evaluate_results_mirav2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis Pipeline \n",
    "1. load the data \n",
    "2. explore the data \n",
    "3. compare the models \n",
    "4. ROC curve & AUC score & PR curve\n",
    "5. F1 score\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>label</th>\n",
       "      <th>weight</th>\n",
       "      <th>bird</th>\n",
       "      <th>fox</th>\n",
       "      <th>lizard</th>\n",
       "      <th>rodent</th>\n",
       "      <th>scrub jay</th>\n",
       "      <th>skunk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133450/000/009.jpg_...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.299275</td>\n",
       "      <td>1.213028e-01</td>\n",
       "      <td>1.918221e-01</td>\n",
       "      <td>5.783474e-05</td>\n",
       "      <td>6.857130e-01</td>\n",
       "      <td>3.487533e-04</td>\n",
       "      <td>7.555791e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133450/000/010.jpg_...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.299275</td>\n",
       "      <td>2.765466e-05</td>\n",
       "      <td>7.763770e-07</td>\n",
       "      <td>8.221242e-08</td>\n",
       "      <td>9.999712e-01</td>\n",
       "      <td>3.195493e-07</td>\n",
       "      <td>8.999244e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133450/000/011.jpg_...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.299275</td>\n",
       "      <td>1.747305e-02</td>\n",
       "      <td>2.256891e-02</td>\n",
       "      <td>4.593492e-04</td>\n",
       "      <td>2.612599e-04</td>\n",
       "      <td>2.382994e-04</td>\n",
       "      <td>9.589992e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133258/000/000.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.517075</td>\n",
       "      <td>4.150726e-08</td>\n",
       "      <td>9.999837e-01</td>\n",
       "      <td>1.076929e-09</td>\n",
       "      <td>1.098083e-05</td>\n",
       "      <td>1.412573e-06</td>\n",
       "      <td>3.953084e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133258/000/001.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.517075</td>\n",
       "      <td>3.727612e-13</td>\n",
       "      <td>9.999995e-01</td>\n",
       "      <td>6.491059e-15</td>\n",
       "      <td>6.489738e-10</td>\n",
       "      <td>3.991921e-12</td>\n",
       "      <td>5.216569e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                path   label    weight  \\\n",
       "0  mirav2_dataset/loc-h500ee07133450/000/009.jpg_...  rodent  0.299275   \n",
       "1  mirav2_dataset/loc-h500ee07133450/000/010.jpg_...  rodent  0.299275   \n",
       "2  mirav2_dataset/loc-h500ee07133450/000/011.jpg_...  rodent  0.299275   \n",
       "3  mirav2_dataset/loc-h500ee07133258/000/000.jpg_...     fox  0.517075   \n",
       "4  mirav2_dataset/loc-h500ee07133258/000/001.jpg_...     fox  0.517075   \n",
       "\n",
       "           bird           fox        lizard        rodent     scrub jay  \\\n",
       "0  1.213028e-01  1.918221e-01  5.783474e-05  6.857130e-01  3.487533e-04   \n",
       "1  2.765466e-05  7.763770e-07  8.221242e-08  9.999712e-01  3.195493e-07   \n",
       "2  1.747305e-02  2.256891e-02  4.593492e-04  2.612599e-04  2.382994e-04   \n",
       "3  4.150726e-08  9.999837e-01  1.076929e-09  1.098083e-05  1.412573e-06   \n",
       "4  3.727612e-13  9.999995e-01  6.491059e-15  6.489738e-10  3.991921e-12   \n",
       "\n",
       "          skunk  \n",
       "0  7.555791e-04  \n",
       "1  8.999244e-09  \n",
       "2  9.589992e-01  \n",
       "3  3.953084e-06  \n",
       "4  5.216569e-07  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the results from model 1\n",
    "# calulate how many animal species are in the results\n",
    "\n",
    "df_train = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/mirav2/outputs_train.csv')\n",
    "df_val = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/mirav2/outputs_val.csv')\n",
    "df_test = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/mirav2/outputs_test.csv')\n",
    "df_test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>label</th>\n",
       "      <th>weight</th>\n",
       "      <th>bird</th>\n",
       "      <th>fox</th>\n",
       "      <th>lizard</th>\n",
       "      <th>rodent</th>\n",
       "      <th>scrub jay</th>\n",
       "      <th>skunk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133495/000/005.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.490316</td>\n",
       "      <td>1.297812e-17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.528587e-19</td>\n",
       "      <td>8.808427e-11</td>\n",
       "      <td>2.021834e-17</td>\n",
       "      <td>3.581323e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133495/000/006.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.490316</td>\n",
       "      <td>8.667226e-09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.061429e-08</td>\n",
       "      <td>6.111339e-08</td>\n",
       "      <td>4.923424e-10</td>\n",
       "      <td>4.014444e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133495/000/007.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.490316</td>\n",
       "      <td>4.965319e-08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.400253e-08</td>\n",
       "      <td>2.855523e-08</td>\n",
       "      <td>3.934005e-10</td>\n",
       "      <td>6.268715e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133495/000/008.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.490316</td>\n",
       "      <td>3.278755e-11</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.555252e-10</td>\n",
       "      <td>6.483605e-10</td>\n",
       "      <td>6.105010e-13</td>\n",
       "      <td>1.098609e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133495/000/009.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.490316</td>\n",
       "      <td>3.769914e-08</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>5.728963e-07</td>\n",
       "      <td>1.987321e-08</td>\n",
       "      <td>1.558845e-10</td>\n",
       "      <td>2.362147e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                path label    weight  \\\n",
       "0  mirav2_dataset/loc-h500ee07133495/000/005.jpg_...   fox  0.490316   \n",
       "1  mirav2_dataset/loc-h500ee07133495/000/006.jpg_...   fox  0.490316   \n",
       "2  mirav2_dataset/loc-h500ee07133495/000/007.jpg_...   fox  0.490316   \n",
       "3  mirav2_dataset/loc-h500ee07133495/000/008.jpg_...   fox  0.490316   \n",
       "4  mirav2_dataset/loc-h500ee07133495/000/009.jpg_...   fox  0.490316   \n",
       "\n",
       "           bird       fox        lizard        rodent     scrub jay  \\\n",
       "0  1.297812e-17  1.000000  5.528587e-19  8.808427e-11  2.021834e-17   \n",
       "1  8.667226e-09  1.000000  1.061429e-08  6.111339e-08  4.923424e-10   \n",
       "2  4.965319e-08  1.000000  3.400253e-08  2.855523e-08  3.934005e-10   \n",
       "3  3.278755e-11  1.000000  1.555252e-10  6.483605e-10  6.105010e-13   \n",
       "4  3.769914e-08  0.999999  5.728963e-07  1.987321e-08  1.558845e-10   \n",
       "\n",
       "          skunk  \n",
       "0  3.581323e-18  \n",
       "1  4.014444e-10  \n",
       "2  6.268715e-11  \n",
       "3  1.098609e-12  \n",
       "4  2.362147e-11  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df_train, df_val, df_test])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rodent       75958\n",
       "fox          52047\n",
       "bird         13072\n",
       "lizard        6336\n",
       "scrub jay     2555\n",
       "skunk         2251\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xdOnSMuspCfsl65o6dSrmz5+P2NhY9OnTB2ZmZujRo0eV38JAoVDg4MGD8PDwQFhYGDp06AALCwsEBgYiOzu7StddV/DqOXppdu/ejaKiojIncpb2xhtv4I033kBRURFOnjyJpUuXIigoCFZWVhgyZIisdalz76fyfhGUTCv5gVzyy7rkB2QJuT9wK2JmZlbufY1KTmQ1Nzd/oeUDgImJCerVq1fp6zlw4ID013x5vxRjY2Nx7tw5ODo6qr3syrZp0yZ069YNK1asUJle2b8o3nzzTbi7u2Pnzp1IT0+HpaWlNC8tLQ2vvPKK9LywsBB3796V9l3J/8FPP/2Exo0bV7iOkgshbt68qVZvJctfunRphVcnllzVam5ujsWLF2Px4sVITk7Grl278NlnnyE9PV06p7C2kLtfn+XVV1+VTv5+8803oaenhy+++AJLly7FpEmTYGJiIo3efvLJJ+Uuo2nTpgAeB+aJEydi4sSJuH//Pg4cOIDPP/8cHh4eSElJkf5AKP3zBnjxnzmNGzeW/qC5ePEifvjhB4SEhCA/P/+Z538SQxO9JMnJyZg0aRKUSiVGjx4t6zUaGhpwcXFBy5YtsXnzZpw6dQpDhgyRNbqijrNnz+Kvv/5SOUS3ZcsWGBkZSfcKKrmK5cyZM7C3t5fqdu3aVWZ56vwF3aNHD0RGRuLWrVsqI3AbNmyAvr5+pVx6b2BgABcXF+zYsQPz58+Hnp4egMcjRZs2bULDhg1VTmaVa82aNahXrx527NgBpVKpMu/mzZvw8fHB2rVrn+uE+sqmUCik902JM2fOICYmpsyhUTlu374NCwsLlavkAKCoqAiXLl2Cvr6+dNimxObNm+Hs7Cw9/+GHH1BYWCj9EeHh4QFNTU1cuXLlqYeXW7RogWbNmmHt2rWYOHFime2qSNeuXVG/fn2cO3cOY8eOlbeheHzl69ixY3Hw4EH8+eefsl9XWV50REruflXX5MmTER4ejjlz5mD06NEwMjJC9+7dcfr0abRp0wba2tqyllO/fn0MGjQI//3vfxEUFITr16/D0dERTZo0QXp6Om7fvi2F2fz8fOzbt++Zy5T7M7JFixb44osvsH379io/FaGuYGiiSpeYmCgdy09PT8cff/yBdevWQUNDA5GRkU+9j8vKlStx6NAh9OvXD40aNcKjR4+kS9J79uwJADAyMkLjxo3x888/o0ePHjA1NYW5ubmsy+PLY2NjgwEDBiAkJAQNGjTApk2bsH//fsydOxf6+voAHg/t29vbY9KkSSgsLISJiQkiIyPLvbS+devW2LFjB1asWAFnZ2fUq1dP5b5VT5o5cyZ+/fVXdO/eHTNmzICpqSk2b96M3bt3IywsrEwYeV6zZ89Gr1690L17d0yaNAna2tpYvnw5EhMTsXXrVrXvyn737l38/PPP8PDwwNtvv11uzaJFi7BhwwbMnj0bWlpalbEZz83T0xP/+c9/MHPmTLi5ueHChQv48ssv0bRpUxQWFqq9vI0bN2LVqlXw9vbGa6+9BqVSiZs3b+L777/H2bNnMWPGjDK/NHfs2AFNTU306tULZ8+exfTp09G2bVt4eXkBeBzMv/zyS0ybNg1Xr15F7969YWJigtu3b+PEiRMwMDCQrsD69ttv0b9/f3Tu3BkTJkxAo0aNkJycjH379mHz5s3l9mxoaIilS5fC19cX9+7dw6BBg2BpaYk7d+7gr7/+wp07d7BixQpkZmaie/fu8Pb2RsuWLWFkZIS4uDhERUVh4MCBau+rF9W6dWscPnwYv/zyCxo0aAAjIyOVP1yeRZ39qg4tLS2EhobCy8sL33zzDb744gt88803eP311/HGG2/g448/RpMmTZCdnY3Lly/jl19+waFDhwAA/fv3l+5nZ2FhgRs3bmDx4sVo3Lgx7OzsADw+h2rGjBkYMmQIPv30Uzx69AhLlix55pWywOPTHPT09LB582Y4ODjA0NAQNjY2+OeffzB27Fi8//77sLOzg7a2Ng4dOoQzZ87gs88+U3sf/CtV84noVIeUXM1S8tDW1haWlpbCzc1NhIaGivT09DKvKX0lSExMjHj33XdF48aNhY6OjjAzMxNubm5i165dKq87cOCAaN++vdDR0REApCtHSpZ3586dZ65LiMdXnfTr10/89NNPolWrVkJbW1s0adJELFy4sMzrL168KNzd3YWxsbGwsLAQ48aNE7t37y5zpde9e/fEoEGDRP369YVCoVBZJ8q5Muvvv/8W/fv3F0qlUmhra4u2bduWueql5AqeH3/8UWV6ydVupevL88cff4i33npLGBgYCD09PdG5c2fxyy+/lLu8Z109t3jxYgFA7Ny5s8KalStXqlzxVrKvS3NzcxNubm7S84qunjMwMCj3ta1atSozvfS68vLyxKRJk8Qrr7widHV1RYcOHcTOnTvLvUKpvP+j0s6dOyeCg4NFx44dhYWFhdDU1BQmJibCzc1NbNy4UaW25H0XHx8v+vfvLwwNDYWRkZH44IMPxO3bt8sse+fOnaJ79+7C2NhY6OjoiMaNG4tBgwaJAwcOqNTFxMSIPn36CKVSKXR0dESzZs1UrtCr6IrPI0eOiH79+glTU1OhpaUlXnnlFdGvXz/pvfXo0SMxZswY0aZNG2FsbCz09PSEvb29mDlzpnQ1aUUqunquvP+jiq4OKy0hIUF07dpV6OvrCwDSe6Wiq3XLe/8IIX+/llbRZ6+Ei4uLMDExka50vXbtmhg+fLh45ZVXhJaWlrCwsBBdunQRX331lfSaBQsWiC5dughzc3Ohra0tGjVqJPz9/cX169dVlr1nzx7Rrl07oaenJ1599VWxbNkyWVfPCSHE1q1bRcuWLYWWlpb0nr59+7bw8/MTLVu2FAYGBsLQ0FC0adNGLFq0SBQWFj51P9BjCiGecTkTERE9t5CQEMyaNQt37typlHPUiKj68Oo5IiIiIhkYmoiIiIhk4OE5IiIiIhk40kREREQkA0MTERERkQwMTUREREQy8OaWlai4uBi3bt2CkZGR2jcLJCIiouohhEB2djZsbGzK3On/SQxNlejWrVvP9ZUMREREVP1SUlKe+mXYDE2VyMjICMDjnW5sbFzN3RAREZEcWVlZsLW1lX6PV4ShqRKVHJIzNjZmaCIiIqplnnVqDU8EJyIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhk0q7sBoueV/GXr6m6hRmg04+/qboGI6F+BI01EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJUK2hqUmTJlAoFGUen3zyCQBACIGQkBDY2NhAT08P3bp1w9mzZ1WWkZeXh3HjxsHc3BwGBgYYMGAAbt68qVKTkZEBHx8fKJVKKJVK+Pj44P79+yo1ycnJ6N+/PwwMDGBubo7AwEDk5+dX6fYTERFR7VGtoSkuLg6pqanSY//+/QCA999/HwAQFhaGhQsXYtmyZYiLi4O1tTV69eqF7OxsaRlBQUGIjIxEREQEjh49igcPHsDT0xNFRUVSjbe3NxISEhAVFYWoqCgkJCTAx8dHml9UVIR+/fohJycHR48eRUREBLZv347g4OCXtCeIiIioplMIIUR1N1EiKCgIv/76Ky5dugQAsLGxQVBQEKZMmQLg8aiSlZUV5s6di9GjRyMzMxMWFhbYuHEjBg8eDAC4desWbG1tsWfPHnh4eCApKQmOjo6IjY2Fi4sLACA2Nhaurq44f/487O3tsXfvXnh6eiIlJQU2NjYAgIiICPj5+SE9PR3Gxsay+s/KyoJSqURmZqbs19DzS/6ydXW3UCM0mvF3dbdARFSryf39XWPOacrPz8emTZswfPhwKBQKXLt2DWlpaXB3d5dqdHR04ObmhmPHjgEA4uPjUVBQoFJjY2MDJycnqSYmJgZKpVIKTADQuXNnKJVKlRonJycpMAGAh4cH8vLyEB8fX2HPeXl5yMrKUnkQERFR3VRjQtPOnTtx//59+Pn5AQDS0tIAAFZWVip1VlZW0ry0tDRoa2vDxMTkqTWWlpZl1mdpaalSU3o9JiYm0NbWlmrKM3v2bOk8KaVSCVtbWzW2mIiIiGqTGhOa1qxZgz59+qiM9gCAQqFQeS6EKDOttNI15dU/T01pU6dORWZmpvRISUl5al9ERERUe9WI0HTjxg0cOHAAI0aMkKZZW1sDQJmRnvT0dGlUyNraGvn5+cjIyHhqze3bt8us886dOyo1pdeTkZGBgoKCMiNQT9LR0YGxsbHKg4iIiOqmGhGa1q1bB0tLS/Tr10+a1rRpU1hbW0tX1AGPz3s6cuQIunTpAgBwdnaGlpaWSk1qaioSExOlGldXV2RmZuLEiRNSzfHjx5GZmalSk5iYiNTUVKkmOjoaOjo6cHZ2rpqNJiIiolpFs7obKC4uxrp16+Dr6wtNzf+1o1AoEBQUhNDQUNjZ2cHOzg6hoaHQ19eHt7c3AECpVMLf3x/BwcEwMzODqakpJk2ahNatW6Nnz54AAAcHB/Tu3RsjR47EqlWrAACjRo2Cp6cn7O3tAQDu7u5wdHSEj48P5s2bh3v37mHSpEkYOXIkR4+IiIgIQA0ITQcOHEBycjKGDx9eZt7kyZORm5uLgIAAZGRkwMXFBdHR0TAyMpJqFi1aBE1NTXh5eSE3Nxc9evRAeHg4NDQ0pJrNmzcjMDBQuspuwIABWLZsmTRfQ0MDu3fvRkBAALp27Qo9PT14e3tj/vz5VbjlREREVJvUqPs01Xa8T9PLxfs0Pcb7NBERvZhad58mIiIiopqMoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZqj00/fe//8WHH34IMzMz6Ovro127doiPj5fmCyEQEhICGxsb6OnpoVu3bjh79qzKMvLy8jBu3DiYm5vDwMAAAwYMwM2bN1VqMjIy4OPjA6VSCaVSCR8fH9y/f1+lJjk5Gf3794eBgQHMzc0RGBiI/Pz8Ktt2IiIiqj2qNTRlZGSga9eu0NLSwt69e3Hu3DksWLAA9evXl2rCwsKwcOFCLFu2DHFxcbC2tkavXr2QnZ0t1QQFBSEyMhIRERE4evQoHjx4AE9PTxQVFUk13t7eSEhIQFRUFKKiopCQkAAfHx9pflFREfr164ecnBwcPXoUERER2L59O4KDg1/KviAiIqKaTSGEENW18s8++wx//vkn/vjjj3LnCyFgY2ODoKAgTJkyBcDjUSUrKyvMnTsXo0ePRmZmJiwsLLBx40YMHjwYAHDr1i3Y2tpiz5498PDwQFJSEhwdHREbGwsXFxcAQGxsLFxdXXH+/HnY29tj79698PT0REpKCmxsbAAAERER8PPzQ3p6OoyNjZ+5PVlZWVAqlcjMzJRVTy8m+cvW1d1CjdBoxt/V3QIRUa0m9/d3tY407dq1Cx07dsT7778PS0tLtG/fHqtXr5bmX7t2DWlpaXB3d5em6ejowM3NDceOHQMAxMfHo6CgQKXGxsYGTk5OUk1MTAyUSqUUmACgc+fOUCqVKjVOTk5SYAIADw8P5OXlqRwuJCIion+nag1NV69exYoVK2BnZ4d9+/ZhzJgxCAwMxIYNGwAAaWlpAAArKyuV11lZWUnz0tLSoK2tDRMTk6fWWFpallm/paWlSk3p9ZiYmEBbW1uqKS0vLw9ZWVkqDyIiIqqbNKtz5cXFxejYsSNCQ0MBAO3bt8fZs2exYsUKDBs2TKpTKBQqrxNClJlWWuma8uqfp+ZJs2fPxqxZs57aBxEREdUN1TrS1KBBAzg6OqpMc3BwQHJyMgDA2toaAMqM9KSnp0ujQtbW1sjPz0dGRsZTa27fvl1m/Xfu3FGpKb2ejIwMFBQUlBmBKjF16lRkZmZKj5SUFFnbTURERLVPtYamrl274sKFCyrTLl68iMaNGwMAmjZtCmtra+zfv1+an5+fjyNHjqBLly4AAGdnZ2hpaanUpKamIjExUapxdXVFZmYmTpw4IdUcP34cmZmZKjWJiYlITU2VaqKjo6GjowNnZ+dy+9fR0YGxsbHKg4iIiOqmaj08N2HCBHTp0gWhoaHw8vLCiRMn8N133+G7774D8PhwWVBQEEJDQ2FnZwc7OzuEhoZCX18f3t7eAAClUgl/f38EBwfDzMwMpqammDRpElq3bo2ePXsCeDx61bt3b4wcORKrVq0CAIwaNQqenp6wt7cHALi7u8PR0RE+Pj6YN28e7t27h0mTJmHkyJEMQ0RERFS9oem1115DZGQkpk6dii+//BJNmzbF4sWLMXToUKlm8uTJyM3NRUBAADIyMuDi4oLo6GgYGRlJNYsWLYKmpia8vLyQm5uLHj16IDw8HBoaGlLN5s2bERgYKF1lN2DAACxbtkyar6Ghgd27dyMgIABdu3aFnp4evL29MX/+/JewJ4iIiKimq9b7NNU1vE/Ty8X7ND3G+zQREb2YWnGfJiIiIqLagqGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRQOzStX78eu3fvlp5PnjwZ9evXR5cuXXDjxo1KbY6IiIioplA7NIWGhkJPTw8AEBMTg2XLliEsLAzm5uaYMGFCpTdIREREVBNoqvuClJQUNG/eHACwc+dODBo0CKNGjULXrl3RrVu3yu6PiIiIqEZQe6TJ0NAQd+/eBQBER0ejZ8+eAABdXV3k5uZWbndERERENYTaI029evXCiBEj0L59e1y8eBH9+vUDAJw9exZNmjSp7P6IiIiIagS1R5q+/fZbuLq64s6dO9i+fTvMzMwAAPHx8fjggw8qvUEiIiKimkDtkab69etj2bJlZabPmjWrUhoiIiIiqonUDk0A8Mcff2DVqlW4evUqfvzxR7zyyivYuHEjmjZtitdff72ye6xTnD/dUN0t1Ajx84ZVdwtERERqUfvw3Pbt2+Hh4QE9PT2cOnUKeXl5AIDs7GyEhoZWeoNERERENYHaoemrr77CypUrsXr1amhpaUnTu3TpglOnTlVqc0REREQ1hdqh6cKFC3jzzTfLTDc2Nsb9+/croyciIiKiGkft0NSgQQNcvny5zPSjR4/i1VdfrZSmiIiIiGoatUPT6NGjMX78eBw/fhwKhQK3bt3C5s2bMWnSJAQEBKi1rJCQECgUCpWHtbW1NF8IgZCQENjY2EBPTw/dunXD2bNnVZaRl5eHcePGwdzcHAYGBhgwYABu3rypUpORkQEfHx8olUoolUr4+PiUGRVLTk5G//79YWBgAHNzcwQGBiI/P1+9nUNERER1ltqhafLkyXjnnXfQvXt3PHjwAG+++SZGjBiB0aNHY+zYsWo30KpVK6SmpkqPv//+W5oXFhaGhQsXYtmyZYiLi4O1tTV69eqF7OxsqSYoKAiRkZGIiIjA0aNH8eDBA3h6eqKoqEiq8fb2RkJCAqKiohAVFYWEhAT4+PhI84uKitCvXz/k5OTg6NGjiIiIwPbt2xEcHKz29hAREVHd9Fy3HPj6668xbdo0nDt3DsXFxXB0dIShoeHzNaCpqTK6VEIIgcWLF2PatGkYOHAgAGD9+vWwsrLCli1bMHr0aGRmZmLNmjXYuHGj9HUumzZtgq2tLQ4cOAAPDw8kJSUhKioKsbGxcHFxAQCsXr0arq6uuHDhAuzt7REdHY1z584hJSUFNjY2AIAFCxbAz88PX3/9NYyNjZ9r24iIiKjuUHukqYS+vj46duyITp06PXdgAoBLly7BxsYGTZs2xZAhQ3D16lUAwLVr15CWlgZ3d3epVkdHB25ubjh27BiAx3chLygoUKmxsbGBk5OTVBMTEwOlUikFJgDo3LkzlEqlSo2Tk5MUmADAw8MDeXl5iI+Pr7D3vLw8ZGVlqTyIiIioblJ7pOndd9+FQqEoM12hUEBXVxfNmzeHt7c37O3tn7ksFxcXbNiwAS1atMDt27fx1VdfoUuXLjh79izS0tIAAFZWViqvsbKywo0bNwAAaWlp0NbWhomJSZmaktenpaXB0tKyzLotLS1Vakqvx8TEBNra2lJNeWbPns07oRMREf1LqD3SpFQqcejQIZw6dUoKT6dPn8ahQ4dQWFiIbdu2oW3btvjzzz+fuaw+ffrgvffeQ+vWrdGzZ0/s3r0bwOPDcCVKBzQhRLmh7Wk15dU/T01pU6dORWZmpvRISUl5al9ERERUe6kdmqytreHt7Y2rV69i+/bt2LFjB65cuYIPP/wQzZo1Q1JSEnx9fTFlyhS1mzEwMEDr1q1x6dIl6Tyn0iM96enp0qiQtbU18vPzkZGR8dSa27dvl1nXnTt3VGpKrycjIwMFBQVlRqCepKOjA2NjY5UHERER1U1qh6Y1a9YgKCgI9er976X16tXDuHHj8N1330GhUGDs2LFITExUu5m8vDwkJSWhQYMGaNq0KaytrbF//35pfn5+Po4cOYIuXboAAJydnaGlpaVSk5qaisTERKnG1dUVmZmZOHHihFRz/PhxZGZmqtQkJiYiNTVVqomOjoaOjg6cnZ3V3g4iIiKqe9Q+p6mwsBDnz59HixYtVKafP39eusxfV1f3mYfQAGDSpEno378/GjVqhPT0dHz11VfIysqCr68vFAoFgoKCEBoaCjs7O9jZ2SE0NBT6+vrw9vYG8PhQob+/P4KDg2FmZgZTU1NMmjRJOtwHAA4ODujduzdGjhyJVatWAQBGjRoFT09P6bwrd3d3ODo6wsfHB/PmzcO9e/cwadIkjBw5kqNHREREBOA5QpOPjw/8/f3x+eef47XXXoNCocCJEycQGhqKYcMef3P9kSNH0KpVq2cu6+bNm/jggw/wzz//wMLCAp07d0ZsbCwaN24M4PE9oXJzcxEQEICMjAy4uLggOjoaRkZG0jIWLVoETU1NeHl5ITc3Fz169EB4eDg0NDSkms2bNyMwMFC6ym7AgAFYtmyZNF9DQwO7d+9GQEAAunbtCj09PXh7e2P+/Pnq7h4iIiKqoxRCCKHOC4qKijBnzhwsW7ZMOlfIysoK48aNw5QpU6ChoYHk5GTUq1cPDRs2rJKma6qsrCwolUpkZmZWOELl/OmGl9xVzRQ/b9gLLyP5y9aV0Ent12jG388uIiKiCsn5/Q08x0iThoYGpk2bhmnTpkn3JSq9gkaNGqm7WCIiIqIa7bnuCF6C5/sQERHRv8VzhaaffvoJP/zwA5KTk8t8qe2pU6cqpTEiIiKimkTtWw4sWbIEH330ESwtLXH69Gl06tQJZmZmuHr1Kvr06VMVPRIRERFVO7VD0/Lly/Hdd99h2bJl0NbWxuTJk7F//34EBgYiMzOzKnokIiIiqnZqh6bk5GTpppB6enrIzs4G8PhWBFu3bq3c7oiIiIhqiOf6GpW7d+8CABo3bozY2FgAwLVr16Dm3QuIiIiIag21Q9Nbb72FX375BQDg7++PCRMmoFevXhg8eDDefffdSm+QiIiIqCZQ++q57777DsXFxQCAMWPGwNTUFEePHkX//v0xZsyYSm+QiIiIqCZQOzTVq1dP5ct6vby84OXlValNEREREdU0z3WfpkePHuHMmTNIT0+XRp1KDBgwoFIaIyIiIqpJ1A5NUVFRGDZsGP75558y8xQKBYqKiiqlMSIiIqKaRO0TwceOHYv3338fqampKC4uVnkwMBEREVFdpXZoSk9Px8SJE2FlZVUV/RARERHVSGqHpkGDBuHw4cNV0AoRERFRzaX2OU3Lli3D+++/jz/++AOtW7eGlpaWyvzAwMBKa46IiIioplA7NG3ZsgX79u2Dnp4eDh8+DIVCIc1TKBQMTURERFQnqR2avvjiC3z55Zf47LPPVO7XRERERFSXqZ168vPzMXjwYAYmIiIi+ldRO/n4+vpi27ZtVdELERERUY2l9uG5oqIihIWFYd++fWjTpk2ZE8EXLlxYac0RERER1RRqh6a///4b7du3BwAkJiaqzHvypHAiIiKiukTt0PTbb79VRR9ERERENRrP5iYiIiKSQfZI08CBA2XV7dix47mbISIiIqqpZIcmpVJZlX0QERER1WiyQ9O6deuqsg8iIiKiGo3nNBERERHJwNBEREREJANDExEREZEMDE1EREREMsgKTR06dEBGRgYA4Msvv8TDhw+rtCkiIiKimkZWaEpKSkJOTg4AYNasWXjw4EGVNkVERERU08i65UC7du3w0Ucf4fXXX4cQAvPnz4ehoWG5tTNmzKjUBomIiIhqAlmhKTw8HDNnzsSvv/4KhUKBvXv3QlOz7EsVCgVDExEREdVJsg7P2dvbIyIiAnFxcRBC4ODBgzh9+nSZx6lTp567kdmzZ0OhUCAoKEiaJoRASEgIbGxsoKenh27duuHs2bMqr8vLy8O4ceNgbm4OAwMDDBgwADdv3lSpycjIgI+PD5RKJZRKJXx8fHD//n2VmuTkZPTv3x8GBgYwNzdHYGAg8vPzn3t7iIiIqG5R++q54uJiWFpaVmoTcXFx+O6779CmTRuV6WFhYVi4cCGWLVuGuLg4WFtbo1evXsjOzpZqgoKCEBkZiYiICBw9ehQPHjyAp6cnioqKpBpvb28kJCQgKioKUVFRSEhIgI+PjzS/qKgI/fr1Q05ODo4ePYqIiAhs374dwcHBlbqdREREVHvJ/hqVJ125cgWLFy9GUlISFAoFHBwcMH78eDRr1kztZT148ABDhw7F6tWr8dVXX0nThRBYvHgxpk2bJn1Z8Pr162FlZYUtW7Zg9OjRyMzMxJo1a7Bx40b07NkTALBp0ybY2triwIED8PDwQFJSEqKiohAbGwsXFxcAwOrVq+Hq6ooLFy7A3t4e0dHROHfuHFJSUmBjYwMAWLBgAfz8/PD111/D2Nj4eXYTERER1SFqjzTt27cPjo6OOHHiBNq0aQMnJyccP34crVq1wv79+9Vu4JNPPkG/fv2k0FPi2rVrSEtLg7u7uzRNR0cHbm5uOHbsGAAgPj4eBQUFKjU2NjZwcnKSamJiYqBUKqXABACdO3eGUqlUqXFycpICEwB4eHggLy8P8fHxFfael5eHrKwslQcRERHVTWqPNH322WeYMGEC5syZU2b6lClT0KtXL9nLioiIwKlTpxAXF1dmXlpaGgDAyspKZbqVlRVu3Lgh1Whra8PExKRMTcnr09LSyj2caGlpqVJTej0mJibQ1taWasoze/ZszJo161mbSURERHWA2iNNSUlJ8Pf3LzN9+PDhOHfunOzlpKSkYPz48di0aRN0dXUrrFMoFCrPhRBlppVWuqa8+uepKW3q1KnIzMyUHikpKU/ti4iIiGovtUOThYUFEhISykxPSEhQ6wTx+Ph4pKenw9nZGZqamtDU1MSRI0ewZMkSaGpqSiM/pUd60tPTpXnW1tbIz8+X7lZeUc3t27fLrP/OnTsqNaXXk5GRgYKCgjIjUE/S0dGBsbGxyoOIiIjqJrVD08iRIzFq1CjMnTsXf/zxB44ePYo5c+Zg9OjRGDVqlOzl9OjRA3///TcSEhKkR8eOHTF06FAkJCTg1VdfhbW1tcp5Uvn5+Thy5Ai6dOkCAHB2doaWlpZKTWpqKhITE6UaV1dXZGZm4sSJE1LN8ePHkZmZqVKTmJiI1NRUqSY6Oho6OjpwdnZWdxcRERFRHaT2OU3Tp0+HkZERFixYgKlTpwJ4fPJ1SEgIAgMDZS/HyMgITk5OKtMMDAxgZmYmTQ8KCkJoaCjs7OxgZ2eH0NBQ6Ovrw9vbGwCgVCrh7++P4OBgmJmZwdTUFJMmTULr1q2lE8sdHBzQu3dvjBw5EqtWrQIAjBo1Cp6enrC3twcAuLu7w9HRET4+Ppg3bx7u3buHSZMmYeTIkRw9IiIiIgDPEZoUCgUmTJiACRMmSPdLMjIyqvTGAGDy5MnIzc1FQEAAMjIy4OLigujoaJX1LVq0CJqamvDy8kJubi569OiB8PBwaGhoSDWbN29GYGCgdJXdgAEDsGzZMmm+hoYGdu/ejYCAAHTt2hV6enrw9vbG/Pnzq2S7iIiIqPZRCCFEdTdRV2RlZUGpVCIzM7PCESrnTze85K5qpvh5w154Gclftq6ETmq/RjP+ru4WiIhqNTm/v4HnOKeJiIiI6N+IoYmIiIhIBoYmIiIiIhnUCk0FBQXo3r07Ll68WFX9EBEREdVIaoUmLS0tJCYmPvOO3ERERER1jdqH54YNG4Y1a9ZURS9ERERENZba92nKz8/H999/j/3796Njx44wMDBQmb9w4cJKa46IiIioplA7NCUmJqJDhw4AUObcJh62IyIiorpK7dD022+/VUUfRERERDXac99y4PLly9i3bx9yc3MBALyxOBEREdVlaoemu3fvokePHmjRogX69u2L1NRUAMCIESMQHBxc6Q0SERER1QRqh6YJEyZAS0sLycnJ0NfXl6YPHjwYUVFRldocERERUU2h9jlN0dHR2LdvHxo2bKgy3c7ODjdu3Ki0xoiIiIhqErVHmnJyclRGmEr8888/0NHRqZSmiIiIiGoatUPTm2++iQ0bNkjPFQoFiouLMW/ePHTv3r1SmyMiIiKqKdQ+PDdv3jx069YNJ0+eRH5+PiZPnoyzZ8/i3r17+PPPP6uiRyIiIqJqp/ZIk6OjI86cOYNOnTqhV69eyMnJwcCBA3H69Gk0a9asKnokIiIiqnZqjzQBgLW1NWbNmlXZvRARERHVWM8VmjIyMrBmzRokJSVBoVDAwcEBH330EUxNTSu7PyIiIqIaQe3Dc0eOHEHTpk2xZMkSZGRk4N69e1iyZAmaNm2KI0eOVEWPRERERNVO7ZGmTz75BF5eXlixYgU0NDQAAEVFRQgICMAnn3yCxMTESm+SiIiIqLqpPdJ05coVBAcHS4EJADQ0NDBx4kRcuXKlUpsjIiIiqinUDk0dOnRAUlJSmelJSUlo165dZfREREREVOPIOjx35swZ6d+BgYEYP348Ll++jM6dOwMAYmNj8e2332LOnDlV0yURERFRNZMVmtq1aweFQgEhhDRt8uTJZeq8vb0xePDgyuuOiIiIqIaQFZquXbtW1X0QERER1WiyQlPjxo2rug8iIiKiGu25bm753//+F3/++SfS09NRXFysMi8wMLBSGiMiIiKqSdQOTevWrcOYMWOgra0NMzMzKBQKaZ5CoWBoIiIiojpJ7dA0Y8YMzJgxA1OnTkW9emrfsYCIiIioVlI79Tx8+BBDhgxhYCIiIqJ/FbWTj7+/P3788ceq6IWIiIioxlL78Nzs2bPh6emJqKgotG7dGlpaWirzFy5cWGnNEREREdUUaoem0NBQ7Nu3D/b29gBQ5kRwIiIiorpI7dC0cOFCrF27Fn5+flXQDhEREVHNpPY5TTo6OujatWulrHzFihVo06YNjI2NYWxsDFdXV+zdu1eaL4RASEgIbGxsoKenh27duuHs2bMqy8jLy8O4ceNgbm4OAwMDDBgwADdv3lSpycjIgI+PD5RKJZRKJXx8fHD//n2VmuTkZPTv3x8GBgYwNzdHYGAg8vPzK2U7iYiIqPZTOzSNHz8eS5curZSVN2zYEHPmzMHJkydx8uRJvPXWW3j77belYBQWFoaFCxdi2bJliIuLg7W1NXr16oXs7GxpGUFBQYiMjERERASOHj2KBw8ewNPTE0VFRVKNt7c3EhISEBUVhaioKCQkJMDHx0eaX1RUhH79+iEnJwdHjx5FREQEtm/fjuDg4ErZTiIiIqr9FOLJb+GV4d1338WhQ4dgZmaGVq1alTkRfMeOHS/UkKmpKebNm4fhw4fDxsYGQUFBmDJlCoDHo0pWVlaYO3cuRo8ejczMTFhYWGDjxo3SFwXfunULtra22LNnDzw8PJCUlARHR0fExsbCxcUFABAbGwtXV1ecP38e9vb22Lt3Lzw9PZGSkgIbGxsAQEREBPz8/JCeng5jY2NZvWdlZUGpVCIzM7PC1zh/uuGF9k9dET9v2AsvI/nL1pXQSe3XaMbf1d0CEVGtJuf3N/AcI03169fHwIED4ebmBnNzc+mQV8njeRUVFSEiIgI5OTlwdXXFtWvXkJaWBnd3d6lGR0cHbm5uOHbsGAAgPj4eBQUFKjU2NjZwcnKSamJiYqBUKqXABACdO3eGUqlUqXFycpICEwB4eHggLy8P8fHxz71NREREVHc819eoVKa///4brq6uePToEQwNDREZGQlHR0cp0FhZWanUW1lZ4caNGwCAtLQ0aGtrw8TEpExNWlqaVGNpaVlmvZaWlio1pddjYmICbW1tqaY8eXl5yMvLk55nZWXJ3WwiIiKqZar9tt729vZISEhAbGwsPv74Y/j6+uLcuXPS/NK3MRBCPPPWBqVryqt/nprSZs+erTLKZmtr+9S+iIiIqPZSe6SpadOmTw0SV69eVWt52traaN68OQCgY8eOiIuLwzfffCOdx5SWloYGDRpI9enp6dKokLW1NfLz85GRkaEy2pSeno4uXbpINbdv3y6z3jt37qgs5/jx4yrzMzIyUFBQUGYE6klTp07FxIkTpedZWVkMTkRERHWU2qEpKChI5XlBQQFOnz6NqKgofPrppy/ckBACeXl5aNq0KaytrbF//360b98eAJCfn48jR45g7ty5AABnZ2doaWlh//798PLyAgCkpqYiMTERYWFhAABXV1dkZmbixIkT6NSpEwDg+PHjyMzMlIKVq6srvv76a6SmpkoBLTo6Gjo6OnB2dq6wVx0dHejo6LzwNhMREVHNp3ZoGj9+fLnTv/32W5w8eVKtZX3++efo06cPbG1tkZ2djYiICBw+fBhRUVFQKBQICgpCaGgo7OzsYGdnh9DQUOjr68Pb2xsAoFQq4e/vj+DgYJiZmcHU1BSTJk1C69at0bNnTwCAg4MDevfujZEjR2LVqlUAgFGjRsHT01O6q7m7uzscHR3h4+ODefPm4d69e5g0aRJGjhwp+8o5IiIiqtvUDk0V6dOnD6ZOnarWieK3b9+Gj48PUlNToVQq0aZNG0RFRaFXr14AgMmTJyM3NxcBAQHIyMiAi4sLoqOjYWRkJC1j0aJF0NTUhJeXF3Jzc9GjRw+Eh4dDQ0NDqtm8eTMCAwOlq+wGDBiAZcuWSfM1NDSwe/duBAQEoGvXrtDT04O3tzfmz5//oruFiIiI6gi179NUkbCwMCxfvhzXr1+vjMXVSrxPk3y8T1Pl4X2aiIhejNz7NKk90tS+fXuVE8GFEEhLS8OdO3ewfPny5+uWiIiIqIZTOzS98847Ks/r1asHCwsLdOvWDS1btqysvoiIiIhqFLVD08yZM6uiDyIiIqIardpvbklERERUG8geaapXr94z78StUChQWFj4wk0RERER1TSyQ1NkZGSF844dO4alS5eiki7EIyIiIqpxZIemt99+u8y08+fPY+rUqfjll18wdOhQ/Oc//6nU5oiIiIhqiuc6p+nWrVsYOXIk2rRpg8LCQiQkJGD9+vVo1KhRZfdHREREVCOoFZoyMzMxZcoUNG/eHGfPnsXBgwfxyy+/wMnJqar6IyIiIqoRZB+eCwsLw9y5c2FtbY2tW7eWe7iOiIiIqK6SHZo+++wz6OnpoXnz5li/fj3Wr19fbt2OHTsqrTkiIiKimkJ2aBo2bNgzbzlAREREVFfJDk3h4eFV2AYRERFRzcY7ghMRERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDNUammbPno3XXnsNRkZGsLS0xDvvvIMLFy6o1AghEBISAhsbG+jp6aFbt244e/asSk1eXh7GjRsHc3NzGBgYYMCAAbh586ZKTUZGBnx8fKBUKqFUKuHj44P79++r1CQnJ6N///4wMDCAubk5AgMDkZ+fXyXbTkRERLVLtYamI0eO4JNPPkFsbCz279+PwsJCuLu7IycnR6oJCwvDwoULsWzZMsTFxcHa2hq9evVCdna2VBMUFITIyEhERETg6NGjePDgATw9PVFUVCTVeHt7IyEhAVFRUYiKikJCQgJ8fHyk+UVFRejXrx9ycnJw9OhRREREYPv27QgODn45O4OIiIhqNIUQQlR3EyXu3LkDS0tLHDlyBG+++SaEELCxsUFQUBCmTJkC4PGokpWVFebOnYvRo0cjMzMTFhYW2LhxIwYPHgwAuHXrFmxtbbFnzx54eHggKSkJjo6OiI2NhYuLCwAgNjYWrq6uOH/+POzt7bF37154enoiJSUFNjY2AICIiAj4+fkhPT0dxsbGz+w/KysLSqUSmZmZFdY7f7qhMnZVrRc/b9gLLyP5y9aV0Ent12jG39XdAhFRrSbn9zdQw85pyszMBACYmpoCAK5du4a0tDS4u7tLNTo6OnBzc8OxY8cAAPHx8SgoKFCpsbGxgZOTk1QTExMDpVIpBSYA6Ny5M5RKpUqNk5OTFJgAwMPDA3l5eYiPjy+337y8PGRlZak8iIiIqG6qMaFJCIGJEyfi9ddfh5OTEwAgLS0NAGBlZaVSa2VlJc1LS0uDtrY2TExMnlpjaWlZZp2WlpYqNaXXY2JiAm1tbammtNmzZ0vnSCmVStja2qq72URERFRL1JjQNHbsWJw5cwZbt24tM0+hUKg8F0KUmVZa6Zry6p+n5klTp05FZmam9EhJSXlqT0RERFR71YjQNG7cOOzatQu//fYbGjZsKE23trYGgDIjPenp6dKokLW1NfLz85GRkfHUmtu3b5dZ7507d1RqSq8nIyMDBQUFZUagSujo6MDY2FjlQURERHVTtYYmIQTGjh2LHTt24NChQ2jatKnK/KZNm8La2hr79++XpuXn5+PIkSPo0qULAMDZ2RlaWloqNampqUhMTJRqXF1dkZmZiRMnTkg1x48fR2ZmpkpNYmIiUlNTpZro6Gjo6OjA2dm58jeeiIiIahXN6lz5J598gi1btuDnn3+GkZGRNNKjVCqhp6cHhUKBoKAghIaGws7ODnZ2dggNDYW+vj68vb2lWn9/fwQHB8PMzAympqaYNGkSWrdujZ49ewIAHBwc0Lt3b4wcORKrVq0CAIwaNQqenp6wt7cHALi7u8PR0RE+Pj6YN28e7t27h0mTJmHkyJEcQSIiIqLqDU0rVqwAAHTr1k1l+rp16+Dn5wcAmDx5MnJzcxEQEICMjAy4uLggOjoaRkZGUv2iRYugqakJLy8v5ObmokePHggPD4eGhoZUs3nzZgQGBkpX2Q0YMADLli2T5mtoaGD37t0ICAhA165doaenB29vb8yfP7+Ktp6IiIhqkxp1n6bajvdpko/3aao8vE8TEdGLqZX3aSIiIiKqqRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGTSruwEiorriyJtu1d1CjeD2+5HqboGoSnCkiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIhmoNTb///jv69+8PGxsbKBQK7Ny5U2W+EAIhISGwsbGBnp4eunXrhrNnz6rU5OXlYdy4cTA3N4eBgQEGDBiAmzdvqtRkZGTAx8cHSqUSSqUSPj4+uH//vkpNcnIy+vfvDwMDA5ibmyMwMBD5+flVsdlERERUC1VraMrJyUHbtm2xbNmycueHhYVh4cKFWLZsGeLi4mBtbY1evXohOztbqgkKCkJkZCQiIiJw9OhRPHjwAJ6enigqKpJqvL29kZCQgKioKERFRSEhIQE+Pj7S/KKiIvTr1w85OTk4evQoIiIisH37dgQHB1fdxhMREVGtUq3fPdenTx/06dOn3HlCCCxevBjTpk3DwIEDAQDr16+HlZUVtmzZgtGjRyMzMxNr1qzBxo0b0bNnTwDApk2bYGtriwMHDsDDwwNJSUmIiopCbGwsXFxcAACrV6+Gq6srLly4AHt7e0RHR+PcuXNISUmBjY0NAGDBggXw8/PD119/DWNj45ewN4iIiKgmq7HnNF27dg1paWlwd3eXpuno6MDNzQ3Hjh0DAMTHx6OgoEClxsbGBk5OTlJNTEwMlEqlFJgAoHPnzlAqlSo1Tk5OUmACAA8PD+Tl5SE+Pr7CHvPy8pCVlaXyICIiorqpxoamtLQ0AICVlZXKdCsrK2leWloatLW1YWJi8tQaS0vLMsu3tLRUqSm9HhMTE2hra0s15Zk9e7Z0npRSqYStra2aW0lERES1RY0NTSUUCoXKcyFEmWmlla4pr/55akqbOnUqMjMzpUdKSspT+yIiIqLaq8aGJmtrawAoM9KTnp4ujQpZW1sjPz8fGRkZT625fft2meXfuXNHpab0ejIyMlBQUFBmBOpJOjo6MDY2VnkQERFR3VRjQ1PTpk1hbW2N/fv3S9Py8/Nx5MgRdOnSBQDg7OwMLS0tlZrU1FQkJiZKNa6ursjMzMSJEyekmuPHjyMzM1OlJjExEampqVJNdHQ0dHR04OzsXKXbSURERLVDtV499+DBA1y+fFl6fu3aNSQkJMDU1BSNGjVCUFAQQkNDYWdnBzs7O4SGhkJfXx/e3t4AAKVSCX9/fwQHB8PMzAympqaYNGkSWrduLV1N5+DggN69e2PkyJFYtWoVAGDUqFHw9PSEvb09AMDd3R2Ojo7w8fHBvHnzcO/ePUyaNAkjR47k6BEREREBqObQdPLkSXTv3l16PnHiRACAr68vwsPDMXnyZOTm5iIgIAAZGRlwcXFBdHQ0jIyMpNcsWrQImpqa8PLyQm5uLnr06IHw8HBoaGhINZs3b0ZgYKB0ld2AAQNU7g2loaGB3bt3IyAgAF27doWenh68vb0xf/78qt4FREREVEsohBCiupuoK7KysqBUKpGZmVnhCJXzpxteclc1U/y8YS+8jOQvW1dCJ7Vfoxl/V3cL9P+OvOlW3S3UCG6/H6nuFojUIuf3N1CDz2kiIiIiqkkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZKjW754jIiIqz7LgX6q7hRph7IL+1d0CPYEjTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDJoVncDREREVDW+/nBQdbdQI0zb9FOlLIehiehfruvSrtXdQo3w57g/q7sFIqrheHiOiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoamUpYvX46mTZtCV1cXzs7O+OOPP6q7JSIiIqoBGJqesG3bNgQFBWHatGk4ffo03njjDfTp0wfJycnV3RoRERFVM4amJyxcuBD+/v4YMWIEHBwcsHjxYtja2mLFihXV3RoRERFVM4am/5efn4/4+Hi4u7urTHd3d8exY8eqqSsiIiKqKXhH8P/3zz//oKioCFZWVirTrayskJaWVu5r8vLykJeXJz3PzMwEAGRlZVW4nqK83ErotvZ72j6SK/tRUSV0Uvu96L4szC2spE5qt8p4T+YUcl8ClbMvc/MeVkIntd+L7stHBQWV1Ent9qz9WDJfCPHUOoamUhQKhcpzIUSZaSVmz56NWbNmlZlua2tbJb3VJcqlY6q7hbpjtrK6O6gTlFO4HyuNkvuyskz+tro7qBu++kHeezI7OxvKp7x/GZr+n7m5OTQ0NMqMKqWnp5cZfSoxdepUTJw4UXpeXFyMe/fuwczMrMKgVd2ysrJga2uLlJQUGBsbV3c7tRr3ZeXgfqw83JeVh/uyctSW/SiEQHZ2NmxsbJ5ax9D0/7S1teHs7Iz9+/fj3Xfflabv378fb7/9drmv0dHRgY6Ojsq0+vXrV2WblcbY2LhGv4FrE+7LysH9WHm4LysP92XlqA378WkjTCUYmp4wceJE+Pj4oGPHjnB1dcV3332H5ORkjBnDQ0lERET/dgxNTxg8eDDu3r2LL7/8EqmpqXBycsKePXvQuHHj6m6NiIiIqhlDUykBAQEICAio7jaqjI6ODmbOnFnmsCKpj/uycnA/Vh7uy8rDfVk56tp+VIhnXV9HRERERLy5JREREZEcDE1EREREMjA0EREREcnA0FTHCCEwatQomJqaQqFQICEhobpbqlO6deuGoKCg6m6jxvPz88M777xTpesIDw+vNfdFexHPes81adIEixcvVnu5ISEhaNeu3XP3VZM9uc+ed/+8qLq8f9XxPPvh+vXrNfb3F6+eq2OioqIQHh6Ow4cP49VXX4W5uXl1t0TP4Ofnh/v372Pnzp3V3QrVQnFxcTAwMKjuNmos7p/ax9bWFqmpqTXy9xdDUx1z5coVNGjQAF26dKnuVmq0/Px8aGtrV3cbRC/MwsLiqfMLCgqgpaX1krqpeZ61f17Uv3X/VuV2a2howNraukqW/aJ4eK4O8fPzw7hx45CcnAyFQoEmTZogLy8PgYGBsLS0hK6uLl5//XXExcUBAB49eoRWrVph1KhR0jKuXbsGpVKJ1atXV9dmVIlu3bph7NixmDhxIszNzdGrVy8cOXIEnTp1go6ODho0aIDPPvsMhU98S31OTg6GDRsGQ0NDNGjQAAsWLCiz3Pz8fEyePBmvvPIKDAwM4OLigsOHD0vzSw4h7du3Dw4ODjA0NETv3r2RmpoK4PHQ9fr16/Hzzz9DoVBAoVCovL6m++mnn9C6dWvo6enBzMwMPXv2RE5OTpm6+Ph4WFpa4uuvvwbw+IuxS4+s1a9fH+Hh4QD+Nzy/Y8cOdO/eHfr6+mjbti1iYmIq7OXu3bvo1KkTBgwYgEePHlXaNtYEhYWFGDt2LOrXrw8zMzN88cUX0rexlz78pFAosHLlSrz99tswMDDAV199BQCYM2cOrKysYGRkBH9//zq3jyry5P4JDw+XPmdPPkJCQgA8HpXq1asXzM3NoVQq4ebmhlOnTqksrzbv32d9XteuXYtWrVpJPxPHjh0rzStvu8s7RL5z585yv3t11apVsLW1hb6+Pt5//33cv3+/wj5LH54rKiqCv78/mjZtCj09Pdjb2+Obb76R6n///XdoaWmV+e7Y4OBgvPnmm2rsIRkE1Rn3798XX375pWjYsKFITU0V6enpIjAwUNjY2Ig9e/aIs2fPCl9fX2FiYiLu3r0rhBDi9OnTQltbW0RGRorCwkLRtWtX8fbbb1fvhlQBNzc3YWhoKD799FNx/vx5cfToUaGvry8CAgJEUlKSiIyMFObm5mLmzJnSaz7++GPRsGFDER0dLc6cOSM8PT2FoaGhGD9+vFTj7e0tunTpIn7//Xdx+fJlMW/ePKGjoyMuXrwohBBi3bp1QktLS/Ts2VPExcWJ+Ph44eDgILy9vYUQQmRnZwsvLy/Ru3dvkZqaKlJTU0VeXt7L3DXP7datW0JTU1MsXLhQXLt2TZw5c0Z8++23Ijs7W/j6+krvo99++00olUqxfPly6bUARGRkpMrylEqlWLdunRBCiGvXrgkAomXLluLXX38VFy5cEIMGDRKNGzcWBQUFQojH+1apVAohhEhJSREODg7Cx8dHml9XlLx3x48fL86fPy82bdok9PX1xXfffSeEEKJx48Zi0aJFUj0AYWlpKdasWSOuXLkirl+/LrZt2ya0tbXF6tWrxfnz58W0adOEkZGRaNu2bfVsVBVzc3OTPqdP7p+HDx9Kn7PU1FSxdetWoampKaKjo4UQQhw8eFBs3LhRnDt3Tpw7d074+/sLKysrkZWVJS27tu7fp31ehRBi+fLlQldXVyxevFhcuHBBnDhx4pnvqyc/gyUiIyPFk9Fi5syZwsDAQLz11lvi9OnT4siRI6J58+bSz8DylHz+T58+LYQQIj8/X8yYMUOcOHFCXL16VfoMbNu2TXpNixYtRFhYmPS8oKBAWFpairVr177AXiuLoamOWbRokWjcuLEQQogHDx4ILS0tsXnzZml+fn6+sLGxUXlzhYWFCXNzczFu3DhhbW0t7ty587LbrnJubm6iXbt20vPPP/9c2Nvbi+LiYmnat99+KwwNDUVRUZHIzs4W2traIiIiQpp/9+5doaenJ/0wvnz5slAoFOK///2vyrp69Oghpk6dKoR4/IsdgLh8+bLKeqysrKTnTwaM2iQ+Pl4AENevXy8zr2Sbdu7cKYyMjMSWLVtU5ssNTd9//700/+zZswKASEpKEkL8LzRduHBBNGrUSIwbN07l/7OucHNzEw4ODirbNmXKFOHg4CCEKD80BQUFqSzD1dVVjBkzRmWai4tLjfqlXpkqCk1Punz5sjAzM1P5WVhaYWGhMDIyEr/88os0rbbu36d9XoUQwsbGRkybNq3C15e33XJDk4aGhkhJSZGm7d27V9SrV0+kpqaWu67Soak8AQEB4r333pOez507V/pMCCHEzp07haGhoXjw4EGFy3gePDxXh125cgUFBQXo2rWrNE1LSwudOnVCUlKSNC04OBj29vZYunQp1q1bVyNPvqsMHTt2lP6dlJQEV1dXlWHkrl274sGDB7h58yauXLmC/Px8uLq6SvNNTU1hb28vPT916hSEEGjRogUMDQ2lx5EjR3DlyhWpTl9fH82aNZOeN2jQAOnp6VW1mS9N27Zt0aNHD7Ru3Rrvv/8+Vq9ejYyMDGn+8ePH8d5772H9+vX44IMPnmsdbdq0kf7doEEDAFDZd7m5uXj99dfxzjvvYMmSJeUeFqgLOnfurLJtrq6uuHTpEoqKisqtf/K9Dvzv/f6k0s//TTIzM+Hp6Yk+ffrg008/laanp6djzJgxaNGiBZRKJZRKJR48eIDk5GSV19fG/fu0z2t6ejpu3bqFHj16PHUZpbdbrkaNGqFhw4bSc1dXVxQXF+PChQuyl7Fy5Up07NgRFhYWMDQ0xOrVq1X+X/z8/HD58mXExsYCeHyo0cvLq9IvAmBoqsPE/5/zUPoXiRBCZVp6ejouXLgADQ0NXLp06aX2+DI9+eEpvQ9KpgGP95eQ8e1CxcXF0NDQQHx8PBISEqRHUlKSyvH20idLyl1+TaehoYH9+/dj7969cHR0xNKlS2Fvb49r164BAJo1a4aWLVti7dq1yM/PV3ltefugoKCgzDqe3Hcl/1/FxcXSNB0dHfTs2RO7d+/GzZs3K23bajteLVaxoqIiDB48GMbGxmXO3fTz80N8fDwWL16MY8eOISEhAWZmZmXev7Vx/z7t86qnpydrGaW3u169erI+x6WVfJbl/pHzww8/YMKECRg+fDiio6ORkJCAjz76SOX/xdLSEv3798e6deuQnp6OPXv2YPjw4bKWrw6GpjqsefPm0NbWxtGjR6VpBQUFOHnyJBwcHKRpw4cPh5OTEzZs2IDJkyfj3Llz1dHuS+Xo6Ihjx46pfOCPHTsGIyMjvPLKK2jevDm0tLSkv1oAICMjAxcvXpSet2/fHkVFRUhPT0fz5s1VHupc+aGtrV3hiEFNp1Ao0LVrV8yaNQunT5+GtrY2IiMjAQDm5uY4dOgQrly5gsGDB6v8MLWwsJBOhgeAS5cu4eHDh2qvv169eti4cSOcnZ3x1ltv4datWy++UTXQk+/Dkud2dnbQ0NCQ9XoHB4dyl/FvNGHCBPz999+IjIyErq6uyrw//vgDgYGB6Nu3r3RC9D///PPMZdaW/VvR59XIyAhNmjTBwYMH1VqehYUFsrOzVU4mL+/eSsnJySqfzZiYGNSrVw8tWrSQtZ4//vgDXbp0QUBAANq3b4/mzZurjOaXGDFiBCIiIrBq1So0a9ZM5ShLZWFoqsMMDAzw8ccf49NPP0VUVBTOnTuHkSNH4uHDh/D39wcAfPvtt4iJicGGDRvg7e2NQYMGYejQoWX+sqprAgICkJKSgnHjxuH8+fP4+eefMXPmTEycOBH16tWDoaEh/P398emnn+LgwYNITEyEn58f6tX730emRYsWGDp0KIYNG4YdO3bg2rVriIuLw9y5c7Fnzx7ZvTRp0gRnzpzBhQsX8M8//8j6S60mOH78OEJDQ3Hy5EkkJydjx44duHPnjkogt7S0xKFDh3D+/Hl88MEH0tWJb731FpYtW4ZTp07h5MmTGDNmzHNfvqyhoYHNmzejbdu2eOutt8pcQVMXpKSkYOLEibhw4QK2bt2KpUuXYvz48bJfP378eKxduxZr167FxYsXMXPmTJw9e7YKO66Z1q1bh+XLl2PlypWoV68e0tLSkJaWhgcPHgB4/Ifmxo0bkZSUhOPHj2Po0KGyRmFqw/591uc1JCQECxYswJIlS3Dp0iWcOnUKS5cufeoyXVxcoK+vj88//xyXL1/Gli1bpCtgn6SrqwtfX1/89ddfUjD18vKS/cdl8+bNcfLkSezbtw8XL17E9OnTpavAn+Th4QGlUomvvvoKH330kaxlq61Sz5CiavfkieBCCJGbmyvGjRsnzM3NhY6Ojujatas4ceKEEEKIpKQkoaenp3KSbmZmpmjSpImYPHnyy269Sj15YmiJw4cPi9dee01oa2sLa2trMWXKFJUrr7Kzs8WHH34o9PX1hZWVlQgLCyuznJKrOpo0aSK0tLSEtbW1ePfdd8WZM2eEEPJOlExPTxe9evUShoaGAoD47bffKnvzq8S5c+eEh4eHsLCwEDo6OqJFixZi6dKlQoiyJ7ffunVLtGjRQnh5eYnCwkLx3//+V7i7uwsDAwNhZ2cn9uzZU+6J4E+eCJqRkaGyf0rv24KCAjFw4EDh4OAgbt++XcVb//K4ubmJgIAAMWbMGGFsbCxMTEzEZ599Jp0YXt6J4KVPshdCiK+//lqYm5sLQ0ND4evrKyZPnlyjTlSuTBWdCO7r6ysAlHmUXDV76tQp0bFjR6GjoyPs7OzEjz/+WGf279M+ryVWrlwp7O3thZaWlmjQoIEYN26cNK+i7Y6MjBTNmzcXurq6wtPTU3z33XdlTgRv27atWL58ubCxsRG6urpi4MCB4t69exX2Wvrz/+jRI+Hn5yeUSqWoX7+++Pjjj8Vnn31W7v6dPn260NDQELdu3VJvB8mkEKIOnFxBREREdcKFCxfQsmVLXLp0Cc2bN1frtSNHjsTt27exa9euKumNdwQnIiKiGuHevXv46aefYGxsDFtbW9mvy8zMRFxcHDZv3oyff/65yvpjaCIiIqIawd/fH/Hx8VixYgV0dHRkv+7tt9/GiRMnMHr0aPTq1avK+uPhOSIiIiIZePUcERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMR1XghISFo165dnVnPi6otfRLVNQxNRFTljh07Bg0NDfTu3fu5Xj9p0iS1vxerqmzfvh0uLi5QKpUwMjJCq1atEBwc/FJ7qEn7g+jfhKGJiKrc2rVrMW7cOBw9ehTJyclqv97Q0BBmZmZV0Jl6Dhw4gCFDhmDQoEE4ceIE4uPj8fXXX7/072qsKfuD6N+GoYmIqlROTg5++OEHfPzxx/D09CzzhZ6HDx+GQqHAwYMH0bFjR+jr66NLly64cOGCVFP6cJSfnx/eeecdhIaGwsrKCvXr18esWbNQWFiITz/9FKampmjYsCHWrl2rsq4pU6agRYsW0NfXx6uvvorp06er9QXJv/76K15//XV8+umnsLe3R4sWLfDOO++ofLFpSa+rVq2Cra0t9PX18f777+P+/fsqy1q3bh0cHBygq6uLli1bYvny5Srzb968iSFDhsDU1BQGBgbo2LEjjh8/Xu7+eNby8vPzMXbsWDRo0AC6urpo0qQJZs+eLXu7iegxhiYiqlLbtm2Dvb097O3t8eGHH2LdunUo756606ZNw4IFC3Dy5Eloampi+PDhT13uoUOHcOvWLfz+++9YuHAhQkJC4OnpCRMTExw/fhxjxozBmDFjkJKSIr3GyMgI4eHhOHfuHL755husXr0aixYtkr0t1tbWOHv2LBITE59ad/nyZfzwww/45ZdfEBUVhYSEBHzyySfS/NWrV2PatGn4+uuvkZSUhNDQUEyfPh3r168HADx48ABubm64desWdu3ahb/++guTJ09GcXFxuet71vKWLFmCXbt24YcffsCFCxewadMmNGnSRPZ2E9H/q5KvASYi+n9dunQRixcvFkIIUVBQIMzNzcX+/ful+b/99psAIA4cOCBN2717twAgcnNzhRD/+6b0Er6+vqJx48aiqKhImmZvby/eeOMN6XlhYaEwMDAQW7durbC3sLAw4ezsLD0vvZ7SHjx4IPr27SsAiMaNG4vBgweLNWvWiEePHqksQ0NDQ6SkpEjT9u7dK+rVqydSU1OFEELY2tqKLVu2qCz7P//5j3B1dRVCCLFq1SphZGQk7t69W24fpft81vLGjRsn3nrrLVFcXFzhthHRs3GkiYiqzIULF3DixAkMGTIEAKCpqYnBgweXOWwGAG3atJH+3aBBAwBAenp6hctu1aoV6tX7348wKysrtG7dWnquoaEBMzMzlWX89NNPeP3112FtbQ1DQ0NMnz5drXOsDAwMsHv3bly+fBlffPEFDA0NERwcjE6dOuHhw4dSXaNGjdCwYUPpuaurK4qLi3HhwgXcuXMHKSkp8Pf3h6GhofT46quvcOXKFQBAQkIC2rdvD1NT02f2JGd5fn5+SEhIgL29PQIDAxEdHS17m4nof/iFvURUZdasWYPCwkK88sor0jQhBLS0tJCRkQETExNpupaWlvRvhUIBABUejipdX/Ka8qaVLCM2NhZDhgzBrFmz4OHhAaVSiYiICCxYsEDt7WrWrBmaNWuGESNGYNq0aWjRogW2bduGjz76qNz6ku15sp/Vq1fDxcVFpU5DQwMAoKenJ7sXOcvr0KEDrl27hr179+LAgQPw8vJCz5498dNPP8leDxExNBFRFSksLMSGDRuwYMECuLu7q8x77733sHnzZowdO/al9fPnn3+icePGmDZtmjTtxo0bL7zcJk2aQF9fHzk5OdK05ORk3Lp1CzY2NgCAmJgY1KtXDy1atICVlRVeeeUVXL16FUOHDi13mW3atMH333+Pe/fuPXO0Sc7yAMDY2BiDBw/G4MGDMWjQIPTu3VvW8onofxiaiKhK/Prrr8jIyIC/vz+USqXKvEGDBmHNmjUvNTQ1b94cycnJiIiIwGuvvYbdu3cjMjJSrWWEhITg4cOH6Nu3Lxo3boz79+9jyZIlKCgoQK9evaQ6XV1d+Pr6Yv78+cjKykJgYCC8vLxgbW0tLScwMBDGxsbo06cP8vLycPLkSWRkZGDixIn44IMPEBoainfeeQezZ89GgwYNcPr0adjY2MDV1bXcvp62vEWLFqFBgwZo164d6tWrhx9//BHW1taoX7/+C+1Ton8bntNERFVizZo16NmzZ5nABDweaUpISMCpU6deWj9vv/02JkyYgLFjx6Jdu3Y4duwYpk+frtYy3NzccPXqVQwbNgwtW7ZEnz59kJaWhujoaNjb20t1zZs3x8CBA9G3b1+4u7vDyclJ5RYAI0aMwPfff4/w8HC0bt0abm5uCA8PR9OmTQEA2traiI6OhqWlJfr27YvWrVtjzpw50uG20p61PENDQ8ydOxcdO3bEa6+9huvXr2PPnj0q54QR0bMphCjn2l8iInouISEh2LlzJxISEqq7FSKqZPwzg4iIiEgGhiYiIiIiGXh4joiIiEgGjjQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnwfyjaeFP6V8DuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['label'].value_counts()\n",
