1_ML_Intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### South African Heart Disease Dataset\n",
    "\n",
    "https://web.stanford.edu/~hastie/ElemStatLearn/data.html\n",
    "\n",
    "A retrospective sample of males in a heart-disease high-risk region\n",
    "of the Western Cape, South Africa. There are roughly two controls per\n",
    "case of CHD. Many of the CHD positive men have undergone blood\n",
    "pressure reduction treatment and other programs to reduce their risk\n",
    "factors after their CHD event. In some cases the measurements were\n",
    "made after these treatments. These data are taken from a larger\n",
    "dataset, described in  Rousseauw et al, 1983, South African Medical\n",
    "Journal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row.names</th>\n",
       "      <th>sbp</th>\n",
       "      <th>tobacco</th>\n",
       "      <th>ldl</th>\n",
       "      <th>adiposity</th>\n",
       "      <th>famhist</th>\n",
       "      <th>typea</th>\n",
       "      <th>obesity</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>age</th>\n",
       "      <th>chd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.73</td>\n",
       "      <td>23.11</td>\n",
       "      <td>Present</td>\n",
       "      <td>49</td>\n",
       "      <td>25.30</td>\n",
       "      <td>97.20</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.41</td>\n",
       "      <td>28.61</td>\n",
       "      <td>Absent</td>\n",
       "      <td>55</td>\n",
       "      <td>28.87</td>\n",
       "      <td>2.06</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32.28</td>\n",
       "      <td>Present</td>\n",
       "      <td>52</td>\n",
       "      <td>29.14</td>\n",
       "      <td>3.81</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.41</td>\n",
       "      <td>38.03</td>\n",
       "      <td>Present</td>\n",
       "      <td>51</td>\n",
       "      <td>31.99</td>\n",
       "      <td>24.26</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>134</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>27.78</td>\n",
       "      <td>Present</td>\n",
       "      <td>60</td>\n",
       "      <td>25.99</td>\n",
       "      <td>57.34</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row.names  sbp  tobacco  ldl  adiposity  famhist  typea  obesity  alcohol  \\\n",
       "0          1  160    12.00 5.73      23.11  Present     49    25.30    97.20   \n",
       "1          2  144     0.01 4.41      28.61   Absent     55    28.87     2.06   \n",
       "2          3  118     0.08 3.48      32.28  Present     52    29.14     3.81   \n",
       "3          4  170     7.50 6.41      38.03  Present     51    31.99    24.26   \n",
       "4          5  134    13.60 3.50      27.78  Present     60    25.99    57.34   \n",
       "\n",
       "   age  chd  \n",
       "0   52    1  \n",
       "1   63    1  \n",
       "2   46    0  \n",
       "3   58    1  \n",
       "4   49    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease_data = pd.read_csv('https://web.stanford.edu/~hastie/ElemStatLearn/datasets/SAheart.data')\n",
    "heart_disease_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient ID</th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Tobacco (kg)</th>\n",
       "      <th>LDL</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Fam. Hist</th>\n",
       "      <th>Type A</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Age</th>\n",
       "      <th>Coronary Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.73</td>\n",
       "      <td>23.11</td>\n",
       "      <td>Present</td>\n",
       "      <td>49</td>\n",
       "      <td>25.30</td>\n",
       "      <td>97.20</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.41</td>\n",
       "      <td>28.61</td>\n",
       "      <td>Absent</td>\n",
       "      <td>55</td>\n",
       "      <td>28.87</td>\n",
       "      <td>2.06</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32.28</td>\n",
       "      <td>Present</td>\n",
       "      <td>52</td>\n",
       "      <td>29.14</td>\n",
       "      <td>3.81</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>170</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.41</td>\n",
       "      <td>38.03</td>\n",
       "      <td>Present</td>\n",
       "      <td>51</td>\n",
       "      <td>31.99</td>\n",
       "      <td>24.26</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>134</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>27.78</td>\n",
       "      <td>Present</td>\n",
       "      <td>60</td>\n",
       "      <td>25.99</td>\n",
       "      <td>57.34</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient ID  Systolic Blood Pressure  Tobacco (kg)  LDL  Adiposity  \\\n",
       "0           1                      160         12.00 5.73      23.11   \n",
       "1           2                      144          0.01 4.41      28.61   \n",
       "2           3                      118          0.08 3.48      32.28   \n",
       "3           4                      170          7.50 6.41      38.03   \n",
       "4           5                      134         13.60 3.50      27.78   \n",
       "\n",
       "  Fam. Hist  Type A  Obesity  Alcohol  Age  Coronary Heart Disease  \n",
       "0   Present      49    25.30    97.20   52                       1  \n",
       "1    Absent      55    28.87     2.06   63                       1  \n",
       "2   Present      52    29.14     3.81   46                       0  \n",
       "3   Present      51    31.99    24.26   58                       1  \n",
       "4   Present      60    25.99    57.34   49                       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use more user-friendly column names\n",
    "columns = [\n",
    "    'Patient ID',\n",
    "    'Systolic Blood Pressure', \n",
    "    'Tobacco (kg)', \n",
    "    'LDL', \n",
    "    'Adiposity', \n",
    "    'Fam. Hist', \n",
    "    'Type A', \n",
    "    'Obesity', \n",
    "    'Alcohol', \n",
    "    'Age',\n",
    "    'Coronary Heart Disease'\n",
    "]\n",
    "\n",
    "heart_disease_data.columns = columns\n",
    "heart_disease_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define features (just 2 for simplicity) and target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# features\n",
    "f1 = 'Age'\n",
    "f2 = 'Systolic Blood Pressure'\n",
    "\n",
    "# patient id\n",
    "patient_id = 'Patient ID'\n",
    "\n",
    "# target variable\n",
    "target = 'Coronary Heart Disease'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a sample of 100 instances to form the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = heart_disease_data.sample(100, random_state=43)[[patient_id, f1, f2, target]]\n",
    "data[target] = data[target].apply(lambda x: False if not x else True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Systolic Blood Pressure</th>\n",
       "      <th>Coronary Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>106</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>56</td>\n",
       "      <td>108</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>442</td>\n",
       "      <td>32</td>\n",
       "      <td>110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>167</td>\n",
       "      <td>55</td>\n",
       "      <td>110</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>27</td>\n",
       "      <td>112</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>230</td>\n",
       "      <td>50</td>\n",
       "      <td>188</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>316</td>\n",
       "      <td>50</td>\n",
       "      <td>190</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>409</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>208</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>459</td>\n",
       "      <td>58</td>\n",
       "      <td>214</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patient ID  Age  Systolic Blood Pressure  Coronary Heart Disease\n",
       "20           21   20                      106                    True\n",
       "179         180   56                      108                   False\n",
       "440         442   32                      110                   False\n",
       "166         167   55                      110                    True\n",
       "67           68   27                      112                   False\n",
       "..          ...  ...                      ...                     ...\n",
       "229         230   50                      188                    True\n",
       "314         316   50                      190                   False\n",
       "407         409   60                      200                    True\n",
       "403         405   58                      208                    True\n",
       "457         459   58                      214                   False\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(f2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a first simple ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "linear_model = LogisticRegression(solver='lbfgs')\n",
