{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a3be1a5b",
   "metadata": {},
   "source": [
    "## <center> Predicting Critical Health Code Violations: Dining in Las Vegas"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "979ea1c1",
   "metadata": {},
   "source": [
    "Restaurants are frequent settings for foodborne illness outbreaks. Periodic inspection of restaurants is crucial to ensure commercial food establishments carry out safe food handling procedures. Predictive analytics can help identify problematic restaurants and maximize the utility of limited enforcement resources. To that end, I develop a machine learning model in this notebook. The resulting model can predict restaurants and other dining venues likely to face critical health code challenges in Las Vegas during the next inspection period.\n",
    "\n",
    "The final model uses five numeric features to predict if a restaurant receives a C grade or below during the inspection (i.e., critical violation of sanitary practices). Three predictors refer to employee characteristics; the remaining two features describe the degree of violations. The model is a good tool if the purpose is to identify as many problematic restaurants as possible. The model could correctly identify 80% of problematic restaurants in a holdout test data set. At the same time, the model misclassified many compliant restaurants as non-compliant ones."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "11d76521",
   "metadata": {},
   "source": [
    "# Initial Data Exploration and Observations\n",
    "I converted all uppercase characters in column names into lowercase characters to facilitate data manipulation. I also renamed the target variable to 'target.'\n",
    "\n",
    "The concise summary of the dataframe indicates that many rows have some missing values. The heat map shows that missing values do not seem to occur because of a systematic reason. As such, the missing values can be imputed without introducing a significant bias to the data.\n",
    "\n",
    "While the dataframe contains many features, my unreported exploratory analyses indicate that most of these features are neither predictive of the target nor necessary. For instance, the information contained in 'current_grade' is already reflected in 'current_demerits.' As such, I focus my analyses on numeric features that tend to contain richer information compared to categorical data. I focus on the following five numeric features: \n",
    "- current_demerits\n",
    "- employee_count\n",
    "- inspection_demerits\n",
    "- median_employee_age\n",
    "- median_employee_tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f5236e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import Python libraries\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', None, 'display.max_columns', None, 'display.width', None)\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize':(17, 4)})\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97039d6b",
   "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>restaurant_serial_number</th>\n",
       "      <th>restaurant_permit_number</th>\n",
       "      <th>restaurant_name</th>\n",
       "      <th>restaurant_location</th>\n",
       "      <th>restaurant_category</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>current_demerits</th>\n",
       "      <th>current_grade</th>\n",
       "      <th>employee_count</th>\n",
       "      <th>median_employee_age</th>\n",
       "      <th>median_employee_tenure</th>\n",
       "      <th>inspection_time</th>\n",
       "      <th>inspection_type</th>\n",
       "      <th>inspection_demerits</th>\n",
       "      <th>violations_raw</th>\n",
       "      <th>record_updated</th>\n",
       "      <th>lat_long_raw</th>\n",
       "      <th>first_violation</th>\n",
       "      <th>second_violation</th>\n",
       "      <th>third_violation</th>\n",
       "      <th>first_violation_type</th>\n",
       "      <th>second_violation_type</th>\n",
       "      <th>third_violation_type</th>\n",
       "      <th>number_of_violations</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6478</th>\n",
       "      <td>DA1546062</td>\n",
       "      <td>PR0013888</td>\n",
       "      <td>Pump N Snack Deli</td>\n",
       "      <td>Pump N Snack</td>\n",
       "      <td>Snack Bar</td>\n",
       "      <td>329 N Las Vegas Blvd</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>89101-2965</td>\n",
       "      <td>8.0</td>\n",
       "      <td>A</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.953746</td>\n",
       "      <td>6.272959</td>\n",
       "      <td>7/1/2015 14:30</td>\n",
       "      <td>Routine Inspection</td>\n",
       "      <td>15</td>\n",
       "      <td>2,152,172,222,909,290,000,000,000</td>\n",
       "      <td>7/6/2015 12:12</td>\n",
       "      <td>(36.171985, 115.139176)</td>\n",
       "      <td>215.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>Major</td>\n",
       "      <td>Major</td>\n",
       "      <td>Major</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14817</th>\n",
       "      <td>DA0984229</td>\n",
       "      <td>PR0022849</td>\n",
       "      <td>Tortilleria San Diego</td>\n",
       "      <td>Tortilleria San Diego</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>235 N Eastern Ave 117</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89101-4542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>15.0</td>\n",
       "      <td>28.561471</td>\n",
       "      <td>2.513798</td>\n",
       "      <td>5/30/2012 10:10</td>\n",
       "      <td>Routine Inspection</td>\n",
       "      <td>19</td>\n",
       "      <td>211,212,214,215,216,000,000,000,000</td>\n",
       "      <td>2/21/2013 22:26</td>\n",
       "      <td>(36.1651291, 115.1164481)</td>\n",
       "      <td>211.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>Major</td>\n",
       "      <td>Major</td>\n",
       "      <td>Major</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10530</th>\n",
       "      <td>DA0946895</td>\n",
       "      <td>PR0002002</td>\n",
       "      <td>Sbarros #551 at Boulevard Mall</td>\n",
       "      <td>Boulevard Mall - Sbarros #551</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>3528 S Maryland Pkwy</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>89169-3054</td>\n",
       "      <td>8.0</td>\n",
       "      <td>A</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.845926</td>\n",
       "      <td>3.101600</td>\n",
       "      <td>2/28/2012 12:00</td>\n",
       "      <td>Routine Inspection</td>\n",
       "      <td>6</td>\n",
       "      <td>222,229,230,233</td>\n",
       "      <td>2/21/2013 22:26</td>\n",
       "      <td>(36.1248998, 115.1350531)</td>\n",
       "      <td>222.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>Major</td>\n",
       "      <td>Non-Major</td>\n",
       "      <td>Non-Major</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      restaurant_serial_number restaurant_permit_number   \n",
       "6478                 DA1546062                PR0013888  \\\n",
       "14817                DA0984229                PR0022849   \n",
       "10530                DA0946895                PR0002002   \n",
       "\n",
       "                      restaurant_name            restaurant_location   \n",
       "6478                Pump N Snack Deli                   Pump N Snack  \\\n",
       "14817           Tortilleria San Diego          Tortilleria San Diego   \n",
       "10530  Sbarros #551 at Boulevard Mall  Boulevard Mall - Sbarros #551   \n",
       "\n",
       "      restaurant_category                address       city   state   \n",
       "6478            Snack Bar   329 N Las Vegas Blvd  Las Vegas  Nevada  \\\n",
       "14817          Restaurant  235 N Eastern Ave 117  Las Vegas     NaN   \n",
       "10530          Restaurant   3528 S Maryland Pkwy  Las Vegas  Nevada   \n",
       "\n",
       "              zip  current_demerits current_grade  employee_count   \n",
       "6478   89101-2965               8.0             A            24.0  \\\n",
       "14817  89101-4542               0.0             A            15.0   \n",
       "10530  89169-3054               8.0             A            24.0   \n",
       "\n",
       "       median_employee_age  median_employee_tenure  inspection_time   \n",
       "6478             32.953746                6.272959   7/1/2015 14:30  \\\n",
       "14817            28.561471                2.513798  5/30/2012 10:10   \n",
       "10530            36.845926                3.101600  2/28/2012 12:00   \n",
       "\n",
       "          inspection_type inspection_demerits   \n",
       "6478   Routine Inspection                  15  \\\n",
       "14817  Routine Inspection                  19   \n",
       "10530  Routine Inspection                   6   \n",
       "\n",
       "                            violations_raw   record_updated   \n",
       "6478     2,152,172,222,909,290,000,000,000   7/6/2015 12:12  \\\n",
       "14817  211,212,214,215,216,000,000,000,000  2/21/2013 22:26   \n",
       "10530                      222,229,230,233  2/21/2013 22:26   \n",
       "\n",
       "                    lat_long_raw  first_violation  second_violation   \n",
       "6478     (36.171985, 115.139176)            215.0             217.0  \\\n",
       "14817  (36.1651291, 115.1164481)            211.0             212.0   \n",
       "10530  (36.1248998, 115.1350531)            222.0             229.0   \n",
       "\n",
       "       third_violation first_violation_type second_violation_type   \n",
       "6478             222.0                Major                 Major  \\\n",
       "14817            214.0                Major                 Major   \n",
       "10530            230.0                Major             Non-Major   \n",
       "\n",
       "      third_violation_type number_of_violations target  \n",
       "6478                 Major                    7      0  \n",
       "14817                Major                    9      0  \n",
       "10530            Non-Major                    4      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in the dataset into dataframe and display three randomly selected rows\n",
    "df = pd.read_csv('TRAIN_SET_2021.csv')\n",
    "df.columns = df.columns.str.lower()\n",
    "df.rename(columns = {'next_inspection_grade_c_or_below':'target'}, inplace = True) \n",
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7cfcd0ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant_serial_number      0\n",
       "restaurant_permit_number      0\n",
       "restaurant_name              65\n",
       "restaurant_location         200\n",
       "restaurant_category         130\n",
       "address                      70\n",
       "city                        236\n",
       "state                       209\n",
       "zip                          59\n",
       "current_demerits            216\n",
       "current_grade               308\n",
       "employee_count               93\n",
       "median_employee_age          34\n",
       "median_employee_tenure      297\n",
       "inspection_time             183\n",
       "inspection_type             221\n",
       "inspection_demerits         254\n",
       "violations_raw              165\n",
       "record_updated              119\n",
       "lat_long_raw                 15\n",
       "first_violation             212\n",
       "second_violation             85\n",
       "third_violation              61\n",
