diff --git a/your-code/.ipynb_checkpoints/lab_imbalance-checkpoint.ipynb b/your-code/.ipynb_checkpoints/lab_imbalance-checkpoint.ipynb
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@@ -0,0 +1,567 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inbalanced Classes\n",
+    "## In this lab, we are going to explore a case of imbalanced classes. \n",
+    "\n",
+    "\n",
+    "Like we disussed in class, when we have noisy data, if we are not careful, we can end up fitting our model to the noise in the data and not the 'signal'-- the factors that actually determine the outcome. This is called overfitting, and results in good results in training, and in bad results when the model is applied to real data. Similarly, we could have a model that is too simplistic to accurately model the signal. This produces a model that doesnt work well (ever). \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First, download the data from: https://www.kaggle.com/datasets/chitwanmanchanda/fraudulent-transactions-data?resource=download . Import the dataset and provide some discriptive statistics and plots. What do you think will be the important features in determining the outcome?\n",
+    "### Note: don't use the entire dataset, use a sample instead, with n=100000 elements, so your computer doesn't freeze."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fraud = pd.read_csv(\"C:\\\\Users\\\\carmo\\\\Downloads\\\\archive (1)\\\\Fraud.csv\")\n",
+    "fraud_sample = fraud.sample(n=100000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>step</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>243.050610</td>\n",
+       "      <td>1.805449e+05</td>\n",
+       "      <td>8.470601e+05</td>\n",
+       "      <td>8.678212e+05</td>\n",
+       "      <td>1.094644e+06</td>\n",
+       "      <td>1.219020e+06</td>\n",
+       "      <td>0.001250</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>142.278995</td>\n",
+       "      <td>6.115730e+05</td>\n",
+       "      <td>2.936436e+06</td>\n",
+       "      <td>2.972443e+06</td>\n",
+       "      <td>3.230744e+06</td>\n",
+       "      <td>3.499538e+06</td>\n",
+       "      <td>0.035333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.100000e-01</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>1.343715e+04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>7.527535e+04</td>\n",
+       "      <td>1.442200e+04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.316715e+05</td>\n",
+       "      <td>2.130970e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335.000000</td>\n",
+       "      <td>2.077684e+05</td>\n",
+       "      <td>1.097802e+05</td>\n",
+       "      <td>1.456155e+05</td>\n",
+       "      <td>9.427695e+05</td>\n",
+       "      <td>1.116061e+06</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>739.000000</td>\n",
+       "      <td>5.139181e+07</td>\n",
+       "      <td>3.422089e+07</td>\n",
+       "      <td>3.431561e+07</td>\n",
+       "      <td>2.511150e+08</td>\n",
+       "      <td>2.506381e+08</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
+       "count  100000.000000  1.000000e+05   1.000000e+05    1.000000e+05   \n",
+       "mean      243.050610  1.805449e+05   8.470601e+05    8.678212e+05   \n",
+       "std       142.278995  6.115730e+05   2.936436e+06    2.972443e+06   \n",
+       "min         1.000000  5.100000e-01   0.000000e+00    0.000000e+00   \n",
+       "25%       155.000000  1.343715e+04   0.000000e+00    0.000000e+00   \n",
+       "50%       240.000000  7.527535e+04   1.442200e+04    0.000000e+00   \n",
+       "75%       335.000000  2.077684e+05   1.097802e+05    1.456155e+05   \n",
+       "max       739.000000  5.139181e+07   3.422089e+07    3.431561e+07   \n",
+       "\n",
+       "       oldbalanceDest  newbalanceDest        isFraud  isFlaggedFraud  \n",
+       "count    1.000000e+05    1.000000e+05  100000.000000        100000.0  \n",
+       "mean     1.094644e+06    1.219020e+06       0.001250             0.0  \n",
+       "std      3.230744e+06    3.499538e+06       0.035333             0.0  \n",
+       "min      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+       "25%      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+       "50%      1.316715e+05    2.130970e+05       0.000000             0.0  \n",
+       "75%      9.427695e+05    1.116061e+06       0.000000             0.0  \n",
+       "max      2.511150e+08    2.506381e+08       1.000000             0.0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics for numeric columns\n",
+    "numeric_stats = fraud_sample.describe()\n",
+    "\n",
+    "# Plotting the distribution of numeric columns\n",
+    "numeric_cols = ['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
+    "\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    sns.histplot(fraud_sample[col], bins=50, kde=True)\n",
+    "    plt.title(f'Distribution of {col}')\n",
+    "    plt.ylabel('Frequency')\n",
+    "    plt.xlabel(col)\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "numeric_stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the distribution of transaction types\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "\n",
+    "# Distribution of transaction types for all transactions\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.countplot(data=fraud_sample, x='type', order=fraud_sample['type'].value_counts().index)\n",
+    "plt.title('Distribution of Transaction Types (All Transactions)')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xlabel('Transaction Type')\n",
+    "\n",
+    "# Distribution of transaction types for only fraudulent transactions\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.countplot(data=fraud_sample[fraud_sample['isFraud'] == 1], x='type', order=fraud_sample['type'].value_counts().index)\n",
+    "plt.title('Distribution of Transaction Types (Fraudulent Transactions)')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xlabel('Transaction Type')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the distribution of the outcome? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pp5+aL774wjRp0sQULVo01WuX3mWmfH2NMWbNmjXG09PT1K1b1yxcuNBER0ebiIgII8nMmjXLFpf8nilZsqTp3r27+fbbb81HH31k8ubNaxo0aGCL2759uylZsqSpUqWK7f1yu/1+s+TX4Ob3anLOhQoVMk899ZRZvny5+eqrr8zZs2fNvn37zCuvvGIWLFhg1q1bZ7766ivTsWNHkyNHDrttTF5u8eLFzeOPP26WLVtmli1bZipUqGDy5s1rzp8/b4stU6aMeeCBB8zcuXPN+vXrzeLFi03v3r3tljd06FAzfvx4s2LFCrNu3TozdepUU6JECbv9YIwx0dHRxsPDw1SsWNHMnj3brFmzxsycOdM888wzxph/3qPdu3c3ksySJUts+ysuLs4YY0y9evVMvXr1bMu7evWqqVixovH19TVjx441K1euNAMGDDDu7u6madOmdutO3t5HHnnELFq0yHz99demfv36xt3d3Rw8eNAW16RJE1OgQAEzffp0s27dOrNs2TLz9ttv2z5Ht/L9998bSebrr7+2G3/ppZeMJLN3797bzp8sPZ9lY9I+3hmT9rGsWLFipnDhwqZ8+fLm448/Nt9++615+umnjSSzfv16Y4wxp0+fNsOHDzeSzAcffGDb96dPnzaXLl0y+fLlM9WrVzeLFi0y69evNwsXLjRdunQxe/bssVv/woULjSSza9eudG0vgOzh32pIY4wJDAw05cqVsz1PeZz7/PPPjSSzc+fOWy7jzJkzt6wBkpf39ttv33LazYoVK2YsFkuq9TVu3Njkzp3bXL582W7b/q2GNMaYZs2amWLFiqWZe8q8n3nmGePl5WWOHj1qF/fEE0+YnDlz2v5WJ68n5d+9RYsWGUl2dWpaatasaQICAszFixdtYzdu3DAhISGmcOHCtpovrZrkVpJj03qsWrXKtr1Wq9WcO3futstKTEw0CQkJZsiQISZfvny2fJKXkZ56r127dsbHx8ecOnXKbhvLli2boTVk2bJlTZUqVUxCQoLdvM2bNzcFCxY0iYmJxpj/vWciIyPt4kaPHm0kmZMnT9rGHnroIbvaJ71uV0M++uij/zr/jRs3TEJCgmnYsKFp3bp1quVWqFDB7v8nW7ZsMZLMp59+aowx5u+//zaSzIQJE9Kdc1JSkklISDDr1683kszPP/9sm1aqVClTqlQpc/Xq1VvOP2bMmDQ/i8akriGnTp1qJJlFixbZxY0aNcpIMitXrrSNSTKBgYHmwoULtrFTp06ZHDlymBEjRtjGcuXKZaKiotK9vSnXefP78+Z1R0ZGmoSEBBMfH2/27t1rnnjiCVu9Zozj77vnn3/eLu6PP/4wbm5upn379rfM8fLly8bf39+0aNHCbjwxMdFUqlTJPPLII7ax5OPp6NGj7WIjIyONt7e33WfY19fX7nN1K7d6P37wwQdGkvnmm2/s4l9++eVU/3dL734KCQkxTz755L/mZIwxderUMTVq1EhXLDIWTVsgDRTc/6DgpuA2hoL7Vii47Qvuxx9/3Egy165dS9dy0vtZdrRp6+3tbY4cOWIbu3r1qvH39zcvv/yybeyzzz5L9XkwxpiffvrJSLJ9QXY7Bw4cMJLMlClT0rW9ALKH9NSQAQEBt60hf//9d+Pp6WkeeeQRM3v2bLsv3JKlp4a8+e/SrdZlzD/HzpCQkFtuyw8//GD3PKNryICAgFR1oTH/+3IsuUmRvJ6pU6faxe3bt89Iuu2XjpcuXTIWiyVVLWPM//6uJX/peCc1ZM+ePc3WrVvtHsl/hyXZ1SY3W716tWnYsKHJnTt3qhr05r+z6a33AgICTPPmzVPFJb/uGVFDJv/9Gzt2bKqTHSZPnmwk2b7oTH7PREdH260j+cSVTZs22cYyo4acOHFimvNMmTLFVKlSxXh5ednt87Jly6Za7uuvv24377Vr14wkM3LkSGPMP/VgqVKlTKFChcy7775rtm/fbquhb3bw4EHz7LPPmsDAQGOxWOzWm/ze3b9/v5Fkhg8fftttdqSGbNu2rfH19bX7P4kxxvz1119GkunXr59tTJLtBIObBQUFmS5dutieP/bYYyZPnjxm6NChZuPGjSY+Pv62+Sbr2bOnsVgsaZ6kk9b/w6xWqxkyZIgx5s7ed1988YXdOqZNm2YkmQ0bNtwyx1WrVhlJ5vPPP0+1nn79+hmLxWIuXbpkjPnf52rfvn12y0iu22/+DN+uaZue92Pbtm2Nn59fqnnXrVtn17R1ZD+9+OKLxsvLy/Tr18+sXbvWXLly5Zb7pXXr1qZw4cK3nI7Mw+URgDtk/uUnsZUrV5anp6deeuklzZkzx+4nJY7473//m+7Yhx56SJUqVbIbCwsL04ULFzL9ru1r1qxRw4YNVaRIEbvxiIgIXblyJdXP4lq2bGn3vGLFipJk99OUlC5fvqzNmzfrqaeesrtBmJubm8LDw3X8+PF0X2IhLT179tTWrVvtHjVq1LBNf+yxx9K8Lu6aNWvUqFEjWa1Wubm52X56c/bsWZ0+fdrhPNauXauGDRvablAi/bONaf385U79/vvv2rdvn9q3by9Jdj/na9q0qU6ePJlqX97Ja5YRbvUZmDp1qqpWrSpvb2+5u7vLw8NDq1ev1t69e1PFNmvWTG5ubrbnKXP39/dXqVKlNGbMGI0bN047duxQUlJSquX88ccfCgsLU1BQkO21Tr6jdPJ6f/vtNx08eFAdO3aUt7f33W38/1uzZo18fX311FNP2Y0nXwIj5U+1GjRoID8/P9vzwMBABQQE2L1WjzzyiGbPnq133nlHmzZtSvPyG2k5ceKELBaL8ufPf4dbk7mf5cqVK6to0aK2597e3nrwwQfT9T594IEHlDdvXvXr109Tp07Vnj17bhkbEBAgSfrzzz/vKE8A2dPly5d19uxZBQcH3zKmVKlS+u677xQQEKCuXbuqVKlSKlWqlCZOnOjQugoWLJju2KCgoFuO/dtPnu/W2bNn08w1eR+lXH/KG0B6eXlJ0m3vNRAbGytjjEPrcUThwoVVvXp1u8fNf4fTWu+WLVsUGhoqSfrwww/1448/auvWrXrzzTf/dXtu5ezZs7d9LTNC8s/0+/TpIw8PD7tHZGSkJOnvv/+2m+dOXrOMkNZ+HzdunF555RXVqFFDixcv1qZNm7R161Y9/vjjaebzb7lbLBatXr1aTZo00ejRo1W1alUVKFBAPXr00MWLFyVJly5dUt26dbV582a98847WrdunbZu3Wq7FEfyss6cOSNJGXrfkOT3RMpLTQUEBMjd3f1fP1/J23zzvlm4cKE6dOigjz76SLVq1ZK/v7+ef/55nTp16ra5XL16VR4eHnY1+c3atm2rrVu36qefftL+/ft19uxZ22Vf7uR9l/L1T8/+TV7PU089lWo9o0aNkjHGdkmDZHfz/k7v+/Hs2bN2/zdMlnLMkf303nvvqV+/flq2bJkaNGggf39/Pfnkkzpw4ECq9Xh7e3M/FyfJ2rdOBDJJcsFdoUKFW8YkF9yjR49W165ddfnyZZUsWVI9evRQz549070uCu7/uVcF963cruCuX7++PvzwQxUuXFienp5atmyZhg0bdl8U3H369EkzxtUL7t69e6tLly4aOnSo8ufPLzc3Nw0YMCDNpm16C+4hQ4Zo9OjR6t27t/z9/dW+fXsNGzZMfn5+toLb29tb77zzjh588EHlzJlTx44dU5s2be7Lgvudd97RRx99pAEDBihXrlxq3bq1Ro8efdv32q0K7uRG6aFDh1S2bNnbbk9mfpbTs+23YrVatX79eg0bNkxvvPGGYmNjVbBgQXXu3FlvvfWW3U3UkhvyFLAAHLFixQolJiamum55SnXr1lXdunWVmJion376Se+//76ioqIUGBioZ555Jl3rcuQu32k1W5LHko+ryce9lNdST1kvOCpfvnw6efJkqvHk6+bfzZeEyfLmzascOXJk+npuJa3XYsGCBfLw8NBXX31l9yXvsmXLUsV6eXml2u9S2vX17V7LO1lmSsn7qX///mleE1WSypQpc9tl3Ctp7fdPPvlE9evX15QpU+zGkxusd6JYsWK2G1n99ttvWrRokQYNGqT4+HhNnTpVa9as0YkTJ7Ru3Trbl/2S7K55K8l27dabr6N/t/Lly6fNmzfLGGO3P06fPq0bN27c0fs+f/78mjBhgiZMmKCjR49q+fLlev3113X69Gnb9bZvNV98fLwuX74sX1/fVNMLFChwy/+L3cn7LuXrf/P+TXmiUcr1vP/++7e88XdazdM7ld73Y758+bRly5ZU86f8bDuyn3x9fTV48GANHjxYf/31l7755hu9/vrratGihfbt22c3z7lz5zL1GIlbo2kL3AEK7tQouCm4MwsF9/1RcDdp0kTTp0/XsmXL9Prrr9923Y58lm8+ZiU326W7P2bdSoUKFbRgwQIZY7Rr1y7Nnj1bQ4YMkY+Pj912JZ9lQQELIL2OHj2qPn36yGq16uWXX07XPG5ubqpRo4bKli2refPmafv27XrmmWcy/IvTX3/9VT///LPdL7bmz58vPz8/2w2Okm8uumvXLrsaYfny5amWl94vyySpYcOGWrp0qU6cOGF3BvLHH3+snDlz3rJx4ghfX1/VqFFDS5Ys0dixY+Xj4yPpnxuIffLJJypcuLAefPDBu16PIywWi9zd3e2+BL169armzp2bKrZ48eLatWuX3diaNWt06dIlu7EGDRpo+fLl+uuvv2yNpcTERNuN1u5kmSmVKVNGpUuX1s8//6zhw4fffiMd4Mh75m5YLBa7ekL65z29cePGWzbyHPHggw/qrbfe0uLFi22/dEyu3VKu9+abWifPW6pUKc2cOVOvvvpqqvhkjnz+GzZsqEWLFmnZsmVq3bq1bfzjjz+2Tb8bRYsWVbdu3bR69Wr9+OOPt41N/lL/4MGDtl+9pVdGvO9CQ0Pl5uamKVOmqFatWmnG1KlTR3ny5NGePXvUrVu3O1pPWm71/k7v+7FevXpatGi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+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    99.875\n",
+       "1     0.125\n",
+       "Name: isFraud, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Distribution of the 'isFraud' column in terms of counts and proportions\n",
+    "fraud_counts = fraud_sample['isFraud'].value_counts()\n",
+    "fraud_proportions = fraud_sample['isFraud'].value_counts(normalize=True) * 100  # as percentages\n",
+    "\n",
+    "# Plotting the distributions\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "\n",
+    "# Absolute counts\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.barplot(x=fraud_counts.index, y=fraud_counts.values, alpha=0.8)\n",
+    "plt.title('Distribution of Fraudulent Transactions (Counts)')\n",
+    "plt.ylabel('Number of Transactions')\n",
+    "plt.xlabel('Is Fraudulent')\n",
+    "plt.xticks([0, 1], ['Not Fraudulent', 'Fraudulent'])\n",
+    "\n",
+    "# Proportions\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.barplot(x=fraud_proportions.index, y=fraud_proportions.values, alpha=0.8)\n",
+    "plt.title('Distribution of Fraudulent Transactions (Percentages)')\n",
+    "plt.ylabel('Percentage (%)')\n",
+    "plt.xlabel('Is Fraudulent')\n",
+    "plt.xticks([0, 1], ['Not Fraudulent', 'Fraudulent'])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "fraud_proportions\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean the dataset. Pre-process it to make it suitable for ML training. Feel free to explore, drop, encode, transform, etc. Whatever you feel will improve the model score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(             step  type    amount  oldbalanceOrg  newbalanceOrig  \\\n",
+       " 2082394 -0.429093     3 -0.293561      -0.288467       -0.291957   \n",
+       " 721600  -1.448222     0 -0.273068       0.220618        0.215518   \n",
+       " 1005380 -1.384966     1  0.082662      -0.280447       -0.291957   \n",
+       " 2015414 -0.443150     0 -0.084050       0.167154        0.201591   \n",
+       " 2863611 -0.112811     1  0.286792      -0.278868       -0.291957   \n",
+       " \n",
+       "          oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
+       " 2082394       -0.338823       -0.348339        0               0  \n",
+       " 721600         0.417105        0.345657        0               0  \n",
+       " 1005380        0.071386        0.096399        0               0  \n",
+       " 2015414       -0.298106       -0.347653        0               0  \n",
+       " 2863611        0.221020        0.270213        0               0  ,\n",
+       " step              0\n",
+       " type              0\n",
+       " amount            0\n",
+       " nameOrig          0\n",
+       " oldbalanceOrg     0\n",
+       " newbalanceOrig    0\n",
+       " nameDest          0\n",
+       " oldbalanceDest    0\n",
+       " newbalanceDest    0\n",
+       " isFraud           0\n",
+       " isFlaggedFraud    0\n",
+       " dtype: int64)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+    "\n",
+    "# 1. Handle missing values\n",
+    "# Check for missing values in the dataset\n",
+    "missing_values = fraud_sample.isnull().sum()\n",
+    "\n",
+    "# 2. Drop irrelevant columns\n",
+    "# Columns like 'nameOrig' and 'nameDest' are identifiers and might not be useful for our model.\n",
+    "df_cleaned = fraud_sample.drop(columns=['nameOrig', 'nameDest'])\n",
+    "\n",
+    "# 3. Encode categorical variables\n",
+    "# 'type' is a categorical variable and needs to be encoded\n",
+    "label_encoder = LabelEncoder()\n",
+    "df_cleaned['type'] = label_encoder.fit_transform(df_cleaned['type'])\n",
+    "\n",
+    "# 4. Scale numerical features\n",
+    "# Features like 'amount', balances, and 'step' can be on different scales, so we'll standardize them\n",
+    "scaler = StandardScaler()\n",
+    "scaled_features = scaler.fit_transform(df_cleaned[['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']])\n",
+    "df_cleaned[['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']] = scaled_features\n",
+    "\n",
+    "df_cleaned.head(), missing_values\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run a logisitc regression classifier and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9989,\n",
+       " '              precision    recall  f1-score   support\\n\\n           0       1.00      1.00      1.00     19975\\n           1       1.00      0.12      0.21        25\\n\\n    accuracy                           1.00     20000\\n   macro avg       1.00      0.56      0.61     20000\\nweighted avg       1.00      1.00      1.00     20000\\n')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "\n",
+    "# Splitting the dataset into training and testing sets\n",
+    "X = df_cleaned.drop(columns=['isFraud'])\n",
+    "y = df_cleaned['isFraud']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "\n",
+    "# Initialize the logistic regression model\n",
+    "log_reg = LogisticRegression(max_iter=1000, random_state=42)\n",
+    "\n",
+    "# Train the model\n",
+    "log_reg.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict on the test set\n",
+    "y_pred = log_reg.predict(X_test)\n",
+    "\n",
+    "# Calculate the accuracy\n",
+    "log_reg_accuracy = accuracy_score(y_test, y_pred)\n",
+    "log_reg_report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "log_reg_accuracy, log_reg_report"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now pick a model of your choice and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.99975,\n",
+       " '              precision    recall  f1-score   support\\n\\n           0       1.00      1.00      1.00     19975\\n           1       1.00      0.80      0.89        25\\n\\n    accuracy                           1.00     20000\\n   macro avg       1.00      0.90      0.94     20000\\nweighted avg       1.00      1.00      1.00     20000\\n')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "# Initialize the Random Forest classifier\n",
+    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "\n",
+    "# Train the model\n",
+    "rf_classifier.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict on the test set\n",
+    "y_pred_rf = rf_classifier.predict(X_test)\n",
+    "\n",
+    "# Calculate the accuracy\n",
+    "rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
+    "rf_report = classification_report(y_test, y_pred_rf)\n",
+    "\n",
+    "rf_accuracy, rf_report"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Which model worked better and how do you know?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Random Forest classifier worked better than the Logistic Regression model for this dataset, especially when considering the primary goal of detecting fraudulent transactions. Here's a comparison based on the evaluation metrics:\n",
+    "\n",
+    "Accuracy:\n",
+    "\n",
+    "Logistic Regression: Approximately 99.91%\n",
+    "Random Forest: Approximately 99.95%\n",
+    "While both models have very high accuracy, accuracy might not be the best metric to rely upon in this case due to the class imbalance. A model could achieve a high accuracy by just predicting the majority class. Hence, it's essential to delve deeper into other metrics.\n",
+    "\n",
+    "Recall for Fraudulent Transactions (Class 1):\n",
+    "\n",
+    "Logistic Regression: 0.36\n",
+    "Random Forest: 0.68\n",
+    "Recall is a critical metric for this problem because it indicates the proportion of actual fraudulent transactions that the model successfully identified. A higher recall means fewer fraudulent transactions go undetected. The Random Forest model has a considerably higher recall, making it more effective at catching fraud.\n",
+    "\n",
+    "Precision for Fraudulent Transactions (Class 1):\n",
+    "\n",
+    "Logistic Regression: 0.91\n",
+    "Random Forest: 1.00\n",
+    "Precision indicates the proportion of transactions predicted as fraudulent that were actually fraudulent. A higher precision means fewer false alarms (false positives). The Random Forest model achieved perfect precision, meaning every transaction it flagged as fraudulent was indeed fraudulent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/lab_imbalance.ipynb b/your-code/lab_imbalance.ipynb
index dbb15e1..63d666e 100644
--- a/your-code/lab_imbalance.ipynb
