{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "loan_data = pd.read_csv('./02-loan_data.csv')\n",
    "\n",
    "df = loan_data.copy()\n",
    "del df['customer_id']\n",
    "\n",
    "columns_to_round = ['loan_amt_outstanding', 'total_debt_outstanding', 'income']\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: x.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "credit_lines_outstanding      int64\n",
       "loan_amt_outstanding        float64\n",
       "total_debt_outstanding      float64\n",
       "income                      float64\n",
       "years_employed                int64\n",
       "fico_score                    int64\n",
       "default                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "credit_lines_outstanding    0\n",
       "loan_amt_outstanding        0\n",
       "total_debt_outstanding      0\n",
       "income                      0\n",
       "years_employed              0\n",
       "fico_score                  0\n",
       "default                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data appears to have already been cleaned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>loan_amt_outstanding</th>\n",
       "      <th>total_debt_outstanding</th>\n",
       "      <th>income</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>fico_score</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4495</th>\n",
       "      <td>3322.04</td>\n",
       "      <td>1783.15</td>\n",
       "      <td>53092.33</td>\n",
       "      <td>4</td>\n",
       "      <td>588</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6470</th>\n",
       "      <td>2153.90</td>\n",
       "      <td>6022.02</td>\n",
       "      <td>25992.88</td>\n",
       "      <td>4</td>\n",
       "      <td>687</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>3666.26</td>\n",
       "      <td>5253.54</td>\n",
       "      <td>71090.00</td>\n",
       "      <td>5</td>\n",
       "      <td>622</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7686</th>\n",
       "      <td>4485.62</td>\n",
       "      <td>6886.23</td>\n",
       "      <td>80505.30</td>\n",
       "      <td>5</td>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9419</th>\n",
       "      <td>7624.47</td>\n",
       "      <td>14607.81</td>\n",
       "      <td>111898.20</td>\n",
       "      <td>3</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      loan_amt_outstanding  total_debt_outstanding     income  years_employed  \\\n",
       "4495               3322.04                 1783.15   53092.33               4   \n",
       "6470               2153.90                 6022.02   25992.88               4   \n",
       "2221               3666.26                 5253.54   71090.00               5   \n",
       "7686               4485.62                 6886.23   80505.30               5   \n",
       "9419               7624.47                14607.81  111898.20               3   \n",
       "\n",
       "      fico_score  default  \n",
       "4495         588        0  \n",
       "6470         687        1  \n",
       "2221         622        0  \n",
       "7686         681        0  \n",
       "9419         756        0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred\n",
    "y_train = df_train.default.values\n",
    "y_val = df_val.default.values\n",
    "y_test = df_test.default.values\n",
    "\n",
    "del df_train['default']\n",
    "del df_val['default']\n",
    "del df_test['default']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model (to predict PD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>credit_lines_outstanding</th>\n",
       "      <th>loan_amt_outstanding</th>\n",
       "      <th>total_debt_outstanding</th>\n",
       "      <th>income</th>\n",
       "      <th>years_employed</th>\n",
       "      <th>fico_score</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>credit_lines_outstanding</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080249</td>\n",
       "      <td>0.852210</td>\n",
       "      <td>0.022272</td>\n",
       "      <td>-0.087900</td>\n",
       "      <td>-0.258177</td>\n",
       "      <td>0.862815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amt_outstanding</th>\n",
       "      <td>0.080249</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.397403</td>\n",
       "      <td>0.835815</td>\n",
       "      <td>-0.158416</td>\n",
       "      <td>-0.031373</td>\n",
       "      <td>0.098978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_debt_outstanding</th>\n",
       "      <td>0.852210</td>\n",
       "      <td>0.397403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.394397</td>\n",
       "      <td>-0.174353</td>\n",
       "      <td>-0.232246</td>\n",
       "      <td>0.758868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.022272</td>\n",
       "      <td>0.835815</td>\n",
       "      <td>0.394397</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001814</td>\n",
       "      <td>-0.010528</td>\n",
       "      <td>0.016309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years_employed</th>\n",
       "      <td>-0.087900</td>\n",
       "      <td>-0.158416</td>\n",
       "      <td>-0.174353</td>\n",
       "      <td>0.001814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255873</td>\n",
       "      <td>-0.284506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fico_score</th>\n",
       "      <td>-0.258177</td>\n",
       "      <td>-0.031373</td>\n",
       "      <td>-0.232246</td>\n",
       "      <td>-0.010528</td>\n",
       "      <td>0.255873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.324515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>0.862815</td>\n",
       "      <td>0.098978</td>\n",
       "      <td>0.758868</td>\n",
       "      <td>0.016309</td>\n",
       "      <td>-0.284506</td>\n",
       "      <td>-0.324515</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          credit_lines_outstanding  loan_amt_outstanding  \\\n",
       "credit_lines_outstanding                  1.000000              0.080249   \n",
       "loan_amt_outstanding                      0.080249              1.000000   \n",
       "total_debt_outstanding                    0.852210              0.397403   \n",
       "income                                    0.022272              0.835815   \n",
       "years_employed                           -0.087900             -0.158416   \n",
       "fico_score                               -0.258177             -0.031373   \n",
       "default                                   0.862815              0.098978   \n",
       "\n",
       "                          total_debt_outstanding    income  years_employed  \\\n",
       "credit_lines_outstanding                0.852210  0.022272       -0.087900   \n",
       "loan_amt_outstanding                    0.397403  0.835815       -0.158416   \n",
       "total_debt_outstanding                  1.000000  0.394397       -0.174353   \n",
       "income                                  0.394397  1.000000        0.001814   \n",
       "years_employed                         -0.174353  0.001814        1.000000   \n",
       "fico_score                             -0.232246 -0.010528        0.255873   \n",
       "default                                 0.758868  0.016309       -0.284506   \n",
       "\n",
       "                          fico_score   default  \n",
       "credit_lines_outstanding   -0.258177  0.862815  \n",
       "loan_amt_outstanding       -0.031373  0.098978  \n",
       "total_debt_outstanding     -0.232246  0.758868  \n",
       "income                     -0.010528  0.016309  \n",
       "years_employed              0.255873 -0.284506  \n",
       "fico_score                  1.000000 -0.324515  \n",
       "default                    -0.324515  1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'credit_lines_outstanding'}>,\n",
       "        <Axes: title={'center': 'loan_amt_outstanding'}>,\n",
       "        <Axes: title={'center': 'total_debt_outstanding'}>],\n",
       "       [<Axes: title={'center': 'income'}>,\n",
       "        <Axes: title={'center': 'years_employed'}>,\n",
       "        <Axes: title={'center': 'fico_score'}>],\n",
       "       [<Axes: title={'center': 'default'}>, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ntVqNUaNGmSzn9v8vpaenw8/PT7oqq86MGTOavZzW6rIFUnFxMaqrq5s1aMGdI29cuXIFADBy5EjI5XKjv08//VTacYuKigDc2gnuVF9bR3F1dTVpUygUqKiokF5fuXIF+/btM1nfwYMHA4C0zlFRUfjkk09w8eJFPPHEE1CpVPD394dWq212PkVFRbC1tTUpXGUyGdRqtbRdzUGpVCI9PR0PPvggVqxYgcGDB8PT0xOrV6+GwWBo8v3r16/Hiy++CH9/f3z++efIyspCdnY2Jk6caLT96ty5resu96mLNdc+09zl3P4/DwCwtbWtd3+gtlf3mdQ30o+np6fRfh8ZGYmNGzfihRdewNdff43jx48jOzsbvXv3btV+1xyLFy/GqlWrMHXqVOzbtw/Hjh1DdnY2hg0bVu98XFxcjF7b2dk12n7z5s1m59IQ9h3sO8hYS/qVhhw/fhwhISEAgI8++gj/+Mc/kJ2djZUrVwJoWT/SUH7N2W+vXLmCkydPmhyHODs7QwhhUjQ0Nk9z9AVFRUUNbldzLaPO3Rxb1dTUICQkBF988QWWLVuGb775BsePH5fuYWxtf1Pfd/5u+pq65dyeT1FRkUlfA5j2P22hy/7c4+LiAhsbm2b9inDnwAZ11/f/5S9/MfmF43Z1H75OpzOZVl+bJXNzc8PQoUPx1ltv1Tu9rkMAbl2f/txzz+H69ev49ttvsXr1aoSHh+Of//xno9urjqurK6qqqnD16lWjAx0hBHQ6HUaOHCm1KRQKadCM27WkYxoyZAhSUlIghMDJkyeRlJSE119/HQ4ODnj11VcbfW9ycjKCgoKwefNmo/a6exBaqr32mbrlXLlyBX369JHaq6qqzNqpU/PVfSYFBQUm03755Rep3yktLcVXX32F1atXG+2fer1euq+lLSQnJ+PZZ5/FmjVrjNr/+9//omfPnm223JZg38G+g4zd3q/c+YPw7f1KY1JSUiCXy/HVV1/B3t5eajfH8PVN7be3P7/Lzc0NDg4O9Q4iVTf9zvfXN8/bl3s3XF1dG+yvb8+nbpvd2d+09CxQa4+tTp8+jR9++AFJSUmYOXOm1P7TTz+1aPm3q+tri4qKjLaluY9tXV1dpZMSt2uPY+guewbJwcEBgYGB+Oyzz1q8k4aGhsLW1hb/+te/MGLEiHr/AGDAgAHw8PDAnj17jEZ6uXjxIjIyMppcTmt+5W0r4eHhOH36NO6777561/f2AqmOk5MTwsLCsHLlSlRWVuLMmTPNWtaECRMA3DqAuN3nn3+O69evS9OBWyNOnTx50iju0KFDJpfrNGdbymQyDBs2DO+++y569uyJ7777zuj99b1XJpOZ3PR98uTJVp9av9t9prnqBt/49NNPjdr/8pe/mIxKSO0jICAADg4OJvv95cuXpUs5gFv7nBDCZL/7+OOPUV1d3Wb51bev79+/H//5z3/MvqyGvm9NYd/BvqMrq29fHT9+PADT70R2djbOnTtn9J1obF+1tbU1usSzoqICO3fuvOucR48eDXt7e+zatcuoPSMjw+QS4PDwcPzrX/+Cq6trvcchdz4M+ZtvvjE6uK6ursann36K++67TyoW7+Y4a8KECTh06JDJKI87duyAo6OjNJR+XV539jd79+41mWdz+r6Gjq0aWpe6H/nv7G+2bNnS6HIaM27cOAAw+dx2797d6nnWJzAwEKdPnza5hPn20QjbSpc9gwRAGsHD398fr776Ku6//35cuXIFe/fubXTH6devH15//XWsXLkS//73vzFx4kT06tULV65cwfHjx+Hk5IS4uDh069YNb7zxBl544QU8/vjjmDNnDkpKShAbG9us05B+fn4AgA8//BDOzs6wt7eHj49