       "first_violation_type        146\n",
       "second_violation_type       267\n",
       "third_violation_type        173\n",
       "number_of_violations        169\n",
       "target                       40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display number of missing variables per column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a2ce55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+sAAAGICAYAAAA5/3GUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAC+iElEQVR4nOzdd3yN5//H8deRYY9GExRFtWipGcW3KvaOkNhK1aZWtEiJIMReNarRpUZLjMRoBCk1StFh02pLzYotIZF1//7wyPklNarIfU6a9/Px6KPOfe5zrs99ktzn/tzXdX0ui2EYBiIiIiIiIiJiN7LYOgARERERERERSUvJuoiIiIiIiIidUbIuIiIiIiIiYmeUrIuIiIiIiIjYGSXrIiIiIiIiInZGybqIiIiIiIiInVGyLiIich9z5syhdOnSlC5dmg8//PCh+44fP96679mzZ63bS5cujZeXV7rHGBkZmW5tPMj69espXbo077zzzj/uO3HiREqXLk1oaOgjv//q1aspXbo0CxcufIIoRUREMi4l6yIiIv9g8+bND3zOMAw2bdp03+f69+9P+/bt0yssXnvtNfr370+JEiXSrY0HqV+/Prly5WL79u3ExMQ8cL/k5GTCw8PJkSMHDRs2NDFCERGRjE3JuoiIyEO4urpy9OjRND3mqf38889cvHiRHDly3PPcgAED6NChQ7rFVq1aNQYMGEDJkiXTrY0HyZYtG40bNyY+Pv6hPft79uwhKiqKRo0akTNnThMjFBERydiUrIuIiDxEvXr1AB6YkG7cuJHcuXPj7u5uZlh2oVWrVgCEh4c/cJ+1a9em2VdEREQejZJ1ERGRh6hevTp58+Z94FD3zZs3U7duXZycnO557n5z1hcvXoy3tzeVKlWicuXKdOzYkQ0bNtzz2kfZ735z1kuXLo2fnx8//fQTnTt3plKlSlStWpXBgwffd3TA7t276dy5M1WqVKF69eoEBATw66+/Urp0aebMmfPQz6ZKlSoULVqUXbt2cf369Xuej4+PZ/PmzRQuXJjXXnsNgKtXrzJ58mSaNGlChQoVqFChAs2aNeOjjz4iMTHxoe3VrVv3vjdF9uzZQ+nSpQkKCkqzPSYmhmnTplG/fn3KlSvHG2+8wejRo7ly5co97/GoPxcRERGzKFkXERF5CCcnJ+rUqcPPP//M5cuX0zx38OBBzp07R+PGjR/pvRYsWMD48eMBaN++Pd7e3pw+fZrBgwcTFhb2r/d7kCNHjtClSxeyZMlChw4dKF26NBs2bKB79+4kJydb99u0aRPdu3fn+PHjNGzYkKZNm7Jx40b69ev3SMdjsVjw8vIiISHhvvP6t27dSnR0NC1btsRisRAdHU3btm1ZtGgRL774Il26dKF58+ZcunSJmTNnMn369Edq91FER0fToUMHPv74Y4oUKUKXLl2oVKkSISEhtGnThqioKOu+T/p5i4iIpAdHWwcgIiJi7xo2bEhYWBjffPMN7dq1s26PiIggV65c1KxZk5UrV/7j+3z66ac8//zzhISE4Oh49yu4R48eNGjQgMWLF9OyZct/td+D/PrrrwwdOpQePXoAd4vg9ejRg507d7Jnzx5q1KhBbGwsY8eOJWfOnKxYsYLixYtb2/k3Q9ZbtmzJvHnzCA8Pp02bNmmeW7duHRaLxfp+X331FWfOnGH8+PFp9u3fvz8NGzZk3bp1DB8+/JHbfpgZM2bw66+/EhAQQKdOnazbv/nmG/r160dQUBAffPAB8OSft4iISHpQz7qIiMg/qFmzJjly5LhnKPymTZuoW7cuzs7Oj/Q+hmFw9epVTp48ad1WsGBBNmzYwJdffvmv93uQbNmy0aVLF+tji8XCG2+8AcCpU6cA2LFjB5cvX+bNN9+0JuoAzz33HG+//fYjHQ9A0aJFqVKlCnv27EkzvDw6Oppt27bh7u5O0aJFgbuf49ixY+9JfgsVKkTRokW5evXqI7f7MImJiYSFhfHSSy+lSdThbg2CypUrs3nzZmsV+yf9vEVERNKDetZFRET+QdasWalduzabN28mOjqa3Llzc+TIEc6cOcP777//yO/Trl07FixYQIsWLXj11VepVasWHh4evPrqq4+134M899xz99xAyJ07N3B3HjnAoUOHAChfvvw9r69cufIjHxPc7V3/4YcfiIiIsCbHERERxMfHp0nMX3nlFV555RVu3brFgQMH+PPPPzl16hSHDh3izz//JCkp6V+1+yAnT57k9u3bJCUl3Xfe/Z07d0hKSuKXX36hSpUqT/x5i4iIpAcl6yIiIo+gYcOGhIeHs3XrVlq0aMHGjRvJmTOntcf6UQwZMoRixYqxbNkyDh48yIEDB5gzZw4lSpRg9OjR1KhR41/t9yD36+m3WCzA3V5kgGvXrgHw7LPP3rOvm5vbIx8TQJMmTRg/fjzh4eHWZH3dunVkz549zXz+O3fuMGPGDJYvX05sbCwABQoUoGrVqjzzzDNcunTpX7X7IDdv3gTgjz/+YO7cuQ/c78aNG8CTf94iIiLpQcm6iIjII/Dw8CBbtmxs3rzZmqzXqVPnkYfAw92EuXXr1rRu3ZorV66wa9cuNm/ezKZNm+jbty9btmzBxcXlkfd7Erly5QKwDgVP7X7b/um96tevz9dff83FixcB2LdvH56entZ2ACZNmsSXX35Jo0aN6NSpE6VLlyZfvnzA3YT/UZL1lJsNqcXFxaV5nLKeu5eXF1OmTPnH9zTj8xYREfm3NGddRETkEeTIkYOaNWuyY8cODh48yKlTp2jSpMkjv/7atWvMmTOH0NBQAPLnz4+npyezZ8/G29ub2NhYjh49+sj7PamyZcsC/z8cPrUDBw786/fz8vLCMAwiIyPZtGkTycnJ9xSqW79+Pfnz5+eDDz6gWrVq1kQ9Li6O8+fPA/dPxlM4OTkRFxd3zz6nT59O87hEiRI4Oztz5MiR+77fwoUL+fDDD7l27Zppn7eIiMi/pWRdRETkETVs2JDY2FiCgoLIkSPHvxoCnzNnThYtWsTMmTPvWZM8JVF97rnnHnm/J1WvXj3y5cvHokWLOHPmjHX7X3/9xaeffvqv3+/111/Hzc2NLVu2sHnzZp577jmqV6+eZp+sWbNy584d6zB1gKSkJIKCgqy94wkJCQ9s44UXXiAxMZHt27dbt12/fp2lS5fe007Tpk357bff+Pzzz9M8t2fPHqZMmcKqVavImzevaZ+3iIjIv6Vh8CIiIo+obt26ODk5sX//fpo3b07WrFkf+bXOzs4MHDiQ8ePH07x5cxo0aEC2bNnYt28fhw4dwsvLixdeeAHgkfd7Ejly5CAgIIB3330XHx8fGjRogIODQ5qK91myPPo9fQcHBzw9PVm0aBHJycn06tXLOk8+haenJ5999hk+Pj7Ur1+fxMREdu7cycmTJ3FxceHq1atcv379gXPm27Zty5YtWxg8eDCenp44OzsTERFBsWLF0lRyBxg+fDg///wzkydP5ptvvqF8+fJcvHiRTZs24ejoyIQJE8iSJcu/+rmIiIiYScm6iIjII8qdOzc1atRg+/btNGrU6F+/vnPnzuTPn59FixYRHh5ObGwsxYsX5/333+fNN9/81/s9qWbNmpE9e3Y++ugj1q9fT7Zs2WjWrBnu7u74+vqSPXv2f/V+rVq1svbK32+tdl9fX3LmzMnatWv58ssvcXFxoWTJkvj7+/P7778zYcIEtm3bds967Snq1KnDtGnT+OSTTwgNDcXFxYVWrVrRpUsXatasmWZfFxcXQkJCCA4OZvPmzSxevBgXFxfq1q1Lv379KFOmjHVfsz5vERGRf8NiPGxymIiIiPwnxcTEcOvWLdzc3O7pAV+1ahUjRoxg5syZNG3a1EYRioiIZG52MWd93bp1NG3alIYNG94z70xERESevpMnT1KrVi1GjBiRZntcXBxLly7F0dGRKlWq2Cg6ERERsfkw+IsXLzJz5kxWr16Ns7Mz7du3p1q1arz44ou2Dk1EROQ/q2zZspQvX57Vq1dz9uxZypcvT1xcHFu3buXcuXP4+vpSoEABW4cpIiKSadl8GHxoaCj79u1jwoQJAMybNw/DMOjfv78twxIREfnPi46O5vPPPyciIoLz58/j5ORE6dKlefPNN2ncuLGtwxMREcnUbN6zHhUVhaurq/Wxm5sbBw8etGFEIiIimUPu3LkZOHAgAwcOtHUoIiIi8jc2T9aTk5PTFLYxDOOeQjcP9mv6BCUiIiIiIiKS7ko98BmbF5grWLAgly5dsj6+dOnSA9dXFREREREREckMbJ6s/+9//2P37t1cvXqV2NhYNm3aRK1atWwdloiIiIiIiIjN2HwYfIECBfD19aVLly4kJCTQunVrypcvb+uwRERERERERGzG5tXgn4zmrIuIiIiIiEhGZcdz1kVEREREREQkLSXrIiIiIiIiInZGybqIiIiIiIiInUmXAnNr1qxhwYIFANSqVYvhw4ezfPlyFi9ejMVioVy5cowdOxZnZ2fmzp3LqlWryJMnDwBt27alU6dO6RGWiIiIiIiISIbw1AvMxcbG4uHhQUREBHny5KFDhw60bt2aTz/9lNWrV5MzZ078/Px4+eWX6dq1K3369KF3795UqlTpMVpTgTkRERERERHJqEwsMJeUlERycjKxsbEkJiaSmJhIiRIlGD16NLly5cJisVCqVCnOnz8PwOHDhwkODsbT05PAwEDu3LnztEMSERERERERyVCeerKeK1cuBg0aRJMmTfDw8KBw4cJUrVqV119/HYCrV6+ydOlS6tWrx61bt3j55ZcZOnQooaGh3Lx5kw8//PBphyQiIiIiIiKSoTz1ZP348eOsWrWKrVu3smPHDrJkycKnn34KwMWLF3nrrbfw8fGhWrVq5MyZk48//piSJUvi6OhIt27d2LZt29MOSURERERERCRDeerJ+s6dO6lRowb58+fH2dkZb29v9u7dy++//0779u1p1aoV77zzDgDnz59n5cqV1tcahoGjY7rUvBMRERERERHJMJ56ZlymTBmmTp3K7du3yZ49O1u2bOGFF16ge/fuDB48mJYtW1r3zZYtG1OnTqVatWoUKVKEpUuX0qBBg6cdkohIGtmfH23rEACIPT3W1iGIiIiIiJ166tXgARYsWMDq1atxcnLi1VdfpVixYsyZM4eSJUta96lbty6DBg1i48aNzJkzh4SEBCpXrmxd0u3RqBq8iIiIiIiIZFQPrgafLsm6eZSsi4iIiIiISEZl4tJtIiIiIiIiIvJklKyLiIiIiIiI2JknTtZjYmJo3rw5Z8+eBeDnn3+mbdu2NGvWjCFDhhAfHw/A3LlzqVOnDl5eXnh5ebF06VLgbkX4Tp060bhxY/r27cutW7eeNCQRERERERGRDO2JkvUDBw7QoUMHTp06BdxN3AcMGEBgYCBff/01gHVptsOHDzNjxgzWrFnDmjVr6NSpEwBjx46lY8eOREREUK5cOT788MMnCUlEREREREQkw3uiZD0kJITRo0fj5uYGwHfffUfFihUpU6YMAP7+/tal2A4fPkxwcDCenp4EBgZy584dEhIS2LdvH40aNQLA29ubiIiIJwlJREREREREJMN7omQ9KCgId3d36+M///yTHDly4Ovri5eXF3PmzCFPnjzcunWLl19+maFDhxIaGsrNmzf58MMPuXbtGrly5cLR8e5y766urly8ePHJjkhEREREREQkg3uqBeaSkpLYuXMnQ4YMYfXq1cTGxrJgwQJy5szJxx9/TMmSJXF0dKRbt25s27YNwzCwWCxp3uPvj0VEREREREQyG8en+WbPPvssFSpUoGjRogA0adKEJUuWcP78eXbt2kXr1q0BMAwDR0dHXFxciI6OJikpCQcHBy5dumQdUi8iIiIi98r+/GhbhwBA7Omxtg5BROQ/7akm6zVr1mTOnDlcuHCBQoUKsXXrVsqWLUu2bNmYOnUq1apVo0iRIixdupQGDRrg5OSEu7s74eHheHp6EhYWRq1atZ5mSCIiIiL/KUqSRUQyB4thGMaTvkndunVZtGgRRYoU4dtvv2XmzJncuXOHl19+mQkTJpA9e3Y2btzInDlzSEhIoHLlyowdOxZnZ2fOnTuHn58fV65coVChQsyYMYO8efM+Ysu/PmnoIiIiIiIiIjZS6oHPPJVk3XaUrIuIiIiIiEhG9eBk/akWmBMRERERERGRJ6dkXURERERERMTOKFkXERERERERsTNPVA1+7ty5bNiwAQAPDw+GDRtmfW7JkiVs3LiRxYsXc+zYMfz8/KzPXb16lbx587J+/XpCQ0OZPn06+fPnB6B27dr4+vo+SVgiIhmCll8SERERkQd57GR9165d7Ny5k9DQUCwWCz169GDz5s00aNCA3377jQULFlCsWDEAXn75ZdasWQNAbGwsbdq0YcyYMQAcPnwYPz8/mjdv/uRHIyKSgShJFhEREZEHeexh8K6urvj5+eHs7IyTkxMlS5bk/PnzxMfHExAQwMCBA+/7uuDgYKpWrYq7uzsAhw4dIjQ0FE9PT9577z1u3LjxuCGJiIiIiIiI/Cc8drL+0ksvUbFiRQBOnTrFhg0b8PDwYPr06fj4+FC0aNF7XhMdHU1ISAj9+/e3bnN1daVfv36sXbuWQoUKERgY+LghiYiIiIiIiPwnPHGBuRMnTtCtWzeGDRvGuXPnuHDhAj4+Pvfdd+3atdSvX986Px1g3rx5VKlSxTqUfseOHU8akoiIiIiIiEiG9kQF5n788UcGDhzIiBEjaNasGe+//z4nTpzAy8uL27dvc/nyZQYPHsysWbMAiIyMpHfv3tbXR0dHs2rVKrp27QqAYRg4ODg8SUgiIiIi6UJFIUVExEwWwzCMx3nhhQsXaNWqFTNnzqRGjRr3PL9nzx7mzp3L4sWLgbuJeLVq1dixYwdZs2YFICkpCQ8PD+bNm0eFChWYO3cuUVFR/2Io/K+PE7qIiIiIiIiIHSj1wGceu2f9008/5c6dO0yaNMm6rX379nTo0OG++1+9ehUnJydrog7g4ODArFmzGDNmDHFxcRQvXpwpU6Y8bkgiIiIiIiIi/wmP3bNuH9SzLiIiIiIiIhlVOvSsi4iIiIiIiPwbqv/x6NSzLiIiIiIiImIT6dSz/sEHH7Bx40YsFgutW7fm7bff5ssvv2Tp0qUYhoGHhwfDhg3DYrFw7NgxRo4cya1bt3B3d2fs2LE4Ojpy/vx5hg4dypUrVyhRogTTpk0jZ86cTxKWiIiIiIiIpKIe7YznsXvW9+7dy8yZM1m8eDGJiYk0bdqUTz75hD59+hAWFkbWrFnp1KkT/fv3p2bNmjRv3pzx48dTsWJFRowYQbly5ejYsSO9e/emRYsWNGvWjHnz5nH79m2GDh36iFGoZ11EREREREQyqgf3rGd53Ld87bXXWLRoEY6Ojly5coWkpCRy5MjB119/TY4cObh58yYxMTHkyZOHc+fOERcXR8WKFQHw9vYmIiKChIQE9u3bR6NGjdJsFxEREREREcnMnmgYvJOTE7Nnz+azzz6jcePGFChQAIvFQkhICJMnT6Z8+fKUKVOGI0eO4Orqan2dq6srFy9e5Nq1a+TKlQtHR8c020VERETk/jSUVUQkc3jiavADBw6kZ8+e9OnTh5CQENq1a0fbtm3x9vbm/fffZ+7cuXh4eGCxWKyvMQwDi8Vi/X9qf38sIiIiIv9PSbKISObw2MPgf//9d44dOwZA9uzZadiwIQcOHODHH38EwNHRkWbNmvHLL79QsGBBLl26ZH3t5cuXcXNzw8XFhejoaJKSkgC4dOkSbm5uT3I8IiIiIiIiIhneYyfrZ8+exd/fn/j4eOLj4/nmm28oUqQIQ4cO5ebNmxiGwcaNG6lSpQqFCxcma9as1kR+zZo11KpVCycnJ9zd3QkPDwcgLCyMWrVqPZ0jExEREREREcmgnmid9Tlz5rBhwwYcHBxo2LAhAwYMYNmyZSxatAgHBwfc3d0ZMWIETk5OHD9+HH9/f2JiYihbtiwTJ07E2dmZc+fO4efnx5UrVyhUqBAzZswgb968jxiBqsGLiIiIiIhIRvXgavBPlKzbnpJ1Ecm4VCRKREREJLNTsi4iIiIiIiJiZ9JhnXURERERERERSR9PvHQbwOTJk7l27RqTJk0CICEhgR49etCvXz+qVasGQGRkJHPmzMEwDIoUKcLEiRPJmzcvoaGhTJ8+nfz58wNQu3ZtfH19n0ZYIiIiIiIiNqVpb/K4njhZ3717N6GhodSuXRuAP/74gxEjRnD06FHrPjExMYwZM4ZVq1ZRoEABPvjgA+bMmYO/vz+HDx/Gz8+P5s2bP2koIiIiIiIidkVJsjyuJxoGf/36dWbOnEmfPn2s21auXEmPHj2oUKGCdVtCQgKjR4+mQIECAJQuXZoLFy4AcOjQIUJDQ/H09OS9997jxo0bTxKSiIiIiIiISIb3RMl6QEAAvr6+5MmTx7pt2LBh1K9fP81+zzzzDA0aNAAgLi6OBQsWWPdxdXWlX79+rF27lkKFChEYGPgkIYmIiIiIiIhkeI89DH7FihUUKlSIGjVqsHr16kd6TXR0NO+88w5lypShVatWAMybN8/6fI8ePaxJvYiIiIhIRqF5ySLytD12sh4eHs6lS5fw8vLixo0b3L59mwkTJjBixIj77h8VFUX37t2pXr26dZ/o6GhWrVpF165dATAMAwcHh8cNSeyEvqxEREQks9F1h4g8bY+drH/++efWf69evZq9e/c+MFFPSkqiT58+NGnShH79+lm358iRg08++YRKlSpRoUIFlixZop71/wB9WYmIiIiIiDyZp7J02z/ZsmULR48eJSkpiY0bNwJQrlw5goKCmDVrFmPGjCEuLo7ixYszZcoUM0ISERERERERsVsWwzAMWwfx+H61dQAiIiIiIiIij6nUA595omrwIiIiIiIiIvL0KVkXERERERERsTNK1kVERERERETszFMpMDd58mSuXbvGpEmTWL16NZ988gkODg5Uq1YNPz8/HB0dmTt3LqtWrSJPnjwAtG3blk6dOnH+/HmGDh3KlStXKFGiBNOmTSNnzpxPIywREZEMS8tgioiIZG5PXGBu9+7d+Pr6Urt2bXr16kXXrl1ZuXIlbm5ujBkzhmLFivH222/Tp08fevfuTaVKldK8vnfv3rRo0YJmzZoxb948bt++zdChQx+xdRWYExERERERkYwqnQrMXb9+nZkzZ9KnTx8AfvnlFypWrIibmxsAderUITIyEoDDhw8THByMp6cngYGB3Llzh4SEBPbt20ejRo0A8Pb2JiIi4klCEhEREREREcnwnihZDwgIwNfX1zq0vUyZMhw4cIALFy6QlJREREQEly9f5tatW7z88ssMHTqU0NBQbt68yYcffsi1a9fIlSsXjo53R+O7urpy8eLFJz8qERERERERkQzsseesr1ixgkKFClGjRg1Wr14NQIkSJXj33Xfp27cv2bJlo3Hjxhw6dIicOXPy8ccfW1/brVs3RowYQceOHbFYLGne9++PJePRPEsREREREZEn89jJenh4OJcuXcLLy4sbN25w+/ZtJkyYQMeOHQkLCwNgw4YNFC1alPPnz7Nr1y5at24NgGEYODo64uLiQnR0NElJSTg4OHDp0iXrEHrJuJQki4iIiIiIPJnHTtY///xz679Xr17N3r176du3L61atWL9+vU4OzuzZMkS2rdvT7Zs2Zg6dSrVqlWjSJEiLF26lAYNGuDk5IS7uzvh4eF4enoSFhZGrVq1nsqBiYiIiIiIiGRUT2XpthTPPPMM77zzDu3atSMxMZHmzZvj6ekJQGBgIH379iUhIYHKlSvz9ttvAzB69Gj8/PyYP38+hQoVYsaMGU8zJLEBDYMXERERERF5Mk+8dJttaek2ERERERERyajSaek2EREREREREXn6lKyLiIiIiIiI2JknmrPeuXNnrl69al0nvUGDBmzevNn6/MWLF6lQoQKDBw/Gz8/Puv3q1avkzZuX9evXExoayvTp08mfPz8AtWvXxtfX90nCEhEREREREcnQHnvOumEY1KpVi61bt1qT9dQuXbpEhw4d+OSTTyhevLh1e2xsLG3atGHMmDG4u7szbtw4KlWqRPPmzR8jCs1ZFxERERERkYzqwXPWH7tn/Y8//gCgW7duXL9+nbZt2/Lmm29an58yZQrt27dPk6gDBAcHU7VqVdzd3QE4dOgQp06dIjg4mNKlSzNq1Cjy5s37uGGJiIiI/Kdp1RURkczhsZP1mzdvUqNGDUaNGkVCQgJdunShRIkSvP7665w6dYq9e/cSFBSU5jXR0dGEhISwbt066zZXV1e6detG5cqVmTFjBoGBgUyfPv3xj0hERETkP0xJsohI5vDUlm5buHAh58+fZ8SIEUyePJl8+fLRu3fvNPssXbqUX375hcDAwPu+x40bN2jQoAF79+59xFY1DF5EREREREQyqnRYuu2HH35g9+7d1seGYVjnrn/zzTc0bdr0ntdERkam2R4dHc3ChQvTvIeDg8PjhiQiIiIiIiLyn/DYyXp0dDRTpkzhzp07xMTEEBoaSoMGDbh69SpxcXEULVo0zf6GYXDkyBEqVapk3ZYjRw4++eQTDhw4AMCSJUto0KDB44YkIiIiIiIi8p/w2HPW69Spw4EDB2jZsiXJycl07NiRSpUqcfDgQQoWLHjP/levXsXJyYmsWbNatzk4ODBr1izGjBlDXFwcxYsXZ8qUKY8bkkgaKsAjIiIiIiIZ1VObs24bmrMuIiIiIiIiGVU6zFkXERERERERkfTx2MPgRUREREREJGPQFNGMR8PgRURERERERGwinYbBb9myBW9vb5o0acL48eMB+Pnnn2nbti3NmjVjyJAhxMfHA3Ds2DG8vb1p1KgRI0eOJDExEYDz58/TqVMnGjduTN++fbl169aThCQiIiIiIiKS4T12sn7mzBlGjx7Nhx9+yNq1azl69CiRkZEMGDCAwMBAvv76awBWrlwJwNChQwkICGDjxo0YhkFISAgAY8eOpWPHjkRERFCuXDk+/PDDp3BYIiIiIiIiIhnXYw+D/+yzz7h48SLvv/8+ABcvXmT//v2sW7eOuXPnAneXa0tKSiI+Pp633nqLyMhIAH744Qdmz57Np59+SrVq1di7dy+Ojo5cuHCBN998k2+++eYRo9AweBERERFb0PxXEXkcOnf83YOHwT92gbk///wTJycn+vTpw4ULF6hduzY5c+YkR44c+Pr68scff1C5cmX8/Pw4evQorq6u1te6urpy8eJFrl27Rq5cuXB0dEyzXURERETsm/1c6Io9UkImD6KfyaN77