoAs1I2NjY1N5IKDg4PR3NwcAwMD0dTUNJFLAwAVOtm/v128CQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDIT/rXpR79MdXBwcKKXBgAqdPTv7U/6UvQJD4uhoaGIiGhpaZnopQGAUzQ0NBTNzc0n/P26sU9Kj8RGR0fjwIEDkcvloq6ubiKXnlIGBwejpaUl9u/fH01NTdUeByLCzyWTj5/JdMbGxmJoaCjmzZsXp5124ispJvyMxWmnnRbz58+f6GWnrKamJv+yMOn4uWSy8TOZxsedqTjKxZsAQDLCAgBIRljUqPr6+njggQeivr6+2qPAOD+XTDZ+JifehF+8CQBMXc5YAADJCAsAIBlhAQAkIywAgGSERY3Zvn173HjjjTFv3ryoq6uLp59+utojMc11d3fHFVdcEblcLubMmRMrV66M119/vdpjMc2tW7culi5dOn5jrOXLl8dzzz1X7bGmBWFRYw4fPhyXXnppPProo9UeBSIiYtu2bdHZ2Rk7duyIF154IT744IO44YYb4vDhw9UejWls/vz58dBDD0WxWIxdu3bFtddeGzfddFO89tpr1R5tyvNx0xpWV1cXmzdvjpUrV1Z7FBj37rvvxpw5c2Lbtm1x1VVXVXscGDdr1qz48Y9/HLfffnu1R5nSJvy7QoCpbWBgICI+/I84TAZHjhyJp556Kg4fPhzLly+v9jhTnrAAkhkdHY1777032tvbY8mSJdUeh2nulVdeieXLl8d///vfmDlzZmzevDkWL15c7bGmPGEBJNPZ2Rmvvvpq/OlPf6r2KBAXX3xx7NmzJwYGBuL3v/99rFq1KrZt2yYuMiYsgCTWrFkTzz77bGzfvj3mz59f7XEgZsyYERdddFFERLS1tcXOnTvjkUceifXr11d5sqlNWACnZGxsLO6+++7YvHlzbN26NS688MJqjwTHNTo6GqVSqdpjTHnCosYcOnQo9u3bN/78jTfeiD179sSsWbNiwYIFVZyM6aqzszM2bdoUzzzzTORyuTh48GBERDQ3N0dDQ0OVp2O66urqihUrVsSCBQtiaGgoNm3aFFu3bo0tW7ZUe7Qpz8dNa8zWrVvjmmuu+cjrq1atig0bNkz8QEx7dXV1x3398ccfj29+85sTOwz8X7fffnu8+OKL8dZbb0Vzc3MsXbo07rvvvrj++uurPdqUJywAgGTceRMASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJPN/AGmNkwmy2yb1AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "finetuned_nlls = get_nlls(finetuned_model, batches).cpu().numpy()\n",
-    "finetuned_svm_nlls = get_nlls(finetuned_model, svm_batches).cpu().numpy()\n",
-    "finetuned_gp_nlls = get_nlls(finetuned_model, mtgp_batches).cpu().numpy()\n",
-    "\n",
-    "# box plot of nlls\n",
-    "plt.boxplot([finetuned_nlls, finetuned_svm_nlls, finetuned_gp_nlls], showfliers=False)\n",
-    "plt.show()\n",
-    "\n",
-    "original_nlls = get_nlls(original_model, batches).cpu().numpy()\n",
-    "original_svm_nlls = get_nlls(original_model, svm_batches).cpu().numpy()\n",
-    "original_gp_nlls = get_nlls(original_model, mtgp_batches).cpu().numpy()\n",
-    "\n",
-    "plt.boxplot([finetuned_nlls, finetuned_svm_nlls, finetuned_gp_nlls], showfliers=False)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pfn",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/private_multitask_pfn/paper_figures/figures_hpobench.py b/private_multitask_pfn/paper_figures/figures_hpobench.py
deleted file mode 100644
index 25c8f45..0000000
--- a/private_multitask_pfn/paper_figures/figures_hpobench.py
+++ /dev/null
@@ -1,420 +0,0 @@
-from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
-import torch
-import json
-
-
-import pickle
-import pdb
-import gpytorch
-import torch
-from botorch.models import SingleTaskGP
-from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
-from botorch.exceptions import ModelFittingError
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from tqdm import tqdm
-import botorch
-import einops
-
-from tabpfn.base import (
-    create_inference_engine,
-    determine_precision,
-    initialize_tabpfn_model,
-)
-from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
-
-from tabpfn import TabPFNClassifier, TabPFNRegressor
-import matplotlib.pyplot as plt
-import os
-
-import traceback
-from collections import defaultdict
-
-
-colors = {
-    "MTPFN": "deepskyblue",
-    "ScaML": "#E78C35",
-    "ICM": "#DA2222",
-    "GP": "#95211B",
-}
-
-baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"] #+ ["random"] 
-
-# model_to_label = {
-#     "different-dust-282": "PFN (0.5 uncorr)",
-#     "royal-firebrand-281": "PFN (0.25 uncorr)",
-#     "peach-plasma-280": "PFN (1.0 uncorr)",
-#     "treasured-lion-279": "PFN (0.0 uncorr)",
-#     "exalted-wave-278": "PFN (0.75 uncorr)",
-# }
-
-# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-
-def get_label(model):
-    if model in model_to_label:
-        return model_to_label[model]
-    return model
-
-
-def get_labels(models):
-    return [get_label(model) for model in models]
-
-
-def get_color(model):
-    return colors[get_label(model)]
-
-
-
-
-
-def plot_bo_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
-        # linestyle 
-        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
-
-                    
-    plt.legend()
-    plt.xlabel("BO Iterations")
-    plt.ylabel("Normalized Regret")
-    plt.title(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    
-    
-    
-
-def plot_bo_ax(ax, exp_dir, models, title, path=None):    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                if torch.isnan(function_results).any():
-                    continue
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results) ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        # linestyle 
-        xs = torch.linspace(0, 40, len(mean))
-        ax.plot(xs, mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
-
-    ax.set_title(title)
-    ax.set_ylabel("Normalized Regret")
-    ax.set_xlabel("BO Iterations")
-    # ax.set_ylim(0.05, 0.7)
-    # ax.set_yscale("log")
-    # plt.legend()
-    # plt.xlabel("BO Iterations")
-    # plt.ylabel("Normalized Regret")
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-    
-    
-    
-    
-
-def plot_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    # one plot for boxplots of mse, one plot for boxplots of nll
-    
-    model_mses = defaultdict(list)
-    model_nlls = defaultdict(list)
-    
-    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-    for model_order in all_models:
-        for function_dir in os.listdir(exp_dir):
-            # check if directory
-            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                continue
-            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
-                results = json.load(f)
-                
-            models = list(results.keys())
-            for model in models:
-                if model != model_order:
-                    continue
-                mses, nlls = [], []
-                try:
-                    for trial_key in results[model]:
-                        mses.append(results[model][trial_key]["mse"])
-                        nlls.append(results[model][trial_key]["nll"])
-                except KeyboardInterrupt as e:
-                    raise e
-                except Exception as e:
-                    traceback.print_exc()
-                    continue
-                model_mses[model].extend(mses)
-                model_nlls[model].extend(nlls)
-    
-    # plot nll boxplots
-    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("NLL")
-    plt.title(title)
-    plt.ylabel("NLL")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    plt.clf()
-    
-    # plot mse boxplots
-    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("MSE")
-    plt.title(title)
-    plt.ylabel("MSE")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path.replace(".png", "_mse.png"))
-    plt.clf()
-    
-
-def plot_bo_function_ax(ax, function_dir, models, title):
-    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
-        bo_results = json.load(f)
-    with open(os.path.join(function_dir, "info.json"), "r") as f:
-        info = json.load(f)
-        
-    for model in baselines + models:
-        for model_try in bo_results:
-            if model_try != model:
-                continue
-            model_results = []
-            try:
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    model_results.append(regret)
-                
-                # remove shorter trials
-                max_length = max([len(result) for result in model_results])
-                model_results = [result for result in model_results if len(result) == max_length]
-                
-                model_results = torch.stack(model_results)
-                
-                mean = model_results.nanmean(0)
-                std = model_results.std(0) / len(model_results) ** 0.5
-                lower = mean - std
-                upper = mean + std
-                # mean = model_results.mean(0)
-                # lower = model_results.quantile(0.25, 0)
-                # upper = model_results.quantile(0.75, 0)
-                linestyle = "--" if model in baselines else "-"
-                
-                xs = torch.linspace(0, 40, len(mean))
-                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
-                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
-                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                print(e)
-                # import pdb; pdb.set_trace()
-                # raise e
-                continue
-                    
-    # plt.legend()
-    ax.set_xlabel("BO Iterations")
-    ax.set_ylabel("Normalized Regret")
-    ax.set_title(title)
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-
-
-
-# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
-# plot_bo_all("/home/yl9959/mtpfn/eval_plot/25-01-28_11-31-32__synthetic__trials_5__seed_0__num_samples/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0", models, "All")
-
-fig, axs = plt.subplots(1, 5, figsize=(12, 2.5))
-
-# add grid
-for ax in axs:
-    # y grid only
-    ax.yaxis.grid(True,  linestyle='-', linewidth=0.5, alpha=0.5)
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__svm/n_target_2__n_source_20/"
-models = ["likely-donkey-479"]
-model_to_label = {
-    "likely-donkey-479": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-title = "SVM"
-plot_bo_ax(axs[0], dir, models, title)
-
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__xgb/n_target_5__n_source_20"
-model_to_label = {
-    "stilted-night-482": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["stilted-night-482"]
-title = "XGB"
-plot_bo_ax(axs[1], dir, models, title)
-
-
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__rf/n_target_2__n_source_20"
-model_to_label = {
-    "stilted-night-482": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["stilted-night-482"]
-title = "RF"
-plot_bo_ax(axs[2], dir, models, title)
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-47__hpobench__trials_3__seed_0__lr/n_target_2__n_source_20"
-model_to_label = {
-    "fallen-cherry-483": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["fallen-cherry-483"]
-title = "LR"
-plot_bo_ax(axs[3], dir, models, title)
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-20-48__hpobench__trials_3__seed_0__nn/n_target_2__n_source_20"
-model_to_label = {
-    "stilted-night-482": "MTPFN",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["stilted-night-482"]
-title = "NN"
-plot_bo_ax(axs[4], dir, models, title)
-
-handles, labels = axs[0].get_legend_handles_labels()
-# legend above plot
-fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=4, handles=handles, labels=labels)
-# plt.legend()
-plt.tight_layout()
-plt.savefig("hpobench.pdf", bbox_inches='tight')
diff --git a/private_multitask_pfn/paper_figures/figures_uncorr.py b/private_multitask_pfn/paper_figures/figures_uncorr.py
deleted file mode 100644
index 011119d..0000000
--- a/private_multitask_pfn/paper_figures/figures_uncorr.py
+++ /dev/null
@@ -1,537 +0,0 @@
-from PFNs.pfns.utils import get_restarting_cosine_schedule_with_warmup
-import torch
-import json
-
-
-import pickle
-import pdb
-import gpytorch
-import torch
-from botorch.models import SingleTaskGP
-from botorch.fit import fit_gpytorch_mll, fit_gpytorch_mll_torch
-from botorch.exceptions import ModelFittingError
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from tqdm import tqdm
-import botorch
-import einops
-
-from tabpfn.base import (
-    create_inference_engine,
-    determine_precision,
-    initialize_tabpfn_model,
-)
-from tabpfn.model.loading import get_encoder, _preprocess_config, get_y_encoder
-
-from tabpfn import TabPFNClassifier, TabPFNRegressor
-import matplotlib.pyplot as plt
-import os
-
-import traceback
-from collections import defaultdict
-
-
-#4477AA', '#EE6677', '#228833', '#CCBB44', '#66CCEE', '#AA3377', '#BBBBBB'
-# colors = {
-#     "PFN (p=0.0)": "#4477AA",
-#     "PFN (p=0.1)": "#66CCEE",
-#     "PFN (p=0.2)": "#228833",
-#     "ScaML": "#CCBB44",
-#     "ICM": "#EE6677",
-#     "GP": "#AA3377",
-# }
-##2166AC', '#4393C3', '#92C5DE '#F4A582', '#D6604D', '#B2182B'
-colors = {
-    # "PFN (p=0.0)": "#C3A8D1",
-    "MTPFN (p=0.0)": "#8C4E99",
-    "MTPFN (p=0.1)": "#6059A9",
-    "MTPFN (p=0.2)": "#4E96BC",
-    "ScaML": "#E78C35",
-    "ICM": "#DA2222",
-    "GP": "#95211B",
-}
-
-marker = {
-    "MTPFN (p=0.0)": "o",
-    "MTPFN (p=0.1)": "^",
-    "MTPFN (p=0.2)": "s",
-    "ScaML": "1",
-    "ICM": "+",
-    "GP": "x",
-}
-
-# marker = {
-#     "PFN (p=0.0)": "1",
-#     "PFN (p=0.1)": "2",
-#     "PFN (p=0.2)": "3",
-#     "ScaML": "+",
-#     "ICM": "x",
-#     "GP": "o",
-# }
-
-##CC6677', '#332288', '#DDCC77', '#117733', '#88CCEE', '#882255', '#44AA99', '#999933', '#AA4499'
-# colors = {
-#     "PFN (p=0.0)": "#CC6677",
-#     "PFN (p=0.1)": "#332288",
-#     "PFN (p=0.2)": "#DDCC77",
-#     "ScaML": "#117733",
-#     "ICM": "#88CCEE",
-#     "GP": "#882255",
-# }
-
-baselines = ["scaml", "mtgp", "gp", "lmc", "mtgp_nuts"] #+ ["random"] 
-
-# model_to_label = {
-#     "different-dust-282": "PFN (0.5 uncorr)",
-#     "royal-firebrand-281": "PFN (0.25 uncorr)",
-#     "peach-plasma-280": "PFN (1.0 uncorr)",
-#     "treasured-lion-279": "PFN (0.0 uncorr)",
-#     "exalted-wave-278": "PFN (0.75 uncorr)",
-# }
-
-# baselines = ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-
-def get_label(model):
-    if model in model_to_label:
-        return model_to_label[model]
-    return model
-
-
-def get_labels(models):
-    return [get_label(model) for model in models]
-
-
-def get_color(model):
-    return colors[get_label(model)]
-
-
-
-
-
-def plot_bo_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
-        # linestyle 
-        plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=1)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        plt.fill_between(range(len(mean)), lower, upper, alpha=0.15, color=get_color(model))
-
-                    
-    plt.legend()
-    plt.xlabel("BO Iterations")
-    plt.ylabel("Normalized Regret")
-    plt.title(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    
-    
-    
-
-def plot_bo_ax(ax, exp_dir, models, title, path=None):    
-    if models is None:
-        models = []
-        
-    # for model in models + ["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]:
-    for model in baselines + models:
-        model_results = []
-        for function_dir in os.listdir(exp_dir):
-            try:
-                function_results = []
-                # check if directory
-                if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                    continue
-                with open(os.path.join(exp_dir, function_dir, "bo_results.json"), "r") as f:
-                    bo_results = json.load(f)
-                with open(os.path.join(exp_dir, function_dir, "info.json"), "r") as f:
-                    info = json.load(f)
-                
-                
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    function_results.append(regret)
-                
-                function_results = torch.stack(function_results)
-                model_results.append(function_results.mean(0))
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                continue
-            
-        if len(model_results) == 0:
-            continue
-        model_results = torch.stack(model_results)
-            
-        # import pdb; pdb.set_trace()
-        # mean = model_results.median(0).values
-        # lower = model_results.quantile(0.25, 0)
-        # upper = model_results.quantile(0.75, 0)
-        mean = model_results.mean(0)
-        std = model_results.std(0) / 2 / len(model_results)# ** 0.5
-        lower = mean - std
-        upper = mean + std
-        linestyle = "--" if model in baselines else "-"
-        # linestyle = "-" if model in baselines else "--"
-        # linestyle = "-"
-        markevery = slice(0, 30, 3) if model in baselines else slice(2, 30, 3)
-        # linestyle 
-        xs = torch.linspace(0, 40, len(mean))
-        ax.plot(xs, mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-        ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
-
-    ax.set_title(title)
-    # ax.set_ylabel("Normalized Regret")
-    ax.set_xlabel("BO Iterations")
-    # ax.set_ylim(0.05, 0.7)
-    # ax.set_yscale("log")
-    # plt.legend()
-    # plt.xlabel("BO Iterations")
-    # plt.ylabel("Normalized Regret")
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-    
-    
-    
-    
-
-def plot_all(exp_dir, models, title, path):
-    plt.figure(figsize=(8, 6))
-    # one plot for boxplots of mse, one plot for boxplots of nll
-    
-    model_mses = defaultdict(list)
-    model_nlls = defaultdict(list)
-    
-    all_models = models + baselines #["random", "scaml", "mtgp", "gp", "lmc", "mtgp_nuts"]
-
-    for model_order in all_models:
-        for function_dir in os.listdir(exp_dir):
-            # check if directory
-            if not os.path.isdir(os.path.join(exp_dir, function_dir)):
-                continue
-            with open(os.path.join(exp_dir, function_dir, "results.json"), "r") as f:
-                results = json.load(f)
-                
-            models = list(results.keys())
-            for model in models:
-                if model != model_order:
-                    continue
-                mses, nlls = [], []
-                try:
-                    for trial_key in results[model]:
-                        mses.append(results[model][trial_key]["mse"])
-                        nlls.append(results[model][trial_key]["nll"])
-                except KeyboardInterrupt as e:
-                    raise e
-                except Exception as e:
-                    traceback.print_exc()
-                    continue
-                model_mses[model].extend(mses)
-                model_nlls[model].extend(nlls)
-    
-    # plot nll boxplots
-    plt.boxplot([model_nlls[model] for model in model_nlls], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_nlls.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("NLL")
-    plt.title(title)
-    plt.ylabel("NLL")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path)
-    plt.clf()
-    
-    # plot mse boxplots
-    plt.boxplot([model_mses[model] for model in model_mses], showfliers=True)
-    plt.gca().set_xticklabels(get_labels(model_mses.keys()))
-    # for tick in plt.gca().get_xticklabels():
-    #     tick.set_rotation(45)
-    # plt.title("MSE")
-    plt.title(title)
-    plt.ylabel("MSE")
-    # plt.yscale("log")
-    
-    # plt.suptitle(title)
-    plt.tight_layout()
-    plt.savefig(path.replace(".png", "_mse.png"))
-    plt.clf()
-    
-
-def plot_bo_function_ax(ax, function_dir, models, title):
-    with open(os.path.join(function_dir, "bo_results.json"), "r") as f:
-        bo_results = json.load(f)
-    with open(os.path.join(function_dir, "info.json"), "r") as f:
-        info = json.load(f)
-        
-    for model in baselines + models:
-        for model_try in bo_results:
-            if model_try != model:
-                continue
-            model_results = []
-            try:
-                for trial in bo_results[model]:
-                    best_init = info[trial]["best_init"]
-                    best_possible = info[trial]["best_possible"]
-                    
-                    results = bo_results[model][trial]
-                        
-                    train_y = torch.tensor([[best_init]] + results["train_y"]).squeeze()
-                    cummax = torch.tensor(train_y).cummax(0).values.squeeze()
-                    # average normalized regret
-                    regret = (cummax - best_possible) / (best_init - best_possible)
-                    
-                    model_results.append(regret)
-                
-                # remove shorter trials
-                max_length = max([len(result) for result in model_results])
-                model_results = [result for result in model_results if len(result) == max_length]
-                
-                model_results = torch.stack(model_results)
-                
-                mean = model_results.nanmean(0)
-                std = model_results.std(0) / len(model_results)# ** 0.5
-                lower = mean - std
-                upper = mean + std
-                # mean = model_results.mean(0)
-                # lower = model_results.quantile(0.25, 0)
-                # upper = model_results.quantile(0.75, 0)
-                linestyle = "--" if model in baselines else "-"
-                
-                xs = torch.linspace(0, 40, len(mean))
-                ax.plot(xs , mean, label=get_label(model), linestyle=linestyle, color=get_color(model), linewidth=2)#, marker=marker[get_label(model)], markersize=8, markevery=markevery)#, markeredgecolor="black")
-                ax.fill_between(xs, lower, upper, alpha=0.15, color=get_color(model))
-                # plt.plot(range(len(mean)), mean, label=get_label(model), linestyle=linestyle)
-                # plt.fill_between(range(len(mean)), lower, upper, alpha=0.2)
-            except KeyboardInterrupt as e:
-                raise e
-            except Exception as e:
-                print(e)
-                # import pdb; pdb.set_trace()
-                # raise e
-                continue
-                    
-    # plt.legend()
-    ax.set_xlabel("BO Iterations")
-    ax.set_ylabel("Normalized Regret")
-    ax.set_title(title)
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.savefig(path)
-
-
-
-# models = ["wobbly-donkey-484", "fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481", "sparkling-microwave-480", "likely-donkey-479"]
-# plot_bo_all("/home/yl9959/mtpfn/eval_plot/25-01-28_11-31-32__synthetic__trials_5__seed_0__num_samples/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0", models, "All")
-
-fig, axs = plt.subplots(1, 4, figsize=(12, 3))
-
-# add grid
-for ax in axs:
-    ax.grid(True, which='both', linestyle='-', linewidth=0.5, alpha=0.5)
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_0__n_target_2__n_source_20__seed_0"
-models = ["fallen-cherry-483", "wobbly-donkey-484", "youthful-wildflower-481"]
-model_to_label = {
-    "fallen-cherry-483": "MTPFN (p=0.0)",
-    "wobbly-donkey-484": "MTPFN (p=0.1)",
-    "youthful-wildflower-481": "MTPFN (p=0.2)",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-title = "0 Unrelated Tasks"
-plot_bo_ax(axs[0], dir, models, title)
-
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_1__n_target_2__n_source_20__seed_0/function_4"
-model_to_label = {
-    "sparkling-microwave-480": "MTPFN (p=0.0)",
-    "stilted-night-482": "MTPFN (p=0.1)",
-    "youthful-wildflower-481": "MTPFN (p=0.2)",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["sparkling-microwave-480", "stilted-night-482", "youthful-wildflower-481"]
-title = "1 Unrelated Task"
-plot_bo_function_ax(axs[1], dir, models, title)
-
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_2__n_target_2__n_source_20__seed_0/"
-model_to_label = {
-    "sparkling-microwave-480": "MTPFN (p=0.0)",
-    "wobbly-donkey-484": "MTPFN (p=0.1)",
-    "youthful-wildflower-481": "MTPFN (p=0.2)",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["sparkling-microwave-480", "wobbly-donkey-484", "youthful-wildflower-481"]
-title = "2 Unrelated Tasks"
-plot_bo_ax(axs[2], dir, models, title)
-
-
-dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-30_16-21-19__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_3__n_target_2__n_source_20__seed_0/"
-model_to_label = {
-    "likely-donkey-479": "MTPFN (p=0.0)",
-    "fallen-cherry-483": "MTPFN (p=0.1)",
-    "stilted-night-482": "MTPFN (p=0.2)",
-    "mtgp": "ICM",
-    "gp": "GP",
-    "scaml": "ScaML",
-}
-models = ["likely-donkey-479", "fallen-cherry-483", "stilted-night-482"]
-title = "3 Unrelated Tasks"
-plot_bo_ax(axs[3], dir, models, title)
-
-
-handles, labels = axs[0].get_legend_handles_labels()
-# legend above plot
-fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=6, handles=handles, labels=labels)
-# plt.legend()
-plt.tight_layout()
-plt.savefig("uncorr.pdf", bbox_inches='tight')
-
-# original
-# # uncorr 0
-# # dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_0__n_target_2__n_source_20__seed_0"
-# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_21-12-23__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_0__n_target_2__n_source_20__seed_0"
-# models = ["fallen-cherry-483", "stilted-night-482", "youthful-wildflower-481"]
-# model_to_label = {
-#     "fallen-cherry-483": "MTPFN (p=0.0)",
-#     "stilted-night-482": "PFN (p=0.1)",
-#     "youthful-wildflower-481": "PFN (p=0.2)",
-#     "mtgp": "ICM",
-#     "gp": "GP",
-#     "scaml": "ScaML",
-#     # "random": "Random",
-# }
-# path = "uncorr_0.png"
-# # title = "4 Total Tasks, 0/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
-# title = "0 Unrelated Tasks"
-# plot_bo_ax(axs[0], dir, models, title, path)
-# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
-
-
-# # uncorr 1
-# # dir = "/home/yl9959/mtpfn/eval_plot/25-01-28_13-31-07__synthetic__trials_3__seed_0__uncorr_final/features_3__tasks_4__lengthscale_None__task_corr_0_8__uncorr_tasks_1__n_target_2__n_source_20__seed_0"
-# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_1__n_target_2__n_source_20__seed_0"
-# models = ["sparkling-microwave-480", "youthful-wildflower-481", "wobbly-donkey-484"]
-# model_to_label = {
-#     "sparkling-microwave-480": "PFN (p=0.0)",
-#     "youthful-wildflower-481": "PFN (p=0.1)",
-#     "wobbly-donkey-484": "PFN (p=0.2)",
-#     "mtgp": "ICM",
-#     "gp": "GP",
-#     "scaml": "ScaML",
-#     # "random": "Random",
-# }
-# path = "uncorr_1.png"
-# title = "4 Total Tasks, 1/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
-# title = "1 Unrelated Task"
-# plot_bo_ax(axs[1], dir, models, title, path)
-# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
-
-# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_2__n_target_2__n_source_20__seed_0"
-# path = "uncorr_2.png"
-# title = "4 Total Tasks, 2/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
-# models = ["stilted-night-482",  "wobbly-donkey-484", "youthful-wildflower-481"]
-# # models = ["youthful-wildflower-481"]
-# model_to_label = {
-#     "stilted-night-482": "PFN (p=0.0)",
-#     "wobbly-donkey-484": "PFN (p=0.1)",
-#     "youthful-wildflower-481": "PFN (p=0.2)",
-#     "mtgp": "ICM",
-#     "gp": "GP",
-#     "scaml": "ScaML",
-#     # "random": "Random",
-# }
-# title = "2 Unrelated Tasks"
-# plot_bo_ax(axs[2], dir, models, title, path)
-# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
-
-# dir = "/home/lily_l/private_multitask_pfn/eval_plot/25-01-29_20-08-47__synthetic__trials_3__seed_0__uncorr/features_3__tasks_4__lengthscale_None__task_corr_None__uncorr_tasks_3__n_target_2__n_source_20__seed_0"
-# path = "uncorr_3.png"
-# models = ["fallen-cherry-483", "youthful-wildflower-481", "stilted-night-482"]
-# model_to_label = {
-#     "fallen-cherry-483": "PFN (p=0.0)",
-#     # "wobbly-donkey-484": "PFN (p=0.2)",
-#     "youthful-wildflower-481": "PFN (p=0.1)",
-#     "stilted-night-482": "PFN (p=0.2)",
-#     "mtgp": "GP",
-#     "gp": "ICM",
-#     "scaml": "ScaML",
-#     # "random": "Random",
-# }
-# title = "4 Total Tasks, 3/3 Auxiliary Uncorrelated\n10 Settings, 20 Trials Each"
-# title = "3 Unrelated Tasks"
-# plot_bo_ax(axs[3], dir, models, title, path)
-# # plot_all(dir, models, title, path.replace(".png", "_all.png"))
-
-# plt.legend()
-# plt.tight_layout()
-# plt.savefig("uncorr.pdf")
\ No newline at end of file
diff --git a/private_multitask_pfn/paper_figures/joint_data.py b/private_multitask_pfn/paper_figures/joint_data.py
deleted file mode 100644
index 9dd0cab..0000000
--- a/private_multitask_pfn/paper_figures/joint_data.py
+++ /dev/null
@@ -1,483 +0,0 @@
-#!/usr/bin/env python3
-import gpytorch
-
-from PFNs.pfns.priors import Batch
-import torch
-from gpytorch.distributions.multivariate_normal import MultivariateNormal
-from gpytorch.kernels import RBFKernel
-import os
-import matplotlib.pyplot as plt
-import numpy as np
-import timeit
-
-import warnings
-warnings.filterwarnings("ignore")
-import pandas as pd
-        
-def get_mtgp_batch(n_samples, n_features, n_tasks):
-    xs = torch.rand(n_samples, n_features)
-    task_id = torch.randint(n_tasks, size=(n_samples,)).unsqueeze(1).long()
-    
-    rbf = RBFKernel()
-    rbf.lengthscale = torch.tensor(0.2)
-    covar_x = rbf(xs)
-    task_covar_matrix = torch.eye(n_tasks)
-    # randomly choose two tasks to have a correlation
-    related_tasks = torch.randperm(n_tasks)[:2]
-    task_covar_matrix[related_tasks[0], related_tasks[1]] = 0.9
-    task_covar_matrix[related_tasks[1], related_tasks[0]] = 0.9
-    
-    covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
-    covar = covar_x.mul(covar_t)
-    ys = MultivariateNormal(torch.zeros(n_samples), covar).sample()
-
-    return xs, task_id, ys
-
-
-def draw_paired(n, target_corr, samples):
-    
-    target_train = torch.linspace(0, 0.25, n)
-    target_test = torch.linspace(0.25, 1, n)
-    
-    source1_train = torch.linspace(0.4, 1, n)
-    source2_train = torch.linspace(0.0, 0.6, n)
-    
-    xs = torch.cat([target_train, source1_train, source2_train, target_test], dim=0).view(-1, 1)
-    task_id = torch.cat([torch.zeros(n), torch.ones(n), torch.ones(n) * 2, torch.zeros(n)], dim=0).long()
-    task_id = task_id.unsqueeze(1)
-    
-    rbf = RBFKernel()
-    rbf.lengthscale = torch.tensor(0.1)
-    covar_x = rbf(xs)
-    task_covar_matrix = torch.eye(3)
-    task_covar_matrix[0, 1] = target_corr
-    task_covar_matrix[1, 0] = target_corr
-    task_covar_matrix[0, 2] = target_corr
-    task_covar_matrix[2, 0] = target_corr
-    task_covar_matrix[1, 2] = 1.0
-    task_covar_matrix[2, 1] = 1.0
-    
-    covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
-    covar = covar_x.mul(covar_t)
-    # covar = covar_x
-    ys = MultivariateNormal(torch.zeros(4 * n), covar).sample(torch.Size([samples]))
-    
-    # add batch dimension to xs
-    return xs.unsqueeze(0).repeat(samples, 1, 1), task_id.unsqueeze(0).repeat(samples, 1, 1), ys
-
-
-def get_constructed(n, pairs, samples=10):
-    source_xs = []
-    source_ys = []
-    target_train_xs = []
-    target_train_ys = []
-    target_test_xs = []
-    target_test_ys = []
-    
-    for i in range(pairs):
-        if i == 0:
-            corr = 0.95
-        else:
-            corr = 1.0
-        xs, task_id, ys = draw_paired(n, corr, samples)
-        target_train, source1_train, source2_train, target_test = xs[:, :n], xs[:, n:2 * n], xs[:, 2 * n:3 * n], xs[:, 3 * n:]
-        target_y, source1_y, source2_y, target_test_y = ys[:, :n], ys[:, n:2 * n], ys[:, 2 * n:3 * n], ys[:, 3 * n:]
-        
-        source_xs.extend([source1_train, source2_train])
-        source_ys.extend([source1_y, source2_y])
-        target_train_xs.append(target_train)
-        target_train_ys.append(target_y)
-        target_test_xs.append(target_test)
-        target_test_ys.append(target_test_y)
-
-    target_train_xs = target_train_xs[0]
-    # target_train_ys = torch.stack(target_train_ys, dim=0).sum(dim=0)
-    target_train_ys = target_train_ys[0]
-    target_train_ids = torch.zeros(samples, n).long()
-    target_test_xs = target_test_xs[0]
-    # target_test_ys = torch.stack(target_test_ys, dim=0).sum(dim=0)
-    target_test_ys = target_test_ys[0]
-    target_test_ids = torch.zeros(samples, n).long()
-    
-    source_xs = torch.cat(source_xs, dim=1)
-    source_ys = torch.cat(source_ys, dim=1)
-    source_ids = torch.cat([torch.ones(samples, n) * i for i in range(pairs * 2)], dim=1).long() + 1
-    
-    xs = torch.cat([target_train_xs, source_xs, target_test_xs], dim=1)
-    task_id = torch.cat([target_train_ids, source_ids, target_test_ids], dim=1).unsqueeze(-1)
-    ys = torch.cat([target_train_ys, source_ys, target_test_ys], dim=1)
-    
-    return xs, task_id, ys.unsqueeze(-1)
-
-
-default_device = "cuda:0" if torch.cuda.is_available() else "cpu:0"
-def get_joint_batch(
-    batch_size: int,
-    seq_len: int,
-    num_features: int,
-    max_num_tasks: int,
-    num_tasks: int,
-    lengthscale: float,
-    hyperparameters=None,
-    device: str = default_device,
-    **kwargs,
-):    
-    xs, task_id, ys = get_constructed(seq_len // 4, 1, batch_size)
-    perm = torch.randperm(xs.size(0))
-    # randomly permute the data
-    xs = xs[perm]
-    task_id = task_id[perm]
-    ys = ys[perm]
-    
-    return Batch(
-        x=xs.transpose(0, 1),
-        y=ys.transpose(0, 1),
-        target_y=ys.transpose(0, 1).clone(),
-        task_id=task_id.transpose(0, 1),
-    )
-
-    
-
-def mtgp_predict(task_id, train_x, train_y, possible_x):
-    mtgp = mtgp_fit(task_id.double(), train_x.double(), train_y.double())
-    covar_factor = mtgp.task_covar_module.covar_factor.detach()
-    v = mtgp.task_covar_module.raw_var.detach()
-    cov_matrix = covar_factor @ covar_factor.t() + torch.eye(covar_factor.size(0)) * np.exp(v)
-    # to correlation
-    std_devs = np.sqrt(np.diagonal(cov_matrix))
-    # Outer product of the standard deviations to get normalization factor
-    # print(cov_matrix)
-    corr_matrix = cov_matrix / (std_devs[:, None] * std_devs[None, :])
-    print(corr_matrix)
-    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
-    return mtgp.posterior(possible_x)
-
-
-
-def joint_visualization():
-    
-
-
-    for seed in range(10, 20):
-        torch.manual_seed(seed)
-
-        n = 50
-        n_test = n
-        pairs = 1
-        xs, tasks, ys = get_constructed(n, pairs)
-        print(xs.shape, tasks.shape, ys.shape)
-        train_xs, train_tasks, train_ys = xs[0, :-n_test], tasks[0, :-n_test].squeeze(), ys[0, :-n_test]
-        test_xs, test_tasks, test_ys = xs[0, -n_test:], tasks[0, -n_test:].squeeze(), ys[0, -n_test:]
-
-
-        fig, axs = plt.subplots(1, 3, figsize=(12, 4))
-        # fig, axs = plt.subplots(1, 2, figsize=(8, 4))
-
-        def plot(ax, pred, title, legend=False):
-            if "NUTS" in title:
-                pred_mean, pred_std = pred.mixture_mean.detach().squeeze(), pred.mixture_variance.sqrt().detach().squeeze()
-            else:
-                pred_mean = pred.mean.detach().squeeze()
-                pred_std = pred.variance.sqrt().detach().squeeze()
-            
-            print(title, pred_mean.shape, pred_std.shape, test_ys.shape)
-            nll = -torch.distributions.Normal(pred_mean, pred_std).log_prob(test_ys.squeeze()).mean()
-            colors = plt.cm.tab10.colors + ("darkgrey",)
-            # colors = colors[:1] + colors[2:] + ("darkgrey",)
-            for i in range(pairs * 2 + 1):
-                if i == 0:
-                    label = "Target"
-                else:
-                    label = f"Auxiliary {i}"
-                ax.scatter(train_xs[train_tasks == i], train_ys[train_tasks == i], label=label, color=colors[i], marker="x")
-            ax.plot(test_xs.squeeze(), test_ys.squeeze(), color=colors[0], label="True")
-            ax.plot(test_xs.squeeze(), pred_mean, color=colors[-1])
-            ax.fill_between(test_xs.squeeze(), pred_mean - 2 * pred_std, pred_mean + 2 * pred_std, alpha=0.2, color=colors[-1], label=r"$\mu \pm 2\sigma$")
-            ax.axvline(0.6, color="black", linestyle="--", alpha=0.5, label="End of Aux 1")
-            if legend:
-                ax.legend()
-            ax.set_title(title + " NLL: {:.2f}".format(nll.item()))
-            
-            
-        # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "dutiful-darkness-309"
-        # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "mild-valley-277"
-        # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "major-monkey-325"
-        try:
-            pfn_dir = "/home/yl9959/mtpfn/ckpt/25-01-15_22-04-28__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_80__attn_standard__task_hier__seed_0__13187" 
-            pfn = load_model(pfn_dir)
-        except FileNotFoundError:
-            pfn_dir = "/home/lily_l/private_multitask_pfn/ckpt/25-01-15_22-04-28__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_80__attn_standard__task_hier__seed_0__13187"
-            pfn = load_model(pfn_dir)
-        except e:
-            raise e
-        pred = pfn_predict(pfn, train_tasks, train_xs, train_ys, test_xs)
-        plot(axs[0], pred, "MTPFN")
-        
-        pred = mtgp_predict(train_tasks, train_xs, train_ys, test_xs)
-        plot(axs[1], pred, "Joint (ICM)")
-
-        pred = scaml_predict(train_tasks, train_xs, train_ys, test_xs)
-        plot(axs[2], pred, "Ensemble (ScaML)")
-        
-        min_y = np.min([ax.get_ylim()[0] for ax in axs])
-        max_y = np.max([ax.get_ylim()[1] for ax in axs])
-        for ax in axs:
-            ax.set_ylim(min_y, max_y)
-        
-        
-        # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        # pred = mtgp_nuts_predict(train_tasks.to(device), train_xs.to(device), train_ys.to(device), test_xs.to(device))
-        # plot(axs[2], pred, "MTGP NUTS")
-
-        # 3 column on top
-        h, l = axs[0].get_legend_handles_labels()
-        fig.legend(h, l, ncol=3, loc='lower center', bbox_to_anchor=(0.5, 1.0))
-        # plt.tight_layout()
-        plt.savefig("joint_data_{}.png".format(seed), bbox_inches="tight")
-        plt.savefig("joint_data_{}.pdf".format(seed), bbox_inches="tight")
-        
-        # break
-        
-import signal
-
-class TimeoutException(Exception):
-    pass
-
-def timeout_handler(signum, frame):
-    raise TimeoutException("Function timed out!")
-
-def long_running_function():
-    signal.signal(signal.SIGALRM, timeout_handler)  # Set the signal handler
-    signal.alarm(600)  # Set the alarm for 600 seconds (10 minutes)
-    try:
-        # Your long-running code here
-        while True:
-            print("Running...")
-            # Simulate work
-    except TimeoutException:
-        print("Function terminated after 10 minutes.")
-    finally:
-        signal.alarm(0)  # Disable the alarm
-
-# long_running_function()
-
-
-def time_up_to(func, number, max_time=600):
-    signal.signal(signal.SIGALRM, timeout_handler)  # Set the signal handler
-    signal.alarm(max_time)  # Set the alarm for 600 seconds (10 minutes)
-    time = None
-    try:
-        time = timeit.timeit(func, number=number) / number
-    except TimeoutException:
-        print("Function terminated after 10 minutes.")
-    finally:
-        signal.alarm(0)  # Disable the alarm
-    return time
-
-        
-def get_times(n_tasks, n_samples_per_task, pfn, device, benchmark_repeats):
-    
-    print("n_samples_per_task", n_samples_per_task, "n_tasks", n_tasks, flush=True)
-
-    def gen_data(n_tasks, n_samples_per_task):
-        n_samples = n_tasks * n_samples_per_task
-        xs = torch.rand(n_samples, 1)
-        task_id = torch.randint(n_tasks, size=(n_samples,)).long()#.unsqueeze(0)
-        ys = torch.randn(n_samples, 1)#.unsqueeze(0)
-        
-        return task_id.to(device), xs.to(device), ys.to(device)
-    
-    def benchmark_mtgp():
-        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
-        mtgp_fit(task_id, xs, ys)
-        
-    def benchmark_scaml():
-        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
-        for _ in range(n_tasks):
-            gp_fit(task_id, xs, ys)
-        
-    def benchmark_pfn(pfn):
-        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
-        pfn_gaussian_fit(pfn, task_id, xs, ys)
-    
-    mtgp_time = time_up_to(benchmark_mtgp, number=benchmark_repeats) if n_tasks < 15 else None
-    scaml_times = time_up_to(benchmark_scaml, number=benchmark_repeats) if n_tasks < 100 else None
-    pfn_times = time_up_to(lambda: benchmark_pfn(pfn), number=benchmark_repeats)
-    
-    return mtgp_time, scaml_times, pfn_times
-        
-        
-        
-def benchmark_time():    
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    benchmark_repeats = 5
-    
-    # pfn_dir = "/home/yl9959/mtpfn/wandb_links/%s" % "dutiful-darkness-309"
-    # pfn_dir = "/home/yl9959/mtpfn/ckpt/25-01-15_22-04-28__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_80__attn_standard__task_hier__seed_0__13187"
-    pfn_dir = "/home/yl9959/mtpfn/ckpt/25-01-16_11-13-16__prior_mtgp__features_1__tasks_3__epochs_50__seqlen_200__attn_standard__task_hier__seed_0__98742"
-    pfn = load_model(pfn_dir)
-    
-    mtgp_times = []
-    scaml_times = []
-    pfn_times = []
-    
-    n_samples_per_task = 50
-    n_taskss = torch.logspace(0.5, 4, 20).int()
-    for n_tasks in n_taskss:
-        mtgp_time, scaml_time, pfn_time = get_times(n_tasks, n_samples_per_task, pfn, device, benchmark_repeats)
-        mtgp_times.append(mtgp_time)
-        scaml_times.append(scaml_time)
-        pfn_times.append(pfn_time)
-        
-        plt.plot(n_taskss[:len(mtgp_times)], mtgp_times, label="Joint (ICM)")
-        plt.plot(n_taskss[:len(mtgp_times)], scaml_times, label="Ensemble (ScaML)")
-        plt.plot(n_taskss[:len(mtgp_times)], pfn_times, label="PFN")
-        plt.xlabel("Tasks")
-        plt.title(f"{n_samples_per_task} Samples per Task")
-        
-        # save csv
-        df = pd.DataFrame({
-            "n_tasks": n_taskss[:len(mtgp_times)],
-            "mtgp": mtgp_times,
-            "scaml": scaml_times,
-            "pfn": pfn_times
-        })
-        df.to_csv("benchmark_time_task.csv")
-        
-        plt.xscale("log")
-        plt.ylabel("Time (s)")
-        plt.yscale("log")
-        plt.legend()
-        plt.savefig("benchmark_time_task.png")
-        plt.savefig("benchmark_time_task.pdf")
-        plt.clf()
-        
-    return
-        
-    
-    mtgp_times = []
-    scaml_times = []
-    pfn_times = []
-    
-    n_tasks = 5
-    n_samples_per_tasks = torch.logspace(1.5, 2.5, 10).int()
-    for n_samples_per_task in n_samples_per_tasks:
-        mtgp_time, scaml_time, pfn_time = get_times(n_tasks, n_samples_per_task, pfn, device, benchmark_repeats)
-        mtgp_times.append(mtgp_time)
-        scaml_times.append(scaml_time)
-        pfn_times.append(pfn_time)
-        
-        plt.plot(n_samples_per_tasks[:len(mtgp_times)], mtgp_times, label="Joint (ICM)")
-        plt.plot(n_samples_per_tasks[:len(mtgp_times)], scaml_times, label="Ensemble (ScaML)")
-        plt.plot(n_samples_per_tasks[:len(mtgp_times)], pfn_times, label="PFN")
-        plt.xlabel("Samples per Task")
-        plt.title(f"5 Tasks")
-        
-        # save csv
-        df = pd.DataFrame({
-            "n_samples_per_task": n_samples_per_tasks[:len(mtgp_times)],
-            "mtgp": mtgp_times,
-            "scaml": scaml_times,
-            "pfn": pfn_times
-        })
-        df.to_csv("benchmark_time_samples.csv")
-        
-        plt.xscale("log")
-        plt.ylabel("Time (s)")
-        plt.yscale("log")
-        plt.legend()
-        plt.savefig("benchmark_time_samples.png")
-        plt.savefig("benchmark_time_samples.pdf")
-        plt.clf()
-    
-        
-def get_specific_times(model_name, n_tasks, n_samples_per_task, device, benchmark_repeats):
-    
-    print(model_name, "n_samples_per_task", n_samples_per_task, "n_tasks", n_tasks, flush=True)
-
-    def gen_data(n_tasks, n_samples_per_task):
-        n_samples = n_tasks * n_samples_per_task
-        xs = torch.rand(n_samples, 1)
-        task_id = torch.randint(n_tasks, size=(n_samples,)).long()#.unsqueeze(0)
-        ys = torch.randn(n_samples, 1)#.unsqueeze(0)
-        
-        return task_id.to(device), xs.to(device), ys.to(device)
-    
-    def benchmark_mtgp():
-        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
-        mtgp_fit(task_id, xs, ys)
-        
-    def benchmark_scaml():
-        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
-        for _ in range(n_tasks):
-            gp_fit(task_id, xs, ys)
-        
-    def benchmark_pfn(pfn):
-        task_id, xs, ys = gen_data(n_tasks, n_samples_per_task * n_tasks)
-        pfn_gaussian_fit(pfn, task_id, xs, ys)
-    
-    if model_name == "mtgp":
-        benchmark = benchmark_mtgp
-    elif model_name == "scaml":
-        benchmark = benchmark_scaml
-    time = time_up_to(benchmark_mtgp, number=benchmark_repeats)
-    
-    return time
-        
-        
-def benchmark_time_slurm(model_name, vary_tasks=True):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    benchmark_repeats = 20
-
-    result_times = []
-    df = pd.DataFrame()
-    if vary_tasks:
-        n_samples_per_task = 50
-        n_taskss = torch.logspace(0.5, 4, 20).int()
-        for n_tasks in n_taskss:
-            times = get_specific_times(model_name, n_tasks, n_samples_per_task, device, benchmark_repeats)
-            result_times.append(times)
-            
-            df = pd.DataFrame({
-                "n_tasks": n_taskss[:len(result_times)],
-                "n_samples_per_task": [n_samples_per_task] * len(result_times),
-                "model": [model_name] * len(result_times),
-                "time": result_times,
-            })
-            df.to_csv(f"benchmark_time_{model_name}_task.csv", index=False)
-            
-    else:
-        n_tasks = 5
-        n_samples_per_tasks = torch.logspace(1.5, 2.5, 10).int()
-        
-        for n_samples_per_task in n_samples_per_tasks:
-            times = get_specific_times(model_name, n_tasks, n_samples_per_task, device, benchmark_repeats)
-            result_times.append(times)
-            
-            df = pd.DataFrame({
-                "n_tasks": [n_tasks] * len(result_times),
-                "n_samples_per_task": n_samples_per_tasks[:len(result_times)],
-                "model": [model_name] * len(result_times),
-                "time": result_times,
-            })
-            df.to_csv(f"benchmark_time_{model_name}_samples.csv", index=False)
-        
-
-
-if __name__ == "__main__":
-    from utils import *#mtgp_fit, to_mtgp_format, scaml_predict, mtgp_nuts_predict, pfn_predict, load_model
-    import sys
-    
-    # # benchmark time
-    # arg = sys.argv[1]
-    # arg_options = [
-    #     ("mtgp", True),
-    #     ("scaml", True),
-    #     ("mtgp", False),
-    #     ("scaml", False),
-    # ]
-    # args = arg_options[int(arg)]
-    # benchmark_time_slurm(*args)
-    
-    # visualization
-    joint_visualization()
\ No newline at end of file
diff --git a/private_multitask_pfn/paper_figures/nlls.pkl b/private_multitask_pfn/paper_figures/nlls.pkl
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diff --git a/private_multitask_pfn/pfn.yml b/private_multitask_pfn/pfn.yml
deleted file mode 100644
index a074b58..0000000
--- a/private_multitask_pfn/pfn.yml
+++ /dev/null
@@ -1,199 +0,0 @@
-name: pfn
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - _libgcc_mutex=0.1=conda_forge
-  - _openmp_mutex=4.5=2_gnu
-  - _sysroot_linux-64_curr_repodata_hack=3=haa98f57_10
-  - binutils_impl_linux-64=2.40=h5293946_0
-  - binutils_linux-64=2.40.0=hc2dff05_1
-  - bzip2=1.0.8=h5eee18b_6
-  - ca-certificates=2024.9.24=h06a4308_0
-  - comm=0.2.2=pyhd8ed1ab_0
-  - decorator=5.1.1=pyhd3eb1b0_0
-  - expat=2.6.3=h6a678d5_0
-  - gcc_impl_linux-64=11.2.0=h1234567_1
-  - gcc_linux-64=11.2.0=h5c386dc_1
-  - gxx_impl_linux-64=11.2.0=h1234567_1
-  - gxx_linux-64=11.2.0=hc2dff05_1
-  - ipykernel=6.29.5=pyh3099207_0
-  - jupyter_client=8.6.0=py312h06a4308_0
-  - jupyter_core=5.7.2=py312h06a4308_0
-  - kernel-headers_linux-64=3.10.0=h57e8cba_10
-  - ld_impl_linux-64=2.40=h12ee557_0
-  - libffi=3.4.4=h6a678d5_1
-  - libgcc=14.2.0=h77fa898_1
-  - libgcc-devel_linux-64=11.2.0=h1234567_1
-  - libgcc-ng=14.2.0=h69a702a_1
-  - libgomp=14.2.0=h77fa898_1
-  - libsodium=1.0.18=h7b6447c_0
-  - libstdcxx=14.2.0=hc0a3c3a_1
-  - libstdcxx-devel_linux-64=11.2.0=h1234567_1
-  - libstdcxx-ng=14.2.0=h4852527_1
-  - libuuid=1.41.5=h5eee18b_0
-  - ncurses=6.4=h6a678d5_0
-  - nest-asyncio=1.6.0=py312h06a4308_0
-  - openssl=3.4.0=hb9d3cd8_0
-  - prompt_toolkit=3.0.43=hd3eb1b0_0
-  - ptyprocess=0.7.0=pyhd3eb1b0_2
-  - pure_eval=0.2.2=pyhd3eb1b0_0
-  - python=3.12.0=h996f2a0_0
-  - python-dateutil=2.9.0post0=py312h06a4308_2
-  - readline=8.2=h5eee18b_0
-  - setuptools=75.1.0=py312h06a4308_0
-  - six=1.16.0=pyhd3eb1b0_1
-  - sqlite=3.45.3=h5eee18b_0
-  - stack_data=0.2.0=pyhd3eb1b0_0
-  - sysroot_linux-64=2.17=h57e8cba_10
-  - tk=8.6.14=h39e8969_0
-  - tornado=6.4.1=py312h5eee18b_0
-  - traitlets=5.14.3=py312h06a4308_0
-  - tzdata=2024b=h04d1e81_0
-  - wheel=0.44.0=py312h06a4308_0
-  - xz=5.4.6=h5eee18b_1
-  - zeromq=4.3.5=h6a678d5_0
-  - zlib=1.2.13=h5eee18b_1
-  - pip:
-      - anyio==4.6.2.post1
-      - argon2-cffi==23.1.0
-      - argon2-cffi-bindings==21.2.0
-      - arrow==1.3.0
-      - asttokens==2.4.1
-      - async-lru==2.0.4
-      - attrs==24.2.0
-      - babel==2.16.0
-      - beautifulsoup4==4.12.3
-      - bleach==6.2.0
-      - botorch==0.12.0
-      - certifi==2024.8.30
-      - cffi==1.17.1
-      - charset-normalizer==3.4.0
-      - click==8.1.7
-      - contourpy==1.3.0
-      - cycler==0.12.1
-      - debugpy==1.8.8
-      - defusedxml==0.7.1
-      - docker-pycreds==0.4.0
-      - executing==2.1.0
-      - fastjsonschema==2.20.0
-      - filelock==3.16.1
-      - fonttools==4.54.1
-      - fqdn==1.5.1
-      - fsspec==2024.10.0
-      - gitdb==4.0.11
-      - gitpython==3.1.43
-      - gpytorch==1.13
-      - h11==0.14.0
-      - httpcore==1.0.6
-      - httpx==0.27.2
-      - huggingface-hub==0.26.2
-      - idna==3.10
-      - ipython==8.29.0
-      - ipywidgets==8.1.5
-      - isoduration==20.11.0
-      - jaxtyping==0.2.19
-      - jedi==0.19.2
-      - jinja2==3.1.4
-      - joblib==1.4.2
-      - json5==0.9.28
-      - jsonpointer==3.0.0
-      - jsonschema==4.23.0
-      - jsonschema-specifications==2024.10.1
-      - jupyter==1.1.1
-      - jupyter-client==8.6.3
-      - jupyter-console==6.6.3
-      - jupyter-events==0.10.0
-      - jupyter-lsp==2.2.5
-      - jupyter-server==2.14.2
-      - jupyter-server-terminals==0.5.3
-      - jupyterlab==4.2.5
-      - jupyterlab-pygments==0.3.0
-      - jupyterlab-server==2.27.3
-      - jupyterlab-widgets==3.0.13
-      - kiwisolver==1.4.7
-      - linear-operator==0.5.3
-      - markupsafe==3.0.2
-      - matplotlib==3.9.2
-      - matplotlib-inline==0.1.7
-      - mistune==3.0.2
-      - mpmath==1.3.0
-      - multipledispatch==1.0.0
-      - nbclient==0.10.0
-      - nbconvert==7.16.4
-      - nbformat==5.10.4
-      - networkx==3.4.2
-      - notebook==7.2.2
-      - notebook-shim==0.2.4
-      - numpy==2.1.3
-      - nvidia-cublas-cu12==12.4.5.8
-      - nvidia-cuda-cupti-cu12==12.4.127
-      - nvidia-cuda-nvrtc-cu12==12.4.127
-      - nvidia-cuda-runtime-cu12==12.4.127
-      - nvidia-cudnn-cu12==9.1.0.70
-      - nvidia-cufft-cu12==11.2.1.3
-      - nvidia-curand-cu12==10.3.5.147
-      - nvidia-cusolver-cu12==11.6.1.9
-      - nvidia-cusparse-cu12==12.3.1.170
-      - nvidia-nccl-cu12==2.21.5
-      - nvidia-nvjitlink-cu12==12.4.127
-      - nvidia-nvtx-cu12==12.4.127
-      - opt-einsum==3.4.0
-      - overrides==7.7.0
-      - packaging==24.2
-      - pandocfilters==1.5.1
-      - parso==0.8.4
-      - pexpect==4.9.0
-      - pillow==11.0.0
-      - pip==24.3.1
-      - platformdirs==4.3.6
-      - prometheus-client==0.21.0
-      - prompt-toolkit==3.0.48
-      - protobuf==5.28.3
-      - psutil==6.1.0
-      - pure-eval==0.2.3
-      - pycparser==2.22
-      - pygments==2.18.0
-      - pyparsing==3.2.0
-      - pyro-api==0.1.2
-      - pyro-ppl==1.9.1
-      - python-json-logger==2.0.7
-      - pyyaml==6.0.2
-      - pyzmq==26.2.0
-      - referencing==0.35.1
-      - regex==2024.11.6
-      - requests==2.32.3
-      - rfc3339-validator==0.1.4
-      - rfc3986-validator==0.1.1
-      - rpds-py==0.21.0
-      - safetensors==0.4.5
-      - scikit-learn==1.5.2
-      - scipy==1.14.1
-      - send2trash==1.8.3
-      - sentry-sdk==2.18.0
-      - setproctitle==1.3.3
-      - smmap==5.0.1
-      - sniffio==1.3.1
-      - soupsieve==2.6
-      - stack-data==0.6.3
-      - sympy==1.13.1
-      - terminado==0.18.1
-      - threadpoolctl==3.5.0
-      - tinycss2==1.4.0
-      - tokenizers==0.20.3
-      - torch==2.5.1
-      - tqdm==4.67.0
-      - transformers==4.46.2
-      - triton==3.1.0
-      - typeguard==4.4.1
-      - types-python-dateutil==2.9.0.20241003
-      - typing-extensions==4.12.2
-      - uri-template==1.3.0
-      - urllib3==2.2.3
-      - wandb==0.18.6
-      - wcwidth==0.2.13
-      - webcolors==24.11.1
-      - webencodings==0.5.1
-      - websocket-client==1.8.0
-      - widgetsnbextension==4.0.13
-prefix: /ext3/miniconda3/envs/pfn
diff --git a/private_multitask_pfn/scaml.py b/private_multitask_pfn/scaml.py
deleted file mode 100644
index 89323a4..0000000
--- a/private_multitask_pfn/scaml.py
+++ /dev/null
@@ -1,435 +0,0 @@
-#!/usr/bin/env python3
-# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-"""Scalable Meta Learning with Gaussian Processes.
-
-Code adapted from https://github.com/boschresearch/Scalable-Meta-Learning-with-Gaussian-Processes/blob/main/scamlgp/model.py"""
-
-import copy
-from itertools import compress
-from typing import Dict, Hashable, List, Optional, Tuple, Type, Union
-
-import torch
-from botorch.fit import fit_gpytorch_mll
-from botorch.models import SingleTaskGP
-from botorch.models.transforms import Standardize
-from botorch.utils.datasets import SupervisedDataset
-from gpytorch import Module
-from gpytorch.constraints import GreaterThan, Interval
-from gpytorch.distributions import MultivariateNormal
-from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
-from gpytorch.likelihoods import GaussianLikelihood, Likelihood
-from gpytorch.means import ZeroMean
-from gpytorch.mlls import ExactMarginalLogLikelihood
-from gpytorch.priors import GammaPrior, LogNormalPrior
-from linear_operator import to_linear_operator
-from linear_operator.operators import PsdSumLinearOperator
-
-
-def validate_meta_data(meta_data: Dict[Hashable, SupervisedDataset]):
-    """Validate the metadata passed to meta-learning algorithms"""
-    if len(meta_data) == 0:
-        raise ValueError("Empty meta data. Needs at least one source task.")
-    task_id_source_0, data_source_0 = list(meta_data.items())[0]
-    X_shape = data_source_0.X.shape
-    Y_shape = data_source_0.Y.shape
-    if X_shape[:-2] != Y_shape[:-2]:
-        raise ValueError(
-            f"The X and Y batch sizes of task {task_id_source_0} are not equal."
-        )
-    for task_id, task_data in meta_data.items():
-        if (
-            task_data.X.shape[:-2] != X_shape[:-2]
-            or task_data.Y.shape[:-2] != Y_shape[:-2]
-            or task_data.X.shape[-1] != X_shape[-1]
-        ):
-            raise ValueError(
-                f"Dimensions of tasks {task_id_source_0} and {task_id} do not match."
-            )
-        if task_data.Y.shape[-1] != 1:
-            raise ValueError(
-                f"The output dimension of task {task_id} is {task_data.Y.shape[-1]} "
-                f"but must be one"
-            )
-
-
-def _get_default_likelihood_noise(
-    batch_shape: torch.Size | None = None,
-) -> GaussianLikelihood:
-    noise_prior = LogNormalPrior(-8.0, 2.0)
-    return GaussianLikelihood(
-        noise_prior=noise_prior,
-        noise_constraint=Interval(1e-8, 1e-2, initial_value=1e-3),
-        batch_shape=batch_shape,
-    )
-
-
-def _get_default_likelihood(
-    batch_shape: torch.Size | None = None,
-) -> GaussianLikelihood:
-    # noise_prior = LogNormalPrior(-8.0, 2.0)
-    return GaussianLikelihood(
-        # noise_prior=noise_prior,
-        noise_constraint=Interval(1e-8, 1e-5, initial_value=1e-6),
-        batch_shape=batch_shape,
-    )
-
-
-def _get_kernel_source_gp(
-    base_kernel: Union[Type[RBFKernel], Type[MaternKernel]],
-    ard_num_dims: int = 1,
-    batch_shape: torch.Size | None = None,
-) -> ScaleKernel:
-    lengthscale_prior = GammaPrior(3.0, 6.0)  # Same prior as `SingleTaskGP`
-    outputscale_prior = GammaPrior(2.0, 0.15)  # Same prior as `SingleTaskGP`
-
-    return ScaleKernel(
-        base_kernel(
-            ard_num_dims=ard_num_dims,
-            lengthscale_prior=lengthscale_prior,
-            # If the outputscale is zero, lengthscales tend to be arbitrary. This
-            # avoids numerical instability when lengthscales get very small/large.
-            # Since our domain is in [0, 1] the lengthscale ranges allow for
-            # anything between
-            lengthscale_constraint=Interval(
-                lower_bound=torch.tensor(1e-4),
-                upper_bound=torch.tensor(1e2),
-                initial_value=torch.full((ard_num_dims,), 0.5),
-            ),
-            batch_shape=batch_shape,
-        ),
-        outputscale_prior=outputscale_prior,
-        # Avoid numerical instability: prior.log_prob is nan for output_scale = 0
-        # If the scale gets too large then we have to add more noise to make the
-        # matrix PSD. Since our model acts on normalized data the maximum output
-        # scale should be ~1.
-        outputscale_constraint=Interval(
-            lower_bound=torch.tensor(1e-4),
-            upper_bound=torch.tensor(1e2),
-            initial_value=torch.tensor(1.0),
-        ),
-        batch_shape=batch_shape,
-    )
-
-
-def _get_default_kernel(
-    base_kernel: Union[Type[RBFKernel], Type[MaternKernel]],
-    ard_num_dims: int = 1,
-    batch_shape: torch.Size | None = None,
-) -> ScaleKernel:
-    # The lengthscale prior is not as restrictive as for `SingleTaskGP`
-    # since the difference signal can have a broad range of lengthscales depending on
-    # how successful the transfer is. This prior is inspired by our study on
-    # meta-learning priors.
-    lengthscale_prior = LogNormalPrior(0.5, 1.5)
-    # The outputscale parameter can attain a broad range of values because the target
-    # y-values are normalized wrt all target and meta observations. This prior is
-    # inspired by our study on meta-learning priors.
-    outputscale_prior = LogNormalPrior(-2.0, 3.0)
-    return ScaleKernel(
-        base_kernel(
-            ard_num_dims=ard_num_dims,
-            lengthscale_prior=lengthscale_prior,
-            lengthscale_constraint=Interval(
-                lower_bound=torch.tensor(1e-4),
-                upper_bound=torch.tensor(1e2),
-                initial_value=torch.full((ard_num_dims,), 1.0),
-            ),
-            batch_shape=batch_shape,
-        ),
-        outputscale_prior=outputscale_prior,
-        outputscale_constraint=Interval(
-            lower_bound=torch.tensor(1e-4),
-            upper_bound=torch.tensor(1e2),
-            initial_value=torch.tensor(0.1),
-        ),
-        batch_shape=batch_shape,
-    )
-
-
-def _compute_target_prior(
-    x: torch.Tensor, source_gps: List[SingleTaskGP], weights: torch.Tensor
-) -> Tuple[torch.Tensor, PsdSumLinearOperator]:
-    """Compute the prior distribution of the target GP.
-
-    Args:
-        x: A `n x d` or `batch_shape x n x d` (batch mode) tensor of features.
-        source_gps: Dictionary containing the source GPs (order needs to match weights).
-        weights: The source weights (order needs to match source_gps).
-
-    Returns:
-        Tuple containing:
-            - The mean function as `n x 1` or `batch_shape x n x 1` tensor.
-            - The covariance function as `n x n` or `batch_shape x n x n` lazy tensor.
-    """
-    if len(source_gps) != len(weights):
-        raise ValueError(
-            f"The number of source GPs, {len(source_gps)}, does not "
-            f"equal the number of weights, {len(weights)}"
-        )
-    posteriors = [task_gp.posterior(x) for task_gp in source_gps]
-    weighted_post_mean_source = [p.mvn.mean * w for p, w in zip(posteriors, weights)]
-    weighted_post_cov_source = [
-        p.mvn.lazy_covariance_matrix * w**2 for p, w in zip(posteriors, weights)
-    ]
-    prior_mean_target = sum(weighted_post_mean_source, torch.zeros(*x.shape[:-1]).to(x))
-    prior_cov_target = PsdSumLinearOperator(*weighted_post_cov_source)
-    return prior_mean_target.unsqueeze(-1), prior_cov_target
-
-
-def meta_fit_scamlgp(
-    meta_data: Dict[Hashable, SupervisedDataset],
-    likelihood: Optional[Likelihood] = None,
-    covar_module: Optional[Module] = None,
-    num_restarts_log_likelihood: int = 5,
-    seed: Optional[int] = None,
-) -> Dict[Hashable, SingleTaskGP]:
-    """Train the source GPs on the given meta-data.
-
-    Args:
-        meta_data: Dictionary containing the source datasets.
-        likelihood: A likelihood. If omitted, use a standard
-            `GaussianLikelihood` with inferred noise level.
-        covar_module: The kernel of the GPs in the stack. If omitted, use an
-            `RBFKernel`.
-        num_restarts_log_likelihood: The number of restarts for the log-likelihood
-            optimization of the model parameters. The first restart corresponds to
-            warm-starting, while the rest of the initial conditions are sampled
-            from the parameters' prior.
-        seed: A seed to make the optimization reproducible.
-
-    Returns:
-        A dictionary of the trained source GPs and corresponding task IDs.
-
-    """
-    if seed is not None:
-        torch.manual_seed(seed=seed)
-    validate_meta_data(meta_data)
-    source_gp_0 = list(meta_data.values())[0]
-    ard_num_dims = source_gp_0.X.shape[-1]
-    batch_shape = source_gp_0.X.shape[:-2]
-    if likelihood is None:
-        likelihood = _get_default_likelihood(batch_shape=batch_shape)
-    if covar_module is None:
-        covar_module = _get_kernel_source_gp(
-            base_kernel=RBFKernel, ard_num_dims=ard_num_dims, batch_shape=batch_shape
-        )
-    source_gps = {}
-    for task_id, task_data in meta_data.items():
-        likelihood = copy.deepcopy(likelihood)
-        covar_module = copy.deepcopy(covar_module)
-        task_gp = SingleTaskGP(
-            train_X=task_data.X,
-            train_Y=task_data.Y,
-            likelihood=likelihood,
-            covar_module=covar_module,
-            mean_module=ZeroMean(batch_shape=batch_shape),
-            outcome_transform=Standardize(1, batch_shape=batch_shape),
-        )
-        task_mll = ExactMarginalLogLikelihood(task_gp.likelihood, task_gp)
-
-        fit_gpytorch_mll(
-            task_mll,
-            num_restarts=num_restarts_log_likelihood,
-            pick_best_of_all_attempts=True,
-        )
-        source_gps[task_id] = task_gp
-    return source_gps
-
-
-def significant_weights_mask(
-    weights: torch.Tensor, std_Y_vals: torch.Tensor, threshold: float
-) -> torch.Tensor:
-    r"""Boolean mask of weights that when rescaled exceed the threshold.
-
-    Args:
-        weights: The weights of the models. shape = (n_source_tasks,)
-        std_Y_vals: The standard deviation of the models' Y-values.
-            shape = (n_source_tasks,)
-        threshold: threshold for weight pruning, $\tau$, according to the criterion
-            $$
-                \frac{w_i \sigma_i}{\sum_j w_j \sigma_j}n_w < \tau,
-            $$
-            where $w_i$ is the weight of task $i$, $\sigma_i$ is the standard
-            deviation of the Y-values from task $i$, and $n_w$ denotes the number of
-            weights.
-
-    Returns:
-        Mask containing True for significant weights and False otherwise.
-    """
-    num_weights = len(weights)
-    w_times_sigma = weights * std_Y_vals
-    norm_weights = w_times_sigma * num_weights / w_times_sigma.sum()
-    return norm_weights >= threshold
-
-
-class ScaMLGP(SingleTaskGP):
-    def __init__(
-        self,
-        train_X: torch.Tensor,
-        train_Y: torch.Tensor,
-        source_gps: Dict[Hashable, SingleTaskGP],
-        likelihood: Optional[Likelihood] = None,
-        covar_module: Optional[Module] = None,
-        weight_pruning_threshold: float = 1e-3,
-    ) -> None:
-        r"""Scalable and Modular Kernel for Transfer Learning with Gaussian Processes.
-
-        Args:
-            train_X: A `batch_shape x n x d` tensor of training features.
-            train_Y: A `batch_shape x n x m` tensor of training observations.
-            source_gps: Dictionary containing the source GPs.
-            likelihood: A likelihood. If omitted, use a standard
-                `GaussianLikelihood` with inferred noise level.
-            covar_module: The kernel of the target GP. If omitted, use an `RBFKernel`.
-            weight_pruning_threshold: Relative threshold for weight pruning during
-                prediction. Weights satisfying the following condition are pruned:
-                `weights * num_weights/ weights.sum() < weight_pruning_threshold`.
-                Defaults to 1e-3. Note that the absolute threshold decreases linearly
-                with `num_weights`. Usually, it is not required to change this.
-
-        Example:
-            >>> pi = torch.pi
-            >>> meta_X = torch.distributions.Uniform(-pi, pi).sample((20, 1))
-            >>> meta_Y1 = torch.sin(meta_X)
-            >>> meta_Y2 = torch.cos(meta_X)
-            >>> meta_data = {
-            >>>     0: SupervisedDataset(meta_X, meta_Y1),
-            >>>     1: SupervisedDataset(meta_X, meta_Y2),
-            >>>}
-            >>> source_models = meta_fit_scamlgp(meta_data)
-            >>> X = torch.distributions.Uniform(-torch.pi, torch.pi).sample((7, 1))
-            >>> Y = torch.sin(X) * X/10
-            >>> model = ScaMLGP(X, Y, source_models)
-        """
-        self._weight_pruning_threshold = weight_pruning_threshold
-
-        batch_shape = train_Y.shape[:-2]
-        n_source_tasks = len(source_gps)
-        # Note that, since we normalize with respect to all meta- and target data, the
-        # target data alone does generally not have mean = 0 and std = 1. This may lead
-        # to BoTorch raising warnings about Y-normalization and can be safely ignored.
-        Y_meta = torch.cat(
-            [
-                gp.outcome_transform.untransform(gp.train_targets.unsqueeze(-1))[0]
-                for gp in list(source_gps.values())
-            ],
-            dim=-2,
-        )
-        Y_all = torch.cat([Y_meta, train_Y], dim=-2)
-        outcome_transform = Standardize(1, batch_shape=batch_shape)
-        outcome_transform(Y_all)
-        # Make sure parent constructor doesn't re-learn the normalizer and only consumes
-        # current state
-        outcome_transform.eval()
-
-        # Cache the posterior mean of the source models
-        if train_Y.shape[-2] > 0:
-            with torch.no_grad():
-                posteriors = [gp.posterior(train_X) for gp in source_gps.values()]
-                self.source_means = torch.stack(
-                    [p.mvn.mean for p in posteriors], dim=-1
-                )
-                # No need to deal with LazyTensor since the target typically has little
-                # data
-                self.source_covs = torch.stack(
-                    [p.mvn.covariance_matrix for p in posteriors], dim=-1
-                )
-
-        if covar_module is None:
-            covar_module = _get_default_kernel(
-                base_kernel=RBFKernel,
-                ard_num_dims=train_X.shape[-1],
-                batch_shape=batch_shape,
-            )
-
-        self.source_gps = source_gps
-        batch_shape = train_Y.shape[:-2]
-        if likelihood is None:
-            likelihood = _get_default_likelihood(batch_shape=batch_shape)
-        # We normalize to zero-mean unit-variance
-        mean_module = ZeroMean(batch_shape=batch_shape)
-        if outcome_transform is None:
-            outcome_transform = Standardize(1, batch_shape=batch_shape)
-        # If input is empty, do not standardize the data
-        if train_Y.nelement() == 0:
-            outcome_transform = None
-        super().__init__(
-            train_X,
-            train_Y,
-            likelihood=likelihood,
-            covar_module=covar_module,
-            mean_module=mean_module,
-            outcome_transform=outcome_transform,
-        )
-
-        # Add weight parameters
-        self.register_parameter(
-            "raw_weights",
-            torch.nn.Parameter(torch.full((n_source_tasks,), 1.0 / n_source_tasks)),
-        )
-        # Use a generic distribution that is constant for `w \lesssim 1` and decays
-        # quickly for `w \gtrsim 1`. This prior is best for tasks functions that live on
-        # relatively similar scales.
-        self.register_prior(
-            "weights_prior",
-            GammaPrior(1.0, 1.0),
-            lambda m: m.weights,
-            lambda m, v: m._set_weights(v),  # pylint: disable=protected-access
-        )
-        # For unrelated tasks, the weights can reach a value of zero and may conflict
-        # with a strictly positive prior. We therefore bound the weights from below.
-        weights_constraint = GreaterThan(1e-10, transform=None)
-        self.register_constraint(
-            "raw_weights",
-            weights_constraint,
-        )
-        self.to(train_X)
-
-    @property
-    def weights(self):
-        # When accessing the parameter, apply the constraint transform
-        return self.raw_weights_constraint.transform(self.raw_weights)
-
-    @weights.setter
-    def weights(self, value):
-        self._set_weights(value)
-
-    def _set_weights(self, value):
-        if not torch.is_tensor(value):
-            value = torch.as_tensor(value).to(self.raw_weights)
-        # When setting the parameter, transform the actual value to a raw one by
-        # applying the inverse transform
-        self.initialize(
-            raw_weights=self.raw_weights_constraint.inverse_transform(value)
-        )
-
-    def forward(self, x: torch.Tensor) -> MultivariateNormal:
-        if self.training:
-            # Use cached values
-            mean = (self.source_means @ self.weights).unsqueeze(-1)
-            cov = self.source_covs @ self.weights**2
-        else:
-            std_Y_vals = torch.tensor(
-                [m.outcome_transform.stdvs.flatten() for m in self.source_gps.values()]
-            ).to(x)
-            mask = significant_weights_mask(
-                self.weights, std_Y_vals, self._weight_pruning_threshold
-            )
-            significant_weights = self.weights[mask]
-            significant_models = list(compress(self.source_gps.values(), mask))
-            mean, cov = _compute_target_prior(
-                x=x, source_gps=significant_models, weights=significant_weights
-            )
-        if hasattr(self, "outcome_transform"):
-            # Transform the source terms to the target space
-            self.outcome_transform.eval()
-            mean = self.outcome_transform(mean)[0]
-            cov /= self.outcome_transform.stdvs**2
-            if self.training:  # return to training mode, if relevant
-                self.outcome_transform.train()
-        cov = to_linear_operator(cov) + self.covar_module(x)
-        # print(mean.squeeze(-1).shape, cov.to_dense().shape)#,squeeze(-1), cov.to_dense())
-        # if mean.shape[0] == 1:
-        #     return torch.distributions.Normal(mean.squeeze(-1), cov)
-        return MultivariateNormal(mean.squeeze(-1), cov)
\ No newline at end of file
diff --git a/private_multitask_pfn/task_embedding.ipynb b/private_multitask_pfn/task_embedding.ipynb
deleted file mode 100644
index 6e79e6e..0000000
--- a/private_multitask_pfn/task_embedding.ipynb
+++ /dev/null
@@ -1,2065 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import gpytorch\n",
-    "\n",
-    "import torch\n",
-    "from PFNs.pfns.priors import Batch\n",
-    "from gpytorch.distributions.multivariate_normal import MultivariateNormal\n",
-    "from gpytorch.kernels import RBFKernel\n",
-    "from gpytorch.kernels.kernel import Kernel\n",
-    "from gpytorch.means.constant_mean import ConstantMean\n",
-    "from gpytorch.priors.lkj_prior import LKJCovariancePrior\n",
-    "from linear_operator.operators import InterpolatedLinearOperator\n",
-    "\n",
-    "import torch\n",
-    "from gen_batch import (\n",
-    "    combine_batch,\n",
-    "    get_hpo_batch_fn,\n",
-    "    get_icm,\n",
-    "    get_lmc,\n",
-    "    get_mtgp_batch,\n",
-    ")\n",
-    "from PFNs.pfns import (\n",
-    "    bar_distribution,\n",
-    "    encoders,\n",
-    "    priors,\n",
-    "    utils,\n",
-    ")\n",
-    "from PFNs.pfns.train import (\n",
-    "    train as train_pfn,\n",
-    ")\n",
-    "import sys\n",
-    "import logging\n",
-    "import os\n",
-    "import datetime\n",
-    "import json\n",
-    "import argparse\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "torch.cuda.is_available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "default_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def train(\n",
-    "    get_batch_fn,\n",
-    "    num_features = 2,\n",
-    "    num_tasks = 1,\n",
-    "    seq_len = 20,\n",
-    "    epochs = 20,\n",
-    "    sample_num_features = False\n",
-    "):\n",
-    "    batch_fn_hypers = {\n",
-    "        \"num_tasks\": num_tasks,\n",
-    "        \"sample_num_tasks\": False,\n",
-    "        \"sample_num_features\": sample_num_features,\n",
-    "    }\n",
-    "    get_batch_fn = priors.get_batch_sequence(\n",
-    "        get_batch_fn,\n",
-    "        priors.utils.set_properties_get_batch,\n",
-    "    )\n",
-    "    criterion = torch.nn.GaussianNLLLoss(reduction=\"none\", full=True)\n",
-    "\n",
-    "    single_eval_pos_gen = utils.get_uniform_single_eval_pos_sampler(\n",
-    "        seq_len - num_tasks, min_len=num_tasks\n",
-    "    )\n",
-    "    \n",
-    "    encoder_generator = encoders.get_normalized_uniform_multitask_encoder(\n",
-    "        encoders.get_variable_num_features_multitask_encoder(encoders.Linear)\n",
-    "    )\n",
-    "\n",
-    "    return train_pfn(\n",
-    "        priordataloader_class_or_get_batch = get_batch_fn,\n",
-    "        criterion = criterion,\n",
-    "        encoder_generator=encoder_generator,\n",
-    "        single_eval_pos_gen=single_eval_pos_gen,\n",
-    "        extra_prior_kwargs_dict = {\n",
-    "            \"num_features\": num_features,\n",
-    "            \"hyperparameters\": batch_fn_hypers,\n",
-    "        },\n",
-    "        num_features=num_features, \n",
-    "        num_tasks=num_tasks, \n",
-    "        seq_len=seq_len, \n",
-    "        epochs=epochs,\n",
-    "        progress_bar=True,\n",
-    "        y_encoder_generator= encoders.Linear,\n",
-    "        steps_per_epoch = 256,\n",
-    "        print_every=1,\n",
-    "        validation_period=2,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def sample_data(xs):\n",
-    "    # randomly sample uncorrelated tasks\n",
-    "    kernel = RBFKernel().to(xs.device)\n",
-    "    kernel.lengthscale = 0.2\n",
-    "    return MultivariateNormal(\n",
-    "                torch.zeros(xs.shape[:-1], device=xs.device), kernel(xs)\n",
-    "            ).sample()\n",
-    "    \n",
-    "    \n",
-    "def gen_batch(\n",
-    "    batch_size: int,\n",
-    "    seq_len: int,\n",
-    "    num_features: int,\n",
-    "    max_num_tasks: int,\n",
-    "    num_tasks: int,\n",
-    "    lengthscale: float,\n",
-    "    hyperparameters=None,\n",
-    "    device: str = default_device,\n",
-    "    **kwargs\n",
-    "):\n",
-    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
-    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
-    "    \n",
-    "    xs = torch.cat((x1, x2), -1)\n",
-    "    ys = sample_data(x1)\n",
-    "    ys += x2.squeeze(-1)\n",
-    "    task_ids = torch.ones(batch_size, seq_len, 1, device=device)\n",
-    "    task_xs = torch.cat((task_ids, xs), -1).to(device)\n",
-    "\n",
-    "    return Batch(\n",
-    "        x=task_xs.transpose(0, 1),\n",
-    "        y=ys.transpose(0, 1),\n",
-    "        target_y=ys.transpose(0, 1).clone(),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Toy Problem\n",
-    "\n",
-    "2 features: $x_1$ and $x_2$\n",
-    "\n",
-    "$f \\sim \\mathcal{GP}(0, k_\\text{RBF})$\n",
-    "\n",
-    "$y = f(x_1) + x_2$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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vBed54YUX9Nu0Wq0ycOBAxdHRUUlMTNRvB5RZs2bpvx88eLDi6OioREVF6bfFxsYqHh4eSteuXfXbjH12C2GrJLMphJUUDCH18PAosu2WLVsAmDZtWqHtr7zyCsA98zqbNm3KAw88oP/e19eXRo0aER0dXeS5du3aRW5uLlOnTi00n3LixIl4enqWag7p2LFjcXFxKfH+QgghREk999xzhb5/4IEHCn0url27Fi8vL/r06cPt27f1jzZt2uDu7s7evXvNOs+UKVP0z1UqFVOmTCE3N5ddu3YZbJ+fn8+OHTsYPHgw9erV028PDAxk5MiR/P777zLtRFRYUiBICCvx9PQEIC0trci2165dQ61W06BBg0LbAwIC8Pb25tq1a4W2165d+55jVKtWzaw5JwXHatSoUaHtjo6O1KtX755zFUfdunVLvK8QQghRUs7Ozvj6+hba9u/PxUuXLpGSkoKfn5/BY9y6davI86jV6kIBI8B9990HYHS5ksTERDIzM+/53AVo0qQJWq2W69ev06xZsyLPL4StkWBTCCvx9PQkKCiI06dPm72PSqUyq52dnZ3B7YoZBQqKw1h/8vPzDfZBsppCCCGswdjn4j9ptVr8/PxYtWqVwdf/HawKIYomwaYQVvTQQw+xdOlSDh06RFhYmNF2derUQavVcunSpUKFABISEkhOTqZOnTrFPrexQLHgWBcuXCh0dzY3N5crV67Qu3dv/bZq1ardU6EWdNnRf9/ZFUIIIWxZ/fr12bVrF507dy7xzVGtVkt0dLQ+mwlw8eJFAKPrYvr6+uLq6sqFCxfuee38+fOo1WqCg4MB8286C2ErZM6mEFb0+uuv4+bmxoQJE0hISLjn9aioKD755BMGDBgAwIIFCwq9/tFHHwEwcODAYp/bzc0N4J5gsXfv3jg6OrJw4cJCmdCvv/6alJSUQueqX78+f/zxB7m5ufptmzZt4vr168XujxBCCGFNw4cPJz8/n3ffffee1/Ly8gzeXDXks88+0z9XFIXPPvsMBwcHevXqZbC9nZ0dffv2ZcOGDYWG2iYkJBAeHk6XLl30U2+MfXYLYasksymEFdWvX5/w8HAef/xxmjRpwpNPPknz5s3Jzc3l4MGDrF27lqeeeoqXXnqJsWPHsnTpUpKTk+nWrRtHjhzh22+/ZfDgwfTo0aPY527VqhV2dna8//77pKSk4OTkRM+ePfHz8+PNN99k9uzZ9O/fn4cffpgLFy7w+eef065dO0aPHq0/xoQJE/jxxx/p378/w4cPJyoqiu+++4769etb8tckhBBC6C1btoxt27bds71gXeqS6tatG88++yzz5s0jMjKSvn374uDgwKVLl1i7di2ffPIJjz32mMljODs7s23bNsaOHUuHDh3YunUrmzdvZvr06SaH4f73v/9l586ddOnSheeffx57e3u++OILcnJy+OCDD/TtTH12C2GTrFwNVwihKMrFixeViRMnKiEhIYqjo6Pi4eGhdO7cWfn000+V7OxsRVEURaPRKLNnz1bq1q2rODg4KMHBwcqbb76pf71AnTp1lIEDB95zjn8vR6IoivLll18q9erVU+zs7O4ppf7ZZ58pjRs3VhwcHBR/f39l0qRJyt27d+857ocffqjUrFlTcXJyUjp37qwcO3bM6NIna9euLfHvSAghRNVWsPSJscf169eNLn3i5uZ2z/FmzZqlGLoUXrp0qdKmTRvFxcVF8fDwUEJDQ5XXX39diY2NNdm/gvNERUUpffv2VVxdXRV/f39l1qxZhZYSU5R7lz5RFEU5fvy40q9fP8Xd3V1xdXVVevTooRw8ePCe85j67BbC1qgUxcIVQ4QQQgghhKhinnrqKX788UfS09Ot3RUhbIbM2RRCCCGEEEIIYXESbAohhBBCCCGEsDgJNoUQQgghhBBCWJxVg839+/czaNAggoKCUKlUrF+/vsh99u3bx/3334+TkxMNGjRg+fLlZd5PIYQQQgghTFm+fLnM1xTiX6wabGZkZNCyZUsWLVpkVvsrV64wcOBAevToQWRkJFOnTmXChAls3769jHsqhBBCCCGEEKI4bKYarUql4ueff2bw4MFG27zxxhts3ryZ06dP67eNGDGC5ORkg+stCSGEEEIIIYSwDntrd6A4Dh06RO/evQtt69evH1OnTjW6T05ODjk5OfrvtVotSUlJVK9eHZVKVVZdFUIIYeMURSEtLY2goCDUailhYA6tVktsbCweHh7yGSqEEFWYuZ+hFSrYjI+Px9/fv9A2f39/UlNTycrKwsXF5Z595s2bx+zZs8uri0IIISqY69evU6tWLWt3o0KIjY0lODjY2t0QQghhI4r6DK1QwWZJvPnmm0ybNk3/fUpKCrVr16b1IzOwc3C2Ys8sK9e9+HflNW5mtnM3/Xq+m+mR2HluWuMvuucZfcnZLcfoa16u2SbP6e9meoJ+TecUk68XCHa5Y1Y7Q0IcEku8b0ld1fiWeN/rWdXNancz28usdgkZRbxxgJRM8/4NZmc4mdWukHQr//dm4r1tiKn3e4Gyft8X9X435z1d2yGpyDYGj21f/jM61G6HCQ4OxsPDo9zPXVEV/K6uX7+Op6enlXsjhBDCWlJTU836DK1QwWZAQAAJCQmFtiUkJODp6Wkwqwng5OSEk9O9F6p2Ds7YV6Jg0z4HcjyKF3DaaYoOJAG0RVznK86mLxLVLiaCTRfjF+R2rsaHaNkXEeA6uOWafN3RxcHk6wWcXUr+T8TV0a7E+5ZEdK4fziWIyQCuZdXA0Yz3AoCDnWORbeIzPLA342aGHeb9G1QX9SY0JN/K/72ZeG8bYur9XqCs3/dFvd/NeU+7O5RsOKqnNYJNd12wJMNBzVfwu/L09JRgUwghRJGfoRVqkkpYWBi7d+8utG3nzp2EhYVZqUcCwD69/C/UkjMM31wQQljXVY15GfJ/i86TgE8IIYSobKwabKanpxMZGUlkZCSgW9okMjKSmJgYQDcE9sknn9S3f+6554iOjub111/n/PnzfP755/zwww+8/PLL1ui+EEJYTFZ60dnbom6yxGfIcFAhhBBC2A6rBpvHjh2jdevWtG7dGoBp06bRunVrZs6cCUBcXJw+8ASoW7cumzdvZufOnbRs2ZIPP/yQr776in79+lml/1WFQ1muT2xiXp05F98ldSPLu8yOXSA616/Mz2GNc1mKudnpsnwflClrzxkVQgghhLAyq14Nde/eHVPLfC5fvtzgPidOnCjDXlVcTmnaYs/bFKZdy6pBHZfb1u6GKC4J9ISwGkVRyMvLIz8/39pdEUWws7PD3t5e5i0LIcqMXJGJMmefribP3USRoDIQn+FBgFtauZ7TWsozq1keGWFhu6Jz/ajneKvsjp+nop4VCgUJy8nNzSUuLo7MzExrd0WYydXVlcDAQBwdiy7+JoQQxSXBphBlqKwvzi3hWlYNa3dBlJMbWd7Uckm2djdEJaXVarly5Qp2dnYEBQXh6OgoGTMbpigKubm5JCYmcuXKFRo2bGhyYXYhhCgJCTarOId085Y/EUKUQLp9sdfbFKKiys3NRavVEhwcjKurq7W7I8zg4uKCg4MD165dIzc3F2fnyrMknBDCNsgtLCEqMFssDGT1iqgyX7PELJXlLunyJ6JykOxYxSJ/LyFEWZL/YSoZp7TynRspimaLAaEQtkrW2xRCCCEqDwk2hUXYp8sFYnmrCkFshV32pISq2s8rhBBCiMpNgk1RYZm7TqMx5lZWlQI6llfav11VJr87IYQQQlQUEmwKszikW7sHoixIIF0OynkOqdXnzFYQMlxXmGvdunX06dMHX19fPD09CQsLY/v27aU65nvvvUenTp1wdXXF29vbrH0URWHmzJkEBgbi4uJC7969uXTpUqE2SUlJjBo1Ck9PT7y9vRk/fjzp6fIBLoSwHgk2hSgHlh7yao0htBVijU0pDlQpSCAobMn+/fvp06cPW7ZsISIigh49ejBo0CBOnDhR4mPm5uYybNgwJk2aZPY+H3zwAQsXLmTJkiUcPnwYNzc3+vXrR3Z2tr7NqFGjOHPmDDt37mTTpk3s37+fZ555psT9FEKI0pIrs0rIKU1LjkcFuo9gYnmIrHQnXNxzyrlDQghjKsLasUWRYLb8KIpClibfKud2cbAza53PxMREQkNDefHFF5k+fToABw8epHv37mzdupUFCxYUaj937lw2bNjAxo0bad26dYn6Nnv2bACWL19uVntFUViwYAEzZszgkUceAWDFihX4+/uzfv16RowYwblz59i2bRtHjx6lbdu2AHz66acMGDCA+fPnExQUVKK+CiFsS26els2nYvnh6A3Sc/JwcbDD2dEOFwe17vlfj+Y1vRjSuiZ2aut+5kmwKcplrU37dDV57uVbKTc+w4MAt7RyPacQQoi/ZWnyaTqzdENOS+rsnH64OhZ9mePr68uyZcsYPHgwffv2pVGjRowZM4YpU6bQq1eve9prtVrS0tLw8fHRb5s7dy5z58413Z+zZ6ldu3bxfxDgypUrxMfH07t3b/02Ly8vOnTowKFDhxgxYgSHDh3C29tbH2gC9O7dG7VazeHDhxkyZEiJzi2EsA2303MIPxzDyj+ukZhmXiLmq9+imflQUzo1sN60KQk2hSgnlsoIVYUqtKLs3MjyppZLcpmf56qmOiEOd8r8PEJYwoABA5g4cSKjRo2ibdu2uLm5MW/ePINt58+fT3p6OsOHD9dve+655wp9b0hpMovx8fEA+Pv7F9ru7++vfy0+Ph4/v8KfD/b29vj4+OjbCCEqnrOxqXxz4AobTsaSm6dL3Ph5ODGmYx2a1fQkW6MlKzefLE0+2X89kjM1rI24wfn4NEZ+dZi+Tf15a2AT6lR3K/f+S7ApRDmqDEMQTZHiNMJSovNU1LNXyuS4ovy4ONhxdk4/q527OObPn0/z5s1Zu3YtERERODnduxRReHg4s2fPZsOGDYUCOx8fn0KZTiGEKK1rdzJ4c90pDkb9feO2ZS0vnu5SlwebB+Job3rK3OQeDViw6yLfHY5hx9kE9l1IZFyXEKb0aICHs0NZd1+vAk3sE8J6LFm1tTSZSUtmNW29Em2x15ysJMWBrL3Wpi29LyQwNG3evHm0a9cODw8P/Pz8GDx4MBcuXChyv7Vr19K4cWOcnZ0JDQ1ly5YtZdZHlUqFq6O9VR7mzNf8p6ioKGJjY9FqtVy9evWe11evXs2ECRP44YcfCg1nBd0wWnd3d5OPmJiYEv8eAwICAEhISCi0PSEhQf9aQEAAt24VvpmZl5dHUlKSvo0QomK4npTJE0v/4GDUHezUKga2COSnSZ1YP7kzj7SqWWSgCVDNzZHZjzRn60sP8EDDGuTma/ni12h6zP+VNUdjyNda/oauIZXj6kyIEiqvIYXib1VynUgTRbBE+alsweuvv/7K5MmTadeuHXl5eUyfPp2+ffty9uxZ3NwMD5U6ePAgTzzxBPPmzeOhhx4iPDycwYMHc/z4cZo3b17OP4HtyM3NZfTo0Tz++OM0atSICRMmcOrUKX328vvvv+fpp59m9erVDBw48J79y3oYbd26dQkICGD37t20atUKgNTUVA4fPqyvaBsWFkZycjIRERG0adMGgD179qDVaunQoUOJzy2EKF837mYyYukfxKZkU9/XjeXj2hPs41ri493n78GKp9uz5/wt/rv5HFduZ/DGT6ewU6t5rE0tC/bcMAk2y5ljiq4qX65X8Yb3FJc1KtLap6vIcy+fuyQVXUmG01pzrmaFWPakCknOcMHbLcva3SgXZTWctjLYtm1boe+XL1+On58fERERdO3a1eA+n3zyCf379+e1114D4N1332Xnzp189tlnLFmypMz7bKveeustUlJSWLhwIe7u7mzZsoWnn36aTZs2ER4eztixY/nkk0/o0KGDfv6ji4sLXl5eQPGH0cbExJCUlERMTAz5+flERkYC0KBBA9zddRX7GjduzLx58xgyZAgqlYqpU6fy3//+l4YNG1K3bl3efvttgoKCGDx4MABNmjShf//+TJw4kSVLlqDRaJgyZQojRoyQSrRCVBCxyVk88eUf3EzOol4NN76f2BE/T+dSH1elUtGriT8PNPRlxaGrbPozjkdalc//CxJslpGCoLIyKY+qtcVVkS+6ixNwSlEgIUqnsmU1DUlJSQEwGfQcOnSIadOmFdrWr18/1q9fX5Zds2n79u1jwYIF7N27F09PTwBWrlxJy5YtWbx4MWvWrCEvL4/JkyczefJk/X5jx441e+mSf5s5cybffvut/vuCJVT27t1L9+7dAbhw4YL+bwrw+uuvk5GRwTPPPENycjJdunRh27ZtODv/fSG6atUqfRVdtVrN0KFDWbhwYYn6KIQoX/Ep2Tzx5R9cT8qiTnVXwi0UaP6To72aCQ/UY3yXusWealBSEmxaiWNKfplnN4vDFgNJoSOBphkqyXxNS7GVZX8sVZFWsptF02q1TJ06lc6dO5scDhsfH2+youm/5eTkkJPzd4n91NRUy3TYhnTv3h2NRlNoW0hIiD7QKximaknLly8vMlBVlMLveZVKxZw5c5gzZ47RfXx8fAgPD7dEF4UQ5ehWajYjv/yDa3cyCfZx4fuJHQnwsmyg+U/lFWiCFAgqE+ZmNStj9lMUT1GBpASawhZZ431ZmsxkVchqTp48mdOnT7N69WqLHnfevHl4eXnpH8HBwRY9vhBCVHWJaTk88eUfRN/OoKa3C+ETOhLkXXnqW0iwaWHFDSAl4CxbFWEpDkMX7tG5fmV6QW9LFUeFEKUzZcoUNm3axN69e6lVy3Sxh4CAAJMVTf/tzTffJCUlRf+4fv26xfothBBV3Z30HEZ99QdRiRkEejnz/cSOpSoGZIsk2LQgCRxLwcQwSGsvBVGgvAK0iprNtGRgbyt/c4uSob6lVhUylMWhKApTpkzh559/Zs+ePdStW7fIfcLCwti9e3ehbTt37iQsLMxgeycnJzw9PQs9hBBClJ5Wq/Di6hNcTEjH39OJ8IkdqV29cgWaIHM2LaY0gWZZzd+0RkVaU+zT1eS5a63dDZtUUQNMUTay0p1wcc8pumEJVaUlfypzgDp58mTCw8PZsGEDHh4e+nmXXl5euLjohmA9+eST1KxZk3nz5gHw0ksv0a1bNz788EMGDhzI6tWrOXbsGEuXLrXazyGEEFXRqiMxHLh8B2cHNd+N70DdGoaXrKrobCcSqeIkKypslSx7ImxJZQ4ei2vx4sWkpKTQvXt3AgMD9Y81a9bo28TExBAXF6f/vlOnToSHh7N06VJatmzJjz/+yPr166v0GptCCFHeYu5kMm/LOQDe6N+Yhv62P+2rpCSzaQGWChRtrUJtVVGVsjzWlpxRBhPeZXiqxV3LqkEdl9sWOZalKtL+k7nVaSt7YPrvaqWG7Nu3755tw4YNY9iwYWXQIyGEEEXRahVe+/Ekmbn5tK/rw9iwEGt3qUzJVVopVaaMpDnLnxTVxj5dRZ67LFEgRFmryGvMWlplDyqFEEJUHisOXeXwlSRcHe2Y/1hL1OrK/RkmwWYplEWgKdlNUdakEq2oDCTAFEIIUdFcvZ3B/207D8CbDzaulAWB/k3mbNqgypQtrWwkUBOlIkN+hRBCiCopX6vw6tqTZGu0dG5QnVEd6li7S+VCgs0SqigBoVNa5a/+WibzAIWo4Mp6jVmpoCyqinXr1tGnTx98fX3x9PQkLCyM7du3l+qYSUlJjBo1Ck9PT7y9vRk/fjzp6elG21+9ehWVSmXwsXbtWn07Q6+vXr26VH0VQljGNweucOzaXdwc7Xh/aItKP3y2gASbQohSkzU2hSlXNdWt3QUhSmz//v306dOHLVu2EBERQY8ePRg0aBAnTpwo8TFHjRrFmTNn2LlzJ5s2bWL//v0888wzRtsHBwcTFxdX6DF79mzc3d158MEHC7X95ptvCrUbPHhwifsphLCMy7fS+d/2CwDMeKgptapV/uGzBWRMVwmUR1ZT5m5aTnyGBwFuadbuhhDFUtZrbQohdBITEwkNDeXFF19k+vTpABw8eJDu3buzdetWFixYUKj93Llz2bBhAxs3bqR169bFPt+5c+fYtm0bR48epW3btgB8+umnDBgwgPnz5xMUFHTPPnZ2dgQEBBTa9vPPPzN8+HDc3QtX7fP29r6nrRDCegqGz+bkael6ny8j2gVbu0vlSjKbokKQbJd12PwamzIHUgjbpiiQm2GdhxlLwwD4+vqybNky3nnnHY4dO0ZaWhpjxoxhypQp9OrV6572Wq2WtLQ0fHx89Nvmzp2Lu7u7yUdMTAwAhw4dwtvbWx9oAvTu3Ru1Ws3hw4fN6nNERASRkZGMHz/+ntcmT55MjRo1aN++PcuWLTNriRwhRNn58rdoIq8n4+Fsz/tDQ1Gpqsbw2QJypVZMFWWupq2yT1eT525kHmm6PbjnlW+H/lKctTYtuQZhebNmgSOZW2tbZH1ZUS40mTD33kxduZgeC45uZjUdMGAAEydOZNSoUbRt2xY3NzfmzZtnsO38+fNJT09n+PDh+m3PPfdcoe8NKchYxsfH4+dXeM6zvb09Pj4+xMfHm9Xfr7/+miZNmtCpU6dC2+fMmUPPnj1xdXVlx44dPP/886Snp/Piiy+adVwhhGXdSc9h4e5LALz9UFMCvaretZAEmzbMGkNpzVlrUwhRCla8qVIcFfmmihAlMX/+fJo3b87atWuJiIjAyeneETXh4eHMnj2bDRs2FAoYfXx8CmU6y1JWVhbh4eG8/fbb97z2z22tW7cmIyOD//3vfxJsCmElX+yPJjM3n+Y1PRnWppZlDppxByKWwYnvICcNHFzB3hkcXHTPHVx0j8BWEPY8OJVtwcCiSLBZBTilacnxkBHTQgghypmDqy7DaK1zF0NUVBSxsbFotVquXr1KaGhooddXr17NhAkTWLt2Lb179y702ty5c5k7d67J4589e5batWsTEBDArVu3Cr2Wl5dHUlKSWXMtf/zxRzIzM3nyySeLbNuhQwfeffddcnJyDAbPQoiycystmxWHrgLwSp9GpR8+m3AG/lgMp9ZCXvY/XrhjuP2FLXDsa+g1E1qOBLV1YgEJNotBhtDqSPZTVBX26br/mI0O/bay5AwXvN2yrN0Ns1zVVCfEwcgHoqi8VCqzh7JaU25uLqNHj+bxxx+nUaNGTJgwgVOnTumzl99//z1PP/00q1evZuDAgffsX5xhtGFhYSQnJxMREUGbNm0A2LNnD1qtlg4dOhTZ16+//pqHH34YX1/fIttGRkZSrVo1CTSFsILP90aRrdHSurY33RsV/e/VIK0WLm2HPz6HK/v/3h7YCjpOgoAWkJcFmn89su7CkS8gKRo2TIYjX0L//4M6YRb52YpDgk0bVxGr0tqnq8hzl4IEovhspRBUQZApTIvO9aOe462iGwph49566y1SUlJYuHAh7u7ubNmyhaeffppNmzYRHh7O2LFj+eSTT+jQoYN+XqWLiwteXl5A8YbRNmnShP79+zNx4kSWLFmCRqNhypQpjBgxQh+Q3rx5k169erFixQrat2+v3/fy5cvs37+fLVu23HPcjRs3kpCQQMeOHXF2dmbnzp3MnTuXV199tbS/HiFEMcWlZBF+WFcUrMRZzZvH4acJkBSl+16lhiaDoOPzENxBdzPPlLbj4MhS+PUDiIuEb/pDs0ehzxzwLr+KuHJFZSbJaop/smahnZIqqz5bco3NYrFwJVr7dLX+Yeg1IUTltG/fPhYsWMDKlSvx9PRErVazcuVKfvvtNxYvXszSpUvJy8tj8uTJBAYG6h8vvfRSic+5atUqGjduTK9evRgwYABdunRh6dKl+tc1Gg0XLlwgMzOz0H7Lli2jVq1a9O3b955jOjg4sGjRIsLCwmjVqhVffPEFH330EbNmzSpxP4UQJfPZnsvk5mtpX9eHzg1KsNZ07AlYOVgXaDp7QacX4aWTMHwF1O5YdKAJYO8EnV6AF45Dm6cAFZxZB5+1hT3v6ap2lwOVUsVqYqempuLl5UXbx/6LvYOz2ftZM9i0RGazOHM2zRkiW1QbU5lNk0MSTRROKWrNQVPDCc1ZZ7O4lTkrWvGU4gab5i57Ym6waU412mJlNi0YbJoTTFp0KG0xCgSV5n0Ppt/7Rb3nzXmPFyezaWvDaBt4n8bLy4uUlBQ8PT2t3Z0KoeAz1NDvLDs7mytXrlC3bl2cnc3/fBXWJX83ISzrelImPT/chyZfYc0zHelQr5jBZtyf8O0gyE6G4I4wai04W+AzKv4UbHsTrv6m+/7Rr6DFsBIfztTnwT/JMFozWDurWRGH0gpR2ZhctkcIIYQQAli4+xKafIUuDWoUP9BMOAMrHtEFmrXawegfLVdNNiAUxm6Ecxt1Gc7mQy1z3CLI2LAqwimtal8kW22opwBse41NqwyRtfAQYCGEEEJY35XbGaw7cROAaX3vK97Ot87Btw9DVhIE3Q+jf7L8siUqFTR9GIYtL7fqtBJsins4pFu7B9Zh7rBRUXXJ3M3SuaopwbwVIYQQooL4ZNdF8rUKPRr5cn/taubvmHhRF2hm3obAljDmZ91czUpArpyKYO0htAVspR/WZCuVSgtUpCJBFamvovzIDRYhhBDCMi4lpLHhpG5d4Wl9Gpm/4+3LujmaGbd0Q13HrAcX7zLpozVIsCnKncnskAwvrLKsURyoJJnKqpTdlJsUQgghhHkW7LqEokC/Zv6E1jIzK5kUrQs00+PBrxmM2QCu5i2jVFFUnaumEpBsohBCmBad62ftLgghhBBWdTY2lc2n4lCp4OU+Zs7VzNfAD2MhLRZ8G8OTG8Ct8k03kWCzArGl4LeqzuusKiy97IktKk2G0paym7ZcfEkIIYSoCj7edRGAgaGBNA4wc5mS3z6C+D/BpZpu6Ky7b9l10Ips54pJlLnyrEhrn27GYrOiXMhQSBslQ8aFEEKICu/yrTR2nk1ApYKpvc3Masb9Cfs/0D0fMB88A8uug1YmwaYRtpRFFLZLArmqq7yym7ZWGKu0pCKtEKKspGVr2BB5k40nY7lxNxNFUazdJVEFfHPgKgB9mvjTwM+96B3ycmH9JNDmQZNB5bbepbXIrfUKxjEln1wvO2t3o9K6keVNLZdka3ejUrHFYZ6WChTt09Xkudv2GrbxGR4EuKVZuxtCCFEmtFqFQ9F3+DHiBltPx5Gt+fv/ZF8PJ1oFe9O6tjetg6vRopYXbk5y6SssJyVTw7rjunU1x3Wua95O+/8HCafBtToM/Fi39mUlJplNUWnYYlAjyoAMPxVClKN169bRp08ffH198fT0JCwsjO3bt5fqmElJSYwaNQpPT0+8vb0ZP3486emmiyF0794dlUpV6PHcc88VahMTE8PAgQNxdXXFz8+P1157jby8vFL11VZdvZ3Bhzsu0OX9PYz66jA/n7hJtkZLfV83WtTywl6tIjEth51nE/hg2wWe+PIPQt/Zzoilh7iUIDfghGWsPhpDliafxgEedKxnRhXZ2BPw24e65wM/rLTzNP9JrtoMkCG0ugJAGjNGAlQkkuERYFvFfYQQtm///v306dOHuXPn4u3tzTfffMOgQYM4fPgwrVu3LtExR40aRVxcHDt37kSj0TBu3DieeeYZwsPDTe43ceJE5syZo//e1dVV/zw/P5+BAwcSEBDAwYMHiYuL48knn8TBwYG5c+eWqJ+2KDoxnTfXneLwlST9Nk9nex5uFcRjbYJpWcsLlUpFtiafM7EpnIhJ5kRMMpHXk7mZnMUf0UkM/PR3XulzHxMeqIedunJnlUTZycvXsuLQNQDGdQ5BVVSGMi8Hfp4ESj40exSaDSmHXlqfBJsVkAylFeaqKHNKK/K8xIowlLasRef6Uc/xlrW7IUSJJCYmEhoayosvvsj06dMBOHjwIN27d2fr1q0sWLCgUPu5c+eyYcMGNm7cWKJg89y5c2zbto2jR4/Stm1bAD799FMGDBjA/PnzCQoKMrqvq6srAQEBBl/bsWMHZ8+eZdeuXfj7+9OqVSveffdd3njjDd555x0cHR2L3Vdbc+xqEhNWHCM5U4NaBV3v8+WxNrXo3cQfZ4fC10XODna0qeNDmzp/Z5ti7mQy85fT7LuQyLyt59l+Jp75w1pSz7eS3V0X5WLXuQRuJmdRzdWBR1rVLHqHff8HiefAzVdXFKiKkFv8VUx5VqStKipKQGcuc5c9ERZgA0OCi/p7l8X7W4oEVR2KopCpybTKw9ziML6+vixbtox33nmHY8eOkZaWxpgxY5gyZQq9evW6p71WqyUtLQ0fn7+DmLlz5+Lu7m7yERMTA8ChQ4fw9vbWB5oAvXv3Rq1Wc/jwYZN9XbVqFTVq1KB58+a8+eabZGZm6l87dOgQoaGh+Pv767f169eP1NRUzpw5Y9bvwpZtPRXHyK8Ok5ypoWUtL357oyfLx7XnoRZB9wSaxtSu7so3T7Xj/aGhuDvZczwmmQELf2PZ71fQaqWYkCieZX8VBhrZoXbR78EbEXBgge75Qx9XyvU0jbH+lY6NkSG0ti0r3QkX9xxrd0NQMdfYlCG0QpSvrLwsOoR3sMq5D488jKuDa9ENgQEDBjBx4kRGjRpF27ZtcXNzY968eQbbzp8/n/T0dIYPH67f9txzzxX63pCCjGV8fDx+fn6FXrO3t8fHx4f4+Hij+48cOZI6deoQFBTEn3/+yRtvvMGFCxdYt26d/rj/DDQB/femjlsRfPVbNO9tOYeiQO8m/ix8ohWujiW7hFWpVDzerjZdGvryxo9/8vvl28zZdJZtZ+KZ/1hLalc37z0jqrYzsSkcuZKEvVrFmI4hphtrsmD9c6BoIXS4rgJtFSLBpiix0szrNDn0MN0e3CtnQQMhhBC2af78+TRv3py1a9cSERGBk9O9w/vDw8OZPXs2GzZsKBQw+vj4FMp0loVnnnlG/zw0NJTAwEB69epFVFQU9evXL9NzW0u+VuG/m8/ql5Z4MqwOswY1s8g8y5reLqwc357vDscwb8s5jlxJov8n+1k5vgNt6lQr9fFF5VbwnnwwNJAAL2fTjffOhdsXwT0AHny/7DtnYyTYrKBk3mbZqSzLn1S24b22zNrzNpMzXPB2y7La+YUwxsXehcMjTQ8NLctzF0dUVBSxsbFotVquXr1KaGhooddXr17NhAkTWLt2Lb179y702ty5c4sswnP27Flq165NQEAAt24VnuOcl5dHUlKS0fmYhnTooMsYX758mfr16xMQEMCRI0cKtUlISAAo1nFtRbYmn5dWn2D7Gd3P8NaAJkx4oG7RRViKQaVSMaZjHbo19GXaD5Ecu3aXZ1Yc4+fnO0uGUxh1Oz2HXyJjAV1hIJOSY+CPxbrngxaAq+VuSmnyNeQpeTjZOaFW2e7ILQk2/0GG0Aph40oxx7EiD6GV4eOiolKpVGYPZbWm3NxcRo8ezeOPP06jRo2YMGECp06d0mcvv//+e55++mlWr17NwIED79m/OMNow8LCSE5OJiIigjZt2gCwZ88etFqtPoA0R2RkJACBgYH647733nvcunVL3++dO3fi6elJ06ZNzT6uLbibkcv4b49yPCYZRzs1Hz3ekodaGC+cVFq1q7vy7dPtGf7FIc7EpjJu+RHWPd8ZLxeHMjunqLjCD8eQm6+lZbA399cuIgu+/3+g1UDIA9DoQYuc//Ldy3x37js2R28mOz8bACc7J5ztnXG2c8bF3gUnOyeaVG/C8y2fJ9A90CLnLSkJNkWZsU9XkedeNSbcX8uqQR2X29buhs2RtU+FEBXBW2+9RUpKCgsXLsTd3Z0tW7bw9NNPs2nTJsLDwxk7diyffPIJHTp00M9/dHFxwcvLCyjeMNomTZrQv39/Jk6cyJIlS9BoNEyZMoURI0boA9KbN2/Sq1cvVqxYQfv27YmKiiI8PJwBAwZQvXp1/vzzT15++WW6du1KixYtAOjbty9NmzZlzJgxfPDBB8THxzNjxgwmT55scEiwrcrXKjy/6jjHY5LxcnHgyyfb0r5u2Q5RBnBzsufrse0YvOgAUYkZPL8qguXj2uNgV3FvVArLy83TsvIP3XInTxeV1UyKhhOrdM97zijVebWKlt9v/s7Ksyv5I+6Pe17Pyc8hJz+HFFL02y7cvcC2K9sY13wc45qPK/ZoD0uRYLMKckrTkuNR9H+elXGtTVE1VeSspiXY4hqzVzXVCXG4Y+1uCMG+fftYsGABe/fuxdPTE4CVK1fSsmVLFi9ezJo1a8jLy2Py5MlMnjxZv9/YsWNZvnx5ic65atUqfbVbtVrN0KFDWbhwof51jUbDhQsX9NVmHR0d2bVrFwsWLCAjI4Pg4GCGDh3KjBl/X8Da2dmxadMmJk2aRFhYGG5ubowdO7bQupwVwcLdlzgUfQdXRzt+eDaMRgHlV4wuwMuZr59qy7Alhzhw+Q4zfj7N/w0NtejQXVGxbTkVR2JaDn4eTjzYvIiM4a8f6NbUbNAbancs0fkyNBlsuLyB8PPhXEvVBblqlZpetXsxqskomvg0ISsvi5z8HLLzssnKzyI7L5uUnBS+PfMtx28dZ/HJxay7tI6pbaYysO7Acn8/S7ApKpWi5q7Z4kW3LbHGsicVeY3Nf7L2vE1rk7U2RUXVvXt3NBpNoW0hISGkpOgyBJMmTbL4OX18fAgPDzf6ekhISKGlW4KDg/n111+LPG6dOnXYsmWLRfpoDb9fus3CPZcAmDsktFwDzQLNgrz4bGRrJnx7jDXHrhNSw41J3StnASZRfN8cvArAmI51cLQ3cSM78SL8uUb3vMf0Ep1r/439/Oe3/5CWq7tu9XDwYOh9QxnReAQ13f9e19PYVIUewT3YcW0HHx37iNiMWN787U2+P/89/2n3H0J9Qw3uUxaq9u3+f6iI8zUrYp9F+ZDiQBWIDay1KSqm/fv3M2jQIIKCglCpVKxfv95k+3379qFSqe55VPRlMUTlcCs1m6lrTqAo8ET7YAa3rln0TmWkZ2N/Zj6km+f6/rbzbDkVZ7W+CNtxPOYuJ6/r5hE/0aG26ca//p9uqZNGA6Bmm2Kf6/ebvzN171TSctMI8Qxheofp7Bq2i1favlIo0DRFpVLRL6Qfvwz5hRdbv4iLvQt/Jv7JyC0jmf7bdBIyEordr5KQYFMIUWwVcY1NYVxRGW25eWGbMjIyaNmyJYsWLSrWfhcuXCAuLk7/+Peaj0KUt7x8LS98f4Lb6bk0DvBg1qBm1u4ST3Wuy1OdQgB4eU0kJ2LuWrdDwuoKljt5uFUQNdxNjMpKOAundevfliSreTD2IC/teQmNVkPv2r1Z98g6nmj8RImLrTnZOTGxxUQ2DdnEw/UfBmBj9EbOJ50v0fGKS26pC2EhUiSojEkGsNKReZul8+CDD/Lgg8Wvbujn54e3t7flOyRECX2y+xKHryTh5mjHolH34+xgG0u7vf1QU2KSMtlz/hYTVxxj/eTO1Kpm+9WVheUlpGaz9a8Md5HLneybCyjQ9BEIKN5w1cNxh3lxz4vkanPpEdyDD7p+gIPaMlWR/Vz9eK/LezzR+Al2XN1B11pdLXLcokhmExmOWtGUxxw/a8xdFGWjPIsDVfVCRKJiaNWqFYGBgfTp04cDBw6YbJuTk0NqamqhhxCWtP9iIp/tvQzA3EdDqe9rO5UJ7dQqFj7RmiaBntxOz+U/P50qNJdWVB1rj10nT6vQLqQazYK8jDeMjYRzGwEVdH+zWOc4Gn+UF/a8QE5+Dl1rdWV+t/k42Fl++Z3mNZozre20cisUJFdGFZy1A2WHdKueXhggQx6FEIYEBgayZMkSfvrpJ3766SeCg4Pp3r07x48fN7rPvHnz8PLy0j+Cg4PLsceisktIzeblNZEoCozsUJtHWllvnqYx7k72LB51P072an6/fJsfI25Yu0uinGm1CmuOXQfgifZFzNXcO1f3NfQx8Gti9jmOJxxn8u7JZOVl0blmZz7q/hGOdo4l7bJNkWBTWI3JLJAMmazwZI1Nyyoqoy+/b1GURo0a8eyzz9KmTRs6derEsmXL6NSpEx9//LHRfd58801SUlL0j+vXr5djj0Vllpev5YXwE9zJyKVJoKe+II8tCqnhxst97gPgv5vPkZiWY+UeifJ0MOoO15Oy8HC2N73cyfWjcGk7qNTQ7T9mHz/yViSTdk0iKy+LsMAwFnRfgJNd5ajUDzYQbC5atIiQkBCcnZ3p0KEDR44cMdl+wYIFNGrUCBcXF4KDg3n55ZfJzs4u8fmtnRm0Fqe0qrtEQ1mSrKKoyqJzi19o5qqmehn0RJirffv2XL582ejrTk5OeHp6FnoIYQmf7rnMkau6eZqf29A8TWMmdKlLsyBPUrI0vLPxjLW7I8rR90djABjSuiYujibep3vf031tORJqNDDr2GfunGHSrklk5mXSPqA9n/T8BGd759J22aZYNdhcs2YN06ZNY9asWRw/fpyWLVvSr18/bt0yvFZbeHg4//nPf5g1axbnzp3j66+/Zs2aNUyfXrL1a0TRSjtM1j7d9hZClkqqhlXGeaoyh1II0yIjIwkMLGJhciEsLC4liyW/RgG6eZp1a7hZuUdFs7dT8/7QFtipVWz+M46dZ8tn2QhhXUkZuew4o1se6vF2JqYRXDsI0XtBbQ/dXjPr2JmaTF779TXSNem08W/Dpz0/xcW+8o1SsuqV2EcffcTEiRMZN24cTZs2ZcmSJbi6urJs2TKD7Q8ePEjnzp0ZOXIkISEh9O3blyeeeKLIbGhlV1Wzs8bIcMKKw+xiTxVoWHWJAtxy+PnkJkvlk56eTmRkJJGRkQBcuXKFyMhIYmJ0d+HffPNNnnzySX37BQsWsGHDBi5fvszp06eZOnUqe/bsYfLkydbovqjCFuy8RE6elvYhPjzcMsja3TFb85peTHigLgBvrz9NWrbGyj0SZW3d8Rto8hVa1PIyXRioYK5m6zFQLcSsYy84voDradcJcAvg056flnhpE1tntWAzNzeXiIgIevfu/Xdn1Gp69+7NoUOHDO7TqVMnIiIi9MFldHQ0W7ZsYcCAASXqgwRpoixYcyitDOMVouo4duwYrVu3pnXr1gBMmzaN1q1bM3PmTADi4uL0gSfoPndfeeUVQkND6datGydPnmTXrl306tXLKv0XVdOlhDTWRujm/r7xYONyq4hpKVN73Ued6q7Ep2bz/rbyWadQWIeiKKw+qnuvmsxq3oiAq7+B2gG6vmrWsY/EHeH7898DMLvTbDwcK+8NYasFm7dv3yY/Px9/f/9C2/39/YmPjze4z8iRI5kzZw5dunTBwcGB+vXr0717d5PDaKVsuyipyjis1BIkQ1Y1leWNDJm3WTLdu3dHUZR7HsuXLwdg+fLl7Nu3T9/+9ddf5/Lly2RlZXHnzh327t1Ljx49rNP5CmTdunX06dMHX19fPD09CQsLY/v27aU6ZlJSEqNGjcLT0xNvb2/Gjx9PerrxeStJSUm88MIL+poVtWvX5sUXXyQlJaVQO5VKdc9j9erVpeqrpf1v+wW0CvRt6k+bOtUse/A7UfDbh/D7x3D1d8jNsOzxARdHO+Y9qls78bs/Yjh6Ncni5xC2IeLaXS7fSsfFwc50Bv7wEt3X5kPBq1aRx83QZDDzoO6m4LD7htEpqJMlumuzKtSEpn379jF37lw+//xzjh8/zrp169i8eTPvvvuu0X2MlW2XrKYQQvxNbq4IYdj+/fvp06cPW7ZsISIigh49ejBo0CBOnDhR4mOOGjWKM2fOsHPnTjZt2sT+/ft55plnjLaPjY0lNjaW+fPnc/r0aZYvX862bdsYP378PW2/+eYb4uLi9I/BgweXuJ+WFnHtLjvOJqBWwev9G1nmoDlpcHwFLOsPn94Pu+fArndg+UCYFwxLusCml+HEKki8CNrSF0jsVL8Gj7fVXU/+56c/ydbINWVlVJDVfKhFIB7ORta7TIuHMz/rnnd8zqzjfnTsI26m3yTILYhX2r5iia7aNKtNhKpRowZ2dnYkJBSeYJ2QkEBAQIDBfd5++23GjBnDhAkTAAgNDSUjI4NnnnmGt956C7X63tj5zTffZNq0afrvU1NTZZ0wUeauZdWgjstta3ejSrNmcSD7dDV57lLxWYiKIDExkdDQUF588UX9SKmDBw/SvXt3tm7dyoIFCwq1nzt3Lhs2bGDjxo36IczFce7cObZt28bRo0dp27YtAJ9++ikDBgxg/vz5BAXdm0Fp3rw5P/30k/77+vXr89577zF69Gjy8vKwt//7cs7b29vodZQ1KYrC+1t1w06HtQmmgV8pRslotbphi5HhcO4X0GTqtqvUUK8HOLrBzQhIvQnxp3SPY3/VA6kWAoMWQr1upfp5pg9owp4Lt4hKzGDR3su80tdCwbOwCanZGjb9GQvACFNrax79GrQaCO4IQUX/f3Aw9iA/XPwBgHc7v4ubg+0Xxyotq12NOTo60qZNG3bv3q3fptVq2b17N2FhYQb3yczMvCegtLPTlSBWFMXgPlWlbHtJMrWy/EnlUpphjpbOakmRJiEE6D6btZmZVnkYuy74N19fX5YtW8Y777zDsWPHSEtLY8yYMUyZMsXgfFatVktaWho+Pj76bXPnzsXd3d3ko2D+7KFDh/D29tYHmgC9e/dGrVZz+PBhs3+3KSkpeHp6Fgo0ASZPnkyNGjVo3749y5YtM/v3UNb2XrjFkatJONmrmdqnYckPFHMYFraCFQ/Dn6t1gWb1htD7HXj5DIxZB4+vhGln4eWzMHwFdHoBancCexe4e1W37+ZXSzXM1svVgTkPNwNg8b4ozsfLNK3K5JfIWLI1Whr6uXN/bW/DjTTZf9/EMCOrmZ6bzqyDswB4ovETtA9sb6He2jarlnicNm0aY8eOpW3btrRv354FCxaQkZHBuHHjAHjyySepWbMm8+bNA2DQoEF89NFHtG7dmg4dOnD58mXefvttBg0apA86RflzSAeNe/meMyvdCRd3WVRZCFsTnetHPUfDy1eZclVTnRCHO2XQI2FNSlYWF+5vY5VzNzoegcrVvOqOAwYMYOLEiYwaNYq2bdvi5uamv/b4t/nz55Oens7w4cP125577rlC3xtSkLGMj4/Hz6/wmrT29vb4+PgYrVnxb7dv3+bdd9+9Z+jtnDlz6NmzJ66uruzYsYPnn3+e9PR0XnzxRbOOW1bytQofbLsAwFOdQgj0KuENybMb4KeJkJ8DTl4QOhRajYKabcBQoSGvmrpH00d03+ekwc6ZugDh6JdweScMXgx1SjZnrn/zAPo29WfH2QRmbTjD6mc6VriCR8Kw1X+trTmifW3jf9PTP0HmbfCsBY0HFXnM+cfmE58RTy33Wky9f6oFe2vbrBpsPv744yQmJjJz5kzi4+Np1aoV27Zt0xcNiomJKZTJnDFjBiqVihkzZnDz5k18fX0ZNGgQ7733nrV+BCGEsIx0e3DPM9mkqJssyRkueLtlWbpnQlQJ8+fPp3nz5qxdu5aIiAicnO5dmik8PJzZs2ezYcOGQgGjj49PoUxnWUpNTWXgwIE0bdqUd955p9Brb7/9tv5569atycjI4H//+5/Vg831J25yPj4NT2d7JnWvX7KDHPoctk8HFGg0AB79EpyKeafbyQMe+hiaDIINL+iynN8MgI7PQ6+3waF4QbBKpWLWw83YdzGRw1eS2HcxkR6N/IreUdi00zdTOH0zFUc7NUNa1zTcSFHg8GLd8/YTwM50SPX7zd/56dJPqFDx3y7/rbTLnBhi9cXrpkyZwpQpUwy+9s8qeqC78zdr1ixmzZpVDj0TBcoyc2lybpsZF98lFZ/hQYBbWpkcu0B5ztusiEueVMY1Nv9J5m0KASoXFxodj7DauYsjKiqK2NhYtFotV69eJTQ0tNDrq1evZsKECaxdu7bQsm2gG0Y7d+5ck8c/e/YstWvXJiAggFu3Cmf/8/LySEpKKnKuZVpaGv3798fDw4Off/4ZBwcjRUv+0qFDB959911ycnIMBs/lIScvn492XgRgUvcGeLs6Fu8A2nzY/tbfF/btJsKD74O6FCPa6veE5w/qgtcT38Efi+DSdhi8BILbFetQNb1dGBtWhy9/u8IH2y7QraEvarVkNyuygqxmv+YB+LgZeb9eO6ibC2zvAvePNXm81NxU/fDZUU1G0cbfOqM9rKViXsUJgxxT8sn1kuHElnQjy5taLsnW7oYoJmsWBxJC/E2lUpk9lNWacnNzGT16NI8//jiNGjViwoQJnDp1Sp+9/P7773n66adZvXo1AwcOvGf/4gyjDQsLIzk5mYiICNq00V107tmzB61WS4cOHYzun5qaSr9+/XBycuKXX37B2dm5yJ8rMjKSatWqWS3QBN3yIDeTswjwdGZc55Di7azJgnUT4dxG3fd93tXNv7TEUFVnL3hkETR5GH55Ee5chmX9YMQqaPRgsQ71fPcGrD5ynXNxqfxyMpbBxrJhwuZl5eaz4cRfhYFMra1ZsNxJi+HganpUw/yj87mVeYs6nnV48X7rjjKwBgk2RZmzT1eR516+BQqq0nDC8sxqyhqbVVtZZ+tl3qawlrfeeouUlBQWLlyIu7s7W7Zs4emnn2bTpk2Eh4czduxYPvnkEzp06KCfV+ni4oKXlxdQvGG0TZo0oX///kycOJElS5ag0WiYMmUKI0aM0AekN2/epFevXqxYsYL27duTmppK3759yczM5Lvvviu0brivry92dnZs3LiRhIQEOnbsiLOzMzt37mTu3Lm8+qp5i8yXhdRsDZ/tuQTA1N4NcXYoxg3xjDvw/Qi4cQTsHGHIEt06hpZ2Xz+Y/Icu4Dz3C/w4Hp7eCoEtzT5ENTdHnuten/9tv8CHOy8wIDQQR3u56VkRbT4VR1pOHrV9XAmrZ2QN6OQYOL9J97yD6cJAl+9eZv3l9YCu+qyLveUKKGri49FmZKB2dkbl7Kz/qrKxOjYSbApRhirCEiiyvmLVUR7Dx4WoaPbt28eCBQvYu3evvmL9ypUradmyJYsXL2bNmjXk5eUxefJkJk+erN9v7NixLF++vETnXLVqlb7arVqtZujQoSxcuFD/ukaj4cKFC2Rm6pb0OH78uL5SbYMGDQod68qVK4SEhODg4MCiRYt4+eWXURSFBg0a8NFHHzFx4sQS9dESvtwfzd1MDfV93XisTdGL3evdvQYrh0BSFDh7wxPfl7iIj1lcqsFjy2DVMIjeC+GPw4TduuJCZhrXOYTlB69yPSmL74/EMLZTSNn1V5SZNX8NoX28XbDx4dBHvgRFC3W7gX9Tk8f7/OTnKCj0qt2L1n7FXyrp3xStlozffyfp2xVkHDhgsI3K0RGVszPOTZvi98oruIQ2L/V5S0OCzSrOKU1LjofcfROiLMi8TSFsX/fu3dFoNIW2hYSEkJKSAsCkSZMsfk4fHx/Cw8ONvh4SElJoyZLu3bsXuYRJ//796d+/v8X6WFopmRqW/X4FgNf6NcbezsxrDU02rB6lCzS9a8OoH8G3HNawtHOA4d/C130h8Tx8/ziM22Z2ESJXR3te7NWQt9ef5tM9l3isTS3cnOQyuyK5fCudo1fvYqdWGb85kpsBx7/VPe9o+v+G80nn2XltJypUPN/q+VL1TZuRQfKGDdxdsZLcq1d1G9Vq7Dw80GZno+T8XTxQyc1Fyc0l848/uDpsGF5DhuD78lQc/KxTvEr+FQhRgdliYSBrr7Ep8zVLzlJzlEu6/IkQovIIPxJDRm4+jQM86NfM3/wdt/0HEk6Baw1dsFeM7GKpOXvByB/gq1664i8/jYcR4WYXIxrRLpivf4vm6p1MvvrtCi/1LsV6oqLc/XDsOgA9Gvnh72lkTvTJ1ZCdAtXqQsN+Jo+3KHIRAP1C+nFftftK1CfNzZskrQonee1atGm6kUlqd3e8H3uMaqNH4VhLFxQrWi1KTo4u8MzKIj8lhaTly0nZ8AspP/9M6vbt1HjmGXzGPYW6nOdwy1VZJeOYkm/tLoh/scWAUAhbdVVjZI6MEKLCyM3T8s0BXVZz4gP1zF978tSPEPENoIKhX5ZvoFmgWh14YjXYO8PFbX8tt2IeBzs1r/TVZWGX7o/iTrqsB15RaPK1rDt+E4DhbY1kNRUFDn+he97hWVAbD6NO3z7Nvuv7UKvUTGpVstERd9eu5XLffiQtW4Y2LQ2HOrXxnzGDBvv24f+fN/SBJoBKrUbt4oJ9tWo4BAXh3KQJQe+/T8ia1bi0bImSmUniggVEDxhI6vYdRY6UsCQJNoVFOKRbuwdVT5UIYivosiclVtV+XiFEpfTLyVhupeXg7+nEoJZB5u10+xJsfEn3vOtruuVJrKVWWxjyV1BxeMnfAYYZBoYG0rymJxm5+Xy293IZdVBY2r4LidxOz6GGuyM9GhsZbhq1B25fAEcPaDXK5PE+i/wMgIfqPUQ9r3rF7k/yTz8R//ZMyM/HtUMHai3+nPpbt+IzehR27m5mH8elZUvqfB9O0P8+wN7fH83Nm9x86SVinhxLzqVLxe5XSUiwKSoss9dpNMLcyqpSQMfySvu3q8rkdyeEsGWKovDl/mgAxnWua15VVk0WrH0KctMh5AHo/p+y7aQ5mg2G3u/onm/7D1zcbtZuarWKN/o3BmDVHzFcT8osm/4JiyoYQvvo/bVwMDa/uGC5k9ajwNnT6LFO3DrBgZsHsFPZ8VwL09VqDUlev564GW8DUG30aGov/waPHj1QmcikmqJSq/EaNIj6W7dQ4/nnUTk5kXn0KPlp5VMwUIJNYRbJXFZOEkiXvfKeQ2rtObMVhQzXFaJs7L90mwsJabg52vFE+9rm7bT1DUg4DW6+MPQrs+dIlrnOU6H1GF3l0bXjIOGMWbs90NCXzg2qk5uv5eOdF8u2j6LUEtNy2HteV2dgmLHCQHei4NIOQAXtnzF5vM9O6LKagxsMJtjTxFqdBqRs3ETc9LdAUfB+YgT+b003fxh6EdSurvi++AL1t27Bf8YMXO+/3yLHLfK85XIWIao4Sw95tcYQ2oqwxqYUB6ocJBAUouIqyGqOaF8bLxeHonf484e/qnuq4NEvwSOgbDtYHCoVPPSxbokLTQasnwT5eWbtWpDd/DnyJufiUsuyl6KU1p+4SZ5WoVWwNw39jVzrHF+h+9qgN1Svb/RYR+KOcCT+CPZqe55pYToo/bfUrVuJfeMN0GrxHjaMgLfftlig+U8OQUH4jDY9DNiS5MqsEqpoRYJMBggyh00Im1IZ5gpLMCtE2TgTm8Lvl29jp1YxrnNI0TskXoSNU3XPu70O9XuUZfdKxs5BFwQ7e0HcSfjjc7N2a1HLm4GhgSgKzN9+oYw7KUpKURT9ENrhbY1kIfM1EPnXUkVtxpo8VsFczaENhxLkbuZ8ZSB1+w5uvvoaaLV4PfooAbPfKfGwWVtTOX4KUSpOaWW/DqB9uuXvzBRFhhMKIYQQ5eer33QVaAeGBlKrmqvpxrmZsHasLmMY8gB0e6McelhCHv7Q9z3d871zISnarN1e6XsfahXsPn+LM7EpZdhBUVInb6Rw6VY6zg5qHmoZaLjRxW2QcQvc/OA+42vZHow9yIlbJ3BUOzIxdKLZfUjbvZubr7wC+fl4PfIwge/OqTSBJkiwKUS5sVRGqDJkloT1lNdw6Ohc6yweLYSwjtjkLDaejAV0y50UaedMuHVWdwE/9GvbmadpTOvRULcr5GXpquaasXREPV93BrbQZbcW74sq6x6KEijIaj7YPBBPZyPDvguG0LYaqct0G6Aoin6u5vBGw/F3M29t2fQDB7gx9WXIy8PzoYcInDsXlZ2N/1soJgk2hShHlT1QlGyysJSyGuoqQ2iFKBvLD14lT6sQVq86obW8TDdOvAjHvtY9f/QLXebQ1qlUMOgT3fqbV/ZD5Cqzdnu+u25+35ZTcVy5nVGWPRTFlJWbz8ZI3Q0So4WBUm7A5V265/c/afRYv974ldN3TuNi78L40PFmnT/v7l1iX38DNBo8HuxP0P/Nq3SBJkiwKYRZLFm1tTQBpyWDVZuvRFvM+bqVpjiQlecp29L7QgJDYQvWrVtHnz598PX1xdPTk7CwMLZvN28ZDGOSkpIYNWoUnp6eeHt7M378eNLTTZd9z87OZvLkyVSvXh13d3eGDh1KQkJCoTYxMTEMHDgQV1dX/Pz8eO2118jLM6+gTWmkZmsIPxwDwDNdzchq7pmjq/DaaIB119MsLp960GO67vn2tyAtwXR7oEmgJz0b+6FV4ItfJbtpS7afiSctJ49a1VzoWM/I582JVbr3asgDRgsDKYrCoshFADzR+AlquJh3rZbw7rvk37mDU8MGBL3/Pir7ylmnpJJcnQlRMhWhwmplUxXXiaw0gXAFJ8GrKIn9+/fTp08ftmzZQkREBD169GDQoEGcOHGixMccNWoUZ86cYefOnWzatIn9+/fzzDOmK1e+/PLLbNy4kbVr1/Lrr78SGxvLo48+qn89Pz+fgQMHkpuby8GDB/n2229Zvnw5M2fOLHE/zbXmyHXSc/Jo6OdOt/t8TTe+fhTObQSVGnqVfd8sruNkCGwJ2cmw9XWzdpncQxek/HT8BnEpWWXYOVEcBUNoh7UJRq02UFtEmw8nVuqem8hqHo4/zPmk87jYuzCu2Tizzp26bRupW7aCnR2Bc+ehdnQsdv8rCrkCKmeOKRocUzTlcJ7yr0gra3GaryQZSmsOwZWg3LZUpYBdAkRR1hITEwkICGDu3Ln6bQcPHsTR0ZHdu3ezYMECXn/9ddq1a0fDhg2ZO3cuDRs2ZOPGjSU637lz59i2bRtfffUVHTp0oEuXLnz66aesXr2a2NhYg/ukpKTw9ddf89FHH9GzZ0/atGnDN998w8GDB/njjz8A2LFjB2fPnuW7776jVatWPPjgg7z77rssWrSI3NzcEvXVHJp8LcsO6AoDTXygnuGL9gKKArve0T1vORL8mlisH1pFy5G4I8w5NIf//vFffon6haspV1HMmFtZLHb28PCnoLKDs+vh/OYid2lTx4f2dX3Q5Cv6IkrCuq4nZXIw6g4qFQxtU9Nwo+i9kHIdnL2hycNGj7XqrG5I9SP1H8Hb2bvIc+fduUP87DkAVH9mIi6hzYvb/QqlcuZrbUB5BJTlzSEdNO7W7kVhWelOuLjnWLsbJXItqwZ1XG6b3VYIUXIStFqHoijk5ZZ9xXND7B3VZq1R5+vry7Jlyxg8eDB9+/alUaNGjBkzhilTptCrV6972mu1WtLS0vDx8dFvmzt3bqFg1ZCzZ89Su3ZtDh06hLe3N23bttW/1rt3b9RqNYcPH2bIkCH37BsREYFGo6F37976bY0bN9Yfr2PHjhw6dIjQ0FD8/f+e/9ivXz8mTZrEmTNnaN26dZG/i5LY/GcccSnZ1HB34pHWRSz1cHkXXPsd7Jygx5sWOf/11Ov8Ev0Lv1z+hdiMv4P1NRfWAODp6EmobygtarSghW8LWvm2wt2xlBczgS2h0wtwYAFsfgVCuuiWRjFhco8GHLlyhPDDMUzu0QAft8qbyaoIfoy4AUDn+jWMV06O+Fb3tcXj4OBssMn11Ov8euNXAEY2GVnkeRVFIX72HPLv3sWpUSN8J00qfucrGAk2rcQxRUOulxmLHZcTpzQtOR6S6LZFEmgWTYapFpac4YK3m/WHakXn+lHP8Vapj3NVU50QhzsW6JEob3m5Wpa+9KtVzv3MJ91wcDKv2MaAAQOYOHEio0aNom3btri5uTFv3jyDbefPn096ejrDhw/Xb3vuuecKfW9IUJAuEIuPj8fPr3C1Znt7e3x8fIiPjze4b3x8PI6Ojnh7exfa7u/vr98nPj6+UKBZ8HrBa2VBURSW7tctA/JUpzo42Zv4fWu1f2c1208ELyMFWcyQoclgx9UdbIjaQERChH67h4MH/er2w9XelVO3T3H2zllSc1M5cPMAB24e0Ld5vf3rPFL/EbNuRhjV/T9w7hfdMii73oGHPjbZvGvDGjSv6cnpm6ksP3iVaX3uK/m5RalotYo+2BzW1sj7MD0RLmzRPTcxhDb8fDgKCp1rdqauV90iz526ZQtpO3aAvT1B8+aiqsTDZwtIsFkGzM1q2lrAKcpfUdlNCTSFLSpOVt5SShNwSlZTmGP+/Pk0b96ctWvXEhERgZPTvcPVw8PDmT17Nhs2bCgUMPr4+BTKdFYVx67d5WxcKi4OdozqUMd041NrIeE0OHnBA6+U+Jy7ru1ixoEZZGh0lV1VqOgU1IlHGjxCj+AeONv/nYHS5Gu4ePciJxNPcur2KY4nHCc2I5a3D7zN7mu7mRk2E1/XIuaYGuPgoqtO++0gOLYMQodBnU5Gm6tUKp7v3oDnVx1n+YErPNO1Hu5OchluDYei73AzOQsPZ3v6NQsw3OhkOGjzoGYbCDA8zDU9N52fL/8MwOgmo4s8b15iIglz3gWgxnPP4dy0acl+gApG3uUWVtzhsxJwCkMX7mUdZNpSxVEhROVl76jmmU+6We3cxREVFUVsbCxarZarV68SGhpa6PXVq1czYcIE1q5dW2g4KxRvGG1AQAC3bhXO+Ofl5ZGUlERAgOEL34CAAHJzc0lOTi6U3UxISNDvExAQwJEjRwrtV1Ct1thxS+v7I7oKtINaBlLN1LDQvBzY+1/d8y4vgWvJAvOVZ1fyv6P/Q0EhxDOERxo8wkP1HiLAzfDP52DnQLMazWhWo5muG9o8lp9ZzueRn7Pvxj6ObzjO9A7TGVB3QMmynHW76rJex1foigU9sx/Uxt93/ZoFUM/XjejEDMIPX+OZroarm4qyVVAY6JFWQTg7GMjGK8rfa2veP9bocTZEbSBDk0GIZwidgozfaNAdUiFu1jvkp6Tg1LQJNZ41XRCsMpGxZxZUGedplheTwyBLsRSEJdd9LK8AraJmMy26xqaVl/8oCzLUt/QkQ1nxqFQqHJzsrPIoTvCQm5vL6NGjefzxx3n33XeZMGFCoYDw+++/Z9y4cXz//fcMHDjwnv2fe+45IiMjTT4KhtGGhYWRnJxMRMTfwz/37NmDVqulQ4cOBvvXpk0bHBwc2L17t37bhQsXiImJISwsTH/cU6dOFer3zp078fT0pGkZZFBSMjVs/jMOgBHta5tufOwbSI4B9wDoUPw5avnafN4/8j4fHP0ABYXHGz3O+kfWMyF0gtFA0xB7tT0TQiew5qE1NPFpQmpuKv/57T9M2zeNO1klHKrf6x1w8oT4U3BmncmmdmoVk7rpAswvf7tCtqb8izlWdSlZGrad1g0rH9Ym2HCjawfhzmVwcIPmjxpsolW0hJ8LB2BUk1GoVaY/41N/+YX0PXvAwYGgef+HyqHqJJrk6sdCShNollWQao2KtKbYp5dibkQldy2rhv4hRFkH21WpurAEqMIcb731FikpKSxcuJA33niD++67j6effhrQDZ198skn+fDDD+nQoQPx8fHEx8eTkpKi39/Hx4cGDRqYfNj/tYZekyZN6N+/PxMnTuTIkSMcOHCAKVOmMGLECH1AevPmTRo3bqzPVHp5eTF+/HimTZvG3r17iYiIYNy4cYSFhdGxY0cA+vbtS9OmTRkzZgwnT55k+/btzJgxg8mTJxscElxa6yNvkpOnpXGAB62DvY03zE6F/R/onnf/DzgaKcZibPe8bF799VW+O/cdANPaTOOtDm9hpzZvPq4hDas1ZNXAVTzf6nnsVfbsitnFkA1D2HF1R/EP5lYdOr2oe77nXcgzXfn3kVY1CfJyJjEtRz9vUJSfjSdjycnT0sjfgxa1jBR1Kshqhg4FJ8Ofl7/f/J2YtBg8HDx4uL7xSrUAmoQE4t/TjXzwnTwZ50ZVa76uBJs2QrKiwlZVpcBE2D4JHoWl7du3jwULFrBy5Uo8PT1Rq9WsXLmS3377jcWLF7N06VLy8vKYPHkygYGB+sdLL71U4nOuWrWKxo0b06tXLwYMGECXLl1YunSp/nWNRsOFCxfIzMzUb/v444956KGHGDp0KF27diUgIIB16/7OpNnZ2bFp0ybs7OwICwtj9OjRPPnkk8yZM6fE/TRGURT9ENoR7YJNZ5EPfQaZd6B6A2g9pljnuZt9lwk7JrArZhcOagc+6PoB45qPK11hn784qB2Y1HIS4QPDaVitIXdz7vLKr6/w48Ufi3+wsOfBzQ/uXoXj35ps6miv5pmu9QD4Yn8UefnWqdZcVa0tWFuzbS3D76Osu7olbcDkENrvzupufjza8FFcHUzfQImf8y7a1FScmzen+oTxJep3RVb5xqpZgaUCRZm/aR3xGR4EuKVZuxtVQlmsDynDUy3vRpY3tVySLXIsS1Wk/SdziwVJYCrM0b17dzSawp/jISEh+szlpDJYmsDHx4fw8HCjr4eEhNyzPqSzszOLFi1i0aJFRverU6cOW7ZssVg/jTl5I4Xz8Wk42asZ0tpEVdn0W3DwM93zXjN1a1SaKSY1hkm7JumyR44eLOyxkLYBbYvesZiaVG/CmoFr+DDiQ1adW8V///gvQe5BRc7BK8TRDbq9DltehV/fh5ZPgJPx5VUeb1ebT/dc5npSFpv+jGNwayPrPAqLuhCfxskbKdirVQwx9js/9SPkZYNfM11xIAOikqM4FHcItUrNE02eMHnOzBMnSN+9G+zsdNVn7S0TeimKQnx0KrnZeTg4qrFzsMPeUY2Dox32jnbYO6hxcC7edIKyIsFmKVWmjKQ5y5/Y4lqbQlRFFXmNWUuToFKI8vX9YV1Wc2BoIF6uJm6S7/8faDJ0F+1NTA81/KfzSed5Zscz3M25S5BbEIt7L6aed73SdtsoBzsH3mj3Bik5KWyK3sQr+15h5YMraVCtgfkHafMUHFoEd6/AH5/rgk8jXBzteLpLXf63/QKL90XxcMsg1GrrBwWVXUFWs3cTf6q7G7j5rSh/r615/5NgJFBbdW4VAD2Ce1DT3fSNgtuffgqA1yOP4NSwYQl7/jdNTj4XDsfz557r3I3PNNnWy9eFTo82oG6rGlYNOiUlUAplEWhW9ODVId3aPRBFkUq0ojK4qqmufwghyk96Th4b/4wFiigMlBYPEct1z3u/Y/TC/d9SclKYuncqd3Pu0sSnCasGrirTQLOASqVidqfZ3O93P+madCbvnsztrGIs8WTnAD1n6J4fWAgZpvcd3bEOHk72XEhIY+8Fy47+EPfKzdPy84mbAAxvZyQbH3sCEk6BnRO0MLxubkpOChujNgK6wkCmZB49SsbBQ2BvT43nSzdCIi0pm4PrLvPtmwf4NfwCd+MzcXC2o0awO9UCXHH3ccLZ3QF7Jzv4659aSmIWW784xYYFJ7h9w3oX6JLZtEEynNZ2WXJ4oah67NPV5LnL/BwhRMX1S2Qsmbn51Pd1o11INeMNjyyF/FwI7qhbIsQMiqIw88BMbqbfpJZ7Lb7q9xWejp4W6nnRHO0c+aTHJ4zaMoqYtBhe2vMSX/f7utDanSY1exQOfALxf8JvH0L/eUaberk4MLJjbb74NZrF+6Lo1cTfQj+FMGTP+VvcycjFz8OJrg2NrK16YqXua9OHjS7P89Oln8jOz6ZRtUa09Tc+rFtRFBI/WQiA99ChONYyMdzchLioFE7uvk50ZCKKVje03tPXhRY9atEkLBBHl3tDOUVRyM3KI3LXdU7sjOHmhWR+eO8ITboE0fHherh4mFimqAxIZrOEKkoG0tYq0paFspgHKERFZ9GlaAyQyslCVE1/FwaqbXxoXk46HP1a97zTC2Yf+7tz37Hn+h4c1A7M7z6/XAPNAt7O3izqtQgvJy/+vP0n03+fjlYx8yahWq3L4gIc/Uq33IsJ4zvXxdFOzbFrdzl6Nal0HRcmFQyhHdqmFvZ2BsIfTRac+kn3vPVog8fI0+ax+vxqQJfVNDU0NfPQITKPHUPl4ECN554tUZ8Pb4xm3f8iiDp+C0WrULNRNQY834JRszvSsmewwUATdFl6J1cHOjxcj5HvdKBBGz8UBc7+Fst3M/8gclcM+Xnld+Nbgk0hRKnJGpvClOhcP2t3oVLav38/gwYNIigoCJVKxfr164vcZ9++fdx///04OTnRoEEDli9fXub9FJXH6ZspnLqZgqOdmqFtTGRqIldBdjL41IdGD5p17FOJp/go4iMAXmv3Gs2qN7NAj0smxCuEBd0XYK+2Z+e1nSw8vtD8nev31GVy83Nh71yTTf08nRnaRjfnb8m+qNJ0WZiQkJqtH6o8zNj79twmyEkBr9oQYjgTv/f6XuIy4qjmVI0B9QYYPV+hrOaIETgEBha7z0c3X+HY5qsANO4YwIi32zP45dbUbVGjWPN7Pau70G9ic4a8cj++tT3IzcrjwI+XWf3uEeKiUoo+gAVIsFkC5ZHVrCiZ04qgrDM8QpQJCbpFETIyMmjZsqXJ6qT/dOXKFQYOHEiPHj2IjIxk6tSpTJgwge3bt1u0X/+uoipsW3H+XquP6jJ1fZv54+NmZChefp6uUA5A2GQwYz3MlJwUXv31VfK0efSt05cRjUaY3aey0jagLbM7zQbg69Nf8/Oln83bUaX6O7t5cjUknDHZ/Jmu9VGpYPf5W1yIl8r4ZWHd8ZtoFWhbpxr1fI1UuYzULWVCq5G6DLUBBcudPHbfYzjZGR9Vl7F/P1knT6JydqbGMxOL3d9jW69yZOMVADo92oBeTzWles3SVecMaujNsP+0pceYxrh4OpJyKxNH55KvVVscEmyKikEuvK3C1tfYlGVPRFX24IMP8t///pchQ4aY1X7JkiXUrVuXDz/8kCZNmjBlyhQee+wxPv74Y4v0x8FBV2vgn2tDCttX8Pcq+PsZbZebx/oTusJAT5gqDHR+IyRfA9fquiVAiqAoCjMOzCA2I5Zgj2De6fSOTSzXAPBw/Yd5toVuCOScQ3OISIgwb8eabaDpI4ACu02vc1q3hhsDmusyX1/8KtlNS1MURT+EdnjbYMON7l6D6F91z1uNNNjkQtIFjt86jr3KnhGNjd8M+WdWs9rIkdj7GpkfasTxHdc4vCEagI6D69G6r4l/a8WkUqto2jmI0bM70v/Z0FIHsOaSK/hikoxj6dinq8hzN3wX1ZrFU4qz1mZFLhJkzUq0MrfWtsj6slXPoUOH6N27d6Ft/fr1Y+rUqUb3ycnJISfn7yV2UlNTjba1s7PD29ubW7d0w9VcXV1tJmgQ91IUhczMTG7duoW3tzd2dqazHJv+jCM9J4861V0Jq2ekCrSi6CqxArSbCI6mF7sHWHF2Bfuu79PN0+w2Hw9H27rJObnVZGJSY9h6dSszD8zkp4d/Mq9gUM+ZuqGZF7fBtUNQJ8xo0+e61WfzqTg2nIxlWt/7qFWt6N+bME/EtbtE387A1dGOAS2MDGc9+T2gQN1uUK2OwSY/XvwRgJ61e+LnanxqSNquXWSfPYvK1ZXqE8YXq6+Ru2I4tE53w6H9oLq06R9SrP3N5ehiT71WxQuCS0OCTRtmjaq05qy1KYQouYpSkbYi31QRhsXHx+PvX7jipb+/P6mpqWRlZeHicu+Ug3nz5jF79myzzxEQEACgDziF7fP29tb/3UxZ/VdhoMfbBRufMxZzCGKPg70ztJtQ5DFPJp5kQcQCAN5o9wZNqzc1u9/lRaVS8XbY20QkRBCTFsPik4t5uc3LRe9YowHcP0a3/MvuOfD0VqNNQ2t50aVBDX6/fJuvfrvCOw9bb75qZfPDX1nNgaGBuDsZCHu0Wt0cYzBaGCg7L5vN0ZsBGHrfUKPnUrRabi/UravpM2YM9j6GK9oa8ufe6xz48TIAbQeG0G5gXbP3tXUSbFYBjin55HqVz7hsIYQQlcebb77JtGnT9N+npqYSHGxkKBq6C/PAwED8/PzQaGQkkK1zcHAoMqMJcCE+jeMxydirVTxmqjDQQd2FNi2fAHfTmRP9PE0lj/4h/RneyPC6hrbAw9GDGR1n8OLeF/n2zLf0D+lPk+pNit6x2xsQGQ4xB+HaQajTyWjT57rV5/fLt1l9NIYXezU0PidWmC0jJ49Nf8YBMLydkf+3rv6mqxrs5AVNBhlssvPaTtI0adR0r0nHwI5Gz5e2bRs5ly6hdnen+rinzO7n6V9v8NuaSwC06V+H9g9VnkATJNgsFhlCq+OQDpryGeYthFXZp+vu3hsb+m1tWelOuLjnFN3QBkTn+lHPUbJd1hQQEEBCQkKhbQkJCXh6ehrMagI4OTnh5FT8IfB2dnZmBTGiYihY7qRXEz/8PIwMIb19CS5sAVS6wkBF+O8f/yU+I57aHrWZFTbL5odc96jdg751+rLj2g5mHZxF+MBw7NVFXEZ7BkGrURDxjW7dTRPBZucG1Qmt6cWpmyksP3iVaX3us/BPUPVsPhVHZm4+dWu40baOkTVhT/xVGCh0KDgY/n/wp0u6JVGGNBiCWmV49J+Sn0/iZ7rCWD5PPYWdt7dZfbxwOJ5fv78IQOs+tenwSD2b/7dQXDJe0sZVxADXId3aPRAVlo0UgrJPV+kDTWGcrLVZsYSFhbF79+5C23bu3ElYmPG5ZEJka/L5+cRNoIjCQIc+031tNABqNDR5zFOJp9h2dRsqVHzQ7QPcHSvGHew3O7yJp6Mn55LOsfLsSvN26vwiqNRweRfEnTTaTKVS8Vy3+gCsOHSVjJw8S3S5SisoDDSsbS3DAVx2Cpz7Rfe8leEhtFdSrhCREIFapWZwg8FGz5W6aRO50dGovbzwGfukWf1LvZPFr99fAKBlz2DCHq1f6QJNkGDTbBUx6BNlx5qFdkqqrPpsraVlLF2JtiDANBRkSuApbFF6ejqRkZFERkYCuqVNIiMjiYnRZaHefPNNnnzy74ue5557jujoaF5//XXOnz/P559/zg8//MDLL5sx/0xUWdvPxJOSpaGmtwsPNDQyNDY9ESK/1z3v9ILJ4ymKwoLjCwAYVH+QVdfTLK4aLjV4te2rACyKXERMakzRO/nUg+Z/zfP77SOTTfs3DyCkuivJmRpWH71e2u5WadGJ6Ry9ehe1Cobeb2To9+mfIC8bfJtAzfsNNilY8uaBmg/g7+ZvsI2Sn0/i558DUH38eOw8ii5ypSgKe1eeR5OdT2B9Lzo91qBSBpogwaaoJKTSqeXZ+rInlmSzwWQpM72yxmzlduzYMVq3bk3r1q0BmDZtGq1bt2bmzJkAxMXF6QNPgLp167J582Z27txJy5Yt+fDDD/nqq6/o16+fVfovKoafjuuymkPb1MLOWGGgo19Cfg7UbAu1jc9pAzgYe5Aj8UdwUDswuVXRw21tzeAGg+kQ2IGc/BzmHJpj3jqlXf66oXN2A9y+bLSZnVrFM1112c2vf4smN8/2i8nZqrURNwDo3sgPf08jQ78LhtC2HqVbH/VfNPkaNkRtAGBoQ+OFgdL27EFzLQY7Ly98RhleOuXfzuy/yY3zd7F3UNPzySbGi25VArYxZs3GWTuraY2qtEKIwkwt2yOENXTv3t3khe7y5csN7nPixIky7JWoTBJSs/n9UiIAQ++vabhRbiYc+VL3vNMLBi/aC2gVLR9H6NZ1faLxEwS5B1msr9qMDFK37yB161ZQq3Bp2VL3CA3FztPTYudRqVTM6jiLR395lMPxh1l/eT1DGhax1q1/M7jvQbi4FQ58DI8sMtr00ftr8vGui8SmZPPLyVjTBZmEQXn5Wn76K9gc3tbI7+/WObgZAWp7aPG4wSb7buwjKTsJXxdfHqj1gNHz3V2hG1LtPXw4aje3IvuXkpjFgb+WOOk4pD7e/pV7qRsJNquIql6RNjnDBW+3LGt3o8qy5cyzNbKaFWX5EyFE1fbziZtoFWhbpxp1qhu5iD4ZDllJ4F3HaDXPAluvbOXC3Qu4O7gzMXRiqfunaLVkHj1Gys8/k7pjB0pmpv61jF/365871q+PS4sWuLRsiVvnTjiaqKhsjmDPYCa3msyHER/yv2P/44FaD1DDpYg57A+8ogs2T66B7m+Cl+EgyNnBjvFd6vJ/W8/zxa9RPNq6ZqXOepWF/ZcSuZWWg4+bIz0bGx76qs9q3tcf3A2vm/nTRV1hoMENBhstBpV9/jyZR4+CnR3VRj5RZN8UrcKeFefIy8knqKE3LbpX/psJMoxW3MMprWpeBFelYaOiZGx2uG0FEZ1rfCFsIYRtURRFnx0aaiy7ps2HQ39l6cKmgNr4TW1NvoZPT+iWRhnXfBzezt4l7lvujRskfvoZUX36EjN2LCnr16NkZuIYEoLv1Kn4T5+O50MP4fBXUJkbFUXKzz8T/847RD04gMTPFqGUcmme0U1H07R6U9Jy05h3eF7ROwS3g5AHQKuBg5+ZbDqyQ208nOy5dCudXecSTLYV91rz13zXIa1r4mhvINTJ18Cfa3TPW40yeIzY9FgOxh7UHaeB8cx10kpdVtOjbx8cAgOL7NupX28QeykZeyc7ej7ZBFUVuJEgmc0iWHsIbQEZSotu/pq77VRnq0iL3lfEgkai7MVneBDglmbtbgghbNDpm6lcupWOk72agS2MXERH7YWkaHD20s17M+GHiz9wM/0mNVxqMLqJ4cqf5khasYKE/3sftLob42p3dzwHDMBryGBcWrW6p8hK3p07ZJ38k6w/T5J5+AhZJ05w+7PPSN+zh8D/m4fzfSVbYsRebc/sTrMZsWkEO67tYE/MHnrW7ml6pwem6dZ1jFgOXV8FN8PZUE9nB0aH1WHxviiW/BpFn6b+lbZ4jKXFpWSx65xuma3Hja2teWkHZCSCmx807GOwyc+Xf0ZBoUNgB4I9DR8nLymJ1I2bAPAZU3QF2uRbmRz6a/hspyH18fKtGnUVJLMpyp2p7JClK4yKCqQYxXAs9T4pSaayKmU35SaFEFXXT8d1Wc2+zQLwdDZys/v4ct3Xlk+Ao/G5ahmaDJb+uRSASS0n4epQ/DlqilZLwrx5JMydB1otrh07EjR/Pg1//43AObNxbd3aYEBmX706Hj174Dd1KnXCVxE0fz5qLy+yz57l6tDHuP3FUpS8kt3IbuzTmKeaPQXA/GPz0eQXkaCo1wOCWkNeFhxeYrLpuM4hONqrOR6TzNGrd0vUv6ro+yPXydcqdKjrw33+RkasFQyhbTkC7O59b+dr8/VVaE0VBkr+4QeU3FycmzfHpXUrk/3SahX2fHuOPI2Wmo2q0byrkTnQlZBc2ZtgK1lNIYSwVbLWphCVT26elg2Rf1WhNVYYKC0BLmzVPb9/rMnjrTizgqTsJOp41im6mI4B2uxsbr40laRvVwDg99qr1P5mGV4PDUTtbKTSqAEqlQqvhwZSb+MvuHfvjqLRkPjxx1wdOYqcqKhi9wvgmRbPUN25OtfTrvPTpZ+K6gB0maZ7fngpZKcabern4awvDrR4n/EKtuJvuXlavj+iq8A9JqyO4UZpCXBxu+55a8MZ9gOxB0jITMDLyYtetXsZbKNoNNwN1y334/PkmCIzz3/uuU5cVAoOTnb0fLJxlRg+W0CCzQrEloJfh3Rr90CUJXPnr1bkpTVKk6G0peymLRdfEkJUTHsv3OJupgZfDye6NDByQylyFWjzoFZ78G9q9Fh3su6w/MxyAF5o/QIO6uJNCcq7e5eYp8aRtnMnKgcHgj6cT/Xx40s1rNTBz49aiz8ncN481B4eZP/5J1eGPMqdb5abt5TJP7g6uPJsy2cBWHJyCZmaTNM7NH4IatwHOSlwbJnJps88UA+1CvZeSORcnPHAVOjsOBtPYloOvh5O9GsWYLjRn2tAyYda7cC3kcEm6y6tA2BQvUE42jkabJO6fQd5t25hV6MGHv37m+zX3fgM/tgQDUDnxxrgWb3iXjuVhASbVYhjSn65nUuCUdshQyFtkwwZF0LYqoLCQENa18TezsD/VVotHNdlGWljOqu59M+lZOZl0qx6M/rW6VusfuReu8bVESPIioxE7elJ7WVf4zVwYLGOYYxKpcJ7yGDqbfwFty5dUHJzufX++9xe9Hmxj/VYw8eo5V6LO9l3WHl2penGavXf624eWgQa45XyQ2q48WBz3XzZL34tWea1Kllx6BoAT7SvjYOh962i/D2E1khhoNtZt/n1+q+A6SG0SSt17/9qI0agdjQckOpOqbBv1QXyNVqCm/rQtIvllvupKORqxwhbyiIK2yWBXNVVbtnNYsxlrQikIq0Qti0pI5e9F3QFVobeb6QK7dX9cPcKOHlCM+PDYq+nXeeHiz8AMLXN1GJlI7NOnuTqiCfQXIvBISiIkO/DcW3XzvwfxEwOAQEEf7kUvzfeAOD2Z5+RsnFj8Y5h58ALrV8A4Jsz33A3u4g5lqHDwCsYMm7pMsQmPNetPgAb/4zjelIRWdMq7EJ8GkeuJGGnVjGyfW3DjW4cg9sXwN4FmhsOJDdc3kCekkdL35Y0qNbAYJuskyfJPvknKgcHqo0wvEZngetnk4i9lIydg5oeoxtXyUJPEmxWMBIEly1Z/sTybHGYp6UCRVsaTmtMRR7qLIQofxtPxqLJV2he05NGAUY+EyO+1X0NHWayMNCiyEXkafPoFNSJjoEdze5DxqFDXBv7FPl37+LctCkha1bjVL9+cX6MYlGpVFQf9xQ+458GIG76W2QeO1asY/Sv258mPk3I0GTw5akvTTe2c4DOL+meH/hEtxSHEaG1vOjSoAb5WoWvfosuVp+qkpV/XAWgb1N/AryMzOM98VfWudlgcPa852VFUfRDaE1nNXXZUc8BA7CvYbxugaIoHP5F9zdr3rUmHj7mzy+uTCTYFJWGLQY1wvJk+KkQQpSdgiq0RrOaGbfhvG65B1NDaK+nXmfrFV0BoZfuf8ns82vi47k57RWU7GzcunWlzsoV2Pv6mr1/afi98goeffuiaDTcmPICudeumb2vWqXW/5yrz68mLj3O9A6tR4ObLyTHwJmfTTad1F0XaK85dp076Tlm96mqSMvW8PNxXUGrMR2NFAbKzYDTukDSWGGgYwnHiEmLwc3BjX4h/Qy20STcInXbNgCqPTnGZL+u/nmbW9fSsHdUc38/I/2qAuSqzQDJHoJTmtbaXbA4yfAIqBjZSCGEsIZLCWn8eSMFe7WKh1samVt28nvIz4XAVhDY0uixVp5biVbR0qVmF5pWN15A6J+UvDxuvvKqPqNZa+FC1G7GM6eWplKrCXr//3Bu0YL85GSuP/MseXfNX3akU1An2ge0R6PVsChykenGDi7Q4Tnd898X6OYTGjtu/eqE1vQiW6Pl20PmB8BVxc8nbpKRm099XzfC6lc33OjsL5CbBtVCoE5nw8f5a7mTB+s+aHR5nrurv4e8PFzatMGlWTOjfVK0Coc3XgGgRY9auHoan9dZ2UmwWQFJMCzMVWHmlFbgeYkSvMryJ0JUFj/+ldXs3siP6u4GRgspyt9DaNs8ZfQ4ydnJrL+8HkC/DqU5Ej9ZSFZEBGp3d2ou+Bi1U/mPWFK7uBC86DPsgwLJvXaNmy+8iDY316x9VSoVU++fCsDG6I1cvlvEkiXtxoOjO9w6A5d2mjxuQXbz24NXycgp2bqglZGiKKz8KwAf07GO8TmRBXNjW43WLUHzL2m5aey8pvsbDGlgeB6yNieH5DW6Ocg+Y0xnNaNOJHLnRjoOzna07lN1s5ogwWaVU54VaauKChPQmUnmrZYfWxgSXNTfuyze31IkSAjbk69VWH9CNxTxsTZG1taMOQR3LoGDG4Q+ZvRYP1z8gay8LJr4NKF9QHuzzp/+66/c+VI31zHwv//FsbaRIi/lwN7Xl+AlS1C7u5N57Bjxb79t9pIoob6h9KnTB62i5ZMTn5hu7FIN2o7TPf/9Y5NN+zULoG4NN1KyNKw+et2svlQFf0QncelWOq6OdjzaxsjQ76RouPoboIJWTxhssu3qNrLzs6nvVZ/QGqEG26Ru2kx+UhL2QYF49Da8/iaAVqtwZJMuq9myZzDO7sVb7qeysf6Vjo2RrKGNq8AZsMqmIg5LliykEEIYduDybRJSc/B2daBHYyM3hAqyms0fBSfDN6py8nMIPxcOwNhmY82qvqmJiyP2jf8AUG3UKDz7G54vV56c77uPmgsWgJ0dKRt+4fbixWbv+0LrF7BT2bHv+j5O3DphunHHyWDnCDEHIeaw0WZ2ahXPdK0HwFe/RZObV/mmO5XEd3/ospqDW9fE09lIUBepez9Svyd4GQ5ICzLxgxsMNvieVRSFpJW6AkM+I0eisjd+PXr5WAJ34zJwcrWnVe9gM3+SykuCTVFipVlL09RFvy1ke4QQQoiqpKAw0MMtg3Cyt7u3QdZdOLte97zNOKPH2Ry9mTvZdwhwC6BvSNHraioaDTenvUJ+cjLOzZrh98brJel+mXDv0pmAmTMBuL3wU9J27TJrv7pedRncYDAACyIWmM6KegZCi7+WzziwwORxh7Suia+HE3Ep2fxyMtasvlRmCanZbD8TD5goDKTN/zvYNFIYKCo5ij8T/8ROZcdD9R8y2CbrxAlyzp9H5eyM92PGs/rafK0+q9mqd22cXKt2VhMk2KywJANbdirLMNLKNrzXllk7YyqVmIUQpZGWrdFftD9qrArtnz9AXjb4N4ea9xtsolW0fHtGl/0c3WQ0DuqiL7QTP/mErBMn/p6n6WhbhVSqPT4cn7G6qrtxs2eTn5Ji1n6TWk7Cyc6J47eOs//GftONO78EqODCFrh13mgzZwc7xnepC8CSX6PQas0b2ltZfX8khjytQruQajQJvHcpEwCi90LqTd2Q5cYDDTYpyGp2rdWVGi6GaxAk/7AW0C13YuftbbRPFw4nkHIrC2c3B1r0NPJvqYqRYPMfJIATwraVJutt7YCwVGT4uBCiDG09FU+2Rkt9Xzda1vK6t8E/CwPdP9ZggRWA32/+TnRKNO4O7ibXKSyQtncvd776GoDA997DMdg2hxz6vjINx3r1yE+8TcL//mfWPv5u/oxqMgqAhScWms5u1mgITf7KqB0wPc9zZIfaeDjZc/lWOjvPJZjVl8pIk68l/HAMAGPCQow3PKFbE5PQ4WB/741ZjVbDxqiNAPps9L/lp6XplzvxHmY8q5mfp+XoZl1Ws3W/2jg6y2c3SLApylBphtlWNJJFNEwybkIIYft+/qsw0KP31zI8x/LGMV3FVHtnaDHM6HGWn1kOwLD7huHu6G7ynJrYWOL+8yYA1caMwbNf0UNurUXt6Ejgu3MASPnxJzL++MOs/Z5u/jRuDm5cvHvRjOzmy7qvp36AlBtGm3k6O/BkJ92Q0Y93Xqyy2c2dZxO4lZZDDXcn+jcLMNwoMwnOb9Y9NzKE9vcbv3Mn+w4+zj48UOsBg21SN21Cyc7GqWEDXFq1MtqncwfjSLuTjYunI6HdJatZQILNKsjcirSy1qaoLCp0VtMCbPF9LxVphbAN8SnZ/HHlDgCPtDKytubx5bqvzYbohiMacOb2GY7GH8VeZc/IJiNNnlNRFOJmzCA/JQXn0FD8Xnu1pN0vN65t2lBtpK6SadzMWWizs4vcx8vJi+GNhgPw5akvTWc3a7WBul1BmweHTK/ROfGBeng42XM+Po0tp+PM/yEqkRWHrgLwRPtgHO2NhDOn1urWhA1oAYEtDDYpGEI7qN4go8O+k9f+CID3Y48ZLXiVp8knYquuT2361cHB0cC85ypKgk1RqUgmrXSsMl+1kgwRreoBray1KUTF9MvJmygKtAupRq1qBhayz06F0+t0z+8fa/Q4BXM1H6z7IAFuRjJNf8k4cJCMg4dQOThQc/7/bG6epjG+06Zh7++PJiaG2599ZtY+TzZ9Eke1IycTT3Is4Zjpxp2n6r5GLNdl5YzwdnVkwgO6yrQf7bxIXn7lSw6YEnk9mT+ik7BTq3iivYklck7oqsfS2vCamLezbuszzsaG0GadOUP22bOoHBzwfPhho6c6+3ss6XdzcPN2ollXIzdtqigJNv9SEedrVsQ+i/Ihw3orDqm+LISwpvUndFVNH2llZG3NM+tAkwk17oPaHQ02uZl+kx3XdgC65U5MUbRabn30IQDeT4zAsU7FWfDezt2dgFmzALjzzXKyzpwpcp8aLjUY0nAIAF+f+tp04/o9dVk4TSYc+dJk06e7hODt6kB0YgbrI6tWZdpPd18CdNV5g7yNjNyJOwnxp3TLyhhZE3Zz9GbylDxa1GhBg2oNDLZJ/lGX1fTo0wf7aoaz+vkaLRHbdEuwtH2wDvYOktX8J7nKEUIUmy0OyxQlV1RGW25eCFE5XUxI42xcKvZqFQNDAw03ivxe97XVKKOFgb47+x35Sj5hgWE08mlk8pypW7eSc/Ycajc3ajz3XGm6bxUePXvgOeBByM8n7u23UfLyitznqWZPYaey40DsAc7cMRGgqlTQZaru+eElkJthvB/ODjzXrT4An+y+WGXW3Tx9M4Xd52+hVsHkHoYDRODvwkCNHwJXn3teVhSFny/9DMAjDR4xeAhtZiapGzcB4D3c+FzlSxEJZKbk4ubtRJPOktX8Nwk2hbAQuSAvW5IBrHxk3qYQ1rX+r8JA3Rv5Us3NwFDWO1Fw/Q9Qqf9eC/JfUnJS+OnST4AuqDJFyc0lcYGu2qrP+Kex97k3CCip1NtZHNkYTfg7f/D9nMPsWXmOs7/HcudmusWL6PhPn47ay4ucs+dIWr68yPa1PGrxYN0HATOym00egWp1ISvp74DJiCfD6lDD3YnrSVmsjbhubvcrtM/2XAZgUMsg6tZwM9xIk61bqgeMFgY6ffs0USlRONk56f82/5a6fQfa9HQcgoNxbd/eYBtFUfhzj66gU/NuNbEzNn+0CpPfCDIctcIphzl+lWWtTVG+cymr+rxNIUTFodUqbIgsYgjtn2t0X+v1AE/Dmc8fL/5IVl4WDas1JCwozOQ5765di+b6dexq1KD6WNPDbc2Rm53HuYNx/PzhcVbOOMTRzVe5G59JUmwG5w7Esfe786x+9whfvbyf9R+f4ND6KGIvJ5f6vPY1auD/xhsAJH76GbnXrhW5z/jm4wHYdW0X0SnRxhva2UOnF3TPD34K+cavUV0d7ZnSQ5fd/HT3ZbI15hWArKjOx6ey7Uw8KhVMMZXVvLAZspPBsxbU626wyc+XdVnNPnX64OFo+Jovea1ubU3vxx5DpTYcMsVFpZAYk4adg5pmD0hW0xAJNis4awfKVWl5k4pCMqxCCCGKEhFzl5vJWbg72dO7if+9DbRaOFkwhNZwdVlNvoZV51YBuqymsUqdANqMDG5/vhiAGs9PQu1mJCtlhtjLyez+9izfvHGAPSvOEXspGVQQ3KQavcc15cHnQrm/fx1qNvLG3skOTU4+Ny/c5fi2a/w8/zi7V5wjJ6vo4a+meA0ZjFunMJScHOJmzjJdaRZoUK0BPYJ7oKCw7NQy0wdvNQrc/CDlOpz+yWTTJzrUJsjLmfjUbFb9te5kZVWQ1XyweQAN/U0kBY7/VRio1UhQ3zt/Misvi61XtgLGCwPlREWRdfw42NnhNcRwG4A/9+gyyve198fFvWIUuipvlaMMpKiQ7NNV5Lkb/s/ZPl1NnnvVmH9QWUllYAtLtwd34xdHWelOuLjnlGOHhBAVWcEQ2n7NAnAxtExDzEFIjgEnT2g80OAx9lzfQ2JWIjVcavBgiOGhiAXufPst+Xfu4FC7NtWGGZ//ZoqiVTjw02VO7v57yKiXnwuNwwJp1CEADx9n/fZ6rXwBXQY3KTaDhCsp3LyYzKVjCZw/GMeNc0n0HNOE4KYlG8qrUqkImD2b6EEPk3n4MCnr1uE9dKjJfSaETmDv9b1sjt7M5FaTCXQ3Mk/WwRk6Pge758DvCyB0OBjJrDnZ2/FCr4a8ue4Ui/ddZkS7YNycKt/l/eVb6Ww+pVvmZUqPhsYb3r4M0XsBFbQeZbDJ7pjdpGvSqelek3YB7Qy2KVjuxL17dxz8DE/5SEvKJjryNgAtewab+ZNUPVbPbC5atIiQkBCcnZ3p0KEDR44cMdk+OTmZyZMnExgYiJOTE/fddx9btmwp8fmtnRm0FnPX2hTFI1lFUZWVZPkTmbcpRPnLzdPqL9wHtzYy9K+gMFDTR8DBcFG4ny7qsm5DGgzBwc7wGoUAeUlJJH2lm6voN/UlVA7G2xo9Rm4+2788rQ80G3cK5NHX2jBqdkfaPhhSKND8J7VaRY1a7jR7oCZ9xzdjyLT78fR1If1uDr8sjGTfqvPkZpcsy+kYHIzvC7ohr7cWLECbmWmyfQvfFnQI7ECeksfyM8tNH7zteHD0gMRzcGmHyaaPtalFbR9Xbqfn8u1f609WNp/vvYyiQJ+m/jQN8jTe8NhfWeP7+kG1EINN1l9aD8Aj9R9Brbo3FNLm5pKyYQMA3o8Zv4Fwat8NFK1CzUbeVK/pbtbPURVZNdhcs2YN06ZNY9asWRw/fpyWLVvSr18/bt26ZbB9bm4uffr04erVq/z4449cuHCBL7/8kpo1jcw1EKXmlFa67KItDrOVSqqGVcZ5qjKHUggh7rX/YiLJmRp8PZzoVN/ATaLcDDi7XvfcyBDaG2k3OBR3CIBHGz5q8ny3lyxBm5mJc9OmePTvX+z+ZqXnsmFBJFEnElHbq+gzvim9nmxCYH0vk0N3DQlq6M2IGe0J7V4LgDO/xbL63SPcuHC32P0C8BkzGofgYPITb5O0YmWR7SeETgBg3aV13Mm6Y7yhize0Had7fmCByWM62KmZ2luX7fvi12hSsytXIuXanQw2nPx/9u47PKoye+D4d0pm0ntIgZAQegmEXqVJE6SJrmBhRcUV14q/dcVdy9rb6rqWtSLYUVSkKB2kd0KvgSQQ0sukT72/Py6JYu5MJiHJzCTv53nykGXeO/POEuGeOec9Rz5f/OBoB1lNUzkkX26q1P9uxSUZpRnsztqNCpXdLrSlGzZgLSxEGxmJ/zXXKK4xm6wc3ybvqecokdV0xKXB5htvvMHcuXOZM2cO3bp14/3338fX15eFC5Vr2RcuXEhBQQHLli1j6NChxMfHM2LECHr16tXEO3cvLTU7a48o3/QgTjZ78qROtPUJcJvi/YkPWQRBqLIsWS6hndwzBo1a4e+sk6vAVCpnhtoqN/354cwPAAyOHkybgDZ2X8t08SKFX38DQMSj8+02WrHHkFvO96/uJ+ucAb2vlikPJtGpf1SdnuOPvPQahs/sxNSHkwgI9aYkv5Kf3jzI1iWnsVrr9iG7Sqcj4sEHAcj/+GMshY6D1oFRA0kMT6TSWll93tWuQffJcyLTd0L6LodLpya1pn2EH4YKMx9vPV+n9+Du3tuUgtUmMbJzBIltguwvPLoUKg3yz237axWX/HRWzlgOjB5IjL9yVr+qhDbohumotMr3Kad3Z2EstxAY7k18z7pX9bQkLruDM5lM7N+/nzFjxvy2GbWaMWPGsHPnTsVrli9fzuDBg/nrX/9KZGQkPXr04MUXX8RqrV9JqAjShMbgylJaUcYrCIIgOFJqtLD+RDbgqIT2K/nXXrMUZ2tabBaWnV0GwI2dbnT4ern//S+YzfgNGYz/0KF12mvWeQPfv7ofQ04FAaHe3PC3vrTuFFKn53CkTZdQZj41gG6Xu4ge3nSRLV+dqrXZzx8FTpqIvmtXbKWl5H/4kcO1KpWKuxLlzrRfn/yaElOJgyeOhl4z5e+3/cfh82rUKuaPlWecLtx2noIyk9P7d2cXC8v5/oA8WuQBR1lNSYI9l/+/73eX4hlXm2Sr/rm11xjIdPEiZTt2gEpF8Azln21Jkji8Sd5T4sg2qJU+sBGquSzYzMvLw2q1Ehl5ZQe0yMhIsrKyFK85d+4cS5cuxWq18vPPP/Pkk0/y73//m+eff97u6xiNRoqLi6/4EgRnNMey0oYgMmQtU2N+kCHObQpC01lzNItKs42EcD8SWytkiQwZcG6z/L2d2ZpbLm4htyKXUO9QRsWOsvtaladOUbxiJQAR8x+t0z7PJefy0xsHqSgxE9E2gBl/70todP072Nqj89Yy6tYujJ/bA5UKjm/P5ODaunV1VanVtJr/CACFX36J+dIlh+tHxY6ifVB7Ss2lLDm1xPGTD3kIUMHpXyDnhMOl1/WIolt0IKVGS3XnVk/3/q8pWGwSQzuE0TfOwQcNF/dB1mHQetudrbk7czeZZZkEeAVwbVvlzGfR9/I5ZL8hQ9C1UT6md/FkIQWXyvDSa+g6VIw7qY3n1KYBNpuNVq1a8eGHH9K3b19uvvlm/vGPf/D+++/bveall14iKCio+is2Vq6r1hWLrKYgCEIV8eGKILQMVSW0U5NaK593PLwEkKDtEAhtp/gcS0/LZYZT20912Bgo9403QZIIuG4CPj26O73HM3uzWf3BESxmG3E9wpg2vzd+QY17RKZD31YMvVHOnO38MYWUA8r9Q+zxGzYM3wEDkEwmct951+FatUpdnd38/PjnVFoq7S8O7wBdJ8vfb3/L8fOqVTw2Qc5uLt6ZyvFLnp1gyTJU8u1eJ7KaAHsvZzV7zABf5Q7DVbM1JyZMxFtbs6GUZLFg+F4uDw++yX7GvmrcSZfB0eh9ml/n34bmsmAzPDwcjUZDdnb2Fb+fnZ1NVJRyLX50dDSdOnVCo/mtRXfXrl3JysrCZFIuF1iwYAEGg6H668KFC4rrBKEhiXJW13NlcyDRmEgQBHeUU1LJ9rPyqIapSQoZGUmCQ/L5SpJmKT5HVlkW2y9tBxw3Bqo8eZLSX38FtZpWDz3k9B4LLpWx8fMTSBJ0GxrNxHmJ6Lyb5oa+5+g2JI6Qs1nrPj1O9nnngzWVSkWrR+cDYFi2DONZx5nFCe0m0Nq/NQWVBdXnCO0a9rD865HvoMjxfezIzq24rkcUVpvEEz8ewWarW0mwO/lgSwomq40B8aEMSgizv7AsD47JgST971JcYjAa2JC2AZC7Jysp3boVS04OmpAQAkaPVlxTlFNO6lG5sVPiSNGg1BkuCzZ1Oh19+/Zlw4YN1b9ns9nYsGEDgwcrH0YfOnQoZ8+exWb77fD26dOniY6ORqdTHqSq1+sJDAy84qs5qs/5UzH+pHm5mgC3obNaokmTIAiC+1l5KBObBEmxwcSHK5SkXjoAeadA6wPdpik+x49nfsQm2egf1Z/4oHi7r1Xw6acABE4Yjy7e/rrfMxutrP7oKBaTjTZdQhhxaxfUmqa7VVWpVAz7U0fadg/Darax6n+HKc6vcPp6n169CBg7Fmw2ct78j8O1XmovZnebDcCnxz7FYnMwfqV1X2g3HGwW2Ok4awrw9OTu+Ou1JF8o4qs9dSsJdheZhgq+vrz3B67t4Hjxgc/AaoKYPvL/VwpWn1+NyWaiY0hHuoV1U1xj+EHOagZNnYrKTlxxZNNFOfHfPYyQqIYv626OXFpGO3/+fD766CMWL17MiRMnmDdvHmVlZcyZI7d6nj17NgsWLKheP2/ePAoKCnjooYc4ffo0q1at4sUXX+Svf/2rq96CgIvGmzjZxVQQhKZVn1mbIM5tCkJT+OlyCe00pawm/DZbs+v14F3zw3mrzcoPZ+Ub8hkd7c8fNGdlYVglz0APnXOn0/vb8s0pCjPL8A3UMfbO7i5pvKLWqBk/tzthrf2pKDax6t3DGCucn8MZ8cjDoFZTumED5QcOOlw7veN0QvQhZJRmsC5tneMnHvqw/OuBxVBe4HBpVJA3j47rBMArq0+SW2J0cvfu49kVx6k02+gfH8KwDg7+XbFZYZ/8wYa9cSfwWwnt9A7TFcvHLfn5lGzaDEDwDOWMvanCwomd8nzaXqPtd2AWruTSYPPmm2/m9ddf56mnniIpKYnk5GRWr15d3TQoPT2dzMzM6vWxsbGsWbOGvXv30rNnTx588EEeeughHn/8cVe9BcFDiSY3grtxavxJLR+yiIxyy/Tuu+8SHx+Pt7c3AwcOZM+ePXbXLlq0CJVKdcWXt3fNs0tC83M+r4xDFw1o1Cqu76UQbFqM8ugIkLvQKth+aTtZZVkE6YMYEzdGcQ1Aweefg8WCb//++CT2cGp/J3ZkcnJnFioVjLurO76BypmlpqDz1nL9/T3xDdJRcKmMNR8ddXokij4hoTpYyXnj3w472/pofbi1660AfHLkE8ddcNuPhqieYC6HPR/Wuo/Zg+Pp0TqQkkoLz6867tTe3cWmUzn8cjQLjVrFs1N7OJ6lemYtGNLBJwR6KAeJpwpOcSz/GFq1lkkJkxTXGFasAIsF75490XdUPh96Ymcm5korIVG+xHZTPhcq1OTyBkH3338/aWlpGI1Gdu/ezcCBA6sf27x5M4sWLbpi/eDBg9m1axeVlZWkpKTwxBNPXHGGU2h4+pK6zZyqC0dn2zxptqKSpjy36ZFnRJvhjM3fE+c2haawZMkS5s+fz9NPP82BAwfo1asX48ePJyfHfnOTwMBAMjMzq7/S0tKacMeCqyw7KGc1h3UIJ9xf4YOp02ugohACoiFhpOJzfH9a7tQ5OWEyeo3yh1vW0lKKlnwLQOidc5zaW/6lUrZ8fQqAAZPb0bpzw403qS//EG8m3dcTrU7NheMFbP3mtNMjUcL/+ldUej0V+/bL51YdmNllJj5aH04Vnqo+C6tIpfrt7ObuD8BU5vB5NWoVL05PRKWCn5IvsfVMrlN7d7VKs5WnfzoGwJwh8XSNruX4296P5V973wZeyomEqnEno2JHEepdM0iUJAnD5S60wTcon+eUbJJcQgv0HNXGcQAsXMEz7+IERWJuaMMTHTo9kwj0hJbijTfeYO7cucyZM4du3brx/vvv4+vry8KFC+1eo1KpiIqKqv764wgyofmRJInlh+RxHIqNgeC3xkA9/wTqmh/i55bn8utFOXByNFuz6Lul2EpL0SUk4D9iRK17MxutrPnwKBazjdiuIfSdEF/rNU2lVVwgY+/sDio4tvUSx7c5HmlSxSsqipDb5Ixl7htvIjmYBx+kD+KmTjcBsPCo/f9uAeg6FULaQUUBHPi81n30bBPM7EFxADy57CiVZvfv1fHuprOkF5QTFejNw2M7OV6cnwJn1wMq6Kdcrm22mll5Th6/Y2+2ZuXRoxjPnEWl1xM4caLimvQTBRhyK9D7auk8KNrZtyMggk2hCbjiTGdLKidsyqymKD9u2Rr7Z02c26wbk8nE/v37GTPmt3JGtVrNmDFj2Llzp93rSktLiYuLIzY2lqlTp3Ls2DG7a8Ws6ubhSIaB83ll6LVqxnVX6Phflgdn1sjf97pF8Tl+SvkJq2QlKSKJ9sHtFddIZjMFn30GQOicO1CpHd9mSpLEr1+fojCrHL8gHWPmdEflgnOajiQkRTB4mvx+d3x/ltJC584/hs+dizowEOPp0xSvXOlw7e3dbker1rI3ay+Hcw/bX6jRwpAH5O93vgPW2pMMj47vTKsAPan55by3OcWpvbtKSm4p7/8q7/Hpyd3w19dSAbXvcnDeYQyEJigu2XxxM0XGIlr5tGJIzBDFNUWXGwMFjBuHxk4j0ROXP2joPDAKL72oqKwLEWwKQiPyhPJWkb1tOcSHBc1LXl4eVqu1RmYyMjKSrKwsxWs6d+7MwoUL+emnn/jiiy+w2WwMGTKEixcvKq63N6ta8CzLk+Ub5THdIpVv4I9+L3c6jekNrbrUeNgm2apna87oZL8xUPGatVgyM9GEhRE0ZUqt+zq5M5NTuy6f07zbtec0HUka25bIdoGYKq1s+eaUU+W0muBgwu6WG9bkvvMuksV+k6EovyiuT7gecCK7mXQr+LUCwwU4/G2t+wj09uKpyXL31fc3p5CS64qujrWTJIknlx3FbJUY2TmCCT2UxyBWM5XDwS/k7wfMtbvsxzNyY6ApHaagVdf82bdVVlK8chVgv4S2vNjE+UPyyKBuw+xUBgh2iWCzhRPjTwSh8YhyXsHdDB48mNmzZ5OUlMSIESP44YcfiIiI4IMPPlBcL2ZVez6bTWLlYbnZ4hSlxkAAh5fIv/acqfjw7szdZJRmEOAVwPj48YprJEmi4HL5duhtt6LWO64wys8oZcvXpwEYMCWBmI6uP6dpj1qtYtRtXVBrVJw/lEfKAefOP4befhua0FDMFy5Q/Mtqh2vndJfPt25I38C5onP2F3p5w+DLUxh+fcWp7OakxGhGdIrAZLXx5LKjTp89bUrLD11iR0o+eq2aZ6fU0hQI4NgPUFkEwW3lzKaC7LLs6nOw9kpoS9atx1ZSgldMDL6/6xvzeyd3ZWKzSUS2CySstb+zb0m4TASbguDB3DFz6uoSZhHg1V9DZbnrO/5EqJvw8HA0Gg3Z2dlX/H52djZRUbVkBS7z8vKid+/enLUzgL6lzKpuzvakFpBVXEmAt5aRnSNqLshPgYz9oNLY7eb5/Rm5ecrEhIn4aJUrJMp376Hy+HFU3t4Ez1QOWqtINomNn53AYrbRtlsofcfH1e1NuUBYa3/6TJD3uWXJaSrLag/y1D4+hM6WZ2nmf/ghks1+w8WE4ARGx44G5LmbDg2YK2c3i9J+y+45oFKpeG5qD/RaNTtS8vnxcrMod2GoMPPcyhMA3D+qA23DfB1fIEmw5yP5+353KZ4xBlhxbgU2yUafVn2IC1T+GTP8eHm25vTpimXfkiRxYrv8YU23oSKrWR8i2GxmRJMg9+OOAaEguCtxbtN5Op2Ovn37smHDhurfs9lsbNiwgcGDBzv1HFarlSNHjhAdLRpeNFc/XS6hva5HFHqtwk35ke/kXxNGgn/N//4KKgvYkC7/jDlqDJT/qZzVDL7hBrQhjrOUZw/kkJNWgpdew7V3dHO7c5r29JsQT0iULxXFJnZ8r/wBzR+F3DILtZ8fxjNnKN282eHaOxPlJjcrz60kq0y5FB4AnR9c86j8/ZbXwFxZ6z7ahvny4LXySI+nfjrG6ewSp/bfFP699hR5pUYSIvy4Z4Ty2csrpGyEzGTQekPv2xWXSJJUXUI7vaNyeazpYgZlO3cBcrCpJPNsEUXZ5XjpNXToJ/59qg8RbAoNwhVNgFq6lhDEeurYk/pqae9XuHrz58/no48+YvHixZw4cYJ58+ZRVlbGnDlySd7s2bNZsGBB9fpnn32WtWvXcu7cOQ4cOMBtt91GWload99tfxi64LlMFhu/HK0qoW1dc4Ek/Xbur+efFJ9jZcpKLDYL3cO60yW05nlOAOOZM5T9ugVUKkLv+LPDPVmtNnb/JJeJJo1t67bnNJVovNSMuq0LqOS5oBdOFtR+TWAgIbfITZfyPvjAYQlrr4he9Ivsh8Vm4fPjtXSb7XsHBLaG4gzYX0sm9LJ7hicwsF0opUYLdy3eS0GZyanrGtPhi0V8vksev/T81B7KH4j8niTJATZA3zngF6a47EDOAdJL0vHV+jIubpziGsOyZSBJ+A4ehK6Nwn8fwLHLjYE69o9E5+3cyDbhSuLORvBcTs5ptMfZZimigU4juMo/uxZN/H8n/M7NN9/M66+/zlNPPUVSUhLJycmsXr26umlQeno6mZmZ1esLCwuZO3cuXbt2ZeLEiRQXF7Njxw66devmqrcgNKJtZ3MpKjcT7q9ncHuFm/JLB6AgBbQ+0EV52P2KcysA+2feAPIvz0QPGDsWXdu2Dvd0YtslDLkV+AR4kTTG8xpORXcIJnG4HJhs/vIUZlPtvS9C/zwblV5P5aHDlO/e43DtXYl3AbD09FIMRoP9hV7eMPxv8vdb/13r3E0AL42a/93Wl7ahvlwoqODez/djsjTeLPXaWG0S//jxKJIkj+QZ0sGJIxip2yB9J2h0MPRBu8uqspoT2k3A16tmWa5ks2H4UV4TfINy+Xhlmbn6fK4ooa0/EWwKTtGXuO4vI6HxiEC68TX1GVJXn5n1FM2pXPf+++8nLS0No9HI7t27Gfi7JhebN29m0eVAAODNN9+sXpuVlcWqVavo3bu3C3YtNIWqEtrre0ajUSpVrcpqdpkE+pr/HpwqOMXJgpNo1Vqua3ed4muYc3IoXi4HpGF3znG4H7PRyt5VqQD0m9jOYzNFg6a1xz9ET3FuBXtXnK91vTY8nOAZchff/A8/dLh2aMxQOod0ptxSzjcnv3H8xL1vg5B4KMuFPY6ft0qon45P/tyPAL2WPakF/HPZEZc0DJIkiedWHudIhoEAby3/mNTVuQu3vCr/2vt2CFQOAMvMZaxNWwvA9A52Oszu2YM5IwN1QAABY8cqrjm9Jxur2UZYa39axYv7pfoSwaYgNIGGLnl1RQmtJ4zNEM2BmofmFAgKgqtUmKysOy43j5qSpHBTbrXII0/Abgnt8pTlAIxsM5IgfZDimsIvv0Iym/Hp0wefpCSHezq04QLlxSYCw73pfo3nZop0PlpGzOoMQPL6dHLTaz//GHbXnaDVUrZjBxVHjtpdp1KpuLOHfHbzyxNfUmGpsP+kGi8YeblMftt/oNJBJvR3OkYG8PYtvVGr4Nt9F/lkW+0Bc0N7Y91pFu1IBeCF6Ym0CvCu/aL03XB+C6i1MOxhu8vWpK6hwlJBfGA8vSJ6Ka4p+l5uDBQ4cSJq75qvLUkSxy+X0HYbFl17d1zBLhFsNkOe1iTIUYAgzrAJgntpDmeFRTArtATrT2RTbrISG+pD79jgmgvOb5YzYr5h0H50jYctNgurzsnzB6e0V56ZaSsvp/AbOftWW1azotTEgbXy2byBUxPQaD373/f4nuF07NcKSYKNn5/AanVcAebVujVBk+RS5dqym+Pix9HavzWFxkKWnV3meCOJN0F4J3kMyM73nN7/yM6t+OckuXz+hZ9PsPFkdi1XNJwPt6Tw9ka5wdJzU7vbH8nzR1VZzaRb5JEndlR1T57ecbpikGgtKaFkrZz5DJ6hXEKbk1ZCfkYpGq2aTgOc6+4tKPPs/9KFBtEUszZd0UBIlBMKgiAILdXyQ3JWZnLPGOWszOHLXWi7T5czZH+w49IO8ivzCfUOZVibYYqvUfzLamwGA16xsfiPGuVwP/t/ScNcaSU81p+OfSPr9mbc1LA/dULvpyXvQimHNtQ+gzbsnrmgUlGybh1GO+OGALRqLXd0vwOAxccWY7FZ7D+pWgOjnpC/3/kulNfetKjKnKHxzBrQFkmCB79O5lRW43eo/Wp3Oi/+fBKAxyZ05vbB8c5dmLEfzq6XR/QMm2932Yn8ExzOPYxWrbX7IUnxqp+RjEb0HTvgnZiouOb4dvm/n/Z9IvD2q/nfh+A8EWwKQhNpqIxQc8gsCa7TVOXQYtamILiOodzM5lM5AExNUuiyaSqHkyvl7xMdl9BObDcRL7XyzXbRd3LAGnzTTag09ruIFudXcOTXiwAMntbeY0ad1MY3UMfQGR0AOZiubfamvn17AsZcC0D+Rx87XDutwzRCvUPJKM1gTeoaxxvpOhUiE8FUAtvfcnr/KpWKZ6d2Z1DCbx1q80uNTl9fVz8lZ/CPZUcAmDeyPfeN7OD8xVtel3/t+ScIbWd32ZJTSwAY23Ys4T7K/w4VVc/WvEHxgxhTpYUze+RMb7dhnlvu7S5EsCkITai5B4oimyw0lMYqdRUltEJLsPpYJmarROfIADpHKTQ2OfUzmEohOA5iB9R42GA0sCl9E2C/hLby9GkqkpNBqyV4+jSH+9mz4jw2i0TrziHEdgut69txa10GRRPWxh9ThYX9q9NqXR92zz0AGFauxHQxw+46b603t3a9FYCFRxc6buKjVsPof8rf7/4ASpwvifXSqPnfrX2JC/PlYmEFcxbt5UJBudPXO2vDiWwe/fYQkgS3DWrLY+M7O39x1hH5ZxbVb/NFFRiMhurS75ldZiquMZ45Q+Whw6DVEjRlsuKas/tzMButBLXyIaZjsPP7FBSJYFMQnNCQXVuvJuBsyGDV3TvR1vW8bnNpDuTqc8ru9HMhAkNBqJ+qElrFxkAARy6X0CbeBAqZnTWpazDZTHQI7mB3tmbRd0sBCBg1Cm1EhN295GeUcmp3FgCDp7dvdo1WVGoVg6e1B+DIpouUFFQ6XO+TmIjfkMFgtVKwcKHDtTd3vhlfrS+nC0+zLWOb4410Gg+t+4GlAra9Uaf3EOKn45M/9yfQW8vhiwYm/GcLS/amN1iX2p0p+dz35QEsNolpSTE8O6VH3X4OquZq9rgBwjvaXbY8ZTmV1ko6hnSkdyvlLttFP8jjTvxHjEAbrpz5rG4MNNROCbpQJyLYFFo0T+iw2uy0wDmRzSUQ9nQieBVagpziSnak5AMoN14py5fPvkGtXWintp+qeLNtq6zEsFxeE/ynmxzuZ9eyFJDks2+R8YHOvg2P0rZ7KDEdg7FabOxdWXtn17B7/gJA0fffY8nLs7suSB/ETZ3k/38XHnUcmKJS/Zbd3LcQDBed2/xlHVr5s/KBa+gfH0KZycrfvz/C3Yv3kVPiOHh2xGK18f3+i9y9eC9Gi42x3SJ57aZeqOtSRp1zEo7LP2tc8392l9kkW3UJ7czOMxV/biWz+bef2xuUR6LkZ5SSfb4YtVpFl8HRzu9TsEsEm01MU1yBpthBG+sG4oqOtK5oAuSp6pOhdGUJrgjK3UwLCthFgCgIdbPycCaSBL3bBhMbWnOYPcd/BJsFonpCRM1SxrTiNA7lHkKtUjMpYZLia5SsXYvNYEAbE43fkCF293LpTBGpR/JRqVUMmtq+3u/J3alUKgZPl9/fyZ2ZFFwqc7jed+AAfHr1QjIaKVj8mcO1t3e7Ha1ay77sfRzKPeR4IwkjIW4YWE2w8fm6vAUA2ob58s09g3liYhd0GjUbTuYw/s0t/HIks07PY7RY+Wp3OqP+vZlHvztEmcnK0A5hvD2rN16aOoYeW18HJOg6GSK72V22K3MXacVp+Hv5c33C9YprSrdswZqfjyY8HP/hwxXXVDUGiu8Vjm+grm57FRSJYLORVAWVf/zyZPoSx229XcKDb7rrEjw297OegtDYRNAqtBTVJbT2xklUdaGtJas5JGYIEb7K5bFF315uDDRjht3GQJIkyVlNoNvQaIIjFQLfZiQqIYh2vcKRJNj1U4rDtSqVirC/yGc3C7/+Gmtxsd21kX6RTE6QzxYuPOJEdnPsvwAVHPoazm+t03sA0KhV3DO8PSseGEa36EAKy83M+/IAjyxJxlDhOJFRbrLwybbzjHh1M0/8eIQLBRWE+el4bEJnPvlzf7y97DeRUpSf8tss2OF/c7j0m5PyCJ4p7afg66X8s1Y1WzNo6hRUXjWbXlnM1uqS725DRWOghuK5d+oeTlNcgTXQfbJFOoMVU1Ad/xIQmoQINGsnylSvVFGqx8e/8ToKOiutIpw4H/slYs46Z2pFgi6nAXYkCM1ben45yReKUKtgUk+FEsDCNLiwC1BBjxk1HrZJNlakrADkElolxnPnKd+3D9RqgmfUfI4qmSkGMlMMaLRq+k+y3z20ORk0rT2ph/M4fyiPrHMGohKC7K71HzkSfccOGM+cpfCbJYTfM9fu2jt63MGys8vYeGEj54rOkRCcYH8TbfpB3ztg/6ewaj7cuw20dW/e1zkqgGV/Hcp/N5zhvc1n+fFgBr8czSQ6yIeIAD2tAvS0CvCmVaD8/aWiChZuT6WgzARAVKA39wxPYNaAtvjo6nl/ufXfINmg0wSI7mV3WWZpJr9e/BWQz7kqseTmUvqrvCb4BuXZmueT8zCWWfAP0Te7RlauJDKbjcDZDKanZzqFq1dbICkCTcEdueLn8moykyKrKbQUyw/J3U2HtA+nVYB3zQVVjYHaXQOBNTM3+7L2kVmWSYBXACNjRyq+RtFSuTGQ//DheEXZH3afvC4dgM6DovALbhmdykOj/arP+e38McVhgx2VWk3Y3XcDUPDZZ9iM9j8gTAhKYHTb0QB8euzT2jcy5mnwi4C807Djv3V4B1fSadX83/jOLJ03hIRwPyrNNs7nlbHnfAErD2eycPt5Xv7lJPO/PcTra09TUGaibagvL92QyK+PjeTOYe3qH2jmnoZDcraS4Y85XPrd6e+wSTYGRg20G4gbli8HqxWfpCT07ZVLuk/skKsCugyJrtu5UsEhkdlsYHUNIN0tw9ncuEuGx5GLFcG08Smq8XuNyZ06jgqCIAhXT5Ikfkp2UEIrSb/rQuu4hHZc/Di8tTWDVZvJhOFHuZuno8ZAhVllnD8sVzUkjYl1+j00B/2vb8fpPdlcOlNE2tF84hPtzxwOnDiRnLfewnIpE8OPywiZqZyVA7izx51sSN/AynMr+WvSX4nysx/o4xMC41+EH+bK8yl7zIBQB9nQWvRpG8K6+SNIyy8jp8QofxVXklv1fUklNhvc3D+W63tGo63rucw/kiQ5KytZodN10Kav3aUmq4nvz8iltjd3Uf7/T5Kk30poZyhnNUsKKrlwshCArqIxUIMSmc0GJDKV9eeoDNLVoyCqNFWA5qnZzIacsekuf+YNSZT6Xj2RoRQE+45dKuZMTik6rZrxPRQCkawjkHsSNHroVnN2Zrm5nHVp6wCY2kG5hLZ0wwashYVoW7Wy22AF4NCGCyBBfM9wQqL86veGPFRAqDeJo9oAsGvZOSSbg+ymlxdhd8wBIH/hQiSr1e7anhE96RfZD4vNwufHP699I4k3yQ2DLJWw6lE5gLsKGrWKhAh/BiWEMaVXDHdfk8CCiV158+Ykvrx7EF/fM4hpvVtffaAJcPhbSN0KWh+47hWHS9elraOgsoBWPq0YFTtKcU1FcjKmc+dQ+fgQeN11imtO7swECVp3DiEwXCSBGlLzu6NzkasJNBsrSHVFR1pHRLda+y5WBFd/CUJjB9stqbuwCFCFluL7A/Koi3HdIgnyqdn8hCPfyr92Gg/eNc8SbkjfQLmlnLYBbUmKSFJ8jaLv5Mxo0IwbUGmVi+PKi02c3CU3Wek9tmVlNav0nRCHzkdLfkYpp/dmO1wbfOMMNMHBmNPTKVm71uHauxLvAuSyUYPR4HgTKhVMekP+cCFlIxz7oU7vwWUqCmHtP+TvR/wNQuIcLq8ad3Jj5xvRqpV/Jg0/yO89cPx4NP7+NR6XbJIcbAJdh4isZkMTwaabEFlRwV21pMBEcH8ieBSEmsxWG8svl9DO6NOm5gKbDY5eDjYSlctff0r5CYDJ7Scrzig0XbhA2Y6doFIRPONGu3s5+utFrGYbreICiO4QXLc30kx4+3nRZ3xbAPasOIfVYr+bv9rXl5DbbgMg76OPHJ7zHBozlM4hnamwVFR3X3UorD1cM1/+fvUCqKwlQHUHG56DslwI7wyDH3C49FTBKQ7mHESr0nJjR+WfSVt5OcWrfgYg2E4JbcaZIorzKtF5a0jordyBWag/EWw2gIYKFEXA6RoimGpCjTCqRpSnNryGLBlPq7B/Xqm+nA04RWAqtBS/nsolv8xEuL+eazoq/DeXvhOKM0AfCB3H1Xg4qyyLPZl7ADnYVFL0ndwYyG/IEHRtWiuuMZusHPlVblKUNLatYtDaUvQcHYtvkI7ivEqObc1wuDbk1ltQ+fhgPH6Csh077K5TqVTc2eNOAL488SUVFifuG4c9AmEdoDRbDuTc2cX9sO/yeJdJ/wat4zmX35ySA+5r4661O6aneM1abOXleMW1xadfP8U1J3fIWc2O/SPxqm9DI8EuEWxepeYUIOoM9s8KVHHLWZuC0BJ58IzZhnbO1ErxSxBaih8OyiW005JilM/MVTUG6joZvGo2/ll5biUSEv0i+9Hav2YgKZnNFP0oZ0aD/6TcXAjg1K4sKkvNBIR5076FZ4i8dJrqkS/7fknDYrJ/j6UNCSH4Jjkzl//Rxw6fd1z8OFr7t6bQWMj3p7+vfSNavVxOC7D3Y8jY79wbaGo2K6x6BJCg581yx2QHik3FrDq3CoCZnWfaXWf4Xv7/KHj6DYoffhgrLKQckEdrdREltI1CBJtXoTECTU8PXsW5TPcnOtEKzYEIKgVBVlRuYv1x+Wb5BqUSWqsZjsslsiTWLDWUJKn6pt1eVrP011+x5uahCQsjYNRIxTWSTSJ5vTzupNe1sagbolGMh+s6NBr/UD0VxabqM4H2hN1xB2i1lO/aRcWRI3bXadXa6rObHx/5mHJzee0bSRghB3BIsOJhsFqcfxNNZe/HkHlIPk887vlal69IWUGFpYIOwR3oG6ncrdaUmlo9EzZomnLTq7P7srGYbYRE+xEZH3hVb0FQVqe/CQ4dOsTzzz/Pe++9R17elYO6i4uLufPOOxt0cy2VpweczZkI1ISrIUp+BUFoaCsPZ2Ky2ugSFUC3GIWb5ZRNUFEgz12Mr9lB9nThac4WncVL7cWYuDGKr1H4rdxcKHj6NFQ65dLG84fzMORUoPfViiYrl2k0anqPlRvcHFibjs1qvzrMKyaGoOuvB2rPbk7rMI02/m3Ir8yvLiWt1bjn5UAu6zDs+cC5a5pKSRZsvBxgXvsU+Dv+ENEm2arPrM7sPNNuuXbRj8sA8Bs21O5M2BOXS2i7Do5u0WXfjcnpYHPt2rUMGDCAb775hldeeYUuXbqwadOm6scrKipYvHhxo2zSHXlKQOhuHWkbhSgnFIQaGnIUjRLROVkQ3MMPl7vQ3thXIasJv5XQdp8Ompr/XlZlNUfGjiRQVzNYNV+6RNnWbQAE32i/MVBVVrP78NbovMW/y1W6DY3GJ8CLkvxKzuzLcbg27G45Y1mybh3Gc+ftrvNSezEvaR4AC48upMRUUvtG/FvBmH/J3697GtLsnw1tcmueAGMxxPSBvnNqXb4ubR2pxakEeAVwffvrFddIVutvM2FvmKG4piCzjOzzxajUKjoPcjC3VLgqTgebzzzzDP/3f//H0aNHSU1N5bHHHmPKlCmsXr26MfcnCIIHEDM2BUcao0mQIAhwLreUA+lFqFUwJSmm5gJTOZyUg0mlLrQ2ycbP5+VOnZPaTVJ8DcNPP4Ek4TtgALr4eMU1WecMZJ41oNao6DnKTtDbQml1GnpdK4+A2b86zeHcTX2HDviPHg2SRMGnCx0+76R2k2gX1A6D0cAXx79wbjN9/gzdpoLNDEtug8JUZ99G40nZCEe/B5Uarn8T1I4b9NgkGx8e/hCAW7vdip+X8hzXsu3bseTkoAkOxn+08vzNqsZA8Ylh+AY6bkYk1J/Td3XHjh2rLpNVqVQ89thjfPDBB9x4442sXLmy0Tbojpoiq+kpmVNP0NgZHkFoDCLo9hx//vOf2bJli6u3IbRAPx6Uu5wO7xRBq4CajX84vRrMZRDcFtr0r/Hw/uz9ZJdnE6AL4Jo2NRuySJKEYZl83jNo+nS7+6jKanYaGIVfkPg39496jGiDzltDYWYZ5w/nOVwbdvfdABiW/YQ5234mVKPWcF/SfQB8dvwziiqLat+IWg3T3ofoJCjPh69mQmWxs2+j4ZkrYdWj8vf950JMUq2XbL6wmdOFp/HV+nJb19vsriv6/vJszSmTUSuUflutNk7ulufBdhksyr4bk9N3M3q9nqKioit+75ZbbuHjjz/m5ptv5sfLqWpBaAzixts13H0sjDgDKbgDg8HAmDFj6NixIy+++CIZGY7HHAhCQ7DZJH44IP+sKc7WBDljBNBjBiicR1t5Tk4WjIsbh05T84a84mAyprQ0VL6+BI4bq/gShtxyzh3MBSBpTGxd30aLoPfRkjhS/jPavzrN4SxN3z698enXF8lspuAzx8fTxsWNo3NIZ0rNpSw6tsi5zeh8YdbX4B8FuSfg+7vkTrBNzWqBH+6GgnPgHwmj/1HrJZIk8cFh+bzpLV1vIUgfpLjOUlhIycaNAATfoDxbM/1oPhXFJnwCvIhLDKvnmxCc4fQdfFJS0hVnNKvMnDmTjz/+mAcffLBBN+auRMbx6jjqVuvKwKEuQZUnNwly6d7F2Vq34u4fJHiSZcuWkZGRwbx581iyZAnx8fFcd911LF26FLO5BZybF1xi9/kCMooqCNBrGdstsuaCiiI4s1b+XqGE1mg1si51HQCTEhyU0AKBY8ei9lMuVzy04SKSBG27hxEW41/3N9JC9Bwdi8ZLTU5qMRdPFTpcW5XdLPr6G6wGg911apWa+3vfD8BXJ78ir8Jx1rRaYAzM+gq03vLPyLqnnLuuodhssPwBOLECNDq44UO5eVEttmVs43j+cXy0Ptze7Xa764pXrASzGe/u3fHu0kVxTVVjoM6DotGIzsmNyun/d+fNm2f309pZs2axaNEihg+v2eVMqD9XBLbOzNoUBKH+PCUb68kfqrhCREQE8+fP59ChQ+zevZsOHTpw++23ExMTwyOPPMKZM2dcvUWhmalqDDSpZzTeXgrn3E6sAKsJIrpCZPcaD2+9uJUScwmRvpGKoyNsRiPFv/wCQND0aYp7qCwzc2LHJQB6jxVZTUd8A3V0Gyafqz2wOs3hWv8RI9B36oStvJzCrx13mx3RZgSJ4YlUWCr45Mgnzm+odV+Y9j/5+53vwIHPnL/2akgSrH4cDn0FKg3c+CkkjHTist+ymn/q9CdCvUPtriv6Tm6KFXSDcul3ebGJtCP5gNyFVmhcTgeb06dP580331TMboJcUjtzpv2hqoLrtIiOtIIgCG4gMzOTdevWsW7dOjQaDRMnTuTIkSN069aNN99809XbE5qJCpOVn4/ImZkZ9rrQHl0q/5qo3ImzqgvtxHYTUatq3g6WbtyIrbgYbXQ0vgMGKD7H6T1ZWEw2wlr70bpzSB3fRcvTe2xb1GoVF08WknXefsZSpVJVd6Yt+PxzbJWVDtdWZTeXnFpCVlmW8xvqcQOMXCB/v3I+pG53/tr62vTib6NXpr0HXZW7yf7R7qzdHMo9hF6j544ed9hdV7FvH8YzZ1D5+BA0WXlu7KndWdhsEpHtAgmNUc7YCw2nznnjCRMm8Le//e2K0qC8vDwmT57M448/3qCbczeihFamL7E/J0oQmhOvUsel3y7nQaXJzbkjrdls5vvvv+f6668nLi6O7777jocffphLly6xePFi1q9fz7fffsuzzz7r6q0KzcTa41mUmazEhvrQL04hyCvJhvOXm1b1qDmupNhUzK8XfwXsl9AWLVsGQNCUKajUNW8XJUni2FY5q9n9mtZiRqETAkK96TRQLnmuLbsZeN11eMXEYM3Px3D5z8KewdGD6RvZF7PNXN2p1Wkj/i6PxanqUFtgf+TKVdvxNmx5Vf5+4uvQy/kk1QeH5AB1RscZhPvY//ek8OuvAQi6/no0gTVH+UiSVF1CKxoDNY06B5ubNm3ixx9/pH///hw/fpxVq1bRo0cPDAYDycnJjbDFls0TA1y3vjkX3Jq7NIJy+yDTTYhZm7Lo6Gjmzp1LXFwce/bsYd++fdx7770E/u5GZ9SoUQQHB7tuk0KzsnS/XEJ7Q+82ykHesR9BskHrfhDarsbD69PWY7aZ6RDcgU4hnWo8bsnNpWybnOUKmjpVcQ/Z54spuFSG1ktNpwEKZ0YFRX3Gx4EKzh/KI/+S/X9oVF5ehM6RZ07mf7IQyWKxv1al4oHeDwDw45kfuVBywfkNqVQw9T2I6Q0VBfD5NLi4z/nrnbV/Eaz9p/z9tU/BgLnOX5q9n33Z+/BSezGnh/05nOacHIrXyueQQ26ZpbgmJ7WEwswyNF5qOvYXP7dNoc53dkOGDCE5OZkePXrQp08fpk+fziOPPMKvv/5KXFxcY+zRLXhi0Cc0Hk88z9ZYe3bVaJmGPvtYFWAqBZki8BQcefPNN7l06RLvvvsuSUlJimuCg4M5f74RMwZCi5FlqGT7WbkRjP0utFUltDWzmvBbCe2khEmKwaph5SqwWvHp1Qt9Qs1gFeD4Njmr2b5vK/S+XnV5Cy1aSJQf7ZMiADiwxnF2M/jGGWhCQjBfuEDJ2rUO1/aN7MvQmKFYJAvvH3q/bpvS+cLMryGorTx785OxsPF5sJjq9jz2HFkKKx6Wvx/6MFzzaJ0ur8pqTuswjSi/KLvripYuBYsFn9698e7aVXFN1Rnj9n0i0Pt4TnWQJ6tXGuH06dPs27ePNm3aoNVqOXXqFOXl5Q29N0FwngeVE3qKltSt1F2DyavN9IoZs03j9ttvx9tbYcahIDSCn5IzsEnQPz6EtmG+NRcUnIeLe0Gllssj/yC7LJu9WXsB+bymkqqyTXuNgUwVFs7sywag++WmN4Lz+kyQkzNn9uZQnGc/maH28SHktlsByPvoY4cjU4Dqs5srz63kXNG5um0qMBru3SJ3LpZssOU1+GQM5Jyo2/P8XkUR/Poq/PgXQIJ+d8KYZ+r0FIdzD7MzcydalZa7Eu+yu06yWCha8i0AIbfcorjGbLJyZq/8cysaAzWdOt/JvPzyywwePJixY8dy9OhR9uzZw8GDB+nZsyc7d+5sjD26nKuzmq5+fUEQ3DcgFQSh5ZAkie8vd6G9obbZmvHXQEDNLNAv539BQqJPqz7E+NcMFCtPnMB46hQqLy8CJ0xQfInTe7OxmGyERPkS1b72kRXClVrFBRLbLRTJJnFwbbrDtSG33ILK1xfjiROUbd/hcG2P8B6Mih2FTbLx7/3/rjU4rcEnBGZ8LHeI9QmBzEPwwQjY8Y48rsRZZXmw/l/wn0TY9ALYLHIQO/HfivNeHanqQHt9++tp7d/a7rqSjRuxZGejCQ0lYPw4xTXnDuZiqrQSGO5N606ioVVTqXOw+dZbb7Fs2TLefvttvL296dGjB3v27OGGG25g5MiRjbBFoSG09I60IsPjYm6ceXZFEOkp408EQXAvhy4aOJ1dil6rZmKincxMVbBpr4T2/G8ltEqqspr+o0ejsXPOuKqEttuwGNEYqJ76Xs5untiRSZnBaHedNiSEkJvkP8v8jz6q9Xkf7vMwWrWWLRe3sDF9Y/021+MGuG8XdBwHViOs/Qcsngz5KfLoEnsMGfDL4/BmD9j2BhiLoVU3mPEJTP8QFBpNOXI8/zhbLm5BrVIzN9HxGc/Cr+TGQME33YRap1Nc8/vGQCq1+LltKnW+Azxy5Ajh4Vd2gfLy8uK1117j+uuda18suDedwYopSGFmVzNXVOZDsJ/IIgv2eZWCWcwsr7e0inDifJwcOi4IQg1f75azYJMSownyUTgnmX0cco6DRgdda459SClK4WTBSbQqLePiamZ/JLNZPq8JBE1TbgyUk1ZMbnoJaq2KzoPsn58THIvpGExUQiBZ54o5vPEig6e3t7s29I47KPjyK8p376bi8GF8eva0uzYhOIE53efw0ZGPeHHPiwyKGYSfVz3GewREwS3fwoHFsPoJSNsGb/cBjR78IyEgUv7VP1Jea7gAyV/LXW0BYvrA8P+DTtfVOcisUnVW87p219E2sK3ddcaUFMp37QK1mpCb/6S4pjivgoxThaBC/Nw2sTr/6f8x0Py9ESNGXNVm3JG7lLC6yz5cyV06lVbxpCZBnrRXoem0pHO5guDpSirNLD8kZxRnDbRz413VGKjDWLkM8g+qGgMNaz2MYO/gGo+XbtuGNT8fTVgY/sOGKb5EdWOg3q3w8VfOIAm1U6lU9B4nZzeP/noRY4X9brNe0dEEXU7o5H/0ca3PfU/Pe2jj34ac8hzeTX73ajYJfe+Aeduh3XD596xGMKTL54JProR9n8ilsgc+kwPNuKFw2w8wdyN0mVTvQPNw7mE2XtiIChX3JN7jcG3h198A4D9qFF4xymeIT+yUs5qxXUIIDBP/9jUl961tE5otR9khbakKi38dzxgIzUJdPkxoqDLU+pTQtqTsZlZZAFF+Ja7ehiAIwE/Jl6gwW+nQyl95tqbNBke+k79PnFHjYUmS+Pn8z4CjEtqfAHlGocqrZubUVGnh9OUGK91EY6Cr1q5nOCFRvhRmlXNsS4Y8FsWOsLvvwrBsGSXr12M8d95ul2AAb603/xj0D+atn8eXJ75kcsJkuoYpd2d1Smg7+PMKMFdCabb8VZL1u1+z5PLa3rdD3OD6v85lFpuF53Y9B8Dk9pNJCE6wu9ZWVlZd+h0yS3ncic0mcfJyCW3XIeLntqm5V6rIzYhsoiAIgmNi1qbrvfvuu8THx+Pt7c3AgQPZs2ePw/XfffcdXbp0wdvbm8TERH7++ecm2qlQX5Ik8dXlEtpZA9oqn5NM3wlF6aALkEsX/yA5N5mM0gx8tb6MiK1ZiWYtKqJ0o3zGz14J7dn9OZgrrQRF+NC6U3D935AAgEr9W3bz0IYLWMxWu2v1HTrgP3o0SBL5Cz+p9bmHtR7G+Pjx2CQbz+58FqvN/nM7zcsbQuIgdgB0myLPyrz2SZj6Lkx7r0ECTYBvTn7DyYKTBOoCebSf4zEphpWrsJWWoouLw2+I8utnnCyktNCI3ldLuyT7FZpC4xDBpgdxp+BXX1KHrmSCx3G2vNKTGy9dTWMgt+pM68bNl4TGt2TJEubPn8/TTz/NgQMH6NWrF+PHjycnJ0dx/Y4dO5g1axZ33XUXBw8eZNq0aUybNo2jR4828c6FujiSYeB4ZjE6rZoZfex05DwkN0ih+1R5buIfVJXQjokbg4+25t/xxb/8gmQ2o+/c2e6MQtEYqOF1GhCJf4ie8mITp3ZlOVwbdvfdABh+Wo45O7vW536s/2P4e/lzNP8o353+rkH229hyynN4J/kdAB7u+zCh3qF210qSROFXXwEQPGsmKjslu1WzNTv1j0Tr1fJ6kriaCDZbkKbsSOtWN+MtnDiv6Z5ER1qhIbzxxhvMnTuXOXPm0K1bN95//318fX1ZuHCh4vq33nqLCRMm8Le//Y2uXbvy3HPP0adPH955550m3rlQF1/9rjFQsK/COUlzBRxbJn/fq+aMQbPNzJrUNYCj2ZqXS2inTVN8PD+jlOzzxajVKrqIGYUNRqNVkzRGPoN7cF06Npv9o0S+fXrj068vmM0ULP6s1udu5duKB3o/AMBbB94ir8L9G7S9uvdVysxl9IzoyYyONcvBf6/i4EF5TI+3N8HTa86UBagsM3MuWX7fXYeKElpXEMGmHe6URRTclwjkWq6m+kDF3RpjXa20ClHC1FBMJhP79+9nzJgx1b+nVqsZM2aM3bnXO3fuvGI9wPjx4+2uNxqNFBcXX/ElNK0rGgMNsNMY6OQqMJVAcFtoW7OUcNelXRQZiwj1DmVg9MAajxvPnafi0CHQaAiarDxZ4NjlrGa7XuH4BorGQA2p69Bo9L5aDDkVnDuY63BtVXaz6JtvsBoMtT73zZ1vpntYd0rNpby659UG2W9j2ZGxgzWpa1Cr1Dw56EnUKsf//hV+KWc1A6+fhCZIed7rmb3ZWC02wtr4Ex7bQhouuJnmdRfTAogguHGJ7pyNwA3LPBsqUPSEDL4nlzoLjuXl5WG1WomMjLzi9yMjI8nKUi7Hy8rKqtP6l156iaCgoOqv2NjYhtm84LTlhy5RbrLSPsKP/vF2BtFXldD2nKnY/fOX878AMC5uHFp1zb+TDcvlrKb/sGFoFaYOWExWTu+Wf0ZEY6CGp/PWkjiqDQAH1qQhOZhl6T9iBPqOHbGVl1Pw+Re1PrdGreGpwU+hVqn5JfUXdmTsaLB9NySj1cgLu18A4JYut9AltIvD9Za8PIrXrgXsNwaC32Zrdh0SLUq/XUQEm0Lz4YZBjdDwRPmpIDSdBQsWYDAYqr8uXLjg6i21OF/vqaUxUEkWpMiNfeg1s8bDFZYKNqRvAJS70EqSRHHVbM2pUxT3kHIgB2O5hYBQb2K72j9DJ9Rfz1Ft0HqpyU0v4eKpQrvrVCoV4ffNA6Bg0SKsRUW1Pne3sG7c0kUur35+9/NUWiobZM8N6ZMjn5Bekk4rn1b8Nemvta4vWroUzGZ8evXCp3t3xTW5F0p+mwk7QMzWdBURbCoQ2UPQGRqga5mbERkeATwjGykIzggPD0ej0ZD9h0Yh2dnZREUp31hFRUXVab1erycwMPCKL6HpHLlo4GhGMTqNmhl92igvOvwtSDaIHQhh7Ws8vOXiFsot5cT4xdAroleNxysOJmO+eBG1ry/+o0YpvsSx6sZA0ajU4gO/xuDjr6Pr5azxgdVpDtcGjB+PvnNnbKWl5H+ifD77j+7vfT+tfFtxoeQCHxz+4Kr325DSitP4+Ig8P/RvA/6Gv85xuatkMlH4zRIAQm6teUa5StW4k3Y9I/D2rznKR2gaItj0QCIYFpzlKWdKPflcoghexfgTV9HpdPTt25cNGzZU/57NZmPDhg0MHqw8AmDw4MFXrAdYt26d3fWCa311Oat5XWIUIX4K5yQl6bcSWoWsJvxWQjuh3QTFzGjxyhUABIwdi9qn5lGSwqwyMs8aUKmgy2BRQtuYksbEolKruHiykJw0++ejVWo1EQ89CEDBF19gyau98Y+flx8LBiwA4OMjH7M+bX3DbPoqSZLE87uex2wzMyRmCOPjxtd6TdFPP2HJykIbEUHAeOX1VrONU3vk0u+uQ0VDK1fy3Ds8oV6asiNtS+EpAZ2zxLnVpuMOJcG1/Xk3xs+3aBLUcObPn89HH33E4sWLOXHiBPPmzaOsrIw5c+YAMHv2bBYsWFC9/qGHHmL16tX8+9//5uTJkzzzzDPs27eP+++/31VvQbCj1GhheXIG4KAxUNYRyDkOGj10r9mNs9hUzJaLWwDlLrSS2Uzxz3IwGjh5suJLnNwp37DH9QjDP0RUCDWmwDAfOvZvBcCBNekO1/qPGoV3YiJSRQX5H33k1POPiRtTXU77xLYnOJF/4uo23ABWp65mV+YudGod/xj4j1rPVUoWC/kfyu839K47UeuVfybPH87DWGbBL1gvSr9dTASbfyCyhu7NkzNgzY0nliWLLKTQ3Nx88828/vrrPPXUUyQlJZGcnMzq1aurmwClp6eTmZlZvX7IkCF89dVXfPjhh/Tq1YulS5eybNkyevTo4aq3INix4tAlykxWEsL9GNjOzs1yVVaz83XgU7N50Ia0DZhtZtoHtadTSKcaj5du24a1qAhNeDh+g2p2qbXZJE5dbgwkxp00jT7j4gA4dzCHouxyu+tUKhURDz0EQOHX32C20+Trj/7W/28MiRlChaWCBzY+QG654+63janYVMyre+UOuXcn3k3bQDsfqvyOYeVKzBcuoAkNJeTmm+2uq2oM1GVwFGpR+u1S4s5dqDd9ia3e1zq66XeHbI8gCIKnuP/++0lLS8NoNLJ7924GDvwtaNi8eTOLFi26Yv1NN93EqVOnMBqNHD16lIkTlecuCq5Va2MgqxmOfCd/30u5G2dVCe117a5TLqFdsRKAwInXodLWbLKXcbKQsiIjel8t8YmiIqEphLX2Jy4xDEmCg+sdZzf9hg7Bt18/JJOJvPffd+r5tWotr414jfjAeLLLs3lo00MuaRhUYanggQ0PkFeRR9uAttyZeGet10hWK/nvy+dNQ+fcoVj2DVBaWMmF4/mA+JDEHYhg00OJDGzjaS5lpM2tvNeduTxjKjoxC0KzcjTDwOGLBrkxUF87jYFSNkJZLvhFQIdrazycV5HH7qzdgBxs/pG1tIySjXIX2yB7JbS75OxQx/6RaLzELWNT6TNezm6e3JlJmcFod52c3ZTPbhYt/R6Tk92iA3WBvHvtuwTqAjmSd4SndjzlcNxKQzNZTTyy6REO5BwgwCuAf4/8N3pN7dVSxatXY0pNRRMURMgsB42BdmUhSRDTMZjgVr4NuXWhHsTfHL8jAjhBcG9Xk/V2eUB4FUT5uCC0LFWNgcb3iCJUqTEQQLI80J7Em0BTs9PmmtQ12CQbieGJiuWJpRvWI1VWoouLw1uhjNpUYeHcQbnEsvMgMTaiKcV0CCYqIQibReLwRscBpG///vgNHQoWC3nvvuf0a7QNbMubI99Eq9Lyy/lf+PDwh1e7badYbBYe3/o42y9tx0frw3tj3qt1piaAZLORfzl7G/Ln2Wj8/RTX2WwSJ7bL3ZO7DhFZTXcg7mCERuPJN/d1JbKIdoiMmyAIQp2UGS38dLCqMVCs8qKKQjgll8jW1oVWKasJYKgqoZ08WbHENuVgDhazjeBIXyLjxcibptZngpzdPPprBsYKi8O1VdlNw/LlGM+dc/o1BkQP4IlBTwDwTvI7rEtbV8/dOscm2XhmxzOsS1uHl9qL/4z6D0mtkpy6tmT9eoxnzqL29yf0ttvsrks/lk9xXiV6Xy3t+7ZqoJ0LV0MEmy2Qsx1pxaxNobloSR98KHHHn3vRkVYQlP1wMIMyk5V24X4MTghTXnRsGViN0Ko7RPWs8fDFkoscyj2EChXj42uOhrDk5VG2YwcAQZOvV3yJqi60XQZH1dohVGh48T3CCIn2w1Rp5diWDIdrfXr2xH/0aLDZyHvnnTq9zk2dbuK2rnLw9sTWJzief7zee3ZEkiRe2/saP6X8hEal4bXhrzEkZojT11adSQ25/TY0Dub9Hv1V/v+qy5BovHSaq9+4cNVEsCk0LyKTdlVccV61uZSItvSAVszaFISrZ7NJfLrtPAB/HhxnP8j7/WxNhTWrU1cDMCBqAK18a2Z3in/+BWw2vHv2RBcXV/PxvAounSkCFXQeKEpoXUGlVtFnvFz+nLzhAhaz4wRAVXaz+OdfqDx1qk6v9Wi/RxnaeiiV1kr+su4vjTKD871D7/HFiS8AeHbos1wbV/OcsT2lmzdjPH4Cta8vobNn211nyK0g7ZjcGKjH8NZXt2GhwTSPu7wG4InnNT1xz0LTEGW9nkN0XxYEocqvp3M5l1dGgF7Ljf3slNDmp8CF3aBSy+c1Ffx8/mfAQQntSrmENuh65axm1biTNp1D8A/xrstbEBpQx/6R+IfoqSg2cWqX49Em3p07EzhR/vPOfeu/dXodrVrLa8Nfo1tYN4qMRTyy+RGe2PoExabieu/99xYfW8z7h+TM5BMDn2BK+ylOXytJEnn/u5zVvGUW2pCaI36qHNuSARK07RYqGgO5ERFsCoJQZ+5YlinUX20ZbfHhhSA0jYXb5azmzf1j8dfbqdQ5vET+NWEUBNZsgHK28CxnCs+gVWsZEzemxuOm1FQqDx8GjaY6OPk9SZI4eTmw6SIaA7mURqMmaYyc3Ty4Nh2bzXHH2PD77we1mtKNG6k4fLhOrxWgC+Dz6z7n7sS7UavUrDi3ghnLZ7Arc1e9959enM7TO57m9X2vA/Bg7weZ1UV5TI89Zdt3UHn4MCpvb0LnzLG7zmKycnyH3Biox0g7HZwFlxDBpiA0EHFD3rhEBrD5Eec2BeE3p7NL2HomD7UK/jwkXnmRzQaHvpG/T1Ie/VCV1RzWehhB+qAajxtWrgLAb/BgtOE1/xvMSjFQnFuBl15DQm/RYMXVug2LQe+nxZBbUd0d2B59QgJBU+SsYfZLLyPZ6jYPXafR8VCfh1g8YTGxAbFklWUxd+1cXt7zMhUW56vpzhae5e9b/s7kZZP54cwPANzV4y7uTry7TvuRJIm89+QOuyE3/wltmJ0zzMDZ/TkYyywEhHoT18P+OqHpiWATUY7qaZrijF9zmbUpNO1ZypZ+blMQhPr79HJWc3z3KGJD7ZQApm6BojTQB0LniTUeliSpugvtxHbKjxevWAE4aAx0OavZvk8EXnrRYMXVvPQael7O1B1Yk1brPMyIhx5E7etLxcGDFC1dWq/XTGqVxNLJS7m5880AfHniS/604k/svLSTvIo8rDbl86PH8o7x0MaHmL58Oj+f/xmbZOOa1tfw+XWf83Dfh+vcaKp89x4qDhxApdMReuddDtceudwYqPvwGNRq8eG0OxHdVDycprgCa6DrAiN9iQ1jgPjMwp2IDKsgCIJnKSgz8cMB+Wb5zmHt7C/cv0j+teefQFczID2Sd4SLpRfx0fowos2IGo9XHj2KKS0Nlbc3/tfWLLG1mKyc3ZcNQJdBYkahu0gc1YaDa9PJTS/h4slCYruG2l3rFR1N+IMPkPPyK+T8+w0Crr3WYUbQHl8vX/456J+Mih3FU9ufIrU4lXvW3QOAWqUmzDuMcJ9wwn3CifCNILM0k52ZOwFQoWJM3BjmJs6la1jX+r1pIO9//wMg+MYb8Yq0n2XPSSsmJ7UYtVZFt6Ex9X49oXGIKEFwGUdZIFEy2QyIzsANqtaMvvj/WxA81le70zBabCS2DqJfnJ0GKKW5cEJu7EPfOxSXVGU1R8WOwterZjBquJzVDBg9Go2/X43Hzx/Kw1RpJSDUm5iOwXV+H0Lj8PHX0XWYHEQdWJNW6/rQ225D37UrNoOBnFdfvarXHtp6KD9M/YFpHaYR5h2GChU2yUZuRS4nCk6wNWMrP5z5gZ2ZO9GoNExOmMyyqct4Y+QbVxVolm7dSvnu3eDlRdhcx+W3VVnNDn1b4ROgq/drCo3DLe5O3n33XV577TWysrLo1asXb7/9NgMGDKj1um+++YZZs2YxdepUli1bVq/XbqkltDqDGVOQl6u30exklQUQ5Vfi6m0IgktcrAimjU9Rna5JqwgnzievcTYkCB7AZLHx2U45gLhzWLyDcSdfgc0MrftCVGKNhy02i+MSWotFHnkCBNZSQtt5UBQqUYroVpLGxHL01wwuniwkJ62YVnH2Z02qtFqin3ma1JmzMPy0nKDpN+A3aGC9XztIH8RzQ58D5J+zwspCcityyavII7c8l9yKXCRJ4vr21xMbYKeLch3YKivJelZ+vdBbb8Ur2n6WvbLUzJm9cjY+cYRoDOSOXJ7ZXLJkCfPnz+fpp5/mwIED9OrVi/Hjx5OTk+PwutTUVP7v//6Pa665pol22jLpDI7nOtXGHc+wiU6qyprjOVV3/PkTBEH4vZ+PZJJTYqRVgJ5JiXZKACXptxJaO1nNnZd2kl+ZT4g+hCExQ2o8XrZrN9a8PDTBwfgPG1bzcYORC8flGYVitqb7CQzzoWN/uZTUmeymT69eBM+Uz1xmPfMMNpOpQfahVWuJ8I2gW1g3hrcZzoxOM7i3173MS5rXIIEmQP6HH2K+cAFtZKTcYdeBEzszsZpthMf6E9nOfgAuuI7Lg8033niDuXPnMmfOHLp168b777+Pr68vCxcutHuN1Wrl1ltv5V//+hcJCQlNuFv31FKzs3aJckKP4WyzJ08qq65PgNsU7098yCII7keSJD7ZJjcGmj04Dp3Wzt+JqVuh4BzoAqD7DYpLVqTIJbITEybipalZuVTVGCjgugmovGo+fnp3NpIEUQlBBEeKGYXuqM+4OABSDuZSlF1e6/pWjzyCJiIcU2oq+R991NjbaxDG8+fJ/+hjACKfeEKx3LuKZJM4ukUuoU0c0abODYiEpuHSYNNkMrF//37GjPntkLparWbMmDHs3LnT7nXPPvssrVq14q67HHemqo0I0oTG4MoGPaI5kCAIgufYl1bIkQwDeq2aWQPa2l9Y3RjoJtD713i4xFTCxgsbAZjcfnKNx22VlZSsWwdA0PU1S2jl2ZqZAHQZLLKa7iqstT9xiWEgwcF16bWu1wQGEvn44wDkf/AhptTURt7h1ZEkiaxnn0Uym/Ebfg0B48Y6XJ9+ooDi3Ap0Plo6Dohsol0KdeXSYDMvLw+r1Upk5JU/IJGRkWRlZSles23bNj755BM+cvITGqPRSHFx8RVfguCM5lhW2hBEhqxlaswPMsS8TaGlWng5qzm9d2vC/O383VqWByfkrKS9Etq1qWsxWo20D2pPt9BuNR4v3fwrtvJytDHR+PTuXePxvAulFFwqQ6NV06GvmK3pzvqMl7ObJ3dlUmYw1ro+cOJE/IYMQTKZ5ECultEprlS86mfKd+5CpdcT9eSTtWYqj26+CEDXwdF46cSYHnfl8jLauigpKeH222/no48+IlxhELGSl156iaCgoOqv2Fi5nlxTUtmYWxUEQfAo4sMVQWhaFwrKWXNM/mDd4biTQ1+D1QQxvSG6l+KS5SnLAZjSYYriDXrxqlUABE2ciEpd89bv1OXGQO16haP3Fc0D3VlMh2CiEoKwWSQOrqk9u6lSqYh6+ilUOh1lO3ZSvOrnJthl3VlLSsh+5WUAwu/9C7pYx+c/i/MqSD0qnzHuMaJ1o+9PqD+XBpvh4eFoNBqys7Ov+P3s7GyiomqWcaSkpJCamsrkyZPRarVotVo+++wzli9fjlarJSUlpcY1CxYswGAwVH9duHCh0d6PIFQR5ayu58rmQKIxkSAItVm8IxWbBNd0DKdTpJ1/M5xoDHSh5AIHcg6gQsWkdpNqPG4tKaH0118BCJxU83GbTeLM5dmaojGQZ+h/fTwAR7dkUFpYe/JEFxdH2L1/ASD75ZexumGVX+5/3sKam4euXTtCnTgmd2xrBkgQ2zVEnDF2cy4NNnU6HX379mXDhg3Vv2ez2diwYQODBw+usb5Lly4cOXKE5OTk6q8pU6YwatQokpOTq7OWv6fX6wkMDLziqzmqz/lTncHcCDsRXOVqAtwGz2qJJk2CIAh2lRotLNkrf/h951AHWc3UbZB/FnT+0GOG4pKVKfLszUHRg4j0q3lurWTdeiSTCV379ui7dKnxeMbJQsqLTXj7eRHbPbQe70ZoarFdQ4nuEITVYmPfL7V3pgUIu/tudO3aYc3LI/uVVxp5h3VTceQohV99BUDU00+h1jmelWk2Wjm+TT5j3EOMO3F7Li+jnT9/Ph999BGLFy/mxIkTzJs3j7KyMubMmQPA7NmzWbBgAQDe3t706NHjiq/g4GACAgLo0aMHulp+OIXGoS+xNflrOtvFVBCEpnWxIrhe14lzm0JL8vXudEqMFhIi/BjRKcL+wqqsZuKNoK/5gaIkSVeU0CqpKqENnDRRscT29B65hLZD31ZoNOLfVk+gUqkYNFWexnBi2yWK82pPOKh1OqKeeQZUKgzf/0DBl1828i6dI1mtZD3zDEgSgddfj9+gQbVec3RLBpVlZgIjfIhPDGv8TQpXxeV/q9x88828/vrrPPXUUyQlJZGcnMzq1aurmwalp6eTmZnp4l0KgiA0LmfGn9T6IYvIKAuC2yupNPPe5rMA/GV4Amq1nf/2y/LhhBxI2iuhPZhzkIulF/HV+jI6dnSNxy15eZTt2gVAkEIJrdlkJeVgLgCdRDdPjxLTMYTYriHYbBJ7V5136hq/gQOIeOQRALJffInS7dsbc4tOKfzmGyqPHUPt70/k3x+rdb3FZK3uxNt3Qhxq8QGJ23OLP6H777+ftLQ0jEYju3fvZuDAgdWPbd68mUWLFtm9dtGiRSxbtqzxN9mC6QzWRntuR2fbGnP2YFN0VG3Kc5ueeEa0Oc7Y/D1xblMQBCWfbDtPYbmZhHA/ZvRxUAJY1RgoOkluDqSgKqs5Nm4svl41z60Vr14DViveiYno4uJqPJ56OA+z0UpAmDdR7YPq9X4E1xk4pT0gN3gqzCpz6pqwuXcTNHUKWK1kPDIf4znnAtXGYM7KIvc/bwEQ8fDDaCMcZPkvO779EhXFJgJCvek8SJwx9gRuEWwKDUPMDW14okOnZxKBniAI7qigzMTHW+Wb+/njOqG1l5VxojFQpaWStalrAZjaYarimt+X0Co5vUduDNSpf2StYyYE9xPZLpD4nuFIEuxZ6VzQqFKpiHr2WXySkrAVF3Nx3jysRUWNu1EFloIC0u+6G1tJCd7duxMya2at11jNNg5c7sDbZ0KcKPv2EOJPSWh0Lrnxb0HlhE2Z1RQzNlu2xv5ZE+c2hebuf5vPUmq00D0mkIk9ou0vTNsB+WfAy08+r6lg84XNlJhLiPaLpm9k3xqPmy5mUHHwIKhUBF5XM9isLDWTfnl0RKcBIkPkqQZOkRtMnd2XQ95F52641Ho9bd55G21MNKa0NC4+/AiSuemaRlpLSrhw91xMKSloo6Jo89+3UGlqn5N5YmcmZUVG/IL1dB3s4L8fwa2IYFMQGpEnlLeK7G3LIT4sEATXyTRUsHin3Dn0/8Z3tn9WE2ptDAS/ldBen3A9alXN27nin+V5ir4DBuAV2arG42cP5GCzSYTH+hMa41eHdyK4k/A2AXToK//57llxzunrtOHhxP7vf6h8fSnftYusF15AkqTG2mY1W0UFF+6dR+Xx42hCQ2m7cCFerWufk2m12jiwRv7vp/e4tmi8RAjjKcSfVAsnxp8IQuMR5byCIFT574YzmCw2BsSHMtJRB9ryAjj+k/y9nRLavIo8dlzaAcCU9rV3oVVS1YW2U3+R1fR0/a9vh0oF5w/lkZ3q/AxN786daf36a6BSUfTNEgq//KoRdwmSycTFBx+iYv9+1AEBtP3kY/QJDkb//M7p3VmU5FfiE6ij+7CYRt2n0LBEsCkIHswtM6cuLmEWAV79NVSWu77jTwShuTqfV8a3+y4C8NiEzo7PRx5YDFYjRPW02xho1blVWCUrPSN6Eh8UX+Nx49mzGE+dAi8vAseNq/F4cX4FmWcNoIKO/UUXWk8XGu1Hp4HyhwZ7ljuf3QQIGD2aVo/OByD7pZco3dY4HWolq5WMvz1G2datqHx8iP3gA7y7dnXqWpvVxv7L80R7j2mLVld7ya3gPkSw2cyIJkHuxy0DQkFwU+LcptAcvbHuNFabxOguregXH2p/odUMez6Svx80D+wEpStSVgAwJUE5q2m4nNX0HzYMTXBwjcfP7JUbA7XuFIx/iCivbw76T2qHWq0i/XgBl84U1ena0LvuImjaNLBauXjffeQvWoRka7gZ6pLNRuZTT1GyZg0qLy/avPM2vn2UP0hRcmZfDobcCrz9vOg+XGQ1PY0INoUGoS9puL+UBOe0hCDWU8ee1FdLe7+C0BIcu2RgxaFLADw6rpPjxSeWQ3EG+EVAjxmKS04VnOJU4Sm81F5MaDehxuOSJFG8sqqEtuZsTUmSfutCKxoDNRtBET50HSo3zdn1U0qdzl/KHWr/hf/IkUgmEzkvv0L6HXMwXcy46n1JkkTOK69g+P4HUKuJ+ffr+A8d6vT1NpvE/l9SAUgaG4vOu+U0gGwuRLApeCxn5zTa42yzFNFAp+Fd7Z9dSyb+vxMEz/L6mlMATO4VQ/eYWmZZ7nxP/rX/3aBV/jeqqjHQyNiRBOlrPl/lkSOYL1xA5eNDwOhRNR7Pzyil4FIZaq2K9r1rn2soeI5+E+PRaNVknjVw4URBna5V63S0+d97RD3zDCofH8r37OH81KkUff9DvRoHSZJE2Z49XLjrLgoWfwZA9AsvKJZ1O5JyIIfCrHL0vloSRziYSyu4LXHXIjhFZ7C6egtCIxCBdONr8jOkLWjsz9UQ5bpCU9ibWsCmU7lo1Crmj60lq3lhL2TsA40O+t2puMRis7DqnJy1nJwwWXFNVWOggNGjUfv61nj89G45qxmfGI7e18vZtyJ4AP8Q7+oy0+1Lz2K11K3qTKVSETLzZhKW/YhP797YysrI/Mc/uPjX+7Hk5Tn1HJIkUbplC2m33kb67D9TtmMnaDREPf0UwdOn1Wk/0u+ymj1Hx6LzEf++eSIRbApCE2jokldXlNB6wtgM0RyoeRCBoNAcSJLEa6vlrOaf+rWhXXgt40V2Xc5qJt4E/jVHlQBsubiF/Mp8Qr1DGdZmWM3XtFoxXB55olhCa5M4vbeqhFY0BmqO+k9sh7e/FwWXykhen16v59DFxRH3xedEPDofvLwo3biRc5OnUPjdd1QkJ2POyMBmMl1xjWSzUbxmLakzbuTCPX+h4sABVDodwbNm0n7NGkJmzarzPs4fziM/owwvbw09R4mspqcSHxE0Q5riCqyBnpOx8ioFs7/yY9pSFRb/xp/7JAiCc7LKAojyK3H1Nq6KCGaFprD5dC57UgvQadU8eG1Hx4sNF38bdzJont1lS08vBWBqh6l4qWtmJcv37sWam4c6KAj/YTXPxWWcKaKsyIjOR0tcjzDn34zgMbz9vRh6Ywc2LDrBvlWpdOwXSWB43e8JVRoN4XPn4j98OJce+zvGU6fIevKpK9ZogoLQtopAGxGBOSsb0zm5E67Kx4eQmTMJnXMHXq2UPzipjSRJ7Ps5FYCeI9vg7Sey8J5KZDaFJpm16ZKMkygnFARBEFyg0mzl2RXHAfjz4Diig2q52d/zIUhWiL8GohIVl2SWZrItYxsAMzoqNw+qnq05bhwqna7G41WzNdv3iUDrJcZHNFedB0bRulMwFrONLd+crteZyyrenTsT/923hN9/P949e6KNiQYvOfCzGgwYz5ylbMdOTOfOoQ4IIGzevXTYuIHIvz9W70AT4PSebHLTS9Dq1PQaE1vv5xFcTwSbgtBEGqr0tSV0oRUaT1OVQ4tZm42voKCAW2+9lcDAQIKDg7nrrrsoLXX8yd7IkSNRqVRXfN17771NtOOW460NZzifV0arAD33j64lq2kqg/2L5O8H3Wd32Q9nf0BCYmDUQOIC42o8bjMaKV6zFlAuobWYraQcyAVEF9rmTqVSMeKWzqg1KtKO5lf/udeXWqcj4v6/0u7bJXTcuJEuhw/RcecO2i3/idhPPib65ZeIfvFFOmzcQKuHHkIbEnJVr2csN7N96RkA+l4Xj49/zQ9OBM8hUj+C0ISaQwmiQyKbLDSQtIpw4nyca0hR1+dtLm699VYyMzNZt24dZrOZOXPmcM899/DVV185vG7u3Lk8++yz1f/bV6GJjFB/RzMMfLhFLid8bloPgnxqKf879DVUGiCkHXQar7jEYrPww5kfALix042Ka0o3bcZWXIw2OhrfAf1rPJ52NB9ThQW/YD2tOwY7/4YEjxQS5Uef8XHs+zmVbd+epm230AZrsKNSqdCGhMhBZadaGl/Vw66fzlFRYiYkypfeY9s2+PMLTUtkNgXBCQ3ZtfVqMpMNmdV09060dZ052VyaA7l61qY7/Vw0p8CwoZ04cYLVq1fz8ccfM3DgQIYNG8bbb7/NN998w6VLlxxe6+vrS1RUVPVXYGBgE+26+bNYbfz9+8NYbRKTEqMZ372WDKLNBrvel78fNA/UyqWt2zK2kVOeQ4g+hNFtRyuuMSxbBkDQ5Mmo1DVv785cnq3ZsX8kKrWY6dsS9J0QR2CED2UGE7uXn3P1dpySnVrM0S3yfM/hszqj0YpQxdOJP0GhRfOEDqvNTUucE9lcAmFP15yC1507dxIcHEy/fv2qf2/MmDGo1Wp2797t8Novv/yS8PBwevTowYIFCygvL7e71mg0UlxcfMWXYN9HW89z7FIxQT5ePDOle+0XnF0P+WdAHwhJt9hd9t3p7wC5MZBOU7Ok0JKfT+nWrQAETZta43FjhYXUI/kAdOovutC2FFqdhhGz5Mzjkc0XyUlz7/9+bTaJX786BRJ0GhhJm85XV44ruIeWd9fnYqrCElSFjV9GqSmuaPTX+CN9Sd3mObVk9clQuvKspgjK3UtLCtibU4DYkLKysmj1h+YbWq2W0NBQsrKy7F53yy238MUXX7Bp0yYWLFjA559/zm233WZ3/UsvvURQUFD1V2ysaNRhz/m8Mv6z/jQA/5zUlYgAJ/7erBp30mc26JX/jne6MZDVinfPnugTEmruLTkXq8VGSJQv4bF22r8LzVLbbmF07NcKSYJfvzqFzea+Hf6P/ppBbnoJel8tQ2fUctZZ8Bgt546liVUFlX/88mQ6g9XVW6jBk2+66xI8iqZAgnB1PCVoffzxx2s08Pnj18mTJ+v9/Pfccw/jx48nMTGRW2+9lc8++4wff/yRlJQUxfULFizAYDBUf124cKHer92c2WwSj39/GKPFxjUdw7mxrxMzAXNOwLlNoFLDgLl2l/149kdsko3+Uf2JD4pXXFNUVUI7dYri42f2/q6EViVKaFuaoTd1ROejJSethKO/Zrh6O4rKDEZ2/yT/PTRoagK+gaIpUHMhunm4iKqwBCnEfQIIncGMKUjMMHJHItCsnShT/YNSLfhbXL0LLlYE08an6Kqfp7GaBbmjRx99lDvuuMPhmoSEBKKiosjJybni9y0WCwUFBURFOd9pdODAgQCcPXuW9u3b13hcr9ej14vKhtp8vTed3ecL8PHS8OL0ROcCul3/k3/tMglC4hWX/L4x0E2dblJcU3nqNMbjJ8DLi8CJE2s8Xl5s4sLJQgA69hMltC2RX5CewdMS+PXr0+z6KYX2vSPwC3av/663Lz2LqdJKq7gAul3T2tXbERqQCDYbgbMZTHcLOIWmV1t3WhFoCu7IFV2Vrybg9JSsJkBERAQRERG1rhs8eDBFRUXs37+fvn37ArBx40ZsNlt1AOmM5ORkAKKjo+u1XwGyDJW8/LOcbf6/8Z2JDXWiu29ZPhxeIn8/6K92l23P2E52eTbB+mCubXut4hrD8p8ACBg5QnHkRMqBHCSbRKu4AIIjRefhlqrbNa05sTOLnNRiNn91ion3JrpNo6gLxws4szcblQpG3toFtZvsS2gYnluD6KbqWirr6aW1bs8DRnEoBZRZZQGNGmi6U8dRQRDqrmvXrkyYMIG5c+eyZ88etm/fzv3338/MmTOJiYkBICMjgy5durBnzx4AUlJSeO6559i/fz+pqaksX76c2bNnM3z4cHr27OnKt+OxJEnin8uOUGK0kBQbzB1D4p27cPf7YKmE6CRoO8jusqWnlwIwtb1yYyDJYqF4+QoAgqbWbAwEV5bQCi2XWq1i5K2dUWtVpB7OY/cK9+hOazFb+fWbUwD0GNmGiLbiQ/bmRgSbDUgEjvXnqAzS1aMgqjRVgOax2cwGDOzd5c+8IYlS36tXnwylJ2U16+rLL7+kS5cuXHvttUycOJFhw4bx4YcfVj9uNps5depUdbdZnU7H+vXrGTduHF26dOHRRx9lxowZrFixwlVvweOtPJzJ+hM5eGlUvHpjTzTOZGRKc2Hnu/L318wHOyW3WWVZbMnYAsCMTsqNgcp27sKSm4smOBj/4cNrPF6cX0FmigFU0KGvCDZbuojYAEbd2gWA/b+kcWq3/WZiTeXg2nQMORX4BuoYOKVmcyvB87l/2sdDXE2g2VjltJriCqyB7pPB8ioFs2iCp8hjA0yhUWhLVVj8G69jYEWpHh9/Y6M9v9A0QkND+eqrr+w+Hh8fjyT99nMUGxvLr7/+2hRbaxFOZZXwxA9HAPjrqA50inTy7/Ftb4C5TM5qdlVu6AO/NQbqF9mPdkHtFNcYfpJLaAMnTUKlq5n5PLtPPtfbumMw/iHudUZPcI0ug6MpzCrjwJp0Nn1+kqAIH6ISglyyl/yMUvb/kgbA0Js6oPcRYUlzJDKbbkJkRQV3JcaeCO6kOWcqBc+RZajkjk/3UGK0MCA+lPtGdnDuwqILsPdj+ftrn7Kb1bTarNWNgW7sdKPymtJSStavB5RnawKcFiW0goJBU9sT3zMcq8XGz+8foaSgssn3YMitYPl/k7FabMR2DRHNq5oxEWw2gIYKFEXA6RoimGo6jTGqRpSnNryGLBm/WBHcYM9VxdmAUwSm7uvzXWkczTC4ehv1Umq0MGfRXjINlbSP8OPD2X3RaZ38u+3Xl8FqgvhroP1ou8u2X9pOVlkWQfogxsSNUVxTsmYNUmUluoQEvHv0qPF4waUy8i+WolaraN+7lcIzCC2VSq1i7J3dCGvtT0WxiVXvHcZU2XQdzEsLjSx/6yDlBhOhMX6Mu7uHGMnTjIlg8yo1pwBRZzA7scb9Zm0KQkvkyTNmG1paRbjil+CeNp3K4cllR7np/Z2sPprp6u3Uidlq474vD3Ais5hwfz2L5gwg2NfJeYB5ZyD5ctmzg6wm/NYYaEr7Keg1yh+IGpbJJbRB06Yp3qif2SdnNdt2D8XbX4w2E66k89Yy8b5EfAK8yL9YyvpPjyPZGu/4RpWKUhPL3zpIcV4lgRE+THkoCW8/8fPZnIm7lavQGIGmpwev+hKbq7cg1EJ0ohWaAxFUeq6+cSEM7xRBhdnKvV8c4N1NZ684W+quJEninz8eZcvpXHy8NCy8o59zY06qbHweJBt0ug5iB9hdll2WzZaLcmOgGzsql9CaLl6kfO9eUKkImny94l5FCa1Qm8AwH667tydqrYrzh/LYvbxxO9QaKyys+O8hCrPK8Q/RM/WhJPyCRHVZcyeCTTfk6QFncyYCNeFqiJJfQYBAby8W/rlf9ZiQ19ac4tHvDmG0uHflzDsbz7Jk3wXUKnjnlt70bBPs/MWXkuH4MkAF1z7pcOmSU0uwSlb6RvYlIVi5O6dh+XIAfAcNxEthRmpOWgnFuRVovdTE9xQfyAj2RbcPYvRtlzvUrk7j5K7GqTYwm6ysevcQuekl+AR4MeWhJALDxT1VSyCCzXrylIBQU1zh6i00OlFOKAgKGnnGrOigLFwNrUbNM1O689zU7mjUKn44kMGtH+0mv9Q9uyR/v/8i/153GoBnp/bg2q51zBZufE7+NfEmiOxud1mlpZLvTn8HwG1db1NcI0lSdRfa4GnTFNec2SNnNdv1CkfnLTp8Co51HhRNn/FxAGxYdIKt357GYmq4D3+sFhurPzhK5lkDOh8tkx9IIiTKr8GeX3Bv4i5dEISrJ2ZsCg40RpMgoXm4fXA8i+b0J8Bby760Qqa9t53T2e71Ye72s3n8/fvDANw7oj23DYqr2xOkboez60GthVELHC5deW4lRcYiWvu3ZlTsKMU1FQeTMaelo/L1JWBMzeZBNpvEmf2ihFaom0FTE0gc0RqAwxsv8u2Le8lOLb7q57XZJNZ/epz0Y/lodWqu/2tPItqKDytbEhFs1kNTZDU9JXPqERo5wyMIjUEE3UJLcU3HCH68bwhxYb5cKKhgxns72HQqx9XbAuC7fRe4a/FeLDaJyb1ieGx857o9gSTBhn/J3/eZDaH2h9ZLksQXx78A4JYut6BRaxTXVc/WHDsWtV/N7NClM0WUG0zofbW07RZWt/0KLZZKrWL4rM5M+mtPfIN0FGaV8/2r+9mz4hxWa937cVitNk7uyuSbZ3dzdn8Oaq2K6+5NJLpDcMNvXnBrItgUPIK48XYNdx8LI85ACkLz0KFVAMvuG8qAdqGUGC3M+XQvjy09REGZySX7KTNamP9tMn9bephKs42RnSN4/aaeqNV1/Lfo9Bq4sBu0PjD8MYdLd2buJMWQgq/Wl+kdpyuusRmNFP/yCwBB06cprjlzuTFQ+94RaLzEbZ5QN/GJ4cx6ciAd+rZCsknsXZXK96/spyCzzKnrLWYrR7dk8OVTu9iw6ASFWeXofbWMv7uH+PCjhRIpnzoSGcer41UKZv+6P9bYKkr1+Pg7d1aoqMyHYD/PPAvrygZH4myte6nLz7wgNIUQPx1f3DWQZ1ce44td6Xy77yJrj2fz+IQu/KlfbN0DvXo6lVXCfV/uJyW3DLUKHh3XmXkj2tf99W22385qDrwHAms28vm9z49/DsD0jtMJ0CmXGZasXYutuBhtTDS+A2p2tLVabKQckLPCooRWqC9vfy/Gz+1BQlI2v359itz0Er59YS+9x7UlNMYPvyAdvoF6fIN01WeCzUYrx7ZmcHBdOuUG+UMinwAvksa0pcfw1uh8RMjRUok/eTemKixBCmnaunadwYwpSMw7EoTG4soPVerCkz9UETyXTqvm+WmJTO/dmn/8eJSTWSU8/sMRluy7wPPTetA9JqjRXluSJL7dd4Gnlx+j0mwjMlDPf2f2ZmBCPbMxx36A7KOgD4ShDztces5wjm0Z21Ch4pYut9hdV7hkCQDBN96ISl3zA7z04wUYyy34BumI6RRSv30LwmUd+0cS3SGYTZ+fIP14Aft+Tq2xxkuvwTdIR2WZGWOZBQD/ED29x7Wl69AYvHTK5eBCyyGCzRZAU1yBNVC0lxYEQRA8Q9+4UFY+MIzFO9N4Y+0pDqYXMfntbfx5SDzzx3YiwLthPxQtM1r457Kj/HgwA4DhnSJ480+9CPOv51GCiiJY8w/5+yEPgm+ow+VfnfgKgBGxI2gb2FZxjTElhYp9+0GtJnjGDMU1VSW0HftGNlkmWGje/EP0XP9AL07tyiLtaD5lBiPlBhNlxSYsRitmoxVDjvzBZGCED33Hx9F5UBQarahmEmQi2KwDUUIr0xmsmILEJ1VC81d1JtRdM5HaUjUW/7o3bnCFixXBtPEpcvU2BA+i1ai5a1g7JiVG8/yq46w8nMmn21NZcegS1/eM4boeUfSLD0VzFUHVxcJyfkq+xDd707lQUHF1ZbO/t+5JKM2CsA4w5AGHSw1GA8tT5LmZs7vNtruu6Ft5JIr/yJF4RdYskTVVWjifnAuIElqhYalUKroMjqbL4CtLwU2VFjnwNBiRJIjpEIRaI4JM4Uoi2HRzriilvVr6EhvGAPGXjVB37tIISjQeck5WWQBRfuJDOKFxRQV5884tfbi5fy5P/XSM83llLNqRyqIdqYT76xnfPZKJidEMbBeK1okb3YIyE6uOZPLTwQz2pRX+9jqB3vx3Vm8GtHOchazV+S1w4DP5+8n/BS9vh8uXnl5KhaWCziGd6RfZT3GNzWjEsGwZACE3/0lxTcqBXCxmG8GRvrSK96z7BsEz6by16Ly1BEf6unorghsTwaaTRFZT+D1PPM/WaM2BXDRapqEDQkfP5ynnLAWhObumYwRrHh7O1jO5/Hwki3XHs8grNfLl7nS+3J1OiK8Xo7q0IsJfj95Lg16rRq9V4335e4tNYt3xbLaczsVikwBQqWBQuzCmJsUwqWf01Zfnmsph+YPy9/3ugvihDpebbWa+Pvk1ALd1uw2VSvkDt5K167AaDGijo/EbNkxxzaldmQB0HhRl93kEQRCamgg2hWbBk8oJPYW7jz1pSO6aydSWqrD4S/V/glIt+FsabkOC4GI6rZpru0ZybddITJZEdp7LZ/XRTNYcy6agzMQPBzKcep7uMYFMS2rN9b2iiQ5qwA/iNr8IhechsDWMeabW5RvSNpBdnk2odyjXtbvO7rqib78FIPjGGag0NY+xFOdXkHG6CFTQeWBUfXcvCILQ4ESw6QRXZzU9sZRWEJobkd0UBPei06oZ0SmCEZ0ieG6qjT2pBew+V0C5yYLRYqPSbL3iV7PVRp+2IUxNiqFDq0b4NzXjAOx8V/5+0hvgHVjrJZ+fkMedzOw8E71G+QM+47nzlO/d67Ax0OndcmOg1p1CCAh1XLYrCILQlESw2UK0+I60IsPjUu48Y9MVWU0RuApCw9Jq1AxpH86Q9uGu2YDVDMsfAMkGPW6EzhNqveRQ7iEO5x7GS+3FTZ1vsruu6LvLjYFGjMArqmbWUpIkTu3OAqDLIJHVFATBvbjvHaDgMjqD2dVbcImWVDYq1I+7ltt6iosVwa7egiA0ju3/kWdq+oTCda84dckXx78AYGK7iYT7KAfJNpMJw48/AhD8J+WANPt8MUXZ5Wh1ahJ6R9R974IgCI1IBJu1cHUJbRV32YcruUun0iqN1nCnEXjSXoWmIz5gEYQGkHsafn1V/n7Cy+BXe3Y1qyyLdWnrALkxkD0l69ZhLSpCGxWF/zXXKK45tUvOaib0jkDnLQrWBEFwLyLYFJpcbV0/hZapLh8mNNTPSX2epyX9jIoPKQShFjabXD5rNUGHsdBTeSzJH3169FOskpX+Uf3pEtrF7rqq2ZrBM2ag0tYMJK1mG2f2yec1uwyKrvG4IAiCq4lg0wGRTRQEQXAsq0w0LxNasL0fw4VdoPOH69+UZ6nUIqM0g29Py91l7+l5j911xvPnKd+9+3JjoBsU16QeycNYbsEvWE/rziH1ew+CIAiNSASbHsSdgl+dwerqLQiNyOnyShfN2GwIV5OhdKfspjs3XxKEZi11G6z9h/z9mGcgONapy95Lfg+LzcLA6IEMih5kd13R0qUA+F9zDV4xMYprTl4uoe08MBK12r2OmgiCIIAINlsUTXFFk72WvkTMvHQXohTSPblTwCoIQh3lnoJvbpHLZ7tOgX53OXVZSlEKK8+tBOCh3g/ZXWczmTD8cLkx0M3KpbkVJSbSj+YD0HmgKKEVBME9iWDTDnfKIgruSwRyLVdTBYvu1hjraomOtAIA6/8Fp9e6ehf1U5INX9wIlQaIHQg3fAhq526n3jn4DjbJxujY0SRGJNpdV7p+PdbCQrStWuE/fLjimtN7s7HZJFrFBRAa41evtyIIgtDYRLDpYUQQ3LhEd86G545lnq5sMNTkPLjUWWimji+HbW/A1zfDrv+BJLl6R84zlsJXN4EhHULbw8yvwcu5Dx2P5h1lffp6VKh4oPcDDtcWVjUGulG5MRD81oW2s5itKQiCG3O/u0BBqCd3DGqEhucRAZ4gCPZ1mgC9bwPJBqsfh5WPgNUD5jtbLbD0Tsg8BL7hcNtS8Atz+vK3DrwFwOT2k+kQ0sHuOlNqKuW7doFKRfCNNyquyb9USm56CWq1io79I+v2PgRBEJqQuDtXILKHoDN4wD/8dSUyPAIiWBUEl9PqYMo7MPY5QAX7P4UvZkBFoat3Zp8kwS9/gzNrQOsDtyyB0ASnL9+duZtdmbvQqrXM6zXP4dqCL74EwH/4cLuNgaqymnGJYfj465zehyAIQlMTwaYHEsGw4CxPOVPqyecSRfAqxp8I9aBSwdAHYeZX4OUH53+Fj8dAfoqrd6Zs+39g30JABTM+hjb9nL5UkiT+e+C/ANzY8UbaBLSxu9ZaXEzRDz8AEPrn2YprbDaJ07tFCa0gCJ5BBJstTFN2pG0pPCWgc5Y4t9p03CFQre3PuzF+vkWTIKFal4lw1xoIbAP5Z+Gj0XB+q6t3daXD38H6Z+Tvr3sFul5fp8s3XdjE4bzD+Gh9+EuvvzhcW7T0e6TycvQdO+A7eLDimoyThZQZTOh9tcT3CK/TXgRBEJqaCDb/QGQN3ZsnZ8CaHQ8sS3aH4E4QhD+ISoS5G6F1P6gsgs+nyVlEVzcOsllh00vww1z5fw++HwY6Dhb/yGqz8vbBtwG4teuthPvYDw4li4XCL74AIGT2bFQq5X/vTu7OBKBj/0g0XuI2ThAE9yb+lhLqTWew1vtaRzf9IiAQBEFoYQIi4Y6V0GMG2Cxy06DPpkLeGdfspyRbfv1fXwYk6HvH5TOmdfPz+Z85W3SWAF0Ad3S/w/FLbtiI+dIlNCEhBE2erLjGVGHh3MFcQJTQCoLgGUSw6aFEBrbxNJcy0uZW3uvOXP0BiejELDQLXj4w4xMY8wxo9PI5dBHL2wAAMb9JREFUzvcGw4bnwFTedPtI2QTvD4XUrfJ50ukfwuS3nJ6lWcVsNfNu8rsA3NnjToL0QQ7XF3z2GQDBM29G7e2tuOb03mwsJhshUb5ExgfWaT+CIAiuIO5QfkcEcILg3q4mqHN1QHg1RPm40GKoVDDsEfjrLugwFmxm2Po6vDcQTq9p3Ne2WWHjC/D5dCjLhVbd4Z7N0Ovmej3d0jNLySjNIMw7jFu63OJwbcWRo1Ts3w9eXoTMnKW4RpIkjm3NAKD7Na3tltkKgiC4ExFsCo1GX2Jz9RaajMgiKhMZN0EQ6iU0AW79Dm7+AgJbQ1E6fPUn+OZWKLrQ8K9XnAmLp8CWVwEJ+vwZ5m6AiE71erqc8hzePiCf1fxLr7/g6+XrcH1VVjPwugl4RbZSXJObXkLehVI0WjWdB4oSWkEQPIO4E2yBnO1I2yxnbQotkidnNRuEGzZzEh1pr84LL7zAkCFD8PX1JTg42KlrJEniqaeeIjo6Gh8fH8aMGcOZMy46E+kMlQq6Toa/7oGhD4FaCydXwtt94etb4NA3UFFU/+e32SBtB6x4WM6cpm0DnT/c8DFM+a9c1lsPkiTx/K7nKTGX0COsB3/q9CeH683ZORT/8gsAobP/bHfdsa2XAGjfJwJvf6967U0QBKGpiWBTaFZqzaS54U23O3HFedXmUiLa0gNaMWuzaZlMJm666SbmzZvn9DWvvvoq//3vf3n//ffZvXs3fn5+jB8/nsrKykbcaQPQ+8PYZ+HebRA3DKxGOLUKfvwLvNYBvrgRDnwO5QXOPV/2MVj3NLzVEz69DvZ/CpUGiEyUy2Z73nRV212btpZNFzahVWl5ZsgzaNQah+sLv/4KLBZ8+vbFp0d3xTWmSgun92YD0P2amKvanyAIQlMSd96XeeJ5TVVhCVKIuMETahJlvZ7DqxTM/q7eheBp/vWvfwGwaNEip9ZLksR//vMf/vnPfzJ16lQAPvvsMyIjI1m2bBkzZ85srK02nFZd5Y61Ocfh+E9wfDnknoCz6+SvFQ9B28HgHwFaH9DqQesNXt7yr1YznPpZvr6KPhC6ToHEG6HdcKglMKyNwWjgxd0vAnBX4l10Du3scL2tspKib5YAEPrn2XbXndmbjcVoJTjSl+gOwVe1R0EQhKYkgk1BEOpOZIiblYpSPT7+RruPF5X5EOznXPm94J7Onz9PVlYWY8aMqf69oKAgBg4cyM6dO+0Gm0ajEaPxt5+N4uLiRt+rQyoVRHaXv0Y9Abmn4cTlwDPrsFwKWxuNDjqOg8SboNP4epfLKnlt72sUVBaQEJTAPT3vqXW9YflyrEVFeLVuTcC119pdV1VC2/2aGNEYSBAEjyLuGAWhgYgb8sbV0stUm6OLFcG08Sly9TZahKysLAAiIyOv+P3IyMjqx5S89NJL1VlUtxTRCSL+BsP/BgXnIW07mMrAUgnmSvnXqi+rCdoMgG5TwCekwbey49IOfkr5CRUq/jXkX+g0OofrJUmqbgwUctttqDTKWdWctGJy00tQa1VitqYgCB5HBJt4ZgltS6YtVWHxlxr1NWrL9AieoymDVFESK1yNxx9/nFdeecXhmhMnTtClS5cm2hEsWLCA+fPnV//v4uJiYmNjm+z16yS0nfzlAuXmcp7d+SwAs7rMIqlVUq3XlO3YgelsCmpfX4JvnGF33fFtlxsD9W6Fj7/jAFYQBMHdiGDTw7n63KbOYMUUdHVnXISGJc5rCoJnevTRR7njjjscrklISKjXc0dFyRmx7OxsoqOjq38/OzubpKQku9fp9Xr0+qZvHOZp3j74NhmlGUT7RfNQn4ecuqZg8WIAgmbMQBOg/O+4qdLC6T2XGwMNE42BBEHwPCLYFFzGURbIUzNEopT2N2LGZsOqLaOvLVVj8W85s22bo4iICCIiIhrludu1a0dUVBQbNmyoDi6Li4vZvXt3nTraCjUdzj3Mlye+BOCpwU/VOlMTwHjuHGVbtoJKRehtt9pdd2ZvNmajlaBWPsR0Cm6oLQuCIDSZFn832FJLaJ2dtSkIguCs+ow/EfM26yc9PZ3k5GTS09OxWq0kJyeTnJxMaelvdeNdunThxx9/BEClUvHwww/z/PPPs3z5co4cOcLs2bOJiYlh2rRpLnoXns9sNfP0jqeRkJicMJlhrYc5dV3e++8D4D9qFLq4OLvrqkpouw9rLRoDCYLgkdwi2Hz33XeJj4/H29ubgQMHsmfPHrtrP/roI6655hpCQkIICQlhzJgxDtcLV0dnMF/V9foSN8y0iE6qilwxY7OxiaZCQnP11FNP0bt3b55++mlKS0vp3bs3vXv3Zt++fdVrTp06hcFgqP7fjz32GA888AD33HMP/fv3p7S0lNWrV+Pt7e2Kt9AsfHjkQ84WnSXUO5TH+j/m1DWVp09TvGIlAOEOssq56SXkpMmNgboMEY2BBEHwTC4PNpcsWcL8+fN5+umnOXDgAL169WL8+PHk5OQort+8eTOzZs1i06ZN7Ny5k9jYWMaNG0dGRkYT79x9tNTsrD2ifNNzaEud+6Tek4LG+uy1Sd6f+JClWVm0aBGSJNX4GjlyZPUaSZKuOAOqUql49tlnycrKorKykvXr19OpU6em33wzsSZ1De8fkjOUjw94nGDvYKeuy33rvyBJBIwbh09iD7vrjlU1BkqKEI2BBEHwWC6/K3/jjTeYO3cuc+bMoVu3brz//vv4+vqycOFCxfVffvkl9913H0lJSXTp0oWPP/4Ym83Ghg0b6vzaIkgTGoMrG/SI5kCCIHgCSWrcjuKN7UD2AZ7Y+gQAt3S5hQnxE5y6riI5mdING0CtJuKhB+2ukxsDySNpul3T+uo3LAiC4CIuDTZNJhP79++/Ysi0Wq1mzJgx7Ny506nnKC8vx2w2Exoaqvi40WikuLj4ii9BcEZzLCttECJD1iI15gcZ4txmy2KTbDy+9XG+O/2dq7dSL+cN53lw04OYbCZGx47msf6POX2eMuc/bwEQNG0a+vbt7a47uz8Hc6WVoAgfWovGQIIgeDCXBpt5eXlYrdY6D5n+vb///e/ExMRcEbD+3ksvvURQUFD1V9V8MFWRyGoKgiBUER+uCE1lbdpafj7/M8/ufJZX976K1WZ19Zacll+Rz7z18zAYDfQM78nLw19Go3Zu/FfZjh2U79qFysuLiL/e53Dtsa1yCW23a2JEYyBBEDyay8tor8bLL7/MN998w48//mi3wcGCBQswGAzVXxcuXGjiXQotkShndT1XnvP0pDOmgtDUxseN569JfwXg8+Of8+CmBykzl7l4V7WrsFTwwMYHyCjNoI1/G/47+r/4aJ37u16SJHLe/A8AwTNn4tXafmlsbnoJOanFqDUqugyKtrtOEATBE7g02AwPD0ej0ZCdnX3F72dnZ1cPoLbn9ddf5+WXX2bt2rX07NnT7jq9Xk9gYOAVX81Rfc6fivEnzcvVBLgNndUSTZoEQbBHpVJxb697eW3Ea+g1erZc3MLtv9zOpdJLrt6aXVablb9v+TtH8o4QpA/if2P+R5hPmNPXl6xbR+WRI6h8fQn/yz0O1x7eKH8o3r53BL6BojGQIAiezaV3hDqdjr59+17R3Keq2c/gwYPtXvfqq6/y3HPPsXr1avr169cUWxUc0BmavgTK2S6mgiA0rfrM2gRxbrMlmhA/gU/Hf0q4TzhnCs8wa9UsknOSXb2tGiRJ4pW9r7DpwiZ0ah1vj36b+KB456+3WuUOtEDo7NvRhofbXVtmMHJ6n/wBfM9rY69q34IgCO7A5emH+fPn89FHH7F48WJOnDjBvHnzKCsrY86cOQDMnj2bBQsWVK9/5ZVXePLJJ1m4cCHx8fFkZWWRlZV1xSBrQRAET+NM6W1tH7KIjLLgaRIjEvl60td0Ce1CQWUBd625i5XnVrp6W1dYdGwRX5/8GhUqXrrmJXq36l2n6w3LV2BKSUEdFETYnXc6XHtsSwY2i0RUQiBR7YKuZtuCIAhuweV3JjfffDOvv/46Tz31FElJSSQnJ7N69erqpkHp6elkZmZWr//f//6HyWTixhtvJDo6uvrr9ddfd9VbaPZ0BnOjPbejG+xGPffWBB1Vm/LcpieeEW2OMzZ/z1P3LQhNLcovisUTFjM6djQmm4kFWxfw1oG3MFsb798eZ5Sby/nntn/yxv43AHi036OMix9Xp+ewmUzkvf02AOFz70bj4CiPxWzl6BZ5ZnjP0SKrKQhC8+AWMwzuv/9+7r//fsXHNm/efMX/Tk1NbfwNeShVYQlSSP1K2ARlFaV6fPyNrt6GUEci0BMEz+Lr5cubo97krQNvsfDoQj4+8jEb0zfyz0H/pH9U/ybfT0pRCo9ufpQUQwpqlZr7k+5ndrfZdX6eoiXfYr50CW1EBCG33upw7Zm92VSUmPEP0dO+d0R9ty4IguBWXJ7ZFJo/fYmtyV+zJZUTNmlWU8zYbNEa+2dNnNts2dQqNY/0fYRXh79KqHco5wznuHPNnSzYuoC8irwm28eys8uYuXImKYYUInwi+Hjcx8ztObfOI0hs5eXkvf8+AOH3zUPtY/+/H0mSOLThIgCJI9ug1rScf8MEQWjexN9mgtCIPKG8VcxXbEHEhwWCB7iu3XUsn7acmzvfjAoVK8+tZMqPU/jm5DeNOpOz3FzOP7b9gye3P0mltZLB0YP5bvJ39c6s5i9ahDU/H6/YWIJnzHC4NuN0EfkZpWh1aroNi6nX6wmCILgjEWy2cGL8iSA0HlHOKwj1E6QP4p+D/slXk76iW1g3SswlvLD7BW75+RaO5h1t8Ner6oa7PGU5apWaB3s/yPtj36/TeJPfM547T/77HwAQ8dBDqHSOR5gc2iCPO+kyOBpvP696vaYgCII7Eh9zC4IHc8fMqatLmEWAV38NdUY5qyyAKL+6z/4VhD/qEd6DryZ+xXenv+O/B/7L8fzjzFo1i84hnRkbN5axcWNJCE6o13OXmcvYmL6RVedXsevSLqySlVY+rXhl+Cv0i6r/WDXJZiPzqSeRTCb8hg4lcNJEh+uLcspJPSKXCfcc1aberysIguCORLDZzIgmQe6nqMyHYD+RQRYEZ1ysCKaNT5GrtyG4EY1aw8wuMxkTN4Y397/JqnOrOFV4ilOFp3gn+R3aB7VnTNwYxsaNpVNIJ4dnK81WM9svbefncz+z6cImKq2V1Y+NaDOCZ4c+S6h36FXtt2jJEir27Ufl60vUv/5V61nPw5suggRxPcIIifK7qtcWBEFwNyLYFBqEzmDFFKRx9TZaFHfMaja0lpal9CoFs7+rdyEI7incJ5wXhr3A3/r9jU0XNrEubR07M3eSYkgh5XAKHxz+gBi/GMJ9wtFpdOg1+t++tHosNgtbM7ZiMBqqnzMuMI5J7SZxXbvriA+Kv+o9mrOyyHn93wC0evhhdG1aO1xvLDdzYoc83q3XtWLciSAIzY8INgWPpS1VYfGX6v8EpVrwt9S6TIw/aXjOztgUarrqn3tB8HDB3sFM7zid6R2nU2wq5tcLv7I+bT3bL23nUtklLpVdcnh9uE84E+InMClhEt3Dute5y6w9kiSR9cy/sJWV4dOrFyG33lLrNSd2ZGIxWgmN8aNNl5AG2YcgCII7EcGm4BSdwYwpSDQtaG5EJ9rG19TZSm2pGot/048b8jRizErzEKgLZHL7yUxuP5lyczlH8o5Qbi7HaDNitBgxWn/7MtvM9IroxYCoAWjVDX/7U/zzz5Ru3ozKy4voF55HpXFc7WOz2ji8UR530mt0bIMFvYIgCO5EBJuC0AQa+tymS0poPWBsRksru22uxLlNoT58vXwZGD3QJa9tKSwk+/kXAAi79y/oO3So9Zrzh/IoKajE29+LTgMiG3uLgiAILiFGnzRDqkLP6gLpKEAQwYMguJfmcFZYZDWFhpb90ktYCwvRd+xI+Ny5Tl1zaKM87qTH8NZodaLngSAIzZMINoUmmbWpL2n6sj5Xj+AQBEEQmr/SrVspXr4C1Gq5fLaWmZoAOWnFZJ41oNao6DHccRMhQRAETybuxgWhiTRURqg5ZJYEF2qicuisMjGCSWj+rKVlZD79NACht9+OT8+eTl23d+V5ADr0a4VfsDg7LwhC8yWCTUFoQs09UBTZZKGhNFapqyihFRpSzmuvYbmUiVebNkQ89KBT12SeLSL1SD4qtYr+E9s18g4FQRBcS9wZCoITGrJr69UEnA0ZrLp7J9q6ntdtLud7Xf0+3OnnQgSGgjsr+OwzipYsASD62X+h9vWt9RpJktj10zkAug6OIjiy9msEQRA8mQg2hZbNAzqsNjctccamqwNIQSaCV/chmc2u3sJVKV63juyXXgag1f89it+QIU5dd+F4AZfOFKHRquk3SWQ1BUFo/kSw2cRsBYXYCgob/XVc0ZFWZ7A2+Wt6qvpkKF1agiuCcrfSkgJ2ESA2P5LJRPqdd5Hz5n+QbJ43E7YiOZlL//c3kCSCZ80k9K67nLpOsv2W1ewxojUBod6NuU1BEAS3IO4gG0lTBJRNTWcwYwrycvU2rqAtVWHxl1y9jXqpy+zN5n7WUxAamwha3UfJ5s2U791L+d69mM6fJ+bll5wqQXUHprQ0Lsy7D8loxH/ECKL+8Q9UKuc+/Ek5mEtuegleeg19J8Q18k4FQRDcg8hsuoi7BaNNMf5EqB8RaNZOlKleyV0aNTVUR1oRKDYvgePGEf3SS+DlRcnataTddjvm7GxXb6tWlsJC0u+5B2thId7du9P6jX+j0jr3mb3NamP3cjmrmTQmFp+A2sejCIIgNAfucUfSzDgbSLpbwCk0vdoCSRFoCu7IFT+XVxNwimDV/QRPn0bcpwvRBAdTefw4qTf9iYpjx1y9LbtslZVcnHcf5rR0vGJiiH3/f6j9/Jy+/uSuLIqyy/H28yJpTNtG3KkgCIJ7EcFmA6trACkCzsblLhkeR5Ru3IvKfBr1ht6dOo4KgtAy+fbrR/x336Jr3x5LTg5pt95G8dq1rt5WDZLNxqXH/k5FcjLqwEBiP/oQbUSE09dbzNbquZp9JsSh8xEnmARBaDnc/07cg4jAsf4clUG6S4lkUwVonprNbMjA3l3+zBtSc3xPTa0+GUqR1XRvuthY4r/5Gr9hw5AqK8l48CHyPvgQSXKPs/iS1Ur2Cy9SsnYtKi8vYt99B3379nV6jmNbLlFaaMQvWE/iiNaNtFNBEAT3JILNBnI1gWZjBamu6EjriL7E87oONpWqTKanBppCw2r0wFR0FxbciCYggNj3/0fIbbcBkPvmm1x67O9YDQaX7suSl0f63XdT+OWXAES/9BK+/fvX6TlMlRb2r04FoP+keLQ6TUNvUxAEwa2JYNNNiKyo4LZEYCK4EZGpbJ5UWi1R//wHkU89CRoNxStWkHLdRIqWLXNJlrNs1y7OTZ9O+c5dqHx8iHn1FYKun1Tn5zm04QIVJWaCInzoMiS6EXYqCILg3kSw2QAaKlAUAaeLiGCqyTTGfEhRntrwGrJkvKE60v6eswGnCEw9T+gttxC3eBG69u2xFhSQ+fgC0m+fjfHMmSZ5fclqJfedd0mfcyfW3Dz0HTvQbul3BE2ZUufnqiw1k7wuHYCBUxLQaMQtlyAILY/4m+8qNacA0ZnxJzqDuQl2IghCbRojcPdUFyuCFb8Ez+Tbrx8JP/5AxKPzUfn4UL5vH+em30DO669jKytrtNe15OaSftfd5L3zDkgSQTfOIP7bb+t8RrPKvp9TMVVaCWvjT4e+rRp4t4IgCJ5BBJtXoTECTU8PXnUGq6u3INRCdKIVmgMRVDZvKp2O8Llzab9yBf5jrgWLhfyPPyHl+skUr16DZLE02GtJkkTptu2cm34D5bt2ofL1JebVV4h5/nnUPvU7R5+dWszhTRcAGDK9PSq1+HBIEISWSQSbbsjTA87mTARqwtUQJb9CQ3nhhRcYMmQIvr6+BAcHO3XNHXfcgUqluuJrwoQJjbvRq+TVujWx77xDm/+9h1fr1lgyM8l4+GHOXDOczCefpHTrViSTqV7P/f/t3Xl4VPW5B/DvLJlJyA7ZMSwJhLCEIARoWC4FI0EQzW0FWjSiFawFrkv6oAjUVLGEetHrUpRHlOX2QVG8ggopgimxRUJRyFSUsAaECBMIZCfM+rt/IFFgZpKZzMw5Z/L9PA+PZDhn5p3XybznPb/f+R3zmTOoWbUKlXdOxZnZs2GrqYE+Lc3jabPX2Kx27PprBYQA+g6PR4+B3Tx+LiIipePFah5SSkOoqm2EiPb+NVNyom1SwRomj2XyieRC26SGNcx3K0DXNYcgKrTtqffkG2azGdOmTUN2djbefvvtdu83adIkrF27tvVnvV4ZJ9DCx49H6M9+hourV6P2nXdhq61F3aYPULfpA6gjIhA+fjzCcycidPRoqF28J+vFi2j423Y0bN2KFoOh9XGVToeoe+5B3JMLoA4O7lCsBz79Dhe/b0ZwaBDGTu/boeciIlI6NptE1GG8xya5YmwOR0KovG7FpHTPPvssAGDdunVu7afX65GQkOCDiHxPHRKC2EcfRczcubj85Zdo2LEDjTs/g62mBvUffYT6jz6CSqeDOjICap0eKr0eqmD91b8HB0NYLWgpNwC2Hy73UKsR+rORiLhzKsJvz4EmvOMnZi+da8ZXfzsFABg7oy9CwnUdfk4iIiVjs+kBf4xq2i/VQt012uev0xn4eoSHyBeCmgBLmNRRUKApLS1FXFwcoqOjMWHCBDz//PPo1s35NE+TyQSTydT6c0NDgz/CdEml1SI0Oxuh2dlIWLIELeXlVxvPHTthNRphu1ADV6sHBGdkIPLOKQi/4w4ExXlv4R5hF9j118OwWwV6DuqGvsPjvfbcRERKxWaTFIEH3hKR+W1hOApK1H6TJk3CL37xC/Tu3RsnTpzAokWLcMcdd6CsrAwajcbhPkVFRa2jqHKk0mjQJSsLXbKyEL9wISxVVbBfvgxhMsF+xQRhNsF+5QqEyQxhsSBkSCb0vXv7JJaDn38PY2U9gvQajJvZDyoVFwUiIpL3kaQMKeVaTbnSN9phCnc85VIpDWVLkx4hYaa2N5QhKRc44q06ZKZJC4R5b0VP6riFCxfiz3/+s8ttKioqkJ6e7tHz/+pXv2r9e0ZGBgYPHozU1FSUlpbitttuc7jP008/jYKCgtafGxoakJyc7NHr+5pKrYauRw9JXrvhYgvKtpwAAGT/ZyrCu3bsuk8iokDBZlPGpJhKq2logS3Cs6XeFY0H3uQnPKlCzvz+97/HAw884HKblJQUr71eSkoKYmJicPz4cafNpl6vV8wiQlIRQuDzd47AarIhsU8kBv1Hd6lDIiKSDTabnUBnWJGWiEjpYmNjERsb67fXq6qqwsWLF5GYmOi31wxER/dV4/S3l6DRqjH+vnTeU5OI6Cd4n003cArtVbp6i9QhEPmFvtEOfaN8F5dS0tRkYzNPeHnT6dOnYTAYcPr0adhsNhgMBhgMBjQ1/Xghc3p6OjZv3gwAaGpqwoIFC7B3716cOnUKJSUluPvuu9GnTx/k5uZK9TYU73KDGbvfPwYAyJrSC9EJoRJHREQkLxzZlDklrkqrq7fBHOl4sQkiV+Sy4I+cG0w54b02pfPMM89g/fr1rT/feuutAIBdu3bh5z//OQDgyJEjqK+vBwBoNBp8/fXXWL9+Perq6pCUlISJEydi6dKlnCbrIWG/On32SrMF3W4Jw60TpblelIhIzthsthNHNemnlHg9m68WB/LmPTbd4e3G1FWD6WphKyIprFu3rs17bAohWv8eEhKCTz/91MdRdS5lW06g0nABao0KE/LTodHwO4KI6Eb8ZqSAoKTphIoh89ueeJNcRzI72lBLdSKAqC1Xmi3XNcNKc7C0CuU7TgMAJtzfH3E9IySOiIhInngk0g5Sj2pK/fpEJN+GlEhpzC1WbH7xAD5bewhWi03qcNx28usa/PO9owCAkXeloN/IBIkjIiKSLzabnYSqtlHqECTFER5pyXnkWYomUi7XphJJ4ezxOtQaL+Povmp89D8GXG4wSx1Su1WfasCOt76BEMCA0YkYdkdPqUMiIpI1HoHTTTQNnXTBj040bZQ8w9HNjuGKtAQAvTJiMPW/MqHvooWxsh4fLP8KF7+X/xmYhpoWbFv5b1jNdvQY0BX/MbMfVCr5nkgjIpIDNpttkMsUVrnEISW5jQb5asEdX1BSrORHPMFCEknu3xW/fHIYImND0HjpCv7vv/fj1MEaqcNy6kqzBZ+89m+0NFoQkxyG3IcHcUEgIqJ24Dcl+Z2r0SG5NZTkP+78v/fW58STkcrONLrJkxTkS9EJobjnqSx0T4uC5YoNxa9/jX+XnJHdwkFWiw3Fb3yNuurLCIvW4855mdAF80QNEVF7sNl0gaOJRESu1TWHSB0CKVhwWBCmPjoE/UcnQghg96Zj+PydI7DZ5HFSx2K2YeeaQzh3vB66YA3unJ+J0CiehCEiai82mwoip+ZXV2+ROgTypXZOr1TywksdGaGU0+imnBdfImoPjVaN8felY9Qv+wAq4Nt/nsUnrxhQf0Ha9QMunWvGB8u/QmX5BajVKkx6JAPduodJGhMRkdIo90iR3ObPFWl19cpbzj5QcSqkPHHKONGPVCoVbr29ByY/kgGtXoPvj9bh3ef+hS+3nYTN4v+TO4f3nsOmoi9x6WwzQiJ0mPpoJpLTu/o9DiIipWOz6YScRhFJvtjIdV7+Gt0MtKaUK9KSK70zYzFj0XDckh4Nm8WOfZ+cxLtL/4Uzhy755fUtJhtK/rcCJesqYDXbcUt6NGYsHo5b2GgSEXmEV7grjP1SLdRdo6UOI3A1aYEwq9RRBBQ5TvP0VqOob7TDFC7vc3baJjWsYfKZ9kvUlqj4LrjrsSE4/tV57N50DPXnW/Dxqwb0GRaH0ff0RVi0b07yXTrbjO2rv0HtuWaoVMDwO3tj2B29oFbL7zuMiEgp2GxSwNA2qWANk9cqhuR9gTbSR0Q3U6lU6Ds8Hj0GdcO+TypxcFcVju8/j+++uYjhU3qj/6hEBIcFeeW1bBY7Du89h92bjsFqtqNLhA4THxqI7v14YpeIqKPYbDrAKbSApqEFtojAWmWSIzwEyGtxHyJyTR+ixdjpaUjPTsTn7xxB9ckG7PnwOMq2nED3tCikDo1DypBYdInQufW8wi5w9lgdjn5ZjRMHzsN0+eqMluT+0ch5cKDbz0dERI6x2VQgTqWl9lLKNaVKHq1UwlRaX6trDkFUqLQrh1Jgi00Oxy8XDEPFnnP4urQKF6uaUHW4FlWHa/H5u0eQ1CcKqUNj0TvzauOp1qigUl0//VUIgZqqJhzdV41jX1ajuc7U+m+hkToMvi0Zt+b0gIrTZomIvIbNZiejqm2EiOYCHd7U0qRHSJip7Q2Vop23PaGOC2oCLFLfSaGN65R98fk2NocjIdR/q2NTYFCpVRgwJgkDxiSh7vxlVJZfwIkD53H+u0acPVaHs8fq8M/3jv2wMaANUkMTpIZWq4ZGp4HdZkfTpR8/y7oQLVKHxiJtRAKS+kbx2kwiIh/gUeUNOIVW3mRxcE4AlHmPTU6hJQoMUXFdMDS3J4bm9kTDxZYfGs8LMJ6sBwQAAVjNdljNdvz0VIlGq0avwd2QNjwBPQd1gyZIed9jRERKwmaTPKart8Ac6dkCDa6mHrKhJCKi9oroFoIhOT0wJKcHbFY7rGYbrBY7bBZ7639t1qt/YpLDoQ/hoQ8Rkb/wG1eheN2mDwXI7U+Ucr1mIJD6uk2uxEx0lUarhkarBr/9iIjkgfNHfoJTaInkrSMLCSl5Cq2SF1AiIiKizovNJvmMrt4mdQh+w1FEx7RNXHCDiIiIqLNis9kJqWrbtwqkpoG3MqDAoORRTW+Q42JOxmauik1ERBTo5HcEQtQBbY2kyfGgW1YkuO1JoEwR7ewNbV1ziNQhEBERkczwyPsHSrxeU4kxk39wWq9yBEqzTURERHQjNptE5DaOEAeYNka0efKCiIiIPMEjRiIv4QG5b3EEMPDwuk0iIqLAxmYTnI6qNH5pOiS4dpF8w5/XUnb26zaJiIiIforNpsJJ3Sjr6i2Svj7djCOsRERERCQHbDZJMq5GgZQ6ZZKN3o94j03vaut3gvkmIiIiuZFFs7ly5Ur06tULwcHBGDlyJPbt2+dy+02bNiE9PR3BwcHIyMhAcXGxx68t9cigVNp7r00iovby5PYnvG6TiIgocEnebL733nsoKChAYWEhDhw4gMzMTOTm5uL8+fMOt9+zZw9+/etf46GHHkJ5eTny8vKQl5eHb775xs+Rdw6ahpYO7a+rt3kpEu/hSqpOBOB1qryGkoiIiEg6kh91v/TSS5gzZw4efPBBDBgwAKtWrUKXLl2wZs0ah9u/8sormDRpEhYsWID+/ftj6dKlGDp0KP7yl7/4OXL56Kyjs85wOqFytHe6tJKmVXvS4Prj/fEkCxEREfmbpEcfZrMZ+/fvR05OTutjarUaOTk5KCsrc7hPWVnZddsDQG5urtPtXWGTRr4g5XWbvGaUiIiIiORC0nlzNTU1sNlsiI+Pv+7x+Ph4HD582OE+RqPR4fZGo9Hh9iaTCSaTqfXn+vp6AIDVboZamDsSvqyo7aa2N7qBsOnatZ3N6nqk0GpxPVXWatE4f26z8/MdNhdvyRbk8iVh1wrX/65px+hTC4Awa9vbOWBTu///wxvsLa7ft0st7fs60Dar0Z6xO5up7RHm9qZJ7eGvqtUizTRaV59rp/u0Ixc+/dxrXH/Wm1uA4NC2g7TC/c9+VbMO8aH+H75uaGgAAAjRgd+bTuZarq7ljoiIOqf21tDAu0jrBkVFRXj22WdverzUuFaCaHzIk0srz3o9CiIixUjGWwCAxsZGREZGShyNMjQ2Xl1cLjk5WeJIiIhIDtqqoZI2mzExMdBoNKiurr7u8erqaiQkJDjcJyEhwa3tn376aRQUFLT+bLfbcenSJQQFBaFHjx44c+YMIiIiOvhOOoeGhgYkJyczZ25gztzHnLmPOXPftZwdOnQISUlJUoejGElJSThz5gzCw8OhUnl+fTw/s44xL84xN44xL84xN455Ky9CCDQ2NrZZQyVtNnU6HYYNG4aSkhLk5eUBuNoMlpSUYP78+Q73yc7ORklJCR5//PHWx3bu3Ins7GyH2+v1euj111/HFhUV1Tr0GxERwQ+gm5gz9zFn7mPO3Mecua979+5Qq7l4Unup1WrccsstXns+fmYdY16cY24cY16cY24c80Ze2jMrSPJptAUFBZg1axaysrIwYsQIvPzyy2hubsaDDz4IALj//vvRvXt3FBUVAQAee+wxjBs3Di+++CKmTJmCjRs34quvvsKbb74p5dsgIiIiIiKin5C82ZwxYwYuXLiAZ555BkajEUOGDMH27dtbFwE6ffr0dWedR40ahXfeeQdLlizBokWL0LdvX2zZsgWDBg2S6i0QERERERHRDSRvNgFg/vz5TqfNlpaW3vTYtGnTMG3atA69pl6vR2Fh4U1TbMk55sx9zJn7mDP3MWfuY86kxfw7xrw4x9w4xrw4x9w45u+8qATXfCciIiIiIiIv46oIRERERERE5HVsNomIiIiIiMjr2GwSERERERGR17HZJCIiIiIiIq8L2GZz5cqV6NWrF4KDgzFy5Ejs27fP5fabNm1Ceno6goODkZGRgeLiYj9FKh/u5Gz16tUYO3YsoqOjER0djZycnDZzHIjc/Zxds3HjRqhUKuTl5fk2QBlyN2d1dXWYN28eEhMTodfrkZaW1ul+P93N2csvv4x+/fohJCQEycnJeOKJJ3DlyhU/RSu9f/zjH5g6dSqSkpKgUqmwZcuWNvcpLS3F0KFDodfr0adPH6xbt87ncQY61mHHWGudY011jHXTOdbHm8muBooAtHHjRqHT6cSaNWvEt99+K+bMmSOioqJEdXW1w+2/+OILodFoxAsvvCAOHToklixZIoKCgsTBgwf9HLl03M3ZzJkzxcqVK0V5ebmoqKgQDzzwgIiMjBRVVVV+jlw67ubsmpMnT4ru3buLsWPHirvvvts/wcqEuzkzmUwiKytLTJ48WezevVucPHlSlJaWCoPB4OfIpeNuzjZs2CD0er3YsGGDOHnypPj0009FYmKieOKJJ/wcuXSKi4vF4sWLxYcffigAiM2bN7vcvrKyUnTp0kUUFBSIQ4cOiddee01oNBqxfft2/wQcgFiHHWOtdY411THWTedYHx2TWw0MyGZzxIgRYt68ea0/22w2kZSUJIqKihxuP336dDFlypTrHhs5cqT47W9/69M45cTdnN3IarWK8PBwsX79el+FKDue5MxqtYpRo0aJt956S8yaNSsgC6Mr7ubsjTfeECkpKcJsNvsrRNlxN2fz5s0TEyZMuO6xgoICMXr0aJ/GKVftKbRPPvmkGDhw4HWPzZgxQ+Tm5vowssDGOuwYa61zrKmOsW46x/rYNjnUwICbRms2m7F//37k5OS0PqZWq5GTk4OysjKH+5SVlV23PQDk5uY63T7QeJKzG12+fBkWiwVdu3b1VZiy4mnOnnvuOcTFxeGhhx7yR5iy4knOPv74Y2RnZ2PevHmIj4/HoEGDsGzZMthsNn+FLSlPcjZq1Cjs37+/dSpRZWUliouLMXnyZL/ErESdvQZ4G+uwY6y1zrGmOsa66Rzro/f4+vtX65VnkZGamhrYbDbEx8df93h8fDwOHz7scB+j0ehwe6PR6LM45cSTnN3oqaeeQlJS0k0f1kDlSc52796Nt99+GwaDwQ8Ryo8nOausrMTf//533HvvvSguLsbx48cxd+5cWCwWFBYW+iNsSXmSs5kzZ6KmpgZjxoyBEAJWqxWPPPIIFi1a5I+QFclZDWhoaEBLSwtCQkIkikyZWIcdY611jjXVMdZN51gfvcfXNTDgRjbJ/5YvX46NGzdi8+bNCA4OljocWWpsbER+fj5Wr16NmJgYqcNRDLvdjri4OLz55psYNmwYZsyYgcWLF2PVqlVShyZbpaWlWLZsGV5//XUcOHAAH374IbZt24alS5dKHRoRdQBr7Y9YU51j3XSO9VEaATeyGRMTA41Gg+rq6user66uRkJCgsN9EhIS3No+0HiSs2tWrFiB5cuX47PPPsPgwYN9GaasuJuzEydO4NSpU5g6dWrrY3a7HQCg1Wpx5MgRpKam+jZoiXnyOUtMTERQUBA0Gk3rY/3794fRaITZbIZOp/NpzFLzJGd/+MMfkJ+fj9mzZwMAMjIy0NzcjIcffhiLFy+GWs1zjDdyVgMiIiI4qukB1mHHWGudY011jHXTOdZH7/F1DQy4rOp0OgwbNgwlJSWtj9ntdpSUlCA7O9vhPtnZ2ddtDwA7d+50un2g8SRnAPDCCy9g6dKl2L59O7KysvwRqmy4m7P09HQcPHgQBoOh9c9dd92F8ePHw2AwIDk52Z/hS8KTz9no0aNx/Pjx1oMIADh69CgSExMDpmC64knOLl++fFPBvHbQcXWtALpRZ68B3sY67BhrrXOsqY6xbjrH+ug9Pv/+9coyQzKzceNGodfrxbp168ShQ4fEww8/LKKiooTRaBRCCJGfny8WLlzYuv0XX3whtFqtWLFihaioqBCFhYUBueS6K+7mbPny5UKn04kPPvhAnDt3rvVPY2OjVG/B79zN2Y0CdeU8V9zN2enTp0V4eLiYP3++OHLkiNi6dauIi4sTzz//vFRvwe/czVlhYaEIDw8X7777rqisrBQ7duwQqampYvr06VK9Bb9rbGwU5eXlory8XAAQL730kigvLxffffedEEKIhQsXivz8/Nbtry37vmDBAlFRUSFWrlzJW590EOuwY6y1zrGmOsa66Rzro2Nyq4EB2WwKIcRrr70mevToIXQ6nRgxYoTYu3dv67+NGzdOzJo167rt33//fZGWliZ0Op0YOHCg2LZtm58jlp47OevZs6cAcNOfwsJC/wcuIXc/Zz8VqIWxLe7mbM+ePWLkyJFCr9eLlJQU8ac//UlYrVY/Ry0td3JmsVjEH//4R5GamiqCg4NFcnKymDt3rqitrfV/4BLZtWuXw++na3maNWuWGDdu3E37DBkyROh0OpGSkiLWrl3r97gDDeuwY6y1zrGmOsa66Rzr483kVgNVQnTicWMiIiIiIiLyiYC7ZpOIiIiIiIikx2aTiIiIiIiIvI7NJhEREREREXkdm00iIiIiIiLyOjabRERERERE5HVsNomIiIiIiMjr2GwSERERERGR17HZJCIiIiIiIq9js0nUSZ07dw4zZ85EWloa1Go1Hn/8calDIiIiUgTWUKL2YbNJ1EmZTCbExsZiyZIlyMzMlDocIiIixWANJWofNptEAerChQtISEjAsmXLWh/bs2cPdDodSkpK0KtXL7zyyiu4//77ERkZKWGkRERE8sIaSuQdWqkDICLfiI2NxZo1a5CXl4eJEyeiX79+yM/Px/z583HbbbdJHR4REZFssYYSeQebTaIANnnyZMyZMwf33nsvsrKyEBoaiqKiIqnDIiIikj3WUKKO4zRaogC3YsUKWK1WbNq0CRs2bIBer5c6JCIiIkVgDSXqGDabRAHuxIkTOHv2LOx2O06dOiV1OERERIrBGkrUMZxGSxTAzGYz7rvvPsyYMQP9+vXD7NmzcfDgQcTFxUkdGhERkayxhhJ1HJtNogC2ePFi1NfX49VXX0VYWBiKi4vxm9/8Blu3bgUAGAwGAEBTUxMuXLgAg8EAnU6HAQMGSBg1ERGR9FhDiTpOJYQQUgdBRN5XWlqK22+/Hbt27cKYMWMAAKdOnUJmZiaWL1+O3/3ud1CpVDft17NnT04VIiKiTo01lMg72GwSERERERGR13GBICIiIiIiIvI6NptERERERETkdWw2iYiIiIiIyOvYbBIREREREZHXsdkkIiIiIiIir2OzSURERERERF7HZpOIiIiIiIi8js0mEREREREReR2bTSIiIiIiIvI6NptERERERETkdWw2iYiIiIiIyOvYbBIREREREZHX/T/GbE2FLmNQfQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1100x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(1, 2, figsize=(11, 5))\n",
-    "fig.suptitle(\"Sample data\")\n",
-    "\n",
-    "x1 = torch.linspace(0, 1, 50).unsqueeze(-1)\n",
-    "x2 = torch.linspace(0, 1, 50).unsqueeze(-1)\n",
-    "\n",
-    "f_x1 = sample_data(x1)\n",
-    "ys = f_x1.expand(50, 50) + x2.expand(50, 50)\n",
-    "axs[0].contourf(x1.squeeze(), x2.squeeze(), ys, levels=20)\n",
-    "axs[0].set_title(\"Contour\")\n",
-    "axs[0].set_xlabel(\"x1\")\n",
-    "axs[0].set_ylabel(\"x2\")\n",
-    "axs[0].axis(\"equal\")\n",
-    "\n",
-    "for x2 in torch.linspace(1, 0, 5):\n",
-    "    ys = f_x1 + x2\n",
-    "    axs[1].plot(x1.squeeze(), ys.squeeze(), label=\"x2=%.2f\" % x2)\n",
-    "axs[1].set_title(\"Line plot\")\n",
-    "axs[1].set_xlabel(\"x1\")\n",
-    "axs[1].set_ylabel(\"y\")\n",
-    "axs[1].legend()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Train single-task PFN on problem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda:0 device\n",
-      "init dist\n",
-      "Not using distributed\n",
-      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7ff916b3c9a0>, 'seq_len_maximum': 20, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 1, 'sample_num_tasks': False, 'sample_num_features': False}}, 'num_features': 2, 'num_tasks': 1, 'epoch_count': 0}\n",
-      "Style definition of first 3 examples: None\n",
-      "Initialized decoder for standard with (None, 2)  and nout 2\n",
-      "Using a Transformer with 1.49 M parameters\n",
-      "Using OpenAI max lr of 0.0012557515027448725.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:286: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast(enabled=scaler is not None):\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 36.05it/s, data_time=0.00251, step_time=0.0207, mean_loss=1.76]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.63it/s, data_time=0.00492, step_time=0.0216, mean_loss=0.567]\n",
-      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast(enabled=scaler is not None):\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 84.63it/s, data_time=0.00575, step_time=0.00705, mean_loss=-0.14]  \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.35it/s, data_time=0.003, step_time=0.0204, mean_loss=-0.181]    \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.29it/s, data_time=0.00241, step_time=0.0203, mean_loss=-0.324]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 98.59it/s, data_time=0.00231, step_time=0.0069, mean_loss=0.00138]   \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.04it/s, data_time=0.00327, step_time=0.0209, mean_loss=-0.318]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.90it/s, data_time=0.00235, step_time=0.0204, mean_loss=-0.292]  \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 98.21it/s, data_time=0.00234, step_time=0.00609, mean_loss=-0.437] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.08it/s, data_time=0.00241, step_time=0.0214, mean_loss=-0.566]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 36.41it/s, data_time=0.00248, step_time=0.0211, mean_loss=-0.258]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 99.01it/s, data_time=0.00235, step_time=0.0067, mean_loss=-0.685] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.85it/s, data_time=0.00243, step_time=0.0213, mean_loss=-0.257]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.46it/s, data_time=0.0031, step_time=0.0215, mean_loss=-0.359] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 99.38it/s, data_time=0.00319, step_time=0.00607, mean_loss=-0.606] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.17it/s, data_time=0.00249, step_time=0.0211, mean_loss=-0.376]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.02it/s, data_time=0.0025, step_time=0.0211, mean_loss=-0.493] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.69it/s, data_time=0.00223, step_time=0.00608, mean_loss=-0.254]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.79it/s, data_time=0.00249, step_time=0.0212, mean_loss=-0.547]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.99it/s, data_time=0.00249, step_time=0.0213, mean_loss=-0.734]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.82it/s, data_time=0.00286, step_time=0.00623, mean_loss=-1.2] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 39.02it/s, data_time=0.00249, step_time=0.0232, mean_loss=-0.946]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.91it/s, data_time=0.00251, step_time=0.0215, mean_loss=-1.02] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.66it/s, data_time=0.0029, step_time=0.00668, mean_loss=-1.09] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.89it/s, data_time=0.00249, step_time=0.0216, mean_loss=-1.21]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.83it/s, data_time=0.0025, step_time=0.0212, mean_loss=-1.24] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.54it/s, data_time=0.0024, step_time=0.00603, mean_loss=-1.34] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.28it/s, data_time=0.0025, step_time=0.0223, mean_loss=-1.53] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:06<00:00, 38.75it/s, data_time=0.00232, step_time=0.0213, mean_loss=-1.6] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:02<00:02, 97.66it/s, data_time=0.00359, step_time=0.00626, mean_loss=-1.65]\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_batch, num_features=2, num_tasks=1, seq_len=20, epochs=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HERE\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(1)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "TRAIN_CONST = 0.5\n",
-    "train_x2 = torch.ones_like(train_x1) * TRAIN_CONST\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "train_y = f + train_x2\n",
-    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
-    "task_train_x = torch.cat((torch.ones_like(train_x1).unsqueeze(-1), train_x), -1)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.sort()\n",
-    "sorted_f = f[indices]\n",
-    "sorted_train_x2 = train_x2[indices]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:10], train_y[:10], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10], color=\"grey\", alpha=0.5)\n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Train Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model is able to fit this setup and make reasonable predictions for varied $x_1$ and constant $x_2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HERE\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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ERERERERERERERKQbMZNM+fvf/86cOXNITk4mOTmZo48+mldeeWWwmyUiMiAUA0VkJFMMFJGRSvFPREYyxUARGWpiZgH6F154AavVyuTJkzFNkwcffJC77rqLjRs3MnPmzMFunohIv1IMFJGRTDFQREYqxT8RGckUA0VkqImZZEpX0tLSuOuuu7jhhhsGuykiIgNOMVBERjLFQBEZqRT/RGQkUwwUkcFkG+wGHI5IJMKTTz5Ja2srRx999EH3CwQCBAKBjvfRaJTGxkbS09MxDGMgmioiMcg0TTweD7m5uVgsQ68aYk9ioOKfiBwuxUARGamGQ/wDxUAROTyKgSIykvU4BpoxZMuWLWZCQoJptVrNlJQU86WXXup2/5/+9KcmoJdeeul1WK+ysrIBim4905sYqPinl156HelLMVAvvfQaqa9Yjn+mqRiol156HdlLMVAvvfQaya9DxcCYKvMVDAYpLS2lpaWFp556invvvZe3336bGTNmdLn/p7PRLS0tFBQUUFZWRnJy8kA1W0RijNvtZsyYMTQ3N5OSkjLYzenQmxio+Ccih0sxUERGquEQ/0AxUEQOj2KgiIxkPY2BMZVM+bRTTz2ViRMncs899/Rof7fbTUpKCi0tLQqgInJQsRIrehMDY+U7icjgi5V4oRgoIn0tVmKFnoNFpD/ESqxQDBSR/tDTWDH0iiD2QjQa7ZRtFhEZSRQDRWQkUwwUkZFK8U9ERjLFQBEZTDGzAP1tt93GmWeeSUFBAR6Ph0cffZS33nqL1157bbCbJiLS7xQDRWQkUwwUkZFK8U9ERjLFQBEZamImmVJbW8s111xDVVUVKSkpzJkzh9dee43TTjttsJsmItLvFANFZCRTDBSRkUrxT0RGMsVAERlqYiaZ8u9//3uwmyAiMmgUA0VkJFMMFJGRSvFPREYyxUARGWpies0UERERERERERERERGR/qZkioiIiIiIiIiIiIiISDeUTBEREREREREREREREemGkikiIiIiIiIiIiIiIiLdUDJFRERERERERERERESkG0qmiIiIiIiIiIiIiIiIdEPJFBERERERERERERERkW4omSIiIiIiIiIiIiIiItINJVNERERERERERERERES6oWSKiIiIiIiIiIiIiIhIN5RMERERERERERERERER6YaSKSIiIiIiIiIiIiIiIt1QMkVERERERERERERERKQbSqaIiIiIiIiIiIiIiIh0Q8kUERERERERERERERGRbiiZIiIiIiIiIiIiIiIi0g0lU0RERERERERERERERLqhZIqIiIiIiIiIiIiIiEg3lEwRERERERERERERERHphpIpIiIiIiIiIiIiIiIi3VAyRUREREREREREREREpBtKpoiIiIiIiIiIiIiIiHRDyRQREREREREREREREZFuKJkiIiIiIiIiIiIiIiLSDSVTREREREREREREREREuqFkioiIiIiIiIiIiIiISDeUTBEREREREREREREREemGkikiIiIiIiIiIiIiIiLdUDJFRERERERERERERESkG0qmiIiIiIiIiIiIiIiIdEPJFBERERERERERERERkW4omSIiIiIiIiIiIiIiItINJVNERERERERERERERES6oWSKiIiIiIiIiIiIiIhIN5RMERERERERERERERER6YaSKSIiIiIiIiIiIiIiIt1QMkVERERERERERERERKQbSqaIiIiIiIiIiIiIiIh0Q8kUERERERERERERERGRbiiZIiIiIiIiIiIiIiIi0o2YSab85je/YfHixSQlJZGVlcUFF1zA7t27B7tZIiIDQjFQREYyxUARGakU/0RkJFMMFJGhJmaSKW+//TY333wzq1at4o033iAUCnH66afT2to62E0TEel3ioEiMpIpBorISKX4JyIjmWKgiAw1hmma5mA34nDU1dWRlZXF22+/zfHHH9+jY9xuNykpKbS0tJCcnNzPLRSRWBULsaK3MTAWvpOIDA2xEC8UA0WkP8RCrNBzsIj0l1iIFYqBItJfehorbAPYpj7V0tICQFpa2kH3CQQCBAKBjvdut7vf2yUiMhAOFQMV/0RkOFMMFJGRSs/BIjKSKQaKyGCLmTJfnxSNRvnWt77Fsccey6xZsw66329+8xtSUlI6XmPGjBnAVoqI9I+exEDFPxEZrhQDRWSk0nOwiIxkioEiMhTEZJmvm266iVdeeYX33nuP/Pz8g+7XVTZ6zJgxmtonIt0a6tOAexIDFf9E5HApBorISDUc4h8oBorI4VEMFJGRbNiW+fra177Giy++yDvvvNNt8ARwOp04nc4BapmISP/raQxU/BOR4UgxUERGKj0Hi8hIphgoIkNFzCRTTNPk61//Os8++yxvvfUW48ePH+wmiYgMGMVAERnJFANFZKRS/BORkUwxUESGmphJptx88808+uijPP/88yQlJVFdXQ1ASkoKLpdrkFsnItK/FANFZCRTDBSRkUrxT0RGMsVAERlqYmbNFMMwutx+//33c9111/XoHEO9/qOIDA1DMVYcaQwcit9JRIamoRgvFANFZCAMxVih52ARGShDMVYoBorIQBl2a6bESM5HRKRfKAaKyEimGCgiI5Xin4iMZIqBInI4TNOksK6VSVmJfX5uS5+fUUREREREREREREREZICt2F3Lvlpvv5xbyRQREREREREREREREYlpq4sa2FzW0m/nj5kyXyIiIiIiIiIiIiIiIp+2qqiBlYUN/XoNJVNERERERERERERERCTmmKbJ23vq2Fja3O/XUpkvERERERERERERERGJOR8UNgxIIgWUTBERERERERERERERkRizv6GVtSWNA3Y9JVNERERERERERERERCRmeANhXt1WjWkO3DWVTBERERERERERERERkZjx5q5a2oKRAb2mkikiIiIiIiIiIiIiIhITAuEIJfWtA35d24BfUURERERERERERES65Q2EcftCxDusJMXZsVqMwW6SyJBQ1thGJDqA9b0+pGSKiIiIiIiIiIiIyBDQ4A2wobSZwjovvk+UMDIMyB8Vz+y8FCZlJSqxIiNacX3boFxXyRQREREREREREZFhxB+KEGe3DnYzpBe8gTBv7a5lX623ywW1TbN9NH5ZYxtJcTaOn5LJlOykgW+oyBCwv2HgS3yB1kw5PKYJrQ3QtL/9v11FOBGR4UoxUERGKsU/ERnJFANFYkZbMMxzGysGuxnDSz/HwG0VLfxnZQl7a7pOpHyaxx/mpS1VPLW+nKbWYJ+2RWSoq/X48fjDg3JtzUzpDV8zbP4vrL4Hmoo/3j5qPCz9Msy9Elypg9U6EZH+pRgoIiOV4p+IjGSKgSIxxReM8PSGCty+0GA3ZXjo5xjo9odYtqOG/Q2HV7KorLGNR1bv55hJGcwfk4phqPSXDH8lg1TiCzQzpef2LYO7Z8Crt0FTSefPmkrat989o30/EZHhRjFQREYqxT8RGckUA0ViSlswzNMbyqn3BAa7KTHL/OS0kMOMgf5QBG+g+1HzgXCE9fubeGjl/sNOpHwkFDF5e3cdT60vx+1XEk2Gv5L6wSnxBZqZ0jP7lsEjl304ha+ruXYfbgv52ve76gmYdOpAtlBEpP8oBorISKX4JyIjmWKgSEzxBsI8s6GcBq9KPh2J8iYfmUlO4vav6HUMdOefwPr9TWyvaCEUMXE5rGQkOslNjSM3xYUJtAbC1Hr87KzyEAxH+7ztD6/az8nTspiWk9yn5xYZKvyhCFUt/kG7vmamHIqvGR6/5sPgeaggF23f7/Fr2o8TEYl1ioEiMlIp/onISKYYKBJT/KEIT64rUyKlDwTCEXYUlfU6BpqPX83T721nU2kzoUh7osUXjFDW2Mbqokae3VjBcxsreGNHDZvLWvo8kdLR/lCUV7ZWs2JXLdGo1raS4aewzkt0ENdtUzLlUDb/F0JtHDp4fiTavv/mx/qzVSIiA0MxUERGKsU/ERnJFANFYsrbe+poblN5p77iX/cQZq9joI9xFf/rz2b1yqayZp7eUI4vGBnspoj0qZ1VnkG9vpIp3THN9gWmDsfqf3yYwRYRiVGKgSIyUin+ichIphgoElP2N7Syo9I92M0YPkyTGWW9TwybwPyqxw+IgVEzQolnI9VteynzbuWN8r/z4zVH8Z/dt7C7+X2K3Ov6qOEHKm/y8fSGcvwhJVRkeHD7Q5Q3Dd7i86A1U7rX1ghNxYdxoNl+nK8J4tP6vFkiIgNCMVBERirFPxEZyRQDRWJGMBxl2c7awW7GsGLxN5HqL+/9cZik+stxhJp4v/ltmoPV7Gx6mxLPxi7339TwMpsaXu54b7M4mZi8mM9N+i1JjozDbv+n1XkCPLexggsX5OG0WfvsvCKDYXe1Z9DHbCiZ0p2g98iOD3h0EykisUsxUERGKsU/ERnJFANFYsaW8mbcPpX36kuWUOsRHX/X2mPYb/S+tzccDbC7+T1+uu5YZqedTo1vHxdP+AmTU44+ovYAVLX4eWFzFRfNz8NiMY74fCKDZWfV4M/CUzKlO47EIzvemdQ37RARGQyKgSIyUin+ichIphgoEjNKGga33M1wFLUnHNHxn1zNYeaokylv3c7SrEs4Oe9LOKxx+MJufGEPYFLi2URzsJodTSso924nGPUBsLXxdQD+vv26jnP9ZukGnNbDb1tZYxvv7qvnhCmZh30OkcFU6/bT4A0OdjOUTOlWfBqMGg9NJbRXP+wpA0aNA9eo/mmXiMhAUAwUkZFK8U9ERrIhEgNbA2F2Vrmp9QRo8AawWiykuOzkpDiZmZtCnF3lamRkC0eiVDX7BrsZw040bhTNcfkk+yuw9CIGRjEpIkojJtdM+RPzMs7scj+XLRmXLRmAtLh8AE7OuxEA0zQp9qxnXd3zrKp5otNxt61ewJjE2Vw/7a+kOLIP56uxYX8To1PimJKtpLfEnu1DYFYKaAH67hkGLP3y4R279Cvtx4uIxCrFQBEZqRT/RIaNYDg62E2IPYMcAz3+EMt31nDfe8W8u7ee3dUe6r1Batx+9tR4eGdPPfe+W8QbO2poDYSP6FoisayqxU84OsiLBwxHhsGm0ZfR+0hmsGH0Zdx97O6DJlIOfWmDCcmLuGziL/jD0bu4YuKvO31e5t3Krzacxj+2X8/2xhWEo70fpf/GjhoaWwd/dL9IbzS3BdlW3jLYzQCUTDm0uVeCPZ4e/1EZlvb9517Rr80SERkQioEiMlIp/onEvBW7a6lx+we7GbFpkGLg9soWHlq1ny3lLd12EociJtsqWnhwZQmby5oxB3s1WpFBUNqoEl/9ZUfWOYQscUR7mFKJYhC2uKgvuLnP2mAYBkuyL+buY3bzu6O2sjjzQqB9bZU9Le/z711f4XurZvNk4U8IRnr+uy4YjvLS1irCEQ02kNixYnftkEkeK5lyKK5UuPw/H46uOdQflwUw4PKH2o8TEYl1ioEiMlIp/onEtLd217KptHmwmxG7BjgGtgXDPL+pgte31xAI9byDLxCK8uauWp5cX47br0W4ZWQpUzKl3wRsSbw47beAcciESvvnBs9PuRO/9QjXnDoIm8XBlZPv5PdH7+Si8T+hIHFux2crax7nB6vn8nTRHQQjPSv7Vu8J8O7e+n5pq0hf21froaR+6MQ7JVN6YtKpcNUTYHcBBuYBgbQ9cGJ3wVVPwqRTBqGRIiL9RDFQREYqxT+RmPT2njo2KpFy5AYoBu6r9fLQyv0U1bUedlMrmnw8vGo/u6qHRj11kf4WCEeocQcGuxnDTlljG8t31rCr2s1rgZn8KftXBA0n0S6SKlEgahr4TAfXBr/HrZsy+cuKfTzwQQnPbqzgnT117Kp209wW7LPZcxbDwnGjr+Jbc57ghwve6PTZ+9WP8oPV8/j2B1PZ2rDskOfaVNbMvlpPn7RLpL+EIlHe2l032M3oRAvQ99SkU+HbO2DzY7D6H9BU/PFno8a114addyXEpQxaE0VE+o1ioIiMVIp/IjHl/X31bNjfNNjNiHmRqInVYhx2DKzzBChvaiMtwUFGopMEZ+euB9M0KWloY8P+pj4rVRQIRXllazUl9W2cPC0Lh01jR2X4Km/yEVV5uz63bn8jv3lld8f71xjLA/wfF1nf5Rrbk0wwPp75URrN5IHImTwdOR4P8QBETWjxhWjxhTrFthSXnfHpCRSkx5ObGofTZj3itmbEFfCrJevY1fwOz5fciSdYj0n7zL77d9/M+KSFnJL3JWaknXjQc7y2vYYUl4PMJOcRt2eoME2TwjovyS47mYlODK1lGNPWFDfi8Q+t9dGUTOkNVyoc9RWii25k5YqXsYV9LD7uZKyJGVpoVESGP8VAERmpFP9EYsLakkbWFDcOdjNinmma7Kp2MzP3wwRJL2Jgc1uQd/bWU1Tn5ZP9vElxNnJTXbjsVpp9QRq8wX7rHNlZ5aa6xcfZc3KHVQehyCepxFf/yEqKY0FBKnWe9lk/FouBy57Ik6Egd7TWkgYstR/PlJzbsSVmkuiyc7XDisUwCEejBEJR3P4Qzb4Q9Z4AtZ4Ate4ALb4Qm8qb2VTejGFAWrwDp92Cw2ohakI4GiUcMYlETcJRE9M0+SiEWgwDwwCrxcBmMbBZLNitBg6bBafNSpx9KRdmv0CcI8QW9/2srPs3AMWe9dy768sAOK0JXD/tr0xIXozV+LgrOBiO8vymCq5YUkCiM/a7iMsa23h7T13H31+8w8qU7CTmjkklLcExyK2T3mpuCw7JATKx/y9lMBgGYXsyYXsyxKfpIVpERhbFQBEZqRT/RIas5rYgKwsbBrsZw8a6kiZmjE7uPKL3EDEwFInyv82VNHiDB5zP4w+zu3rgysk0tYV4Yl0Zn52Zw6Ss/lnDQGQwKZnSP46dlEF2spMXNld1bHul9M/sKP8nGHBczgXcOParVMaNpe1TyzRZLVacNivJLjv5oz7eHgxHKWtqo6S+lbImHy2+EA2tB8bJvnEh44xzcblqqLT8mZbIDgACkVb+vv06ABJsqZw19jvMzziLOGsiHn/7mlUXL8gnzn7kM2YGy9qSRt771DowbcEIm8qa2VTWTF6qi9xUF9nJTpJdduJsVpx2C06bRbNXhqi399QNmUXnP0nJFBEREREREYlp7+2rJzIEH7hjVWNrkH21XiZnJ/X4mBW7artMpAyWYDjKi1sq+czkDBaOTRvs5oj0mQZvgPoh9G9tOFtV8yRvlP8NgD999k/M8c3BMAwuW1zAi1trqPccet0ah83CxMxEJma2J3bd/hCNrUFC4SjBSBSL0T7jxGptn3VitRhYDNo7+E0wMYlGIWKahCNRwlGTUCRKMBwlEI7iD0XwBsJ4/GFafCECYRttbXmk8CtM239w25/t1J7WcDNPFv6YJwt/jMMSz9z0z3LO2O/yyOoo58wZTXZyXI/+bPyhCL5ghEA4SuTDqYgG7TNpLJ3y8AZWi4HdapDotPV54sI0Td7dW8/6Q8xgqGj2UdHsO2C7xTCIs1soSItnRm4yBWnxSq4MAUV13iNaS60/KZkiIiIiIiIiMauy2cfeGu9gN2PYWVvS1ONkys4qN9sru178PWpGeb/6EZoDVeQnzmK/ZxPvVD0IwMLM8whFApw37vuMcub2QycbvLOnHqvFwrwxqX16bpHBsmsAZ3mNZM8U/ZL3qh8C4LZjb+dri7/Gu+++C0BynJ3z5uTy0KoSQpHeJfKT4+wkx9n7vL3QnljwBsLUeQJUu/1UtXyN6pYbCUWDNNj/it+6kYjx8SzOYLSNtXXPsrbuw4TL8vb/3HXqH7lg+jnkJeXhsrswTZNmn4+KphDlTW1UNPtx+0JdtKB7NotBSrydvFQXU7KTyB/lOqK4b5omy3bWsq2i5bDPETVN2oIRdlV72FXtIScljrNmjSYlvn/+juTQotH2BNlQpWSKiIiIiIiIxKx39tQNdhOGpRq3n/0NrYxNT+h2P18wwordtR3vTdPk3eqHKGxZQ2OggorWHQc9dn3d/wDY0vhax7azCm7hpNwbsFr6riPrrd21OKwWZuQm99k5RQaDaZrsrOo6cSl9p6J1V0ciZWH2cfzqlF8QjUY77ZMSb+foiem8s2fodPoahkFSnJ2kODsTPpwJE4ma1Hr8lDX9gpL6Vqpb/PgsO/BaX6XV9maX57l12S3cuuyWLj+Lo4BE5hPBTRMrAEi1LMQwDJoi60iyTmBy/GXYrVDmX0GcLZ6CxLmMSZxKQfIUfOFMGrxBtpS3kBRnY8n4NGbmpmC19C6pEo2avL6jmp1VfZtcrG7x88ia/Zw+I5tJWT2fnSl9Z2tFC439VgrvyCmZIiIiIiIiIjGpxu2nqsU/2M0YtlYVNRwymbKyqJ5AKIppmqyre47/7vvBQfc1MPh4WeWuvVz6R5aX30Mg2kaOazJfn/1fXLYj69AyTXhjRw1JcTbGpMUf0blEBlN5kw+PPzzYzRjWmv2N3L/r5o73r1390kFnT8wfM4rd1V5q3EP395DVYjA6xcXoFBdLxqXhC0Uoqc+msG4xpY3fIRDx0mJ7Br91PUHLvkOez08pfko7bWuOru/42RMpYoPnzk6fF3lXdHkupyWR43dfx9kTruPMmVOY0sPZkOFIlFe3V/fbrNRAKMqLW6o4dXqUWXkp/XIN6VowHGVV0dBeA0/JFBERERGRIaw1EKbeGyAUiZLicpAab8dutQx2s0SGhPKmA+ufS9+pbPZTVOftGOH8afXeAFvL3fjCbp4s/AmbGl7p+MxmcbIg42xc1mTGJs1lbvqZHR2SUTOCgYXWcBNt4WYa/RXsbn6PzQ2v0RysIhBtX1y72reX29csAuCCcT/kM6OvOeySMFHT5JVtVXxu6VgSneoKkdikWSn97xfvfY/GQDlpznweOudN0hMOPqPNYjE4dUYWj60pi5l1u1x2K9NHJzN9dDJR06SlLUSddyJtwQhR0yQSjeCwRchIstEc3oE/0kJF2w7eqvw3ALNSL8Q0o2xvef6I2xKIenmj4i+8UfEXvtVeQY0vzv8a/++su4izdb12i8cf4oXNVf2ewDJNWLazBkAJlQG0bn8jbcHIYDejW7qDEBERERnGat1+snq4kKQMHZFoexmPdSWNNLV1rkltsxhMzUliwdhRZCQ6B6mFIkNDV4vJSt/6oLCB8Rldz055bvM2HtnzKzY3vEbwwwQIwMUTfsqxOZ876DkthhWARHsaifY0slwTmDbqM5w//jbC0SC7mt/hvk+MDAd4ruTXPFfyay4e/xOOzrkSi9H7pHJrIMLLW6q4ZGE+ll6WlBEZbOFIlH11Wh+qv333qJ+xvaaESybezolTxh9y/6ykOI6akM77+4ZOua+eshgGoxIcjEpwAJAUZ+OU6dmMS/9oEfajP7H3vUD7wvOVzT5q3AGa2wK4/WE8/hCBsIkvGDigRGNrqJmIGcJpSWZz3bv4QzYqPSWsafkNhmnDNDrPtPrXxr/wr41/AeC1q1Zw+qQTAQiEIxTXt/LOnjpaAwPT2f5RQsVmNZiWozKR/a01EGbD/qbBbsYhKZkiIiIiMky9s6eOqGkqmRJjiutbWb6z5qBlPMJRk+2V7Ys9z8hN5oQpmcTZrQPcSpGhoVLJlH5X5wmwp8bLpMzO5bEeXP8KX1x2Vsf7LNd4zhv7A2aknXhE17NZHMxKO5W7j9mNL+zhjnXHd0rUPF38c54t+TVHZV3CoqwLGJc0v1fnr2j28X5hPZ+ZnHlE7RQZaHtqvARC0UPvKEdkdGI+X535H+YXjCLe0bNu08XjRlHS0EpFDM+WnJCZwOkzcnA5ur+njLNbmZCZ2OWMxXAkSmswgjcQprktSFNriMa2ROo9Adz+EEtGn9qx7xVch2maNLUFeXTvrRS1vXbA+T77yEmMdV7A7Kx5HJ93MTaL48i/aC+ZJry+vYZEp438USoT2Z/WFDcSigz9GV5KpoiIiIgMQ+/vq2f9/ibmF6QOdlOkh4LhKO/urWNLeUuPj9lR6aa0oY2Tp2cx8SBleESGq8bWIL4hXgpiuFhZWE920uiO999//Q7+b90fOt5fMek3LM688LBLcB2My5bEnUdtBKCmrZDfbmpP3kTNMB/UPMYHNY8BcOWkO5mfcXaPO9rW729ibFoCBenqGJPYEIpE+aAw9mY+xCqrxcKCglE93t8wDM6YlcPDq/bHXMLLYhgcMymdxePSjvhcNquFFJeFFJedvFRXp8+C4SgtvhAtvhCtgTD+UIRgJIppwukzH8JqGBgG/PjtW1he9nDHcfsDz7G/7DleLPsZ4+Mu4uppd5AaP7BJlUjU5IXNVVy+eAxpCQOf0BkJWnwhtlb0/BloMCmZIiIiIjLMrN/fyJrixsFuhvRCjdvPK1urDijp1RPeQJj/bapk3phUPjM5A5vWU5ERIpZHAMeaprYQD6/aT5rHx0/3fZN9rXsByIgbz9WT7mGUI49I1MRm7b/SWdnxE7n7mN20hppYXvFPdja9TY2vEID/7vsB/933A7484z6mph57yHOZJry2vZrPHzX2kKOwRYaCdSVNWnh+AE3JTiQl3n7oHT8hOc7OBfPyeHlrVcz8XbkcVs6aNXpAEssOm4XMJCeZSd2XqF028SHgIVqDrfx02d/5w9pbOz4r9j/Dzzc9w3FJ93HqpCUku3r3d3Qk/KEIz22s4MolBfq90Q9WFTXEzLpDSqaIiIiIDCO7qz28u1cjF2NFKBJlw/4mVhc3HvEDxKayZiqafZw9e3RH7WuR4UzrpfQ9fyjCfe8Vs7KwgXA0SjASpS0QoTUYxoxGiUb/wz6zPZFiNUcR33Q3z6wNAsVA+5pOSXE2kl12Ul120hOdZCQ6yEx09lmiN8E+ivPGfZ/zxn2fX64/hcZAecdn9+y4HgC7JY47Fr1PnO3gM/a8gTBv7KzhvLm5fdIukf7i9odYv1+DZAaOwcJxPZ+V8km5qS4+f9RYXtteTVFdax+3q2/lpbo4Y3YOyXEDl5DojQRHAr8/67v8/qzv8v8+eJRvvnFVx2fvea5nz+rbOH7MOSwel4Z9gAYStfhCvLClkosX5GPVult9psEbYFeVZ7Cb0WMxNWztnXfe4dxzzyU3NxfDMHjuuecGu0kiIgNC8U9EeqKy2cdr26sxY2NQT48NRgwMR46sREO9N8CKXbWsKmpgT42HptYg5od/MaZp0uILsaqogfveK+aDwr4biVXnCfDomlJ2Vrn75HwiQ9lISaYMZAwMRaL87rXdrClpZENpM9sq3BTVt1LjDrC/7R3Wm4917Jsd+C0Gdgzgo+pe4ahJU1uI/Q1tbC5v4c1dtTyxrpy/v13IE+vKeGdvHbuq3NR5ArT4Qnj9Ybz+MG5fiOa2IC2+EG5fCO+HJWDCkWhH7OzKjxYu586lm7lw/I+ZnHLUx98j6ueHaxbywK5v4Ak2HPT4wlrvsJzJ6QtGVAJvGHlvb31MrCPQXwb6PjAv1UVW0uGvNxhnt3L+vDxu+Mx4zpydw6y8lCG1tp1hwJLxaVyyMH/IJlI+7RvHfI4111Z3ivO1jt/wUuU3+eeqFeyu9nT7u6IvVTT5WLazZkCuNVKsKmokGkMPsDE1M6W1tZW5c+dy/fXXc9FFFw12c0REBozin4j0xHt762NmenRvDEYM3FHlZk5+aq+PC4QjLN9Zy54azwFJLae9vYZ0c1uIYLj/6mkHw1Fe3VZNZbOPE6dmaeScDEsef3un+0gwkDHQZbdy6cJ8dtd4sFkM7FYL8Q4r+/0v8E7dXWDCjLijOHv8X0iOdxFnt/JRiAlGoviCEdwfJkca24I0eoPUeQO0BSNUtfipavH3uk2GAXarBYfVgtNuIc5mJc5uIc5uxWW3kuC0keM4nyljLqEtv4j/235hx7FbGl9jS2P7osZz08/gjDHfIDt+Yqfzf1BYT1qCg0lZsb/uVDgSZWNZ84eL+EZJT3QyMSOB2fkpJMVIp6l0Vt7Uxu7q2Bmx3R8G+j6wr0o4JcfZSc6xMy0nmZOnZVFc7+WDwgYavME+Of9htcll57Mzs2NyIfXF47J56LwXeWdvFW+U/Z1lFffgs66jkHXU7jmXySVf5ZiJWYzPSOjz9bs+bUelm1SXnaUT0vv1OiNBnSfA3trYinExlUw588wzOfPMMwe7GSIiA07xT0QOpbypbdiO0h6MGFhc30pOchxZyb0bmfjmztqDdnoEQlFqQ4G+aF6PbClvobE1yDlzclXbWYad4RrvujKQMdBmtfC7S+bwp2V7O7ZtqHuRFaW/BODotGO4Y8bPcObls76s80KxTpsVp816wMLApmni9oepbPZR4/ZT5wnQ2BYkHDGJmOaHM1uMjqRM1IRo1MTsOL49SRwMR/EeMoTame54hcxEJwH7+6x2/7jjk80Nr7K54VUAslwTuGDcD5k26jMd66cku/KPaDT6YGtpC/HcpgoaWz/uqK33BKj3BFhb0sTk7ETGpSeQmxo34Is3y+ExTZO399QNdjMG3XB4FrZaDCZlJTE+I5GNpe3lXftzYM2nWQyD2fnJHDspA6ctdu8Jl4xPY0+tlzMt32R62gn8v62XA+CxvcDuoIe6LTeTlzKKYyam93vC6IPCBpLi7MzITe7X6wx3K4saYq6qQkwlU3orEAgQCHx8t+V2q9yBiIwMin8iI8/akuFXpuRw9VUMXF3cyLm9qKW/raKFXUNs9Gh5k4/H1pZyycJ8jUqWYaWsceQkU3qrL+8D9zR/wH/3/QCAbyz5BhfEXYBhGBw7OYMEl4N3etDRaxgGKS47KS4700f3vNMpGjUJRaOEIybBSJRAOEogFCEQjuIPRfCHorQFw7QFI3gDYVp8IdqCEdqCEfY3tgHzGcMT1Dh/TNCyu9O5a31F/HPnjR3vT8q9EU/wRs6fM71HbQyGo9S4/bQGw/hDUSLRKNCeELJaDCyG0fFfi9GepLJbDRKdNpLj7Fj6eMZgdYuf5zdV0HaQ0l5R02R3tacj2e+wWUiKs5HgsJHgtJEUZyM72cn4jETNZhxCtle6qXUP3CCM4WIoPwtbLQaLxqUxfXQyq4sb2Fbh7tdZ5YYB4zMSOG5SBumJ3S/8HgsMw+C4SRk8t7GCcUnzuGPR+7xX/QhvlP+NVttbtNreIuL+J1Ub/EzJSuTEaVm4+rHE2rKdNSQ6bRSkx95Mn6Gg1u2nsNY72M3otWGdTPnNb37DHXfcMdjNEBEZcIp/IiNLrdtPSX3bYDdjyOirGFhY56XeGyCjBw+fja3Bg44e9Ue8vF72V96p+g8n5n4Bb6iBNbXPdNpnQca5nJZ/E1muCX1emqC5LcQzGyq4dFE+8Y5hffsvI0QkarIvBh++B0pfxcAdjW9x764vA3DelAv5/Wm/5/333u/4fOHYUZQ2tvbb7x+LxcBpseK0QUIPjwmGozS0BqjzBKj1BKhxO7B6/4AJhIwyPLZX8VhfAiPc6bgVlfeyovJeblv18ba/n/13ZmfNZmbWTHwhHztqyjCDYyhp8NHgDWJiYrNYsBj0Km5bLUZHWbHJWYlH3MFZUt/Ki1sqe7WmRjAcpcEbpIHO5YbiHVZm56WwZHwatgFa0Fm6FgxH+aCwfrCbEZNi4Vk4wWnj5GnZLCgYxdt76vp8sfqMRAdTc5KZmpNEimt4DaYZn5FA/igX5U0+khwZnFnwTcYmzu34fVUZ9yWygj9hT+0Sypt9nDo9m/EZPf0t0juRqMkLWyq5aEEeo1Nc/XKN4Wxl0cHXNBvKhvXT1G233ca3v/3tjvdut5sxY8YMYotERAaG4p/IyLK2pGmwmzCk9FUMNE1YU9zIWbNHH3LfZTtrOpVrWF/3As8W/xKXNYmGQFnH9jcr/tXl8RvqX2BD/Qsd778x+zHGJc3vdZsPprE1yLMbK7h4Qf6QWgRV5HAU17fiD2lx7YPpixjoCTbwWOEPO94/fumjWIwDO9dPnJLFQ437h8x6XQ6bhdEprk6dWh/NIqlsSaO8cQqVLV8kbPoJWHZQ5/gVptH1yP+bXrrpoNexRydgM9PwWdd1+XmqZSnJ1nE0RzfgjrSXTFuUdj2jE8YzIXUG7kAedZ5RrCxsYHxGAkdPTCe7l2UlAfbVenl5a1Wf/fm3BSOsLm6kuKGVs2ePVjmwQbSupJHWgOLc4YilZ+HUeAfnz8ujqM7LW7vraDmCtcASnTZm5iUzNTtpWMxC6c5nJmfy2NrSjvJQM9JO5IZp/+Dfu74CQJ3jl0zkt7T5pvO/zZUsHDuKYyak9/msQGj/HfPcxkouWZhPZtLw/nPvS1Utvj5PIg6UYZ1McTqdOJ36H1lkpAtFothH2MgqxT+RkcM0TUoaYvNGtL/0ZQzcU+Nhyfi0bmen7K72UNHUXnJofd3/eGTvrR2ftYWbO+2bZM9gbNI8tjUu6/a6/2/rFR0/XzHpN8xLPwuH9chq+de6A7y2vZrz5ub2+8KcIv1pV/XQKdkyFB1pDAxHwzy05xa8ofYRoyu/sJU4WxyRyIEdu6MSHCwcO4o1xUO31KTDZmFMWjxj0uJZOr792aC8yUdxfTYlDUvw+MIEjUJClnLqHXd9fKBpO2AGy0dCliJCFB30ms3R1TRHV3fatq7xPmgEyjrva8HGJRN/wTlTzuLcmdNITehZAmNnlZvXt9cQ7Ydi87XuAI+sLuXcObkqXzMIvIEwG0o1UOZwxeKz8ITMRArS4lm/v4l1+5t6tZ5KVrKTBQWjmJKdNGLK9OWkxDExM7HTLNWZaSfxowVv8p89t1Dq3UyJcTsLs35Jde0M1u9voqbFzxmzckhw9n1XuD8U4ZkN5Vy6aAxpPYzhI93KwticlQLDPJkiIuIPRXhjR02vat7L0OLxhyiub6XBG8TtD5HsspOe4GBiZmK/3AiJxJp6b3BAF7AcaUyz/Wb/YL9HQpEo7+6twxtq5NXSP/NBzWMdn2XEjWV+xlnkJcwk1ZFNbsI0bJYDH7ACkVZshoMaXyFrap9hY/3LtIWbiZjtoxMf23cbj+27DYCfLHybFEf2YSdDiupaWbe/icXj0g7reJHB5g9FKI7RkYyx4tldz7LPvRqHJZ7fn/AiRxXM6nb/JePT2FnlxuPvOvEw1NitFsZnJHSUfWkNhKn35tLsC4F5CRiQnuBg+uhkclKjRAw3SU4nT+z4D2urVjEmYRZ2MtndtJKtTa/jsqbgi7R0uobDkoiBhUD00Im/KGGeKLyNJwpvg1fat9116p/4zjHf6DLWm6bJB4UN/Z7ACoaj/G9zBefOzWVsev+UyJGurSps6FXZNhkebFYLSyekMysvhc3lzWwtbznoOkhWi8HY9HgWFIxiTNrITHguGjfqgJKfaXF5fHXmf3h477fZ1ricdd7bOWHs7ZSWL6a82cd/15Zy9uzR/VKSqy34YUJl4RhS4odXabW+VtHsY39D7JaojqleKK/Xy759+zreFxcXs2nTJtLS0igoKBjElonIUBSKRPnfpko8gdh4sOvOSIx/zW1B1pY0sbOq60X53t5dx4zcZBaPTyNZiyrLCFbd4h/sJvS7wY6BhXVeat1+0hMOjDWv7NjB/236Ltub3uxIfgBcPeVu5mec3aPzO63tnVS5CdO4YPwPuWD8D/GFPbxdeT+vl/+1074/X38CeQnTuWziL8lPmHlYSZUP9jWQkxw3Yh++Jbbtq/USHiIlpQbKQMfAS6ZfwiUT7iDBlsqFs5cecn+71cIp07N5bmNFn7dlICQ42xdhH0v7LJZTpmcxLefARehPnvzbjp/DkSg1ngCN3iDNviBefxh/OIIvGKUtGKYtGOl0/2qaJoZh0Bpqptnvxu2DFZV/pca/nWAkSsAo7HStW5d9i1uXfQuAhu+6SUtIwjRN6jwBVhU3DtiCvaGIyf82VXLePCVUBkqDN8D2Ss2++6TBvg8caAlOG8dMzGDp+HQqm33UevzUe4PYLAZOm5XsZCcF6fE4bSO7bOvoFBd5o1wds8M/4rDGce2UP/Povh+wsf5F3qz9GaeO/Q4N1Z+lsS3IU+vLOXFqFrPzUvq8TR5/mKc2lHPponz1UXQjlmelQIwlU9atW8dJJ53U8f6jGojXXnstDzzwwCC1SkSGItM0eWlLFRXNPpKHwYJrIyn+mabJprJm3ttb321nSThqsqW8hV3VHo6fnMns/L6/GRKJBVUtvkPvFOMGOwaaJnxQ2MC5c3I6bV9dupvzn5nd8T4/YSan5n+FOemnH/E1XbYkzij4BmcUfIOq1j3ctfncjs8qWnfyxy0Xk2TP4Ojsyzk25yqSHOk9PnfUNHl1WzWfP2osLsfIfhCX2LOr2jPYTRhwAx0DDcPgmJwryEmJI39Uz5Ku4zMSmJGbzI4Y7gROT3RwzpzcHpVosVkt5KW6yEs9+OhmbyBMU2uQxtYg9d4A9d4ADa1pJNhTyUuC6VntyRnTNClpKuXRwm/SENp5YLt+n0yStYAzx3yHo3LPOPwveJjCUZMXNldyycIx5KQcWblJObT3Cxv6pXRbLBvs+8DBYrUYHSUKpWuLxo46IJkCYLXYuWryXaQ6clhReS/Lqv5Alus5Zsb/k6J6H2/uqqXW4+fEKVl9XhrN7QvxzPpyLl9coPvsLpQ3tVHWGLuzUiDGkiknnngipn6piEgPvLevnuL64VMCYqTEv9ZAmNd3VFNS3/NfrsFwlGU7ayis83L6zGziHTH1q03kiFW7h//MlKEQA4vrW9le8XEZlz+vuofb3vxux/tzx36Pk/Ju6Jdrj06Ywt3H7MY0TdbWPcum+lfY1fwOnlA9r5f/tWP2yi1znmZMYvfleD7iDYRZtlNlMCW2NHgDlDfF9gP44RisGLho7Khe7X/ClExKG9rwxuCs8IlZiZwxMweHre/WWUx02kh02g7oCPUFI7j9IXzBCL7QRzNYcrjp+FXE2a20hT3Mumd0p2M8kVKeKPkmT5TAdWPfZFZuLpYBXPsqFDF5blMFly8awyitB9BvKpt9AzbrKJYMhftAGZrGZySQkeig3hs84DOLYeHccbfii7hZVfMEtb5CHAnf4JgJ97C6qJltFW4avEHOnj26z8uHN7WF+N/mCi5ekI9thK3feygfxPisFAD9jYrIsLOr2s26Ei3YF2tK6lt5eNX+XiVSPqm4vpVHV5dS0Tz8R+mLfMQfitDYeuDDg/SPZTtr2FbRwsPFT/KdZTcTjPpwWpK4cfITLEq/psuShH3JMAyWZF3El2b8i+/Ne5ETc6/v9Pkft1zMf3Z/i5ZgTY/Ot6/Wy7aKlkPvKDJEvLu3HvWnDYzUeDuTshJ7dUyc3cpn+zghMRCWjE/j3DmjB6zdLoeV7OQ4xmUkMH10MrPyUpiVl8L00cmMz0hgZk4O5k9NzJ+alH6zjHMmdE7UP7D/ZH628lIKaz0D2sHsC0Z4dmMFrTGYLIsV7++rH+wmiMQUwzBYOLb7dQAvnfBzXLb2Khblrdspj/6lI+ZXtfh5bG1ZvwxOq2z288q2aiUCP6G0oa3LmUSxRsN3RWRY8fhDLNvRs04kGRrc/hBrixvZWtFyxB0kHn+Yp9aVc+ykdBaOHXXYCzSLxIoat18di/3ggfeLeW17NbWeAL5ghLZQBF8wQjgcJs3+IhusD8CH4SWr9d+8sSUeKAEg3mElOc5OsstGistOqstBarydtAQHcfa+m+qfEz+Z88Z9n1lpp/CXbVd1bN/U8AqbGtpXMD41/yuclv9V7BbnQc/z9p468lJdGmksQ15pQ9uwmnU81C0oOLz7qIL0eK5cUsBLW6uo9wT6oWV9x241OHVGdpfrowwVY1LzeeHqewmE/k7SnUmEou1/pl62ct/urzCn/FbOnDFzwGrzt/hCPL+pkksX5WPXaOs+tb+hlfJh0MkoMtCm5SSxpriBprZQl58bhsGvlqzh7coH+F/JnXxQ8xhxtmSuWPx1Xtxc1bGOyinTspg+um9/H+yr9bKxrJkFBb2b6TlcrSwaHgljJVNEZFjZWt5CKKKexb4QDEePaIReWzDMmuJG/KEIKS4HoxLsZCQ6SYt34A2GqXX7KaprZVe1p09Hc0dNk3f31lPe5OOzM3NUp1SGtcrm4V/iazC8vqOGlUWNB2wPG8VssD5A1GgflVsQ+geJziTCEZNQJErUhLZghLZghOoulg1IdNrISHSQGu8gOc52QIw1MLBZDexWCw6rhTi7hTi7lTi79aD1nCckL+LuY3bjC3tYWfM4a2ufpcbXvkjrsvJ/sKz8HyzJuohzx36fOFsiVqPz7X8wHOXFLZVcvrgg5kaTH0prINznZRtkcJimydt76wa7GSOGYRjMyD38DqW0BAdXLB7Djkp3x1ohte5At2vhDbSkOBvnzc0lKzk21gBx2u14bmvlr++s5DvvfgYAn3Udq31XsWndWC7M/zuLxhYMSOmvGrefl7dWce6cXCx9vNbASGWaJu/vi/3SNyKDwWIxWDohnVe3VXe73wm51+GwxvNk4Y95s+KfuKxJXLb4Bl7fXkNRfSuv76jBGwizqI8HZb6/t56CtHgyEg8+uGkkKKlvHTbPrnq6EJFhIxI12VapciV9pbDOS/4oF0mHMdJt/f5GVhU1EgxHD/jMYhgDsqhicX0rj6zez7lzc8mOkQdlkd6qdmsEY3+4cH4eqfF2fMEILruVeIeNcv97PLv/NqLRMJPip/O1+Y8RNT+OLaZp4gtF8PjDuP0h3L4wLb4QzW1BmtpCeAPhjhcNvS9n6LRZcDmsxDusJDhs7f912kj4sCZ/ktPJ8TnXc2LuF1hZ8zhPF92BzXAQNoOsqX2GNbXPAHBszue4cPyPsBgfJ5rrvUGW7azhrNmjD3b5mNLgDfDmrlrKm3zE2a1kJTmZmZfM5KykPl9kVAbG9kr3kJ/lMNwc6awDu9XC3DGpHe/DkSjVbj+7qjzsqHL3e1nE7oxNj+fMWaNjbsCN02blK8cdRZKtkH1Nu3h49/ep9G0jYBTyWOXpbK35B2dPP5qcAbjvLaprZcXuWk6Znt3v1xoJ9tV6qRkBa+CJ9JdpOUmsLWmkoYu1Uz7p6OzL8Ic9vLD/d7xU+gdMopwz58u8X9jA+v1NfFDYQGsgzAlTMvssoRKOmry6rZorlxSM6PvQlUXDJ2GsZIqIDBt7az20BiKD3YxhIxI1WVvSyMnTeveQtLfGwzt7Dj59cyASKR/x+MM8ua5syJdwEDkcpmlS3aLOxf5w6aIxOGwWiuraSwqVeDbx9P7vEI4GmJU8m1/O+gWfOWoKz2+uovnDkgKGYRDvsBHvsHWZwA2EI9R7gjS0BtoTLf4Q4Ui041jTNDGBSMQkGIkSCEcJhCMEQlFM+PB9tON6B5PotDEq4XguyHyPUfEG9+8/ttPn71c/yvvVjwIwY9RJXDX5d1gNO7urISPRyZLx3dedHurWFDeyqqiho6PWH4pQ2thGaWMb7zrrmZSdSG6Ki5yUOJLjbCoHGQOC4Sgrh8FipSOdzWohf1Q8+aPiWTwujVXFDeyq8gzofaFhwNLx6Rw1IS1m/+3HO2wsGZ+GNzCJW+Y9zm83nkW9fz8AO8O3Urf+VhbnnMjRE9OJd/Rvd8+W8hZcdivHTMro1+sMd9GoOaw6GUUGg2EYLB2fzstbqw6570l5N+CLuFlW/g9eLv0jFa07uHbq/yPBYeWdvfVsLm8hEjU5eVpWn/2uqPMEWFPcyNET0/vkfLGmsM5LdcvAJoxrfcXs2beOGWOupiCloE/PrWSKDG2mCW2NEPSCIxHi09rvgkW6sKVMs1L62rYKN4vHpfV4dorbH2LZztoenz9qRjCw9OsDbShi8srWaloD4UMuTjfkKAZKN5rbQvhDSiD3t8rW3dy38ybC0QBnTjqT7+R+B6thJcVl57JFY3hsbRluX/cJDmgfUZw3ykXeKFevrm+aJv5wtH3tlmC4o4xYayBMazBMayCC1x/GEwgRipgds1/KGttnLY3lRSKWCsqdXz7g3DuaVnD7msUd7y+u/imfrT6Jy+YvJNmZcNDYbJomdd4Alc1+WnwhAqEI4aiJxWifffhRqTKbxYLNamCztL+3Wy247O1/dsmuQyQyehn/IlGT5Ttr2F7ZRX21D3kDYTaVNrOJZgCsFoMEp43kOBvJLjvpCQ6mjU4mUaXBhpR1JY3tM7pk2EiJt/PZmTkcNT6dNSWN7Kh093tSJScljpOnZcXObOVuYuDsvBQ2lDbR3AY/XPA6nmADf99+PdW+XdQ6f8K7tRexp/ZGjpmQzpz8lH69z15d3Eicw6r1AI7Azmr3IUfTi4w4h/EcPCU7kXX7ndS6Dz3Y7Mwx32JZ+T8A2NzwGvta1jC/YAkuh5XXt9ewrdKNzWLh+CkZfRZDN5Q2MXdMSr8nuoca0zQHZVDMq6V/ZlPDK1T51/LcFc/16blH1t+gHL6B7tDzNcPm/8Lqe6Cp+OPto8bD0i/D3CvBldp/15eYU+cJUNGscjd9rTezU6IfTl/9dOdu1IziDdXzfMmdbKx/iXTnGDJcY9nd/N4B5zg25yqOzfkcGXEF2Cx9uxjyO3vqsVoszPtEyYkeGYyEhmKg9ECRFmLudw3+Mn6/+TwAZmbM478X/ZeNqzd2fJ7gtHHcpIwejYI7XIZh4LJbcdmtpHWzSPxHZcaa20I0tgZp8Aap8wao8wYIhvMY63sRE5OwUY7b9gJe28sHnOPpojt4uugOvvR6+/ulecdw8thTmBk/kRZ3Mx/UbCEp/gTs0SkYOLEaBlYLYAmQaE/EYWtPmFgMDvngabcajEmLZ2JmIpOzE3HaPiy3cxjxLxiO8sLmSkobe1c+LRI1cftC7cmwDxf9fX9fA+MzEzh+cgap8X37e0h6z+0PsaG0abCbIf0kJd7OaTOymZOfwus7avqllFtWspO5+anMzE0+vA6xIfgcbHGlcvTEdF7Z2r4+QJIjnW/MeYRH936PbY3LcdufIRDZQ2DPLyis83LajOzDKtvbU+/sqcNusTA7P6XfrjFcRaImq7pYo01kyBiCMfBgz8GGYXDKtGweW1vKofLzhmHwwwVv8OsNpwHwz503cu2UPzEz52SiUXhjZw2bypux2wyOmdg3s++C4Sirixo5aVpWn5wvVuyt9VI3wKVaW0NN7Gh6G4CzJp/V5+dXMkW6NxgdevuWwePXQKiLB+KmEnj1Nlj+C7j8PzDp1L69tsSs3dWewW7CsLWtws2icWkkH+IhbHN5MxVNHye0Nta/zEN7bsFq2ImYH4/abgiU0RAo6/Ic71c/wvvVjwBwXM7nOSnvBkY5c/vgW7R7a3ctdqvBzNwePOwNVkJDMVB6SHGvf4WiQR7e892O969d/SKJjsQD9puak8TG0iaqBnjq+qd9ssxYburHs19M08TtD1Pr9lPjCVDTEk+1+2bSQ18lShtt1tU0OP5wwPlSTFhavo7ryzczifZEx1eBfTzO/xHiQYK0mWMIWbqO5wBpxol4zE2EPpwJkmDNpiBhCXmJkylImkSjby57a1J4Z6+TeWNSWRTagOPp63oV//yhCM9trOizP/+oaVJY66WssY1Tp2czNSepT84rh+eDffWEIkNn0XLpH9nJcXxuSQGrixtYW9x0xLNUXA4rU7ITmTE6hZyUw5yJMsSfg6dOPIV1JU0dHVRx1kSumfInfrXhVFqCNQSs29pje9N3eHhVgFOnZzE5u3/imWnC8l01GAbMylNCpTe2VrT0aHaryIAb4jHwYM/BOSlxzMlPYXMPqpZkxBXwqyXruG/XVyl0r+GRvd/jljlPMSN3HOFolBW761hb0kSS095nyeKtFS3ML0gdMQN2TNNk1SCUMXxh/10Eo22McmZy7dxr+/z8SqbIwQ1Gh96+ZfDIZe13ZHR1E/3htpCvfb+rnlBnogBQ1aJZKf0lEjVZW9zY7QKTrYFwR63fPc0f8I8dX/j4eLPzA0JewnRmpZ3KqponaAnWHPSc71U/zHvVD3e8PzH3es4quOWIZqyYJizfWUtagoPRKd2U2hmshIZioPRQU2tQC5X2s/u3/Zz93k1YsPLgeS+Tl5xHJNJ1WbXjp2Ty+NqDJxUGk2EYpLjspLjsHR1pkahJrcdPWaOP0sZ0qt0nE46GCBmVRIxG5jl+ytPEE28aYHSORROw8Eec/AonFxuVvN7NtRvNtzq9b43UsNP9Aju7qMR1TdxMlvrKMQGjh/GvdcyJPLuxol9GuwXDUV7eWkW1288JUzL7/PxyaDVuP7uUNB4xrJb20b8TMhJ5dVsVTYdYH6oreakuFoxNZXxG4pEt8hsDz8HGVU9w1ISlvLD545mRNouDnyx8m5U1j/FM8S9ptb6HLT5Ictv3eXlbNfNa/Bw3KaNfFkA2TVi2s/2+XgmVnglFoqwp1lopMgTFQAzs7jn4mIkZ7Kv19mg9XZctiS/P+Dd/3no5Fa07+Pv26/j67EeZk59LWzDC6uJGVuyuJTHOxviMhMP/fh+KRE0+KGzgrNmjj/hcsWBXtWfAyxiWeDaypvZpAH509L9x2px9fg0lU2JAvTfA9ko3Dd4AzW0h4h1W0hOdjEuPZ1JWYv/UQB2MDj1fc3vANk0geoido2Ba2vf/9g6VuxnhTNOkdoCnDY402yvdLB5/8Nkp7+6tp7a1ircq7uPtqgc6tqc7x3BmwbeYlLIEq+EgwZ7a8dlnx3ztgPNEzQgN/nKWV9xDna+YYs+Gjs/eqryPtyrv46yCW1iUeQGpzpzD+i6RqMlLW6r43NKCruuVDlZCQzFQekEdjP2rvq2elZWvAPDVOf/gyrmndbt/bqqLKdlJ7KmJjb8Xq8VgdIqL0SkuloxPIxI1PywNlsf0trV8vToJAxOL0Z7a+CTLh+/jMXiZRG505bM2LpsGfxlNwUoCkfbyc6Nds3FZ0ilrW0vI7KIknWkHo72zNMWEv/pKATA41H1te/yLPn41Tx/9Kg2R3q1B01sb9jcRjkT7dBFS6Zl399YfskyHDD85KXFcddRYNpY2s25/I4FQ9/dEVovB5KxE5heMOvxZKJ8UQ8/BE7+9nRSXnZZPzGwwDINjcq4kPa6A+3Z9lZboGqzJPyPe/QM2lbUnKc+ePZqEflgb6qOEStQ0mZOf2ufnH242lTX3qLNXZEDFUAw82HNwnN3KKdOzeWFzZY/uI2wWB1+a/i/+su0q6vwl/H37dXxn7rMsHZ+G2x9iZ5WHV7ZVcenCMWQmHXnH/J4aD8dOzCAlvv/KLw4F0ajJ6kGYlfJM0S8AmJC8mBnpiw+x9+FRMmUIq/X4WVnYQHF9a6cA0OILUdXiZ1tFCxmJDo6akN63U3YHq0Nv838/zHz39Kkp2r7/5sfgqK8c/nUl5jW1hQiGD/X/qhyJSNRkTVEjp844cHbKtqoKvr/iBrY2voH5iZhx/bS/MSvtlF5dx2JYyXSN5YpJvwagKVDF37dfS71/f8c+L5f+kZdL/8jJeV/kmOwrSYvL6/X38fjDvLK1mgvn52H55Oi8wUxoKAZKL+yuPvgi23LkMuIz+MOJL/N64etct+DiHo3i/cyUDEoaWmPy95HVYpCZ5CTfFeTGtXdgwTzI7JCPWTAxsfDvUAN7L3uFxkg8vlCYtmCEtkCE1mCY1kC4yxJN4UgYTyDK7vrtbG96iwta1xBv7u1I1BxaFCPkY0zZ8zTkXnEY37h3tpS3EImanDYjWwmVAVJc30pZL9fAkeHDbrWwZHwac/JT2FLeQkl9K1Ut/o7yXxbDYHRKHGPT45mRm9x364HE2HOwsflx5hVcztu76w7YY2rqsXx5xr/5986baAxtJZr8Q9LafkdVC/x3bSnnzM7tm+TTp5gmvLmrFtOEub1dp3AE8YcirCvRelAyxMRYDOzuOXhiZiInT8ti+c7aHp0xyZHBDdP/zl2bzqPev5+/b/8CX5v1MKdMy8brD1PW5OOFLZVcsXjMES8gb5qwsayJE6cO77VTdlS5D2uW6ZEo8WyivHU7AMdk998zgqXfziyHzTTbF3x+bE0ZRXWt3WZS671BXtxSxUtbqg5Y9PmwdQSynnYGfCKQHS7TbK/FeDhW/wMNWxvZVOpmYOyocnca+QZQ0lTF0ffPYUvja5hEKUicw3VT/4+7j9nd60RKV0Y5R/PDBa9z9zG7+fKM+xiXNL/jszcr/sUvN5zMn7dczvq6FzB7GQdKG9tYW/KpBR8HI/6BYqD0So3bP+A3piNRWlw2n8m7hFl5yT3aPznOzlET0vu5Vf1rTsNL2KP+QyZSPmIQxRLyMbX6JY6emM7J07I5Z04uly0ewxeOHc/NJ03i+uPGc8H8PI6dlMHk7ERGxdtx2OyMindwVMF8bpjzLb7r9GP08rHEBOZXPT5g8W97pZu39hzYYSl9zzRN3turP2tpH128ZHwaly0ew1dOnMD1x43nxs+M56YTJ3LZ4jEsnZDetwurx+Bz8MzRSThsXcfPCcmL+Prs/5LqyKE5tJ8i++XY47fTGojw1Ppydlb1z8AM04QVu2vZUamBHwezfn9T3/XfiPSVGIyB3d0HzslPZemEtB6fLss1gRun/wOAUu9mHtrzbTAinDV7NKkuOx5/mJe2VBGJHvm95/ZKN4Hw8I0BkajJ6uLGQ+/Yx14p/SMAyY4s5mec3W/XUTJliPH4Qzy1vpz39tb36h/onhoPD6/aT2XzEa4bMVgdem2NHy5q1dvjzfbjfBrVMZIpmTIwIlGT17ZX09zWXvNyRc1bTPrrGLyh9l+SR2VfzjdnP8Gc9NP75fpTU4/lG7Mf4/dH7+Sqyb/HbmkfTbffu4lH9n6X76ycxp+2XEYw0vP/H1YXN378/89gJjQUA6UXVOJr4MzKTcFps/Z4//ljUslK7vu6vAMhJc7G0fVP93huSCcHiYEfrdcyPiOBJePTOGdOLtcdO56vnTyJ644Zx6WL8jl/qotUf3mPEzgfsWCS6i8nLnzoBUb7yqbSZlYWqr59f9te6aZ+gOtry9DntFlJcdlJirMfNHlwRGL0OdgZcne7RklO/CSunfp/He/3md8nK62KiGny+o4a3ttX3+sBST1qnQlv7KihqM7b5+eOdW3BMJvKmge7GSKdxWgMPNRz8DETM5jTi8Xjp6YexzVT2jvktza+weP7bifObuXcubk4rBYqW/y8tbv2iONmMBxl+zBOOG+vbMHtG9jBf2tqn2FvyyoAbpx2T7/OJlcyZQjZV+vl4VWllDcdXkLE4w/z1PpyNpQeQafaYHXoBY/wJiugjqWRrNat9VL6W9Q0CYQi7Kx083/L9/HC3jX8fNcdH35qcHnB/ZyW8yPCfTBK41AshoWFmedy59JNfGXGAxQkzun4rNS7mR+snsuqmicJRw/dEROJmry6rZpQJDq4CQ3FQOkhXzDC9sqB6zweyawWg/kFqb06xmIxOG16dr8s7tufRqfEccXsBKzNJQxEDLRaDEYlOMgfFc+E5CP7vWEOcPxbVdSgTrB+FIpEWTUI9bVFYvk5eN6YVCzddBqNTZrDV2bc3/F+Z+j7TMtvj53r9zfx4paqfilRGTVNXt5aRXWLBr590urixpgsCSrDXAzHwEM5aWoWE7MSe3zKeRlnce7Y7wGwru45djS+RVqCgzNn5WAA2yrdbK048uexTaXN/ZLMHmzhSJQ1AzwrJWKGeWzfbR3v8xNn9Ov1lEwZAmrdfl7YXMkLmyuPeKpnJGry9u46XtpSdXhTxgarQ8/R88DWJWcfrhkjMSUaNanzKpnS197YUcMvX9rBf9eU8s93ivi/N/fxj3eKuP+DEu5Z8yL/r+JnHfvmBH7Hqt2Z/GfVfv72ViH3vlfEk+vLeG17NSsLG9hW2UJZYxtuX6ij1nVfMAyDKalH8605TzIv/axOnz1R+CO+t2o23/5gKkXudd3epDS2Bnlrd93gJjQUA6WHVhc3HHIxXukbU7KTDqt8TFZyHJcszCcpLjaWJpyUlcjFC/OJN4+ws2uQ7gH/sukxiuoGNqHy1u5admuGWL/YVNaMxx8e7GbISBTDz8EpLjtTc7o/z5TUY/jVkrXkxk/DG25ktefbHDvVxGoxKKpv5cn1ZXj8fT+KOBRpT6gM53I2vdHiC7G1XINiZAiK4Rh4KBaLwVmzcshLdfX4tCfl3cDklKMAeGjPLez3bGFcRgLHTGov6fv2njoqDnMg/EdafCEK61qP6BxD0daKlgG/l1tf+3zHzzfNfLDfrxcbT3mDJBiOEomauBw9L+/wSeFIlA2lzRTVeUmMs5EUZyct3kFaooNo1KSqxU9ZYxul/bC44p4aDzVuP2fNHt27heUGq0MvPg1GjYemEnqXCTdg1DhwjTq860rMa2wLamRPP6hs9rGq6MDRBKalgXrHnwgbPgwMZlr+RlLyZHzBCN4PFxpuDURoDUSo5MCOOavFINVlJzHORoLDhtNmwTRpX8bYYmCzGNitFuIdVlwOK4lOG4lOGy67tdtpmtdM/SPX8EdqfUWsqX2GNyv+1fHZX7ZdBcBlE3/JxOQlZLrGHnD8tooWCuIcTD2cP6yPHElCox9ioGmaFNZ5CUdNHFYLo1Nch/37TIaGlrYQW/QAPmAmZR7+PVFuqourlo7lrd21VLv9ePzhPqmv3NfmFaRy4pTM9vgaY/eAUUyKiLLTuJede+9lWvmXuW72t/qn/M+nmCa8tr0al91KQXp8v19vpPCHIgeuZSYyUGIsBn76HnDRuDR2VXu6rbTjsiXzlZn385dtV1HrK+KN2q9z6Zz7eHN7gHpvkMfWlnHBvDwyk/q2XGWLL8SKXbWcMWt0n543Fq0qahiS9wMisR4DD8VmtXDWnNE8vGo/vmDPkrtfnP4v/rXzi+xtWcU/d97IzTMfYmHBFOo8AfbUeHlpaxVXLBlD8hGs3bW1oplJvZg1M9SFIlHWlQxsCXJf2MNrZX8B4OS8L3UkwfqTkindiETbF4I/fkpmr4+taPbx2rbqjxdrHoS+jxZfiCfWlXHc5AwWFPQw2TBYSQ3DgKVfhldvO/S+n7b0K+3Hy4ikaeP94+iJ6XzlhAlUNPlIirMT77DSENzNP3d+k3C4mRznaH468Y+0Jc/BsLR30JumiT8cpaUtRIsvhNvf/vL4wrT4Q7h9ISJRk4bWIA2tvauFbrUYJDptJMV9+HLaP/45rv1nu9VClmsC54z9LqfkfYmniu6gJVhDkXst0D5b5SPXT/sbk1OOxmn9uBPsjeIgk1LHYW3ez4Andfs4Brb4QizbUdMpWW+zGEzOTmTG6BRyUuIGpMNR+tb7hb1bT02OjOUIS3W5HFbOnN3ecRSNmrj9IRpbg1S7/WyraKE1MHijdA0Djp+S2fn+MMbuAQ0srEg7AZpeBGCX7x5+v7qez039IeMy+v+hNBI1eWFLJRfOzyO3FyMd5eDWFDdq5p0MnhiLgUCne8CMRCcTMxPZV9v96PJEexpfnnEff9n6Oer8Jfxt98ncOu8dlu/00+AN8tT6cs6dO5r8UX2bKN5Z5WFsegLTRyf36XljSYM3wK4qzWqUISrGY2BPJDptnDYjm/9tquzR/jaLg+un/Y1/bL+e/d5N3LPjer4261FOnT6GptYQdd4AL26u4tJF+dith/dsvb+hjRZfiBTX4SdkhpIt5S14AwM7K+X2NYsASHFkc3r+zQNyTSVTDmFLeTMLx44iwdnzP6q2YJiXtlQO6kPyRz4q+1Xd4ufU6dmH7jwbzKTG3Cth+S8g5AN68CBlWMDmgrlXHP41JebVepRM6Q9TspM4Z04ub+yoAaApUMl9u75MW7iZednz+O7Y75LnyiZpbCbvFjZimu1lt1x2K64Ua5cz4qKmiccfprktiDcQpjUQIRiJYjHAwCBimkSiJoFwBF8wQtuHs13aghEiUZMWX+jjBHUXXHYryS4bKS47qfEOliT/jLQEB6W+5Tyy75ZO+96366sdP59d8B2mph5LfuJM1mdfxuLmu3q/AHNfJHX7KAYW1Xl5ZVv1ATO2wlGTnVUedlZ5sBgG6YkORsU7Ppw5aSPVZSctwUFqvOPIvof0ixq3nz01egCPVRaLQWp8+7+vCZmJLB2fzu5qDxtKm6jzDGypSofNwlmzRzM+I6HzBzF0D2gaFsKGk9Dkn/Fl70Xcs+N6AOqNp7l3Ry1Hp/2Ak6aMI97Rv486wXCU5zZVcMmCfLKSezETXA7g9ofYrLVoZDDFUAw82D3gkvFph0ymAIxyjuYrM+/nNxs/C8Bd247n5ws38Oq2JiqafTy3qZKzZuUw4QhmaHbl7T11jM9IIM4+MmdKf1DY0Kcljw8lakZ4YOd3iU+9kM/N/hxOW9/OOJJhZhjEwJ6YmJnIvDGpPV7/zmlN4Isz/slft11NVdtu7tv1Vb4y8z7OmTuax9aUUecN8PqOGs6alXNYC56bZnuVjGMnZfT62KGmfVbKwM4wrmkr7Pj5s2O+jsM6MPfjSqYcQihisqakkZOmZvX4mDd21PQokRI1o7R49xONhklKyMNh678yAburPTS1Bbl4Qf6hb14GK6nhSoXL/wOPXAam5RDXtgAGXP5Q+3EyYtVo8fl+1+iv4E9bL8UbamBG5kyWX72cLWu3ADCvYBTBqNGjxWIthkGKy94x6sI0TaJ+D4bFiuGIP+jNRyRq4g2E8frDePwhPIEwbn+o/X0gjMcXJhiJ4gtF8IUiXfw/MZnZca/ic7zCvtCfDzj/S6V/4KXSPwBQYM9it2HDYYax9GBEjomFiNXJs+HjaPugBMNon0Vjt1hw2Cw4bRZsVgt2a3v5Mru1fXtSXHvSJy3e8fHo9z6IgVvKm1mxq+6QD2pR06TOE+iyEzcr2cnc/FSm5SRhO8wRNtL33t5T123pDoktVovBpIw4RruSqfaE2Fztp2oAZlpmJTs5Y2YO6YkH6VCJkXtAA4PiU+8h0JrE1NRjufuY3bxT+QjPlfyCNtu7LHe/S9HKBzhx0gxm5SYf1sNtTwVCUZ7ZWMHFC/L7vDTOSLKysIGwZt6NKB6Ph3A4TEJCAg7HEBnIESMx8GDPwdnJcYzLiKek/tBlxDNd47hg3A95ruTXADxa+E2unfNX3tjZQGFdKy9treLcObmM+3Ti/Qj4ghHW728aFp2GvVXd4u9Roqsvrat7nnV1L/Cd19/j83M+P6DXlkNraGjAarWSkpLSr/cpvRLjMbCnPjM5g9LGNhp7WC0j3pbCF6f/k99vPp8a3z7uWHc8v1qyjnPmjObpDeXsq/WyqriRoyekH1Z7tle2cNSEdKxHOCt+sG0qa6athyXU+sony7svzrxgwK6rZEoPbCtvYeHYUT2qg7e5rJmiTy0g5PXV0NC0k7aKbeSF0qmmmqfjXmWsP4NzOAcr4GcdfuC//JdR2XM5KvMSCpLn9un3qHUHeGp9ORcvyO++bv5gJjUmnQpXPQGPXwOhNkzA6NSh+WFwsbvarznplCO/psSsQDhC/QCP6B1pQtEAv9xwMgBpcVm8etUrpMSldNpn0bhRbKvoejqnaZqYgVYi3kZ8e94HwDX5aOyZ4/CufQYz3PkGJumoyzAsVkzT7LiptFo+mYTpupRKIBTB7Q93zF5pagvS2BqkqTWIPxzF7Q+D/zTGchoAQaMQt+05Wm0rOp2nNFTL+aaVl4jHBKzdzFGJfhid/ph2Ox/sXE9a3GhcNidJjiTstihWa5hkZ9pBjweIs1sZlx7P5OwkJmYmYBxBDPxgXz2ri498JEitO8AbO2rYWNbM2bNHk5YwRDo4RrB9td4jXuBQBkckEqGuro7i4mJef/11AG6//XYCgQC///3vO+1rsztIXHIJUdPsFAP7gtVisGR8GkvGpXVfviyG7gEnjD+JpA9KOha4PD73KnITJvO37VcDUGy/jrY9v2VPzSJOm55Ncj+WT/AFIzy9oZyLFuSRlaQZKr1V7w2ws8o92M2QfuD3+6mpqWHjxo1s3rwZl8vF9773PXbs2MGTTz7Zad9zzjmHhQsX9nn865UYioEHew4+dlIG+xtKezQA4/jca1lZ8wQ1vn3san6XB/fezBdm/p3Xdxjsq/Xy4tYqzpubS0Fa3w363FjaxJz8FJKOYI2BWPT+vvoBvV7EDPNe1cMAHD3maOzWkfXnPRSYpklLSwvV1dU8++yzBINBTj/9dI4++mjuvfdeKioqOu3/ve99D5fLpRg4AH2BNquFU2dk8+S6sh4PVkt15vC1WQ/zu03nAO2lpX571BZOnpbFsp21rCluJNVlP6xShq2BCEV1XiZnH8EarIMsEG5Plg+kIvc61tY9C8A3Zj+O1TJwcU7JlB4IR03WFjdyyvTsbvdr8YV4d29dx/t9+18iq6L9wWAUMIr2LKUVK3X+EhYz44BzJJDAtpqXmVWTTAWrcafFkzfmZJz2JJyOI/+HVecJ8PSGci5dlI/T1k1CZTCTGpNOhW/vgM2Pwep/QFPxx5+NGtc+hXDelfCpDl0ZeQprWzWKsR+ZpsnTRXd0vL///P8wJmUMkUjn0QZ2q4WjJqSzbGdNp2P9hWsI1RYdeGLD0p5kCXcxEsSMEm6qoW3PB1gT03DkTsOWlAFWe7c3lU67lUy7tctRwW3BME2tHyZYPkyy1Hum4Ah+h4zQd4gSwG17khb7YwC8bkQ422zjaeI/TKm0j4P+SPTDbW2YXEQbb9R/46Dtsps5hIxaPnkjOso+EZvFyvRRJzAueSbb6zMYVzmPrOQEFo9LY9rEUzB6GQPf3lPHhj6+ean3BPjvmlJOnZ7N1JzYvbGLdZGoyXufuLeQ2BAIBLjzzju7/CwUChEIHDgQIBwKcvHCPB5//lW8+7diz5mMI3sSFmcChu3wHg7i7FZm56UwryCVxJ6WrI2Re0AbcNSE9I5ylACTUpbwlRn3848dXwCgzvFzaPkxNatnc/zkTGb24ywVXzDC0+sruGhBHtkq+dUr7++r18y7Yaa0tJT777//gO0f3UM2NR14z9Lc3Izf7+eJJ56gqqqK4447jrlz5+JwOAZ25kqMxMCDyUqKY1ZuClsrerZo6/fnv8Sm+ld4ZO+t7Gp+l9fK/x9nzvw2L0erKKpv5YXNlVy8IL/LEr6HIxQxWVnYwOkzc/rkfLGgrLGt0zqGA2FN7TOUt27HYXHxh9P/MKDXFnjjjTf44IMPDtj+0T3IpxMp0B4f9+/fz5NPPklqairHHnss48aNw+l0YrEMYLWAGI+BPZWX6mJOfgqby3q+wHVO/GROyr2BFZX/BuB/Jb/l4gk/oaktxPr9TSzbWUNSnO2w1pzaUt4S08mUTaXN+AZwVoppmvxl21UALM26lHFJ8wbs2qBkSo9tr3SzZHxatyMo3tpdSyhiUuxez/9t+xyncRpZHHvAfntd1VyZdyf2YIRIqZ96SzMZ0VSsWNnCFkYximyysWMnqRFofJsA0IKf3Vk+PjPpqwecszfqPAFe3VbNeXNzu3+gHMykhisVjvoK0UU3snLFy9jCPhYfdzLWxAwtNi8ddtdoFGN/emr3g6ypfRoDC7847mHOm/bZg+47MzeZ9fsbaWr7eE2TcHNVl/vaUtvricZNWIS/aD2WxFFEvY3YUkdjWO1E/a0QCRFpqcHX8nEnmSUxjYRZp3YseN9T8Q4b8Q4beaM6z2ppDYSpdvupbPZR1XIjdZ6rCUUjQJRVtv8w0bKTa7Byk6WSCXw866aIKP+PEA8SxH2IcBQyqg/Y1hRqr+tZV72Hdz71sYHBcbkXc8+5f2F6D2KgaZq8uauWLeU9vwnsjWA4yivbqvCFIswbk9ov15DubSlv7vTvSmKDx9P1+jYpKSnExcXhdDqZMGECDQ0NGIZBc3Mzl112GXmpLtItrXiBUPVeQtV7O4515M8irmB2j66fl+piZl4yU7KTDm9BzBi5B5wxOplVRQ0ds1MApqQewx2LPuD+3TdT4tlInfOnmIEfsXzXfMoa2zh5elb3A4qOgD/UPkPlwvl5jE7RovQ9Ud7UdsCsfol9paWlXW4/4YQTAJg5cybvvPMOmZmZ1NbWEgqFOPbYYwkEAlRWVhIIBFi2bBnLli0DwGaz8fnPf56xY8cOzBeIkRh4MMdMSmdPrYdAqAdleoB5GWcSiLTyeOHtvFnxT+JtKZw5+3pe2FxFaWMb/9tcyaWL8hnVR+vq7ahys2hc2oiZ/TzQs1KCET9PFv4YgHPG3cK0jGkDev2RzjRN9u3b1+VnM2a0D6i++OKLefrppxkzZgxlZWXk5OSQmJhIcXExra2ttLa28sQTT3Qcl5mZyfXXX09c3AAN1ojxGNhTx07KoKiutdN95KGcO+57JNjTeHH/Xbxf/QiZrnF8ZuLVuH0h9tZ6eXFLFZcszCfjYCV1D6K0sY16b6DXxw0F/lCEDaXNA3rNjwZOAZw99tsDem1QMqXHIlGTdfubDrp2yr5aD2v3baJi/3Ie8v4fAOtYx1hjPMkTjiE3c2lHNvkMrvz4wPz2WSsf+d2Hn5WUL8fbVESiN0yamQpAHHGk19ZzW+0czk/+AvOnfgmn/fBqmBbVtbKysIFjDlWvdLCTGoZB2J5M2J4M8WlKpEgHXzBCaYPK3vSnoubdAJw19ha+svSibvc1DJjkauP9ov04ciZhGAbOgjmEGytxTVyMYT/wpsCRMxlHzuQDttuzxoPFSsTbQKjm4xvRqLeRYPVebCnZRANt2EYdIiF8CAlOGxMzE5n44eKa0ahJY1uQQCiKxfITMpOcnDl7NGOzEnjvE/HPDDRxi5HCRY1t7K6pZ2X5+zT4K0iPK2BT/cukOLIp925nXOISnJZ0Klv30BiooMz3HgCJxnT80TowHYQtlZ3aZGLybuVTzLjnqY5tTx31FOmu7ANioD8U4dVt1RTX928nlGnCil21RKJRFo7tvmyZ9C1/KNInpduk/4VCIbZt20ZaWhpjx44lJSWFefPmkZ2dzeLFi7FaO3feG4bB1Vdf3eW5LrrgfLZu3camnXupryjp2B4s34Y9cxzRtmYsrmSs8Z0fYC2GwZTsRBaOG9U3paZi4B7QYjGYnZfCB4Wd1+1KcqTzlRkP8MDur7Or+R3qnD8jPfgN9tSeQo0nwNmzR/fb+iaBUJRnNlRw/rzcwxqZONK8t3dgOxmlf7jdbjZs2MDRRx+N0+lk7ty5FBYWcvzxxzNu3LgD7tdSU1O57bYDFzmOi4vjc5/7HIWFhezZs4fq6vZRJ+FwmOeff56bbrqJzZs3M3v2bJzOfu5wioEYeDDxDhtLx6fzzp6ez2xdmn0J3lAjL5X+gRf330W6M5+zZ5/O0xvKqfUEeG5jBZctGkNCT2c5dsM0YcP+Jk6d0X3lj+FgX613QNZE+6R3q/7T8fNncj83oNceqcrKyqisrGTp0qUYhsHJJ5/M2rVrueCCC0hMTDxg/1mzZjFr1qwDts+YMQPTNKmoqGDDhg2Ew+2d/HV1dbz//vvMnTuXuro6pk2b1v+lwGI4BvaU02bl1OnZPLvxwJlC3Tk570YAXtx/F88V/4rnin/FnUu24w2EqWrxd8TL3paY3VjazGkxGBc3ljbjDw3crBRPsIG9LSsByI2fRqJ94PsolEzphW3lLSwZl0acrfM/Yq/fz+//fTujg+mMI4WpTGEHO7hi5h+ZnHL0YV1rXP4pkN8+Xa6o9FXMykIyo2n48RNHHFZ3C1Xr/kNriovJUy47rMXr15Q0kpXsZFJWD6aSKakhQ8yeGs8hF9mWI/P9o35DYnQJn5106sEXK6Z98byHHnqIlpYWDIsVe0YBhs2BI2sCjqwJvb6uYbXhyJ4A2ROIm7CQYOVuAvs3AWBxxEM0im/fKmxJmdizxmNLy++Tm0mLxegYCTInP4UTp2ZhtRhEIpFO8W9yUiYA49PhxMnjuSGykMa2IA3eIN7AVXgDYdoCEbyBEB5/GG8g3GX5knAkSr3XT3FzMcUtO9nj/R9uVh+w3yWrLgFgWXYRp0wfjy8YoaE1wPKdtT1eNK8vvLOnvcNLCZWBs66kaUCnS8vh2bx5M//73/+IRqNMmjSJsWPHYrfbOf/88w/rfMnJyRx77DEce+wxlFTV8b/XVtC0f+eHn5qY4QCtm1/BkTMZe9YE4lPTmZXbXsqrJ+v79doQvwecnZ/CmuLGA8p+OqxxXD/tr/x33w/YWP8S9Y4/4jShxXcKT6wr69cShsFwlOc2VnDB/DwlVLqxt8Yz4J2M0rdM0+T5559n8+bNACQmJrJo0SKSkpK49tprD+ucBQUFFBQUcNJJJ1FUVMT7779PUVERdrsdn89HaWkp77//PvPnz2fp0qX9n1QZ4jHwYOaPSaWssa1Xg25Ozvsipd4tbG18g4f2fJsvzfg3581dzJPry2nxhXhhS3vJr8Oa8fgpu6rdHDspo/u1XGOcaZqsLGo49I596KOEGMCctM9it8TeKPdY4na7+c9//tMx03j69OkkJyczdepUpk6d2uvzWa1W5syZw5w5czjjjDNYt24dL7/8MtA+uzkSifDKK6+wbt065s6dy+zZs/s/qRKjMbCnxmUkMH10cq/Xbjsp9wb2tqxkd3P7gMlXy//AeXO/y1Pry2loDfLMxgouXZjfqwT0rio3x05KJ94RO131/lCEjWUDu1bKk0U/6fj523OfHdBrfyR2/oaGgPCHs1OOm/hxR1JhdRH/74G/MDrcvh5KvaWJ4/O+xA35Z/RZUJtQcAYUtP8ydjW/y4yKJqa6p4IJac3gX/M8+60NpEw+kay0A7PbB2Oa8MaOWrKT40bcAnAS+3bXdF1CRfrWlNRjuu08r6io6FQTe+r06VR1rCpy5AzDgjNvOs686R3bfHs+gHCQcFMF4aYKsNqxp+URN3FJr0uAfZrVYnDS1Cxm5/d8yrLNaiErKe6gI8GD4SiNrUHqPAFq3H5qPQGa2tqTIDkp8eSkzORoZgKX0OJ3s7ehkA31L7Or9T+dznPqE+2JqXTHZL437xnstoEvjfDOnnqcNiuz8rRmVX9z+0NsLB3YG1PpnWg0ysaNG3nppZcAsFgsjBs3rk8XDh03OpNvXHcZja1Bdld7qGppY+sL7dcLVu0hWLUHZ1oajrjFJE85qk+uGWviHTam5CSxo/LAh2CbxcFVk39Poj2Nd6seosL4I674p8hs+xuvbq+mzhvg2Inp/dIREYqY/G9zJZcszNei9F2IRs0BL30jfautrY2XXnqJ3bvbZzJnZmYyatSoQxzVOxMmTGDChI8H5rjdbrZu3QrAihUrWLFiBXl5eZx11lnk5ub26bVjncVicPac0Ty1vpzqHiYtDcPgqsl38Y8dX6DEs5H7dt3ETTMf5Px503liXRk17gCvba/m7NmjjzhuhiImW8qbWToh/YjOM5TtqvZQ7zlwjbT+9G7VQx0/XzThRwN67ZGmpqaG+++/v2P2yPTp04lGe1ZarycMw2Dx4sUsXry4Y9vy5cvxeDx4PB6Kiop44YUXmDx5Mueff37/J5aHsROnZrK/oZW2XgxiMwyDL02/l++sbC+j91blfeS4JnHB/PN5Yl0ZLb4Qz26q4OIF+bjsPeujCEdNNpe1cPTE2ImL60qaelxSsi/saf6AbY3tJUCvnfJnLMYArif0CYNz1Ri2uayZ9/fWE4mabK7czH/vfZTMcHtHY4W9gTGLvsCUMWf2y0OZYRhMH3U8l866G2PeSZS5Pu5kyY6kE7drKy998NWOzGhPfFQmxtQIf4khbn+IymaV+BoIWclOxqR1Pap29+7dnRIp55xzDpdfcjELJo7u1za5phxDwpzPYkn4MMkTCRGqK8Gz6gki3sMvieSwWTh/Xm6vEik9PW9OShyz81M4dUY2n1tawFdPnMgNnxnP5YvHcP68XM6cncOZs3P43JKp3Hneuay6+V6avufmp5/5xQHnawju5ftrZvPK3peIRAc+di/fWcteJTP73Qf7Gg4YaS9Dh2ma/PrXv+5IpFitVm666SaOPfbYfrkHTEtwcPTEdC5aMIaf/OQnXHjhhdjt7QNhGhsbee2117jjjju6XNh+JJjfzZpOFsPCeeN+QH7CTAB8ZhnWtPswMVm/v4kXt1QRDPfPQ2Ag1D5Dpblt4GYRxoptlS1aDyqGtbW1cffdd3ckUsaPH8+NN97IxIkT+/W6ycnJfP/73+eooz5OHldUVPCvf/2Lp59+ul+vHYvsVgsXzMtjVHzPB046rC6+OP1fjI6fSjDq489bL8Pp8HHOnFyshkFhXSvv9VEidEt5y6Dcyw6ESNRk1QDPSmkO1PBG+d8A+Nyk35Ls6LpEvRy54uJi/vWvf3UkUk455RQuvfRSUlNT+/W6p5xyCl/96lc71o8Kh8Ps3LmTO++8k5KSkn699nAWZ7dy0rTe/3sxDIO7j9nNSbntZb8eK/whpa3vceH8POIdVhq8QZ7dWNGrElhbypsJRwYuOXEk2oJhNpc3D9j1ghEfTxa2z0o5Kvsy5mac0e3+dZ4Af39rHy9t6Xot3yOhZMohmKbZvkhxi5/ypjYK67w8vq6M57btonlvc8d+21MSiZ/wBdpCtgEpPZQUn8PM+V/Fufg8qnM+Hu1WSy337LiBxzd/g30lL+D11XRzlnblTT7Wlmj0q8SOHZXuLssmSd9bUND1CMNAINBRxzopKYlvfetbLFy4EICjJqSTFNe/Ex+tiWkkzv0siYsuwJ794YO71Y5hs2OaJqH6UsItNZhmz25E4h1WLlmYz9j0w1uHqrcMwyA5zk5uqosJmYlMy0lmWk77YtFj0uJJcdlJdSXx4xNuY8UJK1h2/DJ+ctxvOp3jjbpv85tV32JnVfOAJsSjpskr26opbWgbsGuONLUeP7uqezfVXAZWRcXHtZXnzp3LD3/4QzIyDrEOXR8xDIM5c+Zw22238eUvf7lj+8yZM7FYLASDQdavX099/cgZ9Z+VHEde6sEXfLcaNr4150myXe2/L4p9z5I2+mmsFoOi+laeWl+Ox98/HfutgQj/21xJKEYejAdCMBwd8E5G6Vvr1q3r+Pmyyy7jmmuuweEYmBmzcXFxfPazn+X222/nsssu69j+UedibW0t27dvx+v1Dkh7hjqXw8qFC/J7dW/usiXxtVkPd7x/aM+3yUmxc+qM9s7GDaXNbK1oOeK2eQNhdlcPzwE62ytbaB7ghPHP1x/f8fOCzHMH9NojSSgUorS0FACn08nXvvY1jjvuuAG7fmZmJtdddx233nprp+u6XO33Qbt27aKoqIhIRKWCe2NKdhLTDrP86xkF3+i4x7xv11fZ37aMi+bn4bJbqfMEeG5TzxMqbcEIm8qaD6sdA21tSVO/DUjqyorKf9MQKCPVMZrzxn6/y31M06Sy2ceLWyp5dE0pm8tb+L839/Z5f4nKfB1EUZ2Xbz2+iV3VngP+5zBpojLu22Qb6XyRL/J2uJmi6kVQ3b6QsMWAFJedpDg7iU4bSXEfvezt2502LJa+GbXotCcwZcKFVCdvpHjP03jxMoc5nNl6NLR6iVa+yfYEN6PHnUJayqSDnmdlYQP5o1zkdvMgGstCkSgWw8DaR3/uMnh8wQjr9yv5NxCS4myMOUitd6fTSX5+PuXl5VxzzTWkpHw8m8Nhs3Dxgnxe3FJJvbd/R+NaHC5cE5dgS84i1FQJFhv+fasI1ZV07BM3YTH2zHEY1q5/5SXF2bh4QT6jEga+bFZPWQ0rPznxVn584q3M/uvR7GpaC0Ajr/JgYTHzqn/IeTMXDVh91UjU7KibnZOi8jV97d099UoYD3G5ubkUFRVhs9k477zzsFgGfnySYRjk5ORw++23s3z5chwOB16vlwcffJCWlvZOrvj4eC666CImTJjQ/zW1B9mCsaOo6GbWqsWw8v35L7Oy+nGeLPoJm5of4PgxKdRWnkGdN8Dj68o4b25uv5TkavAGeWdPHadMj71FRfvDhtImWgPq5IllCxcuZN26dRx33HFMnz790Af0A5vNxvTp0/na177G22+/TVJSErt27eLxxx/v2GfRokUcddRRpKfHTsmU/pDisnPB/DyeXFfe4w49ly2Zb895hv+39Qp2N7/HE/t+zOWTfkVLW4hVxY2s2F1ListOwUFmsPfU5vJmZuQmH9E5hppwJMqa4sOfLX84qtv2dfy8IONcLMbwXYtmsNntdsaPH09xcTFXX331oMWX+Ph4TjnlFPLy8ti6dStOp5OXX36ZtWvXduxzxhlnMG/ePJUA66GTpmVR0ezD4w/36ji7xcm35jzFM0U/Z23dszy857t8fsrvuWjBKTy9oZwad4BnNlRwwfzcHj2vrypqYHJWEim9mFU40Dz+EFsGMOlT5t3K62V/AeC8cd8jzpbY8Vk4EqWsyUdRvZeius7l2haPG8VvLprT589BSqYcRLLLzpbyj0dbJDptOKwWbEYjFdE/E8ZPBRW8Gp9LuvNkxgcjtLSFaPGFiJgmTW2hg05dtxiQ4LQR77Disls7/lKtFgOnzYLTZiEpzk5SnI0UV3sC5lCLvOVkzCcnYz5LzShVTZupLHyf3FB7UB/Tmgzb11LBm+wcE+Wk/BuxGp3/6qOmyctbq7hq6dhhswhcWzDM69tr2N/QRtQ0cdgsTMlOYvroJEanuJRYiVFrSxoHNPs9khWkxR/wS6empobMzExOPPFETjzxxIOuDTAqwcHliwtYvrOGfbXefi9XZM8chz1zXPvPGeMIN1VihtsTOf6itfiL1hI3fiGO0VM6tzPezkUL8/tn0eZ+YLNa2fa11TyzsYS719zOurrnCVp2s8Z3LWvWwXXjXmdO7tgBaUswHOW5Te0L66Un6ga9r5TUt1LaqFk/Q5FpmtTW1nLiiScCcNJJJw2JBIXNZuOzn/0sAJFIhEWLFrF8+XKgvRTPww+3jy6+6aabyMoaviU/JmYmkJbgoLG1+yT+0TmX44t4eHH/XbxT+2fOGZ9CRcVxNLYGeWp9OWfNHs24fpiluKW8hbHpCUzKSjz0zsNYWzCsQTExyu/309bW1hEDzzrrrCERA9PT07nooosA8Hg8zJw5k+3btwPtM2g+mkXz4x//eFAS30NFRqKT8+fl8tT68h6X1spPnMnnp9zNf3Z/k7V1zxBnS+D8cT+k2RdiV7WHl7ZWcdkR3gdWt/ipdfvJSh4+g3M2lzf3ujP2SETNCM8V/xqA2Wmn8fkpvx+wa48kdXV1jBo1qiMG9uUaeUdi2rRpTJvWvm7HzJkz2bNnT8egmldffZVXX32V4447jlNOOWUwmxkT4uxWPjszh6c3lPd6YJvTGs/lk34NGKyte4b/7LmFM8Z8g4sX3MgzGyqo8wZ4ekMFF87PI/EQi9KHIiZv7q7hwvn5h/9l+tmqosYBK0kdjPj445ZLAJiccjRz088E2tffW1nUwKay5k5tcVgtTMhMYNHYURw3ObNf7r2VTDmI9AQHf75iHmuKG0l12bFZLbi9ZbRsWUsLc3jCtocV173HzrLkTouKRU0Trz9Msy+ENxDG6w/jCYTw+MN4fGFa/CEiUbP9fS9+wSY4raS6HO3JlXg7qR8mWVJcdpw2S0cQtxgW8tLmQ9p8mjzFVJYsZ5QHEkkgiSTeLfsD+WWtOHAQzR1P3uhjcTnby/h4/GFe31HNeXNzh8QvhSNR1tjGq9uq8QY+/jMOhqNsq2hhW0ULNotBZpKT9ERnx+yhUQkO0uIdwyaZNBx5/CE2x8iUx+Hg03Fgz549PP7445x00kkdU4q7ixUOm4UzZ4/GNE3agpEP41570nlvrbfHi2H2lm3UaJKWXEy4uZpwSzXBip0AhJursLiSaNv5Dvas8WQWTOaShQtIjJFEykesFoNzZhdQ6/45p435Mg/t+j4lrasBeLD4HJbW/pNzZiwekFkqvmCEZzZUcOmifFLjh+7MnlhhmibvakHmIck0TV566SU2btzIF7/4RXJycobkvZLVauW4447j2GOPZe3atVRUVLBlyxYAfD4fb7zxBh988AFnnXUWEydOJC0tbZBb3HcMw2Dh2FG8sePQJW5PzrsRf9jDsop/8GLZz1maeTljHF+irMnHC5srOX1GDlMPs9RDd5btrCEv1TWi7zVXF2lQTCzy+Xzcf//9GIbBF7/4RWw225CMgUlJSVxyySWceeaZrF27lq1bt9LY2D5DwOfz/X/2zjs+yirr499n+qT3XoEUQggJvSooqFgoKva69tVtvrvu+uradtctrm5T133ta6UoYgF7o5cQAgQSEtJ7n0wm0+d5/xgYiKRnZlJ4vvvhs/O0e+8TMyf33nPO77Bu3Tra29u58MILSU5OdsninC3EBGmZNzGUbcUDn2tkhS7jmklP8nbJr9la9wZaeQBLJ99Hh9FKrc7Epvxarp4Zj28/m4N9cahGx/njxJlittm9LqH+etHPOKbbDsDFCb/wat9nC+Xl5bz55pvMmTOHZcuWAX2vg0eKxMREfv7zn1NZWUlxcTHbtjnrKZeUlJCamsrbb7/NpEmTyM7OJjk5+ax2MPdGfIgPs5NC2D2E7DKZIOPqSb8HRPY2beTTqn9isndy5fSfs/FALa0GC+v3VbE6J7bftXN5cxdF9XqPzEeHS6vBwpFa70lSP1dwo+vzNZP+iCAIGK12thyqo6rNmZXup1aQHObLhHBf4oK1KDz8uy05U3pBEAQuyIiitMkAgMmio/nwR4QRjEwGn129hZmxU8mMsLPpQA217c5NQZkgEKBVEqDteXNOFEWnk8Vso8tix2S1IwKITtkUs92B6cSmY8eJTUezzYHBbMdgNvYoX6BSyAjUKAnQnspkCdQqCdDGkj7lNqw2PZZ9zgKpUUQRQ4zzwVo91tpPaaOTo5FG5iTcQGkTfFPUyOLUiEFJkTkcIha7A4coopDJUMqFEfvjcrSugy+ONPQZcWNziNTpTNT1sJkbHahhZlIwE8P9RuUfyLMZb3q/JbpTW1vLhg0bcDgcNDQ0DCoSRxAEfNUKfNUKlyTUzKQQmvRm8qvaOVrX4ZH/roqgKBRBUShD4zEc/ByZbwiWumMgOrA2HKe24ThP7/2UzMxMLrnkEjSasbOI06rkzEwMZsdxB/dOfZnH9i3CYGtDFCzsMfyEit33sHzClUyJCfC4Hes029iQW82amfEE9vK3T2JgFNR2dAvQkBg9bN++ndzcXMCpxx8VFTXCI+obQRCYPXs2AH5+fuzYsYOgoCB27NgBwObNm133rlq1iqlTp46LBfXk6AB2Hm/pFkzTG8sTfk6HtYk9je+xu2ktVyRnoFWdy7GGTj4tqMdss5MVF+TW8RktdnaXtbA4bfxmCPVFe5fFLXUWBsPbxQ/wfbOaJ857mKmRU73a93jBZrOxdu1ampqa8Pf3p6OjY9Q7Yn19fVm8eDGLFi3iySefxOFw0NbW5qpzsH79esAp13PXXXedVTJgMxODqWzpGlQW7MyIVZjtBt4re4LPq5/DXxnKpVnXsHZfFTqjlQ/zndKvKsXQ/o4U1utZlBI+5OdHE3mV7Rgt3pMxtIs2DrV+AUCSfw6RPhO91vfZQlNTE2vXrsVut9PQ0IDD4Rj1c6aEhAQSEhKYNm0azz33HFFRURw4cACTycThw4c5fPgwACkpKaxevfqscyz3x9wJoVS3G6lp610+tjdkgpyrJ/2BIt12OiyNfFv7ChX6fG6c/hofHKhFZ7SyPreaVdmxhPv3ndX3VWEDwb5Kj0jQDocdx5u9Uisc4LOqZ6nqPATABXH3EayOpr7DxJZDdXSYbCjlAksnR5IS4d39W8mZMgC6TK3Y9n9GGMF00cUNN9xAdnI24EwDW5kdy8vbygYUZSUIwgkJr4FvOJmsdtq7rLQbLbSfkBI7+a/LYsdic9DUaaap88wNGAGnpJifeiFatZlg0QKG7vf44Udxw4d83fAKM5hBqTCDDQoHPv5qfnXjL4kMOlPfubnTTEWLgeo2I3U6U48TBtUJyTKVQoZS7vynkAko5TJ8VHICtApCfdXEBmv7lTEbKLkVbWwtbhqW1nydzsRH+XWE+6u5NCtairYeJbR0mr3q/ZY4RUlJCW+99RYAEydOZNWqVW75QxXur2ZpRiTzJ4Wyv6KdfRWtHqkTIfcLJWD+tQBYW6qwtdV2u15YWMjixYsxmUx89NFHaLVa2tramDZtGrNmzXL/gNzE9MRg8qvbMZjhd7N3obe08OzhW2gyHaNO/hfeL2mkoPYGzkuP6HeiNlz0JqdD5coZcZJDZYhY7VJB5tHKpk2bOHDgAODUns7KyhrZAQ2SZcuWuaIoU1NTOXbsWLfrZWVlpKSk0NLSwoYNG5gwYQKtra2cd955roLOYwW5TCA7IWhAUdeCIHDlhMfY0/geAO+VPcp9mW+jUcRzsEbHN0VNAG53qBys1pETHzyqdbA9xfaSlgHLC7mDdnM9+5o2sa8JHjr3V17rdzxhs9n485//jM1mQ61Wc/311496R8rpyOVyfvvb3wLOotE/xGq1Ultbi4+PD4cPH2b37t2EhISgVqu56KKL8PV1v+TfSCMIAhdmRvHmropBbfoviL6eemMJ2+vf5r2yJ6gyFLAq+zHW7aumUW9my+E6Ls2KGZKUtsXmoKhez9S4wP5vHsWYrN6v7bmrYZ3r881p//Bq32cD1dXVvPzyywDExcVx9dVXj3pHyumEhYXx6KOPAlBRUcH+/fu7XS8uLkav12O1Wvn0009xOBwuucS5c+eOqXd1JzKZwMVTo3lrV0W3+hsDfl6Q88iM79hQ+hi7GtZSps/liQNTeTh7D1sOtdPc6ZSWvTQrmvg+6k6ZrQ427q/hqpnxo6a+a73ORHFDp1f6ajPX8VnVvwCI9kllWdw97K9sY3tJMw7RWQ/s0qxowkZAclwQ3V3SfhTT0dFBYGAgOp2OgID+i5wZLXae/6aY43ufJ9LujFaZc+kcLppx0Rn37iptYedx72+CWO0OOoxWdCYrHUYbOqPVeXziX2/R3iIOQuSHyVRWEYIShaDge75nPvMJofsE+XVeR/CLwmxq5Sbb5ahRU0EjrTIVRkUEdmU8DezHB4E4IQIf/3Sw1aPASGzkIgJ9fbGKndhFGz7yoDMMslIuEB/iQ3Z8EIk/0Ki22+1s3boVgEWLFiGX9y6LsPN4i9s3otRKGcszo0kOG38T6bHGpgM1rkyxwRKgVXLbwuQB3z9YWzEWGOo7dXV18dRTTwFOTeo77rjDYwXsKloMbDlc77FoLoVM4Ny0cLLigrBYLJSUlLjqCsjlcs4//3zefffdM5576KGH2LZtGxaLhZ07dwKQk5NDdHQ0kZGRREZGolarqa+vx2az4ePjQ2dnJ3K5nNjY2G5tDVZbtz8beLC6na+ONp6632Hlr/mraTAWA6C1zyLS+ggzEkKYkxyCwk2O697w1yhYMyP+rNwkHC67S1vY4aF5RE5C0KAi4SUbeIrTixnPmTOHiy46cw44FmltbaWwsJBdu3YBMGnSJPz8/Fz25iT+/v787Gc/Y+vWrdTW1lJcXIxarSY7O5v4+HjCw8MJCwtDEASOHz9OaGgoZrMZg8FAWFgYgYHdN8YGYwMHMwc8HYvNwWs7ygZc4LxEt4fnT8gHKGRq7s54ldrGRNeG2PnpEWTGuneDLz3Kn+VTo93a5minXmfi3b2VHgma6I21JQ+zu3E9arkG40NdA/rdG4/2D4b+XuvWrePoUadc6o033siECRM8NUSvUl1dze7du6mursZutzN//nzy8/Opr6/vdt+qVavIzMxk69at7NmzB6PRSFJSEgkJCcTFxREZGUlAQABms5mysjLi4uJob2/HZDKRmJiIUnlqPnRy28XTNnCgFNZ3sOVQff83noYoivzlwKU0GJ3Fzn+U/jyh8vm8t78Gu0NkcpQ/yzIihxR0FRGg5vo5Y8uB/0O2Fjexz4sSX3pLM3/MuwiTXc/q5N+yKPqGM+5RKWTcu2TSgNuUbOApzGYzf/rTnwDnfOjuu+/Gx6f3je+xgs1m4/jx43z11VfY7c650rJly1zz3dP57W9/iyiKfPvtty7JsGnTphEVFeVaC2s0Gpqamujq6iIwMBC9Xo8oisTHx3ezBe5eB3uDihYDG/Nqhjx3cYgO/rB/KW3mGte53808wJZDLdS0G5EJDEha1l+j4KpZ8aOizuv6fVVUDyFjZ7DYHBYe2HUqo/jaxLWU1oZS3+FUF5oU4cfSyRGoFX3/XkyODuCizIErCgzUVkiZKf1QeOxt4k44UtpDDT06UgCmJwSTX9U+JK/lcFDKZYT6qXss+nZ6nYJOs7NWgcFix2C2nZARm85WkzO60o4OvfIAlyrOjDSqoQZLZxmRRKLG2U8iESQ6AIuFSss3fMUrpJHGxVwLnQdOPdz0MS/yFsUUs4AFLGOZ61KD0EqXWoZBZSIgeipH6uaSFBrG/ImhZzhV+mPH8WZ2lw5e07A/zFYHmw7UsHRypNsX0hIDp6q1a8iOFImh43A4eO+991zH11xzjcccKQCJob5cOzuBD/Nr3S51lBjqwzmp4a6oBZVKRUZGBhkZGa57iouLz3guPj7e9bm0tNT1OS8vj7y8PMA5+Zw/fz67du0iPz//jDYeeeQRAP74xz92i4pMS0sjJCSErKysIUsGZcYEsq+8DZ3R2a5cpuRX2R+ytuQh9ja9j1G+l0bxr+yt+AUlTZ1cNCWKSA9qUutNNtbnVnHlDKmGymAwmG3skwoyjzra29v58MMPXccXXHDBCI7GvYSEhDB//nzmz5/vOvfBBx+ccd/SpUsB55zypI00m83s3r2b3budtZruu+8+AgMDeeedd3A4umdpZ2RksGbNGgwGA3/9q7MgrkwmQ61Wk5KSQnBwMHPnznWrzKJKIWNOcihfFzb2fzMwKXA2f5i9j1cKf8zxjj28Vfwr7pvyFg4xiLzKdr4qbEQuE5gc7b5NpaIGPTMSg8dVweX+GG7m+GCpNRSxp3EDAM9e+Jok3TsE8vPzXY6UyZMnjxtHCjgjzOPiThX2tdvtfPHFF2fcl5KSAoDJZMJodG4elZeXU15eDkB4eDg//vGPqa+v73EjcvXq1WRlZXHgwAE2bdrkOh8ZGUliYiLh4eHMmDFjRH4/06MCKKrXD2qNJQgCP57yOo/uWwDAm8d+yQPZH3Px1Cg+PljH0Xo9GpWcRZPCBv1OjR1m6nRGogPHptxQp9nm9dqefzpwMSa7nljfDBZEXevVvsc7oih2+86uWbNmXDhSABQKBWlpaaSlpbnOVVdXn3Hf6fKHJyUSwfm34eSa92Rh+/3797sCdE7nN7/5DWq1mueff56mpibX+ZSUFMLCwkhPTychIcEt7+UJEkN9mTshdMiB8zJBxm9nfM2nlf/i8+pnAfhL/jJ+nPEW+0r9KGl0SssaLDZy4oN6tZt6k42NJ+qUeqMuam+UNOq94kgB2FD6mOtzuvwZdhT6AiYUMoFFKWFMjQ0c0bmd5Ezpg+2VW2lqPUQc2dSH2Xnunj/3eq9KIWNmUgjfH2vq9R5vc3qdgt6w2h00d5pp1IfT2PEYH3aY0BmtKKknVF6ESm4mUXUTFmUJfjYrZovZ5VAB6MKIBudC8IcZLSc5ef95nNftfKQYAibABE92/AILFi7gAo4xn6YQI1edv5p5KdP7fEdRFNle0sLecvc7Uk714SwYCkgOlRFAFEW2DqJIooT76OrqQq/Xo1Qquf322wkLC/N4n4FaJVfNjOPj/LpBaTn3hCBAcpgvs5NDBrQwS0lJcaVB19fXU1lZSXBwMAqFgsWLFxMcHNxts1Gr1WKz2VyL6x9K55zEbrdjt9vPkJcoKioCnBu2V111FWazmaeffhqr1coVV1zBxIkTUan6dkjIZALT4oO6/e2RCTKuTfkjKUFzWVvyEF2K72iTWxC7fsm6fVXMSQ5lZlIwMg9NPvQmG+v3OSW/Rks68mhnV2mLVJB5FNLc3IzNZiMmJoZbb7113EsdrFq1ilWrVmG1WqmpqaG6upqYmBjkcjkLFiygo6PDJXcGzlosXV1dGI1GQkJCznCkgDO6C7rbR4fDgdFo5ODBgwDExsaSkpLiktIICQnhiiuuGJaU0NTYQPIq22jrOlPWpye0Cn9uTvsH/zp0LU2mcv595Gbuy3wbhyOQ/GodXxxtQK2QMSHcb8hjOh1RhN1lrVw2LcYt7Y12ypoNXlt8g3Pu+Nf8FQBMC72ICyde5rW+xxMNDc71zznnnMOSJUtGeDSe5XQ5MIPBQGlpKSaTCYVCgVwuZ/r06RQVFdHe3u56xtfXF5PJGSF70tb9ELPZGRz0/fffdzvf0NDg+vlOmTIFrVbL9u3b+fLLL5k9ezazZs0iKCjIzW95JkvSI6huqxjUHMRfFcaTs3P5x6GraDAe58Wjd3Jf5pssmxzJ50cayKtsRy2XMWfC4OvQ5FW2Ez11bDpT9pa1YrV7z2N8oHkLRpuzBtWF8fchE7wftT+eMZvN6HQ6ZDIZt9xyS7cAu/FIXFycax3c2NhIZWUlvr6+CIKATCZj7ty53RwqarUaURRd6+CysrIe27VYLMjl8m6OFHAGMRYXF1NTU8Mtt9yC3W7n2WefRafTsXLlSlJSUkZNPdM5ySHU60yUNQ89uPeihJ9Qrs/jmG47emsTf86/gDsnv4KvKoX8ah1bi5vRm2wsSgnrdY3earDw/v4arpwRh0bp/e+73SHy/THv7MuV6/NcMrwqx0SMxlSUcoGs2CByEoL63OP2FpLMVx9sr9jDZW+tZrZ2MX9Y/U9mJPU9IbDZHby2oxy9qf+il2OBcH81i1LCzsgSsdgsKGQK2o02ypo7OVLXxL66PURpMqmt245MkGPsrCM0Ihub3UyXTKTWWINCpyO4Q0ejOhA/cxsKEYwOB+EyOW/yJnr0PMIjZ4zjO79tPJzzIOeee2631D6b3cFnBQ0ca9B7/GcBzo1ZKUPF+xyt6+DTw4NLQf8hkszX0N/JYrFQX1/v9YgRu0Pk68JGDg+hUK1KIWNKTADZ8UEeyY44vejg6enKZrOZY8eO4efnR3NzsyuC8+SG4KeffordbicwMJCGhgbsdjsWiwWFQsE111xDW1sb//rXv87ob+HChcjl8l7Tm01We691u460fcdrhfdhEy0EyjLxNzyInEDig7VclBnl0cgWP7WCK2bEESI5VPqkpdPMm7sqPVrET5L5Gvo7NTY2olarz5CrOpvpzQa2tLRQX1+PUqnEZrMRFhaGVqvF398fo9HI2rVrSUtLo729HZ1Oh1qtpqGhgZycHObMmcPOnTv5/PPPu/UVHR1NamrqkOQdShr1fJRfN6hn2sy1/P3gVeitTST553Dn5Jf4rqiTwno9cpnA6uxYYoPds9EnEwRuWZA07utMiaLIm7sr3Z5x2hcV+nz+cegqAH42dT3/s/jiPjXJT2c82j8Y+nuVlJQwYcKEce9MHigOhwNBEFx273QbWFZWhsPhoKWlhcTERBwOh6v+SmlpKd9++y3Tp0+ntLQUo9GIj48PtbW1rFmzhvDwcJ544okz+svJySEgIMCjEjd5lW18WzT4gNB2cz1/P3glHVbns3dOfgmjPpPvTwTBLZgUyszEwTnFZYLArQuTRoWUzWDQGa28vqPcazWhRFHksX2L0J/42f913lFkQs/fUUnmy8lQ3stms1FVVUVy8sD3EcY7vc0BrVYrRUVF+Pr60tTUxKRJk7Db7YSGhiIIAl9//TV6vZ7IyEhqamqwWq2IoojVauXGG2/EYDDwzDPPnNHfggULUCgUIybzdRKT1c7buytdahBD5Wjbd7x49E7X8eLoHxHJjewqdTpqJob7ctGUqD6lueNDfLhieqzXszL2lbd6Jci5rrOcfx2+DpOjBV/bYiJsvyQrNohZycFD2rvwlMyX5EzpA6PFzl+/3Iu/2pcfnzsFlaL/SWRRvZ7Nhwa3cBuNZMQEcH56xKD09XVGK+1dFjrNNgxmO3qTFb3JRqvBQofJ2mNqv0MUae+y0KA3cKR5L8s6es8wKQ8Weejq+wn0UaMzWtlX3kqdzjSU1xsyMkHgkqxoJkW4JzJRom9sdgev76ygY5h/tCRnyth9p+IGPV8VNg6ojopaKSM7LoichGC0qrEXnaXT6fj73//e6/Uf//jHhIeH93jtq6MNHKzu2fFUotvDq4X3YrR3EKpKI6DzURz2APzUCi6eGuVROQV/jYKrZ8XjP8YWxd5kOPWgBorkTBmf7zTe2LNnD1u2bOnxmr+/P/fdd1+/2Xo/ZGNeNeXNg8tyPK7by/MFNyIiMiV4CTenPs/mw/WUNRtQKWSsmRHntkKX0xODOTe1Z7s+XjhS28FnBcMLihkMoijyv3tmYLYb8FUE8bvZu7lyRpzkTBmn7zWeePrpp+ns7Lmo73nnncfChQs9snkmiiJr91YNaV1d3VnAMwcvdx0/NnMbRbVyVw24c1LCyEkIHlSbM5OCWZQytuziZwX1HKntOTvJE3xX+xqbyv8IOH/mAaref16SM8XJeH2v8YLRaOQvf/lLr9fvuuuuIctiu4vGDhNr91b1Wpd6oFTqD/L3Q2tcx0GqaC6MeJa9JRrsokhUgIbLpkX36TiYPzF0SNl/Q8VgtvHajnKPKykcaajm9ZJrsQqNKB2JzPX/B+elThpW4JHkTHEDQ3GmvPDdcWYlhbAwZeDyNt4qyOMJZILA4rRwpsUHubVdi80pJ9bQYaKhw0yLwUybwdJjKqzV7qCyqY620tcIwIdGGskiq9s9ZeGBZE1aPiIaeQqZwOrpscQFjw/NzNGMu7zfkjNlbL9Tp9nGvvJWjtbpMVnPdKoE+SjJjg9iSkzggJzeY4H6+nref/99QkNDaWlpOSM1+oEHHkCrPeUEae4088bOit7b6yrhH4euwmx3btqn8k/MxgnIBYHzJkeQ4cZaAD8kzE/FmpnxI5KOPNqpau1iQ+6ZGsXuRnKmjM93Gq+Iokhubi5bt24lLS2NvXv3druenJzMjTfeOKA5oNlmZ93eKpo7LYMaQ17zZt449gsAUgLnckf6q7yfV0OdzoSfWsFVM+Pc4iRWKWTcvii53+KZYxV3BcUMhsK2rfzf0dsBuDvjNVKD5knOFMbve41HLBYLmzdvpqamhvj4eFeNvpNcccUVZGZmurXPJr2Zd/ZUDimzoqxjP/867KzXEes7mV9kvcfusnb2lDmDJBdNCmN64sAdKhqlnNsWJo+ZOb03MoxPp91cz5/zlmN2dLEo+iZWJz/U6702h4Pihk6mxgbyk/NTBtT+eLUV4/W9xiMtLS2sX7/eldlcU1PT7fr999+Pv3/fBds9RUGtjs8LGobdjl20caB5M59UPEO7xRmMf1nsUxSVT8FscxCoVXJZVnSPtbHBuW975cw4YoO8I4v4ycE6jyoCdVlsfFvUwPb232CU70ElRnPDxNfJjEocdtuSM8UNDMWZ8uLWUm5dkDSoBVOT3szbu733B9VdKGQCy6d6L+tCFEUMFjtGix2zzY4oOvX/VXIZAVoFaoWcWn0ta/duoGlrfbdaLQB/42+sTn6PqVFxXneqqJUy1syIJ9zfc8W4z3aMFjuv7ijDbB2+91typoyPd7LZHZS3dNFptmGy2vHXKIgN0o77QucHDhzgww8/5Id/rk/q2p5kQ241VX3UmTmu28tzBTcAIBMUzNa+Ql2LU35hRkIw8yeFeqyOSmywliunxyGTSQWATyKKIm/vqaSxw/PSN5IzZXy+09mAKIps2LCBI0eOdDsfERHBPffcM6A2OkxW1u6potM8OBne7fVv817p4wBcM/FJskJWsX5fNa1dFkJ8Vaxxk2b14rTwQUdujxX2V7bx3RDkg4aKQ7Tzy50ZACyMupHLJzwMIDlTGL/vNd4xGAw899xzrroEJ7njjjuIiXFvzaXtJc0uB8hgaTSW8XT+KqwOE5MC5vCj9OfZX2Fib3kb4IyinpU0cMmvJekRZLs5uNNTfJRfS0ljzxlFnuClo3dzpO0bkvxzuC/z7R7lvSw2B0fqOsitaKPTbEMll7H110uIDOi/BsV4tRXj9b3GO4WFhaxfv/6MunyPPPLIiBUf70sRYrC0m+t5Ivfcbucy2IDBqEEpF7hwShQTe6nX569RcMPcRI8HLJY2dbLpQK1H2hZFkSN1HXxf3ESd8C86FZ8iQ8lPMteSGDDFLX14ypkyNtz9I0hqpN+gI8/C/dVMix9b2toqhYxVObFela8SBAE/tYJwfzVxwT7Eh/gQG6Ql3F/titCL8Y/hF+f9lCcffZK7f3Y3bbJ21/NWrGwsu4J1Oz+josWzEik/xGx18EFezbA1EyV6Z3dZi1scKRLjB4VcxqQIP7Ljg5g7IZQpMYHj3pECkJ2dzSOPPMJdd93lOneyfkNFRYWr+GlOQlCf7UwMnMWPp7wBgEO0UWj7NZnxzmjt3Mo2Nh+qw2r3zHeups3IrrIWj7Q9Vjlap/eKI0VCYiwjCAJr1qzhkUce4cILL3SdnzlzJqIocuzYsTMczT8kQKNkZXYMauXglj0Loq5jQsBMANYd/y0NpgJW5sTgp1bQarDw0cFabG6wmQeq2vt9h7GI2WYf8sbsUPm86jnX54XR13u1bwkJT+Dr68sDDzzAgw8+SGLiqQhdHx8fDAYD1dXuy26dkxxCsM/QMu4itMmsSPo1AjJKOnbzUuFdzEjSMjfZ6UDZcbyFncdbBmzrdpW29JiNPtqo15m86kg52vY9R9q+QS4ouWri77s5UkRRpKLFwJbDdby4tZTvjjXRabbhp1bw6+XpY64OjYQEQHp6Or/97W+59957XedO1q2qrq7GYPDuPiDA4rQIogL7d0wOhCB1FH+Ze4iZ4Std545wJV2+/8BqF/n4YB07j7fg6CFrUG+ysfO4Z9fXFpuDbzwUFNPSaeb9vBq+OFpPrfA3OhWfIiDjupQ/uc2R4kmkzJQ+MFnt6IzWAXnwf4jDIbKtpJncirahDNWrqBQyVufEEuOlFLHh8mHuF9z38W1UUcVN3ESimMgeRxvykFUsSovxqlRCiK+Kq2bGj8n6DKOZ9i4L/91Z4bYiflJmyvh8p7OV4uJiIiMjAXj++edRqVRcdtllTJo0if/urKDV0Lecjd7SwnMF19NoLCNck8TyyP9jW5F1wBqtQ0UmCFw1K86jNVrGCla7g9d3lKM3DS5SfqhImSnj853ORsxmM6WlpaSmplJQUMDGjRtJSkpixYoVBAf3nd1R225kY17NoPSeHaKDVwt/TEHbN/gqgrg3800UjgTW51ZjsTmYFO7H8qlRw87qu3x6LImhvsNqY7Sxo6SZ3V50pljsRn6fex6dNmefz8wvcl2TMlPG73udbbS2ttLR0UFSUhLr16/n6NGjzJ07lyVLlqBUDn+zvF7nrAkwVIWNcn0e/3fkdkz2TiYEzOKOyf/hYJXZVUNlWlwg56aGDyiiPDM2kGUZkUMah7d4L7eayj6ywt1Jq6maP+ZdhF20cm70raxM/g3gdKIUNejJrWjrJmkZfEIGOTs+iJ8tTR1wP+PVVozX9zrbKCsrIygoCLVazfPPP48oilx88cVMmeLdzfcOk5W3d1cOqK7rQDm9FhJAovoyrLpLUYqxRAdquGhKFAE/qB0iCHDt7IQh7VkPhG+KGjlQ2e7WNs02O3vL2sirasMhQqfyfVoUrwBw1cTfMzdyTT8tDA4pM+UEzz33HElJSWg0GubMmcOePXs81pdGKR/yL6VMJnBOajjLpzp/4T0lnTJcTmakjBVHCsCKGcvY94tjPDr9c1TKAOSCnHnyMGbrtvH9rhcob/aed7rVYOGDAzWYbaM/cmYssa2k2W2OlPGGN22gxOgkJSWFgIAAurq68PX1Ra/X8/bbb/PEE08wwbf/zXl/VSh3ZbxKsCqGJlM5XzX/jOVZ/qgVMuo7TKzbV+0RfXuHKPLp4XqPF64bC+RVtnvNkTLekGzg2Y1arWby5MnI5XJsNhtKpZLy8nL++c9/8vjjj2Ox9O5MjgnSsmJaDIpByA3KBBnXp/6VBL8sDLZ2Xii4FRQNXJYVjVwQKGnq5LtjTcPOLDlU4x65iNGCwWwjr6rdq31+Uvk0nbZWgtWx/GXuIa/27S0k+ycREhJCUlISdrsdhUKBKIrs3LmTJ598ki1btgzbFkUFapidPHA5rh+S5J/DXRmvopH7Udqxlwd3TycxUsfiNGeB9PxqHZ8faRjQOq+gVkdt++itQ1vRYvCaI0UURZ45eAV20UqAMpwLE+5znf+6qJHPChpo7rSglAtkxQVy9ax4bpybSFZcEAr5mNvy6xXJBkokJycTHByM0WjE19eXrq4uNmzYwOOPP05FRe/1Q91NgEbJ8swo3LnNe27MLdyd8ZrruML8EbWau6jQXkqtTs9buys5XKPrZudFEb462uiRDOeyZoNbHSkOh8jB6nZe31FBbmUbVrGddt8HXY6UpXF3u92R4kkGbVlvvvlmvv/+e0+MpV/Wrl3L/fffz6OPPsr+/fuZNm0aF154IY2NjSMynoGQHhXAbQuT+cl5k/jRwmSumhXPxVOjyYob+ULJKoWMldkxXita5E4iAjRcMDmH9Bn3Uh91yuE1RxbC8aMv8emhWgyD1MYeKvU6Ex8eqPWYPM7ZRm27keIG76VLDxbJBkqMFqKiorj77ruZOXOm69zXH7yFwtB/UbxgdTR3TXkFP2UoNYajPHfsHC6d5oO/RoHOaGV9bjVt/WS4DIX2LqvXZV9GG10WG3vLvfczsDksOET3/X2SbKDEaGH69OncfffdqNWn6te9+OKLfUo+xIf4cO6JTb2BopH7ccfkF4n2SaXD2sS/C24h0NfIBVOcEdMHq3XDtmulTQavzVu9wZ6yVq86zss6ctla55SxvCzxVyhknpMAHSkbKNk/idORy+WsXr2aa665xnVuz549fPTRR8PeVJuTHDIsCZtE/yzuynjFdfynvItICDdyYUYkggCF9Xo+Otj/2lkU4avCRrfIKbobURTZWtzstf42lf+RLls7AJdPeASN3CnNvruslcM1HYDzv9uPFiSzJC2CqACNR+pJSHNAidFCaGgod955JwsWLHCde+21186osedJEkN9mTsh1K1tpgbN45n5Rdye/h98FEGu83Xa26mSP8SnRXlsPNC93EBDh4n9bs4e6bLY+OJIvVvaEkWR4gY9b+6u4JuiJoxWO4FaGdXaG9A5nMEvC6KuZ3n8z93Sn7cY9G6+Tqdj6dKlpKSk8OSTT1JTU+OJcfXIM888wx133MGtt95KRkYGL7zwAj4+Przyyiv9PzzCyGQCgVolsUFa0qL8OX9yJHcsmsB56REEar2vX3lS2isueGAp76ORmUkh+GuVpE5YTUvCqYXxLHkE1bpf8+rOQ+yraMXm8PwErLrNyCcH66RsCjewtdh7hUqHgmQDJUYTSqWSiy++mOzsbNc5XcFWRFv/jpAIbXK3xe4zRxZzxfQYgn2UdJptrM+tprnT/TU9DlS1DboQ9HhiV2mLVzcZv6l5iTWbpvH0jqfd0p5kAyVGEyEhIfziF79wHTc3N/Phhx/2+UxWXBApkYOrEeirDOKujFcI1STQZq7h2cPXER1sZnGqc/65q6yVg9Xtgx7/SewOkYLajiE/P5po77J4PdPms9NqpUwLvcijfY2UDZTsn0RPpKWlcfvtt7uO8/LyyMvLG1abMpnA8syoYQV+JvpP466Ml13Hv8tdjEOTx2VZzuzAipYu3t9fg7GfuijNejOfHKrrsV7ASHK0Tk+T3jt170p0u/m+7nUAglUxZIVeAEB+VbtLSnFJWjhzJ4R6vBC1NAeUGE3I5XKWLl3azaHy3nvvodfrvTaGOckhJIe5X6Y1I2Qxv5u1i2T/6QBYacEkz6NWcxfbjOfz6q697Ck7tde5vaTZbZl8oijyxZEGDObhqe+IokhZs4F391ax+XA9bV1WNEoZCbG5HORi132XJz/C6uSHPeIA9iSD/gv5wQcfUFNTwz333MPatWtJSkpi+fLlbNiwAavVc8W4LRYLubm5LF261HVOJpOxdOlSdu7c2eMzZrOZjo6Obv9GEyqFjGnxQdwyP4kLpkR6re6GRinniulxY0raqydUCpnLE5wct5SA+ddSE2KnggqOyvZSrryLb4/n8+auSq8UqC9rNvDxwVrJoTIMihv01LabRnoYfTJWbOBot38S7kMQBFauXMn//u//kpGRwbx581BrBhZRGOubju9pUS8fVT3E6pxIIvzVGK123t9f028NlsFitYvsLj07i9G3Giwcqvbud3Fnw1paTY3YHO5xYEk2UGK0oVarefTRR7nnnnsIDw9n8eLF/T6zdHLkGbrT/RGgCuem1L8B0GQq59F9C0iJFlySON8UNVFYN/Tfsx9KN4xVdh5v8epcuEJ/kGO67QDckvasxxfjI2EDx+M6WMJ9xMbG8uijj7Jy5Uqio6PJysoadptBPiounDK8eiVpQQv5n2mbXMcvHb0THd9x+fRYl7Ts+n1VdJj6/t6UNhn4/EjDqLGPNruDnV6axzpEO88X3OQ6/s30TxFFkR3Hm/n2mDMAcU5yCFlxQV4Zz1iZA4JkA88mli5dysMPP0x2djazZs3C39/fa30LgsBFmWfWMnFX2z+Z+g6Pz9zBRfE/7XatUn0rm6p/xn92baKsRYfdIbL5UB1dluGv93aXtVLaNPT9U1EUKW82sHZfFR/m19KoN6OUC8xJDmFuRgVbWx913XvtpD+zMPp6ZMLYkyMc0ojDw8O5//77yc/PZ/fu3UyaNIkbb7yRmJgYfvGLX1BcXOzucdLc3IzdbncV3T1JZGQk9fU9px/98Y9/JDAw0PUvPj7e7eNyBzKZwJSYQK6ZFU+Ir+fS0gHC/dVcNzthWKm7o4nMmEDC/E79zCan34AifS4iIg5Bh0PzGHZzAR8cqOWTg3Uel1AobZIcKkPF7hDZVuK9dOnhMBZs4FixfxLuQ6lUsmbNGs5bspic+CBEUcTaXIlo79vuPTZzG1NDnFFu+5s/4qOqR1mdE0u4n9Oh8t7+atq63OtQOVzTQbub2xwLbC1uGnJR16Gwq2E97Ranfbh26rVua1eygRKjkYiICO655x6io6MB5+bL0aNHe7xXo5Rz4ZTIQWtdx/tlclv6C67jvx5YyZykYLLiAgH4/GgDxY1Di4jUGa1e09/3FI16E0UN3osIdYh23it9DICZ4avICl3mlX69bQPH8zpYwn1kZ2dzxx13oFAoAOfvzXAyByZF+JOTEDSsMcX6pvPTqe+6jv977BeUGz9kzYw4/NQK2rqsrNtX1W8m9NG6Dr47NjrUC/Kq2j1SW/CHiKLIQ3tmuY5vSfsXokPJpwX17C1vA2B2UghzhlHjZiiMhTkgSDbwbEMul7Ny5UouvPBC17nCwkJMJs8H6mqUci7Lih5UTb7B4K8K5YL4e3lydi4Cp/owyvdwnN/wr6LZ3L8jjS9KP+Lj/OGVHyhp1LNriM7ik5koa/dVsSm/loYOMwqZwIzEYK6dE06j7CX+e+xe1/2/zt7MrIhVQx7rSDMs909dXR1ffPEFX3zxBXK5nIsvvphDhw6RkZHB3/72N3eNccg8+OCD6HQ617+qqqqRHlKfBPmouHpWPPEhnpHeyogJ4OpZ8QT6eF9WzFPIZAKzk7vrFE4OWcz/TNvETMV87uAOLlM3EigvoKSpkzd3VXDMw4u80iYDm0dhOvJo50BVO+1dnp+YupPRbAPHmv2TcB+CIDAjKRhr2V6Mx7bTVfB1n7JfcpmSW9P/xY0nIq73NW1iZ+NrrM6JJdRPRZfFmaGi7ydycDA4RJEdx8+u7JSq1q5hRfkMFrO9iy2Vfwfg5im/JCEwwe19SDZQYrRxMivBaDTywgsvsG7dOr788sseI5rjgn3IjAkcdB9TQpawKNoZKdxuqeOr2v+wODWcjOgARBE+PVxPafPQar8d8HLRdnezvaQZbwaPv1vyv1QbCgBnrRRvI9lAidHGSRt4/Phx/vOf//D6669TWlo65PYWpYQPu75qkn8OT8w6lT2wvvQRdrc+z5oZsYT4qjCY7WzIraamH4mavMp2dhwf2cC7TrPNa7X/9ja+j9nunDfOi7wahWUur+8s51hDJzIBlk2OZN7E0BGTxhnN9g8kG3i2cvL78Pnnn7N27VpeffVVOjs9X483IkDD+ZOHl83XHxqFH0/PL+TJ2bksjbubYHVct+vryn/O1ZsTuWPdf9hfcxS7Y3AyXY16E58VNAx6HucQRY416Hl7TyUfnuZEyUkI4vq50ZTYnuB3ebP5tvYVRBzMCF/BX+YeItJn4uA6GmUM2plitVp57733uPTSS0lMTGT9+vX8/Oc/p7a2ltdff50vv/ySdevW8cQTT7h1oGFhYcjlchoauhfWbWhoICoqqsdn1Go1AQEB3f6NdjRKOauyY5gYMTgt576IDdZy7ewELpwShVI+9tKn+mNShB9+akW3c7G+6ayY+ieaZe1o0HCJSkeCz0FMNgdbDtez5XCdRzXrSxo72XK4XnKoDBCT1T5milKPFRs4Fu2fhPtQK+RMz8lGUKiwd7ZgKPgKh6XvRWpO2MVcnHA/AB9V/IX9Le9yeU6sq4bKxrwat6QOn6S4obNb8bzxjLcLlQK8VvQT9NZmQtRxXJ/xM7e1K9lAibGAVqtl+nSnzvT27dvZvHkzjh5q6C1MCTtjDjkQVic/xJUTHgdgS+Xf2Vj2B86fHEFqpB8OETYfHJpDpazZQJubpRW9RVVrF+XN3susaTc3sK/pAwDmRl6FvyrMK/2OhA08W9bBEu4jLi6OhIQErFYrb7/9NoWFhUNqRy4TuCQrGn/N4O3k6fgpQ3gg+xPX8ZfVL/D7/JlcMT2a6EANZpuDjXk1lDb1bTd3l7aSW9E2rLEMh+0lzV6pe6e3NLOp4s8njgTsLbfxxZEGuix2ArVKVufEkhHj/e/0WJkDgmQDz3aysrLw8/OjsbGR1157jfb2do/3mRETQHZ8kMf70Sj8uDjhF/x2xlf8OnvzGddfP/ZjZryUgeJ3CiKeiuadQ+9QpevbmVjZ0sWG3OpB2TeT1U5uRRuv7yhny+F6mjstKOUC0xOCuGFuDCX2R3kibzoHWz9zPXPdpD9zfcpTKGSeVWTyBoPeWY+OjuaOO+4gMTGRPXv2sG/fPu6+++5uxmnJkiUEBQW5c5yoVCpmzJjBV1995TrncDj46quvmDdvnlv7GmkUchmXTo1mcvTwtP4SQny4fHosV82MHzeyXj0hlwlMjTszstBPG0n89Juok7egRsVi0cj0kN0IAhxr6OSdvZUeLRx3rEHP50fqR42+62hmd1krpn4KEI4WJBsoMVZYMiOD8OkXICjVOAztGA59gb2r76LAS+PuYlH0jQBsLH+S8s5trM6JdUkxbDpQi9nmnu+qQxTJH+NR2AOlqEFPQ4f36kFZ7CaK2rcBkB22HKXcfRNWyQZKjBUWLlzIJZdcAsC+fftYt27dGZruGqWcJekRQ2p/ftQ1nBt9KwDb6t/glzvTuSAjipQIP+yiyCcH6/rdGPwhojh2s1O2e1mqdXPl067PlyT8j9f6HQkbKNk/icGiVqu59tprSU9Px263s3btWnbv3j2ktnzVCi5xg4RNlM8k/jrviOvY5jDzl/zzyEguJTnMF7tD5OODdRyu6XuuurW4ifJm72X6nqReZ+LoMOpiDRSDtZ0XjvwIo02HnyyBROMHNOidm5QLJoZyw9wE4oI9o2TSH9IcUGKsEBUVxa233kpgYCAtLS289NJL1NbWerzfc1LDPaY01BORPhN5Zn4Rf513hJXxfyFQltntelNXPde9fx0Jf09AeFxg0ctL+azkM2o6nBKQDofIwep2NubVYLb270jpstgoqtfzUX4tL20tY1tJMx0mGxqFjDnJIUxPP8zGpoU8npfD4davuj17f9ZGZo5hWa8fIoiD3Ol94403WLNmDZoBFrd1J2vXruXmm2/mP//5D7Nnz+bvf/8769ato7Cw8Az9xJ7o6OggMDAQnU43JjzToiiyv7KNbcUtg9JYTwrzYd6EsHHtQPkhXRYbL20t67FWidVmpDj/ZeLMTjmw41od+w3n02l2OmIuyIgkNdJzRapmJ4ewYJJ3ouXGIu1dFv67s8KjdWYCtEpuW5g84Pv7shVj1QaONfsn4R5KGvVs2n2MriPf4TDpQabAJ20hiuDoXp+xizaeO3wD5fo8FDI1P818B18hhfW51RitduKCtazMjkEhG36mo1op4/aFE1Apxl/W5Elsdgev76zwir72SdaWPMTuxg0APDX3MDOTwlmcNvANY8kGSownCgoK2LhxI3a7nYiICG644YYzipN+lF9LSePgM0kcop1f7sxwHV+e/AjzIq/js4J6ihudUizLM6OZNIiMc5VCxm0Lk9Eo5YMez0hR0qjno/w6r/VX2rGPZw9fD8DdGa+SGjS/x/scDpGiBj11OhNr75qLj6r/6Pr+bMVI2cCzbR0s4R4cDgebN28mNzcXgJkzZ3LRRRchlw/evhTU6vi8oKH/G/vBLtrYUvkPvqt9FbvonBtlBi8jzvEwR+udctxzkp21QHqTsPJRybl+buKQMguHgiiKrN1bRZ3Os4ExdtHGvw/fTKl+HwoxmAjzn1CKsaRG+LEoJRy/IWQIqRQy7l0yacD3j8c5IEg28Gylo6ODt99+m4aGBmQyGVdccQUZGRn9PzgMTFY76/ZV0dLp/UzjQK0SjVLGfw++zGe1v+vzXjkaghQZWEUdQYo04jVLiVBloVL4uGyv6BDpstoxmG00d1q6qUqIWFH45lLq+H2f/SyO+RGXJv4SmTAy89rJ0QFclNlzBltPDNRWDNqZMtI8++yzPPXUU9TX15Odnc0///lP5syZM6Bnx6oBrdMZ+fRwfZ/1JAQBksN8mZUUQswwdU3HKp8eru81WsTusFOU92+XQ+Vr1R5E+f9Q2eqUvZmdFMLcCb1P2IbLsoxIMmMHr8t9NvDxwVqKGzyrY+lOZ8pIM1QbOJrfScKz7DjezM6iOgz5nyJanBIs/rOvQFD0nq1gtnfxj0NXU991DID/mfYBCnsy7+2vxmoXmRThx/LMKGRusJnnpUcwzQsp0SNFbkUr3x/zXsR2p7WVx/YtwiHaOD/2Li5JvJ+chCC3OVNGGskGSgyFkpIS3nrrLQBiY2O5/fbbu13Xm6z8d2fFkOVb/l1wM8W6XQgI3D75/0gLXMRnBfUca+xEEOCCjEjSowb+e3dOajgzEoOHNBZv43CIvLGrglYvyZPZHBaezl9Ng7GEJP8c7st8G5nQ3SFvdzj1u/eUtdJ+YuH/20szBjQXHM224mxcB0sMH1EU2bx5M/v27QPgwgsvZO7cuUNq67tjTex3k8yWwdrOlsq/s6PhHQBSA+eTpvpfDlY6r0+O9ue89Iheg3figrVcOSPOKzVD8irb+LaoyeP9vH/8KbY1vIQgaokyP0WEJoUl6REkDCPS3Z3OlJFGsoESQ8FsNvPSSy/R3Oxcj/3iF7/w+O+Azmhl7d5KDObBKToIAqRHBZAe5Y9dFLHYHJQ2GSht6sTWS/Cxv0ZBRkwAKRH+hPuru11rNXSy6dAxntj2M8oN24b8PkPhtzO+IVgd49U+e0JypriBsWxARVHkeFMneZXtNHWaMVsdyASBMH8VsUFapsYGEuqn7r+hcUxDh4m3d1f2et1qM7J/zx9JJZU3eIOJsRcSYLmR/ZXtAKRE+HFBRiQKD9SVkcsErpgRN+wCfuONmnYj6/Z6vhjceHKmDJXx+E4SA0MUnbIJxyrrMBz+GgC/nEsQ5H1Ht7WbG3jhyC00GkuJ1E7i/mnvU9du58MDtdhFkamxgSxJCx/2IjbEV8VN8xJHrICmJzFZ7by6vdyrMob370hzfX56XiGCIIwrZ8pQGY/vJDE48vPz+fjjj5k6dSorVqw44/pwNstEUeTVons53PoVAgI/Sn+eycFL+PJoA0frnJHW56dHDDiwxl+j4Ob5SWOi1uGhah1fHh1+tPpA+bzqOT6t+id+ihB+nbMZX+Upp5PZaie/RsfB6nbXBoZGKeOW+Un85LwUfAcQxT5ebcV4fS+JgeFwOPjwww85dOgQV155JZMnTx5iOyKb8mvcWh9pc+Xf+LL6BdfxFP/r6Gy8FhCICdRwSVZ0r1llC1PCmJUU4rax9ESHycobw3C2DwS7Q+Sdo8+zX/dPAMItv2Rx/OXMSgoe9t7EeHKmDIfx+l4SA0Ov1/Paa69hMpm499578fHxvBRXc6eZjftr6DQPrOZoYqgP56aG97iva7LaKW0yUN3WRYPejFYpJ1CrJDHUh0nhfsgGKMNY1lbJkbpW/rHrnxxrLaDCsGdQ79QXs8IvZ2ncXYRrk9zWpjuQnCluYDwZUKvd+cd8LCyyvMm6fVXUtPVeZNnhcLCx9HG2N76LDBnXhv8Kje8VfF3YiEOE2CAtl2ZFe0RaIchHyfVzEse1nM1gEEWRd/dWUe/hdGmQnCkwPt9JYuBYbA425lVT09YFCAiCgMNkQLSZkfv1vggt69jPvw5fC8DM8JVcM+lPHG80sPlwPeCUYZg7IXTY47t8eiyJob7Dbme08f2xJq8WSq0xHOXp/FUALIy6kcsnPAwgOVMYn+8kMXjsdrtL3qa0tJTY2FjUaueidbjzEr2lmf87ejs1hqPIBSW3pP2TjOAlfFPUxKETNQAWTQpj+gAzTuYkhzB/lMvEWu0OXttePuCNguHSZq7lz3kXY3EYuSHlaaaHX+q6drypk68LG+myOJ0oPio52fFBTIsL4ro5CQPWMB+vtmK8vpfE4DhpA0VRpKioiPT09EG3YbLaWbu3yq3ZaKUd+3i18D4MtlNzpiTL64j2UPw1CpZnRhEdeGZQolIucOPcJAJ9lG4byw/ZdKCG0ibP1Wgpazaw/tg/qOd1AOLlt3Jz5v2E+Lqn3p3kTHEyXt9LYuA4HA4EwbkO7uzspK2tjfj4eI/2qTNaeX9/dZ8qQ3KZwIJJYUxPCBqR4MJD1Tq+LmykqH0H39W+hsluoLxjP5E+EzHZDbSZa84cs6DkjskvMjFgJnbRjko+ektMeMqZ4h2RSQm3IzlRemZGYnCfzhSZTMYVkx4nWBOHf2UNKU1aasUtrMxewScH66hpN7Iht5rVObEDil4bDO1dVraVNHFeev+anmcDRQ16rzhSJCQknAupVTmxbMqrpabdiMOkd2apOGz4ZC5F7tNztHRywHTuyniF/ztyO/uaNuGjCGJl0oMsTgvn26Imdpe1olXJmRYXNKzxHazWjTtnis5oJd/LhaQ3V/zN9XlV8oNe7VtCYixw0pFy6NAhNm7cSGJiItdddx1KpRJBcNbRe3t3Za9SCn3hrwrj51kbeOvYrzjQspnXin7KLWn/ZEnaElQKGbkVbWwtacZkszNvQmi/C+bcijYmRwcQ7KYNNU+QV9nuNUeKzWHhqQMrsDiMJPvPICfsEsAZKf/F0QYKT9RaCPJRMicphJRIf+TDLJgtITHeOOlI2bhxI4cOHeL8889n4cKFg2pDo5SzYloM7+ytHFDB4oEwIWAmv8nZwm/3npIfK1fdTBS3YDdewPpcG/MmhDIzMbib7bTaRb4uamB1TpxbxvFDjtR2eMyR0thhYvvxFgrbv6FJ9V8QYFrgzdw4+QFkbqhLKCEh0Z2T36uTWSqdnZ3cdNNNxMbGeqzPQK2Sq2fFs7W4mcI6/Rm1sCeE+zJvYigR/iPnjJgaF0iAVsHHBxeQEjhv0M/L8ZwzezQjWWmJccWEMN8BRXGcF3cHPn5Ooxnd7MDc/jlXzojDVy2nxWBhw/5q9Cb3Fws+WK2jssV9adH9IopgaIG2Cuf/j5JENKvdwbZi79UQkJCQALVCzqqcWGKCNAhKDYJKg2iz0HXkGxym3usWpQUt4OpJfwDg+7rX+azqWabFBTEn2ZnR8m1RE0UnNrGGSmmTAYMnNuRG0AbuKGke0obsUNlZv5aj7d8hExQ8mPP5iBX5k5AYC4SGhqJUKikvL2fDhg3Y7c5shlA/NQtShp4NIhcUXJ/6FNmhy7GLVl4uvIc3i/+HhZPCmD/RmcW3t7yNb4qa6E8cwOYQ+aaocchjATxqA7ssNvaWt7qtvf54veinmOzOvzVrJj7h2lDdU95KYb0eAZiZGMz1sxNIjw6QHCkSEr0gCIKrYPdXX33lqqUyGIJ9VVwyNdottfNO4qsM5i9zD5ESeMqhUs9rVGuvo1P2HTuOt7A+t/qMjJjy5i4K63uomzpM+9feZRm+De6BVoOFLYfqeGtvCVuN59Gk/h0IIhlB53FTxoOSI0VCwsNotVoCAwOxWCy89dZbNDa6/3t+Oj4qBRdOieKmeYnMSgphRmIwc5JDuH5OAiuzYz3nSBmEDUwM9eXKGXEo5dLcaaBIllpiXCEIAtMTBiafMDnzNqq07QgIRNYbaTr2ImtmxOOvUdDeZeW9/TV0GN3rUBFF+LqwAYenN9iM7bDr3/DPHHhqAvwjy/n//8xxnje2e7b/fthf0Ybe5J1IRoB6Q2m/mxYSEmcDKoWMS7Ji8NVq8Jm8GJk2ENFipHP/R9j1Lb0+NzviclYn/xaAz6ufZUf9O8xJDiHrhP7/50fqKW3u3SHTHw5RpKC2h4XwUBlhG9jYYaKoYXgOpsFgsnWyvvQRAOZHXku4NtFrfUtIjEViYmK49tprkclkHDt2jN///vc4HM4I65z4oGEV+3U6VP7KtNCLAMhr/oSvqv+PWUkhLEkLB+BQjY5PC+qx9zMfrGjp4vAJibBB4QUbuLu01aM1BE7nWPsOCtq+AWBB1PVE+Tgla+p1JvaccOhckBHJgklhHql9KCEx3liwYIErI+WTTz7h5ZdfHnQbiaG+nJPqXilChUzFPVNe58k5uZwfe5frfLPqKSo0K8g3PsRbu8vYU9aKzXHK/nxX1HQqS84N9s/uENlyuN6tNq6l08yWw3W8sauCosY2GlW/dV1LC1rIrZP/NS7rB0pIjDYUCgVXX301sbGxGI1G/v3vf1NSUuLxfoN9VSxMCeOc1HDmTwojIsBDTpQh2sDIAI2kojMIpNmmxLhjcrQ/Pqr+I3JlMhnp0+6kXXBu4MWZQ9Hr9nLl9DgCtUqnvmFejdujpdu6rBypc+Om4Q8p+RKeyYBPH4S28h90Xu48/0yG874RwGC2sc+LNQQqmnby111XMuX5Kdgc3nPgSEiMVvzUCpZnRiNXqfGZsth13nDoc0R77w7kRdE3cGH8TwDYUPoYn1c/x+K0cNKj/HGIsPlQPVWtQ8+8O1yjc4/TcxTYwO+Lm72aCLin8T3X56Vxd3a7ZtM14rBLtk9C4ockJSVx+eWXu45ff/11RFF0yn1NiUQ7gLlkb8gFBTek/NV1/Enl09y/I42MGB8umhKFTIBjDZ18fLAWm73vzbqvjjZS1jwImRkv2MA2g8VVB8bTtJlreeHIra7jy0849i02B58W1COKkBrpR3r0mbrWoihia6+TAmokJHrgvPPOIzHRGXxRXV1NcXHxoNvISQhmamzPUrHDQSP345LE+3lo+pcEqE7UfBMcGOW7KdOs4KOq/+W/u467bGOXxc7mg3U4jrnH/u043uw2OeomvZlPDtXx5u5KjjV0YpIVUKldjVl+BICM4CXcMfn/kAvuV+B3mA3YurxjqyUkxhJqtZrrr7/edfzWW29hMo0DCfphzgEzYgLIjg/y9Ci9hq2jCbvN/YpDIDlTJMYhCrmMGQMs7imXydGkLXAdF5W9T5vtyBkOFeOJYpbuYldpS7/RiEOi5Et46yqwGgHxxL/TOXHOanTeNwIOlZ3HW7wWyQjQWbmfn/EzFpgXoJBJZaIkJAASQn2YnRyCTOWDIizBdd6ma+jzuWVx95AduhyAz6r+xWdVz7JsciQTwnyxO0Q+zK/ts25VX+iMViqH4YwBRoUNLG82DMupNFgM1ja+qH4egEXRN53adABE0UFX0Va2b3yVY8eOeW1MEhJjhSlTprg+19TUoNc7M8r8NUoumhLFcIKE5TIlf513lOlhl7nOPbBrKolhApdlxaCQCZS3dPHBgVrMtt7nmQ5R5JODtdTpBmBbvWQDt5U0e2Ye2wMvFJxypPwmZwuCIGB3iHxWUI/OaMVPrWBJWkSPz9o7mug68i1rX30Bi8V9xbIlJMYDgiBw5ZVXuo4LCgqG5Hhckh5BXPCZxeHdQagmnsdmbuXRmd93O29QfMNhLuX1gt/z/N4XaOo0oCj7GuGdNcO2f0X1evaVDz/wr05n5MP8Wt7eU0lJozN7Ozykigb1r133rEj6Dbel/9tj0qyW2iLa933Mxo0bPdK+hMRYRqvVcs4557iOx/xayU1zwHNSw4kOHL0F5QeKKIoYi3ey/+P/cvDgQbe3LzlTJMYl0+KD8BtgAfmIkEyMaVN4T/kxn4gf8ULBLVhoYHVOLH5qBa0GCx8cqOlzoTtY9CYbB6vb3dYe4EzXW3vTCS3E/pwVDud9a2/yquRXk97M4VrvRcfUNu4lzhyMgMCUpCn9PyAhcRYxNzmUyAAN2pT5aFMX4D/rcpQhfRfwlAlyrkv5i+v48+pnaTQdZ3lmFIkhPtgcIpvyawa26dcDB6uHYR9GgQ0URZGtJd6tB/VJ5TMYbO1E+aSyIvGBbtcs1QVgs+Cw2UhISOilBQmJs5uHH36YFStW8NBDDxEQcCq7ISnMlznJocNqWybIuC7lz93O/e+eGcSHqFiVHYtKLqOm3cj7+/sO3LHaRT7Iq6W509x7Z16ygVWtXa6NQU/zdvEDNJnKAVgScxsR2gnYHA4+OVRHabMBuUzgoilRaJRnbkSKooipfD8AWh9fVKr+aypKSJxt+Pn58dOf/pQbbriBVatWDUlmSi4TuDQrhiAfzxUhDlRF8sz8In6S+U638x3K9ymx/o3n86dzwZGfnnAGDd3+NXSY+OJI/ZDHKYoiFS0G3ttfzbp91ZQ1GxCA5AgRv+jnyTP91HXvj9KfY3HMrR6T9rIb9VjqndlGYWHulWOTkBgvLFmyhKuvvpr777+frKyskR7O0HHjHFAuE7goMwqVYmy7C6z1xYhmAw67Z9bBY/unIyHRC0q5jDkTQgZ8f2RoFtfkvECMTzpmRxdvHvkJMrGF1TmxaJVyGvVmPj5Y102bdbjsLW/F2o+0w6DIfwesXfRvPE/icN6f/677xtAPW4ubvCp901m2GwGBOlULP1n1E+91LCExBpDJBJZlRKKQy1CGJSAo1a5rdkN7r9GJCpmK383a5Tp+ufAeTI52Ls2KJi5Y69r0G4pDpbTJgG6otapGgQ08UtdBs76PzU43c6TtO3Y1rAPgygmPIped2shwWIyYq50SEqmzF6PRjP0IIwkJTyCXy8nJyXFtaImiSEODM0tv7oQQksN8h9W+TJDzzPwiFsf8yHXuV7syEZVlXDH91Dxzw/7qU5r/PWCy2tm4vwZdVy820gs20OEQ+e5Y04DvHw6V+oPsa9rkOr4s6QEMZhsf5ddRdsKRcllWNLG9RMRbm8txGNpAruS85Su8MmYJibFIcHAwEydOdB0bjUY6OgYnSa1VyVmZHdujY9OdJAdM55n5RTw0/UuWxd3jOn8zKnwQkZ0Rid0bZ9o/ndHKR/m1WO2DX6zaHSKF9R28u7eKDw7UUt1mRCZAapSSsPg3+FZ/GQXtm7GLVjJDzud3s3aTGbJ00P0MBnPFARAdKAMjmT9/vkf7kpAYy6Snp+Pv7+86bmpqctXRGzO4eQ4Y5KNi8Yk6f2MR0WbFVOnMRkmYOpegoCC39yE5UyTGLZkxgYOKkNEq/PlR+nPEKydxmek8WvI34qe2sTI7BqVcoLrNyGcFDTjc5A0wmO0cdVftFFGE3f8Z2rO7X8AbHo6yZgMVLd6Tviku/4gouzOiNDHzYuQyz07uJSTGIuH+amb+QBbR2lKN4eBnmMvzen3OVxnM4zN3EKKOpcVUySN751FnLGDFtBhig7RY7I4hOVQcokh+VfvgX2QU2ECb3cHO4y3DbmegOEQ7Lx09VR9lQsDMbte7jn4PogOZbzCEJGGyuleuUkJiPCKKIps3b+Y///kPpaWlCIIzOi/Ed/hZDSuSfs3cyKtdx88cXI1OPMCVM+LwVctpNVjYkFvdp0O502zjvf3VZ9bz85INPFyro8kLDmOL3cR7pY8DIBMU/GH2PgrrO3hzVwWVrV0oZAIrpsWQGNqzo0t02DEVO53+6pg0/AODPD5mCYnxgMFg4PXXX+eVV17BbB7cdz3EV8WlWdHIZZ4voh6qiWd5ws95Zn4Ra5If5ycMMSvmhP2r15lYu7cSvWlwNebauyzsPN7CqzvK+KyggUa9GYVMYGqslvikj/hCdyH7mte67r808ZfcmvYcvsqgoY13gNg6mrC1VgPgO3EGcrm0DpaQGAhlZWW8+OKLfPTRRyM9lIHjoTnglJhA0qL8e7w22jEW7wS7FUHjR9SkqR7pQ3KmSIxbZDKB+RMHl9Iaoonjuol/xB9/QsQgSve/TIiPyKVZMcgEKGns5LuiJrcVsjwwlE3DnuhqhbYyztRF7A/R+ZzRswXhHQ6RrcXeiWQE52bI/9U+yFd8RSFFRASnea1vCYmxxuzkkG4bhaKlC0QHlroiLHW9a8f6q0K5ffL/uY7/dvBK9NY6VmbHEHfCobIxr4ba9sE5VA7X6gZfV2kU2MADVe2DXoQPh+31p6QuHsj+5IzrvlOWQEAkx4nm4Q8KeH1HudfGJiExVhFFkdbWVkRRZMOGDdTV1aFRylmZHeOWiOurJj7BPVNecx3/u+BmNtc8yMVZvgRoFOiMVtbnVtHSh5yXzmhl86E6HKfXLPGCDTRZ7ezwgsPY5rDwWtFPqDIcRi335adTtvD54Q4+K2jAZHMQ7q/mqpnxJIT49NqGIJPjO/UCZFp/lJGTPD5mCYnxgs1mQ6fTodPpePvttwddayg+xIcLpkQOq97UYFkSdhGTkCNjsJ067V9hWQUbcqswmPsPOjHb7NS2G9lV2sK7eyt5fWcFe8pbMZjtaFR24mMKOK6+hI9bz+f7+u6bm/dnvc95sXd4TNbrdOT+YWiSZyAPikLhN7BashISEqDX67FarRw4cIDvvvtupIczMDw4BzwvPYIAreckHD2FNnU+ipB4VFEpCDLPuD0kZ4rEuCYtyp/J0QH933gakSFZtEY5n4mxhXHs4CvEBqm5cEoUAAdrdOytcI/zoaXTQqU7sjUsw9SuNuuHP4Y+OFyro6XTe4U/D7RswY6VrWwlOuvK/h+QkDiLUchlnD85wrXwVUaloAhLBMBUlou1parXZ6N8JnFj6jOu4xeP3oFd7GJFdswpya8DNYMqyG62Ojgy2Ky9EbaBRoudPeWtwxvDIOiwNPFJxdMAXBB3L1E+3TcLzTY7uyr0vNGUwLfNPthFkcJ6