    "\n",
    "#plot the species distribution from the all data \n",
    "sns.countplot(x='label', data=df)\n",
    "# add labels and species numbers\n",
    "# add number to each species\n",
    "\n",
    "plt.xlabel('Animal Species')\n",
    "plt.ylabel('Number of Images')\n",
    "plt.title('Distribution of Animal Species in the Results')\n",
    "plt.show()\n",
    "\n",
    "# imbalance in the species distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loc-h500ee07133550    15455\n",
       "loc-h500hh06211894    15144\n",
       "loc-h500ee07133326    11264\n",
       "loc-h500hh06211646     8527\n",
       "loc-h500hh07215715     8236\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the location information from the file name\n",
    "df['location']=df['path'].str.split('/').str[1]\n",
    "df['location'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# analyze the location compare the predicted and actual species\n",
    "# plot the species distribution from the all data\n",
    "# group by location and plot the species distribution\n",
    "# select one location to plot\n",
    "df_location_1 = df[df['location']=='X811480B']\n",
    "sns.countplot(x='location', hue='label', data=df_location_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_location_2 = df[df['location']=='X811492D']\n",
    "sns.countplot(x='location', hue='label', data=df_location_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a prediction label with the highest probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the species with the highest probability\n",
    "df_train['pred'] = df_train.iloc[:, 3:].idxmax(axis=1)\n",
    "df_val['pred'] = df_val.iloc[:, 3:].idxmax(axis=1)\n",
    "df_test['pred'] = df_test.iloc[:, 3:].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fox          10165\n",
       "rodent        9833\n",
       "bird          2341\n",
       "lizard        1803\n",
       "skunk          902\n",
       "scrub jay      366\n",
       "Name: pred, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val['pred'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_confusion = confusion_matrix(df_train['label'], df_train['pred'])\n",
    "val_confusion = confusion_matrix(df_val['label'], df_val['pred'])\n",
    "test_confusion = confusion_matrix(df_test['label'], df_test['pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2251,   201,    63,    16,   196,    12],\n",
       "       [   26, 11615,     3,    98,    13,    36],\n",
       "       [   58,    43,   415,     1,    11,     0],\n",
       "       [  438,   859,    63, 18774,    16,   222],\n",
       "       [    8,     3,     9,     0,   588,     0],\n",
       "       [   10,    40,     0,    10,     0,   483]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2251,   201,    63,    16,   196,    12],\n",
       "       [   26, 11615,     3,    98,    13,    36],\n",
       "       [   58,    43,   415,     1,    11,     0],\n",
       "       [  438,   859,    63, 18774,    16,   222],\n",
       "       [    8,     3,     9,     0,   588,     0],\n",
       "       [   10,    40,     0,    10,     0,   483]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify confusion matrix\n",
    "cm = np.load(\"/home/wliao14/CameraTraps/classification/BASE_LOGDIR/mirav2/confusion_matrices.npz\")\n",
    "cm['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "# plot the confusion matrix for multiple classes\n",
    "# https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] # normalize the confusion matrix\n",
    "        # print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    # print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bird', 'fox', 'lizard', 'rodent', 'scrub jay', 'skunk'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = df.columns[3:-1]\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YhkFaWho6duzIdb6advz4cXz9+hVHjx7l+BaVmJhY7XPev38frq6u6Ny5M3bs2MH1vIaGBkxNTbFs2TKex5c0BNTV1XHr1i2u5ys7gVVTUxNv3rwpN4+6ujpSU1O50t+9e8cua02RlZXlOUE0MzOT4zpSUlLw9vaGt7c3Pn78iMjISMyZMwc9evRASkoKR8/Dr3EAKDOWmoyDH58+fcLp06cRGBiI2bNns9NL5jJURVXfD4GBgXjw4AEsLCywYMEC9OnTB02bNi33mIULF2LatGnsvyuzRLtx48ZwcXHB33//jXXr1iE0NBTNmzdHp06d2Hn27t2LUaNGYfny5RzHZmZmQkVFpUpxldQ9r/dFWloax95Ae/fuhZGREQ4cOMDx+lVm4nJF1y/r3pOQkICqqmq1z08Ej3pG6qHu3bvD2dkZixcv5lrFUvIta+/evRzpR44cwdevXyv1LYxfJR9Iv06UZRiGZyOiMpKTk9GzZ080bdoUR44cQYMGDbjy9OnTB//++y+aNWsGS0tLrkdJY6Rr1674/PkzTp48yXH833//Xamy9OzZE5cuXcKTJ0/KzNOtWzc8evQId+/e5UgPDw8Hi8VC165dK3WtyjA0NMT9+/c50p4+fVpu+VRUVDB48GBMnToV2dnZZe65YWtri4YNG3LdS2/evEF0dLRA7qXKYLFYYBiGa1nqzp07uYZAavLbckREBFasWIF58+YhIiKCvTKros3HDA0NOe7Nyk4s9fDwwIcPH7BgwQIkJiZi7NixHP/4s1gsrtfgzJkzePv2bZVjs7GxgaysLPbt28eRfvPmTa5hNxaLxd6Ir0RaWhrXahqg+PWvzGvfqlUrNGrUCH///TcYhmGnf/36FUeOHGGvsCF1B/WM1FOrVq2ChYUFMjIy2EMRAODs7IwePXrA398fOTk5sLe3Z6+mMTMzw8iRI2u9bM7OzpCWlsawYcPg5+eHHz9+YMuWLVzdqpXVs2dPfPz4EX/99Rd7v40SzZo1g6amJhYvXoyIiAjY2dnB09MTrVq1wo8fP/Dq1SucPXsWW7duRePGjTFq1Chs2LABo0aNwrJly9CiRQucPXu2zE2lSlu8eDHOnTuHTp06Yc6cOWjXrh0+fvyI8+fPw9vbG61bt8bMmTMRHh6O3r17Y/HixTAwMMCZM2cQHByMyZMnlzlHpzpGjhyJESNGYMqUKRg0aBBev36N1atXcw3T9e3bF23btoWlpSU0NTXx+vVrBAUFwcDAgGNFzK9UVFQwf/58zJkzB6NGjcKwYcOQlZWFRYsWQVZWFoGBgTUWBz+UlJTQqVMnrFmzBhoaGjA0NMSVK1ewa9curh6Bkt2Kt2/fDkVFRcjKysLIyIiri78iJatuOnfujMDAQEhISODAgQPo1KkT/Pz82CujalK/fv2goaGBNWvWQFJSEqNHj+Z4vk+fPggLC0Pr1q1hamqK+Ph4rFmzpsJhRV5UVVUxa9YsLF26FOPHj8eQIUOQkpKChQsXcg3TlGwPMGXKFPaqmyVLlkBXVxfPnj3jyNuuXTtcvnwZp06dgq6uLhQVFXk2xiQkJLB69WoMHz4cffr0wR9//IHc3FysWbMGHz9+xMqVK6scExEyoU6fJXwraxUFwzCMu7s7A4BjNQ3DMMz3798Zf39/xsDAgGnQoAGjq6vLTJ48mfnw4QNHPgMDA6Z3795c5y2Z2X7o0KFKlaVkNvv79+/ZaadOnWLat2/PyMrKMo0aNWJ8fX2Zc+fOca1kqMxqGgBlPn5dEfD+/XvG09OTMTIyYho0aMCoqakxFhYWzNy5c5kvX76w871584YZNGgQo6CgwCgqKjKDBg1ibt68WanVNAzDMCkpKcy4ceMYHR0dpkGDBoyenh4zdOhQJj09nZ3n9evXjLu7O6Ours40aNCAadWqFbNmzRqOlQElqxrWrFnDdQ0ATGBgIPvvsl77oqIiZvXq1UzTpk0ZWVlZxtLSkomOjuZa/bFu3TrGzs6O0dDQYKSlpZkmTZowHh4ezKtXr7iuUbKiosTOnTsZU1NTRlpamlFWVmb69+/PXp1UYvTo0Yy8vDxXHCX3RkXKuhcBMFOnTuVI4/W6ldSpqqoqo6ioyPz222/Mv//+y3O1UVBQEGNkZMRISkpy1HnplWm/+vX1LCgoYDp37sxoa2szqampHPlKVnodO3aswpirY+bMmQwArhVODFO8AsbDw4PR0tJi5OTkGAcHB+batWtc90JlVtMwTPG9tWLFCkZfX5+RlpZmTE1NmVOnTvFcWbRy5UrG0NCQkZGRYYyNjZkdO3bwrPvExETG3t6ekZOT41iVU3o1TYnjx48z1tbWjKysLCMvL89069aNuXHjBkceXp8/ZcVEhIfFML/0cRFCCCGECBjNGSGEEEKIUFFjhBBCCCFCRY0RQgghhAgVNUYIIYQQIlTUGCGEEEKIUFFjhBBCCCFCRZueiaiioiK8e/cOioqKtbIlOyGEkMphGAafP3+Gnp4exw/z1aYfP35UuFtvZUhLS3P9JpQoosaIiHr37h309fWFXQxCCCH/SUlJqdaOtVX148cPNFRUBwq+VZy5Ajo6Onj58qXIN0ioMSKiSn4cq8X0fZCUqf+/sfD3HzbCLoJAaSuL9gdDTZKSFK/R4ILCImEXQWCyv/L/zb0u+PL5M2xNm1fqRwtrQl5eHlDwDTImYwFJ6eqfqDAPaQ9DkZeXR40RUj0lQzOSMnKQlKn4Z8frOgVFJWEXQaCUlET7g6EmUWOk/sqXEI/GSAmBD5lLSoPFR2OkLm2vTo0RQgghRBSxAPDTAKpD0w2pMUIIIYSIIpZE8YOf4+sIaowQQgghoojF4rNnpO50jdSdZhMhhBBC6iXqGSGEEEJEEQ3TEEIIIUSoaJiGEEIIIUQwqGeEEEIIEUl8DtPUof4GaowQQgghokiMhmmoMUIIIYSIIjGawFp3SkoIIYSQeol6RgghhBBRRMM0hBBCCBEqMRqmocYIIYQQIorEqGek7jSbCCGEEFIvUc8IIYQQIopomIYQQgghQsVi8dkYoWEaIiLcrBrjvI8D4gOdcGCyNcwNVMrMu3SgCf5d6sz1OD7dliNf9zZaOOFpi7sLu+GEpy26GWvWchSVsy90O5w6tkFbAzUMcLHH7dgbZebNSE+F9+Qx6GHfAa10FbBsvi9XnqP/7EFLHXmuR+6PH7UZRqXt2LYFbVs1g4ayHBxtO+LG9Wvl5r9+9QocbTtCQ1kO7Vo3x64dWzmeD921Ay5OnaGvow59HXX07emCO7dv1WYIlbZtSzBatzCCioIs7KwscL2CWK9dvQI7KwuoKMjCuGVT7Ni2lSvPsaNHYGbaBsryMjAzbYMTx4/VVvGrRJzqFQD2hGyDg3lrtGykgj5OdrgVc73MvBlpqfCcOBpO1qYw0pTDormzuPI8ffwIk8b8DnuzVjDUaIhdW/+szeKTGkKNkXrst7bamN2rFXZcfokhwXG4+/oDto4yg46yLM/8K888QeeVV9iPbquv4uO3PFx8mM7O015fGWvd2uFUYioG/RWDU4mpWPu7Kdo1VhJUWDydOX4Yyxf4YZKXH45H3ISltR0muA/AuzcpPPPn5eZBTV0Dk2b4orVJuzLPq6CohBv3X3A8ZGR5v36CdOTQAfjPmolZ/gG4HhcPO3sHDOrfGynJyTzzv3r5EoNc+8DO3gHX4+Lh4zcbvt5eOHHsCDvP9atXMMTtd5y5EIXIKzfQWF8frn1+w7u3bwUVFk+HDh6Ar48X/GfPReztBNg5OMK1T08klxOra99esHNwROztBPj5z4HPTE8cO/oz1tiYGIx0d4P78JG4FX8P7sNHYsSwobgVFyeosHgSp3oFgFPHDmHxXF9Mm+mPs5di0dHWDmN+d8XbN7zjzc3Lg5qGBqZ6+8O4rSnPPN+/fUMTAyP4z18CTS2d2ix+7ZNg8f+oI1gMwzDCLgThlpOTA2VlZbSedQySMvLVOsfff1gh6V0Olpx6zE476WmL6KT3CIp4XuHxTsaaCBrWHj3WX0fqx+LegLVu7SAvI4XJ4QnsfFtHmSHnRwH8Dj6oVjkB4Nh0+2ofCwCDe3aGSbsOWLR6IzvtN0dzdP+tD2bNXVzusSMG/Abjtu0wd8kajvSj/+zBsgX+iH/6jq+y8aKrwl+DpqujLdp3MEPQn8HsNIv2JujTtz8WLV3OlX/+3Nk4e/oU4u89ZKfNmDYZDx7cR/QV3j1IhYWF0NdRx9oNm+A+YlS1yyolyd93Hkc7a5iZmWPT5i3stA7tjNG3nyuWLFvBlX9ugD/OnD6JxAdJ7LTpUybh/v17uHI9BgAwwt0Nn3NycOL0OXaefr1/g4qqKsL37uervAWFRdU+ti7VKwBkfcnj6/j+Lo5oa2qGZWs3sdO62XaAS6++8J+/pNxj3fq5oE07UwQuW1tmHnuzVhj3xzR4TJrOVzk/f85BOyNtfPr0CUpKtf/Fq+TzX8ZxHlhS1f+sYAp+IPfaUoGVmx/UM1JPSUmy0EZPETefZ3Gk33yejfZNVCp1joEWjRD7v2x2QwQo7hkpfc4bz7PQoYky32Wurry8PDy8nwD7Lt040h06OyHhNn/fdL99/YIuFq3haNYCE0cMwqMHiXydrybk5eUh4W48nLo7c6R36+6MuNgYnsfcio1Ft9L5nV2QEH8H+fn5PI/59u0b8vPzoaqmVjMFr4aSWLs5u3Ckd+vugtiYmzyPiYuNQbfunPm7u/TA3V9i5ZnHuUeZ5xQEcapXoDjef+8lwLEr5/vWsWs3xN+KFVKpREzJ0l5+HnUENUbqKVU5aUhJSnB9c8n6mgsNBekKj9dQkIZDC3UcufO2VLoM9zm/5EFDQYb/QlfTh+wsFBYWQkNTiyNdXVMbme/TyziqYk1btMLKjduwJfwg1m8Jg4yMLH7v1x2v/ldxr1JtysrMRGFhIbS0tDnSNbW0kZ6exvOY9PQ0aJbKr6WljYKCAmRlZvI8JnBeAPT0GqGrU/eaKXg1ZJYRq7Z2+bFqa/OONfO/WNPT0qBVOo+2NtLTeJ9TEMSpXgHgQ1ZxvJql3reamtrIzKj++5bUTdQY+UWXLl3g5eVV5vOGhoYICgqq8nkXLlyIDh06VLtc/Cg9BscCiyuNF1dzPXz+UYCopIyKz8kCRGG0j1X6WwDD8PXNoIOFFfoPHgZjE1N0tLHHxh17YNi0Ofbs4p4MKQy84uVKKyd/SZ3xOmbDujU4fPAf7DtwGLIiMEeGV9n5jbWq5xQUcapXAFzvUYbP9229UrK0l59HHUFLe6vg9u3bkJev3vwNQfvwLQ8FhUVcvSBq8tKVGucdYN4IpxJTUVDI2cjI/MLds6ImL42sr/yNHfNDVU0dkpKSeF/q21RWZgY0NLTKOKrqJCQk0K6DhdB7RtQ1NCApKcn1bfn9+wyub9UltLV1kMEjv5SUFNTU1TnSN25Yh3WrV+Dk2Yto2473JEFB0Sgj1oyM8mNNS+Mdq/p/sWrr6HD1grzPyODqLREkcapXAFBV1+D5vs3MzODq5RRbtAMr4UVTUxNycnJlPl/WGK0wFBQyePTuM2ybc34g2TZXw73kj+Ue29FIFQYacjgazz3b/l7KJ9g24zynXXN1JCZ/4rvM1SUtLQ0TUzPcvBLNkX7jyiWYdbSuseswDIOkf+9DS1u4M/SlpaVhZm6BS1GRHOnRUZGwtrHleYyVjQ2iS+ePjICZhSUaNGjATgtavxarVyzF0ZNnYW5hWfOFr6KSWKMjIzjSo6MiYGNrx/MYaxtbREdx5o+KuAjzX2LlmSfyYpnnFARxqlegON627c1w/TLn+/b65WhYWNkIqVREWKgxUkpBQQGmTZsGFRUVqKurY968eexuz9LDNCwWC1u3bkX//v0hLy+PpUuXAgBWrlwJbW1tKCoqwsPDAz8qsS9Fbm4ucnJyOB78Cr/xGoMsGmGAuR6aasrDr2dL6CrL4sDtNwAAL+fmWD7IhOu4gRZ6uJfyEc8zvnI9t/dmMuyaq2GcoyGMNOQwztEQNs3UsOfma77Ly4+xf0zHob/DcPjv3Xj+9DGWL/BD6tsUDBs1HgCwdtkC+E4bz3HMo3/v4dG/9/Dt6xdkZ2Xi0b/38PzJzxUYf65djmuXIpD8+iUe/XsPc2ZOxuOH9/H7KM7zCMM0Ty/sDt2F8LAQPH6chNm+3niTkgyPCX8AAALnzcHEcaPZ+T3G/4GU5NeY7eeDx4+TEB4WgvCwEMzw8mbn2bBuDZYsnI/gbTthYGCI9LQ0pKel4cuXLwKP71eeXt4IDdmJ3aEheJyUBF+fmUhJTsb4iZMAAPPnBsBjzM9VIRMmTkLy69fwm+WNx0lJ2B0agrDQXfDy/rknxdRpMxAZcRFr16zCk8ePsXbNKkRHRWLadC9Bh8dBnOoVAMZP9sSBvaE4uK/4fbt4ri/evU3B8DHF77FVS+bDe4oHxzEPH9zDwwf38O3rV2RnZuLhg3t49sv7Ni8vj50nPy8P6anv8PDBPbz63wuBxlYjaJhGfO3evRseHh6Ii4vDnTt3MHHiRBgYGGDChAk88wcGBmLFihXYsGEDJCUlcfDgQQQGBmLz5s1wdHTEnj17sGnTJjRt2rTc665YsQKLFi2q0VjO/5sOZbkGmNS1KTQVZfAs/Qsm70lgr47RUJThWmKqICOF7m20sfLsE57nTEz5BN+DDzC9e3NM79YMKdnf4HvgAR684b/xxI/eroPx8UM2Nq9fiYyMNLRs3QY79h1FI/0mAID36WlIffuG4xjX7j+/Bf97PwGnjh5Eo8ZNcOlO8Qfb55yPmD9rOt6/T4eiohLatGuPfccvor258L9ZDhrihuzsbKxavhRpaaloY9IWh4+fRhMDAwBAWloqUlJ+7rFiaGSEI8dPY7afD3ZsDYaurh7WrA9C/wGD2Hl2btuCvLw8jBg2lONaAXMXYM78QMEExsOQoW7IzsrC8mWLkZaaChOTtjh+6iwMSmJNTUVKys99KQyNjHD81Fn4+czEti2boaunh3UbNmHAwJ+x2trZIXzfP1gUOA+LA+ejabNm2PP3AVhZ11xPWnWIU70CQN8BQ/DxQzY2rl2O9+lpaNnaBKH7j6OxfnG8GelpeFtqr6DeXX/2mjy4dxcnjhxAI/0muJFQ/JmVnpbKkWf75iBs3xwEaztHHDh5UQBR1SAxGqahfUZ+0aVLF2RkZODhw4fsyV+zZ8/GyZMn8ejRIxgaGsLLy4s9yZXFYsHLywsbNmxgn8POzg7t27fHli0/90SwsbHBjx8/kJiYWOa1c3NzkZuby/47JycH+vr6fO0zUpfwu89IXcPvPiN1Cb/7jNQ1/OwzUtfwu89IXSG0fUa6LeN/n5GoubTPSF1kY2PDMQvd1tYWz549Q2FhIc/8lpac35KTkpJga8s5vlv6b15kZGSgpKTE8SCEEELEAQ3T8KmurK4hhBBSx4jRMA31jJQSGxvL9XeLFi0gKSlZqeONjY15noMQQgipGn4nr9adf+LrTkkFJCUlBd7e3njy5An279+PP//8EzNmzKj08TNmzEBISAhCQkLw9OlTBAYG4uHDhxUfSAghhIgpGqYpZdSoUfj+/TusrKwgKSmJ6dOnY+LEiZU+3s3NDS9evIC/vz9+/PiBQYMGYfLkybhw4UItlpoQQki9I0bDNLSaRkTVxK/21iW0mqb+otU09Retpqkd7NU0LqvBatCw2udh8r8j96JfnVhNQz0jhBBCiCjid+OyOrTpWd0pKSGEEELqJeoZIYQQQkSRGM0ZocYIIYQQIopomIYQQgghRDCoZ4QQQggRRTRMQwghhBChEqNhGmqMEEIIIaJIjHpG6k6ziRBCCCH1EvWMEEIIISKIxWKBJSY9I9QYIYQQQkSQODVGaJiGEEIIIUJFPSOEEEKIKGL99+Dn+DqCGiOEEEKICBKnYRpqjBBCCCEiSJwaIzRnhBBCCCFCRT0jhBBCiAgSp54RaowQQgghIogaI4QQQggRLjFaTUNzRgghhBAiVNQzQgghhIggGqYhIuNKgBOUlJSEXYxap9pxmrCLIFAfbv8l7CIIDMMwwi6CQElJik+Hs5aSjLCLIBCyEE6cxT/ay09jpObKUtvE511DCCGEEJFEPSOEEEKICGKBz2GaOtQ1Qo0RQgghRATRnBFCCCGECBct7SWEEEKIOAoODoaRkRFkZWVhYWGBa9eulZt/3759aN++PeTk5KCrq4uxY8ciKyurStekxgghhBAiiv4bpqnuozrDNAcOHICXlxfmzp2LhIQEODo6omfPnkhOTuaZ//r16xg1ahQ8PDzw8OFDHDp0CLdv38b48eOrdF1qjBBCCCEiiJ+GSHXnm6xfvx4eHh4YP348jI2NERQUBH19fWzZsoVn/tjYWBgaGsLT0xNGRkZwcHDAH3/8gTt37lTputQYIYQQQuqxnJwcjkdubi7PfHl5eYiPj4eLiwtHuouLC27evMnzGDs7O7x58wZnz54FwzBIT0/H4cOH0bt37yqVkRojhBBCiAiqqZ4RfX19KCsrsx8rVqzgeb3MzEwUFhZCW1ubI11bWxtpaWk8j7Gzs8O+ffvg5uYGaWlp6OjoQEVFBX/++WeVYqXGCCGEECKKWDXwAJCSkoJPnz6xHwEBAeVfttTwDsMwZQ75PHr0CJ6enliwYAHi4+Nx/vx5vHz5EpMmTapSqLS0lxBCCBFB/O4zUnKskpJSpX5WRENDA5KSkly9IBkZGVy9JSVWrFgBe3t7+Pr6AgBMTU0hLy8PR0dHLF26FLq6upUqK/WMEEIIIQTS0tKwsLBAREQER3pERATs7Ox4HvPt2zdISHA2JSQlJQFU7XepqGeEEEIIEUE11TNSFd7e3hg5ciQsLS1ha2uL7du3Izk5mT3sEhAQgLdv3yI8PBwA0LdvX0yYMAFbtmxBjx49kJqaCi8vL1hZWUFPT6/S16XGCCGEECKChNEYcXNzQ1ZWFhYvXozU1FS0bdsWZ8+ehYGBAQAgNTWVY8+RMWPG4PPnz/jrr7/g4+MDFRUVODk5YdWqVVUrKyNuv+9dR+Tk5EBZWRnpWZ8qNdZX16l2nCbsIgjUh9t/CbsIAiNuHzH8/bBZ3SIudZuTkwMdDRV8+iSYz+OSz3+t0eGQkJar9nmK8r4hY/cogZWbH9QzQgghhIggYfSMCAs1RgghhBBRJEY/lEeNEUIIIUQEiVPPCC3tJYQQQohQUc8IIYQQIoKoZ4TUG9u2BKN1CyOoKMjCzsoC169fKzf/tatXYGdlARUFWRi3bIod27Zy5Tl29AjMTNtAWV4GZqZtcOL4sdoqfpVMHOKIpNML8SF2A27s84O9WbNy8/8xtBMSjsxDdsx63Ds2H+59rDiel5KSQMDE3/DwZCA+xG5A3IHZcLYzrs0QqkSc6nbb1mAYt2wKVcWGsLO2xI3KxGptCVXFhmjTqhl2bOeM9dHDhxg2dDBatzCCnLQE/toUVIulrxpxqleg5usWAI4fPQJzUxOoKMjC3NREpOKtCmH8aq+wUGOkHjt08AB8fbzgP3suYm8nwM7BEa59enKsEf/Vq5cv4dq3F+wcHBF7OwF+/nPgM9MTx44eYeeJjYnBSHc3uA8fiVvx9+A+fCRGDBuKW3FxggqLp8Eu5ljjOwirdl2AzbCVuJnwAsf/mgJ9HVWe+ScMccDi6X2xbNtZmA9ehqVbzyJo9lD06tSWnWfhlL4YP8gB3qsPwWzQUuw8fB0H1k1A+1aNBRVWmcSpbg8fPAA/n5nwmz0HMbfuwt7BAa59eyGlnFgH9OsNewcHxNy6C1//AMyaOQPHf4n12/dvMGpqhCVLV0BbR0dQoVRInOoVqJ26jYuNwcjhv2PY8BGIu5OIYcNHYKS7G27dEn68pGy0z4iIqol9RhztrGFmZo5Nm7ew0zq0M0bffq5Ysoz7VxvnBvjjzOmTSHyQxE6bPmUS7t+/hyvXYwAAI9zd8DknBydOn2Pn6df7N6ioqiJ87/5qlRPgf5+Rq+GzkPA4BTOWH2CnJRyZh1OX72PBnye58l8K80ZM4v8wJ+g4O23NrEEwb9ME3cZtAAD87+IyrNp5AdsOXmXnObh+Ar58y8W4eeF8lZfffUbqUt3y+xHTyd4GHczMsOmvn7GatWuDvv36YzGPWOcF+OPM6VNIePCInTZ96iQ8uH8fl69x/wx66xZGmDZ9BqZ5evFVzhL8fButS/UKiGbdjnT/HTmfc3Di1Fl2nn59ekJVRRW79/5drXIKa58RvQl/873PyLsd7nVinxHqGamn8vLykHA3Ht2cXTjSu3V3QWwM9wcyUPyNolt3zvzdXXrgbvwd5Ofnl53HuUeZ5xSEBlKSMDPWR1RMEkd6VGwSbNob8TxGuoEUfuTlc6R9z82HZVsDSElJlJvHroLhn9omTnXLjrVUubo5OyM2NobnMXFxsejm7MyR1t2ZM1ZRJE71CtRe3cbFxaB7d848zs4uiI0VbrzVQcM0pM7LzMxEYWEhtLQ4f2lRW1sb6elpPI9JT0/j+mVGLS1tFBQUIDMzszhPWhq0SufR1kZ6Gu9zCoKGqgKkpCSRkf2ZIz096zO01Xl/G4iMScIYVzuYGesDAMzbNMGo/jaQbiAFDRUFdh7PEU5o1kQTLBYLTtat0aezKXQ0hPsNQ5zqtiRWXmUvq1zpaWk8X5tfYxVF4lSvQO3VLa885Z1TlFFjhHBgGAYTJ06EmpoaWCwWEhMThV2kSit9MzIMU+4Nyit/6fSqnlNQSvcYs1isMruRV+w4j4s3HuHK7ln4fHsjDm2YiL0ni8eUCwuLAACz1hzGi+QM3Ds6Hzm3grBh9hCEn4xFYaFojGyKU93WRqyiSpzqFRC/eAlvtLS3Es6fP4+wsDBcvnwZTZs2hYaGhrCLVCENDQ1ISkpyfaPKyMjg+tZQQltbB2mlvj28f58BKSkpqKurF+fR0eH6hvE+I4Prm5cgZX74goKCQmirK3Kka6kpcPWWlPiRm49Ji/Zh2rL90FZTQmrmJ3gMskfOl+/I/PiVfd6h3jsgIy0FdWV5vHv/CUs9++PVu6xaj6k84lS3JbHyKntZ5dLW0eH52vwaqygSp3oFaq9ueeUp75yijAU+l/bWoS1YqWekEl68eAFdXV3Y2dlBR0cHUlKi34aTlpaGmbkFoiMjONKjoyJgY2vH8xhrG1tER3Hmj4q4CHMLSzRo0KDsPJEXyzynIOQXFCIhKQVONq050p1sWiP23styjy0oKMLbjI8oKmIwpIcFzl17yNWbkptXgHfvP0FKSgKu3Trg9OX7NR5DVYhT3bJjLVWu6MhI2NjY8jzG2toG0ZGRHGlRkZyxiiJxqleg9urW2toWUVGceSIjI2BjI9x4q4OGaQjbmDFjMH36dCQnJ4PFYsHQ0BC5ubnw9PSElpYWZGVl4eDggNu3bwMAfvz4ARMTE0ycOJF9jpcvX0JZWRk7duwQaNk9vbwRGrITu0ND8DgpCb4+M5GSnIzxEycBAObPDYDHmFHs/BMmTkLy69fwm+WNx0lJ2B0agrDQXfDynsXOM3XaDERGXMTaNavw5PFjrF2zCtFRkZg23UugsZW2aW80xg6ww6j+NmhlpI3VPgOhr6OGnYeL9yxYPL0fdi4Zyc7fvIkWfu/VEc2aaMLSxADhK8eiTTM9jpU3HdsaoL9Texg2Uoe9WTOc/GsqJCRYWB8WyXV9QROnuvWcMRNhIbuwO6w4Vr9ZM5GS8jPWBXMDMH7saHb+8RMnITn5Nfx9/4s1LAS7Q0PgNdOHnScvLw/3EhNxLzEReXl5ePfuLe4lJuLF8+cCj+9X4lSvQO3U7dTpnoiKuIh1/8W7bs0qXIqKxFTPGQKPj1Se6H/FF7KNGzeiWbNm2L59O27fvg1JSUn4+fnhyJEj2L17NwwMDLB69Wr06NEDz58/h5qaGvbt2wdra2v06tULffv2xciRI9G1a1dMmDChzOvk5uYiNzeX/XdOTg7fZR8y1A3ZWVlYvmwx0lJTYWLSFsdPnYWBgQEAIC01FSkpP9fzGxoZ4fips/DzmYltWzZDV08P6zZswoCBg9h5bO3sEL7vHywKnIfFgfPRtFkz7Pn7AKysrfkuLz8OX7wLNWV5zJnYEzoaSnj4PBWu04ORnPoBAKCjoQR9HTV2fklJFmaMdEJLA23kFxTi6p2n6DpmHZJTs9l5ZGQaIHBqHxg10sCXb7m4cOMhPOaH49OX7wKPrzRxqtvBQ92QlZ2FFcuWIC01FW1M2uLYyTNoUhJrWhpXrMdOnoHfLG9s2xIMXT09rN2wEa6/xJr67h1srczZfwetX4eg9evg2KkzLkReElxwpYhTvQK1U7c2tnYI37sfiwLnY/HCBWjatBnC9/0DKyvhx1tlYvRDebTPSCUEBQUhKCgIr169wtevX6GqqoqwsDC4u7sDAPLz82FoaAgvLy/4+voCANasWYPVq1dj2LBhOHToEB48eFDuXJOFCxdi0aJFXOn87DNSl/C7z0hdw+8+I3WJuH3E1KWucX6JS90Ka58RgymHICHDxz4jud/wOngI7TNSH7148QL5+fmwt7dnpzVo0ABWVlZISvq5z4WPjw9atWqFP//8E6GhoRVOeg0ICMCnT5/Yj5SUlFqLgRBCiOijOSOkTGUtESy9dCwjIwNPnjyBpKQknj17VuF5ZWRkoKSkxPEghBBCxAE1RqqoefPmkJaWxvXr19lp+fn5uHPnDoyNf/6I2rhx49C2bVuEh4fDz88Pjx494nU6QgghhCcWi/9HXUETWKtIXl4ekydPhq+vL9TU1NCkSROsXr0a3759g4eHBwBg8+bNiImJwf3796Gvr49z585h+PDhiIuLg7S0tJAjIIQQUhcUNyj42GekDjVGqGekGlauXIlBgwZh5MiRMDc3x/Pnz3HhwgWoqqri8ePH8PX1RXBwMPT1i7ca37x5Mz5+/Ij58+cLueSEEELqDH57RepQY4RW04iomvjV3rqEVtPUX+L2EVOXJg3yS1zqVliraZp6HoakjHy1z1OY+xX/2zS4TqymoWEaQgghRATxuyKmLjWMqTFCCCGEiCB+J6HWobYIzRkhhBBCiHBRzwghhBAigiQkWJCQqH73BsPHsYJGjRFCCCFEBInTMA01RgghhBARJE4TWGnOCCGEEEKEinpGCCGEEBFEwzSEEEIIESoapiGEEEIIERDqGSGEEEJEkDj1jFBjhBBCCBFBNGeEEEIIIULFAp89I3XoZ3tpzgghhBBChIp6RgghhBARRMM0hBBCCBEqcZrASsM0hBBCCBEq6hkhhBBCRBAN0xBCCCFEqMRpmIYaI4QQQogIEqeeEZozQgghhBChop4RQgghRATRMA0RGbn5hcjNLxR2MWpdRswmYRdBoFS7LxF2EQTmQ+R8YRdBoIqKGGEXQWAkJOrOP3b8ENo/6nwO09ShDVhpmIYQQgghwkU9I4QQQogIomEaQgghhAiVOK2mocYIIYQQIoLEqWeE5owQQgghRKioZ4QQQggRQTRMQwghhBChomEaQgghhBABoZ4RQgghRASJU88INUYIIYQQEURzRgghhBAiVOLUM0JzRgghhBAiVNQzQgghhIggGqYhhBBCiFCJ0zANNUYIIYQQEcQCnz0jNVaS2kdzRgghhBAiVNQzQgghhIggCRYLEnx0jfBzrKBRzwghhBAigkomsPLzqI7g4GAYGRlBVlYWFhYWuHbtWrn5c3NzMXfuXBgYGEBGRgbNmjVDSEhIla5JPSOEEEIIAQAcOHAAXl5eCA4Ohr29PbZt24aePXvi0aNHaNKkCc9jhg4divT0dOzatQvNmzdHRkYGCgoKqnRdaowQQgghIkgYq2nWr18PDw8PjB8/HgAQFBSECxcuYMuWLVixYgVX/vPnz+PKlSv43//+BzU1NQCAoaFhla9LwzT13M5tW2Bq3BzaqvLobGeFmzfK7267fu0KOttZQVtVHu3btEDIjm0cz588fgxd7K3RRFcdehpKcLC2wD9/763NECptx7YtaNe6GTRV5NDJriNuXq841k52HaGpIgdT4+bYtWMrx/Mnjx9FZ3sr6OuoQUddEfbW5tj/957aDKFKJva3QNL+afhwMQA3to2HfTv9cvP/4WqJhN2TkX1hNu6FT4G7iynH8/0dW+P6Ng+knvZF5jl/xO6cgGHO7WozhErbtiUYrVsYQUVBFnZWFrheQd1eu3oFdlYWUFGQhXHLptixbStXnmNHj8DMtA2U5WVgZtoGJ44fq63iV8n2rcFo07Ip1JQawt7GEjcqEau9jSXUlBrCpFUz7NzOGeujRw/h7jYYxi2NIC8jgb82BdVi6atOnOq2qiRY/D8AICcnh+ORm5vL83p5eXmIj4+Hi4sLR7qLiwtu3rzJ85iTJ0/C0tISq1evRqNGjdCyZUvMmjUL379/r1qsVcpN6pSjhw8iwM8bs/wCcDXmDmztHTDEtQ9SUpJ55n/16iWGDugLW3sHXI25Ax/f2fCf5YUTx4+y86iqqcLHLwARl67jxq0EDB81GlP/8EBUxAVBhcXTkUMHMNt3Jmb5B+B6bDxs7RwwyLU3UpLLjnWwax/Y2jngemw8fPxmw8/HCyeOHWHnUVVTwyy/AERevoGbtxMxfOQYTJnogUghxwoAg7u2wZppPbBq73XYjN+Bmw+ScXy1O/S1lHjmn9DPAosnOGFZ2BWYj9mKpWFXEOT1G3rZtmDnyf78Hav3XEeXKaHo6LEde87dw/bZ/dC9Y1NBhcXToYMH4OvjBf/ZcxF7OwF2Do5w7dMTyWXV7cuXcO3bC3YOjoi9nQA//znwmemJY0d/1m1sTAxGurvBffhI3Iq/B/fhIzFi2FDciosTVFg8HT50AH6zZsJv9hzcjLsLO3sHDOjXq+z7+OVLDOzfG3b2DrgZdxe+/gGY5T0Dx3+5j79/+wZDIyMsXroC2jo6ggqlUsSpbquF9bN3pDqPkrW9+vr6UFZWZj949XAAQGZmJgoLC6Gtrc2Rrq2tjbS0NJ7H/O9//8P169fx77//4tixYwgKCsLhw4cxderUqoXKMAxTpSOIQOTk5EBZWRnJadlQUuL9D0xFunWyRfsO5li/aTM7zcqsLXr37YfAxcu58gfOm41zZ07jVsK/7LSZ06fg3wf3EHH5RpnX6WTbES6/9cS8wMXVKifA/6zvro626GBmhg2bgtlplh1M0Kdvfyxcwh3rgrmzcfbMKdxJfMhO85o+GQ/u30fUlbJjdbS1hMtvvTCfj1gBQOu3ZXwdfzV4HBKepWLGhnPstITdk3Hq+hMs2BHNlf/SX2MQ828K5myNYqetmeYC81a66DZ9d5nXubl9PM7HPsfikMvVLuuHyPnVPhYAHO2sYWZmjk2bt7DTOrQzRt9+rliyjPtDdW6AP86cPonEB0nstOlTJuH+/Xu4cj0GADDC3Q2fc3Jw4vTP169f79+goqqK8L37+SpvUVH1P1I7O9igQwczbPzrZ6zmpm3Qp19/LF7KHeu8Of44e/oU7t5/xE7znDoJDx7cx6Wr3N9kjVsaYeq0GZjm6VXtMv5KQoK/921dqducnBxoqyvj06dP1f48rur1lJWV0X19FBo0VKj2efK/f0GkdzekpKRwlFtGRgYyMjJc+d+9e4dGjRrh5s2bsLW1ZacvW7YMe/bswePHj7mOcXFxwbVr15CWlgZlZWUAwNGjRzF48GB8/foVDRs2rFRZqWeknsrLy0Niwl107ebMkd61mzPiYmN4HnMrLpYrv1N3FyTcjUd+fj5XfoZhcOVSFJ4/ewJ7B8eaK3wVFccaD6fSZa8g1tL5u3V3QcLdO2XGevlSFJ49FW6sANBASgJmrXQRdft/HOlRt1/AxqQxz2OkG0jhR14hR9r33AJYtm4EKUneHwNdzA3RUl8d1++9rpmCV0NeXh4S7sajmzNnt3G37i6IjeHdbRwXG4Nu3Tnzd3fpgbvxP+uWZx7nHmWeUxDKitWpewX3cXfO+7h0rKJKnOq2umpqNY2SkhLHg1dDBAA0NDQgKSnJ1QuSkZHB1VtSQldXF40aNWI3RADA2NgYDMPgzZs3lY613jdGunTpAi8vLwDFk2qCgoIEXoaFCxeiQ4cOAr1m1n/dbVraWhzpWlpayEhP53lMRno6tLRK5dfWQkFBAbIyM9lpnz59QiNNZWgqN8TQgf2wat1GrkaMILFj1eJ8s2hpayM9nXfXYnp6GrRKvbm0tLR5xqqroQR1JVkMGdAXa9Zv5GrECJqGshykJCWQ8eErR3r6h6/QVuP9LSry9guM6d0BZi2Lu+nNW+liVM/2kG4gCQ1lOXY+JXkZvD/nj5zIOTi2chi8N51HdPzL2gumApll1K12BXVb+oOzpG4z/6vb9DQe9a+tjfQyuqIFoaz7WLuccqWnVRyrqBKnuq0uVg38VxXS0tKwsLBAREQER3pERATs7Ox4HmNvb493797hy5cv7LSnT59CQkICjRvz/nLEi1itprl9+zbk5eWFXQyBKj2bmmGYcmdY88pfOl1RURHXYuPx5csXXLkcjbmzZ8HQyAiOnbrUXMGro5ZivR53F1+/fMGVS9GY4z8LhkZNhR8rfpa3BIvFAgPeQwQrwq9BW00BV4LHgcViISP7C/aevw8fdzsUFhWx833+lgvr8duh0FAaXc2NsGqqC16mfsS1ROH1jgC1cx9X9ZyCUuVyVSJWUSZOdVsXeHt7Y+TIkbC0tIStrS22b9+O5ORkTJo0CQAQEBCAt2/fIjw8HADg7u6OJUuWYOzYsVi0aBEyMzPh6+uLcePGVXqIBhCzxoimpmatnj8/Px8NGjSo1WtUlvp/3W3paZy9IO/fv4dmqd6PEsU9CaXyZ7yHlJQU1NTV2WkSEhJo2qw5AMC0fQc8ffwYG9auEto/0CWxZpT6NvU+I4PrW1cJbW0drm9K799n8Iy12S+xPnmShHVrVgq1MZL56RsKCou4ekG0VOSQkf2V5zE/8gowafUpTFt3Btpq8kjN+gKPPubI+ZqLzE/f2PkYBvjf2w8AgPvP09HKQAO+7vZCa4yUdBuX/qacUUHdlu5mLqlb9f/qVluHR/1nZHB9oxYk9fJiLaNcPOMoFauoEqe6ra5fV8RU9/iqcnNzQ1ZWFhYvXozU1FS0bdsWZ8+ehYGBAQAgNTWVY4KxgoICIiIiMH36dFhaWkJdXR1Dhw7F0qVLq1bWymTatGlTpR+i7NdhmrCwMJ6zjxcuXAiguBfF2dkZGhoaUFZWRufOnXH37l2O87FYLGzduhX9+/eHvLw8+8VfuXIltLW1oaioCA8PD/z48UOQYQIo7m7rYGaOy9GRHOmXoyNhbWPL8xgraxuu/JeiImBmblFuI4thmDKXiglCcawWiC5d9gpivVQqf3RUBMzMLSuMNS83j/9C8yG/oAgJT1LhZMm5ysXJsiliH5Y/RltQWIS37z+jqIjBECcTnIt5hvKmsLMAyEhL1kCpq0daWhpm5haIjuTsNo6OioCNLe9uY2sbW0RHceaPirgIc4ufdcszT+TFMs8pCGXFeimqgvs4ivM+Lh2rqBKnuq0uflbS8LNHyZQpU/Dq1Svk5uYiPj4enTp1Yj8XFhaGy5cvc+Rv3bo1IiIi8O3bN6SkpGDdunVV6hUBKtkzsmHDhkqdjMViwdPTs0oFEBY3Nzf89ttv7L8vX76MkSNHwt7eHgDw+fNnjB49mt3AWrduHXr16oVnz55BUVGRfVxgYCBWrFiBDRs2QFJSEgcPHkRgYCA2b94MR0dH7NmzB5s2bULTpuUvj8zNzeX4Bz0nJ4fvGKd6zsQfHqPRwdwCVtY2CAvZgTcpyRg7/g8AwKIFc/Du3Tts2xkGABg7/g/s2BqMOf4+GD12PG7FxWLP7hDs3L2Pfc71a1bCzNwCRk2bIS8vDxcvnMM/f+/B+o2beRVBYKZ5emGix2iYm1vAytoWobuKYx33X6wL58/Bu3dvsX1X8cqRcRP+wPatmxHg54Mx48bjVlwMwsNCEPJLrOt+iTU/Lw8Xz5/D/n17sGGTcGMFgE2HYrFrjivuPnmHuIdv4dHXDPrayth5Mh4AsHiCE/Q0FDF+xQkAQPPGarA0boTbj95CVVEWnkNt0MZIE+NXnmCfc5a7Pe4+eYf/vfsA6QaS+M26OYb3MIXnhrNCibGEp5c3PMaMhLmFJaxtbLFr53akJCdj/MTibuP5cwPw7u1b7Aor7jaeMHEStgb/Bb9Z3hjnMQFxsTEIC92F3b+spJg6bQacnTph7ZpV6Nu3P06dOoHoqEhEXb4ulBhLTJ8xE+PHjoKZhSWsrW0Rsms7UlKSMX5CcawL5gXg3bt32BlSfB+PnzAJ27Zshr+vN8aOm4C4uBjsDgtB2J6/2efMy8tDUtIj9v9/9+4t7t1LhIK8Apo1by74IH8hTnVbHfxs6V5yfF1RqcbIy5fCm8BWWxo2bMhuub148QLTpk3D8uXL4excPDnRycmJI/+2bdugqqqKK1euoE+fPux0d3d3jBs3jv33sGHDMG7cOPbudUuXLkVkZGSFvSMrVqzAokWLaiS2EgMHD0V2VhZWr1iK9LRUGLdpi4PHTqFJk+LutrS0NLz5Zc8RQ0MjHDx2CnP8ZmHnti3Q0dXDqrVB6O86kJ3n29ev8PGajndv30C2YUO0bNkK20PCMXDw0Bote1UNGuKG7OxsrFq+FGlpqWhj0haHj59GE4OSWFPxJiWFnd/Q0AiHj59GgJ8PdmwLhq6uHlavC0L/AYPYeb5+/QrvGdN+ibU1doSEY9AQN4HHV9rhS4+gptQQc0Z3go6aAh6+fA9X//1ITv8EANBRV4C+9s+lfJISEpgx1AYt9dWRX1CIq4mv0XVaGJLTPrHzyDdsgI0ze6KRphK+5xbgaXImxi07jsOXHnFdX5CGDHVDdlYWli9bjLTUVJiYtMXxUz+7jdNSUzn2zjE0MsLxU2fh5zMT27Zshq6eHtZt2IQBA3/Wra2dHcL3/YNFgfOwOHA+mjZrhj1/H4CVtbXA4/vV4CHFsa5cvgRpqcX38dETZ365j0u9Z42McPTEGfj7emP71uL7eO36jXD95T5OffcOdlbm7L83bliHjRvWwbFTZ5yPuCS44HgQp7ol5av2PiN5eXl4+fIlmjVrBikp0Z160qVLF3To0AFBQUEwNDSEl5cXe3UNULxawsbGBpaWltiz5+fumhkZGViwYAGio6ORnp6OwsJCfPv2DX/99RemTJkCoLgnaO/evRg+fDj7OFVVVWzcuBGjRo1ip82cOROXLl1CYmJimeXk1TOir6/P1z4jdUld+nXJmsDvPiN1Cb/7jNQ1/OwzUtfwu89IXSGsfUb6/HmZ731GTk/vIrBy86PKS3u/ffsGDw8PyMnJwcTEhD2RxdPTEytXrqzxAtamwsJCuLm5QUlJCTt27OB4bsyYMYiPj0dQUBBu3ryJxMREqKurIy+Pc75ATa3OkZGR4VoLTgghRHwJ61d7haHKjZGAgADcu3cPly9fhqysLDu9e/fuOHDgQI0WrrbNnDkTDx48wLFjxzhiAYBr167B09MTvXr1gomJCWRkZCq1bt/Y2BixsbEcaaX/JoQQQshPVR5fOX78OA4cOAAbGxuOmbpt2rTBixcvarRwtSk0NBTBwcE4duwYJCQk2MvFFBQUoKCggObNm2PPnj2wtLRETk4OfH19KzU7eMaMGRg9ejQsLS3h4OCAffv24eHDhxVOYCWEEEJ+JYxf7RWWKveMvH//nmuXTqB4sl9dCvzKlSsoLCxEv379oKury36sXbsWABASEoIPHz7AzMwMI0eOhKenJ8+4S3Nzc8OCBQvg7+8PCwsLvH79GpMnT67tcAghhNQz4jRMU+UJrJ07d8bgwYMxffp0KCoq4v79+zAyMsK0adPw/PlznD9/vrbKKlZq4ofy6hKawFp/0QTW+osmsNbe9ZSVlTFgy1W+J7Aem9ypTkxgrfIwzYoVK/Dbb7/h0aNHKCgowMaNG/Hw4UPExMTgypUrtVFGQgghhNRjVR6msbOzw40bN/Dt2zc0a9YMFy9ehLa2NmJiYmBhYVEbZSSEEELEDqsGHnVFtTYIadeuHXbv3l3TZSGEEELIf8RpAmu1GiOFhYU4duwYkpKSwGKxYGxsjP79+4v05meEEEIIEU1Vbj38+++/6N+/P9LS0tCqVSsAwNOnT6GpqYmTJ0+iXbt2NV5IQgghRNwI41d7haXKc0bGjx8PExMTvHnzBnfv3sXdu3eRkpICU1NTTJw4sTbKSAghhIgdYf1qrzBUuWfk3r17uHPnDlRVVdlpqqqqWLZsGTp27FijhSOEEELEWR1qT/Clyj0jrVq1Qnp6Old6RkYGmgv556gJIYQQUvdUqmckJyeH/f+XL18OT09PLFy4EDY2NgCKf3tl8eLFWLVqVe2UkhBCCBEztJqmFBUVFY6gGIbB0KFD2Wklm7j27dsXhYWFtVBMQgghRLyI0wTWSjVGLl26VNvlIIQQQoiYqlRjpHPnzrVdDkIIIYT8goZpKuHbt29ITk5GXl4eR7qpqSnfhSKEEELEHb9butedpkg1GiPv37/H2LFjce7cOZ7P05wRQgghhH8SLBZfv2hel34NvcpLe728vPDhwwfExsaiYcOGOH/+PHbv3o0WLVrg5MmTtVFGQgghhNRjVe4ZiY6OxokTJ9CxY0dISEjAwMAAzs7OUFJSwooVK9C7d+/aKCchhBAiVlgs/jY9q0MdI1XvGfn69Su0tLQAAGpqanj//j2A4l/yvXv3bs2WjhBCCBFT4rQdfLV2YH3y5AkAoEOHDti2bRvevn2LrVu3QldXt8YLSAghhIijkp4Rfh51RZWHaby8vJCamgoACAwMRI8ePbBv3z5IS0sjLCyspstHCCGEkHquyo2R4cOHs/+/mZkZXr16hcePH6NJkybQ0NCo0cIRQggh4kqcVtNUe5+REnJycjA3N6+JshBCCCHkP+I0gbVSjRFvb+9Kn3D9+vXVLgwhhBBCxE+lGiMJCQmVOlldmrlbV/DbTVdXiEGIHLIj5gm7CAKj6hQo7CII1IfoRcIugsAUFjHCLoJACCtO2g6+FPqhPEIIIUSwJFCNJa+ljq8r+J4zQgghhJCaJ049I3Wp4UQIIYSQeoh6RgghhBARxGIBErSahhBCCCHCIsFnY4SfYwWNhmkIIYQQIlTVaozs2bMH9vb20NPTw+vXrwEAQUFBOHHiRI0WjhBCCBFX9EN55diyZQu8vb3Rq1cvfPz4EYWFhQAAFRUVBAUF1XT5CCGEELFUMkzDz6OuqHJj5M8//8SOHTswd+5cSEpKstMtLS3x4MGDGi0cIYQQIq7E6Vd7q9wYefnyJczMzLjSZWRk8PXr1xopFCGEEELER5UbI0ZGRkhMTORKP3fuHNq0aVMTZSKEEELEXsnPgfDzqCuqvLTX19cXU6dOxY8fP8AwDG7duoX9+/djxYoV2LlzZ22UkRBCCBE7tB18OcaOHYuCggL4+fnh27dvcHd3R6NGjbBx40b8/vvvtVFGQgghhNRj1dr0bMKECZgwYQIyMzNRVFQELS2tmi4XIYQQItb4nYRah0Zp+NuBVUNDo6bKQQghhJBfSIC/eR8SqDutkSo3RoyMjMrdSOV///sfXwUihBBCCPWMlMvLy4vj7/z8fCQkJOD8+fPw9fWtqXIRQgghRExUuTEyY8YMnumbN2/GnTt3+C4QIYQQQuiH8qqlZ8+eOHLkSE2djhBCCBFrLBZ/e43UpWGaGmuMHD58GGpqajV1OkIIIYSIiSo3RszMzGBubs5+mJmZQVdXF3PmzMGcOXNqo4yEDzu2bUG71s2gqSKHTnYdcfP6tXLzX792BZ3sOkJTRQ6mxs2xa8dWjufDQnagR7fOaKKrjia66ujXywV3bt+qzRAqbce2LWjbqhk0lOXgaNsRNyqK9eoVONp2hIayHNq15o41dNcOuDh1hr6OOvR11NG3p+jECgDbtgbDuGVTqCo2hJ21ZYXxXrt6BXbWllBVbIg2rZphx/atXHmOHz0Cc1MTqCjIwtzUBCeOH6ut4lfJRNeOSDrghQ8R83Bjxx+wN21Sbv4/BlghYc80ZEfMw7290+Heoz3H8/07GeP69olIPTMbmRfmInbXJAxzMa3NECpt25ZgtG5hBBUFWdhZWeB6ZerVygIqCrIwbtkUO7Zx1+uxo0dgZtoGyvIyMDNtIzL1CgDbtwbDpGVTqCs1hINN5e5jBxtLqCs1RNtWzbCz1H386NFDuLsNRpuWRlCQkcDmTUG1WPraRb9NUw5XV1f079+f/Rg4cCACAwPx77//YuLEibVRRlJNRw4dwGzfmZjlH4DrsfGwtXPAINfeSElO5pn/1auXGOzaB7Z2DrgeGw8fv9nw8/HCiWM/h9+uXb2CwUN/x+nzUYi8fAON9fUxoO9vePf2raDC4unIoQPwn/VfrHHxsLN3wKD+5cT68iUGufaBnb0DrscVx+rrzRnr9atXMMTtd5y5EIXIK8WxuvYRfqwAcPjgAfj5zITf7DmIuXUX9g4OcO3bq9x4B/TrDXsHB8Tcugtf/wDMmjkDx4/+jDcuNgYjh/+OYcNHIO5OIoYNH4GR7m64dStOUGHxNNjJBGum/4ZV4VdhM34rbt5/jeOrR0BfS5ln/gn9O2LxxG5YFnoJ5qM2Y2nIJQTN7I1edi3ZebJzvmP1nqvoMmUnOo4Nxp5zidg+2xXdOzYTVFg8HTp4AL4+XvCfPRextxNg5+AI1z49kVxOvbr27QU7B0fE3k6An/8c+Mz0xLFf6jU2JgYj3d3gPnwkbsXfg/vwkRgxbChuxQm3XgHg8H/vW9/Zc3Aj7i7s7B0wsF/59/Gg/r1hZ++AG3F3Mcs/AL7eM3D8l/ft92/fYGRkhEVLV0BbR0dQodQKcfrVXhbDMExlMxcUFGDfvn3o0aMHdOp4JYu6nJwcKCsr4036BygpKVXrHF0dbdHBzAwbNgWz0yw7mKBP3/5YuGQ5V/4Fc2fj7JlTuJP4kJ3mNX0yHty/j6grN3heo7CwEE101bFmwya4Dx9VrXIC/Lfguzraon0HMwT9+TNWi/bFsS5ayh3r/Lmzcfb0KcTf+xnrjGmT8eDBfUSXE6u+jjrWbtgE9xHVjxUAJPn8lOhkb4MOZmbY9NcWdppZuzbo268/Fi9bwZV/XoA/zpw+hYQHj9hp06dOwoP793H52k0AwEj335HzOQcnTp1l5+nXpydUVVSxe+/f1S6rWreF1T4WAK5unYCEp6mYsf40Oy1hzzScuvYYC7ZHcuW/FOyBmAcpmLPlIjttzfTfYN5KD92mhZR5nZs7/8D5mGdYvCuar/J+iF5U7WMd7axhZmaOTZt/1muHdsbo288VS3jU69wAf5w5fRKJD5LYadOnTML9+/dw5XoMAGCEuxs+5+TgxOlz7Dz9ev8GFVVVhO/dX+2yAkBhUaX/+eCpi4MN2ncww8Zf7mNz0+L7eNFS7njnzym+j+/e/3kfe06dhH8f3Ef01Ztc+du0NMLUaTMw1dOLr3Lm5ORAT1MFnz59qvbncVWvp6ysjPknEiArr1jt8/z4+hlL+psJrNz8qFLPiJSUFCZPnozc3NzaKg+pIXl5eUhMiIdTN2eOdKduzoiLjeF5zK24WK783bq7IOHuHeTn5/M85tu3b8jPz4eqqvDmC+Xl5SHhbjycupcuezmxxsaiW+n8zi5IiK9ErEKeG1USb7fuLhzp3ZydEVtGvHFxsejmzBlvd+ceuPtLvHFxMehe6jVxdnZBbCz3h7ygNJCShFlLXUTdfs6RHnX7BWza6vM8RrqBFH7kFXCkfc8tgKVxI0hJ8v7I62JuhJb6Grh+71WNlLs62PXqXKpeu7sgNoZ3HcTFxnDdB91dStUrrzzOPco8p6CUHW8F93Gpe7R0vKRuqvIwjbW1NRISEmqjLKQGZWVmorCwEFpa2hzpWtraSE9P43lMenoatLRL5dfSRkFBAbIyM3keEzg/ALp6jdDVqXvNFLwayopVU6v8WDVLvzYVxTovAHpCjhUAMv+LV5tHXaWnlRFvWhrX66OtXRxv5n/x8spT3jkFQUNZDlJSksj48JUjPT37C7TVFHgeE3nrOcb0MYdZS10AgHkrPYzqZQbpBlLQUJFj51OSl8H783OQE70Ax1YNh/fGs4i+I7xNGzPLuI+1K3jP8roPuOq1dB5t4dYrUP5nVEYZZcvgFUsF79u6TJyGaaq8z8iUKVPg4+ODN2/ewMLCAvLy8hzPm5qKxiSw6ujSpQs6dOiAoKAgYRel5pQa/2AYptwddEs/VzKKx+uYoHVrcPjgPzh7IRqysrI1UFj+cJWxBmPdUBLrRdGIFeBdfn7jreo5BaX0aDKLxeJKK7Fi9xVoqyngytYJYAHI+PAVe88nwsfdAYWFP4/5/C0P1h5bodBQGl0tmmLV1B54+e4DriW+qsVIKiZO9QqUUf4aet/WdeK0z0ilGyPjxo1DUFAQ3NzcAACenp7s50o+GFgsFgoLC2u+lHXYmDFj8PHjRxw/flyg11XX0ICkpCQySn2jep+RwfVNpIS2tg7Xt6X37zMgJSUFNXV1jvRNG9Zh3ZoVOHHmItq2E24DtCTW0t8e378vP1au16aMWDduWId1q1fg5FnhxwoU/yaUpKQk0njUVelvjSW0dXS4Xp+MjOJ41f+Ll1ee8s4pCJmfvqGgoJCrF0RLVZ6rt6TEj7wCTFp1AtPWnoK2mgJSsz7Do68lcr7+QOanb+x8DMPgf2+zAQD3n6ehlYEGfEc4Cq0xolHGfZxRwXuW133AVa+l82QIt16Bct635ZRNi1csZbxvSd1S6WGa3bt348ePH3j58iXX43//+x/7f0VBXl6esIsgdNLS0uhgZoHoaM4JfpeiI2FtY8vzGCtrG1wqlT86KgJm5pZo0KABO23j+rVYvXIpjpw4C3MLy5ovfBVJS0vDzNwCl6JKl72cWG1sEF06f2QEzCw4Yw1avxarVyzF0ZOiESvwM97oqAiO9OjISNiUEa+1tQ2iIznjjYq8CPNf4rW2tkVUqdckMjICNjZ2NVj6qskvKETC01Q4WXKucnGybIrYf1PKPbagsAhv3+egqIjBkG5tce7m0zJ7U4DiL1UyDSRrpNzVwa7XyFL1GhUBG1vedWBtY8t1H0RFlKpXXnkiL5Z5TkEpO94K7uNS92jpeOsTFovF96OuqHRjpORNbGBgUO5DGLp06YJp06bB29sbGhoacHZ2xpUrV2BlZQUZGRno6upi9uzZKCj4Oant69evGDVqFBQUFKCrq4t169ZxnTcvLw9+fn5o1KgR5OXlYW1tjcuXL7OfDwsLg4qKCi5cuABjY2MoKCjgt99+Q2pqKgBg4cKF2L17N06cOMG+MX49vrZN8/RCeOgu7NkdgiePkzDb1xtvUpIxbvwfxeWbPwcTPUaz84+b8AdSkl8jwM8HTx4nYc/uEISHhcDTy5udJ2jdGixZNB+bt+6EgYEh0tPSkJ6Whi9fvggsLl6meXphd+guhIeF4PEvsXpMKI41cN4cTBz3M1aP8cWxzvbzwePHSQgPK451xi+xbli3BksWzkfwNtGKFQA8Z8xEWMgu7A4LweOkJPjNmomUlGSMnzgJALBgbgDGj/0Z7/iJk5Cc/Br+vt54nJSE3WEh2B0aAq+ZPuw8U6d7IiriItatWYUnjx9j3ZpVuBQViamevH8CQlA2HbyJsX3MMaqXGVoZaGD1tN+gr6WMnSduAwAWT+yOnXMGsPM3b6yO351N0ayxGiyNGyE8cDDaGGlhwY4odp5Zwx3hZNkUhrqqaNlEA55DbTG8R3vsv3hf4PH9ytPLG6EhO7E7tLhefX1mIiX5Z73OnxsAjzE/V3JNmDgJya9fw2/Wf/UaGoKw0F3w8p7FzjN12gxERlzE2v/qde2aVYiOisS06V6CDo/LtBkzf75vk5LgP2vmf+/b4ngD5wVgwq/v2wmTit+3/93HJe9bz1/u47y8PNy/l4j79xKRl5eHd+/e4v69RLx4/pzr+qKO5oyUQZRbWbt378bkyZNx48YNZGZmwsXFBWPGjEF4eDgeP36MCRMmQFZWFgsXLgQA+Pr64tKlSzh27Bh0dHQwZ84cxMfHo0OHDuxzjh07Fq9evcI///wDPT09HDt2DL/99hsePHiAFi1aACheYbF27Vrs2bMHEhISGDFiBGbNmoV9+/Zh1qxZSEpKQk5ODkJDQwGgzF1qc3NzOVYp5eTk8P2aDBrihuzsbKxavhRpaaloY9IWh4+fRpP/Go1paal4k/Lz26WhoREOHz+NAD8f7NgWDF1dPaxeF4T+Awax8+zcvgV5eXkY6T6U41qz5y7AnHmBfJe5uioTa8qvsRoZ4cjx05jt54MdW4tjXbO+VKzbimMdMYwz1oC5CzBnvvBiBYDBQ92QlZ2FFcuWIC21ON5jJ8/8Em8aUlJ+7tVgaGSEYyfPwG+WN7ZtCYaunh7WbtgI14E/47WxtUP43v1YFDgfixcuQNOmzRC+7x9YWVkLPL5fHY5+CDUlOcwZ3Rk66op4+DIDrv77kJz+CQCgo64Afe2fe45ISrIww80OLZuoI7+gCFcTXqLrlJ1ITvvIziPfsAE2evdBI00lfM/Nx9PkTIxbegSHox+WvrxADRnqhuysLCxfthhpqakwMWmL46fOsr/opaWmctXr8VNn4eczE9u2bIaunh7WbdiEAb/Uq62dHcL3/YNFgfOwOHA+mjZrhj1/H4CVtXDrFQAGDymOd+Xyn/fxkRPl38dHTpzBbF9vbGe/bzfC9Zf3beq7d7CzMmf/vXHDOmzcsA4OnTrjfMQlwQVXA8TpV3srvc+IhIQElJWVK2yQZGdn10jBqqJLly749OkTe5XP3LlzceTIESQlJbHLGxwcDH9/f3z69Anfvn2Duro6wsPD2XNgsrOz0bhxY0ycOBFBQUF48eIFWrRogTdv3kBPT499re7du8PKygrLly9HWFgYxo4di+fPn6NZs2bs6yxevJg9jlvZOSMLFy7EokXc+xPws89IXVKX3jQ1gd99RuoSfvcZqWv42WekruF3n5G6Qlj7jCw7m8j3PiNze3WocrmDg4OxZs0apKamwsTEBEFBQXB0dKzwuBs3bqBz585o27YtEhMTq1TWKvWMLFq0CMrKvHc9FDZLy5/j+UlJSbC1teVoONnb2+PLly948+YNPnz4gLy8PNja/hyXVFNTQ6tWrdh/3717FwzDoGXLn7s2AsU9GOq/TJSSk5NjN0QAQFdXFxkZGVUuf0BAALy9fw4R5OTkQF+f9z4KhBBC6r+SH7zj5/iqOnDgALy8vBAcHAx7e3ts27YNPXv2xKNHj9CkSdk/w/Dp0yeMGjUK3bp1Q3p6epWvW6XGyO+//w4tLa0qX0QQfl1izGvZ2q/LvyrTGVRUVARJSUnEx8dDUpJzUpuCws+Z/aUnTVX2/KXJyMhARkamyscRQgipn4SxtHf9+vXw8PDA+PHjAQBBQUG4cOECtmzZghUruHfFLfHHH3/A3d0dkpKS1Vo9WukJrKI8X6S0Nm3a4ObNmxyNgps3b0JRURGNGjVC8+bN0aBBA8TGxrKf//DhA54+fcr+28zMDIWFhcjIyEDz5s05HlXZCl9aWpqWOxNCCKk6fn8k779/tnNycjgeZe2inpeXh/j4eLi4cO6K6+Ligps3y96xNzQ0FC9evEBgYPXn0lV5NU1dMGXKFKSkpGD69Ol4/PgxTpw4gcDAQHh7e0NCQgIKCgrw8PCAr68voqKi8O+//2LMmDGQkPj5crRs2RLDhw/HqFGjcPToUbx8+RK3b9/GqlWrcPbs2XKuzsnQ0BD379/HkydPkJmZSVsWE0IIESh9fX0oKyuzH2X1cJS1u7O2tjbXfjYlnj17htmzZ2Pfvn2QkqryPqpslT6yqKio2hcRtEaNGuHs2bPw9fVF+/btoaamBg8PD8ybN4+dZ82aNfjy5Qv69esHRUVF+Pj44NOnTxznCQ0NxdKlS+Hj44O3b99CXV0dtra26NWrV6XLMmHCBFy+fBmWlpb48uULLl26hC5dutRUqIQQQuopCbAgAT7mjPx3bEpKCscE1oqmBFR2x97CwkK4u7tj0aJFXPMrq6pKv9pLBKcmfrW3LqlDo4A1glbT1F+0mqb+EdZqmrUX76MhH6tpvn/9jFkuppUud15eHuTk5HDo0CEMGPBz754ZM2YgMTERV65c4cj/8eNHqKqqcsyrLCoqAsMwkJSUxMWLF+Hk5FSpslb5h/IIIYQQUv9IS0vDwsICERGcu+JGRETAzo57x14lJSU8ePAAiYmJ7MekSZPQqlUrJCYmwroKe9lUf4CHEEIIIbVGGKtpvL29MXLkSFhaWsLW1hbbt29HcnIyJk0q3hU3ICAAb9++RXh4OCQkJNC2bVuO47W0tCArK8uVXhFqjBBCCCEiSBj7jLi5uSErKwuLFy9Gamoq2rZti7Nnf+4CnJqaiuTk5ArOUnU0Z0RE0ZyR+o3mjNRfNGek/hHWnJGNUQ/4njMyo1s7gZWbHzRnhBBCCCFCRcM0hBBCiAiSAJ/DNHwsCxY0aowQQgghIkicfrWXhmkIIYQQIlTUM0IIIYSIIAnw12NQl3obqDFCCCGEiCAWi8XXj9TWpR+4pcYIIYQQIoJ++eHdah9fV9SlXhxCCCGE1EPUM0IIIYSIIGHswCos1BghhBBCRFTdaU7wh4ZpCCGEECJU1DNCCCGEiCBx2vSMGiOEEEKICKKlvYQQQggRKnHa9KwulZUQQggh9RD1jBBCCCEiiIZpCCGEECJUtAMrIYQQQoiAUM+IiPv0LR9FUvnCLkat01SSEXYRBKqoiBF2EQTmQ/QiYRdBoFQHbBF2EQQm++gkYRdBICSE1MVAwzSEEEIIESpxWk1DjRFCCCFEBIlTz0hdajgRQgghpB6inhFCCCFEBInTahpqjBBCCCEiSJx+m4aGaQghhBAiVNQzQgghhIggCbAgwcdgCz/HCho1RgghhBARJE7DNNQYIYQQQkQQ67//+Dm+rqA5I4QQQggRKuoZIYQQQkQQDdMQQgghRKhYfE5grUvDNNQYIYQQQkSQOPWM0JwRQgghhAgV9YwQQgghIkicekaoMUIIIYSIIFraSwghhBAiINQzQgghhIggCVbxg5/j6wpqjBBCCCEiSJyGaagxQgghhIggcZrASnNG6rnwXdtgb9YKLfSU0cvJFnEx18vMm56WiukTR6GLVTsYaDTEwjmzeOY7e/IYnGw7oLmuEpxsO+D86RO1Vfwq2bYlGK1bGEFFQRZ2Vha4fv1aufmvXb0COysLqCjIwrhlU+zYtpUrz7GjR2Bm2gbK8jIwM22DE8eP1Vbxq2z71mC0adkUakoNYW9jiRuViNfexhJqSg1h0qoZdm7njPfRo4dwdxsM45ZGkJeRwF+bgmqx9FUjTnU7sZcJknYOx4cjE3Bjw2DYt9EtN//vnVsgbtMQZB0ej//tHoVtM7pCTVGGI8+0fqa4t2UYsg9PwLOQkVg93g4yDSRrM4xK27Y1GMYtm0JVsSHsrCt3H9tZW0JVsSHatGqGHaXv44cPMWzoYLRuYQQ5adG6j0nZqDFSj508dgiL5s7CNG9/nL0UBysbe4x264+3b5J55s/Ly4WauiamefujTVtTnnnib8di6vgRGDjUHeev3MbAoe6Y4jEcCXdu1WYoFTp08AB8fbzgP3suYm8nwM7BEa59eiI5mXesr16+hGvfXrBzcETs7QT4+c+Bz0xPHDt6hJ0nNiYGI93d4D58JG7F34P78JEYMWwobsXFCSqsMh0+dAB+s2bCb/Yc3Iy7Czt7Bwzo1wsp5cQ7sH9v2Nk74GbcXfj6B2CW9wwcP/Yz3u/fvsHQyAiLl66Ato6OoEKpkDjV7WCHZlgz3h6rDt6FzYxDuPkwFccX9oa+pgLP/HZtdLBzphN2RzyG+dQDGLHqIixaaGHL9C7sPL93boElo62x/J876DDlH0z68xIGOzTHktHWAoqqbIcPHoCfT/F9HHPrLuwdHODat/z7eEC/3rB3cEDMrf/u45kzcPyXuv32/RuMmhphiYjdx9XBws+hmur9V3ewGIZhhF0Iwi0nJwfKysp4+DIDikpK1TpHP2dHtDXtgOXr/mSnOdm0h0uvvpi9YGm5xw7t54w2bdtj4fK1HOlTPEbgy+cchB88yU4bOaQvlFVU8NeOPdUqJwBoKslUnKkcjnbWMDMzx6bNW9hpHdoZo28/VyxZtoIr/9wAf5w5fRKJD5LYadOnTML9+/dw5XoMAGCEuxs+5+TgxOlz7Dz9ev8GFVVVhO/dz1d5i4r4e9t1drBBhw5m2PjXz3jNTdugT7/+WLyUO955c/xx9vQp3L3/iJ3mOXUSHjy4j0tXb3LlN25phKnTZmCapxdf5QQACT5n0dW1ulUdsKXiTGW4unYgEl5kYsaWq+y0hODfcSr2JRaEczeUvAa0x4SeJjCZ+Dc7bXKftvAeaIYW44rfjxv+cEArfVX0mneKnWflOFtYttRG99nHq11WAMg+Oomv4zvZ26CDmRk2/XIfm7Vrg779+mMxj7qdF+CPM6dPIeHBz/t4+tRJeHD/Pi5f476PW7cwwrTp/N/HOTk50NFQwadPn6BUzc/jql5PWVkZZ+NfQl6h+tf7+iUHvSyMBFZuflDPSD2Vl5eHB/fuolPX7hzpjl27I/52bLXPe/d2LNc5Ozs5I/5W9c/Jr7y8PCTcjUc3ZxeO9G7dXRAbw/0BBQBxsTHo1p0zf3eXHrgbfwf5+fll53HuUeY5BaWseJ26OyMuNobnMbfiYuHU3ZkjrXS8okic6raBlATMmmsiKiGFIz0qIQU2xry/4ccmpaGRhgJ6WDQBAGipNMQA+2Y4d+c1O8/NR2kwa6YJyxZaAABDbUX0sDTA+V/yCAO7bkvVQzdnZ8SWcR/HxcWim3Op+9hZ9O9jUjGxa4wsXLgQHTp0qNIxr169AovFQmJiYq2UqTZkZ2WisLAQGlpaHOmamlp4n55e7fO+z0iHhibnOTU0tfA+o/rn5FdmZnGsWlraHOna2tpIT0/jeUx6ehq0tTnza2lpo6CgAJmZmcV50tKgVTqPtjbS03ifU1Cyyou3jLKlp1UcrygSp7rVUJKFlKQEMj5+40hP//gd2ipyPI+JfZyOsWsjscfPGTnHJuL1njH4+CUX3tt+zg07dO05Fu+7hahVrsg5NhFJO0fgyoO3WHs4oVbjqUhJ3fKqq/LuY173gqjfx9XF3xBN3RqoodU0laCvr4/U1FRoaGgIuyhVxio1nZphGK40UThnTahquXjlL50uqrEC1ShbJeIVVeJUt6UHzlksgAHvYb3W+qpYN9EBK/6JR0RCMnRU5bF8rC3+nNIJk/+8DABwbKsHv6EWmLH1Gm4/SUczXWWsnWiPtOxvWHkgvnaDqYTaqNv6QpxW09Sbxkh+fj4aNGhQK+eWlJSETh2bCKWmrgFJSUmuXpDMzPdcvSVVoamlzdULkpX5nqu3RJA0NIpjLf1NOSMjg+tbVAltbR2klfr29f59BqSkpKCurl6cR0eH6xva+4wMrm/UgqZeXrxllI1nLKXiFUXiVLeZOT9QUFgEbVXOXhAt5YbI+Pid5zG+Q8wQk5SGDccSAQD/vsrGt9x8RK0agEV7byHtwzcEjrDC/ktPEXaxeA7Nw9fZkJOVwuZpnbHqYDxX40dQSuqWV12Vex/zuBdE/T6uLtZ/D36OryuEOkxz+PBhtGvXDg0bNoS6ujq6d++Or1+/sp8PCQmBiYkJZGRkoKuri2nTprGfY7FY2Lp1K/r37w95eXksXboUYWFhUFFR4bjG8ePHebaYt23bBn19fcjJyWHIkCH4+PFjmeUsPUxTWFgIDw8PGBkZoWHDhmjVqhU2btzIzn/16lU0aNCA603m4+ODTp06VeEVqj5paWm0a2+Oa5ejONKvXY6CRUebap/XvKMN1zmvXoqEhVX1z8kvaWlpmJlbIDoygiM9OioCNrZ2PI+xtrFFdBRn/qiIizC3sGQ3annmibxY5jkFpax4L0VFwtrGlucxVtY2uBQVyZFWOl5RJE51m19QhITn7+Fk1pgj3alDY8Qm8R62kJNpgKJSrYnCwpKeguK/G8pIcU2YLipiijvxhfjVmV23peohOjISNmXcx9bWNoiOLHUfR4r+fUwqJrTGSGpqKoYNG4Zx48YhKSkJly9fxsCBA9ldblu2bMHUqVMxceJEPHjwACdPnkTz5s05zhEYGIj+/fvjwYMHGDduXKWv/fz5cxw8eBCnTp3C+fPnkZiYiKlTp1b6+KKiIjRu3BgHDx7Eo0ePsGDBAsyZMwcHDx4EAHTq1AlNmzbFnj0/V5cUFBRg7969GDt2LM9z5ubmIicnh+PBr/FTPPHP3lAc2BeGZ08eY9FcX7x7m4IRYycAAFYungevyZyv28MH9/DwwT18/fIV2Vnv8fDBPTx9/HNVwrg/puLqpUgEb1yL50+fIHjjWly/Eg2PP6bzXV5+eHp5IzRkJ3aHhuBxUhJ8fWYiJTkZ4ycWz/afPzcAHmNGsfNPmDgJya9fw2+WNx4nJWF3aAjCQnfBy/vn3ipTp81AZMRFrF2zCk8eP8baNasQHRWJadO9BB0el+kzZiIsdBd2hxXH6zdrJlJSkjF+QnG8C+YFYPy40ez84ydMQnLya/j7/hdvWAh2h4Vgxkwfdp68vDzcu5eIe/cSkZeXh3fv3uLevUS8eP5c4PH9SpzqdtPxexjrbIxR3VujVWMVrB5vB31NRew89xAAsHiUNXbOdGLnP3PrFfrbGmFCTxMYaivC1lgH6/5wwO0n6UjNLp57cvbWK0zoZYIhjs1hoK0Ipw6NsWC4Fc7cesX3qi5+ec6YibAQHvfxf3W7YG4Axo/95T6eyOM+Dg2BV+n7ODER9xJ/uY8ThX8fV4cEWJBg8fGoQ30jQhumSU1NRUFBAQYOHAgDAwMAQLt27djPL126FD4+PpgxYwY7rWPHjhzncHd3r1IjpMSPHz+we/duNG5c/A3kzz//RO/evbFu3bpKDcc0aNAAixYtYv9tZGSEmzdv4uDBgxg6dCgAwMPDA6GhofD19QUAnDlzBt++fWM/X9qKFSs4zlkT+g0Ygo/Z2di4Zjky0tPQ0tgEu/85jsb6xa93Rnoa3r3lnLnfs8vPvQce3LuL44cPoLF+E9xMfAoAsLSyxV8792DtsoVYt2IRDAybYvPOvTCztKrRslfVkKFuyM7KwvJli5GWmgoTk7Y4fuos+95KS01FSsrPvQsMjYxw/NRZ+PnMxLYtm6Grp4d1GzZhwMBB7Dy2dnYI3/cPFgXOw+LA+WjarBn2/H0AVtbC359h8JDieFcuX4K01FS0MWmLoyfOoElJvGlpeFMq3qMnzsDf1xvbtwZDV1cPa9dvhOuAn/GmvnsHOytz9t8bN6zDxg3r4NipM85HXBJccKWIU90evv4CakqymPO7BXTU5PHwdTZcF51B8vsvAAAdNTmOPUf2Rj2BYsMGmNSnLVZ62OLTlzxcvv8W88J+rm5beaB4KCZwhBX01OWRmfMdZ269xsI9wt8vZ/BQN2RlZ2HFsp/38bGTnPdx6bo9dvIM/GZ5Y9uWYOjq6WHtho1wHch5H9v+ch8HrV+HoPXF9/GFSOHdx9UhTsM0QttnpLCwED169MCtW7fQo0cPuLi4YPDgwVBVVUVGRga0tbURHR2Nrl278jyexWJh7969GD58ODstLCwMXl5eHEMux48fx4ABA9g9LgsXLkR4eDj+97//sfN8+vQJKioquHz5Mjp37sx1rVevXsHIyAgJCQnslThbt27Fzp078fr1a3z//h15eXno0KEDbt0q3vwrIyMDjRs3xtWrV2FjY4P+/ftDQ0MDu3bt4hlPbm4ucnNz2X/n5ORAX1+fr31G6hJ+9xmpa4T9jVSQ+N1npK7hZ5+RuobffUbqCmHtMxJ59zXkFfnYZ+RzDrqbG9A+I+WRlJREREQEzp07hzZt2uDPP/9Eq1at8PLlSzRs2LBS55CXl+f4W0JCAqXbVpVZe14yblrZ8dODBw9i5syZGDduHC5evIjExESMHTsWeXl57DxaWlro27cvQkNDkZGRgbNnz5bbiyMjIwMlJSWOByGEEDHGqoFHHSHUCawsFgv29vZYtGgREhISIC0tjWPHjkFRURGGhoaIioqq+CS/0NTUxOfPnzkmwfLaGyQ5ORnv3r1j/x0TEwMJCQm0bNmyUte5du0a7OzsMGXKFJiZmaF58+Z48eIFV77x48fjn3/+wbZt29CsWTPY29tXKR5CCCHiS5z2GRFaYyQuLg7Lly/HnTt3kJycjKNHj+L9+/cwNjYGUDycsm7dOmzatAnPnj3D3bt38eeff5Z7Tmtra8jJyWHOnDl4/vw5/v77b4SFhXHlk5WVxejRo3Hv3j1cu3YNnp6eGDp0aKWX7zZv3hx37tzBhQsX8PTpU8yfPx+3b9/mytejRw8oKytj6dKlZU5cJYQQQnhi/dxrpDqPOtQWEV5jRElJCVevXkWvXr3QsmVLzJs3D+vWrUPPnj0BAKNHj0ZQUBCCg4NhYmKCPn364NmzZ+WeU01NDXv37sXZs2fRrl077N+/HwsXLuTK17x5cwwcOBC9evWCi4sL2rZti+Dg4EqXfdKkSRg4cCDc3NxgbW2NrKwsTJkyhSufhIQExowZg8LCQowaNYrHmQghhBBCP5RXCU+ePEHr1q3x7NkzruXFFZkwYQLS09Nx8uTJijP/oiZ+KK8uoQms9RdNYK2/aAJr7V1PWVkZ0YnJUOBjAuuXzzlw6tCkTkxgrTc7sNaW7OxsHD58GEpKStDX16/0cZ8+fcLt27exb98+nDhxohZLSAghpF4So7W91BipgIeHB+Lj47FlyxbIyFT+23v//v1x69Yt/PHHH3Au9SuThBBCCPmJGiMVOHbsWLWOu3z5cs0WhBBCiFjhd0VMXVpNQ40RQgghRATRr/YSQgghRKjEaMqIcDc9I4QQQgihxgghhBAiioS0HXxwcDCMjIwgKysLCwsLXLt2rcy8R48ehbOzMzQ1NaGkpARbW1tcuHChytekxgghhBAigoSxHfyBAwfg5eWFuXPnIiEhAY6OjujZsyeSk5N55r969SqcnZ1x9uxZxMfHo2vXrujbty8SEhKqdF1qjBBCCCEEALB+/Xp4eHhg/PjxMDY2RlBQEPT19bFlC+/N/IKCguDn54eOHTuiRYsWWL58OVq0aIFTp05V6brUGCGEEEJEED+/S/PrSpycnByOR25uLs/r5eXlIT4+Hi4uLhzpLi4uuHnzZqXKXFRUhM+fP0NNTa1KsVJjhBBCCBFBNTVlRF9fH8rKyuzHihUreF4vMzMThYWF0NbW5kjX1tZGWlpapcq8bt06fP36FUOHDq1KqLS0lxBCCBFJNbS2NyUlheO3aSraTZxVaoMShmG40ngp+XHaEydOQEtLq0pFpcYIIYQQUo8pKSlV6ofyNDQ0ICkpydULkpGRwdVbUtqBAwfg4eGBQ4cOoXv37lUuIw3TEEIIISJI0KtppKWlYWFhgYiICI70iIgI2NnZlXnc/v37MWbMGPz999/o3bt3tWKlnhFCCCFEBAljO3hvb2+MHDkSlpaWsLW1xfbt25GcnIxJkyYBAAICAvD27VuEh4cDKG6IjBo1Chs3boSNjQ27V6Vhw4ZQVlau9HWpMUIIIYSIIGFsB+/m5oasrCwsXrwYqampaNu2Lc6ePQsDAwMAQGpqKseeI9u2bUNBQQGmTp2KqVOnstNHjx6NsLCwSl+XGiOEEEIIYZsyZQqmTJnC87nSDYya+oV6aowQQgghokiMfimPGiOEEEKICKrulu6/Hl9X0GoaQgghhAgV9YwQQgghIkgYq2mEhRojhBBCiAgSoykj1BgRdRpKMlBSKn/r3vqgoLBI2EUQKClJGiGtrz4cmyzsIgiMqvUMYRdBIJhC3j8sV+vEqDVCn4iEEEIIESrqGSGEEEJEkDitpqHGCCGEECKCxGkCKw3TEEIIIUSoqGeEEEIIEUFiNH+VGiOEEEKISBKj1gg1RgghhBARJE4TWGnOCCGEEEKEinpGCCGEEFHE52qaOtQxQo0RQgghRBSJ0ZQRGqYhhBBCiHBRzwghhBAiisSoa4QaI4QQQogIEqfVNNQYIYQQQkQQbQdPCCGEECIg1DNCCCGEiCAxmjJCjRFCCCFEJIlRa4SGaQghhBAiVNQzQgghhIggWk1DCCGEEKFigc/VNDVWktpHwzT13LYtwWjdwggqCrKws7LA9evXys1/7eoV2FlZQEVBFsYtm2LHtq1ceY4dPQIz0zZQlpeBmWkbnDh+rLaKXyU7tm1B21bNoKEsB0fbjrhRQazXr16Bo21HaCjLoV3r5ti1gzPW0F074OLUGfo66tDXUUffni64c/tWbYZQJeJUtxRr2epyrAAwcYgDkk4uwIeba3Fj7yzYd2habv4/hjgg4XAAsm+swb0jc+DeuyNXnmnDOuPekTnIvrEGz84sxGrvAZCRrnvfvVk18KgrqDFSjx06eAC+Pl7wnz0XsbcTYOfgCNc+PZGcnMwz/6uXL+HatxfsHBwRezsBfv5z4DPTE8eOHmHniY2JwUh3N7gPH4lb8ffgPnwkRgwbiltxcYIKi6cjhw7Af9ZMzPIPwPW4eNjZO2BQ/95IKSfWQa59YGfvgOtx8fDxmw1fby+cOPYz1utXr2CI2+84cyEKkVduoLG+Plz7/IZ3b98KKqwyiVPdUqz1M1YAGOxshjU+A7Aq5CJs3NfgZsILHP9zEvR1VHnmnzDYHoun9cWybedhPnQllm47hyD/wejlaMLO83tPCyyZ3hfLd1xAh8ErMGnJfgx2McOSaX0FFRapBhbDMIywC0G45eTkQFlZGelZn6CkpFStczjaWcPMzBybNm9hp3VoZ4y+/VyxZNkKrvxzA/xx5vRJJD5IYqdNnzIJ9+/fw5XrMQCAEe5u+JyTgxOnz7Hz9Ov9G1RUVRG+d3+1ygkABYVF1T4WALo62qJ9BzME/RnMTrNob4I+fftj0dLlXPnnz52Ns6dPIf7eQ3bajGmT8eDBfURfucHzGoWFhdDXUcfaDZvgPmIUX+WVkuTve0Bdqlt+UayiG6uq9Qy+jr+6eyYSHr/BjBWH2GkJhwNw6vIDLPjrNFf+SyFeiLn3P8zZeJKdtsZnAMzbNEE3j40AgA1+g9DKSAe9Jm9m51k50xWWJk3QffymapWTKcxF7r1t+PSp+p/HVVHy+f/oVQYU+bje55wctDHUEli5+UE9I/VUXl4eEu7Go5uzC0d6t+4uiI25yfOYuNgYdOvOmb+7Sw/cjb+D/Pz8svM49yjznIJQEqtTd2eO9G7dnREXG8PzmFuxsehWOr+zCxJ+ibW0b9++IT8/H6pqajVT8GoSx7qlWOtXrADQQEoSZq31ERX7hCM9KvYJbEyNeB4jLS2FH3kFHGnfc/NhadIEUlLF/5zdTPwfzIwbw9KkCQDAsJE6etgb4/z1R7UQRW0Tn4EaaozUU5mZmSgsLISWljZHura2NtLT03gek56eBm1tzvxaWtooKChAZmZmcZ60NGiVzqOtjfQ03ucUhKwyYtXUKj9WTS3esWb9F2tpgfMCoKfXCF2dutdMwatJnOqWYq2fsQKAhoo8pKQkkZGVw5GenvUZ2uqKPI+JjHmMMa42MGvdGABgbqyPUf1sIN1AChoqCgCAQxcTsHjLWUTtmoGcuPVIOrkAV+48x9qwyNoNiPCl7s3o4dOYMWPw8eNHHD9+vNauERYWBi8vL3z8+LHWrlFZrFJTsRmG4UqrKH/p9KqeU1C4ylADsZbYsG4NDh/8B2cvRkNWVpb/wtYAca5birX8/KXTRTVWACg9UYDFAsqaO7Bi5wVoqyviym5vsABkZH/G3lNx8BnTHYX/DfU6WjSH3zgXzFh5CLf/fY1m+ppYO2sg0jI/YeXOi7UaS00Tp9+mEbvGiLjQ0NCApKQk1zeqjIwMrm9eJbS1dZBW6tvS+/cZkJKSgrq6enEeHR2ub1TvMzK4vnkJknoZsb5/X36sGTzyS0lJQe2/WEts3LAO61avwMmzF9G2nWnNFr4axKluKdb6GSsAZH78ioKCQmhrcM5l0FJTREbWZ57H/MjNx6TF+zFt+QFoqykiNTMHHgPtkPPlBzI/fgUABE7uhf1nbyPseCwA4OHzVMjJSmPzPDes2hWBujRNUow2YKVhmvpKWloaZuYWiI6M4EiPjoqAja0dz2OsbWwRHcWZPyriIswtLNGgQYOy80ReLPOcglAS66Uozm7Y6KhIWNvY8jzGysYG0aXzR0bA7JdYASBo/VqsXrEUR0+ehbmFZc0XvhrEsW4p1voVKwDkFxQi4XEKnKxbcaQ7WbdC7P2X5R5bUFCEtxmfUFTEYIiLOc5df8huZDSUlUZREWeDo6ioiO89O4ShpGeEn0ddUW8bI4cPH0a7du3QsGFDqKuro3v37vj69StXvvj4eGhpaWHZsmUAirszSw/hqKioICwsDADw6tUrsFgsHD16FF27doWcnBzat2+PmBjeEyUBICsrC1ZWVujXrx9+/PhRYzFWxNPLG6EhO7E7NASPk5Lg6zMTKcnJGD9xEgBg/twAeIz5uSpkwsRJSH79Gn6zvPE4KQm7Q0MQFroLXt6z2HmmTpuByIiLWLtmFZ48foy1a1YhOioS06Z7CSwuXqZ5emF36C6Eh4Xg8eMkzPb1xpuUZHhM+AMAEDhvDiaOG83O7zH+D6Qkv8ZsPx88fpyE8LAQhIeFYIaXNzvPhnVrsGThfARv2wkDA0Okp6UhPS0NX758EXh8pYlT3VKs9TNWANi09zLGutpgVD9rtDLUxmrvAdDXUcXOw8Ur2hZP64Odi4az8zdvoonfe1qimb4mLE2aIHz5aLRppsux8ubs1X8xYbADhriYwUBPDU7WrbBgci+cufovVyOFiI56OUyTmpqKYcOGYfXq1RgwYAA+f/6Ma9eucXXPXb58Ga6urlixYgUmT55cpWvMnTsXa9euRYsWLTB37lwMGzYMz58/h5QU50v65s0buLi4wNLSEiEhIVzPl8jNzUVubi7775ycHJ75qmLIUDdkZ2Vh+bLFSEtNhYlJWxw/dRYGBgYAgLTUVKSk/Ny/wNDICMdPnYWfz0xs27IZunp6WLdhEwYMHMTOY2tnh/B9/2BR4DwsDpyPps2aYc/fB2Blbc13efkxaIgbsrOzsWr5UqSlpaKNSVscPn4aTUpiTUtFSkoKO7+hkRGOHD+N2X4+2LE1GLq6elizPgj9B/yMdee2LcjLy8OIYUM5rhUwdwHmzA8UTGBlEKe6pVjrZ6wAcDgiAWoq8pgzoQd0NJTx8EUqXD23ITntAwBAR0OJY88RSQkJzBjRFS0NtZBfUIird56h67ggJKdms/Os3HURDAMETukNPU1lZH78ijNX/8XCzWcEHh+/xGk7+Hq5z8jdu3dhYWGBV69esd/EJUomsI4dOxYjR47Etm3bMGzYMPbzLBYLx44dg6urKztNRUUFQUFBGDNmDF69egUjIyPs3LkTHh4eAIBHjx7BxMQESUlJaN26NXsC661bt+Ds7Iz+/ftj48aN5U4YW7hwIRYtWsSVzs8+I3UJv/uM1DX87jNCiCjgd5+RukJY+4w8Tcnke5+RlvoatM+IsLRv3x7dunVDu3btMGTIEOzYsQMfPnxgPx8XF4dBgwZh9+7dHA2RqjA1/TmRUVdXF0DxRLMS379/h4ODA1xdXbFp06YKZ64HBATg06dP7Mev3+IJIYSIH/HZZaSeNkYkJSURERGBc+fOoU2bNvjzzz/RqlUrvHxZPCmqWbNmaN26NUJCQpCXl8dxLIvF4hrO4bUJ1q+THEsaGkVFP7/dy8jIoHv37jhz5gzevHlTYZllZGSgpKTE8SCEEELEQb1sjADFDQR7e3ssWrQICQkJkJaWxrFjxT8OpaGhgejoaLx48QJubm4cjQ1NTU2kpqay/3727Bm+fftW5etLSEhgz549sLCwgJOTE969e8d/UIQQQsQGraap4+Li4rB8+XLcuXMHycnJOHr0KN6/fw9jY2N2Hi0tLURHR+Px48cYNmwYCgqKtxh2cnLCX3/9hbt37+LOnTuYNGkSRy9IVUhKSmLfvn1o3749nJycuPYDIIQQQsrCqoH/6op62RhRUlLC1atX0atXL7Rs2RLz5s3DunXr0LNnT458Ojo6iI6OxoMHDzB8+HAUFhZi3bp10NfXR6dOneDu7o5Zs2ZBTk6u2mWRkpLC/v37YWJiAicnJ455JYQQQgipp6tp6oOa+NXeuoRW0xBS99BqmtpR8vn/4m0W36tpmjVSrxOraerlPiOEEEJIXSdO28FTY4QQQggRQeL0Q3nUV0wIIYQQoaKeEUIIIUQk8bsipu50jVBjhBBCCBFBNExDCCGEECIg1BghhBBCiFDRMA0hhBAigsRpmIYaI4QQQogI4ndLd9oOnhBCCCGkkqhnhBBCCBFBNExDCCGEEKESp+3gaZiGEEIIIUJFPSOEEEKIKBKjrhFqjBBCCCEiSJxW01BjhBBCCBFB4jSBleaMEEIIIUSoqGeEEEIIEUFiNGWEekYIIYQQkcSqgUc1BAcHw8jICLKysrCwsMC1a9fKzX/lyhVYWFhAVlYWTZs2xdatW6t8TWqMEEIIIQQAcODAAXh5eWHu3LlISEiAo6MjevbsieTkZJ75X758iV69esHR0REJCQmYM2cOPD09ceTIkSpdlxojhBBCiAhi1cB/VbV+/Xp4eHhg/PjxMDY2RlBQEPT19bFlyxae+bdu3YomTZogKCgIxsbGGD9+PMaNG4e1a9dW6bo0Z0REMQwDAPickyPkkghGQWGRsIsgUFKS9D2A1H1MYa6wiyAQTGFe8f/+97ksKJ8/5/C1Iubz5+J/P3JK/TsiIyMDGRkZrvx5eXmIj4/H7NmzOdJdXFxw8+ZNnteIiYmBi4sLR1qPHj2wa9cu5Ofno0GDBpUqKzVGRNTnz58BAM2N9IVcEkIIIUDx57KysnKtX0daWho6OjpoUQOf/woKCtDX5zxPYGAgFi5cyJU3MzMThYWF0NbW5kjX1tZGWloaz/OnpaXxzF9QUIDMzEzo6upWqpzUGBFRenp6SElJgaKiIlgCWiyek5MDfX19pKSkQElJSSDXFCZxilecYgXEK15xihUQTrwMw+Dz58/Q09MTyPVkZWXx8uVL5OXl8X0uhmG4/g3h1Svyq9L5eZ2jovy80stDjRERJSEhgcaNGwvl2kpKSmLxoVZCnOIVp1gB8YpXnGIFBB+vIHpEfiUrKwtZWVmBXlNDQwOSkpJcvSAZGRlcvR8ldHR0eOaXkpKCurp6pa9NA9eEEEIIgbS0NCwsLBAREcGRHhERATs7O57H2NracuW/ePEiLC0tKz1fBKDGCCGEEEL+4+3tjZ07dyIkJARJSUmYOXMmkpOTMWnSJABAQEAARo0axc4/adIkvH79Gt7e3khKSkJISAh27dqFWbNmVem6NExD2GRkZBAYGFjheGJ9IU7xilOsgHjFK06xAuIXr6C5ubkhKysLixcvRmpqKtq2bYuzZ8/CwMAAAJCamsqx54iRkRHOnj2LmTNnYvPmzdDT08OmTZswaNCgKl2XxQh6rRIhhBBCyC9omIYQQgghQkWNEUIIIYQIFTVGCCGEECJU1BghhBBCiFBRY4QQQgghQkWNEUIIqUO6dOmC8PBwfP/+XdhFEYjytkTPzMwUYElIbaLGCBEbu3bt4pleUFCAgIAAAZeG1LSrV6+ioKCAK72goABXr14VQolqh4WFBfz8/KCjo4MJEyYgNjZW2EWqVUOHDkVREfeveqenp6NLly6CLxCpFdQYIWLDx8cHgwYNQnZ2Njvt8ePHsLKywsGDB4VYMlITunbtylG3JT59+oSuXbsKoUS1Y926dXj79i3Cw8Px/v17dOrUCW3atMHatWuRnp4u7OLVuNTUVHh4eHCkpaWloUuXLmjdurWQSkVqGm16JqbMzMwq/YuKd+/ereXSCMbLly8xcuRIvHz5EmFhYXj69Cl8fX0xePBgbN68GYqKisIuIt/EsV5LSEhIID09HZqamhzpT58+haWlJXJycoRUstr1/v17bNu2DcuWLUNhYSF69eoFT09PODk5CbtoNSIrKwudOnWCi4sLNmzYgLdv38LJyQnt27fHP//8AwkJ+k5dH9B28GLK1dWV/f9//PiB4OBgtGnTBra2tgCA2NhYPHz4EFOmTBFSCWuekZERrl69ipkzZ+K3336DpKQkwsPD8fvvvwu7aDVGHOt14MCBAIp/rnzMmDEc24QXFhbi/v37Zf7IV11369YthIaGYv/+/dDS0sKYMWOQmpqKvn37YvLkyVi7dq2wi8g3dXV1XLhwAQ4ODgCAM2fOwNzcHPv27aOGSD1CPSME48ePh66uLpYsWcKRHhgYiJSUFISEhAipZDXv5MmTGD9+PFq1aoUnT56gXbt22LNnD/T09IRdtBonLvU6duxYAMDu3bsxdOhQNGzYkP2ctLQ0DA0NMWHCBGhoaAiriDUqIyMDe/bsQWhoKJ49e4a+ffti/Pjx6NGjB7tXLDIyEq6urvjy5YuQS1tznj17BgcHBzg7O2PPnj2V7gEkdQRDxJ6SkhLz9OlTrvSnT58ySkpKQihR7Zg4cSIjIyPDrFmzhikqKmJSU1OZnj17MmpqasyBAweEXbwaJy71WmLhwoXMly9fhF2MWtegQQOmdevWzOrVq5mMjAyeeT59+sR06dJFwCWrOSoqKoyqqirXQ0ZGhlFSUuJII/UDDdMQNGzYENevX0eLFi040q9fvw5ZWVkhlarm3bhxA3FxcWjfvj0AQEdHB2fPnsXmzZsxbtw4DB06VMglrFniUq8lAgMDhV0EgYiKioKjo2O5eZSUlHDp0iUBlajmBQUFCbsIRMCoMULg5eWFyZMnIz4+HjY2NgCK5xaEhIRgwYIFQi5dzYmPj+f5s+NTp05F9+7dhVCi2iUu9VoiPT0ds2bNQlRUFDIyMsCUGoEuLCwUUslqVkUNkf+3d+9BUV13HMC/d2V5s6BEEAnycFXQGEQoj6CJisVgY0DyQIkVFHTQCDQEpQyCmgSVNBBEU7QYeVgTiyi2sYi0ii8yiItEJSIGlIcVAooU5WFZ9vYPh52si0Zxl+tefp8ZZ7jnHu5+YXfGH+fcew4fBAUFcR2BDDG6Z4QAAHJzc7Ft2zZUVVUBABwcHBAZGcm70YL29nZ8/fXXqKqqAsMwcHBwQEhICIyNjbmOphbD5X0FAB8fHzQ0NGDNmjWwsLBQuqfA19eXo2Sql5eXh9zcXDQ0NCgtCsa3p6QAQCaToaamBi0tLUprjrz++uscpSKqRMXIMCeVSpGYmIjly5fDysqK6zhqJZFIMG/ePOjp6cHV1RUsy0IikaC7uxtFRUWYPn061xFVZji9r/2MjIxw5swZTJs2jesoapWWloa4uDgEBQUhIyMDy5YtQ21tLc6fP48PP/wQiYmJXEdUqdLSUgQGBqK+vl5ptIthGN6MeA13VIwQGBoaorKyEjY2NlxHUauZM2dCLBYjIyMDWloPZyilUilCQ0Nx/fp1Xq3SCQyf97Xf5MmTsW/fPjg5OXEdRa3s7e2xYcMGLF68GEZGRrh48SLs7OyQkJCAtrY27Nixg+uIKjVt2jRMnDgRmzZtGnDEi6+jmsMNPaRNMHfuXJw8eZLrGGonkUgQExMjL0QAQEtLC+vWrYNEIuEwmXoMl/e1X2pqKv74xz+irq6O6yhq1dDQIF83RU9PD/fu3QMA/P73v8e3337LZTS1+Omnn7B582Y4ODjAxMQExsbGCv8IP9ANrAQ+Pj6IjY1FZWUlnJ2dYWBgoHD+7bff5iiZaolEIjQ0NCgtId3Y2MiL1VcfNVze134BAQHo6urC+PHjoa+vD6FQqHB+oKXiNdGYMWNw584dWFtbw9raGqWlpXB0dMSNGzeUpjH4wM3NDTU1NRCLxVxHIWpE0zTkiasY8mlONiIiAvn5+fjiiy/w2muvgWEYnD17FmvXrsU777zDu8cJh8v72i87O/uJ5/nyhEZoaCisrKywYcMG7Ny5E1FRUfD09IREIoG/v/9jN4TUVPn5+Vi/fj3Wrl2LqVOnKhWZr776KkfJiCpRMUJ47dKlS3jllVcgEAjwv//9D2vXrsXOnTvlu7sKhUKsWrUKW7duHfCxX0JeNDKZDDKZTD7dmJubi7Nnz0IsFiMsLAza2tocJ1StgYpqhmHAsiwvi+rhiooRwmsjRoxAU1MTzMzMYGdnh/Pnz0NPTw81NTUAALFYDH19fY5TElWpra1FZmYmamtrsW3bNpiZmaGwsBBWVlaYMmUK1/HIINTX1z/xvLW19RAlIepExcgwlZaWhpUrV0JXVxdpaWlP7BsRETFEqVTP1NQUBQUFcHNze+yurnzW2dmJU6dODbgehSa/rwM5deoUfHx84OnpidOnT6Oqqgp2dnb4/PPPUVZWhry8PK4jDtovR/guXbr0xL6GhoawsrJSms4g5EVGxcgwZWtrC4lEAlNTU9ja2j62H8MwuH79+hAmU62VK1ciJycHFhYWaGhowMsvv4wRI0YM2FeTf86BVFRUYP78+ejq6kJnZydGjRqF27dvQ19fH2ZmZrz7eT08PPDee+8hKipK4ZHX8+fPw8/PD//5z3+4jjhoAoEAzc3NMDMzg0AgkE9TPI6xsTF27tyJgICAIUypHjk5OU88v3Tp0iFKQtSJihGioP/jwKcdMQsLC1FTU4OIiAh88sknj31yJjIycoiTqdesWbMwceJEpKenw8TEBBcvXoRQKMSSJUsQGRkJf39/riOqlKGhIS5fvgxbW1uFYqSurg729vbo6enhOuKg1dfXY9y4cWAY5lenLR48eIADBw4gIyODF485jxw5UuG4t7cXXV1d0NbWhr6+Pm+ekhr2hmxLPvJC2717NztlyhRWW1ub1dbWZqdMmcJmZGRwHUulgoOD2Y6ODq5jDBljY2P26tWr8q+vXLnCsizLlpaWspMmTeIymlpYWlqyJSUlLMuyrKGhIVtbW8uyLMseOnSItbOz4zLakGtra2MXLlzIdQy1uXbtGuvl5cUWFhZyHYWoCC16RhAfH4/IyEgsWLAABw4cwIEDB7BgwQJ89NFHWL9+PdfxVCYzM5OX64k8jlAolI9wmZubo6GhAcDDIfz+r/kkMDAQMTExaG5uBsMwkMlkKCkpQXR09LAbyh85ciQOHTrEdQy1mTBhArZu3cq70czhjKZpCF566SVs374dixcvVmj/9ttvER4ejtu3b3OUjDwPb29vBAcHIzAwEGFhYaioqEBERAT27t2Lu3fv4ty5c1xHVKne3l4EBwdj//79YFkWWlpa6OvrQ2BgILKysh57rxDRTBUVFXjjjTfQ0dHBdRSiAlSMEIwcORJlZWWYMGGCQvu1a9fg6uqK9vZ2boKR5yKRSHDv3j3Mnj0bra2tCAoKkq9HsWfPHt5uKFdbW4uKigrIZDI4OTkpfa6JZvnHP/6hcMyyLJqamrBjxw5YWVnh6NGjHCUjqkTFCEF4eDiEQiFSUlIU2qOjo9Hd3Y2vvvqKo2SEkOHu0UXPGIbB6NGjMWfOHCQnJ8PCwoKjZESVaG+aYSoqKkr+NcMw2L17N4qKiuDu7g7g4bbdjY2Nw26unU8++eQTzJgxA3PmzFFo7+zsRHJyMhISEjhKpjq//Bz/mkeLbU3X0tKC6upqMAyDiRMnwszMjOtIaiGTyR57jv6W5g8aGRmmZs+e/VT9GIbBiRMn1JyGqINAIIBQKMSWLVsU/tP++eefMXbsWF4so/3o57i8vBx9fX2YNGkSgIdTjSNGjICzszNvPscdHR348MMPsX//fvl7OGLECAQEBOCrr77i3U62W7ZsQWxsrFJ7X18flixZwsudiocjGhkZpoqLi7mOQIZATk4O1qxZg0uXLuEvf/kL7/Yt+eXnOCUlBUZGRsjOzpavTXH37l0sW7YMM2fO5CqiyoWGhuKHH37AkSNH4OHhAYZh8P333yMyMhIrVqxAbm4u1xFVKjU1Faampli5cqW8TSqVYvHixaisrOQwGVElGhkhhKf6V+28d+8eFixYABMTE+Tn5wMAb0ZGfsnS0hJFRUVKe9BUVlbC29sbt27d4iiZahkYGODYsWOYMWOGQvuZM2fw5ptvorOzk6Nk6lFeXo65c+di165deP/999Hb24uAgABcvXoVJ06cwJgxY7iOSFSA1hkhhKf61xgZP348SktLIRKJ4OLiAolEwnEy9ejo6MDPP/+s1N7S0oJ79+5xkEg9TE1NB5yKMTY2VlqtlA+cnZ2Rn5+PFStW4O9//zveeecdVFdXo7i4mAoRHqFihBCe+uWgp0gkQkFBARYuXAg/Pz/uQqnRwoULsWzZMuTl5eHmzZu4efMm8vLyEBISwqul79evX4+oqCg0NTXJ25qbm7F27VrEx8dzmEx9Zs2ahb179+Ldd99FXV0dTp06BXNzc65jERWiaRpCeCo7OxuLFi2Cjo6OQntmZiZOnz6NzMxMjpKpR1dXF6Kjo7Fnzx709vYCALS0tBASEoI//elPMDAw4Djh4Dk5OSnsF/XTTz/hwYMHGDduHACgoaEBOjo6mDBhAi5cuMBVTJV5XPFYWloKsViMl156Sd7G55VmhxMqRgghvNLZ2Yna2lqwLAuxWKzRRUi/TZs2PXXfDRs2qDHJ0Fi2bNlT9+VbUT1cUTFCCI+kpaVh5cqV0NXVRVpa2mP7MQyD8PDwIUw2tG7evAmGYWBpacl1FPKcuru7IZPJ5EVlXV0dDh8+DAcHB8ybN4/jdERVqBghhEdsbW0hkUhgamoKW1vbx/ZjGAbXr18fwmTqJ5PJ8NlnnyE5ORn3798HABgZGeHjjz9GXFyc0kqeRDN4e3vD398fYWFhaG9vh729PYRCIW7fvo2UlBSsWrWK64hEBWidEUJ45MaNGwN+PRzExcXh66+/xtatW+Hp6QmWZVFSUoKNGzeip6cHiYmJXEdUCYFAoHD/yKP49sj2hQsX8OWXXwIA8vLyYG5ujoqKChw8eBAJCQlUjPAEFSOE8MjTLo/OMAySk5PVnGZoZWdnY/fu3Xj77bflbY6OjrC0tMTq1at5U4z0rxXTr7e3FxUVFcjOzn6me0s0RVdXF4yMjAAARUVF8Pf3h0AggLu7O+rr6zlOR1SFihFCeKSiouKp+j3pL2tN1dbWBnt7e6V2e3t7tLW1cZBIPXx9fZXa3n33XUyZMgV/+9vfEBISwkEq9RGLxTh8+DAWLlyIY8eO4aOPPgLwcP0YkUjEcTqiKnTPCCGEF9zc3ODm5qZ04254eDjOnz+P0tJSjpINjdraWrz66qu8W4E1Ly8PgYGB6Ovrg5eXF4qKigA83LPm9OnTOHr0KMcJiSpQMUII4YXTp09j/vz5GDdunMKeLY2NjSgoKODV/jSP6u7uRmxsLI4ePYrq6mqu46hcc3Mzmpqa4OjoKL8RuaysDCKRaMDRMKJ5qBghhGi83t5eeHt7IzExEf/85z9x9epVsCyLyZMnY/Xq1Rg7dizXEVVm5MiRCtNsLMvi3r170NfXx1//+leFe2YI0RRUjBBCeGH06NH4/vvvMWHCBK6jqFV2drbCsUAgwOjRo+Hm5sbLvWnI8EA3sBJCeGHp0qXyR3v5SiqVoq6uDsuXL4eVlRXXcQhRGRoZIYTwQnh4OHJyciAWi+Hi4qK0DHxKSgpHyVTLyMgIly9fho2NDddRCFEZGhkhhPBCZWUlpk+fDgC4du2awjk+Pcrs5eWFkydPIjg4mOsohKgMFSOEEF4oLi7mOsKQ8PHxQWxsLCorK+Hs7Kw0AkQ3sBJNRNM0hBCiQZ60xw7DMLxbDp4MD1SMEEIIIYRTtI0lIYQQQjhFxQghhGiQiIgIpSXvAWDHjh34wx/+MPSBCFEBKkYIIUSDHDx4EJ6enkrtr732GvLy8jhIRMjzo2KEEEI0yJ07d2BsbKzULhKJcPv2bQ4SEfL8qBghhBANIhaLUVhYqNR+9OhR2NnZcZCIkOdH64wQQogGiYqKwpo1a9Da2oo5c+YAAI4fP47k5GSkpqZyG46QQaJHewkhRMOkp6cjMTERt27dAgDY2Nhg48aNWLp0KcfJCBkcKkYIIURDtba2Qk9PD4aGhlxHIeS50D0jhBCiQbq7u9HV1QUAGD16NO7cuYPU1FQUFRVxnIyQwaNihBBCNIivry9ycnIAAO3t7XB1dUVycjJ8fX2Rnp7OcTpCBoeKEUII0SAXLlzAzJkzAQB5eXkYM2YM6uvrkZOTM+BiaIRoAipGCCFEg3R1dcHIyAgAUFRUBH9/fwgEAri7u6O+vp7jdIQMDhUjhBCiQcRiMQ4fPozGxkYcO3YM3t7eAICWlhaIRCKO0xEyOFSMEEKIBklISEB0dDRsbGzg5uYGDw8PAA9HSZycnDhOR8jg0KO9hBCiYZqbm9HU1ARHR0cIBA//piwrK4NIJIK9vT3H6Qh5dlSMEEIIIYRTNE1DCCGEEE5RMUIIIYQQTlExQgghhBBOUTFCCCGEEE5RMULIMLdx40ZMmzZNfhwcHAw/P78hz1FXVweGYfDDDz88to+NjQ1SU1Of+ppZWVkwMTF57mwMw+Dw4cPPfR1CyMCoGCHkBRQcHAyGYcAwDIRCIezs7BAdHY3Ozk61v/a2bduQlZX1VH2fpoAghJBfo8V1AELIwN58801kZmait7cXZ86cQWhoKDo7OwfcDK23txdCoVAlr2tsbKyS6xBCyNOikRFCXlA6OjoYM2YMrKysEBgYiA8++EA+VdA/tbJnzx7Y2dlBR0cHLMviv//9L1auXAkzMzOIRCLMmTMHFy9eVLju1q1bYW5uDiMjI4SEhKCnp0fh/KPTNDKZDElJSRCLxdDR0cG4ceOQmJgIALC1tQUAODk5gWEYzJo1S/59mZmZcHBwgK6uLuzt7fHnP/9Z4XXKysrg5OQEXV1duLi4oKKi4pl/RykpKZg6dSoMDAxgZWWF1atX4/79+0r9Dh8+jIkTJ0JXVxe//e1v0djYqHD+u+++g7OzM3R1dWFnZ4dNmzZBKpU+cx5CyOBQMUKIhtDT00Nvb6/8uKamBrm5uTh48KB8muR3v/sdmpubUVBQgPLyckyfPh1eXl5oa2sDAOTm5mLDhg1ITEyERCKBhYWFUpHwqNjYWCQlJSE+Ph5XrlzBN998A3NzcwAPCwoA+Pe//42mpiYcOnQIAJCRkYG4uDgkJiaiqqoKmzdvRnx8PLKzswEAnZ2deOuttzBp0iSUl5dj48aNiI6OfubfiUAgQFpaGiorK5GdnY0TJ05g3bp1Cn26urqQmJiI7OxslJSUoKOjA4sWLZKfP3bsGJYsWYKIiAhcuXIFu3btQlZWlrzgIoQMAZYQ8sIJCgpifX195cfnzp1jTU1N2ffff59lWZbdsGEDKxQK2ZaWFnmf48ePsyKRiO3p6VG41vjx49ldu3axLMuyHh4ebFhYmMJ5Nzc31tHRccDX7ujoYHV0dNiMjIwBc964cYMFwFZUVCi0W1lZsd98841C26effsp6eHiwLMuyu3btYkeNGsV2dnbKz6enpw94rV+ytrZmv/zyy8eez83NZU1NTeXHmZmZLAC2tLRU3lZVVcUCYM+dO8eyLMvOnDmT3bx5s8J19u7dy1pYWMiPAbD5+fmPfV1CyPOhe0YIeUEdOXIEhoaGkEql6O3tha+vL7Zv3y4/b21tjdGjR8uPy8vLcf/+fZiamipcp7u7G7W1tQCAqqoqhIWFKZz38PBAcXHxgBmqqqrw4MEDeHl5PXXu1tZWNDY2IiQkBCtWrJC3S6VS+f0oVVVVcHR0hL6+vkKOZ1VcXIzNmzfjypUr6OjogFQqRU9PDzo7O2FgYAAA0NLSgouLi/x77O3tYWJigqqqKri6uqK8vBznz59XGAnp6+tDT08Purq6FDISQtSDihFCXlCzZ89Geno6hEIhxo4dq3SDav9/tv1kMhksLCxw8uRJpWsN9vFWPT29Z/4emUwG4OFUjZubm8K5ESNGAABYFWyJVV9fj/nz5yMsLAyffvopRo0ahbNnzyIkJERhOgt4+Gjuo/rbZDIZNm3aBH9/f6U+urq6z52TEPLrqBgh5AVlYGAAsVj81P2nT5+O5uZmaGlpwcbGZsA+Dg4OKC0txdKlS+VtpaWlj73mhAkToKenh+PHjyM0NFTpvLa2NoCHIwn9zM3NYWlpievXr+ODDz4Y8LqTJ0/G3r170d3dLS94npRjIBKJBFKpFMnJyfKda3Nzc5X6SaVSSCQSuLq6AgCqq6vR3t4u3912+vTpqK6ufqbfNSFEtagYIYQn5s6dCw8PD/j5+SEpKQmTJk3CrVu3UFBQAD8/P7i4uCAyMhJBQUFwcXHBjBkzsG/fPvz444+ws7Mb8Jq6urqIiYnBunXroK2tDU9PT7S2tuLHH39ESEgIzMzMoKenh8LCQrz88svQ1dWFsbExNm7ciIiICIhEIvj4+ODBgweQSCS4e/cuoqKiEBgYiLi4OISEhGD9+vWoq6vDF1988Uw/7/jx4yGVSrF9+3YsWLAAJSUl2Llzp1I/oVCI8PBwpKWlQSgUYs2aNXB3d5cXJwkJCXjrrbdgZWWF9957DwKBAJcuXcLly5fx2WefPfsbQQh5ZvQ0DSE8wTAMCgoK8Prrr2P58uWYOHEiFi1ahLq6OvnTLwEBAUhISEBMTAycnZ1RX1+PVatWPfG68fHx+Pjjj5GQkAAHBwcEBASgpaUFwMP7MdLS0rBr1y6MHTsWvr6+AIDQ0FDs3r0bWVlZmDp1Kt544w1kZWXJHwU2NDTEd999hytXrsDJyQlxcXFISkp6pp932rRpSElJQVJSEl555RXs27cPW7ZsUeqnr6+PmJgYBAYGwsPDA3p6eti/f7/8/Lx583DkyBH861//wm9+8xu4u7sjJSUF1tbWz5SHEDJ4DKuKyVtCCCGEkEGikRFCCCGEcIqKEUIIIYRwiooRQgghhHCKihFCCCGEcIqKEUIIIYRwiooRQgghhHCKihFCCCGEcIqKEUIIIYRwiooRQgghhHCKihFCCCGEcIqKEUIIIYRw6v/WvPOtfekLPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(val_confusion, classes , normalize=True,\n",
    "                        title='Normalized confusion matrix - Validation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's worth noting that the confusion matrix doesn't tell the whole story about the performance of a classification model, as different misclassifications may have different implications depending on the specific problem. Additionally, it's always a good to look at other metrics such as ROC curve, precision, recall, and F1-score, to get a more complete picture of the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def precision_recall(confusion_matrix):\n",
    "    # precision = tp / (tp + fp)\n",
    "    # recall = tp / (tp + fn)\n",
    "    # f1 = 2 * (precision * recall) / (precision + recall)\n",
    "    # https://en.wikipedia.org/wiki/Precision_and_recall\n",
    "    # https://en.wikipedia.org/wiki/F1_score\n",
    "    precision = np.diag(confusion_matrix) / np.sum(confusion_matrix, axis = 0)  # true positive rate = tp / (tp + fp) <- precision\n",
    "    TP_rate = np.diag(confusion_matrix) / np.sum(confusion_matrix, axis = 1) # true positive rate = tp / (tp + fn) <- recall\n",
    "    FP_rate = np.sum(confusion_matrix, axis = 0) - np.diag(confusion_matrix) # false positive rate = fp / (fp + tn) <- fall-out\n",
    "    f1 = 2 * (precision * TP_rate) / (precision + TP_rate) # f1 = 2 * (precision * recall) / (precision + recall)\n",
    "    return precision, TP_rate, FP_rate, f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ROC_curve(thresholds, data, classes):\n",
    "    tpr = [1]\n",
    "    fpr = [1]\n",
    "    for i in thresholds:\n",
    "        confusion_matrix_ = confusion_matrix(data['pred'], data['label'])\n",
    "        precision, TP_rate, FP_rate, f1 = precision_recall(confusion_matrix_)\n",
    "        tpr.append(TP_rate)\n",
    "        fpr.append(FP_rate)\n",
    "\n",
    "    from sklearn.metrics import roc_curve\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    auc_score = []\n",
    "    for i in range(len(classes)):\n",
    "        fpr, tpr, _ = roc_curve(data['label'], data.iloc[:, 3:-1].iloc[:, i], pos_label = classes[i])\n",
    "        plt.plot(fpr, tpr, label = classes[i])\n",
    "        auc = 0\n",
    "        for i in range(len(tpr)-1):\n",
    "            auc += (tpr[i] + tpr[i+1]) * (fpr[i+1] - fpr[i]) / 2\n",
    "        auc_score.append(auc)\n",
    "\n",
    "    # plot the ROC curve using matplotlib\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve for Multiple Classes (Validation)')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    #calculate AUC area under curve for each class\n",
    "    # from sklearn.metrics import auc\n",
    "    # auc = []\n",
    "    # for i in range(len(classes)):\n",
    "    #     fpr, tpr, _ = roc_curve(data['label'], data.iloc[:, 3:-1].iloc[:, i], pos_label = classes[i])\n",
    "    #     auc.append(auc(fpr, tpr))\n",
    "    \n",
    "    # print the AUC score for each class\n",
    "    for i in range(len(classes)):\n",
    "        print('AUC for class {} is {}'.format(classes[i], auc_score[i]))\n",
    "    return auc_score\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fBwU9eK0AwNixYzFp0iQ8++yz+OWXX3Dw4EEcOnQIjRs3trjdgu0D5v9sPhgnANjb25tsP/89LmlsRGSZkv1ZmIgIQGhoqH5CiHbt2kGr1eLbb7/Fjz/+iBdeeEFfrlKpsGnTJgwfPtzkcfInhejYsaN+Hzs7uyL3KU6nTp3wzjvvYNOmTejcuXOx9fO/gGRnZ8Pe3l5fXtiX5sJ6xzp16gR/f38sXboUnTp1wtKlS9GiRQvUq1dPX8fb2xteXl7YunWryWOUZFrv/C9b8fHxRtvi4uLg7e1tVvzmGDRoEN5//30sWrQIH330UaH1Cr6nBZUkMTRXQkICqlatql/XaDRISkoy+WU038N8Hn5+fmjYsCF+++03ZGRklOg+p++++w7BwcFYu3atwefy4PsGAN27d0f37t2RnZ2NAwcOYPr06ejTpw+CgoIQHh4OlUqFsWPHYuzYsUhOTsbvv/+Od955B506dcLVq1fh6ekJpVKJ/v3749VXXzUZT3BwMAAUe6zCzjX/Wrt9+7bJ7c899xw8PT2xZMkStGnTBv/73/8wYMAAuLi4AAD+/fdf/PPPP1i2bBkGDhyo3+/ChQtmvJvGvLy8TF4HCQkJRnW/++47DBgwAB9//LFBeWJiIjw8PErcPpD3s1mtWjWDbaZ+Ni2R/x4/zDGIyHzscSIiq5k5cyY8PT3x/vvv64eq+fr6YsiQIdi2bRvWrl1rtM+5c+fwySefoH79+vqJHHx9fTF06FBs27bN4Ob4gi5evIgTJ04UGstjjz2GyMhILF68GDt37jRZ5/Dhw4iNjQUA/SxcDx6zuGdFPSj/S+mmTZuwb98+HD582Ggo2zPPPIOkpCRotVqEhYUZvQrORGau8PBwODo6Gt1Afu3aNezcudOsWfPMVbVqVYwfPx5du3Y1+GL7oMLe0/yehuIU9lf2oqxatcpgfd26ddBoNEU+8PZhP49Jkybhzp07GDVqlMmb9O/evaufrMQUSZL0DzLOl5CQYHJWvXz29vZo06YNPvnkEwDAsWPHjOp4eHjghRdewKuvvorbt2/j8uXLcHJyQrt27XDs2DE0atTI5PmaSjJNHaswoaGhAPJ+Rk1xcHBAnz598Ntvv+GTTz5Bbm6uwc9I/vtQ8A8YAPDVV18V2mZR8iepePDa+P77743qSpJk1O6vv/5qNBQuv44512f+rJ4P/mweOnQIZ86ceaifzUuXLkGhUJTodwYRWY49TkRkNZ6enpg4cSImTJiA77//Xj9t9uzZs3H27Fn069cPe/fuRdeuXWFvb48DBw7gs88+g6urK9avXw+lUqk/1uzZs3Hp0iUMGjQI27ZtQ48ePVClShUkJiZi+/btWLp0KdasWVPklOQrVqxA586dERkZiSFDhiAyMhKenp6Ij4/HL7/8gtWrV+PIkSOoXr06oqKiUKlSJURHR+ODDz6ASqXCsmXLcPXqVYvfhyFDhuCTTz5Bnz594OjoaDTj3EsvvYRVq1YhKioKb7zxBpo3bw47Oztcu3YNu3btQvfu3dGjRw+L2vTw8MCkSZPwzjvvYMCAAejduzeSkpIwdepUODg4YPLkyRafR1FmzJhRbB1fX1906NAB06dPh6enJwIDA7Fjxw5s2LDBrDYaNmyINWvWYO3atahRowYcHBzQsGHDIvfZsGEDVCoVOnbsiFOnTmHSpElo3LixfhiYKQ/7ebz44ouYNGkSPvzwQ/z333+Ijo5GzZo1kZGRgYMHD+Krr75Cr169Cp2SPH9q+5EjR+KFF17A1atX8eGHH8LPz89gNsT3338f165dw9NPP41q1aohOTkZc+fOhZ2dHdq0aQMA6Nq1Kxo0aICwsDBUrlwZV65cwZw5cxAYGIjatWsDAObOnYsnn3wSrVu3xogRIxAUFIS0tDRcuHABv/zyi/4PDeYcy5Rq1aqhRo0aOHDgAEaNGmWyTnR0NObPn4/Zs2ejbt26aNmypX5b3bp1UbNmTbz99tsQQqBSpUr45Zdf9MNfLRUREYGnnnoKEyZMQHp6OsLCwvDnn39i5cqVRnWfeeYZLFu2DHXr1kWjRo1w5MgRfPrpp0Y9RTVr1oSjoyNWrVqF0NBQuLi4wN/fH/7+/kbHrFOnDl5++WXMmzcPCoUCkZGRuHz5MiZNmoSAgICHeojtgQMH0KRJE3h6epb4GERkAblnpyCi8qewB+AKkTdDXvXq1UXt2rUNHmibk5Mj5s+fL1q0aCFcXFyEvb29qFOnjpgwYYJITEw02Y5GoxHLly8X7du3F5UqVRIqlUpUrlxZREZGiu+//15otdpiY83MzBRffPGFCA8PF25ubkKlUgl/f3/x3HPPiV9//dWg7t9//y1atmwpnJ2dRdWqVcXkyZPFt99+a3JWvS5duhTZbsuWLQUA0bdvX5Pbc3NzxWeffSYaN24sHBwchIuLi6hbt6545ZVXxPnz54s8dlHv/7fffisaNWok1Gq1cHd3F927dxenTp0yqDNw4EDh7OxcZBvmtleQqVnw4uPjxQsvvCAqVaok3N3dRb9+/fQP7ixuVr3Lly+LiIgI4erqKgDoZ0orala9I0eOiK5duwoXFxfh6uoqevfuLW7cuGFwXFMPwH2YzyPfnj17xAsvvCD8/PyEnZ2dcHNzE+Hh4eLTTz8Vqamp+nqmZtWbMWOGCAoKEvb29iI0NFR88803Ru/J//73PxEZGSmqVq0q1Gq18PHxEVFRUWLfvn36OrNmzRItW7YU3t7eQq1Wi+rVq4vo6Ghx+fJlg/ZiYmLEkCFDRNWqVYWdnZ2oXLmyaNmypZg2bZrFxzJl0qRJwtPTU2RlZRVap2nTpgKAmDlzptG206dPi44dOwpXV1fh6ekpXnzxRREbG2s0C545s+oJIURycrIYMmSI8PDwEE5OTqJjx47iv//+MzrenTt3RHR0tPDx8RFOTk7iySefFPv27TN5zaxevVrUrVtX2NnZGRzH1LWs1WrFJ598IkJCQoSdnZ3w9vYW/fr1E1evXjWo16ZNG1G/fn2j98PUOaWlpQknJycxa9Yso/pEVDokITj5PxERlW9TpkzB1KlTcevWLd7vYQPi4uIQHByMFStWWP0ZX5Rn8eLFeOONN/T3rhFR6eM9TkRERGRV/v7+GD16ND766CP9/Y5kPRqNBp988gkmTpzIpImoDPEeJyIiIrK69957D05OTrh+/br+UQNkHVevXkW/fv0wbtw4uUMhqlA4VI+IiIiIiKgYHKpHRERERERUDCZORERERERExWDiREREREREVIwKNzmETqdDXFwcXF1dDZ7STkREREREFYsQAmlpafD394dCUXSfUoVLnOLi4ji7DxERERER6V29ehXVqlUrsk6FS5xcXV0B5L05bm5uMkdDRERERERySU1NRUBAgD5HKEqFS5zyh+e5ubkxcSIiIiIiIrNu4eHkEERERERERMVg4kRERERERFQMJk5ERERERETFYOJERERERERUDCZORERERERExWDiREREREREVAwmTkRERERERMVg4kRERERERFQMJk5ERERERETFYOJERERERERUDFkTp71796Jr167w9/eHJEnYtGlTsfvs2bMHzZo1g4ODA2rUqIFFixaVfqBERERERFShyZo4paeno3Hjxvjyyy/Nqh8TE4OoqCi0bt0ax44dwzvvvINRo0Zh/fr1pRwpERERERFVZCo5G4+MjERkZKTZ9RctWoTq1atjzpw5AIDQ0FAcPnwYn332GZ5//vlSipKIiIjKKyEEMjWZcodBRCY4qhwhSZLcYZhN1sTJUvv370dERIRBWadOnbB48WLk5ubCzs7OaJ/s7GxkZ2fr11NTU0s9Tno08T9fY0KnA3I1+Wt5L6EDxL1lnQ6AuLf6wDZtDqDV5JUhf7socHBxvy5w71j5VUR+AA/8iwL73Nu/wCHzj3G/HQEhCtbNr//g8cX9f+/VFzm5Bscxih0AcjMASWGijQdjzF9GgfYfiKfgMSAAnUDqXTUO/uuPnFzF/fPXn6sGUCgMywplolKR+4ki1gorNCuQAtUffD8L/sdaYJtOA0iKB7YX0a6FYRTcQQgtUlL2QKu9W6L9rV+74F7l54tHmSh4iUCU8H0tH/jJV0Ai73MX0v11ayvpdSWJAnGZoclTT6HdyFdK2FrZK1eJU0JCAqpUqWJQVqVKFWg0GiQmJsLPz89on+nTp2Pq1KllFSI9BKHRQJeVDei0gE6X92VTp4M2NxdpSYl5ZTodIHQQubnQZWcDWh2g1UDk5EKTlAhJbQ+Rm4uc3EwgNwtCkwvt9Xgo3FzyvjTrtIBWe+/YWkCTnXdcrQ4Qee3mxsZD6e0O5GYCCpX+i+3Bu5eRpr2XhN/7paX/F3m/LPL/LbhNKvALTV8H9/dzyRDQKQz3z69T1DEePE5+XbtcwDcFuO1suK9RXA/sV1Ts+jYLlKse4W8iNyo/hhS34DJrTyvl4KbqX8u/3pWjv9I9GnRyB0Bk5BH+VUxFKPg3vlI7fhnsnJWW9jAtlblylTgBMOrOE/f+KllYN9/EiRMxduxY/XpqaioCAgJKL8BHiBAC2qQk6DIzob19G0KrhcjJgS4jE5pbtwChQ/alS1C6ukHk5EBz8yZEbk5esqLJhsjNAbLS8hKVewlL9tmrsKvqBZGdjdy4ZCic1BBaHUS2Rt9ulkqJVEd7/frhGsYJcalLTL63kF2gsEDSLj3w70NIti++jqX+87f+MSuWpHsvW8evTHJQCMAtV2lym/4TkQopL6JOfplzpkCWWkLOA4MoLPkrbmEe9ooxub8FcRV2DqV9JSuEEi7ZLoW3JeXHUbI3ufj4zTiuiSrmHlel0UCrVECrvH9dikIOanTMAlUc7ZSFt2vmH2pytAKOdgooJMv+ozT7+jZZ74Hvhma2KUEHnXT/q7BOAFoh4GRn+ue76BiMCTPfs/z3215V1NQDDxxLp4MkdNCp1Q9skgyXpUI2FSx4cP8H6ml1gINaWfg1IBk2Imm0EPbGo8BMaRBl/i07tqBcJU6+vr5ISEgwKLt58yZUKhW8vLxM7mNvbw97+1L4ZvqI0GZmIuXf49Al3UZuzGXkxlwCsjOQvn3v/WFNVpYTcwNZKiUSvN2gy//pdL23UQL+8/cudF+l1jCmB3/RSgB0Jn6uJVGg3Iz/uI27mhX6XZUmf9EUOIhURCOF71CC3gPjX25F1dWJ+yPPyDwKlaLMOnWEEPAPfRJVG7SxaD+VSoFqDTyhdrSdX+fiwSGSANJztEjLyoVGJ3AjNQtKhflvbEaOFjfTsuFgZ/yl4lxCGtwcDf+DvpGSiTuZGpy7kYpAL2d9TCeupcLXvWT/HySk3PsjiiQhQ+lY4t4+wVwXACz6/B+k1d1/E1sEVzL5UZy/eRetanrp21HC3uQfWK8nZ6JFsOnvDwAgIJCaqUE9fzeD8swcDWr6uBRIDuQnBODuaAdXh8J/F3g6q+Fibzu/K4jKm3L10xMeHo5ffvnFoOy3335DWFiYyfub6D7NnTvIPHYMd3f8Bk3cVdw9eNzsxEijADRKwD4XOBrkgSw7OyhEXnKR7gCotMBdx7zERHuvLqR7d2kU+Bf3/lXoANcMtyJazJPinItcVV6Mie45OFD/ttmJQl2P2lje4WvArugvOEIncPd2dpFfZrZ8cRoZyTnmNVxO1GslQy9eOeMf4ok6LXzlDqPUZWu0uJmajRyt8e+D/+LTcDs9G0rF/YRFQODolWSjBESjFdh0/DpupGY/eJgyUNi9h664HldgVemJi5bcnlSQ0un+sv5etIdXo3JeYpeercGN1Gz0bh6Aou7XupGajWaBnkZb7qTnoJaPCxzVhf+lPCtXixqVXYr9NWqvUqKya+n8wVGSAB9X00kMEZGtkzVxunv3Li5cuKBfj4mJwfHjx1GpUiVUr14dEydOxPXr17FixQoAwPDhw/Hll19i7NixGDZsGPbv34/Fixdj9erVcp2CzRFCIHXzZiRt24qDV88jWwHosnKNKwYbfyHMsM9LhjQq4K6DhGw7INvufk+Nd4oaKp3xX3yzVYCkA5QAlFpAbaK5wqidnBHU9DGjcr86oajXvoP5B3qAubO0/Dr/H1w+af6QrCrBxSd8tkxlp0CL7jXhV9Nd7lCoFGTkaCBEXhJzLTkDV29nQq2ScP1OJo7GJqOyqz2+3nsJAODlrEZSetn/QcBZrUR6jhaOdkoEVHI0e79badnwdFYj6F4PUj4hBK4kZaBVLcOe6qT0bPi7O8LLxV7fjhB5Q2F83BxKFLtKIaFKCfctyN3RDuoih+QQEZEtkjVxOnz4MNq1a6dfz78XaeDAgVi2bBni4+MRGxur3x4cHIzNmzdjzJgxmD9/Pvz9/fHFF19U6KnINXfuIDc2Fun7duPGsuW47KDGpSqeyFEpAfW9j9fF8o/ZISfvVZjmL75U5P52ChWK6x5y9vBEvafaQ6my3mV47lAC9q4+B02ueb1p2gL17BwK/0uth48Teox7DHb2xYx7pgorK1eLjBwt4lMyIZm49m+kZSFXo4NGJ3DuRhrc7w0vu52eg+vJmcjVCly/kwEf17wv5jv/u4naVVyKHRF2N0uDy0kZFsdrKmlyMzHEJzVLg6dCKhuMvdfqBBLvGvd8aLQCdzJy0CG0Cp4KqYyCo7EkSdKfMxERUXkkCVGxRlynpqbC3d0dKSkpcHMrn70HIjcX10aPRsrOnUh1tIeAhERXR1zwrWRUV6PU4FLV27jjDGQ43P8WU9W1Kl5r8hoAQK1Umz1sQpIkVKvXEI4ursVXLkOXjt3C0d+u4EaM5dPNOzjbod+0cNjb0D0iZH3xKZm4mZqtT0TiU7Kg1RneQnw9OROXEtORo9HBxV6F41eT4eqgMrph99/rqVAqJCSlZyPLzCRdLo52StSu4oL/4tNQo7IzWtb0xo3ULHRv4o+ASk5wd7SDl4sa9ir+UYCIiCoeS3IDflMsR9KuxuLk55/h7qFDgFaLk41qFlp352M3keySC/+AmlgeucFoe3l74NiD/t1zzSBJ+u+A4aQhT75YGzWaVjbrWI4udlAVcV8AlR8/HL6KfecTEZ+SCSGAw1fuyBKHk1ppdAO2QN5ws8eqeyAzV4f0bA0aB3gAAJIzcuDhpIaHox08nez0Q8mycrWo6eNSbHs6nYC3iz383B3g6pDXq2OvUkDxEDfgExERkSEmTuVE4pq1WL5xZd6Kv/EMQKlOudAoBeKfcMG8/isw4t7N3OUhQUpPycaFIzeh05jX+ZmVnoOj22JNbmvSIQC1mlWBT5CrzZ835UnNysV/8aaf43Dp1l3Y2ylw6noq3B3tcDkpA+uPXoOvmwMc1UrEJKYDANRKhcnJDUzxd3fQXxvXkzPRPMiwp/bCrbsI8nJCPX83eDqpcSM1C42qeUD1QBJyN1uDmpXzhtJV83SEo1oFH1d7KCWJCQsREdEjiImTjdNptbg8YAAO3bwKVLrffZhQKQO5KiDNUYPUVj5YHrUOgG0lSmm3s3A7Lr3Yer8vO42suxbMKFFA+HP3e91cPOxRK6wKv7TKLDYpA9eSM3AuIQ0Xbt2Fl7M9zt9MQ9LdHByLTUaOVoeASo64eruwmdCKl5CaZbD+YNLUrbE/PJ3sUMvHBQLAk7W84eViDzcHlc38fBAREVH5wsTJhmnS0nDu8ebQKBS41jBYX76i0xWEeNfB8si82QZLO1kSQiAjJQc6nfm3w2lzdVg1+YDFbdV5wvzpnwMbeKF2WJXiK5JVaO89fyc9W4M/LyRi+f4rqFnZGUdjk/X/as28RkwlTQGVHGGnMLyXSHtvxrTWtb1x6VY6ngrxRmaOFrV8XNA82AsKCVAoJPi55w1tc7ZXwc2BExAQERGR9TFxslG3r1zG0vGvAo0N72N6sedjGNFtXZn2LP35wwX8s/NqifevXL34iSQcXdV4emAonNzUJW6HrOdutgZHrtzBv9dTsOZQLNwd7fDvdeOJN/KHyt02MUNbbR8XnL95F80CPVHPzw3xKVmoUdkZ3i5q1Pd3h5NaCYWUl/R4Oqthp+T0zERERGS7mDjZqJXjRgIPfJGsFVIN1Z//oNTaPPrbFRzdegXigV6DnCytfllp4bNHajTxRsTQBlaJj0pHalYuFu2+iAW7L8LVXoW0bI1RnauFPGDUx9Ueg1sFw16lgL+HA7I1OtTxdUWdKrzHjIiIiB4tTJxs0NUTx6G5lzQ5Z2dhd8frWNpsIhyfGPDQx757Jwt7vj+LrHTje4oSLhU+lbdSpcBLk5rDo4rTQ8dAZU+j1SE1S4NsjRa30rIhQcKK/Zfxw5FrBvVMJU0+rvYQAF5vXwtPh1ZBVQ/zH1pKRERE9Khg4mSD/lowR7/8W4d4/HgrHorm/cze/+yBeMRdSDG57fQfccXuHzm8ISr5OxuUObrYwd6J947YshyNDsmZORACOH41GYv2XMSx2GS4O9ohJdO8yTdC/dzwxtO1EVDJEfX83NhrRERERHQPEycbI4TAtTuJAIAqKen4Luk6FO/dABSFD5G7fDIRKbfyhlJpcrQ4sOlSse04uqnRtk8do3LXSg5m3ZNE8kvLysX/TsTjdFwqVh64Umg9U0mTt4saaqUCcSlZGNQyCO8/U4+zERIREREVgYmTjbmbdlu/fL1qOhz7bQRU9gZ1hBC4FZuG7EwNbsel4491500e6/Fngk1+GVaqFAhpUQXO7vYm9iJbdTdbg//9E4efjsdh/6WkQuspJKDgbWoDwwPRtq4PGlfzgIejHSQJ7EkiIiIishATJxsihMDCd3tDibyZ5YZXi4FUs51RvbMHErBj+Rmj8tphPvrlaqGVUK+Vf+kFS6XudnoOtp9OwMVb6fh6b+G9iAoJqOfvhqYBnni/az3OTkdERERUCpg42ZC7WWlQ3rw/HbdbrdYm6x3YdFG/XMnfGZIkoWHbqqjfumqpx0il5262Bp9tO4ufjl/HnYyi70mqUdkZQ5+sgeceqwoHO2UZRUhERERUcTFxsiHx/93vRXoy+zKkF/8yWS89Je+ZOQ2eqoo2Ju5TovJhx5kbiF5+GI2quePENdOTeeSLbOCL5IxcfNG7KSq7coglERERUVlj4mRDdn2zUL9c9zEl4OBmsP3yyUQc//3+g2jrPOFbZrHRw8vR6LDh6DW8veGkQbmppOnxIE+Mi6iDJ2p4lVV4RERERFQEJk42QgiBu0l5s+l5pGdBNWKdwXadVodf558wKPOu5lJm8VHJ/fxPHEatPlbo9s71fdHr8QA42CnRIrgSZ7cjIiIiskFMnGzEDx++q18OuJ0IySPAYPv5wzf1y/Vb+6NJh+pQqXlvi63afzEJo9Ycw620bKNtDnYKeDiq8XmvJniiRiXOcEdERERUDjBxsgHpyXdw9dT93qQcVw0cVY4GdXYWmEXvqd512CthY67ezsCOMzew6mAszt+8a7JOh1AffPxcQ/i4OpRxdERERET0sJg42YCLRw7qlzv/cxEefZ7T90LotDpcO3sHunsP5mncIYBJk8y0OoHdZ29i8s+ncO1OZrH1x3eqgwHhgXB1sCuD6IiIiIioNDBxsgE3Ll3QLysAqGqFAgByc7RYPeUg0m5n6bc/HhVUxtERAGTlavHD4auY9NOpYusqJGDG843QMyyg2LpEREREVD4wcbIBDi6uAAB1rgYAYBcYgOQbGVg99aC+p0mSgCd61IS9E3styoIQAr2/OYCjV5KhEwKae5+DKa+3r4WBLYPg7cJpwomIiIgeVUycbEDM8SMAgMppGQAAdWhd/P3rFX3S5FvDDT3ebMYhemUgISULPx65is9+O1donXp+blgy6HH4uvNeJSIiIqKKgomTDbh1+RIAQCGAbLUEBxdvnPkzb0iYyl6JZ8c+xqSplFy8dRcbj17H6r9jkZSeY7LOh882QBVXe7Sq5Q1ne/7IEBEREVVE/BYoM01urn7ZN+Uu7HME/tl5/yG3zbsEQ6lSyBHaI+vCzbuI+HwPihh9B28XNTrW88XkrvXgYMdp34mIiIgqOiZOMrtWYBpyr7RMSC4uOLDpkr6s/lP+coT1yMnV6vD13kv4dNtZk9sbB3jATiHhjQ618WQtbz5biYiIiIgMMHGS2d3kO/plBQBF67bAvRmuu7zaCGoHfkQldT05E/vO3cLMbWdxu5BheNOfa4jnH6sGNXv1iIiIiKgI/FZuIySRN6PeLWWgviyoobdc4ZRrEzecxOq/Ywvd3rdFdUx7tgF7lYiIiIjIbEycZKbTagEASpF3r1OSQyBwV86IyqccjQ4Xb91F5Nx9JrfXrOyMFdEtUNXDsYwjIyIiIqJHARMnmZ3csQ0A4JKZl0BdTXQGAPgEusoWU3nz6qqj+PVkvFH5K21q4O3OddmzREREREQPjYmTzBIu5j0vKEelhEZ5/wGq9Z7kpBDFOXLlDp5f+JdReVUPR/z5dnsZIiIiIiKiRxUTJ5m5eHnhblIS6sanQEj3JygIaeErY1S2S6sT+On4dYxd94/RtkPvdkBlV3sTexERERERPRwmTjIT954l5JiTixx1JX25UsnhZQ/6bNtZfLnrgsltf7/zNJMmIiIiIio1TJzkJnT6xXO1XtAvK5ScHjtfYfcw1ajsjF9eexLO9ryMiYiIiKh08RunzDJSUgAAkgBwbxID7wAXGSOyDTqdwNK/LuPD/5022vZau1oYFxHCSR+IiIiIqMwwcZKZ0OX1OAkJuONZFwDQNKK6nCHJ6t/rKfjo1zPYfynJaNsH3etjQHhQ2QdFRERERBUeEyeZqR0dkZOZiVS32vqyAqP3KgQhBN7b9C9WHTT90NoxHUIw6ula7GEiIiIiItkwcZKZuDc7xNmQPsi/q8mvprt8AZWxtKxcNJzym1F5NU9HTO5aHx3rVZEhKiIiIiIiQ0ycZJafOAF5vSlPPFsDbt6O8gVURjJyNFj652V8uu2sQfngVkGY1KUeFAr2LhERERGR7WDiJDd94pSnfuuqMgVSdl77/ij+d8Jwljw/dwfsn/i0TBERERERERWNiZPM8nuc8u/fcXC2kzOcUpWVq0XdSVuNyt/rEoqhrWvIEBERERERkXmYOMksf1Y9QII6O1nOUEqVqXuZfn6tFRpV85AnICIiIiIiCzBxklnBe5xy7D3kDKVU/HUxEX2+OWhUHjM9irPkEREREVG5oSi+CpWqAomTp7gpayjWlqvVGSVNDnYKXJ7RhUkTEREREZUr7HGSWcEeJ8UjlsZ2+WKffvnpuj5Y1L8Z7JSP2EkSERERUYXAxElGwmBGPQlajWyhWFVWrhYNp2xDrvb++S0e9LiMERERERERPRwmTjISQmewbudsL1Mk1vXgzHlH3usgUyRERERERNbBxElG2tzc+yuSAu72mfIFYwWfbP0PC3dfNCi7+HEUlHyYLRERERGVc0ycZKTTagusKSGV09t/crU61H53i1H55RldZIiGiIiIiMj6mDjJSKcrOFRPQm5GjmyxlNS+87fQf/HfBmW/j30KtXxcZYqIiIiIiMj6mDjJSBgkTgr4+JSvIW0/HL6K8T+eMCi79HEUFByaR0RERESPGCZOMiqYOEmSBHsnpYzRWGbLyXiDpGlRv8fQuYGfjBEREREREZWecnpXzaOh4DOcAKCyR7Z8wVjgZmoWRqw6ql+f2q0+kyYiIiIieqSxx0lG93uc8hInycafgJuQkoX+iw/i/M27+rLX29fCwJZB8gVFRERERFQGmDjJ6P5znCS4pVyCpLDdj2PpnzGY+stpg7JnGvlhXEQdmSIiIiIiIio7tvtNvQLIycx/bpOEDCdfQHFb1ngKcyz2jlHStPblJ9CihpdMERERERERlS0mTjJKz8of8qaBZ/JZqFUBssZjyowt/2HRnvsPtX0nqi5efqqmjBEREREREZU9Jk62QHLO+0dpW/c4/XjkmkHSNLVbfd7PREREREQVEhMnGyEJAUi2kzgl3c3Gmz/8o1/fNvop1PHlQ22JiIiIqGKynW/qFZ4ovkoZuXo7A82m/a5f/6B7fSZNRERERFShscdJTqJgsmQbidO8Hecxa/s5/foTNSphQHiQfAEREREREdkA2XucFixYgODgYDg4OKBZs2bYt29fkfVXrVqFxo0bw8nJCX5+fhg8eDCSkpLKKNrSIkESAnZ+vrJG0WjKNoOkKcjLCWteDpcxIiIiIiIi2yBr4rR27VqMHj0a7777Lo4dO4bWrVsjMjISsbGxJuv/8ccfGDBgAKKjo3Hq1Cn88MMPOHToEIYOHVrGkZcOSa2Wre3Ptp1FapZGv/5h9/rYPb6dbPEQEREREdkSWROn2bNnIzo6GkOHDkVoaCjmzJmDgIAALFy40GT9AwcOICgoCKNGjUJwcDCefPJJvPLKKzh8+HAZR14aBCSVPCMnP/r1NL7cdUG/fvHjKPTn8DwiIiIiIj3ZEqecnBwcOXIEERERBuURERH466+/TO7TsmVLXLt2DZs3b4YQAjdu3MCPP/6ILl26FNpOdnY2UlNTDV42SQAKZ+eyb1YIfLMvRr++qF8zKBVSmcdBRERERGTLZEucEhMTodVqUaVKFYPyKlWqICEhweQ+LVu2xKpVq9CrVy+o1Wr4+vrCw8MD8+bNK7Sd6dOnw93dXf8KCLC9h8wCgAQdIJVtwiKEQPDEzfr1Gc81ROcG8t5nRURERERki2SfHEJ6IFkQQhiV5Tt9+jRGjRqF999/H0eOHMHWrVsRExOD4cOHF3r8iRMnIiUlRf+6evWqVeO3Fp1CDSjK7uNIy8o1SJoAoNfjtplUEhERERHJTbbpyL29vaFUKo16l27evGnUC5Vv+vTpaNWqFcaPHw8AaNSoEZydndG6dWtMmzYNfn5+RvvY29vD3t7e+idgZVqlPYCy6XEauvwQfj9z06AsZnpUoQkrEREREVFFJ1uPk1qtRrNmzbB9+3aD8u3bt6Nly5Ym98nIyIDigV4ZpVIJIK+nqjxzykiAVAb3Fq3cf9kgaVJITJqIiIiIiIoj6wNwx44di/79+yMsLAzh4eH4+uuvERsbqx96N3HiRFy/fh0rVqwAAHTt2hXDhg3DwoUL0alTJ8THx2P06NFo3rw5/P395TyVhyYJUepD9bI1Wkz66ZR+ff/E9vBzdyzVNomIiIiIHgWyJk69evVCUlISPvjgA8THx6NBgwbYvHkzAgMDAQDx8fEGz3QaNGgQ0tLS8OWXX2LcuHHw8PBA+/bt8cknn8h1Cg/FsJesdCeH0OkE6ry3Vb++fkQ4kyYiIiIiIjNJoryPcbNQamoq3N3dkZKSAjc3N1ljuXz2JNa/PxGQXFEnpToivhkFdbWqpdLW0OWH8fuZGwCAYG9n7Hqzbam0Q0RERERUXliSG8ja40QFiVLrcOr51X78HXMbAGCvUjBpIiIiIiKyEBMnG6FTqErlHqfGU39DSmaufv3vdzpYvQ0iIiIioked7M9xojw6hb3V73FatOeiQdK0d3w7uDvZWbUNIiIiIqKKgD1Ocipwe5l9dhIgWTePnbHlP/3ysUkd4emsturxiYiIiIgqCvY42QIpbzpyaz7H6WZqln75vS6hTJqIiIiIiB4CEycbIUFYdahe84936JcHtwq22nGJiIiIiCoiJk62QlgvcYpLztQv2yklKK3Yk0VEREREVBExcbIREgQULi5WOVbLGTv1y8ffj7DKMYmIiIiIKjImTjIyePKwEJCsMB35iv2X9csR9arA2Z7zfxARERERPSwmTjZByrvHyQqJ0/s/ndIvf9nnsYc+HhERERERMXGyHUL30InToKV/65ffiaoLtYofLxERERGRNfCbtY2QICA95OQQu8/e0i9HP1njYUMiIiIiIqJ7mDjZCJ3C7qH2/3bfJf3yT6+24kx6RERERERWxMRJTuL+9BAuGddLfJisXC2m/XpGv96omvtDhUVERERERIaYONkACfcegFtCn247q1/ePKr1Qw/5IyIiIiIiQ0ycbIQCuhLtl6vVYfEfMfr1ev5u1gqJiIiIiIjuYeJkK0rYSzR67XH98vdDW1gpGCIiIiIiKoiJk42QRMl6nH49Ea9fblnL21rhEBERERFRAUycZCQK3NckZWVavP/+i0n65QV9+bBbIiIiIqLSwsTJJkhQujtZvFfvbw7olzvX97VmQEREREREVAATJxshKS37KDrP2atffjzIEwo+t4mIiIiIqNQwcbIRkgWJz6m4FPyXkKZf/6p/WGmERERERERE96jkDoDukczPYbt88Yd++Z/3I+DuZFcaERERERER0T3scZJTgWfemtvjlJ6t0S8/Vt2DSRMRERERURlg4mQjJDOf49Tn24P65R+GtyytcIiIiIiIqAAmTrbCjB4nIQT+uZqsX1dyQggiIiIiojLBxMlWmNHjFPH5/Zn0fnntydKMhoiIiIiICmDiZCOKy5tWHbyC8zfv6tcbVnMv5YiIiIiIiCgfEyc5ifuzQ4iMrCKrLth1Ub+8d3y7UguJiIiIiIiMMXGyFVpNoZuycrW4npwJAHjj6dqo7uVUVlERERERERGYONkMO3+fQrf9cT5Rv9y7efWyCIeIiIiIiApg4mQrlIXf5JSl0eqXfd0dyiIaIiIiIiIqgImTjSjqOU4r918BAIQFepZVOEREREREVAATJxnptDr9sqQs/KM4GHMbAJBToD4REREREZUdJk4yEgWWC8ub8ieFAIC3O9ct3YCIiIiIiMgkJk5y0mdOkkESVVCrGTv1yy1reZd2REREREREZAITJxkVTJa0SXeMt4vC0ikiIiIiIipLTJxshLqW8TTjlxLT9ct86C0RERERkXxKlDhpNBr8/vvv+Oqrr5CWlgYAiIuLw927d60a3COvQIeSpFAabd5z9pZ+mQ+9JSIiIiKSj8rSHa5cuYLOnTsjNjYW2dnZ6NixI1xdXTFz5kxkZWVh0aJFpRHno6ngUDwTz3HS6PJm0avq4VhWERERERERkQkW9zi98cYbCAsLw507d+DoeP8LfY8ePbBjxw6rBvfou584SZJxj9PmkwkAgDZ1KpdZREREREREZMziHqc//vgDf/75J9RqtUF5YGAgrl+/brXAKhpTz3G6dicDAJCamVvW4RARERERUQEW9zjpdDpotVqj8mvXrsHV1dUqQVUUBrPqpaYZbU+8mwMAaFmT05ATEREREcnJ4sSpY8eOmDNnjn5dkiTcvXsXkydPRlRUlDVjq1CUnu4G61tOxuuXW9XyKutwiIiIiIioAIuH6n3++edo164d6tWrh6ysLPTp0wfnz5+Ht7c3Vq9eXRoxProKdDkpnA1nzftm3yX9cqCXc1lFREREREREJlicOPn7++P48eNYs2YNjhw5Ap1Oh+joaPTt29dgsgiyhARIhrPqJaXnDdN7rLqHDPEQEREREVFBFidOe/fuRcuWLTF48GAMHjxYX67RaLB371489dRTVg3wkVbwOU4PJE5XkvImhhjRtlZZRkRERERERCZYfI9Tu3btcPv2baPylJQUtGvXzipBVRSiwHOcJMX9xEmnu18e7M0H3xIRERERyc3ixEkIYdQ7AgBJSUlwdua9OCVW4C2NvZ2hX+b9TURERERE8jN7qN5zzz0HIG9I2aBBg2Bvb6/fptVqceLECbRs2dL6ET7ChMGE5Pdz2MNX7uiX7Uw834mIiIiIiMqW2YmTu3vedNlCCLi6uhpMBKFWq/HEE09g2LBh1o+woigwVG/5X5cBAN4u6kIqExERERFRWTI7cVq6dCkAICgoCG+++SaH5VlDIZNDnLyeAgAIC6xU1hEREREREZEJFs+qN3ny5NKIg+4lTunZGn3R0NbBckVDREREREQFWJw4AcCPP/6IdevWITY2Fjk5OQbbjh49apXAKhpJkdf9tOXfBH1Zs0BPucIhIiIiIqICLJ554IsvvsDgwYPh4+ODY8eOoXnz5vDy8sKlS5cQGRlZGjE+soTB3BB5H8WJa8n6IlOzFxIRERERUdmzOHFasGABvv76a3z55ZdQq9WYMGECtm/fjlGjRiElJaU0YqwAJP1QvVxtXjbVuJq7nAEREREREVEBFidOsbGx+mnHHR0dkZaWBgDo378/Vq9ebd3oKpS8xOl/J+IAAJ0a+MoZDBERERERFWBx4uTr64ukpCQAQGBgIA4cOAAAiImJgTAYe0bFu/9+SfeG6uVqdXlb+FYSEREREdkMixOn9u3b45dffgEAREdHY8yYMejYsSN69eqFHj16WD3ACuPeUL2s3LzE6fEgTkVORERERGQrLE6cvv76a7z77rsAgOHDh2PZsmUIDQ3F1KlTsXDhQosDWLBgAYKDg+Hg4IBmzZph3759RdbPzs7Gu+++i8DAQNjb26NmzZpYsmSJxe3aAqEzeJAT4pIz9ashVVxkiIiIiIiIiEyxeDpyhUIBheJ+vtWzZ0/07NkTAHD9+nVUrVrV7GOtXbsWo0ePxoIFC9CqVSt89dVXiIyMxOnTp1G9enWT+/Ts2RM3btzA4sWLUatWLdy8eRMajcZk3fJEkiQcuJSkX/dwUssYDRERERERFWRxj5MpCQkJeP3111GrVi2L9ps9ezaio6MxdOhQhIaGYs6cOQgICCi052rr1q3Ys2cPNm/ejA4dOiAoKAjNmzfXT1ZRnunuZhj0OBERERERke0wO3FKTk5G3759UblyZfj7++OLL76ATqfD+++/jxo1auDAgQMWDZnLycnBkSNHEBERYVAeERGBv/76y+Q+P//8M8LCwjBz5kxUrVoVISEhePPNN5GZWXjCkZ2djdTUVIOXrSg4/4PKrzJupGYDAJ6s5S1PQEREREREZJLZQ/Xeeecd7N27FwMHDsTWrVsxZswYbN26FVlZWdiyZQvatGljUcOJiYnQarWoUqWKQXmVKlWQkJBgcp9Lly7hjz/+gIODAzZu3IjExESMHDkSt2/fLjRpmz59OqZOnWpRbHKQJAkrD1wBANSo7CxzNEREREREVJDZPU6//vorli5dis8++ww///wzhBAICQnBzp07LU6aCpLuzSaXTwhhVJZPp9NBkiSsWrUKzZs3R1RUFGbPno1ly5YV2us0ceJEpKSk6F9Xr14tcaxWV3ByCNw/59pVXMs+FiIiIiIiKpTZPU5xcXGoV68eAKBGjRpwcHDA0KFDS9ywt7c3lEqlUe/SzZs3jXqh8vn5+aFq1apwd3fXl4WGhkIIgWvXrqF27dpG+9jb28Pe3r7EcZaVLO39JKpzfT78loiIiIjIlpjd46TT6WBnZ6dfVyqVcHYu+ZAytVqNZs2aYfv27Qbl27dvL3Syh1atWiEuLg53797Vl507dw4KhQLVqlUrcSxyKdjfdDkpXb/s7cIZ9YiIiIiIbInZPU5CCAwaNEjfe5OVlYXhw4cbJU8bNmwwu/GxY8eif//+CAsLQ3h4OL7++mvExsZi+PDhAPKG2V2/fh0rVqwAAPTp0wcffvghBg8ejKlTpyIxMRHjx4/HkCFD4OjoaHa7tij+3sQQgPHwRSIiIiIikpfZidPAgQMN1vv16/fQjffq1QtJSUn44IMPEB8fjwYNGmDz5s0IDAwEAMTHxyM2NlZf38XFBdu3b8frr7+OsLAweHl5oWfPnpg2bdpDxyI35b1nY/HBt0REREREtkcSQojiqz06UlNT4e7ujpSUFLi5uckay8k//8RvX0yHpKiEgMeaYXxSLfRuXh3Tn2soa1xERERERBWBJbmBVR6ASw9LgvbeDHt2Sg7TIyIiIiKyNUycZKQrMJPe9ZS8e5zslPxIiIiIiIhsDb+ly6jgIEm1nRIAcDs9R6ZoiIiIiIioMEycbILAtTtZAIB6fvLed0VERERERMaYOMmpQJdTeq4WAOCoVsoVDRERERERFaJEidPKlSvRqlUr+Pv748qVKwCAOXPm4KeffrJqcBXJ3ey8xMnfw0HmSIiIiIiI6EEWJ04LFy7E2LFjERUVheTkZGi1eV/4PTw8MGfOHGvHV2GIew+9rebpJHMkRERERET0IIsTp3nz5uGbb77Bu+++C6Xy/rCysLAwnDx50qrBPeoKPkJLIC9x8naxlyscIiIiIiIqhMWJU0xMDJo2bWpUbm9vj/T0dKsEVRHp7vU4OfEeJyIiIiIim2Nx4hQcHIzjx48blW/ZsgX16tWzRkwVhzAucrBj4kREREREZGtUlu4wfvx4vPrqq8jKyoIQAn///TdWr16N6dOn49tvvy2NGB9h9zMnHSSo+fBbIiIiIiKbZHHiNHjwYGg0GkyYMAEZGRno06cPqlatirlz5+Kll14qjRgfXffucZKQNzlEJWe1vPEQEREREZFJFidOADBs2DAMGzYMiYmJ0Ol08PHxsXZcFY4A8ESNSnKHQUREREREJlg8Nmzq1Km4ePEiAMDb25tJ08MocI+TUuiQnJkrXyxERERERFQoixOn9evXIyQkBE888QS+/PJL3Lp1qzTiqhAKzg2RrnJArcoussVCRERERESFszhxOnHiBE6cOIH27dtj9uzZqFq1KqKiovD9998jIyOjNGJ8ZAl96iRBq1ByRj0iIiIiIhtVomnc6tevj48//hiXLl3Crl27EBwcjNGjR8PX19fa8VUQAgISavo4yx0IERERERGZ8NDzXzs7O8PR0RFqtRq5ubxHxzLCYEmp4HTkRERERES2qETf1GNiYvDRRx+hXr16CAsLw9GjRzFlyhQkJCRYO75Hm84wcVIpJPliISIiIiKiQlk8HXl4eDj+/vtvNGzYEIMHD9Y/x4kelgQlEyciIiIiIptkceLUrl07fPvtt6hfv35pxFPB3J8cAgBytTr5QiEiIiIiokJZnDh9/PHHpREHSRJ83RzkjoKIiIiIiEwwK3EaO3YsPvzwQzg7O2Ps2LFF1p09e7ZVAqsQhDBY5XTkRERERES2yazE6dixY/oZ844dO1aqAVVUOkjwdFbLHQYREREREZlgVuK0a9cuk8v0cMQDPU6eTnYyRUJEREREREWxeDryIUOGIC0tzag8PT0dQ4YMsUpQFZGABAcVh+oREREREdkiixOn5cuXIzMz06g8MzMTK1assEpQFZGABAWnIyciIiIisklmz6qXmpoKIQSEEEhLS4ODw/0Z4LRaLTZv3gwfH59SCfKRVWConpujxRMcEhERERFRGTH727qHhwckSYIkSQgJCTHaLkkSpk6datXgKpLkTI3cIRARERERUSHMTpx27doFIQTat2+P9evXo1KlSvptarUagYGB8Pf3L5UgH1UFJ4eo5GIvYyRERERERFQUsxOnNm3aAABiYmJQvXp1SBLvx7EmTkVORERERGS7zEqcTpw4gQYNGkChUCAlJQUnT54stG6jRo2sFtyj736PU3xKtoxxEBERERFRUcxKnJo0aYKEhAT4+PigSZMmkCTJ6BlEQN59Tlqt1upBPrIKvIX1/N3li4OIiIiIiIpkVuIUExODypUr65fJWu5nTkoOfSQiIiIisllmJU6BgYEml+khFey0U1j8SC0iIiIiIiojJXoA7q+//qpfnzBhAjw8PNCyZUtcuXLFqsE9+gr0OPHht0RERERENsvixOnjjz+Go6MjAGD//v348ssvMXPmTHh7e2PMmDFWD7BCML5djIiIiIiIbIjZ05Hnu3r1KmrVqgUA2LRpE1544QW8/PLLaNWqFdq2bWvt+B5tBSbYkJRKGQMhIiIiIqKiWNzj5OLigqSkJADAb7/9hg4dOgAAHBwckJmZad3oHnEGtzjJFgURERERERXH4h6njh07YujQoWjatCnOnTuHLl26AABOnTqFoKAga8f3aCvQ4xSfmiVjIEREREREVBSLOzrmz5+P8PBw3Lp1C+vXr4eXlxcA4MiRI+jdu7fVA3y03UucJKBmFVd5QyEiIiIiokJZ3OPk4eGBL7/80qh86tSpVgmoQikwVo+z6hERERER2S6LEycASE5OxuLFi3HmzBlIkoTQ0FBER0fD3d3d2vFVGBKf40REREREZLMs/rZ++PBh1KxZE59//jlu376NxMREfP7556hZsyaOHj1aGjFWCEqJiRMRERERka2yuMdpzJgx6NatG7755huoVHm7azQaDB06FKNHj8bevXutHuSjShScjpxD9YiIiIiIbJbFidPhw4cNkiYAUKlUmDBhAsLCwqwaXEUhQYIkMXEiIiIiIrJVFo8Pc3NzQ2xsrFH51atX4erKmeEsUqDH6U5mroyBEBERERFRUSxOnHr16oXo6GisXbsWV69exbVr17BmzRoMHTqU05GXlACquTvJHQURERERERXC4qF6n332GSRJwoABA6DRaAAAdnZ2GDFiBGbMmGH1ACsMJSeHICIiIiKyVRYnTmq1GnPnzsX06dNx8eJFCCFQq1YtODmxx8RSBSeH4NwQRERERES2y+xujoyMDLz66quoWrUqfHx8MHToUPj5+aFRo0ZMmqxAAjMnIiIiIiJbZXbiNHnyZCxbtgxdunTBSy+9hO3bt2PEiBGlGVvFwi4nIiIiIiKbZfZQvQ0bNmDx4sV46aWXAAD9+vVDq1atoNVqoVQqSy3AR5nBc5w4HTkRERERkc0yu8fp6tWraN26tX69efPmUKlUiIuLK5XAKhrmTUREREREtsvsxEmr1UKtVhuUqVQq/cx6VAIGk0NwVj0iIiIiIltl9lA9IQQGDRoEe3t7fVlWVhaGDx8OZ2dnfdmGDRusG2FFwR4nIiIiIiKbZXbiNHDgQKOyfv36WTWYikzBsXpERERERDbL7MRp6dKlpRlHhcfpyImIiIiIbBdvrLER7HAiIiIiIrJdsidOCxYsQHBwMBwcHNCsWTPs27fPrP3+/PNPqFQqNGnSpHQDLE1Cd3+Zz3EiIiIiIrJZsiZOa9euxejRo/Huu+/i2LFjaN26NSIjIxEbG1vkfikpKRgwYACefvrpMoq0dBSYVI95ExERERGRDZM1cZo9ezaio6MxdOhQhIaGYs6cOQgICMDChQuL3O+VV15Bnz59EB4eXkaRlr70HK3cIRARERERUSFkS5xycnJw5MgRREREGJRHRETgr7/+KnS/pUuX4uLFi5g8ebJZ7WRnZyM1NdXgZYs8HdXFVyIiIiIiIlmUKHFauXIlWrVqBX9/f1y5cgUAMGfOHPz0009mHyMxMRFarRZVqlQxKK9SpQoSEhJM7nP+/Hm8/fbbWLVqFVQq8yYEnD59Otzd3fWvgIAAs2MsdQXG6tnbyX67GRERERERFcLib+sLFy7E2LFjERUVheTkZGi1eUPMPDw8MGfOHIsDkB6YTk4IYVQGAFqtFn369MHUqVMREhJi9vEnTpyIlJQU/evq1asWx1hahFajX1bYmT0zPBERERERlTGLE6d58+bhm2++wbvvvgulUqkvDwsLw8mTJ80+jre3N5RKpVHv0s2bN416oQAgLS0Nhw8fxmuvvQaVSgWVSoUPPvgA//zzD1QqFXbu3GmyHXt7e7i5uRm8bIXQ3e9xUjg4yBgJEREREREVxeLEKSYmBk2bNjUqt7e3R3p6utnHUavVaNasGbZv325Qvn37drRs2dKovpubG06ePInjx4/rX8OHD0edOnVw/PhxtGjRwtJTkZ/u/nTkCiWn1SMiIiIislUWjw8LDg7G8ePHERgYaFC+ZcsW1KtXz6JjjR07Fv3790dYWBjCw8Px9ddfIzY2FsOHDweQN8zu+vXrWLFiBRQKBRo0aGCwv4+PDxwcHIzKyyOlxHuciIiIiIhslcWJ0/jx4/Hqq68iKysLQgj8/fffWL16NaZPn45vv/3WomP16tULSUlJ+OCDDxAfH48GDRpg8+bN+qQsPj6+2Gc6PSrUKiZORERERES2ShKi4GNYzfPNN99g2rRp+okWqlatiilTpiA6OtrqAVpbamoq3N3dkZKSIvv9Tn8sX4GDm9dBKXmjw4wP0SDIhmb8IyIiIiJ6xFmSG5RoKrdhw4Zh2LBhSExMhE6ng4+PT4kCrfAMclb2OBERERER2aqHmgPb29vbWnFUTAXyJoWCk0MQEREREdmqEk0OYeo5S/kuXbr0UAFVJKJA5iQp2ONERERERGSrLE6cRo8ebbCem5uLY8eOYevWrRg/fry14qpwishFiYiIiIhIZhYnTm+88YbJ8vnz5+Pw4cMPHVBFIkSB5zgxcyIiIiIisllWGx8WGRmJ9evXW+twFcO9kXoSwC4nIiIiIiIbZrXE6ccff0SlSpWsdbiKocCsegol73EiIiIiIrJVFg/Va9q0qcHkEEIIJCQk4NatW1iwYIFVg3vkFZhVT+KsekRERERENsvixOnZZ581WFcoFKhcuTLatm2LunXrWiuuCkchsceJiIiIiMhWWZQ4aTQaBAUFoVOnTvD19S2tmCqQAkP12OFERERERGSzLOrmUKlUGDFiBLKzs0srngpF6O9xkop8NhYREREREcnL4vFhLVq0wLFjx0ojlgqNeRMRERERke2y+B6nkSNHYty4cbh27RqaNWsGZ2dng+2NGjWyWnCPPN39oXrMm4iIiIiIbJfZidOQIUMwZ84c9OrVCwAwatQo/TZJkiCEgCRJ0Gq11o/yEaXV3X8AroOdUsZIiIiIiKg4Wq0Wubm5codBFlKr1VAoHn4iNrMTp+XLl2PGjBmIiYl56EYpj9BPDiE4VI+IiIjIRuU/fic5OVnuUKgEFAoFgoODoVarH+o4ZidO+RMZBAYGPlSDdJ/B5BAcrEdERERkk/KTJh8fHzg5OXFSr3JEp9MhLi4O8fHxqF69+kN9dhbd48SLxMoKPgCXby0RERGRzdFqtfqkycvLS+5wqAQqV66MuLg4aDQa2NnZlfg4FiVOISEhxSZPt2/fLnEwFY4QxdchIiIiItnk39Pk5OQkcyRUUvlD9LRabdklTlOnToW7u3uJG6MHFZhVjz1ORERERDaLI6/KL2t9dhYlTi+99BJ8fHys0jCxw4mIiIiIqLwwe14+ZtnWJ8DnOBERERFR6Wjbti1Gjx5d6PagoCDMmTPH4uNOmTIFTZo0KXFc5ZXFs+qRFXFyCCIiIiKSyaFDh+Ds7Cx3GOWG2YmTrsDDWsk6CiajzJuIiIiIqCxVrly5yO25ubkPNZnCo+bhH6FLJZer0S/yOU5EREREZG0ajQavvfYaPDw84OXlhffee0//x/sHh+pJkoRFixahe/fucHZ2xrRp0wAAM2bMQJUqVeDq6oro6GhkZWXJcSqys2hyCLIyDn8kIiIiKneEEMjM1ZZ5u452SovnHVi+fDmio6Nx8OBBHD58GC+//DICAwMxbNgwk/UnT56M6dOn4/PPP4dSqcS6deswefJkzJ8/H61bt8bKlSvxxRdfoEaNGtY4pXKFiZOMxAP/EhEREZHty8zVot7728q83dMfdIKT2rKv7wEBAfj8888hSRLq1KmDkydP4vPPPy80cerTpw+GDBmiX+/duzeGDBmCoUOHAgCmTZuG33//vUL2OnGonpwEn+NERERERKXniSeeMOilCg8Px/nz56HVmu4xCwsLM1g/c+YMwsPDDcoeXK8o2OMkI2Ewqx4zJyIiIqLywNFOidMfdJKl3dLGWfYKx8SJiIiIiMgCkiRZPGROLgcOHDBar127NpRK85Kw0NBQHDhwAAMGDCj0mBUFh+rJiZNDEBEREVEpunr1KsaOHYuzZ89i9erVmDdvHt544w2z93/jjTewZMkSLFmyBOfOncPkyZNx6tSpUozYdpWPVPkRxbyJiIiIiErTgAEDkJmZiebNm0OpVOL111/Hyy+/bPb+vXr1wsWLF/HWW28hKysLzz//PEaMGIFt28p+cgy5SUJUrK/vqampcHd3R0pKCtzc3GSNZfPHn+DMP/uglCrj5ZXz4WTnJGs8RERERGQoKysLMTExCA4OhoODg9zhUAkU9RlakhtwqB4REREREVExmDjJSSd3AEREREREZA4mTjISfPQtEREREVG5wMRJTvduL+MTnIiIiIiIbBsTJyIiIiIiomIwcZIVh+oREREREZUHTJxkJDg5BBERERFRucDEiYiIiIiIqBhMnGRUwZ49TERERERUbjFxsgFMn4iIiIjI2oQQePnll1GpUiVIkoTjx4/LHVK5ppI7AOJ05ERERERkfVu3bsWyZcuwe/du1KhRA97e3nKHVK4xcZKRTpc3OwR7nIiIiIjI2i5evAg/Pz+0bNlS7lAeCRyqR0RERET0iBk0aBBef/11xMbGQpIkBAUFITs7G6NGjYKPjw8cHBzw5JNP4tChQwCArKws1K9fHy+//LL+GDExMXB3d8c333wj12nYFPY4yYp9TURERETljhBAbkbZt2vnBEjm3eQxd+5c1KxZE19//TUOHToEpVKJCRMmYP369Vi+fDkCAwMxc+ZMdOrUCRcuXEClSpWwatUqtGjRAlFRUejatSv69++Pdu3aYdiwYaV8YuUDEyc58TlOREREROVPbgbwsX/Zt/tOHKB2Nququ7s7XF1doVQq4evri/T0dCxcuBDLli1DZGQkAOCbb77B9u3bsXjxYowfPx5NmjTBtGnTMGzYMPTu3RsXL17Epk2bSvGEyhcO1ZMR+5uIiIiIqCxcvHgRubm5aNWqlb7Mzs4OzZs3x5kzZ/Rl48aNQ506dTBv3jwsXbqUE0oUwB4nWTF1IiIiIip37Jzyen/kaLeE8p8fKj0w1E8IYVB28+ZNnD17FkqlEufPn0fnzp1L3Oajhj1OMuIDcImIiIjKIUnKGzJX1i8z728ypVatWlCr1fjjjz/0Zbm5uTh8+DBCQ0P1ZUOGDEGDBg2wYsUKTJgwAadPn36ot+pRwh4nIiIiIqJHnLOzM0aMGIHx48ejUqVKqF69OmbOnImMjAxER0cDAObPn4/9+/fjxIkTCAgIwJYtW9C3b18cPHgQarVa5jOQH3uc5MQeJyIiIiIqIzNmzMDzzz+P/v3747HHHsOFCxewbds2eHp64r///sP48eOxYMECBAQEAMhLpJKTkzFp0iSZI7cNkqhg48VSU1Ph7u6OlJQUuLm5yRrLxomTcenSESgVlfHyivlweohxq0RERERkfVlZWYiJiUFwcDAcHBzkDodKoKjP0JLcgD1OtqBCpa5EREREROUPEyc56e49yKnk9/kREREREVEZYOIkJx27moiIiIiIygMmTjJi2kREREREVD4wcZKT0MkdARERERERmYGJk4wEb24iIiIiIioXZE+cFixYoJ8asFmzZti3b1+hdTds2ICOHTuicuXKcHNzQ3h4OLZt21aG0VobB+sREREREZUHsiZOa9euxejRo/Huu+/i2LFjaN26NSIjIxEbG2uy/t69e9GxY0ds3rwZR44cQbt27dC1a1ccO3asjCMnIiIiIqKKRNbEafbs2YiOjsbQoUMRGhqKOXPmICAgAAsXLjRZf86cOZgwYQIef/xx1K5dGx9//DFq166NX375pYwjtxJ2OBERERERlQuyJU45OTk4cuQIIiIiDMojIiLw119/mXUMnU6HtLQ0VKpUqdA62dnZSE1NNXgRERERET3q2rZti9GjRwMAgoKCMGfOnDKPYcqUKWjSpEmZt1saVHI1nJiYCK1WiypVqhiUV6lSBQkJCWYdY9asWUhPT0fPnj0LrTN9+nRMnTr1oWItLexwIiIiIqKycOjQITg7O8sdRrkm++QQkmQ4s5wQwqjMlNWrV2PKlClYu3YtfHx8Cq03ceJEpKSk6F9Xr1596JitRjB1IiIiIqLSV7lyZTg5OZXa8XNzc0vt2LZCtsTJ29sbSqXSqHfp5s2bRr1QD1q7di2io6Oxbt06dOjQoci69vb2cHNzM3gREREREVUkBYfqLVu2DJIkGb2mTJkCIK93qmPHjvD29oa7uzvatGmDo0ePGhxPkiQsWrQI3bt3h7OzM6ZNmwYAmDFjBqpUqQJXV1dER0cjKyurLE+zVMmWOKnVajRr1gzbt283KN++fTtatmxZ6H6rV6/GoEGD8P3336NLly6lHSYRERERkQEhBDJyM8r8Jaw0WqlXr16Ij4/Xv1avXg2VSoVWrVoBANLS0jBw4EDs27cPBw4cQO3atREVFYW0tDSD40yePBndu3fHyZMnMWTIEKxbtw6TJ0/GRx99hMOHD8PPzw8LFiywSsy2QLZ7nABg7Nix6N+/P8LCwhAeHo6vv/4asbGxGD58OIC8YXbXr1/HihUrAOQlTQMGDMDcuXPxxBNP6HurHB0d4e7uLtt5lByH6hERERGVN5maTLT4vkWZt3uwz0E42T38cDtHR0c4OjoCAC5evIjXXnsNH3/8MTp27AgAaN++vUH9r776Cp6entizZw+eeeYZfXmfPn0wZMgQ/Xrv3r0xZMgQDB06FAAwbdo0/P77749Mr5Os9zj16tULc+bMwQcffIAmTZpg79692Lx5MwIDAwEA8fHxBs90+uqrr6DRaPDqq6/Cz89P/3rjjTfkOgUiIiIionIpJSUFzzzzDCIjIzF+/Hh9+c2bNzF8+HCEhITA3d0d7u7uuHv3rtGzVsPCwgzWz5w5g/DwcIOyB9fLM1l7nABg5MiRGDlypMlty5YtM1jfvXt36QdUhjg3BBEREVH546hyxME+B2Vp11q0Wi169eoFNzc3fPPNNwbbBg0ahFu3bmHOnDkIDAyEvb09wsPDkZOTY1Cvos3SJ3viVKExcSIiIiIqdyRJssqQOTmNGTMGJ0+exKFDh+Dg4GCwbd++fViwYAGioqIAAFevXkViYmKxxwwNDcWBAwcwYMAAfdmBAwesG7iMmDjJipkTEREREZWtpUuXYsGCBdi4cSMUCoV+3gAXFxe4uLigVq1aWLlyJcLCwpCamorx48fr74kqyhtvvIGBAwciLCwMTz75JFatWoVTp06hRo0apX1KZUL25zgREREREVHZ2bNnD7RaLbp162Ywb8Bnn30GAFiyZAnu3LmDpk2bon///hg1alSRz03N16tXL7z//vt466230KxZM1y5cgUjRowo7dMpM5Kw1ryG5URqairc3d2RkpIi+zOdfhg9AbHxp6FUVMbLK+aX+y5fIiIiokdNVlYWYmJiEBwcbDSkjcqHoj5DS3ID9jgREREREREVg4kTERERERFRMZg4yahiDZIkIiIiIiq/mDgREREREREVg4kTERERERFRMZg4yYlj9YiIiIiIygUmTrKS5A6AiIiIiIjMwMRJVuxxIiIiIiIqD5g4ERERERERFYOJk4zY30REREREtq5t27YYPXq03GHIjomTjCRODkFEREREFdCgQYPw7LPPyh2GRZg4yYhpExERERGVlZycHLlDKNeYOMmIiRMRERFR+SOEgC4jo8xfwsLRSm3btsVrr72GsWPHwtvbGx07dsSePXvQvHlz2Nvbw8/PD2+//TY0Go1+n/T0dAwYMAAuLi7w8/PDrFmzjI6bk5ODCRMmoGrVqnB2dkaLFi2we/du/fZly5bBw8MD27ZtQ2hoKFxcXNC5c2fEx8cDAKZMmYLly5fjp59+giRJkCTJYH9bpZI7gAqNmRMRERFRuSMyM3H2sWZl3m6do0cgOTlZtM/y5csxYsQI/Pnnn0hMTERERAQGDRqEFStW4L///sOwYcPg4OCAKVOmAADGjx+PXbt2YePGjfD19cU777yDI0eOoEmTJvpjDh48GJcvX8aaNWvg7++PjRs3onPnzjh58iRq164NAMjIyMBnn32GlStXQqFQoF+/fnjzzTexatUqvPnmmzhz5gxSU1OxdOlSAEClSpWs8h6VJiZORERERESPqFq1amHmzJkAgBUrViAgIABffvklJElC3bp1ERcXh7feegvvv/8+MjIysHjxYqxYsQIdO3YEkJd4VatWTX+8ixcvYvXq1bh27Rr8/f0BAG+++Sa2bt2KpUuX4uOPPwYA5ObmYtGiRahZsyYA4LXXXsMHH3wAAHBxcYGjoyOys7Ph6+tbZu/Fw2LiRERERERkAcnREXWOHpGlXUuFhYXpl8+cOYPw8HBIkqQva9WqFe7evYtr167hzp07yMnJQXh4uH57pUqVUKdOHf360aNHIYRASEiIQTvZ2dnw8vLSrzs5OemTJgDw8/PDzZs3LY7fljBxIiIiIiKygCRJFg+Zk4uzs7N+WQhhkDTllwF552TOPVQ6nQ5KpRJHjhyBUqk02Obi4qJftrOzM9hm7vFtGRMnIiIiIqIKoF69eli/fr1BAvXXX3/B1dUVVatWhaenJ+zs7HDgwAFUr14dAHDnzh2cO3cObdq0AQA0bdoUWq0WN2/eROvWrUsci1qthlarffiTKkOcVU9O5TzrJiIiIqLyY+TIkbh69Spef/11/Pfff/jpp58wefJkjB07FgqFAi4uLoiOjsb48eOxY8cO/Pvvvxg0aBAUivspQ0hICPr27YsBAwZgw4YNiImJwaFDh/DJJ59g8+bNZscSFBSEEydO4OzZs0hMTERubm5pnLJVsceJiIiIiKgCqFq1KjZv3ozx48ejcePGqFSpEqKjo/Hee+/p63z66ae4e/cuunXrBldXV4wbNw4pKSkGx1m6dCmmTZuGcePG4fr16/Dy8kJ4eDiioqLMjmXYsGHYvXs3wsLCcPfuXezatQtt27a11qmWCkmU98GGFkpNTYW7uztSUlLg5uYmayyrXx2HuMSzUCoq4+UV8+FkVz7GyhIRERFVFFlZWYiJiUFwcDAcHBzkDodKoKjP0JLcgEP1iIiIiIiIisHESU4Vq7OPiIiIiKjcYuIkK6n4KkREREREJDsmTkRERERERMVg4iQrDtUjIiIiIioPmDgREREREREVg4mTnNjhRERERERULjBxkhUzJyIiIiKi8oCJk4yYNhERERERlQ9MnIiIiIiIyCqmTJmCJk2aWLTP5cuXIUkSjh8/XioxWYtK7gCIiIiIiKjiCggIQHx8PLy9veUOpUjscZIVB+sRERERke3Lzc0ttWMrlUr4+vpCpbLtPh0mTkREREREFhBCIDdbW+YvISz7o/uPP/6Ihg0bwtHREV5eXujQoQPS09P125csWYL69evD3t4efn5+eO211/TbJEnCokWL0L17dzg7O2PatGlYtmwZPDw8DNrYtGkTJEkyavurr75CQEAAnJyc8OKLLyI5ObnQOB8cqqfVahEdHY3g4GA4OjqiTp06mDt3rr7+3r17YWdnh4SEBIPjjBs3Dk899ZQF75BlbDute9Sxw4mIiIio3NHk6PD1G3vKvN2X57aBnb3SrLrx8fHo3bs3Zs6ciR49eiAtLQ379u3TJ18LFy7E2LFjMWPGDERGRiIlJQV//vmnwTEmT56M6dOn4/PPP4dSqcSuXbvMavvChQtYt24dfvnlF6SmpiI6OhqvvvoqVq1aZdb+Op0O1apVw7p16+Dt7Y2//voLL7/8Mvz8/NCzZ0889dRTqFGjBlauXInx48cDADQaDb777jvMmDHDrDZKgokTEREREdEjJj4+HhqNBs899xwCAwMBAA0bNtRvnzZtGsaNG4c33nhDX/b4448bHKNPnz4YMmSIxW1nZWVh+fLlqFatGgBg3rx56NKlC2bNmgVfX99i97ezs8PUqVP168HBwfjrr7+wbt069OzZEwAQHR2NpUuX6hOnX3/9FRkZGfrtpYGJk5zY40RERERU7qjUCrw8t40s7ZqrcePGePrpp9GwYUN06tQJEREReOGFF+Dp6YmbN28iLi4OTz/9dJHHCAsLK1Gc1atX1ydNABAeHg6dToezZ8+alTgBwKJFi/Dtt9/iypUryMzMRE5OjsFsfYMGDcJ7772HAwcO4IknnsCSJUvQs2dPODs7lyhmc/AeJyIiIiIiC0iSBDt7ZZm/TN1LVBilUont27djy5YtqFevHubNm4c6deogJiYGjo6OZh3jwSREoVAY3WdlzqQR+XGbG/+6deswZswYDBkyBL/99huOHz+OwYMHIycnR1/Hx8cHXbt2xdKlS3Hz5k1s3ry5RL1jlmDiRERERET0CJIkCa1atcLUqVNx7NgxqNVqbNy4Ea6urggKCsKOHTssOl7lypWRlpZmMMGEqWcvxcbGIi4uTr++f/9+KBQKhISEmNXOvn370LJlS4wcORJNmzZFrVq1cPHiRaN6Q4cOxZo1a/DVV1+hZs2aaNWqlUXnYykmTjLiSD0iIiIiKg0HDx7Exx9/jMOHDyM2NhYbNmzArVu3EBoaCiDvQbWzZs3CF198gfPnz+Po0aOYN29ekcds0aIFnJyc8M477+DChQv4/vvvsWzZMqN6Dg4OGDhwIP755x/s27cPo0aNQs+ePc0eplerVi0cPnwY27Ztw7lz5zBp0iQcOnTIqF6nTp3g7u6OadOmYfDgwWYd+2EwcZIVUyciIiIisj43Nzfs3bsXUVFRCAkJwXvvvYdZs2YhMjISADBw4EDMmTMHCxYsQP369fHMM8/g/PnzRR6zUqVK+O6777B582Y0bNgQq1evxpQpU4zq1apVC8899xyioqIQERGBBg0aYMGCBWbHPnz4cDz33HPo1asXWrRogaSkJIwcOdKonkKhwKBBg6DVajFgwACzj19SkrB0QvhyLjU1Fe7u7khJSYGbm5ussax65Q0kJF+EUlEZL6+YDyc7J1njISIiIiJDWVlZiImJQXBwMBwcHOQO55F09uxZ1K1bF+fPn0etWrUs2nfYsGG4ceMGfv7550LrFPUZWpIbcFY9IiIiIiKSxe3bt/Hjjz/Czc0NAQEBZu+XkpKCQ4cOYdWqVfjpp59KMcL7mDgREREREZEsoqOjceTIESxcuBD29vZm79e9e3f8/fffeOWVV9CxY8dSjPA+Jk5yqlCDJImIiIiIDG3cuLFE++3evdu6gZiBk0PISDBzIiIiIiIqF5g4ERERERERFYOJExERERERUTGYOMmJI/WIiIiIiMoFJk5ERERERETFYOJERERERERUDCZOREREREQVyKBBg/Dss8+WahvLli2Dh4dHqbZR1pg4ERERERERFYOJExERERERUTGYOMmIk+oRERERlT9CCORmZZX5SwjLvj3++OOPaNiwIRwdHeHl5YUOHTogPT3dqN6RI0fg4+ODjz76CAAgSRI2bdpkUMfDwwPLli0DAFy+fBmSJGHDhg1o164dnJyc0LhxY+zfv7/QWJKSktC8eXN069YNWVlZFp2HrVDJHcCCBQvw6aefIj4+HvXr18ecOXPQunXrQuvv2bMHY8eOxalTp+Dv748JEyZg+PDhZRixNTF1IiIiIipvNNnZ+GLgC2Xe7qjlP8LOwcGsuvHx8ejduzdmzpyJHj16IC0tDfv27TNKvnbv3o1nn30W06dPx4gRIyyK591338Vnn32G2rVr491330Xv3r1x4cIFqFSGKca1a9cQERGBsLAwLFmyxGh7eSFrj9PatWsxevRovPvuuzh27Bhat26NyMhIxMbGmqwfExODqKgotG7dGseOHcM777yDUaNGYf369WUcuZUwbyIiIiKiUhAfHw+NRoPnnnsOQUFBaNiwIUaOHAkXFxd9nZ9++gndunXDwoULLU6aAODNN99Ely5dEBISgqlTp+LKlSu4cOGCQZ1z586hVatW6NChA5YvX15ukyZA5h6n2bNnIzo6GkOHDgUAzJkzB9u2bcPChQsxffp0o/qLFi1C9erVMWfOHABAaGgoDh8+jM8++wzPP/98WYZORERERBWUyt4eo5b/KEu75mrcuDGefvppNGzYEJ06dUJERAReeOEFeHp6AgAOHjyI//3vf/jhhx/Qo0ePEsXTqFEj/bKfnx8A4ObNm6hbty4AIDMzE08++SR69+6NuXPnlqgNWyJbj1NOTg6OHDmCiIgIg/KIiAj89ddfJvfZv3+/Uf1OnTrh8OHDyM3NNblPdnY2UlNTDV42gz1OREREROWOJEmwc3Ao85ckSWbHqFQqsX37dmzZsgX16tXDvHnzUKdOHcTExAAAatasibp162LJkiXIyckxOr8Hh/SZ+q5tZ2dnsA8A6HQ6fZm9vT06dOiAX3/9FdeuXTM7dlslW+KUmJgIrVaLKlWqGJRXqVIFCQkJJvdJSEgwWV+j0SAxMdHkPtOnT4e7u7v+FRAQYJ0TICIiIiKyYZIkoVWrVpg6dSqOHTsGtVqNjRs3AgC8vb2xc+dOXLx4Eb169TJIjCpXroz4+Hj9+vnz55GRkWFx+wqFAitXrkSzZs3Qvn17xMXFPfxJyUj2WfUezJyFEEVm06bqmyrPN3HiRKSkpOhfV69efciIrafp811QK6Q5Grd+Ao4qR7nDISIiIqJHxMGDB/Hxxx/j8OHDiI2NxYYNG3Dr1i2Ehobq6/j4+GDnzp3477//0Lt3b2g0GgBA+/bt8eWXX+Lo0aM4fPgwhg8fbtC7ZAmlUolVq1ahcePGaN++faEdJOWBbImTt7c3lEql0Zt38+ZNo16lfL6+vibrq1QqeHl5mdzH3t4ebm5uBi9b0SCqM7p/+D7ajXzFoq5XIiIiIqKiuLm5Ye/evYiKikJISAjee+89zJo1C5GRkQb1fH19sXPnTpw8eRJ9+/aFVqvFrFmzEBAQgKeeegp9+vTBm2++CScnpxLHolKpsHr1atSvXx/t27fHzZs3H/b0ZCEJSyeEt6IWLVqgWbNmWLBggb6sXr166N69u8nJId566y388ssvOH36tL5sxIgROH78eJHzxheUmpoKd3d3pKSk2FQSRURERES2JysrCzExMQgODoaDmVOBk20p6jO0JDeQdaje2LFj8e2332LJkiU4c+YMxowZg9jYWP1zmSZOnIgBAwbo6w8fPhxXrlzB2LFjcebMGSxZsgSLFy/Gm2++KdcpEBERERFRBSDrdOS9evVCUlISPvjgA8THx6NBgwbYvHkzAgMDAeTNP1/wmU7BwcHYvHkzxowZg/nz58Pf3x9ffPEFpyInIiIiIqJSJetQPTlwqB4RERERmYtD9cq/R2KoHhERERERUXnAxImIiIiIqBgVbJDWI8Vanx0TJyIiIiKiQuQ/v6gkD4Al25CTkwMg75lSD0PWySGIiIiIiGyZUqmEh4eH/tlDTk5OfP5mOaLT6XDr1i04OTlBpXq41IeJExERERFREXx9fQGg3D64taJTKBSoXr36Qye8TJyIiIiIiIogSRL8/Pzg4+OD3NxcucMhC6nVaigUD3+HEhMnIiIiIiIzKJXKh75PhsovTg5BRERERERUDCZORERERERExWDiREREREREVIwKd49T/gOwUlNTZY6EiIiIiIjklJ8TmPOQ3AqXOKWlpQEAAgICZI6EiIiIiIhsQVpaGtzd3YusIwlz0qtHiE6nQ1xcHFxdXW3i4WWpqakICAjA1atX4ebmJnc4VA7wmiFL8HohS/GaIUvxmiFL2dI1I4RAWloa/P39i52yvML1OCkUClSrVk3uMIy4ubnJfuFQ+cJrhizB64UsxWuGLMVrhixlK9dMcT1N+Tg5BBERERERUTGYOBERERERERWDiZPM7O3tMXnyZNjb28sdCpUTvGbIErxeyFK8ZshSvGbIUuX1mqlwk0MQERERERFZij1ORERERERExWDiREREREREVAwmTkRERERERMVg4kRERERERFQMJk6lbMGCBQgODoaDgwOaNWuGffv2FVl/z549aNasGRwcHFCjRg0sWrSojCIlW2HJNbNhwwZ07NgRlStXhpubG8LDw7Ft27YyjJZsgaW/Z/L9+eefUKlUaNKkSekGSDbH0msmOzsb7777LgIDA2Fvb4+aNWtiyZIlZRQt2QJLr5lVq1ahcePGcHJygp+fHwYPHoykpKQyipbktnfvXnTt2hX+/v6QJAmbNm0qdp/y8B2YiVMpWrt2LUaPHo13330Xx44dQ+vWrREZGYnY2FiT9WNiYhAVFYXWrVvj2LFjeOeddzBq1CisX7++jCMnuVh6zezduxcdO3bE5s2bceTIEbRr1w5du3bFsWPHyjhykoul10y+lJQUDBgwAE8//XQZRUq2oiTXTM+ePbFjxw4sXrwYZ8+exerVq1G3bt0yjJrkZOk188cff2DAgAGIjo7GqVOn8MMPP+DQoUMYOnRoGUdOcklPT0fjxo3x5ZdfmlW/3HwHFlRqmjdvLoYPH25QVrduXfH222+brD9hwgRRt25dg7JXXnlFPPHEE6UWI9kWS68ZU+rVqyemTp1q7dDIRpX0munVq5d47733xOTJk0Xjxo1LMUKyNZZeM1u2bBHu7u4iKSmpLMIjG2TpNfPpp5+KGjVqGJR98cUXolq1aqUWI9kuAGLjxo1F1ikv34HZ41RKcnJycOTIEURERBiUR0RE4K+//jK5z/79+43qd+rUCYcPH0Zubm6pxUq2oSTXzIN0Oh3S0tJQqVKl0giRbExJr5mlS5fi4sWLmDx5cmmHSDamJNfMzz//jLCwMMycORNVq1ZFSEgI3nzzTWRmZpZFyCSzklwzLVu2xLVr17B582YIIXDjxg38+OOP6NKlS1mETOVQefkOrJI7gEdVYmIitFotqlSpYlBepUoVJCQkmNwnISHBZH2NRoPExET4+fmVWrwkv5JcMw+aNWsW0tPT0bNnz9IIkWxMSa6Z8+fP4+2338a+ffugUvG/gIqmJNfMpUuX8Mcff8DBwQEbN25EYmIiRo4cidu3b/M+pwqgJNdMy5YtsWrVKvTq1QtZWVnQaDTo1q0b5s2bVxYhUzlUXr4Ds8eplEmSZLAuhDAqK66+qXJ6dFl6zeRbvXo1pkyZgrVr18LHx6e0wiMbZO41o9Vq0adPH0ydOhUhISFlFR7ZIEt+z+h0OkiShFWrVqF58+aIiorC7NmzsWzZMvY6VSCWXDOnT5/GqFGj8P777+PIkSPYunUrYmJiMHz48LIIlcqp8vAdmH9uLCXe3t5QKpVGf425efOmUUadz9fX12R9lUoFLy+vUouVbENJrpl8a9euRXR0NH744Qd06NChNMMkG2LpNZOWlobDhw/j2LFjeO211wDkfSkWQkClUuG3335D+/btyyR2kkdJfs/4+fmhatWqcHd315eFhoZCCIFr166hdu3apRozyask18z06dPRqlUrjB8/HgDQqFEjODs7o3Xr1pg2bZrN9B6Q7Sgv34HZ41RK1Go1mjVrhu3btxuUb9++HS1btjS5T3h4uFH93377DWFhYbCzsyu1WMk2lOSaAfJ6mgYNGoTvv/+e48crGEuvGTc3N5w8eRLHjx/Xv4YPH446derg+PHjaNGiRVmFTjIpye+ZVq1aIS4uDnfv3tWXnTt3DgqFAtWqVSvVeEl+JblmMjIyoFAYfsVUKpUA7vciEBVUbr4DyzQpRYWwZs0aYWdnJxYvXixOnz4tRo8eLZydncXly5eFEEK8/fbbon///vr6ly5dEk5OTmLMmDHi9OnTYvHixcLOzk78+OOPcp0ClTFLr5nvv/9eqFQqMX/+fBEfH69/JScny3UKVMYsvWYexFn1Kh5Lr5m0tDRRrVo18cILL4hTp06JPXv2iNq1a4uhQ4fKdQpUxiy9ZpYuXSpUKpVYsGCBuHjxovjjjz9EWFiYaN68uVynQGUsLS1NHDt2TBw7dkwAELNnzxbHjh0TV65cEUKU3+/ATJxK2fz580VgYKBQq9XiscceE3v27NFvGzhwoGjTpo1B/d27d4umTZsKtVotgoKCxMKFC8s4YpKbJddMmzZtBACj18CBA8s+cJKNpb9nCmLiVDFZes2cOXNGdOjQQTg6Oopq1aqJsWPHioyMjDKOmuRk6TXzxRdfiHr16glHR0fh5+cn+vbtK65du1bGUZNcdu3aVeT3k/L6HVgSgn2mREREREREReE9TkRERERERMVg4kRERERERFQMJk5ERERERETFYOJERERERERUDCZORERERERExWDiREREREREVAwmTkRERERERMVg4kRERERERFQMJk5ERFQiy5Ytg4eHh9xhlFhQUBDmzJlTZJ0pU6agSZMmZRIPERHZNiZOREQV2KBBgyBJktHrwoULcoeGZcuWGcTk5+eHnj17IiYmxirHP3ToEF5++WX9uiRJ2LRpk0GdN998Ezt27LBKe4V58DyrVKmCrl274tSpUxYfpzwnskREto6JExFRBde5c2fEx8cbvIKDg+UOCwDg5uaG+Ph4xMXF4fvvv8fx48fRrVs3aLXahz525cqV4eTkVGQdFxcXeHl5PXRbxSl4nr/++ivS09PRpUsX5OTklHrbRERkHiZOREQVnL29PXx9fQ1eSqUSs2fPRsOGDeHs7IyAgACMHDkSd+/eLfQ4//zzD9q1awdXV1e4ubmhWbNmOHz4sH77X3/9haeeegqOjo4ICAjAqFGjkJ6eXmRskiTB19cXfn5+aNeuHSZPnox///1X3yO2cOFC1KxZE2q1GnXq1MHKlSsN9p8yZQqqV68Oe3t7+Pv7Y9SoUfptBYfqBQUFAQB69OgBSZL06wWH6m3btg0ODg5ITk42aGPUqFFo06aN1c4zLCwMY8aMwZUrV3D27Fl9naI+j927d2Pw4MFISUnR91xNmTIFAJCTk4MJEyagatWqcHZ2RosWLbB79+4i4yEiImNMnIiIyCSFQoEvvvgC//77L5YvX46dO3diwoQJhdbv27cvqlWrhkOHDuHIkSN4++23YWdnBwA4efIkOnXqhOeeew4nTpzA2rVr8ccff+C1116zKCZHR0cAQG5uLjZu3Ig33ngD48aNw7///otXXnkFgwcPxq5duwAAP/74Iz7//HN89dVXOH/+PDZt2oSGDRuaPO6hQ4cAAEuXLkV8fLx+vaAOHTrAw8MD69ev15dptVqsW7cOffv2tdp5Jicn4/vvvwcA/fsHFP15tGzZEnPmzNH3XMXHx+PNN98EAAwePBh//vkn1qxZgxMnTuDFF19E586dcf78ebNjIiIiAIKIiCqsgQMHCqVSKZydnfWvF154wWTddevWCS8vL/360qVLhbu7u37d1dVVLFu2zOS+/fv3Fy+//LJB2b59+4RCoRCZmZkm93nw+FevXhVPPPGEqFatmsjOzhYtW7YUw4YNM9jnxRdfFFFRUUIIIWbNmiVCQkJETk6OyeMHBgaKzz//XL8OQGzcuNGgzuTJk0Xjxo3166NGjRLt27fXr2/btk2o1Wpx+/bthzpPAMLZ2Vk4OTkJAAKA6Natm8n6+Yr7PIQQ4sKFC0KSJHH9+nWD8qefflpMnDixyOMTEZEhlbxpGxERya1du3ZYuHChft3Z2RkAsGvXLnz88cc4ffo0UlNTodFokJWVhfT0dH2dgsaOHYuhQ4di5cqV6NChA1588UXUrFkTAHDkyBFcuHABq1at0tcXQkCn0yEmJgahoaEmY0tJSYGLiwuEEMjIyMBjjz2GDRs2QK1W48yZMwaTOwBAq1atMHfuXADAiy++iDlz5qBGjRro3LkzoqKi0LVrV6hUJf+vr2/fvggPD0dcXBz8/f2xatUqREVFwdPT86HO09XVFUePHoVGo8GePXvw6aefYtGiRQZ1LP08AODo0aMQQiAkJMSgPDs7u0zu3SIiepQwcSIiquCcnZ1Rq1Ytg7IrV64gKioKw4cPx4cffohKlSrhjz/+QHR0NHJzc00eZ8qUKejTpw9+/fVXbNmyBZMnT8aaNWvQo0cP6HQ6vPLKKwb3GOWrXr16obHlJxQKhQJVqlQxShAkSTJYF0LoywICAnD27Fls374dv//+O0aOHIlPP/0Ue/bsMRgCZ4nmzZujZs2aWLNmDUaMGIGNGzdi6dKl+u0lPU+FQqH/DOrWrYuEhAT06tULe/fuBVCyzyM/HqVSiSNHjkCpVBpsc3FxsejciYgqOiZORERk5PDhw9BoNJg1axYUirzbYdetW1fsfiEhIQgJCcGYMWPQu3dvLF26FD169MBjjz2GU6dOGSVoxSmYUDwoNDQUf/zxBwYMGKAv++uvvwx6dRwdHdGtWzd069YNr776KurWrYuTJ0/iscceMzqenZ2dWbP19enTB6tWrUK1atWgUCjQpUsX/baSnueDxowZg9mzZ2Pjxo3o0aOHWZ+HWq02ir9p06bQarW4efMmWrdu/VAxERFVdJwcgoiIjNSsWRMajQbz5s3DpUuXsHLlSqOhYwVlZmbitddew+7du3HlyhX8+eefOHTokD6Jeeutt7B//368+uqrOH78OM6fP4+ff/4Zr7/+eoljHD9+PJYtW4ZFixbh/PnzmD17NjZs2KCfFGHZsmVYvHgx/v33X/05ODo6IjAw0OTxgoKCsGPHDiQkJODOnTuFttu3b18cPXoUH330EV544QU4ODjot1nrPN3c3DB06FBMnjwZQgizPo+goCDcvXsXO3bsQGJiIjIyMhASEoK+fftiwIAB2LBhA2JiYnDo0CF88skn2Lx5s0UxERFVdEyciIjISJMmTTB79mx88sknaNCgAVatWoXp06cXWl+pVCIpKQkDBgxASEgIevbsicjISEydOhUA0KhRI+zZswfnz59H69at0bRpU0yaNAl+fn4ljvHZZ5/F3Llz8emnn6J+/fr46quvsHTpUrRt2xYA4OHhgW+++QatWrVCo0aNsGPHDvzyyy+F3tsza9YsbN++HQEBAWjatGmh7dauXRuPP/44Tpw4oZ9NL581z/ONN97AmTNn8MMPP5j1ebRs2RLDhw9Hr169ULlyZcycORNA3kyBAwYMwLhx41CnTh1069YNBw8eREBAgMUxERFVZJIQQsgdBBERERERkS1jjxMREREREVExmDgREREREREVg4kTERERERFRMZg4ERERERERFYOJExERERERUTGYOBERERERERWDiRMREREREVExmDgREREREREVg4kTERERERFRMZg4ERERERERFYOJExERERERUTH+D7Kmw+JWdqxIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for class bird is 0.9620625033284056\n",
      "AUC for class fox is 0.996497910354184\n",
      "AUC for class lizard is 0.9963721822899441\n",
      "AUC for class rodent is 0.9960608275331769\n",
      "AUC for class scrub jay is 0.9923252464742164\n",
      "AUC for class skunk is 0.9961513748532839\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9620625033284056,\n",
       " 0.996497910354184,\n",
       " 0.9963721822899441,\n",
       " 0.9960608275331769,\n",
       " 0.9923252464742164,\n",
       " 0.9961513748532839]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ROC_curve(np.arange(0, 1, 0.01), df_val, classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load all the models and predict the labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### some parameters to tune(batch size and weight-decay). \n",
    "Batch size of 1 (Stochastic Gradient Descent): This approach uses a single training example to update the model weights in each iteration. This can lead to a high variance in the updates, but can also provide faster convergence and improved generalization.\n",
    "  -> I tried this but the result is not as good as the last one.\n",
    "Batch size of 32-128\n",
    "  -> still trying to do this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- model1's weight decay = 1e-06, batch size = 80 (original model)\n",
    "- model2's weight decay = 1e-02\n",
    "- model3's batch size = 1 (stochastic gradient descent)\n",
    "- model4's batch size = 30 \n",
    "- model5's batch size = 50 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the results\n",
    "\n",
    "df_train_model1 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/mirav2/outputs_train.csv')\n",
    "df_val_model1 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/mirav2/outputs_val.csv')\n",
    "df_test_model1 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/mirav2/outputs_test.csv')\n",
    "\n",
    "\n",
    "df_train_model2 = pd.read_csv('~/animl-implement/result/0222/20230222_021024/outputs_train.csv')\n",
    "df_val_model2 = pd.read_csv('~/animl-implement/result/0222/20230222_021024/outputs_val.csv')\n",
    "df_test_model2 = pd.read_csv('~/animl-implement/result/0222/20230222_021024/outputs_test.csv')\n",
    "\n",
    "# # #load the model 3\n",
    "\n",
    "# df_train_model3 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/1/20230301_034813/outputs_train.csv')\n",
    "# df_val_model3 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/1/20230301_034813/outputs_val.csv')\n",
    "# df_test_model3 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/1/20230301_034813/outputs_test.csv')\n",
    "\n",
    "# #load the model 4\n",
    "# df_train_model4 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/60/20230301_005312/outputs_train.csv')\n",
    "# df_val_model4 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/60/20230301_005312/outputs_val.csv')\n",
    "# df_test_model4 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/60/20230301_005312/outputs_test.csv')\n",
    "\n",
    "# #load the model 5\n",
    "# df_train_model5 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/60/20230228_234620/outputs_train.csv')\n",
    "# df_val_model5 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/60/20230228_234620/outputs_val.csv')\n",
    "# df_test_model5 = pd.read_csv('~/CameraTraps/classification/BASE_LOGDIR/batch_size_parameters/60/20230228_234620/outputs_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>label</th>\n",
       "      <th>weight</th>\n",
       "      <th>bird</th>\n",
       "      <th>fox</th>\n",
       "      <th>lizard</th>\n",
       "      <th>rodent</th>\n",
       "      <th>scrub jay</th>\n",
       "      <th>skunk</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133376/000/086.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.441790</td>\n",
       "      <td>3.734033e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.090851e-14</td>\n",
       "      <td>9.983020e-12</td>\n",
       "      <td>6.485392e-12</td>\n",
       "      <td>2.562433e-09</td>\n",
       "      <td>fox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133376/000/087.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.441790</td>\n",
       "      <td>1.276096e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.121936e-14</td>\n",
       "      <td>2.174303e-08</td>\n",
       "      <td>5.108855e-12</td>\n",
       "      <td>2.274605e-10</td>\n",
       "      <td>fox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133376/000/088.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.441790</td>\n",
       "      <td>6.296354e-11</td>\n",
       "      <td>9.999992e-01</td>\n",
       "      <td>7.142781e-14</td>\n",
       "      <td>3.530066e-09</td>\n",
       "      <td>4.641423e-09</td>\n",
       "      <td>8.178529e-07</td>\n",
       "      <td>fox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133376/000/089.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.441790</td>\n",
       "      <td>5.639691e-12</td>\n",
       "      <td>9.999999e-01</td>\n",
       "      <td>5.760029e-14</td>\n",
       "      <td>1.694272e-07</td>\n",
       "      <td>1.438960e-10</td>\n",
       "      <td>1.635343e-10</td>\n",
       "      <td>fox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mirav2_dataset/loc-h500ee07133376/000/090.jpg_...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.441790</td>\n",
       "      <td>5.547539e-18</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.231157e-15</td>\n",
       "      <td>3.852544e-08</td>\n",
       "      <td>2.071547e-12</td>\n",
       "      <td>3.465329e-11</td>\n",
       "      <td>fox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25405</th>\n",
       "      <td>mirav2_dataset/X8115CB7/p_012017_465bd72cc8b54...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.405612</td>\n",
       "      <td>2.441563e-02</td>\n",
       "      <td>5.505114e-06</td>\n",
       "      <td>5.270331e-05</td>\n",
       "      <td>9.741090e-01</td>\n",
       "      <td>2.548241e-04</td>\n",
