Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "\n",
    "## Deep Learning\n",
    "\n",
    "## Project: Traffic Sign Recognition Classifier \n",
    "\n",
    "by **Vikas Hanasoge Nataraja**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import numpy as np\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = \"train.p\"\n",
    "validation_file= \"valid.p\"\n",
    "testing_file = \"test.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 1: Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
    "- `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
    "- `'sizes'` is a list containing tuples, (width, height) representing the original width and height the image.\n",
    "- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Summary of the Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "# TODO: Number of training examples\n",
    "n_train = X_train.shape[0]\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = X_valid.shape[0]\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test = X_test.shape[0]\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train.shape[1:4]\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = np.unique(ar=y_train).shape[0]\n",
    "\n",
    "image_depth_dimension = X_train.shape[3]\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "#### So, what do the images in the dataset look like? \n",
    "\n",
    "The images are 32x32x3, meaning they are very low resolution color images.\n",
    "Below, I am showing how the images look like, chosen at random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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urbC5wnl0fOcexGb7D83xNx+8B2Wen+E6JF7iMx24sWNCnFJiYkaRpa49sHWJuDHYOqV0pnwNKdPU+O4qZyDXkM5wd4rt7sNd25c/v0KDnu9/8QpiZ+dsXekCZG7wa7UQ8D9HbE5h9+P7bVXjtcnyN6ydc9+C+AterbDtz1f2uX84mEKZN3O82HQHx5zJ0K4d6grfaU26X+vbx9dwOFLvFkIIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoufcqsZuTX6ucbqx7QK/K99s8GNaSMzpErSGEMJH7z2F2Gxhky8+/9Ff4O+d4jf+Lal7m9lv/CckAeTue08gNrmza47zCdY9IplVq9J9g7vEb37XJ5jc9eLlM3O8upxDGa/fCyGE4sR+f31e4zfhew/vQ2w2wve1La2Gj8l3yOVBIUFyRIrbhr0D+jl4l4TXjOu1XlHHZN0++XiaDqGMT0YeQgipyzDL+mOeofY3gM4AzytIMtnaaSu6PuKW6BpuG3g9f/NV+v81K5L4e5DYwTPNx1Cmrsk86pJ6ZwnqQaIGdSTrub3WyZeY2Pzos08htj16DbF2Y+uQk4To4xmKuXcP7dwz3UVNfZJhf984geD8CufRxTkmad5c2bm13C6hzOIEz5u2dm4dHKAe6tHBHsSqHOu+ndsE7+MGNbxZgnP5yGnsmB5K3BxMV5W4RWueEl8Icq3MzQa7U2wrd1Jce/7w9YU5/v6Xb6DMYsl0fliHyCUInw6wpvdnOE6krt0tttg2jy9R+1fUfu1Akp+TNuznom2BZSqiqV27MaHOsG9PdnDsGpAd1NP7B+bYP4Ov6kmSnbv1S1MREWFH9B87IYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc27VPGVcoUi5ia34N2u2UKZsUc252dpyDx49hTL3aky6/flP/twcr89QiN6QxxKNULx955vfNMePP3gfyuS7KHT1xi9NRBId1ygybZwIuzlAAebeQ0wwu//ksTk+fYai9jdE6N6u7HMvztCY5ZIkOt17gELeorQC2YsFir6Z60Lt3FKi6FabrAgk4TTR3VMzky7Xpkmwu0ASlMfYNtLMGqPkQzQkSBIibo6cURHzagnYb9PMGj9EEQrKWfLxrUtk7s1UvqoUht4WmgC9vT5xOr2Wv4zcU94p9Rbn0XZo235V4jyaRWj2k7l5plihYL9YXUHs6KVNqH3y6SdQpjxGQ5W0xnqND63ZwMH7H0CZyf1HeJ5LBt4SM6OUzBfDxrbPHdLXVld4zxdfPjfHl19+AWWKc5wjl2c2kXlC+t7OPhqqNDM0VXvt3n1DzC9i4n4xcONSTIw7xM0xneK7G6Qzc5yT5PJoqxVC69ZYd2e4xvvLz88g9k+eWfO7FXqUBDqPJlive3t2Hv1Xvv0Yyjx8cACxU5fU+w9//AzKVFfESOTaAA9G3lCFrC988vMQQlitbZ95+RoNBmPSbwekG/k1zcO7u1BmMkCDqLS175klbu+K/mMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEz9HGTgghhBBCCCF6zq06UUyHKJyeX1qRcpZNoczBaAyxbWnFjvvEAOHNJz+G2PLEiiKjFtWP8RgNTx78xt+G2KOPn5jjJCPmH8SQIHX76YSIJGsi1IxiW9eEXLsh9xPtHZrjwzEaOgyGeN6XP/6pOd4QoXZxdgKx5Rjf19503xyvNq+gTFPj9b24t3lLgwfx9oA5BtP0MjMO1667GqX4SzEzjogYDqUpmieMhratZyn5W1aDhhFNbZXmLXFPieIBxhIbS4mhQ0QMh7yh0naLYyU1VGGuLnBxfH4tE56jCwopw5xzbCySeco7paywveZuXKyJ4VjbYPspnWB/Mcdr1xs0Zpg/t8Yh2xMcz9MaDbJm9w4hdvDBx+Z4972PoEwyQfOyKnZzA2mwMfMIcWsFZhYxvYOGB3lu+/JojHU6ffZziC2OrWHE4vwcykQZMVPYxXEidaYLq0t8zyUxqPGdOcvx98TN8Z//g38AMd/Kohb7WlXi+3xxbNvL//j/fB/K/PnnaNqzWvn5F8flhDSDx3dmEPu971hjwIeP70KZo8USYn/y3NbryzdoSlSXOC7hnE/WABAJwV+JzVdgsBJCaN04uFnjmPDyNd7fj7JjiA1zO+jMRrhO2CFrZL+Fifyi4J8C/cdOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0nFs1TxlNJhBbXNrM9PMVCq7LuIDY7uEDc1wvT6HM2Ws09mgae8ttjnW6++2/BbFHH3wAsWJrRa2nxxdQpgp4/d1dKyCfDolwOiLOBc484ez8DRQ5OnoNsdXWCnL37r4HZe6+9x2I3d9W5vjVTz+FMsUWBcDbUxTbT54+Ncc7E2KIc47C2sw9hk3FDFbETcEMTqgvCpyHMTzveoOVryK2XEwcEJIMBcmDwRBiaeqdWLC9tsRsIGpt209I3ZsGz2tcv42JwUpKFOttZk1emNB9s8WxEQxqiEEGhxmqXA9tHx1/UdwMcUNMutywWOGUGeoCx87lyrbzYr2AMtvzOcRWJ9YUIS5WUGa6gyZkswePITa8b2MrYoK0WWC9ysr+5iDFcWIwwHmmDfb6y3UFZVYLnMMGwdYhTXC82bn3EGKNM0Janr+AMusrvL8hGc9CZMeTJsaxZFPiODF2Zjp5igZq4ub41m/9dodSOFav1zg//eX/8n+Z4x/+FI2K5ldkXnPHWY6/9/QBmgT93W+R9eEDa6hyscC12vc/xXY9P7Jr5FmOW46amBcVla1rRYzDmG8Yzk/4vytmxgazGJlH12SceHmM/fb+GzsuPb6HY+PhLppENm78+jorXf3HTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zqxq7L1+gDm5bWDHApiDfChOdSlbZZIFXL19CmWqDX6k2kb3l6d0nUOb+++9D7OrsC4j94Mc/MMdHxyTxaIzfyY8f2N/8znd/A8o83ENt3ualrcOf/uWfQZnlHPUQfv/efIHfQn/0re9B7JtPvmGOd0gy8rOXRFe4wu+OVwtbr9kQvzE+jzEB5Npp6moJet4xLHmnK8ESXhPtVZfzqDjPJfCOiOYmJ/qTnCTdjYL9Lr6pifiIJCgnOcSxTEsSqzb2+kwHEBFdTJraMS4myUlZbLu13+9XNPlqt8Twb62W63x9cRM0a2zDZbQ2x0mKbTMn0/0wsePrcoHzx/oYtdzV4tIcZxnq2yZ3MBl5fuc+xLa51cGdLVG7ekrmnnZl59tHh/h75Qzb9KXTBz17hnP75RkmfB7W9hk/2sMkzYdEVzjaszqm9eoSyjTbNcTYPBrldgxIfFbjEEKb4Hv2MuX9faynuF1a8n+Vl6e4hvyjH35ijl+/wTVXW2F/jxPb9h8c4Jrrtz7APjMhfWZV2LUZ03t/tI/r9KezRy6C44T3gAghhPNLe/2jC1wbnlxhbLWxz4HOfTET59l7ZjNaQybzyzmOxZ+9sPrcJw8w4fvDQ4yNRnZNU32NeVX/sRNCCCGEEEKInqONnRBCCCGEEEL0HG3shBBCCCGEEKLnaGMnhBBCCCGEED3nVs1TZmM0DQitFR8uNijczHJMohi5jKybcxSdsiS/kRNq7xKjlCRB8fYXP/8RxC7W9vF957t/B8rMahRK/+XnvzDHn3yOQsrptzAh+stf/Mwcrys0kPjeb2NizP2BNYf46U9+BmVevHgGscd3raHK/mOs09UJ3l9NkpaXl/b97L73AMqMcnxfXlcbyajhndLFGKWrGQc1S/GQ5OOxM0sZDDHJcE7MGuII61W7TM1tTYxSOnmG4LVjcmLjEgG3LY4lLRFht5EdG/0zCCGEAato5MTiaxS11xWK03mS1i6wxOb2frq8dvH2nJJEueWVTVpcFyxDOTH7cYY80QpNPMpLTEYclbZdj/exj44O0Jgh28G5fO0MFZYk+fH6AueZSWrbdZrhfLjB7h6u3Fph2KAJw50H9yCWJjap92qJScyXFV5rOrR9eTJD45LtKfbbco3voonc2JhiZ0sbXNbtO9OVbxCjGXG7lGRc/uTnP4fYp589N8csUTabkTM3XwwG2C62EV5rQwzGBm6ezlP8xZ0ZmbNy22fyDMs0Ec7l1cbW680Jmjr9/Es0Y3z2xo4d8zWZ+zqtX7qZkJUVxs4uraHZC2J28/49HAPef2CfzXRA9ksd0X/shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEz7lV85T5CgXCKyfWrkoUbo6mKPpuNlagWG7x2lGE+9bh1IqGp3fuQpn19hXEzi5Q0L177zfN8Ue/9l0oM6pOIHZ+aYXgPz/FMovNAdZhbYXZ+e57UObu46cQm8X2vCdHx1Dm5DkKPOeVbR73D/FZ5WMUw7J30W7se61aFMymCXFdcIL/6K1NH8Tb4k1QmIi4k1EKeXdxggLhPLcmCHmGw1SSMKMUNCppnBA8jVndiZmJC7F2R5+Dk7G3gRhWNChYD7Gte9vic4mJ0Uya2meVJigWb5hpxg16EHV79+KmKNoBxJLUto3haIplYmxTbelMyFY4N4QK20/s5tbBBA3AsinGQoZ136ysw8nqCo0SkhJdUPZ2bNufjSZQhhpNuDXGzgDnsAf30FwkGVrDg188JyY2Da5fImdcMpyg0Ux9ie+mJGuh1I1VsykxlqrwfsaufaSkLYjbZUtM5p4/ew2xs1O7XqxJfwzEKLB24/58ieuy00ucW4c5rpvjsb3+psC2f3qF8+/ZZmHrRLYc0zG24ft7ti/fuYumS3GK9fTGZJ+9wnX7qsA5MoS3W2e25FKrjQ2+Pl1AmeNzHOMe3ds3x8MRPpeu6D92QgghhBBCCNFztLETQgghhBBCiJ6jjZ0QQgghhBBC9Jxb1dhlNf5c3boEqQnqSHyixRBCaNY+8TDRyZDvZHOnBcgG+M1/XWI989EIYvO1/fZ5MV9BmaLA72vPt7ZcOkTtGsklGbxksCH6nZp89Ns4QU2e4zNm+qfSNY+WPIPhBJPCLs+Jxs5pOYoS6xkl+HcG3x6ihnxfLm6MLvo5qqnqINoiMgCazNo3g7ijhqtl5dy1OidXh4uT87rUi9400TB0yJLekLZfV6Ur0y0haxcNAX9S1+sRxbvl/YcPIVa7uSAmWlLSNML20uqvNy0m/W0avFaS2rkhHuA8wGIFGb6Xc1uH1SUm/s5b1JsNY3v9iiQ2bxsy1/k5kvx5u83wvMJ1mYZof2Oif4oyq2dLhtj3EqI1LhvULOVunEjJnBwR/dzWvcJfzHGOFrfLfI7J7I+Pse2vnWarJQ2WabljP8bXOACcX6whlpJ1mJ96EjJfLRd4rWcv7Rr5dIFjyXCI48SjB1ZT9+0nuEa+e4i6u/eWts8cX2E735RYz9qPcWx6JHM5e+5eDny1xLFrQ9a/662t+8US9xNd0X/shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEz7lV85QoR6OS3dgm4h7XmPhwmGPCzXZhRYvEOyU0xNwgcsYhKUlyOMowOenTuyhY/9Mf/9wc/8EpCs/bFhMRbpx4++Pvfg/KTMmzGvgExUSIHrV4P4k7LyHmDRF57mVhRZ/NBAXlMUkKG6JLCPnEkWWLlWdGNiE4kxzlQv4b5+0TlLNrXW8IUsc4THlDghBCiBMyTgTb13zC8hBCiKkxijukxi/E1Mk30IiYN5BYFDmjIlKniiQsLgsrDq9JP2YwvxOfAD0h98yu782ZOpnKiLdmOCXjZOPnQ3zDdYPvpXBmAw1xDWhJa4m9wUKKbbrx81UIYbXFNnx2aU1Pzq7OoUy1wtj8yo4BB3sXUCad7EAsbmwbbiPWG9gY96uPvzqNmIIF976IQVwUE0MlUq2BM2wZH+JaJZ3sQ8ybkPkxVtwsr4mR3sit6b58hcnIj8gasqpcm+pohuWXeTUx37qYo0HPlhgAFaX9zbu7ZC1PEo0/3LP1Wq3R4OjyEp9V5YxexjmuAQ5maLpysD81xzsTHBNOiaEKGxsRNk4rMWcrAAAgAElEQVSwSdKtdYlj1MUcDVxen9oxbrXBd9MV/cdOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0nFs1T7lcopFIBEYeKFDMKxTsY8WJsJGITH2Iicxj4tCxJQLIytViZ28KZcYZCkqP3xzb40s0G1kWaEoy8OLXGOvUMjGnizGhNhN917UVWLfk7wBJjXVIyLuoIEZE+kRsH2f2N0cxmmaIdwsaeZB+Rc7zpj0N7aLYpion3m5bFDsz0pwZqliBNesfLTFUwTtilccQjGcRCrwjYgbjqSusU7FFwXXpxkYwMvllMGMpV6+Y3GBDxpyIiPLFuyPJ0CQoOFObusbxnL0l3/1aZqxFxuW2tn2yJW2grrGNLYkhwHq7NMc7u9hnZvc/hFhT2f6+KvDayXwBsdS14fEQ749YHoXW9RnWP6gFg3s27FmxWEwulvn5cIwmFpNDNIxpYttm2Fgsbo7/4x//CGKHO3fM8Z/84R9AmcujlxCLvcHY9V5fIYQQ/LL5YkGMfYhxULZEY53ztb3Y6QLXtbPhCGKb2q0B2P+SyMC0Xtr575yY0WyJAdBgaNt5TkxXuBEaxrrQ5T9jrG8XW1zTXM3t/oitAbqi/9gJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc25VY8e0Mpu1jZXku/w24Le7E68TIx+ktxV+P1xsnSaFlFkXxxB7dvIKYnuPPjLHv/O3MdH4KCKJCP/6h+b4nzx/DmWO39uFGOzCSSLJmmVqd9/lRylqkVDriEmToxa/+a1KvD+mWwwuoa3XX4UQQrXG6/tLlYUSq9468AE69rWYJN2NXR9tSHulWkv/zklibgZL6p073V1EkphT7V9j21lChAAsKWwd7PUjogml5znN0pbo6QryHBr//T7TFZO+zf+mZ+vVkDE1IudFXXV94kZgY6efNqOI9EeiSUlcFnraj1OiaN+4JN8lzu3lFnUxdY3tZ5DYNjzbQ43Y/gxjm9Jq8d6cHUGZarOEmJeJRi255xr7bV2559BtOAuN0ztGRAsYNeTdZCRpudMMpUPU2OVEgxmlNlZxNaC4IY7e4BqydnrPJfFWiMlYmrk+ynRxDUmw3biJjdghUM35tsSCi61tsxeX2F7TBPWsfq7bbNkaj2hVnfi3KYk+ECIhpG7OasmzYmAVOv7PK8J6eREk08rS9YR7X+Xm7de6+o+dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToObdqnsJE37E36CDZFzck2WqW2mulJKNoW+B5hUsCuFmhwDuLUQjeFmhmEB9a8XaUYSWyCI1f8pFNPh7VKPqOSSLXbGAF0O0l1rNao+Cyiu1zWK9QwFoT8e3QJSiPlyhEX5LEkUxOmjgB/ogkkz4lgtyqtLFGou93S4dMnUmCw4Y3SgmBGHsQWp98NRBzESKurohZQ9tWEAvB9rWMJCz1ScxDCKF1f/OqG/y9iKQxjuKBK0NE7SQh+nZj+1FRdDBKYVATpI59xp3LTvPP5auC7v2oi75bSJ+J3UP3JgIhcIOsemDbfj7BxMMhQ4MO3xS3S5IIfIXmEIH0tdgZCayJidaEGH6tnaFDXWEfTVIyLrX2WhdrnLEG5zjfb537xJbUM0/I7OfGxpIYutRkTMgmaIKSuyTQKXk3zBzNG+Bkb5uRWXQiIWvWjx8+NsfTBtd4f7bBOezT17ZdJwm285rNDTCNMpMSPI0O/O7yNRmDamJwAvMR+b3UuxmFEOK4/ZXHIfC6t5WtA3sszOuLzdNwHou15DzX33OyLxgkOC61zlxuzZ5nR/QfOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XNu1TzFi51DCKGtvdiRmKds0aBj4oxEhkM0KSk3KICulqfmeH6ExiX3H+9C7GAHr//z15+b48+nKAy/26IhyM9evDTHzWQCZXYP70Msri9s4PVnUOav/vrPIbY3tPv3Vy+/gDLt3iHE9qf2GVdHp1Bmu8L7Y4LSZGRF3nmC77kmguPgDHe8gYy4Dez7ZEYpTHrsY1SvT2LeQCkiRhA1GSeqCoXnbWvHjrrFPjrIsU1FiYsR4wlW+djVtalwzCvIuFQWtu7MKIWJ37tAheHsZfjn3PE0/77IqxE3COl+MG+mxGwgTkk/Gtp2PppNocxgugOx1fmxOV7P0RBkcHUFseE+9r/MGaocn+F5b758DbHE3c/uwR6UuXf3AcSKxv7e69dvoMznn30CsTbYPjoc47hx9849PG9j38Xq4gLLEOOn8WgGseHYvot8iO+rjdA8pXZzsv6i/25ZVmiG82Zh56LjBc4NV2TcHwzt+xwQA7CiwnUYjvvMNYQ5kBBDMzfwt2RyaMn1I3ctZi40Rf+fENz6cJwSsxYyt67cOFiUONdGUYf7owYrBLIuSN24NB1hf4y8G00IYb21pjhfZxpV/xZCCCGEEEKInqONnRBCCCGEEEL0HG3shBBCCCGEEKLn3KrGLm7w+9rGf1teYZmyJgk9d2wi1WQPv68Pl5g0tSls7PJL1Jvt3/tNiL3/0a/h5X/yV+b4J3/xB1DmJw1JWr5jv5P/8NvfgTK7E9Q15I8/NsffWeI3xj9+jvfzhfv8enBwB8r8+sffgNjYPfeXz59Bmaog3yvHqD0Y7lgtQE3eKUt+3Djdj9dkihuGfVzuRD1M60W/B3ffrVOJXQcdV0PrxJKoEt2m0x7QfKwkNhjYD/9j0qbZ1ZrGjmfbLUl0XGC/9ZpBKqejOr+3hN20/9GOSYwlqbtdsiHODV7zEkWoS08C6riS1MbKGbax4RCXCZvc9o8tSbpdzVErN5qhfv3xXatLS3KSjPwCtXlTpz26c7APZXZ3D7Beka37HknKvn6DdUgzO9cNx6iBSxp8fovzV+a4Js8qI0mMszHq+vOJnUezAZYJJPmx1xDhr4mb5IMd1D4mc6tLLRcnUGZGNGgHY/u2jsb49tZbPK+u/MhMNHBE6wXZyCnkWmS+iNycMsqxbe5g9wtx4nRqGVn3VTi3FqWd74cRah0HzN/BPysy1zJdIZsiJ268PJgSzWuDY3FR2Ht8W019CPqPnRBCCCGEEEL0Hm3shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dm3ap5St8QcI7JCUCrmJIkI5wubzG+8g+Yp2fAYYo1LqL0+eQ5lTp6h4PrhN55C7Hv/ohWCP744hzJFjULX3R1rXrK7i6LvNCbPYWh/7/3v/jYU2Xt6CbGysgrPwRh/b9KgEPX0Z39hji9Pz6AMSxSdzlDcPzm4a46PLn8BZVbEOCdyiVVrUka8W5hPiYcJp9EFpYsom4iUybVJ/mWabLV2v1mXOJZsSEJW7y7CkpgHYga13VrTCm6UQkykGkjnfm2dKF2NbUg5SGdL+jZ/hdeb5Iib48UZjtXzczvuJ+1LKFOtcD5MYtsWZ/kEykQ5SXbuTDzqzRzKXB4d4bVIkvT9sTUz+e6Hj/C8+AOIJYldviTE8ICPXTZ4Z0qSpt/H9UTrjBnml2iC8uoLNC9bnbvk6i2OCQOyftk5RJOz2Y5dAwwGJPkxMU+Bv+F3NEYSb8cf/+kfQcwbiWyIsdZmTpLXb2272xvhmnK5wnd+5YzmfLLwEEJIWkxsHgLOT23rYmQt30bEkse3M9JHG2LlM0xt3x6kmMU8gTkzhMqtDxOyJmjJHqPtYEzm318ImIw8hBDu79nxZDJhVkVonlLWzqTua8yk+o+dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToObdqnkK0+KF1Isw4Q5OCQYRizvXSisXnQxQa7969C7Htc2eW4swOQgjh5NOfYEUzfFT3n75vjp88PoQyLREpR07R3RAxZ0QsD3wm+ihD4fTuDhqjtK0Vb9YbFO1ePvsRxF58+jNzXG3JC0xQ1Dojz72p7XM+vUCx/XJdQKyNbXvIyHsQ7xZowV0NOroI9Mm1/NVoXyBnxfT3bMma/F5dYbvbbmy5tsF2HntBeQhhu7WmTt68JQR+y77qvq9/FcRQHF//t7mGXou+MXtt8jgbIqwPXpTfQYgu3p5nL3GsHsd2LpjlKNjfmeL8FPk23OB5kwNiUlDYd1yROaW4QrOWiy/R1CW4Ouw3aOgwvfsQYkmyCzG4NGnnuetIKTEEqgsMzk+sOdrLz38GZS5O3kCs2i7M8XiGBjXDAzRsi4Zo6nJ8dmqO8zWuX8YTPC8fjM3xupYJ2bvkT/74/4YYrn+JmQlZJA/ykTkeDrCtHE5w3bwtG3eM9WT9IyLmXjAHEyOWiBqjOPO7Gs9bFTi+tM5cZL7ZQJmrxQJilZt/iwJNShryjME4DEqQQiGE3RmuCx4585TF4grKHBdovFS7sbetr5+jfxmagYUQQgghhBCi52hjJ4QQQgghhBA9Rxs7IYQQQgghhOg5typYylL8fr903wGzhNdM1xHVVhdzcYHfsY4P7kNssrbfts6PMOl2u8LY8Y++D7FyYXV+9z54inXYJ9/O+6dOkpGzZJKNS9adsH05+XZ+e3Zijl8//xzKnH6GusJybd9FQ76hntxD7cP+ASYo/+mLT8zxfI0ffNPkx639Rrog33+LG4R8R379F+khRCzmvt9nWq8uic1Z/nCmpiO5QkPlYhXTB5KGV1e2XNmx3XkNAdMVk5ypofVPmWlzye/FsX9W3fR0LRtf/D0SkR07D7S/NOG7uCm+++QexCKnsauItqRYo65js7SxzRqTZ/s+FEIIycTqfkaHqKtelHitzQLn1rMvrO5ue4X13L17grH7VuM+3EXNXZuiDr2o7NyzOLuEMmuynjh9Zeu5uMIE7E2N81riNG/pAfoBREQbXxD9+mJj1z0Z1fCiZnjt3utFhe1D3BwvPvuMRCN3xCYHDA1GVpN5/y6eN57gmHBnYheaxwtsK2WJ/YOt83AVwPRfZM5y16rINLpYY722lY3NZiw5ONbdTz3bGseSkiQ291pAti6ZjnC79K17qJddu3Hhp5/9GMosV1ivtnZzOZTojv5jJ4QQQgghhBA9Rxs7IYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQgghes6tmqfkQ5Lk1yUfL4nou65QhB05B4K6xGSFxwtM1Hn//lNzPCIJEzenFxBr15hQ++xTmyh28foVlNl58B7G9mfmONmdQpk4QaOZtnbPZo73vDxBQffFK5uUfbnApKZtRQwWnPB1dPcBlLn3CGMvzvE5nCztOyQ/B4nbv6L2hUgZ8S4Bcwzqd9LNaKPbD8LVuxSiyce71YDU3Qmsid6aJ053Bkc00Th9Vj4pO4PZ2HizFmKm0PFajU8M3fJaQB26FRM3xPFrkgTbTQ2VnytCCA1xLmh8OZacmLzgbGwTIkd3MPk5M99ZHuGSo7iyib+rNzjXXh1j7OWnX9g65MRMIcZ51CdgjsiaIyImKG1jy0XErSmZjSE2vm+NLSYPHkOZwWQGsWwwgtjBzN4P63oJNeWwJYcDXIuJm6MgCbXflrK07Y6N5w8ibOd7Q98nMYn5GXp4hHXJ5qdfffxLg24OaVjSbXJe7dZ56wrX8sdLvOf5yj73sxUzISRrSHfL0xGW+d4TNAUcx7iW/sEvrFnK2SkaP7Vk3wFP4Wu4p2iVLIQQQgghhBA9Rxs7IYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQgghes6tmqes1ygorZ1wuSSi75pYIMRemE1cEtbrc4idpgfm+N7DD6FMkn4JscXxKdahsgLr4gKNS44ujyF24oxRkowImYnwvHbi7bgkAm+vog9ozNAwkWuKwtrJQyvyfvDwEZR5sziD2ItzvOei9qJ8/JtCzEwlfJkUBbPi5oioYNcGmSEINQnx1yZK/27XIkYf5Oe6xDpU8ytcXalWnJ5oo8wPiJ3nDSpu0o8kZoZD5GWkiRsDWN2Ji4wflzo/Y/FWbIsCYpFr6Kwftw0xEvAifmZ4REx0osS2qXwHzQ2SARqqDMY4z6yOrClBcY5zbblC85RmZY0LiI9Bp37EzGGiBB/gYGrn6XzvLpTJD/Ge88M9cxwP0WAlyshSjAwefu5mBk6dXBeYiYW4MVhfw3bW7R00bp25vMJ17TEZ4+/dsdffG+9DmYys+06XuK5cbu39kGVmYItwmMtZXyOxurL38+IYXV5en2GH93NRVbG9Az6rhzM7fn3v8R6UGaRYhz/75IcQO/YGhsS0itLJNK4b+o+dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToObdqntKUKCKsK2fsQUS9DRERer1lS8q0EQoni60Vni4DCkqnj55CbHc8g9jcibzby0sok1REAO2E7k1JDE+IsLZ1inimf2bGBW1sDUcGOygM3Xl4H2J7h/bZfHn6BsocUaOULhXD58JErV5825ZE3C9uDGrs4d8BEYa3VBTt2jk5j7UD3/a7S4jJtZzZB+sfzNSli7Cdib67XIad5e+ZPU/+rKJfefxLqhAi8je91Js6sf4Y0LxoW9hfqOh7FjdFuUETstj1tThCU4TAzFN8jDQWZmrlZ/K6xbk9zrCtDPZ3IJZNbGx7B+enNTGMKOYX5rgh5mxsrPLzTDZG45ecmLyMDnftdSZYz9FwgnXI7e81zBSlIWujGk1ygjPJYH2bGTZ5F6nam+aIGyXNMoj510KNb6jD2PW/t1peQew4emGODwOaouxM0OxnlGN/uFjb9rJYY3v180AIIfjlWk2MmNj9+fmwLLDuGAkhcaZO+xPc4jy+g2PQR/v2ObQrXOv+2c+/D7HjN7j+Tf3cmrJ5lADGSG+P/mMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEz7ldjR0RuEROB5eRrWYb4bf68N0/SdCYxOT3nBZgtcQE24s1fh89nuB3ucMD+839cIkJDLfnGFtsrRYgXpLMqgTQ9KTkO+491AzOdq0+8GAHy1zE+MXyz0+emeMFSVy53hIdAPk6OHF1L8g3/tsGtYb+vI45PcVbwmVjPtgtibHvk1FH/ddbw3R+Tl8a04To1yeT7SKnYwVbkjU9IiIYryWlCjtSibdNBk71gW4IbYluiiai9zlo1UnfKZBUPIQQu7GzJNqyNUmUu16vzfFijvNVWWwhtt3aWFWTjMWkX2UJzuW50+KlpH9kZC73mrdJgnqhiIxLtWvDdcC6l6QOW387a5y3c5I83oveWP9PU1yK0cTpbi0UJ3ge09j5sZdpD8XNMchxbebpNNUS6OhK1r/Fxvbl09NXWKZYQ2w2w/Xh3aldQ96ZoAZ1U2Id1k53V5CE4Ux3F7n7GeCwEcZDbPu7s5E5PtjFMmmCdfji2V/Z45fPoMx6PcdrRaTfXv/q6Tz6dunrOfqPnRBCCCGEEEL0HG3shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dm3a57SJekvKROTROOxM0WoaxSGR0yo7Y1YSBLQgojFFxsUlee5FWrujDCJef6IJAN3os+8JqkWibY5dffsfz8EFIaHEML55Yk5/uTNp1AGU7uG0DqxfU2SqLI/DUSkGFSLGDOwi0Wpe1+sDuLGoOYY0Ce7JdgFoxIi8O5CQ8TVcczMBq5vjE1DhNrE5KFtraECM35iiZvRSQTPY9ciFcAYTV7rTFeYOQy7PIl5o5mUmbUQ04WydGPo2zq6iE68eonJc+vlwhyvSULfyw3OdStn3NWSfky6R6jc3EBy2XOTIHJ9bxLSksmPjRxpPHDnoQlKSsaqdGDPY/VkRk+lM36oalwnJGTNEYJ1U4jImMBOY+NLmri6J/hkSD7kkHjTFRkcvVMS8nxhFu1oyHW9zcYvm8Ps8XZFjJE2aJ4yv0RDwdF4ao6H7jiEECYkNp1akxVv/hNCCKMRrmOHA3teGnDcWDJTp7W9x89/ignEjy9P8bylew5snKLmjxAia10sw9YAjXuHdD3TEf3HTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ6jjZ0QQgghhBBC9JxbNU9hQt+aiPE93sQjhBAqJ6ZsiSgzaTBWt1ZUvi1RZF5XRNhIqlk21nLkyAnYQwihRJ1mmM52zXFGjF8uLs+xXq19fpPpGMpEDQpKC3f9OM2gTBbjDW5XVlhfBfKMM7wW0aIH/0hzUqgiz71tJPK+TTrYgfAIEQOjkQ8RHxPXhdabpRARcUTMU7IEY3VtDRWKllyLCLOZgQOU6WISQh4o8U6AJ0OvzIKss3WAvi83yCVdxdu+Cuqy75Sf/ewziPk5Ms1Iv4rIdO8MQbIcy8TZEGLebCAZkGuz/ujmlK/OtXMImQZCSubpOLPzX1GjBdiA9OPRwJ7XeoOuwE0e2rUbSypi1pLgs6pb+xzY2EI8nUJCDBzS2D6rosA6VGQuT9083dR4nrg5arJuwWGSGWjgtbxBBzP2oZZy7lpsqmjI2nNdYtvYrl1/n19Cmc10F2JpZvvRgK0XWzQYTII1VHlzegRlXr7B2MYZREVs4c4eMpjPUHsxErseZkLGLQBtuZgtFDqi/9gJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc25XY0e+d/Vaj5Z9xxrjd7mJS1rOtHpbknyxdN+3RyT5cUW+O65JucbVoSXnNeR+pjt3zfGjAe6v/+LNS4itK/u9cks0MIOc6Z9sPddLTFRZsG+K3fWZ5KYmOoOqIBoC910zS2jLsrL75NRpdqtN9p87eDLr678t76bNw/fL9AI+YXFgeheWiJsm57bXgmv/sjpAf2f9A0NvT4dnTB+yHz9JEXYxktC2LKxGqSWdlCVN9slWu7QX8fbEKWqrS5csO86xz6REK1cu7XlZRvRmGSYQrjLbfnIyhzUugfhXP4it0etwAtHBpi3Wazh2urEN0ZZhDUI2tPWqSJtOiB5x4LRyVUMSK48nEPOSpabBOTMmWcWjChPKj8b2HcZk/m2JNmeQ2zZTFrg2EjfHxx8/hVjixsWGrFmZ/CtO7Hkkl32oySgfxz4pPdF6kXmATRiJ88eIica9JGvkxdpqamPmrcCSgbs+ebCL+r1RnkOs9Jphss6MyTqhdGt3Kmdni1a2nnAL5ZR4i7Tk3Re107jH7E13Q/+xE0IIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPSfqlGRXCCGEEEIIIcQ/s+g/dkIIIYQQQgjRc7SxE0IIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0HG3shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ6jjZ0QQgghhBBC9Bxt7IQQQgghhBCi52hjJ4QQQgghhBA9Rxs7IYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc7SxE0IIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPSe9zR/7te9+s/WxLHZ7yxaKhLquIJbFkQ00DZ7nrx1CCO60JMUybLfbYLVC29qLRVEEZdIUH3FT2ftpyMVbci3/aJIEyyRJgrG6Nsd7gwGpUwGxk21pywS8dlzjcy8hEkKc2KeaYdVDE+FzSF253L/AEMIf/eATcjXxNvzuv/wvwUsYzfbN8Wq7gvNq18ZCCGFv3563s78DZdpqA7HEvePlfA5lzs8xVpO+9vThe7YOAevZXJ5BbJqOzfHk7j0oM84gFJZfPjfHp+cXUObwg29D7IO/87vmOL17B8rk5P42pe1t+WwGZSbDCcRGA7xW3NhxiY0vLRsIa1sH0o3D7//+76mP3hD/8L/8r+EJR60dh2MyToYIx+/WzXYtmfzoXOReMps/YG4PIbQB5wuc8vH3WJvypdIY7y802N9rt1aoyNqhZXXwSw6yVmnYvObK+XcVQggpW6uQ6/tKRKye7N27a7E1x3/yX/0X6qM3xEcfvA8P2C0XQ0XWTmyh6VvGhsy1IcPxfGdvzxxPdnBu2NmZQmw4yiHWLi/N8fb8EsocX+CcvCrsuDCb4u/FMd7Pq4291uDBUyjz7ae/BrF7e3ZS/qM//T+hzHiG1/p7f8vOv+P5Syjzw2d/BbGfvDqBWDKw8+3h/Y+hzMff+C2IPXh0aI7fe4RrgP/sP/h3OvVR/cdOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0nFs1TxmlKG7OnNC3JcLiNdGK1l5kSgTezMzEq7C9B8svOy/1ytcQQnBVaIiotS4wVrm6U7EzFW/7GBGnE721fzbzAu1Nqgqfe+Hq3hAhehKROpA/F3hReZNgoYTV3QmMa/ZOxY2xJu0gbLfmkLWxssbgdHpgjoekXZzO0bjkamF/L7TEFIEYJTRbNFk6O7HmJeN7+1BmTATd5XxhjtdHX0KZgvT3amGNZZJsBGWmB1iHdGSH4pY8ZGaIEbtBKCJGUwlxnojJOBu7YnGNv9eQMafyfTsibUjcHKRtwLBIzb7YAGvfVUTcP5ghSJzZdlCxuYiM1Q2ZR/28wmpJ2767fk3adMtM1dz8S43RiMkLVOz65UUIAY1RSDWpmQldA/jnzIxt2JVcsdi7kokbJfeDacB3UBLzFGb4V/t2QOadpkTzoqK0c1FaDaGMN08KIYQsxm1B5WLpAOe1LNZmwRMAACAASURBVEMjtNStNWvS/5Mh1utwYuf3fIqGf+/vjyGWbqyZyUd7eN6rk9cQe/7FZ+b4/i46o11scd2cDnDtkGQ2NszRvGwyxOe3v2PN5RLak7uh/9gJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc25VY5cRXUzmEmqzZOShwm9bvaTHf28fAt+1ep0B08XF5Btj+r27+0Q6jvC8mujS4DpMN0YTstofbIgeqiXfaHt9wpYlFS+xnl4v4PU8IXBNZEwS4TbuN7dEkzUkCZhj9xa9PlHcLJsSv5OvF9drYEKC37KHyCY6JbKDsPR6uhDCemNjEWlPMUtGTLha2ESnxzl+z/94FzVvIbFjTrG6wjqUOFbVTpc22MUko8O9Q4j5flsxvW5KEiJ7vRCpE+u3LLly5GI0mTREUEvFdBvi5mDzhQ9xxRZ55+6Y6fBapotzMTYPsHbgE5uHEELTQTfdMi23OyZTCn0OoPdmv0f1iBamjWdjXOzXHGwepYK963X2rC20dJ6+Xlcobo5hgvOT738ZMRVYVjjuF8G/c/w91u7WGzuH5XvXjxshhNBWOCeHYOtVtjjPDAa4fmsruy44W6yhzHCI8+/DB0/M8d0H70GZDw4fYjVPnB/AwX2s0+IUYs+/sMnHf4ESu7BhzyXDdU+SuhGlQf3jZITnDVyb+TqzqGZgIYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc27VPKUmyUILn5yUlKHpu53ZAPEMCSlRhpaVVZlWzKSkRQFrzkTKzryEJlZN8LxBYh+7v5cQ+HNo3bNizzMmSnBfg5oZpZCnnMReII93SKxu6LVaZwZRkTJlYEY29tko9fG7pSJmP/7dUdMAIpw+O7PJx5dktFlt0BipKGyrikjC65ipxYnJg28xZ2v8vSyHUNgf22ShKTFwqphRydAmTd299wTKNENMarpyiVwj0pGLDO+vcGL7KEahdl1hPdmY46/eNYVx7JwYOnhhiK8BJKkOIeDIyIxSOoyeLbk2MzxxE2fXvxCzphHDPEOqRWI+0Tg1h2FmMF0qxehgOEKLtL6eBOadwsZZ96xich7xRsPWIfeUd0rJTPOgLbIk5hjz4yub5lji74EzCouJARFZnoaWJDsvt0t77JKff1UHnCO3bu5hbbOt0Kkkbu38O8z2oMyiQIOaPTffphs0PRtP5hArUmsad0U2BmmE79QnYA8hhDS1z7kk5imsfXgDtabzwIToP3ZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0HG3shBBCCCGEEKLn3Kp5SkvEorUzZmhbYp5CRJ8gKyQ6QyYV9zEm5oyIsHjDhJOJFX2OUhRzTkfozDAdWfOEmOyvG5LlvtpaEWZVYz29WDWEEDa1jaUp3os3YQghhNI/G+qKQExQmAGOe4cJKVMzQalvM8wlR9wg+D4b9w6Y4LqpUSB8efnGHOekf5BmF+ra/kBDDBCYoUpD6jUcDMxxNhhCmTob4fV3bR9t4wGUWdTY1zLXtzcjFH0vIxx2N1srws5iYuCUE8MD12cqIvquGmLy0uK7qGF8xjIJGYsDjNmyOHqXRMzIC8wTiGkIcyBx5SLixpGSd165d+6NtkIIISZzOVsD+HmFmQZQMzEwJelmNoBrh27zGlpfMOcJdilvNHP9MwiBG6P4GK06O9GZ4rTqo++UDTHW8s0lYQZWJJa7dl6wvk0m5daP+2y+In07pu47NsaMitj6rUl8OaxDvV1A7Pj1F+Z4u72EMq+mY4iNd3fN8U4ygzLD2QOIpbE1epuluCZ4PNuHWHH0BmKvNmtzXJLuuCFr99qbpxAjna5olSyEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTPuV2NHdGW+ISMLHFmQ2KV++6Y6QAa8o3/tvTJlsk3zSnWczLCb3UP9qx+Zso0PkQrk7pqxSw5eE5ejUts3kao+9kSLcLSfWs932LSxsslJpxcuETRDUmCyRLAsm+t/XPOiDiAfHYcaiecUu7jdwzTsrin3jBhB0teX9ikn0mNmq2ItSnfJ0k/9hqfEEKIx9hHp4cPzfHO7B6USQY7EHtxZZOYbhZY902FfTRe27q/foXJUHcL7B+zA6vNu39nAmVGESZD9XrgOCX9qu6SLBeTptbkG/+8gx5J+p13DEu67fVmHfUZXpcWk/fbRVbJNDeBzuXkWvH1Ce4T9idoVzGmVWfaPNDZ0yTmHWYaqllkxVwfZXpEqqe7Psk8TVbP3pcfAmg/FjdFRX0GXCAh8yHVX7r2Q95dShpQ29r1W0s0dnWJc8OAajRtHdiavCH3nGT2WiM2NdS4Hi2Wdu1wXp1AmbNLfH7h1OrnPrz/bShyZ/Yenre2HgF3DrHMzgDXFznR4i1e/cIcl2zf05BOGtn7aUnC967oP3ZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0HG3shBBCCCGEEKLn3Kp5CtNzx05YyIXaKPr04uaWJFWMycWy1CYVz1NMcrgzmULs/T0UTk5cduV6gYkW6+0SYpVLRsw02BkkdgyQgTlK0DzF318IIeyNbZL0/REKPq8STNz8ZmMFpZdrvL+GCHKJr0WonNi2Igk1WZJbb/xQk6TJ4uaIyMvzCYNrIvxlSXeLemvPI79HDXN8h0hwmMpJH90jguc4OjTHb86xFlVzDrHl2gmXmdiZ6dydWVK03UKZ4znGxmdWLH50jPVcPsbEqrOxfTe7RJCPTyqEmhlSVfbd58SIpQQXhhBSl1g1Jkl2xc3BEpTj3MpMNa43+2iI8U1F5tbaGSUkpN2laQ6xtiJGJWC2RRIdk3r5YaIl95xGzKDCmzMRsy/2qNz8xJJJsyTw3kyIGj+RwYSvl7yZGHk3eBq8L2bYJm4ONgKWbsxlbYW9F9/OWBlmyFO5EDMmLIsCYkXA+aksXRsm12ImRJEbFyYjXGdW2zXEQnBr5AqNRNgcFlq7nr84P4IiWYxrfre0DnWJz2XVoMEge9O70wNzvGlxTZ6Sd+jXQi0xluuKZmAhhBBCCCGE6Dna2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFzbtU8pSJZ7luW5d7BhKGxF6ISMWKSoWhx6owYdvI7UOZwjOYiw/UJxLZnc3PclETgTYTuIbZ1SFMUeDMheIjt9ZmgtCFC1Gppn/tohuLRaX4AsWxqbRfGGd7f8RUxjCHmKf5u2PtiJjLe/4YZ6YibA3wMQghew9tSZwFmCOAE16RhEC+FEDnThXyyC2V2D55ArK5RmH2xsKYkG9J8WFv0xksJMUqIOojFAzlvU+BzWJ/afnu1wBdxeoXi7fv37LN5Gt2FMrsHpEO2KA5vXN9qWxyXiOdRCJktlzBHHHFjtA2ZZzpEmBtH5Np5FOOSIGbNJxTuGKnYYEJK+nGCjSWkCjBHxjGaiQ1yNHCp3TwdN9gXmMGJN5+IyLqkJf29cu+rYRMdfV3Xm2vQ1RNZc3ijjq/hyyC6wN6Baz8NMf9ga8HMrVmbGvtVRsyL/No6JedViznE1i2uK6va9pGowok0JeNS7RpaFhEjtATX6ZUbq5hZC51/iytzvJ4/gzInLa4T6soaxmzXaHq4HexAbJLhmJM4U8M8wvtjY7g3g5F5ihBCCCGEEEL8c4w2dkIIIYQQQgjRc7SxE0IIIYQQQoiec6sauzjCb3w3XmRDPivNybfsiRN7sGSFaYpauYOR/U72gHwLnc9RT1csz7AOsf1Wd7qP+pbhPmrXdu9bXd9kiN/pxjF+l7st7XfAy7NjKLM8eQWx+bkttyHfVbcx+W57bJOy35sdQpmqxiZUXmHCZ/92WHJXJkesnS4romoOcVNQ9Zx/B+zPQSQRsO/LTG9ak9hgYvvozsEjKFO2qBO9uEJtwNZlaY1T7FeTAY4Tezv2+rMJlslIAmYvXlluUb9zdoXf7y9Wtm+T/KhhfkX0s5Xtt+kA7+9gH1OUNzt4PyPXlauIKJuITqtoXYMgCeXFDcI0Wl57RcZSlszaz6Mx0dix3/NyWdb9G5b4G4uBZpfqr1niZqddyTPSpnPU0xROQ7Qh2t+IrFW80q8loyXTTXl9W8TujyVb7pJEnL1odp4LUZ2fuDEa+u7cOMneOfMncH10QPR0ZCYKwc0NWbOBIu0W58xojOvRZmXPzVvU2EWkv7eJvec8ZZ4TOGdtnAZtRHwvigI154mrQ7G5gDKrGp9f4zS1dUvWtRnOv8V4D2LjkdW95xm+Zz9vhxBC7bW4UKI7+o+dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToObeqch8S3aQX6FdMMUgE3ZETXOckGfkoJ6YIuTVFyJdoQLJdXkGsTtGAYPrkQ3P89Nu/DmV27j2A2GBm65WS7TWTNtcuwWRFkpFvL15D7PSzT83xy2fPocz5KRrGVC5J8iDgM747QnH6akgSRW9cQlt2zx0E3dJ8v1u8gDeEgI2RiL4bIvX1ZikREfpnGcq+d3dtn2kD9r3FCn9vSxJjD4e2zR4eYLLze3fQ4Oje4cTWgQjDayLCrp1ZS3MJRUJYoOg7iu21sgQ7SEZGhdr1q6PXaPL0am8GMSbATyd2nE2JUUrZoGi+8jnZiWmVuEG6DIIseS8zIPHeZeQ84osSYm8IQhyV2HjODEdqME8hkOv7sWM0wLEkIUnSU7ecSIlPCsutXrv7YUsVbzQVAq5VYu5s0wl4h+xaLHG6Wzswkxdxc7B51PeRlsxXJTMlce98QNp5UxC3rdK+8yTHPhSnaJQyJEm3gzMKS8nWgc0XwfXR1He+wOfR2K01dw/QpGQ1R5O+uLXP4XyzwPPIs/LGUsw8qSCDwoL1P2ceVgVcD28rlgTeX5+NvN3Qf+yEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTPuVXzlJoIdlsnNo5aFCNWxFEliu2edGeIBgsPZhOIDTbWXKCYo7tBE6H4/+6T70Lsw9+ysWh3DGUWAxRALpw8fJqgKQkTojZOFB0RQ5fJ4AnEdsbWMCIboYFE/OmPIHZx9MYc19tTKJNlaMywP8TnsHFi0VXJRMKIjxFduLhBmLDfGx4wE5QkJn8jcmJxbxASQgijHNtwXtvzCvLSB2MUJEcptqkHe1Z0/ZiYpwx3sL0en1hTpaMTbPtFyQwj7JC6LnDsWpd4P6OhHase30dDl/0BGRsLa6C0JSYvJy/RUGVnjGPOMLHPISLuEHFA0XfsHnv+NUTf4nqYAUkXK4yWzK3eRKNl5g3kPB9hpivsr8YNM08J3lwEz8tS0mcyG8sjrHso0WAsdcZEI+L1E5G+vals36qJ+QV7E/CsyFkRMzxhBjgd5r8u3jpsDBc3hzcXCiGAU1FDyhSkTbWpMwpscWCOWatyc/JwhPPcbG8fYsRXK6RuHVuWxIAkwxMrV4c4JUYpLXbAaWbn6ekE56vJANcA67mdpy/LDZRhg2XkzG4iYm4SYpxbt5s5xGrXtwYx7kP2axyrvDli8jX8jfQfOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XNu1TxlDZnVQ/BSYibCbhoUizbOiGE6QxOPwwQFpdurKxto8RGMHzyG2Ee//i2IrVZWqPnjFz+BMi+WKMKcHtwzx7/xPv7e4300eYiCFVyW6wWU2V6tIFbObWznDv7eg80SYq0TyJ6dotFMUuK7ORyjIUaZWxH7q4qI9CP8O0PjzHUaab7fKTF7B+44YepqQuTE4nGMYudscAdjzkhkQgxPogyv1ZY4vhxMrXlKkuL9vXz5EmJfvjo3x1drvHbdMsMY164jfFYRMUuajq2w/f0n+Fzuz3Csqivbry4XWyjz6XM0T3l5hLE0tv397t4Aykzy600eykAE6+LGqMkc2UZ+HsXzmBlH68waIujtIUTU7sNdixjtJMyIiRmCuOOMjC8TYs40bGxfi5mhQ4WxENv+lw+wnccZ1r3e2rpXG5zD4oQYKrn1S0ueJ/GnCdSIBcqRaxGTOn+ivFPeLSlzAHKhkrwnttat/XmksyXEacObLDU5ts1khHMKacIwThQFmhL5fhVCCKVbgw/GOZ4X0NRl4EzVBhkxZ0rJs9ra6w8GeO2aGRO6tXSzxfsbTnCcaGK81qqy6+Rqi0Zo24qsJxobKwoydnVE/7ETQgghhBBCiJ6jjZ0QQgghhBBC9Bxt7IQQQgghhBCi59yqxo4nznTffhNdHPvGv3YfAqcD3KPWS5I80H22OpzdhzLvfevX8bwhfs/7+U9/Zo4vlqh5mwZMANlePjfHP2bf237jKcTu1FYH94//6vtQ5vgUNXZ7jf3ueOfeQyhzeB9j07X9znh5hdqZcoGx8QATMu6M7PfJJxv8hrkkArrIveeMfIMubo6Y6FsgSTzRu3TRzrDk58Md7B/nW1fuDHVjH314D2L3p5joNHYj3GWBmtfFFq/fOGFD06J+oCF6RJ8UlmkW44D9fZDYemUx0ToNsA4XV1bne3aOY97ZJca2JJn7eGIf1nSC00NGdD9J63VaEvC8S7yejsVaoiOhCcP9uyPJjyOq9fKaPqwTS9Ick3Kt7zMN6nCSEselzGmNmDYoTVAXs3Xa2LIg4xk5L/aDSYxzbWiIzs/dMtPTsWTVtB/5k8n7YmMxJMfWPPpOyYjGrnH9qGxxHmBzZOLeXUs0djVZN6euX20LbK/HVxAKwxl6JCQuYTeTELJml7tOGZG5ryRJxKPcXizLsI8u57iG3LjE35MhrglSkhx87vWORD//5A6ukaMa63BS2Pn2ssb1hfevCCGErSv3+uICynRF/7ETQgghhBBCiJ6jjZ0QQgghhBBC9Bxt7IQQQgghhBCi52hjJ4QQQgghhBA953bNU4het3HC0IQpoIlYdDy25fKECBS3mHTba4Yn+5g8cPceGjOcrU4gdr6yCtInj34Dynzn8cd4rWefmeP/6cVnUGZ8BwXk5Zk1Zzk+Q0H5waPvQOxua4Waf/HiEywz/gbEfue9j8zx6s0rKDN/cwqxqkBFbj62TY2Ji1ckwXSSXn+euDlY8vEGhMXYHxuScN4nSI3Q+4PlNQ7rre3bROscVks8c3IXTXuy3Lb9okHzlDTHZOdlZMvRpNDERMYbHjAjA5ZYdbpnE6muaqxneY5i6rKy1zo9v4Qy6xWK5suA4u3VI5sUfVNj3YcBX4Z/rUkiY4Z3SVlh2wjOpIcmoKZJk6/v28SXISTu9xpifMW80pjxi29BbcD+WJPBo07sfBENiIHEdA9ixcaNCWvsC8TbAMazvCGGbS32jyZ4IyZmbMPcKDAEZjdYhBpw+BAzqRM3R5aSZN1uTE/I/FFzZ51ry7Ck93k2dGWwbV6scK0WBxxfHu1YQ5W0wq1DSQzGYD4kSdnZkr90BiTnWzKHVbi+n2/sefQZb/BaSWOfTZzhWuLOzhOITWI0fqkvXprjipgzNdU5xFabmTleL46gTFf0HzshhBBCCCGE6Dna2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFzbtk8BYWTjRM3xhExJEiwmjuJNRcZbFHE2JTEUCW21xru7ECZ8WwGsSsi1EwHNqv9ghhIzEuMbZwA2ouyQwihJdnql1fWPOXOFE1ePnryIcTu5vZaX1ygWUu1QEFpdO+BOR7s47NanKCpTFWg8jwe2feaMoG3d7YJIdS1M1Sh4ntxY5Dn67tfgx439LzKlRtmxBSB+Gz4d15UpC+sFhB78ZrU3ZlDvDlFsfir4znEFltbh4oYGUSk7pEbz7x4PIQQamK6cHppx5cq4D2PR3je7tQ+0+EQTZeaCp9VTcal9dr+5maD7ytM0VyndYYRNTP3EDdGRPqaN8zgHlPE9MgbeTAPD/J7vju0zBmN/F5EKub8scIgw86WxeT6rq9VA2yvb7bYj1J3XjrENh3WxGChtGuMNGJz+/XGFswohRvNkKArSXxYqGETmW3ZxcUN4de1IeBcR42EyPv07aUqsN3FGY77IbLtum5xXE6ImVhYYJ+Jp+76Cfa1osT7yUp7Q5drnH+viDHh2JmXjHLs/1WJ1yoLO2+zYSkiwTp258VkLFnjO3365D2IJZVd/+bseRLTle3GmqMVCzQm7Ir+YyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTPuVWNnU/UGUIILWjqcK+ZR2OIjeNdczwiSXHbGrUzUWy/Ox6OB1AmIcklQ4J1iBL7Le3zL34AZY6+fAmx2H1s/eEHD6HMfaKVWThdzHA0hTKznRHERu774ZQkJy2ZhshpCPMJXjsm35JviX5n5K7PEo0nJFY53V1N1QjipohIgvJBZmObNX4f3rYovPN9O86wr7VMk+I0dizp7/HxGcRWV9d/c7/e4jhRkMZfQ3LujpoU0M/+v+y92ZMt6XXd9+V4hprvrTsP3Y3GQDRBACREAJIhcTJIWpKpyQpZVtiWHdKj/wA/+MF/g1+tCEdYDkfYEoO0TAkSKYGiCQogQICY2I3uRnffeahbw6k6U85+aD1477XAe9w4txQZXL+33PFlni8zv7Eq19pI22GfmU4WtkyFz7PdwzFhd8OOVSnRTCTkGbdE3Ng4PVJH6hB1OGX4a9VMDyXWRsIy+gafoJyqtiAS+RjRuDP9rL9SywSnRIybxdh+hpk9d5CjRjuL8VppbOejiIwvB0c4Tuxs2nlzNCK60YT4AXjdbYXPqg7YR6vai6ugCBfLsSHHvYuI6Bg7olUHKWVEdIVibUxmC4j5fttSESWZi5x3Q000Yqy7R2OrU2PS56TBOuwTj4nU+WMUZL6vKrzn+dJqxxcNalfLgG3R6+zJVBSGOfa/JPMJ0fG80KHmrWrtmJOOcAw6LO9AbFRh3dOtC+b4wgCf8dEMX9huZ/cYV3dvQ5lV0X/shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEzznfBOVEGNo6dWPXkgSmRFjcuuzHyyUqQ4dEUApaSiIMbzs0h5gvHkDs1CUe3Nu6CWXyIQpRJ05Q2k7R5CUjyRArZ1LQEbF4SFHM6c0NmJ40SkmCS5eZOqFJMElCefIDqRMOj0gy2XRBEui62pIc92KNJMQ8JY4zVwb7WkrMi7w4PCOmRBH521Ia25ccE4H3bIGGKkWBCuvEmfugKUoILWlUflhgCZipMNsniqaZovGeFwsr1i4KvL+I9b8r9pAN6P4ZvH8xvOfOmTxUxGimWOIzjoOt6yg712nlzxwsQTk4baxgvBECJj9m/ZGN8ZAEmxmskFhKzFNyZ2iWENMV1o/yge0PSY79I6rIWOUulZH22pGe1DQuRswvsgjrsHDriYaYizETFJpW3Jun0Hfz/LGKNiGxNpgpWFn7PkNPxBgYXZE5rMR2furMxAZDnH83d/Yglm/h2qxobBtuA/bRukHDkcR1tozcdENcXTZGtq6bY6xTQhaaiTMFYv0jitA8JYp9Ana8v9HGDGJRPYFYtbSmNVWBa6qL+5jYPOrsecPRB59H9R87IYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc85V5Z4RYw8vZG6I8HdaoygzcmLq7SGKK3NmGuAErBUxKWhKNE85ntzDOmRW7PjZn/lFKLM3wiq8+YPvmuM/OnoMZe60xDxlaH9vm9zeJnl+iTOH8KYWIYSQ5CisTVwxb1gTQqAC8pYJZJ3et67Jc2+JAYdTecs75cUSE/F2495VQgwQ0oyYoLj3GTX4zmNiGpC4hpeSvhClWIetnU2IdW7sKGconK5rYjbgRNfca4CZrqxinkJ+z/cPYiIVchxMmsg+h7pi5iakP9K623O7Dp9VSa4fdVb8nmY4zos1Qvqonw9Zg6XGKCuVIQYdrg1zkwKMxREafnWNLdeSeSCkZH4f2PkwTnAOy0g7z13zZOdV1Rhj3oSsxfEs9wuaEMLArXuWZD3TEcMY9vwSV1c2utB5tLPXj5hJh1gbYC4UQqga9w6YURkzzHHXionxFZtnKmccFA/w90qyXjytsH1uDOw8U04XUKZp0QSlqWwb7jrsa+MBzts7W9bUZXOMdd8gMW9MVJF15mKJ542H++Z4bxfHqafPcA9QdKcQGw3t2NERU7diPoVYWtoxrsrwWa2K/mMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEzzlXjR35ZDwkLgl2RL75rTv8RnVWzc1xEVB/sp2QH3Tfny8mqKerlvgNcxqT713dt88FSbS4rPERl64OJdlfPzsjyV2dFuEiqxL5pjhUtp51Tb7jJt9oxy7hZLPAZ0WTOxPNgn+D9Qo6gBBCiJyGKCbfpYv1wfQtdWvbQRQwCW/mE3yGEDp3XtVh28xi/L3YJTKPGuwf2xuouXnl5mWIlQv7DfyDu0+hzJLoGrxEgjTzQPN+e00P1bJgzOsKY6Ih3NvcgpiXNczmqI+oiH6HSGPD0OmDyvkBlLl3OofYeLhjjmMlKH+xsDblYiyJOUuajMmsiX6H6vVsMGH9I8Kxuq2x05RuGTIeo75luIFtP8msloXNRayTRn5dEOF8leZYhyqz91wXx/hzLfa/HfcYUuI1MCuJNpa8w4xqdi0sMbzXvUth92JhGjv/Wjoy13akz/huy9pPnuP8WzpNX0K03TXRezYVljsrbbm2Ynp5Mr+ntt+2yQaUSXKcy4e5nVM2R9imi/KExGw9l0Qv2MY7ELuy86o5vnoJn3HU4dx3PEEdeptY/WEUcCxZLlGjmBT2+oclXntV9B87IYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIkO0cEAAAIABJREFUIYQQQgjRc85V5c4MM9LYJ18kYmciiu6qwpXBJIfpEIWazfzMHC+O0Uzh9OAQYtf2XoHY0dEdc/yVb/0ulIkGKAxdOvOS3b1rUOalK2gEMaveM8dPDt+CMuMxirB3FlaUWVb4PHc2sZ7N6ZE5Xp6cYRma7BzFoo0TBS+ZcJhIun3i1udLx8WPBUt0WrpkxDGKpCtiHNQ4Y4acJCgfshea+KSm2KbrAo185kdoZrC/Z8eFag/HicUC+7uvakLGpTQlAnnfQonpUpSSRMouGenmBiZI3h1hrDiz9zxdoFi8JkL+8RCH/sSJw4+ePIIyFTPAumLHWW8OJdZLQgxB2lXcfogxmTd5iGNinkJMF2J3rZSM3QPiqFKDjVYIqUs0PtrCdj7YwLm8dX+XxiuHMBiiqVrikp1HCekLxKRr5PtkPYMy9SkmHs7c40tbknicLMVaYoRW1f4uiSEOiSVuXO9IkmuxPqh5iu8jZE5hica9MVJC5g+2MMoz14bJmJDF2O5iarLkDfiwtyURXiuNrKlLnKMJUjQk86GbW8s5mo0cTycQ87nAS3Ivg22s5+butq1nwL59/Qqu0/OY1OvU7k3IsicsOxwnoqUdXy5d3IYyq6L/2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zruYpaYRCRq/VbplRSotCzc5lmJ/OUKE4y9AQJE6taHExRfOUo0f3IXZt+xMQ+6mPWnHqO4/fgzLTEu9nY/OWOb518yNQZn8HQuGoOzHHr//wAZT57ne/AbE0WAHrh27dgjLXSEuYPXjXHM8naJ6SEsHsaAMFsseVNWaYE2F42eB79k2GaGHFGikKNN/whiApEXg3xODEmzVUCxQkL6anEMtG1jgoRx+DsCT1PFwWEBu21nRhexfNU65WeN7RsW2vKRGs7++joUPpROWnZFyaLtFopnUNfTBAA6KqxrHk4MjW85hcOxvkEBtn+L4mJ9YsJW7mUGZITCyGzmiC+G+INdISc5rIGarQYZIEYzIne7wJUggh1M4IYkja2BBDIYrwWvnYtp9RjnNKRP4G7c1TKuJS4I1ZQgihduYlKbk287AYDOwYkGzjWDKv0dQpctXqSrx4W6MJA3vPvl7suXQNM8lxMf1J/4USkX7lTY8attZlpit+DUR+L0mxz1SNmyM7fOl1Q+qZsfZp54uKGIJARQOaEMUB5zXiIROKhTUFa8gcvTjD9cSysRfLN/egDDOMOT17Zo4TYkIYRViHE2LqUnd2nKgrHBOWDXn3S2eWklyCMqui7i2EEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTPOV+NHfm2tXHfg0Oi1cC/Vw7u+/PpAvUgkwy1XjsuGWJ9hucd3PshxLYv7EPs2odt0vLr1zGJeR1Q85KkNmlqxrSAHX7EPL72kjkejfH+nhyjDq6K7D1fJQnEw6M3IfT08T1zXC7wW+F8gDqDlryuU3fukuiFWLLz1CWrDiQ5r1gfVYnaNf+dPJHchNi/pxBC4kU9RIdXLk8gtrftvosnzXW6wEocnGDbH4xs27+yie310qUrEBvmtl6T42dQZpPodzb3L5rjh08xiWrosB9NF3acmJ5hmao5gtjhM6tRLGrsQ7sjjOUxvufpmU3UPswwufPeAMecNLLtoyRjsVgjZD70cyRTzjE9loellmeJv5Pc9qt8hHNYQtRArAaZ04AnrBJk7PDFeN3xF6valsyJ3mWVlVEc48CUjVAcv5xZLVAyRNFwXOA6oSZjsdfK0TfK1kvuFpmWS6yPJMU3E3mBJNFC+vVwCKipq0mZUGH76dyZRGJH12FpRHRwie3faY79sZmh3ixJbBuuS5yjuwT7Qxbs+reak/lwipq3OHUJ0SvsQ/XsGGIHLmH4dIRzX5piPdsEn1VX23fREo+QosT3FTlNZMOV0iuhVbIQQgghhBBC9Bxt7IQQQgghhBCi52hjJ4QQQgghhBA9Rxs7IYQQQgghhOg552qeEkhCRp9UtCPZCmNimJG6WFGguPK4QmHj5oY1T0iIiLGdoaHDnde/A7HaVevK7dtQZotkGo+dsLYNKPCsmIlMPjbHexdRsH5xj1zr1ApWj+5iAvaDR48gNjmyxg9dhEmaO5KY9sAnxgwhPHPPuSNtgSXLhSSt+lPEi4W0u84ZF9Q1SZBMBNAxJK8nwnCSmHeQ2LayuYOGHaFF4fSEmA08eXxgf28f7+/CJpqgZC5J8mCIQ+VijkLwPLf9YXuIfaYtSMLn0t7PbIKmK6cTTOZetvaeY/J72zsoBI9avNbcJ4snpitnSxxnE1evUaZO+kJh5hgu1rEk5itcKyKGJxn5vaFLiIy9P4TGT5AhhDgdQyxNbP9LiMkac2zqnNlWVKPhWMLmGReK2LMiRl4+FkU490Ux3l8b7BgXR7jmGOekzzTE+KWyY3FLxtToR1jn/H/xxhpivSQZ9ghvfJMQNxNmtOHfVNMwIyGMxYnv29iv2g7rGcXYrlNn/JJk+HtViXNy21hTkpaYBDUB56c6tvVqlti3O9I/ktQ+rYSsrRNixLR05mXLchfKjMbXIXbxEq4d5pVdS1dkrZLA2iiE3L3DPGaj6mpoBhZCCCGEEEKInqONnRBCCCGEEEL0HG3shBBCCCGEEKLnaGMnhBBCCCGEED3nXM1TioYIJ71ZAxVJYyw4s4aOiIhnsznEThIrirywQ8xNJpiZfnaM5iJ3vm1F0fODB1Dm8o1XIHbx+lX7ewM0PAgxCqAbJwRvF2g8MTt9CrHje3fM8eHDh1Dm7OgIf6+2zzge70OZ5QCf++MJ1uGssCLWiAjyqaDbhVoihhfrYzRCQ57OiXpZd2yZ6Yo7jzTpEBPRd13OzPFwA8XV25vYZ4ozrNi8tKLrx0+eQZnZhJigJPZ+0gwF5VlGDBbcTWbESCRN2d/TbN1b8pBb8vwGzh9qZwefy8UtfDeLZzh2NE7YvgxosHJ0SMalyp63M8JnJdZHFDGDo9YdE/MtMuZGrlxMxuAhOW+zce21QVOdpkPzsiRCQ4Cmtf2hrohRAjFHa12HaInBQk6ulfhnFWFfq0ry/DpvUIP9uC6fb37Rldj3xgleq0uImVhjYzWZD9kM6V9hx610xJpg78D3Sf6eyHvxS2RyYkT+R1M7M6E0YYZ1xDyFXCt26+2YtJ/xBhqJRAu7Bq+LGZRpiRlMmdg5pK1I3RMcX9LUzb8p1pPdc+tibYdbo6bBsWQ2w/spXbmWeKCMyBi+2drzNtIPvj3Tf+yEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToOeeqsSvJ9+D+C1i20/Q6gBBCqN13uUx3UC/xW/YHtT1vub0NZa5u7kEsmWLC4DC1icyP38LvbU/fuwOxe9s2SfqA1CH24pkQ4OPq8gw1MNUZJlev5rZcV5GkjSRJZOy+mW428O08mqIecUK0f+6xh5QktGaf/XtNXSyN3QslH5Lv1mP73jPy7XftX3BAjSvTjcU+W3AIYXpq9Z7FEtvrYAP76JgkEfc6nJJobk5PUfvbunrFG6g9ZOPEpczpDFrUBs1IIlf/XX7DFBgk6e3WttUiXNnCZ3B5jLGnRJPs+1Zd4HOvU7yfeMMnwv3giVXF84lZLuvGZ91+fhJzem3W7Egsd/qdgpRJiNSyLHGObKa2/0URjiWhxLbopXEkz3hIInxYZel0aiXR0zCtv3vGEbl2x7Tjbt6OWNL0FsegnIwBVeq0QDXWs2nIg3DXIvnXxRppSWP0TYpp5VaCtLEkRW11U9g+2pE1Mk9mj+UgcTrpH2mKHT7KbLmmmEKZtiT63Mx3bpxTkgTXrInTEdYk4XtTEh36hZvmeGsLyxxO0Gfj6VOcy/1+pSP9PU3xfsZ+DFjic1kV/cdOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0nHM1T4mIKDNySvCYJmhkAk+XnJQlSK5RkNw505VnJ+Ta25hocY8kSR4VTphJjBmqBRqczOfW4CR6QgTX5Dm07p4T9jzJc/Bi+9EY76WKMVH7WW7FsM+mB1DmdIlJ4FmCy4QkgcUTmRj9+Yl3xfpYzPF9jjas2U/omCEBiohrMHTA3+vIeZUzZlguUHBdFJhUfLh1GWJbY2tMVJRYiaLAtrmsrVlD1+JQuViiWPxsZu95PkfTpRNyP3FsxdRbJPl5voW/t7Vp67WxgaJvZrZBch+HyBmqVDUWKkt8VouFS6w60t8LXyRJ/HyDDjpKknm0cyVxBguhJA0o2dk1x95MJQQ0vgohhITMDS0kTia/l6Opk5/riDdEaJkRhL9nNjeRuSjE3iTo+Yni34/Z43SI6ws2P6YNGsbkzmSpJqYrDTFUcR5SP8I0Q6wLtg7zyceZmRjruZ3rf3Q1TNpr7GIdcxdia2vWj1zybLYOK6g5ojMJiXEejcgaIHHmIv5eQgihJcnHvenRvMD+EY1xLLm8/5I5vjRC45Kzk/cgNmMGhm7e9EnTQwhhe0jMYJb2WpPHaLy4KpqBhRBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEzzln8xSGE0CTrWbnlb8hhCTyVUfhZtNg1vnECVibGoWbTycYazdRcLmbjs3x9nALykQVijCb0sbyFusZESFq5QXQKb6+OMV6xgNbbt7h/Z0WaJrx5NQaVEwLFHMz0W6SoIFDFFtRa0tEu3mM57WdfTZMIC/WR9OgwHrmDFWiBNt0WxKjotYbOuA7j1tsP5E3h4jw2kWDouWSmBeNNmxdB67PhhDCeHNIru+MRDJs+6FGE5SnB8/seQ2avIyICUpaLVw98Z6rBo1Yiqk1Ymg30ECmIUL3NEPxtvdrYuPnkowBM2cGVZV4z2J9NDW+l9YZI3Tkb7ZdwPP8rBxF2C5m3gQphPBoYvtfTtx4xjmadGVDjNWJnZ+YERr7C3TizVPI+FI3OCYkbt7kJijMCMLViS1WiEFFUczM8dkU+0dHjFIaUne/LCCvJrCVlr8dWnexNthaN06dCQrzMiFjrr+Y7+shhNCS9hO7xZKfj0MIISY1TZjblm9opN11ZOzo3LiQZjj3dRXOdZkzHBnkWKdlWECsqG3FStLOdzY3ITYY2N+rZjMo02E1Q1nig6jBPAXr0FRkHp08MsfMIG5V1LuFEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTPOVfzFCqKbp4v+m46jPls7l2HglLvwRACCqUjJigniuTjKSonp4k9d0AMFtIM6z7ctALSK8QRpCOizLkTmTPhtDd9CCGExdIaSFTE0KUsUahduksxQTATrLMH70W61ASFxLw5C2lCYq3gu2tr1/axqfD3ScThQExOdG24IQ2Daf+ZILlZWIOTNEPzlGy8A7HBljV5GCR4Mwl5VuOR7aNJjMYsZYWmK7PlgTk+XZ5AmXmJY9X23ivmOL16BcpEHZ6XJih09/eTkLE4y9HgaLhhYxExQRLroyZjPHQ21iG9O04IIQTXDkjH6ojBUePaRkGMPnLye1mK5gmxizEDCTbux94AiK4v8Fq1WyskGS6D2Pji/WEi/+xCCHXJTJas+VTZElMUYqDWEYMxfIdYhpnBRPBsuJWdWA+svXozqigh61ra/+xxTIyKOtL/YtcOmCEQm34j0ha9/werZ0KMUWI3z1TkB5fEwLD1zyYj7bzGdWzq9gqDBOf7IVmTHx1b45J0ieZskzM0GGwrfFa5ew44SoTQEkPIk7n9zXaOa4BV0X/shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dnnm6CcJDD0HxA3RDjWkW/GK5fUkGrZhpis23+iTnI2hohoAxryXXzltCsV+ea3Id/Sbo3sN7i3tzBp6/FTTE74aG4TMnbse2yirShc4mamdWT3HHsJA0mM6fUKIYRAJD3w3Jn+kSXQhaSa0ti9UFjbAK0c1a6SmA/SpL+k/bg2xapEmh1XjTidStth4tFpjYlOZ4XViQ1S1I3tbl2AWFk5HSxJ3F4s8Nv5xdL299LrGkMIXUANg5P50j5KtRUJDv0+aXESYX+MyThROT1wFYjOSKyNjrR09l5WKeNlXC2bEMmg27oOWBNdDsnnGwZE2+l1OA1prw1LyuwSKS9JomOfjDwEXDuweZTes3vueYZjAmrZUI9IJMqhI0qcjk6SXuiHz4Umx/aX0Tz6QmFjbl25d0XWugl5535Jx5orS1oe+UTjbK3W4lhdFdiPBkPbv/OY6XxJ23fDQj4ka9YGdXC16yTTAntN4heoIYRBbmM16W2L06cQOy4n5riZow5+vsSYTwIfAvGTCKizT4j2r3beF8sS9xOrov/YCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInnO+5ikkBtJDJuolAkUvph7mm1Bmc3sbYwNrqJITMTczBClqFJnWTiBLPENCTW56+6Kt1yYxTynaQ4hlp9b4oayZ2QiKRQepvUdmsAKi7BBC7O+PJHttmNieml24ehGHlYaZcjgBsNKqvmBY8uMVjBmYWNzHaA5z5iSwyu8zBTkp1zpzlo6YPDAHpdaJyushCqC7iJgldc4IgvweM2Ly5TpiuhRIQtvKJXdtG5LslSQVjwbEWMqL7Rsc80oiKl8W9jk0JAm1WB/8r7HPHycjlswamj4xWGBGIuADQQzO2JhATbpsLCV3yEzI/LXYtTvfpkMIwRkHtWQsacnc6jOUJzSLOTGIc/Vq2RtkRmgdqZebN1kyctY+0MdK7ikvEmpC5N8nMU+hZn7uHbN+zMxT/CzDTJdKMl+MY5wbSjcXsHmNeJmEOLFzT0Ra52iI69/p0t7zGTEuYWvIPLf3GJOE6PEM19ZFaSufEnOxnBg01h0zdbH18oniQwihbYlZUuvGpRbn7VXRf+yEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTP+Q9unhJ5xSU1YSAnOpFyvolGKfs3X4bYT9+6bY53E6xVkaKwcVajyLR0RhMFMRcpiAEBCC6Jmckgxsz0l3cW9to1MWEgdYhBWEvKEPF24gxVYiKYjYhotyPC83I5N8fT6SmUOVnOIIaifP0t4kVCzUW8KQE1UyHGBa79UF8kFvOmCOTMVU10Img/REzNTBd83YnQPRCTJX/9jpTx1w4hhLaJXRnSR4mxRZra8SWOcEyIyVgyHm3gtWJbr4jUoS5xPEtya5ayMcRri/WRksbfBm8kQk4kTdiPpjEzKiOXalcx3yBlqJeJ639xisuSlLlKuBsaZHge67a5n9+ZeQp5fpkr549DCCEhBkcJGIAx8xQMMQMlb3rC11TPH2m9MZpYL6QZhIh2Sl+IvXN3TPoCW795g8GOuJvUbQWx+RxjG24KoWZGNV4/cvUaxGis5ceu9+tqjUMWxGykqXCuG7p9wT4xCRuTeTSktp6LCp8BGxTSGI1fOm960rI1K86jbWPvhy4vVkSrZCGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXrOuWrsYvKBvU+sSBNnsm/Z3Xf42Ri/dd29egNir772aXP80h4mNi9IosWC6E26yNYhIUkNI0gTGUJdWa3c4ewMyixLkkzWaQqKDr/TbUiWb9DYER1eRxJTl3OriytIcuJyMYdYt0Ct3HRyYo7vPXkMZWYPH0Csqay2kemtxPqg0hz4yJ8k3FxRdwfXZmdFXutFdLdE2+nP+/cnu9/7YPVkdWDJ3KPIDh4scWxHdExei0cTD8esnvb3kgQ1DGxcygIOcrnTKCXk92IyvgzdeVmLemSxPsj0BNKcmrVXprHz8lmmdyHttXbtms3tKdPKEfGRP7WtSZ8hc1br+ozXFIUQQkvq5cezmGiP2DPO3N/BWSJ1rw0KIYTYPVPWt5lmkfV31FI9fyx5/zctrG+LNcL+ZeK1cbSPMj2515yTtSF5n3AeeeXjAfbRYYT6ss7p2ZowhDJVg71mvrDn0cTfZBcSp/Y5tESnlqeoHU+dv0Ndkf4xxrqXbp3Z1HjessDnUrZY+dHI+n2MhmztgGt+v1apyL5nVfQfOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XMialYihBBCCCGEEKI36D92QgghhBBCCNFztLETQgghhBBCiJ6jjZ0QQgghhBBC9Bxt7IQQQgghhBCi52hjJ4QQQgghhBA9Rxs7IYQQQgghhOg52tgJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc7SxE0IIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0HG3shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ6jjZ0QQgghhBBC9Bxt7IQQQgghhBCi52hjJ4QQQgghhBA9Jz3PH/uff/P1DqOROWpaUiS05LTEHLKzkg7Pizq7l2XnxeS8OMJyjTu5i7BQRGKdu34XyMXJPUex/cE4wn15HBKINf4u24b8Hql763+P3Av500DX4fX9I22bEsosZ6cQKxcLc1yRqv/3/91fZg9QfAD+4a/9AnSJurEPvWnwJRSkTTWxbYsRaWMp6YE+lrD2RGIFaZ+L2jbQpsUG27bY11rX9quqwnrmOdbL1b2l/Rjr4J9NR8bBpiHjkhuY8gyvnbLfI2NHkmb299oayrBn5cfGjvze//Wl31cfXRPXd7awcbjXwh42e+crvZQOf87Pa3TaprMrw7V9ch6L+bqz8WWVOtAVxwr33MU417akCtCX2bqE9iv6Fl09SQlWh8S+e7KiCgeTifromvjN//Gn4M1kY7vc/smPfRzOu1piHy0WA3P8pbceQJmDCc5PX/yVv2uOb7z601CmjnAOWxS4Nrv34L45/pM3vgtlvvvNr0BsIz00x+O8gDLjBpvd7aG95yt7W1BmOMLYVjo0x3tDLDNi/89azM3hoMb1xZ3lEmK/88NHEPvdR8fm+EmJ1/r4rX2IffFzH7aBsxmU+bX/4Z+u1Ef1HzshhBBCCCGE6Dna2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFzztU8pSMCYTAOIcYMcUTE1M4kwJsdhBBCTc7zcmdm3sCMGZgiOXExMCkJVHce4DEQOWTMTBf8tci1G1Z3L7hmInpyf01k65CQ87wRTAgh1A2aLniVd13MochyNoHYbGnLVS0K1sX6qEk/6tx7j1Ji2kNeee0aepLgu0tJLGq9WQvp28RIZFFjJYrG1TXCIS9O8H7qtnDHZFwibbGurYidXTtig4IzHEmIMQPr72DWQlwRWjYukfupa3syM2FgJlK+qnkiD4YXiTe1orC2suK8Rk4kl4+eV4Qbs7B6OVMwZuzBjF/gOtQsjT2r6E85eh8/t4cQsPGTItwG7fnvixmhrdKLqFEKK+iC3nRJrJfXPnkNYlu5nXviYgFl3jpEc5FpvWeOK9L/L17YgNh4aM2wItL/nV/W+xTYj5pgz13WaLCSR3jelZE1M/nQ5T0oM6zwvI3WzqPXt9Hk5eLOJsSezOy13jzG53m4xOeXz8/M8Wcv4vNsBjgnH5EJd1E747Uay0yJoco3fnDXXmdyAmV+DSIc/cdOCCGEEEIIIXqONnZCCCGEEEII0XO0sRNCCCGEEEKInnOuGjv2vTt8fcq+GWcfkjuNCPsmvu1IQu3gk4MzTRpqdTqmM/C/2eE3uPEqe2eiBWRgFZj2gSkUEndEnhW55+CfH0k8TOvA9DuF/dZ5McXvhxfzY4gdHR/Y67T4rbVYH6TLQEJtqqdhCa/dtRKaKJu1RZ+wGPtVSXScBfluver8tUjS7ZIkA3fJuaMYh8qGaB28ZpBp7Kjsx3XbjugVWALm1bRHeB5Lfux1NwnRysXkHabuvIyOE2JdRBn2B/aO4TwSe37ab14KMmOT9kTn7RX0pTwx9/OrRWVjqyRXX+XiK5cgWvzUaatWvT8ulDSHTG4JCdHJlRqiRxTrY/fyTYgVE5vg+vAZroG6HdTm3XzJJjK/93tfhzJZjJqwNPcxbHcpWXu2Dern/FzHNOdMrndtc9ccv7S7g9eenEJs7HS3KVmmT57ieY8rOzb+4BDn+3dOMdH4vlsXfHJvAGXCGJ/VgqxNFoUbz8jAdDpFj4k3ZrY9eL3+/x80AwshhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zvuYpK2TT9MYJ7wdRABk5o5KYycDpttWeR3O9Us3yCsYF1OQFRcqZE0A3JNFxScTNkB+VCV+pM4M3v2DiamIYE5xRCTF06DoU2nYkeWW1sMk4y8UZlJnNjiA2P3tmjpel/hbxQiFC3861s4gkz447HEqiyrapjvSFLiIJyv0YQPpVmqBUO8+IsYfr7ySveYhJwvXl0gqsWZ9JVzAgiei4RNqwew4tScrOLBe8mQkbY5nhCTOaiHxCeTKUJOw5uPGlrZkhlVgb2RBCvn2uas/hk2ezdh4TexHoy2yyZWZJpA6+za7oJfYjrmZh9+PvpmHz4QoxNtW2xFTCJ3Nn41n7Ac1M2GnsWlj3lR+y+AB86+27EHv7h9YI7ugZJs++fgnXYftH1kTj8XwGZbaGOB82zsiLOaPh7BtCRIzJcteGU5LsPKvREGQ7swnJuxbHrtPZAcSS3N5Hj2wGAAAgAElEQVTP2RLr/miKa8j7ha3XUYO/V1RoSpIN7Vo3ztA8ZTjA53JxGxOu7+/Yce9givfXFmTd7Aa+6seYRrVKFkIIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPedczVN89voQAgismai/JaJPr93umGkAMWvwpVqyt02IOQRzVOlcjIlOy/kxxE5PnpjjbHQNysRbuxhzb4sZxpQVijJbJxf3JgkhhNDVKOSNEvu02gE+l67F8xYzFPcuZ1boulhgmbOzE3KtqTmezfEZi/XB+igYLJDzuLGHaz81tru6QSGzNwViZgqJF4aHEAakFv4nmW8JMxIY51ZM3ZHnwrTNfhhixhMJMYyJ3f005Ka530H3/EL0PGIG5QqyUZCZp8StvVbdyDzlRTJ1RlQhBGh4zESH9VE/tVJTnZVipF1QIxZSL9+3qPkH1mEVA5CIlXGVT9igQNYA/lJVjXMRMy/zMVZrbnr2fLjnyvOvJe+UF8vjI1zfvPXQxg6e4trp8OgRxK6d2fG0G2EfurLF5lFrABYnpCOzuZX2UdvQsgR/7+JFNBwpnKnaw1OyTh/hWvfAOYfMCuyPE2L4t3HRltvZvAJldsi72ZzbZ5XkYygzL3HczYYbWIeRXYOfLnGblcTYcVv3jJmB2qroP3ZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0nHPW2GGs8/ovkgSbJVb03/gz+R4kBg2oS6M50+mnrSSBd2O/y20KLDO5/z2Iffebv2OOk83PQJmPfu7nIXZhb9sGEqaVIxq7xn7L3ZAEjcv5FGKJS8g4DPgNdUS0ecspJqqcOv3crDiFMovZEmJ1Ye+xJNo8sT4allzaJR8mEjEq9mi85pTJXZiox1+a6s0wltBrPV+DVrd4ntca+u/fQ+CJ2iHZMtMnsSTw7vfYrZBhEPRJ/Kt8pnVCMqc1InnbQyCJaTv3/Fo20Iu1wfRYvt2tIC17P+aOaV9jOnQXS5nGnWl1SNtonSazJW2MNWwfotpf9hz8MelsEdXB2uVSTvo/kzGV7v4aMt5wvSAVFrpDVvdVrr9qCnvxQZgTL4AksuunV26j/itpsK+dnNn11PX9fSjzsQ+hT8PY6bhSoiWtKqxnQTS889nEHF++8gqUCUPUyl2/eNPWYWcH67lJ5t/CrivrCdbz8P7bELt244I53t6/DmUePXkCseMffN8cLxf4e+9NJhB7cobPKkrsWnqY4Xp7XuK6uSrdGgBKrI7+YyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXrO+ZqndCSJohf/EjEw0xW3rUvaSH6PJR5FwTW7OBNvkwTFLhn44ugelHnwxjchdvdP3jTH5RCNRJoRikw//bN/0RyPhyhELxZ4rbq0z72p8T3MSFLxrHYGEsQ8ZRChyLReYlL246OH5nhRo+i0XmK9fP5q/8zFeqHGF64fxcTgiCXiBpMC5t5ARPxghESNIJhpADGV8AE6lrDE6W4MiHAsYWJ078MQkfPY7/nhZTUDBDRPScjvRaSeCXlWmSvGEkwzswYYi8n9iTVC+5EvQtpmzDqSN+1h12bmKdY4BJKMh4CDdwiho8nr7W/SOqwQY1emY4dv16TuHTNLc+08Sph5Cj6rPLGxilybdRnejaI/5ejfs0L28Q+aEF2sRpJigusNt3y6eX0bysxP0Xju8aF9yw8fo/lHE70Esc63O/LOmYnHGTHSu7pr7+cTlz4HZaIprllHlV2PlmdPoczs/gnEmsr2re3NC1Dm1msfhdjw1ZfNcbIJRUKc/gBii8Mjc/y1N78PZd46wuey6HC9/cVP3zLHs7MtKPP7b96B2ONDO17WxIxxVfQfOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XPO1TwlIgYLid9bEnF+CChS9teqOyZIxmvFsb3lmAiUOyL6Lms07chcvYpTFIY+vPMWxKq5NQ6plw+gzNM3vwGxuxcvmuNr1y5hPZco8IycwYk3OwghhPkCRbtpbe+vapdQZhljLDQoKC1mB/a8GuvQFfiMq8LWqyrx3Yj10TbEoMOZFFADDda3vZMI69vEbKBzZgbM4CgisYa4DXgvGObxwHwEYlevjtgUUJsJZ1TCxjx6z6uYp7Q4xjW+L5PzcvLc4d2EEEJnr0X9MMgzbl3BWuYpLxZmauOOuVEK63/+PGISRP7+GzsTFGa61JFYHONcnjoTkjTDZUmSYsybl3Tk/qhRkat7U+Gc0takr8E9E7sWYkqUOBOLKMVnQPxUQtmRumMxZAVjFG5kJdZFkmHbiFprVNIscL2TxmRJ3k3M4cbmLhSZLPG8Mh2Z4zwfQpnxcB9iL4/2IJYN7Dpv+Ue4rn3v374OsfbRXXveyTMo05TEEM814WqAxn27H3oFYuMrdo18+wufhTLxNt7zafYJc/zHMZr73Z1j3X/pZz8BsV/5yBVz/Oa3vgNl3sgziBVb9h7LVOYpQgghhBBCCPFnFm3shBBCCCGEEKLnaGMnhBBCCCGEED3nnDV2iNdnME0KJDEPIUQgnsFEiw35Rj3JN8xxF+H37g2pQ9HgN7ed+zR4dogau+OnjyDmv4GPaqz75MF3Ifbe6y7pZf0pKDMaYUbGPLffexcl3stijnUYDu03v1GM30KflZiMPC1RY1eWE3cMRUK3QL3eYmavVSyVWPW88U+8IRqOhGnQINE4nscS5foQk4zUJNjQRMP++PkawhBQC5QmWCbzGb0DG+PYEPv8evpxMYTA1Ho0EbWnIXohSNIcQuh84nky7rL89b4YG1PF+qD6KAjh+2WySt9+QPMeQkhYv/X6MqJli4mWLCdameHQaoGyHM/rmC7V3WNHtWXP18Z2pO51RfTeSztHlkssA5rXEECsGic4JqQJ3h8bZ+vG3zP+HLvn5484Yp1cHJD+cNGtzTrUWc1IJ33pmj3vE7dvQ5kbOy9DbDS0WrzxDmrLogiTijeP70Hsyb/8l+b4e7/xz6HM06e4hmydXjYab0CZsIGawchpDVviHfHet/8dxAb1zBzf++ZvQ5mP/qW/AbGfuPQfm+PqGr6bZvoQYm8+xjX/t4d2XJhXOP9uMc1gY9/9sx9je6b/2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zruYpTUlE2ImNZVT0TZJSe9VwicLNgpiS+FtOiCi7JUYsVYHGHtPDx+b4zg/fhjLLJYqpBxtWDJuS5Oqz2QnEHr39J+Y4SzHh5OXrVyA2GNp7XBYkAXtJRPPu2ZTkGTcsafkSk52XS3uPDUn4Xi3OILZYWDFsWaGoVawPluQXzH7I34OYCYr34mCC/ZYYHHmXB2YWERGPAkbiHFw2BjmUGUQYG+fWqGhzQIxSSNvvfL7wBK8dpXituRtfJnPsQwtiZlI5g4q2JcYJzGiGuKA07oUxHwhqRuGTWiv38QuF2V7EK5josBeTOJOghFy8q0lDcG2KJRDPBzg/DcYjiGWuP0TEQKJbwQSFuQsxcxEoxsyTUjJOjOyzYuNSuSRzpDOaiQI+T/8eQgghJUnmWzfAsATsHO9wJPOUF8mYdMfhnl27PHmGxnMHp2gS8up1ay7ysUsXoMzOpWsQ65x5SkT6Y3Xn2xC791v/BGJv/Kt/Zo63ilehzCuv/W2IffrvWIO/0S2se3ztBsSCMwkKz9Ck5PS7uN5+57e/ao6P3/sDKPPtL/1vEPv4563J4WduYeLx0xtfgNhXH6M5y9feesccj2vsaxtDHAc33bhwb4FrgFXRf+yEEEIIIYQQoudoYyeEEEIIIYQQPUcbOyGEEEIIIYToOdrYCSGEEEIIIUTPOVfzlNPjxxBLNmwm+iRD0XJVotFG5DLaN9UMyiwXEzzPGSUkNRogzKeHGCOZ75/csWYm9x/dgzJlR0TYW9fNMdFRhubJfYgVE/v8Dt79EyiTZyjoHm5Z0WxNjGDqDptCU1sDl4iYyjQFxtoCBd3TiX0XTUSE4RWayDjdeeiY2YZYGx0xYfDif9LEwPCEXYu9O+bVkiTejAPrxMwiEtKGB6n9gYs59sd0gWZCyempOc4a7DPNEs1+0uDMTGKsUzYaYGxoy6Xk/mYpDhSz1vajRcP6EMaY2U3rrtUSfwXSbUMqX4ZzhZnh+HcHfSiEEJPOlrpY5AfcEEJLYlFi23mWY5tOBxiridlHtbT9D8x4QggpMWdJXB1Ym25qbPtlZdcTNTOHIWYtWWbNL9IczShy0mlKN0c25BnExDyFGbvFzvqFzYa0/8nQ6Fw5Iu0uVAtz2GD3CNMU55lHrW2vb9b41q+f4TrsxmVrntLM0Izjzu9/CWJv/e6/gNho85L9vS/8LShz6Zf/K4htfA7b9Qfi8h6Etj/yMYj95KufN8ePvvYKlHnz3/06xL7zzf/THP90jp3oZz/81yF2WHwPYtH8wBzXxIVsSYzQDud2PXFKjJhWRf+xE0IIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPedczVNOlicQuzC2guSKGBmcTE4hNrrgxJTEBOVkcgCxprWC57TCOh0fonHJfIbXf3rvDXN8dvgUyrTZPsSy7RvmOB+hUHNU4u9VS2vOMj96CGWOnuDvjestc9yRa0dEvF0srHgzjlAQ3NVE6N6iQUVT2mvNCjTE6UhzjDNrGJGROoj1wdpBwhwzPEQM7AX7zIyDGTrgpfDENMG2skmMUQbOXiA6xf5eHqHJUlJawXNb47gUamzDsftbWUsMEEriPpNtWSV9NtiGMuNsF2KJN3SIF1DmlBi/VMwQw737JMW6d+Q9N50vA0XEOiEdqYt9DN9TTExJfKRj7YJUIUu9eUoGZdoWx+oFGfdrZzTRMeMSYsSyMbRzeUTGqaLC3ytK25eZH1dLDA+KxhbcIHXKyBiUuGfaljiWdNRQhbwvF4tIZ2PmOuJ8OT4layxnnpLkYyhz4/Y1iHXBtvPXHxxBmcEeztsvDy6a44Pf/waUefO3vgKxrME6vPqZ/8bW86/9IpRJXsP26v3wCuJTRLrRahsTHHJC9ik7R97a+zUos3vlMsS++uv/kzl+/ff+DZT59HU0a/m5z70GsW989V1z/B4xjTw4w/bxzmNr0PgMi6yM/mMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEzzlXjd18gQn3BhObNLEmKTdnC0wYHpVWezVM8XvlssBv2SeF1aXFNX6vfPgMv4mtpvit/tlTd26N2oDx3g7EfILiKMd7Hu1egdhyYhMYzk9RGzR99h7EQmK/KWaf4EdEC1Q19n3lQ9QPRF5gE0LIAr7nqrLvoiZajjghSW4zp4lM8D2I9UHVGU4H19FSGIPeQPR0LYl5qUxEkp8PiZZlh+juumM7dhQHz6BMRsalyHVJlmaV6c18JCICnq7EWH1q+0dKdAf5GGuRJZvmmEgkw4I8Y/r5visWJ3gxpvtpnPaoJWOCWB9Mgxa78ZsnKCeNw2m7mD6LJc9OnbaT6TFrom9jicbHAzuX1w3O20sv1gkhVO76SYLPhSUfj9xzGI6ws0VkPFsWttc0ZA5LyXNInB6RJU1nidvDChq70JDzmCjSvVflK3+xXNu8CLHBpm1nTYwisb0G3/nRs5k5Xp6g58SFz1+H2PzA6snv/NZvQ5n6Ia5/r38O9XOXvvjz5vj1AfaPe1/+txBLIuuF0W3jc/nYR9EX4uaWXfexRevdB7gvOD62z+alK6gX3P+ZvwCxD9+zvhp3f/0fQ5m3v/wbEPvUf4tJ2V/f/Yg5fvf7d6DM4WQKsbPSjhPLivTtFdF/7IQQQgghhBCi52hjJ4QQQgghhBA9Rxs7IYQQQgghhOg52tgJIYQQQgghRM85V/OUqETB4PTYGhfMiWA4SlA4uTizIslmiLcSkQSpx09s8sDl4hivvZxBrD2dQ2w5seXiBJMK5yM0T4kiK7puiFg8kOSVWxevmuNyiSLa+REmSY8SK0Qd7mCi46o6g1jhEjC33SaUSYg4fUaE7qW7Vk2MJ3Ki7Y/c3x6oAYBYGzRhuBMuM/MUlijXX8obPIQQQsfMU5xZyiBDkfmQmEM0U+wP5aE1S4mXaBuSMv8B1z5TZvxCnoN/NquaroTSPeMWxxsyNIZk6CqfbUCZjRzNIZbE4KSBF4a/B842IYSu82OAzFNeJMwgx7eNiLRXik/EzcxTSF9LnVFRlqKZETuP2Xak7oZqMh02Dc4prTccYfdMmmLqTIHyjKwdiIlbU9n7oSZSpJN68xRWTWbWwt5g4scXUqhVgvL/4Dw+wfY6qm07m83RtOuMxMq5MwmqR1Dm4u6rEFvet2vdo7e+BmVuXyXJyP/qL0Ns9ElrevL13/i/oczWaAixK5c+ZI6LCNe1vv8z7r91D2JvfecuxPZ2rFHg/advQJnxx25B7PrP/jVzPPv6D6HM2++hOUx7B9/z/vVfMMen4ZtQZlbiHqPp1vd/Nv3HTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ6jjZ0QQgghhBBC9JxzNU8JBTH7KKxSejBA8X+XEPF/YbPOpzMUOxczNASZTKyZQrHAMlWBxgXF0RHGSiucHO7sQRkWK4uJOT6cHEKZbPMCxPZ2bWyzvAxljh8+gtji0BrEJDmKXLsETVC6zj7TsiyhTNsQMTz5e4HXczN9d+fF8CGEsrRi4piI2sX6YEYCrXtXrTdcCCF0LToeeEOVNEWRdEvMBmLX7hLSLroFGjGVp2iEFLk2G7WrmUpEzrrAH4fAzWBaOG81nF9MiMgzbpY4LjULa1oxHG9Bmb0RjqlFg890WtvxrCUmL8wkJ3aGDn7cEOslJmYD3iwFjEVCCBHta8832mCGSokzIPEGISGEkJLfa4jZVlnZsWNZoIFETaqZJdZUKYqZAQn+nndU4WYmBFeQ+A/R8cybtTBTmagh9WR9LXr++MJi2JVXHZnEB+H1Z2iOcdGtUWenaOR1NMd5tCrsuHx1B+foeAvXyO2dd8xxR+bHq7/wSxDb/PznIBYGdp4ZTCdQ5Je++IsQG42tUR9ajYQwxKqD6dj3/+DrUOYTV25C7MrH7Jr4wZe/CmUeHZ5A7JXP/6w5vv2L+Aze+ke/B7Hy0QOIXfrL/4k53vmD21BmcoTrdD8s5PkH357pP3ZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0nHPV2GUZfoffuO+HlyRBY5wRbUBuv08+O0adWr1YQKxYWJ1KucTfCxXGihl+U9w5rUO+hckX2xZ1hU+f/sAcP36K1043MRZuvGIOt3bwG+MxSdJcnFp9YHGC3+5nm5hcPY6dxm6JGjuWgrmLUC/Qdu6ZdqslhfUJdBuSdF6sD68HCSGEAFoyfHd1ie+udUmFmaqD/Z7XwXnNXQghdA22xYrp/Fwi5SjHZOcN08+5GNMLgTAuhBC5MYHpk1jyaP+IE/Z7TAzktA850c9GOSaPHlf4rBYuKTp7Luye08hev23JmCrWBs89bsf0VZuPnwmo5I6dR9rBKicy9WXl5ttlSTR2Aftt5jpNFJGrk5v2pfgtEx2cvxQ7kTxA0tvxtJVKkSi5P3atDuqlJOYvkgnRys1ObctbzHEtczglc1hh39WtXSJKI2uu4s4dczyOcN4eXb6Kl9rchJifbZ8Srfr3vvMOxHY3XraBa7tQ5soN7GvemaKYo7788p/7KMSGF+w6tv4o6ttmM9Q2Rtft2n3j49ehTCBz6+m7b0Fsf/tvmuNP3kAvjOW7mGR+4Z7ylR9je6b/2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zruYpw62LWIHIJho+OX4GZU4nBxDLMit27ApiBnCGRiJlYRNH1jUKWBsirqyWKE7NRlYUOdhAA5KSJEA/OrCJIpsS61CeoBnM06FNPrxx7RaUGW2joUoze9cdozHLcIDi9Ghgm0fdomFFR0xQmogJyF1S2I489xqv7w1VqgLfjVgn+O4Sp5OOiXFCTBKbV+5aLbk2S/ydusTDGfm9jTEm3R7s7WMsdolVB2hw5A1WQsAk0DExeUlIouHEJ4+m5ikQArML/wxCCGFAYp3rD4s5Jm4/mmEsS8k9++ewSkXfr4Uro+THLxI0wsD2GZP3FJH30rl3zIw3mM+GrwOrE8mKDYnNQwhhNHJzOfl784zMkX6+iFPmGEOq1a1wz3Ss8sf0wTw3xA1qVjNZ6lYwPaHmOr4OSlD+QqlKXI/ee2zXXbM5vstlhW04L22565euQRmcGUIYunF/TPp/PiRnkm7kbQgvfeqnoMwfffWPIHZ1ZNfg9+/egDKf+uwViH3hpl1LpxExWXvvHlb0krVdmW6g0cwDYuH0E27sGL/0EpRJR2gqM5xjIvorY2uM8uc/jNd6+D1cv8yD3WNs/Rh9VP+xE0IIIYQQQoieo42dEEIIIYQQQvQcbeyEEEIIIYQQoudoYyeEEEIIIYQQPedczVMOD9FIpDy1Zil1hYYnVYHi/2JhTQPaAn+vQY1kiJwTRLtEw46KZLlvOxRvjreteUo2QHFltUCjktC4a3VozBJaFIu3tb3nqEGB7mi4C7Fyw8YWJ4+gzHKK7yZPrdmNNzIJIYSmxTokMQpy286eGxExbEuutazti20afC5ijVDBvitCzRuIkYiXdJP+GDNRtIslSQ5ldohRysu30Djo6mXbhgdjFFODaUgIoXPmE8xsgJm6xGBSwMwOSMxdKiHPJWvxGVendry8++67UOaEjGdJjPecOHOWlHqnEFOOyL7YtD3XaeXPHNRUw4WomUmLHRCuRd55S85raztntWxcJtMaM9/xY8AwH0KZuiWGZo39AW+K8n7FiJmJG4haUk/m/wNXooYxJOSfHzmPGrGsYJTCYOOzvFLOlzF5B365VjXk/yoVvqi6tte6f4hr5Jr00dqtIdnYzcyMGL5H/pXPfgbKbJHY1C1/v/Svvg9lujfvQiy9+pP2eIBjQvQSzveemCw6chKDp3DhAp6X4ro2Y31536638xtodrO/twWxMrb12iQGZ6ui/9gJIYQQQgghRM/Rxk4IIYQQQggheo42dkIIIYQQQgjRc85VDFHMUcc1OXpqjpsGxXIN+X44dFZ3t5ijPisb7kFsNLLJA+tTTIheL/Ab5ihD/Vw8stqf2Rx1ALMZavha+C6XJCft8J5np0fm+N0K7/nyBUz2OL50yRyXBd7fco7P3Ut6sm3UJ3UB9TspSRRb1bapVRXTexCxQ2efX0uFG2JdNKQt+u/3O6Kna6iWxZaLU6KnI7/nNT2k99NYqFDnExc2tWpdYkJR9vctGHOIrmlRYJ/JnO6nJQIemmDaxSIik6lLvFbl7nl6gpreqsR6siTGPuE6+6sfO69zY9UHUwaJ1aFCLnNYkw6SkJfndaLs/XKNnW13TYNLiUVF9Ouk6pnTuDKNWE0E814fmBK9UJfieUVh580ZSTLMcp03ri8zfVLUYB9t3LNiz5MnI2d1aP/U4x91HvwezZIu1sXeCNdKm2PbH2ryzhvS//yc/HSCYzxbIkebO+a4JPq98hTba47LyuDlZah4Izq1EILL1R1evYJ6+STgvB25G4qYFvDoBGOXrTYuJc18k6xVYPS6T5KfVz5NewhhgFo8fzHmJzHa2IZYV9rnUNTkRayI/mMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEz9HGTgghhBBCCCF6zrmap7RLNCrpGmuospijQLFticjUJa5eLNEgoFugmcneyJl4TLFMV+NjGexchljrEgoePMNEi8uzI4g1nRVJsiTGNJ9oZxWsXYrCzSIbQ2y0vWGOd67hszq++xbEyumBrVNyCcokGZHREuOXrvNiexSGMpFpAkk1qW2GWBNFTQxInMgbzX9CaBqMJc6YoSPGNxERMvs33nX496ez0ycQe/MQBc/3XHOJiTo9IsYMjbvHhBi/VCTxd+YSNUNy4vAjzBNcLGYJZ0m/ap3xRLyFouzaGUaFgMlr36+ET/hMTCxqYuBS2r7M7lm8YOBdkTZNEtz78ZWZePi2GUIItXvnaY6mCCkZzxcFzrdFa+ejmPy9mfW/gfvNPCMJhMl5fr717TeEEFi+9dglDM5XNE+paz/fE0hGdFbOj71s7cDO81fvlLH8hbI9xnXRh1+2sXsHOH8cHOL6N4lsu65IH2rJuNxc27fnJdiom0c4j4bJFEKzfWse+JSYgt1aYt27iR07ZkOcixL0JQxtZvtWGePznG3sQGxQ2HvcOEBjpJis+ZOZm8PuP4YyiwrPCx9+BWNLW2528BSKvH2Ae6GjiTU1LJbYPlZF/7ETQgghhBBCiJ6jjZ0QQgghhBBC9Bxt7IQQQgghhBCi52hjJ4QQQgghhBA951zNU+oChYxVa0WLLTHVqIkxSl2584h4NErxWuXMioabOQoiowwFnhs7+xBLNgbm+GKMZiYt8RZ5+NgaxtR1iedFuOfOM/t7V67cgDJb2wOIRZEzZti8CGXGe2jycvrUCjy7xSmUSVMUsAYirG2dGj0ixgxM9u3NGiLyXMT66Ij4v3GGCk2MpgHM8ACvTUw1iCFIlDhDB2K6Ui9QqH12dAixyaFts1GJ18pJtdoVzAUi0l79k2FlmHGB7w8pOa8hj7gb2QFmNEATi5YI+auI9DX3LmpimlHVOKZWrs0k6qMvFDZ0etMT1npbZr7T2ZJpQt4dMxdyhiD1EufRwRjn0b1NdEoovOEQuUFmgpIm1lQiIXWnhlzO9MSbm4QQQkdMnXy1mFFKVWH/aOH5kbdD+gzrf2BcRZ1YEH+eN8QS6yWOcE336o09c7zjTO1CCPUG0zwAACAASURBVOFb5X2Inblm0CTYxo4njyB25TOvmuP9L+PccHbn9yA2fPJzEMv2P2OOuxbv78G7r0MsuDXk1s3b+HsXr0HMd4dL1z8OZd74Y3xWH75t9xjVE+zb127fxHo6D5nDP3wHitRkHbL9yU9A7PTQ3vP9t34AZY4nJxA7dKYuU2JGsyqagYUQQgghhBCi52hjJ4QQQgghhBA9Rxs7IYQQQgghhOg555ugvCUJPRP7HX6SEW1JRRIflu4bf6L9GMSoN1vOjs1xWaI2IN7ahVi2gYm/06G9fjZATUGVkG/Zn7lvg1NMrBoRHVPntHJpTpI7x8/XBoQcdQ7DvVsQm5/ZRJV1MYEyWUfeaSDCQpdkvuvwubRE14AighVFBeIDgQnhUUMVkTIteS8++Tj7KxJNiOw0KFVNNDcd9u3NAfbbkFstQFvit+0DJv1z7Zpp5VZJw83uj57pOik9LcZxIt+wfTkd4XM5I7rlBdEkly7JvH/vIYTQsOTj0uucKyzBfez6TEtlXBhsXLvOyLwTJWROcW2jKFEHz/rHYIzz6MbIz5tkLCG6NH/PHdGNMr2e15elZP5tWNt3icyrAnVGXvsfQgiNGwf53I73V5O6+/7HtcDk+UV+/tU8+iKZTIjmNLXeCjd2rkKZ5Uuo//r+O1azVQ/xnX/73Tcg9mt//j8yx1c/+uegzL2vfBliyZd+G2LXrlq93s0L21BmeRG9G2rXFncv7UGZ0RauR/3G5BOfwfXp6RGut4euL1/+FNZz99IWxGZ/+H1zXH7/O1Amz0ly9ZvXITatrM7vYIbeFIMOx5eR0y1mW/h7q6L/2AkhhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zruYpZYEC69DaKgxHmAg8tChEXSxsrCOJSImWOpQzV4cIzQZGe5hoPBmssAcmAug2w3qFgf3NnfFLWAQ15uFsakWYHZGns5jXV7dkP59tosh0uGPFsKdPHkKZ6ekcYpsXUAzrdfsdMQCIYmyOnU+qy16qWBtMVA8x8u7gPQWeJBkLYSmfsLhkiYcTNDzIYjTtSQY2ltbY39s5miB0rRU3e6OGEHiiaH/X1AOFGSX4WIp9Id3dwYvtWsOYU2KQcUSSuU8rYp7i3qt/BiGEQLyZIMl8rATlLxRmyOMNVWLSDiJqxmFjJRlfWXJwaPwkiXlF5ntm/JI31iQgzbFv+6TiIQT8szRpmyzRuK9DzepeEWOUpb2flhilsN/z2ZY7Yp5Skb7GjIo6P6LwbPV4HhTTPPoieXyI73O6tKYaw+PHUOYMTwtD1x2u7eI8tzXENhWPbDLw7CN/Fco033oLYnf/9R9DbLz7wBxv/100KhtdwXVs2HdmMGReW+X/S9u72Ka3dy9hQd9l0LckhLfvQejwj/6pOb5796tQ5qd/5W9ArLmBydW////8c3N8h6ybdzfRGOXChn3R0eCDb880AwshhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zruYp1RIFyZUTGzcNES0TcXPjBMJRgmLgxJkwhBBCV9lrpUM0ShlsYaxJULwZOwFyRKwS4jSH2ObuFXO8tXkDyoyGeM87O9Y8IWOmEkSEHXnxNhFOE81u2NizRjb1dAplZicnEJuTWLZl655k+G6iCO+5LG17oOJ0sTbqCt8LvhVi0EOE/o17VeyvSAkxefDmBm2LYwLrazkZzUYb1hSoI8YCUXwGsaawsboiRgYt/qA3WfHj1PtlkDa348RwF8Xp8S4aHJ25MeCowGc1rfGd+nE3BDTAoeMZMUZJ3TtsWfsQa4MZHMVx7I5JO6emGvZdlcQEKcRoZpK79hpKNEphY3VNDFUqZ0KSZMQ4KMM6pM6IgRkVteRZtY2d7WrSP1gs1Pa8jvxgFDOjJ2tsUZGxsiRrlZqaVPmLYx24MYpbq5C2INZHHeP7vHdm2/7yGI3nAjEB3B9vmONXiYnWlREaqoTE1mH82Y9BkUuv/yWITf/4dYjd+c1/ZI4/kn0Byoz+81/GOuQbLvCC/5c0s2NJ++Y7UKT96r+A2LNv/oY5fuXzn4cyV//e34HY4+98G2Lf+PqXzXGd4Ht+9YZ/LiF87pMfNcebow/+rPQfOyGEEEIIIYToOdrYCSGEEEIIIUTP0cZOCCGEEEIIIXrOuWrsZktM6FlX9rvjusZk5MuKfJfvvpPPiQ6nmKEmzCdkHWzsQZlsgAm2uwhVaKhJwW/iM6JPuHrRauq8Vi+EEBKilcsT+7q6EhMPRwuSbNk9q7Yh76HE594s3fVLvHbckmst8Lm3sdMHbuA3xm2H50GSZPINulgfFdOWEN2IhyWc982aaTu79vlaj44oQOsYx4RlhnrWaHPLHBc5ahHyTUxa7pMmL56hDq+tsS36AZUlI45JktbhjtX1dkRPNyHP4cj10RlLtkwkNy3RUnntltfmhsATtXuhj3SwLxb2XnxS6pjorxmtay/szRVE/xVcuwbNXQghqsic2ZJruVhT4Hkt0Y6WvimuqBvz7ZxpFiEReAgh9rp+Ni4SbWPtdYw1Gc+o5hUvDxI7ds/kRCwnjd2L5Kc+dhtif3LXJiS/9wTXb0xrOYrt2mxW4Fqt84L2EEIze2qOk1uY0Pv63/uvIfYg/icQO/v2b5njN//3r0OZj9eY+Dv71b9pA4MtKBPluEb20PUFPr4w+533zPHDP/xfoMzDt/81xF75mZfN8bV/8A+hzNkxjnFf+R4mc98b2LXJlSu4vvjINZzff/Kjdi+y8RPYhlZF/7ETQgghhBBCiJ6jjZ0QQgghhBBC9Bxt7IQQQgghhBCi52hjJ4QQQgghhBA951zNU8oKBdCzM2tK0LQoDC2ZcNIlBx50KMBsSUL0gRN5jwe4t63OnpE6YL1aZxzSkvvrSjQXacDMhCVSJ4minemJF76HECCJagghxE6cHrHEpyQGyV0jlticJZjGuifdqTlOYzRPmS1neC13zM0bxLpgxgy+HfC/BqFJSJbbWEQMO5oa+4c334hJJuCmwdikw/533Nm2mJL72xyjkPlibhOEj8fErGWKY0IauyE1wXEpG6KYOoxs7BkZS45m+HsLN3aw1OA0XTFL3Oyee0p8iljva5zBEbu2WB9xQhKNP+c4BDT/YCVZfnJmXlS68xqylEhZovGOGPJ0bj6sWCWY8ZKfn/A0in8OzASFjBOdM0bpyPOsydznzVIadi/khbGxOFrB+IUnol8ps7lYE12N4/e1XWvKVy5xTjk4w1jd2H5UE0O+JMG+ljS2DvXpEZRpL6GZ2K2//VcgNt68aI7T7/wbKHP/f/3HeN4/c0YlN1+GMun16xDzTXg5w7VhvcQ2/Oz1H5rjERkrP/TZ/wxit//L/8IcT8la9Ouv/x8Qezj7IcSuj+0z3Sfz/dbGCGKzxI4T1buPoczepyFE0X/shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEzzlX85TjI2JKUluxcUcE+1mGGd8Tp66MChSdNsxIxAlKp4/ehTIdEXi3gZiZdM83DeiYGn2F32N7bhC/xygMjSJ8gHFkX3OE2tvQkmslsTO/IOYpCRGnhhSbVZZbQWlDjC6IHwaoaP0zF+slJkYCbeufOb4odl7mzBPqEvsoXjuEyF2fmQg0xOyH9ffWGa+0xFhggv4tYeHK5aR/1CzmDI3YAJuQsapeTu0xuz9yLV+Km6LgtdgzjWNniOHNKcKP6H/u+sy8QawP5vXhDY7YvOP7QgghdM4Ehb87YpDlfq8hZhEVaWMZGyfcPBPnbA5jJl3uHqlnD2nDvgQzGyHneZOgusYytN+6Yh19D4g3SmGs6lOE5Z6/LhEfnCcLNN94JbMLrw/d2ocy33p6CrHDmW0vSYrr4XSAZhy+DdcFXntyeBdiYXQNQoOf+1Vb5DbWffDeFYg9/v475nj51ntYz+/9AGJ+LpqTYanbHUNs9BMvmeMLr/0FKHPz5/8ixE4q+wNfe/MbUObdx1+B2HaCc3mU23dxjxixfOPtRxAbPbXvYmsLTWX+vsxThBBCCCGEEOLPBtrYCSGEEEIIIUTP0cZOCCGEEEIIIXqONnZCCCGEEEII0XPO1TxlWaPQ0O8tO+KewkTKnbMSWBRLvDIxJfH+H22F53VMLE6MEkLiHl9CnF8SYlLgYxFmps/SDYhtugz28+IMylTk+UXO4CROsU4pEZB7QXxLBPJthGYKTM9d195oBs9LyPPzAvKmluj7/HEGJMRUI4mJkUBpDYeouQl5nYnrHy3px6z9eEMHBi1DYlPXituWmMoQI4gmt2MCM4uIWuLW4h5EQg1qiKmEO+46NHliPbJpiLmGqwM1eSDvPnLvJ/PjolgrzEgEmwt5d7R/eKOi1c7zYwC9NjHbahvst6WvA/lzc0raVOJMuqIVDZW8KRB7Ksw8pXPXZ2MJHV7c/a1seLJKjPoUPf8HZHD0Yhmk+Hz3ctuw90dbUObd0wXEZqVtw6MhMRMkhiqRM2uJSV+YzHH9e+fhH0MsdaaD1/aGUObyS78KsZ/8pOszJ3h/UYXmIqGzc2Q5xLlv4yU0F4muXjXHizH2hfuHb0LsjTe/ZY6/9/B1KJMtjyD2mQ99CGKHC7vPefR4AmXePsTnMLlj73lzgHP53/8HEKLoP3ZCCCGEEEII0XO0sRNCCCGEEEKInqONnRBCCCGEEEL0nHMVQ3RkHxl5jR3RcFQtfmsaJfab4uEWJkzMNvAb5gy++2cJTEkdiHal9jocqn0g+iBINI7fD2fjqxDb3t42xyf3MYni2QL1O43TB0ZErzDMMGt529jvgJm2iukRqdwiVCsUIgnXfTFpA14oLXm8Pvl4Q3QyPnlvCCQF7oqaFK//Yq98RZlKiCKvpyF1oJIlWy4m57GW6OWzRGIbaqIT7fwzZeMGq+gqsGTkCV6rdnUg3Z0muY5cLA5EayzWBmvDkdeAE+0q08H5GB1eqSz1+YnNqTaP9Brf1yJS95oEUVaI165II/btnLHKY1hVKwfX/jGmMP+b9FpMB8vFeOIF8druHgZLq2c7JGvKJsF2PhzY9prGeB7TrobUjsMt8VaIyUBRHT+G2A8e2iTif0hk4pd2UPN2cdeuY/euvwxlbr38CazDYmqOD48woXfbkLXuu+/Z845/CGXuH78Fsa49MMej+hjKXBvjGnmXvItJauuVohwx7AzwWmFh39eAPONV0X/shBBCCCGEEKLnaGMnhBBCCCGEED1HGzshhBBCCCGE6Dna2AkhhBBCCCFEzznfTLLUVMMZJTAzDnJiXc/N8ZIIQ1k69NYZM3QNih87IihlhioQoxlFMeh17g1LKp6S8zKrwqyZYUWLScQ7SEaMySwbplj3AvmUJEgm90wTynuhMBPyE9MFb8QQhecL38UHJyXvuHF9hL1zZnrkOwTrV74vhBDA7IN5hjBjBvZXqqhz/Z36G+E9J65iTU3Mk2pUN6fRyB4nKJLuyO/VziCKGl2Q5+fLtSRxOxs/mT2Ef4XsWuw1x+4eO5KUXawPZlTiQzzB9gd0+1iJ1doY7e7+StQkiN2zb2dsgUEM29xcx5OKPz+22h1judXujke90QyDGZrBtT6o84tYiXJyBrHaT5wdjuf7WzhfXN6288XNDfy9zZQs5eEdk/kjwjksIePLfGb70Rv3Mcn3D5NvQ2x7284hN6cfgzLF4D+F