z9p5CYnxgEwm4/LLL8fPzw+j0ci7776LTqcjyMd9EdcpgfN4bOZWpoYsA2B/88f89fA5zElvJtRXhcFsZ0NuNfU6U69tVLc5N/NceMEG7jze0mddF3fxQdmTFLZ/j1KmYXHo03y830J5SxdyQWDehFCunhlPuL+633bk/qH45VyKTOWZwtgSEuORwMBAVq9eDUBlZSUffPABdvvgvvfpUQEsTovwxPB6RGUf3lr6ov9O4CdbU/nLrgf51+7nWbuvmPdyK/jgQA0f5tfy/v5q3t1bycvbynjuu4Osy61iZ1kttR3N6OWfUKG9lArtpRTJV7Kt7dfd2vZXhrEi8dc8OTuXOD/v1e8UBAFVdCq+GUu81qeExHggKyuLWbNmAfDtt9+Sl9e7UsOowYNzQI1SzkWZUci86SF3A4JcgU/6QtQx6R7rQ3KmSIx7lk6OICKg/0XX6aROWE1tuNNgxJtCKNn7b1Ij/Tk31akbuPN4CwVuKqSeV+UGx4zKb3jPqz2Xvme22b0rfeNwUFK8AQUK4n0ziffL9FrfEhJjlbhgHzJOOJ4FQUCbMg9FcCwAxqJtWFuqe302J+wS/mfaJhSCigbjcV4tuhe5DFZMiyEhxAerXWRTfi0VLYYBj+dAZdvgMgBH2AbuLmvBbPWetu6m8j9icXQRoAzn3JhbXecdDpFDpTV8tXM/e8pbsTlEogI0/GZ5Gn+7Ottr45OQGMv4+vpy55134uPjQ0dHB3//+9+xWCzEh/hw/mT3bBAGqCK4Nf1Zfj51PXLBGfH3ctFNxMVvI9Jfjcnm4L391Rxv6n2BvKe89ZRd9bANrNMZya9uH14fAyC36UN2NDiz7pJ4mMKKOKx2kehADdfNSWB2cki/Di1rWy12g+fHKiExXklNTWXNmjUAHD16lP/8Z/DyMdnxQcyfGDro5+QygamxgdwwN5Gb5iVy3ZwEzk0LJ8yvd6lFX//AQfdzOie3Eesd71Nm/we7LZey3XwB33edx7eGJWwznc8e6zIKZBdRrb2GSu1lVGmvpFp7La2qf/fa7m9ytvD4rO0sjv0RGsUwbfQgsNQXI9qHWH9QQkKC5cuXu4rRf/jhh+Tm5o7wiPrBw3PA2CAts5LHRoabw2LE0ljmNiWhvpCcKRLjHoVcxqVZMWhVg5NnSE+5huogZzZFpD2YsuovyY4PYsaJ+gJfFTZS2scid6CUNRto7xpm1oZPCAQnw6DTmwXnc1rPGce9ZW10eSGS8STHcp/nQnEZD/Mwd0x+0Wv9SkiMdc5JDcfnhJ0UBAFt6nzk/k6pRFP5fhzW3mVnYn3TuX2yc7FdrNvFi0fvQMTCZVnRJIf5YneIfJRfN2Cb2dZlpahhEJkUI2gDdV1WDla7x7k+EArbtpLX/AkCMm6b/G+0CucE2GJz8FF+DYn137NQXsKl2mNcPDWKq2bGkR41uAxNCYmzHX9/f2655RaXHMsnn3yCKIpMiQlkzoQQt/WT4J/FA9kfAyDi4MOKJ9hjW0Z4cDM2h8jHB+vI68W5LIrweUGDsx6SB22g3SHy5dFGj5fXO9jyBe8U/waAAOsVWAzZqBQylqSFs2ZG3IDq1jgsRoxHv8OQvwWbrsGzA5aQGMdkZGSwYsUKwFmMuaCgYNBtzJkQypL0iAFHNE+K8OOWBUkszYgk3F9NqJ+ayAAN0xOCuXFeEtfPdTpWUiL9SI30Z86EEFblxHL1OdOGZP8cQJVMSyvioJ89iUZ+ahNzScxtPDrze56ZX8Qz84uI0E4YUpvDwVSeh6l0H/rdGxAdUq08CYmhIAgCK1asICUlBYBvvvmGjg431Tr2BF5YB89NDiU2ePRn+nbu+wBTyS46czd5vC/JmSJxVhCoVXL59NjBO1TSbwRAhoxjVZ9Q31XCgomhTI72RxRh8+H6QdcD+CGiCPnD3YgTBJhz19CenXM3nhK21Rmt5FV6th7L6Rgt7cRYT0VB+anct+EhITHe0SjlLEoJdx0LcgXalPkAiGYDjq6+v8upQfNZnfwwSpmGovZtvFp0Lw4sXDI1mknhfthFkY8P1XFsgE6SrceaMdsGuBAcQRu443gzdofno18ArA4zH5T/AYBzom8i3s9ZUM9woii1pb3edW9sfCIpEf5e0eaWkBiPhIeHs2yZU4qruroak8kpuzV/YhiTo92X0RuuTeLPcw9ybvQtrnP7TLdA0MsAfF/czGdHGnqsJdVptrG9pNmjNjC3oo1mDxed39/0Ka8V/QQHdnxt5xFku5m0SH9umptIVlzQgO2YqXSv67PcT5oDSkgMh5ycHPz9nbaurKxsSG1kxwdx2bRoVIret520KjkXT43msmkxBGh61+aP8Hc6Vi7NiuGSrGjmTwwjOczXqYc/BPsnQyD+gscwPmRjw6WV/GlOPvdlvsV9mW8xN/JqpoddxtzIq0+NU34qMCU1cD6/yNrAH2bvczlPLkt6gEBV5KDH4S5Ehx1LbaHrWJBJReclJIaKXC5n1apVABgMBmpra0d2QH3hhXWwTCZw8dRofNWj167Y9afUcBRBUR7vT+HxHiQkRgkR/hqunBHH+/urB1RgDpza2ZrZK/nk4P+y1bSFnUf28ZPMtzk/PQ6jxU55Sxcf5tdy5Yw4wvwGJyV2OkdqO1gwMRSFfBj+zWnXwle/A6sRZ6xNPwgyUGhh2jVD77MfdpQ4Iyu9Remx94jHWZBUlnO+1/qVkBgvZMQEUNyop7TJKR0j0/jiO205ICL37T9qZVH0jcT4pPPi0TtdDpUfpT/P8swovjjaQGG9nk8P12O1O5gS07csQ6fZxq7SVpe8Yr+MgA1s6DANLoNmmDx3+AYajWX4KUNZFv9jAByiU0atSW9ihcY50RcUajTxksShhMRwmTdvHqGhoSQmJqJWn5rnLcuIotNsH1Q9qL5QytSsTH6QIHU0m8r/CECFeSPhgccxdcVRWH8HjR0mlmdGn1Ev5FCNjvToAGI9YAObO83sLvWcVKvN7uC78t1sqf9fEES09nlMVj/AuVOjBh0B6TB1Ymtz2kB1QhaCfOwVTJWQGG38/Oc/5+jRo2RkZAy5jQnhfty6IIn9Fe3kV7e7HMP+GgU5CcFkxgagVgxzg24Y9k+jlLM6J5bvilWoKjXOMQfMdN161cQnhjc2L2FtOO767Dd9xQiOREJifODj48NPf/pTDAYDcXFxIz2cvvHCOthPrWB5ZjTv76/B4QUZrcEgiiKmynzXsWbiHI/3KWWmSJxVhPmpuXx6XJ/RMT9EpfDhgqmPEqVNocPSyIuHb0PfVcXFU6OJDtRgtjnYmFczLKkuk9XOsYZhSoZpg+Dq/57wLPf3fjJAgKvfcD7nAep13t1krG3cS3yH05FSH6XBT+u9wocSEuOJZRmRLrkvALlvUDdHisNkQBR7n6RNDJzFHZP/D5VMS1H7Nn69K4tOWzMXZESSGROACHx5tHFA+vsHKttp7hxgRPQI2MDvjzV5XPrmJNvr3qKy8yAAlyf/Fh+F0xl1pLaDJr2ZBaoqQgUDyBT4Zl/snUFJSJwFpKamdnOktLW1IZcJXJoV3aeO/1A4N+YWfjp1reu4yXIQvWIzldrVtHS18e7eSnaVtnTLhhNF+OpoAzZVgFttoM3uYMuhOo8ExZhtdvaWN/PAril80nALDsGAVkxhzYQnuXZ20qAdKaIo0rn/IxBF5IFRqOO8V+hZQmI8I5PJmDJliis7zG63D0nuxkelYGFKGHcsmsCd50zgnsUT+dGCZGYkBg/fkQLDngPKZAJL0iLIjB1e/ZWRwmHqxFTmrOugSZ6JTOM7wiOSkBgfBAcHd3OkdHR0YLePQgk9L62D40N8mD9p8PWwPI2lugC7rgEQ8Jt+mVeUGSRnisRZR5ifmqWTB5eC66sM5u4prxClnsjFliW0HfoYu72TFdNiCPVT0WWxszGvhk6TbcjjOlzjBs39SUvh+nWg1AIC4hm6iYLzn1IL16+HSZ7L3vi+2HubjAB+JSUANMlamZgkReNISAwVH5WC83uxkfbOVgwHP8Vckd9nYTenQ+VUzaLH9i3EYGvjvPQIsuODAPi2qIn9FX1LhzlEkc8Lepa36REv2sDjTZ1Utw1P5nGg6C0tvFfmjIwMVEWSHbYccG5I7jjeghYLqfJGANRxU5CpNF4Zl4TE2cauXbt49tlnKSsrQ6OUszInFn+NexP9k/yzeWZ+Efdnbex2vkp7DWWaS/mu4nve2VtJdduprJiWTgvfFze51QZuLW6muXOYNf1+gN5kZWtxEy9tO8bHlU+C4LTtwcoUfjPrTbJjY4a0ALY2lLg+aybMcNt4JSQkTmG1Wlm7di2vvfYaRuPQ5j8qhQxftQKNUo5M5ubNLjfYvyVp4UQHjr05VOf+jwAQ1L4ooyaO8GgkJMYnzc3NvPTSS3z88cdeKXA+aLy0Dp6VFMLk6NFTj1O0WTBXHQJAFZOOTOPXzxPuQXKmSJyVpEX5uzb0BkqAKoLbU/5JFFGEisFY9n0CooHV2bEEapV0mGy8n1dNl2VoDpWaduPAI7D7YtJSuP8IXPQnCE7qfi04yXn+f4561JFS0qinxkubjABV+oPsxamTbQ0NRy5pxEpIDItJEX49RufZjTpEmwVLbSFdBV/12cbEwFlcNfH3ruNH9s7jQMsWzkkJY2aiM9Nla0kze8pa+2ynocPE5kN1OAYaHe0FG+hwiGwrbh7y84PB5rDw6L75ruP7s953fd5b3obRasdPqwGZc0NXFTXJK+OSkDjbEEWRqqoqHA4H//3vfzl48CABGiUrsmMGlfE8UOL8MnhmfhFXTniMCG2y63yD+jfst1/Amwc+4ZODNXQYrQDkV+koqte7xQaWNHZyoKrdbe/S2GHi08P1vLajnL2VZRxXrUKv+MR1/aGZm/BXBQ25fdlp2ZNy7ehZ4EtIjCcsFgt1dXW0tbXxl7/8ha4u98gcupVh2j+FXMYlWaO7LkBPqE5k46kikhEEaYtPQsITtLa2otfrOXDgAM8+++xID6dnvLQXuCwjktigUVKQXpAhqJ0OFFV0ive6FUelS80zdHR0EBgYiE6nIyBAmmif7dgdImv3VtHQYRrUc1X12wksrXQdq2ZejMXuw/rcajrNNkL9VFwxPQ6tcvCTsOyEIJakuU+eym6zsfObzShsRmYtPA+5X5jHis27+nSIvLGznLYuq0f7OYlDdPDPQ9dQ03mYKf6LuGXqf864J0Cr5LaFyT083TPj0VaMx3eS8Cw2u4O1+6po7Oju5DVVHsRSXQCAPDAS3ynn9dlOVech/nbwStfxJQn/w/lxd7K7rIVdpU5HyuykEOZOCOkzIjkjJoALpwyumJynbODB6na+Oto47HYGwoflf+bb2lcAWDPhCeZFOYuhNunNrN1bhV0UWTEthqQgZ30AQXGm7FBOQhCLB/G3ZTzai/H4ThLex2w28+yzz9LZ6ZRmveaaa0hLS6OypYsPDtR0k99yN7sbNrD2+EPdzikdCYCNzICbuDL9RgJ9VFw3O4FgX6cdGIoNbO502pYBZwT2giiKlLd0sb+yzZXFZxVqaNT8ChtOmaDl8T/jvLg7kQvDz+5xmLsQVJoeNxKvnBFHfIjPgNoZr7ZivL6XhHepr6/nP/85tdZ64IEH0GpHyYbaDxjOHLCypYv386q9qrIwHETRgWgxIVOfaedUChn3Lhl4oM14tRXj9b0kvMvu3bv59NNPAacE2E9/+tMRHlHveHovsMtiY93eKq/t+/WFaLMCYo/r4MnRAVyUOfA9hIHaCsltLXHWIpcJLM2IQDZIgxIftYBKn1OSXMcL3iJAq+Ty6bH4qOS0dFr4IK8Gs3XwWopH6zqGvXjthiBgUwZg0kaCT4jHHSng3GT0pkHdWfMmDZ2FKOQaVqf1XiDwLPIbS0i4BYVcxqVZMWh+4BhWx091fbbrGrDp6vtsJ95vKjem/s11/Enl0xxq+ZI5yaEsOKG5uqe8le3HW/r8nh6p7eD7Y02DewkP2ECT1c7O454ryHw6uU0fuRwpEwNmMydyDQAdRiubDtRgF0VSg2UkhmgQFKoeJ5ASEhLuQ61Wc9ttt7mO3333XYxGIwmhPizLiPToNGtO5JU8Pa+QCQGzXOesskqsslryOv/EQ/um8D9bZ/Hy9kO0GU7Icw3SBhotdj48UDusuajN7uBwjY43d1fyYX4t1W1GHEInOr8HqdXc5XKkrEr6X5bF/3hYjhTRbsNhcTpqZGofKSJbQsLDREVFsXTpUtfxpk2bRu8aaxhzwIRQn0GrWIwEos2CaLMgCLIeHSkSEhLuZfbs2a7gv7a2No4cOTLCI+oDD+8F+qgUrJ4e53a528HgMHchOuwICqXX18HSjFPirCbCX8PMpOD+b/wBmdl3U612bqbFG4M5VLaWYB8Vl+fEolXKadSb2Xhg8A4Vs9XB0brBF/UbLZisdnb3I9njTjoMNUytVPIrfsWFMfcQqDqzzoPBbOPDAzU88fEo/kMnITFKCdQqWZ4Z1c3pLAgC/nOvch0bj+1AtPctb5gTdjFPzsl1Hb9adC9/P7iGGQnBnJMSBkBuRRvfFzf3uSjPrWjrVxbM0+wua6XL4vnCgwZrG++VPu46vjPjJWSCjC6LjY15NRgsdmb7NLHAtAdLxYHRu5khITHOCAoK4q677nIdb9myBXBGvi1KCfdo34IgcF/mmzw9r5CHp3/F/Mjrul03i+38audM5vz7Oi5+4wryG/IH3LbZZuej/Fp0xqEFxOiMVraXNPPK9nK+Kmyk1WBBJu+gK+AxqjTX0G536lmnBs7nkRnfcU7MzUPq53T0u9fTue8DbLqGYbclISExMBYsWMDcuXMBKCoqGt2bicNg4aQwwvxGb5CKaLei3/Me+j3vIdrcW99KQkKiZwRB4OGHH3Ydb9q0aXRKHnqJQK2SVTmxZwRfegNrcyWduZswlewekXWw5EyROOuZkxxCiO/gJ0oZM+6jMsDAd3zHq3WP8L+7ZxDqp2Z1TiwapYyGjqE5VPIq28bsptje8laMXthkPEldgbN2gAIFM8O6F523O0T2V7Tx350V5FfreGNnxaAl3SQkJCApzJdFqWHdzgkyOf5z1iAPikabMh9B3n9Eikbux4M5nxOqSQCgsvMgf81fSUq0yJI05wbkgap2vi5q7NMGbi9p5lC1rtfrnqTNYCHfjXUEesMhOvig/ElMdj0auR9PzNqJUqamXmdi3b5q2o1WgjQCU8QKACwNpUMq2iwhITE0oqKiuPvuu0lOTmbZsmWu8zMSg4cUpDNYBEEgRBPHlRMf5ck5uZwbfUu368WG99hS+j4zXpzBku+W8OP99/JC7gs0GXrO7us021i/r5qa9sHVuzNZ7Ryu1bExr4bXdpSzr8JZx0mr6cIvYi1lqutosu5z3T838mruyniFIPXgJBt7wqY7JbVo7xxZJ7uExNnGhRdeyIUXXsjs2bOZPHnySA/HIyjkMi7MjEIuG53zK1PpKdsq2kdeZkdC4mxBJpPx8MMPk56ezurVq/HxObuzwsL81Fw+PRa10nvuBVF0YDy2HQBrc8WIrIMlZ4rEWY9CLuP8yRFDynqbMuU22sOcxlNhh50FfyfcX83lOXFDdqi0dVkpbTYMfjAjjM5o5UBlu9f6q23cS7TNucFbGybgpz2VlVKnM/LOnkq2ljRjsTuICdSw/u55RAZovDY+CYnxxPSEYKbFdy9IL8gV+GYsRhHk3BSz6eqxd/YtfxWuTeTBnM9Qy30BqOsq4k95y7Gqv2fpZGdNj8M1HXx+pKHPgvPfFDVSr/O+c/T74iaP1kQApyThxrLfk9v0IQIybk77Bz7yYPaWt7I+twqd0Yq/RsHKyFMOJf8ZK/poUUJCwhNERkZy00034e/vjyiK7N27F4vFwqKUcK/Kw2jkfqxMfpBn5hfx+1n7mBvyU9TEdLvnqP4I9225j4i/RiA8LnDzxh+xrmAdJqudksZO3t1TSZPe3EsPTrtksTlo0pspbtCzraSZtXureHFrKV8dbaSytQsbrSgDvqZCeymFwlUU6N/o1sbPpq7nqolPuGXBK4oOTMd3u47VseNzM1dCYjQzd+5cli9fjkwmw2g0sn///pEektuJ8Ncwb2LoSA/jDBwmPdamcsBZfF6m9h3ZAUlInGXI5XKuvvpq0tPTAaiurqa8vHxkBzWCRAZouDwnzmsOFUttoeuz3witg0dO3ExCYhQRF+zD5OgAjtQOTmJLEASumfRHCpu/5nZuJ0gXRHHZB6Qkr+LynDjez6umocPM+3k1JzJWBpb+llfZzsRwv6G8yoix83gzNg9vMp7E5rBgKt2LH8HUy1tIT70PcC72vy9u5sCJyHGtUs6CSaHMnRBKToLnI0UlJMYzi1Mj6DDaKOvB2Wtrq6Wr8HsEhRrfqRcg0/S+qJMJMv44Zz+HW79kY9mTtJlreKv4V0wMmM2yjH/w5dEWCuv1WO0OLpoShUJ+5qTM7hD55FAd189J8FpacWlTJ6VNnnd072pYx/b6txAQuDblT4QpZrEut4qGDudGZ0qEH0sSNVgL9gCgTp6OoFR7fFwSEhK98+mnn7Jnzx5KSkq4+uqrWZwWjkMUOTjELDqFTEAmEwZVu0SjlDM5OoZV2U8g8ATvH9rKm4f+S5Fh/Rn3/vfgq/z34KuAADjnbjLRD4UYhtaRg59jIRomoBDUWOnEarMh4I9VqAAEHIIOuRhOl/x72pWnOU1+EBydEbyY82PvIsY3zeVEdweWumM4TJ2AgN/0S9zWroSExOCxWq28/PLLtLQ4a9/NmDFjpIfkVmYmBlPWZBh05p6nEEURY4lzDijzDepWy1BCQsL7VFZW8sYbb6BQKLjtttsICwvr/6FxSFSghtU5sWzMq8FsdWMd6B/gMHdhrioAQBWXOWLOZMmZIiFxgnNSwilrNgxapkopU/OH+Qc4fODfBHVBZJ2Rap9dxEXO5fKcODbm1ThrqOTVsOpETZX+qGrtolFvIsJ/bGRSNHSYKKzXe62/Y0XvEucIxoaNwJRzAefE8rtjTeSf2LjIiA5gYUoYWqVckr+RkHADMpnAxVOj2ZBbfYZkntw/HJkmAIdRR9eRr/HNurDfInCZIUuZHHQuH1U8xfd1r3O8Yw8tpstZmvJn9heHcrzJwMYDNVyWFdOjw6TDaOWzgnpWZse69T17wmp38E1Rz/I47qSsI5cPyp8EYHn8/dj0C3n7UCUOEVQKGeemhJMe5YfxyNcgOpD5BqOKTPH4uCQkJPpm6tSp5ObmcuzYMTZv3swll1zCeekRKOUycivaBtyOn1rBtPggpsYGolXJsTtEWg0WDtfoOFrf0ePiNMxPxcykENIi/ZGdJkeTEXMpDy5dzrff3UNpk54vjBV8WPk7zOLptuxUEIxD6MQidGKRlaNjY/dOlAN+BRe/mvYR0b6pg3+wHxwmA+ZKZ/0VVVwGMo2/2/uQkJAYOEqlkszMTL777js+/vhjgoKCmDhx4kgPy20IgsCFU6J4c3fFoBzcnsLaWIa9wylzqJkwS1rnSkiMMDExMURGRlJTU8Prr7/OPffcc9ZKf0UHarliehzv76/BNMhyBwNBFEVMZfvAYUOm8RvRzGRJ5ktC4gRalZyFk4buRZ6cdTutQjsAPseLKav+0in5Nf1UUfr391fTZem7UPNJdh7vWy5nNLG1uBlvlXlp11cQeWJfojFcRWSIMxpnb0Wby5Fy4ZRIlmVEDshxJSEhMXBUChkrs2MI1HbfWRMUSrSp8wEBh6kTw6EvcFj6j+CTy5SsTHqQnLBLAWi31LGh6ibsob9HKReobTexfl81Hb0URC5tMnilfsrO4y29jsFdlOvzePHonVgdJhJ9FlJZeT67y1pxiJAc5suNcxPJiAnA1lyOvaMJZHJ80hYiyKSpnITESBMXF8fixYsByM3N5aOPPgLgnNRwlmVE9qu5r5AJzJ0Qyq0LkpidHIJW5Zy/yGUC4f5qlqRHcMeiCVwxPY45E0LIjA3knNRwrpwRxw1zE5kcHdDNkXI6cplASmQA79z4Y0yPNCI+KtJ8v5E3LtnKL2e8zISA2cT6ZA/pvaN8TjlLFkXfyNPzCnlmfhHPzC/yiCMFwFS+Hxw25P5hUkS2hMQo4dxzzyUuLg6At956a9wVpQ/0UXJuavhIDwPRZsFUkQeAOnEaCv+zMwJeQmI0oVAoWLNmDXK5nM7OTl588UXa29tHelgjRmSAhitmxOKjcv9enK2tFltrDQgytGmLBlS31VNImSkSEqcxJSaAwno9Va1dg35WLlMSkLGM+sJPibKHElBZSzlfkRR3PldMj+X9vBqaOy28v98p+eWr7vvrV9pkoLSpkwmjXO6rtKlzSD+vobKx+inkNJGtmEXmxPsBKG7Qu5xP56aGkx4V4LXxSEicbfiqFazOiWXdviq6Tsvkk/sG4TP5XIzHd+MwdtBV8DU+U85DptL22Z4gCNyY+jSLY27hX4evx+YwU2HYCapLSHW8TWtXAO/sqeSizCgSQ89M491a0kRSmA/+miGETg+AJr2ZPA/Xg6rvKuH5wzdhEy34C1k4Wn6OHgd+agXnpIQxKcLPFXmoDInDEaNDUPkg04zuvw8SEmcT8+fPx263891335GX59zsuuyyy8iMDSTMT83O0mbKm7vPl5RygZRIf+YmhxLo07cNU8plJIT6kBA6/GjHUH8NN8xcyA0s5Cl+RFG9ni+PNtBlMSEXlDQYj9NiqqTVXEuj8TjVnUcI1cShkKmp7ixgZsQqZoWvwlfpfQlVdXwmOOyok3KkiGwJiVGCIAhcddVVfPzxxxw7doz33nsPQRDGVXH6zNhAKlq6ONbgPTWGM5Ar0U6YhbWpHFVM+siNQ0JCohuBgYHceOONbNq0iba2Nl5//XVuvvlmgoKCRnpoI0KEv4Y1M+N5f381etPAgskHgiIwAlXcFARBhtw3yG3tDgUpnFFC4jQEQWBZRiQqxdC+GiGBk0iecRu1yhaUKPGvrKG07htC/dRcMT0OX5WcFoPToWIw929Uvi1qwmYf+XTi3nA4RLaVNHutvw/L/8yhti/I5yAhGc6Chw5RZMcJR0pOQpBXi75KSJytBPuqWJUTe4atVARH4zvlfASV9oRD5RtEx8BSfOP9pvL4zO3dzpXKb6VCeyktju/54EAtu0pbzihMb7Y6+LqwcXgv1At2h8jnR+pxeDD1rsVUzQsFt2MTLagcEwnqehilTMuMxGBunJtISqR/tw1DQaFCk5SDOibNY2OSkJAYPDKZjHPPPZfVq1cjCAJ5eXl88cUXwEkd6Tiun5PAuWnhroyV2xdN4MIpUf06UjxNWpQ/18yKJ8zPF0EQiPKZxJSQ81gUfQNXTHiUn2Wt5YbUp7lm0pP8MnsTi2NuHRFHCoDcNxifjMXIfQJHpH8JCYme8ff35+qrr2bq1Kk4HA42bNhAWVnZSA/LrZw/OQJ/zcjFIwuCgDIsAZ/J5yAI0laehMRoIjExkVtuuYWQkBDa29t5/fXXMZlM/T84TgnxVbFmZjxBbpzjCnIlmoQsZ2DNCCNZYAmJHxCoHV4ar1KhZWLObdQqmymllH+X3ctnVc8S4qviihlx+KkVtHZZeG9/NZ39OFR0Rit7yweute1tDtfqaOm0eKWv6oadGGudhabmRq4hzm8KAMUNnbQbrWgUMuYmh3plLBISEs4U3kuzos+Qr5Fp/fGZcj6CygdV1CQE2cBTfLUKf/489yCrk39LgCoCm+gsut6s/hOd8i/YXnaMtfuqaO40d3uutMnA0bqO4b/UD9he0kxjh7n/G4eA3SHyt7xb+cP+8+mw1qFwRBJueZCpMVHcPC+RhZPCujmrzLWF2PVjR/5RQuJsZerUqaxatQpfX1+ysrK6XYsI0DA9IZgZicFkxgb2WA9qpAj1U7MqOxa1cvQtD0VRxHh8L6LNO3NOCQmJoSGTyVi1ahVTp04lPj6e2FjP17XzJhqlnOVTo5F5OSvO3tWBueowojh6gywlJCQgICCAm2++mZCQELKzs9FoxkYNZE8RqFVy1cx4wv3Vw2rHUl+CTdfgplG5h9E3W5aQGAVkxgaSHHamnMxAcTpUbud77QHs2Pms6l/srn2XYB8VV86Iw1+joK3LyobcavSmvnX495W3UtPef+0Bb2OxOdhV6p2NPZvDQsDxcpaf+N/lEx4BnIvrveWtAOQkBA85o0hCQmJoJIb6cuGUKH64ppRr/fHLuRhVtFMzX3Q4EO0DS/FVytQsir6B307/mhVJv3Gdb1H9gxrtLeRZbuatPUfZVtKM2XYq6+XrwkbaDO7baKtoMbC/0v3ObIPZxs7SRp7a8RRVxh0AyERfFgY/w+3z5rJ0cuQZkmX2zlbM5XkYDn2OTe+9bEAJCYmhkZWVxU9+8hOioqIAsFqtOByjfxMs2FfFJSOwUdgf5vI8rA0l6Pe8N9JDkZCQ6IeTDpXrr78elUoFgNnsmcCUkSA2SMuiVO/VKhFFEcPBTzFXHcJYvNNr/UpISAyNgIAA7rzzTs4991wAHA4HVqtna2+OZnzVCtbMjCM+ZGgytXZjB6bSvXQVfI21tcbNoxs60s6jhEQvXDAlEr9+6pr0hVKh5afT1iJDzmIWM7lcpGPHO/ipZVw5PY4AjQKd0cp7+2vo6MOhYnOIfHigllY3bhK6g30VrRjMA5PvGS4tu950fU5LvgK54PzvcrzJQIvBgkouY1qcJPcgITESpEX595jNJ8idDgHRbkO/ay363esxleUOuF25TMnimFt5YtZO5kde6zpvk9VSobmaDxou5sWdWzlUo8PuELHYHHx8qA6rG6QRDWYbnxc04C51L1EUqWkzsuVQHS9tL2B9/SIaZS+7rv92+jZWTJlJoPbMNGjRZsFw8DPXsdwnyD2DkpCQ8ChqtTMKr6WlhSeffJLf/e531NbWjvCo+icx1JeFKaMn09faVoulrggAuVRsWUJiTCCTyVAqnXOaffv28ac//YnHH38cu907a0dPMz0hmPQof6/01XXoCzghmasMTfRKnxISEsPj5BzQbrfzt7/9jSeffJINGzaM8KhGDrVCzuqc2EHbTdFhx5D3iet4NM0DJWeKhEQv+KgULJ8aNazoPKVMzZ/nHmKK7JTMg2HXOlTyLq6YEUegVul0qORW02Hs3aFistrZmFfTryyYt+g02zxekPkkxWUfosVZwLpG2UJC9CIAbA4Hu8ucmTHZ8UGoR5FUhoTE2UZOQjBzJoT0eE20nYpGtNQdw1Dw9aDa9lOGcOXEx/jTnHyC1afkIhyCjlL5XbxaMZtf7Upnd9VhGjtMw66fYrM7+DC/1i321mJzcLC6nbf2VLJhfzVHmkqpUz3kuj4v8lr+OCePQG3vkTpdhd+7PmvTFyHIR06rW0JCYvA0NJySJXjxxRcpKCgYwdEMjBmJIcPK0HYXot2G8eh3rmOfKeeN4GgkJCSGwvHjx12ff//739PV1TWCo3EfSzMihy1d0x82XQP2Tud6V+YbgjI0zqP9SUhIuBez2UxnZycABQUF/Pvf/x7hEY0ccpnA8qnRzJsYeoaqRW8Yi3e5PmsmzUGm9KzNHQySM0VCog/ign1YMGl40XlymZyk2Xd3O6fb9x4ysY0rpscSqFXSYbKxYX817V29Z590GK1szKvBZB35iJ7tJc1YbJ6Xq2jRFRNZZwDAgoWUGXcCzijvrwsbae60oFLIpKLzEhKjgPkTw8hOCDrjvEzti9+MFa5ju67hhO7z4NI+VHINv53xNY/P3IG/8sxMmLVVV/DLnek89vWf+exw/RmF6gfKF0caqNcNr1hgc6eZb4saeXlbGd8UNVFvqKRO/RNqND/CIisG4OKE+1kz8THU8t4dKebqAuwdTQCo4jJRhkiLaAmJsUZGRgYXXXSR63jDhg1UVlaO4IgGxrKMSLSqkQtUEUUR/e71rmPf7IsHVYNLQkJidHDVVVfh43NqrvPUU0+5NhfHMkq5jFU5sR4rSO+wGOk6LQDJd+pSj/QjISHhOXx8fPjVr37lOm5sbGTLli1jQvrVU8ydEMrFU6P7lei3NJZha3HOl5VRKagiJnhjeANGcqZISPTDjMRgUiOHl8Yrl8kJmH8tLUI7AFq07Mt7ihbrYa6cHkewjxL9CYdKX5r/zXozmw7UuEXGZqg0dpg8Uuj5h4iiyN6Sl04dZ8xBIXPq7uZWtHG0To8gwMWZUSO62JeQkDjF4tRwJkcHnHFepvbFf+7VrmNz1SFsbUPTPPVXhfL4rG08M7+I+zLfOuP6rta/c/l7S/B9MojUZ9Noswy87snW4iYK6/VDGpfJaudQjY51+6p4a3cl+dU6jPYWKrSXUqu5HYusDIB430wezPmcpXF39dmeKIrdCu2p4zOHNC4JCYmRZ86cOdx6662u47fffhuTaXhOW0/jq1awdHLkiPUvmk9FryvCEpD7SHKuEhJjEUEQ+NWvfkVqaqrr3CeffNLHE2MHP7WC1TmxqJXu31aznzYH9J16geRMlpAYo/j4+PDII4+4jvfs2cPBgwdHcEQjT2qkP9fMiifUT9XrPbb2U9K4muTp3hjWoJC0IiQk+kEQBC6YEonOaKWhY3gL38Q5d1FUspaO5qNsZjO2Qx9yQ/KfWJ19CR/k19FqsLBhfzWrc2IJ8+s5ha223cTHB2tZOS0Wmcz7BUK/PdbktjoCffFlzQt8bv6ASuEYV2Y8RXjgJERRJL9ax/bjznTnc1PCSQwdeRkKCQkJJ4IgcEFGJHaHyLGG7k4JQSbDf84ajCW7UARGurIsRJsFQdH7RKovJgTM5Jn5RTSbKnm18D7qupy6+l0Ugh1K2zu4fOflAEw8OJHrp17PY4sfQ/hBbrHN7uDLow0crRucI6XTbKO82UBps4HK1i7sJ7JhrLJyDNoX0DkOd7t/YsBs7sp42eUY7gtBEPDJWIy1sRRlxMQzxiwhITG2SEhI4N5772Xjxo2sXLkSjUaDKIqYzWY0Gs1ID69HJkX4MSUmgIJazwfR/BCZxhe/mauw65tRhsZ7vX8JCQn3cu2113Lo0CHy8/NZvXo1ACaTCbVaPabnOKF+alZMi+GDvBqsdvctkpXhSQgqLYJSIzmTJSTGOIIg8NBDD/HJJ58QEhLCtGnTAKcNHK1zQE8T6qfmmlkJfFvU2OM8U5syH2tgFMrwZARh9OWBSM4UCYkBoJTLWJEdwzu7K4eloy+TyZicei15IZ9gO7YWAQFlWRG1ihouSL+UL4tUNHda2JBbzarsWKICezas5c1d7CxtYcEk7xZgKm7QU9Nm9Hg/W2vfZEvl3wFIS1xJSOAkLDYHXxU2cKzBmRaeFRdIllR0XkJi1CGTCSzPjEIQoOgHWR6CXIFP2kLXscPUSWf+p6iiUlAnZA15MR2mSeBX2R8CUKU/xmdl6zjS+Ua3e463HeeJ75/gie+fAGByWAbvXvE+KjGS/ZWt1LRbsNgciCKIACIuPVeLzYHZ5kBvstJutNLSaaFOZ6TD5Px7YEePKBgxadfTwpYTL9d9jD+e8gaTAmf3+y6iKLp+XJgCfwAAT/dJREFUDoIgQxU5aUg/EwkJidFHWFgYd9xxh+s4Ly+Pr7/+mhUrVnSL2h5NnJsWTmVrF3qTd+r2nW4DZSotMsmRIiExbpg6dSpTp04FnN/1jRs3YjQaufzyywkKChrZwQ2DuGAfVkyLZdOBGmxDlJk9yek2UBE4ctmBEhIS7kWhULBy5UrXscFg4PnnnyczM5MLLrgAufzsyz5TKWRcMCWKiRF+fHmkAcOJvVZBEBAEAVXkxBEeYe9IzhQJiQHip1awMieG9fuqh10vJCfsEtKDzmFb6b+JaY5CaVPCkZ3MT0pkT0089R0m3s+r5rKsGOJDetbT31veSlSghonhfsMay0Cx2h18X9zs8X6q6rcTVt5AGGFMj7uGc2NuoU5n5PMjDbR3WZEJsGBSGDnxQWM6iklCYjwjkwlcNCUKmSD0KQtobakEuxVLzRGsjcfxzb4EmVKNv0ZBuL+aTrMNvcmG0dJ/rSgflZyJ4X6siljCr5cupaPrWd7K3c26vD9y1P7NGfcfbT7CtP+k99hWgPVq5PjiYzsXBBsW4TgqcRJmoRijfBcgQyUmYVe00qHc2OuYpoVeyOrkh/FVhiAXBjblMlceRLSa0CTPkIrNS0iMY0RR5MCBAxgMBt555x3mz5/P0qVLR93cRq2Qsywjkvf3D02acTCIDjtdBd+gjEhGGTFh1P0sJCQk3Ed7ezvl5eVYLBb+8Y9/cPPNN5OUlDTSwxoyCaE+XDotho/ya12ZyoPFbmjHWLIT7aS5yH2D3TxCCQmJ0URhYSFdXV3s2bOHvXv38otf/AJ//+GVFxirTAz3I2aelnc//pLa2lq0E2cNWbnCW0irdAmJQRDhr+HSrGg2HRj6JOkkWoU/y1IfoDHsMNbC3fjgQ2h5BdnyXA4FXkaNzsqmA7VcMCWyx5otogifFdRz/exEAn2UwxrLQNhd2kqH0erRPpraC5GXHiGWGC5TX0NazI/ZVtLM/oo2RJwOreWZUcQEaT06DgkJieEjkwlclBlFiK+KHcebe5QHVMdmICjUmI7vQbSa6dz7PrMWncdFCxa6ZAxFUeR4k4G8yjaqe8iMC9AqmZ0UQkZMAPLTpA8jAzT89qLzWOyrxGR9iLaARNYd/pIPqu/pd+wdyrUAtClf6fUeQy/n/ZXh6K1N/HLaJmJ8e3bW9Ia1qRxLzREAFCFxKENiB/W8hITE2EEQBG666SY2btzIkSNH2LFjBzt27ODHP/4x4eHhIz28biSG+pIVF8jBap3H+hBFB8aS3dj1TTiMOhQhcQjKniVvJSQkxj7BwcHcc889vPrqq3R0dPD6668TExPDj370ozEboZ0c5stl02L4OL920BkqDnMXXYVbEc2dmKsO45O+yEOjlJCQGA3MmDEDrVbL+vXrEUWRZ555hsWLF3PuueeO9NBGhKry41QX7MHhcKCKjMcalDDSQ+qT0Sc8JiExykkM9WXp5EjcFSwXEZKJmJrjOo62h3GBZScxwUewiyJbDtezv7INsYedSLPVwedH6nu85k6aO83srxx4Eeeh0NB6CPOR7/HDj1ahHWX0at7cXUnuCUfK5Ch/rp+TIDlSJCTGGLOTQ7hsWgz+mp7jN1SREwlJOVVUbu/Wr3n++edcdk0QBCZF+LFmZjx3nDOB5VOjmJ4YzOK0cK6cEcct85OYGhfYzZHyQzRKOVfNTGbjbXcjPioiPiqie8DMF9cc5on577Iq6WEm+s8Z9Lsl+E1jZrgzXfv82Lt4Zn4Rj8/axjPziwbtSLE0lmEs3gWAKipVcqRISJwFKBQKLr/8cmJjT33fn3/+eXJzc0dwVD2zKCWcIA8F74gOO6aS3diaK0AQ0KbMQyY5UiQkxj1BQUHcfPPNruPa2lp+//vf09raOoKjGh7JYb6szI5FKR/4ZoHD1EnXkW8QzZ0Ial80E2Z5cIQSEhKjhYyMDC699FLX8bfffssf/vAHHI7hKeGMNQoLC1m3bh0Oh4OpU6dy9+XnMX9i6KDsqLeRMlMkJIZARkwAXRYbW90kexUdNh1bSCZFh14l3hAAQKfpDfxCFqNvXcrW4maa9WbOS49AIe/uA61uM5JfrSM7PsgtY/khoijy1dGGYWfi9IXR3IatcBcB+NOGjlzlHKqPOiPQ/dQKlqSFM8FLcmYSEhLuZ2K4H0mhvhyt66CgVofZ5sDuEIkN0jI5OoC44BQMhiW89tprtLS00Nraik6nIzAwsJvMi59aQXpUAOlRwx9TgFbF0rQpLE2bgsl6JZ8fuYfjjc6aTKIo4sBOVechRNFBq7kGrTwAtdwXvbWZQFUEyQEzXG1dl/KXYY3F1l6PqcTpSFGGJ6NOnt7PExISEuMFuVzO7bffTmVlJa+++ioARUVFzJgxo5t2/kijUsi4KDOKdXurcbg5iMdccQBrUzngLDiqCI5xa/sSEhKjl5CQEB555BG2bdvG119/DcDx48cJDnbKXI0WGzgYEkJ9WJUTy6YDtf3Kg4sOO11Hv8Vh1COotPhOOR+Z6uwsSC0hcTYyY8YMMjIyeOutt6ipqcFms9Hc3OzKUh6LNnAw1NbWsm7dOkRRJC0tjVWrViGTyZgzIZT06AC+O9bkWqOPJsaMM+UPf/gDn3zyCQcOHEClUtHe3j7SQ5I4y5mZFEKXxU5uhXsyNhQyFVOm3cXuY/+honkHRzhCoLGGn2pDMYtm3qmfSYvBwvLMKIJ8uusHbi9pJjnU1yNyXweq2qltN7m93ZN0GhtozH+PMIIxYmSzMROL0R+5IDA9MYhZSSEo5VISnWQDJcY6cplAZmwgmbGBPV738/Pj3nvvZffu3QQGBhIUFMSOHTv44osvmDNnDhdddJHHxqZRylkxLYa95a1sK25GEATkKEjyd2YNJjOjnxaGjq2jkf9v787jo6rv/Y+/z0xmksm+L0BCCDthlYCsCoqCWMQVVGpFKRbFe13K7Q+vtYBUpb3UB4pL1V5FW6+oFVqLuEFBERURQUACyhLWQFgTIHvm/P6IjEYg60xmTub1fDzmIXPmnJnPF5K3M/M55/st3rpKkhQS20phHfq3+DfNDUX+IRhkZGTo17/+tb788kt169ZNkvTcc8/p0KFDuv3225We7v+F2NNiXOqXGac1u7xz1rhpmirf943K87+VJIVl5ciRGNjTOvgDGYiWzjAMDR06VOnp6dq/f7969eql0tJS/fGP1SerPPDAA3I6A3v+/J9qExeu6/u20eL1+8+79p9ZVaHi3I/kLjkp2ezVjZSwiGauNPCRgWjpXC6XJk2apHXr1iksLExJSUnasGGD3n77bfXo0UPXXnutv0v0ifz8fP3tb3+TaZrKyMjQ9ddfL5vth+/+YlwOXdWrlXYdOa3luYd0srTSj9XWZJlvKMvLy3XDDTfozjvrnuscaC4XdUpSt1bRXn3OCzv9Sldf+Jwuz/gPRav6uUONUI0IW6GQ0uX6vzXb9PW+EzWm9iqvdOv9LQfl9vLVIwUnS/WJDxed/+LQYq1YP0OJ7jiVmqV6tyxBFUpRt7Ro/WJgWw1qn0gj5XtkIIKBYRgaMGCAunbtKknav796weM1a9bogw8+0K5du3z6+v0y49U7I9anr/FjpmmqdOc6yV0pe1SiXJ0GyjDIvJ8i/xAsIiMjNWzYMCUnJ8s0TR06dEiS9OKLL2rt2rU6evSonyuUBmQlKC3GO2dNu0tOqmzfN5IkZ+uucqR08MrztjRkIIJFZmamBg8eLKfTqaKiIs/2559/Xhs3blRZWZkfq2u4lOgw3dC3jSJDz30Oc/mhHaoqOizZ7ArvepFsruBcfLouZCCCgWEYysnJUffu3WUYhg4ePChJ2rRpk5YsWaLt27f7fHr/5mSapt5//32VlJQoJSVFN9xwg0JCzp2V7RIjdMvAtl7/7rUpLHNlyqxZsyRJCxYs8G8hwE9c1jVFZZVur1565rSH6bI2d2pw0jhVrvtAktTGSFSbEEkhm/XmzqXadnC8Lu6UqpTo6g+0+4+XaPWOIxra0TuLlpZXuvXupoMNXjyvPk6WVujt7/6idSfnyS674hWvnRX91DplgMa2jVNchLXOPGoOZCCC0eDBg7VlS/WC7J999pk+++wzSdL999+vqCjffOC8uGOSCosrtOvI+ZaYbzrTNCXTLcNml6vTIJXt3aywrBwZIWTfuZB/CEaGYah///764osvJElLly6VJPXp00c/+9nPapy515xsNkNX9EjT/63Zo9KKc59tXRfTXSUZNtnDoxWW2UdmVYWcrbtxVd55kIEIRgkJCZ4/Hz16VIsXL5Yk3XTTTerUqZO/ymqwhMhQjeuXrkVf7dOJ4gpJ1Rlo2OxypnWS+/QJOZLaKiTGC3PYtlBkIIJRv379PO8B161b51lP75577lFsbKwfK2sa0zTldrtlt9s1duxYffjhhxo1apQiI2uf1j80xK6R2dXff67YWtBM1Z5fiz79saysTEVFRTVugLfZbIZGd09Veny41587PDRB0YNu0pH0BO1z/nA2ojNkvT4vH6nn1s/S+5t2qrCk+o3Zl3nHtb3gpFde+99bC3TsdLlXnkuS3G5Tu46c1tvr81S0boEuPmmTQw5VqUplibfrmoFjdFm3FBopXkL+oSVo1aqVfvvb3+qKK65QdPQPZ6KUl1dnU1lZmdfP0Kn+ojBVcT5aaNldelonP1uosn3fyDRN2cNjFN55MIstexkZiJbgiiuu0L333quhQ4d6tu3cuVOVldXTHPjrLO0Yl0OXZ6eoMb2P8sO7dPLzN1R1qvp9rTOtk0LbZNNI8TIyEFYXEhKiGTNm6NZbb1Xv3r09289csed2uz3vBwNdjMuhcTnpSox06tTX7+rkmr/LrCyXYdjk6jhAIbFp/i6xxSEDYXWJiYl66KGHNGbMGCUmJnq2n3nv54vPwb526tQpPfzww3r33Xdlmqbi4uI0bty4Gp/z69I7PVbZAXCFimWuTGmMxx57zNPFBnwpxG7TVb1aadFX+5Rf6P31RbLSL5fSL9fOvR8oem++LtAF+kbfaITDpu4n10jr12iTcVhp2b/Q+9/YFONyKimq8V/MfbHrmHLzvfOGo7CkQlsOFGlLfpHKyg5oUNiXijKSJUnX2G5S977/qUgHlzR7G/mHlsJut6t///7q16+fPvzwQ3355Zdav369+vfvr5deeskzb/K1116rHj16eOU1Q0PsuqJHml5fu1dVXrw6r7KoQCXfVl9dU77vGzniW8semVDHUWgMMhAtRUxMjC655BINGDBAr7zyikpKSrR3715VVlZq4cKFnv3uvvvuGmdy+1r7pEjltI3X2rz6rZ9imqbK87epLG+DJKl404eKGngjTRQfIQPRUmRmZiozM1N9+vTR//3f/2nLli3KycnRsmXL9NVXX0mSUlJSdPvttwf0uipGVblCdq+R+/QJSVLxttWKyB7u36JaMDIQLYHNZtMFF1ygPn36aNWqVVq1apXWrl2rSy65RAsWLNDhw4clVZ98069fv4B+T3XgwAG99dZbkqqvtOnSpYs6dGjc9K6XdEnW0dPlOuiD717ry69XpkyfPl2GYdR627p1a6Of/4EHHlBhYaHntnfvXi9WD9TkDLHp6j6tldiEJkZdstIvV+KgWxWWPVxZodlqozaex9qaSXJufldffvyI/vjBZ9p/oqRRr7H1YJE+3dG0dVLKK93akl+kt77apwWf5mnTng26wliun7v2KMtIVpWqtDe2TP37PxDUjRRfZiD5h5bGMAxdfvnl+u///m+NGDFC3333nU6e/OFKvEWLFmnWrFnatm2bV14vJTpMA7K888VkxbF9Kvr0NRVvXi6zvFiGI0zh3UcEdSOF94BAw4SHh2vKlCm67777lJWVpY0bN9Z4/KmnntKsWbOa9ezbwR0S1D659mkZzKpKleZtqL4iL2+9JFP2mBRF5lwT0B/6fY0MBBomIyND06dP169+9SuVlpYqLy/P89ihQ4f02GOPaf78+QF3pnZhYaGefvpp/c///I++3bZVhmEorXMfhXe72N+l+RUZCNSfYRi66KKL9OCDD+pnP/uZdu7c6TmhUJLeffddPfzww54GcyDZuXOnZs2apRdeeEHHjh2Ty+XSz3/+80Y3UqTqk9lH90hTiM1/7yP9emXKr3/9a02cOLHWfbKyshr9/KGhoQoNZdoMNJ8wh13XXdBab365z6tTZP1U+5h+at93ocori/Xd5peVXhzreexrfa31m4fr35sv0FW6Skcij+i6vtfV63l3HD6lD785pMa8B62ocivvyGltLzilnUdOq9JtqkqFKnXdo9t0t6QISdJxo1ChHQcqO7FXw1+khfFlBpJ/aOn69u2rTp066fHHH/dsCw8P91wG/dRTT+no0aPq1atXo+eV7ZcZp7yjp7X/eOOa05JUtm+LyvZ87bkfkthWrqx+MkJ8M42YVfAeEGg8wzB0ww03aM+ePXrppZc82zt16qSIiAi53W7Nnj1bkjRo0CA5HL7JG8MwNCo7VW+W7lVB0dlTjpmmqdNfvyd36Q+N77CsfnKktA/qRopEBgJNERcXp7vuukurV6/WihUrPNtzcnJkGIa2bdumhQsXKikpSV27dvVbnYWFhZo3b57nfkxMjG644Qa1atVKn+44qi921e/KvpaIDAQar3v37mrXrp2effZZnT5dvc6nzWZTamr12kuvvPKKdu3ape7duzfrVcs/tWHDBv3zn//03O/UqZPGjh2r8PCmL5EQ43KoV3qs1u0+3uTnagy/NlOSkpKUlOSdxbKBQBHuDNH1fdvora/26egp387j6gwJV3bvO+V2u3Xg8BoV7fhE+cqXJHVXd0lS4qlEffTRR/roo4+0XMs15/Y5GthmYI0PsaZp6vOdx7Rm19F6N1JM09Sx0+Xae7xEu4+e1r7jJZ7F6hPt6zTGZWqd1unfKtFRHVWCEnTCKFJSn3EKD4v37l+ERZGBQNNERUVpxowZqqqq0ldffaVt27YpKipKVVVVOnq0ej7+r7+ubmSsWrVKkyZNUqtWrer9/IZhaGR2qv72+W6VV7rrdYxpmirbvUHlB7YqvPsIhcSleZopzrROCmvXt4GjbJnIP6DpMjIyNGPGDJWVlelf//qXUlJSZLPZdOTID1cYf/rpp5KkTZs26c4771RIiHc//jlDqqe6fX3tXp0srZS7vETFuR/LffqYogaMkyM5y5OB4d2GsTbA98hAoGnsdrsuuugiDR06VAUFBVqyZIlat24tSdq1a5ck6fDhwzp8+LA+/vhjDRw4UJdffrnP69q7d69efPFFde3aVWPGjFGbNm20b98+RUREaPLkyYqIqD7BcHCHRMVHOLVsyyHPZ+hgQgYCTRMREaFp06bJ7XZr06ZNWr9+veLi4uR2uz0ZuHnzZknSRx99pF/84hdq166dz+tatWqV/v3vf2v8+PFKT0/3bO/Zs6euvvpqr55M079dvDYfKFRZRf0+p3uTZdZM2bNnj44dO6Y9e/aoqqpKGzZskCR16NBBkZG1X14ONLeI0BBdd0EbLfpqn474uKEiVXeh26QMlFIGqov5a208/JnydyxR1k/el6UqVbe8eItGaZSSlazDjmPqdMEo7SnIU4jrArnNUMlmyJAhGZLNkAwZqqhyq7SySqdLq3S8uFxHT5frUFGpyr7/crHc2KkwW56GuUrUTime1+urvlqhFZqv+ZrS5X/VKX6Iz/8uWioyEDg/u92ufv36qV+/fpJ0zike3G638vPzlZeXpzVr1qioqEg/+9nPlJZW/cVeWlraOd/cxbgcGtoxUctzC877+qZpqqrwkEq++0xmxQ9ztxZvXqboQTcpauCNkumWYbM3dahBifwDahcaGqrrr7++xv2fOnbsmA4cOKC1a9dq27Ztqqio0O23367KykolJCQoKiqq0R9ww+xSlvuAVn36b0k/5G/Zvm8UltFTzladJcMW9FejNBYZCJyfYRhKSUnRpEmTPNvOnJ39YwUFBdq9e7fef/995efnKzk5Wddff72KioqUnp4uh8PR6IwqKirSe++9p9zcXM+23NxcjRgxQpMmTVJlZeU5G9ld06IVH+HUOxvzVVhS0ajXDgZkIHB+NptNvXr1Uq9e1TO/mKYpm80mt7tmgyE/P1/Hjh3TqlWrVFhYqEsvvVQdOnRQRUWF2rRp06T3aHv27NHixYtrTD32+uuv63e/+51+97vfqaqqyusn80jVMwPltI3X6u1NW6agMQwz0CaVPI+JEyfq5ZdfPmv7ihUrNGzYsHo9R1FRkWJiYlRYWKjo6GgvVwicraS8Sks2HtC+BkwRYzMMtU0IV2ZihIrLK1VUUqFdR4pVWlFV63GhDps6JEWqY0qUUqJDVVFl6sjJMv1lxYc6sG2ZXG5TaUaKXtALmqzJ53yO5/W8DuiAspWtG3SDllWeUoUZqlb2ozpslugbd7myHXs1QiMkSZvdh9TWFq6N2qgylekSXVLj+Y4bJ9Sq/21y2MPqPX5fiHY5NGlI/bvwgZgVTc3AQBwT0Bw2b96sjRs36rvvvpPdbtd1112nN95445z7/u53v5NhGFq4cKEOHjyoSy+9VJWVldq9e7eKwlvrcGWYSneuVeXRvbJFJsgIcarq1FGFdx6q0l1fyl1cWOP5wtr3lzOlfXMMs1Z9MmI1rHNyvfcPtLzgPSDQOJWVlVq5cqWOHTum3NxcDRo0SIWFhfrmm2/O2jctLU133HGHKisr9cgjj6hPnz5q3769jh8/rsLCQg0ZMkRFRUX65z//qaNHjyo7O1tHjhxRZWWlxo8fr2eeeeas54zse5VsoRHNMdRaXd+3jdLj6zelRCBmBRkINE5hYaE+/PBDFRYWat++fbrtttv08ssvn/UloyTdeOON6ty5s3bs2KG//e1vGj16tCIjI7Vjxw7Fx8erf//++uqrr/Tee+/JNE317t1bO3bsUIcOHZSamqp33323xvOFhoZq2rRp9foCsbSiSh9uOaTtBae8NvYznCE2TR1e/7UJAjEryECgcb777jt98cUX2rNnj8rLyzVhwgS9+uqr59z3zOfgt99+W1u2bNGVV17pucolOztbGRkZeu+997RhwwYlJiYqMTFRu3fv1tixY7VmzRrP1TBnDB8+XBdddJHPx1hR5dbLn+bpZGnlOR/vmhatUd3PbrCfT32zwjLNFG8gQOEPbrepld8W6Ou9hbXuF2Iz1DsjVn0y4hQZWvNNV3mlW5v2n9CGvYUq+slZK6EOmy7IiFOfjFiFhpz/rOeSskq9n7tbT388R0mFUpI7UvGKrbHPv/VvfayPNVMzz/kcm7XZM33YGQUq0Ct6RaZMTdEUnQirkOwhSs28RAkxHWsdc3NpCc2UpmqJYwIa45tvvtGXX36pvXv3qqrqhya1YRgaN26ckpOTNX/+/HMea4Q4ZVbWvNrQkdJBYe0uUOXRvSrds1GOhHQZjjA5UzvIsAfGuihWb6Z4Q0scE9BQFRUV+vzzz/XFF1/o1KmaX9q1atVK1113nT777DN9+eWXZx3brVs3bdmy5azt48ePV+fOnbV48WIdO3ZMSa3StfO0Q2ZMKxmGzWdjaQirN1O8oaWOC2iIY8eO6ZNPPtG2bdtUXFxc47F+/frpiiuu0MMPP3zOY3v06KFNmzbV2BYTE6OJEyfK5XLp2WefVUZGhiIjI9WnT59GTWG15UCRPvr2cJ0nUTZES2imeENLHRfQEN9++63WrFmjvXv3qqKi5veK1157rbKysjR37txzHpuUlKTDhw/X2Na9e3ddddVVysvL07/+9S9169ZNkZGR6tu3r1wul8/G8VOFJRV6d1O+8gtLz3rMV80Uy0zzBViVzWboki4pykqM1JpdR3XgRM1f8DCHXZ1TI9UvM15RYef+4s0ZYlPftvHq2zZeR06Vac+xYtkNQ/ERTiVHh9baRDnDFRqiq3u319W9X1BVVZVWrVol0zTVvkdfrf4uV6s2rlRVZYoijpz7DMJDOqRP9WmNZspR47hKEuI1IORGRbpSlJp6s1obxAqAwJWdna0uXbpo1apVkqQBAwaouLhYBw4cUOfOnc86q+aM8ePHa836zcr79oczuh0p7eVIaCMZNjmSMuVIymyOIQBAozgcDg0aNMjTSB46dKgqKyt18OBB2Ww2xcfHn/OMbUkaM2bMWc2UYcOGKTExUYZh6Nprr/VsP1RUqkVf7ffqF4IA0FTx8fG68sorPV+QDR06VCUlJdq9e7fatm171peLZzidTg0bNqxGMyUmJkYXXXSRZ6Hze++9t8n1dWsVrbYJ4Vq57bC+PXSyyc8HAD/WqVMntW/f3vM5eODAgSouLta+ffuUnZ2t/fv3n/O4K6+8UkVFRTWaKT179lR2drbsdrs6duyo+++/v1nGcC4xLofG5aTr851HtXF/oUrKff/+kytTgGZ24ESJCksqVF7pVlRYiNomRMhua945pM80U6TqN5F2u117jxXr/W8O1rg8rrS8UIYRouOF3yo2tqOqzArZ3Iaq3GWKdKWc7+kDDlemtMwxAY11rgw8l9LSUrndbpWVlSkuLk6StGjNd8o7UWmp9U+4MqVljglojPrmn2maKi8v18mTJxUZGamwsDCZpqnCwkLFxMTUObd2QVGpFq3f3ywfaOvClSktd1xAQ9U3A880Vo4ePark5GTZbDZVVFSovLzcs4i8L+09VqyV3x7WkZNlTXoerkyp1lLHBTRUQz4HS9Lp06eVkJAgSTp16pTCwsJ8sv6Jt5imqQOFpTpwokSnyioVH+5Ur/TYeh/PlSlAgGoV61Kr2Oa75K2+0uPDNeHCtnpz3V4dPVU9jU2YM0aSlJrYx5+lAYBfhIVVr/cUHv7Dl3BX9MnSXz/P0+ky/39BCAC+YhiG54zrH2+LjY2t1/HJ0WG6vm8bLfpqH3kJwHIcjuoZI368mL3D4fBs97X0+HD9/MIMbckv0mc7jp53PQAA8IUzn4PP/FeSIiMj/VVOvRmGodaxLrX28XeugTGRLYCA4HLaNbZ3a4U7rXPGNQA0J5fTrsu7paqOk7IBIOglRobqhr7pigrj/D0AaCjDMJTdKkYTB2Xqok6JCnXw9R0ABALSGEANMS6HrurdSiHNPPUYAFhFZmKE+raN83cZABDw4iKcuiEnXbHhzXM2NwC0NCH26vVTbxvUTj3bxPi7HAAIejRTAJwlLcal4V3qP78+AASbwe0TlRYTVveOABDkYlwO3ZCTrsRIp79LAQDLcjnturRrivplxvu7FAAIajRTAJxT99Yx6pgS+HMiAoA/2GyGruiRJhfTIgJAnSJDQ3R933SlRNOEBoCmGNIxUX0yYv1dBgAELZopAM5rRNcU5rkGgPOIcTl0ZY802VhABQDq5HLadV3f1moT59tFQQGgpRvWOVlZSRH+LgMAghLNFADnFeawa2Q2Cy0DwPmkx4drWOckf5cBAJYQGmLXNX1a8yUgADTRsE7Jctj5oA4AzY1mCoBapceHq08GCy0DwPn0So9V7/RYf5cBAJYQYrdpTM9W6t6ahZQBoLFiwh2snwIAfkAzBUCdBrdPUHwEi4YCwPkM65zEOlMAUE82m6HLuqVoQFaCv0sBAMvKyYznczoANDOaKQDqFGK3aWR2KusCAMB5GIahUdmprAUAAA0wsH2CRvdIY6oaAGgEu83Qz3qmKSU6zN+lAEDQoJkCoF5SY8LUrx3TfQHA+YTYbbqqdyulxfCBFgDqq3NqlMblpCva5fB3KQBgOQmRobqpf7qGdU4iRwGgGYT4uwAA1jGgXYLyjhTrUFGpv0sBgIAUGmLX1X1aa9FX+8lKAKin5OgwTbgwQyu3FSg3/6S/ywEASzEMQ30y4tQnI05FpRU6VFiqkooqVbpNf5cGAC0OV6YAqDebzdCo7qlMxQAAtQhz2HXtBa2VHB3q71IAwDLCHHaN6p6mK3umKTKUc/4AoDGiwxzqmBKlnm1idUEGM0sAgLfRTAHQIPERTg3pmOTvMgAgoIU57LrugjbMYQ0ADdQpJUq/GNRWOZlxsts4gQcAAACBg2YKgAbrnR6rrKQIf5cBAAHtzBUqqayhAgANEhpi19COSZo4OFO90mNoqgAAACAg0EwB0CiXd0tVVBhTMABAbc40VFrF0lABgIaKDnPoki4pum1wpvq2jZMzhI+vAAAA8B/ejQJoFJfTrlHdU2UzOFMQAGoTGmLXNX3aqHWcy9+lAIAlRYU5dFGnJE0a0k6dUqL8XQ4AAACCFM0UAI3WJi5cgzsk+LsMAAh4zhCbrunTWhnx4f4uBQAsK8xh1+geqRqQlSDO5wEAAEBzo5kCoElyMuPVOZUzBAGgLg67TWN7t1K7RNacAoDGMgxDA9snaHjnZH+XAgAAgCDDggcAmuyybik6drpch0+W+bsUAAhoIXabxvRqpXc25WtHwSl/lwMAltUrPVb7T5Ro28GT/i4FAGBxVVVVqqio8HcZCGAOh0N2u93fZSAA0EwB0GSO778cfH3tHp0uq/J3OQAQ0Ow2Qz/rkaYPthxUbj5fAgJAY13aNVmHikp1opgvwAAADWeapg4ePKgTJ074uxRYQGxsrFJTU2Uw12hQo5kCwCtiXA6N7d1af1+3T+WVbn+XAwABzWYzNDI7Vc4Qm77eW+jvcgDAkkJD7LqyR5r+tTFfRSU0VAAADXOmkZKcnKzw8HC+JMc5maap4uJiFRQUSJLS0tL8XBH8iWYKAK9JiQ7TFd1T9a+v8+U2TX+XAwABzTAMXdIlRRHOEH2646i/ywEAS0qODtNtgzK1Jb9IG/cV6tjpMlVU8T4UAFC7qqoqTyMlISHB3+UgwLlcLklSQUGBkpOTmfIriNFMAeBVWUmRurRrspblHhL9FACo24VZCYoJd+iDbw6pyk1wAkBD2WyGureOUffWMTJNU0Wllaqoqr5SOjrM4efqAACB6MwaKeHh4X6uBFZx5meloqKCZkoQo5kCwOu6t45RWaVbH3972N+lAIAldEmNVnSYQ0s35etkaaW/ywEAyzIMQzEuGigAgPphai/UFz8rkCSbvwsA0DL1bRunAVlcKgsA9dUq1qUJF7ZVVlKEv0sBAAAAUF+mKZ0+Kh3fXf1fpukAWiyaKQB8ZmD7BPXLjPd3GQBgGS6nXVf1aqXLuqXI5eTScQAAACBglZyQPn9WerKP9D9Z0hM9q//7ZJ/q7SUn/F1hvWVmZmrevHl+rWHmzJnq3bu3X2sA6kIzBYBPDemYqL5t4/xdBgBYhmFUz/0/cVCmeqfHKsTG5eQAAABAQNm+THq8m/TeA9LxvJqPHc+r3v54t+r9vMgwjFpvM2fObNTzrl27VnfccUeTahs2bJinjrCwMHXr1k3PPPNMvY+fNm2ali9f3qDXDIQmEIILzRQAPndRpyRdQEMFABokzGHX8C7Jun1IO+VkxinMwZUqAAAAgN9tXya9Ok6qKJFkfn/7se+3VZRU7+fFhkp+fr7nNm/ePEVHR9fYNm3atB+qME1VVtZvPcakpCTPAutNMXnyZOXn52vLli0aN26cpk6dqtdee61ex0ZGRiohgeniEdhopgBoFhfTUAGARokIDdHQjkmaPLSdRnVPVXJ0qL9LAgAAAIJTyQnp9V98vy6Ku46d3dX7vf4Lr035lZqa6rnFxMTIMAzP/a1btyoqKkrvvvuu+vbtq9DQUH3yySfasWOHxo4dq5SUFEVGRqpfv35atqxmg+enV3gYhqG//OUvuuaaaxQeHq6OHTvq7bffrrO+8PBwpaamKisrSzNnzqxx3J49ezR27FhFRkYqOjpa48aN06FDhzzH/nSar4kTJ+rqq6/W3LlzlZaWpoSEBE2dOlUVFRWSqq+E2b17t+677z7PFTGStHv3bo0ZM0ZxcXGKiIhQdna2li5d2ti/cqAGmikAmg0NFQBovBC7TV3TonVTvwxd1ClJDjvTfwEAAADN6uvXpIpi1d1IOcNdvf/XC31ZVQ3Tp0/XnDlzlJubq549e+rUqVMaPXq0li9frvXr12vUqFEaM2aM9uzZU+vzzJo1S+PGjdPGjRs1evRoTZgwQceOHWtQLS6XS+Xl5XK73Ro7dqyOHTumjz76SB9++KF27typ8ePH13r8ihUrtGPHDq1YsUIvv/yyFixYoAULFkiSFi1apDZt2ujhhx/2XJkjSVOnTlVZWZk+/vhjbdq0SX/4wx8UGRnZoLqB8wnxdwEAgsvFnZJkNwytzWvY/4ABANVsNkN928apfVKE/r5un06W1u/SfQAAAABNYJrSmucad+yaP0sX/koyfH9C1MMPP6zLLrvMcz8+Pl69evXy3J89e7YWL16st99+W3ffffd5n2fixIm66aabJEmPPvqonnzySX3xxRcaNWpUnTVUVVXptdde08aNG3XHHXdo+fLl2rRpk3bt2qX09HRJ0iuvvKLs7GytXbtW/fr1O+fzxMXF6amnnpLdbleXLl105ZVXavny5Zo8ebLi4+Nlt9sVFRWl1NRUzzF79uzRddddpx49ekiSsrKy6qwXqC+uTAHQ7IZ0TNSF7eL9XQYAWFpsuFPXXtBG4U7WUgEAAAB8rviYdHyXzl4jpS5m9XElx31R1VlycnJq3D916pSmTZumrl27KjY2VpGRkcrNza3zypSePXt6/hwREaHo6GgVFBTUeswzzzyjyMhIuVwuTZ48Wffdd5/uvPNO5ebmKj093dNIkaRu3bopNjZWubm5532+7Oxs2e0/fN5JS0urs4b//M//1O9//3sNHjxYM2bM0MaNG2vdH2gImilAMDJNOcqLFFZySCo++v1cn81rUIdEDe6Q2OyvCwCBkIHeEh/h1DV9WtNQAVA/LSj/AKDByEA0Vfmpph1fdtI7ddQhIiKixv1p06Zp8eLFevTRR7Vq1Spt2LBBPXr0UHl5ea3P43A4atw3DENud+3Tm02YMEEbNmzQrl27dPr0aT3++OOy2Rr/9XNjavjlL3+pnTt36pZbbtGmTZuUk5Oj+fPnN7qGFoMM9Aqm+QKCSckJ6evXZFvznAYf31W9bY2kuHbVl5v2uklyxTZbOf3bxcthN/TRt4fJcAC+F2AZ6C3J0WH65dAs7TlWrO8OndTJ0kqVV7llmpIzxKbESBasB4JeC80/AKgXMhDe4mziuhuhUd6po4FWr16tiRMn6pprrpFUfaVKXl6eT14rJiZGHTp0OGt7165dtXfvXu3du9dzdcqWLVt04sQJdevWrdGv53Q6VVVVddb29PR0TZkyRVOmTNEDDzygF154Qf/xH//R6NexNDLQq2imAMFi+zLp9V98v1DaTxzPk957QFo+Wxr/itRhRLOV1ScjTqEhdi3LPaQqNx0VAD4SoBnoLXaboXaJEWqXGFH3zgCCSwvPPwCoFRkIbwqPr/4C+nieGjbVlyHFZUquON/UVYeOHTtq0aJFGjNmjAzD0EMPPVTn1R3eNmLECPXo0UMTJkzQvHnzVFlZqbvuuksXX3zxWdOSNURmZqY+/vhj3XjjjQoNDVViYqLuvfdeXXHFFerUqZOOHz+uFStWqGvXrl4cjYWQgV7HNF9AMNi+THp1nFRRIsmUcdb/9M3qW0VJ9X7blzVred1aRevq3q3lDCGSAPhAgGcgAPgM+QcgmJGB8DbDqD6TvzEunNIsi8+fy+OPP664uDgNGjRIY8aM0ciRI3XBBRc0aw2GYeif//yn4uLidNFFF2nEiBHKysrS66+/3qTnffjhh5WXl6f27dsrKSlJklRVVaWpU6eqa9euGjVqlDp16qRnnnnGG8OwFjLQJwzTDJ7JdYqKihQTE6PCwkJFR0f7uxygeZSckB7v9n141ufMA5vkcEn3b2n2y/wOnyzTko0HdKK4wqvPG+1yaNKQdvXevyVmRUscE1AvFsrAQNES86IljgmoE/nXYC01K1rquIBakYEN1lKz4nzjKi0t1a5du9SuXTuFhYXV/wkb+rNl2KSQ4P7Zaika/TPjD2Rgg9U3Ay1xGnheXp4mTZqkdu3ayeVyqX379poxY0adCyUBkPT1a99fzlffSzjd1ft/vdCXVZ1TUlSobuqfofbJTZyHtIUhA4EmsFAG4tzIQKCRyD/LI/+AJiADLS9gM9AVWz0lkmGo7q9VbZIMafxfg/YLavgJGegzllgzZevWrXK73XruuefUoUMHbd68WZMnT9bp06c1d+5cf5cHBC7TlNY817hj1/y5+vLVZr4MNcxh11W9WunrvSe0escRlVU07zyegYgMBBrJghmIs5GBQCOQfy0C+Qc0EhnYIgR0BnYYIU144ydrUfx44p/vf34crupGSodLm7tCBDMy0Kcs0UwZNWqURo0a5bmflZWlbdu26dlnn/V/gAKBrPiYdHxXIw40q48rOV69wJof9EqPVYfkSH387WFtO3RSwTMh4dnIQKCRLJyB+AEZCDQC+dcikH9AI5GBLULAZ2CHEdVTIn29sPoL6B//zMVlVq+R0vsmKSzGbyUiSJGBPmWJZsq5FBYWKj6+9n/YsrIylZWVee4XFRX5uiwgsJSfatrxZSf9GqARoSG6okeaLsxK0Lrdx7U1v0iV7iDuqvxIXRlI/gGyfAbi/MhAoA7kX4vF52CgHsjAFivgMtAVKw2YUn0mf8nx6p+d0CjJFceZ/fAfMtCnLLFmyk9t375d8+fP169+9ata93vssccUExPjuaWnpzdThUCAcDZx7ZHQKO/U0UTxEU5d1i1Fky/K0mXdUpQRH+7vkvyqPhlI/gFqMRmImshAoB7IvxaJz8FAPZGBLVJAZ6BhVH/5HNe2+r80UuBPZKBP+bWZMn36dBmGUett69atNY7Zv3+/Ro0apRtuuEGTJ0+u9fkfeOABFRYWem579+715XCAwBMeL8W1k2e+znozqo9zxfmiqkYLc9jVvXWMruvbRhMuzFCH5EhLv0fxZQaSf4BaXAa2NGQg4EPkX0DjczDgY2RgQCMDAR8jA33Kr9N8/frXv9bEiRNr3ScrK8vz5wMHDmj48OEaNGiQnn/++TqfPzQ0VKGhoU0tE7Auw6i+3PS9Bxp+7IVTAvpsiuToMI3p1Uo7Dp/S+98ctORC9b7MQPIPUIvOwJaADAR8iPwLaHwOBnyMDAxoZCDgY2SgT/m1mZKUlKSkpKR67bt//34NHz5cffv21UsvvSSbzZIzlAHNr9dN0vLZUkWJpHo0HAybFOKSet3o89K8oX1SpG7ql6F3Nx/UoaJSf5fTIGQg0AxaeAZaGRkI+Bj5F7DIP6AZkIEBiwwEmgEZ6DOWWIB+//79GjZsmNq2bau5c+fq8OHDnsdSU1P9WBlgAa5Yafwr0qvjJNOm2kPUJsmQxv+1+jiLiItw6uYLM1RSXqWCk6UqqahSiM2Qw25TRGiIosIsEXXnRQYCTRAEGdjSkYFAI5F/lkf+AU1ABloeGQg0ARnoM5b4hvHDDz/U9u3btX37drVp06bGY6Zp+qkqwEI6jJAmvCG9/gupovj7jT/+3fn+Ej6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-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(1)\n",
-    "N_POINTS = 5\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "train_y = f + train_x2\n",
-    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
-    "task_train_x = torch.cat((torch.ones_like(train_x1).unsqueeze(-1), train_x), -1)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.sort()\n",
-    "sorted_f = f[indices]\n",
-    "sorted_train_x2 = train_x2[indices]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
-    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model is able to fit this setup and make reasonable predictions for varied $x_1$ and $x_2$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Missing Data\n",
-    "\n",
-    "How does the model do when entire features ($x_1$ or $x_2$) are missing?\n",
-    "\n",
-    "Each input is now the concatentation of\n",
-    "1. Task description: 2-bit vector indicating whether feature is present\n",
-    "2. Features: $\\left[x_1\\right]$, $\\left[x_2\\right]$ or $\\left[x_1, x_2\\right]$\n",
-    "\n",
-    "The input is variable-length: could be 3 or 4 dimensional depending on number of missing columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gen_missing_batch(\n",
-    "    batch_size: int,\n",
-    "    seq_len: int,\n",
-    "    num_features: int,\n",
-    "    max_num_tasks: int,\n",
-    "    num_tasks: int,\n",
-    "    lengthscale: float,\n",
-    "    hyperparameters=None,\n",
-    "    device: str = default_device,\n",
-    "    delete_x1=None,\n",
-    "    delete_x2=None,\n",
-    "    **kwargs\n",
-    "):\n",
-    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
-    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
-    "    \n",
-    "    ys = sample_data(x1)\n",
-    "    ys += x2.squeeze(-1)\n",
-    "    \n",
-    "    task_ids = torch.ones(batch_size, seq_len, 1, device=device)\n",
-    "    \n",
-    "    task_info = torch.ones(batch_size, seq_len, 2, device=device)\n",
-    "    if delete_x1 is None:\n",
-    "        delete_x1 = np.random.rand() < 0.2\n",
-    "    if delete_x2 is None:\n",
-    "        delete_x2 = np.random.rand() < 0.2 and not delete_x1\n",
-    "        \n",
-    "    # get accurate task embedding\n",
-    "    if delete_x1:\n",
-    "        task_info[:, :, 0] = 0\n",
-    "    if delete_x2:\n",
-    "        task_info[:, :, 1] = 0\n",
-    "        \n",
-    "    if delete_x1:\n",
-    "        xs = torch.cat((task_ids, task_info, x2), -1)\n",
-    "    elif delete_x2:\n",
-    "        xs = torch.cat((task_ids, task_info, x1), -1)\n",
-    "    else:\n",
-    "        xs = torch.cat((task_ids, task_info, x1, x2), -1)\n",
-    "    \n",
-    "    xs = xs.to(device)\n",
-    "\n",
-    "    return Batch(\n",
-    "        x=xs.transpose(0, 1),\n",
-    "        y=ys.transpose(0, 1),\n",
-    "        target_y=ys.transpose(0, 1).clone(),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda:0 device\n",
-      "init dist\n",
-      "Not using distributed\n",
-      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7efbdc328040>, 'seq_len_maximum': 20, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 3, 'sample_num_tasks': False, 'sample_num_features': True}}, 'num_features': 2, 'num_tasks': 3, 'epoch_count': 0}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Style definition of first 3 examples: None\n",
-      "Initialized decoder for standard with (None, 2)  and nout 2\n",
-      "Using a Transformer with 1.49 M parameters\n",
-      "Using OpenAI max lr of 0.0012557141533970579.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:286: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast(enabled=scaler is not None):\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.33it/s, data_time=0.0026, step_time=0.0267, mean_loss=1.88] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.51it/s, data_time=0.00259, step_time=0.0267, mean_loss=0.959]\n",
-      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast(enabled=scaler is not None):\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:04<00:04, 57.34it/s, data_time=0.00263, step_time=0.0196, mean_loss=0.71]  \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:14<00:00, 17.15it/s, data_time=0.00261, step_time=0.0263, mean_loss=0.737]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.36it/s, data_time=0.00347, step_time=0.0263, mean_loss=0.645]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.42it/s, data_time=0.00262, step_time=0.00761, mean_loss=0.523]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 29.72it/s, data_time=0.00255, step_time=0.0269, mean_loss=0.599]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.90it/s, data_time=0.00302, step_time=0.0264, mean_loss=0.624]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 71.39it/s, data_time=0.0027, step_time=0.00755, mean_loss=0.432] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.19it/s, data_time=0.00258, step_time=0.0271, mean_loss=0.7]  \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.16it/s, data_time=0.00259, step_time=0.0267, mean_loss=0.677]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.94it/s, data_time=0.00266, step_time=0.00857, mean_loss=0.61] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.19it/s, data_time=0.00259, step_time=0.0267, mean_loss=0.637]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.38it/s, data_time=0.00318, step_time=0.027, mean_loss=0.586]  \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 72.30it/s, data_time=0.00313, step_time=0.00765, mean_loss=0.632]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.25it/s, data_time=0.00257, step_time=0.0269, mean_loss=0.597]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.30it/s, data_time=0.00255, step_time=0.0271, mean_loss=0.741]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 73.30it/s, data_time=0.00319, step_time=0.00787, mean_loss=0.472]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.29it/s, data_time=0.0026, step_time=0.0265, mean_loss=0.472] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.22it/s, data_time=0.00255, step_time=0.027, mean_loss=0.316] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.94it/s, data_time=0.00267, step_time=0.00764, mean_loss=0.445]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.23it/s, data_time=0.00259, step_time=0.027, mean_loss=0.252] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 30.38it/s, data_time=0.00344, step_time=0.0276, mean_loss=0.142]   \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.94it/s, data_time=0.00354, step_time=0.0078, mean_loss=-0.16]    \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:11<00:00, 21.86it/s, data_time=0.0208, step_time=0.0627, mean_loss=-0.0567] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 30.48it/s, data_time=0.00257, step_time=0.0265, mean_loss=-0.0749] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.53it/s, data_time=0.00296, step_time=0.0081, mean_loss=-0.109]   \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.89it/s, data_time=0.00253, step_time=0.0264, mean_loss=-0.431]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.27it/s, data_time=0.00255, step_time=0.0283, mean_loss=-0.409]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 81.54it/s, data_time=0.00349, step_time=0.00799, mean_loss=-0.482]\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_missing_batch, num_features=2, num_tasks=3, seq_len=20, epochs=20, sample_num_features=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Missing $x_1$\n",
-    "\n",
-    "Only $x_2$ and $y$ are present in the training data, where the full model is $f \\sim GP, y=f(x_1) + x_2$. This means that the model has no information about the shape."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
-      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
-      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
-      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n",
-      "torch.Size([5, 4]) torch.Size([5]) torch.Size([100, 4])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(1)\n",
-    "N_POINTS = 5\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "train_y = f + train_x2\n",
-    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "task_info[:, 0] = 0\n",
-    "\n",
-    "task_id = torch.ones(1000, 1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_id, task_info, train_x2.unsqueeze(-1)), -1)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.sort()\n",
-    "sorted_f = f[indices]\n",
-    "sorted_train_x2 = train_x2[indices]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_task_info = torch.ones(100, 2)\n",
-    "    test_task_info[:, 0] = 0\n",
-    "    test_task_id = torch.ones(100, 1)\n",
-    "    task_test_x = torch.cat((test_task_id, test_task_info, test_x2), -1)\n",
-    "    \n",
-    "    print(task_train_x[:N_POINTS].shape, train_y[:N_POINTS].shape, task_test_x.shape)\n",
-    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
-    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "plt.suptitle(\"Missing X1\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Missing $x_2$\n",
-    "\n",
-    "Only $x_2$ and $y$ are present in the training data, where the full model is $f \\sim GP, y=f(x_1) + x_2$. This means the model has shape information but doesn't know about the shift."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(1)\n",
-    "N_POINTS = 15\n",
-    "train_x1 = torch.rand(1000)\n",
-    "# train_x2 = torch.randn_like(train_x1)\n",
-    "train_x2 = torch.ones_like(train_x1) * 0.2\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "train_y = f + train_x2\n",
-    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "task_info[:, 1] = 0\n",
-    "\n",
-    "task_id = torch.ones(1000, 1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_id, task_info, train_x1.unsqueeze(-1)), -1)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.sort()\n",
-    "sorted_f = f[indices]\n",
-    "sorted_train_x2 = train_x2[indices]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_task_info = torch.ones(100, 2)\n",
-    "    test_task_info[:, 1] = 0\n",
-    "    test_task_id = torch.ones(100, 1)\n",
-    "    task_test_x = torch.cat((test_task_id, test_task_info, test_x1), -1)\n",
-    "    \n",
-    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
-    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-4, 4)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "plt.suptitle(\"Missing X2\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(1)\n",
-    "N_POINTS = 15\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "train_y = f + train_x2\n",
-    "train_x = torch.cat((train_x1.unsqueeze(-1), train_x2.unsqueeze(-1)), -1)\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "task_info[:, 1] = 0\n",
-    "\n",
-    "task_id = torch.ones(1000, 1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_id, task_info, train_x1.unsqueeze(-1)), -1)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.sort()\n",
-    "sorted_f = f[indices]\n",
-    "sorted_train_x2 = train_x2[indices]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_task_info = torch.ones(100, 2)\n",
-    "    test_task_info[:, 1] = 0\n",
-    "    test_task_id = torch.ones(100, 1)\n",
-    "    task_test_x = torch.cat((test_task_id, test_task_info, test_x1), -1)\n",
-    "    \n",
-    "    pfn_predictions = best_model(task_train_x[:N_POINTS], train_y[:N_POINTS], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:N_POINTS], f[:N_POINTS].squeeze(), f[:N_POINTS].squeeze() + train_x2[:N_POINTS], color=\"grey\", alpha=0.5)\n",
-    "    ax.scatter(train_x1[:N_POINTS], train_y[:N_POINTS], s=100, label=\"Train Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"latent f\", linestyle=\"--\")\n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-4, 4)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "plt.suptitle(\"Missing X2\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Transfer Learning\n",
-    "\n",
-    "Shared:\n",
-    "$f \\sim \\mathcal{GP}(0, k_\\text{RBF}), c \\sim \\text{Uniform}(0, 10)$\n",
-    "\n",
-    "Source: $y = -f(x_1) + cx_2$\n",
-    "\n",
-    "Target: $y = f(x_1) + cx_2$\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def gen_transfer_batch(\n",
-    "    batch_size: int,\n",
-    "    seq_len: int,\n",
-    "    num_features: int,\n",
-    "    max_num_tasks: int,\n",
-    "    num_tasks: int,\n",
-    "    lengthscale: float,\n",
-    "    hyperparameters=None,\n",
-    "    device: str = default_device,\n",
-    "    **kwargs\n",
-    "):\n",
-    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
-    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
-    "    c = torch.rand(batch_size, device=device) * 10\n",
-    "    task_ids = torch.randint(0, 2, (batch_size, seq_len), device=device).float()\n",
-    "    task_id[:2] = torch.tensor([0, 1], device=device).unsqueeze(-1)\n",
-    "    task_ids[-2:] = torch.tensor([0, 1], device=device).unsqueeze(-1)\n",
-    "    \n",
-    "    f = sample_data(x1)\n",
-    "    source_ys = f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
-    "    target_ys = -f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
-    "    \n",
-    "    task_xs = torch.cat((task_ids.unsqueeze(-1), x1, x2), -1).to(device)\n",
-    "    ys = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "    return Batch(\n",
-    "        x=task_xs.transpose(0, 1),\n",
-    "        y=ys.transpose(0, 1),\n",
-    "        target_y=ys.transpose(0, 1).clone(),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Train without missing data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda:0 device\n",
-      "init dist\n",
-      "Not using distributed\n",
-      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7efb5cfe9bc0>, 'seq_len_maximum': 30, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 1, 'sample_num_tasks': False, 'sample_num_features': False}}, 'num_features': 2, 'num_tasks': 1, 'epoch_count': 0}\n",
-      "Style definition of first 3 examples: None\n",
-      "Initialized decoder for standard with (None, 2)  and nout 2\n",
-      "Using a Transformer with 1.49 M parameters\n",
-      "Using OpenAI max lr of 0.0012557515027448725.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 30.88it/s, data_time=0.00285, step_time=0.0271, mean_loss=25.5]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.75it/s, data_time=0.00285, step_time=0.0266, mean_loss=2.25]\n",
-      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast(enabled=scaler is not None):\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.53it/s, data_time=0.0028, step_time=0.00757, mean_loss=1.47] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.74it/s, data_time=0.00325, step_time=0.0266, mean_loss=1.39]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.67it/s, data_time=0.00288, step_time=0.0271, mean_loss=1.14]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.30it/s, data_time=0.00313, step_time=0.00871, mean_loss=1.09]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.62it/s, data_time=0.00286, step_time=0.0268, mean_loss=1.06]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.68it/s, data_time=0.00286, step_time=0.0269, mean_loss=0.925]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.35it/s, data_time=0.00279, step_time=0.00777, mean_loss=0.919]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.64it/s, data_time=0.00286, step_time=0.0275, mean_loss=0.941]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:12<00:00, 20.87it/s, data_time=0.00285, step_time=0.0268, mean_loss=0.821]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.83it/s, data_time=0.00293, step_time=0.00872, mean_loss=1.06]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.83it/s, data_time=0.00285, step_time=0.0264, mean_loss=0.77] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:11<00:00, 21.37it/s, data_time=0.0106, step_time=0.0471, mean_loss=0.722] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:04<00:04, 55.79it/s, data_time=0.00293, step_time=0.00857, mean_loss=0.816]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 23.57it/s, data_time=0.00327, step_time=0.0337, mean_loss=0.695]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 25.62it/s, data_time=0.00319, step_time=0.0263, mean_loss=0.708]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 65.45it/s, data_time=0.00909, step_time=0.0177, mean_loss=0.403] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.75it/s, data_time=0.00286, step_time=0.0279, mean_loss=0.594]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 29.69it/s, data_time=0.00314, step_time=0.0271, mean_loss=0.472]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 65.45it/s, data_time=0.00295, step_time=0.00849, mean_loss=0.259]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.60it/s, data_time=0.00372, step_time=0.0273, mean_loss=0.371]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.74it/s, data_time=0.00286, step_time=0.0272, mean_loss=0.135]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.30it/s, data_time=0.00291, step_time=0.00825, mean_loss=0.0601]   \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.70it/s, data_time=0.00288, step_time=0.0264, mean_loss=0.0139]   \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 23.60it/s, data_time=0.00331, step_time=0.0406, mean_loss=-0.197]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:06<00:06, 37.71it/s, data_time=0.0029, step_time=0.0176, mean_loss=-0.204]  \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 23.51it/s, data_time=0.00291, step_time=0.0427, mean_loss=-0.534]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:09<00:00, 26.71it/s, data_time=0.00417, step_time=0.0276, mean_loss=-0.592]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:07<00:07, 35.97it/s, data_time=0.00294, step_time=0.00872, mean_loss=-0.643]\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_transfer_batch, num_features=2, num_tasks=1, seq_len=30, epochs=20, sample_num_features=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Without transfer learning, there is a large amount of uncertainty"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[10:12], train_y[10:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    # ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    # ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10, color=\"green\")\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-6, 6)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning, C=%.2f\" % c)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The transfer learning really helps the model learn the shape ($f$) as well as the amount of shift ($c$)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(1)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 0.8 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_train_x = torch.stack((task_ids, train_x1, train_x2), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning, C=%.2f\" % c)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(1)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 3.0 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_train_x = torch.stack((task_ids, train_x1, train_x2), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-6, 6)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning, C=%.2f\" % c)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transfer Learning with Missing Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[[ 0.0000,  0.0000,  1.0000, -1.4068,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.4449,  1.3667],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.0879,  0.3745],\n",
-       "         [ 0.0000,  0.0000,  1.0000, -0.8154,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.5876,  0.6553]],\n",
-       "\n",
-       "        [[ 0.0000,  0.0000,  1.0000, -1.0935,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.5568, -0.5042],\n",
-       "         [ 0.0000,  0.0000,  1.0000,  1.8983,  0.0000],\n",
-       "         [ 0.0000,  0.0000,  1.0000, -1.8532,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.0297, -0.4621]],\n",
-       "\n",
-       "        [[ 0.0000,  0.0000,  1.0000,  0.5832,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.0319, -0.5039],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.7601,  0.0962],\n",
-       "         [ 0.0000,  0.0000,  1.0000,  0.5355,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.6945,  0.8844]],\n",
-       "\n",
-       "        [[ 0.0000,  0.0000,  1.0000, -0.6043,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.1699, -0.9647],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.2251,  0.1915],\n",
-       "         [ 0.0000,  0.0000,  1.0000,  0.9712,  0.0000],\n",
-       "         [ 1.0000,  1.0000,  1.0000,  0.7972,  0.0973]]], device='cuda:0')"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def gen_transfer_missing_batch(\n",
-    "    batch_size: int,\n",
-    "    seq_len: int,\n",
-    "    num_features: int,\n",
-    "    max_num_tasks: int,\n",
-    "    num_tasks: int,\n",
-    "    lengthscale: float,\n",
-    "    hyperparameters=None,\n",
-    "    device: str = default_device,\n",
-    "    **kwargs\n",
-    "):\n",
-    "    x1 = torch.rand(batch_size, seq_len, 1, device=device)\n",
-    "    x2 = torch.randn(batch_size, seq_len, 1, device=device)\n",
-    "    c = torch.rand(batch_size, device=device) * 10\n",
-    "    task_ids = torch.randint(0, 2, (batch_size, seq_len), device=device).float()\n",
-    "    task_ids[..., :2] = torch.tensor([0, 1], device=device)\n",
-    "    task_ids[..., -2:] = torch.tensor([0, 1], device=device)\n",
-    "    \n",
-    "    f = sample_data(x1)\n",
-    "    source_ys = f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
-    "    target_ys = -f + x2.squeeze(-1) * c.unsqueeze(-1)\n",
-    "    \n",
-    "    xs = torch.zeros(batch_size, seq_len, 5).to(device).reshape(-1, 5)\n",
-    "    task_id_flat = task_ids.flatten()\n",
-    "    xs[:, 0] = task_id_flat\n",
-    "    for task_id in range(2):\n",
-    "        delete_x1 = np.random.rand() < 0.2\n",
-    "        delete_x2 = np.random.rand() < 0.2 and not delete_x1\n",
-    "        \n",
-    "        relevant_task_ids = task_id_flat == task_id\n",
-    "        xs[relevant_task_ids, 1:3] = 1\n",
-    "        if delete_x1:\n",
-    "            xs[relevant_task_ids, 1] = 0\n",
-    "            xs[relevant_task_ids, 3] = x2.flatten()[relevant_task_ids]\n",
-    "        elif delete_x2:\n",
-    "            xs[relevant_task_ids, 2] = 0\n",
-    "            xs[relevant_task_ids, 3] = x1.flatten()[relevant_task_ids]\n",
-    "        else:\n",
-    "            xs[relevant_task_ids, 3] = x1.flatten()[relevant_task_ids]\n",
-    "            xs[relevant_task_ids, 4] = x2.flatten()[relevant_task_ids]\n",
-    "    \n",
-    "    xs = xs.reshape(batch_size, seq_len, 5)\n",
-    "    ys = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "    return Batch(\n",
-    "        x=xs.transpose(0, 1),\n",
-    "        y=ys.transpose(0, 1),\n",
-    "        target_y=ys.transpose(0, 1).clone(),\n",
-    "    )\n",
-    "# gen_transfer_missing_batch(4, 5, 2, 1, 1, 0.1).x.transpose(0, 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda:0 device\n",
-      "init dist\n",
-      "Not using distributed\n",
-      "DataLoader.__dict__ {'num_steps': 256, 'get_batch_kwargs': {'batch_size': 200, 'eval_pos_seq_len_sampler': <function train.<locals>.eval_pos_seq_len_sampler at 0x7f70301ef240>, 'seq_len_maximum': 30, 'device': 'cuda:0', 'num_features': 2, 'hyperparameters': {'num_tasks': 3, 'sample_num_tasks': False, 'sample_num_features': False}}, 'num_features': 2, 'num_tasks': 3, 'epoch_count': 0}\n",
-      "Style definition of first 3 examples: None\n",
-      "Initialized decoder for standard with (None, 2)  and nout 2\n",
-      "Using a Transformer with 1.49 M parameters\n",
-      "Using OpenAI max lr of 0.0012557141533970579.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch:   0%|          | 0/256 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:286: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast(enabled=scaler is not None):\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.50it/s, data_time=0.00411, step_time=0.0253, mean_loss=18.4]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:10<00:00, 25.49it/s, data_time=0.00323, step_time=0.0244, mean_loss=2.8] \n",
-      "Eval Epoch:   0%|          | 0/512 [00:00<?, ?it/s]/home/yl9959/mtpfn/PFNs/pfns/train.py:525: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast(enabled=scaler is not None):\n",
-      "Eval Epoch:  45%|████▌     | 232/512 [00:02<00:03, 78.91it/s, data_time=0.00343, step_time=0.0084, mean_loss=2.13] /ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