GQ9KSmJH374gcWLF5MjRw769u3La6+9xs6dO1m+fDnPPfccI0eOZMGCBfzvf//DYrFYX2sYBhaLxfr/1P7+WEREREREMhYlZCJP7rHnrD/77LPUqFEDFxcXsmXLRv369Zk/fz4VKlSgaNGiODg40KRJEw4ePEjBggW5dOmS9bWXL1/Gzc0NFxcXoqOjSUpKAuDSpUu4ubk9+VGJiIiIiIiIZGCPnazXqVOHnTt3cvPmTZKSktixYwe9evXiyJEjXLhwAYCtW7dStmxZChcuTNasWfnxxx8BWLNmDbVq1cLJyQl3d3fCw8MBCAsLo1atWk/hsEREREREREQyridaZ33lypUsXLiQhIQEXn/9dfz9/dm+fTszZ87kzp07vPzyy0yYMIHs2bNz/Phx/P39iYmJoWzZskycOBFnZ2fOnTuHn58fV65coVChQsyYMYO8efM+YgQqMCciIiIiIiIZ1YMLzD1Rsm57StbtkQqKiIiIiIiIPAol6yIiIiIiIiJ25sHJ+mPPWRcRERERERGR9PHYS7etWLGCJUuWWB+fPXsWLy8vAgICAFiyZAkbN25k8eLFAERGRjJnzhwMw6BIkSJMnDiRvHnzEhoayvTp08mfPz8AtWvXxtfX90mOSURERERERCRDeyrD4E+cOME777zDsmXLcHFx4bfffqNbt24UK1aMxYsXExMTQ+PGjVm1ahUFChTggw8+IDo6Gn9/f8aNG0elSpVo3rz5Y7SsYfAiIiIiIiKSUaXzMPgxY8bg6+uLi4sL8fHxBAQEMHDgQOvzCQkJjB49mgIFCgBQunRp6/Juhw4dIjQ0FE9PT9577z1u3LjxNEISERERERERybCeOFnftWsXcXFxNGnSBIDp06fj4+ND0aJFrfs888wzNGjQAIC4uDgWLFhA/fr1AXB1daVfv36sXbuWQoUKERgY+KQhiYiIiIiIiGRoT5ysL1u2jLfffhuA7777jgsXLuDj43PffaOjo+nVqxdlypShVatWAMybN48qVapgsVjo0aMHO3bseNKQRERERERERDK0xy4wBxAfH8++ffuYNGkSAOvXr+fEiRN4eXlx+/ZtLl++zODBg5k1axZRUVF0796d6tWrM2LECOBu8r5q1Sq6du0KgGEYODg4PNkRiYjIv5L9+dG2DgGA2NNjbR2CiIiIiN14omT9l19+oXjx4uTIkQOAiRMnWp/bs2cPc+fOZdasWSQlJdGnTx+aNGlCv379rPvkyJGDTz75hEqVKlGhQgWWLFliHS4vIiLmUJIsIiIiYn+eKFk/c+YMBQsW/Mf9tmzZwtGjR0lKSmLjxo0AlCtXjqCgIGbNmsWYMWOIi4ujePHiTJky5UlCEhEREREREcnwnsrSbbajpdtEREREREQko0rnpdtERERERERE5Ol5omHwIiIiIiIi8mAq5CqPS8PgRURERERERGziwcPgn6hnfc2aNSxYsACAWrVqMXz4cJYvX87ixYuxWCyUK1eOsWPH4uzszNy5c1m1ahV58uQBoG3btnTq1Inz588zdOhQrly5QokSJZg2bRo5c+Z8krBEREREREREMrTH7lmPjY3Fw8ODiIgI8uTJQ4cOHWjdujWffvopq1evJmfOnPj5+fHyyy/TtWtX+vTpQ+/evalUqVKa9+nduzctWrSgWbNmzJs3j9u3bzN06NBHjEI96yIiIiIiIpJRpUOBuaSkJJKTk4mNjSUxMZHExERKlCjB6NGjyZUrFxaLhVKlSnH+/HkADh8+THBwMJ6engQGBnLnzh0SEhLYt28fjRo1AsDb25uIiIjHDUlERERERETkP+Gxk/VcuXIxaNAgmjRpgoeHB4ULF6Zq1aq8/vrrAFy9epWlS5dSr149bt26xcsvv8zQoUMJDQ3l5s2bfPjhh1y7do1cuXLh6Hh3NL6rqysXL158OkcmIiIiIiIikkE9drJ+/PhxVq1axdatW9mxYwdZsmTh008/BeDixYu89dZb+Pj4UK1aNXLmzMnHH39MyZIlcXR0pFu3bmzbtg3DMLBYLGne9++PRURERERERDKbx07Wd+7cSY0aNcifPz/Ozs54e3uzd+9efv/9d9q3b0+rVq145513ADh//jwrV660vtYwDBwdHXFxcSE6OpqkpCQALl26hJub2xMekoiIiIiIiEjG9tjJepkyZdi1axe3b9/GMAy2bNnCCy+8QPfu3Rk0aBDdunWz7pstWzamTp3KmTNnMAyDpUuX0qBBA5ycnHB3dyc8PByAsLAwatWq9eRHJSIiIiIiIpKBPdE66wsWLGD16tU4OTnx6quvUqxYMebMmUPJkiWt+9StW5dBgwaxceNG5syZQ0JCApUrV7Yu6Xbu3Dn8/Py4cuUKhQoVYsaMGeTNm/cRI1A1eBEREREREcmoHlwN/omSddtTsi4iIiIiIiIZVTos3SYiIiIiIiIi6UPJuoiIiIiIiIidcXySFy9YsIBVq1bh7OxM06ZN6du3L7t27WLixIncuXOHJk2a4Ovry7Fjx/Dz87O+7urVq+TNm5f169cTGhrK9OnTyZ8/PwC1a9fG19f3yY5KREREREREJAN77GR9165drFu3jlWrVpE9e3beeecd1q5dy4wZM1i8eDGFChWid+/ebNu2DQ8PD9asWQNAbGwsbdq0YcyYMQAcPnwYPz8/mjdv/lQOSERERERERCSje+xh8EePHqVmzZrkypULBwcH3njjDVasWEGxYsUoWrQojo6OeHp6EhERkeZ1wcHBVK1aFXd3dwAOHTpEaGgonp6evPfee9y4cePJjkhEREREREQkg3vsZL1s2bLs3LmT69evc+fOHbZs2cJPP/2Eq6urdR83NzcuXrxofRwdHU1ISAj9+/e3bnN1daVfv36sXbuWQoUKERgY+LghiYiIiIiIiPwnPPYw+Bo1auDt7U3nzp3Jly8fNWrUYOfOnVgsFus+hmGkebx27Vrq169vnZ8OMG/ePOu/e/ToQYMGDR43JBEREREREZH/hMfuWY+JiaFhw4asW7eOxYsX4+zszGuvvcalS5es+1y6dAk3Nzfr48jISJo2bWp9HB0dzcKFC62PDcPAwcHhcUMSERERERER+U947J71s2fPMnz4cFatWkVsbCwrV64kKCiIIUOG8Oeff1KkSBHWr1+Pj48PcDcRP3LkCJUqVbK+R44cOfjkk0+oVKkSFSpUYMmSJepZFxERsSPZnx9t6xAAiD091tYhiDyU/lZE5Gl77GS9TJkyNGzYkBYtWpCUlETXrl2pUqUKkyZNYsCAAdy5cwcPDw8aN24M3F2uzcnJiaxZs1rfw8HBgVmzZjFmzBji4uIoXrw4U6ZMefKjEhERkadCF/4ij0Z/KyLytFkMwzBsHcTj+9XWAYiIiIiIiIg8plIPfOax56yLiIiIiIiISPpQsi4iIiIiIiJiZ5Ssi4iIiIiIiNiZJ0rWFyxYQKNGjfD09GT+/PkArF69mqZNm+Lp6cn48eNJTEwE4NixY3h7e9OoUSNGjhxp3X7+/Hk6depE48aN6du3L7du3XrCQxIRERERERHJ2B47Wd+1axfr1q1j1apVhIWFceDAARYsWMCsWbNYuHAh69atIzExkcWLFwMwdOhQAgIC2LhxI4ZhEBISAsDYsWPp2LEjERERlCtXjg8//PDpHJmIiIiIiIhIBvXYyfrRo0epWbMmuXLlwsHBgTfeeIO5c+dSsWJF3NzcAKhTpw6RkZGcO3eOuLg4KlasCIC3tzcREREkJCSwb98+GjVqlGa7iIiIiIiISGb22Ml62bJl2blzJ9evX+fOnTts2bKFihUrcuDAAS5cuEBSUhIRERFcvnyZqKgoXF1dra91dXXl4sWLXLt2jVy5cuHo6Jhmu4iIiIiIiEhm5vi4L6xRowbe3t507tyZfPnyUaNGDQ4cOMC7775L3759yZYtG40bN+bQoUMkJydjsVisrzUMA4vFYv1/an9/LCIiIiIiIpLZPHayHhMTQ8OGDXn77bcB+OSTTyhQoADly5cnLCwMgA0bNlC0aFEKFizIpUuXrK+9fPkybm5uuLi4EB0dTVJSEg4ODly6dMk6hF5EREREREQks3rsYfBnz56lX79+JCYmEh0dzcqVK/Hy8qJr167ExMQQHx/PkiVLaNq0KYULFyZr1qz8+OOPAKxZs4ZatWrh5OSEu7s74eHhAISFhVGrVq2nc2QiIiIiIiIiGZTFMAzjcV88b948vv76a5KSkujatSsdOnRgxYoVLFy4kMTERJo3b86AAQMAOH78OP7+/sTExFC2bFkmTpyIs7Mz586dw8/PjytXrlCoUCFmzJhB3rx5HzGCXx83dBERERGRpyb786NtHQIAsafH2joEEflXSj3wmSdK1m1PybqIiIiIiIhkVA9O1h97GLyIiIiIiIiIpI/HLjAnIiIiIubTcGsRkczhkYbBx8TE0L59ez766COKFCnCrl27mDhxInfu3KFJkyb4+vpy7Ngx/Pz8rK+5evUqefPmZf369fz4449MnDiRhIQE8uXLx4QJEyhcuDB79+5lwIABFCxYEIBXXnmFiRMn/ovwNQxeREREREREMqonmLN+4MAB/P39OXnyJBERETz77LM0btyYxYsXU6hQIXr37k2XLl3w8PCwviY2NpY2bdowZswY3N3dqVu3Lh9++CFlypRh5cqVfPPNN8yfP5/PPvuMhIQEevfu/ZgHpmRdREREREREMqonmLMeEhLC6NGjreufHzx4kGLFilG0aFEcHR3x9PQkIiIizWuCg4OpWrUq7u7uxMfHM2jQIMqUKQNA6dKluXDhAgCHDh1i586deHp60qdPH+t2ERERERERkczsH5P1oKAg3N3drY+joqJwdXW1PnZzc+PixYvWx9HR0YSEhNC/f38AnJ2d8fLyAiA5OZm5c+dSv359AHLnzk3nzp1Zt24dHh4e+Pr6Pp2jEhEREREREcnA/nU1+OTkZCwWi/WxYRhpHq9du5b69euTP3/+NK+Lj4/nvffeIzEx0TrsPTAwkIYNGwLQoUMHfvvtN6Kjox/rQERERERERET+K/51sl6wYEEuXbpkfXzp0iXrEHmAyMhImjZtmuY1t27dokePHiQmJjJ//nycnJxITk5m/vz5JCUlpdnXwcHh34YkIiIiIiIi8p/yr5duq1ChAidPnuTPP/+kSJEirF+/Hh8fH+BuL/uRI0eoVKlSmtcMHTqUYsWKMXbsWLJkuXt/IEuWLGzevJlixYrRtGlTwsLCqFChAjly5HgKhyUiIiIikvloaT+R/45/naxnzZqVSZMmMWDAAO7cuYOHhweNGzcG7i7X5uTkRNasWa37Hz16lG+++YYXX3yRVq1aAXfnuX/88cdMnjyZUaNGMW/ePFxcXJgyZcpTOiwRERERkcxHSbLIf8cjrbNuv7R0m4iIiIiIiGRUT7B0m4iIiIiIiIiY618PgxcRERER29GcZBGRzOGRh8HHxMTQvn17PvroI4oUKQLAsGHDqF69Ot7e3gCEhoYyffp067JttWvXxtfXl7179zJgwAAKFiwIwCuvvMLEiROJj49n5MiRHD58mGzZsjFt2jRKliz5L8LXMHgRERERERHJqB48DP6RetYPHDiAv78/p06dAuDixYuMHj2a3bt3U716det+hw8fxs/Pj+bNm6d5/eHDh+nWrZt1ffUUixcvJnv27GzYsIF9+/bx/vvvExIS8qhHJSIiIiIiIvKf9Ehz1kNCQhg9erR1PfV169ZRr149mjRpkma/Q4cOERoaiqenJ++99x43btywbt+5cyeenp706dOHCxcuAPDtt9/SokULAKpWrcrVq1c5f/78Uzs4ERERERERkYzokZL1oKAg3N3drY979OhBmzZt7tnP1dWVfv36sXbtWgoVKkRgYCAAuXPnpnPnzqxbtw4PDw98fX0BiIqKwtXVNc3r//rrryc6IBEREREREZGM7qkWmJs3b5713z169KBBgwYA1qQdoEOHDkyfPp3o6GgMw8BisVifMwyDLFlUoF5EREREREQyt6eWrEdHR7Nq1Sq6du0K3E28HRwcSE5OJjg4mF69euHg4GDd38HBgQIFChAVFcXzzz8PwOXLl61D7UVEREREMgpV6ReRp+2pJes5cuTgk08+oVKlSlSoUIElS5bQoEEDsmTJwubNmylWrBhNmzYlLCyMChUqkCNHDjw8PFizZg3u7u788MMPZM2aleeee+5phSQiIiIiYgolySLytD21ZN3BwYFZs2YxZswY4uLiKF68OFOmTAFg8uTJjBo1innz5uHi4mLd3rlzZwICAmjWrBnOzs7W7SIiIiIiIiKZ2SOvs26ftM66iIiIiIiIZFQPXmdd1dxERERERERE7IySdRERERERERE7o2RdRERERERExM48UoG5mJgY2rdvz0cffUSRIkVYvnw5ixcvxmKxUK5cOcaOHYuzszObN29m9uzZJCcn8+qrrxIYGEh0dDTdunWzvld0dDTXrl3j559/Zu/evQwYMICCBQsC8MorrzBx4sT0OVIRERERERGRDOIfC8wdOHAAf39/Tp48SUREBAkJCfTu3ZvVq1eTM2dO/Pz8ePnll2nbti2NGjUiNDSUZ599Fl9fX6pXr067du2s75WcnMxbb71F27Zt8fT05LPPPrO+3+NRgTkRERERERHJqJ6gwFxISAijR4/Gzc0NAGdnZ0aPHk2uXLmwWCyUKlWK8+fPkyNHDrZs2cKzzz5LbGwsV65cIU+ePGnea9WqVWTPnh1PT08ADh06xM6dO/H09KRPnz5cuHDhSY5SRERERERE5D/hH5P1oKAg3N3drY8LFy7M66+/DsDVq1dZunQp9erVA8DJyYlt27ZRu3Ztrl27Rs2aNa2vS0pK4qOPPuLdd9+1bsudOzedO3dm3bp1eHh44Ovr+9QOTERERERERCSjeuwCcxcvXuStt97Cx8eHatWqWbd7eHiwZ88e6tSpw5gxY6zbd+zYQfHixSldurR1W2BgIA0bNgSgQ4cO/Pbbb0RHRz9uSCIiIiIiIiL/CY9UYO7vfv/9d3r06EHnzp2txeOuX7/O4cOHrb3pnp6eaXrKIyMjadq0qfVxcnIywcHB9OrVCwcHB+v21P8Wkacn+/OjbR0CsafH2joEEREREZEM4V8n6zExMXTv3p3BgwfTsmVL63bDMBg6dCirVq3iueeeIyIigsqVK1uf379/Pz179rQ+zpIlC5s3b6ZYsWI0bdqUsLAwKlSoQI4cOZ7siETkvpQoi4iIiIhkHP9YDT5F3bp1WbRoEZGRkUybNo2SJUumeW7QoEFERkbywQcfYLFYePHFFxk7diy5c+cGoEKFCuzdu5esWbNaX3fixAlGjRpFdHQ0Li4uTJkyhUKFCv2L8FUNXkRERERERDKqB1eDf+Rk3T4pWRcRERER27OH6WagkXQiGY+SdRERERERERE78+Bk/bEKzImIiIiYyR56LdVjKSIiZnqkZD0mJob27dvz0UcfUaRIEb788kuWLl2KYRh4eHgwbNgwLBYLkZGRzJkzB8MwKFKkCBMnTiRv3rycP3+eoUOHcuXKFUqUKMG0adPImTMnN2/e5L333uPMmTO4uLgwa9YsXF1d0/uYRURE5BHZQ5IM9pEo67MQEREz/eMw+AMHDuDv78/JkyeJiIjAMAy6d+9OWFgYWbNmpVOnTvTv35+KFSvSuHFjVq1aRYECBfjggw+Ijo7G39+f3r1706JFC5o1a8a8efO4ffs2Q4cOJTAwkIIFC9KrVy/CwsL49ttvmTVr1r8IX8PgRUREREREJKN68DD4LP/00pCQEEaPHo2bmxsARYsW5euvvyZHjhzcvHmTmJgY8uTJQ0JCAqNHj6ZAgQIAlC5dmgsXLpCQkMC+ffto1KgRAN7e3kRERADw7bff4unpCUDz5s3Zvn07CQkJT3asIiIiIiIiIhncPybrQUFBuLu7p9nm5ORESEgI9evXx9XVlTJlyvDMM8/QoEEDAOLi4liwYAH169fn2rVr5MqVC0fHuyPuXV1duXjxIgBRUVHWYe+Ojo7kypWLq1evPtUDFBEREREREcloHrvAXNu2bfH29ub9999n7ty5DBkyBIDo6GjeeecdypQpQ6tWrbh48SIWiyXNa//+OIVhGGTJ8o/3D0RERERERCQDUv2PR/evk/ULFy5w/vx5qlSpgqOjI82aNeOrr74C7vaUd+/enerVqzNixAgAXFxciI6OJikpCQcHBy5dumQdUu/m5sbly5cpWLAgiYmJ3Lp1i3z58j29oxMRERERERG7kRGSZHvxr5P16Ohohg4dSlhYGLlz52bjxo1UqVKFpKQk+vTpQ5MmTejXr591fycnJ9zd3QkPD8fT05OwsDBq1aoFgIeHB2FhYfTp04fw8HDc3d1xcnJ6ekcnmZru2omI/HfYwzld53MRETHTP1aDT1G3bl0WLVpEkSJFWLZsGYsWLcLBwQF3d3dGjBjBt99+y4ABAyhdurT1NeXKlSMoKIhz587h5+fHlStXKFSoEDNmzCBv3rxcv34dPz8/zpw5Q+7cuZk2bRpFihT5F+GrGryIiIiIiIhkVA+uBv/Iybp9UrIuIiIiYgv2MNoBNOJBRDI6JesiIiKSgdlDYqikUEREnj4l65mCPVzIgC5mREREREREHo2SdRERERERERE78+Bk/ZEWNY+JiaF58+acPXs2zfYlS5bQuXNn6+Njx47h7e1No0aNGDlyJImJiQD8+OOPtG7dGi8vL9566y3OnTsHwN69e6lWrRpeXl54eXnx/vvv/+tDExEREREREfmv+cdk/cCBA3To0IFTp06l2f7bb7+xYMGCNNuGDh1KQEAAGzduxDAMQkJCrNvHjx/PmjVr8PT0ZPz48QAcPnyYbt26sWbNGtasWcPEiROf0mGJiIiIiIiIZFz/mKyHhIQwevRo3NzcrNvi4+MJCAhg4MCB1m3nzp0jLi6OihUrAuDt7U1ERATx8fEMGjSIMmXKAFC6dGkuXLgAwKFDh9i5cyeenp706dPHul1EREREREQkM/vHZD0oKAh3d/c026ZPn46Pjw9Fixa1bouKisLV1dX62NXVlYsXL+Ls7IyXlxcAycnJzJ07l/r16wOQO3duOnfuzLp16/Dw8MDX1/epHJSIiIiIiIhIRvZIc9ZT++6777hw4QI+Pj5pticnJ2OxWKyPDcNI8zg+Pp733nuPxMREevfuDUBgYCANGzYEoEOHDvz2229ER0c/1oGIiIiIiIiI/Ff862R9/fr1nDhxAi8vL/z9/Tl8+DCDBw+mYMGCXLp0ybrf5cuXrUPnb926RY8ePUhMTGT+/Pk4OTmRnJzM/PnzSUpKSvP+Dg4OT3hIIiIiIiIiIhmb4799QeoicHv27GHu3LnMmjULgKxZs/Ljjz9SpUoV1qxZQ61atYC7BeaKFSvG2LFjyZLl7v2BLFmysHnzZooVK0bTpk0JCwujQoUK5MiR4ykcloiIiIiIiEjG9a+T9YeZNm0a/v7+xMTEULZsWbp06cLRo0f55ptvePHFF2nVqhUAbm5ufPzxx0yePJlRo0Yxb948XFxcmDJlytMMR0RERERERCRDshiGYdg6iMf3q60DEBEREREREXlMpR74zL+esy4iIiIiIiIi6eupDoMX28r+/GhbhwBA7Omxtg5BREREREQkQ3ukYfAxMTG0b9+ejz76iCJFili3L1myhI0bN7J48WIAjhw5QkBAAAkJCRQqVIipU6eSJ08eQkNDmT59Ovnz5wegdu3a+Pr6cvPmTd577z3OnDmDi4sLs2bNSrNW+z/TMHgRERERERHJqJ5gGPyBAwfo0KEDp06dSrP9t99+Y8GCBWm2BQUFMXDgQNauXUuJEiX49NNPATh8+DB+fn6sWbOGNWvW4OvrC8CsWbNwd3dnw4YNtGnThqCgoH97ZCIiIiIiIiL/Of+YrIeEhDB69GjrmukA8fHxBAQEMHDgwDT7Jicnc+vWLQBiY2PJli0bAIcOHSI0NBRPT0/ee+89bty4AcC3336Lp6cnAM2bN2f79u0kJCQ8nSMTERERERERyaD+MVkPCgrC3d09zbbp06fj4+ND0aJF02z38/PD39+fmjVrsmvXLtq3bw+Aq6sr/fr1Y+3atRQqVIjAwEAAoqKirMPeHR0dyZUrF1evXn0qByYiIiIiIiKSUf3ravDfffcdFy5cwMfHJ832uLg4Ro4cycKFC9m5cycdO3Zk+PDhAMybN48qVapgsVjo0aMHO3bsuO97G4ZBliwqUC8iIiIiIiKZ27/OjNevX8+JEyfw8vLC39+fw4cPM3jwYH799VeyZs1K+fLlAWjXrh179+4lOjqahQsXWl9vGAYODg4AuLm5cfnyZQASExO5desW+fLle/KjEhEREREREcnA/nWyPnHiRDZs2MCaNWsYP3485cqVY9asWRQrVoy//vqLP/74A4BvvvmGV199lRw5cvDJJ59w4MAB4G4F+QYNGgDg4eFBWFgYAOHh4bi7u+Pk5PSUDk1EREREREQkY3pq66znzZuXiRMnMnjwYAzDIH/+/EyYMAEHBwdmzZrFmDFjiIuLo3jx4kyZMgWAQYMG4efnR7NmzcidOzfTpk17WuGIiIiIiIiIZFiPtM66/dI66yIiIiIiIpJRPXid9afWsy4iIiLyX5b9+dG2DgGA2NNjbR2CiIiYQD3rIiIiIiJPSDdzROTxPLhnXcm6iIiIiIiIiE1oGLyIiN1RL4yIiIiIPIh61kVERERERERsQj3rIpmePfTiqgdXREREROTRqGddRCSTs4cbOaCbOSIiIpIZ/WcLzImIiIiIiIj892SxdQAiIiIiIiIikpaSdRERERERERE7o2RdRERERERExM4oWRcRERERERGxM0rWRUREREREROyMknURERERERERO6NkXURERERERMTOKFkXERERERERsTNK1kVERERERETsjJJ1ERERERERETuTaZP1pKQkW4cgdio+Pt7WIYiIiIiISCaXaZP11q1b2zoEli1bZusQ7NKJEyf44Ycf2Ldvn/U/MzVs2JCxY8dy8OBBU9tNLT4+nuPHjwOwbt06Jk+ezNWrV02PY8OGDXZz8+Ls2bN8++23JCUlcebMGZvEkJCQwMKFC+nTpw/9+/cnJCQEwzBMj8Nefj/s4Wcybty4e7YNHz7cBpHcdfv2bY4fP45hGNy+fTvTxmBPcQDcuHHDpu1fv36dXbt2ARAcHMzAgQM5ffq06XH07t2bZcuW8ddff5netj367LPP+OOPP2wdht1ITEzk22+/JSwsLM1/mZmtzx1w93t+5syZxMbGZvqfB9j+fHq/34lz586lW3uO6fbOdu7ZZ5/lhx9+oHz58jg7O9skhiVLltC+fXubtP13586dw9/fn3PnzrFkyRLee+89JkyYQJEiRUyNY+zYsWzdupWiRYtat1ksFhYtWmRaDBs2bGDjxo3MmDGDK1eu0LJlS1q0aIGrq6tpMQwdOpQiRYpw584d5syZg5eXF++//z7BwcGmxQCwfft2pk6dioeHB61ataJ8+fKmtp8iPDyc+fPnExsby/Lly2nfvj3Dhg3Dy8vL1Dj8/f2Ji4ujbdu2JCcns2bNGk6cOMHIkSNNjcMefj9s/TMZOXIkZ86c4fDhw5w4ccK6PTExkejoaFNi+Lvdu3cTEBBAUlISy5cvp3nz5kyfPp2aNWtmqhjsKY5jx47h6+tLXFwcy5cv580332TWrFmULVvW1Djeffdd/ve//wEQERHBW2+9xciRI1m8eLGpcfTt25cdO3YwYMAAkpKSqFWrFnXr1jXt3P7+++8/9PmJEyeaEkeKxMRERo8ezZUrV6hZsyZ16tShatWqODqae3m8Y8cOZs6cyc2bNzEMA8MwsFgsfPPNN6bG8e6773L+/HlKliyJxWKxbm/ZsqWpcaRci964cSPNDXEzfz/s5dwxbdo0/vrrL44cOULPnj1ZtWoVx48fx8/Pz7QYbty4wdSpUzl9+jSzZ89m8uTJ+Pn5kTdvXtNiSM1W59MLFy5gGAa9evXi448/tv5uJiUl0bNnTyIiItKnYSOTqlatmlG6dGmjdOnSRpkyZaz/N1P37t2Nzp07G9OmTTPmzJlj/c8WunXrZuzYscNo2bKlkZycbCxfvtzo2LGj6XE0aNDAiI2NNb3dB9m0aZNRq1Yto2LFikbfvn2NU6dOmdKut7e3YRiGMWXKFCM4ODjNNrPFxsYaYWFhRo8ePYxWrVoZn3zyiXH58mVTY2jZsqURHR1teHl5GYZhGBcvXjSaNm1qagyGYRiNGjVK8zgpKclo0qSJ6XHYw++HrX8mZ86cMb7//nvD09PT2LNnj/W/H374wbh27ZppcaTWunVrIyoqyvqZnDhxwvD09Mx0MdhTHB07djR+++03axw7d+40fHx8TI8jpc3AwEDjiy++MAzDMFq1amV6HCmuXLliLF261HjjjTeMsmXLmtbu6tWrjdWrVxsDBgwwunXrZoSFhRnr1683+vXrZwwfPty0OP4uOjra+PLLL4033njDqFy5suntN2zY0NiyZYtx5swZ4+zZs9b/zPb37zhbad26tTFp0iRj1apV1t+Z1atXmxqDvZw7vLy8jOTkZGscCQkJpl93DBgwwFi2bJnh6elp3Llzx5gxY4bRs2dPU2NIzVbnUz8/P6NOnTpG2bJljTp16lj/a9CggREUFJRu7WbanvXvv//e1iFQsWJFW4dgde3aNWrWrMm0adOwWCy0bduWpUuXmh5H0aJFbTKsOLU///yTtWvXsn79ep577jnee+89GjZsyPfff0/Pnj3ZtGlTuseQlJTE1atXiYyMZM6cOVy6dIk7d+6ke7v3ky1bNgoXLkyhQoX4888/+eWXX+jatSvt2rXjzTffNCWGLFmykCtXLutjNzc3smQxfxZPkSJF+PPPPylWrBgAly9fpkCBAqbHYQ+/H7b+mRQpUoQiRYqwdu1aYmJiiI6Otp47bt++Tb58+UyLJUVycnKaETgvvvhipozBnuKIjY2lZMmS1sevv/46kydPNj2O5ORkDh8+TGRkJEuWLOHYsWM2qZ0zduxYfvzxRxwcHKhatSqjR4/mtddeM639Vq1aAfDll1+yfPly6zmjSZMmtG3b1rQ4UmzYsIF9+/bxww8/4ODgQJMmTahevbrpcTzzzDPUqVPH9Hb/rmTJkkRFReHm5mbTOBITE206nQns59yR8jeSMtIhPj7e9Oufs2fP0q5dO7766iucnZ3x9fWlRYsWpsaQmq3OpykjOxYsWECvXr3Svb0UmTZZj4+P57PPPuPkyZOMGjWKhQsX0qtXL1OHxPfv35/bt29z+vRpSpUqRVxcHDly5DCt/dSyZcvGX3/9ZT0Z/PDDDzaZHpA3b16aNWtGpUqV0rRv5tCnt99+G29vbz777DMKFy5s3e7h4cF3331nSgzdu3enbdu21K1bl1KlStGoUSMGDRpkStupzZw5k/Xr11OkSBF8fHwYOXIkWbNmJSYmhnr16pmWrL/00kssWbKExMREjh07xpdffkmZMmVMaTu1xMREvLy8cHd3x8HBgR9//BE3Nze6dOkCYNp0jR49etj898NefibBwcEEBwenSc5tMXwUoGDBgmzduhWLxcLNmzdZunQpzz33XKaLwZ7iyJcvH8ePH7d+t61du9YmQzeHDh3KlClT6NatG0WLFqVt27b/OCQ8PaQMsy5RogQlS5bkhRdeIHfu3KbHER0dzfXr13FxcQHu3vi0RV2DiRMnkpSUxFtvvUWDBg0oUaKE6TEAVKlShYkTJ/LGG2+QNWtW6/aqVauaGkdcXByNGzemVKlSaa7BzJyKCHc/jy1btlCzZk2bTVW1l3NH48aNGTx4MDdu3GDhwoWsXbuW5s2bmxqDg4MD0dHR1s/i1KlTNukwSZFyPn377bet51MzpwV07dqVjz76yLQc0mLYuhvTRvz9/XFxcWHLli2sWLGCgIAADMNg2rRppsVgL3P6AA4dOoS/vz+nT5/m+eef58aNG8yaNcv03v/Q0ND7bk+5G2+GYcOGMWXKFNPa+ycxMTFcuHCBl156yfS2P/jgA7y9vdPUEEhx8OBB0+Y53r59m/nz57Nr1y6Sk5OpXr06/fv3J2fOnKa0n2Lv3r0Pfd6sHqrvvvuO119/3fo4KSmJb775hoYNG5rSPtz/Z/LOO++k6W03Q/369QkJCbFe9NvSlStXCAoKSvOZ+Pv7m9pLlToGwzCoVq2a6TH8PQ5bfRYAp0+fZvjw4Rw6dIhs2bJRrFgxpk6dygsvvGBqHKGhofd8jy1dupROnTqZGkeK33//nd27d7N48WJu377Njh07TG0/LCyMadOmUblyZQzDYP/+/YwaNcrUc1iKP/74g++//569e/dy6tQpSpYsyfTp002NoXPnzvdsM7teDzz4O87M0RcANWvW5PLly2m2WSwWjh07ZloM9nLugLs1DVKf080ehbFjxw6mT5/OhQsXqFKlCvv372fChAnUrl3b1Djsxd9zyNGjR5OcnJxuOWSmTdZbtWpFaGgoLVu2JCwsDMMw8PT0ZP369abF0KZNGz788EN69uxJWFgYv/32G0OGDGHt2rWmxZBaQkICp06dIikpiRdeeMFmdzN//fVX9u7dS2JiItWqVePll182tX0fHx8WLVpkeiKY2ooVK/jxxx8ZNmwYLVu2JGfOnHh5edGnTx9T4xgwYABz5sxJs+2tt97iiy++MDUOW1/oHjlyhLJlyz5wZQKzej/Cw8OJj49n9uzZDBw40Lo9MTGR4OBgNm/ebEoc9qRz584sXLgQBwcHW4diF2bOnImvr6+tw7A7Fy9eJDk5mUKFCpna7sKFC4mJiWHZsmVpCsomJSWxbt06IiMjTY3njz/+YPfu3ezevZvjx49Tvnx5PDw8TC/WCRAVFcXPP/+MxWKhSpUq5M+f3/QYAH777Td27drFrl27OHXqFO7u7owfP94mscTExJCcnEyePHls0j7Atm3b+P77763XYPXr17dZLPbAVueO1CIjI9m9ezeOjo7UqlUrzc16s1y9epWDBw+SnJxM+fLlefbZZ02PIUVoaCiTJk3i5s2bababdTPH7Bwy0w6Dt1gsxMfHW4d0XLt2LU3lSzPYy5w+uPsFHhIScs9yBGZXZg0LC2Pu3LnUr1+f5ORk+vfvT9++fU1dai9LlizUqVOHEiVKpBmOZuYd7q+++oqPPvqI9evXU69ePUaOHEnbtm1NS9b79+/PsWPHiIqKol69etbtSUlJFCxY0JQYIO2FbuplMVIudM1K1pctW8a4ceMICAjA1dU1TV2Fn376iSNHjpgSx61bt/jpp5+4desWe/bssW53cHAwLUErU6ZMmnOlo6MjDg4O3Llzh1y5cpm+1GLx4sXp2LEj1apVS3ODsX///qbGAXeXfUw9b85isZAtWzZeeOEFhg8fnmZaTXrZunUrgwcPNv377O8iIiJYsGDBPd8pZk9POH78OMOGDePixYsYhsELL7zA5MmTrXUn0lvx4sU5fPjwPdudnZ2ZNGmSKTGkNmjQIOrUqUPXrl2pVKmSzW5yxcfHs3r1av744w9GjRrFF198YfpURIBatWrx3HPP8cYbbzBgwADTK32nOHPmDL6+vpw5cwbDMHjuueeYNWsWxYsXNzWOjz/+mE2bNuHp6YlhGHz00UecOHGCvn37mhpHbGwsc+fOZffu3SQlJVG9enUGDRpk6lRRW587UkyaNIn9+/fTrFkzkpOT+eCDDzh06JCpnTc3b95k/vz5fP/999YbBn379iVbtmymxZDavHnzWLx4MaVKlbJJ+2bnkJk2We/SpQtvv/02ly5dIigoiMjISN555x1TY7CXOX1w98K2adOmlC5d2ibtp/j8889ZsWIFzzzzDAB9+vShS5cupibrQ4cONa2th3Fzc2Pbtm106dIFR0dHUwuITZo0ievXrxMUFIS/v791u6Ojo6m9H/ZyoZuylvfp06dp06YN3bp1sz5n5pI2bdq0oU2bNuzevZsaNWqY1m5qKeu7jx49msqVK9OiRQssFgsbN240fSgtQIECBWxS5O9+atWqRZEiRaznq7Vr13Lo0CHq1q3LyJEjWbhwYbrHkC9fPho3bkzZsmXT3Gw0+8br5MmTmTJlis2+01KMGDECX19f67DRzZs38/777/Pll1+a0n7t2rWpXbs2TZo0SVOsylbWrVvH0aNHuX37Nj/99BNJSUmcPXvW1O9YgMDAQFxcXDh69CiOjo6cPn2aESNGmDoVEe52EKSeQmMYBmfPnr3v1K/0FBAQQI8ePWjcuDFwdxTVqFGjTF/ab+3ataxYscKahLVt2xZvb2/Tk/XAwECyZ8/OhAkTAAgJCWH06NFMnTrVtBhsfe5IsXXrVr7++mvrcoLt27enZcuWpibrQ4cO5YUXXmDatGkYhsGqVasYOXKk6dNFUri5udksUQfzc8hMm6y3bNmScuXKsWfPHpKTk5k/f77pxZECAwMJCgriwoUL1K9fn+rVqxMYGGhqDCny5Mljk56ov0tOTrYm6gAuLi6m9xC99tprNh8G9uKLL9K7d2/Onj1LjRo1GDx4sKlrnP/555+ULVuWt99+m/Pnz6d57vTp06YN+37YhW5cXJwpMaRWtGhRfv75Z959910mTpyIs7Ozqb+fo0aNYty4cXz44YfMnz//nufNHP1x8OBBxo4da33cqFGj+8aU3uzhvJXixx9/THNzq2PHjnh7ezNx4kQ+/PBDU2Iws77Hwzz//PNUqVLFpkWI4G7ylXp+Z4MGDZg3b55p7ffu3Zvg4GB69ux533OF2SMN/P392bt3Lzdu3OCFF17g+PHjVK5c2fRk/ciRI4SGhrJ9+3ayZ8/O5MmT8fT0NDUGuJuATZ48mdjYWOu2woULmz494dq1a9ZEHaBp06Y2OZ8ahpGmtzRr1qymrzkPd38/Uk8JDQgIoGnTpqbGYOtzRwpXV1du3rxpvamUkJCQ5jrZDOfOnSM4ONj6eOTIkaYXuUutbNmyDBw4kNdffz3NTWmzOk9S55BJSUnpnkNm2mQd7iYdZ86cwdHRkatXr5refv78+ZkxYwYxMTE4ODiQPXt202NI0apVK2bOnEn16tXTnJjNrkRaunRpgoKCrBcOK1euNP0mij0MA5swYQI///yztSJrixYtqFWrlmntf/XVV4wfP545c+Zw5coV8ufPT2xsLFFRURQvXtz0ojd//vkngwcPJjY2FsMwSE5OJjY21vQlGLNnz86cOXOYNWsW7dq1Y+7cuaYmI+3atQOgb9++NrmASi179uysWrWKJk2akJyczJo1a2xSKffvw/Lh/0elmC1Llizs2LGDN954A7hblMfZ2ZnLly+TmJhoSgzVqlUzpZ1/0q1bN7p06ULVqlXTDLU2++bK//73Pz788EPatm2Lg4MD4eHhlCxZ0noTMr17/lNG5cyaNctmc7JT27VrFxs3bmTcuHF06dKF2NhYmwzHt4epiHB3NYk1a9Ywa9YsfH192bZtGz/99JPpcTg7O1trowAcPnzYJteE1atXZ8CAAdabfmFhYTY5pxiGwc2bN61z92/evGn6lA1bnztSuLi40KJFC+rVq4ejoyM7duzAxcXFupqEGaOmXnzxRX744Qfc3d2BuyPszJ4OkFpMTAw5c+Zk//79abablayHhYUBWGtbHT9+nFOnTvHCCy+kS49/pi0wN336dH788UfrhWZ4eDh169ald+/epsXwyy+/4OfnZ/3DT5kP8/zzz5sWQwo/Pz9++umnNMNJbVGJNC4ujtmzZ7Nnzx5r1UuzK0x7enqmGQYWGxuLt7c3GzZsMC2G+Ph4Pv30U06dOmWzpQXhbk/t6tWrCQsL4+zZs/Ts2ZO33norTaEkMzRo0IBx48bx+eef06dPHyIjI4mNjSUgIMDUOFKKicDdOblTpkwhKSnJ9MQwpbiJLZ07d45x48axZ88eLBYLr7/+Ov7+/jYdkp6QkEBkZCT79++3ybJYv/76K35+ftb6Cs8//zyTJk0iIiKC5557zpRe77p162KxWDAMg8TERC5fvszLL7/MqlWr0r3t1Nq3b0+JEiXumadvdrJet27dBz5n5hJ/TZo0MfU75EHat2/PsmXL+OKLL3j22Wdp1qwZLVq0ML2wbVhYGCtWrODPP/+kSZMmREZG0q9fP9q0aWNqHG3atGHFihUsWLCAF198kbp169K8eXNTiw0D7N+/nyFDhpAvXz4Mw+DGjRvMmDHD9BV5DMPgq6++4vvvv8cwDKpXr067du1Mvzm8atUqgoODqVu3LoZhsHXrVnr16mXqCBB7OXf803e9Gd8rnp6enDhxghIlSuDg4MDJkyfJmzcv2bJls9lSqQkJCZw8eZKkpCReeuklU39HBwwYwNGjR2nQoAGGYfDtt9/i5ubG7du38fT0pGvXrk+3QSOTat68uREfH299HBcXZzRr1szUGNq1a2d8++231sebNm0yOnXqZGoMKZo3b26Tdu3R338PkpKSTP98Ro4caUyfPt1o1qyZcfv2bWPo0KHGu+++a2oMhmFY209x+/Ztm/yutGrVyjAMw5g3b56xbds2wzAMo0mTJqbHsWjRojSPjx49anTr1s30OHr06GHs27fPuHPnjultP0xsbKytQzAMwzBatGhh0/avX79uREdH2zSGFAcOHDCGDRtmerve3t6mt3k/UVFRtg7BMAzDGDx4sBEaGmr8/vvvxrlz56z/mW3gwIHGRx99ZBw4cMB48803jfXr1xuNGjUyPQ7DMIwTJ04YS5YsMb744gvj2LFjNomhc+fOxu7du41vv/3W8Pf3N6Kioox69erZJJb4+Hjj119/NY4fP276uT3l7yT176Ytf08NwzB++eUXY8mSJcaiRYuM48ePm96+vZw7Ro8ebRw4cMCmMZw9e/ah/5nt0KFDRp06dYxWrVoZXl5exuuvv27s37/ftPbbtWtn3Lhxw/o4Ojra6NSpk5GQkGB4eno+9fYy7TD4vHnzcuvWLfLlywfcvUNj9vrAd+7cwcPDw/rYVvNhAF566SWOHz9u+pDzFCk9hX8f0moYhulra9rDMDB7mc+XkJCAk5OT9XHqf5spW7ZsnDx5kpIlS7J3716qV69OQkKC6XH8fS3cl19+mU8//dT0OA4dOsSbb74JYO1FNfvvZMuWLcyaNYvbt2/bdGpCykgHuHu+OHHihM2mCOzfv5/g4OA0n8n58+fZsmWLTeIBKF++PCNGjDC93ddff50lS5bwxhtvpDlvmF1w7s0336RYsWJ4e3tTt25dmy1JeuDAAQ4cOJBmmy16pIKCgti2bRvly5enYcOGrF+/njFjxpgaA/z/sqCpV8GxxbKgo0aNYuXKlQwfPpyVK1fSpEkTU0d/zJkzhwEDBjxwJJBZhSH9/f0JDg7mzTffvO81mFm/p1u3bqVOnTr3DDM+duwYx44dM7Wgq72cOypUqMD06dO5evUqXl5eeHl5pVlJygy9evXC29ubFi1amN72/YwfP56ZM2dSoUIF4O5377hx41i5cqUp7V+7di3N8s5Zs2blxo0bODo6pst0nkyXrKecEJOTk/Hy8qJu3bo4ODiwfft2XnjhBVNiSBn2XqZMGRYsWEDr1q1xcHBg3bp11vkgZvvjjz9o1aoVrq6uODk5mX6CThnmk1JpOrX4+HhTYkgxcuRIvvrqK+vaiSnDwMxkL/P56tevz1tvvUWTJk2sFb9TL+VmlsGDBzNr1iymTp3KggULWL58uekFkeyJ2Qnx/UycOPG+UxPMlnoJO4BnnnmGWbNmmR4H3K0e3L17d0JDQ+ncuTObNm3ilVdeMTWGuXPnpnl84sQJm8yVThlG/Nlnn1m32SI53bhxI/v27SMsLIypU6fi4eFBq1atePXVV02Nw5Y3bFIbNGiQ9QZj586d77kBmd7sZVnQFOvXr7deF86ZM8f09lPmqL/22mv3PGfmd35K8bDVq1dbO7FSnD171rQ4Dh06RJ06de45r6cwM1m3l3NHq1ataNWqFRcuXGD9+vW0b9+eF198kTZt2phW/HjBggWEhYXRpUsXihYtire3N/Xq1bNZB87t27etiTpAxYoVTV0xqWHDhtZr4+TkZDZt2kS9evUICwtLl5sZmW7Ouj3M/Ug9p/DvbDX3I/Ua1qmZsS5wau3atWP58uXWxyk3VdatW2daDH+vfm6xWMiaNWua5V3S2/3m873zzjs2SVAjIiLYt28fjo6OVK1a1fTK+HA34XjppZesj2/cuMHJkydNn89nL+Lj4/nss884efKkzWoaeHt7s3r1aj788EPKlStHrVq1aNq0KeHh4abFkMKWc9dSS6lpMHv2bKpWrcprr72Gp6enqZ/J35P1Z555hmbNmt1zAZ7ZxMXFERERwcyZM7FYLLi4uBAQEGDaOeTGjRtMnTqV06dPM3v2bCZPnsz7779vLaBllo4dOzJ9+nQKFSpkarspYmJiHrosqNl/uy1atGDNmjU2uRmeWnBw8D01k2bMmMGQIUNMaf/ChQsYhkGvXr34+OOPrdenSUlJ9OzZk4iICFPiSPHdd9/x+uuvp9m2adMmGjZsaGocYPtzB8CZM2dYu3YtX3/9NQULFqRp06bs3r0bBwcHpkyZYloccHcFhfHjxxMXF0eLFi3o16+f6dXpO3fuzFtvvWW9Ho2MjOSLL74wbanDpKQktm/fznfffYeDgwP/+9//8PDwYP/+/ZQoUeKpF9vNdMl6ajExMdy8eTPNNluvCWsr8fHxbNu2jVu3bgFY114dNGiQKe136dKFvXv33rPd0dGRunXrMnv2bFPigLs3bE6cOEGpUqWsw2pdXV1xcHBg3LhxpqxvffXqVa5evWpdFuK1116z2RQFW/rxxx9JTk7G39+foKAg6wVEYmIiY8aMYePGjTaO0Db8/f1xcXFhy5YtrFixgoCAAAzDMHWN4o4dOxIUFMSvv/7KoUOHGDhwIM2aNWPz5s2mxQB3qyYPHDiQfPnykZyczOXLl5k3b16au+5madeuHcHBwezYsYNz587Rp08fGjVqlKl+T+1lWG+K3bt3ExYWxq5du/Dw8MDb25vKlSvzyy+/0LNnT7Zv325KHCnLDC1dupSVK1cyb948jh07xoIFC0xpP0WTJk04deoU+fPnJ2vWrKaPokstZb13wzBstt57ly5duHjxImXLlk2zBJRZv6fTpk3jypUrbNmyJU1Bs6SkJA4cOGDaueP9999nz549REVF4ebmZt3u6OhI7dq1TZtKEx4eTnx8PLNnz2bgwIHW7YmJiQQHB5v6/WIv544OHTpw+fJlWrZsSatWrax5SmJiIrVq1WLXrl3pHsOtW7fYuHEja9as4eLFi7Rs2ZJmzZqxfft2Vq1axerVq9M9htROnjzJsGHDOH36NHB3Wd0pU6aYNkLa7CK/mW4YfIrJkycTEhJi7W2wxRfWH3/8QUhICDdu3Eiz3eyLGYAhQ4Zw48YNTp8+jbu7O3v27KFy5cqmtZ9SdX78+PFp7rbbQoECBRg3bhzlypUD7lbtnzt3LiNGjKB///6mVFXu1KkTGzZsSDOfLzPatWsXe/fuJSoqitmzZ1v/Th0dHU2fmmBP/l7TYMqUKabXNPD19bWLqQm2nruWWteuXfH19WXOnDm0adOGdevWWc8jZgkNDWXSpEnWG9Fm1zN42LBeW5g7dy4+Pj6MGTMmzVJYpUuXplu3bqbFcfbsWdq1a8dXX32Fs7Mzvr6+tGjRwrT2U3zyyScPfC710mHpzV7WezdjNOXDNGzYkN9//53vv/8+zd+Mg4MD/fr1My2OlOvOBQsW0KtXL9Pa/btbt27x008/cevWrTRD4R0cHPD19TU1Fns5d7z99tv3jCg4d+4chQsXNiVRB6hXrx516tShf//+aZZ07tixo2kxpFaiRAlWrFjB7du3SU5ONr3m2LPPPssPP/xA+fLlTRnRmGmT9W+++Ybt27enKRBgtv79+9O0aVNKly5tsxhS/PLLL2zatImgoCB8fHwYPHgwgwcPNj2OoUOHsnnzZpv18MPdk2DqC+zSpUtz+vRpChUqRHJysikxlClThrCwMMqXL29dQg4y38iPAQMGADBv3jxy5MhBp06d6NOnD0eOHOGtt96ycXS2Yw81DX7//Xc++OAD4O4yOzdu3LDJOuu2nruWWpMmTWjcuDEWi4VVq1Zx6tQp64iY5cuXm3KDad68eSxevDhd1np9FCm9g1FRUfcd1mu2WrVq4e3tfU8cQ4YMefrL6zyEg4MD0dHR1r/TU6dOkSVLFtPaT/GwqW3+/v6m9RbZy3rvD0vWzeg9K1++POXLl6d+/frkzp3but0wDFPniqfw8fFh4cKF3Lp1y1ok8+zZs6YNtW7Tpg1t2rRh9+7dpoxifBhbnztSpibMnj2bV1991aZTE4KCgu6pV5QyLcHMwtijRo1i3LhxdO7c+b7XPGYtN526yG+K9LwpnmmT9dKlSxMfH2/TZD1Pnjymrzn7IPnz58disVCiRAl++eUXWrZsaZNq2++++65Ne/jh7nCaadOm4eXlRXJyMuvXr6dYsWL8/PPPpl1c2UvlYHuxbds2Bg4cyKZNm8iWLRthYWH0798/zWoKmUmXLl14++23uXTpEkFBQdaaBmZasmQJ7du3tz62RaKe0m5kZKR17trmzZttOj875QIiR44caYrLLVu2zJRk3c3NzWaJOqQd1nvq1Cnr9pRhvWbNwX1QHImJiRw8eNC0OFIMHDiQzp07c+HCBfr168f+/fuZMGGCqTH8EzNnRbq5ueHk5ETJkiX55ZdfaNasGdHR0aa1/yjM/DzCw8OZPHlymiKdhQsXJjIy0rQY4O6IqUKFCrF//37q16/Pt99+a3pBNYDs2bPTt29fm6ysYS/njtmzZ1unJnTq1Mm63cnJybRrn9TTElL/fSYkJLBgwQLTawikfIemdOTYitlFfjNtsu7l5UXDhg0pVaoUDg4O1u1m3ZWBu3dtZ86cSfXq1dMUVUk9xMQsL730EuPGjaNDhw689957REVFmfpFlcIeevinTJnCvHnzePfdd62FIyZMmMCWLVsYO3asKTHYS+Vge5GcnEzNmjV59913adiwIYUKFSIpKcnWYdlMy5YtKVeunLWmwfz5802vaVCwYEG6dOlChQoV0sz1NPsG5Lhx4xg6dCgjR44E/n/umr0x63xatmxZ6/zo1D8Xs6oo28uw3ofFYfaNLYA33niDsmXLcvDgQZKSkggMDOTZZ581PY6HMXN0ToECBQgODqZGjRpMnToVMH/ll39idjX2NWvWMGvWLHx9fdm2bRs//fSTae2niIqKYtGiRUyePJmGDRvSo0cPm4xis+XKGvZy7niUqQkpS92lF3ualgBYR71u3LiRUaNGpXlu+PDhpk2/unr1KmvXrjVtBEqmTdZnzpzJyJEjbTqs+Oeff+ann35Kc0K2WCym3jBIMWbMGH7++WdefPFFBg4cyK5du5g+fbrpcdhDD3+uXLkYMGAAXl5elCpViri4OHLkyGHK/MIHFWVKYYt6BvYge/bsfPbZZ+zZs4eAgAAWLVpk01ExthYfH8/p06etn8Hx48c5fvy4qcva2Esl/uLFizN//nxy5MhBcnIyV65coVixYrYO6x5mXfjHxMSQM2dO9u/fn2a7Wb8bDxrWm1rv3r2tS0X91+NIcfXqVb7++mtrjZqU4ZL2MrrObPay3ru9yJ8/P0WLFqV06dL8+uuvdOrUia+++sr0OFJGSJUoUYLjx4/bpFAngLOzMz4+Ppw7d448efKYWpfF3s4dD6shMHv27HRN1h9lWkJKUVEzjBw5kjNnznD48GFOnDhh3Z6UlHRPwfD0NHjwYFNHoGTaZD137tymXtjez5EjR9i0aZNNY0jh4OBA3rx5+eGHH8idOzeNGjW6p/CdGeyhh3/37t0EBASQlJRESEgIzZo1Y/r06dSsWTPd2065K7h161Zu3bpFixYtcHR0JDw8/IFfGpnBtGnTWLFiBbNnzyZv3rxcvHjRJjeT7EXPnj0xDOOe+admntPsJclYtGgRoaGhhIaGWiuwd+3aNdMWIEy5oWerGgIpHna+unjxYqaLo2fPnpQqVcr05VDtTeqlUStVqsT58+epV6/ePfNhM5vs2bPz/fffU7p0aSIjI3n11VeJi4szPY7q1aszcOBAhg8fTrdu3Thy5EiaujlmyZo1K9evX6dEiRIcOHCAGjVqmD6azl7OHQ9j1vXxw+oHbNmyxbRkvW/fvpw7d46goKA01yAODg6ULFnSlBjA/BEomTZZf+WVVxgwYAC1atXCycnJut3Mi92XXnqJ48eP28WSXGPHjmXr1q0ULVrUus0Wvfx/7+HfvXu36UnZjBkz+PLLL+nZsyfPPvssS5cuZciQIaYk6ynFbr788kuWL19unSPfpEkT2rZtm+7t26sCBQqkOTEPHTrUhtHY3rVr11i7dq1NY/Dw8CAqKsq6TvTNmzfJkycPRYoUYfz48bz88sumxBESEkJISAhwd47n6tWradu2baZN1o8fP87gwYOJi4tj+fLlvPnmm8yaNcu0Kt+PwtZrWqcwOw57HxllxoX/m2++icVi4c6dO1y5coWiRYuSJUsWzpw5Q5EiRexqmUMzOwr8/f1ZuXIlfn5+rFy5ksaNG9tkXq6vry+nT5+mcOHCzJgxg3379tnkxqw9rKzxMJn1HHY/Zv6dFClShCJFirB27VquX79ObGysdenHY8eOmVaU8H4jUNLzc8i0yXpsbCy5cuW6Z06Qmcn6H3/8QatWrXB1dcXJycmm651+9913RERE2OQOamqXL19m69atuLu789JLL7FhwwZcXFxMjSE5ORlXV1frY1ssnxYdHc3169etx3758mVu375tehxin6pXr86uXbuoXr26TSpKw93aGo0bN7YWdtu2bRsRERF07tyZsWPHsmzZMlPiSEhISLN0Suqbr/bErJEx48aNs9bcKFCgAGPGjGH06NE2WcpO/l/9+vVZsWIF1atXT1Mnx+ypePHx8fzxxx+UKVOGdevWcfToUXr27ImLiwtz5sxJ9/ZT6rH4+vrSqVMn3N3dATh48OBDl5WzBTOXMCtVqpR1LXMzfg5/FxYWluZxyrVxvnz52LVrl+kjUe+3soZZN4Dl37HFDYM5c+awcOFCEhMTyZcvH1FRUZQrV44VK1aY0v79RqDkyJEj3drLtMm6PdzhNnO5g39StGhRmxSU+7v33nuPZs2aAXd7U6tWrcqwYcP47LPPTIuhYMGCbN26FYvFws2bN1m6dKnpF1R9+vShRYsWVK5cGcMw2L9/v83Xnxf78dxzz9GtWzfrl6TZa2kDnDhxgmnTplkfe3h48MEHH/DKK6+YunRa/fr1eeutt2jSpAkWi4WNGzfabEhtfHw8n376KSdPniQgIICFCxfSq1cvnJ2dTRulFBsbm2Y44Ouvv87kyZNNaVse7Pbt20yYMIFnnnnGus0WN+eHDh1KkSJFuHPnDnPmzMHLy4v333+f4ODgNCPr0tvvv/9uTdTh7jzhkydPmtZ+itQjhAzDIDo6Os0IofRWt27dhyY7Zv1+pC4edj9mJeuq2yOPIjQ0lG3bthEUFETfvn35448/+PLLL01r/48//mDYsGFpRqD8+eef6dZepk3WH3SCNPOLc9++fffdbos5bXnz5qVZs2ZUqlQpTS+V2SfGGzduWJeDcnZ2pm3btqYXWQkMDCQoKIgLFy7QoEEDqlWrxrhx40yNoWXLlvzvf//j559/xmKxMGbMGPLnz29qDGK/QkJC2LJli00LZObJk4dly5bRokULkpOTWbduHXnz5uX3338nOTnZtDiGDh1KREQE+/btw9HRkS5dulh7+y9dupRmlEx6CwwMxMXFhaNHj+Lg4MDp06cZMWJEmpsa6S1fvnwcP37c+v22du1am85dvx97uDEM5saxdetWdu/ebfPRa2fPnuWDDz5g6tSptG7dml69euHj42N6HAULFuSDDz6gadOmGIbBmjVrKF68uOlx2HqE0OLFi9P1/R9V6mu9hIQETp48SVJSEi+99FKa1YrSm1nVvJ9UZjyH2RM3Nzdy5cplnU7csGFDU6bM9u/fn2PHjhEVFcXRo0et2xMTE9P1eizTJuupT5CJiYls3rzZ9GVDUt/JTEhI4Mcff8Td3d0mhe/eeOMN3njjDdPb/bts2bKxbds26xqSu3btInv27KbGcPz4cWbMmJFm26ZNm0xZT3Lu3Ln33f7rr78C9lPUS2zL1dXVpmuJw92if0FBQUydOhVHR0dq1KjB5MmT2bhxI++9956psTRu3JjGjRvfs71Xr16EhoaaFseRI0cIDQ1l+/btZM+encmTJ5tWwTjFmDFjGD58OCdOnMDd3Z1ixYqZerPgUdi6uGsKM+MoXLgwN27csHmynpSUxNWrV4mMjGTOnDlcunTJ1JEwKaZOncrs2bMZMmQIFouFGjVq2KTX1NYjhFI6ZwzD4KuvvuL7778nMTGR6tWr8+abb6Z7+393+PBhBg4cSL58+UhOTuby5cvMmzfPtKrwKXV74O51z969e0lMTKRatWp2NQzezHNHYmIiO3fu5Pr16/fEsHz5ctPieBAzC7ulyJUrF2FhYZQtW5YlS5bg5uZmSkHGSZMmcf36dYKCgtKMdnV0dEzXDrVMm6z/vfe6R48eeHt7m7oO7N+/mK5fv26TdQsh7Qnyfs+ZdcE7duxYhg4dyrBhwwAoVKiQaWsmh4eHEx8fz+zZsxk4cKB1e2JiIsHBwaYk6ykOHjzIX3/9RePGjXF0dGTz5s2Zvoqw/L98+fLRvHlzKleunGaOtpkXuwUKFGD27Nn3bO/cuTOjRo2yi5t/Zvc6WCwW4uPjrb3a165dM30+3507d/jqq6+4ffs2ycnJ5MqV655l3MywY8cOZs6cyc2bNzEMI01Nlq5du2a6OBISEmjWrBkvvfRSmr9Zs4u4du/enbZt21K3bl1KlSpFo0aNGDRokKkxwN3RfP369eO1117D0dERd3d3cuXKZXoc9jJCaMqUKfz555/4+PhgGAarV6/mzJkzjBw50rQYAMaPH8/MmTOtyfn+/fsZN26c6TUvwsLCmDt3LvXr1yc5OZn+/fvTt29fWrdubVoM9nLuePfddzl//jwlS5ZM833SsmVLsmbNakoM586dY8mSJdy4cSPN9+rEiRNtcjM4KCiIr7/+mpYtW7J161YCAgIYPHhwurebK1cucuXKxfz589O9rdQybbKeegi6YRicOHHCJneXU8uRIwfnzp2zaQz3Y+YF78svv8z69es5f/48FouFQoUKmdb2rVu3+Omnn7h161aaUQ8ODg6m3URJ6Tlv3749y5cvt44qeOutt+jSpYspMYj9q127NrVr17Z1GA90+PBhW4cAmF/4pkuXLrz99ttcunSJoKAgIiMjeeedd0xp+8cffyQ5ORl/f3+CgoKs5+3ExETGjBljepXt8ePH4+fnx0svvWTTisX2EkefPn1s1nZqnp6eaUZ7hIeHpyl4Z5Y1a9YwZcoUqlSpQlJSEmPGjGH8+PHWUXVmST1CyMHBgf/973/WEULvvvuuaXF89913hIWFWQuG1q5d2/RROXC3tkLqXvSKFSva5Nr4888/Z8WKFdYaD3369KFLly6mJuv2cu745ZdfiIiIsFn7cHddcXd3d9zd3e2iAn2BAgXo1q0bAH5+fjaOJv1l2mQ9dY+QxWLhmWeeYdKkSabG0Llz5zQFos6ePUutWrVMjeFRmPmHefr0aYYMGcKZM2cwDIPnnnuOmTNnUqJEiXRvu02bNrRp04bdu3ebtvzDg/y9Ry4hIeGeIVCSebVq1YqzZ8/y22+/UbNmTS5cuGBqcSi5v5YtW1KuXDn27NlDUlIS8+fPN21pzl27drF3716ioqL44IMPrNsdHR1tsozdM888Q506dUxv117jeO211/jxxx/59ddf8fHx4cCBA1StWtX0OHbs2MGsWbPu6SEzu9Dd/PnzWb16NQUKFADu9tz16dPH9GT9YSOEzJSUlERiYqK1ZlBSUpJNbqLkzZuXyMhI6xz+yMhIm0y5Sk5OTlOM0cXFxfQk0V7OHSVLliQqKgo3NzebxZCYmMjw4cNt1n6KMmXKpPk9SBntYIsiu2bKtMm6PRT1SL2GZsoNA1ssE2ZPRo8eTY8ePazzT8PDwwkICDD155U9e3b69u3L7du3MQyD5ORkzp8/b11yxgxt2rTBx8eHWrVqYRgGW7du5a233jKtfbFv4eHhzJ8/n7i4OJYtW0b79u0ZNmwYXl5etg4t00spPtO7d282bdpkWrKe8n0SFhb2wPmUy5cvNy1xr1KlChMnTuSNN95IM1TT7ATVXuL44osviIyMJCoqisaNGxMQEEDr1q3p3r27qXHYS29hzpw50xR/LFy4sE2WXbSXmxeenp506dLFuhrO119/bf23mQIDAxk2bJh1+H3RokVNm4qYWunSpQkKCrL2pK9cudK0c2kKezl3xMXF0bhxY0qVKpWmALSZU2iqVKnCli1bqFmzZpoYzHb8+HGbtW1LFiOTlhI8evQoH3300T0naLPnj504ceKeGGxxt/1hzJyz3rJly3vW+/T09GTdunWmtA/QtGlTunfvTmhoKJ07d2bTpk3kz5/fugaqGe7cucOiRYuIj48nZ86cGIbBzZs3bTK3UOxPq1atWLx4MW+++SZhYWFERUXx9ttv8/XXX9s6NMDcc8bD3O98kp6mTZvGX3/9xZEjR1ixYgV9+/albNmydjNMz8yfy/16Ji0Wi+nfsfYSR8uWLQkJCaFt27aEhYVx69Yt2rRpQ3h4uKlxtG/fPt0rnD+KMWPGcObMGXx8fHBwcGDDhg3WpATMK+DVqFGj+968sEWNmO3bt7N7924Mw6B69eo2neqUuuaFLcTFxTF79mz27NmDYRhUq1aNd955x9R47OXcsXfv3vtuN7Nyfs2aNbl8+XKabbbsyY6NjWXu3Lns3r2bpKQkqlevzqBBg9J1rXNbyrQ968OHD6ddu3Y2vbscGBjIli1b0gxftcWJ4J+YeT/H2dmZI0eOULZsWeDu3Fezq8E7Ozvj4+PDuXPnyJMnD1OmTDF97ti7777LjRs3OH36NO7u7uzZs4fKlSubGoPYryxZsqS5aHFzc7POdbQHZt8DTpkS8MYbb3D+/HnrOTV1tVYz7Ny5k9DQUFq1akWuXLn4/PPPadGihd0k62b+XFJGQ8XExJCcnEyePHlMa9se48iSJUuaHqmsWbPaZJizvfQW3rlzBzc3N3bs2AHcHdGWPXt2a70Ys5J1Ww91Trne2bdvH9mzZ6du3brW5/bt22faz2XUqFGMGzcuzfTM1My+Ls2WLZu10LCt2Mu547XXXmPbtm3WlQKqVatmnaZglp07d5ra3j8JDAwke/bsTJgwAbi7nO3o0aOZOnWqjSNLH5k2Wc+WLZtNlsVIbefOnURERNh8KRfAeoGZ2tKlS+nUqRO9evUyLY4RI0YwYMAA8uXLh2EY3Lhx455l1NJb1qxZuX79OiVKlODAgQPUqFGDpKQkU2P45Zdf2LRpE0FBQfj4+DB48GBTKl1KxvDSSy+xZMkSEhMTOXbsGF9++aXpQwRPnDjBSy+9lGbb/v37qVixIv/73/9MiyNlSkBsbCzLly9PMyXA3d3dtDgA6w2TlIvd+Ph4u7qJYuaN6TNnzuDr65um/sisWbNMX0vbXuJ47bXXmDx5MrGxsURGRrJ8+XKqV69uagxwd6URIM0awbboJLDFMm33Y+ubF8uWLWPcuHH3nTdv5s8lZXpM6umZtpAy+udBc5PN7Mm1l3PHxx9/zKZNm/D09MQwDD766CNOnDhB3759TYvB3nqyjxw5wtq1a62PAwICaNq0qU1iMUOmHQb/wQcf4OLiQs2aNdOcoNNzUfu/6969O3PnzjW95zi1hQsXEhMTY533miIxMZH169cTGRlpekwJCQmcOnWK5ORkSpQoYfr8mA0bNhASEsKcOXNo06YNWbJkoUyZMkyfPt20GFKGKi5dupScOXPSsmVLWrRokebkJJnX7du3mT9/Prt27SI5OZnq1aubNkTQ3qqO29OUgAULFnDkyBEOHTpEly5dWLt2LQ0bNrSbSuBmDoN/++23adeuXZr6I1999ZXp9WLsJY7k5GRCQkLS/M22b98eR0fb9JnYurewbt269715ZPZccXsZ6nz8+HHTb7jeT79+/fDy8qJOnTo2nZucmJhos7+NFPZy7vD09GTFihXWjr3Y2Fi8vb3ZsGGDaTG8//77ZM+enbZt2wJ3e7Kjo6Nt1pPt6enJ0qVLreevmzdv0qlTJ1OnzJop0/asr1mzBri7PESKlPUTzZI3b16aNWtGpUqV0pwUzbzjXLx48fsus5Q1a1ZTq+O///77D33ezM+kSZMmNGjQAEdHR1asWMHx48dNH4L+0ksvMW7cODp06MB7771HVFSU6UOLxX7lyJGDd99919SlhVLYW9Vxe5oS0KtXL3bs2MFzzz3HhQsXGDBggF1UE7aFa9euWS9y4W4tELPXprWHOM6fP2/9d61atdKs+BIVFWVqBwHYT29h6oQnMTGRzZs3Ex8fb2oMqeOw9c2LESNGkJCQYF1az8xla1Nr06YNX3/9NRMnTqRmzZq0aNHC1LnRKerUqUOjRo1o0aIF5cuXN719sP25I4VhGGlG4GbNmtX0Gxn21pPdtWtXWrduTd26da1FmM0cBWy2TJusP6yyt1kVc9944w3eeOONdG/nYVLWa27SpAklS5a0WRy2+DJ4kJRhtevWrePq1asMGTKEgIAAU+cIjRkzhp9//pkXX3yRAQMGsHv3blN79sU+/X1ooKOjIw4ODty5c4dcuXKxb9++dI/hUaqOm8kepgSk6NWrF61atcLX19emvVIPkjt3btPasof6I/YQx5tvvonFYuHOnTtcuXKFokWLkiVLFk6fPk3RokVNH4kSEBBwz4oro0aNMr238O8F3Hr06IG3tzf9+vUzNQ57uXmxevVqTp06xddff02vXr3Ily8fXl5epq4rDneT5Dp16nDnzh22bt3KpEmTuHbtGlu3bjU1jvXr17Np0yamT5/OxYsXad68OS1atOD55583LQZbnztSVK9enQEDBlinqoaFhVGtWjVTY0gpcpy6J9sWNTdS+Pj48Oqrr7Jv3z6Sk5OZM2cOpUuXtlk86S3TDoN/GHuoZGx2DPayfMnDmPWZeHp68vnnn/Pss88CcOXKFbp162YdjSFia6NHj6Zy5cq0aNECi8XCxo0b2bFjB+PHjzcthnPnzrFkyZJ7zhlmz0W15ZSAv9u7dy9hYWF8//33eHh40KpVK9N7heLj4/n00085efIkAQEBLFy4kF69epl+82D//v0MGTLknvojFStWzJRx+Pr60qlTJ2sdhYMHD/LJJ5/cd65yerKHFVeANDcWDcPgxIkTfPnll6ZPX7GXoc4pbt++zTfffMPnn39OTEwMmzZtMj2G3377ja+//pqIiAgKFSpEixYtbHpj9tChQ4wePZrjx4+nqbWQ3uzl3GEYBl999RXff/+9daWAdu3amdq7vmrVKoKDg+/pyTb7ZtLWrVupU6fOA1d5sYcOhPSQaXvWH8Ye7l+YHYO9rL36MGZ9JgkJCdZEHSB//vx28TshkuLgwYOMHTvW+rhRo0amD88bPHgw7u7uuLu72/SckSNHDvr27UuzZs0oVaoUcXFxNit689prr/Haa68RFxdHREQEAwYMIHfu3LRu3ZqOHTuakjAHBgbi4uLC0aNHcXBw4PTp04wYMYJp06ale9upVaxYkY0bN9q0/og9xfH777+nKXhYvnx5Tp48aXoc9tJbmPomhcVi4ZlnnjF16l0KexnqvHnzZtatW8eBAweoU6cO/v7+NlkBxtPTEwcHB1q0aMEXX3yBm5ub6TEAXL16lQ0bNhAeHs6NGzdo3rw5c+fONTUGW587Ll26hKurKxcuXLCOgk1h9hQae+nJPnToEHXq1LGuGvF3StYzEXtIVs2OwdbLlzwKsz6TKlWqMGTIEDw9PbFYLISHh5t+J1XkYbJnz86qVato0qQJycnJrFmzhrx585oaQ2JiIsOHDze1zfvZvXs3AQEBJCUlERISQrNmzZg+fTo1a9a0STx79uxhzZo1fPfdd9SqVYumTZuya9cu+vbty6effpru7R85coTQ0FC2b99O9uzZmTx5sqlLT86ZM4cBAwY8sA6JWSMv7CWOFAULFuSDDz6gadOmGIbBmjVrTB9qDfax4grw0J7rlJ+dGezl5sXatWvx8vJi+vTpODk5pXkuJWkzw7Rp0x6YhJn5c/Hy8qJJkyb4+fnx6quvmtJmCns5d/j7+xMcHGydSpMipTK+GSNf/96TnTNnTgCOHTvGsWPHTE+OBw4cCEC9evWoXbu2zYsQmiVzHKX8I1svX2JPRo8ezeLFi1m+fDmOjo64u7vTsWNHW4clYjV16lTGjRvH+PHjyZIlC//73/+YMmWKqTFUqVKFLVu2ULNmTZvOz54xYwZffvklPXv25Nlnn2Xp0qUMGTLEJsl6nTp1KFKkCD4+PgQEBFiLAlWrVg0fHx9TYrBYLMTHx1sv7q5du2bqzd+UpOd+dUgyYxwppk6dyuzZsxkyZAgAr7/+uk2WL7N1b+Gj2LJli2lJob3cvJgzZ84Dn+vVq5dp0yIf1ltq5s/l22+/feCc6N69exMcHJxubdvLuSPlGFevXk2+fPnSPHf27FlTYrDXnuy1a9cSGBhInTp1aNGiBVWqVLFJHGZRsi6A/ay9ag+cnZ3p3r073bt3v+c5e6hnIFK4cGE++uij+z43atQoxo0bl+4xREREsGTJEuDuucIW6+DC3SWxUvc6vfjii6a2n9oXX3zB888/T0xMDPHx8dZkPUuWLKadN7p06cLbb7/NpUuXCAoKIjIyknfeeceUtuHuklxwd5hm79690zxnZhJkL3GkyJs3L6NGjbrvc+mdfID99BY+CjOnnWWEmxf2Mg3PzDgeVrzs4sWL6dq2vZw7Lly4gGEY9OrVi48//tj6+SclJdGzZ08iIiLSPYaUnuzmzZvz+uuvp3nOFvUUUsyePZuYmBgiIyNZsGABp0+fpnHjxgwaNMhmMaUnJev3YWbF3Acx++Rsq2Iq/4Y9fGHZQwwiD3O/pRjTw86dO01p558ULFiQrVu3YrFYuHnzJkuXLjV9OawUFouF1q1bc+bMGZKTkylcuDAzZ86kRIkSpsXQsmVLypUrx549e0hKSmL+/PmmVsefNm0aV65cYcuWLZw6dcq6PTExkYMHD1p7ljNLHI8ivZMPsJ/ewkdhRjz2tFzsP7GXn09mieNB546kpCQOHDhg2rlj9uzZ7Nmzh6ioKDp16mTd7ujomGb+enoKDw8nPj6e2bNnWxN3uHseDQ4OpmHDhqbEcT+5cuWiSpUq/PXXX1y4cIGff/7ZZrGkt0yXrP9TgYr+/fub1pscGhpqXYohxdKlS+nUqZPp6wXu37+f4OBgbt++jWEYJCcnc/78+YcucZce7OkzuR97+bISsbX4+Hg+++wzTp48yahRo2xWdTwwMJCgoCAuXLhAgwYNqFatGoGBgabGkOJ+y2IFBASYfjP02LFj1l6hTZs2mZqsN2zYkN9//53vv/+eatWqWW9wOjg4mNrDby9xPAozvlfspbfQXtjTcrFiX1KfO1L/njg4OJi6tGDKDaMFCxbY7Pr31q1b/PTTT9y6dSvNUHgHBwd8fX1tEhPA559/zvr164mPj6dFixYsWLCAggUL2iye9JbpknV7sHDhQmJiYli2bBnnzp2zbk9MTGT9+vV06tSJpk2bmhrTiBEj6N69O6GhoXTu3JlNmzbxyiuvmNa+PX4mIvJgKVXHjxw5goODA3/++adNqo7v3buXSZMm2cXQVXuoLD1t2jT++usvjhw5Qs+ePVm1ahXHjx/Hz8/PlPbLly9P+fLl8fDw4Oeff6ZevXpcvXqVLVu2UKxYMVNisKc47IW99Bbai793CjxoH017y3xSzh3169dPM9LWMAzT5oqn5uPjw8KFC7l165a1M+3s2bOm1Klp06YNbdq0Yffu3dSoUSPd23tUFy9eZPz48bz88sv3PJe6YOR/RaZL1vv373/f7Wb+ERYvXvy+Q1WzZs1qk6VL4O48bR8fH86dO0eePHmYMmWKqRWE7fEzEZEH+3vVcbPPGSm2b9/O1KlTbbaueWr2UFl6586d1hFKuXLl4vPPP6dFixamJesppk6dSnJyMvXq1QPuVsk/ePCg6aMe7CUOW7OX3sJHUbJkSVuHANjPtDd7iSOz/VzCw8OZPHkysbGx1m3/1969x8Wc9v8Df00zKlaobC3Fbmwb63gTcj6uUNNRyVKLHJIloVSKexVRWRS3xf1DTuVYSTnltKx12KVaKSwSiZwadlulmfn90WNmG6Xde7+6ro/m/fxn83E/HtfrVs3MdXi/LzMzM2RkZDAZX8Xf3x8tWrRAZmYmhg0bhlOnTjHvkN+wYUNMnz6d++lbldrez0JDQ+vdIpvWTdZVdu3aVe2X0NzcHMeOHavzsVX3JY4cOVIwL356enooKSmBhYUFsrKy0Lt3b8jlcmbjC/HfpCZCedMk5G1Y/Yzy7jquEhkZiT/++ANHjx5FXFwcnj59Cjs7Ozg5OcHY2JhpFiF0ltbR0QHw59Hq8vJy9TOWrl69itTUVACAkZERoqOjuSzmCCVHbVj8zgplt/Dv1IqzPp3zNqw7f9dUnjBnzhyEhobW+fjv0/eFVQfy9evXIyUlBatWrYK/vz9Onz6Ny5cvMxm7quLiYmzduhXLly/H8OHDMXnyZHz11VdMM/A+ffu/qI+f07V2ss7zl1DV+XXKlCk1vhmwuDvxTRMnToS/vz/i4uLg5uaG1NRUdOzYkXmOBw8eIDAwEDKZTOMXjuW/idDr5gm5efMmLC0tNZ5lZmaia9eu6NOnD5MMvLuOV9WwYUOYmZmhRYsWuHv3Lq5fv44JEyZgzJgxGD9