diff --git a/sigllm/pipelines/detector/mistral_detector_lst_context.json b/sigllm/pipelines/detector/mistral_detector_lst_context.json
new file mode 100644
index 0000000..8eae66a
--- /dev/null
+++ b/sigllm/pipelines/detector/mistral_detector_lst_context.json
@@ -0,0 +1,132 @@
+{
+    "primitives": [
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate",
+        "sklearn.impute.SimpleImputer",
+        "sigllm.primitives.transformation.Float2Scalar",
+        "mlstars.custom.timeseries_preprocessing.rolling_window_sequences",
+        "sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context",
+        "sigllm.primitives.transformation.format_as_string",
+        "sigllm.primitives.forecasting.huggingface.HF",
+        "sigllm.primitives.transformation.format_as_integer",
+        "sigllm.primitives.transformation.Scalar2Float",
+        "sigllm.primitives.transformation.Scalar2Float",
+        "sigllm.primitives.postprocessing.aggregate_rolling_window",
+        "numpy.reshape",
+        "orion.primitives.timeseries_errors.regression_errors",
+        "orion.primitives.timeseries_anomalies.find_anomalies"
+    ],
+    "init_params": {
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
+            "time_column": "timestamp",
+            "interval": 21600,
+            "method": "mean"
+        },
+        "sigllm.primitives.transformation.Float2Scalar#1": {
+            "decimal": 2,
+            "rescale": true
+        },
+        "mlstars.custom.timeseries_preprocessing.rolling_window_sequences#1": {
+            "target_column": 0,
+            "window_size": 13,
+            "target_size": 1
+        },
+        "sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context#1": {
+            "L": 5,
+            "aggregation_method": "mean"
+        },
+        "sigllm.primitives.transformation.format_as_string#1": {
+            "space": false
+        },
+        "sigllm.primitives.forecasting.huggingface.HF#1": {
+            "name": "mistralai/Mistral-7B-Instruct-v0.2",
+            "steps": 5
+        },
+        "sigllm.primitives.transformation.format_as_integer#1": {
+            "trunc": 1,
+            "errors": "coerce"
+        },
+        "sigllm.primitives.postprocessing.aggregate_rolling_window#1": {
+            "agg": "median"
+        },
+        "orion.primitives.timeseries_anomalies.find_anomalies#1": {
+            "window_size_portion": 0.3,
+            "window_step_size_portion": 0.1,
+            "fixed_threshold": true
+        }
+    },
+    "input_names": {
+        "sigllm.primitives.transformation.Float2Scalar#1": {
+            "X": "y"
+        },
+        "mlstars.custom.timeseries_preprocessing.rolling_window_sequences#1": {
+            "X": "X",
+            "y": "y",
+            "index": "index"
+        },
+        "sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context#1": {
+            "X": "X",
+            "first_index": "index"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1": {
+            "X": "y_hat"
+        },
+        "sigllm.primitives.transformation.Scalar2Float#1": {
+            "X": "y_hat",
+            "minimum": "minimum",
+            "decimal": "decimal"
+        },
+        "sigllm.primitives.transformation.Scalar2Float#2": {
+            "X": "y",
+            "minimum": "minimum",
+            "decimal": "decimal"
+        },
+        "sigllm.primitives.postprocessing.aggregate_rolling_window#1": {
+            "y": "y_hat"
+        },
+        "numpy.reshape#1": {
+            "X": "y_hat"
+        },
+        "orion.primitives.timeseries_anomalies.find_anomalies#1": {
+            "index": "target_index"
+        }
+    },
+    "output_names": {
+        "sklearn.impute.SimpleImputer#1": {
+            "X": "y"
+        },
+        "mlstars.custom.timeseries_preprocessing.rolling_window_sequences#1": {
+            "X": "X",
+            "y": "y",
+            "index": "index",
+            "target_index": "target_index"
+        },
+        "sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context#1": {
+            "X": "X",
+            "first_index": "first_index",
+            "window_size": "window_size",
+            "step_size": "step_size",
+            "dim": "dim"
+        },
+        "sigllm.primitives.forecasting.huggingface.HF#1": {
+            "y": "y_hat"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1": {
+            "X": "y_hat"
+        },
+        "sigllm.primitives.transformation.Scalar2Float#1": {
+            "X": "y_hat"
+        },
+        "sigllm.primitives.transformation.Scalar2Float#2": {
+            "X": "y"
+        },
+        "sigllm.primitives.postprocessing.aggregate_rolling_window#1": {
+            "y": "y_hat"
+        },
+        "numpy.reshape#1": {
+            "X": "y_hat"
+        },
+        "orion.primitives.timeseries_anomalies.find_anomalies#1": {
+            "y": "anomalies"
+        }
+    }
+}
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context.json
new file mode 100644
index 0000000..0f6368d
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context.json
@@ -0,0 +1,73 @@
+{
+    "name": "sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context",
+    "contributors": [
+        "Allen Baranov <baranov@mit.edu>"
+    ],
+    "description": "Process rolling window sequences to create long-term aggregated and short-term raw context windows",
+    "classifiers": {
+        "type": "preprocessor",
+        "subtype": "feature_extractor"
+    },
+    "modalities": [
+        "timeseries"
+    ],
+    "primitive": "sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context",
+    "produce": {
+        "args": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            },
+            {
+                "name": "first_index",
+                "type": "ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            },
+            {
+                "name": "first_index",
+                "type": "ndarray"
+            },
+            {
+                "name": "window_size",
+                "type": "int"
+            },
+            {
+                "name": "step_size",
+                "type": "int"
+            },
+            {
+                "name": "dim",
+                "type": "int"
+            }
+        ]
+    },
+    "hyperparameters": {
+        "fixed": {
+            "L": {
+                "type": "int",
+                "default": 5
+            },
+            "W": {
+                "type": "int",
+                "default": null
+            },
+            "S": {
+                "type": "int",
+                "default": null
+            },
+            "step_size": {
+                "type": "int",
+                "default": 1
+            },
+            "aggregation_method": {
+                "type": "str",
+                "default": "mean"
+            }
+        }
+    }
+}
diff --git a/sigllm/primitives/prompting/timeseries_preprocessing.py b/sigllm/primitives/prompting/timeseries_preprocessing.py
index b2ea5bc..6afd0c1 100644
--- a/sigllm/primitives/prompting/timeseries_preprocessing.py
+++ b/sigllm/primitives/prompting/timeseries_preprocessing.py
@@ -43,3 +43,141 @@ def rolling_window_sequences(X, window_size=500, step_size=100):
         X_index.append(index[start])
         start = start + step_size
     return np.asarray(out_X), np.asarray(X_index), window_size, step_size, dim
+
+
+def long_short_term_context(
+    X,
+    first_index,
+    step_size,
+    L,
+    S=None,
+    W=None,
+    aggregation_method='mean',
+    window_size=None,
+    dim=None,
+):
+    """Process rolling window sequences to create long and short-term raw segments.
+
+    This function processes already-windowed sequences from rolling_window_sequences to create
+    raw segments with long-term aggregated segments (rounded to nearest integer) and
+    short-term raw segments.
+
+    Args:
+        X (ndarray):
+            Input windows from rolling_window_sequences with shape (num_windows, window_size, dim).
+        first_index (ndarray):
+            First index value of each input sequence.
+        step_size (int):
+            Step size used in rolling_window_sequences.
+        L (int):
+            Number of long-term windows to extract.
+        S (int, optional):
+            Number of short-term values to extract (last S values). If None, set to L.
+            Defaults to None.
+        W (int, optional):
+            Size of each long-term window. If None, computed automatically
+            from window_size, L, and S. Defaults to None.
+        aggregation_method (str):
+            Aggregation method for long-term windows. Currently only 'mean' is supported.
+            Defaults to 'mean'.
+        window_size (int, optional):
+            Size of each input window. If None, computed from X.shape[1].
+        dim (int, optional):
+            Dimensionality of the data. If None, computed from X.shape.
+
+    Returns:
+        tuple:
+            * processed_windows_array (ndarray): Processed windows
+                with shape (num_windows, S + L, dim).
+            * first_index (ndarray): First index array (passed through)
+            * new_window_size (int): New window size (S + L)
+            * step_size (int): Step size (passed through)
+            * dim (int): Dimensionality (passed through)
+    """
+    if aggregation_method != 'mean':
+        raise ValueError(
+            f"Aggregation method '{aggregation_method}' not yet supported. "
+            "Only 'mean' is currently supported."
+        )
+
+    if window_size is None:
+        window_size = X.shape[1]
+
+    if dim is None:
+        if X.ndim == 2:
+            dim = 1
+        else:
+            dim = X.shape[2]
+
+    if S is None:
+        S = L
+
+    if W is None:
+        if window_size < S:
+            raise ValueError(f'window_size ({window_size}) must be at least S ({S})')
+        W = (window_size - S) // L
+        if W <= 0:
+            raise ValueError(
+                f'Cannot compute W: window_size ({window_size}) must be '
+                f'greater than S ({S}) + L ({L}).'
+            )
+
+    required_size = W * L + S
+    remainder = window_size - required_size
+
+    if remainder < 0:
+        raise ValueError(
+            f'window_size ({window_size}) is too small.'
+            f'Need at least W*L + S = {W}*{L} + {S} = {required_size}.'
+        )
+
+    num_windows = X.shape[0]
+    processed_windows = []
+
+    is_2d_input = X.ndim == 2
+
+    for i in range(num_windows):
+        window = X[i]
+
+        if remainder > 0:
+            window = window[remainder:]
+
+        long_term_aggregated = []
+        for j in range(L):
+            start_idx = j * W
+            end_idx = (j + 1) * W
+            long_term_window = window[start_idx:end_idx]
+
+            if is_2d_input:
+                aggregated_value = np.mean(long_term_window)
+            else:
+                if dim == 1:
+                    aggregated_value = np.mean(long_term_window)
+                else:
+                    aggregated_value = np.mean(long_term_window, axis=0)
+
+            aggregated_value = np.round(aggregated_value).astype(int)
+            long_term_aggregated.append(aggregated_value)
+
+        short_term = window[required_size - S : required_size]
+
+        if short_term.dtype != np.int64:
+            short_term = short_term.astype(int)
+
+        if is_2d_input:
+            long_term_array = np.array(long_term_aggregated)
+            combined = np.concatenate([long_term_array, short_term])
+            processed_windows.append(combined)
+        else:
+            long_term_array = np.array(long_term_aggregated)
+            if dim == 1:
+                combined = np.concatenate([long_term_array, short_term.flatten()])
+                processed_windows.append(combined.reshape(-1, 1))
+            else:
+                combined = np.concatenate([long_term_array, short_term], axis=0)
+                processed_windows.append(combined)
+
+    processed_windows_array = np.asarray(processed_windows)
+    new_window_size = S + L
+
+    return processed_windows_array, first_index, new_window_size, step_size, dim
diff --git a/tutorials/pipelines/mistral-detector-lst-context-pipeline.ipynb b/tutorials/pipelines/mistral-detector-lst-context-pipeline.ipynb
new file mode 100644
index 0000000..450b2c2
--- /dev/null
+++ b/tutorials/pipelines/mistral-detector-lst-context-pipeline.ipynb
@@ -0,0 +1,1536 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9eaccb47-9974-4728-9521-cd467adc33c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings; warnings.simplefilter('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fcfb84d-883e-48f4-b01f-b0e254d78e8e",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates the **Long-Short Term Context Pipeline** (it adds a `long_short_term_context` primitive between `rolling_window_sequences` and `format_as_string`).\n",
+    "\n",
+    "This notebook requires **gpu** to run. See [mistral documentation](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for memory requirements. \n",
+    "\n",
+    "## 1. Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "874a75ae-8eef-4d10-b6e4-d30f83b0bf3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1624, 2)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from orion.data import load_signal\n",
+    "\n",
+    "data = load_signal('exchange-2_cpm_results')\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a7ae8973-dd2b-4f49-bb2b-7ec5ea87a323",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(data['value']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c7d5bd4-b01b-481e-abde-69346cf21dda",
+   "metadata": {},
+   "source": [
+    "if you want a quick test of how this pipeline works, uncomment the cell below\n",
+    "to save time. We will look at a small segment of the time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a7182e87-367e-4b5e-95b5-f319b4af519a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start = 900\n",
+    "end = start + 1000\n",
+    "\n",
+    "data = data.iloc[start: end]\n",
+    "\n",
+    "plt.plot(data['value']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4621be2-9579-43f9-a603-b2f2d54f5e9e",
+   "metadata": {},
+   "source": [
+    "## 2. Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b4943557-000a-47a6-9c54-48acb5fbd79a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-17 17:51:14.324053: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2026-02-17 17:51:14.356448: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2026-02-17 17:51:14.356481: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2026-02-17 17:51:14.356514: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2026-02-17 17:51:14.363562: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2026-02-17 17:51:15.192926: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+      "Loading checkpoint shards: 100%|██████████| 3/3 [00:04<00:00,  1.50s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mlblocks import MLPipeline \n",
+    "\n",
+    "pipeline_name = 'mistral_detector_lst_context'\n",
+    "\n",
+    "pipeline = MLPipeline(pipeline_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "db6f0b21-cb47-4a21-a356-fb48780efca0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading checkpoint shards: 100%|██████████| 3/3 [00:04<00:00,  1.52s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hyperparameters = {\n",
+    "    \"mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1\": {\n",
+    "        \"interval\": 3600,\n",
+    "    },\n",
+    "    \"mlstars.custom.timeseries_preprocessing.rolling_window_sequences#1\": {\n",
+    "        \"window_size\": 70,\n",
+    "        \"target_size\": 1\n",
+    "    },\n",
+    "    \"sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context#1\": {\n",
+    "        \"L\": 5,\n",
+    "        \"S\": 20,\n",
+    "        \"W\": 10,\n",
+    "        \"aggregation_method\": \"mean\"\n",
+    "    },\n",
+    "    \"sigllm.primitives.forecasting.huggingface.HF#1\": {\n",
+    "        \"samples\": 2\n",
+    "    },\n",
+    "    \"sigllm.primitives.transformation.format_as_integer#1\": {\n",
+    "        \"trunc\": 1,\n",
+    "        \"errors\": \"coerce\"\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "pipeline.set_hyperparameters(hyperparameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cc0d29c-573a-400b-ba0c-c03b030bdd20",
+   "metadata": {},
+   "source": [
+    "### step-by-step execution\n",
+    "\n",
+    "MLPipelines are compose of a squence of primitives, these primitives apply tranformation and calculation operations to the data and updates the variables within the pipeline. To view the primitives used by the pipeline, we access its primtivies attribute.\n",
