Code/experts/expert_properties.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "deployment = pd.read_parquet('../data/BAF_deployment_score.parquet')\n",
    "train = pd.read_parquet('../data/BAF.parquet').loc[:deployment.index[0]-1,:]\n",
    "\n",
    "current_expert_properties = pd.read_parquet(f'./expert_info/expert_properties.parquet')\n",
    "current_w = pd.read_parquet(f'./expert_info/full_w_table.parquet')\n",
    "current_deploy = pd.read_parquet(f'./expert_info/deployment_predictions.parquet')\n",
    "current_train = pd.read_parquet(f'./expert_info/train_predictions.parquet')\n",
    "current_perror = pd.read_parquet(f'./expert_info/p_of_error.parquet')\n",
    "\n",
    "with open(f'../ml_model/model/model_properties.pickle', 'rb') as infile:\n",
    "    ml_model_properties = pickle.load(infile)\n",
    "\n",
    "ml_model_threshold = ml_model_properties['threshold']\n",
    "ml_model_recall = 1 - ml_model_properties['fnr']\n",
    "ml_model_fpr_diff = ml_model_properties['disparity']\n",
    "\n",
    "pal = sns.color_palette(['C0','C2', 'C3'])\n",
    "pal = pal.as_hex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>expert</th>\n",
       "      <th>fnr_target</th>\n",
       "      <th>fpr_target</th>\n",
       "      <th>alpha</th>\n",
       "      <th>protected_w</th>\n",
       "      <th>score_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>standard#0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.038</td>\n",
       "      <td>3.962</td>\n",
       "      <td>-1.110</td>\n",
       "      <td>-1.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>standard#1</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.038</td>\n",
       "      <td>3.822</td>\n",
       "      <td>-0.886</td>\n",
       "      <td>-2.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>standard#2</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.042</td>\n",
       "      <td>3.851</td>\n",
       "      <td>-0.910</td>\n",
       "      <td>-2.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>standard#3</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.044</td>\n",
       "      <td>4.338</td>\n",
       "      <td>-0.950</td>\n",
       "      <td>-2.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>standard#4</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.042</td>\n",
       "      <td>4.010</td>\n",
       "      <td>-0.910</td>\n",
       "      <td>-1.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>standard#5</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.041</td>\n",
       "      <td>3.873</td>\n",
       "      <td>-1.068</td>\n",
       "      <td>-3.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>standard#6</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.033</td>\n",
       "      <td>4.038</td>\n",
       "      <td>-1.012</td>\n",
       "      <td>-1.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>standard#7</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.044</td>\n",
       "      <td>4.420</td>\n",
       "      <td>-1.094</td>\n",
       "      <td>-2.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>standard#8</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.041</td>\n",
       "      <td>4.024</td>\n",
       "      <td>-1.027</td>\n",
       "      <td>-1.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>standard#9</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.051</td>\n",
       "      <td>4.123</td>\n",
       "      <td>-0.947</td>\n",
       "      <td>-2.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>standard#10</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.052</td>\n",
       "      <td>4.060</td>\n",
       "      <td>-1.069</td>\n",
       "      <td>-1.269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>standard#11</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.042</td>\n",
       "      <td>3.930</td>\n",
       "      <td>-1.040</td>\n",
       "      <td>-3.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>standard#12</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.036</td>\n",
       "      <td>3.771</td>\n",
       "      <td>-1.069</td>\n",
       "      <td>-2.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>standard#13</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.034</td>\n",
       "      <td>3.930</td>\n",
       "      <td>-1.085</td>\n",
       "      <td>-2.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>standard#14</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.044</td>\n",
       "      <td>3.958</td>\n",
       "      <td>-1.067</td>\n",
       "      <td>-1.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>standard#15</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.041</td>\n",
       "      <td>4.117</td>\n",
       "      <td>-1.001</td>\n",
       "      <td>-2.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>standard#16</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.037</td>\n",
       "      <td>4.168</td>\n",
       "      <td>-1.112</td>\n",
       "      <td>-2.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>standard#17</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.040</td>\n",
       "      <td>4.186</td>\n",
       "      <td>-0.977</td>\n",
       "      <td>-2.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>standard#18</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.034</td>\n",
       "      <td>4.057</td>\n",
       "      <td>-0.834</td>\n",
       "      <td>-1.979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>standard#19</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.047</td>\n",
       "      <td>4.177</td>\n",
       "      <td>-0.926</td>\n",
       "      <td>-1.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>model_agreeing#0</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.037</td>\n",
       "      <td>11.561</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-6.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>model_agreeing#1</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.048</td>\n",
       "      <td>11.922</td>\n",
       "      <td>0.229</td>\n",
       "      <td>-6.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>model_agreeing#2</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.021</td>\n",
       "      <td>12.128</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-7.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>model_agreeing#3</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.057</td>\n",
       "      <td>11.506</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>-5.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>model_agreeing#4</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.055</td>\n",
       "      <td>11.831</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-6.897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>model_agreeing#5</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.037</td>\n",
       "      <td>11.882</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>-6.421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>model_agreeing#6</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.046</td>\n",
       "      <td>11.681</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-5.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>model_agreeing#7</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.040</td>\n",
       "      <td>11.406</td>\n",
       "      <td>0.118</td>\n",
       "      <td>-6.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>model_agreeing#8</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.032</td>\n",
       "      <td>11.289</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>-6.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>model_agreeing#9</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.041</td>\n",
       "      <td>11.923</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-6.455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>unfair#0</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.024</td>\n",
       "      <td>3.881</td>\n",
       "      <td>-4.394</td>\n",
       "      <td>-1.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>unfair#1</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.046</td>\n",
       "      <td>3.979</td>\n",
       "      <td>-3.735</td>\n",
       "      <td>-1.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>unfair#2</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.036</td>\n",
       "      <td>4.149</td>\n",
       "      <td>-3.736</td>\n",
       "      <td>-1.952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>unfair#3</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.023</td>\n",
       "      <td>4.024</td>\n",
       "      <td>-3.487</td>\n",
       "      <td>-2.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>unfair#4</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.034</td>\n",
       "      <td>3.898</td>\n",
       "      <td>-3.985</td>\n",
       "      <td>-2.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>unfair#5</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.034</td>\n",
       "      <td>3.929</td>\n",
       "      <td>-4.121</td>\n",
       "      <td>-2.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>unfair#6</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.031</td>\n",
       "      <td>4.063</td>\n",
       "      <td>-4.164</td>\n",
       "      <td>-2.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>unfair#7</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.040</td>\n",
       "      <td>3.984</td>\n",
       "      <td>-4.464</td>\n",
       "      <td>-2.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>unfair#8</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.018</td>\n",
       "      <td>3.923</td>\n",
       "      <td>-3.705</td>\n",
       "      <td>-2.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>unfair#9</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.037</td>\n",
       "      <td>3.977</td>\n",
       "      <td>-4.330</td>\n",
       "      <td>-2.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sparse#0</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.038</td>\n",
       "      <td>3.780</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>-1.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>sparse#1</td>\n",
       "      <td>0.409</td>\n",
       "      <td>0.031</td>\n",
       "      <td>4.229</td>\n",
       "      <td>-1.206</td>\n",
       "      <td>-2.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>sparse#2</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.033</td>\n",
       "      <td>4.180</td>\n",
       "      <td>-1.032</td>\n",
       "      <td>-2.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>sparse#3</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.057</td>\n",
       "      <td>4.100</td>\n",
       "      <td>-1.038</td>\n",
       "      <td>-2.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>sparse#4</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.041</td>\n",
       "      <td>4.180</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-1.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>sparse#5</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.034</td>\n",
       "      <td>3.863</td>\n",
       "      <td>-1.110</td>\n",
       "      <td>-3.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>sparse#6</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.042</td>\n",
       "      <td>3.975</td>\n",
       "      <td>-1.017</td>\n",
       "      <td>-1.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>sparse#7</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.061</td>\n",
       "      <td>3.813</td>\n",
       "      <td>-1.088</td>\n",
       "      <td>-2.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>sparse#8</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.041</td>\n",
       "      <td>3.946</td>\n",
       "      <td>-0.996</td>\n",
       "      <td>-1.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sparse#9</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.046</td>\n",
       "      <td>4.106</td>\n",
       "      <td>-0.942</td>\n",
       "      <td>-2.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              expert  fnr_target  fpr_target   alpha  protected_w  score_w\n",
       "0         standard#0       0.392       0.038   3.962       -1.110   -1.188\n",
       "1         standard#1       0.492       0.038   3.822       -0.886   -2.306\n",
       "2         standard#2       0.455       0.042   3.851       -0.910   -2.264\n",
       "3         standard#3       0.414       0.044   4.338       -0.950   -2.536\n",
       "4         standard#4       0.454       0.042   4.010       -0.910   -1.567\n",
       "5         standard#5       0.426       0.041   3.873       -1.068   -3.151\n",
       "6         standard#6       0.486       0.033   4.038       -1.012   -1.128\n",
       "7         standard#7       0.505       0.044   4.420       -1.094   -2.381\n",
       "8         standard#8       0.539       0.041   4.024       -1.027   -1.840\n",
       "9         standard#9       0.359       0.051   4.123       -0.947   -2.125\n",
       "10       standard#10       0.357       0.052   4.060       -1.069   -1.269\n",
       "11       standard#11       0.404       0.042   3.930       -1.040   -3.030\n",
       "12       standard#12       0.437       0.036   3.771       -1.069   -2.161\n",
       "13       standard#13       0.473       0.034   3.930       -1.085   -2.192\n",
       "14       standard#14       0.445       0.044   3.958       -1.067   -1.433\n",
       "15       standard#15       0.328       0.041   4.117       -1.001   -2.550\n",
       "16       standard#16       0.414       0.037   4.168       -1.112   -2.086\n",
       "17       standard#17       0.471       0.040   4.186       -0.977   -2.439\n",
       "18       standard#18       0.441       0.034   4.057       -0.834   -1.979\n",
       "19       standard#19       0.467       0.047   4.177       -0.926   -1.709\n",
       "20  model_agreeing#0       0.416       0.037  11.561        0.055   -6.208\n",
       "21  model_agreeing#1       0.541       0.048  11.922        0.229   -6.028\n",
       "22  model_agreeing#2       0.308       0.021  12.128        0.004   -7.068\n",
       "23  model_agreeing#3       0.435       0.057  11.506       -0.112   -5.180\n",
       "24  model_agreeing#4       0.448       0.055  11.831        0.054   -6.897\n",
       "25  model_agreeing#5       0.497       0.037  11.882       -0.060   -6.421\n",
       "26  model_agreeing#6       0.454       0.046  11.681       -0.002   -5.749\n",
       "27  model_agreeing#7       0.387       0.040  11.406        0.118   -6.623\n",
       "28  model_agreeing#8       0.429       0.032  11.289       -0.075   -6.529\n",
       "29  model_agreeing#9       0.456       0.041  11.923        0.001   -6.455\n",
       "30          unfair#0       0.467       0.024   3.881       -4.394   -1.106\n",
       "31          unfair#1       0.528       0.046   3.979       -3.735   -1.782\n",
       "32          unfair#2       0.367       0.036   4.149       -3.736   -1.952\n",
       "33          unfair#3       0.398       0.023   4.024       -3.487   -2.932\n",
       "34          unfair#4       0.389       0.034   3.898       -3.985   -2.139\n",
       "35          unfair#5       0.308       0.034   3.929       -4.121   -2.177\n",
       "36          unfair#6       0.383       0.031   4.063       -4.164   -2.041\n",
       "37          unfair#7       0.378       0.040   3.984       -4.464   -2.314\n",
       "38          unfair#8       0.486       0.018   3.923       -3.705   -2.022\n",
       "39          unfair#9       0.423       0.037   3.977       -4.330   -2.239\n",
       "40          sparse#0       0.395       0.038   3.780       -0.854   -1.188\n",
       "41          sparse#1       0.409       0.031   4.229       -1.206   -2.306\n",
       "42          sparse#2       0.395       0.033   4.180       -1.032   -2.264\n",
       "43          sparse#3       0.387       0.057   4.100       -1.038   -2.536\n",
       "44          sparse#4       0.396       0.041   4.180       -0.887   -1.567\n",
       "45          sparse#5       0.429       0.034   3.863       -1.110   -3.151\n",
       "46          sparse#6       0.373       0.042   3.975       -1.017   -1.128\n",
       "47          sparse#7       0.441       0.061   3.813       -1.088   -2.381\n",
       "48          sparse#8       0.512       0.041   3.946       -0.996   -1.840\n",
       "49          sparse#9       0.466       0.046   4.106       -0.942   -2.125"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_expert_properties.drop(columns = ['features_w_std', 'fpr_noise', 'fnr_noise', 'seed', 'theta']).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_expert_properties['expert'].to_list()\n",
    "\n",
    "new_names = []\n",
    "for expert in current_expert_properties['expert'].to_list():\n",
    "    if expert.split('#')[0] == 'regular':\n",
    "        new_names.append('standard#' + expert.split('#')[1])\n",
    "    else:\n",
    "        new_names.append(expert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = current_perror.loc[deployment.index, :]\n",
    "\n",
    "temp_lp = temp.loc[deployment.loc[deployment['fraud_bool'] == 1].index,:]\n",
    "temp_ln = temp.loc[deployment.loc[deployment['fraud_bool'] == 0].index, :]\n",
    "temp_lp = temp_lp.iloc[:, np.arange(0,100,2)]\n",
    "temp_ln = temp_ln.iloc[:, np.arange(1,101,2)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_ln_sample = temp_ln.sample(n=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtain_exp_class(exp):\n",
    "    exp_type = exp.split('#')[0].split('_')[-1]\n",
    "    if exp_type == 'regular':\n",
    "        exp_type = 'standard'\n",
    "    if exp_type == 'agreeing':\n",
    "        exp_type = 'model_agreeing'\n",
    "    return exp_type\n",
    "\n",
    "melted_ln = temp_ln_sample.melt()\n",
    "melted_lp = temp_lp.melt()\n",
    "melted_ln['Group'] = melted_ln['variable'].apply(obtain_exp_class)\n",
    "melted_lp['Group'] = melted_lp['variable'].apply(obtain_exp_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "melted_ln\n",
    "f, ax = plt.subplots(figsize=(5, 5))\n",
    "sns.histplot(data = melted_ln, x = 'value', hue = 'Group', stat = 'probability', bins = 20, log_scale = (False,10), palette=[\"C0\", \"C2\", \"C3\", \"C1\"])\n",
    "plt.ylabel('Proportion of Cases')\n",
    "\n",
    "plt.xlabel('Probability of FP - Label Negatives')\n",
    "plt.savefig(\"perror_dist_ln.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(5, 5))\n",
    "g = sns.histplot(data = melted_lp, x = 'value', hue = 'Group', stat = 'probability', bins = 20, log_scale = (False,10), palette=[\"C0\", \"C2\", \"C3\", \"C1\"])\n",
    "plt.ylabel('Proportion of Cases')\n",
    "\n",
    "plt.xlabel('Probability of FN - Label Positives')\n",
    "plt.savefig(\"perror_dist_lp.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_25376/728810888.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0mtn_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtp_o\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_o\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpreds_o\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m     \u001b[0mtn_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtp_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpreds_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mfpr_disparity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fpr_train'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtn\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/l2a-env/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/l2a-env/lib/python3.7/site-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m     \"\"\"\n\u001b[0;32m--> 299\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/l2a-env/lib/python3.7/site-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \"\"\"\n\u001b[1;32m     83\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0mtype_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m     \u001b[0mtype_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/l2a-env/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mtype_of_target\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    304\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m'continuous'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msuffix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m'multiclass'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msuffix\u001b[0m  \u001b[0;31m# [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munique\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/l2a-env/lib/python3.7/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0mar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_unpack_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/l2a-env/lib/python3.7/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[0;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0maux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mar\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m         \u001b[0mar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    334\u001b[0m         \u001b[0maux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m     \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maux\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "exp_list = current_deploy.columns.drop('model#0').to_list()\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "fpr_disparity = pd.DataFrame(index = exp_list, columns = ['target_fpr', 'fpr_train', 'fpr_train_y', 'fpr_train_o', 'fpr_deployment', 'fpr_deployment_y', 'fpr_deployment_o', 'target_fnr', 'fnr_train', 'fnr_train_y', 'fnr_train_o', 'fnr_deployment', 'fnr_deployment_y', 'fnr_deployment_o', 'disparity_train', 'disparity_deployment'])\n",
    "\n",
    "old_index_dep = deployment.loc[deployment['customer_age'] > 50].index\n",
    "yng_index_dep = deployment.loc[deployment['customer_age'] < 50].index\n",
    "\n",
    "old_index_train = train.loc[train['customer_age'] > 50].index\n",
    "yng_index_train = train.loc[train['customer_age'] < 50].index\n",
    "for exp in exp_list:\n",
    "\n",
    "    preds = current_deploy[exp]\n",
    "    labels = deployment['fraud_bool']\n",
    "\n",
    "    preds_o = current_deploy[exp].loc[old_index_dep]\n",
    "    labels_o = deployment['fraud_bool'].loc[old_index_dep]\n",
    "\n",
    "    preds_y = current_deploy[exp].loc[yng_index_dep]\n",
    "    labels_y = deployment['fraud_bool'].loc[yng_index_dep]\n",
    "\n",
    "    tn, fp, fn, tp = confusion_matrix(labels,preds).ravel()\n",
    "\n",
    "    tn_o, fp_o, fn_o, tp_o = confusion_matrix(labels_o,preds_o).ravel()\n",
    "\n",
    "    tn_y, fp_y, fn_y, tp_y = confusion_matrix(labels_y,preds_y).ravel()\n",
    "\n",
    "    fpr_disparity.loc[exp, 'fpr_deployment'] = fp/(tn+fp)\n",
    "    fpr_disparity.loc[exp, 'fpr_deployment_y'] = fp_y/(tn_y+fp_y)\n",
    "    fpr_disparity.loc[exp, 'fpr_deployment_o'] = fp_o/(tn_o+fp_o)\n",
    "    fpr_disparity.loc[exp, 'fnr_deployment'] = fn/(fn+tp)\n",
    "    fpr_disparity.loc[exp, 'fnr_deployment_y'] = fn_y/(tp_y+fn_y)\n",
    "    fpr_disparity.loc[exp, 'fnr_deployment_o'] = fn_o/(tp_o+fn_o)\n",
    "\n",
    "    preds = current_train[exp]\n",
    "    labels = train['fraud_bool']\n",
    "\n",
    "    preds_o = current_train[exp].loc[old_index_train]\n",
    "    labels_o = train['fraud_bool'].loc[old_index_train]\n",
    "\n",
    "    preds_y = current_train[exp].loc[yng_index_train]\n",
    "    labels_y = train['fraud_bool'].loc[yng_index_train]\n",
    "\n",
    "    tn, fp, fn, tp = confusion_matrix(labels,preds).ravel()\n",
    "\n",
    "    tn_o, fp_o, fn_o, tp_o = confusion_matrix(labels_o,preds_o).ravel()\n",
    "\n",
    "    tn_y, fp_y, fn_y, tp_y = confusion_matrix(labels_y,preds_y).ravel()\n",
    "\n",
    "    fpr_disparity.loc[exp, 'fpr_train'] = fp/(tn+fp)\n",
    "    fpr_disparity.loc[exp, 'fpr_train_y'] = fp_y/(tn_y+fp_y)\n",
    "    fpr_disparity.loc[exp, 'fpr_train_o'] = fp_o/(tn_o+fp_o)\n",
    "    fpr_disparity.loc[exp, 'fnr_train'] = fn/(fn+tp)\n",
    "    fpr_disparity.loc[exp, 'fnr_train_y'] = fn_y/(tp_y+fn_y)\n",
    "    fpr_disparity.loc[exp, 'fnr_train_o'] = fn_o/(tp_o+fn_o)\n",
    "\n",
    "\n",
    "fpr_disparity['disparity_train'] = fpr_disparity['fpr_train_y']/fpr_disparity['fpr_train_o']\n",
    "fpr_disparity['disparity_deployment'] = fpr_disparity['fpr_deployment_y']/fpr_disparity['fpr_deployment_o']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>expert</th>\n",
       "      <th>fnr_target</th>\n",
       "      <th>fpr_target</th>\n",
       "      <th>features_w_std</th>\n",
       "      <th>alpha</th>\n",
       "      <th>fpr_noise</th>\n",
       "      <th>fnr_noise</th>\n",
       "      <th>protected_w</th>\n",
       "      <th>score_w</th>\n",
       "      <th>seed</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>standard#0</td>\n",
       "      <td>0.391602</td>\n",
       "      <td>0.037777</td>\n",
       "      <td>1</td>\n",
       "      <td>3.961633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.110062</td>\n",
       "      <td>-1.187827</td>\n",
       "      <td>3466490401</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>standard#1</td>\n",
       "      <td>0.491965</td>\n",
       "      <td>0.037992</td>\n",
       "      <td>1</td>\n",
       "      <td>3.822474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.885528</td>\n",
       "      <td>-2.305878</td>\n",
       "      <td>2632592002</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>standard#2</td>\n",
       "      <td>0.454968</td>\n",
       "      <td>0.041866</td>\n",
       "      <td>1</td>\n",
       "      <td>3.850568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.909841</td>\n",
       "      <td>-2.264086</td>\n",
       "      <td>1665848788</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>standard#3</td>\n",
       "      <td>0.414417</td>\n",
       "      <td>0.044101</td>\n",
       "      <td>1</td>\n",
       "      <td>4.338491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.949751</td>\n",
       "      <td>-2.536484</td>\n",
       "      <td>1833563731</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>standard#4</td>\n",
       "      <td>0.453747</td>\n",
       "      <td>0.041983</td>\n",
       "      <td>1</td>\n",
       "      <td>4.010162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.909914</td>\n",
       "      <td>-1.567296</td>\n",
       "      <td>3708884016</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>standard#5</td>\n",
       "      <td>0.425543</td>\n",
       "      <td>0.041190</td>\n",
       "      <td>1</td>\n",
       "      <td>3.872601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.068373</td>\n",
       "      <td>-3.150769</td>\n",
       "      <td>3229269814</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>standard#6</td>\n",
       "      <td>0.485903</td>\n",
       "      <td>0.033293</td>\n",
       "      <td>1</td>\n",
       "      <td>4.038183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.012289</td>\n",
       "      <td>-1.127594</td>\n",
       "      <td>3208863008</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>standard#7</td>\n",
       "      <td>0.505312</td>\n",
       "      <td>0.043776</td>\n",
       "      <td>1</td>\n",
       "      <td>4.420051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.093577</td>\n",
       "      <td>-2.380603</td>\n",
       "      <td>1837441277</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>standard#8</td>\n",
       "      <td>0.538600</td>\n",
       "      <td>0.041218</td>\n",
       "      <td>1</td>\n",
       "      <td>4.024032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.026789</td>\n",
       "      <td>-1.840480</td>\n",
       "      <td>2389033628</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>standard#9</td>\n",
       "      <td>0.359497</td>\n",
       "      <td>0.051295</td>\n",
       "      <td>1</td>\n",
       "      <td>4.123441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.946964</td>\n",
       "      <td>-2.124685</td>\n",
       "      <td>1839888439</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>standard#10</td>\n",
       "      <td>0.357116</td>\n",
       "      <td>0.051989</td>\n",
       "      <td>1</td>\n",
       "      <td>4.060034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.069166</td>\n",
       "      <td>-1.268946</td>\n",
