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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Description\n",
+    "\n",
+    "<p> This notebook describes how to generate training data to train a Diffusion Convolutional Recurrent Neural Network.\n",
+    "DCRNN expects the graph to be represented as an adjacency matrix, with the values representing closeness or connectedness between the nodes.\n",
+    "Additionally, since the nature of the data is spatio-temporal, we also expect a time series of values at every node. \n",
+    "Below we see how to generate data to train DCRNN from the above mentioned sources. \n",
+    "</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What should the adjacency matrix represent? \n",
+    "\n",
+    "The adjacency matrix could be thought of as the kernel in Machine Learning, with each value corresponding to a distance metric. \n",
+    "\n",
+    "For eg., in the traffic data below, the values in the adjacency matrix represent actual distances between 2 road sensors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample data download\n",
+    "\n",
+    "https://drive.google.com/drive/folders/1VB5OGQudoEOHCf0Y-J0wuDByU8OCcsMY?usp=sharing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of adjacency matrix\n",
+      "(325, 325)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "graph = pickle.load(open('adj_mx_bay.pkl','rb'),encoding= 'latin1')\n",
+    "\n",
+    "print(\"Shape of adjacency matrix\")\n",
+    "print(graph[2].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus this particular graph consists of 325 sensors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since traffic data is huge the input data format is an hdf file. \n",
+    "# Other popular formats like .csv,.jsons should be a good place to start. \n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "hdf=pd.HDFStore('pems-bay.h5')\n",
+    "df = hdf['speed']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(52116, 325)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset Description\n",
+    "\n",
+    "<p>\n",
+    "    \n",
+    "The Caltrans Freeway Performance Measurement System (PeMS) standard rollup provides a variety of measurements (number of vehicles, average speed, etc.) for both individual lanes and across all lanes in five-minute intervals aggregated from the raw 30-second intervals. \n",
+    "\n",
+    "For the purposes of this task we use 6 months worth of data scraped from the Caltrans website and focussed on the San Francisco Bay Area to predict traffic speeds upto 1 hour in the future effectively. The Bay Area road network has 325 traffic sensors as of the year 2017 and the traffic speeds at each of these sensors can be seen at 5 minute intervals. \n",
+    "\n",
+    "**Link from which data was acquired:** <br>\n",
+    "http://pems.dot.ca.gov/?dnode=Clearinghouse\n",
+    "\n",
+    "</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>sensor_id</th>\n",
+       "      <th>400001</th>\n",
+       "      <th>400017</th>\n",
+       "      <th>400030</th>\n",
+       "      <th>400040</th>\n",
+       "      <th>400045</th>\n",
+       "      <th>400052</th>\n",
+       "      <th>400057</th>\n",
+       "      <th>400059</th>\n",
+       "      <th>400065</th>\n",
+       "      <th>400069</th>\n",
+       "      <th>...</th>\n",
+       "      <th>409525</th>\n",
+       "      <th>409526</th>\n",
+       "      <th>409528</th>\n",
+       "      <th>409529</th>\n",
+       "      <th>413026</th>\n",
+       "      <th>413845</th>\n",
+       "      <th>413877</th>\n",
+       "      <th>413878</th>\n",
+       "      <th>414284</th>\n",
+       "      <th>414694</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-01-01 00:00:00</th>\n",
+       "      <td>71.4</td>\n",
+       "      <td>67.8</td>\n",
+       "      <td>70.5</td>\n",
+       "      <td>67.4</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>66.6</td>\n",
+       "      <td>66.8</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>66.8</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>67.9</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>69.2</td>\n",
+       "      <td>68.9</td>\n",
+       "      <td>70.4</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>71.1</td>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-01 00:05:00</th>\n",
+       "      <td>71.6</td>\n",
+       "      <td>67.5</td>\n",
+       "      <td>70.6</td>\n",
+       "      <td>67.5</td>\n",
+       "      <td>68.7</td>\n",
+       "      <td>66.6</td>\n",
+       "      <td>66.8</td>\n",
+       "      <td>67.8</td>\n",
+       "      <td>66.5</td>\n",
+       "      <td>68.2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>68.4</td>\n",
+       "      <td>67.3</td>\n",
+       "      <td>68.4</td>\n",
+       "      <td>67.6</td>\n",
+       "      <td>70.4</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>70.1</td>\n",
+       "      <td>68.4</td>\n",
+       "      <td>70.8</td>\n",
+       "      <td>67.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 325 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "sensor_id            400001  400017  400030  400040  400045  400052  400057  \\\n",
+       "2017-01-01 00:00:00    71.4    67.8    70.5    67.4    68.8    66.6    66.8   \n",
+       "2017-01-01 00:05:00    71.6    67.5    70.6    67.5    68.7    66.6    66.8   \n",
+       "\n",
+       "sensor_id            400059  400065  400069  ...  409525  409526  409528  \\\n",
+       "2017-01-01 00:00:00    68.0    66.8    69.0  ...    68.8    67.9    68.8   \n",
+       "2017-01-01 00:05:00    67.8    66.5    68.2  ...    68.4    67.3    68.4   \n",
+       "\n",
+       "sensor_id            409529  413026  413845  413877  413878  414284  414694  \n",
+       "2017-01-01 00:00:00    68.0    69.2    68.9    70.4    68.8    71.1    68.0  \n",
+       "2017-01-01 00:05:00    67.6    70.4    68.8    70.1    68.4    70.8    67.4  \n",
+       "\n",
+       "[2 rows x 325 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we can see a time series for every node with 52116 values at 5 minute intervals."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Data cleaning - handling missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize = (20,8))\n",
+    "plt.title('Distribution of Null values across sensors')\n",
+    "df[df.isnull().any(axis = 1)].isnull().sum(axis = 0).hist(bins = 100)\n",
+    "plt.xlabel('Missing Values')\n",
+    "plt.ylabel('Number of sensors')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This particular dataset does not have null values. However, it must be noted that null values **must** be imputed prior to training DCRNN. \n",
+    "\n",
+    "Popular time series null value imputation techniques can be found below \n",
+    "\n",
+    "https://otexts.com/fpp2/missing-outliers.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p> While DCRNN is a graph convolution, it still relys on a sequence to sequence architechture to learn temporal characteristics.\n",
+    "The underlying method takes the dataframe of sensors with speed values over time and breaks it down into sequence to sequnce steps, based on the horizon defined\n",
+    "</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "DCRNN, the supervised training model expects an x and a y tensor of the following dimensions\n",
+    "\n",
+    "Input/ Encoder sequence\n",
+    "\n",
+    "    - Epoch size  (Number of training sequences)\n",
+    "    - Input Length \n",
+    "    - Number of Nodes (The number of nodes in the graph , equivalent to the adjacency matrix)\n",
+    "    - Input dimension (Number of features per node, in this case speed, time of day, etc.)\n",
+    "\n",
+    "Output/ Decoder sequence (Number of training sequences)\n",
+    "\n",
+    "    - Epoch size  (Number of training sequences)\n",
+    "    - Output Length (Often equal to the horizon)\n",
+    "    - Number of Nodes (The number of nodes in the graph , equivalent to the adjacency matrix)\n",
+    "    - Output dimension (Number of output features per node, in this case speed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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caRys+Gha4AAe9oJjORA1KRBRKQQEMGOQopa0w5Z1wC5eEHJJ6QME0I+D4AAbiZc2qZcLQQki+FabYQYmKZiGhSCFWRyaYAABIAVd0AMY6G5iSZalGXkBQABRcAZIgAC8Yplsw5YwYABLIAZSgHwCkZTUIAAE8ARfMFUaR5qQaZoKcQUieHibMQetKZhR/xWbxVEMOzAANihQZTBxoPAC0ygEgzMIv4gNRBAAwjQ4NhBd8DAJMEA4flYAAlAEt+UDsyQQ1zAEwfQCC8ly2jEQ8vkQ21V4i+gZfBCYrvlVqUKegAQJ8YgHXhEFqogRReB4HlEIFiqYWaWhgASiktiOWlEEApARBfCLIuEIN3ChNFCgKtpH8QBUklgDw5UVMJoR2RB4IKEJPpqVNjBjO7pHhpCVOuoTojAI4VAGTqAFTCoQggAFURB/AgENWfAEvTGkA1ELV/AESkgQkyAFUEAICEEIagUPtUClZuAEWZClGVEKhXWSGdike/QEWWl/PQEEAzAACBAAqZYuzPACDv8AAAHwZRCzAAbwAghwiDEqEE7QAALwAgxAAzmkBI0KAA7gaAbxAgM2BEmVAIiaAFV5EczAl5Jojn6KR+Fwo1nJZz7xA+olNdqAAwkQOi8gALBJCAbgcrjIBFLjDT/wAJcaehtZCwOAPvHQALzSCC+gCaU6YEJAAUQgNdyQAwpQUnmapCKYRLOqR4LgmsaoEz+AUQORDQvwKX7wAA0qEGLgAM5RAzQqi5facsyjAPEQDwrABAthqgIhBPD2QArAXxrRDBEqidV4rnjUBK5pAzC2Ez/AUAMxAKh1UTGGlwWwoPCAoAJhAHNHdy9AARrCBQkwo06Ap/BgsPAgBBr7Zhz/mBHVAKsiiAMSKbF15A22KpitehMZWxADQF1Q4K4l8gDjUgCEORBBwFAFsAM7p3NaoDfacAZFgJ48RhAyS7MFIQBHoBHawHgnSaI+K0d4gKOox641O1HUhQgQ4KYDYVcAcgOviIADEATrZ5cL4Q0FgJYF8bVvK7YZEQ60x11A0HA2wFdpe0dKgKM0EKc8UbQEcbTrVwAydAYKsF6MwAA9QCLQcAMIwGOD0ABHcE/UEATjcgYDkKaNoAA76LXaWrhjexHxsARA1kzceWgE+7h2pA0nmpUimxOLFbZClQ048AAGkJNDUEh0UAANUAAOIADICA9nYAAKQACGmDLK+wAI/0C9KTq4iFUEbxsAQmYRUABkT0uGQCaGwDtHdCC5F9mzODENsDkQlCCStnAGZ9CPPOMHZWCGBBEOelAGsjoN/ktiB/EJjksNqikQmmBjEoEFQIasBMGa+uWX8StHR0C/NJCmleEFQCaNBgGe3DW0HbxG1zC8gnmzkxFgE/YDLFYQFWpYN/B0K/xGjAbCNSCuj6EHrdkDFEwQJvpVxbvDbUQEIPxV6gcZR6xfOhCkB2GjNdBKStxG0xCegql2jxEJLowDuLcQmvC0WexGZdDEhpU7YYEHNYwSnxC0rXUDAmdfAXvHeJzHerzHfNzHfvzHgBzIgjzIhFzIhnzIiGzItP+UyIzcyI7cyIv8yJI8yZRcyZZ8yZWsEAwHZP9kkj3wA0VQBEagBErQBE/wBFJwBZTYFQtwXilRDHvaWjYAvxoRD+EQCYyQy7q8y7zcy778y4oACICgCL9czMbsy8FMB4pAzMfczMWsCIhgB8vszNTMy8sMCDHEzNVczcE8B8qszdtczZCQIuFcztAszeBczsYMzXSQzeq8zcUACe9MzdfszvN8zMuMzvf8zIrQzva8z73MzsOczgCtywJN0AXNy8xwcZEwCaAACswwDThGECzaWhGsFuKmEtPwsBS2yhgRD97QBD0gBCRd0iZ90iid0iqtAzag0i790i7N0jA90zT/DQQ2MNI0ndMoHQQ6cAM6/dMmLdNAPdRCkAMygANEDdQ2jdNJPdNL3dQ6HQT6ytRQ7dI87dNVPdNBYAM7kNVWzdVe/dJPHdYqvdVUTdYoXQNGEA70qBAVbVh1jNHjhhLYEIytVQPexhEg3QRKgAzM8NeAHdiCPdiELdjIUAyAkAN+UNiM3diDfdiJvdiOPdmMfdi1kARL4NeUvdmBfdh9kAOCwNmizQyQDdqjfdrIIAjMGNqnvdmWnQR93dqT/dqZLduuXQxJUAOxbduN7dmmzduOjQy2oARIoNnA/di2sAS7fdyPXQyXXdvM3dzXuNzR/dhLoANFwA322xBSoF+y/zoU8YAFNvACN2BAOzAJ5wcDkOBO440DESs07Z3XSskFTRBoPkC5IMENQgBkKkwRe50Exl3dgF3aki3gA47Yim3gne3cmB3gAu7brK3gpQ0ICm7Yqn0DES7hDE7d1U3bDm7gh53bHN7hxfDZGS7hw13cFS7cSwDgFU7aDA7dGj7dHw7iS/ADV1AERdrWCNG7reWlQuENMpAAPtAEOdAAQuBMBPACxP0J8QCEPdAEOPCQTNVWRY4DDnC7SnkA+RQEA5AAQL4R4RCC+hWltewNTuDiL07gLw7jgpDgax7jNc7cEB7nb07hbU7ag7Dabf7aI07ncp7nIV4DMg7iJf7ba/+e3CqO4kyg5hr+3HN+3IdN43lO2jf+BVdABNfADTx+EBbMXWxcFMOZv3NAmg2AkZiaAKE+Bw4Qi02QAOR4Bg+QLEoJA3rjDQDwgNlFsROWxLiL5ou+4sXw5gWu4cTe54Eu7CZu5zmA532+5xiO7Jf955Ke7HGe24X+4Id+4iBuC0wQ7A/u7eCu7ZAe55Qu6JcuBpleDZxewfp1vUMBlquIkQ3gyvAQAERgVH4boBCFkQwwu/BwBZ/1Ed09YX165k4w7tr+5n2A7Awv7Q0e58su7Mcu6NDO7SQ+7ZHO2x4u6LhN6BvP8dsu6N5+BCEv28L97SeP8tZu6Ofe5+mu7kT/wO6dbhBTyV0EPBQKYO/w4IoyJVAI8MQjy1ALIPRAkG8Z3X1t1xFaAGRIsN0WAdIJv/Is/+Z8gOyDkANXH+flruyIbuhZX+wrfvEQT+3A3fHIju1Uj9oj3+febgRrL9opr/DRjfYzDttx39rIEPMyT/MS0fTcRbdF4bSPxVAGVmLDiAMMxQAHfwNEl/Qvt/Qb0cP6NQRGGvXe8AQDifVaz/lbL+xdr+ETr+FZ3/AWz+dcj/egH/FpD/IS//Uo3gRGUAwrbgtNsPmrn+0k/vJrzvd93+4R4b6t5dFAoQMCACma8Ik/TxCeOWPJv/gDoEuSwEwCAfnwAHMdQQet6QMX//vRmY/7wp71n0/6nZ/6ul/3bU/+pt/nhID6ws77Gc/61+76Xo/xHW77s1/7TXAEtJ/7ec/ZAIGsmK0kSpAxQ5hQ4UKGDRsiW/LjixiKFK8QqcYtHjyOHT1+7MiFxkiSgECeRJlS5UqWKGsZALBFT5QCLwBwbHDGIzQCA7LMNEDgJjwGBARo0fPkAIyNRL94LNOg5VR4gmqQxLqDGtWp8cI9OVLM4ViyDAUOysGn7FqyZ9OyhWu2WK0kSw7GxSuwTw5BePMWQ9vH72BkhG7c6DsYrkCCBhWvFUjX7uPFxZLUmEy5bbG9iTVvblJE7OeHxZqEJe0wct27qRciQ9a4tf9rsxEnVrSIUSNXjl6wjuTDW/hw4aJwHFgA4IyOGBwBGPrITIeBBQG0XMHBkQGXJQQYFCDCrSMAQR4JDSX+kZGN3yNzIEuP0isUI6NpK3Sr5/5rwDn0709otcwA1IsvABFyS60DAyzkMM8IHKig2WgTcML9BLpswAs5M3BBZgQKzb77QKxvwQo9jE1CDxG0DbeKLsqoKa7EaA+P+G7EkaNP6PAoGwOYGI6BLnIUbpIb2qPhBlCI7Gi+Ek3s778DBSLEPxTnYg3KzqCsUkEUG0TsStmmxFJDCDO00LUCH4QwRDKbeBJCydJUc8wVIZLIxRd1k5EqM9qzg0lBuTqjgSX/soEHlBcIuCbIIQdlqRQckLxBkkG9ikK0K6vEY9McOoVyTi07hLBKKU0Ek80RI3RMziyvRHNUVVd1s01NyRTVRDtRbFHP3GIU7oz2eIS0WJW4KMABBB544RLihDQ2JWZyQNIG6ATF9FYyOfUUVFxfhXDLba280xAHxVTRVTM3jJVMcaF0QttV4xVx1Vyn3NXEXn0VA8bdeBP2tzmiJfgkROpwNj0nbCn4o2p2QLKGPi4NJ9N6V+WWy0+vvHfDdzcshFwUzQ0TynwpLJNONS3DTOXP1ryzGHrJnPnbdSk8mcB9+fW3z6nmaG/ghocmWlBtfohYaCazvRjlKu+4MmSo/0MF12NSQd64XBxKJjPn1E6EtWVZY655w7I37PhCr0fcmWc+hQP6N52Kprtu4bwpAkkaxKDY4qhzmJpMqTmuetWPVw3ZWxMR2XrWldd+OeWY2w33agidIKJp0gR6Ql57Cx8RcjXbdhtY3uhob267V2f9pHiW0DsLiqXwHPEc7LjSkNsJv/lrDh3fvBjdA1+8cXRbRRv0VSm3GnjfnxhC88g7lz7ytENPl9c8+XWxZ+HsaM+M1scnHwq9o5i9dpR1xx1K9nl3WTOYyRx+xQ8Z5xpC0SkDG0rmDbfchqBXPf4Vg3pU693X9rc50nHPe7zBQ3v4Rj4K1g0LemOCz24UD/9vSIEItshdDuoQwhEiMH4FPNz6dncnRRjPZNn7XAJ99z+UpTB00AMhhKDwQROiaIEva6AD38YbPrTHCxVE4tC6oLcihANbHeSh+0RIQviNjX4rRFEL87ehHxJGcitimQzl97s72QIKQ8ghZXIoChDaQhSc+yAbmVEKSiAEEgihRCnmWEdm3JEZjgAFMz7hCIQQAiGARAYlBoGEJfynEJpghiYMgZD/AGISzJgEIBAyB4RYkhmRAJUZEHKHSBBkCAcRJTPmQEhHcJIZXNgkK8+AECwg5AyMYIYiZsmMWjJDDIZYgg6aQJEiUKQJWBADFoYphiFQZAlXuMIOxKAR9MH/wwuWYgkg2sOFJHbTWAFrzw+08UQpoDGEdLgSInKAzh66K4ArU2cJy1ILhFwCIXJ0Yz3xqEc6fkgPW6MDP/loyD8GEhSEZAZBHxnJSTLDkZDURCEGUgSDeDKTmxylJD4ZyoxuFCGwVKUjiqGIIdgFpLfM5S6zYEtc6hIhUkCIGBDBDEOU4aUxlSgSWvaEjxqSECBlAkKyMAhmCGKlzAgqM4bKDD9cASFH+NAVtpADLjiVGUNASBTUogeYXjWrarkDFBCCVWZo1aFd/UFshhAEW3B1rF9lRlgRAgS4utWrZVVLU4sBhRyA8Aqa9EMvoapUohoVIUld6k8RwtNXEqIY/4LAgV0YK1Oa2pQZXaWsS5XK0pQKdZOKSIIRXAnSVf6Ro3HVKCgRkkqLalKVCPEDEX5wHYpIgSJY2IIYtoBMMdhWDFmoahAmoZFJcKQWjWLJINqzBW82l0l4uEp7eoDcG12jFvCIhySuMARFGBShD41kISiJkIVe9LXMaG0xGIdOUnqUGalsbys/Gss/uvKot0SGIrKwzl6itKY3ZUYZJvnfsvY0oSBlLBcGUQskpBWpQiXqII6a1Cv4galWHWyFmcGHrp6yGFHAgSA4/FZmQAFqI76rFMAqVmb4AK4cFogP1gmFTt0hCiQ2K4rJquGmPhUhPMYCMhhRA8QsVcKHNf+wYpmRYJ+CtKtlmKkhxEAQIRwEpYxQKWexPN+Q1pfLdoBELYyQBFSOMhLudWVrMYpeiIrXoeSlhBJqQOaEHtK7hbTzHu24Tz0LUiB24Es+BXnPNMrPjOaEUBQQLSdGnpB/XeRfELn3q39xhRDt0YJzNR0fQrCnPTqABpEi4QUO6gCNXGjyYg1M4CcP2LL3bekZ1NlVlMr3lfS1NXzPHImp6boYergBOtN8SfOyc6ERHS8zFnpQPCOjEZRIAhIQStA89tmP1Rb0JwjNDEFfohh+eKfvFLFO+wnZhV2DIcqut59iyFmMBQS38zZniygIodAoU/S91d1oH6ZbZ9ubdOn/Kk0VQ7RHdptGOFci4enf4OC6S/PGdvWNMnWyk0zjtri6HK0YgcQbShgvNyPOrT9/+27d92m32MjkcR/We+LBy3eo+K2rkrMN4AH31QO5ooj2YCHhP2fJJY7Unhto4onbpSeU1OnKi+eA6RqHEsshBPI7NWLkXKy59ZSHcndv3ItSx1cUgpD0DdWb7GibOb6yPrqb41xPOqeKI3oOdLqfpBiTao8NHPFELAjh7Bsa99MB7/QqrjzcwVOEDgRPIKtvUW1rL+DJaZPydz+m44dXoNj/vjKzy7zyloe05SXt9n4NUeHtuULdVc+RaeigWoPgu9+vFPgrMYLw7YQQ2FfF/wjFl9sRV3888mLo9cdQnvh/0b3aogCEzftO854/fl5Cz/HRkx7uU5lEe6RwknBMY/XNxYYPIhYoiGNh7LO/PZl4v3h1bz14yV/Z+n2fA8djT/jt/zxljB91zM97+c0PHik4P1xJOwKZPsKoPuszPaqghPaoJpBwou9LIm4QAr1RnRzhIPMDQN+RPyjpQJuJvsqAPw7svTtxBPqTt5c5QLbonwXZP8NLQUOTgh/YwDESwBocIwbLP8JYwbxIQAU0Ha64BAf8iEkgFglEonA4Ar3hJojLAuarvRIkk0aQQqiDQSihQvYbEUhAwTKCPMv7Ig95wdzrPxWcQRwEQymAQv8C3EG/SJH7+7fbIL2Au76WEIX2gIKPmIbiQsIKiocm0JvUc8If0CMPTD8IycLCI8MY5LhiSMQ74cL6w5kvbETJc40x3JARrJMZLMQN4UTo6zc4vJAfnMM6ZIlaaI8n6IhS0KA+bB0p0BsneKIsoMEo1ELfeUQQ5D9G9KJcNJFI5EWO68GyaMEDwcRV0UQFOkMyWUY2DMHFGMbFIMVSXMCpKIb2kEWOyIJScMUK0gK9QYJWJA4OosVORMQq3BBHQMfh20UoUcdbVBNg9EJRDB5LTI1jRJlknLdPhJArIERQpDl6pJBppMYgpAptoANAUEhAIISH60YkKgO9GQJvGET/c9wQKtwlMlHHjLTCRXRHHeDIKYmELgzFZyTGMHTBrmtHH/JHi1yZlgRItRNItpPDOXQ7UxyObFiCh6QgOoiu3/ABRCESDtKCf8RCkLySjVTETCxDMFTKOxlJSawTSvQieyQNfFwZfVRBmOxHo+xIA6TKSGs7m6TDaowPoeRJ1vGDn8yKrRhKbyhKl0SZpyQTSEBK3GPKYPwLuwxJAolKvXTDaByLYgQQrPQdrTS0K/ABuQwervzK4DNJaRxLspw0nFS4QkjL8VEEhsOK94C4LfBKCKFLCOHLpUTGpmzE0rwTSSDJgIzMwURJY1TJK2TJxSQTLAhNdnTNciNIyrRM/6r4hEjITNYxEkpZkrfcAh8QhaS8y7pszsfMR9T0ItVEEdaUSgUKy7+wys8wzPeTTmFUzOWEECxQTryETN6cTMqsTLPkilAbTtYxDkrhQwz0huQUT+fsS5ShTujMyu/cy+ecEknQgeuct+yMC8JkNxrqT8CUPvK8z9BxUPO0v9eEjN5Uz99kCShwz/ekm2mpFkSAOC4oTyjZzw2JBADVTY8kkxPNzxERUAJVQQOtjO3UjDCiUNX4Nv8MzAg1QB7lz6mcya+x0AtlTw7dtIeJmIl5SxF90A0p0VVhUdOMTgZlwWKI0tUcUCqFRhmtUhrVPwU9TB1t0BFVGx9NnjaUPv8u9cH0VM+yNEiqgIY8NFK6yQakaY8aUBoc4SAmvZInRZkr1UXahJBI2IEWpZAX1VIWpIsgBUMvLT4w9c5EhQxbMFOcyQIyPdMbVY1F1dTBHNI2xdCU2NA5bRhvIAK9KQOI6wIfCCQS1YFUWtFXvZJSKEAAklTYJFRDVZNJyNI74VQyoVU09QsbtSJdIc9WVZtLRdYNCdZONYtfvZNPBdUiTYlISBhSHZp4SILYUVVWvZIThdVBlVUoadZiNdEdCNcp4VUYlR9oZdZaLUxIjRwbwpljxRdlJVd4pRB31Rc2bVOcC9WPQAThxNaheYLz6dZlNVEdsKwV3YGGhZBydaf/W8VRQoVYde1VFOFXlJFYMZTXMaLXOsFXAxzZiNVXNdnYOKSI3BIDLvACMfACLlhZitCCLmhZLaCILMhZmQXCgVsJbCjYorkCvWkCcRQODvICb4WSE71YKH3YWT3Z4AnZyLHYcltXih3MlPWdjnXBj1UMe0KGT9CDHPAD8bQnZqi2fuojhJCEQBokPCsohqKk2MCCHgAFSVozNTuvO0it06KDO2olW8gCJVGlO9Ks/oo1AKMsVuupYgAEH5AsCCsqLUAywmIqwfoxwOolsooCPEgCH+iwxeoUuyKrJ+gUO7ixu4oCwbADFuNcH/iBTKEIaRIDI7AtKTgCivABirDd/35JAor4AYpAgisoSJ9VCSYA2qAtGJFAkiZ6yy9Q2lhtWpSRhKfNV2Gt0qmFi7PFpzdSr4dNWz66tuVktjqLW7x9M2WDKENAhqvVW1fiW/eCL8AdLfrSJbrAAStL3MuKqSizrK7iAvFSMsbaAkGgVQdDLKIKLB9jhr+6XBzbqtBdMju4DCA4CM6tMdeFK7lihiCoqw4TCCcIsQVmhgzTJMN6MMtF4QQTr0EAqSgQXB0gKsZlBpQ6XM6yNdIipEaYg0u1rPhVrfN6X/Ja32QrhDxCAscgKEBihvK9NoHSJ2boXn1CBq21uZr8VyJ9U+UdH3D6DSAQD/oUg+gt3yU2qP+ZSt9jc7Nhw6Q+ENAmMDP5jeMcxrUsq2FFcFyG5d8AczUAQ7UDU7XGYoYjezBkyII+QAIfwLAfs7AeU1210DGEoLEN6zDOuAFR4mDOXTGEcDG8OisS47ErKAZJsAGb0jAUTuCiOiomA+QSQwgoqyxetYG+uDI7rrX67bJci+NIqIOC2KUhVt/wMmJIKuMPYYRJQAJp2zO0hWJM2rZsWwtindgyygK7xRctiN53xV5FVdMDldYspjSj7Yha8DkuJhjoQpLpessy8AFtSwhCZuUBfuU+3mMzYARkQIQt0IHfwmFcLi1djqtd67VOkoTqJS2EGIQ2G+YmnrY8c2KBgAT/I1gCURDfZxZPd/a6+YGQ6hUDq81YE7HiyOFaY/RaMNReFaxmd1YbbFZpbXbW1whpmgRncA7VeHBIc4aUTkMSUFvnHmjpDRHQjoYSjobabT7JkwZDor6Tq/XVbt6MkY5XlascrFUNwfXpa75qYI3azYlpIfXXmb5JaoWHSWgGnI4WSOBMksABhqHPMshqMhHQL7iSSdgBub7elw4QMiITurZrFKGEj56SrgZDqE5QqW6eMtKCHfjpOtmCt3ZpjXVqFvxmsC69LfYIO5hPsxYUoaMUo2trx94QuvaCud6B0b5rc10Vvi43StgBduUfwW5Ewka5km5EpH60xF5sBWrs/9ze2q1+GdiWn8mmbJykSM0uFlvAu9/Qu7c0gx44270ubdI2ba026s2wbS8S7dVubap+COB2w2KQ7cmjbS+6bmFM7OcOncZG71UJb66O7Ar9asoOa8uOhyMobuNmEmhwvbyDPfo0gx1Y79TegS4gbQI/7WmGbgP36+1uakaNbd++yvH+7vLmwS0AcHyx8ABfmfb+7fcmRuGWb5wUZ/wWjmsQvzu9g7c8A+cu8BY/8KmGErpWcBPRBAaHbAf3Ig6PcMO21TLKcHzhggunbrwOEO8WvfiWb5yD3ii4Bp+9hAkicSLhhiCwQBUXcugGKSwvaiL/EL2GELrOchq3cZD2cP+50HHulPC/oPDA/HEDDHIN7+3qbgsjpz4kT/JJ+4IfUIJmwAZv8BluuNYoxxFvUAIkaUI99YYz2AFIivEdCPPQ7oFHZ28IN2kxrdJJiPRyq3HXtjw6r4wzr9E0P1Avx/BFB3JTH/Ib5/IAAXHKzvMkKAZqyIb7hgdNGCdBx5aDxQpBRHRFZ3Qtj/FMf/HD3mthR5FN5+5nLXP+APUv5fEasnRotPBfD50uQHWTlfOsXXbVaHWwfvVimAZsGLgy8GxcF5QLGglVpE9fJ21JRxlMd/cNp/TajvaThHdNH/PA3va8bvZHffaVWXPpC3JqxxlrJ3iOnXeO83Q37PaZfnX/W4CGJg+Hphhxc4+PJUqCim8JDmL3Rt+CK6GEHvj4Ye9xMgn5kUeRS8h3All4yADvhPf3bC+NgD9QWxh4fPGCa39sMsdxscTiOyfLh494bZh4jvAGI9B4ix8OQKD19OD4HeAj6EZ5CDn5LUdtlKn6O1F5Tlf4fUeQl5f5sZBmGPehII/60Mn5s5/0sFf2nj/ynwf6ORT6ayD6Pml6pfemp1f7VWFtyoWSkPf7VEdwqu+BwDeRS+gBrieMlj/JfleMsSd2XekCHdh7BfoCqCf5EWF8yYT7uHe7ua97jjBCvEc4DpoDzP/7HTD8DaEEH1j9tedyjWZ91y83xFd8N9x8/9h0/MGA/JKXfMrHl8uv/Dhf9Sr2+tdoeHAG/aJnvcwm/W4yfdQ3edW/Ek2g/cwH+HrfDOt//Qux/WSH6eMXiN0fVlGvDJqHxskffhUUA+nfeX13+zrvfM8PuOVvClZ8fk2L/ktK/e5fGe4HCGYCBxIsaPBgQWTFSiFZggwhxIgJi/XJIUgiRogKNfnQkvEjQmSXety4CPKkQGS1kih5iDKjQoYOX6IslqTGTJoYFVY0qXOnLS86Jv2MiMyWmB1Ei2pc2NAl05ArW0aViGzJjy9itnLt6vUr2LBiv375kcQWtGvawsWD5zZLKbdy59Kta/cu3rx69/Lt6/cvYLrxvP/NUVo1ZDFKO7IcPrjRB+PGBlU+lUyQp0XLAx9H1rz5k4+SnjdPhSo5ZuXRAm3iNH2aYmbVR4UutXy0jGHPqHPqLq06JVatY4cTL9617Nm0a9sGbu78OfTo0vsOLlxb88bFsotx7Nw7NXbYPm1zh/ybGTLQomX7Di/TtWbWvMn3/H30y1DdtnBff/2ePUvw2RaccQUaSJZZaKnFllvZKDEdhBFKOGF01eWmG3fajcaZfbWAR19s4XV3XnqhjYdde+T995t8AlaF2Ym22YJff4fddqGKH9qW4oYEHvhjgcgpuNxc2VB4JJJJHjkYHTiSp0kP3pF3iXns6dgYjBsWQ6X/lNipF+NpPGLp1HyetahlfbIhlR92tpjh5Jgr9hYgiT4CeedYQkKDDZHwRFKIkoEKOuhfTMI5JpRdjsllh1faKJ6WjNr3ZYd0uueoZGdimOaGa9b4optsXlpmmJbKZqcYXnCx1RZbceGFGFxosVUWW2nRRayz4smVnthww6Bbn0RCKLHFFsukqCL2gMV2VDJrJamPcjqlD8/KBoqJlVI1qouNaRretC8VYwsztogi0CUCiWJLelsMBYlAlJTCzCeOCESIQI6Awowmhgikh0CFaMLMJIAINMdRXQxVsEBmCHSHJMxEgodAXBwMbyN0CPTsGYwwo8gZCkXR0BeIMGNI/xkCRSHQFoUwM4jFzDwhUBaDMAOIR8wwIdAVBvdxxUo3PBRFH8zoIYVAQwgEBcB2rMyM0swQzYzTSatctB1QYLXDVkVIIUYURWzlw1ZGXCGGFEds9cNWSJwtRRK7btXrr8w1YyzeeSeJbMSRLttstY1G+yKkGDpLIrbrEbRuuecyky4z8jJTCiUCwcuMvvTaywwhlCGBTL//Bjwww8zMIRAgRE3c8MMROzLHQkQ4NAfGZ2wsUMcf284M0syIYTLKAs3MDBcuw9xizcwIkvMRPBvsx7NFXG1071E/gQcyeBxhkfUUH221zN5XP/33zDTPzBV+2HJFDhHvzEzygkQ2fP/xL8fc++8np4y+LWfoYDPIbmc6eL2uYq2TGMWY4bCbEaVgC+mBQwTGL38ZLWDpqte98rUvylkuXvOqxSVYAjnGYQdVcjsh3YAFDyjcTW8ufOFzBlMHHfTNcH+LygjJZS6FIKJakuMgMy6XOVBsDl/MkGDoKshAgvnBQz54CMQQyDpm3IGApyPewewVQPjhjhHFoAMOLoI/kxEiZvTDFyFy9r74ReYICrnCDrDghyuALwp8oJ5AfKCyO5ZPj1LjY/WQAQodlKR8UbMjM6AnkPP1THmRWaPNyii8lfgAjfcTSP6Cx0Vm5G6LMZuDvRoxB4YUAYoRWx0nUddAg5ludBP/FB3m9gWKRtzkc5GbFxAvBwodOi6HTQmXjMpAQ/34r4Y5GtyLxFTCrJywmXNL0J7qBsNpUjMwMqThIB5JM5vNcZE88wMz+DC+P4ZzZcjwwUh2cEdx5nGP4eydHxHJh6dFLX2JvMKWbMAsLNgsmwKBJOfMWDF8abJ3ZfCXIszQEI55TJQGNJ29CqjAh0VCiq1MpCQqojElItFlnNMgM4goEAr+sHKY21LgzCWQT9gmcWDCkjIJJ6fRfAtEL7XRftpHzGFiaKY7MlWPmOlMFELTV8CCxhOqqdSlCsYbdtBBRUUaRA9OzqQsZQbjVNqUkdAxUj/o6oY8hEymZMlwX0Vc/7YAtK1jduswNX0NME+T04q2yX905ZZa2/oiEw4VSClkjlugwdTBVnMwd4Bqs3oA1vB84qzQ0utPyspYx8pGFD9Q3JzW6h9MeesmYy2KZPVThhzcVa521ZJPS6VZ3fC1rwf6q1sicQnC0haGhkWs3xZLnsbqdkecJWvhJtvbll72pskEapx+W5W3jimuWJrrTku72c+CNqanaa1rg5SgaRgVsIgYVm3Da6zB4AG3GALNcB/F20pRVyehfc167WNZzKIIuY9KrWWYK60Q6ccMpI0uapVbXfsOSKjZ9et2u+sWbIi3wcfyRnkvd14fpJdw8Q1rEtpLk/eO6cIbmq9xo/+iEgKLmEyQZYp+CedcnPpXwqbVgYune2L3WhdL2D0wcYTEXWm6hQkMdjCQ9wZhGG8HNL07LxCOPCcNv4TD6k0yiUCsrRmjZDdU1kmKS7xiUJ0hBzHGqf++fF8BR7bGNroxjvOUYB4Huc1C1gORtWTk7YACygBicpWDS546K1k3Us5rT8lclCwDd8si7p+X9TOHOOMVw6tdpnDSbCAdKxgetcCCmzMdocHAeXMT7vNr+MzeK5/EyYQDRRBAbZs/O5rUH7HyeQgNWj3XNQeeluuiby1jQNsHzZIGC3KKsWMVxqMWmj42dAbDBx3oGr4+UPWjUA1tG60Ez6Wm9ZikHWX/IND3p4+WqaB/IuvIYtu0tlY0swNs7VKbea8G/nVxgj1s5kyihci+N2CUrYNGFNkHT8MQqv+9ZFe/utynDoLAdVOKIHRbtQTfiYlj7dmHQ9zQZEU0v9u06Iw3OrMUf7Wv4X0cswi70vCwwyTwrXLqeKMP+6azv+kshIT/dN0Ft7h7iwGKmZNo4Q2HKYmBi99MTRxN/K01x3H98kDb/NXtFnHIRf7MJJScx95YOdb3MhiXJ323MdcSKIZA81I1veJHz7bYey6En1M76LMeemdbY/QQX7zLXcfpxtX9cas8naxRl7q8Kx2PI1w964Zvqh+WDvCvAzztd977L88ebcfL/6YUQ2D7cb9dYri7teibkvxz7Y7uu4+57DvpO2j/LvLAsxkegD384QeTeI+BnfHhCfvYgW56oxi8xLjv+eXpPmDNCz3cWPY8uEAPZhzQXkaLbj5beX2qd0tdLKwH1iXEAPvtu94bgNAB9Pds+z1TfuBzB3v5FR78KTMd8hIZd85xHtn+MV/ROQh/cndvFNRHVvXwvj5gccNscZ/hDQYg5IAiwBwUbIcoDMECPt75YUgDPiB7rJ/07RqLIJ9N2Qf9JWCb0AEC6t0Fshb1VR+wkdy8uYUmaAMBxp43CEII1h4FhscEjloE0iARzGBvWGCrtZ/Eyd3nCd/8nQEOeKCMgP+gEUZfD/ZaCZqgVwCgXJSBJrQg1sVDOHjDIOhAEu7ZD+jga4hCDm5HtbmfY/QecIGhF+4ID4YH/10b55WYBsKV8pVYB4aHLSChCGJIG26Y//0aFLoeFWadFWJhDphM7Q2PBBIBIrJhhpHhZJjhrIHhIvYGEWAecO1hwb0hcMVhc80hcNGfIb7GHRZiHjIi8Z1ZEzohr6BgpXmDEbxeIB7bIGZhKNLgD0ziFxYBLo7JGN4geYiCLpJILRSBJc4aJsJExG0HJ+6XEG7YuMwBDtTio9hCHZCiD+qh27lbpKkiglBdCspF4cUiss2iNUrgLTJgMGpJLwahlgDjLoYJMTb/Y5MVwzFCnCbO2jKqmCfOmi1AozQSDjWWoxKaoiOGRB9K2h9OAh2II1O9gQk8JERGpERCZAmUAAl8AAiIgEZuJEd2pEd+JEiCwAeAJEmWJEmKpEmmpEqipEq2JEhipEvGJEeypEzWpEhmZE3KJE3m5EqOJE/GpEj65E+qJEwOZUsW5VCawAnAwWaMSzVSUMeRRz2CxFWkIjf+ITxMQ8oxZDWZQAdgAFiGpViOJVmWpVmeJVqmpVquJVu2pVu+JVzGpVzOJV3WpV3eJV7mpV7uJV+OZQecAHqMix3kAFQOpFRmI9RZpSr+YSnAIle+0Br0pWROJmVWpmVeJmZmpmZu/yZnWuYaCCZhluJhnqI2cmNYYCVcPCY1ecMHdKZrviZsxqZsziZt1mZtfkAp3EEOeFRUvsZUgpxiOiFWqqZS+YFtHidyJqdyLidzNqdkSsFuiqZvIqbfBacJ/qGDEGc1vYFzdqd3fid4hqd4VmYKGFFv8iJ1pp51Vt9wGol2ThN3jqd8zid91qd9MucaSCd6kmZibqNprqI3VlokEMJ7bud9HiiCJqiCLmhd5ud5PspvwsRBphlWggJ4FSgMxSeDbiiHdqiHzqeDYiBB1sl6Ah4r8pi9YSh8fiiLtqiLvihshmjpFeRl0GN69l+Jrt6JqlAUpKiKupCGkiUIvMEbuP9BGqTAGiSpki4pkzapkz7pGtSAD0AplVZpk97AlFqpllIplm6plzYpEyDpl45pknYpmZLpDYjpmX6pma6plrapm75pCmRpnFZpmNbplhaBmuKpkoLAWcro5hnftUXoTkwojg3njxqoWZrAYChCDthMOwIBE9BoUzLEpD6WL27WpaoJEhTj8FFqSogVqBIE/Dmj/LlXP94ApEqGCPxp/o0qehCqVRjqgf3hNCRVolJTkI5lCXiDLdyADgiBsA4rsRarsR6rsQYBEOhADSCrsz7rsSors0IrtVJrEExrtWZrsgKBDQSrtn6rEEhrs4IruQbBDshADpAruF7ruKprtbL/q7uC6w7YQLvG67Mqqw2kq71a6w/UgLfuq7H6qVneAMAS67LWa8EmK7YmrLP2QI7+346+3jTkqq6eZQlogyL4AB9sLMd2rMd+LMh+rB7cAQiKQcieLMqKLB7QAdmkrMumLB7UQcu+LM167MhygcbWrM7ygR7E7MzurM6OrA/kLNDWLB7Ywc8WLczKrMkqrdH6AA40rdOi7M0S7dSi7NFegdVerceGwFk2AddyrM9Kbdh+7NEmbdmCLIX9ZzdWHbBEwhRSbMWaZQlkQyEMgS3krd7uLd/2rd/2bS2AAiXoACD8reEeLuAKbhAULuI2ruHWgihcwuI6LuUm7h3gbeVm/64tBK4mTK7mam7gBgHmfi7lQu4lCAHjki7iQm7npq7qNi7kCgEOuO7r/m3gXm7twm7kfsHo5i7f1kKrmuUa+G7esq7nEi/gRi7qIi/iDsHZsC2Auq13XajcvtCuimUJYMPdMq/flsInTALhci/floLi0i73Gq/5Mq/32kHvii/5UsLxiq/eei8QtK/4oq/8Fm/55u/eQm4QzC7/2sL62u/5ioImeAEB+y7w/inyBi78pm8DG3D8BrDzQu/USe+CVa9SXW9YZi8hJDDyei/4QjDxku8kAAEJK7AoPHAAe28dgLDvmjAKB3De0i8Mq3Dr8q8DT7D8+i8A868L33Dusv8uAufvAgtvA++vDktwCjNvBUMvVvqYBs9tWXqwENeuCIcvEIPCCTdx7UIuCwPxJ7xwC3PxDNOwDdMw/hqxEgewD3vx6waxGhtwEfdw8Jbl8BLvDsOx6q4xDdvCE/8nok4xFZOlFaPx92px/sowH5MuGPOw+o7xFasuI/9xGrsxE+twGy/x/zby58oxJh/wJIPuHZNlHivwJt9vJv8xID+vaf7hpRGyolYxNnwwIo9wGXcxJofxIktyLp9xC39C/c5xDrMxL3PyD/cyGYdyHd9vKY/lKQ9xKhdwMf9xIC9mxMpFsclyIfNqLY8y6WaxJ2tuJS/xMbuvL2+xLiPyMIf/MiTr8TSrcicH8zIvsSgb8TOLZTR/cTxHcDXT8DVfZzbDQ71x8ywb8jffsiLLbzkb8Qq/cwmn8yKb8Th/sjCD8+f68f32M/O+MT1jNOjSMUg77hHjcRKfMzVDNPcGtIkGKJvZgSQYdDdjb0IHMy6rMzD38ENXdOWC8kSvczC3sz2rND+jtDwnM0NLtEPfsx0zMDwbtT8TNfKyNMS6NLCEo0yvKN3WtBjf9E/ntCpDdUTXM0NTtCVfNDFLdR9zdATPsxiTtU4ztTM7NSqLNQ6rte9SNUIO9OBhdVYDqcVydS97dVkDtU7bde76dGGDdVIL9VLjdUaztR6Lglsr80hX/y4RX/bq5nNY7vPr7rE78/Tn6vWhDrRbOOZf5w0Hg+Uh2/RCu69ZmzNkWzRcc29Di7Fjx/VsZy5oz3FlJ3VtU3Mzny9ng6VnrzVif/EqW7MrVzUGw8MlfEFqH7Q327Jri7bj3nZY73bmKjZsG3Yv57Yq//P5SrYK/zY6B3dHizQ+07U0J/dnLzdAN/deW3UADuB0Z2hgW3dXv7Ztx7ZDwzclK/V3MzY6i3dKY/fqmvcQUzZSp7dmkzR7NzUSPzV3Y7Z8UzB9l7Z9qyAL5vdMd7BgJzVhF7iCr+5Of/Qvn3jjXvJQs7jtMrhyo7dtE/h4D3dHFzcGHLcjy7gjZzj/kv/2UGGlGcQtiOv3VvP3YPu3+gL4YV845Xr3f4N3Y0e47pJ3R/t4Hzs4jP+tlAu3lduujvN4ZAv4j2N5/gp5M/0hah+53qw2BrR2f3d59zr5dtN53355CNt5lac1nvevlv84jUeyevszjjfwmJ80lEs4msuvmsvNH7pim7u5scC5nC/5n++tdhewmdN2mBvuph/4pz8ukCt6pv8ul6s4M4/67ya6hZ968Za6o2/4kJs2PPg1pb/5frN69yYyrAswn693p5OzjTc5lYu6n2vysGd0qr81ryevXBO3exf1ol95tVfuo99JQi5kroc4a484Opf4lBt4BC97Txf7nh97jSP/+Ho3el1fu+4Oegiju6E/e/G6+rv/ukZreK2atlZ2u7fHObjXuLgbO7lPtrlHOb3HcLCvu733r6zn+x97tLMT86HrMb6/N7yTuruvNK1D+kA3JsBTN00rOYkzebofvAonfHYvfGI3fCSze1TDem9jsryP9cPrbWa3d4VL/Iuzciu7FmrGxchrNS2bfLijfAnD/MpvvJe7PBYzfUTL/GR3PLX/eqzffAxDvXJHe45P+2cHOm9HvMf31SAXvdEjNNITvNIzvLojvNP3eqG7vcpPfc7HutWHPcuXbrNbtsU/fEmbsqmHNtBnu3Fg54OgfcBf+sn/eqiXe9znOddTstRv/z3V3zXNi31Ia31iT/6Wez2igz1yRz6q5z3xGn6O2bp7Kj6SH/3dC7Cvr/guk76me/4nV37nX36Dm36P772Ecz4W2z6zgz7lRm7eUkLeXsK6RG6regBYesBXdoAHDC/y24Lym8slHH/yL3/220L1K38taAIkLO73c7/2W7/5e//xf4ItfIIk5C0j5G0kUELn1gH8560ksL/7378t5H/7vz9A2GJky5akT7Y+SSI4sOCnIVK0iBGTRaIWLmK4RJxY8WJGiRS/+EBSrJYmbuEmwYOniJBKly9hxpQ5k2ZNmzdx5tS5k2fPmG8wBMXwAURQFCWwXRliy08WgkwIZvHD1P+pLai2pFJ9GnVqU1ullEzS0aRr1atZvVoleEWPLT1XCBpZ21aPFFCTbgCyxdYtXFty99L1C5jv27hza92JEgQQYcGHA/cluNRWlDu2FE8mGIVOHR1RNFe+nNkWZcuYQZfePDr1kLs+zKAOfZq06ct1nhD0QfBJHVu4dX8CYsM3cFu7RctWnbz2atS1RAVhTLu18+a2ev/OfZx3nVp0mjBGnt24beXmr1uuVccGDkDoqzNPPb749vFzPNvvft56fOqToYPCiSHSY202A5ejTgQMUggKhA+GAmGN5QobbC7J/kJMMcYcw7BDwzKMDETC7tAkiNQ+9OussrbCikW1XBz/ogkkJPpBIiSuEOMKGsWwUQwcdeTxhy9+2EGQaRTpIpx4nFDJjkh8ilLKKams0sopgRLKAw+C+gCpOpbSRJGFCDIIIYUEKvOghMhs6MyvCBGLjjXRZMhMNtN0E08714TkNb3MFOUSgigh6BJRbBGUUEMRVdSWQm05tJZLIGEM0kMTHfRRRjNdNNJGNYVUE0rADJUgUAhKVdVVWWW1lLuA0KvVWWmttRThKKtVV12hM1HWXYFdtRZQKGEs2GNZhS4I95Bt9lYwm222Vy9yjbbWWhYUSlsMJrQ2WWKN9XbXXsMVN9ghcpRI3XXZbVciIpMoZhpsTornynvxzVfffW3K/3LboJAipFpzZ71VrF8JbvXVSWJNmFboikXYYVWfHXhighZu+GKFcd04WVF89TjVYSMWWdjomDX5q0+gVdmWaS12GNt/heo2YZLLFZlciVVG192f3SVSCXnpXZLfo5FOWmmc/P034JgvNlgHnqOGleqJIc7Z44pdzvjqqDt2eWeVcf4aa5TNJphrsUGm1uSZaeYWa3DTNndsl1P1Gei9xRAamXnrXVrwwQnXt+ltn8Zb6rrF9ZrtklVe22THFQ+bbJC1vrhsvF9Gu2uWoT5bE7d1zpZmmwne/PKQOS8tXb6D/kGJv4u2t/Dbcc89p8O1Tfzzg7u2+vHMwW55cuErB/8idJkxZ9xa1dle1nlrJX+77eXFhfv0uSG3nnXO9YadXb8BN1r389FHn3ehfI/8E+AjR9767kWufmv53Vce77t1pptzZVM2OdDt73pvM92/UGc3/62OeCILn/je9YN4lc926bPgBQW3PoBhQ2CVg9/xGDa9aGVNhM4aYPxCyLlb6Y9t3/MY9C4nvc8Zz3uke+EBt5XA7C3Qew302APFBy+iBQ6DRTTivjSIgfYJ8IP1w98LRUG/rZ0QhBr7HAsZWMJjwdB6MnQfDXVWwNLFTW4y42EYXegyIPJNiBQ84hvhWKUkLrF+75ta8FJ4OSluzH58fGIdsdhDLQaLi2H0ogD/wQjF0WHPW9pDIPd8eLPmtc51EOybBIdovjhukpO7IyMdp9jE++VxfpFUGxWdSMr8MdJuk3zbGbsYwDomcmMwMyAZddhIWNbSleB7HRsx6cZODpOYMpkjB1nZODsOMliUKyUzgdXHqqlSgIFEoyl3uEcoHnKWyWykGG+IS0hCc1y9xNsaYzfB2hWTne08Zgd/d0cUWjGM2pyYNO/5xylaU5HY1KU9Nee5L3rzeeCsJQ61lcvn7TKgaezZL3/WxnW2k6LDfCdBo7U4PNITigBNGD4d5sxVEtCh4/yfQBGJUWkZVHMIrZlJs0hJdK5LokSs6E3jeFEPyrOK5ORVFP3p/y2QflSffLScIMnGUF5yc4q0bKgNDypOM3o0deZUI0THF8yJ4pSrR9RpPH1qq6Iyj6rmGqraxnrPo14zrLMq5DZl2VSVIsuWY4ybQkeoVOaV1IFYVVdNNdlVwV7wq+4TpR+pycuyKtOpIU1rSNfaz7a26q1LjatRG7tXqLZUqjfTqyT5+kO/XlKdNh3sadX3SWTudLIKe2wrFytUVI6So4Cc6xatqrnPMo+pmL0tIVmKNZcGBa/S2m0rQ7sxdAK2gqh1bu4Ky0SeprK2AY0t9WaL2OoalZ+8TK4Cr/vN3hbvt8CqazjvClOktm6NzH3ue6GrWngadrq0be3HwmvCzP+iNbH7LG85v5vNoGYPpd0k6WaF29nUHZfAAXYYEN0LXwkTLrp1POw0t0vWAWc0uxi+L8e629AN05XBrRyvWvdb1UXeMr1THTFdc2uyB0Z4wjVWWoVDWV/tflhVJJzhf8XaX+4C+VoxdjGPT3ZiyKYYuQiW2XDL6Nn8wtjBCQsfjW2cZX7h2KgXzqeQNYxkjHX4yxlWa4hF92JClpjASv4omUXn5JtBubgknjJuq0wwvWFZy33GF5fB5mXHglmSd24mnIlKaMiiea9qNi+bxXtZ8h6YyMmis3rZKtMc8dnPnZajfCtdsGVuVMydM3Q0Ec1fMy861JQ1spQdjVs3n5L/yQ2Wc+oufeQWxhpZ6OK0p4EdJUCrVdCJXjVsea2rs5r1tadkNGiT/TBIF3TWZk21im9tt1zDutT8O+cVtGraYI/bJ8OGbLFV3W2glnrZjWu2tZ+t4mi7ddojjI4N2H3tJrd6ZNte8KkBPO9dDUEHpQ0suRG+k2Mi4gbScfjDIR5xiUscCD/4gQ18MHGNbzziFf/BDTLOcZFvvOIgH/nJO16khqOc5UHwuMlb3vKSrzzmKC95yGsucovDPOcnB8INMN5zkVd8BzQXOsl/oAOjHx3iRYnb0oW+c5wzneIfnzrVJ34Dgzc34V1n2iezMY1B8AEPZTf72dGedrWj/Q51/5jDGeYwh87cYe11t3vZ2z4HM5Qh7nSg+90Bn3Y70OEMXOi7HQKfeLy7nQtbgDsd7PB3xds972XQAt/nPvnE3yHyd+hM5DWf+MGfwQxxr4PkQ7/2wZtBC1p4POhTT3k9IKIOdYB97Nee98a/HvW4P/vg3y730/s+7SEgYxOI/3vCbwHzw0++8gt/+N4/v+yAoJ24vZ59msyRG9igBjSYgQzxj5/85Tf/+cv/FVCIQhS1KAb64R9/8qv/E6AohS3eL3/9m58koKh/Kdxv/wRw/BIFFC7hE9ov/wZQ/xLlEzRBE+wP/xZwAIuBIIrhAieQAqEDFCJQATMQ/VZmVCTFA/8/8PzCDxkuEANLUP4aUBNGcAXhjyTYDwAlEAbLj85scP5e5RIuoQNzsPxkkAMBkAR/UPyYARqoIRuwT/uYECbmKBy4IRuuoRqosAqt8AqxMAuxkBqmYRqgYRqoQQvFcAyvkAuhoRm+kAzVcAy/DxrcMAzXMA6rsA2bAQ3hUA7X8PuagRmOcBrwEA+nkArv8A/l0AzTkBDVUA/50A4RcQ0DsREL0Q3rEAkhkQy5sAv9sBKzcATIiA000QrpEA0z8ROxsAvdEAxJUQuvARu0wRvigeuaMBZdIolA4A3ewA1wMRd1cRd5sRd9ERfboA1+cRiJkReDsRiRkRiDURiTsRn/d3EZnTEa3QAapbEarTEaj/EakXEZmVEbvfEbc5EawXEcndHpaMYNvCEd1XEd2bEd25EbuEEb5BEe3bEe7bEe43Ee73Ef+VEdD04WARIekoiMCLIgDfIgETIhFXIhGbIhHfIhITIiJXIiKbIiLfIiMTIjaeYNXrEjPfIjQTIkRXIkSbIkTfIkUbIjA3IlX2IgNfIlYTImZXImabImbfImcTInFfINWLInWdIldTIohXIoibIojfIokfIledInmTIWgTIpoTIqpXIqqbIqrdIgl7IptdLrnvIqvfIrwTIsxXIsEzIrt/Isx80PyHIt2bIt3fItk9IP0HIug80bIAQu//EyL/VyL/kSIT/AG+gyMDttDfqyMA3zMBETLNdAMBmzz0ygAxIzMiVzMikzJjvABBozM7PsDUygMz3zM0EzNEVzNEmzNE3zNFEzNVVzNVmzNV3zNWEzNmVzNmmzNm3zNnEzN0HTLDWzN33zN4Ez2CIBC2AxOI3zOJEzOZVTJiIhB4qgCYpzOaVzOqmzOlmyOf2gFpQAOq2zO73zO8HTz7CTILSTO8PzPNEzPdWTk8ZzZLYzOtczPuVzPunTStqzx96zPvVzP/mzP2PiPoUlP/1zQAm0QL0TQJNFQA10QRm0QTMTQSlLQR10Qim0QmURQt1KQi10Qzm0Q7MMQx9GQ/89dERJtETbCUSvRURNdEVZtEXPB0V5RUVddEZptEbvBUbHRUZtdEd5tEdnIhJuYAv4YEj5ABBqYVwAgUj5QA98wDx99Emh1Ee/oAiotEpvYBB2ZRBuoEq5dBqi9EvB1EeLoG4AoQjC9EzRtEfHdFfKNE3d9E1ZdE11pU3htE7t1ELltFbo9E75tE/RJwqYQBDgswnzlFb21E8RNVGXpglogAZwIApSgiULdVYOVVEt9VLvhVEbtVF3QAuKISAntVUqFVNJtVR5QlM3tVFrIAjO4BpiMVRZZVRNdVZpVSZQNVU31QaQQA/CQftgdVVktVaFlVZvFVdT9QaegBG87lf/VSVYh/VZMbVYjRVXdQALRAHhmDVVnBVauRVRpXVajdUHvoAagi1bCWJbuzVd7fRbwdVYa6AI6IAbOs1cbQFd1fVe05Rd23Vab6AJCGFQUYte7RVfCfZL9XVfwRUHpIASbExgzbRgIfZNDxZh27UHtoAZJMxhI9ZNNeEJPPZjQTZkRXZkSbZkTfZkUTZlVXZlWbZlXfZlYTZmPVYHKLZmp7UGhuAMsuG5NHZj0RQQbDZohXZoibZojfZokTZplXZpmbZdbUAJ+qBXB6tnfTZMgbZpsTZrtXZrubZrvbZpbyAKIEGwqLZqv/RqvzZt1XZt2bZt3XZadUAMuKpszRZK/9H2bfE2b/V2b/l2U991DrRhbsn0YVvUFjCCYd8LGQI3d0ChJyKVCe+2byV3cim3cofWB7zAS8l2cFs0HHTgARQAASDABsjVuQDACAYHESqIDhiAJwwhAkqhCSPXcmm3dm1Xb3MACxo3YDmXRY1AAeZAJQxhAGDguQJgCAQnHCBALlXiDByAJ3RAAGLRGzCxeq33erE3e7V3e7m3e733e8E3fMV3fMm3fM3Xeplga5FVEeCLbkkUAHrgJS7hCIwXeZfGGyjgDlzCeXciGxTgCeq26yZ2aHUVDwAzY3t3RWvgBmriDHYgB5qgGl6CG65AB3TgCuTVJZDAVV1CEqDgJf/qoAdygAmgwSWkgBHooOiUAGNfYhB8AAeQwBZeAhSKAAd8oBBm4nhpghKIAAd+oBFcog66gBKE4AZ+ABFgYhqaIAd0IAukthjqgAIsoxngwXmxwQluQAe+wCaigAEkOIARboBtFghaNcvcd0TnwAF8QBNkIgcWAAd6YAAK4BJU4hMG4AAs2AAGYHezgQIE4SWiwABcwgcY4AZ6IAAOwBFUggAGQAFs4AYMAAHYVyWWoAFqYAcAQAHqQCXmYAFewAdsoAGiQCZ0WCa+gAE+uQYaIAtUYgcKQAFgAAcA4AGuwCX8AAEG4IERAAComAkIgAIKgABG+QwegAAEAAdkwAH/GJgmAAAHwDjhxHhfd8Ap/OyM0TgAIgABYkBuVQILFGBs4SEcZKAGVAIAXoCDseEFXkAl+viPXSKQVUIMGKAl4CEebmCd4YEACqAWVEIb1Fkl9MABzsAlmmAJ4OEaEAAJXKILGuATYqKUYYIZFgCAVeIKGABjd+ABglclioABqFgbDCAHbKcZBMAGVAJ/9bd5KeAHXIIPHEALZoIQIoB5n3ncotlYH1USgM2aSZQZzuBzH/YFhOAlCMEBtAERIABxVWISIIB92xmQBRkeLll+IQAZ8plwqxgCdjYHyDkmssAAmgsAmuCh7RcmoIAAYIIAahmTX8IbGGAL4GELFGBn/11iDh6ghFF6fyOAg1VigWciems6jAmYCQBBane6brSUSxV7sRm7sR37sSE7siV7sim7si37soughKMEChyghF/5AED7ABCAAkQhCw4gJhAAC+Dhqd85qgVgAUIbtCPAEPJ5rF3CEUgbHl6grF+iCBwgtg+gAVgaJiD6JXygAYDbARR6B4r3JQrgg48AAGDCGyJAD+Ahr5v3eV9CB2JAJrBhogOb3KIZZ+eArsmNXmshSZV0vdm7vd37veE7vuV7vum7vu37vu/bB3Q6SvoYD+BhAIqgEQRcwFOiDBjAsK+bAaQgnCPADqC6nH1gwAUcSmr7JSCBAho3BnZAJphgAP8kXMD52iWK2yWIAAA+vBF2lrlh4rnhoQkGACZKgQLo4Lrzd3+12yV0oLlhIgoW4IvDG9gG2Ae4gIqXtdQo6ciRvGf2+yaQ4QBw2CUEIQIYFpNhsRoYYJTfmQIWHB4KAKtzIKqHYAAyOK1tWyUuvHGbwAB83CUU4QEG4SZGXCXwwAHAGSZU3Lk/mBEgwMFdYghkvJ4bgAtsHCZyXCaa+cdtul1z4ArYmAnpNckhPdIlvTSW/CZsAAGkABmwwQwKAJ9r4QBqwBC8wQ5gAKZbvAGQYP2WAAEaYMufwAGSYBIgQQgeIKqhodMFIRz6oAawnADKHB7OHB60QQAAgA6awQ//aGDLdeAAtCAbHMKkH1oHPjxSb6AAvEAbKOGFW1nHVYLF4SEHEuAKkGEShiAAIGDG4cEGCIALoIR/t5vbVWIQIgAQED3YpLVfCwFgx+3RJ73f/V3GKt0muGEIRpsCGgAHNBceJoEGHIACFGAHEl4KCoACIAAAAAHTCXq0IeAFmkB6VUIUQpkC3tiq4QEAVNslSkEBiLwZboABDP4GWDgejOAAKOABXoDPYQIGKGDned4BZNgbhGC0HeAF6B0eiGCZXQIATD0chiABar4GLvzN4aEZasABpNsPTvslhgDpXSIHPL7ePQ1Va8AI6mDMZZHf/z3t1V5chiDgcUITHMHs/13CG1g4JqABG1QC41+CGUL8JcKB5HciHD5VJppB7nmCGQ4YJ/4VHpghcKNAAQx/JxDgg8E+7GngB8qgdCXVyNe+8ye97W9H77OMAGyAhelAARR6SiABwStfywghdpsS7T1/9ml/VUC/cETfxiYBABpAAAjAAX5A31sfU2W/9o3f82+fcIRgkvtsEK4gC651+Lu1+I+/+v89+aU/+0GV862/+x/M7bU//LOP+r2//I8c+23iDsxAJrTgyXsCGdb8giThCBxaJaDBCXxACha3QLOAAPz//wGCgEBB2IpcgocwocKFDBsqzHEF3g2BFAX6cIgxo0aEAA549LhjI8ZwSf8UiTyJUuGQIylbunwJM6bMmQ2LALKFM6fOnTx7+vwJNKjQoUSLGj2K1OcQSShzwGgIYAhMA09pWkUoKECECBHhIUKA4IWCAciumj2LNu3MYmXaMqHgpW2ZbHooQFErCQIdeARuYPkLWJBajA12ODnsBE9MUBCEzCxF4BrMJgqwDb6MOfNZm0k7e/4MOrRo0UubVl0YFWaUMppTKvoxKEFXAC+ywUNGIEfr3bx7i6xDgdtCRvHU+hCAkAAS3wwbsL4KSbhMRRSKwbymIArz7dwvFxHDJ7z48eTLmz+PPr369ezbu38PP/55H0xPOoUqtbta2fCYRdiLUBMF6EdggVf/ASddQpAUB88l1yBjBh7FeUOHGcww5EgZgGCkXHLLMcSNJgthIyJCmpRxRzgJZTMJPI2IIQk3LSp0TYkKObfRJN6IUoZg8GgzxxnVKCSJNwjV0kw8fIhhUkLh1JdQMWXBQ511CnljRxk2IpRNkKIkdEMMBo5J5kZfFIFmmmquyWabbr4JZ5xyzklnnXbeiSec0JiGX0Ki5FCAAgFohxA2AvghgwIFtOjDXfC88JFHASAUTxMCKLrDlPA0GkQBCwhAaEaGyNUWIg7xN00M1CQUhQFlvgqrQwgu9IAeHL3AAAIPAKBHAQwowMAWCZXyggMELDAAIwxdQoGyfH24kBcI/2ijkA8vwFONDQ4UkIABdSAURV8NFPCCFwlYlpC1zcGAAw5GlOKQAS80gAAEMMxxwAIMJAAgPAdogVANNAxAbwQAwAtPGQkshMMN8OyQAAUIHPDcGQYkUIADN1ALzxcKHFDAAzmomMUCDMaKcsoqr8zyRvcxlFqDBwDAhR1KKKADQsVQcEARdNBh5AshweOHXFsoMPQNCTRhxxYBFGDlvABkccYPDkDrEAAUbL31tQ3xt1A8AOjWctnbzarQfwgJsMAd8FAScg7cxNODAkOK/UJZ2eBQQIIIbWFycjUg5oQYCHljgKPwULOAsDYIIGI4Qyhg3RMU6ICuNwUwkRDjAP8vtIMNNtRwgALfMuSRqYUk8AAR8XBjQwEMIoAFQjJEkIRwjgzgNRcKLHSDDfDUkgUFZ+hBLSQMIKHiJAQMXUB+fgzQR4MUOGJ29tpvzz1mOXAN/tb52QCAigj18cC3O4eKkNAMkW+kGA2YipA2ApD9ggBGIuTEAhzPBLZqHcBK3SvgWdCWELXBQwBDgwcTHICubDTAcGdYwDQSwo0DCEshSEBOchBQkRokhAmyQwgSCBCPSDwgEgoJgBLg8YQEmI9/BtifEkqIEdgZwG8IOQC0fIAABn2CAoVACO1s5zWEAIIClICH74AnPCpVJyE6kIFC6NAA2xyAcwxhgBcMCMb/MIpRjDl4QTHOiMZiCCA/DPjCQl7QA3jszFkJcZ9CmICAL8EDB1FMiBQQ0L44JqQZFBiEVQIILgZsaIyMdAkCEaJAARQhIVlYWEIQEJEiIKBdnDRAfhLygyR2qCHYSADAEKedKzCAk+0agMOeQICFaOMA2gmHAZrAkFJsCRkPOAPqpJCQJQxAIRCYgxFrBw8Z5Cwh8eilE3+nkOAhpEoJCQAAWImDCBQxCwwAwA60sD94GKArjSynOc9JoJehRircICLwHLazRryxgfCYQwOMaTtBJuQMDmjfRZxEATtkJAnY5CJDEHmGBrgRnQxtyCPhEclJIiQLgLxkRIZQADb5/wiUosTaQogwqSscQDhXSACbjAlLhiwBhVg4F0N2cBqEJACZCjkAMBEiTGLi84jJXGZCHODLLiwAitOc4tpksKYjDOk2UtCBAQhgC4SMs6FUrapVraJOhcRspqj5gRwpIE+F2BEen0CAQeGhgyQGaECP+idCwhHQjBBBdHRlydfIyQgF4PKqVH1oRClZUSNGxAt200gHEzLKhjRjAXMYwIcM8YAtJSSlI0pAFgQQhIY0IQG2QUghIrDRhNg0mMNMSDGPaTufIgSo8BBEBEAh1ihCggIHQQgORKgRbQzACAhZwBf5CtzgCvchMU1IzHZAgGYkRAsOUFY854kQbgCgj/8I4cMDzJCQWnjSnwqBq0BpAjZkFECfwy2nXwEkScAqBJPwCMcAaLBUQbBvotB8VkZ6EKhVIQQGAKgFQiTBRcouRJMNiCpDmoEAGkR1EARQq2hvCo+cmnanyFTmQlgbDwLAQL9XgEAUvZEAHzCoEA3YKzyuIFBEvCCs1CjAC0vRrPLKeMbozKpx81MNABhACE24gQOcoDOwQhetDwBCmvZ6BI014Qdh4dgL3NreuIJ3Ng1g5Q70S+MC3iEC4UQIBE4XAN4iRAuB9RcyKXGp/DVAzESiwIwGsIBIgcRPDoByM6QGgAYsU8AKoQYDyNYQQBQAAgugwAsMvJADkJMJHkT/yAN2mgWE0EC18GiAL+FBCAMwQAAFMAAAHIYQLjRgAcvsAsZeYIADGBMbNXiAAACgAAAMiQsMOFmWb41r7W1hvghRwqXhwQ0nyOCafMDgDbDMv99a6wXMfoENGESHGwCABlGYYRMM9xD/0sQHporHDZrNbBoQMNfZ88av9ymdPmgbW/2CBx2wHA86PAEL41bI5hCiB8Do25AJeTdDAAEFKdS2P99dCCIegL2RzAELhMAIHS6IEGQUG90ImYN+ETGjhMzhf9roghO0kA1HJBwhpchCwxFyjS044Qx+i8QVntAvHDiY3DSvuc1vjnMC7QAAvLkBDfiqjQQ8IedEL7rR/4+O9JQYIgITz0wpGnA6q0aBAchOutWvjvWsz5gGQNbMFXjO1x1AWetkL7vZz472tKt97Wxvu9vfDve4y33udK+73e+O97zrfe9877vf/w74wAt+8IQvvOEPj/jEK37xjG+84x8P+chLfvKUr7zlL4/5zGt+85zvvOc/D/rQi370pC+96U+P+tSrfvWsb73rXw/72Mt+9rSvve1vj/vc6373vO+9738P/OALf/jEL77xj4/85Ct/+cxvvvOfD/3oS3/61K++9a+P/exrf/vc7773vw/+8It//OQvv+AlcQdAoCtsG7E1PJqBRo24Px7uV0j9E+INQpyhDxB3CP1RMv9D85cR7kcNaNRlA9gIc0AHerQQMyQSDrgQ14BG/yN/DFENaYSBxcBD9ycSCWgHiKYQ05CBZ7R+8MCBJtiAI3hG3YVGVdcQ9Qd/KjhuJ3gNgHAGgCAZ/jeA5kd5zQAxXKMxmrIpDuA2GEEDCzBwOBA+CFAD54YQ1XAAk0IpBUAAe8IQWPAAipMQknADDMA1D/ACUYcafbMQlkJHCHEGD4BLjMAAoAYPorAALwCBCDEEjzZCEQA+CvACWMCBlJADEcM1GVWCBSCFKgEAsKUQWQABkXaFEAA+3QQFPKQQk7AAMXUFeBg+eahcEYcAYHcSzQBEXBMBAsCICOEDmfiFl+b/A6zFEMiQAEmEDQeAilsTS/AQi+DTAC+ATwzhBQ8gUQixA7PINWCHAw1gCAxRBi/wAF/4AtimEIrQANSlEDrQAPzGg49XDAVAARAgAC8AAMtYAAjzKHaREQZAAbsoADwjEAbgiBRQA0uFELPVAJ2zNbi1EKcIaAhxBQ1AAQ8QADQAAIUGAUkAFcazEOaYWQqhhLi0BRRQWvAAHBQABA0BAxRgYjdAAQkgEBmzNQAgWfCABV4YAQMAA6i2NQVwcvCgjdbYXoVmYpJGAaVYLRSwABvJjxRAAPTDED1AARGQkmWQah4RMRAQKQKQg/BgBFuzixpxBwjQkwQQAy/glBTg/4YvQJNy5hEFUIRWmQCIqBBLZElv05NY6RFeQwljeQC6so1aaFwZ+T9HECmF1gCREkXp+Dn1g5EU0AAAMGxeSJUU2JAWWZA0dY2NF4wEMHKUEAAU8HPtQ44YYY7oGJODxASFBgD/I4/0uDUvaYoUkI9a4Ig2YCPV8AMQUIgqQQH05AgdeZAR0EQN+ZARCQHYtRAVeZESuSJXYI4GwIDwsAWOCANhhYYEkJH1oQMU8IvutjVign8L4AD9N5M4gH9cQJwJIJwrogBbk48KARwH4H/a6I4iQQheCACL1F5QwAANsD9W2XUYYZUUUD4LAZYJcZYPoBH1OUjB6AAspBCDgP+JvGZCjNkQdqkQNkABDlAE8HgNSMCPNHAygekAtoIaFECYhbl46Vih2vWQVsmWB3mOCZGOMgkPjuCUXhWPeqmZDeAATdeZgFYKhWaiC9EI9YYQg4CTCtEEFGAAD4BlhuCQf/OjCAEcDgABCfAJtSmYCIGRE6kQzJCOp4EM2UlpCEENWgN2ZwCfChEEOroAMzQHhuYQpyidCoENVikA7hcFFBAyDACdCdGdDsEFPNMAD3CkGeFeFAADkigKUMKeGmGV/BijSpSR9NmP91moYkUBjqGQOEkBp8lBAsoQBIoQUnCgS9lv/BgqDcmPBrCJbVmhFpp4WgNhGJQgHFqOH7r/NpO5EGagl5uYmVS6NWlaAJ26KZ6ZEDwJACcYL66ZEDGghhSwQfBwBBTwT7Dppjr6A/DpgLYJJri5EJLAjxM3BA55gAmhCfy4F96wAA/QfwGZlP1yii/UEGLaihHDBQuRjk5QkWd1rN7ZEFY5BBgZqA2BBRJjhRnRp/iKnAkAAc8hqGGJnxkRsAnhBOGZENDAj37glBK6EEgAqehKAXdJnMm5EG+BQw15Pw/LERQKqo1HBDj5kYjaoQoRmSCqqhB7U6+6OFujDUrYmC2aEObor02RpOGwAABwCREAaFZZhMYqpDoaDloTqMyqpM4KPBQgSANAAaPKEEq4TDWgqtTw/wAwsESKCg8DEAFQco8UMKZbS113QAEMUA1dgJP196YYAgEPIApgiwCSqBAVSbEOka/uaRdmUKR1Cg/ziRAD6xB8Cw8O64bwoARZmqyBmxAO+7IKIamEELZHyRDakJ3V45sUIADZoLRLoFUc27GLhw2L2QA0UARYoLWOObIxi6oLdLIzOTQqSw0siw1Ka1D4eCR6Wa0bUQZ4+rNeRYVP6ABBBKSxqaPwAAr86q9EKxFGqxCUei3hsIw0qhD1CnaUCmpxugTxkABg9wkRUIvjyrUNgaXbCw8Gqhu2ZJALcbagY7BKS04NIYt+IBJWuQOkUgYpSbrwkKzxmbeDurf9KP+/ZUCrfJuNFNCe8aCNwDQJENAANIq4Axqx4AKmGVGRQze5yNEIDECEnrq5jFcNO+CXHakYdfSY8XK6IdoQS0CVJzqPsEoB1MKGDvDBsgsPNuoqCnEFPmDDPiCGCsENDMAAwjGtxpRNB9GQbuizEBm88HAGRVpbxruk3vujzICiGUEHN9ofD+Cux3mMNOAAq2I5YxedDdEIEpMQtuAAEcBv08qc3MkzDIEd5ZujntgQ/Hgh+3TDPlCK7xk+ENCbphq0xPqvhIqKaSyWEIAYTKam63e7nOWYcbvAkdrAf3vCGaGEEoWxCFGvBjDHWvOpGXx42HAGTYADEeMAu2iqkDn/wqmbEEUQyazLsvqooxcCw4wgxsx0k6zpEDRAAdgFAD0MkuSohMFaxMAxw/AABPBpJEyMvAnxBY0KD9fQj3OoEFjaaFojGATgrjl6rrjcblvbtf15xPCgyp74CQ/wAE2kxu6qEJazvX5GAaG1EIWGt/CwmFyTwuPYYOCWA11WysMLAYajt2IZAeDGbDJ5lpkoAPy5X6mZEA1pABDYyBD7OTkqjQyBkR9SyQjxPTBQHJrMyZBHDWYKwqUrVacsoghhoESAwpr5P99jRTCcDQ8QAeJINH+xpY3GEGm6A9XgAF5TDB4mTg2wfsH8zX18EcjMpAuRlLgVMebZEEnwsMNa/wTZG0WyrAPamgAnSK4MQalgR76jKgN+fM4MobQEmRBKaLgKobRdMIl/Qan26ZjtKbchnMQJoAn/7LcMcZYQwEnLOLMHHAEjFw6yGKwBmrgm+zlkC74M/DkXDQ/ZkI7LwdEd/XiTsBVzXMoiLJklPQ2FBiCsvMIrMtYwDA9aw2bRTLkY0dMEEKdsNgAJ4KNqJdTD/IYIEAFfYNQMEQ9Ku1cxoNAYkY4GFcYAkKahcgAEgKVn/cUMYZUm2pARcAOspDUJQIHnixBTTAE2wEp/SqsKEYwT3cyHOo5w/a4hDA9bCgB9oL9i6dYYMbA8WQCdVasLgE3E6WAPrbiP3Axl7P/OzxgBD2BgjD2i20oHkS3ZhGcLN/Bb9seP5nzZDVGyqVrS3/OQnk2BFQyGtmrJNJlx+3TaGKE1UGuex4mRcB3bC5HEgpKkx3vUBEuTVugFellEDZGmCWxvUxsBA2cDzo3K3MwQDekAM4LHqDiq1B2+wkgBHpUQjPAAEOCE373ecxvXWtjHFQmw4N234F0N5mii2QCIqBgBZ2jfhp0QBgoAtRsOVmmPAA4PWRABT6W5BT54X8oAMb5cNLme5M0QD466MukNp2iHKa3CFNjL2slMWlMAI4cQrGrTDKHKEXDVCRGnEBAB8VziCyEEXHObKw4PULCMcG2VBsCSluwAFFD/2g/Tk2zlwBDgAPDIvd0MD1rAj0zKCBFQTI1g67d+iqdJ3aUw6l1w67fusDj0UjTphNMA3lA+3mwpClNZ5et95c4epw5wjGl6AIzw67ZulaoV5ql6l5HghTbguI2NkQ0gnGqOVlyzyXDud3/qA3TgCHTQA6Meo1b5ZHXsAz8ACSMtmUHQFlrQKf3IPhTOEN+D4SSnjXp2Bgn4A9k5cwrhowZLpcu46JXeXe95mzfQFlyABPNMXnJEnA6AA2UACYaABe/5bCZO6IDc8DP5Am3RBUygNVRpPsfZ8LxOAQxL3dO66E9YaAsFIlbpYVygCHeABNoYllaJA/oGGFtw5x2a/8TpnddKDxjuq94Lgcu5upiajhBkuwDwuO2oe5dONOoGcAR6AAl8gATm6ACDbe72szXpru58dw03gImiODIgPIugpo1RN8/hMwCWepYMkBDYQOvV6tiXsxDIUAN1P4wb3hDaKMFt+UkIscyNVheorhDLLsDNmokGsMnNcAPtiItGUH/hkJ1hr5Ip7hDJmokJANfaiPoJAbVuVReynY6RvxDf4+o67APLGD4F0C9Bnok3ZZUAOq3oLApGLvhvGMXW6oW3+wAD113meK4I8Rb22EIUUP1uSpyZSADb/OKOGo+FVtJx/3d+sHMBIDSS22/YxEk5QPVb8APhdAaslANFUP+EDGEE7NME4r2FOgAQjOANJAgPUI8XAQDomFPQYcEzPrA5xLOjmcNsPsQQDAdky8OBFYsRLJQDx0kcQsx4A0mQkZCEAGo0QdYS3pYfLCFKtDlwkg6UOH5w0eYQy5B4LR3pEDgwXBAtBb34KArykw4/PeFNIhLzhpZwBeuYDIpSh6iBdHZAAxluCJaCSMoGbUIQSZSHXXpgMyLF5pkd1Aja2tEHZMRrD+N5yQFAIQ4uYR1i83Gm5Zke1bRu5tzZ82fQoUWPJl3a9GnUqVWvZt3a9WvYsWXPpl3b9m3cuXXv5t3b92/gwYUPJ17c+HHkyZUvZ97c+XPo0aVPp17d+nX/7Nm1b+fe3ft38OHFjydf3vx59OnVr2ff3v17+PHlz6df3/59/Pn17+ff3/9/AAMUcEACCzTwQAQTVHBBBht08EEII5RwQgortPBCDDPUcEMOO/TwQxBDFHFEEks08UQUU1RxRRZbdPFFGGOUcUYaa7TxRhxz1HFHHnv08UcggxRySCKLNPJIJJNUckkmm3TySSijlHJKKqu08koss9RySy679PJLMMMUc0wyyzTzTDTTVHNNNtt0800445RzTjrrtPNOPPPUc08++/TzT0ADFXRQQgs19FBEE1V0UUYbdfRRSCOVdFJKK7X0Ukwz1XRTTjv19FNQQxV1VFJLNfXUA/MCAgAh+QQAZAAAACwAAAAAIAPCAYcAAP4AAP4AAP4AAP4AAP4AAP4AAP4AAP4AAP4AAP4AAP4SEvw3N/s8PPw8PPw8PPs7O+I2NqEwMEktLC4jIyQZGRkTExMNDQ0ICAgFBQUBAQEAAAAIAQGEBwfbDw++GBiELCxeOTlKQEBLRERVRUVtSEh7TEyGUlJxVFRbVlZWVlZYWFhYWFhZWVlZWVlaWlpdXV1nZ2dra2tvb29ycnJ0dHR2dnZ4eHh7e3t/f3+CgYGFgoKagYG7goK0hYWVh4eJh4eGhoaHh4eIiIiJiYmLi4uNjY2Ojo6Tk5OVlZWWlpaXl5eampqwnZ2loKCooqKmpqaqqqqsrKyyrq62sLC3srK2tLa5tL+4tNu1tOqzs/K0tPO0tPS1tfW5ufi9vfy+vv6+vv6/v/y/v/e+vum9vcu8vL68vLy9vb29vb29vb2+vb3Cvb3Gvb3Fvr7Cv7/AwMDCwcHFw8PJxsbLyMjMycnNy8vOzMzOzs7Pz8/Pz8/Q0NDR0dHR0dHS0tLS0tLT09PV1NTW1NTX1dXa1dXb1tbb1tbZ2NjZ2dna2trc29vf3Nzh3d3h3d3f3t7e3t7e3t7g4ODh4eHh4eHi4uLi4uLj4+Pk5OTk5OTl5eXl5eXm5ubm5ubm5ubm5ubm5ubm5ubm5ubn5+fo6Ojo6Ojp6enp6enq6urs7Ozt7e3t7e3t7e3u7u7u7u7u7u7u7u7u7u7u7u7t7e3t7e3t7e3u7e3t7Ozr6urq6urq6enq6enq6enr6+vs7Ozs7Ozs7Ozt7e3v7+/v7+/w7+/v7+/v7+/v7+/v7+/v7+/w8PDw8PDx8fHx8fHx8fHx8fHx8fHx8fHx8fHx8fHx8fHw8PDx8fHy8vL08/P09PT09PT09PT09PT19fX29fX39vb39vb39/f49/f4+Pj5+fn6+vr6+vr7+/v7+/v7+/v8/Pz8/Pz9/f39/f39/f39/f39/f3+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7////+/v7+/v7+/v7///8I/wD3CRxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjx5AjS55MubLly5gza97MubPnz6BDix5NurTp06hTq17NurXr17Bjy7aq6YTt27Yj7bPT5N7IJj1m7mnSbrbx45ureFjOfLmhfc4lAn+4vGGbE8VFLqfDsN2JNsjDi/8frJwKQjc9fEc04YF6e4Y9PCgbSafHOYbaPAQfz79/3vII3SPOQOEIREok2e0jTiTaEMTeQtFUog507w30TSTfFBTffAXFMsmEB/FiSYIElRMJhwQNKJCKtkxy30D57effjDS+BeBB8Qn0jX7MmbBPNCcwdwIm0bC3nIwEeRLkck1UJxAvS3pwwi0CxbfcCRnuc0dzPYAokBxcqrfPNVYuR8hA8d13D4/M7ddGc1PUKOecZinXw513NoLme7Uc2UYb3MXXQxvxnRCOlT00YdCO+lVRpkDdHOnoct3sg2gP5eyzB5OTyjgHk1QEuZ86kjbpgSNVelApqR6YEIWpT2j/iugcdNZqa1fKNaffngL1ieQ+QV4jUCxU7vPgQU3GKdCm78XnxkBu7GqpfAOxZ8ueZ1LYYKqT7DOFB4oKNIm08a26XHaMChTjrey2a5VyTwwjL4rT9irtQGBK2UQu1VZYUJBiUijQsQO1miqH59y7jye7KqMwIeBO+6JAQaZqLpIV77Ouuxx3zNSNBuW4j68GWdKDkcoS/K+/wL7nZMHNUrtPOApHGpwjEQ80rqIi8zrtxQSJvLHHRBctFMga8qlwQcM4mXHIHuimrpSpFrsPL07Gd61Ay4lJxa44n0BQk8/Gl+0+rFqMtsIi7/ir0XDHbRPSQSuNZDn68RtL1h5M/yGKQZUst8g+uQRZh0B4LNetJcwJ9HUPk/gmqLCfgn1kgW96MGEu0SkTn7Llro1kz2ZGI/fpqMP0ZhUIidw0kveUuZyynC93iEGZMxfrQKYuZ6RArHoQqzqyC7pP2Lre/qWuMsZXYDtsV/j1y6lXbz1J4TRR4EGLsC4QFJUY1AhwTUgtkDJNNDExQco8kejfBTlyZxv3yEGrQOdM0UQtA81x55lQwMTx9GMN4EChUgSxBhQSNTidKctbDRTIIbwnEEM04VnXy6AG24Kzt23wgyA0TAdDSMISDsYWwjOhCle4FxWx8IUwjKEMZ0jDGtrwhjjMoQ53yMMe+vCHQAyiEP+HSMQiGvGISEyiEpfIxCY68YlQjKIUp0jFKlrxiljMoha3yMUuevGLYAwj3AImxjLa8AgsGAk3yGjGNs4pByMYyQSi4MY60gmOcnSCHfdIIzzuoxhqUIcVhMCE8BVEGVA4AhSKQRArgCOBVtjHPYoxASUUYxx8zKR4/BiFCkiAAiqYQAaY0EgLTAAGE7DAAy9wP4FYwQL7OIMFNIABC9BAk7g0Dic3oAP1QCEDiZAgBqAwEChg4A8CwUAr9/HKgUxAj7mMZmw4aQEv7UMCRRCIDNJIEBjAIJnLbKZAnqnFclhDmrXiJAUK8oIajHMJBXHCBMBZSmdC8yftMB3HznH/hhpsC51yUic7b7mPCRyhIEuY5z4uEId6jvOePgEEBW+ljjXMwAWkBOgb47iPKKyTIC8g6AQmkKB2iMAG48zmQI4Ay3EiIShMoAEba9SOOFzUBS4wpEZrJFCQitQCMMDFPnABgwqQQiBMuIAUzqGOKVygpfvIAQXqIKyenCMGLlDenNohBxrgFKc72OmciKACgVRBoQOJAQ7GmQQZ1FIDItDqPopwgQxkgAJDqMBAwPECDVhgpjbBA07hKad7zKEGX/3q4cRaI3VwQyD3OCdBwJGpgpLyGn3wxEHAAYhE+EayA4lGL3ySBJzC4JFIccQO2JiEQWzkD0pYiU2J6ZF7/9ThBon9qgysydiOTSCjUxkHVnH6hqRIYQMHLcgECJuRhKqECRiYwRo6cg884Da3XzVPb4v226rQIbE5MC4GWKlc5mLEuSg5hwV2xxE94AC7iYWBPrfrMRxwhypHyK1mjyKFCjChArwgyHIfco/5aqyyAkHvODB5kHZkySDgQC1CtEFGRGyAXhgBRA7gm1vz0peP34BBbmlrlP7uAwYjSNCA96GCifaiAsLCRQVYugEKnMEOE9hAdF+UUDyIIAMamMAZCBIJFWCgxnQUiAiioAK7Drkg5dBBBXSsgkcIJAki2IAKVLBMigxCBxzGrpU/rEk5YHe3/NUrNyYQ1neOE/+4+5jEBoT6iA1MQA6auMFTjeAJNVQgCAmuQAWcwAtMBAED3uNFBWQQCW5IwQJSEAgFLLAEUcSCt/cI5RmKgYgYWEA3c9DBBpjgBAFW5BA7CDN2w0vmWgECVZB1CWBDQgT44iHNErSAGtxsWYLImc4bkANkJ1DWBH90CRt4skCOQIHi3ICjAnmCQinQ5oNY4QL7FYgKXrCsDfA2IooQgqrhK+xW00kFKN3HDSowa5JQgKAluYaIsWsEXAukk5ooKGG7q7M576POo9X2DQbyhgsYuyDayEAgCjqDKTh8ClDYACMpEGmEzOCbBJFlt7/tkEcUYdzwjcH6zE0jFbiTmaz/ZokOJjoSNXBYviXW60BkIIJzrJjf4vJ3nVcxkBUMXCBxMPg+mIDWgZCXAsTe8pZZ8FgKPNAgMDj5QPCgAd/swdsSiYQRQM7hJJNcTibfSqo5HMmimHgg4pjADVY8Ah0QJBA63wDPBeLzgQT9SxcA7T4CkYHRjgDQB3F6QoQggoIQfeMQscQRXsB1+MJg7l8fTzR0IAIR1EAUYd9HqAWyChjQIZQxIAUuZjCBEZRdIOAwwggmIANY72MGgSiCCCYQA0UQ5A0rKD0U1FOE6eJvCatfgbL3UXvKT2AGOn1IMRjP4XQT5ewDcYQFLkDYIxhVIO2IQdwhX3egC70dtAeR/zZEwO19VMECWkXE3AWPEExc4J6TqMBLNYX1hmgCCcxvPHbnH3nx4ALpTjAFM1ABE3By6AUIG1ABRQAFswdKUXADGVB24jB7UGAFnuZa+1ABFjACTuAEI3ABgCAQc4ABQ2AF1sdqI6BSE0gBSkAFOnABKZcBFqACT8AEImABrucQVjButvd8MkcQVaABhCUOI2ABMVADI7V9Pfdz+3B3AvEISHcDNEABIsBIApEDFxADqXYBT8Z+CFEFFzACORADWZgdV8dxBSEKSZB/+oddctV/x4FiCHZcBqhQCOg91mABIqAeNFB4czUBLmQDHFUB5ScQLsBROwBtlmAHApGCAv8RBBQwX3ugTAKRATMwEO0wAhj3EBumanAWFJoAUcU0ZmgTBTIgA1GQehMiDkaQIDc2ELEQWxbiBDJAA1LAW3iQAzJgBKa2D06QbwrhCUagVuWmLsmVEKawBGzYhrl1A+0Gh61RDpQoYHUoEAj4WI2YclQgcxOQBMXwjcWwBxmQKRXwdBq3D3JwAUeACCTiiAUlii9wifuQAQ1VTB/lELwAcjHgQp3BC0wwb8zIYb4HjcdhChsQcAPBAul2gBvgQiNQbVYgcxTwVBZQkRZQAY9VAae3D3WQAfiiAhUZA7HQiCplAV2WA8WWAfflSj/YEFXAdfXIGavgBAAZkCHHjwT/KRvSuFjOlAQJZocNORAPORAROU7sVRAaSRAdaRCHMAIc5Y4UQGICAQMyUIkryUwtyRDXNW5uxxmTUJM2CV9SmZOz8QIj4CVWgAH7xZAOCZEyNwQT8GATk5QDsZT7gAcjKRBnIHPumAMTgI37cAgYUI8q2UhZGYz6d1ScEQfLGJZf9QKmQJbIIQpUKAVngAPvNxBsKZRuuSIUGAdJUAHeQ5cCYZcqQAFLsAZRQHskKRDcIAET8ARqYAS2NBCFSZSHmRBSoH9etxl1AJaO6QIqJZnHUQw4MFJcRhBw8HO4oAIJMgQUdAibyIqz12JphYECgQnFNg5JsHoiYASVpQOn/wcOsld6FadtlkAQhxADD4FYjScDJKIZfwCcYdkHxAlGk8CMe+AVUeACGXEELPcRiTBcwSlT9/lFUMCMx7gVR+CHF8GaJBEJBBqWG3mgWnQPN6V/MKB3WdGgGTEOaOgRopChAQkDD2ahWqQINlmhPMELh9AOcOAEVnCiAjEIUBAFyScQ11AFTnA4HjoQqyAFTxCCBKEJUQAFiIAQiCBU+7AKLxoHMkqjGdEL7hmQooiiWPQENsmEPhEEI1UBN1gBrRQNRjYCIrBjA3F+FDACVIgDDuoEFyACRfgCDLYEGOCUGBAD36YCXVkEXxqmxbgR0bCVbQiMWJpF7SADYUmKPf+xA8jlG+UwgFmiAiIQmfuACBSwVpqCAUzgG+qwAxngh+lYj6swAWt1DxdQdo4wAvBDEGIoEEOwAUbgG+dAAxXwTxrRCyTaeIB3qFk0CI55pTqxA0U3DhYQSXyXngOxBhjQIDDAngRRedqmqUD3V/dgAZ9oEK+6D3BZIhagXRphDVXahnfgq1rEBI4ZAwzWqNDmZtJWEO2AAYczAecpELInaUm3ZSOwASO5lxQAA04gpfuwrUPQrtfkkxrxDe8VkDMQn+Y6ReowoQEZqDuRiMoVW1BQdPtwDhnAiBMwlkLAUWr3cA5nBepRDnJgBCOFYQPLagVbECLAfxdRDmMXkAH/+rBStAfB6QK9yhMWK2CxpQgZkKQDQQUX8EgxUGzYNwFCMJXQqhDqQE4FQbAGG7MZ0Q75BV9BMG4wUFU4W0VLsLMvwKQ+a7CVJBAxMAEYGAcVUG//BoMDcg0xQAEccggXgAQTwg1CUK5zMAFEKn0sSrUwK7MUcQ9hC1/msZthlq1fC0XlILE2Wa86QQRVm1HjMAN3VQEXUARicgcTcAETgAEiwKhxMGmfOwLhMw4ycFehWwNspAJtpnow62ETkaBiSZSNiVM52LhRZAc7i1MN2xPaYKkD4Qn8GAtxIAeAORD3EAhv0IPw2gdv0IvqMgdxEGAHYQpVxQ2KORCiIGEU/0EFHNapBMGYq8a7VYQEv4tTRFoZZ8BhR+Cwv5lbXYa+TgQO9GmThBsZc5C7QYBgBTGfujVy9ttEZra+LgADuAoZfUCfOAC+BjGgOCW5BdxEW4fALjB8j5EI9FkDHHoQEgoDVljBTqQN+RuW1PoYlQC5LjADkLcQotCbJMxEcIDBX2U+X7EHAIwSpqCo2BUD2UZd9zDERFzERnzESJzESrzETNzETvzEUBzFUjzFVFzFUixJVpzFWrzFWozFXPzFYBzGYjzGZCzGCiFu8PUCMRADwJkDO2AERoAES7AEHegEUiAFb9gVFgAHK1EMu4pTMbC7GXEP7VAJj3DIiJzIiv+8yIzcyI8wCIPgyJI8yYs8CHdAyZg8yYqAB5ncyYw8CHUQyZ48yqB8yaN8ypNQDKe8ypu8yp6sCHYQyq7sycUwCbPcyaAsyrdMyZy8y5N8B3Ugy77cyLCsy8PMyIpwB8Z8zIwcDYBVCZiAC7gQDdpAYQRhu4nVvWtxAbuWEtpAqF8FA3mMEfegDkyQA0OQzuq8zuzczu78zjcQA+88z/Q8z/Fcz/icz0EAA+icz/7czkJwz/880Oos0AR90DXAAjNw0Ae9zzjA0P+8z/0M0f4sBM860RRNzwEtzxmdzxb90B09zx8d0vUs0SSt0TAA0ic9zzCABO3wjAjRn/rlFtz/nBLi0Im5BQO3Rl3q4ARLoAzRENRCPdREXdRGTdTKUAyDQAOBcNRO/dRFndRL3dRQXdVOndSroAQ/bdVcjdTFAAg0MAhdPdbRINVhTdZorQyDsMZijdZcjdVaDdRuDdVwvdVzbdVJrQQwYNd3fdVffdZ9TdexsARJINeBHdWDzdeH7dVZrdiLLdRJHYuO/dhDrQxLYANHcA4O2xCKm1jUSxT3QAUwoAIycJU3oAnApwLdwkxJKwPlWhBVMNozsNMCwYVMoAJiSLYgcQ5DkMYUO8g9rQSGTdmQrdSwR9xePdXIXdnF0NjDTdxJDdZtvdxmPd3LHdRqzdbXjd3NHdfX/13Xz43ceb3X4f3Y0Q3Y260Mg40E5X3Y6r0Ewp3e3T3Z5l0Mkt3elG3ZOyAFRgCiMG0Qnf1VOSoU6sACFaADTjADFzAERkdshG0K9+BpOJDg6oiJRZUDtIgB/HcBVFgEQjABFTDgG9EO6gtfLErOwY3f7q3UTL3dZc3iVP3d863ifX3e1g3dLH7j1H0IMhADOp7fMy7jzu3iL67X9L3YNk7k783e3x0LTBDfQu7dMn7fRF7WS7ADasDf4HAO/10QU4BdODwUSUABxFsH8lrb8igQTlAB5kMHGMCIQ1cBhhoHGKAHta0C6qEOmggS9+AEHEbBKO4Ehe3iZh3j1A3jhP8e5DIu3fK91D8O3Tzu44k+5IdO6fJt5DR+10lO6E7O5NTt5IMe5Ue+4lSu5FeuBmvA39/A5RIhvrnFqEPRjexUlQtVXAPxnQVBlQIhAQu6Dy1c2xsJfR0h09glrBdRzoKe6Zpu3ICQ6Evd7PJt6eL914++4sqt5JFe7Ssu7UDO7dOO6fLN6JzOBJ4u3qCu7G4N3vJd6oR+6muQ6kaw6l0OhNgFvURRAbY+EEHAURcQqBWgwUXAURagwfte290sEAX3ETsIX0mw2RaB7KEu40v9B4l+CDRA8dEu5Ycu7odu8Yb+3dk+6RqP496O4+C+6Ojd5OSO7mSt3iuf8aMe2JH/PfLf7e7vruqsHhELn1hEWxQfWxAywO+BinMCMQN+aAGiKAMYV9MDAQdCxxFxwGFFEKKF29NIUAwVf/FZj/GizvItT+3ybfHQTughD/Ne39Xqnugnv/Ep/+nkjvVuf/Vmv+40T902f/PxnvMQsfNfNc5AUQM1570bWNuB6pcPJgp6KBCmtK6WoEoGr5xPrxF3kLs6sK4aUc5PIPdhr/Wbz/WVXvf1zfHTLvZVrgyI0OPaLvP2Dfrbzvr1vfbTLvpxD/fm/vZzP+Wub953j/fyLhHvm1vYSRSrQIVW0AcZ65SETxDXMFJVYPxId/QTIALF/wSgpB5MLxBOzxGDQJ83/7C8g9wOmU/7Hc/5Mm7xnj/tJY/kYF/+NDD26X36kk73Mb/s6f/65B3ubT/tTHAE4g/ktt/1ABFN4ECCBQ0eJKhMWSwlS5QhhBgRobIlO9SswZhRipFv5+7tAxlS5MiQZ1ycRPmH5EqWLV2+hNmSlwwKFkbIsbECpAhFI6PVqCnCipQZIC2cWTLBwgQj50KOGCQS0YiYVR/FQJmVhrKqXe+1e4KkmESyEZUVO0SjT1m2Bs+mXdtWbrSzqxo+nMv2LCAag/LqRUvjz1+5yhLJiOGXMNmzDB0uNlvM7mPIEs8qgUG58sG9fTdHZnJk7OeCZ5mIJe1W8t3Ubh3jbV264v/FjBo5euwack3Wk3ty/wYefJ+pOyLHUWAS/Khw5iA1YeXtQgau5iu/QkEde+Bbtdq3F0PU3Tvd1Zpjd1as/Wz4weMFGkacXn2x197rsnZ/OTPs88X4yp8vtNH6cyK7+Sbj77z63KNrttpq26ijj36LIzo8qsMQwzguWGKcfXBRYQJwlDsjw996kSE6F2KwxMSQrhMtP/Bo2EPG8Goc7z7zWkMvxxnjyu+wxGRcsD8EGdRvx9R6lNGJGOdzckAj8bOvyBwdfNA2CYGTI7riXAQztzMowKCCDEYgRbjlwnzpJxVh6AlMGKXs70YbaZTxSPv882zP8IDMUZH4iKSSx/L/EuwPMyVJY9LHKPd8dE89qyzUPiyzxChC3H7rkrc62AQ1JkXwSJM5J2IJlaVvcHgTkDDnvBNHP/H0cdL+/vMxkRoAtU/QIX20cslDkSxGUUQZ5RNAAp9clk5hbVWwUvUuxXQNTSfMbY7o5ki1W2+/BamcHVR84VM524GC2fnC00PGRGhot1ZpkcV1T11lDXRQYOfdTMdjhTXWx3odNcLZz86K9EB+Dw52WouqhfA2bLuqIzo5wMU44wzVMUJFF9Z4tZ0o1O3v3XjthTfPhStrtOQa8O1V3z0bPnhYGQPec2BIC4aU5GdXZplmHqmF+Frg7rBYY6WX9moJj6sIOQqe/3OlAQ8ZFala5UVr1tlQRWo4Od9fZwaaMH+JxXm+rgkswuB+i3liaoW3ZrhslokuWmLg8IguDqb/BpykJzyGImq558Paah8T1/rft9dG9uuwYx57PqFZttnHtG/t08cn2t4zbrffhpbHyxnFO+8tf9sjOpADh53pKTxmYmIM71FH6liuznrx3iW1+6+Wva5B8fwemaHy/k5f7Oyb9xO48yo/332+uKuf2/F+me83ddU3ze2P6EqMvXyMTVLxiHbkzN0I7BH/HX7js49e2ciLZ5Au5JU3lHvhMx9PsaCXM+mpJxbUq0z1eLG7WPACYe5bYDR64QmBTEIgnuiFBCkYDf8LRiMSuIiGKSIhEEQI5IPK8MQhkrCEuCRCFNEQhSIEEpdBaCIamFBMHQRSw2hUAkdxEIgeKsEQIjwEiNGowwgjocNonEEgSfTgHARCBYHI4RHReIQcpiiQNShiCTZgAkaOgBEmUGENUwjjGoiAkSVMQQo4WINHogCSM7QIJoMYn/n02K05vEBFQSgH+6RQhPf1B2t34B0i5UU3zEGuZl+bH0RWIRBSCCSCDaTkBTM4Qbr0IXl22OQGS+hBEOJihNEYpQthKMNotPCFokgEfY7gEB7i8IlBtEQPf4hLXQrEiUiMRDEeUQSH/HIOV8yiQKpQRWRqMRpR4KIMFfEGgUD/MxpriGUSXuCQJ/iyhIj4JROUeYhoDMIKAhFnNKpAzkBMQSBIoIsUrECDM7gzGkWo5mD6YE18PnMweujmPfPZSmvuYCFFEEIs9imQfkbhn1AQSBAGulCB+jMa7SwGFGqwuyn4pZ3vHGc5zxmNdK4Tlb8M6BkQUYxByICb0YzGNKsJ02Sqk5lYdOYykfgIJSCBib+EYiR2GQ095NKHAjliLXMokEAYYQdDwUgUMDIFK6zBClOIKkaqcAYpCEETHtEESFYxIpgcIjpW2GNaw7QHGKgoB2StDjhWsY97WGKQjyjlKV0Jw0TMUCCq1MRSy2nDwBZDETNApBB7GY0jKnaJ/758ohKZqFMrKuMRVqjBHagYDSvGlJrPFMgbpPlZiDbxm790gi8PsQokGJSkArECOQ8x0nROIRAXtSc8o2HbaPyBn2eBwgwG4VuGCuQJ7SJuRR1K1IDqYKC+PYsOMvuEGumBn8+97m5v+9Fo6Ja37bQsDBIT22jMFp3ePKlx0QvOmUZDtDFdA0OG8JDO1pSyzYQsMD34U4HgYRKsVYIRg1iJxTKRh4G9ZTlf2ddW/tUTS4BBgFFpwrySkMIarKAmMRzCs+ChL5gMoSULabZYQIEII64ZFAhZqxVqL2jBa573IJYpvf0GEWdVa44xhIi2RqcG1zBRJc6AuxoQUqXpjf9GSksoU9C6d7TKvGkWsWbNzj62iZHdL1IH3MN4NbYSxehDDBBpYMIqRpGqhKVfo6FKU1pYGY7whBKScMpRYnDDHbQziE0h4miAmBTFCEQBhfWIzObPssmzH7L8lxfnuacYEGYkZM4S6EQzqsRDQPHbVJxp0rU4P4uO8cNm/D3biSo6UNNxqoFTCejwZgZzdRHupHDiq9XADjIi9K0X6eLmAVrQyMq1ofdX6beBujAA9M6jBzgfSiPp0pyWdEZpLSlP58jYwpPxqK1V49w8IjpUUHW4q0KKFEUnBqJg3xSIMMnF1YCJeyL0u+m3p2bD29aGdgSiCRVpsyFbO8rmd7//621tKAiB3f2BwhAObqRqUyrgks62to32m0hEZwrixnhLijEDFcUgEulWOK7dLXJ5T+nhjPY1saMdb3zr+9MwPnbpYgNwXgv810sqscH3lPCFG2oVDTcgzLEtam2TetXRkULGlS4SbdzgTYdgHxVC7iOWU33ku/bRwPtT9fxEwuXWFjpgZN4ammf95paOQhB6jqwo6JzaJ2f0tRkd8aJPPDeYQPpK2qGNpedYHDl404VirQ4quN3eJTcU1+fN7LNjTvH28Tr/TBf2sjQ6gJCu+f+0bsC0r71mbfd8zX4O97kshPKFoXvdud0VT0Rnjnrve1rPMQRyXWzwhQ99zQhN/weR8x7r9G78ym2A+PNEXuUvJr3YT9+WsgP/+JKORef3BHoWJ78tprc+6ole9BnbvSukiE7hRIKJL8Vej+1AgsfIZyLcVUHtvZeRI2rge+BlnzEp95Ejhm/oSXzd4ZkvPX+bOcwzu+drnuh7v/mQvrcDwLhbPr1IPe7zvqrghfAbCW3ABPPTo3tgAo9JusFzvwyyOinKvxogwfprwGPbPGHRP/rLkUmgAcnDuQeECMtLNgJ0PmeLgh0Qwf6QgiDoQYazP8aQu9KLQAlcvapYheh4gpDohVLTwMCJAo9xAvZxv9x7G0I7wfmQvy00uRS8Pvzbkxbkvxg0QBKjwYkQQP+yw0HGO0Phib4dwMJe+8E57LXRA8PrK0Ltow3uU73VyY1iiI4qBIkq6IUoNB8r8JgkgELhwB0rAML4M0EZiYRJ/D039JFIsAEvVA8YlEFLS0POWMPUaD5M/DQp4ME9+cEg9Dmgi5YhrLwj9MMJjIlyuINBwMVBQARYQ0Q9ggOPKQJ1GDwrSMUS5ERh0cRjRJaxe5sVRJZkNLRKMENnC0XVYEbSKMX+cEacQ0VWRJZurL481Is9vD5ZnMUkZI5xWIJeLB878KPo0AEPYT91IEZvZMFNpER8vERtDL5eg0YkkcZPrBtYVMNr/IxsNJRttDRwnI8pKMbFCzqCtAxzPEf/QMQQeWRHwAmEHuONG+CGYXzI+ahEZ9qTSbABkoTIhOzHfjNJZVySgHxDB5RIa6xGiEBIYVFIhpECHbDHt3HIniSdmnQNoeQMiqxI8GGOSkiEjISdR+BIreCKeazHfETJ/jDJqvQ5ovyOnIy2qzQ0S6gBgdwerSQPg9yMm0QWrgyanQTKaPvJfXxFcSzH7fPDo2zE3DCFSmBKwHkOFZmOYdQBXqBKGfFKuMTJlfyfwswPsBRL5JPLyDDLykDLmlFL6JuCwNyTyxRMFHy5maxBo6xLWswNINtLwJmJjstAqcRMH1HM+WjNLyxA1rwBrDwPxozJ0iNHyCRLghAgzywN/zG0Ns2sEipYzZScPN8sSrqsS7tsDiggzdJUGjeJDjgZvDMoTtc8SRmphNlsnNjck+2kTR6xTWpEzvcYRWzcHJW8zcKIhSnIgc1UEOKET9gEu/KUDeVcTiS0SOjEuFVpleq8Tqu0gSP6zhsgUONMS8REOfD8yrBcTz0kSxv8t/Q8zAcdRyp4z+EM0J+xz/fIzVjEz/z8Q6TsimtoQv5UmnEYl+goFwCdTwE90P7YzhjNyg6dNAUNwBlt0MaUtMhEOR9djN58zMiozAPE0Be1tCrY0GXcTboAUrMBTREVzZh4ThQFF47xmDeozhwAIdYcUO00UBnpBVcUFkd6Gx1FEv9McFAkedIwHNMOHQghrR9nw9AuVZAq4FIfedMhNYs2nbsQFdERvUuWqIRSsVKMuQcleJottdP5qIQv9RE03ZM9ndMCpdHzwAQb4NHm8dPKKwZKZRA5JSALrbxYqNMqwdNGNRRQtY9O5cNAhdUpbQlF0MtDxZjBURHxyxDcOYM8jVQbgAMwDVY9JVN6wdFj285hzY9M3VSzcVXL+NRiJTsKNVZSJcJURVVfnQ9WVY9nhUBRq6o1OIMzEFdytSqMsAJyPYNwrQKtMldB7QpxsFWNmR0VqZ157FVVNZRHVVZHvYF+7Q9uNRQzxZxkNTRmtVbL8NYajFY4FQhRbY1KUgb/U+gDGgiEzaykaLAzTuIggbAEEBIhCyOlVZqhhajTGEqwAxOsolqsI7IDC1qiWKiCGAChOrCgmtqs+nIma+oiz2ovlWKpHHgpdSInczovosUtkIoGKfAoe2qoPVCCHLAmIjCuGqGofqKuaMCD0mooQNBarq0mHdiBkcGIG8AIJJCCNYgCJMCIHDjbtJUCJcCIHcCIJEhb/SRRl2ACeZ3Xb0Gf6FCfeVQDbXVUG/isPbGEGzjcbZXWxznWg8jYS3Igw/rXjd0gPBPMNpuwkUXZBluzV1IEZdAETcVFG7IlJOKlo2KsIIJZ/tKvLLKLGaAv/GqynmUyazqDvmKvJIOt/0HohdZ6iJJip9wSiI5KWuXSp+x6AjzAjCB4iH6CguoCW4vSg9ISgon6rWJwAhkYBO7yLr8w2tdC2vBNrSbqq0P4JSiQ2Rsgp9uNMmfK2SuyMqAaIUegAyvIgc9iWdX1PaVKMDRjpRbCICR4jFH6oGjQXDwLpUyKBsnNJGVYWMaIUlilsf3sW9jpoz9yil1VhzXQVs014FIK4L9asJQlLEBI3HRSLNX1siy7Mv2KhJxiJpZS3CZ7L/c1LSQr3yMzr9dShioAhCTQAeLVruNtqMFILqytkST2jxgAIoAqLuptLuyN4u+agmKwBBigJt4KX+EVKfXK4d0NqBt+A024gf8h0Vko4yz5dd2gYqIWrgTmXQJnUtn/LWHPZTM60x9NSII5yzCNXeAb4jM9IwuI5ZyENYtYwF99ZZhFJlY4heAmfY8JpuBts2CSWAVwu2BvYSu3gqvqwJ03yIE9G4geVjIkG+Mna7I4eARlUAQrMNv7cmH6dWE47rIdsoTEBSqBOIQ73isQvrAEPotJIGBeuNxB3kxSBsDhEZbEXQNDM+NmFZ4IFkWBndBlO2Rnw19SVpBtfmQ+rUFqNgtKrmRZfRFe3GRQ4TEVsYEqbY5QHmUZcWZ5NlsxbdxGelzAmGckiWZEDucIbVhwjghDHth81kMrwAFuNh1vntR7Zhlx/kz/QK1k5mwJTLCGdO6WSWi1rJgBVOFgOIhnH0lcNZARTLgBkv7mSp0PTMABlM4PTzhjf54IiE6IgM4fgi5Tgx5HhFZonGNoxoVkmk7OPpzoWEVHkcCD1MRoNiG3jkO3jw7pPTHpX5JqHKBqoBZo1SDYaGNpl84RmJZmRhNq87TmmaNWrtHpUuXpKlnrhg5qSW4QiS5qvG1EYVzqUIkFjjO3j+PgOMCBjJXqG7jq/mDpwV5Vh+61re61wjY0sJZpzhhr8ijracXmgn5s10BowF7ov07pHInshCDnuRbNe0ACu75rMLkGG3i6efRrzSZswS5pq7ZnG00WH2FsJHFsNgXo/8kmxbN23MsuDUXmbAPK7M5uVbimCLmea7pmiUE97eAAB8CbTj1g7eEObMMWFk2QbePO5j3RbuzmEVGIad22z7PgbfSs7JwG7oQQbte2tDOw7oBFbE5F7tCeazXQASgABxIlhdd5bhc5ByGovXmUg/h+bfCume+e7az+TcXuNwVHElHAgbAuvcg27/mGDJyuVm028PfucGQ574eub+Vebm1Tgx1YAmsQB3WwnXMw1P/eGKfJIw4u8Beybdj2EQh3awavaQf/Hx3PEQmn8MKwcJsOVd/G5/XeDkW+ARtXEPh2cvl+a4eNa6IucRE9cSUoBm4YB9PeB1EIJBh/FVxFif+Lo3EciPL+MGMEfxsgl3Ie/w4fRzk3Vw8hV/L3KPIQP0skjzY5h0NYTnMPD3QQx/BpHnErv/LlzPJi0AZxQEo4eGoxD5N6dYETvR11qHEZWXNNzwE2xxw97zc/PzZN6HRDs3PyhnPJLnTC0HC0vnO6iIUzaPIqgXLuPo/P3g77Luosj4Vr2O92mBDnlvTqOIMXUAJhjwncyfQcv4GR2hNPyAFnx2qV7g9ol3b7IIUJf/VILm8jdzQ+T+y0JkJZH/TtqfUdzw9cn2QST/Rq4XVfLwdgBwl1QAJkH3bhGAQvZw7cmQMc2CDvxoFrNxRrX3BqH/hoN7RsH/Lry/NV/4v/Vv9tZ5P1fzedibd1HlH3Km/3QH13cIj3idH3e1crfr8Biq/2gJcRgr/4DX92hEcShd/2hqdyiE9yiS/5KlGDm0d3zz70jcfyHVCCXvd4ed8H8hP5cCN5kx94lPcRld95tRF3hnX6HIF5VH/6AAL3UI/6RLZ4A8p5pSf0KU910GZ3n6+Njv/4kMDAo0813KEDnX92pt8TUXD5q7dsH6F7gT+Pqk/33Xb4vKD5Pt/6Gox1uDedNTD8wxZ7Q9P1iUZ7on9Ctm97dXh7G2p6uZ+PvC/4UcX7uqf6HFh4vZD5sY/TrP8fUcfNM7ABy1cQxGf9N+97Kk9uRDf7UXt8bDFE/8nPMdypgxt4/ZPXe2TR/HT/e8BAfcAY/vwwBdCPeb+fedNHuePXQ9X/fUt7A99feUbJ+Nmv/bq8fd1HenXo/eoXFgnXqbnPgfNv1eL3VOm/P7pXf/tY/tCvvNG/aegPQPcvVTVY/SqBA+wHiGgCBxIsaPDgQGXFeiVZogwhxIgGla1S4lAiRozKluxQs+YjyJAiR5IsaXKkmh1KYl0DV67dvX37xi2RafMmzpw6d/Ls6fMn0KBChxLNeU9dnRuYMjKdWEwUjipNpwpUKCqHVKpTKTZ8qDWjQkA0Bn0F+xRr2abKTOWIQTatxooX4SJUyHAu3YjFlMDAm7dg2LF/If8qi6XGxtLBBAvDUaoY8MKujwHL9Tp5MUePJzdz7hwy5cqWL2PaHFf0NOrUqlez7nnUjuPLVZ9GlT2blI6strn6fRz47WWFuHXvZuvWdtXKsu1KRh5tb1/Lk387L7wG8fJYcWIHj9zbt/LqmT2TL49SJUtxo2VWStT6Pfz48le/5t4dKvHpxYZXX9W8u1jA6cdfdcYJGFx4+t0lnWzQfTcYdchZh11w2tnnm3cMgmeRhtONB9IZZ3xkxUchrnEGiWtUMaKIKJpXEmjpnQOTTaZUMh+OOeqI41F3XIghVFRIuJ8OQkro34N/RdgdbkZKiEtbB06XIIYLOvccX0nmteT/V8XEEk0svAhEikC8xLKWFYhNIpAnvURjSiQCISJQJLhEI4oiAvUhUCKiRKPJW3UUdoZSgAoUh0B6WBJNJXsIdIZAdazpiB0CGSnHI9E8IodCUDSkRp6KvCEQFAJZkUg0h0AazRMCVXFININYIRATAklBFiBTVCTDQ1EAEk0fUQhUBKl74iFsNMRGA8WveJSaLKnNQsHRDR8ZEcUaURzxUQ4fISFFtkh8tMNHSYArhRIvnheaODOSZs2O8co7L1E93rDobvvh4CSTRfb3n34B5tvklcrgosNxBZkJppjRkBlNm9H04olAa0ZT55txRoMIV0kog6eefPqJSaACDaIJ/6OORoNoNIpeXMdCRjgkaTSOyGGpQJhqenM0yK4R6qisPoqqqnu94NCrsc4aDRK2khXIFAIdIZCvwCKr7BN7KLMHEmNh7WiwwwqUtdVi9/xrH1II1HQ0UwQSyxQzLFprNEnLKpATQ6e6qs9A26qdDbBuinM0NEdSx6OJLtrooSajDOhCODjU5515AssnmXDKSaedE1fMppurkGLRwwsH96G6qZ8U4zXt0igTFPDSOzvtsx+Fx71DkpIDv2mV/mWYCilSZMSeR2MxxrhoPGc0lIN8eawoYxKIfzk85DLjKye65uGJFx7n4HXn/EgxdsxAVt8br9pqNGfMicjSdNu9NP8SCklxAxVQmx3FH2VHowPV+he2/5FKgFcz2A3cMkBl8S8a+mOaQKZAlrtFQ36wQsSqnFARHbyPbwL5WTRE5arx7cx7dYiTI+jAkCNcb3Eq49nJokGySImsciG7mJ1w4Qi+eAxibjKexXABvIb9jjDFEJiEYvGGGuBrOoBrooIANiUOFQx1qruiSFjnOtLUrote3NHtbGCJQ8TPVLB6INvcFo0/XC2AayyVMnSwuxv0j40CAWDP6ogsPDbQjtBqWyAcOIX9xCBqU4AVGWllRvXh7VFzEmHPBPKGPD0iDg25VKYcgbj2RSpO3dNeyyqRMho60BJiqRT0nIeqjXEuGsr/E4jlikexixEpamESiCmmc7CE7YZKimGOlgbjoA4p6YiCSeISofjLWMhBjPmyUi+pKJ6OYLGan0FP69wlk2s84Yve/CZ91IEHG4jylccDncRmmctoLOyWRtxd1Aa2g3j2Uoq/NKaUILSfeRZsl/n8pS/1CU3kDDNfSNxNLOBQA1FWqJkM7c5AESRNCVnRmlfUojZtcg1wcrSjQDmKHsipuxyoLV+m2EFJ6xlMuHAJQydN6W54sQNeLiegxYxog7JEzC3hszoJXWh2HPpMewJ0orupqEVTh9HXVYIUHn0qVHECUpEOjKRDeum/VpqWlt4Tq9XBxUz/CSGb8hSnlyno/3J6mkQ40OChvmEmVSFK1LEadTlITeqLYqSNLdpEETeKKmA7epQ9xLU7bIGpfkwRBMROUatl4ao+FcvY6ciUphJ1rFaAuVNh6tSgx0RoHNoa1MJWaa5KIusv74rX8uiVr/sQR2Bjy9HB2sBiaT3sVRfbn7re86CJ1W11KivW0/L2pqblbHQ8O9wtaYcGtn3rHGo7VMx+hSLF9Y1qV+uZ1mZ0H0yArWzDWzvaPjexVjUpcKNJXapAtpiSLRgvgmDZxm52qxm6EloB9NnshLa8EIpFdP0r0OPSxbrrrW52tcsZ7r5OvA4erzr6IF2TnjetuEhvTa/LU99i6MKT9U185/+7oQMzRbP47WxaOezE/mYnwNOtL0tRC6EEK3h16NlrRldBhQfzOF5HkbDGbpsDZFk4CETOMIlLrNbu4EIIR15OiJdbYBnb16yTyW/A9ttQGgQZujXocmmTXGIqb4nGNYaRSoqB4wbfYxU9fvN8jvIHG4DZpUMeUpOfTF/l5ivP8JWvlGOsYZbe1zlYxpCK3yqHGdRZSQD+8ouPNGglmfnM61KzazEhOzhzujVyroEjrnrnPjt5t2LWyJL14+fq9EIIIi7qqSNiYkOjWL+B3iozZxDqCtEB1JFWL4wLXGlLX1MJmO4uHjDR6WWr5iiA8HWfc/AsCw9h2kgOdnVT3eH/ahes1a+ma6yNaOXHHLq3WnbionftxF6rO4rhljWZhU1NYpMHNMd+nTqYrW/UOBvaQra2qokA8D2n+Nz6xIXAuz2EbxP33QeZNUFrneVbPzbX7V5mHfztbmxXN94sHTa912DvNZPmHkjI975T/lF1BELjqtbBwO+J8JjDmuPs1bbME85qIjB8Sx6/+bgVU259JnqZ6c5Oxi9+z6D7fNJlnnfIF5xmkt+Eiyq/uk6O0vJM9RnmeC4CzcFt863g/OBg7zbPKd5xp2eb6X8ZejGL/t9Fc92JGa/7xiXtcFmDnN4jdy0p1oD1wWddHYOoAd5fHnae4uLseh+7WspezMYv/15JvUi7qSFvFrfnBe4bNrjRZ5D4/97919euItSjbmNjU10m53Aq4WO/j6McfvQy93q+eOF4YPM5rbqvvM+L0PMpsz2zhY54cguu9szm2vbMtQPiTX9ZzW+l78T+e0ZFUQ7Zy5720e86+3xfhPCfXvm5NwL5ayr85bP352TnPF08X2DJ/3cOos8O9J1fVgILeu+Esb6lYV+DwYEocN/g3UM7qMMhfJ+F7UD6YQgvoN+QuB/Zyd38ReADTsn6TWDxAR3/EZrEIRroFROA3Z9+xMIdMGDepRUFZhYAnpkAlpwBEh4CKmANWI6F6UAG6hMGcqD/QYYFEloP9ocRDJ99tf/g5n2gfYWgubFf5JUgDt4TCt6g9GEIElZf6qkemrGea6kDEljdDOpbDS5gFOqHTO1gMQ0hC3bgzQWhfalhLxWhEyrZFUoExKUVExLdCPIUFHZHLOABFaYV/GVbHZbYC9ZYDN4EyoXhvo1hIJbFI3hBF0wiJVLiFmiBFsjADFQiJ3aiJ36iJWJiDWwiKJaiKU4iF1wiDsgAF5yiK3riJWaBCmDBK9ZiKGrBKNqiLk7iJdZABNDiLr5iKmrBKrZiMJoiF2BiMR6jMBJjBLAiM5piLM5iNCLjJWLBL1ajJw4jFlCjNsLiGOCCEhpfIYLFISpYIu4DJtgBI6qcI5bhVAz/wgMogAHUoz3eIz7moz7uIz/2oz/+I0AGpEAOJEEWpEEeJEImpEIuJEM2pEM+JERGpD0uQARQH6qVo0aco3al4z5og7K1oxgmICI8olYMwgJIJEqmpEquJEu2pEu+JEzGpEyq5AOs4Q8+nEauVjr2AhiCJKfV4EjC41Q8wEwWpVEeJVImpVIuJVMupQKMgX5gpB3mJF5xZBX0gk8yWw0mQg2sEiTSY1OGpViOJVmWpVmeJUTW5D1JJd9loRZeWutlZSMmIFd6ZVmMAVrmpV7uJV/2pV/S5FqyIYK55VsW272RBk3I5bJtZQ0wD1x0wV9GpmROJmVWplmqJU+x5f8R/2ZhfgRHzoRi/mQCKkJj/gVkWiZqpqZqriZrIiRmEppm1gVVJlU6VgIihKZoqgNpOiZdnGZr/iZwBqdwRuZrPlYxxCZOcmZnciQu/BVu8lgNkiasDIZvDqd1Xid2ZqdMFic5CqYLKmdhpuOmPSd0jmYNTCd18uMCPAB7tqd7vid8xmd8LgADyKd93qd90id+7ud+MsB68ieAyqd+BiiBvucC/GeBJuiAJmiALiiDNqiDPih++qeEEigEIGiF2ieFZuh+0icD8CN3kh1yTsRsWhRHxg55lqdunudkVCc+hmhZIQETWKQdHuc4tuEeEhpDzKhPIYERGueIRsMddkcexv9djhJaLNSBDKCnFPYBDTBpmCmDi94jjJpFkGIGeL7lZ6Zojx3FI7Boi4KoXPGoStGorNHf/O2omdphLPjoHI6Zd47ZjXZJkX7em4JFki5pdvzBk0balNpjlcZFnGKhZnQmXLqWNnQTlz7YUcRCDNTAEESqpE4qpVaqpUpqDPAjBFwqp3aqpApBENwADHgqqZKqEIhqqaaqpYIqDECqqr7qEIAqqsIqrQoBDrCAq9Kqqp7qqOpqqvKqr8IqDsBArwarp7JqrhprpyKrsnIqszYrp4ZqsWbqPm6qrwIrtHpqDmSpFn6mNizqg7VDOTyCDvyBuZ4ruqaruq4rumIBPzL/ALvGq7yaax/swR10y7zm67zuAR7gq77+a7r2gR6cQbkCrMH+Qb326xocLMMKbA4ULMMCLL/6a8Tm68QubMVKbA7MAMZmrLwKLMF6rL7yqxRArMiqK7+G7MnG68Weq7vuI7xGbMuu7LzqALgY6qFmVCUUILg62FGMQyIUQSwMLdEWrdEeLdIWrRaAaNI2rdMO7SrggicIwSA8rdU67SrwAilQ7dV27dFGrR4IrdeObSxErShwLdmSbdQKgdimbddmLSkMQdW6rdVm7dnOLd1ebdYOwfnkbd3iQtj6bd1qrRq0reAa7Sr0ginggeEeLuLywt0S7dLu4wO4LdzKreNa/20R3CzOggRH+lXP+qw6iEPQZu7RTq4+Vq7pxkIvSC3ari7R2u3rwi7rLm7jrm7rTi3e0u7QKm4Q3O7qyu7uBq/rDi/tZq0Q9C3v1i7jLi/UQu4ZAO/hwm3zLm/U6q7kMm3aCq/zFu3mdq7nTp1rgVfoiq44IIL0Hi7q5qPqmm7rakIQGK/pZi32Lq/iVi/vvm/8dm/t/m73ci