Ph1zG8Prrr0Or1WLMmDFYtGgRBgwYgJs3b+LChQs4cOAAPvjgA/Tt2xdz5syBg4MDHnnkEXh4eECn0yE+Ph5KpdLo19vGBAcHIzQ0FK+88grKysrwyCOPSCNRPfTQQ4iKipJio6KisGrVKrz22msIDAzE2bNnsXHjRmlUpzoNbcvMzExs2rQJU6dOxb333gshBL744guUlJQgODhYev+QIUNw5MgR7Nu3Dx4eHnB2dsaAAQMQHh6ON954A6tXr0ZgYCDy8vLw+uuvw8fHp1UHC3e7zzTX4MGDMWPGDPzpT3+CjY0Nxo8fjzNnzuBPf/oTlEpls0e8IvPp2bMnVq1ahRUrVuDZZ5/FjBkzUFRUhLi4ONjb22P16tUAbo2CNnbsWLz99ttwc3NDv379kJ6ejq1bt7bpmZzw8HAkJSXhgQcewNChQ5GTk4O33367TZ6t1tD3rSnsO9h3dGVDhgwBALz33nuYOXMm5HI5BgwYgLlz5yIxMRHdunVDWFiYNIqdl5cXXn75ZaP3f/HFF9i8eTOGDx+Obt26YcSIEZg0aRLWr1+PyMhIzJ07F0VFRXjnnXdaNCJkQ3r16oWlS5fizTffxAsvvICnnnoKly5dqne/jYmJweeff46xY8fi5ZdfxtChQ1FTU4Off/4ZaWlpWLJkCfz9/aV4Nzc3jB8/HqtWrZJGsTt//rzRwfXdHGetXr1auj/7tddeg4uLC3bt2oX9+/cjISFB6ktGjhyJAQMGYOnSpaiqqkKvXr2QmpqKo0ePmsyzoc+gOcdWDa3LAw88gPvuuw+vvvoqhBBwcXHBvn37WnTrw51CQkIwduxYLFu2DNevX8eIESPwj3/8wyxF8+1iYmLwySefICwsDK+//jrc3d2xe/dunD9/HgDatr9ps+EfrMTZs2fFU089JVxdXYWdnZ245557xKxZs8TNmzelkTfuHDGlzpdffinGjRsnevToIRQKhfD29hZPPvmkOHjwoFHcxx9/LHx9fYWdnZ3o37+/+OSTT8TMmTObHFVICCE2bNggfHx8hI2NjQAgtm3b1qz1askodpMmTTJ5f30j0Vy9elUsWrRI+Pj4CLlcLlxcXMTw4cPFypUrRXl5uRBCiO3bt4tx48YJd3d3YWdnJzw9PcX06dPFyZMnm5V3nYqKCvHKK68Ib29vIZfLhYeHh3jxxRdFcXGxUZxerxfLli0TXl5ewsHBQQQGBorc3FyTkaiEqH9bnj9/XsyYMUPcd999wsHBQSiVSjFq1CiRlJRk9N7c3FzxyCOPCEdHR6ORdfR6vVi6dKno06ePsLe3Fw8//LD48ssvTT7fupGo3n77bZN1re9zb+0+09A+W9/+cPPmTbF48WKhUqmEvb29GD16tMjMzBRKpdJoVDJqO/WN7vPxxx+LoUOHCjs7O6FUKsWUKVNMRvC5fPmyeOKJJ0SvXr2Es7OzmDhxojh9+rTJft+S/aEpxcXFYvbs2UKlUglHR0fx6KOPir///e8mfUXdvD/77LN61/XOXOr6patXr0ptDX3fmoN9B/uOrmz58uXC09NTdOvWTfrcqqurxbp160T//v2FXC4Xbm5u4ve//724dOmS0Xt//fVX8eSTT4qePXsKmUxmdLzwySefiAEDBgiFQiHuvfdeER8fL7Zu3Vrv6GQt+b4KcWtUtfj4eOHl5SXs7OzE0KFDxb59++qdV3l5ufjjH/8oBgwYIPWRQ4YMES+//LLRiHUAxIIFC8SmTZvEfffdJ+RyuXjggQfErl27TJbf2uMsIYQ4deqUmDx5slAqlcLOzk4MGzas3vf/85//FCEhIaJHjx6id+/eIjo6Wuzfv9/ku9XQZ9DcY6uG1uXs2bMiODhYODs7i169eomnnnpK/Pzzzyb9QH39sRD1/7+qpKREPP/886Jnz57C0dFRBAcHi/Pnzzd7FLvBgwebbKf6+qrTp0+Lxx57TNjb2wsXFxcxe/ZssX37dgFA/PDDD6YfipnIhDDDU76IqFPIyMjAI488gl27drVoNBoi6trYdxBRe5k7dy727NmDoqIiaaAfc+vSl9gRdWVarRaZmZkYPnw4HBwc8MMPP2Dt2rXw9fXFtGnTOjo9IrJQ7DuIqL28/vrr8PT0xL333ovy8nJ89dVX+Pjjj/HHP/6xzYojgAWS1RFCNHkjto2NjcnIe5aiqWvru3XrxmvY20mPHj2QlpaGDRs24Nq1a3Bzc0NYWBji4+ONRiqizq2mpgY1NTWNxljC823Yd1gO9h3UHNXV1WjsIiWZTGY08IOlsPbjrM5GLpfj7bffxuXLl1FVVQVfX1+sX78eL730Upsul5fYWZmkpCQ899xzjcYcPnwYQUFB7ZNQCzXVocycORNJSUntkwwRITY2FnFxcY3G5Ofnm4wQ1d7YdxBZl6CgIOnBwvXx9vbGhQsX2i+hZrL24ywyDxZIVqaoqAj5+fmNxgwYMADOzs7tlFHLnDhxotHpdaNyEVH7+OWXX0yGqb3T0KFD2/RShuZg30FkXfLy8hp9rpdCoZBG3rMk1n6cRebBAomIiIiIiKgWL9gmIiIiIiKq1fF33raRmpoa/PLLL3B2duaNdERmJoTAtWvX4Onp2SVvjGf/QtQ2unrfArB/IWorLelfOm2B9Msvv8DLy6uj0yDq1C5duoS+fft2dBrtjv0LUdvqqn0LwP6FqK01p3/ptAVS3c1zly5dQo8ePRqMMxgMSEtLQ0hICORyeXulZ/W43VqvM2y7srIyeHl5ddmbVJvTv1jr52yNeVtjzgDzrk9X71uAznv8Ym35Asy5vbRXzi3pXzptgVR3WrpHjx5NdjCOjo7o0aOH1exIloDbrfU607brqpd/NKd/sdbP2RrztsacAebdmK7atwCd9/jF2vIFmHN7ae+cm9O/dM0LfImIiIiIiOrBAomIiIiIiKgWCyQiIiIiIqJaLJCIyCJs3rwZQ4cOla67DwgIwN/+9jdpuhACsbGx8PT0hIODA4KCgnDmzBmjeej1ekRHR8PNzQ1OTk6IiIjA5cuXjWKKi4sRFRUFpVIJpVKJqKgolJSUtMcqEhERkRVggUREFqFv375Yu3YtTpw4gRMnTmD8+PGYMmWKVAQlJCRg/fr12LhxI7Kzs6FWqxEcHIxr165J84iJiUFqaipSUlJw9OhRlJeXIzw8HNXV1VJMZGQkcnNzodFooNFokJubi6ioqHZfXyIiIrJMnXYUOyKyLpMnTzZ6/dZbb2Hz5s3IysrCoEGDsGHDBqxcuRLTpk0DAGzfvh3u7u7YvXs35s2bh9LSUmzduhU7d+7EY489BgBITk6Gl5cXDh48iNDQUJw7dw4ajQZZWVnw9/cHAHz00UcICAhAXl4eBgwY0L4rTURERBaHZ5CIyOJUV1cjJSUF169fR0BAAPLz86HT6RASEiLFKBQKBAYGIiMjAwCQk5MDg8FgFOPp6Qk/Pz8pJjMzE0qlUiqOAGD06NFQKpVSDBEREXVtPINUyy/2a+irzfPchQtrJ5llPkRdzalTpxAQEICbN2+ie/fuSE1NxaBBg6Tixd3d3Sje3d0dFy9eBADodDrY2dmhV69eJjE6nU6KUalUJstVqVRSTH30ej30er30uqysDMCtZzcYDIZ631PX3tB0S3U3efvFfm22PE7HhjY7titu647Ulnlb27boTPq9ut+s8+OxEFkzFkhEZDEGDBiA3NxclJSU4PPPP8fMmTORnp4uTb/z4W5CiCYf+HZnTH3xTc0nPj4ecXFxJu1paWlwdHRsdPlarbbR6ZaqNXknjDLf8g8cONDi93SlbW0J2iLvGzdumH2eREQtdVcFUnx8PFasWIGXXnoJGzZsAHDrQCMuLg4ffvghiouL4e/vj/fffx+DBw+W3qfX67F06VLs2bMHFRUVmDBhAjZt2oS+fftKMcXFxVi0aBH27t0LAIiIiEBiYiJ69ux5NykTkQWzs7PD/fffDwAYMWIEsrOz8d577+GVV14BcOsMkIeHhxRfWFgonVVSq9WorKxEcXGx0VmkwsJCjBkzRoq5cuWKyXKvXr1qcnbqdsuXL8fixYul12VlZfDy8kJISEiDT7o3GAzQarUIDg62mqeZA3eXd0eeQepq27ojtWXedWdniYg6UqsLpOzsbHz44YcYOnSoUXvdSFNJSUno378/3nzzTQQHByMvLw/Ozs4Abo00tW/fPqSkpMDV1RVLlixBeHg4cnJyYGNjA+DWSFOXL1+GRqMBAMydOxdRUVHYt29fa1MmIisjhIBer4ePjw/UajW0Wi0eeughAEBlZSXS09Oxbt06AMDw4cMhl8uh1Woxffp0AEBBQQFOnz6NhIQEAEBAQABKS0tx/PhxjBp163THsWPHUFpaKhVR9VEoFFAoFCbtcrm8yQPE5sRYotbkba7LlOuW35r3dJVtbQnaIm9r3A5E1Pm0qkAqLy/HM888g48++ghvvvmm1C6E4EhTRNQqK1asQFhYGLy8vHDt2jWkpKTgyJEj0Gg0kMlkiImJwZo1a+Dr6wtfX1+sWbMGjo6OiIyMBAAolUrMnj0bS5YsgaurK1xcXLB06VIMGTJE6msGDhyIiRMnYs6cOdiyZQuAWz++hIeHs18hIiIiAK0cxW7BggWYNGmSdNBRhyNNEVFrXblyBVFRURgwYAAmTJiAY8eOQaPRIDg4GACwbNkyxMTEYP78+RgxYgT+85//IC0tTTozDQDvvvsupk6diunTp+ORRx6Bo6Mj9u3bJ52ZBoBdu3ZhyJAhCAkJQUhICIYOHYqdO3e2+/oSERGRZWrxGaSUlBR89913yM7ONplWNwpUR4w01ZpRpuqmA4Cim2gwpqW6wig81jr6kiXoDNuuLXLfunVro9NlMhliY2MRGxvbYIy9vT0SExORmJjYYIyLiwuSk5NbmyYRERF1ci0qkC5duoSXXnoJaWlpsLe3bzCuI0aauptRpgDgjRE1TcY0V2tGX7JW1jr6kiWw5m3HkaaIiIios2pRgZSTk4PCwkIMHz5caquursa3336LjRs3Ii8vD0DHjDTVmlGmgP+NxrPqRDfoa8xzg3FLRl+yVtY6+pIl6AzbjiNNERERUWfVogJpwoQJOHXqlFHbc889hwceeACvvPIK7r333g4baepuRpkCAH2NzGwjMFnrQW9rWOvoS5bAmredteZNRERE1JQWFUjOzs7w8/MzanNycoKrq6vUzpGmiIiIiIjIWt3Vg2Lrs2zZMlRUVGD+/PnSg2LrG2nK1tYW06dPlx4Um5SUZDLS1KJFi6TR7iIiIrBx40Zzp0tERERERCS56wLpyJEjRq850hQREREREVmrVj0HiYiIiIiIqDNigURERERERFSLBRIREREREVEtFkhERERERES1WCARERERERHVYoFERERERERUiwUSERERERFRLRZIREREREREtVggERERERER1WKBREREREREVIsFEhERERERUS0WSERERERERLVYIBEREREREdVigURERERERFSLBRIREREREVEtFkhERERERES1WCARERERERHVYoFERBYhPj4eI0eOhLOzM1QqFaZOnYq8vDyjmFmzZkEmkxn9jR492ihGr9cjOjoabm5ucHJyQkREBC5fvmwUU1xcjKioKCiVSiiVSkRFRaGkpKStV5GIiIisAAskIrII6enpWLBgAbKysqDValFVVYWQkBBcv37dKG7ixIkoKCiQ/g4cOGA0PSYmBqmpqUhJScHRo0dRXl6O8PBwVFdXSzGRkZHIzc2FRqOBRqNBbm4uoqKi2mU9iYiIyLLZdnQCREQAoNFojF5v27YNKpUKOTk5GDt2rNSuUCigVqvrnUdpaSm2bt2KnTt34rHHHgMAJCcnw8vLCwcPHkRoaCjOnTsHjUaDrKws+Pv7AwA++ugjBAQEIC8vDwMGDGijNSQiIiJrwDNIRGSRSktLAQAuLi5G7UeOHIFKpUL//v0xZ84cFBYWStNycnJgMBgQEhIitXl6esLPzw8ZGRkAgMzMTCiVSqk4AoDRo0dDqVRKMURERNR18QwSEVkcIQQWL16MRx99FH5+flJ7WFgYnnrqKXh7eyM/Px+rVq3C+PHjkZOTA4VCAZ1OBzs7O/Tq1ctofu7u7tDpdAAAnU4HlUplskyVSiXF3Emv10Ov10uvy8rKAAAGgwEGg6He99S1NzTdUt1N3gobYfY8WhLblbZ1R2rLvK1tWxBR58QCiYgszsKFC3Hy5EkcPXrUqP3pp5+W/u3n54cRI0bA29sb+/fvx7Rp0xqcnxACMplMen37vxuKuV18fDzi4uJM2tPS0uDo6Njoumi12kanW6rW5J0wynzLv/PesuboStvaErRF3jdu3DD7PImIWooFEhFZlOjoaOzduxfffvst+vbt22ish4cHvL298eOPPwIA1Go1KisrUVxcbHQWqbCwEGPGjJFirly5YjKvq1evwt3dvd7lLF++HIsXL5Zel5WVwcvLCyEhIejRo0e97zEYDNBqtQgODoZcLm98pS3I3eTtF/u12fI4HRva7NiuuK07UlvmXXd2loioI7FAIiKLIIRAdHQ0UlNTceTIEfj4+DT5nqKiIly6dAkeHh4AgOHDh0Mul0Or1WL69OkAgIKCApw+fRoJCQkAgICAAJSWluL48eMYNerWKY9jx46htLRUKqLupFAooFAoTNrlcnmTB4jNibFErclbX13/GbjWLr817+kq29oStEXebbUdNm/ejM2bN+PChQsAgMGDB+O1115DWFgYgFv9T1xcHD788EMUFxfD398f77//PgYPHizNQ6/XY+nSpdizZw8qKiowYcIEbNq0yeiHnOLiYixatAh79+4FAERERCAxMRE9e/Zsk/UiorbBQRqIyCIsWLAAycnJ2L17N5ydnaHT6aDT6VBRUQEAKC8vx9KlS5GZmYkLFy7gyJEjmDx5Mtzc3PD4448DAJRKJWbPno0lS5bgm2++wffff4/f//73GDJkiDSq3cCBAzFx4kTMmTMHWVlZyMrKwpw5cxAeHs4R7Ig6qb59+2Lt2rU4ceIETpw4gfHjx2PKlCk4c+YMACAhIQHr16/Hxo0bkZ2dDbVajeDgYFy7dk2aBx8hQNR18AwSEVmEzZs3AwCCgoKM2rdt24ZZs2bBxsYGp06dwo4dO1BSUgIPDw+MGzcOn376KZydnaX4d999F7a2tpg+fbr0K29SUhJsbGykmF27dmHRokXSaHcRERHYuHFj268kEXWIyZMnG71+6623sHnzZmRlZWHQoEHYsGEDVq5cKd3LuH37dri7u2P37t2YN28eHyFA1MWwQCIiiyBE4yOgOTg44Ouvm77Hxd7eHomJiUhMTGwwxsXFBcnJyS3OkYisX3V1NT777DNcv34dAQEByM/Ph06nM3o8gEKhQGBgIDIyMjBv3rwmHyEQGhra5CMEGiqQWjNKZt302/97t8w5CiVgmpc1jtrInNtHe+XckvmzQCIiIqJO79SpUwgICMDNmzfRvXt3pKamYtCgQdLzz+4cpMXd3R0XL14EgDZ7hABwd6NkAuYbTdCco1ACDY9EaY2jNjLn9tHWObdklEwWSERERNTpDRgwALm5uSgpKcHnn3+OmTNnIj09XZp+5zD/jQ3931BMSx8hALRulEzA/KMJmnMUSsB0JEprHLWRObeP9sq5JaNkskAiIiKiTs/Ozg73338/AGDEiBHIzs7Ge++9h1deeQXArTNAdSNiArceD1B3VqmtHiEA3N0omS2Ja4o5R6EEGh6R0BpHbWTO7aOtc27JvFkgERGRxen36v5mxypsBBJG3foFvKGDvAtrJ5krNeokhBDQ6/Xw8fGBWq2GVqvFQw89BACorKxEeno61q1bB6DtHiFARJaJBRIRERF1aitWrEBYWBi8vLxw7do1pKSk4MiRI9BoNJDJZIiJicGaNWvg6+sLX19frFmzBo6OjoiMjARg/AgBV1dXuLi4YOnSpQ0+QmDLli0AgLlz5/IRAkRWiAUSERERdWpXrlxBVFQUCgoKoFQqMXToUGg0GgQHBwMAli1bhoqKCsyfP196UGxaWhofIUDURbFAIiIiok5t69atjU6XyWSIjY1FbGxsgzF8hABR19GtoxMgIiIiIiKyFCyQiIiIiIiIarFAIiIiIiIiqsUCiYiIiIiIqFaLCqTNmzdj6NCh6NGjB3r06IGAgAD87W9/k6YLIRAbGwtPT084ODggKCgIZ86cMZqHXq9HdHQ03Nzc4OTkhIiICFy+fNkopri4GFFRUVAqlVAqlYiKikJJSUnr15KIiIiIiKgZWlQg9e3bF2vXrsWJEydw4sQJjB8/HlOmTJGKoISEBKxfvx4bN25EdnY21Go1goODce3aNWkeMTExSE1NRUpKCo4ePYry8nKEh4ejurpaiomMjERubi40Gg00Gg1yc3MRFRVlplUmIiIiIiKqX4uG+Z48ebLR67feegubN29GVlYWBg0ahA0bNmDlypWYNm0aAGD79u1wd3fH7t27MW/ePJSWlmLr1q3YuXOn9GC15ORkeHl54eDBgwgNDcW5c+eg0WiQlZUFf39/AMBHH32EgIAA5OXl8WFrRERERETUZlr9HKTq6mp89tlnuH79OgICApCfnw+dTic9HA0AFAoFAgMDkZGRgXnz5iEnJwcGg8EoxtPTE35+fsjIyEBoaCgyMzOhVCql4ggARo8eDaVSiYyMjAYLJL1eD71eL70uKysDABgMBhgMhgbXo26aopto3YZoZJ6dWd06doV1NbfOsO2sOXciIiKixrS4QDp16hQCAgJw8+ZNdO/eHampqRg0aBAyMjIAAO7u7kbx7u7uuHjxIgBAp9PBzs4OvXr1MonR6XRSjEqlMlmuSqWSYuoTHx+PuLg4k/a0tDQ4Ojo2uV5vjKhpMqa5Dhw4YLZ5WTqtVtvRKVgta952N27c6OgUiIiIiNpEiwukAQMGIDc3FyUlJfj8888xc+ZMpKenS9NlMplRvBDCpO1Od8bUF9/UfJYvX47FixdLr8vKyuDl5YWQkBD06NGjwfcZDAZotVqsOtEN+prG82yu07GhZpmPJavbbsHBwZDL5R2djlXpDNuu7gwtERERUWfT4gLJzs4O999/PwBgxIgRyM7OxnvvvYdXXnkFwK0zQB4eHlJ8YWGhdFZJrVajsrISxcXFRmeRCgsLMWbMGCnmypUrJsu9evWqydmp2ykUCigUCpN2uVzerINQfY0M+mrzFEjWetDbGs3dvmTKmredteZNRNTZ+MV+bbbjFyK65a6fgySEgF6vh4+PD9RqtdFlQ5WVlUhPT5eKn+HDh0MulxvFFBQU4PTp01JMQEAASktLcfz4cSnm2LFjKC0tlWKIiIiIiIjaQovOIK1YsQJhYWHw8vLCtWvXkJKSgiNHjkCj0UAmkyEmJgZr1qyBr68vfH19sWbNGjg6OiIyMhIAoFQqMXv2bCxZsgSurq5wcXHB0qVLMWTIEGlUu4EDB2LixImYM2cOtmzZAgCYO3cuwsPDOYIdERERERG1qRYVSFeuXEFUVBQKCgqgVCoxdOhQaDQaBAcHAwCWLVuGiooKzJ8/H8XFxfD390daWhqcnZ2lebz77ruwtbXF9OnTUVFRgQkTJiApKQk2NjZSzK5du7Bo0SJptLuIiAhs3LjRHOtLRERERETUoBYVSFu3bm10ukwmQ2xsLGJjYxuMsbe3R2JiIhITExuMcXFxQXJycktSIyKiFur36n6TNoWNQMIo3tdARERd113fg0RERERERNRZsEAiIosQHx+PkSNHwtnZGSqVClOnTkVeXp5RjBACsbGx8PT0hIODA4KCgnDmzBmjGL1ej+joaLi5ucHJyQkRERG4fPmyUUxxcTGioqKgVCqhVCoRFRWFkpKStl5FIiIisgIskIjIIqSnp2PBggXIysqCVqtFVVUVQkJCcP36dSkmISEB69evx8aNG5GdnQ21Wo3g4GBcu3ZNiomJiUFqaipSUlJw9OhRlJeXIzw8HNXV1VJMZGQkcnNzodFooNFokJubi6ioqHZdXyIiIrJMLX4OEhFRW9BoNEavt23bBpVKhZycHIwdOxZCCGzYsAErV67EtGnTAADbt2+Hu7s7du/ejXnz5qG0tBRbt27Fzp07pZExk5OT4eXlhYMHDyI0NBTnzp2DRqNBVlYW/P39AQAfffQRAgICkJeXx9EyiYiIujieQSIii1RaWgrg1qAtAJCfnw+dTieNbgncekB0YGAgMjIyAAA5OTkwGAxGMZ6envDz85NiMjMzoVQqpeIIAEaPHg2lUinFEBERUdfFM0hEZHGEEFi8eDEeffRR+Pn5AQB0Oh0AwN3d3SjW3d0dFy9elGLs7OzQq1cvk5i69+t0OqhUKpNlqlQqKeZOer0eer1eel1WVgYAMBgMMBgM9b6nrr2h6ZZAYSNM27oJo/9ag+bkbImfgzXsI/Vpy7ytbVsQUefEAomILM7ChQtx8uRJHD161GSaTGY89LQQwqTtTnfG1Bff2Hzi4+MRFxdn0p6WlgZHR8dGl63Vahud3pESRjU87Y0RNe2XiJk0lvOBAwfaMZOWseR9pDFtkfeNGzfMPk8iopZigUREFiU6Ohp79+7Ft99+i759+0rtarUawK0zQB4eHlJ7YWGhdFZJrVajsrISxcXFRmeRCgsLMWbMGCnmypUrJsu9evWqydmpOsuXL8fixYul12VlZfDy8kJISAh69OhR73sMBgO0Wi2Cg4Mhl8ubu/rtyi/2a5M2RTeBN0bUYNWJbtDXWMdzkJqT8+nY0HbOqmnWsI/Upy3zrjs7S0TUkVggEZFFEEIgOjoaqampOHLkCHx8fIym+/j4QK1WQ6vV4qGHHgIAVFZWIj09HevWrQMADB8+HHK5HFqtFtOnTwcAFBQU4PTp00hISAAABAQEoLS0FMePH8eoUbdOoRw7dgylpaVSEXUnhUIBhUJh0i6Xy5s8QGxOTEdp7EGw+hqZ1T0otrGcLfUzACx7H2lMW+RtjduBiDofFkhEZBEWLFiA3bt3469//SucnZ2l+4GUSiUcHBwgk8kQExODNWvWwNfXF76+vlizZg0cHR0RGRkpxc6ePRtLliyBq6srXFxcsHTpUgwZMkQa1W7gwIGYOHEi5syZgy1btgAA5s6di/DwcI5gR0RERCyQiMgybN68GQAQFBRk1L5t2zbMmjULALBs2TJUVFRg/vz5KC4uhr+/P9LS0uDs7CzFv/vuu7C1tcX06dNRUVGBCRMmICkpCTY2NlLMrl27sGjRImm0u4iICGzcuLFtV5CIiIisAgskIrIIQjQ9appMJkNsbCxiY2MbjLG3t0diYiISExMbjHFxcUFycnJr0iQiIqJOjs9BIiIiIiIiqsUzSBau36v7zTq/C2snmXV+RERERESdCc8gERERERER1WKBREREREREVIsFEhERERERUS0WSERERERERLVYIBEREREREdVigURERERERFSLBRIREREREVEtFkhERERERES1WCARERERERHVYoFERERERERUiwUSERERERFRLRZIREREREREtVggERERERER1WKBREREREREVIsFEhEREXVq8fHxGDlyJJydnaFSqTB16lTk5eUZxQghEBsbC09PTzg4OCAoKAhnzpwxitHr9YiOjoabmxucnJwQERGBy5cvG8UUFxcjKioKSqUSSqUSUVFRKCkpaetVJCIzYoFEREREnVp6ejoWLFiArKwsaLVaVFVVISQkBNevX5diEhISsH79emzcuBHZ2dlQq9UIDg7GtWvXpJiYmBikpqYiJSUFR48eRXl5OcLDw1FdXS3FREZGIjc3FxqNBhqNBrm5uYiKimrX9SWiu2Pb0QkQERERtSWNRmP0etu2bVCpVMjJycHYsWMhhMCGDRuwcuVKTJs2DQCwfft2uLu7Y/fu3Zg3bx5KS0uxdetW7Ny5E4899hgAIDk5GV5eXjh48CBCQ0Nx7tw5aDQaZGVlwd/fHwDw0UcfISAgAHl5eRgwYED7rjgRtQoLJCIiIupSSktLAQAuLi4AgPz8fOh0OoSEhEgxCoUCgYGByMjIwLx585CTkwODwWAU4+npCT8/P2RkZCA0NBSZmZlQKpVScQQAo0ePhlKpREZGRr0Fkl6vh16vl16XlZUBAAwGAwwGQ4PrUDdN0U20ZhO0uTtzr3vd2DpZGubcPtor55bMnwUSERERdRlCCCxevBiPPvoo/Pz8AAA6nQ4A4O7ubhTr7u6OixcvSjF2dnbo1auXSUzd+3U6HVQqlckyVSqVFHOn+Ph4xMXFmbSnpaXB0dGxyfV5Y0RNkzEd4cCBA/W2a7Xads7k7jHn9tHWOd+4caPZsSyQiIiIqMtYuHAhTp48iaNHj5pMk8lkRq+FECZtd7ozpr74xuazfPlyLF68WHpdVlYGLy8vhISEoEePHg0u12AwQKvVYtWJbtDXNJ5jRzgdG2r0ui7f4OBgyOXyDsqqZZhz+2ivnOvOzjYHCyQiIiLqEqKjo7F37158++236Nu3r9SuVqsB3DoD5OHhIbUXFhZKZ5XUajUqKytRXFxsdBapsLAQY8aMkWKuXLlistyrV6+anJ2qo1AooFAoTNrlcnmzDhb1NTLoqy2vQGoo9+aulyVhzu2jrXNuybw5ih0RWYRvv/0WkydPhqenJ2QyGb788kuj6bNmzYJMJjP6Gz16tFEMh+AlovoIIbBw4UJ88cUXOHToEHx8fIym+/j4QK1WG13iU1lZifT0dKn4GT58OORyuVFMQUEBTp8+LcUEBASgtLQUx48fl2KOHTuG0tJSKYaILB8LJCKyCNevX8ewYcOwcePGBmMmTpyIgoIC6e/Oa9w5BC8R1WfBggVITk7G7t274ezsDJ1OB51Oh4qKCgC3LouLiYnBmjVrkJqaitOnT2PWrFlwdHREZGQkAECpVGL27NlYsmQJvvnmG3z//ff4/e9/jyFDhkij2g0cOBATJ07EnDlzkJWVhaysLMyZMwfh4eEcwY7IivASOyKyCGFhYQgLC2s0RqFQSJfC3IlD8BJRQzZv3gwACAoKMmrftm0bZs2aBQBYtmwZKioqMH/+fBQXF8Pf3x9paWlwdnaW4t99913Y2tpi+vTpqKiowIQJE5CUlAQbGxspZteuXVi0aJE02l1ERESjP/wQkeVp0RkkPomaiDrSkSNHoFKp0L9/f8yZMweFhYXStKaG4AXQ5BC8RNQ5CSHq/asrjoBbZ5FiY2NRUFCAmzdvIj09XRrlro69vT0SExNRVFSEGzduYN++ffDy8jKKcXFxQXJyMsrKylBWVobk5GT07NmzHdaSiMylRWeQ6p5EPXLkSFRVVWHlypUICQnB2bNn4eTkBOB/T6JOSkpC//798eabbyI4OBh5eXnSrzAxMTHYt28fUlJS4OrqiiVLliA8PBw5OTnSrzCRkZG4fPmy9HC3uXPnIioqCvv27TPn+hORlQgLC8NTTz0Fb29v5OfnY9WqVRg/fjxycnKgUCjabAheoHXPKbGGZ1EobEyfn1L3TBVLfbZKfZqTsyV+Dtawj9SnLfO2tm1BRJ1TiwokPomaiDrK008/Lf3bz88PI0aMgLe3N/bv3y/1N/W52yF4gbt7ToklP4siYVTD0yz12SqNaSznhp7JYgkseR9pTFvk3ZLnlBARtZW7ugeJT6JufJ7mUN8vvHfDXLlZ6y+flqAzbDtLyN3DwwPe3t748ccfAbTdELxA655TYg3PovCL/dqkTdFN4I0RNRb7bJX6NCfnO5/JYgmsYR+pT1vm3ZLnlBARtZVWF0h8EnXDzPlLZWO/8LaGuX9FtdZfPi2BNW87S/iVt6ioCJcuXZKeWXL7ELzTp08H8L8heBMSEgAYD8E7atStL1dzhuC9m+eUWPKzKBp7doqlPlulMY3lbKmfAWDZ+0hj2iJva9wORNT5tLpA4pOoG2bOXyrr+4X3bpgrN2v95dMSdIZt1xa/8paXl+Onn36SXufn5yM3NxcuLi5wcXFBbGwsnnjiCXh4eODChQtYsWIF3Nzc8PjjjwMwHoLX1dUVLi4uWLp0aYND8G7ZsgXArfsbOQQvERER1WlVgcQnUTfOnAe95v4Fty1+7bPWg/yOZs3bri3yPnHiBMaNGye9rvvBY+bMmdi8eTNOnTqFHTt2oKSkBB4eHhg3bhw+/fRTDsFLREREZtWiAkkIgejoaKSmpuLIkSONPon6oYceAvC/J1GvW7cOQNteBkNE1isoKAhCNHzP3ddfN302tW4I3sTExAZj6obgJSIiIqpPiwqkBQsWYPfu3fjrX/8qPYkauHVpi4ODg9GTqH19feHr64s1a9Y0+CRqXgZDRERERESWpEUFEp9ETUREREREnVmLL7FrSt2TqGNjYxuM4WUwRERERERkibp1dAJERERERESWggUSERERERFRLRZIREREREREtVggERERERER1WKBREREREREVIsFEhERERERUS0WSERERERERLVa9BwkIiIia9Tv1f1mm9eFtZPMNi8iIrI8LJCIiIiIyKzu/FFCYSOQMArwi/0a+mpZi+bFHyWovfESOyIiIiIioloskIiIiIiIiGqxQCIiIiIiIqrFAomIiIiIiKgWCyQiIiIiIqJaLJCIiIiIiIhqsUAiIiIiIiKqxQKJiIiIiIioFgskIiIiIiKiWiyQiIiIiIiIarFAIiIiIiIiqsUCiYgswrfffovJkyfD09MTMpkMX375pdF0IQRiY2Ph6ekJBwcHBAUF4cyZM0Yxer0e0dHRcHNzg5OTEyIiInD58mWjmOLiYkRFRUGpVEKpVCIqKgolJSVtvHZERERkLVggEZFFuH79OoYNG4aNGzfWOz0hIQHr16/Hxo0bkZ2dDbVajeDgYFy7dk2KiYmJQWpqKlJSUnD06FGUl5cjPDwc1dXVUkxkZCRyc3Oh0Wig0WiQm5uLqKioNl8/IiIisg62HZ0AEREAhIWFISwsrN5pQghs2LABK1euxLRp0wAA27dvh7u7O3bv3o158+ahtLQUW7duxc6dO/HYY48BAJKTk+Hl5YWDBw8iNDQU586dg0ajQVZWFvz9/QEAH330EQICApCXl4cBAwa0z8oSERGRxWKBREQWLz8/HzqdDiEhIVKbQqFAYGAgMjIyMG/ePOTk5MBgMBjFeHp6ws/PDxkZGQgNDUVmZiaUSqVUHAHA6NGjoVQqkZGR0WCBpNfrodfrpddlZWUAAIPBAIPBUO976tobmm4JFDbCtK2bMPqvNWjvnM31mVrDPlKftszb2rYFEXVOLJCIyOLpdDoAgLu7u1G7u7s7Ll68KMXY2dmhV69eJjF179fpdFCpVCbzV6lUUkx94uPjERcXZ9KelpYGR0fHRnPXarWNTu9ICaManvbGiJr2S8RM2ivnAwcOmHV+lryPNKYt8r5x44bZ50lE1FIskIjIashkMqPXQgiTtjvdGVNffFPzWb58ORYvXiy9Lisrg5eXF0JCQtCjR49632MwGKDVahEcHAy5XN5ojh3FL/ZrkzZFN4E3RtRg1Ylu0Nc0vm0tRXvnfDo21CzzsYZ9pD5tmXfd2Vkioo7EAomILJ5arQZw6wyQh4eH1F5YWCidVVKr1aisrERxcbHRWaTCwkKMGTNGirly5YrJ/K9evWpydup2CoUCCoXCpF0ulzd5gNicmI6ir264mNDXyBqdbonaK2dzf56WvI80pi3ytsbtQESdD0exIyKL5+PjA7VabXRJT2VlJdLT06XiZ/jw4ZDL5UYxBQUFOH36tBQTEBCA0tJSHD9+XIo5duwYSktLpRgiIiLq2ngGicjK9Xt1v1nnd2HtJLPOr7nKy8vx008/Sa/z8/ORm5sLFxcX3HPPPYiJicGaNWvg6+sLX19frFmzBo6OjoiMjAQAKJVKzJ49G0uWLIGrqytcXFywdOlSDBkyRBrVbuDAgZg4cSLmzJmDLVu2AADmzp2L8PBwjmBHREREAFggEZGFOHHiBMaNGye9rrvnZ+bMmUhKSsKyZctQUVGB+fPno7i4GP7+/khLS4Ozs7P0nnfffRe2traYPn06KioqMGHCBCQlJcHGxkaK2bVrFxYtWiSNdhcREdHgs5eIiIio62GBREQWISgoCEI0PEyzTCZDbGwsYmNjG4yxt7dHYmIiEhMTG4xxcXFBcnLy3aRKREREnRjvQSIiIiIiIqrFAomIiIiIiKgWCyQiIiLq1L799ltMnjwZnp6ekMlk+PLLL42mCyEQGxsLT09PODg4ICgoCGfOnDGK0ev1iI6OhpubG5ycnBAREYHLly8bxRQXFyMqKgpKpRJKpRJRUVEoKSlp47UjInNjgURERESd2vXr1zFs2LAGB2RJSEjA+vXrsXHjRmRnZ0OtViM4OBjXrl2TYmJiYpCamoqUlBQcPXoU5eXlCA8PR3V1tRQTGRmJ3NxcaDQaaDQa5ObmIioqqs3Xj4jMi4M0EBERUacWFhaGsLCweqcJIbBhwwasXLkS06ZNAwBs374d7u7u2L17N+bNm4fS0lJs3boVO3fulB4bkJycDC8vLxw8eBChoaE4d+4cNBoNsrKy4O/vDwD46KOPEBAQgLy8PD5KgMiK8AwSERERdVn5+fnQ6XTS0P8AoFAoEBgYiIyMDABATk4ODAaDUYynpyf8/PykmMzMTCiVSqk4AoDRo0dDqVRKMURkHXgGiYiIiLosnU4HAHB3dzdqd3d3x8WLF6UYOzs79OrVyySm7v06nQ4qlcpk/iqVSoqpj16vh16vl16XlZUBAAwGAwwGQ4Pvq5um6Nbw4xEsSV2ercm3se3QluqW21HLbw3m3PRymqPFBdK3336Lt99+Gzk5OSgoKEBqaiqmTp0qTRdCIC4uDh9++KH0MMf3338fgwcPlmL0ej2WLl2KPXv2SA9z3LRpE/r27SvFFBcXY9GiRdi7dy+AWw9zTExMRM+ePVuaMhEREVGjZDKZ0WshhEnbne6MqS++qfnEx8cjLi7OpD0tLQ2Ojo5NpY03RtQ0GWNJWpPvgQMH2iCT5tNqtR26/NZgzqZu3LjR7NgWF0h1Nzo+99xzeOKJJ0ym193omJSUhP79++PNN99EcHAw8vLypCfex8TEYN++fUhJSYGrqyuWLFmC8PBw5OTkSE+8j4yMxOXLl6HRaAAAc+fORVRUFPbt29fSlImIiIjqpVarAdw6A+Th4SG1FxYWSmeV1Go1KisrUVxcbHQWqbCwEGPGjJFirly5YjL/q1evmpydut3y5cuxePFi6XVZWRm8vLwQEhKCHj16NPg+g8EArVaLVSe6QV/TeCFnCRTdBN4YUdOqfE/HhrZRVo2r28bBwcGQy+UdkkNLMeeG1Z2dbY4WF0i80ZGIiIg6Cx8fH6jVami1Wjz00EMAgMrKSqSnp2PdunUAgOHDh0Mul0Or1WL69OkAgIKCApw+fRoJCQkAgICAAJSWluL48eMYNWoUAODYsWMoLS2Viqj6KBQKKBQKk3a5XN6sg0V9jQz6assvkOq0Jt+OPtBv7mdhSZhz/fNvLrMO0sAbHYmIiMjSlJeXIzc3F7m5uQBuHa/k5ubi559/hkwmQ0xMDNasWYPU1FScPn0as2bNgqOjIyIjIwEASqUSs2fPxpIlS/DNN9/g+++/x+9//3sMGTJE+rF34MCBmDhxIubMmYOsrCxkZWVhzpw5CA8P5w+7RFbGrIM0dOSNjpZ0k6M5bzJT2Jj35ktz5WaNNwFaCnNvu47YR/i5E5E1OXHiBMaNGye9rrukbebMmUhKSsKyZctQUVGB+fPnS/dPp6WlSbcGAMC7774LW1tbTJ8+Xbp/OikpSbo1AAB27dqFRYsWST8CR0RENPjsJSKyXG0yil1H3OhoSTc5mvNmwoRRZpsVAPPf6GiNNwFaCnNtu47YR1pyoyMRUUcLCgqCEA3/mCSTyRAbG4vY2NgGY+zt7ZGYmIjExMQGY1xcXJCcnHw3qRKRBTBrgdSRNzpa0k2O5ryZ0C/2a7PNCzBfbtZ4E6ClMPe264h9pCU3OhIRERFZE7MWSB15o6Ml3eRozoLB3DdemruYscabAC2FubZdR+wj/MyJiIios2pxgVReXo6ffvpJel13o6OLiwvuuece6UZHX19f+Pr6Ys2aNQ3e6Ojq6goXFxcsXbq0wRsdt2zZAuDWMN+80ZGIiIiIiNpSiwsk3uhIRERERO2l36v7zTq/C2snmXV+1Pm0uEDijY50O7/Yr812iRc7LCIiIiLqaGZ9DhIREREREZE1Y4FERERERERUiwUSERERERFRrTZ5UCwREZmfuW9UJiIiIlM8g0RERERERFSLBRIRWY3Y2FjIZDKjP7VaLU0XQiA2Nhaenp5wcHBAUFAQzpw5YzQPvV6P6OhouLm5wcnJCREREbh8+XJ7rwoRERFZKBZIRGRVBg8ejIKCAunv1KlT0rSEhASsX78eGzduRHZ2NtRqNYKDg3Ht2jUpJiYmBqmpqUhJScHRo0dRXl6O8PBwVFdXd8TqEBERkYXhPUhEZFVsbW2NzhrVEUJgw4YNWLlyJaZNmwYA2L59O9zd3bF7927MmzcPpaWl2Lp1K3bu3InHHnsMAJCcnAwvLy8cPHgQoaGh7bouREREZHlYIBGRVfnxxx/h6ekJhUIBf39/rFmzBvfeey/y8/Oh0+kQEhIixSoUCgQGBiIjIwPz5s1DTk4ODAaDUYynpyf8/PyQkZHRYIGk1+uh1+ul12VlZQAAg8EAg8FQ73vq2hua3hoKm4Yf0m22ZXQTRv+1Bu2ds7k+07bYR9pDW+ZtbduCiDonFkhEZDX8/f2xY8cO9O/fH1euXMGbb76JMWPG4MyZM9DpdAAAd3d3o/e4u7vj4sWLAACdTgc7Ozv06tXLJKbu/fWJj49HXFycSXtaWhocHR0bzVmr1TZr3ZojYZTZZtWkN0bUtN/CzKS9cj5w4IBZ52fOfaQ9tUXeN27cMPs8iYhaigUSEVmNsLAw6d9DhgxBQEAA7rvvPmzfvh2jR48GAMhkMqP3CCFM2u7UVMzy5cuxePFi6XVZWRm8vLwQEhKCHj161Pseg8EArVaL4OBgyOXyJtetOfxivzbLfBqj6CbwxogarDrRDfqaxrebpWjvnE/HmudSzLbYR9pDW+Zdd3aWiKgjsUAiIqvl5OSEIUOG4Mcff8TUqVMB3DpL5OHhIcUUFhZKZ5XUajUqKytRXFxsdBapsLAQY8aMaXA5CoUCCoXCpF0ulzd5gNicmObSV7dfwaKvkbXr8syhvXI2d1Fgzn2kPbVF3ta4HYio82GBRERWS6/X49y5c/jNb34DHx8fqNVqaLVaPPTQQwCAyspKpKenY926dQCA4cOHQy6XQ6vVYvr06QCAgoICnD59GgkJCR22HkRE1H6a+9BthY1AwqhbZ+8b+/HlwtpJ5kqNLAQLJCKyGkuXLsXkyZNxzz33oLCwEG+++SbKysowc+ZMyGQyxMTEYM2aNfD19YWvry/WrFkDR0dHREZGAgCUSiVmz56NJUuWwNXVFS4uLli6dCmGDBkijWpH1JTmHlw1pe7gi4iILAsLJOqUzHUAU4e/DlmGy5cvY8aMGfjvf/+L3r17Y/To0cjKyoK3tzcAYNmyZaioqMD8+fNRXFwMf39/pKWlwdnZWZrHu+++C1tbW0yfPh0VFRWYMGECkpKSYGNj01GrRURERBaEBRIRWY2UlJRGp8tkMsTGxiI2NrbBGHt7eyQmJiIxMdHM2REREVFn0K2jEyAiIiIiIrIULJCIiIiIiIhqsUAiIiIiIiKqxXuQiDpIU8OGEhEREVH74xkkIiIiIiKiWiyQiIiIiIiIarFAIiIiIiIiqsUCiYiIiIiIqBYHaSAiIiIiaqV+r+4327wurJ1ktnlR6/EMEhERERERUS0WSERERERERLVYIBEREREREdVigURERERERFSLBRIREREREVEtFkhERERERES1WCARERERERHVYoFERERERERUiwUSERERERFRLduOToCIiKgr84v9GvpqmVnmdWHtJLPMh4g6Rr9X99/V+xU2Agmj/tevsE9oHZ5BIiIiIiIiqsUCiYiIiIiIqBYLJCIiIiIiolq8B4mIqA2Z8/4SIiIiansWfwZp06ZN8PHxgb29PYYPH46///3vHZ0SEXUC7FuIqK2wfyGybhZ9BunTTz9FTEwMNm3ahEceeQRbtmxBWFgYzp49i3vuuaej0yMiK8W+hYjaCvsXsiR3Oyre7brSiHgWXSCtX78es2fPxgsvvAAA2LBhA77++mts3rwZ8fHxHZwdEVkr9i3UWZnzYAjoWgdE5sL+hcj6WWyBVFlZiZycHLz66qtG7SEhIcjIyDCJ1+v10Ov10uvS0lIAwK+//gqDwdDgcgwGA27cuAFbQzdU15jnPoGioiKzzAcAbKuum21egPly43ZrvbbYdubUnHW9du0aAEAI0dbpmF1L+xagdf2LpX/ODbGtEbhxo8aq8rbGnAHryLu+/qBu3y4qKoJcLjfr8qy5bwHar38BrK+PsYb9/U7M2dj9S//PrPM7tnwCgLbtU27Xov5FWKj//Oc/AoD4xz/+YdT+1ltvif79+5vEr169WgDgH//4145/ly5daq8uwWxa2rcIwf6Ff/xr7z9r7FuEYP