    "\n",
    "X = data[[f1, f2]]\n",
    "y = data[target]\n",
    "linear_model.fit(X.values, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1170x827 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_and_decision_boundary(data, f1, f2, target, linear_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05469812, 0.00435669]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# w\n",
    "linear_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.85732665])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# b\n",
    "linear_model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction on Unseen Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logit(v): return 1 / (1 + np.exp(-v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55535078])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply model for 62 year old patient with SBP = 158\n",
    "logit(linear_model.coef_.dot(np.array([62, 158])) + linear_model.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Metrics for Binary Clasification\n",
    "\n",
    "For simplicity, we will evaluate the model on the training set, which is not a good indicator for future performance.\n",
    "In the next week, we will talk about test sets and model selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True,  True, False, False,  True, False,\n",
       "       False, False, False,  True, False, False,  True, False, False,\n",
       "       False, False, False,  True, False, False, False, False,  True,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False,  True, False,  True,\n",
       "       False, False, False,  True, False, False, False, False, False,\n",
       "        True,  True,  True, False, False, False, False, False, False,\n",
       "        True, False,  True, False, False, False, False, False, False,\n",
       "       False, False, False, False,  True, False, False, False, False,\n",
       "       False, False,  True, False,  True,  True,  True, False, False,\n",
       "       False, False, False, False, False, False,  True, False,  True,\n",
       "       False])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = linear_model.predict(X.values)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "display = ConfusionMatrixDisplay.from_predictions(y, y_pred)\n",
    "confusion_matrix = display.confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = confusion_matrix[1,1]\n",
    "fp = confusion_matrix[0,1]\n",
    "fn = confusion_matrix[1,0]\n",
    "tn = confusion_matrix[0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tp + tn) / (fp + fn + tp + tn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5454545454545454"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tp / (tp + fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5454545454545454"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "precision_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35294117647058826"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tp / (tp + fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35294117647058826"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "recall_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42857142857142855"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision = tp / (tp + fp)\n",
    "recall = tp / (tp + fn)\n",
    "(2 * precision * recall) / (precision + recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42857142857142855"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import RocCurveDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x12f1e2d90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ba+bo6Mi8efOoX78+qqpq/wDKly/P/PnzcXV11dsEIYQQQogcQ9dtTSNFUXjrrbd46623kuwrVKgQixYtws/Pj3PnzhEbG0ulSpVo2LAhdna6Y0MhhBBCiBzFJsGZJapXr0716tUz63RCCCGEEC8k6boSQgghhMhGJDgTQgghhMhGLLqt2apVqwxrgKIo7N69O8PqF0IIIYR4kVgUnAUGBqIois1PrqpqhtQrhBBCCPGisnhCgDEFhhBCCCGEyDgWBWdXrlzJ6HYIIYQQQghkQoAQQgghRLYiwZkQQgghRDYiwZkQQgghRDYiwZkQQgghRDYiwZkQQgghRDYiwZkQQgghRDaSaQufCyGEyFyqqnLl3xCehEUl2ffv/adZ0CIhhCUkOBNCiBzq+p1QRs/0TbWMvb3cQBEiu5HgTAghcqjgp9EAuDg7UK6kR5L9zk72tG1QOpNbJYRIiwRnQgiRw71UND+TPmiU1c0QQlhI+rOFEEIIIbKRDOk5S0hIIDg4mLCwMDw9PSlQoID2uJ2dxINCCCGEECmxWXAWHx/Pxo0b2bRpE35+fkRHJ451GD16NIMGDQKgZ8+elCpViqFDh1KhQgVbnVoIIYQQIsewSXB29epVPvroI27fvg0kTt8GUBTFrJy/vz8XLlxg586dfPbZZwwcONAWpxdCCCGEyDF0B2fXrl2jT58+REREaEGZk5MTsbGxZuVCQkKIiopCURTi4+OZPHkyjo6O9OnTR28TNKqqsmXLFtauXcvly5eJjIzEy8sLHx8f+vTpQ/Xq1XWf48yZMyxdupTTp0/z5MkTnJ2dKV26NC1btqRv3754eHjofyJCCCGEyLV0DQCLj49nxIgRhIeHo6oqzZs3Z+XKlZw6dSpJ2QIFCrB69Wrq168PJAZSU6dO5cGDB3qaoImOjmbo0KF89tlnHD16lNDQUGJjYwkMDGTDhg306tWLuXPn6jrHjz/+SO/evdmyZQv3798nLi6O8PBwLl68yIwZM+jYsSNnz561yfMRQgghRO6kKzjbuHEjt27dQlEU+vfvz++//06NGjVwcnJKtny1atVYuHAhvXr1AhIDqlWrVulpgmbs2LHs378fAG9vb7788kumTZvGO++8Q968eTEYDEybNo2NGzemq/558+Yxb948AK3H74cffuCrr77Cx8cHgMePHzNkyBDu3Lljk+ckhBBCiNxHV3C2a9cuAAoXLsxnn31m8XFjxoyhUKFCABw+fFhPE7Q6tmzZAkCDBg1Yv349AwcOpGPHjnzxxResWbNGu904adIkwsPDrao/LCyMmTNnAuDu7s7atWv55ptv6Nq1KwMGDGDJkiUMGzYMgKdPn/Lzzz/rfk5CCCGEyJ10BWeXLl1CURSaN2+eYm9ZcvLkyUPLli1RVZV///1XTxMAmD9/PgAODg5MmDABZ2dns/3e3t58/fXXAISGhrJ69Wqr6j98+DCRkZEADB48mIoVK5rtVxSFjz76iJdeegmAPXv2EB8fn67nIoQQQojcTVdwFhISAkCJEiWsPrZ48eIARERE6GkCoaGhHDlyBIAmTZpQqlSpZMu1b9+eggULArB9+3arznH37l1tu1KlSsmWsbOz45VXXgEgKipK+9sIIYQQQlhDV3CWL18+AKtvEwIEBwcDkD9/fj1N4NSpUyQkJACJtzRTYmdnp40NO3/+PGFhYRafw9XVVdtObTzZvXv3ALC3t8fd3d3i+oUQQgghjHQFZyVLlkRVVU6ePGnVcaqqcuDAARRFoWTJknqawPXr17XttBLblitXTjv/tWvXLD6HaQqOv/76i5iYmCRlTpw4gZ+fHwD16tWz6javEEIIIYSRruCscePGAJw7d46jR49afNyiRYu4desWAA0bNtTTBAIDA7XttG6vFi1aNNnj0vLKK6/QtGlTAG7cuEHfvn05duwYz54948GDByxbtowPP/yQhIQEXF1d+fzzz618FkIIIYQQiXQFZ7169dIG33/66afJ5jczZTAYmDt3LlOmTAESB/B3795dTxO026OAtoZnSkwTxIaGhlp1np9//pnmzZsD4Ofnx4ABA6hbty7NmjXju+++4+nTp9SuXZvFixdTtWpVq+oWQgghhDDStUJA0aJFGTlyJD/++COhoaH069ePWrVqUaVKFa2Mn58fixYtwt/fn/379/PkyRNUVUVRFN59913dtzWNa3gCSWZpPs/0VqPpcZbIly8fP/zwA6NHj+bgwYNJ9iuKQtmyZWWFACGEEELoonv5pnfeeYegoCAtQevZs2c5e/astq7m9u3bzWZHGpd46tSpEx9//LHe05ulrEhrnJfpfmtTXWzbto0vv/yS6OhoihQpQvfu3fH29iYiIgJfX1927tzJ2rVr2b59OwsXLrTJUlFCCCGEyH1ssvD5559/Tu3atfn555/NBugnp2jRonzwwQf06NHDFqcmT5482nZcXFyqAZrpep/WDNjfs2cPn332GQaDgfr16zN79myzGZzdu3fn0KFDfPjhh0RERDB48GC2bt2Kl5eXlc9GCCGst+v4LW7cSzoD/UFQZBa0Rgihl02CM4BWrVrRqlUrzp49y8mTJ/H39ycsLIz4+Hjc3d0pVaoUdevW5dVXX8XBwWanJW/evNp2TEyMxcFZWrdAjVRV5b///S8GgwF3d3d+/fVXs8DMqHHjxnz++ed8//33hIWFMX/+fL744gsrnokQQlgvKCyKX1edS7VMvjyOmdMYIYRN2C5K+n+1atWiVq1atq42RW5ubtp2aGhoqnnTTCcBeHp6WlT/5cuXtdxmHTp0SHVMWY8ePZg6dSpRUVEcOHBAgjMhRIaLiTMA4GCv8FaL8kn229kpNK1lfaJwIUTW0RWcbdq0iTZt2pjdWsxsZcqU0bbv37+f4goBAA8ePNC2jSsUpMV0eam08qg5OTnh7e3NhQsXzFYVEEKIjOboYE/fdpWzuhlCCBvQlUpj9OjRNGzYkC+//NKqPGe25O3trW2nNd7NuF9RFMqXT/oLMy3JJZ99np1d4p/U3t7e6vqFEEIIIXQFZ5C4juSGDRt45513aNasGVOnTrUq+75etWrVwtExcTzF8ePHUyxnMBi0lQwqVapkdjs0NUWKFNG2L1y4kGrZ+Ph4bty4AVjeMyeEEEIIYUpXcNahQwfy5MmDqqqoqsqjR4+YN28enTt3pkuXLixcuJDHjx/bqq3JcnNz09bU3Lt3r7a+5fO2bt2qJaxt166dxfVXq1ZNmwCwZ88e7t+/n2LZjRs3auuMNmrUyOJzCCGEEEIY6QrOpk2bxtGjR/npp59o0aIFDg4OWqB29epVJk+eTIsWLRg8eDBbtmyxOvGrpQYOHAgkptL49NNPkyzE7u/vz8SJE4HEZLLWrErg5ORE3759AYiMjOTjjz82W5XA6MyZM0yaNAkAR0dHBgwYkJ6nIoQQQohcTvdszTx58tC+fXvat2/P06dP2bFjB5s3b+bUqVMkJCQQHx/P4cOHOXz4MC4uLrRt25ZOnTrx6quv2qL9QGIai7Zt27Jjxw7Onj1L586d6d27N8WKFePSpUssX76ciIgIIHGc3PMzNY8fP07//v2BxEXLlyxZYrZ/6NChHDx4kEuXLnHu3Dnat29P165dqVSpEgaDgWPHjrF161Ytse3YsWPTXOdTCCGEECI5Nk2l4ebmRvfu3enevTuPHj1i27ZtbNmyRRurFRkZyYYNG9iwYQOFCxfmjTfeoFOnTmnOgrTE5MmTiYiI4NChQ9y9e1dbv9NIURSGDx9Or169rK7bxcWFefPm8cknn3Ds2DFCQkKYP39+knKOjo589dVX9O7dO93PQwghhBC5m83znBkVLlyYgQMHMnDgQG7dusWWLVvYsWOHNlng4cOHzJs3j/nz53Pp0iXd53NxceHPP/9k8+bNbNiwgcuXL/Ps2TM8PDyoU6cO/fv3p06dOumu39PTk4ULF7Jnzx42bdrE+fPnCQoKwsnJiRIlStCoUSP69esnPWZCZKGb98JYvec6sf+f+ys3iI61bik6IUT2p6jGxS4zQXR0NMuXL2fGjBlERUVpC6Bfvnw5s5rwQmjVqhWQOAFBCGG5GavOsfP4raxuRpYoVjAfc79qndXNECJXs9X3d4b1nBmFh4ezc+dOdu7cybFjx7RcYcaYsFChQhndBCFELhFvSACgYfVi1K5YOItbk7mqectnqRA5RYYEZ9HR0ezZs4etW7dy6NAh4uLigP8FZHny5KFVq1Z07tyZxo0bZ0QThBC5WMWXCtC2QZmsboYQQqSLzYKzuLg4Dhw4wLZt29i3b5+WNsMYkCmKgo+PD126dKFt27bJLh4uhBBCCJHb6QrOEhISOHLkCFu3bmXPnj08e/YM+F9ABolrX3bu3JnOnTtL1nwhhBBCiDToCs4aN25MSEgIYB6QeXh40L59e7p06UL16tX1tVAIIYQQIhfRFZyZZsp3dHSkefPmdO7cmWbNmmnrXQohhBBCCMvpHnNWo0YNOnfuTIcOHXB3d7dFm4QQQgghci1dwdn27dspU6aMjZoihBBCCCF0BWcSmAkh0ktVVWatOc/126E2q/NRSKTN6hJCiKyS4UlohRAiOU9Co9lxLGOy+XsVyJsh9QohRGawKDirXLkykJirzHQdTOPjejxfpxAidzDO8Hawt+Prd+rbrF7XvI6UL+Vhs/qEECKzWRScpbT8ZiYuyymEyKHs7BRqV8pdSy0JIURqLArOUkoeK0llhRBCCCFsy6LgbO/evVY9LoQQQggh0scuqxsghBBCCCH+R9dszZMnTwLw0ksvUaRIEauODQgI4NChQ0RERPDBBx/oaYYQQgghRI6hq+esX79+9O/fn23btll97I4dO/jvf//LokWL9DRBCCGEECJHybLbmnFxcQBERERkVROEEEIIIbKdNG9rxsfHc/bs2VTL3L59W7vFmRaDwUBgYCDLli0DkPU4hRBCCCFMpBmcOTg4sHDhwhRnZqqqyooVK1ixYoXVJ1cUhTp16lh9nBBCCCFETmXRbc2vv/6aPHnyoKqq2T+j5x+39J+bmxsff/xxRj03IYQQQogXjkWzNYsWLcrEiRPx9fU1e3z9+vUoikLVqlUpX768RSe0t7fH1dWVkiVL8vrrr1OoUCHrWy2EEEIIkUNZnEqjffv2tG/f3uyx9evXA9ChQwcGDRpk25YJIYQQQuRCuvKc+fj4AIk9a0IIIYQQQj9dwdmSJUts1Q4hhBBCCIEs3ySEEEIIka1Y1HO2YcMGbbtLly7JPq6HaZ1CCCGEELmZRcHZmDFjUBQFRVHMAinj43o8X6cQ4sUUE2dATVDTLmhSXgghRFIWjzkzzWtmyeNCiNxj8bZLrN5zPaubIYQQOYJFwdnw4cOtelwIkbucv/443cfWruhlw5YIIcSLT4IzIYTNjOnvQ51Kha06xtnJPoNaI4QQLyZdqTSEEMKUo6MdeZzlY0UIIfSQVBpCCCGEENmIzYKz2NhYzp8/n+TxuLg4Zs6cyWuvvUa1atVo3bo1U6ZMISQkxFanFkIIIYTIMXTff1BVlRkzZrBw4UISEhI4d+6c2f6hQ4dy5MgRrWxgYCDz589nx44dzJs3j9KlS+ttghBCCCFEjqG75+zbb7/lt99+IzIykpiYGB4+fKjt27x5M4cPHwb+l3JDVVVUVeXu3bt88sknkopDCCGEEMKEruDs8uXLrF69Wvv/unXrmgVby5cv17Y7d+7MkSNH2Lx5M7Vr19aO//vvv/U0QQghhBAiR9F1W3PDhg2oqoqiKHz77bf07t1b2/fo0SPOnj0LgKurK+PHj8fZ2RlPT0/++OMP2rRpQ3BwMDt37qR9+/b6noUQIlM8DI4kNpnM/rFxCVnQGiGEyJl0BWcnTpxAURRq1KhhFpgBHDhwQAvcmjVrhrOzs7YvX758tGzZktWrV3Px4kU9TRBCZJKNBwP4c+OFrG6GEELkeLqCs/v37wNQq1atJPt8fX217caNGyfZX7JkSQCePHmipwlCiEzy772nADg52uPsmDRxrJeHC5VKe2Z2s4QQIsfRFZyFh4cD4Olp/oGckJDAsWPHtP9v0KBBkmMjIyMBMBhk8WMhXiS921SkW8vyWd0MIYTIsXRNCMiXLx8AoaGhZo+fP3+ep0+foigKpUuXpmjRokmOvX37NgAeHh56miCEEEIIkaPoCs7Kly+PqqpmvWQAO3fu1LabN2+e5LjHjx+zb98+FEWhfHn5BS6EEEIIYaQrOGvatCkAly5dYvr06YSHh3Pw4EGzFBqvv/662TEPHz5k+PDhREdHA9CsWTM9TRBCCCGEyFF0jTnr1q0bf/zxB+Hh4cydO5e5c+dq+xRFoWbNmtSsWVN77P333+fo0aPExcUBULBgQd588009TTCjqipbtmxh7dq1XL58mcjISLy8vPDx8aFPnz5Ur15d9zmio6NZsWIFO3bs4ObNm4SHh1OwYEHq1KlD3759tRxuQgghhBDpoSs48/T0ZNq0aXz00UdaT5hRoUKFmDRpktljd+7cIS4uDlVVcXFx4ZdffsHV1VVPEzTR0dGMHDmS/fv3mz0eGBhIYGAgmzdv5uOPP2bIkCHpPseVK1cYMWKENl7O6MGDB2zdupVt27YxdOhQPv7443SfQwghhBC5m+61NZs2bcqGDRuYP38+586dw8HBAR8fH959910KFy5sVtbb25tbt27RsmVLPv/8c1566SW9p9eMHTtWC8y8vb3p0aMHhQoV4uLFi6xYsYLIyEimTZtGkSJF6Ny5s9X137lzh0GDBhEcHAzAq6++ymuvvYaHhwdnzpxh9erVxMTE8Ntvv1G0aFF69epls+cmhBBCiNxDUTNxcct///0XDw8Pm8/QPHz4MO+88w6QmLZj7ty5ZklvAwICePvttwkNDcXDw4M9e/ZY3WM3ePBgLXfbqFGjkvTAnTt3jr59+xIXF4eHhwe+vr44OTml6/m0atUKgD179qTreCHSS1VVrt8JJTwyLsm+9Qf8OXftMQM6VJFUGkIIkQxbfX/r7jmzRpkyZTKk3vnz5wPg4ODAhAkTzAIzSOxJ+/rrrxk1ahShoaGsXr2aQYMGWVz/iRMntMDszTffTPbWaM2aNenVqxdLliwhNDSUo0ePymQH8cI54nefHxafTLWMnZJJjRFCiFwqQ4Kz27dvc+fOHcLCwgBwc3OjVKlSlC5d2ubnCg0N5ciRIwA0adKEUqVKJVuuffv2TJo0iaCgILZv325VcLZlyxYAnJ2d+eSTT1Is161bN+Li4ihQoAAFCxa04lkIkT08CklMDu3q4khhz7xJ9ru6ONKgWrHMbpYQQuQqNgvOQkNDWbhwIWvXrk1xSaYCBQrQvn17hg0bZrPg5dSpUyQkJC66nNxKBEZ2dnb4+Piwfft2zp8/T1hYGO7u7hadY/fu3QA0bNgwyTg6U5UqVeK7776zovVCZE8+VYrw6dt1sroZQgiRK+nKc2Z08uRJOnbsyJw5c3jy5Amqqib7Lzg4mKVLl9K5c2ett0uv69eva9sVKlRItWy5cuWAxHE1165ds6j+R48eERQUBGCWFiQuLo7Lly9z8OBBLly4QHx8vJUtF0IIIYRISnfPmZ+fH4MHDyY2Nhbj3AIPDw8qVaqEh4cHCQkJhISEcPXqVZ4+fYqqqjx58oShQ4eycuVKKleurOv8gYGB2naJEiVSLWu6jFRgYCA+Pj5p1u/v769tlyxZkri4OH7//XeWLVumzdyExOf83nvvMWjQIOztky4KLYQQQghhCV3BWVxcHJ9++ikxMTEAVKlShc8//5xXX3012fKHDx/mp59+4uLFi8TGxvLxxx+zZcsWHB0d090G0wCpQIECqZY1nSX6/HqgKXnw4IG27eTkRN++fTl37lyScqGhoUyZMoVz584xffp0Xc9JCCGEELmXrtuaGzZs4O7duyiKQrNmzVi5cmWKgRlAo0aNWLlypbbe5u3bt7XB9ullmvz2+VmazzNNbfF80tyUREREaNs//vgj586do3z58syZM4czZ85w8uRJfvrpJ4oVSxwkvWvXLn7++WcrnoEQQgghxP/oCs6MeTzy58/P5MmTLeotcnBwYPLkybi5uQGwY8cOPU0wG+uVVl4x0/2WjhGLjIzUtu/cuUPlypVZsWIFzZs3J1++fLi5udGhQweWL1+Op6cnAAsXLjS73SqEEEIIYSldwdnly5dRFIVWrVpZlVjW3d2dVq1aoaoqFy5c0NME8uTJo20b1+xMSWxsrLZtaYLY53P0fv/998kmsC1WrBgfffQRkBj4bdiwwaL6hRBCCCFM6QrOQkJCANKVv8x4jKVjv1KSN+//cjEZx76lxDQ4S+sWqJGLi4u2XaZMGapVq5Zi2bZt22rbZ86csah+IYQQQghTuoIzY4BjeuvPUsZjTIOr9DDeHoW0Az3T/cZbkGkx7SUrW7ZsqmU9PT21HsRHjx5ZVL8QQgghhCldwVnx4sVRVZXjx49bfazxGNP0FulhuiTU/fv3Uy1rOvOyePHiFtVfsmRJq9pjvF1qTIwrhBBCCGENXcFZ/fr1gcRcZ3v37rX4uD179nD+/HkURdHqSC9vb29t2zQhbXKM+xVFoXx5yxZuNk1sm9Ygf4PBoPXOFSlSxKL6hRBCCCFM6QrOevbsiaIkroL8+eefs3///jSP2bdvH6NHjwYSg6Tu3bvraQK1atXSZomm1oNnMBg4eTJxQedKlSqZ3Q5NTYECBbRA7vr162a9b88z5m+DtFcrEEIIIYRIjq7gzNvbm969e6OqKpGRkQwbNoz+/fuzdOlSTp8+TUBAAAEBAZw6dYqlS5fSr18/PvjgAyIiIlAUhW7duukOYtzc3LQ1Nffu3cu9e/eSLbd161YtYW27du2sOkeHDh2AxJmbf/zxR4rl/vrrL23b2nMIIYQQQoANlm8aM2YMgYGBHDhwAEVROHnypNZDlRxjaoqGDRvyzTff6D09AAMHDsTX11dbseDPP/80G8jv7+/PxIkTAciXL5/VvXW9evVi8eLFBAcHs3z5cqpXr07nzp3NymzYsIGNGzcCULt2bWrUqKHzWQkhhBAiN9IdnDk5OTF79mxmz57NwoULzTLqJydv3rwMHDiQ4cOHY2dnk3XXady4MW3btmXHjh2cPXuWzp0707t3b4oVK8alS5dYvny51q7Ro0cnmal5/Phx+vfvD0C9evVYsmSJ2f4CBQowfvx4Ro4cicFgYPTo0fz999+0bNkSJycn9u3bx/bt24HEGazff/+9TZ6XEEIIIXIf3cEZgL29PSNGjKBfv37s27ePY8eOcffuXUJDQ1FVFTc3N8qUKUOdOnVo27atxeO9rDF58mQiIiI4dOgQd+/eZcqUKWb7FUVh+PDh9OrVK131v/baa0ybNo3//Oc/hIeHs2/fPvbt22dWxsPDg99++41y5cql+3kIIYQQInezSXBm5OHhQdeuXenatastq7WIi4sLf/75J5s3b2bDhg1cvnyZZ8+e4eHhQZ06dejfvz916tTRdY527dpRp04dli5dyt69ewkMDERRFEqVKkXr1q15++23Lc6fJoQQQgiRHEV9fn0ikeVatWoF/G/tUiHS4+g/9wm4G2rVMVduBXP++hNa1CnJp2/r+zEjhBC5ja2+v9PVcxYUFMTx48d58OAB9vb2vPTSS9SrV498+fLpaowQwjaeRcbyw6ITJKTzp1ceJ5t2qgshhLCCVZ/Ajx49YuLEiezZsweDwWC2L0+ePPTu3ZuPPvrIbDFyIUTmi44xkKCCnQLtG6W+7NjznB3tadfQumOEEELYjsXBWUBAAH379tUG+T8vKiqKBQsWcPjwYRYvXoy7u7tNGyqEsJ69vR3vd62e1c0QQghhBYtyWSQkJPDRRx8REhKiPebq6kqVKlV45ZVXcHFxARJzmF27do0vvvgiY1orhBBCCJHDWdRztmvXLgICAlAUBRcXF7766is6d+6sLZsUHx/P6tWr+eGHH4iJieHAgQNcunSJKlWqZGjjhRBCCCFyGot6znx9fbXtuXPn0q1bNy0wA3BwcKB3795aFn5IXC5JCCGEEEJYx6LgzM/PD0VRaNiwIXXr1k2xXMeOHSlVqhQAFy5csE0LhRBCCCFyEYuCs4cPHwJQtWrVNMv6+Pigqiq3bt3S1zIhhBBCiFzIouDMuC6l6WLiKSlevDgAz54909EsIYQQQojcyaLgzJjTzHScWUqMMzejoqJ0NEsIIYQQIneyKDizZoUnRVGsPkYIYVunLj8AwMVZMv0LIcSLxqLgTAjx4rj3JJx5my8C0L1VhSxujRBCCGtJcCZEDmIwJDB92RliYg1UL1eITk1ezuomCSGEsJIEZ0LkIGv3+XPlVgh58zgwslct7OyUrG6SEEIIK0lwJkQOEXA3lGU7rgDwftdqFC6QN4tbJIQQIj2sGi184cIFNmzYkGYZo7TKGnXp0sWaZgghnhMbZ2DasjMYElRerVaMFnVKZXWThBBCpJNVwdm2bdvYtm1bmuWMMza//PJLi8pKcCaEPkv+vsydh8/wyO/Mh91qaNegEEKIF49VwZmkxxAi+/Hzf8zGgwEAfNSjJu6uzlncIiGEEHpYFJz5+PhkdDuEEOkQERXH9OVnUVVo26A0PlWKZnWThBBC6GRRcLZkyZKMbocQIgUnLj1g08EAEhKS7gt5Fs2T0CiKFszLu51eyfzGCSGEsDlJHy5ENrd273Uu3QxOcb+dAp/0ri2rAQghRA4hn+ZCZHOGhMSxnm82L0e5Uh5J9hcvlA/vkkkfF0II8WKyKDgLDAykRIkSGd2WJO7du0fx4sUz/bxCZEdVynpS/5ViWd0MIYQQGcyiJLTt27fn559/JjIyMqPbA0BMTAwzZ86kffv2mXI+IYQQQojswqLgzMHBgTlz5tCmTRuWLVtGXFxchjQmOjqahQsX0qpVK2bNmoWDg9x1FUIIIUTuYlFwtnHjRmrVqsWTJ0/4/vvvad26NTNnzuT+/fs2aURAQACTJ0+mWbNmTJ48mSdPnlC5cmXWrVtnk/qFEEIIIV4UFnVNlSxZkqVLl7J48WJmzJjBw4cPmTVrFr/99hs1a9akadOmvPrqq1SsWBFn57QTYEZHR/PPP/9w6NAh9u/fz7Vr14DEJLcuLi588MEHvPPOO9jb2+t7dkIIIYQQLxiL7xsqisKAAQPo0KEDv/zyC+vXryc+Pp4zZ85w5swZfv75Z+zs7ChZsiQvv/wyHh4euLq6kjdvXmJjY4mIiODhw4fcuXOHW7dukWCStElVVezt7XnzzTcZPnw4RYoUyZAnK4QQQgiR3Vk9qKtQoUJ8//33vP/++8yfP59NmzYRHh4OgMFg4Pbt29y+fTvVOkyXgXJ1deXNN99kwIABWTIjVAghhBAiO0n3iPuSJUvyzTff8Pnnn7Nr1y727NnDiRMnCAkJSfNYLy8v6tWrR+vWrWnVqhVOTk7pbYYQOV5CgqxpK4QQuYnu6ZAuLi506tSJTp06AXDnzh1u3rzJ/fv3efbsGbGxseTJkwdXV1dKlChB2bJlJXeZEBa6EPAE/7uhABTycMnaxgghhMgUNs9VUapUKUqVKmXraoXIdSKj45i+InFR89Y+L8kqAEIIkUtYlEpDCJH5/tx4gUfBkRT2zMt7XWRRcyGEyC0kOBMiGzp24T67TtxGUeCTXrXIm8cxq5skhBAik0hwJkQ2E/oshpmrzwHQpVk5XvEulLUNEkIIkakkOBMiG1FVlZmrzxEWHkuZYm70a1cpq5skhBAik0lwJkQ2sufkbY5ffICDvcKnb9fG0UFWyRBCiNxGgjMhsomHwZHM3XABgD6vV6ZscfcsbpEQQoisIMGZENmAIUFl+vIzRMXEU6WsJ12bl8vqJgkhhMgiEpwJkQ3sOn6LizeCcHG255PetbG3U7K6SUIIIbKIBGdCZAPXbicue9ax8csULZgvi1sjhBAiK0lwJkQ24uJs80U7hBBCvGAkOBNCCCGEyEZs+jM9PDyc3bt3c+bMGe7du0dYWBh9+/alc+fOAPz2229UrVqVpk2b2vK0GlVV2bJlC2vXruXy5ctERkbi5eWFj48Pffr0oXr16hly3u+++45ly5YBcPXq1Qw5hxBCCCFyB5sEZwkJCcyePZuFCxcSEREBJAZKiqIQHByslVu6dClBQUHUqFGDKVOm2HSB9OjoaEaOHMn+/fvNHg8MDCQwMJDNmzfz8ccfM2TIEJudE+DYsWMsX77cpnUKIYQQIvfSHZzFxsYyZMgQjh8/jqqqKZaLiYnhyZMnKIrCuXPn6N69O0uXLsXb21tvEwAYO3asFph5e3vTo0cPChUqxMWLF1mxYgWRkZFMmzaNIkWKaD15ekVERDB27NhUn7cQQgghhDV0jzn79ttvOXbsGKqqkidPHnr27MkPP/yQpJyqqrz99ts4OjqiKAqhoaF8/PHHxMfH620Chw8fZsuWLQA0aNCA9evXM3DgQDp27MgXX3zBmjVr8PDwAGDSpEmEh4frPifA1KlTuXv3rk3qEkIIIYQAncGZn58f69evR1EUKlSowN9//813331Hly5dkpTNkycP33zzDZs2baJMmTIA+Pv7a0GVHvPnzwfAwcGBCRMm4OzsbLbf29ubr7/+GoDQ0FBWr16t+5ymtzPz58+vuz4hhBBCCNAZnK1ZswZIDIpmzpxJsWLF0jymTJkyzJw5E3v7xDUDt2/frqcJhIaGcuTIEQCaNGmS4ji29u3bU7BgQZuc0/R2ZufOnalcubKu+kTuERkdR1BYVJJ/MbGGrG6aEEKIbELXmLMTJ06gKApNmzblpZdesvg4b29vWrRowe7du7l8+bKeJnDq1CkSEhKAxFuaKbGzs8PHx4ft27dz/vx5wsLCcHdP39qFxtuZXl5efPXVV4wYMSJd9Yjc5Z+AJ3wz5wjxBhmjKIQQImW6es4ePXoEkK6eo/LlywMQEhKipwlcv35d265QoUKqZcuVS1yvUFVVrl27lq7zmd7O/Pbbb7WxbEKkJeBumBaY2dkpSf655XOierlCWdxKIYQQWU1Xz5lxlqLxFqU1FCVx7UBHR0c9TSAwMFDbLlGiRKplixYtanacj4+PVecyvZ3Zrl07XnvtNesaKwTQvHZJRvWpk9XNEEIIkU3p6jkrVCjxV76/v7/Vx/r5+ZnVkV6medQKFCiQalnTXq7Q0FCrz2W8nVmgQAG++eYbq48XQgghhEiLruCsbt26qKrK3r17zYKktFy6dIkjR46gKAq1atXS0wSio6O17ednaT7Pyckp2eMsYXo78+uvv8bT09Oq44UQQgghLKErOOvYsSOQGOiMHj2a2NjYNI+5ffs2w4cP1wbxt2vXTk8TzPKkmQZfyTHdb01+NdPbma1ataJDhw7WN1QIIYQQwgK6grNGjRrRqFEjVFXl8OHDvPnmm6xZs4ZLly5pZRISEnj27Blnzpxh8uTJdOnShfv372u9Zs2aNdP1BPLkyaNtx8XFpVrWNHhMK5AzZbyd6e7uzrhx46xuoxBCCCGEpXQv3/TTTz/Rs2dP/v33XwICArRkr4qioKoqU6dOZerUqVp54ySCwoULM336dL2nJ2/evNp2TExMqkGXaXCW1i1QI9PbmWPGjKFw4cLpbKkQQgghRNp0L9/k7u7OqlWraN26Naqqav/gfzMyn3+8Vq1arFq1iiJFiug9PW5ubtp2WoP8TfdbMmbM9HZmkyZNePPNN9PbTCGEEEIIi+juOYPEAGnmzJn4+fmxZs0aTpw4wa1bt8wWBPfy8sLHx4cuXbrQtGlTW5wWQFsKCuD+/fsprhAA8ODBA227ePHiadZ94cIFbe1MX19fKlasmOYxxjIlSpRg7969aZYX2Vd0bDz/3n9qs/oehUTarC4hhBA5l02CM6Pq1atTvXp1AAwGA2FhYRgMBtzd3a0a42UNb29vbfv69evUq1cvxbLGhLWKomhJcIVIyZezD+N/J9T2FSu2r1IIIUTOYdPgzJS9vX2qtw7j4uIICAjgyZMnNG7cON3nqVWrFo6OjsTFxXH8+HH69OmTbDmDwcDJkycBqFSpktnt0JSUL1+eWbNmpVnu559/1gI/Y3nTiQrixXT/cTgAhdzz4OCgewQAAI4O9rSonXLvrhBCCKErOKtUqRJ2dnZ8/vnnDBo0yKpjZ82axZw5c/Dy8uLgwYPpboObmxsNGjTA19eXvXv3cu/evWRvWW7dulXLxWZp+g5PT09at26dZrlFixZp25aUFy+WicMaUdzLNaubIYQQIpfQ3R1gOq7MGi4uLqiqalXy2pQMHDgQSOyN+/TTTwkPDzfb7+/vz8SJEwHIly8f3bt3131OIYQQQoiMYJt7NVZQVZU7d+6wZcsWwDwVRno1btyYtm3bAnD27Fk6d+7Mn3/+ydatW5kyZQo9evTQZmqOHj06ye3W48ePU7FiRSpWrEi/fv10t0cIIYQQIr3SvK0ZHx9P165dU1w/U1VVfvzxR3788UerT64oChUqVLD6uORMnjyZiIgIDh06xN27d5kyZUqScw0fPpxevXrZ5HxCCCGEEBkhzZ4zBwcHxo8fD5jnKzO9nfn845b+A3jvvfds8kRcXFz4888/mTJlCo0aNcLT0xNHR0e8vLx4/fXXWbp0KcOHD7fJuYQQQgghMopFEwJq1arFgAED2Llzp9nj9+7dQ1EU3NzcyJcvX5r1KIqCvb09rq6ulCxZkh49euiaqZlc/Z06daJTp05WHVe/fn2uXr2a7vMuWbIk3ccKIYQQQpiyeLbmmDFjGDNmjNljlSpVAmDo0KFWz9YUQgghhBBJ6c5zlt7ZmkKkx7XbITwMzpxM+3EGeW8LIYTIfLqCsytXrtiqHUKk6daDp4z6Jf058dLLzk5S+gshhMg8GbZCgBC2FvI0GgBnJ3sqlCqQKed8uYQ7RTz1p3sRQgghLJUhwdnzszmNjyUkJBAfH09UVBShoaFcvHiRDRs2MG/evIxohsihihXMx6QPGmV1M4QQQogMYZPgbOfOnSxbtoyLFy8SEREh49CEEEIIIdJJd3A2ffp05s6dC6RvcoCiyHgeIYQQQggjXcHZtWvXUgzMjEFXSo87ODjwxhtv2DTPmRBCCCHEi07X2pqrV6/Wgq86deqwevVq/Pz8WLt2LaqqYm9vz5EjRzh37hx79+5l0qRJFCtWDFVViY+P55VXXqF9+/Y2eSJCCCGEEDmBruDs1KlTALi6ujJ79myqVauGk5MTVatWxc3NDYPBwOHDh8mTJw/FixfnzTffZOPGjVSoUAFVVZkyZQpPnjyxyRMRQgghhMgJdAVnDx48QFEUmjdvjru7u9m+qlWrAnD69Gmzx/Pnz8+0adNQFIXo6GhWrVqlpwlCCCGEEDmKruDs2bNnAJQtWzbJvnLlyqGqarKJasuXL0/dunVRVZWTJ0/qaYIQQgghRI6iKzhzcXEBwNHRMcm+MmXKABAQEJDssa+88kqq+4UQQgghciNdwZmHhwdAsuPGSpUqBUB4eDiPHj1Kst94GzQsLExPE4QQQgghchRdwVmVKlVQVZUjR44k2ffSSy9p235+fkn2P3jwAJCF04UQQgghTOkKzho2bAiAv78/s2fPNttXunRp7bbnpk2bzPZFRUWxe/duAAoVKqSnCUIIIYQQOYqu4Kxz5854enoCMGPGDLp168aePXuAxGSzLVu2RFVVdu3axYQJE7h27RrHjx9n8ODBPHnyBEVRqFmzpu4nIYQQQgiRU+gKzvLkycP48eOxs7NDVVUuXrxoNvtyyJAh2NklnmLp0qV07tyZgQMHcubMGa1M79699TRBCCGEECJH0RWcAbRu3ZrZs2dTokQJVFWlZMmS2r6KFSsyduxYIHFsmek/gPfeew8fHx+9TRBCCCGEyDF0L3wO0KxZM3bt2sXJkycpUqSI2b4+ffpQoUIF5s6dy/nz54mNjaVSpUr0799flm4SQgghhHiOTYIzADs7O+rXr5/sPh8fH+khE0IIIYSwgO7bmnoFBwdndROEEEIIIbKNLA3OVq1aRbt27bKyCUIIIYQQ2Uq6bmsGBwcTEBDA48ePKViwIDVr1sTZ2dni469fv863337L2bNn03N6IYQQQogcy6rg7M6dO0yfPp2dO3diMBi0x/PkycOAAQMYMWIE9vb2KR4fHR3NjBkzWLRoEQaDAVVVURQl/a0XQgghhMhhLA7Ozp07x7BhwwgNDTVbcklRFKKiopgzZw7//PMPc+fOTTZA279/P+PHj+f+/ftmQVnhwoVt8DSEEEIIIXIGi8acxcbGMmrUKEJCQrTHnJyczJZeMq6xOWvWLLNjIyMj+eqrrxg2bBj37t3734nt7Ojfvz/btm3T+xyEEEIIIXIMi4KztWvXEhgYiKIo5MmThwkTJnDq1CkOHTrEyZMnGTlyJPb29qiqyoIFCwgPDwcSFzfv3r0769ev13rLVFWlevXqrFmzhq+++op8+fJl6BMUQgghhHiRWHRb8/Dhw9r21KlTadWqlfb/rq6uDBs2DDc3N77//nuio6PZs2cP9evXp2fPnjx69Egrmz9/fj755BN69eolY82EEEIIIZJhUc+Zv78/ABUqVDALzEz17t1bWx3g9OnTjBkzhocPHwKJtzzbtGnDtm3b6N27twRmQgghhBApsKjnLCgoCEVRUs3yb2dnR926ddm6dStbt24lMjISSOxZ++6772SpJiGEEEIIC1gUnEVFRQGYTQBIjnHR88jISFRV5eWXX2bOnDmUKlVKZzOFEEIIIXIHi4Kz+Ph4FEVJM9Gs6eD+okWLsmTJEgoWLKivhUIIIYQQuYhNl29ycPhfrPfRRx9JYCaEEEIIYaUMW1uzefPmGVW1EEIIIUSOlWHBWYECBTKqaiGEEEKIHCvDgjNJlyGEEEIIYb0MC86EEEIIIYT1JDgTQgghhMhGLEqlYXThwgU2bNiQ6n6j1Mo9r0uXLtY0QwghhBAix7IqONu2bRvbtm1LtYxxrNmXX35pUZ2KokhwJoQQQgjx/6wKzlRVzah2iFxm/X5/9p++a9UxkTFxGdQaIYQQIvuwKDhLbU1NIdJj9Z7rPIuMTdexhQvktXFrhBBCiOzDouBsyZIlGd0Om1BVlS1btrB27VouX75MZGQkXl5e+Pj40KdPH6pXr667/j179rBx40b8/PwIDg7G0dGREiVK0KhRI/r370/x4sVt9GxyNmMv7PDuNfHycLH4OEWBymU8M6pZQgghRJaz6rZmdhYdHc3IkSPZv3+/2eOBgYEEBgayefNmPv74Y4YMGZKu+sPCwvjkk084fPiw2eOxsbFcu3aNa9eusXTpUr7//nsZQ2eFqi97UrJw/qxuhhBCCJFt5JjgbOzYsVpg5u3tTY8ePShUqBAXL15kxYoVREZGMm3aNIoUKULnzp2tqjshIYEPP/yQkydPAlC8eHHeeustXn75ZSIiIjh06BA7d+4kNjaWMWPG4ObmRsuWLW39FIUQQgiRC+SI4Ozw4cNs2bIFgAYNGjB37lycnZ0B6NixI926dePtt98mNDSUSZMm0apVK1xdXS2uf/369Vpg1qBBA3777Tfy5v3fuKfu3buzd+9eRowYQXx8POPGjaNRo0ZaG4QQQgghLJUjktDOnz8fAAcHByZMmJAkKPL29ubrr78GIDQ0lNWrV1tV/8qVKwFwdnZm6tSpZoGZUcuWLenbty8ADx8+5ODBg1Y/DyGEEEKIFz44Cw0N5ciRIwA0adKEUqVKJVuuffv2FCxYEIDt27dbXH94eDh+fn4ANGzYEC8vrxTLvv7669r2+fPnLT6HEEIIIYTRCx+cnTp1ioSEBCDxlmNK7OzstJQg58+fJywszKL6Q0JCqFatGl5eXrz88suplnV3d9e2nz59alH9QgghhBCmXvgxZ9evX9e2K1SokGrZcuXKAYlpHK5du2ZR/rZSpUpZfBs0ICBA2/bw8LDoGCGEEEIIUy98cBYYGKhtlyhRItWyRYsWNTvO1sl1165dq23XqFHDpnW/iLYducnyHVcx/H/PpqnwKMn2L4QQQiTnhQ/OgoODte0CBQqkWta0Nys0NNSm7fD19WXfvn0AFCxYkEaNGtm0/hfRgTN3CQ2PSXF//ryOFHS3PAGtEEIIkRu88MFZdHS0tp1W6gonJ6dkj9Pr/v37fPHFF9r/Dxs2jDx58tis/hfde11eoVaFwkkeL+ieBxfnF/4tKIQQQtjUC//NGB8fr22bBl/JMd1vepwewcHBvPPOOwQFBQHQqFEjLaWGSFTI3YVSRWQVACGEEMISL/xsTdMeqri41Mcxxcb+b6HttAI5Szx+/Jj+/ftz48YNAEqXLs3UqVNRFEV33UIIIYTInWzac3b79m02b97MmTNnuHfvHmFhYXz44Yf06dMHgHHjxlGpUiXefPNNmwRHgFlC2JiYmFTrNQ3O9Gbvv337NoMHD+bWrVsAFCtWjAULFuDpKYtyCyGEECL9bBKcRUZG8v3337Np0yYt55iqqiiKYhYQbd++nZUrV/LHH38wbdo0atasqfvcbm5u2nZoaCj586d8+8x0EoCeIMrPz4+hQ4dqtzJLlCjBokWL0pwtKoQQQgiRFt23NZ8+fUrPnj3ZsGEDBoMBVVVRVTVJuYiICC04CgwMZODAgZw6dUrv6SlTpoy2ff/+/VTLPnjwQNsuXrx4us7n6+vLgAEDtMCsXLlyLF++PMWVCYQQQgghrKE7OBs1ahTXr19HVVUKFSrEp59+yuLFi5OUc3R05PPPP9d6rKKjoxk9ejRRUVG6zu/t7a1tmyakTY5xv6IolC9f3upz7d27l2HDhhEZGQkk5jL766+/KFKkiNV1CSGEEEIkR1dwdvjwYXx9fVEUhXr16rFjxw6GDBlCvXr1kpR1cnLi3XffZcuWLdrtzPv377N+/Xo9TaBWrVo4OjoCcPz48RTLGQwGTp48CUClSpXMboda4sSJE4wcOVKbdNCoUSMWLlyYZm41IYQQQghr6ArONm3aBICLiws//fQT+fLlS/OYAgUK8Msvv2gD8vfs2aOnCbi5uWlrau7du5d79+4lW27r1q1awtp27dpZdY6goCBGjhypjZ9r0aIFv//+u9lkBJFUTJwhq5sghBBCvHB0BWenT59GURRatGhBoUKFLD6uSJEitG7dGlVVuXr1qp4mADBw4EAgMZXGp59+Snh4uNl+f39/Jk6cCEC+fPno3r27VfV///33WmBXvXp1fvnlF5vNNs2pfM8FEnA3DDsFXioqOc6EEEIIS+marfnkyRPAfNyXpcqWLQtAWFiYniYA0LhxY9q2bcuOHTs4e/YsnTt3pnfv3hQrVoxLly6xfPlyIiIiABg9enSSmZrHjx+nf//+ANSrV48lS5Zo+wICAti+fbv2/6+//jq+vr5ptsnDw4O6devqfm4voqCwKGavOQ9A91YVKFlYgjMhhBDCUrqCM3t7ewAtfYY1jGO3bLXM0eTJk4mIiODQoUPcvXuXKVOmmO1XFIXhw4fTq1cvq+rdsGGD2ezTH3/80aLjng/ycgtVVfl15TnCo+IoV9KdXm0qZnWThBBCiBeKruCscOHC/Pvvv1y+fNnqY41pNAoXTrrmYnq4uLjw559/snnzZjZs2MDly5d59uwZHh4e1KlTh/79+1OnTh2r67127ZpN2pdb/H30X85cfYSTgx2fvl0HB/sXfhEKIYQQIlPpCs7q16/PzZs3OXjwIHfv3qVkyZIWHXfkyBFtvJotb/0pikKnTp3o1KmTVcfVr18/xbFvc+bMsUXTcoV7j8OZv/kiAAM6VJH1NIUQQoh00NWt8eabbwKJi4iPHDnSLAN/Sk6fPs2nn36q/b+1gZTIngyGBH5adoaYWAM1yheiY+OXs7pJQgghxAtJV89Z9erV6dSpE5s2beLSpUt06NCBHj16UKVKFa1MaGgoV65c4fr16+zatYs9e/ZgMBi0WZ61a9cmNjY2XePWciovLy8gMVHvi2Lb4ZsEhT6jVCFnPuhaldjYmKxukhBCCJEmOzs7HB0dURQlq5uiUdTk1lqyQmxsLAMHDuTMmTMWPTHj6Ro3bsx//vMfYmNjMRgkH5aphw8fArwwKw/ExScQ8iwxkHTL50QeJ5ss2SqEEEJkCnt7e/Lnz4+7u7uuHKatWrUC9Odw1f0t6uTkxKJFi5g6dSpLly4lPj4ewCxQM43/FEVh4MCBdOzYkfj4eDw8PMiXLx92dnbZKmrNDozpRrKzhASV+0EROLkmkDePA14eLvI6CiGEeCGoqkpCQgIRERE8ffqU0NBQSpYsSf78WTtmWnfPmamHDx+yYcMGTpw4QUBAAGFhYcTHx+Pu7k7JkiXx8fHhjTfewGAw4ObmRvHixeWLPBnGyQkVK2b/NBQhT6N5EhaNg73CS0XyYy+zM4UQQryAVFXl3r17PH36lNKlS6erBy3b9JyZKlKkCO+//z7vv/9+imXu379PRESEBGY5RFx84lhBt3xOEpgJIYR4YSmKQvHixYmKiiIsLCxLl2jU9W364MEDq8qrqsqzZ89wc3OTwCyHkddTCCHEi05RFNzc3Hj27Bk2vLFoNV3BWcuWLRk4cCDr16/XlkdKTVxcHAaDwaIF0oUQQgghMlvevHkxGAzaSkZZQVdwlpCQwPHjx/nqq69o1KgRo0aN4sCBAymmxTA+bmcnt7+EEEIIkf3oWZrSVnSNOStSpIiW9iE6Oppt27axbds2PD096dixI506daJq1apJjpNbYEIIIYTIjrJDjKKrC+vAgQMsWbKEnj174uHhgaqqqKpKUFAQixcvplu3bnTs2JE//vjD6vFpQgghhBC5ke77iz4+Pnz33XccOnSIOXPm0LFjR1xcXLRALSAggJ9++omWLVsyZswYIiMjJemsEEIIIUQKbJZKw8HBgWbNmtGsWTOio6PZu3cvmzdv5tChQ8TFxaGqKn5+foSGhnLjxg08PDzw8PDA1dU1W3QhCiGEEEJkBxmyzk6ePHlo37497du35+nTp+zYsYOtW7dy9+5dIDGlRlhYGGFhYTg4OFCpUqWMaIawEVVViYkzYDAknVYcb5A1UYUQQghbyvBFEN3c3OjevTvdu3fnwYMH3L5922y2pnG5J5F9RUTHcf9JZFY3QwghhMgVMmWF6jt37rB7927Onj1L586dyZMnj6TTyAb69evHiRMngMSlJkqWLJlsufj4xB4zOzsFx2RWAbC3U3B1ccy4hlrAuNRVvXr1WLJkiU3rVlWV5cuX89prr+Hl5WW2b8aMGcycOROAxYsXU79+fZud19LluxwdHXF1daVYsWJUq1aN7t27U61aNZu1Q6Rfy5YtCQwMpESJEuzduzerm2MzH3zwAXv27GHOnDk0b9481bLnz5+nR48e2v/Pnz+fRo0aWXSeMWPGsH79esC662vdunV8+eWXAAwfPpwRI0akecydO3fYvHkzR48e5caNG4SFheHo6IiXlxfVqlWjXbt2tGjRQkuz8KKIi4tj1apVbN68mevXrxMXF0fRokVp1KgR/fr14+WXX05XvcePH6d///5WH2fNtfD333/z8ccfp/m5PnnyZObPn89XX33FgAEDrG5TdpRhwdmjR4+01Br//PMPAMWKFaNz586JJ3Zw0MadiRdDvjwOFC2YuxII37p1i6+++opTp07RtGnTrG5OsuLi4ggJCSEkJIRLly6xevVqBg8ezKhRo7K6aSIHWrduHXv27OHVV19NMzAD2Lhxo9n/r1mzxuLgLDOEhITw448/snHjxiST1eLi4rh16xa3bt1iy5YtVKxYkUmTJvHKK69kUWutExISwnvvvad9BxsZn9O6dev47rvv6NKlS6a1ydIx5o8ePWLixIkWlR02bBjr1q3jp59+onHjxnh7e+tpYrZg0+AsJCREG192+vRpbekD43+dnZ1xcXGhRIkSeHp6ykQAke1t2rSJU6dOZWkbPD09+f7771PcHxUVxa1bt9ixYwfXrl0jISGBuXPnUqpUKbMeCyH0CgkJYfLkySiKwueff55m+bi4OLZu3QpAoUKFePLkCbt37yYkJIQCBQpkdHPTdO3aNQYPHqzl68ybNy+tW7emRo0aFCxYkJiYGK5evcrWrVt5+PAhV69epW/fvsyePZuGDRtmcetTZzAYGD58uBaY1ahRgy5duuDq6srp06dZu3Yt0dHRjB07lmLFilnd61++fHlmzZqVZjlVVZk4cSL3798HsKgXMzQ0lHfffZfHjx9b1BY3NzeGDRvGf//7X8aNG2fzuydZQXdwFh4ezu7du9m6dStHjx7VfnkYAzI7OzsaNGhA586dad68Offv3ydfvnwSmAmbunr1apacd8SIERZ92Ojh4uJC69at0yz3wQcfMGXKFObPnw/Ar7/+SteuXXF0zNpbzrlZTrqVCTB9+nRCQ0Np27ZtsgnGn3fgwAFCQ0MBGDp0KBMmTCA2NpZNmzZl+e2n+/fvM2jQIJ48eQJA27ZtGTduHJ6enknKfvzxx/zwww8sW7aMqKgoRo4cybp16yhVqlRmN9tia9eu1X5YdurUicmTJ2vDiTp16kTHjh159913iYmJ4bvvvmPLli1WDTfy9PS06HPp999/1wKz3r17p9lLd+vWLYYPH861a9csbgvA22+/zfz58zlx4gRbt26lQ4cOVh2f3ega+DV8+HAaNWrEl19+yaFDh4iPj9fym5UvX55Ro0axb98+FixYQJcuXciTJ4+t2i2EeI6dnR2ff/65Nobk8ePHHD16NItbJXKKW7dusWbNGgDee+89i47ZtGkTkNgj1bNnT23MprGerDRmzBgtMOvYsSM///xzsoEZJN71+fbbb3njjTcAePr0KT/++GOmtTU9jD/S3N3d+fbbb5MEXj4+Pnz44YcABAQEsHv3bpu34cqVK9qY3DJlyvDFF1+kWn7btm1069bN6sAMwMnJiUGDBgHwyy+/vPD5VHUFZ7t37yY2NlYLyDw9PenXrx9r165l8+bNvPfeexQpUsRWbRVCpMHOzo4mTZpo/3/r1q0sbI3ISX7//XcMBgOVKlWyaMJJWFgY+/btA6Bx48Y4OTnx2muvAYm3E/38/DK0vak5cOAAx44dA6B48eL88MMPFvUaffXVV+TNmxeAXbt2cefOnQxtZ3pdvnyZmzdvAvDGG2/g6uqabLnevXtrExy2b99u0zaoqsrXX3+tLR4+fvx4XFxcki17584d+vXrxyeffMLTp0+BxDsB1urcuTOOjo7aGMEXme7bmk5OTrRo0YIuXbrQpEmTF24mi7CNyMhIVq9eze7du7l27RoRERF4eHhQqVIl2rVrR5cuXdJ8b9y/f5/58+dz8OBB7t27h4uLCxUrVqRnz5507NiRuXPnMm3aNCDpbcy0ZmsGBASwbNkyjh49SmBgIAAFCxakRo0atG/fXvvSMDKdhWnUqlUrwHy2kaWzNS9dusSKFSs4ffo09+7dA+Cll16iefPm9OvXj0KFCqX6t0mvmJiYFPc9fPiQv/76i4MHD3L37l3i4+MpXLgw9evXp0+fPlSuXDnN+n19fVm2bBnnz5/n6dOnFCxYkIYNGzJ48GC8vb15/fXXuXnzJl27duWHH37QjjOdTXfgwAGuXr3K9OnTCQgIIH/+/FSuXJkJEyZQrFgx7Zjo6GhWrlzJrl278Pf3Jzw8nAIFCmhjadK6xWLte8DUgwcP+Ouvvzh06BC3bt0iPj4eT09PqlSpQps2bejUqVOy729LZmvquXaM7/uRI0fywQcfcPz4cZYuXcq5c+cIDg6mQIEC+Pj40K9fP2rVqpXq3yc1oaGh2pdd165dLTrm77//JjY2FvjftdOhQweWLVsGwOrVq6levXq626TH0qVLte333nvP4lv/np6evPHGG5w4cYJ69eppz89Sp06d0m7zWqNYsWIW3UY2Ms7CB3j11VdTLOfm5kaVKlX4559/OHTokNXtSs3atWu1APyNN95IdUzbyZMntTYXKlSI8ePH06pVK2bPnm3VOT09PWnWrBm7d+/mr7/+0iYgvoh0BWfff/89r7/+Ovnz57dVe8QL6NSpU4waNSrJ+qmPHz/m8ePH+Pr6snDhQmbPnp3iGI3jx48zbNgwIiIitMdiY2M5ceIEJ06cYNeuXRanlnjehg0b+M9//qP9gjMKDAwkMDCQbdu28eqrrzJ79mztV7GtGAwGJk+ezKJFi5Lsu3LlCleuXGHFihXMmDGDevXq2eScphMYypQpk2yZDRs28N133xEZaZ6/7vbt29y+fZu1a9cyZMgQPv7442THhxoH+T4fCD948IB169axdetW/vvf/1rU3gMHDjBu3DgSEhITGgcFBXH16lUKFy6slbl06RIffvihFtgaPXr0iF27drFr1y6aNm3K9OnTk+0l0PMeOHLkCB9++GGSv9WDBw948OABe/fuZcGCBSxYsICCBQta9JyNbHHtGE2fPp05c+Zo430h8e+zdetWtm3bxqeffsqQIUOsap/Rpk2btECkZcuWFh2zYcMGIPGWoDE4q1OnDiVKlCAwMJCtW7fy5Zdf2vyaS0tUVBRHjhwBEnuaO3bsaNXx48aNS3cqqF9++cUscLLU8z9u0nL9+nVtu3z58qmWLVeuHP/88w9hYWHcu3eP4sWLW92+58XExPDrr78Cia//p59+muYxjo6O9OnThw8//BA3N7d0n7t58+bs3r0bPz8/rly58sImudcVnHXv3t1W7RAvqH/++Yf33ntP++KqVasW7dq1w8vLi8DAQDZu3Mj169e5du0avXr1Yt26dUludV+4cIEhQ4YQHR0NJP7KbtmyJY6Ojpw4cYINGzawfft2Dh8+bHX7/P39tS/l/Pnz0717dypXroydnR03b95k5cqV2tisKVOm8O233wLQvn17KleurH2xQWK3fMGCBa0aO/n111+zdu1aAFxdXenWrRtVq1YlIiKC3bt3c+jQIUJDQxk6dCibN2+mRIkSVj9HU2vWrOHixYsAeHh4JPureePGjYwZMwZVVXF0dOSNN97Ax8cHJycnrl27xpo1awgKCtJuY3322WdJ6pgyZYoWmOXPn59evXpRuXJlQkJC2LhxI35+fowePRonJ6c02zxx4kQcHR3p168fFStW5NKlS7i6umq9RQEBAfTt21cL3Bs3bkzLli0pUKAA9+7dY+PGjVy7do2DBw8yZMgQFi9ejIPD/z7a0vseAAgODubjjz8mMjISJycn3nrrLWrUqIGzszN3795l9erV3L59m6tXr/Lll18yd+5ci18rW1w7Rps2beLmzZvky5ePbt26Ub16dSIjI9myZQvHjx9HVVV++uknGjVqZFUPjNGuXbsAKF26NC+99FKa5W/fvs3Zs2eBxC9L4w94RVHo2LEjc+bMISIigr///pu33nrL6vbocfHiRS1Ir1ChgtWBwIuQo9PYM2xnZ2fW+5ycokWLatu2Cs5WrFihzYDt1atXmnXWqVOHvXv3mv0gSy/TNC27d+/OncGZyN0MBgOff/659uUyatSoJL/MBw0axPjx41m5ciVPnjzhq6++Yt68eWZlJkyYQHR0NIqiMGnSJN58801tX+fOnenatStDhgzh2bNnVrdx5cqV2gfxvHnzqFGjhtn+Pn368NZbb3Hv3j3WrFnDF198QZ48efD29sbb25vLly9rZRs1apRiot7kHD9+XAvMypQpw8KFC80+KHv37s2vv/7KrFmziIiIYMaMGVb9OobE3sVnz55x7do1Nm/erCXsBPjss8/Il888L93Dhw8ZN24cqqpSsGBB5s+fn+TD691332XIkCGcO3eOP//8k9atW1OzZk1tv7+/v9YTWKJECZYsWWIWVPbp04fJkyezYMECi1YAiYmJYfr06bRv3x5InElmpKoqn3/+OREREdjZ2fHDDz8kuVUxaNAgvv/+e5YvX87p06eZP3++2fswve8BSAx6wsLCgMREl8Y2GvXv35+3336bixcvcuDAAe7cuWPRDD5bXTtGN2/epESJEixevNjsPdqjRw++/PJL1q1bh6qqrF271urgLDIykjNnzgBYnNzYOBEAzF9PSLzFNWfOHCDxx0RmB2fGsViQdq+SrWVWiofg4GAA8uXLl+YPJNNco+m55fq8hIQEFi9eDCTmMx04cGCax5QuXVr3eY2KFy9OwYIFCQoK4tChQwwfPtxmdWcmi4IzYxZgRVHMbs9Ymx24QIECdOvWDUdHR7MxFGXLlk22vKqqxMS+2DMu0kNV1Rci1cjOnTu1D7r27dsne8vEwcGBb7/9lsuXL+Pn58ehQ4f4559/tA/5Y8eOab+wu3fvbhaYGdWtW5cvvviCb775xuo2Gtvn4eGR5EsZEsco9O/fn1WrVvHSSy/x5MkTqwKw1CxYsEDbnjZtWrK/YEeMGMHWrVv5999/2bFjB99//32S8S+BgYFW3dJ1dHRk1KhRyfZsL126VAsIJkyYkOyvSnd3dyZPnkzHjh2Ji4tj3rx5zJgxQ9v/xx9/aEHX1KlTk/T2KYrCF198wblz57TXNjWlS5dOEvQYHT58WOsJHDBgQLJjSOzt7Rk7dixHjx7l33//ZfHixQwaNEj7O+p5D5h+kTdr1izJsXny5OG9995j+vTplCpVipCQEIuCM1tcO8/76quvkn3vfvDBB6xbtw4gSTJSS1y6dEl7vcuVK2fRMcbEs+7u7kmSN5cvX57KlStz+fJlzpw5Q0BAQKYmDTUNQLJDrrWMYLwL4ezsnGZZ0+AtKipK97n37t2rraP9+uuv26Qnzlrly5cnKCiIS5cuERcX90KmE7IoODtx4kSywUJKj6ekWLFidOrUicjIyDS7hlVV5YuZh7j8b7DF9ecUZYq48GGntG8dZLUDBw5o24MHD06xnL29PYMHD+ajjz4CEruajV8wxtlcQKp5j7p27cpPP/1k9S8746/C0NBQ1qxZQ7du3ZKUGTRokDYF21ZiYmK02WC1atVKMaO4oiiMHj2awMBAypYtazZeyBqKovDSSy/RokUL+vTpk+KtJ+OMrIIFC9KiRYsU6ytTpgx16tTh2LFjWpocBwcHEhIStNe9WrVq1K5dO8X2vPPOOxblgKtTp06K+0xnkKXWw+Lo6EinTp349ddfefz4MRcuXNAGwOt5D5j2KsybN48RI0Yk+cxr164d7dq1S7FtybHFtWPKyckpxWz9pUqVIl++fERERGgz4axhOn7JkuDszJkz3L59G0j8ck6u56ZTp05ar7SxtzKzmA7iz+zxbpnFGExbMqzAtIwt0k+Y9g6+++67uutLj/Lly3Ps2DFiYmK4e/duih1A2ZnFtzVT6s2x5sskvV88Ins6d+4ckDjjJ61bJQ0aNNC2TX+9nzx5Ekj8BZvaB7+TkxO1a9e2Oqnn66+/zubNmwEYO3Ysy5Yto1WrVjRt2pRXXnklw3oo/f39tV+hac2SMw6WTsnzKwQkJCTw7Nkzdu3apQW3FSpUYPLkyanOsAwJCdFSa7i6urJnz55Uz2u8tRcZGcmNGzeoUKEC169fJyQkBEjMk5QaSzOOp/bBef78eW37+vXrqaYGMf3Vf/HiRe3vruc98Prrr2uD7GfNmsW2bdto06YNTZs2pWbNmmZj26xhi2vHVJkyZVJti6urKxERERbdZn6eMYEoWNbTZJwIAGh5wZ7XsWNHpkyZQkJCAhs3buTTTz/NtN4N04DMeMs6pzFeu89PgEmOabCq9zW4deuW9qO0Tp06VKlSRVd96WX6Pr13717ODc5S+hBP68P9efHx8Tx9+pTSpUunOahaURQmD2+cK29r/nvT/4W4rWkc12DJbUB3d3fc3d0JCwvTEj9C4mwywKKub0sGIj+vdevW9OjRg1WrVgGJX9oXL17k119/1aZdG79s0/tFmxzT56h3kH9KKwS89dZbrFmzhv/85z9cvXqVnj178ttvv6W4bqHxbw2JH6LGBJSWCAoKSlJHWs/L9DVPTWoDsk3P98knn1jSVOB/7QV974HKlSszcuRIfv75ZyDxNuecOXOYM2cO+fPnp1GjRrRp04ZWrVpZNVHEFteOqZTyWBkZn1d6fiCb9raldZ7Y2Fitt9PR0ZEzZ86keGu7YMGCPH78mKCgIPbt20ebNm2SlDEd/mJN240zf5+vAzAbdG6LMVbWyKxUGsYANLVUOkamwZklt0FTY5rI9vXXX9dVlx6m79P09BZnBxZ9G6X0IWztl050dLQ268mS7lZFUcjjnPvmLLwIgRmgzZ5LKbHg8/LkyUNYWJhZD4fxg8qSL7b0rjDx/fff07BhQxYtWsS5c+e0D/ng4GDWr1/P+vXrKVGiBBMmTLDZenmmAYneD7zUdOvWjbt37/Lbb78RExPDRx99xMqVK5PthQwPD0/3eYyvtekXiyXPy/iapya1z4L0ttk0JQvoew8MGzaMmjVr8ueff5otUffs2TO2b9/O9u3bKVCgAF9//bXFS8bY4toxZcsfFs8z/fJ+foLJ8/bv36+93nFxcfz0008WnWP16tXJBmem7zFLeoGMTP9Oz79PTce3BQQEWFynUUJCAqqqpiunZ2al0jD+4AkPD9eGJKTE9JpOaYUESxnvbCiKQtu2bXXVpYdp76i1ueiyiyyLfBISEoiJiSE+Pl7ypL2gXFxcePbsmcWDSI0D0U2DLGdnZ+Li4iyqwzjINT2M44IePnzI/v37OXz4MMeOHdO+SAIDAxk6dCgrV660KPlqWky/dC359arHiBEjtMHi4eHhjB49mlWrViX5QDZtU/v27Zk+fbrV5zJ97TL6NTOeLzw8nEKFCqUrlYopPe+BV199lVdffZXg4GAOHjzI4cOHOXr0qLYwc0hICKNGjcLV1TXZiQPPs8W1k1lMz5nWF53pLU1rHDp0iAcPHpildQDz21PW3II0Lft8wGFMn/H06VOuX79OUFCQVfnpzp49y5AhQ6hXrx7t2rVLMhs1OyhTpgyHDh0iISGBhw8fptqRYppjT08vf0hIiNZLWrNmzSxdHcj0ffqiLhupK2FLpUqVqFKlitmsNEs9fvyYgIAALR+LePEYs9obZ+akJjg4WEuFYXrRGj+Mn08umhxLyqSlSJEi9OzZk19//ZWjR4+yYMECLUt5TEwMf/75p+5zgPkXgumYneQEBQVx6tQpHj58mK7bTvb29kyYMEELxi5evJjs8zD9AjIGFdYyfe3SunbDw8N131IwtjksLMxmv4D1vAc8PT3p0qULU6ZMwdfXlxUrVmizEVVV5bfffrOoDba4djKL6Y/n1HoyQ0JCOHjwIJD4JX/16tU0/xkTwCYkJGhpZ0yZ3va15G9lZDrL9vnZs3Z2dtpkGFVV2b9/v8X1QuKtu/DwcPbu3culS5esOnbJkiUW/V2e/2dtih3T3kF/f/9UyxonfLi7u+t6f5n2Kqc1jjajmfac60lom5V0Z9NL7yB/42zNF31x0tzMOGvs6dOnWrqDlBgHiQJmaSGMqQ1CQ0NTvcWQkJBgUVoGU9HR0WzevJmff/452V/09vb2NGzYkHnz5mkDYdOz4G5yKleurN32MA7+TsmOHTvo06cPTZs2NZvFZ41KlSrRt29f7f9///33JFnnixQpoo23uXDhQpKM98+bPn06Y8eO5bffftN6IipVqqTdJjLmvkrJ6dOndU8CMr7H4uLi0jzf1q1bGTVqFNOnT9eW99LzHlBVlb///puZM2eycOHCJMcqikKtWrWYPXu29nd9flmxtJ6Xnmsns5gGSKn90Ni2bZt26zGl1CjPM02NYszFZsp0lrNx8lBaVFXVbh06ODgkmy6mR48e2va8efMsvmUaGhpqFkRmx14zwGy1EdP3z/OePn2qzZpNa4JPWkyvz+RS1mQm0x/ytkqNlNmyJNVxbGysdp/7Rci2nBuoqsrT8BiCwqKS/IuITv6Dy/TXUWq9DQaDgfnz52v/b5rCwXTQqOl6d8/bvn271b09dnZ2WnCRWvvy5cunfTE/P7XedPyfNYFG3rx5tQ+706dPp/rrdevWrUDiLd7U0kqkZcSIEVpPU1RUFD/++GOSMsbXLCoqSlvjMDn//vsvf/zxB2vWrGHRokXaWCMnJyetDj8/v1TzZv3111/pfi7Ptxcwew89Ly4ujp9//pktW7bw+++/awPC9bwHFEVh2rRpzJgxg9mzZ6fYc+fo6Kj9fdIak5Xc80rvtZNZTHthUpsta8xtBpYHZ40aNcLLywtI7Bk7evSo2f7y5ctrPV+HDx9O84cOJOaQM04kadCgQbKTGOrWravNgg0ICLCoZyohIYFvvvlG+6HSokWLLJuNmJZy5crx8ssvA7B+/fokYzCNli5dqnWQWJsO5nkXLlwAEq+brP67GFO5uLi4ZEmeNVtIMzKKj4/njTfeoHLlykn+QeIX1o8//pjs/uf/tW/fnnv37vHvv/9q43AycrC0sFxsfAIPQ6IIfhqT5F9kdOL0++cnKrz22mtaZudt27Ylu3SNwWBg/Pjx2pd4/fr1qVu3rra/cePG2i/b5cuXa0slmbp69apZKglLmQYS169fT3Fpnb/++kvrRTJtG5iP07J22r0xM7aqqowaNUqboWdq4cKF2lqYHTt21DX+0tXV1WxG49atW83W2YTEXHLGIOSXX35JNjXJ06dP+eijj7QP7X79+pmNXxs0aJD2o+qzzz7TlmkxNXfuXO0Wlx5t2rTR3mMHDhxg+vTpSYJk45em8QO5QYMG2ueT3veA8cdDWFgYkyZNSjZA3717t3Yb7fn3T0psce1klldeeUX7nDZdMcPUv//+q6U9KVOmjMVfzvb29maTKNasWZOkjDFXlqqqjBgxItUfBKdOndKW31IUhffffz/FsuPHj9eC6b/++ovPPvss2WsUEl//Tz75hB07dgCJt8rSkxQ7Mxnz9oWEhDBmzJgkPy5Onjyp3YYvUaJEshMyrGH8AVqqVKk0Z/VmNOPt5po1a6Zr4kZ2kOaEAAcHB8aPH8/bb7+dYs+BpT0KyZUz/moSWWvcN1+jKg4oCjjYJx+zO9jb4eVViEmTJgGJH6xTp06lT58+xMbGMm3aNPbu3Uv79u0pVKgQ9+/fZ8OGDdptIg8PDyZPnmxWp52dHePHj6dPnz7ExcXxySef8Pfff9OsWTOcnZ05d+4cq1evJiYmBnt7ewwGg1W9rcOHD2f37t1a+3x9fWnRogVFihQhODiYw4cPa7nCChQokCQRrukA5f/+97/06dMHsKxnoEWLFnTu3JmNGzdy5coVOnToQI8ePahQoYKWPsC4ALOXlxejRo2y+Hml5K233mL58uXarbIJEyawbt067W9WtmxZRo8ezcSJE4mNjWXYsGG0atWK5s2b4+Ligr+/P6tWrdK+pCpVqpQkSWr16tXp27cvixcv5t9//+WNN96gZ8+eVKpUiWfPnrFt2zaOHz+uvV6Q/h5yBwcHs/fY77//jq+vL506daJw4cJma2tCYs/VuHHjzOrQ8x545513WL16NaGhoSxfvpzz58/Trl07SpQoQVhYGKdPn9Z+UDg7OzN06FCLnpctrp3MYswxePTo0RRvLZveMra018yoc+fO2m3jXbt2ERoaapb8t1evXhw8eJC9e/fy6NEjevToQePGjWnYsCFFihQhLi6OBw8ecPToUbOet2HDhpnd3nte6dKlmTVrFsOGDSMqKorNmzezZ88eXnvtNWrXro2HhwfPnj3j/Pnz/P3339p4u7x58zJr1qxs3yPTrVs3Vq9ejZ+fHzt37qRbt250794dDw8Pzpw5w+rVq4mLi0NRFMaNG5fsrOl169bx5ZdfAqnPGA0JCdHGRVozuSIjBAQEaLkYk1tb+EVh0WzNWrVqMWDAAHbu3Gn2+L1791AUBTc3N4u68wsXLoy9vT3Ozs64uLhQoEABmamZTZw4cdyics/P5qlevToLFizg448/5vHjx5w9ezbZsWGVK1dmxowZyS5hVKNGDaZPn87o0aOJjIxk586dSd5r3bp1IzAwkKNHj1qUhsXI29ub6dOna+sYnjhxItmp7EWKFGHGjBlJFt5t2LAhHh4ehIaGcurUKU6dOoWiKDRv3tyi7OITJ07EwcGBtWvXEhwczO+//56kTMmSJfntt99s8qFmZ2fHV199pQWRly9fZtWqVfTq1Usr079/f+zs7Jg8eTKxsbHs2bMn2ZyFNWvWZPbs2cn2bo8ZM4awsDA2btxIWFhYkp4fFxcXxo4dy3/+8x/AskzlKTG+x0aOHMmTJ0+0PGXPK1y4MDNmzEiScFLPe8DT05Pff/+dYcOGERISwqVLl5IdBO7m5sbkyZOtWmTZFtdOZmnXrh1Hjx7l4cOHXL9+3WxNSlVVzdbStDSdiFGVKlUoX748169fJzY2lk2bNpktDagoCr/88gs//PADy5cvJyEhgYMHD6bYM+vi4sKoUaPo169fmud+9dVXWbFiBV999RUXL14kMjKSjRs3mt2iNVW1alV++OEHKlSoYNVzzAp2dnbMmTOHwYMHc/HiRa5evcqECRPMyjg6OvLtt98mWWLLWqa951nda2YaoFv7QyE7sTiVxpgxYxgzZozZY8YPoqFDh1q0/E10dDQ3b960KAmteHHUrVuXHTt2sHLlSvbs2YO/vz8REREULlyY8uXL06VLF1q3bp1q9unXXnuNzZs3s3DhQnx9fXnw4AFOTk5UqVKFvn378tprr2kBh7Wzb1q3bs3WrVtZtmwZR44c4fbt20RFReHm5kbZsmVp1aoVPXv2TPZDpUCBAixatIgpU6bg5+dHZGQknp6e3L9/36L1AB0dHbXF3FesWMHp06d58uQJDg4OlCtXjjZt2tC7d2+bfqDVrVuXjh07smXLFgB+/vln2rVrh7u7u1amb9++tGzZkqVLl3L48GHu3r1LVFQUHh4eVKlShTfeeIMOHTqkeEvA3t6eH3/8kXbt2rFq1Sr++ecfQkNDKViwIE2aNElyO8n03Ol9Tjt37mTlypXs27cPf39/nj59St68efH29qZVq1b06tUrxR97et4DtWrVYtu2baxYsYKDBw9y8+ZNwsPDcXV1pWTJkrRo0YJevXppMzCtfV56r53M0K5dO3744QciIyPZsWOHWXB2+vRpbeZuhQoVLF5/01SnTp2YNm0akHhr8/l1m52cnPjmm2/o27cvGzdu5Pjx49y6dUvrrXF3d8fb25tGjRrx1ltvWfVaVKpUibVr13Lo0CF27tzJP//8w71794iIiMDJyYnChQtTs2ZN2rdvT9OmTV+YPJSQ+ONi1apVrFy5ki1bthAQEEBkZCReXl7Ur1+fQYMG2WSSSXaaHWm89VyvXj2L1rnNrhRVx3QqY3D2xRdfWBWclS1bVoKzVBhnfGXmzKzI6DgCH0fg7GjHS0Wz59Tj119/nZs3b1KjRg0t27vIvs6dO0fPnj0BGDduHL17987iFgk9Jk+ezPz58ylbtqzZmqdCZBcPHjygRYsWJCQk8Mcff6S7R1BPrGIc42rtCkrP05WE9sqVK7pOLsTGjRvZsmULpUqV4v33308xz05AQAD//vsvQJbPBMrtPvvsM+Li4qhevXqqCxubfjhZs/SMyJ4GDRrEX3/9xc2bNzl69OgLPZ5H5EzG295Vq1bVfas2q0keC5Gl3NzcOHjwIEuXLk12PBYkJr4cO3asNqHEmLhSZA3j+olTp041W5jc1KlTp7RUGqVLl9byeokXV+HChXnzzTcBWLRoURa3Rghz0dHRrF69GiDVWbovCot6zkwTupnOULE2Y3t8fDwGg4HY2Fiz2Vt6BguLF1ujRo0oWbIkd+/eZdmyZVy6dIk2bdpQtGhRoqKiuHHjBhs3btQWfO7evXuWpBMQ/9OzZ0927NhBQkIC/fr1o3379tSoUQN3d3eCg4M5efIku3fvJj4+Hjs7OyZOnPhCjdMRKfvoo4/YsWMH+/bt49KlS9KLLbKN5cuXExQURIMGDXSnBckOLBpzVqlSJRRFQVEUs5lKxsctVaxYMcaOHYuXl5dZcGaaBVrkvjFnV65cYejQoWkuc9S7d2+++uorCeazgXnz5jFt2rRUV/gwzmBs2bJlJrZMZLStW7fy6aef0rRpU/7444+sbo4QhIeH06ZNG6Kioti0aZPuiQAv1JgzvTnOrC2bHqqqEhYWRkhICNHR0SQkJODg4EC+fPnw9PS0KPVBWqKjo3ny5AkRERFaz4CzszMeHh4UKFBAegjSoVKlSvz999+sWbOGPXv2cO3aNcLCwsiXLx9FihShVq1adOvWTVv/UGS9d999l+bNm7Ny5UqOHz/OnTt3iI2NxdPTk+LFi/Paa6/RpUuXLM95JGyvQ4cObN++nZ07d3LgwAGLFnoXIiPNmjWLoKAgvv766xd6hqYpi3rOTPPFLFmyJNnHLVGgQAG6d+9O0aJFzaboG5eZ0CMhIYE7d+5oU6uTU6RIEV1Jb4ODg7l//36KQWbevHl56aWXzLKpp0du6zkTQgghsosXpufMNCCz5PGUGJ9wqVKlbJ5KIzAwUAvMnJ2dKVCgAA4ODkRFRRESEkJCQgIPHz7E0dHRLPu0pZ49e6aNsVMUBU9PT1xcXIiPjyckJISYmBgiIyO5e/cupUuXlh40IYQQQqSLvi6ebCI8PFxb9zBfvnyULl1aG9NmvN148+ZNDAYD9+/fJ3/+/Fatt5WQkKAFZnZ2dpQtW9ZszUVPT0/u3r3L06dPtbakJwAUQgghhMgRqTSMM/kgcXmh59fxy5Mnj7b0icFg0NbdstTTp0+Ji4sDoFChQmaBGSQGbCVKlNACvsePH1v9HIQQQgghwMbBWXx8fLKPnzhxgrFjxzJ27FhCQkLMlnqwxTmNC9Lmz58/xZl87u7uWvBk7GWzlGl5T0/PZMvY29trvWUxMTHExMRYdQ4hhBBCCLBRcHb06FEGDhyY7KydmTNnMmDAANatW8fZs2eJiooiMDCQW7dukZCQoPvckZGR2nZqi68riqLtj4qKSjGQTI4xmMyTJ0+qg/1N1+VLbWKCEEIIIURKdAdnq1ev5p133uHYsWMEBwcTGhqq7Tt79iwzZ85EVVWzf5AYvBgXy9UjOjpa205rkoGzs7O2bWnPVlxcnBZEmh6fVv2m7RJCCCHEiyGj035ZQldw9vjxYyZOnKj9f548eQgODtb+f8GCBUBir1XFihUZM2YM7u7u2piwsLAw3bc4jWPBABwdHVMta7o/NjbWovpNy6WV/NS0V820XUIIIYR4MRiTaz8/fj0z6Trz6tWrtR6iTp06cfToUS1nWUREBPv37wcSx2P98ccftGnTBnd3dzw9PbVUE9aO/3qe6e3JtPKLme5PLbO5KdNyac3wtLOz015MS+sXQgghRPYRGRmJvb19mh0+GUlXKo2jR48Ciett/ve//zULXo4cOUJsbCyKotCgQQMKFy4MJA7aj4iIwNXVlWfPnunuOTPtfkwrt5jpfkvHu5mWsyR3mbGMLcbTZYSgsCiiYpIGjtm1vUIIIURmUVWVp0+fkj9//izNV6orOLt58yaKotCiRYskvUq+vr7adpMmTbRtd3d3QkNDMRgMqKpq1cD85KQ3OEtP/ZZ0cdrZ2WnPLbtJUFVCn8WQkErT7O1zRHYVIYQQwiqqqnLv3j3i4uJwd3fP0rboCs6MtySNvWKmDh06pG2/+uqr2nbevHkpWbIk//zzD3Z2dri4uBAeHo69vX26gqeEhASt1ycqKirVAComJkYrGx8fb9Gg/fj4eO2YmJiYNI8xlk9ISEj3pADj+TJiUoGXuyNx8Snfcs3jZCeTGYQQQuQKqqpiMBiIjIzUcpqWLFnSJmtx66ErOHNyciI+Pp6oqCizx2/cuMG9e/e0ZY4qVKhgtj9//vz4+voSEhJCvXr10hxon5rg4GDt/Pb29qkGZ1FRUdqEhZiYGIvGu0VHRxMUFKQdk1YC24cPH5KQkKDrXvXDhw/TfawQQgghrGNvb0/+/Plxd3fP8sAMdAZnpUqV4urVq1y6dMnsceNEAIDGjRsnOS4mJoYtW7bw5MkTbt26xaJFi9I95mnz5s38/fffAMyfP19bCSA527dv55dffgFg9OjRVKtWLc36L126pM1Iffvtt1Nd7D0uLo4PP/wQVVWpVasWkyZNsuapaL777jsAFi5cmK7jhRBCCGEZOzs7HB0ds9Wa2LqCs/r163PlyhUOHTrEwYMHadq0KU+ePGHRokVamddee83smISEBCZNmsTjx49RFEV3z1mhQoW0dS8fP35M2bJlUyx7584drWzhwoUtWny9bNmy2jE3btxI9ZgnT55oudsaNWqU7sXdjcs/2XpxeCGEEEJkf7pGf3fv3h07OzsSEhIYNmwYXbt2pX379jx69AhFUShatCjNmzfXys+dO5c333yTVatWAYl5x7p166brCXh7e2vb169fT7Wscb+iKJQvX96i+gsUKEDBggUtqt/f31/bfv5WrhBCCCGEJXQFZ+XKlWP48OHagLorV67w7NkzVFXFzs6OcePGmeUWW7FiBVevXtVmMn7xxReUKlVK1xOoVauWNr7r+PHjKZYzGAycPHkSgEqVKuHm5mbxOXx8fAC4fPlyqssyHTt2TNuuV6+exfULIYQQQhjpzpvwwQcfMHnyZEqXLq0tz1S5cmXmzp2bZK3Nl19+GVVVKVSoED/99BN9+vTRe3rc3Nxo0KABAHv37tVuQT5v69at2mSAdu3aWXWOtm3bAokzMZcvX55smfDwcNavXw8k3gqtVKmSVecQQgghhACdY86MOnfuTOfOnQkLC8Pe3t5sAXBTb7/9Nr169aJZs2Y2zbw7cOBAfH19iYuL49NPP+XPP/80a4O/v782qD9fvnx0797dqvpbt25NyZIluXv3LrNmzaJ27drUrVtX2x8bG8vo0aO1dUUHDRqk/0kJIYQQIldS1OyYLTUdPvroI3bs2AFAyZIl6d27N8WKFePSpUssX75cW4ngu+++o1evXmbHHj9+nP79+wOJtyOXLFmSpP69e/fywQcfoKoqjo6OdO/endq1axMaGsqqVau4du0aALVr1+avv/5Kc6mn1LRq1QqAPXv2pLsOIYQQQmQuW31/26TnLDlxcXGEhYWhKAr58+fXNSPTEpMnTyYiIoJDhw5x9+5dpkyZYrZfURSGDx+eJDCzVMuWLfn222+ZOHEicXFxLFu2jGXLlpmVqVatGrNnz9YVmAkhhBAid7NpcHb06FHWrFnDqVOnePTokdk+Ly8vatasSYcOHbQxXLbk4uLCn3/+yebNm9mwYYM2eN/Dw4M6derQv39/6tSpo+scvXv3pm7duixatIgjR47w+PFjHB0dqVChAm+88QY9evTI0oVShRBCCPHis8ltzeDgYEaNGmU2W/H5ak2Tu9WvX58ff/wx2WWfhNzWFEIIIV5Etvr+1j1bMygoiO7du3Ps2DFttqaqqjg4OODp6Ym7uzv29vZm+44dO0aPHj202ZNCCCGEECKR7tuan376qZYV39XVlX79+tGuXTvKly+v9ZbFx8dz/fp1tm/fztKlSwkPD+fBgwd88cUX/PHHH3qbIIQQQgiRY+gKznx9fTl+/DiKolCqVCnmz59PyZIlk57EwYHKlStTuXJlevTowTvvvMOtW7c4dOgQhw8fplGjRnqaIYQQQgiRY+gKzrZs2QIkruY+a9asZAOz55UoUYKZM2fStWtXDAYDmzZtkuDsOY8ePcJgMGj3roUQQgiR/d2/f98mGRt0jTk7c+YMiqLQpEkTi9eqBChfvjxNmjRBVVXOnDmjpwk5krOzs9myV0IIIYTI/hwcHHB2dtZfj56Dnzx5AkDVqlWtPrZq1ars27ePhw8f6mlCjnTq1KmsboIQQgghsoju2Zp6ScJWIYQQQoj/0RWceXl5AXDx4kWrjzUeY6xDCCGEEELoDM5q1qyJqqr4+vpy48YNi48LCAjA19cXRVGoVauWniYIIYQQQuQouoKzLl26AGAwGBg+fLhF48cePnzIhx9+SHx8PADt27fX0wQhhBBCiBxF9/JN/fv358SJEyiKgpubG/3796dNmzaUK1dOS0Krqir+/v7s2LGDxYsX8+zZMwBq167N0qVL9T8LIYQQQogcQndw9vDhQ3r37s29e/fM1s90cHAgf/78ADx79kzrKTOermjRoqxatUrW1xRCCCGEMKF7tmaRIkVYtWoVPj4+ZutnxsXFERISQkhICHFxcdrjAD4+PqxcuVICMyGEEEKI5+juOTN1+PBhtmzZwrFjx7h//77ZvuLFi1OnTh26du1Kw4YNbXVKIYQQQogcxabBmSmDwUBYWBiqquLm5oajo2NGnEYIIYQQIkfJsOBMCCGEEEJYL93LNz158oQHDx7g4OBAyZIlcXV1tWW7hBBCCCFyJauCM1VVWbZsGStWrMDf31973M7Ojpo1azJkyBCaNWtm80YKIYQQQuQWFt/WjIiI4L333uPs2bPA/1JiaBX9fxqNN998kwkTJpil1RCWU1WVLVu2sHbtWi5fvkxkZCReXl74+PjQp08fqlevrvsc165dY8GCBRw/fpxHjx6RP39+vL296dKlC127drVovVNb1JETZfTrp6oqe/bsYePGjfj5+REcHIyjoyMlSpSgUaNG9O/fn+LFi6daR8uWLQkMDLTofIcOHcp1S6xl9Gtoq7+/XIMpy4jX8O7du7Rq1Spd7bl69WqSx+Q6tM53333HsmXLGD58OCNGjNBdX2BgIAsWLMDX15d79+7h4uJC6dKl6dChA7169SJPnjyZUkdKLA7Ohg0bxr59+1AUJUlgZlahojB48GBGjRqV7kblVtHR0YwcOZL9+/cnu9/e3p6PP/6YIUOGpPscq1ev5rvvviMuLi7Z/XXr1mXWrFl4eHhkaB05UUa/fmFhYXzyySccPnw4xTJOTk58//332uodzwsPD6du3bqpXsOmctuXQka/hrb6+8s1mLKMeg3TG5wpisKVK1fMHpPr0DpHjx7lnXfeISEhwSbB2YEDB/jkk0+IiIhIdn+FChX47bffKFmyZIbWkRqLgrOzZ8/Su3dvLTDr1KkTffv25eWXX8be3p4rV66wePFi/v77byAxAe2ePXsoUqRIuhqVW40aNYotW7YA4O3tTY8ePShUqBAXL15kxYoVREZGAvDjjz/SuXNnq+v39fXlvffeQ1VVXFxc6NWrF6+88gpBQUGsWbOGa9euAdCoUSP+/PNP7OySpsGzRR05VUa+fgkJCfTv35+TJ08Cialp3nrrLV5++WUiIiI4dOgQO3fuJCEhAUVRmD17Ni1btkxSz+nTp3n77bcBGDp0KNWqVUv1vE2bNsXJycmqtr7IMvoatMXfX67B1GXUaxgVFZXqDyNTs2fP5uLFiwB89NFHfPjhh2b75Tq03IULFxgwYADh4eEAuoOzq1ev0r17d2JiYrC3t6dbt27UrVuXiIgINm7cqN0drFixIitXrsTFxSVD6kiTaoEffvhBrVixolqpUiV1wYIFKZb78ccftXJz5syxpGrx/w4dOqRWqFBBrVChgtq/f381OjrabL+/v79ar149tUKFCmq9evXUZ8+eWVV/TEyM2rJlS7VChQpqzZo11QsXLiTZ/+GHH2pt2Lx5c4bUkVNl9Ou3Zs0as/ojIiKSlNmzZ49apUoVtUKFCmqTJk2StEFVVXXp0qVaPTdu3LDuSeZwGf0aqqr+v79cg6nLjNcwLZs2bdLa8N5776kJCQlJysh1aJn9+/erdevW1f5WFSpUUH/99Vdddfbq1UutUKGCWrlyZXX//v1m+xISEtTx48dr5/r9998zrI60WPSTyhgFli9fnoEDB6ZYbuTIkbi7uwNw7tw56yPFXGz+/PlAYq/jhAkTcHZ2Ntvv7e3N119/DUBoaCirV6+2qv6dO3dy9+5dAAYPHkzVqlXN9js5OTF58mTtNsicOXMypI6cKqNfv5UrVwLg7OzM1KlTyZs3b5IyLVu2pG/fvkDismoHDx5MUsY49sXZ2ZnSpUtb1YacLqNfQ9D/95drMHWZ8Rqm5uHDh4wfPx6AAgUKMGnSpGTHX8t1mLrY2Fh+/fVXhg4dytOnT21W79mzZzlz5gwAXbp0STKBUVEUxo4di7e3N5D4fnp+6IAt6rCERcHZ3bt3URQlzcz+Tk5OvPrqq6iqyvXr161uTG4VGhrKkSNHAGjSpAmlSpVKtlz79u0pWLAgANu3b7fqHNu2bQMS3zi9e/dOtky+fPl48803gcTBxjdv3rR5HTlRRr9+4eHh+Pn5AdCwYcNUx568/vrr2vb58+eT7Dd+KZQrVy7X3e5KTWZcg6D/7y/XYMoy6zVMzcSJE7Vg4osvvqBQoULJlpPrMGVHjhyhXbt2zJo1i4SEBPLmzcugQYNsUrfx+gG028rPs7Oz0/aFhoZy7Ngxm9dhCYveFc+ePQMSfwmkpUyZMgA2jXZzulOnTpGQkABAgwYNUixnZ2eHj48PkPjFGxYWZvE5jGOVKlasiKenZ4rlTM//fM+LLerIiTL69QsJCaFatWp4eXnx8ssvp1rW2HMNSa9B0x9NFSpUsOjcuUVmXIO2+PvLNZiyzHgNU3P06FF27NgBoC1VmBy5DlO3adMmrXf4lVdeYfXq1bRo0cImdRuvH3d3d6pUqZJiOdP3j6+vr83rsIRFec5iY2MBLFqCyZiMNqUZDCIp017GtC7WcuXKAYkX+LVr17QPmdQ8fPhQ+6IuX758qmWNXbGA2QwjW9SRU2X061eqVCmLb78EBARo28/P1AsMDNQG1Rpfw/DwcK5cuUJERASFCxemQoUKuTIFQ0a/hqD/7y/XYOoy4zVMzdSpU7XtMWPGpFhOrsO0eXp6Mnz4cHr16oW9vT1BQUG66zQYDNrno7e3d6o9lmXLlsXe3h6DwWB2/diiDktZFJypqoqiKBblLjM21mAwWN2Y3Mo0102JEiVSLVu0aFGz4yz5UDH+CrGk/iJFimBnZ0dCQgL37t2zaR05VUa/ftZYu3attl2jRg2zfaa5luzs7Bg+fDh79+41u1Y9PDzo27cv7733nq4cPS+azHgN9f795RpMXVZeh7t37+bChQsAtG7dOtU8anIdpq5Pnz6MGzfO5s/7yZMnWkdTWu8Pe3t7vLy8ePDggdn1Y4s6LCU3u7OB4OBgbTutW8emvSGhoaEW1R8SEmJx/Y6Ojtpgc9P6bVFHTpXRr5+lfH192bdvHwAFCxakUaNGZvtNvxR++OEHdu3aleRHVGhoKDNnzqRfv35mzyuny4zXUO/fX67B1GXldbhw4UJte/DgwamWleswddWqVcuQgNSa9wf8b4iI6fvDFnVYSoKzbCA6Olrbfn520fNMc92YHpeaqKgoi+s3LWNavy3qyKky+vWzxP379/niiy+0/x82bFiSDzjTLwVHR0eGDBnCtm3b+Oeff/D19WXChAnaZAM/Pz8++eQTbQxPTpcZr6Hev79cg6nLquvwypUr2jikWrVqUatWrVTLy3WYNV6070EJzrKB+Ph4bTutRIOm+02PS43przJLEhkay5jWb4s6cqqMfv3SEhwczDvvvKONy2jUqJGWUsPUw4cPgcQPjIULFzJq1Ci8vb1xcnKicOHCdO/enbVr12q3fI4dO2Y2Mykny4zXUO/fX67B1GXVdbh48WJtO61eM5DrMKu8aN+DEpxlA6Y9HGnlQzHe7wbL3hxgHuFbkm8luQkgtqgjp8ro1y81jx8/pn///ty4cQOA0qVLM3Xq1GTHh65YsYLTp0+zY8cO6tatm2x9RYoU4T//+Y/2/6tWrdLdxhdBZryGev/+cg2mLiuuw4iICG01guLFiye7Ksfz5DrMGi/a96BFEwKMwsLC0hzYZjot+f79+xatHZbWQs05nWlC0ZiYmFQ/LEw/VCzpVk2u/rQYy5h+2Nmijpwqo1+/lNy+fZvBgwdz69YtAIoVK8aCBQtSTbHg6uqqzahOScuWLXF1dSU8PJyzZ8+SkJCQ43MxZdZrqOfvL9dg6rLiOvT19dX+zm3atLH4OpHrMPPly5dP207v9WOLOixlVXA2Z84cizNOq6pq0a8IRVG4dOmSNc3Icdzc3LTt0NBQ8ufPn2JZ04GFqX0Jp1R/Wjl94uLitLXnTOu3RR05VUa/fsnx8/Nj6NCh2q3MEiVKsGjRojRnEFnC3t6e0qVLc/HiRWJjYwkNDc3xr2NWvIYpSenvL9dg6rLiNdy7d6+23bZt23TXk5zceB1mJNP3gyW57YxlTP/mtqjDUlaH4aqqpvoP