Telecomm Churn Analysis Using XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "\n",
    "In this example I will build a classifier for churn prediction using a dataset from telecomm industry. You can find the data set in github in the following links.\n",
    "\n",
    "https://github.com/abulbasar/data/tree/master/Churn%20prediction\n",
    "\n",
    "There are two files \n",
    "- churn-bigml-80.csv training data\n",
    "- churn-bigml-20.csv test data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "from sklearn import *\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code International plan Voice mail plan  \\\n",
       "0    KS             128        415                 No             Yes   \n",
       "1    OH             107        415                 No             Yes   \n",
       "2    NJ             137        415                 No              No   \n",
       "3    OH              84        408                Yes              No   \n",
       "4    OK              75        415                Yes              No   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   Customer service calls  Churn  \n",
       "0                       1  False  \n",
       "1                       1  False  \n",
       "2                       0  False  \n",
       "3                       2  False  \n",
       "4                       3  False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"/data/churn-bigml-80.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check number of records, number of columns, types of columns and whether the data contains NULL values.\n",
    "\n",
    "As we see it contains 2665 records, 20 columns, and no null values. There are three catagorical values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2666 entries, 0 to 2665\n",
      "Data columns (total 20 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   State                   2666 non-null   object \n",
      " 1   Account length          2666 non-null   int64  \n",
      " 2   Area code               2666 non-null   int64  \n",
      " 3   International plan      2666 non-null   object \n",
      " 4   Voice mail plan         2666 non-null   object \n",
      " 5   Number vmail messages   2666 non-null   int64  \n",
      " 6   Total day minutes       2666 non-null   float64\n",
      " 7   Total day calls         2666 non-null   int64  \n",
      " 8   Total day charge        2666 non-null   float64\n",
      " 9   Total eve minutes       2666 non-null   float64\n",
      " 10  Total eve calls         2666 non-null   int64  \n",
      " 11  Total eve charge        2666 non-null   float64\n",
      " 12  Total night minutes     2666 non-null   float64\n",
      " 13  Total night calls       2666 non-null   int64  \n",
      " 14  Total night charge      2666 non-null   float64\n",
      " 15  Total intl minutes      2666 non-null   float64\n",
      " 16  Total intl calls        2666 non-null   int64  \n",
      " 17  Total intl charge       2666 non-null   float64\n",
      " 18  Customer service calls  2666 non-null   int64  \n",
      " 19  Churn                   2666 non-null   bool   \n",
      "dtypes: bool(1), float64(8), int64(8), object(3)\n",
      "memory usage: 398.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check distribution of the output class. As it shows it contains 85% records are negative. It gives a sense of desired accuracy - which is closure to 90% or more. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2278\n",
       "True      388\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Churn.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.854464\n",
       "True     0.145536\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Churn.value_counts()/len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Account length', 'Area code', 'International plan',\n",
       "       'Voice mail plan', 'Number vmail messages', 'Total day minutes',\n",
       "       'Total day calls', 'Total day charge', 'Total eve minutes',\n",
       "       'Total eve calls', 'Total eve charge', 'Total night minutes',\n",
       "       'Total night calls', 'Total night charge', 'Total intl minutes',\n",
       "       'Total intl calls', 'Total intl charge', 'Customer service calls',\n",
       "       'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loaded the test data and performed similar analysis as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 667 entries, 0 to 666\n",
      "Data columns (total 20 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   State                   667 non-null    object \n",
      " 1   Account length          667 non-null    int64  \n",
      " 2   Area code               667 non-null    int64  \n",
      " 3   International plan      667 non-null    object \n",
      " 4   Voice mail plan         667 non-null    object \n",
      " 5   Number vmail messages   667 non-null    int64  \n",
      " 6   Total day minutes       667 non-null    float64\n",
      " 7   Total day calls         667 non-null    int64  \n",
      " 8   Total day charge        667 non-null    float64\n",
      " 9   Total eve minutes       667 non-null    float64\n",
      " 10  Total eve calls         667 non-null    int64  \n",
      " 11  Total eve charge        667 non-null    float64\n",
      " 12  Total night minutes     667 non-null    float64\n",
      " 13  Total night calls       667 non-null    int64  \n",
      " 14  Total night charge      667 non-null    float64\n",
      " 15  Total intl minutes      667 non-null    float64\n",
      " 16  Total intl calls        667 non-null    int64  \n",
      " 17  Total intl charge       667 non-null    float64\n",
      " 18  Customer service calls  667 non-null    int64  \n",
      " 19  Churn                   667 non-null    bool   \n",
      "dtypes: bool(1), float64(8), int64(8), object(3)\n",
      "memory usage: 99.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"/data/churn-bigml-20.csv\")\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.857571\n",
       "True     0.142429\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Churn.value_counts()/len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2501875468867217"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_test)/len(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort out of categorical and numeric columns so that it can be passed to pipeline for pre-proceessing steps. In the processing steps, we are doing the following \n",
    "- replace any missing numeric values with column median\n",
    "- perform standard scaling for numeric values\n",
    "- one hot encode the categorical columns\n",
    "\n",
    "\n",
    "Althought the Area Code is numeric, here I am considering this as categorical since it is a qualitative variable in nature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_columns = ['State', 'Area code', 'International plan', 'Voice mail plan']\n",
    "num_columns = ['Account length', 'Number vmail messages', 'Total day minutes',\n",
    "       'Total day calls', 'Total day charge', 'Total eve minutes',\n",
    "       'Total eve calls', 'Total eve charge', 'Total night minutes',\n",
    "       'Total night calls', 'Total night charge', 'Total intl minutes',\n",
    "       'Total intl calls', 'Total intl charge', 'Customer service calls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"Churn\"\n",
    "X_train = df_train.drop(columns=target)\n",
    "y_train = df_train[target]\n",
    "X_test = df_test.drop(columns=target)\n",
    "y_test = df_test[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipe = pipeline.Pipeline([\n",
    "    ('imputer', impute.SimpleImputer(strategy='constant', fill_value='missing')),\n",
    "    ('onehot', preprocessing.OneHotEncoder(handle_unknown='error', drop=\"first\"))\n",
    "]) \n",
    "\n",
    "num_pipe = pipeline.Pipeline([\n",
    "    ('imputer', impute.SimpleImputer(strategy='median')),\n",
    "    ('scaler', preprocessing.StandardScaler()),\n",
    "])\n",
    "\n",
    "preprocessing_pipe = compose.ColumnTransformer([\n",
    "    (\"cat\", cat_pipe, cat_columns),\n",
    "    (\"num\", num_pipe, num_columns)\n",
    "])\n",
    "\n",
    "X_train = preprocessing_pipe.fit_transform(X_train)\n",
    "X_test = preprocessing_pipe.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>63</th>\n",
       "      <th>64</th>\n",
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       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>2666.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "      <td>2.666000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.024756</td>\n",
       "      <td>0.017629</td>\n",
       "      <td>0.016879</td>\n",
       "      <td>0.009002</td>\n",
       "      <td>0.022131</td>\n",
       "      <td>0.022131</td>\n",
       "      <td>0.016879</td>\n",
       "      <td>0.019130</td>\n",
       "      <td>0.020255</td>\n",
       "      <td>0.018380</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.179352e-16</td>\n",
       "      <td>3.275699e-16</td>\n",
       "      <td>2.915897e-16</td>\n",
       "      <td>5.654392e-16</td>\n",
       "      <td>-5.730183e-17</td>\n",
       "      <td>-2.760149e-16</td>\n",
       "      <td>-2.013893e-16</td>\n",
       "      <td>-2.946297e-18</td>\n",
       "      <td>5.267937e-16</td>\n",
       "      <td>4.839007e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.155410</td>\n",
       "      <td>0.131625</td>\n",
       "      <td>0.128843</td>\n",
       "      <td>0.094470</td>\n",
       "      <td>0.147136</td>\n",
       "      <td>0.147136</td>\n",
       "      <td>0.128843</td>\n",
       "      <td>0.137007</td>\n",
       "      <td>0.140898</td>\n",
       "      <td>0.134345</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "      <td>1.000188e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.933617e+00</td>\n",
       "      <td>-4.962065e+00</td>\n",
       "      <td>-3.933688e+00</td>\n",
       "      <td>-3.101565e+00</td>\n",
       "      <td>-3.456440e+00</td>\n",
       "      <td>-3.100065e+00</td>\n",
       "      <td>-3.672045e+00</td>\n",
       "      <td>-1.819157e+00</td>\n",
       "      <td>-3.672907e+00</td>\n",
       "      <td>-1.191955e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.887477e-01</td>\n",
       "      <td>-6.460883e-01</td>\n",
       "      <td>-6.889229e-01</td>\n",
       "      <td>-6.744811e-01</td>\n",
       "      <td>-6.750593e-01</td>\n",
       "      <td>-6.741347e-01</td>\n",
       "      <td>-6.230740e-01</td>\n",
       "      <td>-5.975267e-01</td>\n",
       "      <td>-6.171222e-01</td>\n",
       "      <td>-4.291724e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.008679e-02</td>\n",
       "      <td>-1.172304e-03</td>\n",
       "      <td>1.083774e-02</td>\n",
       "      <td>-3.730931e-04</td>\n",
       "      <td>-5.467553e-03</td>\n",
       "      <td>-1.177149e-03</td>\n",
       "      <td>-1.327980e-02</td>\n",
       "      <td>-1.903165e-01</td>\n",
       "      <td>-1.925127e-02</td>\n",
       "      <td>-4.291724e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.814391e-01</td>\n",
       "      <td>6.933526e-01</td>\n",
       "      <td>6.805757e-01</td>\n",
       "      <td>6.954009e-01</td>\n",
       "      <td>6.641242e-01</td>\n",
       "      <td>6.947594e-01</td>\n",
       "      <td>6.682549e-01</td>\n",
       "      <td>6.241038e-01</td>\n",
       "      <td>6.716218e-01</td>\n",
       "      <td>3.336100e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.205881e+00</td>\n",
       "      <td>3.471452e+00</td>\n",
       "      <td>3.204795e+00</td>\n",
       "      <td>3.817767e+00</td>\n",
       "      <td>3.393998e+00</td>\n",
       "      <td>3.815532e+00</td>\n",
       "      <td>3.502005e+00</td>\n",
       "      <td>6.325047e+00</td>\n",
       "      <td>3.501544e+00</td>\n",
       "      <td>5.673087e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0            1            2            3            4   \\\n",
       "count  2666.000000  2666.000000  2666.000000  2666.000000  2666.000000   \n",
       "mean      0.024756     0.017629     0.016879     0.009002     0.022131   \n",
       "std       0.155410     0.131625     0.128843     0.094470     0.147136   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "                5            6            7            8            9   ...  \\\n",
       "count  2666.000000  2666.000000  2666.000000  2666.000000  2666.000000  ...   \n",
       "mean      0.022131     0.016879     0.019130     0.020255     0.018380  ...   \n",
       "std       0.147136     0.128843     0.137007     0.140898     0.134345  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "                 59            60            61            62            63  \\\n",
       "count  2.666000e+03  2.666000e+03  2.666000e+03  2.666000e+03  2.666000e+03   \n",
       "mean  -1.179352e-16  3.275699e-16  2.915897e-16  5.654392e-16 -5.730183e-17   \n",
       "std    1.000188e+00  1.000188e+00  1.000188e+00  1.000188e+00  1.000188e+00   \n",
       "min   -3.933617e+00 -4.962065e+00 -3.933688e+00 -3.101565e+00 -3.456440e+00   \n",
       "25%   -6.887477e-01 -6.460883e-01 -6.889229e-01 -6.744811e-01 -6.750593e-01   \n",
       "50%    1.008679e-02 -1.172304e-03  1.083774e-02 -3.730931e-04 -5.467553e-03   \n",
       "75%    6.814391e-01  6.933526e-01  6.805757e-01  6.954009e-01  6.641242e-01   \n",
       "max    3.205881e+00  3.471452e+00  3.204795e+00  3.817767e+00  3.393998e+00   \n",
       "\n",
       "                 64            65            66            67            68  \n",
       "count  2.666000e+03  2.666000e+03  2.666000e+03  2.666000e+03  2.666000e+03  \n",
       "mean  -2.760149e-16 -2.013893e-16 -2.946297e-18  5.267937e-16  4.839007e-17  \n",
       "std    1.000188e+00  1.000188e+00  1.000188e+00  1.000188e+00  1.000188e+00  \n",
       "min   -3.100065e+00 -3.672045e+00 -1.819157e+00 -3.672907e+00 -1.191955e+00  \n",
       "25%   -6.741347e-01 -6.230740e-01 -5.975267e-01 -6.171222e-01 -4.291724e-01  \n",
       "50%   -1.177149e-03 -1.327980e-02 -1.903165e-01 -1.925127e-02 -4.291724e-01  \n",
       "75%    6.947594e-01  6.682549e-01  6.241038e-01  6.716218e-01  3.336100e-01  \n",
       "max    3.815532e+00  3.502005e+00  6.325047e+00  3.501544e+00  5.673087e+00  \n",
       "\n",
       "[8 rows x 69 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train.toarray()).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a basic logistic regression model and decision tree models and check the accuracy. Basic logistic regression model gives accuracy of 85%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8545727136431784"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = linear_model.LogisticRegression(solver=\"liblinear\")\n",
    "est.fit(X_train, y_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "est.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9535232383808095"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = tree.DecisionTreeClassifier(max_depth=6)\n",
    "est.fit(X_train, y_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "est.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print classification report. The report shows that precision and recall score quite poor. Accuracy is 85%. Confusion metrics shows a high number of false positive and false negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.97      0.98      0.97       572\n",
      "        True       0.87      0.79      0.83        95\n",
      "\n",
      "    accuracy                           0.95       667\n",
      "   macro avg       0.92      0.89      0.90       667\n",
      "weighted avg       0.95      0.95      0.95       667\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[561,  11],\n",
       "       [ 20,  75]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we build a similar model using XGBoost. Performance the model is slightly better than logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.044636\tvalidation_0-logloss:1.56424\tvalidation_1-error:0.043478\tvalidation_1-logloss:1.30105\n",
      "Multiple eval metrics have been passed: 'validation_1-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-logloss hasn't improved in 10 rounds.\n",
      "XGBRFClassifier(base_score=0.5, colsample_bylevel=1, colsample_bynode=0.8,\n",
      "                colsample_bytree=0.99, gamma=10, learning_rate=100.0,\n",
      "                max_delta_step=0, max_depth=10, min_child_weight=1,\n",
      "                missing=None, n_estimators=100, n_jobs=1, nthread=None,\n",
      "                objective='binary:logistic', random_state=0, reg_alpha=0.003,\n",
      "                reg_lambda=1, scale_pos_weight=1, seed=None, silent=False,\n",
      "                subsample=0.8, verbosity=1)\n",
      "test accuracy:  0.9565217391304348\n"
     ]
    }
   ],
   "source": [
    "eval_sets = [\n",
    "    (X_train, y_train),\n",
    "    (X_test, y_test)\n",
    "]\n",
    "\n",
    "cls = xgb.XGBRFClassifier(silent=False, \n",
    "                          scale_pos_weight=1,\n",
    "                          learning_rate=0.1,  \n",
    "                          colsample_bytree = 0.99,\n",
    "                          subsample = 0.8,\n",
    "                          objective='binary:logistic', \n",
    "                          n_estimators=100, \n",
    "                          reg_alpha = 0.003,\n",
    "                          max_depth=10, \n",
    "                          gamma=10,\n",
    "                          min_child_weight = 1\n",
    "                          \n",
    "                         )\n",
    "\n",
    "print(cls.fit(X_train\n",
    "              , y_train\n",
    "              , eval_set = eval_sets\n",
    "              , early_stopping_rounds = 10\n",
    "              , eval_metric = [\"error\", \"logloss\"]\n",
    "              , verbose = True\n",
    "             ))\n",
    "print(\"test accuracy: \" , cls.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'validation_0': {'error': [0.044636], 'logloss': [0.685241]},\n",
       " 'validation_1': {'error': [0.043478], 'logloss': [0.68529]}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls.evals_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = cls.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[566,   6],\n",
       "       [ 23,  72]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49532136, 0.5033076 , 0.50276643, 0.4953032 , 0.4953032 ,\n",
       "       0.49530497, 0.49530637, 0.4997355 , 0.49530464, 0.4953086 ,\n",
       "       0.49531177, 0.495819  , 0.49530464, 0.49530464, 0.49530464,\n",
       "       0.49532348, 0.49618483, 0.49530637, 0.4953032 , 0.496843  ,\n",
       "       0.4953032 , 0.4953032 , 0.495819  , 0.4953032 , 0.49530497,\n",
       "       0.49530464, 0.4953032 , 0.49530464, 0.4953106 , 0.4958201 ,\n",
       "       0.49530464, 0.4953032 , 0.49530497, 0.49530464, 0.5033253 ,\n",
       "       0.5011071 , 0.495819  , 0.495819  , 0.49534324, 0.49530464,\n",
       "       0.49532136, 0.50361496, 0.4953032 , 0.49752688, 0.49531996,\n",
       "       0.4953032 , 0.49743274, 0.5039511 , 0.4953032 , 0.49836493,\n",
       "       0.4953032 , 0.49530464, 0.49530464, 0.49532348, 0.49530464,\n",
       "       0.49530497, 0.5039283 , 0.4958608 , 0.5029972 , 0.49530464,\n",
       "       0.50387526, 0.49532342, 0.50241953, 0.4953032 , 0.5013817 ,\n",
       "       0.50372434, 0.49643707, 0.4953032 , 0.49580815, 0.49530464,\n",
       "       0.49530464, 0.49530464, 0.4953092 , 0.4953032 , 0.5025921 ,\n",
       "       0.4953032 , 0.4953032 , 0.49922177, 0.495831  , 0.49530464,\n",
       "       0.4955463 , 0.4953032 , 0.4953032 , 0.4953092 , 0.49759394,\n",
       "       0.49531597, 0.4953086 , 0.49532312, 0.4953032 , 0.4953032 ,\n",
       "       0.4953032 , 0.4953032 , 0.49530637, 0.4953032 , 0.4960941 ,\n",
       "       0.4953106 , 0.50403553, 0.49532342, 0.5001099 , 0.4953032 ,\n",
       "       0.49532342, 0.49530464, 0.49580815, 0.4953032 , 0.50063103,\n",
       "       0.49530464, 0.49530464, 0.495819  , 0.4953032 , 0.50381213,\n",
       "       0.5024414 , 0.49577698, 0.4953032 , 0.49531937, 0.5024465 ,\n",
       "       0.50251037, 0.4953032 , 0.4953032 , 0.4953032 , 0.4953032 ,\n",
       "       0.4953032 , 0.49531072, 0.49580815, 0.50403553, 0.4955463 ,\n",
       "       0.49532172, 0.49530464, 0.49530464, 0.4953032 , 0.4953032 ,\n",
       "       0.49587768, 0.49530464, 0.5016732 , 0.49530464, 0.5036581 ,\n",
       "       0.4968922 , 0.49532488, 0.49702328, 0.4953032 , 0.49530464,\n",
       "       0.495819  , 0.49530464, 0.4953032 , 0.4953032 , 0.4953032 ,\n",
       "       0.49552852, 0.496921  , 0.49530637, 0.4953032 , 0.49969548,\n",
       "       0.49530464, 0.49531996, 0.4955463 , 0.49552852, 0.50340784,\n",
       "       0.50382143, 0.4958201 , 0.49621817, 0.49530637, 0.4955463 ,\n",
       "       0.4953032 , 0.49686763, 0.49531388, 0.50262976, 0.49532348,\n",
       "       0.4955463 , 0.49580944, 0.4968922 , 0.49743342, 0.49532172,\n",
       "       0.50403553, 0.49530464, 0.4953032 , 0.49530637, 0.49531996,\n",
       "       0.5034803 , 0.49530464, 0.49690187, 0.4953032 , 0.4955463 ,\n",
       "       0.4953106 , 0.49530464, 0.4953032 , 0.4955463 , 0.4978502 ,\n",
       "       0.49530464, 0.49643707, 0.4953106 , 0.49530637, 0.4953283 ,\n",
       "       0.4953032 , 0.4953032 , 0.49577698, 0.49531996, 0.4953032 ,\n",
       "       0.49988273, 0.49530464, 0.4953032 , 0.49532652, 0.49530464,\n",
       "       0.49530464, 0.49531096, 0.4953092 , 0.50354415, 0.49530464,\n",
       "       0.49530464, 0.49532792, 0.4953032 , 0.4955463 , 0.49977228,\n",
       "       0.4953032 , 0.4953032 , 0.4953032 , 0.4982413 , 0.49530637,\n",
       "       0.4953032 , 0.49530464, 0.4981815 , 0.49978557, 0.49530464,\n",
       "       0.49530464, 0.4953086 , 0.49530464, 0.4953032 , 0.4953032 ,\n",
       "       0.50369656, 0.5036074 , 0.49530464, 0.4955463 , 0.4958687 ,\n",
       "       0.49531096, 0.4966823 , 0.49530464, 0.4953032 , 0.49530637,\n",
       "       0.4953092 , 0.4955463 , 0.4953032 , 0.503647  , 0.4953032 ,\n",
       "       0.49530637, 0.4953032 , 0.49532482, 0.49533904, 0.4953032 ,\n",
       "       0.4953032 , 0.4955463 , 0.4953086 , 0.4955463 , 0.49530637,\n",
       "       0.49552852, 0.4953032 , 0.4999526 , 0.4953032 , 0.4953032 ,\n",
       "       0.49530464, 0.4955463 , 0.49530464, 0.4953032 , 0.4974299 ,\n",
       "       0.49532488, 0.49530464, 0.4953032 , 0.4953032 , 0.4968409 ,\n",
       "       0.4953032 , 0.4953032 , 0.49532488, 0.4955463 , 0.49682808,\n",
       "       0.49532342, 0.49530637, 0.49530464, 0.4953032 , 0.50050545,\n",
       "       0.49531212, 0.5023832 , 0.49530464, 0.49531072, 0.5035246 ,\n",
       "       0.4953092 , 0.49530464, 0.4953032 , 0.49531996, 0.49530464,\n",
       "       0.495831  , 0.4953106 , 0.4993842 , 0.4953032 , 0.4958764 ,\n",
       "       0.49572074, 0.4953092 , 0.4953032 , 0.49530464, 0.49531072,\n",
       "       0.4962372 , 0.49530464, 0.4953092 , 0.50367874, 0.5025119 ,\n",
       "       0.49535015, 0.4953032 , 0.49530464, 0.4953032 , 0.49531072,\n",
       "       0.495819  , 0.4953032 , 0.49530464, 0.4953032 , 0.49530464,\n",
       "       0.49531996, 0.4953032 , 0.4976884 , 0.49659276, 0.49552852,\n",
       "       0.4953032 , 0.4953032 , 0.4953032 , 0.49530464, 0.49530497,\n",
       "       0.49530497, 0.49531   , 0.49533406, 0.4953032 , 0.4953032 ,\n",
       "       0.49530464, 0.4953032 , 0.5038736 , 0.4953032 , 0.49530464,\n",
       "       0.49530464, 0.4953283 , 0.49531248, 0.4953032 , 0.4953032 ,\n",
       "       0.49530464, 0.4953032 , 0.5017977 , 0.4953032 , 0.49530464,\n",
       "       0.49696875, 0.4953032 , 0.5039511 , 0.4953032 , 0.49532342,\n",
       "       0.49682102, 0.49530464, 0.4953032 , 0.49584877, 0.5038956 ,\n",
       "       0.4953106 , 0.50390285, 0.49530464, 0.4953032 , 0.4960224 ,\n",
       "       0.5038183 , 0.49531248, 0.4953032 , 0.49530464, 0.49532652,\n",
       "       0.49530497, 0.4953032 , 0.50385237, 0.4967842 , 0.49627995,\n",
       "       0.4953032 , 0.49530464, 0.4953032 , 0.4953032 , 0.4953092 ,\n",
       "       0.4953032 , 0.49530637, 0.4953032 , 0.49580815, 0.49530464,\n",
       "       0.4969823 , 0.49530464, 0.4958201 , 0.49530464, 0.49530464,\n",
       "       0.4953032 , 0.49530637, 0.49531212, 0.49685526, 0.49580944,\n",
       "       0.5036435 , 0.49530464, 0.4953092 , 0.49587113, 0.4953032 ,\n",
       "       0.4953032 , 0.49659276, 0.49530464, 0.4953032 , 0.49579862,\n",
       "       0.49530464, 0.49581063, 0.49530637, 0.5033076 , 0.49532342,\n",
       "       0.49655408, 0.50392205, 0.4955463 , 0.5025287 , 0.49530464,\n",
       "       0.4953032 , 0.49530464, 0.49532342, 0.4953092 , 0.50359726,\n",
       "       0.4953092 , 0.4953654 , 0.49530464, 0.49530464, 0.4953032 ,\n",
       "       0.4955463 , 0.495819  , 0.4953032 , 0.4997985 , 0.49530497,\n",
       "       0.5035447 , 0.49530464, 0.495819  , 0.4965742 , 0.49701816,\n",
       "       0.49696875, 0.4953032 , 0.49530464, 0.49580196, 0.49530464,\n",
       "       0.5023832 , 0.4953032 , 0.4953092 , 0.495819  , 0.4955463 ,\n",
       "       0.4953032 , 0.49577698, 0.49974996, 0.49530464, 0.49671346,\n",
       "       0.4955463 , 0.49659276, 0.5038705 , 0.4953032 , 0.49643803,\n",
       "       0.4953032 , 0.49530497, 0.4953032 , 0.49675822, 0.4963314 ,\n",
       "       0.49531248, 0.4953032 , 0.4953032 , 0.49532136, 0.4953032 ,\n",
       "       0.50403553, 0.49658242, 0.50356317, 0.49531996, 0.49531996,\n",
       "       0.49653822, 0.50388294, 0.4953032 , 0.5007544 , 0.50050545,\n",
       "       0.49530497, 0.49530464, 0.49530464, 0.49775854, 0.49530464,\n",
       "       0.49530464, 0.49530497, 0.4953092 , 0.49673262, 0.4953032 ,\n",
       "       0.49530464, 0.49530464, 0.5038602 , 0.49580815, 0.4953032 ,\n",
       "       0.49530464, 0.49532342, 0.4953032 , 0.4999901 , 0.49610895,\n",
       "       0.49532348, 0.4953032 , 0.4953032 , 0.4953032 , 0.4953032 ,\n",
       "       0.4953092 , 0.49531996, 0.49887386, 0.4953032 , 0.4953032 ,\n",
       "       0.5038602 , 0.4953032 , 0.49530464, 0.4953032 , 0.49530497,\n",
       "       0.5004331 , 0.4953032 , 0.49531996, 0.4953032 , 0.4953252 ,\n",
       "       0.5014622 , 0.49686527, 0.49531388, 0.4953032 , 0.49806842,\n",
       "       0.49725476, 0.49530497, 0.49530464, 0.4953032 , 0.49659276,\n",
       "       0.4953092 , 0.4953402 , 0.49531072, 0.4953032 , 0.49530464,\n",
       "       0.49631235, 0.4953106 , 0.49530464, 0.4953106 , 0.502097  ,\n",
       "       0.49712962, 0.49530464, 0.49530497, 0.49530637, 0.5006794 ,\n",
       "       0.49530464, 0.49531212, 0.4953032 , 0.4953032 , 0.4953032 ,\n",
       "       0.49530464, 0.4953032 , 0.4953032 , 0.49532652, 0.4953032 ,\n",
       "       0.49591362, 0.4953032 , 0.49652252, 0.4955463 , 0.49531212,\n",
       "       0.4953032 , 0.49825776, 0.4958687 , 0.4956332 , 0.50390285,\n",
       "       0.49530637, 0.49530464, 0.5035893 , 0.49530497, 0.49580815,\n",
       "       0.49530464, 0.4953032 , 0.4953032 , 0.495819  , 0.495819  ,\n",
       "       0.4953032 , 0.50392735, 0.4953092 , 0.49531072, 0.4953032 ,\n",
       "       0.49530497, 0.4953106 , 0.49532136, 0.49805152, 0.4953032 ,\n",
       "       0.49530464, 0.4953032 , 0.5039283 , 0.50264704, 0.49531072,\n",
       "       0.49531248, 0.4953032 , 0.4953086 , 0.49653822, 0.49738258,\n",
       "       0.4953032 , 0.49531072, 0.5032668 , 0.49530464, 0.49530464,\n",
       "       0.49531996, 0.4953032 , 0.4953032 , 0.49530464, 0.49530464,\n",
       "       0.50403553, 0.4955463 , 0.4953032 , 0.5023832 , 0.49530497,\n",
       "       0.49531096, 0.49530637, 0.49582142, 0.4953032 , 0.49530637,\n",
       "       0.4953032 , 0.4955463 , 0.4953032 , 0.49530464, 0.4953032 ,\n",
       "       0.4968609 , 0.5026335 , 0.49530464, 0.49532792, 0.4953032 ,\n",
       "       0.4953032 , 0.49530464, 0.4953032 , 0.4953032 , 0.495819  ,\n",
       "       0.50350094, 0.49530464, 0.4953032 , 0.500039  , 0.5001839 ,\n",
       "       0.49532944, 0.49531   , 0.4953032 , 0.4953032 , 0.49754012,\n",
       "       0.49572074, 0.4953032 , 0.49530637, 0.4953032 , 0.495819  ,\n",
       "       0.49535576, 0.4953032 , 0.49537733, 0.4953032 , 0.49619558,\n",
       "       0.49892336, 0.49698773, 0.495819  , 0.4953092 , 0.49530497,\n",
       "       0.4953032 , 0.49658677, 0.49530464, 0.49530464, 0.49530464,\n",
       "       0.49580815, 0.4953032 , 0.4953032 , 0.49580815, 0.4953032 ,\n",
       "       0.495819  , 0.49531072, 0.5039283 , 0.50389624, 0.4953032 ,\n",
       "       0.495819  , 0.49926472, 0.49530497, 0.4953032 , 0.4953032 ,\n",
       "       0.49530464, 0.5033546 , 0.4953032 , 0.4953032 , 0.49530637,\n",
       "       0.4953032 , 0.4955463 ], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_prob = cls.predict_proba(X_test)[:, 1]\n",
    "y_test_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9264722119985278"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc = metrics.roc_auc_score(y_test, y_test_prob)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftr, tpr, thresholds = metrics.roc_curve(y_test, y_test_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC, auc: 0.9264722119985278')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 8,8\n",
    "plt.plot(ftr, tpr)\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC, auc: \" + str(auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost cross validation parameters\n",
    "\n",
    "- num_boost_round: denotes the number of trees you build (analogous to n_estimators)\n",
    "- metrics: tells the evaluation metrics to be watched during CV\n",
    "- as_pandas: to return the results in a pandas DataFrame.\n",
    "- early_stopping_rounds: finishes training of the model early if the hold-out metric (\"rmse\" in our case) does not improve for a given number of rounds.\n",
    "- seed: for reproducibility of results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.921533+0.00807297\ttrain-error:0.0365722+0.00345008\ttrain-logloss:0.684847+0.0001037\ttest-auc:0.899072+0.0266443\ttest-error:0.0615158+0.0153549\ttest-logloss:0.685205+0.000253096\n",
      "[1]\ttrain-auc:0.918103+0.00770359\ttrain-error:0.035822+0.00343167\ttrain-logloss:0.676853+0.00028859\ttest-auc:0.897627+0.0269152\ttest-error:0.0615166+0.0142642\ttest-logloss:0.677571+0.000453148\n",
      "[2]\ttrain-auc:0.923362+0.00558459\ttrain-error:0.035634+0.00270392\ttrain-logloss:0.669226+0.000352606\ttest-auc:0.896829+0.0280443\ttest-error:0.0637682+0.0175672\ttest-logloss:0.670414+0.000763792\n",
      "[3]\ttrain-auc:0.923363+0.00572027\ttrain-error:0.034884+0.00273107\ttrain-logloss:0.661607+0.000592359\ttest-auc:0.896745+0.0277124\ttest-error:0.0626424+0.0184112\ttest-logloss:0.663192+0.000821418\n",
      "[4]\ttrain-auc:0.923499+0.00549045\ttrain-error:0.0349778+0.0026816\ttrain-logloss:0.654104+0.000705886\ttest-auc:0.897508+0.0281374\ttest-error:0.0615174+0.0182119\ttest-logloss:0.656008+0.00103398\n",
      "[5]\ttrain-auc:0.923711+0.00533214\ttrain-error:0.0351656+0.00338492\ttrain-logloss:0.646568+0.000766784\ttest-auc:0.898581+0.0283717\ttest-error:0.0615172+0.0177019\ttest-logloss:0.64877+0.00129266\n",
      "[6]\ttrain-auc:0.924853+0.00638966\ttrain-error:0.0348842+0.00361882\ttrain-logloss:0.639399+0.000742947\ttest-auc:0.897607+0.0282272\ttest-error:0.0611426+0.0178303\ttest-logloss:0.641937+0.00148973\n",
      "[7]\ttrain-auc:0.92459+0.00652497\ttrain-error:0.0346966+0.00369596\ttrain-logloss:0.632366+0.00098383\ttest-auc:0.897484+0.0279144\ttest-error:0.0607674+0.0176712\ttest-logloss:0.635199+0.00178034\n",
      "[8]\ttrain-auc:0.924464+0.00621409\ttrain-error:0.0345092+0.00339353\ttrain-logloss:0.625226+0.00103183\ttest-auc:0.898053+0.0282137\ttest-error:0.0607674+0.0176712\ttest-logloss:0.628376+0.0020224\n",
      "[9]\ttrain-auc:0.926523+0.00675586\ttrain-error:0.0342276+0.00318398\ttrain-logloss:0.618486+0.00101266\ttest-auc:0.897991+0.0283456\ttest-error:0.0603916+0.0172168\ttest-logloss:0.621976+0.00251335\n"
     ]
    },
    {
     "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>train-auc-mean</th>\n",
       "      <th>train-auc-std</th>\n",
       "      <th>train-error-mean</th>\n",
       "      <th>train-error-std</th>\n",
       "      <th>train-logloss-mean</th>\n",
       "      <th>train-logloss-std</th>\n",
       "      <th>test-auc-mean</th>\n",
       "      <th>test-auc-std</th>\n",
       "      <th>test-error-mean</th>\n",
       "      <th>test-error-std</th>\n",
       "      <th>test-logloss-mean</th>\n",
       "      <th>test-logloss-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.921533</td>\n",
       "      <td>0.008073</td>\n",
       "      <td>0.036572</td>\n",
       "      <td>0.00345</td>\n",
       "      <td>0.684847</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.899072</td>\n",
       "      <td>0.026644</td>\n",
       "      <td>0.061516</td>\n",
       "      <td>0.015355</td>\n",
       "      <td>0.685205</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train-auc-mean  train-auc-std  train-error-mean  train-error-std  \\\n",
       "0        0.921533       0.008073          0.036572          0.00345   \n",
       "\n",
       "   train-logloss-mean  train-logloss-std  test-auc-mean  test-auc-std  \\\n",
       "0            0.684847           0.000104       0.899072      0.026644   \n",
       "\n",
       "   test-error-mean  test-error-std  test-logloss-mean  test-logloss-std  \n",
       "0         0.061516        0.015355           0.685205          0.000253  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {  'objective': \"binary:logistic\"\n",
    "          , 'colsample_bytree': 0.9\n",
    "          , 'learning_rate': 0.01\n",
    "          , 'max_depth': 10\n",
    "          , 'alpha': 0.5\n",
    "          , 'min_child_weight': 1\n",
    "          , 'subsample': 1\n",
    "          , 'eval_metric': \"auc\"\n",
    "          , 'n_estimators': 300\n",
    "          , 'verbose': True\n",
    "         }\n",
    "\n",
    "data_dmatrix = xgb.DMatrix(data=X_train,label=y_train) \n",
    "\n",
    "cv_results = xgb.cv(dtrain=data_dmatrix\n",
    "                    , params=params\n",
    "                    , nfold=5\n",
    "                    , maximize = \"auc\"\n",
    "                    , num_boost_round=100\n",
    "                    , early_stopping_rounds=10\n",
    "                    , metrics=[\"logloss\", \"error\", \"auc\"]\n",
    "                    , as_pandas=True\n",
    "                    , seed=123\n",
    "                    , verbose_eval=True\n",
    "                   )\n",
    "\n",
    "cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1113f25d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_results[[\"train-error-mean\"]].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install graphviz to display the decision graph\n",
    "\n",
    "```\n",
    "$ conda install graphviz python-graphviz\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a286b2cd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tp7By5UrI5XL07NmzQ8ZUqVT47bffkJeXJxbGadtn//79kMlkrfZTqVT4+OOPW22j7ThA4/f4woULcHd3b3ZMoVBg165dOHjwIJRKJdzd3SGXy9vdpq3z3KmoqAiffvophg8frra/tLQUGzduxPfffw+5XI7u3bu3e04AUFFRgR07diAsLAw3b95Ez549Wy2ObS2WzuTg4ICJEydi9uzZKC8vx9q1a/Htt9/ilVdekSwmenjt27cPISEhmDVrFvbu3YsxY8bAxMSk085/e65vaGiAl5dXh45/6tQpfPPNN9i6dWuH5fz25Nz29GlPG20+a7TNw3Fxcdi3bx8uX76MAQMGiPt/+OEHxMbGIiEhAQkJCfjxxx/Rt29ftfxZVFSEPXv24PDhwyguLoaLiwv09fXVxtdmHG3ivfP6uLi4aDUne3t7mJuba7wGHc3Ozg7BwcH461//iosXL2L79u0YMGAAC+WJiIiIiB5+cQAOtXaQz+4gIiIiIiIiIiIiIq3s2bMHvXr1wu+//459+/bh1KlTGDdunGTxJCQkYO/evfjkk086dNzY2FisW7cO3t7emDZtGqqqqv70mPn5+fDx8UFGRgYOHTqEkJAQ1NfXa91n9uzZrfZratdaG23HKSgowLJly9CtWzccPHiwWTzJycno27cv7O3tsXz5cpSVlcHT0xMnT55sVxsAGs/Tkrlz5+LTTz9V21dcXIwBAwYgLi4OCQkJGD9+fLN+bc2pKWZvb2989NFHWL9+PebNmwd/f3/k5uZqHYtUHB0d8fHHH+Pq1asICAjArFmzUF5eLnVY9JAoKyvD9OnTMW3aNIwaNQrvvvtupxc9Auq5Pjs7u0PHbsr3y5cv77Ccr23ObW+f9rbRdF5tciMAFBYWYu7cuXjjjTcQEhKC+fPni8euXbuGiRMnYvr06eJ26dIlGBsbi20uX76MkSNHwtfXF8uXL0daWhqCgoKQk5Ojdp62xtEm3pauT0tzb2lOzs7OrV6De61nz544ePAgysvLMWDAAHz22WeSxUJERERERPcBQRA0bURERERERERERET0iKurqxP+8Y9/CDKZTFi8eLFQUVEhdUiiuLg4ofFP3R1n/PjxwjvvvCM0NDQIWVlZf3q8+vp6YdiwYUJwcLAgCI3X083NTXj99de17tNav7bG1nYcQRCEc+fOiddzyZIlzWIaP368MGfOHLV9L7zwgjB8+PB2tREEQeN57rR161bBy8tLsLOzU9u/efNmoaioSHy/evVqITo6ul1zaoo5Li5OEARByM/PF+bOnSsAEGbPnq11LPcLOzs7wdfXV0hLS5M6FHrAZWRkCL179xbs7e2FH3/8UepwxN/jL774okPHbcr3giB0SM5vT85tT5+7aaPpvNrkxhs3bghdu3YVnn/++RaPz5s3T4iMjBQyMjLETalUqs0rICBAWL58uVq/QYMGCePGjVPbp2kcbeJt7frcOfe25iSluro6YfXq1YKurq4we/ZsoaamRuqQiIiIiIjo3tghaKjZ5Qq9RERERERERERERNQqQRAwf/58bNy4ETt37sSGDRtgamoqdVgiXV3dDh8zMTERcrlcfJz5n3Xy5ElER0dj3rx5AAC5XI4XXngBGzZsQGVlpVZ9WuvX1tjajgMAAwcO1Pio+ZycHCQmJqrtMzAwgEqlalcbAFo/0j4lJQWXLl3ChAkT1PbX1NTgiSeegLW1tbhv5syZzVYPbWtOsbGxmDFjBvz9/QEANjY2WL16NXR0dPD7779rFcv95OLFizAxMcHQoUORnJwsdTj0gCooKMCYMWPQ0NCAs2fP4sknn5Q6JDHXy2SyDh23Kd83jf1nc357cm57+txNG03nbSs31tTU4K9//Susra2xZcuWZsdzc3MRHx8PT09PuLi4iJuhoaHYJiYmBnFxcejbt69a30GDBuGXX35BbGysOJamcbSJt7Xrc/vc25qT1ORyOVatWoVjx44hLCwMoaGhaGhokDosIiIiIiLqZCzoJSIiIiIiIiIiIqJW/fvf/8auXbsQHh6O0NBQqcPRSnZ2Nr788kusXr0av/76a7PjKSkp+Prrr7Fs2TK1R3dHRUVh06ZNyMzMxLlz5xAeHt4h8Rw4cAAA0Lt3b3Ffr169UFlZiWPHjmndp6V+bY2t7TjaeOaZZxATE4Ndu3YBABQKBQ4ePIilS5e2q422amtr8dZbb+GDDz5odkxfXx8eHh5q++Lj45vNsy3u7u6YPn262j4HBwf0798fVlZWWsVyP3F0dMSvv/4Kd3d3PPXUU1AoFFKHRA+YhoYGhISEQEdHBydOnICrq6vUIWmkKd+3luuB5vn+888/75Ccfzc5V5s+d9vmbnI9AKxcuRLnz5/H8uXLYWJi0uz4Z599hrNnz8LFxQXdunXDjh07IAiCWpummwru3D9w4EAAQHR0tDiWpnG00dr1uX3ubc3pfjFu3DgcOnQIhw4dwnvvvSd1OERERERE1Mk6fukCIiIiIiIiIiIiInooJCYm4r333sP69evx+OOPSx2OViIjI7F7924sXLgQZmZmmDRpEmbOnImNGzcCAD755BMcPnwYv/32G9LT0zFq1Cjk5uZi4cKFcHNzEx9vZ2dnB29vbwCNBWPXr1/XeF6ZTIagoKAWj6WlpQFoLBRtYmtrC6Cx4EzbPi31a2tsbcfRxksvvYRvv/0WoaGhuHjxIhITE/H555/j6aefblcbba1evRpLly6FmZmZxnaCIGDfvn3497//jYkTJ7brHF26dGlxf2ZmJhYtWtTuWO4HZmZmCA8PR69evbBq1SqsX79e6pDoAfLFF18gNjYWly5dQteuXaUORyNN+V5TrgfQLN/3798fpqambeZ7Tbke0D53t7fP3ba5m1wPALt374auri6uXLmC0aNH49y5c+jXrx8++eQT9OvXDyNGjEBtbS3OnDmDs2fPYtasWfj222/x008/iSseGxkZAQAuXLiA5557Thy7e/fuAICMjAwAwIgRI3Dy5MlWx9FGa9fn9rm3Naf7yahRo/Dee+/hzTffxPTp09GtWzepQyIiIiIiok7Cgl4iIiIiIiIiIiIiatHGjRvh6empVth4P1MoFJg7dy7i4+NhYmKCvn374vjx49i0aRNCQ0MRGBiIjRs34oknnoBMJoO7uzv69OmDo0ePYuHChXB3d4e1tTUAwMnJSXy89/fff49//OMfGs+tq6uL2traFo/l5eVBLpdDX19f3GdsbAwAyMnJ0bpPS/3aGlvbcbRhZ2eHU6dOYciQIVi/fj2GDBmCoUOHtruNNqKioqCrq9tm38rKSvz973/Ht99+i6qqKpw/f15c/fFunTx5Erq6uvj73//erljuJ3Z2dnjrrbewatUqvPPOOzA1NZU6JHpAbNy4EbNmzYKvr6/UoWjUVr7XlOsBNMv3AwYMAACsX79eY77XlOsB7XN3e/vcbZu7yfVZWVnIyspCnz598M9//hPW1tZISUnByJEjMWLECFRUVODxxx8Xb/SJi4vDs88+i4iICKxbtw4rVqwAAAQFBUFfXx9RUVEQBAEymQwAUFZWBqDxewBAHKu1cbTR2vVpmntbc7p27RqcnJy0Pl9n+Nvf/oZPP/0U27dvx7vvvit1OERERERE1El0pA6AiIiIiIiIiIiIiO5PJ0+exOTJk8UinPvd7t27oVQqsXz5cixevBiLFy9GTk4OunfvLq7ed+LECaxZswYAkJSUhMzMTKSmpmoc95VXXkFVVZXGrby8vNX+LRVT1tfXAwDs7e217tNSv7bG1nYcbW3fvh0jRozA7NmzcebMGQwePFhcZbE9bTQpLS3Fhg0bsHLlyjbbmpiYYOvWraioqMD69evFYr27VV9fj3/+858IDw8Xr522sdxvpk6dioqKCly8eFHqUOgBUVZWhitXriAkJETqUNrUVr6/m1wPtJ3vNeV6QPvc3d4+d9vmbnJ9U86YNGmSWPTs7e2Njz/+GAqFoln7gIAAxMbGwtnZGbt37xb3u7i4YM2aNYiNjcWsWbNw7NgxfPTRR3j77bfFftqMow1NNy3Y29u3OadNmza163ydQS6XY8KECTh16pTUoRARERERUSfiCr1ERERERERERERE1KLi4uL7/pHrt0tMTISDgwM2btzYahsnJyf8/PPPOHr0KEaMGIHu3bsjNjZW47i6urrQ1W37z+lnz57Fyy+/rLbv//7v/+Di4oL6+nqoVCoYGBgAACoqKgCg1VUwW+rTUr+2xr527ZpW42jjq6++wvfff4/z589DV1cXQUFBmD9/PhYvXowjR45o3aYtf//73zFw4ECEh4eL+1JTU1FdXY0DBw7A0tISo0ePVuujo6ODpUuX4tVXX2021/ZYtmwZ/vGPf6Bv377ivvbGcr+wsbGBTCZDUVGR1KHQA6KkpAQA0KVLF4kjaZs2+b69uR64N/ke0JxztemjTS5vqc3d5HoLCwsAaPb5P2TIkFb7GBsbIyQkBF9++aXa/tdeew2DBg3Czz//jOjoaDz77LOIiYlBamqqWp5ta5y2tHYNgca5tzWn5OTkdp2vs3Tt2hXFxcVSh0FERERERJ2IBb1ERERERERERERE1CIPDw8kJSVJHYbW5HI5kpOTUVtbCz09vRbbrFq1ClFRUTh+/DiMjIywf//+Nsc9f/48IiIi2jx3aGhoswKvbt26wcfHBwCQmZkJT09PAEBhYSGA1ousWurTUr+2xr569apW42hj586dGD9+vFjsNnv2bFy4cAHbt29HaWkpLC0ttWrTloKCAvzyyy9q+8rKylBVVYUlS5bAz8+v1SJaa2vruy7m3bp1K/r27Yvg4GC1/f/5z3/uKhapJSYmQhAEeHh4SB0KPSAcHR2hp6eH5ORkDBo0SOpwNGor399NrgfazvdyuRzLly+Hq6ur1vke0JxztemjTS5vqc3d5Hpvb28AaFYA7erq2upnKwD07NlT7Hu7ESNGYMSIEQCAGzduIDw8HOvWrYOZmVm7xtGktWsINM696bOntTm1FovUrl69Cnd3d6nDICIiIiKiTqQjdQBEREREREREREREdH965plnsHfv3gdmhc+AgABUVlZiy5YtavtLS0uxadMm3LhxA2vWrMHzzz8PIyMjAEBDQ0Ob46akpCAsLEzjtn//fjg4OOCFF15Q21xcXDBnzhwYGBjg9OnT4pixsbHo06dPq0VLLfVpqV9bY2s7jjbi4+NRWlqqti8kJAQ1NTXIy8vTuk1bjh49ilu3bqltCxcuhI2NDW7duoXjx4+32nfixIlaz+d2Bw8ehCAImDlzptr+qKiou45Faps2bYKXl1ezx9oTtUZfXx/jx4/Htm3bpA6lTW3l+7vJ9UDb+b6pMLg9+R7QnHO16XO3be4m19vb2+OJJ55ATEyM2v7U1FTU1ta22u/gwYMICQlp9XhNTQ2mTZuGHj16YNGiRXc9Tktauz5Nc29rTkFBQe06X2fIzc3F0aNHMWnSJKlDISIiIiKiTsSCXiIiIiIiIiIiIiJq0bx582BmZoYFCxZAEASpw2lRWVmZ+HratGlwcXHBsmXLsG7dOly9ehV79+7FSy+9hNDQUCgUCgDA7t27UV5ejlOnTuHkyZMoKSmBQqFARUUFsrKyADQW0jSZMWMGYmNjNW5nz55tNUZ7e3u8/PLLWLduHQRBQHV1NY4cOYLt27dDR0f9z/TLly/H3Llzm/UB0GK/29u11EbbcZqUlJSIbe40adIkHDx4UK0wLiYmBv7+/vDy8tK6TVvn0YZSqcS7776LhIQEcV9RURHWr1/frG1b54qIiMAHH3yA2tpabNiwARs2bMCnn36K+fPnIz4+/q7ik9rx48exbds2/Otf/4JMJpM6HHqAvPnmm4iOjsb27dulDkVNU65vyuNt5XtAc64HcFf5XlOuB5rne6D1nNuefH83bTTlekBzbvzoo4+QmZmJ33//XdwXGRkJHx8fpKSkYOnSpbh06ZJ4LDExEZWVlXjrrbdavC6VlZWYN28ePDw8EBERIa7i3jSWNuNoire163P73DXN6cUXX2wxbqkIgoCFCxfCwcEB06dPlzocIiIiIiLqRLI2/gh7f/6FloiIiIiIiIiIiIg6zYEDBzB9+nSMHz8eO07h0GwAACAASURBVHfuhLm5udQhtSk9PR0ymQyurq7NjlVUVKg9XlulUsHAwKBT4qqvr4dcLm/1uEKhQG1tLaysrNT6FBYWws7OTuPYeXl5GttoO05bqqqqkJ6eDnt7e7U429umI5SWlkJfXx/Gxsb37BwPkq+//hrz58/Hk08+ib1790JPT0/qkOgBJAgCXnzxRezZswdffPFFs5Wr7zet5Xspcz3Qds6923yvbRtNnzXtkZ2dDSMjI7U4VSoVMjIyYGxsDCcnp1b7Hjp0CP7+/ujWrVurbVQqFQoLCzWO0x7aXJ+W5nQ/UKlUWLBgAXbt2oWvv/4azz33nNQhEdEdqqqqoFAooFAokJmZiYqKCigUCpSXl6OsrAwKhQIqlQqVlZWoqalBdXU1lEolamtroVAoIAiC+DSLsrIy8Sa4pnZ3am0/ABgZGcHQ0LDNYzo6OrCwsIClpSVkMhnMzMygq6srttHX14eJiQkMDQ1hamoKc3NzWFhYwNTUVNysrKzE1/x3NxER0Z+2E8CLrR1kQS8RERERERERERERten06dOYPHkyDAwMsG3bNowbN07qkIjoNgUFBbCzs8OyZcuwdu3aDivmo0eTIAh444038MEHH+CFF17Af/7znwfiZg6iB1VCQgJmzJiBmzdvYs+ePRg/frzUIRE91JRKJfLz81FQUICioiIUFRWhuLi42evi4mIUFhaipKRErQD3djKZDJaWljAzM4OpqSmMjIyaFcs2FdUCaFZYCwByubzFz1ldXV21m1NuV1FRgbq6uhaPlZeXo76+HgBQV1eH8vLyZoXETTeWKJVKsXC4aSX70tLSVp/Q0jQXa2treHt7w9raGl26dBG/3v7axsYGtra2MDIyauM7QkRE9EhhQS8RERERERERERER/XkFBQVYtGgRwsLCMGHCBKxfvx6enp5Sh0X0SKutrcVXX32FlStX4quvvsKECROkDokeIsePH8esWbNQU1ODrKysTl3hluhRUVRUBCcnJ/Tp0we7du3iv62I/qSioiLk5OQgOzsbubm5uHTpEvLy8pCVlYX8/HxkZ2ejvLxcrY+urm6rRalNr+9ctdbFxQUmJiYwMTGRaKb3VmVlpbgacWlpqfhaoVCgrKwMRUVFSE1NbbEY+s5CY3Nzczg6OsLOzg5OTk6wtbUV3zs4OMDBwQGOjo6wtraWaLZERESdigW9RERERERERERERNRxIiIi8Oqrr+Lq1at49tlnsXLlSvTo0UPqsIgeKTU1NdizZw9Wr16N7OxsLFmyBO+//77UYdFDqKSkBB988AEOHjyINWvWYMqUKZDJZFKHRfTAq6qqwmeffYa1a9fiww8/xJw5c7i6OpEWcnJykJ6eLm4ZGRlIT0/HjRs3kJ6eDoVCIbY1MDBAv379YGtrCycnJ7GA1N7eHra2trCxsUGXLl3E1XOpY5SVlaGwsBCFhYXIz89HTk4OcnJykJeXh+zsbOTn5yMrKwt5eXlQqVRiPzMzM7i5uWHEiBFwc3NT2+zt7SWcERERUYdiQS8RERERERERERERday6ujp88803WLt2La5fv46//OUvmDdvHv7yl7+wGIXoHkpNTcW2bduwY8cOlJeXY+7cuXj99dfh7OwsdWj0kJs+fTr27NmDPn36YNGiRXjuuece2lUJie6ljIwMbN26Fdu2bUNVVRVef/11rFy5UuqwiO4reXl5SE5ORkpKClJTU5GSkoLk5GTcuHED1dXVAAC5XA4nJye4urrCw8NDLPx0dXWFs7Mz7O3tueLrA6CoqAi5ubnIysoSi7SjoqKQnp6O7Oxs1NfXAwAMDQ3h4eGBHj16wNvbW9x69OgBW1tbiWdBRETULizoJSIiIiIiIiIiIqJ7o76+HgcOHMDWrVvx22+/wcHBAbNmzcKcOXPg7u4udXhED4Xq6mocOHAAX3zxBaKiouDs7IzZs2dj/vz5cHBwkDo8eoRcvHgRn376Kfbu3QtDQ0O8+OKLWLBgAVdpJ2qDIAj45ZdfsGnTJhw9ehS2traYN28eFi9ezEI0euTdvHkT8fHxuHLlCg4fPoyUlBSUlZUBAExNTeHl5SUWb3p6eoqFu87OztDV1ZU4erqXamtrcevWLWRkZODmzZtIS0sTC7xTUlJQWVkJALC0tBR/Tnx9fdG7d2/4+/vDzc1N4hkQERG1iAW9RERERERERERERHTvXb9+XVw5NC8vD8OHD8ekSZMwadIkFvcStZNSqcTx48dx+PBhhIeHo6KiAhMmTMDcuXPxxBNPcCVsklRhYSG+/PJLbNmyBTdv3sSwYcMwZcoUTJ48GU5OTlKHR3TfiI2NRVhYGPbu3Yvr169j5MiRWLhwIZ5++mno6elJHR5Rp1IqlUhISEBcXBzi4uIQHx+P+Ph4lJaWQiaTwcPDA5MmTVJbfZWfKaTJrVu3mq3ifPXqVdy4cQOCIMDS0hIBAQHw9/fHzJkz4efnByMjI6nDJiIiYkEvEREREREREREREXWeuro6/PDDD9i3bx9++OEHlJaWom/fvggJCcHbb78tdXhE963i4mIcPXoUhw4dwvHjx1FdXY0hQ4bg6aefxowZM2Bvby91iERqGhoa8NNPP+G7777DkSNHoFAoEBgYKBb3urq6Sh0iUacSBAHnz59HWFgYwsLCcOPGDXh4eGDKlCl44YUX4OfnJ3WIRJ2ioaEB165dw9mzZxETE4MzZ84gKSkJ9fX1MDU1Ra9evRAQECAWW/r7+8PMzEzqsOkhUV5ejitXriA+Ph6XL19GfHw8YmJiIJfL4efnh8DAQAwZMgSDBw9Gz549IZPJpA6ZiIgeLSzoJSIiIiIiIiIiIrob169fx5o1a7B69Wo4Ozv/6fFmz57dYWM9KGpra3HixAkcOnQIhw8fhq6uLkaPHo1Ro0Zh9OjRXHWLHmlKpRJnzpxBZGQkIiMjcfbsWcjlcowePRpPP/00goODYWdnJ3WYD6WOzO8d/VnxoFKpVHjnnXhcunQEp09/ifLybPTt2xdjx47FuHHjEBQUxFXx6KGUn5+PX3/9FREREfjll1+QmZmJ7t27Y8qUKZgyZQoGDBggdYhE95xCoUB0dDRiYmIQExODs2fPorS0FEZGRujfvz8CAwMxePBg9OnTB926dYOOjo7UIdMjJjU1FXFxcWKReWxsLJRKJSwtLcWfzyFDhmDYsGEwMTGROlwiInq4saCXiIiIiIiIiIiI6G6EhYVh6tSpOHbsGMaPH/+nx5PJZB021oNIEAT06JEGpfIScnOjUFeXiG7dVBg71h+jRo3CyJEjuQIpPfRUKhXOnTuHVatWISYmBiqVCl5eXhg5ciTGjh2L8ePHc4W6TtCR+b2jPyseZEFBwO+/N762tFTBxCQDVVXnUFLyO/T1b2DwYAtMmNBY5NunTx8WdNEDqaqqCqdOnUJERAQiIiIQFxcHuVyOwMBAjB07FsHBwejbt6/UYRLdU/X19bh8+bL4e3Dq1CmoVCo4ODhg2LBhCAoKQv/+/TFw4EAYGBhIHS5RM3V1dUhOTsbp06cRHR2N2NhYXL16FXK5HAEBARg7dizGjh2LESNGQE9PT+pwiYjo4cKCXiIiIiIiIiIiIqK7VVhYiK5du953Yz2oZDJALm98XV/f+FUuV6KhIRWCcAlmZlno0QMYPNgCY8a4Y+TIobCyspIuYKI/KTs7W61QIDY2FtXV1Zg6dSrGjh2Lxx9/HO7u7lKH+Uhifu94L78MfP45UFf3v326uo1fm/bJZEoIQip0dZMwYUIdxo51xqRJfnBysun8gIm0lJ2djdjYWJw+fRqffPIJVCoVunXrJhZ8jRs3DpaWllKHSXTP5eTk4PDhw3jjjTdQWloKJycn8XdgzJgxvDmPHmi5ubniausRERHIzs6GlZUVRo0ahccffxwhISH8GScioo7Agl4iIiIiIiIiIiIiuj/o6ACt/VlaJhOgoyOgvr5pxcZ6ANkwM7sJd/dK9O1riDlzjNCnjw/Mzc07K2QirdTX1yMtLQ1XrlxBbGwszp07hwsXLqC8vByGhobo06cPBg0ahIEDByIoKAgeHh5Sh0zU4b74Ali48H83bGiioyOgoUH233dZcHF5E6NHyxAYGIh+/fqhV69eMDY2vqfxErUkPz8f8fHxuHDhAs6cOYOzZ88iLy8Penp66Nu3L5YuXYoRI0bA0dFR6lCJOkVWVhb279+PsLAwnD59GkZGRnjvvfcwduxY+Pr6Sh0e0T2TmJgorkIdGRkJpVKJ4cOHY8qUKXjmmWf4OUBERHeLBb1EREREREREREQkrV9//RWZmZkwMDDAM888AwMDA5w7dw5JSUmwsrJCSEgIgMbVz3766SfcunULQUFBGDNmjDiGIAiIiorC5cuXIZfL0bNnT4wbN07rGJRKJQ4fPozg4GDk5+fj2LFjcHR0xMSJEyGXy5GXl4fw8HDo6Ohg6tSpMDc3R0NDA6KiomBqaoqBAwdqFYumY5GRkWpjZWZm4sCBA3jllVeQlJSEw4cPw9XVFTNmzGj2GHKFQoFvvvkGGRkZ8PLywqBBg+Dj4wN503K3DwgnJyA7u/39LC3jUFu7CJWVjc9yd3NzQ69evcRt6tSpfJwvdZr09HQkJiYiISFB3JKSkqBSqaCjowMfHx8MHDhQLOANCAh46B7V25TXAbSZ24HW8/ufze2Aen7fsWNHm7kdQIv5/W5ze0tjaZvfN2/e/MDn9dudPQsEBmrfXk8PWLhQiWHDohAXF42YmBicO3cOFRUV0NHRgaenJwICAuDv7y9uXNGaOkptbS2uXr2K+Ph4XLlyBZcvX0Z8fDxyc3MBAE5OTggMDMSQIUMQGBiI/v37w9DQUOKoiTpHcXExvvnmG+zduxcxMTEwNTXFxIkTMXnyZDz55JMwMjKSOkSiTqVUKvHjjz8iLCwMR48eRWVlJYYOHYrw8HA+UYaIiNqLBb1EREREREREREQkraqqKpiYmOCPP/5At27dxP0+Pj44fPgwvL29ERkZid27d2PhwoVIS0vD7NmzMXPmTGzcuBEAsHLlSnh4eGDu3Lm4cOECFi9ejLNnz2p1/qioKMybNw+pqan46KOPkJycDEtLS2zYsAHjx4/Hk08+iRMnTqC+vh7ff/89JkyYgPfffx9vv/02wsLCsHnzZixYsEAcT1MsrR1LSkqCn5+fONaRI0cwZ84cFBQUYP369YiPj0dBQQGOHj2K9957D2+88YZ4vpKSEgQGBmLbtm3o168fQkNDcfDgQXGlz/Xr13fEt6lTTJwIHDsGNDS03VYuB2xtgfffB2bObCyou3nzplhAeeXKFSQmJuLatWtoaGhA9+7d0bNnT3h6eoqbl5cXXFxcmhVIE2mjpKQEP/30E9LS0pCSkoLU1FRcu3YNZWVlAABnZ2f4+fmhd+/eYnG5r6/vI1HkUlVVhUGDBiExMVFjbgegMb//mdwONM/veXl5GnN7eHg4kpKSWszvd5vb7xxL2/xeUlKChISEBz6vA0B6OhAfD1y4ALzzTusrsTeRyxtX8f3jD+C2Hx0Ajbn++vXriIuLQ3x8POLj4xEXF4cbN25AEARYWlrCz88PPXr0gLe3N7y8vNCjRw94enryxg5qUWlpKVJSUpCcnIzk5GSkpqYiOTkZV69eRU1NDfT19cVc7u/vj4CAAAQEBMDGxkbq0Ik63YULF7Bp0ybs2bMHenp6mDRpEqZMmYLHH3+cOZbov6qrq/Hzzz8jLCwM+/fvx3PPPYdFixahX79+UodGREQPBo0FvRAEQdNGRERERERERERE1CEACF988YX4Pjs7W5gyZYogCIJQUVEhdOvWTVAoFOLxOXPmCACEM2fOCA0NDULXrl2FyMhI8fiaNWvadf6PP/5YACDs27dP3LdixQoBgLB//35x38qVKwUDAwNBEAQhPj5eACBs3rxZPK4plrbivHOspvNHRESI+/r16yf0799fLfY33nhDcHNzE9/HxsYKAIT169e36xpIobhYEE6dEoRPPhGE0FBBsLUVBH19QWgs92p509MTBENDQXj7bUFQKts+R01NjbBnzx5h1apVwrPPPisMGDBAsLCwENC4aIVgYGAg+Pj4CMHBwcKrr74qbNy4UThy5IgQFxcnFBcX3/NrQPev6upqIS0tTThx4oTw9ddfC6tXrxaef/55ITAwUOjatasAQNDT0xO8vLyE8ePHC0uWLBE2b94snDx5kj87giCEh4drzO2CoDm/d0RuF4Tm+V1Tbq+vrxcEoXl+bysWTcda+qzQJr+/8cYb4usHJa8rlYJw4YIgbNsmCEuWCMLIkYJgZfW//O3hof7+zk0ma9x69xaEqKj2nbusrEyIjo4WNm/eLCxevFgYN26c4ObmJujo6AgABLlcLnh4eAhPPPGE8Nlnnwnh4eFCfHy8UFpaem8uBt13srOzhTNnzgjff/+98P777wvDhg0TbGxs1P494OfnJzzzzDPC66+/LuzatUuIj48XampqpA6dSFK1tbXC119/LQwePFgAIPj7+wtbtmwRKioqpA6N6L63adMmoVevXgIAITAwUNi1a5dQV1cndVhERHR/2yFoqNnV7fD6YSIiIiIiIiIiIqIW+Pj44OOPP8acOXMgk8nw3XffYebMmQCA3bt3Q6lUYvny5WL7nJwcdO/eHWlpaQgMDESPHj0wbdo0bN26FSEhIVi2bFm7zm9hYQEA6N27t7ivR48eAICAgABxX8+ePaFSqQCgxVWoZDJZq7FoOtaSplU8e/bsKe7z9fXF8ePH1dr98ccfKCgoEFeRCwgIgImJifi4+/tBXR1w7RoQF9e4Xb7c+DU/v/G4iwsQEAC8+CKwbl3LY+jqNq7YOG1aYxt7e+3Oraenh2nTpjXbX1BQgLS0NKSmpiItLQ1paWmIiorCjh07UFRUJLYzNTWFq6srXF1d4ezsDBcXF7i5uWHgwIGwt7eHtbV1O68G3Q+qq6uRl5eH7OxsZGVl4datW0hPT0dmZqa45ebmQvjvUqIGBgZwc3ODp6cnBg8ejBkzZsDT0xNjx46Fri7/O6UlEyZM0JjbAc35vb05szV35ndNuT07OxvOzs7N8ntbsWg61tJnhTb5/Y8//riv8zoAlJQAiYlAbCzwzTdAQgKgUgH6+oCnJ9C/PzBpEuDnB/TpA3TtCkydChw40Hwldj09wMICePddYM6cxlV628Pc3BxBQUEICgpS219dXY2UlBS1bfXq1SgoKBDbWFpaws3NDa6urvDw8ICrqyvc3Nzg7OwMZ2dn2NraQl9f/24vE3WCyspK3Lp1C7m5uUhPT1fbMjIykJGRgerqagCAXC6Hs7MznnrqKUydOhU9evSAl5cX3NzcIG/vDx7RQy48PBwrVqxAWloaJk+ejHXr1mH48OFSh0X0wFi4cCEWLlyIqKgobNq0CbNmzcLatWvx/vvvY8KECVKHR0REDyD+BYqIiIiIiIiIiIg6xWuvvYbZs2fj2LFjeOqppxAREYG//e1vAIDExEQ4ODhg48aNrfbfsGEDpk6dikmTJmHMmDH49ttvYWdn96diMjQ0bLZPT0+vzX6aYvmzccrlcrHAsMmoUaOwd+9eREdHY/To0SgpKUFNTQ3GjRun9bgdqby88dHqSUn/K/S6dAmoqmosyvX2bizuWriwsdhr0CCg6RJcvw58+KH6eDo6jWs39ukDbNgADB7cMXHa2NjAxsYGQ4YMaXasqqqqWXFneno6bty4gZMnT6oVBhkYGMDGxgaOjo6wtbWFnZ0dHB0dYWNjAwcHB9jZ2aFLly6wtraGlZUVH0d8j5WUlKC4uBhFRUUoKChAfn4+srOzkZ+fj0uXLqGgoADZ2dkoLy8X+8hkMjg4OMDV1RUuLi4ICgoSi/uairjtta0gJ5FMJtOY24G283tn5/bKyspW+93L3A40z++jRo26b/J6XR2QnKye1y9cAHJzG487ODQW6i5Z0pjXe/ZsvSC3Tx8gPByoqWl8r6fXmOMXLgTeeQcwN+/Y2A0NDeHv7w9/f3+1/VVVVbh582azws/Y2Fjs378fOTk5aLit6tjGxkbM7/b29nBwcBBzvJOTE7p06QIvLy+t/p1A2quqqkJqairy8vKQm5uLnJwc5OTkIDc3F9nZ2cjLy0NWVpba766BgYFYlO3q6orhw4fD3d0dbm5ucHNzg5OTE79PRG2IiYnB8uXLER0djalTpyI8PByenp5Sh0X0wBoxYgRGjBiB1NRUvPnmmwgODsaIESPw4YcfYuDAgVKHR0REDxAW9BIREREREREREVGnmDFjBlatWoWPPvoI7u7u8PPzE1e9lMvlSE5ORm1tbasFGH369MHFixexYsUKfP755+jXrx+uXLkiyeqpmmK5F3HOnTsXaWlpWLBgAd59911ERkZi7dq1ePLJJztwVi3Lzm4s7Lq9yOvatcaVFy0sgF69Gou7Zs4EfH0bX/93YcoWeXgAJiZAU12OXA7Y2AAffACEhgIy2T2fEgDA2NgYPj4+8PHxabXNlStXkJubi9zcXLFotKCgABkZGTh79qxYTHpnAbaJiQmsra3FAt+m103v3dzcYGJiAhMTE1haWoqvTU1NYWlpCVlnXQSJVFZWIi8vD6WlpaisrERlZSUqKipQXl6OiooKFBcXo7i4WCzcvfN1S9e7qeiuT58+sLW1hb29Pezt7WFraysW53H1zXtDU24H2s7vj3pu9/X17fS8XlrauNLunbm9urqx+NbLqzGXL13amNdvvylDGwEBjcW8cnnjqushIcD//R/g5nbv5tQSY2Nj+Pr6wtfXt8XjNTU1yM7OVisazc/PR1ZWFvLy8sTPgPz8fNTX14v9zM3NYW1tja5du4o3c9z+1dzcXMznpqam4mZhYQFzc/OHboXY6upqKBQKlJeXo6ysDAqFQtxKSkpQXl6O4uJiFBYWoqioSLwho+lr080zQGNxtr29PRwdHWFnZ4eAgADY2trCyclJrdja3t7+of+sJLpXqqur8a9//Qvr1q3D4MGDcfLkSQwbNkySWGpqanDq1CkcPXoU48aNw1/+8pcOG/vUqVOIiorCtWvXsGvXrg4ZU6VSISoqCpcvX8awYcMwdOjQdvcZPHhwi58Dt7d79dVXm7XRdhwAyM3NxbVr1zBy5MhW42qrzQ8//KB2c1xmZiZefvllGBsb4/z580hLS2vWJzAwEB4eHuL7iooKfPfdd7hx4wY8PT0xffp0GBsbtxpTXFwcTp48CX19fcyfPx8AUFpaiu3btyMjIwNPPfUUxowZo/FzVJu532teXl7Yt28fzp8/j+XLl2Pw4MF45ZVX8OGHH/LGTyIi0goLeomIiIiIiIiIiKhT6OvrY+nSpXjttdfw2muvYd26deKxgIAAVFZWYsuWLXjllVfE/aWlpfjuu+8wZ84c7N27F6Ghodi4cSOCg4Mxfvx4HDhwAHPnzu3UeahUqlZjCQ0NvSdx6urqwsHBAV999RW6du2K4ODge/KfgbW1QEpKY2FXbCzw3XdAYWHjMQeHxgKvqVMbV9/19W3c2ltPI5MBvXsDMTGNhb///Gdj0VgLC2pKrnfv3ujdu7fGNnV1dSgoKEBRUVGrBaglJSXIyMjA5cuXUVxcjIyMDNTV1bU6prGxMUxMTGBmZiYWf5mamkJPTw8GBgYwNjaGjo4OLCwsAEBsY2VlJY6hr68PExOTFsc3NDSEUQtV1w0NDSgrK2u2v7a2FgqFQnwvCAJKS0sBNP5HfV1dHaqrq6FUKlFXV4eKigoAjb+/giCgoqIClZWVqKqqQklJSavztrCwgKmpqVohtKOjI/z8/FoskO7SpQtsbGxanSd1Dk25HdCc342MjB6I3D537lx888039yS338u8XlcHZGT8r2C3qYD3+vXG41ZW/7sR46WXGnN7r17Anw2jaaHc3r2BjRsBLeqdJKGvrw93d3e4u7trbNfQ0IC8vDwUFRUhNTVVLEQtLCwUX8fHx4tFquXl5RpXgjYyMoKpqSnMzMxgaWkJS0tL6OjoiHneyMgIhoaGYh6Xy+Uwv21Z4zvf387ExKTZzQuVlZWoaVou+Y553Z7zb39/e/6uq6tDVVUVVCoVVCoVSktLoVAoUFpaKh5vjZWVFczMzNClSxdx8/X1bVYE7enpCVtbW7XPMSLqeNnZ2ZgwYQKuX7+OHTt2IDQ0VNJ4EhISsHfvXmzduhV+fn4dNm5sbCzWrVuHsLAwvP/++6iqqtJYSKqN/Px8BAYG4s0338Ts2bPx4Ycfaiyqba3Pe++9h8OHD6v1u7NdSEiIWhttxykoKMAHH3yATZs2Yd68eS0WtWrT5tq1a5g4caLaTXTPPvssjI2NIQgCnnvuOfzxxx/N+sXGxooFvcnJyRg5ciTMzMyQnp6OmpoavP/++4iOjm72VIzCwkKsWLEC2dnZ2LJlC1xdXQEAxcXFGDRoEIYOHYqsrCxs2LABAwYMwNmzZ+9qXp1t4MCBiIyMxM6dO7FkyRKcOXMG4eHhfCoIERG1iQW9RERERERERERE1Gnmz5+PNWvWoLCwUO0/badNm4a33noLy5YtQ3V1NSZMmIArV64gLCwM27dvhyAI2LJlC55//nnIZDI8/vjj6Nq1K7p27SqO8dJLL+HWrVv46quvWnwMelORoUqlEvc1FSkWFxeje/fuANQfx97UtrCpqhXQGIs2cd4+VtOqR7cX2hQWFkKlUkEQBHH1uc2bNyMsLAz9+/dHTU0NMjIyYG9vDzMzM62v/Z1KSpoXeSUkACoVoK8PeHoCb73VWODVrx/QkYtlDhoE+PgA777bWCj8IGsqtnZo50RUKhUqKyvFwqimlWpLSkrE100FU4IgoLy8HPX19VAqlaiurkZNTQ2u/7cqr6nN7cWyTcVXLVEoFKitrW3xmIWFBXR0dJrtv7PIqmkl4abisabCMz09PXTr1g0AYGZmBl1d3WYrEJuYmMDe3h4WFhbi7UmbtgAAIABJREFU/j/zs0zSay23A5rz+9dff/2nczvQPL9ryu1Nbe7M723lb03HWvqs0Ca/b968GT4+Ph2W18vKgCtXgH37GvP6pUtAVRWgqwt4ezfm89DQxgLeAQPuXf51/X/27jyuqjp//PiLHWRHRAFBRCHZRVxQTB1Fy8m1YipNW7TMyhbH1DJtxrLlW1nNVCbqWNoymWmu2W9cc19ABUEBN0AW2ZEdhPP743aPXLkgJnpR38/H4z7uved8zue8z0U/Hrzvz/vjCT/8AI88cuuqrt9MxsbG6jgfGBjYrGO0Ex9KSkrUarXaCrb1txUVFaljt3acz8vLo7q6Wp0ocfWkCu12fbT/HtTX1ASPqysGa8d67XbtGO/k5ISVlRVmZmY4ODioyci2trYNKhBrx3QbG5tmfVZCiFsjNTWVAQMGYG1tTWxsrHq/Zkg9evTghRdeIDo6ukX7nTt3Lv369cPc3Jy5c+fecEXvuro6HnroIYKCgtSJPO+99x5z5szh/fffv65junTponOcvnYnTpxQ2zS3H4Dz588zceJEPv7440avpTltFi5cyPbt29V7KIB27doBsHXrVh544AFeffVV3NzcANi1axfPPvssPXr0UNu/+uqr/PbbbwQHB5Obm8sbb7zB0qVLmTNnDsuWLdOJp1evXtx///1s3rxZJ45Vq1Zx6NAhdTWEt99+m3nz5rF3714iIiKu+7oM5YknniAiIoIRI0bQt29ffv/9dzw8PAwdlhBCiNZMUZSmHkIIIYQQQgghhBBCCNGinnvuOeWLL75osD0xMVHx9fVVAAVQAgIClNjYWEVRFKWiokJxdXVVHn30UeWnn35SPvroI2XevHk6x3fp0kUBlI8++qhB3/v27VNCQkIUQHniiSeUs2fPKjt27FB69OihAMoDDzygJCQkKPv27VPCw8MVQFm5cqXy8MMPK4ASGBiobNy48ZqxNLXvwIEDOn3t3LlT8fb2VgBl8uTJSlZWlvLDDz8odnZ2CqD84x//UGpqahRFUZS1a9cq1tbW6mejfURGRipZWVlNft6XLyvKmTOKsn69orz1lqKMGKEo3t6KApqHo6OiREQoyksvKco33yjKiROaY26mqqqb278Q4tZrbGxXlMbH9xsd2xWl4fh+rbH9b3/7m97x/VqxNDW2X91Xc8f3tWvX/qlxXVEUJSPjyrgeFaUo/v6KYmSkGde1Y/rixYqye7eilJc384cohBDijnTp0iXFz89PCQkJUQoKCgwdjo6EhAQFUJYuXdpifXp6eirvvvtui/W3Y8cOBVA2bNigs93a2lopLS29rmPmzZunc5y+dvXbNLcfraqqKgVQXnrppUavp6k2WVlZSp8+fZT09HS9x+7bt0+pra3V2fbCCy8of//739X3R44cUb799ludNpmZmYqxsbHSrVs3nTh69eql+Pr66r2Os2fP6mw7f/68AihxcXHXfV2tQV5enhIUFKQEBAQoJSUlhg5HCCGEYX2tNJGzKxV6hRBCCCGEEEIIIYQQt9SZM2d47733Gmz38/MjKSmJ1NRUjIyM1KU2ASwtLUlLS6Ouro7s7GwefvjhBscnJCSwbt06LC0tG+zr27cvx44d09nWuXNnYmJiGrTdv3+/+vrxxx9vsL+pWJra16dPnwZV865eqvTRRx/l0UcfbXBOa2trPv/8c/r37092djbl5eWUlZWxevVqvv76a2bPng1ASQkkJ+tW3m2qQmOvXmCIFT+vWg1cCHEHaGxsh6bH9xsZ20H/+H6tsR30j+9NxdLU2P7TTz816Ks547u1tTUpKSnXPa4fOwZlZWBiAp06gb8/REVpxvWAAGgFRReFEEK0Im+++Sa5ubkcP368waoLrVFmZiZbtmzhwoULREREMGTIEJ39ycnJHDhwgLi4OCIiIhg7diygqRSbkJBAeno6hw4dYvHixbi6ujJq1KgbimfNmjUABAUF6WwvKytj8+bNREVFNfuYwMBAneP0tavfZvfu3c3qp6X8+9//5uDBg3h4eNC5c2fmzZvHE088oVY57tu3r077uro61qxZw+rVq9VtXl5eOtV6AVxdXQkLC8PU9EqK0pw5czh8+DBLly5tUEne3Nyczp0762yLi4tjxIgRDT6L20Xbtm3ZvHkzISEhzJs3j4ULFxo6JCGEEK2UJPQKIYQQQgghhBBCCCFuupiYGFauXMnOnTv55ZdfcHBwaLRtp06d9G7XfvlXPxGsvt27dzNy5EisrKxuPOBraCqWa8V5vZYvX86kSZOYO3cReXlG5OSEc+TIeWJiDnHpkgcwlb17ryRy+ftrEnYnTmyR0wshhF4xMTHMnDmToKCgZo3toH98v1vH9iVLljNlyiQmTkzhwoVwYmPPU1h4CPCgQwcZ14UQQrSc6Oho8vPzadOmjaFDuaYuXboQFxfH008/DcDkyZOJjIxk//79hIeH4+Pjw3333cfnn38OwNixY1m6dCmbNm1i4MCBhIaG8sILL9CjRw+mTJkCwCeffML06dObPK+pqSk1NTV69+3duxcTExO99zG7du3Sm1Db2DFeXl46x+lrV7/N/v37m9VPS1mwYAELFiwA4Pjx4zz66KM89dRTvPfee+pko/peeeUVHnzwQfr166dua9u2bYN2v//+O1lZWZw8eRKAjIwMPvroI7p3764mZCcnJzNo0CBKSkooKSkBNEnTr776Kt999x3l5eU4ODhw+PBhevXq1WLXfCt17NiR1NRUnJ2d6dmzJ+PGjTN0SEIIIVohSegVQgghhBBCCCGEEELcdHV1dXz99dcsWbJE/fKxpUVGRt6Ufm+1oiI4ceJKJcbExCcxMSlk/vz/Ai9jbGyKk1MQ99//FE89NZ9evczR852pEELcVHV1dRw+fJiYmBgZ268hMxMSE3Wr7CYlPYmiFLJiRQAmJqZ07BjE5MlPMXv2fJycpIy5EEKIluPh4XFbJPMCVFRUMHPmTPV9VlYWXbp04fTp04SHh7Nz5061mmtiYiLp6elcunSpyT6nTZvGc88996djsrGxaXRfh0aWO2nsmNraWp3j9LWr36a5/dwMISEhxMTEcM899/DDDz/oTej9+eef+fbbb5vsp7a2lnnz5rF+/Xr1emJjYwEYM2YMTk5OAPj6+rJw4UIee+wx9Vhra2uio6P56quv+Ne//sWMGTOYOnUqR44caanLvOVsbGzw8PAgNTXV0KEIIYRopSShVwghhBBCCCGEEEIIcdP16tWLgoICjI2NDR1Kq5KZeSW5S5O8C2fPavY5OmqqMkZEGPHSS9MJCJhOt241WFubGTZoIYRAd1yXsV2jpgaSk6+M5wkJcOgQ5ORo9ru6aqrujhwJs2YZERY2HV/faZiZybguhBAt5ezZs7zzzjvMnz+fjh07Gryf1iA/Px9FUTAyMjJ0KNfk6urKF1980eh+d3d3/t//+39s3LiRgQMH0qVLF2JiYprs09TUVK2035SDBw/y4osv6mz76KOP8PDwoLa2lqqqKiwsLHT2+/v76+2rsWO0lWe1x+lrV7/NqVOnmtXPzdKmTRtGjx7Nf/7zH737q6urGTBgQJN9zJgxg+nTpxMaGqpus7e3B8DZ2Vmnbd++ffX2YWxszCuvvMK+ffv4+eef9f4sbheKopCXl6cmMgshhBBXk4ReIYQQQgghhBBCCCHELXG3J3yVlMDatVeSd48dg7IyMDUFT88rS6qHhUHPnprEr4Yk6UsI0Xo0JznmTlVYqFtxNzFRU129qgrMzMDHRzOev/EGBARASAi0a6evJxnXhRCiJcXGxrJ8+XKioqJuKBG3pfppDQoKCti9e/c1Ey9bg6SkJGpqahqd7DJ37lx27drFb7/9hpWVFT///PM1+zx8+DBbt25tso2JiQkTJkxokNDr7e2Nn58fAOnp6XTt2lVnf2MJtY0dk5eXp3Ocvnb125w8ebJZ/dxM3bp1w9fXV+++0aNHY2Ji0uix0dHRhIaGMmrUKJ3t2v6uTsb29PRscqLT0KFD2bFjx22bzAuwY8cOioqKuPfeew0dihBCiFbq7v2fFiGEEEIIIYQQQgghhLhJ9C2vfuoU2NhAUJAmuSsqSpPs1aMH3CYr4AohxF3p8mXNhAztmH7kCGRna/a5umrG9IgIeOklzbjerRs0kdsihBDiJnr44YfJzc1tUPnTUP20BgMHDmTmzJns3bu3yeTL1qCsrIyvvvqKadOmqduKior4/vvvGT58OO+88w6LFy/GysoKgLq6umv2mZyczOrVq5tsY2pqysyZM3niiSca7Js0aRJvv/02e/fu1Umq7d69e6OJro0dExMTo3Ocvnb12zS3n5tp7dq1jB49usF2RVF46KGHmjxOURQmTpyos33Xrl0MHDiQ++67jwMHDujsS0lJoaamptE+T5w4wciRI6/zClqPy5cvM2vWLP7yl7/ckmRsIYQQtydJ6BVCCCGEEEIIIYQQQog/6fqWV4fHH4e7vFCxEEK0akVFmkq79cf12FhNUq+26u4rr2iqqvfuDe3bGzpiIYQQV2upJNw7IZkX4IsvvqBnz568/PLLfP7554YOR0dxcTEApaWlAHh4eDBjxgwqKysZMWIE8fHxrF69mmXLlnH+/HkAfvjhBx599FGOHz/O77//TlVVFaWlpSiKQkZGBgDZ2pk3wPjx4xk/fvyfjrFDhw68+OKLfPjhh0ycOBEjIyMqKytZtmxZg1VoZs6cSUFBAUuXLtV7zIYNG/jhhx/U4/T1Xb9NY+e+uh+twsJCACorKxu9nsbaJCcn8+WXX/LEE08QGhoKQEJCAmVlZbz55psN+tm/fz9DhgzRe46tW7fywQcf8Pjjj6t/5mpra0lMTCQwMJCBAwfy8ccfEx4ezr59++jXrx+gqV7r5+dHRUUFCxcuZPTo0QQGBgKQn5/P0aNH2bBhw3VdV2sybdo0EhMTG1QmFkIIIeozUhSlqf1N7hRCCCGEEEIIIYQQQoi7hb4kr5gYqKzUXV49LExTrbF7d7hDcgCEEOKOU1sLqam6ldQTE+HsWc1+R0dN0q52XP/b38DS0rAxCyHErbBt2zbS09MBsLCw4MEHH8TCwoJDhw6RmJjIk08+CUBmZiZbtmzhwoULRERENEjsUxSFXbt2cezYMUxMTOjWrRtDhw5tVgwVFRWsW7eOUaNGkZOTw+bNm3Fzc2PkyJGYmJhw8eJF1q9fj7GxMVFRUdjZ2anH1tXVsWvXLmxsbOjVq1eTcTS17+p+tNLT03F3dycxMZF169bh6enJ+PHjdRIrS0tLWblyJWlpafj4+NC7d2/8/PwMWh13w4YNjB07lqeeeopFixZhamr42m+HDh1i3rx5/Pbbb4SGhrJgwQK8vLwYM2YMycnJAAQEBLBy5Uo1wXTSpEmsWLECLy8vZsyYgbOzM+PGjaN///7MmjWLr776irVr1+Lh4cE777zD6NGjsbe3v+FYFUXh9ddfJyEhgWHDhpGVlcW7777boJ2fnx8FBQVkZmZibGzc4Bg/Pz8mTJjQZN8ODg46bfSdW18/v/76K9988w0//vgjLi4uLFiwgBEjRtChQ4dmtYmNjWXw4MEUFxfzl7/8hd69e+Pk5MS0adPUisj1vfrqq3zyyScNtsfGxjJgwADKysoa7LO0tCQjIwMnJycA4uLimD59OhEREVhYWLB//36io6Oxs7Pj3nvv5dixY/Ts2ZP7778fZ2dnnn76aWxsbBr025xrN6Ta2lpefvllvvrqK3766SfGjh1r6JCEEEIY1jfAk43tlIReIYQQQgghhBBCCCGE0GPDBt3k3ZMnQVEaJnkFBEBgIFhYGDpiIYQQ+hQXQ3y87mSMo0ehvBxMTcHXVzOWa8f2nj01FdaFEOJuVF5eTu/evUlISODMmTN4e3ur+/z8/Dh58iQ7duzghx9+YOrUqZw+fZqnn36aiRMn8sUXX6ht58yZQ+fOnZk8eTJHjhzhhRde4ODBg9c8/65du3jmmWdISUnh448/JikpCQcHBz7//HOGDx/O/fffz86dO6mtreXHH39kxIgRrF+/HoDExETeeustVq9ezaJFi3juueeajKOxffr6AU1S7KRJk3jjjTeIi4sjNzeXjRs38u677/L6668Dmiqh4eHhLF26lB49ejBhwgTWrl1Lr169iIiI0JsAeausXbuW8ePH07dvX7755hs6duxosFiuJTU1FSMjIzw9PRvsKykpwdbWVn1fVVWFxS36Zay2tpa8vDzaN1Kiv7S0lJqaGhwdHZt9THPbNbefG1FVVUVaWhpt2rTB3d29ybbnzp2jc+fOLXLezMxMrKysdD43gKKiIszNzWnTpk2LnMcQ0tLSmDhxIkeOHOG7775j9OjRhg5JCCGE4TWZ0Gv4aVdCCCGEEEIIIYQQQghhQNXVkJJypUJjTAwcPw4VFdCpkybBKyrqSvJuvZwGIYQQrUxmpm7F3foTMuztNRMwAgKujOthYaCn6JwQQty12rRpw3vvvceoUaPYvn27mtCblZVFYGAgpaWlTJ48mbi4OKytrQkNDeW3337jyy+/ZMKECYSHh6MoCtHR0fz0008A9OzZk1GjRjXr/AMHDmTq1KlMnz4dT09Ppk+fDoCxsTHvv/8+48aN49tvvwWgS5cufPTRR9TV1WFsbIy/vz/z5s1j9erVan+NxdFUjPr6ARg5ciSTJk0iKCiIV155BYCwsDB+/vlnNaH3ww8/pKqqinvvvReAN998k7Vr1zJu3Dj1GEMZO3Yse/bsYfz48QQHB/PWW28xdepUzM3NDRqXPp06dWp0X/1kXuCWJfMCmJiYNJlQq6967LWOaW675vZzIywsLPDx8WlW25ZK5gVwc3PTu93BwaHFznGrVVdX8+WXX/LPf/4TNzc39uzZQ/fu3Q0dlhBCiNuAJPQKIYQQQgghhBBCCCHuGpmZuhUaY2Lg1CmoqwNbW02VRm0C7zPPwG1cCEgIIe54JSWQnKw7pu/dq9nn6qpJ1o2KulJ9198fjIwMG7MQQtwORowYgZ+fHwsXLmTSpEkYGRnx/fffM3HiRH744QcqKiqYOXOm2j4rK4suXbpw+vRpwsPDMTIy4p577uGRRx4hOjqa0aNHM2PGjGaf397eHoCgoCB12z333ANASEiIuq1bt25UVVWRmZmpVpu9OrmzsTiuFWNjSaJWVlZ069ZNfe/v789vv/2mvj9z5gy5ublUV1djbm5OSEgI1tbWpKenN/v6W9qzz0J+PgwYAP369eDgwRgWLPgns2fP5t///jfvvvsuUVFRGMk/kkLcMEVRWLVqFW+88QZZWVm88sorzJ07FyuZQSaEEKKZJKFXCCGEEEIIIYQQQghxx7l8GZKSdJN3Dx+Gixc1+7WJXiNHwqxZmtd+fmBsbNi4hRBC6KetvFt/XNdOyLCzAx8fTcLu++9DaChYWxs6YiGEuH0ZGRnx2muv8fTTT7N582YeeOABtm7dyssvv8yMGTNwdXXliy++aLKPzz//nKioKMaMGcOQIUP47rvvbqi6qKWlZYNtZmZmAJSVlf2pOFoiRhMTExRFUd//5S9/YdWqVezZs4fBgwdTWFhIdXU1Q4cOva5+W5KJCaxdC+vXa35PsrBoQ+/eHzBlyhskJETz2GPPsnDhQl577TVGjx6NqamkkQhxvWpqali3bh0ffvghR44cYeLEicyfPx8PDw9DhyaEEOI2I3diQgghhBBCCCGEEEKI21pxMcTH6yZ6xcZCRQWYmWmSvMLC4NVXNcle4eHQrp2hoxZCCKFPdTWkpFypuJuYCMeOQV6eZr9MyBBCiFtj/PjxzJ07l48//hgvLy8CAgIwNTXFxMSEpKQkampq1IRafbp3705sbCyzZ89m8eLF9OjRg/j4eJycnG7hVTQdx82IcfLkyZw+fZrnnnuOBQsWsGPHDt577z3uv//+Fryq69OxI5ibQ1WV5n1VFezeDQcP2lNd/RpGRq9x4kQ2UVEbcHCYwRNPeDNzZhSurq4Gi1mI20VmZibR0dEsWbKEixcvMmLECGJjY3WqiQshhBDXQxJ6hRBCCCGEEEIIIYQQtxVtlUZtotfq1aAo4OCgWVY9LEyzrGxAAAQGQiOr5QohhDCwzEzdirsxMZrq6rW1msSjrl01Y/qIEZoxPTQU2rY1dNRCCHF3MDc355VXXuG1117jtdde48MPPwQgJCSEsrIyvvrqK6ZNm6a2Lyoq4vvvv+f555+nqqqKVatWMWHCBL744gtGjRrF8OHDWbNmDZMnT76l17Fy5Uq9cUyYMOGmxGhqaoqrqyvLly/H2dmZUaNGYWHgX0g6doSamobbq6s1z4oCZWUdMDObTFERfPqpEZ9+moOn516GD7flxRfvITBQfqkSor7Kykq2bNlCVFQUTk5OTJo0iSlTptCpUydDhyaEEOI2Jwm9QgghhBBCCCGEEEKIVklflcbYWCgo0OzXVmlct06T6OXtbdh4hRBCNK6mBpKTdaupb9yo2efqqhnHIyOl6q4QQrQmU6ZM4Z133iEvL4+AgAAAHnnkEd58801mzJhBZWUlI0aMID4+ntWrV7Ns2TIAFEXhq6++4vHHH8fIyIhhw4bh7OyMs7Oz2vezzz7LhQsXWL58Oe3bt9c5b0lJCQBV2pKyQGlpKQAFBQV06dIFgLKysgbttK/z/ijt3lgc14rx6n60Ll26RLU2E/aP/VVVVSiKgpGREYsWLWL16tWEhYVRXV1NWloaHTp0wNbW9vp/AC2kfXuoq7t2u5oao3rvXMjMtCY6+v/4/vuvGTmyPw8//DD3338/VlZWNy1WIVqziooKfv31V1avXs3GjRspKytj5cqVPPTQQwZP3BdC/DnV1dWUlpZSVFRESUkJpaWllJWVUVxczKVLlygtLaW0tJSSkhJqamooLS1FURSKiooAzX1BbW0thYWFVFVVUV5ervZdVFSEoih6z1tcXExdI/8429nZYWJi0mC7ra0tpqaaVE8zMzNsbGwwNjbG3t4eAHt7e4yNjbG2tsbc3BxLS0usrKywsrLC2toaOzs77O3tsbGxoUOHDjg4OGBjY4ONjQ3W1tY39DmKliUJvUIIIYQQQgghhBBCCIMrLNSt0Fi/SqOtLfj6gr8/zJunSfQKDQX5v2YhhGidrh7TExPhxAnNEt9mZuDjoxnL16+H8HBo187QEQshhNDH1taWxx57jKCgIHWbhYUFv/32G2PGjGHmzJnMnDmTgIAAVq5cqZO0eu7cOcaNG8dDDz1EamoqU6dOZcyYMer+7du3c+bMGb799lv+/ve/q9v379/P8uXLAVi4cCFvvfUWqampLFq0CIB//vOf/N///R/FxcUsWbIEgAULFvDOO+9QUFDARx99BMCPP/5IaGhoo3FUVlY2uu/gwYMN+nnggQfYtWsXa9eupbi4mLfffpudO3eye/duSkpKmD9/PnPmzMHV1ZX4+Hj+8pe/6HyWkZGRrFy5kg4dOrTkjwiAykpN1fvMTMjKuvJ89qzm9YEDzevHyEgzmcbeHqZNg1dftaasbAo//+zM6tWrefjhh2nTpg1//etfWbhwIe7u7i1+LUK0NhkZGfzvf//j119/ZdOmTVRWVtK/f3/ee+89HnzwQVxdXQ0dohDiD5WVleTk5JCfn09eXh75+fkUFBSoz9rXKSkpFBQUUFxcTI2+EvZ/sLW1VZNdtcmy2uRZBwcHjIyMcHNzw8zMDEdHRzXJVkubWKtPmzZt9E4EqKuro7i4uMH2+knEgJo8fPnyZXUiVGFhIQBZWVnU1tZSXl6uttMmJjeWZGxkZISDgwP29vY4Ozvj5ORE27Zt9T5rX7dv3x47O7tGPz/x5xk1lgn+hyZ3CiGEEEIIIYQQQgghxPW4fFmTqFt/ifUjRyA7W7NfW6XR31+T7CVVGoUQovXSN6bHxGiSiAAcHXXH84AACAqCRr7TFEII0QoNGzaMVatW4eDg0GBfamoqRkZGeHp6Nth3+fJl6urqyM7O1ru/qqqKdevWYWlpyahRo25K7NeK41ox/hn/+9//yMjIoH///mRnZ1NeXk5ZWRmrV68mKCiI2bNnX1d/RUWQkQFpaZrk3AsXNI/MTM22jAzNRBotCwtwd9c8PDw0z6GhMG5c4+cwNgZF0bSdMQOmTAFLy4btsrOzWbNmDWvWrGHbtm34+/sTGRlJZGQkgwYNMmgVYiFaSklJCTt27GDr1q1s3bqVkydPYmlpSf/+/XnwwQd58MEHG1QVF0LcXLm5uWRnZ5OZmcnFixfJzs4mKyuLnJwcMjIyyMnJISsrSyfhFcDY2FhvMqqPjw9t27bF3t4ea2trbGxsGlSr1Xffc6coKyvj4sWLFBYWqhWJtcm+xcXFepOhtc/1V0UAsLKyokOHDri6uhIaGoqLiwuurq506NCBDh064ObmRocOHfRWHL7LfQM82dhOSegVQgghhBBCCCGEEELcNJcuQVycJtlrxQqIjYWKCjA11VTd1SZ4+ftDnz7g4mLoiIUQQuhTVKSpsqutuJuQcGVM11bdrT8ho3dvzRLfQgghbl/Hjx9n0aJFfPXVVy3e96VLl3jrrbf48MMP1eWjb3cxMTGMGjWKtLS0BokrRUVFrFq1imeffVZne2HhlYq6MTFXqupmZcHp01C/SJ+lJbi5aSZBurmBt/eV19pnLy/9kyFtbKCsTHebiQnU1Wl+L3vjDU3Sb3N/FL/99pua8Hj8+HFMTEwIDw+nX79+9O3bl/Dw8JtSjViIlpaVlcWBAwfYv38/+/bt4+DBg9TV1RESEqImrN97771YWVkZOlQh7li1tbVkZWVx/vx5UlNTSU1NJS0tjd27d3P+/HnKy8vVtpaWlrRv3x43NzdcXFxwc3Ojffv2alKpi4sLzs7OtG3bFkdHRwNe1Z2ptLRUTfDNzs5Wk6mzs7M5evSommStrRoMYGZmRseOHfH09KRTp054eXmpr7XP+qoV3+EkoVdYqbzVAAAgAElEQVQIIYQQQgghhBBCCHHzZWbqLq+ekAAnT2qqPTk4wMSJV5K9evbUX/VJCCGE4dXWQmrqlaq7K1fCuXOa8bx+1V0Z04UQ4s4TExPDzJkzCQoKYufOnfzyyy94eXm1+Hm2bt1KRETEHZUkt3z5ciZNmsSiRYuIjIzEzq4Te/eeZ8+eQ5w4EUdo6OsUFNirSbtpaVBaeuV4b2/9Sbraba6uYGT052Lz8dEkCIMmaffyZQgMhHnz4OGH/3y/ADk5OWzbto3t27ezf/9+Tp48SV1dHV5eXmpyb3h4OKGhoZiZmf35Ewlxg6qrqzl69CgHDhxQk3hTU1MxNjbG39+fvn37MnjwYIYMGUK7du0MHa4Qd5S6ujrS0tJITk4mJSWFpKQkUlJSSE5OJj09nZqaGkCT/Onh4YGnpycDBw5UEz61lV4lSff2UF5erlZSrp+krX2dmppKWb2ZRq6urvj4+ODj44Ovr6/63LVr1zs12VcSeoUQQgghhBBCCCGEEC2npgaSk3WTd48ehfx8zX5XV93l1f39NY8b+ZJYCCHEzVG/kro2gffoUSgv1yT8eHrChAlXxnRvb0NHLIQQ4mY6fPgwQ4YMwdjYmCVLlhAVFWXokFqd6mrIy9NU0a1fUTczU+HgwU9IT99ARcV+wBQIAp7C0fFJXF3NG62s27Ur2NvfvJgjI2H7ds3knIEDNYm8gwffnHOVlpZy7Ngx9u7dy549ezh48CC5ubmYmpri6+tLQEAA/v7+hIWF0bNnT1xdXW9OIOKuV1hYSEJCAjExMfz000/ExsZSUVGBnZ0dvXv3JiIigrCwMCIiInBycjJ0uELcEWpqajh58iTx8fHEx8eTnJxMcnIyp0+fpqqqCoC2bdvi6+urPry8vNTKra6urhjrKzUv7jj5+flqou+5c+dISUnRSfJWFAVjY2MiIyPx8fEhMDCQ4OBgAgMDsbOzM3T4N0oSeoUQQgghhBBCCCGEfnV1dRQXF3P58mVKSkqoqqqivLyc0tJStTJCfSUlJVy+fLnBdgsLC9q0adNgu5WVFZZ/lOxzdHTEwcEBExMT7G/mN5WiRRUWXknw0ibvxsdrvsQ2N9d88axN3g0Lg+7dNcu5CiFurZKSEvLy8qitraX4j7WZtWP61SorK6moqGiw3djYuMH4bGJion5RYmlpiZWVFdbW1pibm2NnZ9dgOWnRujVVSd3eXlOpTzsRQzuu30GFE4W4rWjvx7X338XFxdTV1aEoCkVFRXqPKSoq4urvfs3NzbG2tm7Q1tTUFFtbW+DKvbz23l07zou71+XLlzE2Nr5rE2oqKzX/Zl5J1NVN3j1wQFPNXsvRUTdJ19sbXFxq8PAww9UVvLxAz1/DW+qZZyA7G+bOhd69b+25FUUhOTmZI0eOEBcXx/Hjx4mLiyMrKwuADh06EBwcTPfu3Rk9ejS+vr44Ozvf2iDFbS0vL4+kpCSSkpI4deqU+mcsOzsb0FR+nDBhAsHBwfTs2RNfX1+MZMaxEC0iOzublStXEhcXR3x8PCdPnqS6uhpzc3P8/f11Ene1lVclgV5cS0VFhZrgu2nTJpKSkkhISKC4uBgjIyM6d+5McHAwQUFBBAcHExISQpcuXW6ne1dJ6BVCCCGEEEIIIYS4E1VWVpKbm8vFixfJz8+nqKiI4uJiCgsLKS4upri4WN2mfV1eXq4mBej7wv9WMjMzw8bGRk0Qs7Ozw97eHgcHB+zt7Ru8tre3x8nJiXbt2qkPU1NTg8V/J7p8WbPsa0ICREdrEr7++I4VV9eGSV5+fnD7/D+pEK1TXV0dubm56qOgoKDB+K1vPNcm5ZaXl6tVbgzF0dFRTfy1srLSO4ZrJ3Vo3wcEBNCuXbs7apnt1qSkRFNJvf6EjOPHryzrLZXUhbi5ysrKyMnJ4eLFi+q9+aVLl3Rea8f09PR0Ll261KrGdS0bGxvMzMyws7PDzMwMe3t7vLy81Pt2e3t79bWdnR1OTk44OzvTrl07nJ2dJSlYtEoVFfoq6+puy87WTHYBzSTGtm3RqawbFnblvaenpqJ9a1dR0fom6eTm5nL8+HE1+TI+Pp6jR48C4OTkhK+vL/fcc49OIljXrl31TiYWd76ysjJOnz6tVvtMTk4mKSmJ5ORkCgsLAWjTpg333HMPwcHB6iMkJIR27doZOHoh7gwlJSUcOXKEAwcOcPDgQQ4ePEh2djbu7u46f++Cg4O55557MDMzM3TI4g5z/vx54uPjiYuLUx8pKSnU1tZiY2NDWFgY4eHh9OnThz59+uDm5mbokBsjCb1CCCGEEEIIIYQQt5uCggIuXLhAWloa6enpZGdnk5OTQ3Z2tpr0lZycrHOMtrKio6Njo4mxbdq0USsqOjg4YGxsjIODg1qhS1vBS5tke7X6FXfrKysro7q6usH2+hV9CwsLKSwsVKsC19TUUFpaqlaQrJ/YoE1cuzqh7Wr+/v60a9eODh064OLiQrt27XB3d6djx4507NiRTp066a1IJq69xPrYsVeSd3v3hvbtDR2xELefiooK0tLSuHDhgvrIyckhPj6enJwc8vLyyM3Npa6uTuc4W1vbRic3aF9fXS3X1taWtm3bquM76K+4C7pVGeurrq6mrKxMZ1v9ar4VFRVUVlaqVSMvXbpEbW0tRUVFamXgyspKvWN4YWGh+r62Xkk7Gxsb2rdvT/v27WnXrh0uLi60b98eFxcXOnXqpI7nLi4uN/zzuJNpK+/+9JPm+dQpqKsDOzvw8dGdjBEaavgqgULcrs6ePUtGRgZpaWlkZ2eTlZWljuXaiXa5ubkNqqBrJz1o79PrJ8J6eHioEyIsLS1p06YNFhYWOuO7qampTlX0xiqkaxNw62ssQVg7pgMNkom19/b1qwRXV1dTXFxMamqqOrbXv3+/dOlSg3M4ODjQvn17NcnXxcVFvWf39PTEzc0NDw8PXF1d//TPRIirFRbqJujGxFx5f/o01P+10tJSk5hbv7Ku9rX22ctLJjHeSoWFhSQkJJCYmMjZs2c5e/YsCQkJJCUlqfeQjo6OuLq64ubmhre3t87D1dUVV1dXqb56myosLCQzM5O9e/eqP//MzEyysrI4d+6cOind1dWVgIAA9efu7+9PQEAAXl5et1N1RiFavVOnTrF37141gTcxMZHa2lo6duxIeHg44eHhhIaGMnjwYEOHKu5iFRUVJCQkcPToUTXRPDExkbq6Ojw8PNQ/q+Hh4fTu3bu1FAiRhF4hhBBCCCGEEEIIQykpKSEhIYFNmzaRkpLC6dOnSUlJUb/wtrKyonPnznh4eNCxY0c8PDzw9PTUSUiV6jPX7+qEaG3iRXp6Ounp6aSlpVFTUwOAi4sLXbt2Vav9+Pj4MGbMmNuyolhNjaYiY/3l1I8dg7w8zX6pyCjE9aupqSE5OZlTp06pY7i2MpR2mWATExM8PDzw8PCgU6dO6sSC+gmpUhXq+lVUVJCenq4mQ6empqqv09LSOH/+PKV/lJy1trbGx8eH4cOHq2N6YGCgmtx8O8rM1J10ERMDSUmaJb7NzaFrV90xPTRUU0FQCNG0oqIiEhMT1fH8zJkznDlzhtOnT1NQUABoVpLw9PTE3d1dTTzt2LEjffr0wcPDg/bt20vC0A2qqqoiIyODjIwMDhw4wIULF0hPTyczM5O0tDT131gjIyM6duxI165d6dKli/rs6+tLt27dbst7dqHf1VVyr66cm5amWVFEy9Gx6URcHx/NxBZxd6isrCQ1NZXU1FTS0tLIyMggOzubzMxMYmNjycnJ0ZmEbGlpiYuLC87Ozjg7O+Pk5ETbtm2bfNZWK7/baSf25efnk5KSQkFBAfn5+Q2e8/LyyM/PJycnR51AAmBubo6Li4s6ac/NzY0OHTrg7u5Op06d6NSpE76+vga8QiHuHjU1NWzfvp1ff/2VrVu3kpCQgLm5OX379mXIkCFERkbSq1ev1pIAKcQNSUpKYtu2bWzdupW1a9fi6urKkCFDuO+++3jggQdwdHS8leFIQq8QQgghhBBCCCHEzXb58mXS0tJISEggJiaGxMREEhISOHXqFHV1dToVY7SVQ7y9vaV6iIFof17aii9XV/4xMjLC09MTf39/wsLCCAgIwN/fHz8/v1b18yoshBUrriTvnjgBVVX6E7169AAnJ0NHLETrp60KFhMTw08//URsbKxaadHR0VFnDNc+/Pz8ZPKFgRQWFuqM4Rs3biQpKUlN9NVW77p6PNdXhd5Q6k/G0CbwHj4MFy9q9ru6XpmEoR3X/fykWqAQ11JdXU1KSop6X669R9dW+DM3N6djx44Nqjt6e3sTEBCgd1UKcetoE37rj/Haip2pqanU1tZiamqq3rNrx/eAgAACAwOxsLAw9CWIeiorNcm510ra1XJ01E3SvTph18tLqs2L66etpJ6ZmamugqRNOtWXkKqvurmFhQU2NjbY29vj5uaGjY2NurqGjY0NNjY2aoV1a2trtUI7oCYKaSuvayuya+mrvm5mZoaNjY3e66m/IlJ9ly9fpqSkRH1fVVXFpUuXKCkpQVEUioqKACguLqaurk6txl5WVkZpaSmlpaUUFRVRUlKivtdWZNeuslSfpaWlTuKz9nXbtm1xdnbGxcVFrZysXZVDCGE4lZWV/O9//+Pnn39m/fr1FBYWEhISQmRkJEOGDGHAgAGy2pm44x06dIitW7eybds29uzZA8DgwYN56KGHGDNmDM7Ozjc7BEnoFUIIIYQQQgghhGhJiqKQnJysLuF08OBB4uPjqa6uxtTUFB8fH4KCgggKCiIwMJDg4GC8vb0NHbZopurqatasWUNcXBzx8fGcOHGC8+fPA2Bvb0/37t3p06cP4eHh9OnTBzc3t5seU20tpKbqVmlMTNR86e3oqJvkFRYG3bqBnlWYhRBXycnJ0RnLY2JiKCwsBMDd3Z1x48apY7mfn58kd90mFEUhLS2NEydOEB8fr47lJ0+epKamBjMzM/z9/endu7e65KK/v/8tm7BRWKgZz6OjdSdjmJlpqgnWr6IeHg6S8yBE85SWlhITE8Phw4dZtGiRmrhrZWWFn5+fmugZGBiIv78/nTp1kiXZb1NVVVUkJyeTkJDAiRMn1OezZ89SV1eHhYUFAQEBPP/88/Tq1Qt/f3+pLHcTVVQ0naSbmQnZ2aBNSzA311SUvzpRNyJC8+zpCfLjEq1BWVmZToJvSUmJmuR66dIlMjIyKC0tpaSkRCcBtqKigsrKSioqKqipqVEnmrU2NjY2mJmZYWVlhaWlJVZWVtja2mJjY4Ojo6OaoKxNYLa3t1ff29nZ4eTkhI+PjyT+CXGbqK2tZfPmzTz++OOUlpYSHh7OQw89xIMPPoiXl5ehwxPCYIqLi9m4cSM///wzW7Zsobq6mkGDBhEVFcVjjz2mTsxpYZLQK4QQQgghhBBCCHEjSktL2bNnDwcOHFCTvgoLC7GwsCA0NJQ+ffrQo0cPAgMDCQgIkGpQd6BLly6piWGHDx/m4MGDnDx5ktraWnX55fDwcPr27Uvv3r1vaBnOkhJNpcb6ybtHj0J5ueaLbV9f3UqNI0e24IUKcQerra0lNjaWffv2cfDgQQ4cOMC5c+cwMjLC19eXPn360LNnT4KCgggODsZJSlrfcWpqajh16hQnTpwgNjaWgwcPEhsbS1lZGba2tvTq1UudrDFgwAAcHBxu6HyXL0NS0pWqu9ox/Y9V5ImI0K2kHhgIcgshRPPU1dVx4sQJ9u7dy+HDhzl8+LB6b9ahQwemTp2qTrDr3Llzg4qH4s5UUVGhVmQ+duwY0dHRlJWVYW1tTWhoKL169aJXr14MGDAAd3d3Q4d72ygs1E3QjYm58v70aSguvtLW0vJKFV19lXXd3DTVdaXKvLjb1NXVUfzHX5arK+MCOpVz69MmB+ujTcLVx8HBQZ20YmZmhp2dHfb29g32CSHufNnZ2Sxbtozo6GguXLjAZ599xtixY+VeSAg9ysrK2Lx5Mz///DMbN27EyMiI8ePHM3XqVEJCQlryVJLQK4QQQgghhBBCCHE9Ll++zPHjx9m6dStbt25l9+7dVFVV4erqSlhYGP379yciIoKePXtKtca7WGlpKceOHSMmJoaYmBh2797N+fPnadOmDf369SMyMpLIyEhCQ0MbrfqYmam7xHpMDJw6BXV1YGcHQUENl1lvRavEC9HqnT17Vh3Lt23bRkFBAXZ2dgQFBaljed++fW/FUnqilaqtreXUqVPqWL53716OHj0KQGhoKBEREbz99tvXrMhSVKSptFt/TI+N1VQt1DcZo1cv6NDhVlyhEHeO+mP6jh07yMvLw9bWluDgYMLCwtRHQECAoUMVrcTVY7z2UVlZibe3NxEREfTv35/77ruPTp06GTpcgygs1K2se3WV3dRUKCu70t7SEvr3bzphVwghhBCGt2/fPv71r3+xdu1abG1teeqpp3juuefo0qWLoUMT4rZQVFTEihUrWLRoEadOnaJfv348//zzPPLIIy2x+ock9AohhBBCCCGEEEI0x9mzZ5kyZQp79+6loqICb29vBg8erD7at29v6BBFK5eSksL27dvZvn07O3bsIDc3l7Zt2zJo0CBGjBhBXd2THD+O+igqAiMjzRfh3btDSMiVx12aUyDEDSkuLmbz5s1s2LCBHTt2kJ2djYODAwMHDmTIkCEMHjxYEr3ENRUUFLBz5051PD9z5gzh4eHcf//9jBkzBvAjLg6OHYO4OM14npGhOdbF5co4Hhysefbzgxso3C7EXSsrK4sNGzawZcsWfv/9d/Lz83F0dGTAgAEMGjSIQYMGERwc3OjEKSH0KS8vZ//+/ezcuZOdO3dy6NAhqqur8fX1Ve/ZIyMjG616eTuprob4+IZJutrE3bQ0TTV5LUdH/Ym62tceHppJh0IIIYRovRISEnj99dfZsGEDffr04fnnn+dvf/ubFKUQ4k9SFIWdO3eyaNEifvnlFzw9PXnzzTeZMGHCjawEIwm9QgghhBBCCCGEEI2Ji4tj7dq1/PLLLxw7doxx48apSV9eXl6GDk/cxhRFIT4+nm3btrF161a2b99OVVUJ7dtfol8/CwYMsCYgAEJDoW1bQ0crxO3t4sWLPPnkk2zfvh1FURg4cCCRkZEMHjyYHj16yFLr4oZ8++23bNu2jc2bN5OTk4OpaS51dU64utYQGmqhVlAPCNAkPQkh/rz4+Hg2bNjAunXrOHz4MFZWVgwePJghQ4YwcOBAQkJCJIFXtKjy8nL27dvHzp072bp1K4cPH8bS0pKhQ4cyatQoRowYgYuLi6HDbKCyUpOc21hl3awsOH9es/IHaJJ19SXpaivrenmBtbUhr0gIIYQQNyI9PZ233nqLFStWEBwczPvvv8+wYcMMHZYQd5Rz586xYMECVqxYgb+/P5999hkDBw78M11JQq8QQgghhBBCCCFEfefPn2f58uV89913nDlzBnd3d0aPHs3YsWOJjIw0dHjiDlVSUsIzzzzHr79upKSkhF69ejFx4kTGjRuHo6OjocO7q6Snp7NmzRqmTZtGYmIi69atw9PTk/Hjx+skCZWUlLB582ZOnjyJh4cHw4YNw8PDw4CRi/pKS0tZtWoVX3/9NXv37lXH8REjRsjfKXFT1NXVsXfvXr7+eg/bt3/D+fNJeHh48Oijj/L000/TrVs3Q4d410tPT8fd3V3G9ttMUlIS33zzDT/++CNnz56lffv2jBw5kpEjRzJ06NA7olKquH1oK0OvX7+ebdu2UV1dTd++fXn88cd59NFHcXBwuOkxVFQ0TNK9OmE3Oxu0X/NbWICTk/7KumFh4OkJN74qsBBCCCFaqxUrVvDCCy/g5OTEnDlzmDRp0i2f2FxdXc3u3bvZuHEjQ4cO5a9//WuL9r9792527drFqVOniIqKYvTo0c0+tqqqil27dnHs2DH69+9Pv379rvuYPn366P1Mte1sbGz0tmluPwDZ2dmcOnWKQYMGNdhXVFTEsmXLSEtL44EHHmDIkCE31E9QUFCjfeTn57Nu3TrS0tIIDg5m2LBh2NjY6D2Xtn10dDSvv/66uq2kpITvv/+ec+fO0bVrV8aNG0ebNm3+1LkWL158zX5utZSUFKZPn87GjRuZMGECX375ZZOfkR5NJvTKFFYhhBBCCCGEEELcFaqqqvjxxx8ZNmwYXbp0ITo6mrFjx3LgwAHS09P54osvJJlX3FS2trb897/fkZOTw6ZNm/D392fWrFm4ubnx+OOPs2PHDq4x+V60gA0bNhAWFsYrr7zCv/71LxYuXMiBAweYOHEiH3zwgU7biIgIzMzMeOGFFygqKsLf358VK1YYKHKhdeDAAZ555hnc3Nx4/vnncXNzY/Xq1axZs4YJEyZIMq+4aYyNjbn33ntZtux1zp07xdGjR3nyySdZtWoVfn5+3HvvvXz99deUlZUZOtS7knZ8b2psP378uIztrUhxcTERERF069aNlStX8vDDD7N//34yMzNZsmQJo0aNkmReccu5urry7LPPsnHjRvLy8li9ejVeXl5Mnz4dV1dXHnvsMWpra2/oHIWFkJAAW7dCdDT84x8wciT07AkODtCmDXTpAkOHwpQpmjYJCWBlBZGRMGsW/Pgj7N4NZ85AebkmyffIEVi1Cj77TNMmKkqT1CvJvEIIIcSdqbKyUp1gOm3aNFJSUnj22WcNskrRiRMnWLVqFZ9++imZmZkt2ndMTAwffvghM2fOxNfXl0ceeYTy8vJmHZuTk4Ofnx9paWk8/fTT/PLLL9e8l9N3zOjRoxscV7+dvjbN7Sc3N5cZM2bg7e3N2rVrG8RTUFBAz549OX78OCdOnGD48OF6k5Kvp5/G+jh27BiDBg3C39+fmTNncvr0aSIiIsjKymr085o8eTKfffaZ+j4pKQlfX18+/vhjPvnkE5555hmCg4PJzs6+7nMlJSVdsx9D8PHxYcOGDfz3v/9l06ZNhIeHc/78+ZY7gaIoTT2EEEIIIYQQQgghbmt5eXnKnDlzlLZt2yomJibKiBEjlLVr1yrV1dWGDk0Ipbi4WFm8eLHSu3dvBVC6dOmifPnll0plZaWhQ7ujzZ49WwGUrVu3qtt69OihhIWFqe+rqqqUefPm6Rw3btw4xdzcXElISLhlsQqNmpoa5ZtvvlECAwMVQAkKClI+/fRTJS8vz9ChCaHU1tYqW7ZsUaKiohRzc3PFzs5Oefnll5WMjAxDh3bXmT17dqNje1VVldKtWzcZ21uBkydPKk8//bRiZWWlPPbYY8pvv/2m1NbWGjosIZpUXFysLFmyRImIiFDc3d2Vd999VykqKmrQrqBAUY4cUZT16xVl8WJFmTVLUSZMUJTISEXx91cUa2tF0dTW1TwsLRXF21vTZtYszTHr12v6kH9GhBBCCNGYsrIyZcCAAYqjo6Oybds2Q4ejKIqiHD9+XAGUJUuWtGi/w4cPV95++21FURSlrq6u2b9r19bWKv3791dGjRqlbrt8+bIya9as6z6mU6dOOsdd3e7qNs3tR1EU5dChQ+pn99JLLzWIadGiRUp+fr76fv78+Qqg7Nmz50/3o6+P2tpaJSQkRJk5c6bOcb1791aGDh2q9/OKjo5WfHx8lPbt26vbhg8frhw/flxRFEXJyclRJk+erADK008/fd3nGj58eJP9tAbp6elK9+7ddT6DZvhaaSJnVyr0CiGEEEIIIYQQ4o6Um5vL7Nmz8fLyYvHixbz88sukpqayYcMGxowZg5mZmaFDFAI7OzueffZZDh48SFxcHMOGDePVV1+la9eu/Pvf/6aystLQId6RtNX+unXrpm7z9/cnLS1Nfb9lyxbCw8N1jrvvvvuorq5m2bJltyZQQU1NDcuXL8fPz49JkybRo0cP9e/Lyy+/TNu2bQ0dohAYGxtz3333sWrVKjIyMpg3bx6rV6+mS5cuvPjii6Snpxs6xLuGlZVVo2P7li1bOHXqlIztBhQbG0tUVBQBAQHs27ePTz75hO+//55hw4ZhbCxfWYrWzc7OjsmTJ7Nnzx4ef/xxPvjgAzp16kRk5CbCwmpwd9dUwnVy0lTaHT1aU3l32za4dAn8/GDCBFi0CHbsgORkTWXdigpNld0VK+D99+HZZzXVesPCwM3N0FcthBBCiNZIURQee+wxEhMT2bNnD4MHDzZ0SACY/rEsgJGRUYv2m5CQoFYdNjIywq2ZN0m///47e/bs4ZlnnlG3mZiY8Pnnnze6sk5jxzzxxBM6x13d7uo2ze0HoFevXjq/x9ZXXV3Nfffdh5OTk7pt4sSJgOb+tL7r6UdfHwcOHOD48eOEhobqHNu7d2/+97//ERMTo7M9OTmZo0ePMmLECHVbTEwM48ePJzg4GIB27doxf/58jI2N2bdv33WdS9tXU/20Bh07dmT37t107dqVnJycFulTfjsWQgghhBBCCCHEHaWiooI5c+bQuXNnli9fzrx58zh37hxz587F3d3d0OEJ0aigoCC+/PJLzpw5w4MPPsisWbPw9vZm6dKlKIpi6PDueCYmJjqfc2JiIjY2Njpt7r33XgBOnjx5S2O7W/3000/cc889TJkyhYEDB5KUlMQ333xD7969DR2aEI1ydnbm73//O2fOnOHjjz9mw4YNdO3alWnTplFcXGzo8O469cf2xMREABnbDeDkyZP89a9/JSwsjHPnzrFq1SoSEhKYMmWKoUMT4k95//33SUtLY/bs2Rw58itxcYvx8VnH0qUV7NkDqalQVQWZmXD4MPzyC/zrXzB7tiapd9Ag8PGBP+aYCSGEEEJcl+XLl7Np0ybWr1+Pv7+/ocO5pszMTP7zn/8wf/58tm3b1mB/cnIyK1asYMaMGaxdu1bdvmvXLr788kvS09M5dOgQixcvZv369c0+75o1awDN/7nWV1ZWxubNm6/rmMDAQJ3j9LWr36a5/VyLubk5nTt31tkWFxfHiBEjGvR9Pf3o62WhYdgAACAASURBVCMpKQmgwf9D9+rVC4A9e/ao22pqanjzzTf54IMPdNp6eXkxbtw4nW2urq6EhYXh6Oh4Xee6ui99/bQWNjY2bNiwgZdeeqlF+pOEXiGEEEIIIYQQQtwxdu3aRUhICF988QXz58/n3LlzvPbaaw0SN4Rozdzd3fnss884e/Ysf/vb35g6dSpDhgzh9OnThg7truLk5MT+/ft1tnXq1AkzM7NW+R/Hd5KMjAzGjBnDI488wqBBg0hOTmbp0qV4e3sbOjQhms3CwoLnn3+elJQUPv/8c1atWkVAQMB1ffkoWpa2EpGM7bdORUUFc+fOpXv37uTm5rJlyxaOHDnCQw89JBV5xW3Pzs6O2bNnk5X1IZ98AgkJk3nzTR+ys3/G0xNkQRghhBBC3AyKorBgwQKee+45+vbta+hwrmnHjh384x//IDQ0FD8/P8aMGcMLL7yg7v/000+ZMmUKEyZM4MUXX2T69OksWrQI0Pyu5u/vj6IotG/fnrCwMHx9fcnMzGTPnj1NPvbu3av+X6qrq2uDuJKTk/XG29gxLi4uOsfpa1e/TXP7uR6KorBq1Spmz56tfkZ/hqIoevvQrmh25MgRne1dunQB0FnZbP78+bzyyivY2trqtG3btq3eCs3p6ekMHz78us6lr6+r+2lNHB0dWbVq1Z/62V7NtAXiEUIIIYQQQgghhDCo8vJyXn31VZYsWcLIkSPZsWPHLa/Gu3v3bnbt2sWpU6eIiopi9OjRAGzatIlLly4Bmv9wevHFF2nTpo3ePo4fP87vv/+Oubk5DzzwAB07dmz2+auqqti1axfHjh2jf//+9OnTR12KrCmlpaWsWrWK8+fPEx4eztChQzG76tvnkpISvv/+e86dO0fXrl0ZN25co9cAkJ+fT3R0NK+//nqDfdnZ2Zw6dYpBgwbpPbaoqIhly5aRlpbGAw88wJAhQ3Su4/Dhw3oTW8PDw9UqA839LPLz81m3bh1paWkEBwczbNgwneTv673ultahQwc+/fRTJkyYwKRJkwgODubtt99m+vTpLb50nWioT58+zJkzh5kzZ6rb/j97dx4WVdn+Afw77AqyKCLIqgi4AQIq7hqY/VxyS9PK3jRccslcyqVX07cyJXsrzX1L2zQTNTHfVFQod0UTARcU2fd9Hwbm/v0xzWmGmYFBgUG9P9d1rmHOec5z7jPA/RyGe54THR0NiUTyVPzD5Gm1Z88eLFq0CDY2Njh79qzGXNFYtMnlgGo+ryuf1MeT5PPPP/9cYy7X5jy0GRPk6srndbU5depUreeoKd8Dyjm/Zq6ePn26xnieZJx9XEZGRpgxYwbGjx+PRYsWYcyYMXj11Vexbds2LiBtYv7+/gBkt0bl3N74wsPDMWPGDGRlZWH9+vWYO3euVrmsoWjK54o05YSmuAZUzPWLFy/W+rWpK48rXkt/+eWXtfb7JHlc23FPmzFNLBZjw4YNGrfX9feBXH3GsIbUokULzJs3D6+99ho++OADTJw4ESNHjsT27du1vh00Y4wxxjSLj4/Hp59+io8//viJ/4ZryL50JSoqCvHx8ZgxY4auQ6lTSUkJpk+fjqioKJiamsLHxwcnT57Eli1b8Oabb6JPnz7YvHkzXnrpJYhEIri4uKBHjx44fvw4Zs+eDRcXF+GDmfb29ujZsycA4KuvvsKiRYtqPbaBgQG8vLygr68PIyMjle3p6elq98vMzFS7j/x6W76funaKbbTtR1ulpaVYuHAhfvzxR5SVlcHT0xOnTp0SZrStbz+xsbEqffTv3x9GRkaIiIgAEQnvP8vvNuTi4gJANqmKgYEB+vXrp9Ux//jjDxgYGGDhwoXCOm2PVVc/zY2trS2OHj2q9J7DYyGi2hbGGGOMMcYYY4yxZi0lJYV8fX3J2tqafv75Z53EcP36dXr55ZdJLBbTf/7zHzI2NqbS0lK6c+cOiUQiAkAAaPLkyWr3z87OpqCgIBo+fDglJibW+/iZmZnUoUMH2rlzJ2VnZ9MHH3xAI0eOpKqqqlr3u3v3LnXq1Il+++03Ki4upp9++omcnJxU2tja2pKbmxsZGRkRAHJ1daX09HSN/Y4dO5batWunsn7x4sXUokULmj9/vtr9cnNzydXVld58800KCAggPT096t27t7BdKpWSq6ur8HoqLpGRkfV6LW7evEndu3enS5cuUWlpKQUHB5OXlxelpaU99nk3psrKSlqzZg0ZGhrS5MmTqby8XCdxPCsWL15MACg+Pl5YN3LkSGrVqhVJpVJhXatWrZR+Jzdv3kxubm4kFoubNN7ngUQiodmzZ5NIJKIlS5ZQWVlZk8egLpcTkUour5nP68on9fGk+Vwxl0dERCi1qes8tBkTiIiysrLqzOd1tcnMzKz1HGvL94o5X12uVpenn3ScbUi///47OTg4kLu7u07jeFYtXry41tz+1ltvcW5vAl9++SUZGBjQmDFjKCUlRScxqLs2l6stJzTFNWDNXK9NnieqO4/XvJZWvI5W9KR5XNtxT5sxTd5G0/a6/j6Q03YMawrh4eHk5uZGdnZ2dOnSJZ3EwBhjjD1LfvnlFwJAJ06caFZ96cqvv/5KAKiiokLXoaiIiYkhALRr1y4iItqxYwfZ2dnRnDlzhGXEiBHk6upK33//PRHJ3tvPz88X9vfz8yM3Nzehz8LCQgJAH3/8sbBOIpFQWVlZncugQYNIX19fJU4A9Mknn6g9B037XLp0SWk/de0U22jbj5xYLCYAGq/P5aqrq+mrr74ifX198vPzU9mubT+a+vj8888JAL311lv022+/0RdffEE9e/YkABQREUH5+fk0YcIEkkgkwj4LFy5U+78AIqKqqioaPHgw3bhxQ2VbXcfStp/mZMiQITRnzhxtmu6lWmp2uaCXMcYYY4wxxhhjT620tDTq1KkTde3alR4+fKizOIYPHy68CSeVSik1NZWIiGbMmEHnzp2jpKQkSkpKUluE+ejRI7K2tqYpU6Y81rGrq6tpwIABNHr0aGFdVVUVOTs709KlS+uMOygoSGndW2+9pdLm1q1bRCT7x/706dMJAL399ttq+9yxYwe5ubmpfRPv1q1btb6huHXrVsrNzRWef/zxxwSAzp8/T0REp06dovnz59OjR49ILBaTWCymU6dOkYuLS71ei+rqavL29qYlS5YoHb9379704osvPtZ5N5UzZ86QlZUVDR8+vFm+cf80CA8Pp44dOxIAmj59OqWnp9P+/fvJ3NycANDq1auFN6Xnzp1L3bp1o71799KuXbto5MiRlJSUpOMzaMb+zn31VV1dTVOmTKGWLVvS4cOHGzgo7WmbyxXzuTb5RFsNmc/feustGjhwoFKb2s5DXR/yfmq6evVqnfm8tjby86ztHNXl+5o5Xx5zzVxdM08/6TjbGNLT08nf35/u3bun61CeKfL8XltuLy8v59yujbQ0IoUPuNTHypUrSU9Pj4KDg5U+JNPU1OVzorpzQmNfA6rL9drkeaK683jNa2nF62hFT5rHtRn3tBnTarZRNx7U9feBnLZjWFMpKCigkSNHkqmpKYWHh+ssDsYYY+xZkZ2d3Sz70oU///yTAFBycrKuQ1FRs6D3vffeI19f3zr3O3nyJL377rt06NAhevXVV8nV1VXYpq6gV1tvvPGG2uJnABQSElKvfU6dOqW0n7p2im207UdO20JcuYkTJ5Kenp5K//XpR1Mf4eHh9OGHH9Ly5cvp1q1bNGHCBLKwsKCioiKaOnUqBQcHU0hIiLCMGjWKLCws6MyZMyrHWLBgAf36668aY6jtWPXpp7no1q0brVixQpumXNDLGGOMMcYYY4yxZ5Ovry916dKFMjMzdRqHk5MTffbZZ0rr5MU6tb25KhaLqVevXuTu7k4lJSX1Pu6BAwfo3LlzBIBCQ0OVtn300Udkampaa789evSgPn36KK2bOXOm8PX169fphx9+UNqelpZGenp61LlzZ5X+7t27R7Nnz9b4qfza3lAUi8VKM+oRESUkJBAAioqKIiKiixcvUnV1tVKbuXPn0uLFi4mItH4tLly4QABo//79Su3mzZtHAOp93k3t2rVrZGFhofPi4udFQUEBXbhwoVn+o6TZad+eyM+P6Ouvieoxk+GHH35IJiYmdPr06UYMrm6Pk8u1ySe1qaqqogMHDhCR9jlMnZr5fObMmUozGGozJtU1JijS5h9EmtrIz1NRzXNUl++JlHO+plytmKefdJxtTEVFRdSxY0dhJiLWtDi31yEoSJbTly0j+ru4VRtbtmwhPT09+vbbbxsvNi3VzOdEdeeEJ70GVMzpmqjL9drk+bryuLpracXraHXtHzePazPuaTOm1fVaaPP3gVx9xrCmIpFIaNKkSdSqVSuKjY3VaSyMMcYYe3YUFxeTmZkZbdy4UdehqKhZ0Lto0SIyNTWlyspKjfusWLGCBg4cKNyp6bXXXquzoPfq1av02Wef1boEBwfTp59+SgAoLi5O6ZgA6M6dO2rj0bTPTz/9pLSfunaKbbTtR66+Bb07duwga2trlfX16UdTH4ri4+PJyMiINmzYQESyu+DY29srLWZmZqSnp0fDhg1T2nf79u20b98+rc5H3bEetx9dEolEdOrUKW2a1lrQqwfGGGOMMcYYY4yxp1R8fDxCQ0NhY2Ojk+NHRERgy5YtSE5OxtWrV7F9+3YcO3YMAPDNN9/gypUrcHR0RMeOHWWfrK7h3//+N65du4YlS5bA1NRU6+NWVVVh3759mDVrFg4fPgwA8PT0VGrTvXt3lJaW4sSJExr7GT9+PC5fvowffvgBAFBSUoIjR44I211cXPD6668r7WNnZwc/Pz9YWVkprZdIJFixYgWCg4O1Pg9FRkZG6NChg9K6qKgojBo1Sji3vn37Qk9P+e2sw4cPY/z48cLXQN2vxb179wBA5XvSq1cvAMD58+e1Pm9d6NmzJ3744Qd8++23wjmzxmNhYYF+/frBwcFB16E0f1VVQGQksHgxYG8PDBkC7NkDFBRo3OX8+fNYt24dNm/ejKFDhzZdrArqk8v37t2rlDu0ySfqyPN4165dMWvWLADa5zB15Pkc+CeXL1iwQNhe13ko9qFpTGgo6vJWzXNUl++lUqlSztc0Rinm6ccdZ5tCq1atIBaLMX/+fF2H8lzi3K6F9HTgv/8FvL0Bd3dgzZpam8fExGDBggVYvXo1pk6d2jQxqiHP6TXzOVB3TqjPta8idTldE3W5Xps8X1ceV3ctrXgd3dDqGve0GdPqei20+ftArqnGsPowMDDA999/D09PT7z66quoqqrSaTyMMcbY3r17sXfvXuzfvx9isRgAcPXqVezduxe//vqr0C4tLQ179uzBxx9/jDNnzij1QUQIDw/H119/jW+++QanT5+uVwzl5eU4cOAAysrKkJCQgC1btuDo0aOorq4GAGRmZmLnzp0oKioS9pFKpTh37hyuXbumdSyatqnrKzk5GRs2bIBUKkV0dDTWrFmD77//HlKpVCX+kpISbN26FcuXL8eePXsQHR0txN5UzMzMMG3aNKxZswa5ublNeuz68vb2RmlpKbZt26a0vqCgAFu2bMGjR4/w6aefYsqUKWjRogUAqH3da7p//z4OHTpU6xISEoKgoCAYGxvjwoULSvv36NED7u7uavvWtE9kZKTSfuraKbbRtp/HFR0djZdffrlR+6isrMSkSZPg4eGBOXPmAACOHz+OlJQUpWX27Nlo27YtTp48Kex75MgREBH+9a9/KfUZERGh9bHk6tOPLhERunTpgsDAwCfuiwt6GWOMMcYYY4wx9lRKS0vDp59+CldXV53F4OzsjK5du4KI0K5dO/j5+Qlvxg0ePBgffPABBgwYgJSUFAwbNkzlDd79+/fDwMAAt2/fRkBAAMzMzDBo0CDcuHFD7fEkEgl27doFDw8PzJs3Dw8ePMCDBw8AyIoNFMmLnO/fv68x/pkzZ8LDwwNvvvkmFi1ahFdeeQXbt28Xtrdp0wYikUhlv+TkZAwfPlxp3ccff4wFCxagVatWGo+nLSLCwYMHsWzZMmzdurXWtiKRCH379gUArV8L+RvE169fV2on/1lKSkrS+rx1ZdSoUXjjjTewfPlyXYfCmKrqakAqBc6fB2bOBNq2BUaMAL77DigpUWr64YcfIjAwEG+//baOgq1fLp82bZpSPtcmn9QkkUiEPD5u3DghdzVEPlfM5a+99pqwva7zUOxD05jQUOTnqUibc7xw4YJSztc0Rinm6fqOs01tw4YN+PHHHxEdHa3rUBhTpa8PSCSyr+PigNWrAQ8PIDhYVuxbw4cffoju3bvj3//+d9PGWYM8p9fM50DdOaE+176A6rW5Yk7XRF2u1yYHapPH5eTX0nVdRz+JusY9bca0+rwWdf190FRjWH0ZGhriu+++w/3797F3715dh8MYY+w598UXX2DatGnw9/eHsbExAKB3794IDg5Gly5dAADnzp3D6tWr4ePjgy5dumDs2LGYO3eu0MeKFSvw4MEDLFiwAH379sWKFSu0Pn5ERAS8vb3x2muvYdu2bVi7di0SExPxxhtvYNKkSdi1axcWL16Ms2fPYsqUKQCA2NhYTJo0CQEBAYiMjFTqr7ZYNG2r2VdoaCj8/PywYMECbNy4EV9++SUuX76Mf/3rXyqTFuTn58PPzw/du3fHihUrcPz4cXh6egp/ozalTz75BEZGRnjllVdQUVHR5MfXpLCwEICs8BmQvd6Ojo54//33sX79ety5cwcHDx7EzJkz8eabbwrt9u/fj6KiIvz555/4448/kJ+fL2xLTU0FAGRkZAjHeeONNxAZGVnrcuXKFdja2mLevHlYv3698IG0iooK7N69W+VDxEuWLMH06dM17hMaGqq0X812Ndto249cfn6+0EZReXk51qxZo/S+QW5uLm7evImvvvpK5XugbT+19QEApaWlmDFjBjp06ICwsDAYGBiobadOWFgYgoODIZFIsGnTJmzatAkbNmzArFmzEBUVVa9jhYWFad2Prq1atQo7d+5U+d4+ltqm732SKYQZY4wxxhhjjDHGGtPGjRupoqJC12Gove1XTX/99RcBoLVr1wrrUlJSCAD16NGDcnNziYjo3r17ZGdnR2ZmZpSSkiK0raiooC1btpCTkxOZmZnRsmXLKDs7m4iIfH19SV9fX+WYV69eJQA0d+7cWuPPysoiV1dXAkB9+/aljIyMWttHRESQg4MDFRcXC+vCw8Np9erVwvOFCxdSu3btVPbV5pZfJSUlNGPGDGrZsiUBIEtLS7p69arG9ornp+1rkZSUREZGRuTn50dSqVRo99tvvxEAtbesU3feunbr1i2V29YzplM2NkSA+kVfn0hPj8jIiOjYMaLKSkpISCCRSEQnT57UdeRa5/LOnTsr5fP65BPFXK6Yx+UaIp9rk8vVnYdiH9qMCU9yq3Z156nNOb777rt1vgYRERFCnq7POKsrUqmUOnbsSMuWLdN1KIwpCwoiMjBQzeUikSyfi0REvXsTff01UXY2ZWVlkYGBAf3888+6jpyIZDm9Zj5/3Jyg7hqwtmvzujxuDlRUWx5XvJau7Tr6SfK4NuOeNmOatq+Ftn8f1Pfvmqb05ptv0oABA3QdBmOMsefcsWPHCADt3LlTWJeWlkYTJkwgIqLi4mLq2LEjlZSUCNuDgoIIAF26dImkUilZW1vTuXPnhO2ffvppvWL48ssvCQD98ssvwrply5YRAAoJCRHWGRsbU3V1NRERRUVFEQDaunWrsL22WGrbpq4v+fHDwsKEdb6+vuTn56cU+/Lly8nZ2Vl4HhkZSQDoq6++qtdr0FCio6OpdevW1L9/f62vRRvTlStX6KWXXiIA5OPjQydOnCAiotjYWHJ3dycABIC6detGN27cEPZ7++23ycDAgDp16kTbtm2jQ4cOkZGREQUEBNClS5do3LhxBIAcHR1p3759VFBQUK+4pFIpLV26lEaNGkUbN26k5cuXq23XuXNnsrGxoaqqKrX7fPfdd7X2ra6Ntv2cOHGCJk2aRADIxsaGdu7cSenp6UQkuxb28fEhkUhEvXr1opUrV9KGDRvUvkddn3409ZGTk0O7d++mfv360eHDh+t+gYnogw8+EP4XEBkZSaampsL3W3ExMTER/hbT5lia+qrZj65JpVJatWoViUSi+uy2l2qp2dW+fJoxxhhjjDHGGGOsGbl+/TreffddXYehFW9vbzg4OGD//v1YtmwZAAgzgY0dOxatW7cGALi7u+PLL7/Ea6+9hi1btmDlypXYsWMHPv/8cxQVFeHdd9/FokWL0KZNG6FvMzMztceUz9Zla2tba2y7d+/G4MGDMXjwYOzZswf+/v5ISEjQ2OdHH32EY8eOCcctKCjApk2bsH//fu1fkFqYmppix44d2LZtGzZu3Ij3338fs2fPVpkFDJB9UP2VV14Rnmv7Wjg6OuLTTz/FkiVLMG3aNLz66qu4c+cODhw4AED2/aq5f83zbg68vLzQvk0b4JdfdB0KYzK13UpaPoNgZSUwejRgYYHqPn0wUCTCkMGDmya+J+Tt7Y3IyEh4eHgI+VybfFJRUaGSy9eouXV9Q+Tzt99+W8jlf/zxB5ycnLQ6D8U+tB0THpe686zrHIkIISEhwq3U1ZHn6vDwcADajbPqvg9NSSQSISAgAKVnznAuZ82Lpt97on/y+fXrsuX994GePTGuqgrDBg5sshDr63FygrprwI0bN9Z6bV6Xx8mBNdWWxxWvpY2MjDReRz8Jba6jtRnTtH0ttP37oCnGsMf14osvYubMmSAitbNAM8YYY01h1KhR6NKlC7788ksEBQVBJBLhp59+Em5lv3//fpSXl2PJkiXCPunp6XB1dcWDBw/Qp08feHh4YNKkSdixYwfGjBmD999/v14xWFhYAAA8PT2FdR4eHgCU348Ti8VIS0uDg4ODMJuwIpFIpDGW2rap60t+553OnTsL67p27YqTJ08qtXv48CGys7NRWVkJIyMjeHt7w9TUFMnJyfV6DR4HEbBzJzBoECAPs1u3bjh37hxGjRqFvn374vvvv0efPn0aPRZNevfujd9//11lfZcuXXDv3j0kJiZCJBKpvE+xe/dufP3110p3fSsqKhK+V4cPH36iuEQiEdatW4fq6mrk5OSgXbt2attdu3YNEokE+vr6AKDVPop9y/d7nGMPHz4cw4cPF66pFZmamuLGjRsoKCiAkZERWrZsqfFcG6KfP//8E0OGDKnXnbQ+//xzfP755wAAX19fYXblutR1rPr0pSvp6ekICgrC2bNn8d133zVYv1zQyxhjjDHGGGOMsaeS/BZeT4sxY8Zgz549wnP5G9jW1tZK7eS3abt37x7Cw8OxatUqFBQUYNGiRVi2bJnSm5uArDi1uroaYrFY6U3p4uJiALI3oDX59ttv8fPPP+PatWswMDBA//79MWvWLI3t33//fSxatAg+Pj7CuoULF6JXr144duyYsC4uLg4VFRU4fPgwLC0tERAQoLFPTfT09LBgwQJcvHgRISEhKucHyG6/PmjQIOF5fV6LDz74AL1798apU6dw/vx5TJ48GZcvX0ZcXJzS+Wk67+bCycICePVVXYfBmIypqfZtCwvR8eRJ7BeJYLRtGzB/PvAUFLi0bNlSJZ/XlU/U5XJ1GiKf37x5U8jlc+fORWhoqNbnUd8x4XHJz1NRXed44cIFVFZWKuX8muS5Wk6bcbY5sLKywrCHDzmXs+ZFT0+21EYqlT0Soe3ly9gLoMXq1cD69YC5eSMHWH+PkxPUXQPWdW0ud+XKFcybN09p3RdffKE212uT52tSl8cV6enpYfz48Rqvo5+ENtfR2oxpd+/erddrUdvfB001hj0uKysrVFRUoLy8vNYiDKZecnIyDh8+jHfffRexsbH49ddf4eTkhDfeeEPplsbFxcU4ceIE7ty5A0dHRwwbNgyOjo46jJwxxpoXkUiEDz74AG+//TZOnDiBkSNHIiwsDO+99x4AICYmBnZ2dti8ebPGPjZt2oSJEydi7NixCAwMxI8//qixSFFbJiYmateXlpbWul9tsTxpnPr6+iAipXUvvPACDh48iPPnzyMgIAD5+fmorKzEiy++qHW/j0skApYsAQoLAWtrYOhQYMgQYMgQL1y9ehXTpk3DwIEDMXv2bKxcuRJt27Zt9Jjqy9nZWeO2mtfUDXntKqevr1/rz4C6D5vVtY9iuyc5tjYsLS2faH9t+xk7dmyDHEcbTXmshiaVSrF79258+OGHsLCwQHh4eMMW1Nc2fW/DTCzMGGOMMcYYY4wx1vAOHz5MycnJug5Dq9u0ExEFBATQRx99pLTupZdeoi5duiitu3fvnsrt74hkt71bt24dtW3bltq0aUOfffYZERGlp6eTsbEx7d27V6n94sWLqUePHsLt6dSxsrKi2bNnK637/fff6e7du0rrDh8+TNu2bVPZPzw8XG2/irfZUqTNrX1rmj9/Pk2bNk1lvVQqJXt7e6V1j/taiMVi6tWrF3l6epJEIhHW1/e8m9qRI0fqeysvxhqXjY3q7dkB2e3Z9fSIDA2JRowgEouJSHa7vxYtWijd6lJXtM3lROrzuZymfCInz+XyPK54e8OGzOe///47AVDJ57Wdh7Zjgvw868rnmtrIz1NRbecoz/fy23XWpC5Xy/N0fcZZXSgsLKRWrVrR5s2bdR0KY8qCgogMDFTzuaEhkUhE1Ls30ddfE2VmEhFRZWUltW7dmtasWaPjwGUKCwvV5nNtc4I214Dqrs3V3bK2JnW5Xps8r05t4xGR5utooifL4zXbqBv3tBnTHve1UHde9RnDdKFv37702muv6TqMp1Zdt0IXi8XUuXNnld+H119/nYyMjJo0VsYYexqsX7+eANDw4cMpOjpaWL9v3z4CQBs3blRqn5+fT5s3b6aKigr67rvvhPW///57vd+X2r17t8rfqvv3i6iK0AAAIABJREFU7ycA9ODBA2GdYhv59ZrieweaYtm5c2et29T19Z///IcAUEpKirDurbfeImtra5X4v/jiCzp//jzdvXuXKioq6nXuT2rAAPVvuQCyS3f55XuLFuVkaPgrmZuvpAULvqecnNwmjZOxZ1FlZSX9/PPP1KdPHwJAo0aNUsqfj2Ev1VKzW8fHaxljjDHGGGOMMcaapxEjRmDlypW6DgOpqakAgIyMDGHd/fv3sWDBAty8eVNYV1paihUrVijt+9///hfJycm4ePGisO7cuXPo0qULpk6dqtTWzMwMS5cuRUJCAj788ENs3LgRwcHBsLW1xbx587B+/Xph5oiKigqEhoZi9+7dSjMWAcCSJUswffp0ALJPwR85cgRS+QxrAC5fvgw3NzfheVhYGIKDgyGRSLBp0yZs2rQJGzZswKxZsxAVFVWv1yo/P1+Ir6by8nKsWbMG0dHRwrrc3FzcvHkTX331lUr7S5cuqdxyq76vBSD7vsyYMQMdOnRAWFgYDAwMGvy8G4NEIsGqVaue6pkM2DNOJAL09WUzPA4aBHz7LZCbC/z2G2BkBEB2u7+pU6fis88+0/kt9NTlcgAquTwmJkZtPgc05xNF8lwuz+MuLi4IDg4GUL8cppjLgX/yudzly5fh5eUFNzc3tWOSuvPQZkyQqy2f19VGfp7a5ml5vg8MDFTZpi5XK+bp+oyzurB27VoYGhri9ddf13UojNVOns8+/BB4+BC4cgV47z3AxgYAYGhoiNmzZ+OLL75AZmamDgOVSU1NVcnngHY5QdtrQHXX5oo5XRN1ub6uPK9NHld3La3pOhp4sjwuV9u4p82YpthG3fb6/H1QnzGsqYWEhODy5cuYP3++rkN5amm6FXpSUhIA4Pfff8fdu3dVZkV76aWXUFlZ2XSBMsbYU2LWrFmwsLBATk4OunXrJqyfNGkSHB0d8f7772P9+vW4c+cODh48iJkzZ+LNN98EEWHbtm3C2D5s2DCVux/MnDkTI0aM0HhNKJ+NXywWC+vk7wfk5eUptZW3kT/m5OQI2zTFYm1tXes2dX0VFRUBgNKYkZOTA7FYrDRL79atW3Ho0CFIJBJUVlYiKSlJOJ+m4OCg+SYaVVWyBQDKy00glY5GcfFqfP31FLRtK4GHRzhOn45sslgZe1YkJSVhxYoVcHJywuuvv4527drhypUrCA0NVcqfDa62at8nKSNmjDHGGGOMMcYYa2wikYh++uknnR3/0qVLNG7cOAJAjo6OtG/fPiooKKDIyEiysLAgAPTCCy/Q0qVLqaysTG0ft27dosDAQProo49ozZo1NGrUKEpLS6vz2OXl5eTi4kJEstkLly5dSqNGjaKNGzfS8uXLlWaiUNS5c2eysbGhqqoqKi0tpaCgIOrevTt9/fXXNH36dBo9erTQNjIykkxNTQmAymJiYkK5uepneNA0Q++kSZMIANnY2NDOnTspPT1d2FZSUkI+Pj4kEomoV69etHLlStqwYYPGmc4WLFhAU6ZMUVmv7WuRk5NDu3fvpn79+tHhw4dVtj/OeTel9957j8zMzOj+/fu6DoWxf8hn6BWJiPr3J9q+naiO35eMjAyysbGhyZMnk1QqbaJAlWnK5USkksuDg4NV8nld+UST8vJy2rhxo5DLibTPYYq5nIiEfK6Yy+Pj44mI1I5J6s6jrjFB7sSJE7Xmc23aSKVSrcYsIs35XtMYVTNPP+442xT09fWbxQzVjKkICvpnuq8OHYhWrya6d6/WXYqKisjV1ZWGDBlC4r9nYtcFeU6vmc/lassJj3vtS6Q+p2tSM9fXlee1yePqrqU1XUc/aR7XdtzTZkyTt1G3vT5/H2g7hjW1uLg4srKyounTp+s6lKdaXTMnrl27lgDQH3/8obRffHw8ycohGGOM1fTOO++ovVNIbGwsubu7C9dA3bp1oxs3bhCR7HrHzs6OJk+eTL/88gt98cUXKrOju7q6EgD64osvVPq+ePEieXt7EwB66623KD4+ns6dO0e+vr4EgEaOHEkxMTF08eJFAkCvvvoqff/99zRhwgQCQN27d6fjx4/XGUtt22r2FR4eTh07diQANH36dEpPT6f9+/eTubk5AaDVq1cLdyE4cuSI2mvFoUOHNtw35m+5uUTh4UQ//ii7Mcby5UQ9ehAZGWmepVdxEYlkN0pq00ZK48ZdIS+vQQSAfHx8lMZTxpiqkpISOnjwII0ePZr09fXJzs6OVqxYQUlJSQ15mFpn6BWRwqcJ1NX7Pm6hMGOMMcYYY4wxxlhjW7RoETZv3owjR45gxIgRug5HiVgsRlJSElq2bAl7e/s626elpaFFixawsrLS+hiVlZUw+numSwCorq5GTk4O2rVrp3GfkpISSCQSpeOUlZUhMTERtra29Tp+YygoKICRkRFatmxZa7tHjx7B3Nwcbdq0Ubu9rtfi6NGj8PLyQseOHZ845qb26aefYtWqVfjxxx8xefJkXYfD2D9GjwYGDwYmTZJNHaOlsLAwjBgxAu+88w42bNgAkUjUiEHWjza5/EnzSc1cDtSdw9TlcgC4c+eO2lxenzGpqcYEbcYsoO58r63HGWcb08WLF7Fp0yb89NNPug6FMVUffQSUlQGvvw74+mq9261btzBo0CAMGTIEBw8ehLGxcSMG+WQaKyeoy+ma1JYHa+Z5bfO4ttfST6K+4542+T4zM1Pj9vqcU3P6uyYuLg6BgYGwtbVFRESEMMssq7+PP/4Yq1atQkpKivDzP3XqVPz222/Izs7Gjh07MGvWLAQHB2PJkiXCflKpFCYmJjxLL2tQJSUlwpKcnCx8XVxcjIKCApSUlKCyslLI4+Xl5aioqEBlZSVKS0shlUpRWFgIQJbf5PU68nY1aVoPyGavNjExqXObSCSCpaUlLCwsoKenBzMzMxgaGsLExAQtWrSAoaEhzMzMYGRkBDMzM1haWqJVq1YwMzODmZkZzM3NYWFhITw3MzNriJeS6diwYcNw8OBBWFpaqt2emJgIkUgEJycnpfVVVVWQSqXIyMhQ2QbIrll+/fVXmJiYYPTo0Y0Suzax1BXn4zh9+jRSU1MxYMAAZGRkoKysDKWlpTh06BB+/PHHOvcvLATS04GsLCAjQ7ZkZwNpabJ1mZn/bJcPXfr6spti2NgAYjHw6JHsURMDA9lMvc7OwMKFwMyZgPwS5PLly9i2bRu+//579OnTB6+88grGjx8PFxeXJ39xGHvKFRYW4vjx4wgJCcHvv/+OyspKBAQEYMaMGRg7diwMDQ0b+pD7AEzVtJELehljjDHGGGOMMfbUIiLMmDED3333HTZu3Ih33nlH1yEx1mgkEgnmz5+PHTt2YMuWLZg1a5auQ2KswRw6dAivv/46xo8fjz179jRqIRJjunbw4EFMmzYNBQUFjfFPIcZ06vLlyxg+fDg8PDwQEhKi1QfbGHvWhIWFYfLkyXB1dcXJkyc1Fksx7dRV0Hvr1i306NEDI0eOxPHjx4X9oqKi4O3tjTrqIdhzqqSkBBkZGcjNzUVubi7y8vLUPubk5CA3NxdFRUXIz89X25e+vr5Q9GpqagoTExO0bNkSxsbGMDY2RsuWLWFgYIBWrVoBgPCBA3Nzc+jr6wOA0nZFmtYDQHFxMaqqqtRuKyoqQnV1NQDZBysU45dvKy0tRWVlJcRiMcrKylBRUYGSkhIUFRUp7a+OlZUVLCws4O7ujjZt2qB169Zo06aN0tfyR1tbWy4CbmZu3bqFrVu3Ytu2bQ3ed1FREVatWoX169fDwMCgwfvXlcjISIwePRpJSUnC761cRkYBUlMtkZYG5OfLinLT0v55zM8HUlKAoiLlPq2sADs72WP79qpf+/nJCnnlL+O33wIzZgDqfjUNDQGJBPD3BxYvBsaPlxUDqxMaGoqQkBAcO3YM+fn56NmzJ8aMGYPAwED06tXrmfq+MVab+/fvIywsDCdOnEBYWBikUikCAwPxyiuvYOzYsbC2tm7Mw9da0Mu/hYwxxhhjjDHGGHtqiUQi7Ny5E25ubpg7dy5+++03bN26FQ71mBmSsadBVFQUgoKCcOfOHRw6dAjjxo3TdUiMNagJEybA1tYW48ePh5eXF3bs2IGAgABdh8VYgyooKMDSpUuxc+dOzJgxg4t52TOpT58+uHr1KsaNG4fu3btj9erVePfdd6Gnp6fr0BhrdIWFhfjoo49guGkTLrVujY5t2kB/wQKgTRugdWvZY9u2skfFdRpm2GQyRX9XQCnOtJuTkwOxWAwigre3N9566y0cPnwYSUlJwkyM58+fh5ubm05iZrqVnp6OjIwMpKWlISsrCzdv3kRaWhoyMjKQmZmJtLQ0lJWVKe1jbGysUojq5OQEHx8fWFtbw9zcHFZWVkqz1Do4OMDMzOyZnYG7vLxcafbh0tJSYUbi/Px8FBYW4t69e8jLy8Pt27eVCqFrzozdsmVLtG/fHra2trC1tYWdnR3atWuH9u3bKz3a2dnp6GyfD4GBgfD09ER4eDiOHj3aKMe4evUqPvvss6e+KLSiAsjL+6co99ixKKSnp2PYsF0wNx+K4mJnJCYmIC3tKsrKogCsAyAb0uUFue3by4py1RXsOjn9U6irLVtb1WJeQ0PZjLxDhwIrVwJ9+9bdz8svv4yXX34ZEokEZ8+eRUhICLZt24aVK1fC3NwcQ4YMwdChQxEYGIiuXbvWL0jGmrmMjAycOXMGy5cvR3JyMlq1aoWAgABs374do0eP1vldPuR4hl7GGGOMMcYYY4w9E86fP4/p06cjIyMD//nPf7hwgD0TKioqsG7dOqxduxa9evXCrl270LlzZ12HxVijSU9Px7x583DkyBFMmTIFGzZsaDZvpjP2JEJDQzF79mxUV1dj06ZNeOWVV3QdEmONqry8HMHBwVi7di169uyJ7du3o3v37roOi7FGExoaijlz5kAikeDHN95AoFQK5ObKqoFyc2VLTo5smr6aTE0Bd3fA2vqfYl/FRV74a20tW8zNm/4EdSQiIgJvv/024uPjMX36dHzyyScIDw/HrFmzUFRUhNWrV+Pf//43qqqq8P777yM8PBwffPABqqqqcOTIEWzduhWOjo66Pg3WgKqqqpCamorExEQkJiYiISEBiYmJSEpKEtaJFe5Hb2pqCh8fH6UiUnt7e9jY2MDW1laYVZZnkG1YJSUlwgzHmZmZyMrKQmpqKjIzM4WCa3nRtWJxtYmJCZydnTFw4EA4OzvD2dkZLi4ucHZ2hr29vcrMqKx+zM3Noaenh507d2LixIm6DkcnaptFt+bXikxMCKamX6GqKhTFxZegp2cAW1tPDBkyDePHT4W3txEcHWUFto3lxg1ZgTAgKwbW1weCgmQz8nbs+OT93717F2FhYThz5gzCw8NRUFCA9u3bY8iQIfD394e/vz98fHxgZGT05AdjrAkQEe7fv48rV67g8uXL+PPPPxETEwMDAwOsWLECgYGB8Pf319UHEGqdoZcLehljjDHGGGOMMfbMKCsrw6pVq/DVV1+hZ8+eWLlyJUaOHKnrsBirt8rKSuzduxdr165FXl4egoODMWvWLIhEIl2HxliTOHDgAObPnw99fX0sWbIEs2bNQsuWLXUdFmP1FhERgU8++QRnz55FUFAQ1q9fz7deZ8+VW7duYdasWbhx4wamTJmCpUuXwsPDQ9dhMdYgiAj/+9//sHbtWly4cAHTp09HcHBw3R9GKi9XrRyKj5c9Ki7p6UBqKqBQnChQnALQykr90r490LWrrAj4OSm+KSwsRExMDJycnPjOPU8xqVSKxMRExMXF4f79+7h37x7u37+P+/fvIyUlBVVVVQBks+o6OTnB2dlZeJQXf9ra2sLe3p4LdZ8CJSUlSE1NRXp6OpKSkvDo0SP8+eefSExMRHJyslCgbWBgAEdHR7i7u8Pd3R0eHh5wc3ODu7s7nJyc+EP9WqiqqoKent4z91qJxbLPzagrylVXtCtXcyhV93X79oCDg/IwKpFIdHK3lbQ0wN5eFt/ChcCcObLP+zSG6upqXL9+HWfOnMH58+dx5coV5OXlwdjYGL6+vvD390efPn3Qp08fODs7N04QjNVTQUEBLl++jCtXrghLXl4eTExM4OPjg379+iEgIACDBg1qDtcHXNDLGGOMMcYYY4yx58vNmzexatUqHD9+HL6+vli5ciVGjx7NxZCs2auoqMCuXbvw+eefIysrC9OmTcOKFStgb2+v69AYa3K5ublYu3Yttm3bBlNTUyxatAhz5sxBq1atdB0aY3UKCwvDJ598gj/++ANDhgzBxx9/jIEDB+o6LMZ0QiqV4rvvvsO6desQFxeH8ePHY/ny5fD19dV1aIw9lurqahw6dAjr1q3DrVu3MHz4cKxcuRJ9+vRpnAPKC4DVFfzKK5RqLhkZgGIdgImJ+oLfugqCGWtkVVVVuHv3LqKionD79m0cP34ccXFxQhGntbW1UvFmhw4dhMJdW1tbfp/nGUdESE9PR0JCglDsq1jknZubC0A2s6+8uNfNzQ1eXl7w8vKCh4eHrmZeZA0gP7/uWXTT0oDMTEAq/Wc/+ZCnWJQrH+78/GSPjo5P34T3EgmwZw/wr38BLVo0/fHT0tJw4cIFnD9/HpGRkbh+/TrEYjEsLS3x6quvomvXrvDz84OPjw9MTU2bPkD2XElLS0NkZCQiIyMRGxuLmJgY3L17F1KpFHZ2dvDz88OAAQPQv39/9OzZEyYmJroOuSYu6GWMMcYYY4wxxtjz6ebNm/jkk09w9OhRdO3aFTNmzMCUKVPQprGmL2DsMT18+BC7d+/Gt99+i4KCAsyYMQNLlizhWaUYA5CdnY0vv/wSmzdvhqGhIf71r39h+vTp6Natm65DY0xJUVER9u/fj507dyIyMhLDhg3DypUrMWDAAF2HxlizIJVKceTIEaxduxaRkZEYMmQIjh8/zv/wZ0+VxMREBAQEIDExERMnTsSyZcvg7e2t67DUy88HYmK0KwbOyZFVCilSVwRsZSW7r7e6YmAbG9k9wBnTIDc3F3/99ReioqKEJSYmBmKxGEZGRujcuTNefvllpQLe1q1b6zps1ozl5eUpFfjGxcXh7t27uHv3LiQSCYyNjdGtWzd4e3tjypQp8Pb25vcEmwF5oW5ts+jevFl7ka6mgl0HB8DCQnfn9jwpKytDZGQk/vrrL/zwww+IiYlBaWkp9PX10alTJ3h5ecHT0xMeHh5CsT1f97P6kkgkOHXqFO7du4eYmBjh2qG8vBwGBgZwd3eHp6cnvL294eXlhd69e6Nt27a6DlsbXNDLGGOMMcYYY4yx59vt27fxzTff4MCBA6isrMS4ceMQFBSEgICAZ+42c+zpUVFRgcOHD2PXrl0IDw9H+/btMXXqVMydOxd2dna6Do+xZicvLw9bt27Fnj17EB8fjz59+mD69OmYNGlSc7hVHnuOXbhwAbt27cIvv/wCqVSKV155BfPmzYO/v7+uQ2Os2Tp9+jS2b9+OkydPYsKECZg2bRoGDhzIMy2yZqmsrAwhISHYt28fzp07h/feew9z5sxBp06ddB1aw6o5E7CmGYDj42XrCwpU+1AsAtZmBmB7e8DYuOnPlTW6iooK3LhxQ7j19aVLl5CcnAwAaNeuHby8vITiG29vb3Tp0kUnt7Bnz6bKykrExsYqFY+fPn0aAODk5IS+ffvC398fffr0ga+vL4w5Dz0xdUW66gp2s7KA6up/9tNUqNu1q3LBrpWV7s6NaUcqleLhw4fC79zt27dx+/ZtJCQkoKqqCgDg4OCgNJu2u7s73N3d4eLiwr+HzzGpVIrU1FTExcUJH86Qf1AjISEBEokEdnZ26Natm1Ao7uXlhW7duj3NPzdc0MsYY4wxxhhjjDEGAKWlpTh48CB27dqFixcvwsnJCePGjcO4ceMwYMAA6Ovr6zpE9owrKyvDyZMncfToUYSGhqKkpAQjR45EUFAQhg8fzj+DjGlBKpXi3Llz2L17N44cOQIDAwOMGDECO3fuhPnTds9M9lQiIly9ehVHjhzB4cOHERcXBx8fHwQFBeGNN96ApaWlrkNk7KnxzTffYO/evbhx4wZcXFwwZswYvPzyyxg0aBAXdjGdys/Px4kTJ3Ds2DH873//Q0VFBUaOHImpU6dizJgxug6veahZAKxNMXDNSi7gn2qujh21Kwa2tQX4g7nNTkpKCiIiInDlyhVcuXIFN2/ehEQigY2NjVA42atXL3h5eaFdu3a6Dpc9hzIyMhAVFYVr164JhebZ2dkwMjKCj4+P8HM6ePBgtG/fXtfhNhvl5Zpn0ZV/feUK8He9JgDZ5zRat9Y8i67i13Z2AH+e69kmkUiQnJyM+Ph4xMfHIyYmBrGxsYiPj0dCQgKkf0/FbGVlhY4dO8LOzg7t27dHx44dMXToUOE5e3pVVlYiJSUFaWlpSE9PF34W4uPjkZaWhoSEBJSVlQEALC0t4erqio4dOwrL5MmTn8X3+7iglzHGGGOMMcYYY6ym2NhY/Pjjjzh69ChiY2PRtm1bjB49Gps2bYKJiYmuw2PPmLy8PBw/fhyzZ89GRUUF+vXrh3HjxuH111+Hra2trsNj7KmVl5eHAwcO4PDhwzh//jwCAgIwbtw4jBkzBjY2NroOjz1DJBIJIiIicOTIEfz6669ITU2Fq6srxo0bh9deew2+vr66DpGxp1pUVBQOHDiAY8eOISYmBpaWlhg+fDjGjBmD//u//4MF3zuZNYH4+HgcO3YMoaGh+OOPPyASiTB48GCMGTMGkyZNelpu39v8yQuBaxb9xserFgTn5gKVlap9mJhoNwOwnR3g7g48Lx8QiIoCunRpkvMtLS3FpUuXEBYWhrCwMNy4cQP6+vpwd3fHgAED0L9/f/j5+aFr1648+zprttLS0nDhwgWcP38ekZGRuH79OsRisVBIOHToULz44ovP3Af2FIt01c2iK1+XnAxIJP/sZ2QEtGmjWpTr58dFuuzxlJSUIC4uDgkJCUhKSsKjR4+QlJQkLNnZ2QAAc3Nz2Nvbo127drC3t4eNjY3w3M7ODra2trC1tUWbNm10fEbPn9LSUqSlpSEzMxM3btxAZmYm0tLSkJWVhZSUFGRmZiIzM1Nob2pqCmdnZ7i4uMDJyUlYXFxc4Obm9jy9l8cFvYwxxhhjjDHGGGO1efToEY4dO4ZffvkFN27cgK+vLwYMGIChQ4di8ODBPDsYq7fy8nJERkbiwoULCAsLQ0REBKRSKf773/9i4sSJPLMEY43gl19+QWhoKI4ePYri4uJn/p+wrGnEx8dj2bJlOH36NAoKCtC1a1e8/PLLGDVqFPr3788FKow1goSEBJw6dQqhoaE4deoUqqur0aNHD/Tv3x8DBgzgnM4aTEZGBv7880+EhYXh/PnziI2NhampKV544QVMnDgRo0eP5p+15kDdTMCaZgCWLxkZgGIdhHwWYG1mAJZvfxrv7z58OBAZCSxYAMycCVhbN/ghIiMjERoaijVr1kAqlcLb21u43h4wYABatGjR4MdkrKmUlZUJ40JYWBhu3boFfX199OnTB0OHDsXo0aPh4+Oj6zA10lSoK/86MhJISVH9nIS8CFfTLLryr3mCdNbUYmNjheLetLQ0ZGRkID09HZmZmUhNTUVWVhYqFX6gjY2NYWNjgzZt2igtrVu3RuvWrYWv3dzc0Lp1a5ibm/PEHn+rqqpCcXExcnNzkZeXh7y8vDq/zsjIQElJidCHra2tUGxtY2OD9u3bw9bWFs7OzkLhLhddC7iglzHGGGOMMcYYY0xbe/bswdmzZ3HmzBlkZGTA0tISgwcPRmBgIPr16wcvLy8u8GUqSktLERkZiT/++ANnz57FpUuXUFFRAQ8PDwQEBCAwMBAvvvjis3h7MMaanbKyMpw5cwZnzpzB2bNnER0dDX19ffj7+yMgIACDBg1Cr169eLZHpqK6uhqxsbG4ePEizp49i3PnziE7OxsTJkxAYGAgXnrpJXTo0EHXYTL2XMnPz0dYWBjCw8MRHh6O2NhYGBgYoGfPnhgyZAgGDhyIXr168cyprE5EhAcPHuDq1auIiIhAeHg44uLiYGxsjN69e+OFF17ACy+8gP79+/Pfe8+K/HxZJZvizL+aCoKzs5XvFw+oFgErLt26qRYEt2sH6Ovr5lzlfH2BmzcBAwNZ1d2UKcDChUD37o/dJRHh6tWrOHToEEJCQvDo0SM4OTkhODgYgYGBnH/ZMy0rKwtnzpxBWFgYTp8+jeTkZHTs2BETJkzAhAkT0LNnz0b/gF/NSc01FewmJwPFxcr7ygt15UW5fn6qRbrNIXUx9iRycnKQmZmJ9PR0pKenIysrC7m5ucJSswi1rKxMaX99fX2Ym5vDwsICZmZmwmJpaSl8bWpqCqu/P+hjaWkJkUiEVq1awcDAAC1atICJiQmsrKygp6en9F6ToaEhzMzM1MZtamoKIyMjlfVEhIKCApX1FRUVKC8vF56XlZVBLBZDLBajrKwM1dXVKCoqAgAUFBSAiFBcXIyqqiqUlJSgpKQEpaWlKCgoQHFxMTIyMlBUVISioiKUlJSgoqJC5ZiGhoZKBdGKRdGtW7eGra2t0izJPIFFvXBBL2OMMcYYY4wxxtjjiI2NFQrCwsPDUVBQgBYtWsDX1xe9e/eGv78//P394eLioutQWROqrq7GnTt