+PLMcQugsPWLECMyePRsymQxbtmzBgQMHYG9vzzQDUNmlv7i4GCYmJgCAp0+fclk0EEqO2rC8/oj3bqFqV//kyZP4/fff4eDgAIlEgvT0dEE092Xt7zRCtLa2rvMcQvm+DBkypNZFkuPHj2PChAlMshgbG6NVq1awsrLCjRs3MG7cOCQkJDAZu6qmTZsCACwsLJCXl4cuXbowz8D79O3/oj72ltLayTrPX0LVtUpC6sCur6+PTZs2QSQSYd++fcjPz2falEglIiICQUFBsLS0ZP4LR3XzROh+/vlnKBQKhIaGYsmSJeoFrYqKCvz73//GkSNHEBgYyCTLgAEDuHYdV1m5ciUOHjwIc3NzuLi4YMGCBdDT08Nvv/2GoUOHMpmsC6mz9NSpU3HmzBm0bNkSRUVFmDlzJgYPHsxsfBUfHx84Ozur77/NysrCggULtDbHmTNnsHLlSrx48QJKpVJ91SHLyQfAf7dQtRC+c+dO7Nq1S71wMnLkSLi7uzPLIRS1lSewXPwUyvdl27ZtUCqVWLt2LVq1agUXFxeIxWKkpqYyrxdv2LAhzp8/DysrK2RkZKBTp0549eoV0wwAYGNjg1mzZmH+/PmYNGkScnJy1NeCssL79K2209rJOs9fQtUK/4cffoizZ8/ixYsXGn9vZmbGJEdV0dHR6qsgGjVqxO14i6GhIZcPlgDVzRPhO3fuHC5evIji4mKsXr1a/VwikWDMmDFMs4wbNw6HDh3ieq85UHnse8uWLWjVqpXG88aNG2Pjxo1MMgips/TUqVPh7OwMf39/rk33pFIpevbsiczMTEgkEoSGhqrf+7QxB8+F6KqEslv48uVLlJSUwMjICADw5MkTlJaWMs9RG9blCXp6etDV1cXdu3dx584dtG7dus7HfxPv74vq8+/169c1FjknTZoEFxcXZjmAytrnvXv3IigoCHv37sWIESMwc+ZMphmAypr1goICmJmZ4dtvv8WlS5fe2n+rrkyYMEEQp2+1lUhZHw/3/w03btxQ/xL6+fnh3LlzmDlzJtMV7q+++gpKpbLa5JzH/Z4+Pj4wNDREly5dNFbsWB7PAyoXDSoqKtC/f3/o6empn/fo0YNZhlu3bgm6bp6Q5ORk5r+bb/L398fAgQPRuXNnjdcM1necKxQKJCYm4vz586ioqECvXr3g6enJ7ahzbm4uzp8/D7FYjL59+zJ/Lbl48SKSk5Nx/vx5rk33VFf73b59G2FhYYiPj+dytZ9Qcnh4eCAxMZHpmDXx8vKCr68vysrKkJGRgVmzZmHs2LHMm2YlJycjJiYG3bp1g1KpRGZmJsLCwpjf21xb2Vt6ejqz03Rr167FrVu3MG/ePLi7u8PS0hJt27ZlUq9eVU3fl9DQUNja2jLN4eLigoCAAHUH8tOnT2PNmjXYs2cP0xw8JScn1/r3rD8DqE4DlZaWIj8/H+3bt2e+8Hjp0qW/nA84OTn95b/d+0ZrJ+tC4ODggAMHDvCOAeDtxzhZLxx4enpWeyYSibB161ZmGc6cOYNVq1ZxrZsnpDaFhYXYvn17tZ9Rlr+vqnubq1Id62Vp+fLluHv3LlxdXaFUKrF//36YmZlxOeq8adMmJCYmYujQoZDL5Th16hSmTZsGV1dX5llevXqFw4cPY+XKlTAwMMDo0aPx5ZdfMpukhoaGwsjICCdOnMCePXuwaNEiKBQK5k2qhJJDCAvRgDA2KgAgLy8PRkZGuHLlCkQiEbp37860IWTVsjcPDw/1c1XZG+vFCxcXF+zcuRNbt25FSUkJAgMD4eLigv379zPNAVQ2NOP1fVHJzc1FYGAgHj9+rN7UioqKYnKS6+/UzbMghPIqIWSoauTIkTh06BBGjx6NvXv31vi/uXfvXrWTdu87rZusq5q7ve2XkeUHzaVLl2LQoEGwsbERXMMbbWZra1vjcUUe5QmE1MTNzQ3W1tbVfkb/zt3B78rJkye5laxU5eDggOTkZPVraEVFBaRSKQ4dOsQ8i62tLfbt24fGjRsDAGQyGcaOHYv09HSmOS5cuICUlBT88MMPGDBgAEaNGoVz587h2rVr+H//7/8xyaC6o1q1y6FUKiGVSnHw4EEm4wsthxAWooVE9aGbl1OnTuHq1avVJutisRg9evRg0tStKtXP59ixYzF79mz06NEDdnZ2zP+NXrx4gdTUVJSUlGgsBLM+dq36vqhuGWnWrBmzsav2LKoJj8+Cr1+/xp07dyCXy2FpaQmJhE0V819dgcbyMwcAeHt74+bNm3j+/LlGOVPVHiD1kdbVrKuau61atYrLamFVLVu2xKRJk9QftlU/bLm5ucyzCGHxAqjsaL1+/XqudznyrJsn5O+oqKjA/PnzuWaIiYkRxO+JXC5HRUWFesdYLpdDLBZzydKsWTOND1ENGzbEBx98wDTD4MGDYW5uDldXVyxcuFBdotCrVy+mO/xCudpPKDlUDWV/++03KBQKNGnShOn4QtktVPn000+xZs2aaqV3rE4a1HZdLI8mYr1794a9vT309fXRo0cPjB8/vsbTS3XNz88PBgYG3HsrbN++HR4eHjA0NGQ+tmoyrlQqkZCQoC6xsrGxYXq7iMrVq1cxa9YsNGvWDAqFAk+ePMHatWuZdIWvOhm/ceMGLl68qC43a9++fZ2P/6aNGzfi4cOH8PHxwbp165iPz4vW7ayr8F7VBYBRo0bhv//9L/Maz5q82f382LFjKC8vh6+vL9Mco0aNqnaXo7GxMUJCQphlEMpxRULeJiIiAn369EG/fv24NRETSp+L7777DqdOnYKdnR0AIC0tDQMHDsT06dOZ5gAqjwxeu3YNdnZ2kEgkOHbsGPT19dWdyFnsThUUFKB169bcJoUqycnJ2LNnD+7evYuRI0eqr/YbPXq0Vua4d+8e/P39ce/ePSiVSrRs2RKrVq3CJ598wmR8oe0WCuWkwYkTJ7By5Ur88ccf6g2CP/74A+fPn2ea49SpU/jss89gamoKsViM3NxcLpMhqVSK1NRU5uO+afLkySgvL0eXLl00Poex3OEXSomVh4cHgoOD1ZPzzMxMREREvPUYeF1ITk7GmjVrMGzYMCgUChw/fhzTp09n/jpalRAWD1jRup11lXbt2iE5OZlrc6QPP/yQ6dGe2rz5Rj158mS4uLgwn6wL4S7H7OxsAMC1a9fUz7T5uCIRnsOHD2P79u0AKn82eZzKUe14ZGVlaTxnPVn38fHB559/jh9//BFKpRI+Pj7qmy1YMzMzg5mZGcrLy1FeXo6+ffsyzyASiTB69Gjcu3cPCoUCZmZmWLlyJSwsLJjmcHJyUl/tp1AouF3tJ5QcCxcuxOTJkzFixAgAlfedh4WFMbvCVWi7hbxPGqhERkYiPDwcmzdvho+PDzIyMjTuoGclOjoaaWlp6j/zmni0b98eeXl5XH5HquratSvX8QHghx9+0CixGjRoEJe7xUtLSzV20bt27YqysjKmGTZv3ow9e/ao3/d9fHzg5eXFbbKekpKCuLg4DB06FEqlEjNmzICvry/XxYO6pLWT9aysrGofMlnXOzRr1gz29vbo1q0bGjRooH7Ooxv8pUuX1F8rlUrcvHmT+YsBIIy7HFl9eCLknzp79izvCFxep6qq+prVsGFDjSOjf6djbF1gXddZk5omhQsXLmT+uvb69WucPXsW58+fh0QigZ6eHqysrJgfrRVKjufPn6u/J0DlKTIexzijoqKq7Rbeu3eP+W4h75MGKgYGBrCxscHly5fx8uVLBAQEMOsAX1WrVq3Uu6c8TyrdvHkTzs7OMDY2hp6eHrdaYCG8lgqlxKpp06bIyMjAsGHDAAAZGRnMN/oUCoVGSYKRkRHXMolNmzYJavGgrmntZJ1lDfTbqGqmhCA2Nlb9tUgkgqGhIZe7xSdOnMj9Lkch1M0TUhvVdVR37txBWFgYtmzZwvw6Kt59Lqq+Zr2J10mY+Ph4rF27Fi9fvgTApw+JUCaFoaGhePXqFdzd3aFQKJCSkoKbN28ynxQKJYeuri5ycnLQoUMHAJV1qA0bNmSaARDObiHvkwYq+vr6uHPnDtq2bYuLFy/CxsYGr1+/ZpoBEM5JpTVr1jAd702qhpDt2rXTeH/h8VoqlUrh5eWlUWKl+pqlxYsXIzAwUP2a1apVK0RFRTHNYGVlhSVLlqgnw3v37uV6+kJoiwd1TWsn6zKZDNHR0SgoKEBsbCyWL1+O4OBgpkexDh48yKwz718Rym6yvr4+Nm3aBJFIhH379iE/P5/5C0JISEi1uvnPP/+caQZCarN48WIYGRkhJycHYrEYd+/eRUhICNPrqKq+ZlTtc8FjfAAoKSmBWCyGgYEBswxvio+PR3JyMtc+JEKZFGZlZeHw4cPqPw8ZMgT29vZamyMkJAQzZ85Es2bNoFQqIZPJ8O233zLPIZTdQqEsKvn7+2PVqlWIjo7Gxo0bsWvXLi67c7xPKqm0bNmSa5mEqvt4Xl4eszHfRiglVhYWFtizZw9KS0uhUCjUt42wFBERgdjYWISEhECpVKJXr15YtGgR8xwqQls8qGtaO1kPCwtD3759kZ2djUaNGsHExATz5s3Dhg0bmGV49eoVioqK0KJFC2Zjvo1QdpOjo6PVL4aNGjXiMkkWQt08IbXJyclBUlISvv/+ezRs2JDLz6hQ+lzk5eUhMDAQjx49glKpRJs2bRAVFYXWrVszzQEAbdq0QfPmzZmPW5VQJoXm5ua4e/cuPv74YwDAkydPYGpqqrU5unbtiiNHjiA/Px8KhQIWFhZcmkMKZbeQ96KSp6enxk08kydPRsOGDdGiRQvk5OQwy9G+fXvk5ubiX//6F4yMjNTPeR0/F0qZhEwmQ1paGp4/f878CjnVz+WlS5e4lliFhYUhPDxc42e1Kpanx/T19REYGMhsvL8SERGBuLg49eKBjY0N18WDuqa1k/X79+9jzJgxSEhIgK6uLvz9/eHg4MA0w7NnzzBkyBDutUGAcHaThVC3JYS6eUJqI4TrqITS5yIkJAT+/v7qa+SOHTuGoKAg7Ny5k3kWLy8vSKVSdOnSRWO3kuWumVAmhRUVFXB0dIS1tTXEYjF+/vlnmJiYwMvLCwC7D5q8c8TFxWHmzJkIDg6u8e9Z76gKZbewpkWllStXMht/5syZzMaqjZmZGSoqKiCRSLBt2zb150DVf1kTSpnEjBkzYGRkxOUKucTERISHh9dYasWyxGrMmDEA+P6sCqksoSp9fX0EBATU+HfTpk3D+vXrGSeqW1o7WReLxXj58qX6hy8/P1/94sTKf//7X6bj1UYou8lCqNsSQt08IbXx8vLCxIkT8fjxYyxZskR9HRVLsbGxePr0KYyNjSESidC0aVMsX76caQag8kND1fvev/jiC6xdu5Z5DgBYsWIFpFIp82uwALx1MqjCelL45gkLb29vpuMLJYdq57hnz57V/o7lJEQou4Uqby4qmZmZMT3eW9P3g4cePXqgU6dOAIChQ4eqn/OaDAmlTEImk6lvPGEtPDwcALBgwQKuR6tVnzu3bNkCR0dHDB48mPnCq6os4erVq5BI3o8p46NHj3hHeOfej3/5OjBr1ix4enqiqKgIvr6+yMzMxNKlS5lmMDMzQ2pqKn799Vf4+PjgyJEjzJuJqAhlN1kIdVtCqJsnpDYDBgxQX0cll8u5XEf1xRdfYP/+/di2bRvu37+PKVOmICcnh/nCVp8+ffCf//wH7u7uEIvFSE9PR9u2bfHgwQMAbK/j1NXV5dbFWCiTD5WuXbvi9u3baNeuHVJTU3Ht2jVMmTJF46ivNuRQTYqLi4sxbdo0jb9jWZ6g2i2cPn26+tSc6ngxj4aM6enpWLduHVJTU1FQUAA7OzuEhYWpO15ri8jISERGRmL69OlcavbfVFOZBI8eD5aWlrh69SrXjZKQkBC8fv0aUqkUUqmUW8mqm5sb0tLSEBkZiX79+sHBwYH56/3gwYNha2sLBwcHdO7cmenY/6v62GhOpKxaDKJlnj17huzsbMjlcnTt2hXGxsZMx4+JicHDhw+Rk5ODPXv2YPr06ejQoQOCgoKY5gCAQ4cOYffu3erdZB0dHbRr1w4rVqxgmoN3h2kAsLOz07jvlBChGTlyJA4dOsQ1g729Pfbs2aOuM/3jjz/g7u6O1NRUpjmq7hC+iXVZkeoGjQEDBmhcx8l61zI3Nxfnz5+HWCxG37590bZtW6bjA4Cfnx/Mzc1ha2uLefPmwdHREdnZ2cyPJ/LOERMTg6dPn+LEiRMaP6sVFRXIzs7GkSNHmORQcXFxQXl5ORwcHLhOQKRSKTZv3qzu8fD06VNMmjQJKSkpXPKQP33//ffqMonevXtj4MCBzMZWfQZ89eoVnj17BlNTU4jFYiiVSujo6CAjI4NZFqDy1G1aWhoOHz6MZs2awdHRkdv1YGVlZTh58iQ2bNiA58+f4+TJk8zGlslkOHr0KA4ePIhHjx7B3t4eDg4OXHrD/BXV0f36RGt31gsKCpCZmQl7e3ssWrQI//nPf/DNN98wXcU7e/YskpKS4OzsjMaNG2Pz5s1wcHDgMlnv06cPRowYobGbzKOrMu8O04Aw6uYJqU27du2QnJyMzp07a/yMstxFfv36tcaEtOrXLAnpSkVVY6qqDapY71pu2rQJiYmJGDp0KORyOaZPn45p06bB1dWVWQagsi/M6tWrER0djdGjR2Pq1KnMMwghx/Dhw3Hr1i2cP38evXr1Uu9mi8Vi5qUrALB//37cvXsXBw8exNSpU7lNQF6/fq3RjNHY2BhavHckGI8ePcKFCxcwf/583Lt3D3FxcejQoQOzxpmqz4DLli1DcHCwRg3/X5X61IVPPvkEEydOROvWrbF582Zs2LCBy2T9119/VS8atGjRQt1zg5WmTZvCzc0Nbm5u+OWXX9TzpmvXrjHNoa20drIeHBwMNzc3nDhxAvn5+QgODkZERAQSExOZZVDVyKt2ksvLy5nXzRcVFUGpVGLq1KnYuHGj+s3SwMAAU6ZM0bjyhgUhdJgWQt08IbXJysqq9vPJehd52LBh+OqrrzBy5EiIRCIcOXJEo+aSldu3b2P37t2QyWQaz3mU1AjhCsxdu3Zh//796vrfGTNmYOzYscwnynK5HM+ePUNGRgbi4uLw+PFjLg0Ieefo3LkzOnfujIEDB+LKlSsYOnQonj17hhMnTqg71LP28ccfc5+AdO/eHXPmzIFUKoVIJEJ6ejq6du3KNAOpbt68eeoj8KamprC2tkZgYCA2bdrEZPzIyEjk5uaiuLhYYyIol8uZnwI5duwYUlNTkZWVhcGDByM0NBTdunVjmgGoPIUiFovh4OCA+Ph4mJiYMM/w7NkzHDp0COnp6ZDJZLC3t8eaNWuY5/g76uOin9ZO1svKyuDk5IQFCxZAKpXC2tqa+Q7uiBEjMHv2bMhkMmzZsgUHDhxgXhsUGxuLCxcuoLi4GOPGjVM/l0gkXDrECqHDtBDq5gmpTVhYmEZTNR4CAgJw+PBhXLp0CRKJBF5eXlzqTb/++muMGjUKVlZWzMd+U2FhIUJDQ1FYWIgdO3Zg7ty5WLp0KczNzZllaNasmUYjoIYNG+KDDz5gNr6Kt7c33N3dMWTIEHz22WewtbWFn5+f1uaIjo6GQqFQL2hduHAB2dnZWLx4MdMcQpmALFq0CNu2bcOuXbsgkUhgbW2NL7/8knkOokkmk8HDwwNAZQ8Od3d3JCQkMBt/2bJlKCkpwZIlSxAaGqp+LpFImJeqHjhwAI6OjlixYkW1k2OPHz/Ghx9+yCRHTEzMW9/fVLdN1DVHR0eMHDkSQUFB6oaIPK1cuRL+/v41/l193FjT2pr1MWPGYNKkSVi8eDGSkpKQnZ2N7777Dnv37mWa48yZMzh37hyUSiV69erF7QP4hg0bMHXqVC5jV+Xp6an+WiQSwdDQEJMnT2b64iCEunlCakN9Ff7k4eHB9ERUbby9vTFx4kTExMQgKSkJe/bsQUpKCnbs2MEsQ3BwMK5duwY7OztIJBIcO3YM+vr66N69OwA29xTXhFdXaaHkkEql1fo51PSsrs2cOROOjo4YOHAgt9IVlfv37+PXX39Fv379UFRUhFatWnHNQwB3d3fMmDFDXad+7tw5rFmzhstVmEImlLpoVjlqe93kcVWag4MDUlJS6mUzuZpo7c764sWLsWXLFixcuBAmJiZIS0tDREQE8xxlZWUoLy+HRCLhcheuiq2tLQ4cOACpVIpFixYhJyeHeQ0/IIxjpEKomyekNtRX4U/Ozs5YuXIlbGxsNHaUWTd1Ayrvu+/Xrx9iYmIgEong7u7OdKIOVJYSmZmZoby8HOXl5ejbty/T8VXOnDmDVatWQSaTaRxLZL3oKZQcCoUCxcXF6iOsT58+ZV72BlTuxAmBqhv8q1evkJiYCA8PDwQGBsLR0ZF3NK32zTffICAgAIGBgRCJRPjoo48QFRXFO5bgCGWfk1WO2hY4eVyV1qxZM4wYMQIdOnSAnp6e+nl9PRmrtZN1KysrjW/qypUr1V+zWqlatmwZMjMzYWdnB4VCgdWrV+OXX36Bj49PnY/9ppCQELi5ueH48eO4c+cOlxp+AMjMzMT69etRWloKpVIJhUKBBw8eMG0iJYS6eUJqQ30V/nTlyhVcvnwZly9fVj/jcRUVUHnt48OHD9Wr/T/99BPzRVheO+dvioiIQFBQECwtLbnufgglh4+PD5ydndUnHLKysrBgwQJueXjbuHEjEhISMH78eBgbGyMpKQkTJ06kyTpn7du3x8GDB/H8+XM0aNBA3fuCaBLKjq4QcvDI4OzszHxMnrR2sl4bVitVJ0+eRFpamno3yMPDA05OTlwm60Ko4QcqFw28vb2RlJQET09PHD16VH0nLCtCqJsnpDb1dfX4n8jJycHRo0d5xwBQeQR92rRpKCgogKOjI2QyGVavXs00Q3x8PNauXYuXL18CgLqbcm5uLtMchoaG3PsqCCmHVCpFz549kZmZCYlEgtDQUC6NooRCR0dHYyJoYmLC5aQB0fRm3w1fX1/mfTcI+SvOzs5aVUZDk/UasFol+vDDD/HixQsYGRkBqLzKRLVjxppYLMaRI0dw6tQp+Pn5ISMjg8sbp66uLlxdXVFYWIgmTZogKioKUqmUaYbY2Fj116q6edX9yYQIAfVV+JOlpSXy8vLQrl073lHQqVMn7N27F/n5+ZDL5WjTpo16Z33Xrl0YM2ZMnWeIj49HcnIy02v8atK9e3dERkaif//+GscUWZcnCCVHeXk5kpKScPv2bYSFhSE+Ph5Tp07lWv7Gk6WlJbZv346Kigrk5uZi586dgvgd1nYLFy6Et7c3YmJi0Lx5c9jb22P+/PnMy3kIqY22ldHQZJ0jIyMjODg4YOjQoZBIJDhz5gyMjIzUd0my3D1T1fAvWrSIaw2/np4eSkpKYGFhgaysLPTu3RtyuZxpBiHUzRNSG+qr8Kfbt2/DxcUFzZs312iYxWvhokGDBrC0tKz2PDExkclkvU2bNszuRK5NdnZ2jTv6rMsThJJj8eLFMDIywrVr1yCRSFBQUICQkBDExMQwzSEUpaWlePToEfT09BASEgIbGxvMnz+fdyytJ4S+G+8DodSst23blncELv8W2lZGQ5N1jgYPHqxxPK9qMzfWNSBWVlbw9fXFrVu3IJfLMWfOHC5HSiZMmAB/f3/ExcXBzc0NqampzJvcCaFunpDaUF+FP61Zswapqan49ddf4ePjg6tXr3JpLvdXWH2g8fLyglQqRZcuXTSaArFa/A0LC0N4eDiA6v+fWb6vCSWHSk5ODpKSkvD999+jYcOGWL58OfNTY0JSWFiIpUuXYu7cubyjkCqE0HdDKNavX49p06ZpPPv2228xZ84cjWvl6opq4+5tIiMjBbHYx6NXjraV0dBkvQasPlTV1iDB2dmZ6S+AUI6U9OnTByNGjIBIJMK+ffuQn58PAwMDphmEUDdPSG2or8KfEhMT8fDhQ1y7dg0tWrTA4sWLcf36dQQFBfGOpoHVBHHFihWQSqXVFnRYUZ0eYHH37/uQQ0UkEqG8vFz9c/D8+XNBNIfiRUdHB0OGDIGFhYVGeQKPxpDkT0Lou8FbTEwMnj59ihMnTiA/P1/9vKKiAtnZ2ZgzZw6sra3rPEfPnj0BVPa3+v333+Hg4ACJRIL09HRmn4vfVnKncvz4cUyYMIFJlqq0rYxGayfrSUlJ1SbLO3bswLhx4wRx3zjrYyW8j5QUFRVBqVRi6tSp2Lhxo/r/v4GBAaZMmYLDhw8zyQEIo26ekNrExsbi6dOnMDY2hkgkQtOmTbF8+XLesbg4e/as+vW8cePG2Lx5MxwcHAQ3WWdFV1eXa0d41Uko1QdNbc+h4uXlhYkTJ+Lx48dYsmQJMjIyMGPGDN6xuAkICOAdgdRAqVRCKpVi4MCBCA8PR1FREWQyGe9YTA0fPhy3bt3C+fPnNV4/xGIx099Z1Rxl586d2LVrl3rneOTIkXB3d2eSYdu2bVAqlVi7di1atWoFFxcXiMVipKam4v79+0wy1GThwoVYt26d1pTRaN1kfcuWLfjtt9+QmJiIwsJC9fOKigocPHgQ48aNw6hRozgmrMR6xZ33kZLY2FhcuHABxcXFGDdunPq5RCLBoEGDmOUAhFE3T0htvvjiC+zfvx/btm3D/fv3MWXKFOTk5DAvGREC1euU6jWzvLy8Xh+H+yvdu3fHsmXLMGDAAI0afiGWBmgTJycndOzYERcuXIBCocC6devq9U7QXxHKIgrRFBERgVmzZiEvLw+NGzdGSkoKvv76awwYMIB3NGY6d+6Mzp07Y9iwYdDT04Ouri7u3r2LO3fuoHXr1szzvHz5EiUlJepm1E+ePEFpaSmTsVUntK5fv65RSjVp0iS4uLgwyVCTRo0aYdasWbCzs0ODBg3wySef1HoX/PtO6ybrn3zyCa5evVrtuZ6enlZ3/OZ9pET1IrBhwwbuJxuEUDdPSG12796NPXv2AADMzc2xf/9+uLu7M2lgJjQjRozA7NmzIZPJsGXLFhw4cAD29va8Y1XD6thiTk6Oxn8BfvfOkz+9fv0aZ8+exfnz5yGRSKCnpwcrKyutPgpPhEehUKBfv36YO3cuhg8fjhYtWmjtZsXWrVtx69YtzJs3D+PGjYOlpSXOnj3LpF69Kh8fHzg4OKBbt25QKpXIzMxkngEAfvzxR/Tu3RsAcPr0aa6T44sXLyIgIADGxsZQKBQoLS3FihUr0KlTJ26Z6pJIKZSWhozdunVLEF0U38bZ2RlJSUnMxps1axY+/vhjnDt3DgqFAjY2NpgxY4bGbjsLd+/eRVZWFqRSKRYtWoScnBx88803TCfLMpkMTZo0gUgkQmlpqbpuvj7f4UjeL7a2tkhLS4NEUrneWlFRAWdnZ6SmpnJOxseZM2c0Xrt43av94sULpKamoqSkRKOUieexdCIM8+fPx6tXr+Do6AiFQoGUlBR89NFHWLBgAe9ohKh5enpi8ODB2LRpE9LS0pCSkoIjR45oZUd4FxcX7Ny5E1u3bkVJSQkCAwPh4uKC/fv3M89SXFyMK1euQCQSoXv37jA2NmY6fm5uLgIDA/H48WMolUqYmZkhKioKn376KdMcKi4uLoiMjISVlRUA4JdffsE333yDvXv3cslT17RuZ13lwYMHCAwMhEwm0/hQJZR7ilmvoQilM2tISAjc3Nxw/Phx3LlzB8HBwYiIiEBiYmKdjy2kunlCajNs2DB89dVXGDlyJEQiEY4cOYKhQ4fyjsVN//790b9/f94x4OfnBwMDA1haWnLbMS0sLERoaCgKCwuxY8cOzJ07F0uXLoW5uTmXPKRSVlaWxnvIkCFDBHkChGi3mJgY7NmzB7GxsWjatCkePXqEFStW8I7FhUKhgL6+Pk6ePInZs2dDoVDgjz/+YJ7jxYsXOHbsmHoR+MaNGwDYLgJnZWUhNTVV3RizWbNmzMauiVKpVE/UAaBTp071+gSI1k7WIyIiEBQUxPVDlZCa3AmlM2tZWRmcnJywYMECSKVSWFtbM7s/Wkh184TUJiAgAIcPH8alS5cgkUjg5eWFYcOG8Y6l9Z48eYLNmzdzzbBw4UJ4e3sjJiYGzZs3h729PebPn6+VO2NCYm5ujrt37+Ljjz8GUPmzYmpqyjkVIZpMTU01JoHa3Aiwd+/esLe3h76+Pnr06IHx48djyJAhzHMIYRF4+/bt8PDwgKGhIZfxVVQ34bRp0wYLFy7E6NGjIZFIkJqaWm+PwANaPFk3NDTkdlRSiE3uhPKCLBaLceTIEZw6dQp+fn7IyMhg1ixKSHXzhPyVESNGYMSIEbxjkCrat2+PvLw8ro3Dnj9/jn79+iEmJgYikQju7u40UReAiooKODo6wtraGmKxGD///DNMTEzg5eUFgK4sI0RoevXqBU9PT5iamkJHRwdhYWFo37498xxCWAT+6KOP4OXlhS5dumhs6LEu8YqNjdX4c3R0tPrr+tz/Q2sn6927d0dkZCT69++v8YPHomOuEJvcCaUz6+LFi7FlyxYsWrQIJiYmSEtLQ0REBNMMtra2OHDgANe6eULI++fmzZtwdnaGsbEx9PT0oFQqIRKJmJZX6evr4+HDh+oPLj/99BN0dXWZjU9q5uvrq/Fnb29vTkkIIX9HdHQ00tLS1H/mMVFXjct7Ebhr167cxq5q27ZtvCNwobUN5jw9Pas9Y90xV+hN7ni5d+8ebt26hf79++PBgwfMG7uNGzcObm5uaNy4MeLj4+Hn54eYmBgmdfOEkPdX1ZNSVamuv2Hhl19+QWhoKAoKCtC6dWvIZDKsXr0aXbp0YZaBVFdeXo7bt2+jXbt2SE1NxbVr1zBlyhT1dUyEEGHx8fGBoaEhunTpAn19ffVzJycnpjmcnZ2Rl5fHdRFYaH766SfEx8dDJpNpPK+vJ5S0dmddCKszQm9yx0N6ejrWrVuHV69eITExER4eHggMDISjoyOzDDzr5gkh768PP/wQp0+fxu+//w4AkMvluH//Pvz8/Jhl6NSpE/bu3Yv8/HzI5XK0adNGvbO+a9curbzeTwgCAgJgbm6O8vJyxMXFwdHREcHBwVi/fj3vaISQGqjqs7OysjSes56sr1mzhul4ValupmrXrp3GMXPVgkFubi6XXEFBQfj666/RsmVLLuOzprWT9czMTKxfvx6lpaVQKpVQKBR48OABTpw4wSyDEJrcCc3GjRuRkJCA8ePHw9jYGElJSZg4cSLTyTrPunlCyPtrzpw5kMlkKCgogLW1NS5cuIBu3boxz9GgQQNYWlpWe56YmEiTdU7u37+P1atXIzo6GqNHj8bUqVPh6urKOxYh5C1UfYx4a9myJRISEnD+/HlUVFTAxsYG48ePZzK26grpvLw8JuP9XaampswXTXjS2sl6SEgIvL29kZSUBE9PTxw9ehSff/450ww8m9wJlY6Ojsbd7iYmJswnykKomyeEvH+uX7+Oo0ePYsmSJXB1dcXs2bMxe/Zs3rHUtLTqTRDkcjmePXuGjIwMxMXF4fHjxygrK+MdixDyhvbt2yM3Nxf/+te/NMpUeB0/j4qKwt27d+Hq6gqlUon9+/fj3r17WLBgAbMMMpkMaWlpeP78ucb7COsGcyqenp6YN28ebGxsIJH8OZWtrxN4rZ2s6+rqwtXVFYWFhWjSpAmioqIglUqZZuDZ5E6oLC0tsX37dlRUVCA3Nxc7d+5k3lTDysoKvr6+uHXrFuRyOebMmcO8bp4Q8v4xNjaGSCSChYUFrl+/DicnJ7x+/Zp3LDU6wcWPt7c33N3dMWTIEHz22WewtbVlWh5BCPl7zMzMUFFRAYlEgm3btqkn6ar/svbDDz8gOTlZvXE1aNAg5vOVGTNmwMjISDAngfft24eysjL8/PPPGs9psl7P6OnpoaSkBBYWFsjKykLv3r0hl8uZZsjOzgYAXLt2Tf2MdZM7oSktLcWjR4+gp6eHkJAQ2NjYYP78+UwzCKFunhDy/rG0tER4eDjGjh2LefPmobi4mHazCQBAKpVqfMBOT0+HWCzmmIgQUpMePXqo7+weOnSo+jmvOm25XI6Kigp17xG5XM78tUMmk2H79u1Mx6zNkydP1Ef0tYHWdoM/fPgwdu3ahbi4OLi5uUFHRwft2rXDihUreEfTaq6uroiPj9c4Cs+as7Mztm3bhvHjxyM5ORnFxcWYOHGixhUehBDyJrlcjitXrsDa2honTpzAuXPn4O7ujs8++4x3NAB/Ngsi7J05cwarVq2ihrKEvCemT5+OdevW8Y6B7777DqdOnYKdnR0AIC0tDYMGDYKPjw+zDHPmzMGkSZMEc4XxokWLMGjQIAwYMEArFj21dmddX18fmzZtgkgkwr59+5Cfn8/8uLUQmtwJjY6ODoYMGQILCwuN0gCWpw2EUDdPCHl/5OTkoEOHDrh8+TIA4NKlSzAwMICtrW21q2V4MjAw4B1Ba1FDWULeL0KYqAOVV8h9/vnn+PHHH6FUKjF9+nQMHDiQydhDhgyBSCTCq1evcOjQIZiamkIsFkOpVEJHRwcZGRlMcrzp+PHj2LVrl8Yznt3p65rWTtajo6MxaNAgAECjRo2YN5cDhNHkTmgCAgJ4RxBE3Twh5P2RkJCAiIgIxMbGVvs71qVNL168QGpqKkpKSqo1AtLmEiveqKEsIeSfePToES5cuID58+fj3r17iIuLQ4cOHdC8efM6H1t1zfWyZcsQHBysUb8fHBxc5+O/zdmzZ7mNzYPWTtZbtWqF4OBgdOnSBfr6+urnLJsTCKHJndD07NmTdwRB1M0TQt4fqtsiVB9sePLz84OBgQHt4AoMNZQlhPwT8+bNUx+BNzU1hbW1NQIDA7Fp06Y6HzsyMhK5ubkoLi7W6K8ll8vRokWLOh//bd529zyv7vR1TWsn64aGhgCArKwsjecsJ+tCaHJHqissLMTSpUsxd+5c3lEIIe8BT0/PWifGLHe0nzx5gs2bNzMbj/w92dnZNR7TpNMOhJDayGQyeHh4AKjc5HN3d0dCQgKTsZctW4aSkhIsWbIEoaGh6ucSiQTGxsZMMvyV169f48yZM+jSpQvvKHVGayfrkZGRvCNg4sSJ8Pf3Vze5S01NFUzzBm0mhLp5Qsj7Y+bMmQCA3bt3Q19fH05OTpBIJDh48CDzu7Tbt2+PvLw8Kt0RiLCwMISHhwOofs89nXwghPwVfX19nD59Wl2nfu7cOTRs2JDJ2I0bN0bjxo0FU7+v8uYO+owZMzBp0iROaeqe1naDVzVNeBPLzqynTp3CwIEDIRKJUFpaqm5yR83M+Lp48WKNz4VwRJ8QIlyurq7Yt2+fxjMXFxfs37+fWQZnZ2fk5eXB2NgYenp66hpD6jrOx9WrV9GxY0d6XyGE/CO5ubkICAjA48ePIRKJ8NFHHyEqKkowt4wIwfPnz+Hq6lpvG3Rr7c561drCiooKHDt2DOXl5UwzCKHJHamOPjwRQv6JsrIy3LlzBxYWFgCA69evo6KigmmGt9XyET5Up+XofYUQ8k+0b98eBw8exPPnz9GgQQOuVxsLRdUNV6VSCZlMhsmTJ3NOVXe0dme9Jqx3QHx8fGBoaMi1yR0hhJB34+zZswgKCoKpqSmUSiWePn2KFStWwNramlmG8vJynD59Gr///juAykZA9+/fh5+fH7MMhBBC3o3CwkKEhoaisLAQO3bswNy5c7F06VKYm5vzjsZNfn4+zp49i5KSEgBAkyZN0KRJk3o7f9LanfVLly6pv1Yqlbh58ybz2kIhNLkjhBDybvTr1w8nTpzAjRs3IBKJYGVlBYmE7dvsnDlzIJPJUFBQAGtra1y4cAHdunVjmoEQQsi7sXDhQnh7eyMmJgbNmzeHvb095s+fjx07dvCOxs3KlSvx4MEDtG3bFiKRCIWFhQDq7/xJayfrVe/DFYlEMDQ0xLJly5hmEEKTO0IIIe+GTCZDdHQ0CgoKEBsbi7CwMAQFBaFp06bMMly/fh1Hjx7FkiVL4OrqitmzZ2P27NnMxieEEPLuPH/+HP369UNMTAxEIhHc3d21eqIOVL7PHT58mHcMZrR2si6E+3CF0OSOEELIuxEWFoa+ffsiOzsbjRo1gomJCQICArBhwwZmGYyNjSESiWBhYYHr16/DyckJr1+/ZjY+IYSQd0dfXx8PHz5Uzxd++ukn6Orqck7FV9u2bVFcXAwTExPeUZjQ2sl6ZmYm1q9fj9LSUiiVSigUCjx48IBpJ0EhNLkjhBDybty/fx9jxoxBQkICdHV14e/vDwcHB6YZLC0tER4ejrFjx2LevHkoLi6udmUYIYSQ90NwcDCmTZuGgoICODo6QiaTYfXq1bxjcfXq1SuMGDECn332mcbCRX29YllrJ+shISHw9vZGUlISPD09cfToUebd2M3MzDT+PHnyZLi4uMDX15dpDkIIIf93YrEYL1++VO+A5OfnM7+K89///jeuXLmCTz/9FLNmzcK5c+ewYsUKphkIIYS8G0qlElKpFAMHDkR4eDiKioogk8l4x+Jq2rRpvCMwpbWTdV1dXbi6uqKwsBBNmjRBVFQUpFIp0wxCaHJHCCHk3Zg5cyY8PT1RVFQEX19fZGZmYunSpUzGzsnJQYcOHXD58mUAle8vBgYGsLW11foPdoQQ8r6KiIjArFmzkJeXh8aNGyMlJQVff/01BgwYwDsaN9p2FabWTtb19PRQUlICCwsLZGVloXfv3pDL5UwzCKHJHSGEkHejY8eOGDZsGE6ePImioiJ88cUXuHr1KgYNGlTnYyckJCAiIkLjfUVFJBLV2+OBhBBSnykUCvTr1w9z587F8OHD0aJFC+bzFcKX1t6zfujQIezevRtxcXFwc3ODjo4O2rVrR8cFCSGE/COurq6wsrJCy5YtNZ5//fXXnBIRQgh5n3l6emLw4MHYtGkT0tLSkJKSgiNHjmh9R3htorWTdZlMhiZNmkAkEqG0tBT5+fkwMDBAq1atmGUQQpM7Qggh74arqyv27dvHZWxPT88abxdRoZ11Qgh5/zx69Ah79uxBnz590K1bN0RHR8PT0xMfffQR72iEEa2brBcVFUGpVGLq1KnYuHGjukuuXC7HlClTmN7bN2rUqGpN7oyNjRESEsIsAyGEkHdj3bp1aN68OWxsbCAWi9XP39xprwsXL14EAOzevRv6+vpwcnKCRCLBwYMHUVZWhvDw8DrPQAghhJB3S+tq1mNjY3HhwgUUFxdj3Lhx6ucSiYRJXWFVQmhyRwgh5N0oLS3F0qVLYWhoqH4mEolw/PjxOh9b1XBn+fLlGrv7Xbt2hYuLS52PTwghhJB3T+sm65GRkQCADRs2YOrUqVyzCKHJHSGEkHfj5MmT+PHHH6Gvr88tQ1lZGe7cuQMLCwsAwPXr11FRUcEtDyGEEEL+Oa2brKvY2triwIEDkEqlWLRoEXJycvDNN9+gY8eOzDJMmDAB/v7+6iZ3qampTMcnhBDy7piZmUEmk3GdrAcFBcHT0xOmpqZQKpV4+vQpNU4lhBBC3lNaV7OuMm7cOLi5uaFx48aIj4+Hn58fYmJikJiYyCyDEJrcEUIIeTcmTZqE7OxsWFpaokGDBurnrJu7lZeX48aNGxCJRLCysoJEorXr8oQQQsh7TWvfwcvKyuDk5IQFCxZAKpXC2toa5eXlTMZ+W5M7AwMD5k3uCCGEvBs+Pj68I0AmkyE6OhoFBQWIjY1FWFgYgoKC0LRpU97RCCGEEPI/0trJulgsxpEjR3Dq1Cn4+fkhIyMDOjo6TMYWUpM7Qggh74aqyRtPYWFh6Nu3L7Kzs9GoUSOYmJggICAAGzZs4B2NEEIIIf8jrT0Gf/36dWzZsgWDBw/G8OHD4e/vj2nTpqFdu3bMMgihyR0hhJD6w8XFBfv374eTkxOSk5MBAA4ODjhw4ADfYIQQQgj5n7HZShYgKysr+Pr6QldXF3K5HHPmzGE6UQf+bHKnVCqxcOFCuLq64urVq0wzEEIIqT/EYjFevnwJkUgEAMjPz2d2aowQQggh75bWvoOnp6fD19cXS5YsQUlJCTw8PJCSksI0Q0hICBQKBY4fP447d+4gODgYERERTDMQQgipP2bOnAlPT088ePAAvr6++PLLLzF79mzesQghhBDyD2jtZH3jxo1ISEjABx98AGNjYyQlJTGv6VM1uTt58iTzJneEEELqn44dO2LYsGEwNzdHUVERvvjiCzqxRQghhLyntLbBnI6ODho3bqz+s4mJCfOjgjyb3BFCCKl/pkyZAisrKwwePJh3FEIIIYT8H2ntZN3S0hLbt29HRUUFcnNzsXPnTuY164sXL8aWLVuwaNEimJiYIC0tjY7BE0II+T9ZunQp7wiEEEIIeQe0thv8rFmz8PHHH+PcuXNQKBSwsbHBjBkzNHbbWbh37x5u3bqF/v3748GDB2jVqhXT8QkhhNQf69atQ/PmzWFjYwOxWKx+3rJlS46pCCGEEPJPaO1k3dXVFfHx8cwn51Wlp6dj3bp1ePXqFRITE+Hg4IDAwEA4Ojpyy0QIIeT9tWLFCmzfvh2GhobqZyKRCMePH+eYihBCCCH/hNYeg9fR0cGQIUNgYWEBPT099fOtW7cyy6Bqcjd+/Hh1k7uJEyfSZJ0QQsg/cvLkSfz444/Q19fnHYUQQggh/0daO1kPCAjgHUEQTe4IIYTUH2ZmZpDJZDRZJ4QQQuoBrZ2s9+zZk3cEQTS5I4QQUn+8fv0adnZ2sLS0RIMGDdTPWZ4aI4QQQsi7obWTdSEoLS3Fo0ePoKenh5CQENjY2GD+/Pm8YxFCCHlP+fj48I5ACCGEkHdEaxvMCYEQmtwRQgghhBBCCBEe2lnnSAhN7gghhBBCCCGECA/trHN08eLFGp8LoZ6eEEIIIYQQQgg/NFknhBBCCCGEEEIEhu4JI4QQQgghhBBCBIYm64QQQgghhBBCiMDQZJ0QQgghhBBCCBEYmqwTQgghhBBCCCEC8/8BMWekEZHdo+8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1224x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create heatmap for missing values\n",
    "sns.heatmap(df.isnull(), cbar=False, cmap=\"YlGnBu\")\n",
    "plt.title('Missing Values', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d64f95be",
   "metadata": {},
   "source": [
    "# Cleaning Target Variable\n",
    "To explore the target column, I created a frequency distribution table. The table reveals that the most common values are coded 0 and 1. Additionally, the column contains 40 missings values and some erroneous entries. I slice the dataframe that contains only 0 and 1 values and convert the column to the integer type to address these issues. The resulting dataframe contains 84% of values coded 1, while the remaining 16% were coded 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04f70459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       13143\n",
       "1        2484\n",
       "NaN        40\n",
       "-3          1\n",
       "Goat        1\n",
       "7           1\n",
       "9           1\n",
       "4           1\n",
       "3           1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create frequency distribution table for the target column\n",
    "df.target.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "609ed5a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#slice the df\n",
    "df = df[(df.target == '1') | (df.target == '0')]\n",
    "df.target = df.target.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1a04fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    84.0\n",
       "1    16.0\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create frequency distribution table for target column after slicing df\n",
    "round(df.target.value_counts(normalize=True, dropna=False)*100,0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "59876438",
   "metadata": {},
   "source": [
    "# Cleaning Features\n",
    "The frequency distribution table for the current_demerits columns indicates that the columns contain missing, unusually high, float, and negative values. The column should contain only natural numbers not exceeding 87. To address this issue, I replace values outside this criterion as missing, fill missing values with a mean value, and convert the column to the integer type. I follow a similar procedure to clean the remaining five features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76621ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.00       3920\n",
       " 3.00       3115\n",
       " 8.00       2437\n",
       " 6.00       2198\n",
       " 9.00       1868\n",
       " 5.00        763\n",
       " 10.00       449\n",
       " NaN         216\n",
       " 7.00        111\n",
       " 19.00        73\n",
       " 4.00         60\n",
       " 20.00        50\n",
       " 1.00         48\n",
       " 2.00         35\n",
       " 14.00        34\n",
       " 17.00        23\n",
       " 27.00        16\n",
       " 11.00        16\n",
       " 12.00        15\n",
       " 25.00        13\n",
       " 22.00        13\n",
       " 32.00        13\n",
       " 16.00        11\n",
       " 18.00        11\n",
       " 31.00        10\n",
       " 30.00         9\n",
       " 46.00         9\n",
       " 100.00        8\n",
       " 51.00         7\n",
       " 13.00         7\n",
       " 23.00         7\n",
       " 42.00         7\n",
       " 24.00         6\n",
       " 35.00         6\n",
       " 26.00         6\n",
       " 39.00         6\n",
       " 28.00         5\n",
       " 15.00         5\n",
       " 38.00         5\n",
       " 43.00         3\n",
       " 21.00         2\n",
       " 37.00         1\n",
       " 33.00         1\n",
       " 87.00         1\n",
       " 88.00         1\n",
       " 98.00         1\n",
       " 363.00        1\n",
       " 89.00         1\n",
       " 1214.00       1\n",
       "-8.00          1\n",
       " 48.00         1\n",
       " 3.14          1\n",
       "Name: current_demerits, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create frequency distribution table for current_demerits column\n",
    "df.current_demerits.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f6127d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaing current_demerits\n",