       "      <td>586070738</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>standard#11</td>\n",
       "      <td>0.404098</td>\n",
       "      <td>0.041852</td>\n",
       "      <td>1</td>\n",
       "      <td>3.929550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.039675</td>\n",
       "      <td>-3.030070</td>\n",
       "      <td>1554367775</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>standard#12</td>\n",
       "      <td>0.437323</td>\n",
       "      <td>0.036247</td>\n",
       "      <td>1</td>\n",
       "      <td>3.771496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.068717</td>\n",
       "      <td>-2.161209</td>\n",
       "      <td>257344540</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>standard#13</td>\n",
       "      <td>0.473130</td>\n",
       "      <td>0.033613</td>\n",
       "      <td>1</td>\n",
       "      <td>3.930131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.084521</td>\n",
       "      <td>-2.192027</td>\n",
       "      <td>658583774</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>standard#14</td>\n",
       "      <td>0.445103</td>\n",
       "      <td>0.044235</td>\n",
       "      <td>1</td>\n",
       "      <td>3.958221</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.067125</td>\n",
       "      <td>-1.433115</td>\n",
       "      <td>521166154</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>standard#15</td>\n",
       "      <td>0.328211</td>\n",
       "      <td>0.040773</td>\n",
       "      <td>1</td>\n",
       "      <td>4.117325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.001266</td>\n",
       "      <td>-2.549946</td>\n",
       "      <td>4025201800</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>standard#16</td>\n",
       "      <td>0.414011</td>\n",
       "      <td>0.036561</td>\n",
       "      <td>1</td>\n",
       "      <td>4.167797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.111731</td>\n",
       "      <td>-2.086214</td>\n",
       "      <td>191348845</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>standard#17</td>\n",
       "      <td>0.470720</td>\n",
       "      <td>0.040436</td>\n",
       "      <td>1</td>\n",
       "      <td>4.186220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.976558</td>\n",
       "      <td>-2.438929</td>\n",
       "      <td>3935950435</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>standard#18</td>\n",
       "      <td>0.440826</td>\n",
       "      <td>0.033800</td>\n",
       "      <td>1</td>\n",
       "      <td>4.057117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.834020</td>\n",
       "      <td>-1.978893</td>\n",
       "      <td>461683744</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>standard#19</td>\n",
       "      <td>0.467422</td>\n",
       "      <td>0.046980</td>\n",
       "      <td>1</td>\n",
       "      <td>4.177028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.925796</td>\n",
       "      <td>-1.708592</td>\n",
       "      <td>3358486024</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>model_agreeing#0</td>\n",
       "      <td>0.415869</td>\n",
       "      <td>0.036865</td>\n",
       "      <td>1</td>\n",
       "      <td>11.560946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055145</td>\n",
       "      <td>-6.208379</td>\n",
       "      <td>1573428609</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>model_agreeing#1</td>\n",
       "      <td>0.540890</td>\n",
       "      <td>0.047710</td>\n",
       "      <td>1</td>\n",
       "      <td>11.921783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.229221</td>\n",
       "      <td>-6.028133</td>\n",
       "      <td>3534262596</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>model_agreeing#2</td>\n",
       "      <td>0.307583</td>\n",
       "      <td>0.021319</td>\n",
       "      <td>1</td>\n",
       "      <td>12.128285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004154</td>\n",
       "      <td>-7.068098</td>\n",
       "      <td>3654375080</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>model_agreeing#3</td>\n",
       "      <td>0.434958</td>\n",
       "      <td>0.057312</td>\n",
       "      <td>1</td>\n",
       "      <td>11.505610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.111793</td>\n",
       "      <td>-5.179865</td>\n",
       "      <td>2212505681</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>model_agreeing#4</td>\n",
       "      <td>0.447844</td>\n",
       "      <td>0.054677</td>\n",
       "      <td>1</td>\n",
       "      <td>11.830589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.053906</td>\n",
       "      <td>-6.896718</td>\n",
       "      <td>1744938045</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>model_agreeing#5</td>\n",
       "      <td>0.497303</td>\n",
       "      <td>0.036643</td>\n",
       "      <td>1</td>\n",
       "      <td>11.881908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.059616</td>\n",
       "      <td>-6.420874</td>\n",
       "      <td>949179846</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>model_agreeing#6</td>\n",
       "      <td>0.454414</td>\n",
       "      <td>0.046113</td>\n",
       "      <td>1</td>\n",
       "      <td>11.681172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001913</td>\n",
       "      <td>-5.748559</td>\n",
       "      <td>116833273</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>model_agreeing#7</td>\n",
       "      <td>0.387111</td>\n",
       "      <td>0.040480</td>\n",
       "      <td>1</td>\n",
       "      <td>11.406194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117500</td>\n",
       "      <td>-6.622644</td>\n",
       "      <td>3206757501</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>model_agreeing#8</td>\n",
       "      <td>0.429322</td>\n",
       "      <td>0.031709</td>\n",
       "      <td>1</td>\n",
       "      <td>11.289391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.074787</td>\n",
       "      <td>-6.528976</td>\n",
       "      <td>1061618149</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>model_agreeing#9</td>\n",
       "      <td>0.456439</td>\n",
       "      <td>0.040877</td>\n",
       "      <td>1</td>\n",
       "      <td>11.923252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000903</td>\n",
       "      <td>-6.454504</td>\n",
       "      <td>1853440609</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>unfair#0</td>\n",
       "      <td>0.466574</td>\n",
       "      <td>0.023767</td>\n",
       "      <td>1</td>\n",
       "      <td>3.881495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.394159</td>\n",
       "      <td>-1.105686</td>\n",
       "      <td>3039490906</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>unfair#1</td>\n",
       "      <td>0.528127</td>\n",
       "      <td>0.046467</td>\n",
       "      <td>1</td>\n",
       "      <td>3.979435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.734613</td>\n",
       "      <td>-1.781745</td>\n",
       "      <td>2216609512</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>unfair#2</td>\n",
       "      <td>0.367115</td>\n",
       "      <td>0.036437</td>\n",
       "      <td>1</td>\n",
       "      <td>4.148615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.735605</td>\n",
       "      <td>-1.951751</td>\n",
       "      <td>3207250604</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>unfair#3</td>\n",
       "      <td>0.398001</td>\n",
       "      <td>0.022569</td>\n",
       "      <td>1</td>\n",
       "      <td>4.023672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.487128</td>\n",
       "      <td>-2.931746</td>\n",
       "      <td>1964226736</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>unfair#4</td>\n",
       "      <td>0.389133</td>\n",
       "      <td>0.034034</td>\n",
       "      <td>1</td>\n",
       "      <td>3.897621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.984990</td>\n",
       "      <td>-2.138694</td>\n",
       "      <td>2968221499</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>unfair#5</td>\n",
       "      <td>0.308367</td>\n",
       "      <td>0.034114</td>\n",
       "      <td>1</td>\n",
       "      <td>3.928701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.121403</td>\n",
       "      <td>-2.177379</td>\n",
       "      <td>3471889354</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>unfair#6</td>\n",
       "      <td>0.383132</td>\n",
       "      <td>0.031261</td>\n",
       "      <td>1</td>\n",
       "      <td>4.062524</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.163608</td>\n",
       "      <td>-2.041371</td>\n",
       "      <td>2960007350</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>unfair#7</td>\n",
       "      <td>0.378128</td>\n",
       "      <td>0.040297</td>\n",
       "      <td>1</td>\n",
       "      <td>3.983949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.463943</td>\n",
       "      <td>-2.313500</td>\n",
       "      <td>1358431728</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>unfair#8</td>\n",
       "      <td>0.485520</td>\n",
       "      <td>0.017517</td>\n",
       "      <td>1</td>\n",
       "      <td>3.923116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.705290</td>\n",
       "      <td>-2.021909</td>\n",
       "      <td>1604600292</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>unfair#9</td>\n",
       "      <td>0.422726</td>\n",
       "      <td>0.037322</td>\n",
       "      <td>1</td>\n",
       "      <td>3.976997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.330320</td>\n",
       "      <td>-2.238609</td>\n",
       "      <td>3624037979</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sparse#0</td>\n",
       "      <td>0.394738</td>\n",
       "      <td>0.038082</td>\n",
       "      <td>1</td>\n",
       "      <td>3.779876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.853789</td>\n",
       "      <td>-1.187827</td>\n",
       "      <td>3880081671</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>sparse#1</td>\n",
       "      <td>0.409484</td>\n",
       "      <td>0.031124</td>\n",
       "      <td>1</td>\n",
       "      <td>4.228945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.206014</td>\n",
       "      <td>-2.305878</td>\n",
       "      <td>2322667597</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>sparse#2</td>\n",
       "      <td>0.394963</td>\n",
       "      <td>0.032528</td>\n",
       "      <td>1</td>\n",
       "      <td>4.180318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.032242</td>\n",
       "      <td>-2.264086</td>\n",
       "      <td>590449352</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>sparse#3</td>\n",
       "      <td>0.387061</td>\n",
       "      <td>0.056925</td>\n",
       "      <td>1</td>\n",
       "      <td>4.100499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.038405</td>\n",
       "      <td>-2.536484</td>\n",
       "      <td>1924060235</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>sparse#4</td>\n",
       "      <td>0.395759</td>\n",
       "      <td>0.040508</td>\n",
       "      <td>1</td>\n",
       "      <td>4.180171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.886623</td>\n",
       "      <td>-1.567296</td>\n",
       "      <td>598187340</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>sparse#5</td>\n",
       "      <td>0.428688</td>\n",
       "      <td>0.033630</td>\n",
       "      <td>1</td>\n",
       "      <td>3.863254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.109989</td>\n",
       "      <td>-3.150769</td>\n",
       "      <td>3831694379</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>sparse#6</td>\n",
       "      <td>0.373456</td>\n",
       "      <td>0.041909</td>\n",
       "      <td>1</td>\n",
       "      <td>3.975422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.017243</td>\n",
       "      <td>-1.127594</td>\n",
       "      <td>3467719188</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>sparse#7</td>\n",
       "      <td>0.441042</td>\n",
       "      <td>0.061003</td>\n",
       "      <td>1</td>\n",
       "      <td>3.812846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.087786</td>\n",
       "      <td>-2.380603</td>\n",
       "      <td>1621712414</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>sparse#8</td>\n",
       "      <td>0.512312</td>\n",
       "      <td>0.041202</td>\n",
       "      <td>1</td>\n",
       "      <td>3.946422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.995779</td>\n",
       "      <td>-1.840480</td>\n",
       "      <td>1708008996</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sparse#9</td>\n",
       "      <td>0.466424</td>\n",
       "      <td>0.046172</td>\n",
       "      <td>1</td>\n",
       "      <td>4.106071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.941718</td>\n",
       "      <td>-2.124685</td>\n",
       "      <td>2312516455</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              expert  fnr_target  fpr_target  features_w_std      alpha  \\\n",
       "0         standard#0    0.391602    0.037777               1   3.961633   \n",
       "1         standard#1    0.491965    0.037992               1   3.822474   \n",
       "2         standard#2    0.454968    0.041866               1   3.850568   \n",
       "3         standard#3    0.414417    0.044101               1   4.338491   \n",
       "4         standard#4    0.453747    0.041983               1   4.010162   \n",
       "5         standard#5    0.425543    0.041190               1   3.872601   \n",
       "6         standard#6    0.485903    0.033293               1   4.038183   \n",
       "7         standard#7    0.505312    0.043776               1   4.420051   \n",
       "8         standard#8    0.538600    0.041218               1   4.024032   \n",
       "9         standard#9    0.359497    0.051295               1   4.123441   \n",
       "10       standard#10    0.357116    0.051989               1   4.060034   \n",
       "11       standard#11    0.404098    0.041852               1   3.929550   \n",
       "12       standard#12    0.437323    0.036247               1   3.771496   \n",
       "13       standard#13    0.473130    0.033613               1   3.930131   \n",
       "14       standard#14    0.445103    0.044235               1   3.958221   \n",
       "15       standard#15    0.328211    0.040773               1   4.117325   \n",
       "16       standard#16    0.414011    0.036561               1   4.167797   \n",
       "17       standard#17    0.470720    0.040436               1   4.186220   \n",
       "18       standard#18    0.440826    0.033800               1   4.057117   \n",
       "19       standard#19    0.467422    0.046980               1   4.177028   \n",
       "20  model_agreeing#0    0.415869    0.036865               1  11.560946   \n",
       "21  model_agreeing#1    0.540890    0.047710               1  11.921783   \n",
       "22  model_agreeing#2    0.307583    0.021319               1  12.128285   \n",
       "23  model_agreeing#3    0.434958    0.057312               1  11.505610   \n",
       "24  model_agreeing#4    0.447844    0.054677               1  11.830589   \n",
       "25  model_agreeing#5    0.497303    0.036643               1  11.881908   \n",
       "26  model_agreeing#6    0.454414    0.046113               1  11.681172   \n",
       "27  model_agreeing#7    0.387111    0.040480               1  11.406194   \n",
       "28  model_agreeing#8    0.429322    0.031709               1  11.289391   \n",
       "29  model_agreeing#9    0.456439    0.040877               1  11.923252   \n",
       "30          unfair#0    0.466574    0.023767               1   3.881495   \n",
       "31          unfair#1    0.528127    0.046467               1   3.979435   \n",
       "32          unfair#2    0.367115    0.036437               1   4.148615   \n",
       "33          unfair#3    0.398001    0.022569               1   4.023672   \n",
       "34          unfair#4    0.389133    0.034034               1   3.897621   \n",
       "35          unfair#5    0.308367    0.034114               1   3.928701   \n",
       "36          unfair#6    0.383132    0.031261               1   4.062524   \n",
       "37          unfair#7    0.378128    0.040297               1   3.983949   \n",
       "38          unfair#8    0.485520    0.017517               1   3.923116   \n",
       "39          unfair#9    0.422726    0.037322               1   3.976997   \n",
       "40          sparse#0    0.394738    0.038082               1   3.779876   \n",
       "41          sparse#1    0.409484    0.031124               1   4.228945   \n",
       "42          sparse#2    0.394963    0.032528               1   4.180318   \n",
       "43          sparse#3    0.387061    0.056925               1   4.100499   \n",
       "44          sparse#4    0.395759    0.040508               1   4.180171   \n",
       "45          sparse#5    0.428688    0.033630               1   3.863254   \n",
       "46          sparse#6    0.373456    0.041909               1   3.975422   \n",
       "47          sparse#7    0.441042    0.061003               1   3.812846   \n",
       "48          sparse#8    0.512312    0.041202               1   3.946422   \n",
       "49          sparse#9    0.466424    0.046172               1   4.106071   \n",
       "\n",
       "    fpr_noise  fnr_noise  protected_w   score_w        seed  theta  \n",
       "0         0.0        0.0    -1.110062 -1.187827  3466490401    0.3  \n",
       "1         0.0        0.0    -0.885528 -2.305878  2632592002    0.3  \n",
       "2         0.0        0.0    -0.909841 -2.264086  1665848788    0.3  \n",
       "3         0.0        0.0    -0.949751 -2.536484  1833563731    0.3  \n",
       "4         0.0        0.0    -0.909914 -1.567296  3708884016    0.3  \n",
       "5         0.0        0.0    -1.068373 -3.150769  3229269814    0.3  \n",
       "6         0.0        0.0    -1.012289 -1.127594  3208863008    0.3  \n",
       "7         0.0        0.0    -1.093577 -2.380603  1837441277    0.3  \n",
       "8         0.0        0.0    -1.026789 -1.840480  2389033628    0.3  \n",
       "9         0.0        0.0    -0.946964 -2.124685  1839888439    0.3  \n",
       "10        0.0        0.0    -1.069166 -1.268946   586070738    0.3  \n",
       "11        0.0        0.0    -1.039675 -3.030070  1554367775    0.3  \n",
       "12        0.0        0.0    -1.068717 -2.161209   257344540    0.3  \n",
       "13        0.0        0.0    -1.084521 -2.192027   658583774    0.3  \n",
       "14        0.0        0.0    -1.067125 -1.433115   521166154    0.3  \n",
       "15        0.0        0.0    -1.001266 -2.549946  4025201800    0.3  \n",
       "16        0.0        0.0    -1.111731 -2.086214   191348845    0.3  \n",
       "17        0.0        0.0    -0.976558 -2.438929  3935950435    0.3  \n",
       "18        0.0        0.0    -0.834020 -1.978893   461683744    0.3  \n",
       "19        0.0        0.0    -0.925796 -1.708592  3358486024    0.3  \n",
       "20        0.0        0.0     0.055145 -6.208379  1573428609    0.3  \n",
       "21        0.0        0.0     0.229221 -6.028133  3534262596    0.3  \n",
       "22        0.0        0.0     0.004154 -7.068098  3654375080    0.3  \n",
       "23        0.0        0.0    -0.111793 -5.179865  2212505681    0.3  \n",
       "24        0.0        0.0     0.053906 -6.896718  1744938045    0.3  \n",
       "25        0.0        0.0    -0.059616 -6.420874   949179846    0.3  \n",
       "26        0.0        0.0    -0.001913 -5.748559   116833273    0.3  \n",
       "27        0.0        0.0     0.117500 -6.622644  3206757501    0.3  \n",
       "28        0.0        0.0    -0.074787 -6.528976  1061618149    0.3  \n",
       "29        0.0        0.0     0.000903 -6.454504  1853440609    0.3  \n",
       "30        0.0        0.0    -4.394159 -1.105686  3039490906    0.3  \n",
       "31        0.0        0.0    -3.734613 -1.781745  2216609512    0.3  \n",
       "32        0.0        0.0    -3.735605 -1.951751  3207250604    0.3  \n",
       "33        0.0        0.0    -3.487128 -2.931746  1964226736    0.3  \n",
       "34        0.0        0.0    -3.984990 -2.138694  2968221499    0.3  \n",
       "35        0.0        0.0    -4.121403 -2.177379  3471889354    0.3  \n",
       "36        0.0        0.0    -4.163608 -2.041371  2960007350    0.3  \n",
       "37        0.0        0.0    -4.463943 -2.313500  1358431728    0.3  \n",
       "38        0.0        0.0    -3.705290 -2.021909  1604600292    0.3  \n",
       "39        0.0        0.0    -4.330320 -2.238609  3624037979    0.3  \n",
       "40        0.0        0.0    -0.853789 -1.187827  3880081671    0.1  \n",
       "41        0.0        0.0    -1.206014 -2.305878  2322667597    0.1  \n",
       "42        0.0        0.0    -1.032242 -2.264086   590449352    0.1  \n",
       "43        0.0        0.0    -1.038405 -2.536484  1924060235    0.1  \n",
       "44        0.0        0.0    -0.886623 -1.567296   598187340    0.1  \n",
       "45        0.0        0.0    -1.109989 -3.150769  3831694379    0.1  \n",
       "46        0.0        0.0    -1.017243 -1.127594  3467719188    0.1  \n",
       "47        0.0        0.0    -1.087786 -2.380603  1621712414    0.1  \n",
       "48        0.0        0.0    -0.995779 -1.840480  1708008996    0.1  \n",
       "49        0.0        0.0    -0.941718 -2.124685  2312516455    0.1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_expert_properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_disparity['target_fpr'] = current_expert_properties['fpr_target'].values\n",
    "fpr_disparity['target_fnr'] = current_expert_properties['fnr_target'].values\n",
    "fpr_disparity.to_parquet(f'./expert_info/fpr_disparity.parquet')\n",
    "types = fpr_disparity.index.to_list()\n",
    "groups = []\n",
    "for expert in types:\n",
    "    if expert.split('#')[0] == 'regular':\n",
    "        groups.append('standard')\n",
    "    else:\n",
    "        groups.append(expert.split('#')[0])\n",
    "  \n",
    "fpr_disparity['group'] = groups\n",
    "\n",
    "fpr_disparity['tpr_deployment'] = 1 - fpr_disparity['fnr_deployment']\n",
    "fpr_disparity['target_tpr'] = 1 - fpr_disparity['target_fnr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S0jXk45AaFSxTNF2j1Pzl16lRtE5JKWUr3FbR+kvpfyltaZRSj3wfXv+/ID8HDQK+Qdj8p9DUylGsD6XWiRLTS2r3zfTX9f7v/6W1J18eJeeRK1tyn+T6Kle25PSibcn1Qa6e0vpftGzx/uTn6eDjHpOwYp8OtLSFN/pWtM6S0+XPT2l5yq9HobLyl+yNfEKJ+UqvS1AgT5F0lJxfo5Q+yOUppWxh/txcCfoMmYg/Lkugo5NbaltyY1QgT9F6Sh9Lyfnl2yq//oqmFx9DKeVL+39F88v9X1asnpycHISNGoV/bt7EfV1d+fyCrOR6KpxewbJCyX1GBespNX/Rbcfk0kuus3i+ksuX1l7p6a//n5+bh//0746CU7vxQkcbkJVcVrE+lJJHgTpRan5F8pRSp6y09IqVFRSoUyitzjLaKyyTn5ePyE9aIP+n6XiuraVgeyWfC6GUcyQoUI/cGEoZf6ntllKPIv0ptawC6SWdT8Xr/P+fQwUFCPNyxj8jeuC+pqZ8/QBkBYrXpWjb8nWqKX9B+fnl0gsUaKtonflF0vMqmi4rM3+eIMPkyZOhq6uLt4kbPxL9y7XolCT+n0GGdx9kUKoPDDKUXL+SQYYy8zDI8L/0txhkULQtBhneXpABePNDPYMMb9ZZPJ96gwzF6mSQoVjZtx1kUK49Bhn+vwGlyypUPxhkENPfYpABAD7Ju4y3jcsliIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgtJIIgCJXdCSIiIiIiIiKq+jiTgYiIiIiIiIjUgkEGIiIiIiIiIlILBhmIiIiIiIiISC0YZCAiIiIiIiIitWCQgYiIiIiIiIjUgkEGIiIiIiIiIlILBhmIiIiIiIiISC0YZCAiIiIiIiIitWCQgYiIiIiIiIjUgkEGIiIiIiIiIlILBhmIiIiIiIiISC0YZCAiIiIiIiIitWCQgYiIiIiIiIjUgkEGIiIiIiIiIlILBhmIiIjeU/Hx8ZBIJNDT08P169eLHQ8ICEC9evUqoWfqERoaCnt7e7k0e3t7hIaGvtN+ZGRkQCKRID4+vtQ8J0+ehEQiwezZs4sd69KlCyQSCeLi4ooda9OmDczMzCAIgsL9iY6OhkQiUTh/UYWvmZMnT5abd/ny5WWOudCZM2cgkUgwfvz4UvOkp6dDIpEgLCysIt0t15EjRxAdHY3Hjx+rtd6yFJ7/+/fvl3i8Xr16CAgIeKt92L17N6Kjo99qG0REbwuDDERERO+5nJwcTJw4sbK78U5s27YNUVFRld2NYho2bAhjY2McPHhQLl0mk+HQoUMwMDAodiw3NxfJyckICAioUNBg0KBBSE5OVku/y6JokMHLywuNGjXCTz/9hIKCghLzrFq1CgAwcOBAdXYRR44cQUxMzDsNMrwPdu/ejZiYmMruBhGRUhhkICIies+1b98e69evx5kzZ95qOy9fvnyr9SvC29sbjo6Old2NYjQ0NODn54c///wT+fn5YvqZM2fw6NEjDBs2DImJiXJljh07hpcvX6JVq1YVaqt27dpo1qyZOrqtNgMHDkRWVhZ+++23YscKCgrw008/oVGjRvDy8qqE3lXcixcvKrsLRET/WgwyEBERvefCw8NhZmaGiIiIcvO+evUKkZGRqFOnDnR0dFCrVi2MGDGi2DfB9vb26NixI7Zu3Qpvb2/o6ekhJiYGiYmJkEgkWL9+PSIiImBlZQVDQ0N06tQJd+7cwbNnz/Dll1+iRo0aqFGjBvr374/s7Gy5ur/99lv4+fmhZs2aMDAwQP369TFnzhzk5eWV2/83l0sUzgIo6VH0W/jbt29jyJAhqF27NnR0dFCnTh3ExMTIBQQA4NatWwgODoaRkRGMjY3Ro0cP3L59u9x+AUCrVq2QnZ0ttxQhMTER1tbWGDRoEO7cuYOLFy/KHSssV2jTpk3w9fWFgYEBDA0NERgYiJSUFLl2SloukZOTg6+//hqWlpbQ19eHn58fTp06VerykmfPnmHYsGGoUaMGzMzM0K1bN9y6dUs8bm9vjwsXLiApKUk8n28uXSmqV69ekEql4oyFovbt24d//vkHAwYMqNA4gdeBmE6dOsHMzAx6enpwdHTEmDFjxPMwbtw4AECdOnXEfhaeV5lMhjlz5sDV1RW6urqoWbMmQkJCcPPmTbk2CpcV/fHHH/joo4+gr68v11d1yM3NxbRp08S+mJubo3///rh3755cvk2bNuHjjz+GlZUVpFIp3NzcMH78eDx//lzMExoaim+//RYA5F7vGRkZYtrIkSOxatUquLi4QCqVwsfHB0ePHoUgCJg7dy7q1KkDQ0NDtG7dGleuXJHrQ0JCArp06YLatWtDT08PTk5OGDJkSLHlIYWvw5SUFHTr1g3VqlWDsbEx+vTpU2xcRERFaVV2B4iIiKhsRkZGmDhxIkaPHo3ff/8drVu3LjGfIAjo2rUrDhw4gMjISLRs2RJnz57F5MmTkZycjOTkZOjq6or5T58+jbS0NEycOBF16tSBgYGB+GHnP//5D1q1aoX4+HhkZGTgm2++Qc+ePaGlpQUvLy9s2LABKSkp+M9//gMjIyMsWbJErPfq1avo1auXGOg4c+YMpk+fjkuXLmHlypUVGvvy5cvx9OlTubSoqCgcPHgQLi4uAF4HGJo0aQINDQ1MmjQJjo6OSE5OxrRp05CRkSF+MH758iXatm2LW7duYebMmahbty527dqFHj16KNSXwmDBwYMHxZkGBw8ehL+/P1xcXGBpaYnExES4u7uLx8zNzcXnM2bMwMSJE9G/f39MnDgRubm5mDt3Llq2bInjx4+L+UrSv39/bNq0CeHh4WjdujUuXryITz/9tNi5KTRo0CB88sknWL9+PW7cuIFx48ahT58++P333wG8Xpby+eefw9jYGMuXLwcAudfGm4yNjfHZZ59h06ZNuHfvHszNzcVjq1atgp6eHnr16lWhce7duxedOnWCm5sbFixYAFtbW2RkZGDfvn3iGB4+fIilS5di69atsLKyAgCx/LBhw/Ddd99h5MiR6NixIzIyMhAVFYXExEScPn0aNWrUEPuYlZWFPn36IDw8HDNmzICGRvnfsxUUFBQLUpVEJpOhS5cuOHToEMLDw/HRRx/h+vXrmDx5MgICAnDy5ElIpVIAr/euCAoKwpgxY2BgYIBLly5h9uzZOH78uHhtoqKi8Pz5c/zyyy9yy2YKxw8AO3fuREpKCmbNmgWJRIKIiAh88skn6NevH/7++28sW7YMT548wdixY/HZZ58hNTVVDFxdvXoVvr6+GDRoEIyNjZGRkYEFCxagRYsWOHfuHLS1teXG9+mnnyI4OBhDhw7FhQsXEBUVhYsXL+LYsWPF8hIRAQAEIiIiei+tWrVKACCcOHFCyMnJERwcHAQfHx9BJpMJgiAI/v7+goeHh5h/z549AgBhzpw5cvVs2rRJACB89913YpqdnZ2gqakpXL58WS7vwYMHBQBCp06d5NLHjBkjABDCwsLk0rt27SqYmpqWOoaCggIhLy9P+OmnnwRNTU3h4cOH4rF+/foJdnZ2cvnt7OyEfv36lVrf3Llzi41lyJAhgqGhoXD9+nW5vPPmzRMACBcuXBAEQRBiY2MFAMJ///tfuXyDBw8WAAirVq0qtV1BEASZTCaYmpoKH3/8sTi26tWrCytWrBAEQRCCg4OFzz//XBAEQcjJyRGkUqkQHBwsCIIgZGZmClpaWsKoUaPk6nz27JlgaWkp5hMEQZg8ebJQ9E+0CxcuCACEiIgIubIbNmwQAMidr8LXzPDhw+XyzpkzRwAgZGVliWkeHh6Cv79/mWMuqvC1sWDBAjHtwYMHgq6urtC7d+8Kj9PR0VFwdHQUXr58WWqbhdf72rVrculpaWkljvPYsWMCAOE///mPmObv7y8AEA4cOKDQOAvPf1mPouet8Dps2bJFrp4TJ04IAITly5eX2I5MJhPy8vKEpKQkAYBw5swZ8diIESOE0v5MByBYWloK2dnZYtr27dsFAEKDBg3Enw+CIAiLFi0SAAhnz54tsw/Xr18v9t4oPA9fffWVXJl169YJAIS1a9eWWCcREZdLEBERVQE6OjqYNm0aTp48ic2bN5eYp/Cb0Denz3fv3h0GBgY4cOCAXLqnpyfq1q1bYl0dO3aUe+7m5gYA+OSTT4qlP3z4UG7JREpKCjp37gwzMzNoampCW1sbISEhKCgowF9//VX+YEuxYcMGhIeHY+LEiRg8eLCYvnPnTrRq1QrW1tbIz88XHx06dAAAJCUlAXg9s8DIyAidO3eWq7fwG/jySCQS+Pv7488//0ReXh5SU1Px+PFj8U4D/v7+SExMhCAIOHr0qNx+DHv37kV+fj5CQkLk+qinpyeWK01h/4ODg+XSP//8c2hplTwp9c0xenp6AkCJdylRlL+/PxwdHeWWTKxbtw45OTni8gNFx/nXX3/h6tWrGDhwIPT09Crcl8JNNt98rTdp0gRubm7FXusmJialzgAqzf79+3HixIlijzf3DNm5cyeqV6+OTp06yY25QYMG4uyWQn///Td69eoFS0tL8b3h7+8PAEhLS1O4b61atYKBgYH4vPD92aFDB7mlNoXpRa/73bt3MXToUNjY2EBLSwva2tqws7MrtQ+9e/eWex4cHAwtLa1iG50SERXicgkiIqIq4osvvsC8efMwYcIEdOvWrdjxBw8eQEtLS24qO/D6w7GlpSUePHggl150+vWbTE1N5Z7r6OiUmf7q1SsYGhoiMzMTLVu2hIuLCxYvXgx7e3vo6enh+PHjGDFihNKbSx48eBChoaEICQnB1KlT5Y7duXMHO3bsKHXqduFa8wcPHsDCwqLYcUtLS4X70apVK2zbtg0nTpxAcnIyLCwsxGUb/v7+uH//Pi5cuCB+ACsMMty5cwcA0Lhx4xLrLWv6fuF1e7PvWlpaMDMzK7HMm+mFSyFU2dxTIpFgwIABmDBhAk6ePAkfHx+sWrUKderUqfA4C9f0165dW6m+FJ6Tkl7D1tbWxYIpZb3WS+Pl5SW35KLQm0GRO3fu4PHjx+J74U2Fr7/s7Gy0bNkSenp6mDZtGurWrQt9fX3cuHED3bp1q9C1Ueb9Cbxe2vHxxx/j1q1biIqKQv369WFgYACZTIZmzZqV2Ic33x+Fr7s3f54QERVikIGIiKiKkEgkmD17Ntq1a4fvvvuu2HEzMzPk5+cXWzMvCAJu375d7INfRW6rqKjt27fj+fPn2Lp1q/jtKACkpqYqXefZs2fRtWtX+Pv74/vvvy92vEaNGvD09MT06dNLLG9tbQ3g9fk5fvx4seOKbvwI/C9okJiYiOTkZPFbaOD1XgE1atTAwYMHkZiYCCsrKzEAUfhh9ZdffpE7L4ooDBjcuXMHtWrVEtPz8/Pf+Qe90NBQTJo0CStXroS2tjZSUlIwdepU8bWk6DgLX59vbtKoqMJzkpWVVSxQcevWrWLBgbfxWi9UuLnmnj17SjxuZGQE4PVMo1u3biExMVHudfMub895/vx5nDlzBvHx8ejXr5+Y/ubmkEXdvn27xNddaQEuIiIGGYiIiKqQtm3bol27dpgyZQpsbGzkjrVp0wZz5szB2rVr8dVXX4npW7ZswfPnz9GmTZu33r/CD3NFNxEUBKHE4IAiMjMz0aFDBzg4OGDLli0lzlbo2LEjdu/eDUdHR5iYmJRaV6tWrbB582b8+uuvcssJ1q9fr3B/PDw8YG5ujt9//x0nT57EzJkzxWMSiQR+fn7Ys2cPjh49KjfbJDAwEFpaWrh69So+++wzhdsDAD8/PwCv70zQsGFDMf2XX35RaGPC0ujq6lZ4ZoO1tTXat2+PDRs2ID8/HxoaGnIfVhUdZ926deHo6IiVK1di7NixpW46WdoMjMKlD2vXrpULnp04cQJpaWmYMGFChcalio4dO2Ljxo0oKChA06ZNS81X0nsDAOLi4orlLTruwk0j1aEifSi0bt06NGrUSHy+efNm5Ofni8uEiIjexCADERFRFTN79mw0atQId+/ehYeHh5jerl07BAYGIiIiAk+fPkXz5s3Fu0t4e3ujb9++b71v7dq1g46ODnr27Inw8HC8evUKsbGxePTokVL1dejQAY8fP8ayZctw4cIFuWOOjo4wNzfHlClTkJCQgI8++ghhYWFwcXHBq1evkJGRgd27d2PFihWoXbs2QkJCsHDhQoSEhGD69OlwdnbG7t27sXfvXoX7I5FIEBAQgF9++QWCIMh9Iw28XjIxZswYCIIgd+tKe3t7TJkyBRMmTMDff/+N9u3bw8TEBHfu3MHx48dhYGCAmJiYEtv08PBAz549MX/+fGhqaqJ169a4cOEC5s+fD2NjY4XulFCS+vXrY+PGjdi0aRMcHBygp6eH+vXrl1tu4MCB2LVrF3744QcEBgbKBbsqMs5vv/0WnTp1QrNmzfDVV1/B1tYWmZmZ2Lt3L9atWyf2EQAWL16Mfv36QVtbGy4uLnBxccGXX36JpUuXQkNDAx06dBDvLmFjYyMXZHvbvvjiC6xbtw5BQUEYPXo0mjRpAm1tbdy8eRMHDx5Ely5d8Omnn+Kjjz6CiYkJhg4dismTJ0NbWxvr1q3DmTNnitVZOO7Zs2ejQ4cO0NTUhKenZ6lLMhTl6uoKR0dHjB8/HoIgwNTUFDt27EBCQkKpZbZu3QotLS20a9dOvLuEl5dXsT1CiIhElbnrJBEREZWu6N0l3tSrVy8BgNzdJQRBEF6+fClEREQIdnZ2gra2tmBlZSUMGzZMePTokVw+Ozs74ZNPPilWb+EdBH7++WeF+lK4A/29e/fEtB07dgheXl6Cnp6eUKtWLWHcuHHCb7/9JgAQDh48KOZT5O4SKGOH/6J3g7h3754QFhYm1KlTR9DW1hZMTU2FRo0aCRMmTJDbhf/mzZvCZ599JhgaGgpGRkbCZ599Jhw5ckShu0sUWr58uQBAMDc3L3YsNTVV7F96enqx49u3bxdatWolVKtWTdDV1RXs7OyEzz//XNi/f3+xc1rUq1evhLFjxwo1a9YU9PT0hGbNmgnJycmCsbGx3O7/pV2nwuta9PxnZGQIH3/8sWBkZCQAKHYtSpObmytYWFgIAITNmzeXmEeRcQqCICQnJwsdOnQQjI2NBV1dXcHR0bHY3QwiIyMFa2trQUNDQ24MBQUFwuzZs4W6desK2traQo0aNYQ+ffoIN27ckCv/5l1YylPSa7qoku7KkZeXJ8ybN0983RsaGgqurq7CkCFD5F4HR44cEXx9fQV9fX3B3NxcGDRokHD69Olir7+cnBxh0KBBgrm5uSCRSOTusAFAGDFihFz7165dEwAIc+fOlUsv6f188eJFoV27doKRkZFgYmIidO/eXcjMzBQACJMnTy52Hk6dOiV06tRJfM/07NlTuHPnjsLnk4g+PBJBEIR3EMsgIiIiIjU6cuQImjdvjnXr1il8hwwiRUVHRyMmJgb37t0rcQNMIqLScLkEERER0XsuISEBycnJaNSoEaRSKc6cOYNZs2bB2dm5xDuNEBERVRYGGYiIiIjec9WqVcO+ffuwaNEiPHv2DDVq1ECHDh0wc+bMYrdUJCIiqkxcLkFEREREREREaqHcdsRERERERERERG9gkIGIiIiIiIiI1IJBBiIiIiIiIiJSCwYZiIiIiIiIiEgteHcJon+5v69erewuVIq96Y4qlQ90Vv68mT+8rFLb90xdlC5r9uSaSm3fqFZP6bL6kucqtU3vXrWXd5Uu+1RaU6W2LW+dVrrsbeuGKrUtSCRKl5VU4f2yJVC+749l1VVqO1+m/J+cepo5KrVd68VfSpd9bGCtUts5UP7OH7p4pVLbVHEP801VKp9yw0Tpsm3qfJh/r1VlFnfOKF32joWXGntSMQ6Oqv2NrAjOZCAiIiIiIiIitWCQgT5IAQEBGDNmTGV3g4iIiIiI6F+FyyXog7R161Zoa2tXdjeIiIiIiIj+VRhkoA+Sqalqa+6IiIiIiIioOC6XoA9S0eUS9vb2mDFjBgYMGAAjIyPY2triu+++k8t/8+ZNfPHFFzA1NYWBgQF8fHxw7Ngx8XhsbCwcHR2ho6MDFxcXrFmzRq68RCJBXFwcOnbsCH19fbi5uSE5ORlXrlxBQEAADAwM4Ovri6tvbNK4Y8cONGrUCHp6enBwcEBMTAzy8/PfzkkhIiIiIiJSEYMMRADmz58PHx8fpKSkYPjw4Rg2bBguXboEAMjOzoa/vz9u3bqFX3/9FWfOnEF4eDhkMhkAYNu2bRg9ejS+/vprnD9/HkOGDEH//v1x8OBBuTamTp2KkJAQpKamwtXVFb169cKQIUMQGRmJkydPAgBGjhwp5t+7dy/69OmDsLAwXLx4EXFxcYiPj8f06dPf0VkhIiIiIiKqGC6XIAIQFBSE4cOHAwAiIiKwcOFCJCYmwtXVFevXr8e9e/dw4sQJcZmFk5OTWHbevHkIDQ0Vy48dOxZHjx7FvHnz0KpVKzFf//79ERwcLLbh6+uLqKgoBAYGAgBGjx6N/v37i/mnT5+O8ePHo1+/fgAABwcHTJ06FeHh4Zg8eXKJ48jJyUFOTk6xNF1dXZXODxERERERkSI4k4EIgKenp/h/iUQCS0tL3L37+v7xqamp8Pb2LnUfh7S0NDRv3lwurXnz5khLSyu1DQsLCwBA/fr15dJevXqFp0+fAgBOnTqFKVOmwNDQUHwMHjwYWVlZePHiRYl9mTlzJoyNjeUeK1asUPQ0EBERERERqYQzGYiAYneakEgk4nIIqVRabnmJRCL3XBCEYmlF2yg8VlJaYbsymQwxMTHo1q1bsfb09PRK7EdkZCTGjh0rl/bPzZvl9p+IiIiIiEgdGGQgKoenpyd++OEHPHz4sMTZDG5ubjh8+DBCQkLEtCNHjsDNzU2ldhs2bIjLly/LLc0oj66ubrGlEfe5VIKIiIiIiN4RBhmIytGzZ0/MmDEDXbt2xcyZM2FlZYWUlBRYW1vD19cX48aNQ3BwMBo2bIg2bdpgx44d2Lp1K/bv369Su5MmTULHjh1hY2OD7t27Q0NDA2fPnsW5c+cwbdo0NY2OiIiIiIhIfbgnA1E5dHR0sG/fPtSsWRNBQUGoX78+Zs2aBU1NTQBA165dsXjxYsydOxceHh6Ii4vDqlWrEBAQoFK7gYGB2LlzJxISEtC4cWM0a9YMCxYsgJ2dnRpGRUREREREpH4SQRCEyu4EEb09f1+9WtldqBR70x1VKh/orPx5M394WaW275m6KF3W7Mk1ldq+Ua2e0mX1Jc9VapvevWov7ypd9qm0pkptW946rXTZ29YNVWpbeGPPnIqQVOE/myRQvu+PZdVVajtfpvzkWT3NnPIzlaHWi7+ULvvYwFqltnNQ8h5KitDFK5Xapop7mF/yJt+KSrlhonTZNnU+zL/XqjKLO2eULnvHwkuNPakYB0fV/kZWBGcyEBEREREREZFaMMhARERERERERGrBIAMRERERERERqQX3ZCD6l6vKezIczXJQumwzq7/V2JOKyZYZqVT+2HXl17qruqZTlTX6ui8eqtT2PTNXpcvWvHdBpbbvmnuoVL6q+uuZjdJl6xrdUGNPKkaVvQUAQIDyezJ8qDSEApXKyySaaurJu3U/r4ZK5T1TVihd9kKjwSq1baL5SKXyRIqSqfC9dco91TY0b2Su2l5UHyLuyUBEREREREREVQaDDERERERERESkFgwyUKULCAjAmDFjxOf29vZYtGiRyvWqo57o6Gg0aNBAfB4aGoquXbuqVCcAxMfHo3r16irXQ0RERERE9D5R/qbFRGqydetWaGtrq73eEydOwMDAQKU6vvnmG4waNUpNPfqfHj16ICgoSHweHR2N7du3IzU1Ve1tERERERERvSsMMlClMzU1fSv1mpubq1yHoaEhDA0N1dCb/8nLy4NUKoVUKlVrvURERERERJWNyyXegoCAAISFhSE8PBympqawtLREdHS0eHzBggWoX78+DAwMYGNjg+HDhyM7O1s8XjiVfufOnXBxcYG+vj4+//xzPH/+HKtXr4a9vT1MTEwwatQoFBT8b7fn3NxchIeHo1atWjAwMEDTpk2RmJiocL+PHDkCPz8/SKVS2NjYICwsDM+fPxeP29vbY9q0aQgJCYGhoSHs7Ozw3//+F/fu3UOXLl1gaGiI+vXr4+TJk2KZBw8eoGfPnqhduzb09fVRv359bNiwodj5KrpcoiKio6Nha2sLXV1dWFtbIywsTK6/RZdLSCQSxMXFoWPHjtDX14ebmxuSk5Nx5coVBAQEwMDAAL6+vrha5G4Mby6XeNOePXvQokULVK9eHWZmZujYsaNc+YyMDEgkEmzevBkBAQHQ09PD2rVr5ZZLxMfHIyYmBmfOnIFEIoFEIkF8fDwGDBiAjh07yrWXn58PS0tLrFy5UqnzRURERERE9DYxyPCWrF69GgYGBjh27BjmzJmDKVOmICEhAQCgoaGBJUuW4Pz581i9ejV+//13hIeHy5V/8eIFlixZgo0bN2LPnj1ITExEt27dsHv3buzevRtr1qzBd999h19++UUs079/f/z555/YuHEjzp49i+7du6N9+/ZIT08vt7/nzp1DYGAgunXrhrNnz2LTpk04fPgwRo4cKZdv4cKFaN68OVJSUvDJJ5+gb9++CAkJQZ8+fXD69Gk4OTkhJCQEhXdGffXqFRo1aoSdO3fi/Pnz+PLLL9G3b18cO3ZM1VOMX375BQsXLkRcXBzS09Oxfft21K9fv8wyU6dORUhICFJTU+Hq6opevXphyJAhiIyMFIMjb465LM+fP8fYsWNx4sQJHDhwABoaGvj0008hk8nk8kVERCAsLAxpaWkIDAyUO9ajRw98/fXX8PDwQFZWFrKystCjRw8MGjQIe/bsQVZWlph39+7dyM7ORnBwsMJ9JCIiIiIiele4XOIt8fT0xOTJkwEAzs7OWLZsGQ4cOIB27drJfWtfp04dTJ06FcOGDcPy5cvF9Ly8PMTGxsLx/+9j+vnnn2PNmjW4c+cODA0N4e7ujlatWuHgwYPo0aMHrl69ig0bNuDmzZuwtrYG8Ho/gT179mDVqlWYMWNGmf2dO3cuevXqJfbN2dkZS5Ysgb+/P2JjY6GnpwcACAoKwpAhQwAAkyZNQmxsLBo3bozu3bsDeP1h2tfXF3fu3IGlpSVq1aqFb775Rmxn1KhR2LNnD37++Wc0bdpUhTMMZGZmwtLSEm3btoW2tjZsbW3RpEmTMsv0799f/IBe2NeoqCjxg//o0aPRv39/hfvw2WefyT3/8ccfUbNmTVy8eBH16tUT08eMGYNu3bqVWIdUKoWhoSG0tLRgaWkppn/00UdwcXHBmjVrxCDUqlWr0L1791KXcOTk5CAnJ6dYmq6ursJjIiIiIiIiUhZnMrwlnp6ecs+trKxw9+5dAMDBgwfRrl071KpVC0ZGRggJCcGDBw/klibo6+uLAQYAsLCwgL29vdyHSwsLC7HO06dPQxAE1K1bV9xHwNDQEElJSXLT90tz6tQpxMfHy5UNDAyETCbDtWvXShyXhYUFAMjNHihMK+xXQUEBpk+fDk9PT5iZmcHQ0BD79u1DZmZmuX0qT/fu3fHy5Us4ODhg8ODB2LZtG/Lz88sso0j/X716hadPnyrUh6tXr6JXr15wcHBAtWrVUKdOHQAoNj4fHx+F6nvToEGDsGrVKgCvz+muXbswYMCAUvPPnDkTxsbGco8VK1Yo1TYREREREVFFcSbDW/Lm3RIkEglkMhmuX7+OoKAgDB06FFOnToWpqSkOHz6MgQMHIi8vr8zypdUJADKZDJqamjh16hQ0NTXl8imycaFMJsOQIUPk9jQoZGtrW2K/JBJJqWmF/Zo/fz4WLlyIRYsWiftQjBkzBrm5ueX2qTw2Nja4fPkyEhISsH//fgwfPhxz585FUlJSqXerqGj/y9OpUyfY2Njg+++/h7W1NWQyGerVq1dsfMre5SIkJATjx49HcnIykpOTYW9vj5YtW5aaPzIyEmPHjpVL++fmTaXaJiIiIiIiqigGGd6xkydPIj8/H/Pnz4eGxuuJJJs3b1a5Xm9vbxQUFODu3btlfggtTcOGDXHhwgU4OTmp3JeiDh06hC5duqBPnz4AXn94T09Ph5ubm1rql0ql6Ny5Mzp37owRI0bA1dUV586dQ8OGDdVSf1kePHiAtLQ0xMXFief88OHDStWlo6Mjt4lnITMzM3Tt2hWrVq1CcnJyuUs5dHV1iy2NuM+lEkRERERE9I4wyPCOOTo6Ij8/H0uXLkWnTp3w559/qmU6e926ddG7d2+EhIRg/vz58Pb2xv379/H777+jfv36CAoKKrN8REQEmjVrhhEjRmDw4MEwMDBAWloaEhISsHTpUqX75eTkhC1btuDIkSMwMTHBggULcPv2bbUEGeLj41FQUICmTZtCX18fa9asgVQqhZ2dncp1K8LExARmZmb47rvvYGVlhczMTIwfP16puuzt7XHt2jWkpqaidu3aMDIyEoMFgwYNQseOHVFQUIB+/fqpcwhERERERERqxT0Z3rEGDRpgwYIFmD17NurVq4d169Zh5syZaql71apVCAkJwddffw0XFxd07twZx44dg42NTbllPT09kZSUhPT0dLRs2RLe3t6IioqClZWVSn2KiopCw4YNERgYiICAAFhaWqJr164q1VmoevXq+P7779G8eXN4enriwIED2LFjB8zMzNRSf3k0NDSwceNGnDp1CvXq1cNXX32FuXPnKlXXZ599hvbt26NVq1YwNzeXu81n27ZtYWVlhcDAQHFTTyIiIiIioveRRCi81yARvZdevHgBa2trrFy5stQ7VJTlbwU2/nxfHc1yULpsM6u/1diTismWGalU/tj1mkqXbVNHtetd7eVdpcvqvnioUtv3zFyVLlvz3gWV2r5r7qFS+arqr2flB6FLU9fohhp7UjESqPaniwCJmnry4dAQii/pqwiZRLP8TO+h+3k1VCrvmaL8bNULjQar1LaJ5iOVyhMpSqbC99Yp91SbgdzI/Fr5mUiOQ5GbC7wtXC5B9J6SyWS4ffs25s+fD2NjY3Tu3Lmyu0RERERERFQmLpf4QHTo0EHu9pRFHzNmzKjs7pVo3bp1pfbZw+Pf/61jZmYmatWqhc2bN2PlypXQ0mJMkIiIiIiI3m/81PKB+OGHH/Dy5csSj5mamr7j3iimc+fOaNq0aYnHSrtF5b+Jvb09uJqJiIiIiIiqEu7JQPQvV5X3ZPhQvRKkSpfVk5QcTPy3U3UfDEONZ0qXtb6UoFLbv5p+qXRZn5pci/qu6eU/V6n8Ky0DNfWEqoJHBSZKl+WeCsp5mK/8l2emWqrtLXTtRS2ly9bR/0eltquqC49V25PBo/p1NfXkw/Eu9mTgcgkiIiIiIiIiUgsGGYiIiIiIiIhILRhkqCQSiQTbt28v9XhGRgYkEglSU1PfWZ+qsoCAAIwZM6ayu0FERERERPRBY5CBqpTExERIJBI8fvy4srvyTn2o4yYiIiIioqrlXx1kyM3NrewuvDWCICA/P7+yu0FEREREREQkqlJBhoCAAIwcORIjR45E9erVYWZmhokTJ4q3+bO3t8e0adMQGhoKY2NjDB48GABw5MgR+Pn5QSqVwsbGBmFhYXj+/PXu0JGRkWjWrFmxtjw9PTF58uRy+3TixAm0a9cONWrUgLGxMfz9/XH69Gm5POnp6fDz84Oenh7c3d2RkFB8J/Ljx4/D29sbenp68PHxQUpKitzxwm+y9+7dCx8fH+jq6uLQoUMQBAFz5syBg4MDpFIpvLy88Msvv4jlHj16hN69e8Pc3BxSqRTOzs5YtWoVgNdBmJEjR8LKygp6enqwt7fHzJkzyx0z8Hq5R1xcHDp27Ah9fX24ubkhOTkZV65cQUBAAAwMDODr64urb9zZIDY2Fo6OjtDR0YGLiwvWrFlTrN4ffvgBn376KfT19eHs7Ixff/0VwOslJK1atQIAmJiYQCKRIDQ0VCwrk8kQHh4OU1NTWFpaIjo6Wq7u6Oho2NraQldXF9bW1ggLC1NorDk5OQgPD4eNjQ10dXXh7OyMH3/8UTyelJSEJk2aQFdXF1ZWVhg/frxcAMje3h6LFi2Sq7NBgwZy/VNl3ERERERERO+LKhVkAIDVq1dDS0sLx44dw5IlS7Bw4UL88MMP4vG5c+eiXr16OHXqFKKionDu3DkEBgaiW7duOHv2LDZt2oTDhw9j5MiRAIDevXvj2LFjch+GL1y4gHPnzqF3797l9ufZs2fo168fDh06hKNHj8LZ2RlBQUF49uz17dBkMhm6desGTU1NHD16FCtWrEBERIRcHc+fP0fHjh3h4uKCU6dOITo6Gt98802J7YWHh2PmzJlIS0uDp6cnJk6ciFWrViE2NhYXLlzAV199hT59+iApKQkAEBUVhYsXL+K3335DWloaYmNjUaNGDQDAkiVL8Ouvv2Lz5s24fPky1q5dC3t7e4WvxdSpUxESEoLU1FS4urqiV69eGDJkCCIjI3Hy5EkAEM8zAGzbtg2jR4/G119/jfPnz2PIkCHo378/Dh48KFdvTEwMgoODcfbsWQQFBaF37954+PAhbGxssGXLFgDA5cuXkZWVhcWLF4vlVq9eDQMDAxw7dgxz5szBlClTxIDOL7/8goULFyIuLg7p6enYvn076tevr9A4Q0JCsHHjRixZsgRpaWlYsWIFDA0NAQD//PMPgoKC0LhxY5w5cwaxsbH48ccfMW3aNIXPo6rjJiIiIiIiel9oVXYHKsrGxgYLFy6ERCKBi4sLzp07h4ULF4qzFlq3bi33AT0kJAS9evUSNwV0dnbGkiVL4O/vj9jYWNSrVw+enp5Yv349oqKiAADr1q1D48aNUbdu3XL707p1a7nncXFxMDExQVJSEjp27Ij9+/cjLS0NGRkZqF27NgBgxowZ6NChg1hm3bp1KCgowMqVK6Gvrw8PDw/cvHkTw4YNK9belClT0K5dOwCvgxMLFizA77//Dl9fXwCAg4MDDh8+jLi4OPj7+yMzMxPe3t7w8fEBALkgQmZmJpydndGiRQtIJBLY2VXsPrX9+/dHcHAwACAiIgK+vr6IiopCYGAgAGD06NHo37+/mH/evHkIDQ3F8OHDAQBjx47F0aNHMW/ePPGbegAIDQ1Fz549xXO1dOlSHD9+HO3bt4ep6et7H9esWRPVq1eX60/R2SfOzs5YtmwZDhw4gHbt2iEzMxOWlpZo27YttLW1YWtriyZNmpQ7xr/++gubN29GQkIC2rZtC+D1OS60fPly2NjYYNmyZZBIJHB1dcWtW7cQERGBSZMmQUND8TiesuMuKicnBzk5OcXSdHV1Fe4HERERERGRsqrcTIZmzZpBIpGIz319fZGeno6CggIAED9MFzp16hTi4+NhaGgoPgIDAyGTyXDt2jUAr2czrFu3DsDrvQ42bNig0CwGALh79y6GDh2KunXrwtjYGMbGxsjOzkZmZiYAIC0tDba2tmKAobDPRaWlpcHLywv6+vql5ilUdHwXL17Eq1ev0K5dO7nx/fTTT+LMjGHDhmHjxo1o0KABwsPDceTIEbF8aGgoUlNT4eLigrCwMOzbt0+hMRfy9PQU/29hYQEAcrMDLCws8OrVKzx9+lQcZ/PmzeXqaN68OdLS0kqt18DAAEZGRrh7926F+gMAVlZWYrnu3bvj5cuXcHBwwODBg7Ft2zaF9rRITU2FpqYm/P39SzyelpYGX19fuddk8+bNkZ2djZs3b5Zbf2n9r8i4i5o5c6b4Oix8rFixokJ1EBERERERKavKzWQoj4GBgdxzmUyGIUOGlLj+3tbWFgDQq1cvjB8/HqdPn8bLly9x48YNfPHFFwq1Fxoainv37mHRokWws7ODrq4ufH19xU0nC/eLKKroB9LS8pSm6PhkMhkAYNeuXahVq5ZcvsJvrjt06IDr169j165d2L9/P9q0aYMRI0Zg3rx5aNiwIa5du4bffvsN+/fvR3BwMNq2bSu3p0NZtLW1i42ppLTCfhZNKyQIQrG0onUUlilahyL9ebOcjY0NLl++jISEBOzfvx/Dhw/H3LlzkZSUVKxcUVKptMw2S+p/4fUsTNfQ0Ch2jfPy8irUf0VFRkZi7Nixcmn/VDDYQUREREREpKwqF2Q4evRosefOzs7Q1NQsMX/Dhg1x4cIFODk5lVpn7dq14efnh3Xr1uHly5do27at+M18eQ4dOoTly5cjKCgIAHDjxg3cv39fPO7u7o7MzEzcunUL1tbWAIDk5GS5Otzd3bFmzRq8fPlS/FD75jhL4u7uDl1dXWRmZpb6TTsAmJubIzQ0FKGhoWjZsiXGjRuHefPmAQCqVauGHj16oEePHvj888/Rvn17PHz4UJyer05ubm44fPgwQkJCxLQjR47Azc1N4Tp0dHQAQJy5UhFSqRSdO3dG586dMWLECLi6uuLcuXNo2LBhqWXq168PmUyGpKQkcblEUe7u7tiyZYtcsOHIkSMwMjISAz/m5ubIysoSyzx9+lScRaMoRcetq6tbbGnEfS6VICIiIiKid6TKLZe4ceMGxo4di8uXL2PDhg1YunQpRo8eXWr+iIgIJCcnY8SIEUhNTUV6ejp+/fVXjBo1Si5f7969sXHjRvz888/o06ePwv1xcnLCmjVrkJaWhmPHjqF3795y3363bdsWLi4uCAkJwZkzZ3Do0CFMmDBBro5evXpBQ0MDAwcOxMWLF7F7924xCFAWIyMjfPPNN/jqq6+wevVqXL16FSkpKfj222+xevVqAMCkSZPw3//+F1euXMGFCxewc+dO8UP9woULsXHjRly6dAl//fUXfv75Z1haWpa55l8V48aNQ3x8PFasWIH09HQsWLAAW7duLXWTy5LY2dlBIpFg586duHfvHrKzsxUqFx8fjx9//BHnz5/H33//jTVr1kAqlZa7D4W9vT369euHAQMGYPv27bh27RoSExOxefNmAMDw4cNx48YNjBo1CpcuXcJ///tfTJ48GWPHjhX3Y2jdujXWrFmDQ4cO4fz58+jXr1+pQTF1j5uIiIiIiOhdqnJBhpCQELx8+RJNmjTBiBEjMGrUKHz55Zel5vf09ERSUhLS09PRsmVLeHt7IyoqClZWVnL5unfvjgcPHuDFixfo2rWrwv1ZuXIlHj16BG9vb/Tt2xdhYWGoWbOmeFxDQwPbtm1DTk4OmjRpgkGDBmH69OlydRgaGmLHjh24ePEivL29MWHCBMyePVuh9qdOnYpJkyZh5syZcHNzQ2BgIHbs2IE6deoAeP0NeGRkJDw9PeHn5wdNTU1s3LhRbHf27Nnw8fFB48aNkZGRgd27d1dos8KK6Nq1KxYvXoy5c+fCw8MDcXFxWLVqFQICAhSuo1atWoiJicH48eNhYWEhd/eKslSvXh3ff/89mjdvDk9PTxw4cAA7duyAmZlZuWVjY2Px+eefY/jw4XB1dcXgwYPFW6DWqlULu3fvxvHjx+Hl5YWhQ4di4MCBmDhxolg+MjISfn5+6NixI4KCgtC1a1c4OjoqPGZVxk1ERERERPQuSYSKbAhQyQICAtCgQQMsWrSosrtCVGX8XeT2rFQ1vBLK3gukLHqSl2rsSdWRLTNSqbyhxjOly1pfSlCp7V9NSw+Ul8enZsWWXpHq9PKfq1T+lZZB+ZnoX+NRgYnSZU00H6mxJx+Oh/nKL/k11XqoUtvXXtQqP1Mp6uj/o1LbVdWFxxW7u92bPKpfV1NPPhwOFfyyUxlVbiYDEREREREREb2fGGQoR9FbQ775OHToUGV3761Yt25dqWP28PCo7O6p1aFDh8q8xkRERERERKS4KnV3icTExHfeZmpqaqnH3rxt5L9F586d0bRp0xKPlXW7x6rIx8enzGtMREREREREiqtSezIQUcVxTwait0unQLV9MHI1ld+Doyq7k2uudFkLnXsqtS3NU34Pjpfaqu3/QURl0xAqfpvyomSSit3Bi6gyGOaotudKtq7y+71wTwYiIiIiIiIiqjIYZCAiIiIiIiIitWCQoZJIJBJs37691OMZGRmQSCTcL0BBAQEBGDNmTGV3g4iIiIiI6IPGIEMR/GD//ktMTIREIsHjx48ruyvv1Ic6biIiIiIiqlqqVJChoKAAMpmsWHpubm4l9KZyCYKA/Pz8yu4GERERERERkeitBxlkMhlmz54NJycn6OrqwtbWFtOnTy/xm9nU1FRIJBJkZGQAAOLj41G9enXs3LkT7u7u0NXVxfXr12Fvb49p06YhNDQUxsbGGDx4MADgyJEj8PPzg1QqhY2NDcLCwvD8+XOxfnt7e8yYMQMDBgyAkZERbG1t8d1334nH69SpAwDw9vaGRCJBQEBAueM7ceIE2rVrhxo1asDY2Bj+/v44ffq0XJ709HT4+flBT08P7u7uSEhIKFbP8ePH4e3tDT09Pfj4+CAlJUXueOH52rt3L3x8fKCrq4tDhw5BEATMmTMHDg4OkEql8PLywi+//CKWe/ToEXr37g1zc3NIpVI4Oztj1apVAF4HZ0aOHAkrKyvo6enB3t4eM2fOLHfMwOvlHnFxcejYsSP09fXh5uaG5ORkXLlyBQEBATAwMICvry+uvnFng9jYWDg6OkJHRwcuLi5Ys2ZNsXp/+OEHfPrpp9DX14ezszN+/fVXAK9nmrRq1QoAYGJiAolEgtDQULGsTCZDeHg4TE1NYWlpiejoaLm6o6OjYWtrC11dXVhbWyMsLEyhsebk5CA8PBw2NjbQ1dWFs7MzfvzxR/F4UlISmjRpAl1dXVhZWWH8+PFyASB7e3ssWrRIrs4GDRrI9U+VcRMREREREb0v3nqQITIyErNnz0ZUVBQuXryI9evXw8LCQuHyL168wMyZM/HDDz/gwoULqFmzJgBg7ty5qFevHk6dOoWoqCicO3cOgYGB6NatG86ePYtNmzbh8OHDGDlypFx98+fPFz/EDx8+HMOGDcOlS5cAvP6gDwD79+9HVlYWtm7dWm7/nj17hn79+uHQoUM4evQonJ2dERQUhGfPXt8eSyaToVu3btDU1MTRo0exYsUKREREyNXx/PlzdOzYES4uLjh16hSio6PxzTfflNheeHg4Zs6cibS0NHh6emLixIlYtWoVYmNjceHCBXz11Vfo06cPkpKSAEA877/99hvS0tIQGxuLGjVqAACWLFmCX3/9FZs3b8bly5exdu1a2NvbK3hlgKlTpyIkJASpqalwdXVFr169MGTIEERGRuLkyZMAIHf+t23bhtGjR+Prr7/G+fPnMWTIEPTv3x8HDx6UqzcmJgbBwcE4e/YsgoKC0Lt3bzx8+BA2NjbYsmULAODy5cvIysrC4sWLxXKrV6+GgYEBjh07hjlz5mDKlCliQOeXX37BwoULERcXh/T0dGzfvh3169dXaJwhISHYuHEjlixZgrS0NKxYsQKGhoYAgH/++QdBQUFo3Lgxzpw5g9jYWPz444+YNm2awudR1XETERERERG9L7TeZuXPnj3D4sWLsWzZMvTr1w8A4OjoiBYtWiAxMVGhOvLy8rB8+XJ4eXnJpbdu3Vrug3hISAh69eolbv7n7OyMJUuWwN/fH7GxsdDT0wMABAUFYfjw4QCAiIgILFy4EImJiXB1dYW5+et7dpuZmcHS0lKh/rVu3VrueVxcHExMTJCUlISOHTti//79SEtLQ0ZGBmrXrg0AmDFjBjp06CCWWbduHQoKCrBy5Uro6+vDw8MDN2/exLBhw4q1N2XKFLRr1w7A6+DEggUL8Pvvv8PX1xcA4ODggMOHDyMuLg7+/v7IzMyEt7c3fHx8AEAuiJCZmQlnZ2e0aNECEokEdnZ2Co25UP/+/REcHAzg9bn09fVFVFQUAgMDAQCjR49G//79xfzz5s1DaGioeP7Hjh2Lo0ePYt68eeI39QAQGhqKnj17iudq6dKlOH78ONq3bw9TU1MAQM2aNVG9enW5/nh6emLy5MkAXl//ZcuW4cCBA2jXrh0yMzNhaWmJtm3bQltbG7a2tmjSpEm5Y/zrr7+wefNmJCQkoG3btgBen+NCy5cvh42NDZYtWwaJRAJXV1fcunULERERmDRpEjQ0FI/jKTvuonJycpCTk1MsTVdXV+F+EBERERERKeutzmRIS0tDTk4O2rRpo3QdOjo68PT0LJZe+KG50KlTpxAfHw9DQ0PxERgYCJlMhmvXron5itYlkUhgaWmJu3fvKt2/u3fvYujQoahbty6MjY1hbGyM7OxsZGZmAnh9DmxtbcUAAwAxIFAoLS0NXl5e0NfXLzVPSeO+ePEiXr16hXbt2smN+6effhKXKQwbNgwbN25EgwYNEB4ejiNHjojlQ0NDkZqaChcXF4SFhWHfvn0VGnvRc1k4O6Xo7AALCwu8evUKT58+FcfZvHlzuTqaN2+OtLS0Uus1MDCAkZGRQtfozdeJlZWVWK579+54+fIlHBwcMHjwYGzbtk2hPS1SU1OhqakJf3//Eo+npaXB19cXEolEbkzZ2dm4efNmufWX1v+KjLuomTNniq/DwseKFSsqVAcREREREZGy3upMBqlUWuqxwm94BUEQ0/Ly8kqso+gHuEIGBgZyz2UyGYYMGVLiOntbW1vx/9ra2nLHJBJJiZtJKio0NBT37t3DokWLYGdnB11dXfj6+oqbURYdX9E2iyopT2mKjruw37t27UKtWrXk8hV+c92hQwdcv34du3btwv79+9GmTRuMGDEC8+bNQ8OGDXHt2jX89ttv2L9/P4KDg9G2bVu5PR3KUvRcFo6ppLSi57eksb+Zpuw1KqucjY0NLl++jISEBOzfvx/Dhw/H3LlzkZSUVKxcUWW9hkvrf+H1LEzX0NAodo1Leq2r47UZGRmJsWPHyqX9U8FgBxERERERkbLe6kwGZ2dnSKVSHDhwoNixwqUJWVlZYpoqt45s2LAhLly4ACcnp2IPHR0dheoozFdQUKBwu4cOHUJYWBiCgoLg4eEBXV1d3L9/Xzzu7u6OzMxM3Lp1S0xLTk6Wq8Pd3R1nzpzBy5cvxbSjR4+W23bhZpiZmZnFxmxjYyPmMzc3R2hoKNauXYtFixbJbXZZrVo19OjRA99//z02bdqELVu24OHDhwqPvyLc3Nxw+PBhubQjR47Azc1N4TqUuUaFpFIpOnfujCVLliAxMRHJyck4d+5cmWXq168PmUwm7nHxJnd3dxw5ckQuiHDkyBEYGRmJgR9zc3O51/nTp0/lZtcoQtFx6+rqolq1anIPLpUgIiIiIqJ35a3OZNDT00NERATCw8Oho6OD5s2b4969e7hw4QJCQkJgY2OD6OhoTJs2Denp6Zg/f77SbUVERKBZs2YYMWIEBg8eDAMDA6SlpSEhIQFLly5VqI6aNWtCKpViz549qF27NvT09GBsbFxmGScnJ6xZswY+Pj54+vQpxo0bJ/ftd9u2beHi4oKQkBDMnz8fT58+xYQJE+Tq6NWrFyZMmICBAwdi4sSJyMjIwLx588rtr5GREb755ht89dVXkMlkaNGiBZ4+fYojR47A0NAQ/fr1w6RJk9CoUSN4eHggJycHO3fuFD/UL1y4EFZWVmjQoAE0NDTw888/w9LSssw1/6oYN24cgoOD0bBhQ7Rp0wY7duzA1q1bsX//foXrsLOzg0Qiwc6dOxEUFASpVCpuwliW+Ph4FBQUoGnTptDX18eaNWsglUrL3YfC3t4e/fr1w4ABA7BkyRJ4eXnh+vXruHv3LoKDgzF8+HAsWrQIo0aNwsiRI3H58mVMnjwZY8eOFWfrtG7dGvHx8ejUqRNMTEwQFRUFTU1NhcesyriJiIiIiIjepbd+d4moqCh8/fXXmDRpEtzc3NCjRw/cvXsX2tra2LBhAy5dugQvLy/Mnj1bqR35C3l6eiIpKQnp6elo2bIlvL29ERUVBSsrK4Xr0NLSwpIlSxAXFwdra2t06dKl3DIrV67Eo0eP4O3tjb59+yIsLEy8Awbweqr8tm3bkJOTgyZNmmDQoEGYPn26XB2GhobYsWMHLl68CG9vb0yYMAGzZ89WqM9Tp07FpEmTMHPmTLi5uSEwMBA7duwQb8epo6ODyMhIeHp6ws/PD5qamti4caPY7uzZs+Hj44PGjRsjIyMDu3fvrtBmhRXRtWtXLF68GHPnzoWHhwfi4uKwatUqhW4VWqhWrVqIiYnB+PHjYWFhUezuIaWpXr06vv/+ezRv3hyenp44cOAAduzYATMzs3LLxsbG4vPPP8fw4cPh6uqKwYMHi7dGrVWrFnbv3o3jx4/Dy8sLQ4cOFYNFhSIjI+Hn54eOHTsiKCgIXbt2haOjo8JjVmXcRERERERE75JEqMiGAERU5fz9/5uAEtHboVPwsvxMZcjVLHvvl3+rO7nmSpe10LmnUtvSvGdKl32pbaRS20RUNg2h4ktii5JJKjZblKgyGOY8Uql8tq6J0mUdKvhlpzLe+kwGIiIiIiIiIvowMMhQjqK3hnzzcejQocru3luxbt26Usfs4eFR2d1Tq0OHDpV5jYmIiIiIiEhxb3Xjx3+Dsu548eZtI/8tOnfujKZNm5Z4rKzbPVZFPj4+Kt3VhIiIiIiIiP6HezIQ/cv9ckymUnljvTw19aTiBEiULqshUe1H24tc5WOwUu18ldp+la/8etJXeapNUDPUVX4t7L2nqgUhrarnKF32ZZ5qMfNXecq/1rRUXP4r1Vb+nOfLlO83AGhrKv/zQU9LtXXTj18qdnvpkniZVuw2wG/SlCn/cy1fQ/l+A0CBCt/vaEK1ny0yFSawSlT8maopU77v+RLVfrZoQPnX+fjlqr3BTWuWfZeysuS+Uu33r2lN5Wdk3st6qlLbz5+8ULqsjlS195i+kZ7SZWX5qv3NpK2r/Pu7bUB1ldrOy1f+94GOtmrv7/yCyvt7TVtT+fIPnqn2/jaUKt929kvVfn/rqPBnT4i/Sk0rhMsliIiIiIiIiEgtGGQgIiIiIiIiIrVgkIGIiIiIiIiI1IJBBnrvJCYmQiKR4PHjx5XdFSIiIiIiIqoABhmIKkgQBOTnq7b5FhERERER0b8RgwykNJlMhtmzZ8PJyQm6urqwtbXF9OnTS5yJkJqaColEgoyMDADA9evX0alTJ5iYmMDAwAAeHh7YvXs3MjIy0KpVKwCAiYkJJBIJQkNDAQA5OTkICwtDzZo1oaenhxYtWuDEiRNiG4Xt7t27F97e3pBKpWjdujXu3r2L3377DW5ubqhWrRp69uyJFy/+t+uxIAiYM2cOHBwcIJVK4eXlhV9++aXEen18fKCrq4tDhw6VeW6uXr2KLl26wMLCAoaGhmjcuDH2798vlycrKwuffPIJpFIp6tSpg/Xr18Pe3h6LFi0S8zx58gRffvklatasiWrVqqF169Y4c+ZMRS4TERERERHRO6PaPb/ogxYZGYnvv/8eCxcuRIsWLZCVlYVLly4pVHbEiBHIzc3FH3/8AQMDA1y8eBGGhoawsbHBli1b8Nlnn+Hy5cuoVq0apFIpACA8PBxbtmzB6tWrYWdnhzlz5iAwMBBXrlyBqampWHd0dDSWLVsGfX19BAcHIzg4GLq6uli/fj2ys7Px6aefYunSpYiIiAAATJw4EVu3bkVsbCycnZ3xxx9/oE+fPjA3N4e////u8RIeHo558+bBwcEB1atXL3N82dnZCAoKwrRp06Cnp4fVq1ejU6dOuHz5MmxtbQEAISEhuH//PhITE6GtrY2xY8fi7t27Yh2CIOCTTz6Bqakpdu/eDWNjY8TFxaFNmzb466+/5MZMRERERET0PmCQgZTy7NkzLF68GMuWLUO/fv0AAI6OjmjRogUSExPLLZ+ZmYnPPvsM9evXBwA4ODiIxwo/PNesWVP8MP/8+XPExsYiPj4eHTp0AAB8//33SEhIwI8//ohx48aJ5adNm4bmzZsDAAYOHIjIyEhcvXpVbOPzzz/HwYMHERERgefPn2PBggX4/fff4evrK/bl8OHDiIuLkwsyTJkyBe3atVPo/Hh5ecHLy0uuT9u2bcOvv/6KkSNH4tKlS9i/fz9OnDgBHx8fAMAPP/wAZ2dnsczBgwdx7tw53L17F7q6ugCAefPmYfv27fjll1/w5ZdfFms3JycHOTk5cml5udrQ1tFVqN9ERERERESq4HIJUkpaWhpycnLQpk0bpcqHhYWJwYDJkyfj7NmzZea/evUq8vLyxOABAGhra6NJkyZIS0uTy+vp6Sn+38LCAvr6+nJBDAsLC3HGwMWLF/Hq1Su0a9cOhoaG4uOnn37C1atX5eotDAYo4vnz5wgPD4e7uzuqV68OQ0NDXLp0CZmZmQCAy5cvQ0tLCw0bNhTLODk5wcTERHx+6tQpZGdnw8zMTK5v165dK9a3QjNnzoSxsbHcY9vqWQr3m4iIiIiISBWcyUBKKVzCUBINjdexK0EQxLS8vDy5PIMGDUJgYCB27dqFffv2YebMmZg/fz5GjRpVYp2FdUkkkmLpb6Zpa2uL/5dIJHLPC9NkMhkAiP/u2rULtWrVkstXOHugkIGBQYl9K8m4ceOwd+9ezJs3D05OTpBKpfj888+Rm5srN543FU2XyWSwsrIqcWZIacs1IiMjMXbsWLm0XWe0S8xLRERERESkbpzJQEpxdnaGVCrFgQMHih0zNzcH8Hpjw0KpqanF8tnY2GDo0KHYunUrvv76a3z//fcAAB0dHQBAQUGBmNfJyQk6Ojo4fPiwmJaXl4eTJ0/Czc1N6XG4u7tDV1cXmZmZcHJyknvY2NgoXe+hQ4cQGhqKTz/9FPXr14elpaW46SUAuLq6Ij8/HykpKWLalStX5DbLbNiwIW7fvg0tLa1ifatRo0aJ7erq6qJatWpyDy6VICIiIiKid4UzGUgpenp6iIiIQHh4OHR0dNC8eXPcu3cPFy5cQEhICGxsbBAdHY1p06YhPT0d8+fPlys/ZswYdOjQAXXr1sWjR4/w+++/i8ECOzs7SCQS7Ny5E0FBQZBKpTA0NMSwYcMwbtw4mJqawtbWFnPmzMGLFy8wcOBApcdhZGSEb775Bl999RVkMhlatGiBp0+f4siRIzA0NBT3m6goJycnbN26FZ06dYJEIkFUVJQ4awJ4HWRo27YtvvzyS8TGxkJbWxtff/01pFKpODOjbdu28PX1RdeuXTF79my4uLjg1q1b2L17N7p27Vqh5RtERERERETvAmcykNKioqLw9ddfY9KkSXBzc0OPHj1w9+5daGtrY8OGDbh06RK8vLwwe/ZsTJs2Ta5sQUEBRowYATc3N7Rv3x4uLi5Yvnw5AKBWrVqIiYnB+PHjYWFhgZEjRwIAZs2ahc8++wx9+/ZFw4YNceXKFezdu1duHwNlTJ06FZMmTcLMmTPh5uaGwMBA7NixA3Xq1FG6zoULF8LExAQfffQROnXqhMDAQLn9FwDgp59+goWFBfz8/PDpp59i8ODBMDIygp6eHoDXyzp2794NPz8/DBgwAHXr1sUXX3yBjIwMWFhYqDRmIiIiIiKit0EilLY4nIjeqZs3b8LGxgb79+9XekPNkvxyTFZ+pjIY6+WVn+ktESApP1MpNCSq/Wh7kav8RC+pdr5Kbb/K11S+bJ5qsWND3YLyM5Xi3lPV9v+wqp5TfqZSvMxTbWLeqzzlX2tayl8uAIBUW/lzni9Tvt8AoK2p/M8HPS3l+w0Aj1/qKF3Wy/SaSm1rypT/uZavoXy/AaBAhUmkmlDtZ4tMhe+WJCr+TNWUKd/3fIlqP1s0oPzrfPxy1d7gpjWNlS6b+0q137+mNQ2VLnsv66lKbT9/8kLpsjpS1d5j+kZ6SpeV5av2N5O2rvLv77YB1VVqOy9f+d8HOtqqvb/zCyrv7zVtTeXLP3im2vvbUKp829kvVfv9raPCnz0h/uXnURWXSxBVkt9//x3Z2dmoX78+srKyEB4eDnt7e/j5+VV214iIiIiIiJTC5RJESvDw8JC7rWTRx7p16xSqIy8vD//5z3/g4eGBTz/9FObm5khMTCx2NwwiIiIiIqKqgjMZiJSwe/fuYrflLKTofgmBgYEIDAxUZ7eIiIiIiIgqFfdkIPqX23pctfWFuSqs8Wti8bdKbX+orK/+oXTZW46qLbexzvhT+bbtm6vUtipMn15XqfwprY+ULltH/x+V2laFxZ0zKpW/Y+GldFnrc7tUavtW/U9UKv8h0pYpv28JAORp8JbGFSVIVFs3bf74qtJlb1bzUKltPclLpctaX0pQqe1bru1UKq+KB/lmSpfV11T+nAGAVKL8XhT07l18YqdSeXdj1f72qCwOjo5vvQ0ulyAiIiIiIiIitWCQgYiIiIiIiIjUgkGGKiwgIABjxoyp7G6UqSr0kYiIiIiIiNSDGz/SW7V161beLUFNAgIC0KBBAyxatKiyu0JERERERFQiBhnorTI1Na3sLpQpLy+PQRAiIiIiIiI14XKJKuL58+cICQmBoaEhrKysMH/+fLnja9euhY+PD4yMjGBpaYlevXrh7t27AABBEODk5IR58+bJlTl//jw0NDRw9errnY+jo6Nha2sLXV1dWFtbIywsTKG+LV++HM7OztDT04OFhQU+//xz8dibyyXs7e0xY8YMDBgwAEZGRrC1tcV3330nV9/NmzfxxRdfwNTUFAYGBvDx8cGxY8fE4zt27ECjRo2gp6cHBwcHxMTEID8/X6G+SiQSrFixAl26dIGBgQGmTZumUJ3p6enw8/ODnp4e3N3dkZCQAIlEgu3btwMAEhMTIZFI8PjxY7FMamoqJBIJMjIyxLQjR47Az88PUqkUNjY2CAsLw/Pnz8s9l6GhoUhKSsLixYshkUiK1UtERERERPQ+YJChihg3bhwOHjyIbdu2Yd++fUhMTMSpU6fE47m5uZg6dSrOnDmD7du349q1awgNDQXw+oP1gAEDsGrVKrk6V65ciZYtW8LR0RG//PILFi5ciLi4OKSnp2P79u2oX79+uf06efIkwsLCMGXKFFy+fBl79uyBn1/Zt9CbP38+fHx8kJKSguHDh2PYsGG4dOkSACA7Oxv+/v64desWfv31V5w5cwbh4eGQyV7fhnHv3r3o06cPwsLCcPHiRcTFxSE+Ph7Tp09X+FxOnjwZXbp0wblz5zBgwIBy65TJZOjWrRs0NTVx9OhRrFixAhEREQq3V+jcuXMIDAxEt27dcPbsWWzatAmHDx/GyJEjyz2Xixcvhq+vLwYPHoysrCxkZWXBxsamwn0gIiIiIiJ6m7hcogrIzs7Gjz/+iJ9++gnt2r2+7/Dq1atRu3ZtMc+AAQPE/zs4OGDJkiVo0qQJsrOzYWhoiP79+2PSpEk4fvw4mjRpgry8PKxduxZz584FAGRmZsLS0hJt27aFtrY2bG1t0aRJk3L7lpmZCQMDA3Ts2BFGRkaws7ODt7d3mWWCgoIwfPhwAEBERAQWLlyIxMREuLq6Yv369bh37x5OnDghLrVwcnISy06fPh3jx49Hv379xLFOnToV4eHhmDx5siKnE7169ZI7X3379i2zzv379yMtLQ0ZGRniOZ8xYwY6dOigUHuF5s6di169eokzO5ydnbFkyRL4+/sjNja2zHNpbGwMHR0d6Ovrw9LSstQ2cnJykJMjfx/1vFxtaOvw3uhERERERPT2cSZDFXD16lXk5ubC19dXTDM1NYWLi4v4PCUlBV26dIGdnR2MjIwQEBAA4HUQAACsrKzwySefYOXKlQCAnTt34tWrV+jevTsAoHv37nj58iUcHBwwePBgbNu2TaElCO3atYOdnR0cHBzQt29frFu3Di9evCizjKenp/h/iUQCS0tLcWlHamoqvL29S93L4dSpU5gyZQoMDQ3FR+G3++W1W8jHx6dCdaalpcHW1lYuqFP0Wijq1KlTiI+Pl2snMDAQMpkM165dU+pcvmnmzJkwNjaWe2xdPavCfSUiIiIiIlIGgwxVgCAIZR5//vw5Pv74YxgaGmLt2rU4ceIEtm3bBuD1MopCgwYNwsaNG/Hy5UusWrUKPXr0gL6+PgDAxsYGly9fxrfffgupVIrhw4fDz88PeXl5ZbZtZGSE06dPY8OGDbCyssKkSZPg5eUltzfBm97caFEikYjLIaRSaZntyWQyxMTEIDU1VXycO3cO6enp0NPTK7NsIQMDgwrVWdL5l0gkcs81NF6/lYrmffPcyWQyDBkyRK6dM2fOID09HY6OjkqdyzdFRkbiyZMnco9u/cYrXJ6IiIiIiEgVXC5RBTg5OUFbWxtHjx6Fra0tAODRo0f466+/4O/vj0uXLuH+/fuYNWuWuE7/5MmTxeoJCgqCgYEBYmNj8dtvv+GPP/6QOy6VStG5c2d07twZI0aMgKurK86dO4eGDRuW2T8tLS20bdsWbdu2xeTJk1G9enX8/vvv6NatW4XH6unpiR9++AEPHz4scTZDw4YNcfnyZbklFKoqr053d3dkZmbi1q1bsLa2BgAkJyfL5TE3NwcAZGVlwcTEBMDrWRlvtnPhwoUy+17WudTR0UFBQUGZY9HV1YWurvzSCG0dWZlliIiIiIiI1IVBhirA0NAQAwcOxLhx42BmZgYLCwtMmDBB/Pbc1tYWOjo6WLp0KYYOHYrz589j6tSpxerR1NREaGgoIiMj4eTkJDflPz4+HgUFBWjatCn09fWxZs0aSKVS2NnZldm3nTt34u+//4afnx9MTEywe/duyGQyuaUcFdGzZ0/MmDEDXbt2xcyZM2FlZYWUlBRYW1vD19cXkyZNQseOHWFjY4Pu3btDQ0MDZ8+exblz58Q7RVRUeXW2bdsWLi4uCAkJwfz58/H06VNMmDBBrg4nJyfY2NggOjoa06ZNQ3p6erE7gERERKBZs2YYMWIEBg8eDAMDA6SlpSEhIQFLly4t91za29vj2LFjyMjIgKGhIUxNTcXXABERERER0fuAn1CqiLlz58LPzw+dO3dG27Zt0aJFCzRq1AjA62/R4+Pj8fPPP8Pd3R2zZs0qdrvKQgMHDkRubq7cxocAUL16dXz//fdo3rw5PD09ceDAAezYsQNmZmZl9qt69erYunUrWrduDTc3N6xYsQIbNmyAh4eHUuPU0dHBvn37ULNmTQQFBaF+/fqYNWsWNDU1AQCBgYHYuXMnEhIS0LhxYzRr1gwLFiwoNxhSlvLq1NDQwLZt25CTk4MmTZpg0KBBxe5moa2tjQ0bNuDSpUvw8vLC7NmziwU9PD09kZSUhPT0dLRs2RLe3t6IioqClZUVgPLP5TfffANNTU24u7vD3Nxc3G+DiIiIiIjofSERylvwT/8qf/75JwICAnDz5k1YWFhUdneqNIlEgm3btqFr166V3ZUybT2u2nKJ3HxJ+ZlK0cTib5Xa/lBZX/2j/EyluOVY9i1ky20740/l27ZvrlLbqjB9el2l8qe0PlK6bB39f1RqWxUWd86oVP6OhZfSZa3P7VKp7Vv1P1Gp/IdIW5ZTfqYy5GnwTkMVJUiU/x0IAOaPrypd9mY15b6wKaQneal0WetLCSq1fcu1nUrlVfEgv+wvyMqir6n8OQMAqaRiG3ZT5br4RPkvKQHA3Vi1vz0qi4Oj41tvg8slPhA5OTm4ceMGoqKiEBwczAADERERERERqR2XS3wgNmzYABcXFzx58gRz5sxRuNyhQ4fkbrn45uN9sm7dulL7qezyDSIiIiIiIlIcZzJ8IEJDQxEaGlrhcj4+PsXukvC+6ty5M5o2bVrisTdvm6kOXGlEREREREQkj3syEP3L/X1V+fWgqtIU8lUqXyBhHLSiUh/UUal8A7NrSpc9dttBpba9LZXf20AHqq1Vr0w6BcqvAc7VlKqxJxWj6j4YD6upthaWPhyWt1NVKn+hRhuly5ppPVCp7cokU2HCsgaq7u2v86CjdFlt5KqxJ1WH9ZmdKpW/5dVRTT15t1YkWKtUforpt0qXvdXoU5XaVsW72JOByyWIiIiIiIiISC0YZAAQEBCAMWPGVHY3ShQfH4/q1aurXI9EIsH27dvLzZeRkQGJRKLyEonQ0ND3/q4LREREREREpF4MMgDYunUrpk6dqlBedX0IJ6qI9zkQRkREREREVIgLngGYmppWdheIiIiIiIiIqjzOZID8t8T29vaYMWMGBgwYACMjI9ja2uK7774T89ap83pTNW9vb0gkEgQEBIjHVq1aBTc3N+jp6cHV1RXLly8XjxXOgNi6dStatWoFfX19eHl5ITk5Wa4v8fHxsLW1hb6+Pj799FM8eFB8w6EdO3agUaNG0NPTg4ODA2JiYpCf/78N9tLT0+Hn5wc9PT24u7sjISGhwufk0qVL+Oijj6CnpwcPDw8kJiaKxwoKCjBw4EDUqVMHUqkULi4uWLx4cZn1yWQyzJ49G05OTtDV1YWtrS2mT58uHj937hxat24NqVQKMzMzfPnll8jOzhaPFy6/mDdvHqysrGBmZoYRI0YgLy9PofGsXbsWPj4+MDIygqWlJXr16oW7d++KxxMTEyGRSLB37154e3tDKpWidevWuHv3Ln777Te4ubmhWrVq6NmzJ168eCGWy8nJQVhYGGrWrAk9PT20aNECJ06cEI+XtNxl+/btkEgk4vPo6Gg0aNAAa9asgb29PYyNjfHFF1/g2bNn4tiTkpKwePFiSCQSSCQSZGRkKDRuIiIiIiKid4lBhhLMnz8fPj4+SElJwfDhwzFs2DBcunQJAHD8+HEAwP79+5GVlYWtW7cCAL7//ntMmDAB06dPR1paGmbMmIGoqCisXr1aru4JEybgm2++QWpqKurWrYuePXuKAYJjx45hwIABGD58OFJTU9GqVStMmzZNrvzevXvRp08fhIWF4eLFi4iLi0N8fLz4gV0mk6Fbt27Q1NTE0aNHsWLFCkRERFT4HIwbNw5ff/01UlJS8NFHH6Fz585iwEMmk6F27drYvHkzLl68iEmTJuE///kPNm/eXGp9kZGRmD17NqKionDx4kWsX78eFhYWAIAXL16gffv2MDExwYkTJ/Dzzz9j//79GDlypFwdBw8exNWrV3Hw4EGsXr0a8fHxiI+PV2g8ubm5mDp1Ks6cOYPt27fj2rVrJd7SMzo6GsuWLcORI0dw48YNBAcHY9GiRVi/fj127dqFhIQELF26VMwfHh6OLVu2YPXq1Th9+jScnJwQGBiIhw8fKtSvQlevXsX27duxc+dO7Ny5E0lJSZg1axYAYPHixfD19cXgwYORlZWFrKws2NjYVKh+IiIiIiKid4HLJUoQFBSE4cOHAwAiIiKwcOFCJCYmwtXVFebm5gAAMzMzWFpaimWmTp2K+fPno1u3bgBez3goDAL069dPzPfNN9/gk08+AQDExMTAw8MDV65cgaurKxYvXozAwECMHz8eAFC3bl0cOXIEe/bsEctPnz4d48ePF+t0cHDA1KlTER4ejsmTJ2P//v1IS0tDRkYGateuDQCYMWMGOnToUKFzMHLkSHz22WcAgNjYWOzZswc//vgjwsPDoa2tjZiYGDFvnTp1cOTIEWzevBnBwcHF6nr27BkWL16MZcuWif12dHREixYtAADr1q3Dy5cv8dNPP8HAwAAAsGzZMnTq1AmzZ88WgxEmJiZYtmwZNDU14erqik8++QQHDhzA4MGDyx3PgAEDxP87ODhgyZIlaNKkCbKzs2FoaCgemzZtGpo3bw4AGDhwICIjI3H16lU4OLy+Nd/nn3+OgwcPIiIiAs+fP0dsbCzi4+PF8/v9998jISEBP/74I8aNG6fg2X4duImPj4eRkREAoG/fvjhw4ACmT58OY2Nj6OjoQF9fX+41R0RERERE9L7hTIYSeHp6iv+XSCSwtLSUm1r/pnv37uHGjRsYOHAgDA0Nxce0adNw9erVUuu2srICALHutLQ0+Pr6yuV/8/mpU6cwZcoUuXYKv+F+8eIF0tLSYGtrKwYYSqpDEUXLaGlpwcfHB2lpaWLaihUr4OPjA3NzcxgaGuL7779HZmZmiXWlpaUhJycHbdqUfJ/qtLQ0eHl5iQEGAGjevDlkMhkuX74spnl4eEBTU1N8bmVlVeZ1KSolJQVdunSBnZ0djIyMxGUub/a56PWxsLCAvr6+GGAoTCts8+rVq8jLyxODEgCgra2NJk2ayJ0rRdjb24sBhoqOraicnBw8ffpU7pGTk1PheoiIiIiIiJTBmQwl0NbWlnsukUggk8lKzV947Pvvv0fTpk3ljhX9UPxm3YXr8gvLC4JQbt9kMhliYmLEGRNF6enplVhH0fX/qiisZ/Pmzfjqq68wf/58+Pr6wsjICHPnzsWxY8dKLCeVSsusVxCEUvtYNL2i16XQ8+fP8fHHH+Pjjz/G2rVrYW5ujszMTAQGBiI3N1cu75vXp6w2C8/1m30vOh4NDY1i16SkfSSUHdubZs6cKTfLBADCRo3C6NGjK1wXERERERFRRXEmQwXp6OgAeL35YSELCwvUqlULf//9N5ycnOQehRtFKsLd3R1Hjx6VS3vzecOGDXH58uVi7Tg5OUFDQwPu7u7IzMzErVu3xDJvbi6piKLt5ufn49SpU3B1dQUAHDp0CB999BGGDx8Ob29vODk5FZuxUZSzszOkUikOHDhQ6rhTU1Px/PlzMe3PP/+EhoYG6tatW+G+v+nSpUu4f/8+Zs2ahZYtW8LV1VWpWQJvcnJygo6ODg4fPiym5eXl4eTJk3BzcwMAmJub49mzZ3JjU+b2pzo6OnKvudJERkbiyZMnco+hQ4dWuD0iIiIiIiJlcCZDBdWsWRNSqRR79uxB7dq1oaenB2NjY0RHRyMsLAzVqlVDhw4dkJOTg5MnT+LRo0cYO3asQnWHhYXho48+wpw5c9C1a1fs27dPbj8GAJg0aRI6duwIGxsbdO/eHRoaGjh79izOnTuHadOmoW3btnBxcUFISAjmz5+Pp0+fYsKECRUe57fffgtnZ2e4ublh4cKFePTokbivgZOTE3766Sfs3bsXderUwZo1a3DixIlSAyp6enqIiIhAeHg4dHR00Lx5c9y7dw8XLlzAwIED0bt3b0yePBn9+vVDdHQ07t27h1GjRqFv377ifgyqsLW1hY6ODpYuXYqhQ4fi/PnzmDp1qsr1GhgYYNiwYRg3bhxMTU1ha2uLOXPm4MWLFxg4cCAAoGnTptDX18d//vMfjBo1CsePH1d4s8qi7O3tcezYMWRkZMDQ0BCmpqbQ0CgeI9TV1YWurq5c2v03nhMREREREb0tnMlQQVpaWliyZAni4uJgbW2NLl26AAAGDRqEH374AfHx8ahfvz78/f0RHx9foZkMzZo1ww8//IClS5eiQYMG2LdvHyZOnCiXJzAwEDt37kRCQgIaN26MZs2aYcGCBbCzswPwenr+tm3bkJOTgyZNmmDQoEFyt4pU1KxZszB79mx4eXnh0KFD+O9//4saNWoAAIYOHYpu3bqhR48eaNq0KR48eCBulFmaqKgofP3115g0aRLc3NzQo0cPcTaBvr4+9u7di4cPH6Jx48b4/PPP0aZNGyxbtqzC/S6Jubk54uPj8fPPP8Pd3R2zZs3CvHnz1FL3rFmz8Nlnn6Fv375o2LAhrly5gr1798LExAQAYGpqirVr12L37t2oX78+NmzYgOjo6Aq3880330BTUxPu7u7icg8iIiIiIqL3jURQZCMAIqqy/i5jKcvbpinkq1S+QMLJVhWV+kDxwGZJGphdU7rssdsO5Wcqg7flP0qX1UHV3eBUp+Cl0mVzNcve8+ZtMn16XaXyD6vZqakn9G9neTtVpfIXapS88bQizLQeqNR2ZZKp8F2iBiq+L9T7Ig86SpfVRm75mf6FrM/sVKn8La+OaurJu7UiwVql8lNMv1W67K1Gn6rUtiocHB3fehucyUBEREREREREasEgwwdmxowZcre/LPro0KFDZXdPKYcOHSp1TIaGhpXdPSIiIiIiog8G5yJ/YIYOHYrg4OASj5V3q8n3lY+Pj1J3bCAiIiIiIiL1YpDhA2NqagpTU9PK7oZaSaVSODk5VXY3iIiIiIiIPnjc+JHoX07VjR93XFR+M7/+1vtUavuV1EzpsvpPb6nU9l1zD5XKq2JvuvIb8gQ6V95Gn0TvijTvmUrlX2obqakn79ZLQV+l8qpsBGggyVapbVVoy1Tb2DVPo2reytkk+6ZK5Z+r8Du0MjeVrUx/P6+tUnkHA9WuGdG7wI0fiYiIiIiIiKjKYJCBiIiIiIiIiNSCQQaiIhITEyGRSPD48WOFywQEBGDMmDFvrU9ERERERERVBYMMRERERERERKQWDDJQhQUEBGDkyJEYOXIkqlevDjMzM0ycOBGFe4iuXbsWPj4+MDIygqWlJXr16oW7d+8CAARBgJOTE+bNmydX5/nz56GhoYGr/79JoUQiQVxcHDp27Ah9fX24ubkhOTkZV65cQUBAAAwMDODr6yvmL7Rjxw40atQIenp6cHBwQExMDPLz88XjEokEP/zwAz799FPo6+vD2dkZv/76KwAgIyMDrVq1AgCYmJhAIpEgNDS0zHMRGhqKpKQkLF68GBKJBBKJBNeuXVN4jLGxsejQoQOkUinq1KmDn3/+Wa7MP//8gx49esDExARmZmbo0qULMjIyyrtERERERERElYJBBlLK6tWroaWlhWPHjmHJkiVYuHAhfvjhBwBAbm4upk6dijNnzmD79u24du2a+GFdIpFgwIABWLVqlVx9K1euRMuWLeFYZLfTqVOnIiQkBKmpqXB1dUWvXr0wZMgQREZG4uTJkwCAkSNHivn37t2LPn36ICwsDBcvXkRcXBzi4+Mxffp0ubZiYmIQHByMs2fPIigoCL1798bDhw9hY2ODLVu2AAAuX76MrKwsLF68uMzzsHjxYvj6+mLw4MHIyspCVlYWbG1tFR5jVFQUPvvsM5w5cwZ9+vRBz549kZaWBgB48eIFWrVqBUNDQ/zxxx84fPgwDA0N0b59e+Tm5pZ7jYiIiIiIiN41BhlIKTY2Nli4cCFcXFzQu3dvjBo1CgsXLgQADBgwAB06dICDgwOaNWuGJUuW4LfffkN29uvbX/Xv3x+XL1/G8ePHAQB5eXlYu3YtBgwYINdG//79ERwcjLp16yIiIgIZGRno3bs3AgMD4ebmhtGjRyMxMVHMP336dIwfPx79+vWDg4MD2rVrh6lTpyIuLk6u3tDQUPTs2RNOTk6YMWMGnj9/juPHj0NTUxOmpqYAgJo1a8LS0hLGxsZlngdjY2Po6OhAX18flpaWsLS0hKampsJj7N69OwYNGoS6deti6tSp8PHxwdKlSwEAGzduhIaGBn744QfUr18fbm