3cOXKFWGJiYlBdXU1HBwchALegIAAODg46Dpcxp57mZmZOHfunJDP4+Pjoaenh86dO6N3797o3bs3+vTpA09PTxgYGOg6XNaEUlNTlXJ5ZGQkSkpKYGZmhoEDBwq5vDnPwsXY8yYzM1Mo7o2IiMCdO3cAAC4uLujVq5ewDBkyRLeBsmbhyJEjuHbtGq5fv45r166hoKAARkZG6NWrF1544QUMGTIE/fr14xlFmYziTMB1zQAcGQmoKXwRioC1mQHYzk5273pj44Y7B0dH2fSbckZGsqmBNrEqAAAgAElEQVQ4Dx4Exo+vV9XerVu3sG/fPoSEhCApKQmurq5KRYyMPW+ICNeuXUNISAgOHTqE+Ph4ODs7IzQ0FJ6envXur2ahruLXkZGy5ykpwN/1eYKaRbo1v5Y/2tjIavsZY8oqKioQFxeHvLw8oZi1uLgYhYWFQuFrSUkJ8vPzlZ4XFhZCKpWisLAQAITnzZG5uTn09fWFouGWLVsKxclWVlYwMzODra0tLCwsYG5uLmxr1aqVUsFuq1atdH0qzzIu6GWMMcYYY4wxxhh7UlKpFHfv3sXVq1dx5coVXL58GdHR0aiqqkK7du3g4+MDLy8vdO/eHZ6enujatavaT9mzp0txcTGio6Nx+/ZtREVF4fbt27h58yaKi4vRsmVL+Pn5KRV3Ozk56Tpkxlgd0tPTlXL59evXhd9pb29veHl5Cfl80KBBug6XNQCJRIL79++r5PLU1FTo6emhS5cuQh739/dH9+7doc/TVDH2VMjLy8O1a9eUlvT0dHTu3FnI5d26dUP37t3h6urKv9vPqLKyMsTGxiI6OhqxsbG4ffs2bt26hczMTHTu3Bk9e/YUir179OgB44YsoGTPL8UC4LpmAJYvmZlAzeIfxZmAO3asuyDYzg7QNCNoq1aAwq2vBSIR4OAAzJ0LzJoFWFqq3b2yshKHDh3Cli1bcOHCBbi7u2PixImYMGECevTo8YQvGGPPlhs3biAkJASfffYZBg4ciDlz5mDMmPFISzNCZiaQlSVLA1lZsl/99HTZZODp6bLnNSYJhY0N0LYtYGsLeHn9M3uujY0sJbRtK/uaL2UYa15KS0tRWVmJ/Px8VFVVoVhhquzy8nK1M98C/8yiq4589l9F8pmE5UxMTNCiRQthFmCRSARLDeM7a5a4oJcxxhhjjDHGGGOsMZSVlSEyMhJXrlzBX3/9hejoaNy5cweVlZUwMDCAh4cHunfvDm9vb4wYMQJubm5o2bKlrsNmGuTl5eHBgweIi4vDwYMHcfv2bSQkJICI0KpVK3Tr1g1eXl7w8fGBv78/z+bJ2DNCcdbtyMhIoYi/oKAAjo6O6N69O7y8vNClSxe4u7ujU6dOfGvhZiwmJgZxcXFCAa+8uEs+Nru7u8PT0xM9evRA79690atXL551hrFnTEpKCrZu3YqYmBhER0fj0aNHkEqlMDExQZcuXYQiXzc3N3Tq1Amurq4wNTXVddhMC1lZWXj48CEePnyIO3fuaPwey6/b33nnHc7xrHmRSIDcXCAvT/aouOTkAPfv//Nc3qaqSrkPY2PZzL6tW8se27QBrK1lz4ODAU31HyIRoKcnm7X3zTeBRYsADw8Asg+8bdq0Cbt27UJeXh7Gjh2L2bNn44UXXoBIU/EwYwwAEBYWhq1bt+LYsWOwsvJGdvZ1YZuJSe2z6MrXOToChoY6PAnGGGNNjQt6GWOMMcYYY4wxxpqKRCLBvXv3EB0djVu3biE6OhrR0dFISEgAADg4OKBTp07o1KkT3Nzc0K5dV1hZdUKXLjZwc2ut2+CfcUSEjIwMJCUlIT4+HnFxccLy4MED5ObmAgAMDQ0xbtw4eHp6CkuHDh34H5mMPWeSkpLw008/ISoqCtHR0bh//z7EYjEAwNLSUsjj8kdXV1c4OzvD1taWZ4BsZAUFBUhJScHDhw/x4MED4cMYDx48QGJiIgDA0dERXbt2hZeXFzw9PYUCPp49n7HnT2lpKe7cuYPo6GjExMTg9u3buHPnDpKTk4VZsWxtbYVrdFdXV7i6uqJjx45wdHSEra0t9PT0dHwWz4eKigqkpqYiKSlJKNx98OCB8HXR3/cdNzIyQqdOnYTZl7t16wZPT0+ehZk9mwoLZdN61iz0rVkMnJkJREdr16eBASCVour//g/72rTBe4cPw9zcHDNnzsTMmTPRvn37xj0nxp5BKSkp2LZtF7Ztu4yKigQsXPgGli5dCDMzM12HxhhjrPnhgl7GGGOMMcYYY4yxpiAWq/+/Wm4ucPVqAVJSxMjMJBQWGqK83ARVVS0BiACEApiEli1FcHZ2hoODAxwcHODk5AQnJyc4ODigffv2aNu2Ldq2bcsFBWpUVlYiJycHWVlZSElJQXJyMpKTk5GSkoLExEQkJycjNTUVlZWVAGRFux06dBAK8eRFeZ06dYKzszPPvMsYUyGVSpGUlKRSQBoXF4f4+Hih2NfAwADt27dXyuEODg5wdnZG+/btYWtrCwcHBx2fTfOVnZ2N7OxsZGRkIDk5GYmJiUJeT0pKQnJystItLO3t7ZWKq4cPH45OnTrxjPiMsTpVVFQgPj5eqWBUXkCamJgIiUQCQJbX7ezs4OTkhN69eytdn7dr1w42NjY8C6wWiAg5OTnIzs5GVlaWcL2empqKS5cuITU1FVlZWUJ7MzMzpQJr+dKpUyc4ODhw4S5jNd29C3Tpol1bQ0NAIkEVgMsGBsh8802M3LIFJiYmjRoiY8+D8vJybNy4EevWrYOxsTE++ugjzJgxA4Y8BS9jjLF/cEEvY4wxxhhjjDHGWGOLjAR69lRdb2gou6tlVRVQXa26ffLkaqxY8QDp6clCsVJSUpJQvJSYmIiysjKhvZ6enlDY6+XlBWtra+G5lZUVLC0tYWFhofT4NP5TrqSkBAUFBSgsLFR6zM/PR3Z2NnJycpCRkYGYmBhkZmYiPz9faf/WrVurFEU7OjrC2dkZjo6OcHBw4KJdxliDkUqlwoyCNXO4/MMF2dnZQntzc3PY2dkJOdzW1hY2NjZCLq+Zxy0sLGBubq7DM3w8YrFYJY/Ll9jYWGRnZyMzMxOZmZlCkVeVwm2lTUxM4OTkJORtxRzu5OQEFxcXLtxljDWKqqoqpKamKhWdJicn4/Lly0hJSUFGRgakUqnQ3sTEBNbW1rCxsUG7du1gbW0tPG/Tpg0sLS1haWkJc3NzWFhYCMvTmsMKCwvVLgUFBUI+l+d4+bV7Tk4OqhX+IDI2Noa9vT3s7e3Rr18/2Nvbw9nZGfb29nB0dISNjY0Oz5Cxp9D588DAgeq36esDRIBUComdHUIlEhwqKEDbKVOw4vPP0bZt26aNtYY///wTERERuHv3Ln744QcAwG+//SbMyg0AycnJmDdvnlLeLCgowO7du5GUlPT/7N15fEz3/sfx12TfN1uCLIIgEsQWSpvalWqs1WpFEdRWa7W9aild3CqqF7VUtbT8qinSqFuVlmgUiVhCggTZZCf7SGYiye+P3ExFVuuEfp6Pxzxm5nvO93s+50yMZOZ9vofBgwfTp0+fhx72v3nzJv7+/sTHx9OuXTv69+9fqxlXa6r/Xmo/d+4cR48excDAgMGDB2tODgwNDeXKlSuV9unWrRvNmjWrsC+bN2/mvffeK9d+r8cxJSWFS5cu8fzzz1e5TlU13y+VSkVQUBBnz56lZ8+eeHp6lqvxXo/F45CRkcGnn37K2rVradmyJd9//z3u7u6PvQ4hhBB1kgR6hRBCCCGEEEIIIR6HH3+E0aNLvyeryTvvwIoVD7+GO79Mv/txVlYWubm5FBQUkJ+fj1KpRK1Wk5OTQ1FREZmZmRQXF5OdnQ2Ufqlz92dHarUapVJZYbsWFhYVvvDR19fXfNGlUCioX78+5ubmGBoaYmJigomJCYaGhpibm6Onp1dliK3s3tTU9OEfMCGEqINu3bpV6Xt4dnY2mZmZZGdnU1hYSE5OjuZ9uaCggBs3bpR7Xy8bq2z24DIlJSVkZWVV2K6xsXGFk0Csra01j8ver62srNDV1cXS0lLzXl/23m5mZlbuvfvu93MrK6tHcMSEEKLuy8rKIicnp1wANicnh/j4eDIzMykqKiInJ4fCwkLy8vJQqVTcunVL87v7ne/nt2/fLjdbeZn8/HwKCgrKtZW9d99JoVCUez82MzNDX19f8zu9lZUVCoUCa2trdHR0NO/3lpaWODo6PlXhZCGeGtu2wYQJpY+NjaFXLxgwAPr2BVdXNm3axLRp05g5cyafffZZnTnBddCgQTzzzDO8//77lJSUoFAomDx5MmPGjKF58+aa9Ro0aFDu99SNGzfy8ssvY2NjA8Dy5ctZvHgxwcHB9OjR44HrKi4upmPHjgwYMIB///vfmnZPT08sLS357bffquxbU/21qT02NpYuXbowcOBAduzYUWEbhw4dYv/+/cyZM4fGjRtr2oOCgpg8eTIxMTHl1t+yZQsrV64kJyeHlJQUTfu9HMfQ0FAMDQ1p3749b731FmvXri23vKaa70dxcTFeXl7Y2Njg7+8PQFFREc2bN+eVV15hxYoV93wstKGwsJDZs2ezceNGNm/ezMSJE7VdkhBCCO2SQK8QQgghhBBCCCHE41BUBI6OkJhY9ToKBXz2Gcyd+/jqEkIIIYQQQgghxFNs3z44e7Y0wNutG9wR2N2/fz9Dhw5l6dKlvP/++1ossiJHR0fefPNNzayxKSkpDB06FD8/vypndVWr1SQmJpabdTUuLg4nJyfCw8NrnAW1qKgIPz8/Ro8eXeU6f/31Fz169GDXrl288sormvaZM2eybt06Tp06RadOnSr0q6n+2tSuVqvp2bMn2dnZnD59utKTm48fP46npyc6Ojrl2mfMmIGRkRGfffZZufZp06ZhZGTEzp07NYHe+zmOarUaQ0PDCoHe2tR8P44cOUKvXr0ICAjgxRdf1LQvWbKEVatWkZqaSnh4+D0dC21asmQJH330EQEBAbzwwgvaLkcIIYT2VBvo1alqgRBCCCGEEEIIIYS4NyoV9O5dejXLuykUoKMDW7dKmFcIIYQQQgghhBAP0dChsHQp9OxZLswLMGnSJMaOHVunwrxBQUFs2LCBhIQEQkJC2LRpEz///DP/+c9/OHnyJPb29jg7O/PNN99UuHKQgYFBuRAqQHh4OC+++GK1Yd7bt2/z7bff4urqypQpU6qt7/LlywAVtt2lSxcAgoODK+1XU/21qX3hwoWEhoayYMGCKoOx3bt3rxBgLS4uZs+ePQwfPrxce2FhYblZhu+lltqqTc0ASUlJfP311yxbtozff/+9xnH37NkDUKEeNzc3lEolBw4cuKdjoW0ffPABr7zyCr6+vpqrYwkhhBB3k0CvEEIIIYQQQgghxANKSID33gN7e9i9u8J3Z0Bp248/wvjxj78+IYQQQgghhBBC/DOpVCrWrFmj7TLKcXR0xNXVlZKSEho1akSnTp1wcXHBy8uLt99+m549e3L9+nXGjx9P//79KSoqqnSckpISdu/ezbvvvsuXX35Z6TqFhYV89dVXtGrVihkzZjBs2DCuXLlSbX3GxsYAnDp1qlx78+bNAYiPj6+0373UX1Xtu3btQk9Pj/Pnz9O7d2/MzMx47rnnOH36dLU1Hzt2DIVCQffu3cu1L1u2DHNz82r71uY4Vqc2NR8+fJilS5fi4eFBmzZtGDp0KNOnT6923LLXyc7Orlx7w4YNAYiKiqq0X1XHoi744osvUCqVbNu2TdulCCGEqKMk0CuEEEIIIYQQQghxn8LCwMcHmjeHbdtg5szScO+ECWBg8Pd6urpw4ADUsYlBhBBCCCGEEEII8ZQbOXIkVlZW2i6jHCcnJzp27AhAkyZN6Ny5M61bt6Z///58+umn/Pnnn4SGhtK6dWsCAwNZuXJlhTGUSiVTpkxh/PjxREZG4u7uTmhoaLl1VCoVLVq0YM6cObz88svExMSwYsUK6tevX219PXr0wMDAgKCgoHIz7JbNqurk5FRpv9rWX13tiYmJuLm5sXjxYv744w9Onz7NlStX8PLyIjExscqaf/zxR4YNG4ZCodC0BQUFoVfZWee1rKU2EhMTa6w5Ly8PX19f1qxZg4eHB6NGjWL06NFs2LCBEydOVDl2amoqurq6GNz5IRtgYmICQHJycqX9KjsWdYWNjQ3Dhw/H399f26UIIYSooyTQK4QQQgghhBBCCHEPCgtLZ9rt2RM6d4aICFi3DmJjS69u2aABzJ5duh6UzsxrYQF9+2qzaiGEEEIIIYQQQvwTtWrVStsl3Jf27dsTFhZG06ZN2bVrV4XlpqambN68mdzcXNasWUNubi5Tp04FoKCggC+++ILmzZvz+uuvExsbyyeffFJjkLeMvb09H374IWFhYYwfP54DBw6watUqlixZoqntQeqvrnaAoUOHYmNjA4CLiwurV68mLy+PDRs2VLqtkpISfvrpJ0aMGKFpy8rKYt26dSxcuLDaOmuqpSZls/BWV/OuXbvIz89nwYIFTJ8+nenTp5OcnEzz5s2rnS3ZzMys0vayGY9tbW0rLKvsWNQ1rVu3JjY2VttlCCGEqKOqPxVHCCGEEEIIIYQQQgCQnQ3ffAOrVkFiIgwaBIcOVR7UdXGBPn0gMBAaNoTDhx97uUIIIYQQQgghhBDcuHFD2yXcNxMTE7y9vfn666+rXEdHR4fZs2fz119/8dNPP6FSqThy5AhLliwhKyuLd999F3Nz8wr9Tp48yYwZM8q1ffbZZ3h5eQHw9ttv07VrV3777TeCg4N55ZVXOHHiBNHR0Xh4eDyU+iurHagQPO7evTsAly9frnScY8eOoVaree655zRtc+bMoUuXLvz8888AREdHU1BQwJ49e7CysqJ379411mJoaFjjPlpaWtZYs1KpxM7OjvXr11c5zt2vx2effYa9vT1FRUUVasnNzQXA1dW1VseirklPT9eEn4UQQoi7SaBXCCGEEEIIIYQQohpRUbB+PXz1Fejrw7hxMHcuODpW32/+fLh+vTT027Tp46lVCCGEEEIIIYQQ4k7+/v58/PHHKBQKbZdyX1q3bo2Li0uN6/Xr14/Dhw9jaGjIwIEDiY2N5T//+Q/NmjVj3rx5zJgxo1yw18HBoUKg19nZudxzLy8vTcA3JiaGn3/+mZUrV1YaEH6Q+u+sHSAsLKzccgcHB/T19avcrp+fH97e3ujq6mra0tPTOXTokOZ5dnY2t27d4q233qJt27YVAr1V1VKTsn2rrmZdXV0uX75MYWEh+vr6lY5z9+vh7OxMmzZtAEhISKBFixaaZWUh9coCvZUdi7qkuLgYf39/Bg8erO1ShBBC1FES6BVCCCGEEEIIIYS4S3Ex/PEHrF0Lv/wCLVrAxx+Dry+YmtZujP794dgxkAk3hBBCCCGEEEIIoS3R0dFs2bKFyZMna7uU+7J37168vb1rXO/ChQsMGTJE89zS0pL3338ffX19Vq1axapVq5g3bx4zZ87EzMwMOzs7xo0bV6sa1Go1o0ePplWrVkybNu2h139n7QMGDODEiRPllkdHR1NYWEiPHj0q9C0pKcHPz48tW7aUa9+/f3+55wsWLGD79u1cv3691rXUhq2tbY01GxgYoFQq2bhxIzNnztSsk5WVxc6dO5k2bVqlr8fEiRNZvnw5x44dKxfoDQsLo0OHDhWC0lUdi7rkyy+/JDY2lilTpmi7FK1KSEhgz549zJw5k8jISPz9/XFwcOC1115DR0dHs15ubi4HDhzg4sWL2Nvb079/f+zt7bVYuRBCPHo6Na8ihBBCCCGEEEII8c+xeTO4uZUGcgsKwN8fLl+GWbNqH+YFUCgkzCuEEEIIIYQQQgjtmjdvHnPmzOH48ePaLqWcxMREAFJSUjRts2fP5syZM5rnERERKJVK3n//fU1bfn4+H330ERcuXNC03bx5kzNnzrBmzZoK23nnnXeIjY3lX//6F1988QVOTk78+9//rnWdSqWSSZMm0axZMwIDA9HT+3vevAULFuDr6wtAVFRUjfXXpvZVq1aRkJDAX3/9pVnn8OHDtGnThjfeeKNCfcePHycvL48+ffrUep9qW8vdMjMzASgoKCjXXlPNo0ePxt7envnz57Ny5UouXrzI7t27mTx5MmPHjq2yRltbW2bMmMHKlSspKSnRbDsgIICtW7eWC34+yLF4XIKDg5k/fz7vvPMOrVu31nY5WhMQEECnTp2YPXs2X3zxBatXr+bEiRP4+PiU+7d57tw5evTogb6+PtOnTycrKwtXV1e2b9+uxeqFEOLRU5T9p1eFahcKIYQQQgghhBBCPC1iYmDTptJZeUeNgnfegbZttV2VEEIIIYQQQgghxP0rKiri5Zdf5tChQ/zwww+88MIL2i6JEydO8Omnn7J3717s7e358MMP8fb2xtHRkezsbHr16kXXrl2xsbFh5syZGBsba/oqlUqeffZZzp49S+fOnRk4cCD169dnwoQJmJmZVbvdgoICtmzZwurVq4mJial23Zs3b+Lv78/WrVuZP38+w4YNq7BOmzZtyMjIICkpiXPnztG7d+9q669t7eHh4cydO5cePXpgaGjI8ePH2bx5M3Z2dhVqmDNnDjdu3GDHjh3V7k/ZDL1lAep7PY7//e9/+fbbb/nhhx9o2LAhH330kSbMXJuaL168yNChQ4mKigKgbdu27NixAw8Pj2rrLikp4b333iMiIoL+/fuTnJxMmzZtKg0C1/ZYaMOhQ4cYNWoUXl5e7NmzB11dXW2XpFXvvfceK1asIDAwUBPA7tSpEwqFglOnTqFWq2nfvj0vv/wyH3zwgabfa6+9hp+fH2fOnMHV1VVb5QshxIP6FnijqoUS6BVCCCGEEEIIIcQ/WlhYaYh31y5o0ADOn4d69bRdlRBCCCGEEEIIIcTDoVKpGD9+PH5+fixbtoy33367TgYKVSoV8fHxmJiY0KRJk2rXzcrKwsDAABMTk3vejlqtxsDAoNp19u3bR7t27XB2dq5ynby8PAoLC7G2tr6n+mtbe1JSEsbGxprxKxMTE4OFhQX17vPDrAc5jpWpqea4uDgUCgUODg73NG5RURE3btygUaNGVa7zoMfiUSgqKuKTTz7hgw8+YMyYMWzZsqXGn71/gmXLlrFkyRKuX7+u+bcyduxYDh48SFpaGj///DPe3t4cOHCg3EkI27dvZ9y4ccydO5dVq1Zpq3whhHhQ1QZ69apaIIQQQgghhBBCCPG0UqvB3x9WrYKTJ6FTJ9i6FcaMAT35tEQIIYQQQgghhBBPEUNDQ77//ns8PDxYtGgR/v7+rF69mu7du2u7tHIMDQ1p2bJlrda1srK67+3UJlA5dOjQGte5eybb2tZf29obN25c4zrNmjWr1VgPWktt1VSzo6PjfY2rq6tbbZgXHvxYPGzBwcHMmzeP8PBwVq5cyaxZs1AoFNouq87S1dWlbFLKyMhIoOK/sWeffRYonfFZCCGeVjraLkAIIYQQQgghhBDicUlLg3//G5yd4fXXwcEB/voLTp0CHx8J8wohhBBCCCGEEOLppFAoePvttwkJCcHIyIgePXowcuRIoqKitF2aEE+VS5cuMWzYMJ599lnMzc0JDQ1l9uzZEua9BzY2NgAcP368XLujoyP6+vrVzlothBBPOgn0CiGEEEIIIYQQ4ql39ixMmQJOTqWz8r7+Oly9Crt3Qx2bjEYIIYQQQgghhBDikWnXrh2HDx8mICCAy5cv4+bmxpQpU7hw4YK2SxPiiRYeHo6vry/u7u5cvXqV//73vwQGBuLm5qbt0p44np6eABw9erRc+4ULFygsLKxzs4sLIcTDJIFeIYQQQgghhBBCPJWKiyEgAPr1Aw8PCAqCTz6B2FhYsQKaNtV2hUIIIYQQQgghhBDaMXjwYM6ePcvGjRs5cuQI7u7ueHl58X//93+o1WptlyfEE0GlUrFz50569uxJ3/bt+WD7dpKcnDjXrBkD9+2DpUth/XrYuxeOHYMrV0Cp1HbZWpeTkwNQ7r3mxo0bqFQqSkpKaN++PePGjePo0aPEx8dr1gkODqZly5ZMnjz5sdcshBCPi6KkpKS65dUuFEIIIYQQQgghhKhrcnJg2zb4/HOIj4feveGtt+DFF0GubCeEEEIIIYQQQghRXklJCb///jsbNmwgICCAevXqMX78eF5++WU8PDy0XZ4QddLp06d54YUXyMjIwNvbm5ljx/LcxYsoUlMhLQ2Skkrv09Lgxo3ynU1Nwda29NagATRuDA0bQqNG0L7938uMjbWzc49QUFAQEyZM4Nq1a/j6+rJ8+XKOHDnClClTyMnJYenSpSxZsoSCggLmz5/PkSNHePvtt7l9+zZ79+7lyy+/xN7eXtu7IYQQD+Jb4I2qFkqgVwghhBBCCCGEEE+Fq1dhyxbYtAmKiuDVV2HOHGjdWtuVCSGEEEIIIYQQQjwZrl+/zubNm9m+fTtxcXE0b96ckSNHMmLECLp06aLt8oTQmpKSEkJDQ/Hz88PPz4+YmBiWLFnC5MmTady4cc0DZGaWhnyTk0vvMzMrf5yc/HcfIyOwti4N/NrZlX98Z5u9PVhYPLqd16Ls7GwiIiJwcHCgqVxyTTxEeXl5KJVKlEolWVlZmse5ublkZ2drnufk5FBUVKSZWTo7O5vi4mLy8/MpKCjg9u3b5ObmkpWVRVkOU61Wo6xmNu7MzMwql5mZmaGvr1+h3cDAAFNTU81zc3Nz9PT0MDIywtjYGF1dXSz+9z5gaWmJjo4OJiYmGBoaYmxsjKmpKRYWFlhYWNCwYUMsLS0xNzfH1NQUU1NTrKys7us4ivsigV4hhBBCCCGEEEI8vYKD4YsvYM8ecHSEyZNLb9bW2q5MCCGEEEIIIYQQ4slUWXjRycmJYcOGsXz58nKhIiGeVkqlkqCgIH777Tf27dtXLuQ+cuRIOnfu/PA3WlPYt6wtJQXuzHzdHf69OwTs6lr64amZ2cOvWQgtUqvVpKenc/PmTTIyMsjMzCQjI0Nzu7s9NjaWrKysase0sLDA1NQUExMTTdDV+n9fOJQFbg0NDTExMUFHRwdLS0ssLCzQ1dUFQKFQVBuQrSq0C1WHfZVKJWq1WvO8LFh869YtVCoVhYWF5OXllRsjLy+PwsJCTTg5Ly9P06+quszNzbGxsalws7a2rtBWr149GjZsiImJSXWHU1QkgV4hhBBCCCGEEEI8fVQq6NIFzp+HHj1g1iwYPhz+95mZEEIIIYQQQgghhHhIwnNFDQAAACAASURBVMLC8PPzIyAggOjoaJ555hn69u1L37596dy5sybEJMSTrKioiNDQUAIDAwkMDOT48eOo1Wrc3d0ZMmQII0eOxMPDQ9tllioogIyMqgO/dz8uY2RU+Yy/d4eB7e2hisChEI9DYmIiKSkppKSkkJ6eTnJyMqmpqaSnp5OUlERaWhqpqancvHmzQl8LC4tKQ6k2NjY4OTlhZWWFmZkZJiYmmJmZYWVlpQnwWjyls13f6caNG+Tk5JCbm1thluLs7OwKoeg7b5mZmRUCwaampnTo0IEGDRrQpEkTGjRoQKNGjbCzs6NBgwbY2dnRuHFjDA0NtbTHdY4EeoUQQgghhBBCCPH0SEmBjRth/Xp44QWYPx/atdN2VUIIIYQQQgghhBD/DDt27NAEHpOSkrCyssLLy4vu3bvTrVs3OnfuLDP4iieCUqkkNDSU48ePc+LECYKCgsjOzqZJkyaawHrfvn2xtbXVdqkPpqAAIiKqn/03KQnunrW0qvDvnW2enhL8FfclOzubhIQE4uLiiI+PJyEhQfM8ISGB2NhYzbomJibY2tpia2urCYg2atSIhg0b0rhxYxo0aEC9evU0oV09PT3t7dg/QFng9+bNm6SlpZGens7Zs2dJTU0lOTmZ9PR0UlJSKsw2bGdnh729vebm6OiIg4OD5vkT/15bexLoFUIIIYQQQgghxJMvLAzWroX/+z+wsYE33oAVK7RdlRBCCCGEEEIIIcQ/V0REBIGBgQQFBXHy5EmSkpLQ09PD3d1dE/D19PTExcVF26UKweXLlzl58iQnTpzg+PHjXLhwgdu3b9OkSRO6deuGl5cXffv2pU2bNtouVTvy86sO/N7Zdv065OT83e/O4G91AeCGDUGClv8oSUlJXLlyhejoaK5cuaK5Xbt2jZw7foasra2xt7fHwcEBR0dH7O3t6dGjh2aWVzMzMy3uhbhfKpWKtLQ0kpKSSExMLBfYTkhIID4+npSUFMryq4aGhjg6OtKiRQtatmxJixYtGDhwIE5OTk9bSFsCvUIIIYQQQgghRF3g5+fHqFGjOHDgAC+88MIDj6dQKB7aWHVNdjZ88w2sXl36+fCgQTBrFvTtq+3KhBBCCCGEEOLRuH37Nrm5uQCa2axycnIoKioCID8/n4KCgkr7FhQUkJ+fX+kyCwsLdHV1K11mbm6u+XLc2NgYIyMj9PX1MTMzQ6FQYGVl9UD7JIQQarWaM2fOcObMGc6dO0d4eDjnz58nNzcXHR0dnJ2d6dChA+7u7gwZMgQXFxeZ3Vfck7y8PKKiooiOjubSpUucP3+es2fPEhMTQ3FxMRYWFrz22mu0b98eDw8PPDw80JcZZR+f/PzKA8J3h4WvXwe1+u9+1tZVB4TvfGxrCzo62tu/f5iioiKuXbvG+fPnuXDhAhcuXGDv3r3cvn0bAwMDWrdujYuLCy1atCh3a9KkibZLF0+o0NBQTRA8Ojqa6OhoIiMjNYFwe3t72rZti7u7O25ubri5ueHq6oqRkZGWK6+WBHqFEEIIIYQQQoi64saNG9SvX7/OjVVXREXB+vXw1VelkzW88QbMnQuOjtquTAghhBBCCCEqys7O5saNG9y8eZOcnBwyMzPJy8vT3LKzs8nNzdU8z8nJITs7m7y8PFQqFZmZmahUKm7duqXtXamWpaUlOjo6WFtbY25ujqmpKWZmZlhaWmJhYYGZmRlmZmaYm5tjaWmpeW5hYaG5/HG9evUwMTHR9q4IIeqAkpISYmJiygV8w8PDiY6OBqBp06a4uLjQsmVLXFxcaNWqFS4uLjg6OmJgYKDl6oU2qNVqYmNjiYqK4vLly0RHRxMVFUVUVBSJiYkA6Onp4ezsTLt27XB3d6ddu3a0a9eOZs2aoVAotLwHolbKwr8REZXPDJycDAkJUFj4dx8DA6hXr+rA791tT5LsbPj9dxg6VGuh5atXrxISEkJoaCihoaGcPn2aW7duoaOjQ7NmzXB3d+fVV1/Fzc2Nli1bSlhePDZxcXGaUPn58+eJiIggMjIStVqNrq4urq6udOnShS5dutC1a1fc3d3r0s+nBHqFEEIIIYQQQghRdxUXwx9/wNq18Msv0Lw5zJgBvr4gE7IIIYQQQgghHie1Wk1aWhrJycmkpKSQmppKamoqGRkZREdHc/PmTTIyMsjIyODmzZua2XPvVBZmNTU1xcrKqkIA1tzcHDMzM4yNjbG2ttbMiKujo4OlpSXwd4DWzMxM88Wznp4e5ubmldatq6uLhYVFhfbi4mKys7Mr7VNSUkJWVpbmuVKpRK1WawLGlc0YnJmZSU5ODnl5eSiVSk1AWalUkpeXR25uLllZWSiVSlQqVYVtGhkZaQK+ZSFfFxcX6tevT4MGDbC1taVx48Y0bNiQhg0bVvdSCSGeQteuXdPcyoI5165dIyYmRnM5bmtra5ydnXF2dsbOzo7GjRtrnrdo0ULzPiqePAUFBSQlJREYGEhSUhLJycman4e4uDjN/7llPwOurq60bdtW8/q7urpibGys5b0Qj8Wds/5WN/tvfDzcvv13v5rCvmWP7eygLoTAL1wAd3dwcoJ33oFx4+AR/oxnZmZy7NixcgHemzdvoq+vj5ubG127dqVz5860b98eV1dXmU1d1Dm3b98mOjqa8+fPc+rUKUJDQwkLCyM3NxcjIyM8PDzo0qULc+bMwcnJSZulSqBXCCGEEEIIIcTT5ffffychIQFDQ0OGDx+OoaEhISEhREZGYm1tjbe3NwBJSUn8+uuvXL9+nR49etCnTx/NGCUlJQQFBXH27Fl0dXVp3bo1/fr1q3UN+fn5+Pv789JLL5GWlsaBAwdo3LgxQ4YMQVdXl9TUVH7++Wd0dHSYOHEiUPpFalBQEGZmZnTp0qVWtVS37PDhw+XGSkhIYM+ePcycOZPIyEj8/f1xcHDgtddeQ+euM/jz8vLYsWMH8fHxtGzZkq5du9KmTZsqL8P6KOTlwc6dpUHeyEjo0aP0c8kXX6wbn5cKIYQQQgghni6pqanExcURHx9PQkICiYmJmvBucnIyaWlppKenl+tjbm6Ora0t9erVo2XLlpoQ6p339evXx8bGBktLS6ytrbW0d3VPYWEh2dnZmvBzZfcZGRlcvnyZmzdvkpaWRkFBgaa/vr4+DRs2xNbWFjs7Oxo2bEiTJk2wtbXFwcGBZs2a4eDgUGXIWQjx9MjJySE6OprY2Fji4uKIi4sjNjaW+Ph44uLiNCcdANSrV08T9LW1taVjx440atSIJk2aaN5HzMzMtLg3/zx5eXlcv36dtLQ0EhMTSU1N1fzfm5KSQnJyMklJSWRkZABgY2ODo6MjDg4OODo64uTkpHnfb9mypbzvi3uTmfl3yDcsrHzwt+w+LQ3uPEnLyKjqGX/vDgQ/yt/9AgOhX7/SD8sVCrCwgDlzYNo0eAhX7lMqlRw/fpzAwECCg4MJCQmhsLAQOzs7OnXqRM+ePenRowcdO3aUqyyIJ9q1a9cIDg4mLCyMsLAwjh07hp2dHT179qRv374MGDAAx8d7mUgJ9AohhBBCCCGEeLrcunULU1NTrl69irOzs6a9TZs2+Pv74+LiwuHDh9m1axdTp07lypUrTJgwAR8fH9avXw/AwoULadasGb6+vpw6dYrp06dz8uTJWm0/KCiISZMmER0dzapVq7h8+TJWVlasW7eOF154gYEDB3LkyBGKior44YcfKC4uJjIykiVLluDn58eXX37Jm2++qRmvulqqWhYZGUnbtm01YwUEBDBx4kTS09NZs2YN4eHhpKens3//fj7++GPee+89zfYyMzPp1q0bX331FR07dmTs2LHs3buXLl26EBIS8jBeomrFxMCmTbBlC9y6BaNGlQZ527Z95JsWQgghhBBCPOVSUlI4dOiQJrhbdh8bG6sJi+ro6GhmgrW1taVRo0aaWWHLAl92dnbY2dnJTH+PWWZmpiZcXRa4TkpKIjU1tdLQF5QGv8pCX3fe+vXrV+msxUKIp09ubq4m5BsXF0dqaqomOHr69GnS0tLKzahuampKo0aNNCdk3H2ixt33NjY2WFhYPNYT4euqoqIicnJyKsxcX9kJGzdu3CAlJYVbt25p+uvq6pb7f7YsbH3nyRpt5UNCoQ13Bn+rm/03NbX0knNljIxKP9iubvbfpk3hfmYP/+670ll579yenl7p/SuvwPvvQ6tW9zTkn3/+ya+//soff/zBqVOnKCoqom3btvTq1YtevXrh5eWFjY3NvdcqxBMkKCiIw4cP88cff3Dy5EnUajWtWrWiV69e9O3bl4EDBz7qGairDfTqPcotCyGEEEIIIYQQj0LZ2eB//PGHJtCbnJyMm5sbLi4u5OXl4evrS3h4OKampnh4eHDw4EE2bNjA2LFj8fT0ZPPmzfz4448AdO7cmZdeeqnW2/fy8mLq1KnMnTsXBwcH5s6dC5R+KbxixQrGjBnDd999B0Dz5s0pLi7G1dWVxYsX4+fnV26skpKSKmupbpmrq2u5cYYMGcLEiRNZsWIF7u7uzJ49G4BOnTrx008/lQv0rly5EpVKxbPPPgvA+++/z969exkzZkytj8H9CAsrnY131y5o0ABmziy91av3SDcrhBBCCCGEeMpkZWVx9erVCpdmP3/+PDk5ORgYGNC0aVPNDI3e3t7lLs/erFkzmWWsjrK2tsba2rrC37x3K7sse9nrX3Zp9sjISPbv36+5NHvZZdnvvjR769at5TLRQjxFzM3NcXNzw83NrdLlxcXFpKamkpqaWu4kgTsDqHFxceWeFxYWVhjH2NgYMzMzzM3Nadq0KWZmZpiZmWFlZYW5uTlmZmYYGxtjaGiIiYkJenp6mJubo1AosLKyAtAEg01NTTEwMNCMXVlgWF9fv8rZhPPy8iqtsSxwW0atVpOTk6NpK5vNODc3l9u3b3Pr1i1UKhW3bt0iLy+PvLw8MjMzUSqVmudZWVnk5uaSl5dHfn5+hRrvDkDb2dnh5uZGvXr1sLW11Zww06hRIxo2bFjhSmJC1AnW1rWfbbcs/FsW8o2M/Dv4W/a4suBvVYHfOx/b25fOxAulY+jpgVr99zi3b5fe//ADfP89DBwIixZB9+5Vlpufn09gYCD+/v5s3boVFxcXevfuzZw5c3j++edp2LDhPR4sIZ5sXl5eeHl5sXTpUm7dusVff/2lCfhu2bIFfX19+vXrh7e3Ny+99BINGjR4rPVJoFcIIYQQQgghxBOpTZs2rF69mokTJ6JQKNi5cyc+Pj4A7Nq1i/z8fBYsWKBZPzk5mebNm3PlyhW6detGq1atGD16NJs3b8bb25v58+ff0/Yt/3dGvbu7u6at1f/Ohm/fvr2mrXXr1iQlJdG0aVMMDQ0rjKNQKKqspbpllSmbOap169aaNldXVw4ePFhuvatXr5Keno5arcbAwID27dtjampKQkLCPR2D2lKrwcsLTpyATp1g61Z49VXQ138kmxNCCCGEEEI8JfLz84mIiODcuXOEh4dz/vx5Lly4QHp6OgAGBgY4OzvTqlUrunfvzrhx42jZsiVeXl4oFAotVy8eJSMjI01QtzJqtZpDhw4RFRVFVFQU0dHRbNu2jYSEBEpKStDV1cXR0RE3NzfatWtHu3btaN++Pc2bN5cZOIV4Cuno6GhmhO3QoUOt+uTm5nLz5k1u3rxJZmYmOTk55OXlaYKtCQkJmsDrtWvXNMsLCgrIz8+noKAAtVqNUql8xHt3f8oCxUZGRhgbG2NkZISZmRkWFhZYWlrSoEEDnJ2dKwSWy9axsbGhZcuWmJuba3tXhHj8ysK/1c0mXVAA6eml4d60tNJwbnJy6eOUlNLgb1BQ6fLc3PJ9raygXbvSdatSFugPDIT//hc8PWHePBg+HHR1UalU7N27Fz8/P3799Vfy8/Pp2rUrly5d0nyPIYQonUCob9++9O3bF4AbN24QEBDAvn37mDlzJlOmTOGZZ55h+PDhjBkz5rEE4CXQK4QQQgghhBDiifT2228zYcIEDhw4wODBgwkMDGTWrFkAREREYGdnx/r166vsv27dOkaNGsXQoUPp06cP33//PY0aNXqgmoyMjCq06evr1/jBfXW1PGidurq6lJSUlGvr1asXu3fvJjg4mN69e5OZmYlaraZfv361Hrc20tJg2zb4z3/gmWdg1arSeyGEEEIIIYS4W2pqKiEhIYSHh2sCvFeuXKGoqAhTU1NN8HLo0KG4uLjg4uKCo6OjhC9FpQwMDBg8eDCDBw8u156fn090dLQm6BseHs5PP/3EihUruH37NiYmJrRt25b27dvj7u6Oh4cHnTp1khmdhfgHMjc3x9zcHCcnpwce685Zc7OysigpKdHMkAulMwhnZ2dX6FcWDK5MWRC3MpaWlppZcPX09LCwsNDMEHznMiHEI2RkVDrbrr19zevm51cM/J4/DwEBUFRUfd+yYO+pU/Dyy6jt7dnbujVzwsJIz8mhT58+rFq1ipdeegk7O7sH3y8hnnL169dn/PjxjB8/HqVSycGDB9m3bx8ffPAB77zzDt7e3kyaNIk+ffo8sv9PJdArhBBCCCGEEOKJ9Nprr7Fo0SJWrVqFk5MTbdu2RU+v9M9cXV1dLl++TGFhIfpVTAPboUMHTp8+zbvvvsumTZvo2LEjiYmJj3MXqq3l/Pnz2NjYVLvsfvn6+nLlyhXefPNNPvroIw4fPswnn3zCwIEDH8r+nD0LX34JO3aAmRlMmAArVjyUoYUQQgghhBBPidu3b/P1118THBxMWFgYFy9epKSkBDs7Ozp16sTQoUNxdXWlU6dOtG7dWoK74qEwNjbWzMh7p8LCQqKioggLCyMyMpKIiAj2799PSkoKurq6tGrVik6dOtGzZ098fX0lDCeEuCe6urpYW1sDaO6FEELD2BicnEpvd7p0CeLjazVESUkJJQoFBgkJDEhMZH+XLjT+7jtsW7R46OUK8U9hamrK8OHDGT58OPn5+fz4449s3ryZ/v374+zszMSJE5k0aRINGjR4qNuVvzSEEEIIIYQQQjyRDAwMmD17NocPH+btt99m/PjxmmXt27dHqVSycePGcn2ysrLYsGEDKpWKHTt2YG5uzvr16/nll19ITk5+3LsAUGUte/bsqXbZg9DT08POzo5t27bRrl071qxZw7x58x5ozOLi0gkD+vUDD4/Sq4V98gnExkqYVwghhBBCCAE5OTkEBAQwe/ZsPD09MTU1ZeHChWRkZPDKK6/w66+/kpWVRVJSEgEBAaxYsQIfHx/atm0rYV7xyOnr69O2bVt8fHxYsWIFAQEBJCcnExcXx86dOxkwYABXr15l1qxZ1KtXj4EDB7J8+XKOHz+umWFTCCGEEOKhun696mUKBfxvgpMChYIjJSXscnPj5Pr1WKhUdDxxQsK8QjxExsbG+Pj4EBwcTEREBC+99BKrV6+mWbNmzJ0796F+xyiBXiGEEEIIIYQQT6wpU6ZgaWnJjRs3aNu2raZ99OjR2NvbM3/+fFauXMnFixfZvXs3kydPZuzYsZSUlLBx40ZKSkoA6N+/P/Xr1y839uTJkxk0aBCpqamVbjs3NxcoDeSWycvLAyAjI0PTplQqNeuU3d+4cUOzvKpa6tevX+2yMneOVXbpPrVaXW65SqXSjAHw5Zdf4ufnR2FhIWq1mvj4eM3+3KvcXNi8GVxdYejQ0raff4aLF2HWLJCrkgohhBBCCPHPpFarOXr0KEuWLKFHjx7Uq1cPb29vgoKC6Nq1K1u3biU9PZ39+/ezaNEi+vfvj6WlpbbLFqIcBwcHXn75ZVavXs2xY8fIyclh+fLlNGjQgM2bN/PMM89ofrb/85//EBkZqe2ShRBCCPG0SE//+7FCAWUnuZmbk9SxI8ssLPA0NOSd6dNpERfHa+HheE6bhs7/gr5CiEfD1dWVNWvWEBcXx4cffsju3btp2bIlt27deijjK+78Qq8S1S4UQgghhBBCCCG0berUqbi7uzNt2rRy7RcvXmTo0KFERUUB0LZtW3bs2IGHhwcFBQU4Ozvj5eXFiBEjiIuLIycnhw8++EDTv0WLFly9epXPPvuswuy1x48fZ+rUqZw7d45x48axZMkS4uLimDdvHqdPn2bw4MF8+umnZGdnM3fuXBwcHBgyZAj+/v74+fnh5ubGihUrGDx4cLW1VLfs5MmTdOvWTTOWmZkZEyZM4Nq1a/j6+rJ8+XKOHDnClClTyMnJYenSpSxcuBA9PT327dvH66+/jlKpLLdfffv25dChQ7U67levwpYtsGkTFBXBq6/C7NnQps39vIpCCCGEEEKIp8HNmzfx9/dn7969HD58GKVSSbNmzejTpw99+/ald+/eD/1ypEJo06VLlwgMDOT333/n8OHDZGdn07hxYwYNGsTw4cPp06cPBgYG2i5TCCGEEE+aoiIwMCi9NJ6FBXh5QZ8+3HBzY8KaNew/cICRI0eycuVKHB0dtVamWq3mnXfeoV+/fgwaNOihjv3hhx9y6dIlRo0ahbe390MZU6VSsXbtWnr27Imnp2etrgSiUqkICgri7NmzVfZ7WOuUSUlJ4dKlSzz//PMVluXm5rJz505iYmJo0aIFY8aMweSOmVVCQ0O5cuVKpeN269aNZs2a1Wo7dzt37hxHjx7FwMCAwYMH07RpU6D0yphbt27F3d2dPn36VHtMqxqjNvuVl5fH7t27iY2NpVu3bvTr1w99ff0a634c1Go1X375JZ9//jlbt26ld+/eNXX5FnijqoUS6BVCCCGEEEII8UTr378/u3fvxsrKqtLlcXFxKBQKHBwcyrXfvn2b4uJiUlJSKiyD0g9X/P39MTIy4qWXXnoktdemlprqvB+HDh0iMTGRnj17kpKSwq1bt1Aqlfj5+fH9999X2zc4GL74AvbsAUdHmDy59GZt/VBKE0IIIYQQQjxhUlNT2bdvH35+fhw5cgQ9PT369+/PoEGD6Nu3L82bN9d2iUI8FkVFRZw6dYrAwED8/f05deoUlpaWDBkyhJEjR9K/f3+MjIy0XaYQQgghngRKJXz1FTz/PLi7g44O//3vfxkzZgx2dnZ8/fXXdOvWTdtVcvr0aTp16sSWLVvw9fV9aOOGhYXh7u7OihUr+Pjjj8nIyCgX7rwfaWlpdOvWjZCQED799FMiIyPx9/evNoBa1udf//oXQ4cOrbKfs7NztevUdpz09HT+/e9/s2HDBiZNmsTatWvL1XP58mWef/55zM3NiYuLQ61W07x5c4KDg7G1taWkpISWLVty9erVSvcnLCyMjh071ridO924cYN3332XpKQkNm7cWO57qoyMDLp27cozzzzD999/T+fOnTl58uQ9jVGb/bp8+TIvvvgia9eu5bnnniMgIIB3332XHTt28Nxzz1VZ++Pm7e3N/v37Wbx4MYsXL0ahUFS1arWBXp1HUZwQQgghhBBCCPE4nDt3Dmdn5yrDvACOjo6VBmH19PQwMDCoMiSrUqk4fvz4Qz+rvDLV1VJTnfcqLCyMN954g7Fjx9KiRQt69uxJ//79GTZsGOvXr6+0j0oF27dDu3bw7LOQlAS7dsHly/DOOxLmFUIIIYQQ4p9GrVaza9cuevfuTZMmTZg3bx7W1tZ89913pKWl4e/vz5QpUyTMK/5RdHV18fT0ZOHChYSEhBATE8PixYu5du0aw4YNo2HDhowbN47jx49ru1QhhBBC1HWmpjBrFrRvDzo6bN26lSFDhuDt7c3p06frRJgXoGPHjo9k3EWLFmFgYMCiRYu4du3aA4d5i4uLGTFiBO7u7tSvX59PPvmECxcusHDhwlr18fX1rbJfcXFxtevUdhyA2NhYfHx8yM/Pr7SmOXPmcPDgQaKiorh+/Tq+vr5cvXpVM05gYCCDBw8mJiYGlUqluf322284OTlpXq+atnNnPW3atEGlUnHgwIEK31Pt3r2bkJAQtm/fztKlSwkJCeHYsWP3NEZt9mvOnDl4eXkxaNAgzMzMePXVV+nVqxfvv/9+tfU/bv7+/qxfv56PPvoIHx8fiouL72scCfQKIYQQQgghhHjihIWFMXv2bMaNG8e77777SLYREhLCxx9/jJ6e3iMZX1vCw8NJTk7mq6++4urVq9y+fZsrV66wc+dOVqxYUW7dlBRYuhSaNoVJk6B1azhxonSW3lGj4Ck7NEIIIYQQQogaxMbG8q9//Qt7e3t8fHywtrbmxx9/JD09nd27dzN69GjMzc21XaYQdYKjoyNz5swhODiY69eva4IbzzzzDB4eHmzevJm8vDxtlymEEEKIOm7v3r1MmTKF9957j2+++aZOzvhfzUyk9yUiIkIzbuPGjR94vKNHjxIcHMykSZOA0hOxxo0bx7p161AqlbXqU1W/o0ePVrtObccB6NKlC61bt660nrCwMF577TXatWsHQIMGDVi2bBk6Ojr89ddfAJiZmbFmzRqcnJwwMDDQ3Pz9/RkxYkSttlNGrVbz8ssvY2Njw8aNGytdPmDAAGxsbADw8fEBwMLCotZj1Ha/kpOTNT8TZQwNDVGpVNXugza8+eab/PLLL/j5+fHWW2/d1xgS6BVCCCGEEEII8cQpLi7mm2++YeHChTg5OT2SbfTt2xdjY+NHMrY2vfHGG3z22Wf83//9H23btsXKyoqxY8eSl5fHsmXLAAgLAx8fcHCAjRth4kS4dg127wZPTy3vgBBCCCGEEEIrXnrpJZo3b863337L1KlTiY2N5aeffmLYsGFP5d9OQjxMdnZ2TJ8+nbCwME6cOEH79u2ZNWsWTZo0Yfbs2douTwghhBB1VGpqKpMmTWLixIksX75c2+XUWlJSEl9//TXLli3j999/r7A8KiqK7du3M3/+fPbu3atpDwoKYsOGDSQkJLBp0yZ+/vnnh1LPnj17AHB3d9e0ubm5oVQqOXDgQK37VNZvz5491a5T23Fq4uTkxJgxY8q12dnZ0alTJ6z/dxnF7t27o6NTPg5aeLK11gAAIABJREFUXFzMnj17GD58eK22U2bhwoWEhoayYMECTE1NKyw3MDCgWbNmmufh4eG8+OKL5fazpjFqu1/Dhw/nxIkTfPfddwDk5eWxd+/eOvt7dL9+/dixYwfr16/nl19+uef+MpeOEEIIIYQQQognTpcuXcjIyKjwwYSomUKhYO7cucydO5fCwkL09fU1y4qLoV8/CAwEDw9Yt6402FsHT/gXQgghhBBCPCbXrl3j/fff18zC6+3t/dRdyUSIx8nT0xNPT09Wr17Ntm3b+Pzzz1EoFCxatEgzw5kQQgghBMAXX3yBoaEhq1ev1nYptXb48GF27drF1KlTMTc3Z+jQofj4+LB+/XoAPv/8c/z9/fnjjz+Ii4ujV69epKSkMHXqVBwdHSkpKaGkpIROnTphZmYGlAaEr127Vu12FQoFPXr0qHTZlStXgNKgaJmGDRsCpeHi2vaprN+VK1eqXae249SkXr16lbYnJCQwbdq0KvsdO3YMhUJB9+7da7WdMrt27UJPT4/z58/Tu3dvQkJC6NixI59//jkdO3Yst25JSQnvvvsuBw8evOcxarNfkydP5vvvv2fs2LGcPn2aiIgINm3axLBhw+5pnx6nkSNHMmrUKJYsWcLgwYPvqa/8tS2EEEIIIYQQ4okkYd4HVxbmzc6Gb76B1auhXTs4dAj69tVubUIIIYQQQgjtys7OZtGiRWzatAlnZ2ciIyMf+qV0a6JWq/nzzz/Zv38//fr1Y9CgQQ9t7D///JOgoCAuXbrEqFGj8Pb2rnVflUpFUFAQZ8+epWfPnnh6eqKrq/tQ+j2sdQBSUlK4dOkSzz//fIVloaGhmnDB3bp166aZbevubT3zzDP3PEZtas7NzWXnzp3ExMTQokULxowZg4mJSaVjA9y8eZPNmzfz3nvvVViWlZXF1q1bcXd3p0+fPpUem19++YWcnBzN84SEBGbMmFHtNh82Gxsb5s2bx4wZM3BycmLHjh18/PHHTJo06bH/WxNCCCFE3bRnzx58fHyqnN20rsnLy8PX15fw8HBMTU3x8PDg4MGDbNiwgbFjx9KtWzfWr1/PgAEDUCgUODk50aFDB/bv38/UqVNxcnLSnODUuXNnzbg//PADc+fOrXbbenp6FBYWVrosNTUVXV1dDAwMNG1lv/clJyfXuk9l/VJTU6tdp7bj3I+jR4+ip6fHnDlzqlznxx9/ZNiwYff0+2ViYiKJiYl06NCBxYsXY2NjQ1RUFM8//zxeXl5cunSJJk2aAKBUKpkzZw6RkZG4u7vz22+/0aVLl3sao6b9atSoEX/++Sfdu3dnzZo1dO/evdzfJXXVtGnT6NWrF3FxcTg6Ota6n3z7KYQQQgghhBBC/ENFR8OsWdCkCSxeDEOHQkCAhHmFEEIIIYT4pwsKCqJ9+/bs3r2bdevWcf78ea0EDC9cuMDu3bv5/PPPSUpKemjjhoWFsXLlShYsWICLiwujR4/m1q1bteqblpZGmzZtiI+PZ8KECezbtw9vb2+Kioruq9+9jl3b7c+fPx9nZ+dylzAuU1JSwquvvsqYMWMqvWVmZla5rbLt1HaM2tR8+fJlXFxcWLVqFWvWrGHSpEm0a9eOlJSUKo+nr68va9eurdCekZFB586dOXfuHC+88EKlX/RfunSJIUOGlKv3zJkzjzXMeydDQ0MuX77MG2+8wfTp0xk0aFC1+y6EEEKIf47Y2FjatGmj7TJqbdeuXeTn57NgwQKmT5/O9OnTSU5Opnnz5poTwY4cOcKHH34IQGRkJAkJCURHR1c77syZM7l161a1tztP1rpb2Uy/dyr7XdTW1rbWfSrrV9PYtR3nXhUVFbF48WJ+/vnnKrdRUlLCTz/9xIgRI+5p7NOnTwMwdOhQTcDaxcWF1atXk5eXx4YNGzTrmpqasnnzZtasWUNubi5Tp0695zFqs19bt27Fy8uLCRMmcPz4cTw9PYmPj7+n/XrcXF1dAYiJibmnfhLoFUIIIYQQQggh/kFKSiAwEIYMgVat4MAB+OgjSEqCSr4LFUIIIYQQQvzDbN26lX79+tGhQwfOnz/PpEmT0NPTzkU/O3bsyPTp0x/6uIsWLaJr164YGBiwaNEirl27VqswZ3FxMSNGjMDd3R1fX1/q16/PJ598woULF1i4cOF99buXse9l+z4+PuTn51daT2BgIIMHDyYmJgaVSqW5/fbbbzg5OdGxY8cqt1W2ndqMUdua58yZw8GDB4mKiuL69ev4+vpy9erVKo/pli1biIiIqHTZ7t27CQkJYfv27SxdupSQkBCOHTtWbp3Vq1fzxx9/EB8fr7lt27atytfvcbCwsOCzzz7jzz//5MqVK3h6enL+/Hmt1iSEEEII7atXrx43btzQdhm1FhERgZ2dHevXr9fcfvnlF65cucLrr78OQJMmTQgJCeGtt97i4sWLNG/enOLi4mrH1dPTw9jYuMbbyZMn6dKlS7lbUFAQ9vb2FBUVoVKpNGPm5uYCf4cu71ZZn8r62dvbV7tObce5V/Pnz2fu3Ll4eHhUuc6xY8dQq9U899xz9zS2paUlAPXr1y/X3r17d6D0hLy7zZ49m+HDh3PmzBlUKtV9jQGV79e2bdv44Ycf2LRpE1u3bmXr1q0kJiY+kr8VH6b09HSg9N/xvdDOX99CCCGEEEIIIYR4rPLyYOfO0tBuZCT06AE//ADDh0MtrgwrhBBCCCGE+AfYtGkTb775Jm+99Raff/65VmblvVtZmPhh1hIREcGzzz6rGbdx48a16nf06FGCg4MJCAjQtOnq6jJu3DhWrVrFokWLKr0ccnX9lEolpqamtRo7NDS01ttv3bp1lfthZmbGmjVr0NEpP/eTv7+/ZvauqupZt24dixYtqtUYtT1mr732Gu3atQOgQYMGLFu2jK+//pq//vqrQu1RUVGcOXOGF198kZ07d5ZbplarGTBggGYGMB8fHxYvXoyFhYVmnZSUFMLDw1m8eDFNmzat8hhpS7du3QgJCWHEiBE8++yzmtmyhRBCCPHP5OXlxY8//sjcuXO1XUqt6OrqcvnyZQoLC9HX1690nUWLFhEUFMTBgwcxNjbmp59+qnHc0NBQAgMDa9z22LFjmTFjRrl2Z2dnzSzHCQkJtGjRAkATlK4qUFtZn/9n787Dqqzz/48/2WUHNQRUMBQEETdcU8cUddLcaoYscym1zK9pNprVVGZl05RZ2qiZZYuOPydzSTMnzVLKyiVMVHBBUUABcWEXAeH+/XGGk0cB0dSD+npc17k6577vz+fzvkGP0Hnd77uicWFhYVUes2/fvmrNcyUWLFhA69atGTBgQJXHLV++nIEDB2J3hR8ChYSEAKY7m1woICAABwcH3N3dKxzXq1cvNm3ahJOT01XNUdl5ffbZZ/Tp08f8u+HIkSP59ddfWbhwIdnZ2Xh5eV3R+d0oy5Ytw9fXt8rfzSqiDr0iIiIiIiIit7C0NJg2DQIDYcIEiIyEPXtgyxaIjlaYV0RERERETDZv3sy4ceOYPn06s2fPrhFh3qqkpaXx8ccf8+qrr/Ldd99Z7Dt48CCLFi1i8uTJrFq1yrw9JiaGefPmkZqayvbt2/nggw9Ys2ZNtddcuXIlABERERbbmzdvTkFBAevWrbviceVjqjP31a5/sU6dOl0SxC0rK2PlypXcf//9VdZTvk515qjueQ0ZMsRin5+fH5GRkXh7e1tsLykp4cUXX+TNN9+s8LwcHR258847za93795Nv379LNb+17/+xbZt22jYsCFBQUF8+umnGIZR4XzW4u3tzbp162jZsiUDBgyo8vbRIiIicmubMGEC27dv59///re1S6mWli1bUlBQwPz58y22Z2dnM2/ePI4cOcL06dMZOnQozs7OAJftzgumn++XL19e5WPFihX4+fkxYsQIi0fDhg0ZNWoUTk5OFnduiI2NpVWrVubg6cUqGlPRuFGjRlV5THXnqa5Vq1ZhGAbDhw+32B4TE2Px2jAMli9fbnGxXXX5+vry5z//ma1bt1psT0xMpKSkhM6dO1c4bu/evfTv3/+q5qjsvMD0c312drbFtoEDB1JcXMyJEyeu+PxuhKSkJN555x3GjRtXabi9UoZhVPUQERERERERkZtEUZFhLFtmGB07GgYYRmSkYXz2mWEUF1u7MhERERERqcm+/vprw8bGxjh06JC1S7lEfHy8ARgfffSRYRiGkZeXZwQFBRn5+fnmY0aNGmUAxi+//GIYhmGMGzfOvG/QoEFG3759za9zcnIMwHj11Vct1nnnnXcMoMpHmzZtDDs7u0tq3L59uwFYrHuhqsaVj6nO3FeyflFRkQEYEyZMqLCmi40fP95ifGVrVXWeF89R3fO6WExMjNGgQQMjLy/PvG3z5s3GtGnTzK+ffvppo169ehXWkZ+fbzz22GMGYHh5eRnbt2+v8Lhdu3YZoaGhBmC88cYbFR5jbbNmzTI8PDyMgoICa5ciIiIiVnL+/HkjOjracHNzM1auXGntcixs27bNAIzWrVsb69atMwzDMBISEoyQkBDzz9Dh4eHGzp07zWNGjhxp2NvbG02aNDHmz59vLF++3HB0dDR69OhhrF+/3rjvvvsMwPjss8+M7Ozsa1ZrWVmZ8eyzzxrvvfee8fzzzxuLFi2q8LjQ0FDDx8fHOH/+vHlMv379qhx3uWOqO8+6deuMwYMHG4Dh4+NjpKenm/fFxsYarq6uFf6eUqtWLeP06dMWc02cONEYOnRohed48ToffvihxVrl4uLijKioKGPq1KnG66+/bvTr189IS0szDMP0M3fr1q0NGxsbo127dsbs2bMtfn6vzhzVOS/DMIyCggJj1KhRRvPmzY1Zs2YZo0ePNgYMGGAkJSVVeH7WlJiYaDRr1swICgoyjh8/XtlhnxpVZHbtryJALCIiIiIiIiI1SGYmfPIJzJkDJ07AoEHw9ttQyUXSIiIiIiIiFrZs2UKLFi1o3LixtUu5rKVLl1JYWMiUKVPM29LT02ncuDGHDh2iY8eOTJ8+HYCEhARSU1Or1eF0/PjxPPHEE1Uec88991S4vbS0FDB1oaqIm5tbpePKx1R1TPnc1TnmahiGwYoVKyw6v1W2VmXrVDRHVfNUVnNpaSlTp05lzZo15rHZ2dnMmTOHpUuXVut8XF1dWbBgAc2aNWPy5MmMHTuWX3/99ZLjWrZsSWxsLE2bNmXp0qU899xz1Zr/RrrvvvuYOHEicXFxdOrUydrliIiIiBXY2dmxZMkSxo8fz1//+lcmTpzI9OnTzR1ural9+/aX3O0gLCyMAwcOkJycjI2NDQEBARb7Fy5cyKxZs3B3dzdvy83NxcnJCYDevXtfl1ptbGz45z//yYkTJ6hXr16lx+3YsYOSkhLs/nd7w3/+85+UlpZy6tSpSsdd7pjytS83T58+fejTpw//+c9/LtnXpk0b8vPzq3OqgKm7s4eHxxWvc6EWLVqwceNG0tLScHZ25u9//7t5n6urKzt37iQ7OxtHR0dcXFyueI7qnpeLiwsfffQRZ8+eJTk5meHDh19yN4+a4LPPPmPixImEhITw7bff4u/vf1XzKNArIiIiIiIicpOKi4N582DxYnB0hBEjYPJkaNjQ2pWJiIiIiMjN5MyZM9SpU8faZVRLfHw8fn5+zJ07t9Jjtm/fztq1a+nWrRuNGzcmNjb2svPa29tjb1/1R6cNGzaktLSUoqIic+AAIC8vD4BmzZpd8bjyMdWZe//+/Ve1/uX89NNPFBcX86c//emyNVe2TkVzVPe8LjR58mT+9re/0bp1a/O2p59+mnbt2rFmzRrztsTERM6dO8fKlSvx8vKiR48el9Q0ceJEfv75Z1asWFHheYApHDBw4EA+/vjjCr821la3bl3A9HdUREREbl8ODg7Mnz+fjh07MnHiRL766ivefvttBgwYYO3SKhUYGFjpvgvDvECFP6ddL1WFeaHiC9Ls7OwuO+5aHXOt3HnnnddsrqqCqV5eXn94jupycXEhLCzsD89zPSQkJDBy5EgmTJjAG2+8Qa1ata56LgV6RURERERERG4yZWXw/ffQuzcEB8Mbb8Bjj0ElF0CLiIiIiIhUKTAwkLVr11JWVoatra21y6mSnZ0dBw4coKSkBAcHhwqPmT59OuvXr8fZ2ZkVK1ZUa94dO3awcePGKo8p//A4NTWVJk2amLefOnUKqDxQW9W48uBqdebet2/fVa1/OcuXL2fgwIHmLmRV1VPZOhXNUd3zKrdgwQJat259STDl5MmTfPvttxbbcnJyOHv2LBMmTCA8PLzCQC9Ar1692LRpU5UhkdDQUEJCQirdb03l3/NGjRpZtxARERGpER555BF69uzJpEmTGDRoEN26dWPatGl069bN2qWJ3JaOHDnCG2+8wSeffEJMTAxdunT5w3PW7N/IRURERERERMQsLw8WLIDwcPjzn2H1ati/H556SmFeERERERG5eoMGDeL48eOsW7fO2qVcVsuWLSkoKGD+/PkW27Ozs5k3bx5Hjhxh6NCh5lsQl5WVVWvegwcPsnz58iofo0aNwsnJiZ9++slibGxsLK1atao0FFrVuPIx1Zn7atevimEYLF++nL/85S/VqrmidSqbo7rnBbBq1SoMw2D48OEWx8XExLB27VqOHTtm8Rg7dix33HEHx44dY/369ZWe3969e+nfv3+VX4NVq1YxcODAKo+xlg8++ICQkJCrDmuLiIjIradBgwZ8/vnnbNmyBYC7776brl27smzZMoqLi61cncjt4eeff2bYsGGEhITw3Xff8dFHH12TMC8o0CsiIiIiIiJS4x0+DM89BwEBMGkS/OlPsHcv9O8PNjbWrk5ERERERG52YWFhREdHM2HCBLKysqxdjoWcnBwA8vPzARg8eDANGzZk8uTJzJgxg3379rFs2TIef/xxhg0bRn5+PkuXLiU3N5cff/yRH374gaysLPLz88nLy+P48eMAZGRkWKzz8MMPExsbW+XD19eXJ598khkzZmAYBgDnzp3jq6++YuHChZd0N54yZQqjR4+uclz5mOrMfSXrl38fz507V+XX95dffiE/P5+oqCiL7ZWtVdF5VjZHdc9r48aNvPnmm5SUlDBnzhzmzJnD7NmzGTNmDLt3766y/gsVFhby+uuvs3fvXgBOnz7Nb7/9xrvvvguYQtsTJ07kt99+M4+Jj4+noKCAF198sdrr3CibN29m4cKFvPTSS9jol38RERG5yF133cWmTZv48ccf8fb2ZsiQITRo0IBnnnmGAwcOWLs8kVvO6dOnmTVrFuHh4XTu3Jm9e/eycOFCDhw4wIgRI67ZOjblvzhVosqdIiIiIiIiInL9bNkC770HK1eawrxjxsBjj0Ht2tauTEREREREbjUZGRm0b9+eRo0asW7dOtzc3KxdEtu3b2fq1KmsX7+e1q1b8/rrr9OnTx/27dvHoEGDOHjwIADh4eEsXryY1q1bA+Dg4ECjRo2YPHkydevWZciQIXTp0oVnn32W+fPns2rVKho2bMj06dMZOHAgnp6e1a7JMAyef/554uPj6d27N+np6YSFhTFs2LBLjg0LC+PMmTOkpaVha2t72XHVmbu66z/44IN8/vnn+Pj48Prrr9OvXz98fX0vqfHpp5/m1KlTLF68uFrn+o9//OOK5qhOzW5ubhQUFFwyrlatWhw/fpzaFfwSPGXKFBYtWmQRzC4oKKBr167s2rWLtm3bMnToUEaOHGn+s7xz50569OhBTk4O3bt3p3379tSuXZvx48ebOzrXFLt37+buu+8mKiqKL774wtrliIiIyE3g2LFjfPLJJyxcuJCUlBS6du3K0KFDGTBgAPXq1bN2eSI3pcLCQjZs2MDnn3/OqlWrcHBw4KGHHuKxxx6jbdu2VzvtZ8Ajle1UoFdERERERESkBikqgs8/h5kzYfduiIyECRNgyBCwt7d2dSIiIiIicitLSEige/fu1K9fnzVr1tCgQQNrl1Sl5ORkbGxsCAgIsNiel5eHu7u7+XVRURFOTk7XdO3S0lJOnTpVZTgiPz+fkpISvL29r2jctTqmOo4cOYKHhwd16tS56rWqM8e1rPlysrOzcXR0xMXF5ZJ9RUVFpKSk4OLiQv369a9rHVfrm2++YfDgwbRr1461a9dSq1Yta5ckIiIiN5GysjI2bNjAwoULWbduHefOnaNTp04MGjSIyZMnW7s8kRrvzJkzrF27li+//JL169dz7tw57rrrLh555BEGDx58LS5+VaBXREREREREpKbLyID582HuXMjNhYEDYdIk6NDB2pWJiIiIiMjt5MiRI/Tv35/09HTmzp3Lgw8+aO2SRG4L586d4+9//zuzZ89m2LBhLFiwAEdHR2uXJSIiIjexs2fPsmHDBr788kvWrl2Lv78//fr1o0ePHtx1110VXgAlcrspKytj165dbNq0if/+97/ExMRgZ2dHjx49GDRoEAMGDKjwbid/gAK9IiIiIiIiIjXVzp3wwQewaBF4eMCjj8L48VBDGwWJiIiIiMhtoLCwkOeee45//etfdOzYkbfeeosuXbpYuyyRW9ZXX33FxIkTOXXqFDNmzODxxx+3dkkiIiJyizl//jyTJk3im2++4eDBgzg5OdGhQwe6d+9O9+7d6dix4zW/q4VITWQYBnv37mXTpk1s2rSJmJgYsrKyuOOOO4iKiuK+++7jnnvuwcPD43qVoECviIiIiIiISE1SVgZffw3vvQcbN0Lr1vDEEzBsGDg7W7s6ERERERERk+3btzNlyhRiYmI4ePAgwcHB1i5J5JazZcsWunbtSr9+/ZgzZw6BgYHWLklERERuccePH+f7779n8+bNbNq0iSNHjuDs7EzHjh3p2LEj7du3p127dtRX5xG5ReTm5hIbG8v27duZOXMmJ0+exNvbm27duplD7c2bN8fGxuZGlKNAr4iIiIiIiEhNkJMDn34K774LqanQty889RT07GntykRERERERCq3evVqoqOjGTRoEE888QTdu3e/UR90itySCgsL+c9//sP777/Pjh072Lp1Kx06dLB2WSIiInKbOnr0KJs2beKHH35g+/bt7N+/n7KyMvz9/c3h3vbt29O2bVu8vLysXa5IlYqLi9m1axc7duxgx44dbN++nQMHDlBWVkb9+vV5+umn6d69O61atcLW1tYaJSrQKyIiIiIiImJNiYkwZw4sXAh2dvDII/D009CokbUrExERERERqZ4lS5Ywb948fv75Z0JDQ3niiScYMWKEPtAXuQIHDhxg/vz5fPbZZ5w9e5b777+f8ePH06lTJ2uXJiIiImKWn5/Prl27iI2NNT8SEhIA8PPzIzw8nGbNmhEeHs7QoUNxcXGxcsVyu0pLSyMhIYH4+Hjzn9P4+HjOnTuHu7s7LVq0IDIyksjISLp06UJQUJC1SwYFekVERERERESswzDgu++gd28ICoLHHoMxY0Cfd4uIiIiIyM0qLi6O+fPns2TJEkpLS7n33nv561//St++fXFzc7N2eSI1TkpKCitXrmTFihX89NNPNGrUiDFjxvDoo4/i4+Nj7fJEREREqiU9PZ1ff/2VvXv3snv3buLj481dT4ODg2nevDnh4eGEhIQQHBxMkyZNdPGfXBNFRUUcOnSIQ4cOkZiYSEJCAnv27CEhIYGzZ89iY2NDo0aNaN68Oc2bNyciIoLWrVvTtGnTmnpnGQV6RURERERERG6kc+dg2TJ46y2Ijzc9v/9+U3deERERERGRW0FeXh7/+c9/WLZsGZs3b8bBwYHevXvzl7/8hf79++vDe7mtHT58mBUrVrBixQp27NiBp6cn/fv356GHHuLPf/6ztW7tKyIiInJNlZSUsHLlSvbs2UN8fDx79+7l6NGjnD9/HoC6devSpEkTgoODzSHf4OBggoKCqF27tpWrl5qmsLCQpKQkc3D30KFD/Pe//+XYsWOUlZUBUL9+fcLCwswB8hYtWtCsWbOb7eJSBXpFREREREREboT0dPjgA5gzB/Lz4YEH4JlnICLC2pWJiIiIiMiFkpKSmD59Oq+++ioNGjSoMXPdrE6fPs1HH21k7dof2L59MVBE586diYqKIioqinbt2mGnKxzlFpaXl0dMTAzfffcdGzduZO/evdSpU4dBgwbxl7/8haioKBwdHa1dpoiIiMh1d/78eVJSUkhKSrrkkZCQQGFhIQC1atXC39+foKAg/Pz8LnkeGRlp5TORay0rK8v8ZyEtLY309HSL50eOHKE8y+rt7U1QUBD9+vUjPDycoKAgQkJCcHd3t/JZXBMK9IqIiIiIiIhcT7GxMHs2LF0KdevCmDHw5JOm5yIiIiIiUvMsX76c6Oho1q1bR58+fWrMXDezL7+E++4DW1vw8jpLrVqHyc39mfz8WFxdU/nTn+6gb9/2REVFERYWZu1yRf6QkpIStm7dag7wbtu2jdLSUiIiIujZsyd9+vTh7rvvxt7e3tqlioiIiNQY58+fJzk5maSkJFJTU0lJSSElJYXU1FTz6/LAr4+PD/Xq1cPPz4969erh4+ODv78/Pj4++Pr64uvri4+PDz4+PlY+K8nLy2PXrl1kZGSQkZFBZmYmaWlpZGZmcuLECdLT08nIyDB3bra3t8ff35+AgAACAgJo2LAhDRs2JCAggEaNGtGkSROcnZ2tfFbXlQK9IiIiIiIiItdacTGsXg3vvgu//AKRkTBhAjz0EDg4WLs6ERERERG5nFOnTlH3Gl2Fdy3nulkdOQJBQZbbHBygrAxKS02vbWzyMIyDODomEBh4jsmTGzNoUEt8fOrc+IJFrlBaWhqxsbG89957/PTTTxQWFuLn50eXLl3o2bMnffv2vW27dIuIiIhcKydPniQ1NZUtW7Zw8uRJjh8/zsmTJy3CosXFxebj7e3t8fHxoU6dOtSuXRtvb29q1659yfPyx5133omnpye2trZWPMuaKScnh7y8PLKysjhz5oxXlP1CAAAgAElEQVT5ceHrrKwsTp8+bX6dmZlpDmHb2NiYQ9YXhq79/f3x9/enYcOGBAYG4ufnd7vfwUWBXhEREREREZFr5eRJ+PhjmDMH0tOhTx947jno3NnalYmIiIiIiFiPYYCbG5w9e/ljbW3LgDLKyuyBYmrXXkH//j/QuXMbWrduTbNmzXBxcbneJYtU6tSpU8TFxbFz5062bt3K1q1bSUtLw97entGjR9OhQwe6dOlCkyZNrF2qiIiIyG3n9OnTnDhxwqITbFUB1DNnznBxRtLZ2RlXV1c8PDxwd3fH1dUVV1dXvLy8cHNzs3htY2ODh4cHdnZ2ODs7U6tWLezt7XF3dwfA29sbV1dXHB0dzfNf/PpC7u7uld7JITs7+5JaDcMgOzu7wtd5eXmcP3+ec+fOUVhYSGlpKbm5uYApoFtWVkZeXh4FBQUUFBSQnZ1NZmYm+fn5ZGdnm7fn5eVVWmtVAenyAG+rVq3w8fG53YO61aVAr4iIiIiIiMgfFRcH8+bBv/9t6jI1YgRMmgQBAdauTERERETkxvvuu+9ITU0FwMnJifvvvx8nJye2b99OQkIC3t7eDBw40Hx8Wloa33zzDceOHaNz585ERUUBpg8iY2Ji2LVrF3Z2doSGhtKrV68rqqWwsJDVq1czYMAAPv30U/z9/enfvz92dnacOHGCNWvWYGtrS3R0NB4eHgCUlZURExODm5sb7dq1u2wtVe2raK7U1FRWrlzJ+PHjSUhIYPXq1QQEBPDwww9bdIJ6//33SUlJITg4mPbt2xMWFnZTfwDasSNs21b94/39Sxk2bCc2NivZunUrv/76K/n5+djZ2dG4cWNatmxJREQELVq0oEWLFjRq1AgbG5vrdwJy2ykuLmbfvn3s3r2bPXv2EBcXx549e0hPTwfAz8+PDh060LFjRzp27Ejbtm1xdXW1ctUiIiIicqXKg71HjhwhJyfHHGTNzc0lNzfX/Lq8S+2FAdjy8QD5+fmUlJRY81SqZGNjg5eXF/B7cLg8nOzm5oaXlxf16tUzPy/f5+7ujqenJ+7u7hbB3cqCx/KHKNArIiIiIiIicjXKyuD772H2bPj6awgOhv/7P3jsMVCzKBERERG5nZ09e5b27dsTHx/P4cOHCQoKMu8LCwtj9erVhISEALBp0yaWLl3K2LFjOXToECNHjmT48OHMnTuXF154gTvvvJPRo0fz66+/Mm7cOLZdQSI0JiaGxx57jMTERGbOnMmJEyeYM2cOffr04Z577mHz5s2Ulpby+eef069fP9asWUNCQgIvv/wyy5cv5/333+eJJ54AqLKWyvZVNNdXX33FqFGjOHnyJO+++y67d+/m5MmTrF27ln/84x88//zzgOkD4b1799KmTRuGDRvGqlWraNeuHZ07d+bdd9+9Vt+qGyI5GXbvhtdfh5074XKfbzs4gJ0dZGVBrVq/by8rKyMpKckcqiwPWCYlJWEYBh4eHjRr1oymTZsSEhJCcHAwwcHBhISEqKOvVOnkyZMkJiZy4MABEhMTOXjwIAcOHODAgQOUlJTg5OREeHg4ERERREREmMPk9erVs3bpIiIiIlIDFRcXU1BQYO6WW94pt1xFnXbh0m67F6uss+/FXX3LOweXH+/o6KgLz24eCvSKiIiIiIiIVEdSEixYYHqUlMCQIfDUU9CsmbUrExERERGpeQzDIDw8HID4+HhsbGyYOXMmISEh9O/fH4APP/yQl19+mfvuu8887ujRoxw4cIBDhw7RpUsXEhMTWbBgAQMHDqSoqAgnJ6crquPjjz9m1KhR7N+/n6ZNm/Lpp5/y6KOPcujQIRo3bgzAv//9b4YNG0ZqaioNGjTg4MGDNG3a1CLQW1UtVe2raK5XX32Vl19+mWPHjlG/fn0Ahg0bxvr168nMzARg8ODBLF68GEdHR0pLS/H09GTMmDHMnDnzqr4f10NWFsTHQ2wsLF4Me/dCURE4OkKTJhAZaXqEh0OrVqbfqTp0uHQeW1vTBZNdu8J775mOvRaKi4tJSkriwIEDHDx4kMTERBITE4mJiTF/eO7s7ExgYCABAQEW/w0MDKRevXrUr18fNze3a1OQXFeZmZlkZmZy7NgxkpOTSUlJISUlhaNHj5KSkkJaWpo5ROHo6EjPnj1p2rSpOfQdHBxMw4YN1eVZRERERESsqcpAr3oii4iIiIiIiABbtsDdd0NAADz7rKkLb+3a1q5KRERERKTmsrGx4ZlnnmHkyJGsW7eOe++9l40bN/LUU0+Zj4mPj8fPz4+5c+dWOMecOXOIjo5m0KBBREVFsWTJkj/cEbPWhS1f/8fBwQGAgoKCSsdVVcu1qNPOzs6iQ1P37t3ZsmULPXr0ICsri+LiYnr16nVFc14r58/DgQOQkPB7gPfXXyEjw7Tfzw+io2HCBFOANzTU1GH3Yq6uv4d3y9nbQ/36MGOGaY5rydHRkdDQUEJDQy22FxcXm8OeKSkpJCcnk5yczOHDh9m0aROpqakUFRWZj3dxccHf3x9fX1/8/Pzw8/OjXr16+Pv74+Pjg6+vL3Xq1CEwMPDanoBgGAaJiYmcPn2azMxM0tLSOHHiBOnp6aSnp3PixAmOHz9OZmamxa2NPTw8zMHsli1b0r9/f4uwtp+fn4K7IiIiIiJy01GgV0RERERERG5bRUXw+ecwc6bp9rCffWbqymuv35ZFRERERKrl4Ycf5qWXXmLmzJk0atSI8PBwi9uA2tnZmW9pXx6qvVCrVq3YuXMnzz33HB988AFt2rRhz5491LbC1XVV1XI96hw9ejTNmjXj9ddfZ9OmTbzxxhvcc8891/CMKpadbeq0GxtrGeA9dw4cHCA42BTanTjRdLeS9u2hutllZ2fTRZJHj5p+r3JygldegfHjTV19bxRHR0eaNGlCkyZNKtxvGAYZGRlkZGSQlpZGZmamOTSalpZGbGwsGRkZpKenc/bsWYt5a9euTe3atalTp475+YWvPTw8cHV1xc3NDU9PTzw8PHBzc8PV1RUPD48b9SW4YcrKysjJySE3N5eCggLy8/PJzc0lJyeHgoICcnNzOXPmDKdPn+bMmTMVPr9Q7dq18fX1xdfXF39/f4KDg6lfvz4+Pj7m0LW/vz9eXl5WOmMREUuFhYXm97ucnBzKysrMt1kvv/16YWEh586do6SkhPz8fPN7Z7mLb9NerqCggOLi4grXrerW6g4ODhbd5729vXFzc8PBwYFatWrh7OxsPsbGxgYvLy9sbW0t/t2q6AIpERERuf70EaWIiIiIiIjcdk6cgE8/Nd3q9dQpGDgQPvgAOna0dmUiIiIiIjcXR0dHJk6cyDPPPMMzzzzDjBkzLPa3bNmSgoIC5s+fz/jx483bs7OzcXZ2ZtmyZQwbNoy5c+cyYMAA+vTpw8qVKxk9evQNPY+ioqIqa1m8ePE1r9Pe3p5PPvmEunXrMmDAAJycnK7hGZm67qak/B7YLQ/wJiWZ9nt7mwK7kZHw+OMQHg7Nm5tCuH9E27amdUeNgtdegzvu+OPncq3Z2NiYO/G2bt26ymPz8vI4ceIEp0+f5tChQ5cEUtPT04mPjze/zs3NrTCUVc7T09Mc8HV3d8fb2xsALy8vbGxscHd3x97eHmdnZ2rVqnVJYOvikNaFysNaFzp79qxFN+ILZWVlWbyuLIBWXFxMQUGBOaSWnZ1NXl4eBQUFFoHnizk7O+Ph4WERevbx8SE0NNQiBB0SEmLed63/HoiIVEf5xQenTp0yv8df+F6fk5NDfn4+2dnZ5Ofnk5+fz/Hjx8nNzaW0tPSy8zs5OeHi4oKdnZ354o7y93/AHLKtbFxFsrOzOXHiRIX7Ln7vz8rKMtda1b8LFyqvtfzfrfKHt7c3rq6ueHp6EhwcbPF+XrduXfPFLSIiInJ1FOgVERERERGR28bOnabg7qJF4OEBjz5q6hRVv761KxMRERERuXmNGTOG6dOnc+rUKcLDwy32DR48mBdffJHJkydz7tw5+vXrx549e1i+fDmLFi1i/vz5DB06FBsbG3r37k3dunWpW7euefzjjz/OsWPH+OSTT6hXSZvYvLw8AHM4JT8/H4AzZ87QuHFjwNTh7sJjyv976tQpwNSxtapaqtp38VxgCgYBFl31Tp06RVFREYZhYGNjw/vvv09YWBjFxcWkpKTg6+uLu7v7FXzlLeXkwJ498MUXpvDub7/B2bOmTrkhIabA7rBhpgBv27bg53fVS1VpxAh4+WVTOPhW4O7ujru7O02aNKFDhw7VGnPu3Dlzp9rc3Fxz+CsvL88iDFZQUEBWVpZFp8Zjx45RVlZm7sp47tw5CgsLzXNXFcQqD+ReqKoOjhcHgD09PbG1tcXV1RVHR0dzkMzFxcX898/b2xsvLy+LcJeXlxfu7u7m1+UBMFtb22p9vURErofS0lIyMzP57bffLDqxp6enk5GRwalTp8yB3ZKSEoux9vb2FkHV8lBrUFCQ+b2ufv36uLu7W3RlL78ow8PDAzs7O1xcXGrshQpFRUWcPXuW0tJS88UoeXl55vByQUGBxb9b5V3Ys7KyyMzMJDExkaVLl3LmzJlLLmRxcHAwf/3q1KmDn58fvr6+1KtXD39/f3PX9Xr16uHj44OdnZ2VvgoiIiI1j83Fv9RdpMqdIiIiIiIiIjVdWRl8/bWpG+/GjdCqFYwda/ogu4LGFyIiIiIichXGjh1LREQE//d//3fJvn379jFo0CAOHjwIQHh4OIsXLyYsLIygoCC6devGX/7yF5KTk8nNzeWVV14xj23SpAmHDx/m7bffZtKkSZfM/csvvzB27Fji4uIYMWIEjzzyCJMmTWLnzp3ce++9vPXWW+Tk5PC3v/2NrVu38sADD9C/f39Wr17N8uXLad68Of/85z+JioqqshZ/f/8K923bto23337bYi43NzdGjhxJUlISo0eP5rXXXmPz5s2MGTOG3Nxcpk2bxgsvvMDatWu57777LM6nZ8+eLF68GF9f3yq/3mlpll134+Nh3z4wDOjc2RTaDQ//vQOvfvcREZFbiWEYpKenk5ycTHJyMikpKeb/pqSkkJmZSWZmJmVlZQC4uLiYA6S+vr74+fmZu8mWh3bLw6d16tTB09PTymd4c8nJyeH06dPmx4Udjk+dOkVaWhonTpwgIyOD9PR0i+7utra2+Pj44OPjQ0BAAIGBgXTt2pWAgAAaNWqEr68vNjY2Vjw7ERGRa+4z4JHKdirQKyIiIiIiIrek3Fz45BN4911ITYW+feGpp6BnT2tXJiIiIiJy6+nduzfLli3Dy8ur0mOSk5OxsbEhICDAvO38+fOUlZWRkZFhsb1cUVERq1evplatWgwYMOC61F6dWi5X59X49ttvufPOO8nIyODs2bMUFBSwfPlyIiIieO655wDIy4ODB02B3fIA765dUFAAdnYQGPh7aLc8xBsUdE3KExERsaqzZ89y8OBBEhMTSUxM5NChQ+bgbmpqqrlbub29Pf7+/gQEBHDnnXfSoEEDfH19zcHd1q1b4+bmZuWzkQvl5+ebOyZfGPZNTU3l6NGj/PLLL5SWlgLg5OREQECAOezbuHFjgoODCQkJITg4GBcXFyufjYiIyBVToFdERERERERuH4cOwb/+BQsXgq0tPPQQ/O1v0LSptSsTEREREbk1xcXF8f777zN//vxrPndubi4vv/wyM2bMwN7e/prPby2xsbEMGDCA48ePm7elpcEPP2TzxRfLcHV9nNhY2L/fdNcRDw+IiLDsutumDSjDIiIiN7uysjKSkpLYv38/a9euNQd4U1NTAbCzsyMwMJAmTZoQGBhoDnY2atSIgIAA6tevf0v9jCCmC6mOHz9u7sB8YSfmQ4cOkZycTGlpKTY2NjRo0MAi4BsSEkJoaChBQUHY2tpa+1REREQqUmWgVz/ViIiIiIiIyC1hyxZ48034+mtTR6qXXoIxY6CKBmEiIiIiInKVYmNjmTJlChEREWzevJkvv/zyuqyzfft2/vGPf9xyQZ2dO3eTnp7OI48c5tixQHbuPEpW1nZgN76+z9O2LURH/x7gbdYMdLdpERG52eXm5pKYmEh8fDyxsbHExsYSFxdHfn4+AJGRkTRr1ozevXsTFBREUFAQYWFh6sJ6m7G3tycwMJDAwMAK95eUlJCamkpSUhJJSUnEx8eTkJDAhg0bOHLkCIZh4OjoSJMmTYiMjCQyMpIhQ4Zwxx133OAzERERuXLq0CsiIiIiIiI3rXPnYNkyeOst0y1oO3eGp56C++833X5WRERERESujx07dhAVFYWtrS0ffvgh0dHR1i6pxkpLg4QE0+8ssbGmx4EDBqWl72Jj83fs7Oxp0CCC6OhHee65R6hd29HaJYuIiPxhWVlZbNu2jW3bthEbG8vu3btJTk4GoHbt2rRs2ZKIiAhatGhBy5YtCQ0Nxc3NzcpVy80uLy+P/fv3ExcXx+7du9mzZw9xcXFkZWXRqFEjIiIiiIyMpEOHDnTo0AFvb29rlywiIrefKjv0KtArIiIiIiIiN6Vp02DOHMjPhwcegGeeMd2CVkREREREbozz589ja2ur2xn/T0kJHDxoCuyWB3i3b4fMTNN+Pz+IjPy9625kJISElODg4GDdwkVERP6g8+fPs3v3brZu3WoO8R48eBDDMGjcuDHt2rWjZcuWtGjRghYtWtCgQQNrlyy3mXXr1rF7927i4uLYsWMHhw8fxsbGhqZNm9K+fXs6dOhAx44dadGixS13ZwgREalxFOgVERERERGRW0dsLMyeDd9+C2PGwJNPQt261q5KRERERERuJ1lZlh13ExJg714oKgJHR2jSxBTYLQ/wtmwJusuziIjcKsrKyvjtt9/47rvv2LhxIz/99BNnz57F09OTdu3a0bFjR3MH1Dv0D6DUQCdPnjSHz7du3cr27dvJzc3FxcWFLl26EBUVRc+ePWnVqpUuXhMRkWtNgV4RERERERG5uRUXw+rVMGsW/Pyz6UPxX34BNbISEREREZHr7fx5WLXq9wDvr79CRoZpn5+fZcfdyEgIDQU7O+vWLCIicj0cPnyYjRs38sILL3D69Gl8fHyIiooiKiqKTp06ERoaqvCj3JTKysrYt28fv/zyC9999x3ff/89mZmZ1K1bl+7du9OzZ0969uxJUFCQtUsVEZGbnwK9IiIiIiIicnM6eRI+/hjmzoXjx6FvX3jqKejZ09qViYiIiIjIrSg729Rpt7zrbnmIt7QUgoN/77jbrBm0bw/16lm7YhERkesrPj6eL774grVr1xIbG4uLiwtTp06lZ8+etG7dWgFeuWUlJSWxceNGNm7cyIYNG8jJyaFZs2b079+ffv360aVLF2uXKCIiNycFekVEREREROTmcuAAzJsHH31k6sI7YgRMmgQBAdauTEREREREbgWlpZCc/HtgtzzAm5Rk2u/tbdl194EHoFYt69YsIiJyo8TGxrJ8+XJWrFhBYmIiDRo04L777uP++++nc+fOOOi2WXKbKS4u5qeffmLlypWsWrWK48ePExISwtKlS2nTpo21yxMRkZuLAr0iIiIiIiJS85WVwfffw+zZ8PXX0KQJjBsHo0eDq6u1qxMRERERkZtVTg7s2WPZcfe33+DsWbC3h5CQ37vuRkZC27bg52ftqkVERG6svLw8li5dyrx584iLiyMwMJCBAwcSHR3NXXfdpU68Ihco71z9yiuvEBoayiOPPMLjjz+Ot7e3tUsTEZGaT4FeERERERERqbny8mDpUpg1C/bvh6gomDAB+vUDGxtrVyciIiIiIjeTtDTLjrvx8bBvHxgGeHmZgruRkZYBXmdna1ctIiJiPXFxccyfP58lS5ZQWlrKQw89xJgxY2jXrp21SxOp8bZt28aCBQtYunQp9vb2DB06lLFjxxIREWHt0kREpOZSoFdERERERERqnqQkWLDA9CgpgSFD4KmnTB+qi4iIiIiIXE5eHhw8+HvX3dhY+OknsLODwMDfA7vlAd4779RFgyIiIuXi4uJ4/vnn+e9//0toaChPPPEEI0aMwMvLy9qlidx0srKy+PTTT5k/fz4HDx6kd+/eTJs2jU6dOlm7NBERqXkU6BUREREREZGaY8sWeO89WLUKfHzgscdMHXlr17Z2ZSIiIiIiUlOVd94t77obG2u6w0dZGXh4QESEKbQ7bBi0bg2urtauWEREpGY6evQoU6dOZcmSJURGRjJ9+nR69eqFja56EfnDDMPg22+/5bXXXmPLli3079+fmTNnEhwcbO3SRESk5lCgV0RERERERKyruBhWr4aZM2HbNlOHrAkTTF157e2tXZ2IiIiIiNQUxcWQmPh7x92EBNi1C06dMu3387PsutusmemhDJKIiEjVSkpKeO2113jrrbcICAhg+vTpREdH3/Agb3FxMT/++CNr166lV69e9O3b95rO/+OPPxITE8P+/fuJjo5m4MCB1R5bVFRETEwMu3btokuXLnTo0AE7O7s/POZKjnFzc7vsuhkZGezfv5+77777kn3Z2dksXLiQlJQU7r33XqKioi6Z6+uvvyY3N9f8OjU1lSeffBIXFxfzttOnT7N69WpSUlJo0aIFvXv3xs3N7YrnqU7NF691//33V7m/olouFhcXxw8//ICjoyP33nsvDRo0qPL46+Hbb79l0qRJHDhwgOeff54XX3wRe/3PcBERuUyg1/bG1SEiIiIiIiK3ozffNN3aduhQCAiAX36BX3+F4cMV5hURERERuZ2lpcHGjTB7tun3g/BwcHGB5s1h3Dj46SdTgPfFF+HHHyEvzzTmq69g2jSIjjaNUZhXRETKJSUlMXLkSI4dO1Yj5qkpkpOT6dSpE++88w5vvfUW8fHxPPDAA1bpyrt3716WLVvGrFmzSEtLu6Zzx8bGMmPGDKZMmUJISAiDBw+u9tjMzEzCwsJISUlh5MiRfPnllwwcOJDS0tI/NOZKj6lq3ZMnTzJ58mSCgoJYtWrVJfvPnDlD27ZtiYuLY+/evfTp04e77rrL4pj9+/fTv39/hgwZYn789ttvFiHcXbt2cffdd9OsWTOmTJnCoUOH6Ny5M+np6RZzXW6e6tRc0VoXrlPdWsqdOnWK0aNH8/zzzzNw4EDGjBljlTAvQK9evdi5cydvvfUWb731Fp07d75l3lNEROT6UYdeERERERERuS5++w3mz4cvv4RHH4UnnwQr/b9TERERERGxspISOHjw96678fGwdq1pX3nX3fKOu5GREBYGtmpLIyIiV2j58uVER0ezbt06+vTpY/V5aoLffvuNvn374uPjwxdffEFISIi1S2L37t20bNmSDz/8kNGjR1+zefv27ctdd93Fiy++iGEYpKen4+/vf9lxZWVldOvWjdq1a7N69WoASktLady4MQ8++CD//Oc/r2rM1RxT1bo7duzAycmJli1bMmHCBGbPnm2xf/78+TzwwAPUrl0bgNdee42pU6dyYS7o8ccfZ8iQITRu3Ni87Y477qBWrVrm82rTpg1//vOfefPNN83HdOjQAU9PTzZs2GDetnnz5krnqU7Nla3Vu3dvNmzYcEW1ABw9epR27dpxzz33sHjx4ku+Z9ZU3jH6zJkz/Pe//6VFixbWLklERKynyg696oUkIiIiIiIif1huLnzyCcyaBSkp0KMHTJhgCvR+8IG1qxMRERERkevh/Hk4cOD3gG5srOlR3jDN2/v3gG55YPfBB8HR0bp1i4jIreuvf/0rl2lqdkPnsbbz58/zxBNPcOjQIVxdXa1djpn9/27bdXGH4LS0NL755huOHTtG586diYqKsth/8OBBtm7dyu7du+ncuTP33XcfADExMcTHx/PNN9/g5OTEBx98gJ+fHwMGDKhWPRMnTmTLli0cPXrUvM3Ozo4ZM2bwwAMPEBkZSXR09BWP+fHHH6/4mKrWbdeuHcXFxZWexxNPPGHxulWrVvTr18/8+oUXXuDDDz/kww8/5M4772Tq1KmMGDHC4vvw2WefERcXx7PPPmsx17hx4xgxYgSzZ8/mqaee4oUXXuAf//hHpfNUp+bK1vr222+ZPXs2Hh4e1aoF4JlnnuHtt9/mo48+YtSoUZV+jawlNDSUPXv2kJ+fT7du3bC3t+fnn3/Gzs7O2qWJiEgNo0CviIiIiIiIXLVDh+Bf/4KFC03dsx56CP72N2ja1NqViYiIiIjItZSdDXv3WnbY3bkTCgvBwQGCg02B3ccfN4V327eHevWsXbWIiFyp7777jtTUVACcnJy4//77cXJyYvv27SQkJPDII48Alw9eGoZBTEwMu3btws7OjtDQUHr16lWtGgoLC1m9ejUDBgwgMzOTdevW4e/vT//+/bGzs+PEiROsWbMGW1tboqOj8fDwMI8tKysjJiYGNzc32rVrV2UdVe27eJ5yqamp1K9fn4SEBFavXk1AQAAPP/wwthe0lc/Pz2fx4sWkpKQQHBxM+/btCQsLs0pwb9GiRSxevLhGhXmrMm3aNMaOHYu7uzuDBg1i+PDhzJ07F4BZs2axevVqvv/+e5KTk+nevTsZGRmMHTuWwMBADMPAMAzq1atHZGQkbm5upKWlkZSUVOl6NjY2dO7cmUOHDgHg5+dnsd/HxwcwBYkvVp0xV3tMVetWh2EYfPHFF7zyyiusX7/evL1bt26UlJTwyy+/sG3bNh599FGWLFnCN998Y/7z6ezsDMCvv/7KQw89ZB5b3ok3JSXFPNcPP/xQ6TzVUdla5euU/927XC0AS5cuxd7enj179tCjRw+2b99OmzZtmDVrFm3atKl2Tdebm5sbixcvpnXr1ixZsoThw4dbuyQREalhdLMiERERERERuWJbtsADD0BoKHz9Nbz0kqkz7wcfKMwrIiIiInKzKy2FpCT46iuYNg0aNzZ12+3aFV55xRTmjYw03aHjxx9Nd+yIj4dly0zH9++vMK+IyM2qU6dOvP322zz66KN06NABJycnANq3b2++5f2mTRXvEdsAACAASURBVJuYNm0arVu3JiwsjEGDBjFu3DiLeV588UUOHTrExIkT6dSpEy+++GK11o+JiaFly5Y89NBDzJ8/nzfeeIPk5GQefvhhBg8ezEcffcSkSZP4/vvveeyxxxg6dKh5bEJCAoMHD6ZHjx7ExsZeto7K9lU0D8BXX31FZGQk7733Hu+88w5bt25l+PDh5q8LQFZWFpGRkTRv3pwXX3yRtWvXEhERQadOnXj66aev5FtxTSxatIiQkJAbvu7VyM/P591336V169ZER0czePBg5s2bx9atWwGYO3cu4eHh2NjY0KhRI1q1asXatWsBaNSokTm0Wb9+fdq2bUtoaCiff/45Xbt2rfRx9913A3DixAns7OxwvOg2Ai4uLgCkl99+4ALVGXO1x1S17uUUFBQwZswYHn30URISEoiIiDDv6927N2+99RY//vgjO3bsIDQ0lI0bNzJjxgzzMZ07d8bR0ZGYmBiLLtU5OTmA6WtdPldV81RHZWuVr1PdWo4fP87x48dp3rw5U6dO5fvvv2fnzp0cOnSIbt26cfz48Suq63pr1qwZgwYNYtGiRdYuRUREaiAFekVERERERKRaiopg0SKIiDB9kJ+WBkuXmm6x++yz4OVl7QpFRERERORK5eaaLthbsACeegq6dAEPD1OI9/77YfFiGDYM1qyBw4fhzBnT8bNnm7rxdukCtWpZ+yxERORacXFx4Y033gDg+++/N29PT0+nefPm5OfnM3r06CqDl4ZhsGDBApo0aQJA27ZtGTBgQLXW79atG2PHjgUgICCADz74gDfffJMJEyawYsUKateuzb///W+WLl3K3//+dzZs2EBZWRlgCslNnTrVYr7K6qiqxormAejfvz+jRo0iIiKCjz/+mK+++oo2bdqwYsUK8zEzZsygqKiIrl274urqag4JDxkyhHfffbdaX4Nrae/evTd8zau1dOlSpkyZwrhx4xg3bhzp6ek0btzY3MF28+bNTJ8+HTCFrlNTU0lMTKxyzvHjx3P27NlKH7m5uYCpa2pFSktLAfD19b1kX3XGXO0xVa17Oa6urixYsIC8vDzeffdd8vLyKjyuZcuWxMbG0qBBA5YuXWre3rBhQ6ZPn05sbCyPPvoo69atY+bMmbz88svmcdWZpzoqW6t83urWsnPnTgAGDRpE7dq1AQgJCeGdd94hPz+fefPmXVFdN0LXrl1vqr+fIiJy49hbuwARERERERGp2dLTTZ1358yB/HxTZ94lS6BFC2tXJiIiIiIAxcXFFBQUkJWVRWFhIefOnQNMXc5KSkouOf7s2bMUFRVVOJe3t/cl2zw9PbG1tcXGxgYvLy/s7Ozw8PDAwcGh0pCC1ExpaRAba3okJJi66u7bB4YBnp7QvDmEh0N0tKkDb2Qk/O9OyCJyg5W/h+fl5XH+/HlycnIoKyvDMAyys7MrHJOdnX1Jh0NHR0dcXV0vOdbe3h53d3cAnJyccHFxwdnZmVq1auHq6npJN0m5vfTr14+wsDDeeecdRo0ahY2NDf/v//0/hg8fztKlSyksLGTKlCnm4y8MXnbs2BEbGxuaNm3K4MGDWbBgAQMHDmTy5MnVXt/T0xPAorNo0//dEurCMGFoaChFRUWkpaXRoEEDAHNH4QvHVVTH5Wq8eJ5yzs7OhIaGml83a9aM9evXm18fPnyYkydPUlxcjKOjIy1btsTV1ZXU1NRqn//tKj4+nrlz51a6v379+mzYsIG1a9fSrVs3GjdubNFBuSL29vbY218+FtOwYUNKS0spKiqy+N6Xh2GbNWt2VWP2799/VcdUtW512draMnHiRH7++edL1i/n4uLCwIED+fjjjy22P/PMM7Rv354NGzawZcsWHnzwQbZu3UpiYiKtW7eu9jzVUdFanp6e5nWqU0v5e0bdunUt5u7UqRMABw4cuOK6RERErEWBXhEREREREalQbKyp69Z//gN16sCTT5oeF/1/URERERG5SiUlJZw8eZJTp05x8uRJsrKyyM3NJScnh5ycHIvnOTk5ZGdnk5OTQ0lJCTk5OZw/f77Sjls3UnlYrFatWjg7O+Pu7o6npyeenp54eHiYn3t6euLl5YWnpyeBgYHUq1cPHx8fhYKvg7w8OHjQFNgtD/DGxZku0APw8zOFdaOjTQHeZs1MDxsb69YtcqsoKCggMzOTEydOkJWVZX5Pv/B5+Xt7amoqubm5nDt3jsLCwiovurjR3NzccHBwMF/E4enpSaNGjSze28ufe3h4ULt2berWrcsdd9xB3bp1FQq+idnY2PDMM88wcuRI1q1bx7333svGjRt56qmnmDx5Mn5+flUGLwHmzJlDdHQ0gwYNIioqiiVLllCvXr2rrqlWBe3gHRwcANPfuaup41rUaGdnZxGk7969O8uWLWPLli306NGDrKwsiouL6dWr1xXNe61cGIqu6ezs7CgpKTF/Xy/20ksvERMTw/r163F2drbojFyZHTt2sHHjxirXnDJlCmFhYQCkpqaauzYDnDp1Cqg4WFudMfv27buqY6pa90r16tWr0oA6mILxISEhl2zv1q0b3bp1A+DIkSOsWbOGGTNmmC8Gqe481XHxWq+++qrFOperpXzdiwPeAQEBODg4VFqzNf3www831d9PERG5cRToFREREREREbOSEvjyS5g1C37+Gdq0MXXmHT5ct9EVERERqS7DMMjIyOD48eOkpaWRnJzMyZMnOXnyJBkZGezbt4/MzEyysrIuGXthGPbCoFRQUBDe3t54eHjg6OiIh4eHubtieadcb29vc6gWsHh+ocq6NVYUEC4rKyMnJ8fieflxJSUl5OfnmzsEFxUVcfbsWfLy8iwCa8ePHyc3N5fs7GxzKLn8NsJg6nRXt25dc8D3/7N33/E5Hfz/x18ZEiNbZIgMUSJLaMRorBKrRmh/Ru2Kaq1WbXcV3f1WqRY1bu4WVTdihFZrtEFtgoTETmRPJJHIzvn9kfu66soS8wo+z8fjeoRzznXO51yRTyLX+3yOKgxmY2ODjY0NDg4O2NnZYWdnV26YR/wzeXfLlpKPly5BcTGYmEDjxiVhXdXU3RYtoJxPvxCiCiIjI4mPjycmJoakpCQSExPVF2WkpqaSnJxMamoqOTk5Gs9TTTY3NzfX6O9mZmZ4enpiYmKino5bu3ZtDA0N1VNyjY2N0dfXx8TEBD09PQCNP99LFcC9V0UB4XsnupcOE2dnZ5Ofn68xJTg/P5+MjAyio6NJSkri8uXLZS5CKc3MzAxra2t1X7eyssLKyop69erh4OBA/fr1sbe3x9bW9qE/J+LJGTp0KB999BELFy7EyckJd3d39PX10dPT4/Lly5UGLwGaN2/OmTNnmDVrFitXruTll1/m/PnzWFhYPMWzqLyOJ1HjmDFjuHbtGu+++y6ff/45wcHBfPnll/To0eMxntU/EhLAxgZ0dctfP2LECC5duqQxVbi68vLyYsWKFUyaNEm9LD09nV9++YWePXvy2WefsXLlSvXPt8XFxffd55UrVwgMDKxwvb6+PjNmzCAgIIBPP/2UI0eOaARvQ0JCaN68eblB1ao852G3qey4D+rChQuVrt++fTv+/v4Vrs/Pz2fQoEG4uLgwfvz4h95PVaiOdfTo0QeqxcbGhu7du3P8+HGN7a9evUpBQQG+vr6PVNfjFh4eTlBQEKtXr9Z2KUIIIaohCfQKIYQQQgghhABKpvEuXAjx8fDaa7BvH/j5absqIYQQQojqJy8vj6ioKK5fv861a9eIjY0lPj6euLg44uLiSEhIID8/X719vXr1sLa2VodUhw4dqrFMNcnQwsICHS2OSNXX18fc3LzM8rp16z72Y8XGxpKcnExKSoo6DHfv3y9dukRiYiLJyckar6WVlZU6ANagQQPs7Oxo2LAhL730Eo0aNXoitVYn+flw9eo/U3cjIuDcOfjfEDd694Y+fWDmzJLwrqtrxQEfIURZt2/f5tq1a1y/fp3IyEgSEhKIiYkhPj6e+Ph4kpOTgZKpoNbW1tja2qr7uKurKzY2NuqebmVlhbW1NRYWFlqdRF67dm1q165dZnl5/f5R3bp1S2PyfEpKCikpKerA87Vr1zh27Jh6erGKg4ODRl93cHDA2dmZl156CWdn50onS4onx8DAgMmTJzN9+nSmT5/OggULgJLQZXZ2doXBy/Hjx5OXl8fmzZsZPnw4y5Yto2/fvvTs2ZNt27YxZsyYp3oe69evL7eO4cOHP5Ea9fX1sbW15ccff8TS0pK+ffs+0X/DM2fCb7+V/C6vRw/o1g2srP5ZP2LECFq3bs2BAweq1V0RVBeMZaluHwAMGjQIExMTcnNz6d27N+fPnycwMJA1a9Zw48YNADZu3MjgwYMJDQ3l0KFD5OXlkZWVhaIoxMfHA5CUlKTe59ChQxk6dOh967GxsWHixIksWLCAESNGoKOjQ25uLrt27WLjxo3o3vMD1YwZM7h16xarV6++73Oqst/S2+Tl5ZV7XBXVRYGqizJUcnJyWLRoEf7+/nh4eABw8+ZNzp49C5SEm3/44QdGjhxJixYtgJJgaXZ2NnPmzCn3dcnOzmb8+PE0bNiQJUuWoK+vr7GvxYsXV2k/FdVc0bFUx6lKLSoLFy6kTZs2HD16lFdeeQWA4OBgXF1dGTVqVIXHfdru3LnDsGHDaNGiBUOGDNF2OUIIIaohCfQKIYQQQgghxAvuyhVYtgzWroWRI2HKFHB01HZVQgghhBDad+HCBa5cuaIOd6k+xsbGqieCWVtbqyfItmjRgt69e6vDSDJVtmL29vbY29vfd7vyph0nJCQQHx9PREQE+/btIzo6moKCAgBatmxJo0aN1AHfxo0b4+bm9tSnAT4OCQklgd3w8H8CvJcvQ1ERGBjASy+VhHZ79wZ395LJu895nlmIxyI9PZ2IiAiuXr2q7uuqHn/r1i2gJLCr6uMODg64urrSoEEDWrdujb29PdbW1uWGrF50FhYWWFhY4OLict9t8/Ly1EHp48ePExcXR2xsLEeOHGHjxo0kJiYCoKOjQ4MGDdR9XfWxSZMmNG3aFAMDgyd9Wi+0d955h88++4y0tDTc3d2BktDlnDlzmDZtWrnBSyj5/r1ixQqGDRuGjo4O3bp1w9LSEktLS/W+x44dS1xcHD/++CPW1tYax1XdMeDe6dKq4OetW7do1KgRUBLwK72d6s9p/7vapaI67ldj6f2oZGZmalxslJaWRl5eHoqioKOjw/LlywkMDMTb25v8/HxiYmKwsbHB2Nj4wT8BVWBuDhkZ8N//wsaNoCjg4VFygU/37vDKK3rExsbSr18/tm3bhomJyROp40GcPHmSjz/+GIC1a9fSpEkTevbsiaGhIU5OTsyYMYMZM2bg7u7O+vXrMTY2xtPTk9GjR7Nu3Tq8vb2ZNm0aS5YsYciQIfj7+zN16lT1tNNdu3bRunVr/P39MTU1rXJdCxYsQF9fn759+9KtWzcSExOZM2cOL7/8ssZ2u3btUn+/qMpzHnQbT0/Pco8L8Pvvv7N27VoAduzYgY+PD71798bGxobi4mK2bt3KRx99RMuWLenRoweWlpbs3r0bKPka+umnn/juu+949dVXadWqFRYWFgQHB5eZtn3z5k2CgoJYs2YN06ZNo3///hrrVfsKCwurdD/3q7kqx7rfehV3d3eOHDnClClT8PX1xdDQkGPHjvHnn3+WGxDWhvT0dPr3709ycjJHjhwpd9q+EEIIoaMoSmXrK10phBBCCCGEEOLZVFwMf/1VMpX3t99KwgBnz8qtd4UQQgjx4kpISCAkJISIiAjCw8OJiIggJCQEKJli6OzsjLOzM25ubri7u+Ps7Ezjxo2rRShClHz+IiIi2L9/P5GRkURGRnL58mV1+Mfc3Fz9uXNzc8Pb2xsvL68nFq55EAUFJRfZqSbuhofDqVOgGl5pa1sS2HVzKwnwyuRdIaomPz+fq1evqvu6qsdHRUWhKAoGBgY0aNBA3d/vfbi7u8vFGFqmCvyqerrqe3NkZCTR0dEUFRWhr6+Pg4ODRn93d3fHw8NDpvo+RuPGjcPT01Pj9vYXL16kX79+XLlyBUAdvFRN/czNzcXZ2ZmOHTvyxhtvEB0dTWZmpjrECfDSSy9x/fp1vvnmG6ZOnapefuzYMcaNG0doaCgjR45k3rx5REdHM3XqVM6cOUOvXr34+uuvycjIYMqUKRw/fpyBAwfy2WefcevWLb755hsCAwPx8PDgq6++4u233y63jspqPHHiRJn99OrVi4MHDzJ69Gg6d+7Mp59+yoEDB3jnnXfIzMxk/vz5fPjhh/z6668MGzZMHTZW8fPzY/369eoQ4+Mydy58/TXck2kGwNCwZFnNmjBpUjzr1r1BvXrZbNmyhaZNmz7WGh636OhodHR0cHBwKLPuzp07Gj+/5eXlPfav96KiItLS0soEzVWysrIoKCjQmHR+v+c8yDaPGvJMT0/HwMCg3OnseXl5xMTEULt2bezs7Crcx44dO2jWrBnOzs4VbpOXl0daWlql+6mK+x2rKrWUlpCQQK1atZ7INPqHFR4ezoABA8jMzOT333/H09NT2yUJIYTQnrXAqIpWSqBXCCGEEEIIIV4gWVnwyy+weDFcugRdusB775VM9dLi3Z2FEEIIIZ6aoqIiLl68yKlTp9SPCxcukJubi66uLk5OTnh4eODu7s6gQYNwcXGRUNczSlEUYmNjiYiI4Pz580RERHDhwgUiIiK4e/cuOjo6ODo64u3tTatWrfDx8cHb2/uJh7Rv3y4J7a5aVRLgvXChJHBTowY0blwS2FUFeNu0gXr1nmg5Qjw3srKyCAkJ4dSpUyxfvlwd3K1Vqxaurq7qoKeHhwdubm44OjqiI/8Rfibl5eVx5coVwsPDuXDhgvpjZGQkxcXFGBoa4u7uzvjx4/Hx8cHNza3aTGd8FnXr1o3NmzdjZmZWZl1lwcvCwkKKi4tJSkoqd31eXh5BQUHUrFmTvn37PpHa71fH/Wp8GPv27SM+Pp527dqRlJTE3bt3yc7OJjAwEE9PT2bNmvVYjqOyaBHMng33DA0uQ0+vZMK/oWEiRUXbeestW77+uhdmZjLhWognraCggO+//545c+bQokULtmzZ8sghaCGEEM88CfQKIYQQQgghxIsuKgpWriwJDeTkwIABMHNmSVBACCGEEOJ5lpSUxN9//83Jkyc5deoUISEhZGVlUatWLVq0aIGPjw/NmzfHw8MDV1dX6sgtC557xcXFREVFceHCBc6fP8/p06c5deoUCQkJ6Orq4uLigo+PDz4+Przyyivl3u64KgoL4fLlf6buhoSUPP53F3l8ff+ZuOvuDp6eIHeOF6JqiouLuXDhAkeOHFFfnHHx4kWKioqwsbFRTxT19PSkYcOGckvrF0ROTo56IvO5c+dYtWoV2dnZ1KlTR/0938fHhw4dOkiYqopCQ0NZvnw5K1aseOz7zszMZN68eSxYsOC5CVyHhITQt29fYmJiyvSd9PR0Nm/ezNixYx/pGJmZkJ5ecoFQejoEBsLy5SWB3QdRs2YIX3yRxOTJr8nFDUI8Ib///jtTp04lKiqKDz/8kFmzZj03/U4IIcQjkUCvEEIIIYQQQryoDh+G77+H7dtLJnuNHQuTJkHdutquTAghhBDiyUhJSeHgwYMcPnyYI0eOcObMGXVI09vbW/3w8fGR23ELDQkJCYSEhKgfx48fJy0tDT8/P/z8/PD19aV169bUqFGjzHPT00sm7YaE/BPgPXOm5GI61eRd1dRdb2/w8YHHfMdtIZ57kZGR7N+/n/379xMcHExaWhrGxsY0a9ZMo7+7y5Wr4n+Kioq4dOmSRm8PCQkhNzcXZ2dnfH19adeuHd27d8fR0VHb5VYbISEhzJgxA09PTw4cOMCOHTtwcnJ67MfZv38/vr6+1KpV67HvW1t+/PFHAgICWL58OX5+fjg6OnLjxg1OnjxJWFgYs2fPxtTUFCj5GeH2bc1HZGTZZfc+bt6sfBJvZVQZwtdfhzffTGbHjpmsX7+eFi1a8Pnnn9OtWzcJ9grxGCiKwp49e/jkk084duwY/fr145tvvqFRo0baLk0IIUT1IYFeIYQQQgghhHiR5OdDUBAsXAgnTpQEBt57D4YM+eeX90IIIYQQz5Pc3FymTJlCcHAwly5dQl9fHx8fHzp27EinTp3w9fXFyMhI22WKZ4yiKISHhzNv3jwOHTpEWloa5ubmtG/fnq5de3Pp0mAiI40JC4P4+JLnWFmBl9c/j2bNwNW1JNQrhHgwiYmJ7Nq1iz/++INDhw5x8+ZNzM3N6dChA506daJTp040a9YMXV1dbZcqniF3797l2LFjHDhwgAMHDnDy5Eny8/Np0qQJnTp1onfv3vj5+T1XIdMHderUKbp06YKuri7//ve/GTBggLZLqpbKC+TeuqWwY8e3nDu3i9jYY+jo6GNq6omd3VsYGY0iPd2A27chKQnKi2nY2oK5+YM9wsOhW7eK69TTA1NTePttmDAB7O3/WXf+/HlmzZrF7t27adKkCe+++y6jRo3C3Nz88b9gQjznbt26xU8//cSKFSu4evUqPXr0YP78+bRu3VrbpQkhhKh+JNArhBBCCCGEEC+ClBT48UdYsgRSU8HfHz74ANq21XZlQgghhBCPX1paGr/99hs7d+5kz549NG3alK5du9KxY0fatWsnAV7xWCmKwoULFwgODubAgQPs27ePrKw9WFhk4u2tT79+DXn99UYydVeIR3T+/Hl27dpFUFAQp06dolatWnTu3JkuXbrQsWNHvLy8JMArHqu7d+9y9OhRDhw4wP79+zl16hQ1a9aka9eu9O3bl969e2NlZaXtMp+6wsJCdHV1X4ivt/KCufd7XLtW/qTcmjX/CdqamRVgYVGDWrU0l1f0sLYuCd8+qHPnoEWLsstr1ICCgpILjI4fL6mhIufPn2f58uX8/PPPFBYWMnjwYMaOHUvr1q1laq8Q93Hs2DFWrlzJpk2bMDAwYPjw4bz77rt4eHhouzQhhBDVlwR6hRBCCCGEEOJ5dvYsrFgB69eDkRGMHg0TJ0KDBtquTAghhBDi8crMzGTz5s38/PPPHD58GH19fTp16oS/vz/jxo3TdnniBZKbm0twcDBBQUHs2rWLhIQEHB0dGThwIKNGjcLNzU3bJb6QYmNjsbOzIyIigqCgIBwcHBg6dKhGIO3OnTvs3r2bixcvYm9vT7du3bC/d1yheOouX77M2rVr2bRpE5GRkVhbW9OnTx/69OlD165dX+hJqeLpU02G3rlzJ3/++Sf5+fm0bduWYcOGMXjwYMzMzLRdoihHZaHcyMiSj7m5ZberaFLu/QK4zs5ll9WtC4aGT//co6PByemfv+vrg45OyZ263nsPXn656vu6c+cOGzduZPny5Zw7dw57e3v69+9Pnz596NSpE/py6y8hACguLubs2bPs2rWLjz/+GFdXV9555x0CAgLkwlIhhBBVIYFeIYQQQgghhHjeFBfDb7/B99/D/v0l0zbGj4fhw0He6xRCCCHE86S4uJjg4GB++ukntm3bRnFxMf7+/rzxxhv06NEDY2NjbZcoXnCKonD69Gl27NjBhg0biI6OplWrVowcOZI333xTblv9lOzatYuAgAD+9a9/ERYWRmpqKr/++itffPEFs2fPBiA0NJThw4czf/582rdvz7p165g/fz7Lli1jxIgRWj6DF09GRgavvfYaR48epUGDBgwZMoT+/fvTqlWrF2IqqKj+srOz2bt3L1u3bmXbtm0oikK/fv34+eef0XuYUaqiUg8zKff2bbh5s/Jpuc7OVHlK7qNMytWWjAxQ5czr1y8J8QYEgKXlo+337NmzbN26la1bt3Lp0iXq169P//79ef3112nXrh0GBgaPXrwQz5C8vDwOHz7Mtm3b2L59O4mJibi6urJx40a8vLy0XZ4QQohniwR6hRBCCCGEEOJ5kplZEuCNiYHOnUt+Ud+7d8n0DSGEEEKI50VWVhYrVqxg6dKlREdH07p1a0aNGiXT8US1VlxczIEDB9QB9KKiIgYMGMCMGTPktrtPwezZs/Hz86NLly4AeHt7o6Ojw+nTp8nPz8fLy4uBAwfy8ccfq58zdOhQAgMDOXv2rExWfkouXbrEggUL2LhxI/369WPUqFH4+flJiFdUa6q7BPz000/cuHGDCRMmMH78eExNTbVdWrVS1VBuedNyExPL7q+qIdzqMi1XGxQFuneHceOgb98nE0aOjIxk165dbNmyhSNHjlCrVi3mzZuHn58fLVq0kP4tnluRkZHs37+f/fv3s2fPHjIzM3Fzc2PAgAH06dMHb29vbZcohBDi2SSBXiGEEEIIIYR4Hly7BqtXw8qVMHAgfPABNG2q7aqEEEIIIR6vmzdvsmTJEpYsWUJBQQFjx44lICAAV1dXbZcmxAO5c+cOW7ZsYfHixVy4cIE+ffowe/Zs2rRpo+3SnluffPIJAQEB2NnZATB8+HD27NlDSkoKO3fuxN/fn927d9OzZ0/1c9atW8fIkSOZMmUKCxcu1FbpL4QzZ87w5Zdfsm3bNpo0acLkyZN55513tF2WEA9s1qxZrFixAoDx48czefJkrKystFzV41VZMDcy8uEm5aoeFU3LdXZ+tiflviiioqLYv38/H374IampqVhaWtK5c2c6d+7MK6+8gpubm0ywFs+koqIiIiIiOHLkCMHBwfz111+kpaVRr149unTpQpcuXfDz88PJyUnbpQohhHj2VRro1X96dQghhBBCCCGEeBiHD8P338O2beDoCLNmwcyZ2q5KCCGEEOLxunv3Lp999hlLlizB0NCQ999/n4kTJ2JhYaHt0oR4KMbGxowePZq33nqLX3/9lS+//JK2bdvy6quv8u2338qteZ8CPT09VINtIiIiADAyMtLYpn379gBcvHjx6Rb3Arl48SJTp07l999/x9vbm82bN9O/4J++ywAAIABJREFUf3+Z6CieWV999RX/+te/+OGHH1i8eDGLFy/m3XffZf78+ZiYmGi7PLWcnIrDt5VNyk1KKpn6WpoqhHtv8LZ0CPdFnpT7omjYsCFvv/02AQEBhIWFsX//fv7880+mTp1KdnY2xsbG+Pj40KZNG9q0aUPr1q2fu8C7eD4kJydz4sQJTpw4wbFjxzh9+jR37tzByMiI9u3bM2vWLPz8/GjWrBk6cms8IYQQT5EEeoUQQgghhBCiGsrLg02bYMECuHABfH1h40Z4/XWZTiKEEEKI589vv/3GxIkTSU9PZ/78+bzzzjtlQndCPKt0dHTo06cPffr04cCBA3z44Ye0bNmS9PR06tSpo+3yXhiqiwOOHTumDvECODo6UqNGDczNzbVV2nMrJyeHL774gq+//ppmzZrxxx9/0L17d22XJcRjYWJiwqxZs3j//fdZs2YNH3/8MZs3b+a7777jjTfeeGzHqWxSbmWP8qblVjYp937BXJmWK0rT1dWlefPmNG/enGnTplFYWEh4eDjHjh3jxIkTbN++nS+//BJFUXB2dsbHxwcvLy88PT1p1qwZDg4O2j4F8YL59ddfOX/+PKGhoZw8eZKoqCh0dXVxcXGhdevWDBo0iDZt2uDh4SFTpoUQQmiVBHqFEEIIIYQQohpJSoIVK2DZMrhzBwYOhA0boFkzbVcmhBBCvJgiIyP57LPP+OSTT2jQoMEj72/06NGPbV/Pg9TUVMaNG8fWrVt58803WbRoETY2Nk+9jr///puDBw9y6dIlBgwYgL+/PwAbN25UbxMbG8vEiROpXbs2ADdv3iQoKIiYmBiaNWtGt27dHnsIOS8vj4MHD3Lu3DnatWtH69atq/zmclJSEpcuXaJTp07lrs/KymLz5s3cuHGDNm3a0LVrV2rUqFFmm6+//rrC9VU51qlTp7h27VqZ5W3atKFhw4ZVruVBX4vQ0FAOHTqEgYEBvXr1Un/N3blzh19++YWoqCheeuklhgwZov6cPg2dOnXi8OHDrF69Gjc3N1auXEmPHj2e2vFfZK1btwbg0KFDzJgxQ738woULFBQU0LZtW22V9lw6cOAAb7/9NikpKSxYsIAJEyY81XBMRX0dSi4iyczMVP+9dH+vSk96VPf2tKlTpz7wa1NRjwPN8yt9bgDp6emsWbOGmJgYevXqRZcuXR6qn8L9v9fc6+bNm6xatYrZs2drLL9z5w6ff/55hT25qn37Qc/rcalVqxYTJ07kzTffZPr06QwYMIBevXqxcuVK6tevD1QtlJuY+OCTcks/ygvlqpbJpFzxpOnr6+Pl5YWXlxfvvvsuUPJ1qZqAGhISwqpVq7hx4wYAZmZmNGvWTB3w9fLyomnTppiammrxLMTzICMjg4sXLxIWFkZYWBjnz58nLCyM9PR0GjZsiKenJ6NGjVJPkJZ/c0IIIaodRVEqewghhBBCCCGEeEIKCxVl505F8fVVFFCUl19WlJUrFSUnR9uVCSGEEOJeqamp1XJfz7ITJ04opqamSpcuXZS0tDSt1tKzZ0/l008/VRRFUYqLi5X4+HhFURTl7bffVmJiYtSPnP/9kFZUVKR4eXkpM2bM0NhPq1atlK5duz6WmoqKipR27dopffv2VS8rLCxUHB0dlZkzZ1b63JMnTyqhoaEKoLz33nvlbtOzZ08lICBAY9nIkSOV9u3bV7hN6fVVOdbevXuV9957T4mKilLy8vLUj71791a5lgd9LSwtLZVhw4ZVeN6hoaGKoihKSkqKMmbMGAVQRo8eXe72T8OSJUsUfX19ZfLkyVqr4Xlx4MABxdnZWRkzZoySmJiobNy4UTExMVEAZf78+UpBQYGSk5OjTJgwQXF3d1d++uknZfXq1UqvXr2UmJgYbZdfvfyvDz6Mjz76SNHV1VX+7//+TykuLn6MRT2Y8vq6opT09uDg4HL7u6JUrT8+ivJ6WlV6u6IoSlRUVKU9TlHKnl9OqV8wLF++XLl586b675988okCKIcPH36gY1Xle829Vq1apTRu3FixtrbWWK7qyxX15Kr27YrOS1vS09OVtm2nK4aG7ytz50Yof/yhKMePK8rly4qSnKwo+flaK02Iaqm4uFi5fv26snv3bmX8+PGKn5+f4ujoqOjo6CiAoqurqzg5OSl+fn7K6NGjlfnz5ytr165VgoODlevXryt5eXnaPgXxmOXl5SnXrl1T/vrrL+Wnn35S5s+fr4wePVrx8/NTnJycFF1dXfW/DUdHR6Vr167K+PHjlcWLFyu///67EhkZqdWfQ4QQQoj7+EmpJLMrE3qFEEIIIYQQ4inLyICffoJFiyAuDl57DfbtAz8/bVcmhBBCiPJYWlpWy309q6KioujZsycdOnQgMDAQAwMDrdYTHh5O+/btAdDR0aF+/fokJSURFhaGvb19me2PHz9OaGgos2bN0ljeqlUrli5dSkhICN7e3pUes6ioiMDAQAYNGlTu+kOHDnH48GF27dqlXqanp8fIkSNZuHAhH330EXXq1Cn3uT4+PuSXvsd2KYmJidy+fVtjmaGhIXl5eRVuU3p9VY5lZGTEt99+i66ursbyoKAgunbtWqVaHuS1yM/Px8LCghUrVpSpJSQkhKFDh9Lsf7e+qFevHp988gn/+c9/OHr0aIXn8KRNnDgRa2trhgwZgo2NDTNnztRaLc+6jh07cv36dfXfBw8ezODBgzW20dfXZ+nSpWRkZBAeHo6DgwMBAQFPu9Tqr3v3ko8jR8KgQVBOLyzP8uXL+fzzz1mzZg2jRo16cvVVgWoyq6qvA+rePnfu3Aon5VelP1bkfr0dyu9pVent+fn5DBw4sMIeB/c/v/z8fLp3746FhYV62YgRI5g7dy4mJiYPdKyqfK9RuXLlCmfPnqV379788ssv6uWl+3LpnlzVvl3ZeWmLqakphw59wbBhw/j229YMHnwCV1dXrdUjRHWno6ODs7Mzzs7O9OzZU708NzeXK1eucPXqVa5evcq1a9eIiYnhyJEjREdHk5ubC4Curi42NjY4OTnh4OBAgwYNqF+/PtbW1tjZ2dG8eXOZwlrNpKenk5iYSHJyMvHx8aSkpJCQkEBcXBwxMTEcP36c4uJiAGrWrImjoyOOjo44OTnRpUsXGjduTJMmTWjcuDE1a9bU8tkIIYQQj5cEeoUQQgghhBDiKblyBZYtg9WrQV8fRo2CKVPA0VHblQkhhBBP3p9//klsbCyGhoa8/vrrGBoacvLkSSIiIjA3N1ffCjshIYE//viDuLg4fH196dKli3ofiqKob0+tp6dH06ZN1aHAqsjJySEoKIi+ffuSkpLC7t27qV+/Pn369EFPT4/k5GR27tyJrq4uAwYMwMTEhOLiYg4ePIiRkRE+Pj5VqqWydcHBwRr7io2NZdu2bUyaNImIiAiCgoJwcHBg6NChZUKQWVlZrF+/npiYGBo3bkyrVq1wdXV9qrcwfxzefPNNHB0d2bRpk1bDvAcPHiQ8PJzY2FhOnjzJypUrsbW1pW/fvixZsoQTJ07g7OzM3LlzGTlyJDo6OgBcvnwZKPk830v1OT18+HClgd7CwkLc3d1JTk6uMPS1bds2ADw9PTWWe3h4kJ2dze7duxkwYMDDnTjw+uuvM3fuXH7++WeGDRtGVlYW27dv57vvviuzDVDu+qpo27ZtmWXFxcVs27aNpUuXVqmWB3ktPvzwQ2bMmFFuIM7JyYmXX35ZY5mtrS3e3t7o62v3rZIBAwaQkJDA1KlT6dy5s0avEU+Gqakpr7zyirbLqL4KCuDyZZg9G2bMgNatYcQIGDeuwqeEh4czefJk5s+fr9Uwr6q3l+7rgLq329vb07BhwzL9HarWH0srLCxkw4YNfPHFF5X2dii/p1Wlt3/44YecOnWK1atXVxj6LX1+169f1zg3AwMDGjZsqPGcsLAwevfurVFPVY5VVQUFBcyZM4c1a9Ywb948jXWl+3LpnlzVvl3ZeWmTvr4+69evp1OnTgwcOJCzZ89q/fuNEM+amjVr0qxZM3Wwv7SkpCRiYmLUj+joaG7cuEFwcDCJiYmkpKRQWFgIQK1atbCxscHW1lYd9K1bty4WFhbqj6X/LKru1q1b3Lx5k1u3bpX757i4OHVoNykpSR3GhpJ+aWVlha2tLQ0aNKBly5Z88MEHODo64uDggLW1tRbPTAghhHj65H8NQgghhBBCCPEEFRfDX3/Bd9/Bb79Bo0bwxRcwZgw84vtiQgghxDOlbdu2+Pn5cf36dQwNDYGSiaYjR44kKCgIKAm7bty4kXHjxmFsbEy/fv0YMWIEy5YtA2DOnDk0bNiQyZMnc/r0aSZMmFDlQO/Bgwd5++23uXr1KgsXLuTy5cuYmZkxffp0evbsSY8ePThw4ABFRUVs2rSJoKAgvvrqK+bNm0dgYCDLly/XCNlVVktF6yIiIujcubN6X7t27SIgIIDU1FQURSEsLIzU1FTmzJlDXFwcs2fPVh/v9u3btGnThtWrVzNixAiGDx9OQEAAPj4++Pr68u233z6Wz9PTcPr0ac6ePUutWrW0Woejo6P6VnbW1tZ4e3tjZGQElEz6LCgoYPHixbz11lts2LCBP/74Az09PXXdp0+f5s0331Tvr1GjRgDExMSUe7yCggLWrl3Ll19+yYABA5g2bVqFtV27dg0oCS7dy8rKCiiZdvgoxo4dy4YNGxg+fDhnzpwhPDyclStX0r9//zLbTJkypdz1D+vIkSMaAbP71fIgr8XGjRv5f//v/9G5c2dOnjzJyy+/zOLFi3n55ZepW7duufXExsYyfvz4Rz6vR/X++++zbds2ZsyYQXBwsLbLEaLE/0JQnDgBp07BunXw1lvw5ptgbKyx6b/+9S88PDz48MMPtVDoP1S9PSwsTKOvwz+9/dixY5w4caJMf4eq9UeVe/t6SkoKEyZMqLS3Q/k9rSq9fePGjejr63P+/Plye1x559etWzeNc7uXoihs2bKFjz/+mD179jzwsarqk08+YfLkyRiX+vcClNuX7+3JD9O3KzsvbahRowbr1q3Dzc2Nn376iTFjxmi7JCGeKzY2NtjY2NCqVaty1yuKQkpKCmfPniU5OZnExESSkpJISkoiNDRUI3Raeuq4rq6uRsjX2NgYU1NTTExMMDIyok6dOtjb26v/bGxsjJmZGUZGRhgYGFC7dm0MDQ0xMDB45IsjnpTs7Gzy8/PJzc0lJyeHvLw8srOzSU9P586dO2RlZZGdnU1GRgaZmZnqv2dmZqqXXbt2jZs3b5a52NHAwEAjIG1nZ4ePj0+ZULWVlRVWVlYa/z8QQgghXnQS6BVCCCGEEEI8tLy8PG7fvk16err6l3kFBQVkZGSQn59PSkoK+fn5pKenoygKBQUFZGVlldlPVlYWBQUFZZabmZmV+WWeoaEhtWvXxtjYmBo1amBmZqb+xajqF6WmpqbUrFkTMzMzzM3NNd7Ae5qyskoGGUVEgK8vBAVB794gv58UQjxN6enp6jdb8vPzycjIoLi4WN2bb9++jaIopKenU1xcrF6fkZFR7v5U60ur6E0qHR0dzMzMADA3N0dPTw8TExP09fXVvVz1hpeql5uYmGBqaqq1/i2ejNq1awPw119/4ezsDJTc1trDw4MmTZqQlZXFmDFjCAsLo06dOrRo0YI9e/bwww8/MHz4cFq3bs2qVavYsmULAC1btlRP3KuKjh07Mm7cOKZMmYKDgwNTpkwBSt6o/eqrrxgyZAg///wzUBLM/Oabb9i5cydz584lMDBQY1+KolRYS2Xr3NzcNPbTp08fAgIC+Oqrr/D09GTy5MkAeHt7s3XrVo1A74IFC8jLy6N9+/ZASWh4+/btDBkyRP28Z0WvXr3KTFvVBicnJ/XkLTs7O1q2bKle161bN7p168bQoUMZPHgw+/fvZ8GCBcyaNQtfX18MDAw4ePAgiqKof15V9U0nJyeN4+Tl5fGf//yHr776ilu3bjFx4kS+/PLLSmtLTk5GT0+vzARj1ddRYmLiI527tbU1f//9N23btuXbb7+lbdu2ZaaVqraxsrIqd/3D2rJli0Yw7n61VPW1iI+PJz4+nrlz52JhYcGVK1fo1KkTHTt25NKlS9jZ2ZWp5dChQ+jr6/PBBx88lnN7VLNnz+a1114jOjoaR7mNh6hOFAWKikpCvSdPwsSJ0LUrDBoE/+//kfq/6bIbNmwoM13+aVP19r///lujr8M/vR0gNDS0TH+HqvXH8vr61KlTsbS0vG995fW0+/V2VX9r3rx5pT2u9Pk1b95c49xUsrOz+eCDD9iwYQN3797F09OTvXv34uPjU+VjVcXBgwfR19ev8vePqvTkyrYp77xu375dpWM/SY0aNWLQoEGsXbtWAr1CPGU6OjpYW1vTo0eP+26blZWlDvjevHmzzITZO3fukJGRQVxcHFlZWWRlZREbG0tWVlaZMHB5VL8LgX9+521kZESNGjXU2xgbG5eZ5H3v80rLzMykqKiozPLCwkLu3Lmj/ntBQQF37twhPT0dqPj3OqUZGhpSp04dzMzMMDY2xsjICCMjI0xMTHBwcMDY2JgRI0ZgYWGBpaWlRoBXfp8jhBBCPDwJ9AohhBBCCCGAkl/spaSkqKcUqG6BlZKSws2bN0lPT1eHd1Ufc3JyKtyfgYEB9erVw9DQEBMTE/T09DRCXfeqVasWNWvW1FhWVFREVFRUmW1VkwMyMjIoLCxUB9Sys7MrrEVfXx9zc3PMzMzUIV8zMzPq1q2LlZUV9erVw87ODk9PT2xtbdVvpj2sqChYuRL+/W/o1Qs2bwZ390fapRBCkJubS1paGqmpqSQnJ5OWlsalS5dITU3VCO1mZmaqHxWFcu9lbm5e7kdTU9NyAxmqiydKS09PJzk5uczye3v07du31W8slX6DqTy6urqYmppiZmaGiYmJ+qFaZmlpiaurK9bW1lhaWmJpaUm9evXkVrbVmKurK4sWLSIgIAAdHR1++eUXRowYAZRMg8vJyWHGjBnq7RMTE2nUqBHXrl2jTZs2uLi4MGjQIFatWoW/v/99p+CVZmpqCmje5trFxQUALy8v9bKmTZuSl5cHUO6/dx0dnQprqWxdeVTTXps2bape5ubmVmaq3PXr10lNTSU/Px8DAwO8vLyoU6cOsbGxD/QaVAdVnapcHXh5eRESEoKLiwsbN25k1qxZ2Nvb89lnnzFjxgzeeustBg4cyMWLF/nvf/+rfg6U9O1Vq1bx9ddfk5mZyaRJk5gyZUqFEwfvVdEb4Ko37G1sbB753NasWUPHjh3p2LEj//nPf2jdujWHDh3CwcFBY5vRo0dXuP5BKYrC1q1b1eH5qtRS1dfizJkzAOqQdpMmTVi0aBFvvvkmP/zwA59//nmZ58+dO5edO3dWm8BBly5dqKmrS+L33+PYpo22yxEvsrt3y1+uCg0VF8PevfD77zBhAvm+vnQoLKRr585Pr8ZHVF5/V6msJ33//fcP1ddVyus39+vtqv7Wr1+/Kvc4Ly8vGjRoUObcAOrUqcOqVatYsWIF33//PdOmTWPcuHGcPn36oY5VnvT0dJYuXcrGjRvvu63qNbhfT77fNuWdV3XRtWtXxo4dq3EhkBCielGFVR/moqr8/HyysrLUk20LCwvVAyxU029Vgy5UF1SDZiD33uX3Uj2/PDY2NuX+fxU0B2WoQsGqZargsOp38qoLrVUfVQMy7g0bCyGEEOLpkXc3hBBCCCGEeEEUFhaSkJBAdHQ0N27cICYmhujoaGJiYoiLiyM8PFxj+zp16qhvf1W3bl0sLS156aWX1GHY0h/r1KmDqakp+vr66rDM03b37l3y8vLIzMwkJyen3BDyvR8vXrzIoUOH1ME4FSMjI+rXr4+1tTVOTk44ODjg6Oio/ujo6FjuLaJDQuC772DjRqhXDyZNgvnzn+ILIIR4JuXk5BAbG0tiYiKxsbHEx8eTkJCgXpaSkkJKSkqZCecGBga4urpiaWmJubk59evXx9XVtUzw1dzcXP13AwMD9Rs45U1B1wZVsFf15tbdu3c1Qsnp6enqwPK9QeX4+Hh1qLn0RBoLCwvq1atHvXr1aNCgAfXr18fe3h47Ozv1n21tbeXNKS2YPn06o0ePZvfu3fTq1Yv9+/fz/vvvAxAeHo6trS3Lli2r8PlLly5lwIAB9OvXjy5durBhwwasra0fqabSFxUBVfq3UVktj1qnnp5emVuWvvrqq2zevJnDhw/TuXNnbt++TX5+/jMVjlVRXTjwrKhduzb+/v785z//US+bPn06rVq1Yu/evRw+fJjBgwdz/Phxrl69SosWLQA4cOAA8+bNIz09nSlTpjBr1qwytxw/ceIEEydO1Fj2zTffYG9vT1FREXl5eRpv0qsuhCg97flB/fjjj2zatIlTp06hr6+Pr68v77zzDhMmTGDXrl0a25w9e7bc9Q/jyJEj5Ofn06FDhyrXUtXXorz/g7Rt2xaAy5cvl1k3bdo0pkyZov58VQc1atTAuk4d2ixapO1SxIuuKrcGLyws+ZiVhd2ePfwCmK1aBTNngp7eEy3vcSmvv9+vJ92vr6s8SH+/X29X9bfSE4Ar63FAmXMrTVdXl8mTJ3P06FG2bt1KXl7eQx+rtA8++AAfHx927typXnb16lVyc3PZtm0bZmZmdL4nAF6VnlzVvn3veZX+3qEt5ubm6lDeo15ELoSofgwMDLCwsFBfCCGEEEII8Sgk0CuEEEIIIcRzRDXV9vLly1y6dIkrV65w+fJloqKiSEhIoPB/b7gZGBhgb2+vDqm2atWKTz75BGtra6ytrbG1tS33tunVXe3ataldu/ZDhUTy8/MJDQ0lOTmZ5ORkEhISSEpKIiYmhrNnzxIdHa0xSdLKygpHR0caN25MkyaubNgwjqtX69K2bTG//KJL//4gAyKFECp5eXns37+fyMhIrl+/zvXr17lx4wYJCQncunVLvZ2BgQG2trY0aNCABg0a0KZNG6ysrMpMoLW2tq7wlovPGtUUdSjprQ9KURTS0tLUj3snGKemphITE8Phw4eJi4sjKSlJfVtJ1a037ezsaNiwIc7OzjRq1IhGjRrh7OxMw4YNH+t5ihJDhw7lo48+YuHChTg5OeHu7q6eqKynp8fly5cpKCioMFDbvHlzzpw5w6xZs1i5ciUvv/wy58+f18obp5XV8iTqHDNmDNeuXePdd9/l888/Jzg4mC+//LJKt499GmJjISwMXn0V7pdTuX79+tMp6jFq2rQpTZo00Vimmt4IEBUVxc6dO1mwYIE63NWjRw9u3LjBkiVL+Pbbb1m7di1Tp05l4sSJ6m0cHBzKBL6cnZ1xdXUFIDY2lpdeekm9TnUR2qMGeteuXUvPnj3VX3+jR4/m9OnTrFmzhvT0dMzMzNTbVLT+YQQGBuLv74/ePWG/+9VS1dei9OcHSl7fGjVqlAncrVq1ihYtWtC3b9+HOo8nJTU1lRuZmfzx++/V5mtbvKCaNoXKgpv6+iXTeg0MoHdvLrVuTbMZM7g4aBCNnpEwr0rp/n6/nnS/vq7yIP39fr1dVV9ISEiZY5TX4yo6t4p07dqV4OBgDA0NH/pYpaWmprJv3z6NZRkZGdy9e5f33nsPd3d3jUDv/Xryw/Ttrl27VoswL5T87GNhYSFhXiGEEEIIIcR9ydvLQgghhBBCPIMUReH69eucO3eOc+fOcfHiRS5fvszVq1fJz88HwNbWlqZNm+Lq6sprr72mMV3W1ta2WkxlrE4MDAzw8fGpdJvbt2+rJxvfuHGD6Ohorly5ws8/ryUyMgE4xfHjIcyY4cjq1U1wdXVlxIgReHh4YGBg8HRORAihNYqicOPGDS5evMiFCxe4fPmyOsAbHx9PcXExVlZW6sBo//79qV+/PnZ2djRo0ABbW9vHcgv1F42Ojo56Gu/9FBYWkpycTExMDImJicTFxREXF0dUVBT79u1jxYoVZGZmAtC4cWN1yNfNzQ1XV1c8PDweKnQs/mFgYMDkyZOZPn0606dPZ8GCBep1Xl5eZGdns2LFCiZNmqRenp6ezi+//EJAQACbN29m+PDhLFu2jL59+9KzZ0+2bdvGmDFjnup55OXlVVjL8OHDn0id+vr62Nra8uOPP2JpaUnfvn2rTUgFIDUVeveGGjWgfXvo0wd69gQXl7LbbtmyhTlz5qCrq/v0C31I27dvx9/fv9x1+fn5DBo0CBcXF8aPH6+xztTUlDlz5jB58mSWLVvGwoULWbhwITdu3MDIyAhbW1tGjhxZZp8BAQF8+umnHDlyRCPEGhISQvPmzasU0KpMWFhYmeCYv78/y5cvJzk5GTMzszLblF7/oBRFITAwkH//+98PVEtVXwsbGxu6d++usZ+rV69SUFCAr6+vetn27dtRFIURI0ZobHvw4EF1QFtbNm3aRO3atWnfvr1W6xCiQnp6oKMDXbvC4MHwxhtQpw6NCgow/uorNm3axL/+9S9tV/lASvf3+/UkFxeXcvv61KlTmTRpEkZGRgAP1N/v19tV/e348eMay8vrcZWdW0UuXLhAnz59HulYpf36669lls2YMYN169YRFxdXps7KevLD9u0LFy5UqdanYdOmTWW+RwkhhBBCCCFEeSTQK4QQQgghxDOgsLCQ0NBQzp49y9q1awkNDeXOnTvo6enRpEkTPDw86NevH02bNsXFxQUXF5fnZnJjdWJubo65uTleXl5l1uXn53P9+nUuXbrE5cuXuXLlCseOHeO7776jRo0auLq60rx5c7y8vGjevDktW7aUz5EQz7DY2FjCwsIIDw9XB3gvXbpEVlYWAHZ2dri6uuLi4sJrr71Go0aN6Nq1a5UnWoknQ19fHzs7O+zs7CrcJjU1lcjISPbu3UtkZCShoaH897//VU9Srlu3Lh4eHri6uuLu7o6bmxvNmzeXW2s+gHfeeYfPPvuMtLQ03N3d1csHDRrEnDlzmDZtGrm5ufTu3ZslMrW6AAAgAElEQVTz588TGBjImjVrUBSFFStWMGzYMHR0dOjWrZt6crXK2LFjiYuL48cff8Ta2rrMsVXT9vPy8tTLVF+3t27dolGjRgBkZ2er16u2VU3PAyqtpSp13rsvVYhcdVGWan1eXh6Koqgvwlq+fDmBgYF4e3uTn59PTEwMNjY21aav/O8O3RQUQHAwHDwIH3wA9etDv34l4d7OnUum9168eJF169YxatQordYMEB8fD0BSUpJ62ZUrV/jhhx8YOXIkLVq0IDw8nOzsbObMmVPm+dnZ2YwfP56GDRuyZMkS9UTH0oyMjJg5cyaTJk1ixYoVODk5MX36dGbOnFnu9jY2NkycOJEFCxYwYsQIdHR0yM3NZdeuXWzcuLFMGHrGjBncunWL1atXAyUXogHk5uaWu/9+/fqxfft2li5dqt7X8ePHadasGY0bN9bY5ocffih3vcr9jqVy7NgxsrKy6NKlywPVoqurW+XXYuHChRw9epRXXnkFgODgYFxdXdX/1vbv38///d//MWzYMJYuXQqU3OUkIiICDw8PrQZ6b9++zeeff86YMWOeybuliOeYjg7o6kJxMSxcCMOGQd26GpvUqFGDcePG8c033xAQEFDu9+CnKT4+XqOvQ9neDpTb36vSH6FsX1+wYAELFy6stLdD+f29vH5Wuq8vXLiQNm3aVNjjyju/0ueWk5PDokWL8Pf3x8PDA4CbN29y9uxZdu3apd7ufsdSqWr/r4yqL6u+H5fuyVXp25WdV3WwdetWjh8/zqJFi7RdihBCCCGEEOIZoKMoSmXrK10phBBCCCGEeDLu3LnDiRMnOHz4MCEhIfz9999kZGRgYGDAoEGD8Pb2xtvbmxYtWsibvdVcQkICISEhGo/ExET09PRwcXHB29ubdu3a4evrqxFqEkJUH7dv3yYkJETdk0+fPq0OCJibm+Pm5qYOdrq7u9OsWTOZ4vocun37NuHh4URERKg/XrhwQf1vwdbWVv392dvbm7Zt22oEOIWmcePG4enpWWaa6cWLF+nXrx9XrlwBwN3dnfXr19OiRQtyc3NxdnamY8eOvPHGG0RHR5OZmcnHH3+sfv5LL73E9evX+eabb5g6darGvo8dO8a4ceMIDQ1l5MiRzJs3j+joaKZOncqZM2fo1asXX3/9NRkZGUyZMoXjx4+zfv16goKCCAwMxMPDg6+++opevXpVWktl606cOEGbNm3U+zIyMmL06NFERkYyZswYPv30Uw4cOMA777xDZmYm8+fP58MPP0RfX58dO3YwbNgwjbAxgJ+fH+vXr9f6hO+0NKhoUHaNGlBYWJIHa9kSGjVaxM6d8zh69Cienp5Pt9B7HD9+nK+//prt27djb2/PZ599hr+/P9evX6dz585kZGQwc+ZMLCwsmDRpErVq1VI/9+bNmwQFBbFmzRqmTZtG//79H+jYS5YsYdGiRURFRVW4jaIozJ49m/DwcLp160ZiYiKurq4MHz68zLaurq7cunWL5ORkfv/9d9auXcumTZuwsrLi888/p3fv3hr/RlS3PD9x4gRjxozhwoULpKSksHjxYho2bKixjaenZ7nrgSodS+WDDz4gLS2N9evXayyvSi0P8lr4+fnh6+uLoaEhx44dY9WqVdja2nLmzBk6dOhQ5msIoGbNmsTHx2vt4oyioiL8/f0JDQ3l3Llz1C0VlhTiqWvaFC5fLgnzenvDiBEwcCBUEtS9c+cOLVq0wN7enj179mjtLjWq3n769Gl1Xzc1NeXMmTPq3v7qq6/SqlWrcvt7VXpSeXJzc/n3v/99394OZXuamZlZmX6m6usJCQno6ekBJdODp0yZUmGPK31+8+bN0zi37Oxs2rdvz7lz52jZsiU9evTA0tKS0aNHqycLq1R2LHiw/q+imtCr+vm5or6s6sk3btyoUt9+kPN62q5du0arVq144403ykyoF0IIIYQQQryw1gKjKlopgV4hhBBCCCGqgYyMDP7880/27dvH33//zcWLFykuLsbFxYW2bdvSrl07XnnlFVxcXJ6pWxOL8iUkJHD8+HEOHz7MsWPHCAkJoaCggPr16+Pr60uXLl3o3r07Tk5O2i5ViBfSiRMnOHToEEeOHOH06dPEx8ejo6NDo0aNaNmypfrRvHlzTFUjKcULKzExkTNnzqjD3qdPnyYxMREdHR0aN26Mj48P7dq1o0OHDmVu3/wi69atG5s3b8bMzKzc9dHR0ejo6ODg4KCxvLCwkOLiYpKSksqsg5JpukFBQdSsWZO+ffs+kdqrUsv96nwY+/btIz4+nnbt2pGUlMTdu3fJzs4mMDAQT09PZs2a9ViO87AKCqAq2S0dHVAUMDBIQ1//VxYs6MzIkQ5Ut2vU8vLyiImJKTONVmXHjh00a9YMZ2fnhz5Gfn5+lQJvRUVFpKWlVTrxMisri4KCAszNzR+ohrt37xIdHY2NjU2Fz7148WKl66sqKioKExOTCsOqVamlKq8FlPy8XatWrUeu+WkoKChgxIgR7Ny5kz///JM2bdpouyQhYMgQ8PAo+fgA/y8NDQ2lQ4cOdOrUic2bN2NoaPjkanwIqt5eu3btSu/YAFXrSeWpam+HyntaZX29oh5X1fNLT0/HwMCA2rVr37fGZ6mfPsh5PQ1Xr16lS5cu2NjYcPDgQY1wtRBCCCGEEOKFJoFeIYQQQgghqpuioiJOnz7N3r172bNnDydOnKC4uBhvb286deqEr68vr7zyCvUqGnMmnis5OTmcOnWKI0eOcPjwYQ4ePEh2djZNmjShe/fudO/enU6dOsk0ZiGekPz8fE6ePMnBgwc5dOgQe/fuxcrKinbt2uHj46MO8FYUPBSitPj4eHW49+TJkxw9epSsrCysrKyYM2cOHTt2xMPD44W9SCc0NJTly5ezYsWKx77vzMxM5s2bx4IFC9DX13/s+9eWkJAQ+vbtS0xMjHpKn0p6ejqbN29m7NixT+z4+fmQmQkZGZCeXvJQ/f3ej4sXl0zifRB6eud4//1iFi58+ckUL0Q1lpKSwsCBAwkJCWHnzp28+uqr2i5JiEd2/PhxevbsiYuLC1u3br1vcFaI59H+/fsZPHgwjRo1Ys+ePfJ/yUcUGxvLtm3bmDRpEhEREQQFBeHg4MDQoUM1/k91584ddu/ezcWLF7G3t6dbt27Y29trsXIhhBBCCCHKVWmg9/n5rbYQQgghhBDVXGFhIcHBwWzevJnt27dz8+ZNGjRoQLdu3Zg0aRJ+fn5ya9UXVK1atejQoQMdOnQASqbqHDlyhD179rB3716WLl2KgYEBfn5+DBw4UH3LUCHEw4uNjeW3335j165dBAcHk5OTg52dHR07diQiIgJXV1dtlyieYXZ2dtjZ2eHv7w+U/AwQEhLCoUOHmDt3Lunp6dStW5cePXrQp08funfv/kK8yR8SEsL69es5cOAAO3bseCLHOHnyJF988cVzFeaFktteJyYmsnr1avz8/HB0dOTGjRucPHmSsLAwZs+eXenzc3M1w7fp6SUfVY/S4dx718fGQk5O+futUwdMTMDUtORhYFC1QK++Pvj7Q0BAAdu2LWPRotXExg5g2bJlckGbeKF4enpibGzM0aNH8fT01HY5QjwWbdq04eTJk/Tv3x8PDw/mz5/PpEmTXtgLmcSLJSMjg7lz57Js2TIGDBjAmjVrqs3E4GfVrl27CAgIIDU1FUVRCAsLIzU1lTlz5hAXF6f+OTg0NJThw4czf/58JkyYwLp163Bzc2PZsmWMGDFCy2chhBBCCCFE1cmEXiGEEEIIIZ6goqIiDh48yObN/5+9+46v8e7/OP6KTGIkRvaSILIaBFVKcNde5RajqFGj1davRW/U7DZuxa32uEur6q7GXaHVUqtmI0aGxEjInrKQRCK5fn+c+xw5sg4Nx/g8H4/rkZzrXNf3+pyr8T1pzvv6XP8hMDCQ9PR0/Pz8GDJkCH369MHLy0vfJYqnQGpqKvv27WPnzp389ttvGBgYsGnTJvr370+dOnX0XZ4QTwVFUQgODiYoKIi9e/dy7tw5zM3N6d69O71796ZLly64ubnpu0zxHCguLiY0NJTff/+dvXv3cuzYMQA6duxI37596devH02bNtVzlY9GcHAw3bp1Y8OGDQQEBOi7nKdKXp7CsmXL2Ls3iJCQk9SoYYSzsw/t24/F23sMubkmZGVR4VJQUP64ZmZgaQk1a977/v7Fzq785xo0gPvvpN64MVy/Xv6xjIxUYd9GjSAiQvVVLSgoiHfeeYebN2+ycOFCJkyYgIGBQbWcOyGeRNHR0UyePBkXFxeWLFlC3bp19V2SENUuPz+fRYsW8cUXX9C6dWvWrVuHt7e3vssS4pEJCgpi8uTJFBUVsXjxYgmRVqNZs2axcOFCDhw4wN/+9jcA/Pz8MDAw4MyZMxQWFuLr68uQIUP46KOPNPuNGDGCnTt3cufOHX2VLoQQQgghRHkq7dArgV4hhBBCCCEekUWLFrF27VquX7+Op6cnAQEBvPbaazRr1kzfpYmnWHZ2Nrt372bChAnUqFGDgIAApk6dSosWLfRd2nNLbv34ZIuNjeX7779nw4YNREdH4+zsTI8ePejbty/dunXDzMxM3yWK59zt27c5ePAge/bs4aeffiI1NRVPT09ef/11xowZg7W1tb5LrFYlJSXPZYe+/Px7wdqkpIpDt+rtSi+ZmaCdQSgCjIGKQ7j3L+UFdhs2VHXUrU4tWsCFC9rrjI1VQd5OneDtt2HgQFW49365ubnMmTOH1atX4+fnx6xZs+jfv/9z+fMinl0xMTEsWbKEf//733h4eHDu3Dl9lyTEI3fhwgUmTZrE2bNnGTlyJDNmzMDd3V3fZQlRLRRF4ZdffuGLL77g+PHjjB8/nkWLFmFpaanv0p4pH3/8MfPnzychIQF7e3sARo0axa+//kpaWhq7d+9mwIAB/Pzzz/Tq1Uuz39atWxk9ejRV5CGEEEIIIYR43CoN9D5b954TQgghhBDiCfDHH3+wZs0afv31V8aOHcuECRPkwypRbSwsLHj99dfp27cv3377LWvWrKFly5a0b9+eyZMnM3jwYEzvb5cnHhm59eOTKTMzk++//55vvvmGU6dOYW9vz2uvvcaIESPw9fXVd3lCaDE3N6dfv37069eP1atXc/ToUb755hs+//xz5s6dS8+ePRk1ahT9+/d/Jub3pzGcWTpkW1HotrLnb9yAwsKy494fsC0dunV1rSyUa4ylJVhZlR+M1af69VVfDQxUS61aMHEivPkmVNV4um7duvzrX/9izJgxfPLJJ/z973/Hw8ODGTNmMHz4cIyetBcrxAMICwtj4cKF/Oc//8HZ2ZkVK1bwxhtv6LssIR4LX19fTpw4wdatW1m4cCGenp4MGjSIWbNm0apVK32XJ8RDKS4u5scffmDbxx+zPyqKLr16ceLECdq1a6fv0p4bhoaGmqDuxYsXAahdu7bWNh07dnzsdQkhhBBCCPFXSYdeIYQQQgghqsnJkyeZPXs2hw4dws/Pj6NHj1KrVi19lyWeAyEhIaxYsYLvv/8ea2tr5s6dy7hx4yT48phUdetHAA8Pjwpv/Xju3Dk8PT31Uvuz5vr16yxbtoyNGzdSUlJCv379GDVqFL169ZJ/D+KpU1BQQFBQEFu3bmXfvn1YWloyefJkpkyZQn11alLo5P6A7YOEcq9cgaKi8sctrztueZ1w73/e1hasrcHQ8PGeh8dh8GD48Ufw9ob33oPhw1Wh3odx8eJFFi5cyPfff4+trS1jxoxh9OjRuLq6Vm/RQjwi+fn5BAYG8vXXX/P777/j4+PDzJkzGTJkCIbP4gQghA5KSkrYtWsXX3zxBSEhIXTu3Jk9e/Zgbm6u79KE0FlsbCxdu3blbmwsscXFqpXqK7JsbcHOTvV96ceNG6uudhIPpbwOvWPGjGHv3r2kp6ezfv16Jk2axKJFi/jHP/6h2a+kpAQzMzMKy7u6TgghhBBCCP2ptEOvBHqFEEIIIYT4i86ePcvs2bPZt28fXbt25ZNPPqF9+/b6Lks8hxISEvjss8/YtGkTjRs35qOPPmLIkCFPZTfEp0lVt34EMDAwqPDWj1OnTmXp0qV6qf1Zcf78eRYvXswPP/yAg4MDU6dOZezYsWW68wjxtEpKSmLlypWsXbuW4uJiJk6cyHvvvYeDg4O+S3ssdAnkxsSU/1x6Oty9W/64FQVvK+uUq37Oxgbk7bWs775T5VVeeqn6xrx+/TqrV6/m22+/JSUlhY4dOzJ27FgGDx4s87x4Ip04cYKvv/6a//znP+Tl5dG7d28mTpxIr169MJAwlxAa+/fvZ926dfz6668MHjyYsWPH0rFjR/l3Ip5IeXl5/Pjjj2zZsoVDhw7xf//3f7w9YQJuBQUQFwexsaolLk61xMdDcvK9AWrVAhcXcHLSXlxcwNER7O3B2FhfL++JV1Wg98KFC7Ro0YI+ffqwZ88ezX6hoaH4+vpSRR5CCCGEEEKIx63SQK+0pxFCCCGEEOIh3b59m+nTp7Nu3TratGnD/v37eeWVV/RdlniOOTg4sGbNGj744AMWLFjAiBEj+Ne//sWWLVtoWtV9rkW1Kn3rR7WKbv0YGRn52Op6Fg0bNowdO3bg6+vLli1bGDJkiHTjFc8cOzs7vvjiC2bNmsX69etZvnw5K1euZNq0acyZM+eJvyNA6YBtTMyDdchNTYWSkvLHLR3ILR281aVLroWFNEl7FF57rfrHdHFxYfHixXzxxRfs27ePr7/+mjfffJN33nmHnj170r9/f/r06UODBg2q/+BC6KC4uJhjx46xe/dufvrpJ6Kjo/Hx8WH+/PmMGDECKysrfZcoxBOpW7dudOvWjZUrV/L111/j7++Pi4sLAwYMoF+/fnTq1AljCTgKPcrKyuLnn39m9+7d/PLLLxQUFNCnTx8CAwMZMGDAvQ1btix/gMJCSEiApCRVuDcmRrUkJUFICFy6BLdu3dvez6/8Lr9ubqpfXp9jubm5AFqddjMyMrhz5w6KouDr68vo0aMJDAwkLi4OJycnAI4dOyZ/DxNCCCGEEE8d6dArhBBCCCHEQzhx4gSjR48mMzOTlStXMnz4cOkiI544oaGhjB07lqioKJYsWcJbb70lP6ePQFWdYkDVobeiWz8GBASwbds2vdT+NLt79y4rV67kq6++YtmyZfTr109+vsVzo7CwkPXr1zN37lzq1avH9evXH9mxKgre6hrKTUmB8v78WFXgtrLn69dXPS+eXzdu3OA///kPP/30E4cPH+bu3bt06NCBjRs3SmhDPBY3b97k119/Zffu3fz888/cuHGD5s2b079/f4YOHUqrVq30XaIQT53Q0FC+//57du/eTUREBBYWFvTq1YsBAwbQs2dP6tWrp+8SxXMgJiaG3bt3ExQUxNGjRzEwMMDf358BAwYwdOhQGjVqVL0HVF/xlpQEx49rh35L/yJtZqYd8r0/9OvsDIaG1VvbE+LIkSOMGzeOmJgYxo8fzyeffMLhw4eZNGkSubm5LFiwgNmzZ3P37l2mT5/O4cOH+eCDD7h79y67du1izZo1ODo66vtlCCGEEEIIUVqlHXol0CuEEEIIIYSOFEXh7bff1nTGe/vttx9reCwkJISPPvqI1q1b8/nnn5OZmanpyJeRkcHMmTNJSkpi7dq1mk4UAJcuXaJv376sWLGCTp06ERQUxMyZM/nmm2/o1KnTA9eRnp7OokWLWL16NRMmTGDFihUPtP/AgQM5efIkKSkpgOq8Nm3alOjo6Apfd6tWrSrdLiQkhJYtW+o0ji7nIzMzk7Zt29K+fXsSExM5fPgwrVu35vTp02XGrercR0dHV8t5/6tOnTrFwIEDsbW1Zf/+/dLFrhpNnz6dpUuXEhMTQ+PGjQHo27cvR48eJScnBwMDA8aMGUNgYCDh4eGan5HVq1ezfPlywsPDMTEx0edLeKrs27ePoUOH0rx5c7799lu9BLfU8/HOnTtZuHCh1pwcFRWFp6enVofmYcOGsX37ds3jyuaNB5GWlsbu3bt59dVXWbx4MRcvXuSnn37CsJIPctPS0mjXrh0ffvhhpftVVqOuY5Q3lxYXFz/QOJcuXaJz587UqVOH2NhYCgsLcXNz49ixY9jY2Og8jq41l2ZjY6N5r1LTpR59sLe3p6hoAtbWs8jPNyUnB3JzVU3BylO3LtSrV/ZrvXqqwKz6saOj9vPq557RrIB4Bvzxxx8cPHiQw4cPc+rUKQoKCmjcuDGdO3emc+fOtG3blmbNmlGjRg19lyqeApmZmQQHB3Ps2DEWLVpEUVER7u7ump+nbt26ye/UQjwGWVlZHDhwgMOHD3P48GEuXryIkZERrVu3pnPnznTs2JE2bdpUf9BSPHMUReHq1av8+eefHDlyhMOHD3PlyhVMTU1p27YtXbp0oUuXLnTo0OHp6Q6tDgSrQ8ClOwFHR0N2tmo7S8uyIeDSwWAXF5Dfj4QQQgghhHhcKg30oihKZYsQQgghhBDif2bNmqUYGxsr//3vf/Vy/F69eimffPKJUlJSoiQmJmrWX7t2TWnYsKEycuTICvd74403tNaNHj1a6dix40PV8eeffyoXLlxQAGXKlCkPtO/69euVpk2bKtbW1pp1v/32mzJlyhTl2rVryp07dzTLb7/9pri4uFS6XeltdB1Hl/OxZs0a5caNG5rHH3/8sQIox44d09pPl3Nf2XEet5iYGMXFxUVp166dkpeXp7c6niWHDx9WXF1dFUAZP368kpycrGzfvl2pW7euAigLFixQioqKlPz8fOXtt99WvLy8lK+//lrZuHGj0qdPHyUuLk7fL+HxOn9eUf77X0UpKHio3YOCghRTU1Nl9OjRSsFDjlEd1POxoihl5uQJEyYohw4dUuLi4jRLfn6+5vmq5g1dFRcXKy+//LLm8d27dxVnZ2dlxowZVe7Tv3//KverqMYHGaO8uVQ9j+o6Tq9evZQLFy4oiqIoaWlpyvjx4xVAGTdunGabqsZ5kJrV1q9fr/Ve9SD16ENycrLStOk0pXbtr5SpU9OU1asVZds2RQkKUpSjR1X/9K5dU5TMTEUpKdFrqUI8Nvn5+cqhQ4eU+fPnK506dVLMzMwUQKlbt67StWtXZcaMGcrOnTuV2NhYfZcqngA3b95Ujhw5ovzzn/9Uhg4dqri5uSmoGp4oTZs2VbZt26b1fi+E0J+UlBTl+++/V958803Fw8ND82/VxcVFCQgIUBYvXqwcOnRI32WKJ0RgYKAya9YspVu3boqFhYUCKCYmJkqHDh2UOXPmKAcOHHi2/z6Sl6co+/cryrp1ijJjhqKMGqUor7yiKK6uimJoqCiqHsCKYmqqWtehg6IEBKi2XbdOtW94uKLcuqXvVyKEEEIIIcSz5GulksyudOgVQgghhBBCB3v37qVfv35s3ryZMWPG6KUGZ2dn3nzzTWbNmqVZV1hYyMsvv0xOTg5nz57F3Ny8zH4tW7bEzMyMkydPatZNmjSJ8+fPl9txtrTi4mJ27tzJ0KFDtdYXFhZiamrKlClTdO7Qe/nyZZYvX46ZmRnfffedpuvhyZMnefHFF8t0SnvnnXcwMzPjn//8Z4Xbld5G13GqOh+FhYUkJiZqOq0CxMbG4uLiQmhoKD4+PppzoMu5P3fuXLnH0ZfLly/Trl07hg8fzqpVq/RWx/MqJyeHiIgInJyccHBw0Hc5j9+OHTBsGNSpAwEBMGIE+Pvr1O4zLCyM9u3bM3ToUDZs2PBYO6Tfr7z5GCAlJYVXX32VnTt3lvvfV5d5ozKl5+TDhw/TpUsXrU7A8+fPZ+nSpaSmppY7tnqfoKAg+vbtW+F+hYWF+Pj4lFvjg4xR3lyam5uLj4+PTuNERUURFRXFiBEjNM8nJyfj4OBAs2bNiIyMBMDAwKDScYKDg3WqWU39fhUYGKjVoTckJESnevQlNzeXHj16kJmZyenTp7GwsNBrPUI8aYqKirhw4QLBwcGaJTIykuLiYqysrGjRogU+Pj54enri4+ODh4cHtWvX1nfZopqVlJQQExNDeHg4ERERhIWFERYWxqVLlyguLsba2po2bdrQunVr2rRpI10/hXgKqLtpl16Sk5Np3rw5L7zwAt7e3nh5eeHt7Y2bm1uld7MQT6+8vDwuXrxIeHg4Fy9eJCwsjAsXLpCamkrz5s215vUWLVpgamqq75L1r6gI0tO1u/qW7vR7+TLcvHlve0vLe519/fzKdvkVQgghhBBC6KrSDr1Gj68OIYQQQgghnk6FhYW88847jBw5Ui9h3iNHjhAREUF8fDx//vknu3fvpn///gDMnj2b4OBgNm7cWGEwbNCgQcybN49vv/2WkSNHcuvWLXbt2lVpEPfu3bts27aNzz//nNTU1DKB3gdVVFTEnDlz2LRpE/Pnz9d67qWXXiqzfUlJCYGBgezcubPC7e7fRtdxqjofJiYmWgE0gNDQUPr27asJ84Lu5/5Bzvvj0KxZM1atWsWIESN44403aNWqlV7red7Uq1eP9u3b67sM/bt5E775BjZvhgYNYNQoGD4c2rYtd/OSkhLGjh1Ly5YtWbNmjd7CvPfPx+vWrcPW1lYzJ69cuZLTp0/j6OhI48aNmTdvHqNHj9bUq8u8UZ7y5uTAwMAy23l7e3P79m1+/vlnAgICyjyv3qf0XFbefrNnz+Yf//hHuTXqOkZFc2m/fv10Hqdr165l5ihbW1v8/PwwMtL+k1Zl4/zxxx861Qza71f3n2MXFxed69GHunXrsmvXLlq2bMmHH37I6tWr9V2SEE8UY2NjWrduTevWrXnrrbcAuHXrFmfPniU4OJjQ0FAOHTrE6tWryc/Px8DAgMaNGzNkyBCaN29OkyZNaNKkCdbW1np+JUIXeXl5REdHEx0dzZUrV4iIiNCEvNT/fV1cXPD29qZ///6aoJeTk5O+SxdCPKD69evTo0cPevTooVmXkJDAmgzsKPYAACAASURBVDVriIiIYMuWLVy7do2SkhLMzMzw8PDQhHybNm1KkyZNcHNze+CL7YR+pKWlaeb3yMhIzfx+/39jLy8v3n//fd58803q1Kmj77KfTMbGqiCunZ0qoFuerKx7Ad/Sod/16+H6dSgpUW1nZqYa5/6Qr/qxkxM8Af/PJIQQQgghxNNAfnMWQgghhBCiCj/++CNJSUl89tlnejm+s7Oz5hYb1tbWNGvWTPPc9u3bMTIyIiwsjK5du/Lnn3/SqlUrli9frgkdTZw4kW3btjFq1CjOnj1LREQE69atY+DAgWWOVVRUxJYtW/jiiy9IS0vj7bffZvr06X/5NXz88ce89957On+Icvz4cQwMDMoN6f7VbR7kfCiKwg8//MBHH33Er7/+qvWcrufexsamyuM8bsOHD+fLL7/kyy+/5Ntvv9V3OeJ5VVSk+nrjBqxaBcuXqz7wGzwYxoyBli01mwYFBXH+/HlCQ0MxNjbWT72UnY/9/Py0ujf6+/tTVFTEyZMnOX36NGPHjmXbtm3s27cPQ0NDneaN0iqbk69evVpmeysrK0DVYbY86n1sbW0r3W/79u0MHjy43Bp1HaO00nOpOtCryzjlhZIB4uPjmTx5sta6ysZ5kJore79q0KCBzvXoi42NDQsXLmT8+PF8+OGHz2cncCEeQO3atenUqROdOnXSrCsuLubatWuEhYURERHBjh07WLZsGXfu3NHs4+bmpgmAqb93dHTE0dERMzMzfb2c505JSQmpqanEx8dz7do1rl69SnR0NL/++itJSUma7RwcHPD09MTf35+33npL04lZOjAL8exycHDQ+hvO7du3iYyM1OrOfeTIEeLj4zV3vLCxsdFcvKGe311dXXF0dMTGxqbM3YDEo1FQUEBiYiJxcXGa4K56fo+OjiY3NxdQXYzdpEkTvLy8eP311/Hy8sLHx0e6MFc3S0vV4uVV9rk7dyAxUburrzrwe+yYKvCbl6c9ljrge3/ot0kTqFfvsb0sIYQQQgghnmQS6BVCCCGEEKIKe/fuxd/fH0dHR70c38XFhfr16wNgb29P8+bNAUhMTCQxMZEWLVowb9486tevz+XLl+ncuTP+/v5ERUVhb2+PtbU1f/zxBy+99BLLli3jpZdeKrdD6Jo1a1i4cCGZmZm88847TJs2jYYNG/7l+o8cOYKRkdEDdSX94YcfGDhwYKVdOB92G13Px+3bt3n//ffZtm0beXl5+Pj48Ntvv9GmTZsHOvdubm6VHkdfRo0axfz581EURW/dToXQUId7k5Jg7Vr4179UH+iNGAGjRrFz5078/f3x9PTUa5n3z8etW7fWer579+50794dgAsXLjBs2DAOHDjAkiVLmDlzpk7zBsCdO3fYvHlzpXNyampqmQ+qa9WqBUBycnK59av3MTExqXA/9fxWUY26jFHa/XNpcHAwbdq0eeBx1I4ePYqRkRHvv/++Zl1V4+h6rId5vyqvHn0bPnw477//Prt27eLdd9/VdzlCPHUMDQ01ga6BAwcyZ84cSkpKSEhI0ISJ1OGi/fv3s3btWk24CKBhw4bY29trAr7q7x0cHLC2tqZRo0Y0bNhQgmFVuHXrFmlpaaSmppKUlERCQgLx8fEkJiYSHx9PQkICSUlJFP3vdwgjIyOcnZ1xc3Nj2rRpmjCem5sbNWvW1POrEULom7m5uaZLe2kFBQXExMRoBUajo6M5duwYsbGxWnOMra0tTk5OtG3bFicnJxwcHLCzs8Pa2horKyvpAqsDRVHIyMggPT2dtLQ04uPjNXP7yZMnSUxMJC0tTbN97dq1NQHrbt268eabb2oupHFwcJDgrr6Zmt4L51YkK+teyLd06PfAgXuP1SwtVZ2Cywv8uriA/O4khBBCCCGeExLoFUIIIYQQogpXr16lY8eO+i6jjLNnzwLw6quvagJmzZo148svv2T48OGsXr1a05Fm06ZN+Pv74+/vz+bNm3nxxRc5evQoTk5OFBQUsH79eubMmcO7777L1KlTK+xC+KCys7P56quv2L59u877KIrCjz/+WGnn2L+6TWXnQ83c3Jz169ezdu1a/vWvfzF9+nTeeustzpw580Dnvqrj6MsLL7xAi+xsCl99FVNTU32XI54X8fFVb1NYqPoaHQ2ffQYff8yMWrW42qEDZGRANVxo8Dj4+voSEhKCu7s727dvZ+bMmUDl88bcuXNZv349ixcvJjc3t9I5ubyugsXFxYCqu1h5KupEWHo/9fxWUY26jFHa/XOpeh590HHUz82bN4/du3dX2VWx9Di6HOth3q8epJ7HycTEBF9fX6KiovRdihDPjBo1auDk5ISTkxNdunQp83x6eromkBQXF0diYiIJCQlcvHiR/fv3k5CQQEFBgdZ4Hh4eNGzYUBPyVQd9LS0tqVevHnXr1qVevXqaxcLC4nG+5GqVn59PTk6O1pKdnc21a9dITU3VCnelpaWRkZFBfn6+Zn8DAwNsbGxwcHDA3t4ePz8/+vfvj4ODgyYs7eDgoNcu/kKIp5OZmRmenp7lXjh49+5dzUUEpS8oOH78ODt27CAlJYWSkhKtsRo2bIiVlRXW1tY0bNhQ87hBgwZYWFhgYWFRZn5XX2T2tLl/Xi89v6vn9fT0dFJTU0lPTycjI4OMjAzN7+AApqam2NvbY29vT/fu3bG3t8fZ2VlzMYz6jhriKaYO6fr5lf98QYEq2KsO/YaE3Av8xsXB3buq7UxMwMFBO+RbOvTr7g5P0P+TiedLXl4et2/f5ubNm+Tm5lJcXKz5mp+fT0FBAXfv3uXmzZuA6u/liqJw+/ZtCv/3NzD1PuVRj1UeQ0ND6tatW2Ft9erV01xIaGlpiYGBgeb/K+rUqYORkRFmZmbUrFlTM5b6a926dTE3N5eL44QQQgg9kECvEEIIIYQQVTAwMND6kOZJUe9/t6K7v4vuSy+9BMClS5cA+Pe//82OHTsIDg7GyMiIDh06MGnSJN5++22CgoI4fPgw8+fPZ8KECcycObNau8q8//77tGnTht27d2vWXblyhYKCAgIDA7GwsKBr165a+xw/fpzCwkKtWx/f769sU9X5uF+NGjV47733OHHiBD/++CN37tx5oHO/bt06nY7zuKlvKyrE0+Jp/JmtVasWAwYMYPPmzZp1lc0b6vk4OzubqVOnVjonOzo6lvlAR/3hUEWdjNX73LlzRyvIX3q/euXc5rR0jbqMUR71XDpt2jTu3LnzUONMnz6dqVOn0rJlS631VY0TFRVV5TblvV9V9l5VWT1Pgqfx34sQTzN1ILdVq1YVblM6rJqamkpkZKQm6BQWFqYJP2VnZ2s6Qt6vdBDMxMRE8wG9hYUFBgYGWFpaYmlpWeaDfXNz8zJdytXj3X+nhtLBAjV16ABUc2N+fr4mnJCXl8edO3c0+926dYuioiKys7M14a6KXo+Li4sm0GxtbY23t7cmBKc+p9bW1tjY2JRbvxBCPErqzt/Ozs7lPl9UVERKSoomsKoOrarn+/T0dK5cuUJaWhqZmZlkZ2eXO46xsTGOjo5YWlpiZGREnTp1MDY2pnbt2piYmGBubq4JW9WsWRMzMzPNvpaWluWOd//FZllZWeXWf+vWLc1jdWAsJyeHkpISzdyflZWFoiia96ecnBxiY2MrfC316tXTXKTSqFEjPDw86NSpkybgbGNjozXHi+ecmVnFXX6LiiA9/V5X39Jdfg8cgCtXoHQA0tKybGdf9dKqFcjdqUQF8vPzycrKIjMzs8yiXp+bm8vt27e5ffs2WVlZmu8TEhJ0+v/vGjVqaP7eog7N3j+nq9ff7/7tSrt9+zYZGRnlPnd/SDgrK0trnXq+17X2OnXqYG5uTq1atbC0tMTc3Bxzc3PN3azq16+PpaWl5nv1UlHtQgghhKiYBHqFEEIIIYSoQpMmTQgLC9N3GWU0a9YMgJCQEK31Tk5OGBsba0JgW7ZsoVevXhgZqX79HzduHGfOnGHTpk1kZ2fTs2dPrl+/jqurK1u2bGHatGm888471RLsTU9PZ//+/VrrcnJyyMvLY8qUKXh5eZUJSe3cuZMBAwZUeuvEv7JNZeejMt26dePQoUOYmpo+0LkfO3ZsmeNkZ2frvctaaGgoFywtMfnvf+VDDfH47NgBw4ZVvo2JiapLr5sbjBgBo0axaMECEhMTGfiUdOctrXnz5po5AyqfN9Tz8cqVK1m2bFmlc7KHh0eZY6k/xKkoVKveJz4+niZNmpS7X3lzU+ka1ftVNkZl6tevj6mpqU61lLZ+/XpatmxJ//79yx23snEiIyOr3Gbz5s1l3q9u3rxZ4XtVVfXoU2FhIaGhoQwePFjfpQghSrGystK502BFHW1LPy4sLNSEr7KzsykpKSEmJoasrCytDmBQ/of1FXUBMzU1LdMtsnbt2prut/eHy2rVqoWpqSlWVlaYmJhoOn1ZWFhodaB8ljoOCyEE3AviOjo66rxPdnY2ubm5WvN5bm4ucXFxWkErddj25s2bpKamai6mUF9EAZSZ69XUF1yUpp6bSyvdpRHuzfXqQJn6og9XV1dNmEsd2HV2dn6mug2LJ5SxsSqYa2dXcYffrKx7Id/SoV91l9/r16GkRBUcVod8ywv8OjmBkcQmniVFRUWkpaWRkpJCSkoKoaGhmgswkpKSuHHjhia0W/rOEGp16tTRCqfWrVuX2rVrY21tjaWlJbVq1cLc3BwHBwfN9+rQq5GRkWZOLe936yeN+r1F/d6j/pqTk6MJLufm5nLz5k3N4+zsbM33P//8s+Zclr5QRK1mzZqa89igQQPs7Oxo1KgRNjY22NjYYGVlhY2NDdbW1lhZWcldN4QQQggk0CuEEEIIIUSV+vTpw+uvv05cXBxOTk76LkfDxsaGHj16cOrUKa31V65coaioiA4dOgCq4Ob9wagBAwawZs0aUlNTNR+2x8bGsmrVKpYuXcrSpUuZNm0a77777l+6jfiePXvKrPvHP/7B1q1bSUhIKPOcoijs3LmTDRs2VDpuVdtUNk5l56My4eHh9OvXD3iwc1/ecdTnXV8UReGbb76hT58+ZbqyCaEXxsaq7jt2djB4MIwZA6W6ngYEBDBo0CDCw8Px9vbWX50PYdeuXQwYMABAp3mjXr16zJkzh/fee6/SOfmNN97gk08+0RorJCSEFi1aaAWIS1Pvc/z4ca1ga+n9atSoQY8ePSqssW/fvlWOURn1PKpLLaXPoaIovP7661pjHTlyBH9/f0xNTSsdR5djlfd+ZWNjU+57VVX16Nu2bdu4efMmAwcO1HcpQoiHpO7CaGNjo+9ShBBCVCMLCwu5oEGI6mRpqVq8vMp/vrAQEhIgJORe4DcmRvU4NhZu39YeSx3wvT/06+YG8m/3iZGfn09sbCzx8fHEx8cTFxdHXFwc8fHxJCUlae6KUZqrq6umQ7iHhwcNGzYst5usenmeQqW1atXShI51vQCxIoWFhVqdje9fMjIySE5O5vr16yQnJ5OamkpeXp7WGI0aNcLKyoq2bdvi7OyMk5OT5gIaJycnatas+ZdqFEIIIZ4GBlXcAkDuzyeEEEIIIZ57RUVFuLu789JLL7Ft2za91BAZGYmnpyeTJ09m1apVmvURERG0a9eOX3/9lfbt2wOwbt06VqxYQWhoKEZGRowbN45ffvmFxMREatSoAcBHH31EYGAg586d06xTy8vLY+3atSxZsoSioiI++OADZsyYobVNamoqNjY2TJw4kXXr1pWp9x//+AeZmZls3Lix3Oe2bt1KSkpKmedOnDhB7969SUtLq/S2uhYWFpVuU9k4lZ2PCxcukJ+fz5dffsmAAQM0wcEbN24wcOBAgoKCNLdH0/Xcb9y4Uafz/jh99913jBw5krNnz9KiRQu91SGeQ6U79KpDvA0awKhRMHw4tG1b7m4lJSW8+OKLmJqacujQIb1+sFLRfHz58mVWr17N6NGjafm/MHJERARvvPEGf/zxB8bGxjrNG/erbE6ePn06S5YswcDAgIKCAnx9fdm+fbvWLd/vn4+nT5/Ovn37CAsLq3C/iIgIcnJyKqxRlzEqmkuNjIw086gu4xw4cIA5c+YwcuRIzWsqLi7m4sWLeHt78+677+o0ji7b3M/GxqbMe5Uu9ehTUlISrVq1YvDgwXz11Vd6rUUIIYQQQgghnmhZWfe6+pbu8qtel5IC6ixF6S6/rq7QocO9x87OUMldxMSDKSoq4vr161y9elWzlA7wpqena7atVasWzs7OmsCng4MDjRo1ws7OTnOHDDs7O8zNzfX4ikRlbt26pQlip6WlkZycTFpaGidPniQhIYHr169rdVG2srLS/Pd2dnamSZMmmsXZ2fm5CmMLIYR4qm0BxlT0pAR6hRBCCCGE0MEvv/xCnz592LhxI+PGjXusxz516hSLFy9m165dODo6EhYWpglDgaoL7NSpU+nQoQOmpqacPHmS9evXY2trC6jCYFOmTOH06dOMHz+e8PBw0tLSWL58OY0bN67wuAUFBWzYsIEvv/ySa9euadb/8ssvbNmyhR07dmBlZcVnn31G3759tTqIeXh4kJmZSWpqaplxKwv0vv/++2RkZPDNN99Uek5GjRpV6TaVjVPV+bh9+zYdO3bk/PnztG7dmp49e9KwYUPGjRtXpluxLuf+xRdffKDz/qhdunSJdu3aMXLkSFauXKm3OsRzSh3orVMHAgJgxAjw99fpg6/w8HBeeuklAgICtILyj9P98/Gnn37KgAEDqFevHmfPnqVr167k5OTQpUsX2rZtS/369Xn33Xe1uodUNW9UpLw5WVEU+vfvT/fu3UlOTsbDw4NRo0Zp7aeej5OSkjA0NERRFGbNmkVERESl+73yyisV1qjLGBXNpVOmTNFsU9U4Z8+epVOnTtwu3TXpf8zMzEhMTKR+/fo61aPr6y7t/kCvrvXoS05ODt27dycnJ4c///yTunXr6q0WIYQQQgghhHjqFRSogr3lhX4PH4a7d1XbGRtDw4bagd/SnX6bNVP9HUSfAgJg1iyo4IJWfSgpKSEmJobLly9z5coVrly5wtWrV/n999+5+79z26hRI5o0aYKLi4tWiFP9fYMGDfT8KsTjkJGRoQl0lw53x8bGcuXKFU1HZiMjI1xcXDQB36ZNm9KkSRPc3d1p3LixXhtsCCGEEPeRQK8QQgghhBDVYe7cuSxcuJAdO3YwaNAgfZdTRlJSEjVr1sTS0rLc5/Py8oiNjcXGxqbCbcpTWFhYabfc8ty6dYuioqIHOg7AtWvXqFu3bpV/jL1x40al2+gyTlXnIzs7GxMTE80txypT2bl/2PP+KERHR9O1a1fs7e35/fff5RZl4vG7cAGuX4eePcHU9IF337t3L3//+98ZPHgwGzduxMzMrPpr/Avu3LlDXFwctWrVwt7evtJtq5qzK3L/nFxcXExGRgbW1tblbl/RfFzVfrrUqMsYusyluoyjC13Gqa5jPWmSkpIYMGAASUlJHDp0iGbNmum7JCGEEEIIIYR4tqk7/Jbu6qsO/UZHQ3b2vW0tLe8FfMsL/bq4wKMMGxoYqJYePWDBAnjxxUd3rHJkZWURERFBSEgIFy9eJCIigvPnz2sumLW0tMTV1RVXV1deeeUVPD098fHx0WpqIURFCgoKiI6O5uLFi8TExGgt165dQ1EUTExMGDp0KF5eXnh6euLl5UXjxo0xMDDQd/lCCCGeTxLoFUIIIYQQojooisKUKVNYs2YN//znP/m///s/+YOPeGocP36cQYMG4eTkxG+//ab3cLEQD+u3335j6NChuLm5sW3bNtzd3fVdkhB6Z2trS7169di7dy9ubm76LuepEB8fT2BgIO+++y4XL17kp59+wsnJiREjRmh17bl58yY///wzkZGRODo60r17dxwdHfVYuRBCCCGEEOKpkJ+v3dX3/tBvbCwUF6u2NTUFe3vo0EE79GtrC40bgw4NByql/hu2kZGqs3DXrqpgb8eOf23c+yiKwpUrV/jzzz8JDg4mNDSU0NBQMjMzAbC2tsbHxwdvb2+8vb154YUXcHd3lzvMiEcmJyeHqKgowsLC+P777wkLCyMtLQ2ABg0a4OPjg6+vL23atKFNmzY0bdpUPvMRQgjxOEigVwghhBBCiOq0aNEi5syZw9/+9jc2b96MnZ2dvksSokKFhYV89NFHLFq0iD59+rBt2zZq166t77KE+EuuXr3KsGHDCAsLIzs7W7pNi+dWcnIy06ZNo7i4mA0bNsiHoDoKCgrijTfeID09nWXLlhEaGkp6ejp79uzh888/Z9asWZptX3jhBRYsWEDHjh3ZunUrCxYsYNWqVbz++ut6fAVCCCGEEEKIp15hIWRkaId+jx1TPb58GW7evLetpWXZrr73P66MoSGUlNx7rA72vvgizJ4N/fo91EtISkrShHfVS3Z2NsbGxvj6+tKyZUt8fHzw8vLihRdeoGHDhg91HCGqU3p6OmFhYYSHhxMeHs7Zs2cJDQ2lqKgICwsL2rZtqwn4tmnTRj7/EUII8ShIoFcIIYQQQojqdurUKUaPHk1GRgYrV65k+PDhcuW2eOJcuHCBsWPHcvnyZZYuXcrEiRPl51Q8M4qLi1m1ahXLly9n2bJl9O/fX36+xXOjsLCQtWvXMm/ePOrXr09MTIy+S3rqzJo1i4ULF3LgwAH+9re/AeDn54eBgQFnzpwBVOf5s88+46OPPtLsN2LECHbu3Mm5c+fw9PTUS+1CCCGEEEKI50BWVtmuvqUfX7sG6qyHmVn5IV/1Y09PuHOn7DHUwd42bWDuXOjb91433wrExsZy6NAh5s2bR3x8PDVq1MDd3V0rANmiRQtMTU0fwUkR4tG4c+cO58+fJzg4WBNSv3z5MiUlJTg5OdG5c2e6du1Kly5dcHJy0ne5Qgghnn4S6BVCCCGEEOJRyMvL44MPPmDNmjW0bt2aTz/9lO7du+u7LCG4evUqCxYsYPv27bRr144tW7bQpEkTfZclxCPx2muv8f333+Pj48MHH3zA0KFDMTY21ndZQjwSubm5rFu3juXLl3Pjxg2mT5/O7NmzpUv1Q/j444+ZP38+CQkJ2NvbAzBq1Ch+/fVXze03d+/ejbGxMb169dLst3XrVkaPHs3UqVNZunSpXmoXQgghhBBCCPLz4fp1iIu7t8TGqpb4eEhIgKIi1bbGxve+L4+hIRQXq4K/CxbA4MGaYG9ycjIHDx7k0KFDHDp0iJiYGGrWrMmHH35Ihw4d8PPzk7vFiGdSbm4uZ86c4fjx4xw6dIiTJ09SUFCAm5sbXbp00Sy2trb6LlUIIcTTp9JAb43HV4cQQgghhBDPllq1arFq1SpCQkKwsrKiR48edO7cmWPHjum7NPGciouLY9KkSXh6enLmzBm+++47jh07JmFe8Uz77rvvOHfuHN7e3owdO5amTZuyYsUKcnNz9V2aENUmISGBGTNm4OTkxKeffsqIESOIjo7m008/lTBvNTI0NKR084OLFy9Su3ZtrW06duwIQGRk5GOtTQghhBBCCCG01KwJHh7QowdMmACffAJbt8KRI6pOvgUFqlDvsWOVh3lBFeYFiIqCIUModHdn77hxdHr5Zezt7RkzZgznz58nICCA/fv3k5mZyZw5c+jSpYuEecUzq27dunTt2pW5c+dy8OBBbt68yZkzZ5gwYQLXr19n3Lhx2NnZsWDBAkJCQvRdrhBCiGeIBHqFEEIIIYT4i1q2bMmePXs4efIkhoaGdOzYET8/P27fvq3v0sRz4tixYwwZMgQ3Nzd++eUXvvrqK8LDwxk6dCgGVdwmT4hnga+vL9u2bePq1asMGDCA2bNnY21tzZAhQwgKCqKoqg+uhHgCFRQU8MMPPxDQqxfhTk54rFzJD23akLRiBYsDArA3MdF3ic+8+vXrc/LkSa11zs7OGBsbY2lpqaeqhBBCCCGEEEIHNWqAvT106KDzLiX/W0yuXMH/66+Zn5vLL7t2kZOTw5kzZ1i4cCGvvPIKZmZmj6xsIZ5URkZG+Pn5MWPGDPbv38+NGzfYu3cva9eupXXr1jRr1owZM2Zw8uRJqrhTuhBCCFEpCfQKIYQQQghRTdq1a8fvv//O8ePH8fDwwMHBgalTp0oHN/FI3Lhxg+XLl9O8eXM6duxISkoK33zzDVevXmXixIkYGRnpu0QhHjtnZ2dWrFhBfHw8y5cvJzExkQEDBuDs7Mz06dM5d+6cvksUolJ3797lwIEDjBkzhkaNGjFy5EhqGhjg4e/P661b0+3yZczHj4e2bcHKCmrXBh8fmDIFli2DXbvg3DnIztb3S3kmvPjiixw9elRrXXh4OEVFRbz00kt6qkoIIYQQQgghqomxsebbRGB3rVr8t1Mnzm/cSK3CQv4WGkqPAQOoVauW/moU4gllbm5O7969SUpK4syZM7z22msEBQXRvn17nJycmDlzJnFxcfouUwghxFPIoIorQ+SyESGEEEIIIR7SihUrWLFiBdeuXcPT05OAgACGDRtG8+bN9V2aeIplZ2eze/duJkyYgKmpKcOHD2fy5Mn4+vrquzQhnkhxcXFs376djRs3cvXqVaysrOjRowf9+vWjV69e1K5dW98liudcZmYmv//+O0FBQezZs4esrCw8PT15/fXXGTt2LFZWVto7FBVBejokJ6tuoxoTAwcOqL7Gxt67VaqZGdjZgatr2aVJE6hX7/G/2CfI9OnTWbp0KTExMTRu3BiAvn37cvToUXJycjQd7uvWrUt4eDhOTk4ArF69muXLlxMeHo6JdEkWQgghhBBCPOlycsDCAgwNVY+Liyk2NSWyTh1+yswkvHZt3EaOZPjkyXh5eem3ViGeAWFhYWzevJmtW7eSm5tLv379GD9+PD179qRGDem5KIQQAoAtwJiKnpRArxBCCCGEEI9QSUkJR48e5T//+Q+BgYGkpqbSsmVLAgIC6NOnDy+88IK+SxRPgeTkZH75sG60pgAAIABJREFU5Rd+/PFH9u/fj6GhIf/+97/p27evhBGF0JGiKISEhLBnzx727NnD2bNnqVmzJq+88gq9e/emc+fOuLu767tM8RwoLi7m3LlzHDx4kD179nDixAkMDQ3p1KkTffv2pW/fvri5uT3c4IWFkJBwL+iblKQd/L1+HUpKVNtaWqrCvba2ZYO/zZpBnTrV9pqfNEeOHGHcuHHExMQwfvx4PvnkEw4fPsykSZPIzc1lwYIFzJ49GyMjI9555x0OHz7MBx98wN27d9m1axdr1qzB0dFR3y9DCCGEEEIIIaoWGgqDBlHcoQPHS0r4/NAhDiQl0alzZyZMmMCgQYMwNTXVd5VCPHMKCgoIDAxkw4YNHDlyhMaNGzNz5kxGjx4tFwgLIYSQQK8QQgghhBD6UKMGGBiAkREoiqqhXmmGhgqmpgUoSjoFBTFAAlZWBri7W9C9e13Gj2+GtbW1XmoXj1deXh7BwcEcP36cY8eOceTIEfLy8mjevDk9evSgR48e+Pv7y+3thHhEioqKCA4O5ujRoxw9epRffvmFRo0a8fLLL9OmTRtat26Nn58f9evX13ep4ikRHx9PSEgIZ86c4c8//+TEiRPcvn0ba2tr5s6di7+/P15eXpousE+MggJVCPj+zr/R0ZCdfW87dRi4vMXJSfXLjxBCCCGEEEIIvQsNDaVVq1aMHz+eefPmYWdnp5c6CgsL+eOPP9izZw/dunWjd+/e1Tr+H3/8wZEjR4iKiiIgIIABAwb85THv3LnDkSNHOH/+PC+//DIvvvgihupOx39hn6q2edDjpqSkEBUVRefOnSvc5vDhw5U+D3Djxg3Wr1/PrFmzyjx38+ZNvvvuO65du0aTJk147bXXKvxbdUXj6DrGrVu3WLx4Me3ataNbt24YGxuX2Wbv3r3k5uZqHsfHx/POO+9oxnuQeh+nhIQEPv74YzZv3oyfnx+bNm3C29tb32UJIYR4/CTQK4QQQgghhD6YmJQN8VbFwEChfv3tZGW9SUnJTZo0aUL79u3p0KED7du3x8PDo8o/GoonX3x8PKdOneL48eOcOHGC8+fPU1RUhL29PR06dOCVV16he/fuODs767tUIZ5LwcHBHDlyhOPHj3PmzBkSEhIAcHV1pXXr1pqAb4sWLSTkK0hISODcuXOcOXNGE+JNTU2lRo0aNG3alDZt2vDyyy/j7+9P8+bN9V3uw8vKKtvxV71cuQKlPkirNPDr6qq/1yCEEEIIIYQQzwlFUViyZAlz587l7NmzeHl56bWes2fPsm7dOtavX8+GDRsYP358tY0dEhLCRx99xM6dO1m4cCGff/45mZmZfynAmZaWRrt27fjwww959dVXWbx4MRcvXuSnn36q8O/zuuyjyzaurq46HTc9PZ1FixaxevVqJkyYwIoVK8rUpN6mqKio3OdLGzhwICdPniQlJUVr/aVLl+jcuTN16tQhNjaWwsJC3NzcOHbsGDY2NjqNo+sYly5dom/fvpw7d46goCBmzpzJN998Q6dOnTTbREVF4enpSems07Bhw9i+fXulx7p69Wqlr/9xCgsLY9KkSZw9e5bPPvuMadOm6bskIYQQj5cEeoUQQgghhNCHB2m6Z2QENjawdSt06aK6gvzChQuajq3Hjh0jOzsbY2Njhg0bhp+fnyZMVrt27Uf3IsRfFhMTQ0REBCEhIYSEhBAcHExqaiqGhoa4u7vz8ssv06FDB/z8/PT+h3UhRPmys7MJDw/X/Ds+fvw4MTExAFhaWuLp6YmXl5fmq4+Pj3RYfwZlZWURERHBxYsXNV9DQ0NJS0sDwNbWVvP+7OfnR/v27WnQoIGeq36MsrK0Q76lw79RUXD7tmo7dajX1hbs7LSDvi4uqlscCCGEEEIIIYR4aEVFRYwdO5YdO3bwxRdfMH36dH2XBKi6Bfv6+lZ7oLd37960b9+eOXPmoCgKycnJf6kTcUlJCf7+/tSvX5+ffvoJgOLiYtzc3Bg2bBgLFy58qH103WbgwIE6HTc4OBhTU1N8fX2ZMmVKuYFd9TabNm2qNNC7YcMGlixZQm5ubplAb+/evVm4cCEvvPAC6enpfPjhh2zcuJFx48axadMmncbRdYzevXtjZ2fHxo0bARgzZgwxMTEcPXpUs83EiRN57bXXcHNz06xr1KgRZmZmlR6rimzUY1dcXMySJUuYM2cOI0aMYNOmTRjJHY+EEOJ5IYFeIYQQQojnQUxMDJ9++ikff/wxDg4OT8xYzzMLC8jJqXwbQ0MoKYHx4+HLL6GibO7du3cJDw/n3LlzfP3111y4cIGcnBxN9z9vb2+aNWuGu7s7zZs3p1mzZlhaWlb/ixIVunPnDpcvX+bSpUuar5GRkQQHB2NiYoKXlxctWrTA19eXFi1a4OfnJ2FsIZ5iSUlJhIWFER4eTmRkJGFhYURFRWlu92dra4uHhwdubm64urri5uZGt27dsLCw0HPloiopKSnExMSwf/9+oqOjuXr1KpGRkWRnZwOqD4m8vb21gty+vr7y37Yq6sDvgQPaod9r1yA/X7WNqSnY22uHfEsHfxs3frArpoQQQgghhBDiOVNSUsKIESPYs2cPgYGBdOvWTd8laVy8eBEvLy82btzIG2+8UW3jOjs78+abbzJr1qxqGe/w4cN06dKFoKAg+vbtq1k/f/58li5dSmpqKubm5g+8T3BwsE7b3Lp1S+fjFhYWYmpqWmGgV73NBx98UOHzly9fZvny5ZiZmfHdd99pBXFDQkKIiopixIgRmnXJyck4ODjQrFkzIiMjqxznQcZo2bIlZmZmnDx5EoBJkyZx/vx5Tp8+Daj+ZvPqq6+yc+fOcj+7quxYxcXF5b5+fdu3bx+DBw9m4MCBbNmyhRpyobMQQjwPKg30yuUdQgghhBDPiLNnz/Lvf/+bgICAvxzCrc6xnjfp6XDqFBw/rsqmGBpCRX8nMjJSZVS2bFF15a2MkZERLVq0oEWLFowdOxZQBa/Pnz/P+fPniYyMZO/evSxbtoyCggJAFThq3rw57u7uNG7cGGdnZ5ydnXFxccHW1rbCW4OJimVkZBAbG0tsbCxxcXFcu3ZNE96NjY2lpKQEQ0NDnJ2dadasGS+//DLr16/Hy8sLY2NjfZcvhKhGdnZ22NnZ0aNHD631sbGxREZGEh4ezqVLl4iOjua3334jISGB4uJiGjRooAn4uri4YGdnh5OTE3Z2djg4OGBtbS1/uH+ECgsLSUlJIT4+nsTERJKSkoiNjeX69etER0cTExPD7f91knV3d8fV1RU/Pz9GjRqFh4cH3t7eNGzYUM+v4illaQl+fqrlfvd391UvBw5AbOy9X6bMzMp29VUv5Y0rhBBCCCGEEM+ZFStWEBgYyL59++hS1R+dnxBJSUns27ePhIQE5s2bp/Xc5cuXOXXqFKGhoXTo0IGBAwdqnjty5AgRERHEx8fz559/sm7dOmxtbenfv/9fqicwMBAAHx8frfXe3t7cvn2bn3/+mYCAgAfe548//tBpm2nTpul83L+qqKiIOXPmsGnTJubPn1/meRcXF1q1aqW1Tn2HotLdZCsbR9cxAAYNGqT5Gbh16xa7du3SCiKvXLmS06dP4+joSOPGjZk3bx6jR4/G4H8X/1Z2rCdVz549+e9//0vv3r1p27Yt7777rr5LEkIIoWcS6BVCCCGEeEYMHjyY9PT0agmZVOdYz7rMTDhyBA4fhoMHISJCFeJt3RomTYLVq8vuo+7KO3Zs5V15q+Lq6oqrqyuDBg3SrCspKSE2NpbLly8TFRWl6RZ78OBBEhISKCwsBMDY2BgHBwdNyNfJyYkXXngBa2trrKyssLOzo06dOg9X2FOqoKCACxcukJaWRkpKCikpKSQnJ2uCXrGxsZqQl4GBATY2Nri4uNC0aVM6duyIu7s77u7uNG3aFFNTUz2/GiGEvqjn1Z49e2qtLyws5ODBg8TExGiCo/v27SMpKYm0tDTNdsbGxtjY2ODo6Ii9vT329vY0atQIKysrGjVqRMOGDWnYsCFWVlbSif1/iouLycjI0Czp6emkpaWRkZFBWlqaVng3JSVFc4tFQ0NDzbl2cXGhX79+mrC1q6srTk5Oen5lz5HKwr5FRRAfr+rmm5xcNvB7/brqF6vKwr5Nm0Lduo/9ZQkhhBBCCCHE45SVlcWcOXOYM2fOUxPmPXToENu3b+ett96iTp06vP3226xatQqA5cuX89NPP3Hw4EFiY2Pp0qULKSkpvPXWW4DqbzCKoqAoCtbW1po7oiUlJRETE1PhMQ0MDOjQoUOFz1+9ehVQBUFLs7KyAlQh44fZR9dtHuS4f9XHH3/Me++9V+FnAQ0aNCh3fXx8PJMnT9ZpHF3HAJg4cSLbtm1j6tSpREREsG7dOq0Qt7+/P0VFRZw8eZLTp08zduxYtm3bxr59+zA0NKz0WE+yV155hQ8//JAPP/yQUaNGyV2ghBDiOSeBXiGEEEKIZ0h1BnAlzFu+27fh5ElVfuTAATh3TrW+eXN4+WWYNw+6dQMLC4iMhJUrtfdXd+XduhU6d67++mrUqEHjxo1p3Lhxma6RJSUlJCcnc/36da5fv05cXJym22xISAiffvqpJuQEULNmTWxsbLCxsaFBgwZYWFhgaWlZ5qv6+zp16mBubo6JiQn16tXTS4fJ3Nxc7t69S3Z2NgUFBWRnZ5OVlUVWVpbm+9LrMjMzSU9PJzk5WXMrdYA6depgZ2eHtbU1zs7O+Pn5aUJ66gC0hHaFEA/CxMSkTMhX7c6dOyQmJpKYmEh8fDxJSUkkJCSQkJDA6dOnSU9PJzU1lZs3b2rtZ2RkhIeHBw0bNsTCwoK6detSr1496tatq/ne0tJS67GZmRm1a9fG2NhY81XfCgoKyM/PJz8/n4KCAm7dukVubi45OTmarzk5OWRnZ2utz87OJiMjg6ioKK33L0ATfG7UqBGOjo506dIFBwcHHBwcsLe3x9HRERsbG+lW/zQwNr4XzC1PYSEkJKh+MSsd+j1wAK5dA/XPhqXlvXFsbbXDv+7uD3+FlRBCCCGEEEI8IXbs2IGhoSHvv/++vkvRya1btxg/fjyhoaGYm5vTsmVLhgwZwqhRo2jXrh2rVq2iR48eGBgY8P/s3XlYVGX7wPEvi6AgKi4IqIiouIIiuYXlhhaZS5lmuSZmlku+7r2pvJWWpWaL5V6Zma9KGmGLyqviigsWKKi4IgoIyo4ybOf3x/zmxMAAg9ug3Z/rmgvmnOc85z5n8DnjzH3ux9XVlfbt27Njxw41odfV1ZXatWsD0KBBA5544gkAli1bxrRp00rdr6WlJXl5eaWuv3HjBhYWFlhZWektt7GxASAhIeGutjG2TUX2ey9CQ0OxtLTkySefrNB2+/fvx9LSUv07u5t+ivehU79+fQ4cOICDgwNdu3Yt0Wffvn3p27cvABEREQwbNoyQkBAWL17MnDlzytxXZTdt2jSWLl3K1q1bef31100djhBCCBOq/FctIYQQQoiH5H//+596l661tTUvvvgi1tbWHDt2jOjoaOzt7Rk4cKDavug0UD4+PvTu3VtdpygKn3/+ORYWFrRs2ZI+ffoYHcedO3cICgpiwIABJCUl8dtvv+Hs7Ez//v25efMmv/zyC+bm5gwZMoQaRSqNFRYWEhoaSvXq1enYsaMaR2hoKH/99VeJWIqua9OmjV6MhvqKi4tj27ZtTJ48mejoaIKCgnBxcWH48OF6iZtZWVls2LCBq1evMnz4cFq1avVIJ8vcvg2HD8PBg3DoEOzfr80bcXMDX1+YPRt694b//9xQT8uWULcu3Lz5d1XeSZPgww+hWrWHfyzm5uZqtUdDFQjy8/NJSkoiKSmJ+Ph4kpOT1Uq1KSkppKWlERsbq5cYm5WVVer+LCwsqFevHjY2NmqiL2gThatWrarX1srKCltbW71lubm5akVcnYKCAjIyMlAUhbS0NPLy8sjKylKTwEpjbW1tMCHZw8MDBwcH6tevj5OTk1qluJopXiAhxD+WtbW1WnW9LBqNRq1Cm5SURHJyMmfPnuXmzZtqcuvFixfJyMhQE1/T0tJKJLsWZ2dnh6WlpfqzRo0aWFhYqEm/huLVfZlUVGZmJvn5+SWWF40hNTVVTeDV/SxLlSpV1GTkoknLtWrVwtXVlbp169K6dWu96sV169Z9pN97iAqystK+MRs/vuQ6jQauX9ev6nvpkvZNnS75V6dowm/RxN/evcHA37sQQgghhBBCVDanTp1Sq9Q+CjZt2sSdO3eYNWuWuqxp06ZcuHCBLl26sG/fPvUz4+joaOLi4sjIyCi338mTJzNhwoS7jqu081dQUACAo6PjXW1zt23K2u/dSktLY/ny5WzatKlC2xUUFDB//nx++eUXqlevflf9FO+juHXr1jF27Fi++eYbOnfuzP79+w3OotSuXTvCw8Np0aIFmzZtMpjQW3RflV2NGjXw8vIiMjLS1KEIIYQwMUnoFUIIIYT4f127duXtt98mKiqKixcvqtU3O3XqxOjRowkKClLbFp8GatCgQYwaNUqdCmru3LksXLiQEydOMHHiRKMTekNDQ3n99dc5f/48S5cu5dy5c9SqVYuZM2fi5+dH1apVKSgoYPPmzQQFBakfQkRHRxMQEEBgYCArVqxQk3Dnzp1LkyZNmDp1aolYiq7r3LmzutxQX8HBwfj7+5OcnIyiKERGRpKcnMzcuXO5du0a77zzDqBN0unSpQtr165l1KhRakKwj48Py5Ytuw+v0oOXnw8REX9X4D1wQJsLokvgHTlSm9fRoEH5fZmZabf573/BxQU2bIAyZvIyOUtLS5ydnXF2dqZ9+/ZGbZOXl6cm92ZmZnL79m00Gg3p6enk5eWRlJTEnTt3yMrKUqseGEr4MpSQW7VqVerXr19in7Vq1cLMzAx7e3s1AU2XJFyjRg2qVKmiVqHUJe4aSjwTQohHjbW1tXpjRkXoEnwzMjK4c+cO2dnZ5ObmqmOzblzOyMigoKCA9PR0CgsLS71ZQrd9cQ4ODiVu2AD0KgHb29urCcFVq1alWrVq6hhuY2ODtbU1dnZ2alVhGb/FPbG2Lru6b06ONrG3eMJvSAhcuADp6dp2hpJ9dQ8XF+0UDEJUArobPzQajTqm626IM9T29u3bJZZbWFjo3TgK+jdy6G7+sLe3N9hWCCHEvdON4br33UXfl+tubDbE0I3ROrrPUgyxt7dXf9fd3Ke7MVv3nl0IIR6EqKgonJyc1O9VimvQoAG7du1ix44ddO/enaZNmxIeHl5uv5aWluVWZD169CiTJk3SW7ZkyRK6d+9Oo0aNKCgoQKPR6M3Spps5qXXr1iX6M2abs2fPGtWmIvu9W//617/o2LGjXqLr+fPnycnJYdu2bdSqVYtevXqV2G7GjBlMmzYNLy+vu+6neB9Fffvtt2zevJk///wTHx8f3njjDSZOnEhwcLDB47CxsWHgwIF88803BteXtS8hhBCispJPm4UQQggh/p+NjQ0fffQRAwYMYM+ePWqVvISEBNq2bYu7uztgeBqonTt38vXXXzNy5Eg6d+7M6tWrWbhwIU888QQDBgwwOobu3bvz5ptvMm3aNFxcXNRpoczNzVm0aJFa3a5p06YsWbKEwsJCzM3Nad26NfPnzycwMFDtS1EUVq9ezdatWwH0Yim+rmiMhvrq378//v7+LFq0CA8PD6ZOnQqAt7c3P/30k5rQu3jxYjQaDU899ZS67auvvqq2r6zy82HpUm3uxqFDcOeOtiBbt27wxRfw7LPaXI270bs31KgBn34KxQrQPhaqVKmCg4MDDg4Opg5FCCFEKXTJsUKIYqpWLTvhNzVVO01DQoJ+sm9MDPz/F6pUqaKdksHZ2XDCb+PG2qkahChHbm4uycnJ3Lhxg+TkZLXKuu7mubS0NHWZ7vecnBzS0tLUmzJMRZfwa2trq86MUfyhq66ue9SuXZt27do9MtXjhBCiLIWFhaSkpHDr1i1u3bpFeno6WVlZpKamkpWVRWZmJllZWeosGkWX6cZ6QP1ZGelm7zA3N6dOnTrY2dlhb29P9erV1UfNmjWpWbOm3jLdjdZ169ZVtxNCPDienp5s2LCBzMzMR+Lfm4WFBefOnSMvL0+9EbioefPmERoays6dO6lWrRo//fSTUf0eP36ckJCQMvc7cuTIEgm9uu+EWrVqBWhnLWzWrJm6/ubNm4DhxFpjtjlz5oxRbSqy37uVnJzM7t279Zalp6dz+/ZtpkyZQps2bUok4q5evRovLy+975Mq2o+hPopav349fn5+AIwdO5YTJ06wbt060tLSqFWrlsFtWrZsqX5/V168lVlGRgYnT55k1KhRpg5FCCGEiUlCrxBCCCFEEc8//zytWrXi008/xd/fHzMzM3788Ue9/0AbmgYqISFBbyqoFi1aEBQUxMCBA5kxY0aFYqhZsyYAHh4e6rIWLVrotWnZsiUajYb4+HgaNmwIoHfHNoCZmRktWrTg5ZdfZvXq1XqxFF9XPMbifQFqNY6WLVuqy1q3bs3OnTvV5xcvXiQ5OZnc3FysrKywtbUlLi6uQsf/MBQUwF9/afMzDh2CXbugWjV46in47DPo0weaNLk/+/L3h3Hj7k9fQgghhBDiIbK3h/79Da9LTdWv6hsfr038DQmBs2dBVx3PygoaNvw7wVd315ibG7i6grn5QzscYTo5OTnExcXpPU6fPk1iYiJJSUkkJSVx69YtvW3MzMyoVauWmgilS4p1dHSkZcuW1KpVC2tra71KudbW1tStW1etdq6rSmaoKmNp1XUNVe4tOttGRkYG+fn5aiJxZmamuo1uto6iScjXrl3j9OnTesnIRau/V6tWDQcHB5ycnKhXr576u4ODA66urjRq1IhGjRpRp06d+/JaCCGEsfLz87lx4wYJCQkkJiaSmJjIjRs3iImJ4datW3oJvMXHcB1DCa81atSgTp06NG7cGDs7O/UmB9B+Jmhubq7OcKGbyaJopXRdv4aYm5urnysWVVq1dihZ8TctLQ1FUdRZPHRju26sz8vLIyUlRU1GzszMJD09nevXr+slLOsexVWpUoU6deqoj9q1a1OnTh1atmxJ/fr1cXR0xMnJifr161OvXr3SXyAhhEFDhw5l+vTpfPrppwQEBJg6nHK1a9eO7OxsVq5cyeTJkwHtOPTjjz/i5+fHggULWLVqlfrdRGFhoVH9xsTE6BUsKc7S0pJZs2YxevRog+v9/f354IMPOHTokF5ibXh4OO3btzeYPGrMNsa22blzp9H7vVs7duwosWzWrFl8//33XLt2rcS67du3oyhKiWTTivRTWh+hoaF0794dgMjISL3E5YEDB7JixQpu3LhRakLv9u3bGThwYLn7Krqfymjx4sWYmZkxZMgQU4cihBDCxCShVwghhBCiCDMzM2bOnMnYsWP57bff6NevHyEhIbz99ttqm/KmgQJYvnw5Xl5e9O7dm40bN1K/fv17iqv49NW6u9VLm0avaBxDhgxh0KBBJWIpui4xMfGuYrSwsFCrBgP07NmTLVu2cPDgQXr16kVubi59+vSpcL8Pgq6gmu6RmgoODtC9O7z3HhR5ie+rUmYzFEIIIYQQjzJ7e/D21j4MKZ7wW7TC73vvadtYW0ODBiWr+jo5aSv+NmkibyYfEXl5eVy+fJmYmBjOnz9PbGysXvJuYmKi2rZq1ao0bNgQDw8P2rRpQ8+ePalXrx7Ozs5qQquDg4PBhKyHwdrausQNnqUljt2tnJwcbt26RUREhFqVODExkeTkZGJjYzl27Jj6XMfGxobGjRvj4uKiJvk2a9aM5s2b07x581K/3BdCCEMyMjKIjY3lypUrXLlyhYSEBOLj40lKSuLatWvqzRZFP/Oys7PDycmJ5s2bU6dOHdzd3fUSU3UVaOvUqaNWqa0sLCwsyhzLH+SsS7qbPG7evKmXAF00ITouLo7du3dz48YNNBqNuq2VlRUODg40bNgQBwcHnJ2dcXR0pGHDhjRu3BhXV1dcXFywsrJ6YPEL8aixt7dn4cKFzJo1i27dutG7d29Th6QnPT0dQE34f/nll5k7dy4zZswgJyeH559/noCAANatW8eVK1cAbYGVYcOGERERwf79+9FoNGRlZaEoCnZ2dly/fh1A7z338OHDGT58+F3H6ejoyKRJk1i8eDGjRo3CzMyMnJwcgoOD2bRpE+b/f2PmrFmzSElJYe3atUZtY2yb8vZblK66e9Gb5gy1KWt9eUJCQvj4448ZMWIEy5cvB7Q3i0RHR7Nq1ap77qNt27Zqou2gQYPYvn07X3/9NQBhYWF4enrSvHlzYmJi+Prrrxk9ejReXl6A9vu67Oxs5s6dW+6+zM3NK21C765du1i0aBGfffaZyf4vKIQQovKQhF4hhBBCiGKGDx/OvHnzWLp0Ka6urrRp00atbATlTwMF0L59e9566y1WrVpFhw4dOHXqlFpt42Fq3749J0+eZM6cOSViKbrufsU4btw4Lly4wIQJE1i4cCEfffQRzz777H06moopmsC7Zw/cuqWdDblLF5g9G3x9oUMHyZEQQgghhBAPQFkJv2Ul+8bGaqeTAKhaVZvYWzzh180NmjYFSWB86JKSkjh16hTnz58nJiaGc+fOcf78eS5fvkx+fj4ATk5OamXZp59+GhcXF70k1AeZNPWoqFq1Kg0aNKBBgwZltrtz545ecvTVq1e5evUqV65c4cCBA1y+fJnc3FxAm4zm7u6Ou7s7/fr1o1WrVri7u2NhYfEwDkkIUQlFRERw+fJlYmNj1Z9XrlwhNjZWTX4CqFevHk5OTjRo0ID69evj5eVF/fr11eRRJycnnJyc9CrkCuPZ29tjb29PEyOnwkpJSVGrIut+xsfHc+PGDc6cOcO+ffu4evWqWk3ezMwMJycnmjRpoib5Nm7cmMaNG9OsWTOaNm36IA9PiEpp8uTJHD9+nEGDBhEYGMgup+3KAAAgAElEQVQzzzxj6pAAOHbsGO/9/82N69evx93dHT8/P3bu3MmgQYOYNWsWs2bN4uTJk9jZ2eHh4cHYsWP5/vvv8fb2ZsaMGXz55Ze8+uqrDBw4kK1btxIWFsYnn3wCQHBwMJ07d2bgwIH3JSFy8eLFWFpaMmDAAPr27UtCQgJz586lQ4cOapvg4GBSUlIoKCjAwsLCqG2MafP888+XuV7n999/Z/369QD8/PPPdOzYkeeffx5HR8cSbfbu3WtwfXlOnjzJoEGDyM7O5ujRo3rrqlatalRCb3l96JKyQVuIZsqUKXz++eecPn2apKQkfv75Z8zNzcnKyuK7777j888/p2fPnnTq1InatWuzd+9e9bu6svZVWkV9U/v9998ZMmQIw4cP56233jJ1OEIIISoBs6J3lxpQ5kohhBBCiMfVkiVLmDlzJn5+fixevJg2bdqo677//ntGjx7NF198oU4DBX9PBeXv78+WLVsYOXIkO3fuxM/Pj9WrVzNu3Dij9v3NN9/g7+/P2bNnadGiBQD//e9/eeWVV9TKIJs3b2bYsGF6bWJiYmjRogUrVqxgwoQJaDQaNQ5AL5aRI0fqrTM3N9eLsXhfAO+//z4BAQFcu3ZN/eJ1zJgx/Prrr3qVk5YuXUqXLl2oW7euGtvDcOkSHDwIhw7Bb7/BtWtQvbo2gdfXV/vw8pJZjYUQQgghRCWWlwdxcdo3t/HxkJCgn/R75Qroppm1t/+7mq+bm/YNr5sbNG8ONWqY9DAeB/n5+Zw7d46tW7cSHh5OdHQ0ly5dAqBWrVo0bdoUNzc39dG6dWs8PT2pIef+oYqPj1dfm0uXLhEVFcXvv/9OQUEBVapUoXnz5nh7e9OmTRtat25Np06d7nkGHSFE5WJoHCg6Ztvb26tjtZOTE87Ozurz5s2by7j9iEpNTSU+Pp6EhAT1tdc9Ll68SFpaGoDedbpNmzZ6124hHmf5+fn4+/uzceNGFixYwJw5c0wdUrliY2MxMzPDxcVFb3lmZiZ2dnbqc41GU2JGiQepoKCAmzdvGnwPmZWVRV5eXokq6GVtY2wbY/p4nJ05cwZHR8cS51aj0XD16lVsbGzKvUGwssvPz+fjjz8mICCAUaNGsXr1ar3iQkIIIR5r64Expa2Uq4EQQgghhAFvvPEGCxYs4ObNm3rJvGB4GqhTp04RGBjIunXrUBSFlStXMnLkSPr27UvdunWpW7euuv348eO5du0a3377rcEPYzIzMwH0ppjTTUGlk52dXaKN7vebN28CqHGMGDECMzMzvViKryseY/G+QDsdIaBWQNKt12g0KIqCmZkZK1asIDAwEG9vb3Jzc0t82HY/xcdrk3dDQmDnTm0xM1tb6NoVJk0CHx/o3BlKKaIshBBCCCFE5VOlyt9VeA3JzdXeuVY0yTc+Xvvz5ZdBV7zB3v7vfoom/bq5QcuW2jfOlUVOjrYasQkpikJMTAxhYWHqIyoqiry8PDp06ICHhwcTJ07E09MTT09PqbJbiTg7O+Ps7Ky3TKPREB0dTWRkpPr4448/1BtRGzZsSMeOHenatStdunTB29tbKm8K8Qi4efMmf/31FxEREURERHDq1CnOnj2rTiGuu7G8RYsWdOvWjf79++Pm5kZVE19jxIOhq/xb/HNbndTUVM6fP09wcDAxMTHs37+ftWvXqpV9a9euTZs2bWjXrh2enp60b9+etm3bUq1atYd5GEI8MJaWlqxfv5527drx73//Gz8/P9q1a2fqsMrUuHFjg8uLf7/wMJN5QTtjY2lJtdWrV6/wNsa2MaaPx1mrVq0MLre2tqZ58+YPOZr7788//2TChAlERkaydOlS3n77bVOHJIQQohKRCr1CCCGEEKV488038fDwMDjFzZkzZxg0aBAxMTEAtGnThg0bNuDl5UVOTg5ubm588cUXxMbGkpGRoU4lBdCsWTMuXrzIkiVLmD59ul6/R44c4c033yQiIoLRo0cTEBBAbGws06dP5+TJk0RFRZGens60adMICwtj6NChLFiwgJSUFJYsWUJgYCBt27Zl0aJF9O7dGzc3N7p3787gwYP1YtHFqFt36tQpNcajR4+W6Kt69eqMHTuWS5cuMW7cOD744AP27dvHG2+8QUZGBv/5z39499132bFjByNGjFATjgF8fX3ZsGFDhaZxKk1iIgQEaJN4L12CatWgQwfo1k1bkOzpp8HK6p53I4QQQgghxKMnJ+fv5F5DjyLTi+sl/BZ9ODlpfz7MZJaJE7WViP/zH/D0fCi7zMnJ4ciRIxw8eJCjR48SFhbGrVu3qFq1Kt7e3mqSp6enZ6mJQuLRk5CQQGRkJH/99Zf6uickJGBpaUn79u3p0qULXbp0oUePHo98tS9hnEuXLrFgwQLef/99GjZsWGn6Etrzefz4cb0E3vj4eACcnJzUBMxWrVrRsmVL3N3dS1TwE6I4RVGIi4sjJiaGmJgYTp06pSaHZ2VlYWFhgbu7u/r31b59e3U6dyEeZadPn6Z9+/a89tprzJ8/n0aNGpk6JCH+sa5evcp7773Hd999R+fOnVm7di2tW7c2dVhCCCEevjIr9EpCrxBCCCFEKfr27cuWLVuoVatWqW1KmwYqPz+f+Pj4EstBWy0oKCiIqlWrMmDAgPsed/E4CgsLSUxMNBhjaevu1u7du7l+/TrdunUjMTGR5ORkAgMD8fDwuKtpvZKSIDQUDh7UVuM9eVKbwOvrq31062byYl5CCCGEEEI8Gu7c0SbOGkr2vXAB0tP/bls04dfX9+/fXVzgfk8B2rev9o49gAED4P33H0hi76VLlwgJCSEkJIQ//viDzMxMnJyc8Pb2plu3bvj4+PDEE09IJcd/mPj4eMLDwzl06BAHDx4kPDxcvQHW19cXX19f+vTpU+bnAuLRFRgYyJAhQ/jtt9/w8/OrNH3902RnZ/Pnn3+q/xb379/PjRs3sLS0xMXFhdatW+Pt7Y23tzdPPPEETk5Opg5ZPIZ014Pw8HCio6OJiorizJkzKIqCk5OT+l7B29ubzp07U0WmBBOPmI0bN/Luu++SkJDA6NGjmTFjBu7u7qYOS4h/jHPnzrF48WI2bNiAs7MzH374IcOGDcPMzMzUoQkhhDANSegVQgghhKioiIgIVqxYwcqVK+973xkZGQQEBLB48WIs7/eX4SYUHh7OgAEDuHr1KhYWFurytLQ0tmzZwvjx48vtIzMTjh7Vfp8fEqJN4DU3h/bttXkEPj7Qv/+DPAohhBBCCCH+oVJTtcm98fH6ib+//w5ZWdo2VapAo0baSr7OziUr/Lq6at/AV0STJnDlivZ3S0soKIBnn4UPPgBv77s+nPT0dHbs2MEvv/zCnj17uHnzJg4ODvTu3VtN1LxfNzaKx8ft27c5ePCgmvwdERGBubk5AQEBvPjii1I96zF08+ZN6tatW+n6etxlZGSwd+9eQkJCWLlyJfn5+Tg7O9OlSxeefPJJtVK63GQhTOnmzZuEhYURFhbG4cOHOX78OFlZWdjZ2TF9+nT69OlDp06dHqvPd8XjLTc3l++//56PPvqIy5cv0717d8aNG8fgwYNlvBXiAcjJySEwMJA1a9Zw4MAB3NzceOeddxg1apTcGCKEEEISeoUQQgghjBEeHs6sWbPw8PBg3759/Pzzz7i6ut73/YSEhODj40O1hzmN7UPw7bff4u/vz4oVK/D19aVx48Zs2bKFyMhI3nnnHWrWrFlim6wsCAv7O4H3zz/BzAxatNBW3/X1hT59QIohCSGEEEIIYUK6ZN+iD13i75kzcPu2tp2VFTRs+HeCb/HE3yZNtG/4dRRFO+VGbq7+/u4ysffWrVsEBQXx008/8b///Y/CwkJ69uxJ37598fX1xdPTUyogiQpJTk5mz549TJ06lcTERFq1asXgwYN58cUX8fLyMnV4Qjwy8vPzOX78OLt27WL37t0cPXqUwsJCvLy8mDlzJl27dpWbLESlV1BQwOnTpzl8+DAfffQRcXFx1KxZk549e9KnTx/69OlD8+bNTR2mEOUqLCzkjz/+YO3atezYsQM7OztGjhzJ2LFj8XwAM2UI8U/z119/8c033/DDDz+QlZVF//79ef311+nbty/mFb0BVgghxONKEnqFEEIIIYxx/Phxevfujbm5OWvWrGHIkCGmDumRoigKy5YtIzg4mCNHjmBpacmnn37KmDFjsLKyArTf8x8+DAcPwqFDEBoKeXl/z+Tr6wu9e0Pt2iY+GCGEEEIIIYRxCgvh+nVtld3Ll7WPK1f+fn79OuTna9va2mqr+PbsqU3urVkTxo0rvW8jE3tzc3P5+eefGT58OJaWlvTt25fBgwfTv39/7O3t7/MBi3+iwsJCDh06xE8//cS2bduIi4vDzc2NUaNG4e/vT8OGDU0dokn873//Iy4uDgBra2tefPFFrK2tOXbsGNHR0djb2zNw4EC1fXx8PH/88QfXrl3Dx8eH3r17A9rPE0JDQ/nrr7+wsLCgZcuW9OnTp0Kx3Llzh6CgIAYMGMB3332Hs7Mz/fv3x8LCghs3bvDLL79gbm7OkCFDqFGjBqB9XUNDQ6levTodO3YsN5ay1hnqKy4ujm3btjF58mSio6MJCgrCxcWF4cOH6yVzrFixgqtXr9K8eXM6depEq1at9GY+elRlZWXx66+/EhgYyO7du0lPT8fFxUVNfOzdu7dUNBaPtLNnz7J79252797Nvn37yMzMxNXVlf79+/PSSy/RrVs3SdwSlV5iYiLr169n7dq1XLhwAVdXVwYMGED//v3p0aOHVKAWwgiFhYX8+eefBAcH89///pdz587RvHlzXn31Vfz9/WnUqJGpQxRCCFH5SEKvEEIIIYSx8vPzMTc3lw9b71FeXh5VqlQhPx8iIv6uwHvgAGg0+gm8PXuCfH8jhBBCCCHEYyovD5KTtdV8ddV9Q0K0P69c0SYEl6doYu/778MTTwBw/vx51qxZw/r167l16xY//PAD/fr1w87O7sEek/hHUxSFY8eOsXXrVjZs2MCtW7fw8/Nj/PjxPPfcc49FIqaxbt++TadOnYiKiuLixYu4ubmp61q1akVQUBDu7u4A7N27l02bNvHmm29y4cIFxo4dy6hRo/jqq6949913adKkCePGjePEiRNMnDiRo0ePGh1HaGgor7/+OufPn2fp0qXcuHGD5cuX4+fnx7PPPsu+ffsoKChg8+bNPP/88/zyyy9ER0cTEBBAYGAgK1asYMKECQBlxlLaOkN9BQcH4+/vT3JyMsuWLSMyMpLk5GR27NjBhx9+yDvvvANAamoqp0+fpkOHDowcOZLt27fTsWNHfHx8WLZs2f16qR6arKwsduzYwdatW/n999/Jy8ujV69e9O/fnz59+tCiRQtThyjEA5GXl0dYWBi7du1i27ZtREdH4+TkxODBgxkyZIgk94pKT1EUjh49ys8//8zPP//MuXPnqF+/PgMGDGDgwIH06NEDW1tbU4cpRKWSlZXFvn378Pf3JykpiZYtWzJo0CBeeOEFOnbsKLPDCCGEKIsk9AohhBBCiIenaBLvf/4DOTnaBF4fH+jWDfz8QG5IFkIIIYQQQvD99/Daa8Yl9QJYWEBhIbe6dmVuYSGrjh6lYcOG+Pv7M3bsWKl8JB46XXXo1atXs2fPHho0aMC//vUvJkyYgI2NjanDeyiCg4MZMGAAa9asYdz/V9xOSEhgypQpbN26FdAmO7Rr147IyEg1GWjcuHGsW7eOwsJCHBwc2Lp1Kz169ABg4cKFvPvuuxWKY9myZUybNo2tW7fy0ksv8c4777Bo0SJ++uknXnzxRQDmzp3LkiVLuH37Nubm5pw6dQpPT081CVdRlDJjqVevXqnrivcFqDGEhISo1Yi9vb0xMzPjxIkTAPz73//mww8/BODkyZN4e3uzbNkypk6dWqHjN7XQ0FC+/vprgoOD1STeIUOG8MILL1CnTh1ThyfEQxcdHc3WrVsJDAzk9OnTODk5MXLkSN58801cXV1NHZ4Q5bp06RLBwcFs3bqVw4cPY25uTvv27fH19cXX1xcfHx+qVatm6jCFeKju3LlDeHg4hw4dIiQkhAMHDqDRaAgICGDo0KG0bt3a1CEKIYR4dJSZ0CtzJAghhBBCiAopLIQzZ+DQob8r76amQv368PTT2qq7Pj4we7b2IYQQQgghhBAGHTwI5uYlE3qtrLR3ChYWgo0NOc2bszMtjY2xseR27crUhQtZ0aMHK0wTtRAqKysrhg4dytChQ9VlSUlJzJs3j5UrV1KrVi0WLlzImDFjTBfkA9a/f3+++eYbxo4di5OTE/369WPs2LEEBwerbebOnUutWrX0KvutXbuWtWvXArB7926GDBnChQsX6N27Nxs3bqxwHDVr1gTAw8MDgHbt2un91K3TaDScP3+eFi1aYG1trdeHmZlZmbGUta54X4Ca6NSyZUu9GH799Vf1uYuLC3v27KFXr164uLhQpUoVvfaVUUpKCosWLWL16tXk5ubyyiuvMHHiRDZv3mzq0ISoNFq3bk1AQAABAQHqsvz8fIKCghg7dix79+7F3d2d2bNnM2bMGKneKyodNzc33n77bd5+++0S665du8bWrVsJCAjgypUrmJmZ4e7uTseOHenUqRMdO3akffv2VK1a1QSRC3F3cnJy+PPPPzl+/DjHjx/n2LFjnD9/HkVRaNKkCT169KBnz5706tWLbt26MVu+/BJCCPEASUKvEEIIIYQol25W3JAQ2LMHbt2CevWgc2dt0q6vL3ToADKDkBBCCCGEEMJoFy5AQQFYWmoTeC0toVUr6N4dOnWi4Ikn+Py335g7bx4tW7Zk8bp1apVLISorBwcHli5dysyZM1m4cCH+/v5s2bKF1atX07BhQ1OH90AMHz6cefPmsXTpUlxdXWnTpg2Wln9//WRhYcG5c+fIy8ujSpUqJbZv3749J0+eZM6cOaxatYoOHTpw6tQpateu/TAPo9xYHkSc48aNo3Xr1ixcuJC9e/fy0Ucf8eyzz97HI7q/Pv30UxYuXIiFhQX//ve/8ff3lwq8QhjJ0tKSwYMHM3jwYKKiovjiiy+YMGECn3/+OZ988gnPPPOMqUMUwigNGzZk1KhRjBo1isTERDX58fjx4wQEBJCamkqVKlXw9PTEy8uLtm3b0rZtWzw8PHBwcDB1+EJw48YNTp06xenTp4mKiuLkyZOcOnWKvLw8ateuTadOnXj55ZfVBPX69eubOmQhhBD/MJLQK4QQQgghStAl8B48CHv3wrVrUL06dOkCM2dqE3i9vLTFtIQQQgghhBDirlhZwSuvaO8U7NRJ+5+M/69yeeHCBUaNGkV4eDjz5s1j9uzZBhMBH5QDBw4QGhqKh4cHAwcOVJf/+uuvZGRkqM/j4uKYNGkSNjY2ANy6dYugoCCuXr2Kp6cnffv2pXr16g8kxsTERM6ePUuPHj3KbZuZmcmPP/7I5cuXadasGa+++qoas05WVhZbtmzhypUrdOnShT59+pR6zsvbd1nn6fjx41y4cKHENl26dKFJkybqc2POpTHHpRMREcH+/fuxsrKiX79+Dzy51tHRkS+//JJXXnmFsWPH0rZtWz777LPHslqvlZUVU6dOZebMmcycOZPFixfrrW/Xrh3Z2dmsXLmSyZMnq8vT0tKoVq0aW7ZsYeTIkXz11VcMGDAAPz8/tm3bxrhx4x7qcWg0mjJj2bBhw32P09LSkm+//Za6desyYMAAg5V+K4uffvqJefPmMXXqVGbPnk2NGjUe6v5zc3M5cOAAO3bsoE+fPjz33HP3rW/dmH/27FmGDBmiN+6XpbTxDEqOacUVHeNmzJhh8FpR3rhszP6NvS4Z007XplatWqX2U951sjjd2PzCCy/c87is0WgIDQ3lr7/+olu3bnTu3BkLC4t7jvFBaNOmDatWrWLatGnMmTOHZ599ljNnzlT6Ct1CFOfo6Ej//v3p37+/uuz8+fNqkm9kZCTbt2/n1q1bANSrVw8PDw81ybdt27a0bNkSe3t7Ux2CeMylpKRw9uxZTp8+zebNm4mMjOTmzZsA1K1bF09PT55++mmmT59Op06daNasmYkjFkIIIQBFUcp6CCGEEEKIf4Dr1xVlyxZFGT9eUVxcFAUUxdZWUXx9FWXRIkU5cEBRcnNNHaUQQgghhBDin+DAgQNKnTp1FC8vL+XUqVMPff8nTpxQ+vfvr2g0GsXa2lrJzs5WFEVRzpw5o5iZmSmA+hg2bJi63Z9//qm0bdtWOXLkiJKdna18/PHHiqenpxIfH39f40tKSlKmT5+uVKtWTZkyZUq57c+ePas4OjoqzZs3V6ysrBRAadq0qZKQkKDXplmzZsqvv/6qZGZmKj/++KPi4uKihIaGVnjfZZ2nwsJCpWnTpnrrdI/w8HC1D2POZWnHVVxycrLi7++v+Pn5KbGxseWerwfh9u3byvTp0xUzMzNl2rRpSkFBgUnieJAyMjKUmjVrKh07diyxLicnR2nUqJFiZWWlfPLJJ0p0dLSyefNmZciQIcqdO3eUJ598UiksLFQURfs3Uq9ePWX79u3q9q+//rri5+enJCYmlrr/zz77TAGUiIgIRVEUZc2aNQqgHDt2TG2zbt06vTaRkZEKoHzwwQeKoijlxlLWuuJ9KYqiTJ8+XQGUS5cuqcv69eun2NnZqf18/fXXyt69e5XIyEglJiZGycjIMOp8P0yZmZnKqFGjFDMzMyUuLs5kcYSHhyvjx49XAGXNmjX3rd+iY/57772nN+6XpazxrPiYVlzxMc7QtaK8cdmY/Rt7XTKmXdE2pfVT3nWyqPs9Nt+4cUNp0qSJsmbNGiU5OVmZOXOm0q9fvxLtKhLjw7R//37FxsZGWbFihalDEeKBSElJUQ4cOKCsWrVKmTJliuLj46NUr15d/Xdob2+veHt7K0OGDFFWrVql7N69+76/hxaPr5SUFOXEiRPKli1blEWLFinjx49XfH19FTc3N/VvzMrKShk5cqSyaNEi5ZdfflEuXrxo6rCFEEL8s32nlJGzKwm9QgghhBD/UAkJfyfxgqLY2CiKj4+izJ6tKLt3K4pGY+oIhRBCCCGEEP80hw8fVmxtbZUXXnjBqISqB8HPz09Nyrt+/bq6/PXXX1f27t2rXL16VX3cuXNHURRFKSgoUNq1a6fMmjVLr69OnTopffr0ua/xHTt2TImIiFAAoxJ6/fz81ATGpKQkZdy4cQqgjB07Vq+Nv7+/3najR49WnnrqqQrvu6zztGvXLmXKlCnK5cuXFY1Go2g0GmXXrl2Kq6urur2x57K04yrq8uXLSt26dZURI0aUe54eho0bNypWVlbKW2+9pSZ0Pk4mTJigfPXVVwbXRUdHK+7u7mpSRZs2bZSTJ08qd+7cUZycnJRhw4YpW7duVZYsWaLMnz9fb1td0uKSJUsM9n348GGlXbt2CqCMHj1a2bt3r9KhQwcFUPr166dERUUphw8fVrp06aIAytChQ5UNGzYoL730kgIobdu2VXbs2FFuLKWtCwsLK9HXvn371CSScePGKQkJCcqmTZuUGjVqKIDyn//8R8nLy1O2b99eIhHT19dXL+H+Xly/rij38qeWkJCgdOjQQXFwcFB27NhxX2K6F7rx534m9BYd8wsLC/XG/bIYGs8MjWnFGRrjDF0ryhuXjdm/MWOpMWOusTGXNf4Xdb/H5oKCAqVbt27KgAED1GX5+flK48aNS7Q1NkZTmDt3rmJubq5MnDhRyc/PN3U4QjxwhYWFysWLF5Xff/9d+fLLL5UpU6Yozz33nFKlShX1mlinTh2lc+fOytChQ5Xp06crn3/+ubJ9+3blxIkTZd7oIx4vCQkJyvHjx5Vt27Ypn332mTJt2jRl6NChSufOnZXatWvrJe26u7sr/fr1U6ZOnaosX75c+eOPP5RLly49lu+9hRBCPNLKTOi1vOcSv0IIIYQQ4pGQlAShoXDwIBw6BOHhYGkJ7drB7t3w1FPq7LZCCCGEEEII8dBdu3aN/v374+vry5YtW7C0NM3H11FRUTz11FMAODs7A5CYmEhkZCTz5883OCV4WFgYERERzJkzR295p06dWL58OeHh4Xh7e5e6z4KCAgIDA3n55ZfLja9jx47k5uYadSzh4eEMHz4cT09PQDvN8fvvv88333zD4cOH1XYJCQmkpqbqbWttbY1Go6nQvss7T9WrV2fZsmWYm5ury4KCghg8eLD63JhzCZR6XDq5ubkMHTqU2rVrs3LlylJjfpheffVVqlWrxtChQ2nWrBn/+te/TB3SfXXx4kU++ugjg+tatWrFuXPniI2NxczMDBcXF3Xd1atXKSwsJDExkZdeeqnEtlFRUQQFBVG1alWDfXft2pW//vpLb5nu76SoI0eO6D0fMWJEiTZlxVLaus6dO7N169YSfV28eFHv+bBhwxg2bJjeMltbW86fP09iYiK3b98mOzubwMBAvvvuuxL/Bu7GrFmwfz+MGgWvvAJt2hi/bUpKCr169aKwsJCwsDCaNGlyz/HcK911wczM7L71WXTMNzMzU8f98hgaz6DkmFacoTHO0LWivHHZmP0bc10yZszVaDTlxlze+K/zIMbm/fv3c/DgQYKDg9VlFhYWjB49muzsbGxtbYHyr1Gm9sEHH+Dl5cWIESPQaDSsWbPG1CEJ8UCZmZnh5uaGm5ub3vL8/HxiY2O5cOGC+rh69Sr79+8nLi6OxMREtW3VqlVp1KiR3qNevXo4Ojri5OREvXr1cHZ2pkaNGg/78ISR0tPTSUhIICkpiYSEBG7cuEFSUhKHDx/m2rVrxMXFkZOTo7Z3cnKiUaNGNGzYkCeffJJRo0bRrFkzmjVrRuPGjbGwsDDh0QghhBD3hyT0CiGEEEI8pjIz4ehRCAnRPk6eBHNzaN8efH1h0SLw8YFq1UwdqRBCCCGEEELAuHHjqF+/Phs3bjRJMm9oaChRURAn9wMAACAASURBVFHExcVx7NgxVq1axRtvvAHAl19+ydGjR2nUqBFNmjRh/vz5jB49Wk0qO3fuHKCdEa+ojh07AnDw4EGDCb35+fls3LiRDz/8kBs3bhiV0FsRrq6udOjQQW+Zk5MT3t7eeuf4xRdfZP78+fzwww+MGDGCrKwstm/fzueff16h/ZV3nrp27arXvrCwkG3bthEYGKguM+ZcjhgxotTj0nn33Xc5fvw4a9euVZO5KoMXXniB999/nzlz5jxWCb0RERG4ublRq1atMts1bty4xDLd32LRJN+iNBoNR44cYfHixfceaDnKiqW8OCsqPDycMWPGcP36dZo1a6Yu79mzJ1u2bLkv+wC4dg0++QQWLoSWLWH0aG1yr4GXQqUoCsOGDeP27dscOXIEJyen+xbPgxAfH88ff/zBtWvX8PHxoXfv3uq6mJgYwsLCiIyMxMfHhxdeeAEwPOY7OTkxYMAAo/ZZfDwDw2NacYbGOEPXivLGZWP3X951yZgxV5cIV1bM5Y3/OsaMzWW9noZs27YNAA8PD73lbdu25bfffmPIkCFA+deoyuDFF19k69atDBo0CE9PTyZPnmzqkIR46CwtLWnatClNmzblmWeeKbFeo9EQFxenPmJjY9XfT548SVJSEsnJyRQWFqrbNG7cGGdnZ+rVq4eTkxN169aldu3a6sPe3l7vubVUPrkrOTk5pKSkkJqaSkpKSonfk5OTSUxMJCkpifj4eJKSkvSSdc3NzXFwcKBevXp07dqVXr164eLigouLi5rEK6+NEEKIfwJJ6BVCCCGEeExkZUFY2N8JvH/+CWZm2gReHx+YPRv69oWaNU0dqRBCCCGEEELoO3DgADt37mT//v0mS75s3LixOrVd/fr19ZJDu3fvTl5eHkeOHOHo0aO89tprbNy4kT/++AMLCwuq/f+dkidOnOCVV15Rt2vatCmgrexZVF5eHuvXr+ejjz4iKSmJiRMnMmPGjPt+THXq1DG4PC4ujrfeekt9Pn78eDZu3MjIkSM5efIkUVFRrFq1Sk18M1Z556m4Q4cOYWZmppeYZsy5LOu4dDZt2oSlpSWnTp2iV69eHDt2jA4dOvDZZ5+VSAZ+2GbPns2PP/5o0hjuh/DwcGbNmoWHhwf79u3j559/fiD7OXbsGB9++KHJqnY/KJGRkSQkJHDx4kUaN27MlStXOHbsGJGRkbzzzjv3bT9mZpCXp/397FmYPx/eeQc8PWHsWHj5ZXB01N9my5Yt7Nmzh8OHD1f6ZN69e/eyadMm3nzzTezs7Bg0aBCjRo3iq6++4rPPPiMoKIg9e/YQGxtLz549SUxM5M033zQ45levXh3QJpReunSp1H2amZnh4+NTYrmhMa04Q2OcoWvF3YzLhvZf3nXJmDFXl7xbVszGjv+ljc379+8Hyn49S3PhwgWAEn+rDg4OHDx4UH1e0WuUqfTr1493332Xd999l6FDh1K/fn1ThyREpWJtba1WZC1NQUEBSUlJauLoqVOnSEhIIDk5mZiYGI4cOaImm2ZnZ5fY3sbGRi/Z187ODltbW2rWrKn+bmtrS4MGDbC1taV69erUqlULW1tbrK2tsba2xsbGBnNzc2pW8i9k0tPTKSws5Pbt22g0GjQaDdnZ2aSlpZGVlUV2djbZ2dmkpqaqswlkZmaSnp5OdnY2sbGxatLu7du3S/Rva2urnse6devi5ORE06ZNcXR0xNHREQcHB5ydndVE3so0HgshhBCm8nh9+iGEEEII8Q9y+zYcPqxN3j14EI4d035B4+amrcA7e7b2p729qSMVQgghhBBCiLL9+OOPeHt7q9Oem4Krqyu1a9cGoEGDBjzxxBPqur59+9K3b19AW4l02LBhhISEsHjxYubMmYOPjw9WVlaEhoaiKIpa7S89PV3tW0ej0eDu7k5KSgqTJk1i+vTp1K1b9yEdpXZqcktLS73qsPXr1+fAgQN07dqVZcuW0bVrV5588skK913eeSpu69atvPDCC3rVEStyLg0dF8D169e5fv067du3Z/78+dSuXZuYmBh69OhB9+7dOXv2LA0aNKjw8d0v5ubmTJ06ldu3b2NjY2OyOO5VYWEhx48fJzw8nDVr1pT62twrX1/fB9KvqY0ZM4bU1FTatGmDpaUlHh4evPbaa7z//vtYWVk9sP3qkntPnYLp02HaNOjUCV57DYYNgxo14KuvvuKll16iU6dODyyO+yErK4tx48YRGRmJra0tXl5e7Ny5k6+//pqvvvqKr776imeeeQYzMzNcXV1p3749O3bs4M033yxzzN+8eTPTpk0rdb+Wlpbk6U5kEYbGtOIMjXGGxre7GZeL79+YsdSYMdeYmI0Z/8sam69fv07NmjVLfT1HjhxJly5dDB73jRs3sLCwKPHvxsbGhoSEBPV5Ra9RpjRnzhy+/PJLvv/+e2bOnGnqcIR45FhYWODk5ISTkxPt2rXDz8+v1LYajabMqrIpKSlqYuulS5fIzMxUk1yvXbtm8HpgSI0aNdQbAatWrUqVKlXUG0l0MesqohdVvF1RWVlZpe4/IyODgoIC9Xlqaio5OTncuXOHgoICMjIyjIpbt39dwrKtrS12dnbUrFmT2rVr4+XlpVfhuPjvUlFXCCGEqDhJ6BVCCCGEeIQcPAiHDmmTeA8cAI3m7wTet9+GXr2glEJFQgghhBBCCFFpnTx5kh49epg6DKO0a9eO8PBwWrRowaZNm5gzZw6NGjViwYIFzJo1i9dee42hQ4dy5swZ/vvf/6rb5OTksHr1aj755BNGjx7NtGnTSq00+6AUFBQwf/58fvnllxKJAevWraN79+50796db775hs6dO7N//35cXFzual+GzlNRiqLw008/8cMPP+gtN+ZclnVcoP17Ahg0aJCasOfu7s6nn37KK6+8wtdff83ChQvv6rjulx49evDFFwlqlctHU0dWr07B3NwcMGfrVlPH86gxo1GjaXz33WQsLKqoS4OC7t8ebtwofZ2igC7P59gx7WPSJHjuOYXDhx1Zt65iFbpNYdOmTdy5c4dZs2apyxIS/v53tW/fPrXqe3R0NHFxcUYlME2ePJkJEyZUKJbSxrTiDI1xAQEBQMnxrSLjsqH9GzOWGjPmFm/j4eFR5phc2vhf1tj89ddf4+rqWurreeHChVITektLdCsoKMCxePnpcmKsLKpWrcqzzz7LwYMHJaFXiAfM2tpaTf69G8Ur2ubl5anVbvPy8sjKygK0CbXwdxKuRqPRq2irS7YtTteXIXXq1Cn15jBd4rCOvb09VlZW2NraYmZmRq1atQCws7PD0tJSrSqsa2Nvb4+tre0DvclICCGEEIZJQq8QQgghRCWWnw8REdoEXt3DzQ18fOCLL8DPDxo1MnWUQgghhBBCCHFv0tPTK/10tEXZ2NgwcOBAvvnmG3XZzJkz6dSpE7t27eLgwYMMGzaMsLAwzp8/j5eXF/v27SMgIIC0tDTmzJmDnZ2dwb6PHj3KpEmT9JYtWbKE7t2733PcM2bMYNq0aXh5eekt//bbb9m8eTPHjx/H0tISHx8f3njjDSZOnEhwcPBd78/QedI5dOgQubm5PP300yXWlXcuyzsu3d9S8crHumnoz507d9fHdL/UqlWLd955HO7Ila+Z7l2V8pvcpXr1jGtXWKj9qSgQFGQGfMO2bSkMHgyl5EpWClFRUTg5OfHVV18ZXN+gQQN27drFjh076N69O02bNiU8PLzcfi0tLdWK38Yqa0wrrvgY5+rqWmJ8q+i4bGj/xo6lxrQr2qZPnz5ljslgePwva2wODAwkOzu7zNeztOtjo0aNKCgoQKPR6FWCzMzMpHXr1gb7Ki3GyqROnTpcvXrV1GEIIcphbW2NtbW1eqOCEEIIIcS9kk9ahBBCCCEqkYIC+OsvbeLuwYOwfz9kZICTE3TrBleuQOPGpo5SCCGEEEIIIe4vZ2dnYmNjTR1GhbRs2RJ3d3e9ZbpKigCXL1/ml19+YfHixdjZ2fHss89y5coVvvzyS5o0acL06dOZNGlSicReFxeXEglLbm5u9xzv6tWr8fLyYsCAASXWrV+/Hj8/PzWBbezYsZw4cYJ169bd834NnSeAwMBABg4ciIWFhcHtyjqXRRk6Lt3+iifuubi4UKVKlVKTqR+my5cvc+ZMMi1btjR1KOIxNmIEbNpUdhvdP0EzM+jTB4YNgylTWtK79yyqV5/y4IO8BxYWFpw7d468vDyqVCmZGD1v3jxCQ0PZuXMn1apV46effjKq3+PHjxMSElLmfotWkYXyx7Tiio5x1tbWJca3ssbltLQ0tbJiefs3diw1pp2uTVn9FFV8/C9rbLazsyv39Szt+tiqVSsA4uLiaNasmbru5s2bpSYclxZjZXLmzBlcXV1NHYYQQgghhBDiIZOEXiGEEEIIEyoshDNn4NAhbRLv7t2Qlgb168PTT8PixdpqvG3amDpSIYQQQgghhHhwevfuzfLly0tU16vMtm/fzsCBAw2uy83N5eWXX6ZFixa89dZb6vKaNWsyd+5cqlSpwtKlS1m6dCnTp09n8uTJ6pThTk5OjB49+r7HqigKo0aN0lseGhpK9+7diYyMLFHFcODAgaxYseK+7NvQeQoMDGTNmjXlbl/audT1Xfy4dMf0zDPPEBYWptf+/Pnz5OXl4ePjc5dHc/9s376dBQsWmDoM8Q9lZgaWltqZoby94dVXtQ9dRd///a833377LZMmTcLc3Ny0wZahXbt2ZGdns3LlSiZPnqwuT0tLIzU1lQULFrBq1SqqVasGQKGuFHE5YmJiCAwMLHW9paWlXkKvoihGj2nF5ebmGhzfyhqXb9y4oZfQa8z+yxpLK9rOmH6g5Pjv6OhY6tjs4+ODlZVVqa/njz/+yFtvvWXw+ujv788HH3zAoUOH9BJ6w8PDefnllysUY2Vx6dIl9u3bx6byMvKFEEIIIYQQj53K+79wIYQQQojH1KVLsHo1DB2q/aKkbVuYOxfu3IE5c+DECUhIgC1bYPx4SeYVQgghhBBCPP7GjBlDWlrafUkgvRfXr18HIDExUV0WExPD1KlT+fPPP9VlUVFRZGdnM3fu3BJ9ZGdn8/rrr9OkSRNCQkIMTts+e/Zsrly5wr///W+++OILXF1d+fjjj42OMzU1FYCcnJwS62bNmsW4cePU5yEhIXz88cfk5eWxfPlyli9fzueff84bb7xBZGQkAIMGDWL79u16yW5hYWF4enoave+KnqesrCx69+5d5nGWdS5LOy7dMS1dupS4uDgOHz6sbrN3715atWrFmDFjytzvg5aYmMjy5csxMzMzaRzin0f3T6h5c/jgA7h+HY4ehbff/juZF7TjSHR0NJ9//rlpAi1Feno6oB0/QJtY2qhRI2bMmMHixYs5c+YMW7ZsYfz48WqbTZs2kZGRwYEDB9i/fz+pqalkZWWRmZlpcMwHGD58OOHh4aU+jh49qtf+yJEj5Y5pxcdm+HuMM3StKGtcbt68eYX2b8x1ydh22dnZJdZXZPwvbWweM2ZMma/nyJEjDcYM2kThSZMmsXjxYhRFAbTXqODgYDUhvaLXKFMqKCjgjTfeoFWrVpUy2VgIIYQQQgjxYJnp/mNTijJXCiGEEEKI8l26pK2+e/Ag7Nmj/bLEzg46dwZfX+2jQwdtdRQhhBBCCCGE+KcKCAhg8eLFpSaSPmhhYWF88sknbN++nUaNGrFgwQJGjRrFyZMn6dWrF+np6fTs2ZNOnTpRu3ZtJk+erFZ9BLh16xZBQUGsW7eOGTNm8MILLxi135ycHNasWcOnn37K5cuXy23/+++/s379ejZv3oyDgwMLFy7k+eefx9HREYBWrVqRkpJCfHw8ERERPP3002RnZ5fop2rVqly/fp3atWtz+/ZtpkyZwtGjRxk3bhynT58mKSmJzz77jCZNmhi1b2PPk87IkSPZsGGDwWMs71yePHmy1OO6desWtWvXBrQVLqdNm4aPjw/W1tYcOXKE1atX4+TkVO55flAKCgp47rnnuHDhAhcvXjRZHOKfYcQI2LhR+3uzZjB6NLzyCjRtWv62ixYtYt68eQQGBlaKpMJjx44xf/58du7ciZeXFwsXLsTPz48zZ84waNAgYmJiAGjTpg0bNmzAy8sLf39/vv/+e1xdXZkxYwZ169bl1VdfpVu3bsyePZuVK1fqjfkDBw6kZs2aFY7tX//6Fzdv3ix1TAP9sTktLa3c64Wx43JZ+zf2umRMu6JtDh06pLeuouO/obE5ODgYoMzXsyyKovDOO+8QFRVF3759SUhIoFWrVmoicEVjNBVFUXjrrbdYv349+/fv54knnjB1SEIIIYQQQoj7bz0wprSVktArhBBCCPEAxMfDe+/BH3/A1atgawtdu/6dwOvlBZV4xkIhhBBCCCGEeOjy8/Pp06cPMTExhIaG6k2bbWoajYarV69iY2NDgwYNDLb5+eef8fT0xM3N7a72kZubi5WV1b2ECWgrV+bl5WFvb1/hbW/fvk1sbCyOjo53tb0x50nn1q1b1KlTx+C6ez2XxcXHx/N/7N15fEz3/sfxV/aQPSIrIUFIQoJYoo1Soq2Larm9uqAuylXdLkq1vS3aum1v0YW6pa3qoq2lra23LWqLJSElkUQSaxLZE9n35fv7Y35ziCxCMZbP8/E4j8yc9XNmxplx5j2f06JFi6vap2uptraWiRMnsm7dOnbt2kXv3r0NWo+4/b30EigFjz8OgYFXvvzTTz/NypUr+fjjj3nqqaeufYHXUFJSEkZGRnh6etYZX1RUhI2NjXa/oqICCwuLa7rtM2fOYGtr2+gxDeoem6/kGNec43Jj22/udpoz3+XmuZLjv15Tx+bGns/LqampIScnBxcXl2tS441UVlbGxIkT+eGHH1i7du1NEaQXQgghhBBCXBcS6BVCCCGEuN7S03UdeLdvh23b4MwZXXD37rshJATuuQeuwfeyQgghhBBCCHFbKywsJDQ0lKSkJDZu3EhwcLChSxLimigpKWHs2LH88ssv/PTTT9x///2GLkmIy1JKMX/+fBYsWMDjjz/O119/beiShLhtde3albS0NDZs2MC9995r6HKEEEIIIYQQ10+TgV7pCyeEEEIIcRUyM2HdOpg6Ffz9wd1d1+0kMhL+9jddqHfbNpg3TxfslTCvEEIIIYQQQlyera0tv//+O8HBwfTv35+XXnqJyspKQ5clxJ9y4MABgoKC2Lt3L7/++quEecUtw8jIiHnz5vHrr7+yc+dO3nnnHTkmC3GN5ebm8tJLL2FjY8Phw4clzCuEEEIIIcQdTjr0CiGEEEI0Q04OHDgA+/bpuvD+8QeYmOguVxgaqhtCQsDS0tCVCiGEEEIIIcStTynFypUrmTlzJl5eXqxatYqgoCBDlyXEFSkrK2P+/Pm899573HfffaxYsYI2bdoYuiwhrkpeXh7u7u54enry5ptv8te//hUjIyNDlyXELausrIwPP/yQf//731hZWZGcnIyJiYmhyxJCCCGEEEJcf9KhVwghhBDiShUX64K7L70EvXqBiws8/LBu3N13w/ff60K+hw/D22/rAr0S5hVCCCGEEEKIa8PIyIgpU6Zw5MgR7O3t6devH1OmTCEpKcnQpQlxWVVVVaxcuRJfX19WrFjB559/zs8//yxhXnFLc3BwIC4ujh49ejBmzBh69+7NqlWrKCsrM3RpQtxSUlNTef311/H29uatt95i1qxZnDhxQsK8QgghhBBCCEA69AohhBBCAFBSouvAu307hIVBRARUVYG394UOvKGh4OBg6EqFEEIIIYQQ4s5SW1vL6tWreeONN0hNTeXvf/87L7/8Mp6enoYuTYg6qqqqWL16NW+99RZpaWlMnDiRf/3rX7i7uxu6NCGuqcOHD7NkyRLWr1+PjY0NEydO5B//+Afe3t6GLk2Im9bOnTv5+OOP+emnn2jVqhWTJ0/m2WefxcXFxdClCSGEEEIIIW6sJjv0SqBXCCGEEHeM6mqIitKFdvXB3fJymDJF13V30CCQZjlCCCGEEEIIcXOrqqriq6++4r///S+HDh2iS5cuPPXUU8yYMcPQpYk7SG1tLb/99hsrVqxg8+bN2NnZMX78eJ5//nnatWtn6PKEuKGqq6vZsmULa9asYevWrVRUVDBw4EAeeeQRRo0aRevWrQ1dohA3zLFjx1i3bh3r1q0jPj6eNm3aMHr0aCZMmED37t0NXZ4QQgghhBDC8CTQK4QQQog7U00NHD16Iby7ezcUFYGbG4SE6Dru3n8/yPdsQgghhBBCCHFrOnLkCCtWrGDNmjXce++9jB49mhEjRmBvb2/o0sRtqLa2lv3797NhwwY2bNhASkoK99xzD1OmTGH06NFYWloaukQhDK60tJStW7eyfv16tm7dSnl5OcHBwQwZMoQhQ4bQp08fTE1NDV2mENdMQUEBO3fuZNu2bfz222+cPHlSC/E+8sgj9OvXD2NjY0OXKYQQQgghhLh5SKBXCCGEEHcGfYA3LAz27YNt2yA/H1xdoX9/XYA3NBTk6n9CCCGEEEIIcXspLi7mb3/7Gzt27ABg0KBBjB49mpEjR0pnSPGn7dixgx9++IGktWuZnpNDiqMjdvfcQ5/Jk/G6/36QcKIQDSotLeV///sf//vf/9i2bRvJycnY2dlx7733EhoaypAhQ/Dx8TF0mUJcsX379rFt2za2bdtGREQEtbW19OjRgyFDhjB8+HAJ8QohhBBCCCGaIoFeIYQQQty+Tp/WdeDdvh127IDz58HZGQYMgLvv1nXi7dkTjIwMXakQQgghxM2jpqaGwsJCAPLz81FKUVRURHV1NQB5eXkNLldRUUFpaWmj67W1tcXExKTBaTY2NpiammJpaUmLFi0wNTXFxsYGIyMj6aQphLhm8vPz2bZtG5s3b+bHH3+ktLSUHj16EBoaSmhoKCEhIdJFVTTL6dOn2b59O9u3b2fdunX4+fnxfEgIf01JwTElBRISoKoKLC2ha1cIDISAAN3fwECQ9zYh6rn439W2bdvIz89n+PDhBAUFERISwl133UXLli0NXaYQ9RQUFHDo0CHCwsLYt28f27dvx9XVlf79+xMaGsrw4cNxd3c3dJlCCCGEEEKIW4MEeoUQQghx+7g4wLtzJ+TkgI0N9O17oQOvBHiFEEIIcTsqKCggNzeX3NxcCgoKyMvLIzMzk+LiYvLz8ykqKqK4uJiSkhIKCgooLCykuLiY8vJySktLqaiouGwg19D0geCWLVtiYWGBtbU11tbWWFlZYW9vj42NjTbO1tYWOzs7XFxcsLOzw9HRkVatWuHo6IiVlZWhd0UIcRMoLi5m27ZtWngsMTGRFi1aaOGb/v3706NHDywsLAxdqrgJnDlzhv379/P777+zbds2UlJSsLW1ZeDAgSxatIiOHTvWXaCqChITITIS4uIgNhYOHYLMTN10Nzfw9wc/PwgK0g1dukAjP3wR10ZKSgoeHh7ExcWxceNGPD09eeKJJ+p0yiwqKuLnn3/m+PHjtG3blvvuu4+2bdsasOo7U3V1NeHh4SxatIiDBw+Snp6OmZkZgYGB9OvXj759+9K9e3c6d+6MqXTBFjdQcXExMTExREZGcvDgQQ4cOMCpU6cwNjamS5cuBAcHM2fOHOkuLYQQQgghhLhaEugVQgghxK3t9GkIC4NXX4WUFLC2huDgCwHeHj1ArmAmhBBCiFtJVVUVWVlZpKenk5GRQWZmJpmZmeTm5nL+/HnOnz+v3db/1XfPvZiLiwtWVlY4ODg0GH61srKiZcuWTXbF1YdoraysMDc3B8DBwaHBuo2NjbGzs2twmlKK/Pz8y07Th4srKyspKSlpsltwYWEhJSUlFBcXU1hYqIWUS0pKKCws1ELOl7K0tMTR0bFOyLdVq1a0atUKJycnnJ2dcXFxwd3dHWdnZ5ydnTGSX4QJcdtLSkrSwr2///47WVlZWFhY0LNnT4KDgwkODqZfv34S7LsDlJSUcPjwYS2odfDgQTIzMzEzM6Nv374MGTKE0NBQ+vTpc+VBwrS0CwHfyEjdkJAANTVgbg4dO14I+I4dC61aXZ+dvANt3ryZSZMm8fLLLxMdHU12djZbtmxh4cKFzJ07F4CoqCjGjRvHvHnz6N+/P19++SXz5s1j2bJljB8/3sB7cGdLSkpi//79HDx4kIMHD3LkyBGqqqqwtLTE39+fwMBAAgMDCQgIIDAwsNHPq0JciaSkJKKiooiOjiYqKoqjR49y+vRpamtrsbOz0z4f6Ae5sogQQgghhBDiGpBArxBCCCFuLenpugDv9u3w229w9iy0bAkvvgghITBgAJiZGbpKIYQQQojGZWVlkZycTFJSEikpKaSlpXH06FHS09PJysoiKyurzvzW1ta4urrWCZ829tfJyQk7OzsJMfy/6upqCgoKGgxBX/o3JyeHnJwcsrKyKC8v19ZhamqKs7Mzrq6uuLm54ezsTFBQEO3atdMGW1tbA+6lEOJ6SExMJDw8XAt1Hjt2jOrqajw8POjZsycBAQGMGzeOjh07YiJdVW9ZOTk5WlgrOjqao0ePEhMToz3Xffv2pV+/fgQHBxMUFESLFi2ufRGVlXDixIWAb1wcREVBdrauk29QUN1uvr6+8svlqzR37lxCQ0MZPHgwAEFBQRgZGXH48GEqKysJDAzkb3/7G/Pnz9eWeeKJJ1i/fj1HjhzBz8/PUKWLS1RWVhIXF6cFLfVDTk4OAO3atcPX1xcfHx86d+6s/ZUfZYhLVVZWcvr0aeLj40lMTCQxMZGEhARiYmLIz8/HyMgILy8vunfvTkBAgBYa9/Lykh/9CSGEEEIIIa4HCfQKIYQQ4uaWmQl79ugCvGFhuu+1TE0hMPBCF97+/UGugiqEEEKIm0VWVhaJiYmcPXtWC+7q/549e5aysjIAjIyMcHNzw83NjcDAQDw8PHB2dsbNzQ1XV1ctQNqyZUsDS08hjwAAIABJREFU79GdJz8/XwtYp6am1umYnJGRQWRkZJ3uv/b29rRr1w5PT0/at2+v3fby8qJTp06Ndi4WQtw69F1bw8PDOXLkCNHR0cTFxdGyZUutO2S3bt3o3LkznTp1ol27dhL0vcnk5uaydetW4uLitBBvWloaoOtqHxAQQPfu3enduzfBwcGGD/5t3ly3m298PNTWgo0N+PhcCPgGBekuT2RlZdh6bwELFixg0qRJeHh4ADBu3Dh+/fVXsrKy2LRpEyNHjuTnn39m6NCh2jJffvklTz75JDNmzGDRokWGKl00U2pqqhbyjY+PJyEhgcTERM6fPw+AlZUVPj4+DB8+nI4dO2o/zvLw8MBMOgTc1kpLSzl79ixnz54lKSmJkydPsmnTJs6ePUt1dTVGRka0bdsWHx8ffHx86Nq1qxbgtbGxMXT5QgghhBBCiDtHk4HeK7xWlBBCCCHEn5edDQcPwr59uhDvH3+AiYkuwDtiBHzwga4Tr6WloSsVQgghxJ2soqKCkydPEhcXx+nTpzl9+jSxsbHExMRQUFAAgJmZGU5OTri7u+Pt7c2DDz6It7c3bm5uuLu706VLF6wkfHNTsre3x97eHl9f30bnKS8vJy0tTXv+09LSSE9P5/jx42zdupXk5GSqq6sBcHBwwNvbG29vb/z8/PD398fb21teA0LcQqysrBgwYAADBgzQxkVGRmqdXaOjo9m4cSPZ2dkAmJub06FDB3x8fOjUqZP2t127drRp00aCY9dJVlYWKSkpnDp1isTERE6cOKH9zc3NxcLCAl9fX7p168Y///lPAgMDCQgIwMXFxdCl1zdihG7QKyqCxMQLAd/ISPjxRygu1k3Xd/O9uKOvnx9IB8lGmZiYoG9sExcXB+iujHCx/v37A3D8+PEbW5y4Kh4eHnh4eNQJZYOuG/fFAd/169dz5swZ7aoMJiYmeHh40K5dO+3HWReHfd3c3OQKGDexmpoa7Qd4586dqxPc1f+wUv/+DLrP5h07duTJJ5/UArw+Pj7yQ0ohhBBCCCHETU869AohhBDiuisqgvBwXXh3+3Y4ckT3XVP37rruu3ffDQMGgFzFWAghhBCGUl5eTmxsLNHR0XzzzTfExMSQmZkJ6EK73t7e2pfAnTp10oJb7u7uGMslse9YVVVVJCUlaWGyi4NlKSkp1NbWYmxsjKenp9YBbOzYsfj4+EhXTyFuYXl5eZw4cYITJ06QkJCg3T5x4gSFhYUAGBsb4+rqSvv27Wnbti1t27bF09NTC45169YNc3NzA+/JzScnJ4esrCwyMjLqdMBPSUkhOTmZ5ORkrQu+qakp7du3196T9e/RgwcPvv3em9PSLgR89R19jx8HpcDODrp2vRDwfeopuIMDa5d26J0wYQJbt24lOzubFStWMHXqVN555x1mz56tLVNbW4ulpSWPPPII33zzjaFKF9dJeno6SUlJ9QZ9GLRYH5gHLC0tcXV1xd3dHVdXV+3qGm3atNGustGpU6d6oXDx55w/f57ExEQyMzNJTU2t81f/g7qsrCxqamq0ZZydneuEsvVBbX1Y21ZONAshhBBCCCFuXk126JVArxBCCCGuC314NyxMF+atrYUuXXSdd0NDYcgQsLc3dJVCCCGEuBNlZ2dz6NAhoqKitMtxnzhxgurqalq2bMnjjz+Ov7+/dkn19u3bY2oqFzkSV6a8vFwL+CUmJmrdPWNjY7G0tMTf35/AwEC6detGjx49CAoKknCIELeBzMxMzp49S0pKCikpKVpwTH8/KytLm9fR0REXFxdat26Ni4sLrq6utG7dmtatW+Pg4KB1Er94sLCwMODeXbm8vDzy8/PrDXl5eURFRZGdna0FtbKzs6mqqtKWbdGiBe3atdPC0G3bttUC0vpw9B3dBbmwEKKjLwR8IyPh6FGoqABPT1249+Juvt7ehq74hmgq0BsVFUX37t0ZNmwYW7Zs0ZaJjo4mMDCQjz76iGeeecZQpQsDyc3NJT09vV6ANCMjg7S0NO1vaWmptoyFhQWtWrXC0dGRVq1aabdbt26No6Mjtra2ODg4YG1tjY2NDdbW1tja2mJnZ4e1tfUtdyy/nOLiYm0oKCigoKBAu19YWEhubi7nz58nNze3zm3939raWm1drVq1wtXVFTc3tzrDxQFrT09PWrRoYcA9FkIIIYQQQog/RQK9Qgghbm0lJSVUVlaSl5dHdXU1RUVF2rS8vLxGl2tqmqWlZYMn/S4eb2pqio2NDYB2uTVbW1vppNWI0lLYv18X4N23Txfm9fbWhXdDQ2HwYHB0NHSVQogbpaCggNraWnJzc7XL0ldVVdXpfKNXU1OjdTK7VIsWLbC0tKw3Xn9cbtmyJRYWFlhbW2NmZibHaSFEPdXV1SQkJLBv3z7CwsKIjIzk+PHjKKVwc3PD398fPz8/goKCCAoKokuXLnIcEddVbGwscXFxxMbGEhkZSWRkJOnp6ZiYmNC5c2fttRgSEkKPHj1uvy6TQtzhysrKOHfuHLGxsXWCrBkZGWRlZWn38/LyaOjcfYsWLbRwb8uWLbGxscHU1BR7e3tMTExwcnLC2tq6zvkNExOTBjsV6pe9WGFhYZ0OiHDh/EptbS0FBQXa5/ry8nLKysq08zb6aRcHdxtiZWWFg4MDgYGBtG7dGldX1zrB5ovDzeIK1dTAzz/X7eZ75oyuk6+Dw4WQr76bb69e0MD/t25ls2bNYvr06Xh5eQEwfPhw9uzZQ0FBAUZGRkyYMIEffviBmJgYPD09Afj44495//33iYmJkc7ZolFFRUVkZGRw4sSJRgOqOTk55ObmUlRU1OS5aTMzM6ytrXFwcMDGxgZ7e3vMzMy0cyDm5uZYWVlp56eNjIywv6gzgrGxMXZ2dg2u28rKqt7rWH+cvtSl52P05971x3uA/Px8lFIUFRVRXV1NaWkpeXl5WmD3cvtpa2tbL/h8aRi6VatW+Pj44OLictuFnYUQQgghhBCiARLoFUIIcWNlZ2drJzL1v8a/+CSf/pf6hYWF2v2ioiLt5KD+JGBDXyLdLPQnRi0sLGjZsiUtWrTQOi7Y29vj6uqKtbU19vb2WhcGa2tr7Ozs6pystLKyMvSuXLXqaoiKutCJd+9eXRMYfYj3tdfg/5uhCCFuIfn5+WRmZpKTk0NeXp7WWaWwsFD7Ur6wsLDO+JKSEu3LIf0XPIZmb2+PsbEx9vb2mJuba51w9OEH/X07OzvttoODA76+vjg5OUmYT4hbWHFxMXv27GHHjh0cOHCAP/74g4qKChwdHenbt2+dQf/jACEM7dy5c4SHh3Pw4EHCw8OJjIyktLQUW1tb+vTpQ0hICIMHD6Zv3753djdKIe4w+s/cDXW4zc/Pp7S0lOLiYqqqqigoKKC6uprs7GxKSkooKyujvLwcgIqKijqdJfUaCmHpfzB3Mf37pT5Qpg+i6UPD+h/X2dnZYW5uXudzd0OdhqXr/Q2Wnw8xMRcCvrGx8McfUFYGpqbg43Mh4BsUBL17g6uroau+Krt372bixIkMGjSIN954g127djF16lQKCwuZN28er7zyCtXV1cyaNYtdu3bx4osvUl1dzY8//sjy5ctp27atoXdB3GaKiorqdKrNz8/XzoVffI48Ly+Pmpoa7dyK/hheWVlJSUlJveBtY8d1uPAD64vpz2E3xN7eHiMjI6BuUPjSBhf68+GWlpZa92FbW1vs7e21c982NjbaeRYbGxsJ5wohhBBCCCFEwyTQK4QQ4s8pLy/XLjeWlZVFamoqOTk5DXYhyM3NbfALIf1JPisrq3pBVysrK+1En4mJCXZ2dhgbG2tfCOk7Ezg4ONTrPqCfpyFNTdN/4XWpi4No+hOml+tIUFFRQUlJCcXFxZSUlJCXl0dGRoZ2UlZ/grasrKze9i69PJv+r5OTE61atdK60ri5ueHs7Iyzs/Pln7DrpKZGd+VGfYB33z7ddz9ubhASogvxPvCA7qqOQoibT2lpKUlJSaSlpXHu3DlSU1PJzs4mJyeHrKwsMjMzOX78eL2OLfov5PVf0tjb29cJwdrZ2dXpDHbpsdzBwaFOYK6x8Jx+/ks19uMO/XuN/niuPy7r59d/gZWfn691C9OHIfS3Lw0l6xkZGdG6dWucnJxwcnLCxcUFZ2dnrWNYmzZtaNu2LR4eHjg5OV3V8yHuXKdPn+bNN99kwYIFtGnT5k+vb+LEiddsXbeq6upqwsPD2b59Ozt27ODgwYNUV1fTtWtX+vfvr4V3fXx8tC+qhbjZVVdXEx0drQV8d+/eTVJSEtbW1gwYMIDQ0FBCQ0Px9/eX17UQQogrV10NCQkXAr76jr7p6brpF3fz1Xf0DQoybM3XWEFBAbGxsXh6et7Rn6WFEEIIIYQQQghxw0mgVwghROPS09NJSkoiOTmZlJQUUlNTycrKIi0tjczMTNLT0+sFdFu1akXr1q3rXRpLf7tz587abTs7O6ytrQ20dzcXfcCssUuyXTxOf3m2zMxMKioqtHV4eHjg7u6Oq6urFvR1d3enXbt2tGvXjvbt22uX1Pzz9eoCvGFhuvDub79BQYGuSUv//roA75Ah8P9XMBRCGFhGRgYnT57k5MmTJCUlaaHdlJQUzp07V+dyt5aWlri7u2uXtXVycsLV1RVfX1/tkretW7fGwcHhmh1TbnbV1dWcP3+e48eP17nUcU5ODpmZmdr99PT0eo9l27ZtcXd3x9PTEw8PDzw9PenQoQMdOnSgXbt20oFM1LF+/XoeeeQRfv75Z4YOHfqn12dkZHTN1nWrycvLY/PmzTzzzDMUFRXh6enJ4MGDCQ0NZfDgwbi4uBi6RCGuqRMnTmjB9Z07d3L+/HlcXV154IEH+O9//ysd0IQQQvx5eXl1A75xcbruvhUVFwK++m6+wcHQurWhKxZCCCGEEEIIIYS41UigVwgh7nSZmZkkJiZy9uxZkpKStGHv3r3a5RdNTExwc3PDw8MDFxcX3NzctNCou7s7zs7OeHh44OzsLF8U32Dnz58nIyODjIwMjh49Wid0nZGRQWpqqtZBGMDZ2VkL+Hp6etKuXTu8vLxo3749Pj4+TT5/p09f6MC7fbvuexxnZxgwAO6+W9eJ9zZryCLELSc9PZ3Nmzdz8uRJTp06xalTpzh58qTWYdbS0hIvLy/atGlTJ2Tq4eEhXWWvkYu7HaemppKcnExaWpoWnk5OTub8+fMAmJmZMXjwYC3g27FjR7p06YK3tzcmJiYG3hNhKDk5Odfs3+G1XNetICcnh59++okNGzbw+++/Y2RkxOLFiwkNDcXHx8fQ5Qlxw9TU1HDkyBG2b9/Opk2biI2NZfjw4YwePZoHHnig0UsqCyGEEFesqgoSE+Grr3Rh30OHIDNTN83N7ULAV9/N19cXGrj6ihBCCCGEEEIIIYQAJNArhBB3hoqKCk6ePElcXBynT5/m9OnTxMbGEhMTo4U9zc3NadOmjdbVNTQ0FG9vb9zc3PD29r5jujDejsrKykhPT9ee+9OnT5OWlqaNO3v2LLW1tYDucvd+fn74+/vj7e1NWdk4oqPd2LPHiNxccHLSBXjvvVc3+PkZeOeEuEPl5+dz6tQpYmNjiYyMJC4ujpiYGDIyMrCwsMDDw6POv2X90L59e4zly1ODy8vL047H27dv126fOXMGpRTm5uZ07NgRf39/7Xn08/PD19dXnj8hLlFVVcXGjRtZsWIFv//+O+bm5jzwwAOMHj2a4cOHY2dnZ+gShTC4jz76iA0bNhAWFoalpSUjR45k6tSp3HPPPYYuTQghxO0oLU3Xvffibr4JCbrLPZmbQ8eOFwK+/v7Qowe0amXoqoUQQgghhBBCCCFuBhLoFUKI20llZSXHjx8nOjqa6OhooqKiiImJIT09HdB1AvTy8qJz5874+Pjg4+NDp06d8PHxwd3dHSMjIwPvgTCEsrIyTp48SWJiIidOnCAxMZHExEQSEhLIyVmJsbEpLi6x9OiRzz33ONC9eyABAQG4ubkZunQh7gg5OTlERERw6NAhDh06xB9//KEd1+3t7fH398ff35+uXbvi7+/PoEGDDFyxuFpFRUXEx8cTExOj/fAmNjaWc+fOAWBjY0NgYCC9e/fWho4dOxq46pvTjh07SElJwcLCglGjRmFhYUFERARxcXE4ODgwcuRIANLS0vjll184d+4cd999N4MHD9bWoZRi9+7dHD16FBMTE7p06cKQIUOaXUNZWRkbN27kwQcfJCsri59//hl3d3dGjBiBiYkJmZmZbNq0CWNjYyZNmgRAbW0tu3fvxtramt69ezerlqam7dy5s866UlJS+OGHH3j22WeJi4tj48aNeHp68sQTT9QLixcXF/PVV1+RnJxMp06d6NOnD76+vjdN9+hz586xcuVKVq5cSVZWFsOGDWPs2LH85S9/wcrKytDlCXFTysrK4scff2TVqlWEh4fj7+/PtGnTGDduHLa2toYuTwghxO2sshJOnLgQ8I2Lg6NHISdHN93N7ULAV9/RV7r5CiGEEEIIIYQQ4s4jgV4hhLiVFRYWEhERwZdffkl0dDRxcXFUVVVhYWGBv78/gYGB+Pv706VLF3x8fPDy8sLU1NTQZYtbSHh4OImJicTFxWlBcX2wrHXr1gQG6sK9AQEB9OrVS7pHCvEnVVZWEhERQXh4OIcOHSIiIoIzZ84A4O3tTZ8+fejZsycBAQH4+fnRtm1bA1csboT8/Hwt4HvkyBEiIiKIiYmhqqoKR0dHLdzbp08fQkJCcHBwMHTJBldaWoqVlRWnTp3C29tbG+/r68vGjRvx8fFh586dfPvtt0ybNo2TJ08yceJExo8fz7JlywB45ZVX8PLyYvLkyRw+fJjp06cTHh7erO3v3r2bp556ihMnTrBo0SISEhKwt7dn6dKlDB06lAceeIBdu3ZRU1PD999/T21tLXFxcbz++uusX7+e5cuX849//ENbX1O1NDYtLi4Of39/bV2bN29m0qRJZGdns2TJEqKjo8nOzmbLli0sXLiQuXPnatvLy8sjODiYTz/9lJ49ezJu3Dh+/PFHevfuTURExLV4iq5aTEwM8+bNY+PGjbRq1YpJkyYxdepUPD09DVqXELeaP/74g+XLl7NmzRqMjY0ZP348c+bMkX9LQgghbqy0tAsBX31H3/h4qK0FW1sYOfJCN98ePUB+uCWEEEIIIYQQQojbmwR6hRDiVnL69GnCwsKIjIxk3759HDlyhNraWu6++26CgoIICgrSujRaWFgYulxxmyooKODYsWPExcURGxtLZGQkR44cobS0FBsbGwICAggKCiIkJISBAwfSunVrQ5csxE2rurqaqKgotm/fTlhYGHv27KGwsBB7e3t69eqlHd/79u2Ls7OzocsVN5GqqioSExPZt2+f9tkgPj4egC5dujBv3jwGDx6Mo6OjgSs1HCMjI1auXMnkyZMBSE9P57nnnmPdunUUFxcTGBhIdHS01s118uTJfPbZZxw4cED7N7du3ToGDhwIwFtvvcUrr7zS7O0vWbKEGTNmsG7dOv76178CMHfuXN5++202bNjAqFGjAHj11VdZsGABxsbGHDt2jICAgDqBXqVUo7U0NU3/GFy8Lv32t2/frnUjDgoKwsjIiMOHD2u1v/zyy6xZs4azZ88CuuBfUFAQS5Ys4YUXXriSp+GaSU5O5vXXX+fLL7+ke/fuvPjii4waNQpzc3OD1CPE7SI/P5/Vq1ezZMkS0tPTmTx5MvPmzZPP8EIIIQynqAgSE3UB3xUrICoKiot10/SdfC/u5uvnB3LVMSGEEEIIIYQQQtwemgz0SgtHIYQwoKqqKsLDw9m+fTt79+7l0KFDFBUVYWVlRa9evRgyZAj/+te/6Nu3L66uroYuV9xB7OzsCAkJISQkRBtXXV3NsWPHOHDgAOHh4fzyyy98+OGHAPj4+NC3b1/uvfdehgwZQps2bQxVuhA3jYSEBJ555hn2799PaWkpbdq0YeDAgSxevJiBAwfSoUMHQ5cobnJmZmb4+/vj7+/PlClTADh//jx79uxh165dPProoxgZGREYGMjgwYMZMWIEd911FyYmJgau/Mbx9fVl8eLFTJo0CSMjI9asWcP48eMB+PbbbykrK2P27Nna/Onp6XTo0IGTJ08SHBxM586dGTNmDCtWrGDkyJHMmjXrirZvZ2cHQLdu3bRxnTt3BiAwMFAb16VLF9LS0mjTpk2DP8gyMjJqtJampjWkRYsW2jb1/Pz8+PXXX+vMd+rUKbKzs6msrMTc3JzAwECsrKxISUm5osfgWigpKWHevHksXboUDw8PvvnmG8aMGYPRDQ5tVFZWsnfvXrZs2cKSJUuu6br37t3L7t276datGyNHjryiZSsqKti9ezdHjx4lJCSEvn37XvbfeXOXudx8V7LtjIwM4uPjteD5pYqLi1m7di2urq4MGTIEMzOzK15Pfn4+n332GcnJyQwbNozBgwc3+VhERUWxZ88ezM3NGTZsGG3atOHQoUOcPHmywfmDg4Px8vKqMy43N5cVK1bU6XDdkObMp6/n4YcfvqGfl+3t7Xn++eeZNm0aX3zxBQsWLGDNmjXMnz+fZ555Rq6+IYQQ4sazsbkQ2v3/z+9aJ199N99162DBAlAK7Oyga9cLAV/9sv//2VcIIYQQQgghhBDidiFn7IUQ4gaLjY3lgw8+YMSIETg6OtK/f3+++OIL2rZty7vvvsuRI0fIz89n165d/Pvf/2bkyJES5hU3BVNTU3r06MHTTz/N6tWrSUhIICcnh61btzJmzBjS0tJ4+umnadu2rXa588LCQkOXLcQNU1NTQ1hYGLNnz6Zz58506dIFFxcXPvjgA06cOEFKSgpfffUVkyZNkjCvuGqOjo489NBDvP/++2RlZbF+/XpCQkLYtGkT99xzD66urkyYMIENGzZQrO9wdRt78cUXOX78OD///DMA27dvZ+jQoYDuM5ebmxvLli3Thq1bt3Ly5EnGjh0LwNKlS7G1teWhhx4iNDSU/Pz8P12TpaVlvXFmZmaUlJQ0uVxTtfzZOk1MTLj06jz33nsvpaWlhIWFAZCXl0dlZSVDhgy5onX/WQcPHqR79+6sWrWK//znPxw/flwLq99oMTExrF27lvfff/+arjcyMpL//Oc/zJ49mzFjxlBaWtrsZbOysvD19SU5OZmJEyfy008/MXLkSGpqav70Mpebr7nryc7OZtasWXh7e/Pjjz82WFNCQgI9evTA1dWVgoICOnbsyJ49e65oPefPn6dXr15ERUURExPD0KFDueuuuxrcXk5ODpMnT2bu3LmMHDmSqVOn0qZNG5RSPPbYYzz++OMNDnl5efXWNXnyZD744INGH+/mzHdpPYb68Zu5uTlTpkwhISGBp59+mlmzZhEaGsq5c+cMUo8QQghRh7s7jBgB8+bB2rW67r35+bB3L7z7ri7AGxsLL78M/fuDrS106HBhmc2b4fRpQ++FEEIIIYQQQgghxJ+jlGpqEEIIcRXKy8vVli1b1IQJE5SDg4MCVK9evdQbb7yhDh48qKqrqw1dohDXXXZ2tvruu+/UhAkTlKOjowJUUFCQ+ve//61OnDhh6PJuCZs2bVKtW7dWgFqyZIn6+9//rgC1cOFCbZ6jR4+qbt26qQ0bNqisrCz13nvvKWtra7V69WoDVn77O3funFq4cKHq3LmzAlS3bt3U22+/reLi4gxdmhBKKaWysrLU559/rh544AFlYmKiHB0d1fTp09WhQ4cMXdo195///EcBaujQoSomJkYbv3r1agWoDz/8sM78eXl5atmyZaq8vFx9+eWX2vhffvlFGRkZXdG2P/vsMwWo+Ph4bdy3336rAHXy5Elt3HfffafNk5CQoAC1fPlybXpjtaxcubLJaUqpeuuaP3++AtS5c+e0cU8++aRycnKqV/97772nwsLCVHx8vCovL7+iff+zvvnmG2VsbKyee+45VVNTc0O33ZTY2FilO1Vy7Xh6etZ5726unTt3KkBt3ry5zvjXXntNWVlZqeLi4qtepjnzXcm2KyoqFKCee+65Bvele/fuKjg4WLs/ZcoU1adPn3rzNbaeiooKdfr06Trjzp49qwAVHR1dZ77evXsrHx+fBh+f/fv3N/h6mz59upo5c2a98dOmTVP//Oc/lYuLS4P7pZTu33Rj812unptBUVGRGj58uLKxsVGHDx82dDlCCCHE1cvLU2rvXqXef1+pu+9WqkULpUApMzOl/PyUeuQRpV5/XalNm5TKyDB0tUIIIYQQQgghhLhzfaGayOxKh14hhLjGJk+ejIuLCyNGjCA+Pp5XXnmFM2fOcOjQIV599dVmXSJXiNuBk5MTY8aMYdWqVWRkZPDrr7/Ss2dPFi9eTKdOnejRowfLli2TLr5NGDFiBJMmTQJ0l3P//PPP6dmzJxs2bAB0lwZ/9NFHefjhhxk1ahStW7dm5syZPPjggzz11FPExcUZsvzb0p49exg+fDjt2rVj0aJF3HfffRw+fJjo6GjmzJmDr6+voUsUAoDWrVvz97//nf/9738kJSUxe/ZsduzYQe/evQkICGDVqlVUVVUZusxrYurUqdjZ2ZGTk4O/v782fsyYMbRt25ZZs2ZpnV/Xrl3LlClTGDduHEop/vvf/2qda++77z6cnJzqrHvKlCn85S9/ITMzs8FtFxUVAVBRUaGN03dGPn/+vDaupKREm0f/NycnR5veWC1OTk5NTtO7eF3699XKyso60ysqKup06V2+fDnr16+nqqqKyspKkpOTtf253iIiIpgwYQJz5szhgw8+wNj45j41kZaWxueff86CBQvYsWNHvemJiYl8+eWXzJo1q05n2d27d/Pxxx+TkpJCREQEn3zyyRVt94cffgB0nwEu1rVrV0pKSrTO1FezTHPnu5JtN2XUqFEcPHiQr7/+muLiYn788UdeeOGFZi9vbm6Ol5dXnXHR0dEMHz68To2UAOsGAAAgAElEQVSvvPIKhw4dYvbs2VhZWdVbT79+/eq93mpra/nhhx8YNWpUnfFVVVW88847TdZVVVXFq6++2uh8l6vnZmBtbc2GDRvo168fw4cPJzc319AlCSGEEFfH3h5CQuD55yEsDAoLISYGvvkGHnkEyspgxQp48EFwdQVHxwvzf/klREbCRZ+hhRBCCCGEEEIIIQzh5v7WTAghbhHl5eV89dVX9OvXj/DwcObPn09SUhIHDhxg5syZtG/f3tAlCmFQZmZm3HfffaxYsYL09HR27NhBUFAQc+bMoU2bNjz99NMcO3bM0GXelFq0aAFAly5dAPDz8yM5ORmAX375hfj4eIKDg+ssc//991NZWclnn312Y4u9TSml2Lp1KyEhIQwYMIDi4mK+//570tLS+PDDDwkKCjJ0iUI0ycPDgzlz5nD8+HEOHDhAr169+Mc//kHHjh358MMPKS0tNXSJf4qNjQ2PPfYYEyZMqDPewsKCX3/9lfbt2zN79mz8/PxYsGABc+fOxcbGBoAzZ87w+OOPs379ehYvXsy0adPqrOP333/nf//7H19//XW97R44cIBVq1YBsHjxYs6cOcOuXbtYvnw5APPnzycuLo4DBw6wcuVK3nrrLb7++msWLFgAwPfff8/WrVu19TVUy0MPPdTktPDw8Drr2r17txYoXbhwIRkZGXz33Xfs3buXoqIiFixYQHV1NQBubm4cO3aMe++9l4CAAHx8fLC1tWXIkCFX9PhHREBGRvPnV0oxZcoUBg4cyFtvvXVF2zKEnTt3Mm/ePHr06IGvry8PPfQQ06dP16a///77TJ06lXHjxvHMM88wY8YM7TXQrl07/Pz8UErh4uKivV+kpaURFhbW5LBv3z5OnjwJ6J6rizk7OwO6IPGlmrtMc+e7km03ZcqUKXTu3Jlx48YxevRoPvnkEx577LErWoeeUoq1a9fy0ksvaY+13rfffoupqSnHjh1j0KBBWFtbc8899/DHH380ur59+/ZhZGREv3796oxfsGCBdqxozIIFC3jhhRcana+xem425ubmrF27FhMTE+bOnWvocoQQQohrw9QU/P11Yd5582DzZkhLg/PnYe9eeP118PaGfftgyhTo1QusrXXLjB8P77yjWyY729B7IoQQQgghhBBCiDuIqaELEEKIW1lRURHvvfceH3/8MQUFBYwaNYrvvvvO0GUJcVMzMTFh0KBBDBo0iPfee4/Vq1ezfPlyli9fzj333MNrr73G4MGDDV3mTcvExETrsKjvwGttbV1nnv79+wNw/PjxG1vcbWjHjh3MnDmT6Oho/vKXv7Bv3z7uuusuQ5clxFULDg4mODiY+fPns2jRIubOncubb77J3LlzefbZZzE1vTX/i3jq1Cn+/e9/1xvv6+tLQkICSUlJGBkZ4enpqU2ztLQkOTmZ2tpaMjIy+Otf/1pv+djYWDZu3IilpWW9af369ePo0aN1xnl5eREZGVlv3gMHDmi3x44dW296U7U0Na1v3751uu7qH4uLPfroozz66KP1tmllZcXSpUsJCQkhIyOD0tJSSkpKWL9+fb15m7JqFXzyCQQHw6OPwqhR0KZN4/Pv3buXqKgooqKiMDIyuqJt3WjFxcVMnjyZ6OhorKys6NGjB7/++isff/wx48aNIzg4mGXLlnH//fdjZGRE+/bt6d69O1u2bGHatGm0b98eR0dHQBes79WrF6ALYM+YMaPJbZuamhIQEICJiQnm5uZ1prVs2RKA9PT0estlZmY2a5nmzHel226Ki4sLe/fupV+/fhQVFV31e2lJSQn//Oc/+eabbygtLaVbt2789ttv9O7dm9TUVFJTU+nevTuvvfYajo6OJCYmMnDgQAYMGEB8fDweHh711rlu3ToefvjhOq/H3bt3X/Z4qJ+nsX1pqp7U1NQGazEkOzs7Xn/9dZ5//nneffdd7O3tDV2SEEIIcX04OOg684aEXBhXVQWJibouvXFxEBsLixdDVpZuupubLug7bhwEBYGvL9zkV5kQQgghhBBCCCHErUnOOAghxFWoqqpi6dKldOjQgY8++ohnn32W5ORkCfMKcYXs7e15/vnnOX78ONu2bcPKyorQ0FCGDh1KdHS0ocu76elDQhcHxUDXEdDMzAwHBwdDlHVbyMrKYty4cYSGhtK+fXuOHDnCli1bJMwrbhtt27bl/fff5+zZs0yePJmXX36ZPn36GLqsqxIVFYW3t3eT4bN27drVCfPqmZqaYm5u3uA0gIqKCg4cOMBf/vKXa1ZvY5qq5XJ1XqnIyEgmTJjAuHHj6NixIyEhIdx33308/PDDLFu27IrWZWamyzIcPAgzZoCnp6652aJFcPZs/fnDwsLo0KEDAQEB12Rfrqdvv/2WsrIyZs+ezfTp05k+fTrp6el06NBB63C7a9cu3nzzTUD3Q5uUlBROnDjR5HqfffZZSktLmxwKCwvr/WBHr6amBgBXV9d605q7zNWsuznTm/LZZ58xYMAADhw4QN++fbUrDlwJKysrVqxYQVFREUuWLKGoqEjrrK3vwvvQQw9pn5F8fHxYvHgxxcXFfPzxx/XWp5Riw4YNjB49WhuXn5/P0qVLeeWVVxqtoznzNFVPQ7XcDEaNGkVZWRmHDx82dClCCCHEjWVmdqEz79tv6zrzZmZCaips2wZz5uhCvRMnQteu0KLFhfk/+AC2b4fcXEPvhRBCCCGEEEIIIW4Dt2b7JSGEMKCffvqJF198kXPnzvHMM88wd+5c7QvaG23v3r3s3r2b+Ph4HnnkEUaOHAnA1q1bKSws1OZLSUnhmWee0TpqFRUVsWbNGkxMTHj88ce18ddKbm4uGzduJDk5mYCAAO67775GQwOXulzt+fn5fPbZZyQnJzNs2DAGDx6MiYlJo+uLiopiz549mJubM2zYMNq0acOhQ4e0EMbFgoOD8fLyanSfVqxYUe8StM2tJyMjg/j4eAYOHHjZx6Chmv+MiooKdu/ezdGjRwkJCaFv3751avz2228bXK6px+NaMzIyIjQ0lNDQUHbu3MmcOXPo0aMHY8eO5d1338XFxeWG1HGr6du3LwB79uxh9uzZ2viYmBiqqqrqXTpaNM+qVauYOXMmNjY2bNq0iREjRtzwGho7vut99NFHlz1GXOtjid7ljikNKS4uZu3atZw9e5bg4GCGDBmCmZnZVa83IyPjsmGypo671+rY3Zz90mvsfQQu/953PbVu3ZqFCxcyYcIEpk2bxrPPPsu7775LixYtrvu2/6zIyEi++uordu3axU8//XRdthEREcHChQtv2c7FjYmOjiY9PZ1PP/2U0NBQ2rVrx9mzZ4mIiCA6Opq333672esyMwMTE6ip0Q0Af/wB0dEwaxb4+MBjj+m693bpAufPn6dVq1bXac+urdjYWNzc3JoMOXt4ePDbb7+xZcsWBgwYQIcOHRrs0nwxU1PTZr2m2rZtS01NDRUVFVhYWGjji4qKAPDz87vqZZoz35VuuymrVq3i+++/59ChQ9x9991MnTqV6dOns3nz5itaj56xsTEvvPAC+/fvZ8OGDYCuwyyAk5NTnXn1n4cSEhLqrWffvn1UVlZyzz33aOP++c9/0rt3bzZt2gTAiRMnKC8v54cffsDe3p5BgwbVm6eh+Zqq50o7Yd8oDg4OmJiYcP78eUOXIoQQQtwc3N11Q2io7v6nn8KJE7pOvvpuvm++CTk5uulubroOvv7+4Ocn3XyFEEIIIYQQQghx5ZRSTQ1CCCH+X1FRkXryyScVoMaOHauSkpIMWs/hw4fViBEjVEVFhZo/f76ysLBQJSUl6vjx48rIyEgB2vDoo49qy8XHxytXV1fVqVMnBagOHTqo9PT0a1pb165d1YEDB1RJSYl65513VEBAgEpLS7vscperPTc3V3Xo0EGNGzdODRo0SBkbG6s+ffo0uK7s7Gw1adIkNXTo0DrPVW1trerQoUOdbeiHyMjIRmt76KGHlIuLS51xzaknKytLzZw5U7Vo0UI999xzTe5/YzX/GZmZmcrLy0utXLlSZWdnqxdffFENGzZMVVdXK6V0j0dDj8XlHo/rrba2Vn333Xeqffv2ytnZWW3ZssVgtRjazJkzFaBOnz6tlFJq2LBhysbGRtXW1iqllHryySeVjY1NndfMsmXLVKdOnVRFRYVBar5VVVRUqMmTJysjIyM1Y8YMVVxcbJA6Gju+K3XhONHUMeJ6HEv0LndMaUh8fLzq2LGj2rp1qyoqKlJr1qxRnp6eavfu3Ve83ouPqY253HH3Wh27m7NfF2vofUSpy7/33Ui1tbXKwcFB9ezZUyUnJxukhisRERGh7Ozs1Nq1aw1dyi2ntrZWLVq0SA0cOFBZWFgoKysrFRwcrD755JMrfu+YM0cpCwuloPHB1FT39/XXlXrxxe+Uo6OjqqysvD479yfFxsYq3akSpWbMmKGsrKyarPXVV19V/fv3V6WlpUoppR577DHVoUMHbXpBQYEC1IIFC7RxERERauHChU0O77zzjnrzzTcVoE6cOFFnm2vWrFGAOn78eL16mrtMc+a7km1XVFQooNFj5oABA9TcuXO1+9OmTVPm5uYqLy/vitZzqRUrVignJyellFLp6ekKUH//+9/rzFNbW6vMzMzUhAkT6i3//PPPq0mTJtUZN2zYMOXh4aEN1tbWytjYWHl4eKj77ruvwXkamq+pehqq5Wagf97Dw8MNXYoQQghxa0lNVWrTJqXeflupceOU8vNTythY9yHY1lapoCDd+PffV2rvXqUMdL5BCCGEEEIIIYQQN4UvVBOZXQn0CiFEM6SmpqoePXooZ2dntXnzZkOXo5RSaujQoeqNN95QSum+FE5NTVVKKfXUU0+pnTt3quTkZG0oKyurs1xUVJRSSqnJkycrQE2cOPGa1VVTU6Nmz55dZ1yfPn3UkCFDLrvs5Wpfvny5ys3N1e4vWLBAASosLKzOes6cOaOcnJzU2LFj623jt99+U88995w6c+aMqqioUBUVFeq3335T7du3b7SuFStWqE6dOtULYjWnnoiICBUVFXXZYEJTNV+tmpoaFRISoh588EFtXHV1tWrXrp2aM2eOUkr3eFz8WDTn8biRCgoK1Pjx45WxsbF67733DF3ODbdr1y7l7e2tADV58mSVnp6ubG1tFaDmzZunqqqqVFlZmZo+fbry9/dXX3zxhfr000/VsGHDbokw3nXx/8fCqzFy5EhlY2OjNm7ceA0LunKNHd+bc5y4HscSveYcUxoydOjQemGpJ598UvXv3/+K13vxMbUxlzvuXqtj9+X262KNvY8odfn3vhvt5MmTqmvXrqp9+/bqzJkzBqujuWpqagxdwi2jslKp8+d1Q06OUqdO6YaEhEoVGanU4cO6YedOpbZtU2rjRqXWrtUNX32l1Cef6IaPP9blFPTD8OGXD/ReGuyFRDVqVIy6xr8puyYuDvSuXr1aAerDDz+sM09eXp5atmyZOn36tALUJ598ok0bM2bMZQO9X3/9terZs2eTQ58+fVR6erqysLBQX3zxRZ3tz5w5U3Xv3r3B139zl2nOfFey7csFcR0cHNS0adO0+7/88osCVHx8/BWt51LPPfdcncDs/fffr3x9fevMk5CQoAC1cuXKOuNra2uVh4eH+vnnn5vcxosvvtjg8bs58zVWz6W13CxeeOEF1bZt2yZ/qCOEEEKIZios1H3AXr1aqeeeU+ruu5Wytr7w4djNTfdh+vXXlYqJUer/f7gthBBCCCGEEEKI216Tgd7b67qhQghxHeTk5DBo0CBqamrYt28fHTt2NHRJgO4SwP379wfAyMgId3d3MjIyiI6O5rXXXmvw0uqRkZE88cQTBAQEALBgwQI+//xz9u/f3+zt1tTUsH79esaMGdPg9IMHD9KjR4864/r06cPSpUuJjIwkKCioweUuVzvA/fffj6Ojo3Z//PjxvPbaa9ja2mrjKisr+dvf/oajoyP//e9/663D2tqaJUuWYHzRpe42btzI6NGjG9xmYmIiR44cYfjw4axZs6bOdppTT+/evamsrGxw3c2t+Wrt2bOHsLCwOpcyNjEx4cknn2TRokX861//wtramvbt29dZrqnH40aztbVl9erV9OzZkxkzZjBz5kxDl3RDDRgwgFOnTtUZV1BQUOe+qakpS5cupaCggNjYWDw9PZk0adKNLPPm4uurG8aPh0cegdatm73ozp07+fXXX7VLcxtKQ8f35hwn/uyxpKamBhMTk0anN+eYYmVlVW+59PR08vLy6oyzsLCgoqLiitfbnGNqU/Ncy2P35fbrYg29j0Dz3vtutA4dOrB7926GDBnC/fffT0REhHbZ+JuRsQEuXVtbCxcfigsKdOMAiouhqkp3u6wMyst1tysroaQEamqgsPDCshe/hAoLddMBSktB/1KqqNDdB6iuhqIi3W2lID+/+TU0zuyy+2xqCjY2uttGRmBvf2Gag8NlF9fU1ICtLXh4JLN373sYG68GnJu/ghvg4vfZMWPG8OqrrzJr1izKy8sZPnw4x44dY/369Xz22WecPXsWgG+//ZZHH32UqKgo9uzZQ0VFBcXFxSilSE1NBXT/3vWeeOIJnnjiiWbV88wzz/Cf//yH8ePHY2RkRHl5OZs3b+bbb7+t8/qfPXs258+f59NPP23WMq6urpedr7nbBrTjYXkjL7iHHnqIH3/8kaVLl2JsbMzBgwcJCAigU6dOzVpPWVkZixcvZuTIkXTt2hWA3Nxcjhw5Uuf9Y9GiRQQHB7N//37uuusuQPf+7uvry4QJE+qs88CBAxQXFzN48OBmPRdXo7F6bsbPauHh4SxdupSlS5c2+XlACCGEEM1kYwNBQbph/PgL49PSIDJSN8TFwbp1MH8+2NlB167g7w9+fheWbdHCcPsghBBCCCGEEEKIG04CvUII0YTa2lpGjx5NdXU1+/btw8XFxdAlsXv3bmJjY0lJSSEiIoJPPvkENzc3HnzwQT766CPCw8Np27YtXl5evPbaazz55JMYGRkB0L59e3r27Kmty83NjaCgIExNL/92UF1dzTfffMPChQvJzMxsNNCbkJCApaVlnXG9e/cGICwsrNFA7+VqB/Dy8qqzTHR0NMOHD6dbt27auFdeeYVDhw7x6aefNhgsuzSoV1tbyw8//MD69evrzVtVVcWrr77KZ599xuuvv15nmrm5ebPqaY7L1ayXlpbGp//H3n3H13i3Dxz/RPaOhExJbCJWE2IEMWIHUaP61KZUW7S0yq8oiqdVSp+KParWY9QINSr23jMhIcheRCJD9v374zznlp0TIif4vl+v8zon9/ie6z6J6xz3ue7ru2YN7u7uKhUe7Nq1C6BAPA0bNiQlJYUDBw4wYMCAPOuKez3UaeLEiWhra+Pr60vv3r3VHU6FZGpqKheKvNeysuDSJbhyBcaPh06dYMgQ8PZ+WY1WiK1bt7Jlyxa1FvMWl99VyROq5pL8cuf3wMDAIrd7lZwC8OGHHzJz5kw2bdrE4MGDSU5OZvfu3fz222+vNe6rKMvcXdJxKWVmZvLzzz8XeB8B1d771MHc3Jy///4bFxcXvvzySzZu3PhGny930Wn+AtSi1ikfv86+qoyZf5vcRbevQ0/v5ffyRT0ual3lynmXKwtqVdlX1ce5i3TNzBRFvEX56Se4c6fo9RoaoKmpKDhev15xrcWLFx/g5nafnj17cuTIEcxyVwir0aVLl5g9ezYABw8epHv37hw+fBhvb2+mTJnClClTcHZ2ZuPGjRgbG9OoUSNGjhzJn3/+iaurK9988w2///47//rXv+jTpw/fffedfIHFvn37aNGiBUNzF3Oo4JdffkFLS4vevXvTpUsXoqKimD59ep7P9Mrx4+Pjyc7OVnmfkrZTdZyDBw+yYcMGAPbs2UPz5s3x8vLC2tpa3mbp0qVMmDCBJk2a0LJlS2JjY9mzZ0+ewuDixsnJyeGvv/5ixowZNGvWjG7dulGlShUOHDiAkZGRPIazszNnz55l0qRJuLu7o6ury/nz5zl69GiB//Ps2LGDXr16oaOjU6rfSWkUFc/YsWPf2HO+inv37sm/5zFjxqg7HEEQBEF4t9naKm69er1cduaMorjX319R6Ltpk+KqPi0tcHDIW+Dr7Aw1a6ovfkEQBEEQBEEQBEEQ3ihR0CsIglCMDRs2cP78eS5fvlwhinkBHB0d5TbrVlZWuLq6yl9ie3h4kJmZyfnz57l48SIjRoxg8+bNHDp0CE1NTSwsLAqMFxYWxueff17k82VmZrJhwwb+/e9/ExsbyxdffME333xT5Pb6+vpcvnyZjz/+WF5Wq1YtAEJDQ4vcr6TYc5MkiR07djB79mwOHz6cZ93WrVvR0tLi9u3bdOzYkUuXLuHi4sKSJUsKFB8AnD17Fg0NjUKL+ObMmcNXX32FcTFFgCXFowpVYj5+/Dhbt26lc+fOeHt7M3ToUHx8fIod98GDB4CicDs3S0tFJ7ygoKAC+xT3eqjb559/jr29PV26dClQNC4IeUjSy4q7Y8fg6FFFNZmnJwwaBP37g4GBvHlWVhZTp04lJCRETQErFJff8+eJrKysAnmttPmvsPxenFfJKQBjxoxh8+bNDBkyhGvXruHv78/KlSvp27fva437ul43d5d0XEpz5szhxx9/LHSM0rz3lTdra2t8fHzo128BWloxVKliVWRn2aQkRS09FN1ZVrl97q6yZUFZeGpgALq6ise6ui//iRfXWdbU9OX+hoagrOlTLtPWBmWdYKVKiu2Vche5Ghsrnid/HDo6inFBkYJyNYF+p2hrK36v+enoKDoT164No0bB8OGg/Ditr2/O4cOH8fDwoE2bNuzbt69Asb06uLm5cejQoTzLnJycCAwMJCQkBA0NDRwcHPKsX7t2LUuWLMnzefH58+fo/u8PoUuXLq8Vk4aGBj/99BPZ2dk8efKkyP+TXL58mczMTDl3qLJPSWOr+tzdu3ene/fu/Pe//y3yOAwMDFizZg2pqamkp6dTuZDWzsWNY2hoyLVr10hISEBHRweDXO/j+TVu3Bg/Pz8iIyPR19fn//7v/wrdbsKECXm6sxdlwYIFLFiw4JW3UzUedTl+/Dj9+/fHycmJ7du3q/2iEkEQBEF4L7Vpo7gpZWdDSMjLAt+rV2HjRkUnX1D8p0VZ5Kvs6NusmeIqPUEQBEEQBEEQBEEQ3mqioFcQBKEYS5cuZfDgwTRp0kTdociqV68uTxVuZ2dHs2bN5HVdunSRiwZu3rzJoEGD8PPz45dffmHq1KkFxjp16hRaWlp8/fXXBdalp6ezbt06fvrpJ+Lj4/nyyy+ZPHkyVapUKTY+d3d3Fi5ciCRJ8pfByqmLq1evXuR+qsaekpLC119/zebNm0lNTaVRo0b8888/NG/enIiICCIiImjatCkzZ87E3NycoKAg2rdvj4eHB/fu3cPOzi7P8+7YsYO+ffsW+OL65MmTaGlpldjxtLh4VKFKzKampowePZpbt25haGjI4cOHWbZsGUOGDKFly5ZFjh0TE4OmpmaBrmPKAoioqKgC+xT1elQU0dHR7N27t8gO0YJQgLKwNycH/Pzg0CEYNw4+/BAGDoTu3Tl+/HixFxyUl6Lye2F5wtbWNk9eK03+e9X8/io5BcDKyorTp0/TqlUrFi9eTKtWrfLk1lcd93W8bu6Gko8LXr6XFKW079vlrW/fvtjZHePAgQzs7fN2bTUxURSpAlhbvyyGzd3lNXdBbO59TU0VBbKgWK+trXisr//y++fiimELqQMU1EhH52VBr5aWIu2amCgKeIcPh6ZNC9+vVq1aXLx4kV69etG0aVN+++03hg8fXk5Rl56jo2OR6/Jf/KUs5i1LmpqaxV5gmLtLrar7qLqdquOowsDAoNhi3JKUppuzra1tsevLu4i8pHjKW0ZGBrNmzWLBggX079+f9evXoy+m9BYEQRCEikFTU9GFt2bNvJ18ExIU02Ncvaro6Hv1KqxerZhWRFsb6tR5WeA7btzLK+oEQRAEQRAEQRAEQXhriIJeQRCEIqSmpnL9+vVCp8h+GzRp0oSrV69Sr149tm7dWmhh0MyZM/H19c1TAJCWlsaqVatYsGABz58/Z/z48UyaNKnQ7r6Fsbe35+rVq4wYMYKBAwdy9+5ducuWqoXRxcVuaGjIqlWrWLFiBf/5z3/45ptvGDduHFeuXOHatWsAeHt7y0VxdevW5ddff+Xjjz9m2bJlzJs3L89z/fXXX2zatCnPsoSEBJYuXcrWrVtLjLW4eFShSszVq1fnxYsXTJkyBVAUt9WqVYsHDx4UW9BbWGEHQPb/ChxzT4MMim6Vhb0eFUmrVq14uHv3y0owQcivsDaRSpmZivvUVPjvfxVTWJqbY1avHv3yFftXJIXlifx5TZVcMmPGjNfK76XNKbmtXbsWDw8PPDw8WLduHS1atODUqVM4ODi81riv6nVzt1Jxx1Wa9xJQ7X1bHT78sBJXrgzi7Nmz6g5FqKB0dBTpVUsLundXdOPt0eNloXZx7OzsOHfuHLNmzWLUqFGsWLGCn3/+GQ8PjzcfuCC8x/z8/Jg4cSKPHj1i0aJFTJgwocJe0CcIQvGUnc8zMzNJTk4G4Nn/pkZQritMVlYWSUlJRY6ro6ODofLqqnxyd1k3MDBAV1cXbW1t+XN9YV3YBUEoI2ZmBbv5ZmVBYKCiwFfZ0XfVKkU339ydfJXdfBs1enlF5psQFaWIU1wo9MoSEhKQJImUlBQyMjLIyMggJSWlwPrCFJf7jYyM0C7iP2r5876ZmRkaGhoYGhqio6NT7PuCIAiCIAiCIAiCULZEQa8gCEIRnj17hiRJKhc6VUQGBgb06dOHdevWFbp+0qRJfPDBB3mWnThxgh9++IGEhAQmTZrE1KlTC3QdA7h48SJffvllnmULFy7Ew8ODEydO8M8//3DmzBkGDRrEhQsXuH//foHnep3YK7/ZZ70AACAASURBVFWqxFdffcW5c+f466+/SE9Px/R/c2Hn7zLZqlUrAAIDAwuMk5GRQbt27fIs+/rrr2nevDm+vr7ysvv375OWlsauXbswMzOjY8eOxcajKlViTklJwcbGBh8fnwL7F/d7sLe3Jzs7m/T09Dyd4pRf2jVo0CDPfmfPni309ahILCwsaHLjBmzbpu5QhIpK1a6IWVmK+/h4mp8/z+9v8sus11RYnsif11TJJa+b30ubU5TWr1/Ptm3buHz5MlpaWri7uzN27Fi++OIL9u3b98rjloXC3ktU7axZ0nHlfy8p6X0ESn7vU4fKlSuTkJCg7jCECqxWLViyBP71L6hatfT76+np8dNPP9G/f3++++472rdvj6enJ4sWLaJx48ZlH7AgvMfOnTvHzJkzOXr0KF5eXuzfv7/cOxULwvtMkiTi4+PlW2hoKCkpKaSkpJCcnExCQoL8c1JSEomJifLPz58/Jzs7m+fPnwOKmZBycnLUfETFUxaO6erqyoW/hoaGmJmZYWRkhKGhIdWqVcPQ0BBDQ0OMjIwwMzPD0NAQExMTzM3NqVy5Mubm5ugpp3EQBKEgLS1Foa6zMwwY8HL5mTOK4t6rV+HsWVi5EtLTX3byzV3k26TJq32YL8zGjfD777BwoWJmpHf8oqH09PQ8uT0pKYmUlBSePXumUo5PS0vjxYsXJCcnF3uhRUWhoaGBmZkZxsbGaGlpYWxsLOfx3Pnd0NCQypUrF8jx+fN7JdG0QRAEQRAEQRAEoQBR0CsIglAEa2trdHV1uXfvHu7u7uoO55XVr1+funXrFli+atUqxowZU2B5t27dePz4Mb///juLFy9mw4YNTJ48mS+//DJP4ZeDg0OBgq+aNWsCyN0KAR49eoSvry+//PJLoYVjrxJ7bp07d+b48ePo6urK2169ejXPNg4ODmhraxf6/H369EFTOWf4/8TFxXHkyJE8yxITE0lNTWXChAk4OzsXWoiVOx5VqRKzpqYmgYGBZGZmFuiiUNzvwcnJCYCwsDBq164tr3/y5AlQsEhu586dhb4eFcndu3c58+GH9Jg/X92hCBWVKt1CtLQUBb3GxuDtzW5jY8Zs20bcm4/ulRSWJ/LnNVVyyevm99LmFKUNGzbQvXt3tLQU//UYOXIkV65cYe3atSQkJLzyuGUp93uJqko6rvzvJaq+j6jy3leegoODqVatmrrDECowT0/F7XU1a9aMo0ePcujQIaZOnYqLiwu9evVi3LhxeHp6ii96BeEVpaWlsXPnTpYtW8b58+dp3749p06dom3btuoOTRDeek+ePCE2NpbY2FgiIyOJjY3NU9SV/6bsnJtb/kInAwMDuaDV3t5e/lnZKdHMzAxALqTS09NDX18fTU1NTExMgJcdcnN3zS2McszCKLtC5pe7EzBAcnIymZmZpKenk5qaSk5ODomJiQByEfKLFy/kgjVlcXJSUhJJSUn4+fkVKHgrrOukgYEB5ubmeYrAct+srKywtLTEysoKa2tr8flVEKBgJ9/MTAgKUhT4Krv5zp8PsbGK9TY2Lwt8lV19nZxKP0vWjRsQEQEffwyLFoGPDzRvXnbH9YZlZmYSGxtLTEwMUVFRxMXFERsby9OnT+V8/uDBA549e0Z8fHyezrm55