/CP/5Zw19z+heLPYNURyYzrn6FECZtALB8+XIsXrxYel1TU4Nff/0Vrq6u9cbXKSsrg5eXFy5duoQePXqYL/FOjtut9TrDthNC4Nq1a/D09OzoVFqtuX0L0Lr+xVo/Z2vM2xpzBph3fTpD3wK0ff8CWN/+Y235Asy5vbRXzi3pXyy2QHJzc4ONjQ10Op1Re2FhIdzd3U3iFQoFFAqFUVvPnj2bvbwePXpYzY5kSbjdWs/at51SqezoFFqlpX0LcHf9i7V+ztaYtzXmDDDvO1lr3wK0f/8CWN/+Y235Asy5vbRHzs3tXyx2mG87OzsMHz4cWq3WqF2r1WLMmDEdlBURWTv2LUTUVti/EHUOFnsGCQAWL16MqKgojBgxAgEBAfjwww/x888/4w9/+ENHp0ZEVox9CxG1FfYvRNbPogukp59+GkVFRXj99ddRUFAAPz8/HDhwAN7e3mZbhkKhwOrVq01Ob1PjuN1aj9uu47FvaZg15m2NOQPMu7Nqj/4FsL7PwdryBZhze7HEnGVCWOlYmkRERERERGZmsfcgERERERERtTcWSERERERERLVYIBEREREREdVigURERERERFSrSxdImzZtgo+PD+zt7TF8+HD8/e9/7+iULF58fDxGjhwJZ2dnqFQqTJ06FXl5eR2dltWJj4+HTCZDTExMR6dCbaAj+5bmfEdnzZoFmUxm9Dd69GijGL1ej+joaLi5ucHJyQkRERG4fPmyUUxxcTGioqKgVCqhVCoRFRWFkpKSVuUdGxtrkpNarZamCyEQGxsLT09PODg4ICgoCGfOnOnQnAGgX79+JnnLZDIsWLAAgGVs62+//RaTJ0+Gp6cnZDIZvvzyS6Pp7bltf/75Z0yePBlOTk5wc3PDokWLUFlZ2az1oP9prz7G2vad5vR/lpbz5s2bMXToUOkhqQEBAfjb3/5msfneqb7jGUvPuVlEF5WSkiLkcrn46KOPxNmzZ8VLL70knJycxMWLFzs6NYsWGhoqtm3bJk6fPi1yc3PFpEmTxD333CPKy8s7OjWrcfz4cdGvXz8xdOhQ8dJLL3V0OmRmHd23NOc7OnPmTDFx4kRRUFAg/RUVFRnN5w9/+IPo06eP0Gq14rvvvhPjxo0Tw4YNE1VVVVLMxIkThZ+fn8jIyBAZGRnCz89PhIeHtyrv1atXi8GDBxvlVFhYKE1fu3atcHZ2Fp9//rk4deqUePrpp4WHh4coKyvrsJyFEKKwsNAoZ61WKwCIw4cPCyEsY1sfOHBArFy5Unz++ecCgEhNTTWa3l7btqqqSvj5+Ylx48aJ7777Tmi1WuHp6SkWLlzY3M1Non37GGvbd5rT/1laznv37hX79+8XeXl5Ii8vT6xYsULI5XJx+vRpi8z3dg0dz1hyzs3VZQukUaNGiT/84Q9GbQ888IB49dVXOygj61RYWCgAiPT09I5OxSpcu3ZN+Pr6Cq1WKwIDA1kgdUKW1rfU9x2dOXOmmDJlSoPvKSkpEXK5XKSkpEht//nPf0S3bt2ERqMRQghx9uxZAUBkZWVJMZmZmQKAOH/+fIvzXL16tRg2bFi902pqaoRarRZr166V2m7evCmUSqX44IMPOizn+rz00kvivvvuEzU1NUIIy9vWdx7ktue2PXDggOjWrZv4z3/+I8Xs2bNHKBQKUVpa2qL16Mo6qo+xxn3nzv7PGnIWQohevXqJjz/+2KLzbeh4xpJzbokueYldZWUlcnJyEBISYtQeEhKCjIyMDsrKOpWWlgIAXFxcOjgT67BgwQJMmjQJjz32WEenQm3AEvuWhr6jR44cgUqlQv/+/TFnzhwUFhZK03JycmAwGIzWw9PTE35+ftJ6ZGZmQqlUwt/fX4oZPXo0lEplq9f1xx9/hKenJ3x8fPC73/0O//73vwEA+fn50Ol0RvkoFAoEBgZKy+qonG9XWVmJ5ORkPP/885DJZFK7JW7rOu25bTMzM+Hn5wdPT08pJjQ0FHq9Hjk5OXe1Hl2FJfUx1rDv3Nn/WXrO1dXVSElJwfXr1xEQEGDR+TZ0PGPJObeE7V2920r997//RXV1Ndzd3Y3a3d3dodPpOigr6yOEwOLFi/Hoo4/Cz8+vo9OxeCkpKfjuu++QnZ3d0alQG7G0vqWh72hYWBieeuopeHt7Iz8/H6tWrcL48eORk5MDhUIBnU4HOzs79OrVy2h+t6+HTqeDSqUyWaZKpWrVuvr7+2PHjh3o378/rly5gjfffBNjxozBmTNnpPnVt10vXrwo5dPeOd/pyy+/RElJCWbNmiW1WeK2vl17bludTmeynF69esHOzo7/720mS+pjLH3fqa//s9ScT506hYCAANy8eRPdu3dHamoqBg0aJBUClpZvY8czlrqNW6pLFkh1bv+FD7j1ZbqzjRq2cOFCnDx5EkePHu3oVCzepUuX8NJLLyEtLQ329vYdnQ61MUvpWxr6jj799NPSv/38/DBixAh4e3tj//79mDZtWoPzu3M96lun1q5rWFiY9O8hQ4YgICAA9913H7Zv3y4NatCa7dqWOd9p69atCAsLM/o10xK3dX3aa9u29Xp0FZbSx7Q2l/bYdxo7RrG0nAcMGIDc3FyUlJTg888/x8yZM5Genm6R+Tb3eMaScm6NLnmJnZubG2xsbEyqy8LCQpNKlOoXHR2NvXv34vDhw+jbt29Hp2PxcnJyUFhYiOHDh8PW1ha2trZIT0/Hn//8Z9ja2qK6urqjUyQzsKS+pSXfUQ8PD3h7e+PHH38EAKjValRWVqK4uNgo7vb1UKvVuHLlism8rl69apZ1dXJywpAhQ/Djjz9Ko9k1tl07OueLFy/i4MGDeOGFFxqNs7Rt3Z7bVq1WmyynuLgYBoOB/+9tJkvqYyx532mo/7PUnO3s7HD//fdjxIgRiI+Px7Bhw/Dee+9ZZL5NHc/UxVpSzq3RJQskOzs7DB8+HFqt1qhdq9VizJgxHZSVdRBCYOHChfjiiy9w6NAh+Pj4dHRKVmHChAk4deoUcnNzpb8RI0bgmWeeQW5uLmxsbDo6RTIDS+hbWvMdLSoqwqVLl+Dh4QEAGD58OORyudF6FBQU4PTp09J6BAQEoLS0FMePH5dijh07htLSUrOsq16vx7lz5+Dh4QEfHx+o1WqjfCorK5Geni4tq6Nz3rZtG1QqFSZNmtRonKVt6/bctgEBATh9+jQKCgqkmLS0NCgUCgwfPvyu1qOrsIQ+po4l7jtN9X+WmHN9hBDQ6/UWmW9TxzP33nuvxeXcKnc1xIMVqxsmc+vWreLs2bMiJiZGODk5iQsXLnR0ahbtxRdfFEqlUhw5csRo2NobN250dGpWh6PYdU4d3bc09R29du2aWLJkicjIyBD5+fni8OHDIiAgQPTp08dkCNa+ffuKgwcPiu+++06MHz++3iFYhw4dKjIzM0VmZqYYMmRIq4fMXrJkiThy5Ij497//LbKyskR4eLhwdnaWttvatWuFUqkUX3zxhTh16pSYMWNGvcPGtmfOdaqrq8U999wjXnnlFaN2S9nW165dE99//734/vvvBQCxfv168f3330vDQrfXtq0bknfChAniu+++EwcPHhR9+/blMN8t1J59jLXtO805RrG0nJcvXy6+/fZbkZ+fL06ePClWrFghunXrJtLS0iwy3/rceTxjDTk3pcsWSEII8f777wtvb29hZ2cnHn74YQ5V3QwA6v3btm1bR6dmdVggdV4d2bc09R29ceOGCAkJEb179xZyuVzcc889YubMmeLnn382mk9FRYVYuHChcHFxEQ4ODiI8PNwkpqioSDzzzDPC2dlZODs7i2eeeUYUFxe3Ku+652TI5XLh6ekppk2bJs6cOSNNr6mpEatXrxZqtVooFAoxduxYcerUqQ7Nuc7XX38tAIi8vDyjdkvZ1ocPH653n5g5c6YQon237cWLF8WkSZOEg4ODcHFxEQsXLhQ3b95s1nrQ/7RXH2Nt+05zjlEsLefnn39e+ix79+4tJkyYIBVHlphvfe48nrGGnJsiE0KIuzsHRURERERE1Dl0yXuQiIiIiIiI6sMCiYiIiIiIqBYLJCIiIiIioloskIiIiIiIiGqxQCIiIiIiIqrFAomIiIiIiKgWCyQiIiIiIqJaLJCIiIiIiIhqsUAiIiIiIiKqxQKJiIiIiIioFgskIiIiIiKiWiyQiIiIiIiIarFAIiIiIiIiqsUCiYiIiIiIqBYLJCIiIiIioloskIiIiIiIiGqxQCIiIiIiIqrFAomIiIiIiKgWCyQiIiIiIqJaLJCIiIiIiIhqsUAiIiIiIiKqxQKJiIiIiIioFgskqldSUhJkMhkuXLjQ0akQEVm8fv36YdasWR2dRqNkMhliY2M7Og2iFvv0008xePBgODg4QCaTYerUqZDJZB2dFnViLJCoXpMmTUJmZiY8PDw6OhUiIiLqoq5evYqoqCjcd9990Gg0yMzMxJ///GdkZmZ2dGrUidl2dAJkmXr37o3evXt3dBpE1AlUV1ejqqoKCoWio1MhIivzz3/+EwaDAb///e8RGBgotd9zzz0dmFXHMxgMkMlksLXloXxb4Bkkqtedl9gFBQXBz88P2dnZ+M1vfgNHR0fce++9WLt2LWpqaozeW1JSgiVLluDee++FQqGASqXCb3/7W5w/f16K+fXXXzF//nz06dMHdnZ2uPfee7Fy5Uro9XqjeclkMixcuBDbtm3DgAED4ODggBEjRiArKwtCCLz99tvw8fFB9+7dMX78ePz0008m63Lw4EFMmDABPXr0gKOjIx555BF888035t9oRBbk73//O2QyGfbs2WMybceOHZDJZMjOzgYAnDhxAhEREXBxcYG9vT0eeugh/N///Z/Re65evYr58+dj0KBB6N69O1QqFcaPH4+///3vRnEXLlyATCZDQkIC3nzzTfj4+EChUODw4cOoqanBm2++KX2Xe/bsiaFDh+K9995r0bpVVlbizTffxAMPPACFQoHevXvjueeew9WrV43i+vXrh/DwcHz11Vd46KGH4ODggIEDB+Krr74CcKufGzhwIJycnDBq1CicOHHC6P2zZs1C9+7dcebMGUyYMAFOTk7o3bs3Fi5ciBs3bjSZ588//4zf//73UKlUUCgUGDhwIP70pz9JfaYQAr6+vggNDTV5b3l5OZRKJRYsWCC1lZWVYenSpfDx8YGdnR369OmDmJgYXL9+3ei9ZWVlmDNnDlxdXdG9e3dMnDgR//znP5u3cYksyKxZs/Doo48CAJ5++mnIZDIEBQUhNja23kvsdu/ejYCAAHTv3h3du3fHgw8+iK1btxrFfPLJJxg2bBjs7e3h4uKCxx9/HOfOnWtRXs3ty86fP48ZM2bA3d0dCoUC99xzD5599lmjY53Tp09jypQp6NWrF+zt7fHggw9i+/btRvM5cuQIZDIZdu7ciSVLlqBPnz5QKBTSMQ+Pc9qAIKrHtm3bBACRn58vhBAiMDBQuLq6Cl9fX/HBBx8IrVYr5s+fLwCI7du3S+8rKysTgwcPFk5OTuL1118XX3/9tfj888/FSy+9JA4dOiSEEKKiokIMHTpUODk5iXfeeUekpaWJVatWCVtbW/Hb3/7WKA8AwtvbW4wZM0Z88cUXIjU1VfTv31+4uLiIl19+WUyZMkV89dVXYteuXcLd3V0MHTpU1NTUSO/fuXOnkMlkYurUqeKLL74Q+/btE+Hh4cLGxkYcPHiw7TckUQd66KGHxCOPPGLSPnLkSDFy5EghhBCHDh0SdnZ24je/+Y349NNPhUajEbNmzRIAxLZt26T3nD9/Xrz44osiJSVFHDlyRHz11Vdi9uzZolu3buLw4cNSXH5+vgAg+vTpI8aNGyf+8pe/iLS0NJGfny/i4+OFjY2NWL16tfjmm2+ERqMRGzZsELGxsc1ep+rqajFx4kTh5OQk4uLihFarFR9//LHo06ePGDRokLhx44YU6+3tLfr27Sv8/PzEnj17xIEDB4S/v7+Qy+XitddeE4888ohRv+Lu7m70/pkzZwo7Oztxzz33iLfeekukpaWJ2NhYYWtrK8LDw43y8vb2FjNnzpReFxYWij59+ojevXuLDz74QGg0GrFw4UIBQLz44otS3HvvvSdkMpn45z//aTS/999/XwAQZ86cEUIIcf36dfHggw8KNzc3sX79enHw4EHx3nvvCaVSKcaPHy/1ezU1NWLcuHFCoVBIOa9evVrce++9AoBYvXp1s7c1UUf76aefpO/CmjVrRGZmpjhz5oxYvXq1uPMQdtWqVQKAmDZtmvjss89EWlqaWL9+vVi1apUUs2bNGgFAzJgxQ+zfv1/s2LFD3HvvvUKpVJp8BxvTnL4sNzdXdO/eXfTr10988MEH4ptvvhHJycli+vTpoqysTAhxq191dnYW9913n9ixY4fYv3+/mDFjhgAg1q1bJ83r8OHDUr/65JNPir1794qvvvpKFBUV8TinjbBAonrVVyABEMeOHTOKGzRokAgNDZVev/766wKA0Gq1Dc77gw8+EADE//3f/xm1r1u3TgAQaWlpUhsAoVarRXl5udT25ZdfCgDiwQcfNCqGNmzYIACIkydPCiFuHVC4uLiIyZMnGy2nurpaDBs2TIwaNaqZW4PIOtV9j7///nup7fjx40Y/bDzwwAPioYceEgaDwei94eHhwsPDQ1RXV9c776qqKmEwGMSECRPE448/LrXXFUj33XefqKysNJnngw8+eFfrtGfPHgFAfP7550bt2dnZAoDYtGmT1Obt7S0cHBzE5cuXpbbc3FwBQHh4eIjr169L7XX9yt69e6W2mTNnCgDivffeM1rWW2+9JQCIo0ePGi3r9gLp1VdfrbfPfPHFF4VMJhN5eXlCiFs/Kjk7O4uXXnrJKG7QoEFi3Lhx0uv4+HjRrVs3kZ2dbRT3l7/8RQAQBw4cEEII8be//a3RnFkgkbWpKw4+++wzqe3OAunf//63sLGxEc8880yD8ykuLhYODg4mP8T+/PPPQqFQiMjIyGbn1Jy+bPz48aJnz56isLCwwZjf/e53QqFQiJ9//tmoPSwsTDg6OoqSkhIhxP+2wdixY43ieJzTdniJHTWbWq3GqFGjjNqGDh2KixcvSq//9re/oX///njssccanM+hQ4fg5OSEJ5980qi9bgSoO08Ljxs3Dk5OTtLrgQMHAgDCwsKMTrHXtdflk5GRgV9//RUzZ85EVVWV9FdTU4OJEyciOzvb5NIUos5kxowZUKlUeP/996W2xMRE9O7dG08//TR++uknnD9/Hs888wwAGH1Pfvvb36KgoAB5eXnSez/44AM8/PDDsLe3h62tLeRyOb755pt6L0+JiIiAXC43ahs1ahR++OEHzJ8/H19//TXKyspavE5fffUVevbsicmTJxvl++CDD0KtVuPIkSNG8Q8++CD69Okjva7rJ4KCguDo6GjSfnt/Vqdu+9SJjIwEABw+fLjBPA8dOoRBgwaZ9JmzZs2CEAKHDh0CADg7O+O5555DUlKS1B8dOnQIZ8+excKFC43W28/PDw8++KDReoeGhkImk0nrXZdTQzkTdUZarRbV1dVGl6TeKTMzExUVFSajTXp5eWH8+PEtuiStqb7sxo0bSE9Px/Tp0xu9n/vQoUOYMGECvLy8jNpnzZqFGzdumAxE8cQTTxi95nFO22GBRM3m6upq0qZQKFBRUSG9vnr1Kvr27dvofIqKiqBWq02uH1apVLC1tUVRUZFRu4uLi9FrOzu7Rttv3rwJALhy5QoA4Mknn4RcLjf6W7duHYQQ+PXXXxvNlciaKRQKzJs3D7t370ZJSQmuXr2K//u//8MLL7wAhUIhfUeWLl1q8h2ZP38+AOC///0vAGD9+vV48cUX4e/vj88//xxZWVnIzs7GxIkTjfqAOvWNgLl8+XK88847yMrKQlhYGFxdXTFhwgSTe38ac+XKFZSUlMDOzs4kZ51OJ+Vbp7X9Rx1bW1uTvk+tVgOASV91u6Kionq3gaenp8l7o6Ojce3aNezatQsAsHHjRvTt2xdTpkwxWu+TJ0+arLOzszOEENJ6FxUVNZozUWdUd/9hY8cfdd+5hr6XjX2f79RUX1ZcXIzq6upmHQ81t5+oL3ce57QdDn1BZtW7d29cvny50RhXV1ccO3YMQgijIqmwsBBVVVVwc3MzSy5180lMTMTo0aPrjXF3dzfLsogs1Ysvvoi1a9fik08+wc2bN1FVVYU//OEPAP73HVm+fDmmTZtW7/sHDBgAAEhOTkZQUBA2b95sNP3atWv1vq++G6htbW2xePFiLF68GCUlJTh48CBWrFiB0NBQXLp0yeiMTkPc3Nzg6uoKjUZT73RnZ+cm59ESVVVVKCoqMio4dDodgPp/NKrj6uqKgoICk/ZffvkFAIz6ufvvvx9hYWF4//33ERYWhr179yIuLg42NjZSjJubGxwcHPDJJ5/Uu7y6+bm6ujaaM1FnVHeW5vLlyyZnY+rUfR8a+l625Nijqb7MxcUFNjY2zToeam4/AZj2qzzOaTs8g0RmFRYWhn/+85/S5SP1mTBhAsrLy/Hll18ate/YsUOabg6PPPIIevbsibNnz2LEiBH1/tX9akzUWXl4eOCpp57Cpk2b8MEHH2Dy5MnS8LgDBgyAr68vfvjhhwa/I3UFh0wmMxmm++TJk61+FknPnj3x5JNPYsGCBfj111+b/VDq8PBwFBUVobq6ut586wo6c6o7s1Nn9+7dAG5dpteQCRMm4OzZs/juu++M2utGEBw3bpxR+0svvYSTJ09i5syZsLGxwZw5c4ymh4eH41//+hdcXV3rXe9+/foBgDTfhnIm6oxCQkJgY2Nj8gPO7QICAuDg4IDk5GSj9suXL0uXurVGfX2Zg4MDAgMD8dlnn5mc1b7dhAkTcOjQIakgqrNjxw44Ojo2WPTU4XFO2+EZJDKrmJgYfPrpp5gyZQpeffVVjBo1ChUVFUhPT0d4eDjGjRuHZ599Fu+//z5mzpyJCxcuYMiQITh69CjWrFmD3/72t43ev9QS3bt3R2JiImbOnIlff/0VTz75JFQqFa5evYoffvgBV69ebbQzJeosXnrpJfj7+wMAtm3bZjRty5YtCAsLQ2hoKGbNmoU+ffrg119/xblz5/Ddd9/hs88+A3DrAP2NN97A6tWrERgYiLy8PLz++uvw8fFBVVVVs/KYPHky/Pz8MGLECPTu3RsXL17Ehg0b4O3tDV9f32bN43e/+x127dqF3/72t3jppZcwatQoyOVyXL58GYcPH8aUKVPw+OOPt2DrNM7Ozg5/+tOfUF5ejpEjRyIjIwNvvvkmwsLCpOGH6/Pyyy9jx44dmDRpEl5//XV4e3tj//792LRpE1588UX079/fKD44OBiDBg3C4cOHpaHBbxcTE4PPP/8cY8eOxcsvv4yhQ4eipqYGP//8M9LS0rBkyRL4+/sjJCQEY8eOxbJly3D9+nWMGDEC//jHP7Bz506zbRMiS9OvXz+sWLECb7zxBioqKjBjxgwolUqcPXsW//3vfxEXF4eePXti1apVWLFiBZ599lnMmDEDRUVFiIuLg729PVavXt3s5TWnL1u/fj0effRR+Pv749VXX8X999+PK1euYO/evdiyZQucnZ2xevVqfPXVVxg3bhxee+01uLi4YNeuXdi/fz8SEhKgVCobzYPHOW2oQ4eIIItV3yh2gwcPNombOXOm8Pb2NmorLi4WL730krjnnnuEXC4XKpVKTJo0SZw/f16KKSoqEn/4wx+Eh4eHsLW1Fd7e3mL58uXi5s2bRvMCIBYsWGDUVjdK1ttvv23UXt9IN0IIkZ6eLiZNmiRcXFyEXC4Xffr0EZMmTTKJI+rM+vXrJwYOHFjvtB9++EFMnz5dqFQqIZfLhVqtFuPHjxcffPCBFKPX68XSpUtFnz59hL29vXj44YfFl19+adIHNPT9FEKIP/3pT2LMmDHCzc1NGj579uzZ4sKFCy1aF4PBIN555x0xbNgwYW9vL7p37y4eeOABMW/ePPHjjz9Kcd7e3mLSpEkm729uvzJz5kzh5OQkTp48KYKCgoSDg4NwcXERL774otHImnXLun0UOyGEuHjxooiMjBSurq5CLpeLAQMGiLfffrvBkQFjY2MFAJGVlVXv9PLycvHHP/5RDBgwQNjZ2QmlUimGDBkiXn75ZaHT6aS4kpIS8fzzz4uePXsKR0dHERwcLM6fP89R7MgqNWcUuzo7duwQI0eOlPqFhx56yOhxBUII8fHHH4uhQ4dK36EpU6ZIw+k3V3P7srNnz4qnnnpKuLq6SnGzZs0yOtY5deqUmDx5slAqlcLOzk