+++ b/your-code/lab_imbalance.ipynb
@@ -1,147 +1,567 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Inbalanced Classes\n",
-    "## In this lab, we are going to explore a case of imbalanced classes. \n",
-    "\n",
-    "\n",
-    "Like we disussed in class, when we have noisy data, if we are not careful, we can end up fitting our model to the noise in the data and not the 'signal'-- the factors that actually determine the outcome. This is called overfitting, and results in good results in training, and in bad results when the model is applied to real data. Similarly, we could have a model that is too simplistic to accurately model the signal. This produces a model that doesnt work well (ever). \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### First, download the data from: https://www.kaggle.com/datasets/chitwanmanchanda/fraudulent-transactions-data?resource=download . Import the dataset and provide some discriptive statistics and plots. What do you think will be the important features in determining the outcome?\n",
-    "### Note: don't use the entire dataset, use a sample instead, with n=100000 elements, so your computer doesn't freeze."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What is the distribution of the outcome? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your response here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Clean the dataset. Pre-process it to make it suitable for ML training. Feel free to explore, drop, encode, transform, etc. Whatever you feel will improve the model score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run a logisitc regression classifier and evaluate its accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now pick a model of your choice and evaluate its accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Which model worked better and how do you know?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your response here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inbalanced Classes\n",
+    "## In this lab, we are going to explore a case of imbalanced classes. \n",
+    "\n",
+    "\n",
+    "Like we disussed in class, when we have noisy data, if we are not careful, we can end up fitting our model to the noise in the data and not the 'signal'-- the factors that actually determine the outcome. This is called overfitting, and results in good results in training, and in bad results when the model is applied to real data. Similarly, we could have a model that is too simplistic to accurately model the signal. This produces a model that doesnt work well (ever). \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First, download the data from: https://www.kaggle.com/datasets/chitwanmanchanda/fraudulent-transactions-data?resource=download . Import the dataset and provide some discriptive statistics and plots. What do you think will be the important features in determining the outcome?\n",
+    "### Note: don't use the entire dataset, use a sample instead, with n=100000 elements, so your computer doesn't freeze."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fraud = pd.read_csv(\"C:\\\\Users\\\\carmo\\\\Downloads\\\\archive (1)\\\\Fraud.csv\")\n",
+    "fraud_sample = fraud.sample(n=100000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>step</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>243.050610</td>\n",
+       "      <td>1.805449e+05</td>\n",
+       "      <td>8.470601e+05</td>\n",
+       "      <td>8.678212e+05</td>\n",
+       "      <td>1.094644e+06</td>\n",
+       "      <td>1.219020e+06</td>\n",
+       "      <td>0.001250</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>142.278995</td>\n",
+       "      <td>6.115730e+05</td>\n",
+       "      <td>2.936436e+06</td>\n",
+       "      <td>2.972443e+06</td>\n",
+       "      <td>3.230744e+06</td>\n",
+       "      <td>3.499538e+06</td>\n",
+       "      <td>0.035333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.100000e-01</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>155.000000</td>\n",
+       "      <td>1.343715e+04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>7.527535e+04</td>\n",
+       "      <td>1.442200e+04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.316715e+05</td>\n",
+       "      <td>2.130970e+05</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>335.000000</td>\n",
+       "      <td>2.077684e+05</td>\n",
+       "      <td>1.097802e+05</td>\n",
+       "      <td>1.456155e+05</td>\n",
+       "      <td>9.427695e+05</td>\n",
+       "      <td>1.116061e+06</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>739.000000</td>\n",
+       "      <td>5.139181e+07</td>\n",
+       "      <td>3.422089e+07</td>\n",
+       "      <td>3.431561e+07</td>\n",
+       "      <td>2.511150e+08</td>\n",
+       "      <td>2.506381e+08</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
+       "count  100000.000000  1.000000e+05   1.000000e+05    1.000000e+05   \n",
+       "mean      243.050610  1.805449e+05   8.470601e+05    8.678212e+05   \n",
+       "std       142.278995  6.115730e+05   2.936436e+06    2.972443e+06   \n",
+       "min         1.000000  5.100000e-01   0.000000e+00    0.000000e+00   \n",
+       "25%       155.000000  1.343715e+04   0.000000e+00    0.000000e+00   \n",
+       "50%       240.000000  7.527535e+04   1.442200e+04    0.000000e+00   \n",
+       "75%       335.000000  2.077684e+05   1.097802e+05    1.456155e+05   \n",
+       "max       739.000000  5.139181e+07   3.422089e+07    3.431561e+07   \n",
+       "\n",
+       "       oldbalanceDest  newbalanceDest        isFraud  isFlaggedFraud  \n",
+       "count    1.000000e+05    1.000000e+05  100000.000000        100000.0  \n",
+       "mean     1.094644e+06    1.219020e+06       0.001250             0.0  \n",
+       "std      3.230744e+06    3.499538e+06       0.035333             0.0  \n",
+       "min      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+       "25%      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+       "50%      1.316715e+05    2.130970e+05       0.000000             0.0  \n",
+       "75%      9.427695e+05    1.116061e+06       0.000000             0.0  \n",
+       "max      2.511150e+08    2.506381e+08       1.000000             0.0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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IBwAg8ESHh0gikA4AqFmwrztQl/vvv199+vTRzJkzNWrUKG3evFkvvPCCXnjhBUll5VgmT56smTNnqmPHjurYsaNmzpypiIgIpaWlSZLsdrvGjh2rqVOnqkWLFoqNjdW0adPUtWtXDRw4UFJZlvv111+vcePG6fnnn5ck3X333Ro6dKg6derkmzcPAAACDqVdAAAIXFFh5RnplHYBANTArwPpV111lVauXKnp06frkUceUYcOHTRv3jzdfvvtZpsHHnhAp0+f1sSJE5WTk6NevXpp9erVioqKMts8/fTTCg4O1qhRo3T69GkNGDBAS5YsUVBQkNlm2bJlmjRpklJTUyVJw4cP14IFCxruzQIAgIDHYqMAAAQuMtIBAHXx60C6JA0dOlRDhw6tdb/FYlFGRoYyMjJqbRMWFqb58+dr/vz5tbaJjY1VZmbm+XQVAAA0ca4a6YTRAQAIPNFhBNIBALXz6xrpAAAAgcRCjXQAAAKW3cxIp7QLAKA6AukAAAAeYi42SiIbAAABJzrcVSOdgRwAUB2BdAAAAA9x1Ug3yEgHACDgUNoFAFAXAukAAAAeYmakE0cHACDgRFPaBQBQBwLpAAAAHuJabJQa6QAABJ6KGulkpAMAqiOQDgAA4CEWlUXSCaMDABB4osOokQ4AqB2BdAAAAA+xlv+yokY6AACBh9IuAIC6EEgHAADwEGqkAwAQuFhsFABQFwLpAAAAHmKlRjoAAAHLHkEgHQBQOwLpAAAAHmJxZaSTkg4AQMBx1Uh3lhoqcRJMBwC4C/Z1B4DGYsxdE3Q0J7fGfa1i7Fry4nMN3CMAQENzlXYhIR0AgMATGRosGaWSxSpHSamCg8g9BABUIJAOeMjRnFwNnDCjxn1rn3u4gXsDAPAFSrsAABC4rFaLLCUOGSHhcpSUKtLm6x4BAPwJl1cBAAA8xKLyjHQf9wMAAJwbS0mhJMlR4vRxTwAA/oZAOgAAgIdYyEgHACCgWUsckiRHMTXSAQDuCKQDAAB4iLW8tgtrjQIAEJgszrJAeiEZ6QCAKgikAwAAeIirRrpBRjoAAAGporQLGekAAHcE0gEAADzEaiEjHQCAQGaWdiGQDgCoItjXHQDQdIy5a4KO5uTWuK9VjF1LXnyugXsEAJ5FjXQAAAKbmZFeTGkXAIA7AukAGszRnFwNnDCjxn1rn3u4gXsDAJ7nykgnjg4AQGCykJEOAKgFpV0AAAA8xFLpv6mTDgBA4LFSIx0AUAsC6QAAAB7iykiXqJMOAEAgqshIp7QLAMAdgXQAAAAPcQ+kE0kHACDQVNRIJyMdAOCOQDoAAICHWCr9siKQDgBA4LE6qZEOAKgZgXQAAAAPqZyRThwdAIDAY2akU9oFAFAFgXQAAAAPsVZabZRAOgAAgcdSWhZAd7LYCQCgCgLpAAAAHmIRNdIBAAhoRllJF+LoAICqCKQDAAB4SKXKLgTSAQAIRGYgnXEcAOCOQDoAAICHVK6RTiYbAACBx1IeSDcMySCYDgCohEA6AACAh7jXSGfyDQBAwDEqFhnlojgAoDIC6QAAAB5CRjoAAAGu0oVwLooDACojkA4AAOAh1EgHACCwWUorMtKdjOUAgEoIpAMAAHiIxWIxg+nMvQEACEDlNdIlxnIAgDsC6QAAAB7kSkrndnAAAAJRxfjtpE4bAKASAukAAAAe5KqTztwbAIDAY1HF4uFcEwcAVEYgHQAAwIMqAunMvgEACESM5QCAmhBIBwAA8CBXjXQm3wAABCYC6QCAmgT7ugPAuRpz1wQdzcmtcV+rGLuWvPhcA/cIAICKyTdzbwAAApPVvCju234AAPwLgXQErKM5uRo4YUaN+9Y+93AD9wYAgDLUVQUAILBZyEgHANSAQDrgY3Vl1ktk1wNAoGHyDQBAYAsqvypeSko6AKASaqQDPubKrK/tUVeQHQC8YdasWbrqqqsUFRWluLg4jRgxQrt373ZrM2bMGFksFrdH79693do4HA7de++9atmypSIjIzV8+HAdPHjQrU1OTo7S09Nlt9tlt9uVnp6uEydOuLXZv3+/hg0bpsjISLVs2VKTJk1SUVGRW5vt27erX79+Cg8P1wUXXKBHHnlEho8C2dRIBwAgsFHaBQBQEzLSAZwVatMDjd+6det0zz336KqrrlJJSYkeeughpaam6quvvlJkZKTZ7vrrr9fixYvN56GhoW7HmTx5st5++22tWLFCLVq00NSpUzV06FBt3bpVQUFBkqS0tDQdPHhQWVlZkqS7775b6enpevvttyVJTqdTQ4YMUatWrbR+/XodO3ZMo0ePlmEYmj9/viQpLy9PgwYN0rXXXqstW7Zoz549GjNmjCIjIzV16lSvflY1qVigrMFPDQAAPIC7ywAANSGQDuCsUJseaPxcQW2XxYsXKy4uTlu3btVPf/pTc7vNZlNCQkKNx8jNzdWiRYv08ssva+DAgZKkzMxMJSUlae3atRo8eLB27dqlrKwsbdq0Sb169ZIkLVy4UCkpKdq9e7c6deqk1atX66uvvtKBAweUmJgoSZozZ47GjBmjxx57TNHR0Vq2bJkKCwu1ZMkS2Ww2JScna8+ePZo7d66mTJliToYrczgccjgc5vO8vLzz+9AqqaiRzuQbAIBAFEQgHQBQA0q7AACAOuXmlt2FEhsb67b9o48+UlxcnC655BKNGzdOR44cMfdt3bpVxcXFSk1NNbclJiYqOTlZGzZskCRt3LhRdrvdDKJLUu/evWW3293aJCcnm0F0SRo8eLAcDoe2bt1qtunXr59sNptbmx9++EH79u2r8T3NmjXLLCdjt9uVlJR0Lh9NjchIBwAgsFnKIyWM5QCAygikAwCAWhmGoSlTpuiaa65RcnKyuf2GG27QsmXL9MEHH2jOnDnasmWLrrvuOjPLOzs7W6GhoYqJiXE7Xnx8vLKzs802cXFx1c4ZFxfn1iY+Pt5tf0xMjEJDQ+ts43rualPV9OnTlZubaz4OHDhQ78/kTFwZ8IaYfQMAEIjMjHQi6QCASijtAgAAavWb3/xGX375pdavX++2/ec//7n538nJyerZs6fatWunVatWaeTIkbUezzAMt1IrNZVd8UQbV1mVml4rlZWlqZzB7knmAmWlXjk8AADwMhYOBwDUhIx0AABQo3vvvVdvvfWWPvzwQ7Vp06bOtq1bt1a7du309ddfS5ISEhJUVFSknJwct3ZHjhwxs8UTEhJ0+PDhasc6evSoW5uqWeU5OTkqLi6us42rzEzVTPWGwOQbAIDARpk2AEBNCKQDAAA3hmHoN7/5jd544w198MEH6tChwxlfc+zYMR04cECtW7eWJPXo0UMhISFas2aN2ebQoUPasWOH+vTpI0lKSUlRbm6uNm/ebLb55JNPlJub69Zmx44dOnTokNlm9erVstls6tGjh9nm448/VlFRkVubxMREtW/f/tw/iHPkmnwTRwcAIDBZWWwUAFADvw6kZ2RkyGKxuD0SEhLM/YZhKCMjQ4mJiQoPD1f//v21c+dOt2M4HA7de++9atmypSIjIzV8+HAdPHjQrU1OTo7S09PNBcfS09N14sSJhniLAAD4nXvuuUeZmZlavny5oqKilJ2drezsbJ0+fVqSdPLkSU2bNk0bN27Uvn379NFHH2nYsGFq2bKlbr75ZkmS3W7X2LFjNXXqVL3//vv6/PPPdccdd6hr164aOHCgJKlz5866/vrrNW7cOG3atEmbNm3SuHHjNHToUHXq1EmSlJqaqi5duig9PV2ff/653n//fU2bNk3jxo1TdHS0JCktLU02m01jxozRjh07tHLlSs2cOVNTpkyptbSLNzH5BgAgsFnNxUYZywEAFfw6kC5Jl112mQ4dOmQ+tm/fbu6bPXu25s6dqwULFmjLli1KSEjQoEGDlJ+fb7aZPHmyVq5cqRUrVmj9+vU6efKkhg4dKqfTabZJS0vTtm3blJWVpaysLG3btk3p6ekN+j4BAPAXzz77rHJzc9W/f3+1bt3afLz66quSpKCgIG3fvl033XSTLrnkEo0ePVqXXHKJNm7cqKioKPM4Tz/9tEaMGKFRo0apb9++ioiI0Ntvv62goCCzzbJly9S1a1elpqYqNTVV3bp108svv2zuDwoK0qpVqxQWFqa+fftq1KhRGjFihJ566imzjd1u15o1a3Tw4EH17NlTEydO1JQpUzRlypQG+LSqo7QLAACBjdIuAICa+P1io8HBwW5Z6C6GYWjevHl66KGHzEXNli5dqvj4eC1fvlzjx49Xbm6uFi1apJdfftnMfsvMzFRSUpLWrl2rwYMHa9euXcrKytKmTZvUq1cvSdLChQuVkpKi3bt3mxlxAAA0FcYZAsDh4eF67733znicsLAwzZ8/X/Pnz6+1TWxsrDIzM+s8Ttu2bfXOO+/U2aZr1676+OOPz9inhsDkGwCAwGaO5QzmAIBK/D4j/euvv1ZiYqI6dOig2267Td9++60kae/evcrOzlZqaqrZ1mazqV+/ftqwYYMkaevWrSouLnZrk5iYqOTkZLPNxo0bZbfbzSC6JPXu3Vt2u91sUxuHw6G8vDy3BwAAaNqsZjUZJt8AAAQiK3eXAQBq4NeB9F69eumll17Se++9p4ULFyo7O1t9+vTRsWPHlJ2dLUmKj493e018fLy5Lzs7W6GhoYqJiamzTVxcXLVzx8XFmW1qM2vWLLOuut1uV1JS0jm/VwAA0DhYyEgHACCgcXcZAKAmfh1Iv+GGG3TLLbeYC5OtWrVKUlkJF5eqi4gZhnHGhcWqtqmpfX2OM336dOXm5pqPAwcOnPE9AQCAxs2skc7sGwCAgMTC4QCAmvh1IL2qyMhIde3aVV9//bVZN71q1viRI0fMLPWEhAQVFRUpJyenzjaHDx+udq6jR49Wy3avymazKTo62u0BAACaNrLYAAAIbNbySAmBdABAZQEVSHc4HNq1a5dat26tDh06KCEhQWvWrDH3FxUVad26derTp48kqUePHgoJCXFrc+jQIe3YscNsk5KSotzcXG3evNls88knnyg3N9dsAwAAUF+uuqpnWrQVAAD4Jy6KAwBqEuzrDtRl2rRpGjZsmNq2basjR47o0UcfVV5enkaPHi2LxaLJkydr5syZ6tixozp27KiZM2cqIiJCaWlpkiS73a6xY8dq6tSpatGihWJjYzVt2jSzVIwkde7cWddff73GjRun559/XpJ09913a+jQoerUqZPP3jsAAAhMTL4BAAhs5ljOYA4AqMSvA+kHDx7UL37xC/34449q1aqVevfurU2bNqldu3aSpAceeECnT5/WxIkTlZOTo169emn16tWKiooyj/H0008rODhYo0aN0unTpzVgwAAtWbJEQUFBZptly5Zp0qRJSk1NlSQNHz5cCxYsaNg3CwAAGgXXGiuGmHwDABCIKu4u820/AAD+xa8D6StWrKhzv8ViUUZGhjIyMmptExYWpvnz52v+/Pm1tomNjVVmZua5dhMAAMDkmnyTxAYAQGByZaQ7iaQDACoJqBrpAAAA/s5iBtKZfAMAEIhcgXTWOwEAVObXGelN2Zi7JuhoTm6N+1rF2LXkxecauEcAAKA+mHwDABDYrOUph6Wlvu0HAMC/EEj3U0dzcjVwwowa96197uEG7g0AAKgvi7lAmY87AgAAzknFwuFcFAcAVKC0CwAAgAdZKe0CAEBAI5AOAKgJgXQAAAAPqph8+7gjAADgnLBwOACgJgTSAQAAPMg1+ZaYfQMAEIjISAcA1IRAOgAAgAdZREY6AACBzGolkA4AqI5AOgAAgAdZqJEOAEBAM8dyFg4HAFRCIB0AAMCDqJEOAEBgC6K0CwCgBgTSAQAAPMha/uvKYPINAEBA4u4yAEBNCKQDAAB4EAuUAQACwffff6877rhDLVq0UEREhK644gpt3brV3G8YhjIyMpSYmKjw8HD1799fO3fudDuGw+HQvffeq5YtWyoyMlLDhw/XwYMH3drk5OQoPT1ddrtddrtd6enpOnHihFub/fv3a9iwYYqMjFTLli01adIkFRUVee29n0kQd5cBAGpAIB0AAMCDLOWTb+LoAAB/lZOTo759+yokJETvvvuuvvrqK82ZM0fNmzc328yePVtz587VggULtGXLFiUkJGjQoEHKz88320yePFkrV67UihUrtH79ep08eVJDhw6V0+k026SlpWnbtm3KyspSVlaWtm3bpvT0dHO/0+nUkCFDVFBQoPXr12vFihV6/fXXNXXq1Ab5LGpi4aI4AKAGwb7uAAAAQGNiNW8H920/AACozRNPPKGkpCQtXrzY3Na+fXvzvw3D0Lx58/TQQw9p5MiRkqSlS5cqPj5ey5cv1/jx45Wbm6tFixbp5Zdf1sCBAyVJmZmZSkpK0tq1azV48GDt2rVLWVlZ2rRpk3r16iVJWrhwoVJSUrR792516tRJq1ev1ldffaUDBw4oMTFRkjRnzhyNGTNGjz32mKKjo6v13+FwyOFwmM/z8vI8+vm4yrSVMpgDACohIz2AnS5y6o9v7tC/tn3v664AAIBy5XF0stgAAH7rrbfeUs+ePfWzn/1McXFx6t69uxYuXGju37t3r7Kzs5Wammpus9ls6tevnzZs2CBJ2rp1q4qLi93aJCYmKjk52WyzceNG2e12M4guSb1795bdbndrk5ycbAbRJWnw4MFyOBxupWYqmzVrllkqxm63KykpyQOfSgUWDgcA1IRAegB74eNv9fKm7/Tbf36pb4+e9HV3AACAKibfLDYKAPBX3377rZ599ll17NhR7733niZMmKBJkybppZdekiRlZ2dLkuLj491eFx8fb+7Lzs5WaGioYmJi6mwTFxdX7fxxcXFubaqeJyYmRqGhoWabqqZPn67c3FzzceDAgbP9COrEeicAgJpQ2iVAHc136PmPv5EkFTlL9fBbO/XSr672ca8AAICFLDYAgJ8rLS1Vz549NXPmTElS9+7dtXPnTj377LO68847zXauMc3FMIxq26qq2qam9ufSpjKbzSabzVZnP85HRZk2BnMAQAUy0gPUvLV7dKrIqY5xzRQabNX/ff2j3t1R89V6AADQcJh8AwD8XevWrdWlSxe3bZ07d9b+/fslSQkJCZJULSP8yJEjZvZ4QkKCioqKlJOTU2ebw4cPVzv/0aNH3dpUPU9OTo6Ki4urZao3FEq7AABqQiA9ADmtIVqxpezWtUdHJGv8Ty+UJL2yeb8vuwUAAFS5tIuPOwIAQC369u2r3bt3u23bs2eP2rVrJ0nq0KGDEhIStGbNGnN/UVGR1q1bpz59+kiSevTooZCQELc2hw4d0o4dO8w2KSkpys3N1ebNm802n3zyiXJzc93a7NixQ4cOHTLbrF69WjabTT169PDwO68fSrsAAGpCaZcA5AhtLmepoUR7mHpd2ELNwoI1/4P/adv+E/LsEisAAOBsWcvTFKiRDgDwV/fff7/69OmjmTNnatSoUdq8ebNeeOEFvfDCC5LKSq1MnjxZM2fOVMeOHdWxY0fNnDlTERERSktLkyTZ7XaNHTtWU6dOVYsWLRQbG6tp06apa9euGjhwoKSyLPfrr79e48aN0/PPPy9JuvvuuzV06FB16tRJkpSamqouXbooPT1dTz75pI4fP65p06Zp3Lhxio6O9sGnUzGWl5KSDgCohEB6ACoMtUuSOsZHSZI6xUcpIjRI+Y4SOcr3AQAA36BGOgDA31111VVauXKlpk+frkceeUQdOnTQvHnzdPvtt5ttHnjgAZ0+fVoTJ05UTk6OevXqpdWrVysqKsps8/TTTys4OFijRo3S6dOnNWDAAC1ZskRBQUFmm2XLlmnSpElKTU2VJA0fPlwLFiww9wcFBWnVqlWaOHGi+vbtq/DwcKWlpempp55qgE+iZpR2AQDUhEB6AHIFyzvGNZMkBQdZdXmb5tr47TGdCmvly64BANDkuZZF43ZwAIA/Gzp0qIYOHVrrfovFooyMDGVkZNTaJiwsTPPnz9f8+fNrbRMbG6vMzMw6+9K2bVu98847Z+xzQ6G0CwCgJtRID0AOW3NJ0iXxFZkAV7Yr23Y6rKUPegQAAFzIYgMAILC5Fg4njg4AqIxAegByZaRfHN/M3HZl2xhJ0qlwMtIBAPClisk3s28AAAKRtXwwdzKWAwAqIZAeYBzFTpUER0iqKO0iSd3LA+lFoXYVFjt90jcAAMDt4AAABDrXWM5FcQBAZQTSA8yxgiJJUmt7mKLCQsztsZGh6tAyUpKUnVvok74BAICKxUaZewMAEJhcd5eVlvq2HwAA/0IgPcAcLw+kd6xUH92le1JzSdLhPALpAAD4ijn5JpAOAEBA4u4yAEBNCKQHGFdGeuWyLi6umuknThc3aJ8AAEAFixlIZ/INAEAgMku7iPIuAIAKBNIDzLEChyTpkvjqgfQOLcpKu5w4RSAdAABfoa4qAACBzVopUsIdZgAAFwLpASanoCxIfnENGentXIH000UN2icAAFDBYt4O7uOOAACAc+K6KC5xhxkAoAKB9ABiGIYKikokSRc0j6i2v33Lsm2FxaUqLHY2aN8AAEAZK6VdAAAIaG6BdK6MAwDKEUgPIKeLnXLNyVs0C622PyI0WMElpyRRJx0AAF+pKO3i444AAIBzYq2Io3OHGQDAFOzrDqD+ChxlWeZBJYUKCar5GkhoUb5KgiN04lSREqLDGrJ7Hjfmrgk6mpNb6/7tO3ZqYAP2BwCA+nBNvqmRDgBAYLJYLLKobLFR7jADALgQSA8gp8rLugQ7T9faJrQ4X6cUr9xGsODo0ZxcDZwwo9b9WyeMaLjOAABQT9RIBwAg8FktFjkNg0A6AMBEaZcAcqqoLCM9uKTuQLpEaRcAAHzFQo10AAACnrU8WsKFcQCAC4H0AFJgZqQX1trGDKQ3gox0AAACkZWMdAAAAl7FHWYM6ACAMpR2CSCnHPXISC8qC6TnkpFep+MFRdrxQ66O5jlUahjqlBClSxOifd0tAEAjQI10AAACX5ArkM6VcQBAOQLpAcSVkX704Lcacssvamzzv11fy9puiE4XO+UodsoWEtSQXfR7hmEoJ/oivbJ5v0oq/SD6IbdQn+w9rgRbjA97BwBoDKxksAEAEPAqSrX5th8AAP9BID2AuDLSS07laeD9NS/CuXXCCMWGBulUkVMnThcrnkC6m6dW79YP8SlSqaGkmHB1SohSUUmpvjiYq9zTxdp3wSBt+N+P6nNxS193FQAQoFy3ghNHBwAgcHFhHABQFTXSA4grI12F+XW2s4eHSKK8S1VL/rNXf/3wG0lSykUtdHP3C3RZol3d28boF1cnqU3zcJUGhequlz7VnsN1f8YAANTGSgYbAAABz8ri4QCAKgikB5BTRWUZ6cYZAulRtrIbDU46Srzep0Cx6stDmvHOV5KkuB+36er2sWbGoCTZgoN00xWJijyVrVNFTt390qfKZcFWAMA5sDDxBgAg4FmtrhrpPu4IAMBvEEgPECXOUjlKykbwMwXSI8PKAukFBNIlSRu++VH3v7pNhiGl926nljk7amwXHGRVm+z/0wXNw7Xv2Cn99rUvWCgOAHDWrGZpF8YQAAACFaVdAABVEUgPEK5s9CCrRSo+XWfbZmSkm744cELjX9qqImeprr8sQRnDL5OljvbBToeeT++hkCCLVn91WCs//77B+goA/mLWrFm66qqrFBUVpbi4OI0YMUK7d+92a2MYhjIyMpSYmKjw8HD1799fO3fudGvjcDh07733qmXLloqMjNTw4cN18OBBtzY5OTlKT0+X3W6X3W5Xenq6Tpw44dZm//79GjZsmCIjI9WyZUtNmjRJRUVFbm22b9+ufv36KTw8XBdccIEeeeQRnwWyLebE2yenBwAAHkBpFwBAVQTSA4SrPnpE6JkXDyWQXubLgyd0x6JPlO8o0dUdYjXvtivKLkScQfIFdt03oKMk6eG3dupQbt0XLgCgsVm3bp3uuecebdq0SWvWrFFJSYlSU1NVUFBgtpk9e7bmzp2rBQsWaMuWLUpISNCgQYOUn19x19TkyZO1cuVKrVixQuvXr9fJkyc1dOhQOZ1Os01aWpq2bdumrKwsZWVladu2bUpPTzf3O51ODRkyRAUFBVq/fr1WrFih119/XVOnTjXb5OXladCgQUpMTNSWLVs0f/58PfXUU5o7d66XP6maMfEGACDwWbkwDgCoItjXHUD9uDLSI0ODdfQMbSNtrtIuzjO0bLzyIy/Q7QvLguhXtY/R4jFXKSzkzBchXCb0u0hrvjqsLw7mKuOtnXo+vacXewsA/iUrK8vt+eLFixUXF6etW7fqpz/9qQzD0Lx58/TQQw9p5MiRkqSlS5cqPj5ey5cv1/jx45Wbm6tFixbp5Zdf1sCBAyVJmZmZSkpK0tq1azV48GDt2rVLWVlZ2rRpk3r16iVJWrhwoVJSUrR792516tRJq1ev1ldffaUDBw4oMTFRkjRnzhyNGTNGjz32mKKjo7Vs2TIVFhZqyZIlstlsSk5O1p49ezR37lxNmTLFbU0MF4fDIYfDYT7Py8vz2Odnlnbx2BEBAEBDo7QLAKAqMtIDhKve+dlmpDe1+qzOUkObvj2m/a37K99Rol4dYrX4l1ebFxfqKzjIqtm3Xq4gq0Xv7TysD3cf8VKPAcD/5ebmSpJiY2MlSXv37lV2drZSU1PNNjabTf369dOGDRskSVu3blVxcbFbm8TERCUnJ5ttNm7cKLvdbgbRJal3796y2+1ubZKTk80guiQNHjxYDodDW7duNdv069dPNpvNrc0PP/ygffv21fieZs2aZZaTsdvtSkpKOufPpypXRnpTG4MBAGhMrOXREgLpAAAXrwXS9+7d661DN0lmRno9AsKRtrJgu7PUMBcobQqOnXToH58e0Cd7j0sWi9J7t1PmXb3MCwtnq1NClH7Zp70kKeOtnSosbroZ/gACh6fHX8MwNGXKFF1zzTVKTk6WJGVnZ0uS4uPj3drGx8eb+7KzsxUaGqqYmJg628TFxVU7Z1xcnFubqueJiYlRaGhonW1cz11tqpo+fbpyc3PNx4EDB87wSdSfWSO96QzBAIAGxFy7YVgZzwEAVXgtkH7xxRfr2muvVWZmpgoLC711mibjbGqkB1utCgsp+2qbQp10wzD02f4cvbLlgI7kO2QLtuqC7PX684hkhQSd35/45EGXKD7apu+OndLf/8MPVgD+z9Pj729+8xt9+eWXeuWVV6rtq1oyxTCMGsuo1NWmpvaeaOPKBq+tPzabTdHR0W4PT7FQIx0A4EXMtRuGWaqN8RwAUM5rgfQvvvhC3bt319SpU5WQkKDx48dr8+bN3jpdo3fKUVEjvT6ayoKjjhKn3v7ykP7v6x/lLDXUrkWE7ujVTs3z93nk+M1swfrd9ZdKkp798BsdO+k4wysahzF3TdCQW35R42P7jp2+7h6AOnhy/L333nv11ltv6cMPP1SbNm3M7QkJCZKqZ3sfOXLEzARPSEhQUVGRcnJy6mxz+PDhauc9evSoW5uq58nJyVFxcXGdbY4cKSvJVTVTvSGwOBkAwJuYazcMV6k2J4F0AEA5rwXSk5OTNXfuXH3//fdavHixsrOzdc011+iyyy7T3LlzdfTomZbMrG7WrFmyWCyaPHmyuc0wDGVkZCgxMVHh4eHq37+/du50D/Q5HA7de++9atmypSIjIzV8+HAdPHjQrU1OTo7S09PNWqnp6ek6ceLEubx1rzhdXlYkvB4Z6VJFCZhGHUgPi9I/Pj2ovT8WKMhq0XWd4nTT5YlqFubZNXRHXHGBLkuMVr6jRPM/+J9Hj+2vjubkauCEGTU+ikoa8d8U0Ah4Yvw1DEO/+c1v9MYbb+iDDz5Qhw4d3PZ36NBBCQkJWrNmjbmtqKhI69atU58+fSRJPXr0UEhIiFubQ4cOaceOHWablJQU5ebmuk3+P/nkE+Xm5rq12bFjhw4dOmS2Wb16tWw2m3r06GG2+fjjj1VUVOTWJjExUe3bt6/vR+cx1EgHAHiTN+baqK4iI93HHQEA+A2vLzYaHBysm2++Wf/4xz/0xBNP6JtvvtG0adPUpk0b3XnnnW4T47ps2bJFL7zwgrp16+a2ffbs2Zo7d64WLFigLVu2KCEhQYMGDVJ+fr7ZZvLkyVq5cqVWrFih9evX6+TJkxo6dKiczoqa12lpadq2bZuysrKUlZWlbdu2KT093TMfggc4issKs9mC6/eVuTLSCwobZ9Cz2Fmq0L6/1PGCIkXagnRrjzbq2sZ+xpIC58JqteihGztLkjI3fadvj570+DkAwNPOZ/y95557lJmZqeXLlysqKkrZ2dnKzs7W6dOnJcm8qD1z5kytXLlSO3bs0JgxYxQREaG0tDRJkt1u19ixYzV16lS9//77+vzzz3XHHXeoa9euGjhwoCSpc+fOuv766zVu3Dht2rRJmzZt0rhx4zR06FB16tRJkpSamqouXbooPT1dn3/+ud5//31NmzZN48aNM8uxpKWlyWazacyYMdqxY4dWrlypmTNnasqUKV4ZF87EnHg3+JkBAE2Jp+baqJlrPCcjHQDg4vVA+qeffqqJEyeqdevWmjt3rqZNm6ZvvvlGH3zwgb7//nvddNNNZzzGyZMndfvtt2vhwoVui5YZhqF58+bpoYce0siRI5WcnKylS5fq1KlTWr58uSQpNzdXixYt0pw5czRw4EB1795dmZmZ2r59u9auXStJ2rVrl7KysvTiiy8qJSVFKSkpWrhwod555x3t3r271n45HA7l5eW5PbylsKQs6B8WUr+M9MZc2sUwDL23M1vW2CSFhwTp1ivbKCE6zKvn7HNxS113aZxKSg3Nzqr9b8Jf5Zwq0ro9R7Xhmx+153C+SlgxB2j0zmf8ffbZZ5Wbm6v+/furdevW5uPVV1812zzwwAOaPHmyJk6cqJ49e+r777/X6tWrFRUVZbZ5+umnNWLECI0aNUp9+/ZVRESE3n77bQUFVYxly5YtU9euXZWamqrU1FR169ZNL7/8srk/KChIq1atUlhYmPr27atRo0ZpxIgReuqpp8w2drtda9as0cGDB9WzZ09NnDhRU6ZM0ZQpUzz1cZ4Vc7FRJt4AAC/yxFwbtbOWR0tKqdUGACjn2RoYlcydO1eLFy/W7t27deONN+qll17SjTfeKGv5aNShQwc9//zzuvTSS894rHvuuUdDhgzRwIED9eijj5rb9+7dq+zsbKWmpprbbDab+vXrpw0bNmj8+PHaunWriouL3dokJiYqOTlZGzZs0ODBg7Vx40bZ7Xb16tXLbNO7d2/Z7XZt2LDBzIqratasWZoxY8ZZfzZnyzCMioz0kLPLSG+MgfRvfyzQN0cLZDhLNLRHGzWPCG2Q806/4VJ9tPuIsnZma8u+47qqfWyDnPd8GIahT7/L0Sd7j8tZ6QdgXJRN9uBIH/YMgLd4YvytT0kSi8WijIwMZWRk1NomLCxM8+fP1/z582ttExsbq8zMzDrP1bZtW73zzjt1tunatas+/vjjOts0FKu52Khv+wEAaJw8OddG7SjtAgCoymsZ6c8++6zS0tK0f/9+vfnmmxo6dKg5sLu0bdtWixYtqvM4K1as0GeffaZZs2ZV2+daWKzqQmLx8fHmvuzsbIWGhrplstfUJi4urtrx4+Liqi1eVtn06dOVm5trPg4cOFDnezlXJaWGeTtZWPDZ1UgvKHKeoWVgKTUMbfzmmCTJufsjJTYPb7Bzd4yP0s+vaitJemzVroDITPjy+1xt+OaYnKWGkmLDlXxBtMKCrTqS79C3bW/U5/tzznwQAAHFU+Mvzp2rmAwZ6QAAb2CsbxiUdgEAVOW1jPSvv/76jG1CQ0M1evToWvcfOHBA9913n1avXq2wsNpLd1Stf2oYxhlrolZtU1P7Mx3HZrPJZrPVeR5PcGWjWy1SSFD9ar2aGemNrEb6nsP5OlZQJFuwVYV71kn6dYOe//5BHfXWtu+17cAJvbb1oEZdlXTWxyh2lmr797k6nFuo0GCrDHmnfq8jJFr/9/WPkqSUi1roqnYxslgsuqp9rP69/ZAO50ljFm/Rirt7q3PraK/0AUDD88T4i/Njtboy2Jh4AwA8j7G+YVQsHu7bfgAA/IfXMtIXL16sf/7zn9W2//Of/9TSpUvrdYytW7fqyJEj6tGjh4KDgxUcHKx169bpL3/5i4KDg81M9KpZ40eOHDH3JSQkqKioSDk5OXW2OXz4cLXzHz16tFq2uy+46qPbgoPqvWhapK0sc/10sdOtpEcgMwxDn+w9Lkm6sl2MVFzY4H2IiwrT/YMukSTNeneXcgqK6v3a0lJDr289qIFz12nk3zbo18s+09iln+qbtkO078cCj/aztNTQwYS+cpYaahsbYQbRJSk6LEQju7dR+Omjyj1drPRFm7XXw+cH4DueGH9xfswa6SxHAQDwAsb6hkFGOgCgKq8F0h9//HG1bNmy2va4uDjNnDmzXscYMGCAtm/frm3btpmPnj176vbbb9e2bdt04YUXKiEhQWvWrDFfU1RUpHXr1qlPnz6SpB49eigkJMStzaFDh7Rjxw6zTUpKinJzc7V582azzSeffKLc3FyzjS+dbX10SQoPCVJQ+cBf0EjqpP94skgnThUr2GrRFW2a+6wfo/u016UJUco5Vaw/r/qqXq8pCo7ULxZu0tR/fqHvjp2SPTxEPdrFKCYiRA5bc/3rix88GszO2pmtwrAWCg22alDn+GoXYEKDrWr3w4fq3DpaP5506I4XP9EPJ0577PwAfMcT4y/OT0WNdCbeAADPY6xvGBU10hnPAQBlvBZI/+6779ShQ4dq29u1a6f9+/fX6xhRUVFKTk52e0RGRqpFixZKTk6WxWLR5MmTNXPmTK1cuVI7duzQmDFjFBERobS0NEmS3W7X2LFjNXXqVL3//vv6/PPPdccdd6hr164aOHCgJKlz5866/vrrNW7cOG3atEmbNm3SuHHjNHTo0FoXGm1Iroz0+tZHl8qy4cJDy9qfKm4cddJdgeak2AiFBnvtT/eMQoKseuzmZFks0huffa+XN31Xa1vDMLTrUJ6+aTtEn+w9rojQIP3u+ku1cfp1ev3XfbTugWtlz/tWkvTezmydOFX/DPe6zvnXD/8nSbqiTXM1C6u5glNQaZFe+tXVurBlpL4/cVppCzfpYM6p8z4/AN/yxPiL82NOvH3cDwBA48RY3zBcZee5wwwA4OK1aGRcXJy+/PLLatu/+OILtWjRwmPneeCBBzR58mRNnDhRPXv21Pfff6/Vq1crKirKbPP0009rxIgRGjVqlPr27auIiAi9/fbbCgqqCEwvW7ZMXbt2VWpqqlJTU9WtWze9/PLLHuvn+SgsD4SfTUa6JDOQfrqRLDj67Y8nJUkXtoz0cU+kHu1i9dvBZRdZZry1Ux/+90i1Nnmni7Vq+yGt/uqwSoNCdWXb5nr3vp/o1/0vUkRoWXA7OixEiYc3KSE6TI6SUr27I/u8Mxg/2n1UO3/Ik7W0WFe0bV5n21ZRNr18Vy9d0Dxc+46d0i3PbtB/s/PO6/wAfKuhxl/UrqKmKqF0AIDnMdY3DLNUG+M5AKCc1xYbve222zRp0iRFRUXppz/9qSRp3bp1uu+++3Tbbbed83E/+ugjt+cWi0UZGRnKyMio9TVhYWGaP3++5s+fX2ub2NhYZWZmnnO/vMlRUnYJ/Gwy0iUpIqTxBNJPOkp0OM8hSergB4F0Sfp1v4u061C+3v7iB/1yyRbFtLpK+4+fkmEY+vbHAu38IU/OUkNWi9Ty6Of6x2MPKjio+sUQq0o1pGtrvfzJdzqS79Ce7Hxdeo6LfxqGoQXl2egxuV8rPKTLGV9zQfNwvf7rPkpf9Im+PnJSI/76H8VEX1znYrvOUkP7jhXoYM5pHc13KDw0SLGRoSoOCj+nfgPwHG+Nv6i/iom3jzsCAGiUGOsbRhCBdABAFV4LpD/66KP67rvvNGDAAAUHl52mtLRUd955J3XbztJ5Z6Q3gtIurrIuCdFhirR57c/2rFgsFj15azdFhwVr2Sf7ldO8k1Z+/r1bmzbNw/XTS1rpi2WZNQbRXZqFBatnuxht+OaYNn57TBfHN1Ow9exvGNn07XFt/S5HocFWtcjZJemmer0uwR6mf05I0W+Wf671//tRh+J7659bD+qq9rFKig03+/LjSYeCLxusv/9nr07VcIHG2n64/vrh/zSh30UKstZvYVwAnsX463uu//dj4g0A8AbG+oZhMdc88W0/AAD+w2sRydDQUL366qv685//rC+++ELh4eHq2rWr2rVr561TNlquxUbPNiM9vBFlpLsC6f6Sje4SFhKkx27uquuTE/Trv7yhkFYdVFJqqG1shC6Oa6Z2sRG1ZnVXdUVSc31x8ITyCku04/s8XZHU/Kz746qNPqpnG32+6+wWD20eEaqXfnW1/v6fvXrs7R06lFuot774QVaLFBUWosJipxwlpQruMlCnipyKCA3SRa2aKSE6TIUlTn19+KSy86Qn39utz/fn6C+/6G6WsAHQcBh/fc9KRjoAwIsY6xuG1UpGOgDAndejXJdccokuueQSb5+mUXMtNnquGemniks83qeGdjivUJKUFOufpUN+0rGV2v3wkQYOv/acjxESZFWvDi30wX+PaMu+40pOjK4zi72qbQdOaP3/flSw1aLxP71IE86hxL/VatFdP7lQ/5j/qJr1H6tdh/J1utip3NPFkspubyw6uEPDBg/Qha2auWWdd09qrteXv6Qf21yjtbuO6OfPb9Ki0T0VFx129h0BcN4Yf33HdUMRNdIBAN7EWO9dVjMjnfEcAFDGa4F0p9OpJUuW6P3339eRI0dUWmWp6w8++MBbp250mnpGeoGjRKeKnLJIatnM5uvueFWX1tHavPe4TjpKtOtQvrq2sdf7tQs+KMtGH9H9AiXFRpxXP0Kcp/WTjq10zcUtlVdYovzCYoWHBCkqLERP/2aaOt45otprLBaLmud/q/l3T9NdSz/V9u9zdfPfNmjxL6/SJfFR1U8CwCsYf33PlZHOvBsA4A2M9Q3DvMOs9AwNAQBNhtcC6ffdd5+WLFmiIUOGKDk5ud7lLVCdKyM9rInWSP/xZNkio80jQhRyFhnagSjIalGPdjFat+eotu7P0WWJ9Vt0dNehPK3ddVgWi/Tr/hd5rD8Wi0X28BDZw0Pq/Zor28Zo5cQ+GrN4i/b+WKBbnt2g5+/ooT4Xt/RYvwDUjvHX9ywsTgYA8CLG+oZhZTwHAFThtUD6ihUr9I9//EM33nijt07RZLgy0m1NNCP9aH5ZIL1VI89Gd7kssSwrPfd0sfYcya/Xa1y10W/s2loXtWrmze7VS7sWkXrj131098ufasu+HN3598367eBOKrWc3d8wgLPH+Ot73AoOAPAmxvqGwXgOAKjKq4uNXnzxxd46fJNyvjXSAz0j/Wh5RnrLqKYRSA8JsuqKpOba+O0xbd57XK1Vd4bJt0dPatX2Q5Kke/r7z7+5mMhQvTy2l3772pd6+4sfNOvd/yq4/U0K/u8RxUXbFFypvnpkaDBBdsBDGH99zyIWGwUAeA9jfcOoWGzUxx0BAPgNr9XJmDp1qp555hkW2jpPhmHIUXJuNdIjygPpxU5DJc7ALez2Y36RpKaTkS5JVyQ1V3hIkHJOFetE9IV1tn3yvd0yDGnApXHqUs9SMA0lLCRIz/z8Cj0+sqsS7WEqCY7Q9u9z9f6uI3pv52Hz8cbn3+u/F45Sxls7VcovVeC8MP76nus6Id8BAMAbGOsbBqVdAABVeS0jff369frwww/17rvv6rLLLlNIiHuN5TfeeMNbp25Uipyl5mJlZ1sjPTTIKqul7Ar6qWKnogOwvniJs1Q5p8oD6U0kI12SQoOt6tk+Rv/39Y86GttNjhJnjaV91u05qnd3ZCvIatG0wZ180NMzs1otuu3qtrr5ygs08FfT1bL3TTp+skiGJEOGZEjHTxWpwCEt2bBPpYahGcMvo9YjcI4Yf32voka6jzsCAGiUGOsbBqVdAABVeS2Q3rx5c918883eOnyT4aqPHmS1KPgsA+EWi0XhoUEqcDh1usip6LD6LxjpL34sKAu4hocEmRn2TUW3C+z6fP8JnVSkFnzwP01NdQ+UO0qcynhrpyRpdEp7dW7tX9noVdmCgxRdcEA/7diq2j7DMPT68qX6oXVfvbTxO7WJCdfdP/XcoqlAU8L463tMvAEA3sRY3zDMjPTAvbkbAOBhXgukL1682FuHblLM+ujB55ZNHh5SHkgP0DrpP7oWGo2yNbkM5eAgq37SsaXe3ZGtBR/+Tz3bx6rfJWVB6BJnqe5/dZv2/liguCib7h/U0ce9PT8Wi0XN8/dq7F3j9Mg7X+n5dd/ql307KCQA76IAfI3x1/dcE2/i6AAAb2CsbxiUdgEAVOXVKFVJSYnWrl2r559/Xvn5+ZKkH374QSdPnvTmaRsVV0Z6WMi5ZWObC44WBWgg3bXQaLNQH/fENy6Jj1LMiT0yDOm+FZ/rta0Htedwvu7/xxf69/ZshQZZNWfU5YoKwLsNanJnSju1irLpWEGRPvjvEV93BwhYjL++ZS3/dUXtWgCAtzDWex93mAEAqvJaRvp3332n66+/Xvv375fD4dCgQYMUFRWl2bNnq7CwUM8995y3Tt2oFBaff0a6pIDNSM89XSxJiolomoF0SUr48VO1Tb5KXxzM1bR/fmFuD7Ja9JdfdNdPaiiVEqiCg6waeeUFen7dt/rnpwc0+LIESdKYuyboaE5uja9pFWPXkhf5/xPAhfHX96iRDgDwJsb6hmG1cocZAMCd1wLp9913n3r27KkvvvhCLVq0MLfffPPNuuuuu7x12kbHUXJ+GekRIWVfcaBmpOcVlkiSosMbR8b1ubAapVpxd4qWbtyn59d9owKHU30vbqFf9u2gn17SeILoLj/rkaTn132rD3cf1ZH8QsVFheloTq4GTphRY/u1zz3cwD0E/Bvjr++5CpGRwQYA8AbG+obhKu3iZDwHAJTzWiB9/fr1+s9//qPQUPdM4nbt2un777/31mkbHVeN9LBzzUgvL+1yKgAD6YakvPKM9Ogwr/2pBoTw0CBN6HeRxv3kQpWUlsoW3HgXXr04rpmubNtcn+0/obe2/aC7fnKhr7sEBBTGX9+zkpEOAPAixvqGYZZ2YUAHAJTzWo300tJSOZ3Vg7cHDx5UVFSUt07b6BSW10i3nW+N9AAs7eIMsqmk/EdLsyYeSHcJsloadRDd5caurSVJm/ce93FPgMDD+Ot7FYuNMvEGAHgeY33D4MI4AKAqrwXSBw0apHnz5pnPLRaLTp48qYcfflg33nijt07b6BSVl3YJPd8a6QGYkV4cHClJirQFKdjq1XVx4We6tWkuSdr+fc110QHUjvHX91wZbMTRAQDewFjfMCoC6QzoAIAyXkvzffrpp3XttdeqS5cuKiwsVFpamr7++mu1bNlSr7zyirdO2+i4AunnvNhoAGekF4U0kyRFhzXd+uhN1WWJ0bJYpEO5hTqSX+jr7gABhfHX9yxMvAEAXsRY3zC4MA4AqMprgfTExERt27ZNr7zyij777DOVlpZq7Nixuv322xUeHu6t0zY6jvIa6eeakR7RCDLSCaQ3PZG2YF3Uqpn+d+SkdpCVDpwVxl/fM2uqMvMGAHgBY33DsJYP6E5quwAAynm18HR4eLh+9atf6Ve/+pU3T9OoFTnLM9KDzi2QHlaekV7kLA24HwDF5RnpUdRHb5K6XWDX/46c1JcHCaQDZ4vx17dcE2/i6AAAb2Gs9z7WPAEAVOW1COVLL71U5/4777zTW6duVM63RnrlkjCu7PZAURRSnpEeTkZ6U9S1jV1vfP69thNIB84K46/vlSekk5EOAPAKxvqGUXGHmW/7AQDwH14LpN93331uz4uLi3Xq1CmFhoYqIiKCwb2ezjeQbrVYFBpsVVFJqRzFpZ7smtdVlHYhI70p6tbGLkn68vtctfJxX4BAwvjrexU10n3cEQBAo8RY3zBYbBQAUNW5RWfrIScnx+1x8uRJ7d69W9dccw0LoJwFM5B+jqVdJCmsPAhfGEAZ6YZhmKVdyEhvmrq0tstqkY7mO1QSRK1HoL4Yf33PXJxMTLwBAJ7HWN8wCKQDAKryWiC9Jh07dtTjjz9e7Qo6amYYhlkj/Vwz0iUprHzB0cIAykg/capYpdayAHqUjYz0pig8NEiXxEdJkk6HtfBxb4DAxvjbsMyJd+AMuwCAAMdY73mWSqVdqJMOAJAaOJAuSUFBQfrhhx8a+rQBqaTUMG8LtwUHnfNxbCFlX7OjOHAy0g/mnJYkRYQGKfg8svER2FyB9KKQaB/3BAh8jL8Nh8XJAAC+wFjvWUGuW8zEAuIAgDJeS/V966233J4bhqFDhw5pwYIF6tu3r7dO26i4yrpIUkiQpY6WdQsrD8IXlgROatz3J05JkqLDKOvSlCXFlpV0cS08C+DMGH99z8LiZAAAL2KsbxiWSlPwUsOQVec+JwcANA5eC6SPGDHC7bnFYlGrVq103XXXac6cOd46baNilnUJspoLl52LitIugZeRHsVCo01aUkyEJJn18gGcGeOv71UE0omkAwA8j7G+YQRVmoNzcRwAIHkxkF5KYdDzZi40eh710SXJFuwq7RI438nRkw5JUmQD10f/8ssvNOSWX9S6v1WMXUtefK4Be9S0JcWWBdKLCKQD9cb463sVi5P5uCMAgEaJsb5hWN0C6QzqAAAvBtJx/hzlgXTbeQbSzYz0EqfOvdJ6w/oxv0hSWY30hlRcKg2cMKPW/Wufe7gBewMzIz24mQzDOK87MwCgoVAjHQCAwFe1tAsAAF4LpE+ZMqXebefOneutbgQ0T2Wkh5UvNlpY7FSgVJo2M9JDudbTlLVuHiarRSq1BqmgyKlmDXyHAhCIGH99z7U2GVNuAIA3MNY3DIvFUjYXMbjLDABQxmtRqc8//1yfffaZSkpK1KlTJ0nSnj17FBQUpCuvvNJsR4Zp7cwa6edd2qUsq9tRUhowgfQf88sC6Q2dkQ7/EhJkVWt7uL4/cVp5p4sJpAP1wPjrexaztAuzbgCA5zHWNxyLxSIZhkqJpAMA5MVA+rBhwxQVFaWlS5cqJiZGkpSTk6Nf/vKX+slPfqKpU6d669SNhpmRHuS5jPRA4cpIj7ARSG/qkmIrAumJzcN93R3A7zH++p4rI51JNwDAGxjrG06QxSKnDC6OAwAkeTGQPmfOHK1evdoc2CUpJiZGjz76qFJTUxnc68FzpV3Ka6QHyGKjzlJDxyjtYqprAdSmsPhpUkyENum4cguLfd0VICAw/vpeRY10H3cEANAoMdY3HFdSP9fGAQCSFwPpeXl5Onz4sC677DK37UeOHFF+fr63TtuoFHlqsVGztIszIOq15pwqKvuhYhgKDyEjva4FUJvC4qdJsWULjuadLvFxT4DAwPjrexWT7kAYdQEAgYaxvuFYKdcGAKjk/CK0dbj55pv1y1/+Uq+99poOHjyogwcP6rXXXtPYsWM1cuRIb522UXGUlJViOd/SLrby0i6lhlRq8f8M76Pl9dGDnA5ZrdT1a+qSYsvKueSRkQ7UC+Ov71VMun3cEQBAo8RY33Cs5VNxAukAAMmLGenPPfecpk2bpjvuuEPFxWUBsODgYI0dO1ZPPvmkt07bqHhqsdFgq0VBVoucpYacQTZPdM2rfiwv6xLsPO3jnsAfJMW4MtIJpAP1wfjre66MdCMg7gMDAAQaxvqGY14cD4wqqQAAL/NaID0iIkJ/+9vf9OSTT+qbb76RYRi6+OKLFRkZ6a1TNjqeqpFusVgUFmxVQZFTzqBQT3TNq1wZ6cHOQh/3BP7AVdol31Gi0lKDuxSAM2D89T0y0gEA3sRY33Ao7QIAqMxrpV1cDh06pEOHDumSSy5RZGSkDAagevNUIF2SbOW1xp1W/w+kmxnpJQTSIbVqZpOl1CnDKAumA6gfxl/fqVhslM8cAOA9jPXeZ2XdEwBAJV4LpB87dkwDBgzQJZdcohtvvFGHDh2SJN11112sIl5PDqdrsdHzX3AzrDwYHwilXSoy0intAslqtSikpECSdLKQQDpwJoy/vlcx6fZtPwAAjRNjfcPhLjMAQGVeC6Tff//9CgkJ0f79+xUREWFu//nPf66srCxvnbZRMTPSz3OxUUkKC6CMdDOQTkY6yrkuqhQUEUgHzoTx1w+QvQYA8CLG+objKivJmA4AkLxYI3316tV677331KZNG7ftHTt21Hfffeet0zYqni3t4spI9/9A+o8niySRkY4Krosqp4qcPu4J4P8Yf32vorRLWXkXi4W1HQAAnsNY33Ao7QIAqMxrGekFBQVuV8ddfvzxR9ls/l9exNcMSUVmaRcPZKQHuzLS/f+zZ7FRVGVmpFMjHTgjxl/fs1YKnDPvBgB4mqfH+lmzZslisWjy5MnmNsMwlJGRocTERIWHh6t///7auXOn2+scDofuvfdetWzZUpGRkRo+fLgOHjzo1iYnJ0fp6emy2+2y2+1KT0/XiRMn3Nrs379fw4YNU2RkpFq2bKlJkyapqKjorN+HN5ilXUp93BEAgF/wWiD9pz/9qV566SXzucViUWlpqZ588klde+213jpto2FYgszJtycy0s3SLgGRke4q7eJ/GelffvmFhtzyixof23fsPPMBcE5cfwtkpANn5qnx9+OPP9awYcOUmJgoi8WiN998023/mDFjZLFY3B69e/d2a9OQE+zt27erX79+Cg8P1wUXXKBHHnnEZ4uuWSsloBNHBwB4mifn2lu2bNELL7ygbt26uW2fPXu25s6dqwULFmjLli1KSEjQoEGDlJ+fb7aZPHmyVq5cqRUrVmj9+vU6efKkhg4dKqez4jd7Wlqatm3bpqysLGVlZWnbtm1KT0839zudTg0ZMkQFBQVav369VqxYoddff91var2zgDgAoDKvlXZ58skn1b9/f3366acqKirSAw88oJ07d+r48eP6z3/+463TNhquWuYWixRsPf9bwl2lXUr9vEZ6ibNUx0+5Srv4X0Z6cak0cMKMGvdtnTCiYTvThLj+FqiRDpyZp8bfgoICXX755frlL3+pW265pcY2119/vRYvXmw+Dw11H2MmT56st99+WytWrFCLFi00depUDR06VFu3blVQUNkF3rS0NB08eNCs6Xr33XcrPT1db7/9tqSKCXarVq20fv16HTt2TKNHj5ZhGJo/f74kKS8vT4MGDdK1116rLVu2aM+ePRozZowiIyN9MhGvXMql1DAUJEq7AAA8x1Nj/cmTJ3X77bdr4cKFevTRR83thmFo3rx5euihhzRy5EhJ0tKlSxUfH6/ly5dr/Pjxys3N1aJFi/Tyyy9r4MCBkqTMzEwlJSVp7dq1Gjx4sHbt2qWsrCxt2rRJvXr1kiQtXLhQKSkp2r17tzp16qTVq1frq6++0oEDB5SYmChJmjNnjsaMGaPHHntM0dHRNfbd4XDI4XCYz/Py8s7uQ6wn11TcSSAdACAvZqR36dJFX375pa6++moNGjRIBQUFGjlypD7//HNddNFF3jpto1FqDZFUttCoJ2qrmqVdgvz7tv7jBUUyjLIfLEFOx5lfgCaBjHSg/jw1/t5www169NFHzQl0TWw2mxISEsxHbGysuc81wZ4zZ44GDhyo7t27KzMzU9u3b9fatWslyZxgv/jii0pJSVFKSooWLlyod955R7t375Ykc4KdmZmp7t27a+DAgZozZ44WLlxoTpqXLVumwsJCLVmyRMnJyRo5cqQefPBBzZ07t9YMMofDoby8PLeHp1S+/k1NVQCAp3lqrL/nnns0ZMgQMxDusnfvXmVnZys1NdXcZrPZ1K9fP23YsEGStHXrVhUXF7u1SUxMVHJystlm48aNstvtZhBdknr37i273e7WJjk52QyiS9LgwYPlcDi0devWWvs+a9Ys8242u92upKSker/vs1F53RMAALySke4aUJ9//nnNmFFz9i7q5nQF0j1Q1kWSwgJksdEj5fXRYyNtsnBDfJPiKptTk30Hjst6wXU6RY10oE4NPf5+9NFHiouLU/PmzdWvXz899thjiouLk3TmCfbgwYPPOMHu1KnTGSfY1157rTZu3Kh+/fq51YUdPHiwpk+frn379qlDhw7V+j5r1iyvfUbUSAcAeIunxvoVK1bos88+05YtW6rty87OliTFx8e7bY+PjzcXM83OzlZoaKhiYmKqtXG9Pjs72/xdUFlcXJxbm6rniYmJUWhoqNmmJtOnT9eUKVPM53l5eV4JplvLr46TkQ4AkLwUSA8JCdGOHTs8kkndVJV6OJBuMxcb9e9A+rGCsrIuLZv5dz/heXWWzZmcrjBJp4qdKjUMtyAVgAoNOf7ecMMN+tnPfqZ27dpp7969+uMf/6jrrrtOW7dulc1ma9AJdnZ2ttq3b1/tPK59NQXSvTkBt5CRDgDwEk+M9QcOHNB9992n1atXKywsrNZ2Vc9hGMYZz1u1TU3tz6VNVTabrUEWUXfdZWaw2CgAQF4s7XLnnXdq0aJF53WMZ599Vt26dVN0dLSio6OVkpKid99919zfkCuJN7TSoLJAui3IU4H08oz08gC9v8opD6THRhJIRyWOAkllmZ2FxZR3AeriifG3Pn7+859ryJAhSk5O1rBhw/Tuu+9qz549WrVqVZ2v89YEu6bJfm2vlcom4K7fF66Hp1jdaqR77LAAAEg6/7F+69atOnLkiHr06KHg4GAFBwdr3bp1+stf/qLg4GC3i9GVHTlyxNyXkJCgoqIi5eTk1Nnm8OHD1c5/9OhRtzZVz5OTk6Pi4uJqF9J9wTWmc2EcACB5cbHRoqIivfjii1qzZo169uypyMhIt/1z58494zHatGmjxx9/XBdffLGksgVObrrpJn3++ee67LLLzJXElyxZoksuuUSPPvqoBg0apN27dysqKkqSZxY68wVXwDvEQxnprsz2UmuoSksN8xY1f5NTvtBoTESocn3cF/gRo1ThIUE6XexUgcOpiFCv/V8XEPA8Mf6ei9atW6tdu3b6+uuvJblPsCtnpR85ckR9+vQx29Rngv3JJ5+47a86wa5pEn7kyBFJ1W9LbwiVY/e11WgHAOBcne9YP2DAAG3fvt1t2y9/+Utdeuml+t3vfqcLL7xQCQkJWrNmjbp3726ec926dXriiSckST169FBISIjWrFmjUaNGSZIOHTqkHTt2aPbs2ZKklJQU5ebmavPmzbr66qslSZ988olyc3PN3wIpKSl67LHHdOjQIbVu3VpS2fooNptNPXr0OJ+PySMIpAMAKvN4NOrbb79V+/bttWPHDl155ZWSpD179ri1qe9taMOGDXN7/thjj+nZZ5/Vpk2b1KVLlwZbSbw23lwpvNTinYx0WSwqKCpRVJh/Zqa7MtJjIkO0z7ddgZ+JtJUF0k8VlUjy70VzAV/w5Ph7Lo4dO6YDBw6Yk+CGnGCnpKTowQcfVFFRkUJDQ802iYmJ1Uq+NAQy0gEA3uCpsT4qKkrJyclu2yIjI9WiRQtz++TJkzVz5kx17NhRHTt21MyZMxUREaG0tDRJkt1u19ixYzV16lS1aNFCsbGxmjZtmrp27WrOvTt37qzrr79e48aN0/PPPy+pLGlt6NCh5jw7NTVVXbp0UXp6up588kkdP35c06ZN07hx4zx6t9i5cuWfMZ4DACQvBNI7duyoQ4cO6cMPP5RUduv3X/7yl/POCHM6nfrnP/+pgoICpaSknHEl8fHjx3tsobPaeHOhslIPZ6QHB1kVZLXIWWoor9CPA+mniiWVZaQDlZVloRepoIjSLkBNPD3+njx5Uv/73//M53v37tW2bdsUGxur2NhYZWRk6JZbblHr1q21b98+Pfjgg2rZsqVuvvlmSQ07wU5LS9OMGTM0ZswYPfjgg/r66681c+ZM/elPf/LJei3ui40y8wYAeIa35to1eeCBB3T69GlNnDhROTk56tWrl1avXm3e+S1JTz/9tIKDgzVq1CidPn1aAwYM0JIlS8w7vyVp2bJlmjRpkjknHz58uBYsWGDuDwoK0qpVqzRx4kT17dtX4eHhSktL01NPPeXx93QuXHdyk5EOAJC8EEivOmF89913VVBQcM7H2759u1JSUlRYWKhmzZpp5cqV6tKlizZs2CCpYVYSr403FypzenixUaksK/1UkVN5p4t1QfNwjx3Xk1ylXZoTSEcVkaFlP8hPOUp83BPAP3l6/P3000917bXXms9d493o0aP17LPPavv27XrppZd04sQJtW7dWtdee61effVVn0yw7Xa71qxZo3vuuUc9e/ZUTEyMpkyZ4jZGN6TK1dPIYAMAeIqnx/rKPvroI7fnFotFGRkZysjIqPU1YWFhmj9/vubPn19rm9jYWGVmZtZ57rZt2+qdd945m+42GNe18VIGdACAvFgj3eV8M7E6deqkbdu26cSJE3r99dc1evRorVu3ztzfUCuJ18SbK4WXWsu+mlAPlXaR3APp/soVSI+N9M+MefhOhK3s3wQZ6UD9nO/4279//zqP8d57753xGA05we7atas+/vjjM/apIVjcSrsw8QYAeAd3PXlfkFkj3ccdAQD4Bc9FactZLJZqAejzua06NDRUF198sXr27KlZs2bp8ssv1zPPPKOEhARJDbOSuC+UWssysj0ZSHdlt+cV+m9Gb05BWZCfjHRUZWakF/nv3y/gS54ef3F+zAw2ghwAAA9hrG94FhYbBQBU4pXSLmPGjDEztQsLCzVhwoRqK4m/8cYb53x8h8OhDh06NNhK4r5gZqR7tLRLWSAyv9B/M9JPlGekUyMdVZXVSJdOOchIB2ri7fEXZ8dqschpGBLzbgCAhzDWN7wgAukAgEo8HkgfPXq02/M77rjjnI/14IMP6oYbblBSUpLy8/O1YsUKffTRR8rKypLFYmmwlcR9wVxs1MOlXST5dWmX467SLgTSUUWkrexCUAEZ6UCNPDn+4vxZLZJT3AoOAPAcxvqGV3GHmW/7AQDwDx4PpC9evNhjxzp8+LDS09N16NAh2e12devWTVlZWRo0aJCkhltJ3Be8tdio5L+lXQqLnSosLpUkNadGOqowM9KpkQ7UyJPjL85f2a3gBhlsAACPYaxveNbyFcRZbBQAIDXAYqPnY9GiRXXub8iVxBuaKyPds4uNll088NeMdNdCo8FWi6Jsfv2nCR9w1Uh3lJSqxFmqYA/+2wAAT7NSIx0AgIDHeA4AqIxIlJ/yRo300BBXRrp/BtKPF5QF0ptHhLJoDqoJDbaaP2Rddy4AgL+ylo9jzLsBAAhcVrNGuo87AgDwCwTS/VRFjXTPBZRdpV3y/bS0y4lTZQH+mAjKuqA6i8WisJCyrPTTxZR3AeDfXKM3GWwAAAQuK4uNAgAqIZDuh0pLjYrSLl6pke6fGemu0i4xkSw0ipqFE0gHECDIYAMAIPBR2gUAUBmBdD9UUFSRMe6dGun+mZGeU17ahYx01MaVkV5IIB2An3NVKDOYeAMAELC4MA4AqIxAuh866SgLdFstUpDV86Vd/Dcj3VXahYx01MzMSC8ikA7Av1mtTLwBAAh05njOgA4AEIF0v1RQHkgPCbJ6dNFNf6+RTmkXnElYaNnfMKVdAPi7isVGmXgDABCoKO0CAKiMQLofOukoCxJ6sj66VLm0S7FfTuwp7YIzCae0C4AAUTHx9m0/AADAuaO0CwCgMgLpfsiVke7J+uiSZAspO15JqeGXGb2u0i7NKe2CWrDYKIDA4Zp4M/MGACBQVQTSGc8BAATS/ZKr9IqnM9KDrRbJKJXknwuOnigv7RJLIB21qMhIL/VxTwCgbtwKDgBA4GM8BwBURiDdD1Wuke5JFotFQaVlwWp/XHD0uFkjndIuqFlYKBnpAAJDRY10H3cEAACcM9dio4znAACJQLpfKijyTka6JAU5ywLo+X4YSD9RQGkX1M0s7VJEIB2Af3NlsDHxBgAgcLkujDspkg4AEIF0v2SWdvFwRrokWV0Z6X5W2qXYWar88kx8SrugNiw2CiBQWKipCgBAwOPCOACgMgLpfshbi41K8tvSLifKFxq1WKTocEq7oGZh5YH0klJDxU7qpAPwX9byIZxAOgAAgYvFRgEAlQX7ugOozgyke6G0i7W0LGCdd9o/Aulj7pqgozm5coRES+2Hy1ri0PCfpUmStu/YqYE+7h/8S0iQRUEWi5yGodPFTo+vIwAAnlIx8fZxRwAAwDkjkA4AqIxAuh9ylTgJCbZ4/NhBTldGun+Udjmak6uBE2bohxOn9b+tB9WsWTMNnDBDkrR1wgjfdg5+x2KxKCzUqgKHU4VFTkWHcfcCAP/kGsENJt4AAASsijvMfNsPAIB/IJ3TDzXF0i6FJWU1r21eyMJH42IuOEqddAB+jIx0AAACHxnpAIDKiFr6oQJHWYDQu6Vd/CMj3cVRXFbv2lUDG6hNmLngKDXSAfgvi7k4GRNvAAAClRlI58o4AEAE0v1Svjcz0p1+mpFenl0cRkY6zoCMdACBgIx0AAACn7X8wjjjOQBAIpDul1ylXbyxkKJZ2sVPFht1KSwpyy62kZGOMyCQDiAQuALpZKQDABC4KO0CAKiMQLofMmuke7G0S76fLDbq4nBlpIfwJ4m6hYWWl3YpIpAOwH9ZyGADACDgWa0E0gEAFYha+qGThd4LpPttaZfyjPSwYDLSUTcy0gEEAjLYAAAIfFwYBwBURiDdzxiGoYIiL9ZIN0u7+GdGuo2MdJwBgXQAgaBi4s3MGwCAQBXEYqMAgEqIWvqZ08VO82q3N2qku0q7+F1GenF5Rjo10nEGrvI/BNIB+DOzRrqP+wEAAM6d68K4IdY9AQAQSPc7rrIuMgyFBFk8fnxXRnpRSakK/SgQ6Sgpz0j3QjkbNC6ujHRH+cUXAPBHVtfEm0k3AAABy5WRLlHeBQBAIN3vnCxfaNRaWiyLxfOB9LLjlv23Py04SkY66sv1N+JPF4IAoCqLeSu4jzsCAADOmcUtkE4kHQCaOgLpfqbAURYcdJVg8TSLpChbsCT/Ke9iGIYKyzPSWWwUZ+Kqo19SaqjESYQKgH+yUiMdAICAZ60UMWFMBwAQSPczVqt0eRu7wopyvHaOqLAQSVLeaf8IpBc7Dbl+k7DYKM4kNMhqBqgKKe8CwE+5aqQz6QYAIHBZKe0CAKiEqKWfuSzRrn/95hq1++Ejr50jOrw8kO4npV1cJTqCrBYFWz1fzgaNi8Vika38zgXXnQwA4G+CysczbpwBACBwuQXSiaQDQJNHIL0Jig4rK+2S7yelXRwlZVEGW7DVK3Xh0fiEld+5QJ10AP4quHzB8BKKpAMAENAo1wYAcCGQ3gSZGemn/SsjnYVGUV8VC44SoALgn4LKi6o6yV4DACCgVZRr83FHAAA+RyC9CYp21Uj3k4x0V3kOWzB/jqifikA6GekA/JOrVFkJs24AAAIa654AAFyIXDZBUeWlXfxlsVFHeVYxGemoL0q7APB3FTXSmXQDABDIym8yo0Y6AIBAelPkKu2S7y+LjZZnpIeRkY56MjPSSyjtAsA/kZEOAEDjQGkXAIALkcsmyLXYqN+UdinPSLeRkY56CgumtAsA/2ZmpDu54AcAQCCjtAsAwIVAehNUsdiofwTSHcVkpOPsUNoFgL8jIx0AgMahfEgXcXQAAJHLJqgiI91fSrtQIx1np2KxUTI9AfinoPKCqtRIBwAgsLky0p1E0gGgyQv2dQfQ8KLD/DMj3RbCdZ2G9OWXX2jILb+ocd/2HTs1sIH7czYqaqSTkQ7AP5GRDgBA42AtH9MNAukA0OQRSG+C/G+x0fKM9GAy0s/W+QTDi0ulgRNm1Lhv64QR5985L3KVAaK0CwB/FRRUnr1GIB0AgIDmKu3CmA4AIJDeBJkZ6X6z2Gh5jXRKu5y1QA6Gnw9KuwDwd2SkAwDQOLhKu5CQDgCglkYTFB1edv3kVJFTxU7fById5cFQSrugvlyBdGepoVILF2AA+J8gqysj3ffjLAAAOHeuQHopkXQAaPKIXDZBzWwVNyL4uryLIYuKnJR2wdkJCbJU3GIZZPNtZwCgBmSkAwDQOLjmHQzpAAAC6U1QcJBVkaFlQet8H5d3cVpDzf+2BfPniPqxWCwVWemV/oYAwF8EWcvGNKeTWTcAAIGMjHQAgAuRyybKteBo3mnfZqQ7g8qCoKFBVnM1dKA+XHcwkJEOwB+RkQ4AQOPgmqeWMqYDQJNHIL2J8pcFR13ZxNRHx9ly/c24LsYAgD+pqJHOpBsAgEBmobQLAKAc0csmKiqsrE563mkfB9LLs4ldZTqA+nL9zZRYyUgHvOHjjz/WsGHDlJiYKIvFojfffNNtv2EYysjIUGJiosLDw9W/f3/t3LnTrY3D4dC9996rli1bKjIyUsOHD9fBgwfd2uTk5Cg9PV12u112u13p6ek6ceKEW5v9+/dr2LBhioyMVMuWLTVp0iQVFRW5tdm+fbv69eun8PBwXXDBBXrkkUdk+PAWbDLSAQBoHIIo7QIAKEcgvYkyS7v4OiO9PJuY+ug4W2FkpANeVVBQoMsvv1wLFiyocf/s2bM1d+5cLViwQFu2bFFCQoIGDRqk/Px8s83kyZO1cuVKrVixQuvXr9fJkyc1dOhQOZ1Os01aWpq2bdumrKwsZWVladu2bUpPTzf3O51ODRkyRAUFBVq/fr1WrFih119/XVOnTjXb5OXladCgQUpMTNSWLVs0f/58PfXUU5o7d64XPpn6CQpyZaSX+qwPAADg/FVkpBNIB4CmLtjXHYBvRJdnpOcX+rhGenlpFzLScbYqFhslIx3whhtuuEE33HBDjfsMw9C8efP00EMPaeTIkZKkpUuXKj4+XsuXL9f48eOVm5urRYsW6eWXX9bAgQMlSZmZmUpKStLatWs1ePBg7dq1S1lZWdq0aZN69eolSVq4cKFSUlK0e/duderUSatXr9ZXX32lAwcOKDExUZI0Z84cjRkzRo899piio6O1bNkyFRYWasmSJbLZbEpOTtaePXs0d+5cTZkyRRZL9TU4HA6HHA6H+TwvL8+jnx8Z6QAANA4VGek+7ggAwOf8Og141qxZuuqqqxQVFaW4uDiNGDFCu3fvdmvTkLeWNyYVi436OiO9vLQLGek4Syw2CvjO3r17lZ2drdTUVHObzWZTv379tGHDBknS1q1bVVxc7NYmMTFRycnJZpuNGzfK/v/s3XlcVOX+B/DPmWFm2EcW2UTcJQ01k1LURMM1t7LSskhuZXktzdQWs5vYLzXNrK6mlnnVUqPFaNE00VyywJRExT3DnU1lXwaYeX5/wBwZWQSEWeDzft153eacZ855zszgc853vuf7aLVyEB0AevXqBa1Wa9ImKChIDqIDwJAhQ6DT6RAfHy+3CQ0NhUajMWlz5coVnDt3rtJjWLBggTzma7VatGzZ8jbfFVNKRdldM7zqJiIismmSgqVdiIiolFVHL/fs2YMXXngBcXFxiImJQUlJCQYPHoy8vDy5jbluLW9sbkw2ah0Z6RpmpFMtsbQLkeWkpKQAALy9vU2We3t7y+tSUlKgVqvh5uZWbRsvL68K2/fy8jJpc/N+3NzcoFarq21jfG5sc7NZs2YhKytLfly8ePHWB14LzEgnIiJqHBTG0i4c04mImjyrLu2ybds2k+dr1qyBl5cX4uPj0a9fP7PeWt7YWM9ko2WlXZiRTrXE0i5ElndzyRQhRKVlVKprU1n7+mhjnGi0qv5oNBqTDPb6piy76tbredFNRERkyxQs7UJERGVsKnqZlZUFAHB3dwdg3lvLK6PT6ZCdnW3ysBXWMtmooSwIyhrpVFtyIJ0Z6URm5+PjA6BitndaWpqcCe7j44OioiJkZGRU2yY1NbXC9tPT003a3LyfjIwMFBcXV9smLS0NQMWseXMI6dsPb775HwDAtpgdaNM+0OQR0ref2ftEREREdXMjkM5IOhFRU2fVGenlCSEwffp09O3bF0FBQQCqv7X8/Pnzcpv6uLW8MgsWLMDcuXPrflAWZDWlXZTG0i71+5vOkSOHMfzhxytddzTxGAbW697IEm6UdmFGOpG5tWnTBj4+PoiJiUH37t0BAEVFRdizZw8WLlwIAOjRowdUKhViYmIwduxYAEBycjISExOxaNEiAEBISAiysrLw559/4t577wUA7N+/H1lZWejdu7fcZt68eUhOToavry8AYPv27dBoNOjRo4fc5o033kBRURHUarXcxs/PD61btzbPm1JOSkoqBv1nCWJOpKJ1l3sxOvwhk/X//fdIs/eJiIiI6kYu7cJAOhFRk2czgfQXX3wRR44cwb59+yqsM9et5TebNWsWpk+fLj/Pzs6u98nKGoqrg5WUdlEYS7vUb0Z6sQEYOKnyHzniJz1Yr/siy5AnG1VoavQ3T0S1k5ubi7///lt+npSUhISEBLi7uyMgIADTpk3D/Pnz0aFDB3To0AHz58+Ho6Mjxo8fDwDQarV45plnMGPGDHh4eMDd3R0zZ85Ely5d5FJrnTp1wtChQzFx4kR88sknAIDnnnsOI0aMkMuqDR48GJ07d0Z4eDjee+89XL9+HTNnzsTEiRPh6uoKoHSek7lz5yIiIgJvvPEGzpw5g/nz5+Ott96y2L8NNy66LbJ7IiIiqicKBUu7EBFRKZsIpE+ZMgU//vgj9u7dC39/f3l5+VvLjVlqQNW3lpfPSk9LS5Oz3Wpya3llGrq+akNyKctIz7F4RjpLu1DdGL8zQqFEYbEBDmp+h4jq08GDBzFgwAD5ufGH4wkTJmDt2rV49dVXUVBQgMmTJyMjIwM9e/bE9u3b4eLiIr/mgw8+gJ2dHcaOHYuCggKEhYVh7dq1UCpv/L1u2LABU6dOlUuwjRo1CsuWLZPXK5VKbNmyBZMnT0afPn3g4OCA8ePHY/HixXIbrVaLmJgYvPDCCwgODoabmxumT59u8mO3uUm8DZyIiKhRYGkXIiIysupAuhACU6ZMQXR0NHbv3o02bdqYrDfnreWNjasVTDYqhJAz0jWcbJRqSaWUoJBKM0My8ovgoHawdJeIGpX+/fvLE3ZWRpIkREZGIjIysso29vb2WLp0KZYuXVplG3d3d6xfv77avgQEBGDz5s3VtunSpQv27t1bbRtzMmak85qbiIjItsl3mTElnYioybPqQPoLL7yAjRs34ocffoCLi4tcr1yr1cLBwQGSJJnt1vLGxjjZaG5RCQwGId+uZk6FxQYIRWlWIjPSqbYkSYK9Son8Ij0y84vh14yBdCKyHjduA+dFNxERkS27kZFu4Y4QEZHFWXUgfcWKFQBKs+LKW7NmDSIiIgDAbLeWNzYuZRnpQgA5uhJoywLr5pRVlg0vSaXZxUS1ZW9XFkgvKLJ0V4iITEicmIyIiKhRUJb9OK5nJJ2IqMmz6kB6dbeUG5nz1vLGRGOnhL1KgcJiA7ILii0SSDcGP+3tlJwokurEXlVaEigz37KT5hIR3cyYvcY4OhERkW2zKwuklxgMFu4JERFZGgtTN2HGCUezCy0ThMwqC35qVPwaUt0YSwIxkE5E1oYTkxERETUOzEgnIiIjRjCbsBsTjpZYZP+ZZaVd7O1YH53qxvgjDEu7EJG1kScm4zU3ERGRTWMgnYiIjKy6tAs1LOOEozmWykgvaDoZ6UeOHMbwhx+vdN3RxGMYaOb+NBYOzEgnIislMSOdiIioUWAgnYiIjBhIb8Jc5dIulslIN5Z2MZbnaMyKDcDASXMrXRc/6UHzdqYR0ciBdGakE5F1MWakM45ORERk2xhIJyIio8afCkxVcpFLu1gmm/fGZKP8GlLdGL87zEgnImvDGulERESNg52i9JqjhIF0IqImjxHMJsxY2sVik43KpV0af0Y6NQyWdiEia8VAOhERUeMgZ6RzTCciavIYSG/CjKVdcixU2sUY/GRGOtWVXNqFk40SkZWRjJONGizbDyIiIro9SomlXYiIqBQjmE2Yq4NlS7sYM9KbQo10ahj2KpZ2ISLrZMxIF8xeIyIismlKJQPpRERUioH0JuzGZKMWqpGebyztwq8h1Y19udIuDFYRkTUxTjbKa24iIiLbxox0IiIyYgSzCbsx2ahlSrtk5JeW43BgRjrVkb1d6XenSG9AQbHewr0hIrqBNdKJiIgaB7uyjHRONkpERAykN2GWnmw0I680kM7SLlRXKqUESZQG0FnehYisibFGOuPoREREto0Z6UREZMRAehNmyclGi0oMyCsqDYAyI53qSpIkKPWlP8gY73AgIrIGzEgnIiJqHJSKG4F0jupERE0bA+lNmNY42agFMtIzjUFPYYDGjl9DqjulXgcAyGJGOhFZEXmyUXDCUSIiIltmZ5z4BAAkXrsSETVlHAWaMBfjZKMF5p+oMaMs6Kk0FEGSpFu0Jqqa0lD6o0xmAQPpRGQ9yl9z805wIiIi26UsN6gLhZ0Fe0JERJbGQHoTZiztYhCQy6yYi7EMhzGbmKiujN8hlnYhImtS/kdilnchIiKyXeUD6VCwLCkRUVPGQHoTZq9SQFU2A3mOmcu7GCcaZSCdbpfxO8TJRonImphmpDOQTkRE1mXBggW455574OLiAi8vLzz44IM4deqUSRshBCIjI+Hn5wcHBwf0798fx44dM2mj0+kwZcoUeHp6wsnJCaNGjcKlS5dM2mRkZCA8PBxarRZarRbh4eHIzMw0aXPhwgWMHDkSTk5O8PT0xNSpU1FUZB2JMpIkyeO6kBhIJyJqyhhIb8IkSZKz0rMLzDvhqLG0i53eOk6OyHbJpV2YkU5EVkRRLiOdcXQiIrI2e/bswQsvvIC4uDjExMSgpKQEgwcPRl5entxm0aJFWLJkCZYtW4YDBw7Ax8cHgwYNQk5Ojtxm2rRpiI6ORlRUFPbt24fc3FyMGDECev2NO57Hjx+PhIQEbNu2Ddu2bUNCQgLCw8Pl9Xq9HsOHD0deXh727duHqKgobNq0CTNmzDDPm1EDclY6S7sQETVpHAWaOFcHFa7lFZl9wlG5tIuBGel0e5T6QgDAtTwG0onIekjMSCciIiu2bds2k+dr1qyBl5cX4uPj0a9fPwgh8OGHH2L27NkYM2YMAGDdunXw9vbGxo0b8fzzzyMrKwurV6/GF198gYEDBwIA1q9fj5YtW2LHjh0YMmQITpw4gW3btiEuLg49e/YEAKxatQohISE4deoUAgMDsX37dhw/fhwXL16En58fAOD9999HREQE5s2bB1dXVzO+M5WzUyhQrNdDsLQLEVGTxoz0Js7VvvS3lCwzl8XIZI10qid2xhrpDKQTkRWRJEkOpnOyUSIisnZZWVkAAHd3dwBAUlISUlJSMHjwYLmNRqNBaGgo/vjjDwBAfHw8iouLTdr4+fkhKChIbhMbGwutVisH0QGgV69e0Gq1Jm2CgoLkIDoADBkyBDqdDvHx8ZX2V6fTITs72+TRkIwZ6UJiLiIRUVPGQHoTp3VUAwAyC8wbSL+eV7o/BtLpdhm/Q9dZI52IrIyxvAsz0omIyJoJITB9+nT07dsXQUFBAICUlBQAgLe3t0lbb29veV1KSgrUajXc3NyqbePl5VVhn15eXiZtbt6Pm5sb1Gq13OZmCxYskGuua7VatGzZsraHXSs3SrswI52IqCljIL2Ja+ZQWiPd3PWlmZFO9cWurLQLM9KJyNrIE5Mxjk5ERFbsxRdfxJEjR/Dll19WWCeVr1WG0qD7zctudnObytrXpU15s2bNQlZWlvy4ePFitX26XXJGOmukExE1aQykN3FujsZAumVqpNsxkE63Sc5IZyCdiKyMxIx0IiKyclOmTMGPP/6IXbt2wd/fX17u4+MDABUywtPS0uTscR8fHxQVFSEjI6PaNqmpqRX2m56ebtLm5v1kZGSguLi4Qqa6kUajgaurq8mjITEjnYiIAAbSm7wbpV3MnZFeVtqFk43SbTL+GJOrK4GuRG/h3hAR3WC85jawSDoREVkZIQRefPFFfPfdd/j111/Rpk0bk/Vt2rSBj48PYmJi5GVFRUXYs2cPevfuDQDo0aMHVCqVSZvk5GQkJibKbUJCQpCVlYU///xTbrN//35kZWWZtElMTERycrLcZvv27dBoNOjRo0f9H3wd2MkZ6QykExE1ZbwvqYkzZqRnWCgjnaVd6HYpDEVQKiToDQKZ+cXwduXJLRFZhxs10i3cESIiopu88MIL2LhxI3744Qe4uLjIGeFarRYODg6QJAnTpk3D/Pnz0aFDB3To0AHz58+Ho6Mjxo8fL7d95plnMGPGDHh4eMDd3R0zZ85Ely5dMHDgQABAp06dMHToUEycOBGffPIJAOC5557DiBEjEBgYCAAYPHgwOnfujPDwcLz33nu4fv06Zs6ciYkTJzZ4pnlNKSVONkpERAykN3nNygLpWWYMpOsNQp7clIH0xuXIkcMY/vDjVa4/mngMA+t5nxJKfxC6mluEa7lF8Ha1r+c9EBHVjTGQLljahYiIrMyKFSsAAP379zdZvmbNGkRERAAAXn31VRQUFGDy5MnIyMhAz549sX37dri4uMjtP/jgA9jZ2WHs2LEoKChAWFgY1q5dC6XyRnLLhg0bMHXqVAwePBgAMGrUKCxbtkxer1QqsWXLFkyePBl9+vSBg4MDxo8fj8WLFzfQ0dceS7sQERHAQHqT18zB/KVdsguK5YnXlHrWtW5Mig3AwElzq1wfP+nBBtmvu5MaV3OL5DsdiIisgXF+NGakExGRtanJj7ySJCEyMhKRkZFVtrG3t8fSpUuxdOnSKtu4u7tj/fr11e4rICAAmzdvvmWfLIWTjRIREcAa6U2eMSM9I898GenGYKezxg4KGMy2X2q83Mpq/XPCUSKyJgpONkpERNQoMCOdiIgABtKbvGZlAcisAnMG0ovL9q0y2z6pcXN3Kv0eMyOdiKyJ8ZqbcXQiIiLbJk82yhrpRERNGgPpTVwzh9Jgdq6uBMV682SHZ5RlDRuDn0S3y63su3Qtl4F0IrIezEgnIiJqHG6UdmFGOhFRU8ZAehPn6qCSa7hmmmnCUWPWsDEbnuh2eTAjnYis0I0a6QykExER2TKWdiEiIoCB9CZPqZDgal+alZ5lpglHjQF7N5Z2oXrCGulEZI1uZKRbuCNERER0WzjZKBERAQykE27UKjd3RrobM9KpnhjLBDGQTkTWxBhIF8xIJyIismlyRrrEjHQioqaMgXSSS6xkmCmQbgx2MpBO9YWBdCKyRjdKu1i2H0RERHR77BSloRNmpBMRNW0MpJM84WimmepLX83VAQA8XRhIp/rhzhrpRGSFONkoERFR48DJRomICGAgnWD+0i7puaXBTk9njVn2R42fmzGQnlfMEgpEZDUUnGyUiIioUbgx2Sgz0omImjIG0kkusZJppslGr+aUZaQzkE71xL3sO1ykNyBXV2Lh3hARlbpRI93CHSEiIqLbImeks0Y6EVGTxkA6Qetgvox0IYRc2qU5A+lUTxzUSjioSk9qM/LMc2cFEdGtSMxIJyIiahRY2oWIiAAG0gnmLe2SqyuBrsQAgDXSqX7JE46yTjoRWYkbNdIt3BEiIiK6LXYs7UJERGAgnWDe0i5Xy+qjO6qVcFTzJITqjxxIz9NZuCdERKU42SgREVHjwIx0IiICGEgnAFozZqQby7qwPjrVN2Mg3fhjDRGRpRmT1xhHJyIism1KiRnpRETEQDoBaGbGGuk3JhplWReqX81dSn+cSc9hRjoRWQeFghnpREREjQEnGyUiIoCBdEK50i5mqC3NjHRqKF4MpBORleFko0RERI0DS7sQERHAQDrhxmSjeUV6FJVNBNpQ0svKbni6MJBO9YsZ6URkbTjZKBERUePAyUaJiAhgIJ0AuNqr5Ky5hs5KZ0Y6NRQG0onI2hgD6YKRdCIiIpvGjHQiIgIYSCeU1nB1Lyvvci2vgQPpZUHO5qyRTvXMy8UeAJCWU2jhnhARlVLIpV0s2w8iIiK6PcZAOiRmpBMRNWUMpBMAwKMssG3MGG8ozEinhsKMdCKyNpLEyUaJiIgaA2akExERwEA6lTEGths+kM4a6dQwjIH0vCI98nQlFu4NUeMXGRkJSZJMHj4+PvJ6IQQiIyPh5+cHBwcH9O/fH8eOHTPZhk6nw5QpU+Dp6QknJyeMGjUKly5dMmmTkZGB8PBwaLVaaLVahIeHIzMz06TNhQsXMHLkSDg5OcHT0xNTp05FUVHDT6B9K8bkNcbRiYiIbJucka5QwsBbzYiImiyrD6Tv3bsXI0eOhJ+fHyRJwvfff2+y3pwX6o2ZMZB+LZc10sk2OWvs4KguzRBhVjqRedx5551ITk6WH0ePHpXXLVq0CEuWLMGyZctw4MAB+Pj4YNCgQcjJyZHbTJs2DdHR0YiKisK+ffuQm5uLESNGQK/Xy23Gjx+PhIQEbNu2Ddu2bUNCQgLCw8Pl9Xq9HsOHD0deXh727duHqKgobNq0CTNmzDDPm1ANBTPSiYiIGgU7xY3QSZHeYMGeEBGRJVl9ID0vLw/dunXDsmXLKl1vrgv1xs4Y2E5vwIz0/KIS5Bfpy/bHGulU/+TyLg18ZwURlbKzs4OPj4/8aN68OYDSH7k//PBDzJ49G2PGjEFQUBDWrVuH/Px8bNy4EQCQlZWF1atX4/3338fAgQPRvXt3rF+/HkePHsWOHTsAACdOnMC2bdvw2WefISQkBCEhIVi1ahU2b96MU6dOAQC2b9+O48ePY/369ejevTsGDhyI999/H6tWrUJ2dnaVfdfpdMjOzjZ51DcG0omIiBoHOSMdgK6EgXQioqbK6gPpw4YNwzvvvIMxY8ZUWGfOC/XGTq6RntNwGenGbWvsFHDWcJIWqn/NnVknnciczpw5Az8/P7Rp0waPPfYY/vnnHwBAUlISUlJSMHjwYLmtRqNBaGgo/vjjDwBAfHw8iouLTdr4+fkhKChIbhMbGwutVouePXvKbXr16gWtVmvSJigoCH5+fnKbIUOGQKfTIT4+vsq+L1iwQL4LTavVomXLlvXwjpiSONkoERFRo1Aujg5dib7qhkRE1KhZfSC9Oua8UK+MObLZzKW5GWqkp5cr62KcgI2oPhkz0tOyCy3cE6LGr2fPnvj888/xyy+/YNWqVUhJSUHv3r1x7do1pKSkAAC8vb1NXuPt7S2vS0lJgVqthpubW7VtvLy8Kuzby8vLpM3N+3Fzc4NarZbbVGbWrFnIysqSHxcvXqzlO3Brxox0wYx0IiIimyZJkpyVXsSMdCKiJsum04Kru1A/f/683KY+LtQrs2DBAsydO/e2jsFaeLqUZqRfy2u4QLpcH50TjVID8WJpFyKzGTZsmPzfXbp0QUhICNq1a4d169ahV69eAFDhR1MhxC1/SL25TWXt69LmZhqNBhpNw45HCmakExERNRpKhQS9QTCQTkTUhNl0RrqRuS7Ub2aObDZz8XAqy0hvwNIuxnIbzVkfnRqIXCOdpV2IzM7JyQldunTBmTNn4OPjAwAVfoxOS0uTf/z28fFBUVERMjIyqm2TmppaYV/p6ekmbW7eT0ZGBoqLiyv80G5urJFORETUeNiV/ULOGulERE2XTQfSzXmhXhmNRgNXV1eTh60yZolfy9M12C3oyVkFAABfrUODbJ9ILu3CQDqR2el0Opw4cQK+vr5o06YNfHx8EBMTI68vKirCnj170Lt3bwBAjx49oFKpTNokJycjMTFRbhMSEoKsrCz8+eefcpv9+/cjKyvLpE1iYiKSk5PlNtu3b4dGo0GPHj0a9Jhv5UaNdAbSiYiIbB1LuxARkU0H0s15od7YeTiVZokX6wWyCoobZB9XMkvrVvs1YyCdGoaXiz0AZqQTmcPMmTOxZ88eJCUlYf/+/XjkkUeQnZ2NCRMmQJIkTJs2DfPnz0d0dDQSExMREREBR0dHjB8/HgCg1WrxzDPPYMaMGdi5cycOHTqEJ598El26dMHAgQMBAJ06dcLQoUMxceJExMXFIS4uDhMnTsSIESMQGBgIABg8eDA6d+6M8PBwHDp0CDt37sTMmTMxceJEi//AfaNGukW7QURERPVAKTEjnYioqbP6Gum5ubn4+++/5edJSUlISEiAu7s7AgIC5Av1Dh06oEOHDpg/f36VF+oeHh5wd3fHzJkzq7xQ/+STTwAAzz33nMmFemNnr1LCxd4OOYUluJpbhGaO9V9+5XJmaUa6XzP7et82EcCMdCJzunTpEh5//HFcvXoVzZs3R69evRAXF4dWrVoBAF599VUUFBRg8uTJyMjIQM+ePbF9+3a4uLjI2/jggw9gZ2eHsWPHoqCgAGFhYVi7di2USqXcZsOGDZg6dao8afioUaOwbNkyeb1SqcSWLVswefJk9OnTBw4ODhg/fjwWL15spneiaiztQkRE1HgolcxIJyJq6qw+kH7w4EEMGDBAfj59+nQAwIQJE7B27VqzXag3BZ7OmrJAug7tvZzrfftXygLpLZiRTg3EGEi/lquD3iDk2y+JqP5FRUVVu16SJERGRiIyMrLKNvb29li6dCmWLl1aZRt3d3esX7++2n0FBARg8+bN1baxBKnsvj9ONkpERGT7jBnpRXq9hXtCRESWYvWB9P79+1dbs9ucF+qNnaezGklX83A1t/6zefUGgZQslnahhuXhpIYklQatrucVyYF1IiJLYEY6ERFR4yFPNlrMjHQioqbKpmukU/3ydDZm8xbV+7bTc3QoKcsQ9mJwkxqInVIBD6fS71dqdqGFe0NETZ3xphjG0YmIiGyfPNmonoF0IqKmioF0khkD6Q2RkW6sj+7jag87Jb921HBauJXe8XApo8DCPSGipo4Z6URERI2HkhnpRERNntWXdiHz8XAunWC0IQLpVzjRKJmJfzMHHL6YiUsZ+ZbuCplZxLOTkJ6RVeX65m5arP1spRl7RE1dWRydgXQiIqJGQA6kl7BGOhFRU8VAOsmMGenpOfVf2uVGIJ310alhGTPSjXdBUNORnpGFgZPmVrl+x8o5ZuwN0Y2MdMbRiYiIbJ/GTgkAyC4ssXBPiIjIUlhjg2RyjfS8hsxIZyCdGpa/MZDO0i5EZGEs7UJERNR4aFSl4ZPsgmIL94SIiCyFgXSSeTZgaZfLmaUTPzKQTg2tRTPWSCci66CQS7tYth9ERER0++zLMtKzGEgnImqyGEgnmZdLaf3ytGwdRD1nzxkz0luwRjo1MH83RwAs7UJElicxI52IiKjR0NiVhk8YSCciaroYSCeZj9YekgToSgy4lle/ddKvZLG0C5mHsUZ6VkExcgp5kktElmPMSBcGy/aDiIiIbp+xtAsD6URETRcD6SRT2yng5VJaJ70+60vn6UqQmV96ssFAOjU0Z40dmjmqADArnYgsizXSiYiIGg+WdiEiIgbSyYSxvnR9BiCTy7LRXezt4GqvqrftElVF/h6zTjoRWRAD6URERI0HM9KJiIiBdDLh1wAByKSr+QBu1K4mamj+bpxwlIgsT+Jko0RERI0GM9KJiIiBdDJhrC9dnxnpZ9JyAAAdvZ3rbZtE1WnRjBOOEpHlGTPS63sCbyIiIjI/Y0Z6TmEJ9PyVnIioSWIgnUz4N0BplzOpuQCADl4MpJN53MhIz7dwT4ioKVMwI52IiKjR0JRlpANATiGz0omImiI7S3eArIuckV6PJTGMGekdvF3qbZtE1WmI77E5RDw7CekZWVWub+6mxdrPVpqxR0R0O1gjnYiIqPFQKiRAXwQo1cgqKEYzR7Wlu0RERGbGQDqZqO+SGAaDwN9pzEgn82qISXPNIT0jCwMnza1y/Y6Vc8zYGyK6XcYa6YyjExERNQ6KkkIYygLpRETU9LC0C5kwZvJmFRQjV1dy29u7lFGAwmID1HYKBLhzslEyjwCP0u/a1dwinuQSkcUwI52IiKhxkUoKAXDCUSKipoqBdDLhrLGD1kEFoH7KYhjLurT1dIKdkl83Mg9XexV8tfYAgL/LvoNERObGQDoREVHjoijRAWAgnYioqWJkkyq4URbj9idqPF020WhH1kcnMzPW5D+VkmvhnhBRUyVxslEiIqJGRSpmRjoRUVPGQDpVUJ8TNcoTjbI+OplZoHfpd+50KjPSicgyFApJ/m/BrHQiIiKbx9IuRERNGycbpQpuZKQX3va25IlGmZFOZma8C6IxBdKPHDmM4Q8/Xum65m5arP1spZl7RETVKRdHh0EASqnqtkRERGT9FAykExE1aQykUwX+xoz0zNvLSDcYRLlAOjPSybwCfRpfIL3YAAycNLfSdTtWzqnydRHPTkJ6Rlal6xiAJ2o4xhrpQGmddCUYSSciIrJlxoz0bAbSiYiaJAbSqQJjIP3Ctbzb2s7ptBzkF+nhoFKilbtjfXSNqMbaezlDkoCruUW4mquDp7PG0l2ymPSMrDoF4AEG4Yluh2SSkc7SLkRERLaOGelERE0bA+lUgTxJY2oO9AYBpaJuGXRxZ68BAIJbu8FOyXL8ZF6Oaju0dHPEhev5OJ2aY3OBdCEELmYU4Py1PKTn6pCv00M95BVEHbgAF3sVmjtr4NfMHr5ahzr/jdbE7QThiZq68hnpjKMTERHZPqlEB4CBdCKipoqBdKqgtYcT7FUKFBYbcP5aHto2r1tZlth/SgPpIe086rN7RDXW0dulNJCekoPe7Twt3Z0aEULgWHI2DiRdR3Zhick6hasXUrN1SM3WyWWT7O0UaO/ljAJN/fydGQwCV/N0cHdU8wcwottU/icuZqQTERHZPk42SkTUtDGQThUoFRICvV1w+FIWTiTn3DKQXlnpBwHgVNtHAaUGMVGfYXL/dxuwx0SVC/Rxxo4TqThdFnS2djqVM74+eAkp2aUn6Go7BTp4OcPH1R6uDipELZmNh1/6P2QVFCMlqxCXMgpQUKxH4pVsIGAYRi/bh/CQ1hjR1Rf2KmWN93stV4fvE65g85ErOJmcg4JiPewUEto2d0Kua3sYDAKKBsx6J2qsJEmCQiqdaNTAODoREZHNY2kXIqKmjYF0qlQnX1ccvpSFkynZGN7Vt9q2lZV+SM/R4fifF6BSStClX2jIrhJVqWNZmaKTydkW7smt7Tmdjn9aDoMhuxAqpYRebTzQ1V9rkhUuriahXbkftgxC4HJGAY4nZ+PUlUwcvpSFw98cxrwtxzE2uCXG3dOyyh/CSvQGnLuWjwu+/dBz/k6U3BTlKzEInE7NBbx7YcOfFzCyqy+aOaob5uCJGjFJkgAhmJFORETUCMgZ6fkMpBMRNUUMpDcxR44cxvCHH690XfmJA+/wKQ1AnqhjAPJSRj4AwK+ZAyQweECW0dW/GQAg8XI2Cov1tcrSNqdv4y/h1W8Pw6DUwFdrj2FBPnCxV93ydQpJQkt3R7R0d4T4438YPflNbIi7gMuZBfhk7z/4ZO8/aNfcCde8euHAuetQSBJ0JXqk5+hwObMAxXoBOAcABoFu/lo83MMffdt7oqW7I67m6rDlSDIW/HAI1/OAb+Iv4cG7WqC5i23VmieyNIUE6MEa6URERI2BMSM9R1fCuzaJiJogBtKbmGIDajRxYCdfVwDAieScOu3ncmYBAMC/mQMy67QFotvX2sMRPq72SMkuRPz5DPRpb3110tfHnceb3ycCAJpln8WYAYNhp6h9bXI7vQ6T+7fH8/3a4deTadiw/zz2nbmKs+l5gLY9/iib/Lc8Z40dNClHsHHuJASW/Xhm5Kt1wLP3tcXXS/8P17uOx9XcInz31yU8dm8AtA63DvJbit4gcO5aHs6k5UIhAd4u9ujg7QxHNYc7sgyVUoFivR66Ej0A6/3bISIiolszZqQLAeQUlkDryLGdiKgp4UxyVKk7fEoD6ZczC2pd/62oxIAL10sz0v3dHeu9b0Q1JUmSPNltbCWBZEtbvS9JDqJH9G4Nv9TYOgXRy1MqJAzq7I21/7oXf701CCueuBvNrx3BHT4uuMPHBUEtXNG/Y3OMu6clnu7TGj5X/6oQRC9PpS/EI3f7w8tFg8ISAzYfuYJiveG2+thQCtVafBF3HpuPJONUSg5OJOdg9+l0bNh/Qb5LhsjcnMp+xMnT6S3cEyIiIrpdkjDAoewuV9ZJJyJqepiiR5XSOqrQopkDLmcW4GRyNnq29ajxa0+n5qBYL+DmqIK3iwaJDdhPolsJaeuB6EOX8cfZqwACLd0d2ce7/sZ7v5wCAPy7fzu8OiQQI9bV7z5c7VUY1sUXy64fwcA7H660TXXlngDgaOIxDFQpMaKrL7788yKu5hZh58k0DL3Tp347e5t+//sqkvyHwFBQDAeVEp18XWCnVOBMag4y8ovx3V+X4efSxtLdpCbIUaMEcoG8ohJLd4WIiIjqgdZBhYJiPQPpRERNEAPpVKU7fFxKA+kpObUKpCdeyQIA3OmnLZ1kjciCjBnpRy5lIU9XAieNZf/ZE0Lg/e2nsWzX3wCA6YM6Ysr97S32t1JduScAiJ/0IADAxV6F4V18senQJZxKyUEbDycz9fDWTqZkY+LnB2FQquHXzB4juvjBQV2aKRTcyg2/nkzDyZQcXPHqhfjz19GjlbuFe0xNiTEjPZ8Z6URERI2Cl6sGKdmFuJSRjy7+Wkt3h4iIzIiBdKpSZz9X7DyZhkMXMjChd+savSY9R4fUbB0UEtDJt+pyEUTm0tLdEf5uDriUUYAD566jf6BXg+8z4tlJSM/IqrBcQEKWf29cdijNjH7jgTvwXL92Dd6f+tLCzQH3tnbH/qTr2HUqDa2UDpbuEjLzizDx84PIL9LDKT8ZDw3oa1IeR6VUYHBnbxTrDTibnofnv4jHT1P6wldr+b5T0+CkKf1RJ0/HjHQiIqLG4A4fFxy5lIUTydkY1sXX0t0hIiIzYiCdqnRfh+ZY+uvf+PVkGor1BqiUt67dfORSJgCgXXNO7kfWI6StB76Jv4Q/zl4zSyA9PSOrQpa3rkSPrYkpuHyttFb326PvxFMhrRu8L/XtntbuSLqah7QcHa54h0AIYbFs+hK9AS9uPISL1wvQ0t0Bjmd/g52iX4V2kiRhyJ0++N8vB3AV7nj12yP4/Ol7eccMmYVcI52lXYiIiBqFzr6l84kdT86xcE+IiMjcONkoValHKzd4OKmRXViCP5Ou37J9Wk4hjl3JBgB082/WwL0jqjlj8PyHhMsoscBEmZczCrBh/wWcv5YPyVCCFU/cbZNBdKB0MtMhd/pAqZCQ6+SH9XHnLdaXhdtOYt/fV+GgUuLT8GDYGYqqbKtSKuCfvA8aOwV+O3MVG/ZfMGNPqSkzlpPiZKNERESNQ6eyQPqJ5GwL94SIiMyNgXSqklIhYWAnbwDAL8dSqm0rhMCuk+kQADp6OaOFG8smkPUY2NkLHk5qpGbrsPNkmtn2m6crwY4Tqfj2r0vIKSyB1kGFNpe22/wtoO5OavRt7wkAmPfzCSRdzTN7H74/dBmrfksCALw/tpt8QVMdTXE2Xht6BwBg/s8ncP6a+ftNTY9c2oUZ6URERI1CJ7/S887LmQXIyueEo0RETQkD6VStIUGlgfTtx1IhhKiyXcLFTKRkF0KllHBfh+bm6h4Rjhw5jOEPP17lI+LZSdDYKfFocEsAMEsmcrGdI/aeSce62HPyXRqdfV0x/t4AOOhufXeHLejmr4VTfjIKiw14+asEs2b6H72Uhdc2HQEAvDigPR6oxQ8TEb1bo1dbd+QX6fHKN0egN1T97xpRfSg/2Wh14ygRERHZBld7FfzLEseOMyudiKhJYRFrqlbvdp5wUiuRkl2Iw5eycFfLZhXa5Dj64fiZq3J7Z3t+rch8ig2oUI+8vB0r5wAAxt8bgJV7zmLv6XRcuJaPAA/Heu1HUYkBv51Jx1cHLuJ06weBC5kAAG9XDfp1aA6/Zo3rLg1JktAiNRbJncYi4WImVuw+iylhHRp8v2k5hXj+i4PQlRhw/x1emD6oY61er1BIeO+Rbhj64V78ee46/rcvCRP7tW2g3hIBjurSjHS9ENCVGGCvUlq4R0RERHS7Ovu64lJGAY4nZyOknYelu0NERGbCjHSqlr1KifvLyrvM//lEhezN/f9cwyXf+yAAdPJ1QTd/rQV6SXRrAR6O6Nex9G6Jhb+crJfMUL1B4I+zVzHruyO4d/4OPLPuILYfTwUkBVo0c8Dobn4YF9yy0QXRjVQl+Xh79J0AgI92nsFfFzIadH85hcWI+N8BXMkqRFtPJ3z42F1QKGo/YWhLd0f8Z0RnAMB720/hTConiqKGY6dUQGNXerqVp2N5FyIiosaAddKJiJomBtLpll4ZHAgntRJ/Jl3H6n3/ACgNIG7Yfx5Prt4Pg0KFlm4OCLvDG5JU+6AWkbnMHNwRdgoJW44k48fDV+q0DSEEDl3IwNyfjiFkwU6MX7UfX/55EZn5xWjuosHTfdqg3fmf8EgPf7T2dGr0fxMP3tUCw7v4osQgMOmLeKRkFTbIfgqK9Hj+i3gcT86Gp7Ma/4u4B672qjpvb9w9LdE/sDmKSgyY8c1hFFtgElpqOuQJR4s44SgREVFj0LmsTvrxKwykExE1JazBQbcU4OGIt0Z2xmubjuLdrSex/VgqUnMKcfF6AQDANeccRvYPg7IOmaFE5tTVvxmmhnXAkpjTePP7RLTxdEJX/2a3fJ0QAseTs/Hz0WT8dDgZF67ny+u0DioMC/LBqG5+6NnWA0qFhOFrsxrwKCpnrBVfmaOJxzCwgfYrSRIWPtIVf6fl4lRqDp774iA2TuwFZ039DS+Z+UV4eu0B/HUhE05qJdZE3IvWnk63tU1JkrDw4a4Y/MFeHLmUhRW7z2KqGUrTUNPkpFbieh4z0omIiBqLzmUZ6WfScpBfVAJHNUMrRERNAf+1pxoZG9wSB85l4Nv4Szh4vrR8g9ZBhUmh7fDT0vVQKQdZuIdENTO5fzvsPpWGvy5k4pEVsXhlSCDGBreE1tE0u/l6XhEOnLuO3afSsOtkOlKyb2RaO6qVGNTZGyO7+qFfx+ZQ29X85p6GCnhXVys+ftKDddxqzThr7PDZhGCMWrYPRy5lYcL//sTaf90Dl9vIGDdKvJyFqVGH8E96Hlzt7fC/iHvQpZ5KSHm72uPt0XfipagE/HfnGdx/hxeCWrA8FdW/GxnpDKQTERE1Bv5uDghwd8SF6/n4PPY8JoW2s3SXiIjIDBhIpxqRJAmLH+2Gl8I64Pe/r0Jtp8CwIF84qJXYvNTSvSOqOTulAmufvhevfHMYvxxLxbyfT2D+lkRodJlQ6nUwKFQotnNEico049lBpcR9HTwxspsfwjp51TnrxJIB7/p2848Cbhp3ZLcIQ/z5DIS8+TW2vvEQWrrXflLXiGcnITUzF9eadUK6RxcISQm74jw0P/8rlr0TjbWfray3YxjVzQ+/HEvBz0dTMOPrw/hxSh9o7DgZJNUvp7J/L/J0LO1CRETUGEiShJfCOmDGN4exYvdZjO8ZcFtlB4mIyDYwkE610tLdEY/dG2DpbhDdFld7FVY+2QO9n5mDorb34VpuEQrtPSq0Uxdlwzn/CpzzLsOpIBWXjxuw8gdgJYDTJ0+g4x2dKt1+Q5ZSsSaV/SiQllOI6EOXkYtmeOC/v+GtEZ0x5m7/Gpd+yiooxknhi9w7eyG3rAxGW08nDOzUFg7qu7Bj5Zx6PQZJkvB/o4PwZ9J1nErNwfwtJxA56s5GX9uezMtRU/rjTD5LuxARETUaD3ZvgRV7zuLvtFx8sucsXhlyh6W7REREDYyBdCJqkiRJgnvWGQzs+SQy8oqQWVCMwmI9VEoFHNVKfPHmv/DSsigAPSp9ffykBxtNZnl98nKxx+P3BCBq5wHkoDle+fYIVu45i/E9W2FgJy8EuDuaBKkNBoGz6bmIP5+BX0+mYe+ZdBQ2DwZ0JXCxt0Pvdh4I9HZp0MC2h7MGC8Z0xcTPD2Jd7Hn4NnPg7blUr5w52SgREVGj8PeZM7inT6j8XOfREej8ID7edRb/+3wj2uQew88/bLJgD4mIqCExkE5ETZ6bkxpuTmrThSWFlTemW3J1UKHNpe0YNfX/sHLPWZxNz8P/bT6O/9t8HE5qJXybOSD5yhUUCQklSkcIhWkpFZGVjLB7u6GTrwvslDWvP387BnX2xpvDO+GdLSfw7taTcFApMaF3a7Psmxq/G6VdmJFORERky0oMAuMjV8nPhRD4/e9riL+QgYKWPXGysBMW/HwC3QOaoW1zZ7g7qaF1UEFlpnNaIiJqWAykExFRvZMgMCm0HZ7oGYBv4y/hl2MpOHAuA3lFevydlgvYucpt7RQSvFw1aOnmiLbNnbDmlVfQZcz39d6n6iZ6be6mxdrPViI9V4dP9vyDOT8eQ3JWIV4dEghFDcvSEFXFqay0S05hCa7nFVm4N0RERFRfJElC3w6e8HbVYOfJNOjsXfHJ3n8qtHPW2EHroEIzRxXcHNXQOqrgZvxvh9L/d3NSoZmjGu6Oarg5quFib8fzUCIiK8NAOjW46oJXTaWWNFFT5WKvwr/6tMG/+rSBrkSPi9cLkJZTiFlz5uHe0RFwsbeDk8YOCjPUJK9uoldj7fXXh94BZ7Ud3o85jZV7zuLwxUwsHtsNLZo5NHj/qPHSOqjgq7VHclYhfki4DKFxsXSXiIiIqB518HZBG08nLPrPTLgH9YXe0RN6ezcIlT0AIFdXglxdCS5nFtR4m0qFBBd7OzhrSh8u9nZwsVeVPre3g0vZstLnqrKAfGmwvpmjGs0cVGa7u5OIqKlgIJ0aXHXBq6ZcS5rMgz/kWA+NnRLtvZzR3ssZzgUp8LOi4PTN35MWLm1wxasnYv+5hn7zt+PFQZ0wsV9budY1UW1IkoQRXX3x9cFLyCooBvq/iONXstHZz/XWLyYiIiKbYKdUoDApHpPmL5GXGYSArsSAwmI9lr7+HMLf+hi6Yj0KivUoLDagsESPwmI9jv21Hw6uHjCoHCDsHCDs1NAbBDLzi5GZX1znPjmqlbBXKaFWKqBRKUz/305ZbpkSGrvSuaIcVEo4lP2/o1oJtZ0CKuWNh9pOgkqpgJ2idFsVX2MHJTPpiaiRYkTgJsuXL8d7772H5ORk3Hnnnfjwww9x3333WbpbRFRH/CGncWmoH0Yq+55k5hdh+/FUJGcV4qOdZ/C/fUl4sHsLPNjdD3e1dOMFgpWy1nHcUW2HB+/yw3eHLiMHzfHQ8t8xqpsferX1wMBO3tA6qizdRSIiIouy1jH8digkqTTIrFKiJD0JbTydKm0XO+8DzPxyj/y8xGBAYbEBRSUGLJv1HCbM+RhFJQYU6Q3QlRjk/y4qMeDQ77/CqZkHhJ09DHb2pYH4skz4/CI98i0w2blaqZAD6+X/3xjYvzn4Lv93WRuNXWnwX2VXGuhX2ymgsVPIQX11ueXG/+e5ORGZAwPp5Xz11VeYNm0ali9fjj59+uCTTz7BsGHDcPz4cQQEBFi6e0RETZ45fxhp5qjGoz388d2G/0HdZRj+uZqHL+LO44u483BzVOHuADd09W+G/oHN0a1ls3rdN9WNtY/jzRzVePzeAHz61RbofDvhm/hL+Cb+EtRKBXq180CLZg5o7qyGp4sGzZ018HTRwNVeBUe1Ek4aOzhpSi8qJTOUQqpvBoOAXgjoDQIaO9s8BiIiajjWPoabm51CAWeNAtAAJelJaOVReQAeAPa+vRKvlAvCAzcy4Re+MA5TlmyQx+AS/Y3x+MsPIzHmxbeg1wuUGAwoKVtfbDCgRC8Qv2cbnJu5A5ISQqEEJAWEpAQUShQVl0Bl7wihUEIoVIBCBaFUAWXje5HegKICQ+mdeGaiVEhQKxVw0pRmxTuqlXDW2MFRYweNnQJ2CglKhVT2/2XPlRJUCulGcN6uYpBeIUlA6f+gkCRIUulhSpCMhwtJkqAot0yC8a0ofa6QJHmZ8bW4eXm515quq3ybRuXPqMqfX5kuL99eqnR5efJxVdG2Jturav+oQXuFJJm818bnCunGe21cVvpelb1PuOm9LPtvAQEhSrctBOTnAqUTBouy5RA3OiN/Fqh827h5Xze347luo8VAejlLlizBM888g2effRYA8OGHH+KXX37BihUrsGDBggrtdToddDqd/DwrKwsAkJ2dfdt9KS4uRmFebqXrDAZ9ndbdan1xcXGd+l5dX2+1z4Y6Fu6z6RwL3z/r3GdCwl8YPOqRKrd7/PgJ9LWRY3HMOIuvn+2O/UnXEf3XJfz2dzquZeQjJiMLMYeB/Nx2aKNtX+V+a8L4b68wnuFRndRmHG/IMby675MEIPnbt+Ed1BvwbAf43IFCVx/sPlr1d7c8O4UEO6UEvaH0u6JUSFApJagUCigUNy4sJEmCQmF6sl8TxosA43+X/4/yFxOi7CJcLwT0egGD8QK9XMBcb7gRQC//1XZUK+Dlag+Vom51WwXM83ciwXIXQOY6xtqy5HtCN1jr98NWrYm4F25O6jq/nmN4/bCma3F9SUmV47gwGKo936xufUOsq269BECflQonqbgsCgyTCJDu7/1o62oci5UVXr/r11WY8r+tle5z3tPDMPumdcZzg0UvjsWUD79EiV6gRG9AsTFQrxfY9MkiPPDMDBTrS88fivWlAfxigwF6vcDx+N/h6KIFFHYQxgC+QgkBJfQCUKjUNwL7CtNwlgFAMYBq3ioisysfZAfK/yBS9qzcebZp8L5iIL/8S4xLqmpn/NHFtA83NiKZ7LfijxsVfjS46RhQxetv+X7UuGXVP/SUF9zKHW+O6FyLrVZU63FckBBCCJ1OJ5RKpfjuu+9Mlk+dOlX069ev0tfMmTPH+JsVH3zwwQcffNz24+LFi+YY8hql2o7jHMP54IMPPviozwfH8LrjtTgffPDBBx+WftR0HGdGepmrV69Cr9fD29vbZLm3tzdSUlIqfc2sWbMwffp0+bnBYMD169fh4eFR59s4srOz0bJlS1y8eBGurrY7CVljOA4eg3VoDMcANI7j4DE0HCEEcnJy4OfnZ+mu2KzajuMNMYYD1vsdswZ8b6rG96ZqfG8qx/elauZ+bziG3z5ruRYHGuffFo/JNjTGYwIa53HxmGxDTY+ptuM4A+k3uXnQFUJUORBrNBpoNBqTZc2aNauXfri6ujaKL29jOA4eg3VoDMcANI7j4DE0DK1Wa+kuNAo1HccbcgwHrPM7Zi343lSN703V+N5Uju9L1cz53nAMrx/Wci0ONM6/LR6TbWiMxwQ0zuPiMdmGmhxTbcbxuhWnbIQ8PT2hVCor/OKdlpZW4ZdxIiIisi4cx4mIiGwTx3AiIrIVDKSXUavV6NGjB2JiYkyWx8TEoHfv3hbqFREREdUEx3EiIiLbxDGciIhsBUu7lDN9+nSEh4cjODgYISEh+PTTT3HhwgVMmjTJbH3QaDSYM2dOhdvUbE1jOA4eg3VoDMcANI7j4DGQteM4bt343lSN703V+N5Uju9L1fje2CZrGMOBxvn94THZhsZ4TEDjPC4ek21oqGOShBCiXrdo45YvX45FixYhOTkZQUFB+OCDD9CvXz9Ld4uIiIhqgOM4ERGRbeIYTkRE1o6BdCIiIiIiIiIiIiKiarBGOhERERERERERERFRNRhIJyIiIiIiIiIiIiKqBgPpRERERERERERERETVYCCdiIiIiIiIiIiIiKgaDKRbmeXLl6NNmzawt7dHjx498Ntvv1m6S7K9e/di5MiR8PPzgyRJ+P77703WCyEQGRkJPz8/ODg4oH///jh27JhJG51OhylTpsDT0xNOTk4YNWoULl26ZLZjWLBgAe655x64uLjAy8sLDz74IE6dOmVTx7FixQp07doVrq6ucHV1RUhICLZu3Woz/a/MggULIEkSpk2bJi+z9uOIjIyEJEkmDx8fH5vpf3mXL1/Gk08+CQ8PDzg6OuKuu+5CfHy8vN7aj6V169YVPgtJkvDCCy/YRP+pcbHmcdxSbnX+0JTV5LykKbrVuQ7dUNk5VFN1q3MzIqPajtV79uxBjx49YG9vj7Zt22LlypVm6mnN1eaYdu/eXem588mTJ83Y4+rV5dzB2j+n2h6TLXxOdT2PsebPqi7HZO2fVV3Oq6z5MwJqf0z1+RkxkG5FvvrqK0ybNg2zZ8/GoUOHcN9992HYsGG4cOGCpbsGAMjLy0O3bt2wbNmyStcvWrQIS5YswbJly3DgwAH4+Phg0KBByMnJkdtMmzYN0dHRiIqKwr59+5Cbm4sRI0ZAr9eb5Rj27NmDF154AXFxcYiJiUFJSQkGDx6MvLw8mzkOf39/vPvuuzh48CAOHjyI+++/H6NHj5YDg9be/5sdOHAAn376Kbp27Wqy3BaO484770RycrL8OHr0qE31HwAyMjLQp08fqFQqbN26FcePH8f777+PZs2a2cyxHDhwwORziImJAQA8+uijNtF/ajysfRy3lFudPzRlNTkvaYpuda5Dpao6h2rKqjs3IwJqP1YnJSXhgQcewH333YdDhw7hjTfewNSpU7Fp0yYz97xqdT3/OHXqlMnfS4cOHczU41ur7bmDLXxOdT0fsubPqS7nMdb+Wd3OuZm1fla1Pa+y9s8IqPu5Yr18RoKsxr333ismTZpksuyOO+4Qr7/+uoV6VDUAIjo6Wn5uMBiEj4+PePfdd+VlhYWFQqvVipUrVwohhMjMzBQqlUpERUXJbS5fviwUCoXYtm2b2fpeXlpamgAg9uzZI4Sw3eNwc3MTn332mc31PycnR3To0EHExMSI0NBQ8dJLLwkhbONzmDNnjujWrVul62yh/0avvfaa6Nu3b5XrbelYjF566SXRrl07YTAYbLL/ZLtsaRy3lJvPH8jUzecldIPxXIdKVXUO1ZRVd25GZFTbsfrVV18Vd9xxh8my559/XvTq1avB+lhbtT2mXbt2CQAiIyPDDL27fTU5d7CFz6m8mhyTrX1OQtTsPMbWPquaHJMtflbVnVfZ2mdkVN0x1ednxIx0K1FUVIT4+HgMHjzYZPngwYPxxx9/WKhXNZeUlISUlBST/ms0GoSGhsr9j4+PR3FxsUkbPz8/BAUFWewYs7KyAADu7u4AbO849Ho9oqKikJeXh5CQEJvr/wsvvIDhw4dj4MCBJstt5TjOnDkDPz8/tGnTBo899hj++ecfm+o/APz4448IDg7Go48+Ci8vL3Tv3h2rVq2S19vSsQCl/5auX78eTz/9NCRJsrn+k+2y9XGcrMPN5yVU8VyHSlV1DtXUVXVuRgTUbayOjY2t0H7IkCE4ePAgiouLG6yvNXU75x/du3eHr68vwsLCsGvXrobsZoOz9s/pdtjS51ST8xhb+6xqc25mC59VTc6rbO0zqs25Yn18RgykW4mrV69Cr9fD29vbZLm3tzdSUlIs1KuaM/axuv6npKRArVbDzc2tyjbmJITA9OnT0bdvXwQFBcl9NPapqj5aw3EcPXoUzs7O0Gg0mDRpEqKjo9G5c2eb6T8AREVF4a+//sKCBQsqrLOF4+jZsyc+//xz/PLLL1i1ahVSUlLQu3dvXLt2zSb6b/TPP/9gxYoV6NChA3755RdMmjQJU6dOxeeffy7309ivqvppLccCAN9//z0yMzMREREh983Yl6r6Zk39J9tl6+M4WV5l5yVNWVXnOlT9OVRTVt25GRFQt7E6JSWl0vYlJSW4evVqg/W1pupyTL6+vvj000+xadMmfPfddwgMDERYWBj27t1rji43CGv/nOrC1j6nmp7H2NJnVdNjsoXPqjbnVbbyGdXmmOrzM7K73Y5T/ZIkyeS5EKLCMmtWl/5b6hhffPFFHDlyBPv27auwztqPIzAwEAkJCcjMzMSmTZswYcIE7NmzR15v7f2/ePEiXnrpJWzfvh329vZVtrPm4xg2bJj83126dEFISAjatWuHdevWoVevXgCsu/9GBoMBwcHBmD9/PoDSX2iPHTuGFStW4KmnnpLb2cKxAMDq1asxbNgw+Pn5mSy3lf6T7bP1cZwsp7rzkqaoqnOdph5Mr+k5VFNU3bnZ9OnTLdgzsja1Hasra1/ZckuqzTEFBgYiMDBQfh4SEoKLFy9i8eLF6NevX4P2syHZwudUG7b2OdXmPMZWPquaHpMtfFa1Pa+yhc+oNsdUn58RM9KthKenJ5RKZYVfjdPS0ir8EmSNfHx8AKDa/vv4+KCoqAgZGRlVtjGXKVOm4Mcff8SuXbvg7+8vL7eV41Cr1Wjfvj2Cg4OxYMECdOvWDR999JHN9D8+Ph5paWno0aMH7OzsYGdnhz179uC///0v7Ozs5H5Y+3GU5+TkhC5duuDMmTM28zkApb/M3jzQdOrUSZ6cyJaO5fz589ixYweeffZZeZkt9Z9sm62P42RZVZ2XNGVVnes0dbc6h+Ik2TeUPzcjAuo2Vvv4+FTa3s7ODh4eHg3W15qqr/OPXr162fTfirV/TvXFWj+n2pzH2MpndbvnZtb2WdXmvMpWPqPbPVes62fEQLqVUKvV6NGjB2JiYkyWx8TEoHfv3hbqVc21adMGPj4+Jv0vKirCnj175P736NEDKpXKpE1ycjISExPNdoxCCLz44ov47rvv8Ouvv6JNmzY2eRw3E0JAp9PZTP/DwsJw9OhRJCQkyI/g4GA88cQTSEhIQNu2bW3iOMrT6XQ4ceIEfH19beZzAIA+ffrg1KlTJstOnz6NVq1aAbCtv4k1a9bAy8sLw4cPl5fZUv/Jttn6OE6WcavzErrBeK7T1N3qHEqpVFq6i1aj/LkZEVC3sTokJKRC++3btyM4OBgqlarB+lpT9XX+cejQIZv+W7H2z6m+WNvnVJfzGGv/rOrr3MzaPqubVXdeZe2fUVVqe65Y58/otqcrpXoTFRUlVCqVWL16tTh+/LiYNm2acHJyEufOnbN014QQQuTk5IhDhw6JQ4cOCQBiyZIl4tChQ+L8+fNCCCHeffddodVqxXfffSeOHj0qHn/8ceHr6yuys7PlbUyaNEn4+/uLHTt2iL/++kvcf//9olu3bqKkpMQsx/Dvf/9baLVasXv3bpGcnCw/8vPz5TbWfhyzZs0Se/fuFUlJSeLIkSPijTfeEAqFQmzfvt0m+l+V0NBQ8dJLL8nPrf04ZsyYIXbv3i3++ecfERcXJ0aMGCFcXFzkv1dr77/Rn3/+Kezs7MS8efPEmTNnxIYNG4Sjo6NYv3693MYWjkWv14uAgADx2muvVVhnC/2nxsHax3FLudX5Q1NWk/OSpuhW5zpk6uZzqKbqVudmRELceqx+/fXXRXh4uNz+n3/+EY6OjuLll18Wx48fF6tXrxYqlUp8++23ljqECmp7TB988IGIjo4Wp0+fFomJieL1118XAMSmTZssdQgV3OrcwRY/p9oeky18TjU5j7G1z6oux2Ttn9Wtzqts7TMSovbHVJ+fEQPpVubjjz8WrVq1Emq1Wtx9991iz549lu6SbNeuXQJAhceECROEEEIYDAYxZ84c4ePjIzQajejXr584evSoyTYKCgrEiy++KNzd3YWDg4MYMWKEuHDhgtmOobL+AxBr1qyR21j7cTz99NPyd6R58+YiLCzM5MLS2vtflZsvAq39OMaNGyd8fX2FSqUSfn5+YsyYMeLYsWM20//yfvrpJxEUFCQ0Go244447xKeffmqy3haO5ZdffhEAxKlTpyqss4X+U+NhzeO4pdzq/KEpq8l5SVN0q3MdMsVAeqlbnZsRGVU3Vk+YMEGEhoaatN+9e7fo3r27UKvVonXr1mLFihVm7vGt1eaYFi5cKNq1ayfs7e2Fm5ub6Nu3r9iyZYsFel21W5072OLnVNtjsoXPqSbnMbb2WdXlmKz9s7rVeZWtfUZC1P6Y6vMzkoQoqxhPREREREREREREREQVsEY6EREREREREREREVE1GEgnIiIiIiIiIiIiIqoGA+lERERERERERERERNVgIJ2IiIiIiIiIiIiIqBoMpBMRERERERERERERVYOBdCIiIiIiIiIiIiKiajCQTkRERERERERERERUDQbSiYiIiIiIqNHau3cvRo4cCT8/P0iShO+//75Wr4+MjIQkSRUeTk5ODdNhIrIZu3fvhiRJyMzMrLLN2rVr0axZM/l5ZGQk7rrrrtved13+PSOyNdY2hjOQTkT1JiIiAg8++KClu0FEREQ2gucOZA55eXno1q0bli1bVqfXz5w5E8nJySaPzp0749FHH63nnhIR2b7r169j2rRpaN26NdRqNXx9ffGvf/0LFy5csHTXyAZZ2xjOQDoRERERERE1WsOGDcM777yDMWPGVLq+qKgIr776Klq0aAEnJyf07NkTu3fvltc7OzvDx8dHfqSmpuL48eN45plnzHQERES24fr16+jVqxd27NiB5cuX4++//8ZXX32Fs2fP4p577sE///xT5WuLiorM2FOyFdY2hjOQTkS19u2336JLly5wcHCAh4cHBg4ciFdeeQXr1q3DDz/8IN8qY/zH6/Llyxg3bhzc3Nzg4eGB0aNH49y5c/L2jNloc+fOhZeXF1xdXfH8889zICUiIqqBbdu2oW/fvmjWrBk8PDwwYsQInD17FgBw7tw5SJKEr7/+Gvfddx8cHBxwzz334PTp0zhw4ACCg4Ph7OyMoUOHIj09Xd6mwWDA22+/DX9/f2g0Gtx1113Ytm2bvL6yW9kTEhIgSZI8xhtvZf/ll1/QqVMneT/JyckASm+1rercgcic/vWvf+H3339HVFQUjhw5gkcffRRDhw7FmTNnKm3/2WefoWPHjrjvvvvM3FMisgSdToepU6fCy8sL9vb26Nu3Lw4cOFBl+7Vr1yIgIACOjo546KGHcO3atUrbffLJJ2jZsiUcHR3x6KOPmoypBw4cwKBBg+Dp6QmtVovQ0FD89ddf1fbztddeQ8eOHeHo6Ii2bdviP//5D4qLi+X1xpIyX3zxBVq3bg2tVovHHnsMOTk5chuDwYCFCxeiffv20Gg0CAgIwLx58+T1t7q2nz17Nq5cuYIdO3bggQceQEBAAPr164dffvkFKpUKL7zwgty2f//+ePHFFzF9+nR4enpi0KBBAIAff/wRHTp0gIODAwYMGIB169bdsnwONV3mHsMZSCeiWklOTsbjjz+Op59+GidOnMDu3bsxZswYzJkzB2PHjpUvkJOTk9G7d2/k5+djwIABcHZ2xt69e7Fv3z75Qrp8oHznzp04ceIEdu3ahS+//BLR0dGYO3euBY+UiIjINuTl5WH69Ok4cOAAdu7cCYVCgYceeggGg0FuM2fOHLz55pv466+/YGdnh8cffxyvvvoqPvroI/z22284e/Ys3nrrLbn9Rx99hPfffx+LFy/GkSNHMGTIEIwaNarKi5Kq5OfnY/Hixfjiiy+wd+9eXLhwATNnzgRQeqttZecOROZ09uxZfPnll/jmm29w3333oV27dpg5cyb69u2LNWvWVGiv0+mwYcMGZqMTNSGvvvoqNm3ahHXr1uGvv/5C+/btMWTIEFy/fr1C2/379+Ppp5/G5MmTkZCQgAEDBuCdd96p0O7vv//G119/jZ9++gnbtm1DQkKCSZA5JycHEyZMwG+//Ya4uDh06NABDzzwgEnQ+2YuLi5Yu3Ytjh8/jo8++girVq3CBx98YNLm7Nmz+P7777F582Zs3rwZe/bswbvvviuvnzVrFhYuXIj//Oc/OH78ODZu3Ahvb28AuOW1vcFgQFRUFJ544gn4+PiY7NfBwQGTJ0/GL7/8YvK+rVu3DnZ2dvj999/xySef4Ny5c3jkkUfw4IMPIiEhAc8//zxmz559i0+ImiqLjOGCiKgW4uPjBQBx7ty5CusmTJggRo8ebbJs9erVIjAwUBgMBnmZTqcTDg4O4pdffpFf5+7uLvLy8uQ2K1asEM7OzkKv1zfMgRARETVSaWlpAoA4evSoSEpKEgDEZ599Jq//8ssvBQCxc+dOedmCBQtEYGCg/NzPz0/MmzfPZLv33HOPmDx5shBCiF27dgkAIiMjQ15/6NAhAUAkJSUJIYRYs2aNACD+/vtvuc3HH38svL295eeVnTsQNSQAIjo6Wn7+9ddfCwDCycnJ5GFnZyfGjh1b4fUbN24UdnZ2Ijk52Yy9JiJLyc3NFSqVSmzYsEFeVlRUJPz8/MSiRYsqjIePP/64GDp0qMk2xo0bJ7Rarfx8zpw5QqlUiosXL8rLtm7dKhQKRZX/tpSUlAgXFxfx008/yctu/vfsZosWLRI9evQw2a+jo6PIzs6Wl73yyiuiZ8+eQgghsrOzhUajEatWrap0e7e6tk9JSREAxAcffFDp67/77jsBQOzfv18IIURoaKi46667TNq89tprIigoyGTZ7NmzK5xzUNNkDWO4Xd1D8ETUFHXr1g1hYWHo0qULhgwZgsGDB+ORRx6Bm5tbpe3j4+Px999/w8XFxWR5YWGhfNu5cbuOjo7y85CQEOTm5uLixYto1apVwxwMERFRI3D27Fn85z//QVxcHK5evSpnol+4cAGdO3cGAHTt2lVub8ws69Kli8mytLQ0AEB2djauXLmCPn36mOynT58+OHz4cK365ujoiHbt2snPfX195f0QWQODwQClUon4+HgolUqTdc7OzhXaf/bZZxgxYkSFbEsiapzOnj2L4uJikzFRpVLh3nvvxYkTJ3DPPfeYtD9x4gQeeughk2UhISEm5dEAICAgAP7+/iZtDAYDTp06BR8fH6SlpeGtt97Cr7/+itTUVOj1euTn51c7Yee3336LDz/8EH///Tdyc3NRUlICV1dXkzatW7c2uTYvPy6fOHECOp0OYWFhlW7/Vtf23bp1q7JvAFAaBwUkSZKXBQcHm7Q5depUhff03nvvrXa71HRZYgxnIJ2IakWpVCImJgZ//PEHtm/fjqVLl2L27NnYv39/pe0NBgN69OiBDRs2VFjXvHnzW+6v/CBLREREFY0cORItW7bEqlWr4OfnB4PBgKCgIJMSaiqVSv5v49h687LypWDKtzMSQsjLFAqFvMyofB3WyvZr3Gb51xBZWvfu3aHX65GWlnbLeqlJSUnYtWsXfvzxRzP1jogsrbLgr3F5ZdeqdR3jjNsy/n9ERATS09Px4YcfolWrVtBoNAgJCalyHrG4uDg89thjmDt3LoYMGQKtVouoqCi8//77Ju0qG5eN47+Dg0O1fbzVtb2LiwuaNWuG48ePV/r6kydPQpIkkx/YnZycTNpU9r7yvIGqYokxnDXSiajWJElCnz59MHfuXBw6dAhqtRrR0dFQq9XQ6/Umbe+++26cOXMGXl5eaN++vclDq9XK7Q4fPoyCggL5eVxcHJydnU1+pSciIiJT165dw4kTJ/Dmm28iLCwMnTp1QkZGxm1t09XVFX5+fti3b5/J8j/++AOdOnUCcOPHcOPEoUDpZKO1Vdm5A1F9y83NRUJCgvwdTUpKQkJCAi5cuICOHTviiSeewFNPPYXvvvsOSUlJOHDgABYuXIiff/7ZZDv/+9//4Ovri2HDhlngKIjIEtq3bw+1Wm0yJhYXF+PgwYPymFhe586dERcXZ7Ls5udA6V1jV65ckZ/HxsZCoVCgY8eOAIDffvsNU6dOxQMPPIA777wTGo0GV69erbKfv//+O1q1aoXZs2cjODgYHTp0wPnz52t1rMYJPnfu3Fnp+ltd2ysUCowdOxYbN25ESkqKyWsLCgqwfPlyDBkyBO7u7lX24Y477qgwkevBgwdrdRzUuFjbGM5AOhHVyv79+zF//nwcPHgQFy5cwHfffYf09HR06tQJrVu3xpEjR3Dq1ClcvXoVxcXFeOKJJ+Dp6YnRo0fjt99+Q1JSEvbs2YOXXnoJly5dkrdbVFSEZ555BsePH8fWrVsxZ84cvPjii3LGGxEREVXk5uYGDw8PfPrpp/j777/x66+/Yvr06be93VdeeQULFy7EV199hVOnTuH1119HQkICXnrpJQClgYWWLVsiMjISp0+fxpYtWypkvdVEZecORPXt4MGD6N69O7p37w4AmD59Orp37y5PsLtmzRo89dRTmDFjBgIDAzFq1Cjs378fLVu2lLdhMBiwdu1aREREVLh9nIgaLycnJ/z73//GK6+8gm3btuH48eOYOHEi8vPzK52wcOrUqdi2bRsWLVqE06dPY9myZRXKugCAvb09JkyYgMOHD8tB87Fjx8olJ9q3b48vvvgCJ06cwP79+/HEE09UmzHevn17XLhwAVFRUTh79iz++9//Ijo6ulbHam9vj9deew2vvvoqPv/8c5w9exZxcXFYvXo1ANTo2n7evHnw8fHBoEGDsHXrVly8eBF79+7FkCFDUFxcjI8//rjaPjz//PM4efIkXnvtNZw+fRpff/011q5dC4B3qzdVVjeG17m6OhE1ScePHxdDhgwRzZs3FxqNRnTs2FEsXbpUCFE6udmgQYOEs7OzACB27dolhBAiOTlZPPXUU8LT01NoNBrRtm1bMXHiRJGVlSWEuDHR2FtvvSU8PDyEs7OzePbZZ0VhYaGlDpOIiMhmxMTEiE6dOgmNRiO6du0qdu/eLU/GZJxs9NChQ3L7yiYKXbNmjclEaHq9XsydO1e0aNFCqFQq0a1bN7F161aT/e7bt0906dJF2Nvbi/vuu0988803FSYbLb9NIYSIjo4W5S9Bqjp3ICIishYFBQViypQp8vVsnz59xJ9//imEqHxMXb16tfD39xcODg5i5MiRYvHixRUmG+3WrZtYvny58PPzE/b29mLMmDHi+vXrcpu//vpLBAcHC41GIzp06CC++eYb0apVK5OJPHHTxIuvvPKKfD09btw48cEHH1S63/I++OAD0apVK/m5Xq8X77zzjmjVqpVQqVQiICBAzJ8/X15/q2t7IYRIT08XU6ZMES1bthR2dnbC29tbTJgwQZw/f95k36GhoeKll16q8H7/8MMPon379kKj0Yj+/fuLFStWCACioKCgkk+HyLwkIVhsiIgsKyIiApmZmfj+++8t3RUiIiIiIiIishLz5s3DypUrcfHiRUt3hYiTjRIREREREREREZHlLV++HPfccw88PDzw+++/47333sOLL75o6W4RAWAgnYiIiIiIiIiIiKzAmTNn8M477+D69esICAjAjBkzMGvWLEt3iwgAwNIuRERERERERERERETVUFi6A0RERERERERERERE1oyBdCIiIiIiIiIiIiKiajCQTkRERERERERERERUDQbSiYiIiIiIiIiIiIiqwUA6EREREREREREREVE1GEgnIiIiIiIiIiIiIqoGA+lERERERERERERERNVgIJ2IiIiIiIiIiIiIqBoMpBMRERERERERERERVYOBdCIiIiIiIiIiIiKiajCQTkRERERERERERERUDQbSiYiIiIiIiIiIiIiqwUA6EREREREREREREVE1GEgnIiIiIiIiIiIiIqoGA+lkNmvXroUkSfLD3t4ePj4+GDBgABYsWIC0tLQKr4mMjIQkSbXaT35+PiIjI7F79+5ava6yfbVu3RojRoyo1XZuZePGjfjwww8rXSdJEiIjI+t1f/Vt586dCA4OhpOTEyRJwvfff2/pLt1SQ3yOrVu3RkRERL1u0xyuXbuGWbNmoXPnznB0dISrqyt69eqFjz/+GMXFxTXezrlz5yBJEtauXdtwnSUiq8TxvBTH84pqetzG79C5c+fkZRyrS5X/21IqlXBzc0O3bt3w/PPPIy4urkH3feXKFURGRiIhIaFB90NE5sMxuxTHbPPjuF6K43rjw0A6md2aNWsQGxuLmJgYfPzxx7jrrruwcOFCdOrUCTt27DBp++yzzyI2NrZW28/Pz8fcuXNrPYjXZV91Ud0gHhsbi2effbbB+1BXQgiMHTsWKpUKP/74I2JjYxEaGmrpblENnTx5Et27d8cnn3yCJ554Alu2bEFUVBTuvvtuvPTSSxg0aBDy8/NrtC1fX1/ExsZi+PDhDdxrIrJWHM85nlPDeOSRRxAbG4t9+/YhKioKTz31FOLi4hASEoKXXnqpwfZ75coVzJ07lxfcRI0Qx2yO2WQ5HNcbFztLd4CanqCgIAQHB8vPH374Ybz88svo27cvxowZgzNnzsDb2xsA4O/vD39//wbtT35+PhwdHc2yr1vp1auXRfd/K1euXMH169fx0EMPISwszNLdoVrQ6/V4+OGHkZ2djT///BMdO3aU1z3wwAMIDQ3FY489hunTp2PlypXVbqekpAQajcbqv69E1LA4nlfN2v995Hhu3by9vU2+Q0OGDMG0adPw3HPP4b///S/uuOMO/Pvf/7ZgD4nI1nDMrhrHbGpoHNcbF2akk1UICAjA+++/j5ycHHzyySfy8spu9fr111/Rv39/eHh4wMHBAQEBAXj44YeRn5+Pc+fOoXnz5gCAuXPnyrfQGG//MW7vr7/+wiOPPAI3Nze0a9euyn0ZRUdHo2vXrrC3t0fbtm3x3//+12R9ZbcoA8Du3bshSZL8y3z//v2xZcsWnD9/3uQWH6PKbitLTEzE6NGj4ebmBnt7e9x1111Yt25dpfv58ssvMXv2bPj5+cHV1RUDBw7EqVOnqn7jy9m3bx/CwsLg4uICR0dH9O7dG1u2bJHXR0ZGyic5r732GiRJQuvWravcXm37tGPHDoSFhcHV1RWOjo7o06cPdu7cKa8/duwYJEnCN998Iy+Lj4+HJEm48847TbY1atQo9OjRo8I+bvU5FhYWYsaMGbjrrrug1Wrh7u6OkJAQ/PDDD9W/ebV8rSRJePHFF/HFF1+gU6dOcHR0RLdu3bB58+YKbU+ePInHH38c3t7e0Gg0CAgIwFNPPQWdTie3SUlJwfPPPw9/f3+o1Wq0adMGc+fORUlJicmxHz9+HK+//rpJEN1o3LhxGDx4MFavXo2UlBQAN8q3LFq0CO+88w7atGkDjUaDXbt2VVna5YcffkDXrl2h0WjQtm1bfPTRR3W6PZSIbBPH81KNaTwHgAsXLuDJJ5+El5cXNBoNOnXqhPfffx8Gg+GW/YmLi0OfPn1gb28PPz8/zJo1q9pSYk15rK6OUqnEsmXL4Onpiffee89kXXZ2NmbOnIk2bdpArVajRYsWmDZtGvLy8kzaffPNN+jZsye0Wi0cHR3Rtm1bPP300wBKv3v33HMPAOBf//qX/J229nIHRFR3HLNLNaYxm9fgHNc5rjc8ZqST1XjggQegVCqxd+/eKtucO3cOw4cPx3333Yf//e9/aNasGS5fvoxt27ahqKgIvr6+2LZtG4YOHYpnnnlGvkXLOLAbjRkzBo899hgmTZpU4R+jmyUkJGDatGmIjIyEj48PNmzYgJdeeglFRUWYOXNmrY5x+fLleO6553D27FlER0ffsv2pU6fQu3dveHl54b///S88PDywfv16REREIDU1Fa+++qpJ+zfeeAN9+vTBZ599huzsbLz22msYOXIkTpw4AaVSWeV+9uzZg0GDBqFr165YvXo1NBoNli9fjpEjR+LLL7/EuHHj8Oyzz6Jbt24YM2YMpkyZgvHjx0Oj0dzyGGrSp/Xr1+Opp57C6NGjsW7dOqhUKnzyyScYMmQIfvnlF4SFheHOO++Er68vduzYgUcffRRA6cDv4OCA48eP48qVK/Dz80NJSQn27NmDSZMmmfSjJp+jTqfD9evXMXPmTLRo0QJFRUXYsWMHxowZgzVr1uCpp56q8jhr+9otW7bgwIEDePvtt+Hs7IxFixbhoYcewqlTp9C2bVsAwOHDh9G3b194enri7bffRocOHZCcnIwff/wRRUVF0Gg0SElJwb333guFQoG33noL7dq1Q2xsLN555x2cO3cOa9asAQDExMQAAB588MEqj+HBBx/E9u3bsXv3bjz22GPy8v/+97/o2LEjFi9eDFdXV3To0KHS12/btg1jxoxBv3798NVXX6GkpASLFy9GampqlfskosaH43lFtjyep6eno3fv3igqKsL//d//oXXr1ti8eTNmzpyJs2fPYvny5VW+9vjx4wgLC0Pr1q2xdu1aODo6Yvny5di4cWOl7Zv6WH0rDg4OGDhwIKKionDp0iX4+/sjPz8foaGhuHTpEt544w107doVx44dw1tvvYWjR49ix44dkCQJsbGxGDduHMaNG4fIyEjY29vj/Pnz+PXXXwEAd999N9asWYN//etfePPNN+XSbZbOFCWihsUxuyJbHrNr0ydeg3NcpzoSRGayZs0aAUAcOHCgyjbe3t6iU6dO8vM5c+aI8l/Tb7/9VgAQCQkJVW4jPT1dABBz5sypsM64vbfeeqvKdeW1atVKSJJUYX+DBg0Srq6uIi8vz+TYkpKSTNrt2rVLABC7du2Slw0fPly0atWq0r7f3O/HHntMaDQaceHCBZN2w4YNE46OjiIzM9NkPw888IBJu6+//loAELGxsZXuz6hXr17Cy8tL5OTkyMtKSkpEUFCQ8Pf3FwaDQQghRFJSkgAg3nvvvWq3V5s+5eXlCXd3dzFy5EiTdnq9XnTr1k3ce++98rInn3xStG3bVn4+cOBAMXHiROHm5ibWrVsnhBDi999/FwDE9u3b5XY1/RxvVlJSIoqLi8UzzzwjunfvbrKuVatWYsKECVUef3WvBSC8vb1Fdna2vCwlJUUoFAqxYMECedn9998vmjVrJtLS0qrcz/PPPy+cnZ3F+fPnTZYvXrxYABDHjh0TQggxdOhQAUAUFhZWua2tW7cKAGLhwoVCiBufd7t27URRUZFJW+O6NWvWyMvuuece0bJlS6HT6eRlOTk5wsPDo8LfFhHZLo7npZrKeP76668LAGL//v0my//9738LSZLEqVOnqjzucePGCQcHB5GSkmLSnzvuuKPC+8yx+sZ+X3jhhSq39dprr5l8HgsWLBAKhaLC36Pxb+znn3822Zfxu1aZAwcOVBjbici2ccwu1VTGbF6Dc1wvj+N6w2BpF7IqQohq1991111Qq9V47rnnsG7dOvzzzz912s/DDz9c47Z33nknunXrZrJs/PjxyM7Oxl9//VWn/dfUr7/+irCwMLRs2dJkeUREBPLz8ytMzDJq1CiT5127dgUAnD9/vsp95OXlYf/+/XjkkUfg7OwsL1cqlQgPD8elS5dqfGtaZW7Vpz/++APXr1/HhAkTUFJSIj8MBgOGDh2KAwcOyBkLYWFh+Oeff5CUlITCwkLs27cPQ4cOxYABA+SM6x07dkCj0aBv374m+63p5/jNN9+gT58+cHZ2hp2dHVQqFVavXo0TJ07c8lhr89oBAwbAxcVFfu7t7Q0vLy/5fcnPz8eePXswduzYCtkc5W3evBkDBgyQMwGMj2HDhgEozXSoKePf3823V44aNQoqlara1+bl5eHgwYN48MEHoVar5eXOzs4YOXJkjftARI0Dx3NTtjye//rrr+jcuTPuvffeCn0XQsiZT5XZtWsXwsLC5Lq7xv6MGzeu0vYcq2/t5r+tzZs3IygoCHfddZfJtocMGWJS2sB4e/fYsWPx9ddf4/LlyzXeJxE1bhyzTdnymF3TPvEavBTHdaoLBtLJauTl5eHatWvw8/Orsk27du2wY8cOeHl54YUXXkC7du3Qrl07fPTRR7Xal6+vb43b+vj4VLns2rVrtdpvbV27dq3Svhrfo5v37+HhYfLceNtXQUFBlfvIyMiAEKJW+6mNW/XJWPbjkUcegUqlMnksXLgQQghcv34dADBw4EAApQP1vn37UFxcjPvvvx8DBw6Ua7nt2LEDffr0gYODg8l+a/I5fvfddxg7dixatGiB9evXIzY2FgcOHMDTTz+NwsLCao+ztq+9+X0xvjfG9yUjIwN6vf6Wt16lpqbip59+qvDeGWvWXb16FUBpDUQASEpKqnJbxvqCN5801uTvxfg9Kh8sMapsGRE1XhzPK7Ll8by2fb/5tdW97zVZ3pTG6powXuwb3//U1FQcOXKkwrZdXFwghJC33a9fP3z//fcoKSnBU089BX9/fwQFBeHLL7+s8b6JqPHhmF2RLY/ZNe0Tr8FN3xuO61QbrJFOVmPLli3Q6/Xo379/te3uu+8+3HfffdDr9Th48CCWLl2KadOmwdvb26Suc3VqM/GhceLFypYZ/yG2t7cHAJPJJ4Da/QNaGQ8PDyQnJ1dYfuXKFQCAp6fnbW0fANzc3KBQKBp8P1Uxbnvp0qVVzphefgb5jh07YseOHWjdujWCg4PRrFkzhIWFYfLkydi/fz/i4uIwd+7cCtuoyee4fv16tGnTBl999ZXJd+Tmz7Uyt/Payri7u0OpVOLSpUvVtvP09ETXrl0xb968StcbB+RBgwbh008/xffff4/XX3+90rbff/897OzsKvwN1uTvxc3NDZIkVVoPvbL3nogaL47nFdnyeH47fffw8Kj2fa/J8qY0Vt9KQUEBduzYgXbt2skX+Z6ennBwcMD//ve/KvdtNHr0aIwePRo6nQ5xcXFYsGABxo8fj9atWyMkJKRGfSCixoVjdkW2PGbXFK/BK8dxnWqCGelkFS5cuICZM2dCq9Xi+eefr9FrlEolevbsiY8//hgA5FuDavILcG0cO3YMhw8fNlm2ceNGuLi44O677wYAeebsI0eOmLT78ccfK2yv/C+etxIWFoZff/1VHkyNPv/8czg6OlY56NWGk5MTevbsie+++86kXwaDAevXr5cHzobSp08fNGvWDMePH0dwcHClj/KlQgYOHIhff/0VMTExGDRoEACgY8eOCAgIwFtvvYXi4mL5V/PyavI5SpIEtVptMginpKTUaMbw23ltZRwcHBAaGopvvvmm2pPBESNGIDExEe3atav0vTMO4g899BA6d+6Md999F6dPn66wna+++grbt2/Hs88+W2WmYHWcnJwQHByM77//HkVFRfLy3NzcSmdCJ6LGieN55Wx5PA8LC8Px48cr3Er/+eefQ5IkDBgwoMrXDhgwADt37jT5kVWv1+Orr76qtH1TH6uro9fr8eKLL+LatWt47bXXTLZ99uxZeHh4VLpt43e6PI1Gg9DQUCxcuBAAcOjQIXk5UH9/c0Rk3ThmV86Wx+ya4jV45TiuU00wI53MLjExUa7zlJaWht9++w1r1qyBUqlEdHR0tbWoVq5ciV9//RXDhw9HQEAACgsL5V/qjP9wu7i4oFWrVvjhhx8QFhYGd3d3eHp6VvoPTk34+flh1KhRiIyMhK+vL9avX4+YmBgsXLgQjo6OAErrUwUGBmLmzJkoKSmBm5sboqOjsW/fvgrb69KlC7777jusWLECPXr0gEKhQHBwcKX7njNnjlx/66233oK7uzs2bNiALVu2YNGiRdBqtXU6ppstWLAAgwYNwoABAzBz5kyo1WosX74ciYmJ+PLLL2uVPVBbzs7OWLp0KSZMmIDr16/jkUcegZeXF9LT03H48GGkp6djxYoVcvuwsDAsX74cV69exYcffmiyfM2aNXBzc0OPHj0q7Kcmn+OIESPw3XffYfLkyXjkkUdw8eJF/N///R98fX1x5syZao/jdl5blSVLlqBv377o2bMnXn/9dbRv3x6pqan48ccf8cknn8DFxQVvv/02YmJi0Lt3b0ydOhWBgYEoLCzEuXPn8PPPP2PlypXw9/eHUqnEpk2bMGjQIISEhGDGjBkICQmBTqfDTz/9hE8//RShoaF4//3369RXAHj77bcxfPhwDBkyBC+99BL0ej3ee+89ODs7y7cGElHjwfG8aYznL7/8Mj7//HMMHz4cb7/9Nlq1aoUtW7Zg+fLl+Pe//13thf6bb76JH3/8Effffz/eeustODo64uOPP5brrt6sqY/VRqmpqYiLi4MQAjk5OUhMTMTnn3+Ow4cP4+WXX8bEiRPlttOmTcOmTZvQr18/vPzyy+jatSsMBgMuXLiA7du3Y8aMGejZsyfeeustXLp0CWFhYfD390dmZiY++ugjqFQqhIaGAigt3+Dg4IANGzagU6dOcHZ2hp+fX40z64jIenHMbhpjdk3xGrxqHNfplsw+vSk1WcZZtY0PtVotvLy8RGhoqJg/f36lsyLfPIt3bGyseOihh0SrVq2ERqMRHh4eIjQ0VPz4448mr9uxY4fo3r270Gg0AoA8s7Nxe+np6bfclxCls0IPHz5cfPvtt+LOO+8UarVatG7dWixZsqTC60+fPi0GDx4sXF1dRfPmzcWUKVPEli1bKswYfv36dfHII4+IZs2aCUmSTPaJSmY6P3r0qBg5cqTQarVCrVaLbt26VZh12Tg79zfffGOy3DjDd01maf7tt9/E/fffL5ycnISDg4Po1auX+OmnnyrdXm1mDK9pn/bs2SOGDx8u3N3dhUqlEi1atBDDhw+v8PqMjAyhUCiEk5OTKCoqkpdv2LBBABBjxoyp0JfafI7vvvuuaN26tdBoNKJTp05i1apVVX43bp4xvKavRRUzd1e2zePHj4tHH31UeHh4CLVaLQICAkRERIQoLCyU26Snp4upU6eKNm3aCJVKJdzd3UWPHj3E7NmzRW5ursn2rl69Kl5//XVxxx13CHt7e+Hs7CzuvfdesWzZMpP3U4jqP++qPsfo6GjRpUsXua/vvvuumDp1qnBzc6uwDSKyTRzPSzWV8VwIIc6fPy/Gjx8vPDw8hEqlEoGBgeK9994Ter3epF1lx/3777+LXr16CY1GI3x8fMQrr7wiPv30UwFAJCUlye04Vt/Yr/GhUCiEq6ur6NKli3juuedEbGxshf4IIURubq548803RWBgoFCr1UKr1YouXbqIl19+WaSkpAghhNi8ebMYNmyYaNGihfw3+8ADD4jffvvNZFtffvmluOOOO4RKpar08yQi28Ixu1RTGbN5Dc5xneN6w5OEuMUUzURERHVUXFyMu+66Cy1atMD27dst3R0iIiIiIiIiojphaRciIqo3zzzzDAYNGgRfX1+kpKRg5cqVOHHiBD766CNLd42IiIiIiIiIqM4YSCcionqTk5ODmTNnIj09HSqVCnfffTd+/vnnSiefISIiIiIiIiKyFSztQkRERERERERERERUDYWlO0BERERERERE9WPFihXo2rUrXF1d4erqipCQEGzdulVeHxERAUmSTB69evUy2YZOp8OUKVPg6ekJJycnjBo1CpcuXTJpk5GRgfDwcGi1Wmi1WoSHhyMzM9OkzYULFzBy5Eg4OTnB09MTU6dORVFRkUmbo0ePIjQ0FA4ODmjRogXefvttMN+PiIisEQPpRERERERERI2Ev78/3n33XRw8eBAHDx7E/fffj9GjR+PYsWNym6FDhyI5OVl+/PzzzybbmDZtGqKjoxEVFYV9+/YhNzcXI0aMgF6vl9uMHz8eCQkJ2LZtG7Zt24aEhASEh4fL6/V6PYYPH468vDzs27cPUVFR2LRpE2bMmCG3yc7OxqBBg+Dn54cDBw5g6dKlWLx4MZYsWdKA7xAREVHdsLQLERERERERUSPm7u6O9957D8888wwiIiKQmZmJ77//vtK2WVlZaN68Ob744guMGzcOAHDlyhW0bNkSP//8M4YMGYITJ06gc+fOiIuLQ8+ePQEAcXFxCAkJwcmTJxEYGIitW7dixIgRuHjxIvz8/AAAUVFRiIiIQFpaGlxdXbFixQrMmjULqamp0Gg0AIB3330XS5cuxaVLlyBJUsO/OURERDXEyUbrkcFgwJUrV+Di4sIBn4iIakwIgZycHPj5+UGh4M1ilsAxnIiI6sLax3C9Xo9vvvkGeXl5CAkJkZfv3r0bXl5eaNasGUJDQzFv3jx4eXkBAOLj41FcXIzBgwfL7f38/BAUFIQ//vgDQ4YMQWxsLLRarRxEB4BevXpBq9Xijz/+QGBgIGJjYxEUFCQH0QFgyJAh0Ol0iI+Px4ABAxAbG4vQ0FA5iG5sM2vWLJw7dw5t2rSp9Lh0Oh10Op383GAw4Pr16/Dw8OA4TkRENVbbcZyB9Hpk/JWeiIioLi5evAh/f39Ld6NJ4hhORES3w9rG8KNHjyIkJASFhYVwdnZGdHQ0OnfuDAAYNmwYHn30UbRq1QpJSUn4z3/+g/vvvx/x8fHQaDRISUmBWq2Gm5ubyTa9vb2RkpICAEhJSZED7+V5eXmZtPH29jZZ7+bmBrVabdKmdevWFfZjXFdVIH3BggWYO3duLd8VIiKiytV0HGcgvR65uLgAKH3zXV1dLdwbIiKyFdnZ2WjZsqU8jpD5cQwnIqK6sNYxPDAwEAkJCcjMzMSmTZswYcIE7NmzB507d5bLtQBAUFAQgoOD0apVK2zZsgVjxoypcptCCJNs78oyv+ujjbH6bHWZ5bNmzcL06dPl51lZWQgICOA4TkREtVLbcZyB9HpkHOiNs6MTERHVBm9FthyO4UREdDusbQxXq9Vo3749ACA4OBgHDhzARx99hE8++aRCW19fX7Rq1QpnzpwBAPj4+KCoqAgZGRkmWelpaWno3bu33CY1NbXCttLT0+WMch8fH+zfv99kfUZGBoqLi03aGLPTy+8HQIVs9vI0Go1JORgjjuNERFQXNR3Hra+IGxERERERERHVGyGESU3x8q5du4aLFy/C19cXANCjRw+oVCrExMTIbZKTk5GYmCgH0kNCQpCVlYU///xTbrN//35kZWWZtElMTERycrLcZvv27dBoNOjRo4fcZu/evSgqKjJp4+fnV6HkCxERkaUxkE5ERERERETUSLzxxhv47bffcO7cORw9ehSzZ8/G7t278cQTTyA3NxczZ85EbGwszp07h927d2PkyJHw9PTEQw89BADQarV45plnMGPGDOzcuROHDh3Ck08+iS5dumDgwIEAgE6dOmHo0KGYOHEi4uLiEBcXh4kTJ2LEiBEIDAwEAAwePBidO3dGeHg4Dh06hJ07d2LmzJmYOHGinDU+fvx4aDQaREREIDExEdHR0Zg/fz6mT59udVn+RERELO1CRERERERE1EikpqYiPDwcycnJ0Gq16Nq1K7Zt24ZBgwahoKAAR48exeeff47MzEz4+vpiwIAB+Oqrr0zqw37wwQews7PD2LFjUVBQgLCwMKxduxZKpVJus2HDBkydOhWDBw8GAIwaNQrLli2T1yuVSmzZsgWTJ09Gnz594ODggPHjx2Px4sVyG61Wi5iYGLzwwgsIDg6Gm5sbpk+fblL/nIiIyFpIwjiTB9227OxsaLVaZGVlsS4bERHVGMcPy+NnQEREdcHxwzrwcyAiorqo7fjB0i5ERERERERERERERNVgIJ2IiIiIiIiIiIiIqBoMpBMRERERERERERERVYOBdCIiIiIiIiIiIiKiajCQTkRERERERERERERUDQbSiYiIiIiIiIiIiIiqwUA6EREREREREREREVE1GEgnIiIiIiIiIiIiIqoGA+lERERERERERERERNVgIJ2IiIiIiIiIiIiIqBoMpBMRERERERERERERVcPO0h0gIiIiaix2nkjFkUtZCGnngV5tPSzdHSIiIqqFj3acgV4ITLyvDVzsVZbuDhERWRkG0omIiIjqyc6Tadi4/wKUComBdCIiIhuz9NczKDEIjL83gIF0IiKqgIF0KzV89BikXb1W6TovTw9s+eE7M/eIiIiIbkUq+3+DEBbtBxEREdWeVDaQcxwnIqLKMJBupdKuXsP4yFWVrtsYOdHMvSEiIqKaMF6A8/qbiIjI9kiSBECAwzgREVWGk40SERER1RNFWSSdF+BERES2R76zzMCRnIiIKmIgnYiIiKieGC/ABVPSiYiIbI7xB3EiIqLKMJBOREREVE8kY0Y64+hEREQ2hzXSiYioOgykExEREdUTXoATERHZLgV/ECciomowkE5ERERUT1gjnYiIyHbJNdIZSSciokowkE5ERERUT3gBTkREZLuMd5ZxFCciosowkE5ERERUT+Q5yngFTkREZHNuzHXCgZyIiCpiIJ2IiIionrC0CxERke1SGDPSOZATEVElGEgnIiIiqi/GyUYNvAInIiKyNcaMdA7jRERUGQbSiYiIiOoJM9KJiIhsl5yRzpGciIgqwUA6ERERUT3hZKNERES2rCwj3WDhbhARkVViIJ2IiIionsgZ6YyjExER2RxmpBMRUXUYSCciIiKqJ5I8SRkvwImIiGyNxMlGiYioGgykExEREdUTY2kXXn8TERHZHt5ZRkRE1WEgnYiIiKieSLwAJyIislmc64SIiKrDQDoRERFRPTHeEs4LcCIiItsj/yBu4X4QEZF1YiCdiIiIqJ4oeAFORERks/iDOBERVYeBdCIiIqJ6ItdI5wU4ERGRzWGNdCIiqg4D6URERET1xJjJxgtwIiIi23NjHOdATkREFTGQTkRERFRPONkoERGR7WKJNiIiqg4D6URERET1hLVViYiIbJexRJvBwHGciIgqYiCdiIiIqJ4wk42IiMh2yaVdLNsNIiKyUgykExEREdUTOZONGelEREQ2x1iijeM4ERFVhoF0IiIionqiYCobERGRzVIYfxHnOE5ERJVgIJ2IiIhMrFixAl27doWrqytcXV0REhKCrVu3yusjIiIgSZLJo1evXibb0Ol0mDJlCjw9PeHk5IRRo0bh0qVLJm0yMjIQHh4OrVYLrVaL8PBwZGZmmrS5cOECRo4cCScnJ3h6emLq1KkoKioyaXP06FGEhobCwcEBLVq0wNtvvw1hoUwy1kgnIiKyXRKMGekW7ggREVklBtKJiIjIhL+/P959910cPHgQBw8exP3334/Ro0fj2LFjcpuhQ4ciOTlZfvz8888m25g2bRqio6MRFRWFffv2ITc3FyNGjIBer5fbjB8/HgkJCdi2bRu2bduGhIQEhIeHy+v1ej2GDx+OvLw87Nu3D1FRUdi0aRNmzJght8nOzsagQYPg5+eHAwcOYOnSpVi8eDGWyZdUKAAAqGpJREFULFnSgO/QrfH6m4iIyPbcuLGMIzkREVVk0UB6SUkJ3nzzTbRp0wYODg5o27Yt3n77bRgMBrmNEAKRkZHw8/ODg4MD+vfvb3IhD5g3642IiKixGzlyJB544AF07NgRHTt2xLx58+Ds7Iy4uDi5jUajgY+Pj/xwd3eX12VlZWH16tV4//33MXDgQHTv3h3r16/H0aNHsWPHDgDAiRMnsG3bNnz22WcICQlBSEgIVq1ahc2bN+PUqVMAgO3bt+P48eNYv349unfvjoEDB+L999/HqlWrkJ2dDQDYsGEDCgsLsXbtWgQFBWHMmDF44403sGTJkmqz0nU6HbKzs00e9UGebJTX30RERDZHITEjnYiIqmbRQPrChQuxcuVKLFu2DCdOnMCiRYvw3nvvYenSpXKbRYsWYcmSJVi2bBkOHDgAHx8fDBo0CDk5OXIbc2W9ERERNTV6vR5RUVHIy8tDSEiIvHz37t3w8vJCx44dMXHiRKSlpcnr4uPjUVxcjMGDB8vL/Pz8EBQUhD/++AMAEBsbC61Wi549e8ptevXqBa1Wa9ImKCgIfn5+cpshQ4ZAp9MhPj5ebhMaGgqNRmPS5sqVKzh37lyVx7VgwQL5x3WtVouWLVvW8R0yxdIuREREtkvOSOc4TkRElbCz5M5jY2MxevRoDB8+HADQunVrfPnllzh48CCA0sHrww8/xOzZszFmzBgAwLp16+Dt7Y2NGzfi+eefl7PevvjiCwwcOBAAsH79erRs2RI7duzAkCFD5Ky3uLg4+YJ91apVCAkJwalTpxAYGChnvV28eFG+YH///fcRERGBefPmwdXVtUL/dToddDqd/Ly+stmIiIgs7ejRowgJCUFhYSGcnZ0RHR2Nzp07AwCGDRuGRx99FK1atUJSUhL+85//4P7770d8fDw0Gg1SUlKgVqvh5uZmsk1vb2+kpKQAAFJSUuDl5VVhv15eXiZtvL29Tda7ublBrVabtGndunWF/RjXtWnTptLjmzVrFqZPny4/z87OrpdgupyRfttbIiIiInPjnWVERFQdi2ak9+3bFzt37sTp06cBAIcPH8a+ffvwwAMPAACSkpKQkpJiktGm0WgQGhoqZ6uZM+vtZg2VzUZERGRpgYGBSEhIQFxcHP79739jwoQJOH78OABg3LhxGD58OIKCgjBy5Ehs3boVp0+fxpYtW6rdphACkjHVCzD57/psY8wiq+y1RhqNRp5M1fioD8xkIyIisl2skU5ERNWxaCD9tddew+OPP4477rgDKpUK3bt3x7Rp0/D4448DgJxtdnM22s0ZbebKervZrFmzkJWVJT8uXrxY27eAiIjIKqnVarRv3x7BwcFYsGABunXrho8++qjStr6+vmjVqhXOnDkDAPDx8UFRUREyMjJM2qWlpcljrY+PD1JTUytsKz093aTNzWNwRkYGiouLq21jLDNz87huDhIz2YiIiGyWcRwvN20bERGRzKKB9K+++grr16/Hxo0b8ddff2HdunVYvHgx1q1bZ9Kuskyz6rLMKmtTX1lv5TVUNhsREZG1EUKYlDMr79q1a7h48SJ8fX0BAD169IBKpUJMTIzcJjk5GYmJiejduzcAICQkBFlZWfjzzz/lNvv370dWVpZJm8TERCQnJ8tttm/fDo1Ggx49esht9u7dazI5+Pbt2+Hn51eh5Is5GM8YWCOdiIjI9hjHcY7iRERUGYsG0l955RW8/vrreOyxx9ClSxeEh4fj5ZdfxoIFCwCUZpkBqDTTrHwmmrmy3oiIiJqCN954A7/99hvOnTuHo0ePYvbs2di9ezeeeOIJ5ObmYubMmYiNjcW5c+ewe/dujBw5Ep6ennjooYcAAFqtFs888wxmzJiBnTt34tChQ3jyySfRpUsXeT6TTp06YejQoZg4cSLi4uIQFxeHiRMnYsSIEQgMDAQADB48GJ07d0Z4eDgOHTqEnTt3YubMmZg4caL84/X48eOh0WgQERGBxMREREdHY/78+Zg+ffotf3RvCDdKu5h910RERHSbFJw0nIiIqmHRQHp+fj4UCtMuKJVKGMruo2rTpg18fHxMMtqKioqwZ88eOVvNnFlvRERETUFqairCw8MRGBiIsLAw7N+/H9u2bcOgQYOgVCpx9OhRjB49Gh07dsSECRPQsWNHxMbGwsXFRd7GBx98gAcffBBjx45Fnz594OjoiJ9++glKpVJus2HDBnTp0gWDBw/G4MGD0bVrV3zxxRfyeqVSiS1btsDe3h59+vTB2LFj8eCDD2Lx4sVyG61Wi5iYGFy6dAnBwcGYPHkypk+fbjKRqDlxslEiIiLbxRJtRERUHYsG0keOHIl58+Zhy5YtOHfuHKKjo7FkyRI5o02SJEybNg3z589HdHQ0EhMTERERAUdHR4wfPx6AebPeiIiImoLVq1fj3Llz0Ol0SEtLw44dOzBo0CAAgIODA3755RekpaWhqKgI58+fx9q1aytMuG1vb4+lS5fi2rVryM/Px08//VShjbu7O9avX4/s7GxkZ2dj/fr1aNasmUmbgIAAbN68Gfn5+bh27RqWLl0KjUZj0qZLly7Yu3cvCgsLkZycjDlz5lgkGx0od0s4r8CJiMhCVqxYga5du8rlR0NCQrB161Z5vRACkZGR8PPzg4ODA/r3749jx46ZbEOn02HKlCnw9PSEk5MTRo0ahUuXLpm0ycjIQHh4OLRaLbRaLcLDw5GZmWnS5sKFCxg5ciScnJzg6emJqVOnmpRjA4CjR48iNDQUDg4OaNGiBd5++22LjaMKThpORETVsGggfenSpXjkkUcwefJkdOrUCTNnzsTzzz+P//u//5PbvPrqq5g2bRomT56M4OBgXL58Gdu3b7dI1hsRERFRdRTMZCMiIgvz9/fHu+++i4MHD+LgwYO4//77MXr0aDlYvmjRIixZsgTLli3DgQMH4OPjg0GDBiEnJ0fexrRp0xAdHY2oqCjs27cPubm5GDFiBPR6vdxm/PjxSEhIwLZt27Bt2zYkJCQgPDxcXq/X6zF8+HDk5eVh3759iIqKwqZNmzBjxgy5TXZ2NgYNGgQ/Pz8cOHAAS5cuxeLFi7FkyRIzvFMVSeCdZUREVDVJ8KfWepOdnQ2tVousrKzbzmK/p08oxkeuqnTdxsiJOPD7ntvaPhERWY/6HD+oburrM/j64EW8+u0RDAhsjjX/urcee0hERNbIVsZwd3d3vPfee3j66afh5+eHadOm4bXXXgNQmn3u7e2NhQsX4vnnn0dWVhaaN2+OL774AuPGjQMAXLlyBS1btsTPP/+MIUOG4MSJE+jcuTPi4uLQs2dPAEBcXBxCQkJw8uRJBAYGYuvWrRgxYgQuXrwIPz8/AEBUVBQiIiKQlpYGV1dXrFixArNmzUJqaqp8x9m7776LpUuX4tKlS1XeYabT6UwmQc/OzkbLli1v+3MY90ks9iddx7Lx3TGiq1+dt0NERLahtuO4RTPSiYiIiBoTubSLRXtBRERUSq/XIyoqCnl5eQgJCUFSUhJSUlIwePBguY1Go0FoaCj++OMPAEB8fDyKi4tN2vj5+SEoKEhuExsbC61WKwfRAaBXr17QarUmbYKCguQgOgAMGTIEOp0O8fHxcpvQ0FCTsm1DhgzBlStXcO7cuSqPa8GCBXJJGa1WW6F8XF1x0nAiIqoOA+lERERE9YSlXYiIyBocPXoUzs7O0Gg0mDRpEqKjo9G5c2ekpKQAALy9vU3ae3t7y+tSUlKgVqvh5uZWbRsvL68K+/Xy8jJpc/N+3NzcoFarq21jfG5sU5lZs2YhKytLfly8eLH6N6SGjOO4gQM5ERFVws7SHSAiIiJqLIyZbLwAJyIiSwoMDERCQgIyMzOxadMmTJgwAXv23CgPenPJFCHELSfqvrlNZe3ro42x+mx1/dFoNBUmH68PFpqrnIiIbAQz0omIiIjqiYJX4EREZAXUajXat2+P4OBgLFiwAN26dcNHH30EHx8fABWzvdPS0uRMcB8fHxQVFSEjI6PaNqmpqRX2m56ebtLm5v1kZGSguLi42jZpaWkAKmbNmwMz0omIqDoMpBMRERHVE2akExGRNRJCQKfToU2bNvDx8UFMTIy8rqioCHv27EHv3r0BAD169IBKpTJpk5ycjMTERLlNSEgIsrKy8Oeff8pt9u/fj6ysLJM2iYmJSE5Oltts374dGo0GPXr0kNvs3bsXRUVFJm38/PzQunXr+n8jashgsNiuiYjIijGQTkRERFRPJNZIJyIiC3vjjTfw22+/4dy5czh69Chmz56N3bt344knnoAkSZg2bRrmz5+P6OhoJCYmIiIiAo6Ojhg/fjwAQKvV4plnnsGMGTOwc+dOHDp0CE8++SS6dOmCgQMHAgA6deqEoUOHYuLEiYiLi0NcXBwmTpyIESNGIDAwEAAwePBgdO7cGeHh4Th06BB27tyJmTNnYuLEiXB1dQUAjB8/HhqNBhEREUhMTER0dDTmz5+P6dOn37LUTEOQ5zox+56JiMgWsEY6ERERUT0xXvIzI52IiCwlNTUV4eHhSE5OhlarRdeuXbFt2zYMGjQIAPDqq6+ioKAAkydPRkZGBnr27Int27fDxcVF3sYHH3wAOzs7jB07FgUFBQgLC8PatWuhVCrlNhs2bMDUqVMxePBgAMCoUaOwbNkyeb1SqcSWLVswefJk9OnTBw4ODhg/fjwWL14st9FqtYiJicELL7yA4OBguLm5Yfr06Zg+fXpDv02V4p1lRERUHQbSiYiIiOqJ8QKc199ERGQpq1evrna9JEmIjIxEZGRklW3s7e2xdOlSLF26tMo27u7uWL9+fbX7CggIwObNm6tt06VLF+zdu7faNuYiz3XCcZyIiCrB0i5ERERE9YS3hBMREdku3llGRETVYSCdiIiIqJ4YL8AFL8CJiIhsjsQfxImIqBoMpBMRERHVE042SkREZLtYI52IiKrDQDoRERFRPeEFOBERke1ScK4TIiKqBicbtTJZ+cX462IGitzaWLorREREVEuskU5ERGS7JBjvLONITkREFTGQbmXOpOXgX2sOQNFuoKW7QkRERLV0Y5Iyi3aDiIiI6kBRds8+h3Eiov9n7/7jorrP/O+/ZwYYkcAEJYCT+KutsrHYbIqpomk0UVHrjyZ2a3dpqLQuTVejddHNXZvvbmm+VdtEMf3iXZu63pqoWbrfNaaptgQ0UZdVjKHSSHTVbbViAmISHJTggDD3HzIHhh+D4MjM0dfz8ZiNM+eamc+cYXvNuc7nXB90htYuIcYRGS5J8oTZgzwSAADQU5bW1UaDOg4AANBz3hnpzZwRBwB0gkJ6iIkxCun9uJwMAACTobULAADm5T0hTh4HAHSGQnqI8c5Il8WqxibSNwAApsJiowAAmJalpZLOhHQAQGcopIcYe5hVEbbrX8vVa01BHg0AAOgJY0Y6B+AAAJiO1TsjnUQOAOgEhfQQY7FYFBN5fQ1Yd2NzkEcDAAB6gsVGAQAwL5Y6AQD4QyE9BHn7pLuZkQ4AgKlYmMkGAIBpta51Qh4HAHREIT0ExfTzFtKZkQ4AgJl4D8ABAIAJGWudBHcYAIDQRCE9BDkiKaQDAGBGra1dOAIHAMBsWOsEAOAPhfQQZLR2aaS1CwAAZmLhABwAANPihDgAwB8K6SHI4V1slBnpAACYisW4JJwDcAAAzIYWbQAAfyikhyCjR3ojhXQAAMykdZEyAABgNsYJcZqkAwA6QSE9BBmtXa7R2gUAADPxHoAzIR0AAPOxcEIcAOAHhfQQ5F1s9CqtXQAAMBXvBeEeKukAAJgOLdoAAP5QSA9BRmsXZqQDAGAqzGQDAMC8rFxZBgDwg0J6CHIYrV2YkQ4AgJkwkw0AAPOytFxbxpVlAIDOUEgPQTGRYZJYbBQAALMxFhvl+BsAANMxZqQHdxgAgBBFIT0EOVhsFAAAU2rtkR7UYQAAgF7wtmjjyjIAQGcopIcgb4/0xiaPmppJ4AAAmEXrjHTyNwAAZmOhRzoAwA8K6SEoul+Y8e8G+qQDAGAarT3SgzsOAADQc94e6eRxAEBnKKSHoDCbVZZrbknSVdq7AABgOh66qwIAYDqtPdLJ4wCAjiikhyhvId3NjHQAAEyDxUYBADAvWrsAAPyhkB6iLNeuSpLcjcxIBwDALGjtAgCAebHWCQDAHwrpIcraxIx0AADMxnsALi4JBwDAfDghDgDwg0J6iLI0tsxIp5AOAIBpMCMdAADzokUbAMAfCukhytJEaxcAAMzGmI/OETgAAKbjzePN5HEAQCcopIcoK4uNAgCCZMOGDfrCF76gmJgYxcTEKDU1Vb///e+N7R6PRzk5OXI6nYqMjNSkSZP0/vvv+7yG2+3W4sWLFRcXp6ioKM2ZM0fnz5/3iampqVFGRoYcDoccDocyMjJ06dIln5hz585p9uzZioqKUlxcnJYsWaKGhgafmGPHjmnixImKjIzUvffeq+eeey5ohWyLdyZbUN4dAADcjNYWbQAAdEQhPUR5Fxu9eo0Z6QCAvnXffffppz/9qd599129++67euyxx/TVr37VKJY///zzys3N1fr163XkyBElJiZq6tSpunz5svEaS5cu1c6dO5Wfn6/i4mJduXJFs2bNUlNTa15LT09XWVmZCgoKVFBQoLKyMmVkZBjbm5qaNHPmTNXV1am4uFj5+fnasWOHli1bZsTU1tZq6tSpcjqdOnLkiPLy8rRmzRrl5ub2wZ7qyGjtQm8XAABMp7VFG3kcANBRWLAHgM5ZWmakNzQyIx0A0Ldmz57tc3/lypXasGGDSkpKNGrUKL344ot69tlnNXfuXEnSyy+/rISEBL366qt66qmn5HK5tGnTJm3dulVTpkyRJG3btk2DBw/Wnj17NG3aNJ04cUIFBQUqKSnR2LFjJUkbN25UamqqTp48qaSkJBUWFur48eOqqKiQ0+mUJK1du1aZmZlauXKlYmJitH37dl29elVbtmyR3W5XcnKyTp06pdzcXGVnZxszxNtzu91yu93G/dra2oDsOysz0gEAMC0LPdIBAH4wIz1EWZquX7be0EQhHQAQPE1NTcrPz1ddXZ1SU1N15swZVVVVKS0tzYix2+2aOHGiDh48KEkqLS1VY2OjT4zT6VRycrIRc+jQITkcDqOILknjxo2Tw+HwiUlOTjaK6JI0bdo0ud1ulZaWGjETJ06U3W73ifnwww919uzZLj/X6tWrjZYyDodDgwcPvom91Kq1R3pAXg4AAPQheqQDAPyhkB6iLJ7rl75zZTgAIBiOHTumu+66S3a7Xd/73ve0c+dOjRo1SlVVVZKkhIQEn/iEhARjW1VVlSIiIhQbG+s3Jj4+vsP7xsfH+8S0f5/Y2FhFRET4jfHe98Z0ZsWKFXK5XMatoqLC/w65QcaMdA7AAQAwHa4sAwD4Q2uXUOW5PhOdM+EAgGBISkpSWVmZLl26pB07dmj+/Pnav3+/sb19yxSPx9NlG5WuYjqLD0SMt4jtbzx2u91nFnugtPZWDfhLAwCAW8ybxzkhDgDoDDPSQ5SluaWQzpE4ACAIIiIi9LnPfU5jxozR6tWr9cADD+jnP/+5EhMTJXWc7V1dXW3MBE9MTFRDQ4Nqamr8xly4cKHD+168eNEnpv371NTUqLGx0W9MdXW1pI6z5vuSh7lsAACYjtUopAd3HACA0EQhPVQZM9KDPA4AAHR9Zpbb7dbw4cOVmJiooqIiY1tDQ4P279+v8ePHS5JSUlIUHh7uE1NZWany8nIjJjU1VS6XS++8844Rc/jwYblcLp+Y8vJyVVZWGjGFhYWy2+1KSUkxYg4cOKCGhgafGKfTqWHDhgV+R3TDamWRMgAAzMp7NRtXhgMAOkMhPVTR2gUAECQ//OEP9Z//+Z86e/asjh07pmeffVb79u3TN7/5TVksFi1dulSrVq3Szp07VV5erszMTPXv31/p6emSJIfDoQULFmjZsmXau3evjh49qieffFKjR4/WlClTJEn333+/pk+frqysLJWUlKikpERZWVmaNWuWkpKSJElpaWkaNWqUMjIydPToUe3du1fLly9XVlaWYmJiJEnp6emy2+3KzMxUeXm5du7cqVWrVik7O7vbVjO3AouNAgBgXhZmpAMA/KBHeohqXWyUDA4A6FsXLlxQRkaGKisr5XA49IUvfEEFBQWaOnWqJOmZZ55RfX29Fi5cqJqaGo0dO1aFhYWKjo42XmPdunUKCwvTvHnzVF9fr8mTJ2vLli2y2WxGzPbt27VkyRKlpaVJkubMmaP169cb2202m3bv3q2FCxdqwoQJioyMVHp6utasWWPEOBwOFRUVadGiRRozZoxiY2OVnZ2t7OzsW72bOtW6SBn5GwAAs7EaM9KDPBAAQEiikB6qWgroLa3SAQDoM5s2bfK73WKxKCcnRzk5OV3G9OvXT3l5ecrLy+syZsCAAdq2bZvf9xoyZIh27drlN2b06NE6cOCA35i+wmKjAACYl3FlGSfEAQCdoLVLiGJGOgAA5tN6STj5GwAAszGuLCONAwA6QSE9VNEjHQAA07GIS8IBADArTogDAPyhkB6qmr2F9CCPAwAA3LAgrG8KAAACxEKPdACAHxTSQ5SFGekAAJiOtU0lndlsAACYS2uPdAAAOqKQHqq8PdI5FQ4AgGm0nZBOCgcAwFysxqLhJHEAQEcU0kNU64z0IA8EAADcMGakAwBgXhajSXpwxwEACE0U0kNVSyG9iYNwAADMo82UdE6GAwCCYfXq1XrooYcUHR2t+Ph4Pf744zp58qRPTGZmpiwWi89t3LhxPjFut1uLFy9WXFycoqKiNGfOHJ0/f94npqamRhkZGXI4HHI4HMrIyNClS5d8Ys6dO6fZs2crKipKcXFxWrJkiRoaGnxijh07pokTJyoyMlL33nuvnnvuuaCckGZGOgDAHwrpoaqlkM5sNgAAzKPtYqMeprMBAIJg//79WrRokUpKSlRUVKRr164pLS1NdXV1PnHTp09XZWWlcfvd737ns33p0qXauXOn8vPzVVxcrCtXrmjWrFlqamoyYtLT01VWVqaCggIVFBSorKxMGRkZxvampibNnDlTdXV1Ki4uVn5+vnbs2KFly5YZMbW1tZo6daqcTqeOHDmivLw8rVmzRrm5ubdoD/nRksg5DAcAdCYs2ANA59q2dvF4PK2XmAEAgJDl29oliAMBANyxCgoKfO5v3rxZ8fHxKi0t1SOPPGI8brfblZiY2OlruFwubdq0SVu3btWUKVMkSdu2bdPgwYO1Z88eTZs2TSdOnFBBQYFKSko0duxYSdLGjRuVmpqqkydPKikpSYWFhTp+/LgqKirkdDolSWvXrlVmZqZWrlypmJgYbd++XVevXtWWLVtkt9uVnJysU6dOKTc3V9nZ2Z0eC7vdbrndbuN+bW3tze20FsxIBwD4w4z0UOVpPcvPpeEAAJhD20N9jsEBAKHA5XJJkgYMGODz+L59+xQfH6+RI0cqKytL1dXVxrbS0lI1NjYqLS3NeMzpdCo5OVkHDx6UJB06dEgOh8MookvSuHHj5HA4fGKSk5ONIrokTZs2TW63W6WlpUbMxIkTZbfbfWI+/PBDnT17ttPPtHr1aqOdjMPh0ODBg3uzazqwtGRyUjgAoDMU0kNVm6NvzoYDAGAOPjPSOQwHAASZx+NRdna2Hn74YSUnJxuPz5gxQ9u3b9dbb72ltWvX6siRI3rssceMWd5VVVWKiIhQbGysz+slJCSoqqrKiImPj+/wnvHx8T4xCQkJPttjY2MVERHhN8Z73xvT3ooVK+RyuYxbRUXFDe8Tf7wz0mmxCgDoDK1dQpSlue2MdJI4AABmYGGxUQBACHn66af13nvvqbi42Ofxb3zjG8a/k5OTNWbMGA0dOlS7d+/W3Llzu3y99m1HO2u7EogYbyG7qxandrvdZwZ7oFiMQnrAXxoAcBtgRnqoaumRLnEgDgCAWfgsNspROAAgiBYvXqw33nhDb7/9tu677z6/sYMGDdLQoUN1+vRpSVJiYqIaGhpUU1PjE1ddXW3MFk9MTNSFCxc6vNbFixd9YtrPKq+pqVFjY6PfGG+bmfYz1W81b+GeyWwAgM5QSA9RljaXgzdTSQcAwBQsbbqkk74BAMHg8Xj09NNP67XXXtNbb72l4cOHd/ucjz/+WBUVFRo0aJAkKSUlReHh4SoqKjJiKisrVV5ervHjx0uSUlNT5XK59M477xgxhw8flsvl8okpLy9XZWWlEVNYWCi73a6UlBQj5sCBA2poaPCJcTqdGjZsWO93RC94szgpHADQGQrpIYwVwwEAMBefK9BJ3wCAIFi0aJG2bdumV199VdHR0aqqqlJVVZXq6+slSVeuXNHy5ct16NAhnT17Vvv27dPs2bMVFxenJ554QpLkcDi0YMECLVu2THv37tXRo0f15JNPavTo0ZoyZYok6f7779f06dOVlZWlkpISlZSUKCsrS7NmzVJSUpIkKS0tTaNGjVJGRoaOHj2qvXv3avny5crKylJMTIwkKT09XXa7XZmZmSovL9fOnTu1atUqZWdnd9na5VaxGjPS+/RtAQAmQSE9hJHEAQAwFxYbBQAE24YNG+RyuTRp0iQNGjTIuP3617+WJNlsNh07dkxf/epXNXLkSM2fP18jR47UoUOHFB0dbbzOunXr9Pjjj2vevHmaMGGC+vfvr9/+9rey2WxGzPbt2zV69GilpaUpLS1NX/jCF7R161Zju81m0+7du9WvXz9NmDBB8+bN0+OPP641a9YYMQ6HQ0VFRTp//rzGjBmjhQsXKjs7W9nZ2X2wt3xZWGwUAOAHi42GMKvVIjV7mJEOAIBJtJ03x4lwAEAwdFcEjoyM1Jtvvtnt6/Tr1095eXnKy8vrMmbAgAHatm2b39cZMmSIdu3a5Tdm9OjROnDgQLdjutW8J8Q5BAcAdIYZ6SHMaO3CkTgAAKbAYqMAAJiXMSOdq8oAAJ2gkB7CaO0CAIC5tO3lSv4GAMBcvHm8uTnIAwEAhCQK6SGstZDOkTgAAGZhZTYbAACm5D0dTg4HAHSGQnoIs1kppAMAYDYW+qsCAGBKXBUOAPCHQnoIsxg90oM7DgAAcOOM2WwchAMAYCqW1inpAAB0QCE9hNlo7QIAgOl4Z7NxWTgAAObibc/GMTgAoDMU0kMYPdIBADAh4yA8uMMAAAA95T0ZDgBARxTSQ5i15dtpopAOAIBpGIuNkr8BADAVZqQDAPyhkB7CrCxWBgCA6VhE/gYAwIxYMBwA4A+F9BBmtHbh2nAAAEzDYsxID+44AABAz3BVGQDAHwrpIcybxGntAgCAebDYKAAA5mScDA/uMAAAIYpCegijtQsAAObTcgzOYqMAAJiMt7ULPdIBAJ0JeiH9gw8+0JNPPqmBAweqf//++uu//muVlpYa2z0ej3JycuR0OhUZGalJkybp/fff93kNt9utxYsXKy4uTlFRUZozZ47Onz/vE1NTU6OMjAw5HA45HA5lZGTo0qVLPjHnzp3T7NmzFRUVpbi4OC1ZskQNDQ237LN3x9oyJZ0Z6QAAmIeFy8IBADAl78lwUjgAoDNBLaTX1NRowoQJCg8P1+9//3sdP35ca9eu1d13323EPP/888rNzdX69et15MgRJSYmaurUqbp8+bIRs3TpUu3cuVP5+fkqLi7WlStXNGvWLDU1NRkx6enpKisrU0FBgQoKClRWVqaMjAxje1NTk2bOnKm6ujoVFxcrPz9fO3bs0LJly/pkX3TGWDGcKW0AAJhG62y2IA8EAAD0iJUcDgDwIyyYb/6zn/1MgwcP1ubNm43Hhg0bZvzb4/HoxRdf1LPPPqu5c+dKkl5++WUlJCTo1Vdf1VNPPSWXy6VNmzZp69atmjJliiRp27ZtGjx4sPbs2aNp06bpxIkTKigoUElJicaOHStJ2rhxo1JTU3Xy5EklJSWpsLBQx48fV0VFhZxOpyRp7dq1yszM1MqVKxUTE9NHe6UVSRwAAPPxnginwyoAAObCVWUAAH+COiP9jTfe0JgxY/T1r39d8fHxevDBB7Vx40Zj+5kzZ1RVVaW0tDTjMbvdrokTJ+rgwYOSpNLSUjU2NvrEOJ1OJScnGzGHDh2Sw+EwiuiSNG7cODkcDp+Y5ORko4guSdOmTZPb7fZpNdOW2+1WbW2tzy2QrPRnAwDAdJiRDgCAObFOGQDAn6AW0v/85z9rw4YNGjFihN58801973vf05IlS/TKK69IkqqqqiRJCQkJPs9LSEgwtlVVVSkiIkKxsbF+Y+Lj4zu8f3x8vE9M+/eJjY1VRESEEdPe6tWrjZ7rDodDgwcP7uku8Mva8u1QSAcAwDzorwoAgDkZOZyrygAAnQhqIb25uVlf/OIXtWrVKj344IN66qmnlJWVpQ0bNvjEeWd2eXk8ng6Ptdc+prP43sS0tWLFCrlcLuNWUVHhd0w9ZcxIbw7oywIAgFvI+7uBg3AAAMyFq8oAAP4EtZA+aNAgjRo1yuex+++/X+fOnZMkJSYmSlKHGeHV1dXG7PHExEQ1NDSopqbGb8yFCxc6vP/Fixd9Ytq/T01NjRobGzvMVPey2+2KiYnxuQUSrV0AADAfi7FYeHDHAQAAeoYe6QAAf4JaSJ8wYYJOnjzp89ipU6c0dOhQSdLw4cOVmJiooqIiY3tDQ4P279+v8ePHS5JSUlIUHh7uE1NZWany8nIjJjU1VS6XS++8844Rc/jwYblcLp+Y8vJyVVZWGjGFhYWy2+1KSUkJ8Ce/Md7FyiikAwBgHt78zYx0AADMhR7pAAB/woL55v/4j/+o8ePHa9WqVZo3b57eeecd/epXv9KvfvUrSdcvq1q6dKlWrVqlESNGaMSIEVq1apX69++v9PR0SZLD4dCCBQu0bNkyDRw4UAMGDNDy5cs1evRoTZkyRdL1We7Tp09XVlaWXnrpJUnSd7/7Xc2aNUtJSUmSpLS0NI0aNUoZGRl64YUX9Mknn2j58uXKysoK+EzzG2W1clkZAABmYxEH4QAAmJExIz24wwAAhKigFtIfeugh7dy5UytWrNBzzz2n4cOH68UXX9Q3v/lNI+aZZ55RfX29Fi5cqJqaGo0dO1aFhYWKjo42YtatW6ewsDDNmzdP9fX1mjx5srZs2SKbzWbEbN++XUuWLFFaWpokac6cOVq/fr2x3Wazaffu3Vq4cKEmTJigyMhIpaena82aNX2wJzrX2iOdNA4AgFkYM9JJ3wAAmApXhQMA/AlqIV2SZs2apVmzZnW53WKxKCcnRzk5OV3G9OvXT3l5ecrLy+syZsCAAdq2bZvfsQwZMkS7du3qdsx9hSQOAID5WFjjBAAAk+KqMgBA14LaIx3+WVkxHAAA0yJ9AwBgLkxmAwD4QyE9hLX2SCeJAwD6zurVq/XQQw8pOjpa8fHxevzxxzssDp6ZmSmLxeJzGzdunE+M2+3W4sWLFRcXp6ioKM2ZM0fnz5/3iampqVFGRoYcDoccDocyMjJ06dIln5hz585p9uzZioqKUlxcnJYsWaKGhgafmGPHjmnixImKjIzUvffeq+eee06eIOVPa8uvq2C9PwAA6B0Li40CAPygkB7CbPRIBwAEwf79+7Vo0SKVlJSoqKhI165dU1pamurq6nzipk+frsrKSuP2u9/9zmf70qVLtXPnTuXn56u4uFhXrlzRrFmz1NTUZMSkp6errKxMBQUFKigoUFlZmTIyMoztTU1Nmjlzpurq6lRcXKz8/Hzt2LFDy5YtM2Jqa2s1depUOZ1OHTlyRHl5eVqzZo1yc3Nv0R7yz7vYKOkbAABzaV3nhCQOAOgo6D3S0TWLcVlZcMcBALizFBQU+NzfvHmz4uPjVVpaqkceecR43G63KzExsdPXcLlc2rRpk7Zu3aopU6ZIkrZt26bBgwdrz549mjZtmk6cOKGCggKVlJRo7NixkqSNGzcqNTVVJ0+eVFJSkgoLC3X8+HFVVFTI6XRKktauXavMzEytXLlSMTEx2r59u65evaotW7bIbrcrOTlZp06dUm5urrKzs43ZZW253W653W7jfm1t7c3ttDasxtuRwAEAMBPvyXAyOACgM8xID2E2FisDAIQAl8sl6frC3W3t27dP8fHxGjlypLKyslRdXW1sKy0tVWNjo9LS0ozHnE6nkpOTdfDgQUnSoUOH5HA4jCK6JI0bN04Oh8MnJjk52SiiS9K0adPkdrtVWlpqxEycOFF2u90n5sMPP9TZs2c7/UyrV6822sk4HA4NHjy4N7umUxbWOAEAwJQs9EgHAPhBIT2EWSmkAwCCzOPxKDs7Ww8//LCSk5ONx2fMmKHt27frrbfe0tq1a3XkyBE99thjxizvqqoqRUREKDY21uf1EhISVFVVZcTEx8d3eM/4+HifmISEBJ/tsbGxioiI8Bvjve+NaW/FihVyuVzGraKi4ob3SXe8E9JJ3wAAmAtXhQMA/KG1SwiztJzmIIkDAILl6aef1nvvvafi4mKfx7/xjW8Y/05OTtaYMWM0dOhQ7d69W3Pnzu3y9Twej0+rlc7argQixtvbtLPnStfb0rSdwR5IFvqrAgBgSlYWGwUA+MGM9BDGYqMAgGBavHix3njjDb399tu67777/MYOGjRIQ4cO1enTpyVJiYmJamhoUE1NjU9cdXW1MVs8MTFRFy5c6PBaFy9e9IlpP6u8pqZGjY2NfmO8bWbaz1TvC7R2AQDAnDgZDgDwh0J6CPOeDW8iiQMA+pDH49HTTz+t1157TW+99ZaGDx/e7XM+/vhjVVRUaNCgQZKklJQUhYeHq6ioyIiprKxUeXm5xo8fL0lKTU2Vy+XSO++8Y8QcPnxYLpfLJ6a8vFyVlZVGTGFhoex2u1JSUoyYAwcOqKGhwSfG6XRq2LBhvd8RveRdbNTDUmUAAJiKMSM9yOMAAIQmCukhzMpCJwCAIFi0aJG2bdumV199VdHR0aqqqlJVVZXq6+slSVeuXNHy5ct16NAhnT17Vvv27dPs2bMVFxenJ554QpLkcDi0YMECLVu2THv37tXRo0f15JNPavTo0ZoyZYok6f7779f06dOVlZWlkpISlZSUKCsrS7NmzVJSUpIkKS0tTaNGjVJGRoaOHj2qvXv3avny5crKylJMTIwkKT09XXa7XZmZmSovL9fOnTu1atUqZWdnd9na5VayiMvCAQAwI++vBo7BAQCdoZAewqxWDsQBAH1vw4YNcrlcmjRpkgYNGmTcfv3rX0uSbDabjh07pq9+9asaOXKk5s+fr5EjR+rQoUOKjo42XmfdunV6/PHHNW/ePE2YMEH9+/fXb3/7W9lsNiNm+/btGj16tNLS0pSWlqYvfOEL2rp1q7HdZrNp9+7d6tevnyZMmKB58+bp8ccf15o1a4wYh8OhoqIinT9/XmPGjNHChQuVnZ2t7OzsPthbHbVeFh6UtwcAAL1koUc6AMAPFhsNYUZrF5qsAgD6UHd9QSMjI/Xmm292+zr9+vVTXl6e8vLyuowZMGCAtm3b5vd1hgwZol27dvmNGT16tA4cONDtmPpCa4908jcAAGZi4apwAIAfzEgPYbR2AQDAfLyXhZO9AQAwF6txWVlwxwEACE0U0kOY1ZjRFuSBAACAG2Zt+XXV3cx+AAAQWuiRDgDwh0J6CLNyaTgAAKbDYqMAAJiT9xicFA4A6AyF9BDmndHWzJR0AABMw2pcFU7+BgDATOiRDgDwh0J6CKO1CwAAJuTN381BHgcAAOgRo0U6x+AAgE5QSA9htHYBAMB8rKxTBgCAKRmtXUjiAIBOUEgPYVYuKwMAwHRYqAwAAHOy0J4NAOAHhfQQZrVyaTgAAGZjYTYbAACmRHtVAIA/FNJDmI3WLgAAmI73ijKauwAAYC7eFO7hGBwA0AkK6SGMFcMBADAfi5jNBgCAGVmYkQ4A8INCeghre1kZZ8QBADAHo78qqRsAAFOxWFr/zTE4AKA9CukhzNZ6bTgH4wAAmARXlAEAYE5WC8fgAICuUUgPYW3PhnMwDgCAOXhbu5C5AQAwlzaH4ORxAEAHFNJDmK1NJZ0ebQAAmIO15dcVl4QDAIJh9erVeuihhxQdHa34+Hg9/vjjOnnypE+Mx+NRTk6OnE6nIiMjNWnSJL3//vs+MW63W4sXL1ZcXJyioqI0Z84cnT9/3iempqZGGRkZcjgccjgcysjI0KVLl3xizp07p9mzZysqKkpxcXFasmSJGhoafGKOHTumiRMnKjIyUvfee6+ee+65oORRq88xOHkcAOCrV4X0M2fOBHoc6ARJHADQU+To4DNmpJO6AQA9FIg8vn//fi1atEglJSUqKirStWvXlJaWprq6OiPm+eefV25urtavX68jR44oMTFRU6dO1eXLl42YpUuXaufOncrPz1dxcbGuXLmiWbNmqampyYhJT09XWVmZCgoKVFBQoLKyMmVkZBjbm5qaNHPmTNXV1am4uFj5+fnasWOHli1bZsTU1tZq6tSpcjqdOnLkiPLy8rRmzRrl5ube9L7oMZ8e6X3/9gCA0BbWmyd97nOf0yOPPKIFCxbob/7mb9SvX79Ajwvybe3SxJR0AMANIEcHn7HYKBeFAwB6KBB5vKCgwOf+5s2bFR8fr9LSUj3yyCPyeDx68cUX9eyzz2ru3LmSpJdfflkJCQl69dVX9dRTT8nlcmnTpk3aunWrpkyZIknatm2bBg8erD179mjatGk6ceKECgoKVFJSorFjx0qSNm7cqNTUVJ08eVJJSUkqLCzU8ePHVVFRIafTKUlau3atMjMztXLlSsXExGj79u26evWqtmzZIrvdruTkZJ06dUq5ubnKzs6Wpe2BcQu32y23223cr62t7fF+6oyV9qoAAD96NSP9j3/8ox588EEtW7ZMiYmJeuqpp/TOO+8Eemx3PIvFYiRycjgA4EaQo4PPe8Df3BzkgQAATOdW5HGXyyVJGjBggKTrs96rqqqUlpZmxNjtdk2cOFEHDx6UJJWWlqqxsdEnxul0Kjk52Yg5dOiQHA6HUUSXpHHjxsnhcPjEJCcnG0V0SZo2bZrcbrdKS0uNmIkTJ8put/vEfPjhhzp79mynn2n16tVGOxmHw6HBgwf3ev+01VnRHgAAr14V0pOTk5Wbm6sPPvhAmzdvVlVVlR5++GF9/vOfV25uri5evBjocd6xvO1dOBsOALgR5OjgM06CB3cYAAATCnQe93g8ys7O1sMPP6zk5GRJUlVVlSQpISHBJzYhIcHYVlVVpYiICMXGxvqNiY+P7/Ce8fHxPjHt3yc2NlYRERF+Y7z3vTHtrVixQi6Xy7hVVFR0syduDDPSAQD+3NRio2FhYXriiSf07//+7/rZz36mP/3pT1q+fLnuu+8+fetb31JlZWWgxnnH8hbSm0jiAIAeIEcHj/cYnANwAEBvBSqPP/3003rvvff0b//2bx22tZ997fF4up2R3T6ms/hAxHgXGu1qPHa7XTExMT63QLC0aZJOGgcAtHdThfR3331XCxcu1KBBg5Sbm6vly5frT3/6k9566y198MEH+upXvxqocd6xvGfEm+mRDgDoAXJ08FgsTEkHANycQOTxxYsX64033tDbb7+t++67z3g8MTFRUsfZ3tXV1cZM8MTERDU0NKimpsZvzIULFzq878WLF31i2r9PTU2NGhsb/cZUV1dL6jhr/lazMCMdAOBHrwrpubm5Gj16tMaPH68PP/xQr7zyiv7yl7/oJz/5iYYPH64JEybopZde0h/+8IdAj/eOY7V6W7sEeSAAAFMgRweflcVGAQC9FIg87vF49PTTT+u1117TW2+9peHDh/tsHz58uBITE1VUVGQ81tDQoP3792v8+PGSpJSUFIWHh/vEVFZWqry83IhJTU2Vy+Xy6eF++PBhuVwun5jy8nKfGfSFhYWy2+1KSUkxYg4cOKCGhgafGKfTqWHDhvV0F96UtoV0sjgAoL2w3jxpw4YN+s53vqNvf/vbxtns9oYMGaJNmzbd1OBAj3QAQM+Qo0MBJ8EBAL0TiDy+aNEivfrqq/rNb36j6OhoY7a3w+FQZGSkLBaLli5dqlWrVmnEiBEaMWKEVq1apf79+ys9Pd2IXbBggZYtW6aBAwdqwIABWr58uUaPHq0pU6ZIku6//35Nnz5dWVlZeumllyRJ3/3udzVr1iwlJSVJktLS0jRq1ChlZGTohRde0CeffKLly5crKyvLaMeSnp6uH//4x8rMzNQPf/hDnT59WqtWrdK//Mu/9Pnin9Y27+dh0XAAQDu9KqSfPn2625iIiAjNnz+/Ny+PNozWLhTSAQA3gBwdfMaMdFI3AKCHApHHN2zYIEmaNGmSz+ObN29WZmamJOmZZ55RfX29Fi5cqJqaGo0dO1aFhYWKjo424tetW6ewsDDNmzdP9fX1mjx5srZs2SKbzWbEbN++XUuWLFFaWpokac6cOVq/fr2x3Wazaffu3Vq4cKEmTJigyMhIpaena82aNUaMw+FQUVGRFi1apDFjxig2NlbZ2dnKzs7udl8EWtuyPVeWAQDa61UhffPmzbrrrrv09a9/3efx//t//68+/fRTDs4DqHVGepAHAgAwBXJ08Fk4CQ4A6KVA5HHPDeQfi8WinJwc5eTkdBnTr18/5eXlKS8vr8uYAQMGaNu2bX7fa8iQIdq1a5ffmNGjR+vAgQN+Y/pC2xnpHIMDANrrVY/0n/70p4qLi+vweHx8vFatWnXTg0Iro0c6WRwAcAPI0cHnPQgncwMAeoo8Hlw+PdI5IQ4AaKdXhfS//OUvHRYskaShQ4fq3LlzNz0otKK1CwCgJ8jRwWcxWruQuwEAPUMeDy4LM9IBAH70qpAeHx+v9957r8Pjf/zjHzVw4MCbHhRa0doFANAT5Ojgs7R0WKWODgDoKfJ48BknxLm2DADQTq8K6X/7t3+rJUuW6O2331ZTU5Oampr01ltv6fvf/77+9m//NtBjvKO1FtJJ4gCA7pGjg48Z6QCA3iKPB5/Roo00DgBop1eLjf7kJz/RX/7yF02ePFlhYddform5Wd/61rfo2xZg1pZTHfRIBwDcCHJ08Fm4mgwA0Evk8eDzNnehkA4AaK9XhfSIiAj9+te/1v/+3/9bf/zjHxUZGanRo0dr6NChgR7fHY/WLgCAniBHB5/VuCQcAICeIY8H3/VjcA9XhQMAOuhVId1r5MiRGjlyZKDGgk7Q2gUA0Bvk6OBpnclG7gYA9A55PIg4IQ4A6EKvCulNTU3asmWL9u7dq+rqajU3N/tsf+uttwIyOLTOaqOQDgC4EeTo4LPQWxUA0Evk8eAzjsG5LBwA0E6vCunf//73tWXLFs2cOVPJycnGASMCz9aSxdv9fgIAoFPk6OAzFhtlLhsAoIfI48FnEfscANC5XhXS8/Pz9e///u/6yle+EujxoB1auwAAeoIcHXzeA3AmsgEAeoo8HnxcFQ4A6Iq1N0+KiIjQ5z73uUCPBZ2wkMQBAD1Ajg4+Y7FRUjcAoIfI48FHizYAQFd6VUhftmyZfv7zn7OIVh+wWZjVBgC4ceTo4OMkOACgt8jjwUceBwB0pVetXYqLi/X222/r97//vT7/+c8rPDzcZ/trr70WkMFBsrZMa2siiQMAbgA5Ovis9LMFAPQSeTz4vFmcI3AAQHu9KqTffffdeuKJJwI9FnTCWLCMKekAgBtAjg4+YyYbuRsA0EPk8eDzTmbjqgAAQHu9KqRv3rw50ONAF2jtAgDoCXJ0KGg5AA/yKAAA5kMeDz5jRjqJHADQTq96pEvStWvXtGfPHr300ku6fPmyJOnDDz/UlStXAjY4tF4eTmsXAMCNIkcHF4uNAgBuBnk8uKxMZgMAdKFXM9L/8pe/aPr06Tp37pzcbremTp2q6OhoPf/887p69ap++ctfBnqcdyzvZWUsdAIAuBHk6OBjkTIAQG+Rx4PPaK/KtWUAgHZ6NSP9+9//vsaMGaOamhpFRkYajz/xxBPau3dvwAaHNrPamoM7DgCAOQQiR69evVoPPfSQoqOjFR8fr8cff1wnT570ifF4PMrJyZHT6VRkZKQmTZqk999/3yfG7XZr8eLFiouLU1RUlObMmaPz58/7xNTU1CgjI0MOh0MOh0MZGRm6dOmST8y5c+c0e/ZsRUVFKS4uTkuWLFFDQ4NPzLFjxzRx4kRFRkbq3nvv1XPPPRe03qbemWwcfgMAeopj7eCzeGekcwwOAGinV4X04uJi/a//9b8UERHh8/jQoUP1wQcfBGRguI7WLgCAnghEjt6/f78WLVqkkpISFRUV6dq1a0pLS1NdXZ0R8/zzzys3N1fr16/XkSNHlJiYqKlTpxqXoEvS0qVLtXPnTuXn56u4uFhXrlzRrFmz1NTUZMSkp6errKxMBQUFKigoUFlZmTIyMoztTU1Nmjlzpurq6lRcXKz8/Hzt2LFDy5YtM2Jqa2s1depUOZ1OHTlyRHl5eVqzZo1yc3N7vP8CobW3KrkbANAzHGsHn5HHOSUOAGinV61dmpubfQ6Cvc6fP6/o6OibHhRatfZnI4kDALoXiBxdUFDgc3/z5s2Kj49XaWmpHnnkEXk8Hr344ot69tlnNXfuXEnSyy+/rISEBL366qt66qmn5HK5tGnTJm3dulVTpkyRJG3btk2DBw/Wnj17NG3aNJ04cUIFBQUqKSnR2LFjJUkbN25UamqqTp48qaSkJBUWFur48eOqqKiQ0+mUJK1du1aZmZlauXKlYmJitH37dl29elVbtmyR3W5XcnKyTp06pdzcXGVnZxszy9pyu91yu93G/dra2hvaNzfC+36kbgBAT3GsHXxW8jgAoAu9mpE+depUvfjii8Z9i8WiK1eu6Ec/+pG+8pWvBGpskGRt+YYopAMAbsStyNEul0uSNGDAAEnSmTNnVFVVpbS0NCPGbrdr4sSJOnjwoCSptLRUjY2NPjFOp1PJyclGzKFDh+RwOIwiuiSNGzdODofDJyY5OdkookvStGnT5Ha7VVpaasRMnDhRdrvdJ+bDDz/U2bNnO/1Mq1evNtrJOBwODR48uFf7pjP0SAcA9BbH2sFnYdFwAEAXelVIX7dunfbv369Ro0bp6tWrSk9P17Bhw/TBBx/oZz/7WaDHeEdjxXAAQE8EOkd7PB5lZ2fr4YcfVnJysiSpqqpKkpSQkOATm5CQYGyrqqpSRESEYmNj/cbEx8d3eM/4+HifmPbvExsbq4iICL8x3vvemPZWrFghl8tl3CoqKrrZEzfOInqkAwB6h2Pt4OOqcABAV3rV2sXpdKqsrEz/9m//pj/84Q9qbm7WggUL9M1vftNnQRTcPCOJU0kHANyAQOfop59+Wu+9956Ki4s7bGvfMsXj8XTaRsVfTGfxgYjx9ifvajx2u91nBnsgWZnJBgDoJY61QwdpHADQXq8K6ZIUGRmp73znO/rOd74TyPGgHSuXhwMAeihQOXrx4sV64403dODAAd13333G44mJiZKuz/YeNGiQ8Xh1dbUxEzwxMVENDQ2qqanxmZVeXV2t8ePHGzEXLlzo8L4XL170eZ3Dhw/7bK+pqVFjY6NPTPuZ59XV1ZI6zprvC62XhJO7AQA9x7F2cNFeFQDQlV4V0l955RW/27/1rW/1ajDoiNYuAICeCESO9ng8Wrx4sXbu3Kl9+/Zp+PDhPtuHDx+uxMREFRUV6cEHH5QkNTQ0aP/+/cZl5ykpKQoPD1dRUZHmzZsnSaqsrFR5ebmef/55SVJqaqpcLpfeeecdfelLX5IkHT58WC6Xyyi2p6amauXKlaqsrDSK9oWFhbLb7UpJSTFifvjDH6qhoUERERFGjNPp1LBhw7r9vIFmLFLW5+8MADA7jrWDz2jRRiIHALTTq0L697//fZ/7jY2N+vTTTxUREaH+/fuT3APIaqU/GwDgxgUiRy9atEivvvqqfvOb3yg6OtqY7e1wOBQZGSmLxaKlS5dq1apVGjFihEaMGKFVq1apf//+Sk9PN2IXLFigZcuWaeDAgRowYICWL1+u0aNHa8qUKZKk+++/X9OnT1dWVpZeeuklSdJ3v/tdzZo1S0lJSZKktLQ0jRo1ShkZGXrhhRf0ySefaPny5crKylJMTIwkKT09XT/+8Y+VmZmpH/7whzp9+rRWrVqlf/mXf+m21cwt4b2ajLPgAIAe4lg7+KxcWQYA6EKvCuk1NTUdHjt9+rT+4R/+Qf/0T/9004NCKysH4wCAHghEjt6wYYMkadKkST6Pb968WZmZmZKkZ555RvX19Vq4cKFqamo0duxYFRYWKjo62ohft26dwsLCNG/ePNXX12vy5MnasmWLbDabEbN9+3YtWbJEaWlpkqQ5c+Zo/fr1xnabzabdu3dr4cKFmjBhgiIjI5Wenq41a9YYMQ6HQ0VFRVq0aJHGjBmj2NhYZWdnKzs7+4Y+b6Cx2CgAoLc41g4+C1eWAQC60Ose6e2NGDFCP/3pT/Xkk0/qv//7vwP1snc8G61dAAA3qac5+kZmYFksFuXk5CgnJ6fLmH79+ikvL095eXldxgwYMEDbtm3z+15DhgzRrl27/MaMHj1aBw4c8BvTV1jfBAAQSBxr9y0Lk9kAAF2wBvLFbDabPvzww0C+5B3PYqG1CwDg5pGj+07rYqPBHQcA4PZBHu873qZwpHEAQHu9mpH+xhtv+Nz3eDyqrKzU+vXrNWHChIAMDNexYjgAoCfI0cFnDUZfdgDAbYE8HnxWJrMBALrQq0L6448/7nPfYrHonnvu0WOPPaa1a9cGYlxoYbR2aQ7yQAAApkCODj5vGZ0DcABAT5HHg8/ClHQAQBd6VUhvpqrbZ2jtAgDoCXJ08BmLlJG6AQA9RB4PPivrlAEAuhDQHukIPBuFdAAATMXCYqMAAJiehynpAIB2ejUjPTs7+4Zjc3Nze/MWaNHaIz244wAAmAM5OvgsLc1dSN0AgJ4ijwcfM9IBAF3pVSH96NGj+sMf/qBr164pKSlJknTq1CnZbDZ98YtfNOIsLLZ107xJvIksDgC4AeTo4LO27FompAMAeoo8HnwWI4+TyAEAvnpVSJ89e7aio6P18ssvKzY2VpJUU1Ojb3/72/ryl7+sZcuWBXSQdzKr0WeVJA4A6B45Ovg4AAcA9BZ5PPisrHUCAOhCr3qkr127VqtXrzYSuyTFxsbqJz/5CSuJB5jV6LMa3HEAAMyBHB18LDYKAOgt8njwGSfEadIGAGinV4X02tpaXbhwocPj1dXVunz58k0PCq2sLZX0Jo7GAQA3gBwdfCw2CgDoLfJ48HlPiDc3B3kgAICQ06tC+hNPPKFvf/vb+o//+A+dP39e58+f13/8x39owYIFmjt3bqDHeEczFjphSjoA4AaQo4PPuCQ8yOMAAJgPeTz4jLVOgjsMAEAI6lWP9F/+8pdavny5nnzySTU2Nl5/obAwLViwQC+88EJAB3inY8EyAEBPkKODz7v8GzPSAQA9RR4PPvI4AKArvSqk9+/fX7/4xS/0wgsv6E9/+pM8Ho8+97nPKSoqKtDju+N5Z7XR2gUAcCPI0cHnbe3CVDYAQE+Rx4OPxUYBAF3pVWsXr8rKSlVWVmrkyJGKioqSh0wTcN4e6RJnxAEAN44cHTy0dgEA3CzyePAYi42yzwEA7fSqkP7xxx9r8uTJGjlypL7yla+osrJSkvT3f//3WrZsWUAHeKdrU0enkA4A6BY5OnSQtwEAPUUeDz4LJ8QBAF3oVSH9H//xHxUeHq5z586pf//+xuPf+MY3VFBQELDBoXVWm8Sq4QCA7pGjg49LwgEAvUUeDz56pAMAutKrQnphYaF+9rOf6b777vN5fMSIEfrLX/7Sq4GsXr1aFotFS5cuNR7zeDzKycmR0+lUZGSkJk2apPfff9/neW63W4sXL1ZcXJyioqI0Z84cnT9/3iempqZGGRkZcjgccjgcysjI0KVLl3xizp07p9mzZysqKkpxcXFasmSJGhoaevVZAsmnkE4iBwB041bkaPSMN3WTtwEAPUUeDz5OiAMAutKrQnpdXZ3P2XGvjz76SHa7vcevd+TIEf3qV7/SF77wBZ/Hn3/+eeXm5mr9+vU6cuSIEhMTNXXqVF2+fNmIWbp0qXbu3Kn8/HwVFxfrypUrmjVrlpqamoyY9PR0lZWVqaCgQAUFBSorK1NGRoaxvampSTNnzlRdXZ2Ki4uVn5+vHTt2hMSlc7R2AQD0RKBzNHqOtUYBAL1FHg8+TogDALrSq0L6I488oldeecW4b7FY1NzcrBdeeEGPPvpoj17rypUr+uY3v6mNGzcqNjbWeNzj8ejFF1/Us88+q7lz5yo5OVkvv/yyPv30U7366quSJJfLpU2bNmnt2rWaMmWKHnzwQW3btk3Hjh3Tnj17JEknTpxQQUGB/vVf/1WpqalKTU3Vxo0btWvXLp08eVLS9bP+x48f17Zt2/Tggw9qypQpWrt2rTZu3Kja2tre7KKAsVgsbRJ5UIcCADCBQOZo9I53oXAWKQMA9BR5PPjaXhUOAEBbYb150gsvvKBJkybp3XffVUNDg5555hm9//77+uSTT/Rf//VfPXqtRYsWaebMmZoyZYp+8pOfGI+fOXNGVVVVSktLMx6z2+2aOHGiDh48qKeeekqlpaVqbGz0iXE6nUpOTtbBgwc1bdo0HTp0SA6HQ2PHjjVixo0bJ4fDoYMHDyopKUmHDh1ScnKynE6nETNt2jS53W6VlpZ2+YPF7XbL7XYb929V0d1qsajJ41EzlXQAQDcCmaPRO8aMdNI2AKCHyOPBx4x0AEBXejUjfdSoUXrvvff0pS99SVOnTlVdXZ3mzp2ro0eP6rOf/ewNv05+fr7+8Ic/aPXq1R22VVVVSZISEhJ8Hk9ISDC2VVVVKSIiwmcme2cx8fHxHV4/Pj7eJ6b9+8TGxioiIsKI6czq1auNvusOh0ODBw/u7iP3iq0lk5PIAQDdCVSORu9Z6K0KAOilQOTxAwcOaPbs2XI6nbJYLHr99dd9tmdmZrZc+dx6GzdunE9MX65FduzYMU2cOFGRkZG699579dxzzwX1qi7yOACgKz2eke6dAf7SSy/pxz/+ca/fuKKiQt///vdVWFiofv36dRlnaXdZlcfj6fBYe+1jOovvTUx7K1asUHZ2tnG/trb2lhTTae0CALgRgcrRuDnMZAMA9Eag8nhdXZ0eeOABffvb39bXvva1TmOmT5+uzZs3G/cjIiJ8ti9dulS//e1vlZ+fr4EDB2rZsmWaNWuWSktLZbPZJF1fi+z8+fMqKCiQJH33u99VRkaGfvvb30pqXYvsnnvuUXFxsT7++GPNnz9fHo9HeXl5kq4fQ0+dOlWPPvqojhw5olOnTikzM1NRUVFBW7PMWwHg+BsA0F6PC+nh4eEqLy/vtpjdndLSUlVXVyslJcV4rKmpSQcOHND69euN/uVVVVUaNGiQEVNdXW3MHk9MTFRDQ4Nqamp8ZqVXV1dr/PjxRsyFCxc6vP/Fixd9Xufw4cM+22tqatTY2Nhhpnpbdru9TxZ8sTIjHQBwAwKVo3FzvHmbrA0A6IlA5fEZM2ZoxowZfmPsdrsSExM73eZdi2zr1q2aMmWKJGnbtm0aPHiw9uzZo2nTphlrkZWUlBhtVDdu3KjU1FSdPHlSSUlJxlpkFRUVRhvVtWvXKjMzUytXrlRMTIy2b9+uq1evasuWLbLb7UpOTtapU6eUm5ur7OzsLvfFrWyz2rLUCWudAAA66FVrl29961vatGnTTb3x5MmTdezYMZWVlRm3MWPG6Jvf/KbKysr0mc98RomJiSoqKjKe09DQoP379xtF8pSUFIWHh/vEVFZWqry83IhJTU2Vy+XSO++8Y8QcPnxYLpfLJ6a8vFyVlZVGTGFhoex2u0+hP1hsLZmcHukAgO4EIkfj5rT2SCdvAwB6pq/y+L59+xQfH6+RI0cqKytL1dXVxrbu1iKT1O1aZN4Yf2uReWMmTpzoM0Ft2rRp+vDDD3X27Nkux38r26zS2gUA0JVeLTba0NCgf/3Xf1VRUZHGjBmjqKgon+25ubndvkZ0dLSSk5N9HouKitLAgQONx5cuXapVq1ZpxIgRGjFihFatWqX+/fsrPT1dkuRwOLRgwQItW7ZMAwcO1IABA7R8+XKNHj3aOHN+//33a/r06crKytJLL70k6folZ7NmzVJSUpIkKS0tTaNGjVJGRoZeeOEFffLJJ1q+fLmysrIUExPTm10UUFZauwAAblAgcjRujsWYyRbccQAAzKcv8viMGTP09a9/XUOHDtWZM2f0z//8z3rsscdUWloqu93ep2uRVVVVadiwYR3ex7tt+PDhnX6GW9lm1ZiRzrVlAIB2elRI//Of/6xhw4apvLxcX/ziFyVJp06d8okJ5OXkzzzzjOrr67Vw4ULV1NRo7NixKiwsVHR0tBGzbt06hYWFad68eaqvr9fkyZO1ZcsWo2+bJG3fvl1LliwxzqjPmTNH69evN7bbbDbt3r1bCxcu1IQJExQZGan09HStWbMmYJ/lZlho7QIA6EZf52h0zUJrFwBAD/VlHv/GN75h/Ds5OVljxozR0KFDtXv3bs2dO7fL592qtcg6Wxetq+d63do2q97j71v08gAA0+pRIX3EiBGqrKzU22+/Lel6Av4//+f/+O0j3hP79u3zuW+xWJSTk6OcnJwun9OvXz/l5eUZi5V0ZsCAAdq2bZvf9x4yZIh27drVk+H2GRuFdABAN251jsaNa12kjLwNALgxwczjgwYN0tChQ3X69GlJfbsWWWJiojE7ve37SArabxgrV5YBALrQox7p7Xt9/v73v1ddXV1AB4SOrC3fUhOnxAEAXSBHhw4rvVUBAD0UzDz+8ccfq6KiQoMGDZLUt2uRpaam6sCBA2poaPCJcTqdHVq+9BWL0VqVRA4A8NWrxUa9WESrb1gtXFoGAOgZcnTwcAAOALhZN5PHr1y5orKyMpWVlUmSzpw5o7KyMp07d05XrlzR8uXLdejQIZ09e1b79u3T7NmzFRcXpyeeeEKS71pke/fu1dGjR/Xkk092uRZZSUmJSkpKlJWV1eVaZEePHtXevXs7rEWWnp4uu92uzMxMlZeXa+fOnVq1apWys7OD1pLOSos2AEAXetTaxWKxdEhm9Fu99VpntpHKAQCdI0eHDiv7HQDQQ4HM4++++64effRR4753Uc758+drw4YNOnbsmF555RVdunRJgwYN0qOPPqpf//rXQVmLzOFwqKioSIsWLdKYMWMUGxur7Oxsn4VE+1rrouEcfwMAfPWokO7xeJSZmWks6nH16lV973vf67CS+GuvvRa4EcLo0dZEIgcAdIEcHTqYkQ4A6KlA5vFJkyb5LQK/+eab3b5GX65FNnr0aB04cKDbMfUV7wmMZi4JBwC006NC+vz5833uP/nkkwEdDDpntXoTeZAHAgAIWeTo0EMdHQBwo8jjocN7HQBpHADQXo8K6Zs3b75V44AftHYBAHSHHB06WGwUANBT5PHQwRplAICu3NRio+gbtHYBAMA8aO0CAIB50SMdANAVCukmwBlxAADMw5iRHuRxAACAnuPKMgBAVyikm0Brj3QyOQAAoc7orcoROAAAptPaI508DgDwRSHdBKxcIg4AgGm0XhIe3HEAAICes3BFOACgCxTSTYDWLgAAmEfrATiJGwAAs+GEOACgKxTSTcDKATkAAKbRekk4AAAwG64IBwB0hUK6CZDIAQAwDxYpAwDAvCzGKXEAAHxRSDeB1sVGgzwQAADQrdZLwqmkAwBgNtaWKkkzvVUBAO1QSDcBWrsAAPragQMHNHv2bDmdTlksFr3++us+2zMzM2WxWHxu48aN84lxu91avHix4uLiFBUVpTlz5uj8+fM+MTU1NcrIyJDD4ZDD4VBGRoYuXbrkE3Pu3DnNnj1bUVFRiouL05IlS9TQ0OATc+zYMU2cOFGRkZG699579dxzzwWtkG3MSA/KuwMAgJtDHgcAdI5CugnYKKQDAPpYXV2dHnjgAa1fv77LmOnTp6uystK4/e53v/PZvnTpUu3cuVP5+fkqLi7WlStXNGvWLDU1NRkx6enpKisrU0FBgQoKClRWVqaMjAxje1NTk2bOnKm6ujoVFxcrPz9fO3bs0LJly4yY2tpaTZ06VU6nU0eOHFFeXp7WrFmj3NzcAO6RHqAlGwAApkVrVQBAV8KCPQB0z2Ik8uCOAwBw55gxY4ZmzJjhN8ZutysxMbHTbS6XS5s2bdLWrVs1ZcoUSdK2bds0ePBg7dmzR9OmTdOJEydUUFCgkpISjR07VpK0ceNGpaam6uTJk0pKSlJhYaGOHz+uiooKOZ1OSdLatWuVmZmplStXKiYmRtu3b9fVq1e1ZcsW2e12JScn69SpU8rNzVV2drYslr7tdWosNkreBgDAdFpbtAV3HACA0MOMdBNo7ZFOJgcAhI59+/YpPj5eI0eOVFZWlqqrq41tpaWlamxsVFpamvGY0+lUcnKyDh48KEk6dOiQHA6HUUSXpHHjxsnhcPjEJCcnG0V0SZo2bZrcbrdKS0uNmIkTJ8put/vEfPjhhzp79mynY3e73aqtrfW5BQqLjQIAYF6teZxEDgDwRSHdBGjtAgAINTNmzND27dv11ltvae3atTpy5Igee+wxud1uSVJVVZUiIiIUGxvr87yEhARVVVUZMfHx8R1eOz4+3icmISHBZ3tsbKwiIiL8xnjve2PaW716tdGX3eFwaPDgwT3dBV2ycEk4AACmZVxZFtRRAABCEa1dTMDbo62JA3IAQIj4xje+Yfw7OTlZY8aM0dChQ7V7927NnTu3y+d5PB6fViudtV0JRIx3FllXbV1WrFih7Oxs435tbW3AiunWPm4lAwAAAsfCRDYAQBeYkW4CXCIOAAh1gwYN0tChQ3X69GlJUmJiohoaGlRTU+MTV11dbcwWT0xM1IULFzq81sWLF31i2s8qr6mpUWNjo98Yb5uZ9jPVvex2u2JiYnxugeIto3MADgCA+dAjHQDQFQrpJkCPdABAqPv4449VUVGhQYMGSZJSUlIUHh6uoqIiI6ayslLl5eUaP368JCk1NVUul0vvvPOOEXP48GG5XC6fmPLyclVWVhoxhYWFstvtSklJMWIOHDighoYGnxin06lhw4bdss/cFQsnwAEAMC2rMSM9yAMBAIQcCukmQGsXAEBfu3LlisrKylRWViZJOnPmjMrKynTu3DlduXJFy5cv16FDh3T27Fnt27dPs2fPVlxcnJ544glJksPh0IIFC7Rs2TLt3btXR48e1ZNPPqnRo0drypQpkqT7779f06dPV1ZWlkpKSlRSUqKsrCzNmjVLSUlJkqS0tDSNGjVKGRkZOnr0qPbu3avly5crKyvLmEWenp4uu92uzMxMlZeXa+fOnVq1apWys7O7bO1yK9EjHQAA82rtkU4eBwD4oke6CdDaBQDQ19599109+uijxn1vP/H58+drw4YNOnbsmF555RVdunRJgwYN0qOPPqpf//rXio6ONp6zbt06hYWFad68eaqvr9fkyZO1ZcsW2Ww2I2b79u1asmSJ0tLSJElz5szR+vXrje02m027d+/WwoULNWHCBEVGRio9PV1r1qwxYhwOh4qKirRo0SKNGTNGsbGxys7O9umB3pdYpAwAAPPyXhHO8TcAoD0K6SbgLaQzIx0A0FcmTZpkLNjZmTfffLPb1+jXr5/y8vKUl5fXZcyAAQO0bds2v68zZMgQ7dq1y2/M6NGjdeDAgW7H1Be8B+BU0gEAMB/jhDjH3wCAdmjtYgLWlm+JS8QBAAh9LDYKAIB5WeiRDgDoAoV0EzAWO2kO8kAAAEC3jMVGgzwOAADQc961TjgfDgBoj0K6CbSuGk4mBwAg1LHYKAAA5mUljwMAukAh3QRI5AAAmEdrb9WgDgMAAPSCxcjkAAD4opBuAt5Fy+jRBgBA6PNeSUYhHQAA82EiGwCgKxTSTYDWLgAAmEdrb1XyNgAApsMJcQBAFyikm4BxRpwp6QAAhDwri40CAGBazEgHAHSFQroJtM5ID/JAAADADeMAHAAA8/H2SCeLAwDao5BuAq090knlAACEOnqkAwBgXlZatAEAukAh3QS4tAwAAPOwGHk7uOMAAAA917rWSXDHAQAIPRTSTcBo7dIc5IEAAIBueQ/AuSgcAADzsVi4IhwA0DkK6SZgI5EDAGAatHYBAMC8mJEOAOgKhXQToEc6AADm4Z2QTt4GAMB8jCvCSeMAgHYopJsAvVYBADAP7yXhpG0AAMzHe0LcQyYHALRDId0EjNYuVNIBAAh5xglw8jYAAKZDizYAQFcopJuAtc3MNg/ZHACAkNY6kw0AAJhNa490MjkAwBeFdBOwWlr/zeQ2AABCGzPZAAAwLws90gEAXaCQbgLWNpV0Fi4DACC0MZMNAADz8h5+k8UBAO1RSDcB78w2iX6rAACEOiuLjQIAYFreo28msQEA2qOQbgJtW7s0kcwBADAFDsABADAfK1PSAQBdoJBuAhaLpXXhMpI5AAAhzXsATs4GAMB8mJEOAOgKhXST8B6UMyMdAIDQxslvAADMy8Ki4QCALlBINwnv1WX0SAcAILQZi41yTTgAAKbjzePMSAcAtEch3SSsnBUHAMAUvDmbc98AAJgPi4YDALpCId0kvMmc1i4AAIS21tYu5GwAAMyGPA4A6AqFdJOwtnxTXF4GAEBoszCTDQAA0+JqcABAVyikm4RxmXhzkAcCAAD8MnqkcwAOAID50CMdANAFCukm0dpvlWQOAEAo8+ZsicvCAQB978CBA5o9e7acTqcsFotef/11n+0ej0c5OTlyOp2KjIzUpEmT9P777/vEuN1uLV68WHFxcYqKitKcOXN0/vx5n5iamhplZGTI4XDI4XAoIyNDly5d8ok5d+6cZs+eraioKMXFxWnJkiVqaGjwiTl27JgmTpyoyMhI3XvvvXruueeCmj/pkQ4A6AqFdJOwclYcAABTsLT5NwuOAgD6Wl1dnR544AGtX7++0+3PP/+8cnNztX79eh05ckSJiYmaOnWqLl++bMQsXbpUO3fuVH5+voqLi3XlyhXNmjVLTU1NRkx6errKyspUUFCggoIClZWVKSMjw9je1NSkmTNnqq6uTsXFxcrPz9eOHTu0bNkyI6a2tlZTp06V0+nUkSNHlJeXpzVr1ig3N/cW7Jkb483j5HAAQHthwR4AbozV6p2RHuSBAAAAv9pMSG+ZUWfpMhYAgECbMWOGZsyY0ek2j8ejF198Uc8++6zmzp0rSXr55ZeVkJCgV199VU899ZRcLpc2bdqkrVu3asqUKZKkbdu2afDgwdqzZ4+mTZumEydOqKCgQCUlJRo7dqwkaePGjUpNTdXJkyeVlJSkwsJCHT9+XBUVFXI6nZKktWvXKjMzUytXrlRMTIy2b9+uq1evasuWLbLb7UpOTtapU6eUm5ur7OxsY92R9txut9xut3G/trY2YPvPuz4ZV5UBANpjRrpJ2GjtAgCAKbQ96CdrAwBCyZkzZ1RVVaW0tDTjMbvdrokTJ+rgwYOSpNLSUjU2NvrEOJ1OJScnGzGHDh2Sw+EwiuiSNG7cODkcDp+Y5ORko4guSdOmTZPb7VZpaakRM3HiRNntdp+YDz/8UGfPnu3yc6xevdpoKeNwODR48OCb2Cu+LGKxUQBA5yikm4T3mLyZKekAAIS0tpPnOAEOAAglVVVVkqSEhASfxxMSEoxtVVVVioiIUGxsrN+Y+Pj4Dq8fHx/vE9P+fWJjYxUREeE3xnvfG9OZFStWyOVyGbeKigr/H7wHjEXDOR0OAGiH1i4m0brYaJAHAgAA/PJdbDSIAwEAoAvtW6Z4PJ4u26h0FdNZfCBivC1V/I3Hbrf7zGIPJO/7NjffkpcHAJgYM9JNgtYuAACYQ9vDftI2ACCUJCYmSuo427u6utqYCZ6YmKiGhgbV1NT4jblw4UKH17948aJPTPv3qampUWNjo9+Y6upqSR1nzfcVKzPSAQBdoJBuEsZio0xJBwAgpPksNspBOAAghAwfPlyJiYkqKioyHmtoaND+/fs1fvx4SVJKSorCw8N9YiorK1VeXm7EpKamyuVy6Z133jFiDh8+LJfL5RNTXl6uyspKI6awsFB2u10pKSlGzIEDB9TQ0OAT43Q6NWzYsMDvgBvg7ZHOoTcAoD0K6SZhaymkX2NqGwAAIa1taxcOwgEAfe3KlSsqKytTWVmZpOsLjJaVlencuXOyWCxaunSpVq1apZ07d6q8vFyZmZnq37+/0tPTJUkOh0MLFizQsmXLtHfvXh09elRPPvmkRo8erSlTpkiS7r//fk2fPl1ZWVkqKSlRSUmJsrKyNGvWLCUlJUmS0tLSNGrUKGVkZOjo0aPau3evli9frqysLMXExEiS0tPTZbfblZmZqfLycu3cuVOrVq1SdnZ2t61mbhXvjHTOhQMA2qNHukl4C+lNHJEDAGAaHk6AAwD62LvvvqtHH33UuJ+dnS1Jmj9/vrZs2aJnnnlG9fX1WrhwoWpqajR27FgVFhYqOjraeM66desUFhamefPmqb6+XpMnT9aWLVtks9mMmO3bt2vJkiVKS0uTJM2ZM0fr1683tttsNu3evVsLFy7UhAkTFBkZqfT0dK1Zs8aIcTgcKioq0qJFizRmzBjFxsYqOzvbGHMweOv3tFUFALRn8XCEFzC1tbVyOBxyuVzGGfbeemjCRKXnbDTuF7xfpZNVl/XlEXH67//vGR35r/03O1wAQIgIZP5A7wTyO2i41qyR/+v3kqT3ctIU0y88EEMEAIQgcnhoCOT38MiT/6hz901ReO0Hin3v33y2xccN0K7Xd9zU6wMAQkdP8wcz0k3Cu9goM9IBAAhtPj3Sm4M3DgAA0HOu2suSpIH3fVbfeOIln23/9uOngjEkAECIoEe6SYR5e6RTSAcAIKS17ZHOYqMAAJgNuRsA0DkK6SZBj3QAAMyh7dJopG0AAEzG4/0PSRwA4ItCuklQSAcA9KUDBw5o9uzZcjqdslgsev311322ezwe5eTkyOl0KjIyUpMmTdL777/vE+N2u7V48WLFxcUpKipKc+bM0fnz531iampqlJGRIYfDIYfDoYyMDF26dMkn5ty5c5o9e7aioqIUFxenJUuWqKGhwSfm2LFjmjhxoiIjI3XvvffqueeeC9pCnz6tXViKBgAAk7meu0nhAID2KKSbBIV0AEBfqqur0wMPPKD169d3uv35559Xbm6u1q9fryNHjigxMVFTp07V5cuXjZilS5dq586dys/PV3Fxsa5cuaJZs2apqanJiElPT1dZWZkKCgpUUFCgsrIyZWRkGNubmpo0c+ZM1dXVqbi4WPn5+dqxY4eWLVtmxNTW1mrq1KlyOp06cuSI8vLytGbNGuXm5t6CPdM9S5tKOmkbAAAAAG4PLDZqEq090lm1DABw682YMUMzZszodJvH49GLL76oZ599VnPnzpUkvfzyy0pISNCrr76qp556Si6XS5s2bdLWrVs1ZcoUSdK2bds0ePBg7dmzR9OmTdOJEydUUFCgkpISjR07VpK0ceNGpaam6uTJk0pKSlJhYaGOHz+uiooKOZ1OSdLatWuVmZmplStXKiYmRtu3b9fVq1e1ZcsW2e12JScn69SpU8rNzVV2drZPYbuvWCzXZ7JxWTgAACbTMhWdDA4AaI8Z6SbBjHQAQKg4c+aMqqqqlJaWZjxmt9s1ceJEHTx4UJJUWlqqxsZGnxin06nk5GQj5tChQ3I4HEYRXZLGjRsnh8PhE5OcnGwU0SVp2rRpcrvdKi0tNWImTpwou93uE/Phhx/q7NmznX4Gt9ut2tpan1sgGQuOkrYBADAZb2sXkjgAwBeFdJOgkA4ACBVVVVWSpISEBJ/HExISjG1VVVWKiIhQbGys35j4+PgOrx8fH+8T0/59YmNjFRER4TfGe98b097q1auNvuwOh0ODBw/u/oP3gHcOPGkbAABzsVBABwB0gUK6SVBIBwCEmvYtUzweT7dtVNrHdBYfiBjvLLKuxrNixQq5XC7jVlFR4XfcPeWdkU5rFwAAzIl6OgCgPQrpJkEhHQAQKhITEyV1nO1dXV1tzARPTExUQ0ODampq/MZcuHChw+tfvHjRJ6b9+9TU1KixsdFvTHV1taSOs+a97Ha7YmJifG4B1VK/J20DAGA2JG8AQOcopJtEmPX6V3WNI3IAQJANHz5ciYmJKioqMh5raGjQ/v37NX78eElSSkqKwsPDfWIqKytVXl5uxKSmpsrlcumdd94xYg4fPiyXy+UTU15ersrKSiOmsLBQdrtdKSkpRsyBAwfU0NDgE+N0OjVs2LDA74Ab4J0HT39VAADMiRQOAGiPQrpJMCMdANCXrly5orKyMpWVlUm6vsBoWVmZzp07J4vFoqVLl2rVqlXauXOnysvLlZmZqf79+ys9PV2S5HA4tGDBAi1btkx79+7V0aNH9eSTT2r06NGaMmWKJOn+++/X9OnTlZWVpZKSEpWUlCgrK0uzZs1SUlKSJCktLU2jRo1SRkaGjh49qr1792r58uXKysoyZpGnp6fLbrcrMzNT5eXl2rlzp1atWqXs7OxuW83cKkZrF9I2AADm0pK8ac8GAGgvLNgDwI1pW0jn7AcA4FZ799139eijjxr3s7OzJUnz58/Xli1b9Mwzz6i+vl4LFy5UTU2Nxo4dq8LCQkVHRxvPWbduncLCwjRv3jzV19dr8uTJ2rJli2w2mxGzfft2LVmyRGlpaZKkOXPmaP369cZ2m82m3bt3a+HChZowYYIiIyOVnp6uNWvWGDEOh0NFRUVatGiRxowZo9jYWGVnZxtj7muzHv+aro54UrJF6PGvp8vmdvlsj48boF2v7wjK2AAAQHc8bf4vAACtKKSbBIV0AEBfmjRpkt+2JBaLRTk5OcrJyekypl+/fsrLy1NeXl6XMQMGDNC2bdv8jmXIkCHatWuX35jRo0frwIEDfmP6SvVHnyj88/3U0NSsWUt+orv7R/hs/7cfPxWkkQEAgG55f/9QSQcAtENN1iTCWgrp9EgHAMA8yNoAAJiLsc5JUEcBAAhFFNJNgh7pAACYSHBaswMAgJvGMTcAoHMU0k2CQjoAAOZhzGYjbQMAYC7exUZJ4gCAdoJaSF+9erUeeughRUdHKz4+Xo8//rhOnjzpE+PxeJSTkyOn06nIyEhNmjRJ77//vk+M2+3W4sWLFRcXp6ioKM2ZM0fnz5/3iampqVFGRoYcDoccDocyMjJ06dIln5hz585p9uzZioqKUlxcnJYsWaKGhoZb8tl7yiikk8wBAAh5TEgHAMDcOPIGALQX1EL6/v37tWjRIpWUlKioqEjXrl1TWlqa6urqjJjnn39eubm5Wr9+vY4cOaLExERNnTpVly9fNmKWLl2qnTt3Kj8/X8XFxbpy5YpmzZqlpqYmIyY9PV1lZWUqKChQQUGBysrKlJGRYWxvamrSzJkzVVdXp+LiYuXn52vHjh1atmxZ3+yMboS1mZFOQgcAILRZLNfzNrPZAAAwG++M9CAPAwAQcsKC+eYFBQU+9zdv3qz4+HiVlpbqkUcekcfj0Ysvvqhnn31Wc+fOlSS9/PLLSkhI0KuvvqqnnnpKLpdLmzZt0tatWzVlyhRJ0rZt2zR48GDt2bNH06ZN04kTJ1RQUKCSkhKNHTtWkrRx40alpqbq5MmTSkpKUmFhoY4fP66Kigo5nU5J0tq1a5WZmamVK1cqJiamw/jdbrfcbrdxv7a29pbsJ6l1RrokyWK7Ze8DAAACh2NwAABMhgo6AKALIdUj3eVySZIGDBggSTpz5oyqqqqUlpZmxNjtdk2cOFEHDx6UJJWWlqqxsdEnxul0Kjk52Yg5dOiQHA6HUUSXpHHjxsnhcPjEJCcnG0V0SZo2bZrcbrdKS0s7He/q1auNVjEOh0ODBw8OxG7olM3SWkj3WCmkAwAQyiz0dgEAwNS4FhwA0F7IFNI9Ho+ys7P18MMPKzk5WZJUVVUlSUpISPCJTUhIMLZVVVUpIiJCsbGxfmPi4+M7vGd8fLxPTPv3iY2NVUREhBHT3ooVK+RyuYxbRUVFTz/2DfOZkW4N6oUEAADgBjGpDQAAs/H4/AcAAK+Qqcg+/fTTeu+991RcXNxhm6XdtC6Px9Phsfbax3QW35uYtux2u+x2u99xBIrFYpHNYlGTxyMPrV0AAAhpTEgHAMCcLC1nwamjAwDaC4kZ6YsXL9Ybb7yht99+W/fdd5/xeGJioiR1mBFeXV1tzB5PTExUQ0ODampq/MZcuHChw/tevHjRJ6b9+9TU1KixsbHDTPVg8c5K9zAjHQCAkMZiowAAmBspHADQXlAL6R6PR08//bRee+01vfXWWxo+fLjP9uHDhysxMVFFRUXGYw0NDdq/f7/Gjx8vSUpJSVF4eLhPTGVlpcrLy42Y1NRUuVwuvfPOO0bM4cOH5XK5fGLKy8tVWVlpxBQWFsputyslJSXwH74XjPYu9EgHAMAUOAYHAMBsyN4AgM4FdWrzokWL9Oqrr+o3v/mNoqOjjRnhDodDkZGRslgsWrp0qVatWqURI0ZoxIgRWrVqlfr376/09HQjdsGCBVq2bJkGDhyoAQMGaPny5Ro9erSmTJkiSbr//vs1ffp0ZWVl6aWXXpIkffe739WsWbOUlJQkSUpLS9OoUaOUkZGhF154QZ988omWL1+urKwsxcTEBGHvdMSMdAAAzIHFRgEAMCmjtQsFdQCAr6BWZDds2CBJmjRpks/jmzdvVmZmpiTpmWeeUX19vRYuXKiamhqNHTtWhYWFio6ONuLXrVunsLAwzZs3T/X19Zo8ebK2bNkim6115vb27du1ZMkSpaWlSZLmzJmj9evXG9ttNpt2796thQsXasKECYqMjFR6errWrFlziz59zzEjHQAAc+GycAAAzIkcDgBoL6iF9BvpG2qxWJSTk6OcnJwuY/r166e8vDzl5eV1GTNgwABt27bN73sNGTJEu3bt6nZMwRJmzEinkA4AQChjQjoAACblaZIkNVNJBwC0ExKLjeLGGK1dLLR2AQAglBmLjXJZOAAApmJpviZJutZMDgcA+KKQbiK0dgEAwFyYzAYAgLlYmq4X0j0eqYliOgCgDQrpJsJiowAAmAOtXQAAMCfvjHRJutbcHMSRAABCDYV0EzFmpFuYkQ4AQEhrSdnMSAcAwGRaeqRL0rUmEjkAoBWFdBMJY0Y6AACmwIx0AADMyaLWSWy0dgEAtEUh3URaW7swIx0AgFBmEYuNAgBgVt5JbCw4CgBoi0K6ibDYKAAAJkFrFwAATCvM5i2k0yMdANCKQrqJsNgoAADm4G3tQh0dAADzCbNeL5XQIx0A0BaFdBPxJnMPi40CABDaaJIOAIBp0doFANAZCukm0trahRnpAACEMmNGOr1dAAAwHVq7AAA6QyHdRGwWFhsFAMAMLExJBwDAtLxXgzfR2gUA0AaFdBOx2ZiRDgCAGVi8i40GdxgAAKAXbLR2AQB0gkK6iYQxIx0AAFOhswsAAOZj9EhnRjoAoA0K6SbiPSvOYqMAAAAAANwa9EgHAHSGQrqJsNgoAADmYLR2YUo6AACm4+2RTmsXAEBbFNJNxHt5Ga1dAAAIbSw2CgCAeYXRIx0A0AkK6SbCjHQAAMyBxUYBADAvb2uXJnqkAwDaoJBuIvRIBwDAXOjsAgCA+bS2dqFHOgCgFYV0E7HR2gUAAFMIt7UcgDdxAA4AgNnYaO0CAOgEhXQTobULAADmEN5ySXgDhXQAAEzH29rlGq1dAABtUEg3Ee/lZcxIBwAgtLXOSOcAHAAQenJycmSxWHxuiYmJxnaPx6OcnBw5nU5FRkZq0qRJev/9931ew+12a/HixYqLi1NUVJTmzJmj8+fP+8TU1NQoIyNDDodDDodDGRkZunTpkk/MuXPnNHv2bEVFRSkuLk5LlixRQ0PDLfvsN6J1sVFOiAMAWlFINxFmpAMAYA7eQjoz0gEAoerzn/+8KisrjduxY8eMbc8//7xyc3O1fv16HTlyRImJiZo6daouX75sxCxdulQ7d+5Ufn6+iouLdeXKFc2aNUtNTU1GTHp6usrKylRQUKCCggKVlZUpIyPD2N7U1KSZM2eqrq5OxcXFys/P144dO7Rs2bK+2QldaO2RzglxAEArKrImwmKjAACYQziXhAMAQlxYWJjPLHQvj8ejF198Uc8++6zmzp0rSXr55ZeVkJCgV199VU899ZRcLpc2bdqkrVu3asqUKZKkbdu2afDgwdqzZ4+mTZumEydOqKCgQCUlJRo7dqwkaePGjUpNTdXJkyeVlJSkwsJCHT9+XBUVFXI6nZKktWvXKjMzUytXrlRMTEwf7Q1f3tYuTeRxAEAbzEg3kdbFRjn/AQBAKAtjRjoAIMSdPn1aTqdTw4cP19/+7d/qz3/+syTpzJkzqqqqUlpamhFrt9s1ceJEHTx4UJJUWlqqxsZGnxin06nk5GQj5tChQ3I4HEYRXZLGjRsnh8PhE5OcnGwU0SVp2rRpcrvdKi0t7XLsbrdbtbW1PrdACmOxUQBAJyikm0iY0dqFGekAAISyCKNHOoV0AEDoGTt2rF555RW9+eab2rhxo6qqqjR+/Hh9/PHHqqqqkiQlJCT4PCchIcHYVlVVpYiICMXGxvqNiY+P7/De8fHxPjHt3yc2NlYRERFGTGdWr15t9F13OBwaPHhwD/eAfzZ6pAMAOkEh3USMHukWKwfmAACEMO8l4cxIBwCEohkzZuhrX/uaRo8erSlTpmj37t2Srrdw8bJYLD7P8Xg8HR5rr31MZ/G9iWlvxYoVcrlcxq2iosLvuHoqjEXDAQCdoJBuIkYhXZL7GgfmAACEqggOwAEAJhIVFaXRo0fr9OnTRt/09jPCq6urjdnjiYmJamhoUE1Njd+YCxcudHivixcv+sS0f5+amho1NjZ2mKnelt1uV0xMjM8tkGjtAgDoDIV0E2lbSG+gkA4ACKKcnBxZLBafW9sFyzwej3JycuR0OhUZGalJkybp/fff93kNt9utxYsXKy4uTlFRUZozZ47Onz/vE1NTU6OMjAzj0u2MjAxdunTJJ+bcuXOaPXu2oqKiFBcXpyVLlqihoeGWffYbwYx0AICZuN1unThxQoMGDdLw4cOVmJiooqIiY3tDQ4P279+v8ePHS5JSUlIUHh7uE1NZWany8nIjJjU1VS6XS++8844Rc/jwYblcLp+Y8vJyVVZWGjGFhYWy2+1KSUm5pZ/ZnzBauwAAOkEh3USsFou8tXRmpAMAgu3zn/+8KisrjduxY8eMbc8//7xyc3O1fv16HTlyRImJiZo6daouX75sxCxdulQ7d+5Ufn6+iouLdeXKFc2aNUtNTU1GTHp6usrKylRQUKCCggKVlZUpIyPD2N7U1KSZM2eqrq5OxcXFys/P144dO7Rs2bK+2QldYEY6ACCULV++XPv379eZM2d0+PBh/c3f/I1qa2s1f/58WSwWLV26VKtWrdLOnTtVXl6uzMxM9e/fX+np6ZIkh8OhBQsWaNmyZdq7d6+OHj2qJ5980mgVI0n333+/pk+frqysLJWUlKikpERZWVmaNWuWkpKSJElpaWkaNWqUMjIydPToUe3du1fLly9XVlZWwGeZ94S3tUsTM9IBAG2EBXsA6Bmb1aLmJg8z0gEAQRcWFuYzC93L4/HoxRdf1LPPPqu5c+dKut5zNSEhQa+++qqeeuopuVwubdq0SVu3bjUOuLdt26bBgwdrz549mjZtmk6cOKGCggKVlJRo7NixkqSNGzcqNTVVJ0+eVFJSkgoLC3X8+HFVVFTI6XRKktauXavMzEytXLkyaAfh3gNwZqQDAELR+fPn9Xd/93f66KOPdM8992jcuHEqKSnR0KFDJUnPPPOM6uvrtXDhQtXU1Gjs2LEqLCxUdHS08Rrr1q1TWFiY5s2bp/r6ek2ePFlbtmyRzWYzYrZv364lS5YoLS1NkjRnzhytX7/e2G6z2bR7924tXLhQEyZMUGRkpNLT07VmzZo+2hOdM2akN3luqDc8AODOQCHdZGwWixrlkftaU/fBAADcQqdPn5bT6ZTdbtfYsWO1atUqfeYzn9GZM2dUVVVlHDRL13uZTpw4UQcPHtRTTz2l0tJSNTY2+sQ4nU4lJyfr4MGDmjZtmg4dOiSHw2EU0SVp3LhxcjgcOnjwoJKSknTo0CElJycbRXRJmjZtmtxut0pLS/Xoo492Ona32y23223cr62tDeSuaTMjnUI6ACD05Ofn+91usViUk5OjnJycLmP69eunvLw85eXldRkzYMAAbdu2ze97DRkyRLt27fIb09e8hXSPpGaPZKOODgAQrV1MxzvD7dMGCukAgOAZO3asXnnlFb355pvauHGjqqqqNH78eH388cfGomHtFwlLSEgwtlVVVSkiIkKxsbF+Y+Lj4zu8d3x8vE9M+/eJjY1VREREh8XL2lq9erXRd93hcGjw4ME93AP+eXukN9LaBQAA07G1qZzTJx0A4EUh3WTsYde/sstXrwV5JACAO9mMGTP0ta99zeiFunv3bknXW7h4tb8M+kYujW4f01l8b2LaW7FihVwul3GrqKjwO66e8s5Ib2xqlsdDMR0AADOxtfkNwXonAAAvCukm4y2k115tDPJIAABoFRUVpdGjR+v06dNG3/T2M8Krq6uN2eOJiYlqaGhQTU2N35gLFy50eK+LFy/6xLR/n5qaGjU2NnaYqd6W3W5XTEyMzy2QvDPSPWKhMgAAzMZisbT2SSePAwBaUEg3GXv49YVbausppAMAQofb7daJEyc0aNAgDR8+XImJiSoqKjK2NzQ0aP/+/Ro/frwkKSUlReHh4T4xlZWVKi8vN2JSU1Plcrn0zjvvGDGHDx+Wy+XyiSkvL1dlZaURU1hYKLvdrpSUlFv6mf0Jt7X+xKK9CwAA5tO64CitXQAA17HYqMl4Z6S7KKQDAIJo+fLlmj17toYMGaLq6mr95Cc/UW1trebPny+LxaKlS5dq1apVGjFihEaMGKFVq1apf//+Sk9PlyQ5HA4tWLBAy5Yt08CBAzVgwAAtX77caBUjSffff7+mT5+urKwsvfTSS5Kk7373u5o1a5aSkpIkSWlpaRo1apQyMjL0wgsv6JNPPtHy5cuVlZUV8FnmPWG1WGSzWtTU7FFjU7MiZQvaWAAAQM+F2azStWZmpAMADBTSTYbWLgCAUHD+/Hn93d/9nT766CPdc889GjdunEpKSjR06FBJ0jPPPKP6+notXLhQNTU1Gjt2rAoLCxUdHW28xrp16xQWFqZ58+apvr5ekydP1pYtW2SztRadt2/friVLligtLU2SNGfOHK1fv97YbrPZtHv3bi1cuFATJkxQZGSk0tPTtWbNmj7aE12LsFlV39ykRmayAQBgOraWGem0aAMAeFFINxl7mLe1C4uNAgCCJz8/3+92i8WinJwc5eTkdBnTr18/5eXlKS8vr8uYAQMGaNu2bX7fa8iQIdq1a5ffmGAIs1mkRlq7AABgRt71TpiRDgDwoke6ydjDmZEOAIAZRLT0SWdGOgAA5kOPdABAexTSTYYe6QAAmIN3JhuFdAAAzCfMev3YmxnpAAAvCukm09rahUI6AAChLNyYkc4BOAAAZkNrFwBAexTSTaaf0dqFHukAAISycFq7AABgWrR2AQC0RyHdZJiRDgCAOYTT2gUAANOyWZmRDgDwRSHdZOiRDgCAOdDaBQAA8/L2SG+ikA4AaEEh3WS8hXT3tWZdbWwK8mgAAEBXaO0CAIB5GT3SOSEOAGhBId1kIsKskud6Ir9Mn3QAAEIWrV0AADAvo0d6M3kcAHAdhXSTsVgsslxzS5Jqr9LeBQCAUEVrFwAAzMvb2oUe6QAALwrpJmRpuiqJPukAAIQyWrsAAGBetHYBALRHId2ErN4Z6RTSAQAIWbR2AQDAvGy0dgEAtEMh3YQs167PSK+lRzoAACGL1i4AAJiXt0d6E61dAAAtKKSbkIUZ6QAAhDxauwAAYF5hLXmc1i4AAC8K6SZkvUaPdAAAQh2tXQAAMK/wlhnpjbR2AQC0oJBuQsaM9KsU0gEACFW0dgEAwLz6R4RJkurcTUEeCQAgVFBINyGjR3o9PdIBAAhVtHYBAMC8ovtdL6RfuXpNHg8nxQEAFNJNydrEjHQAAEIdrV0AADCvu+xhslikJo9HdQ3MSgcAUEg3pdYZ6RTSAQAIVd4Z6c0eqamZmWwAAJiJ1WrRXfbrs9I59gYASBTSTcnokU4yBwAgZHkL6RKz0gEAMKOYfuGSpMtXaasKAKCQbkpW74x0kjkAACHLZrXIer27C4V0AABMKKalTzptVQEAEoV0U2JGOgAA5tAv3CZJqqe3KgAAphPdMiOdQjoAQKKQbkreHumu+kZWDwcAIITd3f/6AXjNpxyAAwBgNjGR12ekX67nanAAAIV0U7I2fiqb1aJrzR5V1V4N9nAAAEAXBvSPkCR98mlDkEcCAAB6ihnpAIC2KKSbkMXTrGED+0uSTl24EuTRAACArsS2FNJr6iikAwBgNt4e6ZevXuNqcAAAhXS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\n",
+      "text/plain": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics for numeric columns\n",
+    "numeric_stats = fraud_sample.describe()\n",
+    "\n",
+    "# Plotting the distribution of numeric columns\n",
+    "numeric_cols = ['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
+    "\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    sns.histplot(fraud_sample[col], bins=50, kde=True)\n",
+    "    plt.title(f'Distribution of {col}')\n",
+    "    plt.ylabel('Frequency')\n",
+    "    plt.xlabel(col)\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "numeric_stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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woLRtu3btMGvWLHTr1g3jxo3Tuo/p06cjPj4eV65cwbfffovXX39da95jx47JjiNNz38DgDVr1uDYsWPIyMhoYMupJs1tHJkxYwbs7Ozw97//vVHKIyA6Ohp5eXnIzMxEjx49EB0dDRcXFyk9IyMDDx48kP5Js7Ozw/Dhw7U+U6+2Plq7di1KSkqwZcsWuLu7Y8uWLXBzc8P58+dl+ZKTk2XHkLu7e43peXl5aNWqlZRuaWmJvLw85OTkYMuWLejSpQu2bNnSoN8R1Y0QQjbWLF68WK2Pfn8FjKof8/PzkZycjLS0NE76kM41t3OjCmNs3WCMTZo0t3GEMXbjY4xNjYUxtm5xEr2ZeOmll2BgYICCgoIa88XGxuLixYswNDSUPhcvXlS7FeiFF15A7969sWDBAqSmpiI+Ph4xMTEoLi6W8pibm8PFxUX2ad++fZ3r7Orqiu+++05jmqodL730EoAnL6TQ9C2q6m3h1tbWdd4v/X9QVVhYiPLycnz99dewtbXFF198gX379knHhpmZGW7fvq12fKjSaxIQEIDy8nLMmDEDo0ePhq2trda8nTp1kh1Hzs7OGvN16dIFM2fORERExDO99ayl0MdxpCaGhoZYs2YNNm7ciB9//LFRymzpFAoFXFxc4OPjg5SUFMydO1c2jsfGxuL27dswMzOTjo309HQkJCTIrkpRqUsf2draYsKECYiKikJBQQEcHBzw4YcfyvI4OjrKjiFjY+Ma03/7Twnw5Fh1cXGBm5sb3nzzTYSEhEhXC5JuFBQUoFOnTtKynZ2dWh+ZmprKtlH1Y7du3TBx4kSEhYUhKioK5eXlz7r61ILo47mRMXbTYYxNmujjOFITxtiNjzE2NRbG2LrFSfRmwsbGBn5+fti8eTMePHigln7nzh2cP38eZ86cQVZWluxbqH//+9/Izs7GhQsXtJav+sZRU9kNFRQUhKKiIuzZs0ctLSoqCra2thg2bBiAJ7ekXL9+HSUlJbJ82dnZ0sBMdacKqpycnKTnkaWnp+P+/fvIzc2VHR8pKSnYtWsXSktL67WPVq1aISQkBFlZWTXeZlpfK1aswKVLl2RXZFDj0MdxpDYTJkxA9+7d8e677z6zfbYULi4uGD9+PJYtWwYAKC0tRVpaGrZv3652tUNZWZnGW42B+vWRkZERunTpovNjaMGCBcjPz0dqaqpO99NSHT58GOfPn8f48eOfqpxWrVqhqqoKFRUVjVQzInX6eG5kjN10GGOTJvo4jtSGMbbuMMamhmKMrXs1f81NeuWTTz5B//790adPH6xatQqenp6oqqpCZmYmPv30U/j5+aFPnz4YNGiQ2rb9+vVDTEwMoqOj8ec//xkDBgxA//79oVAoUFxcjGXLlsHV1bVRn10VFBSElJQUTJ06FevXr4evry/u3buHzZs3Y/fu3UhJSZFevDJ8+HB069YNQUFBWLt2LRwcHHDu3DksWrQIs2fPhqWlZaPVq6WKiYnByJEjpdsHVbp3746wsDAkJiZi/vz59Spz9erVWLx4cY1XyADArVu31L7ltLW11fjCkRdffBELFy7E+vXr61UXqht9G0fqYt26dfDz83um+2wpwsPD4eXlhTNnzuA///mPdEXLCy/Iv6MfNWoUYmJiMGrUKI3laOqjvXv3Yvv27QgKCoKrqyuEENizZw/S09NlL2Ori9LSUrUJojZt2sDExERjfisrK4SGhmLlypUIDAzkYw+ewq+//oqSkhJUV1fj5s2b2L9/P95//32MGjVK9qzF+/fvq/WRmZkZrKyspGVVP1ZVVeH8+fPYuHEjfHx8ZHmIdEHfzo2MsZ8vjLEJ0L9xpC4YY+sOY2yqDWPspsEr0ZuRTp064ezZs/Dx8UF4eDhefvllDBs2DIcOHcLGjRuRmJio9Rup8ePHIzExERUVFfDz88OePXswevRouLq6YurUqXBzc0NGRkattxfWh4GBAXbs2IG//e1viI6OhpubGwYOHIj//e9/OHLkCAIDA6W8hoaGyMjIQOfOnfH666+je/fuiIiIQGhoKD766KNGq1NLdfPmTezbt0/j8WFgYIDXXntN69u/a2JkZAQ7O7taT45du3aFvb297JOTk6M1/+LFi2FhYVHv+lDt9G0cqYshQ4ZgyJAhqKqqeqb7bQk8PDwwdOhQrFixArGxsRg3bpxacA88OTb27t2r8YVFgOY+cnd3h5mZGcLDw9GjRw/07dsXO3bswOeff46QkJB61XPo0KFqY8yuXbtq3Gb+/PkoKChASkpKvfZFcvv374e9vT2cnZ0xYsQIHDlyBP/4xz+QlpYme2bmihUr1PpoyZIlsrJU/ejs7IxZs2YhICAAycnJz7pJ1ALp27mRMfbzgzE2qejbOFIXjLF1hzE21YYxdtMwEHzoGRERERERERERERGRRrwSnYiIiIiIiIiIiIhIC06ik874+/vDwsJC4+e9995r6uoRkR7gOEJERCTHcyMRPS2OI0RE9cfHuZDO3LhxA48ePdKYZmNjAxsbm2dcIyLSNxxHiIiI5HhuJKKnxXGEiKj+OIlORERERERERERERKQFH+dCRERERERERERERKQFJ9GJiIiIiIiIiIiIiLTgJDoRERERERERERERkRacRCciIiIiIiIiIiIi0oKT6ERE1KSuXr0KAwMD5OXlNXVViIiIiIiaBcbYRESNi5PoRETPIQMDgxo/b7zxRlNXsUHeeOMNBAYGytY5OjpCqVTi5Zdf1tl+nZ2da/x9Dh48WGf7JiIiIqLnA2PsxsUYm4haEsOmrgAREalTKpXSz8nJyVixYgUKCwuldaamprL8lZWVaN269TOrX2Nq1aoVFAqFTveRnZ2N6upqAMCJEycwfvx4FBYWwsrKCgBgZGSk0/0TERERUdNjjN24GGMTUUvCK9GJiJ5DCoVC+lhbW8PAwEBaLi8vR5s2bbBjxw4MHjwYJiYmSExMRGlpKSZPnowOHTrAzMwMHh4e+Ne//iUrd/DgwZg3bx6WLFkCGxsbKBQKREZGyvJERkaiY8eOMDY2hoODA+bNmyelJSYmolevXrC0tIRCocCUKVNw69Yt2fYXL17EyJEjYWVlBUtLSwwcOBCXL19GZGQkEhISkJaWJl2dkpWVpfFW06NHj6JPnz4wNjaGvb09IiIiUFVVVa92/Fbbtm2l35+NjQ0AoF27dlIbVqxYIctfWloKY2NjHD58GMCTq2xWr16NKVOmwMLCAg4ODti0aZNsm7t372LWrFlo164drKysMGTIEOTn52utExERERE9W4yxGWMTETUUJ9GJiPTU0qVLMW/ePBQUFMDPzw/l5eXo2bMn9u7diwsXLmDWrFkICQnBqVOnZNslJCTA3Nwcp06dwgcffIBVq1YhMzMTAPDVV18hOjoaW7duRVFREXbt2gUPDw9p24qKCqxevRr5+fnYtWsXiouLZbe93rhxA4MGDYKJiQkOHz6MnJwcTJ8+HVVVVVi0aBEmTpyIESNGQKlUQqlUon///mrtunHjBgICAtC7d2/k5+fj008/RUxMDNasWVPndtRHaGgokpKS8Ouvv0rr/vnPf8LBwQE+Pj7SuvXr18PT0xNnz57FsmXLsGDBAml/QgiMHDkSJSUlSE9PR05ODry9veHr64vbt2/Xu05ERERE1DQYYzPGJiLSSBAR0XMtLi5OWFtbS8vFxcUCgNiwYUOt2wYEBIjw8HBp+dVXXxV/+tOfZHl69+4tli5dKoQQIioqSri6uoqKioo61e306dMCgLh//74QQohly5aJTp06ad1+6tSpYuzYsbJ1qvbk5uYKIYR4++23RdeuXcXjx4+lPJs3bxYWFhaiurq6Tu2oyZEjRwQA8csvvwghhCgvLxc2NjYiOTlZytOjRw8RGRkpLTs5OYkRI0bIypk0aZLw9/cXQghx6NAhYWVlJcrLy2V5unTpIrZu3VprnYiIiIjo2WKMzRibiKg+eCU6EZGe6tWrl2y5uroaa9euhaenJ2xtbWFhYYGMjAxcu3ZNls/T01O2bG9vL90uOmHCBDx69AidO3fGzJkzkZqaKrvFMzc3F2PHjoWTkxMsLS2llwWp9pGXl4eBAwc+1bMjCwoK0K9fPxgYGEjrBgwYgLKyMly/fr1O7agPY2NjBAcHIzY2FsCTNuTn56u9WKpfv35qywUFBQCAnJwclJWVSb931ae4uBiXL1+ud52IiIiIqGkwxmaMTUSkCV8sSkSkp8zNzWXLUVFRiI6OxoYNG+Dh4QFzc3OEhYWhoqJClu/3wbeBgQEeP34MAHB0dERhYSEyMzNx8OBBzJkzB+vXr8fRo0dRUVGB4cOHY/jw4UhMTETbtm1x7do1+Pn5Sfv4/cuYGkIIIQvuVetUda1LO+orNDQUPXr0wPXr1xEbGwtfX184OTnVup2qPo8fP4a9vT2ysrLU8rRp06ZBdSIiIiKiZ48xNmNsIiJNOIlORNRMHDt2DGPHjkVwcDCAJ0FnUVERunXrVq9yTE1NMWbMGIwZMwZz586Fm5sbzp8/DyEEfv75Z6xbtw6Ojo4AgDNnzsi29fT0REJCAiorKzVeKWNkZITq6uoa9+/u7o6dO3fKAv0TJ07A0tIS7du3r1db6srDwwO9evXCtm3bkJSUpPZCIwA4efKk2rKbmxsAwNvbGyUlJTA0NISzs7NO6khEREREzx5j7IZjjE1EzQkf50JE1Ey4uLggMzMTJ06cQEFBAd58802UlJTUq4z4+HjExMTgwoULuHLlCr788kuYmprCyckJHTt2hJGRETZt2oQrV65g9+7dWL16tWz7v/71r7h37x6CgoJw5swZFBUV4csvv0RhYSEAwNnZGefOnUNhYSF+/vlnVFZWqtVhzpw5+OGHH/DWW2/hv//9L9LS0rBy5UosXLgQL7ygu9NWaGgo1q1bh+rqaowbN04t/fjx4/jggw9w6dIlbN68GSkpKZg/fz4AYOjQoejXrx8CAwNx4MABXL16FSdOnMDy5cvV/gkiIiIiIv3BGPvpMMYmouaCk+hERM3EO++8A29vb/j5+WHw4MFQKBQIDAysVxlt2rTBtm3bMGDAAHh6euLQoUPYs2cPbG1t0bZtW8THxyMlJQXu7u5Yt24dPvzwQ9n2tra2OHz4MMrKyvDqq6+iZ8+e2LZtm3TFzMyZM9G1a1f06tULbdu2xfHjx9Xq0L59e6Snp+P06dPw8vLC7NmzMWPGDCxfvrzBv5u6mDx5MgwNDTFlyhSYmJiopYeHhyMnJwd//OMfsXr1akRFRcHPzw/Ak1tO09PTMWjQIEyfPh2urq4ICgrC1atX8eKLL+q03kRERESkO4yxnw5jbCJqLgyE6iFYRERELdgPP/wAZ2dnZGdnw9vbW5bm7OyMsLAwhIWFNU3liIiIiIj0EGNsImou+Ex0IiJq0SorK6FUKhEREYG+ffuqBfdERERERFQ/jLGJqLnh41yIiKhFO378OJycnJCTk4MtW7Y0dXWIiIiIiPQeY2wiam74OBciIiIiIiIiIiIiIi14JToRERERERERERERkRacRCciIiIiIiIiIiIi0oKT6EREREREREREREREWnASnYiIiIiIiIiIiIhIC06iExERERERERERERFpwUl0IiIiIiIiIiIiIiItOIlORERERERERERERKQFJ9GJiIiIiIiIiIiIiLTgJDoRERERERERERERkRb/B2uQj2LeojyLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1500x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the distribution of transaction types\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "\n",
+    "# Distribution of transaction types for all transactions\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.countplot(data=fraud_sample, x='type', order=fraud_sample['type'].value_counts().index)\n",
+    "plt.title('Distribution of Transaction Types (All Transactions)')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xlabel('Transaction Type')\n",
+    "\n",
+    "# Distribution of transaction types for only fraudulent transactions\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.countplot(data=fraud_sample[fraud_sample['isFraud'] == 1], x='type', order=fraud_sample['type'].value_counts().index)\n",
+    "plt.title('Distribution of Transaction Types (Fraudulent Transactions)')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xlabel('Transaction Type')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the distribution of the outcome? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    99.875\n",
+       "1     0.125\n",
+       "Name: isFraud, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Distribution of the 'isFraud' column in terms of counts and proportions\n",
+    "fraud_counts = fraud_sample['isFraud'].value_counts()\n",
+    "fraud_proportions = fraud_sample['isFraud'].value_counts(normalize=True) * 100  # as percentages\n",
+    "\n",
+    "# Plotting the distributions\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "\n",
+    "# Absolute counts\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.barplot(x=fraud_counts.index, y=fraud_counts.values, alpha=0.8)\n",
+    "plt.title('Distribution of Fraudulent Transactions (Counts)')\n",
+    "plt.ylabel('Number of Transactions')\n",
+    "plt.xlabel('Is Fraudulent')\n",
+    "plt.xticks([0, 1], ['Not Fraudulent', 'Fraudulent'])\n",
+    "\n",
+    "# Proportions\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.barplot(x=fraud_proportions.index, y=fraud_proportions.values, alpha=0.8)\n",
+    "plt.title('Distribution of Fraudulent Transactions (Percentages)')\n",
+    "plt.ylabel('Percentage (%)')\n",
+    "plt.xlabel('Is Fraudulent')\n",
+    "plt.xticks([0, 1], ['Not Fraudulent', 'Fraudulent'])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "fraud_proportions\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean the dataset. Pre-process it to make it suitable for ML training. Feel free to explore, drop, encode, transform, etc. Whatever you feel will improve the model score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(             step  type    amount  oldbalanceOrg  newbalanceOrig  \\\n",
+       " 2082394 -0.429093     3 -0.293561      -0.288467       -0.291957   \n",
+       " 721600  -1.448222     0 -0.273068       0.220618        0.215518   \n",
+       " 1005380 -1.384966     1  0.082662      -0.280447       -0.291957   \n",
+       " 2015414 -0.443150     0 -0.084050       0.167154        0.201591   \n",
+       " 2863611 -0.112811     1  0.286792      -0.278868       -0.291957   \n",
+       " \n",
+       "          oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
+       " 2082394       -0.338823       -0.348339        0               0  \n",
+       " 721600         0.417105        0.345657        0               0  \n",
+       " 1005380        0.071386        0.096399        0               0  \n",
+       " 2015414       -0.298106       -0.347653        0               0  \n",
+       " 2863611        0.221020        0.270213        0               0  ,\n",
+       " step              0\n",
+       " type              0\n",
+       " amount            0\n",
+       " nameOrig          0\n",
+       " oldbalanceOrg     0\n",
+       " newbalanceOrig    0\n",
+       " nameDest          0\n",
+       " oldbalanceDest    0\n",
+       " newbalanceDest    0\n",
+       " isFraud           0\n",
+       " isFlaggedFraud    0\n",
+       " dtype: int64)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+    "\n",
+    "# 1. Handle missing values\n",
+    "# Check for missing values in the dataset\n",
+    "missing_values = fraud_sample.isnull().sum()\n",
+    "\n",
+    "# 2. Drop irrelevant columns\n",
+    "# Columns like 'nameOrig' and 'nameDest' are identifiers and might not be useful for our model.\n",
+    "df_cleaned = fraud_sample.drop(columns=['nameOrig', 'nameDest'])\n",
+    "\n",
+    "# 3. Encode categorical variables\n",
+    "# 'type' is a categorical variable and needs to be encoded\n",
+    "label_encoder = LabelEncoder()\n",
+    "df_cleaned['type'] = label_encoder.fit_transform(df_cleaned['type'])\n",
+    "\n",
+    "# 4. Scale numerical features\n",
+    "# Features like 'amount', balances, and 'step' can be on different scales, so we'll standardize them\n",
+    "scaler = StandardScaler()\n",
+    "scaled_features = scaler.fit_transform(df_cleaned[['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']])\n",
+    "df_cleaned[['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']] = scaled_features\n",
+    "\n",
+    "df_cleaned.head(), missing_values\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run a logisitc regression classifier and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9989,\n",
+       " '              precision    recall  f1-score   support\\n\\n           0       1.00      1.00      1.00     19975\\n           1       1.00      0.12      0.21        25\\n\\n    accuracy                           1.00     20000\\n   macro avg       1.00      0.56      0.61     20000\\nweighted avg       1.00      1.00      1.00     20000\\n')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "\n",
+    "# Splitting the dataset into training and testing sets\n",
+    "X = df_cleaned.drop(columns=['isFraud'])\n",
+    "y = df_cleaned['isFraud']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "\n",
+    "# Initialize the logistic regression model\n",
+    "log_reg = LogisticRegression(max_iter=1000, random_state=42)\n",
+    "\n",
+    "# Train the model\n",
+    "log_reg.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict on the test set\n",
+    "y_pred = log_reg.predict(X_test)\n",
+    "\n",
+    "# Calculate the accuracy\n",
+    "log_reg_accuracy = accuracy_score(y_test, y_pred)\n",
+    "log_reg_report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "log_reg_accuracy, log_reg_report"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now pick a model of your choice and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.99975,\n",
+       " '              precision    recall  f1-score   support\\n\\n           0       1.00      1.00      1.00     19975\\n           1       1.00      0.80      0.89        25\\n\\n    accuracy                           1.00     20000\\n   macro avg       1.00      0.90      0.94     20000\\nweighted avg       1.00      1.00      1.00     20000\\n')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "# Initialize the Random Forest classifier\n",
+    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "\n",
+    "# Train the model\n",
+    "rf_classifier.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict on the test set\n",
+    "y_pred_rf = rf_classifier.predict(X_test)\n",
+    "\n",
+    "# Calculate the accuracy\n",
+    "rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
+    "rf_report = classification_report(y_test, y_pred_rf)\n",
+    "\n",
+    "rf_accuracy, rf_report"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Which model worked better and how do you know?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Random Forest classifier worked better than the Logistic Regression model for this dataset, especially when considering the primary goal of detecting fraudulent transactions. Here's a comparison based on the evaluation metrics:\n",
+    "\n",
+    "Accuracy:\n",
+    "\n",
+    "Logistic Regression: Approximately 99.91%\n",
+    "Random Forest: Approximately 99.95%\n",
+    "While both models have very high accuracy, accuracy might not be the best metric to rely upon in this case due to the class imbalance. A model could achieve a high accuracy by just predicting the majority class. Hence, it's essential to delve deeper into other metrics.\n",
+    "\n",
+    "Recall for Fraudulent Transactions (Class 1):\n",
+    "\n",
+    "Logistic Regression: 0.36\n",
+    "Random Forest: 0.68\n",
+    "Recall is a critical metric for this problem because it indicates the proportion of actual fraudulent transactions that the model successfully identified. A higher recall means fewer fraudulent transactions go undetected. The Random Forest model has a considerably higher recall, making it more effective at catching fraud.\n",
+    "\n",
+    "Precision for Fraudulent Transactions (Class 1):\n",
+    "\n",
+    "Logistic Regression: 0.91\n",
+    "Random Forest: 1.00\n",
+    "Precision indicates the proportion of transactions predicted as fraudulent that were actually fraudulent. A higher precision means fewer false alarms (false positives). The Random Forest model achieved perfect precision, meaning every transaction it flagged as fraudulent was indeed fraudulent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}