S9mNTU1lX+uWrWqXAirvOBBuQ2AiYkJmpqa6Ovro6enJxfOKim75hamuNxf2PuQUu68L0mSfGFvUlISWVlZch5XXlyivLBEWZicmppKYmIiUVFR8s/5i5oLY2ZmhoWFBebm5tSqVUvO6xYWFlStWhUbGxusrKyoWrUqlpaWRcYvCIIgCIIgCILwLhEFvYIgCEXQ1NTEy8uLtWvXMnLkyLd2CtLdu3fTp0+fAsvyf0Fy8uRJuQjX1NSU6dOn89VXX+Hj48OiRYtYtGgRkydPZvz48RgZGWFjY8OwYcOKfe6MjAw++ugj6tWrx+eff14msed3584devXqBSiKsLt27cqFCxfybHP//n0yMzMLFGZLkkS/fv0KjLl///4Cy6ZMmcKff/5JeHi4yvGoQpWYdXR0SElJYcWKFYwfPx5QTK22ZcsWPv/88yJ/D6NGjeLHH3/k7NmzeYrkrl69StOmTfMUjEmSxM6dO1m9erXKsatDYGAg3t7e6g5DeBtpaoIkKYp5e/WCYcOgWzfQ1qZBYCBPli1Td4RFKixP5M9rqua/18nvpckpud26datAUW6fPn1Yvnw5MTExrzxuWSpt7oaSjyv/e4mq7yOqvPeVl+TkZP7++2+mT5+u7lCE90i3bt3o0qWLXIDYtWtXateuzdixYxkxYsRbPXuGIJSn4OBgVq5cyfr160lMTMTb25uTJ09W6Nk4BKEiiYmJISwsjLCwMC5fvkxUVBSxsbFER0cTExNDbGwsmZmZ8vZaWlpYWlrKRUnm5ubY2dnRqFGjPMVJuYtSHR0d1XiExVMWnRXmTRdUvXjxgsTExDyF0IUVSIeFhXHr1i25sDp3UZ25uTnW1tZUrVoVW1tbudi3WrVqODg4YG9vT7Vq1YosiBOEd5K29stOvrlFRr4s8L16FfbtU3TXzc4GIyOoV+9lga+ra94i4cJcvqw4/wJw/Tq0aAEffqgo7lVj3svMzCQiIoKwsDAeP35MREQE0dHRxMXFyRdlxMbGyhcXKxkYGMj53cLCgsqVK9OrV68CFxYoc7upqSkGBgZqOsriKS/4KO26spKYmFhoflcWSz948AB/f3+ePXvG06dPi3yvtbKywsbGhqpVq2JtbY21tTX29vZyfre2tn7jxyIIgiAIgiAIgvAmiYJeQRCEYkybNo0WLVqwYsUKxo0bp+5wZBEREQBER0fLy4KCgli2bBnDhg3jgw8+AMDf35+UlJQ8hTh+fn78/PPPDB48mKVLl5KdnU1AQAANGzaUC3qVjIyM+O677xg/fjwrVqzgl19+YdGiRXz77bd89913xcaYkpLC559/To0aNfj999/lDoa5TZkyhfj4eKZMmaJS7PPmzaNPnz40bNgQgKdPn3L9+nX27dsnb7No0SJatmzJuXPnaN26NQDHjx/HycmJ4cOH53n+8+fP06lTp2KPoygvXrzg119/LTEeeNn9IC0trdCxSoo5Ozub6dOn88033+Dp6cnt27fZuXMna9euLTZGa2trvvzyS3755ReGDh2KhoYGaWlp7Nu3j61bt+bpdHf+/HmSk5Nf+fUoDykpKXTt2hU3Nzd1hyK8LZTdpjU0oHNnGDQI+vUr0MW3Xr16dO3atdAu2OWtsPwOBfNEYXmtNPmvqPye/4ur3EqTU5T5fc2aNXh7e7N7926WLl0qb3PhwgUaN25MnTp1qFSpksrjQvEdZfJvkz/vlmXuLum4SqLq+7Y6zZs3D0mSSryARxDKWqVKlRg4cCADBw4kICCA5cuXM3fuXGbMmEGPHj3o168fXl5ecidCQRAUIiIi2L17Nzt37uT06dNUq1aNiRMnMmrUKGxsbNQdniBUGFlZWYSEhPDw4UPCwsIIDQ0lJCRELuANDQ3N8xmwSZMm2NraUrVqVZydnbGxscHS0hJra2u5M6zoGlh29PX10dfXL3VBVkpKilxwfePGDbk4LyoqSi7KjoyMJD09HVBMG29tbY2joyP29vZyMZiDgwOOjo7Url1bfNYQ3g+2topb7mk3MjLg/n1Fga+yo+/cufDkyctOvrm7+TZooDj3kp4Ojx69HCcrS3Hv6wv798NXX8H33ytmTHoDkpOTefDgASEhIYSEhHD27FnCwsIICQkhOjqanJwcAHR0dLC1tZXzeb169fDw8MhzEYClpSW2trZFXtwglJ6pqSmmpqY4ODiovI+yQ7KyG35cXBzR0dFyMfbNmzeJiYkhOjpabmCiq6sr53V7e3vatm2Lvb09NWvWpHr16mo/9ycIgiAIgiAIglASjcKmsMql2JWCIAjvg1mzZjF//nx2795Nz5491R0OFy5cYMGCBezevRt7e3vmzp1Lnz59CA4OpmPHjiQmJtKhQwfc3NwwNzdn/Pjx6OvrA3Dt2jXatWtXYCowPT09IiIiMDc3L/a509LSWL16Nb/++iuPcp+czWfdunWsXbuWb775hr59+xa5nZOTE/Hx8ezfv5/OnTsXGzuAi4sLN27coFmzZnTr1o0qVaowcuTIAtOI3bp1i0mTJuHu7o6uri7nz59n1apVBb7E/vrrr1m8eHGxx6yk7KyoLLJLSUmhbdu2JcZz8OBBNmzYwLZt27C0tGTevHl4eXkV+GKqpJjv3r2Lt7c3QUFBODs7s3HjRrkArDiSJDFt2jT8/f3p0qULUVFRODk5MWTIkAKvxZMnT9i4caNKr0d5S09P58MPP2TZsmUVuouRUAEYGkJqqmJKyHbtYOhQ6NsX/jc1bVHu3r3LihUr+O2338op0IKKyu/KKRdz54lr164VmtdUzX/5KfO7sgt4UVTNKcr8rvzCfMKECVy8eJHRo0dz584dYmNjWbJkCTVq1CjVuLlz6urVqwvNp8Xl3bLM3ampqSUeV27530euXbtW4vu2Oh08eBAvLy98fHz47LPP1B2OIJCSksL27dvZtm0bx44do1KlSnTu3Jl+/foVuGhBEN43jx8/5pNPPuHChQsYGRnh5eXFoEGD6NGjB5rKC5wE4T2TlZVFaGgoDx8+LHALCAjgxYsXgOJ8iK2tLTVr1sTGxkZ+rPzZ0dGxyKnLhbfTs2fPePjwIZGRkURFRRV4HBISQnZ2NqDoWKn8e/D09KRBgwY0bNgQsxL+fykI7yRJUhTrbtwIN28qbo8eKZabmUHjxopi323bih5DSwvMzeHHH2H0aMW5m1JKT08nIiICf39/AgIC8uT3R48eyUWdlStXxtXVNU9OV+Z4R0dH8RnpHZORkcGTJ0/kXJ47tx85coSEhARA0eXXwcFB/rvIfXNycqqw3ZUFQRAEQRAEQXjnbACGF7VSFPQKgiCUQJIkxowZw4YNG1i2bBmjR49Wd0hFSk9PJzQ0FAMDA+zs7N7Y82RkZBQ7LeHDhw+pWbNmieMkJyeTmZlJ5cqVVY49ISEBHR0dlU6uRUZGoq+vX+SUYY8ePSq06Ko0ShOPKkqKOTQ0tFRdDJSys7N58uQJVlZWha5/9OgRJiYmFXIa67i4OPr168ft27dV6swpvOe6dAEvLxg4EErZ0UlTU5Np06Yxd+7cNxRc2YiMjMTW1rbEbYrLJa+rpJySO78rpaamEhISgrW1dZFxlTRuWSnL3K3KcRWlvN63S+vIkSN4e3szcOBA1q9fr+5wBKGAZ8+e4evry19//cU///xDixYt6NSpE56enri5uRU6M4QgvEtSUlI4deoUR48exc/Pj5s3bzJs2DD69etHly5d0NXVVXeIglCuMjMzuX//Pnfu3GHr1q3cuXOHx48fk/W/zpBVqlShTp061K5dW76vXbs2tWrVKvHCZuH9k56eTkhICA8ePOD+/fvcv3+fBw8ecPToUflvqmrVqjg5OeHs7Ezjxo1p2LChKPQV3k/Pn8Pt2y8LfP384PFj+F8n3EJpaCjuGzeGpUuhTZtCN0tMTMTf3587d+5w+/Zt7ty5Q0BAALGxsYDiHI6ym7bypszx1atXR09Pr4wPVnibJSQkEBwcLOf23Pf5/6YaNmwo53dnZ2fq168vuvoKgiAIgiAIglDWREGvIAjC65IkiR9++IG5c+cyaNAgfHx83liRlCAILx06dIiRI0eiq6vL/v37cXZ2VndIwjvsjz/+YPTo0Xz00UesXr1adOUQ3ks+Pj58/fXXDBo0iPXr14uuRUKF9/z5cyZOnMjRo0cJCwvD2NiY9u3bywW+DRo0QENZNCAIb6msrCwuXbqEn58fR48e5cKFC2RkZODs7Iynpyfdu3ena9eu6g5TEMpFbGwsly9f5vbt29y+fRt/f3/u3r1LRkYGWlpa9O3bl4YNG1K3bl25wEsUWQplITMzk8ePH8tFvgEBAdy5c4c7d+7w/PlzAOzt7XF2dqZRo0Y0bNgQFxcXnJycxGdq4f0xcSKsWAEZGSVvq6kJ2dnQowd3Fy7k2rVrcuHunTt3CAkJAcDY2Fj+d+Xk5CTn9xo1ahTbcEIQVPX8+fM8Rb7Kv8OgoCCysrLQ1tamXr16NGzYkEaNGtGoUSOaNWtW4mxYgiAIgiAIgiAIxRAFvYIgCGXlyJEjjBgxghcvXjBlyhQmTpworvYXhDcgMDCQGTNmsGPHDgYMGMCKFStE5yShXJw8eZKBAwdiZGSEj48P3bp1U3dIglBuHj16RK1atZgyZQrz58+n0itMfyoI6vTw4UP8/Pzkosf4+HhMTExo1KgRbdq0wd3dnVatWlGlShV1hyoIxYqKiuLKlStcvXqVs2fPcu7cOVJTU7G2tqZt27ZyEa+9vb26QxWENyo5OZkbN25w9epV+Xb37l0kScLGxgZnZ2caNGgg37u4uIiL8gS1iIyMJCAgAH9/f/n++vXrpKamYmhoSNOmTXF1dZVv4oIj4Z3Vpg2cPVvq3X4EFmlpYeHgQIMGDXB1dZVzu5OTk/i/qaAWmZmZBAUFFcjv9+7dIycnBy8vLzmvt27dukLOfCcIgiAIgiAIQoUlCnoFQRDKUnJyMgsXLmTBggVUrVqV77//ntGjR4sTi4JQBp48ecKPP/7IsmXLqFu3LrNmzWLAgAHqDkt4z0RHRzNlyhQ2btyIl5cXK1aswM7OTt1hCcIbk5WVhY+PD9OnT8fX15cOHTqoOyRBeG3Z2dlcu3aN8+fPc/HiRS5cuMDDhw/R0NCgfv36tGjRghYtWuDi4oKzszOGhobqDll4T8XHx3Pz5k2uX7/O+fPnuXDhAuHh4WhqatKgQQNatmxJy5YtcXd3p169euoOVxDeqNDQUI4fP86JEye4fPkyd+/eJScnBxsbG5o3b57nJi74FCq6rKws7ty5w6VLl7h8+TKXLl0iICCArKwsqlatipubG61bt2bKlCloaWmpO1xBKBsmJpCUVGCxpKmJJElUyskBIB0IBh6bmpJeqxZ1v/2Wul5eaBsZlW+8gvAKEhMTuXLlCj4+Ply+fJnw8HAqVapE3bp1cXNzo127dnTo0IGaNWuqO1RBEARBEARBECouUdArCMKre/jwIXPnzmXOnDlUq1ZN7eNURHfu3OG3335jy5YtaGpqcvbsWRo1aqTusAShwktLS2P79u0sW7aMixcv4uzszLhx4xg5ciT6+vrqDk8QAEhJSeE///kPS5YsITk5mVGjRjF58mQcHR3VHZogvLKUlBTWrFnDokWLiImJYfDgwXz//ffiy6bXEBYWxq5duxg/fjwBAQHs3bsXBwcHPvnkkzwXfSUlJXHgwAHu3r2Lvb09Xbp0ER021UiSJB49esTNmzflqdtv3rxJcHAwBgYGNGjQgHr16lG3bl3q1Kkj3xuJQgOhBE+fPiUoKEi+3b9/n8DAQO7du0dGRgZNmzaVp+tt0qQJjRo1ElP2Cu+N7Oxszp49y9ixY7l37x46Ojq4ubnRsWNHOnToQMuWLcVMSMJ7ZeXKlRw/fpzjx48TGxuLmZkZHTt2pE+fPnh5eYnideGtkJCQwNlVq8hYvx6/8HCuJCfzxMKCD9q3p0OHDnTo0IEGDRqoO0xBKBcZGRlcvHiRY8eOcfz4cS5cuEB6ejp16tShW7dueHt7065dO3ExhyAIgiAIgiC830RBryAIr27nzp0MGDCAAwcO0L17d7WPU5ElJCTwxx9/8PXXX9OiRQv69+9P//79qV69urpDE4QKIzMzk2PHjrFz50527dpFcnIyffv2Zdy4cXh4eKg7PEEo0osXL1i7di0LFy4kMjKSPn36MGzYMLp16yZOwAtvjRs3bvDHH3+wadMm0tLS+PTTT5k8efI7d7FVedu3bx+jRo0iLi6OxYsXc+vWLeLi4ti/fz/z589n2rRpANy8eZMhQ4Ywa9Ys2rZty59//smsWbPw8fFh6NChaj4KIbeUlBS2bNnC3bt3CQwMJCgoiMePH5OVlQWAnZ1dngLf6tWr4+DggKOjI1ZWVmqOXigP2dnZREZGEhISQkhICI8fP85TwBsfHw+Anp5enr8VZRGvuABUeN+8ePECPz8/9uzZw759+4iLi+O7776jY8eOuLu7iy7pgoDiQqOAgACOHTvGwYMHOXbsGNnZ2bRr144+ffrg7e2Ng4ODusMUBFlERAS+vr7s3r2bEydOANC+fXt69OhBhw4daNSokZjRThBQfA46d+4cx44dY9++fdy+fRtzc3O8vLzo06cP3bp1w8DAQN1hCoIgCIIgCIJQvkRBryAIr+fJkydUqVKlwoxT0R09epTNmzezd+9e4uPjadasGf369aN///7Url1b3eEJQrnLyMjgyJEj/PXXX3n+XQwYMIChQ4dibW2t7hAFQWWZmZls27aNNWvWcOrUKaysrBg8eDDDhw/H2dlZ3eEJQgFxcXFs2bKFP/74gxs3blCnTh2GDx/OmDFj3ovPZeVl2rRp/PTTT/j5+dGpUycAXF1d0dDQ4MqVKwA4OTkxcOBAZs+eLe/3ySefsHPnTq5fvy46VlVwmZmZPHr0SC7wvX//vnwfGRlJzv+mD9bT08PR0REHBwf5piz4tba2pn79+mo+EkFVcXFxXLt2jdDQUEJDQ+Xi3ZCQECIiIuQCbx0dHezt7alTp06Bjs729vaikEV4b0mSxLFjx1izZg379u3jxYsXuLm54e3tTd++falbt666QxSECi0pKYmDBw+yZ88eDhw4QGJiIq6urgwfPpzBgwdjZmam7hCF99TKlStZv349ly5dwtDQkO7du+Pt7U2PHj3E36UgqCA4OJg9e/awd+9ezp07h46ODj169GD06NF069ZN3eEJgiAIgiAIglA+REGvIAiCOuTuRLpnzx6ePHmCs7MznTt3xtPTEw8PDzFdr/BOW7ZsGX5+fhw7doznz5/j5uYmOlcL75SHDx/y559/smHDBh4/fkzTpk3p3bs3vXv3xsXFBQ0NDXWHKLynwsLCOPTXX+w8eJDjx4+jr6/PgAEDGD58OO7u7uJv8w2YM2cOP/zwA+Hh4djZ2QEwZMgQDh8+TGxsLAAaGhoFZqv4888/GTZsGJMmTWLRokVqiV14fRkZGYSHh8tFn48fP5aLQJXL0tPTATAwMMDGxgZra2usra2xtbXF0tISOzs7rKyssLKywsLCAgsLC4yNjdV8ZO+e+Ph4nj59ytOnT4mNjSUyMpLo6Giio6OJiooiJiaGiIgIYmNjycjIAMDY2DhPYXbugu3q1atjY2MjinYFIZfo6Gj++OMP1qxZQ3BwMK1bt2bo0KH07t0bGxsbdYcnCG+ljIwMjh8/zvbt29m+fTvZ2dkMGDCATz/9lDZt2qg7POE9cfHiRVavXs2WLVsYMGAAAwcOpFOnTujp6ak7NEF4a8XGxuLr68umTZs4deoUs2bNYuTIkWImJUEQBEEQBEF494mCXkFQp6NHjxIWFgaArq4uH374Ibq6uly6dImAgAAqV65Mnz59AIiMjOTQoUOEh4fj7u4ud/cCRWeTkydPcuPGDTQ1Nalfvz6dO3dWOY4XL16wd+9eevfuTWxsLAcOHGDs2LFoamoSExODr68vlSpVYsCAAZiYmMj75eTkcPLkSYyMjGjevHmJsZw4caLQ5YWNA4qCk127djF+/HgCAgLYu3cvDg4OfPLJJ3m+FF2+fDmhoaHUqVMHNzc3nJyc0NTULO2vQ22ysrKYPfsud++ewd9/D4GBR9HSqkSrVq3w9PTE09OT5s2bi2nbhbdabGwsR48exc/PDz8/P549e0b79u3p0qULffr0wd7eXt0hCsIboXyP27FjB/v27SM8PJxq1arRq1cvFi9ejK6urrpDFN5xkiRx48YNfH198fX15c61a6QA6To6pFavTuU2bdBq3BhcXaFJExBFgmWusILe4cOH8/fffxMXFwcoCnpPnTpF27Zt5f0ePXpEzZo16d69OwcOHFBL7EL5iI6OJiYmhuvXr8sFo1FRUXIRaWRkJCkpKXn20dbWxtzcHAsLC8zNzeVb7p9NTEywsrLCzMwMY2NjjIyMMDIyyvN/uneJJEkkJCSQlJREcnIyKSkpJCYmEhkZSVJSEk+fPiU+Pl4u3FU+Vv6c/xxY5cqVsbGxwcrKqkBxtY2NDS4uLlSuXFlNRysIb5dLly6xYMECfH19MTY2ZsiQIXz66adiFgtBKGPPnz9n69atrF69mqtXr+Lk5MSECRMYMWKE+L+nUOYyMzPZtGkTS5Ys4datWzRu3JiTJ0+KTryC8AYEBgbStm1b4uPj6dGjB1OmTBEXbQiCIAiCIAjCu0sU9AqCOqWmpuLm5oa/vz/BwcHUrFlTXufk5MTevXupW7cux48fZ+vWrYwbN44HDx4wcuRIhg4dio+PDwDff/89NWrUYPTo0Vy5coUvvviCixcvqhTDyZMn+fTTT7l//z6LFi0iMDAQMzMzgoOD6datGydOnCA7O5tt27bh5eWFr68vAAEBAfzwww/s3LmT5cuX89lnn5UYy5o1awosL2qcffv2MWrUKOLi4li8eDG3bt0iLi6O/fv3M3/+fKZNmwbAs2fPuHPnDi4uLgwZMoTdu3fTvHlz3N3dWbx4cdn8ospBt25w+LDisZYWVKmSjK7uQ549u8Dz59fQ1Q2jUSN9WrWqRrNmLnh4eODo6KjeoAWhCNnZ2dy7d4+rV69y9uxZzpw5w927d6lUqRJNmzbF09OTOXPmoKOjo+5QBaHc+fv7s3//fvbt28e1a9dwcXGhTZs2eHp60rZtW/Elq1BmVq1axZkzZzh+/Djh4eFYWlrStWtXPurbly4aGmgHBYG/v+J29y6kpip2dHQEJydo2BAaNFDcOzmBmDnglala0Pvzzz8zZcoUeb+cnBz09PQYMGAAmzdvVkvsQsWRnJxMTExMiUWpuR8nJSWRmZlZdpve+gAAIABJREFU6HgmJiZyga+xsTGVKlXC1NQUADMzMzQ0NDAyMkJbWxs9PT309fXR1taWZxHR0tIqskuwoaFhoZ/zXrx4QVpaWqH7JCQkyAW1ysfJyclkZmbK+2VmZpKcnExOTg6JiYmA4v+DycnJJCcnk6rMY/no6+tjbGxcYgG0svuxhYUFlpaWoqOcIJSBy5cvM336dP755x/c3NwYP348/fv3F/++BKEcXLt2jZUrV7JhwwYsLCyYOnUqn332Gdra2uoOTXjLZWdns3btWubPn09kZCT/+te/+Oyzz2jZsqW6QxOEd1pGRgZ79uzh999/58yZM3h4eDBv3jzc3d3VHZogCIIgCIIgCGVLFPQKgrrt27eP3r17s3r1akaPHg1AVFQUEyZMYMeOHSQnJ9OkSRNu3bqFoaEhAKNHj2bt2rWcP3+eFi1aYGlpyY4dO2jfvj0A8+bN4/vvv1c5hsWLFzNp0iR27NhB//79AUVRwV9//cWHH34IwPTp01m4cCGpqalyd9zbt2/TuHFjuRBXkqQiY1F27i0sxvzjKE2bNo2ffvoJPz8/uSOxq6srGhoaXLlyBYD/+7//Y/78+YDiRLWrqyuLFy/mq6++KtXvQd2mT4cFCyD/9+2KRsMS2dmK6a81NLKQpDDAHxOTKOrV06R588rMn99R/gJeEMpTTk4Ojx494saNG1y6dIkLFy5w5coVUlNTMTU1pUWLFrRs2ZIWLVrQrl07uQhEEARYt24dJ06ckAsuDQ0Nad26Ne3bt8fd3R0XFxcxpbqgkqysLPz9/blw4QInTpzgxIkTpKSk0K5dO9q3b0/Hjh354IMP0NDQKHqQyEgICFAU+Crvb9wAZVdQGxsYMACcnRWFvk2biiJfFala0NuzZ0/2798v73fr1i2aNGnC77//zpdffqmW2IW339OnT0lMTOT58+dy4WtSUhKJiYnyz8nJyWRlZZGUlCR3uQVFl7/s7GxSUlLIyMggPT1dLppVtTg3t9wFwfnlLgI2MTFBU1MTAwMDdHV10dXVxcDAAE1NTbm7sLLo2NTUVC5MNjIyonLlynl+NjU1FV10BUENoqOj+eabb9iyZQvu7u7MnDmzVDNJlaWMjAxOnz7N/v37y/zC79OnT7Ny5UoGDBggz7D1utLT0+VZrFq0aKHSDFTKfW7cuEGbNm2K3C89PZ3ffvutyG1UHQcUv+N79+7J5/mKcvPmTU6dOoWOjg49e/aUpwlPTk5m+/btPH78mJYtW9K5c+dCC01Lep6EhATWrl1LaGgoPXv2pFOnTiUeV+vWrfOsf/r0KXv37iU0NJTGjRvTpUuXEs9dFHVcqq4vT5GRkSxcuJDly5fj4OBAYGCg2mIR3n7Hjh1j4sSJBAYGMmbMGKZMmYKDg0O5xpA7r3fu3JkePXqU6finT5/m5MmT3Lt3r8zye2nya2H7FfWeUJr8r8p2r5vblbZu3So/DgsL48svv8TAwKDAOE+fPmXVqlVy85bcy0uTl4uL5++//+b58+clxlNULKUdp7ycOnWK2bNnc+zYMfr378+vv/4qZsATBEEQBEEQhHdHsQW9SJJU3E0QhDKQk5MjOTk5SU5OTlJOTo4kSZK0cOFCydfXV5IkSVq1apVkY2Mjff755/KtR48eUq1ataSNGzdKkiRJ7u7ukqWlpbRnzx5JkiQpLS2tVDGsXbtWAqR79+7JywDpwYMH8s8bN26UACksLExeFhgYKAHS8uXL5WXFxVLU8sLGkSRJmj17tgRI4eHh8rLBgwdLVatWlX8eOHCglJ6eLkmSJGVlZUmGhobSpEmTSnX8FcGuXZKkoSFJUPqbtvZZCZCqV68u9e7dW5o+fbq0fft2KTAwUMrOzlb3oQnvkISEBOn06dOSj4+PNHbsWKlly5aSkZGRBEiamppSw4YNpdGjR0tr166V7ty5I/7+BKEU7t+/L61evVoaPHiwZGdnJwFSpUqVJGdnZ2nYsGHS0qVLpYsXL5b6PV549+Tk5Ej37t2TNm7cKE2cOFFq3bq1pK+vLwGSkZGR1K1bN+mnn36SMjMzy+YJIyIk6cgRSVqyRJLc3SXJ0PDlhxAbG0ny9JSkCRMkaeVKSTp9WpKSksrmed8hkydPlgDp4cOH8rKePXtKxsbG8uf/YcOGScbGxlJISIi8jY+Pj1SnTh35s64gCIIgvA22bt0qWVhYSDVq1JB27typ7nCkq1evSmPGjJEUp7rLzpUrV6RevXpJs2fPlnR1daWUlJTXHjMmJkaqUaOGtHr1aunbb7+VevbsKWVlZam8T1xcXJH7KbcrahtVx4mNjZUmT54s6evrSxMmTCgyrri4OGnUqFFS9+7d83y+kSRJunfvnlS7dm3p77//lpKSkqQtW7ZIDg4O0smTJ/NsV9LzPH36VKpVq5Y0ZMgQqWPHjlKlSpUkNze3El+f3Md0/fp1qWHDhtL58+ellJQU6eeff5YaN24sRUZGlvq4VFmvTo8ePZIGDBggDR8+XEpMTFR3OMJbJiUlRRo3bpykoaEh9erVSwoMDFRbLLnz+urVq8t0bGVuT09PL7P8rmp+LW6/18nbqmxXFrld6e7duxKKhlASIA0aNKjI8by9vSUrK6s8y4rKy68Sz927dyUNDQ2V4iksllcZp7zt27dPqlu3rmRqaiqtW7dO3eEIgiAIgiAIglA2/pCKqdkVBb2CUE7WrVsnAdL+/fslSZKkbt26yUUYEydOlFxcXIrd//r161Lt2rUlQOrUqZMUHR1dqudXpaD3v//9b4FtCivELS6WopaXpqB32LBhUpUqVeSfly9fLh09elSSJMUJHG1tbengwYOlOv6K4PHj0hXxamlJUtWqkrRhgyTl5EjS/v37pfnz50sfffSR5OTkJGlqakqAZGBgIDVr1kz65JNPpDlz5kj//e9/patXr0pJothGKEZOTo4UEhIiHTlyRPrqq6+k7t27SzVq1JBPWFauXFny8PCQxo8fL61atUq6dOlSmXx5KQjCS2FhYdKuXbukadOmSZ06dZJMTU0lQNLW1pZcXV2lYcOGSQsWLJAOHjxY4b4oFspOfHy8dOrUKWn58uXSF198IbVv317+W9DR0ZGaNWsmjRs3Tlq3bp10+/btEr8QLDO5i3zHjBGFvsU4ceKEVLNmTQmQRo8eLUVFRUlbt26VTExMJECaNWuWlJmZKb148UL64osvJGdnZ+mPP/6Q1qxZI/Xs2VMKDQ1V9yEIgiAIgkqysrKkSZMmSRoaGtIXX3xRoc473Lx5s8wLert37y79+OOPUk5OjhQREfHa42VnZ0tt2rSRevfuLUmS4vV0dHSUvvvuO5X3KWq/ksZWdRxJkqRLly7Jr2dRRV+PHj2SqlSpIg0ePLjQ9d27d5dGjRqVZ9mwYcOktm3b5llW0vMsX75cevr0qfzznDlzJEA6c+ZMsceV+7ibNGkiTZkyJc+4bm5uUufOnUt9XCWtryisrKykBg0a5DnvKwjFCQ0NlT744APJwsJC2rp1q7rDkSTpZX4o64JeZW6XJKlM8ntp8mtx+71q3lZ1u7LI7UqffvqpFBoaKt9evHhR6HarVq2S6tSpk6eItri8/CrxfPrpp9Lx48dLjKewWF5lHHVJTU2VJk+eLFWqVEkaO3aslJGRoe6QBEEQBEEQBEF4PcUW9Gq9Tu9fQRBU98knnzBjxgwWLVpE9erVcXZ2RktL8U9QU1OTwMBAMjMzC516DqBp06Zcu3aNqVOnsnLlSlxcXLh9+zbm5ubleRglxvImYhw9ejQNGjRg3rx5HD9+nH//+99069atjI7mzQsPh2vXFDd9fXjxovjttbUhJwc+/xzmzgXlTOw9e/akZ8+e8nZpaWn4+/tz8+ZN/P39CQoKYuPGjTx+/JjMzEwA7OzsqFu3LnXr1qVOnTrUqFEDBwcHHB0dqVq16ps6ZKGCyMjIIDQ0lNDQUEJCQnjw4AH3798nKCiI+/fvy9Mpt2jRgnr16tG2bVsaNWpE48aNy30qPUF4H1WrVo1q1arRt29fQHGhXVBQEJcvX+b69evcvn2bI0eOEBkZCYCpqSnOzs40bNiQPn36UKtWLWrUqCFPXy5UXJIkER4eTnBwMA8ePODu3bvcuXMHf39/IiIiAMX088rfb//+/WnevDlNmjRBV1dXPUHb2ipunp55l0dGQkAA+Psr7q9ehTVr4H/vKdjYgLMzNGjw8v6DD8DQsPyPoZx4eHgQHBycZ9mgQYMYNGhQnmVaWlosXbqUxMRE/P39cXBwYNSoUeUZqiAIgiC8MkmSGDt2LJs2bWLDhg0MGTJE3SHloTzHVpb8/f1p27YtGhoa2NravvZ4p06d4syZM+zbtw9QnA8cNmwYixYtYsaMGRgW8nkp/z5F7VfS2JcvX1ZpHIDmzZuTkZFR5HFkZGQwcOBAzM3NWbFiRaHbREVF8ezZszzLdHV1SU9Pz7Osfv36xT5P165d85xXHDp0KDNnzsTExKTY12fp0qXMmDGDmzdvcvPmTaZOnZpnbDc3N5YuXcrVq1dxdXVV6bhUOe6K4tq1a3h7e9O6dWtOnTpFvXr11B2SUIGFh4fTrl07dHR0OHfuHHXr1lV3SMDLvK6hoVGm4ypzu3Ls183vqubpkvZ71bxd0vP/9NNPQNnkdoDo6Ghu3bqFvb19sa9LUFAQ169fx8vLiy1btsjLL1y4UGReLk1Ozh3LzJkzqVatWqljKe046qSvr8/ChQvp2LEjH3/8MdHR0ezcufONfP4RBEEQBEEQBEH9xCd9QSgnOjo6fPXVV3z77bd8++23/PLLL/K6Jk2akJKSwooVKxg/fry8PCEhgS1btjBq1Ci2b9/OkCFD8PHxoXfv3nTv3p1du3YxevTocj2O9PT0ImMZMmQIxsbGZR6jlpYW69evp0qVKvTu3Vt9hS0q2r1bUbx79ariPiYGNDSgTh2wtobHjxWt7fKrVElRyNumDfj4gJNT8c+jp6eHq6urfJJLKTMzk0ePHhEYGEhQUBBBQUEEBgayf/9+IiMjFe3ZUZwE8vDwkAt8HRwcqF69OtbW1tja2mJgYFBGr4jwJkiSRGxsLDExMXLRrrJwNzQ0lMePHxMVFZXn912rVi3q1q1L9+7dmThxInXr1qVevXpUqVJFzUcjCAIovkiqV68e9erVY/DgwfLyZ8+eycWft2/fJiAgQL7AQ1NTE3t7e2rVqkWtWrWoXbu2XOhrZ2eHpaWlug7nvZScnCzn4+DgYLmA98iRI6SlpQFgaGhI/fr1cXZ2plOnTjRq1IgGDRrg6Oio5uhVVFihb3Y2hIS8LPL194ezZ/MW+np6vldFvsUxNTWldevW6g5DEARBEEpl9uzZbNq0CV9fX7p06aLucFQSGRnJoUOHCA8Px93dnU6dOuVZHxQUxIULF7h16xbu7u7yhXYnT57E39+fsLAwLl26hK+vL717937teHbt2gVAo0aN5GUNGzYkJSWFAwcOMGDAAJX2KWy/ksY+ffq0SuOo4vvvv+fy5cusWbOm0CI1gA8//JCZM2eyadMmBg8eTHJyMrt37+a3335T6TlAcS61Ro0aeZbdunULLy8v+TiKen2Ux5ScnAwgnxtRat68OQBnzpyRz6uVdFyqHHdFYWtry9GjR/H09KRnz57cuHEDIyMjdYclVEBpaWl069YNY2NjTpw4oZbmIa9C1fxubGxcZG5fuXIlNjY2r53fVc3Tquz3Knm7pOdXlao57vfff+fixYvUrFmTmTNnMmzYsAJF15mZmUyfPp21a9fyww8/5FkXGBgIFJ6XS5OTc8dib29PjRo1ioynqFhKO05F0KNHD/7++2+6du3Kd999x6JFi9QdkiAIgiAIgiAIb4Ao6BWEcjR27Fjmzp3LkydPcHZ2lpd/9NFHTJ8+nW+++Ya0tDS8vLy4ffs2O3fuZO3atUiSxIoVKxg8eDAaGhp06dKFKlWq5CnCGzNmDOHh4axfvx4rK6sCz52UlARQoBNGfHw8tWrVApBP8OTeRvn4yZMnAMXGomz9XViM+cdRev78OUCeq8OfPHlCenq6PNby5ctxcnKSu41aW1tjrGxbq2aRkYrCXeXt8mVFAa+NDbi6wmefKe5btwYLC/i//4Nff4V8vwa0tMDOTlHIm6sJ7yvR1taWu/Lml79j68mTJ7l//z5+fn6Eh4fn+T0YGRlRrVo1LC0tsbOzw8rKChsbG2xsbLC2tsbKygpzc3PMzc1F8W8Ze/r0KfHx8Tx58oTY2FgiIiKIiYkhKiqK69evExkZSWxsLFlZWfI+5ubmcnF2s2bN6N+/v/yz6MgsCG+3ypUr07ZtW7mDDCjePx88eCAXjQYHB3P//n35C62cnBxAcfFHtWrVsLOzw8HBATs7O/mxjY0NVlZWVK1aFX19fXUd3lshKyuLuLg4njx5QmRkJBEREYSFhREeHk5ERAShoaGEh4eTmJgo72NhYSEXWa9YsUJ+bG1trcYjeUM0NaFmTcWtV6+Xy7OyIDRUUeC7Y4eiyHf16pfTFRTWzdfFBcTnCkEQBEGoUPz9/Zk/fz6LFy9+a4p5jx8/ztatWxk3bhzGxsZ4e3szdOhQfHx8AFiyZAl79+7l2LFjhISE0KFDB6Kjoxk3bhyOjo7yOS4rKyv5/EpkZCQPHz4s9nk1NDRwd3cvdN2DBw8AsLGxkZcpL8ALCgpSeZ/C9itpbFXHUcXWrVvR0tLi9u3bdOzYkUuXLuHi4sKSJUtwcXEBFOcpN2/ezJAhQ7h27Rr+/v6sXLlSLqwrLUmS2LFjB7Nnz+bw4cPy8qKOS3lMynOeV65c4eOPP5bXKZeHhoaqfFyqHHdFYmxsjK+vLw0bNmTGjBksXrxY3SEJFdC///1vwsLC1DYT4KsoTX6vWbNmkbnd1dUVIyOjN5Lb4dXy+6vk7ZK2U5WqOc7Dw4PMzEyWLFnCiBEj2Lx5M4cOHUJTU1PeZs6cOXz11VeFfoejPP9UWF5WFierGo8ylvPnz3Px4sUi4ykqlvzHVNI4FUWbNm1Yvnw5I0aM4KOPPsLNzU3dIQmCIAiCIAiCUMZEQa8glCNjY2M+/vjjAldL6+rqcvjwYby9vZkyZQpTpkzB2dmZjRs3YmxsTFpaGo8ePeJf//oX/fr1IyQkhHHjxuHt7S2PcezYMYKDg9m0aROTJ0/OM/758+dZv349AL/++is//PADISEhgKLDyoIFC0hMTGT16tUAzJs3j7lz5xIfH8/ChQsB2LZtGx988AGdOnUqMpa0tLRCl1+8eLHAOD179uTkyZPs3r0bgPnz5/Pjjz9y4sQJTp8+TVJSEnPmzOH777/HxsaGDh065DkmT09PNm7cWK5FMfmLdy9dgthYRQ1LvXqKOpTPPoMJE6Co858uLpB7ZittbcX+330H06bBm24+rKOjQ+3atalduzYAI0aMkNfl5OQQHR1NZGQk0dHR8uOYmBgiIiK4ePGiXFiavzBcX18fc3NzLCwsMDc3p27dulhYWGBhYYGxsTGVK1fGyMhIvpmammJiYoKRkRF6enpv9qDVJDw8nOTkZJKTk0lISCApKYnk5GSSkpLkgt3898rH+TsUWFhYYG1tjbW1Ne3bt8fW1hZLS0u54LpatWoVpshdEITyYWJigouLS6FfHKenpxMSEiIXnYaFhcmPb9++TXh4eIELbAwNDbG0tMTZ2ZkqVapgaWmJlZUVZmZmmJqaYmpqSuXKleX8bWpqWuE75hclJyeHxMREnj17xvPnz0lMTJRvCQkJBAQEEBMTIxfwxsXF8fTp0zxjGBgY4OjoKBdIN2/eXH7s6OhItWrVMDMzU9MRViBaWgULfXMX+ebu5qss9NXUBEfHvEW+zs6KqQtEoa8gCIKgJg8fPmTu3LnMmTOnTKZiHjlyZJmNVR58fHyoXbs2n3/+ubpDUUlycjKjR4/m1q1bGBoa8sEHH3D48GGWLVvGkCFDaNmyJT4+PnTt2hUNDQ2qV69O06ZN2b9/P+PGjaN69epyYZudnR3169cHFOe0Jk2aVOxza2lpkZmZWei6mJgYNDU10dHRkZcpL5COiopSeZ/C9itpbFXHKUlERAQRERE0bdqUmTNnYm5uTlBQEO3bt8fDw4N79+7JF4WfPn2aVq1asXjxYlq1avXKMxSkpKTw9ddfs3nzZlJTU2nUqBH//PMPzZs3L/K4lMc0dOhQdHR0OHnypNw4AJAvxKtevbrKx6XKcVc0VlZWTJ8+nRkzZvDjjz+KLr1CHhkZGSxfvpxvv/0WBwcHdYejktLm9+Jye7NmzQBYvHhxmed2eLX8/ip5u6TtVKFqbgfo0qULXbp04ZNPPmHQoEH4+fnxyy+/MHXqVEDRCVlLS6vInO/u7l5kXi5NTrazs5NjAbh582aR8Xh4eBR7/KqMU9Eoi9j/85//sGnTJnWHIwiCIAiCIAhCGRMFvYJQzoKDg/n3v/9dYLmTkxOBgYGEhISgoaGR5ySanp4eoaGhcsFl//79C+zv7+/P3r17Cy2ObNWqFTdu3MizrEaNGgWKBs+fP19g3x07dhRYVlQsenp6bNiwocDyFi1aFDqOh4cHwcHBeZYNGjSIQYMG5VlmaGjI/fv3iY6OJjU1lZSUFHbu3Mkff/zxxk6o5C/evXABnjx5Wbzr6qrotuvqWroZo11dQZKgUiXIyYH+/WHhQsXM1epWqVIlbG1tsVUhmPj4eLnAKXchqvI+KCiIy5cv8/TpU5KSknj27FmRY2lpaWFsbIyZmRmGhobo6uqir6+Pnp4eOjo6GBoaoqmpiYmJCZX/n707D4uq3v8A/mYbkEVABUFlVXYUlcAUyyXTLNcS81aaW5qtZoqalV6zMrXF8pppejUzC80UzZ9b7riAowKyI7LIJij7OsD5/TF3jgzMsAkM6vv1PPPMcNbPGeErnHmfzzE3ByAPstW8Or7217W3XVtlZaXYtbqm4uJipS7Fiq/LyspQWloKmUyGoqIiMQwGAHl5eRAEAQUFBWKAV9W2AfnPiImJiRh87tSpE7p06SIGoGtOUwSiLS0tH9rQHBFphr6+vtpO7QplZWVit29FcDUrK0sMs0ZERODOnTviBQmqGBgYiGFfExMT6Ovrw9DQUBzDDQ0Noa+vL17UoaenB0D92GxgYFCnU7C68VoxLgNAbm4uBEFAXl6eOD5XVVWhoKBAHLfLysrE0G59x2NmZgY3Nzd07doV/fr1E+82YGVlBQsLC/G14v8jagZVIV9AddD3xAlg/XqgrEy+nq1t3aDv/27HSURE1JquXr2K//73v/D392+REG5LbqstnD17Fi+99FK7vPW0Krt370ZpaSkCAgLEaRkZGejZsycSEhLw5JNP4vTp0+ItvKOiopCamireRUqdd999F2+++Waz61IVpqyqqgIAtResqwtg1l6voW03djsNuXr1KgBgwoQJYjDO2dkZ33zzDf71r39h48aN+PzzzwEAW7duxZAhQzBkyBBs27YNAwYMwNmzZ5scHDQyMsLmzZuxadMmfP/991i4cCHmzZuHK1eu1BtQtbKygo2NDVatWoWAgADMmDEDkydPRnR0NH7//XcAgJeXV6OPq7HH3d74+/tj/vz5uHr1Kp5++mlNl0PtSHR0NLKzs1V+1tBeNXV819TYDjRvfG/OuN3Qco3RlLFdwcvLC1KpFC4uLti9ezeWLFmCvLw8bNiwAbt371a7r/rG5a+//rpV6mko0NvQdtqrSZMm4YcfftB0GURERERE1AoY6CVqQ2FhYXB0dKy3Y5udnZ3K6bq68h9XdSe9y8vLcfHiRaxdu/bBC21AfbVIJJIWvaJfKpVi+vTpSEtLE7vKAsCwYcMQGBjYIvuoHd69eBG4e1eeG3F2ludEPv5Y/vygd4G2twfMzIAePYBNmwA1d+pq9xTB06ZQBF2LiorEjoiKAGxRURFyc3NRVFQkhq9kMpkY2CorK0NOTo4YDM7PzxdvJ68IcamiWFcVMzOzOh9GKsJoCuqCxdra2nB0dAQg77ytCKcpOhB37NgR3bt3F7+uHWYjItI0AwMDODo6imNZferraKv4uqCgABUVFSguLhbH7pycHJSXlyM3NxeFhYWorKwEoBzGrUkx9temKjxbMxSsGM/Nzc2hra0tdg92dHQUx+8OHToodRd+lDoOPzKaG/R1dKwb8nV3B2qFw4mIiB7EpEmTkJ2djS5durTI9lpyW23h3r17D1W9kZGRsLa2Fm+/rkr37t1x7NgxHDp0CEOGDEHPnj0hlUrr3a6urq54Tqw+ly9fxjvvvKM0bd26dbCxsUFVVRXKy8vF3z0VF5u5u7ur3JaqdVSt19C2Y2JiGrWdhpiamgJAne+HgQMHAgBiY2MByEPrf/zxB0JDQ6Grqws/Pz/MnTsXb7/9Ng4ePNiofdWmra2N+fPn48KFC/jzzz9RXl6u9v2peUyLFi2Cr68vjh07hvPnz2PKlCm4dOkS4uPj0a9fvyYdV0Pz2yMLCwtoaWnVuesI0b179wDU/b5uz5o6vrfk2A7UHd/Vje1A88b35ozbDe2/MRo7BtZmaGiI8ePHY9u2bQCADz74AD4+PggKChKXiY+PR1lZGfbt2wczMzMMHz5c7bjc1DG5sfXs27dPbS2N2U57ZWlpybGdiIiIiOgRxUAvUSuTSqUICAhA7969cfr0aezfv79V9hMSEoIvvvii0SefHhbh4eHIyMjAzZs3YWdnh6SkJISEhCA8PBxLly5t8vaqqoCYGHlwNypKfnfne/eUw7uffCJ/9vZu+SyIlhawZw8wbJi82+/jRBFuJSKih4u2tjbMzc3ZlZY0Q13QVyYDUlPlv1gpQr7ffQeUl6vv5uvhAai4mwUREVFjtGTg6WEKTwHyuzxFRUVpuoxG09HRQWxsLGQymdoLaz/55BOcOXMGR48eRYcOHfDnn382uN3Q0FCcOHGiwX1PnTq1TqDX0dERbm5uAOQdIxWBGMcxAAAgAElEQVQXrefk5ABQH/hStY6q9RradnR0dKO20xDFXUBqB+RsbW2hp6cnXnS3Y8cOjB49WjxPOXPmTFy5cgVbt25FXl5evc0OGvLss8/i1KlT0NfXV/v+1D4mRadgALh16xaCgoKwdu1asd7GHldD89ujyMhICIIABwcHTZdC7Yy9vT0AeZfyh6V7c1PH91mzZjW4zcaO7QEBAbC1tVUa39WN7UDzxvfmjNsN7b8xGjsGquLq6iqun52djePHjyvNz8/PR0lJCd577z14eHiIIVpV43JTx+Sm1qOuloa2017duHGDYzsRERER0SPq0Ur+EbVD1dXVCA0NhVQqxZYtW8QTZS1txIgRrbJdTZs+fTpyc3Ph4eEBXV1d9O7dGzNmzMDKlSshkUjqXbeyEoiNVe6+e/06UFwM6OkBTk7Ap5/Kg7tPPNF2+Y5H9J+KiIiIqO3o6clDvosX35+mCPnW7ubLoC8RUbvxzz//IDU1Ffr6+njxxRehr6+PkJAQREVFwdzcHOPHjwcApKen48iRI7h9+zb8/PzwzDPPiNsQBAFnzpzB9evXoaOjA1dXVzz77LONrqG0tBQHDhzAuHHjcOfOHRw+fBjdunXD2LFjoaOjg6ysLAQFBUFbWxv+/v7o2LEjqqurcebMGRgbG8PHx6dRtdQ379SpU0rbSk1Nxb59+/Duu+8iKioKBw4cgK2tLV599VVoa2sr1V9UVISdO3ciJSUFTk5O8PX1hZubG3Ra8arhF198EatWrcIXX3yBzp07t9p+WoqXlxeKi4uxadMmvPvuu+L0vLw8/Pbbbxg9ejRWrVqFn376CR3+dyW34i5A9YmLi8PevXvrXUZXVxcBAQF4/fXX68ybNWsWPvvsMwQHB4uhK6lUir59+6oNDalaR9V6DW27sdtpiJWVFUaNGoVLly4pTY+Pj4dMJoPf/25FFR4eXifENn78ePz444/Iysp6oEDvjRs3MPZ/F3qpOy51x1RRUYGXX34ZLi4ueOutt5p0XBkZGQ0ed3u0ceNGODk5wcvLS9OlUDvj4OCA/v37Y+PGjQ9NoLep43tLj+3W1tZ1xvfmjq8tNW43tFxjNHZsV+Wvv/4Sf387dOhQnfkBAQH45ZdfcPv2bZXr1xyXW7uehmpRt532qKCgADt37sTcuXM1XQoREREREbUGQRDqexBRC5DJZEJVVZWmy3ioVVRU1DtfJhOEGzcEYccOQXjvPUHw8xMEQ0NBAARBT08Q3N0FYepUQfjuO0E4d04QysraqHAiIiIi0qyKCvkvioGBgrB8uSD4+8t/OdTRuf/LoqOjIIwZIwiLFwvClSv8ZZGIqJUUFxcLAISbN28qTXd1dRViY2MFQRCEkydPCm+88YZw9epVITAwUDA2NhbeeustcdmPPvpI2LJliyAIghAaGir4+vo2ev+nT58WnJycBADC119/LcyZM0cICAgQDA0NhZdeeknYsmWL8OqrrwpTpkwRtLS0hLFjxwqRkZHCpEmTBADCjz/+qLS9+mpRNy8yMlJpW0FBQYKFhYUAQPj222+FGTNmCGPGjBEACF988YXS/u7duyc4OzsLZ8+eFYqKioSJEycKAAQfHx9h/vz5jX4fmio/P1+wsbERJk2aJFRXV7fafh7EhQsXBPmpbkEoKysTbGxsBIlEIqxZs0aIiooS/vjjD8Hf318oKCgQwsPDBQDC0KFDhfz8fOHs2bOCtbW10KlTJ6GwsFAoKCgQoqKiBABK33st4cMPPxQ8PDyE6upqobS0VHB2dhakUmmd5RYtWiTMmjWrzjqCIKhdT7GcumUaux1BEITMzEwBgDBnzpw6827cuCEYGxsLwcHB4rRNmzYJbm5ugkwmEwRBEGbMmCFYWVkpnQ9dsWKF0KdPH6Vp9e2npKREWLVqlRARESFOy8nJEZ566ikhLy+v3uNSdUxFRUXCtGnThMmTJwtZWVlNPq7GHHd7c+TIEUFLS0vYtWuXpkuhdiooKEgAIPz555+aLqUOxbj+3XffidOaOr5rYmwXBPXja82xvfZ6DzJuN3a5Bx3bY2Njhffff1+4evWquM6AAQPq/exm0aJFQteuXVXOq29cbqie2rU0ph5VtTRnO+3B66+/LlhaWgp37tzRdClERERERNQ824V6MrsM9BLRQ0dVeLdDB/mIJpHUDe+Wl2u6YiIiIiJqd9QFfVWFfHfsYNCXiKiFABCDroIgCOnp6cKkSZMEQRCEwsJCwdHRUSgqKhLnz5o1SwAgXLx4Uaiurha6dOkinDp1Spy/atWqJu3/m2++EQAIe/bsEactWbKkTqBp2bJlgr6+viAIghgQqhnora+WhuqsvS3F/k+cOCFO69+/v+Dt7a1U+9KlSwU7Ozvxa6lUKgaBW9vJkycFiUQivP322+3uovXLly8Lo0aNEgAIhw8fFgRBEKKiogRnZ2cBgABA8PDwUArrzJw5U9DV1RV69eolbNq0Sdi7d68gkUiE4cOHC0ePHhXD0jY2Nkrh0QdVXV0tLF68WBgzZoywdOlS4ZdfflG5nKurq2BpaSlUVlYqrfP999+rXU+xnLplGrudw4cPCy+//LIAQLC0tFT6eVUICwsTnnnmGeHTTz8VPv/8c2HMmDFCenq6OL+4uFiYNWuW4OnpKXz33XfC7NmzhXHjxgmJiYlK26m9n4yMDHFeUVGR0K9fP0FLS0vw8fERPvnkE2H9+vVCYWFhg8dVU05OjrB161Zh0KBBwr59+1S+3409robmtyfBwcGCiYmJMHXqVE2XQu3cW2+9JXTo0EE4fvy4pksR1RzX+/XrJ47tgtC08b3m2P73338rje07duxosfG9seNrzbG99noPMm43ZjlVY3vNMVcQGh7jpFKpYGpqKgAQFi9eLHz11VdCSUlJve+NqhBtY8fl+uqpWcuwYcMaVY+qWpqzHU1S/Fvr6uoKhw4d0nQ5RERERETUfPUGerUEQai3ge8DtwAmInpAMhmwezcgld5/lJUBxsaAlxfg7X3/4eoKtOJdJomIiIjoURcZCURFKT/HxgJVVYCeHuDkBHh4AO7u95/5SygRUaO5u7sDACIjI6GlpYWvv/4azs7OGDt2LLZs2YLly5dj4sSJ4vJJSUmIjY3FihUr8Nprr2Hw4MGIj4/H5s2bMX78eJSXl0NfX7/R+9+2bRtmzZqFmJgY8fbO27dvx4wZM5CQkICePXsCAH799VdMnToVgiAgLi4OLi4u+PHHH/Hmm2+K26qvlvrmaWlpKW1r5cqVWL58OW7fvo3u3bsDAKZOnYqjR4/izp074v5efvllHDp0CLm5uZBIJKiqqoKpqSnmzp2Lr7/+usn/Fk21b98+vPLKKxg9ejR27NiBjh07tvo+H1RycjK0tLRga2tbZ15hYSFMTEzEr5v6vfQgqqqqoFPP7w5FRUWQyWQwNzdXWicnJwddu3atd9tZWVn1LtPY7TRGeno6OnTooFRnTSUlJUhOToaVlZXaZRqSl5cHiUQCQ0PDepdTd1z79+9Hnz594Ojo2Oh9NnRcDc3XtF9++QVz587Fc889h8DAQOjp6Wm6JGrHqqqqMH36dPzxxx/4/vvvlf6fa88aO7639dhe3/iqamxXrFff/wmNHbdbanyvb4wrLy9HSkoKnJycmr39po7L6upR1GJoaCj+/tQcLbWd1lZcXIw33ngDe/bswbZt2zB16lRNl0RERERERM23A8B0dTN1264OIqKGyWRAXJxyePfKFUAiAfr0kYd258yRP7u5Adramq6YiIiIiB4pHh7yh7///WkVFUB8vHLQd88e4LPPgOpqBn2JiJpg0aJFmDlzJg4fPowXXngBJ06cwPvvvw9AHvK1trbGf/7zH7Xrb9iwAf7+/pgwYQKeeeYZ7Nq164GDKwYGBnWmNSYAV18tD1qnjo4OajdiGDZsGAIDA3H+/HkMHz4cubm5qKiowLPPPtvo7T6IF198Ef/88w9eeukl9O7dGz///HOb7bu57Ozs1M6rGeYF0GaBLwD1BrcAwNjYWOU6jfkeamiZxm6nMbp161bvfENDQ7i5uT3QPszMzBq1nLrjmjBhQpP32dBxNTRfk7KzszF9+nQsXLgQX375ZYPfa0Q6Ojr45Zdf0KtXL7z99ts4fPgwfvrpJ1hbW2u6tHo1dnxv67G9vvFV1diuWO9BttvU5RpS3xinr6//QGFeoOnjsrp6WqKWltxOazp37hxmzJiBvLw8HDlyBM8884ymSyIiIiIiolbEQC8RtYnCQiAsTDmoq2h0ZmoKeHoqd9qdOhWYNk3TVRMRERERQX51maqgrzqKAPC+ffcDwH/+KQ//SiRAr14M/xLRY2vGjBm4e/cuxowZg9GjR2PdunXQ1ZWfouzfvz/Wr1+PH374Ae+++664Tl5eHn777TfMmjULERERiI+PBwAcPXoU1tbW2Lx5M2bPnt2mx1FeXq62lqlTp7ZKnW+++SaKi4uhr6+P2NhY2Nvbo6KiokWOpyHLlwMrVwIWFn4YPjwTTz0FJCVdgpdXP0RHR2LKlClYtmyZ2PWYiNpGRUUFfv/9d6xcuRLp6el47733UF1dremy6CGjpaWF5cuXY/ny5SgpKcEPP/yAL774AgYGBpg3bx4WLFjwUHRlJ3pUJCcn45tvvsGWLVvQo0cPfP7554iPj4eWlpamSyMiIiIiojbA3pZE1OIKC4Hz54H16+WhXA8PwMwMeOopYMUKIDERGDEC2LYNuHEDyM2tuzzPSxARERHRQ0sRAPb3l/8CHBgIlJbKf/n99df7oeA9e4ApU+RXtxkayteZPFm+zp498jAwAxlE9AiaO3cuTE1NkZOTAw8PD3H6yy+/DBsbGyxcuBBr165FdHQ0AgMDMWfOHEydOhWCIGDTpk1i59qRI0eiS5cu6NKli7iNOXPm4Pnnn0dWVpbKfRcWFgKQB3IVioqKAAD37t0TpxUXF4uvFcvm5OSI0+qrpTF11txWQUEBACiFc3NyclBeXq7UpffHH3/E3r17IZPJUFFRgZSUFPF4Wlu3bvK7JGVnA3v3Au+/D8yZ8yQSE6/BxeUW/u//+sDdfTbGjHkRBw8eRFVVVZvURfS4io+Px+LFi2FjY4O5c+di9OjRiIuLw+rVqzVdGj3kDA0NsXjxYsTGxuK1117DunXr4OjoiA8++ABRUVGaLo/okXbkyBG89NJLcHJyQlBQEL799lvcuHED/v7+DPMSERERET1GtGrfuq2WemcSEQFAQQEQHi7vurt5MxATI88dmJvLm43V7Lxb43M6IiIiIiIqLwcSEuS/TEdF3e/om5RUf0dfNzd5soqI6CE1b9489O7dG2+99ZbS9OjoaEyYMAFxcXEAAA8PD+zcuRP9+vVDWVkZHB0dMWTIELz00ktITk5GQUEB/v3vf4vr9+rVCzdv3sS6devw4YcfKm374sWLmDdvHsLCwvD6669j+fLlSE5OxocffoirV6/ihRdewJo1a5Cfn48FCxbg0qVL2LlzJw4cOIC9e/fC09MTq1evxgsvvFBvLfXNu3z5Mp588klxW8bGxpg5cyYSExMxe/ZsfPbZZzh9+jTmzp2LgoICrFixAsuWLYOuri7279+P1157TSlsDAAjRozAzp07YWVl1Ur/WsDBg8C4carnaWkBenryBvVaWtUQhAiYmATj+edNsXTpU/Dysm21uogeJ2VlZdi3bx+2bNmCM2fOoEePHpg5cybmzp0La2trTZdHj6js7Gxs3rwZ27ZtQ2JiIrZv3w5/f38YGhpqujSiR8bt27exbds2rFixAk899RTmzJmDyZMnQ09PT9OlERERERFR69gBYLq6mQz0ElGT5OcDERHyvIHiER0NCAJgbQ3MmXM/uOvoqOlqiYiIiIgeUgUFQHz8/YCv4vnWLfkv34qgr78/Q75E9FAaOXIkAgMDYWZmpnJ+cnIytLS0YGurHAatrKxEdXU1MjMz68wD5N10Dxw4AAMDA4xTl0BtIfXV0lCdzXH8+HGkpaVh8ODByMzMRElJCYqLi7F371707t0bS5YsaZH9qHL1qvx8T2NoaQHa2lWoqtIBUAUTkwRMmRKNjz7qC3t7+1arkehRVFpaiqNHj+LAgQMICgpCYWEhxowZg9mzZ2PUqFHQ0dHRdIn0mKiursY///yDMWPGoEOHDhg7diwmTJiA5557DkZGRpouj+ihk5qaigMHDmD//v04ffo0OnfujLNnz8LFxUXTpRERERERUetjoJeImicvT35XYHXh3Zqdd318gFZsBENEREREREDdoO+ePfdDvvr6QM+e9wO+iivt7O0Z9CWidiUsLAw//vgjNm3a1OLbLigowPLly7F27Vro6uq2+PY1RSqVYty4cUhJSakT4MvLy0NgYCDmzJnTovssKwPS04HQUCAuDvj008avq6cHVFYCAwZkwMxsIy5d2oC8vDz069cP48ePx/Lly1u0VqJHyb1793Do0CHs378fR48eRVlZGQYOHIiJEyfi1VdfbdVu3EQNyc7Oxm+//Ya//voL58+fh56eHp599llMmDABY8eOhYWFhaZLJGq3IiMjceDAAfz111+QSqUwMTHB6NGj4e/vj7Fjx0IikWi6RCIiIiIiahsM9BJRw3Jz5ZmAmuHdqCj5vNrhXV9foGtXzdZLRERERET/k58PJCSo7+bbsSPg5CQP+SrCvh4egIODvI0iEVEbkUql2LlzJ06fPo39+/e3SrfWEydOwM/PDx06dGjxbWvSf//7X8yaNQs//vgjRowYATs7OyQlJSEkJATh4eFYunQpTE1NG7WtqiogKwtISwMyMoDbt4HMTCA1Vf58+7Z8+t278uW1teXngbKygOrq+retpyf/r2fKFOCjj+TN4wFAJpOJ/+4HDhyArq4uhg8fjmHDhmH48OHo3r37A7w7RA+30tJSXLx4EadOncKpU6dw+fJl6OjoYPjw4Zg4cSLGjRuHrjwZS+1QTk4OgoKCcODAARw/fhwVFRV44oknMGzYMAwbNgx+fn7s3kuPtYyMDJw6dQqnT5/GyZMncfPmTVhZWWHcuHGYMGEChg8fDn19fU2XSUREREREbY+BXiKqKz0d2Lz5fng3I0M+vXZ4d8AAwNJSs7USEREREVEzNDfo6+io6cqJ6BEVGhqKZ599Flu2bIG/v7+my3moCIKAb7/9FgcPHsTFixehq6uL3r17Y8aMGZg+fbrY0a20VH6OJz1d/pyYeP+14jklRd49V8HAAOjWTX5OqOazo6P89YAB8qCulZU81KuKrq78MWcOsHAhYGNT/7GsXLkSp06dwqVLl1BeXg4nJycxADZ06FB2IKVHXnl5OUJCQvDJJ58o/RwMHToUI0aMwOjRo2FiYqLpMokarbi4GEePHsWJEydw6tQpxMTEQCKRwNfXF8OHD8fQoUMxcOBAGBgYaLpUolaTnZ2N06dP4/Tp0zh16hSio6Ohp6cn/hw899xzePLJJ6HNO+gQERERET3uGOgletylpyt33r1yRd5xpXZ4d+BAoEsXTVdLREREREStKi8PuHmzbtA3MVE+39ub3XwfQ1VVVSgoKEBZWRlKS0uRm5srTqutpKQE5eXldaYbGhrW6TClpaUFMzMzAECHDh1gYWEBPT09cRo9fqqrqxliaKayMuDePSA1VYaUFL06Qd2QEKDmj6y+PtCpk+qQrmKajY38+o7G6N8fuHZNeZqODmBsDMyfD7z3nnx/TVFaWgqpVIrg4GCcOHEC586dQ3l5OaytreHt7S0+Bg8eDHNz86ZtnKgdSU9PR3BwMM6fPw+pVAqpVIqysjL4+/tjxIgRGDlyZKt0LSfSlKysLJw9exYnTpzA+fPnERUVBV1dXTg7OyuN7wMGDICenp6myyVqMplMhvDwcKVxPTo6Gtra2ujbty/8/PwwePBgjBw5stF3USAiIiIioscGA71Ej5Pa4d3Q0PvdU2oHeMeO1WytRERERETUjiiCvnv21A35mpoCvXox6NuOlZWV4c6dO0hPT8fdu3eRl5eH3Nxclc95eXnIz88Xg7n5+fmorq5u85olEgmMjIxgaGgIAwMDmJubw8zMDObm5kqvzczMxNeWlpbo2rUrLC0toaur2+Y1E7WW3FzV3XRrPmdmyhusK5ibKwd1vb2Vw7rW1i07RE+cCOzfL9+mlhbQtSuwZAkwezZgaNgy+ygqKsKFCxcQEhKC0NBQhISEIDMzEzo6OnBzc4OPjw98fX3h5eUFDw8PdGxsGpmojVRVVSEhIQERERGQSqUICQnBlStXUFBQAAMDA/Tt2xe+vr7w8fGBn58fHBwcNF0yUZtISUlBcHCwOL5fvXoVpaWlMDY2xptvvgkfHx94enrC2dmZv+NRu1RUVITIyEiEhYUhNDQU27dvR2VlJSwsLMRx3dfXF35+fvz9hIiIiIiIGsJAL9GjqnZ49/JlIDtb3h3FxUX+QY7is3Y/v6Z3SSEiIiIiosecImFWs5uvVCpPlwGAmRnQs2fdoK+jo2brfsRUVlYiLS0NKSkpSEpKQmpqKrKyspCVlYWwsDBkZGQgPz9faR2JRFInFFvzuWPHjjAyMoJEIoGpqSn09PTQsWNH6Ovrw9DQUOyEaWZmBq1aiUBFELe2goICVFVVKU2TyWQoKioCIO/sm52djcrKSuTl5aGiogLFxcUoKSlBWVmZUvBYVRi5ZuhYS0sLFhYWsLS0RN++fWFpaYnu3bujR48esLOzg62tLaytrVvk/Sd6ULm5ysFcxTCqmJacDNT80TEwUN1Nt+azrS3Q1nmnd94B/vMfwMkJWLYMeOUVoC2aKqampiIkJER8SKVSFBYWAgDs7Ozg6ekpPvz9/et0CidqLcnJyYiMjMSNGzfER1RUFMrLy6Gtra0UQvfx8YGXlxc7kRL9T2VlJW7cuIGQkBBs2LABUVFRqKqqgkQigZubGzw8PNC7d294enrCw8MD9vb2dX4nJWoNFRUViImJQWRkJCIiIsTxPSkpCYIgwMjICP3798c777wDX19fdlcnIiIiIqLmYKCX6FFQO7x76RKQkyP/8MbZWbnzbr9+gIrPVomIiIiIiFpGbq5yyFfxXF/Ql7cIqVdOTg5iY2MRExODW7duieHd5ORkpKeno7KyEoA8TNu9e3dYW1vD0tIS/fr1g5WVFaysrGBpaYlu3bqhS5cuMGypdpntSEFBgRhkzsrKQkZGBu7cuYNr164hKysL6enpyMzMFEPFBgYGsLW1ha2trRjydXJygouLC5ydnWFsbKzhI6KHXWmp+m66iueUFOB/mXYA8rCut7fqkK61NWBnB7TXb819++QXkY8dC2hra64OQRCQlJQkBmwiIiIQGRmJmJgYVFdXo2fPnnB1dUWvXr3Eh5OTE2xsbKCtycLpoZWbm4sjR44gISEBcXFxiI+PR0xMjHgxTY8ePZTCh56ennB3d0eHDh00XDnRw6OsrAzR0dF1xvaUlBQAgImJCdzc3ODk5CSO64rXndjJhJpIEASkpqYiISEBCQkJiI+PR0JCAmJjY5GQkACZTAY9PT04OzvD09MTvXv3Fsd5BwcH/j5BREREREQPioFeoodRzQDvhg3A3buqw7v9+7fcbQ2JiIiIiIgeiKqg740b8vvEm5vL203WDPk+8YQ8wdaaFi8G5s9v/f00giAISExMREREBOLi4sQAb2xsLO7evQsAMDIygoODA+zt7cUQqiKQamdnB2tra3Ynq4dMJsPt27eRkpKC5ORkJCUlISUlRfz61q1bkMlkAAAbGxs4OzvDxcUFrq6ucHFxQe/evdnZlwAAFRXyC6lrd9NVPCcmyoc8BYkE6NxZfVC35jO1DplMhn379iEyMlIM5sTHx4uhS319fTg6OoohMEdHR3GMtbGxETuT0+OnvLwct2/fFv//SEpKQlxcnBj0ysnJgZ6eHuzt7cUgoaKDqKenJ793iFpRfn6+2Ak7Ojoa8fHxiI+PR1JSEioqKgAAnTp1En82e/XqBUdHR9jY2GDQoEHs2v6Yy8/Px/nz55GamoqbN2+K43pCQgLKysoAAB07dlS6AEjRFdrFxQUSiUTDR0BERERERI8oBnqJ2rOqKiAm5n54NypK/pybez/AO2fO/QAvGzsQEREREdFDJzcX2LPnfsg3IgLIypLPMzdXDvl6eMgfLRGqzMkBLCzkLTEDAoBFi9q09aVMJkNcXBykUimkUil27dolBnfNzc3h7u4ODw8PODo6iq/t7e3Z8akVVVZWIiUlBYmJiYiMjERUVBQSExPFB3D/32bOnDnw9vaGq6srdHR0NFw5taTcXPVddRMT5a+zsoDqavny5ubqw7mOjvLXVlaa7VpL6mVnZyt131O8vnXrljgmA4CxsbEY8O3RowdsbGxgZ2cHHx8fWFlZsQPkQ6qsrEzs4p6Wlobbt28jOTkZqamp4iMzMxOKz0n09fVhZ2enFA7s1asXRowYAV1dXQ0fDREpVFVVITk5WSmgqRjnk5KSxLCmlZWVOKbXvIijR48esLa2RteuXdlN+yGVm5uLzMxMZGVliRfwKcZ1xdeFhYUA5L/fOzo61une7+TkBEtLSw0fCRERERERPYYY6CVqLyorgdjY++FdqRS4fh0oLgb09AAnJ+Xuu088If/cmYiIiIiI6JGjqptveDhw5458vqqgr6enPDXXWGfPAkOGyF/r6gKmpsDnnwOzZsm/bmG3bt1CcHAwLly4gODgYERFRaGyshJGRkbo3bs3pk+fjr59+8LT0xNGRkYtvn96MPfu3cP169dx/fp1hIWFYffu3ZDJZDA0NES/fv0waNAg+Pn5YdCgQbCwsNB0uaRCaWn9Id2MDCA1Ffhfk2YA8vMu9QV1BwyQn7OhR1NJSUmdcGfNr1NSUsRQmL6+PiwsLNCtWzdYWlqia9eu6NatGywsLMRQWOfOndGpUyeYm5uzK2Qry83Nxb1793D37l1kZ2fjzp07SE9Px15cd2QAACAASURBVJ07d3Dt2jVkZ2cjPT0dBQUF4jpaWlqwtrZWCvQpuuErAn9WTfk9g4jarTt37uDChQt1gp7JycnIzMxEteKqHQAmJibi2G5lZQUrKytYWlqKY7uFhYU4vvPijtZTUVGhNLZnZWUhIyMDd+7cQWZmJjIzM5XG+vLycnFdfX192NjYiA87Ozvx9eDBg2Hchhd1EhERERERNQIDvW0lKSkJeXl5KCsrQ1FREUpKSlBeXo78/HxUVlYiPz8f5eXlKCkpAQAUFhaisrJSaRuVlZXiFaMKBgYGKq8QNjU1hba2NvT09GBsbIwOHTrAwMAAHTt2hJ6eHkxNTaGvrw9DQ0N07NgR5ubmMDMzYyeBNqIqvHvtGlBSojq86+MD8Dw/ERERERE99poa9J0yBejaVfW2Nm4E3n9f/geagrY2YG8PrF4N+Ps3u8zq6mpcv34dZ86cEUO8GRkZkEgk8Pb2xqBBg+Dj44O+ffuiV69e7PD6ECovL0dkZCSuX7+Oy5cvIzg4GNHR0aiuroazs7MY8B02bBh69uyp6XIfeeXlwN276oO6ISFAjdwe9PWBTp1Uh3QV03r0kOf8ieoTERFRJ0iUnZ2NzMxMZGRkiGHS2ufZjYyMxPCXubm5+FrxtZ2dHYyMjGBkZAQzMzPxtbGxMczMzKClpaWhI24bxcXFyMrKQl5eHoqLi1FcXIzCwkIUFBSgsLAQ9+7dw71798RwV+3Xqt5vRfiub9++dYJ53bp1g5WVFW+fTkSQyWTIzMwUg6E1g6MZGRnIysoSx/uioqI66/fq1UvluN6pUyeYmJjA1NQUJiYmMDIygqGhIczNzZXG+EdZbm6uOKYXFRWJY3xJSQkKCgqQl5eHu3fvKo3nivE9JSWlzvYsLS3Fi2isra3VXljDizGIiIiIiOghw0Bvc+Tn56v8Az4rKwu5ubnIy8tDXl4ecnNzxa/VvZc1A7YSiUTswqMI4NZmbm6u9HVRURFkNVuHQH47IUV3gYqKCvEP4vLychQUFKCqqkrtsZmYmMDe3l4M+CpONKj6I9jS0pK3+mwkmQzYvft+ePfqVXlnGGNjwMVF/jmzIrzr6wvw3DEREREREVETqAr6hoUB2dmqu/n27g189hmwZQtQUaG8LW1toLpa/gfa+vWAn1+jSsjOzsbp06dx4sQJHDp0COnp6ejYsSN8fX3h5+eHwYMHw8/Pj7ftfYQVFhbi8uXLOH/+PKRSKc6dO4f8/Hw4OjpixIgRGDFiBJ599lmYmZlputSHSm6u6pBuzefMTKDmqTdzc+Wgrre3cljX2hp4xPOQ1I5UVlYiOzu7TkhJ3et79+4hJSWlTrOHmgwNDWFkZAQTExN07NgROjo6MDY2hp6entjEQVtbG6b/S6Urlql5brnmueja1DWRqK6uRn5+fp3pMplMKdgmCALy8vIA3G9cUVZWhtLSUqWmFYrz5oWFheI57NzcXLXHbWpqCmNj4zpBaHWvO3fuDAsLC3a+J6JWUVJSojS+3717Fzdv3lQ5rufm5qKwsFDlGFqT4iIORTMeLS0tmJmZwcTEBLq6uuL4rKOjg44dOwK43+BHQfH/gSqK/w9qUozPqhQXF6Oixt9LisZENf8/UHzuqPgcMjc3V5xfWFiIkpISFBcXqz1mXV1dmJiYwMzMTCkIXXNM79mzp9J0CwsLtcdIRERERET0kGOgt7b8/HykpKQgKSkJycnJSE5OFm+5k56ejqysLPFWagCgra0tXgVqZWUlhmAVz4rXDg4OMDc3h4GBgfjHuKZurab4Q1oR9i0oKBCDx7m5uUhKSlL6+u7du+LVyDWPXUdHRzx2Gxsb2Nvbw9bWVrwVmZ2dHaytrTVyjJokkwFxccrdd6VS+R1bvbyUO++6ugJsxERERERERNRKjh4FbtwAoqPvPyvadFpZyVOA6ujqAlVVwMSJwJo1gIouq9euXUNgYCD+7//+D+Hh4ZBIJPDz88OoUaMwcuRIeHl5PfJdFEk9mUyGCxcu4OjRozh27BiuXbsGHR0dDBo0CGPGjIG/vz/s7Ow0XaZGlZYqB3WlUuWwbkqKchNtAwPV3XRrPtvayn98iR525eXlKC4uRl5eHoqKisSuhqo6HAqCIAaqSktLUVZWpjI4WzMsqwheqaKqiYRC7eCYQu1GFIpOwkZGRpBIJGKAWBFOAyAG1Gp3IDYyMoKVlRVMTU3F6SYmJs16H4mI2hvFRQyNGeMVDXzy8/NRXV0tjt01L6SofSGEuiZD6i7KUARqVal98YfirqDA/XG/9gUlium1uw/X7Div6FZsbGyssc9KiYiIiIiI2qnHN9Cbl5eH2NhYxMTEIDY2FnFxcYiPj0d4eLi4TJcuXZQCqopbb1lYWKB79+5imPVx6lKbl5cndidWhHwzMzORmpoqBqDT09PFLsD6+vqwtbWFg4MDxo8fDxcXF7i4uKBHjx4aPpKWoSq8e+WK/FaPJiZAnz73w7uvvSZv9EREREREREQalJwsD/b6+wMqbpNbh56evGPvzJnAqlUIz8xEYGAgAgMDER8fD3t7e4wfPx4jR47EkCFD2AWQ1MrOzsbx48dx9OhRHDp0CLm5uRgwYAAmT56MSZMmwcbGRtMltpiyMnkoV1U3XcVzSoryj6CBgfz8iaqQrrW1PKjLPB8RERERERERERERPcIe/UBvYWEhwsLCcP36dURERIgh3qysLADywKmzszNcXFzg5OSEp556CnZ2drC3t4ehoaGGq384yWQypKWlKXU6TkxMxIEDB8QrhY2NjeHi4gJnZ2d4enqib9++6Nu3L7p166bh6tUrKgKuX79/B1epFAgNld+dtWNH+R1ba3bfdXNjgJeIiIiIiKhdys4GLC2bvFqFvj5My8thaWsLf39/TJ48GT4+PuzCS00mk8lw4sQJBAYGYv/+/cjPz4efnx+OHz8OAwMDTZdXr4oKICenbjfd2s8KEgnQubP6oG7NZyIiIiIiIiIiIiKix9ijFejNzMyEVCrF9evXxcfNmzchCALMzc3h5eUFV1dXuLi4wNXVFc7OzrC3t3+sOuxqWnZ2ttgVWRGuvnHjBpKSkgAAFhYW6Nu3L/r16wcvLy/0798fLi4ubf7haGEhEBam3Hk3NlZ+t1VTU8DTUzm86+4O8PNbIiIiIiKih8SZM8DQofUvo60t785bUQEIAmQAbmprQ3vFCjh9/DFDvNRiysvLcezYMfz66684efIkZsyYgTfffBOOjo5tXktubv1ddTMy5E2u/3djJpibNxzStbMDdHTa/FCIiIiIiIiIiIiIiB42D3egNzExEefPn0dwcDDOnz+P6OhoCIIAa2treHt7w8PDA+7u7vD29oa7uzs/bGvHCgoKEB4eDqlUiqioKERGRkIqlaKsrAwdO3aEr68v/Pz8MHjwYAwaNKjFuycXFADh4ffDu7t2ye+oamYGeHgwvEtERERERPRI2bgReOcdQHHeQ09PnlCsrpYnD3v0QIa1NYKSk3EiIwNV7u54/r338K/XXoORkZFma6dH2meffYbNmzcjPT0dY8aMwYoVK9CvX78H3m5paf0h3cREIDUVkMnur2NgUH9Qd8gQ+Y8OERERERERERERERG1iIcr0CsIAiIiInD06FF88cUXyMvLg7GxMQYMGAA/Pz8MGjQIAwYMgJmZWVuXRq2goqICYWFhuHDhAoKDgxEcHIz09HTo6emhf//+ePrpp7Fy5com34qydnhXKgViYuSf25qbywO7c+bIw7seHq10cERERERERKQ5b78tD/VaWwN9+gB9+8pvxeLhgdN37mDpihW4fPkyJk6ciIULF2LgwIGarpgeI5WVlQgKCsLq1atx5coV+Pv7Y+XKlXBxcVG5fHk5cPeu+qBuaCiQn39/eX19oFMn1SFdR0f56+7d5Rc5ExERERERERERERFRm2mfgd60tDTs3bsXgYGBuHTpEnR0dDBo0CCMHDkSI0eORP/+/aGtrd1au6eHyJYtW3Ds2DH8888/yM3NhYODA8aOnYbw8PeRmGiOlBT5cnZ2QP/+8oe3t/y5a1fN1k5ERERERESaJwgCNm/ejIULF6Jnz5747rvvMHToUE2XRSSqrq7Grl27sGhRAHJzT8DLyxoeHp3QowdgZQXY2Mife/SQn+vQ0dF0xURERERERERERERE1AztJ9CbkZEhhngvXLgAExMTjB8/Hi+++CKGDx8OExOTltwdPWKqqqoQEhKCoKAgBAb+icTElejU6TZGjuyMuXN9MHSop6ZLJCIiIiIionYmOzsb06ZNw/HjxxEQEIAVK1ZAIpG02f7PnTuHM2fOICYmBr/++qvSvLCwMJw9exYSiQQvvPACevToIc77+++/UVBQIH6dmpqKd955B4aGhs2qIzMzEzExMY0OMh87dgzXr1/H4MGDMWDAAOjUSpAWFhbit99+w61bt9CrVy+88sorDdYWFhaGS5cuKR3r3bt3ceDAAaSkpKBPnz4YOXIkjI2N66xb3/sRGhqKhIQElft88skn4eDg0Kjt1JSZmQkrK6t6j6c1ZGdnY968edi3bx/ef/99rFmzBnp6em1eBxERERERERERERERtQrNB3rPnz+P77//Hn/99Rf09PTwzDPPwN/fH5MmTWr2B1FEkZGR2LNnD3bt2oWEhAS4ublh7ty5mDVrlsoP/4iIiIiIiOjxkpCQgNGjR6O6uhq//vorBg4c2Kb7l0ql+Pe//429e/di9erVWLhwIQwNDZGTk4MlS5YgPT0dmzZtgq2trdJ6MTExcHd3R81zNlOmTMHu3bubXEN2dja++uorbNy4EW+88QbWr1/f4Dp37txBUFAQJkyYgDVr1iAqKgoHDhwQQ72xsbEYOnQoTExMkJycjIqKCvTs2RPnz59XGYKtebyHDx8Wp1+/fh1Tp07Fli1b0KdPH2zYsAG7du3CkSNHYG1t3ej3o1evXrh586bKY5FKpejfv3+jtlP7/SopKWnwvWotO3fuxFtvvQU/Pz/s3buX5zmIiIiIiIiIiIiIiB4Nmgn05ubmYvv27di0aRPi4uLw9NNPY968eZgwYQIMDAyau1miOgRBwMWLF7Fp0ybs2bMHBgYGmDZtGubNmwdXV1dNl0dEREREREQakJaWBj8/P1haWuLQoUOwtLRs8xqef/55DBo0CB9//DEEQYCWlhaSkpLg4+OD5557Djt37lS53pw5c/DKK6+gZ8+e4jQLC4tmnU8JDQ2Fvr4+vLy88N577zUY6K2ursaQIUNw7tw5APK75fTs2RNTpkzB6tWrxeNavXo1+vTpg+zsbHz00Uf4+eefMXPmTGzdulVpe+qOt7q6Gv3798eoUaPw1VdfidMHDBgAU1NTHDt2rNHvx/vvv48PPvgA3bp1E+efOXMGc+bMwa1btxq9ndrvVwPnzFqdVCrFCy+8AE9PT/z999/Q19fXaD1ERERERERERERERPTA6g30arf03oqKivDVV1/BwcEBy5cvx9ChQ3H9+nWcOXMGU6ZMYZiXWpyWlhYGDRqEX375BRkZGVixYgUOHToEd3d3TJ48GXFxcZoukYiIiIiIiNqQTCbDxIkTYWRkhCNHjmgkzAvI7yyj6GqrpaWFiooKTJ48GZ06dcKmTZtUrpOZmYnw8HD06tULNjY24qOx51Oqqqrwxx9/iF/7+Pg06WLXs2fP4vz58+LXOjo6eP3117FhwwYUFxdDKpXi1VdfRZ8+fQDIA7ErV66EtrY2Lly4oLSt+o730qVLCAsLQ79+/ZSm+/r64vjx45BKpY1+P7799lvY29tDIpGIjwMHDuCll14Sl2ns+9rU96s1eXt74+jRo7hy5Qrmz5+v6XKIiIiIiIiIiIiIiKiVtVigt7S0FF9//TUcHBywevVqLFq0CGlpafjpp5/g5eXVUrshqpeZmRnef/99xMfHIzAwEJGRkfDw8MDs2bORkpKi6fKIiIiIiIioDaxduxZRUVH466+/0KlTpzbf/5kzZ7Bx40akpqYiJCQEP/30E4KCgrBs2TKEhoYiICAARkZGKtf94YcfcPnyZdjY2MDR0RHbt29vVKfYyspK7NixA+7u7pg7d26za9+3b1+daZ6eniguLsbhw4dhb2+PV155RWm+tbU1vL29YW5urjS9vuONjY0FgDrH5uPjAwBiqLgx74e2tvLprerqauzbtw8vvviiOK2576umeXl54eeff8amTZtw8uRJTZdDREREREREREREREStqMUCve7u7vj0008xe/Zs3Lx5E8uWLYOJiUlLbZ6oSbS1tTFp0iSEh4dj69atOHnyJFxcXLBu3TpUV1drujwiIiIiIiJqJcXFxVi3bh0CAgLg7OyskRrs7Ozg7u4OQRDQtWtXeHt7w9nZGbt374auri4iIiIwfPhwGBsb4+mnn8bVq1fFdYcMGYJFixZh8ODBuH37NmbMmIGRI0eiqqpK5b5kMhl+/vlnuLi44J133sHEiRORkJDQ7NpVravocBwXF4fOnTtDS0urzjKpqakYPXq00jRVx6s41g4dOgAArly5orROz549AUC8KLep7wcABAcHQ0tLCwMHDhSnNWc77cWkSZPw3HPP4d///remSyEiIiIiIiIiIiIiolb0wIHe0tJSLFiwAH379kVCQgK+/PJLjXS/IVJFR0cH06ZNQ0xMDD799FMsW7YMw4YNw61btzRdGhEREREREbWC48ePo6CgAPPmzdNYDfb29ujfvz8AoHv37njiiSfg6uqKtLQ0eHp64tNPP8XJkydx9epVJCQkYMiQIUhLSwMAjBw5EmvWrMG5c+cQGhoKV1dXnDhxAmvXrq2zn/LycvTq1QsffPABJk+ejFu3bmH16tXo0qVLs2vPysqCjo6O0jRDQ0MAQEZGhsp1zp49C11dXXzwwQfitLS0NJXHqzhWPz8/SCQSnDlzRqlTbn5+vvgeNvX9UNizZw8mTpyoFDxuznbak3fffRfnzp1Denq6pkt5ZKSmpmL9+vWorq7GjRs38Pnnn2Pnzp11LgQvLCzEH3/8gRUrVmDr1q1ITU3VUMVERERERERERERE9KjTfZCVExMTMXbsWKSlpSEvL6+lamqUc+fO4cyZM4iJiYG/vz/Gjx8PAPj7779RUFAgLpeamop33nlH/PCpprCwMJw9exYSiQQvvPACevTo0axaMjMzERMTg6FDhzZq+ZauUdUyoaGhajvyPPnkk3BwcGhWPXfv3sXmzZuxdOlSldtuaH5TjqslSSQSLF26FM8//zymTp0KLy8vpWMmIiIiIiKiR8O1a9fg6uoKCwsLTZei0oQJE8QLoZ2dnfHNN9/gX//6FzZu3IjPP/9caVkvLy9IpVK4uLhg9+7dWLJkCQCgrKwMmzdvxpo1a/D6669jwYIF6Ny5c4vUZ2xsXGeaooutlZWVynmffvopgoKClNZVdOKtfbxFRUXisa5atQoBAQGYMWMGJk+ejOjoaPz+++/isdem7v2oSRAE/Pnnn/j111/VHmNjttPePP300xAEAdevX0e3bt00Xc5D7+DBg5g1axays7MhCALCw8ORnZ2Njz/+GLdv31Y6r+Xn54cVK1bg7bffxi+//AJ3d3f85z//wbRp0zR4BERERERERERERET0KGp2oDciIgLPPvssevTogfDw8JasqUFSqRRr167F3r17sXr1arz88su4d+8eUlJSMHbsWKXOLlOmTKkTTM3JycGSJUuQnp6OTZs2wdbWtll1ZGdn46uvvsLGjRvxxhtvNCrQGxMT06gaFXUuXbpUbY3qjkMQBPzrX//CzZs3VdYglUrFQG9T6gGA2bNn4+LFi2oDuw3Nb6n3vrm8vLwQGhqKDz74AFu2bMEbb7zRpvsnIiIiIiKi1lVQUABTU1NNl6FW7e65AwcOBADExsaqXN7Q0BDjx4/Htm3bxGmnT5/G8uXLkZeXhyVLlsDExKTF6rOxsREDvAqFhYUAAHd39zrLL1y4EAsWLEC/fv2Upiv+DVR1C1Yc66JFi+Dr64tjx47h/PnzmDJlCi5duoT4+Pg621NQ9X7UFBwcjIqKCjz99NP1HmdD22lvjIyMoKurK3YwpgczduxYzJo1C6tXr0bv3r0xf/58AIC3tzf+/PNP8bxWRUUFJk6ciBdffBEA8OGHH+Lq1at444038MQTT6j8mSAiIiIiIiIiIiIiai7t5qyUnJyM5557Du7u7jh9+nSbhzI/+eQT+Pr6QiKR4JNPPkFiYiIMDQ3xzTff4OTJk0hJSREf//3vf+us7+bmhvLychw+fPiBak9KSsK0adNQWlra6HUaW2NSUhLc3NzU1qiYr+o4Tpw4gRdeeAG3bt1CeXm5+Dh27JjSbT+bUg8AbNmyBZGRkfUeX33z66u5Lenr62Pjxo148803sWfPHo3UQERERERERK3D2toaKSkpmi5DLalUqvS1ra0t9PT06g3lurq6wtnZWfz6ueeeQ1JSEj777DM4ODjgyy+/FEO3D8rNza3OtJycHAB1A72bN29Gv379MG7cuDrrKOqtfby1j3XIkCH4/PPP8cUXX8DExARBQUFYuXJlk96Pmvbu3Yvx48dDR0dH7fqN2U57k5aWhsrKSnbnbUEdOnQAIP8+UHB3d1caP44cOYInn3xSab1Ro0ahoqICW7dubZtCiYiIiIiIiIiIiOix0eRAb2VlJV555RV07twZ+/fvV3krxtYWGRkpfjCjpaWFbt26ITMzE+Hh4ejVqxdsbGzEh4GBgdK6FRUV6NSpEzZt2tTk/VZVVeGPP/4Qv/bx8VE66d+QptQ4efJk8ZaUtdWcr+o4jI2N8e2338Le3h4SiUR8HDhwAC+99FKT6wGAuLg4XLt2DWPGjFF7fHFxcWrnN1SzJrz99tuYOXMmEhISNF0KERERERERtZChQ4fi9u3buHr1qqZLqWPUqFG4dOmS0rT4+HjIZDL4+fmpXe+vv/7C+PHjlaaZmpri448/xqJFi/Dtt9+Kwd6ioqIHqnHWrFnQ19dXmiaVStG3b1+l8Otff/0FQRAwbdo0pWXPnDkDALCyslJ5vOqOtaKiAi+//DJcXFzw1ltv1VujqvcDkN+xaO/evUrnPpqznfYoKCgIRkZG8PX11XQpjzQdHR2lu1hFRUXVOff51FNPAQCio6PbtDYiIiIiIiIiIiIievQ1OdC7fft2SKVS/P777+jYsWNr1KTWmTNnsHHjRqSmpiIkJAQ//fQTgoKCAAA//PADLl++DBsbGzg6OmL79u1KJ+AVli1bhoCAABgZGTV6v5WVldixYwfc3d0xd+7cZtfflBpDQ0MREBCgcjs156s6joEDB0JbW/mftrq6Gvv27RNvEdiUemQyGT7++GN89dVXao9NsYw6DdWsCevWrUPPnj2xYMECTZdCRERERERELcTHxwd9+/bFqlWrNFpHWloaAPnFtApff/01UlNTceHCBXHaqVOn4ObmhunTpyMuLg7z58/HtWvXxPmRkZEoLi5W+zf34sWLkZSUhI8++gjff/897O3t6/z9npubCwAoKytTuY2AgADMnj0bgDyI+84774jnB8rKynDw4EFs3bpVPNdw4sQJfPXVV5DJZNiwYQM2bNiA9evXY+7cuQgPD6/3eBXHWlNxcTHeeOMNODg44MSJE9DV1QWAJr8fFy9eRFFREZ555hml6U3djuL9ai9KS0uxdu1aTJ06VewqS22jU6dOuHjxotI0Ozs76OnpwdzcXENVEREREREREREREdGjSrepK6xbtw7Tp0+vc5vFtmBnZwdBECAIArp27Qpvb2+xS8aQIUMgk8lw8eJFXL58GTNmzMCuXbtw5MgRpdss7t69G5MmTcLw4cMREhKC/v3747vvvkP//v3r7E8mk2HHjh348ssvcefOHbz99ttYuHBhs+tvSo26urqIiIiAsbFxnRprzm/McQBAcHAwtLS0MHDgwCbXs3LlSsyfP7/e210qltm7d6/K+c2pubVJJBJ88cUXGDNmDGJiYprUbZmIiIiIiIjaJy0tLaxZswajRo3Cr7/+itdee63Na7h06RLWrFkDADh48CAGDBiA8ePHw8PDA8HBwViwYAH8/Pygr6+Pixcv4p9//oGuri6Kioqwfft2rF+/HsOGDYOvry86deqEU6dOQU9PT+3+DA0NsWDBArz11lvYsmULvvnmGyxevBgA8H//93/YsWMHAGD//v3w8fHBmDFjYGVlJa5/8OBB3Lt3D1VVVdDR0cHatWsxbtw4jBw5EhkZGfj444/Fv92vXr2KCRMmoPj/2bvzuCrL/P/jL2QTOOyCLAqKilsphgViibuNLWqm47T3rSybtMn8lk1WM03b5MOsmTSr0d+YlZNLjkmuGG64ZLiQKyoCKovscNi33x98OYgsgqkH9f18PM6Dc+77uq7zuU7ENDdvPndBAXv27KlTR9u2bU1BZqDB/dbsFSAzM5PVq1ezcOFCZsyYwbhx4+qs19LPY/ny5dx3333Y2Nhc9joXfl7/+te/6n1W5vC///u/5OTkNPmH1HJ1hISEmJoD1Dh06BBlZWV1rrGJiIiIiIiIiIiIiFwJFg11ZL1AnZMnT56kW7du7Nixo8lbQV5NeXl5ODs78/bbb/PGG280OObgwYNMmjSJY8eO8f777zNz5kygujtOhw4dyMzMxM3Njbi4OAYPHkx+fj7Hjh3D19cXgJKSEhYtWsQHH3xAVlYWL7zwAi+//DLt2rWr916lpaXY2toybdo0Pvnkk2bv41I1BgUFsXnzZjIyMurUCNQ539Q+LjRt2jQqKyv59NNPW1TP1q1b2bJlC2+99RYA06dP59tvv63TYejCMQ2dv3hPza35WqisrMTb25tXXnmFl19+2Sw1iIiIiIiIyJU3Y8YM5s2bx7p16xg8eLC5y6knOTkZOzu7el0+S0pKSEpKwt7e/rL/v3JpaWm9UGtTjEYjZWVldWqpqKggIyOD9u3bL86cjQAAIABJREFUX1YNF2tov//973/p06cPAQEBjc5ryedx+vRpnJyccHd3/03rtCYfffQRM2bM4LvvvmPChAnmLueGMmPGDObMmUN8fDydO3cG4N5772Xbtm3k5uZiYWEBgJOTE4cOHcLPzw+A+fPn8/HHH3Po0KEW/XsmIiIiIiIiIiIiIgIsBp5o7GSblqx08uRJAPr06fObKrra+vbtS0xMDB06dGDp0qWm4/v27QOqb5cHEBgYyEcffYTRaGT+/PkUFxfzj3/8gy5duvDqq6/yyCOPkJCQwPvvv99gmPdq1jh27Fjc3Nzq1Xjx+Yb2cbGqqipWrlzJ+PHjW1RPTk4On376Ka+//nqj85oz5nJqvlbatGnDLbfcYvreFhERERERkRvDhx9+yNixY7nnnntYvXq1ucupx8fHp16YF8DW1pZu3br9ptBpS0OGBoOhXi2WlpZXLMwLDe937NixTYZ5oWWfR+fOnRsM87Z0ndagqqqKv/zlL8yYMYOPPvpIYd4rbOvWraxatQqA9957j9TUVP7zn/+wfft28vPzefvttykvLwfgscceY/To0SxevJiFCxeydu1aNm/erDCviIiIiIiIiIiIiFxxVi0ZXNOZ4hJdfVsFe3t7xowZw6JFi0zHnJ2d642ruT3e8ePHTV1mc3JymD59OjNnzsTR0dEsNV4YIL6wxlGjRtU7f/GYi0VHR1NaWsqgQYNaVM9LL73E7bffzg8//GAac+LECYqLi/n+++9xcXFhyZIldcZcfH7o0KEN7ulSNV9LVVVVpu9tERERERERuTG0adOGr7/+mmnTpjFu3DieeeYZ5s6di729vblLE2lSWloakydPpktEBGe7dcNn926YOhXatat+eHpWP2pee3hAmxb9zf5NLzw8nFOnTtU5NmnSJCZNmlRv7Keffkpubi6HDx/Gz8+Pp5566lqVKdIsubm5VFZWUlBQQGlpKdnZ2UB1p/W8vLwG51RWVpKbm9vomm3btsXOzq7ecVtbW9P/jlpaWuLk5ASAi4sLFhYWGAwGrK2tf+uWREREREREREREblotCvR27doVgNjYWO68886rUtCV1KNHDwIDA02vL3xew8/PD2traxwdHbn77rtJSEjgn//8J3PnzmXx4sW8/PLLvPDCC1ct2NtYjTExMQ3W2ND5i8dcbMWKFYwZMwZLS8sW1ZOens6mTZvqnM/NzaWwsJBp06bRu3dvrK2t64y5+PzQoUMvq+ZrpaKigkOHDnHfffeZrQYRERERERG5OiwtLZk3bx6DBw/mueeeY/v27Xz11Vf079/f3KWJNGjlypU899xzODo68v7bb+Nz7hykpcHBg5CRUfu48I/tLSygR4/qYG+7dtC+fe3zhl4rbNdizs7OhIWFmbsMuUEUFRWRmZnJiRMnyMvLw2g0YjQaycnJIT8/n4KCAoxGI7m5uXXO5+fnU1ZWhtFopKqqipycHHNvpVF2dna0bdsWa2trDAYDtra2ODg44OLigqOjIwaDAYPBQIcOHUzPHR0dcXFxwcnJCXd3d9zd3RvsZC8iIiIiIiIiInIja1Ggt0uXLvTs2ZMlS5ZcF4HeVatWMWbMGNNrLy8vU4fbGidOnKCsrIyBAwcC1RfoZ82axZ/+9CfmzZvHnDlzmDNnDi+//DJTp07FYDBckxp3797dYI0NnW9oHzWqqqpYsWIFX375ZYvriYiIqHf+lVde4auvvuLs2bMNzm/ofEtrvpbWr19PRkYGo0ePNlsNIiIiIiIicnVNmDCBkJAQnnzyScLCwnjqqad444038PHxMXdpIgAcOHCAWbNmsXbtWiZPnsycOXNwcHBofEJREaSkQHIyZGfDkSO1z8+cgZ9/rn6dkQFlZXXntm0Lrq7g4wPe3tXPG3rt6gq9e1/djYvcANLT0zl//jwpKSmkpKRw/vx5MjMz6zyysrLIysoiMzOToqKiOvNtbGwwGAy4uLiYwq014VdfX18cHBwwGAw4Ozs32BXX0dERKysrU4j2whCss7MzbRrp4O3k5NRoA4j8/HzKy8vrHTcajZT938+UhsLFeXl5VFRUmLoFl5SUUFhYSHFxMUajkby8PPLy8sjOzubs2bNs2rQJo9FIQUEB+fn59d6vTZs2uLu74+bmhpubm+l5zVdPT0+8vb3x9PTE19cXT09PbGxsmvFPTUREREREREREpHVqUaAX4OWXX+b5559n6tSp3HLLLVejpiadO3cOgNTUVNOxuLg45s+fz+OPP06/fv0AOHz4MAUFBcyaNavO/Dlz5rBz505TV42oqCh69uzJE088UWecwWDg1VdfZerUqSxYsIDZs2czZ84c/vd//5dXX33VNK7mFmbFxcUN1vvKK6+QlZXFK6+80qIaQ0NDTXVeXOPF55vax65duzAajQwbNqzO8ZZ8ZldCS2q+VkpKSvjzn//M/fffT/fu3c1Sg4iIiIiIiFwbfn5+REZGsmjRIv7617+yePFi/vjHP/Lqq6/Srl07c5cnN6ljx47x1ltvsXz5coKDg9m0aVO9azgNsrODgIDqB0BTdx4qKqoO+l4YAL74dXw8RETA2bNQWlo7tyb825wAsK8vuLj8tg9EpJUpKChg69atJCUlkZKSQnJyMmlpaaSmppKcnEx6ejqlF/w7Y2tri6enp6nDbLt27ejZs2e9IKqbmxvdunXD2dm5VQZQG7uj2tXumJuTk0Nubi5ZWVlkZGSYwtAXh6JPnDhBZmYmaWlp5Obm1lnD3d2dvn374uPjYwr6dujQAX9/f/z8/PD29r6qexAREREREREREfktLKouvEVfffVOVlRUMHjwYLKzs4mOjsbZ2fnqVXeR3bt38+GHH7Jq1So6duzIO++8w5gxYzh16hRDhw4lNzeXIUOGcMcdd+Dm5sbUqVOxs7Ort87w4cMZOHAgtra27Nq1iy+++OKSF/KKi4v58ssv+eijjzh9+jQA69atY/HixXz33Xd4enpy8OBBvLy86szr2bMnWVlZREREMGLEiGbXGBsby/Tp0xk6dGiDNdacv9Q+XnrpJTIyMliyZEmd4/v27WvRZ1ajpgPvhYHq5p5vbs3XygsvvMBXX33FgQMHCKj5BZiIiIiIiIjc8EpKSliwYAHvvfceRUVFPPHEEzz//PP06NHD3KXJTWLLli3Mnz+f77//nh49evC3v/2NsWPHYmFhYe7S6gaADx9uOACcnQ3nzsFFQbomA8A1x3r1qn7u7Q2tYb9yUzMajZw4cYKEhAQSExNNX5OSkkhMTCQzMxPA1C3Xy8urXkdYHx8f2rdvj5eXF+7u7mbe0c2nqKiItLQ0kpOTOX/+POfOnePgwYOkpqaSlpbGuXPnSE1NpaKiAoC2bdvi5+eHn5+fKeTbqVMn/P396dq1K76+vmbekYiIiIiIiIiI3OAWA080drLFgV6ApKQkwsLC6NKlCxEREY3+xf61VFJSQlJSEvb29s266JacnIydnV2LuwqUlpa2qGtCzW3IXF1dW1wjVHcAbqrGS+3j9OnTODk5NXgx+XLquRIu97O/0qysrPjuu+8YP368WesQERERERER8zAajSxYsIDPPvuM06dPM2TIEKZMmcKDDz5o7tLkBpWXl8eAAQM4cuQIoaGhTJs2jd///ve0adPG3KVdnprw76U6ANe8/r87XQF1w7/N6QDs5QXX6+ckZlVWVsaZM2eIj48nPj6ew4cPc+TIEeLj40lISKCyshKo7j4bEBBAQEAA3t7e+Pj4EBAQwPDhw81+HVN+m7KyMtLT00lJSTF9H8THx5OcnExKSgrHjx/HaDQC1V2WfX196dWrF7179zZ9TwwfPtzMuxARERERERERkRvElQ/01khISOD+++8nMTGx3q2tRFqj/fv38+ijj3L27FlycnLMXY6IiIiIiIi0MgkJCYwePZqjR4/SoUMHHnzwQSZOnEhoaGjr6J4q14XS0lI2btzIsmXL+OGHH8jPz2fgwIFMnDiRF154wdzlXR9KSiAzs/GA8MXHUlLqzq/pAtyc0LCnJ1hZmWefctmKi4vZt28f+/fvJzY2ltjYWHbv3o2lpSVdunShb9++9OnTh+7du9OtWze6deuGg4ODucuW60xlZSWJiYn8+OOPHDp0iNjYWA4dOkR+fj5t2rQhICDA9L3Wt29fbr/9dnx8fMxdtoiIiIiIiIiItF5XL9AL1V1eZ82axYkTJ5g/f74uVkmrVFpayuzZs3n77bcJCwvj3//+N/7+/uYuS0RERERERFqpw4cPs3z5cpYtW8bRo0fx8PBg8ODBDB8+nHvuuUe35JZ64uPjiYyMJDIyko0bN5Kbm0uvXr147LHHeOSRR/Q9c7VlZ1+6Q3DN6/PnoaKi7vy2bRsO/PbqVfd1x45gbW2ePd7kkpOTiY6OZseOHcTExBATE0NxcTHOzs7ccsst9O7dm0cffZR+/fopuCtXXXJyMjExMRw5coTDhw8TExPDsWPHqKysxNvbm+DgYIKDg7nzzjsJCwvD3t7e3CWLiIiIiIiIiEjrcHUDvTUCAgJIS0vjhRde4JVXXsHd3b35JYpcJRUVFSxZsoS//vWvpKam8u677/KnP/3p+r2VpYiIiIiIiFxzsbGxrF27lo0bNxIdHU1ZWRn9+vVj5MiRDB06lNDQUBwdHc1dplxjaWlpREdHExkZyYYNG4iPj8fJyYmhQ4cyatQo7rnnHjp27GjuMqUxRUVNh35rXsfGQllZ3blt2zbd8ffC1x06gI2NefZ4paSlQUQEPPII2Npek7esqKhg3759REZGsmPHDvbs2UNmZiY2Njb069ePkJAQQkNDCQkJISAg4JrUJHIp+fn57N27l927d7Nnzx727NlDWloa1tbW9OnThwEDBjB06FCGDBmCi4uLucsVERERERERERHzuDaB3uLiYj777DPef/99SktLmT59Oi+++CLOzs7NL1XkCqmoqGDlypW89dZbnDx5kieffJI33nhDv0gTERERERGR38RoNBIVFcWGDRvYsGEDJ0+exNLSkltvvZWBAwcSFhbGwIEDdVeYG0xlZSVHjhwhOjqanTt3snPnTk6ePEmbNm3o168fo0aNYtSoUYSFhWFlZWXucuVKayj821gA+OxZKC2tO78mAHxx19+LA8AdOkBrvJa6dy/ccQe4ucG0afD88+DhccXfJi4ujsjISDZv3kxUVBTZ2dl4eXkxZMgQQkJCCAkJoV+/ftheo1CxyJVw+vRpU8A3Ojqaffv2YWFhQXBwMMOHD+fNN9/U97SIiIiIiIiIyM3l2gR6axQUFPDpp5/ywQcfUFxczIQJE5g+fTpBQUEtXUqkxXJycli8eDGffPIJiYmJjB8/nnfeeYfAwEBzlyYiIiIiIiI3oOTkZHbu3El0dDS7du1i3759lJWV4evrS//+/QkKCjI9OnXqZO5ypZnKy8tZtmwZBw4cYP/+/ezdu5fc3FwMBgMhISGEhYURFhbGgAED9MfsUl9RUeOB3+RkiI+vfn7uHJSU1J17cfi3qQ7ANceutrVr4Z57qp9bWYGFBTz2GEyfXh1Ovkzl5eVs2bKFlStXsnbtWpKSknB0dCQ8PJzhw4czbNgwbrnlliu0CZHWISsri59++onNmzcTGRnJuXPnGDRoEA888ABjx47F09PT3CWKiIiIiIiIiMjVdW0DvTVyc3NZvHgxn332GceOHWPgwIE8//zzjBs3Djs7u8tdVqSeqqoqoqOjWbBgAStWrMDe3p4nnniCKVOm0K1bN3OXJyIiIiIiIjeRoqIi9u7dy86dO9m3bx8HDhzg1KlTVFZWMnjwYIKCgrj11lvp0aMH3bt3x93d3dwl3/TOnDlDXFwcx48f5+DBg+zfv59Dhw5RXl5O7969CQoKIjg4mIEDB9KnTx8sLS3NXbLcSGrCv00FgGteJydDTk7d+RcGgHv3bjoA7O1dHcZtqX//G55