+    "\n",
+    "The mistral-detector contains 14 primitives. we will observe how the context (which are the variables held within the pipeline) are updated after the execution of each primitive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "86a2c3f1-78a5-46e2-80f9-53b7a0553583",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mlstars.custom.timeseries_preprocessing.time_segments_aggregate',\n",
+       " 'sklearn.impute.SimpleImputer',\n",
+       " 'sigllm.primitives.transformation.Float2Scalar',\n",
+       " 'mlstars.custom.timeseries_preprocessing.rolling_window_sequences',\n",
+       " 'sigllm.primitives.prompting.timeseries_preprocessing.long_short_term_context',\n",
+       " 'sigllm.primitives.transformation.format_as_string',\n",
+       " 'sigllm.primitives.forecasting.huggingface.HF',\n",
+       " 'sigllm.primitives.transformation.format_as_integer',\n",
+       " 'sigllm.primitives.transformation.Scalar2Float',\n",
+       " 'sigllm.primitives.transformation.Scalar2Float',\n",
+       " 'sigllm.primitives.postprocessing.aggregate_rolling_window',\n",
+       " 'numpy.reshape',\n",
+       " 'orion.primitives.timeseries_errors.regression_errors',\n",
+       " 'orion.primitives.timeseries_anomalies.find_anomalies']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.primitives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8badf3ea-0871-4f52-809d-415c5b2252dc",
+   "metadata": {},
+   "source": [
+    "#### time segments aggregate\n",
+    "this primitive creates an equi-spaced time series by aggregating values over fixed specified interval.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**:\n",
+    "    * `X` sequence of aggregated values, one column for each aggregation method.\n",
+    "    * `index` sequence of index values (first index of each aggregated segment)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "568f7de5-ddec-4418-87e6-2e607aa8f1a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['X', 'index'])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 0\n",
+    "context = pipeline.fit(data, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0761ba7a-610c-49be-8612-a345ca231c82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry at 1312714801 has value [0.58756715]\n",
+      "entry at 1312718401 has value [0.49446836]\n",
+      "entry at 1312722001 has value [0.48571948]\n",
+      "entry at 1312725601 has value [0.34325765]\n",
+      "entry at 1312729201 has value [0.32660771]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, x in list(zip(context['index'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13dd20ab-bd58-4da8-8163-061f43fb3dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(747, 1)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['X'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5ed441c-9b8e-4716-a4aa-cce3d12d7c02",
+   "metadata": {},
+   "source": [
+    "notice that the dimension of `X` is now different from the original shape, which was (1624, 2)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8baad1-eadc-46c2-ae45-79103a809e82",
+   "metadata": {},
+   "source": [
+    "#### SimpleImputer\n",
+    "this primitive is an imputation transformer for filling missing values.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**: `y` which is a transformed version of 'X'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "95fe4b16-df24-4da1-939f-4625f9013d65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 1\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a550f499-92a3-461f-8ed2-99f9b8f2d03e",
+   "metadata": {},
+   "source": [
+    "#### Float2Scalar\n",
+    "this primitive converts float values into scalar upto certain decimal points.\n",
+    "\n",
+    "* **input**: `y` which is an n-dimensional sequence of values in float type.\n",
+    "* **output**: `X` which is a transformed version of 'y' in scalar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5bec1f33-64c7-471d-b0da-f3ae293d3628",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[58]\n",
+      " [49]\n",
+      " [48]\n",
+      " [34]\n",
+      " [32]]\n",
+      "[[0.58756715]\n",
+      " [0.49446836]\n",
+      " [0.48571948]\n",
+      " [0.34325765]\n",
+      " [0.32660771]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "step = 2\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()\n",
+    "\n",
+    "print(context['X'][:5])\n",
+    "print(context['y'][:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e3e3c7a6-193e-4464-b8b6-5072e4426042",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry at 1312714801 has value [58]\n",
+      "entry at 1312718401 has value [49]\n",
+      "entry at 1312722001 has value [48]\n",
+      "entry at 1312725601 has value [34]\n",
+      "entry at 1312729201 has value [32]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, x in list(zip(context['index'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bd11634f-ffd8-4405-9645-857880412310",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.000385004945833"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['minimum']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "166c0752-90e7-4340-8c2d-d49439be3d04",
+   "metadata": {},
+   "source": [
+    "#### rolling_window_sequences\n",
+    "this primitive generates many sub-sequences of the original sequence. it uses a rolling window approach to create the sub-sequences out of time series data.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `X` n-dimensional sequence to iterate over.\n",
+    "    * `y` 1-dimensional target sequence.\n",
+    "    * `index` array containing the index values of X.\n",
+    "* **output**:\n",
+    "    * `X` input sequences.\n",
+    "    * `y` target sequences.\n",
+    "    * `index` first index value of each input sequence.\n",
+    "    * `target_index` first index value of each target sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2810e641-83e7-4096-b8ed-2b8e23284759",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'X', 'y', 'target_index'])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 3\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "700d5a87-34f9-4889-85e4-e2397bad1565",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape = (677, 70, 1)\n",
+      "y shape = (677, 1)\n",
+      "X index shape = (677,)\n",
+      "y index shape = (677,)\n",
+      "[[[58]\n",
+      "  [49]\n",
+      "  [48]\n",
+      "  [34]\n",
+      "  [32]\n",
+      "  [29]\n",
+      "  [25]\n",
+      "  [26]\n",
+      "  [17]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [14]\n",
+      "  [12]\n",
+      "  [ 9]\n",
+      "  [12]\n",
+      "  [17]\n",
+      "  [16]\n",
+      "  [31]\n",
+      "  [53]\n",
+      "  [42]\n",
+      "  [50]\n",
+      "  [64]\n",
+      "  [55]\n",
+      "  [47]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [37]\n",
+      "  [31]\n",
+      "  [36]\n",
+      "  [30]\n",
+      "  [18]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [16]\n",
+      "  [13]\n",
+      "  [13]\n",
+      "  [12]\n",
+      "  [11]\n",
+      "  [16]\n",
+      "  [17]\n",
+      "  [30]\n",
+      "  [55]\n",
+      "  [44]\n",
+      "  [52]\n",
+      "  [46]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [34]\n",
+      "  [16]\n",
+      "  [45]\n",
+      "  [48]\n",
+      "  [30]\n",
+      "  [26]\n",
+      "  [27]\n",
+      "  [25]\n",
+      "  [29]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [12]\n",
+      "  [13]\n",
+      "  [19]\n",
+      "  [24]\n",
+      "  [30]\n",
+      "  [35]\n",
+      "  [51]\n",
+      "  [45]]\n",
+      "\n",
+      " [[49]\n",
+      "  [48]\n",
+      "  [34]\n",
+      "  [32]\n",
+      "  [29]\n",
+      "  [25]\n",
+      "  [26]\n",
+      "  [17]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [14]\n",
+      "  [12]\n",
+      "  [ 9]\n",
+      "  [12]\n",
+      "  [17]\n",
+      "  [16]\n",
+      "  [31]\n",
+      "  [53]\n",
+      "  [42]\n",
+      "  [50]\n",
+      "  [64]\n",
+      "  [55]\n",
+      "  [47]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [37]\n",
+      "  [31]\n",
+      "  [36]\n",
+      "  [30]\n",
+      "  [18]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [16]\n",
+      "  [13]\n",
+      "  [13]\n",
+      "  [12]\n",
+      "  [11]\n",
+      "  [16]\n",
+      "  [17]\n",
+      "  [30]\n",
+      "  [55]\n",
+      "  [44]\n",
+      "  [52]\n",
+      "  [46]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [34]\n",
+      "  [16]\n",
+      "  [45]\n",
+      "  [48]\n",
+      "  [30]\n",
+      "  [26]\n",
+      "  [27]\n",
+      "  [25]\n",
+      "  [29]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [12]\n",
+      "  [13]\n",
+      "  [19]\n",
+      "  [24]\n",
+      "  [30]\n",
+      "  [35]\n",
+      "  [51]\n",
+      "  [45]\n",
+      "  [54]]\n",
+      "\n",
+      " [[48]\n",
+      "  [34]\n",
+      "  [32]\n",
+      "  [29]\n",
+      "  [25]\n",
+      "  [26]\n",
+      "  [17]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [14]\n",
+      "  [12]\n",
+      "  [ 9]\n",
+      "  [12]\n",
+      "  [17]\n",
+      "  [16]\n",
+      "  [31]\n",
+      "  [53]\n",
+      "  [42]\n",
+      "  [50]\n",
+      "  [64]\n",
+      "  [55]\n",
+      "  [47]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [37]\n",
+      "  [31]\n",
+      "  [36]\n",
+      "  [30]\n",
+      "  [18]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [16]\n",
+      "  [13]\n",
+      "  [13]\n",
+      "  [12]\n",
+      "  [11]\n",
+      "  [16]\n",
+      "  [17]\n",
+      "  [30]\n",
+      "  [55]\n",
+      "  [44]\n",
+      "  [52]\n",
+      "  [46]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [34]\n",
+      "  [16]\n",
+      "  [45]\n",
+      "  [48]\n",
+      "  [30]\n",
+      "  [26]\n",
+      "  [27]\n",
+      "  [25]\n",
+      "  [29]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [12]\n",
+      "  [13]\n",
+      "  [19]\n",
+      "  [24]\n",
+      "  [30]\n",
+      "  [35]\n",
+      "  [51]\n",
+      "  [45]\n",
+      "  [54]\n",
+      "  [44]]\n",
+      "\n",
+      " [[34]\n",
+      "  [32]\n",
+      "  [29]\n",
+      "  [25]\n",
+      "  [26]\n",
+      "  [17]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [14]\n",
+      "  [12]\n",
+      "  [ 9]\n",
+      "  [12]\n",
+      "  [17]\n",
+      "  [16]\n",
+      "  [31]\n",
+      "  [53]\n",
+      "  [42]\n",
+      "  [50]\n",
+      "  [64]\n",
+      "  [55]\n",
+      "  [47]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [37]\n",
+      "  [31]\n",
+      "  [36]\n",
+      "  [30]\n",
+      "  [18]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [16]\n",
+      "  [13]\n",
+      "  [13]\n",
+      "  [12]\n",
+      "  [11]\n",
+      "  [16]\n",
+      "  [17]\n",
+      "  [30]\n",
+      "  [55]\n",
+      "  [44]\n",
+      "  [52]\n",
+      "  [46]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [34]\n",
+      "  [16]\n",
+      "  [45]\n",
+      "  [48]\n",
+      "  [30]\n",
+      "  [26]\n",
+      "  [27]\n",
+      "  [25]\n",
+      "  [29]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [12]\n",
+      "  [13]\n",
+      "  [19]\n",
+      "  [24]\n",
+      "  [30]\n",
+      "  [35]\n",
+      "  [51]\n",
+      "  [45]\n",
+      "  [54]\n",
+      "  [44]\n",
+      "  [48]]\n",
+      "\n",
+      " [[32]\n",
+      "  [29]\n",
+      "  [25]\n",
+      "  [26]\n",
+      "  [17]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [14]\n",
+      "  [12]\n",
+      "  [ 9]\n",
+      "  [12]\n",
+      "  [17]\n",
+      "  [16]\n",
+      "  [31]\n",
+      "  [53]\n",
+      "  [42]\n",
+      "  [50]\n",
+      "  [64]\n",
+      "  [55]\n",
+      "  [47]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [37]\n",
+      "  [31]\n",
+      "  [36]\n",
+      "  [30]\n",
+      "  [18]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [22]\n",
+      "  [16]\n",
+      "  [13]\n",
+      "  [13]\n",
+      "  [12]\n",
+      "  [11]\n",
+      "  [16]\n",
+      "  [17]\n",
+      "  [30]\n",
+      "  [55]\n",
+      "  [44]\n",
+      "  [52]\n",
+      "  [46]\n",
+      "  [48]\n",
+      "  [43]\n",
+      "  [34]\n",
+      "  [16]\n",
+      "  [45]\n",
+      "  [48]\n",
+      "  [30]\n",
+      "  [26]\n",
+      "  [27]\n",
+      "  [25]\n",
+      "  [29]\n",
+      "  [20]\n",
+      "  [18]\n",
+      "  [18]\n",
+      "  [15]\n",
+      "  [12]\n",
+      "  [13]\n",
+      "  [19]\n",
+      "  [24]\n",
+      "  [30]\n",
+      "  [35]\n",
+      "  [51]\n",
+      "  [45]\n",
+      "  [54]\n",
+      "  [44]\n",
+      "  [48]\n",
+      "  [48]]]\n",
+      "[[54]\n",
+      " [44]\n",
+      " [48]\n",
+      " [48]\n",
+      " [58]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# after slicing X into multiple sub-sequences\n",
+    "# we obtain a 3 dimensional matrix X where\n",
+    "# the shape indicates (# slices, window size, 1)\n",
+    "# and similarly y is (# slices, target size)\n",
+    "\n",
+    "print(\"X shape = {}\\ny shape = {}\\nX index shape = {}\\ny index shape = {}\".format(\n",
+    "    context['X'].shape, context['y'].shape, context['index'].shape, context['target_index'].shape))\n",
+    "\n",
+    "print(context['X'][:5])\n",
+    "print(context['y'][:5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71ada767",
+   "metadata": {},
+   "source": [
+    "#### long_short_term_context\n",
+    "This primitive builds a **long + short** context representation from each rolling window.\n",
+    "\n",
+    "- **input**: `X` rolling windows, `first_index` indices.\n",
+    "- **output**: `X` updated context sequence (ready for string formatting), plus window metadata.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f83fa233",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'X', 'y', 'first_index', 'window_size', 'step_size', 'dim'])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 4\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc381564-fbe6-4ed3-9cc0-282197a08a80",
+   "metadata": {},
+   "source": [
+    "#### format as string\n",
+    "this primitive converts each sequence of scalar values into string.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**: `X` which is a string representation version of X."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c706585e-7644-4c47-a8fb-3a51728e7273",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 5\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "361d897a-f973-482b-aa98-d5705188c912",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'34,20,45,18,38,16,45,48,30,26,27,25,29,20,18,18,15,12,13,19,24,30,35,51,45'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(context['X']).flatten().tolist()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af618013-fb07-4ba3-9357-e93044a8bb2e",
+   "metadata": {},
+   "source": [
+    "when inspecting the time series, we can see that we have a single list consisting of 140 values (according to the set `window_size`) and it is now of string type, ready to be an input to an LLM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de1a4a3a-42e5-49bf-bdb5-8b0b20c84619",
+   "metadata": {},
+   "source": [
+    "#### HF\n",
+    "this primitive prompts a huggingface model to forecast the next steps.\n",
+    "\n",
+    "* **input**: `X` input sequence.\n",
+    "* **output**: `y_hat` predicted sequence.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>Warning:</b> this step is time consuming depending on the number of windows.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3fca7405-cecf-44e8-97c2-64ae85810dbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 677/677 [07:20<00:00,  1.54it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'X', 'y', 'y_hat'])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 6\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ced35d4d-48d6-4a2e-9792-002bc9bbf8d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y_hat =  [[',32,17,41,48,55', ',52,50,57,53,48'], [',37,42,48,46,43', ',31,35,49,48,38'], [',51,49,49,19,48', ',57,39,50,55,47'], [',41,14,16,42,37', ',41,43,52,53,61'], [',53,44,35,38,41', ',44,40,46,41,51']]\n",
+      "y =  [[54]\n",
+      " [44]\n",
+      " [48]\n",
+      " [48]\n",
+      " [58]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"y_hat = \", context['y_hat'][:5])\n",
+    "print(\"y = \", context['y'][:5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ce2edb-ee17-4cdb-9944-fb743e157212",
+   "metadata": {},
+   "source": [
+    "#### format as integer\n",
+    "this primitive converts each sequence of string values into integers.\n",