    "df.current_demerits.replace([1214, 89, 88, 363, 100, 98, -8.00, 3.14] , np.nan, inplace=True )\n",
    "df.current_demerits.fillna(round(df.current_demerits.mean(), 0), inplace=True)\n",
    "df.current_demerits = df.current_demerits.astype('int')\n",
    "\n",
    "#cleaning employee_count\n",
    "df.employee_count.replace([687, 111447, -7, 902] , np.nan, inplace=True )\n",
    "df.employee_count = df.employee_count.astype('float')\n",
    "df.employee_count.fillna(round(df.employee_count.mean(), 0), inplace=True)\n",
    "df.employee_count = df.employee_count.astype('int')\n",
    "\n",
    "#cleaning inspection_demerits\n",
    "df.inspection_demerits.replace('Routine Inspection', np.nan, inplace=True )\n",
    "df.inspection_demerits = df.inspection_demerits.astype('float')\n",
    "df.inspection_demerits.fillna(round(df.inspection_demerits.mean(), 0), inplace=True)\n",
    "df.inspection_demerits = df.inspection_demerits.astype('int')\n",
    "\n",
    "#cleaning number_of_violations\n",
    "df.number_of_violations.replace('Nevada', np.nan, inplace=True )\n",
    "df.number_of_violations = df.number_of_violations.astype('float')\n",
    "df.number_of_violations.fillna(round(df.number_of_violations.mean(), 0), inplace=True)\n",
    "df.number_of_violations = df.number_of_violations.astype('int')\n",
    "\n",
    "#cleaning median_employee_age\n",
    "df.median_employee_age.fillna(round(df.median_employee_age.mean(), 0), inplace=True)\n",
    "\n",
    "#cleaning median_employee_tenure\n",
    "df.median_employee_tenure.fillna(round(df.median_employee_tenure.mean(), 0), inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bf83b451",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "The next thing to look for is potential collinearity between some of these feature columns. Collinearity is when two feature columns are highly correlated and risk duplicating information. If two features convey the same information using two different measures or metrics, there is no need to keep both.\n",
    "\n",
    "I generate a correlation matrix heatmap using Seaborn to visually compare the correlations and look for problematic pairwise feature correlations. Based on the correlation matrix heatmap, the following pairs of columns are strongly correlated: 'inspection_demerits' and 'number_of_violations.' These two features reflect very similar information. Given that 'inspection_demeritts' contain more nuanced information, I drop 'number_of_violations' from further analysis.\n",
    "\n",
    "Also, I examined the possibility of removing features with low variance. When the values in a feature column have low variance, they don't meaningfully contribute to the model's predictive capability. To make apples-to-apples comparisons between columns, I rescaled all of the columns to vary between 0 and 1. This is known as min-max scaling or as rescaling. But I finally decided to keep all five features in the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb97fcfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore correlations among features\n",
    "corr = df[['target', 'current_demerits', 'employee_count', 'median_employee_age', \n",
    "           'median_employee_tenure','inspection_demerits', 'number_of_violations']].corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "ax = sns.heatmap(corr,vmin=-1, vmax=1, center=0,cmap=sns.diverging_palette(20, 220, n=200), square=True)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, horizontalalignment='right')\n",
    "ax.set_title('Pairwise Correlations Among Features', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c89dedb0",
   "metadata": {},
   "source": [
    "# ML Modeling\n",
    "To determine the predictive capacity of the ML model, I split the data into a training set and testing set. Given that the number of restaurants that receive a C grade or below for their evaluation (16%) is fewer than the remaining cases (84%), I use the ClusterCentroids method to better train the model to recognize the minority class cases. This method addresses data imbalance by undersampling the majority class by replacing a cluster of majority samples with the cluster centroid of a KMeans algorithm. \n",
    "\n",
    "I use the pipeline tool combined with GridSearchCV to assemble several steps that can be cross-validated while setting different parameters. The preprocessing step in the pipeline includes RobustScaler, scales features using statistics that are robust to outliers. The classifier step includes C-Support Vector Classification, Random Forrest, Logistic Regression, and Gradient Boosting classifiers.\n",
    "\n",
    "GridSearchCV runs through all the different parameters fed into the parameter grid and produces the best combination of parameters based on a scoring metric of your choice. The recall is used as a performance metric when there is a need to identify all positive samples; that is, when it is crucial to avoid false negatives. A health code violation is a good case for this: it is vital to find all non-compliant restaurants, possibly falsely including well-maintained venues in the prediction. Thus, I use recall as an evaluation metric when ranking results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9453d2dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import ML libraries\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.metrics import classification_report, plot_confusion_matrix\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "from imblearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8149e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.target\n",
    "X = df[['current_demerits', 'employee_count', 'median_employee_age', 'median_employee_tenure', 'inspection_demerits']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2e9a3a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the pipeline\n",
    "pipe = Pipeline([('preprocessing', RobustScaler()), ('classifier', SVC())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "007f38d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the hyperparameter space\n",
    "parameters = [\n",
    "{'classifier': [RandomForestClassifier(n_jobs=-1, random_state=0, class_weight='balanced')],\n",
    "'preprocessing': [None],     \n",
    "'classifier__n_estimators': [100, 200, 300],\n",
    "'classifier__max_features': ['auto', 3, 4, 5],\n",
    "'classifier__max_depth': [None, 2, 3, 100],    \n",
    "'classifier__criterion': ['gini', 'entropy'],\n",
    "'classifier__min_samples_leaf': [3, 4, 5],\n",
    "'classifier__min_samples_split': [8, 10, 12]},\n",
    "    \n",
    "{'classifier': [SVC(random_state=0, class_weight='balanced')], \n",
    "'preprocessing': [RobustScaler(), None],\n",
    "'classifier__gamma': [0.01, 0.1, 1, 10, 100],\n",
    "'classifier__C': [0.01, 0.1, 1, 10, 100]},  \n",
    "\n",
    "{'classifier': [GradientBoostingClassifier(random_state=0)], \n",
    "'preprocessing': [None],\n",
    "'classifier__learning_rate': [0.01, 0.1, 1, 10],\n",
    "'classifier__n_estimators': [100, 200, 300],\n",
    "'classifier__max_depth': [2, 3, 5]},\n",
    "\n",
    "{'classifier': [LogisticRegression(n_jobs=-1, max_iter=1000)], \n",
    "'preprocessing': [RobustScaler(), None], \n",
    "'classifier__C': [0.01, 0.1, 1, 10, 100]}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39352a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate GridSearchCV object\n",
    "grid = GridSearchCV(pipe, param_grid=parameters, cv=5, scoring='recall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aaaf7ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define resampling method\n",
    "method = ClusterCentroids(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b2b40833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, stratify=y)\n",
    "\n",
    "# Apply resampling to the training data only\n",
    "X_resampled, y_resampled = method.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c9c39242",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit to the training set\n",
    "model = grid.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3625c267",
   "metadata": {},
   "source": [
    "# Results\n",
    "The exhaustive search identified the best parameters: SVC with the regularization parameter of 0.1 and gamma parameter for non-linear hyperplanes of 0.01 in combination with RobustScaler. This model produced a recall score for problematic restaurants of 0.82. Because this test set recall score of the model is the same as the cross-validation recall score during training, the model is not likely to overfit and can be considered reliable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc32f3c3-2172-447f-bcbd-9852be225538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:\n",
      "{'classifier': SVC(C=0.1, class_weight='balanced', gamma=0.01, random_state=0), 'classifier__C': 0.1, 'classifier__gamma': 0.01, 'preprocessing': RobustScaler()}\n",
      "\n",
      "Best cross-validation score: 0.82\n",
      "Test set score: 0.82\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.17      0.28      3286\n",
      "           1       0.16      0.82      0.26       621\n",
      "\n",
      "    accuracy                           0.27      3907\n",
      "   macro avg       0.49      0.49      0.27      3907\n",
      "weighted avg       0.72      0.27      0.28      3907\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1224x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get performance metrics\n",
    "predict = model.predict(X_test)\n",
    "\n",
    "print(\"Best params:\\n{}\\n\".format(model.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(model.best_score_))\n",
    "print(\"Test set score: {:.2f}\".format(model.score(X_test, y_test)))\n",
    "plot_confusion_matrix(model, X_test, y_test)\n",
    "print (classification_report(y_test, predict))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "56c9430f",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "The final model uses five numeric features to predict if a restaurant receives a C grade or below during the inspection. Three predictors refer to employee characteristics; the remaining three variables describe the degree of violations. The model is a good tool if the purpose is to identify as many problematic restaurants as possible. The model could correctly identify 82% of problematic restaurants in a holdout test data set. At the same time, the model misclassified many compliant restaurants as non-compliant ones. \n",
    "\n",
    "There is plenty of room to improve the model's predictive capacity by supplementing it with additional data sources. For instance, employee characteristics in the current model had decent effect sizes. Additional descriptive information about employees, such as experience and training in safe food preparation practices, may improve the model substantially. In addition to employees' ability, motivation and incentives to practice safe food handling (or cut corners to maximize lower quality output in the kitchen) are also essential components that may explain and predict (in)compliance by restaurants. Other projects were able to leverage social media in predicting outcomes of health inspections. For instance, Uppoor et al. used data from Yelp reviews ([see PDF](https://cseweb.ucsd.edu/classes/wi15/cse255-a/reports/fa15/036.pdf)). However, the authors reported that it was more challenging to predict critical violations than minor problems."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "2f2c054dce7f3ebd1e76d58cbab2bcb69b44b6a1fb131f28c8fa296b61f3b710"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}