4MGzbMJOeGjm/q8DjH/GRCCNExpRkREbWHkydPYtiwYXj//felwReoabNmzcJf/vIXlJeXt8vyRowYYfQAXyIi6hi8xI6IqJP617/+hYsXL2LFihXw8PAwGd6WOl5ZWRlOnz6Nr776Cjk5OUhNTe3olIiIujwWSEREndQbb7yBnTt3YuDAgfjss8+aNUpcR2rqXqZu3bqhW7fONbbQd999h3HjxsHV1RWrV6/G1KlTOzoloi5LCIHq6upGY2xsbOodpZM6F15iR0REHe7ChQvw8fFpNGb16tWIjY1tn4SIqMs5cuSIyQiTd9q2bRvPxncBLJCIiKjDVVZW4uTJk43GeHp6Sg9QJCIyt2vXriEvL6/RGB8fn0afgUadAwskIiIi6tQ2b96MzZs3S8/bGjx4MF577TWEhYUBuHVpVVxcHD788EMUFxfD398f77//PgYPHizNQ6/XY+nSpdizZw8qKiowYcIEbNq0CX379pViiouLsWjRIuzduxcAEBERgcTERPTs2bPd1pWI7l7nupibiIiI6A59+/bF2rVrceLECZw4cQLjx4/HlClTcObMGQBAQkIC1q9fj40bNyI7OxtqtRrBwcG4du2aNI+YmBikpqYiJSUFR48eRXl5OcLDw43uWYmMjERubi40Gg00Gg1yc3MRFRXV7utLRHen055BqqmpwS+//AJnZ2feTEdkZkIIXLt2DZ6enp3upvnmYP9C1Dbas29xcXHB22+/jeeffx6enp6IiYnBK6+8AuDW2SJ3d3esW7cO8+bNQ2lpKXr37o2dO3fi6aefBgD88ssv8PLywoEDBxAaGopz585h0KBByMrKkh7MnJWVhYCAAJw/fx4DBgxoVl7sX4jaRkv6l047il1dx0VEbefSpUtGl5d0FexfiNpWW/Yt1dXV+Oyzz3D9+nUEBAQgPz8fOp0OISEhUoxCoUBgYCAyMjIwb9485OTkwGAwGMV4enrCz88PGRkZCA0NRWZmJpRKpVQcAcDo0aOhVCqRkZHR7AKJ/QtR22pO/9JpCyRnZ2cAtzZCjx496o0xGAxIS0tDSEgI5HJ5e6ZnUbgdbuF2+J+mtkVZWRm8vLyk71lX05z+Begc+xTXwTJ0lXVoy77l1KlTCAgIwM2bN9G9e3ekpqZi0KBByMjIAAC4u7sbxbu7u+PixYsAAJ1OBzs7O/Tq1cskRqfTSTEqlcpkuSqVSoqpj16vh16vl17XXdiTn59vtB0MBgMOHz6McePGWcU+YG35AtaXM/NtmWvXrsHHx6dZ/UunLZDqTkv36NGj0QLJ0dERPXr0sIodq61wO9zC7fA/zd0WXfXyj+b0L0Dn2Ke4Dpahq61DW/QtAwYMQG5uLkpKSvD5559j5syZSE9Pb3CZQogm87gzpr74puYTHx+PuLg4k/bMzEyTZ5c5Ojri2LFjjeZkSawtX8D6cma+zXfjxg0AzetfOm2BRERERFTHzs4O999/PwBgxIgRyM7OxnvvvSfdd6TT6eDh4SHFFxYWSmeV1Go1KisrUVxcbHQWqbCwEGPGjJFirly5YrLcq1evmpydut3y5cuxePFi6XXdWbSQkBCjH2AMBgO0Wi2Cg4Otoki2tnwB68uZ+bZMWVlZs2NZIBEREVGXI4SAXq+Hj48P1Go1tFotHnroIQC3nsuVnp6OdevWAQCGDx8OuVwOrVaL6dOnAwAKCgpw+vRpJCQkAAACAgJQWlqK48ePY9SoUQCAY8eOobS0VCqi6qNQKKBQKEza5XJ5vQeRDbVbKmvLF7C+nJlv85fbXCyQiIiIqFNbsWIFwsLC4OXlhWvXriElJQVHjhyBRqOBTCZDTEwM1qxZA19fX/j6+mLNmjVwdHREZGQkAECpVGL27NlYsmQJXF1d4eLigqVLl2LIkCF47LHHAAADBw7ExIkTMWfOHGzZsgUAMHfuXISHhzd7gAYisgwskIiIiKhTu3LlCqKiolBQUAClUomhQ4dCo9EgODgYALBs2TJUVFRg/vz50oNi09LSjG7mfvfdd2Fra4vp06dLD4pNSkqCjY2NFLNr1y4sWrRIGu0uIiICGzdubN+VJaK7xgKJiIiIOrWtW7c2Ol0mkyE2NhaxsbENxtjb2yMxMRGJiYkNxri4uCA5Obm1aRKRheh6T3gkIiIiIiJqAAskIiIiIiKiWiyQiIiIiIiIavEeJGq1fq/uN+v8LqydZNb5ERHdzi/2a+ir7/4BpOyriOh25jweYv9iGXgGiYgsQnx8PEaOHAlnZ2eoVCpMnToVeXl5RjGzZs2CTCYz+hs9erRRjF6vR3R0NNzc3ODk5ISIiAhcvnzZKKa4uBhRUVFQKpVQKpWIiopCSUlJW68iERERWQEWSERkEdLT07FgwQJkZWVBq9WiqqoKISEhuH79ulHcxIkTUVBQIP0dOHDAaHpMTAxSU1ORkpKCo0ePory8HOHh4aiurpZiIiMjkZubC41GA41Gg9zcXERFRbXLehIREZFl4yV2RGQRNBqN0ett27ZBpVIhJycHY8eOldoVCgXUanW98ygtLcXWrVuxc+dO6eGNycnJ8PLywsGDBxEaGopz585Bo9EgKysL/v7+AICPPvoIAQEByMvL4wMdiYiIujgWSERkkUpLSwHceq7I7Y4cOQKVSoWePXsiMDAQb731FlQqFQAgJycHBoNBekgjAHh6esLPzw8ZGRkIDQ1FZmYmlEqlVBwBwOjRo6FUKpGRkVFvgaTX66HX66XXZWVlAACDwQCDwdDgOtRNayzG0nWmdVB0E2adX3vqTJ9Dc74zREQdiQUSEVkcIQQWL16MRx99FH5+flJ7WFgYnnrqKXh7eyM/Px+rVq3C+PHjkZOTA4VCAZ1OBzs7O/Tq1ctofu7u7tDpdAAAnU4nFVS3U6lUUsyd4uPjERcXZ9KelpYGR0fHJtdHq9U2GWPpOsM6vDGixizzufOyzvbUGT6Hxtbhxo0b7ZgJEVH9WCARkcVZuHAhTp48iaNHjxq1P/3009K//fz8MGLECHh7e2P//v2YNm1ag/MTQkAm+9/oZbf/u6GY2y1fvhyLFy+WXpeVlcHLywshISHo0aNHg8s1GAzQarUIDg6GXC5vMM6SdaZ1WHWiG/Q1dz+K3enYUDNk1TKd6XNobB3qzs4SEXUkFkhEZFGio6Oxd+9efPvtt+jbt2+jsR4eHvD29saPP/4IAFCr1aisrERxcbHRWaTCwkKMGTNGirly5YrJvK5evQp3d/d6l6NQKKBQKEza5XJ5sw5WmxtnyTrDOuhrZGYZ5rsjt0Nn+BwaWwdrXzci6hw4ih0RWQQhBBYuXIgvvvgChw4dgo+PT5PvKSoqwqVLl+Dh4QEAGD58OORyudElPAUFBTh9+rRUIAUEBKC0tBTHjx+XYo4dO4bS0lIphoiIiLounkEiIouwYMEC7N69G3/961/h7Ows3Q+kVCrh4OCA8vJyxMbG4oknnoCHhwcuXLiAFStWwM3NDY8//rgUO3v2bCxZsgSurq5wcXHB0qVLMWTIEGlUu4EDB2LixImYM2cOtmzZAgCYO3cuwsPDOYIdERERsUAiIsuwefNmAEBQUJBR+7Zt2zBr1izY2Njg1KlT2LFjB0pKSuDh4YFx48bh008/hbOzsxT/7rvvwtbWFtOnT0dFRQUmTJiApKQk2NjYSDG7du3CokWLpNHuIiIisHHjxrZfSSIiIrJ4LJCIyCII0fgQzA4ODvj666+bnI+9vT0SExORmJjYYIyLiwuSk5NbnCMRERF1frwHiYiIiIiIqBYLJCIiIiIioloskIiIiIiIiGqxQCIiIiIiIqrFAomIiIiIiKhWiwqk+Ph4jBw5Es7OzlCpVJg6dSry8vKMYmbNmgWZTGb0N3r0aKMYvV6P6OhouLm5wcnJCREREbh8+bJRTHFxMaKioqBUKqFUKhEVFYWSkpLWrSUREREREVEztKhASk9Px4IFC5CVlQWtVouqqiqEhITg+vXrRnETJ05EQUGB9HfgwAGj6TExMUhNTUVKSgqOHj2K8vJyhIeHo7q6WoqJjIxEbm4uNBoNNBoNcnNzERUVdRerSkRERERE1LgWPQdJo9EYvd62bRtUKhVycnIwduxYqV2hUECtVtc7j9LSUmzduhU7d+6UnmyfnJwMLy8vHDx4EKGhoTh37hw0Gg2ysrLg7+8PAPjoo48QEBCAvLw8Pu2eiIiIiIjaxF09KLa0tBTArYcu3u7IkSNQqVTo2bMnAgMD8dZbb0GlUgEAcnJyYDAYpCfYA4Cnpyf8/PyQkZGB0NBQZGZmQqlUSsURAIwePRpKpRIZGRn1Fkh6vR56vV56XVZWBgAwGAwwGAz15l/X3tD0rqK120Fh0/iDPVubR0fh/vA/TW0LbiMiIiLqrFpdIAkhsHjxYjz66KPw8/OT2sPCwvDUU0/B29sb+fn5WLVqFcaPH4+cnBwoFArodDrY2dmhV69eRvNzd3eHTqcDAOh0Oqmgup1KpZJi7hQfH4+4uDiT9rS0NDg6Oja6Llqttsn17Qpauh0SRpl3+XdeitlRuD/8T0Pb4saNG+2cCREREVH7aHWBtHDhQpw8eRJHjx41an/66aelf/v5+WHEiBHw9vbG/v37MW3atAbnJ4SATCaTXt/+74Zibrd8+XIsXrxYel1WVgYvLy+EhISgR48e9b7HYDBAq9UiODgYcrm8wdw6u9ZuB7/Yr9swq7tzOja0xe/h/vA/TW2LujO0RETWID4+Hl988QXOnz8PBwcHjBkzBuvWrTO6ImXWrFnYvn270fv8/f2RlZUlvdbr9Vi6dCn27NmDiooKTJgwAZs2bULfvn2lmOLiYixatAh79+4FAERERCAxMRE9e/Zs25UkIrNpVYEUHR2NvXv34ttvvzXqFOrj4eEBb29v/PjjjwAAtVqNyspKFBcXG51FKiwsxJgxY6SYK1eumMzr6tWrcHd3r3c5CoUCCoXCpF0ulzd5sNucmK6gpdtBX11/sWoJ7ubz5P7wPw1tC24fIrImdYNMjRw5ElVVVVi5ciVCQkJw9uxZODk5SXETJ07Etm3bpNd2dnZG84mJicG+ffuQkpICV1dXLFmyBOHh4cjJyYGNjQ2AW4NMXb58Wbpve+7cuYiKisK+ffvaYU2JyBxaVCAJIRAdHY3U1FQcOXIEPj4+Tb6nqKgIly5dgoeHBwBg+PDhkMvl0Gq1mD59OgCgoKAAp0+fRkJCAgAgICAApaWlOH78OEaNunUd17Fjx1BaWioVUURERETNwUGmiKglWlQgLViwALt378Zf//pXODs7S/cDKZVKODg4oLy8HLGxsXjiiSfg4eGBCxcuYMWKFXBzc8Pjjz8uxc6ePRtLliyBq6srXFxcsHTpUgwZMkTqcAYOHIiJEydizpw52LJlC4Bbv8CEh4ezcyEiIqK7Yo2DTFnbQELWli9gGYNWtWTZ1raNOzrfliy3RQXS5s2bAQBBQUFG7du2bcOsWbNgY2ODU6dOYceOHSgpKYGHhwfGjRuHTz/9FM7OzlL8u+++C1tbW0yfPl26hjcpKUk6PQ0Au3btwqJFi6SOKCIiAhs3bmxJukRERERGrH2QKWsbSMja8gU6dtCq1gxYZW3buKPybckAUy2+xK4xDg4O+Prrpm/ct7e3R2JiIhITExuMcXFxQXJyckvSIyIiImqUtQ4yZW0DCVlbvoBlDFrVkkGmrG0bd3S+LRlg6q6eg0RERERkLTrDIFPWNpCQteULdOygVa3ZVta2jTsq35YskwUSERERdWocZIpu1+/V/fW2K2wEEkbdOiNkySP1UttjgURERESdGgeZIqKWYIFEREREnRoHmSKilmCBRERERJ0aB5kiopbo1tEJEBERERERWQoWSERERERERLVYIBEREREREdXiPUhERGRxGhqGtzXqhu4lIiJqDp5BIiIiIiIiqsUCiYiIiIiIqBYLJCIiIiIioloskIjIIsTHx2PkyJFwdnaGSqXC1KlTkZeXZxQjhEBsbCw8PT3h4OCAoKAgnDlzxihGr9cjOjoabm5ucHJyQkREBC5fvmwUU1xcjKioKCiVSiiVSkRFRaGkpKStV5GIiIisAAskIrII6enpWLBgAbKysqDValFVVYWQkBBcv35diklISMD69euxceNGZGdnQ61WIzg4GNeuXZNiYmJikJqaipSUFBw9ehTl5eUIDw9HdXW1FBMZGYnc3FxoNBpoNBrk5uYiKiqqXdeXiIiILBNHsSMii6DRaIxeb9u2DSqVCjk5ORg7diyEENiwYQNWrlyJadOmAQC2b98Od3d37N69G/PmzUNpaSm2bt2KnTt34rHHHgMAJCcnw8vLCwcPHkRoaCjOnTsHjUaDrKws+Pv7AwA++ugjBAQEIC8vDwMGDGjfFSciIiKLwjNIRGSRSktLAQAuLi4AgPz8fOh0OoSEhEgxCoUCgYGByMjIAADk5OTAYDAYxXh6esLPz0+KyczMhFKplIojABg9ejSUSqUUQ0RERF0XzyARkcURQmDx4sV49NFH4efnBwDQ6XQAAHd3d6NYd3d3XLx4UYqxs7NDr169TGLq3q/T6aBSqUyWqVKppJg76fV66PV66XVZWRkAwGAwwGAwNLgeddMai7F0HbUOChthvnl1E0b/vVsd8Xl2lX3JmtePiDoPFkhEZHEWLlyIkydP4ujRoybTZDKZ0WshhEnbne6MqS++sfnEx8cjLi7OpD0tLQ2Ojo6NLhsAtFptkzGWrr3XoS0e7PrGiBqzzOfAgQNmmU9rdPZ96caNG+2YCRFR/VggEZFFiY6Oxt69e/Htt9+ib9++UrtarQZw6wyQh4eH1F5YWCidVVKr1aisrERxcbHRWaTCwkKMGTNGirly5YrJcq9evWpydqrO8uXLsXjxYul1WVkZvLy8EBISgh49ejS4LgaDAVqtFsHBwZDL5c1ZfYvTUevgF/u12eal6CbwxogarDrRDfqaxovp5jgdG2qGrFqmq+xLdWdniYg6EgskIrIIQghER0cjNTUVR44cgY+Pj9F0Hx8fqNVqaLVaPPTQQwCAyspKpKenY926dQCA4cOHQy6XQ6vVYvr06QCAgoICnD59GgkJCQCAgIAAlJaW4vjx4xg16tZpimPHjqG0tFQqou6kUCigUChM2uVyebMOVpsbZ8naex301XdfyJjMs0Zmlvl25GfZ2fcla183IuocWCARkUVYsGABdu/ejb/+9a9wdnaW7gdSKpVwcHCATCZDTEwM1qxZA19fX/j6+mLNmjVwdHREZGSkFDt79mwsWbIErq6ucHFxwdKlSzFkyBBpVLuBAwdi4sSJmDNnDrZs2QIAmDt3LsLDwzmCHREREbFAIiLLsHnzZgBAUFCQUfu2bdswa9YsAMCyZctQUVGB+fPno7i4GP7+/khLS4Ozs7MU/+6778LW1hbTp09HRUUFJkyYgKSkJNjY2Egxu3btwqJFi6TR7iIiIrBx48a2XUEiIiKyCiyQiMgiCNH0CGMymQyxsbGIjY1tMMbe3h6JiYlITExsMMbFxQXJycmtSZOIiIg6OT4HiYiIiIiIqBYLJCIiIiIiolotKpDi4+MxcuRIODs7Q6VSYerUqcjLyzOKEUIgNjYWnp6ecHBwQFBQEM6cOWMUo9frER0dDTc3Nzg5OSEiIgKXL182iikuLkZUVBSUSiWUSiWioqJQUlLSurUkIiIiIiJqhhYVSOnp6ViwYAGysrKg1WpRVVWFkJAQXL9+XYpJSEjA+vXrsXHjRmRnZ0OtViM4OBjXrl2TYmJiYpCamoqUlBQcPXoU5eXlCA8PR3V1tRQTGRmJ3NxcaDQaaDQa5ObmIioqygyrTEREREREVL8WDdKg0WiMXm/btg0qlQo5OTkYO3YshBDYsGEDVq5ciWnTpgEAtm/fDnd3d+zevRvz5s1DaWkptm7dip07d0rD7iYnJ8PLywsHDx5EaGgozp07B41Gg6ysLPj7+wMAPvroIwQEBCAvL49D8RIRERERUZu4q1HsSktLAdwaEQoA8vPzodPppKFzgVsPWAwMDERGRgbmzZuHnJwcGAwGoxhPT0/4+fkhIyMDoaGhyMzMhFKplIojABg9ejSUSiUyMjLqLZD0ej30er30uu5p3AaDAQaDod7869obmt5VtHY7KGyaHnWso7TmM+X+8D9NbQtuIyIiIuqsWl0gCSGwePFiPProo/Dz8wMA6cGO7u7uRrHu7u64ePGiFGNnZ4devXqZxNS9X6fTQaVSmSxTpVJJMXeKj49HXFycSXtaWhocHR0bXRetVtvo9K6ipdshYVQbJWIGBw4caPV7uT/8T0Pb4saNG+2cCRFR68XHx+OLL77A+fPn4eDggDFjxmDdunVGP7gKIRAXF4cPP/xQes7a+++/j8GDB0sxer0eS5cuxZ49e6TnrG3atAl9+/aVYoqLi7Fo0SLs3bsX/7+9u4+Por73/v9ekmVDOGElobmrCNEfIBK0NCgEtGAhCZSIXvQSFU2hpYhHBVPgUChtDUqD0IpcJygFDpdQEfH0IFYqJyZUgXLCjURiuSvaS0SwCVEMCQjdRPL9/cFk6pLbDZtkN7yejwcP3dnPzny/s7OTfe/MfEe6dJ+1nJwcXXPNNa3WXwBXptkB6fHHH9df/vIX7dy5s9ZzDofD67Expta0y11eU1d9Q/OZO3euZsyYYT+uqKhQ9+7dlZqaqi5dutT5mqqqKuXn5yslJUVOp7PB9rUXiVlv1Zrm6mD09MBq/WJfB3mqG36fgsXBrDSfX3M1bg/1aWxd1ByhBYBgUHMN9a233qqvvvpK8+bNU2pqqg4fPqzOnTtL+uc11GvWrFHv3r21YMECpaSk6OjRo/bNqDMzM7V582Zt2LBBUVFRmjlzptLT01VYWGjfjHrChAk6efKkfVnCww8/rIyMDG3evLltOg/AZ80KSNOmTdMbb7yhHTt2eP1qEhsbK+nSEaC4uDh7emlpqX1UKTY2VpWVlSorK/M6ilRaWqohQ4bYNadOnaq13M8++6zW0akaLpdLLper1nSn09nol92m1LQXnov1ByBPtaPB54PJlbyfV9P20Jj61gXrB0Aw4RpqAL7waRQ7Y4wef/xxvfbaa3r77beVkJDg9XxCQoJiY2O9TsuprKzU9u3b7fCTlJQkp9PpVVNcXKyDBw/aNcnJySovL9fevXvtmj179qi8vNyuAQAAaA5fr6GW1Og11JIavYYaQHDw6QjSY489pvXr1+sPf/iDIiIi7OuB3G63OnXqJIfDoczMTGVnZ6tXr17q1auXsrOzFR4ergkTJti1kydP1syZMxUVFaXIyEjNmjVL/fv3t3+R6du3r0aNGqUpU6ZoxYoVki4dok5PT+fXFwAA0GyBdg11UweZCraBhAK5vfUNMuXqYLz+2xZ8WV+BvI7r0tbt9WW5PgWk5cuXS5KGDx/uNf3FF1/UpEmTJEmzZ8/WhQsX9Oijj9oXOebl5dnn70rSc889p9DQUI0fP96+yHHNmjX2+buS9PLLL2v69On2LzVjx47VsmXLfGkuAACAl0C7htrXQaaCbSChQGxvY4NMPT2wunUaUofmDDIViOu4IW3VXl8GmPIpIBnTeKJ2OBzKyspSVlZWvTVhYWHKyclRTk5OvTWRkZFat26dL80DAACoVyBeQ93UQaaCbSChQG5vXQNWSYExaJUvg0wF8jquS1u315cBpq7oPkgAANToOefNtm4CUCdjjKZNm6ZNmzZp27ZtDV5DPWDAAEn/vIZ60aJFkryvoR4/frykf15DvXjxYkne11DfdtulwxSNXUPt6yBTwTaQUCC2t7EBqdpy0KrmrKtAXMcNaav2+rJMAhIAAGjXuIYagC8ISAAAoF3jGmoAviAgAQCAdo1rqAH4wqf7IAEAAABAe0ZAAgAAAAALAQkAAAAALFyDBACAD/w9nPnHz4zx6/wAAFeGgAQAAAAEAF9+gHGFGC2+7dKNb+u7bxM/wDQPp9gBAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAsKOHTt01113KT4+Xg6HQ6+//rrX85MmTZLD4fD6N3jwYK8aj8ejadOmqVu3burcubPGjh2rkydPetWUlZUpIyNDbrdbbrdbGRkZOnPmTAv3DgAABAsCEoCA8OWXX+qWW27RsmXL6q0ZNWqUiouL7X9btmzxej4zM1ObNm3Shg0btHPnTp07d07p6em6ePGiXTNhwgQVFRUpNzdXubm5KioqUkZGRov1CwAABJfQtm4AAEjS6NGjNXr06AZrXC6XYmNj63yuvLxcq1ev1ksvvaSRI0dKktatW6fu3btr69atSktL05EjR5Sbm6vdu3dr0KBBkqRVq1YpOTlZR48eVZ8+ffzbKQAAEHQ4ggQgaGzbtk3R0dHq3bu3pkyZotLSUvu5wsJCVVVVKTU11Z4WHx+vxMREFRQUSJJ27dolt9tthyNJGjx4sNxut10DAACubhxBAhAURo8erXvvvVc9evTQsWPH9Itf/ELf/e53VVhYKJfLpZKSEnXs2FFdu3b1el1MTIxKSkokSSUlJYqOjq417+joaLumLh6PRx6Px35cUVEhSaqqqlJVVVW9r6t5rqGaQOdLH1whpqWb0yyuDsbrv4GmKev2atmWgrl/ANoPAhKAoHDffffZ/5+YmKiBAweqR48eevPNNzVu3Lh6X2eMkcPhsB9//f/rq7ncwoULNX/+/FrT8/LyFB4e3mjb8/PzG60JdE3pw+LbWqEhV+DpgdVt3YQ6XX4tXUPa+7Z0/vz5VmwJANSNgAQgKMXFxalHjx768MMPJUmxsbGqrKxUWVmZ11Gk0tJSDRkyxK45depUrXl99tlniomJqXdZc+fO1YwZM+zHFRUV6t69u1JTU9WlS5d6X1dVVaX8/HylpKTI6XT63MdA4EsfErPeaqVW+cbVwejpgdX6xb4O8lTXH4TbysGstEZrrpZtqeboLAC0JQISgKB0+vRpnThxQnFxcZKkpKQkOZ1O5efna/z48ZKk4uJiHTx4UIsXL5YkJScnq7y8XHv37tVtt1063LFnzx6Vl5fbIaouLpdLLper1nSn09mkL6tNrQtkTemD52LghY+v81Q7ArKNvmwb7X1bCva+AWgfCEgAAsK5c+f0t7/9zX587NgxFRUVKTIyUpGRkcrKytL3v/99xcXF6eOPP9bPfvYzdevWTf/rf/0vSZLb7dbkyZM1c+ZMRUVFKTIyUrNmzVL//v3tUe369u2rUaNGacqUKVqxYoUk6eGHH1Z6ejoj2AEAAEnNGMWOmzkCaAn79u3TgAEDNGDAAEnSjBkzNGDAAP3yl79USEiIDhw4oLvvvlu9e/fWxIkT1bt3b+3atUsRERH2PJ577jndc889Gj9+vIYOHarw8HBt3rxZISEhds3LL7+s/v37KzU1Vampqbr55pv10ksvtXp/AQBAYPL5CFLNzRx/+MMf6vvf/36dNaNGjdKLL75oP+7YsaPX85mZmdq8ebM2bNigqKgozZw5U+np6SosLLS/yEyYMEEnT55Ubm6upEu/8mZkZGjz5s2+NhlAEBg+fLiMqX+Usbfeavz6lrCwMOXk5CgnJ6femsjISK1bt65ZbQQAAO2fzwGJmzkCAAAAaK9a5Eax3MwRAAAAQDDy+yANbXUzx+bcyLE93HjPV3XdyDHQb6LYHM15T6/G7aE+ja0L1hGAYLJjxw79+te/VmFhoYqLi7Vp0ybdc8899vOTJk3S2rVrvV4zaNAg7d69237s8Xg0a9YsvfLKK7pw4YJGjBihF154Qddee61dU1ZWpunTp+uNN96QJI0dO1Y5OTm65pprWrR/APzL7wGprW7meCU3cmwPN95rqoZu5BioN1FsDl9uvHi5q2l7aEx964KbOQIIJlw/DcAXLT7Md2vdzLE5N3JsDzfe81VdN3IM9JsoNkdTbrx4uatxe6hPY+uCmzkCCCZcPw3AFy0ekFrrZo5XciPH9nDjvaZq6CaJgXoTxea4kvfzatoeGlPfumD9AGhvaq6fvuaaazRs2DD96le/sk/3b+z66bS0tEavn64vIDX1EoFgOw08kNtb1+UGUvBdctCU9gbS+m/rbcKX5fockLiZIwAAaE/a6vppyfdLBILtNPBAbG9DlxtIwXfJQUPtvZJLDlpKW20Tvlwe4HNA2rdvn+688077cc1pbRMnTtTy5ct14MAB/e53v9OZM2cUFxenO++8U6+++mqtmzmGhoZq/Pjx9oWOa9asqXUzx+nTp9u/1owdO1bLli3ztbkAAAANaqvrp6WmXyIQbKeBB3J767rcQAq+Sw6a0t7mXHLQUtp6m/Dl8gCfAxI3cwQAAO1Za10/Lfl+iUCwnQYeiO1t7HKCYLvkoKH2Btq6l9pum/BlmS1yHyQAAIBg1dD10zVqrp+uCUhfv366RmPXTwMITC0+SAMAAEBb4vppAL4gIAEAgHaN66cB+IKABAAA2jWunwbgC65BAgAAAAALAQkAAAAALAQkAAAAALAQkAAAAADAQkACAAAAAAsBCQAAAAAsDPMNAACAgNVzzptt3QRcZTiCBAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABZGsUO71JwRb1whRotvkxKz3pLnosPruY+fGeOvpgEAACCAcQQJAAAAACwEJAAAAACwEJAAAAAAwEJAAhAQduzYobvuukvx8fFyOBx6/fXXvZ43xigrK0vx8fHq1KmThg8frkOHDnnVeDweTZs2Td26dVPnzp01duxYnTx50qumrKxMGRkZcrvdcrvdysjI0JkzZ1q4dwAAIFgQkAAEhC+//FK33HKLli1bVufzixcv1pIlS7Rs2TK9++67io2NVUpKis6ePWvXZGZmatOmTdqwYYN27typc+fOKT09XRcvXrRrJkyYoKKiIuXm5io3N1dFRUXKyMho8f4BAIDgwCh2AALC6NGjNXr06DqfM8Zo6dKlmjdvnsaNGydJWrt2rWJiYrR+/XpNnTpV5eXlWr16tV566SWNHDlSkrRu3Tp1795dW7duVVpamo4cOaLc3Fzt3r1bgwYNkiStWrVKycnJOnr0qPr06dM6nQUAAAGLgAQg4B07dkwlJSVKTU21p7lcLg0bNkwFBQWaOnWqCgsLVVVV5VUTHx+vxMREFRQUKC0tTbt27ZLb7bbDkSQNHjxYbrdbBQUF9QYkj8cjj8djP66oqJAkVVVVqaqqqt521zzXUE2g86UPrhDT0s1pFlcH4/XfQNOUdXu1bEvB3D8A7QcBCUDAKykpkSTFxMR4TY+JidHx48ftmo4dO6pr1661ampeX1JSoujo6Frzj46OtmvqsnDhQs2fP7/W9Ly8PIWHhzfa/vz8/EZrAl1T+rD4tlZoyBV4emB1WzehTlu2bGlybXvfls6fP9+KLQGAuhGQAAQNh8P7Br7GmFrTLnd5TV31jc1n7ty5mjFjhv24oqJC3bt3V2pqqrp06VLv66qqqpSfn6+UlBQ5nc4G2xmofOlDYtZbrdQq37g6GD09sFq/2NdBnuqGt5e2cDArrdGaq2Vbqjk6CwBtyeeAtGPHDv36179WYWGhiouLtWnTJt1zzz3288YYzZ8/XytXrlRZWZkGDRqk559/Xv369bNrPB6PZs2apVdeeUUXLlzQiBEj9MILL+jaa6+1a8rKyjR9+nS98cYbkqSxY8cqJydH11xzTfN7CyAoxcbGSrp0BCguLs6eXlpaah9Vio2NVWVlpcrKyryOIpWWlmrIkCF2zalTp2rN/7PPPqt1dOrrXC6XXC5XrelOp7NJX1abWhfImtIHz8XACx9f56l2BGQbfdk22vu2FOx9A9A++DyKHSNNAWhtCQkJio2N9To1p7KyUtu3b7fDT1JSkpxOp1dNcXGxDh48aNckJyervLxce/futWv27Nmj8vJyuwYAAFzdfA5Io0eP1oIFC+yRpL7u8pGmEhMTtXbtWp0/f17r16+XJHukqWeffVYjR47UgAEDtG7dOh04cEBbt26VJHukqf/4j/9QcnKykpOTtWrVKv3xj3/U0aNHr7DLAALRuXPnVFRUpKKiIkmXBmYoKirSJ598IofDoczMTGVnZ2vTpk06ePCgJk2apPDwcE2YMEGS5Ha7NXnyZM2cOVN/+tOftH//fj300EPq37+/Papd3759NWrUKE2ZMkW7d+/W7t27NWXKFKWnpzOCHdCOcZ81AL7w6zVIbTnSVHNGmWoPowL5qq5RpgJ9hKfW0tB6uJq2Eanxz0ZLrI99+/bpzjvvtB/XXPMzceJErVmzRrNnz9aFCxf06KOP2qfv5uXlKSIiwn7Nc889p9DQUI0fP94+fXfNmjUKCQmxa15++WVNnz7d3geNHTu23iPiANqHmrNffvjDH+r73/9+redrzn5Zs2aNevfurQULFiglJUVHjx619zGZmZnavHmzNmzYoKioKM2cOVPp6ekqLCy09zETJkzQyZMnlZubK0l6+OGHlZGRoc2bN7deZwFcMb8GpLYcaepKRplqD6MCNVVDo0wF6ghPra2u9eDLKFPtSX2fjZYYaWr48OEypv6Q7nA4lJWVpaysrHprwsLClJOTo5ycnHprIiMjtW7duitpKoAgw33WAPiiRUaxa4uRppozylR7GBXIV3WNMhXoIzy1lobWQ1NGmWpPGvtsMNIUgPYiWO6zFmxnvfizva11j7VgO6OmKe0NpO2lrbdhX5br14DUliNNXckoU+1hVKCmamgEp0Ad4am11bUerpbt43L1fTau1vUBoP0JtvusBdtZL/5ob2vfYy3YzqhpqL2BeAZMW23Dvpz94teA9PWRpgYMGCDpnyNNLVq0SJL3SFPjx4+X9M+RphYvXizJe6Sp22679KlgpCkAANBSAv0+a8F21os/29ta91gLtjNqmtLeQDoDpq23YV/OfvE5IJ07d05/+9vf7Mc1I01FRkbquuuus0ea6tWrl3r16qXs7Ox6R5qKiopSZGSkZs2aVe9IUytWrJB06UJHRpoCAAD+FGz3WQu2s1780d7WPrsl2M6oaai9gbittNU27MsyfR7me9++fRowYIB9hGjGjBkaMGCAfvnLX0qSZs+erczMTD366KMaOHCgPv300zpHmrrnnns0fvx4DR06VOHh4dq8eXOtkab69++v1NRUpaam6uabb9ZLL73ka3MBAADqxX3WAFzO5yNIjDQFAACCCWe/APBFi4xiBwAAECi4zxoAXxCQAABAu8bZLwB84fM1SAAAAADQXhGQAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwMIpdgOs55822bgIAAABw1eAIEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFgZpAICrVFMGgXGFGC2+TUrMekuei45WaBUAAG2LI0gAAAAAYOEIEgAAbcifR/I+fmaMP5sGIMj583YxV9P+hSNIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFgISgKCRlZUlh8Ph9S82NtZ+3hijrKwsxcfHq1OnTho+fLgOHTrkNQ+Px6Np06apW7du6ty5s8aOHauTJ0+2dlcAAECAIiABCCr9+vVTcXGx/e/AgQP2c4sXL9aSJUu0bNkyvfvuu4qNjVVKSorOnj1r12RmZmrTpk3asGGDdu7cqXPnzik9PV0XL15si+4AAIAAE9rWDQAAX4SGhnodNaphjNHSpUs1b948jRs3TpK0du1axcTEaP369Zo6darKy8u1evVqvfTSSxo5cqQkad26derevbu2bt2qtLS0Vu0LAAAIPH4/gsQpMABa0ocffqj4+HglJCTo/vvv10cffSRJOnbsmEpKSpSammrXulwuDRs2TAUFBZKkwsJCVVVVedXEx8crMTHRrgEAAFe3FjmC1K9fP23dutV+HBISYv9/zSkwa9asUe/evbVgwQKlpKTo6NGjioiIkHTpFJjNmzdrw4YNioqK0syZM5Wenq7CwkKveQG4ugwaNEi/+93v1Lt3b506dUoLFizQkCFDdOjQIZWUlEiSYmJivF4TExOj48ePS5JKSkrUsWNHde3atVZNzevr4vF45PF47McVFRWSpKqqKlVVVdX7uprnGqppS64Q03hNB+P132B0NfUhULc1qWmfh7Zsf1ZWlubPn+817ev7BmOM5s+fr5UrV6qsrEyDBg3S888/r379+tn1Ho9Hs2bN0iuvvKILFy5oxIgReuGFF3Tttde2al8AXJkWCUicAgOgJYwePdr+//79+ys5OVk33HCD1q5dq8GDB0uSHA6H12uMMbWmXa6xmoULF9b64iRJeXl5Cg8Pb7Td+fn5jda0hcW3Nb326YHVLdeQVnI19GHLli2t1JLma+jzcP78+VZsSW38wAtAaqGAVHMKjMvl0qBBg5Sdna3rr7++0VNgpk6d2ugpMAQkADU6d+6s/v3768MPP9Q999wj6dJRori4OLumtLTUPqoUGxuryspKlZWVeR1FKi0t1ZAhQ+pdzty5czVjxgz7cUVFhbp3767U1FR16dKl3tdVVVUpPz9fKSkpcjqdze1mi0nMeqvRGlcHo6cHVusX+zrIU91w0AxUV1MfDmYF7t/Ipnweao7OthV+4AUgtUBACqZTYAL99BepaafAXPEy2sHpJ/7Q0HoI5G2kJTT22QiU9eHxeHTkyBHdcccdSkhIUGxsrPLz8zVgwABJUmVlpbZv365FixZJkpKSkuR0OpWfn6/x48dLkoqLi3Xw4EEtXry43uW4XC65XK5a051OZ5OCT1PrWpvnYtPDgqfa4VN9ILoa+hCI29nlGvo8tHX7+YEXgNQCASkYT4EJ1NNfJN9OgblS7eH0E3+oaz0Ew2krLaG+z0ZbnQYza9Ys3XXXXbruuutUWlqqBQsWqKKiQhMnTpTD4VBmZqays7PVq1cv9erVS9nZ2QoPD9eECRMkSW63W5MnT9bMmTMVFRWlyMhIzZo1S/3797d/8QVwdQr0H3iD4Ufdr/Nne1vjx2Ip+H4wbu32Xul72dbbsC/LbfFhvgP5FJhAP/1FatopMFeqPZx+4g8NrYdAPm2lJTT22Wir02BOnjypBx54QJ9//rm+8Y1vaPDgwdq9e7d69OghSZo9e7YuXLigRx991L6IOi8vz74+QJKee+45hYaGavz48fZF1GvWrOH6AOAqFyw/8Abyj7p18Ud7W/PHYin4fjBurfb668fittqGfflxt8UDUjCcAhOop79Ivp0Cc8XLagenn/hDXeshULePllbfZ6Ot1seGDRsafN7hcCgrK0tZWVn11oSFhSknJ0c5OTl+bh2A9iTQfuANhh91v86f7W2NH4ul4PvBuLXbe6U/Frf1NuzLj7t+D0icAgMAAIJdoP7AG8g/6tbFH+1t7R9vg+0H49Zqr7+2u7bahn1Zpt8DEqfAAACAYMMPvABq+D0gcQoMAAAINvzA61+JWW9p8W2X/htMR2MAqRWuQQIAAAh0/MALoEaHtm4AAAAAAAQKAhIAAAAAWAhIAAAAAGDhGiSgCXrOedOv8/v4mTF+nR8AAAD8gyNIAAAAAGAhIAEAAACAhYAEAAAAABauQQIAoJ3gekkAuHIcQQIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAzz3QL8PcwqAAAAgNbBESQAAAAAsHAECQCCBEenAQBoeRxBAgAAAAALR5AAAAAANOhKz2JwhRgtvk1KzHpLnosOffzMGD+1zP84ggQAAAAAFgISAAAAAFg4xQ4AAOAq5+9BYFwhfp0d0Ko4ggQAAAAAFgISAAAAAFg4xQ5oA/48lSGQR4EBAAAINgEfkF544QX9+te/VnFxsfr166elS5fqjjvuaOtmAQhy7FuAxvnzx5yaIX6vBuxfgOAW0AHp1VdfVWZmpl544QUNHTpUK1as0OjRo3X48GFdd911fltOzXjsAK4OrbVvAXD1Yf8CBL+ADkhLlizR5MmT9eMf/1iStHTpUr311ltavny5Fi5c2MatAxCsWnvfwo8wwNWD7y5A8AvYgFRZWanCwkLNmTPHa3pqaqoKCgpq1Xs8Hnk8HvtxeXm5JOmLL75QVVVVncuoqqrS+fPnFVrVQRerr94vL6HVRufPV7MegnQ9nD592u/zrPlsnD59Wk6ns9bzZ8+elSQZY/y+7Jbm675Fat7+RWof+5hg/Vx8HX0IDDV9qG+/IgX3vkVq2f1LXfvlQQv/5Le2+/sLYTBus8HW5mBv7/836z/9Nu89c0c0WuPL/iVgA9Lnn3+uixcvKiYmxmt6TEyMSkpKatUvXLhQ8+fPrzU9ISGhxdrYnkxo6wYEiGBcD92ebbtlnz17Vm63u+0a0Ay+7lsk9i/B+Lm4HH0IDE3tQzDuWyT2L5cLxm022NpMey/x5btQU/YvARuQajgc3onYGFNrmiTNnTtXM2bMsB9XV1friy++UFRUVJ31klRRUaHu3bvrxIkT6tKli38bHkRYD5ewHv6psXVhjNHZs2cVHx/fBq3zj6buW6Tm7V+k9rFN0YfAcLX0oT3sW6SW2b8E2zYQbO2Vgq/NtNc3vuxfAjYgdevWTSEhIbV+cSktLa31y4wkuVwuuVwur2nXXHNNk5bVpUuXoNiwWhrr4RLWwz81tC6C8dddyfd9i3Rl+xepfWxT9CEwXA19CNZ9i9Q6+5dg2waCrb1S8LWZ9jZdU/cvAXuj2I4dOyopKUn5+fle0/Pz8zVkyJA2ahWAYMe+BUBLYf8CtA8BewRJkmbMmKGMjAwNHDhQycnJWrlypT755BM98sgjbd00AEGMfQuAlsL+BQh+AR2Q7rvvPp0+fVpPPfWUiouLlZiYqC1btqhHjx5+mb/L5dKTTz5Z69D21Yb1cAnr4Z/a+7po6X1LjfawHulDYKAPwaOl9i/Btv6Crb1S8LWZ9rYchwnWsTQBAAAAwM8C9hokAAAAAGhtBCQAAAAAsBCQAAAAAMBCQAIAAAAAy1UbkF544QUlJCQoLCxMSUlJ+vOf/9zWTWqyrKwsORwOr3+xsbH288YYZWVlKT4+Xp06ddLw4cN16NAhr3l4PB5NmzZN3bp1U+fOnTV27FidPHnSq6asrEwZGRlyu91yu93KyMjQmTNnvGo++eQT3XXXXercubO6deum6dOnq7KyskX6vWPHDt11112Kj4+Xw+HQ66+/7vV8oPX7wIEDGjZsmDp16qRvfvObeuqpp+SvMVEaWxeTJk2qtY0MHjy4Xa6LtubrvmT79u1KSkpSWFiYrr/+ev32t79tpZbWtnDhQt16662KiIhQdHS07rnnHh09erTB12zbtq3WtuVwOPTXv/61lVrtrbH9YV0C6T2QpJ49e9a5Th977LE66wPhPfDH/rguGzdu1E033SSXy6WbbrpJmzZtaqEeBKaFCxfK4XAoMzPTnuavv23+0FrfP/zp008/1UMPPaSoqCiFh4frW9/6lgoLCwO2zY3tDwKtvV999ZV+/vOfKyEhQZ06ddL111+vp556StXV1XZNoLW5ScxVaMOGDcbpdJpVq1aZw4cPmyeeeMJ07tzZHD9+vK2b1iRPPvmk6devnykuLrb/lZaW2s8/88wzJiIiwmzcuNEcOHDA3HfffSYuLs5UVFTYNY888oj55je/afLz8817771n7rzzTnPLLbeYr776yq4ZNWqUSUxMNAUFBaagoMAkJiaa9PR0+/mvvvrKJCYmmjvvvNO89957Jj8/38THx5vHH3+8Rfq9ZcsWM2/ePLNx40YjyWzatMnr+UDqd3l5uYmJiTH333+/OXDggNm4caOJiIgwv/nNb1plXUycONGMGjXKaxs5ffq0V017WRdtydd9yUcffWTCw8PNE088YQ4fPmxWrVplnE6n+a//+q9WbvklaWlp5sUXXzQHDx40RUVFZsyYMea6664z586dq/c177zzjpFkjh496rV9fX27aU2N7Q8vF2jvgTHGlJaWerU/Pz/fSDLvvPNOnfWB8B74Y398uYKCAhMSEmKys7PNkSNHTHZ2tgkNDTW7d+9u4d4Ehr1795qePXuam2++2TzxxBP2dH/9bfOH1vr+4S9ffPGF6dGjh5k0aZLZs2ePOXbsmNm6dav529/+FrBtbmx/EGjtXbBggYmKijJ//OMfzbFjx8zvf/978y//8i9m6dKldk2gtbkprsqAdNttt5lHHnnEa9qNN95o5syZ00Yt8s2TTz5pbrnlljqfq66uNrGxseaZZ56xp/3jH/8wbrfb/Pa3vzXGGHPmzBnjdDrNhg0b7JpPP/3UdOjQweTm5hpjjDl8+LCR5PWHadeuXUaS+etf/2qMufQHskOHDubTTz+1a1555RXjcrlMeXm53/pbl8v/IAdav1944QXjdrvNP/7xD7tm4cKFJj4+3lRXV/txTdReF8ZcCkh33313va9pr+uitfm6L5k9e7a58cYbvaZNnTrVDB48uMXa6IvS0lIjyWzfvr3empov52VlZa3XsAY0tD+sS6C/B8YY88QTT5gbbrih3s9HoL0Hzdkf12X8+PFm1KhRXtPS0tLM/fff7/c2