/vXu/sBjAvJK/8Zu79pq/j2m30EjAp3IECC64A4+36vqjlQu4AL+/3gi9Hflf5mi/6Lm8FUyns6u8BLzAv1G/+2q7zmjD/+m4ESzAGn7AEFy//Iq/yrjD+ErAoNPDxai0EO+8EZ/8v5V5w5PJvLGwwzm7pB4NwDNPtCANqCeMC/NKwDKsw7SZwC1Px/nYvDP/vDFuvDYOxAbcwCwsx9D7x9gKxGlvuGMdCFNdj+44tAPOvEi+n+ObYFDQxo45uCPNuHBvAHDuuC1tvCmew+55x/nKxFR/uF6PxER/vGxtyGduvIvOwD8Mu3AaxGKtwIA/y24YxEt+xluYxm7kZH4vXUZxvG5PtJ09xFaMxFpfwJcNyF5ux/0IyIi/wJBNwJetwK69xDwdzKD8wMXvtEA/tKwvzLsMuKateOmpaKvcxK4uw9rovI8tyMztyLeNuNnuxYh0zMouyJM/yD/9yFndz8KaxA3P/cgC/8TKrLTl37zOHHEfigSVMsxNbcxF7cywbsjkn8g7bciML7iMbciRrci+fcw6n80BrMjv/sDELMTxfMx3Ps/PW8/WZMmksoj6rsh+Ls9XGMwJ/MwEHNAKrc0n/My6LtN5i9PwuNESjMy0/9DoPczu79NMmMxxb9DgndEZzbtSlo8l59EfH1ir/Me2SNCGb9A+jNCGrdFOztCXnMkJvs9/yNCU3dE3r9E5HNERPdCe/LlOHMlBrsFD7HUdX3VHvMyD7tCM7NURDNTfb9ErfclV7NdbCdOZqtS9zNe5KNQrjtES7czmTNVy/9Fnzrkaj41rvAymoQVtTs1LDblkb/7RcrzNdG7RgYzZVr7BV8zBW561fM3RB060WQ3Imr7NYv7MnJ/bgLjbtNrZOPvY+vN5ku/VSw3beFvJJj3Zvd7bf+nZeg7FsT69MrzNNB7Zd9zVYs7ZhK/Rr97M8H/fq0jZtPrb25TZl6zXSXvZwZ/b8HvJpu21qL/Jnp3No/7B1Z3Vyj/dyC7R3Jy0Dz3fRbrJ9I25FU/dFt3fmYrc1cWQc8Cx3h1dS5zcRpy5Ba3N5p+15Z7F4B/Z6Q7R/021pzzRgy7dxr/Z4t/ZhUzBv73WFHy6AYxFRFzh0hjQ/K7g/4/VcAzdqC3dvR3giT/hNw/j2vrdzx3dKN/dgc7hze/+4dCM2f/80jvttiatOOnphT6I4YB34irPvggP0kZPtg095Sxt3lSOzjqMwj0e1j0/vc3d4dBPvdLN4dW+52ya5unymUTt5YEH5Wxd5XKf3eG/2cMs4atN4Stv4eI94juO5DH95XSP4fY95kJd5TJ+5lDNzg+ctm5tHNLMjnOu2ZYe4efP5Fat51155iz+6g4ezloP623a5mBM6Z4e5DBN2WCt6X+83mvc3p5NtpNebbXtkpVv66oL3jNu5cwt6jKt6r7u4eht67PI1LwM7aRdwhve4sT8vq0P3s/M0r4v4rI9trXfGTjZ5rj+5is95rE81sd/5tT+tp2Ozr9f4s5f/LbIjt7JbOLOTeqfrObxHO5lPO6w3eprLO62ndTVZJVZ2O0hXM7jr+13z++C+u5XTu4NrOjf7uXMDutqa+qA3O5ive33nNEUzugU7OhInsb9flG0LfIoT/G7TuWeP+6+Xu9Oe+8G/sKjrMsJjLcVndbybsbAvu70nOr5zPAl7/CiHvFI9dmKSvK6bbrVnerqLucKPrcuL+8xb7UGLdtTTd80vO6rnec7XO5CjsJCbOZGHu1mz/NVm+xYe5k2YhtEPfGXv+j0qwEkawHrGwiOIQiyIwiMM7SEMbd3ffd7Hwt7Tvd3jvd637iDowCAQPuDz/eD/feD3veIH/iFgQixg/8Ler8IdTC0cUL7lD60e6D3nB/7nA37oez7oV/4hLG4OnH7nx8LoTz7qmz7pxz6ADe0fTAIuJMINVO0c2H4lxEIlAMLQ9n4s/MHvB//w+z7wC3/tF/8kKNYQJH/xHz/zE7/xL7/0X38k4MHQvkHZ2gEiCMEccL8SDS0eREIsbH/3mz/6q3/5/2H7c/8qnMHUWkH8rz/8pz/5e3/+uz//0wFAPIr1iE6sVVaEzKAikGCsWFYc0mFY8GHEiQ4hxpI4cI4pPDcsDqSYcWPDihovxqLiMI6iVYfWnCmyMlbLWIriOKRpE6dOlopu5lS5ilfMIj1V/gzqsyZQpDuBIlJjEP+KJyFQEMVCJMSAgQhdFywwsACCwygO1WSVahat2jO8RNEYFCut1qmxztJVezdv3bV4HZ6ZOwhiER1r1jCZsmYKE8REECtm7HgN5MSLp9RQUuyJuHNxMO3TJAXRvij79sXRJDoO6tOpV2tqbRq1atauUZ+ptK/SGdROUJdGPZx4cePHkSdXvpx5c+fPoRO/p04coiIOsWfXvp27di1dwX71+iBWlUCxAlVx6ATj4IzsH7pf3wtXlRuDCK9vHyt/LPhW5PNvv/xWccIqJwL8b66/+tqLLb20OsOjGtqy60G/+KqQQbQOweUPGeZq0ELAYlnDKaGgWiqpmhIxJYggmOL/CUWlnqJxxlXgGESIOGZsSkWaShoppIYOsmohkTAaUkiUkDwpSIMQUiglklJ6wyE8IoklEjwcsjIWLLW8wyMcrsxyyy7L1JLLWLwE80w2y1yFIJnaNHPNOmOp5A+H5nDoj0ry3DOWOYjCI44i9OTTT0ATHXTRQBWN5U9IV5HDKt3yZAIssBQwQIGyHI3lEExiweQQh/RwaNRSD4Erh7lWNRVVVUmVNZZURa311FtpZTWWIiibwoo1rFhsjSgQE5ZYY42t4gw1dNhMm0jOaacXcPYBB5HdVttnFWzBWQW1br/NVlzRUCs33HHTBffcbrWJTt556a3X3nunq+667vjt/ze778AL2ADy/C1YO/o0CWIugxnWjiirFm5Y4l482lfihhFW+OKNKQ7C4o0NJkoUISIGud9VcIHY5IaJSqjklbujGI+PYe5OZJlqtpkXUu6gOefsUFbZIYAF7orgi0Um+eeCi5ACsaehjlrqp9XYQVrP2rnn3q257trrr8FOLl/rlt6O6KKPXjrjl8s2iBeh28ZOZp/LXjvu7Tqmu+yk2V46aKXvBpoXlwN3aO7CHbpZ75qJ4nnxnP+O+GyB02YZLsARd6jpqTmfuuolitEGa63DLt3001EvfezHYZ484MpztrvwhzG/+/DCZUc878z5nj3l2gNveYa+1a6Yd7hwvv+78Z4Rjxw718GDPeTLiY97886xX+NzZUSvlvTUwQ9f/PGNW71w6I2OO3fl3wa+7dsDXz9+F1lnnHrf4Z598OF1N7555OsHsuUF0GTOG1rRoifA+2VOc07LHue2172skY+CFbTg18wXOPQNTH24SFj1INc+ENYMfrbzoMYyt7v/jWyEKzPg/wiHO//pTxTJi9sAm/c7ySEwfRvrHQN/5cAHRi2Co7vgEZGYxOZk8G4blB7M5Nc22rVwZSXs4AcZqEIaus9vOmSg8KhoMiveEIDB2xnz8Ic5JyqQhUAM4hCJaLXQGVGJdbSjEpkYtzW+74RhVGD+1DdDE2IxhfQ7Xhv/g+fF48VwfjM7pA2leEYCClCRsdgj0hbIwOvBUXtylOD37hhKUYovj227pNr6+D9Avk+QV0Rh/zx2SC7+7IX6Y6TtWknGGk5SYjhM4w55+ER//VCTQhxi1a7mvVEuk5mnK2XZTvmzKO5NhP1zJO5SWchYrnCWkKukLfknw2vSEJLUdFwO4RZNhhEzc5t8IDLnqMxmzpOeW3vm0tRJwmyacZV1y2Xd9inDbW7Rj0j7phlvGchxmnGXZjznL5/HQw76MJPtNCb24PnJem6Uo9G558/yCcWAkrGfxVuoKwsKMi0ytJuMOyj7EsrKk+qynH6TJDrVKFFhnqyiiHNn5zJK/8eODpWox/lozkJaxZFGsqTS/CcqCQlLXlIUkcp7KRlj6s+ZRrKh7HtoItOpUza2tGw/9ZwnhVpUtRb1qDVLqhiXSs2mxu6p0owrKwfK0pSy7KqRzKpJp4rJrpLxq1YNazDHuleQmTWOyZzgWiFL1La2Tqx8jCr75krCusburv7MK/uqesO+UvOvTt0qNQcbycKK9rAI3Cm/2OnTizY2no+N7G03OtmVvVWlnQ1hZqG4WX1edn6f1SVZXTham5aWrqe1aWrNiUaw5hSxVEXuzxiLmKDKE7fdnaduTcZbjvnWfsCtonBFSlxcGper16WkeWEGRmsGtpdl9Kp0DUtd1/8m1o3Z3a5tvRvgUYIXZOK92DRtCl+Volep6g0ke1HrXh8ql5b7UyzDxsjVmlZ4tVKspIG3E9vCMfa/oBTwie1I4I2BGGPkjW81xUlfDLtYsxB+roQNquACWni+j5TxMG8K0QNWV7A4XplZS4xiJd9RxRdj8YwdnGAjT4zBcI2yZ388vdB6WMcKZK5mnVth6Nq0w3v7cGWLfOGL/TTJS3ZzEpsssScbDMEV7jKVwzzcVwo0ywUTcRfvjMkvBzfP9hszh/HLWv2ijb9AdGeb3xxpC8a5YXMuWJ1/O2WMVbm3Vy6ejcWs6ZBROISDPm+hX3zoEJYZ0IumXKOLSTW0clf/0rUeH6UZZml/Ybq8oqYzp8fraaeCOoRbNnOg62tqMQKbqhtedaK57OrXwdqi2p01gG2d7dTh2mC67hevX4zsX6Paynsubp+BbOxWq3mdPI6xjx0K7WNLO4HWZXfDNgdpbe/bmdQhmwbRDFVzM9XXl2b2xGgcXGIbuuAnI7X9lL1gcu9Y1fZjNS3PTOT6qvtuTdM3v0EONm4XzNv8ArcLYdxIdJs84eddeKobDtuHvzjiHDv4xp1tcXmvO6IatxzHrSeFa5s45EXn2sj9VfKYtZyi4jb4xIM98AevHLY9nfe9/Ty4GGDd5Df/ec5ffHFvtpbR9u6vDRxLdKOvvV75/1JEDIQQd7nPne51t/vcYyBRCNyd732P+4t2EIMc+J3wfg9C4AdfeMXT/UU4gPviIS8EwAs+8pV/UQweX/nFH57ymi885xPvecUHIQYwCL3o+d74zKO+74e3wepZz/gdOD72rUe83PPOw71rHvS1J3wM0s524dtTHePQxiH+sAflL5/5zXf+85ePBYkyAPrVt77y9YCHOsQBDnSowx30cH3xPx8PdojDGbx/BzyMn/3Y1/4ZrDCH7+Mh/O2/fvbrAAcrvGEOdqC//dkv+/RAD9Tv/wBw/MpPDuKgDurAAA+w+sovDqwg/uav/h6w+vSgDxQBDzjQAi/w+fAP/uRP/f888AOZr/zqQA7qwP9K0AT3gADrYAKtQAVZ0AVP0Pz2jw7sQPp4iPoeMAHPYAQd0AabbxC4J62GLwnlpR3OQRy44RqiQRmkcAqpsAqt8AqlkAt0Cgu5sAunMBbowxRwoRdioRi88AyvsBhQRgx7YRXMEA3hUApjgRdwgRRMgRfcMA7hcA5NQRREYQzLUA/jsBgcohgMURAHkShwARDfEBG5EAz70BNIAQ8b0RGxMAqVwRAP0RK9kA9FYRLzkBOxUA15gRfaMBBFsQrVEBdEwRM84Q9DMRW/kD5IgRTG0IkskRQXsQ0rURapMBqugRvGgdaUsBif4x6YcBzA4RuYsRn/nfEZoTEam9ELJMoBpPEasbEZuUEbrsEarkEbsjEcsfEJr6EcuUEc0dEZydEavPEc0xEdn9AaogEYwfEd03EZmdEd7fEdt7Ec63EfszEe57EdAVIc8bEg03EdCRIhx1EbHPIfGRIatUEepRAY9TEitbEc2fEbqZGHrLEgHdIfLxIjnxEcxKEc1OEe1M4YWVI5kFEdYDImZXImabImZ/ILJGoBHGAnebInffIngTIohXIoibIojfIokTIplXIpmbIpnfIpoTIqpXIqqbIqrbIpGaAabXIrZfIcyuErz4ErxXIsYxLbWvIsmUMl1XIt2bIt3bItxUCi5HIu6bIu7fIu//EyL/VyL/myL/3yLwEzMAVzMAmzMA3zMO/SAd5yMRmzMR3TMdEyMpUoLhGzMi3zMjEzMzVzMzmzMz3zMwHTASRzNNGSMkHzNFEzNVVzNVmzNV2zM0WTNGWzGE3zNW3zNnEzN3VzN3mTL2NzNoGT7WqzN4mzOI3zOJEzOf/yN4OzOUGOFzpFOaVzOqmzOq2TNZnTObUz2xzgOr3zO8EzPMXTLhVADLbzPLOtEsRiPNmzPd3zPXtTC9BzPmutEhwgOuEzP/VzP/nzMBdAPukzQCONF8SgQA30QBE0QRV0QRm0QR30QSE0QiV0Qim0Qi30QjE0QzV0Qzm0Qz30Q0E0RP8LNBDaQUBN9ERRNEWPqBKoYCVV9EVhNEZlVEYroQaMwAlcdEZ1dEd5tEfPskYDYRWWAEd9tEiN9EiRND1r4DwMYkhzNEmhNEqldErtCEiBxkmpNEu1dEu59HSs1GGwtEvFdEzJtEyL40tDLEzNdE3ZtE2LFE25Q0iJ1E3ptE7tNEDh1GbU9E75tE/9lCXzFLb29E8JtVANVdIC9WQG9VAZtVEdlagSdZgW9VEptVItFYki1c8m9VI5tVM91WsyNWQ29VNJtVRNFTkqQQas4A9Y9Q8GYRX8bBBa9Q/6IAfm9FRxNVdPVQ2MoFd9VQZ2pV8OQQZ81VePIF50NVmV1VT/j6CFBuEIljVapZVZnRVap/VasZVSm7VgnjVbvfVbCXVb/aVbwbVczVWJooAJBuFJk1Bc+4VczzVe5VV8nMAFXGAGoiA0ItNd+QVe5/VfAfZr6tVe7RUHrKAYzpJfu8NfA7ZhHVZeBpZg7fUFhEAOsKUYFZY7GPZhObZjkSNiJZZgYSAJ+qBEhy9jt2NjPXZlPRZkQ1ZiZeAJHkH4UFY7VJZlcbZhXfZlQ7YGqIAXjK5ms+Nmc7Zo5XVneTZkX0AH1IAbQE5osYNojXZqwRVpk/ZlYeAI7OActA1qHUJqqTZsr9Vqr5ZnY4AJEIFdu8tr+cNaxfZtv5VsyzZpZ0AK/zwh0tgWbOF2b3FVbuf2anPACqJhyfLWbfk2WkXBCRR3cRm3cR33cSE3ciV3cim3ci33cjE3czV3czm3cxX3Bv42dHkWBopgDsbhxAr3cKd1EES3dV33dWE3dmV3dmm3dm33dnH3amNgCQDBZHErdVU3Wlk3d4m3eI33eJE3eZU3d2UACibhd6s1eIV3eam3eq33erE3e6/2BtYgsoBXepN1eLV3fMm3fM33fCU2a+ugHLw3ei+1RM7gblFMGdj3gnBBXrpFCcUXffm3f/33f2E3B84AWW/rex+1HWogAyqgAjIABpxWwEYACcZHEb7nDi4gOhRBA3qhGPcXgD34g/9B2HxrYAruN8AM2FGRoALoADUUYQJU4MREgAjEpx0yIBCGQw4wIDpsQASMUR0e8oeBOIiFeIiJuIiN+IiROImVeImZuImd+ImhGIiZ4HhjwAlmFsVOuFFHIAeIgxQkWMBiWHzUYQP04IZz+DnGwQKAA3zXzm9fFwaQYA/Uwc2ymFFfIAaUYw5wgAaY4BuI4xykoAZqQAq4djiQ4GJRwxKegDjwIAdmgAmuYTii4BHuAAdkYAkGlzgOQQdmIAligThw4QhmQAcSATnCGDk8wQhmIAgcYTjwIH6HQAZ2QBGKQxucgAZswApMthjwYAOiQA+sYR9wWBycQAZuQA2WIwr/LMCP2djo3Dh0XyAI4gCR6dh9KbUOMEAHROE4asACZAAHJmACSAE1TGECKKAGbIACJqCEx2EDBoE4oIAChkMHLkDwRIACIgE1wrkCYkAGKKACrngfluACYAAHRsAC8AA15sACthgGLgAKjuOUjUMNLkAFcuAFLqAKUOMGKMACVGAGRAADpGA4AkGdb2AGKkAEgpkJJmAD1Pk05CADJkAEZoAFMACPk4Omm7mNa/cGICLb6phR50AENKACVqB7UWMKKuB596EdWOAFUGMERuBixUGqUaOd33k44hk11sAChOMeYmAE9HkCzqUcrHof+gAD5GA4mGAJsqUCvngfzuAC/0zBOCSaOKJBjYdDCixgcG8gA+pgOI7AAoK5HM6ZdKxBBHB6jMsYNeRgA3ZgOP4AA6wAORBBA2x4p4vumXm2eS2B34K6UaNBDmwAA6x1BIaAOBABA8pBETJAfsclA2YWq+FZnvcBBm6gizNAGfZhAgx3DjLgdGcABo6jCigAlESACexaho3jCSagOCZgpG9ArIdDHSygsq3AAk53OOggAyKZsW9YA6j5BWQAOWqAhzV7s1/3bAfBd/eNbYe1WOV7vum7vu37vvE7v/V7v/m7v/37v48gkukFCjAgkjuaAhAcwTeAF6zAtomDAqZgH2hbq21bAiwgwRFcA2h5ApR7OP8iYcH3YQSK4DiOAAMwnAIuIAiW+zhy4AJOHAOSYB9u4IWJYwIWGQmou7o1oA/2Abwd+4yHwwZW4DjEwQIWOb1DjrNJ13TXjm1XQVZnNcqlfMqpvMqt/MqxPMu1fMu5vMu7XAc+m17aeQ96+wgc4czPPDTgwALcWx0u4DTaQQMSmsKjWgfQ/Mx3o7c7HDUmYQPudwVw4DhY+s7PnJpR466HwwhGgNAd4XRnHLoXmaWLoxc24A56nIzNuDhsgMaLAwqWGcmT/G8FOJhpluvc6NRRPdWLIMyZQxkooJSHYxA04G6nm+i+wQIgWqt9WZ+NgDhowLaJYAIKGbr3fB/6/H7/nYACmJk4HgEDDoE5EB019gADmLo4Hr3GF/kRMmDOUaMIKn0f7uECfOPHNZ3TiUOnQR3kyLZut1kJ2TbV4T3e5f1XWJ05YIACokAZxCEOXDhdKOAFFEEd8EAFKnsfnOACkgAXeGEJKuDNfwMDlEATJmEIMsC2r8GF2xsQYOA1OJw4jn0fykEEROAOrCEQXOA10tkKxsEUigCni0MEboDQ9TUGKOAMysETOHmjzb23j5wGKmAKlEETiEAEMsDSb3sCdEOYgRw1Nt04DkEDsjrd921nqzgR1Ba0TX3etX7rN2bVn+MciKACNmADLmAGCFgTXgADNqACboCAo6ClM2AE/wYB39maAjYg7pkAvfeBFxx6AyxgBng7xCN8OHqhAkjdGmTgAsg+BjL5HpDA7uOe24lDBca+8jcAAz5ZHYZA7DFABbLaCFz+0Au+HYqA81+gz599H6wBBjBArEu6OIqgvIuDBvRe6rUtYuEYD4adJd+d633/92HG66NDFKjlONQhk43jGsQBNegerw19ONoh8KGjHRD2OKxh9+UlGubYOdJ2H6KBfT0d+6ODAo7c9m//BXbgDR54X7Me+N3f/YWffJpf0iYABjL5Diogxu1lEtzb/GsNEQCi176BBAsaPIgwocKFDBs6fIjwyKBYFCtavIgxo8aNHDt6/AgypMiRJP81FrEEMSVDClFUunwJsyGmERdETMAQ5F7MnTx7+vwJNKjQoUSLGj2KNClPiSWbOn0KNapUqSeVGhzyyKpWhoekWOG1NazYsWTLmj2LNm1SplPbun0LN27Gqmrr2r2LN6/evXz7+mXIVq7gwYQLz0X5N7HixYwbO34MmWhgw5QrW35K16GeOAitJIKp7BteS0hMEbzmJIeUcpFb86wyIbZs2YPEHSFVlIaUfTJmy86xdQSF4cNxxGynJGvZIkhcO3c5+bL06dQvZm5IQwVCEUVgUtBed5AIDRqmDFREocIICxOUPX//sNib+Uw2nJkPZ1yfDVCIWspwxz4TxDBFgQb/DrLVBTc4waATe8SESwZD8NTLBODA5EQF4sDHoULRVQdiiIVdx1B22xEBExRv2PXIDodUYN4+Iowwzj7KTFBDhzouhMcG5xj0iE5D5SDCQBMkcdYFcAA1yY87PbJBMTCBY0F/O155xBp/bMlll15+CWaYYo5JZplmnolmmmquGaYOiDlk4kEioAgfjPtEo4EdBDExwZV+FtSjkwRNIiQp4CgTxx46qXNHHNEcFMkbCCY0QXMCInnQOaIYJM6mA4kChx7tEDQOJvs4soYl55haEDieFqSkTOrwIulA5dAhh2gEWaLOQKtYc88fayg3UDtvDlSMe/tAKWVB6uABx6sD/41TRxxgDRTDCn/qqIYR3n4Lbrjijktuueaei2666q7LbrvulnvENRDFadCcBPFCQ3oiWLmPOCIEwoIFFGiyjw5WCkccBUXuc48TIlQwwQ3K7pPDE0JMYIEILTGkyHweK5KQndqswA1BUFCw7Z+BGpRBHwPNaEEFGIjQB8YWWGAFQb2MgAHGExBLECkbKHckQmdQwBpBOWj3DQw9V0ABHgNBMYEMF1AwwhkaFrT0QReoMMMMSAiE0HdXZ6BCHRTcXIGeA1GQ8z4wvDDBBRVoMALZb1RgkAwy7HNDBRtAveQ+c6R3UwxJqyEwBRnUMKoVFgiZcuWWX14iePXSSQrWZ//gsYQFNyC7AQVH2HFHryoYt08gHkvOegwVMIGHFTY1S9MIVcixAwaYKjTCBsILP0LIMRZ0zwg0YN7hygVpEKCMFuixjyeO13DOPTlYINo9Iqjg3jgzTCDoQJILOQEMDTqxxkDqUPAEQdzgLLcIm7ZDRAVSOrFBDRvu8z4myI9+BsEBDA64NqkdZDggS0QFMmCEe5wjBhQQUgWoMBAWaEAJP4rEBIq3jzNYwCAxiME+VlGFDcShD6yZhAWSMCpNRMxI3WndBACxD6FFgnk87GHlaDC8IAqPTjAYwagGAogMSK0Y/DHI6g4SgxH0ag0XAJmtRLC8fYxABL0aiBMskDT/ntjJIDmgQLN86BrnEQR6LxvdQJiAgf+N4wLtk4MFtEGQc8DNIElYmIAgJhsY7GkCQkoCISuRgUoURARL2EeGjuhFCnRxCYRcyDlgQIHyvc1SFKvgQEyxgc/s44IZ1Nw+BrEBT4RwhAUp4UCYRZAasKAgd7hAjSggwINY4Axo7KUvn0ODERRjmMQshr32YQE1GGQExmEi0PbxxIIwoQLXmoEgCxIFlGmRdQOxxgYO4ZMxmswCk/olZNQ4EDbK6AgEqQLfCEKB3RyhAmGrJwVqSJAggPBSCRFHBXL2vv5MwQL1DFvVHNmngpSDAv1pBwWccJBeSEsZGZDDAnczEEoW/yQDdRgIKffBghwR5B4ZmMMqSWjCZUWJICIQQUFnoIHPSE4EN7BCF/cRT3PqdKeJodciUXSOUPbNhEx0hBO5uQ86XKCjGQROQeaAgYGMwKnF2oACE6KEl+byIOLchxwuoEyeOgad+1CnCNhpvne+bTdFmMAR3grXcg5kB/ssWkKMUCQpZHIfUqgAXOFKB4QehJL3mMLWDHIDU44SgwbJaUYTOhCOepSxITUIBiwqQpS+cqUvY8Ffj4AEXSkjCjagwARisVaxqna1avEpS+nUVRkFYR9FPSpBTDG7giTWIEzQ5lQL0g6rLsQIBywuJw0izkdYAKKsXQxZzYrWfVhBrf843Y3WdKUQJPjRrgixhgXoYFdFYEBaBHECZAnizyqIQAgImWaNBpIIDcg1tY/dKFM/WtmCXPaUGsBFQVSQ0klsADcDkcE1F1KOSg1kl81tsIOt4tqX0ekGE7AGQayAgazU9r+sO4cIDjyQP2CAM76iAJ1+S5DgXnUnY7wRUh/Ml+dG76wXpm5O2zEBF+hqEPyqMUG4i5AyWqBkA1GBCFYxEEvk0rwRqcAFUHsQa1TgBag9xAfLhtF9aJQgkl1sBkVKkP3eYwIqILIUMpBSdVQgB0JKxAWYu48pSE0RIzDqPrgxgUb2Ymgw7rOffxJhGdHpGyKgwBCYEAMMMHfDBIn/5g1wAtdcIgEDMnBCENSTNBRXdcUxGSNNCnoDIv+5LnrQwE0jq0ARcHK6BaFAjDwhAgts8QLHTfIGWIWxhBXnXhjQQUGsoYILzNoGA3nCeQc4A4UMYgIZsMAGRgDlxmaZCX7cR5crUIWBvIDYsLLoPhCxtkJjLaUhvIAFRKo1Mg+no+JwmgTWMwLRiJByo663vRlihR4TZAne3sc5nMCCEczgD3mUgai9yMuCqWDhC4eBkOwggxG8AAqQZJ9BaIDknugAZPeIAcMX/oIz3tss6jCpQeTgJEBkfB/fcNtA7CDqe9zBCVQQeatz2QcD6RycBLnDwQcyiCdIgcB34vR5/zCwQ4W0ow5UQIRC7oDHgSiD4ASZg5PqQGRFsIogdAhjOc7gBCuMIxJJH0gvrOD0gYDDCk6IQ/kqIYUnuHwGih253e+O97wn5gbVRooMXmC5clQgfnovvOEPj/iwKEIDVEdKLy5g9CtBYciJr7zlL495l7wAzkeZwj5TdgNfZ370pC+96U+P+tSrfvWsb73rXw/72Mt+9rSvve1vj/vc6373vO+9738P/OALf/jEL77xj4/85Ct/+cxvvvOfD/3oS3/61K++9a+P/exrf/vc7773vw/+8It//OQvv/nPj/70q3/97G+/+98P//jLf/70r7/974///Ot///zvv///D/+AASiAA0iABWiAB4iACaiAC8iADeiADwiBESiBE0iBFWiBF4iBGaiBG8iBl2cJelAbCEFvCTGC1kBMDDGC9zCCyKMQ6oAIcgAIUUeCK6h0I2UQNGiDBcENxHRqC3EPjkAHd3AtBgFJDlGEBgEOxBRGKHgQ31BMT1gMmoSDDeEIc4AH0VYQ2gCFw/Q/AzGFI9gOWzhMwEVMPyeCUSaGY5iDCAEOgzAHg3AhJOiDHbh71oADgiM8lDYxBYMB1KMQLmABRDcDQlQBMNBvBPENCjNSsSEvB0EFGUB4BWEJMuBswpMBIxB5MkI+vCUCzzQHGQBRj3AB5MYLFqACRzgQRYD/ASa3D0ygAUFkilOAg55AA3goPG7VhQLSd/tQBCLgXwVhBRmQbY6YAbA4Ak+gSQWhCaZYEFPwikIEixYmdRWwiwthDTlgixogAcM4EDoAjcNTUQORA/t1EMqgHuhFAd94iwMhDuk4PMLGVAdxBhkQXYCjjsOzMDNQRQcBBzwDjiPQPkAyiglhAxfAc3Roe8UwARuQARJgZBiwARNANlq0AZFYNhsQjyJQOrHhOMIDA9i1DwJ2AfLjkUG2AVlEEFNwAQwpAi6wHgypBNuxAYeIUxvAXgUxiBBlBRFJED2yAbN1ECqwAVsVA4MjGxC5AfbjiCupAWSmAu44AWknIN8E/1zOtlXatgHcyDUbwB6xsZIRaUUHgQMboAFS+QYJIzgZkDAiEIf7gATCw4oMoQfp2JQrMAJ4SG7B0zi6RgF+GDwV8IsFgUpq5QlkyZfDAR6FqQHD8UAMqW9SNThhhAQJ42xXQxwpJQEbEDe2UpQbIGwB95WKA4zCc5UstQHHg5C0dwMRWXawtgGAB5kWuUAYaZqbaQ1M4GwjEEYiSZKkeRA5cJLAWIww8CrfsAMZUI1FsAFIFQnC03cUoAGqtJOQ5ZMZQGL/NZQEUZRAuQ/jMAXpSAFDKF3FqAJ2NhBysJAVgBg2sAH1eAfCoy0EoQ4WgAEyuJXJ5j5nkJ7mSSp4iP+fB9Ej2nQQ9+COIKYQiOBsIlBO7fAEFnABXRQ8nIcQwfNsRziYBFGYUbUQGUoQ1rCaGKBIBXEIz7gxB5EEG+ACCJGZmyk3G4ABR4Bd4IAEK/kClLOTLuoy9XKaqWl7GomaJ2RaBBE8stlYtPkymmkQkSA4OzAontmbF4ABjSeOwWl2zsakB+EINjcQh8CTe1I6GCBqitCl09mTLpoBFWAa2EmUP2kQ0aCRmqMMzsZtBsENGglCcvBsBSEEpWMBkDQHeWqS/8mOwSMCIwgFEdls9lmmAmoQZ1A6UJqmSreQKpCMvPAmEcoQwbOS3Al0g4OhLsoQHIqdE4KTEZmUCXH/oil6ECtKEFLgonHZcyvJLzu5khQgjab5ozwKexpZonkkKEO6EOmYkUhqEHHgmdLImwPBDcITBaVzqxRDpdA6AlOYENCpSgOxAopGrG65AaJHpgMRoDtQoWqqnWxqEJawklRHBBHZgwQhCisZIPNZn0JqAW8ZPdDaSIFajoKTcKbpBEJZmuBaOgkhlEUgA+aqEFQwOI24EJjasO35QIXTqYQJqhtaseW1ASB2DSsZCOmYo3yEoiq6rQtZj7wVkUKykxIwiKqKq7pKe0YQkeS1TBUZrEYqI9u6SBuAUcl6Z8JTDoMIm1OKkukosfOyAczVDhlDChoAZsHjh9+6DwHa/w7Bc6VFlp3YgrCttAFOtZBZhhAH60YwkJXKigEqgEqkKiAacCz3iRDeSG77oAdc+Q2OWkkGEaAIEQkZgAG8ELcVkIzYaQQO4bDAU5FxcKaReqEDIaoKsbgD8ZZvuwSnKq5vSxCpKrJxgwhc2ZYHUQ7OhkPStQESMA4Lma9Hmqsu23r+4pkucARTsLYUSaStZrMayaJKswFuxLPLugHlIA4LuVXAmUWr4Jnt2hBvsAHg0SNMGhsD8Q0YUAEo26VRO7C48EArYrVryqmtmqftAJFaWhAKC0Ku+jchtAFLcA/nuA+m0JQKAZyCShB4el5FuTwOpUIAOrAHsZrX1LU1G/8Ignu7HjMfUhmb+xAE43pKnqq4DAnAbwAHz9q4xZCOcDZmG9ASmpABF6CllruqxNqsn4cQQslcKXsq9OmHN3u6qMt633ADX+mcDyKkNKsQwlqbCBG5Aeakyuqzy0KfLgy8W3q/rZoDQZwDmXgON/Mj62pSMIUbOzm+oEud92u4FUBgAFuu2XuePBkNN6wQdtCl0aCWA1EDGwAyLwCm+/AEW8u+G+C+A+EICLwPsYABGsBz6xqfgPLDBEElM+lFp6oQK/koVSfEOcCNFCpEGSCewDq13Tqxn/qNdVw9DNkgO5COE9CFcDA47yWUJbsPGmwQrLrJG0C5BzGIaCXC+6D/sBTwxz6KwrQnDnLABDMgOBgQj8Aaw7OLswRxBBswvrmbw3FWOo/SwypFXffAwnyMEC9QvzTxIwrbH4O4mVB7twNRwFIETVe7D9uJEGpwquDAkKhYEHgqAS+MIBOgTfXBSy6wAff6m2qMEFwqoDDrR6aQARlwrYt6EGcMWfOzAfNVEM4WqTf7ji/slAxHA6dGy9SbAe2TuI+sAR+3cFpZmNAoASFqtdy0kxRwhJxcEJ5cHwYKRRtgKaW8D0CkAjqhyqtse9xAqC8cu/Bky7WLtYEbklqsu2E0iLMUzOMQxxPZOgWyp9U4NbfbvCBUDBkgSBRwAV0IzT+cyMBBxVhr/8Xcek2Cw88FoQQh67gbYATqm1JQUgPz+bxpvMb70MHFko5eywJobMeMaiQbEJMEcbBNfBAL2a/7gAkF0qwZ8MISehC0vA9RLAoL3bgIUZgZUE/FWLQWrAFlR7+1q9EE4cl0yxC0az6hSyqZiSQnjdK1pwnk8cd+fZHDCtPa4GzRw8u7SyoLqQTBTJG1dsVATVsZMAGOykkTUAFiakpLzai8cDdq8NTXnLVeuJDMtQLLuRAauVVtPAKHyi+m9aehzLZBaa47qQEyUFB/uYTRTBBcDMoFJZQX8Ky6lbFsyJB7nakwPBB7OgKA4MaDfRCLO5YT8F7QSlAFtZAe/NgD4f/J1hDHVf1KGpABUCbS+xAJ9HkHmr3ZqhcLMlDXXriS1wrasynaBwFEkHXaS+gI9Bk8KKmwFkAwJ2fMMonM5bSaB8t5un1yZ4rcVXwQ/GMBjeioFyBKB9GsGFwQzKYCGkB0MaABRamV67zGO4kBH07I39ir2j0QYnuPvwMk80yT+8DNeg2ZfD2zFpnIQkmxGsq4F5uIG3Cl42CL0KgBQJPf++DJLUrNB5HIQTvg+1AFi7mQJ5zgpPenFjDjlW0BEIreoT3D8gmcGRBYA3HhBqGwwoOSycOaxRriB5HLGhDWA+GoGaAB/4ziVzE82GsQUACRnCeUFHCQBEEFEFlrq7n/vibDkBgAkkBuEFawqf/NUY4A67HujdWG5L0AkWcQ67H+lnUrllwJq9pQ3lN+3rLJ28KT5aF6seSLASDTrBTwCLkO60I5p5/Mshu9rZXgbDCwud1ZlBdgnm2+D2EsPHI+56P33TlwB5FgB+OIsMGjAoEcxDswCW9jy0IwH1ZwMY5ZEIN+cYZeEBXimTcgB1W4A4LjwQYhpuMtP8XYd5UOXBS6pjEwH2egXcJDVciykBgwA28wCYpABUIJyiP4p9H6yICaxiow8UxAoTFwROx58Ptg6xvwsUiunNX4Dc4WVpkilEZ9Bo+gB4bkxsEzAzpnIDY1wCne3gxJ9AbSv4+s/+UDgc7TqpFR7ajcU7lY3ck4ewYQSQFI0AeT8AdJkI4YwKLgXg4auaPlrnrgwONCpAE0AEkg/43ju5BXhfZCNAHxmMCs1C8A3q7jkJnTbiMw8IxBNAJbRylHq6N0QhDa7Ef7cWwDUexwdrDQSAGMZRDWEAPFKES0toLtIDi167sMUcDQWAGcl44wjZWitw/7waga2dKDONYDcQ7sjvf3KvdGbrW9ShDrKqC8cI9cKfla/CnOZrwZQHTAlY792tEy2eB4sJDQOAEuB+mLLujOpvpqX3qBgANbxEyf23MvVU800PRWEAQ3FQcFRQNHUMIGgQS9ygRUvg+WYAPPBHQ54P/9NhDoDCEHOZCL+wAQe25Y21ew4Lgcawy2C2LF4MN9AosZTERjxsUZQ+Kogwjx0RAVIkTAYKKso0ErQTg+lJND3MmHmmpgnLHjTDmIVIrcgxnJxqOFQhwaPKMD50lTNgLBNKjJSMgRMqy0e4iH5lUbvArewXHtZLsiVB4muUqTicEkUDqecYkkClM5OLgZjHUD0Mk4OcB1vHeGxggRI2acoQpRXI44MON+Y9rY8WPIkSVPplzZ8mXMmTVv5tzZ82fQoUWPJl3a9GnUqVWvZt3a9WvYsWXPpl3b9m3cuXXv5t3b92/gwYUPJ17c+HHkyZUvZ97c+XPo0aVPp17d+nXz7Nm1b+fe3ft38OHFjydf3vx59OnVr2ff3v17+PHlz6df3/59/Pn17+ff3/9/AAMUcEACCzTwQAQTVHBBBht08EEII5RwQgortPBCDDPUcEMOO/TwQxBDFHFEEks08UQUU1RxRRZbdPFFGGOUcUYaa7TxRhxz1HFHHnv08UcggxRySCKLNPJIJJNUckkmm3TySSijlHJKKqu08koss9RySy679PJLMMMUc0wyyzTzTDTTVHNNNtt0800445RzTjrrtPNOPPPUc08++/TzT0ADFXRQQgs19FBEE1V0UUYbdfRRSCOVdFJKK7X0Ukwz1XRT5AICACH5BABkAAAALAAAAAAgA8IBhwAA/gAA/gAA/gAA/gAA/gAA/gAA/gAA/gAA/gAA/gAA/gAA/h4e9zU1+Ds7+j4+/D09/Dw86zg4pjIyVS4uMSkpKiEhIRkZGRQUFA8PDwoKCgYGBgICAgAAAAAAAAkBAVgGBsIODucODt0YGLckJHwyM1A8PEE+PkNBQUhISEtLS09PT1VVVVdXV1hYWFhYWFlZWVlZWVpaWlxcXGFgYGViYmliYnxhYY9jY4hqanNvb3Bvb3FwcHJycnR0dHZ2dnh4eHl5eXx8fICAgIKCgoSEhIaGhoeHh4eHh4iIiIqKiouLi42NjY6OjpSUlJaWlpeXl5mZmZ2dnaKfn6iensWentecnMeiorCmpqmoqKurq62trbCwsLKys7KyvbOz3rS08bW19La29bm5+by8/L6+/b6+/L297ry80MG6vsK6ur+6urq6ury8vL29vb29vb29vb29vb6+vr6+vr+/v8HBwcXCwsbExMnGxsrIyMvKyszMzM3Nzc7OztPPz9DQ0NHR0dLS0tLS0tPT09TU1NXV1dbW1tbW1tnY2NvZ2d3b297c3N7d3d3d3t7e4N7e4ODf4OHg4OPh4efi4uTj4+Tk5OXk5Ofl5eXl5ebm5ubm5ufn5+jo6Ojo6Onp6enp6enp6erq6urq6uvr6+zs7O3t7e3t7e3t7e3t7e3t7e3t7e3t7e3t7e3t7e3t7e3t7e3t7e7u7u/v7+/v7/Dw8PDw8PDw8O7u7u3t7e3t7e3t7e3t7e3t7e3t7e3t7e7u7vHx8fHx8fDw8O7u7u/v7/Dw8O/v7+7u7u7u7vLy8vT09PX09PX19fX19fb29vf39/n5+fr6+vr6+vv7+/v7+/z8/Pz8/Pz8/Pz8/P39/f39/f39/f7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v////7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v////7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v7+/v////7+/v7+/v7+/v///wj/AOEJHEiwoMGDCBMqXMiwocOHECNKnEixosWLGDNq3Mixo8ePIEOKHEmypMmTKFOqXMmypcuXMGPKnEmzps2bOHPq3Mmzp8+fQIMKHUq0qNGjSJMqXcq0qdOnUKNKnUq1qtWrWLNq3cq1q9evYMOKHUu2rNmzaNOqXcu2rdu3cOPKnUu3rt27ePPq3cu3r9+/gAMLHky4sOHDiBMrXsy4sePHkCNLnky5suXLmDNr3sy5s+fPoEOLHk26tOnTqFOrXs26tevXsGPLtmplhO3btnEsK4YDEcJJVp49JKS7oRUrM9Ugn828OeXat0XgLuZHxHKDOKw/tCICUEMROIxf/w8pQoQzhp1GLHLOvj1g7qMIVh9PEJGVZdu7fw/PEMcIksc1dId27hVooF3wyaedJpa0UxA0A0FzDTzDTCLNQNJM0gw83HmH0CiXCAReQcVMQk1B/h3USYgIXbIJQstMch5B11woEITwMOjgQAPSd+CPQKaV4EDV+VfeCGoMxN2L8IyAQ3bSXXMNd+Xh0OFBfoxwpBojCoSIluVZAeEyYI6Qw0BclElfbUeONwmY4H0yUIrwTGJdmUlyqKVtfgTp559jDSlQdeAdp2WfHIrgW5PgqfFGktxZyaZ+Bdk5ghXKVSmQpZhGCg80bOawHHeXsnkdqYYS6IqWnUq3YZP/wf9DqKSHwqNGdpdOAuiuvGolqKxd1tkld4vatmOOwUKjpYcEZbdoovxlp6tA3MkhEJ3wUGPbifC0oyV+zki3o7YjnMgdorYSaNugwdrJX4+9xivvVL/OR9C6iRbLH7Ui5EHQlQXhK5A2XTpJULjLYWvvQFy+AY8dIuTJb58CC4QvvgtbHCu883bscVL1Eqgxv/r+KwIhJj87kMH38ifCmQO10yW2eIjABUHwcncHQVziAWvAsWIs8s/wcPzx0Uj/FPJ4+BJr8b7AjpcdjveOIJxAh6g7woQC1RxtrPCQ8rLJyHE3Xg4ikAJPdskM9MnYRGdMdM0+Jm333TItXTXJTwf/bHM7oA490BvgxfelCKLwiwMxsmrJn9maXGsdNdpQWXaY2lDjKTx5SNdJnVoeMnLUe4ctnSJX46366itxxziRQzctQiJ9E/QJnODZaBCVR/osUDu8vww3KWAKAg8xuKN9OdrlSTeMks2HuXKscgtMJdSsZ689SJbQp/nrAtnhb9hW7KgGugNpo5wV6B/U/XFtF+THcX1aochAyRxn4zXrWwIqftxRwyWOowauDUQTx7FC4ohkB4F8D2e+65oVCrG9ClrQLQG8oAY3yJgMcvCDIBTMgNoXwhKa0C5UO6EKV8jCFrrwhTCMoQxnSMMa2vCGOMyhDnfIwx768IdADKIQ/4dIxCIa8YhITKISl8jEJjrxiVCMohSnSMUqWvGKWMyiFrfIRXkdq4tglGETXjCSZnwxjGgE1BBSMBIKaCGNcFQjG0VCgSnE8Y5AWqNAiPGGa3QBCVGQhEGKkYUmZAF88OhC6uCRDDZ0ixgVeAIxUojHSjJHj/DQwgVOUAEWUGADUiBIFzJAARpQIAM3E4gGIggPNmCglRnggAYy0ANL2vKSc8xCB4iwoyxsgHbwOIQG7CiQLGjAePBYJUFcOZA63vKZssFkFjBgQHicYAkCsQEZCUIDGqiSlcwUiDOpKI34QfNP0rRAQWTgA3FCoSBToMA3l/lKcRLzJ9oIhseowf8GHyjjnHIspjoJMgMeiLMJBYGCPOGRgTqIsp7wGOdPBtGFeV1DDjuIQSgBis5cDnQgBRUnBbSRvhP8QJzYHEgTIEoBJwQlCj04o5+0UQcexOCmguSon9JZkJBGFAMzSJwoaHCBz8FDChnYAjWuwYUMQHQIFsjDP3tCjRrEQGVB0sYdenDTmxZBp39SwgoE0oUKFKQGQhDnE3YwSw6cQGVKyMAGNlCBJHz0GTLgQAZkepM+3DQKf2oHHnzQ1a6OD6xAusar2mFOgTzDRhQIpTIAETmDPGMQiXBQY+ERDLX1xAk3pcEiiwKJIsj0CRTUiCCesJI67OCeHGmHHoBQ2K7/7qCaiPVYZKsCDavelA5J2UIHEFoQCgBWIwpVSRQ0sANrxZYPQahtYVOZ26Ptlip5KOwQgjtLVkb0uBlJLkqogQHYaqQPQpBuYWegz+oeLQiHlUoTaltZo2zhAlG4QHyaCV6GtKO9AlGG7uAhXmhQMn0zsuxoC6KMMyaiA8XgyCCGoN7avtO9tnTGDGqbBaTcFx40SAFJxXncFVR0IKS4wD9FcYGVdsACbdADBTrAXG4plA8o4AAHKODIgURiBRvoQAXeKJATaGEFHNhAjwkiDSJcgMYseIRAnICCDqxgBd6dyCGIUGHpNgLDtrSDdG2AW6F8uBkV+CqJxblRgUii/wOJe4SQ74AJIGgAA0zQxBsuYASBQOECF5jCKCphBA2ceBQX2EEkmqGFpArEAhiIwiaGgdt2sKACbCBGImqAgUjAAw9E6IAUplAJiyCiCF2WblrBvKtBQCJmLuGrSJSgXj4c5cPBzIDDvstmgrw5zh3YWbcqMFY/m5XAHVgyPJpgAZICQQUEmcKxLaDmg7AhA/WFBwtmIJA/dKDMEFEEElKtXmGz+k8tOCk8gHABWZfEAgY1iTI2LF0m3PoCA8nCBTDB64i2GR6/hoecPattIAyEDhnw80cDzAGUUWAHXIg4F6bQAcZZYAsJ2YE36dntb1PkEbQm95i5dW4/saCdrf9cNUuIcOKSvKHC7LUvvgeygxNQw7hrHkjA5eyKgbQgCAOpgwYEIoWFEkSZFaDAlZf+gg1dPCE0QPlA+MABB3kb3AuJBBNEXmEil9zkUscKqivc8qHg+kYUAALO4ZECIhCEEHAWeAd6LpCfB33oRdNAYwnBAbWtoM8HeTpCkHCCgkTh2FeHCCWaIAOuq3cGdP+6e4JBhBOcwAebOHnX3A4PV8wgDyuggA06IYodUCAFZX8GE1Dw8FcLZAeEWMIJKFCD+x28BRRQQRZ2pATnZisKKqBAC5Rd+8o/PKcQIQa91atuopxdIJDAgAaOy2yjaqMGcee5z4EuEKEPjPZcU8b/CbidSAyITiCJoLvgD1KJYer8Ai6FR+IbggknNN7x6o2/5NsjiqRLgQs8cAEUgHLiNQgdcAFKkAWc1ElZAAQbcGLQMHtT0AU2YH4CcQEYkAJTMAUqkAGD0DUakARs0AQXsF3wgAJKcCMoYAFPwAVEkAEmCA8bgAEsMAVSgAIY4HoPwQbkZntmN3Oi5AHHBQ04WAM+QAEzBmyRZ3fdh3fw0AgWkHY9YAEnAD5DoAE1gGoZ0GPrdxCjlAJCUAMZUAMjNn8KsQlPsHz4J13nt3/sQQMooDvCRYALZYAnlgwYgAI70gMoIBBLQAFU8wPQBg8XIAMEEQODWATFBg+UoAcC/4GCAmEEFQBgf6BMMrgD6ZMCG/cQFJZq/xYUmPCJxfRlAnENWbADNpAFqjch0MAEI9ZKjigQw8BaA+EMU7ADPLAFuNUHQ2ADTFBqRMdvCqEJTGADQWBu8KAMxJUQnxAFariGtQUE7uaGriENlthMdCgQBvgqbBeDXDBzFCBJxDCOf7ABF3IBGDcQ1yYQd6ABTZAIr3iCKQgPFWBeMoCJMuhQ+bZwDDEKIlcDB6YZo+CM0JhqvkeNzPEJHUBwAvEC6laAHUA1KlBtXTBzFjBLGZCRGXABG3IBZZcHHDAQdrACGDCGzyOPApEB3jUExbYB8VWRENEFXNdAneEKUvCMBf9ZWwCJkM5hjfEVUfEHkRJJkeBoXgPhkQQBkgZxCClQbJBIj+Y1AzYgEC4pSkDYENElcpy3GZJAAzlJbh3Gk84hAypgQF2gAZUllAMxkQMBk/CQBBSQYCSHlAOhlPDABycJD20wc085BBTAjYigAfpYlW15lQuhCfhnVJtRBzj5lTclA3Iils2xCVSoBWwgBO7nZ3UYkWtJlDcye1lgB09gASdGlwJhlyxgAVEgB1pAe484j80ggW/ABBhQS1T5koapEFuAf2HJGXrQmF+ZUpLZHMQgBBRQAS3ASnVgcPAgCivwiknQcoewia3ISSY2EDWQWgJRCSxwI0+QAhVwAkz/YCNEsGTPIHsVoALpKBAsQAkEcQg18BCE5Xg7EI+aIQhe6ZiFxSzDiUWSAI1/4BVZEAMZ0QRlBxKbpp83FVP9qUUDuobLuBVNUHgYUQHxSRKRYAMKqmwNWkXtkFFrOAObhRUTmhESYhKbAKI5SQMJ1qFVpAg5yaE8MQqIQFMT2KJYMwVZgHwDoQxdMAWOWKID4QpbMAUfSBCYoAVTAEwGkQgL5AqHYKNdgKMZQQrzWZBG6aJSNAU5yZw/YQRIuEkYcAERFAwsoAEocAI11pYYYAEqYAEoIAQUCg9TkAEn0IEygCNQgKYogIVltgKcpwRhegJjiowaEQy0VZDCqKVU/6QNKgqNUvYTRTBcDiINO3ABM7ICJxCZiWAB3FeJUeAg11AEG0Chd9BQAuEKFJBW7ZABJwYJK8AkBLECJpgEHcAEDkINPaBiHUEKj+p4gMeoVHQIjimKOlEERvcpGFBRhLAB7jkQcqAB/zQDFzoQacqe3Bc+GNAO7RBpC0GrApEExyYQ0oAB1JURyXCla7gHwlpFUuCYO+kTRTBH/EWnyQoP2lCVFbCefkihSbd0K6ACHfA8bHABFUADU0Cl8ACub9mHBHEC+ocRzpBeBVmf7TpF1+BbX2mox0qvamWvBUENG+CIFdCbAoEEc1QBQSBxEdcFOyINdsAExxlhBcGwSf/gsAMBsRohDWNXkAd6sU70BwoarDwxr8XFWovwSwTRVMJRA92ZPhSABAJBA9WaENdQjwZhszhbZBFbEdowX+plBORGA1MFtFAEBQoqAwtUtB4bUbRYAxWQWnZwAfYmcBkgBBCiDDVgATR7CBngBBPSDEjArnhAAUcafT+rtQWhsxfRDlFQYTezm13WX2bbRNKgsY7JrzohVos7hDuwARZwkUtwLHtQARlQARpwAqQYPpBGARqQAoIEDZ9rAajrAzLFAmrGBG17AhdmEVyqXr3JBvcnXTpYuU2kBwp6UxbLE8oQmQeUQsNgB3fAjTFDCHTggwShDYBAB8DYo3hgB/v/ZRCfMFXNoJgCsQkLFhFcUGGhShCMKV0xaLxNBFrJGwNHWhltUGFNYJ/w8Ju1lWXyi0TPkJ/J27WRgQfDW1hGMGAEgZ+2RXIBrER3UL83NQNlGxmA0JhCkL4DkaAxoLkRjERbR8ExIKONkQgEXFg+MKIGkaEzgEghfETzRsIxkK2PIQmYa1uRtxCbYLIxfER1QMM35Wlh8QcMfBKf8Ks3VQPZFlvc+sRQHMVSPMVUXMVWfMVYnMVavMVc3MVe/MVg7MXdEsZkXMZmXMZjfMZqvMZs3MZu/MZtrBDjpl4yUAM1gJNCUARMwAROAAVQMGpToAVbgFVdgQH6mBLEYFPS/1UDxasR7aANkvAIkjzJlFzJlnzJmNwIhVAIjYDJnvzJl6zJe9AInQzKpuzJjbAIfUDKp9zKlUzKhZAHnOzKtPwImpwHo1zKtezKkkAMu7zLqbzKuvzLn5zKeiDLw0zMoEwMkazMpgzLyOzMoEzKwizNmdwIe5AH0WzNlmzMnJzM3CzJqbwHsxzOnhwMsiYJlSAKohAMytBgBPGghWW+aqFrKqEMiVpbM9CGG9EO1yAFQpAEAj3QBF3QBn3QCA0ENYDQDN3QDK3QDh3REm0ENBDQEn3RBo0EEI3RHD3QG93RIO0DL7ADIA3SFG3RJT3RFZ3SHI0E1IrSLN3QGr3QMf8t0Uiw0jXN0DcN0zl90Cfd0zKN00DN0DPgBNowjQkhz13VxGlhzygBDZ2oz7bWEf4sBVBQDMGQ1Vq91Vzd1V7N1cVADIXAA4Tw1WZ91l0d1mNd1mjd1mYd1q7wBFGA1W5d11sd1oPQA4dg13wdDGqt130d2MVwCDZQA3sd2HUN109w1Yjd1oo9142d2MSQhowd2WeN14Bt2WhdDMMABU5A15qd1sMQBZUd2mlNDHEN2aZ92rNY2qud1lHwA01AOROhBdLVvUXRDlxAAyuwA/EFBJgQBSvAAjnFBjXQ2+xaEF3A2zsw1SnJBlLAAiowBGsLEtSQBHRMk7H1z08A2q//rdVqTdbfDdZiLd7jDd6oLdfe/d2Yfdjn/deFcN5gTdg24N7vnd6u/dqPvd7jHdaUzd+r3d7yDd6d/dnyzdlR0N0Dvt8LTgytDeDsHQVFsAVMICFIfRCSW1g8OhTX8AIXQARSwAMhOBAaoHRQ8ASf0A6cJgRSwFbLqA1ENQQtrgH6lwFUqARIQAEXsOEcoQ30K10m3LjXMAUK3uBrPeB+TQyHYN4Hnt6qfd95bd/sreQ9EN9I7tf0LeVTHtf5HeBODuFenoZP3t/EEOVXjuAG/t6jXeT3ndpgrtlh/eBX7tcS/gYU/gzUcOEGsb61RcRFMZrOmwcjq0r4KBBTcAF+/54HGhCL+bWodrABfZCSLLAj14AC5OcR7fCu6gXCGOHPRP7mcK7kTH7fS87WTe7mDW7mTX4IVT7ng20D9Y3kit3lps3gsj7ZMzDmU67qCz7aad7faw7qkW3r9y3nZ17nckDhzpDnEsHnhbW6RNFS64SPGgBcOVu3IDWV1hSh8BAD+OiqBPF8HGHb6pWlQj4Fv07qPDAIt77k7N7gqN7kvE7mrG7qC44IsK7l+o3fwo7YxN7kYt7v/l7mmd3rUZDu+u3rAi/YX97gxi7ryJ7sTLDseq7c0oW9Q3EB1j4QRjCIGoCMF8ChS+CwGMChRlBsTi0QCPcRMpl//HvuCD/lS/8uCO3OAzQP7+qd6gVP7z1g7weO77GO87Qe2v9+3wGv8/qe8Ae/8HyN5kzf9A3f5A+/4BEv8RQvES1fWExKFCVbEDswRx9PENdFcw6bAealcSm5a03oEWKmXkqAdRXh6U5ADDV/86tu87ce71C+8zLfA+8u60Cf9LXO76ee87d+9PLO98B+8HSv5oyP87q+71N/4FVv9cweETxYW/w8FD5gcwOxCRmoSsjolwkG+mUPiANBCaiU9u7rhBqxB41JBAFpEXLf+Hdv9+qO+2Su92Q+733/9wueCKgo+ETv4Iv99JLN+0af68gv2b6/+HN/4MMgBdFf+JHv5ZP/3pVv+RX/v0zSpZ1E4QpwygaAkAXpCfbIqAxI2AXlH4WnfwLsPwUWMOmsf3cccQiNCQTUmxGPjO62n/sAEUzgQIIFDR4sWIwYIh6CED6EaFChqydRikXE+FDhoB6HMn5MuLDHIJAlCRZLtMOGR5MgFQ57AuViS4wTK86kmVHhkxkWc9YkxpHlz4gKozQhRlQjMSlOkipFaNMn1IPFisGUSRVisShF3sgBG3YLE2fU2sFDm1bt2rRtYryFK4jtXLp17d7FW3fUDgsYUNj50QItCkVrg/noe6LLlh1oM7CJQiEDBSbU0qY4pDZRirydH9WAG5pHsc6l22mb4lRr1YU8AK2W2Po1/+yTxChOpR1sY8fcAhUi6uGw90CUO2oMzf0yJk7YUpknJ8YTN+3dyKFLQdpbYdOn0G8/b44VPPWuX8OKJWu2dFo5od/+WR9f/nx4n/aohWYhyvzH9P2jxQQ092LYQZT/5jotte6gY2g27YhJxLXhdLPtpuGquxDC4Cb0LaXjJlQuK+8sBDG6nsajCsMSsVtwte1Ue/A7EMXjULfyzjtvrLLOiq+OAfs4MMgg7dAgCmjgEYUFCp7hjw0h4yNlhwFjqIGSJ9M6LYvsMozwjxK7LFHGB4XisgcHL/TQuuaIoXHE6aCTDkWoVMyQxQftjJFE7doE8UYc0dtRPjsGvO9KQ//XY6MCDSzYYIVO6Ovv0LsOm5KGwgzNcssHweSSBy8zFBM6Mjc1s8ZiFDFOTRfZXC7PN6kz8VWt6HxwCk3XtLXFVUNNjs8L/fwTLB3Vi++OAfWQNNm8FuHjUf+mGEZZupwRYsoZBjk0U11X5ZTUT12VUyla10zEhzO1Q/XDDH3dVc8H48xw1AxzrfXWNXmljt3kgA1WjmF5XA+PAfGQtmCDD0ZLmiKszSNbbbTcdlYIeejjS4rDdHdNeWEt99sL01VVYn1TrFDWVeEdk7cSp2AiYnGJoRfcGVvlkCuv+s0xPYBL02PAOxAGOmghr2FiyhjkcBjiL3uouMymZU455Dn/IfTBY+0WSbXEkafGF1aURVV53pbrdfnlrl3c2kV+cf5Xvj0GtENouec2LQqju0h67E6fhk4RpjE2eeqN11TEB76vznpdmmE9++QT4w271iXK/kkhlimvvORwxU171rXZ1lm+Pgasg27TT2drCqOzyBvzzP0+nPC/Qc141cFXLTx26rBW98HOM29c4q9hvR3XyWvV283Nibpq8V9vxhnHtuP7Y0CkUceebi6MjmLnINu5RgsmhinRbz7K7+F82gN/uXiScze1kcR9d75d9qceXuPIe51iCfKhY9n/lDczEfUJetHLWaDiI4gBtSF7DwwaG4zWBG1gKnzjQ5/6HmQ+/8AtLyfjwp3h4jc/6Pzug5qrUaw8SBMQhmcKShBgTv43CvINYxSWGx8Ng0EKTQhEEgLRBCl22MNg/DAYkRBFMD4RCYEkQiBILIYmDlGR2SRiE8HYhCIEMptCYCIYlSiEQPIgkC4W8VN1EEgfJAETJVwEjcHQAxMjoQeBsEEgcTwiHgTCBYHc4RHBaMQd9igQOSgiCj+QAliaABYpcEEOXEikHJQAlihsYQtCkINZWAePNlgJL4UYkJMgOEpp4UEGUzKCNCyoBf+hbw8lWkQPXrm+FbakhSRbhA80CBFXCKQTAtGhDX0JRCHyUDeAMI4eiklEJx4xiaJgYjCaaUUsav8xGFW84iYSwaYmyKSMYBRjGilhRoG8sQ/jlMSn7AjHSBCjEUuwyDrx8MdACqQLfaSnIIORBUIuIhiKoINA+BkMOWwzTlOooxMTsc4o2NMjh7hnMBoajC54hBBbEIgTdLMFNvSADRgNhhIE6hBADFSk+3RIHxAa0pFec6BFuMoSkDCMkgrkpFlI6UqN0NKashSlwbgozHxAvi2EkRB8DIZGKfrQiE60otJc50rZsM1D7MAiK5WDPwEq0H4CUp8R9aNX7SnGRjzBCXQMxjrxGIkzipOcwXjjN8MYjDEClQlFWAxYsgAWLrBBDmxwpBz2KocufBQJmDALJtDiCibh5RD/oSRlZA/1hxlMSQiNPdAzXAGPdlBiC0poxDOjiU0sNpO0mJhrXcuIWmJg7ZVqfKs5fzjHOt5RjmgF6yOK0Ygu+GAPSA3rVvcpEDpoUbgDnSpUBSJVRLjCCTCVqEODAVGBTHQLhAAqSJV63WAIwqQKycIODuFdmy63YuT1KU6DoVKBEKGl3lUIEXw7BS/1waTvvW8wuHvRjApkv1woxiNmcJynUje6aVVoVBOq3GBogbhalQNMknCRsD7iq/gMhoVry84jonWdanTuE9yYxh+mM5zBWG1qyZhN0zZRE1CYgYil+UTRNpHGQ/QhMXGsRIX0gQeHEKYSgRnDyg3jhUSW/1gWYEhLAtbyg59DoLBCF59EDAhvksXygRJBgyn5QBlPkkQbwOcDGCZ3octdsHAdHIzi/jOgFMVwI+wQy4GGlbZptW2Hy0niIjZNtsQARA1eqVovolYgs6SmNrfYxCtC08bFgIQmnvCEaDYziDs24qWD/IkhByPInSAGIfa3qkb41lSPICGsTMhCFHKIGDC+X+ZELbUUDSMLSUAyyZSca5IFb06rZh6Uo+yvKa9HEVbOcrLnIwkBuWcHm70S+D7La1z6AK0PKvW1B/igWWco26dO9ZqAbRLnpBDWTi6JQro9o1tTm3nE2DWTLzTuJx9w2NIrdmkaMSAuKNvfnemEDf+mZINNWPCzvcxQLrUNq2/LGzrrhk7DQQSJcK+K3i5p9YRe/Thujzo8WUACwmF1a5EzrnZoq9++7H3vBBJrPZHg979lXhdiSGlANYiEBbmQhJIz3Nolkvi2H+5xkgVdOxTv3Z5S3uuTw2bjsT4hxPcE8p4nmecO79XSmyNslk9vPZIY0BZmPna1KAMIlUKEzq/ubR/U9UGPaHsHMyT1NcF94cmJRMVRXsB7NX01T0c3xumerywYoeq6DjnW86V1ta2c5S33Xl4qMSAtzEUbXya7ZKExBGsBKdrX4ELi2e526MCd9CaHOqsHLzG7myoSPEh61vluv8CbBPBzJ/qsbG3/+AdRXfHhYbznHP94QLm8NJoY0CbZUsHMk5IaSZiSDH72+dAfnvVxz5Dp5d5xWr/7ET84Pd5h333OBR94ftfK7blfs91b/2VaEL3QZV/7dHOd+F4vTSeSv5ZKFKr5o9QGJzAaUXoS8OkC3ss+7HsQSFBA+VuT1SMZSAA/U5GE8WM/8zshX6MK9Rs68mOe9nsQLUBABwS+2VM58yC+fsG/zhiFAZmCtVCGSvi/UWoHKTAasfu8AxSiBNSjDGHAHoSaDvTBCayZCoy9xTNBpku9n+BAWIFA3cuCIthBWBHBKUQ9+tOJi7Ml+0vBFcwLV3DBtCCFyJtB1MkCo3nBHBzB/9LzASCEjh/cPiFcQCIEESP0wCLDQFbTQKhowgfMvVrTgiJwv3cTwUF8tz3EwyTcuuFLQciTD2IIQ7ToAlIoQwjqAqN5AjKkDwM0AiuEFTjMkEhowzh0wj98t0j4ATfUDju8QEU0G/TbwHPDvTv8oGEIRE+UmC3oxN/bOyz8CJtBwUZUwXzrDGnYg0JAxkJIBGirxFHykSlZgmvIQSksEVB8EFRUxb5bQnILNVPMHGykwB44whL0xaVARKLow1V5wlrbAmqEjnbERYk5RxbSQnLjQmH0QvqAhv1oxuzRg8oaECI4kgK8hi5wxznMxlUBR14kmXU8xVQMx3HsxSAcjv90lBiH5Bx4fBCNpEgkLMeauEd8JMb/GMh+PB1CAEj3CIJmmMZ4lBhR1KcHkYQfiEkSvEhvPKGZTMjmkARxpEV6zENbyriKlMX1m5EtIAKXfBmOtEndC8rKCUmRVKD/kIREMEnsaQQuG5DR+Dw2OEjoQMWahJWZFEtt/Mio6MafTDdiIEtToQQfkEindMVDhEU+LEo5nDekVMp344KvNMsmM5WolErjo49PkISrPJ0AmZIC6UoiGIUSCcsSaUuGnBqMzEmadEu4VEtyq0egmMecsMiGxMkiQ8rHhA4ucEzK/LWnrLdgFEYEysfSwDzENJ29mJIakEGCZIPUzJDJlEn/zFTNd7NMVpMEIChL6njLuKw11sS4z6QJFZpF9kNN08yX6QzOD2TOLWTE1wyW2OyMKZhN2hQaShkQGliExqRO6JhJO5BMIGDP64y60RTK4nxPEEnOzUy3ziyKoewN6DTKeeOCIUhPtLHOjiTHwNxO7vwT7xTPLHOGILAWbNFN3vzN+lRP9yRFP8RPnWBLDK2Z+2zFs4wN52wJ/8TLPQnQAdW9Am3K5ZzLFBFMBWXQulCGNGxQoYGGhRmQGUCWCVXRVVnP9rTQK4xOmfRQ+9TMmqGIF83AulQKEy3FDf3FYUjRPekCCiXSC1lSEdWIGJXRkeyM8LzRgyEao6GDrhyC/yTqzR94IyNtU+ggBSdlNfeZGvo0lUpIUhDZ0geJ0218zvyxHfmkxwBV03zpgjTNkD7lUonY05rx0i+dys6QBGcZU6Bphye4GzQtVPVk0/Z8U1hRVMiR0v0szk9Njkr4AeWck0YFVTllQkCVGDqtNUK1UkTlU1e1JVZ9HtdU0GGbUbZYhMOsVKBRnSlRvu+5Bjaw1d80VSAFgmaVmFCNmt581jtN1VGtCV1dFWnVOFglGVn9tUPdVLQR10TFVXLT1hMEC7/6qzaQgzZgV3YtrL/qArCoV8JiV18FU7ooyWFFmC3gHk2UD/Bpg2XlVGglmVItEW4lHkFdS4WtGVRVVf9xSVeSYdj+9Fai+KVi+ARA6AFCMM1fCoZLM6YiEghKSKIlsjFnqqYtugouEAJRyKITS7ETO6fYuqPZ0oNh6IIaSKI8+KF6CgbgyieuIiitejPk2qZCGIKrki4DcyqLQqrtMiqkuqk/eIIhyC/6uqY1Oymuta/y2ieSYK/0IoIi0BKwCAKwcAItkAMtcAKwGAK2ddsteAKwKAKweIIt6Lp9nQsj8VeEkaApoSCCfAODHcsfeLMHoQQgWFw4PVeMA9eMENlguqHWelaSJaJMe0xHmzGWndlrYrRqKgZMuFa5sll0aqv10tkNw6NAoogdoLCiHa6jdTOjNTMFS6tDIIX/57qIqAWqqfWvqhVb9eqpr+0DnjCCi7ja9Vqpm8opgUACnvoumBGvo+ov/QojqH3aiJIqJ0KEdcoCngUCRLjd2g0roSVaPcMzDoOEPDjUN7tZE6MrMvIicKpfacoma6qiIHKCrGgmJAoGz820ZRqmYLDcYSqGil1EXu3Ve/vVwBUaU0IlyxAS8JGDZfXcAH4mfxJd/S0tmi20QWjciYIt+pUt9lUrObqwYLiDRiCGQnDc2m2z41qwMwuG752uptKNLhiEJyAC7fIv7OKv9HII9PpaL0HioKgBNCpb6HXe9qJesd2vLSAGSqCBgOIu7l2qHUYzBGOwlaIDpMUEIPiQ/wprYTvzsDy7sxSWBD6Iifes2fxNtBYD4Q3WjUfAhEmLpgLesUrotE3DCCjVH2wtCp6N2Vod122NXJdg4MZz4AfW10ilC1foNwk2GMqyLMw6EPChgyHgtIEwMB3GYTE2rjdbszrQrUVgAyAgLAy7sxVmXzf2MzKiBEoIgnWqK0RgsUW740q7MQJWCElwgigYhc0NZNMM5Vq6panBZTkwlTKeWOZ55He72Nwg5EA15K1A5FA2VFA2Vz9Nt2qG0QSV5Oj51XZgRkxOli2bkh8QU//wZCHwZuhoXGjOkGde2Ebm0Mk9IX2uGWneZo0g5xO6ZtrI5lh1WM7sAnq2Uoe+Vf9xduTstEdzPmfQoWT+SwZ2LhhmW8xoueBrqAOIZlwgeIMSqYQgOOlwXtTa8GdWS+mVBhFNMOOBjoqCZrWDdrqMbZ+Fzs+Grme0YQOSblWJ/kWcDjaLvuhhzGi16IPc5GhJCbiBK7iQHumgXpNKAII2QOkg4GqWFtUMiWlToelprhyk5kad/juefreXrkWg3pOhxupo5WedQOvWXOrB1ERpjGplGQabcw+cI8iRFtkH0eqvNmyv3mejBgq35saURuwLKWubroq7xji1Tj+2zhzHtiVELuzwGOrPXhPMpliKrj+lzuvuBNN2cAK+7mtDUYYfQLvBFgLRzmogWKfEzm3/yGXs/eRsjEvp3daOyVZS095P0uZDzT6h385PNggC26610AZrLTXuX3zU1Jaypuas1z6UZ+C8HfU8ZLWD2kZp3C4RTBAC4R7tuvZtn+ZQ9FZv6tiEIDDrD7LsXyQG5H5S5Z5T955S54buX5PuiG7pk7jvio5k7EagNyCCLHgGwuyE6+HuK6EGJIi+6RNv8hbrXD7v9F7sAveNoPDv/YRvU5nv+mahAwcK/UZH/hZK5naJYQDwuNZw3gZx31Bxl7hu7H6DIoCCZICGa/AeaqDUCR8aKJgSAuzka7CDILiiDY9viSnx6QYbyq6NKQeRE7fyhMjxrchv9s6IhP7WEa8J/xl38rg+cwLX0+rWiR1P7R5/AmJohiBXi01QJSPPlmKFCxxE1jtI8wfBBA7PECy38bAGdA+vGS0vbibNaTAfZBfnRhj/7z8HbUpf794uii5vc9RW8POAc2JQBmgwvjqoajw/lO15Cxtdcj9/ckAX9EOPcot1dLSUdBJH9CwXAhTPVTZHSxZ/VY6rcvZz7lavdGK/9BvXDU0HSU7vdLCA82FQhgfXBh4RWFMXEjaQgUwMaVY/71eHDk249UKf1gcB91iflU7I9S03cF5nDV8HTUhfy1ov81Y2dt1rA0tnZEzfCmUvCjfP62ePdmmYdrS4Bieodms/kEJw7f8AHzwIAv8icnVzf5ly/3BD//Zw1w5013V0ZffYcPc/BfaGVXfikHEggPjwuPeTP/Y1Z/SkTvBmDxaAfwaB35mFR3gsa/iHLxFNCIKIInch8HlxD/afD/qMT/dFB3GF+PgShXfJJfNDbmWV17030Hk1p+6Wh0pmb3aZp3m06L+b97ecl/pVAfeiXxNNGAKzz/cbb+aJT3tT0fiRx/GOD4mlt72mx295h3qT3xOqH3tZ13eCpvuo8Pel5vqBR4sYBPtkA588qHpy7/kS2YS3p3KRz5DJV/vmiHukt/puDflCFna+7xU5ePyiRvYFHvyqKPyLPnweGcPFZ/xrcHwvypCyl3zK73z/y38QzIf7o2f5pP/ywIcIMRecp+fmNvgB2s8XOQAC5Tf930fQl4d5HGn9tJhE2Md52W/+nQf628/8WSHRvDd+1uD9mvmEIdj4cU79ELf7kiD+npZ73RgG5Hd+tKGD7c996uD3rVj9c65+7AcIeAIHEixo8CDChAoHtruWBwimYBInUqxo8WLFYsQ2CemC8SNIixo3DfEY8iTIYq6eRCmG8mVGYoN6HIJpM9jIkjd3Fvs0pEbNnTBVPoHiUmhIjaRYHkWKktiTGS2dJpVJk+rJYsPe/IiI9aLWOhC/YlTKlGzZlUbRfiwWpcgbOXLn0q1r9y7evHbfFHkyTNkzadra/wmEBmUh4sSKFzNu7PgxZIbX9ACpxFbkxo6XY5I0uXki0amfcVoN+jmn59E4fQJVLZHo2tPElop2DVVq080aZ5oerZWr5dPDxAaXTTu3brXIT7+Nq/c59Oh0+foFLJjwQGiRt3Pv7v0744aUi8vmmFo3sU5Ezic/67u0ao3q2etm3fu08vfHXUu8XRs9b/xpJccP5LEVVmXxzeaeb/kJ2Jx0EUq4V19/QXOdQJIkAh6HHXr4YWLiJaigea7Jt56AK/132W5XvTcff6v9dF97saG3H3/+LYdWizSyOAyBBvI43IjGMYhfUTuyCOFcbbAh15NysNGGlF3IZaUcXTzJBv+WE1JYHTTUDDbQJ5KAeCaaaXLX0B5FlicEFyamR0ScJqqo5Fc9KqhenSaKMmOKSep35Gg6KhigU8QME8wwo0jUiUSjDNMTGwVKIpEmpATzSSQSJSJRJKIEs4kiEgHi6SbBYFKIRHloxUZlq0pUh0R9UBKMJH9IxEarl0Kih0R13vFIMI3coVEWLL2xSDCK0CFRFrt+igivwUwhUReIBFOISVFItAWrg2yxkg0uZTFIMIBoIZESEk1xah/RBtNuMOcGEy+70KLbxxRvBSEXE1rIkUUTcg0hlxNbyKGFE3IVgbDCWjzh5ZcWioldMmpqvDHHCbEJxK17wiknn4GueCD/fC/SGWMxohDRWkWSMupoMJAGk2kwpGgi0aXBhLppp8EkQpQTxZBqKqrBVMJqMHlIVEhEuc5a662R6DEbEy3l4esdwUo0bLFd1yuRHMw66660wVCrYxc1HWKSE9+ySkidTeibrrz0TvFHMX84QZPeuqqbr7WC53334MHEHcwWhAyzBQ+3ehtM28G8jXYwbExbrbxlN/ss48PY8UNNxnrd9KVW70o1rroGQ+u2Ea06mxAtJZLq0el6CimnnoIqqs48Y6qpK50UZbPMzMFFMfPQUWfxmAJNkXHH1Vt/ZkN8gEzyyF8hv2ijGi1CJ87B4/p7MKIE/anQuJeqe+xKE7LS/xAu2dr61PemDmzmEunRqeko97VHEEMPO6hJ55iViGpdK3OfSkS3sOU2uGlkC0DgAiG2QLgsCAJvEiECtDoICHmBsF4izFvLgACUEW6wg3STyOLAZbkIUq4mC3TXSojwQM6RzWygMwnYAlit/wUDEnlYShPsdyupBUNsUFMa05zWvlG971Q+E5UoIBGVot1MU+brmSjAR7PvtSVl8RkGHXwQMt2I7gdrvBGhWOQgEzGpeXasmDLCFL3r8bGPHMqeGy8XjMlVLoMw/BYhgiGIw5lQkdEqBhE6IQQgdHCRHwyhIy/ZSEvOC5HByGB6ahAnLkxQIoS0IQPTdraxBYMOpf9qRB1YIixi/Wp1wSCi6l5Xq0sxUYqEoMRM+GfF242Kfez7mfok8r7y7cxnc4pToyTyCd24DGbxmSOLFnSyzRjqPYg6YxrfeKA2ihNlOGqQoB60vDuycy7Py+PFBKKMKfixnvZsTEP68INLJfN8XcxZM6cZDJlFs4yS1OCeioDQa8aRR2ZETycUyrJq+uhA2DRnQy/TTdl80zfD8UHP2Di6kMJxmzy6qPKc0052vlOP2JHnPWMq04Pkc5/cW6hsPiFRO2U0Tw/Npk5x6ptRFMGa6LRRNs+pmo0CyEVnrANIPXoHmw7KpGSBDZ54VMeV2rGl8YSHJDox07GOtaYkhaj/EIQK1J0y1KpY0dN7gjrRolb0pOksaVbJwtRsdlQ4dejBWYc01cA6VKk1ymuetspV5nl1j4swE1kjW8+G/IGqcR2CWlH2CSNk1qI9fetPNctZlhHVqEhCKkbdqteoqNYpcH0qYKVqWbzyFLUpXewdG4sd7Ui2t32k7GzR45POXpUYmyXuVe90KKcKd7QCKm1dk3vXpH52tbhZbnTzNJzYCgcPwU0tYl2LUt0oFrcT0u1AosBb37J3Y8AlrEN9si4FbXa+bQ2vUF7bXPvGZxRGMO1hq4rfRLF2wDfRr3DswF02ehe+PjWsHKfrm/KaN0LobS+GO9YQQHxXs0PgL3pE/2EEELentUhBMMpETGLd+BfAEbZtcSHMzQJjV0Ci44GDqaKVBitIxnaF8ZLWWWEvXdgVXMgwkrF3DQ4H7bIrdqiKTWZgm6AYykh48oFanN23jvfB1f3KXlHWV5HyoMk/woMPzExdE5+4ywei8JCd1xdiwHOP7XBFkvP8x2sI4gdqBuoQ5PUeUSBB0OhkM09Ca2VDD/W/W9axm3WszSnvJMwOHfOP7FBmj6L5z4X9snglfNs4n3fOdcZOJain51Wvic8+gIScfMLobBJ61hFG9IEVXdxas4wUSHCxZ4EMWh+zxdLFxfQ4NQ1r4eTh1T0GdZtFTV4hk1o61KGzSwfSh/9KsLrbkGnIIJytoD/ZGspJKLddcU1lXftUFOfu9a8fHWphS5rYaDG2T5EtWB4sm43N7jdtGUrvq8K52ni59qkFcg1vM9wx4Bb3ZdHdbiVIPE/K9SZzaU3xXicB2D+mNErMou5K0xjj8j7xMO7Ab4/++9kjH0qkCU5tgz8H4dmGRzucsPCG83whDSEExGXjsopLWhQb5+nLQ85u0Bqd6G8lhRI8Lt2Bn3jSMcI3aPV91ZSvnNlBXzPIX4JVlhWc5nWx+VcF8tKes70gP/cBscZNBKdXvelSrvGgj24iqEvd4tL2ctJhgnVJa127d9hB3NmoB7i7POwhj3liZ272g5v/+uadkEPbM+/2axSC8XKne36JYXekO74qhXet6JcA+hPz/eT5hXzo7W3dwJs+42w8fOJ/tPjcg722pQ952ScvF7TvkRpi1bzmG9L5RsjJZQ18zyiU8PxD/76Mp6/6KJYw/QZF3fU8gX2iZQ/mknPU9j86PPOFs/j0G4n2SQG/a4MvfOJjZxPSQD7yG3IIH7Bf6ETYfjZlHwDeWvWVxdJJ2igwwQDKUfcFCtXFHrQR2HWZnI3hwQ703zitX+P5HtlJnvCdXeWlXR1sAv61XTtowzXsHwbSWhEsYHEloAv61MWVn/cNBTHAIMu4whL0naTB37qJn6SRX1PVoA0OgwWu/6BPDcMe8N8GvocP5pf8TR79qV0JZt4JpqAPMIvcxSBo4aCCzOAQKogX2gkT8CDqPaHYWZ2cCCFfmZ9DGeEOaGE2KWEWNqFsoOGBRaHZTSE8XIMTrF0VetsVIkIdimELyskY3mEE5tr1hV4iNkgZEmEa4mFWqKGCsKGYuWFxwaEcosww8EEhtl8BggUxUOJQ6CHN8aHCBSLPDWIoygZRcWFIPMIYhIEt3uItgoEu+sAO4KIv/iIwBiMu6mIQ9KIwHiMy2qIufgELfEEyPiMw6iIY8CI0VuMv7qIEOKM1biMxGuM2QmM3fuM3goEQSIA3iiMyLmMzoiM4gsEXrIA2sv9jMKpjPMpjNIajPR7jGYzC372ZB36gO4Vg9FSCHrBiK6IgIb4P9B0iUhTCAyzAAUSkRE4kRVakRV4kRmakRm4kR3akR34kSIakSI4kSZakSZ4kSqakSq4kS7akRDKABIxiW6CiwamiMnCbQQoiCiaCDygkLDKkUBQCA7gkURalUR4lUialUi4lUzalUxrlA6iTSgHkl2DbV5ECIOaknl0hT/okesQiVTzAU44lWZalWZ4lWqalWqblApxBfNBktaliF5CCVnYbV/oA+yykFFDFI0DkWv4lYAamYA4mYRYmS0blqFFlVSZcXTbcXeblT0pB6ZGBYVamZV4mZmamZkL/ZWIq5nQI5G4dRmOu2mMiohFIJlaEwWauJmu2pmu+JmFCwLRNpWcOH2gSxHqNJpJdoSLgpWmiZmrCpnAOJ3EWp3GWpGz6I23WJh9qiG5uJQr2JmR+5WkWoGoeJ3Zmp3ZuJ2smp8wtp2eqoihA1nNm2BUugg9oixgagftNxHVyJ3zGp3zO51N651vBJanxoaqVp3miIHqqJ/Sxp0y+Z0UuAAQcKIImqIIuKIMuqAMwQAM0qIROKIM6QAMwAIVmKIU6wINGqIZ+aIM+KIaCKIkmaIeWaImeKIqmKANA6IqCqIWO6It+aIzOaIbWqI1uqIrm6IR2aANgpH3G3z9SpSpm/8F+8qdvnacPSKLS+Vd7UgSBUmSQOhQYtiGTVgUpOMGTtsUwOIEZVp0ppoQlvgcmXpomJmEeHJBsDAMgnGm9LeK6KUeUTuSU8gR+xpkqImmSNUQjLKlpbql7Ain14Z1xaKlMgkWXfmm0PeDrwSnJTSAN2liaXimXCoKboh4QLqpLzKlE1imV3emQ8eE86al5XsMw1IAPJIGqriqrtqqrvqqrIoER+MAMwKqt3iqr1gBGMgCu9iqsIgEQ1KqvDmusGgENpCqxJmsSyGqwKquzLmsQvACyPuuwAquwUmuvWiu2JqsQ0MC1bqutyuqxgmu2GsEMTCu5tqq4omu6tqoRNP9ru/4qvHbcroKrEAwpQOapMpAqhmmDNDQCEQiCwA4swRaswR6swQJCH+xBD8gBwj4sxBLsF2BkA0SsxR7sH/DBwVwsxyZsH7BBwHasyAoCIGTsxo7syCrsEIQsynbsH/TBybasxZqsw8qsyw7BDtSszUKswoLszl7sy24By/6swb6szxLtw9Is0iYtzOrsxF5kxe4sEShMbX7mE1hl9EgCCfJrezUENCTCEgyD2I4t2Zat2Z6t2bqCKGgCEBQC2r4t3JItGGCkA8St3abtKHQCErjt3fZt2aptH4St3w7uMKjtJuwt4Sau2iKB4CZu37pC3iYB3zpu3ELu4U4u5dr/LeQmwQ5gbuaiLeA27ue+LeR2whuI7uiSrSuQwicEbuqS7ihc7uuCbuRO7txeZN1+7hJQbdXKgSo+Ftd27TV8LerO7tiyLia0rfHKLd0ub+HGLuI67/G2bvEaLymsbfRK7/QaQfUar+Vmr/OqrSaAr/ZCLhJ0rvaKLeu6bvo+7ya0QfeObunyQfymrviS7/J+r+02r+7yLnPeJjzkZvAm6fCCbfsOA/Iqr/TerkXmrveOwvh67vKyLv227/VighFIsPOyLvcesP6m7/1qcP6OwvmK8OtScP3ab+zCb/nm7R6k8OeGcPt+sNgycEU6MOXubu+qonoNsPAS7wEnsAl//64NUyQOvy7kRrAFf0IFp+8FZ/ABI/BmwXAMQ+8QxzD2XrEKl/ASNzEIrzAVK64Lh7HjyvAXy24N82/m6vD/Xi1j+vBuFjAZO64Qa28RT+QRq7ASOzETzzHhPrEWjy4H+zHh0rD0mvEMkzD68rEXt/D7EvLdlu4Lz3AWJzIaD8MdS2QeEy4bKyYfGhkcY5jXGvASJ28gJ24mR+QmV/Eeay8KW7AoYPApZ+4ge7AVg3Al2zIXMzIk+63lsvAhjzElt3IwX3IqH8Aq+20n5ysA4xyehTJ7jXIv920dL7Aaz24S46/19jEsy3IU17IlazMS53Ii77Irc7MlA3P4CjMuE//zOhvzNTvuMs8fAKcaNIuyHAfxJ5iyHcezHovzCaOzK8cyFOtzB4fzLBcyOX+xOUvvK6fzNMOtJEd05S70O2fvMSdz387zHjazrdzzD5MyH/OzNePuOrvzBAu0QxN0QtPxFNvyJYevRQdzQ2+wShezOo9wJ0xyOwO0CsOzSfcvM7vxze0cSIc0RcdtNTtvRp+0TwvyTU8wS3/zSyN0T7e0GNd0SjcyTif12U70MD91FQN1A48uR8clAOecUR91b0mzPpM0U/uz/EKwWNNyVFvvVBu0V8NuTOfvTK+zVm8zV79zTj/wTu/1V/81Nt9yGgf1GvtvTTYzzrE1Ur+1Asf/tWMvNkoLNmKbLSBT9UGfcV2XsWI/cGDP7kOfcWEv9mGHNVb7MmNjslwP7lmHKgCbLmXjMxCX8mUvb1OP8Gaj9l2jdl4vcWg78mgrbmkv9mkH9GCP8CO3cGtftVU3dlkLdSo2s/HldmXz9mvb7W8/cHA7d2eX7WfrNUwnt0KPN3Mv8jk/9wNHdzBPd/ku91yT9Q2bNWTbNlF/lf1xt26L9DnDtW/PduZm83ffbWqvtDejd3Ufsn1vsXs79HAvtnyvM31DOHvfN0Yb+Ebvt3mp4ggCeHePdG8bb3hrtnr/cYWn7nkbd3n/bWzL9IZLeILb7YJ3tXTzdH3X+IHHdorL/zOILxYfZiWJS5Zbe3c/ZzYS0/WNK3WLC3Jx8/FxF/OK+3KEz3Vzu3iU3/dqN3mG0/iVwzZ+G7F+5ycA+6GRH3lZ5bOSl/R1i/eYU3OX0/KUn3OVX/STk26WV/GWQzV8W/iXq3CY+7WPU64hB3ni1nY75elaszmSu7mJ7/nZKvpcH7pLB7qL3zmF5zl0z7nm9vmP//nn5jhhx/jzFrr3iroYlzken3mF8SFBQnqAo/r0EjiKe3gZO3kX2zoCc7pNe3p8g3pFYzqiKzKlo62pQ/egz7WqYzOrFzKQ67rdMnpXNfNN0nqJD/iJz66lszKxQ7mmS3mDw3h6J3vaRntWT/+4TY+7l6M6WFO3aO8vky/6kF97f0cPVmp7rVs2ujNvvV96uMPtshN3uVO5r7vvwIOuuks7qdu1u491s1fxs4+zsUu7q2syrHOVXNIlv0ezpHP7v4/tt//4xQ9uwZ8wsKe0sFv4wif2yTs8u291wv8yvLNzj7/8V097wNP2vVNMnn58v785Zse5io882ab8ph88ntf8jBu6ztPuw1Ou0v/0xP94xdtvwz8uzxu9kHM8ABuG0A/9pC+51/8z0k9vxFP9ym9zyzd5X696zA+u+c48Z8P01R971svv1kdy1+c3drOUZAvw2EdWkpc9nB/AAvglA9RtI2zCMGxCI4jtIYj/7eNH/uQPQ+UPw+VLPuVD7iEQQSF4vuZbPuST/uZ3fuZvPiJUwjBUAiIgsB7Qb+u/fuwPQx+Ibe3Dvtjm/jDs/u37fu1TAiFgMBvovuvzPu4jv+33PvMrPx6IrSBIwjBIQh9wb/QPw/RX/yCIbfZvvyR0vxFKP/WHv/eT/ygsgvJ+f/mLP/tz//lrP/VHAh+ILR2oLR4MQSHQv/2LLR9EAkAMi8Rn2DA6BflEEkjQIEKFAwvScTXKiI1CEBsOS7gwokOOGfM0GtYozzBSXfgsCTmy5DA2BVeSLPhyWMyWNGPiGbUpypKaImW6hAn05lCWBbkUtJOoUx0kSJUqGqbI/w7UYXakUrWKdWrVYVxciXojpJDWr1G7bs3qNelVqYneDHOVZROSLImGJUJy4IAEvgwWHFggoWCWgm/wwi18OHHcYVmWbJHCRQ4XKXLkNME8ufLlzJspW35TZAcxZXUkaatkB56kKfDg2cEEDxNreFlgy6ZtG3fs2bVh99YN/Hbu37yN7w6enHjvNpJat4H9Gl4i2NexZ9e+nXt379/Bhwff7hq0RD4Lple/nn179qQ+YQJSyH19++rB8P3rt6+DYV0IGYaQLgqaYqZDhjmEJgNdQlDBAifqYr4Hh2GQDQcXPDDBDBvc0CQpUrrQwwo1VOwxxvJyzLBhENsQEyOiQP/RxBVbnFFGFVFU0IglaGxssWHkWEuttM66KpFRCuGBvra4MotJIYtskq2wxiqLLbScNCoonIiaaaIiLNpSy6J+OkqoMmU6KSWbZhqzI40eYuggOBfaSQqfNsJozjzl9EhPhBrpxI6nMuLzzY0kEaQgPAoSRJJhEl00rD2GKCTSYRgdxlFIFcW00UcvzXTTSF25oy42QP3ir78CWyCCRQtCpJJhKkGkoD5inbXWWyPjgg052KBMjiww8xVYYYmt7Fc2tijiiT+UgYYSZdpRRhN4RNkDNk2Ugcda2BbZtttvaYPNlWfgecYV2GaD59x01y3XXXTVZddceuNt99165d3/N1/YuhVP4IEJLthg2MgzD737GK4PPvnoa1ji9PLTz+ID/Ju44Yk0QSJijSeGLyWQNSZFlBc/Jlli+HZUeeKJ6krZZffC6ljmmeubCIkdbsZ5PZEX9pnmndoIWuj0JupkD6OPRloUm5tuD2aPKb7Y4oxnjgyzrbnu2uutR3vCNGio0aadg9FOW+212W7b7esSPi/q9h6eb+6qreYL66Y5pvru9ID+G/CTjeh5bpaZnntqw4Wu2W/BC9KZZ8hN+mRkymEuWvCkl8b86cf/XhzvvPdWWeuvUfd6NCjGLvvst2GPXfbZaecu7sSjrptxnCsm/e6+d/c5cMhNRpnyghA//170zT8PvvFRdnZ+5uEhzxx3nzm/vvHmMd/p8d6tLp3k01Mvf/VionW99vXZb9/98G4/XnfBwb9YfOxHgZp4y7UXnnDpcZa87sWsetxTHvQmt7/LVY9o/XNZ9jynv81572P1u5rPyFe+r50vfWZ73wdBGMLZxY9y8/ubBfVzP5wBr4T8K+H/jle5lg0QdIozYPei18IFTnATmgvdKJTmwJk5DoAqW94wUKg3DG5Bg6jjINk8KEIpTpGKAyPh/iBGv7wpUXH5q+HhXEg8GMrvEzNkIAGZJ0EG5lCBQhxiAycYxAh+MWpHTCLGlthE1TmrdVGs4h8BGcgrCs6Ed7ujCv/fqMa7Ue9vxStcDAV4RjoejYgxlFwRScbIH/bQjSCDYAEV2UU0InGLeMRZBvUYtj6+LpCtdGUIB9nI+NjthKVE5AO9iEmQafJwY2yhGXk4ye2F8ncI1GXJwnhGHxZTjqAU5vNGecg86hEzquwgK1+ZTW3SLpaLnOUxGybNLhKzabzMnS/3B8xNPnOFN1xjAgmZTB4us4vNTCM7V0jB0YVvmqnk4zW3GVCBvq2bYMxiLbd4SyPmUoedzCQ646nOYo7yd+6cIBvjucNN0rOOQOycM8EpMTvasp8atCYUsTlQla7UiuWRWwsPakiSjhOf05PnIiEqS4mKsqZGtOgPMSr/S41OlKN886hDPfnTjkZzplljYhNPqj6WTpWq3ylo7r6pxYQWk5xHM2c5c+rNnS61pySr5AGD6s2hirKolDzqHEO6MX0WRJxOheo/UVpVve4VO1ctZ1YR6juaxpVhX/VqWME4VqNS1IZdNWpawbjWpbb1efYMnVIX+72mugyVexQbQPka2qr61auAlelWB9vQFxqPjIp1K2PriNmOQharkl0sZfFn2Yo6Fn9MRa1dUxdVP4qWuColrdAKObe6GpW3AbxpL1n7S6R6cq6XbS7+aPtX274Wt/nUbWPLatbqLtd0T90gXqVaXPUK9LjCM61yN+vW67rMsMhFLFZdC83w/2rsrDiEp1Cny184/vC7sZ3vQn0rWOB6dpXrdXBA2+vcmML3t8zdLzK367/opjPAL6vubi9M3eyWNsO97e4bC8w32eq3gvEdn3m5JtyUPpjGgIywTSccNfI+78CZfO45NxzRDov0w+AlrFxHjNwfT3bI9vnkPY/s5PG6GGSdlXGNsdzKG9P3vTqmcj57vMslHzbIOm0yw44I4ijf55KqHeCJcZliSq64t5qtcHm7duUs77mKW1ZZcr18Zx6HeGVjtm+ZxXpmNhfZwIT2cJLdW+J8cjKOH4UyDVss6CrDWM989rQI/ezjHDdtx/gLM4YV7bD7/jW/dV6z1Oj8PEg7V//Sb6Q0gS1tXUejecqa1hj5Ov1pYb8v1GIe9dFKDeZd36e+Gn5ka1OdM0ar+NQi/q9aoz20WzMz12rG9D7tN81gD5vc6ys2Mo8ttGQnctn2aXYAV13aVk+63TSLNXavHdlsS23A3N73evq7zkwrmLNMHHe5ES67c4esy6T+MrtfTTdDOzvi7olkMCuunoBfNN+1/TfA+13Pbhv523R9uMQic/CEr9xtCy90un22blxWu9C1/nO8lTxvW9cb1jR/dMe1+3GNh7yjch4mz/mdYH6eslmfzSvLoQ47lzcM0A73NcTdTEicu1fnuIQttZE+NCTUIOPqefek4bxQo2Pv3l7/tzPBTfcDp6c36nVfW8IWYQMk7J3vfff73wH/dyMYoQg0GELgEZ/4vteglAxQ/OMBT/gaHB7ylff74IWgd8tvHgmDL8LkOR/6wddA86G3vOQpb/rHo171mzdCDQzf+tUbIfOyhzzhf1B62wu+CLXfPeJZ//vAB3/sjbe9DeY+XLsv/2AJU8YhBPEH6U+f+tW3/vWr3wc+6OEOeciDHvjQB+yPn/yq2mIDyJ/+8fdBD3Zgw/f3IH71z3/62tcDG9iABz3EX/70T7/966AL6ED/ws///K8PELAP9iD8+s8A1Y/97KAOvq8AHfD/9CAAuwAP8oD/KvABAWER+IABO/D//7YP/zSQA0cQ+9iP+yawAVOw/vYgD9igC7qg+8DPBV8QAtmADlrwBa9PB/VvD8wvb9DvBQsBfZ6O+ZTwYLSBGqChGZQhGIphCqmwCq3wCrHQCk1CFEZhFFyBGLIwDMWwGMKglCBgDNEQC4khLD5BFEjhC9MwDqlwGEZBFDrhE7wQDOUwDenwEzZhE9xwGPRwD9OQGAqCGBCREOVwDeswEAdREbOwcjZBEzohDyFxDKWwGBAxES9xDPtwEyoRDjsxCxlxFN5QEEfxCtdQFCZREwBRFFNxDk2mEzrBEWPRChlRFNzQFe7oDDsxGJShGZJwCYnRipoQGp7BGZRxGZmxGf+d8RmdEQqVYRqbARqt8RqVcQzMEBu5ERqlkRq7MRyZURqTIRmCURzFEQqTIRiAURnQER2TURmr8R3f8RvdkR65UR3Z0RznER+xMR79MR2nsRzPMSCv0R4N0hqVYR2nEBj7MSGXkRzN8R4h0hmn8SKbQRu3CAIM8hmgQRquoR1mrBhJ0qq04RpQMiVVciVZsiVdkhqoQRqkgRpcsiZtMiXNoJQW4AF4sid98ieBMiiFciiJsiiN8iiRMimVcimZsimd8imhMiqlciqpsiqt8iqdsgFK6QFusiuvQflKMizHQyTJsizN8izRMi3Vci3Z0izLoJTgMi7lci7psi7t8i7/8TIv9XIv+bIv/fIvATMwBXMwCbMw6/IB2rItxXIxh+0tDfMxITMyJXMyKbMyLfMyMTMz/fIBGLMzxdIxNTM0RXM0SbM0TfM0UfMyOdMzWZMYQTM1YTM2ZXM2abM2bVMvV7M1ddPuXvM2ffM3gTM4hXM4+zI3d/M4V24UAoM4mbM5nfM5odM0jRM5qZPcHiA6sTM7tXM7uZMuF6AMqjM8yY0SGKA7zfM80TM9fTMMxLM9hY0SHmA51XM+6bM+7bMwGYA93XM/PW0UyuA/ATRABXRACbRADfRAETRBFXRBGbRBHfRBITRCJXRCKbRCLfRCMTRDNXRD/5MQtIE/QTRE/0V0RLdDErhgJEk0RVV0RVm0RSXBB5hAClC0RWm0Rm30RsXyRQnBFaBARnH0R4E0SIWU3HQ0cnp0Roc0SZV0SZm0lYoUaY60SaV0Sqm0SmnnSTUuSq10S7m0S700O7AU4LT0S8m0TM30R8OUPXjUR8+0Td30TfczTaVmTOG0Tu30TklSTmmGTvG0T/30Tz9NT3OGTwG1UA31UKtKUJ2MUBG1UR31UalIUdmMUSG1Ui31Ut1GUtGMUjG1Uz31U7lDEnaADQShVAWhEFwBzQrBVAUBEIaATUE1VmUVVN+ACWz1VncAQe7jEHbgVm+1CQJmVoV1WD21CZynEJqAWJV1Wf8/1VgZBlmZNVql1VGd9T6gdVqxNVvxtFrt41q19VvBtYqyIAoOAUmXkFvrw1vDdV3ZtX2kIAZiYAeyoBIYE13dQ13bNV/11W3eFV7hNQi6gBjC0l7bA1/39WARlmD61V/hdQaQwA7QhRgJlj0MNmEt9mK3Y2EZ1l9p4AkA4UOZb2LXo2IxtmQvVmM3lmFtYAoeYflEVj1I1mRldl9RNmU31ge4YBSi7mXTI2Zn9mfXtWZtdmNlgAjeoBlWjmcLwmeBtmmzVWiHNmVnoAn0gBrKTWmHgWmddmuZFWqj1mZrQAoSwVzXC2u1lmvRVli99muHdge04Fr4zGyTNW3pVlr/15Zto1YI2CAYskxu6zZaN2EKBHdwCbdwDfdwETdxFXdxGbdxHfdxITdyJXdyKbdyBRcI8DZzbXYGlAAPoIHG/PZvl7UQNLd0Tfd0UTd1VXd1Wbd1Xfd1YTdqawAKBgFkiyt0RZdYSTd2ebd3ffd3gTd4hTd25RU6iAt3c1dYd3d4mbd5nfd5oTd6bRYI5EC0kDd5ZXV5pXd7ubd7vfd7/XVq9UAarPdY5/ZRgYQN4JbGioF8pUgUCqZdllB7wbd+7fd+8fd0h6ANgrV8n/V8EVUbfGADLuACOIAGkPbBUsAJ3mcRWGkPMoBgFIEDSIEY6Td/MTiDNbh7cRZ+Hex6/w3VCS4gD2BDESiABWjsBJTAfbSBAwjhOu5AAwjmB06gGK/hInE4h3V4h3m4h334h4E4iIV4iIm4iI34iJE4iXM4Cn53ZRsBy0C4UFNACLCjExj4wVTYfa6hA/oAhmVYYKABA6gDe6Pubk+XBpzgD65hz6IYUGegBrwDD4KAB6TAGbCDGrbAB3xgC6z2OpwgYmGDEsYYHvhACHggCoI1Cx5hD4JgB6CAb7HjEIhgB5xgGLBDFJpgB4jAOrYji7lDE5hgB4wAEq6DD9Q3CXagCMIFO5RBCnjgB7oAZImBDzogC/ogGeAhhqFhCnYACN7gO7IgA+yYjMv4dWXACCD20//a+E/1QAOIYBO2wwcwYAeEgAIqoBNg4xMowAJ84AcsgAI8GBo64BCwIwss4DqIIANsQAhOwAIiATYogAIuoAZ2oAIu4IlhIwo0YAaEAAUwgA9gAw8wQAWGgAYyoDeyw5O14w0yYAWGYAYyoAtgAwgqAANYYAdQYAO24DoI4Zvn+AJQAJejgAI6oAIoADfuYAMo4AR24AU0AI67AwV2gJjrzozZFggIhNyW+U/x4AQ44AJaoHphgwsuwHi14QVmADZSQAUiFhpWYAVgQ5zJ+TrMGTbkIAM4uR1qQAXguQLiRRpWgKvhARA04A6uIwqiIF0u4IrhgQ004BO0Q6GxIxj/xPg6tiAD+BYINoCEYaMJMgCXpaECfOB1kuEEYHqLuxg27qADiuA6BEEDJHo7EsEDXpimoc6mbVZeKSHhdhpQg+EOfmAD5lYFkgA7EkEDpGEROGB9aYMDnliqy/mc4WEGgKCKOaAY4IECABgPNuBzeSCptaMLLACbUEAK4nqFtWMKKCA7KGCjgSAFsOMaMoAN2hoDPvc68mADugWxYZgDAHm2Zzqaa9iyL/t0ayAKCsF2EQ5redVX3fu94Tu+5Xu+6bu+7fu+8Tu/9Xu/gRVtpkADuqWiLWDAB7wDRkG4tcMCuAAeYJuqZfsEMIDAB9wDFCG3jfs6IsHA4UEFlmA7/5pAAyTcAjTACI57O4YAxCVcAxgYCFAYOyrgNZwABbLjGjwAEOCBuxX7i6/jB1pgO8J4kMkb4TB7BpbAc+sOa11hVVl1yZm8yZ38yaE8yqV8yqm8yq38yq+cCDb7YMT5D3K7CSAhzMOcXusgA9RburUAHlo4sYNDtlOACMQ8zI2XAi4cNiShA+C3BYJgO0Y6zsP8u2FDrq+DCVDAzyHhc1k8O14cHqRgubGDFDpAW3A8l3UcNnh8O7IAA4Y5yBPOjIeADXDZZcsuhki91E09PZZgy8GjGCyAk2HjEDzgWoIABWbMGQ66nDsgzeGhApgAO3pAtpWAAvqYueu8NfCc0f8tYNOvoxE2YKq9Q9Bh4w80wHizI9Fd/DUeYQPYHB6UINLhoR00gLpzPDsuXTtkmtNZzmt5YAug+VxH/dThPd7nJtUFhgYsQAuKARrq4ITNxQJmQBGuoQ9aILKlQAOegAuj4AIyQNf/+wkwQRKSYANkWxkqYAUOQRsGYQZ6g86x487hVxpOAAX2IBkIIQZ03Zu7ABo+YQlgOqGBwM/pFR5qoALaQBo0YZInusWvY9HhoQcuYAuKAROUwKe1BR5ogAJQhdLJvcezA9YLAd1XrmbDdmxJEmvl/eqx3tTpXTyoQQkuoAM6QAN2IFgxQQY0oAMuAAgSuQI6gANSoBDu/Tr/pMAC2n4FomC84WEUDLoDphm34SEFFvw6SOECQj0ZdiADOkCdIbkdnIDuN0AFtv06WADsKT/sLfkakuDrNWAFnh4emMAGsgMFIlsbluDrN2AG7hwRYCMZZkADoLujs2MJwtvX8R7qy21hZ8AJ+GDYq/7ds/73gb8+tn5gNiESeP86rgGStSNaYCPu5xrQr0Mb/H5gtEFgtyMZjp9ggmGNxYPqg4F8Mz37B8YCgNz2hU0KZKAI6CCBF9Pqg//94d90VP19nP/TKIAGIHkPLuAJ1CY1zB/hACIRKXgECxo8iDChwoUMGzp8CDFhk0LDKlq8iDGjxo0cO3r8CDKkyJEk/0tuXEIposqGFrSsfAkzpsNKKDScoKDBSDuZPHv6/Ak0qNChRIsaPYo0qdKeE006fQo1qtSpU1EuPZik0dWtDQ9t6TKKq9ixZMuaPYs2rVqlTam6fQs3rlyNVtfavYs3r969fPv6/duw7dzBhAsbppsSsOLFjBs7fgw5clHBhytbvgy17sM+dRJ2SRSzmLO8lJx8KqhsyhAt0iS77tmFguzZsw9Ba9LJKI8t8HbQnj2EKwoLxIkLkantiVazSpy8fv6SMubp1Ktj1OywB4uEKJTEtLDd7qETHjzwhreIuAoMFIpBfw+RGJ35UTq0mV8HGqAOU4pS2rAHPBTUwEWBBv8ewpUGQEzB4BR/yCTKBkn0RAoFz8QkxQXQwMfhQtJZB2KIhmHXkHbceQdTFnTc1UgRh1xwHgorbFgMBT50iCNDfHRAzUGP7ETUECcQRIFzZ2nQGVCS9MhTIx0QE9MzGGSRY5XwNCGHIFpuyWWXXn4JZphijklmmWaeiWaaaoJJRGIPmYhQdxzCCE8wHuhRkBQVWMknQTsyWZAkQHbyTDF1/LHTNXvUEQxCkdBRyEJFEmnkQdRschA0mBK0SR19aFMQNJXAA4kclFAzqkHPbGoQkjNdMwodCMIjTR52jFYQJdcQ5Eoy7Qgix3IEaeMmQcS4B4+TUBp0DWesEgSNHnb/hEWQDS302eEbTGzLbbfefgtuuOKOS2655p6LbrrqrituE8pEBOdBchI0Cg8VXHACldCeQMgLGFSACTxE9AfPCsURNyQ87UhxwgUVAIEsPENMgUQFGOTrkCLzbbyIQnQq00IzBWVhAbZW/nnQBoAQhIIKGViwAQqAVJABBhmwURApKmhAAXuPINRJBz8LWKlBbVjQWkFDbOcMDRrcWwEfBGVBwQ4ZVKBCGxoaNMQKCGnAwg47ODFQQhWwoIEFHLCQhwU2X4AnQRZ0QdAMMlCQwQUcpFA2HRccJDY8QVzQAXFJ4mGBwxrUkPQbGFhQwQY+gNoFBkCajHnmmicUr0Hz/3ZiQQps9AEFBkAYW3gTeuyx6wrHwUPIxmxg8HoNF0jRBxsnVLDsChqk0MUdRWhQNHcdHH+8Ch6fZ1A7KPCwOYcoG+RBgPCcgEEf8GgSuQ/UtCMEBqM5z4J70OxQAaAEzQ4kBTM0OIUcBF1jAcHwNIMBzjScgKk2SlwApSl0wAcbgsc1KhCFguCPbgcRAg1oMAO3SQ0hxOlYIi6wASa0gxo1qACQLsAFgryAA0/oUSQo4DV4zO4gNqgBPFzRhQ7YARCtkUQGngAqTFDgdPCoAIoIQYFBwCNokYieEY/Ipx4gb4nHQxENUgAqggxiA1IjRgf0VRDXIaQGKdiVHDTQMf+CSOMEPSDICk6wK4JMAQNJ6wmdDjIECywLia6ZXkGqR5AT8BAeUdBAAaGhAfndAQPvIgg1LIAzgzghYQK6AG1mUJAoUABIT5ikJDYgCYOcAArwmMIFoqhGC6QRCh5kCDVoYAH1ya1ScQTSJzoAGniAUIThIUghOqAJFWKAhS5M1pMK4oMXGGQPGdiQBRKIkAy0gY7MbKZrerACYkhzmsQ4AYoy8IaDaNGKQ8vi6yJpAWrtgAYH0ULJCvZNeCSjA7NyI/OmloFIOTMydiQIHq/XhIJ04W8FsQBvmmABsQnUAigqiBGURymFQOMCdKMflbaQAYGKrWqdpMBBpGEBKmn/wwJSQAgpnlWMDdyBgi4hiCQNwoE8EGSW8HjBjQrSjg3gQZe8JIiyCnICFEh0BxwATRcygAIgdCGN8PDnPI+KVL90riByogYsAedCK0JCm+nMQwZUKsJ0wgMPGjBjcAqijQ5obyFP2CkyEfJGgtwBm0l9TD3hcc8T5JMg+zSIUZdQgSboda/tJEgREEq0hTBhSFtIJTy2cIG97lWlU7DoQSTZDi5s7SBBqOVKQ3iQlkTSsQTZAFZZ6tKDaMAONDVIC236yzy+QLFNcAKuiqGFH1SAAsOQ2zvbitvckmWpLEMRS5lqBHhIlaoF+cQFzgoPIFgWHnryqkHCOlaFMOGB/9QtXkHS+ggMdFS3i3lrXOcKj7r2kzdaw9VCFlmQSSkkGRjIg3oVsYFnFaSxCFloF06AhIRkqIDwSIQH+iq3kvKRs/Dw7GVF+NKCjBYeh+CAKAyygl5KogO5IcgOINkQaagXA8vkroc/jBTewmNeQaBAMvSpgZ8NF8KvowYKyGkQQWwgSS8kqHPBKtafvLEYFdAqiPfiXevJVZ/8tC08tEGBGODqEFgkcnqtaxAhWExkBGEBClxBEEogk74IAagGaouQZFxABrVFBAUAa1cBn7QgBpYlZkPbKtK2gwIsoPIWNtDLa1xgCEBKhAa2e1jtLUIFU71fBThJCqH9eNGM7v+JiOfljOEkQQo10ADBVuxNggBhA0bYKzKdoIEdSMEIFlhB0lbw1WHl2CdvTEGoBQoEKjd6LX3wAFEJwoEJnqBS4pUbZjWBPRWcIAPWpUQHUtWzgxmnIKPQABEMkowVZCAFNfmBGgmswAxATyGFiBwGOqACMGeWeVFgZIHjNjeCyMDarRppf2m2OwugoJcq1AAGXsoGhxnMAniCxgw2cIIUYCAFo2FDBi4364QrXCFsaHJBoOBueFBjCi9IwQ4EURBq7EDWauwwEVYAcpDTAEh6sEEKZJAFUEpBfgbhAZZ9QoSOtaMGIQe5DOa4cLNcY6YHuQOTBvFyeDgjbgTRg6z/27GHKXAB5wZBIEEAYaCoI2KYHLflFLRQ4TpF1yCL2EARF6INPXAhlgnZQyHhUQyMFwQPTDI6QRaRqoLkoY3SYMMUugCNSHydIKT4TEGeYXc7qE8SW5gC0Xew3JwrfvGMbzxfgICCrexABpiTxgXs5/jMa37znEeKIjig9qSQQgMTxFYWMlD1zqt+9axvvUJkgPmjbCEFmQvCs12P+9zrfve8773vfw/84At/+MQvvvGPj/zkK3/5zG++858P/ehLf/rUr771r4/97Gt/+9zvvve/D/7wi3/85C+/+c+P/vSrf/3sb7/73w//+Mt//vSvv/3vj//863///O+///8P/4ABKIADSIAFaIAHiIAJqIALyIAN6IAPCIERKIETSIEVaIEXiIEZqIEbyIEd6IEfCIIhKIIjSIIlaIIniIIpqIIryIIt6IIvCIMxqBCU0AeFwF/N4xAIp07T1BA62A46iIMKcQ2JcAeDcHYJ8YMRAUo+yBA62AzTdGtNCAl5sAfUchCg9BBYeBDPME1t1IMI4QzUJIbEoEpACBGQgAd9IG4GoQxjKE03aIYEoYPa4IbS9FzTlHoIAYTJUId2CFML8QyFgAeFcCEKEYdyKIPOlwyDgzyhFjECowFbhxAxgAFZtwNMdAEzEHEF4QwWwEjtMFtHWBBcsAGxBw+UYAMZgP88G6ACpScv6XMQDNNNBIEHG9BRj5ABNsBsGLACWkgQSyBTkeQBS8SLXBCHmtADhIM8eXWDFWBuv4gCD2YQXbABDHQQXMABSzRtU6BKBoEJGHAtBbEFw8hExHhiBFEMFxB5EZEMQ6CMHeABJ2CNAlOOq0haErNgCJGOKQQP0GAB9Xg8juWPS6QBK4BVCNEGGwBeyQWQyLOOVqMICFEHK7ABq6gCLGcQjZAB9HYQP6ABAJaIxkcMFdABALcCKFCRFVA2BcMfDPGPB3kChSMbMHM8M2Be8DBhXUUQzVCTCTEEHVBGBgFRJXkCMSBwJfkExrOJPdQB+WUQl9hRbNABnLX/Ix0QXAjRAh1wVjXQAY5EARWgAceDAvIFD1ygihxAAS1gMMdTAWRHklMHVt+GXPAgAx0wj0rTAezxlWE5lWGEEEIAj2RHBwdDOBtwMCdQiPDgBMfDcw7RB//oAWm5lh1AbyuQl8pGHBUwVpZ5AdJoELdUZJoAj5hJHOEhmh5AHBjUARxgiiPWlW3kBAfzbWlTHL0Uk/MoDTZwPEBVcapImV4olVppPJgVksgHBFO5d8DWAZRnRi25EC+JU3ZZEMkQBd+GAm2UkwqEPICGl0FJV9lIA6ziDEUAcAmxBB2QTpEglpnlAbkklVR5PBxAYwWRlVtplaGyBSQZTgbBBhXZ/wKFRhB2QAFdmRg/0AELuQfHE47zgwEaIIoE8ZM7UBDXwAYDegEAGirKuG0IsSPnpIck2QEY1hCJoIooIE9HNgUZkAFpZJmtCWHHA0UHAZoFIZob0BA1Op1B0AEakEkGcQjk6HAFsZgxkBC3aRA0sKNNYF7PAGrLeTnBqQErIy8dQJzFaXwxWaU1xlkt6pIdAJPSaRCR8I9FECg7qp0dkAEaEHoQCpQ5821kihCQwHQEgQhTaRBSUDgbIGuKYKcq1KfwsCMawAEXcBoGUZ8FwZVXWRDBEJO1VAzfxm4H0Qwo0AG0Bw92UKkGgQSFgwGghAfgphARmimWeQI6mAUdEP85GfCggFo4DFc4GbABhQp2A8oC3TgKbsKlDGGZqqiottSVNFqSNxqsBsECHTAhTzmVHfCMQtoBRIoQRkoQWrCjjTlMqohFUqmKFXCOTEWlVop8lCpgGQcouaoQ0JlHYGoQdYCm55idO3k8p6qtXNOmBAGYKXCI5dqe9GmLHZBIitkBt/eeBdGhRVCpWHioBJGoCEEJYal2SjCVUVgQm6CKAXINGLABZ4cCGLCY1iMxHcBJPtkBEnoQ6civBxGTU1Csc+knrZoQlrkEutmrCcEFhbOq2uScC2GZTaA38zmjBIGjDPGz8xWibKiKhPCPUnoQQ1qk6DqgC/lYqAokUnn/AjzQrFOapd4qfEwwlWT5oi4qN14anXd5riXVrvdzPNJwiczJpt75j/MJEUq0XdqAASjQCR6QYCqwagG7shagDSnQAXBKEAcLDwmLELr5OgMarghxiTw0A2DaDBvAArd0rALiAcUiryJ7EERAmQbRB2jqDO8JhB2aEJHAARswCp17Ad1oqB3ABA9BrgrRonUwqLLas9szrAsRtASxmBwJBR0QeQTLkczqrCYLpomApomJENLwbULkpycADQOKXJR6tVgLfNAQkxoQA03ABZbLkl5bVGB7rmLbsTxUtjzZAdLwvMK5tryyoxDrEHTQAeGxI2QqGwThDGkTtX8quqKg/zcrErjqO7j3iRDSqjzaUJFzahAza6nSqot+GgXtoI4E8QlouRCiihB38KcBDD0bJUMcyrIHcZwYNqC3lVkdQAiu2wFAsDHzQXbN2R9GULC+GpolucJ0UAfbars2WhAjebOgeEXwgAkboAFzqrTPCqanimYJUawEI7WkkqbRJb3Ua3zOEAS+KZYPkkU3mxDmej3oahD1IWFm6q7nCw+4qAFY/JNBWaceShBbMARvPASuaBDUoKI94rAztQMekBtSibl6y6rnZAcbcAEVJriE23N2GgxivBB60KfBYJgE4QMdEJEyoAEiI0CphhAWfBCQ8KsEMQwa4AFw6bALKrAf/P933+ZueLqOChGWjbJ2cDwE1miZ5Wi6XXtkfnt7tSua9UjKNdogRfCPFcBf6jpZOYsQRUy8DPQEHdDAC3GJc9XEZVk4rhzFUmx80GAHUrADhKMBRPe6FAS+XSy+TRCyBFG+Z9vG09yxQfkInSyHVuy7C1GXnYECGdAjM9sfl9ivfiy6BAHDKLArhSzAB/EGygoPz1CSvmgQmMpIfosgFHBOeIozMdABHJvJ5YwQh/DBWrvKn7ABG5BLBtHPBiFAnNUMqgiSBfFtstrFyJMBWYxCNdcDt5ar/MsB8qPLq1lzK8AC87jLTHQCPVplHRAEBSGVfJu0VWvEDFQfMLYQXGn/JNEMD0rUAjtRzdaMfM2At4z0zSX8peLLla2Lk4psvm1EtcKUxtCyAR6wkrBTIJu6rCOTwvebQsTAAeRkAX5k1Pr7wX3bAcEh0DGru0MrSx1wogmxzMO7mEzwCR7QS+3sAxV7AYeoyQYhrevIweH6An99ECOdXh2QlAVxiZiLEAPar/BQCQUirTrcvQ1BroF8AZuQ06ytEKLJAQJVkfMZxBywdxx82v46vJoEpn6sENAq1dYL2q45vVg9fJhQHq7c1XYVztDKht9mPedMxtAyoE+Q1gTht1CGwXFtLBtAAVJZKRRwAXxqWfxsyqOgN28Q2Ho4oNuVlT4m3GfFyShw/6pYZAEU8KnBC0cYbbNXKZUesFN+ewFe6NnwwMiUKVG7isMGoaNOrSq3G922XBCbigKD4M65mxBBC5jCjJcYsFMDyo+DHdxhSxDJAMopbVOlC2ZSDQ+RYLF7cNXM3XvDsAMd1jxhGdIX/rVfjRBUy1nY7YWQAKuW6Z0ziwEBc8jizTIhatgFcZy6iXnszcaXKsgxaZ+CLUCERBBtsKMtbBCnmgE4RwGQq8eI6gG6Kb54Sdrrs6NOjrcN2WQLjqQNGdoI8QgbwAFLedAWrsUtq8V+XawzTNsffrud+LfQ8o7lyAGziMzCbY1I2kUJoQ2WKaIyHl6oOaDLjeOu96kZQP/mMYQBLDroXq3iE/qTbTbWOmm22T2KyOOd7eC3FLB3BKGuUH4l8DjZe+3nLI3lCJEEyNPlBzEFFYl5lmkBcJnAYVk8OhqZZV6SGnCTFx3nPyXAj8ABHKAHkADu4a655ubZo8ev4R7ui1lKCaGjGECt8KAMgu69rW0Q7n08iC6stC2VGhCRp2oBj4Du4G6ZkQrcS2uNNtQBNIC8/ciVGQCgnA4PkXw8oB7qrVesGTAEexAJeiAEYamololqsDwERdCjXByTSDAfXVAxJdlkRo4QSjSvfDegGQAEd4CGRUA4SXwQfErYO1mRzzjsV1jn9lkD88EGi3Q8WkUMAxpqdCD/CYrABbNcAzr4qR2wobbbAScOsitg9FHgt5QZRQaq9XwXlkjr2Q4b187wbdmUENRgmXfNBo3QB09AkkVmmTsQdQbCBqhuindQkYie9wZywjl8EBRtrzEp2H4qPsJr8PwZlhbgBIAgCYLgBCSpAacN8WM08RXve89QA+SIPB7QA6BUrADZwCRZejFZjhRwkD6LpqHiARwAschN8Gg3A6CPPCkQdwpBkrFHqQVFEAXNSPuxJwhh7/ZzieVoAdObDDWQjUxEbECoDYRzlyTJnQkBw+V4eU3nxQbRuLcHD/vBxierEFQb53M8BBXJRBTAsaUPkCVlmUEKDw7roaPQkK8P/w/2D+ucoorwuwFZBxDwBMLTZqEDm4FROswY2PDEwYbw+FDoUNFiBwp6IrbpcCKiQEkYOnT5WNLkSZQpVa5k2dLlS5gxZc6kWdPmTZw5ddYkJCTFCRVCBkXcs8PoUaM8CAlkY+TaQDtIeTTpY9JJloZRppik9ONRyUJCVJxI8SPPyjtDoEX8EyRZRGhD5AzUZgRhyT9AiA1MxANpkjpPTz5KwuIEihlRip1sKnhg2rUoMflAWoSNtIhclLQzGckrXSQkB7Yhgrnkpx9LJTMxjMJGF20N+fhFevTHKIF7hCgrqU0Jl4ZOaiONMvDqRzZCoB03aUdIs4HDggz9aGfIs/+P7drw+JliB5vYcIfYMXlHiLOd6dWvZ9/e/Xv48eXPp1/f/n38+fXv59/f/38AAxRwQAILNPBABBNUcEEGG3TwQQgjlHBCCiu08EIMM9RwQw479PBDEEMUcUQSSzTxRBRTVHFFFlt08UUYY5RxRhprtPFGHHPUcUcee/TxRyCDFHJIIos08kgkk1RySSabdPJJKKOUckoqq7TySiyz1HJLLrv08kswwxRzTDLLNPNMNNNUc00223TzTTjjlHNOOuu0804889RzTz779PNPQAMVdFBCCzX0UEQTVXRRRht19FFII5V0UkortfRSTDPVdFNOO/X0U1BDFXVUUks19VRPVFNVdVVWW3X1VVhjlXVWWmu19VZcc9V1V1579fVXYIMVdlhiizX2WGSTVXZZZpt19lloo5V2WmqrtfZabLPVdltuu/X2W3DDFXdcctMMCAAh+QQAZAAAACwAAAAAIAPCAYcAAP4AAP4AAP4AAP4AAP4AAP4BAfwNDeYXF9kdHeEuLu46Ovg9Pfo7O+01NZ8sLDglJSchISEaGhoVFRUPDw8JCQkDAwMAAAAJAABpAQGpBQWgFBRMMjJEOztVPT16QkKcR0eDUVF1VVVaWFhYWFhXV1dTU1NWVlZYWFhaWlpeXl5gYGJkZGdmZm5paXFtbXJwcHFycnJzc3N1dXV3d3d5eXl8e3t+fn6BgYGDgoKFgoKPg4OLhoaHhoaHh4eIiIiJiYmLi4uNjY6OjZSTjZKgjJHCi4vViYnPjo6/kpKglZWWlpaWlpaXl5eampqenp6goKCioqKlpaWpqKqrq6utrKywr6+ysbG+sbHPsbHGtbW9uLi8urq8vLy8vLy9vb29vb29vb29vb2+vr6/v7/AwMDBwcLAwMu8vNm2tuy1tfK1tfS4uPe8vPy+vv6/v/3BwfrFxejHx9rJyc7JycrLy8vMzMzNzc3Ozs7Pz8/R0dHR0dHS0tLS0tLT09PU1NTV1dXV1dXa1tbh19fk2Nji2dne2dnb2trb29vc3Nzd3d3d3d3e3t7e3t7e3t7f39/g4ODg4ODh4eHh4eHi4uLi4uLj4+Pj4+Pj4+Pk5OTl5eXm5ubn5+fo6Ojo6Ojn5+fo5+fp5+fn5ufm5ubm5ubo5+fm5ufm5ufn5ufp5+fo5+fn5+fo6Ojq6urs7Ozs7Ozs7Ozs7Ozr6+vs6+vs6+vs6+vr6+vs6+vr6+vs6+vs6+vs6+vr6+vs7Ozs7Ozt7e3u7u7u7u7t7e3w8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDu7u7u7u7u7u7u7u7u7u7u7u7u7u7u7u7w8PDw8PDw8PDw8PDw8PDx8fHw8PDw8PDw8PDw8PDx8fHz8/P09PT09PT09PT09PT19fX29fX39vb29vb39/f49/f5+Pj5+fn6+vr6+vr6+vr7+/v7+/v8/Pz8/Pz8/Pz8/Pz8/Pz8/Pz9/f39/f39/f3+/v7+/v7+/v7+/v7+/v7+/v7///8I/wD/CRxIsKDBgwgTKlzIsKHDhxAjSpxIsaLFixgzatzIsaPHjyBDihxJsqTJkyhTqlzJsqXLlzBjypxJs6bNmzhz6tzJs6fPn0CDCh1KtKjRo0iTKl3KtKnTp1CjSp1KtarVq1izat3KtavXr2DDih1LtqzZs2jTql3Ltq3bt3Djyp1Lt67du3jz6t3Lt6/fv4ADCx5MuLDhw4gTK17MuLHjx5AjS55MubLly5gza97MubPnz6BDix5NurTp06hTq17NurXr17Bjy7aqCYTt27YfkTyCZGK5I4Rk8kNSZ7bx44mzaNiA23bwjkeOEGQ+kZCG3gyPZBlpHURDMyDYIf8fTx6u8u0kbU/3LtE69oUbpIscXqbhEQ2tyuvff/b8wk6X8FNQOQMK+M896vxDTiPeCKTeQNR180g6C/FziTD/uFdQK5kYCKF8BN1TCSwIzTPJMAgJM8k7BanjIYUm5kfQfTLyZ+ONXPmHUBYbaLAcEgYq18lA7MQn0BHx+agBeD2CIB9zICh5xDw79qgBCMq9x6OPG2BHSJTMYXcPkj6CQAdB75B55XMC0RHlckeIJ5CR/yinJgjPkWlbgzj26adUdiIhKBKLDKQclllEKR8SGhQykDcayBcllmRkAQuS8WHXZBaJRnrQoUhkcd91Ah3KaZS9TXJffOjdpx0SPRb/d2SkWcCqwXN0/CiqpwLxyiitqzaIhKJHUPjnscgmpZySPsp3D3MJzpofo44KBKmkGjhT0INzbkDggSBscI9BG2wQzkD39cYPc8byE6W2/9D5TyG8/tMNdRnWGwq+4cL7z3129LqoBujVSeq/+CWr8MJDnTfMw9MMlMjBbRL8D7WP8hquQdzGy560Bd1Sb7690fveP2VYHC+IjCYyowbkIBwxuhr8887InfDq660DOcMrjQwHLbROOhZkXcH5bscom9c6uAHHH3v8ciYFXULxP5GQejRBW69M8y0vh4IwlTTfg87I5eg8MJvk/Jzw0HDHDVPRBLUN4sU8F72Ixk9v/9v3nFHfR3WI+ArE6JlN04xe4XRHKfZ9Xgzkbt+FG4zezmy7PbjcnHduEt0vI/EOPzyCIKDIGwQXiqJOG3RfFkNKTfPmNB+BTp3MGZiui8uiFy4dGBa5ARj//Obj48shnq7hkcZcR7m3/4P5QHaXGmkk43qu/fYcgT5QOW+WqYmhzPYo6d8DZWLlJLLPKnZB84wq/qPhR6pyrssJSIiVy0WZn6vMOkL2xlQ+Nu2sWv84m7PedDLuOfCBEEFHFqKHEDpEJwtkG0gkkKAddWSBfRlCWs9CNa4ssOkfoRAh1ziIwYLcQ1RIiAQ7kNCNgVQiVI8SVRa8kYlWwYwQ0TmT0f9YSME6VasVSMheqSIxkHnUCmwQjKIU23KfmE3xilhcTBWzyMUuDgZJLPKiGMeoF2OR8YxoTKMa18jGNrrxjXCMoxznSMc62vGOeMyjHvfIxz768Y+ADKQgB0nIQhrykIhMpCIXychGOvKRkIykJCdJyUpa8pKYzCR/PKTJTr5RCCQYyTg46clS/ukGHRjJA6hgylaeMpUieQAUXEnLG6FSIMMAwzyu4AMnVMIg05CCEKSAooFs4RwE8cYW/sGPYUCACcOIVi2niZxb/oMKEugABEzwgAo8gSBXoMADVNABClhhIBUQokC2MIF/cIECFqgABWJAzXoax5pTuEAODCT/BQscQiCCqIAUBjKFCvRBIOkkCDsHIkt7OlQ2+KRABv/RgSAIxAWhJIgKVoBQdf5joQJpaCTZwaeH+gmfEShICmQgkA40oSBReEBHFdrOkM4SKPeoYdDewQUZnMukJ4XlFFJKkBTAIKRCKEgTYEkBM9CUoTf9iR+uwLB5jOEFKfgmUIMqkKGq9Kj/eEAHlHiPDtAgpBYdiBBqGlYiBMUJMSBln+5hBhik4K6/3GqfUPrVkE5ABflpxQomELsnUKAK75iHFSjA1htEgA4l3ck7WJACQRyLrjG4611zoFc/AcEEArmCTAnCAhuEdAkukKcFOmDZgQQBnhWAwA+I+o9z/6TAAhSQ603wcNeX9okfdJiBZjXr0c7yZx7jEAg/IltbOT3gm97YAykOcg4/HEJAzO0GiXqyhLuuAJlIeYQO5LqEQGykD0xYiRleMFCPAJcGw9XsCyZqXKE9tyrqWIFmyZCUKlwgqQV5gG8z0oTRosQJFXjBGDrCjzvUIL7DPWd943ZfqtBhuDjorzyLK+CNFDgl75hAFDySBxtAeLgq0OmEh1aD4kJFCPGd7lGqIAEnSMAWBOmwQ/ih4n+EQ04C+fA/1CFNF5oDIecAL0LCQcpDXGBmGvHDDU4c3wGveJrmUEF822sUGv+DnErUsQmoOhBYSKBBrZDAWi8AAS/U4f8BF6gADMJY4Dt0wAIWeAAXCBIJE1SAzaxsKRVMgOc9F4QdOJBAnE3QCIEsoQMXMIEJXCyRQOCAyhAu1JWpaQYIu4C+QvHyOB7A2ZD6tsICmcQF8tMINptBEzSQZxBIAQYJ9CDIE5AAFGyRiR5UgMy2kIALIjEOKhxWIBGYQBM+4Qz68sMEEODCMA4xWCbSIQcXeAIUaCcRQegA0xC+waaR5QfdKNcluhUJEE58hxlLAKAUIF5YT63VVK/6H612KjMfANogQyDIFzC0QIQQgXHVoN8CicK//xGBUh9kCxSQsUBMkAKB6OECoIYIInwA7hPre9x+OsEMBFIDCaS7JBH/AGtJvKFlCAO4KF7u6gTGp2NU/0PVrL7Adv9xghoMhAwUCDJtBRIOC/whrC+wgtKtIIULoCgCVUgIDDhKkHdaHOMUaUQQOn5iFoQR5H0S+TrF3RIckLkkYKByirv87oG4oAPvqLkTCIJzfF8AQwIpgc8FYoYKCOQJBkbnmSDQAUkbngTJjYCED7KCkRPkDhYQ0MUzvpBIwJjrJw402MPu+Kx8m8pnH0rMBZKOB9RAxx1w+D/+cO9W4/0feh9I3/legcj+wQIkMsGtD6L4hPgAlgNxwsInD5FLXB7zEFbB6zdPnm7koAMdkMEnxP6PawtEGCqgAzdZ0IlWvECsoT9H/xCg7wJz/wMGfxj/A1iACIKQoQT8loKBgrBggbyjCdwsgcD/wf7nPwAGeQURwwBunSd6bTcQjzABFeBbBBc797ACrXd3AxF7tCcQ97B+ZBMOHVBx/xBOrfUPh4B3vYcQmVABUTUJEuBW/0B8DaEJRIB8mKaCzEcerQABz2UFMCABD+B4QuYHFzABQSAF2gQBJzAFNWABy/QP6QB9UnAFLEAB5vUPEjABJgAFUGACFOAHbVIBP7AFa0V2FUV62sQEV4ADFEB2/2ABVBgFT9ABE2B+DrEFHdd+RDF6A3EFF+BbSzgBLDADDwBnOfd6FPgPsycQi2CDNRADEdABxfQPN/9QASyQAylAAYY2gggRTh1wA0/IAtnDggrxCd0Fg5j2gTOIHOQEZP7Fg6Plg2TmDRPQAQYSA7AUBA9gRjPQbxLAgQKRAv2WAwh3CbIShv/QAxCgYnqQUGn4AgNRVlT3EFMGbvUmFJoQjQMhBZr2D/MwBS7gAlMgflSSDkKgRFwgK//gDOk1EOYABS8AA1VAX3hwAy4QBJvzBOOjEKQQBCxQAx/nYy+HEK3QBKIIbjVwcqXoGuyAjAyligLhg8nVUhkmEFbQdh2wBA/zMMcoHhKweO4UdIRYAUJwCEpEUWklUgORAsqYhvsoBUPHELbAdStgRp1hC07QcgFJZfVXkMf/0Qo6VxAkcFb/0IMXYEYmoAPGVFMRIE8UkJQUIAHJJQGhRwcWIHsmMAEUwALwIowUUFw30G8W4FFXwFYNcQWYt4+aIQxPQJM12XUwiZOycZActgRBtopBORBDWZQhNWIH4ZQEAZUGIQiF11JpBQFcJhAq4AIC0ZXgBJYM8WBcp3qZMQlomZYnNphsORu8mEFbUAEyBpRCSZTrVFM/8ABHZn8DoZcDwZf/cAf+4gUSmVY38AAN+Q8BpW+IeYeKaY8wGDub0WmSiWk1Upmz8QmERwVcYAMmOBCcSZee+VE1tYQPMAVmsAQRQGam2SZRyXMQ0ARjQAXrB5gCMQ4d8ABR/wAGa0VPh+mVt5kQVQCDU+AZdBCZvZkCaQWcxzEMNqBNJ+BRZeCTrWACSvQDZycIzZgO47dNZ7cCUSgQmXACAqEOj0Z4QiAnOJCEtVWgJhB1A3ECl0AQAvoQwoV8LhCSmtEH+hWfmrUH9IlJkxCQeuAVU6CLFiEEoQcSh0BZJhpXKXpJLyqK/agVQgB8FgEBzSgSkWCjvUmhOTpJ/GBXoqgCzIUVP5oR6kB5HvEJWCWZKzCaSTpJiJCWSNoTtiAI91AGUHAFWjoQgSAFUxCAj3IFUXAmUToQwlAFUaCFBKEJVCAF/3QQhyAjwiCmZlCmZ6oRsCADkhlVWypJUZCWPv/5Ez3wh9mUa+rUDX4GfQn2dV+5TYt4nwMBBRRQeBSQAtHSBBVgqSwAaibwkLT4AJEqAWSZEd3AmAFZj4kaSffgApLZaD+hA/8lIOzwAhIwmibQATJyCBGwd8foBAIyDzmwWnzXVNf3AKbFDxRAZo9gAp9gECZAdj9wAUEgIO8QAxPwUxsBC1cqirtXq5EUCL1JjTuhA4GnDtW6ehawoQMxBhVwLhtVENAnED1HEGYwAfzADxMwdwqxrQIRmgTBDhOgkRjhDR8akAGjrpH0BL3JAkX2rkA6b/8QUy6UUBCAoa4FSzZoeJJ2AdrCBRIAASoABYM6cdy6sRO5EeZgYjX/+QIiSrGLNA9GmpavmhM6sLEPkF5SEHg2UwHF8ZwF8XsCAQE1sHRKdwUGwg5mIAQPAAFQRpcxy68yeBHs8Hk1OaM6u0h6YKLpyhNBG2DplQj+BE4UgEwsgHAH8gA+IBArwAIMMQ/iqa1bSxAd0LUVcQ/HF1890HFOOraPBJAm+ps6kbY5do4s8ABRGLAA1ghnSCHewAIRMDOCQAFEQCXj4AMBQwcPYKcJ+KX/gLD/8AMyC7gTwQ+KC2FUtZ6YZrCIy0js0LOSKbI78Vn8arDq8AKxdZTfOhB28ADiVKrXSIgQMAHd1AG/FLwVEAEQUAEyIFd1+Q9CILcUZWUUIQVU/8ZlckhlcHi7iVQHJnpXONsT4cC4pACTzmAGZhCbkvMHZECHLrQHZMBt4UAHZoBjB9EKfDIOuikQn6BkFCGWJ6asAHtiD2m+i/SC6ZsCdloZXkBl4WgQ7xlflAbBhHQOJZq+rgsZF3ZiPQBkBUGimuUCX+fBh8SbE6wC5BoZewCfKWADCGwQNXpXvOvChrR1E3xX+/cY1HZiM/CkBlGkKtCIPlxI4WDDvbl3kFEJuqu+y/eJ7dnEh1QGQTxcTBQWeoDCKNEKuAphLCBxDDawarzGbNzGbvzGcBzHcjzHdFzHdnzHeJzHerzHecxMfPzHgBzIgOzHglzIhnzIiJzIiv+MyArBcVTGAiwAnzegA0IgBETQBE3wBFEQBVRQBaToFRRQHyoxDEwaXytQvhnBDyLSCKzcyq78yrAcy7K8CIEQCIsgy7icy7FMy3awCLesy8CMy4uQCHjgy8F8zK/sy4FAB7aMzM7cCLRMB738y8+MzCdSzdU8zMVMzdicy8NcB8zMzd2sy8MwCeMczMoczuesy768zes8y4tgB3Sgzu8My99sy+Jcz6w8zHbQzPqMy92QbpWQCa3QCt0QDkxGEOAbXwWsFvGmEuEAX8n3yak8D09wAz+Q0Rq90Rzd0R790TTAAh890iQ90iFd0iid0j2wAhid0i7d0T5w0i890xr/LdM0fdMyQAIwcNM3vdI2wNMvvdItDdQu7QMbNdRETdIxLdJJndI+sAI/3dQj/dRRLdUjLdRWrdRQndUlrQJEcA8EmRA7Olxo7NDydhLpcGnJ124MZtFNMA3dENdyPdd0Xdd2TdfTMAyBgH533dd+Xdd5vdd/8NeE7dd5LQxM8NaFvdh4PQx+EAOBwNiS3Q2BDdmTfdnTEAiQHNmXvdiHndhw3dl//dmKLdqFnddMoAKlbdp9ndePzdmsbdjO0ARLENqxDdizvdq33diIrdu7Ldd5bY6+/dtzPQ1NMANC8A452xBUAGHcNhT8cAUrYAIu4FE1oAn4dwIgtAXT/QIT/wtO3c3WAkGJTyBpOMC4HvEOP0BlP3sR/GDRTGDbxA3ces3X813c9T3Y903fvS3f8+3alr3flK3XAS7gcZ3Zm23gA97fAk7a/n3fqK3aD/7bAA7bDT7bRDDhtz0Nsx3fBu7gHz4Mwq3hxG3cOVAFQjClYX0QtDtcbDoU80ACEpADTwADXIhO/EbbrcAPLDABNlDjHrmMKjABOPAEwiuDFLCIQeADrPriHHEPEgxhqIsR7w0FHh7igq3ggW3fDT4MDN7lr43lBf7hguACLGDhEO7loN3lXx7iqT3cFO7YY77fHN4EGX7hTnDldK7mcL7bwb3mCk7ZTZADYFAFQXAOo/8jEVYAYV9cFNJZI3SAjAlGEFAgAY0e6bJiY7Tad3kw3idgIPPQASoAEvxgsSfWwxUNBbWt5fnN6lke4m2e5mHe5YEw53Re5mfO6rFe4nxO4n4+DG/u66xd4YHO4U5w53TuDE6w6mwO6Hs+4oEu6IQ+BoZuDokeEQo8XMs7FA8Al0V1khXAXwMhjANRmC3Vo0Y13qFnhxzR3CeGqBpR5cxO6zDgB65e77ru7LJu63Fe6/qt5biO5v/d683e53Ee7CE+6x+u7MgO4co+72m+67wO7cU+6GAwBtQeBNa+4gaR7ZqFv0QxAeI+ED3QbxWwjxIwxOMnEBMwxCU/3mf9D0D/9xHjC2FLsNwWIe/CPuz13QesLggw4POwru8Dr/BpLggx8O9kbuYCz+sS/+tPz+sID+b8XuIMv/OdbewNH/FEP/FdD+EWj/EZv/ESUfOatadFIZgF8XYItY82d35MBe8uQHUPPRBl4HcdAcPxFQRUShHvHQVEMAw/H/SDL/QFj/VZL+dN/+tIb+/FHvD5bvA8H/UHL+EJX/UUzvCCn+zHvvl7Tvmx/eeS7+dhL/bVfu0QYfZ3RdFBMQNwNxCf8IptTxA4IJqw/6nj/QDSdAnmBPMEcfcdYQdUhgMZS+XzAPiev+dAb/jKT/hDP/qmTexd3vjRPg2HwPSsTvFcD/08/z/1e270nB/4F/4E4n/4Ia79H176pq/xqP8QXABhCUoUwkB4W7AHUrBNsHTyyfSHV2D/NshUAPGgw5Y9USKY4PfvHwUwChWWqeBQ4kSKFR0GUpFC48YUNcZZBBlSIb97UYgM65ZS5UqWLV2ynDZMEIw+L23ebBlzZk2cPW3GFMakyTSfRWEO8xMjkFGm3XTG8NNU6rRDLlgslVo0pjOhRLPiBNr1q9ZhTFQMHQsWqdK0PWM6EYKyrcuYT07OfRkWLd6c07ju5Uu3SQ4wYwwfrhLE3LuEIil64aixj2PKlS1fVmjrRYQJHczMKKGwA6KJ3WZEoNDhShUYCilwafKAwv+DIO8cdggk8VAHzCEbrYisEca03iJJmpQbeKVOGHuUH53p/LlKvV6nx0yK9brMGDynU6969XvKrWK3BwX8PabZ9Mqxsx3vdNiTuOrn37WP3vr1v/vVDy7sMMQUY6y3MYLTozgFFxSpFTskUicCJ4p7jcEFNWEhuBRcaMVCikiSAj/7oosvpkOaK3EY/cZ7T7vnTOwuPvLCc/HFYfrLzzwWyzrLv8BalLGu+razKzkbV2QRxyABFFDAxBZr7DIzgsPDQysZNKOCJtT5pxUTHjiHQi6uvAyWFzRk4RIyR7pHiiHtO1GPFONMEcntstvxkBikK5HGFJU8Ukf72PORLyD/U6TPyB/nezPQ9twDlEUmmxwQyt6mjOzBNTetjAsIKojAAhM6UbBCTkPqRgYNVyBtTRAbtZHOPGGQc0c7bcQTzj1ldAoRq2p070ZBF711O0J3zHVHKGA1dJhlFSV2WEilfXFSSg17skDMMOWIjlO/FSmRO0hdEApnwK3IHBs0VMGPTV+FdlFZda01x0ebTTZWGfhk0Vfxdow02nsXPda+fLd71r6E7S3U0ICvs/baMbKN0jI6gvMWXY03RpedHDRMIWMySZqCWXlhyGNOlOukFq9DtztEhnr7feFf+x52WcWWmy34TviUNXmumBY+b2ehcXYvYokp7u3iyMzgOGqp/62cRwiQx3j3npLjbVbPlPOM4WuGkf1Z35nVS6RmYBdFuq3qguRxYKHXWntRKILg2u1hogh67mKnldtQpZcmsOLK7AgO6qkXZ7wyfpoA+Yqst1YZjxQRicFyW41O62UbEZFBbBbTtnm7tjvXOfC5e8a17O2iwNs+vvPW++8fT3d5cMItxQyPxBsHPniKogBZisn7nhtzze1TnmXVx/I8WNBFR1vtPzn/6u0UWQ/2YBthpx36vZFH3XaHsXdb9921vUyP4LAWPv7GrQDZCcMt5GceKoJw5vIY7vAfADf3vOzRbUegWx6LFmE9gKFvKqlrGM96RLa6OQx2/Xsd/wYYwf+jORB66lvf/RzTh+B4QX4nlBoXQCaEe7hKfxo84P8C6DwOos57zUIgr6axwNLZCHcFNF9g4lZD8d3wdhdsCwZt0T9n2CImF1xiN2BBipRMIiWkgIUUqdgNK3YjEq3oRisikZJDpOSL0yCFIJbQBOkc4hPd+AQiUiKdQGiiG5nACh1SUsduVKJWZkhJHirBFSAQBZDdoMMYI6HHbnAhJYn0IiOvkBIzNKIbizjkJLsxBkQ0gQZPMIwQDPMEK4zBCqAcAxAM0wQrVMEGY2DMFBTiBTU5JhDBGRMKdQkupwWnB+xw4f4wyLwY2CFFiSgmDSm4o0TIQIA4EUZKOpGSKDb/UZpXzOIUnbKHmtUhm1ssoxfBKEYykvGNcZyjOeF4iBsJYSh8xOMjA3mJPv5xnvVMiSMRGYlhLCIIQ9EnHSyJyZRospKXPOQUUjKGRHQDEWRIiUI3yc4lpGAoUchnGQ+hTycUVBDdCIQmO9qNK3z0D1ZICRGcUoUtxIALKO1GECJakz1IVKbdmEJN8oDRmM60GzVNSQ78EgQfOAOoPcWpTqWQkh749Kg3zWk3TjoMKcigf1ZYyklT6lGQipSrG00JT7nAzkC84KILbehDI4pWhBaUkgPN5CMXwQQiMDKgirRnN/JATz9Sco92jCciU/KHIORgNYaZgmGssIUxbKGU/2NI7BiuwIUq+EATjNGEQoQRJscIIjhb2GVoN6WHjATnBpy10DmE8Q9+XCIxixjnGH+qzk+Es41v1EQe/9qN3A4jbcYUJD67ccjgLjKfj1SkJN+6wyvIwA4GtaRacZoSMshRuhIdazfA2g2xCkIYRNABUUZa0m4IwqvdsMIfpApTlaJXvX2waUyk8IJAwDclN41CyuyL1KjuNKg+hW9McuDcKMgpDzYFMILdu96tLvik02iECq5CXvOmZKTZ3a5YNapPKlA3rWPgyg8MaclGxHW4JD7kXSN53G7gYRLfZYIhA1kJ4TKSj7mVJ0jPaVsykqIJKoixds0Y23KKU4tVxP/mkcMYEzzAIBDWDCM1h9k5Z0gBCFNelBRgeJ41EhF6P0wfYSQ2ZmwVrjeH+Kxo1eyhQwAnODPwxpUq4YX8zYB/GNanhh0K0W50uBvV3bNbT4xQZEr0oMZtJHJXPNwZ9/FrxK3EMPbAAmPa2I447oYxtbvjdG46jLIt4zQeQQomLAHUSdYmF5MMZTB2o5q2kOYw/uC6RS3CuTpsBANv5kGmaC8+w/gxAbMSk1lX0GVV/gGWm6VlZTdLGF0uEZg7B0IyT8zMmEFEcCS3Zm73phIZCs4LVjuyeVThyseUQR1SZGt1b3BHxd4Ru3Gta9Px2ii+Hg+wJ2gfeEdbCsm2j5X/m52zZwt72NL+oJirPWamYWYR2u52xCvTCReg6RMutAIQoslMGTDSPrb2eNEM3hRi07pZ8g7SI+jtQ3uTJYh80ffIey1rk99OCj7YuI2m8IOcHwnaSWo5yam98IZfJhLBsYLElQ6SYcAATZHAOM/X3fGph9xRXh42zY2NukXMwOrXeQQMevgihM/85XiJOdYf2O8kTQHn9tl5z4NVcLVPpezDHjrRr32ZSgSnCksHvETCQQN2CSLqcg8WyFPUCKq7m981n1sjvK7DSIh96267u8uDzpS0vxvyx55CDxCfM7ePnuA/V0/mha7whZO56JbJRHCoUJF7hCPwak7HDdhV/yVyW+Ht8W68fST/dYHVfeZst9HwKW/5IKneLRDkVecff3kqh970eiu9rVDPn83fO++tf31lSBEcWdL+9qF9xw9Aprgr5e8Kvxf+5HekfMdvB/nBon+JKj92wBlf8zKXCumzv887GuuzDyqAP58DwHtzPu9jvdZzvb2zjE4IDuORiEzQlPPTpXsgApDJpfabhysQvcWTP/t4hBIUOf8ji/tblBMkPveYBOaLtu67CXz7DgG0ERaEFAPcDioYwfzYPrKjQZ+Yhu+DwPCjDFuowIkIh0zQQF3ihycAmb8jNxHMovkLvu1wQWWavh3ZwiCJQf67nSHMC+iTERwMFh28nf8p0IErtBEfdEMFVEGtaECtMMIjlMDKEIbgiAKHgAUResLgmQKQgQIXEsHrizwUtJEvHJsuNEFFvI4wpL4vI0O6MMNfC7Y5fD41dBgq0AFEFB8fBEXxobsZXMBeu0M85J3LGIbgKESFuAJYCEQUugKQWQJALA7364E4XERIXJRI8EVnq0RL5ES9AcYX/BFJbL5hPIqzmws0XJRiPBoqyAFebJYq2EXtO0UirEMiTEVVZB/LYAc7CIRyDIRDGLdZ1KUyAJkgmIcqbMMUYcTtOEYuHMBJfKB6DJJKiAExPJ9trMFLzLdM9Dx8PLgqqEb7qIJ4BEKABItudItvBEdcxAz/dWgCdTyhOgAZHOASELwChnxEZJwbfWzEe9yRSKABkXQZfvTHY2PG5RDIGyRIR0wShLTGnLECkLw6U9REO3xACJxIMulIjAyePyityPAIctuChDzJGTgk+5gEp7THHCRA8ZmElNQhljRIu3tJ6ojJ6YDGZpFGzLNJ+7ACpkxBntQhiQRKJPS2QyDK+FkEN4uM4VBKtKRHqdyRq3zKtKRJG+FLHboEGWjJDnLIMnTGtgjLnBlLKivL7TjLmyS4rlwOiAQLtmzLPCyOVqiEuAweDNEQDrlLWGvKvgRMGjDNuaNM8tC6vURNwSTMrSQ5y8yJr3yOxZybxvwyK8AB0rSR/7P0zZ0EusO8zJ8EysxcxeKIM88MHs1AEycEwaUMTsDUS6h8zfqjStkki8AMkkuYgcLEvNW0QbDknmisSirjzel0GODETiEkzhrEzON0S8uQguVkzsVJlVVJBHLjghxQz0WJytRclEq4zpLMzh0hUAF1D+8ETyoTT9tUjiEqyOZLz9S7gt5sz/5bS+M8TuQMx/tUOnOoAXZxFxDkAgzdy+rcjgSdyjQ8z6xj0e78Tu1kwAdNzLSQ0L/kjwrljwv9z8l8T7qgzZ+Izw6dT5Hwhj4E0cVRh4/REJHxkPw50R/NGe60jxg1UBel0eerhBpQ0B9h0C0lwhslOQgVovIUy/8XzR5nsIIboFIq89EM/REyzZ4iNVLNrAz7XFKOmYcgABky4E8caDXr/NK5wdLtgIUgXBQjMlQv1aFMmNEgodN7G4ZEDdKXyFGfEVO3cIYruIFBhZQLBdVgsdSeBItJnQo7vdPkpIxKIJc9jRp+YILICdRRDZZDtRFcJVVFxRc1LdMuLVSXgdQGhR5UJcJK5dUIRdOcYdRj81RbdVZB3ZFSLRFjRUUO7dBV/VDHSITOhNWoIR4NKT8rkVJpRVAaKIMU6dJ0ndZknZtmRZ11fdRIrdYHpdYzXNZ39VW7e1YL/dR2vVSYsFYHDBDGGgMu8IIx8AIuMAyD3QKG5QKDvQL/w5gsD6XIiUiHb50a+tEQ+wFBL/jXc2XXK62BkUVUd9UbeBUfeQ2SYd3UU7VXlEW7fB2LaZqGVtiDGPgD0pymbsAiJeuiSyAyITMydPopv2jTVjBaS+Mt3dIrvsqrOrCiRepUFgAjOrAigiKpt2qr6dqktOIz7CKrGzgrkvqokLIwrtKqbmivKsgqmIIqPWACHJAoIAgrOdkDP8MvOcGDpeKvqOjb+4qoHMiBkjGMGjAMIqCCMaACIjCMG0jcxa0CJjAMHTCMJagCi7UMJ8hYjd0YFdIQFgJBMAjZK6UBPrOPS6gB1D3ZgPVKle2Jnn21mEiEkv3ZVOsiUoA1ciLa/y+CIzmaLU+Lo2nQhBmFJ6fdK+Eirqm1K0XDpKB4gRHrWoliqEDzWjwLq5TYgkCABfASL7Vlr5TAKgbjL5pSsCjAA7PoAaK4KSkwML+FKqVKCR9wqvhyFvpa2/YaX7TthvE6W03SM0HQpynoVBr4qOviWq2FrkVTsUegA0/ls+TtK8ECKcByWjf63U47BCwiArQIJ9/l3dz9pmtyNSbiWacYWJ8MkGxl4SY5Us9tnF6KjB6wDXKdhzEoXd794HFqqODF4NrKsRvzg0uggZEKrgmGNAZWtEgwMTNYhGEIhNP1WkBD4EbasOy14vLyqmm4Aj9YghwIXweD2wQTXO6Sk//9whs/YAFA8q/y1SueyoH6LeP0Wq9huIQVgCg65l//7Sosxl7u8rA90wQaEI+DKjFBO7Tm3adFS+JKuAOhOKQbu2BO8+E30mGnaARNWAJTQzKfHeE7krJuYLWeyNTWedka7NTSDVVV3lXXpY4U9kZsbWFtvdh/EIakg2GNIS0NOS0QJANVrjBAzuIMoy7r4jM/q6RpSIQtoAHJ4lpEUzFEa+RH26NLUN2ASglBoOTb+rQiA2HZmoSYmIQOtoUt6qJXS4lWU7voWRTVHQMdGmRizR5YLsN7/TWataF9nc1+5Y8tYOVFsefroOfiXOFZNugXHol0zOVTOYSj5Ag48+X/f27nZk4Rd06RgO4efSYLiw6SeD7lnxjo2sRo8tw3TW2+K7ABaD0af1bpuRnpOV3N5VBVg7Y2Vp2ITNDTheaUSQC3yHiBc7HhMpDoZiFiMEiRTKgBowZYU/0JA7IPpFbqEiEFQv7ovAjpo3hpZS1pUz7plE49ll7qenVlmZZlmpZPPHUIPIBOneYUirM4EBTqlp4bpPaCo66Bug5rg9HoY4VqHSKFGpDnYbtqmMzqM93qjK5qId0Cr+5nxm5dpgbpmKaOmTbr+XxHtv4WZ3C64GABqLNhM7CBnn3qu7ZrvLaPwk7ZvX4+uvZrwE7snBhsr0TtmT3sRVVtblxs0YaU/9zOa/WI7Zyg7MrGU34ggsvG7DXxBsILDhUwvM8ObbvWp9GO7sdeJulu7cCeit+Wj9l+Rnwuotvm1C2oAd2+Hd4+bZntHO0m64I2a5qez1o+bsw4B91b7jwAQdAm72BB6um2EU2wAf5uZci2RNjNOv8G8Bf5BNeW1Jgda5UoZcRuPvHO72Pjguc+7wafBvWe7LJu7+MEg8I9hw/tBPiJbzJ5Bx9Yv/u2cOveEQO/aPQuIAJ/IBcPkgTHbpLT7pjgbsX07hgH74eU8NSr8AnPmR2fZ8kmj+A26w9vAm9Ih3kwnHd41RKnGsjBpfuugTfakf1OERq/cAGvTRkvUy9nEf8bf22BZXAwx9Qezzoxtzvx1nKy44Is722BRnKnUHKa/nAmGIZxUAfj/odPACYqf5dw3QhcjtJ5MAM6b/EaOPBmIXMbMfIHcnNKjfTrMPMFd2Udh3EcZXNK/3FUhnMhZ3TqZhENT3IO73AI3PNhCId0CMcyuDhC5xSOTQElTfRFj/Pt0ARH7/IbePQi73RQP3PW1ARg16FPsIEb77UcR9YGT4kHt+1ipw5nmPNdv51rr/MXQXU8V/VVX7g9dwZvCPF7aAz4pvUFUSEmQHfjUPRS5/Ua2IIUIYUbmPdtn/Ydqfd7L5FOWHZqJw9nn/SvkPZeBXinsHZ4vx0vUHiAHvb/7L7zIvx2cCczcSd3djB3hZgHImj3dO+NQAD0BckfOqiBLbKPXuf37dj3Fx9rdm6WlQ8Sf2f2exP4h8+KgmfWUP+JhDd5SGH4ng9wsVZzwZh4ipcYiz8HjK+YkPd40Rr5kqd3ead3e2f5oX9dna9NmO/3f9d0q99umw/ATy/TSu81a6cBoHcYMID6Lxf6DWVvo/fwHGCCcU/6jP8HDGz6iHt6tF8UUrCBlLcRrTd1k7YPwVcPmT/4DE/z6BP7mSN7BuSCs089ted7Ycfwbpf4t4d7Vpd7uld6h2jCvOe2vY96TbKPT7gB0x98rj59qo95rm/71b/BxqfUx6fDyK98/8wbg7WXfffA/Dw3aKT//H/4Q9Ef/XkgeTvSdxtQfRtB/eYPer2m9ph4fh3qhBuYea2oeWjvBpzX18R3Bi+gAeUnu90nf0kHe5qPeOCfZeG3+3+IReNXs6c//8Bn/hSp/mpN/2O1fbXI/xIBiFY3WATqZvAgwoQKFyqcJoxJk2kMJ1JEOG0YrCURK3LkOIyJio0dR1oc5idGQZIqDU5z5oWGppUjW5KpEVNmx4sZReLk6BCixJ4zm+QAM+Yo0qRKlzJt6nQpmBxMnHk7x+4ev39at3Lt6vUr2LBix5Ita/Ys2rRh+c2jY1Ooz2GfbFyBW/Hipxt17VJ0qDEo34YmUf8GXogXx97ChgUSVGz4IU/HOv86XvgxJODKF0+mrHywJZgZNzU7K/PWc7fJkTVDzox6GlGjT2fTrp006tSqV7P+U9dELfDgwocTLz6WrdtMqFnKpbs8tVzEz1MLo/x6cGfN0RMvn8Y4u+efqwurdu358njy2KeDnqE8fOka78NjtP669fTUsW3z7w9VKlVWYcWVOsYZeCCCCQ7HVh3yPYeXc90N04l0D1aXXmCbEXYdhdy99h17+NG3k3mVoVdihus96MwY7r3mjBkOXkeihUDlB1tR/unYH25UpbObVpUcoiCRRRpZJIMyXjeXh5JNWGF3F6LIl4bgKXZRhze2gkP/YzViaFd5+Z0oIWfssegifGbQMJ92NEZpI3v7IeUFF0dtcRQXXozBxZ1jXGFnnXzu+F9u6bwzoFatVHIko406ehZbdqz5YHNNXvmkpVdKSeaG9HWSQ6ZXbtnlm1/CFeZ0Y15XplDDONONM7YY1IlBtjjj3RbuTWIQKbB000okBh1iUCStdPMJIgbtIewn3WiSEh0tcbHmswaZYVAel3RTiR4GcWEQHbs+UodBe5nRSDeLmHHRFBqBkUg3iJBhkBQGbTGsIN92E0W5gnQTyF5OGFRFQX5Y8ZALEk3hRzd7UGFQEPQui8cUEBu0cDd41NtNxN1grDFRNRwlBBVjTCHE/1E3HEVEFWNQQcRRORy1RMtUMDHobQB6Yyii3jz6M9BB/xMpDdpK2IkNVlD6qdIW2qcdq57m0PSDo1ppK6yydkNrN712AwspBu3aTbG/BtvNIX4tMQ2yyjLbTSbQGhRITNxai622kdCBURARhdvNI9d2Yy666l5s0BjwymsQv91wgS8XH6UQ0RX+AmwQEQMX9EfTQlzM8B4Vc8y4HtPoQQRKHUfRrcMW78u66B1j3Ho3mXdjxR/OWAGDtgIPbvlejT/eTb6Hd5N4vPN2UwWMM/hr+ODg7qq3t3hv2203gtPtbCAY2RDRIc223bCwtAIrLLHGgi02r74K0wlEXGMdnv+cONtPqI+H8vaPFD4L/T8AD8SWOxRtaTegGlzk96pYXSQRU/Pa+roxtrKdD21vG9+y/hWTTPyhOjeQSLaud7du5GF6dKheN+gQLOgRbhh1eEFBRIe8Q+hLeMM6RMD69a+9EOEiVagB7pomuz40THQ5uBgRQ2eQI3ZDCkmMnXdoQBAljs5jROQc5gxihYJcrhu+qxzaavgQHNxQXzJUnPIIly7B6UuFgKNDRoQAQm3ZLXtz26Dc0Ca+ZJGPbMZqxSNAsrau+SqCY2vFArWmwIlUyUxkmIHRSKOmSLbpaayB04Pqd79NjqFHO9NfAEMpyuIMsGiCyOHvuoHF2mnxD93/6EPskPjKek0jB524QQ2ICMslynKX3WDiFHQZy9u5knMTWoHSrOCvUxrki/6iIeO8NazFecwgZEiWujSixkec0HHgChb17EjCSogwhQb5wyVOQq4+hu9Yw7KgH81mED5CMGxke5LSYmUQY0nGaiHCpJPc9BxV0SdqLyKDDCh5JRgVcEaWlIyIMpkjTnLSkzzjjTeiMMqNcvQ488ADDchZwbHVc5+1SmRfJnTApU0tRA9Vj0GdxDQt5YBU9wHopQS6HIJCrVMvKoMMyJmmGQh1RC8lT0S7o0mK4syioNyK/zoqVamyJQ8hNWAVKNUKHWTVaaY6lYros9WuPsgWNbUS/0Rxqh6dooanToopaYBaVMk0b64BPWqGkvqapTJ1R05FVCU6MdXBdrSqVz3aDcgqVq669KtCaeR1xkpTm4ZHr2vFa2Hceim40rUMMbAreeoqIbam1bFg4mtf/dOjcFx0K4lYFGFjG0C26OGwkU2sVnug2MqqFaY+dVIrdHsjW+iAspc0rUxQNVCQIDe5YX2RGT4LXaKOFrNgsqxmUJta/qy2tf9Ih2zDC0Da0mBst90tcIXrpSmB6bnpRa9miGvc0rL3sfVpbk8061u0LjS65qUrHWbw37vidyXiqS+VtLvd2nT3qf9wAnjFK2FH0VbAWr3BwyQU3AyXCsE9gaxYe//A4e7Yogfz1VRvqXRfD8NFvynibH9jMODQBnjGly2wSg58IwUveDYNRtSEg0zheezBwhrGcG5HzFscz8S9l9rwcE3MX6SmuL2kNRFzWexcGIfWDDCwMZWcUePqMnkm2JUMj3vslB/zRhhWEDKcicSWIp/ttkpO753py6kpt7cVPsizZEp8YiqX+S4rFlOW92wmL9cZwDNoNIG1nNwzXynNamYKbobBWgfzQxhx/rSB2NKHR19YdJH1galvWmhDc7nPqI7yoPNaZbBe2TEuVnGrw8zoFwUY0jm17qkoTR5LX1opmd40ojIRVVAzOziinsEjtIqDVGvHz9Q+rqRz7GT/9Vj7RrDwQaypJGz71loxt25vrsEEIxhEGz4BbrdRV32XcSd4osW2zbG9+w88ZKLZ/lYLW/wAbWlf+8k/KDiK5c3IbaeoFQf3Nrj5LO5ZkxvYdjk3WNN9qnXD29Edj/R6d2zve9Mm30+dx79TjpaAD1zD09YqEBBO6GyTBMTVjrm3fxDu61L8w4dOVaJX9VtJspvXLY83zWtO79OOnORPMTmi+EEElKu86muZxx+OLlYcbCyyQOi6qpOeE4aruBVf9zYQdh7snuNEuTsNekGHTlcvf5zGWgd5h0UuG6f7WCqa1vfQrC54r7Al6+hyOdirHYTEY1vRkV082tX+2KU7/7fcmYV7TyWubi8fnjQB7nwlFc5Iyj+W2E6HOm86MYbBs34rbAnEDEBfba5rFfIhF7rmfd4K2z8IFmnP/eTZXnmLtxjzb5V7f2EgexrLYPk3FntOSP9h05Mc9Vt5h2BbP/jXy2AR0mb8pWzB+7A7nj7iBz9EgyD5D0u/5j9fLmbKD90XeB8+deg+maHvk/Ynl/r3tv4/fAI7aB/rcV/9RVYONM51iJ8Ckh/uSQgD3ogwqB/wsZ/wGdj7vV38PeCK0AH9vcj9HWDo6d+8XeD0NR3fYZrfIdv+lMEnEKDV8cM9zIMgzIAIVlsCUkoESgj/NZnGtd0w7KCFUCCl9GBcWP+eihnfZiFfiojZB2qHM9gB/jmU6BnGMBhhzflfsQHg/sCgyskgDcoAvGhYDkJgEDSgdmAhqzFhewlhlBAhD5qg+yFheynhflGKE46hk0ShGOZfHFYhI2nhpQHgPBBBF3phs4FhDeqh+ZXhAgoBGjqJGqbUDzqXLUCiBArB+jnXJDKE29GHHb4YG7ZXHtKHM9xBH1IhCS5cJ96FIKoZAGoF1SGivyliKi6gI5ofJv7hKnoi2bXhLlqIJlYgJ8phk9EhWIUiro0iWJUiFN5BDDAi3tFHK/bFK/YYAGZCHdBiytkiH+EiFOigEIQjL8qfk1wiOQrjJmJgNSbEJ2qHMqL/GzPalxN+46WcYgzY4zSmoTEa2DUuWCyGQ79xYyLO4CHIgD6eow6koy4yJD8Cojv+IlihYyauo7a1Y0lsCqXEY8bNo8/VoyniQT764XVgpGH843YBICwcIkHGGRgeZEKG30KKo0P2RCOwwRrkpE6ugRqoQQ24wE4GpVAOJVHqZE+mwQk8QVEuJVPmZE+qwQy8QFNO5VL25Aw4gBNQpVYGZU/+5FZ+ZVcC5VeOpRrYgAOI5VhO5VEmZVpqZU8+gQmkQVsy5VrK5VxWZVjeZVPmpV5OJS7tXQo2RSxeASy0JLO9pAy80wLqwBOIY2PaRSAwgAEQAGVWpmVeJmZmpmZu/yZndqZnfiZohqZojiZplqZpniZqpqZqriZrtqZrviZsVqYBNMAUBKZgriDgGeZhGmRi6iBj9qIVXuITkGAgIEBsHidyJqdyLidzNqdzPid0RmdyHkBt2mbOMMHfPZVv6CaoIaZiml8PDCcEEoF42gUDSCd6pqd6rid7tqd7vud7NoB1IkUs9gZ3uuQMIkJvQmB4Aqdg2AJ5rmIjTCZ8FqiBHiiCJqiCLuhrGoAVzCcACsl94uc86Od3akeJlaf5BWhgtAGDfiiIhqiIjiiJKucSWGcsKsqEChkYJoIM+At/OoF/RiQsEIGMFsYalKiO7iiP9qiPLugQ2CYALtuKSv9Yi76oDvbAjc6Ijfpnjv4olEaplE4plaJmkKZgLPZPkU7YkcLoAirpjJZEjS4pjlapmZ4pmqZpj17p6eGmg20pi87DIiBpjIbpZ9QHmZapmu4pn/apn6Ynm/6fmyJKOGgUnBrpPDgDC8zADzSqoz4qpEaqpEaqD/TADKjApGaqpkpqpdIApm4qqEpqC2ymASCAqZ4qqqaqqq4qq7aqq74qrMaqrM4qrdaqrd4qruaqru4qr/aqr/4qsAZrrSoAsSrAAWxmoA7ioHZhOByqhN0DOywCDvQBtVartV4rtmYrtu5BHthBDIyBtoaruG6rHtiByowrumrrE2zmAqSru1L/K7dywbS+K732wR7owR2ca73WK7fewLzu67vqAR7oK8CiK74SbMGmqx7cwAuAa8KOa7z+68OKq8BWgcRO7LUKrLxiLMXmq8NybLYKLMKCbLbiQMschQIga/Uta5C8oLOKF1uowyEEgTPUrM3eLM7mrM7mrDC0AinQQCDsrNAOLc/6rA8ELdEmrc6qwWYygNI+bc32bB7QLNRWrTP07CccrdVubc/6ANVu7dMKgy10wg8gLdgSrdhmrdmebdKK7Q/AENu2bStMbdy27diCwdfWLc4KAyy0Ah7krd7urS2obeDurNiS7doWrs4GwcmOQcpqZrICJMtqxWu9LMzOQzrM/6zi6mzfagLQbi7O9i0paC3oOgPTaqbTbm7f/m3p2qzokm7r1mzf9gDggm7awq7tGm3ilq7Y+gDcxu7q1u7mpq0XCK/eHu4dGG/g9uzo7q7i3q7zli7jIsXjZmbkphYARpjlXm7mKm/gdu7nAm8raEIPRG/dnm5mpq7i9m3yxq4zdG75uq/sBpf3Hu/g4u7w6q78Xq0t+K756i371m/dEq8Ag+3h2kEBny3z4u/w3u//Ku70oqzKwuLkPpj2bm9ssUX3yi/4PnDcwq8Hgy36Yqb6Fm4Auy8I7+/sJrACO3DsLnAIx23v/m7rnrD7EnDrHjALc63+vrAL768zRLDjTv+w5GInC2IwnGmw5qLw+IZvDY9v/JbuCF9mCX9vK7Sv+JJvDLPtCssv9L5wD99w/9Jw6dqwD39C8ebw2CLwDYcx7/7w/gpx9WLm9VbU5LoZEiPqBjOx527x1qawFDct6JpxGUOxH4NtF4sx4eawG6ux/zIxFp9xGr9xJ7AxGDevIjNw68oxEfdVLHZaHnPvEmexExeyFrfuFFtmFQPwFe+w1QIyE9OuF8Nx7mLyLD+y+EayGqOxK4ftGvcy1MJwJh/y2XIy5Kbk5CpbKOvxKD9xH/NxFINuKlfmKtctIQ+yIasw/c7yIvNuI78xLj+xLr8xL6txJQNz2H7z89Ky+xr/s/VibwXjwSUssyij89N2MDQTs9JOM2VW8we3cj5rsywP8yVrcgOHcyGPs+0O7iQv9Dm3sS3vskGDrjvTsSdP7izSc3gpsT0rLT5ncTRvLj8TgD9zMUCDtD5XbSKf8UQPsDofNBljs0I3cDlTsiUzckSTc0tDcOPO8WXWsV9NrtRltEYTFkdzcBOntEdncyCjrkx3NNHCsvgONEsrNdq+9Doj9FNzc0M38EMXtFWjLTvHbkX/NEXVJ0sW9VQdNR+XMjafclOn71ajtEBDtVh3cy3vtP1qteqetCTbtc7qMETrNdt+MRCXtWUCNY9MbieAgVozM2AL7Uc7c0gr7kiX//TZXnNfw3UsR7bhjnXhCvMtx3Rfz/Q617RD37Q35/RC43U793QnD0osYt9j1zNSPzNdxzUJzzVlh3XSrrRE+7bhYnVoj7Fw76xm03RXr/NX4zRht7BrkzVsH7P9AKAA1jZk37Zbb3ZlF+5l87Ypdzc2U3Vwg/X+zvBxc65f7/JyF3dzr/ZzGzBoS+90v3NTVbAZuCx2yxZbk3J656xUW7Ygl7ZnAzhTdzY3x7fVirYY8/X6rjc5t/fy/vJg/7fgRvcm17dFy7ZQ7zeXYm4zm/J2Pzhni/SAP7hpW3GJPzF567SF2yyD+7CDmzCEt7aE2+975/WLw/h8U7SGm3VQG/+xdxViWns4VYF4gRs4bvc2Kp84jac4K694Ibd4ayt41ca4I5M2iif5hd/4AFO4eVc1EAfxjyd2kGcnkP0DURv5YPW3M484jUt54H43gQc0UlM5TVt5MBP3hM+4FUO5/aK2V6u2jhP0YZd5ZSr2bQr5U2kjm2d3W+/4/Mq53tL5lts5ghs6fEs6j/s5KwP6lws6cxN6/rJ2nnP6zSJ2oqtWBQvko9t2pGO6iTt1ned2pou5c6M6/3q6Nde4cnM5/+Z4qet5MPf45qo6ZSq6sU3uSr46pPu3rAs4rV+6rU81sPM4hi8vn++1lj/5tUctQwO7YId5eR869cb201UwYTr/O6xDe7VLu1zXOpPf+bfzb7Yf77Z/Oa//M6jLcLibM6k/b75DN7FvLbITgLIfRX2y+7O/OacHuHc7+Z9/O8SPd70b9qaft3FD8sX/u01/O5af+pgffMID4HYyfLs7fLRH/LR7+8pbfIJLesgv9L6bdL8XtsenNsgPvHzf+7EjerKXXAXbJ8pvNJJr98Mf+KzHO7XP+62Xe8aPdtLfPHR7ub8Lu8Cb+mkXvNWSfN8xOmANSdGnvIgnPaWfr8R/OsUrPYt3vM/LMM+3cM1ntq+fttXjPNaHdtwvuLHz9LlT96Kj+f6o6Ngb/R67u9Mv/W7Le3jrOnC7OLnLeLdPPFeL/zuY57qmy6/XB/4RF36cHr7KvzvLM73Liz7MZ/6w63ondILv7kHNkkLNdoKtjO3rx/7sd0LtO4PsxwruO0Mk9C0d0GwrXELNNkLNXgIiDX/xH3/yE78zGL8zIL8zKL8w/AEa48AnOMMnLELNIu0iZP/2d3/Nfr/2c78zeP8nCEPcIHAe1KwgZIIzZIIg1Gz7O8P7x//8O0P937/807/7TwJAkPJhxpkzOgX7VHJWyU/Bg84SLmxoEKFChg6F2bqDI1CkOwXJFLwTyZlHkCJJmnQW0tnIkh9XFvQhZcwVKQoIJCBAAMEBAgcQDBnjZMwYKVdqSsnRZEu4dH1s8YMV6P/fPzNV8bT61wpPVS5YtXL1CnZr139f/2UtW/VKVTqf/n2iw9YtXLl0/72NO/df27x2+U6pakbTP01X/wm2WvhwVcWEDSN+zLhMVShVD1XVvJlzZ8+fQYcOzW9eukNBCqZWvZp169awWmmiEch1bdusYWvqQft279Zqdu48oJOAAwbOrvxx9udKwSYFky9v7uw5cuXMCxKBHQX1nirZC1bZ48w7eGfiyX93RiT8+D1UYPfYPSWPszxTCgIpSN8+fmf6neHvvvz2q++OKGBjAbUoPjqQwADrG/C/Avt7kL87oPjEBxsKYrClKCyM0D8ABRyxQzoEgqIgHDr8SEISRQz/scIJA7zDhxdApJG/8pwRor30CvLxPPfUE2K77oqE7rrpqosOO+qUlM4ZYZr4xAsWlnSuoC1oC2SLglR0hktnvARzy0CEYc6OIMIcs8wACwLjEGcOAWO/OOes805nvAhEoOn8k5NOO52xoiAzEHEGEYIKPTTRRQuyIiMwbAgE0kadQVRRRr80aBFnFnmoUzo+DXVLmWiyCSfievopqDGeKGoKpK6AoYlpzEjnnUOG+WeYzP6pA7Nef61K2H94/cebSKryo6pIvFGW2X+c/QdaaZt9Ntpls/2HlHD+CYeUqhKp6ttwx/2nXG/BFZdcc9tNd11hzvnnHGGqKuwfeu3F/9ewqvi9N9+qwBXN4IMRPpg001DzzWHcYpvt4YkLym03ih0GLjgDfPrpuEtacaaVSwpqpCCQRSbZGZOdQXnkkmGjA7WXVy6oE1ucsaWTgkixGWedefY5505ga2S3m4euGOPa4mt4ad8y0pC3p20TphWBpqb6toxuzFrr11q5w+mvXYvai7HJTi2jTtZMu+yrffDa7YKijntu24KooqgxcArObwKE2rsoMJaaxql37uEn4cUZb9zxxyGPXHLJFz7tbohlk3tuizUnW+O/dzrubtjEvlw1zk1frenU1bZFatatxpr11ruenXS0Lzcb97TXbht2uDvn3XW7Z08t7737Bv8dcMHHIPzWwxOfXPrpqa/e+us7q3z3zSMOnmzUTf8cdNG5L5118G1vpYft3a7be61jJ352rl94X+vbi9c99d7ZJzt++6nmvuIZT29FSR7oAje4HDBhGNBTHPYgGEEJTvB62itebiR2vthcLHzKC93l8KdB3QDwfurrn/+GR0KM/W+A9FMhxkIIO9edbX86893+gDe/FA6wIMczoAeXp0AGOpCCRTTiEZHoGQumL3NM5ODlxPc38rkthqZDnwbXl78d4lB2WqydBs0nQyud8Gn8m18OxSi/4vmQb0AMHOGGqKvoJZGOdbQj9ZYowgym7opzi6Lfppi2KoJwgy9c2ur/dPg6Lqqxhl/kY9jICL8ZRnKFNqTk0liYRkM+jY0H/JtQ4NhAOT7wjqU05SlFk8dHNlGET7zbH4MTyO9B0ombpBgiNfm7LurQkVakZSJpaDoz6pKRdxMgDzvpxlASEZXNdCYqVelLVq7SlX4Eoiy/NsjRFXKAuKyhIoWJRl7WL31h/GYwc2fJM+5SmFscYDI9OIQFihJxpHzmPfF5xGgScprSrKbbYPnBbZqTn/8kZBYTWcz2iVOGvSQkQdM5xhqy7ZIrZGhEFWo6eCpPBXGsZz5BGlIJ7nOb/SyoLXsTUAJgs4QQ5d4Iu2lCLYIzdxdtp0MHWtFKSrSdFF1nRoVH/9PZbRR0NqDnHEWaVKVOjqQv3aM/UXoblbKUatp8qUG3idBcLjKqDnNhOXVKMf319IbhZCdGu/owov5NDcxc6lvhurimUrF7tUzdVB8a1of10Zda/SZQv5bJcabVNlad21jT6VNiErY2x3xnAdvoQTaMMq6VtexoSmM5Jj71pHe9Zl7tekGZJpSxrRFsQ8kJRr0+DLHGVOdiSctDZ6zVb2z46GVxm9t/zFWQdW1laVeD15yGNn1+badQD2vTdOK0fKv16iQnWtaantW1yE0dbYPTBqTqlrtw5e0sTVpSrH5NuM0lLhadCzV3ThewwmMuXV16WOiSNb1bU2771jtUyP960m9usGd3AZzU72bTt9QErmrKC9/6FpabojUuWmHrxdQ+Mr745WlipWvM+wa1vW7D7k7eEGARK3XAJQzvVQ+cmgT3tsLgHS9dH1zdDpdxw/h9L4sXXLX5YjjHb6OufK2r0f0CMcQjNnI+S1zVAkPVsx6k6tMM21uYOrjHZctvcn/s2hvPssUovLBrFcvV2CJzyB4s8pHR3MwkQ3nJne2gk0H725jGGMgzxmSN3TthX3Y5sDsGc4axbGdMXvm6BQQDf4Nz5jQv+o5rPmSbxZvigqyYy1XG3It7S2cLC1qseEbhlgnMZ0l+Wb5hNiunxUpoITcvB8RRnqIZHWskOhr/hpBGcZOV9+RHi5rNUy6upVnjWPZKejVfVe1M0flnYKvmtBAmMxwZQGRZT3vWmV326WxNV1+/8rPDlTOVZ4pq1nq6z6Bu6bVbR2r8mnrYY37sPKNtZmrPm4K0vmW2pYzpp1E61Oh2Bl8P6u8pqdp/5JakD1hA7Ir9UozJLjWgF5plDit8NUGYwRDj/Wp6bxyCC0sEC3wQcpGPnOQlN3nJ5ZODFeDg5C13Ocp7kAMWsPzlNTd5C4CYAJvvPOTysQHIeR50H6R85kI3unxYAHSj8zzmRV+6zZtO86fvvAcsWPnUa+5zpWPd5T3QAQ22znWSe/3nYu+6zKVudpSjXe0v/2dBHDMOOlhznO6Um4c6wiGIPuiB7333+98BH/i/5+EOdDADHehQhzvkQfCNdzzfCW/4MiDeDox//OX7/gQgLgDznYd84bmwBcrjwfKed3zky7AFMiR+8abvfB5Inwc73IH0ru88HupghsPTofW2dzzuzXAF0dPBDrX3/en3kIg70L70xw985EM/+uY73++4Nzzrp099PcieDlsQ/uEVn33nA1/12Nc+4IHPBemfX/CBMJyu4v63uded/o67xzvSMQ5vdGMa/ff//wEwAAUQAP+tFWzBFoRhGAZwARnw/wqwFVoBFpxBARuwAv1vDa7JAjXQ/3KmFTrBABNwAzWwA/8/4RMicAJFcAOHoSCGoQVTUAUzAgIlkAJfcAEL8BNI4WZCsAYZkP+moQVdkAcbkAR1kAaFMACHISNsARaEAQWPEACTsBVwkBRMcAefkANhoxM+cAavEApjMAKtsAv/rxu8YRzUAXHiz2/mr/7YEGH44f7U4RzMYQ7psA7t8A7x8A7HIRzCwRvCYRzyMBAF0Q730BsMMRwGMRHvkA2uSREdkQ71zxANERAf0REjsRvIEBEr8RHlcA4pcRMrsRD9EBQV8RLJsAxJURE7MRUfMRIl8RNZMQ/3kA81MRbxsA99kAxh0RY9URIPkRfzkA8PcReBkQ7PIR3YYR74gR/SMNH/2vAZG+cN52EaqbEarfEasTEb3+Ed2IEd3iEbwTEcr3Ebu3EbxfEcr7ENrgkd2ZEaybEc2zEe39EbvzEe7dEc7TEfuZEe85EduVEd1MEb+3EgCVIc57EeCxIct3EhE1Ib2QEgAxIhG9Id95EfJ3Icu1EgLxIc56gZQQwaQVKulnEkSbIkTfIkUTIlVXIlWbIlXZIk3+CaXnImabImbfImcTIndXIneZIkd6sngTIohXIoibIodXIzPJIA1jAkmbIpRSMmncwppXIqqdKIknIpqzIroREqlUcB3uArwTIsxXIsybIszfIs0TIt1XIt2bIt3fIt4TIu5XIu6bIu7fIu//EyL/VSLhHtI7XyL5uSK4FoMAmzMA3zMBEzMRVzMRmzMR3zMSEzMiVzMimzMi3zMjHTMLESMDlz4wQzM0EzNEVzNEmzNE3zNFEzNVXzMTezM11T1j5zNWVzNmmzNm3zNnEzN02zNV+zN9EsNnUzOIVzOImzOI3zOBeTN31zOQMMOJHzOaEzOqVzOqnTMZWTObEzt/6gOrmzO73zO8FTN/8gO8kTzebB1cIzPdVzPdmzPQszAeahPOVzxDDQPe3zPinzABwAP/kzNNdgPgE0wNbAAPqzQA10MA9ABYJgPw+0QRvTAP4zQCWUu+CAASz0QjE0QzV0Qzm0Qz30Q0E0RP9FdERJtERN9ERRNEVVVEQbQAX+QBiWwDhWdEZptEZt9EZxVEThYEJ5tEd9FMAqQQaUY0qa4An+60eRNEmVdEn/MkiHlG6K9EiZdEqptEqtlNGcNNii9Eq5tEu99EuXKktNa0vBtEzN9EzRNILEtGzINE3d9E3hNE45Y00bq03l9E7xNE+nlE6rxk719E8BNVCxk0+3xk8F9VARNVGZklB7g0qMVFEhNVIlld4YFWoMdVIxNVM1Na4q1asudVNBNVRF9Y4qwQW2oA9QtQ/QpFEDIVX7YA9w4FFHdVZptVYlCAyEIFd11QUEoTcEwQV0NVgLxlaJtViNdXKEwH4CQQj/jrVZnfVZGydZe2NZobVarfVaq0Jab4NasbVbvdVWtdU2uPVbybVc32oKnCAQpNQpw7U2xtVc4TVen+kJUiAFXmAKMiEr29U13lVe/fVf6Yhe67Vea+AKekUq97U1+hVgGbZhIUhgB3ZgB6JemDJhWWNhHTZjNTZyIDZiB3YFlmAP7gEkLXY1MHZjUTZlDaZjPTZiXSAKGuEZS1Y1TlZlbfZmq4JlW9ZjZ8AKbKH+ZjY1ahZniXZjdXZnWzYHwGAc6C5oC2JoizZqGfZokbZlVUAI6uAd6M1pyYRZpfZrjbZqxdZjWeAJDmFdjYxroRZs2ZZcqXZskRYGqCBdFk1t/722bfFWXt8Wbqv2BragG9LMbvPWXD8hCgz3cBE3cRV3cRm3cR33cSE3ciV3cim3ci33cjE3cw2XBvi2cz1WQelAHdJWWe92cL01EDw3dVV3dVm3dV33dWE3dmV3dmF3BZrAD0YWwATXdL8VdWn3d4E3eIV3eIm3eGnXBaRgErprd3m3W33XeKE3eqV3eqm3eneWBsZAt5i3ea/1ea33e8E3fMV3fAf2aumAHbSXdGnVGcaAC+h2xKYBfe1IKxhHX0LSe8k3f/V3f/lXdXHAC4Y1fae1dDf1HmagAiZAAixABZh2xDqACI4oEUjJDiiAcRDBAmCBKfG3fzm4gz04fP9lwAroN8C2d1OJYAL4AhEewASMrAOAwIjuwQLGczAqgHFooAOach5ocYd5uId9+IeBOIiFeIiJuIiN+IiROImVeImZmIedQHhZIAoWAc1KWFM74AY2oxMg2IFfuIjm4QLyQDPMoIYTRh0mIAq4FyT3dnVXgAj0ID4DV31FNQVYIDTooAZg4AnMYTPeoQpmYAaqQGs1gwgotiouAY014w5uAAacIFocoxHsoAZcoAkAdzMEAQdgYAmcYTNaQQhgIAeApTNcGDRIIQhgoAceIZHd9wdcQAfWRTPC4QlggAauIHeH4Q4ugD6iZYzTAQpcgAbAQDSmgAL2OI2fcY09twf/zKCQ61aOQ5UOKiAH4KIzZIACXsAGOgACOqEqWuEBIuCPIeAB6FcdLoAqNGMKIEAzcoACWOAGOiACpuUBHmACWMAFIEACprgqmqACVACbJ+AO3GICTOAGVoACpMAzRtkzwIACBjoFKMAvagACBBoGOqACqkAz/iACHgCPJaADosUJHuACwlkwzMACHqADXoAEKqCOQQOljRkakXlsC3aTqa2KNZUOOsACJKAEsrcqrGAClPcf7oEEUqAqTMAEKDYdjroqyNmcHSOd/2EMKABY+IEFWPgfHgAC/IUdlvof9qACEOMfnKAJ7EUCtvgsKmCENSOhOaMbzlgzqoACALcG/yqAL/5BCCggWtgBAmTggbyhA1j6i8N4MC4gBzSjDypgCz7jEC5ghl+aDWN6Z+/1EjjOpje1G8zggL3WBH5gMw6hAtghESzgfTXBAqa4qTcDnatCBWogiy1gGrC6dOnAAkQXBlTAM64gAuypA55AlLuYM6LgATjjAS66BnBYM+aBAhR7CyZAdDUDmqNFsMXYApg5BVzgM2bguB8bslV3BdI1dyvbfn41WMm7vM37vNE7vdV7vdm7vd37veE7voXAkR1HCiogWiQ6AvRbvy/AFnK7MyLACv4Btc8ZqjtgAvabvxEBq3tbMyKhv//BBILAM4SgAhI8AiqgB3zbM27Awv8TvAKW4B9q4AQ4AwLQmAi0uyq+eA/+QbppmDNmoAQ8Ix3eeru5m29B17npj2uFoVVd9ceBPMiFfMiJvMiN/MiRPMmVfMmXHAco+3HIWQ9i+xGonMrztQwoALxbnAKoQKgtwCyqQgoMHAeqnMoroSoeoMGrYhIuQCtKoLU7A6TLnMqZuSrYejOCoAPm/BFEd8RLHI2fQLg3AxYuwA5aHIzFmIw1gwZIvDOkgJht/MbF9ga4gL7ZkGtlK9M1fdN9Iwie3GCmIQJC+R8C4QLGxbiP1BwMejOk4AK6PLY3IwagGgg6QJCHW83/gc214gkioJg3YxEqQBAM5s41Qw8qIKj/OcPPN8PE/6ERvhzPC323KgAtrELRq4LRERoGIl3SWxYGqmCaSZbiOH3cyZ2TPt1gViACqGAa0sEMVhhgIiAFEGEe8OAE/OIJQNwAm0ACuLwqoqACmEATJuEHKgCqvQECTCAQ7sEPVEAx0nwzdP0f2KEDOsAOvOEPUuDVZyACtkAdWiEIWJozOoAG5jxf/4EFIMAL2IEUcuAFqkLZNYPZ/yEGJMAKpkETaN0CDP0fVOABuODMx5gzsJ0zBKGct73+dLZsz5Zdxb3cnf7p8ebcReMdgEACLuACKuAFhlUTUqACLmACamBY0fkCLKADAkHdNYPXyd4EnEC7baGgvx4G/2D7HzpAwDUDFibAkb3BBSjgAti5kvmBCCIA600AzDfDBK4+8bF+k+fhB6y+AhK+KkBe5BVbqIPg8VOAzYVdWVSgAnA4ozkjCK6bM2IgxY+e4yBWBYjgDmyd6aH+9WH/naT+YD4hElofuSu5M7whHaoC7TejG+pcM+5h7hfnHg5W92+fcboBjg9m6bsBfadgApJ/cSIAkU+f7ug1B8iggasS02P/+8G/02efgnxf1h5gBSrZDiYgxCNnErT8+untEDIYML0//O3//o1n/Ceo/GMtEyq6AwDiQYUe/P4ZPIgwocKFDBs6fAgxosSJFCtavIgxo8aNHDt6/AgypEiJQv8COTuJMqXKlSxbunwJM6bMmTRr2rzZMsilkQ9/LOIJdKOgKldsBT2KNKnSpUybOn0KNerCkjirWr2KNatWrTqlev0KNqzYsWTLmj17lurWtWzbun3LsivauXTr2r2LN6/evf/Uwv0LOLDguDsl5jGzcMshjNPMlb1EpNVBb1BwUGHHN3PIKw86e/YcKJ2QTjxhVPnn4rPnG0w7RHj92kbGe0x+Rg1CRLNujn4H+/4N/KrciDFMLOwQBCOEE2MDdbhwwYrBRK9NTHgwbbd2jMPIeHdywYv3Mur2XJAy8pIFO/8esLACP34gphVqQLkPRU/GVhZ+bIT1wDkYPTFBOtv/HThRb8EtyGCDKg0HUXHHAYGRFGSMtYgOgkgg3T8dmKDOP9M8IAOCJlJ0xwXvJNRIQSLd0IFBDyzhVQVlfDTJihotcsEwGJ0zwRQnDjnVGH0ciWSSSi7JZJNOPglllFJOSWWVVl7ZJA6FEWecQh1QaCKH/3RzQR0HPQEBkWoylKKOB03iYifnTGOGHgXNY4cZ3SgUCRnzMfRAbu3RqNA7nySUzqEGfVJGHvccpE4m/zwyxiXvSIrQOYoiZCNEmcxji58GsUOHGY4ddMk8BgnjDT99jGGbQfdsadAw2f3Do48IzYNHGZsapE6pRhnEQglrrgmGEMouy2yzzj4LbbTS/05LbbXWXottttpK681EEnoJ5j+2wADBBB0IaVA6HfxBwgQQaPIPDuj9YwJsr8X4Dz9PdOBuDbf+c0MUPkBAwbkQIeJdwokwJGY4JYxzkBRpHkvxP20mVMEeBn1IQQQVdLDHAxRMQMEWB8FiQgUiP9CIQp1c0PKgC3kRAWYH3WCcOSuoLAEEdxgkxQMuUACBCV5IYODNXXJqAgwwEAELQ0VXEIEFJ9ARAckSmGlQBFcYpEIKIktgQQdR/0OGBAm94MI/NUhwwWs3/oO1BAKxYDMYE0QAQQUyPHoFBS5WTHjhhnu7NEJfGtRJBB1wgUcTE9BQa9xC1GGHqibI9s8fCf9vMQHnLEjwBB5bdPCArimbcIUZOlRAaEPPQQdd4giJmRA/HcBwOJEXI3QBex5SkMc/pEBggQzv8HPDBI7pbkJ26rzwgJsGge7iAyrgB8UYBs0DQRQHjTOBySt0cOg9QEzgYxQXzJA0+E6MP8HXCdmgQv5Z/6zQawsfQrYg8OMdLICAi3BHAgswYUWR+JBBuDCBhLCABf8QxhUuYIY9YGYSFFjCozTxgBoYBALJ6dwD/PCPl0WidyxsYe9iQLsYQgdM53uUQfxQgZ8N4zwJ2ZxCWNABVY2hAgsbVQdiYBATBPEgUKCAzTaCO4TcIAK6cuF2fneQ4G1MhAZxQgWSpo7/CnjPDBMIx0HeEQGTIWQJ+GqP3TyjgoM44QEuWgIdK1GBSiiuCf+IggRsaBAoQEBV/2gCHR3yjhVAwHpdExTAIuCiVlxgMf9AYOICcQFS/AOCEqQgrnp0kBmQACF2oECIIjA/hVDAC1ZspStPVJxhyHKWw1jcPygAhh7KZocxO4gPEeKECAzrBStICBUikETOGcQbFxBER6IINAr86ZW6waJBtOghIRzkCmo7SAROI4QIOG2cJExID5Y2I4akQwImAx96rECBcTpNaH18QELYEQEh3QMCT1AILH41jQogJiERoMJBmtDGf1iADgZB4AwQwo8KMJSTCJmgQXJ1kA7s/06eFlhM4DpQgy0Q8h/fpKZJT6qXbyVkce+YZEJcQMEdPkKXCKEDBRhqEBKwpqYVSCIOEHKPC+DBIUyQJwxSqRBomgGXKNWMNf+BzQ5o0yDcREhJg/AAZ03TIDlAZ+wUEoQYVSECK6qCBJzF0CjYMyGG5IcVkKaQGtgOml0zqEEQipCFNrRDJHgopxBD0YNY9JNV7AAJmkWEU02DCjOAwAOcMcLTNHWylB2LShUHJrp2oAf/kClNDdIKCSD1HzSwHZp8ClShOiQI+WutIxMSxUZMoJ+V1ctTozrVf1TVm6fxgvMeQoQ2ppMh3rhpBwiFiAr86iBqVcg6r9ABHyzkCf8SCJFBDnGBrdb1oAnVayX56teDCPQfgbCAZHzpyUlcgDQGeUEcH8KOQBlkAqysrX3vy5TLZhRMNXhAt6hagZZ5FiG/fEcHiomQPox3VRAAkwl+epCgDpUjuJsGBJSJ37rcVnhS3WY3u3aaezwgBacKxLwQcoUIyuirCrlBuSBmkBN0QBgGuUQqm6uQcFYAsgrxhgRSAFlBPMB2271rd3HqUIwhhh8POAGMq2ABT85DAjdw0SEqQNt/WGGoiTDBTP8xjgfwERYwy7CZzxwS/W4MTOZwzQ+ewIIKQKFyX/Yl52gwkGWlkggVcMETehABE9jswamdMBQ71IEKvGCcNYD/MZrHkocLjNQgFuBfBxy52651iBT8UiIFXlvjC2BKZPaKzUFsUYEcIMQbJihYoilXz4WMI54NCcQDLECBC5iAxwSV7D+ccOSu2S8FsBbvQA9BsA5AwHGe3GQFJuBXLvTMBMtmaDpU8DF+dcAxXBDco78N7ops4cQIacJA//EOKJCgAy/owxld4OhA1hcHJqh3vVfgojq4oAMpmAIgn+A9hMCAxhzBwcL4wQJ71zsFVQy3V+aBU4SYQUd+IPg/zME1g9TB0fywQxSs0HCEQCCVe4iPyZ15EDvE2yCBiAIV2DsmQyMkERVYYUPuQYcrUHIhdjCjQabh7oPQQUd0gHEi/zAl9CeygwtQ2II6ImFzg8BC5wc5xxagMHGEVKIKUcj4C4js8LCLfexk30sNEnoUF6TAcOyQgPjKDve4y33uUkGEBYJ+FFjk0HBToMDK6Q74wAt+8BZJwZyRYgWwr6kGECa84x8P+chLfvKUr7zlL4/5zGt+85zvvOc/D/rQi370pC+96U+P+tSrfvWsb73rXw/72Mt+9rSvve1vj/vc6373vO+9738P/OALf/jEL77xj4/85Ct/+cxvvvOfD/3oS3/61K++9a+P/exrf/vc7773vw/+8It//OQvv/nPj/70q3/97G+/+98P//jLf/70r7/974///Ot///zvv///D/+AASiAA0iABWiAB2iAl5AHobEQg9MQDvgP3jBLDwGB/ACBENUQ83AIZuAHPscQFjgRgFSBDgGB4zBLk0aCj0AHdjAsCQFIEfGCCXEOs/REFKgQ5kBLOTgMjHSBEqGCeMBrCBEOOihLSWMQPXiELkiEsgRUs/R3CnGBEriEDYeE/3AOgUAHgSAgH0iCCIh63vA2tNNn//IPOVABxdMQKTABMPcCMiQBKnBuB2EOjnMQ/OBY/5UQVmABb4cQlzA0tGMBJsA/XlI9CbEvvWQQdIBl/9AIFNBstjABJhCDBhEEEiVHMhSJVoCEpBADcEM7WWWE7YF2/xBW57VNFmA/CXH/BRYQQwUTBYyEEJowAcxxEFYgQ7c4AXg4DRIwig7hDTjgidDRAalYhrcIiAOFAwuWELu4NOkQAcYIHWvljDFUASYQcQnhBRWQW24DjbSDLy9AAYigEGWQMmJoAgGHEItAAQimEHiGcl44esPwABdQNkpUARfwAGdDLxfAhwsBARcQcc8RAZ2BPNChAqdiEOrVUwYxDga5EDdwAUiEEPB0AR+TAq1Gj0xwHBiUEP8oXQJ3Af20Bfh4EClyAZylECdwAUjFAhfwRn0jjMulW7lmAU1WL9ABATs3j+/4D/cwASuZEClwAcR4EDhwAdfRGfeIj0WkEDYAHTtHBsv2GnBj/wH20gFb+A9EAB3X6BB58Iz4WAI3eQHNZgJHWWqvAQFoWJYSYIoHgUkfRgrQcZbVYRBxGTcRQDb0SG4Z5ZJPRAT2kmtUAxue9BzEyA4teQEFs265NpY1OJJA6SXRAY+kVwP4GHWcdgFrl0T86BD/GJBDORlO8JMd8EQKOT60k2U3E5EoxoorsCnmkANlsxBBcAEYFgnC2JGZ9A8juVYWAx0WEIcxBpn/0JIo+Q/qYAX/CAEtuJuseAJ19g9mMI8SUBg0cAHbaAfQYSwHMQ8TUAEeiBBG+QLcyQXTCZ3A8pMXwDsLkSLI1ID/eAHv9RCHkGsdME33EAUUQAGEVJaH1/8QZXkBHRCDb3kQcbmQDWGgk1GZeZQQgkA76KIQS5CZG0mMKlCRQoCQ58BnmTk4j5kxkdkhkxl6zxGiq0KHm9mPCuGZfEmUkfCMqpaQFXmaiVkBeGcQECmR/wALP6kDC/EIIWcQDtqb//AEF9A3joYIJLmbSuqbFWABbJkQKsmSJ5kQ3fActCgiP1lsCDEOz9ElZhCgCOEDRjoBgEQHusYQ4okoZdkBECgFRopr4FmSccMQXBA3FFABbbkQIqZrsGgLW9KfD1GWuWacLOeSBSqjDpGgBHYB/oEQbTiPvfgPEqqZKwWaBkEFFcmVBlFKPHQ9iWmkeLgxkimiI3oBdoX/EO/gJmWZorn5mUQZnYn5X6bJkNAxBaEqRat5owFahVKjmwZRAlh2AWqUlRcAo7w5pxCgAwH6glIqWFSaEJdwj0EHBPiIggfxCbnGHt35nRk1AVopPABzAXy0EGq6jHDDBZYKBWU5WnPqngpRlkHgAtDaELYYAaLKEIHqEGUpBGQzN4YKl4mKoAIbSPG5arn2B8+oMRE6oZGZivO4jQkBHgb0qbvTsHxZoqXaebT5ADLJqK0qcgDJohtpV7QKZtDBDm1YqeKao//4r94SkrJiLi9TIpuJhshqECkCAffwHDAqnFNaqM+6U/OIqgvRhrBmofYzDhVwApjkqO1xAbQS/54XMJ4KYZTN9g+RRgHmYKeHlBDtuRCRYAEVYAuRJgGwiBAqGbH5egH+ybZRYAZPaooEWpcEuxCLehBaibVNEKY5MJYLQakUel2JiZUKwQ4/iUJL2gHqMI/kOqoZq7Gbpy4VmQJCYAVSu48gexArOqqwKq5cZLINeQHskA7ziFQ4uioVea0QQQYXQIsp0qOdYRDmQDUugrO+mSahZQEX8rPPGrSYiqb3cI9AOpFh+g+Z2jabNK78wIugVZMNYa4JAaZD2pK8s08c+bV0GlcG6yEX4GsL8Yx/EBFlWQMJ4x07h6L/0APMCrCIurvmWwaiired9Y/+yWQXICSaMLZAGv+4DmsQt6p4vsSZijspeIqG3Qu5kat55lADjCmM+iHAmjtCItu5C8G36SWwoosZjVgBEIy6/+CgE3MQVXADJXwDg5gQ76CfK1KtDAUD67WkybukvamzBhG3EsBezkos9CpxYUomB8oQdaCk3UA1BjEDFyCOKVABEOM+O1WuVOujh2oQznCPKFet24kQYCuDPzlQRSqpBnGPeyJ0JnwDqQigMmQBzMmqPdmz7Vu3xojFxkOP+KEDzwgBRlgGLmld/MqwKzuyk/q3DvHCUzWS+GKLECDGJKrAoZcOZvAELwA3lpi+DcG53eu5QnAByRu6KGsQh7wnH9wIUnyEDmy8CyH/lIjRARSwIraIHm1IrLdbwwaxvkukw8TJwwcBBmF6DvQ4iT3cRs8xHw/gnkWarkIZrlYLxQrhoO+KyW3UCk6qSVmsvQjhPr05a9nVELnWlrMDHRQgwEOmcDEwaWv8D7rrPXQrx4GocKyDEHYpQx2gRwehkly0pDubEP1rqfYDHuzIEC0pKIV8EDB0AgWhyIs8euPAphHcmRRsyQvRkiW0yaN7EC88Sh8cRhegj50DH2P6xW9aA7TbJcNgAcXUN0YIy0YaYc/xU7VcnAuhle8FN9qVEExwsVopBJLkSaE8A90pAVUYvQiRqfhyvUVLAhfQeDk7zXypkQfRhjKsEPOY/64HkQnwkakHqq//OcDR+aSfgM7ze7f06DQvcI8vq78WEHX38IzEahD4rDiX2rUPUZgVCynPQSMFbdCipwnQIcbk7KuvqhDh8JPCE9FPxLgXwAQf3L2gZsOlrBAi/QB26kgPIAFJmjgnLcLiQjZgwNK3nC/PQVslUJsO8RxI9QgB+qYnNpBnirXIXLU9BK2P6QLy9BwSUINafBBCfAErIE+Diq8HUZnymSkCe9VvK6YB6gei7NUKgbdOCQHWJa4TYFTzaDts/cfecI8yfRCLMLY8BtAHEQl4agd2fdeX5wwuUF8QdY/RzNf+yNBxnRAv3JuDjRCPgKdlmaO2OAHwIv+9jB2ZQjlN1jmvbmvZ0msB5jKcLZ0QUHCU/+UFFYm+CHGrFNBwAqGSMNeSLem5RZnMCDGSFaDfZ2yMEOqb72oQK9CNF8BiB9EIFgCcCrHLVt22gprVPVmWKhmwQPzVB2oO/wij6hCMt2gBiEjdFVziAbq691CWldrd2xQ385jA4y15Z0oBD35B+znJff3H3wOR3vUP8l280JGjumOZ+/3FfQEdPX0QdsribTngCfEDtAO0CSEF9+i2ZRkBPAlgFwBqlcmk//CmY4uQT9za10OoBrHiC/UIia7oRplQtq2j98gFiq7oWkmxC1GZE8CV4SDcMb6vM47ZZ46oOK7cBGv/pxUgjrcaAY0g6Ylells65A1tEJWQaypQuMfZkhRQZ0seSrTz5FAeeYN6A3YQCXVwA/donGVpAmRcwjkwCRP8mT7gHVswMHqJEF6OEDCkq1I3jxRAA2aggjnwkwH8D0nKvQx5j2jX5kAFoFPKAt7BBcEFHU5cK/OoaGQwCYiQeNDBAhB4purZzrXzEEZpAu4ObPpuQ0cMdnp3AQs74ghBm71oDrmWSwvxDmU50lywCHmwBP/4YWX5AiYXHyJ15QcRt8hNjyAfH+IrxzgulG36HL+rvL+11hfb1sTIBfcIAUSwB5PQBxtfkWqt60YEHb3u6493DogpQzEASCAuQ8n7/4+DyM0x1AFcGZfenC70eK3q8BxbKiIWeosdgHS+mqLPES4GkcttZB6XfWpw45/zeosQkMDewAKsKEOfdoE+eakHMY+puRDre4sS4Lb/qNYIIZQ+m/ZtrbkvPOgpXOy3+ADhyvQyZFdlKeIHUa3vagsnbvWZj+PZegGtWwEwB1T/GNW/Vu5tXfo5O4+On3EG0eC9OAk/meFFH3l/YANKtDmJm3JGJU8qvwU9MFJmYFRCcMAJQQQi7gRu24c0gIhuiTMaNQObuhBmcAOh+A96UAP4qg44gI730AODfxDYX0WHYFQ/YAarq+I/oEQdoAJOQIYJ8fsoSP3WrxCaMAPylP8DXFCDuiVADBEJMwAQjf4NvOfjysCBXnKwQ9jwXysafxxO1BTERIcOLq7ca3gHxkeQIGnYGmjHhreJ/+4FsdJwSUiYThAukTKRy410RKakHGjGxjiEzmr4SWnmxrmJ/LzAwNgBBheODtPdMMPTpzmeWbVu5drV61ewYcWOJVvW7Fm0adWuZdvW7Vu4ceXOpVvX7l28efXu5dvX71/AgQUPJlzY8GHEiRUvZtzY8WPIkSVPplzZ8mXMmTVv5tzZ82fQoUWPJl3a9GnUqVWvZt3a9WvYsWXPpl3b9m3cuXXv5t3b92/gwYUPJ17c+HHkyZUvZ97c+XPo0aVPp17d+nWz7Nm1b+fe3ft38OHFjydf3vx59OnVr2ff3v17+PHlz6df3/59/Pn17+ff3/9/AAMUcEACCzTwQAQTVHBBBht08EEII5RwQgortPBCDDPUcEMOO/TwQxBDFHFEEks08UQUU1RxRRZbdPFFGGOUcUYaa7TxRhxz1HFHHnv08UcggxRySCKLNPJIJJNUckkmm3TySSijlHJKKqu08koss9RySy679PJLMMMUc0wyyzTzTDQbCwgAOw==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "\n",
+    "with open('/home/akash/Downloads/download.gif','rb') as f:\n",
+    "    display(Image(data=f.read(), format='png'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "\n",
+    "def generate_graph_seq2seq_io_data(df, x_offsets, y_offsets, add_time_in_day=False, add_day_in_week=False):\n",
+    "    \"\"\"\n",
+    "    This method generates data in the sequence to sequence format expected by DCRNN. \n",
+    "    \n",
+    "    It also consists of additional functionality of adding temporal features like Time of day and Day of week, \n",
+    "    which can be ignored if required. \n",
+    "    \n",
+    "    :param df: DataFrame of nodes and time series.\n",
+    "    \n",
+    "    :param x_offsets: Offsets derived from the horizon.  \n",
+    "    :param y_offsets: \n",
+    "\n",
+    "    :param add_time_in_day: (OPTIONAL) add temporal feature time in the day.\n",
+    "    :param add_day_in_week: (OPTIONAL) add temporal feature day in the week. \n",
+    "\n",
+    "    \n",
+    "    :return:\n",
+    "    # x: (epoch_size, input_length, num_nodes, input_dim)\n",
+    "    # y: (epoch_size, output_length, num_nodes, output_dim)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    num_samples, num_nodes = df.shape\n",
+    "    data = np.expand_dims(df.values, axis=-1)\n",
+    "    data_list = [data]\n",
+    "    \n",
+    "    # Can be replaced with any features you like. \n",
+    "    \n",
+    "    if add_time_in_day:\n",
+    "        time_ind = (df.index.values - df.index.values.astype(\"datetime64[D]\")) / np.timedelta64(1, \"D\")\n",
+    "        time_in_day = np.tile(time_ind, [1, num_nodes, 1]).transpose((2, 1, 0))\n",
+    "        data_list.append(time_in_day)\n",
+    "    \n",
+    "    if add_day_in_week:\n",
+    "        day_in_week = np.zeros(shape=(num_samples, num_nodes, 7))\n",
+    "        day_in_week[np.arange(num_samples), :, df.index.dayofweek] = 1\n",
+    "        data_list.append(day_in_week)\n",
+    "\n",
+    "    data = np.concatenate(data_list, axis=-1)\n",
+    "    \n",
+    "    x, y = [], []\n",
+    "    # t is the index of the last observation.\n",
+    "    min_t = abs(min(x_offsets))\n",
+    "    max_t = abs(num_samples - abs(max(y_offsets)))  # Exclusive\n",
+    "    \n",
+    "    for t in range(min_t, max_t):\n",
+    "        x_t = data[t + x_offsets, ...]\n",
+    "        y_t = data[t + y_offsets, ...]\n",
+    "        x.append(x_t)\n",
+    "        y.append(y_t)\n",
+    "    x = np.stack(x, axis=0)\n",
+    "    y = np.stack(y, axis=0)\n",
+    "    \n",
+    "    return x, y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since DCRNN is about spatial and also temporal forecasting, we must specify a sequence length or horizon which determines how far we want to predict in the future."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Number corresponding to how far you want to predict in the future. \n",
+    "\n",
+    "horizon = 12\n",
+    "\n",
+    "percent_train = 0.8\n",
+    "percent_test = 0.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we want to predict one hour into the future, and our data constitutes of speeds at 5 minute intervals, we select our horizon to be 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Create offsets for input and output according to sliding window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_offsets = np.sort(\n",
+    "\n",
+    "    np.concatenate((np.arange(-(horizon-1), 1, 1),))\n",
+    ")\n",
+    "\n",
+    "# Predict until the horizon\n",
+    "y_offsets = np.sort(np.arange(1, (horizon + 1), 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-11, -10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_offsets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_offsets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Generate sequences of data using defined offsets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x shape:  (52093, 12, 325, 1) , y shape:  (52093, 12, 325, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# x: (num_samples, input_length, num_nodes, input_dim)\n",
+    "# y: (num_samples, output_length, num_nodes, output_dim)\n",
+    "\n",
+    "x, y = generate_graph_seq2seq_io_data(\n",
+    "    df,\n",
+    "    x_offsets=x_offsets,\n",
+    "    y_offsets=y_offsets,\n",
+    "    add_time_in_day=False,\n",
+    "    add_day_in_week=False,\n",
+    ")\n",
+    "\n",
+    "print(\"x shape: \", x.shape, \", y shape: \", y.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Split sequences train test and val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate number of samples \n",
+    "\n",
+    "num_samples = x.shape[0]\n",
+    "\n",
+    "# For the rest: 7/8 is used for training, and 1/8 is used for validation.\n",
+    "\n",
+    "num_test = round(num_samples * percent_test)\n",
+    "num_train = round(num_samples * (percent_train - 0.1))\n",
+    "num_val = num_samples - num_test - num_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate train test and validation data and write to the output directory for the model to fetch.\n",
+    "\n",
+    "x_train, y_train = x[:num_train], y[:num_train]\n",
+    "\n",
+    "x_val, y_val = (\n",
+    "    x[num_train: num_train + num_val],\n",
+    "    y[num_train: num_train + num_val],\n",
+    ")\n",
+    "\n",
+    "x_test, y_test = x[-num_test:], y[-num_test:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Write to an output directory (OPTIONAL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# out_dir = 'traffic' # Make sure this directory is made before\n",
+    "# datasets = {}\n",
+    "\n",
+    "# for cat in [\"train\", \"val\", \"test\"]:\n",
+    "#     _x, _y = locals()[\"x_\" + cat], locals()[\"y_\" + cat]\n",
+    "\n",
+    "#     print(cat, \"x: \", _x.shape, \"y:\", _y.shape)\n",
+    "\n",
+    "#     np.savez_compressed(\n",
+    "#         os.path.join(out_dir, \"%s.npz\" % cat),\n",
+    "#         x=_x,\n",
+    "#         y=_y,\n",
+    "#         x_offsets=x_offsets.reshape(list(x_offsets.shape) + [1]),\n",
+    "#         y_offsets=y_offsets.reshape(list(y_offsets.shape) + [1]),\n",
+    "#     )\n",
+    "#     datasets[\"x_\" + cat] = _x\n",
+    "#     datasets[\"y_\" + cat] = _y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FINAL STEP - Train newly created data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch.optim\n",
+    "\n",
+    "from torchts.nn.models.dcrnn import DCRNN\n",
+    "from torchts.utils import data as utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adj_mx = graph[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_config = {\n",
+    "    \"cl_decay_steps\": 2000,\n",
+    "    \"filter_type\": \"dual_random_walk\",\n",
+    "    \"horizon\": 12,\n",
+    "    \"seq_len\": 12,\n",
+    "    \"input_dim\": 1,\n",
+    "    \"max_diffusion_step\": 2,\n",
+    "    \"num_layers\": 2,\n",
+    "    \"output_dim\": 1,\n",
+    "    \"use_curriculum_learning\": True,\n",
+    "}\n",
+    "\n",
+    "optimizer_args = {\"lr\": 0.01}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_units = 64\n",
+    "model_config[\"num_nodes\"] = adj_mx.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = DCRNN(\n",
+    "    adj_mx,\n",
+    "    num_units,\n",
+    "    optimizer=torch.optim.SGD,\n",
+    "    optimizer_args=optimizer_args,\n",
+    "    **model_config\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DCRNN(\n",
+       "  (encoder_model): Encoder(\n",
+       "    (dcgru): DCGRU(\n",
+       "      (layers): ModuleList(\n",
+       "        (0): DCGRUCell()\n",
+       "        (1): DCGRUCell()\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (decoder_model): Decoder(\n",
+       "    (dcgru): DCGRU(\n",
+       "      (layers): ModuleList(\n",
+       "        (0): DCGRUCell()\n",
+       "        (1): DCGRUCell()\n",
+       "      )\n",
+       "    )\n",
+       "    (projection_layer): Linear(in_features=64, out_features=1, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(36465, 12, 325, 1)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "/home/akash/anaconda3/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "/home/akash/anaconda3/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: you defined a validation_step but have no val_dataloader. Skipping val loop\n",
+      "  warnings.warn(*args, **kwargs)\n",
+      "\n",
+      "  | Name          | Type    | Params\n",
+      "------------------------------------------\n",
+      "0 | encoder_model | Encoder | 185 K \n",
+      "1 | decoder_model | Decoder | 185 K \n",
+      "------------------------------------------\n",
+      "371 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "371 K     Total params\n",
+      "1.486     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/akash/anaconda3/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
+      "  warnings.warn(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23dc91402f854f9a963c255d7e1cc35e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training time taken -1628875114.473007\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/akash/anaconda3/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
+      "  warnings.warn(*args, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.time()\n",
+    "model.fit(\n",
+    "    torch.from_numpy(x_train.astype(\"float32\")),\n",
+    "    torch.from_numpy(y_train.astype(\"float32\")),\n",
+    "    max_epochs=10,\n",
+    "    batch_size=64,\n",
+    ")\n",
+    "end = time.time() - start\n",
+    "print(\"Training time taken %f\" % (end - start))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model can be saved and predictions are made using the familiar pytorchlightning API. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}