+    "\n",
+    "* **input**: `y_hat` which is a sequence of string values.\n",
+    "* **output**: `y_hat` which is an integer representation version of y_hat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "093edbfa-9881-4a0a-b5b2-14f81696bb6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 7\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c175381f-95f1-435f-bcdb-2371c566aaa3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y_hat =  [[[32.]\n",
+      "  [52.]]\n",
+      "\n",
+      " [[37.]\n",
+      "  [31.]]\n",
+      "\n",
+      " [[51.]\n",
+      "  [57.]]\n",
+      "\n",
+      " [[41.]\n",
+      "  [41.]]\n",
+      "\n",
+      " [[53.]\n",
+      "  [44.]]]\n",
+      "y =  [[54]\n",
+      " [44]\n",
+      " [48]\n",
+      " [48]\n",
+      " [58]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"y_hat = \", context['y_hat'][:5])\n",
+    "print(\"y = \", context['y'][:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e569e052-8beb-419b-b5e4-74540e1beabb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(677, 2, 1)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# (num_windows, num_samples, pred_length)\n",
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a89723d8-195e-492e-8d54-8f9081f60d42",
+   "metadata": {},
+   "source": [
+    "#### Scalar2Float\n",
+    "this primitive converts integer values into float and adds minimum value.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `y_hat` sequence of integer values.\n",
+    "    * `minimum` value to add to shift by.\n",
+    "* **output**: `y_hat` which is a transformed version of `y_hat` in float."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5cc0579a-bd0f-4333-a56a-068c0616d2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_step = 8\n",
+    "second_step = 9\n",
+    "context = pipeline.fit(**context, start_=first_step, output_=second_step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ee049dcb-9da2-443f-8681-f7f60e4ee5cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y_hat =  [[[0.320385]\n",
+      "  [0.520385]]\n",
+      "\n",
+      " [[0.370385]\n",
+      "  [0.310385]]\n",
+      "\n",
+      " [[0.510385]\n",
+      "  [0.570385]]\n",
+      "\n",
+      " [[0.410385]\n",
+      "  [0.410385]]\n",
+      "\n",
+      " [[0.530385]\n",
+      "  [0.440385]]]\n",
+      "y =  [[0.540385]\n",
+      " [0.440385]\n",
+      " [0.480385]\n",
+      " [0.480385]\n",
+      " [0.580385]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"y_hat = \", context['y_hat'][:5])\n",
+    "print(\"y = \", context['y'][:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "108cb8cd-3c16-47aa-8ba8-9b8dd87e62e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(677, 2, 1)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37892f35-1c58-48cf-8704-f14a154a4e42",
+   "metadata": {},
+   "source": [
+    "#### aggregate_rolling_window\n",
+    "this primitive aggregates multiple horizon predictions into a single representation.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `y_hat` n-dimensional sequence of forecasted values.\n",
+    "    * `agg` aggregation method, \"median\" by default.\n",
+    "* **output**: `y_hat` one-dimensional output sequence depicting the aggregated value of forecasts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8527ebb5-102c-4c0e-b849-595157cc9f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 10\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c699ed1b-9619-427d-97d1-518920abd548",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y_hat =  [0.420385 0.340385 0.540385 0.410385 0.485385]\n",
+      "y =  [[0.540385]\n",
+      " [0.440385]\n",
+      " [0.480385]\n",
+      " [0.480385]\n",
+      " [0.580385]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"y_hat = \", context['y_hat'][:5])\n",
+    "print(\"y = \", context['y'][:5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6bb79d6-2db8-4942-b731-8da5973cb272",
+   "metadata": {},
+   "source": [
+    "#### reshape\n",
+    "reshape `y_hat` sequences\n",
+    "\n",
+    "* **input**: `y_hat` forecasted and aggregated sequences.\n",
+    "* **output**: `y_hat` reshaped sequences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "71ba3d53-9647-48e2-810a-7b22a6e3fac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 11\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f9e6efa6-e5b5-4941-9d98-b8c782460d23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(677, 1)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3016be08-e7c2-42f9-a75d-6293569a3053",
+   "metadata": {},
+   "source": [
+    "#### regression_errors\n",
+    "this primitive computes the point-wise difference between `y` and `y_hat`.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `y` target sequences.\n",
+    "    * `y_hat` forecasted sequences.\n",
+    "* **output**: `errors` computed errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d5274fa0-b505-4a71-b4ed-1c41b655db4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'y_hat', 'X', 'y', 'errors'])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 12\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "4f20a106-0dc3-49c1-8042-474bc5020d93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(677, 1)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05b13020-b95b-484d-8930-91d9e38b71ef",
+   "metadata": {},
+   "source": [
+    "#### find anomalies\n",
+    "this primitive extracts anomalies from sequences of errors following the approach explained in the [related paper](https://arxiv.org/pdf/1802.04431.pdf).\n",
+    "\n",
+    "* **input**\n",
+    "    * `errors` array of errors.\n",
+    "    * `target_index` array of indices of errors.\n",
+    "* **output**: `y` array containing start-index, end-index, score for each anomalous sequence that was found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "31d53428-12e8-4cf7-9803-6a203cea9b9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'minimum', 'decimal', 'target_index', 'first_index', 'window_size', 'step_size', 'dim', 'y_hat', 'errors', 'X', 'y', 'anomalies'])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 13\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "ae8e26f0-1052-49a4-92b8-5218241d4e4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "if len(context['anomalies']) > 0:\n",
+    "    pd.DataFrame(context['anomalies'], columns=['start', 'end', 'score'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "037bd699-cd54-4b27-a62e-87bf3c7fc29a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7aa7043935e0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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eP//8M8rLyw0ft6qqKu5zKzHyYvTjEotvfUaNzNatWzFixIiIPKNo3eIGDhwYsW1ZWRl++OGHmOM2el6z2Yxhw4Yl9LxA7NdXXFwc9X579uxBfX09nnrqKTz11FOG24j3fuvWrRg5cmTEa0z0+9XW1oY5c+bgueeew86dOzWfl5EoG+8zE98V/fPb7XYMHz48Qig0+nxKSkowaNCgiNvk50ll7LFYv349Ghoa0Lt3b8O/i/f76KOPxm9/+1vMnj0bDz30EI455hicccYZOP/88yMCvcV49K+PEEJI94ZiEiGEkJxg/PjxajD0GWecgSOOOALnn38+fvnlFxQWFiIQCAAAZs6cicmTJxs+hlgwH3XUUdi4cSPefvttfPTRR3jmmWfw0EMP4cknn9S4X1Il2qLL7/drft+4cSOOP/547LPPPpg7dy4GDRoEu92O9957Dw899JD6mtL1/DKnnnoqnE4nFixYgMMPPxwLFiyA2WzG7373O3WbQCAAk8mE999/HxaLJeIxCgsLNb/LDouOwGgMRrcBWvEnEAhg9OjRmDt3ruG2+gW/np49e8YUhhIZQ3vpjOdI93OL/feCCy7ARRddZLhNLAE3Ga655ho899xzuO666zBhwgSUlJTAZDLh3HPPNfwepfv9jPZ4iTxPsmOPRSAQQO/evfHKK68Y/l0IqiaTCa+99hq+/vpr/O9//8OHH36ISy65BA8++CC+/vprzXdb7Pt60ZYQQkj3hmISIYSQnMNisWDOnDk49thj8eijj+Kmm25Su5PZbLaoXbdkevTogWnTpmHatGlobm7GUUcdhTvuuCOqmDRkyBAEAgFs3LhR41b45ZdfIrYtKyuL6PgEIMLN8L///Q9utxvvvPOOxikRr+wqGmKM69evx7777qveXllZifr6ek2AdEFBAU455RT897//xdy5c/Hqq6/iyCOPRP/+/dVtRowYAUVRMGzYMOy1114pjSkanelyGDFiBL7//nscf/zxKT3vPvvsg9dffz2tYxoyZAjWrFkDRVE0Y9qwYUNan8foeQOBADZv3qxxnLX3eY3eV9H10O/3x/1ODhkyBKtXr454P4y+X0a89tpruOiiizSd/Fwul+H3MBHEd+WXX37RdD70eDzYvHlzQseYREnn2EeMGIGFCxdi4sSJCYm7hx12GA477DD87W9/w7///W/8/ve/x/z58zXHwc2bNwOA5phCCCGEMDOJEEJITnLMMcdg/PjxmDdvHlwuF3r37o1jjjkG//znP7F79+6I7eW233IJFBB02IwcOTJmG3LRMesf//iH5na5LEwwYsQINDQ0aEqQdu/erXZTEwjXgr6s5bnnnos6jlicdNJJhmMSjhx9x64pU6Zg165deOaZZ/D9999jypQpmr+fddZZsFgsmD17doRjQ1GUiPcxGQoKCgAg5cV+MpxzzjnYuXMnnn766Yi/tbW1oaWlJeb9J0yYgLq6uqTziWIxefJk7Ny5E++88456m8vlMhxjOhGuvccff1xz+yOPPNKuxy0oKIj4LC0WC37729/i9ddfx+rVqyPuI38nTzrpJOzatQuvvfaaeltra2vU8jg9FoslYh995JFHItyAiTJp0iTY7Xb84x//0Dzuv/71LzQ0NLS7i6NMOsd+zjnnwO/346677or4m8/nUz+jurq6iOccM2YMAEQcB5cvX46SkhLsv//+SY+HEEJI14XOJEIIITnLDTfcgN/97nd4/vnncdVVV+Gxxx7DEUccgdGjR+Pyyy/H8OHDUVlZiaVLl2LHjh34/vvvAQD77bcfjjnmGIwdOxY9evTAsmXL8Nprr2H69OlRn2vMmDE477zz8Pjjj6OhoQGHH344PvnkE0NHx7nnnosbb7wRZ555Jq699lq0trbiiSeewF577aUJ1D3hhBNgt9tx6qmn4sorr0RzczOefvpp9O7d21AQi8dBBx2Eiy66CE899RTq6+tx9NFH49tvv8ULL7yAM844A8cee6xm+5NOOglFRUWYOXOmuvCXGTFiBO6++27cfPPN2LJlC8444wwUFRVh8+bNePPNN3HFFVdg5syZSY8TCLYbB4KB5pMnT4bFYtEEGKeTCy+8EAsWLMBVV12FRYsWYeLEifD7/Vi7di0WLFiADz/8UC2hNOLkk0+G1WrFwoULEw6EjseVV16JRx99FOeddx7+9Kc/oV+/fnjllVeQl5cHoOOcW2PHjsVvf/tbzJs3DzU1NTjssMPw+eefY926de163rFjx2LhwoWYO3cu+vfvj2HDhuHQQw/Fvffei0WLFuHQQw/F5Zdfjv322w+1tbVYsWIFFi5ciNraWgDBYOtHH30UU6dOxfLly9GvXz+89NJLcDqdCT3/KaecgpdeegklJSXYb7/9sHTpUixcuBA9e/ZM6fWUl5fj5ptvxuzZs/Gb3/wGp512Gn755Rc8/vjjOOSQQzRh6O0lnWM/+uijceWVV2LOnDlYtWoVTjjhBNhsNqxfvx7//e9/8fDDD+Pss8/GCy+8gMcffxxnnnkmRowYgaamJjz99NMoLi5WRWnBxx9/jFNPPZWZSYQQQjRQTCKEEJKznHXWWRgxYgQeeOABdaG6bNkyzJ49G88//zxqamrQu3dvHHzwwbjtttvU+1177bV455138NFHH8HtdmPIkCG4++67ccMNN8R8vmeffRbl5eV45ZVX8NZbb+G4447Du+++G5G507NnT7z55puYMWMG/vKXv2DYsGGYM2cO1q9frxGT9t57b7z22mu49dZbMXPmTPTt2xd/+MMfUF5ejksuuSSl9+SZZ57B8OHD8fzzz+PNN99E3759cfPNN+P222+P2DYvLw+nnXYaXnnlFUyaNMkwtPemm27CXnvthYceegizZ88GEMwYOuGEEzSdyJLlrLPOwjXXXIP58+fj5ZdfhqIoHSYmmc1mvPXWW3jooYfw4osv4s0334TT6cTw4cPxpz/9KW4JX58+fXDSSSdhwYIFaROTCgsL8emnn+Kaa67Bww8/jMLCQkydOhWHH344fvvb36qiUkfw4osvom/fvvjPf/6DN998E5MmTcKrr76KvffeO+XnnTt3Lq644grceuutaGtrw0UXXYRDDz0Uffr0wbfffos777wTb7zxBh5//HH07NkT+++/P/7+97+r93c6nfjkk09wzTXX4JFHHoHT6cTvf/97nHjiifjNb34T9/kffvhhWCwWvPLKK3C5XJg4cSIWLlwYNT8tEe644w6Ul5fj0UcfxfXXX48ePXrgiiuuwD333BMRPt8e0j32J598EmPHjsU///lP/PWvf4XVasXQoUNxwQUXYOLEiQCgCs3z589HZWUlSkpKMH78eLzyyiuacPa1a9di9erVhg5MQggh3RuT0hnJjYQQQgghOcyXX36JY445BmvXro3a3S4dzJs3D9dffz127NiBAQMGdNjz6Fm1ahUOPvhgvPzyy/j973/fac9LspvrrrsOX3zxBZYvX05nEiGEEA0UkwghhBBCEuDEE0/EwIED05Zr1NbWpglJdrlcOPjgg+H3+9Wys45A/7wAcPHFF+Oll17Cli1b4na3I92DmpoaDBkyBAsWLIgofSOEEEJY5kYIIYQQkgDvv/9+Wh/vrLPOwuDBgzFmzBg0NDTg5Zdfxtq1a6O2dU8X9913H5YvX45jjz0WVqsV77//Pt5//31cccUVFJKISs+ePdHc3JzpYRBCCMlS6EwihBBCCMkA8+bNwzPPPIMtW7bA7/djv/32w1/+8peIrnrp5uOPP8bs2bOxZs0aNDc3Y/Dgwbjwwgtxyy23wGrldUZCCCGExIdiEiGEEEIIIYQQQghJGHOmB0AIIYQQQgghhBBCcgeKSYQQQgghhBBCCCEkYbpdYXwgEMCuXbtQVFTEFqeEEEIIIYQQQgjpMiiKgqamJvTv3x9mc8f5h7qdmLRr1y52KiGEEEIIIYQQQkiXZfv27Rg4cGCHPX63E5OKiooABN/Y4uLiDI+mHbS2AosXA3Y74HBkejQkl3C7AY8HmDgRcDozPRpCCCGk+8F5HGkvnM8RQqLQ2NiIQYMGqdpHR9HtxCRR2lZcXJzbYpLVChQUAEVFQF5epkdDcgmXC2hqAoqLOfkghBBCMgHncaS9cD5HCIlDR8f6MICbEEIIIYQQQgghhCQMxSRCCCGEEEIIIYQQkjAUkwghhBBCCCGEEEJIwnS7zCRCCCGEEEIIIaQjURQFPp8Pfr8/00MhXRCbzQaLxZLRMVBMIoQQQgghhBBC0oTH48Hu3bvR2tqa6aGQLorJZMLAgQNRWFiYsTFQTCKEEEIIIYQQQtJAIBDA5s2bYbFY0L9/f9jt9g7vqkW6F4qiYM+ePdixYwdGjRqVMYcSxSRCCCGEEEIIISQNeDweBAIBDBo0CE6nM9PDIV2U8vJybNmyBV6vN2NiEgO4CSGEEEIIIYSQNGI2c6lNOo5scLtxDyeEEEIIIYQQQgghCUMxiXQJat0BrG3wZnoYhBBCCCGEEEJIl4diEukSXLG4Did+VIMdLWy9SQghhBBCCCHZwMUXX4wzzjgj5jbHHHMMrrvuurQ+7x133IExY8bE3W7WrFm44oor0vrc7cXj8WDo0KFYtmxZpocSEwZwky7BrlY/FAC72/wYWJCZADJCCCGEEEIIIWEefvhhKIqS6WEYUlFRgYcffhg//vhjpoeiwW63Y+bMmbjxxhvxySefZHo4UaEziXQJvKHjkzeQnQcqQgghhBBCCMklPB5Pux+jpKQEpaWl7R9MB/DMM8/g8MMPx5AhQ1J+DEVR4PP5Im5P9b0T9/v973+Pr776Cj/99FPKY+toKCaRLoEvJCK5WeVGCCGEEEIIySIURUGrx5eRf8m4go455hhMnz4d1113HXr16oXJkycDAFavXo0TTzwRhYWF6NOnDy688EJUV1er93vttdcwevRo5Ofno2fPnpg0aRJaWloARJa5tbS0YOrUqSgsLES/fv3w4IMPRozDZDLhrbfe0txWWlqK559/Xv39xhtvxF577QWn04nhw4dj1qxZ8HqTy9CdP38+Tj31VM1tgUAAc+bMwbBhw5Cfn4+DDjoIr732mvr3zz77DCaTCe+//z7Gjh0Lh8OBr776Kup79/nnn2P8+PFwOBzo168fbrrpJo34FO1+ZWVlmDhxIubPn5/Ua+pMWOZGugQ+OpMIIYQQQgghWUib14/9bvswI8+95s7JcNoTX/a/8MIL+MMf/oDFixcDAOrr63Hcccfhsssuw0MPPYS2tjbceOONOOecc/Dpp59i9+7dOO+883DffffhzDPPRFNTE7788suoItYNN9yAzz//HG+//TZ69+6Nv/71r1ixYkVC+UYyRUVFeP7559G/f3/8+OOPuPzyy1FUVIS//OUvCd2/trYWa9aswbhx4zS3z5kzBy+//DKefPJJjBo1Cl988QUuuOAClJeX4+ijj1a3u+mmm/DAAw9g+PDhKCsrM3zvdu7ciZNOOgkXX3wxXnzxRaxduxaXX3458vLycMcdd6iPpb+fYPz48fjyyy+Tel86E4pJpEvgCwT/91BMIoQQQgghhJCUGDVqFO677z7197vvvhsHH3ww7rnnHvW2Z599FoMGDcK6devQ3NwMn8+Hs846Sy0XGz16tOFjNzc341//+hdefvllHH/88QCCQsrAgQOTHuett96q/jx06FDMnDkT8+fPT1hM2rZtGxRFQf/+/dXb3G437rnnHixcuBATJkwAAAwfPhxfffUV/vnPf2rEpDvvvBO//vWvNY+pf+9uueUWDBo0CI8++ihMJhP22Wcf7Nq1CzfeeCNuu+02mM1mw/sJ+vfvj61btyb0ejIBxSTSJfCHlG9vIMMDIYQQQgghhBCJfJsFa+6cnLHnToaxY8dqfv/++++xaNEiFBYWRmy7ceNGnHDCCTj++OMxevRoTJ48GSeccALOPvts1a2j397j8eDQQw9Vb+vRowf23nvvpMYIAK+++ir+8Y9/YOPGjaqgVVxcnPD929raAAB5eXnqbRs2bEBra2uESOTxeHDwwQdrbtM7moDI9+7nn3/GhAkTYDKZ1NsmTpyI5uZm7NixA4MHDza8nyA/Px+tra0Jv6bOhmIS6RJ46UwihBBCCCGEZCEmkympUrNMUlBQoPm9ubkZp556Kv7+979HbNuvXz9YLBZ8/PHHWLJkCT766CM88sgjuOWWW/DNN99g2LBhKY3BZDJFlMnJeUhLly7F73//e8yePRuTJ09GSUkJ5s+fb5i/FI1evXoBAOrq6lBeXg4g+FoB4N1338WAAQM02zscDs3v+vcp2m2JEO1+tbW16tiykawI4H7ssccwdOhQ5OXl4dBDD8W3334bddvnn38eJpNJ809WE0n3I6AoEIcaD51JhBBCCCGEEJIWfvWrX+Gnn37C0KFDMXLkSM0/IYKYTCZMnDgRs2fPxsqVK2G32/Hmm29GPNaIESNgs9nwzTffqLfV1dVh3bp1mu3Ky8uxe/du9ff169drHDpLlizBkCFDcMstt2DcuHEYNWpU0uVgI0aMQHFxMdasWaPett9++8HhcGDbtm0Rr3XQoEFJPT4A7Lvvvli6dKlGGFu8eDGKiooSKu1bvXp1hCMqm8i4mPTqq69ixowZuP3227FixQocdNBBmDx5MqqqqqLep7i4GLt371b/ZXMdIel45NI2BnATQgghhBBCSHr44x//iNraWpx33nn47rvvsHHjRnz44YeYNm0a/H4/vvnmG9xzzz1YtmwZtm3bhjfeeAN79uzBvvvuG/FYhYWFuPTSS3HDDTfg008/xerVq3HxxRer2UGC4447Do8++ihWrlyJZcuW4aqrroLNZlP/PmrUKGzbtg3z58/Hxo0b8Y9//MNQvIqF2WzGpEmT8NVXX6m3FRUVYebMmbj++uvxwgsvYOPGjVixYgUeeeQRvPDCC0m+c8DVV1+N7du345prrsHatWvx9ttv4/bbb8eMGTMiXrMRX375JU444YSkn7ezyLiYNHfuXFx++eWYNm0a9ttvPzz55JNwOp149tlno97HZDKhb9++6r8+ffp04ohJtuGXlF6Pn2ISIYQQQgghhKSD/v37Y/HixfD7/TjhhBMwevRoXHfddSgtLYXZbEZxcTG++OILnHTSSdhrr71w66234sEHH8SJJ55o+Hj3338/jjzySJx66qmYNGkSjjjiiIjMoAcffBCDBg3CkUceifPPPx8zZ86E0+lU/37aaafh+uuvx/Tp0zFmzBgsWbIEs2bNSvq1XXbZZZg/fz4CgbA74a677sKsWbMwZ84c7LvvvvjNb36Dd999N6WSvQEDBuC9997Dt99+i4MOOghXXXUVLr30Uk14eDSWLl2KhoYGnH322Uk/b2dhUqL17OsEPB4PnE4nXnvtNZxxxhnq7RdddBHq6+vx9ttvR9zn+eefx2WXXYYBAwYgEAjgV7/6Fe655x7sv//+hs/hdrvhdrvV3xsbGzFo0CA0NDQkFdCVdbS2Al98ARQVAd28zK/BE8BBbwedbH/evxDX7BcZDkckXC6gqQk46ihAOigTQgghpJPgPI60F87nshaXy4XNmzdj2LBhjGPJchRFwaGHHorrr78e5513XqaHo2HKlCk46KCD8Ne//tXw77H2s8bGRpSUlHS45pFRZ1J1dTX8fn+Es6hPnz6oqKgwvM/ee++NZ599Fm+//TZefvllBAIBHH744dixY4fh9nPmzEFJSYn6L5VaR5LdyGYkBnATQgghhBBCCImHyWTCU089BZ/Pl+mhaPB4PBg9ejSuv/76TA8lJhkvc0uWCRMmYOrUqRgzZgyOPvpovPHGGygvL8c///lPw+1vvvlmNDQ0qP+2b9/eySMmHY1PEpAYwE0IIYQQQgghJBHGjBmDCy+8MNPD0GC323HrrbciPz8/00OJSUb7E/bq1QsWiwWVlZWa2ysrK9G3b9+EHsNms+Hggw/Ghg0bDP/ucDgi2viRroWXziRCCCGEEEIIIaTTyKgzyW63Y+zYsfjkk0/U2wKBAD755BNMmDAhocfw+/348ccf0a9fv44aJsly/JKAxG5uhBBCCCGEEEJIx5JRZxIAzJgxAxdddBHGjRuH8ePHY968eWhpacG0adMAAFOnTsWAAQMwZ84cAMCdd96Jww47DCNHjkR9fT3uv/9+bN26FZdddlkmXwbJILIzycsyN0IIIYQQQgghpEPJuJg0ZcoU7NmzB7fddhsqKiowZswYfPDBB2oo97Zt22A2hw1UdXV1uPzyy1FRUYGysjKMHTsWS5YswX777Zepl0AyjOxM8vjpTCKEEEIIIYQQQjqSjItJADB9+nRMnz7d8G+fffaZ5veHHnoIDz30UCeMiuQK2sykzI2DEEIIIYQQQgjpDuRcNzdC9PglAYkB3IQQQgghhBBCSMeSFc4kQtqDV2EANyGEEEIIISTL8XgAn6/zns9qBez2zns+0q2gmERyHq0zKXPjIIQQQgghhBBDPB7g22+B5ubOe87CQmD8+G4jKB1zzDEYM2YM5s2bl+mhdAsoJpGcx0dnEiGEEEIIISSb8fmCQpLdDjgcHf98bnfw+Xy+biMmkc6FYhLJebyyM8mfuXEQQgghhBBCSEwcDiAvr3Oey+PpnOch3RIGcJOcxy85kxjATQghhBBCCCHJ88EHH+CII45AaWkpevbsiVNOOQUbN25U/75lyxaYTCa88cYbOPbYY+F0OnHQQQdh6dKlmsd5/fXXsf/++8PhcGDo0KF48MEHNX8fOnQo7r77bkydOhWFhYUYMmQI3nnnHezZswenn346CgsLceCBB2LZsmXqfWpqanDeeedhwIABcDqdGD16NP7zn/9EfS133nknDjjggIjbx4wZg1mzZhnep66uDr///e9RXl6O/Px8jBo1Cs8995zmtc+fPx+HH3448vLycMABB+Dzzz9X7+/3+3HppZdi2LBhyM/Px957742HH35Y8xwXX3wxzjjjDDzwwAPo168fevbsiT/+8Y/wer1RX0u2QjGJ5DyyM4llboQQQgghhBCSPC0tLZgxYwaWLVuGTz75BGazGWeeeSYCAW0w7S233IKZM2di1apV2GuvvXDeeefBFwoWX758Oc455xyce+65+PHHH3HHHXdg1qxZeP755zWP8dBDD2HixIlYuXIlTj75ZFx44YWYOnUqLrjgAqxYsQIjRozA1KlToYSMAy6XC2PHjsW7776L1atX44orrsCFF16Ib7/91vC1XHLJJfj555/x3XffqbetXLkSP/zwA6ZNm2Z4n1mzZmHNmjV4//338fPPP+OJJ55Ar169NNvccMMN+POf/4yVK1diwoQJOPXUU1FTUwMACAQCGDhwIP773/9izZo1uO222/DXv/4VCxYs0DzGokWLsHHjRixatAgvvPACnn/++Yj3JxdgmRvJefySfsQAbkIIIYQQQghJnt/+9rea35999lmUl5djzZo1GpfPzJkzcfLJJwMAZs+ejf333x8bNmzAPvvsg7lz5+L4449X3T977bUX1qxZg/vvvx8XX3yx+hgnnXQSrrzySgDAbbfdhieeeAKHHHIIfve73wEAbrzxRkyYMAGVlZXo27cvBgwYgJkzZ6r3v+aaa/Dhhx9iwYIFGD9+fMRrGThwICZPnoznnnsOhxxyCADgueeew9FHH43hw4cbvv5t27bh4IMPxrhx4wAEHVR6pk+frr5PTzzxBD744AP861//wl/+8hfYbDbMnj1b3XbYsGFYunQpFixYgHPOOUe9vaysDI8++igsFgv22WcfnHzyyfjkk09w+eWXG44rW6EzieQ8shuJziRCCCGEEEIISZ7169fjvPPOw/Dhw1FcXKyKKdu2bdNsd+CBB6o/9+vXDwBQVVUFAPj5558xceJEzfYTJ07E+vXr4ff7DR+jT58+AIDRo0dH3CYe1+/346677sLo0aPRo0cPFBYW4sMPP4wYm8zll1+O//znP3C5XPB4PPj3v/+NSy65JOr2f/jDHzB//nyMGTMGf/nLX7BkyZKIbSZMmKD+bLVaMW7cOPz888/qbY899hjGjh2L8vJyFBYW4qmnnooY4/777w+LxaL+3q9fP/