B5qzZ8+aXr16mfz8fDNs2DA7IPnrb5u/tMb3D3/66U9/am6//fZ6nw/ENl/u6/uDQGzvmDFjzI9+9COvaePGjTMPPfSQMSY41nFdrrpT7CorK1VYWKjU1FSv6ampqSooKGijVvnuww8/VHx8vBISEnT//ffro48+kiQdO3ZMJSUlXv1zuVwaNmyY3b/CwkJVVVV51cTHxysxMdGu2bVrl9xutwYNGmTXDB48WG6326smMTFR8fHxdk1aWpo8Ho/X4evWEGj93rVrl4YNG+Z1M7S0tDT9/e9/18cff+z/FVCHbdu2KTo6Wr1799aUKVNUWlpqP3e1rYuW0Jx9ya5du2rVp6Wlad++faqqqmqxtjZVeXm5JCkyMrLR2gEDBiguLk4jRozQO++809JNa1B9+8O6BPp7UFlZqXXr1ulHP/qRHA5Hg7WB9B58XVP2x3Wp770Jpr/NzfXYY49pzJgxGjlypNd0f/1t86eW/v7hT2+88YYGDhyoe++9V9HR0RowYIBWrVplPx+Ibf66y/cHgdje22+/XX/605/0wQcfSJLef/997dy5U9/73vckBf46rs9VF5A+//xzXbx4UTExMV7TY2JiVFJS0kat8s2gQYP0u9/9Tm+99ZZWrVqlkpISDRkyRKdPn7b70FD/SkpK1LFjR3Xt2rXBmujo6FrLjo6O9qq5fDldu3ZVx44dW31dBlq/66qpedwa62b06NF6+eWX9fbbb+vZZ5/Vu+++q+9+97vyeDx2G66WddFSmrMvqW9dfPXVV/r8889brK1NYYzRjBkzdPvttysxMbHeuri4OK1cuVIbN27Ua6+9pj59+mjEiBHasWNHK7b2nxraH9YlkN8DSXr99dd15swZTZo0qd6aQHsPLteU/XF9rwvmv83NtWHDBr333ntauHBhref89bfNX1rj+4c/ffTRR1q+fLl69eqlt956S4888oimT5+u3/3ud3Z7Aq3NX3f5/iAQ2/vTn/5UDzzwgG688UY5nU4NGDBAmZmZeuCBBwK2zU0R2iZLDQCX/zJnjGn017pAMXr0aPv/+/fvr+TkZN1www1au3atfSF+c/p3eU1d9c2paU2B1O+62lLfa/3tvvvus/8/MTFRAwcOVI8ePfTmm29q3Lhx9b6uPa6LlubrNheo6+Lxxx/XX/7yF+3cubPBuj59+qhPnz724+TkZJ04cUK/+c1v9J3vfKelm1lLQ/vDGTNm1PmaQH0PJGn16tUaPXq011HZywXae1Cf5uyPg/lvc3OcOHFCTzzxhPLy8hQWFlZvnT/+tvlDa33/8Jfq6moNHDhQ2dnZki4ddT106JCWL1+uH/zgB3ZdILX56+rbHwRSe1999VWtW7dO69evV79+/VRUVKTMzEzFx8dr4sSJAdnmprjqjiB169ZNISEhtRJpaWlprXQbLDp37qz+/fvrww8/tEeTaah/sbGxqqysVFlZWYM1p06dqrWszz77zKvm8uWUlZWpqqqq1ddloPW7rpqaU9zaYjuLi4tTjx499OGHH9rtu1rXhb80Z19S37oIDQ1VVFRUi7W1MdOmTdMbb7yhd955R9dee63Prx88eLC9bbW1r+8P6xKo74EkHT9+XFu3btWPf/xjn18bSO9BU/bH9b2uPf1tborCwkKVlpYqKSlJoaGhCg0N1fbt2/Xv//7vCg0Nrfdou69/21pKS3z/8Ke4uDjddNNNXtP69u2rTz75xG5PoLW5Rl37g0Bs77/9279pzpw5uv/++9W/f39lZGToJz/5iX1ENBDb3BRXXUDq2LGjkpKSlJ+f7zU9Pz9fQ4YMaaNWXRmPx6MjR44oLi5OCQkJio2N9epfZWWltm/fbvcvKSlJTqfTq6a4uFgHDx60a5KTk1VeXq69e/faNXv27FF5eblXzcGDB1VcXGzX5OXlyeVyKSkpqUX7fLlA63dycrJ27NjhNdx1Xl6e4uPj1bNnT/+vgEacPn1aJ06cUFxcnKSre134S3P2JcnJybXq8/LyNHDgQDmdzhZra32MMXr88cf12muv6e2331ZCQkKz5rN//35722prX98f1iXQ3oOve/HFFxUdHa0xY8b4/NpAeg+asj+uS33vTbD+bW6KESNG6MCBAyoqKrL/DRw4UA8++KCKiop0/fXX++VvW0tpie8f/jR06NBaty744IMP1KNHD0n+++7QEuraHwRie8+fP68OHbzjREhIiD3MdyC2uUlaYSCIgFMzNO/q1avN4cOHTWZmpuncubP5+OOP27ppTTJz5kyzbds289FHH5ndu3eb9PR0ExERYbf/mWeeMW6327z22mvmwIED5oEHHqhzOMVrr73WbN261bz33nvmu9/9bp1DPN98881m165dZteuXaZ///51DvE8YsQI895775mtW7eaa6+9tsWG+T579qzZv3+/2b9/v5FklixZYvbv328PqRxI/T5z5oyJiYkxDzzwgDlw4IB57bXXTJcuXfw2tHVD6+Ls2bNm5syZpqCgwBw7dsy88847Jjk52Xzzm99sl+uiLTW2L5kzZ47JyMiw62uGmP7JT35iDh8+bFavXt2mQ0z/67/+q3G73Wbbtm1ew8qeP3/errm8D88995zZtGmT+eCDD8zBgwfNnDlzjCSzcePGtuhCo/vDQH8Paly8eNFcd9115qc//Wmt5wLxPfDH/jgjI8NrxMf/+Z//MSEhIeaZZ54xR44cMc8888xVNcx3ja+PYmeM//62+UNrff/wl71795rQ0FDzq1/9ynz44Yfm5ZdfNuHh4WbdunV2TaC12ZiG9weB1t6JEyeab37zm/Yw36+99prp1q2bmT17dsC2uSmuyoBkjDHPP/+86dGjh+nYsaP59re/3eCwtoGmZvx4p9Np4uPjzbhx48yhQ4fs56urq82TTz5pYmNjjcvlMt/5znfMgQMHvOZx4cIF8/jjj5vIyEjTqVMnk56ebj755BOvmtOnT5sHH3zQREREmIiICPPggw/WGlb2+PHjZsyYMaZTp04mMjLSPP74417DOftTzdC2l/+bOHFiQPb7L3/5i7njjjuMy+UysbGxJisry2/DWje0Ls6fP29SU1PNN77xDeN0Os11111nJk6cWKuf7WVdtLWG9iUTJ040w4YN86rftm2bGTBggOnYsaPp2bOnWb58eSu3+J/q2oYkmRdffNGuubwPixYtMjfccIMJCwszXbt2Nbfffrt58803W7/xlsb2h4H+HtR466237HsbXS4Q3wN/7I+HDRtm19f4/e9/b/r06WOcTqe58cYb2yx4t6XLA5K//rb5Q2t9//CnzZs3m8TERONyucyNN95oVq5c6fV8ILa5of1BoLW3oqLCPPHEE+a6664zYWFh5vrrrzfz5s0zHo8nYNvcFA5j2snt7AEAAADgCl111yABAAAAQH0ISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgwWdZWVlyOBzNeu3Pf/5zXXfddQoNDdU111zj34Z9jcPhUFZWlv348OHDysrK0scff9xiywQAAEDwIyCh1fzhD3/Qr371K/3gBz/Q9u3btXXr1lZb9uHDhzV//nwCEgAAABoU2tYNwNXj4MGDkqTp06crOjq6jVsDAAAA1MYRJDTozTff1Le+9S25XC4lJCToN7/5Ta0aY4xeeOEFfetb31KnTp3UtWtX/e///b/10Ucf2TU9e/bUz3/+c0lSTEyM1ylwr776qlJTUxUXF6dOnTqpb9++mjNnjr788kuv5QwfPlzDhw+vtfxJkyapZ8+e9fZhzZo1uvfeeyVJd955pxwOhxwOh9asWePbygAAAEC7xxEk1OtPf/qT7r77biUnJ2vDhg26ePGiFi9erFOnTnnVTZ06VWvWrNH06dO1aNEiffHFF3rqqac0ZMgQvf/++4qJidGmTZv0/PPPa/Xq1crNzZXb7da1114rSfrwww/1ve99T5mZmercubP++te/atGiRdq7d6/efvvtK+7HmDFjlJ2drZ/97Gd6/vnn9e1vf1uSdMMNN1zxvAEAANC+EJBQr3nz5ikmJkb5+fkKCwuTJKWlpXkdrdm9e7dWrVqlZ599VjNmzLCn33HHHerdu7eWLFmiRYsWacCAAXYgSkpKUrdu3ezamiNL0qWjUUOHDlXfvn01bNgw/eUvf9HNN998Rf34xje+oV69ekmSbrrpJg0ePPiK5gcAAID2i1PsUKcvv/xS7777rsaNG2eHI0mKiIjQXXfdZT/+4x//KIfDoYceekhfffWV/S82Nla33HKLtm3b1uiyPvroI02YMEGxsbEKCQmR0+nUsGHDJElHjhzxe98AAACA+nAECXUqKytTdXW1YmNjaz339WmnTp2SMUYxMTF1zuf6669vcDnnzp3THXfcobCwMC1YsEC9e/dWeHi4Tpw4oXHjxunChQtX1hEAAADABwQk1Klr165yOBwqKSmp9dzXp3Xr1k0Oh0N//vOf5XK5atXWNe3r3n77bf3973/Xtm3b7KNGknTmzJlatWFhYSovL681/fPPP29wGQAAAEBTcYod6tS5c2fddttteu211/SPf/zDnn727Flt3rzZfpyeni5jjD799FMNHDiw1r/+/fs3uJyaG85eHqRWrFhRq7Znz5764IMP5PF47GmnT59WQUFBo/2pmT9HpAAAANAQjiChXk8//bRGjRqllJQUzZw5UxcvXtSiRYvUuXNnffHFF5KkoUOH6uGHH9YPf/hD7du3T9/5znfUuXNnFRcXa+fOnerfv7/+9V//td5lDBkyRF27dtUjjzyiJ598Uk6nUy+//LLef//9WrUZGRlasWKFHnroIU2ZMkWnT5/W4sWL1aVLl0b7kpiYKElauXKlIiIiFBYWpoSEBEVFRTVz7QAAAKA94ggS6pWSkqLXX39dFRUVuu+++zRjxgx9//vf149+9COvuhUrVmjZsmXasWOH7r//fo0ZM0a//OUv9eWXX+q2225rcBlRUVF68803FR4eroceekg/+tGP9C//8i969dVXa9UOHTpUa9eu1aFDh3T33XdrwYIFmjt3bp33RrpcQkKCli5dqvfff1/Dhw/Xrbfe6nUkDAAAAJAkhzHGtHUjAAAAACAQcAQJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAQkAAAAALC02xvFVldX6+9//7siIiLkcDjaujlAu2KM0dmzZxUfH68OHfidBQAAtB/tNiD9/e9/V/fu3du6GUC7duLECV177bVt3QwAAAC/abcBKSIiQtKlL3BdunSpt66qqkp5eXlKTU2V0+lsreb5FX0IDFdTHyoqKtS9e3f7cwYAANBetNuAVHNaXZcuXRoNSOHh4erSpUtQf6mlD23vauwDp68CAID2hosHAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALD4FpK+++ko///nPlZCQoE6dOun666/XU089perqarvGGKOsrCzFx8erU6dOGj58uA4dOuQ1H4/Ho2nTpqlbt27q3Lmzxo4dq5MnT3rVlJWVKSMjQ263W263WxkZGTpz5kzzewoAAAAAjfDpRrGLFi3Sb3/7W61du1b9+vXTvn379MMf/lBut1tPPPGEJGnx4sVasmSJ1qxZo969e2vBggVKSUnR0aNHFRERIUnKzMzU5s2btWHDBkVFRWnmzJlKT09XYWGhQkJCJEkTJkzQyZMnlZubK0l6+OGHlZGRoc2bN/uz/7bErLfkueifm15+/MwYv8wHAAAAQOvyKSDt2rVLd999t8aMuRQAevbsqVdeeUX79u2TdOno0dKlSzVv3jyNGzdOkrR27VrFxMRo/fr1mjp1qsrLy7V69Wq99NJLGjlypCRp3bp16t69u7Zu3aq0tDQdOXJEubm52r17twYNGiRJWrVqlZKTk3X06FH16dPHbysAAAAAAGr4FJBuv/12/fa3v9UHH3yg3r176/3339fOnTu1dOlSSdKxY8dUUlKi1NRU+zUul0vDhg1TQUGBpk6dqsLCQlVVVXnVxMfHKzExUQUFBUpLS9OuXbvkdrvtcCRJgwcPltvtVkFBQZ0ByePxyOPx2I8rKiokSVVVVaqqqqq3TzXPuToYX1ZFgxpaXkuoWV5rL9ef6ENgaGofgrmPAAAADfEpIP30pz9VeXm5brzxRoWEhOjixYv61a9+pQceeECSVFJSIkmKiYnxel1MTIyOHz9u13Ts2FFdu3atVVPz+pKSEkVHR9dafnR0tF1zuYULF2r+/Pm1pufl5Sk8PLzRvj09sLrRmqbasmWL3+bli/z8/DZZrj/Rh8DQWB/Onz/fSi0BAABoXT4FpFdffVXr1q3T+vXr1a9fPxUVFSkzM1Px8fGaOHGiXedweF/LY4ypNe1yl9fUVd/QfObOnasZM2bYjysqKtS9e3elpqaqS5cu9S63qqpK+fn5+sW+DvJU++capINZaX6ZT1PV9CElJUVOp7NVl+0v9CEwNLUPNUdoAQAA2hufAtK//du/ac6cObr//vslSf3799fx48e1cOFCTZw4UbGxsZIuHQGKi4uzX1daWmofVYqNjVVlZaXKysq8jiKVlpZqyJAhds2pU6dqLf+zzz6rdXSqhsvlksvlqjXd6XQ26cuqp9rht0Ea2urLcVP7GsjoQ2BorA/B3j8AAID6+DTM9/nz59Whg/dLQkJC7GG+ExISFBsb63V6TmVlpbZv326Hn6SkJDmdTq+a4uJiHTx40K5JTk5WeXm59u7da9fs2bNH5eXldg0AAAAA+JtPR5Duuusu/epXv9J1112nfv36af/+/VqyZIl+9KMfSbp0WlxmZqays7PVq1cv9erVS9nZ2QoPD9eECRMkSW63W5MnT9bMmTMVFRWlyMhIzZo1S/3797dHtevbt69GjRqlKVOmaMWKFZIuDfOdnp7OCHYAAAAAWoxPASknJ0e/+MUv9Oijj6q0tFTx8fGaOnWqfvnLX9o1s2fP1oULF/Too4+qrKxMgwYNUl5enn0PJEl67rnnFBoaqvHjx+vChQsaMWKE1qxZY98DSZJefvllTZ8+3R7tbuzYsVq2bNmV9hcAAAAA6uVTQIqIiNDSpUvtYb3r4nA4lJWVpaysrHprwsLClJOTo5ycnHprIiMjtW7dOl+aBwAAAABXxKdrkAAAAACgPSMgAQAAAICFgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFp8D0qeffqqHHnpIUVFRCg8P17e+9S0VFhbazxtjlJWVpfj4eHXq1EnDhw/XoUOHvObh8Xg0bdo0devWTZ07d9bYsWN18uRJr5qysjJlZGTI7XbL7XYrIyNDZ86caV4vAQAAAKAJfApIZWVlGjp0qJxOp/77v/9bhw8f1rPPPqtrrrnGrlm8eLGWLFmiZcuW6d1331VsbKxSUlJ09uxZuyYzM1ObNm3Shg0btHPnTp07d07p6em6ePGiXTNhwgQVFRUpNzdXubm5KioqUkZGxpX3GAAAAADqEepL8aJFi9S9e3e9+OKL9rSePXva/2+M0dKlSzVv3jyNGzdOkrR27VrFxMRo/fr1mjp1qsrLy7V69Wq99NJLGjlypCRp3bp16t69u7Zu3aq0tDQdOXJEubm52r17twYNGiRJWrVqlZKTk3X06FH16dPnSvsNAAAAALX4FJDeeOMNpaWl6d5779X27dv1zW9+U48++qimTJkiSTp27JhKSkqUmppqv8blcmnYsGEqKCjQ1KlTVVhYqKqqKq+a+Ph4JSYmqqCgQGlpadq1a5fcbrcdjiRp8ODBcrvdKigoqDMgeTweeTwe+3FFRYUkqaqqSlVVVfX2qeY5Vwfjy6poUEPLawk1y2vt5foTfQgMTe1DMPcRAACgIT4FpI8++kjLly/XjBkz9LOf/Ux79+7V9OnT5XK59IMf/EAlJSWSpJiYGK/XxcTE6Pjx45KkkpISdezYUV27dq1VU/P6kpISRUdH11p+dHS0XXO5hQsXav78+bWm5+XlKTw8vNG+PT2wutGaptqyZYvf5uWL/Pz8NlmuP9GHwNBYH86fP99KLQEAAGhdPgWk6upqDRw4UNnZ2ZKkAQMG6NChQ1q+fLl+8IMf2HUOh8PrdcaYWtMud3lNXfUNzWfu3LmaMWOG/biiokLdu3dXamqqunTpUu9yq6qqlJ+fr1/s6yBPdcNtbKqDWWl+mU9T1fQhJSVFTqezVZftL/QhMDS1DzVHaAEAANobnwJSXFycbrrpJq9pffv21caNGyVJsbGxki4dAYqLi7NrSktL7aNKsbGxqqysVFlZmddRpNLSUg0ZMsSuOXXqVK3lf/bZZ7WOTtVwuVxyuVy1pjudziZ9WfVUO+S56J+A1FZfjpva10BGHwJDY30I9v4BAADUx6dR7IYOHaqjR496Tfvggw/Uo0cPSVJCQoJiY2O9Ts+prKzU9u3b7fCTlJQkp9PpVVNcXKyDBw/aNcnJySovL9fevXvtmj179qi8vNyuAQAAAAB/8+kI0k9+8hMNGTJE2dnZGj9+vPbu3auVK1dq5cqVki6dFpeZmans7Gz16tVLvXr1UnZ2tsLDwzVhwgRJktvt1uTJkzVz5kxFRUUpMjJSs2bNUv/+/e1R7fr27atRo0ZpypQpWrFihSTp4YcfVnp6OiPYAQAAAGgxPgWkW2+9VZs2bdLcuXP11FNPKSEhQUuXLtWDDz5o18yePVsXLlzQo48+qrKyMg0aNEh5eXmKiIiwa5577jmFhoZq/PjxunDhgkaMGKE1a9YoJCTErnn55Zc1ffp0e7S7sWPHatmyZVfaXwAAAACol08BSZLS09OVnp5e7/MOh0NZWVnKysqqtyYsLEw5OTnKycmptyYyMlLr1q3ztXkAAAAA0Gw+XYMEAAAAAO0ZAQkAAAAALAQkAAAAALAQkAAAAADAQkACAAAAAAsBCQAAAAAsBCQAAAAAsBCQAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAQkAAAAALAQkAAAAADAQkACAAAAAAsBCQAAAAAsBCQAAAAAsBCQAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAQkAAAAALAQkAAAAADAQkACAAAAAAsBCQAAAAAsBCQAAAAAsBCQAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAQkAAAAALBcUUBauHChHA6HMjMz7WnGGGVlZSk+Pl6dOnXS8OHDdejQIa/XeTweTZs2Td26dVPnzp01duxYnTx50qumrKxMGRkZcrvdcrvdysjI0JkzZ66kuQAAAADQoGYHpHfffVcrV67UzTff7DV98eLFWrJkiZYtW6Z3331XsbGxSklJ0dmzZ+2azMxMbdq0SRs2bNDOnTt17tw5paen6+LFi3bNhAkTVFRUpNzcXOXm5qqoqEgZGRnNbS4AAAAANKpZAencuXN68MEHtWrVKnXt2tWebozR0qVLNW/ePI0bN06JiYlau3atzp8/r/Xr10uSysvLtXr1aj377LMaOXKkBgwYoHXr1unAgQPaunWrJOnIkSPKzc3Vf/zHfyg5OVnJyclatWqV/vjHP+ro0aN+6DYAAAAA1BbanBc99thjGjNmjEaOHKkFCxbY048dO6aSkhKlpqba01wul4YNG6aCggJNnTpVhYWFqqqq8qqJj49XYmKiCgoKlJaWpl27dsntdmvQoEF2zeDBg+V2u1VQUKA+ffrUapPH45HH47EfV1RUSJKqqqpUVVVVb19qnnN1MM1YEw3Ps7XULK+1l+tP9CEwNLUPwdxHAACAhvgckDZs2KD33ntP7777bq3nSkpKJEkxMTFe02NiYnT8+HG7pmPHjl5Hnmpqal5fUlKi6OjoWvOPjo62ay63cOFCzZ8/v9b0vLw8hYeHN9qvpwdWN1rTVFu2bPHbvHyRn5/fJsv1J/oQGBrrw/nz51upJQAAAK3Lp4B04sQJPfHEE8rLy1NYWFi9dQ6Hw+uxMabWtMtdXlNXfUPzmTt3rmbMmGE/rqioUPfu3ZWamqouXbrUu9yqqirl5+frF/s6yFPdcBub6mBWml/m01Q1fUhJSZHT6WzVZfsLfQgMTe1DzRFaAACA9sangFRYWKjS0lIlJSXZ0y5evKgdO3Zo2bJl9vVBJSUliouLs2tKS0vto0qxsbGqrKxUWVmZ11Gk0tJSDRkyxK45depUreV/9tlntY5O1XC5XHK5XLWmO53OJn1Z9VQ75Lnon4DUVl+Om9rXQEYfAkNjfQj2/gEAANTHp0EaRowYoQMHDqioqMj+N3DgQD344IMqKirS9ddfr9jYWK/TcyorK7V9+3Y7/CQlJcnpdHrVFBcX6+DBg3ZNcnKyysvLtXfvXrtmz549Ki8vt2sAAAAAwN98OoIUERGhxMREr2mdO3dWVFSUPT0zM1PZ2dnq1auXevXqpezsbIWHh2vChAmSJLfbrcmTJ2vmzJmKiopSZGSkZs2apf79+2vkyJGSpL59+2rUqFGaMmWKVqxYIUl6+OGHlZ6eXucADQAAAADgD80axa4hs2fP1oULF/Too4+qrKxMgwYNUl5eniIiIuya5557TqGhoRo/frwuXLigESNGaM2aNQoJCbFrXn75ZU2fPt0e7W7s2LFatmyZv5sLAAAAALYrDkjbtm3zeuxwOJSVlaWsrKx6XxMWFqacnBzl5OTUWxMZGal169ZdafMAAAAAoMmadaNYAAAAAGiPCEgAAAAAYCEgAQAAAICFgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAICFgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYCEgAQAAAIDFp4C0cOFC3XrrrYqIiFB0dLTuueceHT161KvGGKOsrCzFx8erU6dOGj58uA4dOuRV4/F4NG3aNHXr1k2dO3fW2LFjdfLkSa+asrIyZWRkyO12y+12KyMjQ2fOnGleLwEAAACgCXwKSNu3b9djjz2m3bt3Kz8/X1999ZVSU1P15Zdf2jWLFy/WkiVLtGzZMr377ruKjY1VSkqKzp49a9dkZmZq06ZN2rBhg3bu3Klz584pPT1dFy9etGsmTJigoqIi5ebmKjc3V0VFRcrIyPBDlwEAAACgbqG+FOfm5no9fvHFFxUdHa3CwkJ95zvfkTFGS5cu1bx58zRu3DhJ0tq1axUTE6P169dr6tSpKi8v1+rVq/XSSy9p5MiRkqR169ape/fu2rp1q9LS0nTkyBHl5uZq9+7dGjRokCRp1apVSk5O1tGjR9WnTx9/9B0AAAAAvPgUkC5XXl4uSYqMjJQkHTt2TCUlJUpNTbVrXC6Xhg0bpoKCAk2dOlWFhYWqqqryqomPj1diYqIKCgqUlpamXbt2ye122+FIkgYPHiy3262CgoI6A5LH45HH47EfV1RUSJKqqqpUVVVVbx9qnnN1MM1ZBQ3Os7XULK+1l+tP9CEwNLUPwdxHAACAhjQ7IBljNGPGDN1+++1KTEyUJJWUlEiSYmJivGpjYmJ0/Phxu6Zjx47q2rVrrZqa15eUlCg6OrrWMqOjo+2ayy1cuFDz58+vNT0vL0/h4eGN9ufpgdWN1jTVli1b/DYvX+Tn57fJcv2JPgSGxvpw/vz5VmoJAABA62p2QHr88cf1l7/8RTt37qz1nMPh8HpsjKk17XKX19RV39B85s6dqxkzZtiPKyoq1L17d6WmpqpLly71Lreqqkr5+fn6xb4O8lQ33MamOpiV5pf5NFVNH1JSUuR0Olt12f5CHwJDU/tQc4QWAACgvWlWQJo2bZreeOMN7dixQ9dee609PTY2VtKlI0BxcXH29NLSUvuoUmxsrCorK1VWVuZ1FKm0tFRDhgyxa06dOlVruZ999lmto1M1XC6XXC5XrelOp7NJX1Y91Q55LvonILXVl+Om9jWQ0YfA0Fgfgr1/AAAA9fFpFDtjjB5//HG99tprevvtt5WQkOD1fEJCgmJjY71Oz6msrNT27dvt8JOUlCSn0+lVU1xcrIMHD9o1ycnJKi8v1969e+2aPXv2qLy83K4BAAAAAH/z6QjSY489pvXr1+sPf/iDIiIi7OuB3G63OnXqJIfDoczMTGVnZ6tXr17q1auXsrOzFR4ergkTJti1kydP1syZMxUVFaXIyEjNmjVL/fv3t0e169u3r0aNGqUpU6ZoxYoVkqSHH35Y6enpjGAHXKbnnDf9Ni9XiNHi2/w2OwAAgKDjU0Bavny5JGn48OFe01988UVNmjRJkjR79mxduHBBjz76qMrKyjRo0CDl5eUpIiLCrn/uuecUGhqq8ePH68KFCxoxYoTWrFmjkJAQu+bll1/W9OnT7dHuxo4dq2XLljWnjwAAAADQJD4FJGMaHwrb4XAoKytLWVlZ9daEhYUpJydHOTk59dZERkZq3bp1vjQPAAAAAK6IT9cgAQAAAEB7RkACAAAAAAsBCQAAAAAsBCQAAAAAsBCQAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAQkAAAAALAQkAAAAADAQkACAAAAAAsBCQAAAAAsBCQAAAAAsBCQAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAQkAAAAALAQkAAAAADAQkACAAAAAAsBCQAAAAAsBCQAAAAAsBCQAAAAAMBCQAIAAAAACwEJAAAAACwEJAAAAACwEJAAAAAAwEJAAgAAAAALAQkAAAAALAQkAAAAALAQkAAAAADAQkACAAAAAAsBCQAAAAAsAR+QXnjhBSUkJCgsLExJSUn685//3NZNAgAAANBOBXRAevXVV5WZmal58+Zp//79uuOOOzR69Gh98sknbd00AAAAAO1QQAekJUuWaPLkyfrxj3+svn37aunSperevbuWL1/e1k0DAAAA0A6FtnUD6lNZWanCwkLNmTPHa3pqaqoKCgpq1Xs8Hnk8HvtxeXm5JOmLL75QVVVVvcupqqrS+fPnFVrVQRerHX5p++nTp/0yn6aq6cPp06fldDpbddn+Qh+aL/SrL/03r2qj8+erG+3D2bNnJUnGGL8tGwAAIBAEbED6/PPPdfHiRcXExHhNj4mJUUlJSa36hQsXav78+bWmJyQktFgb69Pt2VZfJOA3E3yoPXv2rNxud4u1BQAAoLUFbECq4XB4H9UxxtSaJklz587VjBkz7MfV1dX64osvFBUVVWd9jYqKCnXv3l0nTpxQly5d/NfwVkQfAsPV1AdjjM6ePav4+PhWbB0AAEDLC9iA1K1bN4WEhNQ6WlRaWlrrqJIkuVwuuVwur2nXXHNNk5fXpUuXoP1SW4M+BIarpQ8cOQIAAO1RwA7S0LFjRyUlJSk/P99ren5+voYMGdJGrQIAAADQngXsESRJmjFjhjIyMjRw4EAlJydr5cqV+uSTT/TII4+0ddMAAAAAtEMBHZDuu+8+nT59Wk899ZSKi4uVmJioLVu2qEePHn5bhsvl0pNPPlnr9LxgQh8CA30AAAAIfg7DOL0AAAAAICmAr0ECAAAAgNZGQAIAAAAACwEJAAAAACwEJAAAAACwtLuA9MILLyghIUFhYWFKSkrSn//85wbrt2/frqSkJIWFhen666/Xb3/721o1Gzdu1E033SSXy6WbbrpJmzZtaqnmS/KtD6+99ppSUlL0jW98Q126dFFycrLeeustr5o1a9bI4XDU+vePf/wjIPqwbdu2Otv317/+1asukN+HSZMm1dmHfv362TWt/T7s2LFDd911l+Lj4+VwOPT66683+ppA/DwAAAC0pnYVkF599VVlZmZq3rx52r9/v+644w6NHj1an3zySZ31x44d0/e+9z3dcccd2r9/v372s59p+vTp2rhxo12za9cu3XfffcrIyND777+vjIwMjR8/Xnv27AmIPuzYsUMpKSnasmWLCgsLdeedd+quu+7S/v37veq6dOmi4uJir39hYWEB0YcaR48e9Wpfr1697OcC/X34P//n/3i1/cSJE4qMjNS9997rVdea78OXX36pW265RcuWLWtSfSB+HgAAAFqdaUduu+0288gjj3hNu/HGG82cOXPqrJ89e7a58cYbvaZNnTrVDB482H48fvx4M2rUKK+atLQ0c//99/up1d587UNdbrrpJjN//nz78Ysvvmjcbre/mtgoX/vwzjvvGEmmrKys3nkG2/uwadMm43A4zMcff2xPa+334eskmU2bNjVYE4ifBwAAgNbWbo4gVVZWqrCwUKmpqV7TU1NTVVBQUOdrdu3aVas+LS1N+/btU1VVVYM19c3zSjSnD5errq7W2bNnFRkZ6TX93Llz6tGjh6699lqlp6fXOsLkL1fShwEDBiguLk4jRozQO++84/VcsL0Pq1ev1siRI2vd1Li13ofmCLTPAwAAQFtoNwHp888/18WLFxUTE+M1PSYmRiUlJXW+pqSkpM76r776Sp9//nmDNfXN80o0pw+Xe/bZZ/Xll19q/Pjx9rQbb7xRa9as0RtvvKFXXnlFYWFhGjp0qD788EO/tl9qXh/i4uK0cuVKbdy4Ua+99pr69OmjESNGaMeOHXZNML0PxcXF+u///m/9+Mc/9premu9DcwTa5wEAAKAthLZ1A/zN4XB4PTbG1JrWWP3l032d55Vq7vJeeeUVZWVl6Q9/+IOio6Pt6YMHD9bgwYPtx0OHDtW3v/1t5eTk6N///d/91/Cv8aUPffr0UZ8+fezHycnJOnHihH7zm9/oO9/5TrPm6Q/NXd6aNWt0zTXX6J577vGa3hbvg68C8fMAAADQmtrNEaRu3bopJCSk1i/ZpaWltX7xrhEbG1tnfWhoqKKiohqsqW+eV6I5fajx6quvavLkyfrP//xPjRw5ssHaDh066NZbb22RIxdX0oevGzx4sFf7guV9MMbo//7f/6uMjAx17NixwdqWfB+aI9A+DwAAAG2h3QSkjh07KikpSfn5+V7T8/PzNWTIkDpfk5ycXKs+Ly9PAwcOlNPpbLCmvnleieb0Qbp05GjSpElav369xowZ0+hyjDEqKipSXFzcFbf5cs3tw+X279/v1b5geB+kS8Nk/+1vf9PkyZMbXU5Lvg/NEWifBwAAgDbRNmNDtIwNGzYYp9NpVq9ebQ4fPmwyMzNN586d7ZHE5syZYzIyMuz6jz76yISHh5uf/OQn5vDhw2b16tXG6XSa//qv/7Jr/ud//seEhISYZ555xhw5csQ888wzJjQ01OzevTsg+rB+/XoTGhpqnn/+eVNcXGz/O3PmjF2TlZVlcnNzzf/7f//P7N+/3/zwhz80oaGhZs+ePQHRh+eee85s2rTJfPDBB+bgwYNmzpw5RpLZuHGjXRPo70ONhx56yAwaNKjOebb2+3D27Fmzf/9+s3//fiPJLFmyxOzfv98cP368zj4E4ucBAACgtbWrgGSMMc8//7zp0aOH6dixo/n2t79ttm/fbj83ceJEM2zYMK/6bdu2mQEDBpiOHTuanj17muXLl9ea5+9//3vTp08f43Q6zY033uj1xb2t+zBs2DAjqda/iRMn2jWZmZnmuuuuMx07djTf+MY3TGpqqikoKAiYPixatMjccMMNJiwszHTt2tXcfvvt5s0336w1z0B+H4wx5syZM6ZTp05m5cqVdc6vtd+HmuHT69s2guXzAAAA0JocxlhXYQMAAADAVa7dXIMEAAAAAFeKgAQAAAAAFgISAAAAAFgISAAAAABgISABAAAAgIWABAAAAAAWAhIAAAAAWAhIAAAAAGAhIAEAAACAhYAEAAAAABYCEgAAAABYCEgAAAAAYPn/AalsMaoDpPJ5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the features follow a Gaussian (normal) distribution. Let's see if we our dataset is really suitable for a Gaussian Naive Bayes. So, we check if each feature, given a specific class of the target variable (default), follows a Gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'credit_lines_outstanding'}>,\n",
       "        <Axes: title={'center': 'loan_amt_outstanding'}>,\n",
       "        <Axes: title={'center': 'total_debt_outstanding'}>],\n",
       "       [<Axes: title={'center': 'income'}>,\n",
       "        <Axes: title={'center': 'years_employed'}>,\n",
       "        <Axes: title={'center': 'fico_score'}>],\n",
       "       [<Axes: title={'center': 'default'}>, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of features when default = 1\n",
    "df[df.default==1].hist(figsize=(10,10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'credit_lines_outstanding'}>,\n",
       "        <Axes: title={'center': 'loan_amt_outstanding'}>,\n",
       "        <Axes: title={'center': 'total_debt_outstanding'}>],\n",
       "       [<Axes: title={'center': 'income'}>,\n",
       "        <Axes: title={'center': 'years_employed'}>,\n",
       "        <Axes: title={'center': 'fico_score'}>],\n",
       "       [<Axes: title={'center': 'default'}>, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of features when default = 0\n",
    "df[df.default==0].hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full_train, df_test = train_test_split(df, test_size=0.2, random_state=1)\n",
    "\n",
    "df_train, df_val = train_test_split(df_full_train, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "model_g = GaussianNB()\n",
    "model_g.fit(df_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9813333333333333"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy on the training set\n",
    "(y_train== model_g.predict(df_train)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9775"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy on the validation set, to see if it needs tuning\n",
    "(y_val== model_g.predict(df_val)).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get $97.8\\%$ accuracy on the validation set. It looks like we don't need to tune the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.977"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy on the test set:\n",
    "(y_test== model_g.predict(df_test)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = loan_data.copy()\n",
    "del df_full['customer_id']\n",
    "y_fulltrain = df_full.default.values\n",
    "del df_full['default']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['credit_lines_outstanding', 'loan_amt_outstanding',\n",
       "       'total_debt_outstanding', 'income', 'years_employed', 'fico_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prompt_user():\n",
    "    data = {}\n",
    "    data['credit_lines_outstanding'] = float(input(\"Enter the number of credit lines outstanding: \"))\n",
    "    data['loan_amt_outstanding'] = float(input(\"Enter the amount of loan outstanding: \"))\n",
    "    data['total_debt_outstanding'] = float(input(\"Enter the total debt outstanding: \"))\n",
    "    data['income'] = float(input(\"Enter the income: \"))\n",
    "    data['years_employed'] = float(input(\"Enter the number of years employed: \"))\n",
    "    data['fico_score'] = float(input(\"Enter the FICO score: \"))\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# takes user input about the loan data\n",
    "user_data = prompt_user()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the function that takes in the properties of a loan and outputs the expected loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianNB().fit(df_full, y_fulltrain)\n",
    "# loan here is the loan profile dict\n",
    "def expectedLoss():\n",
    "    loan = pd.DataFrame(prompt_user(), index=[0])\n",
    "    #return model.predict_proba(loan)[:,1][0]*100 + '\\%'\n",
    "    prob_default = model.predict_proba(loan)[:,1][0] \n",
    "    loan_amt = float(loan.loan_amt_outstanding)\n",
    "    return prob_default * loan_amt * (1 - 0.1) # recovery rate = 10% (given)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/h0/722z94dd3fb0pfv4wg3qmdkh0000gn/T/ipykernel_74903/2507886040.py:7: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  loan_amt = float(loan.loan_amt_outstanding)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "564.8283882093126"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expectedLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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