0NJupFXOdDs3MybuhcTextQ8ePBA27a0x9Ga+h8+fKgNPjWt3xZ15FQZ/fo9z9fXlwEDBmiBWbly5Vi+fHmKGdHTI7fcCjPK7NcwLcn9/eUaTF1mv4YGg4EDBw4Aibch05oIkB657TrMSIULF9Z6vtK6A2gwGHj8+DFg/v6wRR2Wsjg4szSQej4AS62cSGSaMDKtZa+M+xVFSTMRpVGBAgW05UrSqt/f31/bNk3kaIs6cqqMfv1M7d27l2HDhmm9IjVq1OCvv/6iSJEiqR53584dFi1axE8//cT+/fvTPI9x0LKjo6PZqgM5VUa/hrb4+8s1mLrMvA4BLly4oPXAtWzZ0qI8oHIdZi3je8Q0WXdybty4oQ3+f/76sUUdlrDotmZuyTCdVWrVqoWjoyNxcXEcP36cPn36JFvOYDBoU7YrVapk1o2fFh8fH7Zv387ly5d59uxZil3+pmuA1atXz+Z15ESZ8foBnDhxgpEjR2oDURs1asTMmTOTXQT9eY8ePWLSpElA4hdJ8+bNUyx78+ZNreehWrVquSJTeUa/hrb6+8s1mLLMug6Nzp49q20/n/A5JXIdZi0fHx/8/PwICgri+vXrKQbmptfP8wmKbVGHJWR0YTbg5uamrcO1d+/eFLtLt27dqiXBa9eunVXnMI6HiI+PZ/ny5cmWCQ8PZ/369UDi0hOVKlWyeR05UWa8fkFBQYwcOVIbyNyiRQt+//13iwIzSFyjzjgA2dfXN9Uu+d9++03b7ty5s1XtfFFl9Gtoq7+/XIMpy4zr0NQ///yjbVetWtWiY+Q6zFqm4wKXLFmSbBmDwcCyZcuAxDFmjRs3tnkdlpDgLJsYOHAgkDiQ99NPP9XWXjPy9/dn4sSJQOKMke7du1tVf+vWrSlZsiQAs2bN4tSpU2b7Y2NjGT16tNZNP2jQoAypI6fK6Nfv+++/175Qqlevzi+//GJVCgBnZ2d69OihtXHUqFFJ2ggwb948Nm7cCCSO4Ulp8eacKCNfQ1v9/eUaTF1GX4fP1wWJtxxNb6mmRq7DrFWjRg1tbODq1av5+++/zfarqsqECRO01ERvv/12kpmWtqjDEooqg7+yjY8++ogdO3YAULJkSXr37k2xYsW4dOkSy5cv1xaT/+677+jVq5fZscePH6d///5A4m2M5CL6vXv38sEHH6CqKo6OjnTv3p3atWsTGhrKqlWruHbtGgC1a9fmr7/+SrYb3RZ15FQZ9foFBATQoUMHbZzm6NGjKV26dJrt8fDwMMuj9OzZM7p3787NmzeBxPUFe/bsSZkyZQgJCWHbtm3al32+fPlYtGgR1apVS++f44WUkdegrf7+cg2mLqM/R41q1apFZGQkXl5eHDp0yOL2yXVoPdPXZfjw4YwYMSLZcnfv3qVVq1ZA4gx209m0RhcvXqRnz57ExcWhKApvvPEGjRs3Jjo6mg0bNnDmzBkgMShet26dWfoMW9aRFgnOspGo/2vvzONqzv4//rqllFZLo9AQIvtkSSgqkdDYRmWQbSyRbQphbGHshmQZZczYqxExKUVZkiVkWoZkSRt10W2ve6v7+6PfPd/P7d7u0m0bc56PR4/u8vl87vks73Pe533eS0kJ3NzcahR0FosFNzc3uLm5iXwna6dy/vx5bN++vcYEen369IGvr6/EumF1cYwvkfq6f/v27cPx48flbo+45+D9+/dwc3MjRZrFYWBggH379mHAgAFy/+a/nfqWwbq6/lQGa6Yh+tHCwkJyfzp37ixiPZEGlUP5qEvlDKgqVL9q1SoSWFWdjh074sSJExIj4OviGJKQK88ZpX5RV1eHn58frl69isuXLxOnX11dXQwYMAAuLi4KC+q0adMwcOBA/PHHH4iJiQGbzYaKigq6desGBwcHODo6Sg3frotjfInU1/0TWELqAgMDA/j7++Ovv/5CSEgIkpKSkJ+fD01NTXTq1AmjR4+Gk5NTrWZ6XwL1LYN1df2pDNZMQ/SjAusbgFoFFFA5bFxsbW0REhKCkydP4s6dO/jw4QNYLBaMjIxgZ2eHmTNnSr32dXEMSVDLGYVCoVAoFEoTggYEUCgUCoVCoTQhqHJGoVAoFAqF0oSgyhmFQqFQKBRKE4IqZxQKhUKhUChNCKqcUSgUCoVCoTQhqHJGoVAoFAqF0oSo0zxnfD4fT548wZMnT5CVlYW8vDx8++23sLGxAQAEBQWhX79+Mpe6oFAoFAqFQvmvUWfK2aVLl3D48GFkZmYKfd6vXz/y2tvbGzk5ORgzZgw2btwIXV3duvp5CoVCoVAolC8ChZUzPp+PtWvXkiKtzJy2LBaLvObxeMjJyQGfz0doaCgSEhJw5swZtG3bVtEmUCgUCoVCoXwxKOxztm/fPly+fJkoZRYWFmJrlnG5XFhaWpLt0tPTsXLlSkV/nkKhUCgUCuWLQiHl7M2bNzh58iRYLBb09fXh7+8PPz8/scqZhoYGfv31V5w+fRqtW7cGAMTFxeHGjRuKNIFCaTLUVISa0jSg9+ffD72HlP8KCi1rXrhwARUVFVBSUsKhQ4fQp08fqfsMGjQIhw4dwrRp0wAAV69eha2trSLNEIJZvb42SKpkryg2NjbEJy85OblefqMhOXToEHx8fKRu16xZM6ipqaFNmzbo3r07Bg8ejAkTJkBTU7MBWqkYzOdp0qRJ2Llzp8g2paWl8PHxgYaGBlxdXUW+9/T0xKVLlwAAp06dwuDBg+u30fWMPDLGYrGgqqoKbW1ttG/fHn369MGkSZPQq1evem7l/5B2f5oC27Ztw+nTp+Ho6IitW7eSz2W91kpKSlBTU4OOjg6MjIwwaNAgODg4wNDQsD6bXWd0794dgOT+NygoCLdu3YK3t7fY79auXQsAcHNzw9KlS+uvsQ0As2/dsWMHJk+e/K/+HXmo73sp67glC/V5zRSynN2/fx8sFgtDhgyRSTETYGpqimHDhoHP5yMhIUGRJlD+BZSXl6OwsBCpqam4fv06vLy8MHLkSISHhzd20xQmOTkZ48aNg6+vL8rLyxu7OU0OPp+PsrIysNlsPHv2DKdPn8aUKVOwceNGVFZW1vvv/xvuz927d3HmzBloaWnV2tWjsrISxcXFeP/+PWJiYnDw4EHY29vjwIEDddvYRqCoqAgzZ87E2rVrkZub29jNoVAaBIUsZx8+fAAA9O3bV+59e/XqhXv37uHTp0+KNEEiOjo6WLhwoVz7aGlp1VNrvmx69+6NsWPHiv2uoqICxcXFSE9PR3h4OLhcLjgcDpYvX46jR4/CysqqYRtbhyQlJSEjI6Oxm9FoSJOxyspKFBYW4vXr14iOjkZJSQn4fD78/f2hpqaGdevW1Wv7mvr9KS4uxk8//QQ+n4+FCxeiVatWNW4r6Vrz+XyUlpYiJycHN2/exMePH8Hj8XD06FGUl5fDw8Ojvk6h3snNzcWjR48auxmUL4Rhw4ahRYsWNX5/7do1JCYmkm2HDRtW47byGKXkRSHljMvlAgDU1NTk3ldVVRVAlTm+vtDU1MS8efPq7fiU/2FsbCzTtf7w4QPmzp2L169fo7KyEps2bUJ4eDiaN2/eAK2Un8GDByu8BL1z506xy6FfAvLIGIfDgaenJ6KiogBULfE6Ojqia9eu9dnEJo2Pjw8+fPiAtm3bSl2+lPVar127FuvWrcO1a9cAAL/99hvGjx8PExOTOmlzfaCojE2ePLlJLMlRmj79+/dH//79a/w+JSWFKGempqaNpkMopBkJHPtTU1Pl3lcgjJJmipQvD319fRw+fJgo5R8+fKg3Hz9K00JXVxfe3t74+uuvAVRZey5cuNDIrWo8MjIycOrUKQDAggUL6myCoq6ujt27dxN/s4qKCvj7+9fJsSkUSsOgkHLWt29f8Pl8REVFobi4WOb90tPTcevWLbBYrHo1C1KaJkZGRkKm4vv37zdiaygNiaqqKr799lvy/ksIjKktR44cAY/HQ4sWLTBx4sQ6PbaKigocHR3J+5iYmDo9PoVCqV8UWta0t7dHWFgY8vLysGXLFuzatUvqPhwOB8uWLQOPxwOLxcLo0aMVaUKD8ubNG1y5cgWxsbFIS0tDXl4eWCwWtLW1YWxsDEtLS0ydOlWhKMSUlBQEBgbi4cOHSE9PB5fLha6uLjp16oQhQ4Zg6tSp+Oqrr6Qe5/3797hw4QKio6ORkZGBoqIitGzZEj179sSoUaMwYcIEqKio1LqditKlSxfcvXsXwP98F8Xx+fNn+Pv74+7du3jz5g0KCwuhra2Njh07wtLSEs7OzlKtr1wuF1evXkVERAQSExPB4XDQvHlztGnTBqamphg9ejQpMVadmqI1xUX8+Pj4kM+YUUbiojUrKythbW1Nzv3y5cvo0aOHxPMICAjAhg0bAABTpkzBzz//LLJNSUkJAgMDERkZiVevXoHD4UBTUxOdOnXCiBEjMG3atEavzNGmTRvyOicnR+K2CQkJuHbtGh4/foz379+Dw+FARUUFOjo6MDExgbW1NSZNmkTcJATIc3+Y5Obmwt/fH7dv38a7d++Qn58PHR0ddO3aFTY2NnB0dIS6unptT52Qk5ODK1euAKjqR+sjcplZJi87O7vG7bhcLq5cuYIbN24gKSkJubm5UFdXh76+PszNzeHo6AhjY2OpvxcdHY0rV64gLi4O2dnZYLFYaNWqFUxMTDBixAhMmjSpRuuguGhNcZGqjx49ItuamZnh9OnTAGqO8HN3d8dff/0FoGq5d/bs2RLPITU1FXZ2dgCAr7/+GhERESLb8Pl8XL9+HaGhoYiPj8enT5+gqqoKAwMDmJubw9nZucFKFJaUlCA4OBgxMTH4559/wOFwUFpaCk1NTejr62PgwIGYOnUquWbS4PP5CA4OxsWLF5GcnAwul4u2bdti2LBhcHZ2Rrdu3WQ6zuvXrxEQEICYmBh8+PABpaWlaN26Nfr27Qt7e3uMGTNGKEm9PGRkZGDkyJHkfWNHwDPb4+7ujnnz5uHXX3+Fv78/OBwO9PX1MXjwYHh4eCA/P59sy3x+xaGQcmZnZ4fevXsjMTERV65cAZvNxvz589GzZ0+RbXNychAREYHjx48jJycHLBYLXbp0gb29vSJNaBB4PB62bt2KwMBAsRFmAkfce/fu4fjx4zhy5AhMTU3l/p0jR47g0KFDIr/BZrPBZrMRGxuL48ePY/369UKz4ur89ttvOHDgAMrKyoQ+z8nJQU5ODm7dugVfX194e3vLLLR1DdPXUFlZWew2/v7+2Llzp4hV9tOnT/j06ROePn0KPz8/rFu3Dt99953YY6Snp2PBggV48+aN0Oc8Ho9EkF66dAnffPMNjh492mDL7EpKSnBwcICvry8AICQkRKpydvXqVfJanKUlJiYGq1evBpvNFvo8NzcXubm5iIuLw4kTJ7BlyxaMGzdO8ZOoJe/evSOvmYoak8LCQnh6eoodHHk8HolMjIqKgq+vL3x9fWFkZKRQu65evQovLy/k5+cLff7x40d8/PgRDx48gK+vL/bu3Qtzc3OFfiswMJDk7Bo/frxCx6oJplzVJGOPHz/G6tWrRcru8Xg85Ofn4+XLlzhz5gy+//57rF27Fs2aiQ4ZpaWlcHd3F5uzMisrC1lZWYiMjMThw4dx+PDhWgWQ1ZYJEyYQ5ezatWtSlTPBtoJ9q5OZmYnly5eLZBkoKytDQUEBXr58ibNnz2LOnDlwd3evV5/qGzduYMOGDfj8+bPIdwKZf/78Oc6cOYOFCxdKjQQuLS3F/PnzyaRZQGpqKlJTU3HhwgXMnz9f4nEqKyuxZ88e/PHHH6ioqBD67v3793j//j2uX7+OPn36wNvbG+3atZPjjP8d7NixQ0jpSk1NRUFBATZt2iTSt0hC4fJNBw8ehJOTEz5+/Ij79++LLFEdPXoUR44cQWFhIfmMz+dDU1MTBw4cqLX23JCsWbMGISEhAKpydllYWMDExAQaGhrIz8/H33//jdjYWPD5fHz+/BlLly5FWFiYXLPh4OBgHDx4EEDVwG1hYYGePXuiRYsWyM7Oxq1bt5CZmYnS0lJs2LABHTp0wNChQ0WOs2fPHvj5+ZH3pqamGDhwIDQ1NZGVlYWoqCjk5OQgNTUV06ZNw7lz5xrFUZi5nCXwQWJy/Phx7Nu3j7zv0KEDrK2t0aZNG7DZbERFRSEzMxNFRUVYv349Pn/+jAULFggdg8vlYtGiRUQxMzAwgJWVFQwMDFBcXIyXL1/i1q1bqKysxLNnz+Dm5oZz587J1H5BxE9iYiJxvGZG9siinE+cOJEoZ9euXZMYUZednY3Hjx8DqLIuDBo0SOj7mzdvYvny5WTA79ixI4YPHw49PT1wOBxER0fj5cuXKCgogLu7O4qLizF16lSZzrUuyc/PJ6XeAIhVciorK/HDDz8gLi4OQFXA0YgRI9C1a1eoqamR6D2B0256ejqWL1+Oy5cvk8FQ3vtz9uxZeHl5kffdu3fH0KFD0bJlS7DZbNy+fRtpaWlgs9n44YcfcPToUVhaWtb6Oly+fBkA0KJFCwwcOLDWx5HEixcvyGtxMvbo0SPMmzePBHZpa2vDxsYGHTt2RHFxMR48eICEhARUVlbizJkzSE9Px7Fjx0QUjq1btxLFTENDA9bW1jAyMgKLxUJGRgauX7+OoqIi5OTkYN68eQgPD0fLli2ltv/rr7/G6tWrkZ+fj2PHjgEADA0NSY5MAwMDqccYNmwY9PT0wGaz8ffffyMjIwMdOnSocXumcsZcfgeqnrNp06aRyY+uri6sra3RsWNHlJaWIj4+Hvfv30dFRQX8/PzAZrOxe/duqW2sDbdv38ayZcuIAmRsbIwhQ4ZAT08PXC4Xb9++xa1bt1BYWAg+n49jx46hR48eGDNmTI3HPHDgAFkNGjZsGExNTcHlcnHnzh08f/4cFRUVOHbsGPh8Pn788Uexx3B3dyfyJkiz1a9fP6iqqiItLQ1RUVHgcDhISEiAo6MjLl68+EWVcHz06JGIcgsAo0aNqnGCVBMKK2ft27fH+fPnsXLlStJZAv+rq1lQUCBUbxOoErqDBw/+K6K0Hjx4QBQzbW1tnDp1SqyF4+HDh1i4cCFKSkrAZrNx48YNufxIjhw5AqBKMROXXkIQgSVYCjl27JiIcnbz5k2imOnq6mL//v0iYcBlZWXYs2cPTp8+jaKiIixbtgx//fWXyLJQfZKSkoIHDx6Q98OHDxf6/sGDB0KK2dKlS7Fo0SKhWbunpycOHTqEX3/9FQDwyy+/oF+/fkLm7evXr+PVq1cAqkzIfn5+Issq8fHxmDVrFoqLi/HkyRPExsaKKD7iEET8BAUFkc5I3sierl27olevXkhKSkJmZiaePXuGb775Ruy2165dIxZVBwcHoUlNVlYWPD09wePxoKSkhDVr1sDFxUVoEF2zZg2CgoKwceNG8Hg8eHl5oX///g22/AIAaWlpWL16NZnpa2lpYfr06SLbXbp0iShm7du3x6lTp8QOqCEhIfDw8EBlZSWSk5Px5MkTcu/kuT+JiYnYsWMHAKB58+b4+eefRaxZa9euxYkTJ7B//37weDx4eHggNDS0VpbWlJQUpKWlAQAGDBhQL7JXWlqKwMBA8r66jOXl5cHNzY0oZjY2NtixY4fIkndoaCg8PT1RWlqK27dv4+jRo1iyZAn5/v379wgKCgIA6Onp4fz58yKJb1etWgUXFxekpKQgPz8fZ8+eFVtFpjoGBgaYN28eMjIyiHIm+ExWlJWV4eDggN9++w1AlRxVn8QJSEpKwtu3bwFUPStMhbaiogIrV64kipmDgwM2b94sMgGPj4+Hm5sbsrOzERwcjMGDB2PKlCkyt1cWKioq4OXlRRSzpUuXir2eubm5WLJkCZ48eQIAOHfunETlLC8vD5qamvDx8cGQIUPI5ytXrsTJkyeJ25Kvry9sbW1FLKCnTp0istahQwd4e3uLJJsuKCjATz/9hLCwMLDZbLi7u+PMmTNynX+HDh2arK+qQDFbsGABXFxcoKqqipiYGLGTI2nUic3V0NAQAQEBRBlQV1cHn88nf0CVxalfv37YtGkTrl69KnUJpy4oLCzEiRMnZP5jWvcECHyFgCohqKndgwcPxqRJk8h7pqIqDQ6HQyJeu3XrJjbvl4qKCry8vIi/y8uXL0nHClRZI3/55Rfy3tvbW2x+lubNm+Onn34iv/Hu3TshS0Z9k5iYCFdXV9Kx9OrVS6SdzMSZc+bMgZubm8hyioqKCn788UfMmDEDQJW1pXrCzb///pu8njVrllh/l759+2Lu3LkAqhTj+Pj4Wp9bbWAq8MxZe3UkzeiPHz9OzOVLlizB7NmzxS6nTJ48mSxJcLlcMuApgjQZ8/HxwbZt2/D9999j7NixROlSVVXFvn37xCo3TJlbv359jZaOcePGCVmvkpKSanUOBw8eJBbHLVu2iF1mVFZWxoIFC/D9998DqJJZQaSlvDx8+JC8rg+rNZvNxpIlS0h+N3V1dSInAk6cOIG8vDwAVTJw6NAhsb6I9vb2QtYfPz8/sh8AYlkTbCuuIkGrVq2IryRQ+/tUW5jLk7LKWPUlzevXr5OlTHNzc+zevVvsykjfvn3h4+NDJk+HDx8WWd5TlNjYWHJve/XqVaOi27JlS3h6epL3slz3/fv3CylmQJWhZe7cucT/r7Kyklj8BZSWlhIDg6qqKvz8/MRWAdHS0sL+/fuJO01sbOwXFxDm5OQEd3d36OnpQUdHB/b29rWqiKKw5UyAkpISxo4di7Fjx6KiogJZWVngcDioqKiAtrY22rVrV6t8aIqQl5cnl1nZzs5ORODGjBkDIyMjZGRkiAyK1WH6bxUVFcn8u0zFIzMzE2w2G3p6eiLbqaur4+LFi2jZsqXIoBYXF4eUlBQAVbM+aQ6Srq6uuHXrFgDgypUrCi9xpaSk4MSJE2K/Kysrw6dPnxAfHy+k/LRo0ULk/mRkZJABvEWLFlJn2CtXrkRQUBCKi4vx9OlTvHnzBp07dwYg7GcTFxdXY5kwFxcXjB8/Hu3bt29QCyJQ5W+0a9culJeXIywsDOvWrRNRrFJTU4my37t3byFrF5fLJcq1qqoq5syZI/H3XFxc4OPjg+LiYoSFhWH79u0KnbO8MgZUDWAbNmyo0ffI2dkZ5ubmyMnJkZqguHv37rh9+zYAiJ1cSSM7O5vMdvX19aVau11