-      "  warnings.warn(\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.66it/s, data_time=0.00413, step_time=0.00793, mean_loss=2.12]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.78it/s, data_time=0.00355, step_time=0.0259, mean_loss=1.96]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.18it/s, data_time=0.00328, step_time=0.0259, mean_loss=1.84]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.56it/s, data_time=0.00339, step_time=0.00842, mean_loss=1.72]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.78it/s, data_time=0.00386, step_time=0.0247, mean_loss=1.99]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.02it/s, data_time=0.00348, step_time=0.0253, mean_loss=1.84]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 70.41it/s, data_time=0.00354, step_time=0.00774, mean_loss=1.62]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.62it/s, data_time=0.0035, step_time=0.0257, mean_loss=1.75] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.32it/s, data_time=0.00357, step_time=0.0255, mean_loss=1.64]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 80.26it/s, data_time=0.00338, step_time=0.00791, mean_loss=1.53]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.36it/s, data_time=0.00353, step_time=0.0252, mean_loss=1.62]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.37it/s, data_time=0.00339, step_time=0.0254, mean_loss=1.74]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.81it/s, data_time=0.00348, step_time=0.00852, mean_loss=1.53]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.67it/s, data_time=0.00341, step_time=0.0245, mean_loss=1.82]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.52it/s, data_time=0.00338, step_time=0.0255, mean_loss=1.54]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.71it/s, data_time=0.00337, step_time=0.00724, mean_loss=1.43]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.01it/s, data_time=0.0033, step_time=0.0247, mean_loss=1.53] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.93it/s, data_time=0.0035, step_time=0.0254, mean_loss=1.48] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.24it/s, data_time=0.00335, step_time=0.0078, mean_loss=1.55] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.82it/s, data_time=0.0034, step_time=0.0243, mean_loss=1.38] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 29.71it/s, data_time=0.0034, step_time=0.0246, mean_loss=1.46] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 69.99it/s, data_time=0.00422, step_time=0.0079, mean_loss=1.31] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.79it/s, data_time=0.00339, step_time=0.0254, mean_loss=1.32]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.74it/s, data_time=0.00348, step_time=0.0254, mean_loss=1.37]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.84it/s, data_time=0.00324, step_time=0.00743, mean_loss=1.36]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.89it/s, data_time=0.00333, step_time=0.0252, mean_loss=1.28]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.95it/s, data_time=0.00341, step_time=0.0252, mean_loss=1.18]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.77it/s, data_time=0.00412, step_time=0.00868, mean_loss=1.18]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.07it/s, data_time=0.00339, step_time=0.0252, mean_loss=1.35]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.23it/s, data_time=0.00343, step_time=0.0258, mean_loss=1.2] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.66it/s, data_time=0.00336, step_time=0.0075, mean_loss=1.18] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.84it/s, data_time=0.00335, step_time=0.0254, mean_loss=1.12]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.86it/s, data_time=0.00345, step_time=0.025, mean_loss=1.03]  \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 70.35it/s, data_time=0.00447, step_time=0.00815, mean_loss=1.05]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.86it/s, data_time=0.00354, step_time=0.0248, mean_loss=1.15]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.62it/s, data_time=0.0036, step_time=0.0256, mean_loss=0.975] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.58it/s, data_time=0.00341, step_time=0.00759, mean_loss=1.12]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.83it/s, data_time=0.00322, step_time=0.025, mean_loss=1.03] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.01it/s, data_time=0.00404, step_time=0.0256, mean_loss=0.976]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.06it/s, data_time=0.00357, step_time=0.00763, mean_loss=1.02] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.68it/s, data_time=0.00329, step_time=0.024, mean_loss=0.977] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.50it/s, data_time=0.00338, step_time=0.025, mean_loss=0.743] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.24it/s, data_time=0.00328, step_time=0.00777, mean_loss=0.738]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.38it/s, data_time=0.00438, step_time=0.025, mean_loss=0.869] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.75it/s, data_time=0.00362, step_time=0.027, mean_loss=0.738] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 78.23it/s, data_time=0.00336, step_time=0.00843, mean_loss=0.794]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.17it/s, data_time=0.00348, step_time=0.0245, mean_loss=0.763]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.41it/s, data_time=0.00351, step_time=0.028, mean_loss=0.805] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 79.23it/s, data_time=0.00349, step_time=0.0073, mean_loss=0.741] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.21it/s, data_time=0.00399, step_time=0.0255, mean_loss=0.612]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 33.08it/s, data_time=0.00337, step_time=0.0256, mean_loss=0.628]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.67it/s, data_time=0.0035, step_time=0.00736, mean_loss=0.628] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.73it/s, data_time=0.00343, step_time=0.0249, mean_loss=0.607]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.82it/s, data_time=0.00344, step_time=0.0292, mean_loss=0.559]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.86it/s, data_time=0.00341, step_time=0.00733, mean_loss=0.624]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.70it/s, data_time=0.00404, step_time=0.0244, mean_loss=0.401]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.13it/s, data_time=0.00456, step_time=0.0254, mean_loss=0.663] \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.83it/s, data_time=0.00337, step_time=0.0116, mean_loss=0.474] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.71it/s, data_time=0.00338, step_time=0.0255, mean_loss=0.586]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.56it/s, data_time=0.00346, step_time=0.0257, mean_loss=0.408]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.54it/s, data_time=0.00369, step_time=0.00737, mean_loss=0.413]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.60it/s, data_time=0.00366, step_time=0.0261, mean_loss=0.303]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.62it/s, data_time=0.00375, step_time=0.0258, mean_loss=0.305]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.31it/s, data_time=0.00349, step_time=0.00859, mean_loss=0.253]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.70it/s, data_time=0.00356, step_time=0.0248, mean_loss=0.313]  \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.72it/s, data_time=0.00404, step_time=0.0258, mean_loss=0.324]\n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.44it/s, data_time=0.00483, step_time=0.0074, mean_loss=0.244]  \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.27it/s, data_time=0.00416, step_time=0.0252, mean_loss=0.344]\n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.06it/s, data_time=0.00334, step_time=0.0273, mean_loss=0.293]  \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 76.40it/s, data_time=0.00385, step_time=0.0079, mean_loss=0.308] \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:08<00:00, 31.62it/s, data_time=0.00408, step_time=0.0285, mean_loss=0.292]  \n",
-      "Training Epoch: 100%|██████████| 256/256 [00:07<00:00, 32.34it/s, data_time=0.00377, step_time=0.026, mean_loss=0.132]  \n",
-      "Eval Epoch:  50%|█████     | 256/512 [00:03<00:03, 77.01it/s, data_time=0.00345, step_time=0.00761, mean_loss=0.307]   \n"
-     ]
-    }
-   ],
-   "source": [
-    "total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = train(get_batch_fn=gen_transfer_missing_batch, num_features=2, num_tasks=3, seq_len=30, epochs=50, sample_num_features=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(4)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "train_x2[10] = 0.2 / 8\n",
-    "train_x2[11] = 0.8 / 8\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 0.01 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "source_tasks = task_ids == 0\n",
-    "task_info[source_tasks, 0] = 0\n",
-    "\n",
-    "all_features = torch.stack((train_x1, train_x2), -1)\n",
-    "source_features = all_features[source_tasks]\n",
-    "all_features[source_tasks] = torch.cat((source_features[..., 1:], torch.zeros_like(source_features[..., 1:])), -1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    test_info = torch.ones(100, 2)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[10:12], train_y[10:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    # ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    # ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10, color=\"green\")\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    # ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-10, 10)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Target: x1, x2\" % c)\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(4)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 0.01 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "source_tasks = task_ids == 0\n",
-    "task_info[source_tasks, 0] = 0\n",
-    "\n",
-    "all_features = torch.stack((train_x1, train_x2), -1)\n",
-    "source_features = all_features[source_tasks]\n",
-    "all_features[source_tasks] = torch.cat((source_features[..., :1], torch.zeros_like(source_features[..., 1:])), -1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    test_info = torch.ones(100, 2)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x1 only, Target: x1, x2\" % c)\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(4)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 0.01 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "source_tasks = task_ids == 0\n",
-    "task_info[source_tasks, 1] = 0\n",
-    "\n",
-    "all_features = torch.stack((train_x1, train_x2), -1)\n",
-    "source_features = all_features[source_tasks]\n",
-    "all_features[source_tasks] = torch.cat((source_features[..., 1:], torch.zeros_like(source_features[..., 1:])), -1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    test_info = torch.ones(100, 2)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x2 only, Target: x1, x2\" % c)\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-06 to the diagonal\n",
-      "  warnings.warn(\n",
-      "/ext3/miniconda3/envs/pfn/lib/python3.12/site-packages/linear_operator/utils/cholesky.py:40: NumericalWarning: A not p.d., added jitter of 1.0e-05 to the diagonal\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(4)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 2.0 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "source_tasks = task_ids == 0\n",
-    "task_info[source_tasks, 1] = 0\n",
-    "\n",
-    "all_features = torch.stack((train_x1, train_x2), -1)\n",
-    "source_features = all_features[source_tasks]\n",
-    "all_features[source_tasks] = torch.cat((source_features[..., 1:], torch.zeros_like(source_features[..., 1:])), -1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, test_x2), -1)\n",
-    "    test_info = torch.ones(100, 2)\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x2 only, Target: x1, x2\" % c)\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7yGPft2vXLk2YMEG7d+9WVFSURbvw8HB5enpaVWPx4sUtnt8f/LRm0PVx+/7999+SpNKlSyfZt3Tp0lYNWDZu3FgbN26UdG/wu3bt2qpdu7a8vb21c+dOFSxYUAcPHlSPHj0e29eTnIs18uTJoy5dumjFihWqW7euzp8/n6q6Jk+erPbt26ts2bKqXLmyWrdurZdffllVq1aVdO9LJe+//77eeustFSxYUPXr11fbtm3Vq1cvFSpU6InP8VE/LwcHh8cuzW6tyMhISZYDzhs2bNCUKVN04MABi/tgW/slh+joaE2fPl1BQUG6ePGixf3KH/78sIaHh4cky4H7x2nRokWqj+Pi4mJxvvfdvXvX/HpG7g8AAAAAWRmD4gAAAACyreQGaW7duqWmTZvKw8NDkydPVqlSpeTs7Kzff/9d//rXv5SYmGjR/lGzsh8cCKtQoYJOnDihDRs2aNOmTfr66681f/58jR8/XpMmTXpkffePNXLkyEfOrH54wDA9B54SExNVpUoVzZ49O9nX/fz8JEmnT5/W008/rfLly2v27Nny8/OTo6OjNm7cqDlz5iS5ZinVaM31zIh9rdWoUSMtXrxYZ86c0c6dO9W4cWOZTCY1atRIO3fuVJEiRZSYmGie9ZxW6XUuPXr00MKFCzVx4kRVq1ZNFStWtHrfJk2a6PTp01q3bp1+/PFHffbZZ5ozZ44WLlyoAQMGSJJGjBihdu3aae3atdq8ebPGjRun6dOna/v27apRo0aK/WfGz+tx/vzzT0n//3u0c+dOPf/882rSpInmz5+vwoULK0+ePAoKCtKKFSus6vPVV19VUFCQRowYoaeeekqenp4ymUzq3r17kt8Fa5QvX16SdPjwYav3uXTpklXtPD09zb+PhQsX1o4dO2QYhsUXAMLCwiRJRYoUSbGvwoULm9s+yNr9AQAAACArY1AcAAAAQI4SEhKi69ev65tvvlGTJk3M28+ePftE/bq5ualbt27q1q2bYmNj1alTJ02dOlVjxowxLy/8sJIlS0q6N+M3LTM/n1SpUqV08OBBPf300ynOkv3uu+8UExOj9evXW8z+fXB59azA399f0r0Z+A9Lblty7g92b9myRXv37tXbb78t6d4A8oIFC1SkSBG5ubmpVq1aKfbzJEurp0ajRo1UvHhxhYSE6P3330/1/t7e3urbt6/69u2ryMhINWnSRBMnTjQPikv33idvvfWW3nrrLZ08eVLVq1fXrFmz9OWXXz5R7Q/+vJo1a2beHh8fr9DQUPOM9bSKjIzUt99+Kz8/P1WoUEGS9PXXX8vZ2VmbN2+2WAo8KCgoyf6P+hmuWbNGvXv31qxZs8zb7t69q1u3bqWpzrJly6pcuXJat26d5s6da7H0/6MULlzYqr6DgoLUp08fSVL16tX12Wef6dixYxZfntizZ4/59ZRUr15dO3fuVGJiouzs/v9ue3v27JGrq6vKli1rVU0AAAAAkBVxT3EAAAAAOcr92asPzlaNjY3V/Pnz09zn9evXLZ47OjqqYsWKMgxDcXFxj9yvQIECCgwM1KJFi5KdgXn16tU012SNrl276uLFi1q8eHGS16Kjo3Xnzh1JyV+z8PDwZAcSbalIkSKqXLmyli5dal42W5J++uknq2fhlihRQkWLFtWcOXMUFxdnXgK/cePGOn36tNasWaP69etb3AM7OW5ubpKU5oFSa5lMJs2bN08TJkzQyy+/nKp9H37furu7q3Tp0uYlsqOiosxLY99XqlQp5c2bN9lltFOrdu3ayp8/vxYvXqz4+Hjz9uXLl6fpHuYPio6O1ssvv6wbN27onXfeMQ9w29vby2QyKSEhwdw2NDRUa9euTdKHm5tbsj8/e3v7JLPdP/roI4s+U2vSpEm6fv26BgwYYHEt7vvxxx+1YcMG8/O03FO8ffv2ypMnj8VnnWEYWrhwoYoWLaoGDRqYt4eFhen48eMWn1+dO3fW5cuX9c0335i3Xbt2TatXr1a7du2Svd84AAAAAGQXzBQHAAAAkKM0aNBA+fLlU+/evfXaa6/JZDJp2bJlT7Sk8zPPPKNChQqpYcOGKliwoI4dO6aPP/5Ybdq0sbiXcXI++eQTNWrUSFWqVNHAgQNVsmRJXb58Wbt379aFCxd08ODBNNclSdu2bUsysClJHTp00Msvv6xVq1Zp8ODB2rFjhxo2bKiEhAQdP35cq1at0ubNm1W7dm0988wzcnR0VLt27fTKK68oMjJSixcvVoECBZIdzLeladOmqX379mrYsKH69u2rmzdv6uOPP1blypUtBspT0rhxY61cuVJVqlQx3we7Zs2acnNz019//WXVfbvvzyR/7bXX1KpVK9nb26t79+5pP7EUtG/fXu3bt0/1fhUrVlRgYKBq1aolb29v7du3T2vWrNHw4cMlSX/99Zeefvppde3aVRUrVpSDg4O+/fZbXb58OV3OxdHRURMnTtSrr76q5s2bq2vXrgoNDVVwcLBKlSpl9Wz7ixcvmmetR0ZG6ujRo1q9erUuXbqkt956S6+88oq5bZs2bTR79my1bt1aPXr00JUrV/TJJ5+odOnSOnTokEW/tWrV0tatWzV79mwVKVJEJUqUUL169dS2bVstW7ZMnp6eqlixonbv3q2tW7cqf/78SWq7f2/00NDQFM+hW7duOnz4sKZOnao//vhDL774ovz9/XX9+nVt2rRJ27Zts1jePS0rSxQrVkwjRozQBx98oLi4ONWpU0dr167Vzp07tXz5covl7seMGaMlS5bo7Nmz5nPo3Lmz6tevr759++ro0aPy8fHR/PnzlZCQkOQ2ET///LN+/vlnSfe+3HPnzh1NmTJF0r1VFx5cpQMAAAAAsgIGxQEAAADkKPnz59eGDRv01ltvaezYscqXL59eeuklPf3004+8r/fjvPLKK1q+fLlmz56tyMhIFStWTK+99prGjh372H0rVqyoffv2adKkSQoODtb169dVoEAB1ahRQ+PHj09TPQ/atGmTNm3alGR7QECAKleurLVr12rOnDlaunSpvv32W7m6uqpkyZJ6/fXXzcshlytXTmvWrNHYsWM1cuRIFSpUSEOGDJGvr6/69ev3xDWmp3bt2uk///mPJk6cqLfffltlypRRcHCwlixZoiNHjljVx/1B8UaNGpm3OTg46KmnntLWrVutup94p06d9Oqrr2rlypX68ssvZRhGhg2Kp9Vrr72m9evX68cff1RMTIz8/f01ZcoUjRo1StK9e8q/+OKL2rZtm5YtWyYHBweVL19eq1at0gsvvJAuNQwfPlyGYWjWrFkaOXKkqlWrpvXr1+u111575G0HHnbgwAG9/PLLMplMyps3r/z8/NSuXTsNGDBAdevWtWjbvHlzff7553rvvfc0YsQIlShRQu+//75CQ0OTDIrPnj1bgwYN0tixYxUdHa3evXurXr16mjt3ruzt7bV8+XLdvXtXDRs21NatW5P9/Lhz5475fuaPM2XKFDVv3lzz5s3TggULdOPGDeXLl0/169fXunXr9Pzzz1vVT0ree+895cuXT4sWLVJwcLDKlCmjL7/80qovetjb22vjxo0aNWqU5s2bp+joaNWpU0fBwcEqV66cRdvt27cnGSgfN26cJGnChAkMigMAAADIckzGk0yXAAAAAAAgC6hevbp8fX21ZcsWW5eCx0hMTJSvr686deqU7NL+2cXRo0dVqVIlbdiwQW3atLF1OQAAAACAFHBPcQAAAABAthEXF5fknswhISE6ePCgAgMDbVMUHunu3btJbl2wdOlS3bhxI9v/vHbs2KGnnnqKAXEAAAAAyAaYKQ4AAAAAyDZCQ0PVokULvfTSSypSpIiOHz+uhQsXytPTU3/++Wey932G7YSEhOiNN95Qly5dlD9/fv3+++/6/PPPVaFCBe3fv1+Ojo62LhEAAAAAkAtwT3EAAAAAQLaRL18+1apVS5999pmuXr0qNzc3tWnTRu+99x4D4llQQECA/Pz8NG/ePN24cUPe3t7q1auX3nvvPQbEAQAAAACZhpniAAAAAAAAAAAAAIAci3uKAwAAAAAAAAAAAAByLAbFAQAAAAAAAAAAAAA5FoPiAAAAAAAAT8hkMmnixIm2LgMAAAAAkAwGxQEAAAAgC7l69apef/11lS9fXi4uLipQoIDq1q2rf/3rX4qMjLR1eVnW1KlT9fzzz6tgwYKZOjh5/vx5TZo0SXXr1lW+fPnk4+OjwMBAbd26NU39TZw4USaT6bGPwMDA9D2RdDBt2jStXbs2U44VFhamt99+W82aNVPevHllMpkUEhKSKccGAAAAAGQ/DrYuAAAAAABwz40bN1S7dm1FRESoX79+Kl++vK5fv65Dhw5pwYIFGjJkiNzd3W1dZpY0duxYFSpUSDVq1NDmzZsz7bjr1q3T+++/rw4dOqh3796Kj4/X0qVL1bJlS33xxRfq27dvqvrr1KmTSpcubX4eGRmpIUOGqGPHjurUqZN5e8GCBdPtHNLLtGnT1LlzZ3Xo0CHDj3XixAm9//77KlOmjKpUqaLdu3dn+DEBAAAAANkXg+IAAAAAkEV8/vnnOnfunHbt2qUGDRpYvBYRESFHR8dMrSc+Pl6JiYmZfty0OHv2rAICAnTt2jX5+vpm2nGbNWumc+fOycfHx7xt8ODBql69usaPH5/qQfGqVauqatWq5ufXrl3TkCFDVLVqVb300ktPXO+dO3fk5ub2xP3YWq1atXT9+nV5e3trzZo16tKli61LAgAAAABkYSyfDgAAAABZxOnTp2Vvb6/69esnec3Dw0POzs4W21avXq1atWrJxcVFPj4+eumll3Tx4kWLNoGBgckutd2nTx8FBASYn4eGhspkMmnmzJn68MMPVapUKTk5Oeno0aOSpOPHj6tr167y9fWVi4uLypUrp3feeceiz4sXL6pfv34qWLCgnJycVKlSJX3xxRdJjn3u3DkdP378sdejd+/ecnZ21rFjxyy2t2rVSvny5dM///xj3vbguaSHCRMmyM7OTtu2bbPYPmjQIDk6OurgwYOSpEqVKlkMiEuSk5OTnnvuOV24cEG3b99O17ok6e+//9bQoUNVrlw5ubi4KH/+/OrSpYtCQ0Mt2gUHB8tkMumnn37S0KFDVaBAARUrVsz8+ieffKKSJUvKxcVFdevW1c6dO5N9v8TExGjChAkqXbq0nJyc5Ofnp9GjRysmJsbcxmQy6c6dO1qyZIl5ifc+ffqYXz9+/LjOnTuX4nlFR0erfPnyKl++vKKjo83bb9y4ocKFC6tBgwZKSEiQJOXNm1fe3t6pvHKWrPn96dOnj9zd3XXx4kV16NBB7u7u8vX11ciRI821JGfHjh0ymUz69ttvk7y2YsUKmUwmZrcDAAAAQCZiUBwAAAAAsgh/f38lJCRo2bJlj20bHBysrl27yt7eXtOnT9fAgQP1zTffqFGjRrp161aaawgKCtJHH32kQYMGadasWfL29tahQ4dUr149bd++XQMHDtTcuXPVoUMHfffdd+b9Ll++rPr162vr1q0aPny45s6dq9KlS6t///768MMPLY7Rq1cvVahQ4bG1zJ07V76+vurdu7d5AHLRokX68ccf9dFHH6lIkSJpPs/HGTt2rKpXr67+/fubB7Y3b96sxYsXa/z48apWrVqK+1+6dEmurq5ydXVN99r27t2rX3/9Vd27d9e8efM0ePBgbdu2TYGBgYqKikrSfujQoTp69KjGjx+vt99+W5K0YMECDR8+XMWKFdOMGTPUuHFjdejQQRcuXLDYNzExUc8//7xmzpypdu3a6aOPPlKHDh00Z84cdevWzdxu2bJlcnJyUuPGjbVs2TItW7ZMr7zyivn1ChUqqFevXimel4uLi5YsWaJTp05ZfOFi2LBhCg8PV3BwsOzt7dN0zR6Wmt+fhIQEtWrVSvnz59fMmTPVtGlTzZo1S59++ukj+w8MDJSfn5+WL1+e5LXly5erVKlSeuqpp9LlXAAAAAAAVjAAAAAAAFnCpUuXDF9fX0OSUb58eWPw4MHGihUrjFu3blm0i42NNQoUKGBUrlzZiI6ONm/fsGGDIckYP368eVvTpk2Npk2bJjlW7969DX9/f/Pzs2fPGpIMDw8P48qVKxZtmzRpYuTNm9f4+++/LbYnJiaa/92/f3+jcOHCxrVr1yzadO/e3fD09DSioqIsarL2z9HNmzcbkowpU6YYZ86cMdzd3Y0OHTo8sv3Vq1cNScaECROs6j8lhw8fNhwdHY0BAwYYN2/eNIoWLWrUrl3biIuLS3G/kydPGs7OzsbLL7/8xDUkdz4PXsv7du/ebUgyli5dat4WFBRkSDIaNWpkxMfHm7fHxMQY+fPnN+rUqWNxLsHBwYYki/fLsmXLDDs7O2Pnzp0Wx1u4cKEhydi1a5d5m5ubm9G7d+9kz+PhflMyZswYw87Ozvj555+N1atXG5KMDz/88JHt77fZsWOHVf2n5vend+/ehiRj8uTJFn3UqFHDqFWrlsW2h39OY8aMMZycnCx+f69cuWI4ODiky/sTAAAAAGA9ZooDAAAAQBZRsGBBHTx4UIMHD9bNmze1cOFC9ejRQwUKFNC7774rwzAkSfv27dOVK1c0dOhQiyXV27Rpo/Lly+v7779Pcw0vvPCCxT25r169qp9//ln9+vVT8eLFLdqaTCZJkmEY+vrrr9WuXTsZhqFr166ZH61atVJ4eLh+//13834hISHmc3mcZ555Rq+88oomT56sTp06ydnZWYsWLUrz+aVG5cqVNWnSJH322Wdq1aqVrl27piVLlsjBweGR+0RFRalLly5ycXHRe++9lyF1ubi4mP8dFxen69evq3Tp0vLy8rK4zvcNHDjQYob1vn37dP36dQ0cONDiXHr27Kl8+fJZ7Lt69WpVqFBB5cuXt/i5Nm/eXNK9ZcKtYRiGQkJCrGo7ceJEVapUSb1799bQoUPVtGlTvfbaa1bta420/P4MHjzY4nnjxo115syZFI/Tq1cvxcTEaM2aNeZtX331leLj49Pl/vAAAAAAAOsxKA4AAAAAWUjhwoW1YMEChYWF6cSJE5o3b558fX01fvx4ff7555Lu3VNaksqVK5dk//Lly5tfT4sSJUpYPL8/8Fe5cuVH7nP16lXdunVLn376qXx9fS0effv2lSRduXIlzTXNnDlT3t7eOnDggObNm6cCBQqkua/UGjVqlKpVq6bffvtNEyZMUMWKFR/ZNiEhQd27d9fRo0e1Zs2aDFvePTo6WuPHj5efn5+cnJzk4+MjX19f3bp1S+Hh4UnaP/wzvf/+KF26tMV2BweHJPdmP3nypI4cOZLk51q2bFlJT/ZzfRRHR0d98cUXOnv2rG7fvq2goCDzFzDSQ2p/f5ydnS2+KCJJ+fLl082bN1M8Tvny5VWnTh2LJdSXL1+u+vXrJ7n2AAAAAICM9eivtwMAAAAAbMZkMqls2bIqW7as2rRpozJlymj58uUaMGBAqvtJblb2/Xt0P+zBWcjWSkxMlCS99NJL6t27d7Jtqlatmup+7/vjjz/Mg6+HDx/Wiy++mOa+UuvMmTM6efKk+dgpGThwoDZs2KDly5ebZ1JnhFdffVVBQUEaMWKEnnrqKXl6espkMql79+7mn8WD0vIzvS8xMVFVqlTR7Nmzk33dz88vzX2nZPPmzZKku3fv6uTJk0kG9jPTk9zHvFevXnr99dd14cIFxcTE6L///a8+/vjjdKwOAAAAAGANBsUBAAAAIIsrWbKk8uXLp7CwMEmSv7+/JOnEiRNJBl9PnDhhfl26N6M1uWWerZ1NXrJkSUnSn3/++cg2vr6+yps3rxISEtSiRQur+rXWnTt31LdvX1WsWFENGjTQjBkz1LFjR9WpUyddj5OcxMRE9enTRx4eHhoxYoSmTZumzp07q1OnTknajho1SkFBQfrwww8zfNB+zZo16t27t2bNmmXedvfuXd26dcuq/e+/P06dOqVmzZqZt8fHxys0NNTiCwylSpXSwYMH9fTTTz92tnZ6zeY+dOiQJk+erL59++rAgQMaMGCADh8+LE9Pz3TpPzW/P0+qe/fuevPNN/Wf//xH0dHRypMnj7p165Zu/QMAAAAArMPy6QAAAACQRezZs0d37txJsv23337T9evXzcs9165dWwUKFNDChQsVExNjbvfDDz/o2LFjatOmjXlbqVKldPz4cV29etW87eDBg9q1a5dVNfn6+qpJkyb64osvdO7cOYvX7s9At7e31wsvvKCvv/462cHzB48tSefOndPx48etOv6//vUvnTt3TkuWLNHs2bMVEBCg3r17W5x3Rpk9e7Z+/fVXffrpp3r33XfVoEEDDRkyRNeuXbNo98EHH2jmzJn697//rddffz3D67K3t08y+/+jjz565Oz/h9WuXVv58+fX4sWLFR8fb96+fPnyJEuCd+3aVRcvXtTixYuT9BMdHW3xfnVzc3vkwPzx48eTvH+SExcXpz59+qhIkSKaO3eugoODdfnyZb3xxhtWnZs1UvP786R8fHz07LPP6ssvv9Ty5cvVunVr+fj4pFv/AAAAAADrMFMcAAAAALKIZcuWafny5erYsaNq1aolR0dHHTt2TF988YWcnZ3173//W5KUJ08evf/+++rbt6+aNm2qF198UZcvX9bcuXMVEBBgMYDYr18/zZ49W61atVL//v115coVLVy4UJUqVVJERIRVdc2bN0+NGjVSzZo1NWjQIJUoUUKhoaH6/vvvdeDAAUnSe++9px07dqhevXoaOHCgKlasqBs3buj333/X1q1bdePGDXN/vXr10k8//ZTssu4P2r59u+bPn68JEyaoZs2akqSgoCAFBgZq3LhxmjFjhsW1+/vvvxUVFSVJ+vnnnzVlyhRJ0ssvv2ye/RsSEqJmzZppwoQJmjhx4iOPfezYMY0bN059+vRRu3btJEnBwcGqXr26hg4dqlWrVkmSvv32W40ePVplypRRhQoV9OWXX1r007JlSxUsWFCSFBoaqhIlSqh3794KDg5O8dxT0rZtWy1btkyenp6qWLGidu/era1btyp//vxW7e/o6KiJEyfq1VdfVfPmzdW1a1eFhoYqODhYpUqVspjx/fLLL2vVqlUaPHiwduzYoYYNGyohIUHHjx/XqlWrtHnzZtWuXVuSVKtWLW3dulWzZ89WkSJFVKJECdWrV0+SVKFCBTVt2lQhISEp1jZlyhQdOHBA27ZtU968eVW1alWNHz9eY8eOVefOnfXcc89ZtJWkI0eOSLr3Hvjll18kSWPHjn3kMVLz+5MeevXqpc6dO0uS3n333XTtGwAAAABgJQMAAAAAkCUcOnTIGDVqlFGzZk3D29vbcHBwMAoXLmx06dLF+P3335O0/+qrr4waNWoYTk5Ohre3t9GzZ0/jwoULSdp9+eWXRsmSJQ1HR0ejevXqxubNm43evXsb/v7+5jZnz541JBkffPBBsrX9+eefRseOHQ0vLy/D2dnZKFeunDFu3DiLNpcvXzaGDRtm+Pn5GXny5DEKFSpkPP3008ann35q0a5p06bG4/4cjYiIMPz9/Y2aNWsacXFxFq+98cYbhp2dnbF79+4kfSb32LFjh7ndd999Z0gyFi5c+Mhjx8fHG3Xq1DGKFStm3Lp1y+K1uXPnGpKMr776yjAMw5gwYcIjj/vwsQ8fPmxIMt5+++0Uz/1BV69eNSQZEyZMMG+7efOm0bdvX8PHx8dwd3c3WrVqZRw/ftzw9/c3evfubW4XFBRkSDL27t2bbN/z5s0z/P39DScnJ6Nu3brGrl27jFq1ahmtW7e2aBcbG2u8//77RqVKlQwnJycjX758Rq1atYxJkyYZ4eHh5nbHjx83mjRpYri4uBiSLGqRZDRt2jTFc92/f7/h4OBgvPrqqxbb7/88ihQpYty8edOiz0c9rGHN70/v3r0NNze3JPve/7k/6OGf030xMTFGvnz5DE9PTyM6Otqq2gAAAAAA6ctkGI/5aj4AAAAAADnE6NGj9Z///EenTp2Sk5NTph5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-      "text/plain": [
-       "<Figure size 2000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(4)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 0.01 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "target_tasks = task_ids == 1\n",
-    "task_info[target_tasks, 1] = 0\n",
-    "\n",
-    "all_features = torch.stack((train_x1, train_x2), -1)\n",
-    "target_features = all_features[target_tasks]\n",
-    "all_features[target_tasks] = torch.cat((target_features[..., :1], torch.zeros_like(target_features[..., 1:])), -1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x1, torch.zeros_like(test_x1)), -1)\n",
-    "    test_info = torch.ones(100, 2)\n",
-    "    test_info[:, 1] = 0\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x1, x2, Target: x1 only\" % c)\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x500 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "torch.manual_seed(4)\n",
-    "train_x1 = torch.rand(1000)\n",
-    "train_x2 = torch.randn_like(train_x1)\n",
-    "\n",
-    "f = sample_data(train_x1.unsqueeze(-1))\n",
-    "c = 0.1 #(torch.rand(1) * 10).item()\n",
-    "task_ids = torch.randint(0, 2, (1000,)).float().squeeze()\n",
-    "task_ids[:10] = 0 # source task\n",
-    "task_ids[10:12] = 1 # target task\n",
-    "\n",
-    "source_ys = f + train_x2 * c\n",
-    "target_ys = -f + train_x2 * c\n",
-    "\n",
-    "task_info = torch.ones(1000, 2)\n",
-    "target_tasks = task_ids == 1\n",
-    "task_info[target_tasks, 0] = 0\n",
-    "\n",
-    "all_features = torch.stack((train_x1, train_x2), -1)\n",
-    "target_features = all_features[target_tasks]\n",
-    "all_features[target_tasks] = torch.cat((target_features[..., :1], torch.zeros_like(target_features[..., 1:])), -1)\n",
-    "\n",
-    "task_train_x = torch.cat((task_ids.unsqueeze(-1), task_info, all_features), -1)\n",
-    "train_y = torch.where(task_ids == 0, source_ys, target_ys)\n",
-    "\n",
-    "sorted_train_x1, indices = train_x1.squeeze().sort()\n",
-    "sorted_f = -f[indices.squeeze()]\n",
-    "\n",
-    "fig, axs = plt.subplots(1, 5, figsize=(20, 5))\n",
-    "\n",
-    "for ax, x2 in zip(axs, torch.linspace(0, 1, 5)):\n",
-    "    test_x1 = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
-    "    test_x2 = torch.ones_like(test_x1) * x2\n",
-    "    test_x = torch.cat((test_x2, torch.zeros_like(test_x2)), -1)\n",
-    "    test_info = torch.ones(100, 2)\n",
-    "    test_info[:, 1] = 0\n",
-    "    task_test_x = torch.cat((torch.ones_like(test_x1), test_info, test_x), -1)\n",
-    "    pfn_predictions = best_model(task_train_x[:12], train_y[:12], task_test_x)\n",
-    "\n",
-    "    mean_preds = pfn_predictions[:, 0].detach().squeeze()\n",
-    "    std2_preds = pfn_predictions[:, 1].exp().detach().sqrt().squeeze() * 2\n",
-    "\n",
-    "    ax.plot(test_x1, mean_preds)\n",
-    "    ax.fill_between(\n",
-    "        test_x1.squeeze(),\n",
-    "        mean_preds - std2_preds,\n",
-    "        mean_preds + std2_preds,\n",
-    "        alpha=0.5,\n",
-    "    )\n",
-    "    # vertical lines per point\n",
-    "    ax.vlines(train_x1[:10], f[:10].squeeze(), f[:10].squeeze() + train_x2[:10] * c, color=\"orange\", alpha=0.5)\n",
-    "    ax.vlines(train_x1[10:12], -f[10:12].squeeze(), -f[10:12].squeeze() + train_x2[10:12] * c, color=\"green\", alpha=0.5)\n",
-    "    \n",
-    "    \n",
-    "    ax.scatter(train_x1[:10], train_y[:10], s=100, label=\"Source Points\", zorder=10)\n",
-    "    ax.scatter(train_x1[10:12], train_y[10:12], s=100, label=\"Target Points\", zorder=10)\n",
-    "    \n",
-    "    \n",
-    "    ax.plot(sorted_train_x1, sorted_f + x2 * c, color=\"green\", label=\"Ground Truth\")\n",
-    "    ax.plot(sorted_train_x1, sorted_f, color=\"grey\", label=\"Latent Target f\", linestyle=\"--\")\n",
-    "    ax.plot(sorted_train_x1, -sorted_f, color=\"grey\", label=\"Latent Source f\", linestyle=\"dotted\")\n",
-    "    \n",
-    "    ax.set_title(f\"x2 = {x2}\")\n",
-    "    ax.set_ylim(-3, 3)\n",
-    "    ax.set_xlabel(\"x1\")\n",
-    "    ax.set_ylabel(\"y\")\n",
-    "    \n",
-    "plt.suptitle(\"Transfer Learning with Missing Data, C=%.2f\\n Source: x1, x2, Target: x2 only\" % c)\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pfn",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/private_multitask_pfn/train.py b/private_multitask_pfn/train.py
deleted file mode 100644
index 2fef09a..0000000
--- a/private_multitask_pfn/train.py
+++ /dev/null
@@ -1,638 +0,0 @@
-#!/usr/bin/env python3
-
-import torch
-from gen_batch import (
-    combine_batch,
-    get_mtgp_batch,
-    get_pd1_surrogate_batch_fn,
-    get_pd1_eval_batch_fn,
-    get_trios_batch,
-    get_hpobench_batch_fn,
-    target_aware_mtgp_batch,
-    gen_mtgp_equal_eval_batch,
-    gen_mtgp_low_rank,
-)
-from paper_figures.joint_data import get_joint_batch
-from gen_axial_batch import axial_train_batch, multitask_line_batch
-from gen_task_batch import task_invariant_batch, task_corr_batch
-from PFNs.pfns import (
-    bar_distribution,
-    encoders,
-    priors,
-    utils,
-)
-from PFNs.pfns.train import (
-    train as train_pfn,
-    ProbabilisticRankingLoss,
-    PairwiseRankingLoss,
-    GaussianRankingLoss,
-    BarRankingLoss,
-)
-import sys
-import logging
-import os
-import datetime
-import json
-import argparse
-import traceback
-import wandb
-import numpy as np
-
-default_device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-
-def train_from_checkpoint(checkpoint_folder, wandb_mode="online", pty=False, epochs=None, seed=None, lr=None, **kwargs):
-    args_path = checkpoint_folder + '/args.json'
-    with open(args_path, 'r') as f:
-        args = json.load(f)
-    args["wandb_mode"] = wandb_mode
-    args["checkpoint_folder"] = checkpoint_folder
-    args["pty"] = pty
-    
-    if epochs is None or seed is None or lr is None:
-        print("Using original epochs, seed, and lr")
-        continue_scheduler = True
-        
-        checkpoint_path = checkpoint_folder + '/checkpoint.pth'
-        checkpoint = torch.load(checkpoint_path, map_location=default_device)
-        args["start_epoch"] = checkpoint["epoch"]
-    else:
-        print(f"Restarting with epochs={epochs}, seed={seed}, lr={lr}")
-        continue_scheduler = False
-        
-        args["epochs"] = epochs
-        args["seed"] = seed
-        args["lr"] = lr
-        
-    for key, val in kwargs.items():
-        if val is not None:
-            args[key] = val
-    
-    random_num = np.random.randint(100000)
-    dirname = create_experiment_dir(argparse.Namespace(**args), random_num, continued=True)
-    args["dirname"] = dirname
-    
-    wandb.init(
-        project="mtpfn",
-        config=args,
-        mode=wandb_mode,
-    )
-    
-    # make symlink from wandb name to dirname
-    if wandb_mode != "disabled":
-        wandb_name = wandb.run.name
-        # wandb_dir = "/home/yl9959/mtpfn/wandb_links"
-        wandb_dir = args["wandb_dir"]
-        os.symlink(dirname, os.path.join(wandb_dir, wandb_name))
-    
-    if pty:
-        return train(**args, progress_bar=True, continue_model=True, continue_scheduler=continue_scheduler) 
-    else:
-        # redirect stdout and stderr
-        sys.stdout = open(os.path.join(dirname, 'stdout.txt'), 'w')
-        sys.stderr = open(os.path.join(dirname, 'stderr.txt'), 'w')
-        return train(**args, continue_model=True, continue_scheduler=continue_scheduler)
-
-
-def train_continue(continue_scheduler, checkpoint_folder, config, dirname):
-    checkpoint_path = checkpoint_folder + '/checkpoint.pth'
-    checkpoint = torch.load(checkpoint_path, map_location=default_device)
-    
-    model, optimizer, scheduler = train(**config, return_model=True)
-    
-    model.load_state_dict(checkpoint['model_state_dict'])
-    config["continue_model"] = model
-    
-    if continue_scheduler:
-        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-        scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
-        epoch = checkpoint['epoch']
-        
-        config["continue_optimizer"] = optimizer
-        config["continue_scheduler"] = scheduler
-        config["continue_epoch"] = epoch
-    return train_pfn_wrapper(config, dirname)
-
-
-def train_pfn_wrapper(config, dirname):
-    train_result = train_pfn(**config, dirname=dirname)
-    total_loss, total_positional_losses, final_model, dl, best_val_score, best_model = (
-        train_result
-    )
-
-    if final_model:
-        final_state = final_model.state_dict()
-    if best_model:
-        best_state = best_model.state_dict()
-
-    results = {
-        "final_loss": total_loss,
-        "final_epoch": config["epochs"],
-    }
-    torch.save(final_state, os.path.join(dirname, "final_model.pth"))
-    
-    if best_model:
-        torch.save(best_state, os.path.join(dirname, "best_model.pth"))
-        results["best_loss"] = best_val_score
-        
-    with open(os.path.join(dirname ,'result.json'), 'w') as f:
-        json.dump(results, f, indent=4)
-
-
-def get_batch_gen(prior_type, **kwargs):
-    hypers = {}
-    
-    if prior_type == "mtgp":
-        get_batch_fn = gen_mtgp_equal_eval_batch
-        hypers["corr_init"] = kwargs["corr_init"]
-        hypers["uncorr_prob"] = kwargs["uncorr_prob"]
-    elif prior_type == "mtgp_1_uncorr":
-        get_batch_fn = gen_mtgp_equal_eval_batch
-        hypers["num_uncorr_tasks"] = 1
-    elif prior_type == "mtgp_4_1_uncorr":
-        get_batch_fn = gen_mtgp_equal_eval_batch
-        hypers["num_uncorr_tasks"] = 1
-        hypers["num_tasks"] = 4
-    elif prior_type == "mtgp_4_1_4_uncorr":
-        get_batch_fn = gen_mtgp_equal_eval_batch
-        hypers["num_uncorr_tasks"] = 1
-        hypers["num_tasks"] = 4
-        hypers["num_features"] = 4
-    elif prior_type == "toy_axial":
-        get_batch_fn = axial_train_batch
-    elif prior_type == "toy_multitask":
-        get_batch_fn = multitask_line_batch
-    elif prior_type == "toy_task_invariant":
-        get_batch_fn = task_invariant_batch
-    elif prior_type == "toy_corr_multitask":
-        get_batch_fn = task_corr_batch
-    elif prior_type == "pd1":
-        get_batch_fn = get_pd1_surrogate_batch_fn(**kwargs)
-    elif prior_type == "pd1_eval":
-        get_batch_fn = get_pd1_eval_batch_fn(**kwargs)
-    elif prior_type == "trio":
-        get_batch_fn = get_trios_batch
-    elif prior_type == "hpobench_lr":
-        get_batch_fn = get_hpobench_batch_fn("lr", train=True)
-    elif prior_type == "hpobench_lr_eval":
-        get_batch_fn = get_hpobench_batch_fn("lr", train=False)
-    elif prior_type == "hpobench_svm":
-        get_batch_fn = get_hpobench_batch_fn("svm", train=True)
-    elif prior_type == "hpobench_svm_eval":
-        get_batch_fn = get_hpobench_batch_fn("svm", train=False)
-    elif prior_type == "target_aware":
-        get_batch_fn = target_aware_mtgp_batch
-        hypers["corr_init"] = kwargs["corr_init"]
-        hypers["uncorr_prob"] = kwargs["uncorr_prob"]
-    elif prior_type == "target_aware_4_1_4_uncorr":
-        get_batch_fn = target_aware_mtgp_batch
-        hypers["num_uncorr_tasks"] = 1
-        hypers["num_tasks"] = 4
-        hypers["num_features"] = 4
-    elif prior_type == "target_aware_4_1_2_uncorr":
-        get_batch_fn = target_aware_mtgp_batch
-        hypers["num_uncorr_tasks"] = 1
-        hypers["num_tasks"] = 4
-        hypers["num_features"] = 2
-    elif prior_type == "low_rank":
-        get_batch_fn = gen_mtgp_low_rank
-        hypers["decay_alpha"] = kwargs["decay_alpha"]
-        hypers["max_rank"] = kwargs["max_rank"]
-    else:
-        raise ValueError("prior_type is invalid")
-
-    return get_batch_fn, hypers
-
-
-def get_batch_gen_and_prior_dl(prior_type, **kwargs):
-    if prior_type == "combine":
-        functions = []
-        hypers = {}
-        for prior_type in kwargs["prior_types"]:
-            get_batch_fn, prior_hypers = get_batch_gen(prior_type, **kwargs)
-            functions.append(get_batch_fn)
-            hypers = {**hypers, **prior_hypers}
-        weights = torch.tensor(kwargs["prior_weights"])
-        get_batch_fn = combine_batch(functions, weights)
-    else:
-        get_batch_fn, hypers = get_batch_gen(prior_type, **kwargs)
-
-    return priors.get_batch_sequence(
-        get_batch_fn,
-        priors.utils.set_properties_get_batch,
-    ), hypers
-
-
-def train(
-    attn_type="axial",
-    num_features=40,
-    num_tasks=5,
-    prior_type="mtgp",
-    lr=0.0001,
-    epochs=50,
-    seq_len=200,
-    single_train_pos_gen_type="uniform",
-    sample_num_tasks=True,
-    save_to_manifold=True,
-    corr_init=0.8,
-    sample_num_features=True,
-    scheduler_type="cosine",
-    lengthscale=None,
-    criterion_type="gaussian",
-    uncorr_prob=0.0,
-    emsize=32,
-    task_embed_type="linear",
-    same_tasks_across_batch=True,
-    full_precision=False,
-    return_model=False,  # not included in config
-    device=default_device,
-    dirname=None,
-    continue_model=False,
-    continue_scheduler=False,
-    **kwargs,
-):
-    if kwargs.get("eval_type", None) is None:
-        kwargs["eval_type"] = prior_type
-        
-    train_config = {
-        "attn_type": attn_type,
-        "prior_type": prior_type,
-        "num_features": num_features,
-        "num_tasks": num_tasks,
-        "lr": lr,
-        "epochs": epochs,
-        "seq_len": seq_len,
-        "single_train_pos_gen_type": single_train_pos_gen_type,
-        "sample_num_tasks": sample_num_tasks,
-        "save_to_manifold": save_to_manifold,
-        "corr_init": corr_init,
-        "sample_num_features": sample_num_features,
-        "scheduler_type": scheduler_type,
-        "lengthscale": lengthscale,
-        "criterion_type": criterion_type,
-        "uncorr_prob": uncorr_prob,
-        "emsize": emsize,
-        "task_embed_type": task_embed_type,
-        **kwargs,
-    }
-
-    # get batch function
-    get_train_batch_fn, train_hypers = get_batch_gen_and_prior_dl(
-        max_features=num_features,
-        min_num_tasks=num_tasks,
-        **train_config,
-    )
-    eval_config = train_config.copy()
-    eval_config["prior_type"] = train_config["eval_type"]
-    get_eval_batch_fn, eval_hypers = get_batch_gen_and_prior_dl(
-        max_features=num_features,
-        min_num_tasks=num_tasks,
-        **eval_config,
-    )
-
-    batch_hypers = {
-        "num_tasks": num_tasks,
-        # "sample_num_tasks": sample_num_tasks,
-        # "sample_num_features": sample_num_features,
-        "lengthscale": lengthscale,
-        "same_tasks_across_batch": same_tasks_across_batch,
-        "target_only_loss": kwargs.get("target_only_loss", False),
-    }
-    # combine train and batch hypers
-    train_batch_fn_hypers = {**train_hypers, **batch_hypers, "sample_num_tasks": sample_num_tasks, "sample_num_features": sample_num_features, "sample_num_tasks_curriculum": kwargs.get("sample_num_tasks_curriculum", False)}
-    eval_batch_fn_hypers = {**eval_hypers, **batch_hypers, "sample_num_tasks": False, "sample_num_features": False}
-
-    if "bar" in criterion_type:
-        if return_model or continue_model:
-            n = 100
-        else:
-            n = 100000
-        ys = get_train_batch_fn(
-            batch_size=n,
-            seq_len=20,
-            num_features=num_features,
-            hyperparameters=train_batch_fn_hypers,
-            device=device,
-        ).target_y.to(device)
-        criterion = bar_distribution.FullSupportBarDistribution(
-            bar_distribution.get_bucket_limits(num_outputs=1000, ys=ys)
-        )
-        if criterion_type == "bar_ranking":
-            criterion = BarRankingLoss(criterion)
-    elif criterion_type == "gaussian":
-        criterion = torch.nn.GaussianNLLLoss(reduction="none", full=True)
-    elif criterion_type == "prob_ranking":
-        criterion = ProbabilisticRankingLoss(reduction="none")
-    elif criterion_type == "gaussian_ranking":
-        criterion = GaussianRankingLoss()
-    # elif criterion_type == "ranking":
-    #     criterion = PairwiseRankingLoss()
-    else:
-        raise ValueError("criterion_type must be one of ['bar', 'gaussian', 'prob']")
-
-    if "axial" in attn_type:
-        encoder_generator = encoders.get_axial_normalized_uniform_encoder()
-    elif attn_type == "standard":
-        if task_embed_type == "feature":
-            encoder_generator = encoders.get_normalized_uniform_multitask_encoder(
-                encoders.get_variable_num_features_multitask_encoder(
-                    encoders.Linear
-                )
-            )
-        else:
-            encoder_generator = encoders.get_normalized_uniform_encoder(
-                encoders.get_variable_num_features_encoder(encoders.Linear)
-            )
-        
-    if task_embed_type == "linear":
-        task_encoder_generator = encoders.Linear
-    elif task_embed_type == "onehot_linear":
-        task_encoder_generator = encoders.Linear
-    elif task_embed_type == "self_attn":
-        task_encoder_generator = encoders.get_self_attention_task_encoder()
-    elif task_embed_type == "feature":
-        task_encoder_generator = None
-    elif task_embed_type == "task_attn":
-        task_encoder_generator = None
-    elif task_embed_type == "task_attn_shared":
-        task_encoder_generator = None
-    elif task_embed_type == "task_attn_opt":
-        task_encoder_generator = None
-    elif task_embed_type == "hier" or task_embed_type == "hier_single":
-        task_encoder_generator = None
-    else:
-        raise ValueError("task_embed_type must be one of ['linear', 'onehot_linear', 'self_attn', 'feature', 'task_attn', 'task_attn_shared']")
-    
-    if single_train_pos_gen_type == "decay":
-        single_train_pos_gen = utils.get_exponential_decay_single_eval_pos_sampler(
-            seq_len - num_tasks, min_len=num_tasks
-        )
-    elif single_train_pos_gen_type == "uniform":
-        single_train_pos_gen = utils.get_uniform_single_eval_pos_sampler(
-            seq_len - num_tasks - kwargs.get("min_eval_len", 0), min_len=num_tasks
-        )
-    elif single_train_pos_gen_type == "uniform_large":
-        single_train_pos_gen = utils.get_uniform_single_eval_pos_sampler(
-            seq_len - num_tasks - 100, min_len=num_tasks
-        )
-    elif single_train_pos_gen_type == "curriculum":
-        single_train_pos_gen = utils.get_curriculum_single_eval_pos_sampler(
-            seq_len - num_tasks, min_len=num_tasks
-        )
-    else:
-        raise ValueError("single_train_pos_gen_type must be one of ['uniform', 'decay']")
-
-    if scheduler_type == "cosine":
-        scheduler = utils.get_cosine_schedule_with_warmup
-    elif scheduler_type == "restart":
-        scheduler = utils.get_restarting_cosine_schedule_with_warmup
-    elif scheduler_type == "restart_slow":
-        scheduler = utils.get_slow_restarting_cosine_schedule_with_warmup
-    elif scheduler_type == "curriculum":
-        scheduler = utils.get_curriculum_cosine_schedule_with_warmup
-    else: 
-        raise ValueError(
-            "scheduler_type must be one of ['cosine', 'restart', 'restart_slow']"
-        )
-        
-    config = {
-        "train_loader": get_train_batch_fn,
-        "eval_loader": get_eval_batch_fn,
-        "criterion": criterion,
-        "encoder_generator": encoder_generator,
-        "task_encoder_generator": task_encoder_generator,
-        "single_train_pos_gen": single_train_pos_gen,
-        "scheduler": scheduler,
-        "emsize": emsize,
-        "nhead": 4,
-        "warmup_epochs": 5,
-        "y_encoder_generator": encoders.Linear,
-        "batch_size": 128,
-        "train_extra_prior_kwargs_dict": {
-            "num_features": num_features,
-            "hyperparameters": train_batch_fn_hypers,
-        },
-        "eval_extra_prior_kwargs_dict": {
-            "num_features": eval_batch_fn_hypers.get("num_features", num_features),
-            "hyperparameters": eval_batch_fn_hypers,
-        },
-        "epochs": 50,
-        "lr": 0.0001,
-        "seq_len": 200,
-        "aggregate_k_gradients": 2,
-        "nhid": 1024,
-        "steps_per_epoch": 1024,
-        "weight_decay": 0.0,
-        "train_mixed_precision": not full_precision,
-        "efficient_eval_masking": True,
-        "nlayers": kwargs["n_layers"] if "n_layers" in kwargs else 6,
-        "print_every": 5,
-        "validation_period": 5,
-        "meta_tokens": kwargs["meta_tokens"] if "meta_tokens" in kwargs else 1,
-        "same_tasks_across_batch": same_tasks_across_batch,
-    }
-    
-    for key, val in train_config.items():
-        # if key not in config:
-        #     print(f"Adding {key} to config")
-        config[key] = val
-
-    if return_model:
-        config["return_model"] = True
-        return train_pfn(**config)
-    
-    # if "checkpoint_folder" in kwargs:
-    #     return train_continue(kwargs["checkpoint_folder"], config, dirname)
-    if continue_model:
-        return train_continue(continue_scheduler, kwargs["checkpoint_folder"], config, dirname)
-
-    return train_pfn_wrapper(config, dirname)
-
-
-
-
-def create_experiment_dir(args, random_num, continued=False):
-    dirname = args.output_dir
-
-    # name of experiment
-    exp_name = "prior_%s" % args.prior_type
-    exp_name += "__features_%d__tasks_%d" % (args.num_features, args.num_tasks)
-    if args.epochs is not None:
-        exp_name += "__epochs_%d" % args.epochs
-    else:
-        exp_name += "__epochs_None"
-    exp_name += "__seqlen_%d" % args.seq_len
-    exp_name += "__attn_%s" % args.attn_type
-    exp_name += "__task_%s" % args.task_embed_type
-    exp_name += "__seed_%d" % args.seed
-
-    if args.permute_tasks:
-        exp_name += "__permute"
-    
-    if args.prior_type == "combine":
-        for prior_type, weight in zip(args.prior_types, args.prior_weights):
-            weight_string = str(weight).replace(".", "_")
-            exp_name += "__%s_%s" % (prior_type, weight_string)
-    
-    if args.exp_name:
-        exp_name = args.exp_name + "__" + exp_name
-        
-    if args.smoke:
-        exp_name = "smoke_" + exp_name
-    
-    current_time = datetime.datetime.now().strftime("%y-%m-%d_%H-%M-%S")
-    dirname = os.path.join(args.output_dir, current_time + "__" + exp_name)
-    if continued:
-        dirname += "__continued"
-    dirname += "__%d" % random_num
-
-    if not os.path.exists(dirname):
-        os.makedirs(dirname)
-        
-    # save arguments as json
-    with open(os.path.join(dirname ,'args.json'), 'w') as f:
-        json.dump(vars(args), f, indent=4)
-        
-    # # redirect stdout and stderr
-    # if args.redirect:
-    #     sys.stdout = open(os.path.join(dirname, 'stdout.txt'), 'w')
-    #     sys.stderr = open(os.path.join(dirname, 'stderr.txt'), 'w')
-
-    logging.basicConfig(level=logging.INFO, stream=sys.stdout, format='%(asctime)s: %(message)s')
-    logging.info("Experiment directory: %s\n\n" % dirname)
-    
-    return dirname
-
-
-if __name__ == "__main__":
-    argparser = argparse.ArgumentParser()
-    
-    # properties of model
-    argparser.add_argument('--num_features', type=int, default=7)
-    argparser.add_argument('--num_tasks', type=int, default=4)
-    argparser.add_argument('--sample_num_tasks', action='store_true', default=False)
-    argparser.add_argument('--sample_num_tasks_curriculum', action='store_true', default=False)
-    argparser.add_argument('--sample_num_features', action='store_true', default=False)
-    
-    # model architecture
-    argparser.add_argument('--attn_type', type=str, default='standard', help='axial, axial_parallel, or standard')
-    argparser.add_argument('--task_embed_type', type=str, default='hier', help='{linear, onehot_linear, feature, task_attn, task_attn_shared}')
-    # hierarchical
-    argparser.add_argument('--meta_tokens', type=int, default=1)
-    
-    # prior properties
-    argparser.add_argument('--prior_type', type=str, default='mtgp')
-    argparser.add_argument('--target_only_loss', action='store_true', default=False)
-    argparser.add_argument('--same_tasks_across_batch', action='store_true', default=False)
-    # for low-rank
-    argparser.add_argument('--max_rank', type=int, default=5)
-    argparser.add_argument('--decay_alpha', type=float, default=0.2)
-    # for gp-based
-    argparser.add_argument('--lengthscale', type=float, default=None)
-    # for unrelated
-    argparser.add_argument('--uncorr_prob', type=float, default=0.0)
-    # for mtgp-bias
-    argparser.add_argument('--corr_init', type=float, default=None)
-    # for combine prior
-    argparser.add_argument('--prior_types', nargs='+', default=None)
-    argparser.add_argument('--prior_weights', nargs='+', type=float, default=None)
-    # toy
-    argparser.add_argument('--permute_tasks', action='store_true', default=False)
-    
-    # eval properties
-    argparser.add_argument('--eval_type', type=str, default=None)
-    
-    # properties of training
-    argparser.add_argument('--seq_len', type=int, default=400)
-    argparser.add_argument('--single_train_pos_gen_type', type=str, default='uniform')
-    argparser.add_argument('--min_eval_len', type=int, default=200)
-    argparser.add_argument('--criterion_type', type=str, default='bar')
-    
-    # training hyperparameters
-    argparser.add_argument('--lr', type=float, default=0.0001)
-    argparser.add_argument('--epochs', type=int, default=None)
-    argparser.add_argument('--scheduler_type', type=str, default='cosine')
-    argparser.add_argument('--batch_size', type=int, default=64)
-    argparser.add_argument('--steps_per_epoch', type=int, default=1024)
-    argparser.add_argument('--emsize', type=int, default=512)
-    argparser.add_argument('--nhid', type=int, default=2048)
-    argparser.add_argument('--n_layers', type=int, default=24)
-    argparser.add_argument('--seed', type=int, default=0)
-    argparser.add_argument('--full_precision', action='store_true', default=False)
-    
-    argparser.add_argument('--target_aware', action='store_true', default=False)
-    argparser.add_argument('--global_with_target_points', action='store_true', default=False)
-    argparser.add_argument('--local_with_target_points', action='store_true', default=False)
-    
-    # identify run
-    if os.path.exists("/home/yl9959/mtpfn/ckpt"):
-        output_dir = "/home/yl9959/mtpfn/ckpt"
-        wandb_dir = "/home/yl9959/mtpfn/wandb_links"
-    else:
-        output_dir = "/home/lily_l/private_multitask_pfn/ckpt"
-        wandb_dir = "/home/lily_l/private_multitask_pfn/wandb_links"
-    argparser.add_argument('--output_dir', type=str, default=output_dir)
-    argparser.add_argument('--wandb_dir', type=str, default=wandb_dir)
-    
-    argparser.add_argument('--exp_name', type=str, default=None)
-    argparser.add_argument('--smoke', action='store_true', default=False)
-    argparser.add_argument('--pty', action='store_true', default=False)
-    argparser.add_argument('--disable_wandb', action='store_true', default=False)
-    args = argparser.parse_args()
-    
-    
-    random_num = np.random.randint(100000)
-    dirname = create_experiment_dir(args, random_num)
-    config = vars(args)
-    config["dirname"] = dirname
-    # if not args.pty and not args.smoke:
-    #     # redirect stdout and stderr
-    #     sys.stdout = open(os.path.join(dirname, 'stdout.txt'), 'w')
-    #     sys.stderr = open(os.path.join(dirname, 'stderr.txt'), 'w')
-    
-    if args.disable_wandb or args.smoke:
-        wandb_mode = "disabled"
-    else:
-        wandb_mode = "online"
-        
-    wandb.init(
-        project="mtpfn",
-        config=config,
-        mode=wandb_mode,
-    )
-    
-    # make symlink from wandb name to dirname
-    if wandb_mode != "disabled":
-        wandb_name = wandb.run.name
-        wandb_dir = args.wandb_dir
-        os.symlink(dirname, os.path.join(wandb_dir, wandb_name))
-    
-    torch.manual_seed(args.seed)
-    np.random.seed(args.seed)
-    try:
-        if args.smoke:
-            args.epochs = 5
-            args.steps_per_epoch = 32
-            train(**config, progress_bar=True)
-        else:
-            if args.pty:
-                train(**config, progress_bar=True)
-            else:
-                train(**config)
-    except Exception as e:
-        print("EXCEPTION")
-        # rename directory to indicate failure
-        new_dirname = dirname + "_failed"
-        os.rename(dirname, new_dirname)
-        
-        if wandb_mode != "disabled":
-            # delete original symlink
-            os.remove(os.path.join(wandb_dir, wandb_name))
-            # create new symlink
-            os.symlink(new_dirname, os.path.join(wandb_dir, wandb_name))
-            
-        traceback.print_exc()  # This will print the full traceback
-        # Re-raise the exception with its original stack trace
-        raise
-    
-    # os.rename(dirname, dirname + "_completed")
\ No newline at end of file
diff --git a/private_multitask_pfn/train_continue.py b/private_multitask_pfn/train_continue.py
deleted file mode 100644
index a1dc7dd..0000000
--- a/private_multitask_pfn/train_continue.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from train import train_from_checkpoint
-import argparse
-import os
-
-if __name__ == '__main__':
-    argparser = argparse.ArgumentParser()
-    argparser.add_argument('--checkpoint_folder', type=str, default="/home/yl9959/mtpfn/ckpt/25-01-08_09-50-25__prior_mtgp__features_3__tasks_4__epochs_500__seqlen_200__attn_standard__task_onehot_linear__seed_0")
-    argparser.add_argument('--pty', action='store_true', default=False)
-    argparser.add_argument('--disable_wandb', action='store_true', default=False)
-    argparser.add_argument('--epochs', type=int, default=None)
-    argparser.add_argument('--seed', type=int, default=0)
-    argparser.add_argument('--lr', type=float, default=0.0001)
-    argparser.add_argument('--batch_size', type=int, default=None)
-    argparser.add_argument('--seq_len', type=int, default=None)
-    argparser.add_argument('--steps_per_epoch', type=int, default=None)
-    
-    argparser.add_argument('--prior_type', type=str, default=None)
-    argparser.add_argument('--eval_type', type=str, default=None)
-    argparser.add_argument('--same_tasks_across_batch', action='store_true', default=None)
-    # for gp-based
-    argparser.add_argument('--lengthscale', type=float, default=None)
-    # for unrelated
-    argparser.add_argument('--uncorr_prob', type=float, default=None)
-    # for mtgp-bias
-    argparser.add_argument('--corr_init', type=float, default=None)
-    
-    if os.path.exists("/home/yl9959/mtpfn/ckpt"):
-        output_dir = "/home/yl9959/mtpfn/ckpt"
-        wandb_dir = "/home/yl9959/mtpfn/wandb_links"
-    else:
-        output_dir = "/home/lily_l/private_multitask_pfn/ckpt"
-        wandb_dir = "/home/lily_l/private_multitask_pfn/wandb_links"
-    argparser.add_argument('--output_dir', type=str, default=output_dir)
-    argparser.add_argument('--wandb_dir', type=str, default=wandb_dir)
-    argparser.add_argument('--save_val_results', action='store_true', default=False)
-    
-    
-    args = argparser.parse_args()
-    
-    if args.disable_wandb:
-        wandb_mode = "disabled"
-    else:
-        wandb_mode = "online"
-        
-    train_from_checkpoint(**vars(args), wandb_mode=wandb_mode)
-    
\ No newline at end of file
diff --git a/private_multitask_pfn/utils.py b/private_multitask_pfn/utils.py
deleted file mode 100644
index 8ca5fe2..0000000
--- a/private_multitask_pfn/utils.py
+++ /dev/null
@@ -1,781 +0,0 @@
-import gc
-
-import botorch
-import torch
-# from ax.fb.utils.storage.manifold import AEManifoldUseCase
-# from ax.fb.utils.storage.manifold_torch import AEManifoldTorchClient
-from botorch.acquisition import LogExpectedImprovement
-from botorch.fit import fit_gpytorch_mll
-
-from botorch.models import SingleTaskGP
-from botorch.models.model import Model
-from botorch.models.multitask import MultiTaskGP
-from botorch.optim.fit import fit_gpytorch_mll_torch
-from botorch.fit import fit_fully_bayesian_model_nuts
-from botorch.optim.optimize import optimize_acqf
-from botorch.posteriors import Posterior
-from botorch.utils.datasets import SupervisedDataset
-# from botorch_fb.experimental.models.scaml import meta_fit_scamlgp, ScaMLGP
-from scaml import meta_fit_scamlgp, ScaMLGP
-from PFNs.pfns.bar_distribution import BarDistribution
-
-from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
-from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
-from train import train as load_model_from_train
-import json
-from lmc import LMCGP
-from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
-from mtgp_nuts import RBFPyroModel, MultitaskRBFPyroModel
-from gpytorch.kernels import RBFKernel
-from gpytorch.priors import GammaPrior
-
-
-class GaussianPosterior(Posterior):
-    def __init__(self, mean, variance):
-        super().__init__()
-        self.mean = mean
-        self.variance = variance
-
-    def rsample(self, sample_shape):
-        return self.mean + torch.sqrt(self.variance) * torch.randn(
-            sample_shape, device=self.mean.device
-        )
-        
-    @property
-    def lower(self):
-        return self.mean - 2 * torch.sqrt(self.variance)
-    
-    @property
-    def upper(self):
-        return self.mean + 2 * torch.sqrt(self.variance)
-
-    @property
-    def dtype(self):
-        return self.mean.dtype
-
-    @property
-    def device(self):
-        return self.mean.device
-    
-    
-class BarPosterior(Posterior):
-    def __init__(self, logits, criterion):
-        super().__init__()
-        self.logits = logits
-        self.criterion = criterion
-
-    def rsample(self, sample_shape):
-        probs = torch.rand(sample_shape)
-        return self.criterion.icdf(self.logits, probs)
-        
-    @property
-    def lower(self):
-        return self.criterion.quantile(self.logits, 0.025)[..., 0]
-    
-    @property
-    def upper(self):
-        return self.criterion.quantile(self.logits, 0.975)[..., 0]
-    
-    @property
-    def mean(self):
-        return self.criterion.mean(self.logits)
-    
-    @property
-    def variance(self):
-        variance = torch.clamp_min(self.criterion.variance(self.logits), 1e-8)
-        return variance
-
-    @property
-    def dtype(self):
-        return self.logits.dtype
-
-    @property
-    def device(self):
-        return self.logits.device
-
-
-class PFNGaussian(Model):
-    def __init__(self, pfn, train_x, train_task_id, train_y):
-        super().__init__()
-        self.pfn = pfn
-        self.train_x = train_x.unsqueeze(1)
-        self.train_task_id = train_task_id.unsqueeze(-1).unsqueeze(1).long()
-        self.train_y = train_y.unsqueeze(1)
-
-    def posterior(
-        self, X, output_indices=None, observation_noise=False, posterior_transform=None
-    ):
-        original_shape = X.shape
-        X_reshape = X.reshape(X.shape[0], -1, X.shape[-1])
-        pfn_outputs = self.pfn(self.train_x, self.train_task_id, self.train_y, X_reshape, None)
-        mean = pfn_outputs[..., 0]
-        variance = pfn_outputs[..., 1].exp()
-        
-        mean = mean.reshape(original_shape[:-1])
-        variance = variance.reshape(original_shape[:-1])
-        return GaussianPosterior(mean, variance)
-
-    @property
-    def num_outputs(self):
-        return 1
-
-
-def load_model(ckpt_dir, best=True):
-    args_json = f"{ckpt_dir}/args.json"
-    with open(args_json, "r") as f:
-        args = json.load(f)
-
-    model = load_model_from_train(**args, return_model=True)[0]
-    default_device = lambda: torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if best:
-        try:
-            model.load_state_dict(torch.load(f"{ckpt_dir}/best_model.pth", weights_only=True, map_location=default_device()))
-        except FileNotFoundError:
-            model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True, map_location=default_device()))
-        except RuntimeError:
-            try:
-                new_state_dict = torch.load(f"{ckpt_dir}/best_model.pth", map_location=default_device())
-                new_state_dict_keys = new_state_dict.keys()
-                new_state_dict = {k.replace("module.", ""): v for k, v in new_state_dict.items()}
-                model.load_state_dict(new_state_dict, strict=True)
-            except Exception as e:
-                raise e
-    else:
-        model.load_state_dict(torch.load(f"{ckpt_dir}/final_model.pth", weights_only=True, map_location=default_device()))
-    
-    print("Loaded model")
-    return model
-
-
-def load_model_from_epoch(ckpt_dir, epoch):
-    args_json = f"{ckpt_dir}/args.json"
-    with open(args_json, "r") as f:
-        args = json.load(f)
-
-    model = load_model_from_train(**args, return_model=True)[0]
-    default_device = lambda: torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.load_state_dict(torch.load(f"{ckpt_dir}/model_{epoch}.pth", weights_only=True, map_location=default_device()))
-
-    print("Loaded model")
-    return model
-
-
-def load_checkpoint(ckpt_dir):
-    
-    args_json = f"{ckpt_dir}/args.json"
-    with open(args_json, "r") as f:
-        args = json.load(f)
-
-    model, scheduler, optimizer = load_model_from_train(**args, return_model=True)
-    
-    ckpt = torch.load(f"{ckpt_dir}/checkpoint.pth")
-    model.load_state_dict(ckpt['model_state_dict'])
-    scheduler.load_state_dict(ckpt['scheduler_state_dict'])
-    optimizer.load_state_dict(ckpt['optimizer_state_dict'])
-    
-    print("Loaded checkpoint")
-    return model, scheduler, optimizer, ckpt['epoch']
-
-
-def to_pfn_format(num_tasks, *args):
-    """
-    Expects task1, x1, y1, task2, x2, y2, ...
-    where x1, x2, ... are [seq, features]
-
-    Returns x, y, where x is [batch, seq, features + tasks] and y is [batch, seq]
-    """
-    assert len(args) % 3 == 0 and len(args) > 0
-
-    results = []
-    for i in range(len(args) // 3):
-        task_id, x, y = args[3 * i], args[3 * i + 1], args[3 * i + 2]
-
-        one_hot_task_id = torch.nn.functional.one_hot(
-            task_id.long(), num_classes=num_tasks
-        )
-        id_x = torch.cat((one_hot_task_id, x), -1)
-
-        # add batch dimension
-        results.append(id_x.unsqueeze(1))
-        results.append(y.unsqueeze(1))
-
-    return results
-
-
-def to_mtgp_format(*args):
-    """
-    Expects task1, x1, y1, task2, x2, y2, ...
-    where x1, x2, ... are [seq, features]
-
-    Returns x, y, where x is [batch, seq, features + 1] and y is [batch, seq]
-    """
-    assert len(args) % 3 == 0 and len(args) > 0
-
-    results = []
-    for i in range(len(args) // 3):
-        task_id, x, y = args[3 * i], args[3 * i + 1], args[3 * i + 2]
-
-        # x is [seq, features]
-        # if len(x.shape) > 2:
-        #     x = x.transpose(0, 1)
-        #     y = y.transpose(0, 1)
-
-        task_id = torch.ones_like(x[..., 0]) * task_id.to(x.device)
-        x_task_id = torch.cat((x, task_id.unsqueeze(-1)), -1)
-        results.append(x_task_id)
-        results.append(y)
-
-    return results
-
-
-def to_gp_format(*args):
-    """
-    Expects task1, x1, y1, task2, x2, y2, ...
-    where x1, x2, ... are [seq, features]
-
-    Returns x, y, where x is [batch, seq, features] and y is [batch, seq]
-    Only over target task (task id = 0)
-    """
-    assert len(args) % 3 == 0 and len(args) > 0
-
-    results = []
-    for i in range(len(args) // 3):
-        task_id, x, y = args[3 * i], args[3 * i + 1], args[3 * i + 2]
-
-        # x is [seq, features]
-        # if len(x.shape) > 2:
-        #     x = x.transpose(0, 1)
-        #     y = y.transpose(0, 1)
-
-        target_indices = task_id == 0
-
-        target_x = x[target_indices]
-        target_y = y[target_indices]  # .unsqueeze(1)
-        results.append(target_x)
-        results.append(target_y)
-        
-    # print([r.shape for r in results])
-
-    return results
-
-
-def pfn_bar_get_best(
-    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-):
-    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
-    train_x, train_y, possible_x, possible_y = to_pfn_format(
-        pfn.num_tasks,
-        task_id,
-        train_x,
-        train_y,
-        possible_task_id,
-        possible_x,
-        possible_y,
-    )
-    target_indices = task_id == 0
-    logits = pfn(train_x, train_y, possible_x)
-    eis = pfn.criterion.ei(logits, train_y[target_indices].max())
-    best_index = eis.argmax()
-    return original_possible_x[best_index], original_possible_y[best_index]
-
-
-def pfn_gaussian_fit(pfn, task_id, train_x, train_y):
-    return PFNGaussian(pfn, train_x, task_id, train_y)
-
-
-def pfn_bar_get_best(
-    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-):
-    # add batch dimension
-    train_x = train_x.unsqueeze(1)
-    train_task_id = task_id.unsqueeze(-1).unsqueeze(1).long()
-    train_y = train_y.unsqueeze(1)
-    possible_x = possible_x.unsqueeze(1)
-    
-    output_logits = pfn(train_x, train_task_id, train_y, possible_x, None)#.squeeze(1)
-    
-    target_indices = task_id == 0
-    best_f = train_y[target_indices].max()
-    
-    ei = pfn.criterion.ei(output_logits, best_f)
-    best_index = ei.argmax()
-    return possible_x[best_index].squeeze(1).squeeze(0), possible_y[best_index]
-    
-
-
-def pfn_gaussian_get_best(
-    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-):
-    pfn_model = pfn_gaussian_fit(pfn, task_id, train_x, train_y)
-
-    target_indices = task_id == 0
-    best_f = train_y[target_indices].max()
-
-    ei = LogExpectedImprovement(pfn_model, best_f=best_f)
-    eis = ei(possible_x.unsqueeze(1))
-    
-    best_index = eis.argmax()
-    return possible_x[best_index], possible_y[best_index]
-
-
-def pfn_get_best(
-    pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-):
-    if isinstance(pfn.criterion, BarDistribution):
-        return pfn_bar_get_best(
-            pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-        )
-    else:
-        return pfn_gaussian_get_best(
-            pfn, task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-        )
-        
-        
-def pfn_bar_predict(pfn, task_id, train_x, train_y, test_x):
-    train_x = train_x.unsqueeze(1)
-    train_task_id = task_id.unsqueeze(-1).unsqueeze(1).long()
-    train_y = train_y.unsqueeze(1)
-    test_x = test_x.unsqueeze(1)
-    
-    output_logits = pfn(train_x, train_task_id, train_y, test_x, None)
-    # BarPosterior(output_logits, pfn.criterion)
-    # mean, variance = pfn.criterion.mean(output_logits), pfn.criterion.variance(output_logits)
-    return BarPosterior(output_logits, pfn.criterion)
-        
-        
-def pfn_predict(pfn, task_id, train_x, train_y, possible_x):
-    with torch.no_grad():
-        if isinstance(pfn.criterion, BarDistribution):
-            return pfn_bar_predict(pfn, task_id, train_x, train_y, possible_x)
-        else:
-            pfn_model = pfn_gaussian_fit(pfn, task_id, train_x, train_y)
-            return pfn_model.posterior(possible_x.unsqueeze(1))
-
-
-def mtgp_fit(task_id, train_x, train_y):
-    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
-    train_yvar = torch.ones_like(train_y) * 1e-5
-    mtgp = MultiTaskGP(
-        train_x,
-        train_y,
-        task_feature=-1,
-        train_Yvar=train_yvar,
-        outcome_transform=None,
-        output_tasks=[0],
-    ).to(train_x)
-    mll = ExactMarginalLogLikelihood(mtgp.likelihood, mtgp)
-    try:
-        fit_gpytorch_mll(mll)
-    except botorch.exceptions.ModelFittingError:
-        fit_gpytorch_mll_torch(mll)
-
-    return mtgp
-
-
-def mtgp_predict(task_id, train_x, train_y, possible_x):
-    mtgp = mtgp_fit(task_id, train_x, train_y)
-    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
-    return mtgp.posterior(possible_x)
-
-
-def mtgp_get_best(task_id, train_x, train_y, possible_task_id, possible_x, possible_y):
-    gc.collect()
-
-    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
-    train_x, train_y, possible_x, possible_y = to_mtgp_format(
-        task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-    )
-    
-    mtgp = mtgp_fit(task_id, train_x, train_y)
-    ei = LogExpectedImprovement(mtgp, best_f=train_y[task_id == 0].max())
-
-    batch_size = max(10, 1000 - len(train_x))
-    possible_xs = possible_x.unsqueeze(1).split(
-        batch_size
-    )  # add batch dimension for EI, split for memory
-    best_value = -1.0
-    best_index = None
-    for i, x_batch in enumerate(possible_xs):
-        max_value, max_index = ei(x_batch).max(0)
-        if max_value > best_value or best_index is None:
-            best_index = i * batch_size + max_index
-            best_value = max_value
-
-    gc.collect()
-    assert best_index is not None
-
-    return original_possible_x[best_index], original_possible_y[best_index]
-
-
-def gp_fit(task_id, train_x, train_y):
-    train_x, train_y = to_gp_format(task_id, train_x, train_y)
-    train_yvar = torch.ones_like(train_y) * 1e-5
-    covar_module = RBFKernel(lengthscale_prior=GammaPrior(3.0, 6.0))
-    gp = SingleTaskGP(train_x, train_y, train_yvar, outcome_transform=None).to(train_x)
-    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
-    try:
-        fit_gpytorch_mll(mll)
-    except botorch.exceptions.ModelFittingError:
-        fit_gpytorch_mll_torch(mll)
-
-    return gp
-
-
-def gp_predict(task_id, train_x, train_y, possible_x):
-    gp = gp_fit(task_id, train_x, train_y)
-    possible_x, _ = to_gp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
-    return gp.posterior(possible_x)
-
-
-def gp_get_best(
-    train_task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-):
-    gp = gp_fit(train_task_id, train_x, train_y)
-    possible_x, possible_y = to_gp_format(
-        possible_task_id, possible_x, possible_y
-    )
-
-    ei = LogExpectedImprovement(gp, best_f=train_y.max())
-    best_index = ei(possible_x.unsqueeze(1)).argmax()  # add batch dimension
-    return possible_x[best_index], possible_y[best_index]
-
-
-
-def scaml_fit(task_id, train_x, train_y):
-    meta_data = {}
-    for task in torch.unique(task_id):
-        if task == 0:
-            continue
-        source_x = train_x[task_id == task]
-        source_y = train_y[task_id == task]
-        source_yvar = torch.ones_like(source_y) * 1e-5
-        dataset = SupervisedDataset(
-            source_x,
-            source_y,
-            feature_names=["x"] * source_x.shape[-1],
-            outcome_names=["y"],
-            Yvar=source_yvar,
-        )
-        meta_data[task] = dataset
-
-    source_gps = meta_fit_scamlgp(meta_data)
-
-    target_x = train_x[task_id == 0]
-    target_y = train_y[task_id == 0]
-    target_yvar = torch.ones_like(target_y) * 1e-5
-
-    gp = ScaMLGP(
-        target_x,
-        target_y,
-        source_gps=source_gps,
-        likelihood=FixedNoiseGaussianLikelihood(target_yvar),
-    ).to(target_x)
-    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
-    fit_gpytorch_mll(mll)
-
-    return gp
-
-
-def scaml_predict(task_id, train_x, train_y, possible_x):
-    gp = scaml_fit(task_id, train_x, train_y)
-    return gp.posterior(possible_x.to(train_x))
-
-
-def scaml_get_best(
-    train_task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-):
-    gp = scaml_fit(train_task_id, train_x, train_y)
-    target_y = train_y[train_task_id == 0]
-
-    ei = LogExpectedImprovement(gp, best_f=target_y.max())
-    best_index = ei(possible_x.unsqueeze(1).to(train_x)).argmax()  # add batch dimension
-    return possible_x[best_index], possible_y[best_index]
-
-
-def standardize_by_task(train_y, task_id, test_y=None):
-    standardized_y = train_y.clone()
-    if test_y is not None:
-        std_test_y = test_y.clone()
-    for task in torch.unique(task_id):
-        task_mask = task_id == task
-        task_ys = train_y[task_mask]
-
-        std = task_ys.std(0)
-        std = torch.where(std.isnan(), torch.tensor(1.0), std)
-        std = torch.where(std < 1e-5, torch.tensor(1.0), std)
-        standardized_y[task_mask] = (task_ys - task_ys.mean(0)) / std
-        if test_y is not None and task == 0:
-            std_test_y = (test_y - task_ys.mean(0)) / std
-
-    if test_y is not None:
-        return standardized_y, std_test_y
-    return standardized_y
-
-
-def create_train_test(target_xs, target_ys, sources_xs, sources_ys, n_target, n_source):
-    train_id = []
-    train_x = []
-    train_y = []
-    for i, (source_xs, source_ys) in enumerate(zip(sources_xs, sources_ys)):
-        random_indices = torch.randperm(len(source_xs))[:n_source]
-        train_x.append(source_xs[random_indices])
-        train_y.append(source_ys[random_indices])
-        train_id.append(torch.ones(n_source) * i + 1)
-
-    # add target task
-    random_indices = torch.randperm(len(target_xs))
-    train_indices = random_indices[:n_target]
-    test_indices = random_indices[n_target:]
-    train_x.append(target_xs[train_indices])
-    train_y.append(target_ys[train_indices])
-    train_id.append(torch.zeros(n_target))
-
-    train_x = torch.concat(train_x, 0)
-    train_id = torch.concat(train_id, 0)
-
-    train_y = torch.concat(train_y, 0)
-
-    test_id = torch.zeros(len(test_indices))
-    test_x = target_xs[test_indices]
-    test_y = target_ys[test_indices]
-
-    return (
-        train_id,
-        train_x,
-        train_y,
-        test_id,
-        test_x,
-        test_y,
-    )
-
-
-def lmc_fit(task_id, train_x, train_y):
-    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
-    train_yvar = torch.ones_like(train_y) * 1e-5
-    num_tasks = len(torch.unique(task_id))
-    
-    best_mll = None
-    best_mtgp = None
-    
-    for i in range(1, num_tasks):
-        # Create model with custom kernel
-        lmc = LMCGP(
-            train_x,
-            train_y,
-            task_feature=-1,
-            num_covars=i,
-            train_Yvar=train_yvar,
-            outcome_transform=None,
-            output_tasks=[0],
-        )
-        mll = ExactMarginalLogLikelihood(lmc.likelihood, lmc)
-        try:
-            fit_gpytorch_mll(mll)
-        except botorch.exceptions.ModelFittingError:
-            fit_gpytorch_mll_torch(mll)
-            
-        mll_value = mll(lmc(train_x), train_y).sum()
-        if best_mll is None or mll_value > best_mll:
-            best_mll = mll_value
-            best_mtgp = lmc
-
-    return best_mtgp
-
-
-def lmc_predict(task_id, train_x, train_y, possible_x):
-    lmc = lmc_fit(task_id, train_x, train_y)
-    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
-    return lmc.posterior(possible_x)
-
-
-def lmc_get_best(task_id, train_x, train_y, possible_task_id, possible_x, possible_y):
-    gc.collect()
-
-    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
-    train_x, train_y, possible_x, possible_y = to_mtgp_format(
-        task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-    )
-    
-    lmc = lmc_fit(task_id, train_x, train_y)
-    ei = LogExpectedImprovement(lmc, best_f=train_y[task_id == 0].max())
-
-    batch_size = max(10, 1000 - len(train_x))
-    possible_xs = possible_x.unsqueeze(1).split(
-        batch_size
-    )  # add batch dimension for EI, split for memory
-    best_value = -1.0
-    best_index = None
-    for i, x_batch in enumerate(possible_xs):
-        max_value, max_index = ei(x_batch).max(0)
-        if max_value > best_value or best_index is None:
-            best_index = i * batch_size + max_index
-            best_value = max_value
-
-    gc.collect()
-    assert best_index is not None
-
-    return original_possible_x[best_index], original_possible_y[best_index]
-
-
-def mtgp_nuts_fit(task_id, train_x, train_y):
-    train_x, train_y = to_mtgp_format(task_id, train_x, train_y)
-    train_yvar = torch.ones_like(train_y) * 1e-5
-    mtgp = SaasFullyBayesianMultiTaskGP(
-        train_x,
-        train_y,
-        task_feature=-1,
-        train_Yvar=train_yvar,
-        outcome_transform=botorch.models.transforms.outcome.Standardize(m=1),
-        output_tasks=[0],
-        pyro_model=MultitaskRBFPyroModel(),
-    )
-    try:
-        fit_fully_bayesian_model_nuts(mtgp)
-    except RuntimeError:
-        train_yvar = torch.ones_like(train_y) * 1e-4
-        mtgp = SaasFullyBayesianMultiTaskGP(
-            train_x,
-            train_y,
-            task_feature=-1,
-            train_Yvar=train_yvar,
-            outcome_transform=botorch.models.transforms.outcome.Standardize(m=1),
-            output_tasks=[0],
-            pyro_model=MultitaskRBFPyroModel(),
-        )
-        fit_fully_bayesian_model_nuts(mtgp)
-
-    return mtgp
-
-
-def mtgp_nuts_predict(task_id, train_x, train_y, possible_x):
-    mtgp = mtgp_nuts_fit(task_id, train_x, train_y)
-    possible_x, _ = to_mtgp_format(torch.zeros_like(possible_x[..., 0]), possible_x, torch.zeros_like(possible_x))
-    return mtgp.posterior(possible_x)
-
-
-def mtgp_nuts_get_best(task_id, train_x, train_y, possible_task_id, possible_x, possible_y):
-    gc.collect()
-
-    original_possible_x, original_possible_y = possible_x.clone(), possible_y.clone()
-    train_x, train_y, possible_x, possible_y = to_mtgp_format(
-        task_id, train_x, train_y, possible_task_id, possible_x, possible_y
-    )
-    
-    mtgp = mtgp_nuts_fit(task_id, train_x, train_y)
-    ei = LogExpectedImprovement(mtgp, best_f=train_y[task_id == 0].max())
-
-    batch_size = max(10, 1000 - len(train_x))
-    possible_xs = possible_x.unsqueeze(1).split(
-        batch_size
-    )  # add batch dimension for EI, split for memory
-    best_value = -1.0
-    best_index = None
-    for i, x_batch in enumerate(possible_xs):
-        max_value, max_index = ei(x_batch).max(0)
-        if max_value > best_value or best_index is None:
-            best_index = i * batch_size + max_index
-            best_value = max_value
-
-    gc.collect()
-    assert best_index is not None
-
-    return original_possible_x[best_index], original_possible_y[best_index]
-
-
-def tabpfn_fit(task_id, train_x, train_y):
-    from tabpfn import TabPFNClassifier, TabPFNRegressor
-    # onehot task_id
-    onehot = torch.nn.functional.one_hot(task_id.long(), num_classes=task_id.max() + 1)
-    train_x = torch.cat((onehot, train_x), -1)
-    train_y = train_y.unsqueeze(1)
-    
-    tabpfn = TabPFNRegressor(model_path="/home/lily_l/private_multitask_pfn/TabPFN/tabpfn-v2-regressor.ckpt").fit(train_x, train_y)
-    # # https://github.com/PriorLabs/TabPFN/blob/c8959619f0b6e62614c5a1aaa8f41c0a5ac725d3/src/tabpfn/regressor.py#L84
-    # output = tabpfn.predict(train_x, output_type="full")
-    # quantiles = tabpfn.predict(train_x, output_type="quantiles", quantiles=[0.025, 0.5, 0.975])
-    
-    # !pip install tabpfn
-    return tabpfn
-    
-def tabpfn_predict(task_id, train_x, train_y, possible_x):
-    tabpfn = tabpfn_fit(task_id, train_x, train_y)
-    
-    test_task_id = torch.zeros(possible_x.size(0)).long()
-    onehot = torch.nn.functional.one_hot(test_task_id.long(), num_classes=task_id.max() + 1)
-    possible_x = torch.cat((onehot, possible_x), -1)
-    # output = tabpfn.predict(possible_x, output_type="full")
-    quantiles = tabpfn.predict(possible_x, output_type="quantiles", quantiles=[0.025, 0.5, 0.975])
-
-    return quantiles
-    
-
-
-
-def plot_multitask(ax, test_info, model_dim):
-    train_x, train_task_id, train_y, test_x, test_task_id, test_y = test_info
-    
-    if model_dim > 1:
-        # pad train and test with 0s
-        padded_train_x = torch.cat([train_x, torch.zeros(train_x.size(0), model_dim - 1)], dim=1)
-        padded_test_x = torch.cat([test_x, torch.zeros(test_x.size(0), model_dim - 1)], dim=1)
-    else:
-        padded_train_x = train_x
-        padded_test_x = test_x
-        
-    # train_x, train_task_id, train_y, test_x, 
-    lower, median, upper = tabpfn_predict(train_task_id, padded_train_x, train_y, padded_test_x)
-    
-    ax.plot(test_x, test_y, label="true", color="C0")
-    ax.plot(test_x, median, label="mean", color="C1")
-    ax.fill_between(test_x.flatten(), lower, upper, alpha=0.2, color="C1")
-    # ax.plot(test_x, mean, label="mean", color="C1")
-    # ax.fill_between(test_x.flatten(), mean.flatten() - 2 * std.flatten(), mean.flatten() + 2 * std.flatten(), alpha=0.2, color="C1")
-    
-    for i in train_task_id.unique():
-        mask = train_task_id == i
-        marker = "x" if i.item() == 0 else "o"
-        size = 100 if i.item() == 0 else 50
-        ax.scatter(train_x[mask], train_y[mask], label=f"train task {i.item()}", color=f"C{i}", marker=marker, s=size)
-
-
-def get_multitask_test_function(seed=0):
-    from gpytorch.kernels import RBFKernel
-    from torch.distributions import MultivariateNormal
-    
-    n_features = 1
-    n_tasks = 3
-    with botorch.manual_seed(seed):
-        # n_samples = torch.randint(10, 40, (1,)).item()
-        n_samples = 50
-        
-        train_xs = torch.rand(n_samples, n_features)
-        train_task_id = torch.randint(n_tasks, size=(n_samples,)).unsqueeze(1).long()
-        test_xs = torch.linspace(0, 1, 100).view(-1, n_features)
-        test_task_id = torch.zeros(test_xs.size(0), 1).long()
-        xs = torch.cat([train_xs, test_xs], dim=0)
-        task_id = torch.cat([train_task_id, test_task_id], dim=0)
-        
-        rbf = RBFKernel()
-        rbf.lengthscale = torch.tensor(0.2)
-        covar_x = rbf(xs)
-        task_covar_matrix = torch.ones(n_tasks, n_tasks) * 0.9
-        task_covar_matrix += torch.eye(n_tasks) * 0.1
-        
-        covar_t = task_covar_matrix[task_id, task_id.t()].squeeze()
-        covar = covar_x.mul(covar_t).evaluate()
-        covar = covar + 1e-4 * torch.eye(covar.size(0))
-        ys = MultivariateNormal(torch.zeros(len(covar)), covar).sample()
-
-        train_y = ys[:n_samples]
-        test_y = ys[n_samples:]
-        
-    return train_xs, train_task_id.squeeze(), train_y, test_xs, test_task_id.squeeze(), test_y
-
-
-if __name__ == "__main__":
-    import matplotlib.pyplot as plt
-    seed = 0
-    test_info = get_multitask_test_function(seed)
-    fig, ax = plt.subplots()
-    plot_multitask(ax, test_info, 1)
-    plt.savefig("tabpfn.png")
diff --git a/rl/function_sampler.py b/rl/function_sampler.py
new file mode 100644
index 0000000..88b0bad
--- /dev/null
+++ b/rl/function_sampler.py
@@ -0,0 +1,24 @@
+from abc import ABCMeta, abstractmethod
+from typing import Callable
+
+from pfns.base_config import BaseConfig
+from torch import Tensor
+
+
+class FunctionSamplerConfig(BaseConfig, metaclass=ABCMeta):
+    @property
+    def restricts_sampling_points(self) -> bool:
+        """Indicates whether this sampler restricts which points can be sampled.
+
+        If True, the sampler's callable will have a `get_candidate_points` method
+        that returns the available candidate points for each batch element.
+        """
+        return False
+
+    @abstractmethod
+    def function_sampler(
+        self, batch_size: int, num_features: int = 1, device: str = "cpu"
+    ) -> Callable[
+        [Tensor], Tensor
+    ]:  # going from tensor of shape (batch_size, n) to (batch_size, n)
+        pass
diff --git a/rl/function_samplers/singletaskgp.py b/rl/function_samplers/singletaskgp.py
new file mode 100644
index 0000000..72e030e
--- /dev/null
+++ b/rl/function_samplers/singletaskgp.py
@@ -0,0 +1,101 @@
+from dataclasses import dataclass
+from math import sqrt
+from typing import Literal, Optional
+
+import torch
+from gpytorch.priors import LogNormalPrior
+from pfns.priors.path_stgp import sample_paths
+
+from .function_sampler import FunctionSamplerConfig
+
+
+@dataclass(frozen=True)
+class Config(FunctionSamplerConfig):
+    # Noise variance distribution type: "gamma" or "lognormal"
+    noise_var_dist: Literal["gamma", "lognormal"] = "lognormal"
+
+    # Gamma distribution parameters (used when noise_var_dist="gamma")
+    noise_variance_gamma_concentration: float = 0.9
+    noise_variance_gamma_rate: float = 10.0
+
+    # LogNormal distribution parameters (used when noise_var_dist="lognormal")
+    noise_var_loc: Optional[float] = -4.0
+    noise_var_scale: Optional[float] = 1.0
+
+    # sample_paths hyperparameters
+    use_rbf_kernel: bool = True
+    lengthscale_loc_constant_add: float = sqrt(2)
+    lengthscale_loc_feature_mul: float = 0.5
+    lengthscale_scale: float = sqrt(3)
+    mean_width: float = 2.0
+
+    # Dummy dimension configuration
+    # If set, sample number of non-dummy dimensions from [min, max] range
+    # E.g., (1, 3) means 1-3 dimensions are non-dummy, rest are ignored
+    dummy_dim_sample_non_dummy_range: Optional[tuple[int, int]] = None
+    # Probability of applying dummy_dim_sample_non_dummy_range logic.
+    # If not applied, all dimensions are non-dummy.
+    dummy_dim_sample_non_dummy_range_prob: float = 1.0
+
+    # Gap discontinuity configuration
+    gap_max_splits: int = 0  # Max axis-aligned splits (0 = disabled)
+    gap_prob: float = 1.0  # Probability of applying gaps
+    # Add to lengthscale_loc_constant_add when gaps applied
+    gap_lengthscale_add: float = 0.0
+    # Probability of applying lengthscale adjustment
+    gap_lengthscale_add_prob: float = 1.0
+
+    def _sample_noise_variance(self, batch_size: int) -> torch.Tensor:
+        if self.noise_var_dist == "lognormal":
+            return LogNormalPrior(
+                loc=self.noise_var_loc,
+                scale=self.noise_var_scale,
+            ).sample((batch_size,))
+        elif self.noise_var_dist == "gamma":
+            return (
+                torch.distributions.Gamma(
+                    self.noise_variance_gamma_concentration,
+                    self.noise_variance_gamma_rate,
+                ).sample((batch_size,))
+                + 1e-4
+            )
+        else:
+            raise ValueError(
+                f"Unknown noise variance distribution {self.noise_var_dist}"
+            )
+
+    @torch.no_grad()
+    def function_sampler(self, batch_size, num_features=1, device="cpu", seed=None):
+        hyperparameters = {
+            "use_rbf_kernel": self.use_rbf_kernel,
+            "lengthscale_loc_constant_add": self.lengthscale_loc_constant_add,
+            "lengthscale_loc_feature_mul": self.lengthscale_loc_feature_mul,
+            "lengthscale_scale": self.lengthscale_scale,
+            "mean_width": self.mean_width,
+            "dummy_dim_sample_non_dummy_range": self.dummy_dim_sample_non_dummy_range,
+            "dummy_dim_sample_non_dummy_range_prob": self.dummy_dim_sample_non_dummy_range_prob,
+            "gap_max_splits": self.gap_max_splits,
+            "gap_prob": self.gap_prob,
+            "gap_lengthscale_add": self.gap_lengthscale_add,
+            "gap_lengthscale_add_prob": self.gap_lengthscale_add_prob,
+        }
+        paths = sample_paths(batch_size, num_features, hyperparameters)
+
+        noise_variance: torch.Tensor = self._sample_noise_variance(batch_size)
+
+        @torch.no_grad()
+        def noisy_eval(batch_inputs, independent_noise=False):
+            # Calculate a spike function: resembles a triangle with peak at 1
+            noiseless_outputs = paths(batch_inputs.cpu())[0]
+            if independent_noise:
+                noise = (
+                    torch.randn(batch_inputs.shape[:-1])
+                    * noise_variance[:, None] ** (1 / 2)
+                ).squeeze(-1)
+            else:
+                noise = (torch.randn(batch_size) * noise_variance ** (1 / 2))[:, None]
+            outputs = noiseless_outputs + noise
+
+            return noiseless_outputs.to(device), outputs.to(device)
+
+        return noisy_eval
diff --git a/rl/run_training_cli.py b/rl/run_training_cli.py
new file mode 100644
index 0000000..b80bade
--- /dev/null
+++ b/rl/run_training_cli.py
@@ -0,0 +1,250 @@
+#!/usr/bin/env python3
+"""
+Command-line interface for training PFNs models.
+"""
+
+import argparse
+import io
+import os
+import sys
+from functools import partial
+from pathlib import Path
+
+import pfns.run_training_cli as original_cli
+import pfns.train
+import torch
+
+from manifold.clients.python import ManifoldClient
+
+from .discrete_eval import evaluate_bo_on_hpob
+from .discrete_pfns_bayesopt import get_acquisition_values_pfn
+
+
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Train a PFNs model using configuration from a Python file",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "config_file",
+        type=str,
+        help="Path to the Python configuration file that defines a 'config' variable or `get_config` function. Path is relative to PFNs/fb.",
+    )
+
+    parser.add_argument(
+        "--device",
+        type=str,
+        default=None,
+        help="Device to use for training (e.g., 'cuda:0', 'cpu', 'mps'). If not specified, will auto-detect cuda, but not mps.",
+    )
+
+    parser.add_argument(
+        "--checkpoint-save-load-prefix",
+        type=str,
+        default=None,
+        help="Path to save/load checkpoint and for tensorboard.",
+    )
+
+    parser.add_argument(
+        "--checkpoint-save-load-suffix",
+        type=str,
+        default="",
+        help="Suffix to add to the checkpoint save/load path. This can e.g. be the seed.",
+    )
+
+    parser.add_argument(
+        "--tensorboard-path",
+        type=str,
+        default=None,
+        help=(
+            "Path to save tensorboard. If not provided, will use the "
+            "checkpoint save/load prefix or the path in the config file."
+        ),
+    )
+
+    parser.add_argument(
+        "--config-index",
+        type=int,
+        default=0,
+        help="Index of the config to use. This is used to select a config from the config file.",
+    )
+
+    return parser.parse_args()
+
+
+def manifold_load(path: str, map_location: str | None = None) -> object:
+    """
+    A wrapper around torch.load with the same API.
+    Loads from manifold instead of local disk, though.
+
+    Args:
+        path: The path to the file to load. The path has the format: manifold://<bucket>/<path>.
+        map_location: The device to load the tensors to. Same as torch.load, e.g. "cpu" or "cuda:0".
+
+    Returns:
+        The loaded object.
+    """
+
+    with ManifoldClient.get_client("ae_generic") as client:
+        stream = io.BytesIO()
+        client.sync_get(path, stream)
+        stream.seek(0)
+        return torch.load(stream, map_location=map_location, weights_only=True)
+
+
+def manifold_exists(path: str) -> bool:
+    """
+    A replacement for os.path.exists that works for manifold paths.
+
+    Args:
+        path: The path to check. The path has the format: manifold://<bucket>/<path>.
+
+    Returns:
+        True if the path exists, False otherwise.
+    """
+
+    with ManifoldClient.get_client("ae_generic") as client:
+        return client.sync_exists(path)
+
+
+def manifold_save(obj, path: str):
+    """
+    A wrapper around torch.save with the same API that saves to manifold.
+
+    Args:
+        obj: The object to save.
+        path: The path to save the object to.
+            The path has the format: manifold://<bucket>/<path>.
+
+    Returns:
+        None
+    """
+    dir_path = os.path.dirname(path)
+
+    assert dir_path != "", "dir_path must not be empty"
+
+    with ManifoldClient.get_client("ae_generic") as client:
+        if not client.sync_exists(dir_path):
+            client.sync_mkdirs(dir_path)
+            print("made path")
+
+        stream = io.BytesIO()
+        torch.save(obj, stream)
+        stream.seek(0)
+        client.sync_put(
+            path, stream, predicate=ManifoldClient.Predicates.AllowOverwrite
+        )
+
+
+def main():
+    """Main CLI entry point."""
+    args = parse_args()
+
+    config_file = args.config_file
+    config_file = config_file[3:] if config_file.startswith("fb/") else config_file
+
+    # Load configuration from Python file
+    config = original_cli.load_config_from_python(
+        config_file, args.config_index, config_base_path=Path(__file__).parent
+    )
+
+    def get_filename(config_file):
+        return Path(config_file).stem
+
+    if args.checkpoint_save_load_suffix:
+        assert (
+            args.checkpoint_save_load_prefix is not None
+        ), "checkpoint_save_load_prefix is required when checkpoint_save_load_suffix is provided"
+
+    config_tensorboard_path_is_none = config.tensorboard_path is None
+
+    # Override checkpoint paths if specified via CLI
+    if args.checkpoint_save_load_prefix is not None:
+        assert (
+            config.train_state_dict_save_path is None
+        ), "train_state_dict_save_path is already set"
+        assert (
+            config.train_state_dict_load_path is None
+        ), "train_state_dict_load_path is already set"
+        assert config_tensorboard_path_is_none, "tensorboard_path is already set"
+
+        # Add suffix if it exists
+        suffix = f"_{args.config_index}"
+        if args.checkpoint_save_load_suffix:
+            suffix += f"_{args.checkpoint_save_load_suffix}"
+
+        path = f"{args.checkpoint_save_load_prefix}/{get_filename(config_file)}{suffix}"
+
+        config = config.__class__(
+            **{
+                **config.__dict__,
+                "train_state_dict_save_path": path + "/checkpoint.pt",
+                "train_state_dict_load_path": path + "/checkpoint.pt",
+                "tensorboard_path": "manifold://ae_generic/" + path + "/tensorboard",
+            }
+        )
+
+    if args.tensorboard_path is not None:
+        assert config_tensorboard_path_is_none, "tensorboard_path is already set"
+        config = config.__class__(
+            **{
+                **config.__dict__,
+                "tensorboard_path": args.tensorboard_path,
+            }
+        )
+
+    # We overwrite the config with the one from the checkpoint if it exists
+    # as there is some randomness in the config and we want to use the exact
+    # same config again.
+    if pfns.train.should_load_checkpoint(
+        config, check_path_exists_function=manifold_exists
+    ):
+        config = pfns.train.load_config(
+            config.train_state_dict_load_path, load_function=manifold_load
+        )
+
+    print("Starting training with configuration:")
+    print(f"  Epochs: {config.epochs}")
+    print(f"  Steps per epoch: {config.steps_per_epoch}")
+    print(f"  Device: {args.device or 'auto-detect'}")
+    print(f"  Mixed precision: {config.train_mixed_precision}")
+
+    try:
+        result = pfns.train.train(
+            c=config,
+            device=args.device,
+            # overrides for filesystem things
+            save_object_function=manifold_save,
+            load_object_function=manifold_load,
+            check_path_exists_function=manifold_exists,
+        )
+    except KeyboardInterrupt:
+        print("\nTraining interrupted by user.")
+        sys.exit(1)
+
+    print("\nTraining completed successfully!")
+    print(f"Total training time: {result['total_time']:.2f} seconds")
+    print(f"Final loss: {result['total_loss']:.6f}")
+
+    if config.train_state_dict_save_path is not None:
+        print(f"Model saved to: {config.train_state_dict_save_path}")
+        # run eval
+        # todo use manifold_save and manifold_load instead of torch
+        device = args.device
+        if device is None:
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        model = result["model"].to(device)
+        acq_function = partial(get_acquisition_values_pfn, model=model, device=device)
+        results = evaluate_bo_on_hpob(acq_function, verbose=True)
+        torch.save(
+            results,
+            "manifold://ae_generic/"
+            + str(Path(config.train_state_dict_save_path).parent)
+            + "/hpob_results.pt",
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/rl/train_rl.py b/rl/train_rl.py
new file mode 100644
index 0000000..3b6ffcf
--- /dev/null
+++ b/rl/train_rl.py
@@ -0,0 +1,2488 @@
+#!/usr/bin/env python3
+"""Command-line interface for running RL fine-tuning of PFN models."""
+
+import copy
+import math
+import os
+import random
+import time
+import typing as tp
+from contextlib import nullcontext
+from dataclasses import dataclass, fields, replace
+from functools import partial
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from pfns import base_config
+from pfns.model import transformer_config
+from pfns.model.encoders import StyleEncoderConfig
+from pfns.model.transformer import TableTransformer
+from pfns.priors.utils import sample_x_around_points
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.tensorboard import SummaryWriter
+
+from .function_samplers import function_sampler  # noqa: F401
+from .utils import load_config_and_model
+
+
+def local_load(path: str, map_location: str | None = None) -> object:
+    """
+    Load a checkpoint from the local filesystem.
+
+    Args:
+        path: The path to the file to load.
+        map_location: The device to load the tensors to. Same as torch.load, e.g. "cpu" or "cuda:0".
+
+    Returns:
+        The loaded object.
+    """
+    return torch.load(path, map_location=map_location, weights_only=True)
+
+
+def local_exists(path: str) -> bool:
+    """
+    Check if a path exists on the local filesystem.
+
+    Args:
+        path: The path to check.
+
+    Returns:
+        True if the path exists, False otherwise.
+    """
+    return os.path.exists(path)
+
+
+def local_save(obj, path: str):
+    """
+    Save an object to the local filesystem.
+
+    Args:
+        obj: The object to save.
+        path: The path to save the object to.
+
+    Returns:
+        None
+    """
+    dir_path = os.path.dirname(path)
+
+    if dir_path and not os.path.exists(dir_path):
+        os.makedirs(dir_path, exist_ok=True)
+        print(f"Created directory: {dir_path}")
+
+    torch.save(obj, path)
+
+
+@dataclass
+class PathGenerationResult:
+    """Result of path generation for RL training.
+
+    Attributes:
+        ys: Tensor of y values, shape [batch_size * sub_batch_size, seq_len]
+        target_ys: Tensor of target y values, shape [batch_size * sub_batch_size, seq_len]
+        predictions: Tensor of predictions, shape [batch_size * sub_batch_size, seq_len, num_features]
+        options: List of option tensors (if choose_next_in_set=True)
+        chosen_options: List of chosen option indices (if choose_next_in_set=True)
+        choice_probs: List of choice probability tensors (if choose_next_in_set=True)
+        current_num_features: Number of features used for this generation
+        draw: Random draw tensor for computing regret, shape [batch_size, draw_size]
+        y_quantiles: Quantiles of y values, shape [batch_size * sub_batch_size, seq_len]
+        draw_size: Size of the random draw
+        joint_steps: Number of initial joint steps used during generation
+        step_entropies: List of entropy values per step (for tensorboard logging)
+        step_max_probs: List of max probability values per step (for tensorboard logging)
+        step_sampled_probs: List of sampled probability values per step (for tensorboard logging)
+    """
+
+    ys: torch.Tensor
+    target_ys: torch.Tensor
+    predictions: torch.Tensor
+    options: list[torch.Tensor]
+    chosen_options: list[torch.Tensor]
+    choice_probs: list[torch.Tensor]
+    current_num_features: int
+    joint_steps: int
+    basemodel_ei_values: list[
+        torch.Tensor
+    ]  # EI values from unfinetuned basemodel for each step
+    step_entropies: list[torch.Tensor]  # Entropy of distribution at each step
+    step_max_probs: list[torch.Tensor]  # Max probability at each step
+    step_sampled_probs: list[torch.Tensor]  # Probability of sampled action at each step
+    binary_features_mask: (
+        torch.Tensor | None
+    )  # Mask indicating which features are binary, shape [batch_size, num_features]
+    bo_batch_size: int  # The bo_batch_size used for this generation
+    seq_len: int  # The seq_len used for this generation (for random horizon training)
+    y_style: torch.Tensor | None  # The y_style tensor used for this generation
+
+    # placeholders to be filled by subsequent computations
+    normalized_avg_rewards: torch.Tensor | None = None
+    unnormalized_avg_rewards: torch.Tensor | None = None
+    draw: torch.Tensor | None = None
+    y_quantiles: torch.Tensor | None = None
+    target_y_quantiles: torch.Tensor | None = None
+    standardized_ys: torch.Tensor | None = None
+    standardized_target_ys: torch.Tensor | None = None
+    draw_size: int | None = None
+
+    def to_device(self, device):
+        for field in fields(self):
+            value = getattr(self, field.name)
+            if isinstance(value, torch.Tensor):
+                setattr(self, field.name, value.to(device))
+            elif isinstance(value, list):
+                new_list = [
+                    item.to(device) if isinstance(item, torch.Tensor) else item
+                    for item in value
+                ]
+                setattr(self, field.name, new_list)
+        return self
+
+
+@dataclass(frozen=True)
+class RewardConfig(base_config.BaseConfig):
+    reward_type: tp.Literal[
+        "raw", "quantile", "standardized", "log_quantile", "rs_equivalent"
+    ] = "raw"
+    standardization_source: tp.Literal["batch", "draw"] = "draw"
+    only_future: bool = False
+    aggregation: str = (
+        "max"  # we can sum instead of mean, because everything is standardized anyways
+        # Options: "sum", "max", "avgmax", "max_imp", "max_sparse", "myopic_X" (where X is 0, 1, 2, ...)
+        # myopic_0: only current position, myopic_1: current + next, etc.
+    )
+    standardization: tp.Literal[
+        "none",
+        "per_step_and_function",
+        "divide_per_step_and_function",
+        "mean_divide_per_step_and_function",
+        "mean_sub_per_step_and_function",
+        "mean_divide_per_function",
+        "top_0.1_per_function",
+        "top_0.2_per_function",
+    ] = "per_step_and_function"
+    standardization_eps: float = 1e-8
+    reward_on_targets: bool = False
+    no_reward_after_peak: bool | tp.Literal["global"] = False
+
+    @classmethod
+    def _loading_kwarg_transform(cls, kwargs):
+        if "quantile_reward" in kwargs:
+            qr = kwargs.pop("quantile_reward")
+            kwargs["reward_type"] = "quantile" if qr else "raw"
+        return kwargs
+
+    def __post_init__(self):
+        if self.aggregation in ("max_imp", "max_sparse"):
+            assert (
+                self.only_future
+            ), f"{self.aggregation} does only make sense with future rewards."
+        if self.aggregation.startswith("myopic_"):
+            assert (
+                self.only_future
+            ), f"{self.aggregation} does only make sense with future rewards."
+            try:
+                window = int(self.aggregation.split("_")[1])
+                assert window >= 0, f"myopic window must be non-negative, got {window}"
+            except (IndexError, ValueError):
+                raise ValueError(
+                    f"Invalid myopic aggregation format: {self.aggregation}. Expected 'myopic_X' where X is a non-negative integer."
+                )
+
+    @torch.no_grad()
+    def compute_reward(
+        self,
+        ys: torch.Tensor,
+        target_ys: torch.Tensor,
+        quantile_ys: torch.Tensor,
+        quantile_target_ys: torch.Tensor,
+        standardized_ys: torch.Tensor,
+        standardized_target_ys: torch.Tensor,
+    ):
+        batch_size, sub_batch_size, seq_len = ys.shape
+
+        if self.reward_on_targets:
+            ys = target_ys
+            quantile_ys = quantile_target_ys
+            standardized_ys = standardized_target_ys
+
+        if self.reward_type == "quantile":
+            rewards_curr_pos = quantile_ys
+        elif self.reward_type == "standardized":
+            rewards_curr_pos = standardized_ys
+        elif self.reward_type == "log_quantile":
+            quantile_regret = (1 - quantile_ys).clamp(
+                min=1 / 10_000
+            )  # clamp s.t. we don't get log(0.) errors
+            log_quantile_regret = torch.log(quantile_regret)
+            rewards_curr_pos = -log_quantile_regret
+        elif self.reward_type == "rs_equivalent":
+            # Transform quantile to equivalent random search size
+            # 1/(1-q) represents how many random samples would be needed on average
+            # to find a value at least this good (e.g., q=0.99 -> 100 samples)
+            quantile_regret = (1 - quantile_ys).clamp(
+                min=1 / 10_000
+            )  # clamp s.t. we don't get division by 0
+            rewards_curr_pos = 1.0 / quantile_regret
+        else:
+            assert self.reward_type == "raw", f"Unknown reward_type: {self.reward_type}"
+            rewards_curr_pos = ys
+
+        if self.only_future:
+            if self.aggregation == "sum":
+                timestep_rewards = torch.cumsum(rewards_curr_pos.flip(-1), dim=-1).flip(
+                    -1
+                )
+            elif self.aggregation == "avgmax":
+                max_so_far = torch.cummax(rewards_curr_pos, dim=-1)[0]
+                timestep_rewards = torch.cumsum(max_so_far.flip(-1), dim=-1).flip(-1)
+                # normalize sums to be averages
+                num_remaining = torch.arange(
+                    seq_len, 0, -1, device=timestep_rewards.device
+                )
+                timestep_rewards = timestep_rewards / num_remaining.view(1, 1, -1)
+            else:
+                timestep_rewards = torch.cummax(rewards_curr_pos.flip(-1), dim=-1)[
+                    0
+                ].flip(-1)
+                if self.aggregation == "max_imp":  # try this with future rewards
+                    # reward for the first one is a little random
+                    # as it is against a baseline that is 0 starting
+                    # for the quantiles it makes sense, but without not really
+                    max_so_far = torch.cummax(rewards_curr_pos, dim=-1)[0]
+                    timestep_improvement = timestep_rewards
+                    timestep_improvement[..., 1:] -= max_so_far[..., :-1]
+                    average_y_first_guess = rewards_curr_pos[:, :, 0].mean(
+                        1, keepdim=True
+                    )
+                    timestep_improvement[..., 0] -= average_y_first_guess
+                    timestep_rewards = timestep_improvement.clamp(min=0.0)
+                elif self.aggregation == "max_sparse":
+                    # todo: think about this more
+                    max_so_far = torch.cummax(rewards_curr_pos, dim=-1)[0]
+                    timestep_rewards[..., 1:] = torch.where(
+                        max_so_far[..., :-1] > 0.0, 0.0, timestep_rewards[..., 1:]
+                    )
+                elif self.aggregation.startswith("myopic_"):
+                    # myopic_X: only look at current position and the next X positions
+                    window = int(self.aggregation.split("_")[1])
+                    # For each position i, compute max over positions i to min(i+window, seq_len-1)
+                    timestep_rewards = torch.zeros_like(rewards_curr_pos)
+                    for i in range(seq_len):
+                        end_idx = min(i + window + 1, seq_len)
+                        timestep_rewards[..., i] = rewards_curr_pos[..., i:end_idx].max(
+                            dim=-1
+                        )[0]
+                else:
+                    assert self.aggregation == "max", self.aggregation
+        else:
+            if self.aggregation == "sum":
+                timestep_rewards = rewards_curr_pos.sum(-1, keepdim=True).repeat(
+                    1, 1, seq_len
+                )
+            elif self.aggregation == "avgmax":
+                # Average of cumulative max across the sequence
+                max_so_far = torch.cummax(rewards_curr_pos, dim=-1)[0]
+                avg_max = max_so_far.mean(-1, keepdim=True)
+                timestep_rewards = avg_max.repeat(1, 1, seq_len)
+            else:
+                assert self.aggregation == "max", self.aggregation
+                timestep_rewards = rewards_curr_pos.max(-1, keepdim=True)[0].repeat(
+                    1, 1, seq_len
+                )
+
+        if self.standardization == "per_step_and_function":
+            normalized_avg_rewards_future = (
+                timestep_rewards - timestep_rewards.mean(1, keepdim=True)
+            ) / (timestep_rewards.std(1, keepdim=True) + self.standardization_eps)
+        elif self.standardization == "mean_sub_per_step_and_function":
+            normalized_avg_rewards_future = timestep_rewards - timestep_rewards.mean(
+                1, keepdim=True
+            )
+        elif self.standardization == "divide_per_step_and_function":  # try this
+            normalized_avg_rewards_future = timestep_rewards / (
+                timestep_rewards.std(1, keepdim=True) + self.standardization_eps
+            )
+        elif self.standardization == "mean_divide_per_step_and_function":  # try this
+            normalized_avg_rewards_future = timestep_rewards / (
+                timestep_rewards.mean(1, keepdim=True) + self.standardization_eps
+            )
+        elif self.standardization == "mean_divide_per_function":
+            normalized_avg_rewards_future = timestep_rewards / (
+                timestep_rewards.mean(1, keepdim=True).mean(2, keepdim=True)
+                + self.standardization_eps
+            )
+        elif self.standardization.startswith("top_") and self.standardization.endswith(
+            "per_function"
+        ):
+            # generalize to any quantile, e.g. "top_0.1_per_function"
+            try:
+                quantile = float(self.standardization.split("_")[1])
+            except (IndexError, ValueError):
+                raise ValueError(
+                    f"Invalid standardization format: {self.standardization}"
+                )
+            # cutoff top quantile
+            quantile_cutoffs = timestep_rewards.view(batch_size, -1).sort(-1)[0][
+                :, -round(quantile * sub_batch_size * seq_len)
+            ]
+            normalized_avg_rewards_future = torch.where(
+                timestep_rewards > quantile_cutoffs[:, None, None],
+                timestep_rewards,
+                0.0,
+            )
+        else:
+            assert self.standardization == "none", self.standardization
+            normalized_avg_rewards_future = timestep_rewards
+        # normalized_avg_rewards_future = normalized_avg_rewards_future.view(
+        #     batch_size * sub_batch_size, -1
+        # )
+
+        if self.no_reward_after_peak:
+            # Set rewards to 0 for all timesteps after the peak evaluation
+            position_indices = torch.arange(seq_len, device=rewards_curr_pos.device)
+
+            if self.no_reward_after_peak == "global":
+                # Find the global peak across all trajectories in each sub-batch
+                # and use it to cut off rewards for all items
+                # Flatten sub_batch and seq_len to find global max per batch
+                flat_rewards = rewards_curr_pos.view(
+                    batch_size, sub_batch_size * seq_len
+                )
+                global_peak_flat_indices = flat_rewards.argmax(dim=-1)  # [batch_size]
+                # Convert flat index to seq_len index (the timestep of the global peak)
+                global_peak_timestep = (
+                    global_peak_flat_indices % seq_len
+                )  # [batch_size]
+                # Create mask: positions after global peak timestep are True for all trajectories
+                after_peak_mask = position_indices.view(
+                    1, 1, -1
+                ) > global_peak_timestep.view(
+                    batch_size, 1, 1
+                )  # [batch_size, sub_batch_size, seq_len]
+            else:
+                assert self.no_reward_after_peak is True
+                # Original behavior: find peak per trajectory
+                peak_indices = rewards_curr_pos.argmax(
+                    dim=-1
+                )  # [batch_size, sub_batch_size]
+                # Create a mask where positions after the peak are True
+                after_peak_mask = position_indices.view(
+                    1, 1, -1
+                ) > peak_indices.unsqueeze(-1)  # [batch_size, sub_batch_size, seq_len]
+
+            # Zero out rewards after the peak
+            normalized_avg_rewards_future = torch.where(
+                after_peak_mask,
+                torch.zeros_like(normalized_avg_rewards_future),
+                normalized_avg_rewards_future,
+            )
+
+        return (
+            normalized_avg_rewards_future,
+            timestep_rewards,
+        )  # both shape: [batch_size, sub_batch_size, seq_len]
+
+
+@dataclass(frozen=True)
+class RLConfig(base_config.BaseConfig):
+    model_path: str
+    function_sampler: function_sampler.FunctionSamplerConfig
+    reward: RewardConfig = RewardConfig()
+    batch_size: int = 16
+    sub_batch_size: int = 32  # in the scaling RL paper, they use 16
+    seq_len: int = 5
+    algorithm: tp.Literal["grpo", "cispo"] = "grpo"
+    eps: float = 0.2
+    eps_low: float | None = None  # If None, use eps for lower bound (symmetric)
+    filter_rewards_up_to_magnitude: float | None = (
+        None  # Zero out rewards for functions with avg magnitude <= this threshold
+    )
+    num_batches: int = 100
+    experience_repetitions: int = 10
+    learning_rate: float = 1e-5
+    min_learning_rate: float | None = 0.0  # No LR schedule, when None
+    lr_schedule: tp.Literal["cosine", "linear"] = "cosine"  # LR decay schedule type
+    warmup_batches: int | None = None  # Optional linear LR warmup for this many batches
+    opt_beta2: float = 0.99
+    opt_eps: float = 1e-8
+    weight_decay: float = 0.0
+    grad_clip_norm: float = 1.0
+    device: str | None = None
+    standardize_y: None | str = None
+    seed: int = 21415
+    independent_noise_in_function_draws: bool = False
+
+    # For choice based RL
+    choose_next_in_set: bool = False
+    choice_set_size: int = 100
+    super_choice_set_factor: float = 1.0
+    choice_set_top_share: float = (
+        0.5  # only relevant when super_choice_set_factor > 1.0
+    )
+    ei_selector: bool = False
+    keep_head: bool = False
+    num_features: int = 1  # Number of input features for choose_next_in_set mode
+    mix_k_features_in_opt: int = 1
+    basemodel_ei_input: bool = (
+        False  # Pass EI from unfinetuned basemodel as input feature
+    )
+    binary_feature_likelihood: float = (
+        0.0  # Probability that each feature is binary (0 or 1) in the choice set
+    )
+
+    # Around train point sampling: sample some options near previous training points
+    around_train_point_share: float = (
+        0.0  # Fraction of options to sample around training points (0.0 = disabled)
+    )
+    around_train_point_std: float = 0.01  # Standard deviation for Gaussian noise when sampling around training points
+
+    # Joint rollout training: keep trajectories identical for a random number of initial steps
+    # None: disabled (default), train on all positions independently
+    # "single": train only on the first position after joint steps (the split point)
+    # "remaining": train on all positions from the split point onwards
+    joint_rollout_training: tp.Literal["single", "remaining"] | None = None
+
+    # Batched BO: select multiple points per batch with NaN y values for pending evaluations
+    # When bo_batch_size > 1, points within a batch don't see each other's y values
+    # and rewards are copied from the last position in each batch to all positions
+    bo_batch_size: int = 1
+    randomize_bo_batch_size: bool = False  # If True, sample bo_batch_size uniformly from 1 to bo_batch_size for each rollout
+
+    # Random horizon: sample seq_len uniformly at random up to the specified value for each rollout
+    randomize_seq_len: bool = False
+    # When randomize_seq_len is True, add a y_style_encoder that encodes the current seq_len
+    # The seq_len is normalized as (curr_seq_len/seq_len)*2-1 to be in range [-1, 1]
+    seq_len_y_style_encoder: bool = False
+
+    # Mixed precision for path generation
+    mixed_precision_path_generation: bool = False
+
+    # Checkpointing
+    checkpoint_save_path: str | None = None  # Path to save checkpoints after each batch
+    checkpoint_load_path: str | None = (
+        None  # Path to load checkpoint from to resume training
+    )
+
+    # Rollback on high loss
+    rollback_loss_threshold: float | None = (
+        None  # Roll back optimizer step if loss exceeds this threshold
+    )
+
+    # Sequence length curriculum: scales seq_len with factors of 2 until reaching target
+    # Options:
+    #   - None: No curriculum, use seq_len for all batches
+    #   - "equal": Train equal number of batches at each curriculum stage
+    #   - "exponential": Train exponentially fewer batches at longer sequence lengths
+    #                    (double seq_len, half the batches at each stage)
+    seq_len_curriculum: tp.Literal["equal", "exponential"] | None = None
+    seq_len_curriculum_min: int = 8  # Minimum sequence length for curriculum stages
+
+    # Filled in automatically
+    tensorboard_path: str | None = None
+    model: transformer_config.TransformerConfig | None = None
+
+    # make it backwards compatible
+    @classmethod
+    def _loading_kwarg_transform(cls, kwargs):
+        kwargs.pop("output_checkpoint_path", None)
+
+        if kwargs.pop("single_position_training", None):
+            kwargs["joint_rollout_training"] = "single"
+
+        return kwargs
+
+    def __post_init__(self):
+        if self.bo_batch_size > 1 and not self.choose_next_in_set:
+            raise ValueError(
+                "bo_batch_size > 1 is only supported when choose_next_in_set=True"
+            )
+        if self.binary_feature_likelihood > 0.0 and not self.choose_next_in_set:
+            raise ValueError(
+                "binary_feature_likelihood > 0 is only supported when choose_next_in_set=True"
+            )
+        if self.around_train_point_share > 0.0 and not self.choose_next_in_set:
+            raise ValueError(
+                "around_train_point_share > 0 is only supported when choose_next_in_set=True"
+            )
+
+
+def unwrap_model(model: torch.nn.Module) -> torch.nn.Module:
+    """Get the underlying module from a DDP-wrapped model or return the model itself."""
+    if isinstance(model, DDP):
+        return model.module
+    return model
+
+
+def transform_logits(model: TableTransformer, logits: torch.Tensor, ei_selector: bool):
+    if not ei_selector:
+        return logits.squeeze(-1)
+    else:
+        # Handle DDP-wrapped models
+        model_module = unwrap_model(model)
+        return model_module.criterion.ei(logits, best_f=0.0) * 1_000_000_000
+
+
+def preprocess_train_x_and_y(
+    train_x: torch.Tensor,
+    train_y: torch.Tensor,
+    standardize_y: str | None,
+    bo_batch_size: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:  # both input tensors are [b,seq,features / 1]
+    """Preprocess training data for model input.
+
+    Args:
+        train_x: Training x values, shape [batch, seq_len, num_features]
+        train_y: Training y values, shape [batch, seq_len, 1]
+        standardize_y: Standardization method ("m0s1" or "m0.5s0.3" or None)
+        bo_batch_size: Batch size for batched BO. When > 1, y values for
+            previous elements in the current batch are set to NaN (as they
+            haven't been evaluated yet in batched BO).
+
+    Returns:
+        Tuple of (train_x, train_y) with appropriate preprocessing applied.
+    """
+    assert train_x.numel() > 0
+    current_seq_len = train_y.shape[1]
+
+    # For batched BO, determine which positions have been "evaluated"
+    # Positions in previous batches are evaluated, positions in current batch are pending
+    if bo_batch_size > 1 and current_seq_len > 0:
+        batch_start = (current_seq_len // bo_batch_size) * bo_batch_size
+        # Positions 0 to batch_start-1 are evaluated (from previous batches)
+        # Positions batch_start to current_seq_len-1 are in current batch (pending)
+        evaluated_y = train_y[:, :batch_start, :] if batch_start > 0 else None
+    else:
+        evaluated_y = train_y
+        batch_start = current_seq_len
+
+    if standardize_y:
+        # Compute mean/std only over evaluated positions (previous batches)
+        if evaluated_y is not None and evaluated_y.shape[1] > 0:
+            mean = evaluated_y.mean(1, keepdim=True)
+            if evaluated_y.shape[1] == 1:
+                std = 1.0
+            else:
+                std = evaluated_y.std(1, keepdim=True)
+                std[std < 1e-8] = 1.0
+        else:
+            # No evaluated positions yet, use defaults
+            mean = 0.0
+            std = 1.0
+
+        # Apply standardization (NaNs remain NaN)
+        train_y = (train_y - mean) / std
+
+        if standardize_y == "m0.5s0.3":
+            train_y = train_y * 0.3 + 0.5
+        else:
+            assert standardize_y == "m0s1"
+    else:
+        assert standardize_y is None
+
+    # Still need to NaN out batch positions even without standardization
+    if bo_batch_size > 1 and batch_start < current_seq_len:
+        train_y = train_y.clone()
+        train_y[:, batch_start:, :] = torch.nan
+
+    return train_x, train_y
+
+
+@torch.no_grad()
+def generate_paths(
+    model: TableTransformer,
+    sampler: tp.Callable,
+    batch_size: int,
+    seq_len: int,
+    sub_batch_size: int,
+    choose_next_in_set: bool = False,
+    choice_set_size: int = 100,
+    super_choice_set_factor: float = 1.0,
+    top_share: float = 0.5,  # only relevant when super_choice_set_factor > 1.0
+    ei_selector: bool = False,
+    standardize_y: str | None = None,
+    argmax_selection: bool = False,
+    current_num_features: int = 1,
+    device: str = "cuda:0",
+    joint_steps: int = 0,
+    basemodel_ei_input: bool = False,
+    basemodel_for_ei: TableTransformer | None = None,
+    mixed_precision: bool = False,
+    bo_batch_size: int = 1,
+    binary_feature_likelihood: float = 0.0,
+    around_train_point_share: float = 0.0,
+    around_train_point_std: float = 0.01,
+    y_style: torch.Tensor | None = None,
+) -> PathGenerationResult:
+    """Generate paths for a sampler.
+
+    Args:
+        model: The model to use for generating predictions
+        sampler: Sampler function taking inputs (batch_size, sub_batch_size) returning tuple (batch_size, sub_batch_size)
+        batch_size: Number of batches
+        seq_len: Number of sequential predictions to make
+        sub_batch_size: Number of sub-batches per sampler
+        choose_next_in_set: Whether to choose next point from a set of options
+        choice_set_size: Size of the choice set when choose_next_in_set=True
+        ei_selector: Whether to use EI selector
+        standardize_y: Standardization method for y values
+        argmax_selection: Whether to use argmax for selection
+        current_num_features: Number of features to use for this batch (constant across all steps)
+        device: Device to run on
+        joint_steps: Number of initial steps where all sub_batches share the same trajectory.
+            During these steps, only one sample is taken per batch (not per sub_batch).
+            At step joint_steps, trajectories are expanded to sub_batch_size copies each.
+
+    Returns:
+        Dictionary with keys:
+            - ys: Tensor of shape [batch_size * sub_batch_size, seq_len]
+            - predictions: Tensor of predictions
+            - options: List of option tensors (if choose_next_in_set=True)
+            - chosen_options: List of chosen option indices (if choose_next_in_set=True)
+    """
+    if current_num_features > 1:
+        assert choose_next_in_set
+
+    if argmax_selection:
+        assert choose_next_in_set, "We still need to implement the argmax selection."