V15hIUk0jOQ2cSIYQQQgghhLSPU089FbW1tXj66afxzTff4JtvvgEAeHRB3jabTf3ZZDIBQEQpXDyMHiPW495///14+OGHceONN2LRokVYtWoVJk+eHDE2/etxOBx488038b///Q9erxdnn3121O1PPPFEbN26Fddffz127dqF448/XuOGisf8+fMxc+ZMXHrppfjoo4+watUqTJs2Leb7J15rsu9fNsAyN5Lz+AIM4CaEEEIIIYSQVKmpqcEvv/yCp59+GkceeSQA4Kuvvkr6cfbdd18sXrxYc9vixYux1157adw4ybJ48WKcfvrpuOCCCwAERaZ169Zhv/32i3ofq9WKiy66CM899xzsdjvOPfdc5Ofnx3ye8vJyXHTRRbjoootw5JFH4oYbbsADDzyg/v3rr7/GUUcdBQDw+XxYvnw5pk+fro7x8MMPx9VXX61uLweYdzUoJpGcxyc7k/wUkwghhBBCCCFZitudlc9TVlaGnj174qmnnkK/fv2wbds23HTTTUk/7Z///GcccsghuOuuuzBlyhQsXboUjz76KB5//PGkH0tm1KhReO2117BkyRKUlZVh7ty5qKysjCkmAcBll12GfffdFwAiRC49t912G8aOHYv9998fbrcb//d//6feV/DYY49h1KhR2HffffHQQw+hrq5OLZ0bNWoUXnzxRXz44YcYNmwYXnrpJXz33XcYNmxYO1559kIxieQ8Pk1mUgYHQgghhBBCCCFGWK1AYSHQ3AzEKM1KK4WFwedNALPZjPnz5+Paa6/FAQccgL333hv/+Mc/cMwxxyT1lL/61a+wYMEC3HbbbbjrrrvQr18/3HnnnZrw7VS49dZbsWnTJkyePBlOpxNXXHEFzjjjDDQ0NMS836hRo3D44YejtrYWhx56aMxt7XY7br75ZmzZsgX5+fk48sgjMX/+fM029957L+69916sWrUKI0eOxDvvvKN2fLvyyiuxcuVKTJkyBSaTCeeddx6uvvpqvP/+++167dmKSRG99roJjY2NKCkpQUNDA4qLizM9nNRpbQW++AIoKgLy8jI9mozyr3UtuOv7JvX3Db/tA6vZlMERZTkuF9DUBBx1FOB0Zno0hBBCSPeD8zjSXjify1pcLhc2b96MYcOGIU///fZ4AJ+v8wZjtQJ2e+c9XxaiKApGjRqFq6++GjNmzEj5cbZs2YJhw4Zh5cqVGDNmTPoGmCKx9rPO0jzoTCI5j76yzRsArIyWJ4QQQgghhGQTdnu3F3c6kz179mD+/PmoqKjAtGnTMj2cLgfFJJLzeHWh256AgnzQmUQIIYQQQggh3ZXevXujV69eeOqpp1BWVpbp4XQ5KCaRnEfvTGJHN0IIIYQQQgjp3qQz0Wfo0KFpfbyuAIuBSM7j04lHDOEmhBBCCCGEEEI6DopJJOfx0ZlECCGEEEIIySLoYiEdSTbsXxSTSM5DZxIhhBBCCCEkG7DZbACA1tbWDI+EdGU8Hg8AwGKxZGwMzEwiOY/emeTWhygRQgghhBBCSCdgsVhQWlqKqqoqAIDT6YTJxOZAJH0EAgHs2bMHTqcTVmvmJB2KSSTn8emcSPruboQQQgghhBDSWfTt2xcAVEGJkHRjNpsxePDgjAqVFJNIzuNTWOZGCCGEEEIIyQ5MJhP69euH3r17w+v1Zno4pAtit9thNmc2tYhiEsl59M4kBnATQgghhBBCMo3FYslopg0hHQkDuEnOQ2cSIYQQQgghhBDSeVBMIjmP3pnkpjOJEEIIIYQQQgjpMCgmkZwn0plEMYkQQgghhBBCCOkoKCaRnCciM8mfmXEQQgghhBBCCCHdAYpJJOfx6YxIdCYRQgghhBBCCCEdB8UkkvP4dOIRu7kRQgghhBBCCCEdB8UkkvMIZ5Ip9LuH3dwIIYQQQgghhJAOg2ISUVle7cHZn9bgxzpvpoeSFMKZ5LQG5SSWuRFCCCGEEEIIIR0HxSSi8s52F5bVePHudlemh5IUwpmUbwmKSXQmEUIIIYQQQgghHQfFJKLSGlJlXP7ccvbQmUQIIYQQQgghhHQeFJOIihCT3DkmxghnkhCTPDkmhhFCCCGEEEIIIbkExSSiIhxJ7hwTY3yhsjaWuRFCCCGEEEIIIR0PxSSiojqT/BkeSJL4FG2ZmyfHnFWEEEIIIYQQQkguQTGJqLTluDMpnJmUwcEQQgghhBBCCCFdHIpJRKUtRzOT/HQmEUIIIYQQQgghnQbFJKKSq84k4URyWhjATQghhBBCCCGEdDQUk4iKyExy5ZgYI4abzzI3QgghhBBCCCGkw6GYRFTCzqQMDyRJvKGytoKQmJRrZXqEEEIIIYQQQkguQTGJAAAURcnZzCR9AHeulekRQgghhBBCCCG5RFaISY899hiGDh2KvLw8HHroofj2228Tut/8+fNhMplwxhlndOwAuwHuACCqw3JNjPEpWmeSh2VuhBBCCCGEEEJIh5FxMenVV1/FjBkzcPvtt2PFihU46KCDMHnyZFRVVcW835YtWzBz5kwceeSRnTTSro2ck5RrmUlhZ1Jwd841MYwQQgghhBBCCMklMi4mzZ07F5dffjmmTZuG/fbbD08++SScTieeffbZqPfx+/34/e9/j9mzZ2P48OGdONquiwjfBnIrMymgKKqjKuxMophECCGEEEIIIYR0FBkVkzweD5YvX45Jkyapt5nNZkyaNAlLly6Ner8777wTvXv3xqWXXhr3OdxuNxobGzX/SCRtkpsnlzKTJA1M7ebGMjdCCCGEEEIIIaTjyKiYVF1dDb/fjz59+mhu79OnDyoqKgzv89VXX+Ff//oXnn766YSeY86cOSgpKVH/DRo0qN3j7oq0SaqMXwF8OSIo+SXhqIAB3IQQQgghhBBCSIeT8TK3ZGhqasKFF16Ip59+Gr169UroPjfffDMaGhrUf9u3b+/gUeYmbToBJlfcSV4lPE4ny9wIIYQQQgghhJAOx5rJJ+/VqxcsFgsqKys1t1dWVqJv374R22/cuBFbtmzBqaeeqt4WCAStKVarFb/88gtGjBihuY/D4YDD4eiA0Xct5MwkAHD5gYKM7h2JYeRM8uRQ5hMhhBBCCCGEEJJrZNSZZLfbMXbsWHzyySfqbYFAAJ988gkmTJgQsf0+++yDH3/8EatWrVL/nXbaaTj22GOxatUqlrC1gzadmJQrpWLCmWQCkG8JlbnRmUQIIYQQQgghhHQYGfeezJgxAxdddBHGjRuH8ePHY968eWhpacG0adMAAFOnTsWAAQMwZ84c5OXl4YADDtDcv7S0FAAibifJEVHmliNiknAm2cyA3RwUk/wK4FcUWEymDI6MEEIIIYQQQgjpmmRcTJoyZQr27NmD2267DRUVFRgzZgw++OADNZR727ZtMJtzKtopJ9GXueWKu8cXciZZTCbYLeHbPX4gP+N7NyGEEEIIIYQQ0vXIiuX29OnTMX36dMO/ffbZZzHv+/zzz6d/QN0QvTPJlSPOJG/ImWQ1Aw5z2InkDijIB51JhBBCCCGEEEJIuqHlhwAwykzK0ECSRGheVhNgNZvUHdqTI2IYIYQQQgghhBCSa1BMIgByNzPJGyrHs4ZcSaLULVfK9AghhBBCCCGEkFyDYhIBkLuZSbIzCQiXuuWKs4oQQgghhBBCCMk1KCYRAJEZSbmTmaR3JgX/9+SIGEYIIYQQQgghhOQaFJMIAANnUo44e/TOJHtoj86VMj1CCCGEEEIIISTXoJhEAACtXSQzyaE6kzI2JEIIIYQQQgghpEtDMYkAAFw5nplkUZ1JLHMjhBBCCCGEEEI6EopJBEDYmVRsC4oxuZKZ5As5kGyhPTkcwJ0b4yeEEEIIIYQQQnINikkEANAWcib1cAR3iVwRY0SZm8UkytyCt7PMjRBCCCGEEEII6RgoJhEAQFtIPCq1CzEpk6NJHK/OmaSWueWIGEYIIYQQQgghhOQaFJMIgLAzqdQeKhPLkcyhWndQTSoLiWB2S26NnxBCCCGEEEIIyTUoJhEA4cwkIcrkSmZSpStooeqbH6xvC1XpwZMjzipCCCGEEEIIISTXoJhEAIS7uZXmWGZSRVvQmdQnn84kQgghhBBCCCGkM6CYRNDoDcAb0l56OnIrM6myLTjQ3iExid3cCCGEEEIIIYSQjoViEsHyai8AYEiBBb2EmJQjzp6qkDNJlLnZ1W5uuTF+QgghhBBCCCEk16CYRPBdtQcAML7cDoclt5w9FSFnUp+8UJmb6kzK2JAIIYQQQgghhJAuDcUkgm/3BMWkQ3rZckpMcvkVNITq8/qIAO7Q+OlMIoQQQgghhBBCOgaKSd0cl1/B97XBMrdDy+1qN7RccPZUhVxJeRag2BYUkeyimxvFJEIIIYQQQgghpEOgmNTNWVnjgVcJlokNLrAgL4e6oamd3PIsMJmEmMQyN0IIIYQQQgghpCOhmNTN+aHOBwAY18sOk8mklom5cqDMrdIVykvKD+/GLHMjhBBCCCGEEEI6FopJ3Zwmb9Dd0ysUYJ1LmUmVwpkUyksCAEfImeTJgfETQgghhBBCCCG5CMWkbo5wIInytnBmUvaLMZVtkc4ke0hXcgcyMSJCCCGEEEIIIaTrQzGpmxMWkxD6X2QmZWpEiVMpZSYJ6EwihBBCCCGEEEI6FopJ3Zw2n86ZlIOZSb01ziRmJhFCCCGEEEIIIR0JxaRuTkiPiRCT/ArgzXJBpjUkhBXbJDFJLdPLxIgIIYQQQgghhJCuD8Wkbo7IRsoPiUhCVAKy350khmcODzkcIJ7lQhghhBBCCCGEEJKrUEzq5kQL4Jb/lq0IvUjSv2A3s8yNEEIIIYQQQgjpSCgmdXPadGKSyWRSBaVsF5OMnEn2HOpGRwghhBBCCCGE5CIUk7o5QjBySPYetaNblucO+ZXg2C2m8NgdagB3RoZECCGEEEIIIYR0eSgmdXOEmJRvjRSTst2ZFKvMjc4kQgghhBBCCCGkY6CY1M0JZyaFb8sVMck4gDv4PzOTCCGEEEIIIYSQjoFiUjfHFSplyzMoc8t2MSlgVOYmArj9gKJk9/gJIYQQQgghhJBchGJSN0ffzU3+OdvFJL9RmVvolwAAX3YPnxBCCCGEEEIIyUkoJnVjFEUJZyZZ5BDr4P+5IiYZdXMDWOpGCCGEEEIIIYR0BBSTugEbGn34z6ZWtfuZwKuEBRljZ1KnDTElYgVwA8FSN0IIIYQQQgghhKQXa6YHQDqe2asa8WWlB4MLLJjYx6HeLjuPHDlZ5haZmWQ1m2AxBUUyN51JhBBCCCGEEEJI2qEzqRtQ6w4AAKpD/wuEWGQC4JD2hNwRk4L/y2VuQHj8rQxNIoQQQgghhBBC0g7FpG6AO6S66MUVtxS+bTJFOpPcWS4mGZW5AUC//OBuvaOFdW6EEEIIIYQQQki6oZjUDRCGJL2Y1OYTYpJ2+9xxJgXHp3cmDSkMVm9uo5hECCGEEEIIIYSkHYpJ3QDhMGrTiUMiYDtPZ+3Jy5VubiGRTM5MAoDBhcEXsK3Z19lDIoQQQgghhBBCujwUk7oB0crcXFKZm0yudHPzRylzG1wQFJO20plECCGEEEIIIYSkHYpJ3QDR1SyizC2KmOTIgTI3RVEg4sQjy9xCYlIzxSRCCOnqbK5uQWWjK9PDIIQQQgjpVlgzPQDSsSiKAndIU0nemZS9YpLcly6izK0guFtvb/FDURRNuDghhJCuQ32rB8c+8BkAYMu9J2d2MIQQQggh3YiscCY99thjGDp0KPLy8nDooYfi22+/jbrtG2+8gXHjxqG0tBQFBQUYM2YMXnrppU4cbW7hVQAhCbVGZCblrpgkD01f5jawwAITgBafghp3AIQQQromu+rDjqRmN3PyCCGEEEI6i4yLSa+++ipmzJiB22+/HStWrMBBBx2EyZMno6qqynD7Hj164JZbbsHSpUvxww8/YNq0aZg2bRo+/PDDTh55buCWVJc2nTPJncNiUkAamr7MLc9iQt/84K7N3KTuxY87GvDdltpMD4MQ0knYreETQEUDS90IIYQQQjqLhMvcDj744ITLhVasWJHwAObOnYvLL78c06ZNAwA8+eSTePfdd/Hss8/ipptuitj+mGOO0fz+pz/9CS+88AK++uorTJ48OeHn7S7IYlK0zKR83V4gxCR3FotJfiU8NqvBfjm40ILdbQFsa/ZjbM/OHBnJFB5fAKc++hUA4Ic7TkBxni3DIyKEdDQe6bxW0eDCyN6FGRwNIYQQQkj3IWEx6Ywzzkj7k3s8Hixfvhw333yzepvZbMakSZOwdOnSuPdXFAWffvopfvnlF/z973833MbtdsPtdqu/NzY2tn/gOYRc5RVZ5hb8P9KZFLpvFpt6/DGcSQAwpMCKb/Z4sY3OpG7D1poW9efaZg/FJEK6Ab5A+CRXwRBuQgghhJBOI2Ex6fbbb0/7k1dXV8Pv96NPnz6a2/v06YO1a9dGvV9DQwMGDBgAt9sNi8WCxx9/HL/+9a8Nt50zZw5mz56d1nHnEp4YZW6ijM2Rg93cAjEykwCgvzOoiFW0UkzqLmyoalZ/bnR5MzgSQkhn4fWHxSR2dCOEEEII6TwynpmUCkVFRVi1ahW+++47/O1vf8OMGTPw2WefGW578803o6GhQf23ffv2zh1shnEHYpS5+XI3MylWADcAOK3Z/xpIepHFpIY2ikmEdAfkMrfdDW0ZHAkhhBBCSPciYWeSjN/vx0MPPYQFCxZg27Zt8Hg8mr/X1iYWgNurVy9YLBZUVlZqbq+srETfvn2j3s9sNmPkyJEAgDFjxuDnn3/GnDlzIvKUAMDhcMDhcCQ0nq6IXKrWFqWbW35OiknBsZkAwywvUaqXza+BpJf1FJMI6XbIzqSKBneMLQkhhBBCSDpJyZk0e/ZszJ07F1OmTEFDQwNmzJiBs846C2azGXfccUfCj2O32zF27Fh88skn6m2BQACffPIJJkyYkPDjBAIBTS4SCRMrgLsrdHMzciUB8mvopAGRjENnEiHdD21mEp1JhBBCCCGdRUrOpFdeeQVPP/00Tj75ZNxxxx0477zzMGLECBx44IH4+uuvce211yb8WDNmzMBFF12EcePGYfz48Zg3bx5aWlrU7m5Tp07FgAEDMGfOHADBDKRx48ZhxIgRcLvdeO+99/DSSy/hiSeeSOWldHnkMrc2v4KAosBs0opFejHJkQOuHjE0o/BtIDdyn0j6CAQUbKqmmERId0PbzY0XlQjpKiiKgm0tfgwusCTcTZoQQkjnkpKYVFFRgdGjRwMACgsL0dDQAAA45ZRTMGvWrKQea8qUKdizZw9uu+02VFRUYMyYMfjggw/UUO5t27bBbA4bqFpaWnD11Vdjx44dyM/Pxz777IOXX34ZU6ZMSeWldHncBqVtIk+oTRWTtPcR4pI7EDyZZ+NJPBAqc7NEGZso3dOX9pGuyc76Nri8YYcCxSRCugdymVtNixseXwB2a07GQRJCJOZvbsPNyxsx++AiXDSyINPDIYQQYkBKYtLAgQOxe/duDB48GCNGjMBHH32EX/3qV/juu+9SyieaPn06pk+fbvg3fbD23XffjbvvvjuVYXdL3Loyr1afAmfoU4/mTJJ/dwcixaZswJ9wmRvFpO6AXOIGAI0UkwjpFshlbooCVDW5MLDMmcEREULSwcYmHwBgfaMvwyMhhBASjZQu35155plqztE111yDWbNmYdSoUZg6dSouueSStA6QtA+5zA3QOnVEnlAsMSlbxZh4ZW6quypLx0/Sy6bqFs3vdCYR0j3w6rIAKxtdGRoJISSdCLOxPu+TEEJI9pCSM+nee+9Vf54yZQoGDx6MpUuXYtSoUTj11FPTNjjSfvRiinxSjuZMsplNsJiCgk22ikmJB3Bn5/hJeml2Ba9cOu0WtHr8FJMI6SZ4pDI3ANjdQDGJkK6AJzR/a6GYRAghWUtKYpKeCRMmJNV9jXQeiYhJ+dZIRSbPYkKLT8laMcYfJzMpLwdCxEn6cPuCNrveRQ5sqWmlmERIN8GrE5N21bOjGyFdAeGspzOJEEKyl5TEpBdffDHm36dOnZrSYEj68Wjn2boyN2Nnkrgtu8Wk4P/xytxcfuO/k66F2xfc0XsX5WFLTSsa25ixQEh3wKc7R22tac3QSLKPZ77chEKHFeeOH5zpoRCSNGL+SmcSIYRkLymJSX/60580v3u9XrS2tsJut8PpdFJMyiJSKXML3qbdJttIpswtWzvSkfQhnEnlxcEGAHQmEdI9EGVuFrMJ/oCCbbUUkwCgvtWDu9/9GTaLCb8bNwiWaFdeCMlSRJkbnUmEEJK9pBTAXVdXp/nX3NyMX375BUcccQT+85//pHuMpB3oA7jlk3KbL7qY5MhyZ48v9LqizY/F+BVEurNI18PtFc6koJjU6PIiEOAElJCujihzG1leCADYUtMSa/NuQ7M76M70+hVVbCckl2AANyGEZD8piUlGjBo1Cvfee2+Ea4lkFr0zSS5zEyKLw2AvyPYA67AzyVhNknOgsvU1kPQh3AnlITFJUYAmN0vdCOnqqGJS76CYtLOuDR4fryDI70Gbh2ISyT08zEwihJCsJ21iEgBYrVbs2rUrnQ9J2olbN4eUT8re0InaZmDvyXYxyR+nzM1mCu/c2foaSPoQzqSiPBvybMFPvpGlboR0eURmUv/SPOTbLAgowE6GcKs5cgDQ5qWYRHIPISYxM4kQQrKXlDKT3nnnHc3viqJg9+7dePTRRzFx4sS0DIykh2hlbgFFgTg/22I4k/TOpmxBdHOLVuZmMpmQZzGh1Z+9IeIkfYgyDofVjOI8G1xeNxravBiU4XERQjoW4Uq0WcwY0tOJtRVN2FrTgmG9CjIynmzJ6JPFJBfFJJKDCENdm19BQFFgzoLvFSGEEC0piUlnnHGG5neTyYTy8nIcd9xxePDBB9MxLpImogVwe6UqAHuUbm5A9rp64pW5AcEQ8VZ/9r4Gkj7EwslhNaMk34aqJjdDuAnpBnglMWlwDyEmZSaE2x9QcObji9GnOA9PTx2XkTEItGVuLPsjuYdHuhja5ldQYKWYRAgh2UZKYlIgwIlJriDEpCKrCU0+Ba1+ISaFT9J2wzK34P/ZKsSIYcVqUBMUxJSsDREn6SMsJllQkm8DwI5uhHQHvKELJHarGUNDbqRMiUm7G9rww44GAA3w+gOwWdKaJJAUcug2y9xILiKLSS0+BQUprVgIIYR0JJmb6ZBOwR3S/UpDKduig5t8odKozK3UHryxqi07hcN4mUlA9rurSPpQy9xsZopJhHQjvAHhTDJhcA8nAGBrhjq6uSTLb6Yz2zzMTCI5jjxPZQg3IYRkJwnr/DNmzEj4QefOnZvSYEj6Ec6kMrsJ21vkMrdQ5hCMS8VGFgd3jQ1N2dkRKxDKTIolJjkoJnUbRAC3KHMDKCYR0h3who7vVrMZQ3sGnUlbMiYmhUWbhjYvehY6MjIOQBfAzW5uJAfx+LXOJEIIIdlHwmLSypUrNb+vWLECPp8Pe++9NwBg3bp1sFgsGDt2bHpHSNqFsAkLp5EocxO32y3G99srJCatb8hOMSlc5hYrM4liUndBzkwqpphESLfBG/ru26xm9Cy0AwAa2jJz3pIdQI2uzJ475TI3+WdCcgU5joHOJEIIyU4SFpMWLVqk/jx37lwUFRXhhRdeQFlZGQCgrq4O06ZNw5FHHpn+UZKUUZ1JujI34ca3RQkdGhUSk7Y0++H2K6rLJ1sIJFDmlm+lmNQZNLq8UBSojqBM4JEykwocQYWUV+MJ6fr4QmVudosJ+bbgdz9T3cvkY06mxWwPnUkkx2GZGyGEZD8pZSY9+OCDmDNnjiokAUBZWRnuvvtudnPLMtyhOWRZyJnUoitzMwrfBoDeeWYU20wIANiUhaVuzEzKDgIBBSfO+xK/nvu52lUpE6iZSVYz8qypLyg3V7fg++316RwaIaQDEaUwNosZ+SGrbZvXD0Xp/ON+mzd7xCQ3M5NIjuOhM4kQQrKelMSkxsZG7NmzJ+L2PXv2oKmpqd2DIunDHToZ9ww5k5pDlqSwM8n4fiaTSXUnrW/MRjFJZCbFKnML/k8xqeNo9viws74NVU3ujC6e5G5uYkGZrJikKAqOfeAznP7YYlQ1udI+RkJI+hFlblaLGXkhZ5I/oKhZSp2JPjMpk7i9FJNI7qIoisaZxMwkQgjJTlISk84880xMmzYNb7zxBnbs2IEdO3bg9ddfx6WXXoqzzjor3WMk7UCUufXKC37UTd7g70JkilbmBki5SVkoJiVS5hZ2JnXCgLopcvlEJkspVDHJZobDFnYnJIO8+NtdTzGJdC1cXj8uevZb3PLmj5keSloRjki7xYQ86epIJgQUWUzKeDc3ySnqYpkbyTG8Ou2o1ZednYUJIaS7k3BmksyTTz6JmTNn4vzzz4fXG5wwWa1WXHrppbj//vvTOkDSPoSYJJxJQkwSFy3tMeTEkVksJoUDuKNvw25uHU+LO7xvZCqnxOcPwB9SFx1Ws5Sbktzkc0ddW/gxA5y4kq7Fy19vxefrgo7iu884AKYYrs5cQpRs2yxm2C1mmE3Biw0ur7/Tc9xkQT3TYpJbOh7TmURyDY9u3kZnEiGEZCcpiUlOpxOPP/447r//fmzcuBEAMGLECBQUFKR1cKT9uENrYuFMavUr8CtKeAIey5lUEtw91iUgJnkDCnyBcOh1R5NYmRvFpI6m1ZP5BYucDeKwWlQxKdnxbK9tVX9udnPxRboWr363Xf3Z5Q2o5aC5jtrNzWKGyRQM4W7x+DPilGyTBOyMl7kxM4nkMB7d9Zw2ikmEEJKVpCQmCQoKCnDggQemayykA1DL3BxhC1KzV5ECuKPfd4AzuNioaovv0jh7US12tfrx5UnlqojTkSRX5sZJSEfRmgVlbvKiyW41q6UuyTqlZGdSc4bbehOSTlZtr8f6qmb19xaPr+uISX6RmRQ83ufbQ2JSBgSUrA3g1q/MCcly5PBtgM4kQgjJVhIWk8466yw8//zzKC4ujpuL9MYbb7R7YKT9+AIKxPm3yGaG3Ry82tPoDahXfWI5k0QHuGZfUHyKte2PtV4EAOxs9WNEUbs0yoRIpMwtn2JSh9PqCYsurRlzJgWf12YxwWJOvT34jjrZmZTZhSAh6eT91bs1v3elVvHhzKTg+Sovxe9/OsiqAG5JTMpUCTIhqeLViUns5kYIIdlJwqv+kpISNWOhpKSkwwZE0od8ZcdhAYptZlS7A2j2KuF2yjHUmGK7CSYACoB6TwDlecZXsn0BBWLa2qxPTewgknImcRLSYciL0kyFvIquRWIxmWoA93bZmcQyN9KFaNI57Vq7lJgUzkwCkHKZazrQBHC7MhzAzTI3ksPoT8F0JhFCSHaSsJj03HPPGf5MspM3t7bh3h+a1N/tZhOKbCZUu4Mh3IkEcFtMJhTbTGjwKmjwKCjPM95OFq2akgw9ThWRmWSOmZkU/J/OpI6jJQsyk0TXIiEipR7ALTmTWOZGuhBeXSck2VGY6xiVuQGZcePI4nrmnUmZL0EmJFX0ZW50JpF4eP0BLN5QjXFDe6DQ0fEVEoSQIDGkhOi0tbWhtTW88Nq6dSvmzZuHjz76KG0DI+3j+m8bUOkKTbJNgDUkJgFBwSecmRQ736g0pDbVxchckNfsTZ3kTPInlZnUCQPqprRJi9KMBXCHdkCHNeRMEIvJJBZQiqJoM5NY5ka6EF6/XkzqOgfFaGVumcgJ0mQmtWZaTJLK3Hxd5/Mm3YMIMYkXBUkc3l61Cxc/9x3mfbwu00MhpFuRkph0+umn48UXXwQA1NfXY/z48XjwwQdx+umn44knnkjrAEn7EYJRYSiYWHYm2eLsAaWh4O76GBNzd0acScH/Y4lJDmYmdTjZEcAdfF4hJokA7mTErdoWj+a1sMyNdCW8umNgVxKTfFla5tbk9iEQyNy5R1Pm1oU+b9I90E8lWeZG4lHRELwgKF8YJIR0PCmJSStWrMCRRx4JAHjttdfQt29fbN26FS+++CL+8Y9/pHWAJDUO7mFTfxZXdFRnkk9Rr/rEykwCgDJ78O917ugikTxPbfIq+OvyBsxa0ZjSuBMlXOYWfRt2c+t45DK3TIW8iivwDqu2zM0XUCIcGdHQTz6a3V2nDIgQT4Qzqevs3+K12ayZF5Pk51SUyKyqzkQuc2MAN8k1PHoBnGISiYMQ0Dl/I6RzSUlMam1tRVFREQDgo48+wllnnQWz2YzDDjsMW7duTesASWpYDT7ZItWZFJDEpNiPI8rcGjzRT+Ry143tLX78e1MbXtrYilZfx7mUwgHcsTKTKCZ1NFlR5iacSTZtmQuQ+CIqQkzKcHguIenEE5GZ1HXEBSEYiwsjqZS5pgt9TlsmQ7jdDOAmOYwww4sZXkfOJ0nXwE0xiZCMkJKYNHLkSLz11lvYvn07PvzwQ5xwwgkAgKqqKhQXF6d1gCQ1jKrNwplJUgB3rDoxJJaZJNe2b2mWWsV34JWkZDKT3BSTOgzZmZSpBao+M8khKamJhnDvrA9mwInclRaWuZEuRFfNTPIHFPXCgk2fmZThAG4gsyHcLHMjuYyITygOzVtZ5kbiQTGJkMyQkph02223YebMmRg6dCjGjx+PCRMmAAi6lA4++OC0DpCkhi90Iu7pMOP+Q4ICX5E19QDuWJlJ8p+2tUgZOh0o4iRS5pZPZ1KH05aFZW4mk0nq6JbYmCob3QCAEb0LAQTzTgjpKggxqSQ/WP7c1kXK3GSRLBvK3PTHm0yKSZoA7k7KMiQkXYg5qpiDssyNxEOUPLMbLyGdS0pi0tlnn41t27Zh2bJl+PDDD9Xbjz/+eDz00ENpGxxJHXHenXdoCX431AkgXObW7FVUAShemVuZIyjIxBSTJLFmhywmdeDJP7Eyt+D/FJM6Djl7JVsCuIHkQ7grGl0AgJEhMYnd3EhXQhyjS51BMamrOJM0YlLo4oH63c/AaxTHGzGGzIpJ4dfv8QfgSzA/jpBsQHx9RRMYT0AbqUCIHuHGbOHFQEI6lZTEJADo27cvioqK8PHHH6OtLZg3csghh2CfffZJ2+BI6oiTrlXSWrRlbokFcIedSdFP4nKZm6w5tXXgXF7oQ4kEcHfkOLo7mm5uGXImiQmEXRKTknYmNQTFpBHlBQB4ZYt0LcR3RDiTuo6YFD732MxaZ1ImnJLiGNinOA8A0JglZW4A4GLmDMkhxLxSNIEBWOpGYqOWuXky20mTkO5GSmJSTU0Njj/+eOy111446aSTsHv3bgDApZdeij//+c9pHSBJDZ/qPAqfiAtt4TI3j1rmFvtx1MykWN3covypIwMTk8lMcvkVKApPLB2BVkzKzGIlXOYmO5NCpS4JLporm4SYFHQmMTOJdCX0ZW5dpZubeF0WswlmXQB3JjOThJiUDmdSk8uLJRur4U9yceTWnX+Zm0RyCTFHLbCa1Xlqs5fzOBIdT8iNqShAK5sOENJppCQmXX/99bDZbNi2bRucTqd6+5QpU/DBBx+kbXAkdXwh8UTu6hbu5iYFcCfoTGpIMIBbxtWBx/JA6PXFEpMcoT8qiC54kfYh24kz0T0JiMxMAsJiUiJX4xVFUTOTRJmbxx/QlIkQkstEikldY99WO7lJJ4K8DDmTAgFFPRb1TaOYdM97a3H+09/go58qkrqfWyfuZyrTjpBUCM9RgUKrCOHmRI5ER3ZjstSNkM7DmsqdPvroI3z44YcYOHCg5vZRo0Zh69ataRkYaR/imGqVMoVSKXMTFuO6WGVuUeaoHRvAHfzfHCMzySnV+LX6FFVcIulDvvqfqTI3d+h5HVIAmOpOSGDRXNfqVSchw3oVqLc3u3xwFFqi3Y2QnMEb2r+7XmaSOI9Flrh2tlNSdgL1LUmfmLS2ohEAsL2uNan7eXQZSZk6PhOSCm5/eI5aYDOj1uNHM8vcSAzkY16Ty4c+bC5OSKeQkjOppaVF40gS1NbWwuFwtHtQpP2Ic64csF2sOpMCCQdwl4ScSW1+JWqQdbRQxI7svhFIoMzNZjapHd2a2M2mQ5AXpZkqnTEucwv+nMjV+MpQ+HbPAjvybBY4Q0IUS91IV0EN4M63A+h6ZW42a6SQ3NlOSVms6V0UnAelQ0zaXR88PjUncTzy+QNqWVyBPXMZUjJeaUyExEONYrAABcKZxDI3EgPZjdlMZxIhnUZKYtKRRx6JF198Uf3dZDIhEAjgvvvuw7HHHpu2wZHUUQO4DTKTmn2KuriI50wqtplUwSZaqVu0MrfOcCbFMxuVhJxVjZyEdAitcplbxjOTwi6iZEJ4RSc3kXNS6AgaNpvY0Y10EUSWhChz6yr5OcJRKJe5hZ1JmRGT7FYzSp1B0a6xnUH+Pn8AVaE8t2TKNuQr9NnwmfsDCk58+Euc/I8vGYxLEsIjRTGEy9y475DoyMc9lrkR0nmkVOZ2//3347jjjsOyZcvg8Xjwl7/8BT/99BNqa2uxePHidI+RpIAawG3Qzc2vhMWVeAHcJpMJJTYTaj0K6j0B9MmPLPuJls3dZnDif3+HC29sbcMDh5SorqdU8Icyk2KVuQFBN1ZFWyBm5hNJDUVRNCGHGStzCy2UZWeSI4kFpejk1qc46CYozLOiqsnNjm6kyyDKwbpaZpJPlGtbDML3O1tMCr2n+TaL+j6315lU1eRWXbjJLI7kK/TF+TbsanBltMyttsWDDVXNAIDdjS4MKM2Hy+vH1ppW7NWnEKY453HS/fCqTWJMKFAvhHIeR6IjZyY1cf5GSKeR9Gre6/Xi2muvxf/+9z8cccQROP3009HS0oKzzjoLK1euxIgRIzpinCRJwgHc4Uma0xJ2GdWGFKB4AdwAUOoIdXSLkpvkieJAMnIm/fOXFny8y40vKt1xnzcWYoJtjbMHF9voTOoo3L4A5CZ5mbryLRZOmswk1ZkUf/IpwrdFzolwJrV0kVIgQtQA7i6XmSScSanlpaUT4YKUxaTGdopJuxva1J+TKdsQbk2r2aQezzJZ5iaXt22tbgEATP/3Skye9wUW/lyVqWGRLEZ8fYMB3MHvN8vcSCxkMYllboR0Hkk7k2w2G3744QeUlZXhlltu6YgxkTQg1tCy2GIyBe3CDV4FNSExKV6ZGyA6uvnx4oYW9M03Y2ihdrdJJjNpT6jFW1M7JwXhAO7Y2xWH3E+NzExKO/or5W1ePxRFiXmVuarJhetfXYXzxg/GKQf2T8s43P7oZW6JXI2PWubGK1vEgO+31+ObzTW47Ijhajv6bCYQUFQHT6nqTOoa+7Y3RplbsuKJ1x/ALxVN2K9fcUqfq3i+PJs5bc6kXaG8JCC5xZFYVNmt5rC4lkExySuVn2yqbsHhI3th4c+VAIBXvtmKX+/XJ1NDI1lKODPJFM5MYpkbiYGb3dwIyQgp1RldcMEF+Ne//pXusZA04VcUiFOuTbewF7lJdaqYFP/xDiwLTozf2+HGn79tiPh7tAoyfWC3oijY4wpu3N4rTKLMzRK3zC3kTIrRjY6khpG7wR3Hhr54QzUWb6jBi0vS1/VROJPs7Qzg1otJvLJF9DS6vDj9scW45721WLyxOtPDSQhvQMrP6WrOJIMyt1Qzkx75dANOeeQrvLFyZ0pjaVPFJK0zSVFSP/dUNITFpKTK3KTSX7XsL4Ol3vJ5YXN1C5pcYZFN7qBJiMBjWObGeRyJjpvOJEIyQkqZST6fD88++ywWLlyIsWPHoqBAOxmYO3duWgZHUkM24ejLwIpsZgABiE0ScSbNGlOEkcVW3LqiEVWuyAlptABuvTOp0auowlNTO2vfE+nmBoS70dGZlH7EgrQk36ZegW/z+NXFS6z71LZ60jYOo8ykZNwJQkzqqxOTeGWL6Pnn5xvVn/c0JVaq++bKHehR4MDRe5V31LBiIlv/hcjh9gU7a1lywFkVi7AzSRKSRQfSBJySMhv3BDN9ftrVgLPHDkx6LGpmkt2C4vzgMcQXUNDq8aPAkdJUC7s0ZW6Ji2NyU4JMBZLLyM6kzdUtWLOrUf3dHq9WnXRLwgHcCAdws8yNxEA0mgAoJhHSmaQ0w1m9ejV+9atfAQDWrVun+RuDFDOPTxJ39GJRsU37uyP6ul/FYjLh0PJgd5pmA1Emmk6jz0yqlpK6mzurzC30ehvoTEo7olSmKM+KNq8fHl8ArV4/ymLcR7iIalvSKSZFlrmpAdwJODDqQmPpURDcxwvzQs4klrkRiepmN/711Wb190TcPat3NuD6V78HAGy59+QOG1ssvNJxWIhJQPD7W5RnM7pLzhDOTIosc1OU4LEhlrgtI77vQlxOljYpMynfZoHNYoLXr6ChzZuymLS7PlVnUtit6QyVubVmcHEli0lbqlvw486ww5miPTFC7jgsytwYwE1iIXdz4/yNkM4jpRnOokWL0jqIxx57DPfffz8qKipw0EEH4ZFHHsH48eMNt3366afx4osvYvXq1QCAsWPH4p577om6fXdENgRZdWJLoa6uLRFnEoDwydxABHJHCeB26ZxJeyRXU3tr3xN1JhXbcs+ZpCgKWv0KCrL8iq0Qapz24OLJ4wvEFW9coStH9a2etDkjwmKSgTMpgcmnmLSKAG/hTGpvW2/StVizq1ET6J5IptbXm2rUn33+AKyWzv9Oi4W8xWxCvs0Csyl4/Gzz+HNeTPIYBHDL4pHLG9spKSNKr3Y3pCYmyQHcJpMJJfk2VDd70NDmRf/S/JQeUw7gTrXMTQhZzRnMyZLdcdtqW7Fqe736e2sSjivSfQhnJgEFoUQOoxxOQoDgvJllboRkhoyvVl999VXMmDEDt99+O1asWIGDDjoIkydPRlWVcYePzz77DOeddx4WLVqEpUuXYtCgQTjhhBOwc2dqOQddETkQWy+2FOmcSYlkJgHhrCWvEikeRS1z0zuTXJIFNU2ZSeZ4mUn23OvmdtPyRhz4VhU2NGb3ybBFLeuwJlxWJhbjAaX94bQCd+g5Nd3ckujoJKzRYkE6tGewbHf1zsh8MNJ90eeBybkv0Vhf2az+3JKhRbMaxmwxw2QywWkPigvtzU2qbnajMYH3oCPxCfeCJCbZLGa1i2ki3RwFQhysSFlMCj6XEK+K0xDCLQtbqXRzc9jM4Qy4DIrjsmPAF1Dw4U8V6u9c9BEjwmVupnCZG8UkEgVfQNF0F+ZxhZDOI+Ni0ty5c3H55Zdj2rRp2G+//fDkk0/C6XTi2WefNdz+lVdewdVXX40xY8Zgn332wTPPPINAIIBPPvmkk0eevYg1j80UWXYYKSYl50wCIkMQ9bme4jnaYjiTmtrpFPIl60zKYPhosry6uQ1+BXjop+b4G2cQUeZWYLck3DHILf09XaVuHoMyt2QCuEUZkD20IJ0woicAYNX2epZgEBW3T7svJeJMWr0rLEg2uTMjvHh0pWCi7KmlHU6VigYXjn3gM5z1+BL4/Jk7thqVuQGphXCLxUdVk1vTyj5R5ABuACjOC4dwp4LHF8Ce5nAul9sXSPi9lgXEbMiA8+ouLMm/t2c/JF0X1TEsl7nl0EVB0rl4dBd7WOZGSOeRUTHJ4/Fg+fLlmDRpknqb2WzGpEmTsHTp0oQeo7W1FV6vFz169DD8u9vtRmNjo+ZfV8cXkuetBkJRkc6K5EhQTLKYTHBaRAii9qAtnFD7lAQnrb/qGZxER2QmZaDMrSTkTGrIwUnI9pbsPhnKZW55CWYUuTpATIpZ5paQmKTtBjeohxODeuTDF1Dw7ebatIyR5D5ub3LOpDaPH2srmtTfM3WlVL9/O5Nw7UXjv8u2o8nlw4aqZiz82dhF3Bl4DcrcACAvhdcoxEF/QEF1c2Lh6jLhAO7gWEra6Uza0+yGomjP44m62+QcOTUDLoPlZPqFnkymHHskuxGHW7sl7IynM4lEI0JM4oVAQjqNjIpJ1dXV8Pv96NOnj+b2Pn36oKKiIsq9tNx4443o37+/RpCSmTNnDkpKStR/gwYNave4sx1xEjaK3Cm0plbmBoRP6E16Z1JINPr9cCc+O7EXpu9bCCC2Myl9ZW6xt8tFZ5Jge0t2T7LlMjdnos4k6YSfPjEpsswtL0FnQiCgqIH18oJ04oheAIDFG3Kj/TtJHUVRsKGqKa4bJbLMLfZk9cedDZrHzNSVUq9Pu3/nt7PMLRBQ8Oqy7ervL329pX0DbAcegzI3IHlnkj+gaBYfqeQmyZlJQPvFJCFOFTisqmsy0dwjufRXzUzKkDMO0AZw/2pwKa49fhQePf9gAAzgJsa4xXnZbFLzI1sYwE2ioD8/87hCSOeR8TK39nDvvfdi/vz5ePPNN5GXl2e4zc0334yGhgb13/bt2w2360qoi2MDoaU4xQBuICxENXsVrKn3qid2j3QFaWihFc7QdvrMJI2Y1M5JQdiZFCczyZZ7mUmCuizvQNcWWtg4bZYkMpPS70wSV7adtnA/gUTFJDnLQ25RffjIkJi0sSbiPqRr8daqnZg09ws88un6mNsJ0VIcMpviTFZXbqvT/B5v+45CH1JdILp7pVhetHRTDXbUtaHAHgzzXryhBhuqMlOS64viTErGmQhEllpVSMHXieLSlbkJMak9ZW5A8LhUEGq7Gm2BtGZXIxatDTvExGdut5hRpJa5Zd6ZdMTIXnjj6omY8eu9MKjMGRoXF30kEjWAm2VuJAH0zqRMnW8J6Y5kVEzq1asXLBYLKisrNbdXVlaib9++Me/7wAMP4N5778VHH32EAw88MOp2DocDxcXFmn9dHXG+NeqUlWoANxB2Jn1e4cZJH9fggs+DiyX5pA9ALYfTd3Orliaz7XcmBf+Pm5kUKjlo8ytRg8KzGb27K5sQV4LybGZ1ARXP7SAH4ta1tl9M8vgCqmAktz0Xi0l9aVLE/SUxSc5dmTA8mJv08+7GdpUDkezn283B49jqnbFLoMW+26vQASC+M2lrbavm94w5k3Rlbvn2xL6r0fh4TfB8fdqYAZg4MrMOvmiZScmWuek/y9ScSdoA7vY6k2RBKOwuMt6Hrnx5GaY9/x121AX3OXHcc9gsce/bGehzuwCEBTIeX4kBYrewm7VlboqSvXMikjk8fu1xhJlJhHQeGRWT7HY7xo4dqwnPFmHaEyZMiHq/++67D3fddRc++OADjBs3rjOGmlPIAdx69GKSPQlnkrAav7o5eNV2Za0XTd6AKtIIYSo/dBVJn5mkdSa1b1KQaJmb/HqbcuSqVr404d7UnL0nxHDHIEvC3dNcUohxTXP7xSSxUDOZgKK85J1JXulqll1yN/QqtKu/16ZB9CLZy8Y9QVdNRWNsN4pwJoXFpNgigUv3XcjUYl5csRXuHWc7xSTxOgb3cKqdD2tSyBhKB9HL3EKtxBN0JukXHql0dFPLbUPnyeL84PGoMcVFjUfKgosVoh0IKNhZF9x3hQgmxBv5vpkUk4yyrQqk10SBgOgRWZx2S9iZ5FMANyvdiAFuqekAEJz7pdJIgRCSPBkvc5sxYwaefvppvPDCC/j555/xhz/8AS0tLZg2bRoAYOrUqbj55pvV7f/+979j1qxZePbZZzF06FBUVFSgoqICzc3Z3fmqMxFlbkYB3IU6K1IyYpK4OiTHLn1W4ZauIAX/IMQQnxJ2LQUUBTWSmORXAFc7LkgmWuZmMZlQFBpwQ47kJskOqg2NWSwmecOLJ7F4i9/NLb3OJCEmFTmsMEv7cqJlLl51MWrSdD40mUwocQadBfUUk7o0G0MlWvEEBDFZ7VWUmDNJ/13IuDMpdFwusLevu5fsBupRYAcAVKepZDVZopW5idfYmuBr1AuDFY2pO5McaXImyY6yghhiUpPbp54P61uDzyWOs3ZZTMrglXq5ZE8gXpMvoETknRBiVOYGMISbGCOOIWUFYYc63UmEdA4ZF5OmTJmCBx54ALfddhvGjBmDVatW4YMPPlBDubdt24bdu3er2z/xxBPweDw4++yz0a9fP/XfAw88kKmXkHUIA45RAHe7ytxCJ/RKSRT6eKc7fNIPLVbypRO/KNNq8CjquMRf25ObJExPiWhhxXaRm5T9E1ZfQIFs6NqYzWKSdOXcGVq8JeVMSsMCtKEt+BhC+BHk2cPiVqyr3nrXhkypWAy2Zi64lnQsdS0edT+sbvao7hIj3GqZW1BAaY7jqAi3ig/uW5nKcNC7QkR3r1TFJJ/kBhLvRaacSepr0zWWSLa0Ky1lbjpnUrvL3KRjU/j1RO6fstgtBHrZJSU+70xeqff6ta4BIJi1J0jVJUe6LmoWpxkwx+gmTAgQPl4WOKzqMbgxjnuYEJIerPE36XimT5+O6dOnG/7ts88+0/y+ZcuWjh9QjhMO4I6dmWRC/MwhGb2rCQAW7XajV17w9tD6HTZT8HH9SrDUrQRAdcibXGo3wR8IdoRr9iooN85Nj0uimUkAUGQzAwigMcsDrQFE5DptaMpeMUm+2ixKzOKW/sjOpLSIScHnk/OSAKhX4xUluFARizE9Hn/kFXNBqXAmpbgYJNnPpmqto7Wq0Y1BPZyG2+rL3PwBJea+JVxxvQod2FHXlrGrpPpSsMIYwkRijxcWOcR3JB0lq6mgOgvNOmdSkqHTQuizW83w+AKplbnpMpOK2ykmuX2yuygcwN3k8qIoL3y8q5PEbiF8h4V+i5pNBATFNf2xsjMwciZZLWbk2cxweQNocftUl1tnsGJbHUwADh5c1mnPSZJDHLeE473AZkKrX0EznUnEAI9U5laSb0NVk5tiEiGdRMadSST9iPW6sTMpfKPNDE1pTzwKDUKYmnwKNjcHJ+zipG+SriIJZ1K9R4hJZhSEHqc9k4JAyBGQiJgU7uiW/Ve09JV4u1uzd8zygqU4tLiJlw/i1nVza29WRjQxKd9mUQPoY5UjGWV5CEryg4ubejqTuiz6LmSxypvE/l7qtCW0b7WFjjflobK4TLVm1y/k29sqXpSWWaUyt3R1ZkwWdxRnoSq+JNixTojgw0IZUFVNqTuT8tLlTJIDuEPOz9vf+QkH3/kxft4dDouvM3AmCVdcvs0Ch9WiOoIy1TktWrZVJvKc3D4/LnjmG1zwzDcxnYgkc7j8YSe76A4snPEscyNGeKQMz/YK+YSQ5KCY1AUR4dSGmUlSOUAyeUn6+xohP15eaNvW0CSyNTQBcFpN6uM0t0PcCZe5xX8NJSHLVGMOBHB7dKHl2ZzzJJdSqGGzcU7ecobRzvo2HPK3hVjw3faUxyCuxOvFJJPJpC5UYrmlPL7I8gtB2JnEzKSuysY9LZrfY5U3CeEi32aRFsHR9y0RwF0ecjJlqjW7XjAVLsJUF/A+taw5XOZWnaEyN3EMyrNFcyZpX+Pm6hZ8s6km4nGEKChcaS5vIG7eWsRYdJlJRY7g8SNVR5osAsruN19AwYptdervchmucFGKfU+ErQt3UqZCuKOJ9qI8uqHNi8oUcqpSoaHNi1aPHy0ePxrbgu/H9tpWvLR0S9KfOekY6kJOdpspfDGwQJ03Zv88jnQ+6sXNkDMJgPr9JkGCbmq+JyT9UEzqgsTq5mY1h11D9iQ/fb0zyamzBdnDbno1hNsVEpGEQ8lpManlcu1zJgX/T8aZlM3CjMCtK3Orz+Ixh7u5mSVnUmwxSR+0Wt3swUML16U8hobQZMGodEMtvYuxgNK3TZdhZlLXRziThA5eGUtMUgPnLeq+FcuJJ5wqamB3hhfydqs2gDvlMjdf2JnUsyD42hpdPvX2zkTObZOJFjJ++YvLcO7TX2NnvbZznxB8+pfmqa6zZBsE6J1JRVJWkXBzJYNRGbFglzR+eZziWCUyiPJCJ+XCdgqI7cWozA0Ii37T/70Sh835BD/taujwsciibovbhy3VLTjyvkWY9fZP+L8fdse4J+ksakJiUpnDrLrnhaO9hWHtxACPP1QhYTWjOC+xi5vdjXP+uRRH3/8Z9jRl5uIP6bpQTOqCeGM4k4BwbpItSWdSgW4iOKzIovldfjyn3pkU+j9f40xKXUxKJjNJOJPqcyAzSb++q/coaklfthFuxRq2FRtdCapt8eD4Bz/DwwvXG175Lc5LPcND2JiLDcWk4G2xSpHCIbeRO5LqTKKY1GXZuCcoJo0eWAogtjPJJQkXiexbbTpnUnOG8hv0IfOqsJDieNRMvtAVYCG+dGSpW0OrF3Pe/xlrKxo1t+vdQAKjwGpFUbCtphWKEnSiyAj3YnGeTRWR61qSe3/0YymUBKBURBw5tFqfy7WrPryfyplJRmVuQPs7+LUXfUdBgShHrG52Q1GAdZVNHT4W+T2oa/Xg4ue+VX/frRMZSWaoDYlJPRzhOWdhaP7JMjdihEc6P6vOJGYmqSiKguVb67CnyY1HPl2f6eGQLgbFpC6I6kyK8ukWpigm6Z1JQwu1E1zZ6ZSny0wSZW75FpP6OO1xJolSvkTK3ERAeLUr+y3sIoBbuKkCAJqy1NYtuwJiOZOWbqzBxj0teOf7neqCvEhaHLV6k1/g7Kpvw5KN1VEzk+TniFVmEiuAu8QZykximVuXpTp0he7gQaUAgIrG6ItJ1ZlkSyxwXizow5lJmVrIh8vSgPbn1IRLlkwwm00oC31Palo67mrn+6t345+fb8Kjn27Q3C6+vxHOJCmwWtDq8avb1+tcR0IULMyzSiJyct97fcmdLRQwLT9+MkQrcwOgcVbJ4xTCt15MKkzgWNiRxCtzE6TyPiWLvN//tKsRW2rCwmIry9yygjpPpJjEMjcSC/l4ycykSOSqgDdX7FSPyYSkA4pJXRBvSJCwRhFaRAh3smVuRVJmUp4F6JuvfQCHgTOpza8rc7NKZW7tyExKpsytV2hCIjrKZTNCTCqymdRSwWwtz5MX1yIzyejkvaMuOFmvb/WqramfuWgcbj91PwCpdYK65j8rcf7T3+DbLcH8k5hlbjEW/N4owbBAuMyNzqSui5hgDe0ZzMpJJDMpGDgv9q3oi1/hVOmlOpMyW2Jks2rFpFQznPTfGZGb1JEd3cT7rHc/yaWHMupr9GhdKOGftd9pUYJYlGdVxTH9NkAw8y2aCKc6k6SxFDriO9iiIQdwFzq0r2+XRkySurmJzCQhJtl1brQMCZruKGVuhY7OF5NkgXF3g3G5I8ksNQbOpLDDPDvnQySzyMeYcGYS524C+bjX5Pbho58qMzgaIBBQ2AChC0ExqQsiDD9G3dyAdpS5Sc6kHnazenIXyBb2fL0zqYPK3BJ5CcKZVOPK/klIqDIGDrMJpfZQdkeWlud5NIvr4Mm71eOPuOIhrqLXSou5gwaV4nfjBqn3STYUcGtNMDh5e23wsWOLSYmUuUUP4ObVra6Jzx9QS7aG9gp28YqZmWRY5ma8b/j8AVUMKM+WzCRdmVsskTWRx7Oag4/Xs7DjnUli0qn/LkfNTDII4JZFF71ALN6LojwbSlUxKVIcm/bcdzjk7oWG3d5cBmHgxe0QceTFUYHOwVPR4FKFecNubiIzSZS5ZaBrmkw00b5AJ5J1tjNJLhcMPj+P9dmAKHPrKYlJfUIXL6tyYB5HOp9w7ELYKc+5WxiRoyf4asOeDI0EWFvRiEkPfY4j/76IgeBdBIpJXRA10yLKp1ssnEkW479Ho1CasPdwmFFi1yo58vOpZW76bm5SmVt7at9FjlBizqRQLkMOOZPsFhNKs/xKnDtKQKx+QbCzLij4yNFPDqsZBXaLughMxtWgKErEYtBITCpMIoBbvxgFgNL8UJkbnUldEo8keg4JtYSvbHKri3Q9cvfCeEKlS7KUq5lJbh+UDOSfyWVpgOza8ac0Hp8u0LtHKIS7I51JQvTVL/blJgAy4Yyg8ARaKyYZl7kVOawoi1LmtrWmBUs31aDN68fKbfWav/kDiiqY5MnOpHYId3LZhrw/Wcwm+AKKGqIqv65Wjx9unz+izK0oSne7zsIT+u7YdMfZyDK3jj/WyvvELn0Qe4beH6JFiEll0gXL3qGLgpVtdDOQSDzSuaCEZW4R6MWkjsw4jMXm6hac+dgSbNrTgqomNzbpOuqS3IRiUhdEVI9FK3NLR2ZSmcOMEt0E3m4UwK3v5iY5k5raUeYmnEnRXqOM7EzKxGIuHh/udGF1XfCk5xb5JmaoYlKNO4BPdrmyrtxNdgVYLUFxCIi0Fu+o007Y7dZghxaTyaSWACXTWrzZ7VMFU4GxMym2ewRIzJnEzKSuiVs6/gwozYfdaoY/oGB7bavhcUIOWBaCTDQxqU2auAnnjqJETug6A48ur0aM3R9Q4ErhGCxEE9WZVCCcSR33PXGrYpLemWRc5maUmWTk4BE0y2VuBcYi8rs/hjt96XcP2a4vC1tF7XAmyTlDBw4oARB0PfUtzgMQdnzqX0tDq1cqc9M6kzLnjgu+YQ7dcTbZMrcWtw/v/rBb8/1KFm2Zm9aZFKs7I+k8jJ1JwX25si275kEkOwiXBVvU2AV+n8O06BxAyTaYSBdfrNujXuwAOnbeQDoPikldkMTL3JJ73EIpMynoTAo/gAmA9Gc1QLrRa1DmJjKT2hXAHfw/ET1M1N17FaAhy8IbNzX5cOWSevxxaT0AQOhFdqnM7aUNrbh0cT3u+7HjO90kg+zUAGDYQUNRlIg23HnSjplK3oqRUyjlMjeda0NGiEkub8CwCx3JbURZks1igt1qxr59iwAAy7bWYfK8L3D+019rtheCRp4tfjc3sb/k2cxw2i3qcSoTzgevLmTeabdAaPBN7uQnlPow5fB3uCPL3KKISV5jZ6GcmSSEwfpYmUnCmZRnU7/3+m3eldrG6z9HWZTTZialvqiRnUlDexXgvWuPxFc3HocBpfkAwq6aBt0469u8qtiiD+DOdDc3m1V7nNUHi8dzJs39eB3++O8V+PN/V6U8FnlRJTKT1JD8JD6n5VvrcNqjX2HZltqUx0KMMermxjI3EgtxLmAAtzF6Ab42yQYT6UJ/7qztwPJ40nlQTOqChMvc4gVwJ+dMclpNEPfo4TCjWCpzs5kBk+QSKg+5gfaEOqi1yWVuoQllSzuEnWQCuPMsJlVAq86yicjahuCBdVdrsORElLk5pDK3VbXBE+KO1uwSNMK24uCCpVgNPQyfLBravBEnjzypjXfPkDMpmbyVhMWkBDoYhRfakTWfhQ6r2vack5Kuh5h8irKk0QOD7o/HF23AuspmLNlYo1ncyi6YeOHuLqnMyGQyxXUydSR6953JZEKhQRlYoujL5tTvcAeWuQkxyePXCrvRgp2FSBFQwp3NYpW5NUvd3EQAt9jG6w/g1e+24addjdL2+nK7sDBpkc6ribgjo+HR5UHt178YvQod6FcadCbtqm+D1x9Q3UaqCNbiCZe52bOjm1s4z8TYQSaI9/1YsGw7AOC9HytSHossqAnHVP+QQJeM2Pvej7vxw44GvLVqZ8pjIcaIAO4yR/i71CfPov7NE6UUmXRfPP7wxU21uzDnbSriuOcMnRPqMuQI0l/Q6Mh5A+k8KCZ1QcJlbsZ/F2JOsmKS2WRS27P2sGvL3By6xyoPnfj3hMSbVk03N+FaSl3YEYKZJYEyNwAoz9KObpuaggdWrxJ0b4XL3Eyq80tMmxqzKIhbURRN4CGA8AlcWjjpS9wAnZgUKin5ZnMt/vjKCqyRFmzRMArGFYs2o9tiuS/CC+3I/chkMrGjWxdGn7czOlRKtKk6XMNf2eiK3F7KTGqMIhJEZNaE9sXMOJNEDpvUZrsd4oJPF6bcI/Qdru7AyalHygySBQe9O1Igu6+EYCY7jeSfXV6/6lAMdnMTzqTg67nlzR9x4+s/ah4/mjMpL0pXuVTeZ48uOF3QX3ImieOSyQQM6RHsSFjV5FYvtqjOJLXcLjMXJPQCpEAfLB5PTDpkaA/151SdcEbvwYCQQJeM6CeCY0V2FUkfdQZlbqV2k9qBeE+WXRTsLPa4Usu56w54DLq58SJgGDEnEc7WulYPAp0oyor9Vi8mZSq7iaQXikldEJ8S25k0pocNFhMwuixyAR4PIQTpA7j1JXVhZ1JITApNJvOtJpSFJgjt6VIWSKLMDQjnJlW7ssvdI8QkAGjwKOEyNwvU90nQnoypdCOHF4vFuKhTl0/g+hI3QLvw6xUqL3hjxU68++Nu/PvbrXGfu143QSjKs2rcAPLtQGLOJKMAbgAoiRLGS3Iffd7OASExSaaiIbhQDAQUjVNEdPxqaDPet1RxIQvKjIxKOcPh9Ck4ZkQ3t9DjqblnHbioljOJGjVuMa07UmAymaQQ7uB7Ln+H5Z+31rQCCJYkFtqt6mcrhJofdjQAAC49YhjOGz8YQGT2kLov6WrHixMotY2GJ4rrSohJO+td6usozrOpop7c7j6ym1tmFleqaK97Lfrf44k5csbSVxuqUxqL0Xewf0nYmZToYl2IlFUUk5KmxRfAj3Vew/faryjq3FAuczOZTOidJ3KTsmse1xksqXLjkP/twQOrmzM9lKxEPj+LeZvbx4iCbTWtWLKhWj1eDSgLHusCSvSLYelmR10rDvnbQjz08TpVzBfHcopJXQOKSV0Q1ZkU5dMd28uO70/vjev2L0z6sVVnki4zSa/R6MUkucxNXG2qbYdLyJ9EmRsQvsJVk2XOpM1NUrchT9i+LWcmCRqzKO/JLTkFhBBjZC1O1JkkEIv3WOiFHaMSNwAJlRZ5orSsFqjOJF7h6nK4dHk7e/Upili4V4ScSVrx1BK145egTc1M0jpDMlHm5lVFicgsn1TK3NQy6tB3ZlBocrq7oU3jIEonRs4kRVEiSsFkhJ1fuIjk73B9a3gh++X6YIvkQ4b2gNlsUsvchDNJiEpnjBmA3iHxWy9IhPclnTOpHQHc+uB0Qb9QAHdlo0t9TWVOmyqCiXb3NotJvW9ROz7vdKCK9rrXoncOxPt+yAvDL9enUUwKCXRev6I5t8WCzqTUOf+zOpy6sAYf7ox87+o9iurGlru5AeHcpE92uXH7yka0dNDxJhsRkQjf13IuYoRc8lxot6rO1M4STLKVq15ejvOf+QZrdgcvipTm29TzQWcJOau216O62YNP11apx99BISctA7i7BhSTuiBish+r01lhsunbIY7q60CJzYQxPWyaMrk2v1boEGJSrTsAb0BRy9zyrSZ1gtDiU+DypyaQ+EMLgcSdScFJfrZlJm1uDk9s6z0BTTe3ErvemZQ9YpK8uFPL3AwCuHcaiklyALdD87eqJpd+8wj0JWfRxKQitewuhpgUo5sbAMmlwBNeV0O4SYSAZLOEQ7gFoszN7dWKp6X5xh2/BGoAsi6zJhMZDobOpBSdKoGAAr9OTCovciDfZkFACV6B7AjcGjHJG3GbkZikd4PJ5bG+gIKlG2uwdGON6nA5clQvAFCFwoY2LwIBRb1fqdMW1e0oB67LpCMzSS9wlkrjE7kXpU67entFqEOZLNoXZDyAO7TP6F7LxBE9AYTPIc0eX8zSC/kz/ypFMclI2OtXkhcOpU9Q8BWdGaua3NhW04p5C9fxPJEg34e61/57U+TxQlxkLLGZYNVN8ISY9NjaFrywoRWvb4mcX3RVxAXZbItqyBbkCwtms0m6uNl9O7opioJN1UEn27qK4P/59nDHUqPIiI5AzIcaXV61AcLgHkEBn86krgHFpC5IvG5u7eH2McVYcXpv9HNGBhbLlDnMsJiCeT+17oAqNjmtJhTbTAhVyxm6k/a4/NgdJ2w6HMCdmJoULnPLnhNxrTuAeqnUTy5zkwO4BW1+JWuCJ+WrQCJ4XZR0yCdvsbiUy9C0AdxaZ1JVY/yrvOIEKBYg0cWk+AtmfacrPcxM6rq4daVoAHDw4DLNNmJhLoQnswmwmk2qjb7N6ze00YvthbgwMOTemfvxOmza07llCkb7eFhMSs6p4g2Ej5+izM1kMmFw6CrjttqOEZOMnElaMSnyfFQgdXQDIr/D5z/zDc57+mvV4XLEyHIAYQE5oASFAvE8pU6bVC6mL3OL4kxqR/B6NDFJzgMRr6nUaVMFzt0hATTfFulEy1Sr7Gii/fDyQnx+wzFYcvNxAABFiWxhLSOXO1Y0ulIqYTF6/JJ8mxpKn6iLrCW0QPL4Avjbe2swb+F6vLZ8R9Lj6c7oL0IC4TlhT0fkOVmUuQnWN3afEibxXrXH0d+V8ejOc0axC92NZrdPdc2KyIkCu0UVk2pbOue9cYWO/41SQx4xZ6CY1DWgmNQF8SbgTGoPiQg4FlO4nG2PK6ApczOZwrlJ+hOjL6Bg/P/24Oj396j3MSLZMrdenRDA3ewN4Jqv6/HBjvjuGgDY3KSdtNZ7AmrZld1sQpk98sVlizvJ7Y0MvjVyJonFTv9QwKn+Pnpn0p5mt+p8iIZohX3i6L4osFtw1F7lhtsJMcnlDagLaj3xnElqZlI3npB0VdwGJVLXHDcS10/aCzNP2AtAuMxNFgtMJhOK82J3+tO3Zv/TpFEY1bsQFY0u3PDaDx30iozx+iJLOVMN4PZKiz85GHpwz44Vk2QRIexMCt5mMhkH6ItOYaK0K5prxB9Q0KvQjn1CrjS71ayKL5tDYewWc7AjXzRxKLozqR1iUpQA7hLpOCvaO5c57SgrCN6+O7RoEK447TgylJkU5bUAwJCeBehZYFc/w1jvlUuXG5iKyG9U6lecb0v6PWqVRKelG2sAMD8pWVoM5nhiTtjDQEwSziTBpqbu4zoR8+E6d4Ah3AboO0aqx8luPHeTS3CFy9rpsKrxEp3V0c2lOpN8qjt2cM8CAKk3UiDZBcWkLoi4YJtiJVvakHOTWv3hMjcgPFHQZxitqfdBAeAJADujuJMURYG4V6Jlbj07wZn08sZW/G+7C1ctrU9o+03NBmJSSEgxciYB2RPCbXQl3igzSVwF7lssiUkxnEn+gBL3SoVwJk0c0Qs/3DEZVx09wnA7Oaw12gJFdW1EUSXjlTOR3CUcmizvjw78adIo7N23GIBU5qZzGplMJnWyarRv6DOTehflYd65YwAAG6o615lklL2TiGvPCJ8kysolKKKTmAizTjdyZpXqTJIyr0wGFzjkMrdAQFFFP72ADQCHj+gFs/R6RMnYlpqgmFSab4PJZIqagRTNmVTUnsykKHlQQrRXFGB7SLwrddrU/XFPaHIuO5MyHUgbdscZH2dNJpNUEhjLmaQ9/6VSpmH0WRTlWcOfbZJlbkDY8cUyt+RoNRCTxJxQ34AEAPrka79fG7uTmBSaQ3uV7MrPzBb0F4eMugt3N2QxScSfOO0WNRewtrPK3ELnHH9AUQV34UxqdPk6LGuRdB4Uk7ogopubvt68sxFiUmWbXw3odoYO9NFCuL+tDh/cormI5FsTLXMr7wxnkjQx8idw5UgO3waCwZNqNzeDzCQAaGxHB7z28tryHbjp9R+CrbQNFjrCViyXUgiHRm9JTJLbZ/dwasUkQNuO3QjR1rvEaTPs4iawWszhEF6XD1WNLvzfD7s0C2K9NVpPOJ+Ei4Suhj6AW0aIn6LMzShguVTXQt7oseUSuvKQiNHo8nZqS16jMje9aydRxPfFZNKWrgpnUkeJSXJmlRCrPX5jAUfglMqWGl1etTR6WC+nus3Jo/vh1IP6Y/pxIzX3FZNt4UwSn3VRlDK3jshM8kYJ4M6zWdR9VjjBypx2tTxPnHrkfa/QblUvvDS2ebG9tlUNkO4MPDrXgBGJOIPcOiEsFTHJKDeqOM+WdCmgUbkcLzokh5EzqS5mmZuuQUJbAM1ZcoGto5HzRVnqFom+LFguB+6uVDdHHh8L7Bb0CLlYO8uZ1CYdt9VqhZI8dQ7RWdlNpOOgmNQFUbu5dYKWZKB3qJSH6tu3tYQPJM6QAyRamdsySUzao28RF0Ius0+4zE11Sfk7zCJcLC0kdoZes9sfPWR8e2gb0bWtQRPAbUKexQRdRAAaMzhxmvnf7zH/u+2Y+/E6wxIhcSWoIY4zSV5wWS1mXHbEMJxyYD+1zCRed5wGtYNRpBClJ7xA8GL2/63B9H+vxMKfK9W/xw/gZmZSVyXsNopc4PYpCQo/e5rd8PoDUgv68H4SK09LTJ5kd4jsKNG3lu9Iwvu4HMAd3wVihE8EKZu1bqBwZlJLu8YaDdmZ1GjgTDKiQOpgJj6jArsFvYvCx6JzDhmER847GHv10Qavi+/9pj1CTAoea4R7RS9IxMtMSqblvCBaZhIQXiipzimnTQ0OFzilMjez2aTufz/ubMBR9y/ClS8tT2o87UEVxmJMSoSY9J9vt2PWW6s1or9AvM/iHFKXZOZHIKBoHEXycwvhL1EXmdHjsBw6OYyiDGpilLnJWZ0iBmBzc/fITZLfq2zrSpwNeHQNNYyc8pmgvtWDqjgXSDuKPQYNbZxSAHdn5RW1GR5zw+esGgPRi+QWFJO6IGrr5k74dPNiqDnCmbQ1dLI3Aao4YuRMUhQF31WHD/x7opSkyRf1EzVf9cm3wATA5QdqO8jd0yxZNTc2+eHyKzjz0xoc9n9VhuVpYkIwoig4idaXuQFA35Ctuyg0Cc+GzKTnF29RF8t2o8wk6eTdGnI+aMrcdAuuW0/ZD4+e/yu1PXN8Z1K4u1I85DKTDZXB8qJN1eEFb7wA7lilTCS3MRJEBb0KHLCaTVCUoLhplBEmBAYj15pwqsi5NbKjJNkJbqPLiw9WV2iygxLFa5BXU5himZvXoDMcEMy9AYJOmY4Q62VnUjiAW5QpGn93C4X7yuOTjhlaAXrMwFLD+wrB6ZfKxuD9QseBaJlJ7jiZScm0nFcfMwExSXTLLHXa1ZJcQb5OJBWLq2+31EJRgO+31yc1nlQJBJRwN7cooj0AFIUEztdX7MBLX2/F15tqI7YR3ytxPkn2inarQYlfgd0Cq8UslbnF/054/QHD0owGnieSItnMpBFFVswbX4L/HF2GUcXBz2tjY/codZPDyikmRaJ3mZc4M+9MUhQFpz+2GMc9+HlGOmnuMcgjctotakVAZzmCjOYtBQ4LenSyqEU6DopJXRChN3RGmVt+AmLSthafuq24mt3DQEza3OzXnCSjiUlyCVmizqQ8iwl9Q+GN25o75qAuCz0bm3x46pcWrKn3od6jYIvB1TPx2oerYpICUXUiHF9/H1eCOw8uwiHlwYNuppxJ8pUFjz+AhWuC7h45b0bvTFIURXUm9SkxdibJ9C4KukF21LXhyc83YsKcT3DFi8s028jZJ4mJSWEHxq6G4MKrsiEsVsVb5IQFAy4SuhqxnC1mswl9RKlbo8vQeRIuc4vcN9SypxiduJLhptd/wFUvL8e9769N6n6A8T5emGKZm3gsq+77MqA0H2ZTsLwvnrMwFbSZSSKAO3aZW4GUmSTE4LICm+rmAcILDj0DQg0DtteGxRogLCa5fVoxIeyY0Y6lwG5NuuW8IFZotdiPxIWVMqcNpQXa15JntxjeZ0tITG90+dp9XFMUBW+v2okNVU1Rt5E7AEYT7YGw8CbYXB2ZLSbe576h80myZRpiQWcxm9Tuo+IcURwjLH3V9npN9oqRKwkA6lkOHRdZbDaSnWOJSQBwxpB8TOjtUC/CdZfcpDaWucVEH72QDWVuda1ebK1pRbPbh92h+WdnYnQudjqyw5lUYLeqYlJNC0O4cx2KSV0QMe/ujDK3qSOD5Q0TyiNLjvTOpHxpQEYB3HKJGxA9LNuvcSYl/iIHFUSW3aUTWUz6bLcbj/0cngzXeyJfi5gQDCuyqNsIZ5IIhD603I6pIwtQbMusM0kf1Pfqd9sBaBfi+pBXty+gLnb6FIVDb43KioBwrtKjizbg3vfXYneDCx+tqdQs2hpdXjUTRH8l3gixQNnd0KYuEiobwyeucJZHFDFJdSZxkZAtLN1Yg1e+2drux1GdLVHEiD7FwX22ssElbSuXuUUPZxeTp2gL+mQnuO/9WAEAeG7xlqTuBxgHcKtlbkleLY2W42O3mlVn4dYO6OgmZ+WEnUmxy9zkAG7VmZRvx5kHDwAAjB/WI+rzidciEMJhgRTqL19pdhk414CgKClaziebm5RImZs6vnw7ihxWTY6V3pkk7rNZcmYKZ1OqzP9uO/40fxXOenxJ1G2idQDUI0QdgewgFYjvYb+S4OdjJOTGQpSwFdgtYREplPVXGCUP67XlO3DGY4sx663V6m3R8qboYI2Pfgrj1sUA1Hpii0mCESFn0qam7lHmJmucFJO0KIqCxrbgGyS+x9kQUSB3N83EOKJnJglnUueMqU3nCHXaLTCbTehZEJxj0ZmU+1BM6oKIAG5bJziTrty7AC8fVYanJ5ZG/E1kJonOE7KLyajMbWNoUlAYEp2iZSbJZW6JOpMAYHBh8CSzvcPEpPBr+arKA/l8rxeTFEVRgyaFM6lBFpN0n53IY2o0EKU6A/0V4HD4rZyZZNUE6slXb/tEyUyS6V0U2WUJCIcg17d68OFPwUV1gd0S8yq3QIhJ6yrDV84rpDK6RAO4Wzx+dpzIEm547Xvc8ubqdndFU0O1o+yPYsG6bGudYWZSmTO60GiUmQSkLia1x2VqJEqEA7hTzEwyOPAOLAu+X7vq038FVuNMCpXmGZUeyhSo4oBfFZB7FzkwdcJQPD11HJ6eOi7q80WISaHPzWYxq8evZgMxyUgoj9YBLh5G5YmCCDHJGew2VyrdHm3f2yKFpO+oa5/w9+9vtgGIHVotHzdjlrnpnElbdGKSXyqXE+eTZEV+sb8XOqzq8wlHrRBY9a/lrv9bAwB4e9Uu6XGM5xCZ6paXS+jFI/3cqDZGALfM8NBFuO7iTGqV3reObCSTi9S3etVzrjg2qBd7MuhMyrSYZORMype7uXVaALd2fxXnZpa5dR0oJnVB1ADuTvh0rWYTjujjQKHBgqxc13nDaeBMksWkHSGR5+CeoRbHCTiTkhKThDOpgwIbjVxDwlFUr8tpavIp6hW6EVKZm0dX5qY+TihsMlZLWEVRMH9TK5bXpP/AHK22Wl7IyYuZ+lavOnHPs5k1JWnRnSBhwalngR1DQh2ixILnhtd+wI2v/6g+VyKINuDLttSpt4kwREVR4gZwF+XZ1DIVlrplB2KC1N5QSzWAO8r+ePqY/gCA5xZvxtKNNQCMy9yMJolG3dyA1MWkYb0K1J+TFTWNRIkiNYA7SbdMFGcSAJSHcobSXeYmZ+4AUK9Ax8oUAsIB1C1un5rD1qckD3arGb/er0+EICMTISYVhF2QQnSQxaFYLqmiGOVTRiiKojk2Gb2+Yt3YRdmCXLaXr3PFifvI+8+OdjiTFEXBjzsbNL/rufN/a9RSZYvZFLP7ZoSYpOsMKOdu9AuVuSXb2lp1Jjms6oJGvC9FBqKfXFYtY1S2IaA7KTZ6MalOEpMURVHnhGWxursAGBAK465o6x7inRzATWeSFhFh0KvQrp5z1U68Gfw+bpfFpAzMH43OxXJ5WUOb17DRQbpx6Y6Xwj3WszA4jvach0h2QDGpC6IGcCdRAtYRDHBaNDuYpswtJI7UShOJHa3BA86YHtHFpG/2eHD3qkb192TK3AZ3eJmbdrx98834zYDgpLchytW3AqtJbXfb5lfUEG+7TiUrEs6kGJlJaxp8uGl5I274tiHqNqkirhwUOrQTfr0wJLdLF84kp92quV8izqSTRvdTO0TtCDkdvg4t6AGoQlM89g51iFtbEXYmVTW5cc97P+OQv32inuyNnBaAyNYQAgCvnmQajy/cWa294p6R20jmhP374qyDByCgBMt5AO2+WxIjxNIVzZmUYihomSRmbDLIkomFUSetEjUsP5ibc9bji/HMl5viPpaYeFoNvi+9QhNDI2t9e/DoJruJZiapIo7bq7ob