Case Study 2- kNN Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# kNN Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Underlying stochastic process\n",
    "np.random.seed(123)\n",
    "x = np.linspace(0, 1, 200)\n",
    "y = np.sqrt(x) + np.random.normal(0, 0.1, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'x' : x, 'y' : y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.108563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.170623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.128549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015075</td>\n",
       "      <td>-0.027848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020101</td>\n",
       "      <td>0.083916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  0.000000 -0.108563\n",
       "1  0.005025  0.170623\n",
       "2  0.010050  0.128549\n",
       "3  0.015075 -0.027848\n",
       "4  0.020101  0.083916"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot of x vs. y\n",
    "plt.scatter(df['x'], df['y'], c = 'gray', alpha = .5)\n",
    "plt.plot(df['x'], np.sqrt(df['x']), c = 'orange', linewidth = 3)\n",
    "plt.title('Scatter Plot of X vs. Y')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into test and train\n",
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(df, test_size = .2, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.120603</td>\n",
       "      <td>0.221891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.753769</td>\n",
       "      <td>0.893580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.572864</td>\n",
       "      <td>0.858152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.165829</td>\n",
       "      <td>0.337234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.115578</td>\n",
       "      <td>0.457550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.085427</td>\n",
       "      <td>0.510958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.492462</td>\n",
       "      <td>0.739697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.331658</td>\n",
       "      <td>0.580447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>0.633166</td>\n",
       "      <td>0.876951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.547739</td>\n",
       "      <td>0.579553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            x         y\n",
       "24   0.120603  0.221891\n",
       "150  0.753769  0.893580\n",
       "114  0.572864  0.858152\n",
       "33   0.165829  0.337234\n",
       "23   0.115578  0.457550\n",
       "..        ...       ...\n",
       "17   0.085427  0.510958\n",
       "98   0.492462  0.739697\n",
       "66   0.331658  0.580447\n",
       "126  0.633166  0.876951\n",
       "109  0.547739  0.579553\n",
       "\n",
       "[160 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>y</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.108563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.170623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.128549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015075</td>\n",
       "      <td>-0.027848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.025126</td>\n",
       "      <td>0.323654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.030151</td>\n",
       "      <td>-0.069028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.035176</td>\n",
       "      <td>0.144661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.040201</td>\n",
       "      <td>0.327096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.045226</td>\n",
       "      <td>0.125990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.050251</td>\n",
       "      <td>0.156279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y\n",
       "0   0.000000 -0.108563\n",
       "1   0.005025  0.170623\n",
       "2   0.010050  0.128549\n",
       "3   0.015075 -0.027848\n",
       "5   0.025126  0.323654\n",
       "6   0.030151 -0.069028\n",
       "7   0.035176  0.144661\n",
       "8   0.040201  0.327096\n",
       "9   0.045226  0.125990\n",
       "10  0.050251  0.156279"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort dataframe based on x column\n",
    "train.sort_values('x', inplace = True)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020101</td>\n",
       "      <td>0.083916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.095477</td>\n",
       "      <td>0.347613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.390758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.130653</td>\n",
       "      <td>0.452170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.155779</td>\n",
       "      <td>0.114829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.185930</td>\n",
       "      <td>0.500018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.251256</td>\n",
       "      <td>0.371846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.261307</td>\n",
       "      <td>0.685553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.266332</td>\n",
       "      <td>0.436267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.361809</td>\n",
       "      <td>0.717726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y\n",
       "4   0.020101  0.083916\n",
       "19  0.095477  0.347613\n",
       "20  0.100503  0.390758\n",
       "26  0.130653  0.452170\n",
       "31  0.155779  0.114829\n",
       "37  0.185930  0.500018\n",
       "50  0.251256  0.371846\n",
       "52  0.261307  0.685553\n",
       "53  0.266332  0.436267\n",
       "72  0.361809  0.717726"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort dataframe based on x column\n",
    "test.sort_values('x', inplace = True)\n",
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import kNN Regressor\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create X_train and y_train\n",
    "X_train = train['x'].values.reshape(-1, 1)\n",
    "y_train = train['y'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.00502513],\n",
       "       [0.01005025],\n",
       "       [0.01507538],\n",
       "       [0.02512563],\n",
       "       [0.03015075],\n",
       "       [0.03517588],\n",
       "       [0.04020101],\n",
       "       [0.04522613],\n",
       "       [0.05025126]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:10, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>pred_k3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.108563</td>\n",
       "      <td>0.063536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.170623</td>\n",
       "      <td>0.063536</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.128549</td>\n",
       "      <td>0.090441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015075</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>0.090441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.025126</td>\n",
       "      <td>0.323654</td>\n",
       "      <td>0.133096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.030151</td>\n",
       "      <td>-0.069028</td>\n",
       "      <td>0.133096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.035176</td>\n",
       "      <td>0.144661</td>\n",
       "      <td>0.134243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.040201</td>\n",
       "      <td>0.327096</td>\n",
       "      <td>0.199249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.045226</td>\n",
       "      <td>0.125990</td>\n",
       "      <td>0.203122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.050251</td>\n",
       "      <td>0.156279</td>\n",
       "      <td>0.169303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y   pred_k3\n",
       "0   0.000000 -0.108563  0.063536\n",
       "1   0.005025  0.170623  0.063536\n",
       "2   0.010050  0.128549  0.090441\n",
       "3   0.015075 -0.027848  0.090441\n",
       "5   0.025126  0.323654  0.133096\n",
       "6   0.030151 -0.069028  0.133096\n",
       "7   0.035176  0.144661  0.134243\n",
       "8   0.040201  0.327096  0.199249\n",
       "9   0.045226  0.125990  0.203122\n",
       "10  0.050251  0.156279  0.169303"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create kNN regression model, k = 3\n",
    "knn_model_k3 = KNeighborsRegressor(n_neighbors = 3, \n",
    "                                   weights = 'uniform',  #all points in each neighborhood are weighted equally\n",
    "                                   algorithm = 'brute',  #algorithm used to compute the nearest neighbors (for small sample)\n",
    "                                   metric = 'minkowski', #standard Euclidean distance when p = 2\n",
    "                                   p = 2)\n",
    "\n",
    "#Fit the k-nearest neighbors regressor from the train\n",
    "knn_model_k3.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for train\n",
    "train['pred_k3'] = knn_model_k3.predict(X_train)\n",
    "\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>pred_k3</th>\n",
       "      <th>pred_k5</th>\n",
       "      <th>pred_k10</th>\n",
       "      <th>pred_k25</th>\n",
       "      <th>pred_k50</th>\n",
       "      <th>pred_k100</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.108563</td>\n",
       "      <td>0.063536</td>\n",
       "      <td>0.097283</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.170623</td>\n",
       "      <td>0.063536</td>\n",
       "      <td>0.097283</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.128549</td>\n",
       "      <td>0.090441</td>\n",
       "      <td>0.097283</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015075</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>0.090441</td>\n",
       "      <td>0.105190</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.025126</td>\n",
       "      <td>0.323654</td>\n",
       "      <td>0.133096</td>\n",
       "      <td>0.139707</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.030151</td>\n",
       "      <td>-0.069028</td>\n",
       "      <td>0.133096</td>\n",
       "      <td>0.170475</td>\n",
       "      <td>0.150561</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.035176</td>\n",
       "      <td>0.144661</td>\n",
       "      <td>0.134243</td>\n",
       "      <td>0.170475</td>\n",
       "      <td>0.172969</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.040201</td>\n",
       "      <td>0.327096</td>\n",
       "      <td>0.199249</td>\n",
       "      <td>0.137000</td>\n",
       "      <td>0.179285</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.045226</td>\n",
       "      <td>0.125990</td>\n",
       "      <td>0.203122</td>\n",
       "      <td>0.195933</td>\n",
       "      <td>0.204153</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.050251</td>\n",
       "      <td>0.156279</td>\n",
       "      <td>0.169303</td>\n",
       "      <td>0.245941</td>\n",
       "      <td>0.204153</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y   pred_k3   pred_k5  pred_k10  pred_k25  pred_k50   \n",
       "0   0.000000 -0.108563  0.063536  0.097283  0.117141  0.244848  0.363983  \\\n",
       "1   0.005025  0.170623  0.063536  0.097283  0.117141  0.244848  0.363983   \n",
       "2   0.010050  0.128549  0.090441  0.097283  0.117141  0.244848  0.363983   \n",
       "3   0.015075 -0.027848  0.090441  0.105190  0.117141  0.244848  0.363983   \n",
       "5   0.025126  0.323654  0.133096  0.139707  0.117141  0.244848  0.363983   \n",
       "6   0.030151 -0.069028  0.133096  0.170475  0.150561  0.244848  0.363983   \n",
       "7   0.035176  0.144661  0.134243  0.170475  0.172969  0.244848  0.363983   \n",
       "8   0.040201  0.327096  0.199249  0.137000  0.179285  0.244848  0.363983   \n",
       "9   0.045226  0.125990  0.203122  0.195933  0.204153  0.244848  0.363983   \n",
       "10  0.050251  0.156279  0.169303  0.245941  0.204153  0.244848  0.363983   \n",
       "\n",
       "    pred_k100  \n",
       "0    0.504854  \n",
       "1    0.504854  \n",
       "2    0.504854  \n",
       "3    0.504854  \n",
       "5    0.504854  \n",
       "6    0.504854  \n",
       "7    0.504854  \n",
       "8    0.504854  \n",
       "9    0.504854  \n",
       "10   0.504854  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#kNN Regression Model for k = 5, 10, 25, 50, and 100\n",
    "k_list = [5, 10, 25, 50, 100]\n",
    "\n",
    "for k in k_list:\n",
    "    #Create kNN regression model\n",
    "    knn_model = KNeighborsRegressor(n_neighbors = k, \n",
    "                                    weights = 'uniform', \n",
    "                                    algorithm = 'brute')\n",
    "\n",
    "    #Fit the model \n",
    "    knn_model.fit(X_train, y_train)\n",
    "    \n",
    "    #Predict the response for train\n",
    "    new_col_name = 'pred_k' + str(k)\n",
    "    train[new_col_name] = knn_model.predict(X_train)\n",
    "\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CK4TzsfjkcjlSqdSE4UWSJBGNRkmlUuRyuQt9O+eEWTV58Qsv8pPf+gk//Z2fktmfOe/XSCaTrF27lmeeeYYnnniCRx99lK1bt5LL5ZBlmXA4TLVaZXBwENM0T/07EKJAME0QukUgEFwMhG65uFyOuuWMQ80feughHnroodM+/qtf/SqzZ8/m7//+7wFYvHgxa9eu5Qtf+AIPPPAAAP/wD//ARz7yET74wQ96n3n00Uf5+te/zic/+ckzHaJAcNUwHfNXzhcTLT6O41Cr1TBNE13XSSaT5HK5SXOIGsf6fL4J39c0jUKhQK1Wu1C3cc7U8jWe/dyzpHen3Z9zNV74+xd44z+98bz9rseGtUWjUW9BN02TYrFINBrF5/OhqirlcplUKkV/fz+RSISBgQH279lBh+8o4ep2lNRL3Ft+Fmn4G9D3xvMyPoHgXBC6RSCYPgjdcuXrlovB5apbLniO94svvsi9997b9NoDDzzAn/zJnwBgGAabNm3iU5/6lPe+LMvce++9vPjiixOes1arNX3h8vk8APV6nXq9flbjbHzubD9/pSHmo5npOB+pVIo9e/aQTqe9HKL29nbmz59PPB6/YNe9WHNRKpW8+3Ich1KpRDqdplKpYNu2t6gdO3aMUCg04TkURUFVVQzDQNf1ce/XajVUVUVRlPP67HAch3w+T7VapV6v4/P58Pv9RKPRMxYY6/9pPYNbB7FsC0V27yd7MMvw9mHaF7af1ZjH4jgOO3fupFwuE4/HqdVqVMoVlIyCYztYikV+Q55qoYp/oR99toZaPUI49Sqd1a202a+TePkYimQBIAEhoHB0DXrnvWd8v9Ppb0xwdSJ0y+WJmI9mpuN8CN1ydeiWxrkazpXTOUetUEOSJbSQdlrnP1m3VKtVdF2nbtSx0hZlq4yqqscjDqaPbrnghvfQ0BCdnZ1Nr3V2dpLP56lUKmQyGSzLmvCYXbt2TXjOz3/+83z2s58d9/rjjz9OMBg8p/E+8cQT5/T5Kw0xH81Mx/mQZRlNcx9UmUzmouUZXoy50DSNYrHo/dzS0jLOS7x371727t076TlkWR4nek9+f926dec81gsxH1bF4tBPD0343qNffpT4G8+fUNF1nUKhgOM4qJtUiq+58x5Ew+eU8DlpfAeKPPQ7j9LVcRjqgHL83wSUDj/D0yO/OuNxlMvls78JgeA8IHTL5Y2Yj2am43wI3XLl6pYzwbEcynvK5DflqRyogAShhSFiq2P4e/2n/HxDtwB0d3djlS2Gvj9EcEBD10tc+87t9Hdtp9XajZ/ctNAtl2VV80996lN8/OMf937O5/P09fVx//33E41Gz+qc9XqdJ554gvvuu2/S8I6rCTEfzUyn+XAch5deeomhoSHi8XiTZ85xHFKpFF1dXaxateq8hG9N5KGWZZnly5ePE57nk8Z9Dg4OUq1WKZVKBAIBJEnCcRwqlQrBYJBgMEgkEiEYDDI6OjrOiw6wceNGKpUKkUgETdMwDINCoUAgEOCmm246J0/72O9GLpdj48aNZLNZSqUSlmV51UgVRSEUCtHS0nJa10ylUjz3yHPYto2qqtiOjWEYmHUTSZKwXrYI6AGW3LeEhXctRNXP7nE+MjLCCy+8QFtbG5VyieRLWyitLxBQqvilCppcZey3KH0gTFfH+PM4Djj2ibIhMWcvtWqVQDB4RnPc2AkUCK4khG658Ij5aGY6zYfQLVePbln3i3UUDhWIXxOHIF4+uizLJBIJent76Yv3kduUY//j+3FGHSJEiHRE3JOMgvmoSWBpgEVvW0TXjV3jvhMn65bCwGYile2UHgsSHFRokatIZaiuPUb3b56+U6fV3nPBdcsFN7y7uroYHh5uem14eJhoNEogEEBRFBRFmfCYrq6uCc+p6/qE4Rc+n++cHy7n4xxXEmI+mpkO85HNZkmn08RiMWS5uT5io/BGOp2mXC6fc//EZDLJpk2bmlpaGIZBrVZjy5YtrFq16oK2ZVi8eDHJZJLR0VFvV8g0TWq1Gj6fj0QigWEY7N27l7a2NuLxuLdgDA0Nkc/nWblyJatWrfJaexSLRVRVpbu7+7y2lVBVlb1791Iul7FtG8uyCAaDSJKEz+ejXC5jWRaVSoW9e/fS1TV+MWngOA579+4lfyCPpmlYlkWtWsN2bBRV8c4/uG6QkZdG2Pp/tjJz9Uz6VvfRc3MPemT883FCjCyx8kssc35MfHQfkdpOntl0O36tddKPVArNu3NDB7vYu3UuA/t7KNdC1JwAjq+FwJIY/R9IkEylTnm/Y7nUf18CgdAtlzdiPpqZDvMhdMvVoVs2/3wzh/7tEJpPI/9UHnumDR3ud7Bu1Bk9NErm5xk2H9hMS6wFXdPHfR8apHemWffX64jNjLH4bYuZecdMZFUep1uixk70SImXX7qZwvA8dPlE2Pfo0NTpeI4jkax3MFSfidV6M6PSPBKtF1a3XHDDe/Xq1fzyl79seu2JJ55g9erVgBuSsXz5cp566ine+ta3AmDbNk899RQf/ehHL/TwBILLjotVeOPk6pyNh4+u624ecKXCrl27mrzX57toSiKRYOnSpQwPD2NZFuVy2atUGY/HCQaDJJNJ6vU60WjUE7aNCqKNXpq33XYbt9122wUt6JLP50mlUvj9fkZHR9F13Tu/JEnouk6lUqG1tdXzAE8mMBoFWuSMuyAZhuEa3YqChIQkS95OuGma5DI5jr50lGMvHUOSJTqu6aD/9n7m3DPHXagAbAty2yH9EqSO/8vvJAwsPn7dQ7tnkh2Z3OgGKBcDAJSsIC9vuI1taxZTsXSqth9HUpBlmZAWoli0iFcqTRVYz1VQCQQXA6FbBILzi9AtV75uyaQz7PvhPlTVNS1rlRrmdhNll0KdOo7jULWrBINBLNOiWCyitWlITHE/jkNu7xFe+qvXeC2YZuF1rzB3/hrCmunpFiRIHk2w99V54z5eLfmplnX8Qfd7VbKCDFtzOFqZwaFSFwPVXmwlhK7rzO2Yy7Fjx5jpv7C65YwN72Kx2JSXcODAAV555RXa2tro7+/nU5/6FMeOHePb3/42AH/wB3/AP/3TP/GJT3yCD33oQzz99NP84Ac/4NFHH/XO8fGPf5z3v//93HTTTaxYsYIvfvGLlEolr1qoQCA4ga7rqKpKvV6fcAfFMAxUVZ3wvTNhqpYWAJFIpOnB1FgsUqmUFzYVj8fP2Tvb1dVFV1eXt8vUuDdJkqhWq+7DW9PGLegnt92YKM/qdDmdhbkhLPx+P7btGsljURSFWq2GJEme93syarUahmFQPlKmXq9jWiayLBO8Nkhla8UdE46bj328CItZd0WNYzsMvzrM8JbDHPjZE9z5/qP4Si9BegOYxcmvWdHYvObGie8fiZqtU7EC5NId7BpczWi9BeslG9u0T7TlGNOJw7ZtUqkUM2bMEBVYBZcUoVsEgkuL0C1Xvm7Z//R+6pk6iqpQN0/oFs+wlibXLe0L25n30DxqyRF2//hlKiMpMDJgZMExAShnYctAgt1r38R9v/UEwYibV+3YEhufXj7+/o/rlrUHbsfo7eBoZQaj9RZ8Po16vY5lWUiyhGPb3mcsy7rguuWMDe+NGzdy1113eT83cpbe//73881vfpPBwUEOHz7svT979mweffRR/vRP/5R//Md/ZMaMGfzf//t/vZYcAO9+97tJJpN8+tOfZmhoiOuvv55f//rXFzQPQyC4XInFYsTjcQYHB5s8unCikmR3dzexWOycrnM6HupisUitVmtq69AI7arX6wwODpLL5Vi5cuVZL2KxWIxEIjHh/ZqmiWEYtLW1Tbhgnw8v+ukuzA1hYds2sixjWZbn+QX3gS7LsrfoTCUwisUi6UNpCim32JllHa9ofo1K65JWKvsqmBWTkBmieqSKWa3jGDkwiscXqwyYJVKD8MzhNHe+/Xk0/9RVNzc9vZxqyS1mYjo+qrafzgdyFNQ+jv6iFcsG0zLxl/34ZB92vYJdsr3ctQaN/2+EqTVC5M5VUAkEZ4vQLQLBpUXolitbt9imza4f7cKoGziG06RbUNxicDi4EXuS5F5LsuhbqXD9HWlaA7+C1Atg72PBm2UO7ZrFzpcXk0+Pr39RLgR5bd01rHpwPY4Dr669luxIq6dbak4QgxB1glg2DByeS2JmgpJcwnGq3j3aYwxu03SN+0AgcMF1yxkb3nfeeeeUjd+/+c1vTviZLVu2THnej370oyJESyA4DSRJYtGiReRyOZLJJNFo1Cu8kc/nCQaDLFq06JzDkU7XQ61pGtu2bZswtGts2NTJBVXOx/1ms1l8Pt+k3u1z9aKfzsLc8EZHo1Hi8TgDAwMEg0Hyx/KoBRW9T0cKSNRqNUKhELVaje7ubqLRKNlsdpw3OplMsmPHDowhAxxQZDen2/bZlKQS0bYo/kidmZERFrRsRcvuoLQrz9CeDoYOdWGZzR7r9GA7z/zobu56x9MTGt82Gjv33cT2nSuoWDoVO4CqhZH7ZWr9PvSSDvIo2BayJGOXbSQklIqC6biF3iRJwnZsuA8CrQH6ZveRK+WoWBVyuRyzZ88+Z0ElEJwtQrcIBJcWoVsuD91SLBbdlLbjDvXT1S2v/PQVkgeTbtTbcd3iaA5O0MGWbRSfgmSbtGhlErER+ma/zpKF6wj5c9BcKgNFtZmzbD+zl+5nYH8vOzYsJnWs2QFyYMccOpbX2fzccgb2tnm6xbRNLMc6sXNvW8h52TP2FUXxnAmyLGOaJrZt4/e7mw6KolCtVi+obrksq5oLBFc7iUSClStXeh7NQqFw3gtvTOWhBigUCnR3dwNMGto1UdjU2TDZ/fb39xOPx8nn8ziOc1696FPlio1dmFeuXOnda2OhLewtUHykiOM4KCGFwMMBfK0+FEUhGAzS0dHBunXrxnmjFyxYwKuvvkomkyEwGqAqV7EdG022aOlOc1NkEzO0QyRCg8iSA0Xcp/gyWLBsF6ahMnCgm01P3US1fKIVx+hQG0//8G7uescz6PFeiK+i6F/GK0f9HBtNkHomj63bWKaJWSljOTX639RPySlRpoxluiFZiqxgmia6rRMNRxmQBk4YNBr4un1E2iL42nyYhkm9XCccDp8XQSUQCASCyxehW07oFn/az+GvHGbEP0LPm3uILHQrel9K3WIYBrIsUyqVvPoxjVD5qXTLvDnzWP/19Z6zwzAMpA6Jzt8w6FL20qMdZIZ+iITvuG45TSQJeuceo3fuMZKj1/PMj+6hZkfJV2Sqtp//+o+VyJKMpbm6xaxV0TUddaZKbb8bLSDJEmpRpVAoEAqFSCQSDAwMYFmWN1+qqno91Q3DoF6/sLpFGN4CwWVKIpEgHo9fsMIbk3lsG+FPgUCARYsWuXk6F6FoymT3m0qlWL9+/Xn3ok+VKzZ2YR7bRiKRSLBixQr+61v/hU91vcxm0aT2Qo3EbyeYMWMGHR0d7NmzZ5w3+uDBg2zbto1apUh4Twpzi0lcqaBTQpXqLJuzjWsiW6ccs6qZ9C88Qks8x1M/uIdqOQRaC2itZMxWnt78Ee76/JuoWBVeeuklRoujWC9Z2FUbJFB9KiEpBKuhaBcJh8PYtk1FraBIire7rVkaQSeIprm5UgC+Vh/tiXZvQatUKsTjcW677bYLWkFWIBAIBJcHQrfo1IfqHHzsIE7eoZgpcuiRQyz6fxdhOuYl0y0NB8HRo0fJZDJeBfaWlpZT6pbNP9pM8XAeXTbQ7TJRpcJddz7J3I49ZzdpShDaV0B8lfuvfRVxfwfd9bVs/v5mDMvAr+s0UscbuqVSreBb5SM8I8zIwRH3PUVFLsiEQ2ECwQCtra1e1AFAMBgkEAhcVN0iDG+B4DJGkqQLWil6Mo+tLMvcdNNNJBIJstnsRSmaUhwuktqZwnEcojOixOa5C8uF8qKfbRVWJasQqAbwdfiwbAvHdpAMCf1JnZkfmsnBoYNN3mjVLhCubKLD2kC7fzfWsMVLG24hNCZiXFZsZi89cOpBh2ZB/BaiN93KPXffwNN/O0glc2J8w3sL/PsH/53AbwQYzg4j52Rq62tomoaiuhdsXdZK4oEEhUKB5cuX097ezpNrn6SULCHLMrZtE4/GGdk14hnikiTR0tvCnDlzKBbdAm59fX3cfvvtdHRM0PRbIBAIBFclV5Nugeb7reaqrPmbNfgUHy2tLRSLRYyCwdCOIUKzQp5uicfj46L4Toez0S2FwQIBKcCtt95KPp+nWq1iGAaapuH3+4lGo6xbt25C3ZKob2D3K63ENQlZcnOm27vTzJl9Bkb3cd1C4lZI3AKxZSCfME+TySS7Nq7lcPgwxXIRCQnHdpp0i96i0/fWPsx2k2sWXMPLz70MgGM7yLLMrNoszD6TXDWHqqreTn4jfP5i6hZheAsEgik52WOrKArr1q0jHo8D579oykSVOA89e4gX//7FpuPiS+Lc+OEbaZ/fPqUX3XEcju08xoE1B6gMVKDmLoStc1tZ+s6lBNoCE16zkStmGAaAF1rVqEw6NlcMYGRkhFAoxOuPvo6E2wPTx4nFrzZSY81frkFZOso1bxggnttJzNhKyDxEpRggM9JKZqSV7S8tbaoODrDi/vWEW5orkjuSCm03IjUWrPgtEOzx3o8C9/xNgaf+7Ckq6Qo1o0Y2k8VKWVR+WEG5S8HaZWFaJnbVzXHSIho9b+1B8SuUSiUikQitra3EemKYedM79/wZ8wmOBpETMqZlUq1UUWMqo6OjnsC55ZZbhNEtEAgEgovOdNAtJxvNtmWz7m/XUUm7nUl0TUdr0zDrJvNa5rHorkVEo1G2/vtWnv7Z06gRlWv/8FoSCxITnu986JZ1/7yOw786jOpT6VjawTW/dQ2d13Q2XSubzVIa2cEi/74m3QJw7FgPuwtvaAohv+aWrUzmLziVbjmZsfnqwXgQ/w1+6q/Wm3RLdF6Uvvf0oYQV0uk07T3txDpjVEYr3nmOPXEMngR/p594Z5w5i+dgdBtkcpmLrluE4S0QCE7JWI9tI7R47Hvnq2jKRJU429vbGfjqwLhjUztSPP7xx5l11yyue991BOPBcV70ZDLJlqe3sP3vt2NbNpIsoWka4XCY1M4U+aN5Zn1wFrt37yafzyNJrsEcj8dZuHAhgUCA/fv3A+4iJ8sywWCQ9vZ2yuUy0WiUHTt2APDCCy8g1SVGfjZC0B9E13TAgXoRjDTURlFrabQXCiy97icoqo1tyTz38zs4tq930jm55tatzF56EEOKkGI+tehNzLrpt5DabwY1OOV8Rnoi3PP5e3jqz54i/Xoay7LQNA0rbVH5jwqSJXnFRgzDoPuWbnxRH9VqtcnbH2wPkibtnbc6WsUpOui6jo5OMBhkwa0L6Lujb5zAEQgEAoHgYnMpdctEFcRf/farjGwdaR7HcSd9fahOS0sLe365hy3f3eLuhB82OPbnx5jx+zNIJBLe+RzH4cCBA+esW4y0waZvb/J0kbHFILk9SWJJnBveFaE9+gqMPE9k6FkerB2DCaLu9746v+nnlo4MXbMGvZ/PRrc0ODlfvVarEVgVINAfwE7bGDWD4Kwgs26ZhazKTbqlZU5Lk+HtnhCqQ1WqQ1Uyr2ZQ/Apam4ZsyyDB0cGj9KX76P5Q92mN72wRhrdAIDhnzke492SVOI9sPUJyf5KW1pbjxmwzB585yJF1R5hxywxi/THii+J0LOvwcr+PPHoEWZZRFRXbsalVa5h1k1AoxMjjI2z1b8UKWJ5BHovFGBwcZGhoyCu0IcuyV/Uyl8uRzWaJx+MUi0Xy+Ty6rtPW1kbyuUHMYhKjVEZVqyhmDmzDG6sE1Gs+hg930TNngL2vzpvU6DYcndYlNUaX38R3Mg/jRObT1hZn5YqVSGcQPh/pjnDzp27m8B8eRrVUkNzWHrItY9rHi6fIChYWoetCE3r7A+2BpnOW02VKI6Ux9yXRMauDzs7OcQJHIBAIBILpxoXQLWbSJPVsioGRAXb6djJjxgzi/XFUv8run+2e9DypnSkyBzK8+JUX3cg0y0L1qUh5CWfAYdB0K5LPnz+fAwcOsGfPHur1+jnplsLGAj7NB7aNXU1j1I6gqlWST+V46pkK97z7KXy6QSBcQdHGj7mUCzFwoHm3uvvaJK9btzLizOdgseesdUtjzGPz1XVdJxQKUewuEpobwm/5qdfrGKaBruhNumXxby5maMsQjjV5MTerarlRkIBt2xgjBqVkadLjzxfC8BYIBOeFicK9o9Eo+Xye4eHhKYuoTFWJU0trWJZFsVhEa9OQGP95y7A4tOYQDg5m3aR1cSvSKokSJRg+3kMSkCUZTdOoVquk02kcx0E7qNG6uhXbtimVSu7Ob3c3R44cAWDu3Lmk02nK5TK2bePz+bBtm2q1SkC16PMfIWZspT21k5fWziLhhNxQcQN65gyQ6E3y6vPXNY33yOt99MwZ4PDr/QDYSNTsAFU7QNUOUpfCqLOClFYHOFSwsG2bJd29XH/99Z7H+0yK06gxlcR7EuR+kqOerYPk5nrZVdtrraHOUTFkg3KyPM7bH2xv9lCXU2UvXK5BMHF6XmyBQCAQCKYD51O32FWb/d/ej1k0wYGCUWD/wf1k27LjdYuEl1Lm4FAYKfDzP/o5udGcF5nW+MjA9wZoX93Oocwh9gf2oyxQQIaWlpaz1i090mF2vZIhZpTwOUUkbE+3AFi2wuP/fj/g1phZfPNOlq3ehqyc6H29+7UFVKygp1tMPUxh1p1YJctr03UuuuXkfHVJkojH49RqNcrlshu9Z1mUy+VxUQqd13bypn9+E4fXHWZ07yhDW4YwK+ak17qYCMNbILgKOdMH4OkyNrQrmUxO2HpiIk/yVJU4i3uLXs6SWTdZ8OACYjNjbP/+durlE7urNaPmhmcZBsPPDlNfUyd2V4x6pu4V4Ghg2zamaSIrMhzD6+moKArlcpnh4WFs211gVFVlRs8MjvzkCKWdaVQzj18dZf51W7j55hdRLBvHljiwYzbFTKjpOotv3klH3wiSbPPKszd4rx/eO4tYps7uQ9dgSGHKdR+W7aAkFIJ9QfQuidiyGJ2dnaRSKbq6urjnnnuQZfm0w9rGous6/nY/kfdHGPjOALVRN+dN093QOtuyURe6hWZ6enrGnevkHe/M3gy2aTe9Fko037tAIBAIBOeL6a5b8jvyrtENbpeQMbrl5GJnN/3BTbz23dcopAuebmmkfKnK8eJfY3RL+gXXiK7X6/i2+2j9zdZT6pb+/n5qtRqykSbOLqLVVwlXXqGtPMS6/7qNQLVrzCRAMFymXBjvQLctme0vLeXIgZnMvG+YTKSbUZaw/eUgZs1GlmRUVUVdoKIFNTo6Os6bbjm5AF4wGCSRSJBKpahUKliWNaluifREWPrOpQBYdYv07jTZQ1kOPnOQ9O70hNe8GAjDWyC4yjibB+DZXGOisPHBQTdcauXKlU3XmqwSp123KR8sI8uy50HtXdFL3y19zL57Nlu/t5V9v95HtVptDs+SJOrlOrkncsiSG26lqAq+mI/4A3H2fG2PV3jNGrSwKzZyQPbCmSqVCo7jEFFydFaeZPSJNMoGmTbnxC7vgef7kXN15izbz9qf30al2GycRtvzJGa4+Vx919m88nIvaO2gtZEzJB59+mZq9vFekxLImk3oLSHCsTCO41AqlchkMrS1tXHDDTd4i9eZzGuDsYVkZn14Fvu/s5/8vrzbQsMHyvUKrfNaWb58OXPnzh0nZk7e8R7r8ACQVRl/qx+BQCAQCM43l4NuKR1sDlMeq1vGMvue2cx7aB57n9vLkb1HmnWLUceyLarVqqdbwN0ssG0bSZIwUyb5H+ZpeWsLalydVLd0VncdL4R22Lt2Lh/l1z95kFIu3DSm3jnHuPXNa3n8e/eTHWl1X1QCoLd5umWwJrH1O2m01RqyX4ZqGVlye2VLkkTouhDFYhGfz3fedUsikaBcLpNKpSiXy57B3dLSMqluGYviU+hY1kHHsg7mv3E+mX0Zckdy3vumabJ+/XrmPjh3qq/IeUEY3gLBVcRUD8BsNsuSJUsIh8Nn7E0e64nWNI2dO3dOGDaeSCS8BTQejze9d7Jn03EcMq9nqFfrXlsNWZHpvLYTAH/Mz83/z83Me+M8fvHFXyAdlPCN+JBsCSTXiy3Jkpu7YxgElADhuWG0mRqO7CA57nGO7WAcNPAv9hMiTadvF+3yVnp9+2hRUowebGXX+gdRJ0gV2vfaXPa9Nv5Bbdg62hIfL/D/4Ou+m5vvfDutrzxGZl8GANWuwUHXe4vjjjW0KIQv7vNCw+r1Om1tbV77k6nC8Seb1wZjC8kMZ4Zx7nUI3BZAlVVMTFTNrXK6a9cuYrHYuEXw5B3vkwkmguetD6tAIBAIBA0uF91SPlAGxzWSHcchuCiIz/ahZE7sXLfMaeHmP7wZx3HIqlmMuoGu6ciS7OkWWZdRZikYe13dguSO1WsvJoFVsMj8IEPszTFa+wrjdAuV8fc7fLiT5//zduq18a3GFi7fRcZJ0Hanxf4fzELS22mJdzeFyKt2DZ/sQ94kgwOhkFsTxjAMpA4JO2JTNy6Mbjl69CjFYhHLsvD5fFiWRTAYnFK3TIYkSbTNa6NtXpv3Wr1eZ0d5B4klF6Z391iE4S0QXCVM9QAMhUIcOnSIY8eO0dbW5lXIPB1vcuPBeWjtIcqHy/jn+SmHynR2dk74II1Go6RSKXK5nBfeNZlnM/1immql6vVsjM6OooW1pmtv2b2F0VmjSLMlVEel/mwd56iDoiiYloksy1iWmycdnhfG5/ch98hIAxKqVMfnZOk6sJV7rn2ciJR0T+xrzBm8/MTN49p7Nc0rEnUpRNn0U7WDVOwAlixjLOxiRtsMqtkqC3I5+m7t8wxvWZY9L7GsuPnn7de309Lf4uUv1et1Vq1aRWur632eKhx/snkdSyKRYMWKFTz22GNUq1XXS69ANBglHo8TDAYnXQSD7cGmnLSTEfndAoFAIDjfXGjd0thBt22bTCZz1rqlRW+hPFL2QsYdHKx+i46FHdyy5BYyGzP4Aj4Wv30xo7lRtmzZwpB/CNM2scqWm/qlae5/b9cILghScApIWQl/mx8pJFF6ueSGdB/XLYF6mcgvMzz84R+hBwwYb09jmTKpgTiHds1k/9a5OI57bw3dUrF1giscfqT9EfkBHx0dHbT+RoChXw1h1k1i3TFsy6aaqU6oWwACSoDOeztRu9WLolts2yYSiZxSt0xXhOEtEFwlTPYALJVKHDt2zM15lmXC4TCKopwyDAhOeKKTG5Pkf51HlmSKG4uUy2Vy0RwBOYAv4CO6LErPwz1e24pCoUCtdqI3xUSeTdM0MQ+5+VKN4mj5YJ5kMul5StevX8/o6CiyLBMKhdxwrDttak/VUI4pXnhWwwsd7k4TzW5k9bKjHB5uRz1eSaR+yCRopcc9EdOD7YwOtTe9ZiNjSH4MYpTrfkqmRigUpWyWcXAgAOoqhdaOVnRdp1gsUqvV6Lulj9e+/RoAqs/dYa5VXU+7JEtEF0W9kLF8Pk9PT0/TQjRZOH6DieZ1omNCoRDRaBRFUZr6ewKTLoKKpjBj9QyOvnB0wvOK/G6BQCAQnG8upG4Zu4Oey+Uol8uMjIx46+RYTqVbDr540A31xnF3r30Q6HYjxXYO7mTl21aO0y1qTKXt7W1UtlWoF+vYuk3XTV1UE1UMw8D3Bh/9M/uIqaME8htYuXI7B3/RRmHwhKPbqcHh3TOZf/2ecfdZKQb49bcfpFr2YyNTs4PUlYinW9rmJ6heX6Uc4/g8OsRiMdrubEOaKbFi+QrmXj8Xy7B45VuvsPvnu5t0S2MzXA2ptN/QTjqTnla6ZboiDG+B4Cphogeg4zikUinq9TrhcJhKpYJt214Bi6k8iWM90Rw4YRwr8vH2VAWLqlpFsiVGN4zi7/TTvrrdLR4ypkd0g5M9m3JBxspaqIrqeYLlfpldu3bR3t7e5AUvlUrYto2qqoQiIbgHrOcttEN1JCOHRoGe+AD3Wv8JQHWBn4Gnf9PbwTXrKsNHOumZPdg0pmyyFRsVQwpTIwyxKD1/tJjh3aNUf13GKVtgld2DA6D9hoYSUbwWH2PvNdoZJTYzRu5QDgmJcDiMWTcxDIOWZS1IukS1Wp20h+hE4fhjmWxeT/4OWJZFS0uL9/say1SL4Oo/Xc3uubs59vIx0ruaC5O0zm2d9JoCgUAgEJwNF1K3jN1BDwQCBAIBarUaqVSKYLA5fepUuuWHP/qhm4Mtu7VjgjODzJw1s2lHdiLdovaotPa34jgO5XIZJ2wzv1VCHd1I3N5Of/EAIen4ehuDxe9WePoHd5MaiHtjyIy0jJu3uhRm3UsPMlyZTV2OYEoh6raJT/bhaA5KX43u3+lm38F9OJaDqqr4fD5Pt/jb/LTPcjcdFE1h+UeWE+2N8sKXXvB0i6qqyLJMZHmEdCY9LXXLdEQY3gLBVcJED8BGWLOu69i27baVUt3HwqnCgPL5vOeJHh0e9V5vVNk0TdML8ZYVmfz2PG2r2sb1iB7LWM9m7oUcBI8b9BL4Yj7a57eTSqU4evSod21N0wgGgxSLBVrVAl3K63S27KDjN/aw+9m57H11PoFQhZV3r/Ou4w9ViXenmhavo3tm0DN7EEuJIXffhdTxBgqjs5F6iiimhVKroc5SqQP1ljrmXSasA1/dhxpTab27FV/CR61WIxKJoGkaqVSq6V77bukjd8gt6KFrOi2tLRSLRUIrQqTT6Sl7iJ4cjj92cZuo9/bpfgfGMtUiqPpVlr5rKUvftZT0njSb/s8m0q+n6bqhizn3zJn0mgKBQCAQnA0XUrc01lBj1GD40WHK+9xQcaPHoPWtrURnRIFTr6+apmEP256xLkkSPdf1eLvmjfGcUrdEdtAlv064mIUJ+mYDKKpF77yjTdolm2pp0i103okSWUr6Zz9BieYxKhWc43qsXq8TXRklsjqC7JOJRt1Iu6l0S4P5b5xPoD3Axm9sZOTYCLV6DX2WTvDmIInOxLTULdMRYXgLBFcJEz0AG7lNsixTqVS8AiUNpvIkNjzRsilTz4+pct3oEW3bmJbp9YkuHCwwMjRCKBIa5xUde856qU5tR43c87mmPKLo4qgXut0IRQ/LGdorrzA/uIEWZQthOdt0vpvv3ciNd25Bkm1kuTlBubF4WagYhNm5543YCz/JNR+6j1BnBIDiT55HksrYTp1SqUQwEPTm6Ih0hPJDZQLVADMWzuDo4FHMiun1AW14zRctWgRANpslsCBAvV53K5gioWs6Pbf0sOK3V5yyRcrYsLZkMkk0GkXT3HZgk+2Sn853oMHpLoIA7fPbuf9/349t2sjqeA+0QCAQCATnyoXSLY0ddMdxOPTdQ1SHqiimglSVqO6ssn9gP7N+exb+uf5Trq+FVAFjxGgKvw7NPhGq3hjP6eqWU9GSyHq6RQl0kFXuQn7nr5DkE0XcRnenqGQrlEolr2p6UAvCzVCfWUeqSd58OI4zpW4Zq01mrJzBjJUzTru123TSLdMFYXgLBFcJEz0AZVnGcRyKxSJ+v39caNZEnsTCsQKD/zHIuh+so9RVQl0w/jESaA8Q7A6S3pjGdtyq4pIsETNjLF+5fMq+jZlfZqgdqI0LKYouiUJ1hJlspG/oZ/SbzxBOjgkNn8T+U1Sr6eeKHSGrXYtxw01kX+rGlAIYRh0dnf0vVBk99jwPffkhJEmiMFjAwfGqabbPbEfXdXRdp7+/n1QqRSaTQfbJaJqG4ziel7uxcw2wdu1aLzQuL+dhFFcsaDrXve+6085LSiQSrFy50isKUygUptwlP5nzsQiORRjdAoFAILhQnC/d0uDk3dPCzgLVoSoAiqqg6RpOzcGsmez7xj563tfDjBtmTLm+Fg8U3Q4qjtvTWvbJBHrHdAI5C91yMg3dUggtJ7noGjJ62dMtPjtEcbhMpDviHX/0xRNVwDVNw9/hZ/6fzqdUKnm6BTgt3TJR+7axvc9PxXTTLZcaYXgLBFcRJz8A6/U6qqpi2zY9PT1NBUUm8iQmdyZZ89k1VPZVqHfUKR0sUXy96OZKH3/m+bv8zPvYPJLJJHJWxlf24dgOsizT5++b8iEb0kNYhy3MuukWHcPCZ+cJ+LLcEfpHotl97oFDp3/PhtxKWr2GPbkehp1FjNTaCYcj9Pf3o3TswUzVUFUVwzAw6ya5QzlKIyVCHSGKg8UT+Uw+FT1+YiEPhUIEg0Fv8bnvvvtobW3FMAzPA5xKpcYVcdHerrH/u/sZzY9y/X+7no5lHad/M7i/w3g8flre5sk+fy6LoEAgEAgEF4tz1S1jiUbdLh4DRwdo0Vo49uNjJ9503PaikXCESDSCYzu0HGnhto/dNuX6Wj5Ybio6Fu5XiddfpKW4hdbaZsLmfvfA86FbEv0QBDW4C9t2PN2SPZhtMrz3P7/f0y1IEFnsvne2usUwDA4dOsTQ0BA33ngjc+bMOSNDV+iWEwjDWyC4yjj5AVgsFnnllVcYHR3FsixCwRCmZY7zJB5ed5gX//5FzJrpnSscDpMdzrqhTMcLbahtKslk0i10sjrB0HMnVpvk9iS8c/Kx5fYNE9GqaLVBtFoOH2UkHK5b9SpRZ99p3V/ZaeGoMYdS+GbqLbdQVvsolkocGj5EMBhE1y3K5TK1Wo3O+zs5/L3DyLLsha8B5A7nUHwKVs3NUXdsB1mV0dubPeiS5BZJKxaL6LrutdCAiYu4lEolcmoO/zv9VMoVXtdeR31eZfHixWe0cJyJt3kiznURFAgEAoHgYnEq3RIOh92Iskl2QG3LJvtSlufWP8fo4VEG9wxy1Dzq6RbbtjFNE0VRCEfCaD43yTq7K0thoEC0Nzrp2JLbBolqNWrHdcuNLeu5ZnTrGd3fmegWv9+Pv9uPuc/0dEv2YJa+1X3umA9myR/Je7oFjkcMHudsdEsqlfKqvqfTaZYsWSJ0y1kiDG+B4Cqk8QBMJpMMDAxgGAbZvVmOPXkMqS4RvyfOwjct9DyJ+57Yx4YvbxjXx1nXdFpaWsjn8xiGgYNDuDXseSErLZVmw3tHEtuyT+Ru23VIvwxDT8Hw02SeGMaXu76pHeWd73iGrpmTu4rrcpQRljDMUtK+6zEDc0iPjtLT3uOFqzcWV8ty+2U28rxiy2LM/+P57P7CbiRZ8o7PHsziC7qjaPSudGQHNTr+kWkYbkuyk8PaTm6D0mh/0ghx8/ncQmxHjhwhn89P2f7kQnCui6BAIBAIBBeLiXRLsVgkk8ng8/lobW1lxoyJw8K3/fs2Rp8cRe1wtUBLrFm3KLJCpCvCm7/6Zl74f1/AKBjeZ/f+ai83fvjGEycbo1vqh9aQWduFz3E83dI5Y/iU93IuugXA3+0nvyfv6ZbswSzgGs5bvr7F0y22Y6NFNAJ9gabrn61uUVVV6JZzRBjeAsFF4HQLUVzMc47tZdna2kpuYw6r7la9zD2dI/KWCD6fj0q2wqb/s2mc0Q1QM2oUS27BEAcHCYlYf4yFCxeSSCSoqJWm481KnexrG2kLvABDT8DIs2CWvPczwzc3HT9z8UG6Z51kdKth6LgDOu+GzrtRW64llC/QW6sxR9dxHIc1a9Y0VcDUdf14BdEimqY1VUHVO3X81/ip73SLngHkDuUItLoLVaPnthVqzhVv/A4KhYJXmKRpbsYUcRnb/qRR+dRx3DCxaDRKuVyetP2JQCAQCAQXm+muW9ra2ujo6KBYLJLNZgGIx+Pemts4byVTYc8vTvS5nki3qKrK9e+5nu6+bubcO4ddP93lHX/gqf1c9xYNJf3kON2SPNANTqd3rKzYtHc3t9sELohuMU0T3a+j+lQy+zM4jsPh5w8ztGXI0y21ao3OmzvHFSQTuuXSIQxvgeAC0+jhOFmRiktxzpPDicqHy9Szde/hWS1VWfODNfTf3o9yWKFcKKNrJzyjkesjyAWZ7P6sVzHTJ/mwHZtqoMqGDRs8T2ikS6Ww/xDUklBLMvKdz9F20+4Jx5VNNveDbu3IgKxB/BbousddtNpvBvnEnrgETR5Qx3HGVcCUJIl4PE6tVqNQKHh5S42+2eEZYepH6kjHE9Wzh7KEOkPHz++GZdU76hMW9ggEAm7/zpMWnrFFXBo9OnVd945rVHv3+Xz4fL5J258IBAKBQHAxuRx0S/H1ItnDWdRZKmgwNDTEM888Q1dXF4nEifZWu362C8twHeeGYZDL5Zp0Cyr45vsYjg2TTCaZ99A8dv34VaimoJbEGEpy6AufYM6yA+PGNHKkuUZLe3faLeh6gXVLWSu7YfHhMBISpaES33/4+16tnYZucfwOLIZqtSp0yzRBGN4CwQVkrHe28dCs1+sMDg6Sy+XOKEyn4SkeGhpix44dbqj0WZ7z5HCi/PY8lmlRrVaxHRtFUbDT7n+PvXwMI2PQ0tqCrul039RN/oY8vrSPgX8eaGqh4fP76JndBsm1VF/6Ho6yk4ShUcjM9a49cqSTRRMY3o4tkU224PYjawG9ndY3vRvuvB/UwLjjJ2OyCpiKohAIBJAkiWAwyOjoqFeco6O7g82bNnvnKBwtkO/OU6/XvbYlC25cgNwtjyvsMW/ePDZs2DBuHGNbYASDQWzbnc/G77JWq3ltUBoe6InanwgEAoFAcLG4HHRL5uUMx352zNUtRpXgO4IE4gHKL5cZHBnkSPUI22PbWfHuFez75Yn6MKVSCW2uxqw3zkKP68iaDDLIjoEz8jzVl/6DuLKTLr2FocFu73N7Xpk/oeGdPNowvF3dklgxB+7+pGt0X0Dd0ndNH8osBZqDCnEcB7N+ot3abX90G8VEUeiWaYQwvAWCC8RERSrA9SgmEgnP+3s6YTqNY5PJJENDQ9RqNVpbWwmFQl57qzM5ZyOcSJVVSgdLpNamMAzDM7oBzKSJjAyDrpezWCyitWnMWDWDncZOjDaD+Oo4+U15VLuMz87S3XOE24f/FAUDCu61OmbMYv/WE4Z38lgCx4Gm4bVcS4F7sGJ9oLV7nuGWmx/AUXRyJ/WSPNV8TVYBc9asWSxcuNANwRpzPqNosJkThnelXGHr41uplqo4toMkS0TqEW5deCvXXHNN02cbOVcnM3YhzWaz7oJomkiSRK1Ww+fzeb+nWq02afsTgUAgEAguBpeDbrFzNoO/GATH3cG2LRtpn4STdahuqCIHZBRFoTRa4oUvvkBbW5t3DqNuMP/N8wl2BwiZ+2mrbKS1tpFY7bUm3TL/+l6GDp4wvEeH2hkdaqOta9R7zQxdz2jpZmiPe7ql4013Qle363C4wLrliO8I6/7XOi8NsGa4RecMw8CxHULzQxTiBRYtXCR0yzRCGN4CwSloeGzB9bi2t7efVj7LybvKY5EkiWg0elphOmO9z40HXDAYpFQqYRgGvb29hEKhMzqnruvIjszef96LMewuXJZlocgKEm4ej52yqQ/WsapWU7utrhu72P/COtqMTdx86x52HPGRPhJF1UxuvvlZd/EaQ0ffSNPPRlUjV1pMy7Wroes+6Lob/B1knjsE/he84wLtAQpGgZfXvnxWoWlnUgFTj+gE2gJURivUjBrZzIkQelmVsR2bglPwQug7OzsnuOLEY1i5ciU7d+6kWCxSKBTw+/2Ew2Hi8TihUOiU7U8EAoFAIDgTrlTdoigKR75/BNu0se0TuqV+tE7tSA0JN0QbiSbdoigKMgZdi1Pc7P8VbcOb0OzMpNfqmTNAMFKmXAh6r+3ZcTMrb+n1dEv6dQc7/PSJ+ZEl4ovi5xRSfya6pf+2fuqVOhu+tKFJt/h8PhJ3JWi9s5WhoSGvCJrQLdMDYXgLBFMw9gEqyzLPP//8aT9AxxapmAhN004ZpnOy97lUKuE4DpqmoWka5XKZVCrlFb44nXOCG06kHFYoHnaLdjiOg4MDEjg4WLaFWlcpbikCIMugmDlawkNEXv5fPFjZ7p2r5x0yhUwEf7CKHhx/3VC0TCBaJVvoQgl1oQa7Sfb+BS2rFjQdlznQvAhqHdo5h7udSQXM2MwY5dEyxWIRy7KaQuhlSaZzfie5cu6Mi4k0FtIZM2awadMmDMOgra0NXde9HPOJ2p8IBAKBQHCmXMm6xZ/1U9hXGKdb6sm66yxXVK8qeEO3SIUkUn2UDrvAgzc/SqyaP8UMgiw7zLr2EFteWO3plkOZKDdc85toIbfV2Mj25pZhrXNbyRazF1W3zL1vLv4WP8987Rn0uE7LjBZarm0h0OuGuesB/YyiFBoI3XLhkM/mQ1/5yleYNWsWfr+flStXTpgn0ODOO+/0igSM/femN73JO+YDH/jAuPcffPDBsxmaQHDeaHhsBwcHCQTch1ggEGBwcJD169eTTCan/PzYIhUTYRjGKcN0TvY+j20vIUkSuq57vR3HnlPTNLLZLMPDw1640FgkSUI+4oZjGYbhVgBFwrEdt3iGJBPQbGpbdhE1dtJee5l2Zw8zE48j5bc3nUtWbGLxXJPRbTsSSXsOB8PvZUv7P1Jc8jBp5pKz2nDUEIdePjxubJn9JwxvB4eiVvQWbl3XkWXZC01rVNM8+b7OhdjMGGbddOfQp3pGN4AaUtFatSbP/JkgSRJz5szhzjvvpL+/n2q1SjqdplKp0N3dfdFbcggEVxtCtwiuBq503RKtRE+pW/zWYJNuUSuHkMwCfQsOE2uf3Og+WbeM3PvXpOUTuqVaqrHlJ1u8sY1sa47mSyxNNDkcLoZuAQjNDxF6IMSc982h+6Fuz+j25kzolmnFGe94P/LII3z84x/nq1/9KitXruSLX/wiDzzwALt376ajo2Pc8T/5yU+8fnEA6XSa6667jne+851Nxz344IN84xvf8H4WOQOCS8nJHlvAC/s53ZyksUUqxuZKNc5/OmE61WqVctmtXtnwGDfaS9Req1F9rUq9XGdf2z7iN8dhmXvdrVu3kk6nJw1zKqfKlA6UaGltcXOCagYSNrpTIKSWCUoFFHO897lr5uCkYy3Lnewvz+OQsYCkfA2J3gWEQm5l8OCcUdRNOaqVKikrRXJNktElo16+0MKFCxnZNUKtVkOWZRwc7KBNIpYYN79nEpp2JrTMasG2bRzbQVabfZKBGYEz8sxPxpmEkQkEgvOD0C2Cq4HLQbcoioKiKJSHy6RH0vgcH9lkFl3WeWbXMzAfLMeaNDzbGDJOS7eoPhPTbjZxlq5s3jSAqXWLT4fWa1oZfWWUlJXCtExS/5Ri78BepGMSxi4DWZa9QmaB/gCHUofOOUz/TDkfUQpTIXTL+eWMDe9/+Id/4CMf+Qgf/OAHAfjqV7/Ko48+yte//nU++clPjjt+bFEDgO9///sEg8FxC5iu63R1dZ3pcASCC8LJHtuxHsrTfYBOVqWy0c7hVGE6yWSS1157jVQqRTqdxufzEQwGCYVClI6WyD6TdT/rgJE2OPrLo8T1OMoihXw+P2WY06HnD4HjoEsGmpbFsYa58Q3PMDoY4/CumRNPigTtYwuLSEFG5GvYU5jFodoC6v5+8vkCra2tJBIJb/ECCM0Kef0fbcdGRydCBDkoc/DgQXZs2kFhT8HdOZIlFFkhFoldsIVkIrqu60KSJRwc6vW6mxMmyyBBsM/N8zodb/+pOJMwMoFAcO4I3SK4GpjuuqVWq1Eul7EOWJSeLGH5LGzbBqAgFUg5KeKr4vS9vW9C3eI4DqN7Rpt0i2RnkLDHjePudz/F5qeXkxqIAzD32r20dmTPSLcAhG8IM7RhyNUtuo4syaR+msKoGW5KnqJ6umVffh+lSmlSp8SF0C2N89q2TTabJRgMNrX/AqFbphtnZHgbhsGmTZv41Kc+5b0myzL33nsvL7744mmd42tf+xrvec97xn2516xZQ0dHB62trdx999381V/9Fe3t7WcyPIHgvHEqD6LP5yOdTjMwMAAwqfdvsiqV3d3dU+ZbNcLFSqUSkUiEcrmMqqruTnethj/rR1XccDBZlnEcB13XsfZb1GbXiEajbhjW8bCuhrd7z45NxOdUOPj9F2BoFKwKEjB32X4WL9/O4d39kxrekdYCKbmPnLaKYfk67JblIKvYCRt5YID2cJhwOOLmYfn9zfPV5qOuun0hdc0Nv6ocrqAv1qlUKuQP5bEtm0AwgOM41O06eSdPLpejtbV13FjOx0JyMiWnRD1Yp16vewVbFEVB0zQCvQFRTEQguAwRukVwtTCddUslX0Ef1CltLlE+UnZ1i3pct1gWRt1d03Ov5pjxlhkT6pbCq49T32d4umWy/daWjgxtnaPc+Y5nOPx6HwU1gb50Hs/53n5GusVxHPKBPMRAN3Uvd9yx3NB20zKRkAgEA2gdGtlKlkwmQygUumi6JZlMsnPnTjKZDOVyGb/fTygUEkXQpjFnZHinUiksyxpXGa+zs5Ndu3ad8vMbNmxg27ZtfO1rX2t6/cEHH+Rtb3sbs2fPZt++ffzZn/0ZDz30EC+++KLX2mgstVqtyWOUz7s5G/V6fdK8lFPR+NzZfv5K43Tno/FH3Qg/iUajAONem84hKRPdg6IoXkXMRr/CxrGlUomRkRGKxSJbtmxh9+7dtLe3M3/+fOLx+Ljzt7S0sHLlygnnZKL5dRyHnTt3euFioVCIwcFB6vU6uq5Tq9WoHK0gKzIBNeD1VDQMg+TWJKEbQuRyOTf0KRCgv6XGDPkVruEF2o5tI/9aiOyeNzVdc9aSgwAkepvzvyx8GHILhhRjOBrgQO5+AkaARCJBBBnpuHHf3t5OpVKhpaWFXC6HpmlNv/NqtYrdbqOWjxc+caC4r0ghUcA0TfxVP0Wn6IZ5yzIts1uo++oMDQ0RjUa9BW/s76urq4tgMHhe/mZTqRQbN27E7rbxH/FTq9WwHdttXWLb1CI1KskKgUCAefPmNbXiEM+OZs51PsQ8Cs4nQrdcPQjdMj11i6ZpZH+eJXck5/WlHqtb0um0u97WTajCvpf2Mf86mnSLdMwivX0WWKu9ayqqhWUpXjutxmvX3LuLY/p9jARvZEtfnLKpEyicuW6pVCoUi0ViN8WwN9jedRr55T7Vh+246Wld93QR6Y5QKBQuum6pVCp0dXUxMjLiRSbUajUSiQT1el3oltPgYuoWyTmDLP+BgQF6e3t54YUXWL36xJf/E5/4BM8++yzr16+f8vO///u/z4svvshrr7025XH79+9n7ty5PPnkk9xzzz3j3v+Lv/gLPvvZz457/Xvf+x7BYHDc6wLBlcaxrx+jNjBxuFLvBzro7d5Pp7mRLmsjYWeg6f3X1l7L9peWej8HwhXe8nv/iSQ72Cj87NvvIZXsoSbFMKUTf09t97bRsqrlrMec35gn9euU97MaU+n/WD8Awz8ZprSj5L0XXR4l/tB4MXChqRyqMPid5jx2SZWY/cnZF30sVyvlcpnf+q3fIpfLeYJYIDhbhG4RCC4t9UydI185MuF7eq+OmTexCnU0p4ju5Fiy/BVuufOZccdueno5r28+0Q2ld+4xkByO7u2jLoUx9Shd7+nC7J8L59Fp4tgOyV8kKW0v4VhuFXX/DD/+fj+yLhOYFUDr1qa1o0ZwYTkT3XJGO97xeBxFURgeHm56fXh4+JR5TqVSie9///t87nOfO+V15syZQzweZ+/evRMuYJ/61Kf4+Mc/7v2cz+fp6+vj/vvvP2uhVq/XeeKJJ7jvvvsmDdO5mjjVfIz1tEWjUXw+H4VCgcOHDwPQ19dHNBqlXq+Tz+cJBALcdNNNE3pWU6kUe/bsaSoGNpUnFib29p7pQ2+iexg73rlz57Jv3z4qlQrhcJh6vc7o6CjZbJZQKERPT48Xeug4DqlUiq6uLlatWnVOD+CRkRFeeOEF2tra3HAs22HgZwNkX82idWnEH45THizT1tKGrrkhS9nREaiOoDtZFuz8Lte3bp5k3uDgzuZQ8r5lBZy5H8TqegCn8246Rndx8FsbUGQF9bjH1rZtCmoBM2V6nmKfz+dVTa1Wq1SrVW6//Xbq9fq432cwGCTX4e7CN7zA9WydA9sOEO4MkzuYw6yZBANBd8d7TgvhcJjBwUHa2tq8ELrT+W6cKblcjueff55AIEBkSYSkemLX37ZtlFaFarXK3XffPWGOk3h2NHOu89HYCRQIzgdCt1w9CN0yfXTL2Hse2TYCEpimSXt7u6dblJkVZqzeSPWFAYbXx5CxAMgcCMKd46+RHjyRxuEoAVqvu5a5v3krkfV95LMmw7FhlN4oweOh3NVqlUOHDuHz+c5at4yOjtLa2sq8987Dqltk01mODR3DF/FhYVEza3TM7PDC1G3bvui6ZWzouuM4blRkpUK9Xhe65TS5mLrljAxvTdNYvnw5Tz31FG9961sB90v21FNP8dGPfnTKz/7whz+kVqvx3ve+95TXOXr0KOl0mu7u7gnf13V9whwJn893zl+g83GOK4mJ5sNxHPbu3UulUvGqXjqOQy6X86pY5vN5Wlpa8Pv96LrbR3Dv3r10dXU1PdyTySSbNm0a1/NwaGiIfD4/YbuCsT0qJ6vafSomugcARVG88abTaVauXMnu3bu9fpiFQoFoNEpXV1dTvl+jcEk6naZcLp+yCEVjviaqEBkKhVBVFdM00XWd/M48mU1uq63a0Rqjj40i2zKqU0YuHcWuDNFaz7oOXgmSB9vhlomvmx5sp5iLYvtaQE8gB7uY9SdvR14Q93oLti9rP1HZu9HHWpYJ9YUoV8ve7kyjNYjjOBQKBbq7u2lvb0eSJLq6upruLxqNsjawlvQjaRzDLbJmWibmd02K8SJOykH1qV4xs0BPgHq9TigUYvXq1UiSdMGqaVqWhWmaaJrmGv3Xt5B9NevetyLT/YZubNnGtu0pnw3i2dHM2c6HmEPB+UTolqsPoVsuvW4Ze436sTqyJLvO/OO65ab7X2TuzMeQJYfU3HaeWH+/95l8OkopFyIUOxEFZ9ka6ew8rHAHcrALyRch8Rt3E1zaxQ1LXUfayHMjTeHifr+fQCBAsVg8a92ybt06BgcHCYVCXr57uVqmalQBvNzwxjUvhW4Ze15JkjxjPJ1OC91yhlwM3XLGVc0//vGP8/73v5+bbrqJFStW8MUvfpFSqeRVC33f+95Hb28vn//855s+97WvfY23vvWt4wqPFItFPvvZz/L2t7+drq4u9u3bxyc+8QnmzZvHAw88cKbDE1wETq6cCXjVKhsP3EaPxsYDaaJqmie3vmg8DB3HIRgMks1m2blzZ1Pri0bxjpMXvJOrX57NPTQYO95rrrmG2267jXQ6zbp162hvb6erq2vCHL7TrVh5qgX45HYeI0+7vSIlx8Zn52D7XjqkHMqo2+5GPun86cF2jKoPzX8i56RoRRiSb2TrwTcwLEewTQnJloi0yFgtzRVBO5d1IskStmMjS8fPLkFHbwfHjh2jWCx6Xu1qtTphpdOJKmAuXryYbX3byGzNICF5Rr014nq5zbrpfof8OnqHTjrritiWlpYLGsI1tm+prut0PtBJYX8BI2MQmhMisDRArV4TrYIEgssUoVsEQrdcXN3iaQG7irnnIKF6Cv9x3XLTvRuZP2uPd+72rlE0v4FR1bzXBg9207VsmCH5RvKRO9h3aA4D1TSSLKFZdcJhg7Z5J7oPnLyON+YkHo9Tq9XOWrcsWrSIoaEh9u7diyRJ3hzWajXv975v3z66u7sJBoNeIbOLrVsaO92N343jOOe9kJvg/HDGhve73/1ukskkn/70pxkaGuL666/n17/+tVe45PDhw01hJgC7d+9m7dq1PP744+POpygKr732Gt/61rfIZrP09PRw//3385d/+ZfiCzNNmahyZqMI1diH0thCDhM93E9eREqlEqlUinK57PZTdhyKxSIzZsxgzpw5Ey54wBn1qJzqHsYydrySJHnVIIPBIKZpTriATVaxcqyXuFQqsWPHjlMuwI12HvmRfdhHDhFxMmh27kTbjDGVGdq6RskmW7Ct4xU3HYnhI53os2UO1pdy0FjGiDWDtrY46YNpZNVGlWRsx4ZZbvGgsQt/a3srLXNbyOzJoGkaSBBbGiMUCtHb2+uF5TUqlp6q0mmDeDxO25I2cttyOI6DZVrguItd4/dl1A2kmMTA8ACtra1Tti05X4wVDKFQiFQuBW8BpahgRkwOHj7InDlzREVQgeAyRegWgdAtF1e3zPFvp8tcj29oNwPp486o47qlo6857UOSHbpmDXJ410xqToCyHeGJPe/C6uukrS2OWlWxUpbbNsuxqVVryDGZXCVHIuzqjskM//OhWyKRCD6fD9M0KRaLnlHbeGaMjo5Sr9cJh8OXTreM+Q42Ot0I3TI9OWPDG+CjH/3opCFaa9asGffawoULm/oJjiUQCPDYY4+dzTAEl4iJPIuNh5BlubuXsiyjqie+XhM93McuIqVSiWPHjnnnVBQF0zQpFAps2rTJe/Cdjrd3qh6VU93DWCZbjNrb2xkaGmp6sMOJ3K2TWzaM9RI3cq1s26a/v98797gFWM+SSP2cB+Qfolov833r3VPeS1tXGp9WZ/BwN3W5hbrcyrr0nbSs7Mc0TWqZDNHjC4VaVzFlV1jIkkzHog6y2SwbN25k1apVnpd29R+s5rFPPObNQ8vNLVSrVUqlEr29vTiOwy233EIoFDrtEKpcLoe2WCO+LM7ojlEcHFcIOG7YlO24osXud50LK1asOO0QvHOh0bd0rFc7EAjga/VRqVQ8IZVKpS7KeAQCwflH6JarG6FbLq5ukUruOr73yNymsfiDVaJtJ/Jh6+hk9ZswFt3G6IEEpu3u1toZm3i0BVVVSSQSHBk4ApKrWzRNw0k443TLZP3Hz1W3VCoVZs+ezcDAgBfZ4DgO5XIZwzCwLItyuUw4HL7kugUQumWac1aGt+DqZiLPoq7rBINBzxsYiUS8B/RkD/fGImIYhveADwaDTWE/fr8fwzDYtWsX8+fPP21v79ncQ4Op+h7Onz+ffD4/7sE+UdjSyeFllmWRTCaxLMurtBsKhcBxiNZ3068+S+LoOqQjbvVPH1CraEyJEqTlutuRpCW89t08luWgSir2PocWXO9uMBjEMAyi4SgjxRHvo5ZpkSwnqdpVkskk+Xyenp4eFi1axOI7F2N9zmLrY1uxOi1qrTWsikV3dzfz5s1jw4YNdHR0nFFeS61Ww3Isen+nF2O7gWIrSEUJ44iBXbaxHRur1aL3zl50v+7utl8kxnq1wRUwsiwTi8Vob2+nXC6f9q6EQCAQCKYXQrdcPN0yluHDzW38OvpGkMKzoPc3yIZu44W9PooVE61Pw+YIYLt52AUJxVTQihrDm4fJbc8Brm4xDANf1MeuXbuadMtU/cfPRbeYponjuLVpGrnsgLcLXi6X6ezsRNM0oVsEp0QY3oIzZjLPYjQaJZvNAhCNRr2ck4ke7nBiETl06JCXZ2UOmpQ2lEAGeblMbEaMtrY2UqkUM2fOPCtv75ncw2SLEUB5f5kNv9iAUTMI3Rai4qs0PdjHhi1NFF5WLBYBCIfD1CoFtPQa5tX3kKiuQ7dTE44zn25eQB0kHF8LUqALyd8JvjCxe+8lHvSx7dH/pFgsYhgGxaEi+WN5uhd0k0gk2Lp1K1JN8nZwLNOiWq2iSAp+3a3G6fP5mkLHlj24jKUPLB1XTGVsKN6Z0BAstVoNgqAHdaROCX2u+/syTZN6vU6sJUapVDotIXK+aHi158yZ442l8V2SJAlVVU97V+JKZ6oCOwKBQDAduZC6pfF+47PhcHja6BZwDbTJDNILoVsaOA4kj3Y06ZbOd3wYHr4LJIkW4Oa4u2OeHE5St+tg49Z50XRKL5UY3T7qhag3dIvt2ET7o5g+c5xuSSQSxOPxC6JbxqYlNH4fkiR53yOhW6Yv00m3CMNbcFZM5llcuHAh4Ia6pNPpKXNpxobKjIyMYO2zKD9dxrEcLNvCl/PR/qft6LpOsVhE07Sz8vae6T1MNF7bskn9IkWb3y3OUXysyB1/dwdySJ7wj/jkPDC7bmMezBJe9zocrpCQMiy8ZwMzFh6ecoy5dAxHUqjLLZTtKFWiOLYPra4R1jV0JFpmtuAL+Yh1xNA0DbPu5q3F9seIKBFKR0pgQsWoHJ8sd7F3ZIdQa8hbTILBoFcZdayX9Hw9sMcKlkZ4X8NzPFawNBaMi5kr2fBqNyqbn8yZ7EpcyZyPyrwCgUBwKbgQuqVRyMqyLGq1Gj6fj3g8Pi10y8mfm8ggnUq3AOhKjbnKZsK7Bwnkk/jkGkpvEm1myiuQOhGOEuRQ4VYGS0uoSid0izJ/dlOP7bHjWvPMGvL78qg+FbNuktqSQlEUd00+rltsx8YX9qG0KDimI3SL0C2nZLrpFmF4C86ayR7kwGl7lhKJBDfeeCODLw6Sf8LN+5GQUBUVJadglSwKVgFFUfD7/eO8vT6fj2KxSC6XIxQKsXDhwjPyYp3OYgRQOFrAzJvgbg5jGzYDTw6w/PeWT3jexgPRL1co/edzpF/MoBh5tOPF0er42PDYCrpnDeLT6+NP4O+A3ocZev1GRpSKG0LuU72iaLVqDbNuMuvGWWhhN7Sp64YuDj5z0As7Gn1tlNHX3FzqklRCudbNp7ZtN5RLbVW9dhfhcNjzkp5JztmZ0BAs2WyWfD7v5UTZtu0Jlvb2dq/Fx8UsCnK2uXNXE+erMq9AIBBcKs6nbkmn09RqNS/ENxQKebvmhcKl1y0ncyqDtKFbAkqVRPkZEpXnaK1uZO1Tqzmyrxc48dmb7t3I/Ov3NJ/guG7JRe7i+Z0aQzuyGL6Sp1tM1WTboW2Eu8NNa0VjXP039LP78G4AVJ+KpmnUqjW3qNpx3aLICr5uH4ZhCN0idMspmY66RRjegnNisgf5mTz4etp68L3q9s5reI/NukmtVuPg1oPY7TbxeBzDMOjo6PC8vUePHiWTyVCv173ee7t370aSpDP6Qzod72jmQGbca3t/vZcl71hCoC3Q/EY1SWTkEe6wvkF1bYYXnr1lwj+0uuFj5GgHvXOPudcwEwyqqzA6HqT32rcRT3Sy9yvfwrLKXnVxOFFcxDAM6p11HMdxe1AeN7zH3R8SvpqP0qYShmG47U9wkEMy5XLZ89A3Fu0L6SVNJBKsWrUKn8/Hnj17yGazaJpGOBwmFot5OekXoyroWM42d+5q4Uwq8woEAsF05nzoljlz5rBkyRL2rtuLXtfR5+iUrBLDw8PYto1hGJdct5wRY3RLYmQrMm7BuYGD3Qzs6x13+LYXljH32r3k7Pg43fLkX/6Eg/95sGknVpZkYotjVCqVSfOO2xecaNsnIREOhzHrZpNucXCw22yhWxC65VRMV90iDG/BJWfL17YQUAPU1Br1et0tZIGDLMlYWYtAr2vYjm175TgOyWTSe/CFw2FM0xznxTpfeR3Z/dlxr9l1mx0/2uHueleG4OhP4fCPYGQNQccmCKzZ9oYpz/v6/iUc6LieId9K9Ph1aLpObjTH0Q0bWbx4MfkjbtjVuJAuya3IanVanod3xqoZBDuClEfK466jazoY4PgdqtUqju1g+23C4TDxeNwtlnKcC+0lTSQSPPDAAyxYsIDdu3eTz+e9xfl0W3ycb842d+5q4XT7xzZ2cAQCgeBKRpIkAocD5H+ap16vY4dsgm8LooU0LMvC73fD4y6lbpkIx3YwSgZaWEOqDk+oW7xjHXht7XXjzlG1A4zmO/n31/+Clpuub9ItvYFeDv3XoQl1S3humGA0OOnOdNv8tqafdU2npbWFYrHo6RZHcWi5voXO3k6hW4RumZLpqluE4S24ZDiOw4ENB3j98deRZZmWlhaSySS2bSPJbiEwvabT19eHoigkk0m2bNnCPffcw+7duzFNk554D/ntecrRMuF54SYvluM47N69+7zkdUxkeGNV2Pujx1jS/v8jUFtDU3Nt3IrkQ4e6m+8ZiRpRanIrNWIMHvGx6J2LCOUk7LSN1q9597Bjyw7q+fqkVTJlWUaOy56H1xfwcfdf3c3+J/ZTTpWpFWoMbhz0jtc1Ha1No16vk81k0To0+vr6mnKDLpaXVJIk5syZw+zZs6dNwYuzzZ27GjiT/rHC8BYIBFcqDaO4OFpkz4/3eLrFzJjUd9dRr1eJRCK0t7dPqlv6+vq8dU5RlAumW8aOObkjycE1Bzm8ZhdG8giJzj3c+dB3UX2TFx07uncGmeHWcbql5kjoAR2SbUSiUQDvHjY9sgnHdpDV8TnHoTkhfJpv0p3pcFcYLaJhFAzvtdaeVt71v9/F3nV7eeXZV7D7bPqX9AvdchyhWyZnuuoWYXgLLgmNRWb393aTS+eQZDevW5IkrzWHJEnohk46naZcLlOv18lkMl4/6dZYKwe/fpDKgFs0rPOeTjru6SAajXL06FF3MTTNc87rcBznRKi5VYbyMFQGwchgAz/76y5aOh7gmtXbmDH/qPe5I6/349gSDjI1KUpFaiH+nrmM/rSCX/dTLuRRigqD3xqkfMTdpY4sjDDzfTPde3jyqBtW5djI0gSFMzo1fD5fk4c30h3huve5HmrbtPnp+37atIhJSGg+jVhLDLXTrXg5mZcUIJvNXtDF5byHy50jZ5s7d6UjcskEAsHVztgiTaNrR8kdzTXpFn1AZ/ZvzsayLFKp1IS6pa2tbdLdt7PVLZZhsfexvZSGS8y+ezatc1qxLZvdP9vN4A8H+a//+A+M4SOebgFI5mBP9zwW37wLgEImzKvPX08pH6J37jEWLN/HS+vuIyN1Y0gtqHrA0y267hY8y+/IYxs2kk8isylD8tdJKhlXj52sW3wxH1qbRq1Wm3StkCSJ2XfPZvd/unneiq5wx5/fQbA9yLUPX0v36m7Wr18vdMtJCN0yMdNVtwjDW3DRGVvswBl18Gk+LNOiUqlgaRYBKYCiKlimReZwhmDRrVrZqBI6MjJCqVRCHpI9oxsg+XySxJ0JfD4fmUyGcDjc5FmeKK9jbBuQ/U/uZ2DjAB3LOljwpgVIsvte6eBezJHXabMPI48UJ7yn7EgrLzx6C7/xu78gGCmDEuTQwAPQNgv0DhQL5tzUwYoPruCpDU9RypY8r3DD6AYo7C6Qey1HZFmE0pYSPtWHWTebcrzdAUPglgDxeHxSD6+syvTf1s/eX+0d956u6SxbtYxsJDuhlxRg7dq1E3rdp9OCcyGYbovqdOBMcsnOtm2LQCAQTFcaumV01yjGJoPywTISEpVKBdM0CYaCWGmL/O48GS2DaZsT6pZGQbCTORvd0mDbI9vY8YMdABx85iAPfulBtn7jefb94mUChcMYzsS6ZeBAD4tv3kV+NMLTP7iHSikM/k5Gd97LruFeKnKdYLtNWJZRfSpz3zWXtV9a6xnUdt0mtz2HVbYYfHQQnOPFcVV1nG6JLIwAnHJn+rr3X0cwEaScLDPvoXlEe6Pee6fa3QWhWwQnmK66RRjegovK2GIH8XicoYEhqpUqlmW5LTn6Lcq7yoRCIQzDwDIsAoEAsix7ISPxeJxCocDQxiFkR/Ye7LZhUx2qYoQNtx90LOaFmoztbThR9cuBjQNs+NIGHBwOPHuAcnmAhYs2Exj5OZl1B5Dyt9EI+FZ9JrYtY1vNu9B1U+OVvQ+z9P33o3U8zMi/Pw4Bd3g+GZa+cSlt7W10XNPBoXWHkGRpwt3skadGMB0TK28RjUYpFtze3P2/3U9pa4nS0RLaEo22JW2nzN+ZddesCQ1vgJ75PSybt2yclzSVSk1ZBXL58okruQuuXEQumUAguFpp6JbiaJHCfxWoV+quPmnoFsuiXHJ1y8FvHMTWbNrf3Y4aUsfplpGREa/91FiKxeIZ65YGB5464OaZm1Ws/D6e+O3HKI8UkICJE9VcUscSJEe6ePI/f5O6PBNfdy9Ibp/qeoWmEN3YrBgLfmMBWx7dQu1gzdMtqTUpaqNu2HgjTTAUDnm6xRfwEeoNEbs1RjKZPOVaofgUFr1l0aRjnmx3V+gWwclMV90iDG/BRWVssYP8QJ5yrozt2CiyAhL4FviobK9QLru7wIqi4BQdnMiJfonRaJRoNMrI0AhBO4isnDBcSwdLlPvKSJJEJpOhUqlg2zayLBMMBonH4wQCgXE5Rrv/czdGtYSRP4zPTHLoX37CDb/3nwBkRq5tuof27jThlgL7XpuHjUJNbsNQ2jGIkNoik74jTufmfU0p32pApeemHgC6ruvi2IZjTa0yxu5m15I1jvzoCJqmEQwG3SJqEQtllkKoL0RMjZ12zld8UXzSgmvB9uA4L+npVIHcs2fPuHMJrnxELplAILgaaegWNa1Sr9SpVqtNuqXR6srTLY5C8YkisXfFxumWfD5PtVolEDjRDaWRN36mugVgcO8ukjs34zOTaE4eCYdyYeL7iLTlKWYiWI7q6ha5ne//aDVGxURRFFp0E11TJvzste+9lpaWFnpu7WHPnj2ebqkmq8dvAkzTRPfrBINBWme10v7f2skUMpimSV2t0x0/P2uF0C2C02U66hZheAvOGcdxyB7IMrJ9BMd2rU1ZkUksSdA6p7Xp2LGe3OFdw+7ipShISEgBieisKFWtilV3W1koikItVcNRHM9rLMsyHfEOhlPD1OQauqYjyzK2bTOyY4TYrBi2bXseLUVRsCyLYrFIrVYjkUicyOuoF+HYfzHw5Hp8RhLtuLVcyQeP3xsMHepquoeWHoPEXV3sqswldzgBxx/yEiCVILsnS3J9Ej9+pOMW9YzVM1COL2gz3zCTbd/fRthwW2VYmoUW1LCyFrZtY5ruIhhuDSMhoWs6N3/kZuIr42ecvyNJErPunOWFoXmvyxL+Fv+440+nCmQ6nW4qbCK4ehC5ZAKB4GqjoVvqQ+5O91jdAnh5pJZ1QrcYgwaFPQUCswOebuns7KRUKpFMJuno6GjafVMU5Yx1S23Pt1Fe3Uy4fs+U45+zbD8Lbxmi3HUNv/7aPEqpE7oFG28cxWKRcF+Yeqne9Pm2+W30ruhFkiRW/OYKDjxyAKPi5sc2tFdDt7R2tnLrn9xK9/JufEHfRVkrhG4RTMV00y3C8BacMwfXHOSlf3gJB7f/dsNT69N83Pr/3kr/rf3esY1iB8VikfLRMop8YvHyJXwoioKv3YedsjHrJqZpYqQNWue2elVCi8Ui5WNlNEVD0zRMyz1OkiX8aT+tra3kcjkcx3EXR8nNOVIUhVKpxNDgUW6fVyW2/Ydw9Gc4ZgXF+G/j7qtS8pNPx0gPtrs721Ireusc/A9/ko2VfXR+KEifprHnC3uopY57oSWobqhS2F8g3h73QrVm3jHTO68/5uf+/30/B545gOW3yLfmObr5KEP/MYQkS+i67rYicaBerxNsCTLrzlmo+tn9uU5keAfaAl4O+1hOtwrkZJXWBVc+IpdMIBBc7ji2w9GXjjK8dZhKuYKv1ce8B+fR3tk+TpA3dEv6QBrLsjzdIkdlWt/VSv65PPYOGyQ83SLLMs5Wh55bezzdYhom8XiceDxOqVTydt+6uroolUpnpFuwKuhAcXTOpPdoo9C50mL5n/05xdAK1j+7Fn1JmdLzo80HHm9PKrVL3PG/76B2uMbmr22mPFLGF/Kx4mMrvDnpmtHF0geXsutXuzAMw9Neuq7TsaSD2z97O4FQAF/Qd9HWCqFbBKdiOukWYXgLzgnHcXjt269RM2oUi25Oj2M7SLKEpmms/cJa3nnDO/EF3Qdio9jBgQMHMFNmU4i1Eleo1WoEO4JQcXOeFEWhVW0lHo97VUJt26a0sYTkuJ5MWZaxbAvHdrAMi8zRDN3d3SSTScrlMrquoygyrc4+bgisZYG+mWCqCCn3upViYII7g9RwF5s3301O7aNih0FSmDVjJq3XJDBf2I3P5y4srctbGXps6PiEQOlACcu0qBk1VJ+KHtHpuq551zzSE+Ha377Wm8PsDVmeO/gcyZ1JqpUq+ULem8fe63vJ5DNnHRIT64vRMqeFzP6M5xgJh8M4jjOpwDhVFUiBQCAQCC5Xdv98Ny/9y0tNumXTTzex7L8vY8k1S5rW21gsRnt7O/sO78NxK4gBEL49jByS8d3hI3xDGN8BHwMvDKAoCn6/H2fEYWjbEEbAoPhokfpgncjCCLf99W1EO6NUq1UMw6BWq7F58+Yz0i0Nculo088OCobSSlVqg744oftm4HTdT210FNM0iS6IMnqy4e2A3q0TeFOAXCXHvNXzmLFqBvmjecJdYS9ar8GyNy9jZP2IpydMy6ROHelWibUvrD0vLdC8oZ1GT3OhWwSXE+KbKDgnsgeyZAYyZDNZLMtC9anIqozt2NSqNUYOj/DS11/i9o/eDrhep/lz5nPgmQMYBwwURQEZcMCKWug+nUhfhMzBDJqmIcsyqS0pSnNL2LKNz+fDsizktIyDQzabJRQKUTNqbrET0yL7apbEzQm3bUfpAH3Wr5mvbqBVGZnwHvLpE9U13dZfMWpyO0/uuJPiUA0UsC0DWYLF71hMIBRoesi33NDC8OPDmHXT9QBbJo59YrFYetvSCXtaNpAkidbWVha+ayEH/seBpnl0HAdztsn69evPqAXaybTf1M6+jfs8gWHWTdauXTtuYTydKpBdXV1kMpmzGodAIBAIBJcSx3HY8r0t43SLOWyy45s7yL8zz6pVq7y1UZIk+tv62WhuxLEdHMkBGaS4RLlcRtM0eq7tQbpGIrM7g1NyvN1ga6eFbMmYgyYyMrV9NX7+sZ9z6/93KwO7Bhh6bQg7YVOMFWltbT1t3dIgn4416RZTbcORFHxRHy0PtyApJ/KdVVXF1+1D1mRswwbAMi3sFhtuh0qxwubNmxkaGnK1Qf/EeqPzmk5CHSHKI2V306VQpOXNLUR7oufUuvVkxrZvm6qnudAtgssJkfAgOCeOvHiEYrGIZVlomoYv6ENrdw1mTdOwLIutP9hKcdhtZzH0yhAvf/Zl7LW2975lubnNkf4Ivb29tC5sxTRN/AE/bW1t2AWbwgtutRDbtglKQXxpH36/n3q9Tno0Ta1ac8PUNR++tMFM45fcVvkkb7b+P6yn07z43eXs2rhwwnvIplupEiVt93OkvohBo5dMNUByR9ZdlI5X6lSjKm03tVGtVgmFQl5YmC/qQ5upUa1WMS0THLcaqE/1UavWSIVTJJPJKefRcRySUhK1T/UcDkgQXRylZ0EP5XKZXbt24TjOlOeZiGQyyXB0GKlHQlEUIjMidN7ZyeDgIOvXr28aW6MKZDAYJJlMukVkbJtqtepVJJ0/f/4Zj0EgEAgEgulA9mCW9LG0p1sa660sy5h7TI795Bg7tu5oWm+lUYm2tjZPt9g+G0u3CIfD9Pb2EgwGKZaL9NzSQ1tbG3C86NqgRW1PDVVRCQQC+P1+KskKj/3xY+z6110UNxUp/aqEdqji6Za3+z7BCv8vpjS6HUlhgOvYnby5SbeUKwbBRUF639+LHnV3f3O5nKdb8sU8iTe4RqtlWphRE98DPhyfQ2trK7FYbEJtMBZJllj9p6sJxoOUjTKRN0SYedtMdN2tt9MoanauumX9+vUMDg4SDAZpb28nGAwK3SK47BE73oJz4sDzB9wwHp8KErQub6VtRRt7/nEPju2gqirVSpV1X1pHOBbm8POHgRPVJkdHRzEtE82vMXPZTEzbpBgtEp4bRs/pyJKMoiio+1W67+0mNCNE8r+Sbhsxji9spkXAr+N3suhWkp7CHu4KP47jwDP/dRfDx4ujbVlzI60dWTr7h93Bt6+EWe9lYH8/w9YOz6OqSJJbodMysatuERVN02i5pYV1L67DNN0crkbRk3g8jj3Lxn7N9hZvTdOQFZmW2S3YcXvS/psNGsVB+t/az9FvHMWqWDiKQ+TWCLVajUgkMmkrkaloVPus1Cos+cMl2FUb2S8jSRJhJzxhb9BTVYGcLnkyAoFAIBCcKfvW7WvSLU1IUNtRY+s/bGVe7zw6+jsojZQ4tuFYk25RZij09vYSDoep1+uegTfzjpls2bIFRVEIBoNIWQlJkpADx1ufOhPrlsjLQ9x67c/RlPpEQz7Bcd2SCt3NL/5rLSOpkSbd4n+rH7PbJGtmifvjGIbB888/36Rb/PP89M7oJXksSbWliqS6RVwTiQR+vx9d16fsGw7QsayDO/7hDp555hlCoRCSJOE4TlMB3XPVLVNVKRe6RXC5IgxvwVmTPZglsz+DYzteKHVkcQQ9odO2so30i2mv//bgy4Pjcm90XaetvY1isYi2QCOTy6CqKj09Paz4nyvY8JkNVEoVd1dZ8ZFfkyf4UJDslizgLl6KU6ajbRhfqYKCu2DlUjEcW2L35gWe0d1g945rCd95HaElvwfR+TiOw6Ed33TbgijHz3m8RUhj7I7j4GvxEbk+gj/gR9M06vU6pml6HtVya9ktUKK4O9aK6uZEdT3QhRJTTrn4NBarWG+M3o/0MrR9CDNmMlQbQj4gEwi44e0ntxI5FSdX+1QCJ3K1puoNOlUVyHr9FMJAIBAIBIJpytCWoSbdooZVrJLl7czKskxtsMYTf/wEujaxbgktC2HbNqOjo00GXsQXYdMXNrm6wecbZ9g3dEtMHSVcz3u6xaho/Pzf3sKdb19DvKc5kbskdcPs327SLbvWrkUtq6iK2qRb/B1+iiU3CtHvd/VKIBAYp1sc3aEcK+NTfUQiEeLxOKFQCJhaG4yl0cvc5/NRKpWa6vDI8vnTLWMRukVwuSMMb8FZMfzaMGs+swZZlpFkCdux8QV9hGa6D+6OezrIbslSL9WRZGnCNg7BRJCZS2YSmxmj6w1dmI7Z9KAsvqvIlm9t8c5f3F9k71f2IjsmmpVCt0YIBHPc944n+NW3HvLOa9ZVBg508+rz1wNgoVKV2jC1TpxiP7fd/N/geO/vbDZL7nAOTddwbMdbSBwcJCR8qg/LtgjeFHS9wrqOJLn/nTFjBiMjI2iahiRJWG+wKGwqeAttZEGE0Gx3cW7035ysUEgj/yqfzzNSGMHsNo8XV3FbiuTzeSRJolQqnfbvyHEcUqkUxWIRXdcnLKbWqPY50cI4napACgQCgUBwrliGRXZP1tMVsiTT8+YeAI7++Ci2YXvpZbI0cTamrumsfvNqwgvCExb9ivREGM2MeucHmnSLrhewrfHnrtd8PPG9+5h77V6uuXcng77byLe/hd5r30aio8M7rmGY6iNuB5SGbpEiEpZkEY2eKNymadqUuqW9vZ1AIDClNjgt3TIy4tW9EbpFIJgcYXgLzhijZPDs557FNm1Un7vDW6vWaFvY5hXyUIMqibsSHP7ZYXS/7oZ0HUeLaFz/geuZc9+cKfvoLX7bYvY9vo98IU+tWiWkVvBbSTR7FAm3MMiSldtpSWTxh6pUSyf6Ur/wy1vJ11spWjECLf0EQ2GCPhWpLpHamaJjmbuI5YfzWBU3zwsVAkoA27axHdsLCTNVk/ab28eNT5IkYrEYuVwOTdMI3x2mtr+GkTGQQzKxu2OuZ9lxQ+5LpRJr166dsFBIPB6nvb2dbdu24TiOG6J2fG4URUGWZWRZ5tixY8yePfuU/QcboVgDAwOeZ/hkrzacqPY5USVQgUAgEAiuJIa3DiM7sqdbNF0jNDeEGlTRO3UOf/cwhWOFcbplLMF4kK7ruiZt8dl9TTeDewYn1C0Pvf+XqD6LI3v6qBsq219c5n3ORqZsR1i36W62pT/Cje+/mQU3LCDYHmw6f61WI78tT/65PIqqeLrFP9NPV08XhmGQy+UIBMZ3bDlZtzRal50cJi50i0BwYRCGt+CMSe5IYtUsACQkwuEwtmojL5OpVqtomoZhGNjzbAKdAYJW0OvVPfve2Vz/gevxx/xTXQIAVVe54d0dFP5qI5JzFKVuNL2v+Q0W3vA6AK0dGQYPdFNzQpRoo1yJUbfc8fls10HQGMPRl456hndma6bJ840EwflB8ofymKaJHbFxrnXIFrLE9fi4MTa8xpFIhEKhQO8f9DK0ewjDbzBQGEAuuZXJu7u72bFjB+VymVgsNmHlz97eXl599VUcx3F7hR73GtdqNTRNI5FInFa+VKMoSeNalUqFQqHgeYh7e3sJhUJetc/u7m5isdik5xMIBAKB4HKnnC7z8j+97OkWs25CG5iyiWzLEIXou6LIT8uox05oBgB/i59gIkjLrBYW/eaiSY1ugMQsk5g0NE63RFoLxOI5JAkW37wTAJ9WZ/2a21zd4ozRLaM1dnxrB/t+vI87Pn0HlXSFQ88fojhYRG1RSf8q7Rq3x3WL4ziYLSYDAwNUq1Wq1SqZTIZ4fGrdksvlCIVC48LEhW4RCC4MwvAWnDHZA9mmnzVN496/vpdDyUPkcjlkWXZztWf0sPofVjP4+CC2aTPvwXkkliTcvtXZ7OR9GS0Djv4M9v4r/emn6G67l2R5fDuK+dfvQdVMinaMbKyHAWsBpuR3W5PZFrIko+muE8Csm26+Fa7hfcPv3gDAsaePnfB8axr+eX6c2x38dTc3qlwuU6vVKJfLAJRKJcLhsDcGwzDw+XwsXLiQTZs2se/APiRdIuB3Pc2VSgXbthkaGiIcDjNjxoxJC4XMnz+f1la3onulUqFWqyHLMuFwmHg8TiAQIJ1OT5kvNVFRkkQigWEY1Ot1LyddlmUKhQLBYJBFixad0hMtEAgEAsHlim3aPPeXz1FOuWu5pmmEI2ECNwSadEvvzF4W/e0i1KJKLV8jGA8SSoSQfbIXbu3ozvgQ6DG6JX5kE2r5oXFjuO72V2l8pGjH2FW9mf1L76DW0oGxrUx9V2WcbpGKEk9+4smm8zg4TbrFsizMVhN1oYqqqsiyjM/nO23dsnfvXiRJ8nbIhW4RCC4cwvAWnBZjc3yGdg15OdA1o4a+TGf3kd1e8YpwOMyCBQuYM8cNJe9f1O+dZ8q+jP4c7P032P8NqLmtIiQJbv2NdTz+7/dTLpwIt5J9DvPfvIhM/yf45RaTQlcFw87gYCEhnShypijYYTdnq0FpuETucI5qpkr+aN7zfNeMGuocFcuw0HUdwzDw+/0Eg0Hv3tLpdFMFz4bnddasWbz++uuecW8YBrIsE4vFCIfDHD582A1nP4mxhUJmzpxJKBTyFr/G/DTys6rV6inDqyYqShIKhejt7fUqfWazWQKBAD09PeP6YQoEAoFAcCUwVrcMPDvA6L5RT7cUi0XkDpnI4ggwXrcwZlk8U93SEpcIRspNmuWme19mxqJhnN53kG17K7/cYlJXHdfIjZv47/YTmBWg+mwVRVHcqMExumUsY3fsa0YNdb6KvlpHDwrdIhBMd4ThfYmYrFjFdOTkRWf4hWEouAVGSqUSUT1KW7DNC0PK5XLs3LmTaDTa9HA8OZTI5/NhGmWUYz+BgWfAenXC6wfCFe74zed48vv3YjrtEOxj6YfuIvDQamrZLMHXnyGypJXaUzXPC93oy2lbNh1v6kDZoFAdrXphVAMbB0jucBdJXdNpaW2hqlWptlaRbbeaecNjCzAwMABAsVikUqkgyzL5fN7zvObzeSqVCnPmzAGaF59SqYSiKNRqNWq1Gn5/c5h9o1CIpmnE43EGBwe9th4Nxi6W0Wh00oiBRo5WYyFtEAqFCAaDVCoV0uk0N954I/PmzZu23zmBQCAQTC8uV91ilAyG/m0IxVQ83SJFJea8bw7+mP+86xZJdlj9xhd5+cmbsG2Z6x6q0P/mP4ZZvwV6O9Jx3TLOWF2kk5+ZJ/VyivoOtzBtvV73dMvYlLmGbqkoFWo313AkR+gWgeAyQBjel4CRkRFeeeUVUqkUjuMQCARIJBIX3It3NotmKpVi06ZN3qIjI3M0c5R6re72SVRUOhd2et7MyfosnhxKFLQG6C7+gq7yr9Hs7NQD19povf13eNObf5v9LweI9ETov93dRY/FYsTjcQZqA6h+Fbs+xkPsgNKt0LKwhcyhDKk9KRzbQZIl1v94PWpeRZXcPwFd01nyniXsYQ+RSKSpEihAd3c3lmVRr9dJp9OEw2GvfUgikWB4eBjTNN3+3SdVcFdVN/yr0UfzZBqFQvx+P4sWLSKXy5FMJolGo16+fGOx7OjoYN26dRN73hMJr8poo7roWBoOiXA43NQbUyAQCASCqbicdUv15Sp22aZWr3m6ZfFvLSbY6u5IXwjd0jHX5E33zoc5H4TW65rea+iWiYzV6NIotY4a3W/vZvgHw2R2Zzzdomka4XDYa3GmazrLP7ac15KvCd0iEFwmnJXh/ZWvfIW/+7u/Y2hoiOuuu44vf/nLrFixYsJjv/nNb/LBD36w6TVd16lWq97PjuPwmc98hn/7t38jm81y66238i//8i/Mnz//bIY3rdm9ezfPPfccxWIRRVFQVZVqtUqpVPKKVVyIRWzKUKkprrdnzx5v0SmXywzuGKRadit1m6aJrdjYkeZwqIn6LOZyOVLJEeYGdjFr9O9pr2049aA77oC5vwf9bwfFTxBYNrf5EEmSvId+qiOFfdj1DNu2jdwhE39LnFKpRD1RdwuRqO57qZ0pL6QqGHKLv828bSbHNh9D1/VxD/9QKOR5bpcvX048Hm8SAFMtHI3z1Wo1FEVpeu/kQiGSJLFy5Urvd1UoFLweoR0dHd7vY7JCJ/F4vGlBH7tIiaIkAoHgakXolrPnctctA88PYFQNT7dIcySkzlP3hz4fumUixuqWiYxVSZIoG2WCvxEkfEPYrcLer5EzcthHbcyNJjo61773WrpWdLH3mb1CtwgElwlnbHg/8sgjfPzjH+erX/0qK1eu5Itf/CIPPPAAu3fvpmNMn8GxRKNRdu/e7f18ssfqf/2v/8WXvvQlvvWtbzF79mz+/M//nAceeIAdO3aMC2+5nBkZGeG5556jUCgQiURQVRXLsqhWq55Hcay39XwxUajUyQ++yRaxdDpNLBajXC5z7NgxSkdKXsVLSZKwAhZHh4/Sr/c3tXto6rNoZFD2fIn7qv9CuDo85VhttRV57gdg3u9BbNFp3V8ikWDlypXoVZ3dX9+NkTGILo8y7x3zqBpV8vk8M5bPoPCrAmbN9HpeWraFUTeIVCO0///Zu+84uarz8P+fW6aXbTPb1BsqCBBIRoCFMSCKcSOODcExxnYMSQxJbJI44IZbYsexHRKbhJ/tEPeve4spBmREFcJqRkha9a4tM1umz9yZe+/vj9HM7mzT7mp3tSs9b7/0Mnvnzp0zR6t7nnPuOc+ZUUf97HpCR4a++UNxBHnBggUD/n76j2APtu+k3+8nkUigKMqAEeG+iULC4TChUKhilD8YDPLiiy9WJB+BgSP1a9asOeXosyQlEUKcSyRuGbvpHrccO3SMXE8OTdV645YlJkeOHGH27DMft/TvrDY2NpJOp4nH49Q31KM0FvfBLmUdzzgzeK7xMPv82dQtqxs29pC4RYipZ9Qd769+9avceeed5dHghx9+mEcffZRHHnmE++67b9D3KIpCY2PjoK/Zts2DDz7IJz7xCd7+9rcD8N3vfpeGhgZ+9atf8Wd/9mejLeKUZNs227ZtI5lMEggEymtZdF1H0zTS6TSGYRCJRE657cJoP7d/tkgYempVf6VR5ra2NvL5PHpSJ68UE3aggFZbTAISjUYr9nA0DINa9RjVe/4JTvyUgJketpxd+nL2K9ey6Jr7qK4b/HdlOOFwmLV/upbXrX0d2WwWt9uNbdusX7+eqqoqNJeGc6aT5I4klm2hqVpxGzHLIpPNEFfjRKPRYW/+LpeLRYsWDVpPpxrBrqmpYdGiRXR0dAwYER5s9F5RlIrfgZ6engHJR/qe23ekfqgGfajPEkKIs5nELWNzNsQtuc7iE9vy1mAKaHVTJ27p31ntG7coSrHTffz48fJTaV3XyeVyHDt2jEQiwerVqyVuEWIaGVXH2zAMNm/ezP33318+pqoqa9euZcOGDUO+L5lMMmfOHCzL4pJLLuFf/uVfOP/88wE4ePAgbW1trF27tnx+VVUVq1evZsOGDWdNA1bK2lhaN9OXoijlKT2l7RjG+3NHcuMbrNHUdZ1kMkk6ncblcpHsTJ58M8X9I2uo2HbL49Kpy7xAfc9PqGcXHB26bHklQJv3Bk543szhHi9NjU1U1TaM+bv2v+mX1i85HI7iHpMNJtZrVmUjfPI7Uk155HWoUeih9sQsGUnDcd55540pOc1QyUdKKkbqGbxBn8qJcIQQYiJI3DJ2Z0Pcomf0yk63TwNt+sQt0WiUfD5fHiCwbRvDMAgGg6TTaYlbhJhmRtXxjkajmKZJQ0PlTaahoYGWlpZB37N48WIeeeQRLrzwQmKxGF/+8pe54oor2LFjBzNnzqStra18jf7XLL3WXynLYkk8Hgcgn8+Xt08YrdL7xvr+U0mlUti2ja7rWJY1YM2MpmmYpllOJBGNRium64z1xpNKpcqjv6VpR305HA4SiQSpVAqfz1deT1Pa/7GmpoYjR45g2zZ23CZ/vLJ+HOHi9C+3mmF24ofM734Mt905bJm6mcMx79uJ+q8nm1dJ9CTweDwsXLiwIolHqSxjrYfSWjTDKK7tsppOPukuNcL2ycQdikrV7Cqi0SidnZ1UV1ezevXqis/2eDw8/fTTp/z9GOy9pXKX3uvz+crT2wZLWnKq7zLYthy5XK78FKJvGcfyWacy0f9Wphupj0qnWx9Sj2I8SdwydmdF3JKsfL8SVMrT1qdD3JLJZHC73eVrlP4edF0nEAhI3DJC0k5XkvqoNJlxi2IPdlcbwokTJ5gxYwYvvfQSl19+efn4Rz/6UZ599lk2btw4osItXbqU2267jc997nO89NJLvP71r+fEiRM0NTWVz7vllltQFIUf//jHA67x6U9/ms985jMDjv/whz/E6/UOOC5On23btH6/lezh3uQyKLD4HhdL3I8yu7AOnaFHvC00TmiXc9BxE13q0uIG3WfA0f86Sr5r4D+Q5vc3455x9qzLE2K6SqfTvPvd7yYWixEMBs90ccQ0J3HLuS36RJT4pnj5Z/+Ffua+xWRB/v+mTdwihJjaRhO3jOqJdygUQtM02tsrE020t7cPuRaqP4fDwcUXX8y+ffsAyu9rb2+vaMDa29tZsWLFoNe4//77uffee8s/x+NxZs2axfXXXz/mQC2fz/PUU09x3XXXDTkt5nTYts3LL7/M4cOHyWaz5PN5nLoTO2ljFSxSSgqP30MoFMK2bYLBIA6HA8Mw6OzsxOVycdFFFzF37txRjZ6WPretrW3AeqjSNKbGxkYWLlzI5s2byWQyBINBdF0nlUqVR+ijm6N07+ou7o1tWXjUDMtft5MbtN+gFIYeu7FdDVgLPog1/4M0eGbQwKlHhKPRKJs2bSqXpTQ6HY/H8Xg8rFq1asipU9FolL1799LZ2VneBqO0H2U6ncZsNDHaDCzLQlEV3C43mqbhb/ZjWAZXXnnloJkzJ/r3YyT61ktp6xDDMEgkEqesl/E0FepiKpH6qHS69VF6EijEeJC4ZewGi1tcLheqqmKaJolEArfbPWXjlmQySeRgBDNjluOW+c51XJN+DEWZPnFLaYuwXC6Hw+GgqakJn89HNpslm81K3DICU6EuphKpj0qTGbeMquPtdDpZuXIl69at4+abbwaKnbB169Zxzz33jOgapmmyfft2brrpJgDmzZtHY2Mj69atKzdY8XicjRs38td//deDXmOwbROg2Die7i/QeFxjKEuXLiUej9Pd3Y3ZYRL9VRQrYxWncrl0Qm8PYdfZ5WQifTNZdnR00NnZybJly1i6dOmoEk2UPjcajQ6ZLXL37t1kMpnyZ5cmQoRCIaLRKK6IC4/bhWpEqHJGqa2OsvaqYRqv0OVw3j0os96JpjnR+r88xI3Wtm327dtXURYoTllyuVxEIhH27dtHY2PjgIY8EolU7N1ZavgKhQKZTKa4NipklKdqOZ1ONF3DUe0gZaRoamqirq5u2ABhIn8/TqWpqYnLLrusvBYrmUye0eQjZ7IupiKpj0pjrQ+pQzGeJG45PX3jFlVVyeVymKZJoVDA5/MRDofJ5/NTMm5pamoiaSawlBNUOaO41Qzza3dMu7glHo/jdrvx+XyEQqHy9PpEIiFxyyhJO11J6qPSZMQto85qfu+993LHHXewatUqLr30Uh588EFSqVQ5W+h73/teZsyYwRe+8AUAPvvZz3LZZZexcOFCenp6+Ld/+zcOHz7MBz/4QaC4xvbDH/4wn//851m0aFF5W47m5uZyI3m26JvEYvuvtqOZGrpTx+l04vV66Xy8k/MuOG/YTJZHjx4lHo+Pat/MUyXPcDgcwyYyqQ64ybZuop4omqM41XzZpbvQnZVrbyxUjmtXUL36MwTmXjOmOhprUpXhsqDOnDmTjo6OYiKY2hxHXziKA0dxv2/TwgyY02a7Ckk+IoQQoyNxy9j1jR8ikQiZTAZFUQiFQsyfP5/t27efMgP3mYpbQvFf0x3LoTh6p5P7q5MV506HuMUwDPL5PLW1teX95KfTNlsStwjRa9Qd71tvvZVIJMKnPvUp2traWLFiBU888UQ5yciRI0dQVbV8fnd3N3feeSdtbW3U1NSwcuVKXnrpJZYtW1Y+56Mf/SipVIq77rqLnp4e1qxZwxNPPHFW7YVZEg6Hqa2u5XD+MN6QF1VV0R06Rs7AylsktybxXuMdUSbL0eybOdyNr28Wzb50O8Os5GPMTPyMX0evw7J7/16r6mLl/y4oXlq9b+Go92aO9+i8wXM+gTHWz2izYJacquGrqqoik8nwusteR+73OaJ/jFIoFFBUhebzmkcVEJxp/bOgCiGEGJrELadnqPiho6Nj1Bm4JytumZX6GWYsy+7C2yteD5zseE+nuGXVqlUcP358SjwxHiuJW4QoGnXHG+Cee+4ZcorW+vXrK37+93//d/793/992OspisJnP/tZPvvZz46lONNO/GgczdbQXL0TmVRVRVEVoi9HCV4WrFjXA8WpbqqqlqdBDLeVxlD63vhyiRyxIzFUXcXpcaLrenmU2mHGmJH6Oc3pX+AkRaLbj2WqFdcK1sXIag0c872DVu+bMdXieiNdzww6nW6kSqPkpbL0l8vlsCyLRCJR0QiPtOHz+Xy85WNv4bG/fYyCUcDpd/Lmj7wZb60ktxFCiLOVxC2nZ7COU9/22rbtCY9bhvrsweIWgI6e+or3aLqJHahin//90y5uWbNmjTwxFuIsMKaOtzg9nXsHbluhO4pTzrNdWXq292AFerfusG2bXC6H3+/H5XKV1/aMdd/Mbd/exq5f7MK2bQr5Aq46F943ezGMAyxzr6M5/Sia3Zu9PN7VL/GL28m+po/S6b0KW+ktYzwep6mpadAkHyNVVVVFKBSitbW1YuoVFLcYOXz4MKqqsmXLFhwOB6FQiCVLlpyy4TMMA13XcblcVDdU8yeP/AmdezppuKgBV2DsDa4QQghxLurbXnu93ootx8Y7bimxbZtYLEY2m8Xn85Hr3j9o3AKQjPnL/23gRWuYwSsNd03LuEWeGAtxdpCO9xnQtbdrwDEFBb/fTyFfoP35dvQ36cWp0H0yWZamaJX2PhzLCG10d5RdP99FzsiRTCYxDAM1kiPcdph3vPs7eMzsgPeUOt45giRoIBcIUOhqIKzlcTqVAQlPTmcUVlEUlixZQiwWIxKJlJOqxGIxjhw5AsDs2bOpqqoin8/T2tpKLBbj0ksvHbLhG6xx9dX78NX7xlxOIYQQ4lzWt73u6ekpDuZPQNxSEolEymu+Hfk2Fhd+znx7PVrKHPT8RLe/HLekTA9el5dQbvrGLUKI6U863mdA/yfetYtq6drbhcvporqmmmR3Erog4S9u1eH3+ysyWZ7OzXjfE/vIGTl6unuwzQxVSgSP3YHSbfHKE5fyhpufG7BV5eHO5XTryzG1AJZt4a7XyGazdHd3o+v6uK836p9UJR6P09XVhcPhYPbs2fj9xVFsl8tFOBwmEomwe/duFi9ePKDhG8/GVQghhBCQ7kyjObVye71r1y6SyWR5i7HxjFug2OneuHEjVuoYlzgeZ1bhcVTyMESTfjC/ilf2voOsy49lW2g5A8trSdwihDijpOM9yUzDJHa4NzGZjc38P5lP19e7yMfyOJ1OautqCRpBMrMyGIYxbpksjZTB4ecOk4p34S0cwU8UxbbKr5/YP4PXXrqAC16/HRuNY/qV7LDfwnGjGlNPA6AqKg2LGrCCFjU1NVx44YW43e4h1xuVpoWNdl1S36QqkUiELVu2UFVVNSBxTd+MoRdccMGwWVCnSxISIYQQYqp67cevseXbW7BMiwvfdyEX33YxV155JTNnzmTz5s3jGrfAye26XnuBBbFvcJ6yDs3MD35en7gltmsm2XQHKMW4xe12E1wepK6uTuIWIcQZIx3vCTLUjbv7YDe2WdxDMmfkSCVTvHr8VVLNKeIH4jidTvx+P6kdKV7/vtdzsPXguGWyPPzUH8md2Ep17hgq1qDnbN9wIemmxTT/6d+yZU9XcfQ1cqjiHFe9C2eVk1QqhdvtHnLdUd9pYYVCAV3Xy2ubRlL+0pqmXC6Hqqo4nc5Bz+ubMbShoWHE21aMtXEVQgghzjYjaROP7T/Gs19/llw2h23ZrP/39XQ6O1lx1Qrmz59PIBAY3z2bsx3EXvoYq1q/i0MZvMNt4uCwdi2OCz/Gph1RkhuS9GzoqDjHv8BP/QX1ErcIIc4o6XhPgOFu3N17uwHK072VagV/jZ/A1QFaXmkhl81RyBf3x+7Z1MOaW8chk6XRAzv/lf3f3I4j15sobeaio5x38V6e+enVWLZKRq0nq8/gpSc0rlrrA7pQDZVCpnK/blfYhcPpGDZRSmlaWDqdpqqqCofDUbG2aTTbd40mAQmMbNuK021chRBCiLPFSNrESCTCC794gWw6i+7QUXUVy7bY+/O95Ny5crs+Lns2n4xb7Jb/pNpKl6eUmwWNztY6LEtB0aDD93q65ryTjozOkkI1Hb/aSW5/bsDnNb6pEZfLRTKZlLhFCHHGSMd7nJ3qxu1v8WNjk0wmMU2T+oX1xRuvC2ovrqV7czeGYZBMJtn7+F6WvXPZ2DNZmjnY8xDs+GcKyTjdbe+qeHnBhfsJzYoSvjLP5nUXgObBpTmwcybtv2uHFZBqTVW8R9VVnDVOcsbQiVJs26alpYV0Ol2RMKTv2qbR7Oc5XMbQsawdG8/GVQghhJjORtImhkIhWlpaiO8vzswrdYRVRcU8ZNK9r5uWqt52fTziFoyuiiXcmaSHJ394Ham4n5RdS3chBJoHty9C9dpqtDka6d3p4vasWu87a1fV4pnhObl1mMQtQogzRz31KWKk+t+4XS4XqqqWb9zpdJojLUfI5/PF0U6HjrOmdxpS3RV1oICu6xiGQaI9wcavbSTdmR5lQSw4+H347WLY+vdgdNEdqQa7zylAT9N5/DL/RY5c9GYcC4OYpomRN3A6nXS+0kn2WJbu/d0V73OGig1uPB4nFAoN2mjEYjGi0eigo9x91zbFYrEB7x1MKWOo1+slEomQzWaxLItsNkskEhnV2rGR/B21tLRg2/YpryWEEEJMZyNtE3t6eohGoxBhYEIzBTIbMkQikRG36wMLMjBu6e+PL1xER2w2rdZSuu1Z2Kob0zTJZXIknkmQ+EMCgEKhUI5bQq8P0fz25nJnV+IWIcSZJB3vcTSSG3f8RJy8kce2bFRFxVHjKJ/jafbgm+tDVVVsy8ayLA79/hC/+cBvOPrS0ZEVovVJeGIlbLgdUofLh7vaawGwUcgoYTqqlvBM5l30FKqLn32lB0u1UBUVv8+PWTDperqLfGtxkMAyLbDB0eg4ZaORy+UoFAo4HI4Br0FxbVOhUBjVfp6ljKFNTU1kMhk6OzvJZDI0NTWNaqR3vBtXIYQQYroaaZvY2dmJkTEw2oxBr5M7kiO5d+hp3MMaIm4psVDZm72RF7a/iUihiYLtwD75P9MyURUVn9tHy29ayuU2DAPfEh8Nb2ogl89J3CKEmBJkqvk4GsmNOx8rrvdRVAXLtnBWVybeqLuijsT+BIqqoKrFcRHbstnx0x3MumLWoNe1bZvEsY24dtyPq2v9oOd0t9eSUWpI67OxNS+BhV50v046nSaXy2E7bXyX+3DvdJNMFff3VroUTNPEtm3y+TyKoqA2qKdMlDLatU0jNR5rx0bydzTc2nUhhBDibDHSNtG2bawOC9M0y7FJX5ZlEX8ujvOuwZOJ9TeSuOXI7tnsPHAFEe1ScskAuiOJZRuY5sl9uzXQNA2fz9cbt6BgWRa2baMuVuns7BxRgjeJW4QQk0E63uPoVDfuTDyDbdg4A06cTie5bA69uvKvILAkAAFwZpzojt7X4kfi2LY94GYdPbGP3Kb7aUr+YshM5Ub15XS57iTtTpHL5nCqUDWvirrZdeUbek9PD7VvqeXAgQPkOovrty3LKq4jKuRRFZVAIMA1t1zDjCUzhm00xnttU1+ntXaMiWtchRBCiOlmJG2ipml0PdtF5087KeQL5TXeiqYUd2mxi9O7tR6Nnm091FxdM+xnjiRuOXDiTbz88lvoSlrFuMWZQdM1PJoHy7IIrgyinK8Q+36MbDaLaZoVcYvltPDP87P8guU0NjaesrMrcYsQYjLIVPNxVLpxx2KxAWttbNum+1g3TqcTh8OB3+9H0zRiRqxi7U+0K8qsP59F8/nNKH0WUpmGSaYz03tByySx9SsEnl3JjOTPBm284sosnlc/yhPZfyJyXC1/pmEYaCGtXMZ0Ok0wGMQoGDgudqDrenlEW1VVXE4Xtm1j6ibNi5tPOVI7nmubxtup/o6GWwMmhBBCnE1G0iY625zs//X+ihjCMi2qV1aj1+nlzrnf72f7D7ZjFQbvTI8mbnnyxbdi4B3wmQAo4FrhonZWLd5lXoy8MSBuqV9Zj4VFJBIZ0RNmiVuEEJNBOt7j6FQ3bodR7HArKLicLhrnNdI8q3nA2p8r33Ilf/L//QmaU6u4fqK1mDiEjuexf/c6Arv+AZcdH1COlF3N71N/zq+Mz2CEr6fj5Qix7uIN2x/w43K7sKqs8mcGg0Fs2+bw4cNkm7LkXDkymT6d/JMJ3wgXk6qNxHitbRpvU7lxFUIIISbTSNpE7ZhWjluqa6pxuV2YlokZNgmuCeJynzzudJFqT7H38b0DP2gUcUtKv5rkkRTxWLw3bnEVP9MwDPwr/dTOrsW2bZLzkhTUAul0uhy3qA6VuivqRr32WeIWIcREk6nm46x04y7ttZhIJMrri9x5N/uc+8rn1s6qZc2aoffp9jf7iR3qbTCSR47RkPh7OPSDAUlFAQq2zg7rRrabbyar6BjJDLv/azeJfQkKZoGckUPTNAIzAqxYtQK/308qlWLnzp3EYjFUVcXn8+G40kHsNzEcDgemaaLpGqqq4mhyjDqxyLjs5znOhvs7kv0whRBCnEuGaxPPW3gez333ufK5LqcLZ62TmiU1XPwXF+NyufhD9A90tnSWz9nx4x3MXzsfh8cB2Q7Ycu+I45Z8Okvra62YBZNELkHOyOHwOZixdgY1tTXUn1eP7zwfu1p2EYvF0Kt0wu8Jk92Txeg2qKqrIrA8gLvejWVZo177LHGLEGIiScd7hGzbHnAjBga9OQ914972v9sqrumr9w279ifQFDjZ8bYhdYTEY1+D128Y9Nz9xkVssW8jpTQAoGk26V1pjD3FDKQKSnkqViFYYNeuXVx66aUcP368vEVFKpXCsiy8i7wULihg7i2OLpfWVHnneEe9huh01zZNlKnauAohhBDjYTzilsiOCIVMoeK613zuGhouaii3lxe//2Ke/qeny6/nYjlafrmLCy7dCFs/CvmeQcs3WNySSqWIvxLHtMxy3FJzYQ32cpusN0vjqkZ2795dEbcoHoXA6gC2bVNbW0tPTw+2bY957bPELUKIiSId7xGIRCLlUcZCoYCu63g8HgAymUz5WCgUKo88DnbjTkVSFT/76n3Dfq6/0Q/5BPS8CkYXyU5twDkJbQ67vH/J5mPV5DfnMXZF0cM6nms9GLsMsEHV1PL2Zaqq0riikXQ6zbZt20gmk1RVVeF0OvF6vSSTyeJarTf6SUfTZNuKU5rwQ/MFzWfVGqKp2rgKIYQQp2O84pbWLa0VP1fPr6ZxRWPFsfCyMM2va+bEH04UD+QTtDz8MOazG0CZgy8YZvZ5R3B5i0+e+8YtDocD/WQkWigUyB7MYnaaFXFL3co6fGEfkUhk2Lil1AHNZDJks1kSicSYE6JNVRK3CDG9Scf7FCKRCBs3biSdTlNVVYXD4SAej7N7924AZs+eTV1dHfl8ntbWVmKx2JBrgVIdo+h4mwaB7C+hYz9QTKaR6PGXX7YdVRzwvY8tidXU+RrQOvbRvbEbTdMwUya53+awWi10RzHLp64Vn3bXX11P9YpqckaOaDSKbdtUV1ejKAqhUIhcLkc6ncbtdtP03iYyv8qgOlRmXD+DZcuXyciqEEIIMYWNZ9zSv+PdvLJ50M+86L0XceIPxyC+BxL7KGCz6w9Ly6/v++NCrn//Bg4F7yjHLV7v0XKnGSDVliL/bB5d641bnNVOfHN9FftVDxe3AOTzeSKRCLW1tbL2WQgxpUhytWHYtk1LS0t5SpPL5UJRlOK6Il1H0zTi8TiKouByuQiHw6TTaVpaWgZkngRId6QrfvaGvYN/cPc2+N3r8Me+QanTDdDTUcPLT6zm5Y0foqXwOL6L7sPjCxCNRjF3mqiKilkwi39OmChKcT9LVVHxVHk4/7Pn03BdA4qq4HQ6y9uT5fN5AHw+HzNmzMDv95PP59EDOq43uFjygSVc+ZYrZQ2REEIIMYWNZ9ySiqTo3t9dcazpkqZBP7e66hBzG38Bib30jVtKepLn0zbjOYKv+3g5bgkGg+i6TiKRIN4ZJ/tUFsXojVucTifhK8MoarHjPJK4pVQHZzohmhBCDEaeeA8jFosRjUYr1tCURlZLa4bS6TS5XA63210xIhuLxSqmA5mGSbYnW3H9AU+8rTzs+CK89lmwCwSq+72u+zjY+m7oDsOBQzTszHDpPZeye89ujh86jqZpWHZxuw1VVTFtE03VcLlc1K2sQ3X0jrMYhoHH48Hv99PT01OeZubz+fB6vWQyGfL5PIsXL+baa68tb9MhhBBCiKlpPOOWnT/dWXFt3aMTWhKq/MA+ccuFl7g4seNGjKyzz5t8UHUBuMNEDpisuLIySZjX6yWfz5N+Ng0xQKEct9ReVEvd6+vKl5K4RQgx3cldaRi5XI5CoYDD4SgfKxQKWJaFpmnFjq5lUSj0Jh5xOp0UCoUBWTT7r++Gfh3vnh3w5OWw/VNgF6/nDaRRNQtQILAI6q/CdofI5/PkcjmO/eEYbU+1ccmSSwj4AzidzuI+4boDTdPKydQ0TaNmZU35o0r7PobDYVasWDFgi4pcLkcymQTgwgsvlMZLCCGEmAbGK25JdaQ48NSBimvPu3Yeqt4nHugXt/iqUlx769MsXrmbucuOULNsYUXccuiVQ/T09BAKhXj961/PkjlLqHJU4T7kRjmmVMQtvmYfM981szx4IHGLEOJsIE+8h+FyudB1nXw+Xx4pLjUKpmkCxSfLut5bjUNl0UxHKqeZO/3O4lYblgktX4FXPwmWUXGOotq4qrxk9EvBUUXOKDYshmFgWzaKqvD8fz/Pwe0HSSaSmKaJw+FAVVQs28LIGeQLeXS/DrVgWRaGYRCPx8v7Pg61RUVjYyPd3d2EQv1Gt4UQQggxJY1X3LLjpzuwClb5Z9Whcv67zi/+MEzcUh2OccmfFODyr3PiQD1PfuzJctwS7YpiPmlS31RP5g8Zdv5oJ6Zpojt03G53OW4paAX0a3UMy8BpOSVuEUKcNaTjPYyqqipCoRCtra3lKU0ul6ucRdO2bQKBQLmxKo3IDpZFMx2t7Hh7Qh5IH4OX/hw6nmMARYNl91Gz5hoym9vJGTl6unvKjZSqFzvXmWyGXU/sQlO1YmKRkzlEVEUt/pwFz3IPmUyGZDI56L6Pg21R4fV6efzxx4nFYpimKdtWCCGEEFPceMQttm1z9IWjFdddeONCPLUji1tY/inQnBA6QU9PD2bhZNyiqKjdKocjhzn2nWNDxi2emzzo1TrpdHrI/aolbhFCTEfS8R6GoigsWbKEWCxGJBIhGAzidDoJBoP09PQAEAwGsW2bXC5XMSLb/0bfv+Ptcx2Fx24Ho2vgBweXwuXfgbrXEdqzg+Ob20gmi0+06y+rx9PoofXxVlRFxaE7yGVzKPogDYtSnEJWt7KOVatW4XK5hmyI+m9R0dpazGT6/PPPD7rtiBBCCCGmlvGIW5KtSYxk5ZPsxW9bDMd+Ay+//5RxCxQ77/uP7EetU9G6tXLnOn8ij9lZTAI7WNxStbyK+tfVk06nJW4RQpx1pON9CkNNaVq8eDFQ3C+ys7Nz0BHZvsprvG0L4jvxdj41SOOlwNK/hws/B1pxW4xFNy3i2NZjdDzTQc2FNTS/rRnVqZI6lCK+K17O8GlZVjETqFa5rimwMACu4vSzhoaGEX3nSCTCpk2bAPB4PDidzhFtOyKEEEKIM+t045bOPZ0V13NXOfAd+wTs+Y9BPm1g3AK9Sd6qF1UT+0Os99ovdGJkjUHjFkVRqL+uHqfTSSKRkLhFCHHWkY73CAw2pak0Jav/saGmNKWjaTCz0LUZjC68gcon4PjmwOXfh/o1FYedPicr/m4F8Yvj1NXVlROGzHznTPZ9bR/ZziyKomDb9oCtQBRFofrKahRdGbDmfCilrUgymUx5pLnvtiORSISWlhZCoZBM3xJCCCGmoNOJWyo63maWWu1xlD3fG/ghQ8Qt0JvkLTA/UNHxLmQKYDJo3FJ9STXuejfZbHbQXDlDkbhFCDFdSMd7hPpPaSoZ7Jht2+z4yQ72/N8eFEXB3+wnum1PsdNtFrcU8wb6ZDmf9aew+lvgHHgtGDxZiubRmPO+ORz98VGyB7KYTpPg8iDN1zcTezVG5kSGqouqyNfmaQoNXHM+lNIodTAYHJCZfbhtR4QQQggxdYwmbumr3PE2uqBrM3ULtgw8aYRxi2u2C82pYRpm+TVVVVFVlUKhUNwObJ4PT5OHhhsbhs2VMxSJW4QQ08WY9lt46KGHmDt3Lm63m9WrV/PKK68Mee43v/lNrrzySmpqaqipqWHt2rUDzn/f+96HoigVf2688caxFO2Ms22bLd/cwvbvbycXy5HtyRL9w2aIbih3ugG8gUxxWtbrHoY1Px2y8YLeZCmxWKxidNhd72bhPQtp+kgT4Q+G8d/ghyoIXxum8dZG8s35IdecD2WwrUj6Gmq7NCGEEGKqkrhlZKyCRfeBbkgdLsctdU19noCPMm5J5pPMes8s3I3uitdVVUXTNWreWUPz7c003NSAUTCIRCIStwghzlqjfuL94x//mHvvvZeHH36Y1atX8+CDD3LDDTewe/du6uvrB5y/fv16brvtNq644grcbjf/+q//yvXXX8+OHTuYMWNG+bwbb7yR//3f/y3/PNIpRlNNyy9b2PN/e4o/2Bb0bIf0kQHneRtq4LrHoPaSIa9l23Z5StiMGTMGJEspbbFRW1vLokWL6OjoqFjPNdya86H0fbo+mKG2SxNCCCGmIolbRq7nQASrY2tF3FLbcDIfjW8OXPmLUcctadLMumsWdo9N55ZOul7twu12s+TtS3CvdEvcIoQ4Z4y64/3Vr36VO++8k/e///0APPzwwzz66KM88sgj3HfffQPO/8EPflDx87e+9S1+/vOfs27dOt773veWj7tcLhobG0dbnCnFtmx2/mxn8QcrD12bIBcdcJ7pqCN/3a+xaxYx1HhuaU1SNBotZ+f0eDwEg0EymcygjdR555034jXnQ+m7FUn/RmosU8CEEEKIM0nilqGlIim69nVRf349LneG6E/vgXTvk2N/TQKXxyBX+waylzxCsGb+mOOWglbAd7mPOW+dU45b+nbUJW4RQpztRtXxNgyDzZs3c//995ePqarK2rVr2bBhw4iukU6nyefz1NbWVhxfv3499fX11NTUcM011/D5z3+eurq6Qa+Ry+UqpgzF43EA8vn8kCOep1J631jfD5DqSJGNZSnkEuixLahmcsA5CRpIO2fx7B92UHegjUWLFhEKhSrOiUajbNq0iUwmQzAYxOFwYBgG0WgUp9PJokWLCIfD5Qam9FowGMTn8+Hz+QAoFArlRqfUqAWDwVM2agsXLqS7uxvbtsvJSgzDIJFI4PF4WLhwIYVCYcz1NB2Nx+/H2ULqopLUR6XTrQ+pRzGeJG4ZWmRnhPUfX49t2zj8Oa666r947bE5FefUNXayS3krr8VuQ3/xVerqjkvcMk1I29RL6qKS1EelyYxbFLt/KuxhnDhxghkzZvDSSy9x+eWXl49/9KMf5dlnn2Xjxo2nvMaHPvQhfve737Fjxw7c7uKanx/96Ed4vV7mzZvH/v37+djHPobf72fDhg1omjbgGp/+9Kf5zGc+M+D4D3/4Q7xe70i/zrjLHMoQ+d5+aqw9aORRNQtFsTELGhYqcXUeWaUWZ6OTmR+cecbKKYQQU1U6nebd7343sViMYDB4posjpjmJW4bW+v9ayezPoNvpctzSl4VK6B0zyF5wxRkpnxBCTAejiVsmNav5F7/4RX70ox+xfv36cuMF8Gd/9mfl/77gggu48MILWbBgAevXr+faa68dcJ3777+fe++9t/xzPB5n1qxZXH/99WMO1PL5PE899RTXXXfdkAk6hhONRnll63eotVpQKWbvDNQkcLgM2o410V6Yjan5wTYoUKCjo4PGxkYymQyNjY1cdtllKIpCLBbj+eefx+Px4HK5SKVStLa2ljOa27ZNKpXCtm1UVWXWrFkEg0Hy+TzxeByPx8OqVasABow+9z+n/4j1YPWxevVqTNMc8ajz2ep0fz/OJlIXlaQ+Kp1ufZSeBAoxFZytcYtlWvzw6z/EJEK1tacct5QUbJ2eGTPINS/CjkbJ5XI4HA6JW6YRaZt6SV1UkvqoNJlxy6g63qFQCE3TaG9vrzje3t5+ynVOX/7yl/niF7/I008/zYUXXjjsufPnzycUCrFv375BG7DSPo39ORyO0/4FGss1bNsm8tpPmNX+M3ZzXvm4vyqJWqVwdP8cLMWFQytubeGqLU6B6urqor6+ns7OTtLpNNXV1ZimSaFQwOl0AtDZ2YlhGHi9XhRFwbIsDMPA6XSiqirxeJzq6mrcbjcul4tIJMLevXsByGQyhMPhcqOjaVr5nH379tHY2HjKBqmurk7+UfYxHr9jZwupi0pSH5XGWh9Sh2I8SdwyuK4jXRiJE1Sbu1GxKl4zbBetzhk4Xl+DX1FQVRWPx0M6nZa4ZRqStqmX1EUlqY9KkxG3jGo7MafTycqVK1m3bl35mGVZrFu3rmIKV39f+tKX+NznPscTTzxRHtUczrFjx+js7KSpqWk0xTtjUnt/xvmRj5KNVzaq+YCXV6tuwrSdxW3ATk7q1/zFhiSdTmPbdsU2F32zc+ZyOdLpNC6Xq9zQGIaBaZq43W7cbjfpdLr83tJ+la2trbS2tg6apKT/npZCCCHE2UrilsEdfepXBIxdAzrdGdtHm7kA+0ofpmZimsUn4YqiSNwihBCnadT7eN97771885vf5Dvf+Q67du3ir//6r0mlUuVsoe9973srkpj867/+K5/85Cd55JFHmDt3Lm1tbbS1tZFMFhOPJZNJ/vEf/5GXX36ZQ4cOsW7dOt7+9rezcOFCbrjhhnH6mhOo9Sk8f7idbMxBT7Q3Y2ZOqeY159XYDcWEIdiU9+BWVAVN07Asi2w2W7HNRd89uwuFApZlldeL2bZNLpdD0zQcDkf5Gn0ThpS2GTMMQ/a0FEIIcc6TuKWf1qdIPPsdlH6d7pxSTXthLvr5HtSwimVZWFbvORK3CCHE6Rn1Gu9bb72VSCTCpz71Kdra2lixYgVPPPEEDQ0NABw5cgRV7e3P//d//zeGYfDOd76z4joPPPAAn/70p9E0jVdffZXvfOc79PT00NzczPXXX8/nPve5Kb/nYu7A82z/l09zpOXN5NK9Zc2pdXQxh7zTxDLj2B4bO2WXt9dwzHJgmiaKopDJZJg9e3Z5mwtFUViyZAmxWIyenp7yyLKiKOU1VqqqlhtDVVXR9d6/xtJ0LqC8vqo/2dNSCCHEuULilj4iG7CfvZno8esrDpfiFsudw1hqYOZNLMsikUgQCARwOp0StwghxGkaU3K1e+65h3vuuWfQ19avX1/x86FDh4a9lsfj4Xe/+91YinFG2V1/5Jm/+TLdrZVbb2S1MDFlLkY2h+Ivro3SLtYwXzKxbAur0cIMmhjpYiNSVVXFkiVLKqZWhcNhVq9eza5du0gmkyQSCdxuN36/n7q6Ojo7O0kmk9i2TSAQKDdEfferBGhra6tYK9X/HNnTUgghxLlA4hag+1VYfxOpboVM0lM+nNXCmMvORzFTOBY4sF02WMUOsmEY5Uy9uVxO4hYhhDgNk5rVfDqIxWLlbJiDrTUCILGPxM/fQXfr6orDGaWObmsWhYKBZVtUNVSRzCbRFmn45/kppAtkfVm6u7vx+/3Mnz+flStXEg6HB3xEOBwmFAoxc+ZMNm/ejGEY1NbW4nK5yOfz9PT0ABAMBstTueLxOF6vl6VLlwLFLHuRSIRgMFieylU6p3+jKYQQQojpZ6RxC89cD/keIsfmlg9nlDpirjkErlRxZB24VBeJRAJFUfD7/ZimWU6qJnGLEEKcHul4nxSNRgF4/vnny1PCQ6EQS5YsqWxg8nF49m30HCtUXsBdj+q/CEc8QTaXQPWoqE6VWm8tAIVCAa1aQzd1VFXlqquu4oILLhi2EVEUhfnz5xMIBGhpaSEajZJMJtF1ncWLFwPFDKCdnZ3ouk5TU1NFeVevXl1+XyKRGPQcIYQQQkw/o41byBYzu3e11xWPn4xbqpptEqliZ1vTNGpre+MWRVHw+XwStwghxDiQjjcQiUTYtGkTUJxC5nQ6yefztLa2EovFWL16dfGGb1vw0nsgvovujj5bizhrsWtWopo2brebbDZLYGaAefPmladT5XI5CoUCqqqSTCZpaGgY8chtaRQ5FouRy+XKo9rAgGP9p34N9j4ZMRZCCCGmr9HELcnfvo8TL5rk88twe3K0HmyqiFsal9fTXt1OTU0NTqdT4hYhhJgg53zH27ZtWlpayGQy5X02S9tmhMNhIpEILS0thEIhlO2fgeP/B0BPtLr4fj2AEVhBsidW3jIjX8iTTqfLiUgA3G43ANlsFofDMeoEIYqiUF1dPeD4YMdG8j4hhBBCTD+jiVuSv/8Mv/tXjbyxsvf9egCjqjduyWfzJHuSFAqFin2yJW4RQojxNertxM42sViMaDRKMBgc8FrfvSPTu78Pr322/FpPpBoTnfbCbNo6omTSGTRN693PsjrPsWPHSKVS5feUEoSEQiFJECKEEEKIURtN3LLzO78jb/Ru0TVY3DLzgpkEAgFisZjELUIIMYHO+Y53aSrVcHtHOvJteP74ofIxI+sgFfeTdJyHYenFqVizVGzLLo46O10Ezw+SSqVoa2vDNE2y2SyRSEQShAghhBBizEYatzj+8Dcc2T27fNxGqYhbTNNEC2i4qlw0NDTg9/slbhFCiAl0zk81Lz+hzufLx0rZNguFAmahwGXWN1CtZPn1nmg1KX0uOQJYZhqHw4F+lY69zcaZdhJaFcJ9oZu2tjaSySRtbW14vV5JECKEEEKI0zLSuOXYrloK+d4wr2/cous6lmlh+AwymQxer5cZM2ZI3CKEEBPonO94V1VVEQqFaG1txeVykUql6OzsJJ1OY1kWC9UXaKjaBoBlKRQMnT3ta8lqjdhmcW9uO2CTM3OYy0z0gE4ykMSNm7lz59LW1sbFF19Mc3OzJAgRQgghxGkZadzy9GvXlt+T1cIVcQsFsGyLtDvN/v37CQQChEIhiVuEEGICnfMdb0VRWLJkCT09PViWxbFjx8pTuNxWF28M/hqA3ZsXs/3FC8jmfcScK0ABy7KwTAs1qKJpGgCappFMJsnlcoTDYbxeL83NzZIoRAghhBCnbSRxS9vhBiLH6gEwFSdpfR6OoIN8Zx7LtMrX0cO6xC1CCDFJzvk13lDcvmLlymLGz2w2C4BlmVxb9XNcSoboiTq2PHMJecNBQpuHpWhgF/e4RAG1rliNiqLgcDjwer0YhkFra6skJBFCCCHEuBoublFyBTY+cVn53IQ2D9XnYuatM8txCycfYqshVeIWIYSYJOf8E+8Sp9MJwLx589A0jRnWy8xObsW2YeuzFwOQtKpJFry4teLTbtM00XUdfZaOYRi4XC5UVcU0TSzLwrZtZsyYIdO0hBBCCDGuhopbXl6/mnTCC/TGLfOuCaM1aSgzFPQjOqZpwhzQA7rELUIIMUmk431SLpcDwO/3o6uwuOO7ABzfN5Po8TCW4iCuzEJVVAzDQFEUFJ+C73U+rHoLtVAcNc5kMqiqSjAYRNd1fD7fmfxaQgghhDgLDRa3HN8/g4OvzQcoxy3euV7MBSaZTAb3tW6UY0rxKfkcJG4RQohJJB3vk1wuFwD5fJ4Z1u/xmkexTJVtz60AIKHOQVFdVNdUM+fuOUSSEVo7WlEcCtWBaurq6tD14hYduq5j2zbZbLZ8XSGEEEKI8dI/bsl3xHj58evKryfUOWhuD2+874105bo4ceIEBaWAY76DcCAscYsQQkwy6XifFAwGAUjGOpnLdwDYv30Bie4AecVPygricju55uPXMOcNc+jp6eHll1+mq6uLpqYmVLV3ubxt20QiEZqammSdlBBCCCHG1cb/3MiJrSc4dvQYlpknaR5ja/LN5ddLccvCP53HstctA5C4RQghzjBJrnZSaT3TfMcW2ne4+e3/vIVNT68CIE4TmqYz84KZzHnDHBRFoaamhlWrVlFVVUU0GiWbzWJZFtlslkgkgtfrZcmSJbJOSgghhBDjKtuTJd2RphAr4Ep0kU9Wvh6nicCSIJe/5/Li0jiJW4QQ4oyTJ979zOjZyjOPXYFtFRseQ/GDK0x1wM8V91xR0SCFw2FWr15NS0sL0WiURCKBrus0NTWxZMkSwuHwmfoaQgghhDgH+NVusHp/NhQ/7lkzufEzb6K+vr7iXIlbhBDizJGOdx9+6xgHn8mUO90AanABtf5aZlw6g/rl9QPeEw6HCYVCxGIxcrkcLpeLqqoqGTEWQgghxITSyKAVunsPKLDobRfwun96Hw63Y9D3SNwihBBnhnS8+2jqWcf2ljm9B1Qnum8mnjovq/5y1ZDvUxSF6urqiS+gEEIIIc55y/9sOXOvm0vbM79lpvlc+XjdHAX3n38btME73SUStwghxOSTjjfw7Oee5fgrx7GOB9DI977gnc3r77uS5tc1o7ukqoQQQghx5tWdV0fQCNB48HFcxHpfWPpR0JxnrmBCCCGGJL1JABvsfLqy0w3Mu2kVs9fMPkOFEkIIIYQYQuZYZacbYP77z0xZhBBCnJJkNS8p9EsJqugsvmXNmSmLEEIIIcQwlPiuygN6AIKLz0xhhBBCnJJ0vE9S+na8FVh+XYKa+bVnrkBCCCGEEEMY0PGuWgaSIE0IIaYsmWoOrLxrJcvP/wHq0ccB8AbSuM7/8zNcKiGEEEKIwSnxnZUHqpadmYIIIYQYEel4A/5GP17/dtT6nt6D0oAJIYQQYqoa7Im3EEKIKUummgPYNkqs/8jx+WemLEIIIYQQw5G4RQghpp0xdbwfeugh5s6di9vtZvXq1bzyyivDnv/Tn/6UJUuW4Ha7ueCCC3jssccqXrdtm0996lM0NTXh8XhYu3Yte/fuHUvRxiZzAqUQrzwmI8dCCCHEWUHiFiGEEGfaqDveP/7xj7n33nt54IEH2LJlCxdddBE33HADHR0dg57/0ksvcdttt/EXf/EXbN26lZtvvpmbb76Z1157rXzOl770Jf7zP/+Thx9+mI0bN+Lz+bjhhhvIZrNj/2aj0X/UWPeBd9bkfLYQQgghJozELUIIIaaCUXe8v/rVr3LnnXfy/ve/n2XLlvHwww/j9Xp55JFHBj3/P/7jP7jxxhv5x3/8R5YuXcrnPvc5LrnkEr7+9a8DxVHjBx98kE984hO8/e1v58ILL+S73/0uJ06c4Fe/+tVpfbkRi+2o/Dm4DBSZhS+EEEJMdxK3CCGEmApGlVzNMAw2b97M/fffXz6mqipr165lw4YNg75nw4YN3HvvvRXHbrjhhnLjdPDgQdra2li7dm359aqqKlavXs2GDRv4sz/7swHXzOVy5HK58s/xeHG6VT6fJ5/Pj+YrAaB1v1YxAmEFllAwDOLxOLlcDpfLRTAYRDmHtuko1eNY6vNsJPXRS+qiktRHpdOtD6lHMZ4kbpG45Vwl9dFL6qKS1EelyYxbRtXxjkajmKZJQ0NDxfGGhgZaWloGfU9bW9ug57e1tZVfLx0b6pz+vvCFL/CZz3xmwPEnn3wSr9c7si/Tx5rMS9T1+XnXCYV9jz8+6uucjZ566qkzXYQpReqjl9RFJamPSmOtj3Q6Pc4lEecyiVvOPXIvriT10UvqopLUR6XJiFum5XZi999/f8VodDweZ9asWVx//fUEg8HRXcy20X/9PrB6DyX1OeRyOYLBIA6Hg3w+Tzwex+PxsGrVKkKh0Ph8kSksn8/z1FNPcd111+FwOM50cc44qY9eUheVpD4qnW59lJ4ECnE2kbhl4sm9uJLURy+pi0pSH5UmM24ZVcc7FAqhaRrt7e0Vx9vb22lsbBz0PY2NjcOeX/r/9vZ2mpqaKs5ZsWLFoNd0uVy4XK4Bxx0Ox+grzLbhzTsodP2RXS//nJCjg45sE+H6cHmKlqZpuFwuIpEI+/bto7Gx8ZyZvjWmOj2LSX30krqoJPVRaaz1IXUoxpPELRK3nOukPnpJXVSS+qg0GXHLqDJxOJ1OVq5cybp168rHLMti3bp1XH755YO+5/LLL684H4qP8kvnz5s3j8bGxopz4vE4GzduHPKa40pRwNuM3bCWA4638rL1fpzV8wc0UIqiEAwGiUajxGKxiS+XEEIIIU6LxC0StwghxFQx6qnm9957L3fccQerVq3i0ksv5cEHHySVSvH+978fgPe+973MmDGDL3zhCwD83d/9HVdddRVf+cpXePOb38yPfvQjNm3axDe+8Q2g2DB8+MMf5vOf/zyLFi1i3rx5fPKTn6S5uZmbb755/L7pCBUKhSFHLpxOJ4lEoiJBihBCCCGmLolbJG4RQoipYNQd71tvvZVIJMKnPvUp2traWLFiBU888UQ5yciRI0dQ1d4H6VdccQU//OEP+cQnPsHHPvYxFi1axK9+9SuWL19ePuejH/0oqVSKu+66i56eHtasWcMTTzyB2+0eh684Orquk8/nB50SZhgGmqaRy+Vob2/H5XJRVVV1zkzfEkIIIaYbiVskbhFCiKlgTMnV7rnnHu65555BX1u/fv2AY+9617t417veNeT1FEXhs5/9LJ/97GfHUpxxVVdXR1tbG+FwuKJhsm2bSCQCwKZNmzBNE13XCYVCLFmyhHA4fKaKLIQQQohhSNwicYsQQpxpo1rjfS5YtGgRXq+XSCRCNpvFsiyy2SzHjh0rZ63z+XzU1dXh9XppbW1l48aN5cZNCCGEEGKySNwihBDTg3S8+wmFQqxevZqmpiYymQydnZ3l/dmCwSAzZ87E5XKhqioul4twOEw6naalpQXbts9w6YUQQghxLpG4RQghpodpuY/3RAuHw4RCIWKxGLlcjlwux6ZNm/D5fKfMGlpdXX1mCi2EEEKIc5LELUIIMfXJE+8hKIpCdXU1DQ0NuFwuTNMcNmtooVCQrKFCCCGEOCMkbhFCiKlNOt4UE5CU9riMxWIDpl65XK5y1tDBGIaBruuDZhQVQgghhBhPErcIIcT0c85PNY9EIrS0tBCNRlFVleeff35Axs+qqipCoRCtra2DZg2Nx+M0NTVRVVV1pr6GEEIIIc4BErcIIcT0dE4/8Y5EImzcuJHW1lY8Hg8AHo9nQMZPRVFYsmTJoFlDI5EIXq+XJUuWyL6YQgghhJgwErcIIcT0dc52vG3bpqWlhXQ6TTgcLk+3GirjZzgcHpA1NJPJ0NTUxOrVq2U/TCGEEEJMGIlbhBBiejtnp5rHYjGi0ShVVVUoilKxPmqojJ/9s4a6XK7y+4UQQgghJorELUIIMb2dsx3vXC5HoVAYNuNnIpEYkPGzlDVUCCGEEGKySNwihBDT2zk71VwyfgohhBBiupC4RQghprdztuNdyvg52DYcpYyfoVBIMn4KIYQQ4oyTuEUIIaa3c7bjPVjGT0AyfgohhBBiypG4RQghprdztuMNlRk/+zZgkvFTCCGEEFONxC1CCDF9nbPJ1UpKGT87Ozt58cUXufLKK6mrq5MRYyGEEEJMORK3CCHE9HROP/EuURSlvCZKttkQQgghxFQmcYsQQkw/0vEWQgghhBBCCCEm0Fkx1byU3TMej4/5Gvl8nnQ6TTweH3KPzHOJ1EclqY9eUheVpD4qnW59lO7j/bM2C3E2kbhl/El9VJL66CV1UUnqo9Jkxi1nRcc7kUgAMGvWrDNcEiGEEOMhkUjItkjirCVxixBCnF1GErco9lnwWMGyLE6cOEEgEBjzOqd4PM6sWbM4evQowWBwnEs4/Uh9VJL66CV1UUnqo9Lp1odt2yQSCZqbm1FVWQ0lzk4St4w/qY9KUh+9pC4qSX1Umsy45ax44q2qKjNnzhyXawWDQfkl7EPqo5LURy+pi0pSH5VOpz7kSbc420ncMnGkPipJffSSuqgk9VFpMuIWeZwghBBCCCGEEEJMIOl4CyGEEEIIIYQQE0g63ie5XC4eeOABXC7XmS7KlCD1UUnqo5fURSWpj0pSH0JMDvm3Vknqo5LURy+pi0pSH5Umsz7OiuRqQgghhBBCCCHEVCVPvIUQQgghhBBCiAkkHW8hhBBCCCGEEGICScdbCCGEEEIIIYSYQNLxFkIIIYQQQgghJtA51fF+6KGHmDt3Lm63m9WrV/PKK68Me/5Pf/pTlixZgtvt5oILLuCxxx6bpJJOjtHUxze/+U2uvPJKampqqKmpYe3ataesv+lmtL8fJT/60Y9QFIWbb755Ygs4iUZbFz09Pdx99900NTXhcrk477zzzqp/L6OtjwcffJDFixfj8XiYNWsWH/nIR8hms5NU2onz3HPP8da3vpXm5mYUReFXv/rVKd+zfv16LrnkElwuFwsXLuTb3/72hJdTiLOFxC2VJG6pJHFLL4lbKkncUjTl4hb7HPGjH/3Idjqd9iOPPGLv2LHDvvPOO+3q6mq7vb190PNffPFFW9M0+0tf+pK9c+dO+xOf+ITtcDjs7du3T3LJJ8Zo6+Pd7363/dBDD9lbt261d+3aZb/vfe+zq6qq7GPHjk1yySfGaOuj5ODBg/aMGTPsK6+80n77298+OYWdYKOti1wuZ69atcq+6aab7BdeeME+ePCgvX79envbtm2TXPKJMdr6+MEPfmC7XC77Bz/4gX3w4EH7d7/7nd3U1GR/5CMfmeSSj7/HHnvM/vjHP27/4he/sAH7l7/85bDnHzhwwPZ6vfa9995r79y50/7a175ma5pmP/HEE5NTYCGmMYlbKkncUknill4St1SSuKXXVItbzpmO96WXXmrffffd5Z9N07Sbm5vtL3zhC4Oef8stt9hvfvObK46tXr3a/su//MsJLedkGW199FcoFOxAIGB/5zvfmagiTqqx1EehULCvuOIK+1vf+pZ9xx13nDUN2Gjr4r//+7/t+fPn24ZhTFYRJ9Vo6+Puu++2r7nmmopj9957r/36179+Qss52UbSgH30ox+1zz///Ipjt956q33DDTdMYMmEODtI3FJJ4pZKErf0krilksQtg5sKccs5MdXcMAw2b97M2rVry8dUVWXt2rVs2LBh0Pds2LCh4nyAG264Ycjzp5Ox1Ed/6XSafD5PbW3tRBVz0oy1Pj772c9SX1/PX/zFX0xGMSfFWOriN7/5DZdffjl33303DQ0NLF++nH/5l3/BNM3JKvaEGUt9XHHFFWzevLk8revAgQM89thj3HTTTZNS5qnkbL6PCjGRJG6pJHFLJYlbekncUkniltMz0fdRfVyuMsVFo1FM06ShoaHieENDAy0tLYO+p62tbdDz29raJqyck2Us9dHfP/3TP9Hc3Dzgl3M6Gkt9vPDCC/zP//wP27Ztm4QSTp6x1MWBAwf4/e9/z5//+Z/z2GOPsW/fPj70oQ+Rz+d54IEHJqPYE2Ys9fHud7+baDTKmjVrsG2bQqHAX/3VX/Gxj31sMoo8pQx1H43H42QyGTwezxkqmRBTm8QtlSRuqSRxSy+JWypJ3HJ6JjpuOSeeeIvx9cUvfpEf/ehH/PKXv8Ttdp/p4ky6RCLB7bffzje/+U1CodCZLs4ZZ1kW9fX1fOMb32DlypXceuutfPzjH+fhhx8+00U7I9avX8+//Mu/8F//9V9s2bKFX/ziFzz66KN87nOfO9NFE0KIc5LELRK39CVxSyWJWybPOfHEOxQKoWka7e3tFcfb29tpbGwc9D2NjY2jOn86GUt9lHz5y1/mi1/8Ik8//TQXXnjhRBZz0oy2Pvbv38+hQ4d461vfWj5mWRYAuq6ze/duFixYMLGFniBj+d1oamrC4XCgaVr52NKlS2lra8MwDJxO54SWeSKNpT4++clPcvvtt/PBD34QgAsuuIBUKsVdd93Fxz/+cVT13BnvHOo+GgwG5Wm3EMOQuKWSxC2VJG7pJXFLJYlbTs9Exy3nRE06nU5WrlzJunXryscsy2LdunVcfvnlg77n8ssvrzgf4Kmnnhry/OlkLPUB8KUvfYnPfe5zPPHEE6xatWoyijopRlsfS5YsYfv27Wzbtq38521vextXX30127ZtY9asWZNZ/HE1lt+N17/+9ezbt6/ciAPs2bOHpqamad14wdjqI51OD2ikSo17MbfHueNsvo8KMZEkbqkkcUsliVt6SdxSSeKW0zPh99FxSdE2DfzoRz+yXS6X/e1vf9veuXOnfdddd9nV1dV2W1ubbdu2ffvtt9v33Xdf+fwXX3zR1nXd/vKXv2zv2rXLfuCBB866bTlGUx9f/OIXbafTaf/sZz+zW1tby38SicSZ+grjarT10d/ZlB10tHVx5MgROxAI2Pfcc4+9e/du+7e//a1dX19vf/7znz9TX2FcjbY+HnjgATsQCNj/7//9P/vAgQP2k08+aS9YsMC+5ZZbztRXGDeJRMLeunWrvXXrVhuwv/rVr9pbt261Dx8+bNu2bd9333327bffXj6/tC3HP/7jP9q7du2yH3roIdlOTIgRkrilksQtlSRu6SVxSyWJW3pNtbjlnOl427Ztf+1rX7Nnz55tO51O+9JLL7Vffvnl8mtXXXWVfccdd1Sc/5Of/MQ+77zzbKfTaZ9//vn2o48+OsklnlijqY85c+bYwIA/DzzwwOQXfIKM9vejr7OpAbPt0dfFSy+9ZK9evdp2uVz2/Pnz7X/+53+2C4XCJJd64oymPvL5vP3pT3/aXrBgge12u+1Zs2bZH/rQh+zu7u7JL/g4e+aZZwa9D5S+/x133GFfddVVA96zYsUK2+l02vPnz7f/93//d9LLLcR0JXFLJYlbKknc0kvilkoStxRNtbhFse1zbA6BEEIIIYQQQggxic6JNd5CCCGEEEIIIcSZIh1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFmKLWr1+PoiisX79+xO/59re/jaIobNq0aeIKJoQQQgjRj8QtQgxPOt5CnINaW1u57777uPrqqwkEAqdsKF966SXWrFmD1+ulsbGRv/3bvyWZTE5egYUQQghxzlq3bh0f+MAHOO+88/B6vcyfP58PfvCDtLa2Djj3jW98I4qiDPhz4403noGSC9FLP9MFEEJMvt27d/Ov//qvLFq0iAsuuIANGzYMee62bdu49tprWbp0KV/96lc5duwYX/7yl9m7dy+PP/74JJZaCCGEEOeif/qnf6Krq4t3vetdLFq0iAMHDvD1r3+d3/72t2zbto3GxsaK82fOnMkXvvCFimPNzc2TWWQhBpCOtxDjzLIsDMPA7Xaf6aIMaeXKlXR2dlJbW8vPfvYz3vWudw157sc+9jFqampYv349wWAQgLlz53LnnXfy5JNPcv31109WsYUQQggxzqZD3PLVr36VNWvWoKq9k3VvvPFGrrrqKr7+9a/z+c9/vuL8qqoq3vOe90x2MYUYlkw1F2IIn/70p1EUhZaWFm655RaCwSB1dXX83d/9Hdlstnyeoijcc889/OAHP+D888/H5XLxxBNPAHD8+HE+8IEP0NDQgMvl4vzzz+eRRx4Z8FnHjh3j5ptvxufzUV9fz0c+8hFyudy4fI/u7m4uvfRSZs6cye7duwEIBALU1tae8r3xeJynnnqK97znPeVON8B73/te/H4/P/nJT8aljEIIIYQ4PWdz3PKGN7yhotNdOlZbW8uuXbsGvU6hUJBlcWJKkSfeQpzCLbfcwty5c/nCF77Ayy+/zH/+53/S3d3Nd7/73fI5v//97/nJT37CPffcQygUYu7cubS3t3PZZZeVG7hwOMzjjz/OX/zFXxCPx/nwhz8MQCaT4dprr+XIkSP87d/+Lc3NzXzve9/j97///WmXPRqNct1119HV1cWzzz7LggULRvX+7du3UygUWLVqVcVxp9PJihUr2Lp162mXUQghhBDj51yJW5LJJMlkklAoNOC1PXv24PP5MAyDhoYG7rzzTj71qU/hcDhOu4xCjJV0vIU4hXnz5vHrX/8agLvvvptgMMh//dd/8Q//8A9ceOGFQHHN9Pbt21m2bFn5fR/84AcxTZPt27dTV1cHwF/91V9x22238elPf5q//Mu/xOPx8I1vfIM9e/bwk5/8pDzl+8477+Siiy46rXK3tbWxdu1aMpkMzz33HHPmzBn1NUpJS5qamga81tTUxPPPP39aZRRCCCHE+DpX4pYHH3wQwzC49dZbK44vWLCAq6++mgsuuIBUKsXPfvYzPv/5z7Nnzx5+/OMfn1YZhTgdMtVciFO4++67K37+m7/5GwAee+yx8rGrrrqqovGybZuf//znvPWtb8W2baLRaPnPDTfcQCwWY8uWLeXrNDU18c53vrP8fq/Xy1133TXmMh87doyrrrqKfD4/5k43FEe1AVwu14DX3G53+XUhhBBCTA3nQtzy3HPP8ZnPfIZbbrmFa665puK1//mf/+GBBx7gHe94B7fffju//vWvufPOO/nJT37Cyy+/POYyCnG65Im3EKewaNGiip8XLFiAqqocOnSofGzevHkV50QiEXp6evjGN77BN77xjUGv29HRAcDhw4dZuHAhiqJUvL548eIxl/n2229H13V27do1INPnaHg8HoBB121ls9ny60IIIYSYGs72uKWlpYU/+ZM/Yfny5XzrW98a0fX//u//nm9+85s8/fTTXHbZZWMupxCnQzreQoxS/4YGGNABtSwLgPe85z3ccccdg16nNN1rIrzjHe/gu9/9Lv/xH/8xYDuN0ShNMR9sn8zW1lbZmkMIIYSY4s6muOXo0aNcf/31VFVV8dhjjxEIBEZ0/VmzZgHQ1dU1LuUVYiyk4y3EKezdu7diZHjfvn1YlsXcuXOHfE84HCYQCGCaJmvXrh32+nPmzOG1117Dtu2KxrGUyXMs/uZv/oaFCxfyqU99iqqqKu67774xXWf58uXous6mTZu45ZZbyscNw2Dbtm0Vx4QQQghx5p2tcUtnZyfXX389uVyOdevWDZp/ZigHDhwAit9TiDNF1ngLcQoPPfRQxc9f+9rXAHjTm9405Hs0TeNP//RP+fnPf85rr7024PVIJFL+75tuuokTJ07ws5/9rHwsnU4POdVrpD75yU/yD//wD9x///3893//95iuUVVVxdq1a/n+979PIpEoH//e975HMpkcdv9vIYQQQky+szFuSaVS3HTTTRw/fpzHHntswHT6kng8PmB5nG3b5X2+b7jhhtMqoxCnQ554C3EKBw8e5G1vexs33ngjGzZs4Pvf/z7vfve7T5m984tf/CLPPPMMq1ev5s4772TZsmV0dXWxZcsWnn766fJ0pzvvvJOvf/3rvPe972Xz5s00NTXxve99D6/Xe9pl/7d/+zdisRh33303gUCA97znPeXXSo3Qjh07gGJn+oUXXgDgE5/4RPm8f/7nf+aKK67gqquu4q677uLYsWN85Stf4frrr+fGG2887TIKIYQQYvycjXHLn//5n/PKK6/wgQ98gF27dlXs3e33+7n55psB2LJlC7fddhu33XYbCxcuJJPJ8Mtf/pIXX3yRu+66i0suueS0yyjEmNlCiEE98MADNmDv3LnTfuc732kHAgG7pqbGvueee+xMJlM+D7DvvvvuQa/R3t5u33333fasWbNsh8NhNzY22tdee639jW98o+K8w4cP229729tsr9drh0Ih++/+7u/sJ554wgbsZ555ZsRl/t///V8bsP/whz+Uj5mmad922222ruv2r371q4pyD/Wnv+eff96+4oorbLfbbYfDYfvuu++24/H4iMslhBBCiIl1Nsctc+bMGTJmmTNnTvm9Bw4csN/1rnfZc+fOtd1ut+31eu2VK1faDz/8sG1Z1ojLJcREUGzbtie1py/ENPHpT3+az3zmM0QiEUKh0JkujhBCCCHEkCRuEWJqkzXeQgghhBBCCCHEBJI13kJMA5lMhlgsNuw5tbW1OJ3OSSqREEIIIcTgJG4RYiDpeAsxDfz4xz/m/e9//7DnPPPMM7zxjW+cnAIJIYQQQgxB4hYhBpI13kJMA62treXs40NZuXIlNTU1k1QiIYQQQojBSdwixEDS8RZCCCGEEEIIISaQJFcTQgghhBBCCCEm0FmxxtuyLE6cOEEgEEBRlDNdHCGEEGNk2zaJRILm5mZUVcaGxdlJ4hYhhDg7jCZuOSs63idOnGDWrFlnuhhCCCHGydGjR5k5c+aZLoYQE0LiFiGEOLuMJG45KzregUAAKH7hYDA4pmvk83mefPJJrr/+ehwOx3gWb1qS+qgk9dFL6qKS1Eel062PeDzOrFmzyvd1Ic5GEreMP6mPSlIfvaQuKkl9VJrMuOWs6HiXpmkFg8HTasC8Xi/BYFB+CZH66E/qo5fURSWpj0rjVR8y/VaczSRuGX9SH5WkPnpJXVSS+qg0mXGLLKATQgghhBBCCCEm0FnxxFsIIUSRbdvEYjFyuRwul4uqqip5eiyEEEKIKelciluk4y2EEGeJSCRCS0sL0WiUQqGAruuEQiGWLFlCOBw+08UTQgghhCg71+IW6XgLIcQ0cKoR4UgkwsaNG0mn01RVVeFwOMjn87S2thKLxVi9evVZ2YgJIYQQYuqRuGUg6XgLIcQUd6oRYdu2aWlpIZ1OEw6Hyw2by+UiHA6X3x8Khc7a6VtCCCGEmBokbhmcdLyFEGIKG8mIsMPhIBqNDrouSlEUgsEg0WiUWCxGdXX1mfkiQgghhDjrSdwyNMlqLoQQU1T/EWGXy4WqquUR4XQ6TUtLC9lslkKhMOQ2GE6nk0KhQC6Xm+RvIIQQQohzhcQtw5OOtxBCTFGxWGxEI8KGYaDrOvl8ftDrlF53uVyTUWwhhBBCnIMkbhmedLyFEGKKyuVyIxoRdjqdhEIhYrEYtm1XnGPbNvF4nFAoRFVV1WQUWwghhBDnIIlbhicdbyGEmKJcLteIRoTdbjdLlizB6/USiUTIZrNYlkU2myUSieD1elmyZMlZlaBECCGEEFOLxC3Dk463EEJMUVVVVSMeEQ6Hw6xevZqmpiYymQydnZ1kMhmamprOyi05hBBCCDG1SNwyPMlqLoQQU5SiKCxZsoRYLEYkEiEYDOJ0OjEMg3g8PmBEOBwOlxu8ofbNFEIIIYSYCBK3DE863kIIMYWVRoRL+2EmEgl0Xaepqam8H2ZfiqKcVVtvCCGEEGL6kLhlaNLxFkKcE2zbnrYjqufaiLAQQghxrpO45ewjHW8hxJR3uo1PJBIpj7wWCgV0XScUCg068jpVnekR4ekcAAghhBCTSeIWiVsGIx1vIcSUdrqNTyQSYePGjaTTaaqqqnA4HOTzeVpbW4nFYmdtAo/xUGq02traOHLkCMlkEtM0p2UAIIQQQkwGiVvOnKket4w6q/lzzz3HW9/6Vpqbm1EUhV/96lenfM/69eu55JJLcLlcLFy4kG9/+9sDznnooYeYO3cubreb1atX88orr4y2aEKIs0yp8WltbcXr9VJXV4fX66W1tZWNGzcSiUSGfb9t27S0tJBOpwmHw7hcLlRVxeVyEQ6HSafTtLS0DMi8ea6ybZuenh7a29s5cOAAzz//PI8//jhPPvkkO3bsoLu7G7fbPaq/AyHONIlbhBCTReKWyTXd4pZRd7xTqRQXXXQRDz300IjOP3jwIG9+85u5+uqr2bZtGx/+8If54Ac/yO9+97vyOT/+8Y+59957eeCBB9iyZQsXXXQRN9xwAx0dHaMtnhDiLDFY46MoCrZt4/V66enpYdeuXcM2PrFYjGg0Ouj0IkVRCAaDRKNRYrHYRH+dKS8SifDCCy/wzDPP8NRTT/Hoo4+yfft2YrEYqqri9/vJZrO0trZSKBSGDgBsG4/VAfnEmfsyQvQhcYsQYjJI3DK5pmPcMuqp5m9605t405veNOLzH374YebNm8dXvvIVAJYuXcoLL7zAv//7v3PDDTcA8NWvfpU777yT97///eX3PProozzyyCPcd999oy2iEOeMqbh+Zbz0b3xSqRTRaJR0Oo1lWdi2TTKZZObMmcyfP3/Qa+RyOQqFAg6HY9DXnU4niUSCXC43kV9lyus7rS0YDJYb9EKhQDKZJBgM4nA40HWddDpNNBpl9uzZBAIBOo7v58TWQ9Rae3Cn/ogWeZnrjQjxAxr6+e8/a34fxfQlcYsQU4fELRK3jIfpGrdM+BrvDRs2sHbt2opjN9xwAx/+8IcBMAyDzZs3c//995dfV1WVtWvXsmHDhkGvmcvlKn7h4vE4APl8nnw+P6Zylt431vefbaQ+Kk3F+ohGo+zdu5fOzs7yGqK6ujoWLVpEKBSasM+drLpIpVLl75VMJmltbSWfz5enXZmmSSKRYPPmzXg8Hurq6ojH4+XGPBgMomkauq5jGAYul2vAZ+RyOXRdR9O0cb132LZNPB4nm82Sz+dxOBy43W6CweCob+ila+VyOZxOJ0D5+4zleoNdf9euXaTTaUKhELlcjmw2i8vlolAokE6nyWQy6LqOqkKDu4u6wkbmdrRRld9FtXIctaV35LhUmrad/8e2roWj/n2cSv/GxLlJ4pbpSeqj0lSsD4lbJG451+OWCe94t7W10dDQUHGsoaGBeDxOJpOhu7sb0zQHPaelpWXQa37hC1/gM5/5zIDjTz75JF6v97TK+9RTT53W+882Uh+VpmJ9qKpavrF1d3dP2jrDyagLp9NJMpkEoL6+fsDrpQQZw31nVVUHBL39X3/xxRdPu6xT8XdjNFwuF4lEcZpVU1MTAA47QU1DOzXWH6m1dlNj7sFBuvgGk2EXK9Vae9k1ht/HdDo9luILMW4kbpnepD4qTcX6kLhF4pbxMB3jlmmZ1fz+++/n3nvvLf8cj8eZNWsW119/PcFgcEzXzOfzPPXUU1x33XVDTu84l0h9VJpK9WHbNi+//DJtbW2EQqGKkUPbtolGozQ2NnLZZZeNy3SZwUaoVVVl5cqVAwLP8VT6nqWslA6HA03Tyq9lMhl8Ph+BQIATJ05QU1NDXV1dOftnPB7H4/GwYMEC9u/fTyaTIRAI4HQ6MQyDRCKBx+Nh1apVpzXS3vd3IxaLsWnTJnp6ekilUpimWS6Ppmn4fD6qq6tH9JnRaJRNmzaRyWRwOp10dHRgGAa2bZeTrOTz+dP+Dh0dHbz00kvU1VShpXZDdAO11m6anUepc4w9AYmPCAG/n2hn56h+H0tPAoU4m0jcMvGkPipNpfqQuEXiFolbiia8493Y2Eh7e3vFsfb2doLBIB6PB03T0DRt0HMaGxsHvabL5Rp0+oXD4Tjtm8t4XONsIvVRaSrUR09PD52dnVRVVaGqlUN3pcQbnZ2dpNPp094/MRKJsHnz5ootLQzDIJfLsXXrVi677LKKbRnGe+3W0qVLaW9vp6Ojg0AgUJ6qlcvlcDgc1NXVEY1GMQyDYDCI2+0GQNM0XC4XkUiEzs5OVq9eze7du4lGoySTSXRdp6mpaVy3ldB1nX379pXXcpmmidfrRVEUHA4H6XQa0zTJZDLs27ePxsbGIevGtm327dtHJpMhFApx9OhR8vk8Pp8PKI6ulgL3aDQ64HpWwcIqWKi6iqIpg39Oph06X6bu2HreaD5NTcc+dLLgGf13N22VtlwTbfnZtBXm4mp+Awm7nhpNG/Xv45n+9yWExC3Tm9RHpalQHxK3SNxyqrhlRM6CuGXCO96XX345jz32WMWxp556issvvxwoTslYuXIl69at4+abbwbAsizWrVvHPffcM9HFE2LamazEG/2zc5Zuji6Xi1wuRyaToaWlpTx6fbr7Vg4mHA5zySWX0NnZSS6XwzCMcqbKUCiEpmkkk0mcTueA+uib/fOCCy5gzZo1E5rQJR6PE41GcbvddHV1lbOZlsricrnIZDLU1NSUM5IOdUPvm6DFMAzS6XTF9VwuF6l4itiBGHTDvlf2Yb5kYsZMkq1J0p1p6JOwU1FtVCuOanWhFjpR8x3oajezFx/hgiu24x5lNcTyQU4Ys4iyiMOpJo4mQxTsYhITl8vFArWJ1uPHcbpceDweSQQjphWJW4QYXxK3SNzicrlIp9PlAYdTXQ/TgO5t0PkyRE/+SR0EwH3yz2hMlbhl1B3vZDLJvn37yj8fPHiQbdu2UVtby+zZs7n//vs5fvw43/3udwH4q7/6K77+9a/z0Y9+lA984AP8/ve/5yc/+QmPPvpo+Rr33nsvd9xxB6tWreLSSy/lwQcfJJVKlbOFCiF6uVwudF0vJ+zozzCM8o3kdAy3pQVAIBAo3zjz+Xw5u2RphDmfz9Pa2kosFmP16tVjbsTmz5/PsmXLOHr0aDlLZelmnkwmMQyD2traQb9v38ZcUZQxj6SPZES8FFi43W4syypPLyvRNK1cjkKhMOwNPZfLkc/nMU2TVCo14O86vydPfH0cAwNN0zAMA6VO6T3HzIDRffJPD3a+B9O2MCs+JciODctxuvIsWTX4ulSAvKXTnp/B8dxsjqSb6VbPo8fwY1lW+e/ZUkw0VcGyrPL7TNMkGo1SX18/Lr+PQoyVxC1CnFkSt0jcUrpWoVDA6/UO7Nimj0F0Q28nu2szWGPr+E7luGXUHe9NmzZx9dVXl38urVm64447+Pa3v01raytHjhwpvz5v3jweffRRPvKRj/Af//EfzJw5k29961vlLTkAbr31ViKRCJ/61Kdoa2tjxYoVPPHEExO6DkOI6aqqqopQKERra2vFiC70ZpJsamqiqqrqtD5nJCPUyWSSbDbL3r17Bx1hDofD5RHl/uu6RkpRFJYuXUo8HiedTuNwOLBtm1wuR09PDw6HY8hGdjwa85GOiJcCC8uyylPLdL33FmuaJqqqYtv2KcuUTCbp6uqio6MDM2uS/GOSdCKNy+lCtVSye7PYlo3iVbAsE6eSQc8dhVSs2Nk2MyP+fq9tWM688w/g8hgAdBdqacvP5WhmBhFrAUnHfLCcpFpSpPekcVtuzHyeglnAVMziHqWWjWmbYENGy3C85jh22CZ1SYquri5mz5592r+PQoyVxC1CnFkSt5xbcYtlWaTTaQqFAn6/H6fTWb6WruvkcxlC6mECrd+BvVsg+hKkj475+w4Wtyiai1w+RzKXLK6jt1Nks9nyGvbS97Ntm0KhAIDH4yGVmti4ZdQd7ze+8Y3Dbvz+7W9/e9D3bN26ddjr3nPPPTJFS4gRUBSFJUuWEIvFiEQiBIPBcuKNeDyO1+tlyZIlpz0daaQj1IZhDDnC3Hfa1LBTik4hHA6zevXqckOSSCTQdZ3Zs2cTCoWIx+PYtj3ujXnffSKHGhEvfadgMEgoFOLEiRN4vV6SySSaphU7picbXJ/PRy6Xo6mpiWAwSE9Pz4DR6Egkws6dO8t7flovWLAXDMugoBRw6gpOUnj0DB4zi24lULEgMfrvZ6GRzAX43sN/S+jSALG5PiJKMQmKw+HANE20Vo3EU3HyiTxYUHAUsEwLLDBtE1VRSxcrllezyGVy4IRCtkA4HB6X30chxkriFiHOLIlbzq24xefzYZom2WwWy7IIBXUa7D3M9h9jdvIoAWMXOjnYWSxzPuegq70eI+cc/svZCiZOYsoMMs4FpB3zONDppjNhlffstiwLt9uBjUU+nUfNq+RjeaysBQbkrTzooNgKdt7GtEwsl0XSSpIv5Ml6sxMat0zLrOZCnOuGuqGPZ+KN4UaoARKJBE1NTTidzklZuxUOhwmFQgOmTkWjUTZu3Djujflwa8X6joivXr0aqAwsSmu6UqkUuq5TKBTKCZm8Xi/19fW8+OKLA0ajzzvvPP74xz/S3d1NY2MjbTtaye6PEdDTOEnhVtI41Vx5z0ks8FUlCdQk8Vcl8Vf3/r/TZWBZKpalYJla8f89C7ECK2jZdB47N5qkDRWn0wUKHN0K1hYLfYGNscxAD+rkN+eJ/SFW/H6qgqZqxfJqOi6ni2w2W+zQnCyQqqrlqWqFQoGAN8DKlSvHLRGMEEKI6UnilnMjbolGIzhzR5kXPQGHeqAzj8vOoekmytxWvJfsJpn0Em2dSeeJENHWOuJdVRV5aSrofnDWYDtriGXUirgFwCyY2NksaTNdHDRAIW0Vt/dS1OJJ3WY3uqbj0B1ks1lydg4UsC0bVVGxdIsOdweFmQUCN0xs3CIdbyGmqaFu6OM1QjfUCHWpIfJ4PCxZsqQ8yjjRa7dKZeo/+jxRjflwa8X6joj33Uaib1mOHTtGd3d3OZNpdXU1M2fOpL6+vjzFLegPEn0mSvv2dg77DrN14SuE5hxinv8oM+xDHNtTzXFH85BlvPiNW1iyavfgLzqqIXRZ8U/dZRC6tNh42Tbdi3fzhxd/i6ZWrudSFRVzj0lhe4Euswtd18sNl67pKEoxQ7qmajgcDizLIp/PF9/rUPF4PChKcc2Ux+1h2bJlzJs3bwy1L4QQ4mwjcUvRdI5b+j5FP3ToEDtf20qwsJd57mLcEnYdYutTF3F8/wyK3UydzMm0452tdezYsHyYynKAs+bkn+riH9WJjU02kyWV6xq4Dl3XcLvdxWUGZnFAQGHqxi3S8RZiGjudxBsjMVTjoKoqq1atIhwOY9v2pKzdOlU5h2rMx7pVyFizsPYtSzabxTAMnE4nbrebYDDIiy++WB6N7v79IVIbj+A242g9cZwn0rjcORwL2unUnbS2VG5N1DSvlaq6GKpm0jz/BOEZ0d4XA4sg/HoIXVH8/+ASUCq3bSmNdh89ehRzqUl+a3GfTqfTiaYXGzNN1/BoHnK5HFXBKlzuYuBhWzahZSHCF4dp62wrf79kMomqqtTW1uLz+cjn8xQKBZy1TpYuXSpTzIUQQpRJ3NJbzukYtzitHqpyO/CktnIRfyQUPISuFFO3Fgyd5359Je2HB99WcQDdB87akx3tWnD4gcrvmTNyxXX5mSzZXBZN1UYVt6iqimVZJFPFpHaljriiKLjcLjRNKydYc3vcEx63SMdbCDGs/o2Dpmm8+OKLhEIhYPzXbo21wRmsMR9pgpHBPrO0VswwiknHSu8vZSYtjYg7ncU1SR0dHfh8vnJ5BwQWtk289Y94237KFdoePAf28fsnLyGYP3kbPvkVjayTgzsGjraqmsVlb9qA25vDVl0odasg9AEIX1HsbLuHHyHvu+7L5/PhX+2H+WC0G9idNtYxC5ViR922bDStOIrscDhQVIUVH1jB4rctHhAUpFIpjh07RmdnJ4VCAZfuwqN6uPTSS2WKuRBCiEknccvExC21HTvxmn2SoPV5+JzPOXj2l1cROTZ4u28rKoqjutjBdp18qq32zihQdRVv2FvR785msnR1dGF6TNwBN2aqN6FrXsvj9DrRHcUYyjRNlIJCXXNd70yFPtcK2SEMw8A0TQqFAslUklw2h2VbaIqGpmgsWL5gwuMW6XgLIU6p7w25NEWnr/GaNjWWPTXzmTyHnjlEPp3H3+TH6XNimRbRtiivbXuNbDJL7YJa6ubXDUgwEgqFOHjwILt37yYej6MoCg6Hg1AoxOLFi/F4PBw4cAAoNnKqquL1eqmrqytOFQ8G2bmzmB3kpZdeqixvqA56tkPkBeh4HiLPE8ycYCVgFjSe/e0bKORHdgs2bZ26ZQVa/O+C0BVc9Mb3gz7yXSz7r/sC8Pv9JElSNbuKTCaDx/Sgv6YT2x4rdqDdLnSHTt2SOl73oddRM6+mfL3+DfS8efOGDHCEEEKIyTaV45bhrnWqxGhnKm4ZjpF1sv7nb6Szta58zLR1TIefmpXgmn0ezsxy2rZGyq97672EFoeoW1xHaHGImvk1aM7enrxt27zwwgvYrXa5Ho8cOUIymcTj8ZDJZPD5fcyePbtcd01NTaxZs2ZEgx59By5Kccuam9aM6O/pdEjHWwgxLk537dZIGpz+jVjsSIznPv8cydYkADY2hXwB0zJJxBPk83mcTiepZ1M0vaWJ0BWhcoKRzZs3Y9s2+/btK5/n9/upqqqitbWVtrY2DMMgn8+jqipud7GjG4vF6OnpIRQKkUwmicfjuFwu6mr8+LK78Bz/KVbrXiz2oBbi2DZYpoZZ0IieaKb9SANHds8mnfBWfBePP0Mm6cEG8rabrO3FwI+BjxwaxopGDH89qy9aDZpr0MyiQxls3VcoFCKXy5HJZNA0jaySZcabZ6BdoqF2qyxdspRZy2dRu7D2lH+HpwpwhBBCiKnmTMQt/ZU6gNlslldffXXYxGiTFbf0lg2wi+WwgWR3gGhrHZap4XQbFPIa+ZyTA6/Np6OjsTJu0TQa3tWAurSBS04OGBzdfpRER4LQohD1s+tPO25JpVLE43FyudyoZymcqbhFOt5CiHHT/0mobdsj6iCONBNn3z01j750lJf//WUK2eL+i6V1QKWpRIZhoGt6MSunrtH+ZDvVK6rRvToOh4M9e/aUr1VdXY1lWaRSKQzDoKmpiaNHi9OpFixYQGdnJ+l0Gsuyysk5zGw39doJmh37qc7soCa1BwoWZkHF4SrexA/smM+OjctIdgeGrDMLlYLmYMafdHE0HqJ170wo+NA1HdMysTUb9zyVWctnsWLFCgBeeOGFUY2uD7buy+fzMWPGDKLRKKlUcX/LVCrF7PNnj1uGWSGEEGIqm8y4pb++T8vT6TTRaBS/34/P58Pn81WUcSLiFvVIK0d3ziSd9HJYUziqLULViuudeyLVRI+HMQvagHJbqGQtD1nLS9Zyk7O8oDmKe35bNrjB/SYXs1fMHjxu2aITOjJ+ccusWbOmTdwiHW8hzkFjXY80GqOZfjXSTJyxWIyqYBWvfv9Vdv50Z/mcnJGjp7sH0zTRHcUslnkjj2kV95Esjfp2vdxF+OpweesMp9OJ1+tFVdXyVljpdJr29vZyso3Svpt2po2q3KvUWbuoNrYTtA6iWjZWWmXP1kVs2nUN3R21YBfXY1tmZWKzEltxksNHuuAmY3mxHD6UyzVOZM/D4XMQfEOQfD6Pz+ejvr6eaDRKY2Mj1157LZ2dnWMaXR9sb1PbLq7jrq2txePxUCgUWLNmDbNmzZKEaEIIIaaU6Ry3DJZMrv/Tcl3X6erqIpPJcPz4cWbMmFHufJe++3BxS9uJNgo9BRS/csq4JR31suWZSzi698Kht/Hqo3/cYqo+CgUT0zJRFbW4f7ZtoSoq3gYv3rd4mblspsQtg5COtxDnmPFcj1TSv0E0DINXXnllxDfakWbiTHYl2fbgNlo3t/Z+NjbJZBLTNPGGvCgoxSfeLgOloFBIFjAMA4/mofOlTvyr/CSTSXS9ePvruzWFoii4XK7i2iGli5nOAyxJ/B+h2E685rHKQilgWQov/GbNyW0zevXtdNuaB8VVB846cNWi6D7MVJp4NIIaVvFc7UGp7k16oqoqTqeTZDKJw+GgtraWiy++GEVRxjy63n9v09LIemk03DAMQqEQbrd72jReQgghzg3TOW4ZbC/wwZ6W27aNruvl5GjRaBSv14uiKORyxRl9mqZhdVvk9ufIxDIAKA6FfCxP/EAcNWcQCCRwrXyZ+bO3U+c7SDrpof1wI23xemy7gVzGRduhRmx76LZ+uLjFxgbLQlEUVEVFUZXyf1sei8DNAerm1kncMgTpeAtxDhluPVJPTw/Lli3D7/ePajS5f4NYWncDMHPmzBHdaIca2Sw1bKZpggF/+MIfyLZlKz4/n8+jLdSYc9Mc/FX+8vuPHDlC7FCM9C/SmKaJZVkU0gWiL0QxGg2CweKTZdM00XUdH500qi00uFpocO2iSus8+QGDf2/bhs3rVlV0um3AsFxkbR8GPgpaFU5nkNqaWhQUVF2lbnEds+a4caVd1C6pLTe2pmlWNCr5fJ7a2try9ic9PT1jHl3vm8H12LFj5YEKh8OBaZrlGQGvvPLKiNakCSGEEJNhOsctmqYNuhd4T08PJ06cwOFwlDv+LpcLr9dLMpnE6XSSTqfJJDKktqSIHYmRaE+g9+gU4gVyaq64NzUGLiWJnyR1JHE4DMhC24vQ9uJyYJg9s086VdxSous6TocTV40LzadBAdAgZ+coqAWUgIKyTKF+Qb3ELcOQjrcQ54jh1iP5fD4OHz7M8ePHqa2tLWfIPNVo8mANYiKRIBqN4na7y1tXlQx1oz3VyGYunUN9VqXaqK5oxPKFPM4rnHT6O2mLtKF1aXi9XkKhUDkJR3ZGlsKRApZtgQntT7XDIgi/MUetbysh6zVmOvYTUKL9v96w9v1xIXv/uIis5cHUqkgaDrK2F9NSsWwLl9OF2+HG1EyW3r6URVctwlPjQXNq9PT0EHkmUt7mo8Tr9ZLL5Uin0+TzeS677DJqaorZxE9ndB2KSWQuvfRSfve735HNZstrvgKBAKFQCK/XO6I1aUIIIcRkmM5xS+mJbGlrr76fv2nTpnLHW9MGxi25XA4zbXLokUPkW/MYhoGdt3G6C3j1GA47gUdNoVN57RHVKUpF3JJzeLFnOjAdJgFvgBp/kLyRZ+bymcxdPZeq2VUoqkIsFuPlTS8TaAiUO72lvyOJW0ZOOt5CnCOGWo+USqU4fvw4hUIBVVXx+/1omnbK9TdDNYiapuF0OstPcEtTpUoGu9GeamTTfsUmfzxPj6OH6ppqXE4XtsvGc6OHXFUOV8yF0+lEURSSySS5XI4ZM2YwY8YMrNdbdB7phEIal5KiSk+h74+T3pejesFx5t+4FScGbYcbUFWbUHO0nFykvwIeOqwF7Dq6kF3rLyKRd1AoWPj9ftKkcVzmQG20sXIWzaFmAt4AueoczVc142/wl6/Tv8Eu1U9pqns8Hqe5ubliBHiw0fW+SlPVB3utb937fD6CwSCaplXs7wmcck2aEEIIMVmmW9xSyBRQIgqmy8TdMPCJbKnT39PTg9PpHDJuObrlKInfJXCnLTyluEWNo1m54t7Uo+hfWmgYtpe05cHSq0kavXGLMd/AcZmOqZmotsqMBTOora2ls7OTxW9YTENDQ/k6gRkBGrsaaW1trYgbJG4ZHel4C3GOGGzk0bZtotEo+Xwev99PJpPBsiy8Xu8p19/E4/FBG8TSOmVVVUmn0+RyuYrR0aFutEONbDraHRQOF3C6nRiGQTKZpGpBFcF3BunMdtIcasY0TZLJJF6vF6/XSzqdIhfdwdLadpbOeZmuq2z2bphbnFNl9n7mif0z+L9vvQ3LVMuZO/3VSWYvPoy/OknDghS52oUcN8+j3V5MUpuPYjvY/+R+VFvF5bIxrXTxe79BR5mroGvFrOnhRcVG387YA75r3wY7EokQDAZxOovfLx6PD7otxlCd9dLfYzwep6mpiaqqqmF/B0zTpLq6GlUdmPztVKPPQgghxGSZLnHLE48+QWJLAnOrCbliR96/yk/zLc109nTS0tJCXV0dLS0tpFIpGuoayEVzpKIpPG4PTpykOlO0bvwj3kgS/WicWUoSDWNA3ALgDaQJz4ygOwqkE14ySQ/BuhiNs9vp6W7k6PHzaG8Pk7P8FBQvrno3VtjCV+XDa9tEu6M0X9bM0exRLNsqxy1+v3/I7ypxy/iQjrcQ54jBRh5L04NcLheWZaGqajnp2KnW3ww1hai0TimRSABQKBTKr53qRtt/ZJMcHP3FUTS92CnWdZ0CBc77y/PY07GHqqoqVFUlVFeHyzhKQ+EFZrkO0FS1B5/aAwlAhbmXqXQdCtDZWjfgM/O53vJb6PSk5pFoWY3iqkPdVc2cq+finevFm0uQSXRy5OdHMKIGlmWhO3QcuoOqS6soXFQoJ0EJBAI4nU6i0eiQ3zUcDrN69eryOrNEIoGu6zQ1NQ06VW4sjV5/4zH6LIQQQkyGqR63GCmDw48fpvu73TiSDpyqE8WrFDvwO9O0fL4F50wnnWYnHd/v4Pj+4yhZhQ6rA7NgUsjFUDiKR01RRwpdKSaV8Q3SjM87/yC1DV00zGkjWBun1NQbSgCj+nJ8896F0vBGqLqASxSV7q5uWo+3cuz4MZLpJO3t7cRyxTqpM+twh90E24MSt0wy6XgLcY4YbOSxUCiUG65MJlNOUFIy3EjiUDdDRVEIhUKk02my2Ww5sdlIbrT9RzZPrDtBIdXbAKqqiv9aP0qNgn68g1mFQ9Smt1JtbMXt6xjyu6uaxVXvWM8fn19BZ1stPR3F9UcWOln85JUATn8T7kA9ep9yWabNwacPDtiuzOPxkMlkyOfzOOod1F5Ty9ETR7Ht4tPtUsNf+q7AoPuChsNhQqHQiLdIGW2j1994jD4LIYQQk2Eqxi2pSIp9T+yja28X0V1RUrEUhXgBp9M5YAq4VbDIHsxiGAadgU7MRBqflsZhx3BYcTTt1E9pq8I9XHXzc/iqisnfDCXIEet1dKnLCZ3/TmaefwNOtXKvbQUwLZODRw6W17K7XC6OHj1Kd3d3sawUYy6JWyaXdLyFOEcMNvKoqiq2XdyOy+12D5iaNdxIYjAYHPJm6PV68fv9+P1+TNOks7PzlDfaXCKHnbHRVI18Po+SUuj6Q1f5ddUuEFrQxapljzJ79+dZVjgAsZF/f5fHYPnanRzPL6I1W8+hZy8idthR/p6Fgo57kHaj73Zl5YZVBY/HQ5482hs1FF3B6XRi23Y5KUvpuwK88MILQ26DoijKqNYljbbR62s8Rp+FEEKIyTBV4pbFixejdCls+vkm9v9uP1ahNw+Mqha31CrtY92XahfQzB489OBJpQjY6WI28BFwug3Ov+w1Zl50ghPWeXSa56M0XE3GMRcbhUgkQrInwExl4PTrwdayu1wuZs+eTTQapbu7u/gZErdMOul4C3EO6T/ymM/n0XUdy7Jobm6uyOR5qpHEU90Ma2pquPTSS3E6ncPeaK2CxUtfeYmjLxzFxqa7qxtDM3DkFRxW/OSfGE41wVuv/D+8VhoyI/u+ObWGmHMFEXUZr7bXktJmAQp58sy9fS6ul+JE1hezi9s+m4V/shAjYhA/Fid2qNirL+SL+4DrDr1iNFvTNebcMgd1XrHRu+6666ipqcEwjPJ3jUajA7KnGobB4cOHaWtr45JLLmH+/PmjbjBG2+j1dbqjz0IIIcRkOZNxi9PpJP5anE2f21SOCfrTHXrx/GyOuiV+ZiyNEvtDG5ljWXQ71XuiOcibFVAUG0WxUVWLmoZuauck8SyoozBzLls7l/FC/mTcks0zL9yMW1FRGD6p2FBJ6Xw+H16vt3y+xC2TTzreQpxj+o88JpNJtm3bRldXF6Zp4vf7yefzIxpJ7HszjLRH6O7uRlM16ufUc/5F54/oZrj3sb0cfeEo2CaK0U1QacdKdeCwUyjY5fMWXbwHbyA97LWySg2t5nkcSM1GabwaNbgETmYM7Socxuss3vJyuRymbdJwXQM1q2rIRXNk/BnmXDOnnMWza38Xu3+zm9ZdrcTUGHpBx0yZxX23vTrhq8LUrKgpJ3ZzuVzlLTRg8BHnVCpV3m6ko6ODzs5Oli1bxtKlSye14Tid0WchhBBiMk1U3DJUJ862bI68cIRNP9pE/Gh86ILZJkq+m/NXHGfOonXMrd+CioU9DxJdQTrbasmmPQDojgJuXwa3N4vHl8XtzaI7C/3ilncQDy4hPkTc0nft+XBT6ofbxktRFPx+v8QtZ4h0vIU4B5VGHiORCCdOnChnC+/u7sbhcFBTU8PMmTPLjVD3gW52/nwnuXgOX5OPeGucE/UnsHM22VgWc49J/Lk4hVQBh9NBm6eN0J+FCN868Kac7cnSsaODugVVOI3X2PGNddAZBaMLbGvQm5LuLHD+pTsHHLf0KrocF3K8sJhWexkZfQ4+v5/Ozk5q3bW4Tt6QSxlLTbM45Nw3GYuzxonlsShkChVT02oX1HL5Ry6np6eHZ555Bq/XO2RiD2DAa/1HnEvbn5TWlum6Ti6X4+jRo8Tj8SG3P5kopzP6LIQQQkym0cYtwwmFQixfvpxoNIqiKNTV1VFdXY2iKBzdcJRXv/fq4B1u24J8DLUQZe7SvTSFNhBqOoE3UDkNT1EgWBcnWDfwGsW45WL2n0bcAsNPqR9JQrLSeX1J3DLxpOMtxCSwbXvcR+lO95ql/STT6TS1tbXU19eTTCbp6ekBig2Tw+EgG8/yzKeeIRcrjqpaWyyiHVFe2PIC+UKeZDKJYRjYlo2i9q513v797TSvaqZ2QS0AZq7Azu8/w66fbMJMRCAXBXtki51WXrMJlzcHmhfqr4SGa6HxGtTqFdQpKnosRuPJeggGg7z44osVa7hKGUuTySS2bRMIBMoNzqmmpp0qsUcikSh/bl99R5z7bn9S2h/Utm0MwyAYDJJOp4fc/kQIIYSYbNM5brFte8jrlrYbK61f1jStuH558RLaf9/Oaz98rW+JoZAqxivZCH7vYWYv2seCC/fjr0oNev0BJG4RfUjHW4gJ1v8m3z9JxZm45mDTiYDyaGpbWxvPPPMMjY2N2DtsEpEELmflyKhhGMRisXKmb1VXsWyLXDZHIV+guqaalp9u4fLbujn21LNs+VE36S57QFn6ap5/ggvXvEom5aZgOEDVqFmygMCyO4qNVt2loDkr3qPAgBHQwdZwBYPBcuMcDAaxbZtcLjfk1LS+AcKMGTOGXBPm8XiwLGtAw9N3xNm27fL2J6XzTNNEVVUcDgcOh2PItVpCCCHEZJrucUs4HB70utFolM2bN5NOp/HpPrp/001yX5KDxkG2aFuorqnGpSvljja5KJhpqsI9LL/yNWadd5RT9jFVB4Quh4ZrJG4RA0jHW4gJ1Hd0tpSkIp/P09raSiwWG9U0ndINta2tjZ07d1IoFMZ8zcESb/SdUuT1eos3WFQOPX0IMhQbpD6d71QqVZnpG9BQcOlZ1HwnaudODv0kSeSJFOm4b5BS9KPAijf+kaqFi6hpvKbYaIXXYGseekoj5Ik0VVWOU46uDrWGa/HixQBkMplhM60PFiB4PB6CwSCZTKZiTdjChQt55ZVXBpSh74iz1+vFsqzi3uRQbjxL26CURqAHW6slhBBCTJbpHrdomjbkdffu3Us6nSYUCnHku0dI7U+hAr4+cYttJct5VKvDPSy//DVmLhqmw62oULMS+sUtMYlbxCCk4y3EBBlqdNblchEOh8s3yZFM0ymdG4lEaGtrI5fLUVNTg8/nw+Vyjfqa/RNv9J9SBJBOp0nvTKMZGoZZXEvlrHUyY/UMel7pwUyZ+Oq8+Hw5Aq4TNIb2Mn/GRn7/g6uwUeDkbhun7HTrAXCFWPnBxVTd9l/grO73vTePaYR8qEQcwLBT3YYKOmKxGB6PhwsvvLDc8FRVcmiiNgABAABJREFUVVUkO+mrb/bUnp4ebNumUCigKAq5XA6Hw1H+e8rlckOu1RJCCCEmw9kQt2iaNuC6JZ2dnVQFg2Rf2UN+51GCVg8OK45SSjle2iVMgUuu3sx5F+8ZvMNddX556jj1V0ncIkZMOt5CnEJpxBaKN766uroRrWcZajsHKN7chtsKoq++N9TSDc7r9ZJKpTAMgxkzZuDz+UZ1zf6JN3K5XPn6Zo9J6tUUxnGDQqyAqhUTehiGQXBBkNffex7+3/2WOmMzTcoO3Fak4tqLLt7Lni3nDfnZ4bkp5lzRxL4di0l019K4ah4XvPsCaubXVJw3HqPuQyXiGKpuRhJ0nDhxgjVr1ozod6A0gr1r1y6SySSJRAK3243f7ycUCuHz+U65VksIIYQYjXMtblEUhUKhUE5ApigKPt3H4RcPU5uspXmRi+b88/g7X6V7Y4EjrzbgtwavD91R4NLrX2HO0sO9B92N0HgdNF0HjWvB03TK7y1xixiMdLyFGEbfqTuqqvL888+PeORyuO0cYPitIEr631BTqRS2beN0OnE6naTTaaLRaDnxxUiuCQMTbxQKBSzLghR0/7ibfCqPrumoHhXFtnGRQLO6WD7jNzh+8yyrygUceO3FK1s4sH0+hbx+8hSVHH6UYICVf7WG5be8FUVVWXTy+w3WEIznqPtojFfQ0VdpBHvmzJls3rwZwzCora3F5XL9/+zdeZhcVZn48e+9dWuvruqlqtckZOkmCYEQkphmHUACQRmVccFlFGQUxxmYUfk5CC6AiOK4IKMyMjriruDKqOwEAgmERBJCQpJusm+9VfVS+17390dRN129d6e70915P8+TR/vWrVvnnm7uec+pc95DIpEY0fYnQgghxEicanGLyWQqmgatJlSOPHKI2PajqMlOtv1xA9szISy2FJl0Obms2u/a575tIxXVXQA43FEUs4lW5Wy6LSuoPudayuf9HcMt7pa4RYxE/7++Ebj//vuZO3cuNpuNxsbGAdcJFFxyySUoitLv31VXXWWc89GPfrTf61deeeVYiibEuCmMXLa2tmK35/dhtNvttLa2smnTJvx+/5Dv7z06O5ChtoIo6PtA7b29RCHrZSwWMxqswjUtFgs9PT20t7cb04V6K0wncjgc+P3+fAMWzNH5UCfpaBqzmsVjDlKSbqYs+Tc86V0sbvgbp9U+P2y9uTxRznxnE9p8J9l5Z5JcdDHOd7wV28dX0FNXjg5G2YLBYL+yDXTffcveuyEZTyMJOjKZzKjXNCmKwvz587nkkkuYM2cOiUSCzs5O4vE4NTU1k74lhxCnGolbxKngVIhbTEETb/zmDVr/2Er8mThdf+nC/5Cf1ENdpP6nmZa7/4y6+RnciZ249DbUTH5Lr1TCMmCne/6Z+5m35CAJj5tDpRfyetV/8mLNX2iq/g6vZ9ew85hKTteHLNtA99237BK3CBjDN94PP/wwN998Mw888ACNjY3cd999rFmzhubmZiorK/ud/8c//tHYLw7y6yvOPvts3ve+9xWdd+WVV/KTn/zE+FnWDIiTqe/IJWCsrRnpyOVw2zmMZJpOIpEw1iwVRowL20uYTCZMJpPx0C1c0+12s2PHDjo7O4dcX1SYTrTjbzvYdd8OMm0duJUQDi2KRUlQWPIEsPgtuzn7otcGHfCNU07AsoJdXbUcTjag+3zUXV+Hz3l8fXcikeDo0aM888wzRKPRorItXLgQi8Vi1HEikTjhUfexGMnelyeypmmw9VsyYizExJG4RZwKToW4JXssS/RPUdLBNOlkAlOqG5sSwqG+GbeMhmaiqjFH95JF/KjzHehWX34KvC0ftyjkM4lL3CJxy3gadcf73nvv5YYbbuD6668H4IEHHuDRRx/lwQcf5NZbb+13fnl5edHPDz30EA6Ho18DZrVaqa6uHm1xhJgQfUcue49ujnTqTu8kFQNt5zDcNB2/38/27dsJBAJ0dnZiNptxOBw4nU5jbZPJZEJRFLLZrDGS3dXVRUdHBx6Ph/LycjKZDK2trfj3+qnVa5m7fC7Vy6ohcgBf9+M4Ht9CeWcKRcv2K0P9sj3MOf0IVXPai45nsNFjOZsWfQk7O+sIpL04HE5C0RBlZWX4fD6czuKkaplMBr/fTzqdpqqqylj7dPDgQZqamnC5XGiahqZpOJ1OMpnMhDUkg3G73TidTtra2vD5fNhsNuP3M15rmgZbvyWEmBgSt4hTwUyMW3qvi27f3s76Lz+BOdFKud4BemDAuKUv1ZQjl1XJYSKjukmqLrILbKTOLiPmKyEUkrhlOBK3jJ9RdbxTqRRbtmzhtttuM46pqsrq1avZuHHjiK7x4x//mA984AP9/rjXrVtHZWUlZWVlvPWtb+Xuu++moqJiwGskk8miEaNQKD+NJJ1ODzo9ZjiF9431/TPNqV4fvUc2dV03GrDC/2qaRiwW48iRI2QyGdxu94ANUWlpKStWrGDPnj10dnYa2zlUV1fT0NBAaWnpgHUcCAR45ZVXiMViuN1uYrEYZrPZSExSXl5ONBqlu7s7nxAtm8XlctHW1kYoFMJsNhOLxbDb7VRUVOCIO9jzP0342cl+c4JVq9dT37AeXYeWV9+NovdvCN56zbNFHW5/dg5H0ouJl/0dncrpuNzlKIpCZXmObGsrLpcLl8uF2+3GZrMVNfq6rtPR0UEul8Pr9ea3ICP/95VIJIyR8Dlz5pDJZOju7jZ+B3V1dQOOuldXV+NwOMbtbzQQCBi/p2AwSFdXF263G6/Xi9lsJhwOY7fbqa+vL8oIeqr/t9LXidaH1KMYTxK3nDpO9fqYaXFLRUUFR9fvZ93vf4Q1GCJyrBMlEwWMHUyHVLfgGPMva6HdvICQehGtbbNx4qB6YQmaR6NV4pZT9r+VviYzblH0gRYqDKKlpYW6ujpeeuklzjvvPOP4LbfcwvPPP8+mTZuGfP/mzZtpbGxk06ZNrFq1yjheGE2eN28e+/bt4/Of/zwul4uNGzca+8f1duedd/LlL3+53/Ff//rXxpYCQgiw5TrxJbaw98E46c4U6pt7ZWjmDFf9019JxGw8+Yv+6xLfcsVm5iztoMO0jA7TcjpMy0iqZf3OE2K8xWIxPvShDxEMBnG73Se7OGKak7hFiOnFluvEG99Cx1+76GoyGXFLX1WntVFR3Ymi6pgtGWyOBMFQBW2h01Hn1pA9ewkpU/mA7xViPI0mbpnUrOY//vGPOeuss4oaL4APfOADxv8/66yzWLp0KQsWLGDdunVcdtll/a5z2223cfPNNxs/h0IhZs+ezRVXXDHmQC2dTvP0009z+eWXD7o+41QykvroPdJWGGW12WwAxloXTdOoqKigoaGhaC/FvgqjgYX1I4ONxI71/MEMdA8VFRXU19ezd+9e2trajHKHw2FUVaWtrY1IJILH4zFGOkOhEHa7nZUrVw55nyMRDAZZv349drvdmJIUjUaNpBaFTJ6LFi3irLPOoqKigpdffpmDBw8SiYSY7Whntmk7s5VtVKiHeW39UrKdS4oyKWbSGlueXUF5VffxOgVcVXau+loN2wMX8X9dZVT4KlEUhXQkwrEjR7Db7SQSCWpra/PZQ9X8VXO5HF1dXZxxxhns37+feDxOSUmJMUXN7/fT09PD3LlzKSkpQdd1wuEwR44cwWKxYDabicfjzJ49G5fLBeT/hnp6eigvLycWi43q72k0dF3n5ZdfNn7XvadoJRIJAoEAVVVVXHLJJcb99ibPjmInWh+FbwKFmAokbpk+JG6ZGXGLuaeb9f/3d4Q63YN2umvnt3DRu9ajmHT08pXoVVewPVDH3l5xS1LiFolbRmgy45ZRdby9Xi8mk4n29uL1nu3t7cOuc4pGozz00EPcddddw37O/Pnz8Xq97N27d8AGzGq1DrhGwmw2n/Af0HhcYyYZrD78fj9btmwp2qswFArxxhtvADBnzhwqKipIp9PGNKLBMiD23vpiqKQaYz1/MAPdQ+/yNjQ0EAqFCAQClJSUGO8Jh8M4nU6qqqqMtT1WqxW/38/evXuprq4+oaQT2WyWTCaDxWIxruNyuYw1Uul0mlAoxNKlS6murqYncARrx6NcbNsCx/Zx7MUaAmEHXWo9VscsDjedNuDnHN0zm6P7FoCjEqw+FJuP2neeSXhePQcPPoe71GE8sM1ms7EmqzDdKpVKGVlT0+k0mqZRV1dHZWWl8fuJRCLGFDWz2WxkMw0EAoRCIcLhsJFsxWw2G/tvwvGEIWeffTY2m23Cknr09PTQ2dmJx+MpaqAURcHhcFBZWUksFiMejw+5xkmeHcXGWh9Sh2I8Sdxy6pG4ZXrFLT51Mw41DMCxfbU8++iVZFIDd080c4azLjnE6X9/Ouqsn0PNFSg2Hz09PRK3IHHLiZqMuGVUHW+LxcKKFStYu3YtV199NZAfMVq7di033XTTkO/93e9+RzKZ5MMf/vCwn3P06FE6OzupqRl4g3pxcg20V6Gu6wSDQWNtUSgUorS0dNhsmoWtL/o2In2Taoz1/NHcAxTvt9jR0cGqVatobm429sMsjBhXV1cXrfcb7V6Jhfoa6KE8WIZKRVGMkXmfLYS79aeway2e9udZlc2wY+OZ7Nx43kAfd/xzUdDNHnJmL1h9mOwVRb+PmuU1A25NYbVajaykhWmRhfVCfZN3KIrSLwOm2+3mxRdf5ODBg8TjcdLptPG56XSaZDKJyWTi2LFj1NTU4HQ6jUQkNpttQpN6jMe+pUKIqUniFgESt0zFuKVRT5NKWHjj1dNp2V9LZ+vA+RFULcdpK7OUnlWP98K3UrHkrShq8XIOiVuKSdwydY16qvnNN9/Mddddx8qVK1m1ahX33Xcf0WjUyBZ67bXXUldXxz333FP0vh//+MdcffXV/RKPRCIRvvzlL/Oe97yH6upq9u3bxy233EJ9fT1r1qw5gVsTE2WgvQoL2SoLD9zCHo2F7IoDPdwHawh1XcfhcNDT08Pu3buNRm8kjc5wW2UMdQ8Fvct71llnceGFF9LZ2cmLL75IRUUF1dXVA67hG+mDbriR74G281D0LK7YdlJ/20G2uQ1LLszr9iS181twuErYuu4c/Ef7b4sDkMVEPOcipZahVVcR9WfR4zpKMo0l0YXL5cJqsaJqKlVnVRFJRPo1oIVGKZlMEolEKC8vR1VVEonEgJlOB8qAuXDhQpqamohEIthsNuLxOLlcDl3XUVUVVVXp7u4ml8tRW1tLLBY74UycI9E3YNB13WjUCgHZRGQiFUJMDolbhMQtJyducad2UJHYSGl0A25ayO1UCLSVk4z7aD9czb4dCwb8drsQt5iq3NR8ZCmtTgdHMhm0vRG8PS/1mykwUMdf4haJW6aiUXe83//+9+P3+7n99ttpa2tj2bJlPPHEE1RVVQFw+PDhfusJmpub2bBhA0899VS/65lMJrZv387PfvYzenp6qK2t5YorruArX/mK/MFMUQONtBXW7xQe7IVzCgZ6uPdtRKLRKIFAgFgsZjzYIpEIs2bNYv78+SNudEYycjua0UJFUYyHqMPhIJPJDNiADbZVRO9R4mg0yq5du4Yd+V60aBGRnjYs7X9lrvYa9vZdvPLYWW+OCJe++Q/2ba8fsPxpbMRyJcRyLuJZO5pmxu6z4/1wFeUtKu2PtJPNZEkmkmTSGUrLSpm7ci6aTcNjHXgfT6fTSV1dHYcPHwbyQYqmadTU1IxoupzFYsHlcqEoCj09PcY0L13XURQFTdPI5XLEYjEOHz7MnDlzhty2ZLz0DhicTmfR36Cqqui6zvz58ye8IRVCTAyJW4TELZMft1SmN2PWw8Y1w90u1v/5IoL+ge+zb9ziXOKi5uoawpqCx+EYcqbAYPuPS9wicctUM6bkajfddNOgU7TWrVvX79jChQuLUvT3ZrfbefLJJ8dSDHGSDDSyqGkaqqqSzeb3VFRVFU07/uc10MO9dyMSjUY5duyYcU2TyUQmkyEcDrNlyxZKSkrI5XLjNrVmsGlRQ5UXoKKiwtgrcaCtIvqOdPYeJU6n03R1dZHL5ZgzZ45x7d4j3/t2voR3Vge+o3/myujT+A952LruHLrbLxnyfnRUUkoJCdxktHIsNWUkOmJk4ilsFhOaWaPsbWVU1lSi1Co4K520P9NO7EiMWE+MmCnG/HfPNxqTwfbxjEaj1NXVoes6559/Pk6nc8Rrl5LJpNHgJRIJnE4nZrPZaLTS6TTZbBZFUVBVlTPOOGNUa9/GqnC/bW1t7N27F0VRjDVg8XjcCKQCgcCklEcIMf4kbjm1SdwyeXGLkktCKn+t7o5Sdr58Jv6jPhIxW1G5iuIWUzk5k51sJksmk6b68gpqV9cSDof7zRTwer20tLTwyiuvcO6551JaWipxi8Qt08akZjUXM8NAI4u919Louk5JSYnxgB7s4V5oRFKplPGAdzgcRdN+bDYbqVSKpqYmzjzzzDE1OiO9h4LBygsYiUv6PtgHmrbUd11XNpvF7/eTzWaNLW6cTif2zFG8iQ0sYz2lLbtRWvLBXialsf7/LiKVsAx8E6oVbJVgq0KxeiGTQ1GSVLzdjVajURYrI7Mtgy1nIzY7hnfx8alsjjkOKt9fSSAQINeTI5lLsnHXRg71HDJGgRsbG43Gt7CPZ01NDfX19WzevJnKyspRJZTo/fsu/G4L5TGbzUYCllmzZpFOp/vtmTuRvF4vJSUlxv2kUilUVcXj8VBRUUEsFhvxdMCZbqh1fkIIMRVJ3DI5cUuhRMGAh91/W8yBXfPyW6YUDBC3ZCNRtNNMmGvMmEwmapbVUN9Yz/bt2/u1L4UZBuFwGL/fTygUora2VuIWJG4ZylSKW6TjLUZtsJFFt9tNT08PAG6321hzMtDDHY43IocOHTLWWfXeEiGZTOJyuSgvLycQCACMqdEZzT0M1hgVeL3eQR/svactDbSuKxKJAOByOShJNTO76y8siO7AmTk0YBnbD1X363SncJC1uvEsn4VZXUD0UAx0UDWV+ovqWH7DclKm1PGHy7UeOjo6eOGFF4oam94j9TZHPvOm2WzuN4Wrb7IRj8dTNBVvNHr/vgvfMvT+diGbzeJ2u41ELJM5ZTMYDBKPx5k/fz6AsU6q8DepadqIpwPOZOOVmVcIISaTxC0TG7dkUhrd/lKc7hg7X17C3teOL4NL4SCllJK1eLGVVBttu7PKyYJl1Sy8eiG6Sy+KMzo6OvrNFOg7wwCQuEXilmFNtbhFOt5iTAYbWVy4cCGQn+rS2dk55Fqa3lNlOjo6jHUz2WzW6Ah6vV6sViuRSIRUKjWmRme09zDc2p/BHuy9P7fvui5Fz1Clv8Y8x5PMNe/AaevJn/hmW6DrkM2Y0MxZ4xqth6rRUd6ciuUho3nxvKUa32UVRNIRrA4Tly6/lHJPOSazCZMlv37LgaOovAMl4eg9Up/NZjGZTDgcDmN7kd6jpOP1wC78vnt6egiFQsRiMVwuF7lczvh9V1RUEA6HJyU5SW+F6YMWi2XAPS8lQ+j4ZeYVQoiTQeKW8Y1bALIZlT3bTuf1jWeSTuY7yX3jFl21kdNzZFIZEj09LF6zmMu+cBmabfAuiMQtw5O4ZXhTMW6RjrcYs8Ee5MCIp3T4fD6WL19OZ2cnyWTSmCrjdDqN0efCnolWq5XS0tKiRicUCqHrOm63m9NPPx2v1zsu9zBcIzjcgz2ZTJJLx6nO7qKyewPexIv5JCMDDIa27K/lpUfPJ5sxseCsfZx5SRPUvZW2+MV021QSyfwelFVXVFF5aT5zuV234/f72bNvDxdeeOGQ5e07Pa1vJtfCCH1hlHQ0yV5Gy+fzce6552I2m9mzZw89PT1G8hKPx0MsFht1IDIexrp27lQxmsy8QggxVUncUjro633jlrLIRnY+v4DDRys5pJ+Hyx2lftkeak5rIxJ0Emjx8vrGs4j0uMhhQrf6UB01dMfNRtyCkp+R52304pzvJJQN4TjbgcnaP9FbbxK3DE/ilqFN1bhFOt7ihAz2IB/Ng2/+/PmcccYZHDlyBLfbTSaTIRQK0d7eTi6XI5VK4fV6SaXy2ToKjc6BAwdobm4mFAoRDofZvn07LS0t/aZODdc4jefoKJk4tD5J2b5f887Mo5h7YkOeHux0s+HPF5JMW4nrbjZtX8hLRz9E1Zpawu1hkukQmlkDBUoWlhSVeaQNTd/paZqmGdOlYrGYMUJfqJeJHiX1+XysWbOG008/3fj9FUZrR5ppdLyNde3cqWI0mXknc42bEEKMlsQtfQwSt2xe+5ainVOC/lKO7as7/jZdI667SWsVJHQXpqwZl+4y4hbVrOK9yEvFeRVornx3w5KwSNwyTiRuGdpUjVuk4y1Omt6Ny6xZswiFQnR3dxOJRMhms5jNZrLZLFarlXQ6zfPPP8/y5cuZP38+gUDA2N6itLR0wOkjwOSs60hHoOUxOPIHYk3P4j/kxDerA0dJfMi3dcSrePSP76Q7UUVGdWGxWDFpJpQwHP310fwUtpyOqqloTg1bTXFG0NE0NL2np7W0tJBOpwEoKSnB6/UWPXQmY5RUURTmz5/PvHnzpkTCi7GunTtVjGYbG+l4CyFmqpkYt9DyKGSi9M4oE+52sX/Hgn5vS+UsRHPu/DTyXnGLRcfIIK7ndGyVNuZ8eA72GnvR+yVuGd/ySNwyuKkat0jHW5wUAyU7KGQCTSQSxnYNvacV9fT00NnZyeLFi4nH40NOH9myZQuZTGbi1nWkgnDsr/lGq/VxcqkUr7+8hF2bVqPnFBRV5y2Xb2b+kgOkEhayWRN2V4ygtpDD+gpaTavY84SJVDiFyW7CrKrH04Eq+W1O0pl8I5PTc7jqXf0enqNtaAoj7j09Pbz88st0dXVRU1NTtDao9yhpIenMRDYu4zpqf4LGunbuVCBT2oQQp7qZFreQTRS9HGipoLOtAnSFY/tr0fV8GdOKizgeEpSSVMxoFg2TqThucZzmoO7SOiLhCLlUjqqlVdhd9r4lkLhlnEncMripGrdIx1tMur7JDjRNIxKJ0NLSQjgcpra2FqfTSTqdxu/3G//RaJpGMpnkwIEDhMNhZs2aNeD0kZKSEg4ePEhJSQm1tbVDrusYzQNZ0yMoB38Oxx6Btqcgl59C1tVWzstPXkrQX2qcq+cUNj/ZyOanGkkrbuJ6KWlPGb4PzqV2US32gzrJnX8z1kAVyl7YN1ZVVRTyWSkz6QzO+uLRuLFOI1IUhbKyMlauXMmmTZsIBAIDjpJWVlby4osvDjjqPlUanIkw1rVzM91oprSNNXusEEJMVTMtbunrjVcb2LJ2JZDf/SujuokrpcTxkFOtWCwWPF4XnUc7898gKqDZ8zPxyhvLcZ+ZX9ueCWQoKSkhHA5jc9rGZfqzxC1Dk7hlYFM1bpGOt5hUfZMdxGIx2traiMVixjSl1tZW6uvrCYVCRXtk6rpOKpXC4XDQ1dVlrBHq+3DRdZ14PE5lZeWw6zqGfRhnonD0L5gO/porY49j+lvxf5z7X5/H5icbjZHhNz8FrBVgrwFbNZrJij2dwZrLYd5o5vRzT+eZPzyDoirk9ByqoqJaVBZ8cgHdW7oJvBggl8uhqApOl5OkOUmqKj+iPl7TiIYaJa2srGTPnj2DjrqvWLFi1J83nUyl0eypQqa0CSFOVTMtbukrlbDw2vplYPWCvQalb9xiN/Pun72baCLKk798EkeJg5K6EmPddkEhy/fChQvZtWvXuLcVErcMTuKW/qZq3CIdbzGpeic7iMViRXsyappGIpEgFotx8OBBdF3Hbrcb/1Fks1lUVcVqzY++RiIRksmksX9iQSKRnz5ltVpJJBL99jYcao2RrusEu9qh5XEcgb9g7ngCJRtHBXI5hUPNp5GIW3F5IuRyKn97ehW6rry5fYabtObF4p6NxdYrERoYa0yS/iTPfv5ZFPLlSCaSWCwWat9Vi63aRs1VNXiWeTi2+Rgep4elq5biPMPJwfaD4z6NaKBRUrfbzYsvvjjkdLg9e/aM+TPF9CVT2oQQp6LpHLcMJYeKX13K9jeuJFYyb9C45cz3n4m91I5NtzGncQ6tra2YnMVZyXt/gzhv3jxKSkompK2QuEWMxlSMW6TjLSZVIdmBpmm0tbX1Gxm2Wq3EYrH8tha5nJHwQNd1Y/uIkpISXC4XXV1dpNPpogZM13Uj42VbWxupVIpcLoeqqjgcDrxeLyaTqf+6jlyaYPMfSDT/lPLYC5gpToyWy6o8/6eLaTtYffyzUEjiJmWqIK1VkNVVlDIF02yN9NE0ZtPACR0AFBRcLheZdAbz6Wbsi+1GJtSIOULN5TVF67lmNcyakGlEfUdJe3p6hs0C2dnZOeCekWLmkyltQohTzXSNWwaSQ6NNWUqn4xK67BfS+YbOoQ2HUFIxSlULVosVZ7UTV5ULVVOpWlrFon9YBIzuG8SJbCskbhGjMdXiFul4i0lVGCGORCLGnoyFP35FUY6P9m7LkNqdwq/4UTQFXKC5NWxlNnpO78E93004HCYUCmE2m4se/haLBZPJRDAYpKSkxNiGIhKJkEgkcDgczJ07F0+JC9qfg0MPkTv0OzzpbgZadaTrsOXZFbQdrEZXVLBWEs2WEM44MVvsxhpt37kVVF9VTaArgNfp5cy5Z2JxWsgkMzz7hWfJJrPFdWGxUn9RPSVXldAV7BpyJG6yphGNNAukxWIZ8HUx88mUNiHEqWQkcUsymURRFDKZjJG0qTD12uv1oqoqHo9n0uIWgEiPi0iPi/JZIcynraY5vpTdwQXYclUEXwrSs62ddDiNpmuksikikQiWcgt/94W/o3Ru6YDXHM03iBK3iKliKsUt0vEW42Yke08Wkh3sa95HYneClD+f1duxyoFiVfKjxa1OYjtikIS0ksZkMmGKm1CDKpGjEcLbw1jfYqX+7+txOp10dnYaD//q6mpj5NlkMpFKpVAUBZPJZDx8K0wtLFc3ofz5gxA/BjDglKxI0MmB1+ezf+d8eiK1xNUySiobyOZUwp0BTGaT0el21buoeUdNfnm31UpbsI36knp8s/PTnlbdtIqN927MZy15U+XSSi6+/WJMFlO/aVOF/UAne2RupFkghRBCiOluNHHLgQMHyOVymEymovfncjnKy8vRNI329nYikQgOhwOXy0VFRQUmk4lIJEI0GqW+fuLillxWJZPWOLJnFvt21NPatoA4ZZTOWoRzdimHDx2GSJRcYm+/elBVlUQ8get0F57Thk58Nth0b4lbhBie/CWKcTHQNhu9957MZXN07e2i/bV2gi8H6X6pm1g4hslkQlEVEgcTWC62oEZUshuz2Kz5bJiFB7dJzTdGmWyGXC5H/OU4vB0WLVqExWIhkUiQSqVIJpNs3boVn89HNpslEAgQi8XQUm2cad3K6ZV/o0I9CocGvxc9p7Bt/TJ2bFlFUvUSV0rJKPks4yWKGV3NoNpU1Ey+2XPMcTD7mtnE4jECgQDRaJREIsGGDRuYPXs2ixYtYu4lcymdW0qgOYCe07GX26l7Sx2Kmr+/wkic3+8fNCvnia5FGU2AMVQWyOrqarq7u0+oLEIIIcTJNFzcUlCYYt3R0UFnZyeapmGxWMhms8a32rW1taiqmv+i4M2Oud1uJxAIEIlESKVSxrffw8Ut/jY/oT0htK4gtdp+GsteZ3H5NsyHjidJO7avjp2bzkBVc2jmLKEuN5Ggk7TqIaVWFMctaZXO5k4SnYminVQAspksqVSKbDZLzpyje243GzZsGDbm6P0NosQtQoycdLzFCeu7zUbvbJKdRztxNDkI7g6SiR9vNMrcZWSS+SlZiq6Q8+fI/SlnTLcqZAF1OV0Eg0Gi0Wg+A7iqYjFb0FQN/y4/m/XNNDQ00NHRYTRwgUCAeDxOjdfBKu9OKqNPUZ7ZjtL76+ZBHIsv4NlHL6d1bw2otnx5VBN6JmV0klfeuJI3Um9gVa2YVTOaSytKuFLY29PpdBbvvznXN+j0reHq8UT38BxtgDHUGq6GhgY2b948pnIIIYQQJ9to21ufz8dFF13Ek08+SSAQIJ3Oz8ZzuVx4vV4cDgd+v5958+axcOFCtm7dyp49e0in01gsFsrLy/F4PIRCITZvzscte9btoWVTC8lYkkh3BEvcgs2kYkl04Un5Mesh4sBOGnjj2bk0LHuDhSuaCbR4Wf9/f2fMoEvoDqI5DzHdM2jcoqpq0U4qkO90JxIJFK+CY7ED0wITpXNKRxVzSNwixOhIx1uckL7bbPTOJlnmKGPnt3aipTXKy8tReg2zFrJNdnV1kclmMGtmTJoJPZffeqPQoJXVlVHx7gpadrcQ2xjLb72lqvk9JNs0uqu7eeGFF/B4PHg8HmwWlfL4izTom5kffB1NSQ97D817L+aFp8+iO1KBnntzzbYC2WyGXCKHxWpBURUsPgtn//vZVJxdgXO7k56eHuPhX2iI7XY78Xgcl8uF2+0GGNH+m0PV44ns4Vn4/NEGGEOt4Zoq62SEEEKI0Rpre1tZWcmaNWtY/8J6ApsDcBQySoZWvZVkJonFasEz10PboTayySyupAu7bifbmkVJKiTUBM4FTroSXTz1g6dIrU+hmTXI5bAnQjjVHhxqGJVcvzKnk2Z2bVrCrk1LAMiaXARTLiI5N6jHc830jVsAMpkMOT2HZtKMgQDNraHMVnCc5sBV55K4RYhJIh1vcUJ6b7NRNL0np3P0t0dR4gqpXCqfvbtP4gur1Up5RbkxDSudSqOoClabNZ8FtKKEc+88l5e2vMTceXMJeoK0P9NuvL/zpU5sp9mIZCOcXp6kfM8jlEU34Xb6cbvCQ5Zbd8xCmfuPBM3/wDP//QqhYAgdHZMpn3EcBUwmE1k9i75AZ/7q+ShehT2xPexev5tMJmNsC+J2u4lGo2iaRjweN6aTFepjJPtv9q3HQjbUwihvSUnJyPfw7H2fY2wYh8oCmU4PP5ghhBBCTEWDxS0w/J7Zbosb60tW0pvSpFIp9JyeH5i3WDC7zHT1dNH+t3YCnYH82m41UvT+wPr88rdMJkOpU8GebcGa9aNqQ++1DaCb7CiOOnR7HT2hNOFEOB+3KH3illyWbHWW0y49DbVcJdIcIRPJ4HA6SGpJHF4HZaeV0dLagtlklrhFiEkkHW9xQgbLJtnxbAeRvRFUVc2PtubyI7juOW6qz66m6uwqKpdUEvVHWXfnOsLtYXK5HGaHGZvDhqvGxcpPriRjzRjXL1lcUtTx1nMpkj8+RLmpixYStADQCIC3NsD5f/8iTnfMOD+FnTbzhbiX/iulp78TFJUd391g7KmZTCbJZrOY1HzSNMWiYLvUhlqjEnfEcSpObLb8NK50Ok0mkyGRSJDN5qdr2Ww2Y9pZYTsRoGj/zcHWK/Wux2g0aqxNL2wpYrfbjSypo3EiAcZUygIphBBCjIeRZsHu297Gu+M8fcvTRNuilJeXk0lnjDZaM2vGrL5cLoee01G1/mlb9VwKJ35clm6smSG2/1KgrLKLLGbaO+ajuedidlUBCpl0mlQ6asQt6hwVS7kF2wIb2CChJNA1nUxlBqfTSfXF1Ubc0tHRQSKRoCfYI3GLECeBdLzFCRkom2S4OYz/OT+Qb4AUVcFebueq/7oKV5Wr6P0Wl4Wr/vsquvd3Yyu14Z7lLnrQ9vT0GNe3VduwlJqh048124El14ViGnjddqDFy2M/uYrTlhwkYq0i7FiC8y0XcvYljZS+OT0pm8pyaN0h9JyO2WJGURQjyYharuK40oGz2kk4HCaTyRj3W9g+ZNasWXR0dGC327FYLDidTtxud7+GopBRMxqNsmHDhgHXKxXqMRQK0dHRYdSnyWQim80SCoVQFIVoNDri342u68a6d6vViq7r/co2WIAhhBBCzEQjzYLd+7VkKMm6O9cRbcu3wQrKoB33fuupdR1LLjho3GLSspzRuIuS0jA2Z4IcECmbTVfFJWR8a1jsmU/Hug4OrjuIntWNjn3pGaX43uujq6cr/y16LoOqqrhtbolbhJiipOMtTkjfbJKZcIajvz2Kruug59cW2ew2LvniJf063QUWp4Wqs6qGvH64ZQvzbZtxzzvMG21zhi1XUnfSmSxl76Yl2BxuyivKie0Js33HdsoXlJNNZYl2RMnGskYDadJMeHweXGe5cK1ygQlisRg9PT14PP2311AUBY/HQywWw+v10tPTA1A03cpkMhEKhfB4POzatWvQ9UqrVq2ioqKC119/HV3XcTgcxzO6m0yoan5t+7Fjx5g3b96w66UKU7FaWlqMkeGSkpJ+o9oDBRhCCCHETDWSLNg1NTX59j0Qo+mRJvY+sZdsMlt0HYvLwtxL5+LwOfId4kyOVCRFx44OItsjZGLdlFq6qCp9A6sWpuNIJZlccdhdO7+Fv7v6BRRVpz1XzxupC9jRs5DaqqVcfPHFlJaWoigK85fNZ9lHl9G5p5NQd4htTdsoqy/LzxB0u0gmk6TTaXK5nMQtQkxh0vEWJ6RvNsnYczEysfz0q8IDfOUNK6k6c+CO9ZAyUZRDD3Nu+H8wZzdDFGY1mogFFFr215HLFk/jymAmqpcT08tJk9/qw6SZ8iOmKKBDx/YOOrZ3GO/RzPltQZKJJGWLy5j/8flFU6YikQjZbJbu7m7Kysr6FbEw8jpnzhxSqRRHjx41tgfJZDJks1mcTqcxzWuw9UrNzc3U1tby2muvoet6vuxvjhonk0ksFgs+n29E66X6JiWJx+OEw2FjhLiurg6n09kvwBBCCCFmupFkwZ7lnsWm727i0LpD5DL9k525alxc/o3LsZXail/IROHQNtLNP8PcU5xFO53SOLhrLj2BUgA8FUFqzupku34Ve1MXENKriMVj2Dy2/GzBXluqAthKbdS9pY5avZaAPUBraytWe/7b7Gw2S1dXl8QtQkxx0vEWIzLUXoper5czzjiD7S9s58jfjoCOkSSt/tJ6Vl27asTXAqD7Ndj7P3Dgl5AJ03syl0nLctG7NqDroOdUWo/W8Vrzcvzm+SQXN2B2WOCJINl9CVRFxWK1kMvmjDXmfSkouFwuMukMaoNKd3c3HR0dxhQmh8NBJpMhHA5TVlZGNBrF5Tr+zX1h5LW6uhpFUXjhhReIRCKYTCY0TcPpdKIoCkePHmXWrFlDrleqqamhrKyMTCZDPB7Pr91SVWP9ld1up7Ozc8jpVQMlJfH5fPnkdek0iUQCv9+PqqqEw2EcDgeLFi0adcZRIYQQYiobSdzS3NxMMBjMr9N+sy3XmjRe/uHL9N6BVEc31nTbSm1cfOfFxZ3uIeKWArMlQ8OyveQUC3viS2hKX8761NmoJvObndUYZrOZyspKEonEoG1934EDs9kscYsQ04R0vMWwhtpLETBea/lrC7quY9bM+XVDFW4u/n8XFz0cB71Ww2x80bWw94fQOfxei1nPMkwNH8O15iqCa18mE4uRTaVIZxPY19hxh9xY/BZS7SmS3UnUTP8kJwVWi5W5y+dSelEpTW/kH/6FfbgrKiro7Ow01ih1dnYajVLvkVe3282OHTvweDzMmjWLbDZrTIWKRqO88cYbxojvYOuVdF3H6XRit9sBjPoprM9KJBLDTq8aKCmJ0+mkrq7O2GKjp6cHu91ObW1tv/0whRBCiOlupHFLIdu1y+Wiob6Brie72PfkvqJrJVNJIpFIfsvQxXaclzp5bd9rLFJHF7dk3Pm4JVx2FVvXvkxMj5FKpcgl00WdVZPJRCaTGbKtL2yftXv3bmM6uMQtQkx90vE+SYb91neKCAQCbNmyZcA1Pm1tbSiKQi6XwxK0kDuWw2w2k0nnt9o64wNnYC+zG9caaF9GW7yZyqM/oPTIBmCIDJ8AVh/M/TAsuB6t9CwA3LrOrFlHaW1tpaSkpKjhKHxmTU0NS+cvZddfdtFzpAerw4rL40Kza2g2DVeVi1nnzSKaitLub6eyshKHw2E0HIWkawCRSIR4PI6qqsaUtEWLFhEKhYyGo28Do2n56eyF7cdstuKpaYXRZ6/XW7TurPd5fRvLnp6eAf92BsvW6nQ6cTgcxONxOjs7Wb58OfX19VPyb04IIcTUM9PiFo/Hg5pV6Xylk2M9x9h37z5cqgurpVdStVSSYDiIbYmN0y47DWel88245UcTHrcM1dZDvvOtaRqtra0StwgxTUjH+yTo6Ohg27ZtBAIBdF3Hbrfj8/kmfBRvLI3mnj17iqb/6LpulPnAgQOYTCYWL17M4ccPg0J+erfFQtacJTU/ZWSk7D2VqMpbQlViLbWhv+BONw1TagVq3w4LPp7/X5Ol+NVeU67C4bCxViuZTBoNTGVlJdv3bydQFSBTkSGhJTB7zcxbNK+ovrsj3aiqSmlpKap6/Btyp9NJTU0N2WyWdDpNZ2cnLpeLmpoa43fW3t4+6PYkVmt+X/Kurq58dvZBGqbS0tJh151VVlby4osvDjiK7/P5hszWqiiKMareN6GMEEIIMZiZGLekAin2/2w/qe4U6PnOJDawlFuMfbEdKxxYG6zUnlZKVeI5ajsnL24Zqq0vSKVSErcIMY2MqeN9//33881vfpO2tjbOPvtsvve977Fq1aoBz/3pT3/K9ddfX3TMarWSSCSMn3Vd54477uBHP/oRPT09XHDBBfzgBz+goaFhLMWb0pqbm/utp0kkEkSjUYLBII2NjRPSiA017Wqoz+vs7DQaukLSsUhXhGRrkngojuJX2P6r7ZgwHX+TApWXVtId7jamKQWDQWIdO3mL9gyz2p/CrIeHLrC9Nt9oLfgYOIfOYl6YclW4v3A4jKZp1NTUUFlZaTTChZHvVCrFoUOHaGtrY/ny5cyfP9/YamOwh7/T6TQamhUrVuD1eosCgOEaDo/HQzgcJhQKYTab+zVMhfVKo72X3hlGGxsb+40+D5WtVQghThUSt4zdTIhbYrEY6XSaaDSKyWSiY3cHXX/oIpt4M1O5kv+WN5VKkUlnsNqtnPXJszgSeYHTFYlbJG4RYnyMuuP98MMPc/PNN/PAAw/Q2NjIfffdx5o1a2hubqaysnLA97jdbpqbm42f+45YfeMb3+C73/0uP/vZz5g3bx5f+tKXWLNmDbt27eo3vWU66+jo4IUXXiAcDlNSUoKmaWSzWRKJhLHfYlNTE16vd1xH9Qaa4t33wTdYI1YYDY1Goxw7dozY7hjJdcn81hnpHLquEzPHcNgdmLR851tzalSel+94JxNxaN2Mfce3uSL5NAoD77sNoKOQqngr1iX/nh8lVkf+5+nz+fB6vUUj4263mxdffLFo5Lt3I9zR0UFnZydnnHEGixcvHvbhD1BTU8OCBQv6/X6G254knU7T0NCAw+Ggs7OzqGHqG0SM9F6gOMNoU1MTF1544bCjz5KURAhxKpG4ZexmQtxS6Fiqqko8HicdTXPwjwex5WxG3AL5/bczmQyqVeGSf7NQmvkMiyVukbhFiHE06o73vffeyw033GCMBj/wwAM8+uijPPjgg9x6660DvkdRFKqrqwd8Tdd17rvvPr74xS/yrne9C4Cf//znVFVV8cgjj/CBD3xgtEWcknRdZ9u2bUQiEUpKSoypPZqmYTKZiMXySTb8fv+w2y6M9nP7ZouE/g++wRrNwghwIBDIjwRvzqDo+SlY+ZlYijFFy26ygwLeC7zoepDTeRLvpi9AdA9D7baYVL0csVzBXv1izm28BusY711RlKJ66+npKUrY0bcR1jSNZDLJkSNHCIVCNDY2Dvnwt1qtNDQ0DFhPI9mepDDiPJJpc8PdS99zCxlGg8HgkKPPkpRECHGqkbhlbGZC3JJOp3E4HADkEjkIg75OJxfJkdKOxy0Aei6Nr7KVK9/+K7xtrw5ZRolbJG4RYixG1fFOpVJs2bKF2267zTimqiqrV69m48aNg74vEolw2mmnkcvlWL58OV/72tdYsmQJAAcOHKCtrY3Vq1cb53s8HhobG9m4ceOMacAKWRs1TUPTiqu9MF0omUwSj8dJJBJDJtQYy+eO5ME3UKNZUVHB4cOHicVimGNmYtHY8Rf149fJZrP5ZCU1Wd6y+NfUdT+BmQSkBy9bl/UtHHO+i05LIx2BrnGfStQ7YYeu60WNcGHdVyqVwu12E4vFjJHXgR7+1dXVdHd34/V6B/28kTYcYwlOBks+UlDIMFrYsmOg0eepmghHCCEmisQtYzcT4har1UqmLUPw8SDZYNZIOKZox+OW8nk6f/ee56lJPoFZSTBUsSVuGdu9DETiFnEqGlXHOxAIkM1mqaqqKjpeVVVFU9PAySYWLlzIgw8+yNKlSwkGg3zrW9/i/PPPZ+fOncyaNYu2tjbjGn2vWXitr2QyWbQnYCgUAiCdThtbQ4xW4X1jff9wotEouq6jaRq5XA6TyVT0uslkMpJgvPbaa8RiMWNNU0VFBQ0NDUM+PIf63MJ1CtOOejObzYTDYaLRKE6n01hPE4vlO9jz5s2jvb09v1dkixkdnVwuRzaTXxelVCroNTmqy1tZUbOTJbM3oGUzg5YnjYMDysW0lvwDGft8UqkU4UAXdrud+vp6Y+oaHF/b03va0mgewIW1aKlUPslbPB7HZrMZ1yj8HjRNo6SkhEAgQGdnJ6WlpTQ2NhZ9tt1u55lnnhn272Og9xbKfSJ/W73vZaBtOZLJpPEtRO/PcTqdOJ1OgKK6PRET/d/KdCP1UexE60PqUYwniVvGbibELZpJI/hYkHQwbcQtkG8PHaYoFdo+3n3pb3CkYzDIrqMSt4yNxC1Tl9RHscmMWxR9oKfaIFpaWqirq+Oll17ivPPOM47fcsstPP/882zatGlEhVu8eDEf/OAH+cpXvsJLL73EBRdcQEtLCzU1NcZ511xzDYqi8PDDD/e7xp133smXv/zlfsd//etfG1OKxPjr+L8OIjsixs8lZ9hZ/o5dLEj/GY9+aMj3hpTZHDC/nSPaJWQV+5DnCiFOXbFYjA996EMEg0HcbvfJLo6Y5iRuObVFm6K0/76915Ecdr0LR64NqynKhe/cQN2CYwO+V+IWIcRIjCZuGdU33l6vF5PJRHt7e9Hx9vb2QddC9WU2mznnnHPYu3cvgPG+9vb2ogasvb2dZcuWDXiN2267jZtvvtn4ORQKMXv2bK644ooxB2rpdJqnn36ayy+/fNBpMSdC13VefvllDh06RCKRKEr2kc1mCYVCKIqC1+ulrq7OGN3UdZ1EIkEgEKCqqopLLrmkaMuIkX5uW1tbv/VQhWlM1dXV1NfXs2XLFuLxOG63G03TiEajxv6NZrOZ8L5wfppWLo1H6+bcymc5M/Xa4J+Nil73DnL1N2L3XcwZisIZDD8iHAgEeOWVV4yyFJKqhEIh7HY7K1euHHQUPRAIsGfPHjo7O8lkMmQyGSOLaWHamaIoJJNJzGYzNTU1OJ1OEokEiUSCiy66aMBpYxP99zESveulpKTEWIsVDoeHrZfxNBXqYiqR+ih2ovVR+CZQiPEgccvYTfe4xaSbaH+ynWQqacQtJUonVnOc2YuPsHBFE2WVPcWfLXHLuJK4ZWqS+ig2mXHLqDreFouFFStWsHbtWq6++mogP+1l7dq13HTTTSO6RjabZceOHbz97W8H8lOCqqurWbt2rdFghUIhNm3axL/8y78MeA2r1TrgtBWz2XzCf0DjcY3BLF68mFAoRHd3fr/oZDJJNpslk8lgt9ux2+1UVlYaDVTfbTC6u7tRFIVzzjlnVIkmCp8bCAQGzRbZ3NxMPB4v2vcS8kGL3+8nfTSNKZqglBZKzD2o5Jg9++CAn5czl6M2/DNKwydRnHMGnL012INW13X27t1bVBbIT1myWq34/X727t1LdXV1v+lbfr+fLVu29MuCmslkiMfjRsNps9lwOp14vV5jmlo4HKampoaKioohp4VN5N/HcGpqajj33HONtViRSOSkJh85mXUxFUl9FBtrfUgdivEkccuJmY5xS0VFBXt/s5fEjgRqNl4Utyy7eBsLVzShqsWTPSVumRgSt0xtUh/FJiNuGXVW85tvvpnrrruOlStXsmrVKu677z6i0aiRLfTaa6+lrq6Oe+65B4C77rqLc889l/r6enp6evjmN7/JoUOH+PjHPw7kE2V8+tOf5u6776ahocHYlqO2ttZoJGeK3kks/H4/8XjcGC2ePXs2r7/+uvHL65vJ0mq1EolEaGtrY9OmTaPaN3O45Blms3nQRCbZeJbEHw+R3XsYn96NYso3VhZbCndFsOjcqGkWTfrbmHXB56mqnTumOhprUpWhsqDOmjWLjo4OrFYrqVSKdDpNeXm5sS/rdNquQpKPCCHE6EjcMnbTMW7p2dJDZnsnJdkWbL3iFpszwcLlzUWdbolbJp7ELUIcN+qO9/vf/378fj+33347bW1tLFu2jCeeeMJIMnL48OGiKUXd3d3ccMMNtLW1UVZWxooVK3jppZc444wzjHNuueUWotEon/jEJ+jp6eHCCy/kiSeemFF7YRYM9gAKBoM0NTWRTqexWCz9MlkWMkP6fD7C4fCo980c6sHX3t4+YOZJV9dOun+zD0sg3v96dX4j82e3ZRlHXe/jmL6MeCLJAkfpmOtntFkwC4Zr+DweD/F4nJUrV3Ls2LEpMfI6Vn237BBCCDE4iVtOzHSKW9yRnbT+5RDlmXC/6zUs24NqygESt0w2iVuEyBt1xxvgpptuGnSK1rp164p+/s53vsN3vvOdIa+nKAp33XUXd91111iKM+0M9ADyeDx4vV5aW1spKSkpWtej6zrJZBKXy2VktxxqK42Rfq6u6/T09BAKhcjlcqRSKWxWK+XJvzE7/Es2/66GdKBiwGvVNRyj3b6aI873ErEszE+H8vtPeGuNwj6VhRHzvpLJJLlcjnA4XNQIj7ThczqdXHjhhTLyKoQQpxCJW07MdIlbmp5wokTn9bvOrIajLHxLs8QtQoiTakwdbzH+FEVh0aJFBIPB/JrqNx/gmUzGSKhRGCkebPR0NPx+vzGFK51O09PdSWnoac4peQ5Pbj+Blgq62s7s9z6zPYfrTBc7l34Gi30BFs1CahynPfVuyHtPvYL8NLZDhw6hqipbt2416mTRokXDNnypVApN04ygQEZehRBCiLGbanHLzpeXcHBncae76rQ2LnrvFg5bL+OJ7H2Y7fMlbhFCnDTS8Z5CCmuaXn31Vbq7u4lEIpjNZlwul5FQA4ofxmPh9/vZtGlTPpmHu4Ra6zZOy/4Et34E8rOwOLBzftF7LCVp5v6jhVbvVeDwYQHi8Xi/dVcnOu2pb0NeSKoSDAY5fPgwAHPmzMHj8ZBOp2ltbSUYDLJq1apBG75CcpITHdUWQgghxHFTJW451DSH7RuWFr1HMYPnbTU8af8+msOHGYlbhBAnl3S8pxifz8fq1asBaGtrw+fzGdO04MQfxkYyj2iUxe5m5oV+hiuz33g92OnmUNNp7H2tHoCMbiZCJaXnLyFYPwdzOk0wGMRut7N06VJcLte4T3vqm1QlFArR1dWF2Wxmzpw5uFwuID+9y+fz4ff7aW5uZuHChf0avr5ZUGVqlhBCCDF+TnbcEgk6+dtTq4yfM7qZiFJJ1fuWEayvkbhFCDFlSMf7JNN1vd+6HVVVOeecc9i0aRPhcNiYpjUeD+NgTw9a22Ncof+W0u79Ra91tpXz7MOXkUlr5FQ7YaqJ6mVYLFYqVtWiqmpRo9HS0sKFF144ZDkGur+RlLt3UhW/38/WrVvxeDz9Etf0zhh61llnDZkFte+o9ljLJoQQQpyqpkLcks2YCLR4aTtUxeHm00inzEVxy2n/MJfylfntvyRuEUJMFdLxniAjeTj2Xq+UyWTQNM1Y+zPcVhqjnhql69DyONa/fY7z0q/3ezmT0tj46PkkMm5CSjWWkjnEI1E0k4pzvhNLmcU4d6jtMUZzf8MprGlKJpOoqorFYhnwvN5rx6qqqka0bcWJlk0IIYSYSaZ63KLrcPiN2ex9rYFAi5dsxgRARnEUxS3ly0vxXli857bELUKIqUA63hNgJA/HovVKHg9ms7lo7U9hv8tx2fuwYz1s+xwENmIf5JQNz13CkdA5pLRysrkcFvKfoagKZSvK+p0/XKKUkd7fSIwmAQkMv23FeJZNCCGEmO6mctyixi20tVfTtGURrQdqjFNSuo2YNouUejxu0co06t5VN+AlJW4RQpxs0vEeZyN5OHq93vx6pVisKKFG7+lQvfe7HHMmy+Au2HYrHPvLoKf06DU8t+N97N5ahWbSMJmyWG1WzJb89hbus92UntP/84dKlGKsxxrh/Q1nqIyho107Nt5lE0IIIaazqRa36K/eSrx5LfGInb2vNbJ/53zQj5+SxkYwV0U4W4KmH49bnBVOPKs95N7cq7sviVuEECebdLzH0UgfjmeeeSaBQGDAUeCRTocaUuwY7LgD9v8E9IEboJ5cJX+Lv439gVUEn4mgKFnS6TSaplFaUcrbvvs2Nry2YcCR4eEajWAwOK73N1jG0LGsHRvvsgkhhBDT1VSLW5I7f8XLjzXSsv9d/U5J61Z6cpXEKQNFQVFyZEuzVJxVwfn/cD5zV83lqbVPEQqFjC24et+nxC1CiJNNOt7jaKQPx87OTjKZDGazecDrjHm/y1QQdv0nNN8H2fiAp8RNNRwsuY4DufNoj3TS89cOssksCgqqqlJSUsL5nz4f3ZUfXtY0bdSNRjKZHPf7G6+1YxNRNiGEEGI6mkpxS0+bhRceuZxo0FV0SlbJTymP6WWkUml0JYf1fAuWBRZUu8oFl17AwoUL6ezsBCRuEUJMXdLxHkcjfTjquj6qtT/D0bMZ4jvvx9b8FdR054Dn5Cxetuv/QKvlHegBE9l4lvRjaWwZG7pdR9fz/2ZfNptj5mO8tv41VFU17qm7uxtN00bUaIx2bdNIjcfasYkqmxBCCDHdTJW45cgbs3n58XPJpI+HpbpqIaRXk9KqUUwmLDYTJZVuKi6vQKvSyGazZLNZLBYLGzZsIBAISNwihJjSpOM9jkb6cPR6vSNa++NyuDiy8QjOSiflC8oH/MzuN/6C+bX/hyu9Z8DXs4qdxLx/xbH8do4++ALNDzZjMVl4M3caqkkFHZKpJI45DoILgnQe7qS8vJxUKkVZWRnBYBBN01iyZAnV1dXDNhrjubaprxNaOzbBZRNCCCGmk/GOW0bSdvaNW5q3ns7WZ1ccvx4mso65mDwN5CIRtFpY8N4FWCuOl0/XdTo6OrBarbzyyiuk02mJWyRuEWLKk473OBrpw7G0tHTYtT9zK+fyxL8/QfhYGIC33PgW6q+sP/5hsaMkXv4UZW1/HLAsOV3loHY5r/Nu1EAtK9qjhJ4OYcJkNKSqqpLL5UilUuhWncy5GY61HsNms5HJZCgtLcVqtVJZWYnf78fv97Nw4cJhR2rHc23TeJvKZRNCCCEm03jGLcO2nQPELbv/tphtzy/Lfx4KMcVLmBpsPg9X3XMJtnIbmzdvJhgL4k4c/0y/3088HieVSpFKpSRukbhFiGlBPdkFmEkKD0eHw4Hf7yeRSJDL5UgkEvj9/qKHY2HtT01NDfF4nM7OTuLxONXV1Zwx6wx2fG+H0ekG2PqjrcQ6Y5BNwOt3o/9lIbZBOt2Hsufwm9AXWJ/4IC5fPdFolOcffJ5kOImrxIXVaiWby5JKpUin0+hmHc/fezC5TJSUlGCxWIhGowBEo9F+yTtGYrD7q6mpOenbXkzlsgkhhBCTZTzilmHbzkHilr2v1Rud7pju4Vi6ga5sHd5z6yh7bxlNh5vI5XKcccYZVFdXG5/Z3d1NIpFA0zQURZG4ReIWIaYN+cZ7nI0mmUZh7U+Xv4vW11rpfr2b7r92c6zjWL/rZlNZtt//O85d9hWI7GOgcc3uXA2bs/9Iq76EjDlDOhYjcDBA+0vthF8OY7FYMJlMWMwW3CVuSueWkqpOodQrlFSWcOjQIaMhM5lMAHR2duJ0OsecWGRc9vOcAFO5bEIIIcRkGUvcMuK2s/Vp+Nu/GHFLoMVL05aFtOyrI5sxkdJt9Oh1JClBV3UsK804L3PS3d1N++52o/NfUVHB0qVLcTqdbN++HZPJhMPhkLhlipRNCDEy0vEeIV3X+z3sgAEfgCN9OCaCCXb/YTd7n9hLJp4Z/MOzSQju5MAjx6h3BPHWFr+cwsmm2NvYZ7oClHyCFJPJROhwiH2P7kNP6SgoxvTyZDJJ1YerOGfNOWzfvh2Hw4Gu66iqSjabNRoxgHg8bjRaY0necaJrmybSVC6bEEIIcSImIm6BEbad8XbYejMc+jWQ73DveOlM2g7WAJDDRHe2kpjiozD50vV3LjL1GVpbW9F13fg222q10tbWRigUYvHixUSjUTwej8QtQohpRzreI1DYxzIQCJDJZNA0DbvdDuQf8IVjXq/XGB0e6uGo6zr7ntzHqz9+lUxiiA43OkQPQ3A36GkAnv715Vzwjhfx1fmxuRIctb6NN+wf4Y1gN2YUtDd/o8muJPHH4ihJBdWkoud0VEVFVVVKG0rRvTpvvPEG6XQas9mMoig4HA4ikQgmk8lowHK5HOl02pjSJMk7hBBCiKltvOOWEdNzsO9/4dXPQboHXYdX1q5k77aG/MtAQq0kYppFNJ1GVRUUBRyrHFiWWogH4yiKgt1uJ5PJYLFYivYUl7hFCDGdScd7GH6/n02bNhGLxfB4PJjNZkKhEM3NzQDMmTOHiooK0uk0ra2tBIPBQdfbHHz+IDt+tYNIa2TYz7W7M8zyPoYab6b5lUVFr734lwvAWkbZO88gtMCH1+bF4UgajU8unqP7T93oMR2T2UQul0Mz5b/tVhSFqiuq0Nyase6pkM3U6/WSTCaJxWLYbDbgeHIVj8cjyTuEEEKIKW4845ZRCe+Dl69Hb99ALqeg5zS2rlvOvu0LANAt5YSU2URTGhbVgsmUI5PN4FrmwnFuvgMNYLfbSaVSuFwu49vqwpptiVuEENOZdLyHoOs6TU1NxGIxYzS4MHVL0zTj4V7IolkYkW1qasLr9RY97KMdUTb91yZy6dzAH6ZAxcIK6lbWUOtdR6n/VpRcjFxOobujjI7DVW+eZwbPYnDOof2FNA6PQoAAbreb0M4QHds7yB7NgpJvqHK5HKqiYrFYKF9VjudsD675LnK5nDGNKxgM4vP5cDqd1NXVEQgEiMfjQH6a1pw5c4bc/1IIIYQQJ994xi0j/9Ac7HkAfct/sO25hezZ9j6yGdPx19+MWxTnHGypFOlUGNuZNspqywhmg2TdWSKRCKqqYjKZSCaTWCyWfuWxWCwStwghpjXpeA8hGAwSCASK1jkVRlYLo7CxWIxkMonNZuuXRbP3lK1D6w8N2umee+lczvzgmZS4u+Hlf4L2tcZrqqpzwd+/yJO/uJJYtgHcS8CU/2yLZiG3PkfldZW07m4l8UyCbDrf6VZVlayexaSaKJldwsIbF2KyH28IU6kUZrOZhQsXsmvXLmOLCrvdTmVlJZ2dnQBceumlNDQ0yIixEEIIMcWNZ9wyItHD8PI/kWt5jo2Pn8vhptOKX3fUHY9bFGj8ZCNlbyljz4E9BAIB9Eh+IEDX9fyOK9ksdrud6upqnE5n0aUkbhFCTHfS8R5CMpkkk8lgNpuNY5lMhlwuZ2TPLJxTMFgWzcPrD/e7vsPnYOW/rKRuZS3sfxDWfwYy4X7n2Xy1XHrvx9n8exsdr3eQSafz32SrKlpEg42gHlCxmPOjwXpON77x1twa9R+vL+p0996bc968eZSUlPTLZjpnzhy6u7uZN2+eNF5CCCHENDCeccuQdD0ft2z5DPGeDC89dsnxmXkAJgeULgWbDx2dTDrNoo8soubS/JrrqroqDhw4QHNzM2azmUwmY2QmN5vNOByOPh8ncYsQYvqTjvcQrFYrmqYZa4kAIzN4NpsF8t8sa9rxakylUv2yaIZbw3Tv6y669op/XkHDVQ0oiTZ4/u+h5bGBC1H/STjnm7jNLs6e42f3rt3s/N5OYgdiKKqS37vyhSjRaJRsNovZbEZVVHJ6jowpg/lyM+3hdnw2HxaLhVQqRSgU6rc3Z99spg6Hg8cff5xgMEg2m5VtK4QQQogpbrziliHFW2HTx6HlMVoPVrPxsfNJxnq913kauM8AVSOZShJPx7GtsHHIcohjzx3D6/VSWVnJnj17iMVilJWVYTabSafTdHR0EAqFOHr0KD6fxC1CiJlFOt5D8Hg8eL1eWltbjbVShYd7JBJB13VjqwsoHpH1eDzksjm2/M8W9j6+t+i6Vo+V+rfVo7Q8Di9fB8lA/w93zILGB6HmcqA4WcqcD8zh8P8cJhVOkUgk8knVVFM+scib7cuc987BdbqLtp42ID+1bKi9OftmM21tbQVg/fr1A2Y/FUIIIcTUcqJxy7COPQYvX0cu3smOF89m16Yzjr9mskPp2aiuKpZ/YjmWORa27dqGPWentLTU6Fy3tLTQ1NSEzWZj1qxZRsfYarUya9Ysjh49CkjcIoSYeaTjPQRFUVi0aBHBYNBYS2SxWHC73fT09ADgdrvRdZ1kMtlvRLb5r839Ot0As8+tQX3tP6Dp3oE/eP71sPw7YMk3ggMlS5n9/tkc/MlB0CGZSKJox0d05350LiWnlwDgM/uIxWKsXLkSq9U6ohFgv9/PK6+8AuSzi1oslvHPfiqEEEKIcXWiccugsil47TZoupdY2M6Lf1lNoMV7/HXHbPAswVHp4YLPXUDFwgo2bNhAUk9SWVlZ1Ll2u920tLSgquqA5ff5JG4RQsxM0vEehs/no7Gxsd9aooULFwL5/TA7OzsHHJEdaF032TinaV+Apuf6v2arhsYfQd3fFx0eKFmKq95F1eVVHHvsmJG9PJfLUX5OudHphuNrt6xWK1VVVQyn0MmPx+NGg1cYMR+X7KdCCCGEmDAnErcMKHoY1r8Xuv5GMm5h7cOrifS48q+pVig7G2xV1K6q5dxPn4u1xEpPT0+/uKUgm80a2csLSd56k7hFCDFTScd7BAZaS1SYktX3WOGhnoqm6Hyjs/hCyQDzZz+Cr2SATvesd0Hjj8Fa0e+lgZKlAPgu8WGqMnFo6yGy2SxVi6uoWl7cSI127Vahk+92u/slWjnh7KdCCCGEmHBjiVsG1P4cbLgGkgGScQvr/nDp8U63rRrKzka12Dj7o2ez8J0LizKpDxS3QH7NuaZpZDKZoiRvBRK3CCFmqv7zfEbg/vvvZ+7cudhsNhobG9m8efOg5/7oRz/ioosuoqysjLKyMlavXt3v/I9+9KMoilL078orrxxL0SZMYS1RVVUVpaWlRjn7Hivo2NEBeuEnHSL7uOpdt9N42bMUtXGqFVZ+Hy7604CdbihOltJX2aIy3Oe6MZ1lwnOmB8V0/OKFtVter3dka7cYurGE/Eh0JpMZXfZTIYQQ4iSSuGX4uKWIrsPue+HZy0l0h9n4+Ln88f730NVWDooKpWdCxUqcNWWs/sZqFr2reKr6UHFL4VvpwjffxR8rcYsQYuYadcf74Ycf5uabb+aOO+5g69atnH322axZs4aOjo4Bz1+3bh0f/OAHee6559i4cSOzZ8/miiuu4NixY0XnXXnllbS2thr/fvOb34ztjqaItm35pGboGeh6lSrPc7jLeopPKmmANS/D6TfCAI2fruv09PSQSCRwOp0Eg0F0Xe93nsViweVyEQ6HSSQS5HI5EokEfr9/ZGu3ehmqsYQxZD8VQgghTiKJW0YpE4WXPgSv/j9a9lXx6IN/z8Gd8/KvaU7wXQjOedjK7Kz+z9VUNBz/0kDiFiGEGNyop5rfe++93HDDDVx//fUAPPDAAzz66KM8+OCD3Hrrrf3O/9WvflX08//+7//yhz/8gbVr13Lttdcax61WK9XV1aMtzpTVtq0NsnHo3AzpENWntRWfUPcOOO8XRgK1vgprkgKBgDEdKxKJkEwm+22xUVZWRkNDAx0dHUXruUa0dquP3hlR+zZSo85+KoQQQpxkEreMQvQwPP8O6NlOd0cZ6//vInLZN7+jsVVB2TmgmlE1lQtvuxBHxfH9tiVuEUKIoY2q451KpdiyZQu33XabcUxVVVavXs3GjRtHdI1YLEY6naa8vLzo+Lp166isrKSsrIy3vvWt3H333VRUDDz1eirTdZ22/W10vrEfc+hVlFx+alNVr453ZN5/kFl0Kx6zm4HGc3tvHebxeIwtODKZDIlEgu7ubjRNw2QyUVpaypw5c6iqqqKhoYFQKDTytVsDKGRE7enpMUagrVbrgPtoCiGEEFPZTI1bDr1wiO7D3XRv62ZneGfRvtyjpes6sViMXHg/zkP3o2ZywJkc2DXX6HRnHAvQXfVYLBZqzqlh6YeXUja/zLiGxC1CCDG8UT2pA4FAPolXnyyTVVVVNDU1jegan/vc56itrWX16tXGsSuvvJJ3v/vdzJs3j3379vH5z3+et73tbWzcuLHf+h/AyIRZEAqFAEin04NONRpO4X1jfT/k62fPnj2EnnsarecgClkALLYU5ZXdpHGwUb2RtiMrMbc9T0VFBQ0NDXi9x7fk0HWd3bt3E4vFijJwms1mKioqjGlY8+fPp6WlhUgkwo4dO9i9e3e/62UyGWO0t9Coud3uYRuf0tJSzjnnHLZs2WLsE65pGtXV1TQ0NFBaWnpC9TQdjcffx0whdVFM6qPYidaH1KMYTzM1btm3dh8tf2uh29/N67teH3OnMpVKEY1GUVJ+SnP7UZlT9HoOE93KXJLxUhx1MequncXpy0/H5XUZ5Za4ZWqStuk4qYtiUh/FJjNuUfSBFt8MoqWlhbq6Ol566SXOO+884/gtt9zC888/z6ZNm4Z8/9e//nW+8Y1vsG7dOpYuXTroefv372fBggU888wzXHbZZf1ev/POO/nyl7/c7/ivf/1rHA5Hv+OTaU76GXJPbuSNLacbx2rnt7D8PfvYZLuNqFp3EksnhBBTWywW40Mf+hDBYBC3232yiyOmuZkat7Q93EZsT2zU7xuIXQ/gzh1EoTgczGCn21RPFhvWWiu119UWJXAVQggxurhlVN94e71eTCYT7e3tRcfb29uHXef0rW99i69//es888wzQzZeAPPnz8fr9bJ3794BG7DbbruNm2++2fg5FAoZyU/GGqil02mefvppLr/88kEzYw5G13VefvllSlp+wjm5n/JM++ri1ytM/LTlBpxlNnQ9QDKZxGw2U11dTTwep7q6mnPPPRdFUejo6OCll16ivLwcVVWJRqO0traSTqeNvSk7OztRVZWSkhJqa2txOp1GOQKBANXV1dTX17Nlyxbi8Thut9uY9hUKhbDb7axcubLom/bxrI+ZSOrjOKmLYlIfxU60PgrfBAoxHmZq3LJ+63paelrw+/34fL4xfePd3d2NljyCRz/S77V4zkFbajYmi4piTqOdq+Hv9EvcMo1IfRwndVFM6qPYZMYto+p4WywWVqxYwdq1a7n66qsByOVyrF27lptuumnQ933jG9/gq1/9Kk8++SQrV64c9nOOHj1KZ2cnNTU1A75e2IqiL7PZfMJ/QGO5Rk9PDxVtP2JJ7pfoOYXu9uPrniI5D5stZ6BnzNhzOTRNw263E4vF6OrqorKyks7OTmKxGKWlpTidTmN/S4vFQmdnJ6lUCofDgaIoJBIJstksJSUlpNNpOjs7cTqdxjYhhf0qE4kE8Xi8qEE2mUxYrVb8fj979+6lurp62MZ6POp0JpH6OE7qopjUR7Gx1ofUoRhPMzVuqTqzCpPZRKw5xuyFs1FNo9ukJhFPYNuzicrcq/1e82fqOBg7DV1XsVZYcZ3jwlRhkrhlmpL6OE7qopjUR7HJiFtGnY3j5ptv5rrrrmPlypWsWrWK++67j2g0amQLvfbaa6mrq+Oee+4B4D//8z+5/fbb+fWvf83cuXNpa8snGXO5XLhcLiKRCF/+8pd5z3veQ3V1Nfv27eOWW26hvr6eNWvWjLZ4k0/X0XbdxZLMLwEIdbnJpPPVGsz56MpWoZQrKLpOLpcD8olArFYrsVgMXdeL9pfsnZ2zpKSEWCxmjBjruk4ymcRkMmGxWMjlcsRiMZLJJDabDcBo9BKJBBUVFf0aqN6NXDAYpLS0dJIqSgghhJh8MzFuWfK+JaTTaXoe6+H8t58/umBR14ls/H+4Fn2v30tbE1fwWux8lEgME+Aod6BZ8jGNxC1CCHFiRt3xfv/734/f7+f222+nra2NZcuW8cQTTxiJSw4fPoyqHh95/cEPfkAqleK9731v0XXuuOMO7rzzTkwmE9u3b+dnP/sZPT091NbWcsUVV/CVr3xleuy5uOse0pt/yqu7l5FOWji2N7+GO6TU4U+5wZHN13IaIpEILpcLi8WCyWQimUySSCSK9pcsZOcMBoP4/X5jqlahkTObzaiqSi6XM66RyWSM4qRSKaOxG6whtlgshMPhokQvQgghxEwkcUsfu+7BdfA7/Q7vUD/A2s7lZLMxI1mQxC1CCDF+xrT/xE033TToFK1169YV/Xzw4MEhr2W323nyySfHUoyT743/JvTc13nmoSvJpI5XZVidQ1fSg66nMflMoObXMaXTacLhMCUlJaiqiqIoxONx5syZU7S/pM/no7GxkVdffZXu7m4ikQhmsxmXy0VFRQWdnZ1EIhEsFguqqhrbiBQygXq9XiKRiNH49ZVKpYoaTSGEEGImk7jlTW/8N7z2hX6Hd9puYH3bMjKZICaTCVVVJW4RQohxNvaNH2eoYDBINpsdfj/JA7+CV25k58vnFXW6e5hDKF2GrmfzI+hl+bn/hdHcdDpNNBrFZDKhaRoej2fA/SV9Pp+xdUlbWxs+nw+bzWasiUomk4TDYWO/zEQiYexXuWzZMpqbm2ltbe2XdKXQyNXU1BQ1mkIIIYSYfkYbt/T1N+XjbO9uJJvNTxEvfEstcYsQQowv6Xi/KRAIALB+/XoymQyapuH1elm0aBE+n6/45JbH4eXriASdHNp9mnE441pEOlVKJhJGQUFVVLTq/AitxWIxpmjFYjFcLhfz589nxYoV/a//JlVVOeecc9i0aRPhcBhFUYzpXna7HUVRcDgcdHV1oWkaNTU1RnkVRTGmfbndbiwWC6lUymjkBmo0hRBCCDE9jCVu6Su88Gt0d64g0rEbRVEwm83ouo6maRK3CCHEOJOON+D3+3nllVeA/BQyi8VCOp2mtbWVYDBIY2Pj8UYm2AQvfoBMUuHFv1yIrucbAb1kIbp9LjY1QSKRwGwxo9k15l44l65wF7FYDE3TjC00zj//fJYvXz5sI1KYvtXU1EQgECAcDqNpGnPnzmXhwoVGw9h3pHuw9/Vu5IQQQggx/YwlbkHPFl1DP+tOsrP/hTrbMVpbWykrK8NisZDNZgkEAhK3CCHEODvlO966rtPU1EQ8Hje2+yhkHff5fPj9fpqamvB6vSjpEKy/mlC7znO/u4pY2AFA1n4aPekKbEtSaHM0cs/kyCk5qv6+ipLyElxlLiOZSDabJZvNsmDBghGP3Pp8PrxeL8FgcMDGarzfJ4QQQoipaSxxC+nifWZjc/6ZLd2XEtj7HLFYjJ6eHjKZDNXV1TidThwOh8QtQggxzk75jncwGCQQCOB2u/tlyyzawqKnm9LXrkMPNvPiX95mdLozlio605VkSTL3krnYK+xQmd/bu9vWjTPqxOl0YrPZ0HUdv98/pnVKiqKMaQuNsb5PCCGEEFPPaOMWQs1F50QqrmJd99uJxdvweDzGdQqd71mzZkncIoQQE0Ad/pSZrTCiO9QWFplMBm333dDyVw43z6HHXwpACgctMR/xRIKad9XgqnRhMpmoqqrC5XIRjUZpa2sjm82SSCTw+/2yTkkIIYQQYzbauKW3buby0IErOHzkKA6HA6vVKnGLEEJMklP+G2+r1YqmacaelZCfxtV7ilWl0ozr4HfI5RS2v7gUgBxmQtrp5HJZbO+0EfFFiEajOJ35b7jr6upoa2sjEonQ1taGw+GQdUpCCCGEOCGjiVt6SykeXi25g2R3lGw2Q0tLC3V1dRK3CCHEJDnlO94ejwev10traytWq5VoNEpnZyexWIxcLkc2FeHSyu+DAoebTiPSXYKOQthyOtmcBftl4J7rJhKJ0NLSwqxZs7DZbDidTubOnUtbWxvnnHMOtbW1sk5JCCGEECdkNHFLQQ6VXeV3Ekt7gSgul0viFiGEmGSnfMdbURQWLVpET08PuVyOo0ePGlO4stksF5U+g1tpB+DArnkAxLQ5pFU39loLydOOr4uKRqMkk0lKSkrwer2YTCYcDge1tbWyXkkIIYQQJ2w0cUvBAfcN9FiXoekJcrmcxC1CCHESnPJrvCGfRXPFihUAJBIJAHK5HPPcfpZZn80fj9poP1RFWnERN9UA4F7lJp1Ok0wmUVUVk8mEyWQiEolw9OhR/H4/Xq931AlJhBBCCCEGM5K4pSBoXsQR5/sAyGQypFIpiVuEEOIkOOW/8S6wWCwAzJs3D5PJhNmU44LwN1AyOQAON88hq6sETXNBUVDNKomKBFosX4WZTAYATcv/HA6HUVWVhQsXyjQtIYQQQoyr4eIWgIxuYot2I6piQtd1Ojs7jThF4hYhhJhc0vEGnr/refat30ewJ4hJM6EoCvZMC3/JnAecZ5wXzFYR1zXsGjhOd5DIJHA6neRyOYLBIIqikEqlUFUVj8eDw+EwGkYhhBBCiPFS2ErM5crvqDI7/BtcmYNF57ySuIqjWTezS/PJ12KxmMQtQghxkkjH+00m1QSAntMxqTnsmWNFr6cVJzFTDZpqIpVKYW4wE81Gjcyibrcbn8+HxWJB0zTMZjNdXV399tgUQgghhDhRVqsVgHQ6jVWJMyfy66LXg6Z6Dtrfi8vmxO/3o2ka2WxW4hYhhDhJpOP9pt5Tr1ymdlQyRa/3MAeb3Y7L6SJBAm2WRro9v5VHISmJ0+k0zk8kEmiaZjSMQgghhBDjxe12AxAKhVhk+wtmPVL0+mY+Rt3suSxcuJDm5mZaWlqMLcgkbhFCiMknHe8+NDWHLdNadCxOGTlTCW6XC6fHyer/txrn6U5efvllurq6qKmpQVWP56nTdZ1QKERNTY0kKBFCCCHEuCusw/ZY09RFfl/02hHlXJLOM1n25h7cPp+Pnp4eiVuEEOIkko43sPKTK1nyoSU899xznOfbgevQX4zXdFR2VH6bWQsvpryinJLaEkzm/LT0lStXsmnTJgKBAG63G4vFQiqVIhQK4XA4WLRokSQoEUIIIcSEOa+6GfO+hPGzjkp71Y00ntWIz+cD8p30srIyiVuEEOIkko434Kx0YimzYPGZqVN/g+ILGq+lat7DRZd8dMCGyOfz0djYSFNTE4FAgHA4jKZp1NTUsOjNUWYhhBBCiAmh65S0/6boULrmH1hxyT9K3CKEEFOMdLx78eZeR4ntLTpmWfJpGGL01+fz4fV6CQaDJJNJrFYrHo9HRoyFEEIIMaEkbhFCiOlDOt69nJZ+qviAezH4Lhj2fYqiUFpaOjGFEkIIIYQYgMQtQggxfajDn3KKyGWozm4qPlb/iSFHjYUQQgghTgqJW4QQYlqRjndBdB8aqeJjp33w5JRFCCGEEGIoErcIIcS0Ih3vNynBXcUHbFVgrzo5hRFCCCGEGILELUIIMb1Ix/tNSmh38QHPGSenIEIIIYQQw5C4RQghphfpeL+pfwO25OQURAghhBBiGBK3CCHE9CId7zcpoT5TtmTkWAghhBBTlMQtQggxvUjHGyCXgfAbxcfc0oAJIYQQYgqSuEUIIaadMXW877//fubOnYvNZqOxsZHNmzcPef7vfvc7Fi1ahM1m46yzzuKxxx4rel3XdW6//XZqamqw2+2sXr2aPXv2jKVoYxM5gJJLFh+TKVtCCCHEjCBxixBCiJNt1B3vhx9+mJtvvpk77riDrVu3cvbZZ7NmzRo6OjoGPP+ll17igx/8IB/72Md49dVXufrqq7n66qt5/fXXjXO+8Y1v8N3vfpcHHniATZs24XQ6WbNmDYlEYux3NhrBncU/W31g807OZwshhBBiwkjcIoQQYioYdcf73nvv5YYbbuD666/njDPO4IEHHsDhcPDggw8OeP5//dd/ceWVV/If//EfLF68mK985SssX76c73//+0B+1Pi+++7ji1/8Iu9617tYunQpP//5z2lpaeGRRx45oZsbMVknJYQQQsxIErcIIYSYCrTRnJxKpdiyZQu33XabcUxVVVavXs3GjRsHfM/GjRu5+eabi46tWbPGaJwOHDhAW1sbq1evNl73eDw0NjayceNGPvCBD/S7ZjKZJJk8PsUqFAoBkE6nSafTo7klAEzdO4pGILIli8mmUoRCIZLJJFarFbfbjaIoo772dFWox7HU50wk9XGc1EUxqY9iJ1ofUo9iPEncInHLqUrq4zipi2JSH8UmM24ZVcc7EAiQzWapqqoqOl5VVUVTU9OA72lraxvw/La2NuP1wrHBzunrnnvu4ctf/nK/40899RQOh2NkN9PLxfFNlPb6eeeRHAfaHh/1dWaip59++mQXYUqR+jhO6qKY1EexsdZHLBYb55KIU5nELaceeRYXk/o4TuqimNRHscmIW0bV8Z4qbrvttqLR6FAoxOzZs7niiitwu92ju5iuoz39JfSgikIOgIhpDslkErfbjdlsJp1OEwqFsNvtrFy5Eq935q+jSqfTPP3001x++eWYzeaTXZyTTurjOKmLYlIfxU60PgrfBAoxk0jcMvHkWVxM6uM4qYtiUh/FJjNuGVXH2+v1YjKZaG9vLzre3t5OdXX1gO+prq4e8vzC/7a3t1NTU1N0zrJlywa8ptVqxWq19jtuNpvH9gd01XbSiRAbHnuQakcX7cFafJU+Y4qWyWTCarXi9/vZu3cv1dXVp8z0rTHX6Qwl9XGc1EUxqY9iY60PqUMxniRukbjlVCf1cZzURTGpj2KTEbeMKrmaxWJhxYoVrF271jiWy+VYu3Yt55133oDvOe+884rOh/xX+YXz582bR3V1ddE5oVCITZs2DXrNCWGyEzLNoyl+Do7S2n4NlKIouN1uAoEAwWBw8solhBBCiDGRuEXiFiGEmCpGPdX85ptv5rrrrmPlypWsWrWK++67j2g0yvXXXw/AtddeS11dHffccw8An/rUp7j44ov59re/zVVXXcVDDz3EK6+8wg9/+EMg3zB8+tOf5u6776ahoYF58+bxpS99idraWq6++urxu9MRymQyg45cWCwWwuFwUYIUIYQQQkxdErdI3CKEEFPBqDve73//+/H7/dx+++20tbWxbNkynnjiCSPJyOHDh1HV41+kn3/++fz617/mi1/8Ip///OdpaGjgkUce4cwzzzTOueWWW4hGo3ziE5+gp6eHCy+8kCeeeAKbzTYOtzg6mqaRTqcHnBKWSqUwmUwkk0na29uxWq14PJ5TZvqWEEIIMd1I3CJxixBCTAVjSq520003cdNNNw342rp16/ode9/73sf73ve+Qa+nKAp33XUXd91111iKM64qKipoa2vD5/MVNUy6ruP3+wF45ZVXyGazaJqG1+tl0aJF+Hy+k1VkIYQQQgxB4haJW4QQ4mQb1RrvU0FDQwMOhwO/308ikSCXy5FIJDh69KiRtc7pdFJRUYHD4aC1tZVNmzYZjZsQQgghxGSRuEUIIaYH6Xj34fV6aWxspKamhng8Tmdnp7E/m9vtZtasWVitVlRVxWq14vP5iMViNDU1oev6SS69EEIIIU4lErcIIcT0MC338Z5oPp8Pr9dLMBgkmUySTCZ55ZVXcDqdw2YNLS0tPTmFFkIIIcQpSeIWIYSY+uQb70EoikJpaSlVVVVYrVay2eyQWUMzmYxkDRVCCCHESSFxixBCTG3S8SafgKSwx2UwGOw39cpqtRpZQweSSqXQNG3AjKJCCCGEEONJ4hYhhJh+Tvmp5n6/n6amJgKBAKqqsn79+n4ZPz0eD16vl9bW1gGzhoZCIWpqavB4PCfrNoQQQghxCpC4RQghpqdT+htvv9/Ppk2baG1txW63A2C32/tl/FQUhUWLFg2YNdTv9+NwOFi0aJHsiymEEEKICSNxixBCTF+nbMdb13WampqIxWL4fD5jutVgGT99Pl+/rKHxeJyamhoaGxtlP0whhBBCTBiJW4QQYno7ZaeaB4NBAoEAHo8HRVGK1kcNlvGzb9ZQq9VqvF8IIYQQYqJI3CKEENPbKdvxTiaTZDKZITN+hsPhfhk/C1lDhRBCCCEmi8QtQggxvZ2yU80l46cQQgghpguJW4QQYno7ZTvehYyfA23DUcj46fV6JeOnEEIIIU46iVuEEGJ6O2U73gNl/AQk46cQQgghphyJW4QQYno7ZTveUJzxs3cDJhk/hRBCCDHVSNwihBDT1ymbXK2gkPGzs7OTF198kYsuuoiKigoZMRZCCCHElCNxixBCTE+n9DfeBYqiGGuiZJsNIYQQQkxlErcIIcT0Ix1vIYQQQgghhBBiAs2IqeaF7J6hUGjM10in08RiMUKh0KB7ZJ5KpD6KSX0cJ3VRTOqj2InWR+E53jdrsxAzicQt40/qo5jUx3FSF8WkPopNZtwyIzre4XAYgNmzZ5/kkgghhBgP4XBYtkUSM5bELUIIMbOMJG5R9BnwtUIul6OlpYWSkpIxr3MKhULMnj2bI0eO4Ha7x7mE04/URzGpj+OkLopJfRQ70frQdZ1wOExtbS2qKquhxMwkccv4k/ooJvVxnNRFMamPYpMZt8yIb7xVVWXWrFnjci232y1/hL1IfRST+jhO6qKY1EexE6kP+aZbzHQSt0wcqY9iUh/HSV0Uk/ooNhlxi3ydIIQQQgghhBBCTCDpeAshhBBCCCGEEBNIOt5vslqt3HHHHVit1pNdlClB6qOY1MdxUhfFpD6KSX0IMTnkv7ViUh/FpD6Ok7ooJvVRbDLrY0YkVxNCCCGEEEIIIaYq+cZbCCGEEEIIIYSYQNLxFkIIIYQQQgghJpB0vIUQQgghhBBCiAl0SnW877//fubOnYvNZqOxsZHNmzcPef7vfvc7Fi1ahM1m46yzzuKxxx6bpJJOjtHUx49+9CMuuugiysrKKCsrY/Xq1cPW33Qz2r+PgoceeghFUbj66qsntoCTaLR10dPTw4033khNTQ1Wq5XTTz99Rv33Mtr6uO+++1i4cCF2u53Zs2fzmc98hkQiMUmlnTgvvPAC73jHO6itrUVRFB555JFh37Nu3TqWL1+O1Wqlvr6en/70pxNeTiFmColbikncUkziluMkbikmcUvelItb9FPEQw89pFssFv3BBx/Ud+7cqd9www16aWmp3t7ePuD5L774om4ymfRvfOMb+q5du/QvfvGLutls1nfs2DHJJZ8Yo62PD33oQ/r999+vv/rqq/ru3bv1j370o7rH49GPHj06ySWfGKOtj4IDBw7odXV1+kUXXaS/613vmpzCTrDR1kUymdRXrlypv/3tb9c3bNigHzhwQF+3bp2+bdu2SS75xBhtffzqV7/SrVar/qtf/Uo/cOCA/uSTT+o1NTX6Zz7zmUku+fh77LHH9C984Qv6H//4Rx3Q//SnPw15/v79+3WHw6HffPPN+q5du/Tvfe97uslk0p944onJKbAQ05jELcUkbikmcctxErcUk7jluKkWt5wyHe9Vq1bpN954o/FzNpvVa2tr9XvuuWfA86+55hr9qquuKjrW2Nio//M///OElnOyjLY++spkMnpJSYn+s5/9bKKKOKnGUh+ZTEY///zz9f/93//Vr7vuuhnTgI22Ln7wgx/o8+fP11Op1GQVcVKNtj5uvPFG/a1vfWvRsZtvvlm/4IILJrSck20kDdgtt9yiL1mypOjY+9//fn3NmjUTWDIhZgaJW4pJ3FJM4pbjJG4pJnHLwKZC3HJKTDVPpVJs2bKF1atXG8dUVWX16tVs3LhxwPds3Lix6HyANWvWDHr+dDKW+ugrFouRTqcpLy+fqGJOmrHWx1133UVlZSUf+9jHJqOYk2IsdfHnP/+Z8847jxtvvJGqqirOPPNMvva1r5HNZier2BNmLPVx/vnns2XLFmNa1/79+3nsscd4+9vfPillnkpm8nNUiIkkcUsxiVuKSdxynMQtxSRuOTET/RzVxuUqU1wgECCbzVJVVVV0vKqqiqampgHf09bWNuD5bW1tE1bOyTKW+ujrc5/7HLW1tf3+OKejsdTHhg0b+PGPf8y2bdsmoYSTZyx1sX//fp599ln+8R//kccee4y9e/fyr//6r6TTae64447JKPaEGUt9fOhDHyIQCHDhhRei6zqZTIZPfvKTfP7zn5+MIk8pgz1HQ6EQ8Xgcu91+kkomxNQmcUsxiVuKSdxynMQtxSRuOTETHbecEt94i/H19a9/nYceeog//elP2Gy2k12cSRcOh/nIRz7Cj370I7xe78kuzkmXy+WorKzkhz/8IStWrOD9738/X/jCF3jggQdOdtFOinXr1vG1r32N//7v/2br1q388Y9/5NFHH+UrX/nKyS6aEEKckiRukbilN4lbikncMnlOiW+8vV4vJpOJ9vb2ouPt7e1UV1cP+J7q6upRnT+djKU+Cr71rW/x9a9/nWeeeYalS5dOZDEnzWjrY9++fRw8eJB3vOMdxrFcLgeApmk0NzezYMGCiS30BBnL30ZNTQ1msxmTyWQcW7x4MW1tbaRSKSwWy4SWeSKNpT6+9KUv8ZGPfISPf/zjAJx11llEo1E+8YlP8IUvfAFVPXXGOwd7jrrdbvm2W4ghSNxSTOKWYhK3HCdxSzGJW07MRMctp0RNWiwWVqxYwdq1a41juVyOtWvXct555w34nvPOO6/ofICnn3560POnk7HUB8A3vvENvvKVr/DEE0+wcuXKySjqpBhtfSxatIgdO3awbds249873/lOLr30UrZt28bs2bMns/jjaix/GxdccAF79+41GnGAN954g5qammndeMHY6iMWi/VrpAqNez63x6ljJj9HhZhIErcUk7ilmMQtx0ncUkzilhMz4c/RcUnRNg089NBDutVq1X/605/qu3bt0j/xiU/opaWleltbm67ruv6Rj3xEv/XWW43zX3zxRV3TNP1b3/qWvnv3bv2OO+6YcdtyjKY+vv71r+sWi0X//e9/r7e2thr/wuHwybqFcTXa+uhrJmUHHW1dHD58WC8pKdFvuukmvbm5Wf/rX/+qV1ZW6nfffffJuoVxNdr6uOOOO/SSkhL9N7/5jb5//379qaee0hcsWKBfc801J+sWxk04HNZfffVV/dVXX9UB/d5779VfffVV/dChQ7qu6/qtt96qf+QjHzHOL2zL8R//8R/67t279fvvv1+2ExNihCRuKSZxSzGJW46TuKWYxC3HTbW45ZTpeOu6rn/ve9/T58yZo1ssFn3VqlX6yy+/bLx28cUX69ddd13R+b/97W/1008/XbdYLPqSJUv0Rx99dJJLPLFGUx+nnXaaDvT7d8cdd0x+wSfIaP8+eptJDZiuj74uXnrpJb2xsVG3Wq36/Pnz9a9+9at6JpOZ5FJPnNHURzqd1u+88059wYIFus1m02fPnq3/67/+q97d3T35BR9nzz333IDPgcL9X3fddfrFF1/c7z3Lli3TLRaLPn/+fP0nP/nJpJdbiOlK4pZiErcUk7jlOIlbiknckjfV4hZF10+xOQRCCCGEEEIIIcQkOiXWeAshhBBCCCGEECeLdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFEEIIIYQQQogJJB1vIYQQQgghhBBiAknHWwghhBBCCCGEmEDS8RZCCCGEEEIIISaQdLyFmMLWrVuHoiisW7duxO/56U9/iqIovPLKKxNXMCGEEEKcsiQ+EWL0pOMtxCmq0AAO9K+tra3f+X/+859Zvnw5NpuNOXPmcMcdd5DJZE5CyYUQQggxU7W2tnLrrbdy6aWXUlJSMmwH/6WXXuLCCy/E4XBQXV3Nv//7vxOJRPqdl0wm+dznPkdtbS12u53GxkaefvrpCbwTIYppJ7sAQoiT66677mLevHlFx0pLS4t+fvzxx7n66qu55JJL+N73vseOHTu4++676ejo4Ac/+MEkllYIIYQQM1lzczP/+Z//SUNDA2eddRYbN24c9Nxt27Zx2WWXsXjxYu69916OHj3Kt771Lfbs2cPjjz9edO5HP/pRfv/73/PpT3+ahoYGfvrTn/L2t7+d5557jgsvvHCib0sI6XgLMRFyuRypVAqbzXayizKst73tbaxcuXLIcz772c+ydOlSnnrqKTQt/9hwu9187Wtf41Of+hSLFi2ajKIKIYQQ4gRMh/hkxYoVdHZ2Ul5ezu9//3ve9773DXru5z//ecrKyli3bh1utxuAuXPncsMNN/DUU09xxRVXALB582YeeughvvnNb/LZz34WgGuvvZYzzzyTW265hZdeemnib0yc8mSquRBDuPPOO1EUhaamJq655hrcbjcVFRV86lOfIpFIGOcpisJNN93Er371K5YsWYLVauWJJ54A4NixY/zTP/0TVVVVWK1WlixZwoMPPtjvs44ePcrVV1+N0+mksrKSz3zmMySTyXG5j+7ublatWsWsWbNobm7u93o4HCabzQ743l27drFr1y4+8YlPGJ1ugH/9139F13V+//vfj0sZhRBCCDEyMzk+KSkpoby8fNj3hkIhnn76aT784Q8bnW7Id6hdLhe//e1vjWO///3vMZlMfOITnzCO2Ww2Pvaxj7Fx40aOHDkyLvcjxFDkG28hRuCaa65h7ty53HPPPbz88st897vfpbu7m5///OfGOc8++yy//e1vuemmm/B6vcydO5f29nbOPfdco+Hz+Xw8/vjjfOxjHyMUCvHpT38agHg8zmWXXcbhw4f593//d2pra/nFL37Bs88+e8JlDwQCXH755XR1dfH888+zYMGCotcvvfRSIpEIFouFNWvW8O1vf5uGhgbj9VdffRWg37fitbW1zJo1y3hdCCGEEJNrJscnw9mxYweZTKZffGKxWFi2bFlRfPLqq69y+umnF3XQAVatWgXkp6zPnj17jHcixMhIx1uIEZg3bx7/93//B8CNN96I2+3mv//7v40p2JBfk7Rjxw7OOOMM430f//jHyWaz7Nixg4qKCgA++clP8sEPfpA777yTf/7nf8Zut/PDH/6QN954g9/+9rfGlKobbriBs88++4TK3dbWxurVq4nH47zwwgucdtppxmsOh4OPfvSjXHrppbjdbrZs2cK9997L+eefz9atW40GqLW1FYCampp+16+pqaGlpeWEyiiEEEKIsZmJ8clIDRefrF+/vujcwc4DJJYRk0KmmgsxAjfeeGPRz//2b/8GwGOPPWYcu/jii4saNV3X+cMf/sA73vEOdF0nEAgY/9asWUMwGGTr1q3GdWpqanjve99rvN/hcBRNiRqto0ePcvHFF5NOpwds1K655hp+8pOfcO2113L11Vfzla98hSeffJLOzk6++tWvGufF43EArFZrv8+w2WzG60IIIYSYXDMxPhmp0cQn8Xh80PN6X0uIiSTfeAsxAr2nXgMsWLAAVVU5ePCgcaxvZnC/309PTw8//OEP+eEPfzjgdTs6OgA4dOgQ9fX1KIpS9PrChQvHXOaPfOQjaJrG7t27qa6uHtF7LrzwQhobG3nmmWeMY3a7HWDA9VyJRMJ4XQghhBCT61SJTwYymvjEbrcPel7vawkxkaTjLcQY9G2AoP9DO5fLAfDhD3+Y6667bsDrFKaBTYR3v/vd/PznP+e//uu/uOeee0b8vtmzZxclYCtMw2ptbe23/qm1tdVYHyWEEEKIk2smxyd99Y5P+mptbaW2trbo3GPHjg14HlB0rhATRTreQozAnj17ikaM9+7dSy6XY+7cuYO+x+fzUVJSQjabZfXq1UNe/7TTTuP1119H1/WiRnOgDOQj9W//9m/U19dz++234/F4uPXWW0f0vv379+Pz+Yyfly1bBsArr7xS1MluaWnh6NGjJzTdTAghhBBjdyrFJ32deeaZaJrGK6+8wjXXXGMcT6VSbNu2rejYsmXLeO655wiFQkUJ1jZt2mS8LsREkzXeQozA/fffX/Tz9773PSC/B/ZgTCYT73nPe/jDH/7A66+/3u91v99v/P+3v/3ttLS0FG3NFYvFBp0CNlJf+tKX+OxnP8ttt93GD37wg0E/v+Cxxx5jy5YtXHnllcaxJUuWsGjRIn74wx8WbTn2gx/8AEVRitZ9CSGEEGLyzMT4ZKQ8Hg+rV6/ml7/8JeFw2Dj+i1/8gkgkUrT/93vf+16y2WxRuZPJJD/5yU9obGyUjOZiUsg33kKMwIEDB3jnO9/JlVdeycaNG/nlL3/Jhz70oWGzen7961/nueeeo7GxkRtuuIEzzjiDrq4utm7dyjPPPENXVxeQzxD6/e9/n2uvvZYtW7ZQU1PDL37xCxwOxwmX/Zvf/CbBYJAbb7yRkpISPvzhDwNw/vnnc84557By5Uo8Hg9bt27lwQcfZPbs2Xz+85/vd413vvOdXHHFFXzgAx/g9ddf5/vf/z4f//jHWbx48QmXUQghhBCjNxPjE4C7774bgJ07dwL5zvSGDRsA+OIXv2ic99WvfpXzzz+fiy++mE984hMcPXqUb3/721xxxRVFXyI0Njbyvve9j9tuu42Ojg7q6+v52c9+xsGDB/nxj398wvcixIjoQohB3XHHHTqg79q1S3/ve9+rl5SU6GVlZfpNN92kx+Nx4zxAv/HGGwe8Rnt7u37jjTfqs2fP1s1ms15dXa1fdtll+g9/+MOi8w4dOqS/853v1B0Oh+71evVPfepT+hNPPKED+nPPPTfiMv/kJz/RAf1vf/ubcSybzeof/OAHdU3T9EceeUTXdV3/whe+oC9btkz3eDy62WzW58yZo//Lv/yL3tbWNuB1//SnP+nLli3TrVarPmvWLP2LX/yinkqlRlwuIYQQQoyPmRyfFMo92L++1q9fr59//vm6zWbTfT6ffuONN+qhUKjfefF4XP/sZz+rV1dX61arVX/LW96iP/HEEyMuvxAnStF1XT8J/X0hpoU777yTL3/5y/j9frxe78kujhBCCCGExCdCTEOyxlsIIYQQQgghhJhAssZbiGkiHo8TDAaHPKe8vByLxTJJJRJCCCHEqU7iEyFGRjreQkwTDz/8MNdff/2Q5zz33HNccsklk1MgIYQQQpzyJD4RYmRkjbcQ00Rra6uR3XMwK1asoKysbJJKJIQQQohTncQnQoyMdLyFEEIIIYQQQogJJMnVhBBCCCGEEEKICTQj1njncjlaWlooKSlBUZSTXRwhhBBjpOs64XCY2tpaVFXGhsXMJHGLEELMDKOJW2ZEx7ulpYXZs2ef7GIIIYQYJ0eOHGHWrFknuxhCTAiJW4QQYmYZSdwyIzreJSUlQP6G3W73mK6RTqd56qmnuOKKKzCbzeNZvGlJ6qOY1MdxUhfFpD6KnWh9hEIhZs+ebTzXhZiJJG4Zf1IfxaQ+jpO6KCb1UWwy45YZ0fEuTNNyu90n1IA5HA7cbrf8ESL10ZfUx3FSF8WkPoqNV33I9Fsxk0ncMv6kPopJfRwndVFM6qPYZMYtsoBOCCGEEEIIIYSYQDPiG28hhBB5uq4TDAZJJpNYrVY8Ho98eyyEEEKIKelUiluk4y2EEDOE3++nqamJQCBAJpNB0zS8Xi+LFi3C5/Od7OIJIYQQQhhOtbhFOt5CCDENDDci7Pf72bRpE7FYDI/Hg9lsJp1O09raSjAYpLGxcUY2YkIIIYSYeiRu6U863kIIMcUNNyKs6zpNTU3EYjF8Pp/RsFmtVnw+n/F+r9c7Y6dvCSGEEGJqkLhlYNLxFkKIKWwkI8Jms5lAIDDguihFUXC73QQCAYLBIKWlpSfnRoQQQggx40ncMjjJai6EEFNU3xFhq9WKqqrGiHAsFqOpqYlEIkEmkxl0GwyLxUImkyGZTE7yHQghhBDiVCFxy9Ck4y2EEFNUMBgc0YhwKpVC0zTS6fSA1ym8brVaJ6PYQgghhDgFSdwyNOl4CyHEFJVMJkc0ImyxWPB6vQSDQXRdLzpH13VCoRBerxePxzMZxRZCCCHEKUjilqFJx1sIIaYoq9U6ohFhm83GokWLcDgc+P1+EokEuVyORCKB3+/H4XCwaNGiGZWgRAghhBBTi8QtQ5OOtxBCTFEej2fEI8I+n4/GxkZqamqIx+N0dnYSj8epqamZkVtyCCGEEGJqkbhlaJLVXAghpihFUVi0aBHBYBC/34/b7cZisZBKpQiFQv1GhH0+n9HgDbZvphBCCCHERJC4ZWjS8RZCiCmsMCJc2A8zHA6jaRo1NTXGfpi9KYoyo7beEEIIIcT0IXHL4KTjLYSY8nRdP+HR0PG4xslyqo0ICyGEENOZxC0StwxEOt5CiCnN7/cbo6aZTAZN0/B6vQOOmk7kNU62kz0iPJ0DACGEEGKySNySJ3FLf9LxFkJMWX6/n02bNhGLxfB4PJjNZtLpNK2trQSDwREl3xiPa5yqCo1WW1sbhw8fJhKJkM1mp2UAIIQQQkw0iVtOrqket4w6q/kLL7zAO97xDmpra1EUhUceeWTY96xbt47ly5djtVqpr6/npz/9ab9z7r//fubOnYvNZqOxsZHNmzePtmhCiBlE13WampqIxWL4fD6sViuqqmK1WvH5fMRiMZqamvplzRzva5xKdF2np6eH9vZ29u/fz/r163n88cd56qmn2LlzJ93d3dhsNhwOB62trWzatAm/33+yiy3EkCRuEUJMBolbJt90i1tG3fGORqOcffbZ3H///SM6/8CBA1x11VVceumlbNu2jU9/+tN8/OMf58knnzTOefjhh7n55pu544472Lp1K2effTZr1qyho6NjtMUTQswQwWCQQCBQNDVI13USiQTRaBSr1Yrf7ycYDI7qGgWKouB2uwkEAkNe41Th9/vZsGEDzz33HE8//TSPPvooO3bsIBgMoqoqLpeLRCJBa2srmUwGn89HNBrl1Vdfpa2tjZ6eHvRcFkK7mZ1+FmJHT/YtCQFI3CKEmBwSt0yu6Ri3jHqq+dve9jbe9ra3jfj8Bx54gHnz5vHtb38bgMWLF7Nhwwa+853vsGbNGgDuvfdebrjhBq6//nrjPY8++igPPvggt95662iLKMQpYyquXxkvyWSSTCaD2WwG8sFzIBAgFouRy+WM+2xraxt0DVHfa/RlsVgIh8Mkk8kJuYfpove0NrfbTTAYJJvNkk6nSaVSxlQ3TdOIxWIEAgG8Xi//n70/j5PrrA98/885p/a1l6retUuWLO+WcNvYZhU2kMzgrBDuXJbJkBluPHeIfjdMnCHsM2TIDHFCuCEhYSB3wkBICDMTGxsjEN5kGckyXqSWJVlLt3qrqu7at7P9/ijVUVd39apWq1v6vvPyi6iWU6celc7zfZ7zPN+vnh+lNPFjEok/p0M7jWmfwm3nuR3IndmC6+Z/e9X8HsXaJXGLEKuHxC0StyyHtRq3XPY93gcOHGDPnj0Nj91///187GMfA6BarXL48GEeeugh53lVVdmzZw8HDhxoesxKpdLwg8tmswDouo6u60s6z/r7lvr+q420R6PV2B7JZJITJ06QSqWcxBvt7e1s27aNWCx22T53pdpC0zRcLhfVahXDMBgZGUHXdWfZla7rFItFjh49SjQapb29nWw263TmkUik4Rher3fGZ1QqFVwuF5qmLeu1w7Ztstks5XIZXddxu934fD4ikciSsprWv5fH4wFwvs9Sjtfs+MeOHaNYLBKLxUin00xMTGCaJrZtYxhGbWbdNuj2JbkueIp26wRdE2do8Y+D/8KBrMbjJgce4Ujm1kX/HlfTvzFxbZK4ZW2S9mi0GttD4haJW671uOWyD7xHR0fp7OxseKyzs5NsNkupVGJychLTNJu+ZmBgoOkxv/CFL/CZz3xmxuM//OEPCQQCl3S+TzzxxCW9/2oj7dFoNbaHqqrOhW1ycnLF9hmuRFuoquoEqx0dHbO+bq7vXD/GbLPDqqryzDPPXNqJsjp/G4vh9XrJ5XJomsaWLVvwWpO0Wsdps16j1TxOi3USFwufYW+1T/DSEn6PxWJxsacuxLKSuGVtk/ZotBrbQ+IWiVuWw1qMW9ZkVvOHHnqIvXv3On/OZrOsW7eO++67j0gksqRj6rrOE088wTve8Y5Zl3dcS6Q9Gq2m9rBtm+eee47R0VFisVjDzKFt2ySTSbq6urjzzjuXZblMsxlqVVXZtWvXjMBzuSWTSZ599lkGBwcJBAK43W4sy6JSqeB2u+nu7kbXdc6fP09rayvt7e1O9s9sNovf72fLli2cOnWKUqlEOBzG4/FQrVbJ5XL4/X527959STPtU38bmUyGQ4cOkU6nKRQKmKbpnI+maQSDQVpaWhb0mclkkkOHDlEqlfB4PIyPj1OtVrFt20myouv6JX+H8fFxnnvmp6wLpyD5HC3GAJ3aaaKuySUdD6CktJNXugmHQiRTqUX9Hut3AoW4mkjccvlJezRaTe0hcYvELRK31Fz2gXdXVxdjY2MNj42NjRGJRPD7/WiahqZpTV/T1dXV9Jher7fp8gu3233JF5flOMbVRNqj0Wpoj3Q6TSqVIhqNoqqN+RHriTdSqRTFYvGS6ycmEgkOHz7cUNKiWq1SqVQ4cuQId955Z0NZhuXeu9Xd3c0NN9zA2NgYhmGg6zqqqhIMBonFYgQCAc6ePUu1WiUSieDz+YDacq96EpNUKkV/fz/Hjx8nmUySz+dxuVx0d3cva1kJl8vFyZMnnb1cpmkSCARQFAW3202xWMQ0TUqlEidPnqSrq2vWtrFtm5MnT1IqlYjFYgwODqLrOsFgEKjNrtYD92QyOe/xph0cioOQfA6SB+gce4YHjCNokwZo1P5bBN1yM1zp4XxlHQlrE2PGJjo33c758+fZECov+vd4pf99CSFxy9om7dFoNbSHxC0St0jcUnPZB9533XUXjz76aMNjTzzxBHfddRdQSxKwa9cu9u3bxwMPPACAZVns27ePBx988HKfnhBrzkol3phe0qJ+cfR6vVQqFUqlEgMDA87sdSKRYGBggGQy6cwwL0fNxK6uLrq6upxg1+Vy4fV6URSFcrlMPp/H4/HMaI+p2T9vuukm7rnnnsua0CWbzZJMJvH5fExMTDjnWD8Xr9dLqVSitbXVyUg62wV9albTarVKsVhsOJ7X66VYLDod95zHM4owcRiSB2qdVuo5KI04Ty+2E0hV2zhf7uN8dT0JewujpThVo1bWxOVy4fF46ARM0ySZTNLX1yeJYMSaInGLEMtL4haJWyRuWdq5k8/nOXnypPPn06dP8+KLL9LW1sb69et56KGHOH/+PH/zN38DwL/5N/+GP/uzP+PjH/84//Jf/kt+/OMf83d/93c88sgjzjH27t3LBz/4QXbv3s0dd9zBww8/TKFQcLKFCiEu8nq9uFwuJ2HHdNVq1bnIX4q5SloAhMNh58Kp67qTXbI+w6zrOiMjI2QyGfr7+5fciUWjUeLxOCMjIw0dKYBhGFSrVdra2pp+36mduaIoS55JX8iMeD2w8Pl8WJaFpjVOwWqa5pyHYRhzXtArlQq6rmOaJoVCYcbfdf1YhmEQCAQudhC2DYXTkHj2YoeV/jnY5pK+d8XyMlzuZUTfwFBlHecK3biCXViWRblcrgUVLg3VqgK1Tss0a5/l9/spFovOTP2l/h6FWCqJW4S4siRukbhF4paaRQ+8Dx06xFvf+lbnz/U9Sx/84Af5xje+wcjICOfOnXOe37RpE4888gi/8zu/w5/8yZ/Q19fHX/3VXzklOQDe+973kkgk+OQnP8no6Ci33norjz322GXfhyHEWhSNRonFYk0v6PVMkt3d3USj0Uv6nIXMUOfzecrlMidOnGg6wxyPx50Z5en7uhZKURR27NhBJpMhkUgQiUSc/U7pdBq32z1rJ7scnflCZ8TrgYVlWaiqimmauFwXL7GmaaKqKrZtz3tO+XyeiYkJxsfHsSyLYrGIYRiEQiE8Ho9zLLdm4S/+nB32z2l95f+DzM+gPLrk75rUOzlfWceYuYlzxR5ySh8+fxBN0yjYBWyPQSQScZK+mKaJpmmoqophGA3lUjRNo1wuk8lk2LRp0yX/HoVYKolbhLiyJG6RuEXilppFD7zf8pa3YNv2rM9/4xvfaPqeI0eOzHncBx98UJZoCbEAc13Qs9ksgUCAHTt2XPJypIXOUFer1VlnmKcum5pridJ84vE4/f39TkeSy+VwuVysX7+eWCxGNpvFtu1l78yn1omcbUa8/p0ikQixWIzh4WECgQD5fB5N01AUBdu2qVQqBINBKpUK3d3dRCIR0un0jNnoRCLB0aNHsSwL27YJBoOYpkm5XMZLjg3RceLqa/SGzxGbPI3GhTIWY7N/j2YsVwtJtjBubaEcvJWi/wZSOZMzZ85gWRY+nw/3hc62XgbE5XJhmiaWZdHS0uIkYqlTVRW/v1ano1qtous6oVBoWX6PQiyVxC1CXFkSt0jcInFLzZrMai7EtW62C/pyJt6Ya4YaIJfL0d3djcfjWZG9W/F4nFgsNmPpVDKZ5ODBg8vemc+1V2zqjHh/fz/QGFhUq1VUVaVQKOByuTAMw9nrFQgE6Ojo4JlnnpkxG33dddfx85//nMnJSbo6OzAmXiVmPUd362k61ZO0uRJLazxFhZabIXYXtN+J3d7PMz8fY2R0lHjnxe/W2lr7+zp79iyWZWFZFvl8nkgk4nznTCZDMBikt7eXVCpFJpNx9m6pquosVasnWLnnnnuWLRGMEEKItUniFolbFuUqjVtk4C3EGjXbBX25Zuhmm6Gud0R+v58dO3bgdrtXZO9W/Zymzz5frs58rr1iU2fEp5aRmHouQ0NDTE5OOiVEWlpa6Ovro6Ojw1niNnU2evDMcVLHvkfMPs693rN0l87gCy6xprU3DvE3Oh0WbbvAHQJqHfPg4CCDQ0NOttGpgsEgmzZtIpvNsnXrVlKpFPl8nkqlQiAQcGaVXS4X7e3tFItFCoUCfr+f3t5e587iunXruPfee+esYyqEEOLaIXFLjcQtTVwjcYsMvIVYwy4l8cZCzNY5qKrK7t27icfj2La9Inu35jvP2TrzpZYKWWoW1qnnUi6XqVareDwefD4fkUiEZ555hmKxSG+bSot+gGjuFULll4iop1BD1tIaIHoDxN4I8btr/xveCk2+Y322e3BwkNHRUXw+H+l0mlgs1tCZeb1eVFWlr6+P22+/vaH9qtWqU+LEMAxaWloIhUK4XC4qlYrz+3jjG98og24hhBANJG65eJ4St1x7cYsMvIUQc5reOWiaxjPPPEMsFgOWf+/WUjucZp35fAlG6p81vaOJRqPO3qBqtZb5sv7+enmM+oy4x+MBYHx8nGAw6Jxvw7nYNuROUnz1v7N++HvssgcIjI80nCsLnPA38GK334G7600XOqw7wdM67/um7vsKBoP4fD40TXNmhXt7e51ObOps/2yz9VP/jiKRCNlstunvQwghhFhJErdI3LJa4xYZeAsh5jX1Iqbr+oznl2vZ1HLW1Jwvwci2bdsYHx93llbpuo7b7aa1tZW+vj62b9+O3+/n9ddfB2odq6qqBAIBZ6lSJBLh6NGjADz77LMXz3f7NuKuYRh/ChJPQ+Ip7NIY+kQEfaiDnw9voJi7fkHfQ7d95K1WTG83OTOKO9RD37oNUzq8F+Y/iA2DQ4Pk8/laAhXyVDPVWkfldlHQCxQ9RSLRCADFYpFQKMThnx1ecMdaZ5kWI6dGOFo4yi3vv2VxbxZCCCGWgcQtlx63UB4jAGxY1LeAvNXKiLEZPbqb89WN+Lv7ufvetzS9oz2b6fvVAdLpNPl8Hr/fT6lUIplMEggEAOZdpdBsMD7X7+NykYG3EGJZXOrerYVk4pyvE5s6E/zSSy/NmmBkaGiIJ598Ep/PRz6fd/b/6LpOOp3GMAxGR0edDJeqquLz+YDaHqr6MqdcLkcqlSIS9NDrPkWr/gqRsy/RMngSKDF2rpMX9t9GIXMvlqliGtocZw82ChXLS9nyUyWIroQxFQ+mYeJyu/C4PbS0qoylxzB0wykB4nK7UOYYIeu6zkRqolZeQy3UHjTAKluU7TKKolCyS9g++2Itz1YYnxxf0N/dVJZlURovkR3Mzv9iIYQQ4gqRuGVm3LJYlq0yVu1iuLqBcXsbafcNlJQY+XyeFrWFtrY2br3+RoCmGdFn02y/eiwWo1KpUCqVavFMoeDctV6uzPiXmwy8hRDLZvqMom3bC7rQLjQT51w1NafOOheLRZLJJKFQiGAwOCMZR7VaJZfLoaoqlmURDAZRFAWPx+PUnkwkapk4t2zZQiqVolgsYllWLSmLnSeaexbf0AjuRI6QPYmqmFSAhKKS4DpSo+2Mn5u7pq+JRtUOUDJ9lK0AZcsHisu5y6wqKrZtYmPj8/qcO9ITExNUq1Vsy0ZRa+cdCoXwepongrEsC9uyUV2q85jm0vD5fFSrVUzTxLRMTNPE5/fNeSwhhBDianEtxS2tpWfprJygx3WKjuI5NIzawdXpZza7iu1j3NzC2WIvw/omzpd7Udxh5zt6bA+GUcC2bbq7u7n11lsBePrppxe1KqDZfvV6ZvJkMkmhUKBcLlMoFFi3bt2yZca/3GTgLcQ1aKn7kRZjMcuvFpqJc7aamtNnnV0uFxMTE5RKJc6fP9+wD6hSqVCpVFAUhWKxiN/vdz5TURS8Xi+FQuHi3WSXiy09fuzXjmOfPYe/ep7yqMHo6W5yug/wAQu82KteTHcL+aqHXMWNqQZRVBXDNDAtE1VRcV2ooenMZhs6Pq+PWCxGVa+SnkxjmrU74KpLxbItKuUKhm7Q0trSdMCsqiqKqmDZFqpyoYe1a9/X7XGjmRq2bdPW1obP75vz7rkQQgix0iRuWVzcsrU3QLAwQEv1Zdqso4TNMyjYi28UXyeVyB0cS8V4ZbyVomcrLreXTKl2l97tdhMJBFBVlWq1Snd3N9lslq6uLt7+9reTSqWWtCqgWU1227bRNI22tjb8fj+GYXDPPfewbt26VX+nu04G3kJcY5ZzP1Ld9A6xWq3y/PPPL/hCu9RMnPXPnj7rbNs2LpfLSTJS3wekKAqGYTj1KesX8anHslM21itJAhMThMhgqftIZW2KucCFV3UvuF10vJhaFG+4G8XbDq4A4ViQUNymlD6NS6t9psu6WDPT6/OiKiqlcgl/0E88HGf7ddsJR8IcO3oMc9IkGok27r22IZPNEG2Ncv3O62d0QLZtc+zoMSYmJ4hGolQqFWd5lm3bmIZJJBLhxttuJNpyaVlcTcPk0KFDbHzrxks6jhBCCAESt8wVtwBomobfTtLrPsk67+tsTJ8haI3M+NyFyNqdFEO76LzxV1A63gShLXgVBd/AANkf/xhMG90oOUna6hnBVVXFMAyy2SxtbW3cdtttKIqy5FUB02uy11cE1O/iV6tVYrEYPp9vzQy6QQbeQlxT5tqPlE6n2blzZ22Z8SJmk6d3iPV9NwB9fX0LutDONrNZ79hM06wNSpvU1Eyn0wwPD+N2u50O1Ov11pKI5fPOMqxKpeJkxDR0A7/bj23Z6EWdUDVN4Pw5Si+X0MdNQkotI6gNZPAvqG1toGr5KNsBSqafqhLG4wtjY3PLW27h5l+9mUA8QDAeJJ1OY/24tpda0zRcLhemaTqdimEYuC03W3Zu4dZbbyUej5NOpzlmHqMv0Ne0HULlEKVSia43dTWdXW+/q52DBw8yOTlJPp/H7DDxu/3ouo5f8xMMBRlvHWdT/6ZLWq6l6zon1BN03da15GMIIYQQIHFLPW4xTdOpSW2aJlFXmi71OF3KAJ3KcSLtiYsfsMAKX5atkDR6OF/dxLnyBlLq9bT33YhlWby1/a20hFuc13Z1ddHV1TVn3DJ1eXk9blnqqoCpmeeHhoZqcYtp4na7a9viLuxff/755xe0l361kIG3ENeIufYjBYNBzp49y/nz52lra8Ptdi9oNrlZh5jL5Ugmk/h8PqcERN1sF9qFzmzWS2RM/fxDhw45HZimaQQCAWKx2MUkHJMlzAmT1GQKzdKYOJrCPp7HssbxksdDHkWpOilF3AucOLVQqNp+tE4bT1cfwxk3hu3Gtm0M3SAYCNLe0Q4b4MYP3Ui882I7RqNR4vH4jBqigUCAcrlMIpFwlmmpam1p+KXMrkMticwdd9zB448/7iwPsyyLcDhMLBYjEAgsaE+aEEIIsRKu1bilUqk4xwDIZjJ0h8us8x6lUzlGhz1AREstuj0N28WYvp7Bykbs2D28eN5PxfJh2zblcplYLEYoFGJiYmJGLCFxy/KQgbcQ14jZ9iMVCgXOnz+PYRioqkooFELTtHn338zWIWqahsfjcWZC60ul6ppdaJcys1nvPNPpNB6PB4/Hg6Io5PN5yqUyLYUW7OdsSq+VUMwiac8YfqVAhDytdmXxZbLQcEU9WK0+MnYIz/ZNFOMGBaPAjh070E6fRrGsWnkP3Gy8bqNT8mL6jPdcNURzuZyzTKveeUHz2fWpptawnI3H4yEYDBKJRJwZ63rNS2DePWlCCCHESrmW4pZ6Xere3l5GR0fQimeJ516h1/UaHcoAvsDYotvPUIJMuq5nqLKJU7kezOhtpIoFcrkcO7p2ULFOY12IW1RVJRaLoet601hC4pblIQNvIa4RzWYebdsmmUyi6zqhUG2psmVZBAKBefffZLPZph3i1P0+U5dK1c12oV3MzGZ7e7vTefb09GCaZq22o9uP8hoUDo9gFF4noJXo0bK4tAszzgvMKxKIFOnbnsTbu45Juhk3Oym1duNaF6wtaSsUKJVKxCMRTp8+DVysA6mqKq2trUSjUZLJ5Kx1JRdbQ3T67PrUNrdte94allD7DZimSUtLS0PnWDff7LMQQgixUq6FuCUQCBAI+HGXz9Kaeo5NoXNEfEcI+idqH2IuvL0sVwt6650M6Vs5W9pIyuxD09wE40HUQIFKqcS6deskbrmCZOAtxDWi2cxjpVKhWCzi9XobsmHC/PtvZltCVN+nlMvlADAMw3luvgvtQmc2h4aGnM5TVRR6wzqTB14hf8QkXK3Qqui1O9oL3OcE4A2ZtG4Ns/5tm9n0znehtt8CiuokYBkdHeXcuXPk83mKxSKTk5NkMhlisRiA0w4+n4/29nZn1nyuupKLqSE612xzNptdUA3L5Zh9FkIIIVbC1Ry3rGsz0MyDdCtH6XWdIuhN1w5YZsEr8gytBaPtLqzYm/BveCdq6814FZXNtk27xC2rkgy8hbhGNJt5NAzD6bhKpZKToKRurpnE2S6GiqIQi8UoFouUy2VM03T2Ks13oV3ozGZp4hTt4/sJJkcwziUYHYhi5AL4oGmHpWoWoZY8qmbi8er0bhkitiVPMbiDjPsGzlX7CG9+A/e+6c1NE4Dous7JkyedPWEtLS2EQiGGh4eZmJggGAzi9/uxbZtQKATQMAM8VxmU6TVE57LY2ebplmP2WQghhFgJV1PcUp58nZ7qT1jHCdrSR/CZY7VqpItQVaNkPLcw6bmF04X1BLrfwD33vkniljUUt8jAW4hrRLOZR1Wt3dHN5/P4fL4ZS7PmmkmMRCKzXgwDgQChUIhQKIRpmqRSqQVdaOudYjFRRCnWjuduceOPlHCffZHqsVNEz6R5dcRNpeildgmbvbxXS8ckO3YNsGHHOcpKkFHrOgarWzjufhsj6+5EudBJauUyqYnJpjPks+0Ja2lpIRKJMDw8DMAv/dIv4Xa7qVarDZ3UcpdBWcxs83TLMfsshBBCrIS1FLdMH8y7rCwtlRcJFw+x2zhC5NWh2hOlpodpqmiFnbgl7b6FyJS4xXaVSaYmJG5ZY3GLDLyFuIZMn3msJ9HQdZ3W1lanRmS9puRcM4nzXQxbW1u544478Hg8C7rQWoZF5uUMmX/IMHFsnICrgsfK4LYyaHZhyo3s0Lzfs2fLeTbuGsK7bQNZ3y/xpLGNV4dMAoEgJiZ6UcdbrTp7uOaaIZ8tuQvU9kW1t7dTrVadPVJTNcueWq1WOXv2LKOjo9x+++1s3rx50R3GYmabp7vU2WchhBBipazmuAUu3pFNDJ+mKzJCa/UIrdUXCOknURaaWOaCoh0l57+djPdWhiVucVxNcYsMvIW4xkydeRwdHWVgYIDBwUEGBwfxeDyEQiGi0Si6ri9or89SL4aVXIXc+Rx6SWf85+c5/eghSmOjuMvjdNkZFH1xHZaluAn0uoi8WeWM926y6+8kHIkAUC6XUdXTTl3N+j6vurlmyBdSDqNarc7o/JrNOBcKBafcyPj4OKlUip07d3L99devaMdxKbPPQgghxEpaLXFLA7MCyedQxn7MnbnH0MzDqJOLyIQGVNUWUq5bOFPaxGuZbtokbpnV1RK3yMBbiGvQ1P0/1WqVdevWkU6nKRQKTExMkMvl2LZtG7t27Zr3whqLxbjxxhtJJpMoikJ7ezstLS1NL4a2bTP64ignf3CC808fxS4moJKE6gTYtQ5r5g6p2b6EC1c0iqsnQDnejtXThb0lhBkK4Uml8EzpjOqJU/L5PB6PpyEZy3wz5AtJ7FF/3VTTZ5zr5U/qx3G5XFQqFQYHB8lms7OWP7lcLmX2WQghhFhJVypucVgmTL4Ao/tg7MeQeBrM2rrx5sPbJtwRKi1vZEi/jtOlTUyaPbg0N8HOIB6XxC3zuRriFhl4C7EC5kpScSWO2WxWMxqNUqlU0HWdZDKJZVm4XC5nCVczC90HVJos8fo/HeTU//4ZhfNjtcG2VZ37JBXQNBPT0GrnrGiEukJ0395Hxx27ad/dT7Cr1uFMbYdIJMIzzzzTsIernjilUqmQy+Wc5VPlcnnePULzJfbI5XLO5041dcZ5avmTen1Q27apVqtEIhGKxeKs5U+EEEKIlXatxy0A5M/A6BMX/ttXu0kwi2LOTzEXbDxn1YvStgva+yF+F0SuB9VFxLbZnMtRrVadO/YvjL/A+NFx2tranHMPloIUUgXShTThcBjdp1MySrXyqT4/nX2dpI6nmraVv+Bn/Ezj8erPTUxM4PF40Idr7VaXTCYpnC3gbnFjKAbDw8OUiqVaEjbFxrZtzJKJR/MwOT7J4fRhdu3atebjFl3XKQ+VyQ3naNvQdlk/SwbeQlxmy52kYjmO2Wz/j6IomKbJxMQEhUKB48ePk8vl6OnpaXrcZDLJ4cOHG/YB6brOyMgImUyG/tuvx3r1Z7z2/cOcPzyJXS0u6LvFepJcd9trrNs+jNLRT6r8NkrqLoLX3UHrts6mF/jpM6DN9nBpmobf70dRFAKBABMTE3MuL5saIPT29s66J8zv92NZ1ozzmjrjbNu2U/6k/jrTNFFVFbfbjdvtnrX8iRBCCLGSrtW45c5dO4mZL8PIEzD6Q8idmPOcTEPj3PF1nHp5C4mhDkABTyt4Y7X/PC2gaBdePXrhv+Yq1QrpyTTj5rhTV7yeWd20TPJanteU11BUBY/HgxkyOfzo4YZj2NgYei3ru2Ea5HP5GcczDANVU8GGHz/644ZM7Lquk0wlSWkpsKFYLKKqKhWltiTdtm0sy2I4UEvOlrASjLePz7qkfa2wLIvx8XFezb/Kvf/+3sv6WTLwFuIyapakomFwuohlOlPrSR89ehTDMJZ8zGb7f6YvKYJajcfZjnvixInGjJmWgW9wAF48iTU2zME/1slNzp8Ize3V8QXK9G0dYsOdIVpvvQs690L8HnCHaJ8yAM5kMguaIZ9tD9fGjRvZvn37vIlTmgUIfr+fSCRCqVRq2BO2detWnn/++RnnMPVOeSAQwLIsNO3C3XvbplKpOGVQ6nfOmyVJEUIIIVbKtRS3KLZJi3KS63w/I5R9jvafnATm3qdtGiqJ83HOHV/PueMb0K0Lg+z2GHjaQHVdHABXDVTVwuV2ocxTnNvr8dLS2kI+n6darWIYBoqq4A/4CQVDzsBZVdWmx6tUK857bctGURU0tVZX3LRM53hen5dgIEg6nZ5xDi63qxYflSu1pHXYTolWGxvTMnFpLmewXi/tJhZOBt5CXCazlXPwer3E43FncLeQ5cX11yYSCUZHR6lUKrS2thIMBvF6vYs+5vT9P9OXQteTeQQCAbxeb8Nx82N5Ms9nSEwkCPtK6O5D+EtnmHjVoJiuF6X0XvivuWg8zbZbTrLhDjeeDW+FrrdDx5vB27jE51JmyJeaiGO2oCOTyeD3+7n55pudAXM0Gm1IdjLV1Oyp6XQa27ZrHZ+iUKlUcLvdzt9TpVKZNUmKEEIIsRKu1rilLpVK0RUq0FU8QGvlMK2VF3DZhdnbw1IYO9fJ669uJnE+jm0pVIx2LK2jNthuawfV0/CeZgPg+nJyr2fuPt7r8eJp8zh3rWcbZE9Xv1tumiYutwvVpWLZFoZeu7sdCUecu94utwvbap68VkEhFAph6Aa6rtfawK691rRMVEXF4/GAApZpoahK09rlYnYy8BZiHvUZW6gtdWpvb1/Qfpa5yjkoikIkElnQ8uKpA8H6wCwQCFAoFKhWq/T29hIMBhd1zOn7liuVSsPxp96NVRQFv+3nxD+cIPWN8xTODKGnB/HaWSwq1HYXuZjvcqK5TDbcmGDLnj7a7/gVlO77ILhuQd97qTPki03EsZCgY3h4mHvuuWdBv4H6nfdjx46Rz+fJ5XL4fD5CoRCxWIxgMDhvkhQhhBBiMSRuuXjcbPIswfxBbq78N+LGEUKl8XnbITcZ4vSrmzn96iaKhZYLS8fjEIiD5p/1fbqlky1mMWwDd8B9cbm4XiVbzNLmb8Pn9c36/joPnnlfU2djky6ksVwW/uDFc9PQcPvdlCtldHSirVFnAG9ZFqpXdc5xKnfAjcvvIpfNkclmMI3ahIbb5a5Nfmi1WE+v6PiDfvxR/7wTA6tdvT00jzb/iy+RDLyFmMPUO66qqvLUU08t+I7rQso5zLe8ePpAsFAoYNs2Ho8Hj8dDsVgkmUw6CbvqxyyXy6TT6Vnv9E6vZelyuWozpS4XxWIRzdRwn3Fz/uAgamaM8qkEWnWSIkVUbAILbD8bBVeLh+guF97dO9n81vfTHu+o3YXOV/Dq6aYd/HLOui/GcgUdU9XvvPf19XH48GGq1SptbW14vd4FJXcTQgghFupajVucVWRYhKvH2VA5SKR8gMgTp1Cw2DRPu1mmyvDrPQwc2cH5wS2UCaOrLYTaevF4L9yFNixUS2+8C61A161dbH7HZs4YZ/AlfE0TsSYSCbq7uxc8cb9Q6XSan/zkJ86d/unK5TKlUom3vvWtTtyi6zqPPvoo7373u2f9u7Ztm9OnT8+IW+r5bQKBwIpnNb9c6u3xhne/4bJ/1pIG3l/5ylf4oz/6I0ZHR7nlllv48pe/zB133NH0tW95y1v46U9/OuPxd7/73TzyyCMAfOhDH+Kb3/xmw/P3338/jz322FJOT4hlMXXGNhKJUKlU8Pv9C77jupByDvMtL54+EKwvFap3Nl6vl2KxSKVSwefzOfuCXnrpJQqFwpzLs6fugx4eHkbXdayshftoGfVklqqZxW2lUWyD2ed3Z3K3mLg3aBQD6wjt2EV4ZxxUGE8kKL9whEAgQCqVmvPcLscAeCGWI+hoRlEUNm/eTDgcdgKifD6/tNqhQohFk7hFXAuuxbgFIB622B55ne7qd2jLHcJtZWc9P0PXmBxvZWKsjcmxNvKZIAUjwniim5LRiuqLYXs1sGvf18xk0DSNqn5x6bjX46X3pl6u23MdG960gWBHbc/0xE8mJG4Rc1r0wPs73/kOe/fu5atf/Sr9/f08/PDD3H///Rw/fpyOjo4Zr//e977n1IuD2v6KW265hV/7tV9reN073/lO/tt/+2/On2Wvo7iSps/YAs4s7ELvuM5Xhmohy4vL5XLtDrSmOTPG9bqOmqahaZpz0a3PqJbLZTRNW9Dy7Hg8TntLlJGDj/LzfcdJHtZx26V528frr9C5YRRVtSlWAqT1TujsJtXVQqktjN/vd5aS1bndbk6cOEFbWxuxWKzh3NLpNDt37nSWiZXL5cvSkcz7vZYh6JjLUvedCyGWTuIWcS24luKWWFsLubM/YvzFf6S19Dztymm4ELroFTcVy3PhnBVyk2Emxtpq/422kZ2IYlguynYIXWslV/Vi2rXJAZ/Ph1LPQq6AotZqWQd6A3Tf0U2gO4BpmVRCFbR2De9OL3k7j57WJW4RC7LogfeXvvQlPvKRj/DhD38YgK9+9as88sgjfP3rX+f3fu/3Zry+ra0xWdK3v/1tAoHAjA7M6/XS1dW12NMR4rKYPmNbTy4BC5+5nL4sanoZqvmWFycSCV566SWSySSpVAq3200gECAYDDp7mzRNc8ppjI+PUyqVnAzc9TqW0zvdtpY2TnzvICf+8Vnyg6NQToBdmzWeqyCEotpsvvEU63eeR+mLkwvfybB9A0OTISYm0miahmmatEajxOPxhkF3fb+ZrutEIhGnA/B6vQSDQc6ePcv58+dpa2vD7XYTDAYxDOOydSSziUQiBINBRkdHicfjFzphxfkOy7EXe7H7zoUQl0biFnEtuJrjllgshlIagZHHYeQHKCNPENHT+Mpuzg5s5Gz6NvSKm/HBTvLpmdVUbFSqSghDa6WshSmaGrpuoBi1dnJfyOatuS7u8dX8GlwHVq/Flju3EA6HnecKhYLELWJJFjXwrlarHD58mIceesh5TFVV9uzZw4EDBxZ0jL/+67/mfe97X0NQDrB//346OjpobW3lbW97G5///Odpb29veoxKpdIwY5TN1paU6LruLDtZrPr7lvr+q8213h5TlzvZtu10YPX/re8pGhwcxDAMIpHIrPWld+3axYkTJ0ilUk4Zqq6uLrZt20ZLS0vTNk4mkxw6dMhZLlbfv1RPTNLW1kahUGBychKv14tpmvh8PifD5+DgIKqq4vf7aW9vJxTwsd53FvXg9/nBH/4luZG5y2UAqJpF98YRIu0ZTF+AyvowE233kmi7C8N2Ew6GURSFdSGblpY20una4Lu9vR2fz9fQ6ZfLZQqFgjMzW3+uUCgwMjKCaZrYtk0wGETTNCYnJ52/g97e3qaz7l1dXQQCgWX7jSaTSefvKZPJMDExQSQSce7O53I5/H4/W7dubchkfq3/W5nuUttD2lEsJ4lbrh3XentcrXFL28i3Mf/po7hyr174PpAaiXHutdt4/aUt6NXmtwx020fRClJRotieVmxbdRKD+f3gdtcGyfV95qpaq20dWBeg7c42PNs8nDl3Bj9+iVuucisZtyj21Oh4HsPDw/T29vLss89y1113OY9//OMf56c//SkHDx6c8/3PP/88/f39HDx4sGFvVX02edOmTZw6dYrf//3fJxQKceDAAafu7VSf/vSn+cxnPjPj8W9961sEAgtN+yTE1c1t52grHMFz9jjWuSQTQxFSw7F536e5TDbfeoZYv4t09BbGtdsoqTOXYwpxORSLRd7//veTyWSIRCJX+nTEGidxixBrh9vO0WEeodM4RKf5Ah7y5NMhzh1fTyYVxdA1UiMxSvmZmWcsNKpKmIrSQlWJYM5R0hQFPHEPni4Png4PqltF9an4NvhwhSTvtFicxcQtK/rr+uu//mtuuummGQlN3ve+9zn//0033cTNN9/Mli1b2L9/P29/+9tnHOehhx5i7969zp+z2Szr1q3jvvvuW3Kgpus6TzzxBO94xztm3Z9xLVlIe0ydaavPsvp8tTIJ9b0uLpeL9vZ2tm3b1lBLcbr6bGB9/8hsM7FLff1smn2H9vZ2tm7dysmTJxkdHXXOO5fLoaoqo6Oj5PN5otEo69evxzAMstksfr+f3bt3z/k9FyKTyfDUU0/h9/udJUmFQoFUKkWpVMIwavUdd+zYwU033UR7ezvPPfcc586dxV08ySbvq8RyJ0gc8nH26EZMIwLM/+9CCwTpuyPGzg/ew8tpH8+PpYi1xXArCma5zNmzZ519RD09PYRCIacMRblcplwuc+ONNzIwMECpVCIcDjtL1FKpFOl0mt7eXlpaWrBtm1wux+DgoDPTbBgGGzZsaPgNpdNp2traKBaLi/o9LYZt2zz33HPO3/XUJVrlcplkMklnZydvectbmtarlGtHo0ttj/qdQCFWA4lb1g6JW9Z23LJOfZlwYQjL0MhNhnnl3HWMnuskk2iZ9XNtVxh8HdjeOJMFqFR0PG4PCqBaFsVSEVVVUVoUWre00rK5hUBfAF+Xj6pVnRG3BMIBiVuuQSsZtyxq4B2LxdA0jbGxsYbHx8bG5t3nVCgU+Pa3v81nP/vZeT9n8+bNxGIxTp482bQD83q9TfdIuN3uS/4BLccxriaztUcikeDw4cMNNZaz2SyvvfYaAOvXr6e9vR1d1xkdHSWbzc6aTXNq6Yu5slku9fWzafYdpp7vtm3byGazJJNJZ29PIpEgl8sRDAbp7OzE5XI5e3YSiQQnT56kq6vrkpJOmKaJYRh4PB7nOKFQyNkjpes62WyWm2++ma54K/nX/4nekb9mt+sFAqFRRs908eT334RpzF2PsKWrwPXvjtJ6y914t74Db9cmFEUhnU6TfP0nRKNR54Lt8/nw+/3k83nn3161WnX2ZOVyObq7u9m6dSstLS0zMmCuX7+eWCxGNpt1Solks1lyuZxzdygajTbsTaovS7/lllvw+XyXLalHOp0mlUo1fF+o7WkKBAJ0dHRQLBYplUpz7nGSa0ejpbaHtKFYThK3XHskbllbcYs/OMrw6z288ORtZFO75vwcW3GBL47i7aj9r+YHBaJbIhSzw7R2tNN6fSvuFje2bTM0NETRKOIL1kp8eTyei3FLQuIWuXY0Wom4ZVEDb4/Hw65du9i3bx8PPPAAUCs6vm/fPh588ME53/vd736XSqXCv/gX/2LezxkaGiKVStHd3b2Y0xMrpFmN5XryrPo+mGw2S0tLy7zZNKeWvlhINsvFvn4x3wEa60SPj49zxx13cPz4caceZn3GuKurq2G/32JLRdTbq9lFebYMlYqi4PP58FoT9Hqepe3V/wGp/YSMAvVUImPnOuccdOtKANMXJnJvC+Xd1zPY2U1wxw6iU9qsWWkKRVGIxWIN+xSr1SqKosxIuDJbBsxkMsn+/fs5efIkiqI4v5v68dLpNKdOnaK7u5tgMOgkIvH5fJc1qcflKsUhhLjyJG4RIHHLaopb7OR+Mq9FmTjdw+nkjaQT96BXZh+46EqAClHKRDCUEG68hNwh4hvj9N3Vx6a3baLkKvHkk0/S3t7eMBDtdHVy/vx5iVvEqrHopeZ79+7lgx/8ILt37+aOO+7g4YcfplAoONlCP/CBD9Db28sXvvCFhvf99V//NQ888MCMxCP5fJ7PfOYz/Mqv/ApdXV2cOnWKj3/842zdupX777//Er6auFya1ViuZ6usX3Cn1mic7eI+W0do2zaBQIB0Os2xY8ecTm8hnc58pTLm+g51U8/3pptu4p577iGVSvHMM8/Q3t5OV1dX0z18C73QzTfzPaOcBxDST9BeOUB7+QAR/XjtQFNu4GRSEV56+maGTqxr+CwLlYoSgA4vbNyAtiFO1w0deIPeWTv+2TrQYDBIb2+vc+con89jWVbTeo7NMmDGYjHC4TButxvDMCgUCti2jaqqeL1eLMticnISy7Lo6emhWCxecibOhZj+feud6tQkNZcjE6kQYmVI3CIkblkdcUvihRhH9t9NaqR5EkKoxS1lK0jRDmNobVh48Ua8tFwfRQkoVJQKkZ0R7vjFO5y4w0pbErdI3LImLHrg/d73vpdEIsEnP/lJRkdHufXWW3nsscfo7OwE4Ny5czP2Exw/fpynn36aH/7whzOOp2kaL730Et/85jdJp9P09PRw33338bnPfU5+MKtUs5m2+v6d+oW9/pq6Zhf36Z1IoVAgmUxSLBaxLAvbtsnn8/T19bF58+YFdzoLmbldzGyhoijORTQQCGAYRtMObLZSEVNniQuFAkePHp135nvHdVtwpZ4kNvp1+pQX8FuJhmOahoZedZGbDHPy59s4c2wjXEiTaCoeqmorFaUF19Y47nsDqMFafcqFdPxz1fGslwUBePOb30woFFrwEqpMJkOpVGLTpk0MDw9fKOHhplQqYZommqZhWRbFYpFz586xfv36OcuWLJep3zcYDDb8BlVVxbZtNm/efNk7UiHE5SFxi5C45crFLbalMDbUyWtHts24OVA3NW6pEEK3LNxdbkJbgvTc1kNgXQBFu7iPWeIWiVvWqiUlV3vwwQdnXaK1f//+GY9t3769obTQVH6/n8cff3wppyGukGZ3RF0uF6qqYpq1MlWqquJyXfx5Nbu4T+1ECoUC58+fd46paRqGYZDL5Th8+DDhcBjLspZtac1sd3XnOl+A9vZ2p1Zis1IR02c6p84S67rOxMQElmWxfv36hlrW8XicyfFzJF/4KrHQceLDjxDX0xcOXvuf7ESY11/ewsjZLjKJFmz74udbrihFK0zRDoMrgqqqBK4LEHpnCJfHRbVanbWj8Xq9DA4OMjg4yLp16xZUx9OyLDo7Oxe1r6X+923bNtVqlWAwiMvlcv7+dV3HNE0URUFVVXbu3LmovW9LVf++o6OjznIyv7+WMbVUKjmBVDKZXJHzEUIsP4lbrm0St6xc3GLbkE60MlnoZnK8lZM/30YxOzNz//S4xdvqpfXeFpRNCppHQ9d12tramn5XiVskblmrJGe+WLRmM4ter5dAIEA+n8e2bcLhsHOxnO3iXu9EqtWqc4EPBAJOx1DfG1StVhkYGODGG29cUqez0O9QN9v5Ak7iktku7FNnOqfv6zJNk0QigWmaTombVl+F9vKzxMpP02K+gJbSsZNQLnmxLT/ZiTCjZ7sZOdPF5FjbxXNUXCj+DvDV/lNVL+5qBS2fx1ANAv0BWu9sJd4ZJx6P8/LLL8/obOoz9YVCgXK5zNNPP826deuc5Vf9/f1O51uv41lPRPL888/P28bT1f++K5VKw10Gt9tNNBp1ErD09fWh6/qMmrmX09TlZFD7LamqSjQapb29nWKxuODlgFe7ufb5CSHEaiRxy+WNW0hBKe/n9Vdu4NTLWyhkZvbftuJC8V2MWyK97WzujzFhTlBwFyBYiwdisZjELQsgccvCraa4RQbeYtFmm1mMRCKk02kAIpGIs+ek2cUdLnYiZ8+edfZZTS2JUKlUCIVCtLW1kUwmAZbU6SzmO8zWGdXFYrFZL+xT9ws129eVz+cB6Ann6TF/zNbJV4mrp1CwyaYinDyzmfGhOInzHVSKMzthAy9lJUpFaUXxtBEMhfF6puxligS57V/cRs/berBclnNxyWQyHDt2rKHjnzpTX08EEgwGZywda5ZsZOpSvMWY+vddv8sw9e6CaZpEIhGnLMdKLtmsLyfbvHkzgLNPqv6bdLlcC14OeDVbrsy8QgixkiRuWf64pfYeGD3bxYmfb+X8qT5sq/Gzm8Ut4fYwN77vRra9exuqS206KJK4ZX4StyzMaotbZOAtlmS2mcXt27cDtaUuqVSq6cW9bupSmfHxcSchhGmalMvl2nLpQADbttF1nWq1OqPTcbvd5PN5MpkMwWCQ7du3L3gWa67Z0bn+Qc52YZ/6uQ37uoBQ9Tjrqj/h7sh+WrWLWdGqZTcv7L+d069sbvpZF5diRcAVRlVVLNvCqFTRjTQtrS20dLWw9V1b2fqurfiivhnHmD5LDjgz9X6/n1KpRCgUcmrJTt87tVwX7PrfdzqddspzhEIhLMuiUqngdrtpb293SpOt5N6k+nKyem3O6SRD6PJl5hVCiCtB4pbliVssUyU11sbYYCevv7KZ/GS44bNmi1uUiI12m8Zdv3UXPZt6Gtp0epwhccv8JG6Z32qMW2TgLZZstgs5sOAlHfF4nNtvv51UKkWlUqFarWJZlpOkZHx83MnOWCgU2Lx5s9PpDA0NMTk5ia7rTu2948ePO6UhLuU7zNcJzndhr5RLRKpH2ab8nHj6KfzmaO2JKblNRs508fzj/RRzF/c+2SjoahQ10I0W6mUyU6RSruDxeEABl99Fy+0thK8Pk1NzdHV38eZ3vLnpRXfquU7t+L1eL4VCAZfLRalUcpZ21b/zYpK9LFY8HufOO+/E7XZz4sQJ0uk0Ho/HSXZSLBZnnbW/nJa6d+5asZjMvEIIsVpJ3NIy6/OzxS22CpPjrQy/3sPouU5Sw7GGkqWzxS2+sA/NrxHoC9B+dzuBDQESiQSnhk7RvbF7zvOVuGV+ErfMbbXGLTLwFpdktgv5Yi58mzdvZufOnQwODuJ2u0mlUlhWbal0vQalZVkcPXqUcDhMPB53slrWL3yhUAjDMGbMYi1kX8eyzY5aJiSfhXN/T/zc3/N2cxiKjS+plDycfnUzQyd7SQx11N5mqxTtMCU7iuVux7RVtLJGyGVRrVYJbQgR3R4ltC1EcEPQyezpKrtIF9NO7dG5TJ0lHxwcpFwu4/P5CIVCxGKxhn1Jl3uWNB6Pc//993Pddddx/PhxstmsM3Ew36z95bLUvXPXisVk5l3JPW5CCLFYErdMMUfcUi27OfXyFk69vIXcRKTxbbPELdGAgvd6L3139RHdPPO8FzNAlrhlbhK3zG21xi0y8BZXzNTOpa+vj0wmw+DgIIZhOEt5SqUSXq+XWCzGxMQER44c4e1vfzvHjx/HMAwnmyXUSrxMncWybZvjx49f3n0dlgHjT8Lg38PgP0K5NkOsUtv7lE1FGTrVS2o4RrXiYWK0DdPQMGwXBStM0QpTIYymufFFfMRubSOwMUApVCIajxIuh4l3xZdlGVF9lnxwcJCnn36aYDBIJBKZcUFaiVlSRVHYvHkzmzZtWhUJL5a6d+5asZgyNjLwFkJcra72uKVadjN6tpvh090MvrYeo3pxmNAsbvF4PWgujUCHF22HRrw/TlEvEonNjC1A4pblPh+JW2a3WuMWGXiLK6JZsgNVVZ0ahKVSCVVVa0usgfHxcXRdZ3JyknK5TKFQoK2tbdZZrKGhIRKJBIZhLP++DkuH0R/XOq2h70MliV5xM3Syl3OvvYl0ogW3x6Ba9lDK+523mYqPstJKUYmiK0FMTFxuFwFNI3JdhN5f6cXTUvu+5XKZTCYDGsu6jEhRFNatW8e6desYGRmZ8fzUWdJ60pnL2bks516sS7XUvXPXAlnSJoS41l1tcUudbcPY2S6OPr+T8cGOhlKlTtxiz4xb/N1+ghuDtNzWQmB9gEqlUotbkLhlpUjcMrvVGrfIwFusuOnJDlwuF/l8nkQiQbVaZf369fh8PnRdJ5FIOP9ovF4v+Xye8fFxCoUCoVCo6T8Yt9vN5OQkoVCoYWa52b6OBV+QLZ0O4wW0n30Phv83VCdr32UozsDhexh+vRfLnHlX2tKCVJRW8lYE3fajKAoer4eIx0s2m8Ub9tJ1fxdt/Y2dscfjQVEUwuEwmUxmWZcRLWSWtKOjg2eeeabprPtq6XAuh6XunbvaLWZJ21KzxwohxGp1NcUtU42d6+DlZ28mMXRxgDZb3BLvjlBuL9NxcwehTSFcgcYhhMQtV4bELc2t1rhFBt5iRU1PdlAsFhkdHaVYLKLrOqVSicHBQafu5NQamfUlI7FYjFwux/j4OKFQaMbFJZ/Po+v6gvZ1zHkxtkxIPAVnv4194vtcnyyTGVTIpqIkzm9hfKiDTHJm52G5Qqj+XvB3o7rD+LBx6YYzK+72uOnY3cGIZ4T2ne34I/4Zx6hWq7jdbrZv387Ro0eXfRnRXLOkHR0dnDhxYtYskLt27Vr0560lq2k2e7WQJW1CiGvVWo1bXOf+nruqKTgz82XjQ3FefvYmxs911t42W9xiW/Te2cvNv3oznj4P+/fvxx/w4/LOHD5I3HLlSNwy02qNW2TgLVbU1GQHxWLRqclYnxnWdZ1iscjp06cB8Ptrs61T62NGIhEikQjZbJZyuYzff3HgWt9/5Xa7CYVClMvlGbUNm+0xqmQrnHj0BOOvjuP1n6ctcBAteZD0kEJyJEYm+TYulK1sSleCVNQ2ClYECNLia8Hrrs1qB2NBund3E+4O4w646bqti2BHkKeffpqRkRF8Yd+sM3GbNm0iHA5flmVEzWZJI5EIzzzzzJxZIE+cOLHkzxRrlyxpE0Jci1Zr3OK837LIn/0R2uB38SX+N2qlVvpr6nDCtiGfDpFOtHLi51sZO9tVi1u0mXGL6lbpuKmDnl099N3ZR7Aj6JznQu4gStwiVovVGLfIwFusqHqyA5fLxejoaMPMMEA4HHY6MYBAIIBhGE69xFgshqqqdHZ2UigUSCQSdHR0NMxiBYNBLMvizJkzTpmPem3NWCyGpmlOh5Z6LcXJH5zg7L6XqE6chdIwml3hHADb5vwuVYLorhhVrR1T8aJoCi1bQ5gxk5aOFq6//no6dnbQsqml6YzaQmfiLucyoumzpOl0et4skKlUas7yZeLqJUvahBDXmtUWtwC1kfTkixQHvo46+PeE6yVLAdtSGDnTzeuvbiI93optK5TyfkxDuxi3eGtxC4Bm1+5WV7QKd/9fd7PpbZua3tFezB1EiVvEarHa4hYZeItls5ASGPVkB/l8nmKx6Mzm1qmqSjAYRFVVMpkMuVwOr9dLKBSivb0dTdPI5/OYpkksFiMWi1EoFBpmseLxOE899RS5XI5wOIzL5cI0TfL5POVymUAgwPre9Rz/2lO8/k+HoTQMRn5qie3Zv6O7BcvbRaroAZe/tnQ86qbjnhitt7ei+TXK5TKlUonOezrnXPqzmJm4lVpGtNAskPXkMeLaI0vahBBXi7UUt2zcuJGoMgYv/TGc/TbkXiNAbQx+7rX1TI63Uil5GH69tyGxq+1uwfI1xi1TuaNu4m+M47neQ/yueNNBd53ELWItWk1xiwy8xbJolu2zWQmMerKD06dPY1kWmnZxuFtflhWJROjp6WFgYIBAIEBPTw+WZZFMJikWi1hWrb51LBbjlltuwev1zlhy5PP5sG2barWKoihomobb7aY6NEJw/DyZs1WGx6oL+m5VAuiuOEU7Siy+HsVj43ZnaN/RTmhjiMD6gFNb27ZtLMtykq7MN6s227KpbDbL2NjYis/MLTQLpBBCCLGWrYW4xePxYBSG2eIZ4A2ZAZRHXmz4DrYNBx+7k9Ovbmp4fGrc0h5bh6HrGIUUHuXioNsddhN7c4zW3a1UjAqpVEriFiEuM/kliks2PdvnXCUw6kuVxsfHSaVSuFwuPB4Ppmk2LMuqzwx7vV6SyaQzW+x2uzFNE5/PB8DPfvYz+vv76ejoIJPJcOrUKYaHh4nH45imSTKZpJycIHD8JL7X8thZgwo2zSpIKopNqDWHy21QKLdhhrswYxtx98bwdCi0d/v4xff8IoVCgZ/85CcEAoGGi3yhUHBmgHVd54UXXmB0dHTefSRTZ+ISicSsWTkvdS/KQmb2F5IFsquri8nJmdlRhRBCiLVgtcct1WKaXutFtnt/xrrAMVTFguzF858YbeP4C9s5c3Sj85ih+KlqMSpqO7rtxbRMlAs7vVVVRVEVFJ9CsDdIeGeYtje0UaqWGBoZkrhFiBUiA29xSaZn+1xICYx4PM69997L448/TjKZRNd1NE0jFAoRi8UIBAIkEgn6+vq47rrr+OEPf0i5XMbtdmNZFuFwuOF1hw8fJhAIkEqlyOfztcF2qcCOlmH6xo4w8A8eLL322c3mXzWXyc7+o3TcOMmQ+wZeq97OpL2OQCBILBbD7/fX9ge5a8vIDMMgGAySTqed71woFJyEK5Zl0draSjQaXVT9zcUEAou10Jn9hezh2rZtG88///ySzkMIIYS4klZ73HJ75Cni7qdwUWp6/mPnOvjp996CaWgYeChYUfJWFN32O3fJNU1FjatEbopw8703o3pVzmbOktNzdHR0SNwixBUiA29xSaZm+1xMCYyOjg7uv/9+nn76afL5PNFolFAo5NTArCfpcLvdBINBIpFIQ3KR+me53W5OnDhBW1tbbe+UNsRN+j+xVTtE9tUgT33/TVjm7Mudop1Vbvw/ozyXuZ8fV9YR0IJoXg33hb1VlUqFeDyOYRh4PB6eeuopDMPAMAzn+VgsRiKRoFwuo6oqPp+PeDyOz+fD6/UuqP7m9EAALu5bCofDZLPZxdfwvGCxHeN8e7hWyz4ZIYQQYrFWc9wSKmaZTaXk4fVXNvPygTdQ1rpJlDxUbB+a5gIVVBvsqA2bIX5HHLvFJhAIcLJyEqNwMW6pL3mXuEWIlScDb3FJFprUolkJjI6ODu69917nQjkxMTEjScfY2BimadLS0jIjIUh9CZJmTHKL7yV6Ek9SPZelkA3y0uCNnBtYj23PvNj7o2XW3dFF3317iL/pF3n6wHMk06+gaTaapqEoCi6XC03TKBQKDA4OEggECAQC+P1+PB4Puq5jGAblcplEIsHk5CRut9uZ1Q4Ga+U3Flp/c3q5kqn7wlRVxePxMDQ0NH8Nz2mWMrMPc2eB1HV9wZ8vhBBCrCarKW7ZkPkJYeMU+Gaeh1F1ce619YwPdpAY6SRX2oYS6MOOxchOTFIlB4oNyoVY450RvNu9FAoFxvVxAgWJWyRuEauNDLzFJVloUotmz8H8af5nO76eLmMfPciGkedoNUcY+XGcl4d3zVpr20YhvNGi5a19bP+Vj9LRsxmolaFIpVL09PQwPj7uZCzVNA3TNDFNk3K5TDgcds5HURS8Xi99fX2Mj4/j8XhQFIX29nanfudUUzvx2fYr1QMBXdcZHh52vm/9PEqlEoVCgdHR0UV1YEud2a8/L7PEQgghriZXKm5RbJNw7hnu5Xts7HgFrWI2PX617Oa1I9dx/PAOsuUYFTWGr3UT3rYAAIauU9Wr+Hw+qlYV9+1uApsDeLu8ErdI3CJWORl4i0uykKQW3d3dRKPRWY8x14Vy+vHt108x8U8vUT6XRbOrQCujtM567LLlo0QbvlvX0ffh29ix8/qG5Un1jqO9vR232+3M2JbLZaC2JKxarTY9f0VRiEajZDKZC3uqtKbLqeqdeKFQ4Omnn266X6neWY2Pj8+oEVoPAPL5PIODg2zfvn1By7Zs23YSvHi9XmzbnrNzFUIIIa52Kx23BI1Buko/oLP4BF4rBdOqWhlVF+lklGrFw8jpbo6+dAO5agdVVxyXP0goFMLruTiAtywL27Jpv72d9n/WTmoyRbFYpFAoABK3CLGaycBbXJKFJLXYsWPHkstKKIrC9Vt68L3+d6T/6izJYyGAOWtuG3go2K0UrCgl00Xvfb285z+8h9a21lnrc+q6TjAYJBAIkE6nSSaTVCoVKpUK1WqVyclJYrHYjM+qzxqHw2EymcyMfU6appHNZolGoxw9enTW/Up33HEHoVCIs2fPEgqFZgQC1WqVSCRCLpdb0LKt+lKs4eFhZ2Z4+nIymH9mXwghhLiarFTcEk18h56RHxLjRNPXmYbGKwduYODQ9VRN38W4RXcRDAZpa2/D7XY7mckBNI9Gz609eNo8tF3fhs/nIxgOStwixBohA29xyWKxGDt37uT48eNkMhlUVZ2x52nRbBsSz2Cf+Esm//cLnPnJTgw9NOvLLRRKdgtFpQ2jvQVUBTtsEd8dJnpdFFVTm3ai02emi8UiiUTCWTJVLwVSLBaBWsmwUOjieVSrVdxuN9u3b+fo0aMMDQ1RrVadDsw0TYLBoLO/arb9SsePH2fdunUcO3aMSqXi1PCcWq6ko6ODcrk87yzv9KQkpVKJXC7nzBD39vYSDAYXPLMvhBBCXE0uZ9zCyb8kNvj3xMzmWckBUiPtPPPI3YxP9lGw26gQwgZMy8Tt1mp3ei/8X/eubnp299C+vZ3WTa0omsLTTz/NyMgIXq9X4hYh1hAZeIsFmW2Pz9RyD/XkFaFQiOuuu47Nmzc3HezOWZexMoF18m84+d1/YvBIrXMyjZubnpPboxNZ5+J8qY10sBfv1hZCG3xYquVc9Lu7u+e86E+d+R4fH3cyfnq9XqrV2h6qQCDgfLdUKkUwGERRlIYOYNOmTei6zpNPPkk+n3cymdZfOzQ0RF9f35z7lTZs2EBHRwfFYtHpBFVVdcqVaJqGYRhzzvI2S0oSj8epVqvouu4kVVFVlVwud8kz+0IIIcRqtJJxC6f/Pzj1l5A5Ouc5Vctufv7i/Tz30+soWC0oqqdW59SuDbpVRcXj9WBZFh27O+j/l/20bp65nU7iFolbxNokA28xr9lqKXZ0dHDixIkZy5AymQzHjh0jEonMmDVueqz2dm7syhA6/12Gf/osxw5uY3KsY9bzcQdUbvzlPq77wP9B1rWOH/zgB0QuXPRLeqnpRd/j8ZBOp5t2mvUyFEeOHGF0dBRFUTAMwzkGwPDwMAD5fJ5SqfYZU5ekAYyPjxONRunr68M0TWcpVKFQ4LXXXnOWWs22X8nj8dDX18fIyAjhcLjhGPW26+7uJhKJzPpdmiUlCQaD9Pb2OiU20uk0fr+fnp6epc/sCyGEEKvUSsUtrcm/g3PfBWue/ca+Ds5OfohDP9hOIe+ioo2jYNb2a1O7u+32ugl2BoncEMGz3UP/L/ejKApjY2MSt0jcIq4SMvC+QuacPV1Fkskkhw8fntFJDQ8PMzAwgM/na5gRnavcw/SlRH6tRNvE47S9vI+hv/My8LPrMfQ7ZjkTBXydbH7nTm75t+/F11rL1hm17Xkv+pFIhJdffplUKjUjOUj94h2Px7n55ptJJBKEw2E8Hk9D3c3u7m5M00TXdVKpFKFQqGFJWn1/VTQanTGz63K58Hg8Tt1vn6+xbkh9v5LP53NmsXO5nLPvrFKpOJ1lR0cHzzzzTNNEJ/F4fNYyKfX966VSiVQqxe23387WrVtX5W9OCCHE6iNxSy1uiecfp2fon4gODc19IooLen+RatcH+PkT3Zx87DQALreNz++jUq7U4pUeL90PdBNZH3E+U+IWiVvE1UkG3lfA+Pg4L774IslkEtu28fv9xOPxyz6Lt5ROsz4zXF/+Y9s2tm3jcrnIZrMzalRC83IPzlKiQoEtLSlahr7J8BN5XjnRg23vnv0EXEEIbsDbuZn+/9+b6X1D74zPmuuirygK+XzeSRQyPTlIf3+/0+b15Vler3dGJxQMBp3lWbt27SIWizW031x1Qb1eL6FQiImJCXRdb+jApu9XUhSF/v5+Z3Y9l8s5+85mm6mf+l3mKpOiKIqzGmB6JlchhBBiNhK3pOgt/hUdpf2ozF0T2g5tY1T/TU4evYX8AZv0mTTYpy9+FgqhUAjDNAi8IUDfO/vw+r0St0jcIq4BSxp4f+UrX+GP/uiPGB0d5ZZbbuHLX/4yd9zR/E7lN77xDT784Q83POb1ep1yTVD7R/ypT32Kr33ta6TTae6++27+/M//nG3bti3l9Fa148ePz9hPUy6XKRQKMy6oy2m2ZVfzdZqpVMq5sBYKBafcVn0vz8TEBG1tbbS2Nu5Bml7uITMxRmD0u+wxHmfof3k59PxOLHOWxBiKCr4ugpu2seWf3U3rljY6b+5E8zTPZV5fcjX9ot/V1UWxWCSbzTZcsD0eD+FwmEQiwZEjR9izZw+qqs5bYgRqM8hbtmyZMzt6s44jGo2Sy+XIZrO43e45M6g2qxEaiUR45plnGoKJ+udOnam/++67L7lMihBCXG0kblm6az1uuZ/HiKaaZyZ3qG7o+2UKLR/m2W8qJAdSwOSsL9/y5i30/UIfQ9mhWvmsYl7iFolbxDVg0QPv73znO+zdu5evfvWr9Pf38/DDD3P//fdz/PhxOjqa78uNRCIcP37c+fP0f/xf/OIX+dM//VO++c1vsmnTJv7gD/6A+++/n6NHj85Y3rKWjY+P8+STT5LL5QiHw7hcLkzTpFwuYxgGwIxlTsth+lKpuWZPp6vPhhYKBc6fP+9coF0uF5VKxVm+5fF4mpZ78FkJePFPiJz4C9afUTnww36yqUjzz8ILwQ303fsG1r1pO5vetmnWwfZ0zS76tm2zf//+hhneqZ2wrutMTtY6xttuu82ZvZ+txIjX62Xbtm0Lyo4+vePQdZ1t27YRCARIpVINM8LNgojpNUKnLgmbK9FJNpu9rGVShBBirbka45bhw8NkR7JkX8hySjuFy3V5FjCm02kOHDhAqVTC7/ejaRqWZZHVs2iaRtFb5JmBZ7jhhhuWtV/JZDIMDAxQqVQIBoNomkbVrHKicIJzPz7Hjh07ZgzEDMMg+0K2lpk7bJKqpkgmk06Wb9VWUfIKFbPCmTNnKHWWGv6uqtUq1UqVUf1lcpN/ijX8A6JGjgSQYGvT8yzSCj3vJLDpPViZMEe/dpTSxOzZzDWPxu6P7mbzns0AbLQ3StwicYu4hiz6Sv2lL32Jj3zkI85s8Fe/+lUeeeQRvv71r/N7v/d7Td+jKApdXV1Nn7Ntm4cffphPfOITvOc97wHgb/7mb+js7OT73/8+73vf+xZ7iquSbdu8+OKL5PN5wuGws7TH5XLVOq8Ls7GJRGJB9Q4X87nTs0XC3HuapnK5XFSrVSf7ZyAQcJZuud1uTNPEMAySyeTF5ywLb/pZdnt/SmT/0xgVlZ8/fQuvHbkO7Gnnh0JVbaekxbF723nXQ+9i0xs2Lem7Tr/oj42NNSyjmj554PV6yefzjI6OcvDgQWcCYra757PVxKx/9nwdR32p11L2yM21JAwaZ+o7OztnXfYlSUmEENeaqzFuOfHICYYODpEcT3L40OGmy6cvlY1NMlHrQzSXRkkpNTxnmiYltUTBXSAby87aPy3lcycmJqiUK3g8HgpKYeqTTFYnmfzhJG1tbQ01ri3LIjmexMYmq2Zr/aZZq0uto2NjY+kWlmVR1IoMuYbw+/1OVnGqKSJqilcfmUDBBnbMcn61uKWoxilbQWKxGG73a/N+r947e7ntX95GuDvsPCZxi8Qt4tqyqIF3tVrl8OHDPPTQQ85jqqqyZ88eDhw4MOv78vk8GzZswLIsbr/9dv7Tf/pP3HDDDQCcPn2a0dFR9uzZ47w+Go3S39/PgQMHmnZglUqloTxUNpsFQNd1p3zCYtXft9T3z6eetdHtds+YmVYUBZ/PV8vKXSqRz+cxDKNhuc5SZ/rqnxuJ1O4y15ce1YXDYZLJpLM0q67eDm1tbQwODlIq1WaGp56Hoih4PB5s2yaXyZE6MoR74OcEMgN4rRw/113olXdQzAaplBqXMZmKl4q7k/Dd22i/p490KU13Xze9t/Q6n11fYrTUdqgviatWq3g8Hme5Wn2CwDRNPB4PsViMfD7PsWPHiEajtLS00N/f3/DZfr+fH/3oR3P+PlpaWti1axcnTpxomB3u6upi27ZttLS0YBgGwWDQWR1QX+mwmO/SrCxHpVJxJnF0XW/6Hertd6m/8cv9b2WtkfZodKntIe0oltPVGreYpun057ZtY1nWoo8xH8MwqFarKKoy844lCppau/ttWiaGaWDbNqZloqnaJd2Br3+uc4xpE/b1vlCv6g2fU28Pj9tDqVRyzmXq4BxAVVSwwTANLFPHZyfxm2O4KYE5+3mZipey1klF68DCfaE/9jirAKaev2mZBGNBtv/CdoKdQdq2tBFZV4vD5vr7lrhF4paVIO3RaCXjFsWePhKbw/DwML29vTz77LPcddddzuMf//jH+elPf8rBgwdnvOfAgQOcOHGCm2++mUwmw3/5L/+FJ598kldffZW+vj6effZZ7r77boaHh+nu7nbe9+u//usoisJ3vvOdGcf89Kc/zWc+85kZj3/rW98iEAgs9OuIS2CbNqWzJfIv56meyeDNjuC3EqjMfUGuKBGKSgf0dBD7xQ68nbPXdhRCXHuKxSLvf//7yWQyzoShEEt1tcYto98ZpXiiuOj3iRqVKgFrnIC98LilorQAC5v89/X56HxvJ5p/YdvlhBBr12Lilsue1fyuu+5q6Oze+MY3cv311/MXf/EXfO5zn1vSMR966CH27t3r/DmbzbJu3Truu+++JQdquq7zxBNP8I53vGPZlktNlclkePLJJ8lkMs5eqakzyIZhkM1mCQaDtLa2EolEnL3Y2WwWv9/P7t27Z10yNNfnPvXUU/j9/qYzjuVymXK5zL333ouu686sZ732dUtLC4FAgBdeeKE2C32+SunFEvZ5G59dJaKlaFUmLyzLas5WNPD3YQc3oLj8dN4VQtmhYNq1Gpbt7e1s27bN+W7JZJJDhw5RKpUW3Q7JZLLhOxiGQaFQQFVVCoUCoVAIy7KoVCq43W66u7sJBoNYlsXExARvfOMbm+75u9y/j4WY2i710iHVapVcLrfk38dSrIa2WE2kPRpdanvU7wQKcaWshbjl54mfk+hKcObMGTZu3HhZlppXqhUGBwedJcMul2vG0u5qtYrb7cbn8+HxeFA1Fcu0nDvWXV1dteXci/zcocGh2t1Q18yBq2nUtrj1revDMi0mJycplUpYloWmaXh9XtwuN2NjY1iWhWVbmKaJgkLEU6bDfY4WZQSFOVYJKG7s6A3YrbdRMgPYk5Nw4TNUVcXv99Pa2up8t1KpxOjoKIZpEN0SpWtPF6ZiStwiccuqJO3RaCXjlkUNvGOxGJqmMTY21vD42NjYrHuhpnO73dx2222cPHkSwHnf2NhYw8zx2NgYt956a9NjNCubUD/2pf6AluMYzbS3txOPxykUCrhcLorFIl6vF03TnEG3oigEAoGGvdiqqjqZLF9++WUnk+ViPneu5Bm5XI7u7m4sy2qoe+lyuZx9RKWDJaqHq5SzZUzTxG/niLom8Cu5uT/cFYLgRpRAH6huOm/u4I4H7yDUFZp1v5Bt25w8eZJSqdRwvpqm4fV6SSQSnDx5kq6urhlL3xKJRNPanYZhkM/n0XWdXC6H2+0mGKzty6ovndL12pK1YDA459//5fp9LER3dzd33nmnswcqn89f0T1QV7ItViNpj0ZLbQ9pQ7Gcrta4Zfe/2o2u6zz66KPsefeey/LvxrZtnn766VoislLJ2Wc8NW7xKrV8MVPrYtu2TblcJpFIEOgKLDpuqX/ubHFLIpGgu7ub7du38/zzzxMsBumJ9jhxS6VSqZU7c8cvJCUr0Gm+yO7Qk/RqR+f+8PB1cN2DsOkD4Lm4/W6usmb1862OVBvO141b4haJW1Y1aY9GKxG3LGrg7fF42LVrF/v27eOBBx4AajOe+/bt48EHH1zQMUzT5OWXX+bd7343AJs2baKrq4t9+/Y5HVY2m+XgwYN89KMfXczprWpTk1hAbSBZqVSc5GSBQAC/309HR8eCM1ku9nNnS56xfft2jh8/3pCArZqtkj+ap/BcgdxQDpdLJaROEmAEj1KZ9fPae5K0Xx/Hu+UXcHXdhjvowRP0EO4NE11/saOaLXlcfU/6fFkwpyegmyuJXF9fH2NjY0429ng83rBffS2Vq2iWvX2hiU6EEOJaI3HL0q21uKW+czIWi5FMJvG4YIfnAJusf6DdNTbnZ+rxd+C+8f+Brj21kqZNzknilqWRuEWIixa91Hzv3r188IMfZPfu3dxxxx08/PDDFAoFJ1voBz7wAXp7e/nCF74AwGc/+1nuvPNOtm7dSjqd5o/+6I84e/Ys/+pf/SugdlH62Mc+xuc//3m2bdvmlOXo6elxOsmrxdTMk4lEglKphKIoxGIx1q1bxyuvvLLoTJaL/dxm2SLdbrfTadiGzdiPxkg8lcDQDdwuhbCaJKCPoFGdsb2prWuCDTvOEF8/Qa7zTga0d9H35vfT2dm5pDZaTBbMqebr+FpaWrBtG6/XSy6XcxLDrcVyFXMFAEIIIRpJ3LJ0ayFumd5va3aZmzw/ZkP67wnYqVkjXQs3I/77eFl/O7fdLHHL5SRxixA1ix54v/e97yWRSPDJT36S0dFRbr31Vh577DHngnXu3LmGJUWTk5N85CMfYXR0lNbWVnbt2sWzzz7Lzp07ndd8/OMfp1Ao8Fu/9Vuk02nuueceHnvssauqhnfdbDN/9ZqVuq47mSynlu+qX9Tj8Ti5XG7R9b7nmnEcGxtD13XyL+dJ/jhJNV1FsQ2C1jDB6jiq3ZitLxAusn3XABuvP4sa9nE++AADgX9OTvdTKpWaLqdbqPrsbr3jnq6+b2z6cwvp+FwuFzt37iSRSEi5CiGEuEZI3HJpVmvcMr3Pd1l5rqt+ly2Jf8JjZWY9blVt4XzwAYbrcQsStwghVsaSkqs9+OCDsy7R2r9/f8Of//iP/5g//uM/nvN4iqLw2c9+ls9+9rNLOZ01p9nMXzQadfZih8NhZw94fflUpVIhFAo5S42aLVta7Ofatk06nWYyOUni7xMYZw1cqknQHMFrjKJOqauhKDY33fMS67YNEm7NUXRv4GzwQcYCe7AU74VlT4lLXvY0tR2a7e3KZDK0trZSLpdJp9NOJ7zQjq+rq4vt27fLkichhLiGSNxyaVZb3JLNZp3kbmF3kXWFv6en8D9x2bNnei+4NjAU/DWJW4QQV8xlz2ouFmb6nqb6hbhez9vtdjszxbMtW1qMRCJRWzo2mmDkuyMUB1JE1ARhdbIh02cgUiDckuemN75MvC9BKfwGfma8izPV64loLXhsN9VKedmWPc21tyuRSFAul7Esi6eeegqXy0UsFmPHjh3EYrE5O76pe6FkyZMQQghxaa5U3FK/s66nT9FbfJyd3udqW+FmUQq/gZclbhFCrAIy8F5F6nuajhw5wuTkJPl8HrfbTSgUashkOduypYVKJBIcPHiQQq5A+Qfj+E+fptU11lASLN6X4A3veJ5oey1F/nlu5cfavyVj3Ijf7yfir5XPuBzLnprt7TIMg3K5jM/no62tzcn8OTIyQiaTob+/f95kLGtlL5QQQgixFqx03FIsFukI6WxV/o6e8KNz1uA+z60c135Z4hYhxKohA+9VJh6Ps2fPHgBGR0eXPZOlbdsc/N5Bzv3PY/gSo/jMMTxT7nC7PAZ3vusAfVuHQFE4ZbyBY8o/x93xBtxuNwFdJ5PJ4Pf7ufnmmwmFQpdl2dPUvV3lcpmXXnoJTdNmZP6Mx+POLPg999wzZzIW2QslhBBCLK+ViFsGBgawCue5y/tDetP/GxW9+WtReN3YLXGLEGJVkoH3FdasNqSqqtx2220cPHhw2TNZnnv+BUa/9iNarGTDknIAl9vgrb/6E1p7Jsm0/yqHim9jQm+ftdMYHh7mnnvumfM85qp9OZ/60qp0Ok2hUFhQqY7FlK24lHMTQgghrkUrHbdkx0/QNfxF3mj+EK3YfEm5iUau/ZckbhFCrGoy8L5MFnJxnLpfyTCMhr0/85XSWOwsaHl0iPEf/BnPfPk8wSlJ0+pcboM3/crTlLbey3PV+9i64S0Ujh8nEvEuqi7lYr7fQi22VMdC9kIt17kJIYQQV4PVFrdQTsCxPyI88GWidrnpSyzcnA/8Ij+XuGXB5yaEuHJk4H0ZLOTiOHW/UjQabbr3ZzGzoLMyCoz9z//KU//1GHpZmV6GGwCXV6f3F4q8cv0X0N2d6HbJ+YzF1qWc2gYL+X4LsdRSHbNZznMTQggh1rrVFrdw7Etw7Itg5FGbvMS0NQb0exht/7DELRK3CLFmyMB7mS3k4hiLxRgYGKBYLM6596eeDXRJmSwtA079NcnH/pgn//vNGPrMv2obhU0PZDgTezNH7ADBlE0gUNuH1dbWBoCu62iaNuO9c3Ua9f1YC/1+85mvVMdi9o4t97kJIYQQa9lqi1t4+dNQHm3+ElvjhHUPPzd+kUTRTxCJWyRuEWLtaDaRKJZo+sXR6/WiqqpzcSwWiwwMDJBOp0kmkwva+7OEk4DBf8R+5EaO/8WX+PH/d8uMQbeNQoFWSr90Ha/2PEDZH0PTNCYnJ3G5XOzYscPpDLLZLLZtT/uIWqcRi8WadhqZTGZZv1+9VEcgEGgozVEul0kkEovaO7bc5yaEEEKsVaspbuHRG+Fn/6bpoNtCZUC/m/+e/QOeqvwLCrRL3LLEcxNCXDlyx3sZLfTimEqlFrX3Z8ESz2If+V2Kr7/Ic4/dyfi5zoanbaCixtG2bSZyZzellhLFYpFKpYKiKPh8Pnbu3EksFiOVSgHgcrkWXeZisXubFmK59o5djnMTQggh1qLVErcoyWebPm2jMOrbw7nIB0lWWrCTSXSJWy753IQQV4YMvJfRQi+Otm0v694fuzCI/rO9eIb/nomRdn7y9+9GrzSeg+WJU1nfRc+HbsMX8NXeZ9vOOZumiWmaeDwenn76aZLJJKqqOs/XZ5UX0mks996muuXYO3a5zk0IIYRYa1ZD3DJbD67H9/Bk9t0YwZ14XV6CLggEAhK3LNO5CSFWngy8l9FCL46xWGx59v6YZQqHPovv9T/GY5cpF7089T/vbRh0VwmQ19Zzw6++CWWXwlhiDK+/lvGzPlts2zbj4+N4vV4OHTqEruu0tbVRrVZpbW0lk8ngcrm44YYb6OrqmrfTWM69TdMtee/YCpybEEIIsZZc6bilmQk2czzwm2y55V/hOX6c1JTPlLhF4hYh1jIZeAM//exPGTo4xPj4OH/3l3+Hqi5t67uNzcTEBJVyBY/HQ8M0rl3rwLw+Lz/4+g+oVqukJ9MMmoO4XC5UVcWyLAzDQNM0iq1FhjxDs34SpVHszKsoZgn4pRmv0G0PRdd6aO8m/LYgWr/G9u3byT2fm7EEK5FIUCqVqFarVKtVfD4fhmHQ0tKC1+ulo6ODRCJBIpFg+/bt887U1vc2ZTKZRS/3utxW87kJIYQQK2mhg7qWlpZL6zttG4a+j3noYwRL55q+JGvFOBn8l2Si95FIpjCOH2f79u1NP1PiltVxbkKIxZGB9zJSUAiFQhi64cwSTx9Qh0IhFBS8Hi8trS3k83mq1SqGYaCoCl6fl1AohNczy5IhPQeZV6CSbLo8y0IjY3eRMVrw9wW58aPb0BWd4eFhuru72blzJ0NDQ6RSKXK5HIZhUC6XnRnvcDiMoigUCgVaWlooFAqEQqEF1b+catnreS6j1XxuQgghxEpZzKBuyX1n5igc+r9hbB8zc41D1fZzxHwPL+TuJGC2sL5FJRwOS9yyRs5NCLFwMvC+BDY2hm5gWRaqquJyuxY1oPZ6vHjaPDOO0bTatm1C7gTkT4FtNTkXKNjtZOxuLFy4wgrut7rJVXJkMhkmJyepVquEQiHa29u5+eabCQaDvPTSS2iaRiAQ4OzZs7hcLhRFcUpxpFIpgsHgkhOLXHI9z8tkNZ+bEEIIcTnYtj2j31vMoG5RfadRglf/Y60et6U3OReF16w38YL5y1SI4PYaFItF0um0xC1r7NyEEAsjA+8Fmj7ItiyLfKE2uLYtG0VV8Hg8zuB6oQNqBWXWpCaOcgIyL4NRaPp0VQmT1DsxlCCKoqAGVCL/PEJJKzE8PIxpmrjdbtrba+U3RkdHyWazXH/99RQKBaLRKLZto6oqpmk6nRhAqVRyOq2lJO+41L1Nl9NqPjchhBDiUkwfZFerVY4fP04ymcQwDGfvdn1wvdBB3YL6zpEfws/+r9rNgiYmXDewb/Kfk9E243LVQlFN0ygUChK3zGE1n5sQYn4y8AZ2/5vd3PB/3MBPfvIT3vrWt84YCKdSKU6dOsXExASmaWIYBqVCiaA3yPrYelwuF4ZhkMvl8Pv83HrbrbS3t1/6iZUT8PLn4Pz/bPp0SWnhdOA3SAfuxRgZoVAo4A/4