+
+    if basemodel_ei_input:
+        assert (
+            choose_next_in_set
+        ), "basemodel_ei_input only works with choose_next_in_set"
+        assert (
+            basemodel_for_ei is not None
+        ), "basemodel_for_ei must be provided when basemodel_ei_input=True"
+
+    # Sample which features are binary for this batch (consistent across all steps)
+    if binary_feature_likelihood > 0.0:
+        binary_features_mask = (
+            torch.rand(batch_size, current_num_features, device=device)
+            < binary_feature_likelihood
+        )
+    else:
+        binary_features_mask = None
+
+    super_batch_size = batch_size * sub_batch_size
+
+    ys = None
+    target_ys = None
+    predictions = None
+    options = []
+    chosen_options = []
+    choice_probs = []
+    basemodel_ei_values = []
+    step_entropies = []
+    step_max_probs = []
+    step_sampled_probs = []
+
+    for step_idx in range(seq_len):
+        # Determine effective batch size for this step
+        # During joint_steps, we only sample batch_size trajectories (one per batch)
+        # After joint_steps, we sample super_batch_size trajectories (one per sub_batch)
+        is_joint_step = step_idx < joint_steps
+        effective_batch_size = batch_size if is_joint_step else super_batch_size
+
+        # At the transition from joint to split steps, expand trajectories
+        if step_idx == joint_steps and joint_steps > 0:
+            # Expand from [batch_size, seq] to [super_batch_size, seq]
+            # by repeating each trajectory sub_batch_size times
+            ys = (
+                ys.unsqueeze(1)
+                .expand(-1, sub_batch_size, -1)
+                .reshape(super_batch_size, joint_steps)
+            )
+            target_ys = (
+                target_ys.unsqueeze(1)
+                .expand(-1, sub_batch_size, -1)
+                .reshape(super_batch_size, joint_steps)
+            )
+            predictions = (
+                predictions.unsqueeze(1)
+                .expand(-1, sub_batch_size, -1, -1)
+                .reshape(super_batch_size, joint_steps, current_num_features)
+            )
+            # Expand options and chosen_options for PPO training
+            for i in range(len(options)):
+                options[i] = (
+                    options[i]
+                    .unsqueeze(1)
+                    .expand(-1, sub_batch_size, -1, -1)
+                    .reshape(super_batch_size, -1, current_num_features)
+                )
+                chosen_options[i] = (
+                    chosen_options[i]
+                    .unsqueeze(1)
+                    .expand(-1, sub_batch_size)
+                    .reshape(super_batch_size)
+                )
+                choice_probs[i] = (
+                    choice_probs[i]
+                    .unsqueeze(1)
+                    .expand(-1, sub_batch_size, -1)
+                    .reshape(super_batch_size, -1)
+                )
+
+        if choose_next_in_set:
+            if ys is None:
+                x = torch.zeros(
+                    effective_batch_size, 0, current_num_features, device=device
+                )
+                y = torch.zeros(effective_batch_size, 0, 1, device=device)
+            else:
+                x, y = preprocess_train_x_and_y(
+                    predictions,
+                    ys[:, :, None],
+                    standardize_y,
+                    bo_batch_size=bo_batch_size,
+                )
+
+            # we make sure the rollouts within each sub-batch get the same choices in each step
+            total_opts = round(choice_set_size * super_choice_set_factor)
+
+            # Check if sampler restricts sampling points by checking for get_candidate_points
+            if hasattr(sampler, "get_candidate_points"):
+                # Validate incompatible options
+                if around_train_point_share > 0.0:
+                    raise ValueError(
+                        "around_train_point_share > 0 is not supported with samplers that provide candidate points"
+                    )
+                if binary_features_mask is not None:
+                    raise ValueError(
+                        "binary_feature_likelihood > 0 is not supported with samplers that provide candidate points"
+                    )
+
+                # Use candidate points from the sampler
+                candidate_points = sampler.get_candidate_points()
+                # Build options from the candidate points for each batch element
+                opts_list = []
+                for batch_idx in range(batch_size):
+                    candidates = candidate_points[batch_idx].to(device)
+                    n_candidates = candidates.shape[0]
+                    if n_candidates >= total_opts:
+                        # Sample without replacement
+                        perm = torch.randperm(n_candidates, device=device)[:total_opts]
+                        opts_list.append(candidates[perm])
+                    else:
+                        # Sample with replacement if we don't have enough candidates
+                        indices = torch.randint(
+                            0, n_candidates, (total_opts,), device=device
+                        )
+                        opts_list.append(candidates[indices])
+                opts = torch.stack(
+                    opts_list, dim=0
+                )  # [batch_size, total_opts, features]
+            else:
+                # Calculate how many options should be sampled around training points
+                num_around_train = (
+                    round(total_opts * around_train_point_share)
+                    if around_train_point_share > 0.0 and predictions is not None
+                    else 0
+                )
+                num_uniform = total_opts - num_around_train
+
+                # Sample uniform options
+                opts = torch.rand(
+                    batch_size,
+                    num_uniform,
+                    current_num_features,
+                    device=device,
+                )
+
+                # Sample options around training points if applicable
+                if num_around_train > 0:
+                    # Get unique train points per batch
+                    if is_joint_step:
+                        # During joint steps, predictions already has shape [batch_size, step_idx, num_features]
+                        train_points = predictions
+                    else:
+                        # After joint steps, take every sub_batch_size-th trajectory
+                        train_points = predictions[
+                            ::sub_batch_size
+                        ]  # [batch_size, step_idx, num_features]
+
+                    # Use shared utility for sampling around training points
+                    around_train_opts = sample_x_around_points(
+                        batch_size=batch_size,
+                        num_samples=num_around_train,
+                        num_features=current_num_features,
+                        centers=train_points,
+                        std=around_train_point_std,
+                        device=device,
+                    )
+                    # Concatenate with uniform options
+                    opts = torch.cat([opts, around_train_opts], dim=1)
+
+            # Apply binary feature restriction to options
+            if binary_features_mask is not None:
+                binary_mask_expanded = binary_features_mask.unsqueeze(1).expand(
+                    -1, total_opts, -1
+                )
+                opts = torch.where(binary_mask_expanded, (opts > 0.5).float(), opts)
+
+            if super_choice_set_factor < 1.0:
+                raise NotImplementedError("Please use super_choice_set_factor >= 1.")
+            if not is_joint_step:
+                opts = (
+                    opts.unsqueeze(1)
+                    .repeat(
+                        1, sub_batch_size, 1, 1
+                    )  # using repeat instead of expand as we have to copy for the next line either way
+                    .view(super_batch_size, total_opts, current_num_features)
+                )
+
+            autocast_ctx = (
+                torch.amp.autocast(dtype=torch.float16, device_type="cuda")
+                if mixed_precision
+                else nullcontext()
+            )
+            # Compute base model EI and add as additional feature to options
+            if basemodel_ei_input:
+                with torch.no_grad():
+                    # Get logits from the base model (unfinetuned)
+                    basemodel_logits = basemodel_for_ei(x=x, y=y, test_x=opts)
+                    # Compute EI from base model - using best_f=0.0 as in ei_selector
+                    basemodel_ei = (
+                        basemodel_for_ei.criterion.ei(basemodel_logits, best_f=0.0)
+                        .detach()
+                        .view(super_batch_size, total_opts, 1)
+                    )  # shape: [batch, num_opts, 1]
+                    # Create augmented options with EI as additional feature for model input
+                    opts_with_ei = torch.cat([opts, basemodel_ei], dim=-1)
+                    # Add 0s to train_x for the EI column
+                    x_with_ei = torch.cat(
+                        [
+                            x,
+                            0.5 + torch.zeros(x.shape[0], x.shape[1], 1, device=device),
+                        ],
+                        dim=-1,
+                    )
+
+                # Subselect y_style for joint steps (every sub_batch_size-th element)
+                # y_style is already in expanded shape [super_batch_size, 1]
+                current_y_style = None
+                if y_style is not None:
+                    if is_joint_step:
+                        # Subselect from [super_batch_size, 1] to [batch_size, 1]
+                        current_y_style = y_style[::sub_batch_size]
+                    else:
+                        current_y_style = y_style
+
+                with autocast_ctx:
+                    logits = transform_logits(
+                        model,
+                        model(
+                            x=x_with_ei,
+                            y=y,
+                            test_x=opts_with_ei,
+                            y_style=current_y_style,
+                        ),
+                        ei_selector,
+                    )
+            else:
+                # Subselect y_style for joint steps (every sub_batch_size-th element)
+                # y_style is already in expanded shape [super_batch_size, 1]
+                current_y_style = None
+                if y_style is not None:
+                    if is_joint_step:
+                        # Subselect from [super_batch_size, 1] to [batch_size, 1]
+                        current_y_style = y_style[::sub_batch_size]
+                    else:
+                        current_y_style = y_style
+
+                with autocast_ctx:
+                    logits = transform_logits(
+                        model,
+                        model(x=x, y=y, test_x=opts, y_style=current_y_style),
+                        ei_selector,
+                    )
+                basemodel_ei = None
+
+            if super_choice_set_factor > 1.0:
+                # Select a super set of options consisting of:
+                # - the best top_share * total options by model score
+                # - plus the first (1 - top_share) * total options among the remaining non-best ones
+                k_best = max(1, round(top_share * choice_set_size))
+                k_first_non_best = choice_set_size - k_best
+
+                # Get indices of top-k according to logits
+                topk_vals, topk_inds = torch.topk(logits.squeeze(-1), k=k_best, dim=1)
+
+                # Build a mask for selected top-k to find non-best candidates
+                top_mask = torch.zeros(
+                    effective_batch_size, total_opts, dtype=torch.bool, device=device
+                )
+                top_mask[torch.arange(effective_batch_size).unsqueeze(1), topk_inds] = (
+                    True
+                )
+
+                # Indices of non-best (complement of top-k)
+                all_inds = (
+                    torch.arange(total_opts, device=device)
+                    .unsqueeze(0)
+                    .expand(effective_batch_size, -1)
+                )
+                non_best_inds = all_inds[~top_mask].view(effective_batch_size, -1)
+
+                # Take the first k_first_non_best from non-best in their original order
+                if k_first_non_best > 0:
+                    first_non_best_inds = non_best_inds[:, :k_first_non_best]
+                    combined_inds = torch.cat([topk_inds, first_non_best_inds], dim=1)
+                else:
+                    combined_inds = topk_inds
+
+                # Subselect options and logits to the combined set
+                opts = opts[
+                    torch.arange(effective_batch_size).unsqueeze(1), combined_inds
+                ]
+                logits = logits[
+                    torch.arange(effective_batch_size).unsqueeze(1), combined_inds
+                ]
+                # Also subselect EI values if basemodel_ei_input is enabled
+                if basemodel_ei_input:
+                    basemodel_ei = basemodel_ei[
+                        torch.arange(effective_batch_size).unsqueeze(1), combined_inds
+                    ]
+
+            logits = logits.squeeze(-1)
+            if argmax_selection:
+                sampled_inds = logits.argmax(1)
+            else:
+                sampled_inds = torch.distributions.Categorical(logits=logits).sample()
+
+            # Compute entropy, max prob, and sampled prob for tensorboard logging
+            # Entropy: -sum(p * log(p)), using clamp to avoid log(0)
+            log_probs = logits.log_softmax(
+                dim=-1
+            )  # shape: [effective_batch_size, num_opts]
+            probs = log_probs.exp()  # shape: [effective_batch_size, num_opts]
+            entropy = -(probs * log_probs).sum(dim=-1)  # shape: [effective_batch_size]
+            max_prob = probs.max(dim=-1).values  # shape: [effective_batch_size]
+            sampled_prob = probs[
+                torch.arange(effective_batch_size, device=device), sampled_inds
+            ]  # shape: [effective_batch_size]
+
+            step_entropies.append(entropy)
+            step_max_probs.append(max_prob)
+            step_sampled_probs.append(sampled_prob)
+
+            pred = opts[torch.arange(effective_batch_size), sampled_inds]
+
+            options.append(opts)
+            chosen_options.append(sampled_inds)
+            choice_probs.append(probs)
+            # Store the EI values for reuse in training loop
+            basemodel_ei_values.append(basemodel_ei)
+        else:
+            if ys is None:
+                full_train_x = torch.zeros(effective_batch_size, 0, 2, device=device)
+            else:
+                train_x, train_y = preprocess_train_x_and_y(
+                    predictions[:, :],
+                    ys[:, :, None],
+                    standardize_y,
+                    bo_batch_size=bo_batch_size,
+                )
+                full_train_x = torch.cat((train_x, train_y), -1)
+
+            full_test_x = torch.full(
+                (effective_batch_size, 1, 2),
+                torch.nan,
+                device=device,
+            )
+            logits = model(x=full_train_x, y=None, test_x=full_test_x)[:, :, 0].squeeze(
+                1
+            )
+
+            p_cdf = torch.rand(*logits.shape[:-1], device=device)
+            pred = torch.stack(
+                [
+                    unwrap_model(model).criterion.icdf(logits[i, :], p)
+                    for i, p in enumerate(p_cdf.tolist())
+                ],
+            ).clamp(0, 1)
+
+        target_y, y = sampler(
+            pred.view(
+                batch_size, 1 if is_joint_step else sub_batch_size, current_num_features
+            )
+        )
+        # Flatten the result back to effective_batch_size
+        y = y.view(effective_batch_size)
+        target_y = target_y.view(effective_batch_size)
+
+        if ys is None:
+            ys = y.view(effective_batch_size, 1)
+            target_ys = target_y.view(effective_batch_size, 1)
+            predictions = pred.view(effective_batch_size, 1, current_num_features)
+        else:
+            ys = torch.cat((ys, y.view(effective_batch_size, 1)), 1)
+            target_ys = torch.cat(
+                (target_ys, target_y.view(effective_batch_size, 1)), 1
+            )
+            predictions = torch.cat(
+                (predictions, pred.view(effective_batch_size, 1, current_num_features)),
+                1,
+            )
+
+    return PathGenerationResult(
+        ys=ys,
+        target_ys=target_ys,
+        predictions=predictions,
+        options=options,
+        chosen_options=chosen_options,
+        choice_probs=choice_probs,
+        current_num_features=current_num_features,
+        joint_steps=joint_steps,
+        basemodel_ei_values=basemodel_ei_values,
+        step_entropies=step_entropies,
+        step_max_probs=step_max_probs,
+        step_sampled_probs=step_sampled_probs,
+        binary_features_mask=binary_features_mask,
+        bo_batch_size=bo_batch_size,
+        seq_len=seq_len,
+        y_style=y_style,
+    )
+
+
+def compute_curriculum_schedule(
+    num_batches: int,
+    target_seq_len: int,
+    mode: tp.Literal["equal", "exponential"],
+    min_seq_len: int = 8,
+) -> list[int]:
+    """Compute the sequence length for each batch based on curriculum.
+
+    The curriculum scales the rollout length with factors of 2 until reaching
+    the target sequence length.
+
+    Args:
+        num_batches: Total number of batches to train
+        target_seq_len: Final sequence length to reach
+        mode: Curriculum mode:
+            - "equal": Train equal number of batches at each curriculum stage
+            - "exponential": Train exponentially fewer batches at longer sequence
+                lengths (double seq_len, half the batches at each stage)
+        min_seq_len: Minimum sequence length for curriculum stages (default: 8)
+
+    Returns:
+        List of sequence lengths, one per batch
+    """
+    # Clamp min_seq_len to not exceed target_seq_len
+    min_seq_len = min(min_seq_len, target_seq_len)
+
+    # Generate stages: min_seq_len, min_seq_len*2, min_seq_len*4, ..., up to target_seq_len
+    curriculum_stages: list[int] = []
+    current = min_seq_len
+    while current < target_seq_len:
+        curriculum_stages.append(current)
+        current *= 2
+    curriculum_stages.append(target_seq_len)  # Always end with target
+
+    n_stages = len(curriculum_stages)
+
+    # If we have fewer batches than stages, trim stages to only include
+    # the last num_batches stages (prioritize longer sequences)
+    if num_batches < n_stages:
+        curriculum_stages = curriculum_stages[-num_batches:]
+        n_stages = num_batches
+
+    if mode == "equal":
+        # Equal batches per stage
+        base_batches = num_batches // n_stages
+        remainder = num_batches % n_stages
+        batches_per_stage = [base_batches] * n_stages
+        # Distribute remainder to later stages (longer sequences get slightly more)
+        for i in range(remainder):
+            batches_per_stage[-(i + 1)] += 1
+    elif mode == "exponential":
+        # Exponentially decreasing batches for longer sequences
+        # First stage (shortest seq) gets the most batches, halving each time
+        # Sum of geometric series: 1 + 1/2 + 1/4 + ... + 1/2^(n-1) = 2 - 2^(1-n)
+        geometric_sum = sum(1 / (2**i) for i in range(n_stages))
+        first_stage_batches = num_batches / geometric_sum
+        batches_per_stage = [
+            max(1, int(round(first_stage_batches / (2**i)))) for i in range(n_stages)
+        ]
+        # Adjust total to exactly match num_batches
+        total = sum(batches_per_stage)
+        diff = num_batches - total
+        if diff != 0:
+            # Distribute the difference across stages, starting from first stage
+            # to maintain the exponential property as much as possible
+            for i in range(abs(diff)):
+                idx = i % n_stages
+                if diff > 0:
+                    batches_per_stage[idx] += 1
+                else:
+                    # Only subtract if stage has more than 1 batch
+                    if batches_per_stage[idx] > 1:
+                        batches_per_stage[idx] -= 1
+                    else:
+                        # Find another stage to subtract from
+                        for j in range(n_stages):
+                            if batches_per_stage[j] > 1:
+                                batches_per_stage[j] -= 1
+                                break
+    else:
+        raise ValueError(f"Unknown curriculum mode: {mode}")
+
+    # Build the schedule: list of seq_len for each batch
+    schedule: list[int] = []
+    for stage_idx, stage_len in enumerate(curriculum_stages):
+        schedule.extend([stage_len] * batches_per_stage[stage_idx])
+
+    return schedule
+
+
+def run_rl_training(
+    rl_config: RLConfig,
+    device_override: str | None = None,
+) -> dict:
+    # Detect if we're in distributed mode
+    if "WORLD_SIZE" in os.environ:
+        dist.init_process_group(backend="nccl")
+
+    distributed = dist.is_available() and dist.is_initialized()
+    rank = dist.get_rank() if distributed else 0
+    world_size = dist.get_world_size() if distributed else 1
+
+    # Get local rank from environment variable (set by torchrun)
+    local_rank = int(os.environ.get("LOCAL_RANK", 0)) if distributed else 0
+    torch.cuda.set_device(local_rank)
+
+    print("Training distributed? ", distributed)
+    print("Rank?", rank, "Local?", local_rank)
+    print("World?", world_size)
+
+    is_main = rank == 0
+
+    if is_main and distributed:
+        print(
+            f"Running in distributed mode: rank {rank}/{world_size}, local_rank {local_rank}"
+        )
+
+    # Set seed (different per rank for diversity in trajectory generation)
+    if rl_config.seed is not None:
+        seed = rl_config.seed + (rank if distributed else 0)
+        torch.manual_seed(seed)
+        np.random.seed(seed)
+        random.seed(seed)
+
+    writer_path = rl_config.tensorboard_path
+
+    # Only create writer on main process
+    writer: SummaryWriter | None = None
+    if writer_path is not None and is_main:
+        writer = SummaryWriter(log_dir=writer_path)
+        print(f"Tensorboard logging to: {writer_path}")
+
+    if is_main:
+        print(f"Loading base model from {rl_config.model_path}")
+    base_train_config, model = load_config_and_model(
+        rl_config.model_path, map_location="cpu"
+    )
+
+    if (
+        rl_config.choose_next_in_set
+        and not rl_config.ei_selector
+        and not rl_config.keep_head
+    ):
+        # edit model head to be a simple 1 size prediction
+
+        base_train_config = replace(
+            base_train_config,
+            model=replace(
+                base_train_config.model, decoder_dict={"standard": (None, 1)}
+            ),
+        )
+        model_with_single_output = base_train_config.model.create_model()
+        og_statedict = model.state_dict()
+        filtered_og_statedict = {}
+        for n in og_statedict:
+            if "decoder_dict" in n:
+                print("removing", n)
+            else:
+                filtered_og_statedict[n] = og_statedict[n]
+        model_with_single_output.load_state_dict(filtered_og_statedict, strict=False)
+        model = model_with_single_output
+
+    # Add y_style_encoder for seq_len encoding if enabled
+    if rl_config.seq_len_y_style_encoder:
+        if not rl_config.randomize_seq_len:
+            print(
+                "Warning: seq_len_y_style_encoder is True but randomize_seq_len is False. "
+                "The y_style_encoder will always receive the same normalized seq_len value."
+            )
+        # Update the model config to include y_style_encoder
+        # This ensures the encoder is saved with the model and can be loaded later
+        base_train_config = replace(
+            base_train_config,
+            model=replace(
+                base_train_config.model,
+                y_style_encoder=StyleEncoderConfig(num_styles=1),
+            ),
+        )
+        # Recreate model with the y_style_encoder
+        model_with_y_style_encoder = base_train_config.model.create_model()
+        og_statedict = model.state_dict()
+        model_with_y_style_encoder.load_state_dict(og_statedict, strict=False)
+        model = model_with_y_style_encoder
+        print(f"Added y_style_encoder: Linear(1, {model.ninp}) for seq_len encoding")
+
+    # We do this s.t. we can load the model w/o base_train_config
+    rl_config = replace(rl_config, model=base_train_config.model)
+
+    sampler_factory = rl_config.function_sampler.function_sampler
+
+    try:
+        # Setup device based on distributed training config
+        # TODO test which of this is right for distributed, might want to use "cuda" on all.
+        if device_override:
+            device = device_override
+        elif distributed:
+            device = f"cuda:{local_rank}"
+        elif rl_config.device is not None:
+            device = rl_config.device
+        else:
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+        if is_main:
+            print(f"Using device {device}")
+
+        model.to(device)
+        model.train()
+
+        # Create old_model for PPO-style training
+        # For DDP, we need to access the underlying module
+        old_model = copy.deepcopy(model)
+        old_model.requires_grad_(False)
+        old_model.to(device)
+        old_model.eval()
+
+        # Store a separate copy of the basemodel for EI computation (never updated during training)
+        basemodel_for_ei = None
+        if rl_config.basemodel_ei_input:
+            basemodel_for_ei = copy.deepcopy(model)
+            basemodel_for_ei.requires_grad_(False)
+            basemodel_for_ei.to(device)
+            basemodel_for_ei.eval()
+
+        optimizer = torch.optim.AdamW(
+            model.parameters(),
+            lr=rl_config.learning_rate,
+            eps=rl_config.opt_eps,
+            betas=(0.9, rl_config.opt_beta2),
+            weight_decay=rl_config.weight_decay,
+        )
+        # Set up learning rate scheduler with optional warmup
+        warmup_batches = rl_config.warmup_batches or 0
+        main_batches = max(1, rl_config.num_batches - warmup_batches)
+        eta_min = (
+            rl_config.learning_rate
+            if rl_config.min_learning_rate is None
+            else rl_config.min_learning_rate
+        )
+
+        # Create main scheduler based on lr_schedule config
+        if rl_config.lr_schedule == "linear":
+            # Linear decay from learning_rate to eta_min
+            main_scheduler = torch.optim.lr_scheduler.LinearLR(
+                optimizer,
+                start_factor=1.0,
+                end_factor=eta_min / rl_config.learning_rate
+                if rl_config.learning_rate > 0
+                else 1.0,
+                total_iters=main_batches,
+            )
+        else:
+            # Default: Cosine annealing
+            main_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+                optimizer,
+                T_max=main_batches,
+                eta_min=eta_min,
+            )
+
+        if warmup_batches > 0:
+            warmup_scheduler = torch.optim.lr_scheduler.LinearLR(
+                optimizer,
+                start_factor=1e-8 / rl_config.learning_rate,  # Start from near-zero
+                end_factor=1.0,
+                total_iters=warmup_batches,
+            )
+            scheduler = torch.optim.lr_scheduler.SequentialLR(
+                optimizer,
+                schedulers=[warmup_scheduler, main_scheduler],
+                milestones=[warmup_batches],
+            )
+            if is_main:
+                print(f"Using linear warmup for {warmup_batches} batches")
+        else:
+            scheduler = main_scheduler
+
+        if is_main:
+            print(f"Using {rl_config.lr_schedule} LR schedule")
+        scaler = torch.amp.GradScaler(
+            enabled=True,
+        )
+
+        # Load checkpoint if available
+        start_batch = 0
+        if should_load_rl_checkpoint(rl_config):
+            start_batch = load_rl_checkpoint(
+                model=model,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                scaler=scaler,
+                checkpoint_load_path=rl_config.checkpoint_load_path,
+                device=device,
+            )
+            if is_main:
+                print(f"Resuming training from batch {start_batch}")
+        else:
+            if rl_config.checkpoint_load_path is not None and is_main:
+                print(
+                    f"Checkpoint file {rl_config.checkpoint_load_path} not found or load/save paths are identical and file doesn't exist. Starting from scratch."
+                )
+
+        # Wrap model with DDP if using distributed training
+        if distributed:
+            model = DDP(
+                model,
+                device_ids=[local_rank],
+                output_device=local_rank,
+                find_unused_parameters=False,
+                broadcast_buffers=False,
+            )
+            if is_main:
+                print("Wrapped model with DistributedDataParallel")
+                if writer is not None:
+                    writer.add_scalar("config/num_gpus", world_size)
+
+        eps = rl_config.eps
+        target_seq_len = rl_config.seq_len
+        batch_size = rl_config.batch_size
+        sub_batch_size = rl_config.sub_batch_size
+        super_batch_size = batch_size * sub_batch_size
+
+        # Compute curriculum schedule if enabled
+        if rl_config.seq_len_curriculum is not None:
+            curriculum_schedule = compute_curriculum_schedule(
+                rl_config.num_batches,
+                target_seq_len,
+                rl_config.seq_len_curriculum,
+                min_seq_len=rl_config.seq_len_curriculum_min,
+            )
+            if is_main:
+                # Log curriculum stages
+                stages = []
+                current_len = curriculum_schedule[0]
+                stage_start = 0
+                for i, length in enumerate(curriculum_schedule):
+                    if length != current_len:
+                        stages.append(
+                            (current_len, stage_start, i - 1, i - stage_start)
+                        )
+                        current_len = length
+                        stage_start = i
+                stages.append(
+                    (
+                        current_len,
+                        stage_start,
+                        len(curriculum_schedule) - 1,
+                        len(curriculum_schedule) - stage_start,
+                    )
+                )
+                print(
+                    f"Curriculum schedule ({rl_config.seq_len_curriculum} mode): "
+                    f"{len(stages)} stages"
+                )
+                for seq_len_stage, start, end, num_batches_stage in stages:
+                    print(
+                        f"  seq_len={seq_len_stage}: batches {start}-{end} ({num_batches_stage} batches)"
+                    )
+        else:
+            curriculum_schedule = None
+
+        batch_losses: list[float] = []
+
+        for batch_i in range(start_batch, rl_config.num_batches):
+            # Determine seq_len for this batch (curriculum or fixed)
+            if curriculum_schedule is not None:
+                seq_len = curriculum_schedule[batch_i]
+            else:
+                seq_len = target_seq_len
+            if rl_config.seed is not None:
+                # We re-initialize the seed for every batch
+                # As randomness might be different in the fitting
+                # step.
+                seed = rl_config.seed + batch_i + (rank if distributed else 0)
+                torch.manual_seed(seed)
+                np.random.seed(seed)
+                random.seed(seed)
+
+            # Save model/optimizer state before this batch for potential rollback
+            batch_rolled_back = False
+            if rl_config.rollback_loss_threshold is not None:
+                model_state_before_batch = {
+                    k: v.clone() for k, v in unwrap_model(model).state_dict().items()
+                }
+                optimizer_state_before_batch = copy.deepcopy(optimizer.state_dict())
+
+            # Calculate current number of features for this batch: (batch_i % num_features) + 1
+            if rl_config.mix_k_features_in_opt > 1:
+                all_features = list(range(1, rl_config.num_features + 1))
+                num_full_copies = rl_config.mix_k_features_in_opt // len(all_features)
+                remainder = rl_config.mix_k_features_in_opt % len(all_features)
+                # Take all features num_full_copies times
+                current_num_features_list = all_features * num_full_copies
+                # Sample the remainder
+                if remainder > 0:
+                    current_num_features_list += random.sample(all_features, remainder)
+
+            else:
+                current_num_features_list = [
+                    ((batch_i + 1) % rl_config.num_features) + 1
+                ]
+
+            start_generation_time = time.time()
+
+            gen_results: list[PathGenerationResult] = []
+            for feat_idx, current_num_features in enumerate(current_num_features_list):
+                # Sample seq_len for this rollout if randomization is enabled
+                # Use synchronized RNG to ensure same seq_len across distributed workers
+                if rl_config.randomize_seq_len and seq_len > 3:
+                    seq_len_rng = random.Random(
+                        (rl_config.seed or 0) + batch_i * 10_000 + feat_idx
+                    )
+                    current_seq_len = seq_len_rng.randint(3, seq_len)
+                else:
+                    current_seq_len = seq_len
+
+                # Compute normalized seq_len for y_style_encoder: (curr/max)*2-1 to be in [-1, 1]
+                # Create y_style in expanded shape [super_batch_size, 1] upfront
+                # and subselect for joint steps in generate_paths
+                if rl_config.seq_len_y_style_encoder:
+                    normalized_seq_len = (current_seq_len / seq_len) * 2 - 1
+                    y_style = torch.full(
+                        (batch_size * sub_batch_size, 1),
+                        normalized_seq_len,
+                        device=device,
+                    )  # [super_batch_size, 1]
+                else:
+                    y_style = None
+
+                # Sample bo_batch_size for this rollout if randomization is enabled
+                if rl_config.randomize_bo_batch_size and rl_config.bo_batch_size > 1:
+                    current_bo_batch_size = random.randint(1, rl_config.bo_batch_size)
+                else:
+                    current_bo_batch_size = rl_config.bo_batch_size
+
+                # Compute a synchronized seed for sampler feature selection
+                # This ensures all distributed workers use the same dimensionality
+                sampler_seed = (
+                    (rl_config.seed or 0) + batch_i * 1_000 + current_num_features
+                )
+                print(
+                    f"{sampler_seed=}, {current_num_features=}, {current_bo_batch_size=}, {current_seq_len=}"
+                )
+
+                sampler = sampler_factory(
+                    batch_size,
+                    num_features=current_num_features,
+                    device=device,
+                    seed=sampler_seed,
+                )
+                if rl_config.independent_noise_in_function_draws:
+                    sampler = partial(sampler, independent_noise=True)
+
+                # Check if the sampler provides its own num_features
+                if hasattr(sampler, "num_features"):
+                    current_num_features = sampler.num_features
+
+                # Sample joint_steps for joint rollout training
+                # Use current_seq_len (not seq_len) to ensure joint_steps < current_seq_len
+                joint_steps = 0
+                if rl_config.joint_rollout_training is not None:
+                    # Sample a random position to train on (0 to current_seq_len-1)
+                    # joint_steps = training position, so trajectories are identical until then
+                    if rl_config.joint_rollout_training == "remaining" and distributed:
+                        # For "remaining" mode in DDP, use a synchronized seed across all workers
+                        # to ensure the same joint_steps value, enabling proper gradient synchronization
+                        sync_rng = random.Random(
+                            (rl_config.seed or 0) + batch_i * 10_000 + feat_idx + 1412
+                        )
+                        joint_steps = sync_rng.randint(0, current_seq_len - 1)
+                    else:
+                        joint_steps = random.randint(0, current_seq_len - 1)
+
+                gen_res = generate_paths(
+                    model=model,
+                    sampler=sampler,
+                    batch_size=batch_size,
+                    seq_len=current_seq_len,
+                    sub_batch_size=sub_batch_size,
+                    choose_next_in_set=rl_config.choose_next_in_set,
+                    choice_set_size=rl_config.choice_set_size,
+                    ei_selector=rl_config.ei_selector,
+                    standardize_y=rl_config.standardize_y,
+                    current_num_features=current_num_features,
+                    device=device,
+                    super_choice_set_factor=rl_config.super_choice_set_factor,
+                    top_share=rl_config.choice_set_top_share,
+                    joint_steps=joint_steps,
+                    basemodel_ei_input=rl_config.basemodel_ei_input,
+                    basemodel_for_ei=basemodel_for_ei,
+                    mixed_precision=rl_config.mixed_precision_path_generation,
+                    bo_batch_size=current_bo_batch_size,
+                    binary_feature_likelihood=rl_config.binary_feature_likelihood,
+                    around_train_point_share=rl_config.around_train_point_share,
+                    around_train_point_std=rl_config.around_train_point_std,
+                    y_style=y_style,
+                )
+
+                generation_time = time.time() - start_generation_time
+
+                start_draw_time = time.time()
+
+                draw_size = 100_000
+                draw_x = torch.rand(
+                    batch_size, draw_size, current_num_features, device=device
+                )
+                # Apply binary feature constraint to draw samples if applicable
+                if gen_res.binary_features_mask is not None:
+                    binary_mask_expanded = gen_res.binary_features_mask.unsqueeze(
+                        1
+                    ).expand(-1, draw_size, -1)
+                    draw_x = torch.where(
+                        binary_mask_expanded, (draw_x > 0.5).float(), draw_x
+                    )
+                draw, _ = sampler(
+                    draw_x,
+                    independent_noise=True,
+                )
+
+                def compute_quantiles(
+                    y_version, use_draw: bool = True, draw=draw, draw_size=draw_size
+                ):
+                    y_view = y_version.view(batch_size, -1)
+                    if use_draw:
+                        # Use draw samples to compute quantile positions
+                        sorted_ref = draw.view(batch_size, draw_size).sort(1).values
+                        ref_size = draw_size
+                    else:
+                        # Use batch data itself to compute quantile positions
+                        sorted_ref = y_view.sort(1).values
+                        ref_size = y_view.shape[1]
+
+                    quantiles = (
+                        torch.searchsorted(sorted_ref, y_view).float() / ref_size
+                    )
+
+                    return quantiles.view(batch_size * sub_batch_size, -1)
+
+                def compute_standardized_ys(
+                    y_version, use_draw: bool = False, draw=draw, draw_size=draw_size
+                ):
+                    # Standardize per function (per batch) - z-score normalization
+                    if use_draw:
+                        # Use draw samples to compute mean and std
+                        draw_view = draw.view(batch_size, draw_size)
+                        mean = draw_view.mean(dim=1, keepdim=True)
+                        std = draw_view.std(dim=1, keepdim=True)
+                    else:
+                        # Use batch data to compute mean and std
+                        y_view = y_version.view(batch_size, -1)
+                        mean = y_view.mean(dim=1, keepdim=True)
+                        std = y_view.std(dim=1, keepdim=True)
+                    std = std.clamp(min=1e-8)
+                    y_view = y_version.view(batch_size, -1)
+                    standardized = (y_view - mean) / std
+                    return standardized.view(batch_size * sub_batch_size, -1)
+
+                use_draw_for_reward = rl_config.reward.standardization_source == "draw"
+
+                # Compute quantiles and standardized values for REWARDS
+                # (respects standardization_source setting)
+                reward_y_quantiles = compute_quantiles(
+                    gen_res.ys, use_draw=use_draw_for_reward
+                )
+                reward_target_y_quantiles = compute_quantiles(
+                    gen_res.target_ys, use_draw=use_draw_for_reward
+                )
+                reward_standardized_ys = compute_standardized_ys(
+                    gen_res.ys, use_draw=use_draw_for_reward
+                )
+                reward_standardized_target_ys = compute_standardized_ys(
+                    gen_res.target_ys, use_draw=use_draw_for_reward
+                )
+
+                # Compute quantiles and standardized values for PLOTS
+                # (always uses draw for quantiles, batch for standardized)
+                plot_y_quantiles = compute_quantiles(gen_res.ys, use_draw=True)
+                plot_target_y_quantiles = compute_quantiles(
+                    gen_res.target_ys, use_draw=True
+                )
+                plot_standardized_ys = compute_standardized_ys(
+                    gen_res.ys, use_draw=True
+                )
+                plot_standardized_target_ys = compute_standardized_ys(
+                    gen_res.target_ys, use_draw=True
+                )
+
+                draw_time = time.time() - start_draw_time
+
+                # These rewards make sense, but they do have the problem that randomly roll-out might just be disadvantaged by earlier mishaps in it
+                # This could be fixed by keeping all trajectories the same until we break out into sub trajectories and then only train at the break-out point
+
+                gen_res.normalized_avg_rewards, gen_res.unnormalized_avg_rewards = (
+                    rl_config.reward.compute_reward(
+                        gen_res.ys.view(batch_size, sub_batch_size, -1),
+                        gen_res.target_ys.view(batch_size, sub_batch_size, -1),
+                        reward_y_quantiles.view(batch_size, sub_batch_size, -1),
+                        reward_target_y_quantiles.view(batch_size, sub_batch_size, -1),
+                        reward_standardized_ys.view(batch_size, sub_batch_size, -1),
+                        reward_standardized_target_ys.view(
+                            batch_size, sub_batch_size, -1
+                        ),
+                    )
+                )
+
+                # For batched BO, copy reward from last position in each batch to all positions
+                if gen_res.bo_batch_size > 1:
+                    reward_seq_len = gen_res.normalized_avg_rewards.shape[-1]
+                    normalized = gen_res.normalized_avg_rewards.clone()
+                    unnormalized = gen_res.unnormalized_avg_rewards.clone()
+
+                    for batch_start in range(0, reward_seq_len, gen_res.bo_batch_size):
+                        batch_end = min(
+                            batch_start + gen_res.bo_batch_size, reward_seq_len
+                        )
+                        # Copy reward from last position in batch to all positions in batch
+                        normalized[..., batch_start:batch_end] = normalized[
+                            ..., batch_end - 1 : batch_end
+                        ]
+                        unnormalized[..., batch_start:batch_end] = unnormalized[
+                            ..., batch_end - 1 : batch_end
+                        ]
+
+                    gen_res.normalized_avg_rewards = normalized
+                    gen_res.unnormalized_avg_rewards = unnormalized
+                gen_res.draw = draw
+                # Store plot versions (not reward versions) for tensorboard logging
+                gen_res.y_quantiles = plot_y_quantiles
+                gen_res.target_y_quantiles = plot_target_y_quantiles
+                gen_res.standardized_ys = plot_standardized_ys
+                gen_res.standardized_target_ys = plot_standardized_target_ys
+                gen_res.draw_size = draw_size
+
+                gen_results.append(gen_res)
+
+            # TODO put all the relevant var's, that is for stuff without plotting just normalized_avg_rewards, I believe, into the gen_res and then generate multiple gen_res and cycle through them during training
+            # when we do the features, we should enable it to just be a subset of the features
+            # and when we run the trainings with this setting we should maybe think about reducing the batch size!?
+
+            if writer is not None:
+                # Log current curriculum seq_len
+                writer.add_scalar("curriculum/seq_len", seq_len, batch_i)
+
+                def avg(
+                    values: list[float],
+                ) -> float:
+                    return sum(values) / len(values)
+
+                # Aggregated metrics (average across all gen_results)
+                avg_reward = avg(
+                    [gr.unnormalized_avg_rewards.mean().item() for gr in gen_results]
+                )
+                writer.add_scalar(
+                    "avg_reward",
+                    avg_reward,
+                    batch_i,
+                )
+
+                writer.add_scalar(
+                    "reward_metrics/std_mean",
+                    avg(
+                        [
+                            gr.unnormalized_avg_rewards.std(1, keepdim=True)
+                            .mean()
+                            .item()
+                            for gr in gen_results
+                        ]
+                    ),
+                    batch_i,
+                )
+                writer.add_scalar(
+                    "reward_metrics/std_min",
+                    min(
+                        gr.unnormalized_avg_rewards.std(1, keepdim=True).min().item()
+                        for gr in gen_results
+                    ),
+                    batch_i,
+                )
+                writer.add_scalar(
+                    "retrieved_y/mean",
+                    avg([gr.ys.mean().item() for gr in gen_results]),
+                    batch_i,
+                )
+                writer.add_scalar(
+                    "retrieved_y/last",
+                    avg([gr.ys[:, -1].mean().item() for gr in gen_results]),
+                    batch_i,
+                )
+                writer.add_scalar(
+                    "retrieved_y/max",
+                    avg([gr.ys.max(1).values.mean().item() for gr in gen_results]),
+                    batch_i,
+                )
+
+                # Regret metrics
+                for name in ["noisy", "noiseless"]:
+                    regrets = []
+                    for gr in gen_results:
+                        max_draw_per_function = gr.draw.max(1).values
+                        used_ys = gr.ys if name == "noisy" else gr.target_ys
+                        regret = (
+                            (
+                                max_draw_per_function[:, None]
+                                - used_ys.max(1).values.view(batch_size, sub_batch_size)
+                            )
+                            .mean()
+                            .item()
+                        )
+                        regrets.append(regret)
+                    writer.add_scalar(
+                        f"retrieved_y/final_{name}_regret_v_{draw_size}rs",
+                        avg(regrets),
+                        batch_i,
+                    )
+
+                # Noiseless regret at step increments of 5
+                step_increments = list(range(5, seq_len + 1, 5))
+                for step_cutoff in step_increments:
+                    regrets_at_step = []
+                    for gr in gen_results:
+                        max_draw_per_function = gr.draw.max(1).values
+                        used_ys = gr.target_ys[:, :step_cutoff]
+                        max_y_up_to_step = used_ys.max(1).values.view(
+                            batch_size, sub_batch_size
+                        )
+                        regret = (
+                            (max_draw_per_function[:, None] - max_y_up_to_step)
+                            .mean()
+                            .item()
+                        )
+                        regrets_at_step.append(regret)
+                    writer.add_scalar(
+                        f"retrieved_y/noiseless_regret_at_step_{step_cutoff}",
+                        avg(regrets_at_step),
+                        batch_i,
+                    )
+
+                # Distance to incumbent (Euclidean distance to best point seen so far)
+                # Averaged over groups of 5 steps
+                # Skip this logging when using randomize_seq_len as sequence lengths vary
+                if not rl_config.randomize_seq_len:
+                    for group_start in range(0, seq_len, 5):
+                        group_end = min(group_start + 5, seq_len)
+                        all_distances = []
+                        for gr in gen_results:
+                            for step_i in range(group_start, group_end):
+                                if step_i == 0:
+                                    # At step 0, there's no previous incumbent
+                                    continue
+                                # Incumbent is the argmax of target_ys among steps 0 to step_i-1
+                                incumbent_indices = gr.target_ys[:, :step_i].argmax(
+                                    dim=1
+                                )
+                                batch_indices = torch.arange(
+                                    gr.predictions.shape[0],
+                                    device=gr.predictions.device,
+                                )
+                                incumbent_x = gr.predictions[
+                                    batch_indices, incumbent_indices
+                                ]
+                                current_x = gr.predictions[:, step_i]
+                                # Euclidean distance
+                                distance = (
+                                    (current_x - incumbent_x).pow(2).sum(dim=-1).sqrt()
+                                )
+                                all_distances.append(distance.mean().item())
+
+                        if all_distances:
+                            avg_distance = sum(all_distances) / len(all_distances)
+                            writer.add_scalar(
+                                f"incumbent_distance/steps_{group_start + 1}_to_{group_end}",
+                                avg_distance,
+                                batch_i,
+                            )
+
+                # Quantile metrics
+                writer.add_scalar(
+                    "retrieved_y/mean_quantile",
+                    avg([gr.target_y_quantiles.mean().item() for gr in gen_results]),
+                    batch_i,
+                )
+                writer.add_scalar(
+                    "retrieved_y/last_quantile",
+                    avg(
+                        [
+                            gr.target_y_quantiles[:, -1].mean().item()
+                            for gr in gen_results
+                        ]
+                    ),
+                    batch_i,
+                )
+                max_quantile = avg(
+                    [
+                        gr.target_y_quantiles.max(1).values.mean().item()
+                        for gr in gen_results
+                    ]
+                )
+                writer.add_scalar(
+                    "retrieved_y/max_quantile",
+                    max_quantile,
+                    batch_i,
+                )
+
+                print(
+                    "average max quantile",
+                    max_quantile,
+                )
+                print("avereage reward", avg_reward)
+                print("generation took ", generation_time, "seconds")
+
+                writer.add_scalar(
+                    "retrieved_y/max_standardized",
+                    avg(
+                        [
+                            gr.standardized_target_ys.max(1).values.mean().item()
+                            for gr in gen_results
+                        ]
+                    ),
+                    batch_i,
+                )
+
+                # Per-feature metrics
+                for gr in gen_results:
+                    num_feats = gr.current_num_features
+                    local_draw_size = gr.draw_size
+                    max_draw_per_function = gr.draw.max(1).values
+
+                    writer.add_scalar(
+                        f"{num_feats}_features/avg_reward",
+                        gr.unnormalized_avg_rewards.mean().item(),
+                        batch_i,
+                    )
+                    for name, used_ys in [
+                        ("noisy", gr.ys),
+                        ("noiseless", gr.target_ys),
+                    ]:
+                        writer.add_scalar(
+                            f"{num_feats}_features/retrieved_y/final_{name}_regret_v_{local_draw_size}rs",
+                            (
+                                max_draw_per_function[:, None]
+                                - used_ys.max(1).values.view(batch_size, sub_batch_size)
+                            )
+                            .mean()
+                            .item(),
+                            batch_i,
+                        )
+                    writer.add_scalar(
+                        f"{num_feats}_features/retrieved_y/max_quantile",
+                        gr.target_y_quantiles.max(1).values.mean().item(),
+                        batch_i,
+                    )
+
+                # Rollout distribution metrics (entropy, max_prob, sampled_prob)
+                # Averaged across all steps and all samples in the batch
+                # Note: tensors may have different shapes (batch_size during joint steps,
+                # super_batch_size after), so we concatenate and compute mean safely
+                all_entropies = []
+                all_max_probs = []
+                all_sampled_probs = []
+                for gr in gen_results:
+                    if gr.step_entropies:
+                        # Concatenate all step tensors (handles different shapes)
+                        entropies = torch.cat([e.flatten() for e in gr.step_entropies])
+                        max_probs = torch.cat([m.flatten() for m in gr.step_max_probs])
+                        sampled_probs = torch.cat(
+                            [s.flatten() for s in gr.step_sampled_probs]
+                        )
+                        # Mean across all samples
+                        all_entropies.append(entropies.mean().item())
+                        all_max_probs.append(max_probs.mean().item())
+                        all_sampled_probs.append(sampled_probs.mean().item())
+
+                if all_entropies:
+                    writer.add_scalar(
+                        "rollout/entropy",
+                        sum(all_entropies) / len(all_entropies),
+                        batch_i,
+                    )
+                    writer.add_scalar(
+                        "rollout/max_prob",
+                        sum(all_max_probs) / len(all_max_probs),
+                        batch_i,
+                    )
+                    writer.add_scalar(
+                        "rollout/sampled_prob",
+                        sum(all_sampled_probs) / len(all_sampled_probs),
+                        batch_i,
+                    )
+
+            # training actually starts here
+            start_training_loop_time = time.time()
+
+            old_model.load_state_dict(copy.deepcopy(unwrap_model(model).state_dict()))
+
+            losses: list[float] = []
+            nan_encountered = False
+
+            # Tracking for eps clamping analysis - per repetition
+            per_rep_stats: list[dict[str, int]] = []
+
+            # Tracking for zero variance filtering
+            zero_variance_filtered_samples = 0
+            total_filtering_samples = 0
+
+            # Build list of (gen_res_idx, seq_idx) pairs to iterate over
+            if rl_config.joint_rollout_training == "single":
+                # Only train on the first non-joint position (the split point)
+                training_steps = [
+                    (gr_idx, gr.joint_steps) for gr_idx, gr in enumerate(gen_results)
+                ]
+            elif rl_config.joint_rollout_training == "remaining":
+                # Train on all positions from the split point onwards
+                training_steps = [
+                    (gr_idx, sep)
+                    for gr_idx, gr in enumerate(gen_results)
+                    for sep in range(gr.joint_steps, gr.predictions.shape[1])
+                ]
+            else:
+                # No joint rollout training: train on all positions
+                training_steps = [
+                    (gr_idx, sep)
+                    for gr_idx, gr in enumerate(gen_results)
+                    for sep in range(gr.predictions.shape[1])
+                ]
+
+            for _rep_idx in range(rl_config.experience_repetitions):
+                # Per-repetition tracking
+                rep_total_samples = 0
+                rep_ratio_above_eps = 0
+                rep_ratio_below_eps = 0
+                rep_clamp_active = 0
+                random.shuffle(training_steps)
+
+                for gr_idx, i in training_steps:
+                    gen_res = gen_results[gr_idx]
+                    current_num_features = gen_res.current_num_features
+
+                    with torch.amp.autocast(dtype=torch.float16, device_type="cuda"):
+                        optimizer.zero_grad(set_to_none=True)
+                        if i == 0:
+                            train_x = gen_res.predictions[:, :i]
+                            train_y = gen_res.ys[:, :i, None]
+                            full_train_x = torch.cat((train_x, train_y), -1)
+                        else:
+                            train_x, train_y = preprocess_train_x_and_y(
+                                gen_res.predictions[:, :i],
+                                gen_res.ys[:, :i, None],
+                                rl_config.standardize_y,
+                                bo_batch_size=gen_res.bo_batch_size,
+                            )
+                            full_train_x = torch.cat((train_x, train_y), -1)
+                        full_test_x = torch.full(
+                            (batch_size * sub_batch_size, 1, current_num_features + 1),
+                            torch.nan,
+                            device=device,
+                        )
+                        if rl_config.choose_next_in_set:
+                            # Prepare options with base model EI if enabled
+                            if rl_config.basemodel_ei_input:
+                                # Reuse the precomputed EI values from generation
+                                basemodel_ei = gen_res.basemodel_ei_values[i]
+                                # Create augmented options with EI
+                                opts = torch.cat(
+                                    [gen_res.options[i], basemodel_ei], dim=-1
+                                )
+                                # Add 0s to train_x for the EI column
+                                train_x = torch.cat(
+                                    [
+                                        train_x,
+                                        torch.zeros(
+                                            train_x.shape[0],
+                                            train_x.shape[1],
+                                            1,
+                                            device=device,
+                                        )
+                                        + 0.5,
+                                    ],
+                                    dim=-1,
+                                )
+                            else:
+                                opts = gen_res.options[i]
+
+                            # Use stored y_style from path generation (already in super_batch_size shape)
+                            with torch.no_grad():
+                                logits_old = transform_logits(
+                                    old_model,
+                                    old_model(
+                                        x=train_x,
+                                        y=train_y,
+                                        test_x=opts,
+                                        y_style=gen_res.y_style,
+                                    ),
+                                    rl_config.ei_selector,
+                                )  # shape: superb x num options
+
+                            logits_new = transform_logits(
+                                model,
+                                model(
+                                    x=train_x,
+                                    y=train_y,
+                                    test_x=opts,
+                                    y_style=gen_res.y_style,
+                                ),
+                                rl_config.ei_selector,
+                            )  # shape: superb x num options
+
+                            log_p_new = logits_new.log_softmax(1)[
+                                torch.arange(super_batch_size),
+                                gen_res.chosen_options[i],
+                            ]
+                            log_p_old = logits_old.log_softmax(1)[
+                                torch.arange(super_batch_size),
+                                gen_res.chosen_options[i],
+                            ]
+                            pred_ratio = (log_p_new - log_p_old).exp()
+
+                        else:
+                            logits_new = model(
+                                x=full_train_x, y=None, test_x=full_test_x
+                            )[:, :, 0].squeeze(1)
+                            with torch.no_grad():
+                                logits_old = (
+                                    old_model(
+                                        x=full_train_x, y=None, test_x=full_test_x
+                                    )[:, :, 0]
+                                    .squeeze(1)
+                                    .detach()
+                                )
+                            # criterion computes the neg log likelihood, that is why the ratio is inverted
+                            # Note: old_model is always unwrapped, but model might be DDP-wrapped
+                            nll_old = old_model.criterion(
+                                logits_old, gen_res.predictions[:, i]
+                            )
+                            nll_new = unwrap_model(model).criterion(
+                                logits_new, gen_res.predictions[:, i]
+                            )
+                            log_p_new = -nll_new
+                            pred_ratio = (nll_old - nll_new).exp()
+                        normalized_avg_rewards = gen_res.normalized_avg_rewards.view(
+                            batch_size * sub_batch_size, -1
+                        )
+
+                        # Filter low-magnitude functions per time step
+                        # Compute average magnitude (L1 / sub_batch_size) per function
+                        rewards_at_step = normalized_avg_rewards[:, i].view(
+                            batch_size, sub_batch_size
+                        )
+                        avg_magnitude_per_func = rewards_at_step.abs().mean(
+                            dim=1
+                        )  # [batch_size]
+
+                        # Zero out rewards for functions with low average magnitude
+                        # and scale up remaining rewards to maintain gradient magnitude
+                        if rl_config.filter_rewards_up_to_magnitude is not None:
+                            low_magnitude_mask = (
+                                avg_magnitude_per_func
+                                <= rl_config.filter_rewards_up_to_magnitude
+                            )  # [batch_size]
+                            effective_batch_size = (
+                                batch_size - low_magnitude_mask.sum().item()
+                            )
+
+                            # Track zero variance filtered samples
+                            zero_variance_filtered_samples += (
+                                low_magnitude_mask.sum().item()
+                            )
+                            total_filtering_samples += batch_size
+
+                            # Expand mask to super_batch_size and zero out rewards
+                            full_zero_mask = (
+                                low_magnitude_mask.unsqueeze(1)
+                                .expand(-1, sub_batch_size)
+                                .reshape(-1)
+                            )  # [batch_size * sub_batch_size]
+                            # Create a copy of rewards and zero out low-magnitude functions
+                            rewards_for_loss = normalized_avg_rewards[:, i].clone()
+                            rewards_for_loss[full_zero_mask] = 0.0
+                            # Scale up by batch_size / effective_batch_size to maintain gradient magnitude
+                            if effective_batch_size > 0:
+                                rewards_for_loss = (
+                                    rewards_for_loss * batch_size / effective_batch_size
+                                )
+                        else:
+                            rewards_for_loss = normalized_avg_rewards[:, i]
+
+                        # Compute eps bounds (asymmetric if eps_low is specified)
+                        eps_lower = (
+                            rl_config.eps_low if rl_config.eps_low is not None else eps
+                        )
+                        eps_upper = eps
+
+                        # Track eps clamping statistics for this repetition
+                        with torch.no_grad():
+                            n_samples = pred_ratio.numel()
+                            rep_total_samples += n_samples
+                            rep_ratio_above_eps += (
+                                (pred_ratio > 1 + eps_upper).sum().item()
+                            )
+                            rep_ratio_below_eps += (
+                                (pred_ratio < 1 - eps_lower).sum().item()
+                            )
+                            # Clamp is active when pred_ratio is outside bounds
+                            rep_clamp_active += (
+                                (
+                                    (pred_ratio > 1 + eps_upper)
+                                    | (pred_ratio < 1 - eps_lower)
+                                )
+                                .sum()
+                                .item()
+                            )
+
+                        if rl_config.algorithm == "grpo":
+                            # GRPO: min(ratio * advantage, clamp(ratio) * advantage)
+                            goal = pred_ratio * rewards_for_loss
+                            clamped_goal = (
+                                pred_ratio.clamp(1 - eps_lower, 1 + eps_upper)
+                                * rewards_for_loss
+                            )
+                            # GRPO formulation is a maximization and we minimize
+                            loss = -torch.min(goal, clamped_goal)
+                        else:
+                            # CISPO: stop_grad(clamp(ratio)) * advantage * log_p_new
+                            assert rl_config.algorithm == "cispo"
+                            clamped_weight = pred_ratio.detach().clamp(
+                                1 - eps_lower, 1 + eps_upper
+                            )
+                            # Maximize weighted log probability, so negate for minimization
+                            loss = -clamped_weight * rewards_for_loss * log_p_new
+
+                        loss = loss.mean()
+
+                        scaler.scale(loss).backward()
+
+                    losses.append(loss.detach().item())
+
+                    if torch.isnan(loss):
+                        if is_main:
+                            print("nan loss")
+                        nan_encountered = True
+                        break
+
+                    if (
+                        rl_config.grad_clip_norm is not None
+                        and rl_config.grad_clip_norm > 0
+                    ):
+                        scaler.unscale_(optimizer)
+                        torch.nn.utils.clip_grad_norm_(
+                            model.parameters(), rl_config.grad_clip_norm
+                        )
+
+                    scaler.step(optimizer)
+                    scaler.update()
+
+                if nan_encountered:
+                    break
+
+                # Store this repetition's stats
+                per_rep_stats.append(
+                    {
+                        "total_samples": rep_total_samples,
+                        "ratio_above_eps": rep_ratio_above_eps,
+                        "ratio_below_eps": rep_ratio_below_eps,
+                        "clamp_active": rep_clamp_active,
+                    }
+                )
+
+            training_loop_time = time.time() - start_training_loop_time
+            mean_loss = sum(losses) / len(losses) if losses else float("nan")
+
+            # Synchronize mean_loss across all workers for coordinated rollback decision
+            if distributed:
+                mean_loss_tensor = torch.tensor(mean_loss, device=device)
+                dist.all_reduce(mean_loss_tensor, op=dist.ReduceOp.AVG)
+                mean_loss = mean_loss_tensor.item()
+
+            # Check if batch should be rolled back due to high loss
+            if (
+                rl_config.rollback_loss_threshold is not None
+                and mean_loss > rl_config.rollback_loss_threshold
+            ):
+                # Rollback model and optimizer state to before this batch
+                unwrap_model(model).load_state_dict(model_state_before_batch)
+                optimizer.load_state_dict(optimizer_state_before_batch)
+                batch_rolled_back = True
+                if is_main:
+                    print(
+                        f"Batch {batch_i} rolled back: mean loss {mean_loss:.4f} > threshold {rl_config.rollback_loss_threshold}"
+                    )
+
+            batch_losses.append(mean_loss)
+            if is_main:
+                print("mean loss", mean_loss, "after batch", batch_i)
+
+            if writer is not None and not math.isnan(mean_loss):
+                print(f"rank {rank} is pushing times for batch {batch_i}")
+                writer.add_scalar("loss/mean_batch_loss", mean_loss, batch_i)
+                writer.add_scalar(
+                    "optimizer/lr", optimizer.param_groups[0]["lr"], batch_i
+                )
+                writer.add_scalar("time/generation_time", generation_time, batch_i)
+                writer.add_scalar("time/draw_time", draw_time, batch_i)
+                writer.add_scalar(
+                    "time/training_loop_time", training_loop_time, batch_i
+                )
+
+                # Log eps clamping statistics - per repetition
+                for rep_idx, stats in enumerate(per_rep_stats):
+                    if stats["total_samples"] > 0:
+                        ratio_above_eps_frac = (
+                            stats["ratio_above_eps"] / stats["total_samples"]
+                        )
+                        ratio_below_eps_frac = (
+                            stats["ratio_below_eps"] / stats["total_samples"]
+                        )
+                        clamp_active_frac = (
+                            stats["clamp_active"] / stats["total_samples"]
+                        )
+
+                        writer.add_scalar(
+                            f"ppo_clipping/rep_{rep_idx}/ratio_above_eps_frac",
+                            ratio_above_eps_frac,
+                            batch_i,
+                        )
+                        writer.add_scalar(
+                            f"ppo_clipping/rep_{rep_idx}/ratio_below_eps_frac",
+                            ratio_below_eps_frac,
+                            batch_i,
+                        )
+                        writer.add_scalar(
+                            f"ppo_clipping/rep_{rep_idx}/clamp_active_frac",
+                            clamp_active_frac,
+                            batch_i,
+                        )
+                        writer.add_scalar(
+                            f"ppo_clipping/rep_{rep_idx}/ratio_within_bounds_frac",
+                            1.0 - clamp_active_frac,
+                            batch_i,
+                        )
+
+                # Also log aggregated stats across all repetitions
+                total_samples_all = sum(s["total_samples"] for s in per_rep_stats)
+                if total_samples_all > 0:
+                    total_above = sum(s["ratio_above_eps"] for s in per_rep_stats)
+                    total_below = sum(s["ratio_below_eps"] for s in per_rep_stats)
+                    total_clamp = sum(s["clamp_active"] for s in per_rep_stats)
+
+                    writer.add_scalar(
+                        "ppo_clipping/total/ratio_above_eps_frac",
+                        total_above / total_samples_all,
+                        batch_i,
+                    )
+                    writer.add_scalar(
+                        "ppo_clipping/total/ratio_below_eps_frac",
+                        total_below / total_samples_all,
+                        batch_i,
+                    )
+                    writer.add_scalar(
+                        "ppo_clipping/total/clamp_active_frac",
+                        total_clamp / total_samples_all,
+                        batch_i,
+                    )
+
+                if per_rep_stats:
+                    print("  PPO clipping stats per repetition:")
+                    for rep_idx, stats in enumerate(per_rep_stats):
+                        if stats["total_samples"] > 0:
+                            print(
+                                f"    rep_{rep_idx}: "
+                                f"above_eps={stats['ratio_above_eps'] / stats['total_samples']:.4f}, "
+                                f"below_eps={stats['ratio_below_eps'] / stats['total_samples']:.4f}, "
+                                f"clamp_active={stats['clamp_active'] / stats['total_samples']:.4f}"
+                            )
+
+                # Log whether this batch was rolled back (1) or not (0)
+                writer.add_scalar(
+                    "training/batch_rolled_back",
+                    1 if batch_rolled_back else 0,
+                    batch_i,
+                )
+
+            if is_main and total_filtering_samples > 0:
+                zero_variance_filtered_share = (
+                    zero_variance_filtered_samples / total_filtering_samples
+                )
+                writer.add_scalar(
+                    "reward_metrics/zero_variance_filtered_share",
+                    zero_variance_filtered_share,
+                    batch_i,
+                )
+
+            scheduler.step()
+
+            # Save checkpoint after each batch (only on main process)
+            if rl_config.checkpoint_save_path is not None and is_main:
+                _save_checkpoint(
+                    model=unwrap_model(model),
+                    base_train_config=base_train_config,
+                    rl_config=rl_config,
+                    output_path=rl_config.checkpoint_save_path,
+                    batch_i=batch_i,
+                    optimizer=optimizer,
+                    scaler=scaler,
+                    scheduler=scheduler,
+                )
+
+        # Save final checkpoint only on main process (legacy path for backwards compatibility)
+
+        return {
+            "losses": batch_losses,
+            "model": unwrap_model(model),
+            "base_train_config": base_train_config,
+        }
+
+    finally:
+        if writer is not None:
+            writer.close()
+
+
+def _save_checkpoint(
+    model: torch.nn.Module,
+    base_train_config: base_config.BaseConfig,
+    rl_config: RLConfig,
+    output_path: str,
+    batch_i: int | None = None,
+    optimizer: torch.optim.Optimizer | None = None,
+    scaler: torch.amp.GradScaler | None = None,
+    scheduler: torch.optim.lr_scheduler.LRScheduler | None = None,
+):
+    """Save a checkpoint to the specified path.
+
+    Args:
+        model: The model to save
+        base_train_config: The base training config
+        rl_config: The RL config
+        output_path: Path to save the checkpoint
+        batch_i: Current batch index (optional, for resumable checkpoints)
+        optimizer: Optimizer state (optional, for resumable checkpoints)
+        scaler: GradScaler state (optional, for resumable checkpoints)
+        scheduler: LR scheduler state (optional, for resumable checkpoints)
+    """
+    checkpoint = {
+        "model_state_dict": {
+            k: v.detach().cpu() for k, v in model.state_dict().items()
+        },
+        "base_train_config": base_train_config.to_dict(),
+        "config": rl_config.to_dict(),
+    }
+
+    if batch_i is not None:
+        checkpoint["batch_i"] = batch_i
+    if optimizer is not None:
+        checkpoint["optimizer_state_dict"] = optimizer.state_dict()
+    if scaler is not None:
+        checkpoint["scaler_state_dict"] = scaler.state_dict()
+    if scheduler is not None:
+        checkpoint["scheduler_state_dict"] = scheduler.state_dict()
+
+    local_save(checkpoint, output_path)
+
+
+def should_load_rl_checkpoint(
+    rl_config: RLConfig,
+    check_path_exists_function: tp.Callable[[str], bool] | None = None,
+) -> bool:
+    """Check if we should load a checkpoint.
+
+    Returns True if:
+    - checkpoint_load_path is set AND
+    - Either load_path != save_path, OR the file exists
+    """
+    if rl_config.checkpoint_load_path is None:
+        return False
+
+    if check_path_exists_function is None:
+        check_path_exists_function = local_exists
+
+    return (rl_config.checkpoint_save_path != rl_config.checkpoint_load_path) or (
+        (rl_config.checkpoint_save_path == rl_config.checkpoint_load_path)
+        and check_path_exists_function(rl_config.checkpoint_load_path)
+    )
+
+
+def load_rl_checkpoint(
+    model: torch.nn.Module,
+    optimizer: torch.optim.Optimizer,
+    scheduler: torch.optim.lr_scheduler.LRScheduler,
+    scaler: torch.amp.GradScaler,
+    checkpoint_load_path: str,
+    device: str,
+    load_function: tp.Callable | None = None,
+) -> int:
+    """Load a checkpoint and restore training state.
+
+    Args:
+        model: The model to load state into
+        optimizer: The optimizer to load state into
+        scheduler: The scheduler to load state into (or fast-forward)
+        scaler: The GradScaler to load state into
+        checkpoint_load_path: Path to load checkpoint from
+        device: Device to map tensors to
+        load_function: Custom load function (defaults to local_load)
+
+    Returns:
+        The batch index to resume from (checkpoint batch_i + 1)
+    """
+    print(f"Loading checkpoint from {checkpoint_load_path}")
+
+    if load_function is None:
+        load_function = local_load
+
+    try:
+        checkpoint = load_function(checkpoint_load_path, map_location=device)
+
+        if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint:
+            model.load_state_dict(checkpoint["model_state_dict"], strict=True)
+
+            if "optimizer_state_dict" in checkpoint:
+                optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+
+            if "scaler_state_dict" in checkpoint:
+                scaler.load_state_dict(checkpoint["scaler_state_dict"])
+
+            if "batch_i" in checkpoint:
+                start_batch = checkpoint["batch_i"] + 1
+                print(f"Resuming from batch {start_batch}")
+
+                if "scheduler_state_dict" in checkpoint:
+                    scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
+                else:
+                    for _ in range(start_batch):
+                        scheduler.step()
+
+                return start_batch
+            else:
+                print("Checkpoint does not contain batch index, starting from 0")
+                return 0
+        else:
+            raise ValueError(
+                f"Checkpoint does not contain 'model_state_dict'. Keys: {checkpoint.keys() if isinstance(checkpoint, dict) else type(checkpoint)}"
+            )
+    except Exception as e:
+        print(f"Error loading checkpoint: {e}")
+        raise e
diff --git a/rl/utils.py b/rl/utils.py
new file mode 100644
index 0000000..2208bc4
--- /dev/null
+++ b/rl/utils.py
@@ -0,0 +1,33 @@
+from typing import Tuple
+
+import torch
+from pfns.model import transformer
+from pfns.train import MainConfig
+
+
+def load_config_and_model(
+    path: str = "tree/pfns/runs/singletaskgp2_clusteredx_0/checkpoint.pt",
+    map_location: str | None = None,
+) -> Tuple[MainConfig, transformer.TableTransformer]:
+    """
+    Load a config and model from a checkpoint file on the local filesystem.
+
+    Args:
+        path: The path to the checkpoint file.
+        map_location: The device to load the tensors to. Same as torch.load, e.g. "cpu" or "cuda:0".
+
+    Returns:
+        A tuple of (config, model).
+    """
+    if map_location is None:
+        map_location = "cuda" if torch.cuda.is_available() else "cpu"
+
+    checkpoint = torch.load(path, map_location=map_location)
+
+    config_dict = checkpoint["config"]
+
+    c = MainConfig.from_dict(config_dict)
+    model = c.model.create_model()
+    model.load_state_dict(checkpoint["model_state_dict"])
+    model.eval()
+    return c, model.to(map_location)
diff --git a/test_convert_prior_to_x_only_format.py b/test_convert_prior_to_x_only_format.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/model/test_attention.py b/tests/model/test_attention.py
index 8e6d8db..b5ea993 100644
--- a/tests/model/test_attention.py
+++ b/tests/model/test_attention.py
@@ -2,31 +2,31 @@
 
 from pfns.model.multi_head_attention import MultiHeadAttention
 
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Testing attention on {device=}.")
+n_batch = 7
+nhead = 4
+n_seq_q = 534
+n_seq_kv = 316
+embed_dim = 128
 
-def test_attention():
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print(f"Testing attention on {device=}.")
-    n_batch = 7
-    nhead = 4
-    n_seq_q = 534
-    n_seq_kv = 316
-    embed_dim = 128
+dtype = torch.float16 if device == "cuda" else torch.float32
 
-    dtype = torch.float16 if device == "cuda" else torch.float32
+x_q = torch.normal(
+    torch.tensor(0.0),
+    torch.tensor(1.0),
+    size=(n_batch, n_seq_q, embed_dim),
+)
+x_kv = torch.normal(
+    torch.tensor(0.0),
+    torch.tensor(1.0),
+    size=(n_batch, n_seq_kv, embed_dim),
+)
+x_q = x_q.to(device, dtype)
+x_kv = x_kv.to(device, dtype)
 
-    x_q = torch.normal(
-        torch.tensor(0.0),
-        torch.tensor(1.0),
-        size=(n_batch, n_seq_q, embed_dim),
-    )
-    x_kv = torch.normal(
-        torch.tensor(0.0),
-        torch.tensor(1.0),
-        size=(n_batch, n_seq_kv, embed_dim),
-    )
-    x_q = x_q.to(device, dtype)
-    x_kv = x_kv.to(device, dtype)
 
+def test_attention():
     att_ref = torch.nn.MultiheadAttention(
         embed_dim,
         nhead,
@@ -105,20 +105,6 @@ def test_attention():
     y_ = att_multi_test(x_q, x_kv)
     assert torch.sqrt(torch.nn.functional.mse_loss(y, y_)) < 5e-5
 
-    # Caching.
-    att_test = MultiHeadAttention(
-        input_size=embed_dim,
-        output_size=embed_dim,
-        d_k=embed_dim // nhead,
-        d_v=embed_dim // nhead,
-        nhead=nhead,
-        device=device,
-        dtype=dtype,
-    )
-    y = att_test(x_q, x_kv, cache_kv=True)
-    y_ = att_test(x_q, use_cached_kv=True)
-    assert torch.sqrt(torch.nn.functional.mse_loss(y, y_)) < 5e-5
-
 
 if __name__ == "__main__":
     test_attention()
diff --git a/tests/model/test_pfn_caching.py b/tests/model/test_pfn_caching.py
new file mode 100644
index 0000000..1f4ec8a
--- /dev/null
+++ b/tests/model/test_pfn_caching.py
@@ -0,0 +1,477 @@
+"""Tests for PFN caching functionality.
+
+Tests verify:
+1. Gradient flow through test_x when using cached training context
+2. Output consistency between cached and non-cached modes
+3. Batch broadcast behavior with cached KV
+4. Sequential vs batch evaluation equivalence
+"""
+
+import pytest
+import torch
+
+from pfns.model.transformer import TableTransformer
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Testing PFN caching on {device=}.")
+
+dtype = torch.float32  # Use float32 for gradient tests
+
+
+@pytest.fixture
+def minimal_transformer():
+    """Create a minimal TableTransformer for testing."""
+    model = TableTransformer(
+        ninp=32,
+        nhead=2,
+        nhid=64,
+        nlayers=2,
+        batch_first=True,
+        cache_trainset_representation=False,
+    )
+    model = model.to(device=device, dtype=dtype)
+    # Add small non-zero values to weights to make outputs more interesting
+    with torch.no_grad():
+        for p in model.parameters():
+            p.add_(0.01 * torch.randn_like(p))
+    return model
+
+
+@pytest.fixture
+def sample_data():
+    """Generate sample training and test data."""
+    n_train = 10
+    n_test = 5
+    n_features = 4
+    batch_size = 1  # Base batch size for training data
+
+    train_x = torch.randn(batch_size, n_train, n_features, device=device, dtype=dtype)
+    train_y = torch.randn(batch_size, n_train, 1, device=device, dtype=dtype)
+    test_x = torch.randn(batch_size, n_test, n_features, device=device, dtype=dtype)
+
+    return {
+        "train_x": train_x,
+        "train_y": train_y,
+        "test_x": test_x,
+        "n_train": n_train,
+        "n_test": n_test,
+        "n_features": n_features,
+    }
+
+
+def test_cache_output_consistency(minimal_transformer, sample_data):
+    """Test that cached and non-cached outputs are identical."""
+    model = minimal_transformer
+    train_x = sample_data["train_x"]
+    train_y = sample_data["train_y"]
+    test_x = sample_data["test_x"]
+
+    # Non-cached forward pass
+    model.cache_trainset_representation = False
+    with torch.no_grad():
+        output_no_cache = model(x=train_x, y=train_y, test_x=test_x)
+
+    # Cached forward pass: first populate cache, then use it
+    model.cache_trainset_representation = True
+    model.empty_trainset_representation_cache()
+
+    with torch.no_grad():
+        # Populate cache with training data
+        _ = model(x=train_x, y=train_y, test_x=test_x[:, :1, :])  # dummy test
+
+        # Use cache for actual test
+        output_with_cache = model(x=None, y=None, test_x=test_x)
+
+    # Clear cache
+    model.empty_trainset_representation_cache()
+    model.cache_trainset_representation = False
+
+    assert torch.allclose(output_no_cache, output_with_cache, atol=1e-7), (
+        f"Cached and non-cached outputs differ. "
+        f"Max diff: {(output_no_cache - output_with_cache).abs().max()}"
+    )
+
+
+def test_cache_gradient_flow(minimal_transformer, sample_data):
+    """Test that gradients flow through test_x when using cached training context.
+
+    This is critical for acquisition function optimization in BoTorch.
+    """
+    model = minimal_transformer
+    train_x = sample_data["train_x"]
+    train_y = sample_data["train_y"]
+    test_x = sample_data["test_x"].clone().requires_grad_(True)
+
+    # Enable caching
+    model.cache_trainset_representation = True
+    model.empty_trainset_representation_cache()
+
+    # Populate cache with no gradients on training data
+    with torch.no_grad():
+        _ = model(x=train_x, y=train_y, test_x=torch.zeros_like(test_x[:, :1, :]))
+
+    # Forward pass using cache - should allow gradients on test_x
+    output = model(x=None, y=None, test_x=test_x)
+
+    # Backward pass
+    loss = output.sum()
+    loss.backward()
+
+    # Verify gradients exist on test_x
+    assert test_x.grad is not None, "test_x.grad should not be None"
+    assert not torch.all(test_x.grad == 0), "test_x.grad should not be all zeros"
+
+    # Cleanup
+    model.empty_trainset_representation_cache()
+    model.cache_trainset_representation = False
+
+
+def test_cache_gradient_correctness(minimal_transformer, sample_data):
+    """Test that gradients are identical with and without caching."""
+    model = minimal_transformer
+    train_x = sample_data["train_x"]
+    train_y = sample_data["train_y"]
+
+    # Test 1: Compute gradients without caching
+    test_x_no_cache = sample_data["test_x"].clone().requires_grad_(True)
+    model.cache_trainset_representation = False
+    output_no_cache = model(x=train_x, y=train_y, test_x=test_x_no_cache)
+    output_no_cache.sum().backward()
+    grad_no_cache = test_x_no_cache.grad.clone()
+
+    # Test 2: Compute gradients with caching
+    test_x_with_cache = sample_data["test_x"].clone().requires_grad_(True)
+    model.cache_trainset_representation = True
+    model.empty_trainset_representation_cache()
+
+    # Populate cache
+    with torch.no_grad():
+        _ = model(
+            x=train_x, y=train_y, test_x=torch.zeros_like(test_x_with_cache[:, :1, :])
+        )
+
+    # Forward with cache
+    output_with_cache = model(x=None, y=None, test_x=test_x_with_cache)
+    output_with_cache.sum().backward()
+    grad_with_cache = test_x_with_cache.grad.clone()
+
+    # Cleanup
+    model.empty_trainset_representation_cache()
+    model.cache_trainset_representation = False
+
+    # Compare gradients
+    assert torch.allclose(grad_no_cache, grad_with_cache, atol=1e-7), (
+        f"Gradients differ. Max diff: {(grad_no_cache - grad_with_cache).abs().max()}"
+    )
+
+
+def test_cache_batch_broadcast(minimal_transformer, sample_data):
+    """Test that cached KV with batch_size=1 can be used with larger query batches.
+
+    This is essential for efficient acquisition function optimization where
+    we cache training context once and evaluate many test points in parallel.
+    """
+    model = minimal_transformer
+    train_x = sample_data["train_x"]  # (1, n, d)
+    train_y = sample_data["train_y"]  # (1, n, 1)
+    n_features = sample_data["n_features"]
+
+    # Create a larger batch of test points
+    batch_size = 4
+    n_test = 3
+    test_x_batch = torch.randn(
+        batch_size, n_test, n_features, device=device, dtype=dtype
+    )
+
+    # Compute reference: sequential evaluation (one at a time)
+    model.cache_trainset_representation = False
+    reference_outputs = []
+    for i in range(batch_size):
+        with torch.no_grad():
+            out = model(x=train_x, y=train_y, test_x=test_x_batch[i : i + 1])
+        reference_outputs.append(out)
+    reference_output = torch.cat(reference_outputs, dim=0)
+
+    # Enable caching with batch_size=1
+    model.cache_trainset_representation = True
+    model.empty_trainset_representation_cache()
+
+    # Populate cache with training data (batch_size=1)
+    with torch.no_grad():
+        _ = model(
+            x=train_x,
+            y=train_y,
+            test_x=torch.zeros(1, 1, n_features, device=device, dtype=dtype),
+        )
+
+    # Evaluate all test points in parallel using cache
+    # The test_x has batch_size=4, but cache has batch_size=1
+    # This requires broadcasting in the attention layer
+    with torch.no_grad():
+        # Expand train context for batch
+        batch_output = model(x=None, y=None, test_x=test_x_batch)
+
+    # Cleanup
+    model.empty_trainset_representation_cache()
+    model.cache_trainset_representation = False
+
+    # Compare outputs
+    assert batch_output.shape == reference_output.shape, (
+        f"Shape mismatch: batch={batch_output.shape}, ref={reference_output.shape}"
+    )
+    assert torch.allclose(reference_output, batch_output, atol=1e-7), (
+        f"Batch output differs from sequential. "
+        f"Max diff: {(reference_output - batch_output).abs().max()}"
+    )
+
+
+def test_attention_caching():
+    """Test MultiHeadAttention caching produces identical outputs and correct gradients."""
+    from pfns.model.multi_head_attention import MultiHeadAttention
+
+    embed_dim = 128
+    nhead = 4
+    n_seq_kv = 316
+    n_seq_q = 534
+    n_batch = 7
+
+    att = MultiHeadAttention(
+        input_size=embed_dim,
+        output_size=embed_dim,
+        d_k=embed_dim // nhead,
+        d_v=embed_dim // nhead,
+        nhead=nhead,
+        device=device,
+        dtype=dtype,
+    )
+
+    x_kv = torch.randn(n_batch, n_seq_kv, embed_dim, device=device, dtype=dtype)
+    x_q = torch.randn(n_batch, n_seq_q, embed_dim, device=device, dtype=dtype)
+
+    # Test output consistency between cached and non-cached
+    y = att(x_q, x_kv, cache_kv=True)
+    y_ = att(x_q, use_cached_kv=True)
+    assert torch.allclose(y, y_, atol=1e-7), (
+        f"Cached and non-cached attention outputs differ. "
+        f"Max diff: {(y - y_).abs().max()}"
+    )
+
+    # Gradients should fail for train part (caching requires no grad)
+    x_q_grad = x_q.clone().requires_grad_(True)
+    with pytest.raises(AssertionError):
+        att(x_q_grad, x_kv, cache_kv=True)
+
+    # Gradients should flow through test part when using cache
+    x_q_grad = x_q.clone().requires_grad_(True)
+    y_ = att(x_q_grad, x_kv, use_cached_kv=True)
+    y_.mean().backward()
+    assert x_q_grad.grad is not None
+    grads_with_cache = x_q_grad.grad.clone()
+
+    # Compute gradients without caching - should match
+    x_q_grad = x_q.clone().requires_grad_(True)
+    y_ = att(x_q_grad, x_kv)
+    y_.mean().backward()
+    assert x_q_grad.grad is not None
+    grads_without_cache = x_q_grad.grad.clone()
+
+    assert torch.allclose(grads_with_cache, grads_without_cache, atol=1e-7), (
+        f"Attention gradients differ. "
+        f"Max diff: {(grads_with_cache - grads_without_cache).abs().max()}"
+    )
+
+    # Cleanup
+    att.empty_kv_cache()
+
+
+def test_attention_cache_batch_broadcast():
+    """Test MultiHeadAttention layer directly for batch broadcast with cached KV."""
+    from pfns.model.multi_head_attention import MultiHeadAttention
+
+    embed_dim = 32
+    nhead = 2
+    n_seq_kv = 10
+    n_seq_q = 5
+
+    att = MultiHeadAttention(
+        input_size=embed_dim,
+        output_size=embed_dim,
+        d_k=embed_dim // nhead,
+        d_v=embed_dim // nhead,
+        nhead=nhead,
+        device=device,
+        dtype=dtype,
+    )
+
+    # KV with batch_size=1
+    x_kv = torch.randn(1, n_seq_kv, embed_dim, device=device, dtype=dtype)
+
+    # Query with batch_size=4
+    batch_size_q = 4
+    x_q = torch.randn(batch_size_q, n_seq_q, embed_dim, device=device, dtype=dtype)
+
+    # Cache KV with batch_size=1
+    with torch.no_grad():
+        att(x_kv, x_kv, cache_kv=True)
+
+    # Reference: compute each batch element separately by expanding KV
+    x_kv_expanded = x_kv.expand(batch_size_q, -1, -1)
+    with torch.no_grad():
+        ref_output = att(x_q, x_kv_expanded)
+
+    # Test using cache with batch_size=4 query
+    # Batch broadcast is supported - verify it works correctly
+    with torch.no_grad():
+        cached_output = att(x_q, use_cached_kv=True)
+
+    assert cached_output.shape == ref_output.shape
+    assert torch.allclose(ref_output, cached_output, atol=1e-7), (
+        f"Cached attention output differs. "
+        f"Max diff: {(ref_output - cached_output).abs().max()}"
+    )
+
+    # Cleanup
+    att.empty_kv_cache()
+
+
+def test_deterministic_seeded_outputs(minimal_transformer, sample_data):
+    """Test that with same seed, outputs are deterministic."""
+    model = minimal_transformer
+    train_x = sample_data["train_x"]
+    train_y = sample_data["train_y"]
+    test_x = sample_data["test_x"]
+
+    torch.manual_seed(42)
+    model.cache_trainset_representation = False
+    with torch.no_grad():
+        output1 = model(x=train_x, y=train_y, test_x=test_x)
+
+    torch.manual_seed(42)
+    with torch.no_grad():
+        output2 = model(x=train_x, y=train_y, test_x=test_x)
+
+    assert torch.allclose(output1, output2, atol=1e-7), "Outputs should be deterministic"
+
+
+def test_cache_invalidation_on_data_change(minimal_transformer, sample_data):
+    """Test that cache is properly invalidated when training data changes.
+
+    In a real BO loop, training data grows as new observations are added.
+    The cache must be invalidated when this happens to avoid stale predictions.
+    """
+    model = minimal_transformer
+    train_x = sample_data["train_x"].clone()
+    train_y = sample_data["train_y"].clone()
+    test_x = sample_data["test_x"]
+    n_features = sample_data["n_features"]
+
+    # Step 1: Populate cache with initial training data
+    model.cache_trainset_representation = True
+    model.empty_trainset_representation_cache()
+
+    with torch.no_grad():
+        _ = model(
+            x=train_x,
+            y=train_y,
+            test_x=torch.zeros(1, 1, n_features, device=device, dtype=dtype),
+        )
+
+    # Get prediction with original cache
+    with torch.no_grad():
+        output_original = model(x=None, y=None, test_x=test_x)
+
+    # Step 2: Clear cache and repopulate with DIFFERENT training data
+    model.empty_trainset_representation_cache()
+
+    # Create modified training data (add noise)
+    train_x_modified = train_x + 0.5 * torch.randn_like(train_x)
+    train_y_modified = train_y + 0.5 * torch.randn_like(train_y)
+
+    with torch.no_grad():
+        _ = model(
+            x=train_x_modified,
+            y=train_y_modified,
+            test_x=torch.zeros(1, 1, n_features, device=device, dtype=dtype),
+        )
+
+    # Get prediction with new cache
+    with torch.no_grad():
+        output_modified = model(x=None, y=None, test_x=test_x)
+
+    # Step 3: Verify outputs are DIFFERENT (cache was properly updated)
+    assert not torch.allclose(output_original, output_modified, atol=1e-3), (
+        "Outputs should differ after training data change. "
+        "Cache may not have been properly invalidated/repopulated."
+    )
+
+    # Step 4: Verify the new output matches a fresh non-cached computation
+    model.cache_trainset_representation = False
+    model.empty_trainset_representation_cache()
+
+    with torch.no_grad():
+        output_fresh = model(x=train_x_modified, y=train_y_modified, test_x=test_x)
+
+    assert torch.allclose(output_modified, output_fresh, atol=1e-7), (
+        f"Cached output with new data should match fresh computation. "
+        f"Max diff: {(output_modified - output_fresh).abs().max()}"
+    )
+
+
+def test_cache_with_batch_first_false():
+    """Test caching works correctly with batch_first=False configuration."""
+    model = TableTransformer(
+        ninp=32,
+        nhead=2,
+        nhid=64,
+        nlayers=2,
+        batch_first=False,  # Key difference from other tests
+        cache_trainset_representation=False,
+    )
+    model = model.to(device=device, dtype=dtype)
+
+    # Add small non-zero values to weights
+    with torch.no_grad():
+        for p in model.parameters():
+            p.add_(0.01 * torch.randn_like(p))
+
+    n_train, n_test, n_features = 10, 5, 4
+    batch_size = 1
+
+    # For batch_first=False, shape is (seq, batch, features)
+    train_x = torch.randn(n_train, batch_size, n_features, device=device, dtype=dtype)
+    train_y = torch.randn(n_train, batch_size, 1, device=device, dtype=dtype)
+    test_x = torch.randn(n_test, batch_size, n_features, device=device, dtype=dtype)
+
+    # Non-cached forward pass
+    model.cache_trainset_representation = False
+    with torch.no_grad():
+        output_no_cache = model(x=train_x, y=train_y, test_x=test_x)
+
+    # Cached forward pass
+    model.cache_trainset_representation = True
+    model.empty_trainset_representation_cache()
+
+    with torch.no_grad():
+        # Populate cache
+        _ = model(
+            x=train_x,
+            y=train_y,
+            test_x=torch.zeros(1, batch_size, n_features, device=device, dtype=dtype),
+        )
+
+        # Use cache
+        output_with_cache = model(x=None, y=None, test_x=test_x)
+
+    # Cleanup
+    model.empty_trainset_representation_cache()
+    model.cache_trainset_representation = False
+
+    assert torch.allclose(output_no_cache, output_with_cache, atol=1e-7), (
+        f"batch_first=False: Cached and non-cached outputs differ. "
+        f"Max diff: {(output_no_cache - output_with_cache).abs().max()}"
+    )
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/tests/priors/test_convert_prior_to_x_only_format.py b/tests/priors/test_convert_prior_to_x_only_format.py
new file mode 100644
index 0000000..8188d40
--- /dev/null
+++ b/tests/priors/test_convert_prior_to_x_only_format.py
@@ -0,0 +1,424 @@
+# pyre-strict
+
+import torch
+from pfns.priors.convert_prior_to_x_only_format import get_batch
+from pfns.priors.prior import Batch
+
+
+def create_simple_traditional_get_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    single_eval_pos: int,
+    hyperparameters: dict | None = None,
+    n_targets_per_input: int = 1,
+    **kwargs,
+) -> Batch:
+    """
+    Simple traditional get_batch function for testing.
+    Creates linear functions: y = sum(x) + noise
+    """
+    # Generate random input features
+    x = torch.rand(batch_size, seq_len, num_features)
+
+    # Create simple linear relationship: y = sum(x_features) + small noise
+    y = x.sum(dim=2, keepdim=True) + torch.randn(batch_size, seq_len, 1) * 0.1
+
+    # For traditional format, target_y is the same as y but potentially repeated
+    target_y = y.repeat(1, 1, n_targets_per_input)
+
+    return Batch(
+        x=x,
+        y=y,
+        target_y=target_y,
+        single_eval_pos=single_eval_pos,
+    )
+
+
+def create_complex_traditional_get_batch(
+    batch_size: int,
+    seq_len: int,
+    num_features: int,
+    single_eval_pos: int,
+    hyperparameters: dict | None = None,
+    n_targets_per_input: int = 1,
+    **kwargs,
+) -> Batch:
+    """
+    More complex traditional get_batch function with optional attributes for testing.
+    """
+    x = torch.rand(batch_size, seq_len, num_features)
+    y = x.mean(dim=2, keepdim=True) + torch.randn(batch_size, seq_len, 1) * 0.2
+    target_y = y.repeat(1, 1, n_targets_per_input)
+
+    # Add some optional attributes
+    style = torch.randn(batch_size, 3)  # 3 style dimensions
+    y_style = torch.randn(batch_size, 2)  # 2 y_style dimensions
+
+    return Batch(
+        x=x,
+        y=y,
+        target_y=target_y,
+        single_eval_pos=single_eval_pos,
+        style=style,
+        y_style=y_style,
+    )
+
+
+class TestConvertPriorToXOnlyFormat:
+    """Test suite for the convert_prior_to_x_only_format wrapper function."""
+
+    def test_basic_conversion_functionality(self) -> None:
+        """Test that the basic conversion from traditional to x-only format works correctly."""
+        batch_size = 4
+        seq_len = 10
+        num_features = 3
+        single_eval_pos = 6
+
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+            get_batch=create_simple_traditional_get_batch,
+        )
+
+        # Check that x-only format fields are properly set
+        assert x_only_batch.y is None
+        assert x_only_batch.target_y is None
+        assert x_only_batch.x is not None
+        assert x_only_batch.test_x is not None
+        assert x_only_batch.target is not None
+
+        # Check shapes
+        assert x_only_batch.x.shape == (batch_size, single_eval_pos, num_features + 1)
+        assert x_only_batch.test_x.shape == (
+            batch_size,
+            seq_len - single_eval_pos,
+            num_features + 1,
+        )
+        assert x_only_batch.target.shape == (
+            batch_size,
+            seq_len - single_eval_pos,
+            num_features + 1,
+        )
+
+    def test_x_format_contains_concatenated_features_and_y(self) -> None:
+        """Test that x contains the concatenation of training features and training y values."""
+        batch_size = 2
+        seq_len = 8
+        num_features = 2
+        single_eval_pos = 5
+
+        # Create traditional batch first to compare
+        traditional_batch = create_simple_traditional_get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+        )
+
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+            get_batch=create_simple_traditional_get_batch,
+        )
+
+        # Extract training portions
+        x_train_traditional = traditional_batch.x[:, :single_eval_pos, :]
+        y_train_traditional = traditional_batch.y[:, :single_eval_pos, :]
+
+        # Check that x in x-only format is [x_features, y_values]
+        expected_x = torch.cat([x_train_traditional, y_train_traditional], dim=2)
+        torch.testing.assert_close(x_only_batch.x, expected_x)
+
+    def test_test_x_has_nan_for_y_values(self) -> None:
+        """Test that test_x contains NaN for y values (to be predicted)."""
+        batch_size = 3
+        seq_len = 7
+        num_features = 2
+        single_eval_pos = 4
+
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+            get_batch=create_simple_traditional_get_batch,
+        )
+
+        # Check that the last column (y values) in test_x contains only NaN
+        y_column_in_test_x = x_only_batch.test_x[:, :, -1]
+        assert torch.all(torch.isnan(y_column_in_test_x))
+
+        # Check that the feature columns don't contain NaN
+        feature_columns_in_test_x = x_only_batch.test_x[:, :, :-1]
+        assert not torch.any(torch.isnan(feature_columns_in_test_x))
+
+    def test_target_contains_features_and_target_y(self) -> None:
+        """Test that target contains test features concatenated with target y values."""
+        batch_size = 2
+        seq_len = 9
+        num_features = 3
+        single_eval_pos = 5
+
+        # Create traditional batch to compare
+        traditional_batch = create_simple_traditional_get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+        )
+
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+            get_batch=create_simple_traditional_get_batch,
+        )
+
+        # Extract test portions from traditional format
+        x_test_traditional = traditional_batch.x[:, single_eval_pos:, :]
+        target_y_test_traditional = traditional_batch.target_y[:, single_eval_pos:, :]
+
+        # Check that target in x-only format is [x_test_features, target_y_values]
+        expected_target = torch.cat(
+            [x_test_traditional, target_y_test_traditional], dim=2
+        )
+        torch.testing.assert_close(x_only_batch.target, expected_target)
+
+    def test_multiple_targets_per_input(self) -> None:
+        """Test conversion with n_targets_per_input > 1."""
+        batch_size = 2
+        seq_len = 8
+        num_features = 2
+        single_eval_pos = 5
+        n_targets_per_input = 3
+
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+            get_batch=create_simple_traditional_get_batch,
+            n_targets_per_input=n_targets_per_input,
+        )
+
+        # With multiple targets, the target shape should be expanded
+        expected_target_shape = (
+            batch_size,
+            seq_len - single_eval_pos,
+            num_features + 1,
+            n_targets_per_input,
+        )
+        assert x_only_batch.target.shape == expected_target_shape
+
+        # x and test_x should still have the same shapes as single target case
+        assert x_only_batch.x.shape == (batch_size, single_eval_pos, num_features + 1)
+        assert x_only_batch.test_x.shape == (
+            batch_size,
+            seq_len - single_eval_pos,
+            num_features + 1,
+        )
+
+    def test_preserves_optional_attributes(self) -> None:
+        """Test that optional attributes from the traditional batch are preserved."""
+        batch_size = 2
+        seq_len = 6
+        num_features = 2
+        single_eval_pos = 3
+
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=single_eval_pos,
+            get_batch=create_complex_traditional_get_batch,
+        )
+
+        # Check that optional attributes are preserved
+        assert x_only_batch.style is not None
+        assert x_only_batch.y_style is not None
+        assert x_only_batch.style.shape == (batch_size, 3)
+        assert x_only_batch.y_style.shape == (batch_size, 2)
+        assert x_only_batch.single_eval_pos == single_eval_pos
+
+    def test_hyperparameters_passed_through(self) -> None:
+        """Test that hyperparameters are correctly passed to the wrapped function."""
+
+        def test_get_batch_with_hyperparams(
+            batch_size: int,
+            seq_len: int,
+            num_features: int,
+            single_eval_pos: int,
+            hyperparameters: dict | None = None,
+            **kwargs,
+        ) -> Batch:
+            # Check that hyperparameters are passed correctly
+            assert hyperparameters is not None
+            assert hyperparameters["test_param"] == "test_value"
+
+            return create_simple_traditional_get_batch(
+                batch_size,
+                seq_len,
+                num_features,
+                single_eval_pos,
+                hyperparameters,
+                **kwargs,
+            )
+
+        test_hyperparams = {"test_param": "test_value"}
+
+        x_only_batch = get_batch(
+            batch_size=2,
+            seq_len=6,
+            num_features=2,
+            single_eval_pos=3,
+            get_batch=test_get_batch_with_hyperparams,
+            hyperparameters=test_hyperparams,
+        )
+
+        # If we get here without assertion error, hyperparameters were passed correctly
+        assert x_only_batch is not None
+
+    def test_kwargs_passed_through(self) -> None:
+        """Test that additional kwargs are correctly passed to the wrapped function."""
+
+        def test_get_batch_with_kwargs(
+            batch_size: int,
+            seq_len: int,
+            num_features: int,
+            single_eval_pos: int,
+            hyperparameters: dict | None = None,
+            extra_param: str = "default",
+            **kwargs,
+        ) -> Batch:
+            # Check that kwargs are passed correctly
+            assert extra_param == "test_extra"
+
+            return create_simple_traditional_get_batch(
+                batch_size,
+                seq_len,
+                num_features,
+                single_eval_pos,
+                hyperparameters,
+                **kwargs,
+            )
+
+        x_only_batch = get_batch(
+            batch_size=2,
+            seq_len=6,
+            num_features=2,
+            single_eval_pos=3,
+            get_batch=test_get_batch_with_kwargs,
+            extra_param="test_extra",
+        )
+
+        # If we get here without assertion error, kwargs were passed correctly
+        assert x_only_batch is not None
+
+    def test_empty_hyperparameters_default(self) -> None:
+        """Test that empty hyperparameters dict is used when None is passed."""
+
+        def test_get_batch_checks_empty_hyperparams(
+            batch_size: int,
+            seq_len: int,
+            num_features: int,
+            single_eval_pos: int,
+            hyperparameters: dict | None = None,
+            **kwargs,
+        ) -> Batch:
+            # When hyperparameters=None is passed to wrapper, it should become {}
+            assert hyperparameters == {}
+
+            return create_simple_traditional_get_batch(
+                batch_size,
+                seq_len,
+                num_features,
+                single_eval_pos,
+                hyperparameters,
+                **kwargs,
+            )
+
+        x_only_batch = get_batch(
+            batch_size=2,
+            seq_len=6,
+            num_features=2,
+            single_eval_pos=3,
+            get_batch=test_get_batch_checks_empty_hyperparams,
+            hyperparameters=None,
+        )
+
+        assert x_only_batch is not None
+
+    def test_single_eval_pos_boundary_conditions(self) -> None:
+        """Test behavior at boundary conditions for single_eval_pos."""
+        batch_size = 2
+        seq_len = 6
+        num_features = 2
+
+        # Test with single_eval_pos = 1 (minimal training data)
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=1,
+            get_batch=create_simple_traditional_get_batch,
+        )
+
+        assert x_only_batch.x.shape == (batch_size, 1, num_features + 1)
+        assert x_only_batch.test_x.shape == (batch_size, seq_len - 1, num_features + 1)
+        assert x_only_batch.target.shape == (batch_size, seq_len - 1, num_features + 1)
+
+        # Test with single_eval_pos = seq_len - 1 (minimal test data)
+        x_only_batch = get_batch(
+            batch_size=batch_size,
+            seq_len=seq_len,
+            num_features=num_features,
+            single_eval_pos=seq_len - 1,
+            get_batch=create_simple_traditional_get_batch,
+        )
+
+        assert x_only_batch.x.shape == (batch_size, seq_len - 1, num_features + 1)
+        assert x_only_batch.test_x.shape == (batch_size, 1, num_features + 1)
+        assert x_only_batch.target.shape == (batch_size, 1, num_features + 1)
+
+    def test_device_consistency(self) -> None:
+        """Test that tensors maintain device consistency."""
+
+        def get_batch_with_specific_device(
+            batch_size: int,
+            seq_len: int,
+            num_features: int,
+            single_eval_pos: int,
+            hyperparameters: dict | None = None,
+            **kwargs,
+        ) -> Batch:
+            device = torch.device("cpu")  # Force CPU for testing
+            x = torch.rand(batch_size, seq_len, num_features, device=device)
+            y = torch.rand(batch_size, seq_len, 1, device=device)
+            target_y = y.clone()
+
+            return Batch(x=x, y=y, target_y=target_y, single_eval_pos=single_eval_pos)
+
+        x_only_batch = get_batch(
+            batch_size=2,
+            seq_len=6,
+            num_features=2,
+            single_eval_pos=3,
+            get_batch=get_batch_with_specific_device,
+        )
+
+        # All tensors should be on the same device
+        assert x_only_batch.x.device == x_only_batch.test_x.device
+        assert x_only_batch.x.device == x_only_batch.target.device
+
+
+if __name__ == "__main__":
+    # Run a simple test if executed directly
+    test_instance = TestConvertPriorToXOnlyFormat()
+    test_instance.test_basic_conversion_functionality()
+    print("Basic test passed!")