5uWLVqFTIzM+XGTURERERE9L5gkIGU0qxZM0gkEvG5r68v0tPTUVBQgJSUFHTp0gV2dnYwMjJCQEAAACAzMxMAYGVlhU8++QQrV64EAOzcuROvXr1C9+7d5drw9PQU/29hYQEAqF+/vlzaq1ev8PTpUwDAqVOnMGXKFBgaGoqPwhkGL168KLFeAwMDGBkZics51EXRMfr6+hZ7XjiT4dSpU7hy5QqMjIzE8ZiamuLVq1fFlokUysnJwdOnT+UeOTmq3V+ciIiIiIhIUQwykFq9evUKH3/8MQwNDbF27VqcOHEC27ZtAwC5Kf6DBg3Cxo0b8fLlS6xatQo9evSAvr6+XF3a2tri/wsDGiWlyWQy8d+YmBikpqaKj3PnziE9PR16enol1ltYT2Ed6qTIGEtSdFyNGjWSG09qair++usv9OrVq8SyM2fOhLGxsdxjxYoVah0XERERERFRabQquwNUNR09erTYc2dnZ1y6dAn379/HrFmzYGNjAwDi/glFBQUFwcDAALGxsfjtt9/wxx9/qNynhg0b4vLly3ByclK6Dh0dHQBAQUFBhcqUlF+RMR49ehQhISFyz729vQG8Hs+mTZtQs2ZNVKtWTaG+REZGYuzYsXJp/9y8qfBYiIiIiIiIVMGZDKSUGzduYOzYsbh8+TI2bNiApUuXYvTo0bC1tYWOjg6WLl2Kv//+G7/++iumTp1arLympiZCQ0MRGRkJJyenYssGlDFp0iT89NNPiI6OxoULF5CWloZNmzZh4sSJCtdhZ2cHiUSCnTt34t69e+I+EmWxt7fHsWPHkJGRgfv374uzIhQZ488//4yVK1fir7/+wuTJk3H8+HFxM8vevXujRo0a6NKlCw4dOoRr164hKSkJo0ePxs1SAge6urqoVq2a3ENXV1fh8RMREREREamCQQZSSkhICF6+fIkmTZpgxIgRGDVqFL788kuYm5sjPj4eP//8M9zd3TFr1qxit3IsNHDgQOTm5hbbDFFZgYGB2LlzJxISEtC4cWM0a9YMCxYsgJ2dncJ11KpVCzExMRg/fjwsLCzk7l5Rmm+++Qaamppwd3eHubm5uPcEUP4YY2JisHHjRnh6emL16tVYt24d3N3dAQD6+vr4448/YGtri27dusHNzQ0DBgzAy5cvFZ7ZQERERERE9C5JBEEQKrsTVLUEBASgQYMGWLRokUr1/PnnnwgICMDNmzfFjR3/bcoao0QiwbZt29C1a9e32oe/S9kkUlE7LjooXba/9T6V2n4lNVO6rP7TWyq1fdfcQ6Xyqtib7lh+plIEOqt2vYmqAmneM5XKv9Q2UlNP3q2XQvn7+pRFpsJ3SwaS8mf2vS3aMtU2MM7TqJoz+kyyVVvu+FyF36G5mlKV2q6q/n5eW6XyDgZcokrvPwdH5f/OVBT3ZKB3LicnBzdu3EBUVBSCg4P/lQGGD2GMREREREREb+JyCXrnNmzYABcXFzx58gRz5syp7O6UKTMzU+6WmG8+ii6NKKoqjZGIiIiIiEhduFyCqAz5+fnIyMgo9bi9vT20tN7vCUFcLqEcLpcgen9xuYRyuFyiauFyiXePyyXoQ8DlEkSVTEtLS6VbYhIREREREX1IOJOB6F9O1ZkMN15ZKV3WRi9LpbZVcSvHUqXy1rq3lS5ruLL4bVsrIntAlErlP0SGOY9UKp+ta6Kmnnw4tGS5KpXP19BRU0+qFr3850qXfaVloMaeVIzF3XMqlb9Ts77SZbNlqs08MdRQbeaLKvTznipd9oU27yRF/37mO5erVP5ex+Fq6smH413MZOCeDERERERERESkFgwy0DsRHx+P6tWrV3Y3iIiIiIiI6C1ikIHeiR49euCvv/6q7G6onUQiwfbt25Uq++eff0JLSwsNGjRQa5+IiIiIiIgqC4MM9E5IpVLUrFmzsrvx3njy5AlCQkLQpk2byu4KERERERGR2jDIQGqTkZEBiURS7BEQEFBsuUR0dDQaNGiAuLg42NjYQF9fH927d8fjx48Vbm/lypXw8PCArq4urKysMHLkSPFYZmYmunTpAkNDQ1SrVg3BwcG4c+eOeDw0NBRdu3aVq2/MmDEICAgQnwcEBCAsLAzh4eEwNTWFpaUloqOjxeP29vYAgE8//RQSiUR8roghQ4agV69e8PX1lUs/d+4cNDQ0cP/+fQDAo0ePoKGhge7du4t5Zs6cWawcERERERHR+4BBBlIbGxsbZGVliY+UlBSYmZnBz8+vxPxXrlzB5s2bsWPHDuzZswepqakYMWKEQm3FxsZixIgR+PLLL3Hu3Dn8+uuv4q0mBUFA165d8fDhQyQlJSEhIQFXr15Fjx49Kjym1atXw8DAAMeOHcOcOXMwZcoUJCQkAABOnDgBAFi1ahWysrLE5+VZtWoVrl69ismTJxc7Vq9ePZiZmSEpKQkA8Mcff8DMzAx//PGHmCcxMRH+/v4VHgsREREREdHbxiADqY2mpiYsLS1haWmJ6tWrY+jQofD19ZX79r+oV69eYfXq1WjQoAH8/PywdOlSbNy4Ebdvl3/rwGnTpuHrr7/G6NGjUbduXTRu3BhjxowBAOzfvx9nz57F+vXr0ahRIzRt2hRr1qxBUlKSwoGAQp6enpg8eTKcnZ0REhICHx8fHDhwAABgbm4OAKhevTosLS3F52VJT0/H+PHjsW7dOmhpaRU7LpFI4Ofnh8TERACvAwr9+vWDTCbDxYsXkZ+fjyNHjsjNuCAiIiIiInpfMMhAb8XAgQPx7NkzrF+/HhoaJb/MbG1tUbt2bfG5r68vZDIZLl++XGbdd+/exa1bt0rdzyAtLQ02NjawsbER09zd3VG9enWkpaVVaByenp5yz62srHD37t0K1VGooKAAvXr1QkxMDOrWrVtqvoCAADHIkJSUhFatWsHPz08Mkrx8+RLNmzcvsWxOTg6ePn0q98jJyVGqv0RERERERBXFIAOp3bRp07Bnzx78+uuvMDIyUricRCKR+7c0Uqm0zOOCIJRYR9F0DQ0NCIIgdzwvL69YGW1t7WJ9lMlkZbZfmmfPnuHkyZMYOXIktLS0oKWlhSlTpuDMmTPQ0tLC77//DuB1kOHChQu4cuUKzp8/j5YtW8Lf3x9JSUlITExEo0aNSj2vM2fOhLGxsdxjxYoVSvWXiIiIiIioohhkILXasmULpkyZgs2bN8PR0bHMvJmZmbh165b4PDk5GRoaGmV+yw8ARkZGsLe3F5ctvMnd3R2ZmZm4ceOGmHbx4kU8efIEbm5uAF4vdcjKypIrl5qaWma7JdHW1kZBQYFCeatVq4Zz584hNTVVfAwdOhQuLi5ITU1F06ZNAfxvX4Zp06bBy8sL1apVkwsylLUfQ2RkJJ48eSL3GDp0aIXHRUREREREpAwGGUhtzp8/j5CQEERERMDDwwO3b9/G7du38fDhwxLz6+npoV+/fjhz5gwOHTqEsLAwBAcHw9LSsty2oqOjMX/+fCxZsgTp6ek4ffo0li5dCgBo27YtPD090bt3b5w+fRrHjx9HSEgI/P394ePjAwBo3bo1Tp48iZ9++gnp6emYPHkyzp8/X+ExFwY7bt++jUePHpWZV0NDA/Xq1ZN71KxZE3p6eqhXrx4MDAwA/G9fhrVr14p7L3h6eiI3NxcHDhwocz8GXV1dVKtWTe6hq6tb4XEREREREREpg0EGUpuTJ0/ixYsXmDZtGqysrMRHt27dSszv5OSEbt26ISgoCB9//DHq1auH5cuXK9RWv379sGjRIixfvhweHh7o2LEj0tPTAbz+kL59+3aYmJjAz88Pbdu2hYODAzZt2iSWDwwMRFRUFMLDw9G4cWM8e/YMISEhFR7z/PnzkZCQABsbG3h7e1e4fGlatWqFgoICMaAgkUjQsmVLAECLFi3U1g4REREREZE6SYQ3F6YTvQPR0dHYvn27UksUqGL+vnpVpfI3XlkpXdZGL6v8TG/JrZzyZ8SUxVq3/LuclMZw5VSV2s4eEKVS+Q+RYU7ZM4nKk61roqaefDi0ZLkqlc/X0FFTT6oWvfznSpd9pWWgxp5UjMXdcyqVv1OzvtJls2WK7+9UEkONZyqVV4V+3lOly77QrqbGnhC9n8x3KvYFY2nudRyupp58OBzKWdKuDpzJQERERERERERqwSADvZcMDQ1LfRw6dKiyu1cqDw+PUvu9bt26yu4eERERERHRW6VV2R2gD1N0dDSio6NLPV7WMopatWqpv0Nqsnv37hJvhQkAFhYW77g3RERERERE7xb3ZCD6l1uxV7XyvU13K132nqmLao1XovOP7JUuK9XOV6ltR8ObSpe1TtunUtu33D5WqXxVdeGxndJlPapfV2NPKkaV9d6Aamu+/35eW6W2HQyUf53TuydIJCqVl/DPzSqF+9y8eybZqv1MfGSo/M/khKuqrdHvaK/8ni05mvoqtU0Vxz0ZiIiIiIiIiKjKYJCBiIiIiIiIiNSCQQb6V5BIJNi+fft7W58yoqOj0aBBg0rtAxERERERUUUwyEBUgqysLHTo0AEAkJGRAYlEUuZmlMoQBAHz5s1D3bp1oaurCxsbG8yYMUOtbRAREREREb1LvLsEUQksLS3fehujR4/Gvn37MG/ePNSvXx9PnjzB/fv333q7REREREREbwtnMlCli4uLQ61atSCTyeTSO3fujH79+gEAduzYgUaNGkFPTw8ODg6IiYlBfn7pO/ifO3cOrVu3hlQqhZmZGb788ktkZ2fL5Vm5ciU8PDygq6sLKysrjBw5UjxWdLlEnTp1AADe3t6QSCQICAjAH3/8AW1tbdy+fVuuzq+//hp+fn7ljjktLQ2xsbH473//i86dO6NOnTpo0KAB2rZtWyzvmjVrYG9vD2NjY3zxxRd49uxZufUTERERERFVBgYZqNJ1794d9+/fx8GDB8W0R48eYe/evejduzf27t2LPn36ICwsDBcvXkRcXBzi4+Mxffr0Eut78eIF2rdvDxMTE5w4cQI///wz9u/fLxdEiI2NxYgRI/Dll1/i3Llz+PXXX+Hk5FRifcePHwf+j737jq/xfB84/jnZJ3s0kiBEEJLYmyCxvkatmiVtxIiqvWq0Smxam1KlTWK0adWomrWitgghJYIIoVIptRIRWb8//Jw6sk7OCZG63q/X86rzPM9139f9nIie+9wD2Lt3LwkJCWzatImmTZvi6urK2rVrVfelp6ezbt06+vbtm2+bf/31V1xdXdm2bRvlypXDxcWFAQMG8M8//6jdFxsby5YtW9i2bRvbtm3j4MGDzJkzJ9/yhRBCCCGEEKIoSCeDKHK2tra0adOG77//XnVuw4YN2Nra0qJFC2bOnMmECRPo06cPrq6utGrViunTp7Ny5cocy1u/fj0pKSmsWbOGKlWq0Lx5c5YtW8batWu5ffs2ADNmzGDMmDGMGDECNzc36taty8iRI3Msz97eHgA7OzscHR2xtbUFoH///gQFBanu2759O48fP6ZHjx75tvnq1atcv36dDRs2sGbNGoKDg4mIiKBbt25q92VmZhIcHEyVKlVo0qQJH374Ifv27cu13NTUVB4+fKh2pD1NzTcfIYQQQgghhCgM0skg3gi+vr5s3LiR1NRnH4jXr1/P+++/j76+PhEREUybNg1zc3PVERAQQEJCAo8fP85WVnR0NNWrV8fMzEx1zsvLi8zMTGJiYkhMTOTWrVu0aNFCp5z9/f25cuUKx48fB55Nv+jRo4davbnJzMwkNTWVNWvW0KRJE3x8fPj22285cOAAMTExqvtcXFywsLBQvXZyciIxMTHXcmfPno2VlZXasfvH2Tq0UgghhBBCCCE0Jws/ijdChw4dyMzMZPv27dStW5dDhw6xYMEC4NkH8qlTp9KlS5dscSYmJtnOZWVloVAocqxHoVCgVCoLJecSJUrQoUMHgoKCcHV1ZceOHYSFhWkU6+TkhIGBAW5ubqpz7u7uAMTHx1OpUiUADA0Ns+X/8toVL5o4cSKjR49WOxdy0FijnIQQQgghhBBCV9LJIN4ISqWSLl26sH79eq5cuYKbmxu1a9cGoFatWsTExOS6ZsLLPDw8CAkJITk5WTWq4MiRI+jp6eHm5oaFhQUuLi7s27ePZs2a5VuekZERABkZGdmuDRgwgPfff5/SpUtTvnx5vLy8NMrRy8uL9PR0YmNjKV++PACXLl0CoGzZshqVkRNjY2OMjdU7FQyNtC5OCCGEEEIIIQpEpkuIN4avry/bt2/nu+++44MPPlCdnzx5MmvWrCEwMJDz588THR3Njz/+yKRJk3Itx8TEhD59+vDHH39w4MABhg0bxocffoiDgwMAgYGBzJ8/nyVLlnD58mVOnz7N0qVLcyyvRIkSKJVKdu3axe3bt3nw4IHqWuvWrbGysmLGjBkaLfj4XMuWLalVqxb9+vXjzJkzRERE8NFHH9GqVSu10Q1CCCGEEEIIUZxIJ4N4YzRv3hxbW1tiYmLo3bu36nzr1q3Ztm0be/bsoW7dujRo0IAFCxbk+o2/qakpu3fv5p9//qFu3bp069aNFi1asGzZMtU9ffr0YdGiRSxfvhxPT0/at2/P5cuXcyzPwMCAJUuWsHLlSkqWLEmnTp1U1/T09PD39ycjIwM/Pz+N26qnp8evv/7KO++8Q9OmTXn33Xdxd3cnNDRU4zKEEEIIIYQQ4k2jyMrKyirqJIQozgICArh9+zZbt24t6lRy9PVu3eJ9bXdoHfu3bSXdKi9Cf9xz0TpWaZiuU93lzW9qHVsy+jed6r7l/j+d4our8/e1n6bkaX29EDMpGNO0hzrFPza01Dr2anJpnep2NdP+51y8flm5rHWkKYX872axYp56T6f4JGObQsrk7WGTpNvvxHvm2v9O3hNbXqe627tEaR2bqm+qU92i4FzL6/Z+a0LWZBBCSw8ePCA8PJz169fzyy+/FHU6QgghhBBCCFHkZLqEEFrq1KkTHTt2VK2l8KK2bduqbbn54jFr1qwiylgIIYQQQgghXi0ZySCElvLarnL16tWkpKTkeM3W1vYVZSSEEEIIIYQQRUvWZBDiPy4u9opO8VnoNg9XFFyGDv2/Vx6W1Knu64mGWsf+r0KsTnUXJf0s7dfRyFBIf702Ssbs0zr2j/Lddapbqf9E+1jFY53qLq7S0G0/ZEOeFlImBXf2rovWsQ0stZ9rDnDXwFHrWFNFsk51FyVd1hfQZW2B4uzGEyed4ncc0f7/1z5qcUunukXx8jrWZJDpEkIIIYQQQgghhCgU0skghBBCCCGEEEKIQiGdDOKNp1Ao2LJlyxtb3qvi7+9P586dizoNIYQQQgghhNCYdDKIt05CQgJt27YF4Nq1aygUCiIjIwut/LCwMDp16oSTkxNmZmbUqFGD9evX53r/kSNHMDAwoEaNGoWWgxBCCCGEEEIUBelkEG8dR0dHjI2NX1n5R48epVq1amzcuJFz587Rr18//Pz8+PXXX7Pd++DBA/z8/GjRosUry0cIIYQQQgghXhfpZBCv1MqVKylVqhSZmZlq5zt27EifPn0A+PXXX6lduzYmJia4uroydepU0tNzX+k9KiqK5s2bo1QqsbOzY+DAgSQlJand89133+Hp6YmxsTFOTk4MHTpUde3F6RLlypUDoGbNmigUCnx8fPj9998xNDTkr7/+UitzzJgxNG3aNN82f/rpp0yfPp1GjRpRvnx5hg8fTps2bdi8eXO2ez/66CN69+5Nw4YNcy1v3rx5ODk5YWdnx5AhQ0hLS8s3ByGEEEIIIYQoCtLJIF6p7t27c+fOHQ4cOKA6d+/ePXbv3o2vry+7d+/mgw8+YPjw4Vy4cIGVK1cSHBzMzJkzcyzv8ePHtGnTBhsbG8LDw9mwYQN79+5V60RYsWIFQ4YMYeDAgURFRbF161YqVKiQY3knT54EYO/evSQkJLBp0yaaNm2Kq6sra9euVd2Xnp7OunXr6Nu3r1bP4cGDB9ja2qqdCwoKIjY2lilTpuQad+DAAWJjYzlw4AAhISEEBwcTHBysVQ5CCCGEEEII8arJ5t7ilbK1taVNmzZ8//33qikBGzZswNbWlhYtWtCsWTMmTJigGtXg6urK9OnTGTduXI4fvtevX09KSgpr1qzBzMwMgGXLltGhQwfmzp2Lg4MDM2bMYMyYMYwYMUIVV7du3Rzzs7e3B8DOzg5Hx3/3su7fvz9BQUF88sknAGzfvp3Hjx/To0ePAj+Dn3/+mfDwcFauXKk6d/nyZSZMmMChQ4cwMMj9r6GNjQ3Lli1DX1+fypUr8+6777Jv3z4CAgJyvD81NZXU1NRs517l9BAhhBBCCCGEeE5GMohXztfXl40bN6o+/K5fv573338ffX19IiIimDZtGubm5qojICCAhIQEHj9+nK2s6OhoqlevrupgAPDy8iIzM5OYmBgSExO5deuWzmsc+Pv7c+XKFY4fPw48m37Ro0cPtXo1ERYWhr+/P6tWrcLT0xOAjIwMevfuzdSpU3Fzc8sz3tPTE319fdVrJycnEhMTc71/9uzZWFlZqR0rvl6Z6/1CCCGEEEIIUZhkJIN45Tp06EBmZibbt2+nbt26HDp0iAULFgCQmZnJ1KlT6dKlS7Y4ExOTbOeysrJQKBQ51qNQKFAqlYWSc4kSJejQoQNBQUG4urqyY8cOwsLCClTGwYMH6dChAwsWLMDPz091/tGjR5w6dYozZ86opnlkZmaSlZWFgYEBv/32G82bNwfA0NBQrUyFQpFtfYsXTZw4kdGjR6udu3XzRoHyFkIIIYQQQghtSSeDeOWUSiVdunRh/fr1XLlyBTc3N2rXrg1ArVq1iImJyXXNhJd5eHgQEhJCcnKyalTBkSNH0NPTw83NDQsLC1xcXNi3bx/NmjXLtzwjIyPg2eiClw0YMID333+f0qVLU758eby8vDRtMmFhYbRv3565c+cycOBAtWuWlpZERUWpnVu+fDn79+/n559/Vi1GqQ1jY+NsUyPuylQJIYQQQgghxGsinQzitfD19aVDhw6cP3+eDz74QHV+8uTJtG/fHmdnZ7p3746enh7nzp0jKiqKGTNm5FjOlClT6NOnD4GBgfz9998MGzaMDz/8EAcHBwACAwMZNGgQJUqUoG3btjx69IgjR44wbNiwbOWVKFECpVLJrl27KF26NCYmJlhZWQHQunVrrKysmDFjBtOmTdO4rWFhYbz77ruMGDGCrl27qnapMDIywtbWFj09PapUqZItDxMTk2znhRBCCCGEEKI4kTUZxGvRvHlzbG1tiYmJoXfv3qrzrVu3Ztu2bezZs4e6devSoEEDFixYQNmyZXMsx9TUlN27d/PPP/9Qt25dunXrRosWLVi2bJnqnj59+rBo0SKWL1+Op6cn7du35/LlyzmWZ2BgwJIlS1i5ciUlS5akU6dOqmt6enr4+/uTkZGhNt0hP8HBwTx+/JjZs2fj5OSkOnKaEiKEEEIIIYQQ/yWKrKysrKJOQog3VUBAALdv32br1q1FnYrW4mKv6BSfRc5rYIhXJ0OHQWZXHpbUqe7riYb535SL/1WI1anuoqSfla51bIZCBgVqo2TMPq1j/yjfXae6lfpPtI9VZF+U+G2QhpFO8YY8LaRMCu7sXRetYxtYRuV/Ux7uGjjmf1MuTBXJOtVdlGySbmode8+8dCFmUnzceOKkU/yOI9r//9pHLW7pVLcoXlzLl3/ldcj/GQmRgwcPHhAeHs769ev55ZdfijodIYQQQgghhCgWZLqEEDno1KkTHTt25KOPPqJVq1Zq19q2bau25eaLx6xZs4ooYyGEEEIIIYQoejKSQYgc5LVd5erVq0lJScnxmq2t7SvKSAghhBBCCCHefLImgxD/cVdjdZsnfz/DWutYa/37OtXtkKj9XNjbJarqVLcuLjzIeeFSTXlYXS+kTF4vvazsW8EWhOO5nVrH3qreXqe6RfGiyxoaULTraIRd134urJujbnP0y+vlvAiyJlIMLXSqW5f3zCHmgE5136rcKv+bXhGnGye1jk1wrleImRTMvQwbneJt9O9pHZuSZapT3UW5boouz02XZybePref2msd29DdshAzyZlMlxBCCCGEEEIIIUShkE4GIYQQQgghhBBCFArpZBDFlkKhYMuWLW9seUIIIYQQQgjxtpFOBiH+X0JCAm3btgXg2rVrKBQKIiMjX0ldV65cwcLCAmtr61dSvhBCCCGEEEIUBelkEOL/OTo6Ymxs/MrrSUtLo1evXjRp0uSV1yWEEEIIIYQQr5N0MogisXLlSkqVKkVmZqba+Y4dO9KnTx8Afv31V2rXro2JiQmurq5MnTqV9PTcV6eOioqiefPmKJVK7OzsGDhwIElJSWr3fPfdd3h6emJsbIyTkxNDhw5VXXtxukS5cuUAqFmzJgqFAh8fH37//XcMDQ3566+/1MocM2YMTZs21bjtkyZNonLlyvTo0UPt/IMHD9DX1yciIgKArKwsbG1tqVu3ruqeH374AScnJ43rEkIIIYQQQojXSToZRJHo3r07d+7c4cCBf7elunfvHrt378bX15fdu3fzwQcfMHz4cC5cuMDKlSsJDg5m5syZOZb3+PFj2rRpg42NDeHh4WzYsIG9e/eqdSKsWLGCIUOGMHDgQKKioti6dSsVKlTIsbyTJ59tObV3714SEhLYtGkTTZs2xdXVlbVr16ruS09PZ926dfTt21ejdu/fv58NGzbw1VdfZbtmZWVFjRo1CAsLA+DcuXOq/z58+BCAsLAwvL29NapLCCGEEEIIIV436WQQRcLW1pY2bdrw/fffq85t2LABW1tbWrRowcyZM5kwYQJ9+vTB1dWVVq1aMX36dFauXJljeevXryclJYU1a9ZQpUoVmjdvzrJly1i7di23b98GYMaMGYwZM4YRI0bg5uZG3bp1GTlyZI7l2ds/23vWzs4OR0dHbG1tAejfvz9BQUGq+7Zv387jx4+zjUrIyd27d/H39yc4OBhLy5z3p/Xx8VF1MoSFhdGiRQuqVKnC4cOHVed8fHxyrSM1NZWHDx+qHampqfnmJoQQQgghhBCFQToZRJHx9fVl48aNqg/B69ev5/3331dNGZg2bRrm5uaqIyAggISEBB4/fpytrOjoaKpXr46ZmZnqnJeXF5mZmcTExJCYmMitW7do0aKFTjn7+/tz5coVjh8/DjybftGjRw+1enMTEBBA796985xa4ePjw6FDh8jMzOTgwYP4+Pjg4+PDwYMH+euvv7h06VKeIxlmz56NlZWV2vH1118XvKFCCCGEEEIIoQWDok5AvL06dOhAZmYm27dvp27duhw6dIgFCxYAkJmZydSpU+nSpUu2OBMTk2znsrKyUCgUOdajUChQKpWFknOJEiXo0KEDQUFBuLq6smPHDtXIg/zs37+frVu3Mm/ePFXOmZmZGBgY8M0339CvXz+aNm3Ko0ePOH36NIcOHWL69Ok4Ozsza9YsatSoQYkSJXB3d8+1jokTJzJ69Gi1c3/evKl1e4UQQgghhBCiIKSTQRQZpVJJly5dWL9+PVeuXMHNzY3atWsDUKtWLWJiYnJdM+FlHh4ehISEkJycrBpVcOTIEfT09HBzc8PCwgIXFxf27dtHs2bN8i3PyMgIgIyMjGzXBgwYwPvvv0/p0qUpX748Xl5eGuV47NgxtfJ++eUX5s6dy9GjRylVqhTw77oMy5YtQ6FQ4OHhQcmSJTlz5gzbtm3Ldz0GY2PjbDtk3HkNO2YIIYQQQgghBEgngyhivr6+dOjQgfPnz/PBBx+ozk+ePJn27dvj7OxM9+7d0dPT49y5c0RFRTFjxowcy5kyZQp9+vQhMDCQv//+m2HDhvHhhx/i4OAAQGBgIIMGDaJEiRK0bduWR48eceTIEYYNG5atvBIlSqBUKtm1axelS5fGxMQEKysrAFq3bo2VlRUzZsxg2rRpGrf15REIp06dQk9PjypVqqid9/HxYfHixbz33nsoFApsbGzw8PDgxx9/ZMmSJRrXJ4QQQgghhBCvm6zJIIpU8+bNsbW1JSYmht69e6vOt27dmm3btrFnzx7q1q1LgwYNWLBgAWXLls2xHFNTU3bv3s0///xD3bp16datGy1atGDZsmWqe/r06cOiRYtYvnw5np6etG/fnsuXL+dYnoGBAUuWLGHlypWULFmSTp06qa7p6enh7+9PRkYGfn5+hfQk/tWsWTMyMjLUFnj09vYmIyNDdpYQQgghhBBCvNEUWVlZWUWdhBDFTUBAALdv32br1q1FnUq+rsbG6hR/P8Na61hr/fs61e2QGKV17O0SVXWqWxcXHuTcGaYpD6vrhZTJ66WXlX16UUE4ntupdeyt6u11qlsUL/pZ6TrFZyiKbiBn2PXyWse6OSbrVHd5vZw71jWRYmihU926vGcOMQfyvykPtyq30ileF043Tmodm+BcrxAzKZh7GTY6xdvo39M6NiXLVKe6lYrsC4S/Lro8N12emXj73H5qr3VsQ/ecd7krTDJdQogCePDgAeHh4axfv55ffvmlqNMRQgghhBBCiDeKTJcQogA6depEx44d+eijj2jVSv2bkbZt26ptufniMWvWrCLKWAghhBBCCCFeHxnJIEQB5LVd5erVq0lJScnxmq2t7SvKSAghhBBCCCHeHLImgxD/cbquyaCLK0nOOsVXML9RSJmI/7qjt1x1im9U8mohZSJeh3ce6PZ+3bHS7eelqKRiolN86X/Oah37t20lnepOzjLXOtZMkaRT3cXVwXjt1+8A8C6j/b//T9Ft+2sjUrWOtUxJ1Knuh8oSOsWLt0c6hjrFG5BWSJm8Xq7ldfvdogmZLiGEEEIIIYQQQohCIZ0MQgghhBBCCCGEKBTSyaAlHx8fRo4c+UrrCAwMpEaNGlrFBgcHY21tXaj5FKbX8fyEEEIIIYQQQrxe0skghJa06chJSkpi6NChlC5dGqVSibu7OytWrHg1CQohhBBCCCHEaya7SwiVtLQ0DA11WwBF5G3UqFEcOHCAdevW4eLiwm+//cbgwYMpWbIknTp1Kur0hBBCCCGEEEInMpJBB+np6QwdOhRra2vs7OyYNGkSzzfrWLduHXXq1MHCwgJHR0d69+5NYuK/q+WGhYWhUCjYt28fderUwdTUlEaNGhETE5NrfXFxcVSoUIGPP/6YzMxMjXLcsmULbm5umJiY0KpVK27c+He1/ufTMb777jtcXV0xNjYmKyuLXbt20bhxY1W72rdvT+wLOxRcu3YNhULBpk2baNasGaamplSvXp1jx46p1X3kyBG8vb0xNTXFxsaG1q1bc+/ePdX1zMxMxo0bh62tLY6OjgQGBmrUJoD79+8zcOBAHBwcMDExoUqVKmzbtk11fePGjXh6emJsbIyLiwvz589Xi1coFGzZskXtnLW1NcHBwRq1MSwsjL59+/LgwQMUCgUKhUKj/I8dO0afPn3w8fHBxcWFgQMHUr16dU6dOgXAmDFj6NChg+r+RYsWoVAo2L59u+pcpUqVWLlypcbPSgghhBBCCCFeF+lk0EFISAgGBgacOHGCJUuWsHDhQlavXg3A06dPmT59OmfPnmXLli3ExcXh7++frYzPPvuM+fPnc+rUKQwMDOjXr1+Odf3xxx94eXnRvXt3VqxYgZ5e/m/d48ePmTlzJiEhIRw5coSHDx/y/vvvq91z5coVfvrpJzZu3EhkZCQAycnJjB49mvDwcPbt24eenh7vvfdeto6Nzz77jLFjxxIZGYmbmxu9evUiPT0dgMjISFq0aIGnpyfHjh3j8OHDdOjQgYyMDLXnZ2ZmxokTJ/jiiy+YNm0ae/bsybddmZmZtG3blqNHj7Ju3TouXLjAnDlz0NfXByAiIoIePXrw/vvvExUVRWBgIJ9//rmqA6Egcmtjo0aNWLRoEZaWliQkJJCQkMDYsWPzLa9x48Zs3bqVP//8k6ysLA4cOMClS5do3bo18GytikOHDqme9cGDB3nnnXc4ePAgAH/99ReXLl3C29u7wG0RQgghhBBCiFdNpkvowNnZmYULF6JQKKhUqRJRUVEsXLiQgIAAtc4CV1dXlixZQr169UhKSsLc/N+9omfOnKn6wDhhwgTeffddnjx5gonJv3thHzt2jPbt2zNx4kSNPsg+l5aWxrJly6hfvz7w7EO9u7s7J0+epF69esCzzpC1a9dib2+viuvatataOd9++y0lSpTgwoULVKlSRXV+7NixvPvuuwBMnToVT09Prly5QuXKlfniiy+oU6cOy5cvV93v6empVm61atWYMmUKABUrVmTZsmXs27ePVq1a5dmuvXv3cvLkSaKjo3FzcwOePePnFixYQIsWLfj8888BcHNz48KFC3z55Zc5dvTkJa82WllZoVAocHR01Li8JUuWEBAQQOnSpTEwMEBPT4/Vq1fTuHFjAJo2bcqjR484c+YMtWrV4tChQ4wdO5ZNmzYBcODAARwcHKhcuXKO5aemppKamprtnLGxbvtdCyGEEEIIIYQmZCSDDho0aIBCoVC9btiwIZcvXyYjI4MzZ87QqVMnypYti4WFBT4+PgDEx8erlVGtWjXVn52cnADUplXEx8fTsmVLJk2aVKAOBgADAwPq1Kmjel25cmWsra2Jjo5WnStbtqxaBwNAbGwsvXv3xtXVFUtLS8qVK1fg3J+PZMjLi/HPy3ix7bmJjIykdOnSqg6Gl0VHR+Pl5aV2zsvLS/XeFER+709BLVmyhOPHj7N161YiIiKYP38+gwcPZu/evQBYWVlRo0YNwsLCiIqKQk9Pj48++oizZ8/y6NEjwsLC8hzFMHv2bKysrNSOr7/+Wut8hRBCCCGEEKIgZCTDK/DkyRP+97//8b///Y9169Zhb29PfHw8rVu35unTp2r3vrjQ4vMOixenJdjb21OyZElCQ0Pp378/lpaWBcrlxU6QnM6ZmZllu96hQwecnZ1ZtWoVJUuWJDMzkypVqhQod6VSmW9uLy8yqVAoNFprIr+ys7KysrX7+VoZL9b18rm0tLQ8c8zp/SmIlJQUPv30UzZv3qwaHVGtWjUiIyOZN28eLVu2BJ5NmQgLC8PIyAhvb29sbGzw9PTkyJEjhIWF5bn158SJExk9erTauT9v3tQqXyGEEEIIIYQoKBnJoIPjx49ne12xYkUuXrzInTt3mDNnDk2aNKFy5cpaf/utVCrZtm0bJiYmtG7dmkePHmkcm56erlpQECAmJob79+/nOtQe4O7du0RHRzNp0iRatGiBu7u72mKNmqpWrRr79u0rcJymZd+8eZNLly7leN3Dw4PDhw+rnTt69Chubm6qdRvs7e1JSEhQXb98+TKPHz8uUB5GRkYFGhmRlpZGWlpatvU09PX11Tounq/LsH//ftUIGG9vb0JDQ/Ndj8HY2BhLS0u1Q6ZKCCGEEEIIIV4X6WTQwY0bNxg9ejQxMTH88MMPLF26lBEjRlCmTBmMjIxYunQpV69eZevWrUyfPl3reszMzNi+fTsGBga0bduWpKQkjeIMDQ0ZNmwYJ06c4PTp0/Tt25cGDRqo1mPIiY2NDXZ2dnzzzTdcuXKF/fv3Z/tmXBMTJ04kPDycwYMHc+7cOS5evMiKFSu4c+dOgct6mbe3N02bNqVr167s2bOHuLg4du7cya5du4BnOzTs27eP6dOnc+nSJUJCQli2bJnadJPmzZuzbNkyTp8+zalTpxg0aFCBt+90cXEhKSmJffv2cefOnXw7KSwtLfH29uaTTz4hLCyMuLg4goODWbNmDe+9957qvufrMvz666+qTgYfHx/VqBgPD48C5SmEEEIIIYQQr4t0MujAz8+PlJQU6tWrx5AhQxg2bBgDBw7E3t6e4OBgNmzYgIeHB3PmzGHevHk61WVubs7OnTvJysqiXbt2JCcn5xtjamrK+PHj6d27Nw0bNkSpVBIaGppnjJ6eHqGhoURERFClShVGjRrFl19+WeB83dzc+O233zh79iz16tWjYcOG/PLLLxgYFM4MnY0bN1K3bl169eqFh4cH48aNU40qqFWrFj/99BOhoaFUqVKFyZMnM23aNLVFH+fPn4+zszNNmzald+/ejB07FlNT0wLl0KhRIwYNGkTPnj2xt7fniy++yDcmNDSUunXr4uvrq/rZmDlzJoMGDVLdY2VlRc2aNbG1tVV1KDRp0oTMzEzZVUIIIYQQQgjxRlNkvTwxXQjxn3I1NrbI6r6S5KxTfAXzG4WUifivO3rLNf+b8tCo5NVCykS8Du880O39umOl289LUUnFJP+b8lD6n7Nax/5tW0mnupOzzPO/KRdmCs1GcP7XHIwvr1O8dxnt//1/im5TLY1Izf+mXFimaL/ANsBDZQmd4sXbI52CjWJ+mQHZ13MrDlzL6/a7RRMykkEIIYQQQgghhBCFQjoZiqm2bdtibm6e4zFr1qyiTk8n69evz7Vtnp6eRZ1erg4dOpRr3ubm2n+DI4QQQgghhBDFhWxhWUytXr2alJSUHK/Z2tq+5mwKV8eOHalfv36O1wq6OOPrVKdOHSIjI4s6DSGEEEIIIYQoMrImgxD/cQ9O79Up/q5VOa1jHW+G61T31/d7ah3bzeOiTnWn6xnpFK8LXeabF9e55m+zs3ddtI6tbnet0PIoTu5l2OgUb6Nf8K2ZVbFJN3Wq+555aZ3idWGYqf08+TS9t3M75NN3tP83EKDWO3FaxyakOuhUt43RQ61jTRQ5f5ElxJvm/P2yWsd6Wl8vxEwKpijXkpI1GYQQQgghhBBCCFFsSCeDEEIIIYQQQgghCkWhdDL4+PgwcuTIwijqlcnKymLgwIHY2tqiUCjynTuvSZtcXFxYtGiRVvm8imemUCjYsmULANeuXdOonXnx9/enc+fOhZJbcRMWFoZCoeD+/fsABAcHY21tXaQ5CSGEEEIIIcSb7rWPZHj5w9vrsmvXLoKDg9m2bRsJCQlUqVJF5zLDw8MZOHBgnvcUVXudnZ01bmduHRKLFy8mODi40HIqTh/UGzVqREJCAlZWVq+sjitXrmBhYVFsnokQQgghhBBC5Oet2V0iNjYWJycnGjVqVGhl2tvb53k9LS2t0OoqKH19fRwdHXUq41V+wH7TGRkZ6fz88pKWlkavXr1o0qQJR48efWX1CCGEEEIIIcTrVOCRDMnJyfj5+WFubo6TkxPz589Xu75u3Trq1KmDhYUFjo6O9O7dm8TERODZN+bNmjUDwMbGBoVCgb+/P/BsOsMXX3yBq6srSqWS6tWr8/PPP2uc18GDB6lXrx7GxsY4OTkxYcIE0tPTgWfD/ocNG0Z8fDwKhQIXFxeNykxPT2fo0KFYW1tjZ2fHpEmTeHEzjpenSygUCr7++ms6deqEmZkZAwYMyLW9AJmZmYwbNw5bW1scHR0JDAzUuL2XL1+madOmmJiY4OHhwZ49e9Suvzw64d69e/j6+mJvb49SqaRixYoEBQUBUK7cs5WTa9asiUKhwMfHR/XcXpwu4ePjw/Dhw/PM+f79+wwcOBAHBwdMTEyoUqUK27ZtIywsjL59+/LgwQMUCgUKhUKj9j59+pRx48ZRqlQpzMzMqF+/PmFhYarrz0dHbNu2jUqVKmFqakq3bt1ITk4mJCQEFxcXbGxsGDZsGBkZGaq4vH5OIf8RKGfPnqVZs2ZYWFhgaWlJ7dq1OXXqVL7teW7SpElUrlyZHj16qJ1/8OAB+vr6REREAM/+Xtja2lK3bl3VPT/88ANOTk4a1yWEEEIIIYQQr0uBRzJ88sknHDhwgM2bN+Po6Minn35KREQENWrUAJ59KJw+fTqVKlUiMTGRUaNG4e/vz44dO3B2dmbjxo107dqVmJgYLC0tUSqVwLMPXZs2bWLFihVUrFiR33//nQ8++AB7e3u8vb3zzOnPP/+kXbt2+Pv7s2bNGi5evEhAQAAmJiYEBgayePFiypcvzzfffEN4eDj6+voatTUkJIT+/ftz4sQJTp06xcCBAylbtiwBAQG5xkyZMoXZs2ezcOFC9PX16dSpU47tfV7+6NGjOXHiBMeOHcPf3x8vLy9atWqVZ16ZmZl06dKFd955h+PHj/Pw4cN813f4/PPPuXDhAjt37uSdd97hypUrpKQ8257o5MmT1KtXj7179+Lp6YmRUe5b9+WVc2ZmJm3btuXRo0esW7eO8uXLc+HCBfT19WnUqBGLFi1i8uTJxMTEAGBubp5nzgB9+/bl2rVrhIaGUrJkSTZv3kybNm2IioqiYsWKADx+/JglS5YQGhrKo0eP6NKlC126dMHa2podO3Zw9epVunbtSuPGjenZ89mWiHn9nGrC19eXmjVrsmLFCvT19YmMjMTQ0FCj2P3797NhwwYiIyPZtGmT2jUrKytq1KhBWFgYtWvX5ty5cwCcO3eOhw8fYmlpSVhYWL5/J4QQQgghhBCiKBSokyEpKYlvv/2WNWvWqD4Ih4SEULr0v3s+9+vXT/VnV1dXlixZQr169UhKSsLc3BxbW1sASpQooZqLnpyczIIFC9i/fz8NGzZUxR4+fJiVK1fm+4Fq+fLlODs7s2zZMhQKBZUrV+bWrVuMHz+eyZMnY2VlhYWFRYGnEDg7O7Nw4UIUCgWVKlUiKiqKhQsX5tnJ0Lt3b7VnEBcXl629z1WrVo0pU6YAULFiRZYtW8a+ffvy7WTYu3cv0dHRXLt2TfXsZ82aRdu2bXONiY+Pp2bNmtSpUwdAbTTH82kfdnZ2+T6fvHLeu3cvJ0+eJDo6Gjc3N+DZ+/iclZUVCoVC4/cgNjaWH374gZs3b1KyZEkAxo4dy65duwgKCmLWrFnAs6kHK1asoPz/7/narVs31q5dy+3btzE3N8fDw4NmzZpx4MABVSdDfj+n+YmPj+eTTz6hcuXKqmehibt37+Lv78+6deuwtLTM8R4fHx/CwsIYM2YMYWFhtGjRgqtXr3L48GHatWtHWFgYo0aNyjE2NTWV1FT1fdBTnz7FOI+OIyGEEEIIIYQoLAWaLhEbG8vTp09VHQEAtra2VKpUSfX6zJkzdOrUibJly2JhYaEaeh8fH59ruRcuXODJkye0atUKc3Nz1bFmzRpiY2PzzSs6OpqGDRuiUChU57y8vEhKSuLmzZsFaaKaBg0aqJXZsGFDLl++rDbs/mXPP8Rrolq1amqvnZyc1Ibs5yY6OpoyZcqode68+J7k5OOPPyY0NJQaNWowbtw4rdcByCvnyMhISpcurepg0NXp06fJysrCzc1N7efi4MGDaj8Xpqamqg4GAAcHB1xcXNQ6CxwcHNSerTY/py8aPXo0AwYMoGXLlsyZM0ejn1OAgIAAevfuTdOmTXO9x8fHh0OHDpGZmcnBgwfx8fHBx8eHgwcP8tdff3Hp0qVcO95mz56NlZWV2rEgKFSj3IQQQgghhBBCVwXqZHhxPYKcJCcn87///Q9zc3PWrVtHeHg4mzdvBp4NT89NZmYmANu3bycyMlJ1XLhwQaN1GbKystQ6A17M9eXzr5qZmZnG9748vF6hUKieRV5yeh/ya2fbtm25fv06I0eO5NatW7Ro0YKxY8dqnOtzeeX84lSQwpCZmalan+DFn4vo6GgWL16cZ0555antz+mLAgMDOX/+PO+++y779+/Hw8NDVUZe9u/fz7x58zAwMMDAwID+/fvz4MEDDAwM+O677wBo2rQpjx494vTp0xw6dAgfHx+8vb05ePAgBw4coESJEri7u+dY/sSJE3nw4IHaMbrv+xq1SQghhBBCCCF0VaDpEhUqVMDQ0JDjx49TpkwZ4NmCgs+/Wb148SJ37txhzpw5ODs7A2RbDO/5fP8XRwN4eHhgbGxMfHy8VnPNPTw82Lhxo1pnw9GjR7GwsKBUqVIFLu+548ePZ3tdsWJFjdd0gJzbqysPDw/i4+O5deuWahrBsWPH8o2zt7fH398ff39/mjRpwieffMK8efMKLcdq1apx8+ZNLl26lONoBiMjowLVUbNmTTIyMkhMTKRJkyY65fYiTX5ONeHm5oabmxujRo2iV69eBAUF8d577+UZc+zYMbVn8MsvvzB37lyOHj2q+ll9vi7D8+k/Hh4elCxZkjNnzrBt27Y8/44YGxtjbGysdi5LpkoIIYQQQgghXpMCjWQwNzenf//+fPLJJ+zbt48//vgDf39/9PSeFVOmTBmMjIxYunQpV69eZevWrUyfPl2tjLJly6JQKNi2bRt///03SUlJWFhYMHbsWEaNGkVISAixsbGcOXOGr776ipCQkHzzGjx4MDdu3GDYsGFcvHiRX375hSlTpjB69GhVbtq4ceMGo0ePJiYmhh9++IGlS5cyYsSIApWRU3t11bJlSypVqoSfnx9nz57l0KFDfPbZZ3nGTJ48mV9++YUrV65w/vx5tm3bpvo2vESJEiiVSnbt2sXt27d58OCBVnl5e3vTtGlTunbtyp49e4iLi2Pnzp3s2rULeLYORFJSEvv27ePOnTs8fvw4z/Lc3Nzw9fXFz8+PTZs2ERcXR3h4OHPnztV4gcacaPJzmpeUlBSGDh1KWFgY169f58iRI4SHh+c6uuBF7u7uVKlSRXWUKlUKPT09qlSpgo2Njeo+Hx8f1q1bh7e3NwqFAhsbGzw8PPjxxx9VUzuEEEIIIYQQ4k1T4E/gX375JU2bNqVjx460bNmSxo0bU7t2beDZN+XBwcFs2LABDw8P5syZw7x589TiS5UqxdSpU5kwYQIODg4MHToUgOnTpzN58mRmz56Nu7s7rVu35tdff1Vtr5iXUqVKsWPHDk6ePEn16tUZNGgQ/fv3Z9KkSQVtnho/Pz9SUlKoV68eQ4YMYdiwYQwcOLBAZeTWXl3o6emxefNmUlNTqVevHgMGDGDmzJl5xhgZGTFx4kSqVatG06ZN0dfXJzT02Vx9AwMDlixZwsqVKylZsiSdOnXSOreNGzdSt25devXqhYeHB+PGjVN9c9+oUSMGDRpEz549sbe354svvsi3vKCgIPz8/BgzZgyVKlWiY8eOnDhxQjUCQRua/JzmRV9fn7t37+Ln54ebmxs9evSgbdu2TJ06VeucXtasWTMyMjLUOhS8vb3JyMiQnSWEEEIIIYQQbyxFVn4LLQghirUHp/fqFH/XKv+Ovtw43gzXqe6v7/fUOrabx0Wd6k7XK7ppJu88uKp17B0r1/xvEm+Us3ddtI6tbnet0PIoTu5l2OR/Ux5s9O9pH5uk/YLSAPfMS+d/0ytimJma/025SNMzzv+m/6DTd7T/NxCg1jtxWscmpDroVLeN0UOtY00UKTrVLcTrcv5+Wa1jPa2vF2ImBXP0lm7/v9aopPb/r+j6woL5r4r2cwmEEEIIIYQQQgghXlAsOhkGDRqktoXhi8egQYMKXF58fHyu5Zmbm2u8jeGrsn79+lxz8/T0LNLcCtOhQ4fyfB+Ko7Zt2+banlmzZhV1ekIIIYQQQgjxShVod4miMm3atFy3W7S0tCxweSVLliQyMjLP60WpY8eO1K9fP8drL2/NWJzVqVMnz/ehOFq9ejUpKTkPMbS1tX3N2QghhBBCCCHE6yVrMgjxH3c1NrbI6k5Dt3UNHqZbaB1rZ3BXp7p18RTd5i7bp2g/muqBUrc5vEXpcZaZ1rGmiuRCzKT4cLh9Vqf42w7VCymTgrPf8bXWsffb9NWp7rd1fQFRcMq0RzrFpxhq/++YLr8ToWh/L+qSe1qWbt+BWulpt0taUTNZptuC9U+GziikTAouJctU61ilIu/d5kThkzUZhBBCCCGEEEIIUWxIJ4MQQgghhBBCCCEKhXQyiBy5uLiwaNGiok5DCCGEEEIIIUQxIp0M4j8vMDCQGjVqFFp52nbA+Pj4MHLkyELLQwghhBBCCCHeNNLJIIQQQgghhBBCiEIhnQxvuKysLL744gtcXV1RKpVUr16dn3/+GYCwsDAUCgW7d++mZs2aKJVKmjdvTmJiIjt37sTd3R1LS0t69erF48f/rtzq4+PD0KFDGTp0KNbW1tjZ2TFp0iTy2mgkPj6eTp06YW5ujqWlJT169OD27dsAXLt2DT09PU6dOqUWs3TpUsqWLUtWVpbWuebV/hefwb59+6hTpw6mpqY0atSImJgYAIKDg5k6dSpnz55FoVCgUCgIDg7O97kHBgZSpkwZjI2NKVmyJMOHD1c9u+vXrzNq1ChVeQB3796lV69elC5dGlNTU6pWrcoPP/ygKs/f35+DBw+yePFiVdy1a9cIDg7G2tpare4tW7aoygU4e/YszZo1w8LCAktLS2rXrp3tWQshhBBCCCHEm0C3PWLEKzdp0iQ2bdrEihUrqFixIr///jsffPAB9vb2qnsCAwNZtmwZpqam9OjRgx49emBsbMz3339PUlIS7733HkuXLmX8+PGqmJCQEPr378+JEyc4deoUAwcOpGzZsgQEBGTLISsri86dO2NmZsbBgwdJT09n8ODB9OzZk7CwMFxcXGjZsiVBQUHUqVNHFRcUFIS/v7/aB+aC5ppX+729vVXlfvbZZ8yfPx97e3sGDRpEv379OHLkCD179uSPP/5g165d7N27FwArK6s8n/nPP//MwoULCQ0NxdPTk7/++ouzZ59tE7dp0yaqV6/OwIED1Z7VkydPqF27NuPHj8fS0pLt27fz4Ycf4urqSv369Vm8eDGXLl2iSpUqTJs2DUDtPcyLr68vNWvWZMWKFejr6xMZGYmhoaFGsUIIIYQQQgjxOkknwxssOTmZBQsWsH//fho2bAiAq6srhw8fZuXKlQwcOBCAGTNm4OXlBUD//v2ZOHEisbGxuLq6AtCtWzcOHDig1sng7OzMwoULUSgUVKpUiaioKBYuXJhjJ8PevXs5d+4ccXFxODs7A7B27Vo8PT0JDw+nbt26DBgwgEGDBrFgwQKMjY05e/YskZGRbNq0Sa2sguSaX/tf7GSYOXOm6vWECRN49913efLkCUqlEnNzcwwMDHB0dNToucfHx+Po6EjLli0xNDSkTJky1KtXDwBbW1v09fWxsLBQK69UqVKMHTtW9XrYsGHs2rWLDRs2UL9+faysrDAyMsLU1FTjPF7M55NPPqFy5coAVKxYMdd7U1NTSU1NzXbO2Fj2hBdCCCGEEEK8ejJd4g124cIFnjx5QqtWrTA3N1cda9asITY2VnVftWrVVH92cHDA1NRU9aH9+bnExES1shs0aKA2wqBhw4ZcvnyZjIyMbHlER0fj7Oys6mAA8PDwwNramujoaAA6d+6MgYEBmzdvBuC7776jWbNmuLi4qJVVkFw1bf/L5To5OQFka7OmunfvTkpKCq6urgQEBLB582bS09PzjMnIyGDmzJlUq1YNOzs7zM3N+e2334iPj9cqhxeNHj2aAQMG0LJlS+bMmZOt7S+aPXs2VlZWasfXX3+tcw5CCCGEEEIIoQkZyfAGy8zMBGD79u2UKlVK7ZqxsbHqw+aLQ+cVCkW2ofQKhUJVljaysrLUOiRyOm9kZMSHH35IUFAQXbp04fvvv89xB4aC5Jpf+/Mq98X4gnJ2diYmJoY9e/awd+9eBg8ezJdffsnBgwdznaYwf/58Fi5cyKJFi6hatSpmZmaMHDmSp0+f5lmXnp5etrUw0tLS1F4HBgbSu3dvtm/fzs6dO5kyZQqhoaG899572cqbOHEio0ePVjv3582bmjRbCCGEEEIIIXQmnQxvMA8PD4yNjYmPj1ebGvBcXt9o5+f48ePZXlesWBF9ff0c84iPj+fGjRuq0QwXLlzgwYMHuLu7q+4bMGAAVapUYfny5aSlpdGlSxet83teb17t15SRkVGOIzTyolQq6dixIx07dmTIkCFUrlyZqKgoatWqlWN5hw4dolOnTnzwwQfAsw6Oy5cvqz2fnOLs7e159OgRycnJmJmZARAZGZktHzc3N9zc3Bg1ahS9evUiKCgox04GY2PjbB0wd2SqhBBCCCGEEOI1kU6GN5iFhQVjx45l1KhRZGZm0rhxYx4+fMjRo0cxNzenbNmyWpd948YNRo8ezUcffcTp06dZunQp8+fPz/Heli1bUq1aNXx9fVm0aJFq4Udvb2+1hR7d3d1p0KAB48ePp1+/fiiVSq3zg/zb36dPH43KcXFxIS4ujsjISEqXLo2FhUWeaxQEBweTkZFB/fr1MTU1Ze3atSiVStXzdnFx4ffff+f999/H2NiYd955hwoVKrBx40aOHj2KjY0NCxYs4K+//lLrZHBxceHEiRNcu3YNc3NzbG1tVXV8+umnDBs2jJMnT6rtfpGSksInn3xCt27dKFeuHDdv3iQ8PJyuXbtq91CFEEIIIYQQ4hWSNRnecNOnT2fy5MnMnj0bd3d3Wrduza+//kq5cuV0KtfPz4+UlBTq1avHkCFDGDZsmGohyZcpFAq2bNmCjY0NTZs2pWXLlri6uvLjjz9mu7d///48ffqUfv366ZTfc4XR/q5du9KmTRuaNWuGvb292taSObG2tmbVqlV4eXlRrVo19u3bx6+//oqdnR0A06ZN49q1a5QvX161Q8Tnn39OrVq1aN26NT4+Pjg6OtK5c2e1cseOHYu+vj4eHh7Y29sTHx+Pra0t69atY8eOHaptLwMDA1Ux+vr63L17Fz8/P9zc3OjRowdt27Zl6tSpGrdfCCGEEEIIIV4XRdbLE8LFf56Pjw81atTIcc0EXc2cOZPQ0FCioqIKvWyhnas6TKvRVRpGOsU/TLfQOtbO4K5OdeviKbpNUbFP0X7B0AdKB53qLkqPs8y0jjVVJBdiJsWHw+2zOsXfdqheSJkUnP0O7Relvd+mr051p+nJNDKhGWXaI53iUwy1/3dMl9+JULS/F3XJPS1Lt4HWVnoPdIovKibLJukU/2TojELKpOBSsky1jlUqHhdiJkITruXLv/I6ZCSDKBRJSUmEh4ezdOlShg8fXtTpCCGEEEIIIYQoAtLJIArF0KFDady4Md7e3oU2VeJVWb9+vdqWmC8enp6eRZ2eEEIIIYQQQhRbsvDjWygsLKzQywwODlZbsPBN1rFjR+rXr5/jtdy2qBRCCCGEEEIIkT9Zk0GI/7iiXJOhOM8nfVuVjNisdeyt2tm3VS2Iv56W0Do2M0u3gXl/3ND+Z/V/FYru75h56j2d4pOMbbSOTUx7R6e6Sxje0Sm+uNLLKtiWyi/KVGTfZrogbB/d0Dr2HwtnneournSZaw5v73xz+39itI7927aSTnXvvKT9fPO2bkX3+7w4c7p+VOvYhLKNCjEToQlZk0EIIYQQQgghhBDFhnQyiFfi2rVrKBQKIiMjgWdTNBQKBffv33+l9RTWvUIIIYQQQgghCk46GcRr0ahRIxISErCysgKereFgbW2tc7nOzs4kJCRQpUoVncsSQgghhBBCCKEb6WQQeUpLSyuUcoyMjHB0dEShUBRKeQBPnz5FX18fR0dHDAz+W2uYPn36tKhTEEIIIYQQQogCk06Gt1BmZiZz586lQoUKGBsbU6ZMGWbOnKmaTvDTTz/h4+ODiYkJ69atAyAoKAh3d3dMTEyoXLkyy5cvVyvz5MmT1KxZExMTE+rUqcOZM2fUrr84XSIsLIy+ffvy4MEDFAoFCoWCwMDAfPN2cXFhxowZ+Pv7Y2VlRUBAQLYpEPfu3cPX1xd7e3uUSiUVK1YkKCgo1+cQEBCAm5sb169fz7f+wMBAypQpg7GxMSVLlmT48OGqa6mpqYwbNw5nZ2eMjY2pWLEi3377rer6wYMHqVevHsbGxjg5OTFhwgTS09NV1318fBg6dCijR4/mnXfeoVWrVgBcuHCBdu3aYW5ujoODAx9++CF37rydi6UJIYQQQggh3nz/ra9/hUYmTpzIqlWrWLhwIY0bNyYhIYGLFy+qro8fP5758+cTFBSEsbExq1atYsqUKSxbtoyaNWty5swZAgICMDMzo0+fPiQnJ9O+fXuaN2/OunXriIuLY8SIEbnW36hRIxYtWsTkyZOJiXm2+rC5ublGuX/55Zd8/vnnTJo0Kcfrn3/+ORcuXGDnzp288847XLlyhZSUlGz3PX36lN69exMbG8vhw4cpUSLvVe1//vlnFi5cSGhoKJ6envz111+cPXtWdd3Pz49jx46xZMkSqlevTlxcnKoz4M8//6Rdu3b4+/uzZs0aLl68SEBAACYmJmqdKyEhIXz88cccOXKErKwsEhIS8Pb2JiAggAULFpCSksL48ePp0aMH+/fv1+h5CSGEEEIIIcTrJJ0Mb5lHjx6xePFili1bRp8+fQAoX748jRs35tq1awCMHDmSLl26qGKmT5/O/PnzVefKlSvHhQsXWLlyJX369GH9+vVkZGTw3XffYWpqiqenJzdv3uTjjz/OMQcjIyOsrKxQKBQ4OjoWKP/mzZszduxY1evnOT8XHx9PzZo1qVOnDvBs9MPLkpKSePfdd0lJSSEsLEy1TkRe4uPjcXR0pGXLlhgaGlKmTBnq1asHwKVLl/jpp5/Ys2cPLVu2BMDV1VUVu3z5cpydnVm2bBkKhYLKlStz69Ytxo8fz+TJk9HTezagqEKFCnzxxRequMmTJ1OrVi1mzZqlOvfdd9/h7OzMpUuXcHNzyzdvIYQQQgghhHidZLrEWyY6OprU1FRatGiR6z3PP6AD/P3339y4cYP+/ftjbm6uOmbMmEFsbKyqzOrVq2Nq+u9e0g0bNnwl+b+YW04+/vhjQkNDqVGjBuPGjePo0ez79vbq1YukpCR+++03jToYALp3705KSgqurq4EBASwefNm1XSHyMhI9PX18fb2zjE2Ojqahg0bqq1H4eXlRVJSEjdv3sy1bRERERw4cEDtuVeuXBlA9exflpqaysOHD9WO1NRUjdoohBBCCCGEELqSToa3jFKpzPceMzMz1Z8zMzMBWLVqFZGRkarjjz/+4Pjx4wBkZWW9mmTzyS0nbdu25fr164wcOZJbt27RokULtZEPAO3atePcuXOq/DXh7OxMTEwMX331FUqlksGDB9O0aVPS0tLyfaZZWVnZFrx8/sxePP9y2zIzM+nQoYPac4+MjOTy5cs0bdo0x7pmz56NlZWV2vH1119r3E4hhBBCCCGE0IV0MrxlKlasiFKpZN++fRrd7+DgQKlSpbh69SoVKlRQO8qVKweAh4cHZ8+eVVv7IL8P8EZGRmRkZGjfkDzY29vj7+/PunXrWLRoEd98843a9Y8//pg5c+bQsWNHDh48qHG5SqWSjh07smTJEsLCwjh27BhRUVFUrVqVzMzMXMvy8PDg6NGjap0xR48excLCglKlSuVaX61atTh//jwuLi7Znn1unS0TJ07kwYMHasegQYM0bqMQQgghhBBC6EI6Gd4yJiYmjB8/nnHjxrFmzRpiY2M5fvy42k4ILwsMDGT27NksXryYS5cuERUVRVBQEAsWLACgd+/e6Onp0b9/fy5cuMCOHTuYN29ennm4uLiQlJTEvn37uHPnDo8fPy6U9k2ePJlffvmFK1eucP78ebZt24a7u3u2+4YNG8aMGTNo3749hw8fzrfc4OBgvv32W/744w+uXr3K2rVrUSqVlC1bFhcXF/r06UO/fv3YsmULcXFxhIWF8dNPPwEwePBgbty4wbBhw7h48SK//PILU6ZMYfTo0ar1GHIyZMgQ/vnnH3r16sXJkye5evUqv/32G/369cu1g8bY2BhLS0u1w9jYWMOnJ4QQQgghhBC6kU6Gt9Dnn3/OmDFjmDx5Mu7u7vTs2ZPExMRc7x8wYACrV68mODiYqlWr4u3tTXBwsGokg7m5Ob/++isXLlygZs2afPbZZ8ydOzfPHBo1asSgQYPo2bMn9vb2agse6sLIyIiJEydSrVo1mjZtir6+PqGhoTneO3LkSKZOnUq7du1yXLvhRdbW1qxatQovLy+qVavGvn37+PXXX7GzswNgxYoVdOvWjcGDB1O5cmUCAgJITk4GoFSpUuzYsYOTJ09SvXp1Bg0aRP/+/XPdIeO5kiVLcuTIETIyMmjdujVVqlRhxIgRWFlZ5dk5IYQQQgghhBBFRZH1OifUCyFeu6u5LBL5OjzOynsNjfyYKpILKROhqZIRm7WOvVX7PZ3q/utp3lvJ5iUzS7eOtz9uaP+z+r8KRfd3zDz1nk7xScY2Wscmpr2jU90lDO/oFF9c6WVpP1UwU6GvU922j25oHfuPhbNOdRdXKVmm+d+UB6WicEZqFjf2/8RoHfu3bSWd6t55qbzWsW3diu73eXHmdD3vL+vyklC2USFmIjThWl77vyOakq9DhRBCCCGEEEIIUSikk0G8EQ4dOqS2VePLx6u2fv36XOv29PR85fULIYQQQgghxH+BQVEnIARAnTp1iIyMLLL6O3bsSP369XO8Zmho+JqzEUIIIYQQQojiSToZxBtBqVRSoUKFIqvfwsICCwuLIqtfCCGEEEIIIf4LZOFHIf7j1h3S7a94o5JXCykT8TqkYaRTvCFPtY5NxUSnuo15onXshjPldKq7e804neJFwTkkRmkde7tE1ULMpGCK8uf8SZZSp7pNFCk6xevC9uF1rWP/sSxbiJmIN92tVEed4ksa/1VImbxeh2/qthhf49KyaKXQjCz8KIQQQgghhBBCiGJDOhnEf8q1a9dQKBSq9R3CwsJQKBTcv38/39jg4GCsra0LJY8tW7ZQoUIF9PX1GTlyZKGUKYQQQgghhBBvOlmTQfynODs7k5CQwDvvFHz/9p49e9KuXbtCyeOjjz6ib9++DB8+XNZ6EEIIIYQQQrw1pJNB/Kfo6+vj6KjdXD6lUolSqdt8V4CkpCQSExNp3bo1JUuWzPGejIwMFAoFenoymEgIIYQQQgjx3yGfcMQrk5WVxRdffIGrqytKpZLq1avz888/A/9OY9i9ezc1a9ZEqVTSvHlzEhMT2blzJ+7u7lhaWtKrVy8eP36sKnPXrl00btwYa2tr7OzsaN++PbGx/y508/J0iYJ4ebrE2bNnadasGRYWFlhaWlK7dm1OnTqVZxlhYWGqkQvNmzdHoVAQFhamKnvbtm14eHhgbGzM9evXefr0KePGjaNUqVKYmZlRv359wsLC1Mo8evQoTZs2RalU4uzszPDhw0lOTi5w+4QQQgghhBDiVZNOBvHKTJo0iaCgIFasWMH58+cZNWoUH3zwAQcPHlTdExgYyLJlyzh69Cg3btygR48eLFq0iO+//57t27ezZ88eli5dqro/OTmZ0aNHEx4ezr59+9DT0+O9994jMzOz0PP39fWldOnShIeHExERwYQJEzA0NMwzplGjRsTExACwceNGEhISaNSoEQCPHz9m9uzZrF69mvPnz1OiRAn69u3LkSNHCA0N5dy5c3Tv3p02bdpw+fJlAKKiomjdujVdunTh3Llz/Pjjjxw+fJihQ4cWenuFEEIIIYQQQlcyXUK8EsnJySxYsID9+/fTsGFDAFxdXTl8+DArV65k4MCBAMyYMQMvLy8A+vfvz8SJE4mNjcXV1RWAbt26ceDAAcaPHw9A165d1er59ttvKVGiBBcuXKBKlSqF2ob4+Hg++eQTKleuDEDFihXzjTEyMqJEiRIA2Nraqk3dSEtLY/ny5VSvXh2A2NhYfvjhB27evKmaVjF27Fh27dpFUFAQs2bN4ssvv6R3796qxSMrVqzIkiVL8Pb2ZsWKFZiY6LaVmhBCCCGEEEIUJulkEK/EhQsXePLkCa1atVI7//TpU2rWrKl6Xa1aNdWfHRwcMDU1VXUwPD938uRJ1evY2Fg+//xzjh8/zp07d1QjGOLj4wu9k2H06NEMGDCAtWvX0rJlS7p37055HfaVNTIyUmvv6dOnycrKws3NTe2+1NRU7OzsAIiIiODKlSusX79edT0rK4vMzEzi4uJwd3fPFpuamqp2Lu2pEYZGxlrnLYQQQgghhBCakk4G8Uo8//C/fft2SpUqpXbN2NhYtY7Ci9MPFApFtukICoVCbSpEhw4dcHZ2ZtWqVZQsWZLMzEyqVKnC06dPC70NgYGB9O7dm+3bt7Nz506mTJlCaGgo7733nlblKZVKFAqF6nVmZib6+vpERESgr6+vdq+5ubnqno8++ojhw4dnK69MmTLZzs2ePZupU6eqnXvPfzJd+gVqlbMQQgghhBBCFIR0MohX4vnihvHx8Xh7e2e7/uJijZq6e/cu0dHRrFy5kiZNmgBw+PBhnXPNi5ubG25ubowaNYpevXoRFBSkdSfDy2rWrElGRgaJiYmq9rysVq1anD9/ngoVKmhU5sSJExk9erTauY3hRjrnKoQQQgghhBCakE4G8UpYWFgwduxYRo0aRWZmJo0bN+bhw4ccPXoUc3NzypYtW+AybWxssLOz45tvvsHJyYn4+HgmTJjwCrKHlJQUPvnkE7p160a5cuW4efMm4eHh2daE0IWbmxu+vr74+fkxf/58atasyZ07d9i/fz9Vq1alXbt2jB8/ngYNGjBkyBACAgIwMzMjOjo624KYzxkbG2NsrD41wtAoq9ByFkIIIYQQQoi8SCeDeGWmT59OiRIlmD17NlevXsXa2ppatWrx6aefarUbhJ6eHqGhoQwfPpwqVapQqVIllixZgo+PT6Hnrq+vz927d/Hz8+P27du88847dOnSJdtUBF0FBQUxY8YMxowZw59//omdnR0NGzakXbt2wLM1Kw4ePMhnn31GkyZNyMrKonz58vTs2bNQ8xBCCCGEEEKIwqDIysqSrzmF+A9bd0i3v+KNSl4tpEzE65CGbtNjDNF+fZNUdNvtxJgnWsduOFNOp7q714zTKV4UnENilNaxt0tULcRMCqYof86fZCl1qttEkaJTvC5sH17XOvYfy4KPfhTF161Ux/xvykNJ478KKZPX6/BN7RcXB2hcuuBTkcXbyVWHhew1pffKaxBCCCGEEEIIIcRbQToZxFujbdu2mJub53jMmjXrtZcjhBBCCCGEEP81siaDeGusXr2alJSch4ra2tq+9nKEEEIIIYQQ4r9GOhnEW6NUqVJvVDlCCCGEEEII8V8jCz8K8R93NfbtXAjI8Wa4TvF/la5bSJm8XiWvhOkUf6uCT6HkUdxk6NDnrk+6TnXHPCyjdeydR4Y61e1Vquh+P+hlZWgdm6nQ16nu649Lah1b1vSWTnUXV/b3LusUn6Gv/aK0Rbnw44UHutVd1VL7v2MZiqL7LtDpxkmd4s86ttM6toThHZ3qtkm6qXXsPfPSOtUt3i4WT7T/WX1k8k4hZlIwsvCjEEIIIYQQQgghig3pZBD58vHxYeTIkUVWv7+/P507dy6y+oUQQgghhBBCaEY6GcQbb/HixQQHBxd1GioKhYItW7YUOM7FxYVFixYVej5CCCGEEEII8aaQhR/FG8/KyqqoUxBCCCGEEEIIoQEZySA0kpmZybhx47C1tcXR0ZHAwEDVtfj4eDp16oS5uTmWlpb06NGD27dvq67nNN1h5MiR+Pj4qF7//PPPVK1aFaVSiZ2dHS1btiQ5OTnHeB8fH4YPH55rPgAXL16kcePGmJiY4OHhwd69ezUegfD06VOGDh2Kk5MTJiYmuLi4MHv2bODZaASA9957D4VCoXodGxtLp06dcHBwwNzcnLp167J37161nK9fv86oUaNQKBQoFAoAAgMDqVGjhlr9ixYtUpULEBYWRr169TAzM8Pa2hovLy+uX7+ebzuEEEIIIYQQ4nWTTgahkZCQEMzMzDhx4gRffPEF06ZNY8+ePWRlZdG5c2f++ecfDh48yJ49e4iNjaVnz54al52QkECvXr3o168f0dHRhIWF0aVLF/La+CS3fOBZh0jnzp0xNTXlxIkTfPPNN3z22Wca57NkyRK2bt3KTz/9RExMDOvWrVN96A8Pf7ZjQVBQEAkJCarXSUlJtGvXjr1793LmzBlat25Nhw4diI+PB2DTpk2ULl2aadOmkZCQQEJCgka5pKen07lzZ7y9vTl37hzHjh1j4MCBqk4KIYQQQgghhHiTyHQJoZFq1aoxZcoUACpWrMiyZcvYt28fAOfOnSMuLg5nZ2cA1q5di6enJ+Hh4dStm/82gAkJCaSnp9OlSxfKln22VVTVqlW1yqdVq1b89ttvxMbGEhYWhqOjIwAzZ86kVatWGrU1Pj6eihUr0rhxYxQKhSonAHt7ewCsra1VZQNUr16d6tWrq17PmDGDzZs3s3XrVoYOHYqtrS36+vpYWFioxeXn4cOHPHjwgPbt21P+/7ebcXd3z/X+1NRUUlNTs50zNjbWuE4hhBBCCCGE0JaMZBAaqVatmtprJycnEhMTiY6OxtnZWdXBAODh4YG1tTXR0dEalV29enVatGhB1apV6d69O6tWreLevXta5QMQExODs7Oz2of5evXqaZQLPJueERkZSaVKlRg+fDi//fZbvjHJycmMGzdO1XZzc3MuXryoGsmgLVtbW/z9/VUjIxYvXpznKIjZs2djZWWldnz99dc65SCEEEIIIYQQmpJOBqERQ0NDtdcKhYLMzEyysrJyHLr/4nk9Pb1sUx/S0tJUf9bX12fPnj3s3LkTDw8Pli5dSqVKlYiLiytwPi/XrY1atWoRFxfH9OnTSUlJoUePHnTr1i3PmE8++YSNGzcyc+ZMDh06RGRkJFWrVuXp06d5xuX3bODZ1Ixjx47RqFEjfvzxR9zc3Dh+/HiO5U2cOJEHDx6oHYMGDdKg1UIIIYQQQgihO+lkEDrx8PAgPj6eGzduqM5duHCBBw8eqIb129vbZ/v2PTIyUu21QqHAy8uLqVOncubMGYyMjNi8ebNWOVWuXJn4+Hi1xSefr52gKUtLS3r27MmqVav48ccf2bhxI//88w/wrIMjIyND7f5Dhw7h7+/Pe++9R9WqVXF0dOTatWtq9xgZGWWLs7e356+//lLraHj52QDUrFmTiRMncvToUapUqcL333+fY97GxsZYWlqqHTJVQgghhBBCCPG6SCeD0EnLli2pVq0avr6+nD59mpMnT+Ln54e3tzd16tQBoHnz5pw6dYo1a9Zw+fJlpkyZwh9//KEq48SJE8yaNYtTp04RHx/Ppk2b+Pvvv/NceyAvrVq1onz58vTp04dz585x5MgR1cKPmoxwWLhwIaGhoVy8eJFLly6xYcMGHB0dsba2Bp7tMLFv3z7++usv1bSOChUqsGnTJiIjIzl79iy9e/dWjax4zsXFhd9//50///yTO3fuAM92nfj777/54osviI2N5auvvmLnzp2qmLi4OCZOnMixY8e4fv06v/32G5cuXdL62QghhBBCCCHEqySdDEInz7eFtLGxoWnTprRs2RJXV1d+/PFH1T2tW7fm888/Z9y4cdStW5dHjx7h5+enum5pacnvv/9Ou3btcHNzY9KkScyfP5+2bdtqlZO+vj5btmwhKSmJunXrMmDAACZNmgSAiYlJvvHm5ubMnTuXOnXqULduXa5du8aOHTvQ03v212X+/Pns2bMHZ2dnatasCTzrmLCxsaFRo0Z06NCB1q1bU6tWLbVyp02bxrVr1yhfvrxqAUl3d3eWL1/OV199RfXq1Tl58iRjx45VxZiamnLx4kW6du2Km5sbAwcOZOjQoXz00UdaPRshhBBCCCGEeJUUWXntEyjEf8SRI0do3LgxV65cUe3S8La4Ghtb1CkUCcebBZsi87K/Sue/M8qbqOSVMJ3ib1XwKZQ8ipsMHTZb0iddp7pjHpbROvbOI8P8b8qDV6mi+/2gl5WR/025yFTo61T39ccltY4ta3pLp7qLK/t7l3WKz9A30jr2H8uy+d/0ilx4oFvdVS21/zuWoSi6TeCcbpzUKf6sYzutY0sY3tGpbpukm1rH3jMvrVPd4u1i8UT7n9VHJu8UYiYF4/oaPgvJFpbiP2nz5s2Ym5tTsWJFrly5wogRI/Dy8nrrOhiEEEIIIYQQ4nWS6RLiP+nRo0cMHjyYypUr4+/vT926dfnll18AmDVrFubm5jke2k7REEIIIYQQQgghIxnEf5Sfn5/aug8vGjRoED169MjxmlKpfJVpCSGEEEIIIcR/mqzJIMR/XHFekyEly1TrWKXisU51p6H9/GFDnupU99tKmfZI69gUQ4tCzOTtsTnKVevYHp7ROtWdpifb6xZUOrqtwWFAmtaxp++U06nuWu/E6RRfXCWkOmgd62R8O/+b8hB5V/v3rIbd2/l+CfG66LruSYJzPa1jX8eaDDJdQgghhBBCCCGEEIVCOhmEEEIIIYQQQghRKKST4T/MxcWFRYsWFXUaeQoODsba2rqo0ygSYWFhKBQK7t+/D2j2LAIDA6lRo8Yrz00IIYQQQgghtCGdDEK8IXr27MmlS5eKOg0hhBBCCCGE0JrsLiHEG0KpVMruFkIIIYQQQohiTUYyFGM+Pj4MHTqUoUOHYm1tjZ2dHZMmTeLFDUMeP35Mv379sLCwoEyZMnzzzTdqZURFRdG8eXOUSiV2dnYMHDiQpKQk1XV/f386d+7MvHnzcHJyws7OjiFDhpCW9u8K1U+fPmXcuHGUKlUKMzMz6tevT1hYWIHasnv3btzd3TE3N6dNmzYkJCSormVmZjJt2jRKly6NsbExNWrUYNeuXarr165dQ6FQ8NNPP9GkSROUSiV169bl0qVLhIeHU6dOHVW5f//9t1q9QUFBuLu7Y2JiQuXKlVm+fLlG+TZs2JAJEyaonfv7778xNDTkwIEDAKxbt446depgYWGBo6MjvXv3JjExMdcyc5ouMWfOHBwcHLCwsKB///48efJEo/yEEEIIIYQQoihIJ0MxFxISgoGBASdOnGDJkiUsXLiQ1atXq67Pnz+fOnXqcObMGQYPHszHH3/MxYsXgWcdEG3atMHGxobw8HA2bNjA3r17GTp0qFodBw4cIDY2lgMHDhASEkJwcDDBwcGq63379uXIkSOEhoZy7tw5unfvTps2bbh8+bJGbXj8+DHz5s1j7dq1/P7778THxzN27FjV9cWLFzN//nzmzZvHuXPnaN26NR07dsxW/pQpU5g0aRKnT5/GwMCAXr16MW7cOBYvXsyhQ4eIjY1l8uTJqvtXrVrFZ599xsyZM4mOjmbWrFl8/vnnhISE5Juzr68vP/zwg1qHzo8//oiDgwPe3t7As86X6dOnc/bsWbZs2UJcXBz+/v4aPROAn376iSlTpjBz5kxOnTqFk5OTxp0gQgghhBBCCFEUFFkvfkoSxYqPjw+JiYmcP38ehUIBwIQJE9i6dSsXLlzAxcWFJk2asHbtWgCysrJwdHRk6tSpDBo0iFWrVjF+/Hhu3LiBmZkZADt27KBDhw7cunULBwcH/P39CQsLIzY2Fn19fQB69OiBnp4eoaGhxMbGUrFiRW7evEnJkiVVubVs2ZJ69eoxa9asPNsQHBxM3759uXLlCuX/f8/W5cuXM23aNP766y8ASpUqxZAhQ/j0009VcfXq1aNu3bp89dVXXLt2jXLlyrF69Wr69+8PQGhoKL169WLfvn00b94ceDYqIDg4WNXJUqZMGebOnUuvXr1U5c6YMYMdO3Zw9OjRPPP++++/KVmyJPv376dJkyYANGrUiMaNG/PFF1/kGBMeHk69evV49OgR5ubmhIWF0axZM+7du4e1tTXBwcGMHDlStRBko0aNqF69OitWrFCV0aBBA548eUJkZGSOdaSmppKamqp27s+bNzE2Lp770adkmWodq1Q81qnuNIy0jjXkqU51v62UaY+0jk0xtCjETN4em6NctY7t4RmtU91pesXz91JRSsdQp3gD0vK/KRen75TTqe5a78TpFF9cJaQ6aB3rZHxbp7oj72r/ntWwezvfLyFeF6cbJ3WKT3Cup3Ws6/9/5nqVZCRDMdegQQNVBwM8G8Z/+fJlMjIyAKhWrZrqmkKhwNHRUTVkPzo6murVq6s6GAC8vLzIzMwkJiZGdc7T01PVwQDg5OSkKuP06dNkZWXh5uaGubm56jh48CCxsbEatcHU1FTVwfBy+Q8fPuTWrVt4eXmpxXh5eREdrf4/uC+21cHh2T/qVatWVTv3vNy///6bGzdu0L9/f7W8Z8yYoVHe9vb2tGrVivXr1wMQFxfHsWPH8PX1Vd1z5swZOnXqRNmyZbGwsMDHxweA+Pj4fMuHZ+9Pw4YN1c69/Ppls2fPxsrKSu34+uuvNapPCCGEEEIIIXQlCz/+xxkaqn/roVAoyMzMBJ6NbHixg+Ll+zQpIzMzE319fSIiItQ6IgDMzc21zvHlATYv55lT7i+W8/zay+dezBueTZmoX7++WjkvtyM3vr6+jBgxgqVLl/L999/j6elJ9erVAUhOTuZ///sf//vf/1i3bh329vbEx8fTunVrnj59dd9yT5w4kdGjR6ud+/PmzVdWnxBCCCGEEEK8SEYyFHPHjx/P9rpixYoafVD28PAgMjKS5ORk1bkjR46gp6eHm5ubRvXXrFmTjIwMEhMTqVChgtrh6OhYsMbkwNLSkpIlS3L48GG180ePHsXd3V3rch0cHChVqhRXr17Nlne5cpoNL+zcuTNPnjxh165dfP/993zwwQeqaxcvXuTOnTvMmTOHJk2aULly5TwXfcyJu7t7ju9vXoyNjbG0tFQ7iutUCSGEEEIIIUTxIyMZirkbN24wevRoPvroI06fPs3SpUuZP3++RrG+vr5MmTKFPn36EBgYyN9//82wYcP48MMPVdMN8uPm5oavry9+fn7Mnz+fmjVrcufOHfbv30/VqlVp166dLs0D4JNPPmHKlCmUL1+eGjVqEBQURGRkpGqqgrYCAwMZPnw4lpaWtG3bltTUVE6dOsW9e/eyjQbIiZmZGZ06deLzzz8nOjqa3r17q66VKVMGIyMjli5dyqBBg/jjjz+YPn16gfIbMWIEffr0oU6dOjRu3Jj169dz/vx5XF21n0MthBBCCCGEEK+SdDIUc35+fqSkpFCvXj309fUZNmwYAwcO1CjW1NSU3bt3M2LECOrWrYupqSldu3ZlwYIFBcohKCiIGTNmMGbMGP7880/s7Oxo2LBhoXQwAAwfPpyHDx8yZswYEhMT8fDwYOvWrVSsWFGncgcMGICpqSlffvkl48aNw8zMjKpVqzJy5EiNy/D19eXdd9+ladOmlClTRnXe3t6e4OBgPv30U5YsWUKtWrWYN28eHTt21Ljsnj17Ehsby/jx43ny5Aldu3bl448/Zvfu3QVpphBCCCGEEEK8NrK7RDHm4+NDjRo1WLRoUVGnIt5gVzVcgPNNJLtLvF1kd4nXT3aXKF5kd4niR3aXEELkRHaXEEIIIYQQQgghhNCAdDKIV6pt27ZqW0S+eMyaNauo08vVrFmzcs27bdu2RZ2eEEIIIYQQQryRZE2GYiwsLKyoU8jX6tWrSUlJyfGara3ta85Gc4MGDaJHjx45XlMqla85GyGEEEIIIYQoHmRNBiH+44rzmgyieNFlHQuQtSyE5opybQIhCuJxlpnWsaaK5Pxv+g8yTXuoU/xjQ8tCyuTtkZxlrlO8mSJJ69h37l/Rqe471hV0in8byZoMQgghhBBCCCGEKDakk0EIIYQQQgghhBCFQjoZijEXF5e3dvvK4OBgrK2t87wnMDCQGjVqqF77+/vTuXPnV5pXQbzchpfzzcmb1gYhhBBCCCGEeJF0Moj/rLFjx7Jv376iTkNjxS1fIYQQQgghhHiZ7C4h/rOebzlZXBS3fIUQQgghhBDiZTKS4Q3m4+PD0KFDGTp0KNbW1tjZ2TFp0iRe3BDk8ePH9OvXDwsLC8qUKcM333yjVkZUVBTNmzdHqVRiZ2fHwIEDSUr6dwXY58Pv582bh5OTE3Z2dgwZMoS0tH9X3n769Cnjxo2jVKlSmJmZUb9+fY23z3w+JWDbtm1UqlQJU1NTunXrRnJyMiEhIbi4uGBjY8OwYcPIyMhQxd27dw8/Pz9sbGwwNTWlbdu2XL58OVv5W7Zswc3NDRMTE1q1asWNGzdU1/KbfpCVlcUXX3yBq6srSqWS6tWr8/PPP+fbpszMTEqXLs3XX3+tdv706dMoFAquXr0KwIIFC6hatSpmZmY4OzszePBgtWf/spfzzcjIYPTo0ar3fty4cchmMEIIIYQQQog3mXQyvOFCQkIwMDDgxIkTLFmyhIULF7J69WrV9fnz51OnTh3OnDnD4MGD+fjjj7l48SLwrAOiTZs22NjYEB4ezoYNG9i7dy9Dhw5Vq+PAgQPExsZy4MABQkJCCA4OJjg4WHW9b9++HDlyhNDQUM6dO0f37t1p06ZNjh/6c/L48WOWLFlCaGgou3btIiwsjC5durBjxw527NjB2rVr+eabb9Q+4Pv7+3Pq1Cm2bt3KsWPHyMrKol27dmqdH48fP2bmzJmEhIRw5MgRHj58yPvvv6/xs500aRJBQUGsWLGC8+fPM2rUKD744AMOHjyYZ5yenh7vv/8+69evVzv//fff07BhQ1xdXVX3LVmyhD/++IOQkBD279/PuHHjNM5v/vz5fPfdd3z77bccPnyYf/75h82bN2scL4QQQgghhBCvm0yXeMM5OzuzcOFCFAoFlSpVIioqioULFxIQEABAu3btGDx4MADjx49n4cKFhIWFUblyZdavX09KSgpr1qzBzOzZPs3Lli2jQ4cOzJ07FwcHBwBsbGxYtmwZ+vr6VK5cmXfffZd9+/YREBBAbGwsP/zwAzdv3qRkyZLAs7UDdu3aRVBQELNmzcq3DWlpaaxYsYLy/78na7du3Vi7di23b9/G3NwcDw8PmjVrxoEDB+jZsyeXL19m69atHDlyhEaNGgGwfv16nJ2d2bJlC927d1eVu2zZMurXrw8865Bxd3fn5MmT1KtXL8+ckpOTWbBgAfv376dhw4YAuLq6cvjwYVauXIm3t3ee8b6+vixYsIDr169TtmxZMjMzCQ0N5dNPP1XdM3LkSNWfy5Urx/Tp0/n4449Zvnx5vs8MYNGiRUycOJGuXbsC8PXXX7N79+48Y1JTU0lNTc12ztjYWKM6hRBCCCGEEEIXMpLhDdegQQMUCoXqdcOGDbl8+bJqakG1atVU1xQKBY6OjiQmJgIQHR1N9erVVR0MAF5eXmRmZhITE6M65+npib6+vuq1k5OTqozTp0+TlZWFm5ubas0Ac3NzDh48SGxsrEZtMDU1VXUwADg4OODi4qK2/oCDg4Na3gYGBqrOAwA7OzsqVapEdHS06pyBgQF16tRRva5cuTLW1tZq9+TmwoULPHnyhFatWqm1a82aNRq1q2bNmlSuXJkffvgBgIMHD5KYmEiPHj1U9xw4cIBWrVpRqlQpLCws8PPz4+7duyQnJ+db/oMHD0hISFB1gOTU3pzMnj0bKysrtePlaR1CCCGEEEII8arISIZiztDQUO21QqEgMzMTeLbmwIsdFC/fp0kZmZmZ6OvrExERodYRAWi8SGFO5eeXd05yak9O7cutzS96Xtf27dspVaqU2jVNv/X39fXl+++/Z8KECXz//fe0bt2ad955B4Dr16/Trl07Bg0axPTp07G1teXw4cP0799fbcpHYZs4cSKjR49WO/fnzZuvrD4hhBBCCCGEeJGMZHjDHT9+PNvrihUrZvvAnxMPDw8iIyPVvjk/cuQIenp6uLm5aVR/zZo1ycjIIDExkQoVKqgdjo6OBWuMhjw8PEhPT+fEiROqc3fv3uXSpUu4u7urzqWnp3Pq1CnV65iYGO7fv0/lypU1qsPY2Jj4+Phs7XJ2dtYoz969exMVFUVERAQ///wzvr6+qmunTp0iPT2d+fPn06BBA9zc3Lh165ZG5QJYWVnh5OSk9v6np6cTERGRZ5yxsTGWlpZqh0yVEEIIIYQQQrwu0snwhrtx4wajR48mJiaGH374gaVLlzJixAiNYn19fTExMaFPnz788ccfHDhwgGHDhvHhhx+q1mPIj5ubG76+vvj5+bFp0ybi4uIIDw9n7ty57NixQ5em5apixYp06tSJgIAADh8+zNmzZ/nggw8oVaoUnTp1Ut1naGjIsGHDOHHiBKdPn6Zv3740aNAg3/UYACwsLBg7diyjRo0iJCSE2NhYzpw5w1dffUVISIhGeZYrV45GjRrRv39/0tPT1XIrX7486enpLF26lKtXr7J27doCT1sYMWIEc+bMYfPmzVy8eJHBgwdz//79ApUhhBBCCCGEEK+TdDK84fz8/EhJSaFevXoMGTKEYcOGMXDgQI1iTU1N2b17N//88w9169alW7dutGjRgmXLlhUoh6CgIPz8/BgzZgyVKlWiY8eOnDhxQuNv/LURFBRE7dq1ad++PQ0bNiQrK4sdO3aoTbMwNTVl/Pjx9O7dm4YNG6JUKgkNDdW4junTpzN58mRmz56Nu7s7rVu35tdff6VcuXIal+Hr68vZs2fp0qULSqVSdb5GjRosWLCAuXPnUqVKFdavX8/s2bM1LhdgzJgx+Pn54e/vT8OGDbGwsOC9994rUBlCCCGEEEII8TopsnKbAC+KnI+PDzVq1GDRokVFnYooxq5quECnELpKw0ineEOeFlIm4r8uHcP8b8qDAa9ubRwhXvQ4yyz/m3Jhqsh/oej/ItO0hzrFPza0LKRM3h7JWZqts5YbM0WS1rHv3L+iU913rCvoFP82cn1hQf5XRUYyCCGEEEIIIYQQolBIJ4PQSdu2bdW2gHzxmDVrVlGnp7VBgwbl2q5BgwYVdXpCCCGEEEII8UaS6RJCJ3/++ScpKSk5XrO1tcXW1vY1Z1Q4EhMTefgw5+F6lpaWlChR4jVnpD2ZLiFeF5kuIV4XmS4higuZLlFwMl3i9ZPpEm+X1zFdQjoZhPiPK8pOBmXaI53iUwwtCimT18s447FO8an6poWUScGdvqP5wqcvq/VOXCFm8no5/pn39rB5+atUbZ3qPvePi9ax1Wyv6VS3Ln9Hi+vfz6L2FO23Fc7Iyn/76rwoFbr9bhLFiy6dcbp2xKVkaf/vWPRdzXZAy01rvV1ax0Yom+lUdxml5tuVC6GLuMeltI5tUdWkEDPJmUyXEEIIIYQQQgghRKGQTgYhhBBCCCGEEEIUCulkeAP5+PgwcuTIV1pHYGAgNWrUeKV1vC7Xrl1DoVAQGRlZ1KkIIYQQQgghxFtNOhlEseLv70/nzp2LOo3X7m1ttxBCCCGEEKJ4kU4GIYQQQgghhBBCFArpZHhDpaenM3ToUKytrbGzs2PSpEk83whk3bp11KlTBwsLCxwdHenduzeJiYmq2LCwMBQKBfv27aNOnTqYmprSqFEjYmJicq0vLi6OChUq8PHHH5OZmZlnbsHBwVhbW7Nt2zYqVaqEqakp3bp1Izk5mZCQEFxcXLCxsWHYsGFkZGSo4u7du4efnx82NjaYmprStm1bLl++nK3c3bt34+7ujrm5OW3atCEhIQF4NsUjJCSEX375BYVCgUKhICwsTBV/9epVmjVrhqmpKdWrV+fYsWOqa9evX6dDhw7Y2NhgZmaGp6cnO3bs0Oi9OH/+PO+++y6WlpZYWFjQpEkTYv9/x4bMzEymTZtG6dKlMTY2pkaNGuza9e+qxs/fi/v376vORUZGolAouHbtWqG0WwghhBBCCCHeFNLJ8IYKCQnBwMCAEydOsGTJEhYuXMjq1asBePr0KdOnT+fs2bNs2bKFuLg4/P39s5Xx2WefMX/+fE6dOoWBgQH9+vXLsa4//vgDLy8vunfvzooVK9DTy//H4vHjxyxZsoTQ0FB27dpFWFgYXbp0YceOHezYsYO1a9fyzTff8PPPP6ti/P39OXXqFFu3buXYsWNkZWXRrl070tLS1MqdN28ea9eu5ffffyc+Pp6xY8cCMHbsWHr06KH6AJ6QkECjRo3U2jt27FgiIyNxc3OjV69epKenAzBkyBBSU1P5/fffiYqKYu7cuZib578n8J9//knTpk0xMTFh//79RERE0K9fP1W5ixcvZv78+cybN49z587RunVrOnbsqNZ5ogld2i2EEEIIIYQQbwqDok5A5MzZ2ZmFCxeiUCioVKkSUVFRLFy4kICAALXOAldXV5YsWUK9evVISkpS++A8c+ZMvL29AZgwYQLvvvsuT548wcTk371Rjx07Rvv27Zk4caLqQ60m0tLSWLFiBeXLlwegW7durF27ltu3b2Nubo6HhwfNmjXjwIED9OzZk8uXL7N161aOHDmi+oC8fv16nJ2d2bJlC927d1eV+/XXX6vKHTp0KNOmTQPA3NwcpVJJamoqjo6O2XIaO3Ys7777LgBTp07F09OTK1euULlyZeLj4+natStVq1ZVPTdNfPXVV1hZWREaGoqh4bP9pt3c3FTX582bx/jx43n//fcBmDt3LgcOHGDRokV89dVXBXqe2rb7RampqaSmpmY7Z2ys/b7sQgghhBBCCKEpGcnwhmrQoAEKhUL1umHDhly+fJmMjAzOnDlDp06dKFu2LBYWFvj4+AAQHx+vVka1atVUf3ZycgJQm1YRHx9Py5YtmTRpUoE6GABMTU1VH4gBHBwccHFxUevkcHBwUNUXHR2NgYEB9evXV123s7OjUqVKREdH51quk5OTWs55yau9w4cPZ8aMGXh5eTFlyhTOnTunUZmRkZE0adJE1cHwoocPH3Lr1i28vLzUznt5eam1SRO6tPtFs2fPxsrKSu34+uuvC1yOEEIIIYQQQmhDOhmKmSdPnvC///0Pc3Nz1q1bR3h4OJs3bwaeTaN40YsfjJ93WLy43oK9vT316tUjNDSUhw8fFiiPlz90KxSKHM89r+/5ehIvy8rKUutMyamM3GLzyunl9g4YMICrV6/y4YcfEhUVRZ06dVi6dGm+ZSqVynzveTF/UG/T86knL7bhxekhOeX+vExN2/2iiRMn8uDBA7Vj0KBBBS5HCCGEEEIIIbQhnQxvqOPHj2d7XbFiRS5evMidO3eYM2cOTZo0oXLlylp94w3PPkBv27YNExMTWrduzaNHjwoj9Rx5eHiQnp7OiRMnVOfu3r3LpUuXcHd317gcIyMjtcUkC8LZ2ZlBgwaxadMmxowZw6pVq/KNqVatGocOHcqxY8DS0pKSJUty+PBhtfNHjx5Vtcne3h5AtYgjPBsdUVCattvY2BhLS0u1Q6ZKCCGEEEIIIV4X6WR4Q924cYPRo0cTExPDDz/8wNKlSxkxYgRlypTByMiIpUuXcvXqVbZu3cr06dO1rsfMzIzt27djYGBA27ZtSUpKKsRW/KtixYp06tSJgIAADh8+zNmzZ/nggw8oVaoUnTp10rgcFxcXzp07R0xMDHfu3Mnxw39ORo4cye7du4mLi+P06dPs379fo86NoUOH8vDhQ95//31OnTrF5cuXWbt2rWqnjk8++YS5c+fy448/EhMTw4QJE4iMjGTEiBEAVKhQAWdnZwIDA7l06RLbt29n/vz5GrdX13YLIYQQQgghxOsknQxvKD8/P1JSUqhXrx5Dhgxh2LBhDBw4EHt7e4KDg9mwYQMeHh7MmTOHefPm6VSXubk5O3fuVO32kJycXEitUBcUFETt2rVp3749DRs2JCsrix07duS43kFuAgICqFSpEnXq1MHe3p4jR45oFJeRkcGQIUNwd3enTZs2VKpUieXLl+cbZ2dnx/79+0lKSsLb25vatWuzatUqVc7Dhw9nzJgxjBkzhqpVq7Jr1y62bt1KxYoVgWfTIH744QcuXrxI9erVmTt3LjNmzNC4vbq2WwghhBBCCCFeJ0WWNhO/hRDFxtXY2CKrW5mm2xScFEOLQsrk9TLOeKxTfKq+aSFlUnCn75TTOrbWO3GFmMnr5fhnhNaxf5WqrVPd5/5x0Tq2mu01nerW5e9ocf37WdSeov0UtowsfZ3qVip0+90kipd0NP8S52UG6DZiMiVL+3/Hou866FR3a71dWsdGKJvpVHcZ5S2d4oXQVNzjUlrHtqhqkv9NOpKRDEIIIYQQQgghhCgU0skgsmnbti3m5uY5HrNmzSrq9ArVoEGDcm2r7MoghBBCCCGEEAVjUNQJiDfP6tWrSUlJyfGara3ta87m1Zo2bRpjx47N8ZqlpeVrzkYIIYQQQgghijdZk0GI/zhd12S4+KCM1rGVreJ1qrsoXXhQVuvYphzQqe47Vq46xesiFe3n6RnzpBAzEUIUtnsZNlrH2ujfK8RMig+L1Ls6xT8yttM6VpffxwDlLu/UOvZWRd3WJiiunK4f1Sk+oWyjQsqk4HRZB0PWa3m7uJYv/8rrkOkSQgghhBBCCCGEKBTSySCEEEIIIYQQQohCIZ0MbzkfHx9GjhxZ1GloLCwsDIVCwf3794s6lVcmMDCQGjVqFHUaQgghhBBCCFFg0skg3ljFrQNECCGEEEIIId520skgBJCWlvZW1CmEEEIIIYQQr5J0MggyMzMZN24ctra2ODo6EhgYqLq2YMECqlatipmZGc7OzgwePJikpCTV9evXr9OhQwdsbGwwMzPD09OTHTt2aFTvwYMHqVevHsbGxjg5OTFhwgTS09MB8Pf35+DBgyxevBiFQoFCoeDatWuq2IiICOrUqYOpqSmNGjUiJiZGrexff/2V2rVrY2JigqurK1OnTlWVDaBQKPj666/p1KkTZmZmzJgxI998z58/z7vvvoulpSUWFhY0adKE2P/fuSE8PJxWrVrxzjvvYGVlhbe3N6dPn1aLz63OOXPm4ODggIWFBf379+fJE/UV+sPCwqhXrx5mZmZYW1vj5eXF9evXNXrGQgghhBBCCPE6SSeDICQkBDMzM06cOMEXX3zBtGnT2LNnDwB6enosWbKEP/74g5CQEPbv38+4ceNUsUOGDCE1NZXff/+dqKgo5s6di7m5eb51/vnnn7Rr1466dety9uxZVqxYwbfffqv64L148WIaNmxIQEAACQkJJCQk4OzsrIr/7LPPmD9/PqdOncLAwIB+/fqpru3evZsPPviA4cOHc+HCBVauXElwcDAzZ85Uy2HKlCl06tSJqKgotfjc8m3atCkmJibs37+fiIgI+vXrp+q4ePToEX369OHQoUMcP36cihUr0q5dOx49epRnnT/99BNTpkxh5syZnDp1CicnJ5YvX666Pz09nc6dO+Pt7c25c+c4duwYAwcORKFQ5PuMhRBCCCGEEOJ1U2RlZWUVdRKi6Pj4+JCRkcGhQ4dU5+rVq0fz5s2ZM2dOtvs3bNjAxx9/zJ07dwCoVq0aXbt2ZcqUKQWq97PPPmPjxo1ER0erPjAvX76c8ePH8+DBA/T09PDx8aFGjRosWrRIFRcWFkazZs3Yu3cvLVq0AGDHjh28++67pKSkYGJiQtOmTWnbti0TJ05Uxa1bt45x48Zx69Yt4NmogpEjR7Jw4UKN8v30008JDQ0lJiYGQ0PDfO/PyMjAxsaG77//nvbt2+daZ6NGjahevTorVqxQnWvQoAFPnjwhMjKSf/75Bzs7O8LCwvD29s633tTUVFJTU9XO/XnzJsbGxhq1MycXH5TROrayVbzWsUXtwoOyWsc25YBOdd+xctUpXhe67MtuzJP8bxJCFJl7GTZax9ro3yvETIoPi9S7OsU/MrbTOlaX38cA5S7v1Dr2VsVmOtVdXDldP6pTfELZRoWUScGlZJlqHatUPC7ETMSbzrV8+Vdeh4xkEFSrVk3ttZOTE4mJiQAcOHCAVq1aUapUKSwsLPDz8+Pu3bskJycDMHz4cGbMmIGXlxdTpkzh3LlzGtUZHR1Nw4YN1b6R9/LyIikpiZs3bxYoZycnJwBVzhEREUybNg1zc3PV8XxExOPH//4SrVOnjka5AkRGRtKkSZNcOxgSExMZNGgQbm5uWFlZYWVlRVJSEvHx6h+yX67z+XN40YuvbW1t8ff3p3Xr1nTo0IHFixeTkJCQa56zZ89W1f/8+PrrrzVupxBCCCGEEELoQjoZRLYPzgqFgszMTK5fv067du2oUqUKGzduJCIigq+++gr4d9HCAQMGcPXqVT788EOioqKoU6cOS5cuzbfOrKysbEP+nw+q0WQqwIs5P78/MzNT9d+pU6cSGRmpOqKiorh8+TImJv9+K2BmZpZvPc8plco8r/v7+xMREcGiRYs4evQokZGR2NnZ8fTpU7X7ClLnc0FBQRw7doxGjRrx448/4ubmxvHjx3O8d+LEiTx48EDtGDRoUIHrFEIIIYQQQghtSCeDyNWpU6dIT09n/vz5NGjQADc3N9V0gxc5OzszaNAgNm3axJgxY1i1alW+ZXt4eHD06FFenK1z9OhRLCwsKFWqFABGRkZkZGQUOO9atWoRExNDhQoVsh16etr9yFerVo1Dhw7luiPEoUOHGD58OO3atcPT0xNjY2PVlJK8uLu7Z+swyKkDoWbNmkycOJGjR49SpUoVvv/++xzLMzY2xtLSUu3QZaqEEEIIIYQQQhSEdDKIXJUvX5709HSWLl3K1atXWbt2bbah9yNHjmT37t3ExcVx+vRp9u/fj7u7e75lDx48mBs3bjBs2DAuXrzIL7/8wpQpUxg9erSqI8DFxYUTJ05w7do17ty5oxqpkJ/JkyezZs0aAgMDOX/+PNHR0fz4449MmjSp4A/h/w0dOpSHDx/y/vvvc+rUKS5fvszatWtVu1pUqFCBtWvXEh0dzYkTJ/D19c139APAiBEj+O677/juu++4dOkSU6ZM4fz586rrcXFxTJw4kWPHjnH9+nV+++03Ll26pNEzFkIIIYQQQojXTToZRK5q1KjBggULmDt3LlWqVGH9+vXMnj1b7Z6MjAyGDBmCu7s7bdq0oVKlSmq7I+SmVKlS7Nixg5MnT1K9enUGDRpE//791ToCxo4di76+Ph4eHtjb22db3yA3rVu3Ztu2bezZs4e6devSoEEDFixYQNmy2i/kZ2dnx/79+0lKSsLb25vatWuzatUq1bSN7777jnv37lGzZk0+/PBDhg8fTokSJfItt2fPnkyePJnx48dTu3Ztrl+/zscff6y6bmpqysWLF+natStubm4MHDiQoUOH8tFHH2ndFiGEEEIIIYR4VWR3CSH+467GxuoUL7tLFJzsLiGEeBPJ7hIFJ7tLvF1kdwnxNpDdJYQQQgghhBBCCFFsSCeDeCUGDRqktoXki8ebuNtBcctXCCGEEEIIId5EBkWdgPhvmjZtGmPHjs3xmqWl5WvOJn/FLV8hT4d8WgAAlyNJREFUhBBCCCGEeBPJmgxC/MftinyqU7ybxY1CyuT1sr97Uaf4v+0qF1ImBVdc10Uozs+8uLqaXFqneFezm1rHxibpVndlU+3Xi0nT021r3gwdvmPRJ12nuoUQQoiiJGsyCCGEEEIIIYQQotiQTgYhhBBCCCGEEEIUCulkEK+Ui4sLixYtKuo01AQHB2Ntba16HRgYSI0aNbQu79q1aygUCiIjIwEICwtDoVBw//59nfJ8E5+dEEIIIYQQQuRFOhnEW2/s2LHs27dP9drf35/OnTtrXV6jRo1ISEjAyspKp7zCw8MZOHCg6rVCoWDLli06lSmEEEIIIYQQr5LsLvGGe/r0KUZGRkWdxhsnIyMDhUKBnp7u/WTPt6osLEZGRjg6Oupcjr29fSFkI4QQQgghhBCvj4xkeM18fHwYOnQoQ4cOxdraGjs7OyZNmsTzTT5cXFyYMWMG/v7+WFlZERAQAMDGjRvx9PTE2NgYFxcX5s+fr1aui4sL06dPp3fv3pibm1OyZEmWLl2qdk98fDydOnXC3NwcS0tLevTowe3bt1XXz549S7NmzbCwsMDS0pLatWtz6tQp1fWjR4/StGlTlEolzs7ODB8+nOTkZNX1xMREOnTogFKppFy5cqxfv75Az+b+/fsMHDgQBwcHTExMqFKlCtu2bQP+neKwbds2PDw8MDY25vr16zx9+pRx48ZRqlQpzMzMqF+/PmFhYWrlBgcHU6ZMGUxNTXnvvfe4e/eu2vUXp0sEBgYSEhLCL7/8gkKhQKFQZCsvPy9Pl3gx90qVKmFqakq3bt1ITk4mJCQEFxcXbGxsGDZsGBkZGapyXpwu4eLiAsB7772HQqFQvRZCCCGEEEKIN4l0MhSBkJAQDAwMOHHiBEuWLGHhwoWsXr1adf3LL7+kSpUqRERE8PnnnxMREUGPHj14//33iYqKIjAwkM8//5zg4GC1cr/88kuqVavG6dOnmThxIqNGjWLPnj0AZGVl0blzZ/755x8OHjzInj17iI2NpWfPnqp4X19fSpcuTXh4OBEREUyYMAFDQ0MAoqKiaN26NV26dOHcuXP8+OOPHD58mKFDh6ri/f39uXbtGvv37+fnn39m+fLlJCYmavRMMjMzadu2LUePHmXdunVcuHCBOXPmoK+vr7rn8ePHzJ49m9WrV3P+/HlKlChB3759OXLkCKGhoZw7d47u3bvTpk0bLl++DMCJEyfo168fgwcPJjIykmbNmjFjxoxc8xg7diw9evSgTZs2JCQkkJCQQKNGjTRqQ14eP37MkiVLCA0NZdeuXYSFhdGlSxd27NjBjh07WLt2Ld988w0///xzjvHh4eEABAUFkZCQoHothBBCCCGEEG8SmS5RBJydnVm4cCEKhYJKlSoRFRXFwoULVaMWmjdvztixY1X3+/r60qJFCz7//HMA3NzcuHDhAl9++SX+/v6q+7y8vJgwYYLqniNHjrBw4UJatWrF3r17OXfuHHFxcTg7OwOwdu1aPD09CQ8Pp27dusTHx/PJJ59QufKzveorVqyoKvvLL7+kd+/ejBw5UnVtyZIleHt7s2LFCuLj49m5cyfHjx+nfv36AHz77be4u7tr9Ez27t3LyZMniY6Oxs3NDQBXV1e1e9LS0li+fDnVq1cHIDY2lh9++IGbN29SsmRJ4Fknwa5duwgKCmLWrFksXryY1q1bqz2Xo0ePsmvXrhzzMDc3R6lUkpqaWihTHl7MfcWKFZT//31pu3Xrxtq1a7l9+zbm5uZ4eHjQrFkzDhw4oNbx89zzqRPW1tZ55pWamkpqaqrauadPFRgZ6banvBBCCCGEEEJoQkYyFIEGDRqgUChUrxs2bMjly5dVQ+Xr1Kmjdn90dDReXl5q57y8vNRinpfzooYNGxIdHa0qw9nZWdXBAODh4YG1tbXqntGjRzNgwABatmzJnDlziI2NVd0bERFBcHCwav0Cc3NzWrduTWZmJnFxcURHR2NgYKCWe+XKldV2cchLZGQkpUuXVnUw5MTIyIhq1aqpXp8+fZqsrCzc3NzU8jp48KAq9+jo6Byfy+tmamqq6mAAcHBwwMXFRW0tCAcHB41HfuRm9uzZWFlZqR0/ffeFTmUKIYQQQgghhKZkJMMbyMzMTO11VlaWWqfE83OaeB6XUxkvnw8MDKR3795s376dnTt3MmXKFEJDQ3nvvffIzMzko48+Yvjw4dnKKFOmDDExMWr1FZRSqdTonhfLz8zMRF9fn4iICLVpFYDqw7umz+lVez7t5DmFQpHjuczMTJ3qmThxIqNHj1Y7F3ZRu/dECCGEEEIIIQpKOhmKwPHjx7O9rlixYrYPys95eHhw+PBhtXNHjx7Fzc1NLSancp9PffDw8CA+Pp4bN26oRjNcuHCBBw8eqE1pcHNzw83NjVGjRtGrVy+CgoJ47733qFWrFufPn6dChQo55uju7k56ejqnTp2iXr16AMTExKgWP8xPtWrVuHnzJpcuXcpzNMOLatasSUZGBomJiTRp0iTHezw8PHJ8LnkxMjJSGyHypjA0NMw3L2NjY4yN1adGGBk9fZVpCSGEEEIIIYSKTJcoAjdu3GD06NHExMTwww8/sHTpUkaMGJHr/WPGjGHfvn1Mnz6dS5cuERISwrJly9TWbQA4cuQIX3zxBZcuXeKrr75iw4YNqnJbtmxJtWrV8PX15fTp05w8eRI/Pz+8vb2pU6cOKSkpDB06lLCwMK5fv86RI0cIDw9XdUCMHz+eY8eOMWTIECIjI7l8+TJbt25l2LBhAFSqVIk2bdoQEBDAiRMniIiIYMCAARqNUADw9vamadOmdO3alT179hAXF8fOnTtzXTsBnnWI+Pr64ufnx6ZNm4iLiyM8PJy5c+eyY8cOAIYPH86uXbtUz2XZsmV5lgnPdnI4d+4cMTEx3Llzh7S0NI3a8Kq5uLiwb98+/vrrL+7du1fU6QghhBBCCCFENtLJUAT8/PxISUmhXr16DBkyhGHDhjFw4MBc769VqxY//fQToaGhVKlShcmTJzNt2jS1RR/hWWdEREQENWvWZPr06cyfP5/WrVsDz4bib9myBRsbG5o2bUrLli1xdXXlxx9/BEBfX5+7d+/i5+eHm5sbPXr0oG3btkydOhV4NtLg4MGDXL58mSZNmlCzZk0+//xznJycVPUHBQXh7OyMt7c3Xbp0YeDAgZQoUULj57Jx40bq1q1Lr1698PDwYNy4cfl+cx8UFISfnx9jxoyhUqVKdOzYkRMnTqhGazRo0IDVq1ezdOlSatSowW+//cakSZPyLDMgIIBKlSpRp04d7O3t/4+9+w6L4mrfB37v0mEpYgFUFBBBVLCgsaDYiKLE3mMkRhN71FhiTLGX+CoaU82rEdTXEnsSY7CgaOyKoqjYAEUjVmygUp/fH37dnyud3YXo3p/r2ivZmXnmPrMM7u5hzhkcOHCg0MegTyEhIdi5cyecnZ1Rr1690m4OERERERFRDgr5twxaNxAtW7ZE3bp18c033+h0vy4uLhgzZoz67g9EL4RHazdcwsP6mo5aUrLK3zuvVf2dsjV01JKiS4N5sWvN8EyHLSma1/k1f13Fp1bWqt7N6nqxa+NStMuuYRlX8EZ5yFBqd8ecLC1GixohU6tsIiKi0uT20mT0+sIrGYiIiIiIiIhIJ9jJQCVi1apVGreZfPlRq1at0m5evmbPnp1n29u3b1/azSMiIiIiIvrX4N0lSlhkZKRe9nvlyhW97FdXOnXqhEaNGuW67tVbOf7bDB06FL169cp1XWEntiQiIiIiIjIEnJOB6A338MQurerTzO2KXZtiVkarbG1cfOysVf3rOheFobqXWVar+rLG93TUktdLxdgdxa6dcmOAVtlD2tzQqr602D1J0qr+gaVTwRsR6cDDbNti19oqH+qwJfSmu/qkYrFrq1pq916QnGlf7Fp742Stsl9XnJOBiIiIiIiIiF4b7GQgDSKCwYMHw97eHgqFAtHR0XrJiYyMhEKhwIMHD/SyfyIiIiIiIip57GQgDeHh4QgLC8PWrVuRlJSE2rVr6yWnadOmSEpKgq1t8S/lMyRTp05F3bp1S7sZRERERERE+eLEj6QhLi4OTk5OaNq0abHqRQRZWVkwNs7/1DI1NYWjo2OxMnQlPT0dpqampdoGIiIiIiKiNwmvZCC1AQMG4OOPP0ZiYiIUCgVcXFyQlpaGUaNGoUKFCjA3N0ezZs1w7Ngxdc2LYQ/bt29HgwYNYGZmhr///hsigv/85z9wc3ODhYUF6tSpgw0bNuSoe3m4xJIlS+Ds7AxLS0t07doVCxYsgJ2dnXr9i7/mr1y5Ei4uLrC1tUWfPn3w+PHjQh1fy5YtMXLkSIwdOxblypXD22+/DQA4d+4cOnToAJVKBQcHB/Tv3x93795V16WmpiI4OBgqlQpOTk4ICQlBy5YtMWbMGPU2CoUCW7Zs0cizs7NDWFiY+vk///yD3r17o0yZMihbtiw6d+6scVeQyMhIvPXWW7CysoKdnR38/Pxw9epVhIWFYdq0aTh16hQUCgUUCoXGfomIiIiIiP4t2MlAaosWLcL06dNRuXJlJCUl4dixY/j000+xceNGLF++HCdOnIC7uzvatWuH5GTN2Vg//fRTzJkzB7GxsfDx8cGXX36J0NBQ/PTTTzh79iw++eQTvPfee9i7d2+u2QcOHMDQoUMxevRoREdH4+2338asWbNybBcXF4ctW7Zg69at2Lp1K/bu3Yuvv/660Me4fPlyGBsb48CBA/j555+RlJSEFi1aoG7dujh+/DjCw8Nx69YtjVtWTpgwAXv27MHmzZuxY8cOREZGIioqqtCZAPDkyRO0atUKKpUK+/btw/79+6FSqRAYGIj09HRkZmaiS5cuaNGiBU6fPo1Dhw5h8ODBUCgU6N27N8aNG4datWohKSkJSUlJ6N27d5HyiYiIiIiISgKHS5Cara0trK2tYWRkBEdHR6SmpuKnn35CWFgY2rdvD+D51QY7d+7EL7/8ggkTJqhrp0+frr4yIDU1FQsWLMDu3bvRpEkTAICbmxv279+Pn3/+GS1atMiR/d1336F9+/YYP348AMDDwwMHDx7E1q1bNbbLzs5GWFgYrK2tAQD9+/dHRERErh0SuXF3d8d//vMf9fPJkyejfv36mD17tnrZsmXL4OzsjIsXL6JixYr45ZdfsGLFCvXxLV++HJUrVy5U3gtr166FUqnE0qVLoVAoAAChoaGws7NDZGQkGjRogIcPH+Kdd95Btf+7rYyXl5e6XqVSwdjYuNSHmBAREREREeWHnQyUp7i4OGRkZMDPz0+9zMTEBG+99RZiY2M1tm3QoIH6/8+dO4dnz56pv5S/kJ6ejnr16uWadeHCBXTt2lVj2VtvvZWjk8HFxUXdwQAATk5OuH37dqGP6eV2AkBUVBT27NkDlUqVY9u4uDg8ffoU6enp6s4SALC3t4enp2ehM1/kXL58WaPtAPDs2TPExcWhbdu2GDBgANq1a4e3334bAQEB6NWrF5ycinY/9bS0NKSlpWkuS0+HGeeeICIiIiKiEsBOBsqTiACA+i/vLy9/dZmVlZX6/7OzswEAf/75JypVqqSxnZmZWZ5ZueW8ysTEROO5QqFQ5xXGy+180daOHTti7ty5ObZ1cnLCpUuXCrVfhUKRo70ZGRkaOb6+vli1alWO2vLlywN4fmXDqFGjEB4ejl9//RVffvkldu7cicaNGxeqDQAwZ84cTJs2TWPZxMH9MWlIcKH3QUREREREVFzsZKA8ubu7w9TUFPv378e7774L4PkX5+PHj2tMeviqmjVrwszMDImJibkOjchNjRo1cPToUY1lx48fL3bbC6t+/frYuHEjXFxccr0jhru7O0xMTHD48GFUqVIFAHD//n1cvHhR49jKly+PpKQk9fNLly7hyZMnGjm//vorKlSoABsbmzzbU69ePdSrVw+TJk1CkyZNsHr1ajRu3BimpqbIysoq8HgmTZqEsWPHaix7dm5/gXVERERERES6wIkfKU9WVlYYNmwYJkyYgPDwcJw7dw4fffQRnjx5gkGDBuVZZ21tjfHjx+OTTz7B8uXLERcXh5MnT+KHH37A8uXLc635+OOPsW3bNixYsACXLl3Czz//jL/++ivH1Q26NmLECCQnJ6Nv3744evQo4uPjsWPHDgwcOBBZWVlQqVQYNGgQJkyYgIiICJw5cwYDBgyAUqn5q9O6dWt8//33OHHiBI4fP46hQ4dqXHXRr18/lCtXDp07d8bff/+NhIQE7N27F6NHj8b169eRkJCASZMm4dChQ7h69Sp27NiBixcvqudlcHFxQUJCAqKjo3H37t0cQyJeMDMzg42NjcaDQyWIiIiIiKiksJOB8vX111+je/fu6N+/P+rXr4/Lly9j+/btKFOmTL51M2bMwOTJkzFnzhx4eXmhXbt2+OOPP+Dq6prr9n5+fli8eDEWLFiAOnXqIDw8HJ988gnMzc31cVhqFStWxIEDB5CVlYV27dqhdu3aGD16NGxtbdUdCfPmzYO/vz86deqEgIAANGvWDL6+vhr7CQkJgbOzM/z9/fHuu+9i/PjxsLS0VK+3tLTEvn37UKVKFXTr1g1eXl4YOHAgnj59ChsbG1haWuL8+fPo3r07PDw8MHjwYIwcORJDhgwBAHTv3h2BgYFo1aoVypcvjzVr1uj1dSEiIiIiIioOheQ28J3oX+Cjjz7C+fPn8ffff5d2U3Jo2bIl6tati2+++aa0m1Kghyd2aVWfZm5X7NoUs/w7o/Tp4mNnreo9rK/pqCVUEu5lltWqvqzxPR215PVSMXZHsWun3BigVfaQNje0qi8tdk+SCt4oHw8sizahL1FxPcy2LXatrfKhDltCb7qrTyoWu7aqpXbvBcmZ9sWutTdO1ir7deX2f3ey0yfOyUD/GvPnz8fbb78NKysr/PXXX1i+fDl+/PHH0m4WERERERERFRI7Gehf4+jRo/jPf/6Dx48fw83NDd9++y0+/PDDQtUmJiaiZs2aea4/d+6ceuJGIiIiIiIi0g92MtC/xrp164pdW7FiRURHR+e7XpciIyN1uj8iIiIiIqI3ATsZ6I1gbGwMd3f30m4GERERERGRQePEj0RvuPi4uNJuQqmoGK/dhKE33JoXu/boLTetst9yiC927e2MclplVzC5W+zazTHaHXdX7+If9+vs1D2XYtfWKXtFq+wMFP8WtyZI1yo7Kc2h2LVOZre0ynb8J6rYtTcr+Ra80RtIKVla1Yui+Dc0E+j3dtaUk3G2dr/fmcrX8/bZ2VreeM/x7pli194ul/ewXyJdKomJH3kLSyIiIiIiIiLSCXYyEBEREREREZFOsJOBiIiIiIiIiHSCnQxEpSArKwvZ2dml3QwiIiIiIiKdYicD0f/ZsGEDvL29YWFhgbJlyyIgIACpqanIzs7G9OnTUblyZZiZmaFu3boIDw9X10VGRkKhUODBgwfqZdHR0VAoFLhy5QoAICwsDHZ2dti6dStq1qwJMzMzXL16FWlpafj000/h7OwMMzMzVK9eHb/88ot6P+fOnUOHDh2gUqng4OCA/v374+7d4k/MR0REREREpE/sZCACkJSUhL59+2LgwIGIjY1FZGQkunXrBhHBokWLEBISgvnz5+P06dNo164dOnXqhEuXLhUp48mTJ5gzZw6WLl2Ks2fPokKFCggODsbatWvx7bffIjY2FosXL4ZKpVK3qUWLFqhbty6OHz+O8PBw3Lp1C7169dLHS0BERERERKQ149JuANG/QVJSEjIzM9GtWzdUrVoVAODt7Q0AmD9/PiZOnIg+ffoAAObOnYs9e/bgm2++wQ8//FDojIyMDPz444+oU6cOAODixYtYt24ddu7ciYCAAACAm9v/vwXgTz/9hPr162P27NnqZcuWLYOzszMuXrwIDw8P7Q6aiIiIiIhIx9jJQASgTp06aNOmDby9vdGuXTu0bdsWPXr0gJGREW7cuAE/Pz+N7f38/HDq1KkiZZiamsLHx0f9PDo6GkZGRmjRokWu20dFRWHPnj3qKxteFhcXl2snQ1paGtLS0nIsMzMzK1JbiYiIiIiIioPDJYgAGBkZYefOnfjrr79Qs2ZNfPfdd/D09ERCQgIAQKFQaGwvIuplSqVSveyFjIyMHBkWFhYa+7GwsMi3TdnZ2ejYsSOio6M1HpcuXYK/v3+uNXPmzIGtra3GY/HixYV4BYiIiIiIiLTHTgai/6NQKODn54dp06bh5MmTMDU1RUREBCpWrIj9+/drbHvw4EF4eXkBAMqXLw/g+ZCLF6KjowvM8/b2RnZ2Nvbu3Zvr+vr16+Ps2bNwcXGBu7u7xsPKyirXmkmTJuHhw4caj6FDhxbm8ImIiIiIiLTGTgYiAEeOHMHs2bNx/PhxJCYmYtOmTbhz5w68vLwwYcIEzJ07F7/++isuXLiAzz77DNHR0Rg9ejQAwN3dHc7Ozpg6dSouXryIP//8EyEhIQVmuri44P3338fAgQOxZcsWJCQkIDIyEuvWrQMAjBgxAsnJyejbty+OHj2K+Ph47NixAwMHDkRWVlau+zQzM4ONjY3Gg0MliIiIiIiopHBOBiIANjY22LdvH7755hs8evQIVatWRUhICNq3b4927drh0aNHGDduHG7fvo2aNWvi999/R/Xq1QEAJiYmWLNmDYYNG4Y6deqgYcOGmDlzJnr27Flg7k8//YTPP/8cw4cPx71791ClShV8/vnnAICKFSviwIEDmDhxItq1a4e0tDRUrVoVgYGB6iEaRERERERE/yYKeXkgORG9ceLj4kq7CaWiYvzfWtXfcGte7Nqjt9wK3igfbznEF7v2dkY5rbIrmNwtdu3mGO2Ou6t38Y/7dXbqnkuxa+uUvaJVdgZMi11rgnStspPSHIpd62R2S6tsx3+iil17s5KvVtmvK6XkfgVdYYmi+J3jAkXBG5FOGWdr9/udqSz+vy2lKVvLi7wd754pdu3tcjW1yiYqLLdq1fSewT+HEhEREREREZFOsJOBiIiIiIiIiHSCnQxEREREREREpBPsZCAiIiIiIiIineDEj0RvuGd/Ltaq/kaNt4tdu/+6dhPLdDP/o9i1pTmBUsW4fVrV36jmr6OWlKzTyS5a1fvYX9FJOwyJzdPbWtU/sqigo5aUrCdipVW9pSK12LXnHlbVKrum7dVi18anVtYq283qulb12rBOu1fs2sdmZbXKrnDnbLFrb5evpVU2EdG/DSd+JCIiIiIiIqLXBjsZ9MDFxQXffPNNieW1bNkSY8aMKbG8/ERGRkKhUODBgwfFqp86dSrq1q2r0zYVVVhYGOzs7Eq1DURERERERK8jdjLowbFjxzB48ODSbobe6aNzY/z48YiIiNDpPouqd+/euHjxos73q1AosGXLFp3vl4iIiIiI6N/CuLQb8CYqX758aTfhtaVSqaBSqUq1DRYWFrCwsCjVNhAREREREb2O3vgrGTZs2ABvb29YWFigbNmyCAgIQGrq8wmfQkND4eXlBXNzc9SoUQM//vijui49PR0jR46Ek5MTzM3N4eLigjlz5qjXT506FVWqVIGZmRkqVqyIUaNGqde9OlwiMTERnTt3hkqlgo2NDXr16oVbt25p7Ktu3bpYuXIlXFxcYGtriz59+uDx48fFOub09HR8+umnqFSpEqysrNCoUSNERkaq178YDrB9+3Z4eXlBpVIhMDAQSUlJ6m0yMzMxatQo2NnZoWzZspg4cSLef/99dOnSBQAwYMAA7N27F4sWLYJCoYBCocCVK1fU9VFRUWjQoAEsLS3RtGlTXLhwoVBtf3W4xIABA9ClSxfMnz8fTk5OKFu2LEaMGIGMjIxC7c/FxQUzZ85EcHAwVCoVqlatit9++w137txR/0y8vb1x/PjxHK/Pq23K7+eT2xCZunXrYurUqer1ANC1a1coFAr1cwD4448/4OvrC3Nzc7i5uWHatGnIzMzUyM/rXCMiIiIiIvo3eaM7GZKSktC3b18MHDgQsbGxiIyMRLdu3SAiWLJkCb744gvMmjULsbGxmD17Nr766issX74cAPDtt9/i999/x7p163DhwgX873//U38x3LBhAxYuXIiff/4Zly5dwpYtW+Dt7Z1rG0QEXbp0QXJyMvbu3YudO3ciLi4OvXv31tguLi4OW7ZswdatW7F161bs3bsXX3/9dbGO+4MPPsCBAwewdu1anD59Gj179kRgYCAuXbqk3ubJkyeYP38+Vq5ciX379iExMRHjx49Xr587dy5WrVqF0NBQHDhwAI8ePdK41H/RokVo0qQJPvroIyQlJSEpKQnOzs7q9V988QVCQkJw/PhxGBsbY+DAgcU6FgDYs2cP4uLisGfPHixfvhxhYWEICwsrdP3ChQvh5+eHkydPIigoCP3790dwcDDee+89nDhxAu7u7ggODkZ+N1rR9udz7NgxAM87tpKSktTPt2/fjvfeew+jRo3CuXPn8PPPPyMsLAyzZs0CULRzjYiIiIiIqLS90cMlkpKSkJmZiW7duqFq1ee3nHrxBW3GjBkICQlBt27dAACurq7qL3nvv/8+EhMTUb16dTRr1gwKhUJdDzy/MsHR0REBAQEwMTFBlSpV8NZbb+Xahl27duH06dNISEhQfwlfuXIlatWqhWPHjqFhw4YAgOzsbISFhcHa2hoA0L9/f0RERKi/bBZWXFwc1qxZg+vXr6NixYoAns9zEB4ejtDQUMyePRsAkJGRgcWLF6Pa/93CZOTIkZg+fbp6P9999x0mTZqErl27AgC+//57bNu2Tb3e1tYWpqamsLS0hKOjY452zJo1Cy1atAAAfPbZZwgKCsKzZ89gbm5epOMBgDJlyuD777+HkZERatSogaCgIEREROCjjz4qVH2HDh0wZMgQAMDkyZPx008/oWHDhujZsycAYOLEiWjSpAlu3bqV67EA2v98XgyhsbOz08iYNWsWPvvsM7z//vsAADc3N8yYMQOffvoppkyZUqRzjYiIiIiIqLS90Vcy1KlTB23atIG3tzd69uyJJUuW4P79+7hz5w6uXbuGQYMGqecAUKlUmDlzJuLi4gA8v0w/Ojoanp6eGDVqFHbs2KHeb8+ePfH06VO4ubnho48+wubNmzUub39ZbGwsnJ2dNf7KX7NmTdjZ2SE2Nla9zMXFRf0FFgCcnJxw+3bR739+4sQJiAg8PDw0jm3v3r3qYwMAS0tLdQfDq3kPHz7ErVu3NL7MGhkZwdfXt9Dt8PHx0dg3gGIdDwDUqlULRkZGuba1qG1xcHAAAI2rAV4sy2+fuvr5vCoqKgrTp0/X+Fm9uDrkyZMnRTrXACAtLQ2PHj3SeKQVcmgJERERERGRtt7oTgYjIyPs3LkTf/31F2rWrInvvvsOnp6eiI+PBwAsWbIE0dHR6seZM2dw+PBhAED9+vWRkJCAGTNm4OnTp+jVqxd69OgBAHB2dsaFCxfwww8/wMLCAsOHD4e/v3+u8wSICBQKRYHLTUxMNNYrFApkZ2cX+Zizs7NhZGSEqKgojWOLjY3FokWL8s17dbjAq+3ObzjBq17e/4v9FOd4Xt3Xi/0VZV+5taWo7SuoDUqlMsfrU5h5I7KzszFt2jSNn1VMTAwuXboEc3PzIp1rADBnzhzY2tpqPOat215gO4iIiIiIiHThjR4uATz/Mujn5wc/Pz9MnjwZVatWxYEDB1CpUiXEx8ejX79+edba2Nigd+/e6N27N3r06IHAwEAkJyfD3t4eFhYW6NSpEzp16oQRI0agRo0aiImJQf369TX2UbNmTSQmJuLatWvqqxnOnTuHhw8fwsvLS+fHW69ePWRlZeH27dto3rx5sfZha2sLBwcHHD16VL2PrKwsnDx5UmNSRlNTU2RlZemi2a+98uXLa0yc+ejRIyQkJGhsY2JikuP1ql+/Pi5cuAB3d/c8913Ycw0AJk2ahLFjx2osk93Li3NIRERERERERfZGdzIcOXIEERERaNu2LSpUqIAjR47gzp078PLywtSpUzFq1CjY2Nigffv2SEtLw/Hjx3H//n2MHTsWCxcuhJOTE+rWrQulUon169fD0dERdnZ2CAsLQ1ZWFho1agRLS0usXLkSFhYWGvM2vBAQEAAfHx/069cP33zzDTIzMzF8+HC0aNECDRo00Pkxe3h4oF+/fggODkZISAjq1auHu3fvYvfu3fD29kaHDh0KtZ+PP/4Yc+bMgbu7O2rUqIHvvvsO9+/f17i6wcXFBUeOHMGVK1egUqlgb2+v8+N5XbRu3RphYWHo2LEjypQpg6+++kpjiAfw/PWKiIiAn58fzMzMUKZMGUyePBnvvPMOnJ2d0bNnTyiVSpw+fRoxMTGYOXNmkc41ADAzM4OZmZnGsmevXIVBRERERESkL2/0cAkbGxvs27cPHTp0gIeHB7788kuEhISgffv2+PDDD7F06VKEhYXB29sbLVq0QFhYGFxdXQEAKpUKc+fORYMGDdCwYUNcuXIF27Ztg1KphJ2dHZYsWQI/Pz/4+PggIiICf/zxB8qWLZujDQqFAlu2bEGZMmXg7++PgIAAuLm54ddff9XbcYeGhiI4OBjjxo2Dp6cnOnXqhCNHjmjMC1GQiRMnom/fvggODkaTJk2gUqnQrl07jYkbx48fDyMjI9SsWRPly5dHYmKiPg7ntTBp0iT4+/vjnXfeQYcOHdClSxeNOS8AICQkBDt37oSzszPq1asHAGjXrh22bt2KnTt3omHDhmjcuDEWLFig7kQoyrlGRERERERU2hRSlIH2ZLCys7Ph5eWFXr16YcaMGaXdHCqCZ38u1qr+Ro23i127/3q1gjfKRzfzP4pde7tcTa2ytVExbp9W9Teq+euoJSXrdLKLVvU+9ld00g5DYvNUuwloH1lU0FFLStYTsdKq3lKRWuzacw9zv5KssGraXi12bXxqZa2y3ayua1WvDeu0e8WufWymXcd6hTtni117u3wtrbKJiP5t3Kpp9/m8MN7o4RJUfFevXsWOHTvQokULpKWl4fvvv0dCQgLefffd0m4aERERERER/Uu90cMl3gSJiYkatzd89aGvIQpKpRJhYWFo2LAh/Pz8EBMTg127dmk9WWWtWrXyPJZVq1YVaV9///13vq8NERERERERlSxeyfAvV7FiRURHR+e7Xh+cnZ1x4MABne9327Zted5+0cHBoUj7atCgQb6vDREREREREZUszslA9IaLj4srtWyHXcu0qr8VMFBHLTEcDrdOaVV/y6GOjlpiQGaM1q7+q0W6aUcxpMG84I3yYIZnOmxJybqfVabYtWWM7muVfe2ZU7Frnc2TCt4oH6lS/Kv8rBQpWmUnZxb/DlT2xslaZWtDm/kcAOBG+brFrn2WXfzfTwBQKR9rVf+6eiqWxa61UDzRKrv8/UvFrr1TprpW2dq4/sxRq/rK5jd11JKSVTF2h1b1N7za6qglJask5mTgcAkiIiIiIiIi0gl2MhARERERERGRTrCTgd4ILVu2xJgxY3SyrytXrkChUHC+ByIiIiIioiLixI9Er3B2dkZSUhLKlStX2k0hIiIiIiJ6rbCTgegVRkZGcHTUbgIcIiIiIiIiQ8ThEvTaSU1NRXBwMFQqFZycnBASEqKxPj09HZ9++ikqVaoEKysrNGrUCJGRkQCAhw8fwsLCAuHh4Ro1mzZtgpWVFVJSUnIdLnH27FkEBQXBxsYG1tbWaN68OeJeumtDaGgovLy8YG5ujho1auDHH38s9PHExMSgdevWsLCwQNmyZTF48GCkpPz/WbwjIyPx1ltvwcrKCnZ2dvDz88PVq1eL8IoRERERERGVDHYy0GtnwoQJ2LNnDzZv3owdO3YgMjISUVFR6vUffPABDhw4gLVr1+L06dPo2bMnAgMDcenSJdja2iIoKAirVq3S2Ofq1avRuXNnqFQ5b+/1zz//wN/fH+bm5ti9ezeioqIwcOBAZGZmAgCWLFmCL774ArNmzUJsbCxmz56Nr776CsuXLy/wWJ48eYLAwECUKVMGx44dw/r167Fr1y6MHDkSAJCZmYkuXbqgRYsWOH36NA4dOoTBgwdDoVBo8xISERERERHpBYdL0GslJSUFv/zyC1asWIG3334bALB8+XJUrlwZABAXF4c1a9bg+vXrqFixIgBg/PjxCA8PR2hoKGbPno1+/fohODgYT548gaWlJR49eoQ///wTGzduzDXzhx9+gK2tLdauXQsTExMAgIeHh3r9jBkzEBISgm7dugEAXF1dce7cOfz88894//338z2eVatW4enTp1ixYgWsrKwAAN9//z06duyIuXPnwsTEBA8fPsQ777yDav93T1svL68895eWloa0tLQcy8zMzPJtBxERERERkS7wSgZ6rcTFxSE9PR1NmjRRL7O3t4enpycA4MSJExAReHh4QKVSqR979+5VD28ICgqCsbExfv/9dwDAxo0bYW1tjbZt2+aaGR0djebNm6s7GF52584dXLt2DYMGDdLImzlzpsZwirzExsaiTp066g4GAPDz80N2djYuXLgAe3t7DBgwAO3atUPHjh2xaNEiJCUl5bm/OXPmwNbWVuOxePHiAttBRERERESkC7ySgV4rIpLv+uzsbBgZGSEqKgpGRkYa614MhTA1NUWPHj2wevVq9OnTB6tXr0bv3r1hbJz7r4OFhUW+ecDzIRONGjXSWPdqfl7Hk9fQhxfLQ0NDMWrUKISHh+PXX3/Fl19+iZ07d6Jx48Y5aiZNmoSxY8dqLPvn+vUC20FERERERKQLvJKBXivu7u4wMTHB4cOH1cvu37+PixcvAgDq1auHrKws3L59G+7u7hqPl+8Y0a9fP4SHh+Ps2bPYs2cP+vXrl2emj48P/v77b2RkZORY5+DggEqVKiE+Pj5Hnqura4HHU7NmTURHRyM1NVW97MCBA1AqlRpDMurVq4dJkybh4MGDqF27NlavXp3r/szMzGBjY6Px4FAJIiIiIiIqKexkoNeKSqXCoEGDMGHCBERERODMmTMYMGAAlMrnp7KHh4d6zoVNmzYhISEBx44dw9y5c7Ft2zb1flq0aAEHBwf069cPLi4uuV4V8MLIkSPx6NEj9OnTB8ePH8elS5ewcuVKXLhwAQAwdepUzJkzB4sWLcLFixcRExOD0NBQLFiwoMDj6devH8zNzfH+++/jzJkz2LNnDz7++GP0798fDg4OSEhIwKRJk3Do0CFcvXoVO3bswMWLF/Odl4GIiIiIiKi0sJOBXjvz5s2Dv78/OnXqhICAADRr1gy+vr7q9aGhoQgODsa4cePg6emJTp064ciRI3B2dlZvo1Ao0LdvX5w6dSrfqxgAoGzZsti9ezdSUlLQokUL+Pr6YsmSJeo5Gj788EMsXboUYWFh8Pb2RosWLRAWFlaoKxksLS2xfft2JCcno2HDhujRowfatGmD77//Xr3+/Pnz6N69Ozw8PDB48GCMHDkSQ4YMKc5LR0REREREpFcKKWiQOxG91uILMQGlvjjsWqZV/a2AgTpqieFwuHVKq/pbDnV01BIDMmO0dvVfLdJNO4ohDebFrjXDMx22pGTdzypT7NoyRve1yr72zKnYtc7meU/8WxipkvM2zYVlpUjRKjs5077YtfbGyVpla6PCnbNa1d8oX7fYtc+yi//7CQAq5WOt6l9XT8Wy2LUWiidaZZe/f6nYtXfKVNcqWxvXnzkWvFE+Kpvf1FFLSlbF2B1a1d/wyn3S+H87t/+7Y50+8UoGIiIiIiIiItIJdjIQ6dHs2bM1bm358qN9+/al3TwiIiIiIiKd4i0sifRo6NCh6NWrV67r8rs1JhERERER0euIczIQveFKc06G0vTXRe3Gm/lXv1XsWm3HLpNheSJWxa61VKQWvJGeGGena1WfqTTVUUteLxko/nGbQLvXnOh1cCNNu/kBKpoVf36A+NTKWmW7WV3Xqp6Kjv+mFh3nZCAiIiIiIiKi1wY7GYiIiIiIiIhIJ9jJ8Bpq2bIlxowZU9rNKLYBAwagS5cu6uev+/EURmRkJBQKBR48eFDaTSEiIiIiItIbTvxIpW7Tpk0wMTEp7WboVdOmTZGUlARbW9vSbgoREREREZHesJOBSp29vX1pN0HvTE1N4eio3URGRERERERE/3YcLvEGCA8Ph62tLVasWIF//vkHvXv3RpkyZVC2bFl07twZV65c0dg+NDQUXl5eMDc3R40aNfDjjz+q1125cgUKhQJr165F06ZNYW5ujlq1aiEyMrJQbcnKysKgQYPg6uoKCwsLeHp6YtGiRfnWvDxcYtKkSWjcuHGObXx8fDBlypRCHUN+0tPTMXLkSDg5OcHc3BwuLi6YM2eOev3Dhw8xePBgVKhQATY2NmjdujVOnTqlXn/q1Cm0atUK1tbWsLGxga+vL44fPw4AuHr1Kjp27IgyZcrAysoKtWrVwrZt2wDkPlxi48aNqFWrFszMzODi4oKQkBCNtrq4uGD27NkYOHAgrK2tUaVKFfz3v/8t1HESERERERGVBnYyvObWrl2LXr16YcWKFejRowdatWoFlUqFffv2Yf/+/VCpVAgMDER6+vNbtCxZsgRffPEFZs2ahdjYWMyePRtfffUVli9frrHfCRMmYNy4cTh58iSaNm2KTp064d69ewW2Jzs7G5UrV8a6detw7tw5TJ48GZ9//jnWrVtXqOPp168fjhw5griXbrt49uxZxMTEoF+/fkU6htx8++23+P3337Fu3TpcuHAB//vf/+Di4gIAEBEEBQXh5s2b2LZtG6KiolC/fn20adMGycnJ6vZVrlwZx44dQ1RUFD777DP1UI8RI0YgLS0N+/btQ0xMDObOnQuVSpVrO6KiotCrVy/06dMHMTExmDp1Kr766iuEhYVpbBcSEoIGDRrg5MmTGD58OIYNG4bz588X6rUkIiIiIiIqaRwu8Rr78ccf8fnnn+O3335Dq1atsGzZMiiVSixduhQKhQLA87/429nZITIyEm3btsWMGTMQEhKCbt26AQBcXV1x7tw5/Pzzz3j//ffV+x45ciS6d+8OAPjpp58QHh6OX375BZ9++mm+bTIxMcG0adPUz11dXXHw4EGsW7cOvXr1KvCYateuDR8fH6xevRpfffUVAGDVqlVo2LAhPDw8AKDQx5CbxMREVK9eHc2aNYNCoUDVqlXV6/bs2YOYmBjcvn0bZmZmAID58+djy5Yt2LBhAwYPHozExERMmDABNWrUAABUr15dY9/du3eHt7c3AMDNzS3PdixYsABt2rRRH6OHhwfOnTuHefPmYcCAAertOnTogOHDhwMAJk6ciIULFyIyMlKd/6q0tDSkpaXlWPbieIiIiIiIiPSJVzK8pjZu3IgxY8Zgx44daNWqFYDnfx2/fPkyrK2toVKpoFKpYG9vj2fPniEuLg537tzBtWvXMGjQIPV6lUqFmTNnalw5AABNmjRR/7+xsTEaNGiA2NjYQrVt8eLFaNCgAcqXLw+VSoUlS5YgMTGx0MfWr18/rFq1CsDzqwvWrFmjvoqhKMeQmwEDBiA6Ohqenp4YNWoUduzYoV4XFRWFlJQUlC1bVmPfCQkJ6n2PHTsWH374IQICAvD1119rZI4aNQozZ86En58fpkyZgtOnT+fZjtjYWPj5+Wks8/Pzw6VLl5CVlaVe5uPjo/5/hUIBR0dH3L59O8/9zpkzB7a2thqPxYsXF/i6EBERERER6QKvZHhN1a1bFydOnEBoaCgaNmwIhUKB7Oxs+Pr6qr+gv6x8+fJ49uwZgOfDDRo1aqSx3sjIqMDMF1dH5GfdunX45JNPEBISgiZNmsDa2hrz5s3DkSNHCnlkwLvvvovPPvsMJ06cwNOnT3Ht2jX06dMHwPPhGNocQ/369ZGQkIC//voLu3btQq9evRAQEIANGzYgOzsbTk5Ouc4/YWdnBwCYOnUq3n33Xfz555/466+/MGXKFKxduxZdu3bFhx9+iHbt2uHPP//Ejh07MGfOHISEhODjjz/OsT8RyfF6ikiO7V6968aLn3NeJk2ahLFjx2os++f69Ty3JyIiIiIi0iV2MrymqlWrhpCQELRs2RJGRkb4/vvvUb9+ffz666/qSQtfZWtri0qVKiE+Pl59ZUBeDh8+DH9/fwBAZmYmoqKiMHLkyALb9ffff6Np06bqS/wBFOoKg5dVrlwZ/v7+WLVqFZ4+fYqAgAA4ODgAABwcHAp9DHmxsbFB79690bt3b/To0QOBgYFITk5G/fr1cfPmTRgbG6vnaciNh4cHPDw88Mknn6Bv374IDQ1F165dAQDOzs4YOnQohg4dikmTJmHJkiW5djLUrFkT+/fv11h28OBBeHh4FKqzJC9mZmY5hkbc5VAJIiIiIiIqIexkeI15eHhgz549aNmyJYyNjTF79mzMmzcPnTt3xvTp01G5cmUkJiZi06ZNmDBhAipXroypU6di1KhRsLGxQfv27ZGWlobjx4/j/v37Gn8B/+GHH1C9enV4eXlh4cKFuH//PgYOHFhgm9zd3bFixQps374drq6uWLlyJY4dOwZXV9ciHVu/fv0wdepUpKenY+HChRrrCnsMuVm4cCGcnJxQt25dKJVKrF+/Ho6OjrCzs0NAQACaNGmCLl26YO7cufD09MSNGzewbds2dOnSBbVq1cKECRPQo0cPuLq64vr16zh27Jh67ooxY8agffv28PDwwP3797F79254eXnl2o5x48ahYcOGmDFjBnr37o1Dhw7h+++/L/RdMoiIiIiIiP6N2MnwmvP09MTu3bvVVzTs27cPEydORLdu3fD48WNUqlQJbdq0UV/Z8OGHH8LS0hLz5s3Dp59+CisrK3h7e6tvIfnC119/jblz5+LkyZOoVq0afvvtN5QrV67A9gwdOhTR0dHo3bs3FAoF+vbti+HDh+Ovv/4q0nH17NkTH3/8MYyMjNClSxeNdYU9htyoVCrMnTsXly5dgpGRERo2bIht27ZBqXw+Pcm2bdvwxRdfYODAgbhz5w4cHR3h7+8PBwcHGBkZ4d69ewgODsatW7dQrlw5dOvWTT3RZVZWFkaMGIHr16/DxsYGgYGBOTpIXqhfvz7WrVuHyZMnY8aMGXBycsL06dM1Jn0kIiIiIiJ63Sgkt4HgZLCuXLkCV1dXnDx5EnXr1i3t5pAOxBdxuMqb4q+L1bSq969+q9i1VooUrbLJsDwRq2LXWipSddiSojHOTteqPlNpqqOWvF4yUPzjNoF2rznR6+BGmqNW9RXNbha7Nj61slbZblacB6uk8d/UonOrpt1n5MLg3SWIiIiIiIiISCfYyUBFMnToUI3bO778GDp0aGk3D7Nnz86zfe3bty/t5hEREREREb3ROFyCiuT27dt49OhRrutsbGxQoUKFEm6RpuTkZCQnJ+e6zsLCApUqVSrhFpU+DpcoHg6XoJLC4RKGhZf2EuWPwyWoKPhvatGVxHAJdjIQveE2H83Sqj5AubPYtakqB62yn5jkvBUr/Xs5Xj+m3Q4unyt26c2W72uXrQWr9Ada1aea2umkHfR6cLh1qti1D8u6a5WdbmRe7NpMhYlW2Upka1VPJeuZWGhVb654qqOWEOUv8WnFYtdWsbihVfa1Z07FrnU2T9IqWxuck4GIiIiIiIiIXhvsZCAiIiIiIiIinTDoToaWLVtizJgxOtnXlStXoFAoEB0drZP9aZsfGRkJhUKBBw8eFHufunx9XjdhYWGws7NTP586dSpv6UlERERERFQAg+5k0CVnZ2ckJSWhdu3apd0UAEDTpk2RlJQEW1vbArfNq0Ni06ZNmDFjhs7a9Dp9Ue/duzcuXryol32vWrUKderUgaWlJZycnPDBBx/g3r17eskiIiIiIiIqSexk0BEjIyM4OjrC2Ni4tJsCADA1NYWjoyMUCkWx92Fvbw9ra2sdtur1YWFhoZc7Zezfvx/BwcEYNGgQzp49i/Xr1+PYsWP48MMPdZ5FRERERERU0gymkyE1NRXBwcFQqVRwcnJCSEiIxvr09HR8+umnqFSpEqysrNCoUSNERkYCAB4+fAgLCwuEh4dr1GzatAlWVlZISUnJdbjE2bNnERQUBBsbG1hbW6N58+aIe+l2gqGhofDy8oK5uTlq1KiBH3/8sdDHc/ToUdSrVw/m5uZo0KABTp48qbH+1asTrl69io4dO6JMmTKwsrJCrVq1sG3bNly5cgWtWrUCAJQpUwYKhQIDBgwAkHO4hIuLC2bPno2BAwfC2toaVapUwX//+1+N3OvXr6NPnz6wt7eHlZUVGjRogCNHjiAsLAzTpk3DqVOnoFAooFAoEBYWVuBxPnz4EIMHD0aFChVgY2OD1q1b49Sp/z8z94urI5YtW4YqVapApVJh2LBhyMrKwn/+8x84OjqiQoUKmDVrlsZ+FyxYAG9vb1hZWcHZ2RnDhw9HSsr/v+3gq8MlXhUZGYm33noLVlZWsLOzg5+fH65evVrg8Rw+fBguLi4YNWoUXF1d0axZMwwZMgTHjx8HAMTExECpVOLu3bsAgPv370OpVKJnz57qfcyZMwdNmjQpMIuIiIiIiKikGUwnw4QJE7Bnzx5s3rwZO3bsQGRkJKKiotTrP/jgAxw4cABr167F6dOn0bNnTwQGBuLSpUuwtbVFUFAQVq1apbHP1atXo3PnzlCpVDny/vnnH/j7+8Pc3By7d+9GVFQUBg4ciMzMTADAkiVL8MUXX2DWrFmIjY3F7Nmz8dVXX2H58uUFHktqaireeecdeHp6IioqClOnTsX48ePzrRkxYgTS0tKwb98+xMTEYO7cuVCpVHB2dsbGjRsBABcuXEBSUhIWLVqU535CQkLUnRrDhw/HsGHDcP78eQBASkoKWrRogRs3buD333/HqVOn8OmnnyI7Oxu9e/fGuHHjUKtWLSQlJSEpKQm9e/fOt80igqCgINy8eRPbtm1DVFQU6tevjzZt2iA5OVm9XVxcHP766y+Eh4djzZo1WLZsGYKCgnD9+nXs3bsXc+fOxZdffonDhw+ra5RKJb799lucOXMGy5cvx+7du/Hpp58W+NoDQGZmJrp06YIWLVrg9OnTOHToEAYPHlyoq0aaNm2K69evY9u2bRAR3Lp1Cxs2bEBQUBAAoHbt2ihbtiz27t0LANi3bx/Kli2Lffv2qfcRGRmJFi1aFKqtREREREREJenfcW2/nqWkpOCXX37BihUr8PbbbwMAli9fjsqVKwN4/iV1zZo1uH79OipWfH6v1fHjxyM8PByhoaGYPXs2+vXrh+DgYDx58gSWlpZ49OgR/vzzT/UX9Ff98MMPsLW1xdq1a2Fi8vze0h4eHur1M2bMQEhICLp16wYAcHV1xblz5/Dzzz/j/ffzv9/7qlWrkJWVhWXLlsHS0hK1atXC9evXMWzYsDxrEhMT0b17d3h7ewMA3Nzc1Ovs7e0BABUqVMj3r/cA0KFDBwwfPhwAMHHiRCxcuBCRkZGoUaMGVq9ejTt37uDYsWPqfbq7//97eqtUKhgbG8PR0THfjBf27NmDmJgY3L59G2ZmZgCA+fPnY8uWLdiwYQMGDx4MAMjOzsayZctgbW2NmjVrolWrVrhw4QK2bdsGpVIJT09PzJ07F5GRkWjcuDEAaFyh4erqihkzZmDYsGGFuprk0aNHePjwId555x1U+7/7zHp5eRXqmJo2bYpVq1ahd+/eePbsGTIzM9GpUyd89913AACFQgF/f39ERkaie/fuiIyMxPvvv4/ly5fj3Llz8PDwwMGDB/HJJ5/kuv+0tDSkpaVpLMtIN4aJqVmh2kdERERERKQNg7iSIS4uDunp6RqXmNvb28PT0xMAcOLECYgIPDw8oFKp1I+9e/eqhzcEBQXB2NgYv//+OwBg48aNsLa2Rtu2bXPNjI6ORvPmzdUdDC+7c+cOrl27hkGDBmnkzZw5U2M4RV5iY2PVEwe+UNDl86NGjcLMmTPh5+eHKVOm4PTp0wXm5MbHx0f9/wqFAo6Ojrh9+zaA58dcr149dQeDtqKiopCSkoKyZctqvE4JCQkar5OLi4vG3BEODg6oWbMmlEqlxrIX7QSed2C8/fbbqFSpEqytrREcHIx79+4hNTW1wHbZ29tjwIABaNeuHTp27IhFixYhKSmpUMd07tw5jBo1CpMnT0ZUVBTCw8ORkJCAoUOHqrdp2bKleqjO3r170apVK/j7+2Pv3r04duwYnj59Cj8/v1z3P2fOHNja2mo8Ni7/ulBtIyIiIiIi0pZBXMkgIvmuz87OhpGREaKiomBkZKSx7sVQCFNTU/To0QOrV69Gnz59sHr1avTu3TvPiR4tLCzyzQOeD5lo1KiRxrpX84tzPLn58MMP0a5dO/z555/YsWMH5syZg5CQEHz88cdF2s+rnSYKhUJ9PPkdc3FkZ2fDyclJ/YX7ZS9fcZFbm/Jr59WrV9GhQwcMHToUM2bMgL29Pfbv349BgwYhIyOjUG0LDQ3FqFGjEB4ejl9//RVffvkldu7cqb5SIi9z5syBn58fJkyYAOB5p42VlRWaN2+OmTNnwsnJCS1btsTo0aNx+fJlnDlzRj2Xx969e/HgwQP4+vrmOSHnpEmTMHbsWI1l4acN4teciIiIiIj+BQziSgZ3d3eYmJhojMm/f/+++haF9erVQ1ZWFm7fvg13d3eNx8uX9vfr1w/h4eE4e/Ys9uzZg379+uWZ6ePjg7///jvXL60ODg6oVKkS4uPjc+S5uroWeDw1a9bEqVOn8PTpU/Wyl48tL87Ozhg6dCg2bdqEcePGYcmSJQCed6AAQFZWVoH7yI+Pjw+io6M15kt4mampaZEy6tevj5s3b8LY2DjH61SuXLlit/P48ePIzMxESEgIGjduDA8PD9y4caPI+6lXrx4mTZqEgwcPonbt2li9enWBNU+ePNG4wgL4/x1LLzqPXszLMHPmTNSpUwc2NjZo0aIF9u7dW+B8DGZmZrCxsdF4cKgEERERERGVFIPoZFCpVBg0aBAmTJiAiIgInDlzBgMGDFB/2fPw8FDPubBp0yYkJCTg2LFjmDt3LrZt26beT4sWLeDg4IB+/frBxcUl379ajxw5Eo8ePUKfPn1w/PhxXLp0CStXrsSFCxcAPL8rwpw5c7Bo0SJcvHgRMTExCA0NxYIFCwo8nnfffRdKpRKDBg3CuXPnsG3bNsyfPz/fmjFjxmD79u1ISEjAiRMnsHv3bvU8AlWrVoVCocDWrVtx584djbssFEXfvn3h6OiILl264MCBA4iPj8fGjRtx6NAhAM+HNSQkJCA6Ohp3797NMXfAqwICAtCkSRN06dIF27dvx5UrV3Dw4EF8+eWX6rsxFEe1atWQmZmJ7777DvHx8Vi5ciUWL15c6PqEhARMmjQJhw4dwtWrV7Fjxw5cvHixUPMydOzYEZs2bcJPP/2E+Ph4HDhwAKNGjcJbb72lng/kxbwM//vf/9CyZUsAzztw0tPTERERoV5GRERERET0b2MQnQwAMG/ePPj7+6NTp04ICAhAs2bN4Ovrq14fGhqK4OBgjBs3Dp6enujUqROOHDkCZ2dn9TYKhQJ9+/bFqVOn8r2KAQDKli2L3bt3q++44OvriyVLlqgv4//www+xdOlShIWFwdvbGy1atEBYWFihrmRQqVT4448/cO7cOdSrVw9ffPEF5s6dm29NVlYWRowYAS8vLwQGBsLT01M9yWGlSpUwbdo0fPbZZ3BwcMDIkSMLbENuTE1NsWPHDlSoUAEdOnSAt7c3vv76a/Vf6rt3747AwEC0atUK5cuXx5o1a/Ldn0KhwLZt2+Dv74+BAwfCw8MDffr0wZUrV+Dg4FCsNgJA3bp1sWDBAsydOxe1a9fGqlWrMGfOnELXW1pa4vz58+jevTs8PDwwePBgjBw5EkOGDCmwdsCAAViwYAG+//571K5dGz179oSnpyc2bdqksV2rVq2QlZWl7lBQKBRo3rw5AKBZs2aFP1giIiIiIqISpJDiDPAnotfG5qPaDYMJUO4sdm2qqvidQQDwxMRGq3oqWY7Xj2m3g8vnil16s2X+d+XRJ6v0B1rVp5ra6aQd9HpwuHWq2LUPy7oXvFE+0o3Mi12bqcg5kXVRKJGtVT2VrGei3Txb5oqnBW9EpAOJTysWu7aKRdGHS7/s2jOnYtc6mxdu0nh9cPu/u+Ppk8FcyUBERERERERE+sVOhn+h2bNna9yy8eVH+/btS7t5OrNq1ao8j7NWrVql3bxiqVWrVp7HtGrVqtJuHhERERERkV7x3nb/QkOHDkWvXr1yXafr20SWpk6dOuW4hecLr96C8nWxbdu2PG+Dqc08EkRERERERK8DzslA9IaLj4sr7SYQFcrN9ArFrnU0va1VtmXGo2LXXle4aJVtZ/yg2LWO4T9plX24+Yxi17pZXdcq+3UVn1pZq3pDfd2o6G6ll9eqPjWj+Lew5nlKRZEqqmLXWimKd1c7Kj7OyUBERERERERErw12MhARERERERGRTrCT4TXWsmVLjBkzRif7unLlChQKBaKjo3Wyv4KcP38ejRs3hrm5OerWrVsima9SKBTYsmVLqWQTERERERG9idjJQAAAZ2dnJCUloXbt2iWSN2XKFFhZWeHChQuIiIgokczX3dSpU0utQ4aIiIiIiKgweHcJAgAYGRnB0dGxxPLi4uIQFBSEqlWr5rlNRkbGa3uXCSIiIiIiIkPEKxleE6mpqQgODoZKpYKTkxNCQkI01qenp+PTTz9FpUqVYGVlhUaNGiEyMhIA8PDhQ1hYWCA8PFyjZtOmTbCyskJKSkquwyXOnj2LoKAg2NjYwNraGs2bN0fcS3cqCA0NhZeXF8zNzVGjRg38+OOPhToWhUKBqKgoTJ8+HQqFAlOnTlXnr1u3Di1btoS5uTn+97//4d69e+jbty8qV64MS0tLeHt7Y82aNRr7c3FxwTfffKOxrG7dupg6dar6+aVLl+Dv7w9zc3PUrFkTO3fuLFRbX7h+/Tr69OkDe3t7WFlZoUGDBjhy5Ih6/U8//YRq1arB1NQUnp6eWLlypXpdbq/tgwcPoFAo1D+jyMhIKBQKREREoEGDBrC0tETTpk1x4cIFAEBYWBimTZuGU6dOQaFQQKFQICwsrEjHQEREREREpG+8kuE1MWHCBOzZswebN2+Go6MjPv/8c0RFRakvn//ggw9w5coVrF27FhUrVsTmzZsRGBiImJgYVK9eHUFBQVi1ahUCAwPV+1y9ejU6d+4MlUqFu3fvauT9888/8Pf3R8uWLbF7927Y2NjgwIEDyMzMBAAsWbIEU6ZMwffff4969erh5MmT+Oijj2BlZYX3338/32NJSkpCQEAAAgMDMX78eI38iRMnIiQkBKGhoTAzM8OzZ8/g6+uLiRMnwsbGBn/++Sf69+8PNzc3NGrUqFCvXXZ2Nrp164Zy5crh8OHDePToUZHmskhJSUGLFi1QqVIl/P7773B0dMSJEyeQnZ0NANi8eTNGjx6Nb775BgEBAdi6dSs++OADVK5cGa1atSp0DgB88cUXCAkJQfny5TF06FAMHDgQBw4cQO/evXHmzBmEh4dj165dAABbW9si7ZuIiIiIiEjf2MnwGkhJScEvv/yCFStW4O233wYALF++HJUrP79Xd1xcHNasWYPr16+jYsWKAIDx48cjPDwcoaGhmD17Nvr164fg4GA8efIElpaWePToEf78809s3Lgx18wffvgBtra2WLt2rXrIgoeHh3r9jBkzEBISgm7dugEAXF1dce7cOfz8888FdjI4OjrC2NgYKpVKPUTjRSfDmDFj1Pt8Yfz48er///jjjxEeHo7169cXupNh165diI2NxZUrV9Sv2ezZs9G+fftC1a9evRp37tzBsWPHYG9vDwBwd3dXr58/fz4GDBiA4cOHAwDGjh2Lw4cPY/78+UXuZJg1axZatGgBAPjss88QFBSEZ8+ewcLCAiqVCsbGxvkOa0lLS0NaWlqOZWZmxb9XNhERERERUWFxuMRrIC4uDunp6WjSpIl6mb29PTw9PQEAJ06cgIjAw8MDKpVK/di7d696eENQUBCMjY3x+++/AwA2btwIa2trtG3bNtfM6OhoNG/ePNc5Ee7cuYNr165h0KBBGnkzZ87UGE5RHA0aNNB4npWVhVmzZsHHxwdly5aFSqXCjh07kJiYWOh9xsbGokqVKuoOBgAar2VBoqOjUa9ePXUHQ2779/Pz01jm5+eH2NjYQme84OPjo/5/JycnAMDt27cLXT9nzhzY2tpqPBYvXlzkdhARERERERUHr2R4DYhIvuuzs7NhZGSEqKgoGBkZaaxTqVQAAFNTU/To0QOrV69Gnz59sHr1avTu3RvGxrmfAhYWFvnmAc+HTLx6NcGr+UVlZWWl8TwkJAQLFy7EN998A29vb1hZWWHMmDFIT09Xb6NUKnO8RhkZGer/z+31UygUhW5Tfq9FXvsTEfUypVKZox0vt+9lL3fqvKh/8XoXxqRJkzB27FiNZf9cv17oeiIiIiIiIm3wSobXgLu7O0xMTHD48GH1svv37+PixYsAgHr16iErKwu3b9+Gu7u7xuPlS+v79euH8PBwnD17Fnv27EG/fv3yzPTx8cHff/+d65dhBwcHVKpUCfHx8TnyXF1ddXjkwN9//43OnTvjvffeQ506deDm5oZLly5pbFO+fHkkJSWpnz969AgJCQnq5zVr1kRiYiJu3LihXnbo0KFCt8HHxwfR0dFITk7Odb2Xlxf279+vsezgwYPw8vJStw+ARhtfngSysExNTZGVlZXvNmZmZrCxsdF4cKgEERERERGVFHYyvAZUKhUGDRqECRMmICIiAmfOnMGAAQPUfyH38PBQz7mwadMmJCQk4NixY5g7dy62bdum3k+LFi3g4OCAfv36wcXFBY0bN84zc+TIkXj06BH69OmD48eP49KlS1i5cqX6bgdTp07FnDlzsGjRIly8eBExMTEIDQ3FggULdHrs7u7u2LlzJw4ePIjY2FgMGTIEN2/e1NimdevWWLlyJf7++2+cOXMG77//vsYVFQEBAfD09ERwcDBOnTqFv//+G1988UWh29C3b184OjqiS5cuOHDgAOLj47Fx40Z1R8WECRMQFhaGxYsX49KlS1iwYAE2bdqknkvCwsICjRs3xtdff41z585h3759+PLLL4v8Wri4uCAhIQHR0dG4e/dujrkXiIiIiIiIShs7GV4T8+bNg7+/Pzp16oSAgAA0a9YMvr6+6vWhoaEIDg7GuHHj4OnpiU6dOuHIkSNwdnZWb6NQKNC3b1+cOnUq36sYAKBs2bLYvXu3+s4Kvr6+WLJkifpy/g8//BBLly5FWFgYvL290aJFC4SFhen8SoavvvoK9evXR7t27dCyZUv1l/2XTZo0Cf7+/njnnXfQoUMHdOnSBdWqVVOvVyqV2Lx5M9LS0vDWW2/hww8/xKxZswrdBlNTU+zYsQMVKlRAhw4d4O3tja+//lrdkdGlSxcsWrQI8+bNQ61atfDzzz8jNDQULVu2VO9j2bJlyMjIQIMGDTB69GjMnDmzyK9F9+7dERgYiFatWqF8+fI5buVJRERERERU2hRS0IB/InqtxWs5GSdRSbmZXqHYtY6mhZ8gNTeWGY+KXXtd4aJVtp3xg2LXOob/pFX24eYzil3rZmWY873Ep1YueKN8GOrrRkV3K728VvWpGcUfLsnzlIoiVVTFrrVSpOiwJVQYbi/9MVZfeCUDEREREREREekEOxlI52bPnq1xa8uXH+3bty/t5uXwurWXiIiIiIjo34q3sCSdGzp0KHr16pXrusLcDrKkvW7tJSIiIiIi+rfinAxEb7i1B7X7FT8dm17sWmNjhVbZqak5b6FaWLW9rLTK/m39xWLXDv5QuwlQ95/MLnat0ki719xIWfz6CuVNtMqOjX1Y7Fr7stp1CN6//6zYtT61bbTKPnP2cbFrHRwttcp+cL/4v98VK5prlf04Nf9b8uZn9+YorbJNzEyLXWtsot3fZ8wsi/+6Pb5X/N8RABAp/r8tFtbFH3MNAM9SnhS71sxKu3NNsov/PvjpWHetsm8/Lv68CE/StLvg+Mat4v+OOZY3KnijfKQX/+0b5lreedtIi5ctI1O7bFOT4p9rD4r/VgAAOH+++HMLZWYU/98GALh17W6xa2vWr6JV9pljV4pd+3ZHD62yjx2+pVW9NpJv3S927R8/e+mwJbnjcAkiIiIiIiIi0gl2MhARERERERGRTrCTgYiIiIiIiIh0gp0MRERERERERKQT7GQg+hfKyNBixiQiIiIiIqJSwk4GoiLYsGEDvL29YWFhgbJlyyIgIACpqakAgGXLlqFWrVowMzODk5MTRo4cqa5LTExE586doVKpYGNjg169euHWrf8/I+3UqVNRt25dLFu2DG5ubjAzM4OI4OHDhxg8eDAqVKgAGxsbtG7dGqdOnSrx4yYiIiIiIioMdjIQFVJSUhL69u2LgQMHIjY2FpGRkejWrRtEBD/99BNGjBiBwYMHIyYmBr///jvc3Z/f9kpE0KVLFyQnJ2Pv3r3YuXMn4uLi0Lt3b439X758GevWrcPGjRsRHR0NAAgKCsLNmzexbds2REVFoX79+mjTpg2Sk5NL+vCJiIiIiIgKpN3NnokMSFJSEjIzM9GtWzdUrVoVAODt7Q0AmDlzJsaNG4fRo0ert2/YsCEAYNeuXTh9+jQSEhLg7OwMAFi5ciVq1aqFY8eOqbdLT0/HypUrUb58eQDA7t27ERMTg9u3b8PM7PlNo+fPn48tW7Zgw4YNGDx4cI42pqWlIS0tTWNZRropTEy1vOk0ERERERFRIfBKBqJCqlOnDtq0aQNvb2/07NkTS5Yswf3793H79m3cuHEDbdq0ybUuNjYWzs7O6g4GAKhZsybs7OwQGxurXla1alV1BwMAREVFISUlBWXLloVKpVI/EhISEBcXl2vWnDlzYGtrq/H4beUcHb0CRERERERE+eOVDESFZGRkhJ07d+LgwYPYsWMHvvvuO3zxxReIiIjIt05EoFAoClxuZWWlsT47OxtOTk6IjIzMUWtnZ5dr1qRJkzB27FiNZb+dMM23fURERERERLrCTgaiIlAoFPDz84Ofnx8mT56MqlWrYufOnXBxcUFERARatWqVo6ZmzZpITEzEtWvX1FcznDt3Dg8fPoSXl1eeWfXr18fNmzdhbGwMFxeXQrXPzMxMPbTiBRNTKfwBEhERERERaYGdDESFdOTIEURERKBt27aoUKECjhw5gjt37sDLywtTp07F0KFDUaFCBbRv3x6PHz/GgQMH8PHHHyMgIAA+Pj7o168fvvnmG2RmZmL48OFo0aIFGjRokGdeQEAAmjRpgi5dumDu3Lnw9PTEjRs3sG3bNnTp0iXfWiIiIiIiotLATgaiQrKxscG+ffvwzTff4NGjR6hatSpCQkLQvn17AMCzZ8+wcOFCjB8/HuXKlUOPHj0APL/6YcuWLfj444/h7+8PpVKJwMBAfPfdd/nmKRQKbNu2DV988QUGDhyIO3fuwNHREf7+/nBwcND78RIRERERERUVOxmICsnLywvh4eF5rh8yZAiGDBmS67oqVargt99+y7N26tSpmDp1ao7l1tbW+Pbbb/Htt98Wub1EREREREQljXeXICIiIiIiIiKdYCcDEREREREREekEOxmIiIiIiIiISDeEiAzWs2fPZMqUKfLs2bMSr2c2s5nNbGYzm9nMZjazmf3vydYVdjIQGbCHDx8KAHn48GGJ1zOb2cxmNrOZzWxmM5vZzP73ZOsKh0sQERERERERkU6wk4GIiIiIiIiIdIKdDERERERERESkE+xkIDJgZmZmmDJlCszMzEq8ntnMZjazmc1sZjOb2cxm9r8nW1cUIiKllk5EREREREREbwxeyUBEREREREREOsFOBiIiIiIiIiLSCXYyEBEREREREZFOsJOBiIiIiIiIiHSCnQxEREREREREpBPsZCAiIiIiIiIinTAu7QYQUcm6dOkSDh48iJs3b0KhUMDBwQFNmzZF9erVi73P1NRUREVFwd/fX4ct1ZSVlQUjIyP18yNHjiAtLQ1NmjSBiYlJkff3wQcfYNasWahYsWKR6u7fv4/Lly/DyckJlStXLlLtgwcPsH79eiQmJqJq1aro2bMnbG1tc902KioKvr6+Rdr/q27fvo2zZ8/C19cXNjY2uHXrFpYvX47s7GwEBQXB29s73/r4+Hjs378fSUlJMDIygqurK95++23Y2NgUKj8lJQVRUVEa55qvry9UKlWxjykzMxM3btxAlSpVir2Porp16xbS0tKKnTlt2jSMGDEC5cqVK3LtnTt3YGdnV+RzPDMzE3v27FGfa61atdL4/XnZ3bt3i9W2l2VlZamzlEol0tLS8NtvvyE7OxutWrWCg4NDvvUv/g15+VyrX78+FAqFVu0iInodiQh27dqV4/Oan58f2rRpU+x/G+/fv48//vgDwcHB+W6XnZ0NpTLn36Kzs7Nx/fr1PN8PRQRXrlyBs7MzjI2NkZ6ejs2bNyMtLQ0dOnQo1ntN69atERoaiqpVqxapLjo6GpcuXYKTkxP8/Pzyfc3S0tKgVCrV77VxcXFYtmyZ+n1t0KBBcHV1zbV248aNaN++PSwtLYvUvpedOnUKJ06cQMuWLeHq6oqzZ8/ihx9+QHZ2Nrp27Yp27drlW7979+4cn9c6deqk1Wd7rQkRGYQHDx5Ip06dRKFQiJ2dnXh4eEj16tXFzs5OlEqldO7cWR4+fFisfUdHR4tSqcxzfXp6ukyYMEGqVasmDRs2lGXLlmmsv3nzZp71N27cED8/PzEyMhJ/f39JTk6WoKAgUSgUolAoxMPDQ27cuJFn9qlTp3J9mJiYyObNm9XPczNp0iRJTU1VH8NHH30kSqVSFAqFKJVK6dq1qzx9+jTP7O7du8vGjRtFROTs2bNSrlw5KV++vDRq1EgcHBzE0dFRzp07l2utQqEQNzc3mTVrlly/fj3PjLzs2bNHrKysRKFQiJOTk5w6dUoqV64s1atXF09PTzEzM5Pt27fnWpuSkiI9evRQv8ZKpVIcHR3FyMhIVCqVfP/99/lmZ2RkyKhRo8TCwkIUCoWYmZmJqampKBQKsbCwkNGjR0t6enqRj0mk4HNNROSHH36QNm3aSM+ePSUiIkJj3Z07d8TV1TXXukePHkm/fv2kSpUqEhwcLGlpaTJ8+HD1a+Dv75/v78jDhw9zPB48eCAmJiZy5MgR9bLc/Pzzz/Ls2TMREcnOzpZZs2apfzctLS3lk08+kaysrDyzP/74Y9m6dauIiFy7dk1q1KghRkZG4uDgIEZGRuLt7Z3neaRUKqV169ayatUqdRuKIjo6WhwdHUWpVIqPj49cu3ZNateuLVZWVqJSqaRMmTJy9OjRXGuzsrJkwoQJYmlpKUqlUv37pVAopGrVqvL7778Xqg2PHz+WyMhIWbt2rfz6668SGRkpjx8/LvKxvCojI0OuXr2q9X6K4ubNm8XOnDp1qty5c6fY2bdv3y7y72ZGRobs2LFDli5dKjt37pTMzMx8t9emfSIimZmZEh8fr/59ePbsmfz666+yZs0auXnzZqH2kZKSInv37pW1a9fK+vXr5fjx45KdnV2oWp5rz/Fc09+5dv36dalbt64YGRlJnTp1pG3btvL2229LnTp1xMjISOrXr1+szwUiBb+HPnz4UHr27Cnm5uZSoUIFmTx5ssbrnN/ntfPnz0vVqlVFqVSKu7u7xMfHi6+vr1hZWYmlpaWUK1dOLl68mGf2b7/9luvDyMhIvv/+e/Xz3PTt21cePXokIs9/R9u2bSsKhUL92aNBgwZy//79PLNbtWql/ry2f/9+MTMzEx8fH+ndu7fUq1dPLC0t5eDBg7nWKhQKsba2lo8++kgOHz6cZ0ZeNmzYIEZGRlK2bFmxtraWXbt2iZ2dnQQEBEi7du3EyMhIVq1alWvtrVu35K233hKFQiFGRkaiVCrF19dX/ZltwoQJRW6PrrCTgchA9O/fX7y9vXP9B/Dw4cPi4+MjwcHBxdp3QW9aU6ZMEQcHB5k3b5588cUXYmtrK4MHD1avv3nzpigUijzb3bRpU/n999+ld+/e0rRpU2nevLlcv35dEhMTpXnz5jJixIg8s198QXzxxeXlx8sdBrlRKpVy69YtERGZNWuWlC9fXjZu3Cj//POP/PHHH1KpUiWZPn16ntkvv6G2b99e3n33XUlLSxOR550WgwYNkrZt2+bZ7o8++kgcHBzE2NhYgoKCZPPmzQV+qHrBz89PRowYIY8fP5Z58+ZJ5cqVNV6n8ePHS9OmTXOtHTx4sPj5+Ul0dLScP39eunfvLp9++qmkpqbKL7/8IpaWlnm+4YmIjBo1SipVqiRr167VeFO/f/++rF27VpydnWX06NGFOo5XFXSuLVq0SCwtLWXEiBHy3nvviZmZmcyePVu9Pr8PSCNHjpQaNWrIt99+Ky1btpTOnTtL7dq1Zf/+/bJv3z6pXbu2fP7553lmv/iS/OqjqOfa4sWLxcrKSkJCQuTAgQPy3Xffia2trXz33Xd5Zjs5Oak7rHr16iUBAQHqD9j37t2Td955R3r06JFrrUKhkMDAQDE1NZUyZcrIyJEj5eTJk3lmvapt27bSo0cPiYmJkdGjR0vNmjWlZ8+ekp6eLhkZGfLee+9JQEBArrUTJ04ULy8v2bJli4SHh0vz5s1l7ty5EhsbK1999VW+nWEi+u3QEin4fCtuh5aIdp1a2nRoiWjXqaVNh5aIdp1a2nRoiWjXqcVzjedaSZ1rnTp1ktatW+f6B5QbN25I69atpXPnzrnW5vbzevnx999/53uejRo1Sjw8PGT9+vWyZMkSqVq1qgQFBak/u+T3ea1z587SqVMnOX36tIwZM0Zq1qwpnTt3lvT0dElLS5POnTvLe++9l2d2fp/XXv7clpuX30PHjx8vrq6uEhUVJSIiMTEx4uXlJZ988kme2XZ2dnL58mUREWnRokWObb/88kvx8/PLs93Tp0+XevXqiUKhkFq1asnChQvl7t27eea9rH79+jJz5kwREVmzZo3Y2dlpfLacP3++1K1bN9fa3r17S5cuXeT+/fvy5MkTGTFihPqzfEREhJQtW1a++eabQrVD19jJQGQgbG1t8+1hPXTokNja2ua6rkyZMvk+bGxs8n3Tcnd3lz/++EP9/PLly1K9enUZMGCAZGdn5/vFz8nJSQ4dOiQiz78sKRQK2bVrl3r97t27xc3NLc/sOnXqSFBQkMTGxsqVK1fkypUrkpCQIMbGxrJz5071stwoFAr1m1bdunXll19+0Vj/66+/ipeXV57ZFhYW6jctJycnOXHihMb6Cxcu5Pmav8jOyMiQDRs2SIcOHdQfrj799FM5f/58nrkiIjY2NursjIwMMTY21vjiePHixTyzy5UrJ8ePH1c/T05OFnNzc/VVHd9//32eb3gv6l/9EPyyXbt2Sbly5XJdV69evXwfNWrUyPdcq1mzpkYHyMGDB6VChQry1VdfiUj+nQzOzs6ye/duERH5559/RKFQaHwQ/PPPP8XT0zPP7EqVKklQUJDs3r1bIiMjJTIyUvbs2SNGRkYSGhqqXpabl8+1hg0byoIFCzTWL1myRHx8fPLMNjc3l/j4eBERqVy5shw5ckRjfUxMTJ6v+YvsO3fuyPz586VWrVqiVCqlfv368uOPP8qDBw/yzBV5/u/Diw6OJ0+eiJGRkUb+mTNnpGzZsrnWVqxYUfbt26d+fv36dVGpVOovBNOnT5cmTZrkma3PDi2R/L/4adOhJaJdp5Y2HVov6ovbqaVNh5aIdp1a2nRoiWjXqcVzjedaSZ1rVlZWEh0dnee+T5w4IVZWVnkec14/s8L8vKpUqSJ79uxRP7979640atRI2rZtK8+ePcv3XCtfvrz69U1JSRGFQiF///23ev3BgwelSpUqeWYHBgZKUFCQ+nx5wdjYWM6ePZtn3YvjflFXq1Yt+fXXXzXW//nnn1K9evU8662srCQ2NlZERBwcHHK8/pcvXxaVSlVg9vHjx2XYsGFiZ2cnZmZm0rNnT9mxY0e+bbeyspKEhAQRed4RZ2JiIqdPn1avj4uLyzPbxsZGzpw5o36ekpIiJiYm6o6/lStX5vu5RZ/YyUBkIGxtbXN88XjZ4cOH8/zSaWlpKePGjZOwsLBcH9OmTcv3TcvCwkL9D+gL//zzj3h6ekq/fv3kn3/+ybPe3NxcEhMT1c+trKzk0qVL6udXr14VCwuLPLPT0tLUHxBe/pJf2Det27dvi4hI2bJlJSYmRmN9QkKCWFpa5lnfqFEj+e9//ysiz788b968WWP9jh07xNHRMc/sV99or1+/LtOnTxc3NzdRKpXSvHnzPLPLlSunfuNJTU0VpVKp7qwReT6MJK8vnXZ2dhqXNKanp4uxsbH6tbh48aKYm5vnmW1lZZXnEBQRkZMnT+b5AcnMzEzef/99mTp1aq6PIUOGFPlcO3PmjDg4OMhnn32W7wckMzMzjXPN0tJSLly4oH5+5cqVfH/e9+7dky5dukirVq00/rpW1HOtXLlyOV6//D5kiIj4+PjI2rVrRUTEy8tLdu7cqbH+4MGDYm9vn2f2q+fawYMHZeDAgWJtbS2WlpbSv3//PLNfPl/S09PFyMhI/RckEZHY2FgpU6ZMrrXW1tYSFxenfp6VlSXGxsaSlJQkIs+HGeX3mmvToSWiXaeWNh1aItp1amnToSWiXaeWNh1aL2cXp1NLmw4tEe06tXiu8VwryXPtxc8rNxEREXket42NjcydO1f9c3n1sWTJknzPFUtLS/Vr/sKjR4+kSZMm0rp1a4mPj8+z3sLCQmP4jUqlUv+xQ0QkMTFRzMzM8swWEVmwYIFUqVJF4w9TxXkPfXX7K1eu5Pu5pXXr1vKf//xHRESaNm0qy5cv11i/YcOGPDtIcnsPffr0qaxYsUJatmwpSqVSqlatmme2o6Oj+g87ycnJolAoNDp6jh49mudnxfLly2sc65MnT0SpVMq9e/dE5Plnh4Jec31hJwORgXjvvffEx8dHjh07lmPdsWPHpG7dunl+kWjatGm+l1sVdJmnq6urxtUHL/zzzz/i4eEhAQEBedZXqVJF44194sSJ6n88X2Tn9yHjhW3btknlypVl9uzZ6i8yhXnTmjVrlixatCjHB4YX2Xl9eRIR2bp1q9jb20toaKiEhoaKi4uLLF26VA4cOCDLli0TZ2fnPMfLvfzXn9zs2rVL3n333TzXd+7cWd555x3Zv3+/DB48WBo0aCBBQUGSkpIiqamp0qNHDwkMDMy19u2339YYWjFv3jxxcnJSPz9x4kS+r/k777wjbdq0yXXM6s2bN+Xtt9+Wjh075lrr6+srP/74Y577PnnyZIEfpl/9OYk8/7Lq4OAg/fv3z7O+YsWKGl+O+/btq/EzOHPmTL4/7xd+/PFHqVixoqxevVpECv8BacWKFfLbb7+Js7NzjquOzpw5IzY2NnnWh4aGSuXKlWXPnj2yYsUK8fLykl27dsk///wju3fvFm9vb/nwww9zrc3vXEtJSZGlS5fmObRGRKRNmzYyaNAguX79ukybNk3c3d3lgw8+UK8fPnx4nh1iTZs2VV8mKvL/LxV9ISYmJt/XXJsOLRHtOrW06dB6kV3cTi1tOrREtOvU0qZD60V2cTu1tOnQEtGuU4vnGs+1kjrXRo4cKc7OzrJ+/XqNjpAHDx7I+vXrpUqVKjJq1Khca1u2bClz587Ns13R0dF5DncQEfH09JQ///wzx/LHjx9LkyZNpE6dOnmea9WqVdO4cuHHH39Uz5MgIhIVFZXnl+VX21izZk0ZPHiwpKamFvo9dMiQIfLJJ59IhQoVcnQIHj9+PN/PLQcPHhRbW1uZMmWKfPfdd1KuXDn58ssvZdWqVTJ58mSxs7PL83Ut6PPapUuX8h1q+d5770mjRo3kf//7n3Ts2FECAwOlcePGEhsbK+fPn5cWLVrkecVO165dpXv37pKSkiLp6ekyZswYcXd3V68/fPhwoV5zfWAnA5GBuH//vgQGBopCoZAyZcqIp6en1KhRQ8qUKSNKpVLat2+f56Q4s2bNkqlTp+a578TERBkwYECe6wcNGiQDBw7Mdd3169fF3d09zzetTp065dvB8f3330vr1q3zXP+ymzdvSvv27aVZs2aFetOqWrWquLi4qB+vtmPhwoXSuHHjfPexYcMGqVy5co5xhubm5jJmzJg851jI7cNRUVy8eFHc3d3V4wP/+ecf6dSpkxgbG4uxsbGUL19e4wPTy6KiosTe3l4cHR2lSpUqYmpqKmvWrFGv//777/OdvyMxMVFq164txsbGUrduXWnXrp0EBgZK3bp1xdjYWD2+NTejR4/O95Ljy5cvS8uWLfNc37dv3zzrz5w5I+XLl8/zXAsMDJTFixfnue/Q0NB8v2y/7OzZs1KnTh3p27dvoT8gvfyYNWuWxvolS5ZIvXr18t1HSEiIWFpaioWFhZiammpcItulS5c8J6fT9lw7evSo2Nvbi1KplAoVKsjZs2elUaNG4ujoKBUrVhQLC4tcOxlFnneWmZmZyVtvvSX+/v5ibGwsCxcuVK+fN29evr/f2nRoiWjXqaVNh5aIbjq1itOhJaJdp5Y2HVoi2nVqadOhJaJdpxbPNZ5rJXWupaWlydChQ9X/jpubm4u5ubkolUoxNTWVYcOGqedIeNV///tfWbRoUZ7tunnzZr6f5z7++OM8v9A+evRIGjVqlOe5NmTIEFmyZEme+54zZ4506NAhz/Uve/LkiQwZMkSqV68uRkZGBZ5rLVq0kJYtW6ofS5cu1Vg/ffp0adGiRb77OHjwoDRu3DjH+3GlSpXy/Ryq7XvozZs3JSAgQFQqlbRv314ePnwoI0eOVA9tqV69usYVIS+Li4uTatWqibGxsZiYmIidnZ1GZ1xoaKh89tlnxW6bNtjJQGRgYmNjZdmyZTJ79myZPXu2LFu2TD0OTV+uXLki4eHhea6/ceOGhIWFFWvfR48ezTGMoSCLFi2SLl265PlFt7AOHTqUY56F3GRmZsqRI0dk7dq1snr1atmzZ49G735uIiMjJSMjQ6v2iUiOiYd27dolf/zxR4ETEt24cUP++9//ynfffVeoD5KvysrKkm3btsnkyZNl8ODBMnjwYJk8ebL89ddf+d4lQVunTp3KcfeSl505cybPD1j37t3Ld/bpbdu2aVzCWJC0tDT55JNPpG7dujkuPy2qP/74I9/foRfu378v69atk6+//lpmz54toaGh+c7mLSISFhZWrLtKvOzx48dy/PhxdUfG06dPZenSpfLdd98VOH/IqVOn5PPPP5dx48YVOHb1Vdp0aIlo16mlTYeWiO46tYraoSWifadWcTu0XmQX9wO5Nh1aItp1avFc47lWUufaCw8fPpSIiAhZvXq1rF69Wnbv3l3su4AVVnJyssYY/1e9uLtKccTHx+d7N7Dc/PbbbzJmzBitvsSLPP8yXtjPfLdv35bDhw/LwYMHc1xBlJsrV64U+u40RREXFycxMTEFfhZMTU2VHTt2yB9//KH1HVV0SSEiUno30CQiIiIqvuzsbGzfvh2HDx/GzZs3AQCOjo5o0qQJ2rZtm+u93nXh9OnTiIqKwgcffJDr+rNnz2LDhg2YMmVKruuTk5OhVCphZ2eX6/q//voLFhYWaNmyZYFtSU9Px2effYY9e/Zg06ZNed7PvbC2bt0KExOTfO/N/uDBA+zYsQMJCQnIzs5W34u+oPuyL1++HH369IGZmVmx2paSkoILFy7A09MTKpUKz549w6pVq/D06VO8/fbb8PT0zLf+9OnT+PXXX5GWloZ27drh7bffLnQ2zzWeayV1rhG99kq7l4OISt7cuXOL3RuuTS2zDS87Ny/uG17Stcw2vGwiotfZiBEjNOahKqlaZhtetq6xk4HIQLx8uVWZMmXUMwB36NChwMvXtKlltuFlF6SgiUL1VcvsNzt7zZo16tusFoc29cxmNrOZrav6ly/rf/k9uHbt2hoTeOq6ltmGl61P+rmui4j+dWxsbNCiRQt8/vnnSE9PR1paGgBg3759ePr0qd5qmW142USlYfjw4bh7926O/y+JemYzm9nM1lV9jRo1ULVqVbz77rt49uwZrl27BgC4cuUKMjIy9FbLbMPL1id2MhAZiMuXL2PEiBFISUlBeno6fHx80LZtW6Snp+PMmTPIysrSSy2zDS/b3t4+34e/v79eaplteNnVqlXD+++/j//+97/IzMyEQqEAAPzvf//Do0eP8q3Vtp7ZzGY2s/VR//DhQ6xfvx6+vr7Izs5Ghw4d4OHhgbS0NGzfvl09H4iua5lteNn6xIkfiQxQmTJlEBERgdjYWAwcOBBly5ZFSkoKmjRpgu3bt+utltmGkW1lZYVhw4bB29s71/VXr17FtGnTcu2s0KaW2YaXfeDAARw4cAD79+/Htm3b4OjoiA4dOmDlypXYv38/fH19c63TRT2zmc1sZuuj/tmzZzA3Nwfw/D04KioKSUlJCAgIQO3atXHu3DlUrlwZFy5c0Gktsw0vW69KdbAGEZWYihUrSq9evWTRokWiUqnU47ZUKpXExcXJ1atXZeXKlTqvZbbhZTdt2jTfe0rnN85em1pmG172y+zs7OT333+XmTNniomJiZiamkqNGjVk8ODBeq9nNrOZzWxd1ZuYmMhbb70ln3zyiVhaWqpv0/3iPTgtLU3+/vtvndcy2/Cy9cm45Ls1iKg0/Pbbbzh06BAOHjyIZ8+eoX79+mjfvj0yMzNx69YtNGnSBO+9957Oa5lteNlBQUF48OBBnuvt7e0RHBys81pmG162n58fmjdvjqZNmyI7Oxt169ZFx44d8fXXX+PIkSO4e/cu9u3bp5d6ZjOb2czWR/aNGzfU78GZmZlo0KABGjZsiPT0dJw4cQLOzs5o1qyZzmuZbXjZelUqXRtEVKrs7OwkPDxc5s6dKyYmJmJhYSEuLi4SHBys11pmG142kT6tX79exo4dK40bNxalUine3t7y5ZdfioWFhVy8eFGv9cxmNrOZra/6F+zs7OT06dPy66+/ipmZmVStWlXMzc3F399fr7XMNrxsXWMnA5EBsrOzU18Cb21tLefPn5dDhw7J119/rddaZhteNlFJsbOzk6VLl8rw4cPF2NhYLCwspGXLljJ58mS91zOb2cxmtr6yXx2ymJSUJGvXrtVrLbMNL1vX2MlAZID2798vaWlpIvL//yEqiVpmG1723Llz5eHDh0Wq0UUtsw0v+9UPWAcOHJA1a9bI0KFD9V7PbGYzm9n6qE9MTJSsrCwREalVq5YkJiYWKlPbWmYbXrausZOBiIh0KiMjQ/3/ZcqUUX+46tChg9y4cUNvtcw2vOyXrV69WlJTU0WkeB1i2tQzm9nMZra+6oleR+xkICIinbKwsBB/f3+ZNGmSWFlZqcefFubDlTa1zDa87LwkJiZKZmZmqdQzm9nMZrYu6y9duiTh4eHy5MkTERHJzs4ukVpmG162LvHuEkQGYOzYsYXedsGCBTqrZbbhZQPA5cuXsX//fuzfvx/p6enw8fFB8+bNkZ6ejjNnzqBq1aowMjLSeS2zDS87L87OzkWu0VU9s9/c7IyMDLRt2xY///wzPDw8ipytTT2zDSsbAO7du4devXphz549UCgUuHTpEtzc3PDhhx/Czs4OISEheqlltuFl64NCRKTEU4moRLVq1UrjeVRUFLKysuDp6QkAuHjxIoyMjODr64vdu3frrJbZhpf9qjJlyiAiIgKxsbEYOHAgypYti5SUFDRp0gTbt2/XWy2zDSv722+/zXW5QqGAubk53N3d4e/vn2eHhTb1zDas7PLly+PgwYOoXr16rvsoiDb1zDas7ODgYNy+fRtLly6Fl5cXTp06BTc3N+zYsQOffPIJzp49q5daZhtetl6U2jUURFQqQkJCpGPHjpKcnKxelpycLJ07d5b58+frrZbZhpNdsWJF6dWrlyxatEhUKlWO2Y6vXr0qK1eu1Hktsw0v+wUXFxexsrIShUIh9vb2UqZMGVEoFGJlZSUODg6iUCikWrVqeU6EpU09sw0re+zYsTJx4sRc91sY2tQz27CyHRwcJDo6WkQ0h5DFx8eLlZWV3mqZbXjZ+sBOBiIDU7FiRTlz5kyO5TExMeLk5KS3WmYbTvaxY8fk22+/lT59+oixsbGULVtW3nvvPTE3N5eDBw/mm6lNLbMNL/uF1atXS8uWLeXy5cvqZZcuXZLWrVvL2rVr5dq1a+Ln5yfdu3fXeT2zDSt75MiRYmNjI/Xr15fBgwfLJ598ovEoiDb1zDasbJVKles8NUePHhV7e3u91TLb8LL1gZ0MRAZGpVJJREREjuURERGiUqn0Vstsw8sWeX7rrvDwcJk7d66YmJiIhYWFuLi4SHBwsF5rmW1Y2W5ubnLy5Mkcy0+cOCGurq4iInLgwAFxdHTUeT2zDSu7ZcuWeT5atWqVa56u6pltWNkdOnSQL7/8UkSevx/Hx8dLVlaW9OzZM88ONF3UMtvwsvWBnQxEBqZ///5SpUoVWb9+vVy7dk2uXbsm69evL9SHeW1qmW142SKa9we3traW8+fPy6FDh+Trr7/Way2zDSvbwsJCjh07lmP50aNHxcLCQkREEhIS8rxsVJt6ZhtWNlFJOXv2rJQvX14CAwPF1NRUevToIV5eXuLg4KBxFY6ua5lteNn6wE4GIgOTmpoqw4YNEzMzM1EqlaJUKsXU1FSGDRsmKSkpeqtltuFli4js379f0tLSRKTotyXUppbZhpXdoUMHqV+/vpw4cUK97MSJE+Lr6ytBQUEiIvL7779L7dq1dV7PbMPKJipJSUlJMnnyZAkKCpL27dvLF198ITdu3NB7LbMNL1vXeHcJIgOSlZWF/fv3w9vbG2ZmZoiLi4OIwN3dHVZWVnqrZbbhZROVpJs3b6J///6IiIiAiYkJACAzMxNt2rTBypUr4eDggD179qhvKafLema/+dndunVDWFgYbGxs0K1btxz7e9mmTZtyLNOmntmGlU30pjAu7QYQUckxMjJCu3btEBsbC1dXV/j4+JRILbMNL5uoJDk6OmLnzp04f/48Ll68CBFBjRo11LdeBXLenlVX9cx+87NtbW2hUCjU/19U2tQz27CyXxYeHg6VSoVmzZoBAH744QcsWbIENWvWxA8//IAyZcropZbZhpetD7ySgcjANGzYEF9//TXatGlTorXMNozssWPHFnrbBQsW6KyW2YaXTUT0JvP29sbcuXPRoUMHxMTEoEGDBhg3bhx2794NLy8vhIaG6qWW2YaXrQ+8koHIwMyaNQvjx4/HjBkz4Ovrm+PSdxsbG73UMtswsk+ePKnxPCoqCllZWeq/EF68eBFGRkbw9fXVaS2zDS/7ZVlZWQgLC0NERARu376N7OxsjfW7d+/WWz2zDSubqKQkJCSgZs2aAICNGzeiY8eOmD17Nk6cOIEOHTrorZbZhpetD+xkIDIwgYGBAIBOnTqpL+cDABGBQqFAVlaWXmqZbRjZe/bsUf//ggULYG1tjeXLl6sv1bt//z4++OADNG/eXKe1zDa87JeNHj0aYWFhCAoKQu3atTXO18LQpp7ZhpUNABs2bMC6deuQmJiI9PR0jXUnTpzQaz2zDSfb1NQUT548AQDs2rULwcHBAAB7e3s8evQo30xtaplteNl6oe+ZJYno3yUyMjLfh75qmW142RUrVpQzZ87kWB4TEyNOTk56q2W24WWXLVtW/vzzzwK300c9sw0re9GiRaJSqWTEiBFiamoqQ4YMkYCAALG1tZXPP/9cr/XMNqzsd955R9q1ayfTp08XExMTuX79uoiIbN++XapXr663WmYbXrY+sJOBiIj0QqVSSURERI7lERERolKp9FbLbMPLdnJykgsXLhS4nT7qmW1Y2Z6enrJ69WoR0bzV6ldffSUjRozQaz2zDSv76tWr8s4774iPj48sXbpUvXzMmDHy8ccf662W2YaXrQ/sZCAyUKmpqRIbGyunTp3SeOi7ltmGk92/f3+pUqWKrF+/Xq5duybXrl2T9evXi4uLiwQHB+utltmGlz1//nwZPny4ZGdnF7itruuZbVjZFhYWcuXKFRERKV++vERHR4uIyMWLF8Xe3l6v9cw2nOyMjAwJCwuTGzduFJihy1pmG162vrCTgcjA3L59W4KCgkSpVOb60Fctsw0vOzU1VYYNGyZmZmbqGlNTUxk2bJikpKTorZbZhpfdpUsXsbW1FVdXV3nnnXeka9euGg991jPbsLJdXV0lKipKREQaNGggixcvFpHnlyWXKVOmwGxt6pltWNkvd1AUlTa1zDa8bH3gxI9EBmbMmDG4f/8+Dh8+jFatWmHz5s24desWZs6ciZCQEL3VMtuwsrOysnDs2DHMnDkT8+bNQ1xcHEQE7u7uOe5SoctaZhteNgDY2dmha9euhdpW1/XMNqzs1q1b448//kD9+vUxaNAgfPLJJ9iwYQOOHz+Obt266bWe2YaV3ahRI5w8eRJVq1YtMEeXtcw2vGy9KK3eDSIqHY6OjnLkyBEREbG2tlaPS/3tt9/Ez89Pb7XMNrxsMzMziY+PL3A7Xdcy2/CyiUpKVlaWZGRkqJ//+uuv8vHHH8uiRYskLS1Nr/XMNqzsdevWiZubm3z33Xdy8ODBIg1Z1KaW2YaXrQ/sZCAyMNbW1pKQkCAiIlWrVpX9+/eLiEh8fLxYWFjorZbZhpfdoEED2bVrV4Hb6bqW2YaXTaRPXbt2lYcPH4qIyPLly+XZs2clVs9sw8p+mUKhyPFQKpXq/+qrltmGl60PHC5BZGA8PT1x4cIFuLi4oG7duvj555/h4uKCxYsXw8nJSW+1zDa87FmzZmH8+PGYMWMGfH19c1z6bmNjo5daZhtGdv369REREYEyZcqgXr16UCgUee4/t3vRa1PPbMPK3rp1K1JTU2FjY4MPPvgAgYGBqFChQp71r9KmntmGlf2yhISEItfoopbZhpetD+xkIDIwY8aMQVJSEgBgypQpaNeuHVatWgVTU1OEhYXprZbZhpcdGBgIAOjUqZPGB3oRgUKhQFZWll5qmW0Y2Z07d4aZmRkAoEuXLvm2LTfa1DPbsLJr1KiBSZMmoVWrVhARrFu3Ls9Os+DgYJ3WM9uwsl+mzfh6bcfmM9uwsvVBISJS2o0gotLz5MkTnD9/HlWqVEG5cuVKrJbZb3723r17813fokULvdQy2/CyifTp4MGDGDt2LOLi4pCcnAxra+tcr4RQKBRITk7WaT2zDSs7N+fOnUNiYiLS09M1lnfq1Emvtcw2vGydKsmxGURERET6cP/+fVmyZIl89tlncu/ePRERiYqKkuvXr+u9ntmGk61QKOTWrVuFytB1PbMNKzsuLk58fHw0xte/+P+CxtlrU8tsw8vWBw6XIDIAY8eOLfS2CxYs0Fktsw0vOzdPnjzJtWfdx8dHr7XMNpzs06dPIyAgALa2trhy5Qo++ugj2NvbY/Pmzbh69SpWrFiht3pmG1Z2QkICypcvn+/+86NNPbMNK3v06NFwdXXFrl274ObmhqNHj+LevXsYN24c5s+fr7daZhtetj6wk4HIAJw8eVLjeVRUFLKysuDp6QkAuHjxIoyMjODr66vTWmYbXvbL7ty5gw8++AB//fVXruvzG6OvTS2zDS977NixGDBgAP7zn//A2tpavbx9+/Z49913863Vtp7ZhpVdtWpVPHjwAEePHsXt27eRnZ2tsT6/Mfba1jPbsLIPHTqE3bt3o3z58lAqlVAqlWjWrBnmzJmDUaNG5Xi/1lUtsw0vWy9K5foJIio1ISEh0rFjR0lOTlYvS05Ols6dO8v8+fP1Vstsw8t+9913pWnTpnL06FGxsrKSHTt2yMqVK8XT01O2bt2qt1pmG162jY2NXL58WUREVCqVxMXFiYjIlStXxMzMTK/1zDas7N9//12sra1FqVSKra2t2NnZqR9lypQpMFubemYbVradnZ363HRzc5Pdu3eLiMjly5cLvI20NrXMNrxsfWAnA5GBqVixopw5cybH8piYGHFyctJbLbMNL9vR0VGOHDkiIiLW1tZy4cIFERH57bffxM/PT2+1zDa87AoVKsiJEydERPNL4/bt26Vy5cp6rWe2YWVXr15dRo8eLampqQXm6Lqe2YaV3axZM9m8ebOIiPTt21cCAwNl//79EhwcLLVq1dJbLbMNL1sf2MlAZGBUKpVERETkWB4RESEqlUpvtcw2vGxra2tJSEgQEZGqVavK/v37RUQkPj6+wJ51bWqZbXjZH330kXTp0kXS09NFpVJJfHy8XL16VerVqyejR4/Waz2zDSvb0tJS3SlRHNrUM9uwssPDw2Xjxo0i8nxiPy8vL1EoFFKuXLlc35t1Vctsw8vWB3YyEBmY/v37S5UqVWT9+vVy7do1uXbtmqxfv15cXFwkODhYb7XMNrzsBg0aSHh4uIiIdO7cWfr37y/Xr1+XTz/9VNzc3PRWy2zDy3748KH4+fmJnZ2dGBkZibOzs5iYmIi/v7+kpKTotZ7ZhpXdtWtX+fXXXwvM0Ec9sw0rOzf37t2T7OzsEq9ltuFla0shIlLyM0EQUWl58uQJxo8fj2XLliEjIwMAYGxsjEGDBmHevHmwsrLSSy2zDS971apVyMjIwIABA3Dy5Em0a9cO9+7dg6mpKcLCwtC7d2+91DLb8LJf2L17N06cOIHs7GzUr18fAQEBharTRT2zDSP7l19+wfTp0/HBBx/A29sbJiYmGusLuh+9NvXMNqzsl127dg0KhQKVK1cu1Pa6qmW24WXrTKl1bxBRqUpJSZFTp05JdHR0of7yo6taZhte9gupqakSFRUld+7cKdFaZhtW9tOnT7X664029cx+87Nf3H8+t0dh7kevTT2zDSs7IyNDvvzyS7GxsRGlUilKpVJsbGzkiy++kPT0dL3VMtvwsvWBnQxERET0WsvKypLp06dLxYoVxcjISD0G+ssvv5SlS5fqtZ7ZhpVNVFKGDBkiFSpUkMWLF8upU6fk1KlTsnjxYnF0dJQhQ4borZbZhpetDxwuQWRgUlNT8fXXXyMiIiLXezbHx8frpZbZhpE9duzYfNvzsgULFuisltmGl/2y6dOnY/ny5Zg+fTo++ugjnDlzBm5ubli3bh0WLlyIQ4cO6a2e2YaXnReFQoGvvvqqwOzi1jPbsLJtbW2xdu1atG/fXmP5X3/9hT59+uDhw4d6qWW24WXrg3GJJxJRqfrwww+xd+9e9O/fH05OTlAoFCVSy2zDyD558qTG86ioKGRlZcHT0xMAcPHiRRgZGcHX11entcw2vOyXrVixAv/973/Rpk0bDB06VL3cx8cH58+f12s9sw0re/PmzRrPMzIykJCQAGNjY1SrVq3AL53a1DPbsLLNzc3h4uKSY7mLiwtMTU3zzdWmltmGl60XpXL9BBGVGltbW/Xt4UqyltmGlx0SEiIdO3aU5ORk9bLk5GTp3LmzzJ8/X2+1zDa8bHNzc7ly5YqIPL/16ovL38+ePStWVlZ6rWe2YWXn5uHDh9K1a1dZsWJFkWu1rWf2m5s9bdo06du3rzx79ky97NmzZ9KvXz+ZOnWq3mqZbXjZ+sBOBiID4+LiIufOnSvxWmYbXnbFihXlzJkzOZbHxMSIk5OT3mqZbXjZvr6+snLlShHR/NI4depUadasmV7rmW1Y2XmJiYmRqlWrFqtW23pmv5nZXbp0EWtraylXrpy0adNG2rRpI+XKlRMbGxvp2rWrxkOXtcw2vGx94HAJIgMzY8YMTJ48GcuXL4elpWWJ1TLb8LIfPXqEW7duoVatWhrLb9++jcePH+utltmGlz1lyhT0798f//zzD7Kzs7Fp0yZcuHABK1aswNatW/Vaz2zDys7LgwcPtBr3rE09s9/MbDs7O3Tv3l1jmbOzc6H2r00tsw0vWx848SORgalXrx7i4uIgInBxcclxz+YTJ07opZbZhpcdHByMvXv3IiQkBI0bNwYAHD58GBMmTIC/vz+WL1+ul1pmG142AGzfvh2zZ89GVFQUsrOzUb9+fUyePBlt27YtsFbbemYbTva3336r8VxEkJSUhJUrV8Lf3x9r1qzRWz2zDSv76dOnyM7OhpWVFQDgypUr2LJlC7y8vNCuXbt8c7WpZbbhZesDOxmIDMy0adPyXT9lyhS91DLb8LKfPHmC8ePHY9myZcjIyAAAGBsbY9CgQZg3b576zVDXtcw2rOzMzEzMmjULAwcOLNZfbrSpZ7ZhZQOAq6urxnOlUony5cujdevWmDRpEqytrfVWz2zDym7bti26deuGoUOH4sGDB6hRowZMTExw9+5dLFiwAMOGDdNLLbMNL1svSmxgBhERGaSUlBQ5deqUREdHS0pKSonVMttwsq2srCQhIaGILdVNPbMNK5uopJQtW1Y9V82SJUvEx8dHsrKyZN26dVKjRg291TLb8LL1QVny3RpERGRIrKys4OPjgzp16hT413Bd1jLbcLIDAgIQGRlZxJbqpp7ZhpVNVFKePHmivtJhx44d6NatG5RKJRo3boyrV6/qrZbZhpetD5z4kcjAZGVlYeHChVi3bh0SExORnp6usT45OVkvtcw2vOzU1FR8/fXXiIiIwO3bt5Gdna2xPj4+Xi+1zDa87Pbt22PSpEk4c+YMfH19c3RQdOrUSW/1zDasbKKS4u7uji1btqBr167Yvn07PvnkEwDPJ8S1sbHRWy2zDS9bHzgnA5GBmTx5MpYuXYqxY8fiq6++whdffKGeIGby5MkYNWqUXmqZbXjZffv2xd69e9G/f384OTlBoVBorB89erReaplteNlKZd4XZioUCmRlZemtntmGlU1UUjZs2IB3330XWVlZaNOmDXbs2AEAmDNnDvbt24e//vpLL7XMNrxsvSiVQRpEVGrc3Nxk69atIvL8/uCXL18WEZFFixZJ37599VbLbMPLtrW1lf379xe4na5rmW142UREb6KkpCQ5ceKEZGVlqZcdOXJEYmNj9VrLbMPL1jV2MhAZGEtLS7l69aqIiDg6OkpUVJSIiMTFxYmNjY3eaplteNkuLi5y7ty5ArfTdS2zDS+biIiI/j04JwORgalcuTKSkpJQpUoVuLu7Y8eOHahfvz6OHTsGMzMzvdUy2/CyZ8yYgcmTJ2P58uWwtLQscHtd1TLb8LJfvRf9CwqFAubm5nB3d4e/vz+MjIx0Xs9sw8omIqJCKO1eDiIqWRMnTpRZs2aJiMj69evF2NhY3N3dxdTUVCZOnKi3WmYbXnbdunXF2tpaVCqV1K5dW+rVq6fx0Fctsw0v28XFRaysrEShUIi9vb2UKVNGFAqFWFlZiYODgygUCqlWrZokJibqvJ7ZhpVNREQF48SPRAbuyJEjOHDgANzd3Ys8o7Y2tcx+87OnTZuW7/opU6bopZbZhpe9Zs0a/Pe//8XSpUtRrVo1AMDly5cxZMgQDB48GH5+fujTpw8cHR2xYcMGndYz27CyiYioEEq7l4OIStbevXslIyMjx/KMjAzZu3ev3mqZbXjZRCXFzc1NTp48mWP5iRMnxNXVVUREDhw4II6OjjqvZ7ZhZRMRUcHyvocPEb2RWrVqheTk5BzLHz58iFatWumtltmGl01UUpKSkpCZmZljeWZmJm7evAkAqFixIh4/fqzzemYbVjYRERWMnQxEBkZEctyDHgDu3bsHKysrvdUy2/Cys7KyMH/+fLz11ltwdHSEvb29xkNftcw2vOxWrVphyJAhOHnypHrZyZMnMWzYMLRu3RoAEBMTA1dXV53XM9uwsomIqGC8uwSRgejWrRuA57NnDxgwQOPuAFlZWTh9+jSaNm2q81pmG172C9OmTcPSpUsxduxYfPXVV/jiiy9w5coVbNmyBZMnT9ZbLbMNL/uXX35B//794evrCxMTEwDP/yrdpk0b/PLLLwAAlUqFkJAQndcz27CyiYioYJz4kchAfPDBBwCA5cuXo1evXrCwsFCvMzU1hYuLCz766COUK1dOp7XMNrzsF6pVq4Zvv/0WQUFBsLa2RnR0tHrZ4cOHsXr1ar3UMtvwsl84f/48Ll68CBFBjRo14OnpWag6XdQz27CyiYgoHyU5AQQRlb4JEyZIamqq+nlCQoIsXLhQwsPD9VrLbMPLtrS0lKtXr4qIiKOjo0RFRYmISFxcnNjY2OitltmGl01ERET/HhwuQWRgTp48iRUrVmDo0KF48OABGjduDBMTE9y9excLFizAsGHD9FLLbMPLrly5MpKSklClShW4u7tjx44dqF+/Po4dO6YxBEPXtcw2vGwAuH79On7//XckJiYiPT1dY92CBQv0Ws9sw8omIqIClHYvBxGVrLJly8qZM2dERGTJkiXi4+MjWVlZsm7dOqlRo4beaplteNkTJ06UWbNmiYjI+vXrxdjYWNzd3cXU1FQmTpyot1pmG172rl27xNLSUmrVqiXGxsZSt25dsbOzE1tbW2nVqpVe65ltWNlERFQwdjIQGRgLCwv1Zck9e/aUqVOniohIYmKiWFhY6K2W2YaX/arDhw9LSEiI/PbbbyVay+w3P7thw4by1VdfiYiISqWSuLg4efz4sXTq1El+/PFHvdYz27CyiYioYOxkIDIw3t7esmjRIklMTBQbGxs5ePCgiIgcP35cHBwc9FbLbMPL3rt3r2RkZORYnpGRIXv37tVbLbMNL1ulUsnly5dFRMTOzk59BU50dLRUrVpVr/XMNqxsIiIqmLK0h2sQUcmaPHkyxo8fDxcXFzRq1AhNmjQBAOzYsQP16tXTWy2zDS+7VatWSE5OzrH84cOHaNWqld5qmW142VZWVkhLSwMAVKxYEXFxcep1d+/e1Ws9sw0rm4iICsaJH4kMTI8ePdCsWTMkJSWhTp066uVt2rRB165d9VbLbMPLFhEoFIocy+/duwcrKyu91TLb8LIbN26MAwcOoGbNmggKCsK4ceMQExODTZs2oXHjxnqtZ7ZhZRMRUcEUIiKl3QgiInpzdOvWDQDw22+/ITAwUOPuAFlZWTh9+jQ8PT0RHh6u01pmG172C/Hx8UhJSYGPjw+ePHmC8ePHY//+/XB3d8fChQtRtWpVvdUz27CyiYioYLySgYiIdMrW1hbA879OW1tbw8LCQr3O1NQUjRs3xkcffaTzWmYbXvYLbm5u6v+3tLTEjz/+mOt2a9asQadOnXJcHaFNPbMNK5uIiAqhJCeAICIiwzFhwgRJTU1VP09ISJCFCxdKeHi4XmuZbXjZhWVtbS1xcXGlUs9sw8omIjJknPiRiIj04uTJk1ixYgUA4MGDB2jcuDFCQkLQpUsX/PTTT3qrZbbhZReWaDlCVJt6ZhtWNhGRIWMnAxER6cXJkyfRvHlzAMCGDRvg4OCAq1evYsWKFfj222/1Vstsw8smIiKifw92MhARkV48efIE1tbWAJ7f9rJbt25QKpVo3Lgxrl69qrdaZhteNhEREf17sJOBiIj0wt3dHVu2bMG1a9ewfft2tG3bFgBw+/Zt2NjY6K2W2YaXTURERP8e7GQgIiK9mDx5MsaPHw8XFxc0atQITZo0AfD8L9X16tXTWy2zDS+biIiI/j0UwlltiIhIT27evImkpCTUqVMHSuXzfu2jR4/CxsYGNWrU0Fstsw0vuzBq166Nv/76C87OziVez2zDyiYiMmTsZCAiIiIiIiIinTAu7QYQERERFVWZMmWgUCgKtW1ycrJO65ltWNlERFQ07GQgIiKi184333xTavXMNqxsIiIqGg6XICIiIiIiIiKd4N0liIiI6LUXFxeHL7/8En379sXt27cBAOHh4Th79qze65ltWNlERJQ/djIQERHRa23v3r3w9vbGkSNHsGnTJqSkpAAATp8+jSlTpui1ntmGlU1ERIUgRERERK+xxo0bS0hIiIiIqFQqiYuLExGRo0ePSsWKFfVaz2zDyiYiooLxSgYiIiJ6rcXExKBr1645lpcvXx737t3Taz2zDSubiIgKxk4GIiIieq3Z2dkhKSkpx/KTJ0+iUqVKeq1ntmFlExFRwdjJQERERK+1d999FxMnTsTNmzehUCiQnZ2NAwcOYPz48QgODtZrPbMNK5uIiAqhtMdr/L/27h40ir0L4PCZ9UaR4DeC2SIWKgZBg1iJpLAWBMUgiCjBxoiksBJsRBHEYquAjRaWQgqt1MaPQjsJqCAIsZAoGDCCGgnB3f/bXfAN3jF3d1zmzvNAIDubw2+rhT2ZnQEAaMfCwkI6duxYqtVqKcuy1NPTk2q1Wjp+/Hj68eNHofPa1WoDkC9LKaVuLzoAANo1NTUVk5OT0Wq1Yvfu3bFt27Y/Nq9drTYAv2bJAAAAAHTEX91+AQAAS3Xu3Lnf/ttGo9HRee1qtQFYGksGAKB0Jicnf3r8/PnzaDabsX379oiIePPmTSxbtiz27NnT8XntarUBWBpLBgCgdB49evT3741GI1atWhW3bt2KdevWRUTE58+fY2RkJIaGhjo+r12tNgBL1N3rTgIAtKder6dXr14tOv7y5cvU19dX6Lx2tdoA5Kt1e8kBANCOL1++xMePHxcdn5mZia9fvxY6r12tNgD5LBkAgFI7dOhQjIyMxMTERExPT8f09HRMTEzEqVOn4vDhw4XOa1erDcBv6PapFAAA7Zibm0ujo6NpxYoVqVarpVqtlpYvX55GR0fTt2/fCp3XrlYbgHxZSil1e9EBANCuubm5mJqaipRSbN26NXp7e//YvHa12gD8mrtLAAD/Cb29vbF+/frIsuxffWBsZ167Wm0Afs01GQCAUmu1WnHp0qVYs2ZNbN68Ofr7+2Pt2rVx+fLlaLVahc5rV6sNQD5nMgAApXbhwoW4efNmXL16Nfbt2xcppXj69GlcvHgx5ufn48qVK4XNa1erDcBv+JMXgAAA6LS+vr509+7dRcfv3LmT6vV6ofPa1WoDkM/XJQCAUpudnY2BgYFFxwcGBmJ2drbQee1qtQHIZ8kAAJTa4OBgjI+PLzo+Pj4eg4ODhc5rV6sNQD63sAQASu3Jkydx4MCB6O/vj71790aWZfHs2bN49+5d3Lt3L4aGhgqb165WG4B8lgwAQOm9f/8+rl+/Hq9fv46UUuzYsSPOnDkT9Xq98HntarUB+GeWDABA6c3Pz8eLFy9iZmZm0W0IDx48WOi8drXaAPwzt7AEAErt/v37ceLEifj06VP8//9OsiyLZrNZ2Lx2tdoA5HPhRwCg1M6ePRvDw8Px4cOHaLVaP/38zgfGdua1q9UGIJ+vSwAApbZ69eqYnJyMLVu2/PF57Wq1AcjnTAYAoNSOHDkSjx8/7sq8drXaAORzJgMAUGrfv3+P4eHh2LhxY+zcuTN6enp+en5sbKywee1qtQHIZ8kAAJTajRs34vTp07Fy5crYsGFDZFn293NZlsXbt28Lm9euVhuAfJYMAECpbdq0KcbGxuL8+fNRqy39m6DtzGtXqw1APu+sAECpLSwsxNGjR//1B8Z25rWr1QYgn3dXAKDUTp48Gbdv3+7KvHa12gDk+6vbLwAAoB3NZjOuXbsWDx48iF27di26kF+j0ShsXrtabQDyuSYDAFBq+/fv/+VzWZbFw4cPC5vXrlYbgHyWDAAAAEBHuCYDAAAA0BGWDAAAAEBHWDIAAAAAHWHJAAAAAHSEJQMAAADQEZYMAAAAQEdYMgAAAAAd8T9EvtYK3G9CuwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop = pd.read_parquet('./expert_info/full_w_table.parquet')\n",
    "\n",
    "w = prop.drop(columns =['fp_beta', 'fn_beta', 'alpha'])\n",
    "w.index = pd.Index(new_names)\n",
    "w = w.div(np.sqrt(np.square(w).sum(axis=1)), axis = 0)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10, 9))\n",
    "sns.heatmap(w.T, robust=True, cmap = 'coolwarm', vmax = 1, vmin = -1, cbar_kws = dict(use_gridspec=False,location=\"top\"))\n",
    "plt.title(\"Normalized Weight Vector Heatmap\")\n",
    "plt.savefig(\"w_heatmap.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(5, 5))\n",
    "sns.scatterplot(data=fpr_disparity, x='target_fpr', y='target_tpr', hue='group', style='group',  palette=[\"C0\", \"C2\", \"C3\", \"C1\"], s = 70).set(title = 'Recall vs FPR')\n",
    "\n",
    "plt.ylim([0.3,0.8])\n",
    "plt.xlim([0.01, 0.09])\n",
    "plt.savefig(\"Target_Performance.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aJVpikCxVFv4X9T/surNLV+br7IuRDUfqnidnJWPI/iFIU6ZZIkRCCLFKlGiJQTiOQ3fP7hDxcjZ4rl+5Pjb33IxPmn2C2a1n6+o1q9oMYr7YUmESQojVoVHHxCBSgRR+Ln7Y2GMjVl1ehXXd1ukGPvWp0wcAcOnpJcwLmkerQxFCSB4cY6zCTXpMS0uDk5MTUlNT4ehIo2RLIludDQEngJAv1CvPVGWCAwc7oZ2FIiOEkJIpq1xALVpSIlKBtMByasUSQkjB6B4tIYQQYkaUaAkhhBAzokRLCCGEmBElWkIIIcSMKNESQgghZkSJlhBCCDEjSrSEEEKIGVGiJYQQQsyIEi0hhBBiRpRoCSGEEDOiREsIIYSYESVaQgghxIwo0RJCiBnI1XK8uTmalmmRrc62UETEUijREkKIiWUqM7E4cjFSFam6ZKtlWrxSvMKyyGXIVGVaOEJSlmibPEIIMaFMZSYmHZ+EyKRIRD2NwrZe2+AodsQrxSt8uP9DPEp/hCeZT7Cq0yraXrKCoBYtIYSYiFwtx6GHhxCZFAkAiEuLw7ADw3Dt2TVdkgWAs0/OIuJRBORquQWjJWWFEi0hhJiIRCBBsFcwxjQeoyvLTba5SRYAJjSbgE4enSARSCwRJiljlGgJIcSEZEIZxjQeg48af1Tg8fHNxuPDBh9St3EFQomWEEJMTCqQYqjfUHDg9Mr5HB8f1P8AdgI7C0VGLIESLSGEmFDu6OKQAyFg0J/eo2EahOwPwSvFq3xTf0j5RYmWEEJMJDfJ5h34BADejt66nx+kPcCwA8Mo2VYglGgJIcREFBoFbjy/gYSMBF3ZhGYTsK/fPr17to/SH+H2i9uQa2jUcUVAiZYQQkxEKpAiwDUAKzquAI/jYUKzCfiwwYfg8/gY3Xg0Pmr8EfgcH992/hZNqjaBVCC1dMikDHCsAvZdpKWlwcnJCampqXB0dLR0OISQciZLlYWkzCS4ylz1RhdnqjKRnJUMVztX2AlpQJSllVUuoJWhCCHExOyEdvBy8gKP0+80lAll8HT0zFdOyjf6v00IIWZQWDKlJFvx0P9xQgghxIysItGuW7cO3t7ekEgk8Pf3x6lTpwqtO2LECHAcl+/RsGHDMoyYEEIIMYzFE214eDimTJmC2bNnIyoqCu3bt0evXr0QHx9fYP1vv/0WiYmJusejR49QuXJlDBw4sIwjJ4QQQopn8VHHrVu3RosWLbB+/XpdmZ+fH/r374/FixcX+/rff/8dAwYMQFxcHDw9PQ16Txp1TAghpKxygUVbtEqlEpcvX0aPHj30ynv06IGzZ88adI5NmzahW7duRSZZhUKBtLQ0vQchhBBSFiyaaJ8/fw6NRgNXV1e9cldXVyQlJRX7+sTERBw4cABjxowpst7ixYvh5OSke3h4eJQqbkIIIcRQFr9HCwAcp7/DBWMsX1lBwsLCUKlSJfTv37/IerNmzUJqaqru8ejRoyLrE0IIIaZi0QUrqlSpAj6fn6/1mpycnK+V+ybGGDZv3oyQkBCIRKIi64rFYojF4lLHSwghhJSURVu0IpEI/v7+OHLkiF75kSNHEBQUVORrT5w4gXv37mH06NHmDJEQQggpFYsvwRgaGoqQkBAEBAQgMDAQGzZsQHx8PMaNGwcgp9s3ISEB27Zt03vdpk2b0Lp1azRq1MgSYRML08qL3/WEJ5GUQSSEEFI0iyfaQYMGISUlBfPnz0diYiIaNWqE/fv360YRJyYm5ptTm5qaij179uDbb7+1RMjEStxu1rzQY75Xo8owEkIIKZzF59FaAs2jtX1aubzYREstWkJIUSrEPFpCCCGkvKNESwghhJgRJVpCCCHEjCjREkIIIWZEiZYQQggxI4tP7yHEWDSFhxBiCyjREptEU3cIIbaCuo4JIYQQM6JESwghhJgRdR0TYqVoPWdCygdKtIRYMVrPmRDbR13HhBBCiBlRoiWEEELMiBItIYQQYkaUaAkhhBAzokRLCCGEmBElWkIIIcSMaHoPIVaMpvAQYvso0RJipWgxCkLKB0q0hJAykZatgoDPQa7SQirkQ6NlsJfQRxAp/+i3nBBiVpkKNWIS07Dm37s4E/scjAFiAQ+9G7tjao96qGovhljIt3SYhJgNDYYihJhNpkKNfVEJeO+Hczh9LyfJAoBCrcW+qAR0X3USMUnpUKg1lg2UEDOiREsIMZvnGQp89ceNQo9nqzQYvjkSag0rw6gIKVuUaAkhZpGpUGN9RKyuFVuY1GwVjsY8hVZLyZaUT5RoCSFmodEyHLuVbFDdf64nIkOhNnNEhFgGDYYipDxQZes/F0otE0ceHAcoNVqD6irUWlB7lpRX1KIlpLxY6JbzsBJaxlC3mr1Bdeu7OUIsoI8jUj7RbzYhxCwcJUKM7VC72HocB4xq6wVJBZ/ik6nKRLoyHRqt/gjsDGUGMpQZFoqKmAIlWkIqoLRsFV5lKZFpxvuiHMchqE4VBHg6F1lvVFtvSEWUZP8X9T+89+d7eKl4qUu2GcoMTDk+BeOPjqdka8Mo0RJii1TZ+g956utj8tT8xwFotQyZCjWO30rG9N3/4ZNfruDrP27g5pNUZMjNk3BlYgG2jGyJtxq7g8fpH5MK+Zjc1Qeh3evBQSI0y/vbgtwkuz1mO55kPsHQf4bipeIlMlWZmHJ8Ci4kXcCV5CuUbG0YDYYixFYVdj92pa/+89lJ0Gi1eJqmwKAN5/Dohf7AqT1XEhBYxwUbQvzNkvAcJEIsebcx5vdriL//S8TzDAW8q8jQvYEreBwHmZg+hqpIq+h+fpL5BB/8/QFcJC6IfhGtK68kqQSO4wp6ObFy9BtOyp0spRpKdc5oV5GABzsR/ZpnK7UYsO4sktLkBR4/F5uCT7ZfwQ8f+ptl/eHcBD48yMvk57Z1MqEMH9T/AACw5soaAMDTrKd4mvVUV6dLrS5Y1G4RZEKZJUIkpUSfQKTcyJCrkSZXYePJ+7gQ9wIAEODljI871IaTVAj7Cto9yQDsufK40CSb6/S953iSmo16EoeyCYzoyIQyDK4/GK8UrxB2M0zvWItqLbC43WLYCe0sExwpNUq0pFzIUKgRdvYBVhy+rVcenZiGbeceYkq3uhjTvjbsy1M35eyk1z/LU193GU+9DUicdIcYA7aff2jQKbecicNXbzegXgAL0DItbjzPv1zlk8wnyFJnQcKXgMejYTW2iP6vEZun0mjxb/TTfEk2rzX/3sX+64lQqQ1bQMHqCaX6jzyJFRInvWNZTIinxbRmcyWmyqGidYfLXO7o4ktPL+U7lpSZhKH/DMULxQtoteXk97eCoURLbJ5CrcXqf+8UW++7o3cNXqmoPNFqGRylhnWbO0mF4NOnQplSqBWYdmIaLiRd0JV1qtkJYxqN0T3PHY2crcku6BTEytGfFLF5ianZeJiSVWy9xy+zEf+i+HrljUjAw4AWNQyq+2FrT9iLK+a9bEvRMi2GNRwGAZfTXd+lVhcs6bAEY5qMwZQWU3T1+vn0s1CEpLToRgyxeSkZSoPrPs9QmDESC8t7zzYPiZCPkUHe2HQqDpnKwvd9rVvNHn7VHc0VHSmEVChF86rN8X2377Hnzh7MbztfN7o4dzSyQqPA8IbDadSxjaJES2yei73I4LpV7MVmjMSCitlEwE7Mx/YxrfHhTxcKTLaeLnb49aM2kFXwFZosJTfZtqjWAhKBRFeed+oPJVnbRYmW2Dx3Jyk8XeyK7T6u6SyFR2XTTpHIUmVBwBOAx/Eg4L3+c8pUZYIDZzVTMsQCPhpUd8TpmV3wy/mH2HslAWlyFdydpBge5IlejdwhFfJpQQQLkhbyZYkSrO2je7TE5okFPHzWrV6x9SZ3rQuxCUf6ZKmycD7xPHrt7YVnWc+g1uYsY5ipysTWm1sx4uAIpCnTTPZ+pSUW8OFsJ8JHHWpj3/ggHJvaCdtHt8Y7zWtAJhaA9+YaiYQQk6BES2yekM9DN79qmB7sW2idKd3qoldjdwhNtBVbbpINjQhFclYyBv8zGM+ynkGulmPrza1Yf209Yl7EYMyhMVaVbIGchOskFcFRKoSTnRB8mptJiFlR1zEpF+wlQgwP8sKA5jWw4dR9ROauDOXpjLEd68BJIjDpYhUMDK8Ur6BhOfc7U+QpGPzPYAS4BuDQw0O6elnqLGi0GjDGqFuWkAqKY4xVuNnpaWlpcHJyQmpqKhwdaZRleVNWax1nqjJx6MEhzDk7p8Djno6e+LnXz6gkpsXgCbFGZZULqEVLyh07kQB2hg9ENppMKEOwVzCy1dlYErlE71hVaVVKsoQQAHSPlpBSe571PF+ZUquEXCPXdS0TQiouSrSEGCl3dPFPN37KdyxVkYoh/wzBs+zXo5EJIRUTJVpCjJCpysQv0b9g/bX1urJaDrUwvOFw3fPn2c8x+O/BeCF/gQo4FIIQ8v8o0RJiBD7HR+vqrSHm56w0VcuhFn7u/TPGNxuPeUHzdPXqOteFTCij+7SEVGA0GIoQI0gEEvg6+2JT8CbMPzcfG3tshLPYGRzHIdgrGABwMO4gVndeTSv7EFLB0fQemt5DSkGulkPAE4DP6S9fmKnKBI/jQSooeg1iQojl0PQeQmxA3gXg86JWLCEkF92jJYRYjUxVJrJU+TeHyFJlIVOVaYGICCk9SrSEEKuQqcrEwbiDWBK5RC+pZqoyseLSCvwV+5dJk226XIVXWUq8ylJCo9Wa7LyEvIm6jgkhFpebZOeem6sr+7zV5wCAFZdWYPed3bryPnX6lKprPlOhRkxiGn46HYdHL7JgLxZgQIuaeKuJO0R8HkQm2niCkFyUaAn5fxlyFbQA4p7ltJpqV5WBAwd7Cf2ZmJuAE2DP3T265/vu7YNCo4CAJ8CfsX++Lr+7D/19+hv9PulyFUaFXcTFBy/1yi/EvcDiAzHY8VEb1Kkqg0jAN/o9CHmTUaOOa9eujYsXL8LFxUWv/NWrV2jRogXu379vsgDNgUYdk7w0Wi1eZauw4O9o7P8vCUpNTjeiWMDD203d8eVbDeAoEYJP+7WaVboyHWOPjMWN5zcKPO5X2Q8/Bf8ER5Fxf7PpchXG/3IFJ+/mXzIzl6NEgBPTO8NZVgaLZROLK6tcYFQfyYMHD6DR5F/DVaFQICEhodRBEVKWXmWp0Oe70/g96okuyQKAQq3FnssJ6LP2NNLkKgtGWDE4iBywofsG+FX2y3esnnO9UiVZAHieoSwyyQJAmlyNTWfiIFfRGtXEdErUJ/bnn6+7cA4dOgQnJyfdc41Gg6NHj8LLy8tkwRFibulyFb764waepMoLrfP4ZTbm/xWNBf0awl4iLMPoKh4+x0dNh5qIeRGjV17DvgYEnPFd+Aq1BtvOPTCobvjFRxjXoQ5A/6uJiZSo65jHy2kAcxyXb+1WoVAILy8vrFy5Em+//bZpozQx6jomuV5lKaGUZ8Exz33YVLkarZed0asnFvBwcXY3OErp09dcckcX5x34lNc7Pu/g81afGzUQKkOuwsy91/H3f4nF1uU4IGZ+T0iEdJ+2vLPKBSu0/z8E3tvbGxcvXkSVKlXMEhQpf7TywluMefEkBS8AYS7RiWlo4S6BZFmN14Uz8t/+UKi1uJecgRaezmUYnRVSZes/F5pm5auCkmydSnXAAw93X90FkDNACoBRyVbI58HF3rD7ri4yEdTaCrdgHjEjo/pi4uLiTB0HqQBuN2te5HHfq1HGn/zNBAAYlARKMhSQgT58AQAL3XL+OzvJpKeVq19/Gcsd+MSB0xsglbdOSYiFfIS08cLWsw+Lrfuef03wadwbMSGjb3ocPXoUR48eRXJysq6lm2vz5s2lDoyQEstNAIDBScDP3RGAsth6Ah6HOlXtjQyMFEcmlOHLNl8CAGJfxeoNfNrQfQPGHhkLD3sPzAmaY/QcWldHMdrUrozz918UHoeIjzHta0MqoildxHSM+m2aN28e5s+fj4CAALi7u9MWYMT2/H8L2FkEqDLT9I/JU3Hr6466p6lyNeYdiKXpPWaWm2wZY7AXvf5S4yBywMbuG8FxXKkWqnCQCPFjSABCNl3Af49T8x8XC7B9TGs40LxpYmJG/Ub98MMPCAsLQ0hIiKnjIaTsLHQDB+DNO3eS7xroF3z+BPP6NoJDRRxx/GaXvDy14J9zlfKebWGJNG/iLQ0nqRA7PmqDK/Ev8dOpODx+mQWZWIB3mtfAu/41IRbwIKbFKoiJGZVolUolgoKCTB0LISWTNwm8+aFvwiQgFvAgEFXAJJsrb5d8Xit99Z+b+J6tucjEArSvWxVNalbSzZ6QiQUQ8mnpRWIeRv1mjRkzBr/++qvJgli3bh28vb0hkUjg7++PU6dOFVlfoVBg9uzZ8PT0hFgsRp06dei+cDmnlcvzPRiQkwQWuuX/0F/p+/pYYYnCQBwAAX0IlztOUiEq2YlQyU5ESZaYlVEtWrlcjg0bNuDff/9FkyZNIBTqf9tftWqVwecKDw/HlClTsG7dOrRt2xY//vgjevXqhejoaNSqVavA17z//vt4+vQpNm3aBB8fHyQnJ0OtVhtzKcSGvDlq2ffiWZTqrmluC0yeqp+op94GJE4Fv4YQQkrIqET733//oVmzZgCAGzf01yUt6cCoVatWYfTo0RgzZgwAYM2aNTh06BDWr1+PxYsX56t/8OBBnDhxAvfv30flypUBgFajshGlmr5jakV1I0ucTDY/tFzI2yWc90sJfSEhxCBGJdrjx4+b5M2VSiUuX76MmTNn6pX36NEDZ8+eLfA1f/75JwICArBs2TL8/PPPkMlk6Nu3LxYsWACptOAPR4VCAYVCoXuelpZWYD1iPuZYiEKTng5MfZDzRJEK3v+avj5IScA06AsJIaVWqnHs9+7dQ2xsLDp06ACpVArGWIlatM+fP4dGo4Grq6teuaurK5KSCh5Ycf/+fZw+fRoSiQT79u3D8+fP8emnn+LFixeF3qddvHgx5s2bZ/iFEZtwr0uw7mffi298MaMkQAixEkYl2pSUFLz//vs4fvw4OI7D3bt3Ubt2bYwZMwaVKlXCypUrS3S+N5NzUQlbq9WC4zj88ssvuk0NVq1ahffeew/ff/99ga3aWbNmITQ0VPc8LS0NHh4eJYqRlHM2MmLWKtC/FSElYtRQu88++wxCoRDx8fGws7PTlQ8aNAgHDx40+DxVqlQBn8/P13pNTk7O18rN5e7ujho1aujtHOTn5wfGGB4/flzga8RiMRwdHfUehOgIpfkfpGD070RIiRmVaA8fPoylS5eiZs2aeuV169bFw4fFryWaSyQSwd/fH0eOHNErP3LkSKHzdNu2bYsnT54gIyNDV3bnzh3weLx88ZCKQ5OeDjY7Kae1RS0uQogVMarrODMzU68lm+v58+cQi8UlOldoaChCQkIQEBCAwMBAbNiwAfHx8Rg3bhyAnG7fhIQEbNu2DQAwZMgQLFiwACNHjsS8efPw/PlzTJ8+HaNGjSp0MBQpH4obtcw0AFfGu/8QQkhxjGrRdujQQZf4gJx7rFqtFsuXL0fnzp1LdK5BgwZhzZo1mD9/Ppo1a4aTJ09i//798PT0BAAkJiYiPj5eV9/e3h5HjhzBq1evEBAQgKFDh6JPnz747rvvjLkUYiOyOQGyOQGUfCEywYdaIAJPIsn3IIQQa1Oijd9zRUdHo1OnTvD398exY8fQt29f3Lx5Ey9evMCZM2dQp04dc8RqMrTxu+3IVqrxIlOJ74/H4q9rT5CuUKO6kwRDWtfCsEAv2In4tGoTIcQoZZULjEq0AJCUlIT169fj8uXL0Gq1aNGiBcaPHw93d3dTx2hy5SXRWutm6qaSpVTjXGwKxm2/DJUm/69pVQcx9n0aBHcnCfg8SraEkJKx+kRry8pTojVkM3VbTbRJqXK0X3aswCSby6OyFAcnd4BMTFubEUJKpqxygdGfTnK5HP/991+BG7/37du31IGRii1LqcaGk7FFJlkAePQiG1HxL9GubtUyiowQQkrGqER78OBBDBs2DM+fP893jOM4aDSaUgdGKja1luHv/xINqrvr0mM086gE+4q4XywhxOoZdWNrwoQJGDhwIBITE6HVavUelGSJKfA5DllKw36XMpVqaCvcDRBCiK0wKtEmJycjNDS00NWbCCkxVbbeQ8qpUNPZsHnRnpXtIKKRx4QQK2XUp9N7772HiIgIE4dCKrw8G7VzHDAiyMugl41q5w2JiG/e2AghxEhG3aP93//+h4EDB+LUqVNo3Lhxvo3fJ02aZJLgiOUYMnXInKOZOQB9mlbHjyfvI+55ZqH1+jRxh5PU/Pdms5RqqDUMHAdotYCDRAAer1TbzhNCKgijEu2vv/6KQ4cOQSqVIiIiQm+nHY7jKNGWIXNupl7U1KGy2MTdTsTHb+MCMXxzJG4+yb+H8DvNq2NB/8awN+PUniyFGmlyFX48cR8HbyYhS6lB7SoyjGrnjS71q9G0IkJIsYyaR+vm5oZJkyZh5syZ4NngQgHlZR6tORU3R7fU83NV2frP5anASt/Xz/Ns3M4AqDUM8w7EIk2uhoezFMOCclaFcjDjSONMhRoRt5MxeedVqAsYbdXA3RE7PmoNJzuR2WIghJiPVc+jVSqVGDRokE0mWWJFFroVfixP0uUACGcn4au3G0ClZRALeBCWweCnpFQ5Ju28Ck0hQ5qjE9PwyS9X8MOH/nAsg+5rQohtMurTavjw4QgPDzd1LIQUSSzkw14sKJMkmy5X4dujdwtNsrnOxqbgVbbK7PGQEnhjBHu+3hNCyphRLVqNRoNly5bh0KFDaNKkSb7BUKtWrTJJcIRYipDPw8Ebhu1rG37xEUK716X1lq1J3t4S2p+YWJhRifb69eto3jzn/t2NGzf0juUdGEVIkfJ+ABZxj9ZSlBpt8ZUAvMpSQq1loKm8hJCCGJVojx8/buo4SEUjLGYxColT8XXMSMsYHKUCpGWri63rUdkOQmrNEkIKYVSiDQsLw6BBgyCVWu6DkJhfWUzhsVYcOAwK8MDGU3FF1uPzcurRnFoLKmgEe1HPAYt+iSMVj1HTe9zd3ZGZmYmBAwdi9OjRCAoKMkdsZkPTe6xQQQNWLPxhmJKhQOeVEUW2aj9sUwuf96xv1mlGpBiq7KJHsL9pdpLFf7eIdSirXGBUf9fjx4+xfft2vHz5Ep07d0b9+vWxdOlSJCXRoINyx0yjN7OUamQq1Dh26yl2RsbjxP10ZGqFyGbCnA9BK/ggdJAIsPeTIFR1EBd4/D3/mpjZy4+SLCGkSKXe+D05ORnbt29HWFgYbt26hZ49e2L06NHo06eP1c6zpRZtCeRtLZioJZCpUOPHk7HYfPoBMhSvW4uOUgHGdaiDYUFeZl3tqSRUGi2Uai1O33uOfVEJkCs1qF1VhtHta8NRIjAoyaZlq6BUa3HsdjKylRr4uTuiYXVHCPkcRAJao7nUqEVLjGTVC1bkVa1aNbRt2xa3b9/GnTt3cP36dYwYMQKVKlXCli1b0KlTJxOEScqLDLkac/+6id2XH+c7lpatxrJDt5GcrsDUHvWsoqUo5OcsjtGjgSuCartAC0Ak4EEqLD5BMsaQmq3C1N+u4ditZOT9SuvuJMH8fo0QWMfFar5U2DQrH8FOKjajm5xPnz7FihUr0LBhQ3Tq1AlpaWn4+++/ERcXhydPnmDAgAEYPny4KWMl5cDjl1kFJtm8ws4+wMusnEUgtHK5QQ9z4zgODlIhnKTCYpNshlyFV1lKvMxS4cy9FDxPV+DNfqPEVDk+2nYJp+48g1xFeziXSu6thtzHm0k1dwR73gchZcior9J9+vTBoUOHUK9ePXz00UcYNmwYKleurDsulUoxdepUrF692mSBkjJS1AjOUo7eTJersP5ErEF1N5yMxRe9/CBB0ZsbAMaPjlZrtJCrNMjNgRzHQcznQSgw7vtnhlyFJ6ly/HAiFlcevgQAtPSujAX9GyE1W4UJv0Yh9Y1VpL7Ydx2nP+9i1PsRQmyDUYm2WrVqOHHiBAIDAwut4+7ujri4oqdGECtV2P2uvN1xQIlX3NFoGW4k5N+FpyDXH6dCqdFCYqbb/JkKNX6PSsBPp+N02/DVdJZiRJAXPmhZC/aSkv1pZCjUWHnkDraceaBX/iAlC79deoxxHWtj26hW+GDDeWTnacG+zFLhbGwKujdwLfU1EUKsk1GJdtOmTcXW4TgOnp6expyelGMCA+eb8ks4LzVDocbNhFREPXoFIZ9D1/quqCwTwUEiyLdaWYZCjY+2XsK5+yl65Y9fZuObf2Lw17Un2D6mtcH3iBVqDX679Chfks3rhxP34eFshxFBXvla9RcfvEDX+tVoLq4p0bKLxIoY3V44ceIE+vTpAx8fH9StWxd9+/bFqVOnTBkbKWdEAh461KtiUN1OvtUgFRk2IlfLGHqsOoFBG85jyYFbWPB3DDqtiMCILZF4miaHOs9SitlKNdZHxOZLsnlde5yKxftjkKkoflUoIGcLv/URxXeJ/3jyPj5o5YE386mIz4FWLjWhN+/H0j1ZYmFGJdrt27ejW7dusLOzw6RJkzBhwgRIpVJ07doVv/76q6ljJGVtdtLrx9Tbr8un3tY/VkJ2IgHGtK9dbFIR8DiEtPGE2MCpL0q1Fk9S8w+IuhL/Cn3WntG7L6plwC8XHhZ7zr1RCdAaOPPtXnIGktMVxdaLf5GFlAwl6rk66JX3bOROa4QTUo4ZlWgXLlyIZcuWITw8HJMmTcLkyZMRHh6OJUuWYMGCBaaOkZSlokZwmmD0pr1YgG/6NSr0OMcBKwY2hcjIAUlvepahwMrDd3St04cpWXiVVfy2dnKVFtcTChj8VYCUzOKTbK4XWUq96Tx1q9nD08XO4NcTQmyPUZ9m9+/fR58+ffKV9+3blwZAkSLJxAL0b14D20e3RnOPSnrHWnlXxq6xgejewBUyE84t3ReVoPtZoTZ8Ko1cVfzuPdlKNZykIoPPWc1BrGthO0mF+Gl4AOwM7CInhNgmoz7NPDw8cPToUfj4+OiVHz16FB4eHiYJjFgREw8skYkFaOvjgiY1W0Gl0SI1W4VKdiIIeBwcpfkHIBU2fUejZVBptEiTF30vNVulwassFWRiAWpVtgPHId+81oL4VLMvto5GC7g6ilHTWYrHL4teorKeqz2kQj7iX2ThXf8amBFcH852ItrHlpByzqhEO3XqVEyaNAlXr15FUFAQOI7D6dOnERYWhm+//dbUMRJLMtNAEo57nVRd7AteSxgAeBJJocd2RcZj5t7rBr2fgJ9zD1TI56G9TxWcvPu8yPpNazqhUgFJP198HLDr4iN82skHX+wrOpapPXxR2U6EK191BwODvdjyK18R0zJ08ZSifq9J+WNUov3kk0/g5uaGlStXYteuXQAAPz8/hIeHo1+/fiYNkJDCdK5fzaB6HpWluq5oR6kQc/s2xFvfndabz5qXkM9hQf9GBi2NqNIyRNx+hq/7NMBn3ethzb938rWWeRzwRW8/+FSzh4NUaLL7z8Q6mWuBFWK7jL4R9s477+Cdd94xZSyElIhUyEf7ulVwqpjW6Zh2tSHOk9yqV5JizydB+OSXy3iYkqVXt0YlKb4b3Aw+1ewNmtdqL+bjw0BPDN8ciRXvN8WxqZ3w64V4RMW/BMcBLb0q44OWtXA5/iUcJZRkCamISr17jy2i3XvKj5QMBfp9f6bQ+6Pd/Krh2w+a5xtcpdJooVBrcedpOo7//4L/7epWQeMaThDyeSVKiJkKNfp/fwZ3kzPgXUWGD1p6oM7/39+9nZSOHZHx6FK/GqYH+1rFRgnEfLRyuUEtWuo6tg5llQsMTrTOzs4Gz/V78eJFqYIyN0q05YdGy3LWUI6Ixc6Lj3Qjer2ryPBRe2/0bVbDoC5gxlip5rK+zFLik+2Xcf6+/u++kM9heJAXPutWz6QjqYl1okRrW6xum7w1a9aYLQhCjMXncahkJ8KUbnUxqWtdZCk14PM4iPgcJEI+BHzDWqalXTDC2U6EDSEBeJmlRPjFR0iTq+FdRYb3WtQEnwdKsoRUYAb/9dOWd8SaSUU5v8qWTGiOUiEcpUJM7VEPGm1Oa5ZWfCKEGP2ppNFosG/fPsTExIDjOPj5+aFfv34QCOibO6nY+DweDGxIE0IqAKOy4o0bN9CvXz8kJSXB1zdn67Q7d+6gatWq+PPPP9G4cWOTBkkIIbaCpu+QNxmVaMeMGYOGDRvi0qVLcHZ2BgC8fPkSI0aMwNixY3Hu3DmTBklsh1ylgUKlAcdxkIn5tOoRqVBokBMpiFGJ9tq1a3pJFsgZlbxw4UK0bNnSZMER25EuVyFLqcGW03G4mZgGAY+H4IaueLtpdQh4OQOTCCGkIjIq0fr6+uLp06do2LChXnlycnK+9Y9J+ZepUGPtsXvYeOq+3qpIx28n45t/YrBxmD+aeTgbvL8sIYSUJ0b16y1atAiTJk3C7t278fjxYzx+/Bi7d+/GlClTsHTpUqSlpekepHzLUqqx9ewDbDh5v8CF+jMUagzffBEJr4pecN/WyJUaZCrUiLidjM2n47DnymO8ylIiXV78FnyEkIrFqJWheHnuu+VOX8g9Td7nHMdBozF8W7KyQgtWmE6mQo3Wi44iQ1H0DjrBDd2w/L0mBe7OY2syFGrsi0rAqsO38TLP3rYCHoe+zapjXt+GtAIUITbA6hasyOv48eOmjoPYqJN3nxWbZAHg35in4BuwdrC1y1Co8cv5h1h84Fa+Y2otw94rCYhPycLWUa1okQpCCAAjE23Hjh1NHQexUfFvLMpfmNylEm09+ag1Wqw4fLvIOpcevkTEnWfo1dDNoI0JCCHlm9FzL06dOoUPP/wQQUFBSEhIAAD8/PPPOH36tMmCI9bPxV5kcF07G0+yKo0WOy8+gkpT/N2WjSfvI92Alj4hpPwzKtHu2bMHwcHBkEqluHLlChQKBQAgPT0dixYtMmmAxLp1b+AGIb/4Vlsr78qAje8TpVBpcf1xqkF1YxLTDPp3IYSUf0Yl2m+++QY//PADNm7cCKHw9aCPoKAgXLlyxWTBEevH44ABzWsUW29Kt7q2PxCKg8H3mQU8rsBR2ISQiseoRHv79m106NAhX7mjoyNevXpV2piIDXGQCPHV2w3RzqdKgcc5DpjbtyGa1KxUtoGZgZ2Qj16N3Ayq275eVWiNzLTZ2QVPhSqsnBBi3YxKtO7u7rh3716+8tOnT6N27dqlDorYFnuJAD+G+OPn0a3Q1scFVexFcHeS4IOWHjgxrRPe869p0J6w1o7H49ChXlVUlhV/X3pCZx+jpvikp6cjPDwc6enpeuVpaWkFlhNCrJ9Rn34ff/wxJk+ejM2bN4PjODx58gTnzp3DtGnT8PXXX5s6RmIDZGIB2tetiiY1ncCBA0NO96mtjzJ+k5DPYdPwAAzeeB5ylbbAOpO61oVXFVmJz52eno6FCxdi6dKl2LVrF8LDw+Hg4IC0tDQMGjQIBw8exK1btzB79mw4ODiU9lIIIWXEqAUrAGD27NlYvXo15HI5AEAsFmPatGlYsGCBSQM0B1qwgpSGXKlBUpocSw/ewpHop1Brc/6EGtdwwqSuPgisU6XELfjs7GyEh4dj5MiRurJevXph165dGDhwIA4ePKgr37JlCwYNGgSpVGqaCyKkgiqrXGB0ogWArKwsREdHQ6vVokGDBrC3tzdlbGZDiZaYQrpcBQ4cUuUq2An54PM42IsFRs+dzdtyzcXj8aDVvm459+rVS9fSJYSUjk0kWltFiZZYq4KSbS5KsoSYltUl2gEDBhh80r179xodUFmgREuslUarRVZWFio5Oem1ZHk8HlJTU22m14gQW1BWucDgUcdOTk66h6OjI44ePYpLly7pjl++fBlHjx6Fk5OTWQIlpLzLUqrxMjUNAwcO1EuyAKDVavH+++/TqGNCbJBRXceff/45Xrx4gR9++AF8fs4eoxqNBp9++ikcHR2xfPlykwdqStSiJdYmU6lGamoaRoUMxaFDdI+WkLJgdS3avDZv3oxp06bpkiwA8Pl8hIaGYvPmzSYLjpCKIEupxr0nL3Dgrz/0kmzPnr3w7MVLBAf31JUdOHAAe/bsocUrCLEhRiVatVqNmJiYfOUxMTH5urwIIUVjDBgffgMtO/dC6LTpAHKS7I9bfsbMP+5gQ9h2XbL9/PPP8e6779LUHkJsiFGrCYwcORKjRo3CvXv30KZNGwDA+fPnsWTJEr15gISQ4t1LzsDjl9kI2fYffv40FA38/NC9d1+M/OUGYp9lIPZZJraEbceR/X+ib/93qNuYEBtjVKJdsWIF3NzcsHr1aiQmJgLIWZZxxowZmDp1qkkDJMQQWUr1//9XAyGfBx4HyETGz2ktS3KVBgCQmq1CyLb/MO+tTrokCwCxzzIw8pcbmNixE+6/0qBqZUtGSwgpqVLPo01LSwOAAm8knzlzBgEBARCLxaV5C5OjwVCmI1fLIRFI8pVnq7MhFZi/e1Ol0SJNrsJ3/97FnisJyPj/PWAbVnfEJx3roFP9ala/znJiajYCFx8zqO6ZmV1QoxJ1GxNiClY9GCovR0fHQgPs1auXblN4Uv5kqjLxZ+yfyFRl6pVnKDOw//7+fOWmptUyPEtXoOfqU9h67qEuyQLAzSdpmLAjCgv/idYrt0YykQABns7F1mtRqxIcLPylQaFWQK6W5yvPUmVBo9VYICJCrF+pE21RKuCiUxVGpioTqy6twoLzCzDjxAxdUs1QZmDCsQmYe24ufrr+k1mTbaZSjTFbL+FZhqLQOjsiH+HsvedW/bvoIBFgXr+GEPEL/3MU8jnM69sQDhLLJVqFRoG41Djce3VPL9lmqbJw9slZvJC/oGRLSAHMmmgJoJXLi33YGrlajr9i/8KuO7sAACcTTmL6ielIV6ZjwrEJuPz0MgDgp+s/4eTjk1CoC0+EpZHwKhvRiWnF1lsXEYt0ufW2ajmOg3cVGXaMbQ1Xx/y3Wao5iPHrmDaoU80eHGeZe84KjQJxr+Iw7OAwjDo0Cvde3YNCrUCWKgsRjyIQGhGKIfuHULIlpADWffOqnLjdrHmhx3yvRpVhJKYhEUjQp04fRDyKwJknZwAApxJOIWhHkF69Hp490KFmB4gFpr9HzxjDP//lDMSrU1WGDIUaT9P0E3rTmk64m5yBq49eQci37kFRdiIBGtdwwrGpnRAV/xIRd56BMaCTb1W0qOUMAZ+DWMAv/kRmwhjDmCNjkK3Omb876tAo/NTjJ8SnxeOL01+AgSEpMwmfHv0U4W+HWyxOQqyRVbRo161bB29vb0gkEvj7++PUqVOF1o2IiADHcfket27dKsOIiUwow8pOK9G2etsCj/fw7IH5bedDJiz5vqyGYAxQqLVoXMMJv4xthl3jWqCm8+tBQn2auCFsdDOEjW4COxEfGhuY3i0S8CETC9CublXM6lUfX/Suj/Z1q0ImFlg0yQIAA8PKjish5OVsZp+tzsbQ/UMx6/QsMOR0y1cSV8KqjquoRUvIG8yaaA3p5goPD8eUKVMwe/ZsREVFoX379ujVqxfi4+OLfN3t27eRmJioe9StW9dUYRMDyYQyLOu4LF+5gBNgUftFZkuyAMDjcehYryp+HN4Q009NxNr/luCXj5qhprMUfZq4YWrvGhj0zwBceXkA2z9qBgv1uBqNz+OBz7OK78EAAKlAiiZVm2B9t/W6ZJtXJXEl/Nr7V7jJ3CDk5z9OSEVW4r9kxhgePnxo0BJwhgxAWbVqFUaPHo0xY8bAz88Pa9asgYeHB9avX1/k66pVqwY3NzfdI+9ykBWJJe8BZygzMPnY5HzlaqZG6PFQs486blJLjM9PT8LVZ1dx6OEBrP1vCX77xB9Te9fA2H+HITEzEd9fW4MrL/dDyxXw76DKzv8ghZIKpGhWrRm6e3bPd2xko5FwlblSkiWkACW+R8sYQ926dXHz5s1iW5HF7TSiVCpx+fJlzJw5U6+8R48eOHv2bJGvbd68OeRyORo0aIAvv/wSnTt3LrSuQqGAQvH6/l3u3N/ywhL3gDOUGZh4bCIuPX29g5OAE0DNcgYdnUw4iRknZmBZx2Vmbdnm7TU59PAAXiieIz4tHk+znurKhTw+eIU1aRe6vf55dpK5wiwXcgc+HYg7kO/YD9d+QCu3VvCp5FPgvGpCKrISt2h5PB7q1q2LlJSUUr/58+fPodFo4Orqqlfu6uqKpKSCP/Tc3d2xYcMG7NmzB3v37oWvry+6du2KkydPFvo+ixcv1tvmz8PDo9SxV2RytRwHHhzQS7I9PHvg3JBzCKr+ekDUyYSTOJ1w2myjjh1EDvi+6/doXu31F42LSRf1kuxU/2l4t957Zk32FUFukp15aqbunqyQJwSHnC8w2eps3WjkgubZElKRGXUTaNmyZZg+fTpu3LhhkiDevJfLGCv0/q6vry8++ugjtGjRAoGBgVi3bh3eeustrFixotDzz5o1C6mpqbrHo0ePTBJ3RSURSNDbuzdGNsxZ1zp34JNEIMGqTqt0yXZyi8loV6OdWUYd53IQOWBd1/WoWyl/78rHTT7GQN/3YC+iJFtaPI6HZReX6Q18+qP/H9jYY6PeAKmFFxZCwKPJDITkZdRfxIcffoisrCw0bdoUIpEo304iL168MOg8VapUAZ/Pz9d6TU5OztfKLUqbNm2wffv2Qo+LxWKrWwbS1smEMnzc9GPUc66HzrU661qMMqEMqzqtwqnHp9C+ZnuztyS1Wi2y1VlIVabmOxaXGqdf8OY9WHlq0c+FtNRhLgFPgF/e+gVD/hkCLdPmDHyyc0MVaRWs77Yen/z7CTwdPfFDtx8o0RLyBqP+ItasWWOSNxeJRPD398eRI0fwzjvv6MqPHDmCfv36GXyeqKgouLu7myQmc7DFubKGkAll6O7VHWK+OF95Z4/OZm3JAjlJNkWegqH7hyI5Kznf8cMPD4PP42NO4JzXCT/vPdk3rfR9/TPdr9Uj4AngaueKX9/6FVqtVje6WAghmlRtgs3Bm+Ht5A0nsZOlQyXE6hiVaIcPH26yAEJDQxESEoKAgAAEBgZiw4YNiI+Px7hx4wDkdPsmJCRg27ZtAHKSvJeXFxo2bAilUont27djz5492LNnj8liMiWepHwPDHkzyerKzZxkASBdlY6h+4ciMTNRVzaiwQhEPYvCtWfXAAAH4g5AyBPiyzZfgtqnpZObbBljeqOLpQIp/Fz8Cv1dIKSiM7qPR6PRYN++fYiJiQHHcfDz80O/fv0gEJTslIMGDUJKSgrmz5+PxMRENGrUCPv374enpycAIDExUW9OrVKpxLRp05CQkACpVIqGDRvin3/+Qe/evY29FGKjBDwBgqoHYc/dnC9Z0wKmYWC9gdAyLT49+imikqMg4AnQp3YfC0dafhTWLUxJlpDCGbVN3o0bN9CvXz8kJSXB1zenu+3OnTuoWrUq/vzzTzRu3NjkgZpSedomz5B5suW5VZ2pysTyi8vh7eSN9/KMLs5QZmDS8UkY23gsmlZrmrNlX757tK+AlfVfP596G5Dk6fqke7SElGtllQuMSrRt2rRBtWrVsHXrVjg752zv9fLlS4wYMQLJyck4d+6cyQM1pfKUaEnO1BMGlm/glUKjgJZp8++Lq8rOuVf7+QNgqdfr8tlJlFwJqUDKKhcY1XV87do1XLp0SZdkAcDZ2RkLFy5Ey5YtTRYcIYawE9oVWE7dmYQQa2DUPFpfX188ffo0X3lycjJ8fHxKHRQhJvXmMotvTuMpqC4hhJiIUS3aRYsWYdKkSZg7dy7atGkDADh//jzmz5+PpUuX6i1xSF2zxCoUNK1HnprTfZwr4ynwbVOa2kMIMSmj7tHy8uwqkruCU+5p8j7nOA4ajfVtmWXxe7QFtZjo3qD55N6TNRTdqyWkQrDqe7RbtmyBh4dHvh1ztFot4uPj4eXlZYrYyjdazJ4QQioEoxLtqFGjkJiYiGrVqumVp6SkoFu3blbZiiUVXN4vM/JU/VWg3pzWQwghJmRUoi1s0f+MjAxIyvGcTWKjiusGljhRVzEhxGxKlGhDQ0MB5NyH/eqrr2Bn93pahUajwYULF9CsWTOTBlgulHQxe4A++AkhpJwoUaKNispZHJ8xhuvXr0MkEumOiUQiNG3aFNOmTTNthOWFoYvZA3TPlhBCypESJdrjx48DAEaOHIlvv/2Wpu4Q20VfZgghZcToUceE2CzqlieElCHaobms0KhXQgipkCjRlgUa9UoIIRWWUWsdE0IIIcQw1KK1FBqMYzXS5SpotcD5uBTIVRr4ujqghrMUdiI++Dz6LkoIKR1KtJZA3cRWIzVbha9+v4EDNxKh0rxe9tunmj0W9m+ERjWcIBPTnwkhxHj0dZ1UWOlyFQb9eA5/Xnuil2QB4F5yBob8dAFR8a+gVNOSooQQ41GiJRWSUq3F9vPxuJWUXmgdjZZh6m9X8yVhQggpCUq0pEJSabTYevZBsfWepikQFf/S/AERQsotSrSkQtIyhqQ0uUF1z8WmwIhtmwkhBAAlWlJBFbT7VGH4fK5E9QkhJC9KtKRCYozBp5q9QXW7+7maORpCSHlGiZZUSHYiPsZ2qF1svTpVZfCqIiuDiAgh5RUlWlIh8Xk8vNXYHZ19qxVax0EswIZhAZCK+GUYGSGkvKFESyosmViAtUOaY1av+nB1FOvKBTwOvRq54eCU9qjpLIWAVocihJQCxyrgcMq0tDQ4OTkhNTWV9tQlUKq1UGm0eJaugFKjhZujBBwHOEiElg6NEGJGZZULaG05UuGJBDyIBDxaapEQYhb0yVLOaLUMGQo1XmYp8eB5FuzEfDRwdwTHAXYi+t9NCCFljT55y5EshRq3ktIx58+buJ6Qqiu3FwvwQUsPTOlWD/YS+l9OCCFliT51ywmFSoMrj15h+OZIaLT6t90zFGr8dDoON5+kYePwANhTFykpQ4wxqNVqaDS0OQMpW3w+HwKBwOILztAnbjmh1jJM3hGVL8nmde5+Cg5cT8SAFjVon1VSJpRKJRITE5GVlWXpUEgFZWdnB3d3d4hEIovFQIm2nDh/PwUpmcpi6208dR89G7nBQUKJlpiXVqtFXFwc+Hw+qlevDpFIZPGWBak4GGNQKpV49uwZ4uLiULduXfAs1MCgRFsOqDVanLz73KC6d55mQMCnJEvMT6lUQqvVwsPDA3Z2dpYOh1RAUqkUQqEQDx8+hFKphEQisUgc9IlbTpSknUBtClKWLNWKIASwjt8/y0dASk3A56Gzb1WD6jZwd4RSrTVzRIQQQnJRoi0nArwqo6qDuNh6YzvUplHHhBBShijRlhMCPof1Q1tAVMT91071qqJ7A1fweNR5TIgt6NSpE6ZMmWJz5yb6KNGWE2IBHw2qO2Lf+CC09q6sd8zZTohJXX3wv6EtaJlBQkphxIgR6N+/v6XDIDaGPnXLETuRAA2rO2HDsADIVRo8epEFiZAP7yoy8HkcJELa7o3YJo2WITLuBZLT5ajmIEEr78rgU89MsRhj0Gg0EAjoo96SqEVbDjlJhXB1lCDAqzIa1XCCTCygJEts1sEbiWi39BgGbzyPyTuvYvDG82i39BgO3kg023vu3r0bjRs3hlQqhYuLC7p164bp06dj69at+OOPP8BxHDiOQ0REBADg888/R7169WBnZ4fatWvjq6++gkql0p1v7ty5aNasGX7++Wd4eXnByckJH3zwAdLT03V1MjMzMWzYMNjb28Pd3R0rV67MF9f27dsREBAABwcHuLm5YciQIUhOTtYdj4iIAMdxOHToEAICAiAWi3Hq1CmDzk3MhxItIcRqHbyRiE+2X0FiqlyvPClVjk+2XzFLsk1MTMTgwYMxatQoxMTEICIiAgMGDMCcOXPw/vvvo2fPnkhMTERiYiKCgoIAAA4ODggLC0N0dDS+/fZbbNy4EatXr9Y7b2xsLH7//Xf8/fff+Pvvv3HixAksWbJEd3z69Ok4fvw49u3bh8OHDyMiIgKXL1/WO4dSqcSCBQtw7do1/P7774iLi8OIESPyXcOMGTOwePFixMTEoEmTJgadm5gP9ScQQqySRssw769oFLSoKEPOfPB5f0WjewM3k3YjJyYmQq1WY8CAAfD09AQANG7cGEDOAggKhQJubm56r/nyyy91P3t5eWHq1KkIDw/HjBkzdOVarRZhYWFwcHAAAISEhODo0aNYuHAhMjIysGnTJmzbtg3du3cHAGzduhU1a9bUe59Ro0bpfq5duza+++47tGrVChkZGbC3t9cdmz9/vu48hp6bmA+1aAkhViky7kW+lmxeDEBiqhyRcS9M+r5NmzZF165d0bhxYwwcOBAbN27Ey5cvi3zN7t270a5dO7i5ucHe3h5fffUV4uPj9ep4eXnpkiwAuLu767p9Y2NjoVQqERgYqDteuXJl+Pr66p0jKioK/fr1g6enJxwcHNCpUycAyPdeAQEBup8NPTcxH0q0hBCrlJxeeJI1pp6h+Hw+jhw5ggMHDqBBgwZYu3YtfH19ERcXV2D98+fP44MPPkCvXr3w999/IyoqCrNnz4ZSqb/2uFAo1HvOcRy02pzFYxgrfDOQXJmZmejRowfs7e2xfft2XLx4Efv27QOAfO8lk8l0PxtybmJelGgJIVapmoNh69IaWq8kOI5D27ZtMW/ePERFRUEkEmHfvn0QiUT5tvs7c+YMPD09MXv2bAQEBKBu3bp4+PBhid7Px8cHQqEQ58+f15W9fPkSd+7c0T2/desWnj9/jiVLlqB9+/aoX7++3kCo0pybmBfdoyWEWKVW3pXh7iRBUqq8wPu0HAA3p5ypPqZ04cIFHD16FD169EC1atVw4cIFPHv2DH5+fpDL5Th06BBu374NFxcXODk5wcfHB/Hx8di5cydatmyJf/75R9fSNJS9vT1Gjx6N6dOnw8XFBa6urpg9e7beOr21atWCSCTC2rVrMW7cONy4cQMLFiwwybmJedG/NCHEKvF5HOb0aQAg/0YYuc/n9Glg8vm0jo6OOHnyJHr37o169erhyy+/xMqVK9GrVy989NFH8PX1RUBAAKpWrYozZ86gX79++OyzzzBhwgQ0a9YMZ8+exVdffVXi912+fDk6dOiAvn37olu3bmjXrh38/f11x6tWrYqwsDD89ttvaNCgAZYsWYIVK1aY5NzEvDhWATvw09LS4OTkhNTUVDg6Olo6HELKJblcjri4OHh7e5dqe7KDNxIx769ovYFR7k4SzOnTAD0buZsiVFKOFfV7WFa5gLqOCSFWrWcjd3Rv4EYrQxGbRYmWEGL1+DwOgXVcLB0GIUahe7RmkqHMwEv5S72h9RqtBinZKchUZVowMkIIIWWJEq0ZZCgzMO7fcQg5EIJXilc5C3trNXghf4EP/vkAnx3/jJItIYRUEJRoTSxLlYVx/47DtWfX8DDtoS7ZvpC/wJD9Q5CUmYRziefw2fHPkK3OtnS4hBBCzIzu0ZoYA0OgeyCuPbsGAHiY9hAD/xoItVaNFHmKrl77mu2hZVpLhUkIIaSMUKI1MZlQhhGNRgAAfvjvBwDA06ynenVmtJyBAXUHQCaUvflyQggh5Qx1HZuBTCjD6Maj0du7d75jg3wH4b1671GSJYSQCoISrRlotVqkK9Nx+Wn+/R7PPTkHuVpOC30TQkgFQYnWxLRaLVLkKRiyf0i+LmMAiE+P1xuNTAipuDp16oQpU6YYXD8sLAyVKlUyWzxlae7cuWjWrJmlwygTlGhNTM3UCDkQgqTMJF3Z5OaTMarR6w2bH6Y9xKhDo6BmakuESIhNUWqUiEyM1H0xZYwhMjESSo2ymFcSazZt2jQcPXrU0mGUCUq0JqbWqjGj5QzwuJx/2hktZ2Cw32CMbTIWHzf5GAAg4Akws9VMqLWUaAkpilKjxKRjkzD68Ggsu7gMWqbF0otLMfrwaEw6NomSrQWoVCqTnMfe3h4uLhVjtS9KtCZmJ7RDG/c2WN1ptd7oYplQhpGNRuLTpp/ih24/oEnVJpAKpJYOlxCrlZtkzz05BwDYHrMd7//1Pn6J+QVAzngHcyXbTp06YeLEiZgyZQqcnZ3h6uqKDRs2IDMzEyNHjoSDgwPq1KmDAwcO6F5z4sQJtGrVCmKxGO7u7pg5cybU6tdfpjMzMzFs2DDY29vD3d0dK1euzH/NSiVmzJiBGjVqQCaToXXr1oiIiDDqGmJjY9GvXz+4urrC3t4eLVu2xL///qtXJzExEW+99RakUim8vb3x66+/wsvLC2vWrNHV4TgOP/zwA/r16weZTIZvvvkGAPDXX3/B398fEokEtWvXxrx58/SuNzU1FWPHjkW1atXg6OiILl264Nq1a7rjb3YdjxgxAv3798eKFSvg7u4OFxcXjB8/Xi+xGxKvNaJEawa5yfbNKTwyoQzDGg6jJEuIAa4mX8WZJ2egxev55rdf3tb9rIUWZ56c0c1ZN7WtW7eiSpUqiIyMxMSJE/HJJ59g4MCBCAoKwpUrVxAcHIyQkBBkZWUhISEBvXv3RsuWLXHt2jWsX78emzZt0iUlAJg+fTqOHz+Offv24fDhw4iIiMDly/oDJkeOHIkzZ85g586d+O+//zBw4ED07NkTd+/eLXH8GRkZ6N27N/79919ERUUhODgYffr0QXx8vK7OsGHD8OTJE0RERGDPnj3YsGFDgZvJz5kzB/369cP169cxatQoHDp0CB9++CEmTZqE6Oho/PjjjwgLC8PChQsB5HTvv/XWW0hKSsL+/ftx+fJltGjRAl27dsWLFy8Kjfn48eOIjY3F8ePHsXXrVoSFhSEsLKzE8VodVgGlpqYyACw1NdXSoRBSbmVnZ7Po6GiWnZ1t1Ou1Wi1bfGExaxTWqNDHkgtLmFarNXHkjHXs2JG1a9dO91ytVjOZTMZCQkJ0ZYmJiQwAO3fuHPviiy+Yr6+vXizff/89s7e3ZxqNhqWnpzORSMR27typO56SksKkUimbPHkyY4yxe/fuMY7jWEJCgl4sXbt2ZbNmzWKMMbZlyxbm5ORk9HU1aNCArV27ljHGWExMDAPALl68qDt+9+5dBoCtXr1aVwaATZkyRe887du3Z4sWLdIr+/nnn5m7uztjjLGjR48yR0dHJpfL9erUqVOH/fjjj4wxxubMmcOaNm2qOzZ8+HDm6enJ1Gq1rmzgwIFs0KBBJYr3TUX9HpZVLqAFKwghVonjOMxoOQOXki7ptWRz+Tr7YnrL6eA482yX16RJE93PfD4fLi4uaNy4sa7M1dUVAJCcnIyYmBgEBgbqxdK2bVtkZGTg8ePHePnyJZRKJQIDA3XHK1euDF9fX93zK1eugDGGevXq6cWhUCiMupeZmZmJefPm4e+//8aTJ0+gVquRnZ2ta9Hevn0bAoEALVq00L3Gx8cHzs7O+c4VEBCg9/zy5cu4ePGirgULABqNBnK5HFlZWbh8+TIyMjLyxZ2dnY3Y2NhCY27YsCH4fL7uubu7O65fv17ieK0NJVpCiFVijGHZxWUFJlkgpxt5+cXlmNFyhlmSrVAo1HvOcZxeWe57arVaMMbyxcD+f5Q0x3EGTeXTarXg8/m4fPmyXrIBcgYOldT06dNx6NAhrFixAj4+PpBKpXjvvfegVCr14ntTQeUymf4CO1qtFvPmzcOAAQPy1ZVIJNBqtXB3dy/w/nJR05MK+jfXarUljtfaWMU92nXr1sHb2xsSiQT+/v44deqUQa87c+YMBAJBhZmLRUhFcjHpom7gU2G2x2zHpaeXyiiiwjVo0ABnz57V+9A/e/YsHBwcUKNGDfj4+EAoFOL8+fO64y9fvsSdO3d0z5s3bw6NRoPk5GT4+PjoPdzc3Eoc06lTpzBixAi88847aNy4Mdzc3PDgwQPd8fr160OtViMqKkpXdu/ePbx69arYc7do0QK3b9/OF6ePjw94PB5atGiBpKQkCASCfMerVKlS4mspbbyWZvFEGx4ejilTpmD27NmIiopC+/bt0atXL70b9gVJTU3FsGHD0LVr1zKKlBBSlppVa4a21duCl+djytf5dVcrDzy0rd4WTas2tUR4ej799FM8evQIEydOxK1bt/DHH39gzpw5CA0NBY/Hg729PUaPHo3p06fj6NGjuHHjBkaMGAEe7/W11atXD0OHDsWwYcOwd+9exMXF4eLFi1i6dCn2799f4ph8fHywd+9eXL16FdeuXcOQIUN0rUMgJ3F169YNY8eORWRkJKKiojB27FhIpdJiewi+/vprbNu2DXPnzsXNmzcRExOD8PBwfPnllwCAbt26ITAwEP3798ehQ4fw4MEDnD17Fl9++SUuXTLui1Fp4rU0iyfaVatWYfTo0RgzZgz8/PywZs0aeHh4YP369UW+7uOPP8aQIUP07nkQQsoPEV+E77p8h8DqOX/jH/p9iF19dmGo31AAQGD1QHzX5TuI+CJLhgkAqFGjBvbv34/IyEg0bdoU48aNw+jRo3WJBwCWL1+ODh06oG/fvujWrRvatWsHf39/vfNs2bIFw4YNw9SpU+Hr64u+ffviwoUL8PDwKHFMq1evhrOzM4KCgtCnTx8EBwfr3d8EgG3btsHV1RUdOnTAO++8g48++ggODg6QSCRFnjs4OBh///03jhw5gpYtW6JNmzZYtWoVPD09AeR0+e7fvx8dOnTAqFGjUK9ePXzwwQd48OCB7t62MYyN1+LMOtSqGAqFgvH5fLZ371698kmTJrEOHToU+rrNmzezgIAAplKp8o1cK4hcLmepqam6x6NHj2jUMSFmVtpRx7kUagWLTIzUjejVarUsMjGSKdQKU4RJ8sj9bPz3338tHYpBDIm3wo86fv78OTQaTb5vOK6urkhKSirwNXfv3sXMmTNx6tQpCASGhb948WLMmzev1PESQsqeiC9CS7eWuuccx+k9J8Y7duwYMjIy0LhxYyQmJmLGjBnw8vJChw4dLB1agWwt3lwW7zoGUOBovYL63DUaDYYMGYJ58+blGwJflFmzZiE1NVX3ePToUaljJoQQS2rYsCHs7e0LfPzyS9GDyHKpVCp88cUXaNiwId555x1UrVoVERER+Ub/WgtbizeXRVu0VapUAZ/Pz9d6TU5OLrAfPz09HZcuXUJUVBQmTJgA4PXQeoFAgMOHD6NLly75XicWiyEWi81zEYQQYgH79+8vdN1hQ++DBgcHIzg42JRhmZWtxZvLoolWJBLB398fR44cwTvvvKMrP3LkCPr165evvqOjo27ycq5169bh2LFj2L17N7y9vc0eMyGEWIPcgUfE+ll8wYrQ0FCEhIQgICAAgYGB2LBhA+Lj4zFu3DgAOd2+CQkJ2LZtG3g8Hho1aqT3+mrVqkEikeQrJ4QQQqyBxRPtoEGDkJKSgvnz5yMxMRGNGjXC/v37dd/WEhMTi51TSwghhFgrjjEbWL/KxNLS0uDk5ITU1FQ4OjpaOhxCyiW5XI64uDjdqm+EWEJRv4dllQusYtQxIYQQUl5RoiWEEELMiBItIYSYwZkzZ9C4cWMIhUL079/foNeMGDHC4LrEdlh8MBQhhJRHoaGhaNasGQ4cOGDwNnfffvutTWz7RkqGWrSEEGIGsbGx6NKlC2rWrFnkHqx5OTk5FVk3dy9ZYlso0RJCrJrywQNk37yZ76HMs7eqqXl5eWHNmjV6Zc2aNcPcuXMB5Cwb+9NPP+Gdd96BnZ0d6tatiz///BMA8ODBA3Ach5SUFIwaNQocxyEsLAwajQajR4+Gt7c3pFIpfH198e233+q9x5tdx506dcKECRMQGhqKKlWqoHv37ma7ZmI+1HVMCLFaygcPENuzV6HH6xw8AJGXV9kFlMe8efOwbNkyLF++HGvXrsXQoUPx8OFDeHh4IDExEb6+vpg/fz4GDRoEJycnaLVa1KxZE7t27UKVKlVw9uxZjB07Fu7u7nj//fcLfZ+tW7fik08+wZkzZ6hb2UZRoiWEWC1NZmapjpvTiBEjMHjwYADAokWLsHbtWkRGRqJnz55wc3MDx3FwcnKCm5ub7jV5dxHz9vbG2bNnsWvXriITrY+PD5YtW2a+CyFmR4mWEEKM0KRJE93PMpkMDg4OSE5OLvI1P/zwA3766Sc8fPgQ2dnZUCqVaNasWZGvCQgIMEW4xILoHi0hhLyBx+Pl66Z9c6ecN7dm4zgOWq220HPu2rULn332GUaNGoXDhw/j6tWrGDlyZLEDnGQyWQmjJ9aGWrSEEPKGqlWrIjExUfc8LS0NcXFxpTrnqVOnEBQUhE8//VRXFhsbW6pzEttALVpCCHlDly5d8PPPP+PUqVO4ceMGhg8fDj6fX6pz+vj44NKlSzh06BDu3LmDr776ChcvXjRRxMSaUaIlhFgtfjHdpsUdN9asWbPQoUMHvP322+jduzf69++POnXqlOqc48aNw4ABAzBo0CC0bt0aKSkpeq1bUn7R7j20ew8hZmGq3XuUDx4UOLqYL5NZbGoPsR3WsHsP3aMlhFg1SqbE1lHXMSGEEGJGlGgJIYQQM6JESwghhJgRJVpCCCHEjCjREkIIIWZEiZYQQggxI0q0hBBCiBlRoiWEEELMiBItIYQQYka0MhQhxDakxAKKdEDsALiUbt1hQsoSJVpCiPVLiQXWtnj9fOKVcpVsVSpVvv1tSflBXceEEOunSC/6uYnt3r0bjRs3hlQqhYuLC7p164bMzEyMGDEC/fv3x7x581CtWjU4Ojri448/1tu8/eDBg2jXrh0qVaoEFxcXvP3223r7zj548AAcx2HXrl3o1KkTJBIJtm/fjocPH6JPnz5wdnaGTCZDw4YNsX//ft3roqOj0bt3b9jb28PV1RUhISF4/vy5Wf8diGlQoiWEWK+UWODJVeD5Hf3y53dyylNMv3F6YmIiBg8ejFGjRiEmJgYREREYMGAAcjc6O3r0KGJiYnD8+HHs2LED+/btw7x583Svz8zMRGhoKC5evIijR4+Cx+PhnXfegVar1Xufzz//HJMmTUJMTAyCg4Mxfvx4KBQKnDx5EtevX8fSpUthb2+vi6ljx45o1qwZLl26hIMHD+Lp06d4//33TX79xPSo65gQYp3e7C7Oa+9Hr382cTdyYmIi1Go1BgwYAE9PTwBA48aNdcdFIhE2b94MOzs7NGzYEPPnz8f06dOxYMEC8Hg8vPvuu3rn27RpE6pVq4bo6Gg0atRIVz5lyhQMGDBA9zw+Ph7vvvuu7r1q166tO7Z+/Xq0aNECixYt0pVt3rwZHh4euHPnDurVq2ey6yemRy1aQoh1MrR72MTdyE2bNkXXrl3RuHFjDBw4EBs3bsTLly/1jtvZ2emeBwYGIiMjA48ePQIAxMbGYsiQIahduzYcHR3h7e0NICeR5hUQEKD3fNKkSfjmm2/Qtm1bzJkzB//995/u2OXLl3H8+HHY29vrHvXr19e9H7FulGgJIdZJ7GDaegbi8/k4cuQIDhw4gAYNGmDt2rXw9fVFXFxcka/jOA4A0KdPH6SkpGDjxo24cOECLly4AAB693EBQCaT6T0fM2YM7t+/j5CQEFy/fh0BAQFYu3YtAECr1aJPnz64evWq3uPu3bvo0KGDqS6dmAl1HRNCrJNLnZxuYUV6zj3ZvN3FAzYCVeqZbaoPx3Fo27Yt2rZti6+//hqenp7Yt28fAODatWvIzs6GVCoFAJw/fx729vaoWbMmUlJSEBMTgx9//BHt27cHAJw+fdrg9/Xw8MC4ceMwbtw4zJo1Cxs3bsTEiRPRokUL7NmzB15eXhAI6GPb1lCLlhBivVzqANWb5STVvKrUyyk3Q5K9cOECFi1ahEuXLiE+Ph579+7Fs2fP4OfnByCnZTp69GhER0fjwIEDmDNnDiZMmAAejwdnZ2e4uLhgw4YNuHfvHo4dO4bQ0FCD3nfKlCk4dOgQ4uLicOXKFRw7dkz3nuPHj8eLFy8wePBgREZG4v79+zh8+DBGjRoFjUZj8n8DYlqUaAkh1u/N7mETdxfn5ejoiJMnT6J3796oV68evvzyS6xcuRK9evUCAHTt2hV169ZFhw4d8P7776NPnz6YO3cuAIDH42Hnzp24fPkyGjVqhM8++wzLly836H01Gg3Gjx8PPz8/9OzZE76+vli3bh0AoHr16jhz5gw0Gg2Cg4PRqFEjTJ48GU5OTuDx6GPc2nEsd8x6BZKWlgYnJyekpqbC0dHR0uEQUi7J5XLExcXB29sbEomk9Ce0gpWhRowYgVevXuH333+3yPuTkivq97CscgF19hNCbEM5WgmKVCzU50AIIYSYEbVoCSHEQGFhYZYOgdggatESQgghZkSJlhBCCDEjSrSEEEKIGVGiJYQQQsyIEi0hhBBiRpRoCSGEEDOiREsIsWrZ2dklKrclnTp1wpQpUwyuHxYWhkqVKpktHmIelGgJIVYrPT0d4eHhSE/X33M2LS2twHJCrBElWkKIVUpPT8fChQsxcuRIDBo0SJdU09LSMGjQIIwcORILFy6kZEusHiVaQojVyc7Oxp49e7B06VIAwIEDBzBo0CBkZGRg0KBBOHjwIABg6dKl2LNnj8m7kTt16oSJEydiypQpcHZ2hqurKzZs2IDMzEyMHDkSDg4OqFOnDg4cOKB7zYkTJ9CqVSuIxWK4u7tj5syZUKvVuuOZmZkYNmwY7O3t4e7ujpUrV+Z7X6VSiRkzZqBGjRqQyWRo3bo1IiIiTHptpOxRoiWEWB2pVIoBAwagZ8+eurIDBw7AyclJl2QBoFevXnj33Xd1m7Cb0tatW1GlShVERkZi4sSJ+OSTTzBw4EAEBQXhypUrCA4ORkhICLKyspCQkIDevXujZcuWuHbtGtavX49Nmzbhm2++0Z1v+vTpOH78OPbt24fDhw8jIiICly9f1nvPkSNH4syZM9i5cyf+++8/DBw4ED179sTdu3dNfn2kDLEKKDU1lQFgqamplg6FkHIrOzubRUdHs+zsbKPPkZqaynr27MkA5Hv06tWLpaWlmTDi1zp27MjatWune65Wq5lMJmMhISG6ssTERAaAnTt3jn3xxRfM19eXabVa3fHvv/+e2dvbM41Gw9LT05lIJGI7d+7UHU9JSWFSqZRNnjyZMcbYvXv3GMdxLCEhQS+Wrl27slmzZjHGGNuyZQtzcnIywxWXX0X9HpZVLqBNBQghVsvR0RG//fYbnJycoNVqdeU8Hg+7du2Cvb292d67SZMmup/5fD5cXFzQuHFjXZmrqysAIDk5GTExMQgMDATHcbrjbdu2RUZGBh4/foyXL19CqVQiMDBQd7xy5crw9fXVPb9y5QoYY6hXr55eHAqFAi4uLia/PlJ2KNESQqxW7sCnvEkWALRaLd5//32Eh4fDwcHBLO8tFAr1nnMcp1eWm1S1Wi0YY3pJFgAYY7p6uT8XRavVgs/n4/Lly+Dz+XrHzPmFgpgf3aMlhFil3CSb954sj/f6Iyt3gJQ1jDpu0KABzp49q5dQz549CwcHB9SoUQM+Pj4QCoU4f/687vjLly9x584d3fPmzZtDo9EgOTkZPj4+eg83N7cyvR5iWpRoCSFWJzs7G3v37s038Ck1NTXfAClzjDouqU8//RSPHj3CxIkTcevWLfzxxx+YM2cOQkNDwePxYG9vj9GjR2P69Ok4evQobty4gREjRuh9cahXrx6GDh2KYcOGYe/evYiLi8PFixexdOlS7N+/34JXR0qLEi0hxOpIpVK8++67+PzzzwHkJNnw8HDY29sjPDxcl2w///xzs406LokaNWpg//79iIyMRNOmTTFu3DiMHj0aX375pa7O8uXL0aFDB/Tt2xfdunVDu3bt4O/vr3eeLVu2YNiwYZg6dSp8fX3Rt29fXLhwAR4eHmV9ScSEOGbIzYNyJi0tDU5OTkhNTYWjo6OlwyGkXJLL5YiLi4O3tzckEolR50hPT8eePXvw7rvv6t2LTUtLw969e/OVE/Kmon4PyyoX0GAoQojVcnBwwKBBg/K1WB0dHQssJ8QaUdcxIcSqFZZMKckSW0GJlhBCCDEjSrSEEEKIGVGiJYSYVQUcb0msiDX8/lGiJYSYRe4qSllZWRaOhFRkub9/b670VZZo1DEhxCz4fD4qVaqE5ORkAICdnV2+ZQoJMRfGGLKyspCcnIxKlSrlW9ayLFGiJYSYTe7SgbnJlpCyVqlSJYsvYUmJlhBiNhzHwd3dHdWqVYNKpbJ0OKSCEQqFFm3J5qJESwgxOz6fbxUfeIRYglUMhlq3bp1ueSx/f3+cOnWq0LqnT59G27Zt4eLiAqlUivr162P16tVlGC0hhBBiOIu3aMPDwzFlyhSsW7cObdu2xY8//ohevXohOjoatWrVyldfJpNhwoQJaNKkCWQyGU6fPo2PP/4YMpkMY8eOtcAVEEIIIYWz+KYCrVu3RosWLbB+/XpdmZ+fH/r374/FixcbdI4BAwZAJpPh559/Nqg+bSpACCGkQmwqoFQqcfnyZcycOVOvvEePHjh79qxB54iKisLZs2fxzTffFFpHoVBAoVDonqempgLI+UcmhBBSMeXmAHO3Ny2aaJ8/fw6NRgNXV1e9cldXVyQlJRX52po1a+LZs2dQq9WYO3cuxowZU2jdxYsXY968efnKaY9HQgghKSkpcHJyMtv5LX6PFkC+SeyMsWIntp86dQoZGRk4f/48Zs6cCR8fHwwePLjAurNmzUJoaKju+atXr+Dp6Yn4+Hiz/uOaS1paGjw8PPDo0SOb7Pq29fgB278GW48fsP1rsPX4Adu/htTUVNSqVQuVK1c26/tYNNFWqVIFfD4/X+s1OTk5Xyv3Td7e3gCAxo0b4+nTp5g7d26hiVYsFkMsFucrd3JysslfjlyOjo4Uv4XZ+jXYevyA7V+DrccP2P418HjmnYBj0ek9IpEI/v7+OHLkiF75kSNHEBQUZPB5GGN692AJIYQQa2HxruPQ0FCEhIQgICAAgYGB2LBhA+Lj4zFu3DgAOd2+CQkJ2LZtGwDg+++/R61atVC/fn0AOfNqV6xYgYkTJ1rsGgghhJDCWDzRDho0CCkpKZg/fz4SExPRqFEj7N+/H56engCAxMRExMfH6+prtVrMmjULcXFxEAgEqFOnDpYsWYKPP/7Y4PcUi8WYM2dOgd3JtoDitzxbvwZbjx+w/Wuw9fgB27+Gsorf4vNoCSGEkPLMKpZgJIQQQsorSrSEEEKIGVGiJYQQQsyIEi0hhBBiRuUi0ZZkmz0AOHHiBPz9/SGRSFC7dm388MMPesdv3ryJd999F15eXuA4DmvWrDFj9KaPf+PGjWjfvj2cnZ3h7OyMbt26ITIy0pyXYPJr2Lt3LwICAlCpUiXIZDI0a9bM4E0jrCH+vHbu3AmO49C/f38TR63P1NcQFhYGjuPyPeRyuU3ED+SsAjd+/Hi4u7tDIpHAz88P+/fvN0v8gOmvoVOnTgX+P3jrrbdsIn4AWLNmDXx9fSGVSuHh4YHPPvvMZn6HVCoV5s+fjzp16kAikaBp06Y4ePBgyQNjNm7nzp1MKBSyjRs3sujoaDZ58mQmk8nYw4cPC6x///59ZmdnxyZPnsyio6PZxo0bmVAoZLt379bViYyMZNOmTWM7duxgbm5ubPXq1TYV/5AhQ9j333/PoqKiWExMDBs5ciRzcnJijx8/tplrOH78ONu7dy+Ljo5m9+7dY2vWrGF8Pp8dPHjQJuLP9eDBA1ajRg3Wvn171q9fP5PHbs5r2LJlC3N0dGSJiYl6D1uJX6FQsICAANa7d292+vRp9uDBA3bq1Cl29epVm7mGlJQUvX/7GzduMD6fz7Zs2WIT8W/fvp2JxWL2yy+/sLi4OHbo0CHm7u7OpkyZYhPxz5gxg1WvXp39888/LDY2lq1bt45JJBJ25cqVEsVm84m2VatWbNy4cXpl9evXZzNnziyw/owZM1j9+vX1yj7++GPWpk2bAut7enqaNdGaO37GGFOr1czBwYFt3bq19AEXoCyugTHGmjdvzr788svSBVsAc8WvVqtZ27Zt2U8//cSGDx9u1kRrjmvYsmULc3JyMnmsBTFH/OvXr2e1a9dmSqXS9AEXoCz+DlavXs0cHBxYRkZG6QN+gzniHz9+POvSpYtendDQUNauXTsTRf2aOeJ3d3dn//vf//Tq9OvXjw0dOrREsdl013HuNns9evTQKy9qm71z587lqx8cHIxLly5BpVKZLdaClFX8WVlZUKlUZlk4uyyugTGGo0eP4vbt2+jQoYPpgod5458/fz6qVq2K0aNHmzTmN5nzGjIyMuDp6YmaNWvi7bffRlRUlM3E/+effyIwMBDjx4+Hq6srGjVqhEWLFkGj0djMNbxp06ZN+OCDDyCTyUwT+P8zV/zt2rXD5cuXdbeu7t+/j/3795u869tc8SsUCkgkEr06UqkUp0+fLlF8Np1ojdlmLykpqcD6arUaz58/N1usBSmr+GfOnIkaNWqgW7dupgk8D3NeQ2pqKuzt7SESifDWW29h7dq16N69u03Ef+bMGWzatAkbN240abwFMdc11K9fH2FhYfjzzz+xY8cOSCQStG3bFnfv3rWJ+O/fv4/du3dDo9Fg//79+PLLL7Fy5UosXLjQpPGb8xryioyMxI0bN4rcEtRY5or/gw8+wIIFC9CuXTsIhULUqVMHnTt3zrcHubXGHxwcjFWrVuHu3bvQarU4cuQI/vjjDyQmJpYoPosvwWgKJd1mr6D6BZWXFXPGv2zZMuzYsQMRERH5vpmZkjmuwcHBAVevXkVGRgaOHj2K0NBQ1K5dG506dTJd4EXEY2z86enp+PDDD7Fx40ZUqVLF5LGWJKbS/D9o06YN2rRpozvetm1btGjRAmvXrsV3331nqrCLjKc08Wu1WlSrVg0bNmwAn8+Hv78/njx5guXLl+Prr782cfSFx2Sqv+VNmzahUaNGaNWqlQkiNTye0sQfERGBhQsXYt26dWjdujXu3buHyZMnw93dHV999ZWJozd9/N9++y0++ugj1K9fHxzHoU6dOhg5ciS2bNlSorhsOtEas82em5tbgfUFAgFcXFzMFmtBzB3/ihUrsGjRIvz7779o0qSJaYP/f+a8Bh6PBx8fHwBAs2bNEBMTg8WLF5s00Zoj/ps3b+LBgwfo06eP7rhWqwUACAQC3L59G3Xq1LHqaygIj8dDy5YtTd6iNVf87u7uEAqF4PP5ujp+fn5ISkqCUqmESCSy+mvIlZWVhZ07d2L+/Pkmizkvc8X/1VdfISQkRNcKb9y4MTIzMzF27FjMnj3bZNvTmSv+qlWr4vfff4dcLkdKSgqqV6+OmTNn6rZpNZRNdx0bs81eYGBgvvqHDx9GQEAAhEKh2WItiDnjX758ORYsWICDBw8iICDA9MH/v7L8f8DMsB2iOeKvX78+rl+/jqtXr+oeffv2RefOnXH16lV4eHhY/TUUhDGGq1evwt3d3TSB/z9zxd+2bVvcu3dP9yUHAO7cuQN3d3eTJllzXkOuXbt2QaFQ4MMPPzRp3LnMFX9WVla+ZMrn88FyBuJaffy5JBIJatSoAbVajT179qBfv34lC7BEQ6esUO6Q7k2bNrHo6Gg2ZcoUJpPJ2IMHDxhjjM2cOZOFhITo6ucO6f7ss89YdHQ027RpU4HTAqKiolhUVBRzd3dn06ZNY1FRUezu3bs2Ef/SpUuZSCRiu3fv1psakJ6ebvL4zXUNixYtYocPH2axsbEsJiaGrVy5kgkEArZx40abiP9N5h51bI5rmDt3Ljt48CCLjY1lUVFRbOTIkUwgELALFy7YRPzx8fHM3t6eTZgwgd2+fZv9/fffrFq1auybb74xefzmuoZc7dq1Y4MGDTJL3OaMf86cOczBwYHt2LGD3b9/nx0+fJjVqVOHvf/++zYR//nz59mePXtYbGwsO3nyJOvSpQvz9vZmL1++LFFsNp9oGWPs+++/Z56enkwkErEWLVqwEydO6I4NHz6cdezYUa9+REQEa968OROJRMzLy4utX79e73hcXBwDkO/x5nmsNX5PT88C458zZ45Z4jfHNcyePZv5+PgwiUTCnJ2dWWBgINu5c6fNxP8mcydaxkx/DVOmTGG1atViIpGIVa1alfXo0YOdPXvWZuJnjLGzZ8+y1q1bM7FYzGrXrs0WLlzI1Gq1TV3D7du3GQB2+PBhs8Wdy9Txq1QqNnfuXFanTh0mkUiYh4cH+/TTT0ucqCwVf0REBPPz82NisZi5uLiwkJAQlpCQUOK4aJs8QgghxIxs+h4tIYQQYu0o0RJCCCFmRImWEEIIMSNKtIQQQogZUaIlhBBCzIgSLSGEEGJGlGgJIYQQM6JESwghhJgRJVpCDMAYw9ixY1G5cmVwHIerV6+Wyfs+ePDALO/n5eWFNWvWmPSchJCCUaIlxAAHDx5EWFgY/v77byQmJqJRo0aWDon8v7lz56JZs2aWDoOQQtn0NnmElJXY2Fi4u7sXuhOIIUy9NRshxDZQi5aQYowYMQITJ05EfHw8OI6Dl5cXOnXqhAkTJmDChAmoVKkSXFxc8OWXX+pt/eXl5YVvvvkGI0aMgJOTEz766KNi3ysyMhLNmzeHRCJBQEAAoqKi8tWJjo5G7969YW9vD1dXV4SEhOD58+e644bE9qb4+Hj069cP9vb2cHR0xPvvv4+nT58CyOm+5vF4uHTpkt5r1q5dC09PTzDGEBERAY7jcOjQITRv3hxSqRRdunRBcnIyDhw4AD8/Pzg6OmLw4MHIysrSnYMxhmXLlqF27dqQSqVo2rQpdu/erTuee96jR48iICAAdnZ2CAoKwu3btwEAYWFhmDdvHq5duwaO48BxHMLCwor9dyakTJV4GwJCKphXr16x+fPns5o1a7LExESWnJzMOnbsyOzt7dnkyZPZrVu32Pbt25mdnR3bsGGD7nWenp7M0dGRLV++nN29e7fYbRYzMjJY1apV2aBBg9iNGzfYX3/9xWrXrs0AsKioKMYYY0+ePGFVqlRhs2bNYjExMezKlSuse/furHPnzrrzGBrb6tWrGWOMabVa1rx5c9auXTt26dIldv78edaiRQu9nU66d+/OPv30U714mzdvzr7++mvGGGPHjx9nAFibNm3Y6dOn2ZUrV5iPjw/r2LEj69GjB7ty5Qo7efIkc3FxYUuWLNGd44svvmD169fXbce3ZcsWJhaLWUREhN55W7duzSIiItjNmzdZ+/btWVBQEGOMsaysLDZ16lTWsGFD3XaQWVlZBv6fJaRsUKIlxACrV69mnp6euucdO3Zkfn5+TKvV6so+//xz5ufnp3vu6enJ+vfvb/B7/Pjjj6xy5cosMzNTV7Z+/Xq9RPvVV1+xHj166L3u0aNHDAC7fft2iWLLTbSHDx9mfD6fxcfH647fvHmTAWCRkZGMMcbCw8OZs7Mzk8vljDHGrl69yjiOY3FxcYyx1wnx33//1Z1j8eLFDACLjY3VlX388ccsODiYMZbzxUIikeTbem/06NFs8ODBhZ73n3/+YQBYdnY2Yyxnz9OmTZsW9U9LiEVR1zEhRmrTpg04jtM9DwwMxN27d6HRaHRlAQEBBp8vJiYGTZs2hZ2dnd4587p8+TKOHz8Oe3t73aN+/foAcu4jlyS2vO/r4eEBDw8PXVmDBg1QqVIlxMTEAAD69+8PgUCAffv2AQA2b96Mzp07w8vLS+9cTZo00f3s6uoKOzs71K5dW68sOTkZQE4XuFwuR/fu3fWuZ9u2bXrX8uZ53d3dAUB3HkKsHQ2GIsSMZDKZwXWZAVtDa7Va9OnTB0uXLs13LDcBlRRjTC8pF1QuEokQEhKCLVu2YMCAAfj1118LnB4kFAp1P3Mcp/c8t0yr1equBQD++ecf1KhRQ6+eWCwu8rx5X0+ItaNES4iRzp8/n+953bp1wefzjTpfgwYN8PPPPyM7OxtSqbTA92jRogX27NkDLy8vCASF//mWJLYGDRogPj4ejx490rVqo6OjkZqaCj8/P129MWPGoFGjRli3bh1UKhUGDBhg1HXmfV+xWIz4+Hh07NjR6POIRKICW+qEWAvqOibESI8ePUJoaChu376NHTt2YO3atZg8ebLR5xsyZAh4PB5Gjx6N6Oho7N+/HytWrNCrM378eLx48QKDBw9GZGQk7t+/j8OHD2PUqFF6yaYksXXr1g1NmjTB0KFDceXKFURGRmLYsGHo2LGjXte3n58f2rRpg88//xyDBw/WfRkwloODA6ZNm4bPPvsMW7duRWxsLKKiovD9999j69atBp/Hy8sLcXFxuHr1Kp4/fw6FQlGquAgxNUq0hBhp2LBhyM7ORqtWrTB+/HhMnDgRY8eONfp89vb2+OuvvxAdHY3mzZtj9uzZ+bqIq1evjjNnzkCj0SA4OBiNGjXC5MmT4eTkBB7v9Z9zSWLjOA6///47nJ2d0aFDB3Tr1g21a9dGeHh4vrqjR4+GUqnEqFGjjL7OvBYsWICvv/4aixcvhp+fH4KDg/HXX3/B29vb4HO8++676NmzJzp37oyqVatix44dJomNEFPhmCE3hgghejp16oRmzZpZ5TKG5oxt4cKF2LlzJ65fv27ycxNSXlGLlhBSrIyMDFy8eBFr167FpEmTLB0OITaFEi0hZWTRokV601jyPnr16mXp8Io0YcIEtGvXDh07djRZtzEhFQV1HRNSRl68eIEXL14UeEwqleab4kIIKR8o0RJCCCFmRF3HhBBCiBlRoiWEEELMiBItIYQQYkaUaAkhhBAzokRLCCGEmBElWkIIIcSMKNESQgghZvR/Kx5UE7PajMAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(5, 5))\n",
    "sns.scatterplot(data=fpr_disparity, x='fpr_deployment', y='tpr_deployment', hue='group', style='group', s = 70, palette=[\"C0\", \"C2\", \"C3\", \"C1\"])\n",
    "fpr_model = 0.0451\n",
    "tpr_model = 0.5206\n",
    "sns.scatterplot(x= [fpr_model], y = [tpr_model], label = 'model', s = 70, color = 'black', marker = 'X').set(title = 'Recall vs FPR')\n",
    "plt.ylim([0.3,0.8])\n",
    "plt.xlim([0.01, 0.09])\n",
    "\n",
    "plt.savefig(\"Deployment_Performance.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_pred = current_deploy.drop(columns = 'model#0')\n",
    "model_pred = (current_deploy['model#0'] > ml_model_threshold).astype(int)\n",
    "labels = deployment['fraud_bool']\n",
    "exp_pred_lp = exp_pred.loc[labels == 1]\n",
    "exp_pred_ln = exp_pred.loc[labels == 0]\n",
    "model_pred_lp = model_pred.loc[labels == 1]\n",
    "model_pred_ln = model_pred.loc[labels == 0]\n",
    "\n",
    "exp_corr_mod_fp = []\n",
    "exp_corr_mod_fn = []\n",
    "exp_fp_mod_corr = []\n",
    "exp_fn_mod_corr = []\n",
    "\n",
    "expert_correct_model_fp = pd.DataFrame(index = exp_pred_ln.index, columns = exp_pred_ln.columns)\n",
    "\n",
    "expert_correct_model_fn = pd.DataFrame(index = exp_pred_lp.index, columns = exp_pred_lp.columns)\n",
    "\n",
    "expert_fp_model_correct = pd.DataFrame(index = exp_pred_ln.index, columns = exp_pred_ln.columns)\n",
    "\n",
    "expert_fn_model_correct = pd.DataFrame(index = exp_pred_lp.index, columns = exp_pred_lp.columns)\n",
    "\n",
    "for exp in exp_pred.columns:\n",
    "    expert_correct_model_fp[exp] = ((exp_pred_ln[exp] == 0) & (model_pred_ln == 1)).astype(int)\n",
    "    expert_correct_model_fn[exp] = ((exp_pred_lp[exp] == 1) & (model_pred_lp == 0)).astype(int)\n",
    "    expert_fp_model_correct[exp] = ((exp_pred_ln[exp] == 1) & (model_pred_ln == 0)).astype(int)\n",
    "    expert_fn_model_correct[exp] = ((exp_pred_lp[exp] == 0) & (model_pred_lp == 1)).astype(int)\n",
    "\n",
    "exp_corr_mod_fp = expert_correct_model_fp.mean()\n",
    "exp_corr_mod_fn = expert_correct_model_fn.mean()\n",
    "exp_fp_mod_corr = expert_fp_model_correct.mean()\n",
    "exp_fn_mod_corr = expert_fn_model_correct.mean()\n",
    "\n",
    "groups = []\n",
    "for expert in exp_corr_mod_fn.index.to_list():\n",
    "    if expert.split('#')[0] == 'regular':\n",
    "        groups.append('standard')\n",
    "    else:\n",
    "        groups.append(expert.split('#')[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>L1E1M0</th>\n",
       "      <th>L1E0M1</th>\n",
       "      <th>L0E1M0</th>\n",
       "      <th>L0E0M1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_agreeing</th>\n",
       "      <td>0.155447</td>\n",
       "      <td>0.067265</td>\n",
       "      <td>0.026927</td>\n",
       "      <td>0.022251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sparse</th>\n",
       "      <td>0.204192</td>\n",
       "      <td>0.162204</td>\n",
       "      <td>0.031945</td>\n",
       "      <td>0.038935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard</th>\n",
       "      <td>0.224050</td>\n",
       "      <td>0.173651</td>\n",
       "      <td>0.036481</td>\n",
       "      <td>0.039977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unfair</th>\n",
       "      <td>0.231754</td>\n",
       "      <td>0.168877</td>\n",
       "      <td>0.028282</td>\n",
       "      <td>0.040551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  L1E1M0    L1E0M1    L0E1M0    L0E0M1\n",
       "group                                                 \n",
       "model_agreeing  0.155447  0.067265  0.026927  0.022251\n",
       "sparse          0.204192  0.162204  0.031945  0.038935\n",
       "standard        0.224050  0.173651  0.036481  0.039977\n",
       "unfair          0.231754  0.168877  0.028282  0.040551"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complementarity = pd.DataFrame(index = exp_corr_mod_fn.index)\n",
    "\n",
    "complementarity['L1E1M0'] = exp_corr_mod_fn\n",
    "complementarity['L1E0M1'] = exp_fn_mod_corr\n",
    "complementarity['L0E1M0'] = exp_fp_mod_corr\n",
    "complementarity['L0E0M1'] = exp_corr_mod_fp\n",
    "complementarity['group'] = groups\n",
    "complementarity.groupby('group').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs_over_all_cases = pd.DataFrame(index = exp_list + ['model'], columns = ['cost'])\n",
    "lam = ml_model_threshold/(1-ml_model_threshold)\n",
    "for expert in exp_list:\n",
    "    fn = (1 - exp_pred_lp[expert])\n",
    "    fp = exp_pred_ln[expert]\n",
    "\n",
    "    costs_over_all_cases.loc[expert, 'cost'] = fn.sum() + fp.sum()*lam\n",
    "\n",
    "mod_fn = 1- model_pred_lp\n",
    "mod_fp = model_pred_ln\n",
    "costs_over_all_cases.loc['model', 'cost'] = mod_fn.sum() + mod_fp.sum()*lam\n",
    "\n",
    "costs_over_all_cases.reset_index(inplace=True)\n",
    "\n",
    "for expert in costs_over_all_cases['index']:\n",
    "    if expert.split('#')[0] == 'regular':\n",
    "        costs_over_all_cases.replace(to_replace=expert, value= 'standard#' + expert.split('#')[1], inplace=True)\n",
    "\n",
    "\n",
    "costs_over_all_cases.sort_values(by = 'cost', inplace = True)\n",
    "\n",
    "groups = []\n",
    "for expert in costs_over_all_cases['index'].to_list():\n",
    "    if expert == 'model':\n",
    "        groups.append('C7')\n",
    "    else:\n",
    "        if expert.split('#')[0] == 'standard':\n",
    "            groups.append('C0')\n",
    "        if expert.split('#')[0] == 'model_agreeing':\n",
    "            groups.append('C2')\n",
    "        if expert.split('#')[0] == 'unfair':\n",
    "            groups.append('C3')\n",
    "        if expert.split('#')[0] == 'sparse':\n",
    "            groups.append('C1')\n",
    "\n",
    "f, ax = plt.subplots(figsize=(9, 9))\n",
    "\n",
    "sns.barplot(data = costs_over_all_cases, orient = 'h', y = 'index', x = 'cost', palette=groups, width = 0.7)\n",
    "plt.xlabel(\"Loss over Deployment Split\")\n",
    "plt.ylabel(\"Expert\")\n",
    "plt.title('Expert vs Model Performance')\n",
    "plt.savefig(\"Loss_over_deployment.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "CK_model = pd.DataFrame(index = exp_pred.columns.to_list(), columns = ['expert', 'ck'])\n",
    "CK_model['expert'] = exp_pred.columns.to_list()\n",
    "for expert in exp_pred.columns:\n",
    "    CK_model.loc[expert, 'ck'] = cohen_kappa_score(exp_pred[expert], model_pred)\n",
    "\n",
    "\n",
    "CK_model.loc['model', 'ck'] = 1\n",
    "CK_model.loc['model', 'expert'] = 'model'\n",
    "temp = fpr_disparity.reset_index().loc[:,['index', 'disparity_deployment']]\n",
    "temp.loc[50] = ['model', 0.29]\n",
    "\n",
    "for expert in CK_model['expert']:\n",
    "    if expert.split('#')[0] == 'regular':\n",
    "        CK_model.replace(to_replace=expert, value= 'standard#' + expert.split('#')[1], inplace=True)\n",
    "for expert in temp['index']:\n",
    "    if expert.split('#')[0] == 'regular':\n",
    "        temp.replace(to_replace=expert, value= 'standard#' + expert.split('#')[1], inplace=True)\n",
    "\n",
    "a = temp['index'].to_list()\n",
    "groups = []\n",
    "for expert in a:\n",
    "    if expert == 'model':\n",
    "        groups.append('C7')\n",
    "    else:\n",
    "        if expert.split('#')[0] == 'standard':\n",
    "            groups.append('C0')\n",
    "        if expert.split('#')[0] == 'model_agreeing':\n",
    "            groups.append('C2')\n",
    "        if expert.split('#')[0] == 'unfair':\n",
    "            groups.append('C3')\n",
    "        if expert.split('#')[0] == 'sparse':\n",
    "            groups.append('C1')\n",
    "\n",
    "f, ax = plt.subplots(1,2, figsize=(9, 9), sharey = True)\n",
    "\n",
    "sns.barplot(ax = ax[0], data = CK_model, orient = 'h', y = 'expert', x = 'ck', palette = groups)\n",
    "for bar in ax[0].patches:\n",
    "    if bar.get_width() == 1:\n",
    "        bar.set_color('black')    \n",
    "\n",
    "ax[0].set_title(\"Cohen's Kappa: Expert vs. Model\")\n",
    "ax[0].set_xlabel(\"Cohen's Kappa\")\n",
    "ax[0].set_ylabel('Expert')\n",
    "\n",
    "sns.barplot(ax = ax[1], data = temp, orient = 'h', y = 'index', x = 'disparity_deployment', palette = groups).set(ylabel = None)\n",
    "for bar in ax[1].patches:\n",
    "    if bar.get_width() == 0.29:\n",
    "        bar.set_color('black')    \n",
    "\n",
    "ax[1].set_title(\"Predictive Equality: Old vs. Young\")\n",
    "ax[1].set_xlabel(\"Predictive Equality\")\n",
    "\n",
    "plt.subplots_adjust(wspace=0.05, hspace=0)\n",
    "    \n",
    "plt.savefig(\"Cohen_Kappa.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}