       "      <td>1.162371e-03</td>\n",
       "      <td>rodent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25406</th>\n",
       "      <td>mirav2_dataset/X8115CB7/p_012018_c2db62d7f437f...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.405612</td>\n",
       "      <td>1.743998e-05</td>\n",
       "      <td>1.116437e-04</td>\n",
       "      <td>2.672028e-07</td>\n",
       "      <td>9.998643e-01</td>\n",
       "      <td>2.683630e-07</td>\n",
       "      <td>6.039379e-06</td>\n",
       "      <td>rodent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25407</th>\n",
       "      <td>mirav2_dataset/X8115CB7/p_012019_65f79c99e81c8...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.405612</td>\n",
       "      <td>2.129185e-03</td>\n",
       "      <td>8.929301e-06</td>\n",
       "      <td>2.575501e-05</td>\n",
       "      <td>9.900455e-01</td>\n",
       "      <td>4.031762e-06</td>\n",
       "      <td>7.786605e-03</td>\n",
       "      <td>rodent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25408</th>\n",
       "      <td>mirav2_dataset/X8115CB7/p_012020_a6069a5ac4295...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.405612</td>\n",
       "      <td>2.783561e-08</td>\n",
       "      <td>1.167460e-09</td>\n",
       "      <td>1.727278e-08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.149348e-09</td>\n",
       "      <td>9.478033e-09</td>\n",
       "      <td>rodent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25409</th>\n",
       "      <td>mirav2_dataset/X8115CB7/p_012021_80f508216a1a4...</td>\n",
       "      <td>rodent</td>\n",
       "      <td>0.405612</td>\n",
       "      <td>7.014449e-07</td>\n",
       "      <td>2.165211e-06</td>\n",
       "      <td>4.650492e-05</td>\n",
       "      <td>9.999205e-01</td>\n",
       "      <td>2.463862e-08</td>\n",
       "      <td>3.019620e-05</td>\n",
       "      <td>rodent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25410 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    path   label    weight  \\\n",
       "0      mirav2_dataset/loc-h500ee07133376/000/086.jpg_...     fox  0.441790   \n",
       "1      mirav2_dataset/loc-h500ee07133376/000/087.jpg_...     fox  0.441790   \n",
       "2      mirav2_dataset/loc-h500ee07133376/000/088.jpg_...     fox  0.441790   \n",
       "3      mirav2_dataset/loc-h500ee07133376/000/089.jpg_...     fox  0.441790   \n",
       "4      mirav2_dataset/loc-h500ee07133376/000/090.jpg_...     fox  0.441790   \n",
       "...                                                  ...     ...       ...   \n",
       "25405  mirav2_dataset/X8115CB7/p_012017_465bd72cc8b54...  rodent  0.405612   \n",
       "25406  mirav2_dataset/X8115CB7/p_012018_c2db62d7f437f...  rodent  0.405612   \n",
       "25407  mirav2_dataset/X8115CB7/p_012019_65f79c99e81c8...  rodent  0.405612   \n",
       "25408  mirav2_dataset/X8115CB7/p_012020_a6069a5ac4295...  rodent  0.405612   \n",
       "25409  mirav2_dataset/X8115CB7/p_012021_80f508216a1a4...  rodent  0.405612   \n",
       "\n",
       "               bird           fox        lizard        rodent     scrub jay  \\\n",
       "0      3.734033e-09  1.000000e+00  4.090851e-14  9.983020e-12  6.485392e-12   \n",
       "1      1.276096e-11  1.000000e+00  1.121936e-14  2.174303e-08  5.108855e-12   \n",
       "2      6.296354e-11  9.999992e-01  7.142781e-14  3.530066e-09  4.641423e-09   \n",
       "3      5.639691e-12  9.999999e-01  5.760029e-14  1.694272e-07  1.438960e-10   \n",
       "4      5.547539e-18  1.000000e+00  1.231157e-15  3.852544e-08  2.071547e-12   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "25405  2.441563e-02  5.505114e-06  5.270331e-05  9.741090e-01  2.548241e-04   \n",
       "25406  1.743998e-05  1.116437e-04  2.672028e-07  9.998643e-01  2.683630e-07   \n",
       "25407  2.129185e-03  8.929301e-06  2.575501e-05  9.900455e-01  4.031762e-06   \n",
       "25408  2.783561e-08  1.167460e-09  1.727278e-08  1.000000e+00  7.149348e-09   \n",
       "25409  7.014449e-07  2.165211e-06  4.650492e-05  9.999205e-01  2.463862e-08   \n",
       "\n",
       "              skunk    pred  \n",
       "0      2.562433e-09     fox  \n",
       "1      2.274605e-10     fox  \n",
       "2      8.178529e-07     fox  \n",
       "3      1.635343e-10     fox  \n",
       "4      3.465329e-11     fox  \n",
       "...             ...     ...  \n",
       "25405  1.162371e-03  rodent  \n",
       "25406  6.039379e-06  rodent  \n",
       "25407  7.786605e-03  rodent  \n",
       "25408  9.478033e-09  rodent  \n",
       "25409  3.019620e-05  rodent  \n",
       "\n",
       "[25410 rows x 10 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict the species with the highest probability for model 2 to model 6\n",
    "# create a for loop to do this for all the models\n",
    "\n",
    "def predict_species(df):\n",
    "    df['pred'] = df.iloc[:, 3:].idxmax(axis = 1)\n",
    "    return df\n",
    "predict_species(df_val_model2)\n",
    "\n",
    "def predict_species(df):\n",
    "    df['pred'] = df.iloc[:, 3:-1].idxmax(axis = 1)\n",
    "    return df\n",
    "predict_species(df_val_model1)\n",
    "\n",
    "# model_list = [df_train_model1, df_val_model1, df_test_model1, df_train_model2, df_val_model2, df_test_model2 ]#, df_train_model3, df_val_model3, df_test_model3, df_train_model4, df_val_model4, df_test_model4, df_train_model5, df_val_model5, df_test_model5]\n",
    "# for i in range(len(model_list)):\n",
    "#     predict_species(model_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create confusion matrix associated with each model\n",
    "# create a for loop to do this for all the models\n",
    "\n",
    "def confusion_matrix_models(df):\n",
    "    cm = pd.crosstab(df['label'], df['pred'], rownames = ['Actual'], colnames = ['Predicted'])\n",
    "    # cm = confusion_matrix(df['pred'], df['label'])\n",
    "    return cm\n",
    "\n",
    "for i in range(len(model_list)):\n",
    "    globals()['cm_model{}'.format(i+1)] = confusion_matrix_models(model_list[i])\n",
    "cm_list = [cm_model1, cm_model2 ]#, cm_model3, cm_model4, cm_model5, cm_model6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Predicted</th>\n",
       "      <th>bird</th>\n",
       "      <th>fox</th>\n",
       "      <th>lizard</th>\n",
       "      <th>rodent</th>\n",
       "      <th>scrub jay</th>\n",
       "      <th>skunk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bird</th>\n",
       "      <td>1991</td>\n",
       "      <td>415</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fox</th>\n",
       "      <td>8</td>\n",
       "      <td>9460</td>\n",
       "      <td>6</td>\n",
       "      <td>69</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lizard</th>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>1689</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rodent</th>\n",
       "      <td>248</td>\n",
       "      <td>233</td>\n",
       "      <td>46</td>\n",
       "      <td>9749</td>\n",
       "      <td>35</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scrub jay</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>262</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skunk</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  bird   fox  lizard  rodent  scrub jay  skunk\n",
       "Actual                                                 \n",
       "bird       1991   415      59      10         48    326\n",
       "fox           8  9460       6      69          4     39\n",
       "lizard       63    48    1689       3         17      0\n",
       "rodent      248   233      46    9749         35    130\n",
       "scrub jay    31     0       3       1        262      2\n",
       "skunk         0     9       0       1          0    405"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_list[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ROC_curve_models(models, animals):\n",
    "    fig, axs = plt.subplots(1, len(animals), figsize=(20, 4))\n",
    "    plt.subplots_adjust(wspace=0.3)\n",
    "    auc_scores = {}\n",
    "    for i, animal in enumerate(animals):\n",
    "        auc_scores[animal] = []\n",
    "        for j, model in enumerate(models):\n",
    "            fpr, tpr, _ = roc_curve(model['label'], model[animal], pos_label=animal)\n",
    "            auc = np.trapz(tpr, fpr)\n",
    "            auc_scores[animal].append(auc)\n",
    "            axs[i].plot(fpr, tpr, label=f'model {j+1}')\n",
    "            axs[i].plot([0, 1], [0, 1], 'k--')\n",
    "            axs[i].set_xlabel('False Positive Rate', fontsize=12)\n",
    "            axs[i].set_ylabel('True Positive Rate', fontsize=12)\n",
    "            axs[i].set_title(f'ROC Curve for {animal}', fontsize=14)\n",
    "            axs[i].legend(fontsize=10)\n",
    "            cell_text = [[f'{auc:.3f}' for auc in auc_scores[animal]]]\n",
    "            row_labels = [f'{animal} AUC']\n",
    "            col_labels = [f'Model {j+1}' for j in range(len(models))]\n",
    "            # change the position of the table\n",
    "            # so that it is centered below the plot\n",
    "            # and not overlapping with the plot\n",
    "            # (x, y, width, height)\n",
    "            table = axs[i].table(cellText=cell_text,\n",
    "                                    rowLabels=row_labels,\n",
    "                                    colLabels=col_labels,\n",
    "                                    loc='bottom',\n",
    "                                    bbox=[0.2, -0.5, 0.7, 0.3])\n",
    "            table.auto_set_font_size(False)\n",
    "            table.set_fontsize(8)\n",
    "            table.scale(1, 1.5)\n",
    "            # add color to the table associated with the max AUC score\n",
    "            for (row, col), cell in table.get_celld().items():\n",
    "                if (col == np.argmax(auc_scores[animal])):\n",
    "                    cell.set_facecolor('lightgreen')\n",
    "                    # set font color to red \n",
    "                    cell.set_text_props(color='red')\n",
    "    plt.xticks(fontsize=12)\n",
    "    plt.yticks(fontsize=12)\n",
    "    fig.suptitle('ROC Curves and AUC Scores', fontsize=12)\n",
    "    plt.show();\n",
    "    return auc_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [df_val_model1, df_val_model2 ]#, df_val_model3, df_val_model4, df_val_model5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load all the models and predict the labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> some parameters to tune(batch size and weight-decay). </b>\n",
    "\n",
    "`Batch size`\n",
    " \n",
    "- Batch size of 1 (Stochastic Gradient Descent): This approach uses a single training example to update the model weights in each iteration. This can lead to a high variance in the updates, but can also provide faster convergence and improved generalization. (model 3 )\n",
    "- Batch size of 32-128\n",
    "\n",
    "`weight-decay`\n",
    "- Common values for weight decay hyperparameter can range from 0.0001 to 0.1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkpoint 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- model1's weight decay = 1e-06, batch size = 80 (original model)\n",
    "- model2's weight decay = 1e-02\n",
    "- model3's batch size = 1 (stochastic gradient descent)\n",
    "- model4's batch size = 30 \n",
    "- model5's batch size = 50 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = {\n",
    " \"dataset_dir\": \"/home/wliao14/CameraTraps/classification/BASE_LOGDIR\",\n",
    " \"cropped_images_dir\": \"/home/wliao14/crops\",\n",
    " \"multilabel\": false,\n",
    " \"model_name\": \"efficientnet-b3\",\n",
    " \"pretrained\": true,\n",
    " \"finetune\": 0,\n",
    " \"label_weighted\": true,\n",
    " \"weight_by_detection_conf\": false,\n",
    " \"epochs\": 50,\n",
    " \"batch_size\": 80,\n",
    " \"lr\": 3e-05,\n",
    " \"weight_decay\": 1e-06,\n",
    " \"num_workers\": 4,\n",
    " \"logdir\": \"/home/wliao14/CameraTraps/classification/BASE_LOGDIR\",\n",
    " \"log_extreme_examples\": 3,\n",
    " \"seed\": 3495\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = {\n",
    " \"dataset_dir\": \"/home/wliao14/CameraTraps/classification/BASE_LOGDIR\",\n",
    " \"cropped_images_dir\": \"/home/wliao14/crops\",\n",
    " \"multilabel\": false,\n",
    " \"model_name\": \"efficientnet-b3\",\n",
    " \"pretrained\": true,\n",
    " \"finetune\": 0,\n",
    " \"label_weighted\": true,\n",
    " \"weight_by_detection_conf\": false,\n",
    " \"epochs\": 50,\n",
    " \"batch_size\": 90,\n",
    " \"lr\": 3e-05,\n",
    " \"weight_decay\": 1e-06,\n",
    " \"num_workers\": 4,\n",
    " \"logdir\": \"/home/wliao14/CameraTraps/classification/BASE_LOGDIR\",\n",
    " \"log_extreme_examples\": 3,\n",
    " \"seed\": 4153\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_model1 = np.load(\"/home/wliao14/CameraTraps/classification/BASE_LOGDIR/mirav2/confusion_matrices.npz\")\n",
    "df_model1_train, df_model1_val, df_model1_test = df_model1['train'], df_model1['val'], df_model1['test']\n",
    "\n",
    "# read in the data \n",
    "df_model2 = np.load(\"/home/wliao14/animl-implement/data/models/experiment1/model1/confusion_matrices.npz\")\n",
    "df_model2_train, df_model2_val, df_model2_test = df_model2['train'], df_model2['val'], df_model2['test']\n",
    "\n",
    "# df_model3 = np.load(\"/home/wliao14/animl-implement/data/models/model3/confusion_matrices.npz\")\n",
    "# df_model3_train, df_model3_val, df_model3_test = df_model3['train'], df_model3['val'], df_model3['test']\n",
    "\n",
    "# df_model4 = np.load(\"/home/wliao14/animl-implement/data/models/model4/confusion_matrices.npz\")\n",
    "# df_model4_train, df_model4_val, df_model4_test = df_model4['train'], df_model4['val'], df_model4['test']\n",
    "\n",
    "# df_model5 = np.load(\"/home/wliao14/animl-implement/data/models/model5/confusion_matrices.npz\")\n",
    "# df_model5_train, df_model5_val, df_model5_test = df_model5['train'], df_model5['val'], df_model5['test']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix and PR curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>path</th>\n",
       "      <th>label</th>\n",
       "      <th>weight</th>\n",
       "      <th>bird</th>\n",
       "      <th>fox</th>\n",
       "      <th>lizard</th>\n",
       "      <th>rodent</th>\n",
       "      <th>skunk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dataset1/X811480B/p_000232_5416f9c75606bc2093d...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.768660</td>\n",
       "      <td>1.330003e-14</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.619855e-17</td>\n",
       "      <td>9.253115e-15</td>\n",
       "      <td>3.150770e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dataset1/X811480B/p_000230_7d5ceeb53e8005c3402...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.768660</td>\n",
       "      <td>3.278565e-09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.435326e-12</td>\n",
       "      <td>2.199840e-13</td>\n",
       "      <td>3.103492e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dataset1/X811480B/p_000372_c78c93a541ae0475f2e...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.768660</td>\n",
       "      <td>1.693232e-13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.881097e-15</td>\n",
       "      <td>7.300760e-14</td>\n",
       "      <td>2.338386e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dataset1/X811480B/p_000373_cca1bf9d54a27dc9996...</td>\n",
       "      <td>fox</td>\n",
       "      <td>0.768660</td>\n",
       "      <td>1.599664e-11</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.949020e-13</td>\n",
       "      <td>1.200667e-10</td>\n",
       "      <td>1.041570e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset1/X811480B/p_000582_e6e45cc4e3a11ca78dc...</td>\n",
       "      <td>bird</td>\n",
       "      <td>0.619698</td>\n",
       "      <td>8.839586e-01</td>\n",
       "      <td>0.001933</td>\n",
       "      <td>1.005150e-03</td>\n",
       "      <td>1.130881e-01</td>\n",
       "      <td>1.484074e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                path label    weight  \\\n",
       "0  dataset1/X811480B/p_000232_5416f9c75606bc2093d...   fox  0.768660   \n",
       "1  dataset1/X811480B/p_000230_7d5ceeb53e8005c3402...   fox  0.768660   \n",
       "2  dataset1/X811480B/p_000372_c78c93a541ae0475f2e...   fox  0.768660   \n",
       "3  dataset1/X811480B/p_000373_cca1bf9d54a27dc9996...   fox  0.768660   \n",
       "4  dataset1/X811480B/p_000582_e6e45cc4e3a11ca78dc...  bird  0.619698   \n",
       "\n",
       "           bird       fox        lizard        rodent         skunk  \n",
       "0  1.330003e-14  1.000000  5.619855e-17  9.253115e-15  3.150770e-17  \n",
       "1  3.278565e-09  1.000000  7.435326e-12  2.199840e-13  3.103492e-12  \n",
       "2  1.693232e-13  1.000000  1.881097e-15  7.300760e-14  2.338386e-22  \n",
       "3  1.599664e-11  1.000000  3.949020e-13  1.200667e-10  1.041570e-15  \n",
       "4  8.839586e-01  0.001933  1.005150e-03  1.130881e-01  1.484074e-05  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_train = pd.read_csv('~/animl-implement/data/models/experiment1/model1/outputs_train.csv')\n",
    "df2_val = pd.read_csv('~/animl-implement/data/models/experiment1/model1/outputs_val.csv')\n",
    "df2_test = pd.read_csv('~/animl-implement/data/models/experiment1/model1/outputs_test.csv')\n",
    "df2 = pd.concat([df2_train, df2_val, df2_test])\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bird', 'fox', 'lizard', 'rodent', 'skunk'], dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_df2 = df2.columns[3:]\n",
    "classes_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Checkpoint 1\n",
    "### compare mutilple models with different hyperparameters\n",
    "### compare the performance of the models on the validation set\n",
    "\n",
    "# plot the confusion matrix\n",
    "plt.figure(figsize = (20, 10))\n",
    "ax = plt.subplot(2, 3, 1)\n",
    "plot_confusion_matrix(df_model1_val, classes=classes, normalize=True ,title = \"Model 1 - batch size 80\")\n",
    "ax = plt.subplot(2, 3, 2)\n",
    "plot_confusion_matrix(df_model2_val, classes=classes_df2, normalize=True,title = \"Model 2 - batch size 90\")\n",
    "# ax = plt.subplot(2, 3, 3)\n",
    "# plot_confusion_matrix(df_model3_val, classes=classes, normalize=True,title = \"Model 3(batch size 1)\")\n",
    "# ax = plt.subplot(2, 3, 4)\n",
    "# plot_confusion_matrix(df_model4_val, classes=classes, normalize=True,title = \"Model 4(batch size 30)\")\n",
    "# ax = plt.subplot(2, 3, 5)\n",
    "# plot_confusion_matrix(df_model5_val, classes=classes, normalize=True,title = \"Model 5(batch size 50)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show();\n",
    "\n",
    "# calculate the precision and recall for each class\n",
    "precision1, recall1, fpr1, f1_1 = precision_recall(df_model1_val)\n",
    "precision2, recall2, fpr2, f1_2 = precision_recall(df_model2_val)\n",
    "# precision3, recall3, fpr3, f1_3 = precision_recall(df_model3_val)\n",
    "# precision4, recall4, fpr4, f1_4 = precision_recall(df_model4_val)\n",
    "# precision5, recall5, fpr5, f1_5 = precision_recall(df_model5_val)\n",
    "\n",
    "# plot the precision and recall for each class\n",
    "plt.figure(figsize = (10, 10))\n",
    "#show all the classes as annotations\n",
    "plt.plot(recall1, precision1, 'o', label = \"Model 1\")\n",
    "plt.plot(recall2, precision2, 'o', label = \"Model 2\")\n",
    "# plt.plot(recall3, precision3, 'o', label = \"Model 3\")\n",
    "# plt.plot(recall4, precision4, 'o', label = \"Model 4\")\n",
    "# plt.plot(recall5, precision5, 'o', label = \"Model 5\")\n",
    "for i in range(len(classes)):\n",
    "    plt.annotate(classes[i], (recall1[i], precision1[i]))\n",
    "for i in range(len(classes_df2)):\n",
    "    plt.annotate(classes_df2[i], (recall2[i], precision2[i]))\n",
    "    # plt.annotate(classes[i], (recall3[i], precision3[i]))\n",
    "    # plt.annotate(classes[i], (recall4[i], precision4[i]))\n",
    "    # plt.annotate(classes[i], (recall5[i], precision5[i]))\n",
    "\n",
    "plt.xlabel(\"Recall (among all true positive)\")\n",
    "plt.ylabel(\"Precision (among all positive predictions)\")\n",
    "plt.legend()\n",
    "plt.title(\"Precision-Recall Curve\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bird', 'fox', 'lizard', 'rodent', 'scrub jay', 'skunk'], dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85049124, 0.93064437, 0.93677205, 0.99145734, 0.71584699,\n",
       "       0.44900222])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.6988417 , 0.98685583, 0.92802198, 0.93372282, 0.87625418,\n",
       "       0.97590361])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bird', 'fox', 'lizard', 'rodent', 'skunk'], dtype='object')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.89676797, 0.85614849, 0.93671679, 0.97609562, 0.90419162])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88313539, 0.98334444, 0.94321767, 0.88084232, 0.84357542])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's worth noting that the confusion matrix doesn't tell the whole story about the performance of a classification model, as different misclassifications may have different implications depending on the specific problem. Additionally, it's always a good to look at other metrics such as ROC curve, precision, recall, and F1-score, to get a more complete picture of the model's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curve\n",
    "**We care about false negative rate, since we don't want to miss out any non-native species**\n",
    "- plot ROC curve : The ROC curve is a graphical representation of the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of a binary classification model, as the threshold for predicting positive class is varied.\n",
    "\n",
    "false negative rate = 1 - true positive rate (among all true positive)\n",
    "\n",
    "- AUC score : measures the overall ability of a classification model to discriminate between positive and negative instances across all possible classification thresholds.\n"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_ROC_curve_models(models, classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F1 score: \n",
    "F1 score is a performance metric that measures the accuracy of a classification model. It is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives. In general, the F1 score ranges from 0 to 1, with 1 being the best possible score, indicating perfect precision and recall.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score for model 1 is 0.928677375940929\n",
      "F1 score for model 2 is 0.915158766531502\n"
     ]
    }
   ],
   "source": [
    "# The class F-1 scores are averaged by using the number of instances in a class as weights\n",
    "from sklearn.metrics import f1_score\n",
    "for i in range(len(models)):\n",
    "    print('F1 score for model {} is {}'.format(i+1, f1_score(models[i]['label'], models[i]['pred'], average='weighted')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ground Truth vs False Negative "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gWgG/5bQQgOu62LYNAuXiVSJxbe+QIMMwMAyfTiI5HRJsp450XQzTxFAsz56OC0PlBYdkbm6OXC6HaRqMjIwSiUT8r7FJiVPOU8svE8mOYJeL1PPLICDY1YsVifl+cLQQUCyUmJqawHVcerJZstkepAIVBwHYjsPE+DiVcoVwJMLIyAiWpcaBHwKYX1hgeXkZQwiGhoeJx+NqaBNQLJWZmpjAcRy6Mxl6e3uVqbS6rsvExCTlUolQOMTI6B4CisQrApaWllhcXEQIGBwcIplMKqFNGIJyscjM9BTSdQmFwwwMjmBaphJNVAFUalUmxseo1+skEkmGhoeUq/BvhfoKLzAEUC6VmZudZf/+/USjMcbHx/D9rAohcKolqitzOOUirl2nllsgmO7FiqWo5ea9kz78xpVMT0+R7Eqxd99eJicnqNVsBazZY3VlhUK+wKHDh6hVqywtLfkft3jprm7bTE9NsWfvXlLpNGNjp1TxPpCS2ZkZorEY+y65hOnpKSqVqhLxKgSs5XLkcjkOHT6E47gsLiwoE6+ObTM1OcnIyAg9PVnGTp3yW1YHxUKBZLKLfQcOsry0SKlUUKplPz83R8AKcvDAQXK5VQr5gjr54gxoc95mvJZfgVA4RCgUJJVKk1/L+y0LJAgrQKh7ACMURjp1pOtghaJYoSiuXW90cft7dmvdcajXasRjMWLRKI7tUK/XfdPUTjNu44kEAcsiGotRLBb9lgV42srlMoFAgHAoTLq7m2Kh4H3mtzbAcSTVaoV4PEE0EsZ1XWq1qs/KPARQKBSIx+IELIt4PN4KO78RAsqVKqZpEg5HSKe7KZVKjXOX/Ue6ku5MD5meLMVCAdO0CASCvvfANZESyuUS8UScUChIKBSiXC4rEXZnQ3dr7wDCMJBSIqXEcR0MU4U6kMQIhNvOimucN9s4r1qVmq4QAmEIXFd6x9gJlNEGXty69XrjXFepSNx6GEIgaaQ7x2mN/SlRTDbiUcpGekMoNeZsGEZDm3cEqprx6uJKT6sy55MLT8/K8hKzM9MMDo0QiUaRrhLqGunOwG2Uc9KV6o2JnwZ1UuArBCkhkUjg2A6FQpGVlWVSqbQaGUnK1jmtwrQwAiGccgGnnPeM27TwM8tLwDINIpEo+fwaudwaoVCYQFCNyTlSQiKZpFwqUSyVKJVKJOIJJeJWSohEIiChUMizvLRIsqvLb1mAF6+mYRCLxcmvrZFbyxMIBggFg35LAzx9yWSScqVMsVSiWMiTSKgxpislhMNhDGFQKBRYWlwkkUwq0y0rhGBleYmxUyfI9vYRi8dxHMdvWS0MAYlEnEIhT75QpG7bRKMKzP85B7Q5bzMSCIVDDA4NMTU1iWPbjIyMqNLLg2GYWOE4hhUklMpSL67iVMsEU1k1WqhCMDg4SLVaYWZmhuGREXUm5gBdXV2ku7sZHxsjFouR7u5WIm4lYFomw6OjzM7OUiqVGN2zRwltAAjo7x/Admymp6cYGhomFAopEa9SQjyeoKcny/j4GOFwmExPjxJhJwHDNBjds4eF+Xny+Tx79uxRpNns9R6VSiXC4TDFQoGpiXHqtZoylQcpIZvtxRAmExMTZDLdxGJRJeL2bOhu7R1ASujJ9tCT7WlcUCUvSYQZIJQZ8JYphWJE+mLNj5QYJ5ISQqEQ+w8caFxQJew8HUIIhoaHgCHvmkr6JKRTKdLpVOOCWtqCwQCX7N/fcU0ZBAwMDjAwOOC9Vyzskskkya5k44I62pAwPLoH2LN+SUpl4lYClmWx95K969cU0XY2tDnvEOomgPacrVIuX0e2/qcm6sat2mGnsjbQ8fpSUWZ8+TSoHHZnQndrazQajUajGNqcNRqNRqNRDG3OGo1Go9EohjZnjUaj0WgUQ5uzRqPRaDSKoc1Zo9FoNBrF0Oas0Wg0Go1i6HXOGo1Gc4EQHRkGCaXJSax4nPTVVxHu66dw8iQrjz/utzzNNqLNWaPRaC4A+t/6VtJXX8Xigw9Rmpxk9APvJ5TJABC/ZB+BZJL5e+/1WaVmu9Dd2hqNRqM4ZiRC+uqrvDdSkjhwoGXM1cVFALqvvQYzFPJLomab0eZ8ASEVfVyQe+NpNBcQ0SFvL/f8sWMsPPAAqSuvAKA8M8uJv/ochRMnEZZFMJ32U6ZmG9Hd2lsgROO0YwGi4Tuy+d4/WZhCELYMTEORI18auC7KnEJzJlSWqLI2UF+fyjTzRqtMaX8+RyID/QDkX3wRISWRhlkXT5xAAG6tCngt7Pa/2yy3YL0sE81H242GENA6UBaEjzEuTvN6t5FtPy42PO8G2py3oJlwm5kIvIQt8TexpCIBrhhIKFdQSiAaMKk7kmLNUcqopYSgZRANGMqFWwdtFUGVUDrMGlRt16dfbvQbubLxcJGOi7RdZN0701jQPM1scwF/PmHrlMsARPoHkHUbMxwGoDQ2hmEYxPZ4p0I5hcImc5PtbxpPQnpnHTcJWg3L3qKQ8yMNGI1CRJX0J0TnYzfQ5rwFUm7xAF/dWUoImIKkqWaUCWClWOOZ2QKmQu7sSMlAIsSh3piyne8CtU9EAoUHLiTMrFVwfAxAKSXCdXEd2TBnB6dpzgKk29aClRuez5HiqTGQkvR115K+7loA7GKR0swMB37mpzEjEZxqldrqakdctZ/I1DwVVgKu3NAybOXZNoF+GqRYV+MXWyWpph/sBmqW9D6zPpba+Rr8SyzNAtyvNsLZMIWnz3GlUjMZHFe2wkxFg9nUBakY7UW2ijQ7Y/3x5kbLuXF+sWd8ctMRhe3SNvrzuVJZXGTh/gfIvvYW72+6Lgv33Y+UEjMaBWDx/gdw6vUzqt1Ynm0U2VFnaF7zwZ1VS3cvJc5eLtqcNduOOu1mtbRoNC+HxQcfJH/8OOFsluKpU9ilEgC5Z54hf/wEhePHfVao2U60OWs0Gs0FQnVhgerCAsF0mnA8TmV+npm77lZ/XERz3mhz1mg0mgsBIei56SZSV15BIJkE4Pk//CMG3/F2KnPzLH3ve9qkX0Foc9ZoNJoLgP43vYn0tddsum5GIvS+4fUgBEsPP7z7wjQ7gkJTdzQajUazFeG+Ps+YpSR/9CilicnWZ83X2Vteg7B0e+uVgjZnjUajUZzIwAAAhVNjTP7TV6guLbU+W3zwQWqrqwjTbG3pqbnw0eas0Wg0imNGIwDUc6tbfu5UKgAYgcBuSdLsMNqcNRqNRnGah1skL73U2w2sbY1g8vAhIv39IGXrPs2Fjx6g0Gg0GsUpnDhJbXmZYHc3ox94f2tW9sGf+9nWSVSrTz/dakFrLnx0y1mj0WgUR9o2k1/55/WWcWNrzaYxF46fYP7eb/slT7MD6JbzDiCAWq1KrVZHGAaRSKRt71p/EYDjOJQrFZCSUDiMpcgMT4nErdcwDBNMC7fmaRSWhWGqM5ZWqVSwbRvLtAhHwn7LaSGAWr1OrVoFIYhEIhiGGvVvATiuS6VSxnUloVCIgELjo65jY9drjdcOgWAIMxhUYv9IAdTrdYqnTrHyZ39O9soriGSzWPEYtdwa1YUFimNjvmlzXZdypYLruoSCQQLBoC9aToeUkkq5jOO6BAIBQhfImddqlMqvMFzpMjU1jW3buI5D/8AAXV1dKuRzELCaW2VxfgGEIB5PMDg44PuZjxJwq2XKi1OEUlkMK0R1dQ4hTG8WaveA7xUcr9JVY2JsHNE40md0dJRQKKRG3CKZnZmhUqkgXZdsby/d3d1KaBMC8vk15mZmEYYgEo4yNDyEUKHyIKBeq1JcXUa6DoWVJTJDe0hkskglNvWQVMJhYgcPMv7Qg5QeephstkeJeAUoFIvMTE0hDINwKMTQ8DCGafotC/DybLFYZGpyEmEYWJbF6OgolmUpE36nQ4Gc8cpCAKViidXVVfbv309XKs3k5CSqHMHsOi7zc/Nke3vZt28fs7Mz2LbjsyoBrotdWmu0lqG2toQZihHODuNUS7i1su8VCICVlRXqdp2DBw8CsLS0pETcCqBaq7O0tMjevfvI9vYxOTGhQpAB3rGKC/PzdKVS7N+/n4XFeSqVihp7n0sIRWJkBkcJx5OE40miyZQSu215rWabCpLL3v8+fvD3fo9D/+ojdF1xOYYiLdSF+XniiQQH9u9naXmJYqmkRrw2WFhYIBqLcfDAAcrlMoVC4YLYdF+3nLcZIaBUKhEOhTBNg3g8zuTEuN+ygEZGtx0cxyEUDhMOBXEcB9uxCQT8rEl6x9MFkz04NW9Ci1uvEIinEYaBYQVx6zXMcBw/+xmFgHK5TCwawxAQjkSoKDIBRwioVqoErACWZZFIxKlUvDOAd/s0nU3a8E4Hq9fqRCIRApaFlF5XrTIIcBybtcU5Un1DmFZAiVazEFCt1iBgtS70XX45XH45/W95C/ljx8g9+xzFU6eQ7u6fWedKSa1WI51OY1kmAkGtVvM/0TWQEmrVKolEEtM0CAaDVKtVVeSdEW3OO4AVCOA4Do7jUqvVCATVGVszDIFhGNj1OnXbbr33GyEEWAFoHLsnDAvp1L0D7F0XYfjfTSaBgGVRKpdxXRenXiegyHg9gGmZuFLiOA7VWg3L8tKdCoWQEGCYJvV6Hdt2EAJMRbo+wUt/lcIapmURicWbBz/6jsQLp5knn+SZP/kTEvsugd4sw9deixEMkjxyhOSRIzilMrnnn2fum9/cVX0CgWma1G0bx/F64NSKVy9f2HYdx3FxHEeZOTZn48JQeQEhJSTicQzTYHllhVxulUxGjfEhCY1WVYJcLke5XCGRSHgtGb/FNfDGIAVWrAu7XGh0ZUvMUMT/bkYJyVSKXC7H8soq1WqV/oEBJcJOSoiEIwQDAVZWlimVS2QU2S1KAoZhkOzqIp9fQ0pJNBJVbmJOrVIhGIlhmBauD63QLZEQDocIhUIsjE8ydew4tm1Tvv8BwkNDxC/ZR9fll2NFo3Rfd+3um7MBXV1dFPJ5TGEQCoeJRiJq1AgBBHR1eXnWClhe2otGfS9KzgX/m0yvMCQQCAQYHhkhv5YjFAwxMDCoVGLo7+/3WgrlIiMjo0q0nAGQYEWTmMEQgUQaMxjGqRQJdmURZgAVcnw8Hqe3r4/V1RXS3d0ku7qUiFvPAAUjo6OUikUMYTA4NKyEtia9vb0Eg0EK+TxDw8MEAgEFYtRDSkkoEiESTyrRnd1E4rXqR0ZHqVbKICWj+/YR27uHxMGDJA4exIpGfRXY05MlEomQz+cYGhpSaIIkIKE7kyEWi7KWy9HX10ckElFH3xnQLecdQALJZJKuxrFuUqpgKx7NysPo6Ij3XiFtIAkk0g1BkmCqt3FZooLKpoJstodstgcAV6Hwk0AsFiN+yT7vvWLaLNNkeHjYe6+QNgAkRJNpJFIpcwYvnBLdGYZf9zpie/cSHRlBtHcdS0nh1Bhrzz7rizbTNBgaGmy9Vyn4JGAaBoODg61rrkL6zoQ25x1CucKnDdUyUAftwqQiXYsbUDlzqxy3KmsDlDPldiJDQ/TeemvHtfLsLGvPPsfa889jl0r+COMCiFfU1nc6tDlrNBrNBUI9lyP37HPknnuO2vKy33I0O4g2Z41Go1Gc6tISpz7/vylPT/stRbNLaHPWaDQaBRGG4a1WkNI7r3ltrXOseQuk4/eGQprtQpuzRqPRKMjgu95J8tAhVh5/nOLYOMPv+eGzfue53/+DXVCm2Q0UWUOj0Wg0Go2miW45azQajYLknn6G8tQ0lfl57HyeuW/d47ckzS6izVmj0WgUpHDiRMf75Ucf9UmJxg90t7ZGo9EoTmRggCO/+Al6brppy89H3vdejvziJ7BisV1WptkpdMtZo9FoFKXr8ssxIxGCXUmEZREdGabbvr7jHsMyiQwMIC6QAx0054aOTY1Go1GUzKuvJ9TT03of27OH2J49W94rHQenXN4taZodRpuzZttRaae8di0XwPnqmpeIxJ/4lRteyy2uvxxc28at1xFCICzLO0J1i7XMTqnE0iPf8+VMZ83OoM15C4RoZHQBopHLZPO9f7JoSFCWgGnQFbYwDXVUOi5EAkbjtXoFlwAM01A6XkHddCeAsGXi+Lh5snQlUrpgSKQLUoDTZs+iEXitMqX9+SyM/c3fAhAZHGTPhz/E0kMPsXj/A6e9f+PflG0Xm2XZlr+/4Z6Oa+egc6fw87dl24+LDc+7gTbnLWhPvLKZiH1uDjY1+a3jTMRDJkf64koV5BIIGAauKxmfXkK6Uh2nkZ4x7xvKNHaC8lvQZppBpaA0DwG9cb+OKPR+Vbqy8XCRjgu2i1v3WrfNOoPYYHTnmwSrS0uM//0Xqedy5/W9rQwYPBPeqldJbjTti5j2cBCiEYe7GD7anLfAlY1TpdoegH99Zw02ZijVMIQgoMrZ0G0YhsBxHR5+8iSu4yoVhsFggL2DGUDNuN3ubtrtRoBvPTWyURhIJOAiEUgpcE2BcJufrZ9QJ5qV6/bnMxAZGiKQiFNbWcUuFjGjEcxohPBA/2m/s/b8C1sJBcBt0yJlZ8uwWcZtGc8+BK8SlULZ+dJovNgtTdqcNduKmoV4Z/NFKNI0kFIdLRc6vqS7tppLc7xZbqOY7ldd17F959C73nXW72xpzpoLEvWaORqNRqPRXOTolrNGo9EoyOzddzN/77241RrScTj2qU/5LUmzi2hz1mg0GgVxSmUc1tctu7m6j2o0u402Z41Go7kAMIJBuq9/FfVcjtwzzxLbs4eem28i3NdH8eQpZu66S29C8gpCm7NGo9FcAIx+4ANEBvpZfPAhjECAoXe9EzMSASBx6CCYBpNf/kd/RWq2DT0hTKPRaBQn0NVFZKAf6TjUczlSV16JGYkgXZeVxx/HrdVI7N+PFY/7LVWzTWhz1mg0GsWJjgwDsPL977P61FNeSxkonjrF7De+2TpeMtjV5ZtGzfaizVmj0WgUJ9TdDUB5ZhZhWUT6vY1IiqfGADBCIQB9MtUrCB2TGo1Gozj1tTUAUpdfRrg32zLhwqlThHp6iI2MePfl875p1GwvuuW8Q4i2h4qorE1ZNh471P5eES6UdKeiPpW15V88ilurEdu7l8yrXw1AZWGB2vIy+37iIwjLorayQm1lxRd9KocdqK9vK7Q57xCudKlVq9TtupIJolarUatVkVIqp08IcF0HV6VTpCQIIbBMEySYpkE4FCBgmX4r68CVklqtSr1eUy9egXq9Tq1axVUs3QnAcRyq1Qq2YyulDaBeLDJ2++3YpRIA1cVFZu+6u/W5W6sxe/c32g4C2GV9dp1qtYorXeXCDpr6Kjiumvq2Qndr7wRSsrK8QrVWRSBId3cTDvl1ck4nAiiVSqyurgIQi8fpSiZ91bSRWq3G4sIC8USCZDLpV3nTgWmaZFJxhAHzi3l6uhOYpoEhBAvLearVuu/VcgGs5nKUGwV4Op0mEokok+4qlQrLKysgJZFolFRXlzKbi7uuy9LSErZjYxommUwGy7KUCbvc2hqzDz/CqUe+R8/ICAHbbmmb+frXKY5PYBcKvmirVqssLy8jpSQSjpBKp5SJV4FXniwtLSGlJBgM0p3pxhCGEnF7JnTLeZtpJobp6SmCVoBSucTc7KwqaRWQzM/NU6/XMU2DqclJHEedFqqUkpWVZSYnxigU8ggFAk5KSMQj7B3O0JtJYgVMRge6EQgGsl0k4/4bYLPlNzU5iWVZ1Os1pqenFUp3sLCwQKVUwrIspqcmqdfV6FUSQLHo5dNQMMjq6gqruZwSYSfwKg7TU5MYhkG9Xmfq1CkiQ4MkDh4k3NvL2gsv+mLMTZaWligWiwQCFtPTU1QqFSXitcny8jKFQoFgwGJ+fo5yueJ7Rfpc0C3nbUYIKBQKCATZ3iyBYIjjx4+yb+8ev6V53Yq2Q6FYYO/efSQSMcbGxrFtm5AZ9N1gwCuILMsiHk8gFMlBAjAFrKyVCFgGhvC6uEXr2X+lQkC5UsF1HLLZXqrVCk899SSw32dlTYORFPJ5+voH6MmkmRgfp1KtEgwG/ZaHEJDPrxEKhcn29FCpVMmtrtLbk/FbGgivZVqv1+kdHCT7mpvpftWrMAOB1i12ocDCffez+vTTuy5PSkl+bY1MTw+92R4mJyYplytEohEl5mNIYG1tjVQqRW9vL0vLy5SKJeKxqAryzog25x3AdSWiccasYQikq1AyaJ3+3n5m6s7re132MP2R1Fnve3p1gheDFivLy/iVu/+fN/4QqXAMR7r89jf+kXK9xuJyHtMy2dOb4bd/8EdJxsP83WP3s1AtEAkFzv5HdwHpSoQQ7I/3cdOeA+R6r+cbpRco2/7vySylRLKeLxDCt/HRrXBdF6OhTRiGMtoE3jwCIQSDP/g2UpddtukeKx5n4AffhpSS3DPP7Ko+b06kXO/hEkBj3Lk9BG/pOUxf+OxrsJ/NTfJiYYYfGfImvZWdOl+bebzjb/WEErw+eykA35p/hpVa8SwiJUJ4ncRCGEipTk/hmdDmvM1IIBqN4DouxUKBtbU14omE37JamJZJKBSiWCggXZdQKIhp7vykpku7hjiSHDzrfau1Ei8UZhqFuT+854pXM5RMA7BUKvBH3/4q4PlJyLT44JU3AnDXU99nLLdEOBzw7Wzmj+1/CzErxINLR7m36hXMMRng6vReSMO9zx7dfVFbYBiCcDhCqVgkaJlYlkkg4H+rGZp5NsZaLkehWKRcKhKJxfyWBXh1hGAwQN+ll7WMefbJJxELC1SXlokMDZE8fIhQTw/9b3kza889h9zFiZSGEEQiUYrFEuFQCNMwCYZCm+o2R5KDHE6cPf/n6iWOFma5MXOwdW22sspjKydb75NWhJsanz+8fPyM5iyASCRKqVQkXwhj2zbhcFj5VjNoc95+JN5kl1SapZVV6rUaAwMDqNB4loBpGPT0ZMnlVqlUKvT29mGZ5o4n1qP5WQp2pfX+2vReTGGwVi/zYn6mdX267C0FiUajhIKhHVZ1dv6vm97M3z3xANOrK9TqNuXqeis0noxi5A3mFvwbn+wNJ0lYEeJWmGAwQHemhxfnx/gX8xFCoRA1x/FHWBsSz5x7enpYXlpiaXmFnp4soZAa5oyEZDLBWi7K8vIKhmHQnU4rkWcBAoEAw4cOA1Ccn+f4332BTHc3EiiOj7P2/PPsv+2jGIEAwe401cWlXdWXyWRYWFhgeWWF7kyGSGO/73aO5Wcp2tXW+6tTe1r5/1hhtnV9pry66btvH7iGp3IT1F37JenrznQzPz/P8vIKsViMaCyqRJf72dDmvM1IvNrk4NAQ5XIR07SUmTEL4EpIpVOEQkFc1yUajSGE2HF935p7tuP95V3DRMwg0+UVPn/qgdb1kBng8uQwhw/3czQ/S9gIcWX3CC+szSCRDEe9nZKeW5vGdj3j6QklGGh0mT+9OtHxbzmY6Gc0lgEpmdqT55FTx6idRyaPBIL8+zf+MJ/4x8+xVqgwZ6y1PiuVa0zOLFOq1BACLusb4tUj++mOxHh6bpKHx4+Tq5Q6/p4pDF677zBX9I+wUi7yf158gsFIN+lQnIJd4VRxoXVvKhhjXyxLJphgqZZnrLjIcs2b+BM0LAYiaUzh9XqkAlH2xLLIAYl0BBP1VYJGCFu6dAfjDES8noDx4iJ5e/3kooOJAYKGRbHtt01hcCQ5xEA4RcWtMVFaYqy4uClsekNJDicHiVthJkvLnCzOd1TA2pESkskkgUAAx7GJRGIYhhozZiVgWRZDIyNUymUCwaAyqyskICSkh7xWZ/HkKbobxtyktrJCdXmFUKYbK57YVXOWQDweb0xCrBOJRjG3qOzfu/Bcx/tLk0NEzCAzlRX+bvyBjs9M0TlPuSsQ5dbey7hr9smXpC8WizEwMEC9XiccCWOZaszCPxvanHcAibcOttmdrcjwVQsBXu0RQKpViUwFonz0kjcA8PfjD/K+kRswhcGfvXgX2XCCD47eBMB/eupLrLmeyVyZGuGHhq4D4N89/rfekg4zyEf2vZZL27rS3/mh63hk4ji3/f0nN5nmVjw4fowbRvbznitexee+dy+PTZ6iXl839lKlRqlSwxCCX3vLe/jpG9/YMYltNr/KT/zv/8ELC17PQCwY4s9+5KO86cDlrXvef9WNhBMmV3SNcrwwx18c99auXpPay/tHbiRodGbRseICf3nyW/SEkvzcgR9oXb++ez/Xd+/nPzz5ea7OXsL7h71w+s/P/AMhw+In974egDtnn+TuuacAiJhBfmrfGzGE4K65pzhVXCAbSvKRva9jIJzq+N3vLZ/gHyYfxG0k5tf2HOFdg9dhbOgyeHzlFH83/sCmeQzNd5FopHVBpXQngYBlEUgmlNRWb2wukrnqSla++11cez0dRgYHCWW8Sqsfm5BIIBwJE46Etz3sjhVmORDv59bspTy8dIxc/ez5djf17SR6KdUOIfFMWTVjhk5tCspr8SPDr8YUBnXXwT1Ppe8aupZLk4M40uX7q2M8snSccr3Gq0f28z/fd9s5/Y3n5qb4348/gEDwG299X2tmdhOBN878I1dcz8/c+CYEgocnjvOZh+9hobBGfyLFF3/iF4k2xlb/7c1vbRnzCwsz3HP8Wa4b2ssVXaMdvysQvGvwOoKGxVwlx12zT7bG3PbEslyT2stavcTdc0+1egFOFOe5e+4p3C3S3ExlldnKKkDHuN+hxEDLXB9fOYkAfnT0ZgbCKapunQeXjvLc2hSulFzffQnvGLgWgO5gvGXMDy8f43+P388Tq94ez9em93ZUiDaicrpr5Qu/hWxBcXIKpMSMRLjkto8y8ANvpfv6VzH8w+9m9H3vBbwtPuu5nC/6dipen81NcqwwS8CwWunvpSyNUDndnQ7dctYoy1q9zF+euIe58ioS6Auf22YpcSvMTT0HAPjm3DPcOft9pOvytZUEv/HmH+E1ew8xmuphfHVzV+1Gfv/eO3j35ddx3dBefvjyV/Hg+LFN9/z8LV4L9rtjR/nRv/kTAG5/+hHuuO3/JhWJ8oNHruEfn36Ej77aa70+PHGcH/3rP8F2XV69Zz+3/8Qvdvy9dDDGVHmZqTL849QjrNSKBA2LK7pGCBoW/eEuHlw6yp2zT3Jj5gBBw+JEYY47z9Dt99jKSd4xcC0j0QwRM0jZqbUm6I2XFlms5jkQ72c02gN4vRZP5cYxBHxg5GZelb6EV6Uv4WszTzAQSbVM/WRhgSdWxnh85RTzlRyWYVCX/o9zv9KoLiyw8MADZG+5hUAySeqqqzo+l67L1P/5qk/qdpavTD3KLx56B9ek93L/4gt+y9k1tDlrlOW7i0eZ3WKCyNnIhpOt7uX+cIr3Dt+AlJKliDerUyB4zd6DjD9xdnNeKhX4b9/5F/7jW36Ef/+md/Ohv/3Tjs8tw2Rvdy8AQcvit3/wg63PqnadkBXgNXsO8tD4MRIhr0v3jmcfw2ks53hydpylap5MaH1G/3KtwGdP3sNAJM3VqT0MRtLsiWZbXdyGOP8Or8dXTvH2gWsxhOBQYoAnV8darejHVk4B0Nu21OVwcoADiX4EkAx4umNWiP5wiuOFOequTcCw+NHRm/nhoes5VVzg0ZUTPLk6vitL8y5GFr/7INXFJbKvuZlQTw8IgbRtihOTLHz721QWFs7+Ry5AZiurPLR8jJszB3n30Kv42swTfkvaFbQ5a5Rlobp29pu2oGkm4I1Ht8iuv4ycxzKezz5yLz9+3S1c0t3Lx256c8dn3dEYluGZ5auG9vGqoX2bvh8LhshE463384XOf9fiBnO2hMlP7nt9yzzrrsN4aZGQYRG1XtoM9ly9xInCHPvjfRxODLBUKxC3wrhS8v3VUwAkA+HW/Td0H9jy74QMi4pT54sTD/HOwWvpCkQJmwGOJAc5khzkbf15/vLEN1mq+bdj1SuZ/NGj5I8eRZgmVjRKvVBQc+xsm7lz9vtck9rDaLSHa1J7/ZazK2hz1iiLvWGzgPZZ1oG2iVLpYOea1JXqujH84fNfZa66gnRdbr/rcVzbAUFrYtM56XAdfuvuL/PZD36MH7vmNR2fLRbzrRbyb975D/z1o9/Z9H0pIRWJtt4f7h3kq88/4f07TJN98d6O+69N720Z85cmH+J7yyeQSH79svees+ateHzlJPvjfRxKDLbWhr6Qn24tcWles6XLbz79RRzp0NwzpLmsSDbC7YnVU3x/9RTD0QwH4wMcSQ6yN5alJ5TgNT2H+efpR1+WVs060eFhoqMjBOJx6rk1imNjlGdnL6rjIYt2lbvnnuKHBl/FDRn/d73bDfSEMM0Fw1xlvcX5qu69GEKQCIS5vGu447756hp2Y9zzhsx+XClxpeSN+y/nrz/0s/yvH/tZ+hJn362onW8cfZpvn3h+0+xkV0qen58G4EeueDVCCGzXZbgrw+d+7N/y1x/6WV677zBLpQLHl+YB+Dc33Mpr9hxkMJnmP9z67k0zsvsbM6XLTo2Hl47jSJcjCW/J0umwxNk3knkyN44tHZKBCDf3HALo2NyhucbcEgbXpva0wu2WniP8m0vexEf2vA5DGLxr8Dp+56oP8ZuXv5/ZyirfnH+a/3HsTsYaS7FGogpse/kKIfva17Lnx36U7GteQ+qqq8i+7rXs/fEP03399X5L23UeWHyR+eqaMtv67jS65ay5YJiv5Ki5NkHD4gcHruY1PYeIWaFWa65JxanzL9NP8q6ha3lt9jCXJgcpOVVGrvMmO33j6NPMrK2e9+//57u+xNd/+j+0urGb/H/v/jJf/IlPcM3gHh75+H/hxcVZrh/eh2WYzBfWeKgxiez/981/5NMf+GkSoQh/95GPt75fdmpEzPVu9sWa1yKKmEF+Yu/rqLp1LksOe2te6RxzztcrJBo7Jo1EM3zqxDdOq7/i1HlubYoru0aJW2EqTp1n1yZbn0+Ulnhi9RTXpPbyvpGbuLnnMEHDJBvyJuJ9Y+5pHOny3NoUr8teStQK8euXvZcX8zMEDJPRmDdu8Pza9HmHrWYzoWyWnhtv2PyBEPS+9hbWnn22dYTkxYAjXe6YepTbLnmj31J2Bd1y1lww1F2Hz564t9UNmwxEOJ6f46tbTBD51tyz3DH1OFXXJhNKMNKYhXz7U4/wC//0uZf0+0cXZ7fstn5k4jgf+9Knmcot0x2Nc9PoASzD5PGpU7z/f/03Ko29re968Sk++oW/4JnZSeqOw9Ozk/yXb/4j440NPpoTqR5eOsbjK6eouw5XdI1wVdco9y08z9O5cQAOxvtbre3vLD5P3bUJmwH2x/vO+m9obyk/nRun7nbOrP7SxMPcv/gCUkqGImmyoSSulNw99xR3zXmzwY8X5vjSxEMU7SoRM8jVqT1clhzGlS4PLL7IPQu7u7/zK5XYnj2tSV8TX/5HXvjT/87M1+8EQFgWkaEhnxXuPs/np3k+f3FU/oTc2Oy4SHBdF9d1vcPfazWq1Srlcpk9e/bwGy/cjuPYuI6LK931llmz6eIDovFQdct2U3jjknVnd5JTJhSn7jqs1ctnvC9gmAxGu7Aw+Iuv3stqobBjcWgKg32ZXnrjSY4vzTGXP/2aU4HAlZJQKMBDH//PZEIJnlgd4/Nj97XuCRoWfeEupssrrdndW2EIQcwMIwRnDY9zJWwG6A+ncHGZq+SoOpt3VQsYJplggq5AlJJTZamap+TUtuX3zxVD+Lh+tVksuBIpXaTrIh0XabtI2+G/3foxnvnd3/PmazVPMGucONN8PhP9b34T6WuvpXDyFBNf+lLr+iUf/deEMhnmvnUPy4+ew9h+Q6eLRErv2XUlrpBkfuzH2LNnD7/yxN82bpWNbuO2ws6HMs9oBM8uFSdb0/7bwuuxEobAMk2CAYv/eOBHmJycJBwOEwqFCAa9cwpM09yWo251t7bmgmSpem6zgeuuw2RpGem6rFW2x7hOhyNdji3OcmxxdsvP7/23v8FIKsNkbolf/srfcGplkfdceT3djZna4xu2yKy5NhOls2/F6ErZsSXndlBx6pwqLrQmhG1F3XWYbdvgRLPNGN48gvpaZyWvvrpKKJNBWDt/YI3GP7Q5ay4ufJxL8if3/Qt/+O6PsDed5faf/KWOz17Mz/DI8nGflGk0GtXQ5rwFhgBXeFszNruTpY9d2jR/Wnib4KuKwOveVmoypfS6faUQ7BvK4Pp41NBjKyf5yX/8c67t38tVfXvIRGJM5pephmt8b+WEd26vb+q2Rmx4VhHftG0MHNH2aP9IrB8nuvH5XP58bGSEwbe9rXU91OstvUseOkQole74zszXv37aP2TQPH95vWxr3bJRsyL4qqU9TETbY5d+XpvzFggaCVmA0RzPUiDFitb/1EQIME01BRqmwasu3+O3DACmWGRqZREaZxQEyia5fJlKrb4tY1XbhZSSdDJKMGD5diTm2RB4+dTvQlzIdeNzG5pan9Fm0s3ncxHcuCfY3U2wu3vTx+G+PsJ9nZMAZ+/cwpybNOqlzbKtfUxVNN5vKcuHwG0FnyLprhmHBrunSZvzFrjQOkBgq4MEfEMopGUDQqwfHKAazQLRNE1l6zYnJheZn1/FMNVZQOE6LtddtY9sOq5kvAJK5InWoQrthyvI9c+ab1uV63PV+xL+YWf6SqtM2+K+jbo7GgJ+hK9o67FUgFb8Sa/Bthtoc96CZkJtPbc+wNfZ2iofdyY3PKtGs0xUTV+zRr66VmRhMQemQpN8bIdqzZulrVq4NfHTP9oNuJm2tjrZqmNIrPn6HMqSmbvuZuauu7dP50aNbb+/sRLRbtR+tpx9TXcbZmvvdhmizVmjUQDDMMAwEQoNC0hpKNOtqNFcbKjTh6bRaDQajQbQ5qzRaDQajXJoc9ZoNBqNRjG0OWs0Go1GoxjanDUajeZCQghCPT1EBgZa7zWvPPRsbY1Go7kQaBwV2XXFFVixGADP/+EfMfSud1FZmGfxwYfUWRisedloc9ZoNJoLgIEfeCupK6/cdN0IBcnecgsAi999cLdlaXYI3a2t0Wg0ihMZ6PeMWUrWnn+e4vh467Pi2BgAPTffjLB0e+uVgjbnHWKLPfCVQut76SirrX2bNrfxUKyXU9mwQ21tkf5+AAonTzJ1x/+htrzS+mzp4UeorawgDINQT8YXfSqHHaivbyt0NWsHEIDtOFTLZUzLJBSO+C2pAykllUoZ15VEIhEMw1CrDJeScqWM67qEI1FMhfQJoFavUa9WCQS9A9ZV0CYlhIMBanUHDEksFAQhKFZqSNdVplSqlMs4jkM4EsE0TSXCDrzgqdfr1KoVAsEgwWBIKW1GOAxAZWVly3Flt1YDwDD9KdIrlTKOrV68NqlUKjh2nWA4TMAKKKdvK7Q57wCulMzPzyNdF8dxyGQyxGIxJRKEELC2lmctlwMgEomQ6cmgSuktgLVCgdWVVYQB4VKZnp4eJWakCqBu15mbnfMqNFLS19dHIOBjZpcgDIOedIyedJzjE4skYiGSsQgSSbhcY3FpzS91LQRQKBRZXl7CMAyCpRI92R6E8L/zzqtM28zOzmIYBq7r0tfXp0zFC2DhxAmyt9xC6rLLWH7+eYSxfvZk12WXeadTSUl1cXFXdQmgWCqxtLiIYRgEikWy2awyFX4BlMtlFhYWMAwDw8jT19enZAViI/7njFcYAq91sLgwTyKRAGBmZloFbwFAupKF+XlM0yQejzMzPY1jO37LWkdKFufnMQxBIh5nZmYa27YVqTrAWm6NtVyOrmSCYrHI6uqq73EbCllkuxP0dicxTYNMKk44ZBEMWGRTcRBCgUm8ksWFeZCSRCLO7OwMtWpNjXgVUMgXyK2u0pVMUKlUWFle8T1eG9KwHZvn7rmX0twcViTCoR//cVJXXQXAoZ//OQbf8XYAVr7/JE61uusalxYXcRyHZCLO/Nwc5XJ51zWciaWlJWzbJplIsLq6QqlUUqUtcka0OW8zQkCpWMI0TLq6kqTSaXKrOSXSggBs26FSqZDs6iLTnaZcKWM7jjr6HJdypUyyq4vudJpqtUq9XvdbmoeAYqFAJBolmUwSiUQoFAq+hp2kUeFayVNqmp2UmKZBwDKRCrQPBOC4klK5RKKri3Qq5XUhN7pi/UYAxWKBcDhMMpkkFouRL+SVyBMIqFZrOPUaU/98B+XZ2Y6PjUAAgPyLR5n/znd2XZ4rJaVSkWQySSqVwnZsqpWKMuYngWKxSCKRIJXqwrIsyuWyKvLOiO7W3gkMgWw0VaQrvROHFEI0DkqVgFChedCOAIEXft7xdepM4xB44SXdRtxK6Xv4CQGVah1XSqQE0zSIRkLMLuYQQtDfk8SyDOp1f3tHvFhsP3xZKDFU0URg4Mr1eDUMNbSthxtUF5c4+lefY8F1ueI1N2PF4tTXclTnFyhNTfmqT7Z3zQhxXsdW7zRCtJXHEiWGUs4Fbc7bjJQQi8UwDIPFxUXy+QLpdLcyCdWyTGKxOCurK5TLJWLRGAGFll9YpkEsHie3ukqtWiUSjRIIBvyWBXhxG08mmZmeZmFxkUqlQk+2R4m49SoJcr0a45XnOK7bfOsrhiGIJxLkcjkc2yYcDhEKBn1W5SGBeKPLc3FxkWKxqEyelRJCoSCBYJCl5UXsuk2pXGIlFERKsKJRQtksZijkS5e2EIJEMsna2hogCQaDRCJhBYZRPASQTCTI5/MYhsB1XaLRiBJxezbUKZVfIUggHA6T7e2lWCxhGAa9vb1KJNZmSznb28vS4iKlUpmBwUEMRSZHNFvK2WyWxcUlSqUSAwMDWJYa+sDL6OVUimKxRDwep6urS4m4dRyXxZUClZrNxOwyqYRXAE3OrmLXHV/duRmvPdksiwsLFEsl+voH1JlwJSEej5Hu7qZQLBGNRkmnU0rEqwRM06S/vx8jk+GyD36QwokT5B5+mOxrX0vmhlcjDAOnXGbyn75CaXJy1zV2ZzIszM9TLJbo7etXbnVKdyZD3XEoFkuk02ki0Yg6zfozoM15BxBApqeHeq2GYZoELEuZtCCBWDRKYGAAKV2CgaBSXVASiEajDAwEcKVLIBD0us38FsZ6QdnX149t17EsS4lZn0J45jw1u4LtuCyv2hSKXiuqVrd9VuchgUg4TP/AAK7jEAgGve5Gv4XhaTMMg96+Pux6HdMysUy18myqu5tDP/tvMcNhapOTWF3JljEDmJEIAz/wVo5/9q92dQtPCYRDoY54NRSJV/D0BYNB+vv7cR0HKxDAEGrMJD8b2px3AAkYQhAKhVrvVUICwUCg471KSCCgqD7PoA1MU7W4ldh2o4UsoVrzJtGJ1v/8R4I3hNIYRlEn7BrxahiYiubZSF8fZjhMfW2N/IkTdF93XavFPPXPd9D/A28l2N1NMJ2mtry8q9pUjldQX9/puDBGxi9AZNtDRbS+l46y2sT6s2jOt1LEmJsoG3aorS06MgzAyuOPU56eJrZnLwD5Y8cojo9TnvQmhAUayzd3G5XDDtTXtxXanDUajUZxjKDXoneqNaxYrLVNZ3HM22M7lO0BQDoK7VmgeVnobm2NRqNRnObOXz0330TqKu9kKum6FMfG6LnpRm+HMKC2uuqXRM02o81Zo9FoFCd/9CjVxSVCPZlW13X+6DGccpmem28GoHDiBHah4KdMzTaizVmj0WgUR7ouY1/4Ar2vfx3h3l4KJ06y/Nhjrc+L4+PMfP1OHxVqthttzhqNRnMB4JTLWxrwi//jz3F92IBEs7Noc9ZoNBoFEabZWsd8Rly3tce2q8o+9JqXjTZnjUajUZC+N72J9NVXndd3nvv9P9ghNZrdRi+l0mg0Go1GMXTLWaPRaBQk9+yzVDYcEam5eNDmrNEogDAEhikQhum3lBbSVG6DsYuK8tQUZZ+OgtT4jzbnLTAEyMbDoLHlm/Te+1pYCTAU3X+ueXiGyuMkKmvbM5AhFY9gKHTWrCtdErEwoHbY+bVLqWzfLpX1MsNtE9Pc21yIttftz+eDEIT7+whlemDDRDFhGMQP7GfyH750Wp2tsozNZVnzaO2tNPlZ5vmZ7mR7PDbi0NhFD9DmvAVtea5jL1a/jVlKieP6KeL0WI3D6R0VztnbgJeh1G4Djg6kkQNpv2VswhCCcs1hbrWMIdQKQ1dKervCREOWL8c7tofGxnLidOWG2OLaudL/trfRdcXl5/295m+dqSw7l3t2ExVS2mk17JI4bc5b4Erv1LXms5TrrWe/yichwXGhXHeUSLjtSATxkHcMW81Wz5wtUxA01d30XrT9X0XypTrPnFw6t2U9u4jruLzqcNYzZx9+v1khkI0TFWR7udG8p/U/GhVsrwyR51mWmNHoWY25vrq6KRzaf1+yXpa5srNl6G4RgC3ZfpV5+Jtn2yt8ou3ablUEtTlvQfvpJRtPMvE1sQCuki1n2UqwrlTLZpoFUvO1arS3WFTUB2C7LoVS3euCUAnHpe54oeZ32J3p1KOtWtXnazyRwUEAimNjzH/7O4y+/32UpqeZ+8Y36bricnpuuIH5++4/4988l5OZmtpOp3+32Kol7ycvJc5eLtqcLyBaXWKKlZEK9mS3UCyoLlyaA24q4ao+WLF9hHq8U6dKExNU5uYoTU4SHR2lvrbG4gPfJdzXR/+b38TaCy+onSE154xa/VQajUaj2YRd9A60iO3dizAMqotLmKFQ65xnu1DEjEQIN0xcc+GjW84ajUajOOWZWZCS6PAwg+96JyuPPU7PzTcx+v73U56eITI0CFJiF4t+S9VsE7rlrNFoNIpTXVxk8cEHATBMi/L0NLXlZYRpEh0ZRhgGxYkJ7FLJZ6Wa7UK3nDUajeYCYOH+B1j5/pOEenoaR0j+PT033UQo20NtZYWF+x/wW6JmG9HmrNFoNAoSTKcJJBLYxSLVpSUA7EIBu+CNP9vFIrPf+IafEjU7iO7W1mg0GgXpvu5aRj/4ATI33tC6FhkcJHPjjSSPHPZRmWY30Oas0Wg0FwjR4WF6X/daUldc4bcUzQ6jzVmj0Wg0GsXQY847RPtm/Cpu6tXcilHu5n5054ho2+xCbrWvoM8041bFXb3a051S+mRjjyVB516RrRMh/EflPCvo1KcSyqa5NpqtUFX1bYU25x1AAJVqlfxajkAgQFdXWp1cJQRO3aaQX0YC8XgCKxDwW5VH43CPYr5AtVomGAwRiycQQiiRo5pRWCwWKBaLRKMx4vG4CtIAT1+tVmMtt4ppmnSlu72w8xsJ8UiQuuNSrTv0piKAd0jKarGG4/i/56sAiqUixUKBSCRCIpFUJl4B6vU6udVVwmW1lkp5dS1JPrdKrVYn2dVFKBRSJuya+lbXctSqVWKJBNFIVBl9Z0J3a28zAnBdl5npaSrlCktLy6zmVpXZclMAa2urFIsFSsUCSwvzSjWcq5UyC/Oz1Gt1FhfmKRUKahhMg0q1yvT0DNVqjbm5OSqVihJx67XkJbMzM5RKZZZXVlleWlJCWyoW5MBQkmQ0SMAyGO1LEIsGCAUtJfaiFUCtXmdmeppqrcb8/ALlclkFaS1mZ2cplopUyhW/pWwiv5ZncXGJcqXM3Owsjm37XdfqoFAosLCwSLlSZW52jrpdV0rf6dAt5+1GQKlUJL+2xqFDh1heXWV2ZoZMOoXjtza8lmkgEMRMmORWlqnaZb8ldeC6LtFojO6eHk4eO0qpVCTZ1YWjQl1XwFouR71eZ3R0hPHxCVZXVhiMDPgftwJqlRqrq6vsP3CAcrnM7MwM2WyPfyEnQZiCaCRAOGBimQaxcIBQwEAIQcAycFWoGQrIr61RrVYZGRlhamqK5eVlRqJD/scrkDxyhDf+7v8fIQTCNAGI7dnD4Y//wqZ7X/iTP91teSwvLxEMhcj2ZHjhhRdJd3eTSCaU6O0CWFleJhgM0JvNcuLkSUqFIql0SqlGyVbolvM2I4ByuYJlWUSiEZKJBMXGvriqEE8kCIcbXYuO4407q4CEcDhCpidLfi2HlJJoNKZGAU4zbstEIhEi4TDhcJhyRY3KjQCqtRrCMIhEoiSTXZT83i2qMTCfy1fIleqAxHYkY/NFcmsVoiGLZDTg+5yHZryGQmEi4TCRSJRyWY14BW9+iBUOY4ZCGFajPSUERjC46bHbuFJSqVSIxWJEo1Fc6VKr1ZRpmUoJlUqZaDRGNBrBClhUazW/ZZ0TuuW8A5iWheN4de56vY5lqhPMUkqKhQLBYJCe3n6OH30ex7ExDAXGnQUYhsHi4gL53CrZ3n5iiYQyk8IkYJomtVoVANu2MRWKW9M0cV0XiaRu1zEbrSw/kVJSLNutCqBhQK5UI79Wobc7SiRokZNVn1WCZZo4tg0049X/sKvMz5N/8UUc2yFfyJNMJpFSksvl6E53K9EwFQhMw8BuhB0SDNNUQht4oyamYWLbXnnsOi6GAnF7LqhTsrxCkBLisRiBYIDp6WmKxRLZ3l6FEqugUi6SX8sRDAaJRKIYQo0OFAGsrq4wceokya4ubLtOtVIhFA6r0UUmoauri/zaGlPT09SqVfoHBtSQJiEcChGJhJmdmaFarZLNZv2W5SEah9TjFZZ9qQjJsDfevFaq+z7uLCUkurpYWVlhanqaUqlIb1+/7/G6+tTT5J56Gtd1OX78OJFIhHq9DsC+fft81wde1KXSadZyOWy7TiQaJRqJqJFfARr6VlZWmHS9CkQsGvW7s+ac0Oa8zUggGAwyMDBIuVQiHk/QnelWKDEI4okkhXweCfQPDGFaatQkJV6tuyfbSyQWa7S2lAk4JBCLx+nt66NWq5Hp6SGRSCgRt81W/cDAYGMmeZR0Oq2ENoRgPlemVvdma68F6hgC5lfLlCp13wfXJBCJROjr76dardLdnSGZSCoRdhKvN2lgYIBCoYBpmqTTab9lddDUYzsOAwODBINBdXKthK5UCtd1sR2H3t4+wuGwOvrOgDbnHUACya4u4vE4QggMw1AoMUjCkSjBUBjwMr4ySG9pVyyeWO8GVWQZVRMhBN3d3biuizAMDCGUkSfx5hNEozEQYKqS7gSsFKqtRabzq2UMIXBcdVYTCyFIp7txXUfJeI3F40QiUbXiFU9bIBCgpyeLlC6GYfq+LK4dCQQsi55sFtnIs0KhuD0T2px3kObYhooJQSlTbkMIw8vbCmXwTQihdtyajQ1mfNbRQVNMYy27o0KzdCNC8TyrYrzSGK4wBAI1euA24g2nrM90Vy38ToeaJbRGo9FoNBcx2pw1Go1Go1EMbc4ajUaj0SiGNmeNRqPRaBRDm7NGo9FoNIqhzVmj0Wg0GsXQ5qzRaDQajWJoc9ZoNBqNRjG0OWs0Go1Goxh6h7ALkAtlhxsV0GG1TUgJUr1t23T8al6paHPeAiFobSEpGrlfNt/7panxbAiU3NqyuQW28DGMtqI9uFTStRGVtZmGwApamKZaKh1HYDZOtPJDmWxLWEJucb35sWjdtl6G7FI+aZZbsK5xy+1xN9zTcW2nxJ0Dfv52ezz6UYZoc94CA3CFZ4SuAgm0iWkIIkE1968VCAQQUjBFGc0CXIVIPB2KVWra6YoGedXBnlY4qoIrJel4EPAnbps/KUWjIG88muVH657m63Zj3iW97T/jtl0zoOPULUMAcmtD8iNhtszQxyS3qf4i1h+7gYJFqf8002zH3vzN82h90APeDxvC22BeRZoJ1lTRYlqlqK8qTo/K+gSEgyZ7snG/lZyW58cWcRz/TriSUoJ0kY5Eui7SdnBrLrzByxeubOSPhvmJXS5LtkpWG88d2eocktZZJX6kSwXyREcngg/FmjbnLZByiwf4686y40lZlNQn17vdVUSwdeGoEqrKk1Lywtgidt32UwTSBSFdpOMiHQe36rR93ta9LDc877S01v8aJ3bKtue23z9j+vOpzGsGk19sWWGRu5dXtTlvQePY2U2vwb/EokBF8oyorO9C0AZq61NRW5Nq3cau2gi/+kCl9FrPjttoOUtkbb2ysHEYt92fd1Umm8uzre7ZGIoXacO5Az/iTJuzRqO5oBECNWYiisags5D+a9Fc8Oh1zhqNRqPRKIY2Z41Go9FoFEObs0aj0Wg0iqHNWaPRaDQaxdDmrNFoNBqNYmhz1mg0Go1GMbQ5azQajUajGNqcNRqNRqNRDL0JyQ7RvidCa/tPRWju2QDqaYMLQF9b3LqKiVM57FTWJjs2wG5eBAz823msDZXDrkMb6m1Fq7q+06HNeQcQQLFQZHVlhUAwSE82iyGEEhlKALZts7i4gOs4dPdkCYdCSmiDhj7HYWlhAduxyWR6CIfDSuhrbt+3urJCIV8gFo+TTqeV0AaevnK5zMryEqZp0ZPtxTQNJfQJwHFclhYXqNfrdGcyRCIRJbRJCelUlGrdIWAKumJhpISAZbCYK7GWr/h64IwAKtUqy4uLGIZBTzaLZVlKhJ0AHNdleWmRarVKujtDLBpVQht4+lwpWVpYpFqtkOzqIplIKqPvTOhu7W2maX6Tk5OYlkk+n2dhYUGp4woXFhaoVKpIBDPTU7iKVSUXFxYolcuAYHpqCsd1ldkNsVQqMTMzSyAYZHFxkUKhiAoHhXmHZ0imJidBGJTKZebmZtVJdwKWlhbJFwoIw2B6agrbtpWI12x3jMN7s3QlwtRsl3LdwQqa9GcTOL4eRdesEHrxKvFMenZ21j9BW7CyssJabg3TtJiZnqZWqysRr01WV1bJ5VaxLIu52Tmq1apS+k6HNudtRggoFgrUqlV6erIku7qYn51VIjF4rReH1dVVkl1d9GazLC8vY9fVKCQF4Louq6urJJJJenuzrKyuUq/V/JbmISCXy2EYBtmeDKZpsrq6okbYCahUKpRKJTKZHrq7M8zPzXmf+a0NcF3J6soK8USC3myWtbU1KpWqz8poHdDrOC4B06BcrrOcKxENBZheLFAu1/zt1hZQrdYoFgp0d3fT09PD/Px88yPf8XqSlonGYvRmeyjk85TLJTXENVhdXSYSiZHtyWI7NsViUZ1K6xnQ5rwDVGs1LMsiELCIRCJUqwoUQg0cx8VxHELBIKFQENdxcV3n7F/cJRzXxbFtT18wiHRdX8/qbUcAtWqVUDDkxW8wqE7FAajXbQzDIBAIEI1GqNW8dKdCv4h0JbZtEw6FCAYDuFLiOD4e89gSJsmtFsmXGnnUdYlGAgQsg+n5nDcW7SMCvMqzEAQCQSLRKPVaTZlxZykl9bpNKBQiEAgAKNMjAt6QRb1WJxQOEQhYWKZFvV73W9Y5oc15BwiFQtTtOq6UVCoVgqGg35JamKaBYRjYtk3ddhCGwDBMv2W1MA0DwzSp121sx0UIgWmqoU8CwWCQarUCQK1WJRBUJ24DgQC24+C6LuVKhUBAHW3CEJiWSa1Ww20MU1iWGlNeanWnY5JQdzLKWrFKrWr73gKUQCBg4bheJbpSqWAFLGVafkIIApblxWvj6ExVxsPB6xixAgFq1SpSehXEZiVCddTIHa8gpIRYLEY0GmPs1Bi1Wo2BgUElEqsETNMk09PDwsI8S0tL9PRksQJqZCYJ3oSXnh6WlxbJ5VboznQTDAaU0IeEVDpNPr/GyVOncOo26b5+JbRJCaFwkK6uLiYnxqnbdQYGBtTQBhhCkOnJsrK8TKGQpyudIhwO+S3NQwgcV+K4EgxBOGixuFr0W5WH9CqE6XSKqalJbNuhv3/Ab1UtBDTKkwUq5TLJZBfRaFSNZn2DTCbD/Pw8J0+dwrIs4vH4BTFjW5vzNiPxWgSDg4NUKlWEIUgmEkolhu7uboKNLuNoLKbMTPIm6XQaKxDw9EWjGIYaM44lEAmHGRwcol6vk+4OEIlGlYhbzwANBgYGKJXKCAGJREKpQjKdSmGZJq7rEolEMEw1KoVCCGYX13AciRCCmcU1yhVbiSVUsqGvr3+AcqmEBBLxuN+yOkgmkxiGgeM4hCMRAgFFKtMNEokkQggcxyEYDBEIBpXSdzq0Oe8AEohGo0QiUcDrWlElMUggYFmkulKAWtpgvXKjqj6EIJ5IIKVsFd6q6JNAOBwmFAoDaoVds9emqxGvfncXdyCgWFqfO5AvVFvXVUAC4VCIUNDraVAtXg3TJNnV1ahJqKMNmvqMDn0XCtqcdwgJrYSgUmIFtbWB+vpAjY0ptkLlsGvXpjk/VI7XFqrHrer6NqAnhGk0Go1GoxjanDUajUajUQxtzhqNRqPRKIY2Z41Go9FoFENPCDtX5Ibn3f755klIqs4GaR3B5auKrVFYW8ckKQX1Ka2tiV/afC4TNK9stDlrtgddQL0yUTxeZfsreYFNx1WUC2zF0SsWbc5tbLk8RvHCSaO5qLlA8ueFYHZyi1dbvd0VFOyxkUjELsakNuctEBvfyOZJvprTomBm0mwDisfren1a7L4DNsNEAO6GD9rDq7Exh5AghUQ0x6h2LUzlptdyg8ZN74Xs/PftMtL1fl6VZCfk+iJzKb0DP8B7lju0ReBFb85CiI4Ws5RyPUE0U0fHETA+JxdVUutGmoWTqvpAXW0StUqidhSP11Zu9XMPVdmuQZ6+X7hZf9h1s2urYbUMV0DbLnfe9Q1lX+Od8GG4QIU5Ns3QEAivUtW61ilqpzYkuujNuVnraZq067qIZvgLiXBppGl/SyehaOF4QaBw2LUKIIU1qoyQ3jGo0vUxh8pGc8qV3hGTroS2U1ibXaGi4c5it1v57QFjCIQrW5WEjoZJ83+NVr2fZV6zY0GNfeu97uxm78dp72uv7GwDF7U5NwOz/dE8zk4AwhVIXK+G2ZFyfaCRn1U16WaaVFFfS5sax0JvxmC9QqgYKscreEGWioapB23/jlGUEunimbLrIl0Xt+a586W/8is+idqM67q4rusdF1uvU61WW2fN/9drP+KzuguX9gbednZxC7lTHeaKI6XEcRxs28a2bSqVCtVqlUwmQzgc9lueRqO5QNjK9CqVCpVKhUKhQL1ex2mcs+26/tQQ28dI23VHo1EK//J1r4IBSOk1TqSU+FfbUWuqg2j0fRimgWWaBC2LUCBA9/vfj5SSSCRCKBQiGAxiWRaGYWAYL38LkYu25dxe22k+CyE4fmqSy48c4Pif/PdWZpLNbis/EWAawncZp8MQ4LpQa/Q8qIQpBJYpcBUOu45pDQphCK9BWHfU63aQQMAQ1Bx/86cEXCmREhwpcZDYrsvgj/8Yf3DsfubzFcxm17Y/g85t4SMbL12u6E/y4dSNGG2nq0kMkLLjmmr52Q+EITAQGHhmbQpBIBCgXq97n+9AReaiNeeNNAO3WQZ5PVSykemk76bYrIepWIA3cZHYjq8V7s1IwPQSuoph1x5UKuoDb8zNdlCulJYSLANsx/W14tWcRNo0aVeCi8CyLFYqNuMrVQKmuT7JyQ+DbpRjAoErXQxXcvmgIBQKYRmGN2YvXW9muRSNcQz/Itxo/LQqFWrPmAWGMDCFZ86WZVGr1c7+5ZfIRW3OW9V22idBqDJBG/xvuJ87/ldkNqOYq7QhNzwrSXNykIoipVBjPl1z9jMCYYAhvZbV6tIc5VyBuml6tyGQho8t5+a4uHQpFMOEw2EChomLi5TC69Y2O7u//cBoVGScs965C4iGMRsNUxYCo9FybrIT4XVRm3N7gLbWq8m29w2j8eaC+TxfW6yv1FCR5nw5n4eqNiNZ7/lQOOzUq9B4tOZBKti/KRuVBulzADbDSADC8AzYEIJgMIgQIKwAhmWCEAjR6APzYU22xDNmpItbq2G7DpFIhIBpIIXXU9gawTPwNUyb5qzCREQhQGBgCIEpjFbrORAIdAyLrt+/PZF70ZrzxrXNrdetRfq0FeyKlOxS3TFnr/LghZOaGtUOO1DToKVoGKDCLWd80tb8SdFM+20FtWE0Cm/DxAgEMAIBMI31ZVS7XYNtdmtLibQdcF2EMLyJTKaFIxwk3vBAs/u7tWzFB5rmbPiZ5loR3JgUJgSmsd6tbTZ7Q3bAmOEiNmfonPreNGG3OcjRbsp+t5obSN9XW5+ejn0YFKO5o4+C0hozY9WdELa+I5LfQramWWnwM3Zlo1fB8zLRaiFbloWwDIRpYQSDCMNAGoZXgPtgzl6rWSKFg2vXMS2LUCiEaQiENHANieEauG27f+zmdpXt+NHzvxEp2sJAgCEERmNirmiY85ZDo9u03vms5vzss8/yN3/zN1x++eX8+I//+Mv6sUKhQDwef1l/YytWVlb4r//1v/Ke97yHG2644Zy/1wzEzmUGnUsO1gvN9syvQMpRkK12z1EDdeNLxdDajKrxSmPM2V9jFqKpo9Fqbqz8sCzLa3GZBsI0vWfDaHzBh35tV3ibpEiJME0M09NoGSaOBCElrikxpPQmheGDzAbNlrOvyU52tog9c/Z6PwzYtFyq6SW71q39h3/4h/zVX/0VsViMH/7hH35J5prP5/mxH/sx3vve9/JTP/VTL0nomVhdXeV3fud3GB0dPWdzbjfk0wdm+5j0+lXhx0CIpNnHqCbNLX3ayFx9JdH+vjN+bf6Rx6guL5/TT8SHh+i+8nIAJu78BtI51+kijabNSwg7IxSi9w2vB8AuFFh84Lsdn4d7e0ldczUAC9+5D6dcPv8f2eFFnemrryK2dy92scjs3d84r+9eKAc9+dOyl629v4COFrFAYBgGwhBgGI2H6e3Q5VPLWSK8nXhcw9NhGpimiSGEV7QYArOxp7Xf9dmXv0p4G2gM6bR2dmtUuoy219D5ejs5ozkXi0W+8IUv8Au/8Av88R//MZ///Of5mZ/5mfP+kbm5Ob761a/y3ve+9yUL3Uk2Tgxbfw1KtxpUY0P6TF5yCelLD5/xK6svHD1ncw5lMvRcew0Ak3d/6zzM+aVjWBbpq69qva/MzFA4ear1PpDqan2+9NBDL82ctwkrHmf0A+8DYPauuylNTgEQ7u8jcegg9dWcb9pemYhWt7podYFKb9GN8MwZIRpDzKJl1L51a+OC2+x29569CoSBIY1G49rdsqK927R2pvNXxnq3fpshb3zsFGc05y9+8YsUCgU+9rGP8dBDD/Hnf/7nZzTnQqHAc889x9VXX00wGASgXq+zuroKeGa/srJCOp2mWCxSq9VIp9Mdf2NtbQ2AZDK56W+/8MILuK7LwYMHSaVS5/tvPSM7ebrIxY50XVaefW7Lz+qFwi6reXn0vvGNFMc+h/Rpp6czIQyDUE8PAEYg2LpeHJtA2g62jxWHi42mIa/vBSw2P3ZdVOfvt1qCtB2qtdGYt1Fm6sorCPeeuSdt+bHHqK2u0vfWtyAAu1Jh/tvf6bgnmE7Tff31gFchrjc8Y9vZOCGM3a0snNGc//Iv/5IjR45w5MgRPvShD/Hxj3+cBx98kJtuuqnjvlwuxwc+8AG+9a1vYds2yWSSd7zjHfzlX/4l999/Pz/wAz8AwCc+8Qn+03/6TywvL/PLv/zLfOELX2gZd5O3ve1tBINB7r33XgCWlpb4uZ/7Ob7whS903HfbbbfxyU9+sjVjbudodl34PObs48zJ82N9Ckmrt9a2GfvKV8/6jXC2h9jgAIF4nPLCAsWpaexiqe2+zu+0DhQwDFKHDxLOZnGqVQrjE5Tn5tsMtHGfZRK/5BJC2R6ccpnK7Czlmdmz/XM6CGW6SV1zNSuPP771vW33R0dHiPT1gyGozMxSmpra1No3g0Hie/cQ6M5QW1lm7YUXiY2OYgSD1FZWqC4trf92Tw+RgX6sWJzqwgLlmRnskhc+VixGuG0IIdiTIVQsUF1YoLayjHRs3HodBIT7+ggkEgAUTp5saRKGQfySSwBavy0AMxKh+9AewpkMtdUcxakpKkubezvio8PEhoYwAhaFiSmKU9O4O7hJQ3v3q3/L98SG1+K88umHj1zN/lTmrPd9a+IE902d4v9z/euIWoGOzxzpMlPI88jcFE8vniU9+0hs7z6Shw+d8Z780aPUczlSV633VlUXF8m1Ve6tWKzVW7X61JM7Z84+c1pzfvHFF7nvvvv4nd/5HQA+/OEP8+/+3b/jf/7P/9lhzrVajfe85z0cO3aMr3/967z61a/mX/7lX/jIRz7CpZdeyq/8yq9wzz33cOutt/K7v/u7fPjDHz4vgR//+Mf58pe/zJe+9CVuvfVWTpw4we///u/zmc98hne+853KdpVrzo+B17+W/ltu2lTKLj/1NGN3/MtpBxWtaISDH/kQ4Z7OAs6pVDn+hX+gODUNQLC7m4EfejehDfetPvkkM3fdfU6DlsXxcWKjo2RveQ1rzz2HU6lseZ8RCjH0jrcT37+/43p5ZoaJ27/c6voOdHUx+r73Euzubt0THf0+sZERgt3dLD30MPPf8VoN2de+lp6bbtz0W6tPPcXMnXeROHSI/je/qXW979Y3UJmb4+Rf/w3pa64mddVV1HM5jn3q04R7exl4m1dhHv+HL1E8dcr77eFhht/zwwBM3H471aUlonv20P/2d2DFoh2/O3X3N5l/+NHW+z3vfgfdV1zecY90Xaa/dS/zD33v9IF6kfO6oX3cMrTnrPfNlQrcN3WKH95/KalQ5LT3ffHFp/itB7+5nRK3Hem6rD3//Jaf2cXipmu9r389+aPHvMrlRcRpzfmzn/0shmHwEz/xEwBkMhl+6Id+iC984Qv84R/+Id2NAuXb3/4299xzD7fffjtvepNXOHzgAx/ghRde4PnnnycQCDA0NNT6G8PDw+csznVdMpkMv/d7v9cy4e7ubn7rt36LL3zhCzz33NZdparQ3EdXte7y9mWWu7GER5gmfTdvNpbC+ATFqWkCiTj9r73ZuzYxSf7kKeKjIyT27qH7yitYePQJStMzW/7twTe+gXBPhtpanrn7v4sRDJK9/jqCXUlGfvCtPP+XnwMh6H/HOwj1ZHCrVXLPP08gkSC+bx+pq67CKZeZ/859Z/135J5+GjMcJtzbS/Y1NzP3rW9t2UrK3nIL8f37ka5L4dhxnGqV5JHDRAYGGP7hdzP2d14v0MAPvLVlzKXJSYRpkb766k1/L5BMtoy5NDFBcWyM6OgosdFRUldeycoT36cyO8PyY4/Rfd11AKw99xylyckt/x35F1+k/y1vRpgm8Uv2tcy52Wp2SmWKp8Yww2EG3vlOzEiEer7AyrPPExseJDY0yNBb3uRde+4F0pce9oxZSqbvvQ+7WKT7qiuIjwwz9OY3svz0c16hKzbsL6DK3owNRNssail3Z9jiodlxlqvrww0/uPcgAcNkoVTkwdkJGmJ4cXnBe9m4b7qwxn1TYyCgLxrjhv4RIlaADxy6kgemx/jG+PFd0Q/nX55I22b6q187/d/bMAvaisfJvPrVLDzwwMvXh7rLAjeypTk7jsPnPvc5rr/+esrlMseOHQPg1ltv5Utf+hJ/9Vd/xS//8i8D8MQTTwDw1re+teNv/Pqv//rLFmcYBn/yJ38CwNTUFM8++ywvvPACDzQiqazwGJoQkFvNUatV6cn2+i2nhRBQrdaYnZnBdRx6+/qIxWI7atDCNBl84+s3XZ++5zsUp6YJ9/SQO3YcaducuP2fWJibJdzXyxt+9d8DEMn2nNacI31e2NrFIrkXj1EvFilOTdN14JLGMhdBYt9ewn1el+/UV79G4bhXcA29650kjxyh64ormL/v/rPnWglz3/gWez70o6Suvppj3/gG8Q1fMUMhuhuT1pa/9yjz3/42AJW5Ofrf8maiw8MEu7sRhiC2x2sxrTz6KHPfugcJdF93HX1vemPH3wxluikcP4Fr20zdcQdISWRsnNiPf7jxeYbcs8/iFEstcz52770EVlY7thhs4lSrFI6fIHHoIPG9e5lrXI9fsg+A3PPPI12X1NVXYUYinjn8r7+llvO6Dy/9mdsI92TIXHM1K8+9QKTRne7aNvkTJynNzpE7epzeG17lhUk4hFMqUq1WWVyYx7Ft4skkqXS3b+toN+K6kqWFWaqVMuFIhExPnzeBa4f5q2ce63j/huF9BIImL64s8mv3fR0pJV2RANGgV1Q3D6SYLK7w+4/eQ6XubWZ/dXaAv377BwG4oX9k18xZALbtMDs7Ta1aI9PTQ1dX17aVJ83equ5XX8/qU09Rz+fPW5/juMzOTlOpVEmlUmQyGb/nu50TW5rz1772NWZmZpiZmeHgwYObPv+Lv/gLfumXfgkhBC+++CKRSGTb1i9vbGXec889/NzP/RzPPvsspmly6NAhbrnllm35rZ1kZWWZibExIpGoUuaMhLnZWQzTIBqNMjkxwcFDhzbVVrf1J6Uk3zbDuUmtMd8gf/IUhbFxYsOD9Nx0A/2pLrJH1md5G2eYV7B27ATR/j6iA/1c8YmfpbKwyNrJUyw88hi1nDc7OZRZ78pO7L+E+D7PhMyo11VrxWKEMhmqi4tn/bfMPf8c5iOPMPzqVzP6th9k7ZlnOj4Pdqdb1fRgd5r+t7zF+zeE1idoxUZGcKrrXeL5th6g3PPPbTLnwslTFMfGiQwOkr3lNYQyGaJtPVDCMBCw6TjCqckp9l2yd8t/R+7ZZ0kcOkiwu5tAVxIQrVb82nPPAuvh5lSrHT0fzbH8+N5R7/5jJ+i7+UaMQIDDt/0r6oUChbEJFh97gsKE13oXQpBbXQEg3Z1hYvwUkWiUcDji+8xggHqtSrVSJp3JMjs9QTgSJdmV8u2IR/DyTSoSZH82St2VrJZrHVWZwVSYEwvenIOnFmepOg4h0yQbje2eSAGLiwvUanVS6RTT09OEwmHCodBpo1WYJpktlryWJicpT093XCsc8yoZsdFRsq9/HdP/56vnN/dGwNLSIpVyhe7ububm54hGo0QiERWS3RnZ0pw/85nPEIvFuOuuuzbVvD/5yU/yqU99im9+85u8+c1vZnBwkHK5TD6fJ9GYZAIwMzPDqVOnuPbaa7f8YcMwqGwxZjczM8PevXsBbzLYD/3QD3HgwAHuuecebrjhBiKRCBMTE3z6059Wrru4Hcu0SHZ17eipJeeLV8u1KeTzDI2MkIjHGDt1inrdJhQK7lhilfU6x//uH077eSAW4+BHfoxQxjMHu1KhMD5B6tDmiuFGFr73KOFMN12HDyIMg3C2h3C2h+z11zH/4MNM3/MdAvH1wqp9okk7xhYtzE0IWMvlyH/5ywxdey19V125qSZvxdYrqYkDB7b+rWDAW/faoH2czSmVcSoVzLYzxa14nD0/+kGCjZUNTqVCeWqqY0xbCKhUq633yWQXzy0tntacCydPtn4nvndfq0JRW1lpTZKzYl64meEwPdddszk4hMCwLAoTk8ze/12y11+HGQoRiMdJX34p6csvpTA+wbHP/z3SdYknElhWANd1qNWqSphyk2AoRE/fACtLC5RKRSzLUqD70ztBaqVUJx6ycFzZOqXJEMLbE7uxDvcNw/sINSqxM4Xza12+HKTrrbBJpVJ0p9OMj41TLpUIh0OnjV9hmvS+/nWbrs9/575N5gww981vcclP/iu6Lr2Ulce2mIh5Jn3Sy7PxRIJ0OsX8wjzFYpFoNKJA/J6ZTeY8Pz/PHXfcwQc/+EFuvvnmTV9IJBJ86lOf4s///M9585vf3DLfr33ta3zwgx9s3ferv/qr3HnnnRw7dqy1k4pt263Pk8mk1821uEhPY/nH9PQ0ExMT7Gl09z300EMUCgU+8YlP8IY3vKH13W83ugrb/55qJJJxqtUqKyvLtDbBUIDmMZiWZWGZphJLyPpeezOhTDfScTj2d19k9fgJlot53va73mRE9wzjf3apzMkvfwUrEiFxyV4Se/fQdegAViRC7003MP/IY63uWLde58X//mdbdl+fy9KoZuWGXJ6VRx+l+8YbGXpNZx6p59bXEp/6289TmZtjI1LK1vgueLOwa3lvSVm4r6/DmAF6br6ZYDqNtG3G/+FLlCYnCXR1cWDDhLP2Vp5pmThnWAcuHYf8iy+SuuoqYpfsa40F5559dv3f0pgFW56Z4YXPfX7rv9P4zZl772P2vu96cwX27SF18AChTDfx0RFSRw6x8uzzRKMxisUiUxNjZHv7CYXDShl0KBgimUxRKhYo5PNEYwlfC3AhIF91CFh1YkGrcZytJ+hgKssvXvlm7Msl/bEEh9M9re/dPz22axolEsdxsCyrVc67jTPdTxt0UlIc26yxnlvd8vbq4iIr33+S9DVX0/emN25aWnUWgZ6+QADDMDAM09fekPNhkzn/zd/8DfV6vTURbCOHDx/mlltu4Z/+6Z+Ynp7m3e9+NzfccAOf+MQnCIVCXHnllXzlK1/h85//PH/0R39EJBJprUm+/fbb6e7u5gMf+ABveMMb+L3f+z0++tGP8mu/9mvMz8/zH//jf+xY37x//36EEHz605/m2muvpbe3l69//ev8/M//PIZhUFB4jaxheLVYv41vI5ZlYlkW5XIZ0zQwLRPTMn0tIyPZLADFqWmWjx5j/NQJrnr/+9ZvOI04MxTiyl/8OQBm73uA2fsfZOWZ50g8s4cDH/4gwjCI9GapzM8DXus4efhwy4AyN7ya2N69uJUqk//8z2cdc5ZAJBplcWGehe9+l+jhw4Q3rLevrqwgbRthWaSuvIKZGW+sPH7JJXRf/yqQkpk776QyO9u6r+cNb6C2lkcKQfa1r930u+GsV/CWpqdbk7xSV16xSVtzbwGASq1OONJp8hvJPfucZ86jox3XWn9j3puEFO7rI9KbpTQ7hxCC4be9hVB3mvLcPFPfuIeDP/EhYoMDFCenOfq3f0f+5Clmv30/V/7Sz2MEAkT7+1l9/gXyuRxTk+Ok0t1092SVGW8GKJeKrK2u0jc4TCgUplarKnG6WvOkKG9v5/Ux565ghFsG93bca7sun3n6ezywi+ZsCO9M6FKpRC2ZRCIJBs/cC+fW64z/w5fO63cW7r+f5KVHiAwM0HXpkXP+nhAQCocpFYvUU13UalWCbUNMKrPJnD/zmc/Q39/PWxpjZVtx2223cf/99/PpT3+a3/iN3+COO+7gYx/7GO9973txXRfDMLjtttv4N//m3wDeDOuf/umf5rOf/Sx33nkni4uLvP3tb+dXf/VX+f3f/33uuOMOYrEYv/Zrv8bRo0c53piwc/jwYT71qU/xZ3/2Z1xzzTUAXHHFFdxxxx381m/9VqsFrSJSgmmaHQWm30i84YS+vj4WFhZYXlykv38Aa8fXip+Z6sqKt0Z2eIihH3gz/dbbGG5MqoLTjzk71SqFiSkSe0cZeMPr6Dp8iMr8ArERbzzWLpcpzcwg6jUKR48SP3iQwXe8ne5XXYcRDLa6iRfuP4fJYAAS0qk0+bU1Xnzuecpf/jLXfPSjnbfYNvPfuY++N97qGd+ePdilEpGBAQCKp05Rb7Tkl773KD033Uiop4d9H/3X3vcdB7de7+hmr62sEBkcJDo8TP9b30IgHifWGPrxAshASjAcG+k4CNNk37veSfrAAVbvO/0s9NLkJPW1NQKNCnF5erqj5b/65JN0X3M1wUyGQ//6I97kve50q7t74RFvKdXasRPER4aJ7xnh8p//GIWxCULpVOvfsHbyJNKVLC8tUC6XCIXDzE5P0j8wRCAYVKL1HAgEqdWqTI6fRLoumWzv+iE4fiK88yoqdYdwwFifEFZY5evjL1Co2NRdl5linqcWZ5kt7l6XdpPe3l6mp6c5ceIEmUwP0Wh023scnHKZxQe+6+WrK688r+9ms1mmpqY4fvwE0WiUeDyufJc2bGHOTz/99Fm/dNttt3Hbbbe13mezWW6//XbK5TKnTp1idHSUWKxzUsInP/lJ/viP/5harUZXVxcAv/3bv81v/uZvcurUKS655BIsa/MQ+E/91E/xUz/1U0xOThKNRltLuG699dbWPfv27VOuheq6kkQySTQWQ5Uu7SbJri5vjFlKgqGwnzs4AF6XaLArSXzPKMOvuRm7VOLEl7/CyFveSLi7m+TB/Sw8uvVY09hX7mD0HT9I8sAl3sSwxszhyuISE1+7E6dSxTIFs1//OpliifTVV7VmbkvXZfG732XxwYfOSacEAsEAwyPDOLYNU9OUZ2aJDPR33Lf86KMgBNnX3Eygq4tAI72vPf88s3fd3bpv4b77cCtlUldfTSCZpDw1zdKj32Pw7W+HQKCVphfuux8rkSA2MkL66qtxymWmv/pVel//BgJdSRIH9rP65JPIWp2lxx6j5/rrCadSdO3dy8oZzBm8lnJzmVbumWc7PpOOw9Ttt5N905uI799PvFHpcapVJr5+N7mjXiV67sGHsaJRstdfSzCZbO1/7lQqzHz7fvInvZZcb/8gPb19GMJAAqZlKWHMAFYgSN/gMK7jYBgGwVBo15ZTnQlDCApVm6rtYDsSp1FhOJlb4S+eeJiK7a9GCURjMUZGhnFdl0AwdMYJnC+HlccfJ331VR37ApyLvkg02tJnWQFM01Il2Z2RbT0yMhKJcOmll57x80ikcwF9MBjk0KEz7xoDnNf6aFUwTQvTVOtUzmbrOdqY0bmTifTkl78CXz77ffVCgWOf/3usWIxALEZ5cQGk5LnjJzoELn3/KZa+/9SG7xY5/vdfIpCIE0qlEJZFbW2N6vJKR2vYrVaZvftuFr7zHUI9PbiOTXVxCXmWeQt2schzv/8HHdfCoTCEvNen/vZvt/ze8ve+x8oTTxDKZDACAaqLi1tuWrL8vUdZffRR7xxdvN24mmPOTmP3r3o+z/jffxErFsOMRqkueN3Nay+82PG3JLBw77dZuv8BzHAYtzHmPHPnXczcedeWOhfuu4+FMxh4fW3N2zglGCXUk8EulaguLXeO0UvJ1De+xez9DxBKp7EiEerFItXllY6NI8Ib8r5KJaQQEAqF/JaxJY6UOHbjoJ5GoDmu9N2YmwghiETWN6nZsYmlrsvct+5h5H3nt/HUbunbbtRyDs2uoWICtYvFLXcIOhfq+QL1/NnnIDjVKqWpqZf0G03ONeykbW85IQzAikY58H99DIDS+Djz99yLU6uRuXF9yVJ5pnNt97mGj2vbuNs8H6NeLFI/y287lSqlM22HqmKi02wLuxW1hZMnKZw82VoOea5ciElPm7NG4wN2qcTq958kfe01xPbuZd+/3tvx+fKjj24yZ43mQmbqn/+ZqX8++33SdXnhD/7A20BkC1ed+NLt265NRbQ5azQ+MfuNb7D23HPERoYJDw0jDEF1aYni2DiFEyf8lqfxgdf+3f884+ev/8Ind0mJxm+0OWs0PlKenqY6M40rH74gu940Gs3OsHN7Nmo0Go1Go3lJaHPWaDQajUYxtDlrNBqNRqMYesz5rGw1EqjOXtnqIRUcO1U3rtQLq61RMVYBkP7GrXcsaceVCydSNUqjzfmckK39LNYzoh85UKib8dX1P1qVKdXDTkV9SservzTLhHWD1pX2VyQ+Ras2Z41Go3kZiI5XYkNBfsbzmdRjo1S/pCtWYfWj50ibM7SOy2sipUQ2osOV7btASqRP3WgCcKVEjQ37NiMa4eRu6ubzmYYmVcPOi1da23eqhpBteUCleKWR3pANff6Fnhc0ngnLRre2S+PoxPbmtewoTHZZpNzwAFwX13Vbx8Z6eaRxhKzfiVGu5w1VMBCNsKIVZjt55K42Z7yAFkIghEAiGgl2PQ23drV1pa/G42fePhsSWonW94zdxrquCyDs/BayBU1trpQIxQS6rKc519eDl0HIhj27EoSLcAWO4+C4Esd2sF2JsF0wGo2B3S5IpPQO8nBdsB2k67QqEI5slHfI9Xzi86EfrquWOQth4CIRSIxGw63dlJuvNzb0Xg4XrTlvFYhCCMIBA8dx2P/z/7YV4KqdeKXRaNShWblvfwawbZvPve0nCYVCBINBLMvCsiwMw9jWQvxccButZNu2qdfrVCoVqtUqtm3juC6243gm7biekdPsyvWnNWI0zq/2teRtn2fkuiAEUoj1XtW2w1/a4327uGjNuZ32jGIIodQZzBqN5sJlcHCwZcbtj92mvaEhpWyZdSgU4vAvfmLX9bxSaDfkpkEbxvasUL5ozbm9K3srfv3ZL+K4jnfgeqPbx0+EANt1KVUdXzL3mZBSkghbCAE129+u/41ICaYhCJiCqiJH7G0kZBkb5jaog9GIS1W6FzdiCKg5Psdro4EpRMOEDcHIQB8fz76J7/yHXyVgWYRCIQzDwBCN8WkfMonrukjAdhyKxQKv+S//BYDL/vqPwHUaRtMcX/Evwm1Hcv3eFCOZKH5HLdCY4+fFq2GYWKZJKGjxGwe3Prqy6S0vl4vWnGHr7upWoDaevWkeG2Zc+mQ+YtNMUEUQ6y9Ukyda/+t4oSYqylNR0wYEwicrka2JcgKxbm6uNzQG3tnpjvQM0ZTgNgv6XQ5Xb3zeazU7roswDOzmWeaNSWG0mbOf69ql3Dym6ytSIIU3ObIZRhu17cTEsIvanJtG3N6CXk+UspHpmwMP+LuMsTn+IhU0aNlYQgLqaWtmKs1LQrXo3ISvAgVCyNZkqmYRIXFb5hyOxRBAwLQwDcNrfQlj12U3Dc91XRzXJRAMrJtza8prMzilMvHud9ZtNjfOVL/Xs7W3GSFEa0D/dFPi24261dXj545EzZqtMlmniWwUTs1JMX7rWUdKkC1Bfmf109HQp6A8qXrQAX6Kk21dwK2Xcn2yUCwewxBeV6hliPXx513Jw81wEa3loa7bXDLltsy5+W9QMar9L0qa5W17uXv64VA9W3sHaV+W2Oze6WxL+48y3T2bkB1PyqGiLhUS1JlQMcw24PMQqaehIUI23jRbzrbtYBrevBUME9NwG93auxvx6zOM1825qZG2Eg7ahs98LWcaYaRC+muOjTU7LTd0xbXHpZ4Qtg2cfpnUFqmhtQa1ubhA9RLVX1TIT6dDNW06JW0PfsarbIxFNo25fZmN43gTrQQ0NvgwMBqTi3ZeWONZ0NqwxZupLXGFbDPn0+HTEJpo631TIoO0GXNzF7hd4KI15ybt69M6N9BY3+2n434lUosKGtrZWJP0ScYWdNS9FByu12wPfqW5VtnhvWm1UJvXnca2eY5wEdLAMLz1ssLdRcGNuV4Sr9XsNFv57ZlDQqv7tsMYfXFn7/9CjdIW2mbYbyhDNvaA6G7tbeRM0947JoS1tZ3VSTIajcYvNnW6tWaEyY4Kv8QFVyCN5uSx3Wd9B7DGbGzpdppza0GK/2Vbs9tfCKHOZM7WfFfPpE+3iVVz7pJeSrVN7MTWaxqN5iKjOV9z40UpcPG2ffRrQmlr7kzbtJDNLec2/CwKBetdIYoUyYL1SXwbJe3UxjLanBu0Zmy3pdL210q0ljuXYCuDbPWEbVgPrrnwUXEK7wb8SnVCdLaeW97cfq2ZOToE7s6Ki2b51Sq7zjRfU7SL9LuAkQ1/Vqzl3F5paF7ewcJYm/MmRNsrgRRbpGif0q7YxckI50dbmClWc+g8Y0AtbZtQWZ7K2nwT177MZl3Jlo1jcdo3O0bnsBybKgmb86qCkeynpLa6iqC97bw7bM+cb80mBLTmViiX5IW6ZtpEUVnARsNXC5XTXUuTVE9bk5cTr6ebuXLay+fxEKd5fcYHO9/j1xhib71uv64KF0K62wrdct4BhIC5uTlWlpexLIvR0b0EgwElEqwQgrXcKksLc4CgO9NDMtWNStnJcRwmJ8ZIJrvozvQot657cX6BcqXE0PCoUpUbIWBpcYmFhQUMw2BkdJRIOKxEzAoBlUqVyfFx6rZNb28vmUxGCW1NpHSZHJ8gFA7T29e/M+nuJSQXQ8Dy8grzc3MgBCMjI0SjUSXCLhowGeqOMLlcJmQJ+lMRkJLFQo3FfM33SqwA6vU64+Pj1Go1ulJdDA4MoM4eaKdHt5y3GYFXCM3OzDAyuodgMMTk5Di7sazxXJBSUq1WyWT76EqlWZif9c55VQTXdZiZmmR2ZoZqrep75m5HCMHy8hITE2MU8nm/5XQg8Da8mJqaZHBoiHg8zsTYmDrhJ2F2doZgKMzIyDDTU1NUq1V1ikgpmZ2ZYWZ6imqloky4CcBxXCYnJ+jr76erK8XY2JjvTUApvQNb9vREySZCWIYgHDCZXa2wXKwxlI6oEYYC5ucXEEKwZ88eVpdXKBSKamg7C7rlvM0IAYV8HssKEItGcVIpjh096resDjI9vRQLeaYnx0kku9Rq/SHozmQolooqNeYBr2ITjUToHxhgLZfzW04HQkC5XEYgiEVjBINBpqYmvc/wNyibBlMulugfGCARj1Or16lWq4RCIR+VrSOBrlQXpWKP31I6EAJCe/dy7bXX0js6Sm0txxN33tnY2MSvAz8AAa6UzKxWCFoGCJjNVeiOhxjNRClWnW1bUvRykBKKxQLpdIpYNIoVCFAulUgk4qoVL5vQ5rwDNPeXFsI7lUalbtnmWa7RWJQ9lxxk7MRR6vU6gUDAb2kACEMQS8SxLAvl3BmIxqKUyxVALXMGGtPmvWUdhlhPd6qEouTMx7T6iRCCeDyOFVBj+KlJ31vfSuqqqzquveXVr2bxwQdZuO9+n1Q1uosdSaFqt1ZrmIZgrVznxHyJkUyEaNCiVHN8b6VK6TaO8/SEqxS/Z0J3a28zUkI8nqBarVIqlVleXiaRTPotqw3B/Mw0a7kcVsDapUUd54M3J3InjmDbHsT6Zg4KISWEIxEc26ZUKrK0tEg8FvdbFuAVhqYpCIcj5Nfy5AtFTEMQDAb9ltbB+iYSagzzRAb6O4zZbZ0iBZkbbiCQSPghaxNN8x3pjpIMW9QdV5kyRQiIRmOsra1RKpWpVqqEI5ELwqB1y3mbkUA4EmJwYIixsTFM02RkZFSZw+oNQ5Do6mJxfg6AwaERLEVazU2k9DJUKBT2d+/90xAMBIjF1TC+JhIIWBbDIyNMTU6CEOzZs1eh8BMMDAwyNTXB+Pg4g0PDhMIhpQpJKSESiWAYQolwi46MAODW6zzzl5/h5OOP0XflVdz48z+HMAwiAwPUfZ77IKUkX7GxHclKqcZAVwSJ191drvvfakZCb28fk5MTnDx1inQ6TTweUyJ+z4Y25x1B0NObpbs7hTAMTEudrjIpJbF4gkgkBkgM0/Rb0iaklPQPDHTse64KruuFXzQaU657VgLdmW66kgmEEGqlOyAcDrF3716k62JaAVTrs3FdSba3t9Vz4zeBhNfjVhofx8yvsW/PXkR+jcr8AuHeLFbS/5azK2F8qYTjSio2lKqFxnX/ww+8dBcMBdmzZxTpuhimiRAXRoexNucdwjAMzKA32UWNZLqOV3CrZ8rtGIa6+l7fe4T+SOqs9z29OsEL+RneN3IDAGWnxv+ZerwjPWRDCd7UfxkA35x/hpVa8WVpE8IgoGi6Qwgsy+ulUU5bA6XSXePowXqh0IhXbxjAzq9BbxahSMXacZv7iIN9GlP+yKXXcSB19sl23xw/yr0TJ7jt8GuJhyzKTp2vzXTmmZ5QgtdnLwXgW+eYZ5rpDtRNexvR5ryDXCiJQHN+XNo1xJHk4FnvW62VeDE/y809B1vXZsqrPLp8svU+GYhwY8b7/KHl4y/bnEHtdKeyNtW5kMPu9cOX8LqhfWe9b66U59uTJ3nz4KWta7OVVR5bacszVoSbGnnm4XPMMxdi2Glz1mjOk6P5WQp2pfX+2vReTGGwVi/zYn6mdX26vLLpu+8aupanVieoufamzzSarYjv28fwj7yn9T48MABA12WXExnsrCROfvkfd1HZufPgzBgrlVLr/dv3HSFgmMyXCjw4M9a6/sLywqbvvn3gGp7KTVC/yPKMNmeN5jz51tyzHe8v7xomYgaZLq/w+VMPdHxmbhjf6gpEeWPfZXx95skd16l5ZRBIJglsseIjlOkmlOn2QdH585mnH+l4f+vIfgJBkxdWFvh/vvPVM363KxDl1t7LuGv24sozF8bIuEbzCuBofhaAN/ZdRioQ9VmNRnVkvYZTLp/X45XGsYKXZ27NXkrXRZZndMtZo9klns55O3YdTPTzzqFr+dtT96PajGWNOszdcy9z99zrtwxfebaRZw7E+3nHwLX87/H7L5oso1vOGs0u8o+T38OVkuu697EnptZWkRq1CKbTxEZHCWUyfkvxla9MPYorJdek9zIavXjyjDZnjWYXmSmv8uDiUQTwnuHr/d+kQaMs3dddy+gHP0Dmxhta1yKDg2RuvJHkkcM+KttdZiurPLR8DAG8e+hVu3yqsn9oc9ZodpmvzXyfslNjT6yHa9N7/ZajuYCIDg/T+7rXkrriCr+l7Cp3znp5ZjTawzWpvX7L2RW0OWs0u0zRrrZma9/Uc8BnNRqN+hTtKnfPPQXADZn9PqvZHbQ5azQ+cP/Ci8xX1i6aLjqN5uXywOKLzFcvnjyjzVmj8QFHuvzT5Pf8lqHRXDA40uWOqUf9lrFr6KVUFyCK7CmveZk8tzbNc2vTXHoOW4FqNDtF+yEffhwhu/GQESk7FbS/eyE/zQv5aQ4nBluf7ZTeDhliw2MX0Oa8AbMR8IbwIseQXoJtjyhfOlWEd5h5LKTGZvcbMQyvsylgqtfl1FRkGTuzx+5vPPX3rdfmpr4ol//7ib857eefPfFNTEO0dBmKBV9Tjmq6mgi88yF8qa9KgRReASE3PNr1YXjh13wIcX7hmTxyhMQBb25C87CL2J49HP74L2y69+if/mm7vJbbNssx1/Betx9h+5qDzeVJcr2g2+H4/sWHP7/F7zdUSMkvPfrXhCyD+hZn7X7y2Dd3Vty6kv+3vXuLjeOqwwD+nZnZm+/e+BY7tmMnrRNHJAVZSCkIQhIeeWwpD6hU0KpQUFulKlHVIqVFlFJRSFUEPIAopVgVCJK0JCBQ4KFURI2AljRtumkSx7Hj29pb27H3MjOHh91x1uvNDbw7/9jfr4rtnVnL/87ZPd+cObMzue8KSmkopeACZbsbHcO5wHzGAQBkbA3HdaFdnb0Be66hVN7XclIq27G7Wt5n8HXui1ayLzA/l3bE1aegUB025R4Nyb3YxNYHYOpSxrf6vL5B51JZaw2tgYb8q2q6ufdGLhQNDegbmFBUhgGVuyPV5YUKRuEyXG4nvfAl+13nHmo3+91xL/9OQ3Wo4BeAhZ7Op85GKSBlu/hg4pJ/ReTXAwBKQSmFqnAIPc2Ba/3K/43hXCD/xa3zXtQL632oqZCEGm5GXnuKohYfUpTG6xYl1gZk63O1RpEBVnl4O+65UM7+W1rM/9KPJMfGMH3q/Rsr5yrL9TWec5mMMNQayLjax2zOGzl7RSnAdt0r/8oyYjgTEQmU+M8JJP5zwtcapO6UrQY8W5uIiEgYhjMREZEwDGciIiJhGM5ERETCMJyJiIiEYTgTEREJw3AmIiIShuFMREQkDMOZxPL/OkU3J+nbTXp99L9huy4vhnOJqNwF7otdyk8CZahcbfLqUwBcx0XGtst5E5jrpl0N27ZFtq0CkLFtuI4rbrvlt6v3WBKtNVzHhuvIa1sFwGG7/k8UALgutGMDWour70p4+c4ScV0XFwbPw7ZtdHVv9LucJSbjcQwPXcDGW25FOBwR0xkZCpiensHAwAC066KpqRnNLU0Q8ZZXCulkEoMDZ6C1RiAQRFvHegQCARHbz1DA0NAwJuNxAEDn+vWorq4WsftlKGBm9hIGzp2D6zhoaGzE2rUtENGuALTr4sLZGGAomIaJ+sZmVFbVwC3TdZSvxlDAxYujmBgfg4ZGR8d61NbWiGhXpYC5uXmcO3sGju0gumYNWltbRdysAsheFdtOJ/Hh6AVo14EVDKGmad3C3b0k48i5BJQCRi5exGQ8Djtj+13OIoahMDs7g5HhIaSTKRGh4lEAXFdjeHgYzc3N2LR5M4aGh5BOZ2R04bm7DkUbmrB+w61Izs9hfm5OREekFJBKZTA2Oopbe3qwtq0V586elVBa7iYGGiPDw4hG16C3txcjFy9ifj4pol2VAhzHwex0AqYyEApXIByphBYQzApAOmNj5OIwNmy8Be3tHTh37oyUfRpAZ/u62po6bNmyBeNjY5idnRXxugMAKIXUpRkoAPVt65FJzsPJpLxbWYjGcC4BrYHGpia0tq2DMrybqsrguhqRSASdXV0IhUOiwhkAbMeBnckgHA4jFAzAdRxkMhm/y1oQiVSgtq4e58+cRjqdRigUEnE/RQUgmZyHZVmwrABqqmswPz/n3c7Xd46jkU6nEYlEEAhYcHX2sRRau6iNNiDatBbzc7OYmhiDIWB0pRSQSqZgWiYCgQCqa2qQnE9CuzIOz7paI5VKIVIRgWVlt1cqlfK5qnwagXAFMsk5TJw9Ba01zEAIkvrkK2E4l0goFIRhyty8pmkhEAiKfHmq3GR9fm2GIWc7ptIpOI6NDT29qKuPYmJ8FMrwvxMHAMMwsztbCnBcN3sfYAjphlS2bXXejQsltatSCtGmtaisqUV1bT3mLs2I2HHVyB7t8mpxXRfKULlt6T8FBUMpuLn6NDQMwxSx4wAA0BpzH06gMtqE5lu3wrQCSF2aFnG061o451xC2tVwXcfvMorKnvwiqzYNIGCaqKiowGQ8jnQqhWAoiECg9Dc2vx5KKcxdmsXE6Aja2jthZzKIVFSKGDlrDVRUROBqF1OTk5iZmUFdXZ3fZQHItqtlGqisqsRkPA7tOAhYgexRBxEUMpk0Ri8MoGVdJ2Y/TKCiqgoijjloIBKJAFCYmopjfj6JmppaSNnrMgygqroaU5OTMA0DhmEiEolIeEssMAwDdjqJzPwctOuK2Zm+Fjm7riuM1hrhSAS1dfV+l1KUaZqIrmmAaVoQ8S7P0QDa2tbBcRyMT4xj/fouWJYlokKtNapr6lBVU4OLQ4OoqKpCtKFJxA5YdoRloKt7AybGx2FnMujs7BLTSWoNrG1tg2EojI6NoaOzE8GgjKM3WmtEKipRG23A+MgQguEw6huaobWMdlVKobt7AyYnp5BKJbG+q0vMW1ZroLm5BYFAABcvXkRHRwcikbCU8gAAldEWKGVgZnwYkZo6hCqrRRwVuRaOnEvEdYGqqurs2bICXwemaWFdezu0FjHwW6ABBIIBbOjuzj4ScvjOYxgKLa3rvM/JiXqTawA11dWo6bk1+1gZYradBhCwLHR3deUOvctqV0Ah2tiCNU1roXPtKqVpNYCqqkr03HpLdoGwdrUsE13rO0W2qwZgWBZq13bkFmhR9V0Nw7nEpLzBixFdm5L4CecsLW2PJo8GACXzgFj2U/VK7HyfFrazlY/t+v+R2q5XI7O1iYiIVjGGMxERkTAMZyIiImEYzkRERMIwnImIiIRhOBMREQnDcCYiIhKG4UxERCQMw/madJEf/flA+833MXpZRG4/kUXRDdHehThuLvIvzOFzfcUatYwl8Qph1+XypfxU3rLyUtlOQGtxF+LxrhCkvAfSCNteVyR520msDYAW1bbX0Zt79/CUtD31om/Iezf7V+eidvU7pLUvVz/jyDmPukoDiOoDiEgclffVo71rTS86AFeesNG5/+D9/YI/u9Df5X1jP1eEusLPJcaRM7IvUu+Fmv+CVUpB67zHPtXnydbpcxFFKGR3LJX3QBp1ueORNGABsHiPXOC2U0t+kCX7ulPw63YL3l2jvEPbSulcXdkNZmDp+1aVaWMu+jv5P2oAhlr8PKWz18f21otob5V73/pUTEEoq1wHVzQvvKctYwe96sM5fyMbhoHqcHaThAMmXANwXe9G55f3d/183ZqGiHfNFVmCy6upkHFf6EIKgCl4uwGy27Wxxr/7QmudG59qDe3mRqo6L5wNA5YCTNOApRQMlc3Fcu9kG7ndF1cDSmcPW3s1mmZu11XYHHRF0MSWtTV+l5EbdGS3l6EMWJa5KDfyw9pbtix/V8s/K6AktNZwXRe2bSOTySCdTiOVSiGZTKKrq8vv8ohoBRgdHUUoFEIwGIRlWTBNE4ZhLOsI63q4rgvXdeE4DtLpNNLpNJLJJNra2spax0pTyvZd9eHsOA5s214IZ+9fOp2GbdtwHAeu694EZzYSUbl5fUOx7/m8DrvYSKtcCv9+YR1ezX7UJpm3nQzDgGmaCAQCCAaDCIVCC8HshbNhGMsWzqv6sHb+C9QwDFiWtSiIDcOA4zii7/NKRP7x+gsvkL1/Xn+R/XTF5T7Gk/9zqXg1FB5yzQ+PwoD26qXFvG3mhXMgEFgYKZvm4sPcy2VVhzOARXtEruvCsqyF5ZZlMZyJ6Iq8viE/nPMfF3bapejEr6UwnK82eqal8reTaZqwLAuWZS0E9HKOlvOt6nAuPFzh7TV6G7vwzUZElC9/hOw4DoDL87vecj+DOd/1BDODurj8XPCOsnqj5sIpi+WyasM5+zGpxYecLMtatIeUH8wMZyIqlB/OhaPo/PUeP+ebC78znK9ffk54/7xgLjwJbLm24aoNZ0/+XFB+AxSOmBnORFTI28EvDOf8/iL/Zz9HzVrrRX0dUHzu23suZRXbofHCOP+Q9nK37ao9W9tzpT1fBjMRXY/8/iH/LO38PsTvEWnh388PaSk1Sld48ZFi0wPLuQ1XfTgDWBLCDGUiuhHFRsr5o2oJCqfyCpfxTO3rc6Uz3TlyLrHCzcHNQ0Q3SnK/USxEvGWS6/bTlYK3lDszDOcbwE1FRMUUflb4ZnC1YLlZ/h/KwbfzBBjOREREsvCWkURERMKs+o9SERFJ0t/fjwMHDtzQ77zyyiulKYZ8w3AmIhJkaGgIx48fX3jsOA4GBgZgGAa6urrQ0tKCiYkJnD59GlprbN++3cdqqVQ450xEJNg3vvENvPHGG3j55ZexadOmheWxWAx33nkndu/ejWeffdbHCqkUGM5ERELF43E0Njbirbfewkc+8pEl648dO4ZPfvKT+PDDD1FRUeFDhVQqPCGMiEios2fPQmuNdevWFV2/Zs0a2LaN8fHxMldGpcZwJiIS6rbbbkNdXR1+9KMfLVmntcZ3v/td9PT0oLOz04fqqJR4QhgRkVCWZWHPnj144okncPToUezatQsNDQ0YGRnBgQMH8M477+DFF1/0u0wqAc45ExEJ19/fj3379iEWi8F1XQSDQWzduhXPPvssduzY4Xd5VAIMZyKim8Tc3BzOnz+P7u5uBINBv8uhEuKcMxHRTSAWi+Hw4cN44403EAwGEYvF/C6JSogjZyIiwWZnZ/H1r38dv/zlL6G1xsaNGxGLxdDb24uPf/zjeO655xCNRv0uk5YZTwgjIhLswQcfxMGDB7F3717Mz8/jtddeAwDce++9eOyxxxCJRPDjH//Y5yppufGwNhGRUIlEAj//+c/xs5/9DN/5znfQ09OzsO7hhx/G888/jz/84Q8+VkilwnAmIhLq5MmTAIDdu3cXXb9jxw4MDg7igw8+KGdZVAYMZyIiodra2gAAf/7zn4uuP3ToEJRSaGpqKmdZVAaccyYiEqqzsxNbtmzBU089hebm5oXltm3jj3/8I/bv34/t27ejurraxyqpFHi2NhGRYP/617+wc+dOJBIJ1NbWIpPJoKqqCmNjY6ivr8frr7+O3t5ev8ukZcZwJiISbnx8HN/73vdw/PhxDA4Ooq2tDX19fdi7dy8aGxv9Lo9KgOFMRCSU67oYGhpCa2srTNNcsn5mZgbHjx/HZz7zGR+qo1LiCWFEREJNTU2ho6MDFy5cKLr+tddew86dOxGPx8tcGZUaTwgjIhJkdnYWTzzxBFzXRTKZBADs27dvyUlftm3jb3/7G8LhMGpra/0olUqI4UxEJEhVVRVSqRR+8YtfwJt17O/vh1Jq0fMMw0BdXR2efvppWBa78pWGc85ERELF43E0NDTg3Llz6Ozs9LscKiPOORMRCRWNRjEyMoL29naMj48vLE8kEjhz5oyPlVGpMZyJiIRSSmF+fh533nkn7r777oXlb731FjZt2oT7778fo6OjPlZIpcJwJiIS7Gtf+xr+8Y9/4Itf/OLCsr6+PjzzzDM4dOgQ7rnnHh+ro1LhnDMRkVAzMzNobGzEkSNHin6W+cUXX8SXvvQlxONx3tN5heHImYhIqFOnTiGVSqGvr6/o+h07dgDAovloWhkYzkREQm3btg0VFRV49dVXi67/05/+hGAwiO7u7jJXRqXGD8cREQkVCATw+c9/Hvfddx9isRg++9nPorGxEWNjYzhy5Aiee+45fPWrX0UgEPC7VFpmnHMmIhLMcRw88MAD+OlPf7pouWEYuOeee/DCCy8gHA77VB2VCsOZiOgmMDY2hn//+994//330draim3btmHDhg1+l0UlwnAmIiIShnPORESC9Pf348CBA9i3bx/WrVuHL3/5y9f8nVdeeaUMlVE5MZyJiAQZHBzEsWPHMD09Ddu2cezYMb9LIh/wsDYREZEw/JwzERGRMDysTUQkyEsvvYTf/va3N/Q7Bw8eLFE15BeGMxGRIPF4HKdPn154nMlkEIvFYFkWenp60NLSgomJCZw8eRKO4yxcwpNWFs45ExEJdt999+Htt9/Gr3/960WX6RwYGMAdd9yB7du3Y//+/T5WSKXAcCYiEmp8fBxNTU1455130Nvbu2T9m2++idtvvx2JRAKVlZU+VEilwhPCiIiEOn/+PACgpaWl6Pra2lrYto3JyclylkVlwHAmIhLqtttuQzQaxQ9/+MMl61zXxbe//W1s3rwZ7e3t5S+OSoonhBERCWWaJvbu3YtvfvOb+Mtf/oJdu3ahoaEBIyMjOHjwIM6cOYOXX37Z7zKpBDjnTEQk3O9+9zs8+eSTePfdd5FOp1FZWYmPfvSjeOaZZ3D77bf7XR6VAMOZiOgmkclkMDQ0hI6ODhgGZyVXMrYuEdFN4O2330Z/fz8OHz4MwzBw4sQJv0uiEuLImYhIsOnpaXzlK1/Bb37zGwDAxo0bEYvFsGnTJmzduhUvvPACmpqafK6SlhtHzkREgj3wwAM4evQonnrqKTz66KMLyx966CEcPnwYjz/+uI/VUanwbG0iIqEmJyfxq1/9CocOHcLnPvc5/OQnP1lYd//99yMUCjGcVyiOnImIhHrvvfcAAJ/+9KeLrv/EJz6B4eHhRdfippWB4UxEJJR3cZEjR44UXf/73/8eSimsXbu2nGVRGfCwNhGRUO3t7di2bRuefPJJ1NfXwzt/N5lM4tChQ3j++efxqU99itfVXoF4tjYRkWAnTpzAzp07MT4+joqKCmitEQqFkEgk0NTUhNdffx233HKL32XSMmM4ExEJl0gk8IMf/ADHjx/H4OAg2tra0NfXhz179qCurs7v8qgEGM5ERII98sgjuOuuu9DX1+d3KVRGPCGMiEiokydP4vvf/z7+/ve/+10KlRnDmYhIqMbGRgSDQYyNjfldCpUZz9YmIhIqGo1i//792LNnD86ePYuPfexjaGtrg1Jq0fPuuusunyqkUuGcMxGRUPF4HA0NDdd8HrvxlYcjZyIioerq6vDPf/7T7zLIBxw5ExERCcMTwoiIhEmn0/jWt76F7du3Y/Pmzbj77rsxMDDgd1lURhw5ExEJs2vXLhw9ehSVlZUIh8OIx+Ooq6vDqVOneO/mVYIjZyIiQd58800cPXoU9957LyYmJjA8PIyXXnoJ09PTePrpp/0uj8qEI2ciIkH279+Phx56CFNTU4suzbl79244joO//vWv/hVHZcORMxGRIIlEAsFgcMk1s7u7uznvvIownImIBEmlUggGg0uWh8NhzM3N+VAR+YHhTEREJAwvQkJEJJDrute93DA4zlppGM5ERMLMzs7CNM2i64ot53m9Kw/DmYhIkG3btuELX/iC32WQz/hRKiIiImE4UUFERCQMw5mIiEgYhjMREZEwDGciIiJhGM5ERETCMJyJiIiEYTgTEREJw3AmIiIShuFMREQkDMOZiIhIGIYzERGRMAxnIiIiYRjOREREwjCciYiIhPkvN4YjJ0Q7ZI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=487x464>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = Image.open('/home/wliao14/AuTKP.png')\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New experiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = 'bird'\n",