5UYAsehfqt1OdvwYiQ5qmZuRMylKaLcRgYCCs55YgrOfXKqex+OVudksJhSEhCN5nNGETJn2TOL1ZWj6UgZ/QMFzSzZj2dY6dZyx0ItJ22pb1X0XCAu0gJSZlORCUTiKChzhc5J43sK8yHLEnysaNfcX41GzAIsdsFvMKHRY1cylrpab5PIrOOXjatyyPD3zCr0OIgsjzT4FYqoUz5nUO0/EAygagarZG8Cdqxqxuq5riXouZiZFZVd98PguXwRQm6dk8Pu4rUZ2JnXuOAIBBdVGAdwOC0rzberxuKoDMg71uHQB3MIZfdjwngCAhT9XptRchGQPFJO6IOEA7syOw2ExYWBBeELrtEQ6k+o9ijpp3qlzJtW4AwhIVzv/b3sbpnxWize3BU8cybiSAGBQYXAs21s6PoAbAC4c4USZ+jq1J3+5/W2RzaR+EavaQgukiDK3+M6k7SHH1e629E80xNVW2SEBRF6JF/bZ+lavmivhDHXLEQueaJlJsjPplAP7YWCZcCa1wesPqBP4gwaW4MqjRyQ07n36FkXc5gsoeOqLTahudqMmJJLFKpnLhtr7jmTR2iqc99TX2FrTEn/jDCMv9GJd6XN5/fAHon9XgPiZOwBw04n7aH6XhYuyGMJQW5Syp1SdSXInsLW7o4cd61EU405aYhxtXj8Wb6jGim31eCVUshQL8VhGpVfCBWg0gW0Pbq9eTPJBURTDHCsZcTzZVe9SS1v7JCgmOe1WjZtS/lnN1nFFOpOMjm09QrkQ8bpUAkGnzcpt9Vi+NeykjCcmleTbVadmmdStLlqZm8zO+tTL3L5Yt0fzu75UocXjg2xWilXiBkC3rQn+gKIRu8TnbbOY0FNkfqQYwF3gsKjiUbEugFv+XL9YV625v7jSL85tvYvy8MIl4/HSpePRK3QxpKudJ5ZUubG63odXNrWlpVNjhDPJrUg/B79H+RaT5uKjEaV2E0LTIk3Z10sbW/Hs+lbcsrwxyj1zE3Zzi44It+4nNXrJhnnbdqmMuLOd7Q1tXnVtJV9zd9qtMJtNqvDWGa4gvZOzIJQjOH5oD/QtzkOTy4fPftljdFeSI1BM6oKIA0hndHOLh6hrB7RlbmUOM8RvdZ4AXH5FnRAcWGaDCcFytrpQeViVy4/rv9FeGUtWTBLOpF2t4WyidCLEpJsPLMKf9ivAFXsXoNRuXOamdiyxm2E2mVAS2q4q5MZy6NYkiWQmCbt3m1/RXMVKlle+2YqJ936qyaQRCwW9mKRf6JRKbg3hTBInDhF4Gs1J0LvIgV/v1we/3q8PDhnaQ81g2VnXhooGFwJK8PnevHoiTjuof0KvZa8+kWKSEbGCYeXSva7Ida+uwtJNNTj1ka8yPZS4yO6OaJOzVo8PR/x9Ec5/+uuYjxUOTTbeHwGgvMihK9FMMoBb53oqSXFfkgOo9W6JmPeTRCj5u1qUF+4yJrqbVSdwhVJ05jJyJpWHxKR0l7m5/dqJqC+gwOUNhLvxRRGnhbOxtsWDTXuCx7K+JYmJSQDQvyR8lVsWaVSnUQIB3ACwV59CAMAvlfFFwEaDfTpeZlKZtH/KZW7OiDI3rfMHaN9C4qv1WqGlrkU79madEyuWaAtonU0jyoPvmZybpJaOWi2qUy/Z1tbhAG4rivTOJIPyxa82aBc4QpQUDien3YIJI3ri4MFlXbZZg5zf2JCGBiDRArcB+SJb/MmdyWRSoxTkfM0lVcH3//s6rxqdkG48AQXfVXvUuXaqPPVLC/6zKb6gqyhKu8rc5n+7DZc+/11G2tN3BjtD7nXZmSTmopnMMctkZtKe0EWdMqdNc74QFxnE/DpWbt7qnQ1JC/ZG6F2r4hxqNptw6kH9AADvfL8r4n6dRUWDC2t2dS3xubOhmNQFEesHW+a1JDVcGtCWuVlMJlVoqXUHsLM1HL7d02FWnUtikvBzvS+iC0iyWlkvhxn5FhMUhF1Q6UJRFFXoOWVQHq7fvwg2swmldmNnUp2u/a3YTrxEfQB3UQLd3CokR5JR97hE+WB1BXbWt+HL9eGJtJggD+qRr8m9iHQmRWYmiewOsZiJVuZmNpvUYFyz2RQWk+pb1XK0gaX5MCfxwRfl2TCoR37c7WI5k0Q5UyaDHDsScbW50eXDniY3Ah0gtKaLRMSkbbWtqG5249sttTHzhcJukuifvclkUhe2gE5Mkko69QhbdzR3iJFoEItUnUliAmuRuooBwe+avlV8k9sXd9LtjZEx1qtIlLl1jDPJbjWrx/xGlzeus6woz6ZmQ4hQ5UTL3ADjkglACvZO0JkkSm1/qYj/uRnt00blYVoxyW74cyLOpFTFJEVR8P2Oes1t+vwifeB4PGfS+YcOxt59ivDXk/bB0J7BixZy5znViWYzq6+zyeXTlMLFQw7gPuXA/ti/fzF+c0BfAOHzk/x93qbrTFgZKpcUziS5u1+pM7q4nMtIGkZa8oncuvNLvSSM1OrmRfEQTV6Eo1uIPIIPdrYvVy8aD//UjN8tqsWDP6XeBKLWHcA9PzRh1orGuBc3PQFA3strXIGkXGJPfr4Rn6ytwqJfqlIcbXazW5S5SRcACuwW9YJ6Jr6TXn9A04yivs2LOe//jLkf/dIpzy8u6vQqdKjH/nybRZ2/D4jjTPpg9W6c8shXmPnf79s9Fv28Qj5unj4m2F114ZrKjIl+U5/9Bqc9+lW7czi7MxSTuiBeJZucSeGDhlM3Ke4lJgKugHoFaYDTorviFDyFbm2OvKJiSTITymQytSs3aUeLH7tbo3RxCYTLC4skFU+IRPrMJP0VuBJd2GRENzc1gDv6xLlKujrXnpp6sdAVHZAAoDZ0Mi5z2jW5HHpXQJlUfqA6k0I2q0uPGIbj9umNQ4ZGb8ktI5/sxAlvQFl8YUjPPn2L1Z+L8yKvzgOxFzpd9YqzYETvsFgyed4XGH3Hh9hQlbhg0ZloytyiTBDFfqcosUuL3Ak4kwBgpCwmSfu7EJPcvgAO+dtCLAjlKgHhwEl9CHLKZW7SJCsRUcIfUODy+lHTIq5O2iNE2LCYFF4wxxOC1Dw+c+eVuYnMpDyrWRV4fqloilvmBoTdSUDw4oPIdUqEAaWRJROAnJkU/gxjOZOEmLSuoinuAlDfScxuMRs2GyiJ4kbSZCZF2fdkGtq8aNSFsC/fWqt2zYxGRaML1c0eWMwmjB1SBiCy5EyfERWv+2bvojx8eP1RuOKoERhWbiAmCSea1YKS/HBjhGQWii1SAPcRo3rh3WuPxIEDSwFALauubHSrn6cIex8VOkaK40k4D1A6HqgZLV1LTGqV1KSKNJTQ65v0GjuTEhST8rXzxFU1Xs3jv7+jYxaHj60N7pdPrE29NFxc8PMp8edreqe5Vwk2cUkERVGwKySCdgX3hT+goKpJ+7nuMnAmmUymjOYm7apv03SeXl/ZhH9+vgn/+HSDmuGXKomILqKaoEdBeM4uH6/E8W5nFDHphtd+AAB8srb9AmREmZskJu3fvxgFdgvcvoDqMOtMFEXBxj0t8AWUiBxAkjgUk7og4iJ2pjOTAGBElDI3ABgayjDa2OhTnUkiYylSTAr+fWzP8IQ4Fa1M5CZtMRCnYuH2Kzjp42qcurDG8CqSEHlMCLqrBCVxytxEyKS+c4luHaAGcHeGM0ksAuQTthBSehTYo5b9ALJbw6sJ4AaAKYcMxrMXH6I5kcRCnOwqGlxqKc4gaXGYKHJu0pjBZYbbxBKTYmXjdAXkE31tiwctHr8msyWbkBfb0dw98uvZHWPSlkhmEgCM6B0u7ZS3lUPl9zS58bQUYJ3uzCSX5EyqaHTFDas87+mvccTfP1VF2B4FkUKCmGBulrKy4oVnG4V5C4SYVNPsSau7LexMsmD8sKAQ/e3m2rgB3IA2qL+8yAFrHHeMjFiYWM0mzWetZiZJ7eGjde8DgOG9CmE1m9Dk9qmLumjo9+loAky0Mje561wsZ5LVbFKP1fJiQlEUXP7iclz18vKYC54fdgRLzkf1Lgx3VmtpnzNJZnionHq9JGqHQ87NsEjOumREfnH8MBLWypw29bPdUdcKRVFU8XpUqFSxolFkJmlLuIGu60ySxaTKdDiTIjKTwsc2EWsQL3xbIOaJO1v9uGtVIy5dHDxvHdIr+Pkur/FqSuDSxT4l4c+9NcnumoIW6X2tjtK5WCDykqymYNMWIDiHfH1LG/67PbZ4X9fqVR26a3bnvph061s/YvzfPsE1/1mpOknEeb6frnFCSQZzk/SuRjk2YtX21OdXb63cif1v/xBvr9oZczsxB3HaLWrHSqeUn6FerDXIzVtf2aS5GNCerDRFUTTzFwAolMZhMoXzm6IJWx1Jo8un5msmkmtIjMkCuYGkm2zp5gaE294DkeLWqOLg39Y3+jTOJCBYkgaEJy9CTDq8d+JXlo3YNzQJWF2XnJi0xxVAozeY6/RzfeR9hchTaDNpOsxFK3MTV+BEQHdfp/bNiRbA3eRVEFAUPLOuBc+u16rosgVdDrVMFjHhrmp0Y3N1C95etRM1oUVmmdOuTpqByDyPcAC3J1wKoFfGEqR3kQM2iwm+gKKKGwPb4UwymYAxA0sMt4klKMTq2tUVEAujY/cuV2+TXWnZhOxMiibIyLkQIpjTiHjd3ATRytz0jpH1Vc1q9xYhLkRb0LfHmQQAlQ3RP59AQMGyLbWobvbgu821AKCWe2nGEtqv5YyjeHlHIoDbauBMEm1+fVHaqaeKcCY5rGYcKotJ3vifn+xMSjR8WyAmuKVOm+azLjQIao4lTNqtZnUf+iVO3pX+fUtMTAp/tqUG2RiG9ymwY0jovfluS616e12rF7UtHiiKNjxWz+qdQTFp9IASdd/SHx/1mUnJiEkHDAgep3/a2agKk3rxsEw9Lie+rwmxzig/ymQyqfvL1ppWtHj8qrNgZO/gBYlKNTNJW8INSK3Iu1g3N1ksSYczKSIzyaDMTX9xLRqiE+5LG1vxr/Wt6jzszCH5GFQgLhymX0wSIhYAfL0ntc+7WXKZG3UulhF5SfnWcDzE7lY//rKsAX9Z1YZaT/T5nnwO7ArOpK83BY9X//t+F/783+/hDyhqltmAUu38MJN5l9trg++7OC7I+Vort9Wn/Lifrq2CP6Dg0ziOIbd0QUvM2WXxO5yZFDlHemHpFs3v7Tmfe/1KRDMU/QVlUXGwKwPOJPkiSLrzHrsTFJO6INnSzQ3QnnT1J0whJm1oinQmDQ45iB5f24JFu93YGhKbxvYKT5xjuXSiMboseGD/oTa5g6MsBq2qjZw8NIUmBsW6oCq5zE1W94XYI67ACRFNYNeVBApnkoLghO7u75tw56omzVW3yjb5Cl97nEnB96aqyYWbXv8Bf5q/Sr2iVVZg11wJ1y/kyqQcGRFSmm9PzImkx2w2oV+oBv6b0KJ4UFnyzqQxg0thMQezbwZGuX9iZW5d64qzQIhJN0zeB9cePwpAOGg225DdDtGs63LYo2gZbETCZW5SGaDe2XLTifvg2L3LccCAoGD56drKYFhqBzmThE19VwyRrNnjUxfBa0MlcT1DHcWMxiITr0RNdSYZfF9E+VEij5MMbqmEbPywYCvhVdvr1fKsRMvckhWThKNxsM4NGXYmhT9Dd5TPWyBK3dbGKVHUl5xFawwgf3al0TKTdAty+T49C+w48+BgVsU/P9+kOhfkMNaqGILyj0JMGliiPmekMykxYcyIUb0L4bCa0eT2qc45l/oeh3IGQ+ca/fPGQry/ooObHuFk21bbqgpPNosJw3qJEjhtmVuBI1JM6mrnCW2ZW/ozk2QxKekyt1BUgjwXLM8zY1J/B/qHLtDtihJN0B7k9+SLitTEJI0zyR17jK0hAS7fYlKFtg1NfviV4JwwVrWd7DCsanLn/IJZ/r5vrWlFVZML/oACq9mkumMF4tiYCYFXOJNGD4i8gLlye33KjytKf+OVu4s5SL7Nos5hZfF7YOi8tqu+LcJJvGRjjeb39uwz+vBtQOvqBsIiYCbK3OT9qSrHvxuZJAvkBpJuwrkWmXcmyVd09XlDI4WY1OiPcCZNG1WAcT1taPIq+OPSejUzaVhhai4XwUE9ggfV9Y2+pOzJcubR9wZClJjMFOnEFXEVyRPQtnbVB03214tJum9mnsUEMb/a0BheUAuHVbM3oJmc1KWYmeTxBdQrwFVN7ojFTw+nHSX54QWLfiEuW/3b60wCIp1IqZS5DSjNx9t/nIgXLxkftZuTUcitoLTLl7kFPyen3YI+xcHJWLYGESbWzU0uc4vvTIoVwA1AI0DW6ESSq44egeemjVe7C376yx7N5ClaN7dGlzfhUjCfP6Be2RPdFGO9rgZpMbu2QojA0cvcZOJ1dPOpYpLx90VkEu1Jo5gknEl2qxlDezrRu8gBjz+Ab0MCcywxUBaCkgnfBoBRfYrw36sm4PHfj9XcLjKTmgwDuI33pURDuEVGj8ConDD4PBZVnJHLjuWf9cJWkSSg9Ciw49zxg1Fe5MDO+ja8vmIHAO1Van0uiUBRFNWZdEAMZ1JEZlIS7VetFjP27x8UaH8MldTp3V89UnCMive32GDfB6BxJsnCkxAiK1QxSRwzw4uiVDs1ZjvaMrd0OJOC/6vuc+kiY52u/D8evfO0291/SDG+O7U3eudZ1Lnkzg4Qk+S51peVqR3rmiUBLJ4zyeWTxKTQe7OpKfz9WtcSy5mk/R7/nMOlbi6vX3Per23xqG6WviV5muYwQKadSdHFpB921Kvn0mRQFEXtTLppT0vMx3BJ3U7FsUl2JvUpcsBiNsHrVzQiSpvHrzblEOeT9ogsRvlOemdS/wyISS99vRVXvrRMK7Zm6bw3F6CY1AURGkkSc7cO5fTBwYnY5Xtr28qLPKUadwC/NARPjAMKwt3N/n1MDwwpsKDVr8ATCNaLD3BaUBRlgp0IffIt6JNnRgDJlbrJTp9VNbHEJO3Y8i0mVRiSc5P0V+D65ccO4AaAXqGJk3ivAGB1XXAsevt5qs4kfcCxfsFeWmDTOpMiurmFJ/hqZlKCGUlGHL1Xueb3VMrcgOCip39pPg4d3gPnjBuIK48arvl7rKvmXfWKMxCcnLRKtfV9ioLfVRFCm8xV/85AU+YWJ4AbiONM8iXmTJInqNEmVcft0xsA8PXGGizZELyqV17kiLgCJxaxiqJtLR8LOW9AiEmxXpf8nRUZSD0MnEmy8BDePvak0RMSxKM5+cIh3Onbb8LBy8EwapGbJK78xnImDekZPudEE5JjccjQHhH3610UfI2yJd8Vx+W2d5+gmLS+Mnb3p4gytxiOSbFAkI/HRi2gBRazCUWh/bGswI48m0U9Dt7xzk/45OdKrTMpyr4uh2/v169YbbqgP1a06NwWyZS5AVCDsUU+k0vn/hLngs+S6FAVz5k0WONMCgtPQogU3dxa1Mwk2ZmU+10/n1nXggdWa4PiW/1pdiaFHk9kZla7AmoOpb78Px7lOjFp/9Lw5you0HW0M2lzkx/+FDJlmlPITAqWuQVf80bpouIvCTqTgNzOTdJn2jS7fWoDCbmTm0DNTMrAdzKWM8nlDcR1qRpR1eRWjz0efwBbaqKXIstOTnGulwO4rRazmncnH/d/qWxCQAleGNqvX1DQb5czSWpWILwFejFJ7dwcJTOprsWD4x/8DA99vC7lccj4/AHc9/5afPhTJd77cbd6O51JqUMxKQdZsa0OcxZuxPt7jE9goptbnAvuncaDh5Tg8xN74df9tZNyp9WMgaETfrNPQbHNhH1KwpMBu9mEEwaEF0EDnMFWn33y2/fCDgy5k36oS/wEIwtBm5r9Ed3ZRJmb3plkMpnUTm1yqZwQe3pEKXNzGCiBwtL9c0N43D+GXoM+GDPVAG791WQ9RQ6rZhGqF2HksGpRktQeZ9K0icM0C6qeBtkvyeCwWnDf2QfhgsOGaG6PtdARTqxMdATpaNy+AMQ8ON9uURfOFY0u/Gn+Sky891NNR6VMI++fcnCijHBaAXGcSV6t0yEWfzhmBOxWMy4/crjh30eUF2JYrwJ4/AHc8/7PAIAjRvaKyFXKs1nU54sWIB45zvB3OywmxXAmGTyu0ffGqMwtnqNIXAm1RnMmhYSWeA6nZAhnJgWPIwfqcs9iZSb1LnKox6hky9yiMaxXsOxxU3WLuuiO50wSV16juX0EEWVuMYTO/qHvqtzBqEwO4DY47goxU+wPUycMxfH79IbbF8BVLy9X80iA6GVuYvE2uIcTeTZL2CHUoh27vswt2fW2WIT9uLMeQKQz6YLDhsBkAj78qTJht0WszCQAGNIj+P2Sy9yK86zqvtPi8aPZ7UNr6NwmXygRLoiGHM3Wc/kV3P19Ex79uQVvbA3vpy1JOpN8AQXfVXsiOpAJRJlbX6cFdnOwTKsq9Lj6xiTx6J0f3sdtprDbHZDFpPa7qfTIYlIAqTnBUwngzpPK3DY1SR0+E3AmiU62P+VwbpIQxob0dKrdqoU4ZnSxoDQ/7JSPh9vnT2sJoBCThpUXaC4qifPRqhRK3Tbt0c7F1lVGF6TkphDH79sHBw0qxVm/GqjZRu6YLBDH0n37FaM8dD5vz/vi8oUb8Yj3QQ7gBsLnsGjl+6u212Pjnha8tnxHyuOQ+WFng3ox76ddDertDOBOnSyRG0gyLNlQjX8u2Yb/qzI+gajd3LIggBsIdo4ZUmg8eRtZHD6onDUkH3m6RcoJA8InCJGj1M+ZujgBhEvdkslN0otHq3T3bfQYO5MAbQh3qy+Alze2qhMJISb1yde+JqP8SXEVbo0UAP5TfUhM0k1G4rWajUY8MclkMmkyOqKVuQWU8ELe2Q4xyW4149+XHwq71YwjR0UuzlOlROfKiOUAUJ1JLbl7xTkasovHabeid3G4vftnoZKtj+K0Ce9M9PtnkyvyM2lNsJubmOTEC+AGgBt/sw9+uP0ENRhYj8lkwsmj+wEIT/gmjuxluG2yZZPCmWS3mtVJV6zXZfS4ZQmKSdVNiXVzi/Z9KQ85k9JZ5iYcZGISrs89i+UsM4ey0oDI7KNUEbk6TS6f6saJ50wS36uaFk/M0oREu7kBwP2/OwgPnztGc+W7wG7BsF4F6FXoMA5dV91MdvXxn7xwLA4cWAKvX8EiyeUTTfgSt4uSWFFCWasP4NY57/RCWTyEaPj99gZc8Mw3WLop6PgT7/GoPkU4KfSde/TTDQk9ZkNbHGdSj7AzSWxblGdDgcOquroqGtpUd4B8bhOfcVWTW82gyiXkC1Dz1jSrbqE2SfSocQc0Ado7W/0R3W1f3dyG3y2qxZNRgnzcUv6PmPeIi2H68v949JK226vEqnF0d5YzCQCqU5hvyQHc8e7fpilzC75GuXxvXUvQZfzZL1W44Jlv1EYQAFDRGJyHHRlyea+PIUBkO6LMtG9xnnp8E6Hi4ngkk0wo/mUvLMMhf1uolni1h4a2sKt/UJlTcwH28BHB3D+5u1uibKrW3ieWmCRnJg3rVYC3/zgRvzmgr2YbcS7dLnWe04hJaTifC2dSvj3skNIff4WotbveZXiBsCV0gbCy0fjvybJkQ7X6s+zuojMpdSgm5SDjhgZt/ssalIiWjYqiQJznsiGAOx6jpCtJ5w+PtKn+qqdNvUo1NCRI9c1vn5h0YCiEe3USziR92Zg+NynsTDISk4K3NXgUPLymBbeuCB6se9hNasme3olkNhBNhJgk25t3tgZQ6w6o9nMRAF4Xo7tHLBKZ8JfGKHOzW82qE0mU4jhTDOAWjBvaA1/deCyenjquXY8jU6Sz2Zpj5IuJK+9Nbl9OLhJiISYcdmuw3XbPgmAdvaKEnQD6MMZMot8/jYQTWUyqbfEY1uz7A4ramSwvTpmbIFq4suCUg/ppfj8iipiUbAi3HECtXsFLgzNJFoUF8QO4Q93c4mQmpcOZpCgKlmysVjsLimONvmNPPGfZvWeNxu2n7odDhpa1e0xAcD8QrqAtNS3w+gNqTmE0Z1KZ065+r2pilI7qxaT/b++849yozvX/zKhLq+2973q96947xTYY7NC7IfSElhtCfqEkgXBDSSEESCAJhJCbQEJugMANJCFAAIMxxQ2DwQXcvbbX23vXSprfHzPnaDQaSaNd7WrL+/18+GBrtfJopJlzznOe93ltEUTuyhw3zp1TECSwC4KAf99yIt65fbmusMW+e6zzHiC7Mk+aLH9X1dOJcLvRzLGUrZTE8sykbk/QfEQr/MYqJpVnJSHZbobH58cH+5vw78/lcgT1Ob7p5EkA5A5H0dpXe31+LgKFy0zKT5VzVzxeP/Ypiz3mYipQdT8K5AEGxpHcZDvcNjO8fmlUuTmNot6AOtrtw/8dlu8xWuGkQWn6sbN1ACf8uxE3ftgW9HOWb7mvQ39jilU/2kyB8v7aXh/6fRIv/TIqJtlMge5m6hI3AMOWmeSXJF76x+am0ZxFesRS5tanKnNjziT1p9LlA2ra+/GHDw7hg/1NeG1noHSHbTyw8ehQU7fhvL7RBnOO5KYExKQv6piYpONMiiGi4P19ssDwl03VQz5OJs5kJlnh0rj5l5ZnBD0nFthGFbsHRhKT+jUNC/RgjQXU96uAmOQOCORDcOzwZiRmE76zqhIXzy/E7KLUoOdkK/lNXr+kO+6w7plevxSX5h4fqMQkNZ19Xt35IhGdMSA3EFpmF6bCLAqo9wDHNLZj9bg/GgK4ozFDEXYWZVpQmRI6wTMJAs4ukgeJOYqjaHHW0EqdShSHUyxtblmZGwt8DHEmhQngBsDL3Fo9frxxTL4pX1bmwPMr0oMWAunWyJ8XE5O0Tex+/UUX3q2Vb7BTUuTJbZsyMXyntg8/+awjaHcxEnpOD9apig2C6o5Beq4OtkhlYXou29DEP0BeuERbzMdCLA6nFIeF5+aMtgyhoaIO3wbkXJUsTUeULYdaRo2Ipl2g6k0SezzBz9FmRgAIej9GnElGqMpx885vk7OTwmb0MOu90UmR2q7ORIzjbb1hF8965ySSS0VNtB3ISN3cAHVm0tAnfBsPNOOrv9+Mn73+JYCAS6dAk5sWrUvY7KJUXHtCWdxcjQBQqpQbHmrq4TkPdosY1vEiC7XyZxCpS1qH5vsdLoA7Ek6rOexxnDYtB5lJNixR7uWMBSXpIc8NZ/lnj3NnknK/9/oldPZ70dE3gGOtPSHOpHAZZ+EwiQKevGI+SjPCO9Gm5LlhEgX0Dvi46BgO9b2DhahrMZtELlay8gd2LguDxKRQZ5IgCKhkQetj0P2hLY1nrmetmMRK3d6rk8/3u3X9XEACAuVb6k6zapgwYhMDzqS6Xj/fsDMJoV1xI5GtlP9PSw3+TJlQ1TkgoWMgfuOXupFKsSuQ+xQr3QNqMSnyArZX3c0tjNC2p6GLu3TYPUaSJD7+LSxNg9Ukot/rT0jXrHjArvGcZDsXxNl4x0qy1BgNxVe7RY+2xi7yaGFCEWsYw8b8FIcFU5UcoiODEpNkgXtFpZzRGKmhA3NeR5o3M9fufuV1JUnCl7XyawaVuQ1hPGfijMNqwgXzCvHQxbND5g9mk8hz6WraQs9Llyp/L9JGmhF6PT58Ut0W9ueRxmciPCQmjUEcVhOm58o3gW0twYOQes03hJzqEeOsIjseXpiCXy9JDfucO2e58bcV6Ti/RL7ZXFhi548NBrbr1eOTDIssbKK1Ile+uW5v1uzCRnQmyf/ex00eVHf7YBWBu+e4Q8SzDHtksSRL83P2Lz29rwdbm+TBkglubGJ288Z2/H5vD27b2hbtLQIIXcwAwH+tqMALNyzBb6+YByB4EapX7sLKHpgddajOpEQjigJfjMez5flogC+KVBOOHI0I0jvgG1R9/3CgFTujOZMA/Tp8VjoFRC5xjAVBEHDxfDmTYPX03LDPq8iR791Gg1ADQeEi8pSFbkefFwt+/Dbu/9fukOfrnRM9MUm9W8qCOKPtzPFOoSMQwK09P0w0ynBZg3ZbjWRexZsyLiZ18db1JemuiA5Htsvb2GW8RDFe303G104sw9YfnIpKJRCcMa841LXV2jOgKyLXdwYWdIC8WGGiSmu3B5f/fjNWPrw+JN9Db2yJxrKKTNx7zvSgx9SfvcUk8tI0bQmIFuaMcllNETPy2OuxbBnmYuIlIa096FEWN9og2Url2t47iHDdRKMtjWcCSY8m+6hWcUGbVd/1vxwILACZmNQQRmBhmUk2kxBwJvX4AuHbVlHXmR2O0/JtSLUKOCUvWExIsohIUeZj2k7CQ4G9PwFAIROTopSpHer0YmuTJqBe3X3XI/F7qx5snmo3BeaTWt470MJdj2zx39Hn5eNhQaqTl+geaIy9xGo0UMeFbHtIUwl9Z5I87kVzAatLdKsjhFobhQlFRco9g8Uq5Kc6gkppo7kptTAH0eoZOQDkEq1wZV98EyqC85ptfh1o6IIkSTjW2ovOfi+sJhGTspKQlSSf0yFlJqk2wyLBNolqdJqL9Kg2JiKV+Bvh06OtPIdRj2i5hoQ+JCaNURYUyXkCH7cET9AGVDcn8xhwJpkEAReVOkIyg9TYTAIWZVn5BEMQBNxY5cKiQTqUksyBDmvNBmvdmdNnWbYVFgFo8Ug42h2YoHRGcCYxG/bLSqjlsmwrXDoLoMwo1m5t55JvTHFhZa4Nawps+Gq5A3fOcuPqyfIip2NAnpywieBrx/oNZUTpZSaVZriwuDyDD8zq/BU9V0CapnxmKJlJw0m0lvBqmKsgUonKWIRNNO2qzyhHtcPHbiEfHdC3BY807PvJyhT1Jom9GjGpVmdywkr4zKIAcxwX7NefVI4XbliCW06dHPY5s5UsmM+Ptod9jhr1ZCzJZuauiuZuD/744aGQ5+tmJumUtKWqROGidCcXLiIJpkxcsEQL4I6D6HpM09mFiUaCIASFTkfrxjccMDHpcFMPqpUJfmlm5Ewm5vgLt/MpSVJImZu21XU80HNopTgtmKwsLLLdNv756u1Is5IHtROAfb8ON/dgR007BnzSoHbe9cjUOCW1n3dA2ItcWqbuzhYJ5kxiLlQWXKx2JnVrHJ0MJtKNhDNpZ+sAlr3awMvRhkprf/CilM2NmDOJzT/YvEftZPrboV7uOOrizqQwYpIvICblOgPOpFjDtxl3zHTjk3OyUayTyzkcIdzsfLjMAneqh3uvjK9/2Iq177YEiVqdKreUhMg5l8yZ5DSLvMyNsSBNfo8vbQ9kG7JrlLmSUp0WOKwm7kTRCr1jBdZNMTfZHlK6rSsmcWdS5Hmb2nG+r6ErZA4RK+zeV8ydSfJxFKQ6UJDmgCjIc5BYRBqvz4+jypi4qCwDJlGAzy+hOcxYy95DJOd1SYYLJlFAt8eHuo4+7lgrTHPAYhLjEsDNM5OiiUmp4Tu6danc5kN1JrHX1zpe2XqZcpMGB4lJY5QFxYqYpGlvr95IHC3d3EYbgiDwCYthMUmZOGU7RExTavM/VYkzrMxNz57N/i02VVuVr1/+kmGPTUxanmvD0yel4cllafjp/BTcWOVCjuo52lDuJ76MvhvFnB/q3f4SzU1XvQj16+ysaCf/o9WZ5IrhuNh7Cjdwj1V6dco11JOyVVPlHbBPj7TF/Np/23oUV/zPZsPZQEZgYhLbxdJr+dujmbzodXTrU+UQxRNRFLC4PCNi6dXMglQAwM6adkPZFWpnEhAq+GpfQxs26rabdY9HvahOd1oDeUcRXEVePxPhwjmTFNG1yxPzrquWY5pyA/V7UOcmxatMMRZKMwICBgvwZI+Fg2UMhZuY9w74QtwJvSOY37BAyZQqSnfyY9XLymjQOJOAQCel11Vtlhlskj6/ZHCZVep8JyD0muViUpQFcrTwbUa+JpNL60w60tyjKnMLHkOqFDFpJEKO19X243ivH6/XxGcnvUWZ45S7g0u3mHjCSuiPdMnvXd2UpH1A4hmUrHyr2yuhW8fZxsUkMZB/Wdfr42JKuDKuSIRzMg1HbhJzFDnNAjK5mBT59Y/3+OAHcES1AdmtccU3RhKTeAA3eAA347xC+froUd0rmAjMxj5WQjQpW75WxrozKTfFFiImZeuUubF7R7fHF7bM1ueX0KIa83x+CTuPG9voCYdWTGLnvyzTCYspkH0Yi+De1OWBzy/BJArITbbzsTZceS8rc4sk4ljNYsCt1tCNVkVUY05mdk7DuVSN0GsguwlQi/Wh56RHVeY2VGcSm99M1zRTYUIrdXQbHCQ3jFHmK86kPR3+oB0ONhkVILt+CH2YwGO06xnLTEq1ipiTIU8uWQh3r1firp8ynd2xi0odWJYt35wtArAqP3TQA4BcR2xikt4OnlkUuKClDcCs7go/4Xl9Ry2WP/QuPlBCCMuVG2tmki3Eyq8WHvTWi9qOV/HITBoO9Fpnh0O9SB5PBMrcVEGyqjK3VdNkMWkwA+yTGw7gg/1N/Ds1VAZ8fj4xYVkEWicHEMhMKs+SJ87HdSYfgVbuI//drMxJgs0sorPfi8PN0XeItTbx05TPhKEV67R/1ytxY6/HJnhpLgt3FUUK2xxQFjXhxDImunp8fu4EAeQJ+pZDLTHt+IY6kwKfVWGa2pmUgDI35bt1uLmbO2JYjlI42C5vuJ1P9rmpHcXaks3h5HSlNHNhaXrEY61XlZow5iqBqv/Yfjzk+X+5bjFuP70ST1w+b1DHlaEpZ9Fes6URnEkfH27BXzcfARAoc2OB2uHITw3e7GFOwKJ0+Tu383g7fH4JSTZzSE4Ly0yqbukZsrshGqzpRjRXjFFYe/tKpSlKk8aZxDbRqrmYpJ+l1BMlWJpNuWwmgc951GJSrM6kSAxHR7delTOJiUmRMpP8kgSmNamfpxWTIr2G8lHDbhZCytzmpZlRqNmfZII1cyaxMubyTKWsaQyKSZIkBYX/p6tEZrfNHDJPBeROjEzQ2VETKhA98NoXmPejt7D9WFvQ458NsayfZSYVKveMK5aU4AdnTMX1J5cDCO4aaRRWfpWVJIdVs/tvuLmZ0fIynpvU0MnL/Vj1QYrDwl2qg3Ub9w0YcyYVRTgn3UFlbkNzJrFrozDNESRIVin3bnImDY6Ei0mPP/44SktLYbfbsXjxYmzZsiXsc3ft2oULL7wQpaWlEAQBjz766Mgd6CgjO8mGDIvsdlHvdrDxiVxJkWG5SU390ScZkiRxZ1KaVcRsJZeIhXCvr+tHj09CgVPEzLTQAS3LbsL/npyGP5+Uhr8sTw/bje6/piShyGXCN6boL0q0mUmZYZxM7L3t14hJbPKpx/99cgzVzT34uLoVAHDKlCwsLc/ADSeXhTxXEATcvLICa6bn6mZtLNDsQI8HZ1IGy4LpHl8DDRNe1MIa241KdVowI18WBmO1OXt9ft6eOF47PeoJBRMTImUmsUlSrY4tul+ZaCVCiDCbREzPl0M4Pz8WfQdU60z67zOn4UfnTueTM20pEjsnbBIYTkwCAsGgqU5rwI0S4bMe4M4k/Y0Ku8XEF9/q4/rr5mpc8ruN+MpjGwzlb7H8BjVhnUkJKHMrSnNCFOTvGmtZH9WZpGQm1XX04ekPD2F/g+xeYV3Q9MqwhluQULOyKhvvf3clbj2tkt8DtN+Frv5ABovaCcBcR3pOqtIMF24+ZbJuGYoRrGaRl5oBoU608ghi0h0vfY67Xt6BL2o7uPAczZmkDXgPBHDLix22gTK3ODWkDDEzyYZ0lxWSNLjW37HAxJto4c1GYTmLrMNu54CEPp8U6kzqlr+n2sBu1uWtS+Vg0MtNUpe55SlzofpePxdTjHZyM0KZ4rLa1hy/TSAmAjnMAjJt0TOT1IHdzar5Js/ZVMJNI4tJgQDuJLMAtQE+wybg5LTg7yHLvmMujtwU+Ts9KXvslrm19gzwrJscTZkbu7fqMZOVlde0hfzs7S/q0d47gNc0jkoj43IkWBwCG1PTXFZcf3I5//tgxCTmQGLvNdw9mhHo5hZ5fOS5SY0qZ5JStiyKAt8gGmyHSl7mFmXzluVLacd9AEHNHI7rxBbEAhPFspJsfONUEAJ5dxTAPTgSKjm88MILuPXWW3HPPffgk08+wezZs7F69Wo0NDToPr+npwfl5eX42c9+htzc8AGnEwW2G3FMLSYpziQzuZIiEnAmRS/F6PZKXKRLtYqYpXSg+6LNC0mS8G+lQ9uZhfawHYMEQcDJubaInejSbSLePyML35vp1v253STwgG9zhI4nbOeKOZOKlJDIVo/Ecw20aCe+Ocl2PHfDEtygtF7WcvvqKjx55XzdwNlpykKZMVozk5wxOKYyeMvzseNM+vhwC57fciTic9jiT/0ZTVcEpCVlGbxjU3O3Jyab87HWXn4vitdODyvvsltEPsHRq58PEZN0nUksTyAx381ZhakADIpJmh3G4gwnrlxayhe9TZrzyzrXsPIfbTmAGhamn+a08M86opjkixzADQSygdQ7ma/tkPM8Djf34Mo/bI7aJr69dyCkG5ha+CtIsDPJahb5Z8iuC6OZSW/trsd9/9qNu1/ZifV7GjD3R2/hsXX7As4ZlXAykmVugJKdZRb5guXT6tag7ohMGE7SOAHml4YvYUsK0zktFtSl09pAWfY9P9LSE9SVCQjsYlc396icScYykxjs+SkOS1AXOL2NFCCwKBnu3CS1M2moJaVAwKVdnGTiYkVTn5/nLk5VuqUd7/Gj3xfYYCtVuuMy4UjtuNFzTfWrurll2UWIkDdB2XxFW8Y1FE5TIgW2NA6gPsJmWiyoM5OyDDiTwjm12HkqdQdK/cIRKHMTIAgCUlWCW6pFwPL0wDljU7LGzv5QZ5LiqGzo7I96Dx5tsGs5w2WF1SwGBXBHEqojZRSysY51MEtTgrLrhrAB5vdLfOzS65gKRHbhhIPde5kglR3FmWS0vKyCO5O60NItfyfUuajzlI2C7770+aCyk4x0lQPkeQ0gl7lpQ8XVDt2hOpPYvCQzyYY8RWRNdVi44EoB3IMjoWLSL37xC1x//fW49tprMW3aNDz55JNwOp344x//qPv8hQsX4qGHHsKll14Kmy28Ej1RKLTLo4ZaTOrhuz4JOaQxA8snajawq8dK3Kyi3E2DdfDo9Umo7fVj3XH55nRG0eB2XmOBTV4ybOE7njDrOCvDq3CbwUxNehOqvgFfyKAWrnWyESwmkU+ogej21kQRi8iV6WKiytjZtfjWc5/i+3/fEbF9bI/OrtG0/GRsuGMlHr10DtKcVu5CicXmrN7FilQ2FQts8ptks/CF3Gs7akOcLmwSxfIh9AQnVuaWCCECAGYpE9ydOtZ7LVpnEoOVXoZzJrEWxHotkxmso1ua2pkU4fNii/VwAdzycQWLSV39Xnxc3cJ/3tnnxUf7m3V/t2/Ah2uf3oL7Xw3tUhfsTAoIN4nITAKAc+fk8z/bLSJy3JHv/9rd8+1H2/DGTllk++RIG8/0SEmQM0kNc1n9/dManP7LDXwR0aDZHWdku+18t12NIAR3ihws6twk7eedm2yH3SLC6w92s/UN+HipR117b8D5FWVsy9V0s1Q7mdjuORA+A2qkcpPqlNItjz+Q2TgUmDiUYRP5/OiYqjysyGWCyyxAghzCzcrcmJOpQZlbBItJod9f1s3NbhJgFgOCzK62Af7vx4tClwnzMyyQAL7pN1T0MpOa+/26+ZEAgjoGq3ORWLYUc7rvaQ/f7ZBtAjoUFxML4U4yC7CZBJyYBpSkObCoLJ0L7Q2dfajlGUPydzrZbuH358ODdJokCuamYqKD+p6gl5fEYBmF2jK3Ho+Xb1CxjS8WoD+UBhKd/V7uXgw3j2b3yqMxlbnF5kwyWl7G3Gr7G7vQ2sMykwL3vB+fOwOlGU7UtPXiZ69/afh4Gb0eY+V2ucl2WEwCBnxSiJin3lhq6OzHQIRubNFgY1mW28ZLmtNcVn4+hxI2PpFJmJjk8Xiwbds2rFq1KnAwoohVq1Zh48aNcft3+vv70dHREfTfeIE7k1QDPqtnZ24UQp/0GAK41SVugiDAbhJ457XXjvWh1ych1yFidlrkHc94wMWkCGHdJUpuE5ucZNpFVdBl6Ps93NwNbQ6w2za098IWsgAitstOJMXpkctS1GSMscykxs5+7siJNGHp0QngBuQJm91igigKUbNe9DiomqjWx2mnh038ku1mLJ2UgfPm5MMvAd996bOgEGrmpGDOpI4+b1CJHKAK4E6Q0MlyXmoMdCYJl32QpRPo7PNL/Dxdf1I5Ll9cjK+dEFqqyrj2hFKsqMrCyinZBp1JTEwKf//JdFuDjuvD/U0Y8EkozXDimmWlAIAP9jfq/u7bX9Tj3T2N+PsnNSE/U5ez5ajEDGscu/HFwlmzAmKSX4p+n2Ntlhl9A368+rlcXtGocgoElbmNsDOJccWSEtyxugq5yXYca+3Fd17YDp9f4ru2eos3Jq6oS7+SrOa43P/VziRtWaMoClz8+kyVfdKmCtyt69A/v3rYzKYgAVadscTKawUBmFOcqvv7LDdpOJ1J/T4JLZ7IDqBYYc6kVKvI5zdHugL3TYdJQLEyrzzS7eUB3CxjqUFxSHUPRHMmyf9nG55srlrTE/8yNwA4W9nk+9eR+IxDPcpGhMscaOTikwKbjiHPV5e5Keej3yeBnaZFmfL9kolperAyN7si4rMOwSys3GUW8O7Ni/G3G5dyB6TsTJLHl/yUgNuOiU1DLRcaaZg4W5ktX19qx20kZ9KMgmQIgjzWqkUivXImlpszlHkeK6e1mcWwAgoTk6qbYxCTWFadMu6z9xxu88doZlKZcu9s7OznG2/q7q9pLiu+/5WpAIC9g7in9RoUtUyiwF2hRzTnRe2OlaShRSeonUlMZE13WrlIRwHcgyNhYlJTUxN8Ph9ycoLDRHNyclBXVxfmt2LngQceQEpKCv+vqKgobq+daAqU+2eNypl0sFO+6MrcozOnZrSQGUOZGxOT1MGHrEvIRw3yoDMlxRy2xC2esNykSLt3bMLH3lmwmBS6ONHLdhiKMwkAFpWlD+n3R4LvranCyqosQ8GwGWOsm9sXtQHRPJKY08tbXIf/vLMNBDNrOdQU+E4Npga9x+PljhwGE0nYd/Oes6fDbhGxt74Lh5Qga59f4pOobLcNbqUUR1vqlmhnEp8IdvZFLVGJ5kxSd19TB5JX5brxk/NnYnKOftksAKyZkYdnrl2EzCSbockUK3MzRxKTNM6k9XvksvUVVdk4sSITAPB+mFB2rb1djdqZVJjmxPT8ZEzNS46agTNcqAUHI3qJnkOM7bg2dvYFMn0cFh72vLQ8Iw5HGjt2iwnfXFmBZ7++CA6LCR/sb8JfN1frhm8zFpbK9/z5JWnczRiPEjcgsjMJAJZNkr9Xd7+8k9/7WlXtwOvaew13cwOCS93cameSsgisynGHfR3mbtgbwRE6VBo0jp9o3cSM0KrMhdJVzqSjytzSaRIgCgJKlJK2g50+dCqOm8oU5kzyo88HqOWjBp3NK3VmEgBMSQ3+jsTTmQTIjnERcvfdw13h3T96HOv24esftOLD+sAYFnAmibCIAtIUYSfcZ6B2JjX1+/HA55349uY2/tgiJfrgYKePC1XhXsNhChaR1MIbc6qrs+8CmUmB67VAcWMMtcX6SLNPmadOVlzvyXYLF66zI4hJbruF56rtUJWV641zTExq7x1897L23uiidZmq3LCtx5hwFeiiGexM0pvfSZLEy8uiOXeTHWa+mcjundqcRXY/HIzQYtQhBQTurxsPNuOXb+3lm4DdmmzbwXZ0G/D50apsMmS5bZitlKpPyXPz62YonesmMuM+pvnOO+9Ee3s7/+/o0aOJPqS4wcvcVM6kQ53yn8tJTIpIwJlkpMxNvrGkWAMrhnyn/PubG+WBoFSni9twwJxJ2s5uatiEj5FpE3npW61OVxN9MWloC7SL5xfhnNn5+O+zpg3pdYaTVKcVT1+7CGfMzIv6XLYL1tQ99JbnI0GQmBRBzOFlbhEG+iwDwcxa1GVusU5Amrr6sfin6/C1Z7YGPc4mXWySluayokRxl9Uo5S1qJ4fTakaeMnH+7Ggb3t5dzz+7cALNSMF2jwd8Ep/chCPcDmNmUqgtm01kXVZTRPeQHoYCuGMpc1PyxTbslYWjFVVZWDIpA2ZRQHVzT8juo/r4GUmqXB71Z2USBfzr5hPx6rdOTKjz8W83LkVush2/uGRO1OdGCiBt7vagRQk/TbZb8PwNS3HraZX42YWz4nWog2Jyjhu3r64CAPx5YzW/l+iJSRfNL8Stp1XivnOm8+suSafD0mBQd3TTu2a/u6YKi0rT0dnvxT3/2AUg2JlU264W66IfE1s8iYJ8LTGYYHa6pqOiGuacON7eFzGX5rOjbTjhsY14syn28UTrMB6qM6nPJ3EHTZramcTEJKW8irmed7QG3lcFdyb5gsK32XFJkoQ7t7XjoR2yuKbOTAKAKSnBc400a3zvydl2E07Klcfvlw7HJqA8uKMT62r7cfmGVv6YOjMJADKVDb5wuUnqAO7DnV78bk833qiRryOHSUCuw4RMmwgJ4UvdesOUuaXrnCsmWh9q6uYbMGoxibmUhpo9Ewvr9zTgm//7CQ94HgzMFcPEWlEUuIMmJ0IANwDMVDoM7w7aZAsd58oyXVwIH2ykgV72nZZke2DDQH1MkeCZScp7DTiTQo+z3+vnpXbRnEmCIPBMrQ7l+5KmEZPY+W3q6g/JpYsGK9W2G4iVYI6tX63bh8fW7cNTGw4CCGy6sA20wQqhzHFmEgWkOiw4oSIT79y2HPecPR1pzkDnOm10ABGdhIlJmZmZMJlMqK+vD3q8vr4+ruHaNpsNycnJQf+NF/QCuA8pOy9lSdEv3IlMLGVurYp9We1MYi1n2S6VVsAZLpZkWWESEDHIu1hT4hitzG04nElWs4hfXTYXXz8xfInNWIK3PPf6Q4KBE4UkSXj83f14e3d9yM/UE5RIjqLeMGVuanj5UxRRaF99Jy+DOaTqFtOhdJYxyrbqVnT2efHh/uagvCeW61Woyi1hZScsK4XZoQVBzrFhAYu3vfgZrvvzx/hQyeoxWsc/XFjNIhcoo4lt4YQvtmhQW/eZGBMu+DMSbHIaacLoNRDArXYm9Q34eCnf7MJUJNnMPO/qfZ1StzaNsFaSocpG0rx/URRCummNNIvK0rHprlMNCdJqtIsfSQJ2Hu/gPytIdeCWUydH7MQ3Uly8oBAOiwn7Grrwz8+OA5DzLbRYzSJuOXUypuYl8wVrvJxJmSpXl941a7eYcO850wEAe5Uueeod/7qOPr5QMnJtsCyNZIclyHG8enoO3r19BW45dXLY301xWvj5iZSb9KNXd6OmvQ837IxdCNI6jIcqJrENM5PS2IMJJEe6gsUkNrf4XMljdJsDHdlaPRLPUQoclw+Hunx47mAvHv+yG+/W9vPMJOZMmjrMziQAuKRUvo+8dLgXvhg2g1pV80O2EdGjykwCAi73cB3d1AHcLZrzk6QknU9TzsHutjBiksaZxOaiemHlzLHCXDhuuzlI1M1LHfkyt2ue3op/76jVzcIzQr/Xh8PK5sNkVR5nhZKLWBnBfQsAxUoplzpTTW8+k6V0YwQGX+rGstmi3Wem5spr0S9qjTkYeWYSD+AOL/Cwph2AMUdQvqbpQLozeNzJSLLBJArwS4FOdUbhAdwGNu60uXtMQNRGFzBnUlNXP37w8o4gx1kk2MZbZpKVb0KVZyXBYpIjTIzkRhL6JExMslqtmD9/PtatW8cf8/v9WLduHZYuXZqowxpTFCjzq44BidevHyRnkiEyYxCT2LlNs4WKSYyRciatLrBj1/k5WFsWvnNQvtMEs2qOkWUXkac4qSKVuU3KCmQIJap0ZLTisJr4DvVoyU3aU9+Jh/6zB3e9vCPkZ8HOpPADY7jMJDVGHCsNHX0481cfYMVD63GstQfHlcGerfVjKXU70BgQN1/8+Ch+vW4fth5u4ZPJ0gw9MakH+xu6sFtZkDsspqAdNwYLgm7rlT/DwYgu8SJaNxYGcyZp8530uqZxMckZuwiR4bLCJAqQIkwYWWtmcwQRJ1B+188n70k2Mw/7XqB0/vpCZ0dW60w6Z3YglyhRLrJ48vMLZ2HV1Gw8+/XFIT/79IjsflCLpaOBZLsFZ86SxbLGzn6ku6w4a3Zk8YzlssTLmZSpEtXCfQ/Yv9nWM4AejxdtvcHOpFjK3NjiSvtcQRBk90IU1x/LTdpbH7pRw1CLYkbmIWrqeuLrTGJ5SSwXks2PjmqcSaxz2yFFZEqxikizBlrVa8vIGvv8QY6dH3/Wga6B4CYxVcnB35G0YRCTVuXbkGoVUNfrx/v1xsfvbEfgWGqVjTitM4m5vtWbumrUYpKWJDMTk+TvmV5ukiRJvFMeE9pOzbehwClidUGoqMs2GT5XAqe1YyArczOS1xdv2PgcC/sburDpYAt8fglumzlIyH78q/PwyjdPiComFaYG5gkMvflMRpIt4PgdpDvFaDYb63ps5Jx4fX4+zjMRKcNlgyhAV+BhAo5JFAw5lNWZWkCoM8kkCny+EavTvFenyUs4inSaOPR7fby8ngmJtcp395/bj+N/Nx/Bo2/vNXQs6rwkPQaTD0rIJHR2duutt+L3v/89/vSnP+GLL77AN77xDXR3d+Paa68FAFx11VW48847+fM9Hg+2b9+O7du3w+PxoKamBtu3b8f+/fsT9RYSisssIF0pvarp8aHd4+eTkjI3OZMiwZxJfT6ErVNnqIMpGQUa90/xCDrB7BFKTAB5oVegErsybSJywjiTfH6JhyUvnRTI54jXjvJ4IkNn8Z5ImEDT0NkflC/UN+DDgUZ1mVmEMjdWzx4pM0kVzByulvyLuk54fH70eHy4+o9bAMhCDVsYx9Ju9UBD4Nj/54NDeOStvbjr7zt49xkWXg0EFt5f1nXi/Cc+xDVPy6VxLAMqTzNJYmVarHMWawWcCAKOr8jfp2jOJHWZWxt3JsV+/YoGJoxev1LmFkHYyeSOKQ+OtjI3mYM7PNiiv04n94A5k752Qhmeu34JVk8PuJSt40BMumRhEf7n6oWYnJ3ES6mYSM3KHYvSHGF/P1FctiiQNfnIxbO5wBwOtngbqsOVkZEU2ZkEyGUl7Fweb+sLykzyeP3c2RhtkQcEdsgH6wyrVDokReqkqS7PXN8Qm9uVbQqxqUA4V4xR2jQbZqxLGXtdJiZNTgn+PFOtcqt6JrqwmAUmtDT1+4PynQ50+tA+EFzmlmQRuePJbRFgGQa3oc0k4Nxi+Tv5nxi6uqmFoJ1KaV+3xiU0SRHDDnRGLlHTg52n6WnhnUmtHom/Rp4yr1uYacWHZ2ZHFJPYWJ2rGQPzElDmxmCbOEZp7xnAWb9+n88pJuckBTkFM5JsmFOUGvV12JhT09YLv19CR99AyBgnCnKLeD5+DVJQ6DAoWk/LY86k6GJSc7cHkiSLOqzk16RujqKZQzAnuBE3EABelsxeV69EL4dnKsZ2XoxEKTCKNBspdR196FFFkVQoziS2WclEn53HY3MmhetwG61DHhGehM7O1q5di4cffhg//OEPMWfOHGzfvh1vvPEGD+U+cuQIamtr+fOPHz+OuXPnYu7cuaitrcXDDz+MuXPn4rrrrkvUW0g4BcogXtPj4+HbOXYRrnEw8R5OXGYBbAMs2q4g21lT5xSpxRoRQKFzdIl3anEr027iVnStM+k/u+rg8fqR4rDgBCXEFEDCy0dGIxk6gceJRL1YUk8m9jd0BQUZRxJyAgHckZxJ8gD7zpcNmPbDN/DnjYdDnqPuGMeErDNm5g5qAqJ2JjH2NXRxB11ZkJgkTxI/2NfE8yGAwPtRT5IAoK5dPg527lIH4eCJF6wri1FnUrjMpOZuD+9mx0p7Uh2De1/ZUT4vXuYmhh9fslQ7u8eU74V6x5HtlOuVWbQri43JOUlYOikjaNLnjbAoG2sIgoCnrpqP3105Hws1zQr0dmcTzbziNNx/7nQ8unYOVk7Jjvr8FVOykeq0YEVl9OcaISiAO8zcRhAE7iiqbe/lgjGDLa6NuBFPrszCN1ZMwve/MmVQxxtwJoUXk9SL1XX1kXPTtLBNIdZJbagB3AFnkjzua0vNnIpwkmUT+XMA2ZkEBBqDHFScSUUuEwTIXc72deiLLDbVptgURaQajhI3xsk58ndoU6Px8VvdoW2n4hrSOpMmuZmYNAhnkhKOzHKj9qvO1SfNHvx6dxeqlXOabRejbiQCAeGEkacpSWXXSKTNoeEiWj6gltqOXj7+AeBdG2OlgJf29eKhN/dg7v1v4V9KyS5z0qY55dIn5oKMtZyLwcppo2WzsY7H+xo6o34ObI6QpZSbMZio/8/PaoLmESw70mgZv9qZlOa06jYTMuqk1sJCtF0GXKqTc5KCnHR17X08VsJmFvnmIRNCWUOc+o5+Q5u8jVGcSdkGIx2IUBKuONx8882orq5Gf38/Nm/ejMWLA/bv9evX45lnnuF/Ly0thSRJIf+tX79+5A98lFDoDFhsmfWYStyiIwiBtq7RxCT284wwZW75TlPQxGg0wDKcTIK8e5iniI4NvX54lYUny9wBgKuXluC0aTk4c2Yebj2tMjEHPcphO0KNcWp1P1TU5XZ1qsHvS2U3vEqxfjd3e7gjR0uPAQuy2oXg9Uu4/1+huQdMTHLbzZhdmIIHL5yJH583k/+u0QmIJEk4oIhGJ1ZkBi0c2QRJXVfPJhcezftjYpK29p8Ja8wBk8gyNy60Rfk+hXMmsQW2zy9xRxITaHJTIjtHwhEoadQ/Jk8MAdwerx+7lTyIQtUCh+2M1+l8J7SZT+oJaEff6MgqixfT81OwenouF98A+bxGanOdKARBwFVLS3He3AJDz59XnIZP//s0XLIwPt1zM1UB3GKErqksD6ZW40ziP0+xB3VqC4fFJOJ7a6ZgySC76bFsj8OqRgRa1GU06xsGeA6REeqVTaGZafJ1MtQyN9bJTetMYjBnkiAIqFIFZjO3drY92JmUYhX4azC3jVYnChKTlMyg9GEUkxZkWiFCLtHTK/fXgzm2AGBnq9JZSpOZxALID3R4dZtzRBKTmC7AGrp0eSV0KuLJBe+04JFdXfjxZ/I9tMDghmVVjjuoC6R2LMhwWWE1i0NusT4YYhWvOjX3/NkGXEh6sE2lvgE//rb1KHx+CWy/bX6JXHbNxtORciYVpjngtpsx4JN0c0vVsM3CbE3WHptD/P79Q9y9BYTfgAqHOjMp3aV/3EazM7UwMchIybPdYsJ7d6zEe3eskP+tzn7+HUiyBRqq1CrznBaV4GekXDBamVsgM4mcSbGScDGJGBrFyiC0o3WAO5OoxM0YrP1tc5SJmJ6YlG4NOJtKR2HYebErsNMnCgIy7CJMgty2t0mZOK7f34JdxzvgtJpw7QllMJtEPH75vIjhohOZCqV0Ydcg6v6Hg+DW14EBfp8SQLuoLB1mJQMn3K4ND+COMOnQTmC09fRAIBz71tMq8Y+bT8TahcUwiUJQiZwRGjv70dnvhSgA/3P1Anx2z+k4TdU5KT/FHjRBYh1RtDBxbEVVFi5ZUIizlewddp6Y+JKWQGdSYKcv8rkJNzG0mERepsfs22zHTpuTYZRoTjLmDoqUGeOwmvjEkeUAqe3r7Nhauj0hwexM5EtViXxswWWknGEsor6+8lMd48YVqre7PViSHWbML0nD1LzkiGJbfkogD0bPBXHmzLwR6f7HvuMNnf3cNajG55f4QijHCvT4gIvfbcb2FmNuiFpFDJmRxpxJ8Slz04pDDKc51EUEBDrcBsrclIWfOdBBdrfi6DkpJ3gcsatuZ+xnTBwbDlKsIi8n29Rg7DyrA7hZmZvWmVSSZIJJkIWgBp3PIVKZG+uu6zKLSFaCp7RRBNua5X9XG60QDkEQ8KPzpvO/p2pKuUVRULlDR77ULRa6FCHBYTHhO6sqcdH8wkG9js1s4mOb1nF06lR5fjFZ6cKozvwbDB0Gm2AIgsDdSdFK3diGk7a8+MSKQDXBl3WdfD7Hy9wssZe5hZsTBZzUsZ2X7hjEJEAuZy9IdUAU5PvkkRZZkHfaTNxB1azMHdSfpZF5ebQyNy6YjZIN47EEiUljnNNz5RvWv4/24eVqeWCYnkrhyUbIVBIg66NMxFiZm3q3ThACuUQj1cktFsoVQZFN6EyCgBzl+GuV97PhoBxGfMG8Al2BgAhmbnEqAODTI21o7x3AturWyL8wzKh3ZdQ7jPuV0NfKXDcvUQs3AWBuH2eEzKQMzXejqy90B5aJSdpuHLF2x9ivlLgVpTtht5hgt5h4W18gOC8JkCdsepMU5kwym0T8/KLZ+PapFQAC54mXgyU0Myn03EiShN3HO9Dv9aGtx4NfrdvHBTC98h62w/b5sTYAgR27PAPuCz3Y5xXOfcccbtYooZ7se8dccurSrRSHhecnaHOTAgHigc9lyw9W4fVvn8TF3PGG2pmkzYwgZARBwEs3LcW/v3ViRLFNr8xNndVxlirQfTjJctsgCLKTs0mnxXhrjwc+vwQBwBsLRSxKN6HfD7xSHf0+2eP1o0YJ4F6kdHVt7vPH1KVMS7dOu/upKtHIoRKTKlWPM/Epxx48l3KZBf4YO1ZtB1qb6nNcmGnF5rOycM+cyEHKQ2VJlvFSN0mS0KpyJjX0+dHY50OPL9iZZBUFlChCz36dkj6tM0m9KakO7WZRBExg0hJLlEJFths/OX8GZhel4qxZod95tig/noDcpM4+4w48FmY9pygV31412VCpVDjCORLPmJmHV791Ih68aBaAgAN98GVuxgK4gUDTmyOqmAA9mAs9yx18DV1zQhm+uH8Nzzhir9M3hDK3cDlxbL4SzUmtxu+X0K0IXLF8dmaTyOcizLXlsspNPJhAVtfeh2bVvXWXgdykgDNJ/z0aaTZD6ENi0hhnXpoJ01LN6PfLg3amTcT5JaPPJj8aYdbicIM3APgkiecJZGos2PlcTBp9ZYXLc224qcqFO2cFJmcshJtN+Bo65QGqPHN8LtLiDROT9jZ04ro/bcWFv/0I7+0NbW8eCZ9fwn+/shN//ODQkI8nvDNJHnwnZydFrXM3UuZmNolBgk6vZkcICJS5afNe2E6P0c4xLG+JBS0CCPq3SzSZCYIgBJVQMbTiGDsPHX1e9Hp8AQfMKAjgZkKfJEn44T924YxfvY9fvLUXv35nP37x1l7sUXJX9CaGJyg7k3e9vAPvftnAFwf5Q3Qm6YVjA+BdVcxRynoXa8qD1A4ydZe92vZgIa1NZ1c3M8nGd3DHI1mq3eZwTjtC/t5EcxWpv1fs/qhexMwuTNH9vXhjMYmBMPv20IUJW9SkOS1Iswg4u0Be3NREmIsw9iqCRZZdRIXbDAGy4zjWjnBqtGISAJxbHPheqh+vCnImyXOiXGfw3MhlFpDjCH4s1ykGCSnajpA5DlPEEsZ4sDRbPs8bDTiTen0SmJbEcqKqu3y8YYs6l7Q8Qgh3r3Ju2Tub5DbhBOU4zigKnGPWcbe218ejCNQYdSYxLl9cgn988wRdF0Y+zxAafgeGJElQf6yxlNbxEqc4BPkXhBHqk2xmzChI4ZtSmTqNLWKho1fJTDLQNZKV6UebH0XKeHRYTXyTrbpZnj/FKiY5rCbucg63sRwtT1GPHpXzONbOnjnKvZzNCZNsZjkXTyWEtnTFVuYWzZlE3dwGD4lJYxxBEHB1ReAmefNUF5wUvm0IJgZFmsC19vvBpmjaev4rK5xYlGnBGYWjT7yziAK+P8uNpdmBm2ZgsiJPVBo1rUaJyGS77ShMc0CSgK2HZVfSO1/Ux/Qa24+24tlN1fjZ61/Cq8r5aen24LKnNuFvHx81/FpqZxLLn+n1+HgHrcnZSVHr3HmZW5S2rX/+2iK8/92VvC0va/kOyB1XWJ6N1lnBhKBPj7bxfysSLC9pksqFMkMlJpVlhk4ImZikdsto34/bZuaP1XX0qcSkBAZwK+eysasfPr+ER97ci2c3VQMA/m/bMXyscb7pOZPuOmMqzpyZhwGfhEfe2sMn6oN1JrHJbXWYndIBnpkUeYxZPT0n6O/advc8+0C1M9434OeZGon8XEYa9cRWe56I2GCL5Jq2Xi5M/vdZU1GY5sDDF8+Oa+ldNFhWjV42GF/UKDvkhVHaywNAU58Pn7cMYG+7fK+tSjbDLAbyEI92DT6Eu9sbcBQxzikO3EPUQdQs9FtNuWZDzWUR+OYVI8MmoizBLu656fL5ru72Re3iy96zRQCmKm7/6i5fSGYSAFQoOaX7O0I/A+ZkYiWJCzOteHxpKn40Lxn3zQ2I5AFnkh8dA6FiUjybvOSnjlyZW7/XD7VprjbMRsUrn9aEbLKxvJ14dIVUO5NKMuT7rEtn3pM5xEYrAWdS9GNmc5djrZGdSe065d9qmCO8upk5k+TvtpEOagyWZajNmWToOamjwUrcRMF4yR0/nmQmJslzQqciRrG5w6Gmbu56AoBDzd383wsH+0yzogRwN3X1B83PieiMPksFETPnFDnwuz3dsJsEXFZOk1GjsDK14z3hbxrNqi4n2p201QV23daso5Vc1tGNOZOUG2u0Ns9EgLnFaUFCyialVNAonx+Trbgenx/HWuUFT26yHf/ZVYeNB5vR3N2PSxaED62VJAm3vfgZstw2tHYH7OJMRDjQ2AVJkq3KGUm2gDVZZzfJ6/PzQOVoYlKay4o0lxWFaQ7UdfThaEsPz7Bh1uosty3E4VShtECvaevFxoNNWFGZHdFdwF5L3bUly21DbrIddR19ut1c2AJ8eVUW3toti3v9A8HXtCAIyE2242BTNw43dfP3HW5yNhJkuKw8F+CypzZhy+HAd6m528NLvhh6u4xWs4jvrqnCv3fUYtfxDkiSPHHLCbPzFo1yZrtv7sGAzx8iGnkNBHADwDJVZ0ggdFcyN5mVIwUmpuz9mkRBd5I/XgkWk8iZNBTyVQHcXr/8XV1Qmo4PvnfKiB+LfO9t1xWTmDMpy2UF0IciVSMV2ckRen3dvKkdmxo9mKyIOcwhVOo243ivB4e6fJifGfJrhuDOJNWCT91khOX5AIBb9RxWWlemafriMgu8xJ6RaRNR7jbj4+bYOnrFk1SrgCSzgC6vhOM9flQkh1/gshK3VJuIEpcJHwGo7vaGZCYBwKRk+VzpOZPY89eWOfHwQgvK3WZYRAFXTgqeq+c6Ax132z2hc9LCGJ1JkchXdTcbbrRjsZ6YdLSlB//vhe0AgFOmZHOnDSuJM+LyiYa6y9331kyBJEG3dJqVjrd0y3lnsWasGQ3gBtRiUuTPIVrGIxPHqlu0ziTjAk5BmgO7azuilrk1d3vg8fphNWBa6FJ1cotVyGdiPNtgTFJiSZjoxTKSLCYBbrsFLd0eVDf3YFp+MvbVd2LX8Q58ZWYubGb59/q9Pj7HCOdMynDZ4LCY0Dvgw46adswtTovpmCcyZGEZBzjMAtatzsSrqzJGXVex0Uw+F5PC7+gxMSnTPvYXNyFiUierwyZnklHmakKA99R38vakRthxLFDX/drOWpz/xIf42jNbef7SkZYe3Y4wjOrmHvz9kxr87r2DQRNBtmBh9eVsksQmAHrZCGoLcqQyNzWsjE09+WFOqCKdhbAgCFg5JQsA8Iu39mLO/W9GLPFj70kdCAkAt6+uwhkzc3HS5KyQ37lgXgEWlKThhpPL+WN6Czi268RyfKwmMaqINpyYTSKfHG453AKzKODH583g7rcBTXBruJboRWlOuKwmvvub7bZHDMiORG6yHQ6LCV6/xEsX1bBjiuZMsprFiLui+TrOpLZexcrvsIyogyTRZKvuv9oyUSI2WJlb74CPf1fTElTKylyc9cri+c1ddfj5G19iwOfnziTmgihQhJdOr6TrSgECOT/7lDI3LiYpbp/DXYPvdqgnkADAW6szccUkB26eGrzo/uEcN+akW3BZmfx9TbMKSFEJTklmEdlaZ5LdhFnpic3zFASBizLHopQUtvUHQsmLlXN8oMPHS9+CQ8nl97Wt2YMGTae4XtW5rUqxwBJGnGAOs9peX1AXOUaBM37LNb0y4+GiV9NkQa+E+pmPDvM/MycKEAjgjrVESo9ClTNpen4yzpyVh6rc0IwuJqb4Jeh2hIwGc2kb6RRbkMpa3fdFdMKwjMeUMPcyVv4fcCYpHWBjcCZdf1I5zpqVhzNn5en+PM1p4ZtIRgOqYw3fVsPEpG5PcK4nK+HfWSPPpdNdVj73PNragxuf/Rin/XID/t8L2/En1feK5U5ZTELYz8YkClgzIxcA8OK2YzEf80SGxKRxgiAIw15vPt5gYlJtjw/+MAt41iUlYxhb1o4UuXyy4keXV+JiQjaJSYZZXJ4OQN69YuGJWw4FHCW17b0Ra7c/rwmISX/dfASSBOyu7cC7XzYAkO3JkWr11ZO/TpWlt769H5Ik8U5uTEyqynHzY9SKVKzszCQKUQOVGXq27HDh24xTpmQDAHbWdKCjz4sn1h8I+/rs/akDIQHgovmFeOLy+bqi16zCVLz0jWVYWJrOH9ObsLLF3Z46+fNJcSZetFhemQVBAE6anIkXblyCK5aUhN0NC5d/IIoCpqgyhbRCXCyIosB3hQ9p2ppLkoQBxe0RLTMJAP5w9QKIAnDT8kkhP+MlQKrPiZUehpswj1dcNjMK0xxwWk28pTwxOOwWU9DOejRRczjRlrnd8Ow2PLH+AP74waFAmZtyrA6zwDMZj+qUuumVZFUpAkaZUmJ2qHPwZW5dA/pi0uRkM348LyWkxP9rk1145dQMpCmPC4IQ5E5yapxJImRX0CVlDpxdZMd9c4c3aDsSzJGuV1J4rNuH9XXyZ8OcSWlWgediblYEPasYfK5mpJoxL8OCPh/w2O7gNu+szM1hjnzP5Jt9Pf6gskJA7h4cz/iKAlU56HCjFZO0AlZn3wBe2Boo71ePOywzKR5lbmXKuJbmtERsdGAxiVzkjVVs8/r83I1jJIA7222DxSTA55dQH2Hep9flVE2Jpsytl3WANRu/9y0qS8dvvjovbLdMQRC4oy2ak4qhdibFSq7mOJJ4mZt8DMzln+GycXf61kMt+M+uQPTEp0fa4PNLaO8ZUIVv2yLO+1hlwL+2HzcUzUDIjP0VMkEMkhyHCBHAgBTo2KaFOZPGh5jEArglsPxJl9U0pA4ZE43p+Sn44zUL8NfrF3OXzMaDzfznF/12I8741fu6glJXvzdo1009IKuFoUidPeo69Adxj8+Plm4P9tYHwrcBOaDZZhZxrLWXB3MzmB3bZTUZFlUK+Q5Q4DjYBCacq2JpeWaQq6a7P7QbHAD0eLzchjxYQWStMhH4r5WhAgabJDFnUqIcC2oeuWQ2vrh/DZ79+mLML5HFsHlK0LuWSJb1qXmBxZlWiIsVVup2sDFYTPL5Je5+MiI+LqvIxMd3n4bbT68M+RkP0VQFwLYbbKk8Hvn7fy3D698+aUK+93jDSj6AxLrcclTND/yqQOW/f1LDszvUXYUKIjhmajSl+AKAyUppVal7+JxJsaDOQ0qyBLq5AXLepEkQYBUF/HpJKq6uCC1XHimYM0kvK/O2rW245v1WPHugB62KoJNqFVGs/E6TMh+ck24Jij0QBAHfmynfg58/1Itq1WfBzq0zigDPnfI6ZW7xLHEDAgvyzj5vTN3VBkNfiDMpeA7zn131XHQAgsWkDi4mDf2+WJrpwiMXz8Zvr5gftXSNiRPRsoy0MPELMCaAiaLAhb1jEeZ9rMwtXJYg2wCqaevFgM/Pz7nDGt91S3mm/twgHN39sXdyY+RqmogwF/lkTWliRpKVz0u1DXG+rOvEY2/vxez738S/d9QCCJQxhmNxWTqK053o7PfiNeV3iOiM/RUyQQwSixjoOBIuhJuJTFn2sX+p8ADuPj8Xk7LD7EIQ4TllSg4qc9xYVCYv/lmJms8v8Z2+v38SapHdVdMOI92bmTijh3anzGU1IUPZ3f7r5iNYpwSCs+Brh9WEZZPkzlrrvmgI+l3WsSLcTpQeRTqTrL31wW4oLQ6rCd85rRInKp3Hegd8uhlO7L0l2cyDzkj40Xkz8O9bTsSVS0pCfsbeJ+uOlupIfMizIAghjqN5KmcS200FwGv/9VB3O8sbZCc3xiQ2YWwKFh/VHYaMltGlu6y6z9ULJ44WMjqeyXbbQzoVEoPjW6dU8D8nsisP21lXd5YD5PvPF7XyZkOWSkwqjOKYUVPsMnGnCnMmHe7yRSyRjkSXTgB3rKidSS6zgFSrALaWHU2bccyZVKNznjc3KqHtn3SgWZn7pdkCZW6MBZmhY8fiLCuWZVvhk4A3awLfu16dwG49mJOrc0Di89GTcqw4o9CGb06Nr2NRHmPlz2u4S920zqRPj7YFlegfVsQjJhYcbg4IFV398ucRj25uAHDh/EIs0XQa1YO5rCNt7OnBwredVlPUUnBGQLjS3yj0+yVe5hZuAyzbbYPdIspz0NZe9HmVzKQYnElGKFecs+pN0UgEytxiP46qHHfQJiQTpKbnp8CkEgMzXAExiW2Yzi+R51CHm7vxgtLU5u+f1AAIFvD1EEUBlywoBAD87+bqmI97ojJ67vAEkQDyo4RwjydnUrayU+jxA/u65QkO5SUNnkqlhKy6Wc45Ui8YdqjK2RjMlhutbXtEZ5Jm4pfmsnKR5JG39sIvAZcuLOKDKQCcMlXurPXOl8Gd59hrxSImqSc+fr8Ev1/Cl8rCKFL79puWT8JfrlvMSwP31nfC4/Xjxmc/xjVPb0FTVz9qFZeKdkcqFqxmEdPzU3TdCExkYeut0VpONTUvGZlJVrisJpw3p4A/HtmZpC5zG6oziU0Yg3cf1UGq0QK4o8HCUFu6PXwC3h5l95UgjHDKlBwsLJXvfwtK9EtGRwJ2H6tv7wsRtZg7MlNVklfkCi8msVzHFIsAkwCsyLMF/Z4IOUS7sX9wHYji4kxyBxaMLrMAQQh0dMsYRZtx4TKT+jUZdX/eL9//Uq0iki0i0qyBc7NQR0wCgJW58ufC8q0A42VuSRYRbuU5Xyod+4pcJjyxNG1YGr3kj1CpW59SKlSW6cK0vGS09Qzghmc/Rr8ieDAxi216HW4KzH/iWeYWC4MWk3qVErcYNsOYM+loaw96PKHuwi6PF2wfJ1zpnCAIKEmX51aHm7v5WB2uNH6wBFzLxsQkXuZmjf3zS3NZcc2yUv53JjY6rCY+9waAdFWZG2NhaTqy3DZIUqD5DC8tNrDmuWRhESwmAZ8caePZTERkRs8dniASQLQQbrY7lTmKJkODxWYSuCj2mTyXJTFpCBSlOyAI8oDZ1OUJyjr69EhbUJvSnTXt+M27+wEAF8wr5I8XpDpQqpRlVObIi3i94GOGdhcx3WXFbFUo+JrpufjReTOCxBSWWbStupW7PwCgvjN2MSkv1Q6n1QSP14/v/G07DjZ1odvjg9Uscgt0JNgkYG99J37//kH8Z1c91u9pxNrfbcTnNW3yvzFEZ004puUHi12jocxND6tZxEs3LcPL3zwBswpT+OORnElTct1gH3k0sTIaZWEyk5q65e93ks0c8ViMkGy3cOfGPqU0kwVwU6kXMVSeuXYRvrOqEvecPT1hx8DEpM5+L3dfaFGLSZGCoZlT5dxiBzaflYW7ZwcWUzaTwOcxhweZm6TXzS1WmEMKAFyKo4C5bUbTZlw4Z1JDX/DfW3iZm3xjLVbenwBgXob+PWpJttJQodHDnZx9BsvcgEBHNyYmpca5TEmNuvPhcMJcMm67Gb+7cj5SnRbsrOnAB/uaAARK95ljqKatl5dp8fyhhIlJsQltbGMk2WH8eJmr5tG392HOfW8FNWkBgDala6/DYoooDlUqYeLv72viWT+xdHMzwqQwG03hGEoANxCct6jOBJ5TFJgXZSRZUZQevIE2KcuFKTrh6kD0MjdAdgp/ZYYcRP7sRnInGWH03OEJIgGwSVi4MrfGceRMAgKTux2d8gSHwrcHj81s4tkv1c3dQWKSx+fHJiVLqW/Ah2ue3oL23gHMLU7FjcvL+XmfVZiCBy+chZuWT8J/rZDLM6pjcSY5rbjvnOl49VsnYvNdp+LJK+eH2KsLUh0oSnfAr4R9Mxo6WJmb8e+AxSTiJ+fPgFkU8I/tx/Ht57cDkIUwI6VPrN593RcNeGzdPgCA22bGgcZuPPa2/PehZv6EozjdGfR9H80OmNJMFypz3JiuEsDCdXMD5E4n7LmTc/QnUUZhu4+Nnf1BeRpNmg5UQ4VNflmZJA/gJjGJGCIumxnfXjUZM1Vi7EiTZDPzRRRzqp40ORM/v3AWFpamYdXUbFRmB3bUCyM4k9j8pMBlQqbdFNIRrGwIuUkev8Q7lMXLmcTWfcwNPZrmT+w8N/T5g9xItT2BuZ5DJfykKYIOy02qSjEjJYzIMy3VDLdFQKdXwu42ORuQOZOilbkBgY5urGPf8IpJSqfXYXYm9XoCLpmidCfPmmSlUmyDbFp+MheNWKl/J+/mNrJjAst/jLSxp0fHIHL/ClVCiMfnx1PvHwz6Oe9yGmXz6/y5+QCA//vkGBe1hsuZdKy1JyQLSw8uJg1SDExzWfHkFfNx6pRsnDsnnz8+uzCV/znDZeVd8QLHmRTWKW90A/2qpXJUwivba3iZIRGe0XOHJ4gEUGjQmTSabNpDIU+xne9RNhay3ZSZNBRKM+VB7HBzT0gXNhYG+EVtB5q6PEhzWvDnry2C227hOzwzC1OwuDwD3//KFP5Y5ADuUGeS1SxiRkFKRIfRlFx5YP2yLiAm1XfE7kwCgPPnFuKn588EAOxSgsan5oYvcVPDhI6NB5vh8fpx0uRM/Pj8GQCAfiW3YyjdyCIhCAIWlgU6vo0F0SI72Y6/XrcYL960NGpo6FNXLsBLNy0Nm11lFLfdwkW3PUo5DgBVaHB8BOhK5TjZvzGRA7iJ8QkT6lmJc7bbjksWFuHFm5bhf65eCLMYmFewuciRLh/6NCVXzEUTrj18Kevo1hW7M4mVuAFDE5NcZhGn5dswOdmMqhT5eGany9fy9LTR0+Qj3SrAbgIkALW9gfPF/jw52YxzigNjUKoihE1Nld/DsuzwYrpJELBYKYHb1OiBxw+wjzJamRsAlLuDz1OqdWjlxJFgzqRhF5NYGLQibKidr5Ik8Q2y/BSH6mddkCSJb2aMeJlbRiAb0uc3nkPGhLFYxshSTVbe6ztq0aCa5/FOblE2v5ZXZiMvxY62ngG8vrMOQPzFpKwkG9x2M/xS5GxPRtcQArgZa2bk4g/XLAx6/2o3fprLCofVFLTJNVRnEiDnLi2blIGrlpbE9B2YqIyPFTJBDJICl3wJ7O8I3dEb8EtoVKzPWaNoZ20o5DiC3wc5k4YGmwhUN3ejUWk96lJquzcfbAEAXnM9qzCVdyW5+ZQKnDEzF5cuLOavxSYwjZ39urXzHq+ftzd1K4Ozug12JKYqA+uXtQFxoI6LSbF/B86Zk8+PAYicl6RGXetuM4v48XkzsGxSZtBzhsuZBACLSgNiUtoodiapWVaRiYWq4w5HfqoDCww8zwhzlY5yWw+38sfUrXXjAXMm7WvohN8vcfdGrOImQYxWWLkMa9KQHeFeW5pkQoFTRI9PwnMHgxdqzJnEnNR6vwsAhztjdyaxEjeriBDHU6z8/oQ0vHl6BqzK61xf6cSGr2TiopLhu6fHiiAIXLhTl7rVKuc4zyHi8vKA04HlGH1tsgs/X5CMW6dHFutZqdumRg96VaKgkTK3ypTgRXc4B1Q84B0124c5M2kguORK3RGso8+LHqUkKzfFrhKTetDv9WNAOX/xCuA2Sm6yHRaTgAGfFLKBFwm2EVicod/ZVo85Ram4+8yp+N/rFmN+SRq8fgl/3XKE/5x3couyyWISBaxdWBT0WLzFJEEQeKaiNjdpwOfHjmPtQU0AhlrmFg51Rzer4tguUHKTMlxWpDqtfE5qFoWg0n+jziRBEPC/1y3GD86chow4zXnGM+NjhUwQg2R+hhVWETjY5cOedvmmvbN1AG8c68M7tf3o9wOZNjHsJG6soX0fkSa3RHSYmKR2Jp0xU6613lPfidZuD3bWyO4d1mENAE6oyMQTl88PEoNSHBbuytBzJzV09kGS5Lbsc5TFvlExaUpeqDOJlbkNpqOf3WLi7xMwLiaVZbp4S+Vvr5qMkgwXstw2nhcFDC2AOxoLSgOBvCO92zmWYOLV1sMt/DEuJrnjVOamCIt76rqw6WAzqpt7kGQzY+WUrLi8PkEkmirFscncGZE2b8yiwLt2/fbLbvxuTze2t3gw4JdQ3yu7NgvDzENYidlgnEndA0Pv5KZGndcnCAKKk8y6DRESSYFSsnZUJSbVKec412nCrHQLTs2zIcMmYkaaPCbbTQIuKXMiKUoODctT+rxlgLu+rCL4uBeJymStM2n4M5OOD3dmUhhn0sGmbu5KSnNaYLeYuFCxv6GLl7gJApA0iADnoWASBR7qfCSCA8fnl4JKwauVTnQsDNsIgiDgupPKcUJFJq5WAqf/+MEhNCvjLSuxilbmBgBfXVQclC8V78wkALyJiraj249f3Y2zf/MBfvPOfv5Yl4cFcMd3/WQ2ibj7zKn4yoxc3iWYZU8xh/+UXDfuWF2FBy+cFZSXGctm2Gi7b41mSEwiJjQpVhHLlQ4c/zraB69fwjXvt+KmjW344SfywvuCEruhicBY4JxiO1IsgfdCAdxDo0TZgapu7uYde6py3XznZOvhFuw8LjsuZhREF1zYROtAQ3DAYXvvAHYrJWU5KTacP7cA+Sl2nDQ5M+Q19GCW3z31nfApXdgalADu3EE6Qc6fF+g0NjXPWE6P1SzirjOm4qqlJbj+pHL+uNqdlD9MZW5AoNwPCA50JIJZVBYQk5jFO97OJHaNNHX14/H18gT03Dn5cI7wwoEghgttqUW0svKLSh0ocIpo6PPjgc87cd0HbTje44MfsiARrhFIiVLmVt3lC3IGGIGHb0fIZBtvVCjlZDvbAkIAK3NjuUW/W5aKLWdnxewOmpJihgCgqd/PxSqHwe6Xk0PEpOEsc5O/i7XtcmfW4YKFQTsUQaFMlcm3r0F2SucqLqkq5Xr5oraDh28nWc1RS7yHAyO5ST96dTfm3B8IzWZ5lyUxOJPUnDkzD9PyktHR58XDb+4BoC5ziy4mZSfb8dhlc/nfnXEWcQDwMvo3d9fD65MF2IbOPjy35SgA4Nfv7ucNB5gzaShlbuG47qRy/PaKQEZoifJ5VSgbk4Ig4JsrK3Dh/EIuUgJyqR4RfybO6EEQYTi7SB5U/3WkD1uaPGhSQrfrlbyki0pHj0V7qBS5zPjjInkwd1pMIS01idgoVdX/szr3LLeNL8bf39fEA4ZnFEQPg63KCYg+DJ9fwhmPvY8bnt0GAMhLduCCeYX46M5TMUsVRBiJkgwX7BYRfQN+VDd3o6XHwy3kgxUUF5Wm44aTy3HH6qqYwqy/dmIZ7j93RlBQOGsLDAB5w1jmZhIF3HfOdKyamoNTp2YP278z1pmWlwyX1YTOPi/PNGrsjG9mkstm5m2RP9wvh9Vftqg40q8QxJiiSismRXECW0UBP52fgiVZVthNsiDx6lElU8ZpCiuAF7lMEAH0+iQ+bzFKQEyaOOL6QiXXaGtjQEyqUwK48xT3l1kUYBrEhoPTLPKyw0+aPcpjxl4nxSoiWyUYDqczKSfZDlEABnwSL9EfDlg3N1ZylWy38DFk4wH5vs86uE5THM77GjrR0i2fu5EucWMUK8HYh5v1O5f1e3148eOj8PklvLm7Dj6/hGNK9zdW3horJlHAfefKHSif33oUBxu7DGcmMVZWZeOhi2bhzJl5WFpubLMxFs6fWwC3zYzPj7XjdxvksPBnPjwMjyIsebx+3PevXQCGr8xNjyuWlOCaZaW4QbVJyWCbtFaTGFOnPcI4JCYRE55T822wm4Dqbh8e/DzYujk7zYLKlPEVCDs/3Yz1i0S8/PV5I3KTH8+wSUNnnxf7G+TvjlpMenZTNQZ8ElKdFr5wjgTvcKUKPm7o7EONKiTTaYt9t8kkClyo+rKuk4dvZyZZQ7q/GUUUBdx1xlR8c2XFoH5fzdJJGShIdWBuceqw7GKpuXpZKf7n6gVxzxMYT5hNIuaVyCWBrNSNOZPi6WZUOzdOmZJtSHAliLHCpKykIFezkYzC5bk2PL8iHSfnyM/9zRfyYnZBZvjFpFUUeJeyQzHmJnExyTJxxKT5mfKcbk+HF+1Kt7GAM2no48IUJftoW7PS1j0GoU4dRTCcmUkWk8jnL2zuMhyou7kxWG4SE5NYaXthmgNuuxkDPgnbj7YBSFw5+rQ8eSx6cduxoFI2xkcHmtGtuK4+PdKGuo4+eHx+mEWBi2ODYWFpOpaWZ0CSgA8PNAe6ucXQmOLiBUV4/PJ53A0WT/JSHLjnHFnwevTtvTjW2oO/bKoGAHx3TRUAYMO+JvR7fbxUcbjndIBctnnvOdP5Bq8a5qbKSbFR6dowQWISMeFxmUXuPvqsVR407pvrxrnFdvxwztDabI9WSp0CqobY9YmQJ0hs4tCs7KRlq8Qkxoz8FEODGBN89qqcSdpMA6P5RFpYidfmg82BvKRR0s3Pbbdg3W3L8eKNSxN9KIQCCyt/fWctgPiXuQHAFUtLMKcoFT85fwaeunJ+3F6XIEYDVrPI22kDsd1vT1bK71mI8wUlkX+XuWGqY8xNmojOpCy7CeXK+fq4yQOPX0Kj4ujKdQx9WTQ1VV74f6qISUbCtxlqZ5I9ht8bDKy76j7VfCPeaLu5AcG5SQCQp5TaC4LAO8NuOSQLTaxpyUhz4fwClGW60NjZj0ff3hfy8zd31fM/bz/axku7CtMcMA9yg47B5o+fVLfGVOY2Ulw4rwDT85Mx4JPw36/sREefFwWpDtx48iS47Wb4/BIONnaj2zNyYlIk5hen4ZZTJ+M+RQQj4g+JSQQB4I4Zbj6IJ5kFrC1z4rHFqZgfYTeQIABgXnFa0N+zkuzIS3HggrmBTKFlFRnaX9OlMlcW+A43d/PgSta6N9ttw40nl+PqpaWDOk5W1vXspmq8sr0GwPCGXceK3WIa8iSMiB/nzyuAxSRg08EWfHSgKWMRSfgAAB7fSURBVOBMiqOYtLIqG6988wRcvriEPntiXMJCuN02c0xOgeW5gblHvkPEkqzIc5EyJQco1hDuiSgmAcBC5XxuaRpAQ68fEuRcqvQ4dO6dmip/Fs1KZILRMjcAyI6DmGUUllu3dxidSf16YlJWsHtEPQ9h+YtbDsmO2ES5521mE+45exoA4A8fHML/e/5TPP7ufnx0oAl+v4S3vwiISV39Xqz7ogEAUJxhPHw7HPMVV/C26lYewJ3iGD1rEUEQsGZ6LgDg3T2NAIAzZubCJAq8scbe+k5098uffaIrIERRwK2nVeKUKTkJPY7xDM3eCAKynfiB+ckwC8CFpY5h3xEixg9nzgp0NbOYBF6T/Yu1c/Dx3avwtxuX4roTQ+u49chKsiHdZYVfCljPa5XWvUsnZeDOM6YOWgA6fXouLllQCL8E/GP7cQBADnXzI8JQmObkGUb3/2s3+pSuT/Hq5kYQEwFWypkV4722yGXm7pnzSxxRGwYwZ9LhLtkNMOCX4PVL8EkSfrmrE2/W6HftYt3cYhE8xgMLlFK3rU0eXuKW4wifSxULrMyNEUu4OXPJa8O4hwO28N9fP4xlbgMsMylwDhZrnNvqnETWeatVceQksuvqiqps3HJKBQQBeGX7cTz0nz24+o9b8Ojbe9HY2Q+3zcyFn39+Jm/QlQwyL0nNnOJUCILc1XefMg9MG0XOJABYPSM36O+su28ld7t18RB11yCiGYixBYlJBKFwar4d287Jxj3jtLSNGB5WVgWCnAd8UlA5W2aSXPJmNTiZFAQBlUo3Clbqxsrc8g1kLkXjvnNm8PIlAIZynIiJyzdXVsBqFvGlkuHltJqo2xpBxMBC5X6r7exmhO/NcuP0fBuunRx9gVqqdHQ73OlDu8ePE//diEvXt2Dd8X48trsb39rUhvreUNcSa1+fNIG6uQHAggxZFN/ZOoAvlK5uTJAbKoXO4Ne5YpLxcXZOuhWvn5aBl1amR3/yEKngzqTOmLsAGiUgJgXOydziNPzxmgWYlOVChsuK6arW7doy/kSVuTFuPb0KL964FBfPL8S0PLm061fvyN1Hrz+5nJekNXXJDqLBdnJTk2y38MgDljuU7hpdmziTs5N4uWJ+ih1zilIBgM9fd9d2wOOVhepEO5OI4Yc+YYJQMZyBh8T4xGE1wWk1occTW3lBOKpy3Nh0sIV3dGNlbvlxKElzWE144cYl2HSwBVsPt+Cri0uG/JrE+CUn2Y5VU7Px2o46APHNSyKIicCisnT88+YT+MIrFlYX2LG6wNh9v8wdcCa9ebwP9X1+1Pf50bNbdjb0++Uw7x/NC16sd03AAG4AKEkywW0W0OmV8M8j8obNtNT4CBeCIOBrk51441gfHl6UgmXZsd03p8bpOKJRkZ0EQZDbzzd1eeLaXIHByvW1JZ6nTMnByqpseP1SUBOQyhw3MlxWnkGZSGcSY0FpOhaUpqOuvQ+n/eI9dPZ7MSXXjZuWT8J7exuDnlsShzI3AJhbnMo3cZZNyuDC32hBEAScMzsfj63bh3PmFPBNVOZMYgHqQOIzk4jhh1bOBEEQQ+QPVy+EWRRww8nGytkiwTI2Pj/aDgA4rpS5xcOZBMiTgKWTMnDLqZNH3W4XMfo4c2Y+/3NmEn1fCCJWZhWmDrvDoshlQoZNRL8feGRnoGxpd1ugu9vzB3twrDt406NngmYmiYKAaWnyIvdjJSh7Wmr8Fr0/nJOMD8/MillIGknsFhPv6LavYXhCuHuVMkq7OdT1JQhCSDdZu8WEP31tEQrT5PlOaZzEmXiQm2LHQxfPwvySNDx66RxYzSJWVmXhllMqcPq0HFw4rxAnV2bG5d9aoTjep+Yl48kr54/KLmQ3n1KBP16zALeeVskfm6w4k1oUMdBmFgfdMZgYO5BcSBAEMUSWTsrAx3evisuCYXG5bJveVt2KHo8XtUqZmzpXgCBGipVTsvifa9v1c1cIgkgsJkHAecV2/GFfD+p6/UE/S7YIqEoxY2vTAP59rA83VgUW6F0TVEwCgOmpFmxuHAj6ezwZjQKAlsnZblQ392BffReWTYqPEKKmz6PvTIrEjIIUrLttOb6s7cTMgpS4H9NQWDMjD2tmBHIyzSYRt55eFfd/5/RpOfi/byzDtLzkmM7dSGIxiSGh1llJNqQ6LbwLHZW4TQxILiQIgogDqU4rTOLQJ4/lmS4UpDrg8fmxYW8jt3tTvhGRCJxWMw9PzR6GMgiCIOIDC28GgDSrgGSldO2UPBvOLJTL5dbX9gf9zkR1JgHAjLSAeGQ3BUoFJxJVSgfZHTXtw/L6fd7QzCQj2MwmzC5KhRiHOdVYRBAEzC9JG7VCUjjk3M9APlzSKChTJIYfEpMIgiBGEYIgcKv081uPAgBcVhPvEkcQI81rt5yENdNz8eBFsxJ9KARBhGFqqgXTlVKtlXk2XFXhhAhgbZkDK/JkIfjjJg86BwLOpS7lz7F0HBsvzFCVtVWlWGAaA06ieLO4LAMA8NH+pmEJ4e71hHZzI8Y3N5xUjhkFyZiS645L9AMx+qHVCUEQxCjj5MlZeG7LUazfI4c75qU6xoRlnhiflGcl4ckr5yf6MAiCiML3Zrrx4I5OfL3ShakpZnxjiosLRaVJJhzu8uHDBg/WKMHezJnknIDOpHK3GXYT0OeLb17SWGJhaTqsJhHH2/twuLlnUEHxkWDd3BwxOpOIscuqaTlYNS0n+hOJcQNJxQRBEKOMZRWZULu78+LQyY0gCIIY35yca8O/T8vE9FQLREEIchytyJXdSepSt25FTEqaYN3cAMAsCryDW7zzksYKDqsJ80pSAQAf7G+K++v3K863sVauRRCEcUhMIgiCGGWkOCy4fXUg1LE8zruFBEEQxMTi1HxZTHqjpg99PllE6prAziQA+MEsN66pcOLCkombSXhihVxW/+G++IpJXp8fHl/4bm4EQYwPJqavkyAIYpTzXysqcO6cArzzRX1Q9xCCIAiCiJVl2VbkO0Qc7/Xj9WN9mJlmQWOfHyYByHdMzMX+/Ewr5mdaE30YCeWEikw8/OZefHSgCV6fH+Y4tXLv8wayuciZRBDjF3ImEQRBjFIKUh24cmkpsqiLFkEQBDEETIKAteVOAMBzB3vw0uFeAMDKXBvSbLQcmKjMLEhBqtOCjj4vPq5ujdvr9il5SQBgm4AB7wQxUaCrmyAIgiAIgiDGOWvLHBABbGkawF8O9AAALiqbuCVeBGA2iThlSjYA4K3d9XF7XXUnN2ogQhDjFxKTCIIgCIIgCGKck+sw4coK2Z3U5ZWQZhVwSh45Xyc6pyvdt97aXQ9JkoJ+1tXvxf6GrphfkzmT7NTJjSDGNSQmEQRBEARBEMQE4N45bvx4XjIKnCJune6GVSTXyETn5Mos2MwijrT0YE99Z9DP7njxM5z2y/fwYYzd3mra5DLKbCrTJ4hxDYlJBEEQBEEQBDEBEAQBV0xy4sMzs7lLiZjYOK1m3tXtha1H+eP9Xh/e+bIBkgQ8/eGhmF7zUFM3AKA8Myl+B0oQxKiDxCSCIAiCIAiCIIgJytXLSgEAf9lUjW3VrdhZ045Pj7ShX+nK9s6XDTiuuI2MwMSksixX3I+VIIjRgznRB0AQBEEQBEEQBEEkhpMrs7CiKgvr9zTiwt9+BACoynHzn/sl4PktR3Dr6VWGXu9goyImZZKYRBDjGXImEQRBEARBEARBTGB+cMZU2MyBpSHLT2IlcM98dBit3R5Dr8WcSZPImUQQ4xoSkwiCIAiCIAiCICYwk3PcWHfbcrx7+woUpDr44/eeMx1Tct3o6PPisXX7dH/3P7vqMOf+N/GP7TXo9fh4AHcZZSYRxLiGxCSCIAiCIAiCIIgJTmGaE2WZLnzntEoAQEGqA5OyXPjvs6YBkDOVdhxrD/m957YcQVvPAO78+w68t7cRAJDisCDNaRm5gycIYsShzCSCIAiCIAiCIAgCAHDB3AL4/H5U5SZDEAScUJGJr8zIxes763DTX7bhnzefgIwkGwBgwOfHlkMtAIAejw83/WUbAKA8ywVBEBL2HgiCGH7ImUQQBEEQBEEQBEEAAERRwNqFxZhTlMof+9mFs1CW6UJNWy+ufnoLmrr6AQCfH2tHj8cHt90Mtz3gUyhKc470YRMEMcKQmEQQBEEQBEEQBEGEJcVhwe+unI90lxU7azpw/hMfYvPBZmw80ARADup++pqF/PlVue5wL0UQxDhhVIhJjz/+OEpLS2G327F48WJs2bIl4vNffPFFTJkyBXa7HTNnzsRrr702QkdKEARBEARBEAQx8ajMceP/vrEMRekOHG3pxdqnNuHhN/cCAJZOysCC0nS8/F/LcOWSEqxdWJTgoyUIYrhJuJj0wgsv4NZbb8U999yDTz75BLNnz8bq1avR0NCg+/yPPvoIl112Gb7+9a/j008/xXnnnYfzzjsPO3fuHOEjJwiCIAiCIAiCmDiUZbrw6rdOwmWLgsWipeUZAIC5xWn40XkzkKlkKhEEMX4RJEmSEnkAixcvxsKFC/Gb3/wGAOD3+1FUVIRvfetb+P73vx/y/LVr16K7uxuvvvoqf2zJkiWYM2cOnnzyyZDn9/f3o7+/n/+9o6MDRUVFaG9vR3Jy8jC8oxGipwfYsAFwuwG7PdFHQ4wl+vqAzk7g5JMBJ9WzEwRBEMSIQ/M4YqiMgvncnrpO/HnjYWQm2fD/Vk2mwG2CGCV0dHQgJSVl2DWPhDqTPB4Ptm3bhlWrVvHHRFHEqlWrsHHjRt3f2bhxY9DzAWD16tVhn//AAw8gJSWF/1dURJZLgiAIgiAIgiCIoVCV68ZPzp+J75xWSUISQUxAEiomNTU1wefzIScnJ+jxnJwc1NXV6f5OXV1dTM+/88470d7ezv87evRofA6eIAiCIAiCIAiCIAhiAmKO/pSxjc1mg81GNbsEQRAEQRAEQRAEQRDxIKHOpMzMTJhMJtTX1wc9Xl9fj9zcXN3fyc3Njen5BEEQBEEQBEEQBEEQRPxIqJhktVoxf/58rFu3jj/m9/uxbt06LF26VPd3li5dGvR8AHjrrbfCPp8gCIIgCIIgCIIgCIKIHwkvc7v11ltx9dVXY8GCBVi0aBEeffRRdHd349prrwUAXHXVVSgoKMADDzwAAPj2t7+N5cuX45FHHsGZZ56J559/Hh9//DGeeuqpRL4NgiAIgiAIgiAIgiCICUHCxaS1a9eisbERP/zhD1FXV4c5c+bgjTfe4CHbR44cgSgGDFTLli3DX//6V9x999246667MHnyZLzyyiuYMWNGot4CQRAEQRAEQRAEQRDEhEGQJElK9EGMJB0dHUhJSUF7ezuSk5MTfTiDp6cH2LABcLsBuz3RR0OMJfr6gM5O4OSTAacz0UdDEARBEBMPmscRQ4XmcwRBhGGkNI+EZiYRBEEQBEEQBEEQBEEQYwsSkwiCIAiCIAiCIAiCIAjDkJhEEARBEARBEARBEARBGIbEJIIgCIIgCIIgCIIgCMIwCe/mNtKwvPGOjo4EH8kQ6ekBuruBgQHAZkv00RBjif5+wOMBOjoArzfRR0MQBEEQEw+axxFDheZzBEGEgWkdw91rbcKJSZ2dnQCAoqKiBB8JQRAEQRAEQRAEQRBE/Ons7ERKSsqwvb4gDbdcNcrw+/04fvw43G43BEFI9OGMKB0dHSgqKsLRo0eHtUUgQYx16FohiOjQdUIQxqBrhSCMQdcKQUTHyHUiSRI6OzuRn58PURy+ZKMJ50wSRRGFhYWJPoyEkpycTDdogjAAXSsEER26TgjCGHStEIQx6FohiOhEu06G05HEoABugiAIgiAIgiAIgiAIwjAkJhEEQRAEQRAEQRAEQRCGITFpAmGz2XDPPffARl1DCCIidK0QRHToOiEIY9C1QhDGoGuFIKIzmq6TCRfATRAEQRAEQRAEQRAEQQweciYRBEEQBEEQBEEQBEEQhiExiSAIgiAIgiAIgiAIgjAMiUkEQRAEQRAEQRAEQRCEYUhMIgiCIAiCIAiCIAiCIAxDYtIoZMOGDTj77LORn58PQRDwyiuvRHz+Bx98gBNOOAEZGRlwOByYMmUKfvnLX8b8mvfeey+mTJkCl8uFtLQ0rFq1Cps3bw56TktLCy6//HIkJycjNTUVX//619HV1TXUt0wQg2I0XyulpaUQBCHov5/97GdDfcsEMSgSda2ouemmmyAIAh599NGgx2lcIUYLo/k6oTGFGE0k6lq55pprQq6DNWvWBD2HxhRiNDGar5V4jCskJo1Curu7MXv2bDz++OOGnu9yuXDzzTdjw4YN+OKLL3D33Xfj7rvvxlNPPRXTa1ZWVuI3v/kNduzYgQ8++AClpaU4/fTT0djYyJ9z+eWXY9euXXjrrbfw6quvYsOGDbjhhhsG/2YJYgiM5msFAO6//37U1tby/771rW8N7o0SxBBJ1LXCePnll7Fp0ybk5+eH/IzGFWK0MJqvE4DGFGL0kMhrZc2aNUHXwXPPPRf0cxpTiNHEaL5WgDiMKxIxqgEgvfzyyzH/3vnnny9dccUVQ3rN9vZ2CYD09ttvS5IkSbt375YASFu3buXPef311yVBEKSampqYj5Eg4sloulYkSZJKSkqkX/7ylzEfD0EMNyN9rRw7dkwqKCiQdu7cGXJd0LhCjFZG03UiSTSmEKOXkbxWrr76auncc88N+5o0phCjmdF0rUhSfMYVciaNQz799FN89NFHWL58+aBfw+Px4KmnnkJKSgpmz54NANi4cSNSU1OxYMEC/rxVq1ZBFMWQEh+CGAsM17XC+NnPfoaMjAzMnTsXDz30ELxe71APmSASwmCvFb/fjyuvvBJ33HEHpk+fHvJzGleI8cRwXScMGlOI8cJQ5l/r169HdnY2qqqq8I1vfAPNzc38ZzSmEOON4bpWGEMdV8wxHxUxaiksLERjYyO8Xi/uvfdeXHfddTG/xquvvopLL70UPT09yMvLw1tvvYXMzEwAQF1dHbKzs4OebzabkZ6ejrq6uri8B4IYCYb7WgGAW265BfPmzUN6ejo++ugj3HnnnaitrcUvfvGLeL4VghhWhnqtPPjggzCbzbjlllt0f07jCjEeGO7rBKAxhRgfDPVaWbNmDS644AKUlZXhwIEDuOuuu/CVr3wFGzduhMlkojGFGDcM97UCxGdcITFpHPH++++jq6sLmzZtwve//31UVFTgsssui+k1Vq5cie3bt6OpqQm///3vcckll2Dz5s0hN2aCGMuMxLVy66238ufOmjULVqsVN954Ix544AHYbLa4vh+CGC6Gcq1s27YNjz32GD755BMIgjDMR0oQiWMkrhMaU4jxwFDnX5deein/88yZMzFr1ixMmjQJ69evx6mnnjoch0wQCWEkrpV4jCskJo0jysrKAMhfmPr6etx7770xL5BdLhcqKipQUVGBJUuWYPLkyfjDH/6AO++8E7m5uWhoaAh6vtfrRUtLC3Jzc+P2PghiuBnua0WPxYsXw+v14vDhw6iqqhryeyCIkWAo18r777+PhoYGFBcX88d8Ph9uu+02PProozh8+DCNK8S4YLivEz1oTCHGIvGYf6kpLy9HZmYm9u/fj1NPPZXGFGLcMNzXih6DGVcoM2mc4vf70d/fH9fXWbp0Kdra2rBt2zb+83feeQd+vx+LFy8e8r9FEIlgOK4VPbZv3w5RFMnlR4xZYr1WrrzySnz++efYvn07/y8/Px933HEH/vOf/wCgcYUYfwzHdaIHjSnEWCce869jx46hubkZeXl5AGhMIcYnw3Gt6DGYcYWcSaOQrq4u7N+/n//90KFD2L59O9LT01FcXIw777wTNTU1+POf/wwAePzxx1FcXIwpU6YAADZs2ICHH344qPY+2mt2d3fjJz/5Cc455xzk5eWhqakJjz/+OGpqanDxxRcDAKZOnYo1a9bg+uuvx5NPPomBgQHcfPPNuPTSS8O2sSWI4WS0XisbN27E5s2bsXLlSrjdbmzcuBHf+c53cMUVVyAtLW0kTg1BBJGIayUjIwMZGRlBx2GxWJCbm8t3vGhcIUYTo/U6oTGFGG0k4lrp6urCfffdhwsvvBC5ubk4cOAAvvvd76KiogKrV68GQGMKMfoYrddK3MaVIfWCI4aFd999VwIQ8t/VV18tSZLc6m/58uX8+b/61a+k6dOnS06nU0pOTpbmzp0rPfHEE5LP5zP8mr29vdL5558v5efnS1arVcrLy5POOeccacuWLUHH1tzcLF122WVSUlKSlJycLF177bVSZ2fncJ8SgtBltF4r27ZtkxYvXiylpKRIdrtdmjp1qvTTn/5U6uvrG4nTQhAhJOJa0UOvDS2NK8RoYbReJzSmEKONRFwrPT090umnny5lZWVJFotFKikpka6//nqprq4u6NhoTCFGE6P1WonXuCJIkiQZl54IgiAIgiAIgiAIgiCIiQxlJhEEQRAEQRAEQRAEQRCGITGJIAiCIAiCIAiCIAiCMAyJSQRBEARBEARBEARBEIRhSEwiCIIgCIIgCIIgCIIgDENiEkEQBEEQBEEQBEEQBGEYEpMIgiAIgiAIgiAIgiAIw5CYRBAEQRAEQRAEQRAEQRiGxCSCIAiCIAiCIAiCIIgRYsOGDTj77LORn58PQRDwyiuvxPwaf/vb3zBnzhw4nU6UlJTgoYceiv+BRoDEJIIgCIIgiEGwfv16CIKAtra2RB8KQRAEQRBjiO7ubsyePRuPP/74oH7/9ddfx+WXX46bbroJO3fuxBNPPIFf/vKX+M1vfhPnIw2PIEmSNGL/GkEQBEEQxBhlxYoVmDNnDh599FEAgMfjQUtLC3JyciAIQmIPjiAIgiCIMYkgCHj55Zdx3nnn8cf6+/vxgx/8AM899xza2towY8YMPPjgg1ixYgUA4Ktf/SoGBgbw4osv8t/59a9/jZ///Oc4cuTIiMxLyJlEEARBEAQxCKxWK3Jzc0lIIgiCIAgirtx8883YuHEjnn/+eXz++ee4+OKLsWbNGuzbtw+ALDbZ7fag33E4HDh27Biqq6tH5BhJTCIIgiAIgojCNddcg/feew+PPfYYBEGAIAh45plngsrcnnnmGaSmpuLVV19FVVUVnE4nLrroIvT09OBPf/oTSktLkZaWhltuuQU+n4+/dn9/P26//XYUFBTA5XJh8eLFWL9+fWLeKEEQBEEQCeXIkSN4+umn8eKLL+Kkk07CpEmTcPvtt+PEE0/E008/DQBYvXo1/v73v2PdunXw+/3Yu3cvHnnkEQBAbW3tiByneUT+FYIgCIIgiDHMY489hr1792LGjBm4//77AQC7du0KeV5PTw9+9atf4fnnn0dnZycuuOACnH/++UhNTcVrr72GgwcP4sILL8QJJ5yAtWvXApB3H3fv3o3nn38e+fn5ePnll7FmzRrs2LEDkydPHtH3SRAEQRBEYtmxYwd8Ph8qKyuDHu/v70dGRgYA4Prrr8eBAwdw1llnYWBgAMnJyfj2t7+Ne++9F6I4Mp4hEpMIgiAIgiCikJKSAqvVCqfTidzcXADAl19+GfK8gYEB/Pa3v8WkSZMAABdddBGeffZZ1NfXIykpCdOmTcPKlSvx7rvvYu3atXz38ciRI8jPzwcA3H777XjjjTfw9NNP46c//enIvUmCIAiCIBJOV1cXTCYTtm3bBpPJFPSzpKQkAHLO0oMPPoif/vSnqKurQ1ZWFtatWwcAKC8vH5HjJDGJIAiCIAgiTjidTi4kAUBOTg5KS0v55I891tDQAMDY7iNBEARBEBOHuXPnwufzoaGhASeddFLE55pMJhQUFAAAnnvuOSxduhRZWVkjcZgkJhEEQRAEQcQLi8US9HdBEHQf8/v9AIztPhIEQRAEMb7o6urC/v37+d8PHTqE7du3Iz09HZWVlbj88stx1VVX4ZFHHsHcuXPR2NiIdevWYdasWTjzzDPR1NSEl156CStWrEBfXx/PWHrvvfdG7D2QmEQQBEEQBGEAq9UaFJwdD2LZfSQIgiAIYnzw8ccfY+XKlfzvt956KwDg6quvxjPPPIOnn34aP/7xj3HbbbehpqYGmZmZWLJkCc466yz+O3/6059w++23Q5IkLF26FOvXr8eiRYtG7D2QmEQQBEEQBGGA0tJSbN68GYcPH0ZSUhJ3Fw0FI7uPBEEQBEGML1asWAFJksL+3GKx4L777sN9992n+/PMzExs3LhxuA7PECMT800QBEEQBDHGuf3222EymTBt2jRkZWXhyJEjcXndp59+GldddRVuu+02VFVV4bzzzsPWrVtRXFwcl9cnCIIgCIKIN4IUSQ4jCIIgCIIgCIIgCIIgCBXkTCIIgiAIgiAIgiAIgiAMQ2ISQRAEQRAEQRAEQRAEYRgSkwiCIAiCIAiCIAiCIAjDkJhEEARBEARBEARBEARBGIbEJIIgCIIgCIIgCIIgCMIwJCYRBEEQBEEQBEEQBEEQhiExiSAIgiAIgiAIgiAIgjAMiUkEQRAEQRAEQRAEQRCEYUhMIgiCIAiCIAiCIAiCIAxDYhJBEARBEARBEARBEARhGBKTCIIgCIIgCIIgCIIgCMP8fzUu7egLCl8LAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index, y, yhat, errors, anomalies = list(map(context.get, ['target_index', 'y', 'y_hat', 'errors', 'anomalies']))\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(14, 4))\n",
+    "plt.plot(index, errors, label='residual (error)')\n",
+    "\n",
+    "# highlight all anomaly spans\n",
+    "first = True\n",
+    "for start, end, score in anomalies:\n",
+    "    plt.axvspan(start, end, color='r', alpha=0.2, label='anomaly span' if first else None)\n",
+    "    first = False\n",
+    "\n",
+    "plt.title('Residual over time (highlighted anomalies)')\n",
+    "plt.xlabel('time')\n",
+    "plt.ylabel('residual')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "366f4ec4-9ce0-4b07-8e4f-6a875732628b",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "orion310",
+   "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": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}