cbW4MCwDPaPXBuJqbdmW3+93loslEglee+01dF3H7XajKAqBQIB8Po+maU4HZlkWuq5TKpUkeYcQQgixBiQSCecOtmEYGIZBPp/H5/PR0dGB2+1G13VGRkbIZDL09/cTj8cvfVBXGoMX9sLZbzV9uqi0cyL4ESZC76BYGqQyJeYwDANdl7hFCHH1koE3EOwI4mn14Il7iK6PNgy8E4kER88fpWgVadnQgsvl4uzZs5T0EmpARWvTCAQDAITtMIlEgvP582y6ddOlLf85+x146f+C6gTEpj2nuLCv/394JfNWzo+licf9dEW6OH/+PFW9ChYUi0Usq7Yk3ePxEIvFnPOp1+bMZDIATjbTWCxGpVKhWCzi810sOZbNZolGo5K8QwghhFjlEokEBw8epFgsEo1Gnbgll8s5OWe8Xi9er9cZ0A4MDDTECUty9ju1u9zViZnPXYhbjtTjlrDSEHN4PB6JW4QQV72lpe++Rti2zcDAAMVikXg8jtfrRdd1KpUK4XAYwzBIJpPYtg1c7BjqyTyWpJyEp38dnnlf884rfje860WUW7/AdTtvIxAIkEgk0DSNnp4efD4f+Xwe0zTRNI1IJEJvby/BYLDhMB6Pp6Ejs22bYDBIb28voVAIXa/tx3K5XKxfv96ZDRdCCCHE6iRxi8QtQojVS+54zyGTyZBMJhv2ORlGbd92fT9RsVikUqk4M61LSebhGPw+/OxfQ3l85nPuFrjti7DlN0GpzZdMT4ii6zrBYJD29nba2toYGRkhGo065zZVtVrF7Xazfft2jh496mQz9fv9dHR0kEqlAHjrW9/Ktm3bZMZYCCGEWOUkbpG4RQixesnAew6VSgXDMBqWntdLhNVnZuuvqauXEVtUMo/qZK3Uxpn/3vz59b8Gu74M/s5aspR02kl+EovFuOeeezh9+jTHjx8nm81SrVZJJpNOrcu+vr5Za3Nu2rSJcDg8I5vp+vXrmZycZNOmS1wyL4QQQogVIXGLxC1CiNVLBt5z8Hq9Tp3Ieofk9XqdZB4ej6eWsfxCRs6pHcOCk3mMPAHPfQhKwzOf87TBG/5f2PBeYGaylHpG0o6ODk6cOEGxWKSlpcVJmlIoFMhmswwNDRGPx+eszTk9m2kgEOAHP/gBmUwG0zSlbIUQQgixykncInGLEGL1koH3HKLRKLFYjJGREeLxOIqioCgXE4Lkcjmi0VoytnK5PKNjmJOlw0t/AEf/c/Pne/853PEX4O8CZiZLqXdSw8PDDAwM4PP5GmaIvV4vfX19DA0NAbVka3PV5pxeomJkZASAp556qmnZESGEEEKsLhK3SNwihFi9ZOA9B0VR2LFjB5lMxtlL5PF40DTNKXERCASYmJiYtWNoKn8anvkNSB2c+Zw7Crv+FDb9n3ChM5qeLGVqJxWJRBgeHkZVZ+bJq88KF4tFdu/e7WQxnW8GOJFIcOjQIQD8fj8ej6dp2REhhBBCrB4St0jcIoRYvWTgPY/piUDqs68bN25k+/bteDweZ5nTgpY0nX8Env0/QG+SPbTrPrjzryHQ1/Bws2QpdVP3bE1NllJXT5ri9Xrp7Oyc9/vWO8tSqeR0eIqiLH/ZESGEEEIsO4lbJG4RQqxOMvBegGZ7iRa9b8i24JXPw8ufBuzG51Q33PpHsP3/dmaLp2qWLKXO5XLhcrkwDKMhWUrdYpOm1DvLSCQyI8Pp9LIjU5d4CSGEEGJ1kLilRuIWIcRqsqQ63l/5ylfYuHEjPp+P/v5+nn/++Vlf+7WvfY17772X1tZWWltb2bNnz4zXf+hDH3L2IdX/e+c737mUU7ts6nuJOjs7aWlpWVznVc3Ak78EL3+KGZ1XaCvcdwB2/LumnRc0Jktp9pzX63VmkKeqJ02JxWILTpoyV2cJtZlowzCWVnZECCGEuAIkbpG4ReIWIcSVtuiB93e+8x327t3Lpz71KV544QVuueUW7r//fsbHm9RwBPbv389v/MZv8JOf/IQDBw6wbt067rvvPs6fP9/wune+852MjIw4//2P//E/lvaNVpvMMfhhP5z/XzOfW/9eeNcL0Lar6Vtt2yadTlMulwkGg2QyGWzbnvE6j8dDKBQil8tRLpexLItyuUwikVh40pQL5uosYYllR4QQQogrROKWRZK4RQghLotFLzX/0pe+xEc+8hE+/OEPA/DVr36VRx55hK9//ev83u/93ozX/+3f/m3Dn//qr/6Kf/iHf2Dfvn184AMfcB73er10dXUt9nRWt+HH4elfAyPX8LCNSnnHZ/Dd+vsoTZKLwMwSHIZhkM/nqVQqM0pstLa2sm3bNsbHxxv2cy04acoUUzOiTu+kllR2RAghhLiCJG5ZBIlbhBDislnUwLtarXL48GEeeugh5zFVVdmzZw8HDhxY0DGKxSK6rtPW1tbw+P79++no6KC1tZW3ve1tfP7zn6e9vX0xp7cq2LZNJpNBff3rhI9/HMU2G56vEOY57WNMnL2BWOGZph3MbCU4DMOgXC4zOTmJy+VC0zRaWlpYv349nZ2dbNu2jWw227CfCyCdTi94j1c9I2o6nXZmoL1eb9M6mkIIIcRqJnHL/CRuEUKIlbGogXcymcQ0zRlZJjs7OxkYGFjQMf79v//39PT0sGfPHuexd77znfzyL/8ymzZt4tSpU/z+7/8+73rXuzhw4MCM/T+AkwmzLpvNAqDr+qxLjeZTf99S3w+19jnx2mt0jn2Z7cY/zHg+ZW/gcOA/oIY34TcMRkZGSKfT7N69m1gsBtQ6wGPHjlEsFhsycLrdbtrb251lWJs3b2Z4eJh8Ps/LL7/MsWPHaG9vZ9u2bc6xRkdHOXHiBKlUyqlpOf01zbS0tHDbbbdx+PBhyuUy+Xwel8tFV1cX27Zto6Wl5ZLaaS1ajt/H1ULaopG0R6NLbQ9pR7GcJG6Zm8QtVy/pmy6Stmgk7dFoJeMWxW62+WYWw8PD9Pb28uyzz3LXXXc5j3/84x/npz/9KQcPNqnvOMUf/uEf8sUvfpH9+/dz8803z/q6119/nS1btvCjH/2It7/97TOe//SnP81nPvOZGY9/61vfIhAILPTrLDvFNrm1+mesN34y47kh7V6OeB/EUmSPkRBCzKZYLPL+97+fTCZDJBK50qcj1jiJW+YmcYsQQlyaxcQti7rjHYvF0DSNsbGxhsfHxsbm3ef0X/7Lf+EP//AP+dGPfjRn5wWwefNmYrEYJ0+ebNqBPfTQQ+zdu9f5czabdZKfLDVQ03WdJ554gne84x2zZsacjW3bHDzwFBuHP8E6+7kZzz+beTPPlX6R1tYClpWjUqngdrvp7u5G0zTK5TL33nsv0WiU8fFxnn32Wdra2lBVlUKhwMjICLquO7UpU6kUqqoSDofp6ekhGAw655FMJp2Z/bGxsRl1K+uv6erq4s4775x16dWltMfVSNrjImmLRtIejS61Pep3AoVYDhK3NCdxy9VP2uMiaYtG0h6NVjJuWdTA2+PxsGvXLvbt28cDDzwAgGVZ7Nu3jwcffHDW933xi1/kP/7H/8jjjz/O7t275/2coaEhUqkU3d3dTZ+vl6KYzu12X/IPaCnHSKdG2Trye3Tbhxoet2yVn+R/lUPZ21EUC9M0cblc+P1+isUiqVSKvr4+8vk8pmnidrsJBoNOfUuPx0MqlaJarRIIBFAUhXK5jGmahMNhdF0nlUoRDAadciaRSITR0VGgtvRKnZYEpf6aVCpFsVict6blcrTp1UTa4yJpi0bSHo2W2h7ShmI5SdzSnMQt1w5pj4ukLRpJezRaibhl0eXE9u7dy9e+9jW++c1vcuzYMT760Y9SKBScbKEf+MAHGpKY/Of//J/5gz/4A77+9a+zceNGRkdHGR0dJZ/PA5DP5/nd3/1dnnvuOc6cOcO+fft4z3vew9atW7n//vsXe3orzyzj/9n76LYaOy8TN48V/zUDxpuAWkdvWRZQ60S8Xi/FYtHZh1TvkOvZOTOZDOVymWKx6MwY27ZNpVJB0zQ8Ho9zjKn7xupZQ6vVqtS0FEIIcc2TuGUaiVuEEOKKWHQ5sfe+970kEgk++clPMjo6yq233spjjz3mLBM6d+5cw2zln//5n1OtVvnVX/3VhuN86lOf4tOf/jSapvHSSy/xzW9+k3Q6TU9PD/fddx+f+9znVn/NRUuHp38d78RPGx42FR9Pq7/Lq+kwlpVH13UsyyKXyxEOh/F4PM5SrUwmw6ZNm5xMnvXsnJlMhkQi4SzVqnc4brcbVVWxLAtN06hUKhiG4Xx2tVrF4/EAOO+dTmpaCiGEuFZI3DKFxC1CCHHFLHrgDfDggw/OukRr//79DX8+c+bMnMfy+/08/vjjSzmNK8u24LkPw/n/3fCwoQR5PvApXj0fxDAyuFwuXC4XlmVRrVadjfe2baPrOqFQaEaZi3g8Tn9/P0eOHGFycpJ8Po/b7SYUCtHe3k4qlSKfz+PxeFBVFZer9tc4tV4l1LKDxuPxGXulpKalEEKIa4nELUjcIoQQV9iSBt5Xs0wmg2mac9eOtG049G/hzN82PKwT4CfK7zOYbMM0a0ut6rO9fr8fXdcplUpkMhncbjexWIx77rlnRj1MqHVi9dIl9Y7I5/M5e6IqlQq5XM6pl1kul516lddffz1Q2+yfSCSIRCLOUi6paSmEEEJcPSRuEUKItUEG3hckk0kAnnrqKad2ZCwWY8eOHTM7mFf/E5z4fxsf0/zkb/s2nPGSHzuGoii43W5s23aWR/n9ftxuN6VSie7ubt72trfR0dEx6zmpqsptt93GwYMHyeVyKIriLPfy+/0oikIgEGBiYgKXy0V3d3fD+fb39zMwMEAymSSXyzV9jRBCCCHWHolbhBBibZGBN5BIJDh0qJZkxO/34/F40HWdkZERMpkM/f39Fy/4Q/8LXvpE4wFUN/Y9/4ASvIte/TwjIyO0trbi8XgwTZNkMkmxWMSyLBRFIRgMcuutt87ZedXVl29N74g2btzI9u3b8Xg8VCqVpjPd8XjcSXgy22uEEEIIsbZI3CKEEGvPNT/wtm2bgYEBSqWSU+6jnr0zHo+TSCQYGBio1ZXMDsCz/2LaERSyN/y//Pz1EMnkTygWi6TTaQzDoKuri2AwSCAQcJKJmKaJaZrz1g+d6lI6IkVR5i29IYQQQoi1QeIWIYRYm675gXcmkyGZTBKJRGaUqajXjkwmk2ST54g+9x4wcg2vSa3/9xw420WxOEI0GnWOU+/E+vr6CAaD+Hw+bNsmkUgsKUGIdERCCCGEkLhFCCHWpkXX8b7a1Gd056wdqev4XvhXkDvR8NxZ5R6++8p6zp07RyAQwOv1omkanZ2dhEIhCoUCo6OjmKZJuVwmkUhIghAhhBBCLJnELUIIsTZd8wNvr9eLy+VC13XnMdu2KZfL5PN5crkcW9iPN/WjhvdltK0ci+zFNC0Mw2B4eJhCoQBAMBikt7eXaDRKPp9ndHTUSUzSsO9KCCGEEGIRJG4RQoi16Zpfah6NRonFYoyMjOD1eikUCqRSKSepiMcY512xb8CUid6q2sLR9s9jlGuzzaFQiHw+z/DwMH19ffh8PoLBIBs3bmR0dJTbbruNnp4eSRAihBBCiEsicYsQQqxN1/zAW1EUduzYQTqdxrIshoaGnCVcpmlwX8s/4FFKDe8ZaPk9Kq5OXK4ylmU5+6IKhQKVSoVwOEwsFkPTNAKBAD09PbLPSQghhBCXTOIWIYRYm675peZQy765a9cuAMrlMgCWZXFL5BU2uF9teO2w7z4mfP0AGIZBtVqlUqmgqiqapqFpGvl8nqGhIRKJBLFYbNEJSYQQQgghZiNxixBCrD3X/B3vOo/HA8CmTZtqM75qljsz/wPsi68pmBFeVP9PAtT2U6VSKVyuWhMahgHg/DmXy6GqKtu3b5dlWkIIIYRYVhK3CCHE2iJ3vC+ol+QIhUKEQiF2VL+F224swfHT0vtIZi1s26ZSqVAsFgkGg4TDYWfmuFqtYhgG0WiUUCjkdIxCCCGEEMtF4hYhhFhb5I73BV6vFwBd1wnZo3QXf9Dw/IjnzWTCbyHk9ZJIJHC5XJim6WQWjUQixONxPB4PLpcLt9vNxMTEjBqbQgghhBCXSuIWIYRYW2TgfUEkEgEgm81yq+sbKFjOcyZefmb+Bn19fWzfvp3jx48zPDzslPKoJyUJBoPOe8rlMi6Xy+kYhRBCCCGWi8QtQgixtsjA+4L6fqaYN0VnYV/DcyeU+1GDvezYsYN4PE48HiedTvPcc88xMTFBd3c3qnpx1b5t22SzWbq7uyVBiRBCCCGWncQtQgixtsge72nuaH0RZUpmEp0Ak90fob+/n3g8DtQ6u9bWVnbv3k00GiWZTFIu10p0lMtlEokEgUCAHTt2SIISIYQQQlw2ErcIIcTaIHe8p1DtKsHxv294zNz8m9zZ/wtNO6J4PE5/fz8DAwMkk0lyuRwul4vu7m5nllkIIYQQ4nKQuEUIIdYOGXhP0WMeQKlONDzmu+H/hjlmf+PxOLFYjEwmQ6VSwev1Eo1GZcZYCCGEEJeVxC1CCLF2yMB7ig36Dxsf6Hw7hLfO+z5FUWhpabk8JyWEEEII0YTELUIIsXbIHu86o0i7dbTxsa2/dWXORQghhBBiLhK3CCHEmiID77rc8YbkJKBA7y9csdMRQgghhJiVxC1CCLGmyMD7AiU7bdY4tAlcweYvFkIIIYS4giRuEUKItUUG3hfM6MAiO6/MiQghhBBCzEPiFiGEWFtk4H2Bkj3W+EBUOjAhhBBCrE4StwghxNoiA+8LlOxA4wPRG67MiQghhBBCzEPiFiGEWFuWNPD+yle+wsaNG/H5fPT39/P888/P+frvfve77NixA5/Px0033cSjjz7a8Lxt23zyk5+ku7sbv9/Pnj17OHHixFJObWmMEuRPNT4mM8dCCCHEVUHiFiGEEFfaogfe3/nOd9i7dy+f+tSneOGFF7jlllu4//77GR8fb/r6Z599lt/4jd/gN3/zNzly5AgPPPAADzzwAK+88orzmi9+8Yv86Z/+KV/96lc5ePAgwWCQ+++/n3K5vPRvthgzMoMCkR0r89lCCCGEuGwkbhFCCLEaLHrg/aUvfYmPfOQjfPjDH2bnzp189atfJRAI8PWvf73p6//kT/6Ed77znfzu7/4u119/PZ/73Oe4/fbb+bM/+zOgNmv88MMP84lPfIL3vOc93HzzzfzN3/wNw8PDfP/737+kL037Q9cAABCoSURBVLdgmWkJSoIbwR1amc8WQgghxGUjcYsQQojVYFED72q1yuHDh9mzZ8/FA6gqe/bs4cCBA03fc+DAgYbXA9x///3O60+fPs3o6GjDa6LRKP39/bMec9llXm38syzXEkIIIdY8iVuEEEKsFq7FvDiZTGKaJp2dnQ2Pd3Z2MjAw0PQ9o6OjTV8/OjrqPF9/bLbXTFepVKhUKs6fs9ksALquo+v6Ir5RjTb5SsMMhBnagVmtks1mqVQqeL1eIpEIiqIs+thrVb0dl9KeVyNpj4ukLRpJezS61PaQdhTLSeIWiVuuVdIeF0lbNJL2aLSSccuiBt6rxRe+8AU+85nPzHj8hz/8IYFAYNHHe3vxEFMXaL10Rufc+R9cwhlePZ544okrfQqrirTHRdIWjaQ9Gi21PYrF4jKfiRBXnsQtK0euxY2kPS6Stmgk7dFoJeKWRQ28Y7EYmqYxNjbW8PjY2BhdXV1N39PV1TXn6+v/OzY2Rnd3d8Nrbr311qbHfOihh9i7d6/z52w2y7p167jvvvuIRCKL+Upg26gv/CJm+iWsiZdwUyTv2kClUiESieB2u9F1nWw2i9/vZ/fu3cRiscV9xhqk6zpPPPEE73jHO3C73Vf6dK44aY+LpC0aSXs0utT2qN8JFGI5SNwiccu1StrjImmLRtIejVYyblnUwNvj8bBr1y727dvHAw88AIBlWezbt48HH3yw6Xvuuusu9u3bx8c+9jHnsSeeeIK77roLgE2bNtHV1cW+ffucDiubzXLw4EE++tGPNj2m1+vF6/XOeNztdi/tB3TnX6DrOo8+8gidUZux8RLxjrizREvTNLxeL4lEgpMnT9LV1XXNLN9acptepaQ9LpK2aCTt0Wip7SFtKJaTxC0St1zrpD0ukrZoJO3RaCXilkUvNd+7dy8f/OAH2b17N3fccQcPP/wwhUKBD3/4wwB84AMfoLe3ly984QsA/Lt/9+9485vfzH/9r/+VX/iFX+Db3/42hw4d4i//8i8BUBSFj33sY3z+859n27ZtbNq0iT/4gz+gp6fH6SRXjKIwkrYJt7TP6KAURSESiZBMJslkMrS0tKzsuQkhhBBi0SRukbhFCCFWg0UPvN/73veSSCT45Cc/yejoKLfeeiuPPfaYk2Tk3LlzqOrFlB9vfOMb+da3vsUnPvEJfv/3f59t27bx/e9/nxtvvNF5zcc//nEKhQK/9Vu/RTqd5p577uGxxx7D5/Mtw1dcHMMwZp258Hg85HK5hgQpQgghhFi9JG6RuEUIIVaDJSVXe/DBB2ddorV///4Zj/3ar/0av/Zrvzbr8RRF4bOf/Syf/exnl3I6y8rlcqHretMlYdVqFU3TqFQqjI2N4fV6iUaj18zyLSGEEGItkrhF4hYhhLjS1mRW88upvb2d0dFR4vF4Q8dk2zaJRAKAQ4cOYZomLpeLWCzGjh07iMfjV+qUhRBCCHGNkrhFCCHWBnX+l1xbtm3bRiAQIJFIUC6XsSyLcrnM0NCQk7UuGAzS3t5OIBBgZGSEgwcPOp2bEEIIIcRKkbhFCCHWBhl4TxOLxejv76e7u5tSqUQqlXLqs0UiEfr6+vB6vaiqitfrJR6PUywWGRgYwLbtK3z2QgghhLiWSNwihBBrgyw1byIejxOLxchkMlQqFSqVCocOHSIYDErWUCGEEEKsKhK3CCHE6id3vGehKAotLS10dnbi9XoxTXPOrKGGYUjWUCGEEEJcERK3CCHE6iYDb2oJSDKZDACZTGbG0iuv1+tkDW2mWq3icrmaZhQVQgghhFhOErcIIcTac80vNU8kEgwMDJBMJlFVlaeeempGxs9oNEosFmNkZKRp1tBsNkt3dzfRaPRKfQ0hhBBCXAMkbhFCiLXpmr7jnUgkOHjwICMjI/j9fgD8fv+MjJ+KorBjx46mWUMTiQSBQIAdO3ZIXUwhhBBCXDYStwghxNp1zQ68bdtmYGCAYrFIPB53llvNlvEzHo/PyBpaKpXo7u6mv79f6mEKIYQQ4rKRuEUIIda2a3apeSaTIZlMEo1GURSlYX/UbBk/p2cN9Xq9zvuFEEIIIS4XiVuEEGJtu2YH3pVKBcMw5sz4mcvlZmT8rGcNFUIIIYRYKRK3CCHE2nbNLjWXjJ9CCCGEWCskbhFCiLXtmh141zN+NivDUc/4GYvFJOOnEEIIIa44iVuEEGJtu2YH3s0yfgKS8VMIIYQQq47ELUIIsbZdswNvaMz4ObUDk4yfQgghhFhtJG4RQoi165pNrlZXz/iZSqV45plnuPfee2lvb5cZYyGEEEKsOhK3CCHE2nRN3/GuUxTF2RMlZTaEEEIIsZpJ3CKEEGvPVXHHu55kJJvNLvkYuq5TLBbJZrOzluq4lkh7NJL2uEjaopG0R6NLbY/6dXx68ighriYStyw/aY9G0h4XSVs0kvZotJJxy1Ux8M7lcgCsW7fuCp+JEEKI5ZDL5SQ7s7hqSdwihBBXl4XELYp9FdxWsCyL4eFhwuHwkpdbZbNZ1q1bx+DgIJFIZJnPcO2R9mgk7XGRtEUjaY9Gl9oetm2Ty+Xo6elBVWU3lLg6Sdyy/KQ9Gkl7XCRt0Ujao9FKxi1XxR1vVVXp6+tblmNFIhH5EU4h7dFI2uMiaYtG0h6NLqU95E63uNpJ3HL5SHs0kva4SNqikbRHo5WIW+R2ghBCCCGEEEIIcRnJwFsIIYQQQgghhLiMZOB9gdfr5VOf+hRer/dKn8qqIO3RSNrjImmLRtIejaQ9hFgZ8m+tkbRHI2mPi6QtGkl7NFrJ9rgqkqsJIYQQQgghhBCrldzxFkIIIYQQQgghLiMZeAshhBBCCCGEEJeRDLyFEEIIIYQQQojLSAbeQgghhBBCCCHEZXRNDby/8pWvsHHjRnw+H/39/Tz//PNzvv673/0uO3bswOfzcdNNN/Hoo4+u0JmujMW0x9e+9jXuvfdeWltbaW1tZc+ePfO231qz2N9H3be//W0UReGBBx64vCe4ghbbFul0mt/+7d+mu7sbr9fLddddd1X9e1lsezz88MNs374dv9/PunXr+J3f+R3K5fIKne3l8+STT/LP/tk/o6enB0VR+P73vz/ve/bv38/tt9+O1+tl69atfOMb37js5ynE1ULilkYStzSSuOUiiVsaSdxSs+riFvsa8e1vf9v2eDz217/+dfvVV1+1P/KRj9gtLS322NhY09c/88wztqZp9he/+EX76NGj9ic+8Qnb7XbbL7/88gqf+eWx2PZ4//vfb3/lK1+xjxw5Yh87dsz+0Ic+ZEejUXtoaGiFz/zyWGx71J0+fdru7e217733Xvs973nPypzsZbbYtqhUKvbu3bvtd7/73fbTTz9tnz592t6/f7/94osvrvCZXx6LbY+//du/tb1er/23f/u39unTp+3HH3/c7u7utn/nd35nhc98+T366KP2f/gP/8H+3ve+ZwP2P/7jP875+tdff90OBAL23r177aNHj9pf/vKXbU3T7Mcee2xlTliINUzilkYStzSSuOUiiVsaSdxy0WqLW66Zgfcdd9xh//Zv/7bzZ9M07Z6eHvsLX/hC09f/+q//uv0Lv/ALDY/19/fb//pf/+vLep4rZbHtMZ1hGHY4HLa/+c1vXq5TXFFLaQ/DMOw3vvGN9l/91V/ZH/zgB6+aDmyxbfHnf/7n9ubNm+1qtbpSp7iiFtsev/3bv22/7W1va3hs79699t13331Zz3OlLaQD+/jHP27fcMMNDY+9973vte+///7LeGZCXB0kbmkkcUsjiVsukrilkcQtza2GuOWaWGperVY5fPgwe/bscR5TVZU9e/Zw4MCBpu85cOBAw+sB7r///llfv5YspT2mKxaL6LpOW1vb5TrNFbPU9vjsZz9LR0cHv/mbv7kSp7kiltIW/+t//S/uuusufvu3f5vOzv9/e/cTEkUfx3H88+Q22sHoIK4GFrgQRX8IjMIKpGtQx4Ji8RIS0jWSJFawZAnpEkUUUbckoi4pUVkdKrrULgSZYUt1aQUhaKkgdb/PIdxt0h6eXfc3q+77BXMZfyPf+TL85vNz1tmwNm3apL6+Pk1PTwdVtjPF9GPnzp16+fJl7mNdqVRKQ0ND2rt3byA1LyRLeR4FXCK3+JFb/MgteeQWP3LL/LieR0Ml+S0L3MTEhKanpxUOh337w+Gw3r59O+cx6XR6zvHpdNpZnUEpph9/OnHihFavXj3r4lyMiunH06dPdfXqVSWTyQAqDE4xvUilUnr06JEOHz6soaEhjY2NqbOzU5OTk4rFYkGU7Uwx/Th06JAmJia0e/dumZmmpqZ09OhRnTx5MoiSF5S/zaNfv37Vjx8/tGLFijJVBixs5BY/cosfuSWP3OJHbpkf17mlIp54o7Ti8bgGBgZ0584d1dTUlLucwGUyGUWjUV25ckV1dXXlLqfsstms6uvrdfnyZbW0tOjgwYPq7u7WpUuXyl1aWTx58kR9fX26ePGiXr16pdu3b2twcFC9vb3lLg0AKhK5hdzyO3KLH7klOBXxxLuurk5VVVUaHx/37R8fH1dDQ8OcxzQ0NBQ0fjEpph8z+vv7FY/H9fDhQ23ZssVlmYEptB/v37/Xhw8ftG/fvty+bDYrSQqFQhodHVUkEnFbtCPFXBuNjY1avny5qqqqcvs2bNigdDqtnz9/yvM8pzW7VEw/Tp06pWg0qiNHjkiSNm/erG/fvqmjo0Pd3d1atqxy/t75t3l05cqVPO0G/gO5xY/c4kduySO3+JFb5sd1bqmITnqep5aWFg0PD+f2ZbNZDQ8Pq7W1dc5jWltbfeMl6cGDB38dv5gU0w9JOnv2rHp7e3Xv3j1t27YtiFIDUWg/1q9fr9evXyuZTOa2/fv3a8+ePUomk2pqagqy/JIq5trYtWuXxsbGcjdxSXr37p0aGxsX9c1LKq4f379/n3WTmrm5/3q3R+VYyvMo4BK5xY/c4kduySO3+JFb5sf5PFqSV7QtAgMDA1ZdXW3Xr1+3N2/eWEdHh61atcrS6bSZmUWjUevq6sqNf/bsmYVCIevv77eRkRGLxWJL7ms5CulHPB43z/Ps1q1b9vnz59yWyWTKdQolVWg//rSU3g5aaC8+ffpktbW1duzYMRsdHbW7d+9afX29nT59ulynUFKF9iMWi1ltba3duHHDUqmU3b9/3yKRiB04cKBcp1AymUzGEomEJRIJk2Tnzp2zRCJhHz9+NDOzrq4ui0ajufEzX8tx/PhxGxkZsQsXLvB1YsD/RG7xI7f4kVvyyC1+5Ja8hZZbKmbhbWZ2/vx5W7NmjXmeZ9u3b7cXL17kftbW1mbt7e2+8Tdv3rR169aZ53m2ceNGGxwcDLhitwrpx9q1a03SrC0WiwVfuCOFXh+/W0o3MLPCe/H8+XPbsWOHVVdXW3Nzs505c8ampqYCrtqdQvoxOTlpPT09FolErKamxpqamqyzs9O+fPkSfOEl9vjx4znngZnzb29vt7a2tlnHbN261TzPs+bmZrt27VrgdQOLFbnFj9ziR27JI7f4kVt+WWi55R+zCvsMAQAAAAAAAaqI//EGAAAAAKBcWHgDAAAAAOAQC28AAAAAABxi4Q0AAAAAgEMsvAEAAAAAcIiFNwAAAAAADrHwBgAAAADAIRbeAAAAAAA4xMIbAAAAAACHWHgDAAAAAOAQC28AAAAAABxi4Q0AAAAAgEP/AjuMhzCaiTRkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization of the Results\n",
    "plt.figure(figsize = (12, 10))\n",
    "plt.subplots_adjust(hspace = 0.5, wspace = 0.5)\n",
    "for i in range(1, 7):\n",
    "    plt.subplot(3, 2, i)\n",
    "    plt.scatter(train['x'], train['y'], c = 'gray', alpha = .5)\n",
    "    plt.plot(train['x'], np.sqrt(train['x']), c = 'orange', linewidth = 3)\n",
    "    plt.plot(train['x'], train.iloc[:, i + 1], c = 'purple', linewidth = 3, alpha = 0.7)\n",
    "    plt.title(train.columns[i + 1])\n",
    "    plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>pred_k3</th>\n",
       "      <th>pred_k5</th>\n",
       "      <th>pred_k10</th>\n",
       "      <th>pred_k25</th>\n",
       "      <th>pred_k50</th>\n",
       "      <th>pred_k100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.108563</td>\n",
       "      <td>0.063536</td>\n",
       "      <td>0.097283</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.170623</td>\n",
       "      <td>0.063536</td>\n",
       "      <td>0.097283</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.128549</td>\n",
       "      <td>0.090441</td>\n",
       "      <td>0.097283</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015075</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>0.090441</td>\n",
       "      <td>0.105190</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.025126</td>\n",
       "      <td>0.323654</td>\n",
       "      <td>0.133096</td>\n",
       "      <td>0.139707</td>\n",
       "      <td>0.117141</td>\n",
       "      <td>0.244848</td>\n",
       "      <td>0.363983</td>\n",
       "      <td>0.504854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y   pred_k3   pred_k5  pred_k10  pred_k25  pred_k50   \n",
       "0  0.000000 -0.108563  0.063536  0.097283  0.117141  0.244848  0.363983  \\\n",
       "1  0.005025  0.170623  0.063536  0.097283  0.117141  0.244848  0.363983   \n",
       "2  0.010050  0.128549  0.090441  0.097283  0.117141  0.244848  0.363983   \n",
       "3  0.015075 -0.027848  0.090441  0.105190  0.117141  0.244848  0.363983   \n",
       "5  0.025126  0.323654  0.133096  0.139707  0.117141  0.244848  0.363983   \n",
       "\n",
       "   pred_k100  \n",
       "0   0.504854  \n",
       "1   0.504854  \n",
       "2   0.504854  \n",
       "3   0.504854  \n",
       "5   0.504854  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0064160082722704405"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model evaluation on train, k = 3\n",
    "#MSE - Train\n",
    "np.mean((train['y'] - train['pred_k3']) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08009998921517056"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSE - Train\n",
    "np.sqrt(np.mean((train['y'] - train['pred_k3']) ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06287837319381542"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MAE - Train\n",
    "np.mean(abs(train['y'] - train['pred_k3']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs. prediction - Train\n",
    "plt.scatter(train['y'], train['pred_k3'], c = 'gray', alpha = .5)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Prediction')\n",
    "plt.title('Actual vs. Prediction - Train - k = 3')\n",
    "xp = np.linspace(train['x'].min() - 0.2, train['x'].max() + 0.2, 100)\n",
    "plt.plot(xp, xp, c = 'red', linewidth = 3, alpha = .7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>pred_k3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020101</td>\n",
       "      <td>0.083916</td>\n",
       "      <td>0.075593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.095477</td>\n",
       "      <td>0.347613</td>\n",
       "      <td>0.462013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.390758</td>\n",
       "      <td>0.371331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.130653</td>\n",
       "      <td>0.452170</td>\n",
       "      <td>0.246011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.155779</td>\n",
       "      <td>0.114829</td>\n",
       "      <td>0.307931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y   pred_k3\n",
       "4   0.020101  0.083916  0.075593\n",
       "19  0.095477  0.347613  0.462013\n",
       "20  0.100503  0.390758  0.371331\n",
       "26  0.130653  0.452170  0.246011\n",
       "31  0.155779  0.114829  0.307931"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model evaluation on test\n",
    "#Prediction on test\n",
    "test['pred_k3'] = knn_model_k3.predict(test['x'].values.reshape(-1, 1))\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.018307568509933876"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model evaluation on test, k = 3\n",
    "#MSE - Test\n",
    "np.mean((test['y'] - test['pred_k3']) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13530546371057556"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSE - Test\n",
    "np.sqrt(np.mean((test['y'] - test['pred_k3']) ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11564144349619829"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MAE - Test\n",
    "np.mean(np.fabs(test['y'] - test['pred_k3']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs. prediction - Test\n",
    "plt.scatter(test['y'], test['pred_k3'], c = 'gray', alpha = .5)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Prediction')\n",
    "plt.title('Actual vs. Prediction - Test - k = 3')\n",
    "xp = np.linspace(test['x'].min() - 0.2, test['x'].max() + 0.2, 100)\n",
    "plt.plot(xp, xp, c = 'red', linewidth = 3, alpha = .7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1,\n",
       " 5,\n",
       " 10,\n",
       " 15,\n",
       " 20,\n",
       " 25,\n",
       " 30,\n",
       " 35,\n",
       " 40,\n",
       " 45,\n",
       " 50,\n",
       " 55,\n",
       " 60,\n",
       " 65,\n",
       " 70,\n",
       " 75,\n",
       " 80,\n",
       " 85,\n",
       " 90,\n",
       " 95,\n",
       " 100]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#K-fold cross validation to choose the best model\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#Grid\n",
    "k_grid = [1] + list(range(5, 101, 5))\n",
    "k_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(k_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_errors = np.zeros(shape = len(k_grid)) #to save cv results\n",
    "cv_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#K-fold cross validation\n",
    "for i in range(len(k_grid)):\n",
    "    #Create kNN regression model\n",
    "    knn_model = KNeighborsRegressor(n_neighbors = k_grid[i], \n",
    "                                    weights = 'uniform', \n",
    "                                    algorithm = 'brute')\n",
    "    #K-fold cross validation\n",
    "    scores = cross_val_score(estimator = knn_model, \n",
    "                             X = X_train, y = y_train,\n",
    "                             scoring = 'neg_root_mean_squared_error',\n",
    "                             cv = 10, \n",
    "                             n_jobs = -1)\n",
    "    #Save the result\n",
    "    cv_errors[i] = scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.15645708, -0.12495769, -0.11697998, -0.11893709, -0.11860594,\n",
       "       -0.11885396, -0.11973165, -0.12430683, -0.12676141, -0.12952545,\n",
       "       -0.13246729, -0.13718217, -0.14071639, -0.1453413 , -0.14958209,\n",
       "       -0.15448813, -0.16005707, -0.16583446, -0.17074027, -0.17459761,\n",
       "       -0.17970638])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The best hyperparameter\n",
    "k_grid[np.argmax(cv_errors)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Results\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(k_grid, -cv_errors, 'o', linestyle = '--')\n",
    "plt.xticks(k_grid)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('RMSE')\n",
    "plt.plot(k_grid[np.argmax(cv_errors)], -np.max(cv_errors), 'ro')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>pred_k3</th>\n",
       "      <th>pred_k10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020101</td>\n",
       "      <td>0.083916</td>\n",
       "      <td>0.075593</td>\n",
       "      <td>0.117141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.095477</td>\n",
       "      <td>0.347613</td>\n",
       "      <td>0.462013</td>\n",
       "      <td>0.355116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.390758</td>\n",
       "      <td>0.371331</td>\n",
       "      <td>0.355116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.130653</td>\n",
       "      <td>0.452170</td>\n",
       "      <td>0.246011</td>\n",
       "      <td>0.315164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.155779</td>\n",
       "      <td>0.114829</td>\n",
       "      <td>0.307931</td>\n",
       "      <td>0.343032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.185930</td>\n",
       "      <td>0.500018</td>\n",
       "      <td>0.392220</td>\n",
       "      <td>0.378415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.251256</td>\n",
       "      <td>0.371846</td>\n",
       "      <td>0.570465</td>\n",
       "      <td>0.571321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.261307</td>\n",
       "      <td>0.685553</td>\n",
       "      <td>0.519634</td>\n",
       "      <td>0.613490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.266332</td>\n",
       "      <td>0.436267</td>\n",
       "      <td>0.519634</td>\n",
       "      <td>0.606632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.361809</td>\n",
       "      <td>0.717726</td>\n",
       "      <td>0.550028</td>\n",
       "      <td>0.558300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y   pred_k3  pred_k10\n",
       "4   0.020101  0.083916  0.075593  0.117141\n",
       "19  0.095477  0.347613  0.462013  0.355116\n",
       "20  0.100503  0.390758  0.371331  0.355116\n",
       "26  0.130653  0.452170  0.246011  0.315164\n",
       "31  0.155779  0.114829  0.307931  0.343032\n",
       "37  0.185930  0.500018  0.392220  0.378415\n",
       "50  0.251256  0.371846  0.570465  0.571321\n",
       "52  0.261307  0.685553  0.519634  0.613490\n",
       "53  0.266332  0.436267  0.519634  0.606632\n",
       "72  0.361809  0.717726  0.550028  0.558300"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create kNN regression model, k = 10\n",
    "knn_model_k10 = KNeighborsRegressor(n_neighbors = 10, \n",
    "                                    weights = 'uniform', \n",
    "                                    algorithm = 'brute')\n",
    "\n",
    "#Fit the k-nearest neighbors regressor from the train\n",
    "knn_model_k10.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test\n",
    "test['pred_k10'] = knn_model_k10.predict(test['x'].values.reshape(-1, 1))\n",
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01548972975259189"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model evaluation on test, k = 10\n",
    "#MSE - Test\n",
    "np.mean((test['y'] - test['pred_k10']) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12445774283905317"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSE - Test, k = 10\n",
    "np.sqrt(np.mean((test['y'] - test['pred_k10']) ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10115601389487006"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MAE - Test - k = 10\n",
    "np.mean(np.fabs(test['y'] - test['pred_k10']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs. prediction - Test\n",
    "plt.scatter(test['y'], test['pred_k10'], c = 'gray', alpha = .5)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Prediction')\n",
    "plt.title('Actual vs. Prediction - Test - k = 10')\n",
    "xp = np.linspace(test['x'].min() - 0.2, test['x'].max() + 0.2, 100)\n",
    "plt.plot(xp, xp, c = 'red', linewidth = 3, alpha = .7)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}