+Gioqao9ZW0NZGYSEwOuvw733Nnvt0tJSpkyZwurVq8nMzKRfv37cf//9jBgxgpCQEHW5lpvKwoULWbduHevWraOkpIS77rqL8ePH88ADD+BzLQL7IiIiIiIiIiJyrZkn0HuhHTt28I9//IP//ve/WFlZMWzYMCZMmMD48eNxcHC4Em8hN6HDhw+zfPlyvv76a06dOsVtt93Gs88+y8MPP6zvKxEREREREWk18vPziY2NZfHixezfv58jR45QWFgIgLu7O927d6dHjx4EBgYSGBhIt27d8Pf3x9HR0cyV3zjS09NJTEzk5MmTHDt2jOPHjxMXF0dcXBxGoxEAV1dX+vTpY+qo/NBDD2FjY2PmykUu0lQA+MiRuq9TU+HCa7+2tuDmVr/Db2Mh4PbtwdISZs+GWbOgtLR+PVZWUF4OAQEwbRpMngyNNHOIiYnhs88+Y8WKFfTo0YPx48czfvx4AgICrtKHJXL9KCwsZN26daxcuZIff/wRo9FIWFgYzzzzDBMnTqRt27bmLlFERERERERERK4M8wd6a6SlpbFy5UqWLVvG9u3bMRgM3HfffTzwwAMMGzZMtyiUJpWXl7N7925++OEHli9fTkJCAt26dWPixIlMnDiRPn36mLtEERERERERkUuqqqoiKSnJFCg9evSo6XlSUhI112rc3Nzw8/PD398ff39/OnXqhL+/Px06dMDHxwdPT08FTqkOQaWmppKSkkJiYiJJSUkkJiaaHgkJCaYAtbW1NZ07dzZ1SA4MDDQFqj08PMy8E5ErrKgIMjLg/PnqR0ZG9SM9HdLS6r/Oy6s739oa2rUDL6/qsHBJSePvZWFR/XB2rg71vvgieHtTVFTEf/7zHz777DP27t3LrbfeylNPPcWLL754dfcuch0rLi5m06ZNfPPNN6xatQpHR0eeeOIJnnvuObp27Wru8kRERERERERE5LdpPYHeC6WkpLBixQqWLVvGrl27sLCwIDQ0lFGjRjFy5Ej69+9PmzZtrtbby3Xk888/Z+PGjWzevJnc3Fy6dOnCxIkTmTBhAv369TN3eSIiIiIiIiJXTGFhIadPn64TSL3wkZKSwoXXcnr16oWnpyc+Pj54eHjg5eWFh4cHrq6uuLi41Pt6PaioqCAnJ4fs7Gyys7NNz7Oysjh//jzp6ekkJydz/vx50tLSOHHihGmutbU1vr6+phB0586d64Si/f39sba2NuPuRFqxkpLakG9aWnXQNyMDli6FX36BiormrdOmDVhasrd3b6bGx3OgpITx48czZcoU7rzzzqu7B5EbTFpaGgsXLuTzzz/nzJkzjBgxgj//+c+Eh4ebuzQREREREREREbk8rTPQe6HMzEwiIyPZsGEDGzZsIDk5GXd3d958803CwsIICgrCysrqWpQirUB8fDw7d+5k586dbN68mXPnzjF48GDuvvtuRo4cSWBgoLlLFBERERERETGLkpISzp07R2pqKufPn+fAgQOkpaWRkpJCeno6KSkpZGZmkndxp03AwsLCFO51dXXFxsYGBwcHHBwcsLGxwdnZGSsrK5ydnXF1dQXA0tISJyenemvVnK9RXl5Ofn5+vXHZ2dmm53l5eWRkZFBaWkpBQQGFhYWUlJSQl5dHSUmJKbzbUO1Q3bHY09MTDw8PvL29ad++PZ6engQFBZlCzd7e3lhaWrboMxWRSxg9Gtata95YGxsoLaUU+NnamvKhQ+k9bx4eXbpc1RJFbnQVFRX8+OOPfPzxx0RFRTF48GA+/PBDbr/9dnOXJiIiIiIiIiIiLdP6A70X+/XXX9mwYQPvvfce2dnZODg4cMcddzBw4EAGDBhAaGgobm5u5ihNrrDi4mIOHDjArl272LFjB7t27cIzJYV/tGnDYV9fyocMYfIXX2Bra2vuUkVERERERESuGxd2uW2o2212dna9YG1ubi5lZWXk5eWZgrglJSUUFhbWWbux8O7FIV8AJycnU8DW0dERDw8PU5DY3t4eW1tbnJ2dsbGxabSr8PXUXVjkhhQUBAcPNnzO2hrKy8HCgmxfX/6Tm8va0lL6/+lPvPTaaw3+QcDVtHnzZiIiIhgxYgSjR4++omtv376drVu3cuzYMSZMmMCYMWNaNL+kpIStW7dy4MAB7rzzTsLCwlo8JyQkpN4fLVxq3eascaHU1FSOHTvG4MGDTcf27t3LyZMnGxwfGhpK586dmzXmYlu2bKnzPi2p2Wg0smzZMhISEggNDWXEiBENdmD/8ccf6/yhyJkzZ3jhhRewt7evN/bgwYNs27YNGxsb7rnnHjp06NCi89fKtm3bmDVrFtHR0Tz++OPMnj0bd3d3s9QiIiIiIiIiIiItdv0Fei8UHx/Pjh07iI6OZseOHRw9epSqqiq8vb3p3bs3vXr1Ijg4mODgYHr27EmbNm3MXbI0Ijc3l19//ZWYmBiOHDnC4cOH+eWXXygpKcHJyckU2h7VoQN3/PQTllFRkJoKv/89jBwJI0ZAx47m3oaIiIiIiIiIiMjNw9cXkpOrn9cEeAF69IC77ybv9tt59ptvWLZuHZMnT+att97Cy8vLLKU+++yzfPHFF3z55Zc8/fTTV2zdmJgY/vrXv7JixQo++OAD3nvvPbKyshoMhTbk/PnzhIaG8uc//5mxY8fy4Ycf8v777zcZrG1ozpEjR1i9erVp3qXWbc4aNdLT0/n73//O/PnzeeaZZ/jkk09M57p27cqpU6ca/Wz69etHt27dmhxz22231XuvsrKyOu/T3JqPHz/OvffeyyeffMKgQYNYs2YNM2fOZMmSJQwaNMi01rFjx+jVqxcX/g5k0qRJLF26tM57ZmRkMHPmTJKTk1mwYAF+fn719vD00083ed4cli1bxvTp06mqquLf//43I0aMMHdJIiIiIiIiIiJyadd3oPdi6enpxMTEcODAAfbv38+BAwc4efIklZWVuLi40KdPH3r06EFgYCA9evSge/fudOrUCSsrK3OXftNISUnh+PHjHD9+nLi4OI4dO8ahQ4dISkoCwMvLi6CgINOjX79+BAYGNrxYfDzMnAnr10N+PgQEwPDh1Y+RI8HZ+Rru7MZx5swZvv/+e4YNG8bq1avx8/Pj4YcfrhOIz8/PZ+3atRw9epSOHTsycuRIOipQLSIiIiIiIiJyc3FwgMJC8PeHu++GoUNhyBDw8OD48ePcc889lJeXs3jxYsLDw81aamxsLH379r3igd7Ro0cTFhbGrFmzqKqqIiUlBR8fn2bNraysJDw8HDc3N1avXg1Ud1F//fXX+eCDD1o0p0uXLkyaNIkPPvjgkus2Z40L7d27F1tbW/r27cu0adPqBG1ffPFFXnrppTp73rp1K5MnT+b06dNs2rSJiIiIJsc09F4LFy6s8z7NrXn06NH4+Pjwr3/9yzT3iSeeID4+nm3btpmOTZ48mYceeoguXbqYjnl4eNC2bVvT64SEBG6//XbuvvtulixZ0uA/j4SEBN54441Gz5tTdnY2zz//PMuXL2fu3LlMnTrV3CWJiIiIiIiIiEjTmgz0XncpVw8PD+6++27uvvtu0zGj0civv/7KgQMHOHjwIHFxcURERJD8f50jbGxs6NKlCz169KBr164MGjSITp064e/vj6Ojo7m2cl0rLS3lzJkzJCYmmh4nT55kzZo1pluYOTk50b17dwIDA5kyZYopwNuiDh0BAbBsGRQVQXQ0REZWP778EiwtoW/f2oDv4MGg4PYlrVmzhqeeeor09HSefPJJ0tPTmTVrFmfPnuW1114Dqm8f9+ijj/KXv/yFP/7xj3z11Vf06tWLefPm8dhjj5l5ByIiIiIiIiIics3Mm1cd4r2oK+mpU6cYPHgw/v7+rF69mvbt25upwFo1TR0sLCyu6LqHDx/mrrvuMq3d3DAvwLZt29ixYwdr1qwxHbO0tOTTTz/ljTfewMHBodlzHn/8cebMmcMbb7zB3r17m1y3sfMXrnHhe99+++2UlpY2uIe5c+fWuzPe6tWrGT9+PAAGg+GSYy7U2Hs1Z98ODg6kpKSQnZ1dZ66trS0lJSWm16mpqcTGxvLmm2/SoUOHBvdVWlrKxIkTcXNzY8GCBU2OiYqKavC8ubm6urJ06VJCQ0P505/+RElJCTNmzDB3WSIiIiIiIiIicpluiPSjwWBgwIABDBgwoM7xvLw84uLiiIuL4+jRo8TFxbF582Zmz55tGuPm5oa/vz9+fn506tQJLy8vvLy88PT0xNvbm/bt2+Pp6XlTdfjNzMwkLS2NtLQ0UlJSOH/+PCkpKZw9e5bExEQSEhJISUmhsrISADs7Ozp16kTnzp358MMP6d69O927d8fb2/vKFWVnVxvcBUhPhy1bqsO933wDf/87uLtX/3Jh+HAYNaq6a4jUc9999/HUU0/xwQcfsGjRIgCCg4NZuXIlr732GqWlpUyaNImJEyfywAMPAPDyyy+zb98+nnnmGfr370+vXr3MuQUREREREREREblWnnii3qGioiJGjx6Nn58fkZGRGAyGa19XCyQnJ7N+/XrOnj3LwIEDGTZsWL0xcXFx7N69m9jYWAYOHMi4ceMAmD9/PmfOnOHnn3/m888/x9vbm/vvv7/Z7/39998DcOutt9Y5XlBQwNq1a5kwYUKz59xyyy2medu3b29y3cbOX7hGQ+/dkIuDupWVlXz//fesWLECoN51+YbGNEdz9j1hwgQeeOAB3nzzTb7++mseeeQRjEYjq1atqtPt95///Cd79uyhY8eOdO7cmTfffJPHH3+8Ttj79ddfZ+/evfzrX/9qMFh94ZjGzrcWL774IgAvvfSSAr0iIiIiIiIiItexGzql6uTkRP/+/enfv3+d40aj0RRMrekum5SUxN69e0lOTiYtLY2ioiLTeAsLCzw8PPD09MTT0xM3NzdcXFxwdXXF1dXV9Lxz5864urpiY2ODs7Mztra22NvbX+ttA1BWVobRaKSoqIji4mJycnLIzs42fT19+nSd1xkZGaSkpJCenl6nO4LjGqH4AAAgAElEQVSVlRWenp60b9+eDh06EBwczLhx4/D39zc9PD09r/0GPTxgwoTqB0B8fG333hkz4Nlnq7v71oSAR44EZ+drX2crZWdnV+d1r1692LBhAwDr16/n2LFjhIaG1hkzatQovv32WxYuXMicOXOuWa0iIiIiIiIiItK6vP/++6SlpREVFdXqw7xRUVEsXbqUKVOm4OjoyNixY3nssceYN2+eaczHH3/M6tWr+emnn0hMTGTIkCGkpqYyZcoUevXqRVVVFe3btyc4OBiDwUBycjLx8fFNvq+FhQUDBw7k5MmTAA02P4iLi2twbmNzaq7DxsXFXXLd5qxxuaKjo7GwsGgwyNuSMRdrbs2TJ0/mm2++4dFHH2Xfvn0cPnyYzz//3BTCBggPD6esrIxdu3axZ88ennzySb755hvWr1+PpaUlAEuXLsXKyopff/2VoUOH8vPPP3Pbbbfx8ccfc9ttt9UZ09j51uTFF1/kl19+obi4mLZt25q7HBERERERERERuQw3dKC3MQaDgd69e9O7d+9GxxiNRs6dO0d6enqdTrXp6elkZWWRmJjIwYMH64RiKyoqGlzLwcHBFPK1srLC+YJgqaura73xTk5OpouKUN1p+OK1CwoKTMHbmtBuXl4e5eXl5OTkNLl/Z2dnU/jYxcUFHx8fbrnlFnx8fPDw8KjTpdjDw+OK36LuqggIgMmTqx/FxbBjR23A98svwdIS+vatDfgOHgw3UdflS7G0tKSqqgqAI0eOANT7ZUzNbQWPHj16bYsTEREREREREZFWo6ysjM8//5wZM2bg4+Nj7nKaZDQaefrpp4mNjcXBwYF+/fqxYcMG5s+fz6OPPmr6g/Z58+YxatQoLCws6NSpE0FBQURERDBlyhRTcNPX19fUOGLu3LlMnz69yfe2srKirKyMtLQ0LC0tsbGxqTcmJSWlwbmNzalpHpGSknLJdZuzxuVavnw548aNa/K6cXPGXKy5Nbdv357t27czYMAA5s6dy4ABAwgLC6szZ+TIkYwcORKAgwcPMmnSJCIjI5k9ezYzZ87k3LlznDt3jqCgIN58803c3NyIi4tj8ODBhIeHc+zYMQDTmJ9++qneeV9f32bv7Vr58MMPWb16Nb///e/NXYqIiIiIiIiIiFwGJRobYTAY6N69O927d2/2nKSkJHJycigpKSE3N5eSkhIKCwtN4ducnBzKysrIz88HoLy83PS8RlVVVb1Arre3d72LmBd2/23bti12dnY4OjpibW2Ni4sL1tbWGAwG7O3tsbW1NXURdnFxqXeLtBtO27a1wV2A9HTYsqU63Pvtt/D3v4O7O7z3HowaBf7+Zi23tXFzcwNg165dphAvgL+/P9bW1g2G0EVERERERERE5OYQFxfH+fPnGTt2rLlLuaSlS5dSVFTEK6+8YjqWkpJCly5dOHnypCnQu2XLFhwcHIDqP3Y/c+YMeXl5ja47depUnnvuuWbV0FQHYy8vrxbNqWn64OXldcl1m7PG5aiqqmLlypV8/fXXv2lMQ1pS88KFCwkPDyc8PJxFixYREhLCtm3b8PPzqze/b9++xMTE0L17d5YuXcrMmTPZt28fAGPHjjVdDw0MDOSjjz7iD3/4A/Pnzzd9f9R8r198/t13323R/q4Fb29vtm/frkCviIiIiIiIiMh1SoHeK8jPz6/BC4ZiZh4eMGFC9QMgPr463DtjBjz7bHV335oA8MiRcEEH5ZtRSEgIANu2bavzy45Dhw5RVlbWotvkiYiIiIiIiIjIjSUrKwsAd3d3M1dyaYcPH8bb25t58+Y1Oc7X15eNGzcSERFBeHg4Xbp0ISYmptHxVlZWWDXz7l8dO3akoqKCkpISbG1t65zr1atXi+bUNIfo1asXx44da3Ldxs5fuMbliI6OprS0lEGDBv2mMQ1pzr4B/t//+39899137N27FysrKwYOHMizzz7LH//4R9asWdPg2vb29owZM4ZFixYBmO6i165duzrjaq59Hj9+nFGjRtUbc+H51qrm31EREREREREREbn+KNArN5+AAJg8Gf7nf2D3boiIqA74/utfYGEBQUG1Ad/Bg6GZF+evJxd3GMnIyKCkpISqqir69u3L448/zvfff09SUpIppL5jxw66devG5MmTzVGyiIiIiIiIiIi0Av7/d7erY8eO4e3tbeZqmmZpacnx48cpKyvD2tq60XFvvPEGW7duZcOGDdjZ2bFy5com1927dy+RkZGXfO9XXnmFnj17AnDmzBm6du1aZ0xjodrG5mRkZJjmHT16tMl1Gzt/4RqXY8WKFYwZMwZLS8vfNKYhzdk3wOLFi/nd735nClX/z//8D7/88gsLFy4kJycHFxeXBtfv0aMHgYGBAKavFwe3/fz8sLa2xtHRscExF55vrTp16mTuEkRERERERERE5DK1MXcBImZjZQV33gkffAC//AKpqbB0KQQHw7ffwogR4OUFEyfCF19AQoK5K74itm7dyqpVqwBITU3lP//5D9u3byc/P5+3336b8vJyFixYwGOPPcbo0aNZvHgxCxcuZO3atWzevBkbGxsz70BERERERERERMzFz8+P2267jS+++MLcpVxS3759KSgoYMGCBXWO5+TkMH/+fABOnz7NO++8wyOPPIKdnR0AlZWVTa4bFxfHihUrmnzUhIKfeuopbG1tiY6OrrNGUFCQKTB6scbmxMTEmOZdat3mrNFSVVVVrFixgvHjx/+mMY1pbs2xsbHk5OTUGTNmzBhKS0tJS0trdP1Vq1YxZswYALy8vBg1ahS7d++uM+bEiROUlZUxcODABsdceL412rp1K+PGjTN3GSIiIiIiIiIicpksqqqqmjrf5EmRm0Z5ed1uvvv31+/mGx4OTXT6EBERERERERERuVE8+eSTrFy5kk2bNhESEmLucgC4++672bBhA/369ePdd9/ld7/7HUePHmXs2LHExcUB0Lt3b5YsWUK/fv1M85566im++uorOnXqxIwZM2jXrh0PPfQQd955J87OzqxatYqOHTvyzjvvMGbMGJydnVtUV1VVFa+99hqHDx9m5MiRpKSk8N5779UZ07NnT7KyskhOTsbS0rLBOT179uTRRx9t9rrNWaPGunXrWLx4Md999x2enp68++673HvvvXh5eZnGvPTSS2RkZLBkyZJG99qcMTXvFRUVVe99mlNzYWEh06ZNY8+ePTz99NMcOnSI8+fP8/HHH9O5c2f27dvH0KFDyc3NZciQIdxxxx24ubkxdepUU2i7RmxsLNOnT2fgwIHY2tqya9cuvvjiizrdp2NjY1m5cmWj51uDoqIixo4dy8GDB0lNTTV3OSIiIiIiIiIi0rjFwBONnVSgV+RypKfDli3V4d516+DMGTAYYM4cGDkSdFszERERERERERG5gZWVlfHAAw8QFRXF119/zdixY81dUpMSExOxsLDAz8+vwfP5+fk4OjqaXpeUlGBra3tFa6ioqCAjI4P27dvXO2c0GikrK8PV1bXZc5o7pjlrNMfp06dxcnLC3d39N41pjubUXFhYSGJiIl5eXvU+t5KSEpKSkrC3t8fX1/eS75ecnIydnV29dZp73pxSU1MZN24ccXFxbNq0idtuu83cJYmIiIiIiIiISOMU6BW56uLjq8O9M2ZAfj4EBNR27h05ElrYtUNERERERERERKS1Ky8vZ+rUqXz++ec899xzzJ49GwcHB3OXJXLTWL16NZMnT8bZ2ZmIiAgCAwPNXZKIiIiIiIiIiDStyUBvm2tXh8gNLCAAJk+GrCzYvh0mTICYGJg0CdzdoX9/mDmzOvRbVmbuakVERERERERERH4zKysrPvvsM5YtW8ayZcvo3Lkzn3zyCeXl5eYuTeSGdurUKSZOnMjYsWMJDw/n559/VphXREREREREROQGoA69IldTejps2VId5F23Ds6cAYMBBg+G++6r7t7bqZOZixQREREREREREfltMjMzmT17Nh9//DGdOnXib3/7Gw8++CAWFhbmLk3khnH27Flmz57NF198gb+/P3PnzuV3v/uducsSEREREREREZHma7JDrwK9ItdSfHx1uDcyEjZsgLy86u6+w4dXP0aMABcXc1cpIiIiIiIiIiJyWU6dOsXrr7/OsmXL6NOnD1OmTOHhhx/GYDCYuzSR69aOHTv47LPPWLlyJe3bt+f111/nySefxNra2tyliYiIiIiIiIhIyyjQK9IqlZfDwYOwZg1ERMD+/WBhAUFBtQHf8HDQRVkREREREREREbnO7Nu3j3/84x9899132NjY8Oijj/Lcc89xyy23mLs0ketCfn4+S5YsYcGCBfz666/079+fKVOm8Mgjj2BjY2Pu8kRERERERERE5PIo0CtyXcjIgKio6u6969dDUhIYDDB4MPzzn9Cpk7krFBERERERERERaZGsrCz+/e9/8/nnnxMXF0dISAhLly6lc+fO5i5NpNUpLCxk/fr1rFy5kh9++IHKykr+8Ic/8Nxzz9G/f39zlyciIiIiIiIiIr+dAr0i16X4+Opwb2QkLF8OAQG1nXtHjAAXF3NXKCIiIiIiIiIi0ixVVVX89NNPfPvttyxatIjbbruN8ePHM378eLp3727u8kTMJj8/nx9//JGVK1eydu1aSkpKuOuuu3jwwQd5+OGHcdF1YBERERERERGRG4kCvSLXvZgYWLMGIiJg/36wsICgoNqAb3g4WFubu0oREREREREREZFL2rRpEytXrmTVqlWcP3+eHj16MHz4cIYPH87gwYNxdnY2d4kiV1VlZSX79u3jtddeY8eOHZSXlzNkyBDGjx/PuHHj8PT0NHeJIiIiIiIiIiJydSjQK3JDyciAqKjqzr3r10NSEhgMEBoK994L998PumWhiIiIiIiIiIi0chUVFWzfvp21a9eyefNmDhw4gIWFBbfffjvDhg1j2LBhhIWFYWtra+5SRX6zEydOsHnzZiIjI4mKiiIrK4uHHnqIESNGcN999+Hu7m7uEkVERERERERE5OpToFfkhhYfXx3ujYyEDRsgLw8CAmq7944YAbotm4iIiIiIiIiItHKZmZns3LmT6OhoIiMjiYmJwcrKisDAQO68804GDhxIcHAwvXr1wsLCwtzlijSqoKCA/fv3ExMTQ3R0NNu2bSMtLQ17e3vCwsJMHamDg4PNXaqIiIiIiIiIiFxbCvSK3DTKy+HgQVizBiIiYP9+sLCAoKDagG94OFhbm7tSERERERERERGRJiUkJLB9+3b27NnD7t27iY2NpaysjPbt2xMaGkpISAj9+/enT58+tG/f3tzlyk2quLiYw4cPc/DgQdP36uHDh6moqKBDhw6EhIQwYMAAQkNDueOOO7DWtVkRERERERERkZuZAr0iN62MDIiKqu7eu349JCWBwQChoXDvvfDii+auUEREREREREREpFmKioqIiYkxhSZ3797N2bNnAfD09OSRRx6hT58+3HrrrfTu3RtbW1szVyw3msTERH799VdiY2NNjxMnTlBeXo69vT3BwcGmAG9ISAi+vr7mLllERERERERERFoXBXpF5P/Ex1eHeyMjYcMGaNeutnPviBHg4mLuCkVERERERERERJotIyPDFKz8+uuvOXLkCEVFRVhZWdGtWzd69OhBYGAg3bp1IzAwkMDAQHXzlUsqKirixIkTnDhxgri4OCIiIjhy5Ag5OTlYWFjQqVMnU3i8T58+9OnTh65du2JpaWnu0kVEREREREREpHVToFdEGlBeDnPmwJo1sGsXWFhAUFBtwDc8HHT7NxERERERERERuY5UVFRw4sQJYmNj+fXXX4mLizOFMgsKCgBwdnauE/ANCAjA398fPz8/fH19sdY1sZtCRkYGSUlJJCUlkZCQUOd75cyZM1RVVdGmTRv8/f2555576N27N7feeiu33norTk5O5i5fRERERERERESuTwr0isglZGRAVFR159716yEpCQwGCA2Fe++F+++Hzp3NXaWIiIiIiIiIiMhlO3v2rCmwWfM1Li6OhIQESkpKALC0tMTb2xt/f3/8/f2566678PPzo2PHjnh6euLp6YmFhYWZdyKXUlBQQHJyMmlpaSQkJJCYmGgK79YEeAsLC03jvb296datmynoXfO1a9eu2NramnEnIiIiIiIiIiJyg1GgV0RaKD6+OtwbGQkbNkBeHgQE1HbvHTECXFzMXaWIiIiIiIiIiMgVkZKS0mDoc/v27eTk5JjGWVlZ0b59e7y9vfHy8sLLywsfHx88PT3x9fXF09OTdu3a4ebmhpubG23atDHjrm48RqORrKwssrKySE1NJS0tjeTkZFJTU0lNTSUlJYV9+/aZujED2NjY0KFDB1MX5k6dOuHn54efn5/pmEK7IiIiIiIiIiJyjSjQKyK/QXk5HDxYHe5dswZ27QILCwgKqg34hoeDbkUoIiIiIiIiIiI3oLy8PM6dO0daWhrnzp3j/Pnzpu6vKSkppKSkkJaWRkZGRr25rq6uuLu74+7ubgr5urm50a1bN5ycnHBwcMDFxQVHR0cMBgMGgwEnJyecnJywtLQ0w26vvvz8fIxGI8nJyeTl5WE0GikoKCAvL4+srCwyMzNNod0Ln2dlZZk6Kdews7PDy8sLb29v2rdvj6+vL/369cPT0xNvb29T8FrBahERERERERERaSUU6BWRKygjA6Kiarv3JiaCwQChodXh3ldfNXeFIiIiIiIiIiIi11xpaSnnz583hVAzMzNNj4sDqidPniQvL4/CwsJG17Ozs8NgMODo6IizszNt2rQxBX3t7e2xtbXF1tYWe3t7LC0tcXJywtXVtc4aLi4uWFhY1FvbxsYGBweHesfLy8vJz8+vd7ygoIDS0lLT68LCQkpKSigpKaGwsJCKigry8vIAyM7OBqqDu+Xl5RiNRlNo98Juxxeqqb8m8HxxAPrC1zXPPT09cXZ2bvTzExERERERERERaYUU6BWRqyg+vjrcWxPwbdeutnPv8OFw0S8RREREREREREREpFplZSW5ubmmrrVGo5G8vDxyc3NNr41Goykkm5OTQ1VVFUajkbKyMoqKiiguLqasrKzOOGg8nAu1gdyGXBwKhvoB4LZt22JnZ4e1tTUGgwELCwtcXFwATOFjBwcHbGxssLe3N3UfdnV1NT339fXFyckJg8FA27ZtL/szFBERERERERERuY4o0Csi10h5OcyZA2vWwK5dYGEBQUG14d7wcLC2NneVIiIiIiIiIiIiIiIiIiIiIiIiIteaAr0i0gpc2Ml340bIzYWAAHXzFRERERERERERERERERERERERkZuBAr0i0sqUl8PBg9Xh3sa6+Q4aBDY25q5URERERERERERERERERERERERE5EpQoFdEWrnMTPjpp+qA74YNkJgIDg4wYEB1uPe++6BXL3NXKSIiIiIiIiIiIiIiIiIiIiIiInK5FOgVketMfHx1uDcyEjZuhNxcmDy5tnuvq6u5KxQRERERERERERERERERERERERFpCQV6ReQ6Vl4OBw/CzJmwZQtUVUFQUG24d9AgsLExd5UiIiIiIiIiIiIiIiIiIiIiIiIiTVGgV0RuEJmZ8NNP1Z17N2yAxERwcIABA2oDvsHB5q5SRERERERERERERERERERERERE5GIK9IrIDSo+vjrcGxkJGzdCbi4EBNSGe4cPB1dXc1cpIiIiIiIiIiIiIiIiIiIiIiIiokCviNwEysvh4MHagO+WLVBVBUFBteHeQYPAxsbclYqIiIiIiIiIiIiIiIiIiIiIiMjNR4FeEbkJZWbCTz9Vh3s3bIDERHBwgAEDagO+wcHmrlJERERERERERERERERERERERERuDgr0iogQH1/bvXfjRsjNhYAA+OCD6nCvq6u5KxQREREREREREREREREREREREZEblwK9IiJ1VFTAgQPV4d5Zs6CqCoKCajv3DhoENjbmrlJERERERERERERERERERERERERuHAr0iog0KjMTfvqptnNvQgI4OMCAAbUB3+Bgc1cpIiIiIiIiIiIiIiIiIiIiIiIi1zcFekVEmi0+vjrcWxPwzc2FgIDacO/w4eDqau4qRURERERERERERERERERERERE5PqiQK+IyGWpqIADB2oDvlu3QmUlBAXVhnsHDQIbG3NXKiIiIiIiIiIiIiIiIiIiIiIiIq2bAr0iIleE0QhRURARUd29NyEBHBxgwIDagG9wsLmrFBERERERERERERERERERERERkdZHgV4RkasiPr62e+/GjZCbC507w9//DsOGgZubuSsUERERERERERERERERERERERGR1kGBXhGRq66iAg4cqA73vvEGVFZCUFBt595Bg8DG5re9R2Eh2NtfmXpFRERERERERERERERERERERETkWlKgV0TkmjIaISoKIiKqO/cmJICDAwwYUBvwDQ5u+brBwfD44zB1KlhYXPGyRURERERERERERERERERERERE5KpRoFdExKzi46s790ZGwqZNkJMD3t7Vwd777oNhw8DN7dLr1IR4w8NhyRLo2PHq1i0iIiIiIiIiIiIiIiIiIiIiIiJXigK9IiKtRkUFHDhQG/DduhUqKyEoqLZ776BBYGNTf66VVfV8a+vqx9y5MHnytd+DiIiIiIiIiIiIiIiIiIiIiIiItJQCvSIirZbRCFFREBEBGzdCQgI4OMCAAbUB3+Dg6rHW1lBeXjvXwgLGjIEvv4R27cxS/rVSUFBAaWkp2dnZlJeXk5+fbzqXnZ3d6LymzrVt2xY7O7smj1tZWeHo6AiAq6srAE5OTlhaWl7WPkREbia5ublUVlaSmZlJbm4uAGVlZRiNxnpjKyoqyMvLa3AdOzs72rZtW+94zc9le3t7bG1tMRgMWFtb6+e0iMhlqvlZXFxcTFFREdnZ2Y3+fC4sLKSkpKTe8ZqfyReysLDAxcUFqP6Z7uHhgbW1temYiIhcvpqfxxf+d3bNtZDGflYD9a6tXMzGxgYHB4cGz9X8dzjU/ty3trbGYDDUOy8iIiIiIiIiIiL1KNArInLdiIuDTZuqw71RUZCfD506wYgR1cHdi1lbg6MjLFpUHe5tJdLT08nMzCQrK4vc3FyMRiPZ2dkYjUbTIzc3l7y8PNPr/Px8cnJyqKqqMv3yKS8vj4qKCjPvpmEODg7Y2Nhga2uLvb09dnZ2GAwGHB0dcXFxwcvLC4PBgIuLC46OjhgMBgwGA87Ozri5ueHu7o67u3ujvyATETGXnJwc0tLSyMjIIDs7m9zc3P/P3n2HRXGubQC/KYKKCqggqBQVEQFFwYItNtQQUTGKemJN7N3EGGPyJTHGY0xOEk2MRhN7Eo1dsSQoChZEUSxUURRFBGkK0ts+3x8c5rDLlllABvT5XddcsjOzM88scr/v7L47I2R2RkYGMjIy8OLFC7n5OTk5wpcvsrKyUFz+CygSMTExga6uLkxMTGBgYIAmTZrA2NgYJiYmMDExER4bGxsLP5uamqJjx45o3rw5DwpmjNU5+fn5SElJQWJiItLT05GRkYHnz58r/TcjIwOZmZnCYK+yL2HUtLIBYw0bNkT9+vVhamoKExMTmJqayv1clt2mpqYwNzdHixYtYG5uDn19/RqvmTHGKouI8OzZM2GKj48X+tHZ2dnIyMgQHmdlZSEzM1N4XPb+SNmXLKTKbW2UfeGu7H0TQ0NDGBkZwcTEBI0aNYKRkRFat24NIyMjGBkZCe+hGBkZoUmTJmjatClMTU3RtGlTpV/wY4wxxhhjjDHGGGOsjuIBvYwxVuetXQusWKF+nXr1ADMz4M8/gQEDRG86KSkJjx49Qnx8PB4/fownT54IAwGSk5ORlJRU4Uq3zZo1g5mZGZo1ayY3OLXs5w4dOgg/GxsbC1dped2VlJQgMzNTGOycnp4u93P5eWlpaUhPT0dycrLcFXVatWqFli1bwsLCAhYWFrC0tETLli1hY2MDGxsb2NraKr3yMGPs1ff06VPExsYiNjYWjx49QkJCAp48eYLHjx8jISEBGRkZwrr169dHy5Yt0aJFC5iZmaF58+awsLBAx44dYWZmBgsLC5iZmcHU1PS1yZTi4mI8e/YM0dHRSElJQUpKClJTU5GWlobk5GThcVJSUoXX0srKCi1btoS1tTVatWoFa2trtGvXDu3atYONjQ0POGOMiZKWloaYmBjcuXMHcXFxiI+Px8OHD/Ho0SMkJiYKX5YwMDBAq1atYGlpCXNzc3Tt2lXoG5qbm6Nly5Zo3rw5GjZsKPERVb8XL14gOTlZmJKSkpCSkoKbN28iOTkZiYmJePr0qfClwPr168Pa2hrW1tawsbGBtbU12rdvjw4dOsDe3p7PUxhjKiUnJ+Px48d4/Pgxrl27JuTN06dPhb5hUVGRsL6+vj7Mzc2F90bKD0YtmxSX2djYSHiEtVdeXh4yMzOFgc/Pnz+XGwhdfnr+/DnS0tKQkpKCnJwcYRtNmzYVzmlatmwpfBmkdevWsLa2hpWVFVq3bg0DAwMJj5QxxhhjjDHGGGOMvaZ4QC9jjNV5/fsDFy5oXk9PD5DJgBkzgPXrgf9+iJ+cnIy7d+8KAwLKposXLyI/P/+/T9WDpaUlWrVqhRYtWsDS0lIYGFD24UerVq1gbm5e4Ta67OV69uwZnj59iqdPn+LWrVtyg66fPn2KJ0+eCLezBwBzc3NhgG/Z4IU2bdrA1tYW9vb2/PtjrI5LSkrC8ePHERsbi/v37+P+/fuIjY0VPsCuX78+2rRpg9atW8sNMm3VqhWsrKzQqlUrNG/eXOKjqNtyc3OFAXZPnjxBfHw8EhMThcHT8fHxePbsGQCgXr16GDx4sDDA187ODg4ODmjbti1fBZix1xAR4cGDBwgPD8fdu3eFAbwxMTFIT08HUHoniLK+W1l/rqxPZ2NjA0tLS+jo6Eh8JLVXUVGRkMWPHj3Cw4cPER8fLzyOi4sTBuFZWfzsF9AAACAASURBVFnB3t4eHTp0gIODAzp06IBOnTrB0tJS4qNgjL1MxcXFePToER48eIDHjx8L+VA2gDc+Pl54rwQAXFxc0LJlS5iZmQnvl5ibm8PCwkK4Yri5ubmER8QAICcnRxhwfevWLeFLemWDsZOSkpCYmCh8aVpHRwcWFhawsbGBlZUVrKys5NpcOzs7NGnSROKjYowxxhhjjDHGGGOvIB7QyxhjdVpuLmBiApS78osYSaam+NTGBofj4oTBngYGBmjdurVwVVcPDw+0bdsWlpaWaNu27WtzFcZXUV5eHpKSkvDgwQNhSkxMFOY9fPhQuB2nqakpHB0d4eTkhLZt28LLywsODg48sIyxWiYjIwP3799HZGQkQkNDERUVhYiICDx9+hSGhoZo1aqV3N9y2WRrawtdXV2py3/tPX/+XMhjf39/4ee4uDgQEQwMDGBnZwcnJyfh9+jo6IiOHTvy74+xV0hRURHu3r2L0NBQhIaG4s8//xQG7ir2ycp+5hx/uYqLixEfH48HDx4gMjISUVFRcn1o4H+/m1mzZsHNzY37yozVQeX/1hWnqKgo5OXlAfjfnSvK3hsp+7nssY2NDV/N+xVT1k8v/55J+Z8fPXokXOnd1NRU+P/g4eEBR0dHODs7w8TEROKjYIwxxhhjjDHGGGN1GA/oZYyxOu3vv4G33gL09YH/3ma3vCJdXbwAkC6TIQ1Amq4uChs3hm7z5qjXujVSx45FGycn2Nvbo2XLlnw1r9dUXl4eYmNjcffuXdy7dw93794VrgqXlpYGQ0ND2Nvbo1OnTnBxcYGLiws6d+7MVydjrIakpaUhJCQE165dw7Vr13Djxg0kJSUBAExMTODk5AQnJyc4OzvDyckJgwYNkrhiVllZWVm4c+cOIiIiEBkZKfybkJAAAGjcuDFcXFzQvXt3YbKzs5O4asaYWHFxcQgKCsLly5cRFBSEqKgoFBcXw8jICJ06dcK0adPQpUsXODs7w8jISOpymYJnz57h1q1buHXrFm7fvo29e/eiqKgIDRs2RNeuXdG7d2/06dMHvXv3hpmZmdTlMsb+q6ioCPfu3UNERAT27t2LiIgIPHz4EMX/fQ+lefPmaN++Pezs7IR/7ezs0K5dOzRt2lTi6lltU1BQgEePHiE2Nhb37t3DvXv3EBsbi7Nnzwr/p8zMzNCxY0c4OTmhc+fOcHZ25oG+jDHGGGOMMcYYY0wsHtDLGGN1We7+/Ug5ehRnHz5EZFISwhISkFJcjGwDA1g4OsKha1c4OTnBwcEB9vb2aNOmDfT19aUum9UhV69exd27dxEVFYWwsDCEhYUJA8vMzMyEwb2dO3dGt27d+OqRtdiDBw+wevVqrFq1Cq1bt64122LyCgsLERISgqtXr+LatWsICQlBXFwcAKBt27bo0aMHXF1d0blzZzg6OsLKykriillNyMjIEAb43rx5EyEhIYiIiEBRURGaNm0qDO7t0aMH+vbtC1NTU6lLZpVU3fn63nvvcVZLRCaT4datWzh//rwwiDcpKQkGBgZwc3ND79690b17d3Tp0gV2dnZ8hdc6qKCgAJGRkbh16xauXr2KoKAgREdHQyaTwd7eXhjgO3DgQLRr107qctlLxNlde6SkpODatWsIDw9HeHg4IiMjER0djcLCQujr62P06NFwdnaGvb29MHCXB1my6lBUVISHDx8Kg3zL7qASERGBFy9eAACsrKzg5OSETp06wdnZGa6urujYsSP3ARhjjDHGGGOMMcZYeTyglzHG6pIHDx7g0qVLCA0NRVBQEG7evAmZTIY+ffrAzc0Nbm5uwlUaDQ0NpS6XvaIyMzMRHh6OqKgoREZGIjQ0FDdv3kRubi4aN26Mzp07w83NDX379sWAAQP4CmW1xMGDB+Hj44NTp07B09Oz1mzrdVdcXIzbt2/D398fly5dwoULF/DixQuYmJigW7duQr737NkT5ubmUpfLapGioiLcvXsXQUFBQt/gzp07AAAHBwesXLkSgwcP5ivL1THVna86Ojqc1TUoNTUVgYGB8Pf3x4kTJ5CYmIgmTZqgR48e6NOnD/r27Ys+ffqgQYMGUpfKXpKsrCxcvXpVyOWLFy8iMzNTuB27h4cHhgwZwgMIXzGc3dLIzs7GrVu3EBoaKkzR0dEgIlhaWsLJyQmOjo7Cv66urmjYsKHUZbPXUGJiovD+Sdm/Ze+hGBkZoUuXLsJ7em5ubnB0dOQ7aDHGGGOMMcYYY4y9vnhAL2OM1VZFRUW4evUq/P39cfHiRVy7dg1ZWVkwMjJCt27d0KtXL7i7u6Nnz56wsLCQulz2misuLkZ4eDiCg4Nx9epVXLlyBXfv3gUA2Nvbo2fPnhg4cCCGDBnCV5qSUFpaGpo3b17rtvU6iomJwYIFC3D58mXk5uaidevWGDBggDDxlfxYZTx79gwXLlxAYGAgNmzYAB0dHbi4uGDw4MEYMWIEevfuzVcAqwM4q+uWmzdvYv/+/fj7778RFhYGAwMD9OnTB8OGDcPQoUPh4uLCg3JeY0VFRbh8+TL8/Pxw+vRp3Lx5E3p6eujduze8vLzg4+MDGxsbqctk1YCz++WLj49HQEAAAgMDce3aNeGK2JaWlsLdCsom/kITq+2Ki4sRERGBkJAQ4c4sUVFRKC4uhpmZGXr06IHevXvjo48+4jttMcYYY4wxxhhjjL1eeEAvY4zVJpGRkfD394e/vz8CAwORnZ0NGxsbDBgwAO7u7nB3d4ezszO/mc/qhPT0dGFw7+XLlxEUFIT8/Hw4ODhg7dq1GDhwIJo0aSJ1mYzViJKSEgQHB8PX1xfHjh3D3bt3MXHiRGEAr52dndQlsldMeno6Ll68iICAAPj5+SEmJgbNmzfH8OHDMWLECAwbNgyNGjWSukzG6qSwsDDs378f+/fvx71792Bra4tRo0Zh6NCh6N+/P4yMjKQukdVSqampOHPmDPz8/HDixAk8f/4cPXv2xLhx4zB27FhYWVlJXSJjtUZSUhICAgKE6f79+6hfvz7c3d3Rq1cvYfAuf2GUvSpyc3Nx48YNXLt2DdeuXcOFCxeQmZmJN954AwMHDsSAAQPQtWtX/oIeY4wxxhhjjDHG2KuNB/QyxpiUioqKcPbsWRw6dAinTp1CYmIiTExMMGjQIOGWrO3bt5e6TMaqRV5eHi5dugR/f39899130NXVRc+ePeHt7Y0xY8agTZs2Upeo1tmzZ/H48WMAgKGhId5++20YGhoKV9IxNTXFqFGjhPUTExPxzz//ICEhAX369MHgwYOFZUSEH3/8EXp6enBwcMCQIUNE15GXl4djx45h5MiRSElJwalTp9CyZUuMGDECaWlp8PX1ha6uLnx8fOQGTMtkMpw/fx6NGjVC9+7dhTrOnz+PW7duVail/DInJye5GpVt6/Hjxzh8+DAWLlyIqKgoHDt2DNbW1pg4cSJ0dXWF52ZnZ+P3339HfHw8Jk6ciI4dO77SH0hevnwZu3btwuHDh5GWlob27dtj1KhRGDFiBN544w2py2Ovkbt37+LYsWPw9fVFcHAw6tWrBw8PD0yZMgUjR46EoaGh1CVWq7LM1pTXmrJaVUaKoS6v9fT0kJycLGT29OnTASjPV021qFsWEBBQqawG5PO6ffv26NGjxyuf2ZqkpaVh+/btWL58OaytreHj44Nx48ahe/fufBVeprWioiL4+/tj//79OHr0KDIzM9GnTx+cOXMG9evXl7o8SdS17C7rb3N2V5+SkhIEBQVh9uzZuHPnDgwMDNCjRw8MGjQIAwcOhLu7+2v798FeT1u2bBEGtaekpAjvGY4aNQpeXl58NWrGGGOMMcYYY4yxV4/aAb0gInUTY4yxSsjPz6cTJ07QtGnTyNTUlABQt27d6KuvvqIrV65QcXGx1CUy9tKlpqbSX3/9RdOmTaOmTZsSAHJzc6Ovv/6a7t27J3V5SuXk5JCTkxMBoPv378stc3BwoJiYGOHxuXPnaObMmXTjxg3av38/NWrUiObNmycs/+STT4iI6Nq1a9SjRw/RNQQGBlL79u0JAH3//fc0a9Ys+uijj6hhw4Y0ZswYmjhxIk2YMIF0dHRoxIgRwvMiIyNp7NixBIB++eUXuTp+++03pbWUX1Z+vrJt+fr6kpmZGQGgdevW0bvvvkteXl4EgNasWSM899mzZ2Rvb08XLlyg7OxsAkDdu3enJUuWiH4N6oKEhARas2YNdejQgQBQp06daO3atRQVFSV1aYwREVFKSgpt376d3nzzTdLT06OmTZvS/Pnz6dq1a1KXVm1ycnI05rWYrFaVkZpoyuvffvtNLrOJVGe1plpULYuMjKxUVhNVzOvRo0cLmf06Cg4OpsmTJ1P9+vXJ2NiYLl++TDKZTOqy2CskPz+ffH19ady4cdS8eXNatmxZhfx6HdS17B4xYkSdye7a3N/Ozc0lX19feu+994RjXb58Ofn5+VF2drbU5TFWK8hkMoqIiKCffvqJPD09ydDQkPT19WnQoEH0448/0qNHj6QukTHGGGOMMcYYY4xVj52kZswuD+hljLFqNn36dDI2NiYdHR1yd3en7777juLi4qQuizFJFRYWkp+fH82cOVP4ALdLly70888/U2ZmptTlyfH19SUAwofvRESJiYk0duxY4XFWVha1bdtW7sPn6dOnEwAKDg4mmUxGzZs3F5atXr1aqxp++OEHAkAHDhwQ5n388cdU+l2sUp9++ikZGhpSSUmJMC8sLExucEBZHQEBARVqUVymWKPitsrX4O/vL8xzdXUlNzc34fGKFSvIxsZGeFw2MOFVcf78eRo+fDjp6elRs2bNaOHChXT9+nWpy2JMrYSEBFq7di05ODgIA9C3b99OhYWFUpdWZeryWmxWK8tIsdTl9aFDh4R5n376qZDXyvJVm7xWrLMyWU1UMa9DQ0NfucwW49ixY+Tq6koAqGvXrvTrr7/y4DL20q1atYpat25Nurq6NHLkSLpx44bUJdWoupTdhoaGRMTZXRkymYz8/f1pwoQJZGRkRLq6uuTu7k5r166V+6IkY0y5Fy9e0L59++hf//oXGRsbC1+S3rBhAz1//lzq8hhjjDHGGGOMMcZY5akd0Ct/zzbGGGOVkp+fj99//x29evXC1atX8eWXX+LRo0cIDg7G0qVLYWtrK3WJjEmqXr16GDp0KH799VckJSXh7NmzcHNzw/Lly9G6dWvMmzcP4eHhUpcJAPDy8kLHjh3xww8/lH77CcCePXswZcoUYZ29e/ciLy8PH330EebPn4/58+cjKSkJ7dq1Q2xsLHR0dNChQwccO3YMAPDhhx9qVYOxsTEAoFOnTsK8Dh06yK3j4OCAgoICJCYmCvMMDQ3l1imrY/z48RVqUVymWKPitgCgQYMGwr7LODo6Ij4+Xnh8//59pKamorCwEABgZGSEx48fizzy2omIcPLkSfTt2xf9+/dHdnY29u3bh8TERPz0009wc3OTukTG1GrVqhWWL1+O6OhoBAcHo1u3bpgzZw7s7Ozw008/ITc3V+oSK01dXovNamUZKZa6vHZxcRHmOTg4CHmtLF+1yWtNdYrJaqBiXru4uLwSmS3WuXPn0KtXL3h7e8PW1haXL1/GjRs3MHPmTBgZGUldHnvFffbZZ4iLi8OBAweQlJQENzc3jB8/HjExMVKXViPqUnYXFBQA4OzWxtOnT7F27Vq0b98eHh4eiI+Px/fff4+EhAQEBwdj+fLlsLe3l7pMxmq9xo0bY9y4cdizZw9SUlLwzz//wMXFBStWrEDLli0xdepUXLp0SeoyGWOMMcYYY4wxxlg14wG9jDFWBVlZWfjiiy9gZWWF6dOnw8bGBuHh4Vi8eDGsrKykLo+xWklPTw+DBg3C1q1bkZCQgK+++grnzp1D586d0b9/f5w9e1bS+nR0dLBs2TJER0fj1KlTAAB/f394enoK60RGRsLS0hIbN24UppMnTyI2NhaTJk0CAPz888/w9vaGh4cHMjIyqlxX/fr15R7Xq1cPAJCTk6P2eT///DOaNGmitJbyyypbo56enjAYAwAGDhyI3Nxc4YPFwsJCDBkypFLbrg3Onj2Lrl27YsSIETAxMUFQUBACAwMxZswYGBgYSF0eY1pzd3fH9u3bERsbi9GjR2PFihWwtbXFunXrUFxcLHV5WlOX12KzWlVGVpZiXgOlmV1deV2ZOhWzGqiY18+fP6/zmS3G/fv3MXToUAwePBhNmjRBSEgIDh06hF69ekldGnvN6Ovr4+2338bVq1dx6NAhREZGwtnZGbNnz0ZmZqbU5b1UdSm7NeHs/p+QkBCMHTsW1tbW+M9//gMvLy9EREQgKCgIs2fPhqWlpdQlMlZnGRgYYNiwYdi2bRuePHmCdevWITIyEv369YOjoyM2b94sfAGBMcYYY4wxxhhjjNVtPKCXMcYqoaioCD///DPatWuHDRs2YOHChYiPj8dff/0ldWmM1SkmJiZYvHgxoqOjcebMGRgZGcHDwwOenp4ICwuTrK6JEyeiVatW+P777xEZGQknJyfo6+sLy/X09BATE4OioiKV2+jSpQvmzZuHwMBAuLq64tmzZzVRutI6bty4obSW8suqq8YZM2Zg6dKlmDNnDg4cOICvv/4ab775ZpW3W9NSUlIwefJkeHh4wNbWFjdv3sSJEyfQu3dvqUtjrFpYWVlh/fr1ePjwIWbMmIFPPvkEPXr0kLosranLa7FZrZiRUhGb19XVrijm9eeff15nM1sMIsKWLVvQpUsXpKSkICAgAH5+fujWrZvUpbHXnI6ODkaPHo2wsDBs374dx44dQ+fOnREQECB1aS/Ny8ju2t7XfpWz+9q1axg2bBh69uyJx48fY/v27Xjy5AnWr18PJycnyepi7FXVpEkTzJ49G9evX0doaCj69euHJUuWoG3bttiwYYPa/GSMMcYYY4wxxhhjtR8P6GWMMS0dPXoUjo6OWLZsGaZOnYrY2Fh8/vnnsLCwqPFaLl68iNWrV2PSpEnCbTwB4OTJk9i7d68wffvtt3K3087IyMD333+P06dPo6SkpNrrSk9Px/bt27Fy5UocPnwY2dnZop8rtvbFixeLqv/27dvYsGEDtmzZgoSEBAClHziW30fZFBcXp/aYvv766wrzxdbz9OlTBAYGingFlNdcFQUFBTh9+jS+/fZbXL58uUKNyl4LTa9HddPR0YGHhwdOnTqFc+fOIT09HV27dsXUqVORnJxcY3WUMTAwwJIlSxAQEIBly5bh3XfflVvu4uKCnJwcbN68WW5+RkYGNm3ahIKCAvz+++/CFcWSkpJw+PDhmjwEABDqaNy4cYVaFJdVV436+vqwtLTEjh070LlzZyxdurQajqRm7dixAw4ODrhw4QJ8fX1x9OhRudsv1wQx+a6Yj0DV8lcsTZmiTFZWFrZs2YKPP/4YW7durVC3IlWZm52dje3bt+Pzzz9X+UG12PrUtTfaHqO6jM/KylJ73Nq2a9XNzMwMa9aswe3bt2FqaoqFCxciLy+vRmuoCnV5LTarFTNSCtrkdXW1K4p5vW7dujqZ2WKkpqbirbfewvz587Fw4UKEhIRgwIABNbb/8pleRlOft7zq6htq0x8FIOTtqVOn1A4O0rRdxTagqvWpaiO02Y66dcS0NS+Drq4uJk+ejPDwcHTv3h2DBw/G+++//0oOzHoZ2V3b+9qvYnY/ffoUkyZNQs+ePZGbm4vTp0/j6tWrmDRpktIrHr9MhYWFOHv2LN5///1q3/bFixcr9MmrqqyvKbYvXf45mvqnBQUFatfRpp+rLivF9GHF7EtMP7h8Lqtqk8S0NWLaXk3nUzVxvqUNV1dXbNmyBQ8ePMD48ePx0UcfwdnZWdKaGGOMMcYYY4wxxlgVEZG6iTHG2H9lZWXR1KlTCQBNmjSJHj16JGk9169fpxEjRlBBQQF9+eWXZGhoSDk5ORQdHU06OjoEQJgmTJggPC89PZ3atWtHkydPJl1dXerRo0e11+bs7EzBwcGUk5ND33zzDXXu3JkSExM1Pk+b2gcNGqS2/tTUVJo+fTp5enrK/a5kMhm1a9dObh9lU2hoqMravL29qUWLFnLzxNSTkpJCS5cupQYNGtCiRYvUHr+qmqsiOTmZ2rRpQ7/99hulpqbSsmXLaPjw4VRcXExEpa+HstdC0+vxsslkMvrrr7/I1taWzM3N6cSJEzVew4sXL8jY2Ji6d+9eYVl+fj5ZWVmRgYEBffvttxQVFUX79u0jHx8fevHiBeXl5VHv3r2JqPRYzMzM6MiRI8LzZ86cSZ6envT06VOl+16/fj0BoNu3bwvzfvvtNyrtupXatm1bhXXCwsIIAH311VdEREIdMpmsQi2KyxRrVNwWEdHSpUsJAD148ECYN3z4cGrcuLGwnU2bNpG7uzsFBARQWFgYvXjxQtNLXWsUFBTQjBkzSEdHhz744APKzs6WpA6x+V4+H4mIbt68Wen8FUtTpihz584dsrCwoPbt25OBgQEBoHbt2lFSUpLK5yjL3Dt37pCdnR2dPHmSsrKyyNrams6fP1+p+tS1N9oco6aMLzt2VcetTbtWE2QyGZmampKrqyvFx8dLVoe2VOW12KxWzMjyqpLXISEhwrxt27YJ6yjLV23yWrFdqUxWE1XM67t379apzBbr3r17ZGdnR23btqXLly/X+P4VM11Mf71MdfUNtemPlrlz546Qt3v27FGauWK2q6wNUMx/betT1kaI3Y6Y3NbU1tSU3bt3U6NGjWjYsGGUlZUlSQ0vU3Vnd3X1tRWzu6z/zdktb+/evdSsWTNq06YNHTx4sMb3ryg0NJRmzZold75UHcoyvHyfvKrK9zXF9KUVn6Ouf1q2nqp1xG5HU1aK6cOK2Vd6errGfrBiLitrk8S0NWLaXk3nUzVxvlVVcXFx5OPjQ9OmTaPMzEypy2GMMcYYY4wxxhhjyu0kNWN2eUAvY4yJ8OTJE+ratSuZm5vT8ePHpS6HiIg8PT2FDwBlMhk9efKEiEo/vAwICKD4+HhhysvLE573yy+/UHp6OhERrVq1igDQpUuXqq2ukpIS+uijj+Tm9ejRg4YMGaLxudrUrq7+uLg4at68OU2aNKnCPk6fPk2LFi2iuLg4KigooIKCAjp9+jTZ2tqqrOvXX3+l9u3bVxg4IKaekJAQun37NgFQO0BBXc2VVVJSQn379qWRI0cK84qLi8nGxoaWL19ORKWvR/nXQszrUZMyMzNpypQppKurS999912N73/OnDm0ceNGpcuioqLI3t5e+DDQycmJbty4QUSlH+5bWlrSgQMH6LvvvqPPP/9c7rllg8qVHdPly5fJxcWFANDUqVPpwYMHFBAQQK6urgSAIiMj6fLly+Tu7k4AaNy4cXT37l26cuUKjR07lgCQs7MznThxQqhjwoQJFWpRXFa+RmXbCgwMpLZt2xIAmjFjBiUlJdHevXupSZMmBIBWrlxJRUVFdOTIETIyMpL7oNTDw0Pt4M3q9N8orJRRo0ZR48aN6dixY9VXUCWIzffy+VhSUkIuLi6Vzl8xxGSKquMpGzCTkpJCM2bMIAD03nvvKV1fVeZ6enrS9OnThcdTp06lfv36Vao+de2NNseoKePLjl3VcYtt12pSbGwsOTs7k62tLcXFxUlWh7ZU5bWYrFaWkWWqktfDhw+Xy+xx48bR77//XiFfNdWibtmVK1cqldVEpDSvyzK7NsnJIXr+vHLPTUhIIBsbG+revTslJydXb2EiKWY6keY+L1H19g3F9kcV6y5PMXPFbldZG6CY/9rUp6qNELsdMbmtrq2padevX6cWLVrQ4MGDKT8/X7I6Xpa6kN0A6kx2i+1vZ2QQVfZ7a8XFxfTBBx+Qjo4OzZ8/v1YNNi/7265OZRlevk9eFYr9VTF9abF9XE3b1qavrCkrNfVhxe7rl19+UbmNMoq5TFQxm8W0NZraXjHnUy/7fKs6tWjRghwdHSk2NlbqUhhjjDHGGGOMMcZYRTyglzHGqiI1NZU6dOhAdnZ2dO/ePanLEVhbW9OaNWvk5iUlJVHPnj3p8ePHSp9TUFAgd8Wfhw8fEgAKCwsTvd/i4mL666+/VC4PCgqivXv3ys1bsGABAaDr16+rfJ6m2olIrnYi5fUXFBRQ9+7dyd7eXunVNS9fvkwlJSVy8+bPn09Lly5Vus+YmBiaO3cuvf/++3IDBxRfS1X1lK2rbmCBpporKyAggABUGIT++eefk5GREWVnZyu9Up2610Mq69evJ11d3Rrf75AhQ+i5hlFEDx8+VHrVvKKiIpVX08vPz6d9+/bVyMDRoqIiKigoUFmjqmWVdfr0adqxYwfdu3ePLl68SIcPH6Z33nmHvv7662rbhzpNmhD17Em0cSNRSoq2z20iydUbFVUm34OCgghApfK3jKYrg4nJFGX++OMPuceJiYmkq6tLDg4OFdZVlblERF26dCF3d3fh8axZs+Su4iW2Pk2vpbbHqCrjr1+/LnfsisetTTtS09LT08nV1ZXs7e0pIyND0lrE0pTX6rJaXQ6+Tnnt5+cnZHZtEhFBZGBANGIE0b59RLm54p5XWFhI3bt3J0dHR7lBRzVNMdPF9Hmr2jdU1l/X1B9V1KVLF7nHipkrZruKOUhUmoXK8l9MferaCG22o24dTW2NFG7dukXGxsY0Z84cSet4GTi7tacuu8X2t8+cIWrQgGj8eCJfX6KCAnH7lslkNH36dDI0NKTdu3dX4ShejsjIyGof0KusX14VyvqrmvrSYvu4mratbV9eVVaK6cOK2ZfY96cUc5lIPpvFtDVi2l4x51NVPd+qSU+ePKHu3buTubk53blzR+pyGGOMMcYYY4wxxpg8tQN6dcEYY0wlmUyGMWPGoLi4GJcuXYKdnZ3UJeH8+fPYtGkTHj9+jJCQEGzZsgW+vr4AgA0bNuDq1auwsrJC27ZtsXPnztJvb/yXgYEB2rRpIzwO+B+hegAAIABJREFUCwuDl5cXOnXqpHG/xcXF2LVrFxwdHTF79myV68XExMjtEwC6d+8OALh06ZLK52mqHYBc7arq//TTT3Ht2jV89NFHMDIyqrCfXr16QVf3f82fTCbD4cOH8fbbb1dYt6ioCP/3f/+Hb775psIyxddSVT1iaKq5TGJiIlatWoWzZ8+K2u7hw4cBoEI9zs7OyMnJwalTp9CrVy+5ZepeDyktXrwYGzZsEP6v14Tbt2+jbdu2MDExUbuejY0NrK2tK8zX19dXOh8ACgoKEBwcjLfeeqtaalVHX18fBgYGKmtUtawyQkNDMW3aNEyePBl2dnbo27cvRo8ejY0bN6Jp06bVsg9NiouBkBBg0SLAwgIYOhT4/XcgK0v98/bu3Ys9e/ZU+JuoSdrku2I+xsTEAECl8rd8vqsjJlOUeeedd+QeW1paws3NDaampnLz1WUuALz99tu4cuUK/vjjDwDAkSNHsGTJEq3rE9PeaHuMytja2sodu+JxV2c7Ut2aNm2KkydPIisrCwsWLJC0FjHE5LW6rFaXg69TXg8dOlTI7NqmsBA4eRKYMAFo1gyYNAn4++/SzFflP//5D6KionDkyJEaa4PKU5XpYjJIbN9Qkdj+uhhvv/22kLfZ2dkVMlcMxRwESrNQMf/F0NRGVBdNbY0UXFxcsHXrVmzevBnnzp2TtJbqxNmtPU3ZrU3W5eUBhw4Bo0YBzZsDM2YAgYGATKb6OV9++SX++OMP+Pr6YvLkyVU/oBqQmJiI7du3qzyPvnv3Lnbv3o0PP/wQR44cEeYrZnh1nYcq669q6meK7eNq2nZl+/KKxPRhxexL7PtTirms2CaJaWvEtL1iz6cqc74lhZYtW+Ls2bOwtbXF8OHDkZ2dLXVJjDHGGGOMMcYYY0wkHtDLGGNq7Nq1C8HBwTh06BBatGghdTkASj/UdHR0BBGhRYsWcHNzg729PQCgf//+WLZsGfr27YuEhAS8++67GDp0KEpKSipsh4jw8ccf45dfflG7v6KiImzduhUdOnTAggULMHr0aMTGxqpcv0GDBrh+/brcvHbt2gEA4uPjVT5P29r379+vtP69e/dCX18f4eHhGDRoEBo1aoQ33ngDN27cULrfoKAg6OjoKB3Et2rVKixZsgSNGzdWWbemesQQU3NAQABWrlyJjh07wtvbG/Pnz9e43bLfk6Wlpdx8c3NzAKUfXipS93pIbd68eZg/fz7y8/Nf2j5CQ0MxePBgLFmyBFOnTsXHH3/8UvYTEhKCNWvWQF9f/6VsXyphYWFISkrC1q1bcf/+fRQXF2PPnj1Yu3Ytxo8fX2N1EAElJaUDEs6dA6ZNKx0A9tZbwO7dQG6u/PrFxcX4+OOPMXz48BqrURlt8l0xHxs0aAAAWuWvsnxXpzKZAgA6OjoV5j1+/Bienp5y8zRl7qxZs9ChQwdMnjwZH3zwAbZs2YJ//etfWtcnpr3R9hiVadasWYVjV3bcQNXbkZfBwsICGzduxJ9//ombN29KXY5SoaGhnNeVpCyvY2NjhcyujWSy0nzPywP27y/NdFNTYMqUigPQcnJy8N133+Gjjz4ScrSmqcp0MRmkbX9W2/66GLNmzRLydsyYMRUyVwxlOQhAaQ5qIrZfXlWa2hqpjB07Fm+++Sa+/PJLqUupMs7uytOU3dr2t4uLS3M1K6v0C3ADBwItWgCLFwOXLpUuKxMZGYk1a9bg+++/x9ChQ6v5yF6OsnPorl27Kj2PXr9+PWbPno3JkydjwYIF+OCDD4R+mGKGl7UliYmJuHTpktopKChIZU3K+qua+pli+7iatl3Zvrw6qvqw2uxLUz9YMZcV2yQxbY2Ytlfs+VRl3u+SSuPGjeHr64vMzEx89tlnUpfDGGOMMcYYY4wxxsRSd/nel33tYMYYq+1cXV3p3XfflbqMCjIzMwkArVq1SuU6t27dIgcHBwJQ4dab2dnZNHPmTAJAJiYmFBISUuH5+fn5tGnTJrK2tqZGjRrRxx9/TKmpqRpri4+PJzc3N5LJZMK8kydPEgD66aefRB2fmNobNmxYof6EhAQCQF26dBFurRwTE0OWlpbUqFEjSkhIqLCvhQsX0vz58yvMDwwMpJUrVwqPVd3aV109ZdTd1ldMzVlZWdS2bVvhFpjTp08nABQcHKz2dXR1dSU9Pb0K80NCQgiA0uNW9XrUFvr6+hVuIV2dQkJCqHHjxmRsbEz79+9/aft5VclkMvr+++9pwIABZGhoSEZGRrRlyxYqEHsf32rQsCFR6fCDilO9ekQ6OqXrTJpUeovhoqLSWxejmm/PW1li810xH+Pj48nAwEBU/lY23yuTKcqcP3+eWrduTVlZWcI8sZmbkpJC7dq1IwD09OnTKtenrL3Rdhtib2Ov7LiJxLUjUurYsSMtWbJE6jKUCgkJ4byuJGV57e7uXuOZLUZEhOpcB4gMDIjMzIgWLSK6eLH0OUeOHCE9PT1KSUmRtHZNma4sg7Ttz+bn54vKc7FZVV5Z3vbq1atC5lZ2u+fPn6+Qg5q2I7aNEFuPpnXUtTVSOnnyJOno6NCTJ0+kLqVKOLsrr7qy+8wZ9blar17pvy1bEi1fThQdTTR37lzq2LGjXD+ztomMjBT69Irn0EQVz6Pt7Ozk+nbe3t701ltvCY+VZfgPP/xAANRO+vr6KmtU1l/V1JcW28fVtG1t+8qaslJdH1abfYnpB5fPZXVtUhlVbQ2R6vebxJxPiT3fqm3Wr19PjRs3VvmaMMYYY4wxxhhjjLEat5PUjNnlAb2MMaZC2Yfply5dkrqUCsQM+Cozf/58atCggcrlPj4+pKurS/n5+URE9Pfff5OJiQkBoA8++IBevHih9HlXrlyhbt26yU2BgYFEVPqh+yeffEIrVqyg27dv09ixY8nY2Fjltipbu2L958+fJwD0888/y63z8OFDAkBjxoyp8PzmzZtTcXGx3Lxp06bRN998Q4cOHRImLy8vMjY2pkOHDtHZs2c11lOeug/CxNS8ePFicnV1Vbo/db+HiRMnEgDhd1umbPDioUOH5OZfvHhR6etRm3h7e1OHDifVfgDN0+s9GRpW7nkGBolS//cmIvH5riof1eVvVfNd20xRZsmSJXTs2DG5eWIzd/v27dSlSxcqKioiotIB/l5eXsJ2qlJf+ddT222IGTym7LhVUWyXpfbFF1+Qvv5Tyf+2eeJJ28nI6A79t0skmcpkutj+bPlMF9PH1nbg7fbt24W83bZtW4XMrcx21WWhqu1o2y+v6oBeTW2NlGQyGenrt5H8b4un13WSUdu2CRQaKvVfgmrlB/SqO4cuz8/PjxYuXEgHDx6kcePGUbt27YRl2rzvokib/rSmvqrYPq6mbWvbV9a23Sjfh61sv1xZP1gxl9W1SUTi+92VOZ8Ss7w2KiwsJB0dHTp8+LDUpTDGGGOMMcYYY4yxUjuJVI/ZfbXuPccYY9XIwsIChoaGuHPnDvr06SN1OZXm4OCg9lbDQ4YMQUBAAAwNDQEAb775Jh4+fIgNGzZg3bp12LVrF5YuXYoFCxbI3eLW2toaCxYskNtW27ZtAZTezrB///4AgLi4OPj6+uI///mP1rfI1VS7Yv1l64aGhsqtY21tjXr16ind/6hRo6Cnpyc3LzU1FWfOnJGbl5mZidzcXCxatAhOTk4YNGiQ2nrEElOznp4eYmJiUFRUhHr16lVYT9XvoWPHjgBKb/FuZ2cnLE9LSwMAODo6yj3v4MGDSl+P2iQ6Ohpvv30Ja9a8JXUprJYyMtK8jr5+6S2GGzcGvL2Bxo2PYN++WQBSX3p91UVVPqrL36rmu7aZoujXX39F165dMXLkSLn5YjN3165d8PT0FG6fPXPmTGzbtg0ZGRkwMTGpUn2Kr2dlj1EVZcetimK7LLX79+9j0KAp8PPzk7oU9pqKjAScndWvU68eUFQEtGwJTJ4MlJRsw19/rUT//o9rpsgqKp9BYvuz5TO9TZs2SvO8Knbt2oV3330XAPDee+/h+vXrcpmrLVVtgCZV6ZdXhqa2RkpPnjxBcXEcAgPPC209Y5Xh7w8MGaJ5PQMDoLAQsLMDmjSZgG7dTODquuXlF1gN1J1Dl/nss89w/vx5+Pn5oUGDBjh06JDG7V67dg3+/v4a9z158mTR/WlN/UyxfVxN246Ojha1ncoq34etbL9cWT9YMZfVtUnatDWVOZ8Ss7w2ioyMBBGhTZs2UpfCGGOMMcYYY4wxxkTQlboAxhirrfT09ODl5YVt27aBiKQup9KOHDmCUaNGqVweERGBESNGyM0zNjbG//3f/+HRo0dYtmwZ1q1bhzZt2uDrr79GdnY2AMDS0hJTp06Vm6ysrOS2U1hYiPHjx6NDhw6YN29etdeuWL+FhQWGDRuGK1euyK1z7949FBUVVRiYTUQYM2ZMhW2eOHECCQkJctPcuXNhZmaGhIQEtYOalL2e6oip2cXFBTk5Odi8ebOwPCMjA5s2bVL7e5g+fToMDQ0RFBQkt+3Q0FB06dJF7sMrIsLBgweVvh61SUxMDLy9vaUug9VBenqArm7pwIRRowBfXyA9Hdi9G1i0yBHp6WlSl6gVTfmoKn+rku/aZIqyeokIU6ZMkZt//vx50ZkbFhaGjIwM4bmjRo1CYWEhkpOTAWiXeepez8puQ922lR23Ktq2Iy9TdnY2Tp48iWHDhkldCmNKGRiU/jt3LnDxIpCQAKxdC/j4dEJCQgJu3LghbYEilc8gbfqzZZmuKs+rIiwsTO6xYuZqQ1kboC4Hy6tKv7wyNLU1UvL19YWRkRF69OghdSnsFVaWqxYWwJw5QGgocO8e8M47PbB//36kp6dLW6BIys6hgf+dR8fFxWH16tWYNGkSGjRoAACQyWQat3v37l0cPHhQ7XTo0CGt+tOa+pli+7iatl2VvrIY5fuwld2Xsn6wYi4DyrNZ27amsudTYpfXJps2bUL79u3h4uIidSmMMcYYY4wxxhhjTAx1l++t0QsJM8ZYLXT9+nXS09OjTZs2SV2KnKioKAJA8+bNE+bFxMTQ4sWL6caNG8K8iIgI6tmzJxUWFhIRUW5uLq1evZrCw8OJiCgtLY369etHGRkZaveXk5ND33//PVlYWFCzZs1o7dq1GmvMzs6mKVOm0Lhx4yg5OVnpOsuWLaPp06eLqp2I5GpXVX9ERAQ1atSIgoKChHmbN2+mjh07CrdoLBMUFEQFBQUaj6Ws1hYtWgiPFV9LVfUQET19+pQA0KxZs5RuW1PN+fn5ZGVlRQYGBhQVFUX79u0jHx8fUbd0XLp0KTk5OZFMJiMiory8PLK3t6dQhXulBgUFkbGxsejXQwrZ2dk0bNgwqctgtVzDhv+7NbCeXumkr0/k6Um0axdRdrby5w0bNkwub6QiNt8V87E8MflbRjHfNRGbKWX5TkR05swZ6tmzJ23YsEGY1q9fT7NmzaKffvpJ6X4UM5eI6N133yULCwsqKSkhIqKVK1dS586dhcdi6hPT3og9xjLqMl7x2MsftzbtiFQ+/vhjMjExobS0NKlLYa+xiAj5277Xq0eko0NkZEQ0aRKRr2/F58hkMurSpQuNHj265gsuR1mmi+nzatOfLaOpv66pP1o+t4lKM7d8virLXDHbVdYGKMt/TdtRrFWxjdBmO+rWEdPWSCE3N5dsbW1pzpw5ktbBXg1nzsjnqr5+6b/GxkSzZhFdvEj0326QIDMzk6ysrGjs2LFCH6m2uXz5MpW+1U1y59DffvtthfPosLAwAkADBgygzMxMunDhAllaWlLTpk0pKyuLXrx4oTTDq0P5vqa6fmb5XBbbPy1bT9U62vRzVWWl2D6spn2VbUfdNogq5jJRxWxW19aIfb+pPE3nU9qcb0ntn3/+IR0dHfrzzz+lLoUxxhhjjDHGGGOM/c9OUjNmlwf0MsaYBl988QXVq1ePTpw4IXUpREQUHBxMo0ePJgBkZWVFu3btooyMDAoNDSVjY2MCQAMHDqTly5fTN998Q7m5ucJzs7OzqWvXrqSjo0Pdu3enH3/8kbKyskTvOy8vj3766SeytbVVu962bduod+/edPjwYbXrOTg4kLm5OYWEhGisnYjkav/ss89U1n/79m0aPHgwff755/Tvf/+bvLy8KDExscJ6S5YsEXHUpRQHDii+lqrqOXXqFI0fP54AkLm5Of3222+UlJSkdc1RUVFkb29PAMjJyUnuwyh1ZDIZLV++nLy8vOinn36iFStW0O7duyust2TJEpo0aZLYl6PG5efn01tvvUUPHz6UuhRWy5UN6NXVJRowgGj7dqLnzzU/LyoqihYtWvTyC1RDm3xXzEei0g/BxeavorJ810RsppTle3FxMRkZGRGAClP9+vUpPT1d6X6UDdbKycmh6dOnk7OzM61fv55GjhxJDx480Ko+MW2l2GMkUp/xoaGhSo+97LjFtiNSOXXqFOnq6tIvv/widSnsNVd+QK+hIdH48aWDeDV9B+n06dOko6NDv//+e80UqkBVpovp8xKJ788qUtZfF9MfLZ/bRKWZW5a3M2bMUJq5mrarKQe1qa88VQN6xWxH0zpi2hopzJ8/n0xMTCghIUHqUtgroPyAXiMjomnTiE6fJvrvn79K586dIwMDA5o/f77kg9wVXb16lYYNG0YA6NSpU0Qkfw6t7Dz6vffeI319fbKzs6PNmzfTwYMHycDAgAYNGkR+fn5yGV6dX7Yq39dU188sn8ti+6dl66laR+x2lGVlGbF9WE37KtuOpn6wYi4rtkma2hox/f8yms6nqnK+JYWgoCBq3LgxTZ48WepSGGOMMcYYY4wxxpg8tQN6dUj9beTr7j3mGWOsmhARZs2ahV27dmHTpk2YMWOG1CWpVFBQgPj4eDRs2BCtWrVSuV5GRgYMDAzQsGHDSu2nsLAQBmX34lTiwYMHaNu2rcbtZGdno6ioCKampi+l9sTERDRo0ACmpqZKl8fFxaFNmzYat6NOVV9LRZpqjo+Ph7W1tdbbLSkpQVpaGlq0aKF0eVxcHJo0aYJmzZppve2XLTU1FWPGjEF4eDieP38udTmslhs6FPDyAsaNK71VsDb09PSwYsUKrF69+uUUVwViMvLo0aPo3LmzqPytKk2ZUj7fq1tubi4ePXqEjh07Vqo+se2NpmOsLtXdjlSHM2fOwNvbG+PGjcOOHTukLoe95mJigA8/BP71L2DUKMDISPxzP/zwQ2zcuBF///03BgwY8NJq1IbYDCqjqW+oiqb+uiJluV2WtxYWFi8lz2szMW1NTfnhhx/w4YcfYt++ffDx8ZG6HPYKuHQJ+PFH4J13AE9PoH598c89fPgw3nnnHXh6emLXrl1o0qTJyyu0mjx69Ag6OjpKz6OzsrLQuHFj4XFBQQEMDQ1rpK6SkhLo6empXK4sl8X2T5OTk9WuUx39XLF9WE37ys3NFdUPrkqbJLbt1XQ+VZPnW1W1e/duzJ49G2+++Sb279+PevXqSV0SY4wxxhhjjDHGGPufXQCmqVrIA3oZY0wEIsIXX3yB1atXY8KECdi4ceNr96E2Y1L4559/8N5778HQ0BAnTpyAk5OT1CWxV9jOnTsxY8YMjB8/Hr/99lutGmDJWE3ZuHEj3n//fUyYMAE7duxQO9CEsdpOJpNh4sSJ8PX1xZ49ezBq1CipS2JMNCLCl19+iVWrVuGHH37AkiVLpC6JMQBAUFAQxowZA0NDQ2zduhVDhgyRuiTGWDmpqalo0aIFPvzwQ3z99dfcn2eMMcYYY4wxxhirfdQO6NWtuToYY6zu0tHRwapVq+Dn54cLFy7Azs4O33zzDfLz86UujbFXUkxMDMaNGwdPT0/07dsXoaGhPJiXvXTTpk3D2bNn4e/vj06dOuGff/6RuiTGalRcXBwWLlyIDz74ADt37uQP/1mdp6uriz/++APTpk3D6NGjMXv2bOTm5kpdFmMaJScnw9vbG6tXr8amTZt4MC+rVfr06YPw8HD06NEDQ4cOxYgRIxAbGyt1WYy99oqKivDrr7/C0dERvr6++Pbbb7k/zxhjjDHGGGOMMVYH8YBexhjTwpAhQ3Dnzh0sXLgQX375JTp06IBff/0VMplM6tIYeyWkpaVh8eLFcHZ2RmRkJPbv34/9+/ejadOmUpfGXhP9+/fH7du30adPH3h6emLEiBF48uSJ1GUx9lIVFxfjxx9/ROfOnXH27FmsXbsWurp8qsheDXp6eti4cSP27duHgwcPolu3brh+/brUZTGm0qFDh+Ds7Izw8HCcO3cOc+bMkbokxiowMzPDgQMHcObMGcTHx8PR0RFTpkxBTEyM1KUx9topLCzE7t270bFjRyxZsgTTp0+Hl5eX1GUxxhhjjDHGGGOMsUriT2kZY0xLjRo1wsqVKxETE4NBgwZh7ty56Ny5MzZt2oSsrCypy2OsTrpx4wZmzpwJGxsbHDlyBFu3bkV4eDh8fHykLo29hiwsLLB7924cP34c4eHhcHJywqeffoqUlBSpS2OsWhUWFmLHjh1wdnbGihUrsGLFCgwcOFDqshh7KXx8fHDz5k1YWlqid+/emDt3LhITE6UuizHBrVu34OXlBR8fH4wZMwbh4eF44403pC6LMbU8PDwQGhqKLVu24MqVK3BycsLIkSNx/PhxlJSUSF0eY6+0e/fuYfny5bCyssLs2bPh6emJu3fvYu3atVKXxhhjjDHGGGOMMcaqgAf0MsZYJVlZWWHHjh24ffs2evXqhWXLlqFVq1YIDw+XujTG6oT8/Hzs3r0b7u7ucHNzQ3BwML799lvExMRg6tSpfHVIJjkvLy9ERkZi+fLl2Lp1K9q0aYNFixbh0aNHUpfGWJXk5OTgxx9/hJ2dHebMmYM+ffogIiICn3zyidSlMfZSWVtbw9/fH7/88gtOnjwJOzs7LFu2DGlpaVKXxl5jd+7cwfjx4+Hq6ork5GScOXMGmzdvhpGRkdSlMSaKvr4+3n33XURHR2Pv3r3Iy8uDt7c3bGxs8Nlnn+Hhw4dSl8jYKyM/Px979uzBwIED0aFDB+zduxdz587FgwcPsGHDBrRu3VrqEhljjDHGGGOMMcZYFekQkbrlahcyxhj7n4yMDOzcuRPvv/8+evbsibFjx2Ls2LGwtbWVujTGao2ioiKcO3cOBw8exOHDh5GdnY3Ro0dj7ty56N+/v9TlMaZSXl4etm3bhu+++w6JiYkYNWoUpk6dijfffBP6+vpSl8eYKLdu3cLOnTvxxx9/ID8/HzNnzsTSpUv5g3/2WiooKMDmzZuxZs0a5OXlYdq0aZg3bx4cHBykLo29JgIDA7Fp0yYcPnwYDg4O+Oqrr+Dt7Q0dHR2pS2Osyh48eICtW7di586dSE5ORr9+/eDt7Q1vb29+j4QxLeXl5cHPzw/Hjh2Dr68vsrKy4OXlhRkzZmDYsGHQ09OTukTGGGOMMcYYY4wxpp1dAKapWsgDehljrJqdPXsWf/75J44dO4Znz56hW7duGDNmDMaOHQs7Ozupy2OsxhUWFuLMmTM4dOiQ3N+Fj48PpkyZAgsLC6lLZEy0oqIi7Nu3D1u3bsWFCxfQokULTJo0CdOmTYOTk5PU5TFWQWpqKvbs2YOdO3fi1q1baN++PaZNm4ZZs2ahefPmUpfHmOSys7OxefNm/PLLL4iLi8PAgQMxd+5cjB07VurS2CvqxYsX6NWrF6KiouDu7o5FixZh/PjxfHcK9koqLi7GyZMnceDAAZw8eRIZGRno2rUrRo0ahS+++ELq8hirtZ49e4YTJ07g6NGj8PPzQ35+Pnr16oXRo0dj4sSJ/D4KY4wxxhhjjDHGWN3GA3oZY0wK5a9EevToUaSlpcHJyQlDhgyBh4cH+vfvj0aNGkldJmMvzaZNm+Dv749z587hxYsX6NGjB1+5uhIeP36Mw4cPY+HChYiKisLNmzcxceJEuUEfWVlZOHXqFKKjo2FlZYWhQ4fCyspKwqpfDw8ePMDu3buxa9cuPHz4EF26dMHIkSMxcuRIuLq68hX2mGQeP36M48eP49ixYwgICECDBg3g4+ODadOmoU+fPvx/8yVQzOpjx47B2tqa87oOkclk8PPzw6ZNm3Dq1CksWLAA48aNQ69evXigJauygoIC+Pn5Yf/+/Th27BgmTJiAefPmoWvXrlKX9lrj7K5ZRUVFCAwMxNGjR3Hs2DHo6+tj0KBBGDhwIAYNGoRWrVpJXSJjksnLy0NwcDACAgIQEBCAq1evQk9PD4MGDcLo0aMxcuRItGjRQuoyGWOMMcYYY4wxxlj14AG9jDEmteLiYgQGBuLUqVPw9/dHREQE9PX10atXL3h4eMDDwwPdu3fn27azOi0lJQVnz56Fv78//P398fz5cwwYMABDhw7FqFGj+EPvSjh+/DimT5+O1NRUrFu3DmFhYdixYwfWrFmDFStWAABu376NyZMnY+XKlejXrx92796NlStXYuPGjZgyZYrER/B6kMlkOH/+PA4cOIDjx48jISEBrVu3xogRI7Bu3ToYGhpKXSJ7xRERbt24Ad8TJ+Dr64sbN26gUaNGePPNN+Ht7Y3Ro0ejYcOGUpf5ylKW1ampqThx4gTndR318OFDvPXWW4iOjkbr1q0xduxYjBs3Du7u7jwgnolWWFiI06dPY//+/fD19cWEFy/w73r1kGVvD9vPPwccHUsn/j8lCbHZDQCdO3fm7K5mRIRVq1YhICAAV65cQUFBAdq3b4+BAwdi4MCBGDBgAF+BlL3yCgoKEBISgs8++0zu72DAgAHw8PCAp6cnGjduLHWZjDHGGGOMMcYYY6z68YBexhirbVJTUxEYGAh/f3/4+fnh0aNHaNiwIbp27Qo3Nze4ubmhf//+sLGxkbpUxpQqKSnBnTt3EBoaiqCgIFy6dAnR0dHQ1dVFly5d4OHhgVWrVsHAwEDqUuu8FStWYO3atfD398fgwYPh5uYGHR0dXL9+HYWFhXBxccGTW9tzAAAgAElEQVS4cePw5ZdfCs+ZOHEiDh48iJs3b8LR0VHC6l9PkZGROHHiBI4fP44bN27A1dUVffv2hYeHB/r168cDfFm1+fXXX3Hp0iUEBATgz4QE2Ojp4bmlJRr264c2Y8eiXu/eAA+GqRGKWQ1ALq8BoGPHjpzXdUxkZCQOHDiA/fv3Izo6GmZmZsIgm+HDh/PVJFkFDx48EL7cdvr0aWRmZsLR0RFTpkzBu66uMA8OBm7fBo4cAYgAExOgSxfAxeV/k7MzwH3oGiEmuwsLC/Hvf/+bs/slysvLE84r/f39cfHiRRQUFMDS0lJ4f8TNzQ19+/aFqamp1OUyVmmJiYnC+yehoaEIDQ1Ffn4+fHx84OHhgaFDh/LdjBhjjDHGGGOMMcZeDzyglzHGars7d+4gKCgIwcHBuHLlCqKjoyGTyWBra4tevXrB3d0dU6dOhbGxsdSlsteQTCZDXFwcbt26hZCQEFy5cgXDg4NhXFSEOw0aQNa5M8wGDYJr375444030KhRI6lLfqWsWrUKX3zxBRISEtCqVStMnjwZfn7/z959h0V15u0Dv+kgvYPSQapSVCxBo3Gtq0az0axJVpMYX/Mm2TU9W/J7U82+m12zMXUTs+6upphieWOKa0nsDUSqdEV6lzaUoT2/P2bPcQYGGAQ8KPfnus4FM2fmme8J5jsPzH2ecwCVlZXYt28fli9fjh9++AGLFy+Wn7Njxw488MADePrpp/Hmm28qWD394x//wNGjR3HkyBEUFxfD2toat912G+bMmYO4uDhMmjSJqy6RQTo6OnDx4kWcPXsWR48exdGjR9HU1ITbb78dc+bMwV0mJgisqgKSkzVbWZnmiePGacJi0hYTAwQEcEXIIda9VwPQ6dcAYGRkxH59E0tNTcUPP/yAgwcP4tSpU2hvb0dMTAwWLFiAuXPnYvr06ezno1BFRYUcQjxw4AAuX74MOzs7zJ07FwsXLsSSJUv0X6WisRHIyQEuXgQSEzVbcjLQ1ASYmgLBwUBEhGYF38mTgdhYnqAxDAzp3fv27YOZmRl79w2kUqlw+vRpxMfHIyEhAfHx8SgvL4eJiQnCwsIQGxuLqVOnIioqChEREbCzs1O6ZCIdnZ2dyMvLQ1paGhITExEfH4/z58+joaEBlpaWiI6OxtSpUxEbG4u4uDj4+/srXTIREREREREREd1YfQZ6eW13IqIRIDQ0FKGhoXj44YcBAA0NDTh37hzOnj2Lc+fO4dVXX8UTTzwBPz8/REZGyltUVBSCgoJgbGys8BHQraK+vh5paWlITU1FamoqUlJSkJ6eDpVKJX+AOn36dPzM2RlhBQUYk5cHnDsHnD+vWWlMCotFRwMLFih9OLckExMTSCdkZWRkAECPEPWsWbMAAJmZmTe2OOph3bp1WLduHQAgLy8PR48exbFjx/DBBx/ghRdegLGxMcLCwjBlyhTExsYiNjYWUVFRXMV3lBNCICcnBwkJCTh//jwSEhKQlJSElpYW2NjYYObMmXjyySfxzDPPwNS0l1/pamt1g2J79wJ/+hPQ2QnY2mrCYlJQTNqsrG7sgd7itPu1hP365iXNv3/3u99BpVLhyJEjOHDgAHbt2oU//elPMDExwcSJExEXF4fbbrsNcXFxvNrGLaarqwsZGRk4deoUTp8+jdOnTyMvLw/GxsaIiYnB6tWrsXDhQtx2222992aJre213rt2rea+zk6goEC3d3/yCSCtDOvo2LNvh4YCJibDe+CjTPfenZGRgbi4OJ3HsHcPLxsbGyxYsAALtH6fLCoqQnx8vLzt2rULjY2NAABfX19MmDBB3latWsW5NN0wBQUFuHjxItLT0+UtIyMDarVa/l0vNjYWK1eulH/XMzMzU7psIiIiIiIiIiIawRjoJSIagezs7DB//nzMnz9fvu/777+XA5a7d+/G//7v/6KzsxNjxoxBeHg4QkJCEBISguDgYIwfPx7BwcFcKZV6JYRAUVERcnJy8P333yM7OxtZWVnIz88HADg6OiIyMhKxsbFYt24doqOjERERgTFjxvQcrLT0WuggMRH46CPg8mX9oYOwMIAB9CHj5OQEADhz5owcLAA0H2qbmZnxkrQjTFBQEIKCgrB+/XoAQHFxMRISEuSVx/7v//4P9fX1MDMzQ2RkJCZMmICIiAhMnDgR4eHh8PHxUfgIaDjU1tYiPT1dDgJcvHgRSUlJqK+vh7m5udyL169fj9jYWISFhcHEkPCWoyMwc6Zmk7S1Abm5uj17zx7dFSFXrdL062nTADe34TvwUYr9+tZgY2ODZcuWYdmyZQA0l9A+ffq0fMWNrVu3or29HePGjcOUKVMQHR0tb7yU9s2jo6MDX331FZKTk5GUlISEhATU19fDxsYG06ZNw7333ovbbrsNM2bMGJorqZiYaFZQDwgA/vNvC0DPEzROndLMt9VqwNwcCAq6FgyOiQGcnQdfC8mcnJzYu0cAb29veHt74+677wag+X32ypUrcoAyLS0N+/fvx1tvvYWHHnoIgYGBCA0NleffQUFBGD9+PLy9vXlCNF2X2tpa/Pvf/0ZeXh5ycnKQm5uLrKws1NfXAwC8vLwQERGBn/3sZ3jiiScwYcIEhIeHw4onzBERERERERER0QAx0EtEdJNYsmQJlixZIt9ubW3FxYsXkZKSgosXLyInJweffPIJrly5gvb2dgDAuHHjEBwcLId8/f394ePjA19fX7i6uip1KHSDtLW1obCwEIWFhSgoKEBeXh5yc3PlD5+am5sBANOmTUNISAhmzZqFiRMnIjIycmDBwbFjNZt28KCuDti+XRM6OHwYeP99rgo5DKZNmwYAOH78OJ5//nn5/vT0dLS3t2PGjBlKlUYG8PLygpeXF+666y4AuquyJiUlIS0tDYcOHUJpaSkAwN7eHhEREZgwYQKWL1+OwMBA+Pv7w9zcXMnDIAMIIVBcXIxLly4hLy8PmZmZcni3pKQEgOZkHunnq72C15CuMGdurrmEe0TEtRUhAd0TMz76CCgv19zv6anp0dqXfQ8PB4yMhq6mUYb9+tY0duxYrFy5EitXrgQAtLS0ICEhAadPn8aFCxfw+eef47XXXkNXVxfmzJmD6OhoTJw4EaGhoQgJCYEzA5iKk050y87ORkpKCpKSkpCeno6Ojg5EREQgOjoay5cvR1xcHCIjIw07sWKo6DtBo70dyMnR9O2MDE3gVzoZVOrd2v2bvfu6TZs2DS+88AJ79whjZGQEf39/+Pv7yydXAEB7ezv27NmDixcvIjc3F8eOHcPf//53OXRpYWGBgIAAjB8/HuPHj0dAQAB8fHzg4+MDb29vhrRHMbVajeLiYhQXF6OwsBBXrlxBTk4O8vLykJeXh+rqapiZmcHPzw9BQUGYNm0aHnzwQXn+zn87REREREREREQ0VIy6XwK0mz53EhHRyNPe3o78/HxkZ2cjJydH3nJzc1FaWipfPtTKygqzZ8+WA74+Pj7w8/ODh4cHxo4dq38lVhoxhBCorKxERUWFHNqVgrvSh09lZWU6P+/AwEA54C1tISEhcHFxGf6C9a0KmZysuyqkdmBs+nSAoXMAwLPPPos333wTly9fhr+/P5YuXYrjx4+jvr4eRkZGePDBB7Fnzx6kp6fLQewPPvgAW7ZsQXp6OsOetwDtFVzT0tKQkZGBo0ePAtBcFtrb2xuBgYEIDAxEUFCQHPQdN24c3LjC6g2lUqnkfnzp0iU5wHvo0CG0trYCAKytrREaGoqIiAidFZh9fX0Vrl5L99UgExOBrCygqwuwswMmTtTt2VOmAJaWSletqO69GgD7NelobGxEamoqtm/fjqSkJGRkZMgnVzk7OyMkJAShoaE6J+P5+vrC1tZW4cpvHVVVVfJJbllZWTq/L6lUKgDXrlIhrah833333Tz/b0onZ0gBX+3ebW8PTJjQ8ySNUX5SnSG9G9CcdMPefXOrqqqST3CVApq5ubnIz89HTU2N/DgbGxs54Ovl5QVvb2/4+voiNjYWHh4e8hVS6ObS2tqKiooKlJaWoqSkBMXFxSgoKEBRUZG8lZeXy38/sbCwgK+vr7yys7TK87x582BqyvVRiIiIiIiIiIho0LYDeLC3nQz0EhGNIt1XbD127Jj8fXFxMdra2uTH2tjYwMvLC25ubhg3bhzc3d3h6ekJT09PeHh4wN3dHU5OTnBycmL4d4jV1NTg6tWrqK6uRmVlJUpKSlBRUYGysjIkJSWhtLQUlZWV6OjokJ/j5OQkh7N9fX3h5+enc3tErsjc2QkUFOiGxs6f73tlyIgIZWu+wY4dO4Z169bh8uXLWL9+PV577TWEhISgoaEBL7/8Ml544QV0dHTg2WefxdGjR/Hcc8+ho6MDe/fuxd/+9jd4e3srfQg0TBoaGpCXlyeHRrW34uJidHV1AQAsLS3h5eWFcePGwcfHB+PGjZO/9/T0hLu7O1xdXXkp2H50dHSgqqoK1dXVchCgqKgIxcXFKCkpQWFhIYqLi+XV3wBNQE8KWS9YsED+3sPDQ8EjGYTGRs1qkNo9OykJaG6+dmKGds+eMQO4ESeMjAD6evXRo0fxyCOPsF9Tr4QQKCwslAOlmZmZ8veFhYVyqEjfHM/X1xdeXl4YO3Ys3NzcGCgE0NzcjPLycpSVlcknuBUUFMjblStX5AC1mZkZ/P395RWSpZPcQkNDR+aceTAaGoDUVN2Qb/ferT3XnjoVcHdXuuobwtDebWpqil//+tfs3bew5ubmHuFO7duFhYXyiVkWFhZwdXWV+6+7uzvGjh0LV1dXeW7t7OwMJycnODo6Du2VFqiH2tpaXL16FTU1NaiqqkJlZaX895KkpCRUVVWhtLQUDQ0N8nOMjIzg6ekpr8rs5eUln+Quhbhv2vk6ERERERERERHdLBjoJSKioXX16lVUVVXJwVPpAxTpa05Ojvx9Y2Mjamtrex3L1NQUtra2cHBwgLW1NSwsLGBlZQVLS0uYm5vD2toaJiYmsLOzky9haGdnp3OZ2+63u4/dXUdHBxobG3vc39TUpBNqlm63traipaUF7e3tUKlU6OrqkkNbdXV1EEKgoaEBKpUKKpVK79iAJlhna2srf8Dn5OQkf699n4uLC5ydneHs7Aw3Nzd+CAhcC5Lt2KEJIly4ALS0AGZmwPjxupcVjokBeOlqoj61trbKH3ZLQdWKigpkZGTIJxNUVlairq6uz55mb28Pe3t72NrawsLCAmPGjJF7+JgxY2BhYQFHR0fY2NjAzMwMQO+92dLSskewuLd+LfVlQPNBvhACdXV1cn/u7OxEQ0OD3LdbW1tRX1+P+vr6Po/HwcEBYWFhctDZxcUFLi4u8PDwkG97eHjwkrr9KS3VDY9JK0ZqrxAp9ewJEwC+zxENiFqtRklJCcrLy1FZWYnk5GT55K+qqiqUlZWhpqZGJ8AkMTIygoODAxwdHeHo6CjPt62trWFubg57e3uYmprC3t5e7nXSXLy77r2wt56t/btAQ0MDqqur0dbWhqamJjQ3N0OtVqOhoQFqtRq1tbWoq6vTWzugCTi7ubnphOfc3NwQHR0NNzc3jB07Fp6ennp/N6B+dF+dPSMDSEvT7AsKuta7J08GoqMBGxtl6yUaYaQTwGpqauSg6dWrV3v9/urVqygsLNQ5Ube7MWPGwNraGra2tvLfPaR5tTT3NjY2hr29PYBrfxvR7s9Sn9dH3/wbgM7fPLRJc2uJNAcHNCvRd3R0yPN07fcE6e8mjY2Ncu/v6+9E9vb2sLGxkYPR0t9Kevve2dkZrq6uvR4nERERERERERHRCMNALxERKU8KuqpUKjQ0NKC+vl4OwKpUKtTW1kKlUskfELW3t/cI0kof+NTX18urT2p/gNRdS0uLvJJOdw4ODvIlVCXSB2KS3oLFwLUAg62trRxOs7GxgY2NDezs7DBu3Dj5dvcwGw2Bjg4gO1s3MHbuHFBVpdkvre6rHRoLCFC2ZqKblPSBfm1trdy/pU263dDQIIezpN4thbRqa2vlD/gB3TCuNqn3d6cvPKsdCpb6uaOjoxxokPZL/dvKygr29vaws7OTQ8iOjo469/HkiWF28mTPkG9rq/4TMyZNAnhJa6JB6+zsRF1dnRySra2t7fF992BtfX092tvb0dDQIM+91Wq1vLqtpLfwrr6erX3yna2tLVxdXeX+LJ38YW9vD3Nzczg6OsqBY31f6QZrbwd27rwW8E1OBqqrNfu059vSKr/h4UC337GIqG9qtRpNTU2oq6uDSqVCU1MTmpqaUFtbK3+vUql0Tibu7OyU/96hLzirHZaV+rs+vc2/AU2o1tjYuMf93fu8NBeXTgyR+rt0Aglw7e8m0gkkNjY28kndHh4esLe3l+/Xd+IfERERERERERHRLYaBXiIiIholSkuvBQ6k0FhmJiAE4Oh47XLC0hYaCnAFNyKiG086MUO7Z/PEDCKika+v+baDg6Zfa4d8p0wBLC2VrpqIiIiIiIiIiIiIaKRgoJeIiIhGsfp6zeWCtYMH588DajVgbq65hPCGDZrgQUwMwMt0EhEpRwqKaffsvk7MiIhQumIiImpoAFJTdUO+Fy4ALS2AqSkQHKwb8l22TOmKiYiIiIiIiIiIiIiUwkAvERERkY72diAn51po7NNPgatXNav1+vrqBsamTgXc3ZWumIho9OrrxIzuAV+emEFENDJIK7Frh3zPnwfKyzWrsEvhXql/h4UBxsZKV01ERERERERERERENNwY6CUiIiLql/aqkFJo7PJlzT6GDoiIRhbpxIytWzX9OikJqKnRf2JGbCzg4aF0xUREBAC1tcCOHdfm3NnZQGcnYGMDhITo9m+epEFEREREREREREREtx4GeomIiIiuS23ttRXFuocObG2ByMieQV8rK6WrJiIanfo6McPRsedqvjwxg4hIeW1tQG6ubu+WTtIANCfWaffuiAggIEDZmomIiIiIiIiIiIiIrh8DvURERERDpnvoIDFREzpobgZMTYHg4Gthg3XrAFdXpSsmIhq96uqA9PTeT8wIDtYN+k6aBIwZo3TVRESkfZJGRobmJLvMTEAIwMFBM9eW5tzh4cCUKYClpdJVExERERERERERERH1h4FeIiIiomHV2QkUFOiu5puQAFRU6K4qJgUOwsMBIyOlqyYiGp30nZiRnAw0NWlOzLjrrmsh32nTADc3pSsmIiIAqK8H0tKuBXwTE4ELF4CWFsDMDFi9+tp8e/p0nlhHRERERERERERERCMRA71EREREijh8WDfkm5UFdHUB9vbAhAm6Qd8JEwALC6UrJiIavaTVILdu1XwtK9Pc7+l5LSAm9e2wMMDYWNl6iYgI6OjQrLyekQHs2HHtpDrg2ol12j2c/ZuIiIiIiIiIiIiIlMVALxEREdGI0NAApKbqriqWmAi0tmpWFRs//lpYbPJkIDoasLFRumoiotGptla3V2ufmGFnB0ycqBsS4+XeiYhGhtLSnvPt7GzNVTVsbYHgYN2TNGJiAGtrpasmIiIiIiIiIiIiotGBgV4iIiKiEUtaVSwx8Vrw4OxZoLpas19aWUzali1Ttl4iotFMpdL0bO2QWFIS0NwMmJpqQmLaq0HOmAG4uChdNRERtbUBubm6J2mkpGj6uokJ4OurG/KNiAACApSumoiIiIiIiIiIiIhuPQz0EhEREd10pEu/awd9MzMBBwfdsAEvHUxEpKyODqCwUDfkq++S72vXavp3eDhgZKRszUREpNHbnFsIwNFR07M3bNAEfCdMACwslK6YiIiIiIiIiIiIiG5uDPQSERER3RLq64F//eta4CA9HVCrAXNzIChIN+Q7aRIwZozSFRMRjV7dL/n+2WdAVxdgb68JhWmv5Bsby5AYEdFIUV8PpKVdm3Pv2AG0tgJmZsD48br9e/p0wNVV6YqJiIiIiIiIiIiI6ObBQC8RERHRLam9HcjJMfzSwVOnAu7uSldNRDQ6NTQAqam6Id8LF4CWFt2QmBQUi4kBnJ2VrpqIiDo6gOxs3f4dHw9UVmr2SyuxSyFfXkGDiIiIiIiIiIiIiHrHQC8RERHRqKJ96WBpKyvT7PP01A0bMHBARKQcKSSmfan3c+eAqirNfikkph30DQhQtmYiItLovhJ7YiKQlaVZjd3ODpg4UXfezStoEBEREREREREREREDvURERESE2lrdsEH3wMHq1dfCBlOmAJaWSldMRDR6SSdmaAfFMjMBIQAHB01ATAr53n+/ZlV2IiJSnkqlOVFDe96dnAw0NeleQWPDBs2c29NT6YqJiIiIiIiIiIiI6MZioJeIiIiI9NAOHGzdCiQlAc3NgKkpEByse+ngGTMAFxelKyYiGr3q64G0NN2g7/nzmpBvUJDuSr4xMYC1tdIVExGRpPsVNL77TnO/o6PulTMiIjQr+5qbK1svEREREREREREREQ0XBnqJiIiIyADSpd+1V4SMjwcqKzX7tS/9LgV9w8MBIyNl6yYiGq3a24GdO6+FfJOSgJoazT7tni2tvs6VIImIRoa6OiA9vedq7K2tgJkZMH687rw7Opon1xERERERERERERHdGhjoJSIiIqJBGMil37mqGBGRsrqvApmRAVy+rNnXfSXIyZOBsDDA2FjZmomISHOSRk6O7rxb38l10ol1kyfz5DoiIiIiIiIiIiKimw8DvUREREQ0xBoagNTU/lcV27CBl34nIlKa9kqQUt9OSwPa2gAbGyAkRDfoO3Om0hUTEZGktFR3vp2YCGRlAV1dgJ2d5mQ6KeS7YQNgZaV0xURERERERERERETUOwZ6iYiIiOgG0F5VTAqMHT6s2cdLvxMRjSzde3ZiIpCcDDQ1aUJh2itATp0KuLsrXTEREUkaGzU9XDvkm5wMqNVAcLBuD4+NBTw8lK6YiIiIiIiIiIiIiDQY6CUiIiKiEaSvlSJtbTUhhO6XhOdKY0REyqit1Q2M9bUyJHs2EdHI0r2HZ2Ro5uFqNWBuDgQFXevdERGaK2s4OytdNREREREREREREdGtjIFeIiIiIhrh+lop0tRUd6Wxxx4D3NyGr5aKCsDJCTAzG77XuEV1dnaioaEBAFBbW6vztbm5GWq1Wu/z+tpnZGQEBweHHvcbGxvD3t5evm1vbw9jY2PY2NjAzMwMVlZWsLS0HNTxEFEvVCogO1s3JJaUBDQ3X+vZUjgsPByYPh1wdVW6atKjo6MDjY2N6OrqQn19PYQQqKurAwDU1dVB39+MpOf0xtHRUe/9ZmZmsLGxgYmJCezs7HT6e2/PIaJhoD3vzsjQ9PJz54CqKs1+7StrSH08IkLZmqlfLS0taG1tRWtrK+rq6tDc3AwAUKvV8vfdNTQ0oLOzU+8+W1tbmJqa6twn9W/t76U5eW9zdiIiIiIiIiIiIuqBgV4iIiIiukmVluqGfM+fB8rLrwUNtFeFDA8HjIwG/5p/+hPw978Db78NLFky+PFGuKamJly9ehU1NTW4fPkyVCoVVCoVGhsbUVdXJ99WqVSoq6tDY2MjVCoVWlpa5CBuX0EBpUkBMilsYGpqCltbWzg4OMDGxkbeHBwcYGtrC09PT3m/nZ0dnJ2d4eLiwoACUX86O4GCAt2Qb0KC5iQJQNO3N2y41reHqmdL2tuB9euBTZsAb++hG3eEqq+vR1VVFaqrq1FUVCT3Z+2ttrZW57ZKpUJraytaWlrQ1taGpqYmpQ+jB+nkDClIJvVm7c3R0bHHff7+/nB1dYWzszOsra2VPgyim5M075ZCvtorssfF3bjV2EtLgYYGIDR0eMYfQRobG1FdXY2qqirU1dWhvr5e7ucNDQ1y/5b2Sbebm5vR1NSEtra2Pk+MU4p0koaDgwNMTU1hZ2cnz62lvm1nZwd7e3t4eXnJ+6V5t7OzM0xMTBQ+CiIiIiIiIiIiomHDQC8RERER3UJOnuz70u/aQd8pU4CBrtJ6zz3Arl2AEMC8ecC7795UgYKamhqUl5ejvLwcZWVlqKysRHV1NWpqalBdXS2Hd2tqanD16lW0trbqPN/S0hI2Njbyh+zaoVdHR0dYW1vDxsYG1tbWsLCwwJgxY+SQrPbKXFIoS/pAXwrW6mNubt5rAKu30Fn3ELG0ErC00pgUcpDCa+3t7VCpVGhra5PDydph5fr6ejQ0NKC8vFzvypMmJiZwdnaGk5OTzlfpe09PT7i5ucHT0xMeHh5wd3dnEIEI0ASzpHDY1q3Xera9PTBhgm7Pjo0FLCyu73VSU4GoKM3zf/974LnngDFjhvZYhpFKpUJxcTEqKytRVFQk924ptCv1ben7jo4OnedLwVcbGxs5KCWduKAdnpJ6sdS3tVc71w5gGRkZwdraGubm5nrrlR7TXV+r90qhM6kfS6u6S6sDA9d6eX19PTo7O1FXV4eGhgb5RBMp2NY9qKzN0tJS7s8uLi5y0FcKiY0bNw4eHh7yV67mTtSHxkbNar47duhfjV075Dt1KuDuPvjX/Pxz4IEHgMcfB156CbjJVvBub29HRUUFiouLUV5ejuLiYlRXV8tbZWWl3M9LS0t7PL+/Exns7OxgbW0tX43C0tISVlZW8rzc3NxcnrMDPa9qoW3MmDGw6OV9V+rH2qR5tXScKpVK7vvde7q04nt7e7vcu7VDyo2Njaivr0dRURHa29t7vJbUt6XeLX3v5uYGd3d3uYd7e3v3+jsGERERERERERHRCMVALxERERHdwqSggfaqkBcuAC0tgJkZMH687mWDY2IAZ+fex/P1BQoLNd+bmWlWnXz4YeCPfwRcXG7MMfVCrVajsLAQx48fl0MCpaWlqKysRElJCSorK3VW6DI3N4ebm5vOB+H6wqjS9wEBAQyh/oe0QtrVq1dRVVXVIwit/X11dTXKy8t1gsfGxsZwc3ODm5sbJk2ahLFjx8LDwwN+fn7w9fWFr69vr+EKoltaYyOQkqK7AuRgerZkxw7goYc0YWETE8DNDfjrX5A8FYoAACAASURBVIFf/nJoVwK+TjU1Nbhy5QqOHTuG4uJiOewlfdXuH6ampnB3d5fDqNqBJn1BVR8fHwWPbGS4dOmSzskr2gHoqqoq+XZVVRUqKyuh/bcw6aSMsWPHyl/Hjh0LPz8/3HHHHQyKEWnTtxp7YiJQVqbZ7+nZcyXf0FBNXzbUb38LvPmmpndbWwOvvw488ogmRKywjo4OFBUVIT8/H/n5+SgqKkJJSQnKyspQUlKC8vJyVFRU6PQYV1dXuLm56fRuqX+HhITIIVUHB4dROzdsbW1FXV2dTh/vflKidg+vqKhAW1ub/Hxra2t4e3vD3d0dXl5e8PDwwKxZs+Dv7w9/f3/Y2toqeHREREREREREREQ9MNBLRERERKNMRweQna0bGDt3Dqiq0uz39LwWMpBCYwEBgEqlWem3+xzZzExzWeFXX9WsFjZMgYL29nbk5+cjLy8PBQUFOtuVK1dQXl4OIQSsra3h4+MDNzc3jBs3Tv7q7u4OT09PeHp6ymEwunGamppQUlKCiooKlJWVySslJycny2GPKunfIDQrsEnhXu2gr7+/P4KDgxkio9Hjenu2tqefBt5/H5ACPsbGml4eEwO89x4wY8awHoJarUZubi4uXbqEK1euyGEv6Xtp1dpx48bBy8tLDh1pf5VWG3R3d9e7+i0NDWn1TCl8J/Vt7YB1aWmp3K9dXV3h5+cHf39/+Pn5yd+PHz8efn5+PBGGCABqa3VDvhkZQHo6oFYD5uZAUJBuH4+OBnqb58ydCxw5cu22sTHg5we88w6wZMkNOZz6+npkZ2fj0qVLyM/Px08//YTLly+jqKhIXiFdmo9LJwFob56ennKwtLcVcGlwKioqUFFRgaKiIrmXS8Hq0tJSJCYmyo91cXGBv78/AgIC5JCvNN/28fHhey4REREREREREd1oDPQSEREREQEArlwBkpN1t4ICzb4FCwBXV+Czz3p/vrEx4O+vCYctWnTdZdTW1uLy5cu4ePEiMjIycPnyZfl2a2srAM0lw8eOHYuAgACdzdPTE3Fxcfzg+SalVqtRUlIi/8xLS0tRVlYm3y4oKEBnZycAwNHREQEBAQgPD0dERIT8byAiIoKXiKfRobT0WjBMCoplZmqCug4OmmDv5MnA2rXAxo3A6dM9xzA11awoed99wObNgIfHoErS178vXryI7OzsHv/vavdtqZ9Pnjx5UK9PN45arcaJEyfk/ty9bwOAmZkZvL29dfp0eHg4IiMjYWdnp/ARECmsvV1zFQ3tkG9yMlBdrdm/dOm1kzTCwzWbkZFmVfarV3XHMjHR9PI77gDefVfznCFQW1vbo59nZGQgPz8fQgiYmZnBxcUFM2fO7DEn9/f353x8BOs+59beLl26hLq6OgCaK5oEBQXp9PCIiAiEh4fDyspK4aMgIiIiIiIiIqJbFAO9RERERES9qq0FkpKAf/1LsxpYeblmtcjeSIGCxYs1wd7uq0TqDF2LlJQUpKamIjU1FSkpKcjKyoJKpQIA2NvbIzg4GOPHj0doaCjGjx+P4OBgBAUFMQg0SrW1teHKlSvIzs5GTk4OcnJykJubi+zsbJSWlgLQBMgCAgKwZs0aREZGIjIyEr6+vgpXTnSDSD07ORlISdF8TU3VBHf76t1mZpr+/dvfAr/7HdBPKL69vR0ZGRlITk5GUlISkpOTkZaWhqv/CZnZ2NggJCQEISEhCAsLk78fP348A0CjQF1dHXJycpCVlYWsrCzk5OQgMzMTeXl58mXgvby8MHHiRMTExCA6OhoxMTEIDAxkAJBGNyGA/HxN796xQ9PHr1zR7HNy0oR6T57s/flmZkBXF7BuHfDHPwIGXo1C6ukXLlxAUlISLly4gPT0dNTX1//npZ0QGhqKsLAw+WtYWBh8fHxgOkxX5iBl1dTUICsrC5mZmcjOzkZGRgaysrJw5coVdHV1wdTUFEFBQYiJiUFMTAwmTZqEn/3sZ0qXTUREREREREREtwYGeomIiIiIDLJ+vSZc0N7e/2PNzDShhMceA157DbCzQ0FBAeLj4+UQ77fffgsAcHZ2RnR0NCIjIxEeHo7g4GCEhITA3d19mA+IbiWNjY3Izc2Vg2OffvqpvIKcg4MDoqKi5IDvlClTMGHCBIZQaHT49lvgzjsNe6yJCTBunGa13lWr5Lvb2tqQmJiIxMREJCcn45NPPkFbWxssLS3lUGZkZKQc3PX29h6mg6GbWUdHB65cuSKHxFJSUpCUlCSv4GxnZ4eoqCg8+OCDmDp1KsLDw2FsbKx02UTKqqvTBHtTUoDvvwcOHuz/OWZmgJUV8OqrwOOPa07q+I/Ozk6kpKQgPj5eDu+mpaVBrVbDysoKkZGRiImJQVRUFEJDQxEeHg43N7dhPEC6mbS2tiIrKwvZ2dlIT0+X/w2VlZXBz89PDvdOmjQJM2bMgKOjo9IlExERERERERHRzYeBXiIiIiIig0RFaVZ6HKA6KytssrbGX6urYWxiAl9fX4SHh2PDhg2IiIjgJXlp2DQ2NiInJwcXL16Uw4gpKSlQqVSwtrZGdHQ0Jk+ejMmTJ2P27NlcyZduTd98A6xYYfjjjYwAIVAbFYWdt92Gvbm5OHXqFFpaWmBvb48JEyZg7dq1CA8PR2xsLCwsLIavdhoV2tvbkZOTI/fpbdu2oampCba2tpg2bRri4uIwefJkzJo1Cw4ODkqXS6ScN94AXnwR+M9K1/0yNgaCgqD+61+RYG+PU6dO4Y033kBtbS1sbW0RGRmJyZMnIyIigj2dBqW2thY7duyQ+3hmZiaEEAgICEBcXBxmzpyJuLg4REREKF0qERERERERERGNfAz0EhERERH1q6MDsLbWHyAwNtZsnZ2aVXkBVADIAFBgbY2OoCDYx8Zi3KpViJ45E2PGjLmhpRNp6+rqQkZGBs6ePYszZ87g7NmzyMrKQldXF/z9/TFjxgzMmTMH8+bNg7+/v9LlEg3eK69oLr3eVwDMyAhdJiZAZyeM/9PHGwBcGjMGB+bNg+cvfoEZM2YgODj4xtRMo1pHRwfS0tJw6tQpnD59GqdOnUJhYSFMTU0RFRWFWbNmYcGCBZg9ezbnFDS63Hsv8PXXmjm3AbqMjWHc1QUA2AfgDS8vrHnhBcycOZMrYNOwqqqqwunTp3Hy5EmcOnUKiYmJaGtrg5eXF2bNmoV58+Zh0aJFGDt2rNKlEhERERERERHRyMNALxERERFRv9LSgMhIeeVGAOi0skKlnR2SOztx5upVZAsBk7AweM2di8kzZ2LGjBnw8fFRuHCi/tXX1+PcuXM4e/asHD5oampCUFAQ5s2bh3nz5mHu3Lm8bDDdnJYvB779VtO7TU01fby9HYAm7FVhZobEtjZkGRmhMzAQTjNmwH/RIkxetIj/5mnEKCkpkQO+R48eRWpqKiwsLDBr1iwsXLgQCxcuxIQJE5Quk2h4BQYCly/r3mdsDJiYXOvrRkYoBZAqBCpcXGAZFQW3WbMQ/otfwHPixBtfMxGAlpYWJCQk4MSJEzhx4gSOHz+O1tZWREVFYdGiRVi8eDFuu+02mJqaKl0qEREREREREREpj4FeIiIiIqJ+nTuH1s8+wwWVCt9mZ+OL5GRcaW6Gv78/5s+fj/nz52Pu3LlwcnJSulKiQVOr1Thz5gwOHTqEw4cPIzExEQCwadMmrFy5EkFBQQpXSDQAfn6ASoWu4GAU2triZGUlvs3NRVJTE4SfH+YtWoSFCxdi7ty5sLOzU7paIoOUl5fjwIEDOHDgAA4dOoTq6mp4eXlh2bJlWL16NWbOnMnVR+nW0tIC2NgA/1lxFzY2aPX2RqaZGY6UleFUVRUKxoyB/7x5mL9kCRYtWsQT62jEamlpwdGjR7F//37s378feXl5sLe3x+LFi7F69WosWrQIFhYWSpdJRERERERERETKYKCXiIiIiKgvNTU1+NWvfoUff/wRxsbGmD9/PpYuXYp58+YhMDBQ6fKIhl1tbS1++ukn/Pd//zeqq6sRHR2NlStXYuXKlQgJCVG6PKI+Hdy1C5/u24dvvvkGTU1NuP3223HXXXdh4cKFCA4OVro8okHr6urC+fPn8e9//xu7d+9Gamoqxo0bh1WrVmH16tWYOnUqjIyMlC6TaHCqq4E9e1Bhb4+v09Px8b59SE1NhZeXF+655x4sWbIEM2fOhLm5udKVEg1Ybm4u9u/fjz179uDEiROwt7fH3Xffjfvuuw933HGH0uUREREREREREdGNxUAvEREREVF3nZ2d+O677/DRRx/h0KFDWLp0Ke6++24sW7YM9vb2SpdHpIjOzk4cPXoUu3btwt69e1FRUYHIyEisX78ea9eu5f8bNKLk5ubi3XffxXvvvYcZM2bgl7/8JVatWgVPT0+lSyMaVpmZmfjiiy/w5ZdfIjs7G/7+/oiPj4eLi4vSpRFdl87OTuzZswfvvvsuTp48CScnJ6xcuRL33XcfV6OmW05xcTG++OIL7Ny5ExcuXMD//M//4LHHHoOHh4fSpRERERERERER0Y3BQC8RERERkaSyshLbtm3DRx99hKKiIixYsAAPPPAAVq9erXRpRCNKZ2cnTpw4gZ07d+Lzzz8HANx///147LHHEBkZqXB1NFoJIXD48GG8/fbb2L9/P/z8/HDkyBFedp1GreTkZHz66ad4//33cf/992Pjxo3s0XTTaGhowLZt2/DOO++gsLAQK1aswLp167BgwQKYmZkpXR7RsMvKysLs2bNRX1+Pe++9F08++SSioqKULouIiIiIiIiIiIZXn4FeLm9ARERERKNCfn4+HnzwQfj4+GDz5s1YuXIlsrOzsX//foZ5ifQwMTHBnDlz8NFHH6G4uBivv/46jh07hqioKNx+++348ccflS6RRhEhBL788ktMmDABCxYsQHNzM3bv3o3c3FyGeWlUi46OxubNm7FlyxacOXMGUVFR+NnPfoYzZ84oXRpRn37/+9/D29sbL730Eu68807k5uZi9+7dWLJkCcO8NGqEhoaioKAA7733Hs6fP4/o6GjMmzcPCQkJSpdGREREREREREQKYaCXiIiIiG5pNTU1ePrppxEaGoqzZ8/igw8+QHFxMTZv3oygoCClyyO6Kdjb22Pjxo3IyMjA4cOHYWtri3nz5mHx4sVITU1Vujy6xcXHx2PmzJm47777MGXKFKSkpOCnn37CihUreBl2ov945JFHkJ6ejoMHD0IIgbi4ONx3330oLCxUujQiHV1dXfjnP/+Jjz/+GP/v//0/FBUV4e2330ZAQIDSpREpwtLSEuvXr0dqaioOHDiAtrY2TJ8+HevWrUN5ebnS5RERERERERER0Q1m8vLLL/e1v8+dREREREQjVUdHB958802sWrUKmZmZ+NOf/oRt27Zh8uTJN2zVr7a2Nhw9ehTvvvsuurq6MH78+CEd/8SJE/jkk0+wdetWmJiYIDQ0dNBjqtVq/PTTT9i9ezeMjIwwduxYgwJz/T3PkHEH8trl5eU4f/48/Pz8euyrq6vD+++/jy+//BImJibw8/PrMU5NTQ2++OILfPPNN7h69Sq8vb1hbm4+4HEA4Pvvv0diYiLS09ORnp6O/fv3IyYmBmZmZkhISMDx48flfdJmY2MDR0dHg8cBAJVKhU8//RR79+5FS0vLkP97MoSRkRECAgJw//33Y86cOdi3bx9eeuklXLlyBXFxcbC2tr7hNdGtq7KyEo888gieeOIJBAQEYPfu3Xjsscfg7u4+7K890vu3dr+cPn36kPRpQ8cdql4tSUlJwddff43k5GR4eHjAzs5uQOM0NjbKfSg0NFTnPb63HqyvD/d3XIa8bwz0uIaakZERAgMD8cADDyA6Ohrbt2/HH//4R6jVasTFxcHExGTYayDqS2JiIu666y58/PHHKCwsxLx582BpaXlDXns4+/pgenr3+Z2fn9+A/l81pM8Cmh729ttvY9asWTr39zffNXQua8h7g6Fza2Doe6ih7139zceHk9TDH3roIYSFheGjjz7C5s2bYW5ujunTp8PIyGjYayAiIiIiIiIiohsiBcD/9bpXCNHXRkRERER007l06ZKYPn26sLS0FC+99JJobGxUpI7ExESxYcMGAUB8/PHHQzr2+fPnxbJly4RarRavvPKKsLCwEE1NTYMas6KiQvj7+4uPP/5YVFVVieeee04sWbJEdHR0DOp5how7kNeurKwUVlZWYuPGjT321dTUiMDAQLFmzRoxd+5cYWxsLKZOnarzmKSkJDFhwgRx5swZ0dTUJN544w0RGRkpSktLBzSOEEJkZmYKIyMjAUDeVq9eLYQQoqurSwQGBursk7bExESDxxFCiKysLBEUFCS+//570djYKD7//HNx7NixPn8uN0JXV5f44osvhK+vr3B3dxc//PCD0iXRLeLHH38Unp6ews/PT+zevfuGv/5I7t/d++VQ9GlDxzW0V1dWVopnnnmm114thBBVVVXi4YcfFosXLxYFBQV6H9PfOFlZWcLDw0OYm5sLACIwMFCUlZUJIfruwd37cG/HJTHkfWMgx3WjqNVq8eabbwpbW1sxefJkkZeXp2g9NLq98847wsLCQsyZM0ekpaXd8Ncfrr4+mJ6ub37n4+Nj0BzPkD6rbcWKFcLd3V3nvv7mu4bOZQ15bzB0bj0cPdTQ967+5uM3WlNTk3jppZeEubm5WLBggaisrFSsFiIiIiIiIiIiGlL/En1kdhnoJSIiIqJbyunTp4WLi4uIiooS6enpSpcjUlJShiUQtnjxYvHaa68JITQftpeUlAx6zJkzZ4o777xTvt3R0SF8fX3Fb3/7216f09nZ2efz+ttvyBjdxcfHCwB6wwt/+9vfRE1NjXz71VdfFQDEyZMn5deKiooSzz//vM7zpk6dKubPn2/wOJL/+q//EkeOHBGFhYXy1tLSIoQQ4uDBg2Ljxo1CrVbL28GDB4Wfn1+PuvsaRwjNz/vhhx/Wec6sWbN6jKOU+vp6sWbNGmFsbCzeeustpcuhm9yOHTuEmZmZuOeee0RdXZ1idYzE/q2vXw62Txs67kB6dXx8vPzfT1+vzs/PFy4uLuJXv/pVn8fb3ziLFy8WKSkporKyUqxfv14AEOvWrRNCXOvB+fn5ffbhvo5L2m/I+8ZAjutGy8nJETExMcLV1VXEx8crXQ6NMl1dXeKpp54SJiYmYtOmTaKzs1OxWoajrw+mp+ub3z3wwAMGzfH664/atm7dKsaPH98j0NvffFdfHx1ID9V+b+jttbQNRw8dyHtXf/NxpcTHxwt/f38REhIirly5onQ5REREREREREQ0eAz0EhEREdGtr6qqSri7u4slS5aI9vZ2pcuRXbx4UQAQf//733XuLykpEdu2bROvvPKKOHz4cI/nZWdni+3bt4tnnnlG7NmzR2ff+++/L4yMjMSKFSvEhx9+KL755pshqRVAjw+Jv/rqKwFAfPXVV3qf85vf/KbP5/W3/6uvvjLoMfpqNWQ1sn379omlS5fKt//xj38IAOLzzz/Xedz27dt7hAr6GkcIIf7whz/Iq3f5+/uLf/7zn6Krq6vXMTo7O4Wnp6c4derUgMeRQg+ffPKJEELIq7iNRFu2bBFGRkZcrZcGLCMjQ1haWoonn3xS6VKEEL33byGuv4cPtn/r65eD7dO99eHu4w60V6vVar29+tlnn+31v6s+vY1TXV3do1fGxsaKGTNm9DqWvj7c13F99dVX/b5vbNmy5bqOSwlqtVrMnj1bBAYGDnpVfyJDfPPNN8LIyEhs375d6VKEENc/L9fXz48ePTronq5vfufq6mrwHK+3/ihpa2sTq1atEg0NDeKpp57qEejtTt98V9tAe2hf703dX2sgPbS/n5c2Q+sb6LxeCRUVFcLPz08sXrxY6VKIiIiIiIiIiGhw+gz0moKIiIiI6Bbw+9//Hubm5ti5cydMTUf2NPfIkSPYuXMnHn30Udja2mLFihVYu3Yt3n//fQDAli1b8M033+Cnn35CQUEB7rjjDpSXl+PRRx8FAISHh0MIAXd3d0yePBk2NjYoLS3F5cuX+3xdIyMjxMXF9fkYT09Pndtubm4AgJycHL2Pz8vL6/N5/e3Pyckx6DEDJYTA119/jVdeeQUHDhyQ77eysgIAnD9/Hvfee698f2Bg4IDGAYDZs2ejvb0dZ86cwblz5/DQQw/hs88+w7///W+YmJj0GOvUqVMwMjLCjBkzBjzOhg0b8Nlnn2HNmjW4cOECLl682KOekeKJJ57AuXPn8OijjyInJwfm5uZKl0Q3iSeffBIRERHYvHmz0qX0aTA9fLD9W1+/HGyf7q0Pdx93qHq19D6dlpaGuXPnIj4+HpMmTcKWLVswadIkg8YAAGdn5x73FRUV4bHHHuv1Ofr6cF/HdfLkSfn9obf3jcLCwiE9ruFkbm6OL774AmFhYXjjjTfwyiuvKF0S3cI6OjrwxBNPYM2aNVi7dq3S5fTKkJ7+xBNP9Ojnvr6+8h/YtXs6AIP7ur753UcffYS77rprSI7t1VdfxZNPPglbW9s+H9fXfFfbQHso0PO9obfXMrSH9vfz6s7Q+gY6r1eCm5sbPv30U8yaNQvfffcdli5dqnRJREREREREREQ0DEZ20oGIiIiIyAB1dXX47LPP8M477/T7gbXSVCoV1q9fj9TUVFhbWyMmJgYHDhzABx98gDVr1mD69Ol4//33sXDhQhgZGcHPzw/R0dH47rvv5ECv9KH2uHHjMGXKFADAW2+9haeffrrP1zY1NUV7e3uv+01MTHqEL8eMGQMAKCsr0/ucioqKPp/X3/6ysjKDHjMQTU1NeOqpp/DZZ5+hubkZEydOxMGDBxEbG4u4uDiYm5vj2LFjEELAyMgIAFBfXz+gcQBgwYIFWLBgAQAgJSUFq1evxuHDh/GXv/wFv/vd73qM9/XXX+Ouu+6SX1NiyDju7u44ceIEZsyYgbfeeqtHKHik+fOf/ww/Pz989913+MUvfqF0OXQTuHTpEg4dOoTvv/9+xARn9BlsDx9s/9bXLwfbp3vrw93HHapeXVJSgujoaLz44otwcnJCTk4O5syZg9mzZyMrKwvjxo0zaJzujh8/DlNTUzz11FO9PkZfH+7ruMrKyrB27do+3zf8/PxQUlIybMc11Dw8PLBx40Z8/PHHePHFF0f0/290czt06BAKCgrw8ssvK11Krwzt6U8++WSPfu7n5wcnJycAuj0dAL788kuD+3r3+d1tt902JMd27NgxmJqa9jtef/NdbQPtoYDue4O+16qtrTW4hxry8+rO0PoGOq9XSlxcHBYtWoStW7cy0EtEREREREREdIsyVroAIiIiIqLBSkxMREtLC5YvX650Kf3auXMnWlpa8Pzzz+Pxxx/H448/jrKyMgQGBsorSB09ehSbNm0CAGRkZKCoqAi5ubl9jvub3/wGzc3NfW4NDQ0DrrezsxOAJgCkj7QSWW/P62+/h4eHQY8ZCGtra2zduhWNjY1466230NjYKIehvb29sWnTJiQmJuKhhx7CDz/8gDfffBMvvfTSgMbpLioqComJifDy8sLOnTt77BdCYPfu3bj77rv7rL2vcbZt24bZs2dj3bp1OHPmjLwq5Ejk5eWFKVOm4OTJk0qXQjeJs2fPwtzcHPPnz1e6lD4NRw8fSP/W1y8H26d768Pdxx3KXr1ixQo5CBccHIy//vWvUKlU+OCDDwweo3sNL774Ivbt29drnb314b6Oy8PDo9/3jaioKFy4cGFYjmu4LFu2DGVlZcjPz1e6FLqFnT17FmFhYfD391e6lF4Z2tMBw+fkwMD6evf53bRp0wY9x6urq8N7772HF154od/HGjrfvZ4eCui+N+h7LQAG91BDfl7dXc97V3/zeqX9/Oc/x5kzZ5Qug4iIiIiIiIiIhglX6CUiIiKim15tbS2MjY3lD4BHsosXL8LT07PXy8ICmlW+Dh48iO+++w6zZ89GYGAgEhMT+xzX1NQUpqb9T+/PnTuHX//61zr3bd68GbNnz0ZnZyfUajUsLCzkfdIH7eHh4XrH8/b27vN5/e0PDw9HVlbWdb12f4yNjfHkk0/i9OnT2L17tzz+c889h6lTp+LgwYM4efIkVq9ejbNnz/Ya0OhtnO7GjBmD5cuX4x//+EePfadOnUJbWxtuv/32fuvWN84///lPfPnll0hISICpqSni4uLw+OOP49tvvx3Af5Eby9nZGVevXlW6DLpJ1NXVwd7e3qA+pqTh6OGG9m9Af08dbJ/urQ93H9eQcQzl4uKic1tadTw7O9vgMbQ9++yzePrppxETE9PrY3rrw30dl3RMfb1vxMTEICkpaViOa7g4OzsD0MyfiIZLbW3tiJ+bG9rTN27caPCcHDC8r+ub3z3yyCODnuM99dRTiI2Nxb59++T7cnNz0draij179sDBwQFz587VeU5/893r6aGA/vcG7ddSq9Wwt7cH0H8P7e/n1f33nM2bN1/3e1df83qlubi4oK6uTmfVeCIiIiIiIiIiunWM7E/KiIiIiIgM4Ovri66uLmRnZ193+PNGMTExQXZ2Ntrb22FmZqb3Mf/zP/+DY8eO4cCBA7CyssLu3bv7HTchIQGHDx/u97XXrFnTI9AbEBAgf19UVISgoCD5dnV1NYDeP+wOCwvr83n97Q8PD0dmZuZ1vbah5s+fjyNHjuh8iD979mzMnj0bAJCfn499+/bhL3/5y4DH6S40NBTBwcE97t+1axeWL19u8KXNu4+zfft2LF68WA6HrFu3Do8++ijq6urg4OBg0Jg3WlZWlt7LNRPp4+3tjerqaly9enVEB8CGo4cb2r+ff/55vT11sH26tz7cfVxDxjFU90Ccj48PzMzMYGtra/AY2mJiYnDnnXf2+Zje+nBfx6UdEO7tfcPW1lbu10N9XMNFCsf5+PgoXAndyry9vbFr1y50dXXB2HhkXiTO0J7+xhtvGDwnBwzvGwDpngAAG4VJREFU6z/88EOP+d358+exbdu2Qc3xqqqqcOjQIZ376uvr0dzcjI0bNyIiIqJHoFfS23z3enoo0Pd7w/z582FhYWFwD+3v5+Xj46Pze05AQMCg6uttXq+07OxseHt7M8xLRERERERERHSLGpl/TSUiIiIiGoDJkyfD29sb27ZtU7qUfkVFRaGpqQkffvihzv11dXX44IMPkJ+fj02bNuFXv/oVrKysAABdXV39jpuTk4Ndu3b1ue3evRuenp544IEHdDZvb28AgIWFBU6dOqUzbmJiIqKjo3v9MPvhhx/u83n97Q8ODjboMYORnp6OZcuW6d3X1taGX/7ylwgJCcFjjz123eNI9u7di+XLl+vcJ4TArl27elyieCDjpKamoq6urkftFRUVBo95I/3444+4fPlyj/8WRL2ZO3curKyssH37dqVL6dNw9HBD+zegv+cOtk/31oe7jztUvXrhwoU4e/aszn25ublob29HXFycQWNo27t3L9auXatz37Fjx3Ru99WH+zoufcek733Dw8NjyI9rOP3rX//CtGnT4O7urnQpdAtbsmQJysrK+g22KsnQnj6QOTlgeF/XN79bvnz5oOd43333HYqLi3W2Rx99FK6uriguLsaBAwd6fW5v893r6aH9vTekp6cDMLyH9vfz6v57jre396Dq0zevV1pHRwc+//xzLF26VOlSiIiIiIiIiIhouAgh+tqIiIiIiG4KH3zwgTAzMxMJCQlKl6Lj9OnTAoDYsmWLEEKI1tZW4e3tLczNzcWf//xnkZGRIb788kuxatUq0dDQIFJTUwUAMWfOHFFfXy+OHz8uPD09hZOTk2hsbBQNDQ0iIyNDABCPPfbYkNb6zDPPiIiICNHV1SWEEKKlpUUEBweLxMREncc999xz4uGHHzb4eYaMa+hrSwCIDRs26NzX3NwsNm3aJNLS0uT7qqurxaxZs0RdXV2PMVQqlVi7dq245557REVFxYDGyc7OFk888YS4cOGC/Jj09HQxbdo00dbWpvM6p06dEvb29kKtVveowdBxHnroIeHh4SE6Ozvl+yIjI3VujxQNDQ0iNDRULF26VOlS6Cbz/PPPC0dHR1FcXKx0KUKInv1biMH38KHo39375VD0aUPHHUivLi8v19ur09PThY2NjTh16pR834cffijCwsJEe3u7weMIIcShQ4fEtGnTxLvvviveffddsWXLFrFhwwbxzjvv6Dyurz7c13F119v7xvUcl1IOHTokjIyMxN69e5UuhUaBpUuXigkTJojm5malSxFCXP+8fLjm5Prmdy+//LLeOV73vi5E3/2xu+eee064u7vLtwc6b76eHiq9N/T1WhJDemh/P6/e9FffQOb1SvvLX/4iLCwsRG5urtKlEBERERERERHR9fuX6COzaySE6DPvO4xZYiIiIiKiIdPV1YXFixcjNTUVJ06c0LmkqlLi4+Px4osv4sCBA4iJicHrr7+OxYsXIzMzEytWrEBOTg4AICIiAp988ol8ae+HH34YO3bsgJ+fH5599lm4uLjgvvvuw8yZM/H1119j/fr12Lt3L7y9vbFp0yYsX74c9vb2g65XCIHf//73uHjxIhYsWICysjKEhYVhzZo1Oo8LCwvD1atXUVpaChMTk36fZ8i4hr42AOzfvx8///nP4ebmhtdffx1Lly6Fh4cHmpqaMGvWLCQnJ2PKlClYtGgRXFxcsG7dOtjY2MjPr6mpwTfffINt27bh2WefxV133aUzviHjXLhwAXPnzkV9fT3uuOMOTJ06FU5OTvjNb34jr+Imeeqpp1BdXY1PPvmkx7EYOo50ieRz585h/fr1SE9Pxx/+8Af4+/sb+uO9IVpbW7FixQqkpKQgISEBXl5eSpdEN5GmpiZMmzYN5ubmOHLkyJD0tevVW/8GMKgebm9vP+j+3b1fOjg4DLpPGzquob16//792L59O7788ssevRrQrDr+9NNPIy4uDhYWFjhz5gy2bt0KT09Pg8e5cOECbr/9djQ1Nek8x9LSEiUlJXBycpLv66sPG3Jc/b1vSAw9LqVkZmZi5syZWLhwIT7//HOly6FRID8/H1OmTMH8+fPx2WefwcTERLFaBjMvP378eI9+/tvf/hYffvjhoHq6vvldZWUltmzZ0mOO172v99dnu3v++eexY8cOlP//9u49KKr7/v/4axUQgV2wKHflogg4FkUdVC4JSVE0UdNk4i1t47eXyR+ZTux/SabTTqfTSdL0j7Spf2SmbdqvaaLGjCUTjaAkpiqiwRuSBlYbWdAFuayy7OqCXPz+kd85P/BKjHoQn4+ZMwt783VA3zmB1/mcc+ckDe94d7Af/ehHtz1Dh/tnDWeG3ur7dTv5vslxvZXKy8u1bNky/e53v9OLL75odRwAAAAAAADcvv+V9D83epBCLwAAAEYNn8+n4uJiNTQ0qLS0VHl5eVZHuqnGxkbZbDZNmTLlmsd8Pp/sdrv5eU9Pj8aNG3dPcvX396ujo+OGl+L2+/3q7e3VhAkTvtHrbvX4cJ9zK52dnQoJCVFYWNh1Hy8tLVV2drbS0tK+1fv09PSoqalJYWFhSkxMvOH7NDQ0yOFwKDo6+lu9j/R18aOxsVFxcXHXfP2t1tbWpieffFJ1dXWqqKjQnDlzrI6E+5DL5VJBQYEmTpyojz/+WAkJCVZHuiGrZ/jN5uXtzuk7+ZzhaG5u1vjx4+/6PLvVHDbcaL+G+98Nw73ar2+iqqpKK1asUGZmpnbt2jWiSmoY3fbs2aPHH39cy5Yt0zvvvHPPjme/qZvN9MHuxjwfzvHdjeb6t3Wr412Dx+O57Rn6Tf+s4czQ4X6/hpvvmxyPW2Hbtm36wQ9+oNWrV+vvf/+7bDab1ZEAAAAAAABw+yj0AgAA4MFx8eJFrV27VmVlZfr1r3+tl156SUFBQVbHAka1nTt36ic/+YnCwsK0fft2ZWVlWR0J97HGxkYtXbpUHo9H//jHP8yVcQF8c1euXNEbb7yhl19+WSUlJdq8efMty3TAnbZ371498cQTSktL0+bNm5Wenm51JOC+0Nvbq1/96ld6/fXX9fzzz+vNN9/UmDFjrI4FAAAAAACAb+emhV5++gMAAIBRJTw8XB9++KE2bNigV199VTNnztTWrVutjgWMSk6nU6tWrdJjjz2mwsJCHT58mDIvvrXk5GQdOnRIJSUleuyxx7R8+XKdPn3a6ljAfae6uloFBQV68cUX9fLLL6u0tJQyLyzx0EMP6fjx4woJCdGsWbP00ksvWR0JGPH27NmjuXPn6k9/+pPeeOMNbdiwgTIvAAAAAADAA4CfAAEAAGDUsdlseu6551RXV6fc3FytXr1aCxcu1P79+62OBowKHR0dWr9+vWbOnKn//Oc/+uijj/T++++PqMvL4/5mt9u1ceNG7d69Wy6XS1lZWVq/fr28Xq/V0YAR7+zZs3r22Wc1f/58hYaG6vDhw/rNb35DEQyWSk5O1t69e/Xqq6/qrbfe0saNG3WLK8cBD6QzZ87o2Wef1aOPPqrk5GR98cUXWr9+vdWxAAAAAAAAcI/wk3wAAACMWlOmTNHGjRtVVVWl4OBgPfTQQyopKdGHH36o/v5+q+MB953jx4/rueeeU0pKiv71r3/pr3/9q2pra7Vs2TKro2GUKi4u1tGjR/WHP/xB//znPzVt2jT98pe/lNvttjoaMOKcOHFCP/vZz5Senq6DBw+qtLRUn3zyiWbNmmV1NECSFBwcrPXr18vpdOrHP/6xcnNz9d5776m3t9fqaIDlamtr9dOf/lTp6ek6fPiwysvL9dFHH2nq1KlWRwMAAAAAAMA9RKEXAAAAo978+fO1d+9ebd++XWPGjNFTTz2ltLQ0vfLKK2ptbbU6HjCi9fT06N1331VeXp5ycnJUWVmp3//+93I6nVq3bh0rPuKuCw4O1gsvvKBTp07p5z//ud5++22lpqZq7dq1qqqqsjoeYLn+/n498sgjmjVrlqqqqvTHP/5RX3zxhVasWGF1NOC6YmNjVV1dralTp2rdunVKS0vTa6+9pvPnz1sdDbjnduzYoUWLFik7O1sHDx7Un//8Z9XU1Gjx4sVWRwMAAAAAAIAFbLe4tBnXPQMAAMCo89VXX+mtt97S22+/Lb/fryVLlmjjxo2KjIy0OhowIvT19emzzz7T1q1btW3bNnm9Xn3/+9/X888/r6KiIqvj4QF3+fJlvf/++3rzzTdVXV2t119/XatWrVJycrLV0YB76tixY9q8ebM2b96sWbNm6YUXXtD3vvc92Ww2q6MBw9bU1KQNGzboL3/5iy5fvqwnn3xSzzzzjBYtWqTg4GCr4wF3jdPp1KZNm/Tb3/5Wixcv1i9+8QuVlJQwwwEAAAAAAEa//5X0Pzd6kEIvAAAAHliBQEBbt27Vli1b9Mknn6i4uFhPP/20nnjiCU2YMMHqeMA9V15erg8++EClpaXq6OjQnDlztHLlSq1bt07x8fFWxwOuUVVVpeXLl+v8+fNasGCB1qxZo5UrV/L3FaPWl19+qS1btmjz5s06efKk0tLStHr1ar3yyitWRwO+Fb/fr3feeUfvvvuuDhw4oOjoaK1cuVJr165Vfn4+VwTAqOB2u7Vlyxa99957OnLkiOLj41VRUaEZM2ZYHQ0AAAAAAAD3DoVeAAAA4FY2btyorVu3avfu3RoYGFB+fr6Ki4tVXFysefPmaezYsVZHBO6K06dPa/fu3aqoqNAHH3ygefPm6emnn9bKlSuVlpZmdTzglnp7e7V7925t2bJFpaWl8vv9mj9/vpYsWaKSkhJmOO5rgUBA+/btU3l5uXbu3Km6ujolJiZq1apVWrNmjXJzc62OCNxxLpdLmzZt0qZNm1RbW6ukpCQtXbpUS5YsUXFxsRwOh9URgWEZGBjQ0aNHVVZWpo8//liHDh2Sw+HQU089pWeeeUZFRUUcowAAAAAAADx4KPQCAAAAw+X1erVjxw6VlZWpoqJCLS0tmjBhgh555BEtWrRIxcXFmjZtmtUxgdvm8Xi0Z88es8R7+vRphYeH6+GHH9aGDRuUmppqdUTgtnV3d6usrEw7duxQeXm5zpw5o+joaBUXF6ukpEQlJSVKSEiwOiZwU3V1dSorK1N5ebn27t2rQCCg7373u1q8eLFWrFihgoICVivFA6O2tlbbtm3Tzp07VV1drbFjxyo/P98s+GZnZ1sdERjC4/Fo165d2rlzp8rLy9XW1qbExEQtWbJEy5Yt09KlSzVu3DirYwIAAAAAAMA6FHoBAACA23X69GlVVFSooqJCu3btktfrVVxcnObNm6e5c+eqoKBAeXl5CgsLszoqcI3+/n7V19fryJEjOnLkiCorK3Xs2DHZbDbNnj3bXIW6sLCQYgFGpcEzvKysTD6fT/Hx8SooKFB+fr4KCgqUk5NDORKW6evrU01Njfbv368jR47o3//+t5qamhQREaGioiItX75cS5Ys0ZQpU6yOCljO4/Ho008/VUVFhbZv367m5mY5HA7l5uaaM724uNjqmHjANDc3q7KyUvv377/mWHvZsmVavny55syZI5vNZnVUAAAAAAAAjAwUegEAAIA7obe3V4cOHVJVVZWqqqp08OBBtbS0KCgoSNnZ2Vq4cKHmz5+v2bNnKzMzU8HBwVZHxgPG5XLpxIkTqq6uVlVVlT7//HP5fD5FRERo3rx5ysvL04IFC1RYWKioqCir4wL3VCAQMMs2lZWVOnTokHw+n6KiorRw4ULl5eUpNzdXOTk5mjRpktVxMYqVlpaqqqpKBw4c0OHDh9Xd3a3Y2FgtXLhQBQUFKigo0Lx587gMO3ATAwMDOnr0qPbu3at9+/bpwIEDamtrU1FRkQoLCzV//nzNmTNH8fHxVkfFKBIIBFRbW6vq6mpVVlZq3759Onv2rEJCQjR37lzl5+ersLBQRUVFcjgcVscFAAAAAADAyEShFwAAALhbmpqadODAAR06dEgHDx7U0aNHdfnyZYWEhGjNmjXKzs7WrFmzlJ2drZiYGKvjYhT5/PPPVVNToxMnTphbZ2enbDabpk+frgULFmjBggVauHChZs6cSTEMuEp/f79OnDihyspKHThwQJWVlWpqapIkPf7445o9e7Zmz56tnJwcpaWlsbIevrHe3l7V1dXp+PHjQzav16usrCzl5eUpPz9feXl5Sk9PtzoucN9zOp167bXXVFlZqVOnTkmS4uLiNGfOHOXk5JhbWlqaxUlxv+jq6tKxY8d07Ngx/e1vf1N9fb36+vrkcDjMGV5YWKjc3FyNHz/e6rgAAAAAAAC4P1DoBQAAAKzU3t6umpoa1dTUqL6+XidPnpTT6VRra6skKSQkRFOnTlVGRoamT5+uadOmacqUKUpOTlZycjK/HB7l+vr61NzcrMbGRrlcLjU0NMjpdOrkyZM6efKkurq6JEl2u13Tp09Xenq6MjMz9cMf/pCSIXCXXbp0SbW1tTp+/LhOnDghp9Mpp9Ops2fPSvp6fhv/JjMyMpSenq6UlBSlpKQoKSlJQUFBFu8B7pTu7u4hc/rUqVOqq6uT0+mUy+XSwMCAxo4dq9TUVGVmZiozM1PZ2dmaPXu2srKy+LsAjAAXL15UTU2Njh07ptraWtXX1+vLL79Ue3u7JCkiIkIZGRnKyspSVlaWpk6dqtTUVOXm5lqcHHdCd3e3GhoazK2+vl719fWqq6uT2+2WJIWGhiojI0OZmZmaMWOGeWLP5MmTLU4PAAAAAACAUYRCLwAAADASeb1es7Q5uMD51VdfmSVOSYqNjVVycrJZ8n344YeVlJSkuLg4xcTEsPLqCHf+/Hm1tLSYpd2mpia5XC41NjaqsbFRbrdbfX19kqRx48YpOTlZ06dPNwve6enpysjIUEJCgsV7AsDg8/nMcq9R6qyvr9d///tfdXd3S5KCgoKUlJRkFnxTUlL06KOPKj4+XomJiZysMQJ5vV653W653W65XC599tlnZoG3paXFfF5UVJSmTZumzMxMZWVlmeWv9PR0hYSEWLgHAG7H+fPnVVdXp7q6OrPkW19frzNnzqivr0/h4eFKTU29ZktOTlZcXJzi4uKs3gX8PxcvXtSZM2dUWVlpzm9jGzzHo6OjNX36dM2YMWNIiTslJYX/twIAAAAAAMDdRqEXAAAAuN9cuHBhSPnT5XKpqalJjY2NOnz4sPm8sWPHKiYmRnFxcUpISFBsbKwSExMVExOjxMRExcbGauLEifrOd76j6OhoVnO9g3w+nzwejzo6OtTa2qrW1la53W61tbXp7Nmzamtrk9vtVmNjo/ma8PBws9g3eBXmKVOmKCUlRfHx8XyPgPvcuXPnzLnd0NBgfuxyuXTy5EnzeVFRUUpISFB8fLwSEhKGbPHx8Zo4caKio6OZ3d9Sb2+vPB6POa/Pnj2rc+fOmbdut9u8vXTpkvk6u92uoqIipaamDillp6SkaMKECRbuEYB7pbe3V2fOnNGnn346pBja0NBgXmlD+nq19tjYWE2ePFlxcXFKSkoy57lxXG7M9ODgYAv36P7V2dmp9vZ2dXR0qKWlRW63W83NzWpubjY/drvd5kmRYWFh1y1hG5vD4bB4jwAAAAAAAPAAo9ALAAA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      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 50,50\n",
    "\n",
    "xgb.plot_tree(cls, num_trees=0, rankdir='LR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These plots provide insight into how the model arrived at its final decisions and what splits it made to arrive at those decisions.\n",
    "\n",
    "Note that if the above plot throws the 'graphviz' error on your system, consider installing the graphviz package via pip install graphviz on cmd. You may also need to run sudo apt-get install graphviz on cmd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a28afc750>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] =15, 15\n",
    "xgb.plot_importance(cls, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.01025054, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.00170039,\n",
       "       0.00204219, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.10118318, 0.04199324, 0.00574934,\n",
       "       0.03693115, 0.07571935, 0.00976875, 0.07445166, 0.03749925,\n",
       "       0.        , 0.03856089, 0.02044625, 0.01984736, 0.02129499,\n",
       "       0.10545638, 0.10491905, 0.10734642, 0.18483971], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(categories='auto', drop='first', dtype=<class 'numpy.float64'>,\n",
       "              handle_unknown='error', sparse=True)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoder = preprocessing_pipe.transformers_[0][1].steps[1][1]\n",
    "one_hot_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0_AL', 'x0_AR', 'x0_AZ', 'x0_CA', 'x0_CO', 'x0_CT', 'x0_DC',\n",
       "       'x0_DE', 'x0_FL', 'x0_GA', 'x0_HI', 'x0_IA', 'x0_ID', 'x0_IL',\n",
       "       'x0_IN', 'x0_KS', 'x0_KY', 'x0_LA', 'x0_MA', 'x0_MD', 'x0_ME',\n",
       "       'x0_MI', 'x0_MN', 'x0_MO', 'x0_MS', 'x0_MT', 'x0_NC', 'x0_ND',\n",
       "       'x0_NE', 'x0_NH', 'x0_NJ', 'x0_NM', 'x0_NV', 'x0_NY', 'x0_OH',\n",
       "       'x0_OK', 'x0_OR', 'x0_PA', 'x0_RI', 'x0_SC', 'x0_SD', 'x0_TN',\n",
       "       'x0_TX', 'x0_UT', 'x0_VA', 'x0_VT', 'x0_WA', 'x0_WI', 'x0_WV',\n",
       "       'x0_WY', 'x1_415', 'x1_510', 'x2_Yes', 'x3_Yes'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoder.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('imputer',\n",
       "                 SimpleImputer(add_indicator=False, copy=True,\n",
       "                               fill_value='missing', missing_values=nan,\n",
       "                               strategy='constant', verbose=0)),\n",
       "                ('onehot',\n",
       "                 OneHotEncoder(categories='auto', drop='first',\n",
       "                               dtype=<class 'numpy.float64'>,\n",
       "                               handle_unknown='error', sparse=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_pipe.transformers_[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 96 candidates, totalling 960 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  26 tasks      | elapsed:    2.2s\n",
      "[Parallel(n_jobs=12)]: Done 176 tasks      | elapsed:    9.3s\n",
      "[Parallel(n_jobs=12)]: Done 426 tasks      | elapsed:   26.9s\n",
      "[Parallel(n_jobs=12)]: Done 776 tasks      | elapsed:   50.4s\n",
      "[Parallel(n_jobs=12)]: Done 960 out of 960 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score=nan,\n",
       "             estimator=XGBRFClassifier(base_score=0.5, colsample_bylevel=1,\n",
       "                                       colsample_bynode=0.8,\n",
       "                                       colsample_bytree=0.99, gamma=10,\n",
       "                                       learning_rate=0.01, max_delta_step=0,\n",
       "                                       max_depth=10, min_child_weight=1,\n",
       "                                       missing=None, n_estimators=100, n_jobs=1,\n",
       "                                       nthread=None,\n",
       "                                       objective='binary:logistic',\n",
       "                                       random_state=0, reg_alpha=0.003,\n",
       "                                       reg_lambda=1, scale_pos_weight=1,\n",
       "                                       seed=None, silent=False, subsample=0.8,\n",
       "                                       verbosity=1),\n",
       "             iid='deprecated', n_jobs=12,\n",
       "             param_grid={'learning_rate': [0.1, 0.01, 0.05],\n",
       "                         'max_depth': range(2, 10),\n",
       "                         'n_estimators': range(60, 220, 40)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='roc_auc', verbose=True)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {\n",
    "    'max_depth': range (2, 10, 1),\n",
    "    'n_estimators': range(60, 220, 40),\n",
    "    'learning_rate': [0.1, 0.01, 0.05]\n",
    "}\n",
    "\n",
    "\n",
    "cls = xgb.XGBRFClassifier(silent=False, \n",
    "                          scale_pos_weight=1,\n",
    "                          learning_rate=0.01,  \n",
    "                          colsample_bytree = 0.99,\n",
    "                          subsample = 0.8,\n",
    "                          objective='binary:logistic', \n",
    "                          n_estimators=100, \n",
    "                          reg_alpha = 0.003,\n",
    "                          max_depth=10, \n",
    "                          gamma=10,\n",
    "                          min_child_weight = 1\n",
    "                         )\n",
    "\n",
    "grid_search = model_selection.GridSearchCV(\n",
    "    estimator=cls,\n",
    "    param_grid=parameters,\n",
    "    scoring = 'roc_auc',\n",
    "    n_jobs = 12,\n",
    "    cv = 10,\n",
    "    verbose=True,\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRFClassifier(base_score=0.5, colsample_bylevel=1, colsample_bynode=0.8,\n",
       "                colsample_bytree=0.99, gamma=10, learning_rate=0.01,\n",
       "                max_delta_step=0, max_depth=7, min_child_weight=1, missing=None,\n",
       "                n_estimators=60, n_jobs=1, nthread=None,\n",
       "                objective='binary:logistic', random_state=0, reg_alpha=0.003,\n",
       "                reg_lambda=1, scale_pos_weight=1, seed=None, silent=False,\n",
       "                subsample=0.8, verbosity=1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 60}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9102904111517148"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.149544</td>\n",
       "      <td>0.008753</td>\n",
       "      <td>0.005663</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': 2, 'n_esti...</td>\n",
       "      <td>0.841768</td>\n",
       "      <td>0.869040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876419</td>\n",
       "      <td>0.878930</td>\n",
       "      <td>0.868219</td>\n",
       "      <td>0.870282</td>\n",
       "      <td>0.873559</td>\n",
       "      <td>0.875427</td>\n",
       "      <td>0.878467</td>\n",
       "      <td>0.866327</td>\n",
       "      <td>0.873710</td>\n",
       "      <td>0.004520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.206796</td>\n",
       "      <td>0.004109</td>\n",
       "      <td>0.005612</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': 2, 'n_esti...</td>\n",
       "      <td>0.840643</td>\n",
       "      <td>0.869152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.878967</td>\n",
       "      <td>0.880101</td>\n",
       "      <td>0.869855</td>\n",
       "      <td>0.870329</td>\n",
       "      <td>0.874219</td>\n",
       "      <td>0.873556</td>\n",
       "      <td>0.877827</td>\n",
       "      <td>0.863693</td>\n",
       "      <td>0.873758</td>\n",
       "      <td>0.004891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.274844</td>\n",
       "      <td>0.005021</td>\n",
       "      <td>0.005403</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': 2, 'n_esti...</td>\n",
       "      <td>0.839238</td>\n",
       "      <td>0.870052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.880513</td>\n",
       "      <td>0.880447</td>\n",
       "      <td>0.870322</td>\n",
       "      <td>0.872164</td>\n",
       "      <td>0.875337</td>\n",
       "      <td>0.873125</td>\n",
       "      <td>0.877370</td>\n",
       "      <td>0.869071</td>\n",
       "      <td>0.874927</td>\n",
       "      <td>0.003851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.348550</td>\n",
       "      <td>0.004721</td>\n",
       "      <td>0.005465</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': 2, 'n_esti...</td>\n",
       "      <td>0.841206</td>\n",
       "      <td>0.869040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.882023</td>\n",
       "      <td>0.883071</td>\n",
       "      <td>0.870990</td>\n",
       "      <td>0.870443</td>\n",
       "      <td>0.874660</td>\n",
       "      <td>0.872436</td>\n",
       "      <td>0.877211</td>\n",
       "      <td>0.868695</td>\n",
       "      <td>0.875305</td>\n",
       "      <td>0.004721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.190961</td>\n",
       "      <td>0.005856</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': 3, 'n_esti...</td>\n",
       "      <td>0.850990</td>\n",
       "      <td>0.902215</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898055</td>\n",
       "      <td>0.898527</td>\n",
       "      <td>0.892299</td>\n",
       "      <td>0.889783</td>\n",
       "      <td>0.897592</td>\n",
       "      <td>0.894907</td>\n",
       "      <td>0.899457</td>\n",
       "      <td>0.886795</td>\n",
       "      <td>0.894677</td>\n",
       "      <td>0.004167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1.731979</td>\n",
       "      <td>0.043035</td>\n",
       "      <td>0.009813</td>\n",
       "      <td>0.002623</td>\n",
       "      <td>0.05</td>\n",
       "      <td>8</td>\n",
       "      <td>180</td>\n",
       "      <td>{'learning_rate': 0.05, 'max_depth': 8, 'n_est...</td>\n",
       "      <td>0.888270</td>\n",
       "      <td>0.941464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933009</td>\n",
       "      <td>0.934481</td>\n",
       "      <td>0.924315</td>\n",
       "      <td>0.920438</td>\n",
       "      <td>0.923470</td>\n",
       "      <td>0.922971</td>\n",
       "      <td>0.931564</td>\n",
       "      <td>0.918574</td>\n",
       "      <td>0.925977</td>\n",
       "      <td>0.005074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.649170</td>\n",
       "      <td>0.017030</td>\n",
       "      <td>0.008285</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.05</td>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>{'learning_rate': 0.05, 'max_depth': 9, 'n_est...</td>\n",
       "      <td>0.889170</td>\n",
       "      <td>0.947650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.928464</td>\n",
       "      <td>0.930433</td>\n",
       "      <td>0.920389</td>\n",
       "      <td>0.920887</td>\n",
       "      <td>0.921828</td>\n",
       "      <td>0.920205</td>\n",
       "      <td>0.929431</td>\n",
       "      <td>0.914081</td>\n",
       "      <td>0.923693</td>\n",
       "      <td>0.004857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1.041405</td>\n",
       "      <td>0.014829</td>\n",
       "      <td>0.008429</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>0.05</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>{'learning_rate': 0.05, 'max_depth': 9, 'n_est...</td>\n",
       "      <td>0.887314</td>\n",
       "      <td>0.943432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.931600</td>\n",
       "      <td>0.933729</td>\n",
       "      <td>0.922440</td>\n",
       "      <td>0.920672</td>\n",
       "      <td>0.922962</td>\n",
       "      <td>0.922058</td>\n",
       "      <td>0.930330</td>\n",
       "      <td>0.917792</td>\n",
       "      <td>0.925106</td>\n",
       "      <td>0.004886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1.433208</td>\n",
       "      <td>0.040642</td>\n",
       "      <td>0.009050</td>\n",
       "      <td>0.001179</td>\n",
       "      <td>0.05</td>\n",
       "      <td>9</td>\n",
       "      <td>140</td>\n",
       "      <td>{'learning_rate': 0.05, 'max_depth': 9, 'n_est...</td>\n",
       "      <td>0.886865</td>\n",
       "      <td>0.941970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933385</td>\n",
       "      <td>0.935089</td>\n",
       "      <td>0.924040</td>\n",
       "      <td>0.920403</td>\n",
       "      <td>0.923843</td>\n",
       "      <td>0.922945</td>\n",
       "      <td>0.931817</td>\n",
       "      <td>0.917976</td>\n",
       "      <td>0.925825</td>\n",
       "      <td>0.005390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.509487</td>\n",
       "      <td>0.142202</td>\n",
       "      <td>0.006411</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.05</td>\n",
       "      <td>9</td>\n",
       "      <td>180</td>\n",
       "      <td>{'learning_rate': 0.05, 'max_depth': 9, 'n_est...</td>\n",
       "      <td>0.888270</td>\n",
       "      <td>0.941464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933020</td>\n",
       "      <td>0.934491</td>\n",
       "      <td>0.924304</td>\n",
       "      <td>0.920444</td>\n",
       "      <td>0.923470</td>\n",
       "      <td>0.922971</td>\n",
       "      <td>0.931564</td>\n",
       "      <td>0.918574</td>\n",
       "      <td>0.925982</td>\n",
       "      <td>0.005076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.149544      0.008753         0.005663        0.001029   \n",
       "1        0.206796      0.004109         0.005612        0.000706   \n",
       "2        0.274844      0.005021         0.005403        0.000287   \n",
       "3        0.348550      0.004721         0.005465        0.000429   \n",
       "4        0.190961      0.005856         0.005865        0.001291   \n",
       "..            ...           ...              ...             ...   \n",
       "91       1.731979      0.043035         0.009813        0.002623   \n",
       "92       0.649170      0.017030         0.008285        0.002033   \n",
       "93       1.041405      0.014829         0.008429        0.001413   \n",
       "94       1.433208      0.040642         0.009050        0.001179   \n",
       "95       1.509487      0.142202         0.006411        0.001713   \n",
       "\n",
       "   param_learning_rate param_max_depth param_n_estimators  \\\n",
       "0                  0.1               2                 60   \n",
       "1                  0.1               2                100   \n",
       "2                  0.1               2                140   \n",
       "3                  0.1               2                180   \n",
       "4                  0.1               3                 60   \n",
       "..                 ...             ...                ...   \n",
       "91                0.05               8                180   \n",
       "92                0.05               9                 60   \n",
       "93                0.05               9                100   \n",
       "94                0.05               9                140   \n",
       "95                0.05               9                180   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'learning_rate': 0.1, 'max_depth': 2, 'n_esti...           0.841768   \n",
       "1   {'learning_rate': 0.1, 'max_depth': 2, 'n_esti...           0.840643   \n",
       "2   {'learning_rate': 0.1, 'max_depth': 2, 'n_esti...           0.839238   \n",
       "3   {'learning_rate': 0.1, 'max_depth': 2, 'n_esti...           0.841206   \n",
       "4   {'learning_rate': 0.1, 'max_depth': 3, 'n_esti...           0.850990   \n",
       "..                                                ...                ...   \n",
       "91  {'learning_rate': 0.05, 'max_depth': 8, 'n_est...           0.888270   \n",
       "92  {'learning_rate': 0.05, 'max_depth': 9, 'n_est...           0.889170   \n",
       "93  {'learning_rate': 0.05, 'max_depth': 9, 'n_est...           0.887314   \n",
       "94  {'learning_rate': 0.05, 'max_depth': 9, 'n_est...           0.886865   \n",
       "95  {'learning_rate': 0.05, 'max_depth': 9, 'n_est...           0.888270   \n",
       "\n",
       "    split1_test_score  ...  split2_train_score  split3_train_score  \\\n",
       "0            0.869040  ...            0.876419            0.878930   \n",
       "1            0.869152  ...            0.878967            0.880101   \n",
       "2            0.870052  ...            0.880513            0.880447   \n",
       "3            0.869040  ...            0.882023            0.883071   \n",
       "4            0.902215  ...            0.898055            0.898527   \n",
       "..                ...  ...                 ...                 ...   \n",
       "91           0.941464  ...            0.933009            0.934481   \n",
       "92           0.947650  ...            0.928464            0.930433   \n",
       "93           0.943432  ...            0.931600            0.933729   \n",
       "94           0.941970  ...            0.933385            0.935089   \n",
       "95           0.941464  ...            0.933020            0.934491   \n",
       "\n",
       "    split4_train_score  split5_train_score  split6_train_score  \\\n",
       "0             0.868219            0.870282            0.873559   \n",
       "1             0.869855            0.870329            0.874219   \n",
       "2             0.870322            0.872164            0.875337   \n",
       "3             0.870990            0.870443            0.874660   \n",
       "4             0.892299            0.889783            0.897592   \n",
       "..                 ...                 ...                 ...   \n",
       "91            0.924315            0.920438            0.923470   \n",
       "92            0.920389            0.920887            0.921828   \n",
       "93            0.922440            0.920672            0.922962   \n",
       "94            0.924040            0.920403            0.923843   \n",
       "95            0.924304            0.920444            0.923470   \n",
       "\n",
       "    split7_train_score  split8_train_score  split9_train_score  \\\n",
       "0             0.875427            0.878467            0.866327   \n",
       "1             0.873556            0.877827            0.863693   \n",
       "2             0.873125            0.877370            0.869071   \n",
       "3             0.872436            0.877211            0.868695   \n",
       "4             0.894907            0.899457            0.886795   \n",
       "..                 ...                 ...                 ...   \n",
       "91            0.922971            0.931564            0.918574   \n",
       "92            0.920205            0.929431            0.914081   \n",
       "93            0.922058            0.930330            0.917792   \n",
       "94            0.922945            0.931817            0.917976   \n",
       "95            0.922971            0.931564            0.918574   \n",
       "\n",
       "    mean_train_score  std_train_score  \n",
       "0           0.873710         0.004520  \n",
       "1           0.873758         0.004891  \n",
       "2           0.874927         0.003851  \n",
       "3           0.875305         0.004721  \n",
       "4           0.894677         0.004167  \n",
       "..               ...              ...  \n",
       "91          0.925977         0.005074  \n",
       "92          0.923693         0.004857  \n",
       "93          0.925106         0.004886  \n",
       "94          0.925825         0.005390  \n",
       "95          0.925982         0.005076  \n",
       "\n",
       "[96 rows x 33 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  11 out of  25 | elapsed:    1.4s remaining:    1.8s\n",
      "[Parallel(n_jobs=12)]: Done  20 out of  25 | elapsed:    1.7s remaining:    0.4s\n",
      "[Parallel(n_jobs=12)]: Done  25 out of  25 | elapsed:    1.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=<generator object _BaseKFold.split at 0x1a28595e50>,\n",
       "                   error_score=nan,\n",
       "                   estimator=XGBRFClassifier(base_score=0.5,\n",
       "                                             colsample_bylevel=1,\n",
       "                                             colsample_bynode=0.8,\n",
       "                                             colsample_bytree=0.99, gamma=10,\n",
       "                                             learning_rate=0.01,\n",
       "                                             max_delta_step=0, max_depth=10,\n",
       "                                             min_child_weight=1, missing=None,\n",
       "                                             n_estimators=100, n_jobs=1,\n",
       "                                             nthread=None,\n",
       "                                             objective='binary:logistic',\n",
       "                                             ran...eg_alpha=0.003,\n",
       "                                             reg_lambda=1, scale_pos_weight=1,\n",
       "                                             seed=None, silent=False,\n",
       "                                             subsample=0.8, verbosity=1),\n",
       "                   iid='deprecated', n_iter=5, n_jobs=12,\n",
       "                   param_distributions={'learning_rate': [0.1, 0.01, 0.05],\n",
       "                                        'max_depth': range(2, 10),\n",
       "                                        'n_estimators': range(60, 220, 40)},\n",
       "                   pre_dispatch='2*n_jobs', random_state=1001, refit=True,\n",
       "                   return_train_score=False, scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folds = 5\n",
    "param_comb = 5\n",
    "\n",
    "cls = xgb.XGBRFClassifier(silent=False, \n",
    "                          scale_pos_weight=1,\n",
    "                          learning_rate=0.01,  \n",
    "                          colsample_bytree = 0.99,\n",
    "                          subsample = 0.8,\n",
    "                          objective='binary:logistic', \n",
    "                          n_estimators=100, \n",
    "                          reg_alpha = 0.003,\n",
    "                          max_depth=10, \n",
    "                          gamma=10,\n",
    "                          min_child_weight = 1\n",
    "                         )\n",
    "\n",
    "skf = model_selection.StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)\n",
    "random_search = model_selection.RandomizedSearchCV(cls, \n",
    "                                   param_distributions=parameters, \n",
    "                                   n_iter=param_comb, \n",
    "                                   scoring='accuracy', \n",
    "                                   n_jobs=12, \n",
    "                                   cv=skf.split(X_train,y_train), \n",
    "                                   verbose=3, \n",
    "                                   random_state=1001 )\n",
    "\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9373604289197603,\n",
       " {'n_estimators': 60, 'max_depth': 7, 'learning_rate': 0.1})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_score_, random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.409287</td>\n",
       "      <td>0.043595</td>\n",
       "      <td>0.013272</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>180</td>\n",
       "      <td>8</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'n_estimators': 180, 'max_depth': 8, 'learnin...</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>0.930582</td>\n",
       "      <td>0.953096</td>\n",
       "      <td>0.938086</td>\n",
       "      <td>0.934334</td>\n",
       "      <td>0.936987</td>\n",
       "      <td>0.008662</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.398917</td>\n",
       "      <td>0.004048</td>\n",
       "      <td>0.009777</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>180</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'n_estimators': 180, 'max_depth': 2, 'learnin...</td>\n",
       "      <td>0.850187</td>\n",
       "      <td>0.893058</td>\n",
       "      <td>0.893058</td>\n",
       "      <td>0.874296</td>\n",
       "      <td>0.857411</td>\n",
       "      <td>0.873602</td>\n",
       "      <td>0.017709</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.969388</td>\n",
       "      <td>0.009220</td>\n",
       "      <td>0.013126</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>180</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'n_estimators': 180, 'max_depth': 5, 'learnin...</td>\n",
       "      <td>0.925094</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.953096</td>\n",
       "      <td>0.938086</td>\n",
       "      <td>0.934334</td>\n",
       "      <td>0.935488</td>\n",
       "      <td>0.010011</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.571424</td>\n",
       "      <td>0.021647</td>\n",
       "      <td>0.008289</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>140</td>\n",
       "      <td>4</td>\n",
       "      <td>0.05</td>\n",
       "      <td>{'n_estimators': 140, 'max_depth': 4, 'learnin...</td>\n",
       "      <td>0.900749</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.917448</td>\n",
       "      <td>0.938086</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.921238</td>\n",
       "      <td>0.012269</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.355145</td>\n",
       "      <td>0.048288</td>\n",
       "      <td>0.005679</td>\n",
       "      <td>0.001454</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'n_estimators': 60, 'max_depth': 7, 'learning...</td>\n",
       "      <td>0.934457</td>\n",
       "      <td>0.930582</td>\n",
       "      <td>0.947467</td>\n",
       "      <td>0.936210</td>\n",
       "      <td>0.938086</td>\n",
       "      <td>0.937360</td>\n",
       "      <td>0.005628</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       1.409287      0.043595         0.013272        0.001676   \n",
       "1       0.398917      0.004048         0.009777        0.000498   \n",
       "2       0.969388      0.009220         0.013126        0.000999   \n",
       "3       0.571424      0.021647         0.008289        0.001412   \n",
       "4       0.355145      0.048288         0.005679        0.001454   \n",
       "\n",
       "  param_n_estimators param_max_depth param_learning_rate  \\\n",
       "0                180               8                0.01   \n",
       "1                180               2                 0.1   \n",
       "2                180               5                 0.1   \n",
       "3                140               4                0.05   \n",
       "4                 60               7                 0.1   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0  {'n_estimators': 180, 'max_depth': 8, 'learnin...           0.928839   \n",
       "1  {'n_estimators': 180, 'max_depth': 2, 'learnin...           0.850187   \n",
       "2  {'n_estimators': 180, 'max_depth': 5, 'learnin...           0.925094   \n",
       "3  {'n_estimators': 140, 'max_depth': 4, 'learnin...           0.900749   \n",
       "4  {'n_estimators': 60, 'max_depth': 7, 'learning...           0.934457   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.930582           0.953096           0.938086           0.934334   \n",
       "1           0.893058           0.893058           0.874296           0.857411   \n",
       "2           0.926829           0.953096           0.938086           0.934334   \n",
       "3           0.926829           0.917448           0.938086           0.923077   \n",
       "4           0.930582           0.947467           0.936210           0.938086   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.936987        0.008662                2  \n",
       "1         0.873602        0.017709                5  \n",
       "2         0.935488        0.010011                3  \n",
       "3         0.921238        0.012269                4  \n",
       "4         0.937360        0.005628                1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(random_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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