    "predict = 'skunk'\n",
    "\n",
    "df_filter = df_val[(df_val['label'] == label) & (df_val['pred'] == predict)]\n",
    "path = df_filter['path'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are total 326 Falsely Predict as skunk \n",
      "Ground Truth is bird, but it predict as skunk\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=124x124>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = Image.open('/home/wliao14/crops/'+random.choice(path))\n",
    "print(f'There are total {len(path)} Falsely Predict as {predict} ')\n",
    "print(f'Ground Truth is {label}, but it predict as {predict}')  \n",
    "im "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'p_000484_658ae117b5984b3c37edcdd8345b1519.jpg___crop00.jpg',\n",
       " 'p_000485_2250e40411c945ef3ab19fc4311cdbb1.jpg___crop00.jpg',\n",
       " 'p_000487_fad17d5bebf9b5227423330dca4a60f9.jpg___crop00.jpg',\n",
       " 'p_000488_f5b018ab16c00c4b5abb6d3903d2374f.jpg___crop00.jpg',\n",
       " 'p_000490_6520558a670b883bb9bdd111c9109495.jpg___crop00.jpg',\n",
       " 'p_000491_c105a747fe4c2d39aca65c7d2befade1.jpg___crop00.jpg',\n",
       " 'p_000493_f6c24a303f44db2ffe0554b0387d627c.jpg___crop00.jpg',\n",
       " 'p_000494_2608a33bce1638a4aee05059dc72488c.jpg___crop00.jpg',\n",
       " 'p_000496_63ec6fffd9d3ee743df628b7e2333302.jpg___crop00.jpg',\n",
       " 'p_000504_5899ca45aa4e8020390b95b1f38b3cee.jpg___crop00.jpg',\n",
       " 'p_000505_20a4a5554756b68aae7b5c17fea17390.jpg___crop00.jpg',\n",
       " 'p_000506_5602b195cdea6327f500f2830f884c3e.jpg___crop00.jpg',\n",
       " 'p_000512_ec7f820b51f3bcdc8dd87fe82b1bd25d.jpg___crop00.jpg',\n",
       " 'p_000515_e7078e8cf67fb8d56262324aaec5030f.jpg___crop00.jpg',\n",
       " 'p_000520_915643d62077e5a6609a988b979c3d97.jpg___crop00.jpg',\n",
       " 'p_000521_6106ad2943950ce2103facbb9192bb74.jpg___crop00.jpg',\n",
       " 'p_000523_8fdbefb63817bdebebfeb1f74aa7cce0.jpg___crop00.jpg',\n",
       " 'p_000538_6aba1bc6a9576c8ed44018de29e4a12b.jpg___crop00.jpg',\n",
       " 'p_000539_b8787f9de5efb7b909f1931d01589bbb.jpg___crop00.jpg',\n",
       " 'p_000541_a1eea152cb373ebede955e9b1b9ea971.jpg___crop00.jpg',\n",
       " 'p_000545_584f031bf01ad45894b9a7c9e3861aab.jpg___crop00.jpg',\n",
       " 'p_000550_ec6af9b5c0d47778ffb82ebb422cb408.jpg___crop00.jpg',\n",
       " 'p_000551_39d47b2592440f74705037bc89bc8863.jpg___crop00.jpg',\n",
       " 'p_000559_2a401d8f80e129e04b4c218638989fd7.jpg___crop00.jpg',\n",
       " 'p_000560_9949b253fe6caa6471708e3c0c704251.jpg___crop00.jpg',\n",
       " 'p_000568_fd016a0bb2024a40196ab2a5e3c47f8e.jpg___crop00.jpg',\n",
       " 'p_000569_e0bbf70539645d7cb9f6f023c96d93c8.jpg___crop00.jpg',\n",
       " 'p_000571_5c355bad854b5707d0e3e6d377b7aadb.jpg___crop00.jpg',\n",
       " 'p_000572_ece6f15b85f3d618ce5d748962b3d520.jpg___crop00.jpg',\n",
       " 'p_000577_2d51bc55c54cb21180eb331a547db495.jpg___crop00.jpg',\n",
       " 'p_000582_7b364d3c2400b0308c9e23f3c88d4f2b.jpg___crop00.jpg',\n",
       " 'p_000583_d9ab6319ca29ad32dccd15d2fdae862b.jpg___crop00.jpg',\n",
       " 'p_000586_3081d304f6e9aef04240bf0c279b94a1.jpg___crop00.jpg',\n",
       " 'p_000595_a34967f85cd2ef5ab0998e7f36f85ac1.jpg___crop00.jpg',\n",
       " 'p_000596_9196ffed71a8be08b2cd86968c66217a.jpg___crop00.jpg',\n",
       " 'p_000599_596aff4bd41bdb48e29f5fc234d47e24.jpg___crop00.jpg',\n",
       " 'p_000603_b33a861b016529d6cc02c8aaa6b04cb2.jpg___crop00.jpg',\n",
       " 'p_000604_b13574c79f36e74d15a4f235edbaad6d.jpg___crop00.jpg',\n",
       " 'p_000606_eb47e5bd812df60064b42558d47b4a6a.jpg___crop00.jpg',\n",
       " 'p_000608_0bb84846fdf01372ec47a1090846dfbc.jpg___crop00.jpg',\n",
       " 'p_000613_6e864d4fbaefb0c13e7b642bf08d4014.jpg___crop00.jpg',\n",
       " 'p_000614_4cf0776703d06961a4fc67750b89f06d.jpg___crop00.jpg',\n",
       " 'p_000616_b19b77d5fc8418c2c7a64dd67da5d83b.jpg___crop00.jpg',\n",
       " 'p_000617_4773661e5a3eb1fdc69e6d8ec3a1f84e.jpg___crop00.jpg',\n",
       " 'p_000619_903d13820dbb0659842d780fa5dc9cde.jpg___crop00.jpg',\n",
       " 'p_000620_9ae726571f3fce31d8f898df56d057de.jpg___crop00.jpg',\n",
       " 'p_000632_84944f7f33b2f5233cc9186ccc59e0b4.jpg___crop00.jpg',\n",
       " 'p_000637_1d1622b783d367799606ab82d83901cc.jpg___crop00.jpg',\n",
       " 'p_000638_870b0ca9641fd1e5bbbeb6c3088999fc.jpg___crop00.jpg',\n",
       " 'p_000640_a612667f0fa94d8d55419d4c42161d0e.jpg___crop00.jpg',\n",
       " 'p_000647_af03fb1f8bde6bc9adb692340de5f2ee.jpg___crop00.jpg',\n",
       " 'p_000649_9e85d45cc6a496e3de41d9a36c778613.jpg___crop00.jpg',\n",
       " 'p_000650_e44c5cbb3779d59142e6306e12627a74.jpg___crop00.jpg',\n",
       " 'p_000658_39a70f2cfcb77ad42be60b914adb1ace.jpg___crop00.jpg',\n",
       " 'p_000662_e7b289d0e8bea02081b632ba57401aae.jpg___crop00.jpg',\n",
       " 'p_000667_7591f452b6b3a9ccaeb8f66e2a2750b5.jpg___crop00.jpg',\n",
       " 'p_000668_bfd65b05fa6ae3c74af8484ea2f591e5.jpg___crop00.jpg',\n",
       " 'p_000673_14bd1663d67e367478380894862ca0a8.jpg___crop00.jpg',\n",
       " 'p_000674_e09d035d2dbf360a762ac141346bd611.jpg___crop00.jpg',\n",
       " 'p_000676_bbb90c3dc9bcc925a3e4dc6574d574c2.jpg___crop00.jpg',\n",
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       " 'p_005769_7a70e88bf58e0f1eba6d66c81bbd6fbc.jpg___crop00.jpg',\n",
       " 'p_005770_31da8beddaa2b1047807a711a042a585.jpg___crop00.jpg',\n",
       " 'p_006389_cf26d81b3ceedf1a524383d2db9d5011.jpg___crop00.jpg',\n",
       " 'p_006468_1c79f183eed0eefb33e2757440bddedb.jpg___crop00.jpg',\n",
       " 'p_006478_3c430358c6554638f38c647c4ea9f602.jpg___crop00.jpg',\n",
       " 'p_006490_6e5e5d837fd15e433bcd313fb347edf3.jpg___crop00.jpg',\n",
       " 'p_006561_a9fa1fff16aadce8f84652184f12385e.jpg___crop00.jpg',\n",
       " 'p_006579_df849efb1f0a8b05cbde2afbc7abe381.jpg___crop00.jpg',\n",
       " 'p_006603_bb00484d628c8c57b265ea37f32dd346.jpg___crop00.jpg',\n",
       " 'p_006609_7caeafcc0ea9a76ecbf4b5a2e89ca030.jpg___crop00.jpg',\n",
       " 'p_008837_5e4dc8ee15fd41cd8980b90922967309.jpg___crop00.jpg'}"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([i.split('/')[2] for i in path])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Previous experiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = 'bird'\n",
    "predict = 'skunk'\n",
    "\n",
    "df_filter2 = df_val_model2[(df_val_model2['label'] == label) & (df_val_model2['pred'] == predict)]\n",
    "path = df_filter2['path'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are total 6 Falsely Predict as skunk \n",
      "Ground Truth is bird, but it predict as skunk\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=177x177>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = Image.open('/home/wliao14/crops/'+random.choice(path))\n",
    "print(f'There are total {len(path)} Falsely Predict as {predict} ')\n",
    "print(f'Ground Truth is {label}, but it predict as {predict}')  \n",
    "im "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'p_000509_fc3081d0a744e55f92588d02096fd641.jpg___crop00.jpg',\n",
       " 'p_000688_77d066e4f790616e04b78ec30a82db5d.jpg___crop00.jpg',\n",
       " 'p_000826_c2f12d32d604901b0c1d3fa06e56e63d.jpg___crop00.jpg',\n",
       " 'p_011174_2e5657f790b39f31d2599fba93f439c1.jpg___crop00.jpg',\n",
       " 'p_011239_fb2ff072b07161a24e19570f95f015cc.jpg___crop00.jpg',\n",
       " 'p_011292_a3cedb475af3d98c8c163b5141487f60.jpg___crop00.jpg'}"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([i.split('/')[2] for i in path])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New experiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = 'scrub jay'\n",
    "predict = 'bird'\n",
    "\n",
    "df_filter = df_val[(df_val['label'] == label) & (df_val['pred'] == predict)]\n",
    "path = df_filter['path'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are total 31 Falsely Predict as bird \n",
      "Ground Truth is scrub jay, but it predict as bird\n",
      "path:mirav2_dataset/X811494B/p_001826_0f458d7c64817280f5eadec96ee82163.jpg___crop00.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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zGSRgqn7Ftu+gOM/znFOoJTwyw5IIJJVpcNt2ISBiXuVEZlLmPBdGYWEWbmakwpSrmHOvyrjsJTJXdaqaHoY5zvP58/Px4+fhpq13UZEGoOm27XtvOzM7kj1ViUEIUBIHRSj+7ZGAR0Tdl4itc0TCwt2m5RgqjevCERhIIgG4+o9IH+OLiBhID88gSqbCmoRJkiWBTAITMTPztPrYUf2EStvnOZiZifr1aLLQ1/DqT8xpY4wjWfe93263230HQGASJlHWxtJYms+HjRnmwryxTmIW3fc9CjdIZGRWOp7p7tQ5zSxCrmieEenh0+qLKJiuPhHz2++/VYBe7x+pvbWtB7KykUpIkOmEw+YNDBWmFOaCf9vWVfW0eZ4nPBo3YnbPtCRQQlk27T09MikietfbvotIY5mMyj6bcBME5xHJIkTkhMIRPTMzVTWCwi2z6g9hUWG5bXtm+piIkeZuFuaU4e4iJCJJSPMM4wCFE8jMPIOZaetloJmpBcV4IPI8jvPxPJ/HPAeEm27aGpGw9t67SJtux3G6x0lTfS4cCAAIVYQnU2vNL59RgBOrKFNiJiKzzmK1FSbA7p6BIFZh4abaAykSz+OPyHCLisJUfhMYYwgraZWllBHuCVgVTMwcwHQPs1eLjFVba9u23263TVvFynQmIvf5fH6NuYNz33vb28p9w4kEyR6wSPaYz8PPQR7VJpUEESvLOQcTKfGZDo/kSMDMkni6mVkSMUgYiIyIeZxVVPKKmSqqIgKmDAQykEn4jvavJpvwr9OeyQimkMKHIwjJIGa+b3uam1kTbSLwXZoQkcrWdO+toUG1Zca+77e9n+cpQqraN22i1vV221vn51cKV9Lvz/ORmYgVBn09oio2hig3M0vz8zyP5/P5eIznYeOs7n/1o5MCEUzEiXBnSES4GZquE5sZBQRE5rCMOB/P4/FM884ydGfZWFpTvd/f97d30W5jBLLaqzqexyt5ympLCidTZpq71YlanTYkwc6HuxGnEFSEGRFmRj9/PIhE2rbvYG7MmeAMqKqZzXSflh4MSSFNcneLlIRoJ0lPWmhF98atmmOqOjO13ptHGcCrG1HJivomwp62aZt2kkIaMzME040SCU7iAFskzCmxtc6gJIS5EGe4jeFm27ZJk1ddVT74cZxpnh5gNpAIGKTEZSu6tdYaN5WmrMrMP7++6mJKa/XZz/O0adNNRIhJSSvKAzCzzU2EynO5m0ehT9F7V1Yw0sKTlJt0ZSKIEgmgREkMoVzAakQQEi7UVYU4WhcPll4ACNt5nmNQrJ8/H7OyW6pMFAIBCY3n4e5zDB8zXjZrLsiY7JSxPh0JJdzy6vMBWG2wXE2EOro5zcaEuTDvfZO9M7OIbvttv937tm9tj76/vb1t2lRVX09XwSq4mAY054yre8Zg0CrGCc6CxkIkTHp1+uPr60uktYBqd3ebEUh3T451V86JSOVWxT0RVUzQQNs6SDLTwuGz8MhiDBRWV0emjvWcU4k5QSSqjEhiUm7Use1NhJNz+pjhEYiI8sHM1S3STbd2b2Z2zBGWXSPn8BkIdOncFEFHnpxMRJExbQAFIa9kW5hVmYAiB4lqMhETKIkxbBaUqyzEjKpkMqv1DOaqW9vWIwLnKcOFMqLwd4tAOoA5IgjMiWmejn3T3nYAFTuRHFlGnAMzzEEBqiQLxEmZcIs5SHYwRbinZXoSVw3w+HxGRJoD1LVRkrJS0hFHtQCIpPeuSa4a0zJdVYFARAqa7KoayAyy8hO5QJK6TVV6lj8WEDN3baL9uFFEiIhqB3gM6x3v7++//fbbqnW21ldczkwgL24Lf+P+lDnO8HBvqvW5mTWTwtPM5/Teu2pv223b9qabSAu3zDyOY5zn8/n0YQyixqqZtHBdsxCLJHDZsZloAgEIXi2QOd29SSunNecUEJEQjcwkFwCqwsyiTbvOmNNhZubmAeUKBuV0u4i8ytipMzMDeZ5n1Wd92+pt4OKqwMHMyqLEwiIivfUu2lRX5o7FwaNMy8S2R2aaBYcQV7KhQCNaxXhmRfnhlpOKC4co/0skTMxFZOmtEVG6Adj67Xa7CWhWIyAiQpwocpYh3996V3a3vmlrAgRREqVnZMCnreo4aYadx/F4PCICnuXMRKS1jeGP8aACv5KUhTZ24RQVIQLMxhlDiVtrBdSPmUocFK9+1bLIRHowkSbVBSRtIoJdq2hO8HHOjOfWb/rbVtQ+d9efNBILcnuBw4UFuntGNm5NW0T4OcdxPLz13npXovSYEZPZti1MrDWowOdh8lC+NyL3s8vvsjn78+nPiPCU44gv/yzopOoZuAmTUnYVsUFfX5ZxPh9fj69jnGBW1ZD0zEjaedMuvVtjwE80msxn5HAj5432ZBokP4cjoMRCbed2T9ksxM//TefWNup0JJ2BEzEBZ5mBT54b2DqO4YVWtta6bXW+7/u+771rE1DCb/d7JbsO68wk4hlzzng+e+8FjjKDWU5LGm42LCcTt64iSFjCtPmXDfdny7b1ziQ+DkradDuPaW323ntr2reu3Hu/3W5meRwHs3z8/ps2/vr6cTw+M12Jv35+mtkTjbK76//+r6/H46l/u1ESMaFVCnF8fv34+fPncRzFGei977JPf47xufP+4/Mkor31bdvGmMdxbNv21//4+3meREm+9wgR4d4tc855fv1JRErExGSJSCJqUHImcAY9zc4g3+/YOZnf337/5z//Oebcbh/atyBK+b3f/zP1TkRJpnPOvAg+K1xeMN6r4rELta/os5pvqzjmqHNxAcjuARxIJpIiKIjI7Xar+F75ATM/n8+IiOt5MpMTEcGShf7UI66ot3oe35h/QSwiP58PAKfNYZNF+tuttWYRYI6wwycN6r3PcM1I4n2/V3S43SgC7mkWzIhzhkZwIMDJnAyAgm63m6purd/3fdu2JlzwqqqyIABPZQGJBFJEzsOFWIgXFWZVS1k0gPB4zEdetIGm+jQ7n89n0Nv9ft/fRMSGPR4PglQGr6rbtvXbvm2bqm77ViXO/W1vTVRp2xtn/Pz5E0wsbb1QwJFBsMdTVYnT5xznaXNSZBelvjWWisUCimnGmEQUaTaf5qtjkhnux3EQEQVFAcmZdEWw8pS/blCuAg5XA6xqvhctNcJaE1Xe975tvfKy5/PrdtuYpXfVWO2gEAFlMISYmDgdxUotv+/uCGrSe29ExAxmMCsRQGGGYn0zaeaCM0VAhN61964XrFhgsbubD3ekR6ZHmDtVEc6sVuBnMrMyJSVlUpTD40Yk4XBLF1KSw08LP47jnBNNbhI7dmL2dE8DwRQplIITBsttvxXS2XTfej7ozBgECic3OGcmKEWoEZGy7vveWrvvt1vftMmLu5Tpq7+TxsysGkiAXWcFVcoUaqxERAKqWikibNi0AaKKU3CodGl8eV8wcyoJhJv23m9v9/3tfdv31pSYn8fjPJ/MrApPnTbd3dKHWwYRIQnTowygejnbtmnjV8X2AqgzE4hXl3jOyczmMW16sEoRWxkIH6eqBthnuIUjymzM7KZlmhERSaALKik+DRGxcmMhzgjKzCC57XtE1P3urQnzeRzjPPd9b71rIdr1z4v8FJFApZ8F1Vdnk4m0teuTrH4jEV5HhElVNRNVUgBeFXHvvbEUSpyZEAHwl7/8xcyqM8mL2cYASLKarQ1xJzDzNAvz5KTCVUUhXHMTFoGuOTOakEBUqSlUSOX8OoGsbo2+7diae8ww8ngcZ0TsrZeDjICyNumUnI5MMGRre2G8W99779u299aJkrG64RFgLvo0XX8QDgKiMlEg3Pu2ZQQBczgoAAhRSkuEm50RyPz9t98+3n7jxPP5nOdU6bf3WwR632/3t9v727bfpGkCI6fFTAoSBmcVnAGvhoKZEQmmqcGmJ0i0xRgZgSAh3lpXFmOec1bBKEqFxGUEgxiU7rruWuuqhhWgxnkSxN2teqHSiQhBdjEiAplx0QwyA8Pdpi+eQFlWZt623pjP87Tz5My9Nc6MOf/8xz/s7e1+v6808xWIr27SeoqVQ0cBpFIRvM6WJGXKeqGgcdYZZdUOFABpzFwnL1Dgw3jZ3/v7+5xzHGfB4OVIAEQsxsO+7wXv+eMxxqj28WrYrDZaRkQ2AaTLvhEVy5BVwKy9V50bBI9wJCszt+fj/Pp8mpltFhFzOiWLtL3fiEiSsvoNzFUd895VtWkXVuKkLBgdzBARAoC6egwEwGkZHjZGpRaZyQwGCfOcbjbA1FoT6YWXK7euWzHlwkJIhJtI2/fW99t2u7etk/AMt3pGGwAI5NUrDj/O8zzPYdPMMswtmS2z6DKN8yvNAynMrW+Z+TzS58xq5fty4UmszF3Um1cHqMZUstozxGFeXIJwcIIZWox64swUbQsQYVTPscxmzlnDJ1VcMhEoWhdQ4wkRIs5I8znP8Yw086FRCDkzRQoorjyAE5ygyPrDiTSPyLkIa5nJ7v7CWYm4cKSK3SYWcfHA3SPSzKiaLq2V71yYU+aac0gACJ81u1Tt8kCOaoUyr9kDdiJK4kAu+kOxQ3oTkSJWUcT7/T7GGGM8n8+y3dvt1lv759cfRZXA1cpfSE3vXG6DmZlrAoGZ0Xei5QWVdWFCEdUtT6aAl93DzcwRySAhdXhMc54QCUQTnec4z5npdIPu0qQJyb4rJz2/nr339/f3xu35fP78epKoRgTS3E9/mKdXbysdACVNOxM8bZ42j+Mw9zE9HaGkQsXMB4ES6RGUBFDdWRAiq2FvvtJ0BqlIU5U3rq5YZtpI91lTFyKqlMW3F5Xe2tZvRMTNX1UEEUGo7GfOOS/SVmaCkxkiPM9j27b3+43oXlXKPI+IeHt7Y2TY1Cbd3W3YPG2eVnbA3x5EJLR6QkTkOdfnAtxXYzeTWtvqC2burRfpg5mDhQiJAGUhAKpCRHMO92pMej0PZRJRJsiTCI21NQGJW/HpkZHnMcNy26CbApzEi23M1JWZiSyHWwKpQhmS4e7Hw2BzjmPbts+vH8wMgpmbWXpAxGMKEXERqlsXpdVGcpvu7sj01oF9ax3ICGPWctmrB4V0yzm8IKFt28boFU/qEpnVMUgCh/mcs2JOk+buMyaSN83QeBzjjz/+OKfd3t7eMrk3z/Ck6ka+3W51I7StSjTCiOjPP/+sFgaIwIRks7BqRIkwIT0sJhEJ8d43ZRljmA9cVNGttSYq+8qSw5IQvTUCYgaTqDKzKkNEFholAplmVj13y1CWKtqSwk8GEPDMoNCqjNMjQ5rurbU553EcBV42ZUK4Dd33veJpTT+8nFPZZUWuF7sHQCbV1+6eMz0TmUyUQWaOtN5cZQ3uiEgUcLporUREYV6e6ZVrx+JfVt8IRAhCvbqIZAQTvd6kmxUuowRm2kUtiZk3UiY+EAADGJ8PZlawAj59zKcf4ynyPB7FMXGb5pMSCI7Apk1YRLlvtCln5hwWNp5PmvMMd992ImIwZZh5caQIawj4xUj9y75tfSOV1lpRDCPMwz8/H0AUKkREPs2nZeYzVUSI+DxPHx4Rf37+/PPPn8eY0z2IdOuGZBVq0pif4yz8khc7KcccK20lEJFIU+1uOc3dfWPposwoIk/ZlO47gOfz+XzGK4/atG3apMmcsyxMpLW2pfkY5u5dGrMGB7O2LlsT1X5GJT/zOc4ILyrai23vacX0y0yPCeCmH5lhNplpznEcT2bu/XYcz4r1+vHxUcNKz+ezWKsvHKeOeJlOxVxV1b64GFWy7ftelmpmZe/ff2vOmaJzzjCrk21mFvMX3ukL+2TmdI+I5/NZBvdCo4pM+TokuPhOdVo8U7Vlph0nEXVidpznmWakyswWV+WpSqqtEXOIpO7CEvMc5gOR93vfu7bGogkNSYxp53wkbSJta/u+NQCPx4MiWSgizjNZdbv11loy3eX9fn+/M1Q1qo0GzDnHIIsqzBVS5SDVJTWzRxyVxxNRxDGGPZ9PCz/GwOMhvb9Vo7LrtrXbfcszmVmo5zeyJjPv+x5xxFw0amHUoAz7mRf7od5Smcjz+Xw+n0Dc73cRqcJIRLb7dhzH8/kE+HZTEXKnuubPzyczf3z8rqruM2L7+HjT5PHP6YjeG/MWaY/jCweO4yBZ9QAz+0XqKyCp7Od2u23b5hd9vX5Af/vtt+NYJNDKHsqTFab/sq36PERk56qEXrS8usTVRSSSgiHrY4uI13TEBW1mZs3llOVVxZffxgzYJCLsos9wQkCt4j1o0wbh3nvrHUTm3vQXdzjCk4LMNRAWCIOKAsoasuCxaMGcDE+kCPjWmDoRtU1a196bElfV8r6/7x/759c9IijRpJp0qaqtLxw30iJ04YuizGsqozJXkWnhYCISVS0fRkW/4PXnnFUpFsMFdcE79b/+/T+qkTbGkEzYnHN+Ph//+fGR4QZEmruHZbjXTeuiQWhNiJKJA4mLFriqwJVX4boR4T6P4xARd69P/TjOYZ7EDCpYZ3qFZUAAxhiHyO3t7X5/2yExjzPha9yPqYgveSGW9aKQf6mby9esls8YfkET9fZUtIk6i2rroF+T1ywKikicY05bygJjGnpRRary1eti8pw1m4aIqFJElLQG2LI4XgvB8rmyTxHhmvVegyAsIn2/+bSIyKAMYlZmFdIjD5FWxbyqJsHMpluGdVEAPoeZaR2VJjYCEbCoOTIwFSVFFPDwxC9kqrVqLW69FzhgZoFQVW3bG94iLCOKEWNjopXtcaZXt73SZ2KocuXghYZUxlJdPmkbwyMTyyRXrJzzReygKqda3xpAROc4j/PcM24f761mjuutFZ863X1GhHtQLDCuiagIgyw90yOv93fd1krHabFXF8od1/WPiMJMVDozg8Tcq80x5rm3LkIOT85t763rmOcYj8ypChJiFlgm2CPe273IcpfMSawJQWpgYhVpGhEwsvCioYAAIn2cxznO4eZI0iJrBgW9//6bXUyw4QtwJ6KbtoJ7MsksmAdBiGgOB6h1rRzgApAr0bpISlfKaGbVC1GRCj1xkbH3fZ8yw7y3XqoSDIoWx3EwVpuo6t3pNsYYPkpVwsLdXaoXIhrmxQknEWkK4JhjjMEBElbVbWvbdrvdbpVWctOi1vKccs2qe8S2NaBlxDzO43iaTSFERGvC3CDcN+1d4xpM9kRGkRnstDk96qRqbxFCYbG4LUKcDPTbVl2DiGCW6r4z83mex3lEJqn0feu9K4uqznO+0LkaaE37NcBPsih6yEA6AiMMQPlpCnol7jW5RyqkUhN2IiS9OVia6KoZEh7E3HovlIukUqE458PijIgZB8i160J4tEtjX+N17MgWVHd81kv7Gu/1SIBALNpq3KpeUR+PR52SCmpXDypUtbxdDXC9fP56wKMubda8Ae/7rspv97f72y7CZqOYjjU1QbFSmQrldHUgC1asF5U1NS4RIcRljr33Gj9YgcY96ovFDIEVqVCZIJxJIijoSyWKoa5CKp5hI4ZbzmitNdlUtt5u+/bW97215hnEyqq37V76CIUc0QPMzBDOnHNH5NZkKdcguOl220VkxpxumeGFt6AG/xYttdilcA9zRAaBmIkbZ/b2ZmZzzjRnEPFyYNu21RSYshR/u81t2zbmnaj8K4GZ0uuIU4JUwZlwTlYCCE41sEjMC0uuAPyq9Ct6Ft9ehJjZQ4mxAEWzyCAWomy9n+OZRPe+k9KPx5/XmzRWIQJREoFJtbfM/Oc//uTWm24iCmBa1HTs8fmzcugL0+Nt29YgZUXtn1+fr8x6MXOZi0rDkMp1Sqeq6muPAwBBiASgcFTiWEG8wp8IE6X5+JU7ulcF5+5Ug7ZlZNf74KsZumI/AUxCi4tGkZu2UWATfCKxqJyi263fb713G9PPkYmJiDl+Ph+r9kRV2/Y4Ho/juOdduQvdle6E3bNndGBjIYhABMyk1SDJw0c3K8Qk05nRumx9613v9z0zpUvfN2bGRMXRUlVIgKVJMxI2dxZ5NfaCVoO+6gxqypnknpLhQY5AEGLf95ISqHTZnyWalcJSFtmaiFImISg9QMQJuEUGaxeWZLLpkcX4AYBAWviwOW0qEoVKEC1in4CIwgnJyMhAESSB0pPK6QNQTw+35/lU1be+89WtsOkRQY1b24i433bV3tsuoh4AWXXI2n7j1h00I4XALK2vlmZFUT3O2VprzABNC/MsRA1EIiLKDdy+zexVxcKkL4uspvOryI8IokUbi7BgcXc7x3Ec1bNREVVl4Rq9qgCExTkC9VugUOgMT1QiziKiLRGe7gXvoiKD7kqbcBfhDHiMOeYI8wmrjzDJ3e0Y5+f5dZzHLu+UCqgZno85DB5sKe+/fbBKEE7zGCMIc6aTthYR1dhfrenqBTNzpL0qhgJWcXlud7eooVHPrCB61RNJxY2+0g+yxAz3OZEpICFO0GLACAtzI3EkFe3BItIBuKF3rS6ugDLSY3oNMbdQ7XOO43gcXsN7r8mndSuv0L9YuapKS9lhASkJJDhiutuAF28BhM/HT1AwaNu2bWvaiIjGGMdxTIut1FREb7ebalfZWFoEgJlJzApYa631Tsyv6S5ibqrLR74Kn4Iq4uLaVPIn1xRiwTFmdru9vywyExk1kE6vftGrqqKLAPLLU8bSMykMOSIKm6TV7A937/sbM2fNIn5T3OJv7dGISKDaM6DmVTcRksmQ55w2xu+//17qQEEYNocbhEMonDI4JgY8TuPTItmTb/cPYgZTREwHq9zu+13443mOMcZxjhqEuBJZ8+F16imIaJiZG4BC3SognOcZYY5099Z0qZGscXuUVYbHmmaZkwAWLXG1OqJECyKgzMywMTfNsHC3sCICMyfAPOfErKnpq4N3zOPr8eWoKvMFn91uNwA1gxuxkvu8MBZfYjgiQpnqMd3Cbapy6x3hj8enx/ztt/e+b9K0dwIwJ7n7GJNZe3ORNDOkCpc7b9t2C8+I+Pn5h1658mUYi0lTCg7a0ySIQbr1ft9nxnT7inOcP3myqnbVri04EZmcGRoRyrM1EGeks4aIkLiqptuPP/4AaNtuu/wGxZeQ+xhEkxDaOMOglDycmnRSyzHNBxGBEBSP45OIpEk2PHCO6dXvHjRN3MMDzgFV/ei7iOSX56dZDsvwDPMgNCU9n9i2vsstPHA8x2N+HLe7d71Hf8uQZwSaKiX5c3Bz+6d//PWvf/vLX3t/8zV8PWOE7bcUftrxwx4hcXu/B/M/xic5cYGLz0Vyr2Bqh/kFXSUiEUzBCrcz3RpjbzKnPb8+I0xVd+975o0oNjEziwOibdu1Scw47Zw+bv3W970GIVqe242Zt/DCgGeRPNzLFzCQ45j+eMx5mlujvakU/sKgJlqnrkaYijpznMP8aL6ZWUJ95msQEamEDjDzG3NLSu23RtS3d+LfpvVJz/OY54OHfbA0pjvhXXL/+wcXGiAz3za+v20xx9fja799FDQhImb2nE+YMSAleEKkt7d7QQCnzayROJWuWlINuKpjRBbEoJsmXEAiFBUpplXWrNop2S0rjy4fqUIh1FRz27ZquCoxSLlKsJkRrTUhPs9zDNO2XaXkoiK/9P9UpAQzL05GRISfs3QjZnjgpZomL/YNAPfoorzviORb671fHmixLccYt7//R40MAwiiQoUy8//3eBYE/fHxUQVpRAD5akHJJROnyqr6lc+vr6/znCL08fGhqud5Pp6fEaFaHdoFyJznPI4BWtgtyZrieOHYr5DSey9SHBF1W82LoqUSUbVDY3G1MsLNLNKYad/3e/8QEUfWBXnFnLLvqw8ZF8WYn8egb9NqC9u6Ils5PCJaLUTg8+fDLHz1Wsl4TjuV+ff335UJlAgXXeAMYRVYlUb33qmtiuIqGaDMcA93WwDQJc9V5bxyaU2A+bKwxpnUWFS5qASVPRX3UUiJChcjbUxEpsgUuHZhIuKaDo+cNtLDZxBlk01V53SzKHmAFaoiLaxwYyJiojLHymhtTDNznyUmW1pE2tvWVmMp3EZEMRrb3hEtM6NLnU5cPYyMRfWov1a01er1uSsDkXvrb/sNwHE8jjERwQAnlHjdSWKBUJBeDwDuGWHHcRzPAaB1eXUptm0rlIOfsQo+gJkDaWYWvgQHL7pTPScRUbrbHHO6JzNnYl51awLEOeeYczLjtm23222TLiKzECazzFBqRDSr98gAZf0pC4xLFq84uYulxoJIRDLTkplJ2Jgzh81gFt3a1hmAchMBUbTGvQkzZ1ykJQe4syDnrIt/f9uTb8dxnEWIKYsMAqn0Jpq96JjDLEbcbjchUlC9Z/YU5sphgZDWQcrMIhzCESEXLv8NdiZmVTZi4r6IFwKqiVeaIKJqzDNpFbpKumQ/r5GUUrfAlYMqMaq484UgWPglW1w5PFhJi6NjmZkivPWuLPBw94MXLHwNiHMB8j8fX7+fp93vC4YgisxzziZqYyJTmyS80lgh7lvrXYv6GmlzzvM43GvWVPf9fp7n5+fDbAA1DTebblu/3fYbqKRIbnPOp//ITJQCxL5l5nOcNY9r7kGI4tMXXwlQzznnsBkBCkJSIEnYI+a0kr0C0LvmGodiZlaigvNeNU1ZaOFcuKR1IqIJqa7rH9/GMpWRPj2dmYmUkhHJRL29lUo389IjEaHGGXmMSQUOlrBeOCKkpAkKQAlCkYk85nmeCSRCt21j5hKCcvfTpsxSoTWORCAsEktigJlPLrERB21Aunvl8tU6I0oREqX6p4ggcUYoZ0ENiEiP9EBkExXiKzcAgXvb0oNYKIv6Gi/diNLpOlEC2JqXlMJjPAqwZyUWZRVc4lJr8oi1CTfmopQyywWp4sKbPEb8+PHj8+O3j4+P7bbv91sTrT7v5xiZOcaItHpmVVWtSNqLxDnG+Pz6sbTNY723QkOYtVrwc569bwCXPNAqTuecZtNLqGh/+3gnIj2exzh//vzpEUHITFYpFx4RrS7jxa5GomR3ns/notLEInBVfAxZSHN1LgoizcyaM2GVKj3LIq9Pt7g1/g2ze/216AQvUZRjFoDgPkdiirCo9iYsOY7jPGcmmLqQFim8Xos4LeP5fBLRnKeZ9d4jM9JVe4uLEFaI1y4N0txdL2+U5hnpFT9uLdMjwmwCMBurTOP0Ga/D55aUiSTe8UpKwlFi9OklrizhOcaYvtqave9jPAkLbSzGUEy7EiZLD2erwn8ViUxE3EVEhFTqoifl8fUgQFkaU6pEkQFtQuQ19V1J5HmeBTP/39vWb/t//v0/3t/fW2vqzip0HkgPn+M4r6QKiOzalBsX+hIRtrRQ+7Z/fX19fT3NxrqvFsfx8/39vUTMztMKK3ZP9yylAwA1/eiX9MWcSxJxEs1zvBwbL3nNxqRU/XLtqgrhZHJ4hG/btt+31gTA4/HQ0qS8EgAQRcS+7xFRShsFWYPZzISYEqWFRECRKX1aCe9mZpOaOrq9v79v23bMfhyPz68/5pyJKdq2RtvOohE03WcmgwOUzI1J779d+OVF/WaVjbZC0DxZI8zd58V0lKuZ8rZvBTrYqHM808mcc+e6lu5WaBYy65x5TI8XVy3TMjObLz1IAGbh7gKRpRhNUWJ2FswsxJRZnPMuSswUWQovAJZoYuQLWlrQelsAllwj3kBQItIoEUi3cRxZSgFpbiwvSCuvAfC6K//rf/0vvmROCw4josZy33Y7h48ZmX3rdRi21ggYzzHneZ7nOE9O7K3brzG6TsT1/Od5/vbx+0pbk4ik9zWky2ZgKjXr0hY7juOcY9u28hHr2k5bBQ2SiJpuve8lR5hAgfCt9xvdI1xEiLko0OP5ICJHVnfkxjeUhBVQFPS6DqrFaQlhdp82Y+VOa1465jjmGMysQr2JCmWYG2XsGSLctu1W5gj4cX5u2DKNlYQaoRO0t7et728fVsn6cRzuk7U3wMzGOAFGQJNAwl16DXsXRgCgqzIoIjzSzKqRxcwmYWaUWY2ZzCz57rpPCLo61xEzMjPhtG1CPRNhMx0pSw231PDnnO6pqijdm0j+F13VNamol+5bxRoiIqPM9IxMIDwJsuRzgxKtNUoQMsLnTE9EBDwOHGXcr4yqXoKEH8fzn3/+8f7bRynvVKR7/vwJINKJsW/bb7/91ns3s1LrezwexzGO48zM8lX/3//7v1prHx8fEXGepxn2vb29vdUkIWgh6i/rBJZQuSPztdCANxEp2dc6qx6xAmhGZgrP2616GSt7ecGKV48uWcDMnaiYCXVuX7V25gu0usr52621Nj/PIn+LSKGhItXDIxFure37tm2dmcymu/38mQkTkdv9re/p8fz6+vE8fn58fGSIaFdpCEW2vm/v77+L/qhmw/SRmULFk/JKc92nVh0EoNeAdyTXLL02VfUxj3gUvgNgztnbR0SM4xijtiWoXj3ymgx6Pk/VZFJkugXLamhmZk0JqfRMUlWz8DDmEun/pXxXxr1Egi+WxovhVxlPZoKJhJtq3ezIpWhYuemcU4U4aU47x1l6fHvv/E2X/+vrS0Tu93vZwdfX19fX1z//+U8R2batAI7ee+kL1BR2LkHeZdMF9ReWVO/8/f09M1tbv/jjxw8iut/3+/3u7s/j8VohkIuEOkty2+ZSmtTe2oUex6USISKNBaJPG6+mRtUfpcFVhhUelVCqcs0fHkfU+FthXl9fX3mtHamowhczuvCsV774QqAysyJGJY7V7i/jPo7j68v7JjUppurwNV1tZk2bqnIp5BNHxOPx+O0veTwez+Ornvnnz5+12IWvxrq+AKcqbOugIRLa4AvKZ5Uqd5FUQTaXEl8B7gngPKa7h4UIRJKFmYQp0qen+RJ5B5YG0qzf0t5JpDoHWFIKC81fOGgxpjKWHhJRIxCEhXXrECbmViwppzXMQZTpswwE4IQjOCGiJPIyoAKxX8AKiyRwjvHPP/5I4O3tbcw5zRpqg4lGsLs/HmvautgJAFQa7/J6/8xfZm6WVw27gsbn52ciSprVzBYaEAG3F/BEVPrSq4lV8Ye/hSBmRhMR6W3ftlt9sw5G8adGLVVyBxi0qALuHmO4exlxKWbVqa4cYJl+eGbacUw7Iy2SPFhEiBcxh5lAUfNZ005mTvjWtLe2b3WO4F50mUYkKH1KAauoKIuS0ufX1/M4phkRsYhmZPbMzFwBToEkAnNNBYE8ELlUTpBAkjI3Ycpidwlqwg3M3LqoKq/O3sxEMXYJNX3CRDQrgX8dSuaIsIxt49731lpknud8NTB79jp/QYil/VLypwuVJHfOUGIICzfh1loDg8g5gkqMPJ15kJCsMM9IV9E6h5VglR+dc+77XjepvNrPx1etHdCt8xzJuPVb9Ty/vr7OcWTlyu4CiCgp0tI8CqYuAnZm7nt/VWCFyGTmeT6P43GeJ12N2fDJzNKUv+nKMr9m2rB0ppkJpSZTbeGdSS8PWlLMEZk25hJUSa6QW411m1GvuO/7xjtL88VoIVRu5lm8CjumuZeSqLnXhgC5WuH1nsx9rOFuB4xXd4BBotx6vxHJ7XaLSAvOyMR0jzNnJrn9ow6PtoU3pdeejYL9XVcazlKtbjBf2YMkEyG1N7E50mvEEslMxK3SOSZQpkfktt0qj0QyQFGwUckDVEeBW9tFmYuWcH9/a5v23sz9nIenJVJYdOt1IhsSF/s/gb5v63YJWUZeKflOKpBIIJyIBUyg0tCioCzVR2FOTkbISupx5ZEvvM3dIQxhN1vpGiGQSclK2mWOWj2QrWsJY2TC3MvtPR9nOadh59fXl7tHvH389l5bbNw90+csusnhbszsjkyeYxSoiYtX8MoIK6GStRNj5dZ761IFM+pzoHLIxuLuAqoVQ/DANaAXEXExpomouogrR7+gX1yEgYBLW4mmxay/JkWpiYCQRBbzGM+6aGTEfN86aSOWIJK9vfV2J6LpY5zTfSBngOYwMyM6C6hv14wKUdYY2hzjPJ/6ynABcC4Rv0JSCJCmHBpMM9woSTlKjJ9fsbVoMX7b35KzJvAzyWagBlCBcMzIpKgUIZCeMc14zqQ45/w6vlYq3beGbZ1IIlH1DJynZ+jW3R2VvGcWyRBAWNbkQJXhQQmEmUcptiYLJYOSEcyUlFdPDFxUJh9z6pxecHSEZwybz+ezKhh921ZE5l+JXWFGZn4cx+PxeHwdde4B9JsWnFr7K0rEONOfz+Pnz58/fvyYcxa7Ps1xSXZ+L/+/FyiVRK7ooa231np/VXittWw4z/maZ6+8lplrWJ6Zl9ZIrI0wC0TL7L1/rx3j2n6yNNwusgjWNjR/pbPM7O7P57O+2QDm1JaRY8nKcBLz19fXc5xjWOYlqeo558w4V1Gx0FHqXXlJaJu7K4Bq07k7PNysUit3B1NRo0f6c44gbNvWZWfmVvyUihWusfTAQYxYap65Iq5IcjKISBKcBCQSeB5fw4ecMmwcxxERve1ZY/iF/TIJK18rjxKoVZ9rKocAAhHNp5V5oZb8BHm6mTuIPZM9iZWTSUAURF16hUXhRUkuYHm735Ag4cYNwM+vrwD2fb+1388x+rZFhKedc3w9Hz8+vz4/P90qGXX3tWyvtRYx6uRH2BhHhM55jjE+Pz//+PMfP378AHC73TJXV/397feXy3v5hvJnqyoHMagmZvRS6SZQE+29ZyK9hNGDQU1VmJronCdxqsiuPTPbJc0ELB7nqmOuiSh3P+aYc76/37kVDz2T1x+HH/PIzIbGyem/BgNb38Z8fH7aeZ7MJZXAIInIc/gY02IyM3GdsVnYKl/wn1ASbV2bx9Kf0takQliGh5uHEWdCn8eXqKKJxTzncdihrckmu9xFpOs6D4lSVq9JLkJQOJyKLJbEmRBhYsrSlk1wccvXNp90M3NyCIiTOCNy+hK+gqyWYsu0LEECzuUmo26STK/XAFMphnrNtYhkMsKzVBaEy/Uq1tCWkhSKWXZQR5+YVHW6Vy5IRD++fsyYn89HRHx+fv78+XWeZziez+cFtEs9vSfB0+ZSiHWfj8dn+STzcZyPet3q2L8w1IUKvQ4aEV3jUXINKGc1jYoWfZa8whbmM4d7zDFtzguLQEmpMGWmq6golShO1hK7KA5+HsdgZnZ5kS3cp/u0cE6pGquo76xSkzGZRXtgEkZG1elKEXZ+zUH0YOokiqQk3rZtmI9h0yfIgLS0CIsJEemLG1oLKpMStY+EmXXftohA0eoulAfAcZ4l4VRb78ac3JSbIhaAJ6JFWaRVKAAAgtxyDMtEVKkOZkauMbwKmoDw1rX6YU4ukATAteUuVyEc/iKkqGpp02PJ/awgAqCFaulnMfOqawjgqvMDQQTmRQ9k5lpEUB+eLyX3UhQZY9SOYvel1ne73f78888fP34UAfT5fJ7ndPcM6r2rFtd59S3LFpnO13zmCoVYGFZrer/f9IKretd935HtVe1WtljXv1J5Bqnqq5VXjm3btvudS9FjjFnD1AvC68rciMhDIpKvhapxlU15DYTE1eN+4ZQl4l8IaL15+kb52fe9MJrXpyufnc9h5qc5UoSDpQMcSc/HOWN6zICxhCqxQFQC3Fq7bbuqjnnM43T3MYYoFR1dn7OZGZDMLVpznlD1fUtWNHHAA537X9//cn/b39r+xgAs4hhHZKYo7/u+9Z1AP39+nedU2bZ9613jBgCPczXfVvZD6+gzmErJ95Fw2nrv1DHAMUWkcZtMYMp0DzttZoaZhQ1ENkZPVOvvyY3hDdGo1TbDcG/EQIAUIkJJyDA7zIB0F5GtCYOF4fuNbZzmozFtQuF2/nhE2E3klhw//+sz1d2nmWeAlZm1KYDb/S2X0DgpMcAeNueMPMJVtq1vm4i45/M0G+4Df/v9P9vWzUJE3t7epDV3b8THcRzH4Qbh3krEzFylvb/dlnfM2G8fFSiZGNQis0iMfKPWmcxgQaWPPh5K3CWTEz4f2TNTVWXfkVlE3dZbaYiJyKYbg8YYcRg8qJ8zTiQbZYDTR7q0dsugUp+KEE4Vaun0nPGmh+fsG2+bup9fn39EoPctHV01AjYDwQ27QNKTbqjxqYiAacAiwqfrUphrurWOSLNJBGUhBAHzHLlaxiTMW+9N9bbtt23XrBAjmSvV1Uttdt93kSbcVFv4mgiz7HWC6VprfHnK5TJfzZiFh19b2QCO8HR4eppzsUuWJ2RClBKBYwJS+84y1oapVZxGgpK4KIwNSwc2I/xaXPNrRIFWU3EW1qiqIgtpL9eiLNKatEbC5TwWcuGR12uKiHAvqgKRuGe1B8Pxwi+rFXS73fq+u/v59ShvLVejAZc6Q7UxXgllrhnw8r7wGnvQKk20d063MMfFsY8Iiuy91QKbV4FSr1IpaSl52JzjediYAGQj5OpYAEhdr2slzZRJxL1M05Ee+5sDUO2997I/s9A1nC5EyFhjPEvoAWvenxKydA+TIqlds4hr9iMzFyljdXHMLNNfbh9Y8zSxWnyFvkaF5PqcqloDDwCBiix1oa+1V/UKEO4lar1i02sMNzO3JgAn06+XAhMJRU2ellKoZpjFGMNO/2ptK/AzI3iJSLF77Xz1kmtjgjvMfM5BlNVgKSMsRFaEPCqUY79tqlrzk0qcTEFgEe2q2rD2mi9VhEjLXGmRgJi09peVNZtZ4ZeqyqxLlgkgcCYyce1n/tURqBBfiWMWjejbo+w1Iqab+yyNydrSUAcsMzLpVSXVW4ya2M3ApfxGQBH/kFjDkBHVLIjCJzMTiagbX6VMuHt1qpWlzPTzxxeRIKTrBjAlIzxsbeYFwASiFIbQ0pMsbqWVOr0ZX+JnJXSo8zy5uthc7OaapKSEr0Egz0KF0tzOgVyzrbSWfkTEqJSiukCZ1+IBt8waXgVfr+dmK9Grw3H908siD2epLW7I4Bp2VQWf4+me6UFCQRmW47TznMNCCNybIAlgZAnJFWcy3D3hFEFZskEV7BzVrgQLCXENGbLs2YylsGtQJDO47SvxIlBto7cl9YEXpBCWybHuvTEznAGuUqR3qW1C7j5nKXHKT/oZnxhj5Jx5ET5eA8TMfJ7na7bJr22+svpbq9R1D0pntkzJnPAIL15tkaOFKT/P85WeMkFFuamq1twdIglIc2Wp9TLcMN2QDHCdQybloslGpkUQgjwYEQGz5xwiwhDlyczhTkkIWkNdKMcS6Z40AWLVShznca5dlL1vvcelU6XpsW1bay2z2PBRZQsBy+CImzQgm6iyRKmaI4WYSlEzHEDNA2QWGhz1hl5BENcClTnn6tpVbVsaLNdUFxHNaUlM4JqAjxAS9kxKpkxQExCSMxmQhChTE9669L6k88Nt1g7Mq/+BSBAjOJ2SvGjToCQWVW6iLDXX15BR2QgAFmhriq3AOM+wLPp+WrhUf60Qqcx0Kx1XFYoABaonSxCR1loz8/pOZprF43Ecczyfz1tbA/LfLY8vrp18k7jRNS2FlfyweBiYpfaEu8MzHQSq01PN4JgmIqS1TQG18SDN++02zzHn4ARlCXGxqnITIkFOBIFFW8lktL2zkDO4lP5iLmHum24iQtDwRKbw2hng7qtwEr+A/MzMPPxVUbXWqIZVqDazRkQoU25d931z93PJFCURWSI8zabNmREiIiQMJm7XEqTXrnauFSy4yJElL55w81HU/wWCmFtase+lUV6aPqEtbemKp4lqZ5aI6R6lQpmZ296V1g4fIJjQ244bq8i2tX1rImWOXs45M9MhxMoqosxKJMyKeNC1/bj4EL1J8VwAZKQZIqySEFVlq2tVm31WQyADHgmRJsS1AzmTMhjZ2sbMGYW58HUUVzr4WrO04K1kf4lMXAOBrzyn7LLM9MXo40sfnohUGqQYG0ZEJY9NxJnki3cVTVSaMrNlZBRUFx6x/fY7PGBOa2q7fL1rSi1gUyZWYW7CnJEfb7+5u+2e7j5tTicPEG96Y2YlTaNkEiElrtlnZg4KCo6IEi41szO4975v275tr5ykQMpKKHUe59RWissCquGE6kpNj7SsyXeuTkhN8kZ6lIQ1+MVYNQOlTV8EKmk1WfVvCXt+U8F6Jdp8jZK01uDae2eVOTXnyEwQRZpKeyVGBUxsm/R+63BmLvFVTmgNi2S4pzIXo5VZmRTMUJ5xEFEvnchNt23rqiJyjuecc57jmkPQ8lwwCfMwm1YSRgQkIRkrllUZIES0NDkaAL+IF0Q1KLgOSl0cAMxKkN7Jx9frOsg37WC6+jeVO74AF4KGI2J6VTk1zjsNAGclbWsTXYQjIF2aaFlnZChzJZHhXvUTJdJ8joHIFKoZxfQAFz8BVReluTK3TcPckihIGro2ThcShiAoE3npFJSyMBF7yWFbjWYMtLdX/36Beb4SaE5kpn79/IHwsBVMSx5t+Hi/vxW5OswdTsEFfE+qVdEhQREhigiLsNvtlgjipT1epWJmCn5Jpemlnni5zJcg6q/xcJG+73utnxGR0qXITL/wsyiplWQmEhWJIzPD50o3GNQlk89jVgVb9/ga1yEgSnREFMWDUVVROs4cYzwfn14cmcaAuDtdNUdNaSQhiDNT+yLGI7IWYdUbrs8TV9rHLCVxVtHjZV7Mi3v8+n5V3K8j135Jvq/Su9rfxegrPSqiFz/aH4+HEqsIK1OxE5iA4KtI9+qjd34RyRrL1rqARp6WSEBA5/SorA/BSUjKiEz65z//2Xvf2sYJMwufVGz2EBVd+tcrBPB1pgJrraubrQH2j/tfAFROj0scjxbXCQD0JXL6n//5n733+uu+79X4+vz8rE4DM7+9ve37PqONMea0SIj04qRVropS/9gXy5qI9n0XSD1nIcMi8hoxsV8bJZbqYWuNoGDSS4/mlew/zgPAnPM4z8zctk37RkR0HB5RCSu6iogSB/L94762FxAioob3iCg1QMS8JqG+vn6eIr33MQZz9fcyM22GnV8RsaVH1PB7Vt3Xu9amJgBwzDlqc5uytNYmF+t9HcvWapciRITFYBeDidLC5vB5nq/gJS/19Wv294VRyyV8LIIqUaVCCnPdaWYW/jUxt/wz8+fnn7p1IjrnIKIqa+ou3N/3bdt8zDKAMqk5/c8//zzO8/7+9vvvyiQWy3G4xzgGZ83LvCJeKyHc2+3Wu0bEtOE+6965+5jHnOMKaxtXZnzFkLWOxFfS3EXVfHjMn5+kjbdtG6dlZmutFC/HGHXUy4Eyq08v0Edq62uuiDznBCWThFQ/fqGMX8ezBmdvetOtt9YcmeP8Op5+sdQukILJTUHMMGMGFSqx3Ht4YZHKsfZ0uGdmi2oedFVue1fVQBUQFLXMm2h1jIlAVJeJM0q9LSKk5ktErrLsGpxgBWI8B0o/AxlJsLCMOWf9VOnPns+jylVV9aKTMMmQ41SVXvhlFW12cXmW5/A1pVQesWJU+by47tkryanvjDFKzmXamDY8Swi2JnUqHJmDujaAfUzLQIlPh0N4URHdAViGhZ9znGPMMRhERP/r6+cff/xxjPMv5tvt7bf7W+dK+GpAaolgvATG9v4WkUTllRBh5hPA8/ks5oRHTR4vNJqvafRKgFavj2nb9iYqIlq+qggHrTV3R3IxQN29JkLk4ixVNGm9JsyTmUokjf5VBWVtMyYwM4uLKjFra6LqEc/j+Pz6Osco10iXBlVcpJ66aJFwn2ZLWr0isKgws4XTK99tW70Z3bR1FiVzmjEzQEnhkfAAkqrubXxlL69SLIgy8+3tLRcqTCIqIhTpcPRic9XokE9PX4RlIDnc7RzjON2z9lSGrNX1l5PSYr2r9qy+MC9FvVq+W+DDK5N+OUgrTuuV5LzeMFEW3eIY53EcFpBr76eqEjDGBIhaJ4JTAKghKkc2bsV6rPBqZg/z4/E8n8+SC0TkHz///PPnj3NO7dvf3MGipXMrNY221MXAFNPTfLIBoLRzAnNNAopykbKr5CVO1aa9996xdXdPWy1WAdW+j1rIrKpKQghY2ON4ylKrDyYtc1RVEmYVAVkkpu3325XxZKSZ1Yqkpe4XgQjM6Rd7LwMpTZVImpbi49fz8TieNd9Tq4DL1kt2hxLXQDsRQ6jAdjRt2jsJs6tm1KCxZ74FIs3SmQOgaTaX7mcO84higtBEghicb7e3zKxaG4BZIFJYwpFBJYhR4hNp7p7SOc1y+LB5jHHOYRbmXsiFW9qcdo5YVR9bPlWVLkfIrLUFUaUDYJUi/xJRTdRVAKlL+qq1K+3ma4Tj1UoGUKvuw8xszFn5n7IAyYlAwmyWcpyIGBGrlDnW+QhkTX7ubT/HGOc5nkeYc8LmHGMk8X5/2wi3j3cu5dhYe5k814rmJkqAEBubkxe+G25zzjEOZu7ZABADoizBzKyNpIGX+NFr1XOVg0RUMw0g0lcGzb8mPyyj0pp2nUsR8au5cooIkbLUXpP1uDo9EYHCPspAJ4/KCH2c/nyc53mME8KxZmgQVR2XPly4SjHSIhOcwbyExJpyU06S2odZU9AkIqeZDYzDIt3MYRFmMct0PCv4ioOJEBTFLH5ZJNEMc7pYg8wsrEySUVIfYT6PcT4ej8dReLy/uoLlU21MN1tkx8RpXyLCbdF5mNb27dUWWMK+u4iUQlVeMTdzTcrW2a779D2JvIx1jDGfz+fzPMwtwO55HJZJjKDk8zybyLSR2QDo1nNOZRKRgoGCUA71MR4/Pz/nGF20a4PRNNvub2+//+V2u93f397e3ki4+KxhYeEWIVcFk5IcfHXvEGFzntegyFJuUdWlCSEFscmoSu7CFopVRETcWzUVl6YFVXUyx3lUU2uJ2UI4SWruAbWaHZ4AiK9I1OZUW/sjkEkZhYeme7il9+CIMec1uXfkpRZZNTZdOmDMTBGkuWCXyPDiist1O7BmYiMyHbyJCLLWMHN1R4mFJJhlHFbizkREtVCPAF4ix3QVAUBxtK72XSJoVpoYsxLz53EcX8fzHGMp7hFBcobnvwi7EkVlRpGJqoUBJGe13xc3imlMKXpyQYmaDdfmSbvWUOBCyviSeMWLNIkx5nGeh7uJsIpiSezFGIOx9LF8zVIxmhBzB7S30qOqp9Wty3mScL15VpFszTf9eHt/f39/f+/7JiJYZN48n0e1FgOoTkFBv/uumVmy3kvOWVWa+hhgkqbt2lta58oL7ryqvZoOI6L3t7fKT2rmSQCuVRdXbcsfHx/7ft+2TaWXEhIXb34JQxtAzEpUPc/SO6SXEhSTMDlTtn3x9R+Px9fX12vYqq77q4TMUoTvvV1btcMz0jLBFBcPkYl50KJleEyKFGZiVckEQR0clglPoiQBSnKJBIZaFVegA0vdGKkpk6vqZ/xq0HlthX6M5xhjFOAgnMTM5O5XyrsWOCZRUmTmtrfMtAV3SwlzvIpZwlL6S0idgrLUFxb23Vu3awlmfX+h6PScc0ZaCdlztXw8MynMQLS1vV3rNfbWgyBNe+9t34jILgaCtnZ7f3N3BsVisvXWmv7tf2y3vXXJzGFG197pcw5OMBODityWxbvkRTsEQZqwcm/bvu/bftNLG+NCCcoik3XphAlx1RgMsgvd09JvqOpkzplRhLn+17/+9XZ727aNL3Glqw1U92ysbhvlcRzH8/z6+qqmiHBXLW00YQ6jgmyO5ziD0PbtRUbKzHrT5TIruHevTDnTS3A7loh2YYvFlEzLq7HReYNChFKE2Dxn+JwRrELJSSKNkZLlSCgyFvbJl8j0hY8GEGFrLU0mddGaBVsdZ1rVyHSbcwIca2oJnIzIIt3s2+pERwSRA1xszt47QTh/FdqLOrt2VfnLEP0SDogXBPsqwiKG/bkQXBIAJQ2ApMyYczJR1y0ixjhFmhJn1+pj7vteDA66Biml8rRpz8fDMnrvXdv7f/ydiMYYj/PL3Wv243wexNmleNpMnARKIQl+OW+++pwl13273V71bv5CZzn89GvotpQu6zN+Pr6q2FeeN0YjxEb4/dZyr4kq/+32tu1t33trGhHH8XmejzHGl3+oapNOIfY5bYzabXge1hrpjVUFmhZz5HR4d4G5JH7bbv2jb21vokLkw0WkqwppHZfWmoqeb2uWagRCWHojVWM2xDOecSwlMVUlNkp99GeYp3uOkGBESrRbSDxLzkYbepJMN8tgcNf7unbOMcOednyez+eTGdXeSVEXGmMYjMzPM72YGpm1jEwTNRGRRFkrRTKyBMAB8k3RRdblzkzJAMNPb01pkh/TcrRrKbT1r5VWSHicz+NZ53+ebq7T2jn6q7MAgDyFOUvBK0WyqTThZhakAyTS7m276S0t8CD6j07KIoh2DGX5XW+qXVg9YeFb2+7/43ePHLlkdsbPP1pr994/fvvNPcYY5xx+Z5tx2nweRykZqfbIdPhOnpw2I9177/f7/fff/3K/36+yJAsuNTOCMLMiyUMSzBIRYxx+jVWUK9VSNWldS0iuens1GjLm8Xg8aptRYeDHcZDczexMMECeiChtsfK1fd9Y1Skponjw5/Ford3v95oxFWIb08ybrpF+4cbfGJODCuyUFw6Hb0K9lbC+2m4La42kTAZ58aojw32h64Sa/mLWlknMW19KVDUg8/JMhRhU8ldOZWWWS+CZXtjW6/Fv38HVUF0J37dHXlNddNE3Xz82w+qDvzK8C7ycq+c7Z/UOysfsVXdrIyIklZaUikZY751Yq7tDRGAVkaZPLgjq2qrp7uE53RJc9QArhPsSFOWzsP2aox9jbGNY+DhNz3OM4ddehXC3OQ0xxnDL1trttsSAyg1HBPBLCxyJiCAhMKloFcqwGZ5FTKoyXC2tUZP2Krr9+XzGw6fbmIe7997e3t5ExAMeyHn6tVtAQMWioATzTRqLUF4KzlXa721X1dvt9vb2tvctIo7jPJ8HM+/9drvdtlabWpaIhbJAWzS3MW1MD6cEY80N1n1y9xChRLZlQwwCWJYUdMS0fb97hptThFCq6BJDYyqYY85pPsxsugXB/FpQjahK7mWvL+N7PV7f/O//ZDmXRWa8/gBQUUhida0rASV3n+bfcUe69IzqUezJwizLrfLWW9tUlixHppNw0OIHia4cQERUaNtalwUCZDWQkZSewBhTtHPrrEKiLKLbpq35/PESUM3MOawWEhzH+Ho86kVFWtBCue0cNgbAt/f3v/7++9///vemvWKxIz1RekJJSzundP3K2M4Tc3Kt1Jiz2NbLA69UQ5VL7jUizvMc85hzjqERUVIQImLlRcf0ac6cqmWUxzhJmJsiKMwzPIWbyP39vQooBLyyLmYtjm1TaVvt6DvpdJunW9sXPHnpeK/WRd2YV+PxZRPSG0VSgpOzBhczPCMoZviw0ZgoVahGXvAsQsV51nm7fJj7GtyJFyJYVutRbnfNyryc3HeL/P5f+7a+6uVicek4vszu5Sa/P+f3f5Jv6wvyIvIREXnsO5JJRDJAFOQsfG3pkwJZR6bINS9bsNx0Z0BIiQjJ041YGZHJ4bMo+EyIRLmAWhalqpvX2/hzDCWU5oYBXNuFfRqDet8+3t5///jt4+1dRM3s8/MzIkpjrXiMNSIXYiAKygg7bRzznDaJyNOLQqcvOWGz4U5mVmMfn5+ftYughB+2re/7XuVYRIRYKVdl5rRZV7CGQ2vFqBBIRVW3tmUmXYvSmVSFqHsmcevUJIUIAnEvzDPs5Xjo2wNXq/cFzsna+SBBRbIiCpAwaQByjPOcY3hh5xpz9YLOx8Pdp33Lr69XzFwwUKU108aYo/QgE371BfAKu2WZvzwl/fr+97f9+u/LIr8bH8kvc+QLCHvl+99fq2y9sxARhFtrTPJ6zt5baxvL6lMUs6l1EUkv7btpBOmaKp2FVQWCJHhMDwyb5mPOk/yRYQyiROGXpevEBQ/nL80FG+7Tukhr7e3t436/V7KxqLfXj8kvpQAB8PN41j+5e+l/lEdfmUbRJZnhPsfw8kPP51cxIVSVZU0GEtG27aoNraW5i2AlQjndfMxt257jpGMtkCLhUgv3acws2ppuW9+IaLrBpLAuTxrVoWOgCWef81mv+Crf9JpIrHtWTj0u5ac5J5KFKJFEqLFcj/g6DpsTVONjOWyUfx3n43tuV07x9fVll+GXma6f/GWK/+IRv81Y42VY//0s0TWX+MqM6dLcsus6vp4zrmGGlxFfd5QAVI9nhve2rQlx1JPfqaZWPTNThNznnHzYWTfezARqvasGM5sF28R5WMasEdVaaw5rrT33r7bd6vlVlVXdjIl6/6a9S6YMFdl6r/XINuZXfNZGn9qzXSa5rqqXQIOd83wVAwEnQf0BEgSd4RkE4BzneZ5jHGOM02ZJjhAzkQFIogCSiNyWfCeFaqk35KS0tBwZYdJURFrvRBszQFtGOnn4ok4W15pVPXIezzyJKm+LcEqpMe4IJtp6p0VRk3Ge5Rj7JeRVGvAzgzmLP2jh53k8P7+O46gYsbetbUqCGFZarmWXcclUvHK+2ldSGd6rHqGrxgKtAbHvkfS/WWQCIOXXX7L+gyJRRs2lTp+Z2aRxbcp6Td78ax753Vm+wLxXPjPcZrN93zPTIolonNb7gWslQG+bfDUiahjHcYzjqPCi0itdK/w1C8NLvCSpGuUKd2WOfa85uIgsoLkph/OvfMMdHigGmzuJUOXhRAnI5UoiwjIsEhQeaztH37RvS0CwcGIAOsYB9IraYxyvaY+CWHlNfqFWt5zn1ESNAOGqSXuuPQARMd0svIJ7NZgKV7NzfJr13kV7EmrYZE4/5wwkq9SCryC8X2Ho1UCTb1vr4mqv5UVgyW/+w90fx/OPH39+fn7+5S9/2bRJ49aWzKn7rFGHOp2JRa/PzJroWMZ57Wgvs1gw9b/8cL6q8v/+kG/7e14PXKshXu75dUfpCtB0kR1X8NI1gveyyzLKXn3q/FVU5TpDT3cP5BijxBCrOON4jnPJ4y5EMHAhFReTmvQFHCqSealBFxep911aM7Pb7f728V5Aow23MW1O4rUqpSy+967SiKgG3ksrQRYDzddxumg9dXPrU5ToXERoreLCBTivJttlSXF1Jwu5fTweOKgWixRaVJTHSry+vr5+/vxZtXC7NGoZ2ZomcWWoHg/PsMDt7d5v+8fHx+rl09IG+Hbh1qMuX0kk1tO+LMbdwwKFFl4FxNJdIIKwZXx9fS3MyCwzz3Gu28+Lu2A2/ntSmPAad7ts6pccTwmkXFH1V7ZXRykvRcJv3/+1derF08lLEZPW4vZfij9EpKql0Y1/lc8UEZKWmQGUkZlZ3zfVTkQ/P/88zrP0E93dMpiZ7ABqV9qvm1t9tdcbxLdSjIsZpNLaRtf4srRNRPZ9f/v8+Pj4uN1ulaf2Te14RERtza5H06uD+i0bqbdaUGNJoJ/nWcTKfd9rhTpfBKj1ziKiuENXv+GVXfHqmKzAkakBoACdXsL6NUwYMeaUOUUkzM/n4dNIV6lPXB1sTk/ldBsx1WQQMxEr0UyPXFqa+FYKlKt4kV++Ox4A6RGXjfrVIuu9Tx8YkenKnKWwP+3falgiimu3BdHrIkRR+pYn4wAQ3+DFFeiW5/sOMWblH//mIPOCVF++/HXDvln2L3k+vmY//u3ncTV7KtS+/imp4J72nQHtbtONiOJ8MnMRAoozkZFETnFVVFkyRevVqbRu5xJKPm2tPfjL73/zjFq7KyJcf5jLR+S1Fuj5fE4xAEX0/DeU936//6X/fhyHTzs8fFp6TOKqx5dF9n1tNGKTGlYV19dhZVrqAC8GCvx0ISLqXbettU0z0yKCIuGgJR8TNs/zqcz5tpYJaG8MeK5Bvcx0nzIJwsSawBzncRzz80c5iQr9ICpXWHqGmUmv0o9YRV2oZhbMCuWooa1rIP+1p4iJmDxhRUt0T/xytO7F2K3hh7WKtxxgocp07UYoi/1++v8t/2NGIlHE/oy8Es2LLXL5DNRQyqKYxAVpvb7zb8+Mb5kJis3xMlmmiGA2M8s1l7q09TOTI3BxVQJJICxssta+gdKXY48EMK7RR0cmrddN4PPxs9ssFlmuX0BrTXszs1FNWGCYVWXz22+/qWprGpcKQ4lAvt3eYgYnw5GWENQXjRdnWVvTV+JSgwflGgAIN3qJvcoirZ3jgYi80g6v7Mys7PLVUzHRrTUXMT3cZ4RtsYFpXGXd1hohMgwgeFjG+Xh8fX7S18/qjeLC8MoOighS7/M7+iPCETLd5jzPOeY5yosoS7EmQNxUheC1EbXyyJgvI1ianyuP9NfrihIzx5CIWM2gS3bxih54fUHXyqqX63plhN893OuL16NC9veo/TL3V5TIb5nr65//LWKsyHbp4SbWMG5/UZxqygo1lEAg0OpEZU0pgIkTJxwEECtzEpTApK01z8o//ZzznLP7bLkxUgBzn9OrhDjnEFFmnm77vt9v7713FvGIadPPY085n8OGp4MhQsoQSnZzSk7CWnNOC4uXK/+T1ppKL1MYY61yibURdml95LfUp7ZRnM/n+TwiLiRc1eE5rZRNk2AWtQlh2xoHI4qCHunu8wwbfEUuXMnWq5n2Krler05EBImI2nZZIyMIrx5PuiGq4WNMFNPCPUVA8d1iSjjB3TO9tIWZuWTl6dpc+W+P1xVb4aVOziXY/DK+7xb5b8XQN4tc2cgrT8W/thOvN7mSK/cFfXx/qtdRqdwqM5iJhQoryMyidQ43v4p3FYIgPegCUwUEQmvd3YEl9zXNgFOt//63v6pq69u2bW3rbeu1MXM+n7U+sAqPOWd5uuM4WmvP2/n+/r7v++sz/vO//hxj2JgMIpbaThLmc4zacqRVOuGqbGJRY0RVm25mdhwLyVttA/PycGvnIyAi2pu21kZjVVahaVwWyQIKd0vzSlWOcxJRRFfV3l1cASRxRJC7ZIrqtm33+73cZIk7Vu31LcguyaF5bS53ZFh1apboGWUQM3FIgIC0WYwJXFPPtHC+ZRmXxAYyExSlGwHAbdXa8Q1K/H800CVvEb/wxe8/XHyCf/v1f0srv/tR+cbS/f4qfv0wvYa+r6tRiNP1cVbl3rVHBJxmOMpuG0l59HgVb/UXYlCpf0YE0VK54aatt9vtxiJb2aJqMlkGhY9xPs9jTu+9gwkO8QBRIVNfz8ewuTajBcxsfP4kIk5qrGaWFuaTmRGZiIDrsJOIamt7vTdmJmJzF80iwZ9zRIRbjDHuiibSmhJjzuFIFmlo7nbOM9KBYjYQL/dT8zMgQiZe4fIRP+ZeWhq1SH5tx+jgvW9b6/u2zzmf08Zx1pzuuoLXlEztDouqeJgiruIuq2GYQiwsTIDN8ChK37xk/pgItSM4q6ZbFgP6VeNHhEYFwXzZcdlMZWkvl/Z6zG977r+bXcCLxo/L7f2bXX8vyf/NdvMSumBm6AZa/va7c10LQH3N6EQEUaous05CRxJzDSEwc0wrCjMiQJCkIoO9aJqs2jZV7bfbbb/fPt5/X3ne1lk1qs88RrqdNs2CVWmpARKHb1xHFMccxxx5IeRqdsn4lvy40eK09zI8/ec//0m0uLe03LwI1wYAfz1eV/zVPnH3OcdwI+ZSGi7xJBHhhi5aPMhXlBcRwClrLcD0c2Zmmg+3MRZf1cwGfiWRdAEfS8jW/725TEQUiWu4PrPUVRay2ERJOSN9DBszIzgx25WtZ0auxMDdzcb10X6h0/ELLPxV/xZcUh/2Vdu+PFZJYP6bF/y35PL7R0B8zx+WUa5e1Dff+fr18j2OX9UVvrVYcSFT7rOmz8YY1bV6vcor80kEwGAwwEmMChqETKwlUdp73++3t7e3//k//2cyXZk9VWcyIvQKra9zWO/kv/7rv7ZtK8H2xVkJAmBjagnDgBCZ1z6Irosmq6cTM4fZPAczv9/uaBwYt9st5mDgt72Lb+fzCM77reebBNEQWFhV++GL/l14gQeRNFM9mA1o2nvfARwWzHT/7aNYbSx0+jHizHQPn1bLOuzcNnv+1+N/n7d9L1d00DF1mg90ZOaIER5EpCwOo10QSUFVbnVtGot6Pc3+HOf0YZRo5EACkicTMURSeCYs2ZKDOu20Qnc6Eh4ZiUjHTxERbi/bzVUPRG9KKIwjfbo7RZT+7OUd5RLLZJ7s69jQKnXr5u2iHkmcIqIrP46IuL+/xaIPRm+t9w6KOafNpbiDS1mOVtSovDsiE5mNG4EwU7S5e7inGUe0JOIEcuWjdSqJnKor5+bCwvu+F+GhOGb3+33bW0lclCcKd04S4n4apU5yNVdUTmFu/nF/V1IFGitrz75qr2fTSWRYmzejZZMIntNnWiJdC3n2uTbicqL3TsAYY811gvZ9v91uRQbxG70E5rZtY3qNdFT/aqkDvtAipV81EK7OhKrWVC9f3AIzqyhgY4wxbM5j3/mihLl748WYfHmOKFmdyIhISxtmY2aSYHERgDWbpoSU5Rep0spIdy9wRoiJX4ESJXX+8mQJFG+vvghkpdq6CJQ1t7ZU3gD6NbLzrzL38fJSXLFojdGs1+Z/2UGm13Ku1xw3M3u4mfm4yurrSV5BP7+V5K9H+XK/tgzWWXqFlH/NEIKIdu0ism1971tR4LpKEy2HxcyVZua1fGS4zasBG7Gqd042syDkpCr88rUxXKgK/aI+1sIPZo6wegOq3NblCkJiVcnETJJ1fFrbtm1vvXCKg0+6OlpENIe94tdVduTLyNx93++vz3wFGRIRW0MLKFChKpjzPHm7nXGm5xymSw0sC5vILKG6Jc4UkVEbTjzMYhzDxuRktK21Fq8Ev8JSiY4Q8tpNSY6MZDC+VbKvPvW/ZYHfi5V6qJZUwK9DQpem2fe4/Ko14pULJhH/Gm9vC/H9zvq5RNGJVKS0KTPCxpzn8PlLtdqvIVr8/9t7l+1YchxbcONBM3cpIjNv9/3//6tJV1XEkdzNSAA9AEmjSye7umc9EFeuSB3JH2ZGEM+NjSVU/5JCqi2f1RKZccx3YXEkek5tvxPRvu/bnlW+/PD2+fmrlD0dKuBKC+jYUCmqRVL+SKS5ixkJRzsyH5Lerb7d8tFkoi3zBBFGPfcZeitbaw3mJMrMm5a99Fav7uQyB9DC63mYWcMBYNct4UMPPPKrcnPCDO6gZMJUj6hSdaH7yYPNzEW3Hli4t9YvNyJ0UJ5mlSyJLYk7g8WaBupu3+FM5AZvI2RhVtVaa+q75Er3GQXD4cGBntILGALhmU4MTvL4ZEP1yMYzRNeanJ1KORUlHffeeozs4gWYX6KTGLzfmpRz+QhA80Ubl33fE1OTBd+AM3PNfsXEnaQTcp71OInKek6mvpypsVUo0ePpPBEjUbDU2VP6pS9i7vMDhMDh5HCPGk9GHKC3N9uUSykcSl5bhoTo/Z4iMngTRFWrtVQi5h5hrXlu3J9vJQLUm6ezct8icrpoKItyAOawi/w4s5JrbtbMLHr2p2w0K90ZHc+Sayp5d59WmyjDQp419TQXPjqbmnXEdd4GkOTVbmYeYUBbaMGmlpqPnojcTmYW0sgIBxTm7ayBSCQvAWQOuFt19+AQdFPC0UUvZjEw3FIBDN5h9DL30rvkcPc8MDNiZuZ0s2AxTfOwkoFOtpZ9/P0552uyT+p226ahb9bHn6VumzwzPvAG8yHEaFOc/5wpghlnEF8DmsbzvzR6kvMOoE8Wu9nMAA9vzcjdW2VrJ4KVOe532XYStxYII4TBg4JIDOEt851NmrAKQMms6wQn75PUVNMyEiEVQq5mJzM7s26iwRbjnKT3p8myEg4mUd32nYuaWTHbE5wFqsf5+fn5+fHRWst4yswoukYPcmJRkTnyvDN8EgEq4jya7vKMZqK+FN/3W621gZKPSi6854vimTo1zM08GJLscIGEomzb1ot42cQZjmrmxkpJNSSgGDU9C2cHACcP9ORRfmlmdxwuICYCUXYk1MwWDcZiEGX2LlqkXI66MSiAKYVAjDxO/uZWtsJCwwOAu501LRkRcp5zpKdOVEQwSWkGiGk+nKkX51OaT2yVSNAFpE2J1D73k0UkPBmirQ3OSyJK3l5rCaE9zayeRyJXJBHZRGnHaq1BVWPbmIKIxJOtAkwkxkalaESgI/MbtQJr0YujERF62+5h0yjxmGqY8z6lFNVt62MHwt1dvLbWah/33MuGABL9MV2W7K1hpvv9j6wD5YtHbgX5g4iolNgmpbGXshMJc+PBlpZPPwfxESBMwuGUBOAhmT+0xlpUVaC92mXmCEIEIUlHOWc8ESNAERapDQEQD68A4aA5TDl5aWOqn3EYOvkgjRbbobR6raV/aCr0q/IyIGec/BI9Xtm0hMfxeOaTPI6j1mRcIhEJWA8jgIicG37J3LTO9BrlTBnNn17EcXjKq/uL/htmZgXZWZfcE4lIcFXdyaMdz0ertdYc5iUiAUkLEOTgyAo6l36PIkJCCSnIS8pBvBR9hlrCVsystQqAKUYaEhQd6Gu1VqlKnZKl0+N2NiOzLbodYeYiqlw+z4/OHyeSMwDNjIBkIC+DTDuBcZQ8yUREGgN5NY0O0vHxPugqo/t8mS9Eaqvhln4MIh/QVvYIysgdAFEI94QiwASmjEnN3VKLdCost4ZhzlLWuuOX+fa4vrE1Sx1MyfV9BRbomzRW/yjpkNU51EeZZcHGz6hu9B/26YilFOKYVrg3A8nLVIcphV8CmnUREXDF8rkG5qi3irfW0jl638p5POt5qV4KDZac2tSpfZ7PrJaVUp5HZeZt2/KBAAEpIrLfCrGu1eYzW/Y+PgAwSJSIKJPaZz0/Pn5FBMJUB4tptSYiUkpiRYlo20oKdba9mtnn5yepprYzq2k4SungthxKB4xprWGtna36tulWbj6qMqWUfdeRjOjMkRHRqrV6lg2baHCY2ePx4IMTYJKxrS0c8dUz7DYi2vdt329E9Hg8kg1hqDSLHvEEwpio4x7H6n9aG19omrZrg3kQMGOg3fqLwV+EYOa4Z+k5vVEAylL2bSWtA1Br/euvvx7Pj7zm5/Np1kbnU8vSa74yJSBka53TphvfvPJ8DlOXzyXpt03M0SLHEdFaR5ZEhMiWG8HMqpet3/f9fn/LS5oVE5pIFw9mMq8fH3Xbtj/++GO735j1PE9Qzd4aZBYzIS6giFAWoIhiMMX6eVYiwEO7Q0brlvQfns8nM2+3m6pabRHx9va2mfcmNFCL5mgcYORU6wiPaNXdqzHM7aze/vN+v9/vd4JsY8pka56Dp90DYyRWEi38sb/nqa3ceM7XPuvUi11MuDP2FqOIyNnZAIIzz4L97e7usNbs7BhIbxGxSUnPMTwtIPWgOyUxyWl7NynStyMiipwMkqkiEZHU9xjw/bTxgXDrvM59husi+MwsY8AURq6nPY6ENWlnxuoPf3iElwOdsfBxqcyebZh6CAuSbcpx5tJXicwZZLGAOdassLISRGRimiSlysyYe8JARBBcdCu65bwpACTMpADq84DUweHoxGPGnjnMj2bMrFvmi4qIKLHWfu8M6FmTW0dC05pyag1mPp9PEbnfmIM+j8Pdt22jgJCCCR7pDWbFOaloO87T3RLR0U6Pez56Ve1OSX9ekrhKHz1c+77fbrd/bH8erT6fT24ttGdhqhuzmlezDhvznsTgHFZLNDpFmODoaRp3p047HS0yAzSoONG3dUgkqYCIewCIDLXdXTr31xXpJyYgzBPThVGkTrGm6PkK5uwf75/Tc/WqZYC1ch0fj+M4AtaJT4WZhIS7v0eYn5ZwJA73JeU5bTGPlPsMtKdyWiUS6B7ukPIZZV29bflP1Q2jTxdgsxDJSR5SVFKDbNvGtz2PUw7reDyO2lom643ywKRwj4SAE0uyoQuzMJhKAPj777/T69MzjfVWJsd9uFF4YnRV1c1Os3acEVE9kukm3ST3y0SYWYTnBNDZ9RwRANXaIj5nX4XozAALUe+byaPec2OtKTGLkgqAs9WoJ4A6OC3qqIsAED8dkcTkImKEUIZI7QMbOr11htfM3NnMHJxzRPIQco8MmjuFRwShjwMrkkc8W9cyXeCXHnPHYp0B7Fk7kAHbcW/jAoQWetJm6VifrbZwIJKKKZKYcDCnpU7iid4H0nrGbDMaP8wrWheWFRPKyYw+DotyihpR5oAyQyc8ENOE3kdGzDmDXYomIDdPl5aSTXMUupeNiM7zjJBt247zJPecl0I505OFBCXHVCm74zwb4NGiNX97+yN/r4ZgJi6KOaNqUILc7/dMNVtSTRDaWT052XmU9kdG2t3NmgdPg5KgnI4fJioFE4sO6m1lzGV5ZHBHPc9WOzP2rgWjaebj+fCZVHWb6sETiY2wCET4UAKHNUTAWvjASGfCzwzJbppJRBZiDaYgeDY/eCMPYaSHllUsOKXRwYDrjY4wxEz+OQHIUwSP4H7C80llQSEGoPo4jvM4zvPMM5jgt7yj9M+GUNlil69E46rSpvz5si6XsSunl8iarrxmt1H5m23bqDEzb2Xbtx0Ac69n3m73pAoTEULvxROR2p4wD25hFnnqBLdtT+mspzmFOqswFQrl4DcRCWvH83D3ZCFtzbb9lhVLdUJm16JZ7+oV2Uqptb7d7gxKMGwpLCIeqOeZ0uBu3hzmQiSiuvN5HvAGa9kQwCAm/uvzM0v19/vbvueoR09ijTQNmTCfTatnnwvex9gkvDlvr7UWibHo55sD6H3jLFlcaZmjRI5QQmS5yh1myWmnTBwgEJiDiZhDmJjS2WhubpaEINlv3s5KRADHNbyMU+ElaWLMHoPoKe7uAjrFCN7LaPhqZ7XoKbN+1LXz3ZgP+HpwL6hF9JI6AQRzN7d6pBW6OvPzuSU67rtqHBFeBxVkBm36nbNqOiMV1p2Zs70eAKkhSFXvb2/ZmTj61EZl2Cu0MHFrlVm4R4Ft0xLmJhZOyqLcnd0GZhKDZxItebJUYc0IxiANcLOo9UwqfA68v7+rbLXW46jugMFO+3V8JvDiOI48Z5k0cG8gTy5DLMwhPLjbOgfftqkqcAVr82XMDNQrnebEkAAhuDWPakerRz3raeYZHnQvuGfmmFo451czNfcIEoKI+Bg+g4GW6ATP0T3gUZ01RPJfjsJMgHmiLb/uXGcr7hcclzXsvWmLl8a9zTTN8UyBHfUcTRRxVuM+qCoighGw7hO7e5bXMsGetc3M2oxkX+/EXZXi9Cy7He+IXJoSmd4RBt4vP6eTR6ru5d6NuG5E5JLJkD0NZill9ij2VfsNUvTBvBERSV5HpCwkVMYIb/cs8GQeN8nrSilixtumItR9gjQiHx8fj8djE31/f89iYNInFJbW2q+//mbmP//8MyUyIsxykEJjgQiVUiad15RFEfnH9o+ccpcJ/Wxq+fLUrs0e1iS3Oj/wWc+jnuvpj56ac3c3Pd2dOBjgYPcAS7bnUSC1ZEpQzh8SEaRvmSlG5OQDmqgFJhZQhBHc3e/SVYK7W4KrEQhnRaSuTSjLwJjaSB10T2vrtGYfjwcAM0veax/gncfjWfr4nO6Jml2Ay6TZmNF0jAA5xYgvsu12ieBrqSYZ21KldTddaaR7cqTrrCImHvc2/tR7dkVKKhTVrZQtE8wRkQnd06OUAunNayoaEWerM5ibuqlPxdxEpEiOoEtGXRKn3rNLRHq/J+yvabFiroV4i9CoRyXh5uev0y2M/3gXkVOF9+fHx8d5tkw1P9sppPfbH1Hw34/zGV7e36OUk/DUcr/ft08rgiIAzL3B+/indBMFdB5nZrNTFh/+AQIUmbzM3Ht6YxJB4ewui7fkrRQRgqgpec+W+9mTIA42E3cygwVHxNMGy0+nnEh2XNqFW2venFUhfeiiqPxlTyRySAiiACTBGx1zeukekBKRDmLcAFp34UwIZ6ufz8ffH78mvVbegkh1z1kznRYwlsCJiJhKOIIkzMKa2aaqIDbP6Y7sgbNWoFdBHB14JpL6fs/qGBGxUEkeV2KwC2iX/S1nYbnjCK7t0//+448/3v64Mam7i+h+e0uEK5G4KG1v274Llw51QB96GVZrrUl1C5TPs4GChILjxIkIZ4/N/6DgerTWtB1oJ6HyXm4CpXB7tOY6aqOe56CUvZRiXpk5Ux6js+VEBTMXy7F0XjLNK0qDA6RrmtZEBCT1tPBnQTGr59lVYCmybWqdcQUJbc8qTjrr+xiXt5qGL4ZpCkFEdNqQhf/E4nql47Jlq/9EY03VchXKx+eklqKOUF/CglGLWT22iMzzEdGV/Y4RbZznmRzs6Tt+MQ7fV0ToGK4ds0fbzOwixOoeB66k57ydXMknkSBSpmGyE8BALEocUFWRIqLBRqmMb7ckhlDZIoJymmmnOZAEiE0bFaNGZYNcyt0xuGWoZ5cyguzRPZ7Iw9NzYZtu28aMnKdq1jTpWhjYiwjfmBnWjs+M+xi9D7tFa+4wQKTH16Xs+75bwlUHIAU5Tph0wBm9vO3JLUGgokX3LV0KHEcwndZaeOQMZqaISA5tOFnkcADLGZn53/ElmdFGRKhwRCSBYkQMqp2RfksIBGKOSYPzKohTdmNZq/RzQpbikqHksRgX02uM1LmqLj8yHZvZkvF4PGqtZi0LeiPAyPJCvjHDhZ66yzks3udPugj1EUHa0TxDsvvhyQsessjjv118eYTGZUjkvu/kIUq6FWUBRgP/oCrZtztlOxuYWW+3G7OCJTmDcqgfgMjJS+5hRhHdUgvVWoVZmEAe7gjkuL/WzQCCg1h023TbWGBW3dgC6q1RhBDnc8hwpbU2LigQECZ02syk29tcYtu27XaP5EYLb9VVt9sNRfecn5qY/vf3+3DSoxQpY3gvM1o7x6nKAA0AaHRGT/Uww/CpANYf8pW26EJZ8Vp0iVpn8h1Ioil8Mxr7rd7qQSUm3PjS2UNSM0QAZ8RA7tEne6fXkVkCAOYGcp7wnWyFs+/fGNQTezHljC9DdKEEc2zZdaI6tOqVFpAjHTQRThqlwpLdz9GnpRTR7tJtWmjT/JZMHUcESJhzrnwJSs81mtWk8DiezzyZFBACs+QNOpkQCZNZWKv5rDhzGIgIG9A8AnkEZe2jVlPvqDt277QQKS4gjw7KmpuYcAEiEY4Y8Gk2i9oM4H2/b/udiJyYmYuWyUAcI2FxxVyjFztGo+fQfzz+v6//0cClavTJuTjyqVNeV6mleAkC8kO+GPHVIveJCrio/RBI0gggsluKBSwZwxKoD39IXpsUx6nGvhyGL3b/N/c18akLXHx8gs1adn+ANEGuMvOVRJEJ+wxiVJOBVqRoWHDO9dNNiBLi4IRsYzUz6llbmnhZWsg2cmBIZkxHZ1yyNdk4vTT3Eb36qhGdn7xrHK+tYageb80V/aD0nWcCIASWhDwmpMUnF0QOFvWICGqOk4SthVlsm6jcRKS51Vpz8huBiSFyGUoWCoCz6EuRcGqMhNnqJPHgJcoU7mRz+7KFUwqn3oqRagaQvk6eAcsMjr/wCs388yo068qTwr30lr5CMjY6EXrbDoBR40kKkz6Qq53ZPsKMRFthwIguzb3kule5XMqt8EHcwMxFNUsBiwadD4Ex6s7LYwQzWGdVJvE4Usrm4gQR3UgUoKBiIYg2jnGCUKGFti3D/0A4MyfiPZn03273/KJmpzW3drp7hJdSKCl9OTYtzH1Q/XGYuzcLdOjpxX2am6KbZOMyiLgUMR7YJJVAJAqyb6qZmx21O+ZeGWglk/h9oF33YwHaiFQ1GRJ5kKelROc3aykByGC9aWO4kFBS+J91sOt1KFFHDPR0dFzly3O12sjUYww1M9A92QMIgEVWY41FKX5fAHJ0dv5I4Ohsvt1SUx9PRtFHjmc9obV2JrdMmicAaWGH2KUHkgXI6UemPzp/GM7yUq2MCKb1c4LGzXaFNMaq8gj1WCxhuVJYihYpXSL3vcP7S8nSfzMPmCqI+2TIyApTxLYlSbgxTcy5na221tizT7q206OZ56RvRhFhghKnbpZRQbftNAM36Q56smQBZultsm7bLc1KmlQAx3E8Hg9Vba0lenSxMt5aAiPI3CWCmbftRsJmBmKAGLhtuxRNJIgMPF8KOobRmYAuZp6/37aNIpmDfbZMTG/9sqQLar8elTq84eoWwO84JJJYr8h4gm0qg4uTct6pD3wuQbJOScQzagE6hwT1M2LuyAEjx1FH5tXX4H11DKY+IKJx0C4dSXRd/7z3qdHdLfJuM/+9tFWgQ5BUuEzvKLuapsHJwJlJy76ZBYJZSngaE3dnHWXhUvZaq9nTIzLHHL2ASdtWIgqyeFf7c0sGstTuohmbpweszJrh4IQ4iZK0DvVN23yej1QCvTs2LdfsqwLw+fnpoxUrby8hpbgK//2pNLewxswSfZY0EQlNFOeDlkiQB7UfRsJ2QOpVRG63W1hkZv7xeExrlSo2lgzINLVdFw6lCCCbZngwUOLiVKGISPLLKdBTOGxwtH5BEI/XdIWbJcjx+4ziOXd5tm1ExAphXE/U+rHe5yZdqcf54vmg5uVdDR7B07Ocn4bFCZlflJsthYWvDDgzEyQhBOEU4UQuWopKrXacp9ZE/fDUUyy9Vz2i+nHMAo8Uvd9vVh9mlRm32y1REKBIl08WOWHu8OSGcMuOlSwHWEIkz/NM0L62luhLjDu5NiM/azpzZtVdYjAI9nCs6LYVgJNrISKax1RCjwf9+Sb50My8NSPqMQ1zNoLxaHj1CJxn9WYTUB3L9O0p1njt2k4B8iWmzqxEnulVC2bb7mtVF+uur2pscXxBRBSSsSHIKXrNHcjOVUr65Ojcx11WVvW8ys2wy/03C6jies2q86ZfOHRkFkEd5MQx0+lTjifQq/+LsszTO1U8AnCA2kKFYNaIyBCkAkQWlvrHduLmXpIBuzt18hI3Zi5EtdbH45F7cbvv+crRGdeP07zx7g2XwpxGuNZ61loTcM3MGiQgCpJI6ACagyyyLSqYOZicEEykhTzYkJrf3T3LMNhEqNZjVt+L5Fy0cPez9nGZzcIc2YVjZh4HM6uFtN7lCOCsZu18Pp+/Pn49zyMpNIkSX4MkScrHONOTk5E13az8AQBRUA7w7ORr3YLqa5/yKpSruC/HEshJb8lvF3D3JIGe7gAyfdjCzAdhTGT6cHzURe28SioARI7afMn/A2CSS/NRZw4mUKe3nOLLQUsvT/p/s8bIo4pNJB4EzySWAzhazaodmcEsnXUpOWKS6mnCpqoiV1spMydGzt3NKzmY9Wj11+Pjv3/9xczv7+9FShLM9pmKgJQSJEHiIDd/no2IWDaQeyBqM49m3h6PLpEshUeuy93JDSwkatlfklsQERG1NfMkg2IPr63ORyMicy57Z67WTOXj+TxqNRmzI9uYGSjSVHXMDZhpoCP8fDwen5+foxGEpwcZCxBrqskOE1w13NJsNc3fpZm+1WxyTZmYRzn/69SIKPEBHQ9mFrDe5RmM4HxGrbmZn2ebzsIUOmCm0FNGF4FckWPfbPd6neM1HQS83gQNyku+mq9LFyOKZIrkoGwg88yP1ZpTZiOqO4Jp33ctUrTMZ5sfmg+5W/yB7cBI/Z7+7C3bwgm/svBqzTNxmpMyVcDUOk/EOSOWWW2fbR7MrOdpzF4KqTKRCBcVc0GzJCfPvv2IiGTqQJFIpI2be5jU2sichctI03qOxvFmrbVqPm8s+1bTibzdbqnG2ljd6NePzOS5+3B7X9KHMiL3PACRs6LWzVuqgtl2zcO4pgR82WkaVnI1tfOXjonJzf+mrcT4TdJfkVnk8IDVvcuvmD9PsVvN9NVF+00QFwm+kpeLNo0+ppd52NYpkR0cnacIoDwz4wYAhIX3rCbCwjgYcHCP5OYVmlmm83KqnKC3hhJReg4i9PZ2m0p07gtGY1AZA9xrbedZIyjzjuNeiFlFotc1KPS//vtvVb3dtvRMHQQWEoaLe8sGYo8+xwpERZkonBzColSKZsRQ20FEwsWtJlGhuwc8OZzCQZm+s0w2U2YrEWTNWrUZlc6Ucozi8hdVl+o8/6qqHcI2dm4atPyQrumH9ouIuc+ruMTwR+PVc1XVNLiMVMTIQS1TmUXEyIH2iieC08cg6iXBRAeF0/DRsfwvmL94gUu5ZZHLSxBHGi8vv7+LrnEWM4bIj7JG/ZPSH2YS6iwUibhBBAmIAQ5gZOPBrXlEjQ7P76FkwvAzf5KKrbmxykb9mZ+tNmuUmWjN8bXSstvFzCO28pa+YzqUo31YmDkdIn2cRwmHMKllVu9sOWOutxq2duZuTvgThq3MzFReSpKrMncT1idIqlYXM8turHRJnaAqhuyV8aPVOrLHlhxwCDCFJ437aLcTBhMynKdBvIkYwxK6zzdtCjNlNhJAVzFMESG4jPgXPfSiuq7/dmkeggwe9jdPWgpWT6clWIcygz2FMgO4XgEfv6FeQlj82lRm+YN8mwSQZQqWLDu+VLboq3pNERTq9TZgNDCkMkXXcJSik2w9QZ1qMCK8B78EOOsFDW6tJfXFBMgdrU+qzO1r7jaMcbKtnda8ZkGBuGi4NK9nTvQWVilk4vX59ud7hJuZMilBCJk3hlkbE0ead4vaR6HkUzuOZ/pJPQYnT8rmVM7ZleLuZkcajloxS4Wz8zyhsWYBRK12nj3pmNUgYAb+XU3qOs53+HmZBvL0oAe2pT/uaVm8F2O6zP779SWfMtWzswEIv+SGX638knSk7P+egj402eWYrpqPefUspx7vantwPvWqxvqa/PWqQb9Y+S9/mmxsIkJKIgIi8xpTE3PnM513nVVZ9EQeZVqHiMhrxOgMHmW2eYVEpKULayIM3WP6aZkzen4m/MS1sGrZttIagfw8z7TG6u0vh7bzCessy+fZWjsBsECiAc7q+86qAJ3VboU7kdxeNoEAwSre2s6C5u04ReR+vzPo+fFUDh48aex+z2Mb7o/PzGLyefB5RjoUgKEm606SuBHEzM7noaq37R4RYRQRm9wz7oMEM5dBxjdmbhoAFVERs54MFwoR6a2J4Q5o56WBI2qtJMJEze08zxYuIuooUgBkdAAgLE60dKR7C23SBWYpLOJkOBImlcAj58yVdj6CqwzTTThXdL35quj4vHQkLGDwBvKPYzSMq4LZiUAcjBw3oUwk7uJSRAqpqrWBA0paIpC1VIIQIdlYiZkwQnN8+H86nEiUN2YR3qlsvN1qrUJFobVFbaeIqYqqfpo7i3GYGYFUd2GGe/F4f38vIkSk/JbDNqm2umnZhM5yHMd/f/6nfBLColk6GQzqSTszo05HeU1ISJuSFYJ5XmXxvfq590RW97YBQ1CmcIFqyW17ZarnWf9ikub5TrR5bzIhml2qrbXe5RI8jiYRUcZevsyVuU7tohVTa+R/ALR5PUmzu5A+UtYLvAO1ewwYgMyqXXAGWOn4mHNSKEVERI3OoHkpyIU3i0GrIowIHs9hWoCumxcigPnEiCiR3jMjs2pcXsjD5hIp/ckstBzzGqa5yyhEhKgh0wUQUt1USiZhSimEpLSZSTEhojDHzIF0Au1mZspCdPnEXdn3Jvl8mbXWHA6PaHa73VKbKw913XDOesD6LNydEO6cxpcHN5PDGkhGhwijW2fL4TPn4e7HcTxbF9xSyqabiKTTCXQ87dGOapWIksoR3SGb3lPfvFp7/ixHplGHGGKUaUZDTHrrRG45jQU9N5HSmDPY0q8kEDGSAdB6dQtJAELZMy1CnIXanLI0nwkvG5C2nr5I/wLajUUi8494WTw2+AqGMKnGR0UqXQKA9+y6Z2EabgyI0MEDWSRO/iqSII+ZrwWRR/QwCyhaQEER5JEoWPJgJiGl8DAnRRHdto1JGbSXzd3NPNyBIAFTpG/vEUwIwClsdLa839/cveZhVvJAgAK0aYmIMD+Q+DzzZmFtxxZBjqaztmFm4bSmaqPHBE7IKgvcXalPG6Ec1DZiw2rmAA19k5D34zierROgkWDjkoUDS+4AWEQc9cjhZD2CPo+YWZi4fMdaK3OICKsTTawSpKi7Rz9Ijj5fIByDaC9obngAxD0qitFZm+1UIpJBGUVwjwSIsbi2S6HFJp5oMEzQRZI2ZeuSyC8qEItgrpy86xu/SPPcFPq2xvm8CCpmVnIWS4gIYFXtiY60JPCB4ae8keNw1Q0Wkmih/J8UVRXiFm5uYUYUHFSYCtOtaK21eZ92amZWG4ZZy8veYrOAE4vorWwRQR5WtsOaOxq7We9rN3IditDcOxaQRqol+/GYuSseEODeGifF6HhqL1EhOsCxIVp4wzX1cuqJ+Xxnigd9b/LZD0JEJ3SgTU/7XcadfAabctEy+XTGMRKGYxf7uIy8MGdyJlncBixdlF0LBhhB8NKxzF18l8+8ZAsj0GBm1AOLFZ5f0bNXmPKReereUblK4fzY30rkfIarsZ5CyaPYkS2gpRThfVytdDMxQZYdQRfEMdqfaqsUEarKrNnvxoVzwkFHb1ktRVjAAlHcVNCqZ1YPjjDiENJJXdSaA2zuzYyYrTpxMFFRxb7DzVSqEHNOhQrFnLgLsPQ6Zlc/ShGZTA8tTBRmYtWDIhbgpc8nSJl/F+SZQ0ioeEe/Oaza6ZDmzWEkxEIR0MjJEOTu3qLQPl2c3vOF+egvbymh2gBA4uROnnmlSDqKAPU6p6OnvEZvSpJiFk32SHOPCTv3iMX37LZ9JudnRIKB+QEwXLceYk8A0Tgb0//rhxyXmNKidKeaXFTa5fbNkzbF9Ive5dGgqGPa7vQ1kwggIpK+JzOX/SsoABchFrjbcRyAnw9n5pzD1YkZ3AnwzLi2SuQ5vSmpOZgCYQjLBLckt8imt9tNRB3hYcQMd7MIdv74uzfOElgLSM0qkZnXLDxozuoa6PGrhMDMwgpyoiBC9ogSEcS6B+05PMUpYSbC8ACTinT/nwjMupeuCymOdrIzAKdg7iMiKZhztFFLmp5LQ3THZYx+56tKO2H94RTuyZKbleSYJjHdr1m0y11sESJSiGMMYU4p9+G99paa7GOIHhv7LLekXCYjDfrPiTq2UetIL2LKIn1L9ExpA8DDf51Krme2LUarTb/GDqVBbybsLmMAHqDQosJSRLXjzXhSMmE4p1OJ5nX1TmVkswEX0VAN4VQNYZHMiVb9+fmICHi/VwYhzK3Wo7aGVs9wUykk0Zg9QilPCG/bVpR0386zGT4BwJwCKhLcK4JmNSIej0eEG0zf/7i31uppSbLQvLlRRGRP4JIYlhTN9Ey6mUtDjMSy9jF6JEIzPiFk6/faq5ZP3HvKHquFSmTSzMfmcGnqXcMzMRtd2fXAbmqarKlcuP9hN2e4sAStKmTLYOueBPBpTKcEpfpMTqkpi0GYwM3EXTp1R3N1SIal/o1bmS+KCCllVXVzZR11fs50DRGX1NIas48X9CzO8EPOwUGSh3m+LK/ALAuthDGuuZRynmfyIKZqaq2Z1X2f1t+JM0ta3cnO8NoogrdsMOS0nFaPqbP3+3spZ6JId/NSihQ249ZQ62FWazs+Pn5FWAsf2ETu6ENmJu6YRRFhQcTe2mnWcuqEjzJxECjcIsgd3lVXki7qVqRoplXP4znA453gOQ9x9qBgsIUMH8UorkGq/Lry+V5S1Q/6izOKgYacjteUhtzyTTQiaq0w94XbLgZ8M4VxSuR3i+nuWVhLB3mSq+SRmzZ6WuT5V3RtRMPQdz2dNIW+8EHOZrcplDR8Rxm84nyBKoQmd9So+OdnphEYD+carkO0hrCRliAv+6g1IrZt/+OPP7ZtS+9RlT8+PvZ9K6WksGZjA4C//voLOUSWBMJvNzSPAO/7nm5vMMPb/b7ni0XodtvAlCC0brhay1mD1asOmr02wGqWSel8puyc3OWZhWKm5jY5QM7W3J1VkBw30QcLnK3GQNe2er7s9xJPzCcuA1HCzF7708lfzGHWfvVJxQiiKCJlJ+iCor0AXdNmpjTm0+8bPOss3rMKqtoN3HDt1v6XrMXkJ+QvEnB2qTfvFzpctKWagkuZpWHv6pCIiKq9xEDeYbzdUMTC8jhfs+rU9BdT+FJG8wYnrtkG++is++V7bVDrEseUbQ1uz5aTOrpKYs7+zdQFnkQ3PdEoEZYPNoGhSTeY9HyZTatu7Twf4bvvyvjjbVcnKR0tMGTErEVWM5ygCdU+z3Yc53EcSRO4OmqB2RtDRMSDfMNGD3UYwFSGRplBWVo3tE6roqrkYKZoHoDXzrDtFDnfgIMEfFpdBXcKxqhTYW5MF3SereIAQFkcyb33QPoUTOkDoXdHp4IHESeXW88T5mjB3iAWtpQB0zPpwjsuYKSJmbLhKrOA2be/TpYdPGwRkeyVq1QRBJluugAZncAkAlm3T3xkJqu7R0jERJpDZEoRkcfjQaORPSIQwURl246nJ5e8cGHuKS0hVpZgCWbAGR1EBFDzx3YruomFwSn9AAAk7AhvLWd8gCTADlfZTmsWVC2SFSwNCMFBxOHneX4+2mfPwIvIZmFmVu206HQP1AsfxEH6X//1f0WOuavWmhNxsv/OM5qbMtsV5a3PmzEKDjHrVJz9KLtn91MeTSYmUo/kUTM4hXXBNXMAlNOcwF16nBJxSDnGdIpZNz0LD/nABLBNIE3PBCbtOiJgo5UwgKQfDkhJmcRr2Jrq8lqpL2NxD6Yn6mM2jCy6BwYnHjMOCUvLbz6crEW1s06LLGMc03cDQkSTmz4/cBpoX/pv5ofn56e+nLG5iGzb5mbZo5ysKTOPVkohAgsijHlWzz3IpbAUTvbFm97QYVzIzlQhAkmiKMwRLDlXt1l4eGvNvE9zY+bWzo9fv379+mXhnbH2X//7aIeZHfW0cCZS3fY9tu2Zmls/Pz8BeM7EdcpAbSb5ppKffU/OXddkSzqYy3BgaUAimHkbfCBhyEGINkYkYUDbeYy9wRhRtvph6Jyjl06cpnkisi5h6jadx3t7fWy6UDTstI5OoFjsIL4t64U3jGAa8vqqNbxg5uKRLDxjQuxL61m+eCYR19/HkLkJh5tSOCUSA28REWUwFfKScyCiJMlJO5sPOSV79jBRb6buz4EHhBE5uKL7UG2/6X4rZZN6Xm3X84s8XHUjEgS7ASFnPWq1iChg5hzfZonRcW/n8fz169fn56cI/etf/3rbS5D36e9ZVlUpmpEW1eM0M6UlXksr+mWbu81Kexat0lBUTKzC3uMgGuiSXiaa5Y0KCrbq1StFJhLdLXQTIVEuwtmaad7CW6xtZe5IaCZ6A39EAgTj2rmZ4wEAOIVkNgQ+iKaQv0kVB6s9Ru45xfS6wmUkrdATIkuOhrIquERIaTuyLhaARxI2MMheefZjpCp7r0Nc/dRJnJSPO8vK6MVSIqLMdUwTP/di37ZVZXYPyqz0OY0dkj13MDlj3X3WteddJw1cZnmn87zdN6c4rXp6PfXJrQFk4YZgUd121i2ThMR0VjtPc28B2vbSvxz0+evvx/Pz169fz49fAfvzzz//eLv9n//rT9v38zyjNQgYwiIEEBd3FClWm4pSOOOlcWSeDAESlMHEXetE6a2olHMKaPRkAQCEuJRSRGNMWqUGJS1cpHRRa601tMJFWJQUjmjh1aMFDJCO4pxa0N0B/9IiiF4URM8GgcdMpExV8yijYNSZOZwCAW+ZAyVcR3FabYzsN4GS9szcui5KaO3qR0ZHAHXLG8Gvhn4VqdU0p0hpwjiIfTRDuXtr1jHCfPUPTBEnIg5IZ2BW7XQABECJI4I8SlESjQjNiXnIMZlOnWulN7lmAVg4q6U9f060fVI1a9YaQQN0HgdQO+8Aa9l1u+1aSmbKRES329m8WSNztdAiu+7EUY/n8yBB7LdSZP/z/e3tXvZNHwqv1qKGAwwOZlJmqJRgojFaom/wzIiN8wdggg772Sp7d5BbazUiasv4hgeXF49pIBERg65gLbOmitURIfnAqi35iJc0ckokM8dw4NZd5x7rXTvHzHlYUrth8uBkRiZmW/dFScrEWe26dCd1m92vilkyKM5/DaHMT3iRvt/5ABhJrhjTQKbXmFOgaaEr8os8EtPyyDAd8+0zj5afM8Pq6U3mCxA67YmIMHWqDBHpuAw4yLmPRCKS7fPz0yySPMNzzuWoWBbdk/jZzIBQ1dvbfW1KISIRFqX393e4KYHgpUjZhDyeH5+V97PWWms4FWhQBIJItu1GcTqHxvYPMzvjbHE6PMKL12ZPUKOAMmuIQCQoiEOLAd6ataDWxIwjEkGTEEZ4VvS39/f3sr19fn7+Lb+ej/iMamYKERaUcAqjmhFQ5gUDbmwWVlQjam2+3KEA0hPuEIIMRZMFZQlEn9mQB4sDROYByXjGe5ySPGYGC0OAhyYmQnJX9zRGRETwEKw9XzBhv6/VP+pcGckJDBD/uY2KgDXJIxSWHcrU46t5mgCEqpSiAGqtx/Fsraa7JKNzJV9J1HsJaN9A1JiCzSgKkwgJswkRc8cKOpQ2b/g8Gvi43W7B/Hj+ncZdt0JE7Mxz3lSmiok3Lc2DZA+vR6ub8tsfQoGIiuSYwHl8/JeK7Pu+bZtqkz/v7xt/fpbzPCkgQe2087Mxl/vtX/v2jwQQpRD/+tU8ftXjiLNGhEkzbaQaokCIWETTqcN42wAU0X3fixaMDojuRI9uoFpbRFi76FpomT/KI+v7fD7raZ+fn5/HR63VW0pMZNoOEdJNxWvw8m/UzIs/sZZeqM+zjgVakVkaH7NRl7d4RHTqiaVo9OXrvlzA/Ijf/vL777+kD2e2f2L86HWl4xiDUHj6i/G7vsS0X0DPUBARJPMSqKcVJQiTZicQSFiFs/OkRTdxZkbG047NOioRmdnz+WzUGV/J4zwaU+159eCk3Sql7JvmjKJt28L//PXrFzM/n08AQp0zJ8sf/cSO8kFrjUJ8sKYD8AFmnVkUnTEXaUREjgDqGh6kzDl/hZdhivMxjbxDcoynO0V53M3seNbPz8/qZ0Rkt0M3dsCaROyCNX8YW/hFmXyXgNyzlrwACz4x/Ot1ppClA5DV9BgDDKfm+y6LufiVi+JFv/3294nP40BOb8wp0t5nJlDPIhFRzzPn3vhoHR4HaUmwv4J6cswR935Y6gPKiD08gHQMY1RaAbvpTlntf316yaITM13P3MJrrc3rEFBPlr1dIVv2UIlI2bTs223f7u+3933fG97OVuWhYtohee5B4NZCJNxFZLZGWXiJQpGhAlS3++1NRJAEQxYRpJ2LI8HB1tP9iYvOu2pu2dPrtbXWKtd87tOhyfoVutvXb/s8z+M4a60Ik+EAzWBTWVprGMmIufG8VO2+q5NVaOaTtepElCyJ4wUWCf5I7C1Rj7pntPxN1unfsFzMC5vXObUgvrX95wdm7SR/ljEtAK8cFTxWSmQqj/yBFn6VVRZnAZCTdvK6FTgYBFIJYnMIgoUQ3Gqt1XYVSr6KwS2BUbOZdgaL2bHqwb1mRsi+RqGAcumg3CCzsNNObcLFC7LbC8wrPKADX4jBnIS3JEIik2VYRO7bnnOZsloYlp1f/fCChp7JZ1xrpUDLjfFITdtyOBxzskyhZ92YiHLC3NRJM8rhGtmakJo8ZSVGInfK39xpa22KwmoB58fSSIX0/ci01mKdp+HjgRrsQpxtG/GV02Je8yqI34WVFlX95cCsavs8rw75LxL85b/zrtP/mZednzNF8EUc+71k2EUDWJ6lA6nhrZ3QIrITkRkOP2stRAS5mJLmAIMMNtNhiOj8VgLx5oGekNoGEITB7m7NQyI4TjTVpmrlLlJ0u+0J8Ku1WoQRLLzPQwlPeEiyDJz1mYIho/DRWnt8frbWvJm762UmfKlDJJrQesO2u1vtJFUn1VJKjhGK7hRPdJN3oPKV8eKblBRcBLUc+RZXBXwmjQE4jcEwi3qY27meY1qAFJ3KZIE20uB6pjFdKI+rctci8ZqgwWuxeP15vZjVOq8yvf7mi7+4vmxesK9B0uJRzKuaB+mLOPYPTMiSIBwkRNRtYhA7QARDGAczhwJlgjx6N2m2cOSB37ZNUy9moiPDbSQ/ZbCIcslWbHcnkoig3rzefQ8RJRUuKluBsZ31eR7P4xkRLfl0UuMYCyRD01uZM42Ga9vaeZ7MnB6n7mXrD4W6DIlIEU10hbvPzolcFRVIcFrXVaOxtw9zzG3oyUgiZQF69pgHwsUX3ZPfbgMDIa/Wahq7WNbUVXE1/F+9QTNe8Y4LuMwlLZjtKQfWWV9e6+NDzr4LIhaf4UUHD6hH7llHVsRLxWt+/HxBmmwbVBBT+BbHkb98O4BOwdeb05SZSUXGUOtqLVo1c2JO/mk3ZAo9bzY/30eHcX6mmdVa7/pOxMrM4LDpFEF1c88KuzJpL/ZC8riDyS2OVh/nUa1torqVqPAWFpEd3B0vq5ou7Hme7qBglW0rt4ggSIs2Ebstk7PznmutVjt1cWst5dLMeiwWPTojorQ5eUsiXURaa5ZZ2TwlmcpmsUBza2bCHBRMlHQWjikZ1wjVLxroy++7aaaL0m19QVeoi9SOz3lxQ/Owmg0en28S6a+lv+86dZXRaW1XpZhrlfj1viYwZR4nHgw4v9XBdP1amDXDbwjv+54gwNba4zjSQWLm9FCzxDI/WVX3vZOYAcj8Zcu5T4QcO0JEmSbOLtii/fXB4sQevcNdljtKLaiq+/1+4UeBZH9gcOQY4GTPO04XaVrSPZjmRcMM2VynPcpJDF8pJSGM3axL5womP8zsOI5MMvkA+7TWspo4zVCCsnnsqJnlzG8L90GdlJ6RJ3idKCJyXJkMXH5qYjNbmeKxeJnozYqrS4qpA5ImoW+gdElNEMP0Lpg54ajzw+ct4MoifVWQ6yvXn+crp+6Zn8YDSMsDNtZd81ojIilyvmjEeRl55cysQ+KTrX6/3yLieZ6JW6eZVxdEdE73bdsgF3VMxh/nec7HO7Vm4mLz8om6WnX3x+NRSslJWYDnkHSIGghmZ63mnjeSd4EsbIdXN/Lukjq8VStESU2amvI//uM/ROTt7e3t7S2fTCf3pVkMGAe4Hwi63KCuJFi/JGiYLxbQ6aSbWTZIaLKQEzFRn4Dh7oTOyDM2Twf2W0Z1HwtBmU0m9VfFk+ezZz1mcEMBgoWPHv2e1kYE4dLoX4Rp9mTOlX9dHdnfLno17t9LNrS4HN+1vohMdUJLFGWDhDJey4+5xlX11hCBnEfrdD4EZTjIw9IRm+r8y8lxd56CrsoEEUH8RvGDKRnPwBPXPAo+S4Q65IGBxBZR0i4kt20+0vrrV0tm0LyBxS2JiPM8Fcm/11keYFMDz6/Jei4YmbgJNTPE9YDyYE2xTmXu7hnVhgqYMq/rQtFyGuvkAOwboD3ZSzQQhDM9sR6YLysiOtlT9K+IyHblgfrJvwIe4QP/dlmTQS5MCxZ9fbjj+b5s0r+7mPGal88fb5kszplCZGaNsBzmvHq6661NBTk9y6sMK5zcOAgGgUjqcRCRCokwBTrrkTeg5G3k57gnAuXl86kbJYkIroocScGXsqcgkiQGYhXhrehtp42N/Xw+j+fzPI4LRzJQpzyEuxtDUCN+tsg500m1WmSToumkHkf99fnQ7OeI15Tbi0Qu6mRuEpNOFjYejXDAhcKhAakMZaggM1ItKNJKB4u4O40TRkQcgSX2nEdnej/f5YCIEho0O/bXawVAYxpSDC6ysoye5YV5YnoFcxumO7gqmKk55gXEYqnXn38rYePJ9K/mb0nKuYXzletfRaQBvc+femsaAvAQUg6OFhFG4eLI8Y/9Y5dOBro2iGO57O7pRuoHApCdnJnda9HcGcEsUZTAUa21p53+/Ouvv9IRstpmrURAlpcNSlyIwURkL8XTtR34klzunvw2mm1mc3LbfI7DD4spIp5tfmlkk+2g8wV232hsfFfsKkVEUCiYQzgijFDDW/QGu9U+XkprYXhat4dfayeXBvIgoskpEeN/1Hf3en30oOYlR8gjAe6vOvvLmqrxt9oRixLFqM2sovn9hynucyjKF/mLVxM/d4GExmA8nkoOoH3fhYIdSIlM7Dq5QERkdvGmSZ1HLpY8/2UcQDSAvZGM4MLNjGEIYhdxdzuS8cxO/vu//8rsZr9H6xQmyiIjV33bNjODO91uw4R2+GYGJLOCqpMpvj/rxV2ZnQmRDmWCaEb2f/WLfYz/iBhB7txLlSBypt582/kkolnDSIHw4k2oqi1UYLNBYmIBV7kBEIx50LHYcR7zYCJ5CNB7F+y06TJOacskyHoCc2/yEH/RcF+EckrM+OvXghBGvL+K+Hok+DU/lWte3hehROd/SVYd6WAzYNeNEKAW4QOBzwALCdGFmMeiktfnaWbZ7Y6OtOYWrV92FOHR67gxFwp2c4oIcz+OmqTxuxYiClEzg0eKez8664la7r2IZDDn7n///XcO6tN1dgERga83pJDRKHz37Yk2Vdp3nUHfrVuOwUIEjZb7zDJmK9l499y970ooJV6mhL3WtWkpEY1rWHM3l/4b2Ma+HzMNGRF5xOfn02tZJX+eKuS365K/EAJN2C5GXCVMTJx/6oCIHHnbpw7YfOzrzs0M+cSeRR8CUiavS8Z0NNquWYsgQJwd114JS5IrIrILpYd3q0Np+fADiWbqujRISUphAnEEkMSkpHlJxT5DQDayb2HeSy+qGP6Ye5/zY7Udn0c7mluMTKq4x9nqX3/9/TwOM9fJqY9hOFx42rJJdwmgRzDeIRffIu705K5e7P58qYtbr5wIa2aQiRJPRN5b7ld3dtWCX07zerUREZwQv/QXMkMJEILgEY5og1SD+ohMXtXtlLmJ8JvHb0UfxhJjfVeTvxVQWmx0fIvZp1WYNmF1S4io83GOnoQpkbVjTSU713KkHEWHaCioiKgQwObV3c4U8WFtgOR6HTZ61SwRKUl5bR4B92TGL6V8PH9R1hcogmjbtgSkjX4u4SXb5e6bjnJgQPmqgj4ej9aah88ejBxcZKMNRqlwq5WIbrcbEZ3niTARIRBF7kFH5nGmDoNAYu5We6nGw2utGtn6TRGIFuY1wiKEH+85oVJEimwkZFa7IdakGvRaa6ue3KE8OwkjLMb4IQKrZJhIC1GJu98aiCjAtdU+/KaDP7LYVZzEaSJr7FG8tZ5eIaJNNxJhmkiRUc8Ant7CQkPn1011EiMo+SJnAIhtSBumcvJEXHMHXph7s5xXAFdxE0MEJ39lz12kejkRFq7gwpStTZ/H3yb7rjsxebOH4Va2bd/AktPFzOVebqXcrdbj8bCeBG3Z1RgwcICdKBwnAmAHkUUlC2Y2bx5USilld49WzxpQSDzIEwsLttY+7KQ/trd/3c0/C/FRGwJvb2+lFG/NzT4/P7N/PCfHSlEw0XnUt/L3389q57/KRu9KBQRXM5GzsDOF0gIJATAnw0+FMaO8PPcysMrxu7gyvq0RuLzoD+bO/TdtqKrmLFLyLhlrFmbVYV9j0nbRDeRlT88slpBl3hRp79JfP3xVhF9+810dfn/N1JqRvHWv78rXz8uYejrvYibOaAQ08S3VMFcqjlROAPIMq6iqWm3fy0XMbP6ikmmE10TUHWy6Iht31wHjMqtm0ZoBXOtBnEmhLAcSS89o6hiQhenmERFRrXUUftqslD4ej8yuv99v//znP//5z38Wpr8Dx/NTRAprUVYimlgyItr3XcbkQLxWddcq84TrAsjyw9zmWPy8iEHr+G1NC0wjKZi91OSXP/DFQM83rp/AyVODpBwq03a4eyCyJxwACMmwSDzGAuSt5eeneBHFWocEQBS4KuY9Cr0uo7dsB67xBisd3iqUXwSUhp9TREN6sw7NZoZAxwPwGEqLjh0BDcZrdCk3UK01YQM93ERMT+C0Zz6xTXTbNi2pMjN2NACeozmtByspz6M1NNwdDbUmRTeYFJ2UVUDu0ZRChESotXaeT7NOiJKY3bS67n6eSZVW3+/3fd/2fX+73e/3e2HKSIaZZdN92zWx7ABqrUJ82/Ys78xmVk+8Aki0FC2OryVdjFIpLZ7WPOI9KcCxKoBp9dZNwmsr1lST83PWc3KdadXkGCImGRMq3QcWKNGz+bGEDrr7Bp7gBb07/5S/8fBv13kZ/dfTFRHhS56Ivhj08bExMNUY0AfMhMMEUL8+RgyVX9uZbRr5URwwEAX2t7d93/dSYonz0qClefHiXJixK3Nm7AH0AzteDAz+raQFc6dsLLZan9mFrcxKRCIEa3Yep7tZjTCzPi/wfr9vmwJe69EGrkeE9n1X3f/4x/9xu92Y2Wr12ipzSz/SHeDIiE9Gx/RqIDBoJFZYSiklCL7wokyFtIjC5WMxczbOpmnwTkbVEbW5m+uHmBkHjZe90D/IMt13ygGGozZFeRWRqahiMd+Ql7bA+bMtzMLf16qt51pFbUr5bELz14L4+nMMVIG7H+cxyQKmg5RmZ/2iWJB7eXea7BkDq7Vt233fSymdwnickLJvQUhw+FGZiExk8Mm1RGcTETMy1DjsyGvpuFouRORuxIHgLKXlEzvr0awyew58mfoiBWbf9+M4ks0nHco07v/rn/+83+8R8evXr/N81uN8PB7P51O1ZE5GVSSfU28fHslARHDmq0UiIlnmiyqpeLJSj+ebm93GCAkMf18Gp0Dayi+16SHTF2K8C/TAL/KcxQdg8SNXzRERtY87JBAlmHnVVX1H8zfAFOHc+3zl6nfGa5Hwyz/nb1ZZnLc8TsXL77+8bNWRGHjVtswtldFSgqW6swqigDh61oyZlUX4Aq3No+hI0lvf33ZSwoHssDvPk4U4nQxzj5bqVqCULJ4tKZ9aWBBlu0RrLV27zhYUQIO3k1tr5TY4tLTngInDo93vd/PKgoA1O2s7QPdt145LFLnvm7f6rI+Epb29vQERDs3sA03qoFpn/p2ZE+6RKi3PkI9nR8MTT302eRRibV0dBdmEjFF3nl5qMJdkhzFzLKm4VbetCvLlXQiW0TXh2W0CInJ0dMVAc5CwRgS7Nw8KCIhAnTOTOIm4EUHpLBIQQR7EObxt6t3MgV94R7p8mMzy9Iz6l7+2MbaYXlcRyR5iishG1GSeLi+9iIt8BxgED2LkZO+umWo9hk9/ibu1BsrtCwny8JxdMCrOQhpsOdsoca45diki3PsogghvrbEAkdi2RkTuYhbHcbzLnUbpBYPJw92A2Lby9nYHsqmtAnG77XvZcqIABksUgPf39/v77TzP43jqDHsnp8JqKFPfxohVx5dhPbtzt1YDilc7te7BquHWP3nkKMVYX+BjYdS7MKxe1zQDezbPxvzAKce0jF+NWqd7ul55LHZ/VTbrpa6a8vs95j+VrzTFfJK0eJb0JdZGmed5OpRmlk/+ix8ZEXsp/K3VgUZdnpfyYzo/vx4PuTrlqZe0hl4XZkBhV9+Z3tfUb3efjmOMwGq9mrBte2ut1gO4p2zs+06jbSjvKGFmAD4+PgCo6tvb2x9//HGe51mf53km3FFE/vzHP1IOq3nHw0nOth4rKT5mVL968VM7ph6ttT7Hut1u+SHpaE4nPUaERN0eUVxVwWubMzySQZQ99ykPRjrIebc9V1zrcRx62+O1y7PrzpFtkbH69c/BtwuyZNYtp+ivn4PX9HUe6462egUHRR/UfB3X/CIzm8EKBp9Cf6PT5P3KN7p7DhPO/DMPcs3uMpaCpQh+Seq+5+eLXPD+KZ3I4T2ErPQnSKye9YzYBkPO7Xbbtu1pj/Fs+oeIlPf3Lb/0ftuyDF1r/fXrFxHcPfMzOVIpvz1HHWRWNbcvNeivX7/+9ef/fvvjTg/89fffrbW3t7egt1qPWmtzRJDe7vdMi4iqu7OIDc/M3GPx3jIFsd32VYG1ThWOKRYxfK++66gREbApIos758koQpfhu5KFq9JKz3UK0LTp27b5a0Azf+Clo48WsBm/NhBOJTTyo5hfAfzGa5x3R4t9+O1/12+fquv7y1aN/uWf84TQgpqrR6+x5ZaLCF7rqKuCNLMiBGRyx5i5c8QtAItmDiDdRwedMbpWkji6Z939/f1dVff93hOoz6eeR7Ft3b5YbNqXS1o93b7Lwo6uqlprH8/PtAy63XYwuXuH1DEhyMJV1SPMRksvcXPzFuplnv6cV5d7ORUbhqP5qmwuXZKdYqN66XP7+6MfhQ0sNjFGJMQDDzG/Jdxp8OitP7xIZKe/MkvWTXQnkIj6sJvozBeZF0zvGz3ROBP1XQJHGbmDbiIuCYuIsC5/a6gxddWUsynTQhxwT7ZLBPGg7Ah4M6OWqJY53M4R1t/ORbTs3a1qZ7VwXxJJeeCKUGstmhkMIiKRPVzM7MLh4Z3yDkA0N8nBaqNdblYvtZRt27Z9Z+bzPE9rLdwJIjpai7IMmV5xmLlIiiOLqIjmHPpHO43RvLXw09rzeOZE4c/Pz1LK7XbTctuDqfVh1qS+kVlewUhlAUTBZAhzezweNIooKY65ten35Kaln5s31u0dYjnBMQ3lIPT5DfIKr0nmWHkRFr01J7hfimfBSUwtmLmWALJycwnQgopd9db1gtd5iqs+WL90qhyrsbqJUxvNxompQqbunL//cgGruZ+1eJH+iWnB7/uNiCw8WwVmuiOdFWWGPd2tWs3CGVPHXqsqEc/OKhn50LLrbLTQUtITy6SSiAToeZyfnx+fj4e5i2rRXbhkt3TAPHtig62FcVYLRBgqW77sOM+z1uM4Pj4/z1rPs2d/ssYjIpk9kKS5EeZ9lA1UlVrDMjoln9fz+ZwSmSp34gDo1X/HCrLEWoa5fs53rPvt5quUzD2bz3qK0SrEcy/XH7BEYF+M7/xhSv+UwvVndye5vvSLvV4l8rdrveD1UK2H0BamkPWjvnzFvNkkWYyIMJvgGnPbto1bqwuqoy8PCRgo0EGkhE4vluy/QcjmFhqDy0lISZIYIsWRBeW2R0St9VnPz+NoEeV227Zt421VHD5Ie/Lg8cgDTobRFn48j4+Pj1+Pz+oWQKJA+lHPacapNmNk5nrbrLBwIZXZpRARIMQ4yj6muuoYID9P9mwccXfiy1KPLfH1iS9S8qJ48Gq1Z2AkozGof2n0bftuuwGgm0JKfguwnOdBr9jY/K6MV+ZBWtT2V1lfZff7mtiZ1W1a9XGqvXlakh9nRjbzlmnxvebuMnMSVdJknTVzIjdL/z4nCWFx6zdzIb6XzXsbv7AKZd2FBUwOFpHtto9Q1dJDzQI6M2fYISyt1edRPx/HcTbiYJWybxrZQJN7NGedSJr9tPxEECmJ5oRyfdrzPM5Wm5tn7+Lt5maFizs0xTGLjynLAMysjCwDlkm/AHQEqhgx4/SQpvJ4gVeN1rvpaBL1jSGiXr1ftM78ximR8+umRE61TURY5Hv9oSvXLgYBINzh4ami0HGf8y0+vrFLAXNkir6zb0TqeqI+mRov1jwy6YwIXiZMzHO1iuMXP1hylDBLZKOrSOctH80ATBzmFo1UabxGBq33vIXcwax5Zr4gv3GHFdVS9mCy5AgvylIqXLQEUzh12dItIm43zXO1bVspmSeRiPbr16dZfT6fnS0oOJysBRedYC4zby3TLArw8EXzqSqRRFCQB1EQqrVkXlaW9/f381nzdlSKOoLdwpBD4wGAqVorXEQE4RaeKENmLmM056rD8tzzkrWZGvt5HKoKusz6FMdY7PtcPKGZQ7Zikkj91lwu7hde9WsssSqGol3du1V7zVM0NVP/EH75xssYvvITLcKJ9WNjcRzXNTU0Ld7kl6/+oiPnra0n3EYp7HieEZGAayxZMAJUJFtQqps5WFXLhjAtWzCZg1VKKazF3fdyq7WetZ4DvGNeze15Hu7uIBLOcIqEbaFciwHB/PJMvmyxmeUkBDN7HE+r7f3+tt03byHMFNA37BW8KfHW0RLneZytRTSFFBCbt2bRjAVFuR5bOCf/mNscFe8IiY5W2oU3AG7EzLrX7EK2aMKy3YoUztxV6+3bAIiJAQb57lpboPaZdsEUgXBHcHMEkYmSUgibe6115641U0mkFwGgmSXgJ4Ukz1BQ74GaesvG+ck9FlWWzjje3/IMJgYxQQKRvPgewVzSFZviwkVFpD2NB2uhWcsBAMzaLCJArEIS4LPaLUjLfn+35/PZnv0M66azhBgUFqaSt9NgICFTN7hTkLIjO09SlPX5fLr18nREiMhtu4cybje+34mIakVrAIfLTXYlbbXZ80niGptqO8/zr/K8bbsw6nHixJaNWo+HfT4/Pj7O89xvt/d//LnfbyICJgMFyfk4n5+P1pqAmNisvr/fvZlX20T3bdtA/vn4tF9/Pt7s8ZD/+lv/+/Pt2UjlPfTm5ASEg0inTzYFOS2Cj6r5zDiOo59DF7Mm5h3R5J58/9TbrvNwgCgS6BFLsXFu4Qppi5F3tLj6B4jWIcH5zysqH7rqUrfrXaxac02Pudf53nm//JrlnkoOQOeQkSVCWiKt3m85tKy7J4ntdL7z81V1zt6avkfeb2K3ZuZ/NTUxuqpXnaplJ6JSStEdQGu9AhxBwiVT0yIFw/Le73smqGMU3tJN5DHtBsMdGjZEM/YqpShxZtQ54LXRgI5P8oVMfk1zwTkej5mZf/36xSAVSdo9H8XhANqASzpBgGotHg8fO6srynK+bd/3FJ3MOA4n8iIlW03PurtT/cwNFgLCwz2sgSgQiGBEs0bhfaJ7eDIpE2BeA04cxJ1lkiKIY2BqAuTETByEIA5m9MnSSPc/n5W/9kEsY4e/IWqnKfwukTHJNukyQ/NPWIy4mbXwiBDt2elmLRWVqGrhs/oF1SlFCye+MCddpArgRX+vto9G7SChd0QMEke4R3MTLsQqzMUBpgycRWQreynldk9nFdbMYUHBKqyUKRZwFFcArJmTDgqvrYrIfb8p8/k8zvNZ69HaSRSl6LYrM9xbXOmC5t5SPTncjBqitVZUmbYIbu0UQpYAnbh6nOYQfdv2JPx1d2uNCESux3EwM48cBxFlX0e+LiXS+9iVPA2pF50IzFDl8bzW8KVbPICSehWvTlLa2S/KbBVrMIlIOpUczHHhgBK3MlvG8j5puLMY1SBeEtSXio0QeWmHnRew1oqm/xej2knLX220ys8nlron271ybr2P5NSqtjHsz7xmM9sy/zy6bYbueSHQWk97bZ7wn/BEMPi2ycZSk9ys7Fm1S5pZEUnax+p2tpr9nwwVAhDuZm5JLWkIMHFRQ7TW4BFbRMRxHI9fH5mKkZEWHe5WD4U9Ab9mkabTA+Jruge10kCjPY7jeR7VmhR9f3+/3+/V7DiOx3HkneojK/HlKppFaD73jMH71YjAwMzNTvOWOJ0+ZFk4IrIBaEhGZIongOfzyA1b7dGq56fX37eBQaBOgjpyMYJR21y1lHlkV8iSxMGr8Z3fQlcp7zdwivVd6wJ6BQVDMlZZbK3BbQa/XXa9S978WB8Il6nw5mmZKS1/jc++rBjhoJmVsuucWskq4mlJW/V9Y2ZWLdOZTg3iy+yB6WOkJK033s0uj8KEezWbZbllcy0inueZpFM0KKlW7yLt+yQYqh5mpqpS9Nnq6eZMm5b9ft9utzg6QjRvXx+Ph6qq8TyU7pYtVHXwoXVd0rchlYevIWwMEBfnIFXHNI6z+jeP11S6UwL8Bec7CKRzz5Z2d3AyCocsonMcZ37LRBZjZOZXvTt/bq1OsVhfsB6MVSJTSGJJyKe0JRY/uzz7WUpBH1nVeUhiZIDjd3H3vPf5cww/dVWx83GV7SYiZdtUtWy509u2397eMzEs86tba/Vo5/H0UeuKCIGwG1r94x9/mlnn2ssnEJHR5LZtnUrqrKOY14EE/XUj8xXXZENLy5is4V1xuru7JXH9yMLq/n67v+U02O12I5Eg6sluIiXWkcfqgXa6BRgI2fmMUkWtW96nsVC4Z7GcImQgw3MqdxDhJsXMhKm3MbtRuMxRlkTW9z2EwNQpcMc0ILdBiFWKBpM7RYupqKw3NK5nI5+Uq17sePNGsNjZVa3GtzX/1F3+NZE03h69UVNGliT5L6nv0ItXHTP6bo1miZGIMu0QC4lXLPA5GYDAzAbs+05bYWYuW9n3PHjMrMMTQFDmg4iouVez5/mJJU4iohYNgerVwyGYGWhrZm5wUmGDPz4/z+cBdy2czGpOIQxRJmHqfiexuYeF1VoPM7PsEAtDRXY/JyK4CR3twIHbdieVQjsze0Q9jufzedQOH/Hw7r4Q9/tPsY6InCGAhbUovftSMvOZjnZ2bHjihAOS/TSgjD8CgI6RPlNR5bOeFmTKBI+EfP6zjR1Kt7KUEkzTOsTY77e3+zy+czquu7+9vcWI333hip4WfFVaw1H+Ko4Y3IppM33pce7Rq/AsK+RraAKtM6xZ+h7n2Z7HI3XtVNKr7Xt/f09xTKxXRuLbtuH2lt9+u91mjV5EsvYTg8JZRNK+TQ0y/d28qsfjgRGPz9QmgHqctG0wfz6fx+NZRIqo8zVLqpQiRdX0tERL9WRkO2vmE1prLZ0KjHOlMx1mf3/8nTfIoCOinefj8TgeT3fP+FF9wA27nq+1tTqd+hxRIR2uRjNhO10KG9OB8oZXAzFVff53JYCcnsAqpuMTYrVuzJwcDGbJZcz7vlvSSbrTkp6dYpdvTIjhVIeLVntpHmVm67Dn/hzM7DgOG7j6KbIxE0zcPRwRmdcWgzvJRsZnnodheXpKRQZglMZQ7FgSYTzi8Xl0MYBneRfmnupwGvT0+BNvMH/ZAakMwQuT6jQXUzoxIAq5mynlGdzIcBgS3QggySfDurlwdyWmQJKC91NhZrVm1k9Vc8iIu1t4tBrQvJfWWr4rV6rMINIZbcx7S4mcxbq5ncyUp3YtEk47OBGsWFx4oivWxgrPWaznfFf3nxZuCQCT9aWnRR2Gl0h56pjp5M3eYVqcyPn5M4yYF/nleuatLTb3q985r5Do+k36TbOqOd84rw3f3AN3t1g/rd+CiCTEIUG7s6WEmZ94eVzjMCd+Ja/2Fae4dPeuF/b+/p6Q5/wWGqkVd+fow1Xhftv2zB6JSKLdrMfV7u4t/Dzq8/n8/PxM/I4y+1Ln3O+3BD1d7UQ73Fse0T7hu53NqwSn4tSsprO8PJc19UDDsVVlEclaJF4L0GsQhwXZFREqZW5Ar4OMzRhPkxcyLlQ7p4Qxc/qjzNQ7sJENnU5ugiBCi4EeZ6ZB3QuARde9B15Gh6w/5D1OJdQPwgCtZJopMYUYhZx5hr8sIvKYHuQUg4iAamGmbL2ebKApUTR4aWLBBqSezjA5E0YJ3t73N6Ycddiyip1fQGHdUe9GyEaotxy8xVu9b1vGr7fbrTALM7lnaB0RrdZmbcuGaM2uAQqDhXvEmEYKCZi1rN0p9zpCbgUzbVu532+3+6219jyfzRsAjeYGDC0OROYQI9FyhMmN6770E37RXljQN0yKQcZASwvOLDxMc9O9oqHPfMlBTAsYg7gbi5s1d3fKE70yjsYCV9NBWDivMz9BF87cudNEZMt8uy9Ka1WZU0H2Y8CXuvWXtt3L9/AB61gNxaoO59tpeBcisr3tq/ayTu1u7+/v68nhkVqvVt3QasZDtnz+6LyDT5fUlxTB/Py+TXMQ1u1WzzM9y8/Pz+dxAHAzDmDblMWGQY+MHLN0Ln3c0Uxx6MKzPG9nNm8l3Q8z2/OjZbBYe449IjzarGlpj2AGyDpFlS/MaX+I3QCYIdiX9G8s1m2VSJrA+tZiyRFMiUyd9F04DBEjAb7uLi0+0KrzZMG/rLJlo/Y/7fvYmitTuB656cnFYm2xJCCnsM63/1ZHytJDOE/yfOOX64ylFWSVoYiYjPlpQFNHqurbP/41P9O9V3EjQkveV2oxB1pmPFp7AbnN65w5uNZazlif5eJuMQI5AzhbtPqfUvIyTMlDBdq0ZB5g7u9U+Znsy3sJJi4Ka5SlyUhRTqzaYIlnoj6LcgC9AYB6Pi/FALPHgIOZrdF4GeUz/vrzuGl0v+3F8Vo3FSv+fmyY8yUKQDI4d8mLkdpZn+xvrSe+qajrG+mapXoJTTjhq4Rl1k36z/1I/FvWGIwjElc8Pu/q+2MBkBhHby8HEkMj2gDR5JdSR4XLcQ52LswbDwDUs2b29QLjZQbF/7zmhQcAMOgLir4b7Guu3f/0gSqY7AxE8Pr7r1t+sQPI3NH8UmIedeRlvymjyJvleIckmZgJkX+zU4T229//uxW/UT1fXvH/6fO+r9/v0JgI3wG/UyNqjqYaVxUjaT9ff30EZ1T0tfqSguVLUaDn0jPSf92g9Xr6G8O//uVa3aclonWiz3h//v+LVzPvIhYCwfUwWBhrYeZoFu6SHDXuTGxdSAlETggzIOerL47BuPep1CK7uDIaLgX1EMlkkwcgwkEw8zG0iX4vkfkgM7CYa+hIng8U6JByFnEzTHavCd5hJj/jd0IkfLGmxLLHnhQbdIkArbs49unL+q0CxrcgF8AqkUzDf/33Yq6gwItQ/D/rSGL/qknT1mQhC19VJrlePsn4HvqmmZbff7+Q77ojgycQodXLBP2/WqNykZSzBBAoJ8Y1+PWtYxNUdHWl1ltDxPVp+VdmsRbjMUT+JRXu/6iJftbP+lk/62f9rJ/1s37Wz/pZP+tn/ayf9bN+1s/6WT/rZ/2sn/WzftbP+lk/62f9rJ/1s37Wz/pZP+tn/ayf9bN+1s/6WT/rZ/2sn/WzftbP+lk/62f9rJ/1s37Wz/pZP+tn/ayf9bN+1s/6WT/rZ/2sn/WzftbP+lk/62f9rJ/1s37Wz/pZP+tn/ayf9bN+1s/6WT/rZ/2sn/WzftbP+lk/62f9rJ/1s37Wz/pZP+tn/f9w/d8OWWPk4p6dHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=219x219>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_path = random.choice(path)\n",
    "im = Image.open('/home/wliao14/crops/'+random_path)\n",
    "print(f'There are total {len(path)} Falsely Predict as {predict} ')\n",
    "print(f'Ground Truth is {label}, but it predict as {predict}')  \n",
    "print(f'path:{random_path}')\n",
    "im "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X811494B'}"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([i.split('/')[1] for i in path])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}