dXXH27FkAVXKzYsUKuX+TKQOyOlgD/1OExVFeXo68vDwkJyfj4cOHQjUoN23aJNKfMBURDw8Psb5kAuzs7DBgwAA8efIExcXFCA0NhbOzMwBhGUtISEB5ebnYYw0aNAjBwcEwNDSEhoaGbCdcR5iYmKB79+5ITk5GcnIyXr9+LWIx5vP5xOqjoqIi4gv9559/ktcLFiyQ6EvWt29fDBs2DNHR0cjMzMSTJ09gZmZWZ+fTtm1brF+/HhkZGVKPy1T+pY1J5ubmGDFiRI3fu7q64vz58+DxeIiMjERhYSEZLyMiIkj1htGjR0v0/1RWVsbChQvJ0uiVK1dEFMJ/M4IJnKLUmXLGRFlZGYaGhv+aum6SsLa2hrW1tdTtiouLhTKRy1MqRlNTEz169MDz589RUFCAKVOmYNasWbCyshLpRGpahoqNjSWvZdHSe/bsCVVVVXC5XMTHx9fYqcpKYmKiXNbCTp064cCBAyJL28zlTgsLC6l+e5qamrCwsCCloB4/fkyUs0GDBuH3338HUDXjz8zMxIQJEzB48GChDNE6OjrQ0dGRue11SatWrWBpaYmoqCiw2Ww8evRIxA9L0ow+KSmJBEx06tRJ6sCnoqKC3r1749GjR+ByuUhISMCAAQPq6GxqRlCyzcrKSmr6HFkd5PPy8oSiEGtTnkngKwoAPXr0kOoDq6enh/bt2yMzMxOZmZnIzs6W22dGsHQGgDyrsiCvIqylpYXNmzeLXM/09HQSAKCrqytTpJu9vT1ZHouNjSXK2TfffAMVFRXweDzExcXB2dkZ06dPh6WlpVCwh5KSUqMmwZ04cSJZlgsJCcGyZcuEvo+NjSWR01ZWVkJWxPLycjJhBCA24K06pqamiI6OBoA6V86MjIxkCn7h8/lkwi54X1FRUaPvk7TMCa1atULfvn3x5MkTck0Elmt5xx+m+4bgufoS0NDQkGvCJYl6Uc6aCvVRxLyoqAhv375FWloa0tPT8fbtW7x48QIpKSlCg0N1PztpeHh4YMGCBaioqEB2djZ2796N3bt3o127drCwsIClpSWGDh1ao7Ii8K0CgDNnzsi1jl9aWorPnz/LlKKjNqipqZHQ7l69esHKygpWVlZiZ59MBVfWztzExIQoZ+np6eRzGxsbmJmZkdIvoaGhCA0NhYqKCvr37w8LCwtYWVnVmTDVlgkTJpCs+SEhITUqZ82aNROJsmTe95cvX8odffv+/fvaNJkgTsZKS0vx6tUr/P777yTCNCUlBQ4ODrXKa5iXl4fU1FSkpaUhLS0Nb968wYsXL/D69WshOZNX5gDh6xcVFSX39cvKypJbOWNe87qaFKioqEBDQwOtW7dGjx49MHjwYIwbN06ssl4bGWO6czD3b926NebPn0+WtBISEuDp6QkWiwUTExNYWFhg+PDh6N+/v0KTP0VxcHDA3r17UVFRIVY5kzQBysrKEooYlzdSV1EZk0ZlZSXevXtHxqTU1FS8evUKSUlJItGBkmRElmeha9euRJl69+4dUc6YcrRr1y6Z0moJyMrKknnbpk67du3qLEJXIWmpq4va1MNpKysrERwcjHPnziExMbHGgs3Kysq19i+wsLDAkSNHsGnTJqG8X1lZWQgICEBAQABUVVVhZWWFOXPmiJSfYPqB1Ia8vDyFlDNmDjBF4HA45LWs+biY2zGvg5KSEo4cOYJt27YhODiYdEw8Hg8PHz7Ew4cPsW/fPnTs2BGTJ0+Gi4uLxJpr9cXIkSOhra2N/Px8hIeHY+PGjSQHHdNJ2cLCglTlEFAX972uUVNTQ+/evbF371707t0bO3bsQGFhIby8vMBms2VaCuRyubhw4QICAwPx8uXLGrdTROaAxrl+BQUF5LU8tXzrarLJLB4uq3LIlDGmjALAsmXLoK6ujsOHD6O0tBRAlRLw/PlzPH/+HL6+vtDV1cXYsWMxf/78Runv9fT0MHToUNy9exepqalISkoiFh4ej4fr168DqDrP6kt71c9XXhTdvyZevHgBX19fREZG1pgEXh75kKW/ZT4vzGdfETkSpMZpjL63rqnL2twKKWc2NjYKp8JgsVj4559/FDpGfVJUVAQ3NzeRDNssFgv6+vro2rUrevfuDTMzM2RkZAg5vsqLlZUVIiIicPv2bYSHhyM6Oloohw2Xy0V4eDjCw8OxZMkSodkfUwDHjBkjdy6h6oN+Y1Eb60dNyjJQJSy7du3CkiVLEBISgqioKCEnZqBqBvjLL78gICCgxsjA+kRVVRX29vYkaWFMTAwZIKTlXWLe9169esmdv6ym6NC6Yvbs2UhPTyeW3KNHj6Jdu3YS8/QJ0lUwU0EAVcp2+/btYWxsjD59+mDw4MGIjo4mVpvawLx+Q4YMkTs9Rm1yqzEDef4tAxLzOlXv81ksFhYsWABHR0eEhYXh5s2bePToEVHUgCoF5dy5cwgKCoK3t7dE36b6YuLEicS/MCQkhChn9+7dIwrUmDFjRHwwmeeupaUFV1dXuX63Y8eOCrRaPGfPnsW2bdtE+j5NTU106dIFJiYm6N+/P1lxqSuY/TPzOjFXjZycnOQ+58a0qtYldXkeCh+pNoPpv4nt27cTxUxDQwMuLi6wtLQkec6YCByFFUFVVRWjRo3CqFGjwOfzkZycjAcPHuDu3bt48OABEYLDhw/DzMyMmNi1tbXJMUxNTaUmW2yqMGdmzBm+JJjbMa8Dk6+//hqurq5wdXVFfn4+Hj16hJiYGERFRRELcGZmJjw9PeUO7a4Lvv32W1L/MCQkBCNGjACfz0doaCiAqkGBmRVbAPN827Zt22hFeiWxZs0axMbGkvD37du3Y8CAATX6T3p4eBDFrHXr1pg9ezaGDh0KY2NjkYjbmzdvKtQ25vXr2rVrg1w/NTU1YungcrkNHoTCPGdZrTqyyJiuri6cnZ3h7OwMLpeLZ8+e4f79+7h16xaZgJeWluLHH39EVFRUjcepL2xtbaGhoYGioiKEhoZi1apVYLFYQhMgcQEhzHby+fxGl7HY2Fhs3bqVjL22traYOHEi+vbtK7LEXlJSIvNxmRbdmmBayJh9NfO1paUlRo0aJfPv/leRpjsppJzJkg8KqIrWY7PZZO2dxWLB1ta23pIv1hXZ2dkkWSSLxcLJkyfRr1+/GrdndmB1obQK/DZMTEwwe/ZsZGdnY/78+WSQCw4OJsoZM/iC6QQqic+fPze5wvPMGZesuWyeP39OXssShKKtrQ1bW1vY2tpiw4YNOHv2LLZu3QqgquPLyspq8KWXgQMHwtDQEOnp6YiKikJ5eTmePXtGZMbOzk5sKhDm+TL9PiSRl5cHLS2tes1ezkRVVRU7d+7E1KlTUV5ejtLSUqxZswYBAQEibfj7779JUEiLFi3g7+8v8Z7KqsDXRG2uX25uLnR1dWu9aqChoUGUs6Kionop3SSJ2sgY04opi4ypqqrCzMwMZmZmWL58OR4/foxFixahoKAAhYWFiIyMrPN6otJQU1ODnZ0dgoKCkJWVhcTERBgbGxMFv2PHjmKTR7dr1w7NmjVDeXk5CgsLZQoCKSwshKqqar0o3r/++isZX77//nts2rSpxm2rVw+QNC69e/dOqiWd+bwwS3oZGhqS1EUpKSlSlbOKigoUFhY2WiBWY8EMxmBa0MWhUO98+vRpmf4CAgIQFRWF6OhozJw5EywWC7dv34ahoSFmzZqlSBPqlYSEBGLS7tGjh0TFDBDOXySPcnbz5k3MmzcP1tbWJKmqONq2bYtFixaR98xINaaie/v2baFQenEkJCRgyJAhMDU1xZQpU2oV6VYfMM8jOjpaanqEgoICoTw5gs6loqICa9asweTJkzFs2LAaBYHFYmHGjBlCVhxJtT7F7V9XCJYt8/PzERsbi7CwMJHvqmNqakpM6WlpaVIH27KyMtjZ2aFv374YPXq0yNJhfdGzZ0+hNB8JCQliqzE8e/aMvB42bJhERYDP55NgD0D88ra0+8N83p48eSK2FA6TnJwcWFpaol+/frC3t5e6vTiYy+aSal7WF4aGhkS54HA4QhHSNcF8FpkD+PHjxzF9+nSYm5vj6dOnNe4/cOBAoajRxpIxpkIYGRmJO3fuEEW5plRJ6urqQhGIguAjSbi7u6Nfv34YMWKEUBqOuoCZv9HJyUnitswxCZA8LknLN/b+/XuS6636NWHKkbgyXtWJiIiAmZkZBg4ciIULF0rd/kuBmU5MWt/RMFPn/6dNmzZYv349Vq5cCS6Xi7Vr10otfNyYMAd0aeb/GzduCA0U8ig7fD4f0dHRyMrKQkhIiMR9mZYC5uxtyJAh0NfXB1Dls1NTPiTB7wky8BcXF6NDhw5NZs2fOXstLi4WKV5dnYMHDxLTvbGxMYm2U1ZWxosXL5CUlISPHz9KXP6qrKwUur/yRN8xLT+KJptkKmA3b94knZy4ck0CNDQ0hGapu3fvltgB+/n5ITc3lzjhNmSVDjc3N7Rv3568/+WXX0Tknylz0qxip06dkpq+Rtr96dKlC/HP5HK5QomcxXHgwAHweDyUlZVBQ0OjVpZnpp8aM7q4IWEmzN63b5/EPiciIoKkkmjWrBkpDA5U9TWPHz9Gbm6uUO1XcTAHI3lkjGltUFTGzMzMyDMYGRkpVLtVUh5LplJ3/PhxiQNrbGwsKQuXnZ0tdVIvL7LKSEFBgUj/KWnSHhISInHVZf/+/WQC9O233wpZBceMGUMSpCclJUl8FrhcLg4dOkTa2JBVShobXV1d4meakZEhVGeYSUVFRcMqZwJ++OEHdOjQgZTyaKowQ4uzsrLwxx9/iGxTWVmJwMBAuLu7C30uz1q/oAYiUGU29vLyEnKmFfD27VshYWPmpVFRUcHixYvJ+4MHD+LkyZMi1oSSkhJs2bKFzJKaNWsmt4NrfbN8+XIyWz558iR8fHxEBo/y8nJ4e3uTArMsFgvr1q0T2oZZNmXz5s1is//z+Xzs2rULnz59AlDlVM9UIKTBXJKqXjxaXr7++muimF68eJEsaVYv11QdV1dX0lFGR0dj1apVIhZHPp+PwMBAIed5V1fXBlXK1dTUsHHjRvK+sLAQP//8s9A2TJl78uSJ2MLnguoG1cP1xcmcLPdn2bJl5PoGBARg165dIpbW8vJyHDlyBBcvXiSfLV26VOzxpMEcrBsrGMrFxYXUt4yPj8fSpUvFTkAjIiKwevVq8n7BggVCUd1MGTt//jz+/PNPsZOD69evk3uppqYmUk5KEsx7+OHDB4UUNBaLBQcHBwBVS7WCCVD1ck3VmTx5Mvk+JycHc+fOJSW4mMTFxQlFI9vZ2Qkt/9UFTBnx9vYWG6mZkpKC6dOnizzz4sYVAVwuF4sXL8br16+FPi8vL8eePXtI6UANDQ3Mnz9faJuWLVuSCgJAVWUPcQoah8PBypUriQuBhoYGqdDyX4DFYpGcd3w+Hxs3bhS5J4WFhXB3d2+cPGcsFgtWVlY4c+YMIiMjJVa5b0w6d+4MCwsLkkzw559/RmhoKExNTaGpqYns7GzcuXOHDKKCZIyAfOHTqqqqWLt2LcmY7O/vj8jISAwfPpz4PgmKdJeVlQEARowYIZIc18nJCY8fP8aVK1dQWVmJnTt34sKFCxg+fDhatWpFCp8L6sMBVeb3xkwOKY4hQ4bAzc2NzK4OHTqES5cukcLnHz9+RFRUlJDVZPHixSJRSc7Ozvjzzz+RnJwMDoeDqVOnYtiwYejWrRtatWqFT58+ITo6mnQUKioqIgqeNJhLVFevXoWWlhbatm0LY2Nj2NjYyH3uEydORFxcnFCHK0tlio0bN2LDhg3g8/m4evUq7t69i5EjR8LQ0BCfP3/Gw4cPhZY8ra2t6yyTtTxYWVlh1KhRZKAODQ3FlClTSJSkwPE/JSUFfD4fbm5usLCwQM+ePdG8eXNkZmYiKiqKWAykyZws98fS0hKLFi3C0aNHAQC//fYbrl27Bmtra7Rt2xY5OTm4e/eukJVrxowZtY44ZCZ9behyYQJat26NvXv3YtGiRSTj+6hRozBy5Eh07NgRJSUlePDggdASmrm5uUjFDhMTEzg5OcHf3x98Ph/r16/HqVOnMGDAAOjr66O4uBhxcXFCy2tubm5yRYdrampCV1cXHA4HmZmZWLx4MQYMGAB1dXXMnDlT7nOfMGECKV0mkLOa3AYEqKmp4eDBg5gxYwaKiorw/PlzjB07FlZWVjAxMUFZWRmSkpJw7949sk/79u0l+oPVFhcXF7L8//TpU4waNQq2trbQ19dHXl4eEhMT8fjxY6IkV5eRmq59p06dkJqaigkTJsDW1hbGxsbEP1BQXlBJSQlbt24V626wbNkyPHv2DA8fPkRZWRk8PDxw4sQJDB06FBoaGsjIyEBERAQJPFBSUsL27duFkhXLQkZGhlBw1KlTp2RKpNxUmDVrFqnO8+DBA4waNQp2dnZo1aoV0tPTyTVqtLUswQPS1BPQ7d69G3PmzCEDW1xcnFC2aAHdunXDzp074eTkBB6Ph5SUFLkiscaNG4fc3Fzs3LkTPB4PbDZbaJbOxM7OrsYkf7t27UK7du1w4sQJ8Hg8pKamEsFioqamBg8Pj1p1bg2Bm5sbWrZsib1795LqCwIrGZMWLVpg8+bNYjtXQY03V1dXUr3g3r17Qh2ogDZt2mDbtm1yB6l069YNpqamiIuLA5/PJ5ZgGxubWilnY8eOxfbt24nlpnq5ppqYOnUqtLS0sGnTJnA4HHA4nBqfnylTpmDz5s116ssjDz/99BPu3btHBkYvLy/89ddfaN68OZSVlXHo0CHMnTuX9A3R0dFkgsRk4MCBWLlyJSmeLq52oKz3Z8WKFWjdujX27duHkpISfPjwAefPnxc5nrKyMn744QeFJpRGRkbo0qULXr9+TRTxxkipYWFhgd9//x0eHh54//498vPzheqaCmCxWJgzZw5+/PFHsdnlN2zYAC6XS/YVlEmqjsC6X93qIgtOTk7EH/fWrVu4desWtLS0atV/de7cGX379iWKsbhyTeLo2bMnLly4gBUrVuD169fg8XiIiIgQa901NTXFgQMH6iXgaty4cSR/HAB8/PgRFy5cENlOXV0dq1atQmJiIilOn5iYWGN/sn37duzatQvx8fEkYTcTLS0tbN++XWhZm0mzZs3g5+eHLVu24OLFi0K57qqjo6MDLy8viYXYv1SGDh0KT09P7NmzBxUVFcjJyREZ27p06dJ4ypnAJNxYA4SstG7dGoGBgfD390dYWBhevXqFwsJCqKmpQU9PDyYmJrC1tYW9vT2aNWsGc3Nz3L17FyUlJbh+/ToxocvCjBkzMHz4cPz55594+PAhUlNTUVhYCHV1dejp6cHMzAwODg4SFQglJSWsXLkS3333HQICAhATE4OMjAwUFhaiRYsW6NSpE4YOHQonJ6cmn/x3+vTpsLOzw4ULFxAdHY13794hPz8fLVq0QJcuXWBlZQVHR0eJHeBXX32FgIAAhIWF4fr16/jnn3/w8eNHlJeXo1WrVjAyMoK1tTWmTJlSqwSCLBYLvr6+OHDgACIjI8Fms4VmqvKira0Na2trkhRT2oyeyZgxY2BhYYHAwEDcuXMHKSkp4HA4UFFRgYGBAQYMGICpU6fKnQOvrtHX18fSpUvJBCMtLQ1HjhwhCo+RkRGCg4OJZf3t27coKSmBuro6DAwM0KNHD2K1YLFY6Ny5M968eYO0tDQ8ffpUKEGzPPdn5syZJN/cvXv3kJqairy8PDRv3hyGhoYYPHgwnJyc6sRHZsKECdi/fz94PB6io6Olls6pLwYOHIjw8HAEBQUhMjISz58/R25uLpo1awZDQ0OYm5vD0dFR4tKciooKdu7cie+++w7BwcF49uwZMjMzUVZWRuoqW1paCi0NysuKFSugq6uLoKAgZGRkoKKiAjo6OrWOOJ84cSJRzqqXa5JEt27dcPXqVYSGhiI8PByJiYn49OkTKisr0aZNG/Tp0wfjx4+Hra1tvUZDe3h4wNLSEufPn8ezZ8/w8eNHsFgs6OjooEuXLhg0aBAcHR3x1Vdf4dq1a0Q5Cw4OrrFP0dbWxrlz53Du3DlcuXIFb9++BZ/PR4cOHWBjYwMXFxepFk9VVVVs374dM2fOxMWLF/Hw4UN8+PCBRCV37doVI0aMwHfffdfkMgU0JHPmzIGFhQXOnDmDmJgYZGdnQ11dHUZGRrC3t4eTkxNY/EZIVJaRkYHx48ejrKwMPXr0IA8OhUKh/Bf4+PEjrK2tweVyYWdnB29v78ZuEoVCaUI0WEAAl8tFdnY2goKCMH36dOIEV5ulHwqFQvk306ZNG+JLGBUVVauUHBQK5ctFIcsZsxiuvPD5fOjo6CA8PPw/l4iOQqFQ0tPTYW9vDx6PhxUrVjS5qGkKhdJ4KGQ54/P5tf7T0tKCj48PVcwoFMp/EkNDQzg7OwOoKv0mKc0BhUL5b6GQ5UyeJUllZWWoqanBwMAA5ubmmDx5ssxOmBQKhfIlUlBQAHt7e7DZbKxevbrR6zZSKJSmQaMEBFAoFAqlivDwcCxduhS6uroICwsjyWEpFMp/F4WWNZOTk2UuFkyhUCgUUUaPHg1nZ2dwOByp5aMoFMp/A4UsZ8uWLUNERAS6du2K5cuXw9bWti7bRqFQKBQKhfKfQyHLWXx8PPh8Pl69ekVN8RQKhUKhUCh1gELKmaC+HVBVMJpCoVAoFAqFohgKKWedOnUirwXFvykUCoVCoVAotUch5Wzx4sWkNubu3btRUVFRJ42iUCgUCoVC+a+icCqN8PBwbNy4EXl5eTA2NoajoyO++eYbdOjQAdra2vVa/JVCoVAoFArlS0Mh5Wz+/PkAAA6Hg4SEBGJFk6sBLBb++eef2jaBQqFQKBQK5YuimSI73717lyhkgv80py2FQqFQKBRK7VFIOWvXrl1dtYNCoVAoFAqFAuD/ABwOwv8RbxrsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(linear_model, X.values, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:dm4dh]",
   "language": "python",
   "name": "conda-env-dm4dh-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}