2N137pvj17/3NShzdoxz8iDbNcdNhAnY59kcYhd37XO4jb6LoSbP6t6jexB7UPu5HM/7wms3INYuBua4muJaflU0AwshhBBCCCFEz9HGTgghhBBCCCF6jjZ2QgghhBBCCNFztLETQgghhBBCiJ5zvuYpBDAJILrfmBh7dO3CHC9KZqpBTElAM8yExsTkgZTqwJgBS8XkzNad17RYh6LB+5m785i0sm6ZwYK9Vk4cK5iJRVV78wtiNMME+RE2qzix76IlxhNU0A1CXi4zF+shykhLd++AGpCQvpb4ts+MPUg7b9oVzA1IJRrSNpLE1iHPsJ7MMKZ2AmjStcMgR6F7mlljomWBfaYoicGRM0JKyTNuybjkjTSoeQN9fnitxJmepGxQIBfzXdQbYon1wsxTfKdk75f5bqAhyGrmZTgur2jGQdrPKq2Fen2s8JPUbGQF4xD6HM7bcISOsyvMf7jICWBkJe+UF0pF1kXHU2uoEqdk/mjQ8u+1Vy6b45++vg9lLo3Hz60Ta78NjeG53sQuJXNY3E4gViyn5ni+vQdlyukCYmcza3ry9jvv4rWnWNEL23ZdGZG+0IwgFIqBXbO+cfwEyjzpTiE2rnH9cupMcj70Mt7zazv4vi6/fNMcdwtcO6yK/mMn/l/23izGtuw+71t7OnPNt+qO3bfnZnezOYhiU5Qo0ZREWIotxzEMI4MSAwEyvOQxTzKC5CUPQfxg+Dl+CZQAAQLYEewolGRLCiWaEsXm1PN857q3hlOnzrynPNAv///3Mff4sm4lG/x+b+uPtfdZe+817FW1v+8vhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCEazrlq7KhWDhJnMo0Y+Qq/8jo1/NY1IcKYCn6PtJMkOw8VasIq/x01kz4wyZKrmCQkESnRyhW5bQM5DPR0IYQA+ckjoodKMWm51wIVOdPYYRvSjCRldjeC5sqt8PyQeFfigMfKosRnkLXsNNGKsa/EJCl1mdu+WCxZwl08zg8/9swrqtWh4gB3HI6PIsckpiHy9XDMEJlBKCsbTBO8V3WG064fW0x7SOdBuGZSh8wTCUmSHse2XUmCdVoJPq/MTUQRm5jEmUFnQJ+onkywrPdUK2jzVnuaD0+a/uPfw9gqUzob75D4e9XM36AJZW0iWtKH/P6PY+Q4NzeumiieSeW8ZpDK0ldol1bRx0uyuQGxU6exmx5iouxBgXqzybZNXN3fRs1WSvR6vp+VZMGqKpzPmT4wdYtyl7zjnQxxzRoWdg3pnWLm7zrg9ZTV0JTnC/y95QLv39Jp45O0A3WOH6AW8PjYagFHJ7egzjOX1iH29N41iE2O75tyj7z3dOfY9rXavYd0Hn0d1X/shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDja2AkhhBBCCCFEw4lkRiGEEEIIIYQQzUb/sRNCCCGEEEKIhqONnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCEajjZ2QgghhBBCCNFwtLETQgghhBBCiIajjZ0QQgghhBBCNBxt7IQQQgghhBCi4WhjJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGo42dkIIIYQQQgjRcLSxE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhqONnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDjpef7YP/6d/7z2saQVmXJVz+C4uILDQhRappx1t6DOSYGXd+fekSlfWt+AOt21DsSO8znE2i7WDhHUOSmx7WVu99ObrRbUSbvY9iS159ps4+/1e12IZS7W6+L1hXIJoaouTLkOFR5H/jYQkW5VVAtTnocE6szDGsQ+vmuf18HdfajzP/zDf4w3QjwSf+c/+W3osJN7H5jyEyWO0es72If7mz1Tvnc6gToPCuwHraI05Z0+tvOJXRzvp8c4Rm+ejE15bYB9P2q3IXYysn0/THF87J8MITZ0XX97axPqdMg8cWff9vNiUkKdQRvbXtX2mi9fwt9rBzzXJ/sjiH30wI7R0RzHe0Ta7jsMzngh3LpzR2P0jPhXf/5ncIsP7hyYciveheMeHN+D2DLN7HH5KdR57/v/EmJv/uCHpjzOsZ1rGzsQm6S4Ps06r5ny0RjXgb/xK1+E2Bc+OzDldi+DOt0Mf6+/sLfvL/71X0Cd//Ub34HYNLIX2Vr8FdTpLMYQy0s7l5RFAXWSGIdHlODEt7HztCn/u1//Dajz1DY+jD/5o9835T9/612o8+3vf09j9Iz4la/8KozRrQsXTbkmd/vShW2IPXlxz5QvXMSxPSkWEDs6uGPKcY79Yq2P779lhO90i9z26z3Szl73CrartH04G+BFr5NY5uYJ/8784zrk3aHt5rMOru1phvNE7K6ZDQS29iWkYsuN5ZTUqch4Lypfxjq/+KVXVxqj+o+dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoOOdqnvLR3UOIJYkV9lclGiAkEeoF08waM9QBDR0mOR5XOgHkgwgNAqpTjE2WaPzQdWYiWUBRZpWiqUTsNJ8HOYpAkzmey2s+Jy28vs4cTRGymRWwtlM0YUhjFHS3Iqv/jclzqImgtCCq4KK2wt06wa438erREMJoYftDjbdTnCGLJRptzBb2uUzJcRPyN6KktrFOC/v0Rh/FzbeGzsAhwv7a9uYmIYQ4R9uOgTMmyvHywp3bRxA7HFox+qBNBNfs72JT28/HAc0URhWK2KPEtn27i2Oo3SLzRGGvrxri03lA7nun1YNYFttrjogNSk2cUXwoIvOEOEPI7Y2dGH9tC80N0rV1iJ3Obf/MybhqtdGAJI5cX2QGASSYkn4Xt+z54wzXhm4fJ/7MuRKcDnHtu3N8Cxt2aueXT+5jnUlC3gsqe29YLy9KHNu5m3QqYgbX7+AcF/sXhRDCbPLAlP/ij/53qPPnS3zPunFk5zg2z4uzYzxGY60ktn2jRwz/ZmMco4ctO0bn5B25TcbH+oY1IWpn2GMrYrpyevIAYr4PTxc4J5TkzWA6c4ZDMxwfnTCAWNy2a2urhXW6HWxD4g1VIpxLqpK8x7p9APE2oUYpbBao3LaqIMflxEApTuw6HceP/n83/cdOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhnOuGrvjCSb5jSr77S77jDUjH7fGThNW1PjNKvtQtnaJsRdLkpibtCJn3867xL8kp3ioIjyuct/zkjyEISa5wNPEtj0mCRpDTEQwsf3eO/H6iBBCXOH984kVU/LNv9d2hBBCnbB22WKS4t8UqgS/oz6e2+czHeG36+LsWCcJfS92rS6mPcdv6esMtXLTlo3lC9TBRgs810XX8RZjrLNPBkif6ODaiW3DR/uop3v/HiZlrl2/rsifwAZEuzZI7P2r56jN7RCdaGvd6mCjGd6rbobjauQ0UQ9GqL8Yxain6aRksoJxi8fVRHcX+eSuJMGtODsqotEsCtvPDh+8D3W+9/23IHbnwU1TPpkfQ53tBJ9nWdr+w/TXBRGXVDVq7JLOpilPptiHf3j/NsRuj+y8cOM91MpFRHf7+RevmvLOBdQ6PbuJ88SdkR2Tixm+O6RkHa2dpi7NUE/HJG9pwPMHd/7hMc6NZUGSpLsuM12SFwxxZrAk9H4daxU4ro6J/ms8tprJTg/XgW6X6Kh7du1LyPqYEK8D9kIaOd+E8Ryvb5qfQKyYuL5PXpLLHsbqyLY9beGcF1d4H9LYjq0yYD9nGvDE7THYCsZ0d0xznpf23tQFViprbFfsTtZi7/crohVYCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhqONnRBCCCGEEEI0nHM1T1mSBNSxE2W2EnQWqImYs3AGJ9MChcZxjYrkLLWizIhlHSTmIkVOEpa636wT/L2SJfl1ib9JPm9qIhNhJmBybiZYdwcSMSfxMoEEiTER0fOcjeT87tiUCEOTDEXzpUuMWc5YemxxVnTbexB74pJNYnqBJLg/IOr/0diakmwO0BynPiVGLM4cYlFgvxjXJBlqjefPJ7bfHU6wD3e6OOf0elZo3iLjg5k6JZG9D2s9NIvY7GPfH3vTBXJcmxgsDMf2uOMF3qvhAg9c38BEuH4SoCYoTC2uhOTnSlkSY4alNU+ZHe5Dne//2b+A2Af37tnzkKS/n3/ueYjNZtagY5hjH9tM8FxtMu+XLWsctPf0VajT2kHjh1bHnmtvjiYoWcDfa23aeaJb4bzx3CU0L3owOjDliXckCSHUJY6ZONjnVZIE03WN96rV6UMsdmYQVYrvJfmYmOv4RPQB76c4O6oKn8vCjZFWis9pThKb1767nJD3aGKal2W2L3Y7uKb0+9sQ6/XWINZ2S+RpjOtvq0P6vjM4GaOXWEg7+D4Rd7wJGRqczYmJVNe9e7ZaeO50BQMw9vyqEue4iOwV0JwF10e2L8gru59gv7cq+o+dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoOOdqntIhme99RvuYCA0jJnZ0aeC7zhQlhBAiIj6MndgxzfAWlGS/W5ZE9O1iFTMzqUjbnaA7jlmGeTzOV2Mien8/Qwihldj7XtXkuECEmu42lAEFpWWB54qJl0Jd28az+1LkC4jVlW1XVmMbxNkxDyiKngzss3qih+P4wgifS7uwzzwi082UjKuTuTUuWCyxznSGKuwR6YvzpW0rGx97WxsQ2+pbtXhcoTlTQgTWtTNU2VxHY5blbASx4cgq5JctFLp3iPlU5AyOehneq4oIvAMZf366JDYp1CjFnyqWmcpjhQn7gzOn6pC1obsYQyx2BmDxjJiNlGTczuxxh1M0BFnbwuPqGmNdZwAyJWZJB+/cgtjx7MiU8wUaT0QlmqB88oa9NwUxfpljE8J0dmzK1RLnynJOTNxadg7opDh/dlK8L5PhAcSmwRpbXPzsF6DOxegGnuv1N005Gfrp0wAAIABJREFUT3HOE2dHSd49J0vX78gcDEZ3IYTKv3dFxDyFGNv59+2ijWYjFennizGOmTi1c3rSwv7a6aLZj/fR2928AHWSLjZifv++rdPBNaW/jue6kNg2JGQtYv/Nqty7gzdGDCGEuiBmSewdwMUqYia4IO/SSxdrkee8KvqPnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA3nXDV2NUnoWTpNXRKTvSZLjO0SCsZEe8WkHplLwhuzBOLk+9cWyeBdRPYbZva9bUYakXixHLlm1vYktd9tVxHRMFQkYWJtz1+TaykL/Ca8cvemZvc4xjawttfuOVck+XFKxHkt1/ZWG7VH4uwYTR5ArJrYZMcXWQJ68sz9E45n+O3+ZoL9bq1nNSm35kSn2sUE28UEdUVtpydt9Uky1BjbkDuN0u5gE+osF/h7VWLHSKeFdTKSxLjr5rjRDNs0nKCmJ3Nz1foaavra5FP96ZIk0C3sQ4zIvBuYzs/NE0wTLR4vXpueEc15RrTc/lklRIOaEl1cO3b9rEZNSlmRxLwT1Pl1j9815Q528xCW+O4wWVid7c4FvOaX91BLVo6txvXmwX2o83GO43Y2snNjSeazNZLgfdCysaMT1AIejDGhfFEQrVPvBVPOLn8e6jyxjW04/vCuKdfpLtQRZwjR2NXuXWZExkJFPQT8fMoSbONRudPY1W3s00RKFrIOeU93a35C3gE6rROIbW7YdXrWQR3e0QcfQ6yKbN9PMmzTlavPQ6yeuGve3oE6/R6ukWlmb2BBkp/Pia5/PMbE6cuZncC6Xfy9kxkeN3LC3uSnWEf1HzshhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCEajjZ2QgghhBBCCNFwztU8xRulhIAJqGuSPLuOMKFn8CJvktgxjonI1BmcMOOEkgjIyxLFlD6pd0ISCqYpCtYzZzjSTUidFsbaXZf4kOQdPp0QUwRn1lAQA4SE7fGdkJcld6ZpjGuSGN6VI5aUnZw+cvemJqYr4uwgIy0M3OOc5PigOlMUgi+ntv+cJtjvNtfR8KB0ph1TYi60JH1xdwcNVbx/w6CLUx6O7BBCZA0I5gu8M6MDYmbSt+eP19GsJUvQFKGb2WvcICL6qo33oR3bdu2soXECG6OTIbbdS7znxIyG5LOFs9ME2uLMqIlxQe3myYgkwY7JWkQsjvDcNTG6cudihjk1M1SZfQKxO2/b5NnDKfa7VoIGBGtrru9vX4I6m/cgFDqJGyMdrPTWJ5jku5y4JPAZtikh92p4bE1XTp15SwghLHKyjpK1ru0M06bkHWdrdwtilzdtbFIrQfnjJJ/h/Bq3bb9bkoTXFUla7k3rIrI2EN85MHCpiWFWgUMtRMRIL3dtTSqsk5E54Oi+NQX6OMbxvyyw8VFs544WMVkb30ezlunVY1OeX7kKdS5d3oNY2rbj6nSG7zNHpzhuj4/RCCl3pkq9Fo7jETFPKdzcy/rCqugtWQghhBBCCCEajjZ2QgghhBBCCNFwtLETQgghhBBCiIajjZ0QQgghhBBCNJxzNU/ZPziGmN9ZJkQF6g00fhy0wmlmlJISs4YstZecRHjuKMbbEhHBeje2gtKtPv7epe1diK21rtjjOijCjlK0KahjF0vxXi0qPNciDEz54BjbeXR6B2KnuRWBjsb4eykRzMaBKHJdtSInpjWBCHKdyUpyrj32Z4+MjLXtQd/WaaEwvEecfCJnsnL7FI87XOAznwxtPz9FD4bQ6WNHGHTw71QzJ/JOY+ybxXICsbywQvfJMQqnZ0Ocz9q5vVezjW2owywsspYdk5sRjrWYmEjVbgrPF3izmCHOXgdNVhZLe2/aCYr7F8Rsqipt24mPlThLiKlGnVkTojrBfp4keJw3Palr7J1VQAOgxP1eQgyOamKi1c2wX8+rhWsTtj0i6/QgtmNte3KIx936CGLTyBqH7Dzzaajz7AVcR9+df2zKZYEmSIdH2IZZbsdkSUwRYrKwsZifFhYljveNdZxz9tZ3TPnOHJ+pODvGxDylXtp+nhMDsIg4yEXuoTOjlJiM7dKZ5E3LBdSZLXGMRmM0DqncpB4T15WYjPcKzBGhSqiI4VAUOWNC8i+o4QGOtZs33jXlrW0cC9euX4fYpWv2nbwk77X3Dw+wDcf47lAu7boZ1biO1mSOi2M754xn1NZtJfQfOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRrOuSqW3v4Ak37G7ltWlug0It/ve40dk+FlCdHduXPFRMuSJhhb6+E36a/s2oTIX3v1Wajz0hWSDHHeM+Ue0e/l5GPkwiWmTNokCW2LJE3tbpry7SuYFPoH+/g9749c8sWPjx5AndYUv9tuk2SStbseqjNguqLEa+yYQkmcFVWEU8LS3fNuihrNFkn8Pc/td+RrS3y+9xf4rfm6039VNSbzTIj+pJxjv0vcfDJd4u9lyQBikTvX5AQ1dmGJ+hafOP3+fRxrgx2Mxe6edolmmAyPsHCPoiLzZ4tphhckEa7TNTAxB8+ZGrmSRHaPk5SsDS23rrVinCfbZO6M3drD/tJbVrjOJJldDzMigqkrXFMS0hf7LXv+JdEZlWTaf+HyZVN+YoTJj+cnqIONnM527QQTHX9ugOv9jdpq6m6P8NxE4hpqpw9keqiU6emY30CwAz7PUYvf7WKi9vWBfVdJSo3Rx8mcJPmuXKymejrsG4mb+KOU6OnInFC64+jvBeyw0RLPVblk56EkOtiV5n02wzy8n+PdDCFf4LvnaGLX6f0HqIu7ce8exPbuPGHK7S6O/9MhvgMsiUaxdP4RJdE2RhW5f5W9D7NCCcqFEEIIIYQQ4mcWbeyEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoOOdqnjI8waSNXjbJBJg1jdk9KTMWiAOaPHghahJjnSt9VGpf76Eg+ZdffNGUf+kzz0OdTokC67C0Ym1mghK10GAhOME6MzdgcsuqOjLllKQsTnpreGDX1hufoFD0z/7i+xCbDFGMnrgkxoEl52R/Zoj9cyYPWpwZFTEXSUd23JYVGccdYszgTQoGaOxzgSQaHzs99zxFIfNwhu1slSTRuDMuqGMcV9Ec216PbPLTfIrX7I2YQgihdgZH4zEmMY76fYgt3D2NF5gkNlT4e52+vTcRMYxaknnwgBi/zLyRBhmPbH6BEakx+liJiFFC6swN4pqsfSwJtntUNTHVID4QITgTlJgYjpUV9jGfNDmEECpnshIRs4EdMk/053dMeXn3DtRpDy5ALO1a87Ji8S7UubLzBMSe2bSmTsfEUKlkJjLRw9ewhLm/kWEUORuJdILzxKDG9b3jDKmSMc5n4uxg3jTeQI6911IDEjdI6xzHEDOei/wYXWG9CiGE2hulhBBqb/ZB5qCYvMDFqV3z4wT7ZiAJylM3jnyi8xBCqEpiXuZiyykaOI0XQ4idTGy9rI3vHCx5fBxw3OYueXtJ2hnI9fitSPFT/N9N/7ETQgghhBBCiIajjZ0QQgghhBBCNBxt7IQQQgghhBCi4WhjJ4QQQgghhBAN51zNU6pqJen9ajWc0J9oOSlxbCt2W7i3fWUXzQ3+q6+8DLEXv3TdlDstFDJHrR7Eis6nTLnevYLH7e5ADKwLiBg2WqLwPJ5YoXTn/j7UuR7uQSzJ7bnqpwdQ553hZYh99OcoTo1GztiCiEcDEfL6p1+v0F/EozMghjyXO7YPJwGF97MZmoQs3RidE5OghIjFNwfW4GQW43G9DI/b6OJ4mGRWvH0ywuvL0XMlLOfW9CghZkZbl7HvzyenpjwaocFCfkrMDdbsPa4THB/TmgjIcyvUrk/xYjYHOG63N9AsqS7nptyO8PeIR0bwtWRw9HhJU+w/sTMJqsjSnmRoXpR6gwVi0FMxo4TUHpcR84+IrPfLHNenqTNsmhddqPNq7xrEeqOPTHmti9f8ha//FsT8Ff7R7/9TqFPjchh++YVfMeXj6R9AnbcP8R2gqNy9ISYTCZl3mQlR5IyQygXOu3GEz7nTs+80WTKHOuLsYK+jPsbMONjcWbtxVEZ49ooZ8ri+Qo1ZiFFKRdaZ2I2amIz3NCPzkptz6phtOdj/l2xbo5rNL8TMxM1fZYHmKWWOxmvliV230wzHR0Lm3SjGdvlpjxm/1BV51619UeYpQgghhBBCCPEzizZ2QgghhBBCCNFwtLETQgghhBBCiIajjZ0QQgghhBBCNJxzNU+JiNmH1xBGRBjK7VN8jAi8yWEbqf3B17bRRODvffXnIfbSVz4FsbhvjQrynQ2o077yaYhl61+2gS4xSiH36lGJLth7k3bvQJ0y/lOI7RbW+OHVCrvLvxc/B7F4hPW+9ZffNeV6TkTf9cOf86omOeLRGKRsHFnxb7VYYh0y2OLU9uE8RyHzcInn2u5ZQXIUUKA8naMAuigwdn9uRdH9bBPqRCUaHlSVPdfa7tPYzmdegtjk8LYtT9+COssx9v1+184dddqBOnWMx3njiXhJxPclCrXbFT6LbvrwOScixg9RxEyPxOOiIuth3bHmGHVGjBlSnJdr9zhrsv6WZG1N3LlaxESgJOP9pMTxfrqwhiobF7ehzqd20Lxo+ZY1OBg89WWo88Rrfwdig007Zm5++C2o89EP3oNYr2vb8NKlq1Dn3uQTiJ0s3fxJ1jBuN0Ses7MqOo5xzpu1ce7odKyJhTeRE2cLNZBa4Zaz9xs4Fz0P+z07L9clHlgTYw/W9jix4z0h61PSItsJeC9Y5V0e5yFvIPPjOqSd3gwqw3mpCDgHVc5kpSDvJczwhM6p7j2WmU/VK8UefV3Vf+yEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoOOeqseNg2kZghRCT5mUkeKFjvyn+jc88CXX+2pdfwXNtYCMWa+um3H3661AnX3sGYmVsv3dnn2OXOfm9qf2e3iegDCGE9Q1MTjp3PxDtXII6WfUaxFrBJk1Osx9imwLqfm69iPf0+x99aMrju/gNc0C5QKhp0nLxuCgnmHz8bm61LF3y/XlFHl62ZvviOnmWUYL9dTmz9faPMFnoe/dOIJbEqBfo97dMuYeyn7BwyUlDCKHVsUmSt65egTrpBur1ek6P2L+/D3VOD+5DbDyy9727RZK5x3gfCpdgNmF/qiPauYhoK+alnSdmRJu3kjJHCcofLzWOmditBUVEEvN2ehgb2DWsmqF2BntKCF6i5fthCCHMSSLgZYFzx5pbR5/ZGUCd0ztvkHbZMdp68YtQ50aJff/OPTsm17/+61Bnc/93ITY/ed+UP3vtVaizf/gAYq8fDu15SqLLYWOGJat28+x8OYE6ORnbSdvOJyTfszhLyETpNVR0lqTSPPf+RhPX4w/6119ah7WB6OWjxHkdkAMXROPuTTQyokmLEoz5+aTIF1CHad5id0UJWRCzFOfPwmn4CjJPlWSOS4jm3D+vlTV2K0RWRf+xE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDed8E5QTwSVLbIzHPVxEGJOT9xOMPbFrhdkvvHIdj9tEZXHdw1tV9awxw+tvH0KdezM0PGhv2f302oUtqPPOj96H2Py+Pe4zexehzhe/isYv33ZmDW/84EdQ50tP70HsCSfe3BpgMvfnl/i3gRefwmSy65dtW8cPRlAnqvBe+UfPhKji7Fh8gAm139m0wvvdNaK8J6LoztyKm6+0UbRckb8tzZb2uHaEv7dBBNdlgoYj6y6RajRGs4GYKME3Ll4z5d6FC/h7ZM5p9ewY2b6EpivzUzR+Wc6sOLy7hiYWF7poKtEPXtSOx/UyvMeTBQrB7zsDl1mB4vSKCNZBCK4x+lipF9jPF86goyDP9+LTn4bY5zafMOWk6kKdCP2xwicfvmvKkxzNBtIMzxW3+xC7dOkFU34Bp4lQnrwLsesv/Zop772K9+V3/7ffgdjrziDmv/yP/wuo89RruJb/6P/8Z6a8scQb8+VX0Tjs4+9acybi+7S6SZirV5Ek8MzIxk+NEUlsLs4O7h8V/b8Wf7rfI0Y7bh4uiRkHe29OE1xvfagk72qk24UQ7IHlAvurNyoLIYTcmaVUJR6XxjjefXLwkqxh8QoGLnWJ44MleI+9i1QIoXaGKszkha+Q9Qp1VkP/sRNCCCGEEEKIhqONnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA3nXM1TmFBzFckwF6JaaWFMMsAPiGnA1V1rVLJ1ZQdP3cUfjDfRXCTpXzXl04/HUOf1178HsVnLCkF3nt+EOvXBBxDbXFih+8niAOp8+DaKPn/vO39pym+98zbUGd1FIepvfWrXlPfWr0GdjR4ed2H9PsSefMq2/fDdfahTFyjA92YN0VkqjgXwCxGaBvzesZ0mko2rUKedEqONuRVYH1X47CYVSoSXc9uH8xxV2Z0MBd5xdwNifecasFgcQ52sjYYj63vW9MSLq0MI4f7wCH8vtnPOYGsX6qzt4PgYPbDjYT5eQJ04RlMip00PdY1zXk6m+ZRMqqUThxPdOfVF8WNU3imPl2qM7hvHN96zdVI0JNi7guO2f8H2z9F9nJdvHKLZ1nB4z5TTNv4e8S4LWQvXyMvr9nrWH+C6NovRuOuVX/p3THnzWRwf5QzPdXSybcrx3mWo8/yv/yrEHnz/T035zo0PoU7vha9A7OVrtl3D8Xehzox4mdTEPqEKtmJVoIlFTkwevPtFVck85XHC5kA0OFlxovQnW8WYJYRQrXD+mBilhBhjRWXX4JoZ9ET4Lhg5o5LZEt/x4grP5Q2AqMlLRrYvbv3Lye+x+xK5dTsi+4mqxnaCcVgI6PjHNjDsuDNcOPUfOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRrOuWrsGKskKGefCvvPVhNynp31HsQ+88Izpry5jvqaOsFvaaPOOsQ6F1405dc+i+eaHuK53j20GrfhGHU/W2NMfnrZaWyWEWoK3vkI9TvXtqy24vIvPgF1Hnz4BxC7fdsmc35l+xLUCXPUAm60UB90advqCNf621DndDKEWBS5b7sl4HmsfOXntyD2g9etlmyzjdPG5hp+X58v7bM6XuJYmE5QIzJ2yZUjovXqd7HvZy2MLY9t0u1FjvPEBaI9am3a+3Dr4AbU+d6bb0BsY3DRlF9+5kWo09/D8bcY2yTGc5JIPcrwHrcGVttU1lOoM1nieKzmeFN7ThTVJyKpUYEx0I5IBvtY+dF3vgmxdz60GrvTKWqovE4mhBCmCzv+RiOcg0djjPnlNlliAuHDMY7tZ7ZRT75+52PbziH24cHLX4XYk7/8OVPubaKe7to6rjP9qU2SXg9w3b743C9C7It/+++a8h//k38CdcItXA+/sGP1gR+jHDF8gtLDwFRwsdP5FDmO7QWZZ1uJ0xAFmk1anCOrvslEq9SkOi5XZKeJmMYO1/faLRclORk5UyidVo4dVzItt1tEaubGQa7Za+P8eUIIofYXE0KI3fiACS7wBOVeX07bQK6ZxWAd/SledfUfOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRqONnZCCCGEEEII0XDO1TxlBX0nrcO0+F5oyBLubpIknBcqKzbu1JjAcDknyReJaUDWscldW9tosNJfIzFnXHBEdMwnLUya2nr2l0z5zg9vQZ1LkxaezCWvLXdRUB4PXoDYJPciUEzmng8xee3aEhM3X09t8ugftQZQZxxhd4xjK8rnyerFWdH+Zex3z71rDXmiBAXDWYyxum3NPnwC7BBCSDvYX4+6XgCNpijxAmP5GMdyMbGmJO0BGiptXkYzk+OZNXD4wTvvQZ17t+9C7Khtf6/TQWOGV64/CbHB9p4pLyaY/LiYoaFSq2vHUUISxRcFGixM2Xw5sOeazNHRYZrjnFq5ZYTknBdnyJ98848gdng6MuXZEvsBey6RW12zBMdjVeBYmxXW8OvkBA3AZq0LEHvu2hcgdm1qx1YyQIOVL/z1vwuxvWetUVGUo+nKlR2cz7ontg/HZA6K+7sQu/5rf8uUn/nB/w11Pvg2roe766+a8mtPPwV1Tt/+BGIPiHuKf17lgry/LPDAOrYPP4lkniJ44m+frDuEECKXfJwZieRkfqly18/o+xsxRnE7A2pAQk4WPeL/qmrciWAdllOcTaqJa/u/hU2Ob9Wjov/YCSGEEEIIIUTD0cZOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhnOu5imPmqV9Fb+MmGSTH5AD15xYtJNmUKcgRimtGE0QQnDmEKSly5IJQ22sIALoWehDbJgm7iy4Lx8EbOeeM4jZfnEN6nzjzfexmakVv0cRnjurMdZZ4DMczGwbugn7mwIeV0F/kHvK4+TKZ9Fs4GuvnJjy9++jOH9Rk6nE9dc1Vw4hhGqA4+9qYs81WeBxswJNUObzA4jFrr9sO5OSEEJYW0Ozhnv3btrfe4CGQHGB/bUorXnKZHQIdaLkWYgNdqwRxPT4AdSZjE8gVp9aY4uoj+NqQow0Jl7UHkLo+mdI/+yH1wzzHlOZizPjtddeg9ibH75tyjMyB3uTmxBCSCI7jvoxzq+3b6MhyLsfD025JvP5c89sQWyndQdiizvWnOniZ38Vz0WuOevauSPO0JDr4h4aI/XuW6OXpNuFOoHch/SqHbcv/sq/D3Vu/PAfQuz4ll37vvjCl6HOrdEIYoe3cc4pnGlFlaMx0nIxh1hZO8MYYnYlzhA6Bz7qu4s/7tHOw40JV3zfrr2ZCVZJElzL/Y9GEXEEIgaGfjopqdcPMThZ6V4RIxa4PnJfmNEMiflDV14Oz3Dd1H/shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDjnqrHLiMYmRH5vudp3pvBlK/kmfp5hYtVpx2rXFgnWCQE/6K2Jho/Vg+NIAsPYX2OF38lvziYQa938oSl3ya06JZq3/LLVTc0y8v1wjcmP49ReX13nUKdmuily32v3m3mF11fT+26Pk8Lu8XKyj4mGv/v+bVO+VWCC+04L/0a0vmm1K2kX+0rCdGozqxFJa9TOzKfYX2djTKidOf3cxt41PG5JEnhP7LmqimgDWG9000RV4HGHp6cQ67dskuTW1s5D2xRCCPOxHUe9DLW5/Rq1D4dTTOY8d+N7QpKRsyzXdeXGrSR2j5W/97f/OsT+6h3br/cf4PMdDrH/VE5j11riHNyp8FwHd60m7DRbhzrPrz8NsfoAtdxx1+pLr3/116DO1tMbeJxf81PUym7vPgOx/uBDU07a7B0Aidy7w+Wv/jrUefUH34DY9/75t015fgPb+anLz0HsvdF7ENs/sfNeTealKsd12g/KWDrY/1/CtF2gU2NVavZmZGM18bioiHgtKUn/cYfG5PdaCW4nqsTWW+aopwt+/QghxMH5SZDX74qtT66hNPU508q5H4jIOsffPUl0BY3dasq/R0f/sRNCCCGEEEKIhqONnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA3nXM1TOm0U8fsE5TzpIOJjCdmjziq8vIOJFWpOSZ31DhGBLoYQSxY2seqyQuOCI6LenMRWGNpJ0LzhWg+FoZudW6bcv4gGC4s2ClH35/a+37+LbYrJ/eukVjRf5pg0OW5jO/OyBbGi50weBmh+0U7xZhXRuXbRn3m+88/ehtg3b1jBc76Gz2mjjc9zy4mUC9LHlhWO93awpivFEg2BRse3IRZI0t2tKzYh+TxC8fb7b/wlxG7t28TiiwUzJED8vHSwj2OmXH4HYnuXdk352YuYSD0/xSTGwwd2DiqnaASRtnEMLUnS8rE3ziDeUFmMzzD3ynZiZCXOjrUdNCppD+za0xnjqnnnnXcgNg52bRiU2H9GUzQ9ijt2jt/YuwJ1vrCGfXh2+y2IPfV5m7D75/7a16BOq7fCOhDj+rux9yTEelsHppwR85RVjAziixch9qnf/o8g9uAjO1d98hY+hy985pcgdriJc9wfjE9MeVzifJbPcS4OzhAjJYYV4vHiPTse3b+GGFjRROM+OTgz+yKGdSUxF3FbhZi8pxfEGCVymcaZWUuWkX2Be1dIYmKyxn7PrT1xwgwbieFf4Y0CVzNPYbfUPwuWBJ4Bp/opllH9x04IIYQQQgghGo42dkIIIYQQQgjRcLSxE0IIIYQQQoiGo42dEEIIIYQQQjScc3WmmM+IqNeZp6CZSggxifnE91GMlzKeouHB3btWkDyfvgB1Lj5xGX8vQaFmKO6Y4qzehSrx089ibGiNRPKP0YDk9fffhdjH8Q9N+ZXnX4M6F6+iiH06Ojbl995A84brlzch1ks3TLmc3Yc6Icb7Mu/ifXj7yDq2HB4fQJ1ugmrRMrXC2pqpVcWZ8SffPIHYexNn9oNeA+F0hGNtnNq+UcVoqtNjxgWJfeanIzQNWczmEOuubUOsv3PNtiHD39u5QOYlNzVO5mhwtCxw3NZO8ZxlaOhw/eozELvqjJDWN/C4eI7X7Mf2fDKFOssl3vcyoCFNGex96HfxXkUFtmEWWfOUakWxuHg0qoiYDeS23/VqXA+nR2jk8+49Ow+n8RrU2YiwLz5zZcuUL+7h+C9v/SnEOp0exJ7/xS+Z8ublDaizmpEAVtp9/hrE/oabl57v4v0klgsEPK5//dcg9sLX3jPlO+/9I6hTn9yE2Nd+7imIvTE/MuX3S5yXjk+PINaJT025rHHuEmcHMypZZVakBh0rHEeNWFwbIv/SHEKoKuwHFTFPyVpuDSHXl5ekTzmzFPYun5I1OXbv81GJF1gS46A4svUS9hxKNA4rXYyap5C2V4/4nCneXOenWEf1HzshhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCG3TidYAAAgAElEQVQazrlq7EhuwhBF/jtS8v1rhDGffHER8Bv/A/KD79z+xJSHdzEJ6PWnUacWpscQKg7fNOXBznWo88IF1K5d2/yCKa996qtQ542/+j7Ejvetxu7TX/oFqLO1+xLEZpnVxbx07RTqZNMBxJ7uWa1DNX0T6kxZwud9/Db4uz+w9/3waAx1kgX53tudX+qdx8tf3cG/9Uw7NmF4p4NatnQdkybXfavNydpdqJOmOAUtTuxYOz3ahzrse/etyywZ8SUbSPC4AWl73LL6mY/u4DxB/yoWW3VOfx31QpeJ7nZj3WqPkgjnrrXdSxDrH9wz5cM796COTy4bQgj9DLVO/paWFY62fAWNK0zp4kxJElzrwmxoihFKr0KHrKMdp6nrX3wZ6jyxhRq77oFd67YOP4Q6+RDbef3n/xbEnvyKXQ+T3qP9vbmu8PeGNWq5d9acrnB0CHXiLmrzEDIWujjH7X35K6Z8+V/+X1Dnwzc/gdje+m9C7Beet1rcwxtvYKtiollyc6/+ov94YRotr79eOUO5q0akclTX7HWiEfEwCBXxNWAv6rmdUJIMddtZjJrT2K2HdYVzUJrg+lS5e5WmxHsjIWJ/l3C9JknMq5xo4yFRO9HOkXcOBnv2j1JHCcqFEEIIIYQQ4mcYbeyEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoOOdqntImmY3j2O4tYyLOT1JMF5q44yoikizJ1d2ZWJHkO6/fgTpPXBlCbOsyikXz8cem3Jr/K6jz2UuvYiN6T9tyiuYNn77+G3hc+HVXZgJMvOivX/h5Gxg/AXXqGz+AWHH7LVMenqCJxe2A5hA/+gjv3/DAJj9e5Nj2TszSwtp60aqCY/FIDCN8BmVl+9Qsxz5WTvC44cILktG0p08Eyd2Z7SvzKYqd1y/sQmzrymWIxZmdT2jybGISEmf2GtsdTOh9LcX5bOkSQ7dx2ggxMXqKwNiCzINdNDjavnjVlCdHaPI0mWIC9k6K56pb1mBhXuCcmhDzGR8pNEYfK1WCnWrqutS7dzFJddW/ALFrT9l+sP7E81CnTZLSXx3Y8dB97wbUmfTRZOm13/wtiO0+vYpRCcP2s3yJ/fw73/kmxP6nb33DlP+D//RTUOf67lWIteNHczPoPW/fAT7/H/5nUOfkf/xv8cD3XofQZ596ypTf3UITpKf30PztKZdg+qBLJiZxZrAZkKS8Xu1k7p2YG29gzK913nAwhBBist6HGs1Tityb2BETlBSNg6Larg5Jwn6PrL+u7RWrQ25D4YxRyiWutWiUQiDvJWB+E3gScR9ZddZYyVBlRfQfOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRqONnZCCCGEEEII0XDO1TwlTfHnvLAwiojElKgPq9qKNyNy7pKInYeVFX3/4Q00dMi+9SOI/eqraDiytm3PXw6/A3Xi4QHEqh1rLhLvPAN1ok0UusONKIgIdIGi+XBqr7H85CNs071/DbHh+LYp3yjQQOJPR2OIfeuNDyDWW9q29tc2oU47g1DwPWQl4at4ZMo2PuO6sgLkeoF9Os3QMCdxpitxgmYjnQTHbTWzfTgm43jz4lMQywbYp0Jij6XmOxUxcHGuJ5evPA11nrmMc8JkYQ0cTqc4HjMyx8W+n9coTmdz3PoFa5SwtfsA6sxu3oRYWKDRRBrZAbjZxfuSFtiG+dwK60uZpzxW2t0diF2+8qwpF+ES1Ln63HMQW7pHVWVoqrO7xDk3fvP/MOV7d76PbXrlVyF28dM4ZiLw8cC+v4odRZpinWuXL0LsN1+zZmLPbPShTgJmRqQNtEl4XOz+fn7x5c9CnZc+g/PL23/8ZxBrZ9Ys5bVPvwx1Lm3iPLieLUx50GJGZeKsYF0jAhMU1sfIuVaZTkkdH6LdlaytccC+UTtDlSLHOaEu0WQpcoZ43iwxhBAiYshVO0MzZizi9wAhhFAW1jwlIsZo1M7Etcs/qxBCqMm6zdq1kgkK29TU3uzm0dF/7IQQQgghhBCi4WhjJ4QQQgghhBANRxs7IYQQQgghhGg456qxY1qZBL5bZYkBGU5jRxIt1uRcLj95ePtoBHW6b70PsQ3y3fGrz9nv4i9sYyLl5RDPNU/vmXK5hnq6cnsPYlVsH1c5x2+a0zlqBttTG6tPUQ9VlROIHWRWe/DOCeovvvGtNyF24yZq+Dq1uzcp9oVoBW1FkupvEY+TgoyjrtNjXNnApLjPXV2DWOYkdadL1O89uIN98XRi9V+bO5iM/MJFjCUZmc4q16fI9+8R0W1udO31vPQi6lb6HdTmbNZWj7icr0Oddgu1hlFhj4vZN/7kq/vUnWtjBzVFw2O8x/kQY72ePddaF0Wv0RyTlh+4TLH5T5ixxdnQItrqvU2bHHg8xee0ODmBWFm6+TTBJMP9Ja6RB29+aMrrBc4JL19+FmKTd97Gdu3bWF3jWpTnCxKzY6YkdbZneM2/mNgxOfnm70OdP/2Tb0BstrDn90mbQwihWmAsmtlY54gkj3/3LsRGIxxHs/ftuH3x5RegzrVdfJ+Yz+1xLZYoWjxWzi4BNVFfsZCL1WRNqVY5MJB83eRayhLff2sXo9o1tia7ekwqF7GU73DN7H3x4ddcUwkcbcRDoU+dJVxnurtHRG/JQgghhBBCCNFwtLETQgghhBBCiIajjZ0QQgghhBBCNBxt7IQQQgghhBCi4ZyreQpLKOh3lsxgpWJaR6duzKjZAGuDLQ8XKPh8/fYxxMroFsQWS2su8CsvY+LYrIMi5XJoxdNVcR/q1PEbEKvcFVXekCSEsHTJpEMIIbStyUPRQUOHOyUK8r89t8Lzb3wXjWDuvncbYu0FmrpU3sSCJmgkSZlBDau/RTxOlgU+g64zuul3sU9vrqHRRlHZ/nn/GI19JsTYIzhDh3YLMhiH4cEdiFVDTJzuRcpsDqLJT11/ZbY+EyamdslWq5IcSUTmmDidiNOJgjx25y9mU6izXOCc4EXtIYSwnFnTmlkHx1pOzHUSZ8TAErmKs+OP/5f/GWLv7FtDrhtHaEBy+xhNOybuca4/eQ3q1PMxxLL7+6acYL778O4f/lNs5zfRlCR0rWFL1sPxHhKSJD2xc05GDEHiDPt52rHz2bLCfl4GNJEpEmsQ0yZmTfUE3x2Ob/zQlKeH96BOtUDzmSJBs7JqZMf3wZtvQZ0XP48J0CvnZNVNzvXV72cOtqbgqy1Jbr3CuZnFBrfdcIYg7PdYgm1yNnxPJ4nGVzBGYceVZI1EIxFilLJCG9gSzQxj/L1hx7H79xOcUXyrWCU8yh3203ip6C1ZCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhqONnRBCCCGEEEI0nHNV0NbEoKNOHr63pLnjfUZ5ojSkGn5nnlBWKLgeLvAXX7/5AGLzkTWDeO8+ip1feuF5iG13Lptyp0CzkWJ5CLFlYX8vzvCa84wIz1u7pvjJLTzu3fsfQux7960xygc30ZghyfH+1UQMWzllaO3NVEIIEXlgXmwLJiziTJnnC4i1U/uMowSfwSwnph2FFeyfnqKhQ56j60LsjJEO7uxDnfv7OB5xdkFBMtU6s4mifrh4exVNdMQqEWW2N0+hQnfS92H0MbE4E/KTWLFcmvJiigYSbMlIU2tiEUXsSYiz4nf/+b+A2D03tk7naJizIMZIVWZj1yJci/YrfJ5X+natSzI895ica5HgeGhdecmUtz77Gaiz/fQliA3WrFlZv4cGJFULzVN6bXs9KTFKSUIHYnFq57MeeeeYvv8jiP3l77l1+4U9qHM0JeYpBY61wo2/43Vsw80TnGfXNu1MkaUyT3mcRCsalax0LliKVjsPrCGwprGT/6R2+vUJQcMTPH8d4/sivMuTNrCltia/B++ZzCyNnosEV+LRnil/hA83cFkV/cdOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhqONnRBCCCGEEEI0nHNV0DJxZRxZMWXMZJJEXOlPxcSPCRE2Ju7AkvxeQUSLJ0sMfvfIGj+8RcwhLtw8hdhVJ/q+NkDx9qULKKYuSytGnxcokL81XkLso7k1n7h3iEYX8/kQf6+wRhpZhO0sK7wvFYl5MSwThjKjCfSLkHnK4yQvsU/NSzv+0gz7Zpzic5lObV8cL9GYJU+J6ZEzzMmJAJqZ6MQxMSXBWhCpYzIHuNPHZC6pyNl9P0/JuUmI/D4xSkHdOUyEVYSVmJUJM40q3L2Zzcl9IcY5fg5gGnpxdlRZG2JJ2z6Ddo3jOEqIeZkbf7MDrLO4+Cz+3ivXTXm+xLVvskDzlBEx99p48RVTfuVv/hbUabVwzYrKvin3t9CUJKlx/T3af8eUZ3McC+21DGJrA2uo0tu4CHXKCk2dbrTs+f/qvRtQZ17hoDke4nw5d+8hrfs3oU783GsQe23T3qtOxiYTcVaw/5j4FYR5Y7B3IPD2ohPso5m18KmavIfhyaFOucLvsfd71s6V2r7KQsO8y9i9eviZ6L2i5mj+3My8jJrWrNCIFdF/7IQQQgghhBCi4WhjJ4QQQgghhBANRxs7IYQQQgghhGg456qxY7mly8gGa/aNMTlX7IQqTMvCvkXGb4XZYXhcSRq/dPviOkLtwz5KD8JwemDKN45xf715gnq2rG0f13CCuoODEX6Xv3SPmeQPp8lQ69olB6ffABMFD88CbUv0cT08UaV4vLAk2JXT6xQ5duo0Ru1Mmtq+0d/E8ZH1UMviNZplSb7nh0gIJI9yKFyi5nYH9YHzGg9c1jaxMcu5vUQZUyic2LffRi3LoEW+uXfatZq0KWP6udJppHD4h2mBSZrznDzn3P7mcjHC40psV+V0mayOODuee+oaxHrH9lmdTlFrzWJxasdt2sLxePvkLsTe/eS2KX/y4BjqTMn4WHTXIPZ8/3Om/FyM88RTLWz74oGNnVTY+bskKXs0sxr3UOJiNJrhWhQ7rVy6wH4+7+DYvlPbe/qdD8dQZ0Y0/AuSZH5W2N+Mpng/X5uR957YtiGOlaD8ccLeYz1cI0a0VysowNi5VtF/Mdg710q66RV0Y0wbT7WGPrE5zSq+yn1h0VW0+PRsK9WqV8h2Tt9rwTjk0d999R87IYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGo42dkIIIYQQQgjRcM5VQVsydwOvF1wppTBGSX5PqnWMnHBy5aSDTIUZP7wNc2q64kSRET6GKkERe760QvejMUn4XLD75+87MYchSaAxqTgRc5JQRcSjcC48jGTiZKJgmak8VqhxkO0/szmK/8t8ALGtvu3XdYkjeVZhP/cOR7M59vPZgphD5GgSMnMni0gi9TxHk4KuS/gcFdj2nFxP4kwKWi2c8y5s4HiPg72eYU3MG2o0T5nPbHkxnkEdlkg9yrANmXOIyQs815KZrjg3JiaQF2fH9csXMJjavnE8wmfXznAclYntB1lCEldXOD4mM9vPib9RKIgpSSCGKtWpNQGryLi6fOEyxLo25zaYLoUQQj7HNrQHW6bcI28YVYLjo+OMZTrk7WlZ4gW20o6L9KFOTebdmBjGJJWN1ZU/dwhVQUyWIvvuEPubJx47qxmQYAhfu1Z7D1slgfejmpJwoxSect3VIqd6eML1OCbGetzNzzdqxd97RMh780qmNczY0bWV+UGuiv5jJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGs65mqcw0aKXNjLZfUQEkGDGQYSUFRNJulDCxJxEtRjHRFTuBJ0lMQ2ISRsSd1xNjiuIADPPurZOiSYW3sgghADXzM1NHq7UrJjBCvk5LwINAU1xmFicnd//5SFeRZgqHh32XNwYmZFnPifjr+WMBNoZ/h2pTQxBhqOJKS+WaIoyL1g/xzYkziylJoYg7RQNXHod266aGBJMsVmhldlzbXawTe0YD/SX06rwXh1P5hA7OrEHLpdo1lKSv9/VxBAj8Y8+IgYu5B77iJ/fxNmyMWhDbH1szTFyYnKzIGNm6Z5VyubXipj9JM6Mg6yP1G+MrA3lfGTKsykxBZuh2cdH739kysPqGM89xdj8xM4BxZLdK7yeXn/dlF987hrUiXNse3BjOWZ/Tyf3KiEGR4kzemJr7ZLMl6UzY4oLYmQnzg76ruufFXn3PMP3m1U8UFYzPFnxOLJeQDXqk0Lf+n0lrLGSgQurQ35uBVO+VfYvKx/Hbh/UeXSjQK3AQgghhBBCCNFwtLETQgghhBBCiIajjZ0QQgghhBBCNJxz1diFCL9bXyX3otf4hBBC7bRk0YrfrfsE5SwLYEyTDhJcNSrpYwknnR6pKpkuhujuXKLoktyXkiVt9Eng6be7eP/K2reTHEbgeczrh1aKInLfQa+jBOWPkyjBfhB5TVgLp40ZSSpcB9thslYL6rTInFC6cy2YLod8zx/F2EGryp6/E2Pba9LvXNNpn25nqFPrZ1a/0yH513OSfHzubvF8QZKtEy1Q5RIPw7MKIZQkmXtJNDYLNwcwHSzTgPi5F+ZYcaZkRBOaOo1bShKN0yS/bhzFbDz6wfDjHzBFus6xdbvC/pkEqxWvpxOoU0z2ILbR27VtiLegTt1BHfq0fep+H8dHm+lS3b2ajFC/N9x/ALHxxF5zRRZSpntPUhLz8zN5VylyvB7/jsHmXXF21Ew35gYJ016tkqyb5ien71MQwTrk11ZNgL5aJeetwDOiQ8R7U9C7soIGjdWhrTxT6wbn/7HqcugrKkG5EEIIIYQQQvzsoo2dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDSf6aZLgCSGEEEIIIYT4/x79x04IIYQQQgghGo42dkIIIYQQQgjRcLSxE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhqONnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCEajjZ2QgghhBBCCNFwtLETQgghhBBCiIajjZ0QQgghhBBCNBxt7IQQQgghhBCi4WhjJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGo42dkIIIYQQQgjRcLSxE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouGk5/ljv/3bf7/2sXarb8qz6SEcN55NIJYv56ZcVPh7cYTB2l1ymuItKIoFiZUQq9y+uK6wTsgxVka2HEVQJURszx0l9vcCXh/bqRdFYcoVOa4u4dGEyIVIM0Oo8fqKGs9VuRipQomTxAWwzl9+6y9o08S/PZ975dPwZMZzNx7Is4vjBGKRe3Z1jY+prnLSClePnLtOsCNs7j0Jseee/7QpL6dHUOfWh29DbHQ8tL9Xs7mExCobqyJsZxLjfaj8PSX3uC5xXvL3KiKjlEyDYe/JKxCbj+w1L/1zDyFUpGF4b/Ca33znHY3RM+If/P2vw0Mo3Hi4c4D9fDEnY83115j08xYZa63Mrpsx6Xdz6NQhTArSf4Id31mCa3KW4Rzg2z6ezfD3crzmyM0nKVmA2Xgv3aKVZdjO9X4PYm03ALfIGhbneF9uLAqIHbh1ejGZQ521TgaxrWDX6b0M6/yjP3xdY/SM+K9/5x+wjm5g732BrBd1ePi7Exkd8ANxjOeO/EteCKEm83ftxjJres0WGrfm+/OQZv64nn9fJL9Hb587riKVYnaP/X2g7WS/yN64/ZqM54pJLHLPh707/Pf/3X+z0hjVf+yEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTDOVfzlCxGMf54YmNxjILhLG1DrHCiaKILDUmEAmEvkqwD/h7De3iEEEIorVi0ZqJodoed6LsmIkkuTnVtr1CsWhADl8q3cyUDhABCXjAyCSHERNVaEaMZf34uREVq13bmTyPOEKLM9oJkJt6uCmLMUPrYin9Hgh/AMVoRA5JygfNL6fpiTdTUVYmdqtu1JghrvRbUGTmzkRBCGE+9mQExYSBGCTDi2XNgInMfY0YQdLwzgyM/T+Dz6rRwLk6cEcOSjH9xdtwdLyE2c2Zi8yVZB8gUH5wZRzvDfj4nz3OSe0Mu7CtzYpQyK3Ase5OlOMbrS8nU0c78cVhn0MHrSZ15SkLWX7YCnzozoTzHGzqZoZlJaNuXgBMyRtMU31XCDO/7wIXigMeNRzgXz9zDv1/hPRZnR0nMN8DMhJmJMUM8Z5rHDI64s50/90P9XP5NkBmF+XeAVRYjbBZ772NrERzHjMpI6/2IqclLuX8fDiGEOFnh+pjhCX1ezjCGnIm//rIJ+tHQf+yEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoOOeqsWt38Xv3rG2/H57N8PvwcYEJymuv9yLiq4J83Zr671jJ970ZSd65ZLoYl5CRfStcE21ep9M15VbWhTol0SLMF1NTLmqiPWLfX0OMaOWIQCF1wgaWxNwnbQ0h0O+9k9jrIbANq+gD00Q5VB8nCUtwDRH2rT5LPu77HfZN9s29T7LNvqWnyUlJzLe1JHq6kvx9q7+1Y8qXL+9CnfjG+xCbze/ac7PhuMrf00jiWC7b8Alg8foipn0gNwt0DTQxPMYWToPF5i5xdrx/7wBilescWYrPqU1irdSuyRHR2OUFJv4+nVuN1pSsDURiFyo2T7hja9I5ExJbi+zry3ob1+0uOa7lXnv8fBNCCAVZ12K33s/mU6hD8qGHydSOh0WO+raYiPE3E7yeDafF6/awzu0Zao2PZvY351RwKc6Kkmi7UrcWVTWZq1da2MjAYiGvHWXjkcih2XiAhpH3TLJkwRwQk0ole2ctnCcDWzLJO6sfttEKesEQQii99p6utcQ7giVzd/r/JMKxTaZL8qOPPkb1HzshhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCEajjZ2QgghhBBCCNFwztU8JUnQJOR0fGjLE0zwWbFkgU6EyUSZTHroDQEiopKsCjyyIkpwL06tSPLetQ7G+v2OKafk+uYLNCBIW/ZxFSwh8wLV24tgY1S4SRO822BJ7igTADMbizhx94q4SrBkkl4fG7FE8eIMwWfgn2ZFzVMengSb1UmY+NiLzFkuVJpgm5l22FhdE3cDcv5+xyYo32z1oc6ih7EHzqAin7NEwEz07cYDaRMzHMKKKz4bZkizghFLnqORRg6OEfp74eNko7cGMW86NujjWsvE/94UYbrE/pqTw0pn9pHG2GErYqJTkLW1dEYezLwhTsirSm1j0wU29GSB/TVxSb07rQ7UYe8OQ/duMl2yhZQZRNWujNfikxqHEEKc4vknS3s9NTETW5L3idL9Zl4oQfljpcK+X/ok38QRJKbrqF8bVjsu9usv9f8jZkYReTfzzl3sZGxNdqevSJ0peWcdHh6ZcjvGNq1vbUMsceZCMUtsTtvuDduIwSAzBWRmN+4ny4DXx0zj/MstrbMiWoGFEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTDOVfzlE/u3IRY7Mw3ItIkEG4GNEqImdsAMehYuOMSooiMSEb7zc0NiO3u7Zpy2lqHOu3FGM+f23YtZyjwrolRQtWzRizFOnl8xPBg4YTSp6MR1Dk+xXYucnsc87Dx4vsftwFDy6UVkDIBa0Luuz/9TyMoFQ+H3l73qKoKHzAaaOC4ZWOtJp3Km7MkGTEWyJjJCwrWfRvY77Vj7IsbuR0P1a17WIf0891Oy5Q/OT3BNiUZxCJnLhQTAXsg5kI4Hsjf6phpBjVUsRQlPtOyJOdyB8bkfoqzo83GaGqfezFDEzJmLeQNsSpiQpbT9dc2IiJ1khjXpyTBfl24dSZ4s4gfHwmR+dz2T2YIMl9iH47dPJSQ9ZeNj8K1qyDXXBLTDO91kZFJ1huVhRDCKXEKq5wbRb0k5hDkXnlDjDSVC9ljhayR/hUVTFFCCCUZf6nrGxE5d83Mi/w7FhtXpA/XpH/68V6V2M9z8r5dFXNXxuPmMxyj87FdNwtintIi7wBZy5qeJcTdJCJjzb/zU5M+clxNzZIeehh9mUbTuEf/v5v+YyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTDOVeNXVmSxJnum/GqIgnKyXfA/jt5lvgwkOTZSWKTkcZ9TPb6/PVdiK2XPYjNhvumfPDeO1Dn4GQCsdJpA5gujnxqHUr38W6e4uPrdDHWXd8z5d2tPaizMdiE2IOTB6Z8MplCHZpwtsDrqfz33SThbEISsibuW+QiZ/oLcWawceRgCYSZDtaLLXsdTJpcEI3I1GlumESMaXXaRDfitZxxgnWukkSn2+MDU57duwN1ssEOxJ504+hwdAp1hgvUAjmJXYiJPondYkxPzuZBNmaYNufh52LJ6UEfKPnOY2VSoCZl6WI5GVdFjQ+mDHZwtdtEy0b6q+95MdV2ssOwXppZXWpBdHGLnGh63IBIyXrY6+O6FrkE5SW5vrrCNrRcxyYSn1AQ7VHh1nf2rlIQfVJMzoXD7+G69BBCiFzbWb53cXZ4PWYIIcReV0V11BgC3R0ZQwlJNI7nJppQ0u+WXvMaQpi7d7/ZFN9rlwuicc/d+zzRaDPddunuX04ubznFvULWsmO71+tAnQ5552+1+6ZMXkVDTTwgKjrtuUTjbPml+jlf8dG16vqPnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDjna55CRN9gZkASLbJkj5R3GhoAACAASURBVLWLUWOBCBMBtzrWGOWpp16EOv3lJxC7+8a3IHZ6YBN9VzkxQaEKci+GJaYy5KjYXWRriSLXcrnAdo7s9Zw+2Ic6ve2LELviYhs9TGJ+ODzC35sQ4T7oallyV/KcfWLMSs4MjxOWhL5yfZj7q7CgNy4hRjvE4MhTFMQ8KSH9hyQo9sZE3S7OCVsViqnLW++bckoMHaoRJh/v9qxBzJVtNGZZDA8gVnkDJWKoRB+OE9ZT0yWm8CZiezg9EXi32m08zonKST55cYackr4Y+UHp3XhCoEM0caYaTOcfEzecwpmZRGTdLmnyY2L8kNoxGUX4WpKTtdX3z5iYp2xtocHR+sCOyZIkc1/O0PRoNrfr33yJic2ZyUMV2+ubLnCNZvNgTUwlvEmNn5tDwGTL/yZoz02ftDgr/PtpCCHkboJlr7oxmcAreD+kbhx4LldtQUztTkZoiDd1pnkhhJCf2r5fkbWWGbEEF6tZHWbutcIakpMJbe7mvVkL54RWF8f2YG1gyr21dXIcGigmxLDNw8ZjTRxVfH/4acao/mMnhBBCCCGEEA1HGzshhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCEazrmap9TEEMD7IkQZihHbbSKmXljxZk6E2r11NAR59skXbGD/Hajz0ftvQKwcoeDZu0gwAXmaodlA2rEizKyPokwqfndmKdUMha+z+QRilRO6lzOsM757A2KLqRWeb16+DHU2+yjkH09RVJ64W8P03cyUoyjdPY4ebrYhHh1mgeIfVcVqsZAfDkQkXbOH7mLUqyXCeYLo1UPi6m2kpONNjiG0mNux1o6JaUgAR6BQj62h0vXtXahzUqBZw70T24ZiiRfTIkJtuH3EiImEQsnuqhuUzKuFuVT5e/wTnpg4I9opGgBF3qiImJQUZIAwgxNP1sJ+lzvTs4KYItTE9GhJDBx6rv/s9tHMaKe3gbFB35bXulBnexPNi7LEHjeZ4Do6nqIp2GgyNOWTOZowHC3wXEdz++6Ql7hmLnOcSyLyDGP3nNn0WZHnHMf2HSpexZ1CPDIVMQqs3IRak/fFiEzWtX9WpE5FjDaWC9unjo5xnTu5j0Z6NTEOqp37HV+3sQ2VnxfIcWyu8k4ibA2juIoVMRgsZhibj605THeM92BtE42Y+hubEEucsVvkDSJDoMZkvu1R/OjrqP5jJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMM5V41dQUQbSdIx5V4Lv8HP56hJcdKrkA1Qy/Kp5z8FsfruW6Z87+0fYTun+H00y5iYOS3A2sUrUOfCU09BbLC+ZcppBx8D0zHV7rvtfI7f808O70Ps5KbVzw3vY518gd/45y6R8jHRBmxcuQaxCxv4DfO++77bf28eQgihxm+R/XfG8cofW4tHgX7vvsItZ9qA4HQdVUBtUFUT7ar/1pz+HvaVkvydKstaprxRov5zuH8bYl6Tsv3ss1AnnzPNgh1bnQWOhYudAcROTqw275QkLK54ZviHQocMOVeVu/HtyyGEsiTzRLDXSPUX4sxok0Tc/pYXRMtGuhSsMl4LHUIIEdHhRT7pPelkWRdjW+uoU3li56opX2x1oM5mjf2uM7NjOb+HiZUnH9+BWHDzUE3uZ6+H80u/a+eS3W1c++4TH4H53ZumPKxx3fZJ2kMIIfIvOSGEUNl7WpIk5oEklAc5F0mQLM6OnGhOYTTUq621uNZhv1jMca4+cPq50/v3oE4xOYGY19OxZrEZniXijp2+LGYad9JfUYNGbgzRknofj4KMx6ggz8atdeUC30u8r0cIIZTkXq1tWF1vkuHYZh4aPhF9Uj08+flPQv+xE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDedczVOoht8JsxdLkvSXGDq012xS72effg7rnH4AsZvv2uTjzCilDihabO1hsvPrL75sypuXn4Q6SZckUvbKU7K9Tliyx9omSU7amJB1Yw0Tsq7t2sTia7fwvtz74H2ITY5tIvNiNIQ6pwkKQ7cuXYLYbG6F7iczYlBDOkjmROUZEdaLs4MmpfYwnxTmhePG7XyxWnL5xJ0MErQGkvg0BGoIshbb+aQkRin1BA1V1i9aY4SrP/clqDM9ugGx+diK0RcjFKdfvojjY7JjDaI+eoAJkktqOOTKETFFeXgO+BBCCJAPlVRaLsl86ZqVJOe6rPzMMZxMILZ0ycALZhBAxlHkEhunxD2llbYhNnHLdJ7hcZ95As3EvvryFyCWzO35b330IdR58ADHWjW2hkNRiWttkuIa6SerfIbjP4zQsC11RhBZB5MT9zdw/f2cm0t6Ec6DHx/i2rogbjeFfy8ghhU1tbbwJmQyT3mcMNMOb/zGHkFN5u/gxijJZR/u30NDvNGtT0y5HONaFIgZVk2SnUfOYCjr4HtYSmKxXwvYv5LI+z2YsTFDIHKq2l1PwQxPiCmYv2T2flFUI4gxQ7PIXU/XmSWGwI1R/FAulKBcCCGEEEIIIX520cZOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhnOuKvcuEVgvC5vhfblE0WlGstX3uuumPIimUOfu22/i742tmLKuUcSYObOREEJ46nNfhNj27gVTLojwfEn8DiInkG0R54m6xmuOnQtCQsSVeY1i2LK3ZsqDZ16FOlcyFMjfefOHpjw9QZH58uQAYvPBOsR2t6yofDK/C3VyJgSPXX+IifBVnBkgWg4BzFK8ODiEEFpt7D+Jc9WoiPlH2cLx59vA/FxKEo2zPsQyZzSx2EcThijGafDC85825cH1J6BO5wKKxU8PrDnL/jvvQZ0wGUPo0mDXlI+nOI5PZigE96YINTFdYmL4QOqBgpz1BTJX+WGbJDI4epzkoQWxWWH7Rk4eb1Jhn8rcM2aeRHmJ/fWktGvB9Rd/Aer82i//TYhdO8T14v33vm3K3RzXtay7B7Gdy58y5RdfeRHqrO3uQCzO7HhfThdQ58YHtyB2cOOmKQ8ffAR1ZvvvQmxj147tl4jJWk7+xn7rwT7EvHlKRI5j8zPMqaSOODu8KWAIIVR+HSUmN+y4urLP/P4BGu0c3cS+WB7bsRaR8Y/uWyEkbTTEa/X7rg6u98zbq4Lzr9bvwOiJmpQQM6jMme2leC3FEtfRYmrns4qY30QLnBznJ2hIc5LYa0zIO067M4BY5d75V/Gx+0lodAshhBBCCCFEw9HGTgghhBBCCCEajjZ2QgghhBBCCNFwtLETQgghhBBCiIZzruYpIM4PIYTIxloZNqkocP+5vm4FyZN7KB6dHp7izzkNZtxfgzpXX/oMxPobPYjt37XGCP9Pe+fxZMl5Zfcvzcvnyvt2aINuACQMBySHGjEmYqSYkDbaaak/UhttRgotpIUcHQwJDFz7ruouX/Xq+XRaUJt7z5noElHdERlzfru88WW+zC8/W5Xn3MdH+HtFguLNa9vXzfHOBgq8OykR0c7s9Z//8BDKPNo/hNjcGcSsXEMjiLtb9yC2dt+Kymdf/yOUqSZoZjI5RmOU9dv2+r0uGgAcz1Cw2s+cGBZ1qOIqoeYbFiq8J+dFtW0bccCXV0Xshb5eNpym+Htr6J0SpvtP7e8NsI/2N29AbP3uu+a4zLC9JukGxDbfs4YOZy/3oMx4gILrhcze/M7CMp43O4ZYXvl6IIJypmonMRCsk9OyNo6DsXuFVfljZN/idUzHOOZGTnjfInMtm1tbrv/lBc478zm2119/fNMc/93fEHOxCRoEvPjuS4gV81Nz3HdGQiGEcP9n/w5iP//7f2OON+/jeaGLIVj14LQT/uoI6+Hll0/M8Xef/0co8+Xv/gFiL55ZQ6WV+C6U+fQmjkFJmELs21cn5jhihmNknK1rb56iPvomqYgJip9JY9LwKmISMhpbs4/BPq6v5qdoShTNffvBa6ddNEFJifld4dpLXaIRS5LhWtcbppVsnUDqKklsJ4W5KYRQsf9LVb6d4++x+/TVXo7RjLEuyfsihirDU2tu026jmVi8juuJ4Ixf6Lx9SfQfOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRrOW9XYzUqSdNslEKzZd8A91HV0WlZncLaL3x1XBdELxfab4uUb96HM4uYKxPae/glin3/1jTnOa5KkOaAe4tVzmyT5/ie/gDL3d/A751fff2V//xvU2LUz1P20gv1e+OnXn0OZefFXEPvpdasFWD58BWVOn2MS1XyMiczHLjH8ygrW8fB4ALHEfds9GaHuQFwdr1cGhMDEVzFovQLo7iKiH/DvNwSmu8MyvQwFdTskeXZx7NpsgkPe2p0HEGuvrtp7In8CK8nw2d25bY43bqN2dfdL1BmVZ1YjsbWCeqHjDmqdjl1yZfb+6N/vmAbEvx/yviqiM6i9/qImoiVxZaREltpxuhGmw+ulqPWoXEbygmjz0hTb4nbnI3P8Xow68fOX30CsOEedaDezCbvvf/gplPnobz+GWPu6feaTgP0jqfCZ08rO0xlZBbW2cSzZ+WurjY/6/x5/r3sTYtV//0/meH8Xk5hvBTxvrbsNsSyz2qaSjLvzAvVPfl1VlHykEFcDm+vywo6L+RT13hmZ1y7ObLue+DkthFBNURPmE6BHzKCgh793MsEE3rnT6y31cK3bS3FNN3bJwCcTXBsmRHfX7di+3emiXrck8/1sZq+fz7FeWgnWQ6djxbhxC5+vKPFaocR7L6Z2Th6en0KZrI/Pk8Vuzf8j5lH9x04IIYQQQgghGo42dkIIIYQQQgjRcLSxE0IIIYQQQoiGo42dEEIIIYQQQjSct5ugnCQ29hGS9zBkC0Q4ObVGG7MLNNXwyRFDCCHq2ITkm+/cwXsq0cTjydNHeF9rVvT9qw8/hDKdKSYM/+xLa8Ty/NlTKLPRuw2xvb0De+1VLPPLn6ERy2KwIt2vv/gdlHn58hnEhtdtovbla9egzGj/BGLlFF/i/MIKSFdX1qDMcg/FqbPcim99gnlx1bA+6s0x8P22S5I0ubLlWELRksTq1MYi8tI3SftZnOAYML2wwvPe9i281r33IeaF5lXFhMwkSWvPCqBX738AZU5fvoDYuetH7Tkxh1lGcfogt2YUszkK372IPoQQamKe4pMY8zJkgI7sNJKQpMni6ihLfMe166MVeecXxBQhcnNyQcw4WhmaeNy89a/Mcb+HBiuPTn8DsZMxGpz89a/+g732pz+BMo+PfoDYs69sMvD5AmYjP2mjqUu2YvvkT+9i/6/GexCbnA3NcX6Gzxwv/Api73xgzS4uTp9AmfkAx67lVUxa3m3Z+ruYYH0ys6nIGU0kGZrKiKsE30Ge23d8doTrvoUFbFOTc2sIUo6IiQfpt7V751kfTQjLFE1CyvkIYn6cYGvrghiJjEf23rOYWLGluA3xSdlDPIMyFZl/xyO71k2JOduMJIaf5/Zaiz3cc1TknQaWtNxVw2SEhjFzkgA98QYu5N4vi2ZgIYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGo42dkIIIYQQQgjRcN6qeUpZoUA4VP4Qs8K3YhT6zkbWNKDMUUgZR3iteMEaEHSXWlCmnp9BjJm6LG9umOPeMgpfO328h27vsTk+n6C4Mi9Q6F7MrEFFur4MZbI+1lXHZbBfWkRjhhcHeA+T2u77tzZQRJ92H0Ksnl5AbD61wvOoxrrqpiisHU7svUcxvi9xdTC5ro/VxJihICcmsW37cXWZq6MlyWIP2/S1Hvar2dERXt2bJd1+AGWy5UWIRYXta0cnr6DMk8ExxG6uWLOG61vXoczm3fcgNnZGE8UFmhJtbN6E2EnX9ttXZNzICxS1YwRjdY3vubqEoUr5l2u+xSWIEzQ8qIMdF7M2/s12MsP5N0psP8oLNANYXsAx9/btVXN8erILZZ7togHJ1vYOxB78a2teMl3cgDKPfoPmXoNX1gzioBpCmcfLOHYsuqo5+Bb79sku/t7owq4LNrsHUOajPhq4rN+yc93Sn3AeLU7RTGG1h9daW7Bj1cUI1yqBmC548xR10TcMGTtbzlSq28J12HiM67Dp0BrP1QUuRplBVuL6druP81zdxv4RtzOInZ9Zkx62BpgzIxE3H7XJ2jMk+HtTt9YtyLXLmsxizuSss7gERUYzvNZsag1j+h2slyjFcbCc45jqR95qisYv+QjXyJ0ld68xrnEui/5jJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMN5qxo7po7yyfzKCG8pjvGL8Hpiv4mNyHfHdUAtQrtvv2NNMryrOMZv25f7+B3w7r79Dv8Z0be1J/gd/rH7XnnpDmpnui2sh6Rl9+F1jd8K0/zdia2/OCH1WeEev6rtN74+gWIIIaQd9laJDse/nwqfryjIeU5zGbeUWPWNQjPA1+6I6eJI8nH3DXxC/oxUsyScThuwtISam94c2/5kjIlVu9t3zHG6jX3t6Qn2Ua9HOjjApOKHA9S3bH/0iTlOVjFB8tpd1PmdvbRjycGj51CmO0Qdzo7T3JxPsQ6KnCnqSKJYHyNNoSaJcCuXiD5E+nvhmyTHph8S9wp6RCPSamFfK50gkuQPDiuLOI8WudWXPnv2HZQZEb3Q7V9g/1t1WvHuLdSl9lKMddwU8uU3/wfKVF89gVj+zGqWXg5RK7uxjlrAv/3kF+Z4dPRbKDMeYj3cvXvfHK9dexfKPN/9PcSqASYfv7Fux5PDk30oM/T9MYTgO3Mcq4++SRKyxoo6VhO6vHkbyhydDiBW5lYDWpP3y5JZR5nTaGVkbU0mZbYCKEq3BiBrcpagvHTaOHbtkgS9lrsmqnDmexG5NQc7ryJzmNfm1US/VyeoeWP3Duc6vX4IIUxnOJd33Ro5Sti8fTnUu4UQQgghhBCi4WhjJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMN5q+Yp8zlJeOvklHmNQkOWnLB2Qk0m+i6IVLPtDEgiIjpN2isQ29xA0fejP35jjr/6DMXOSY0Jg8vIGo7sLOLvZUSo2XZJLycVSQhJnrlyZgYRE8xWWO9lbhMr1m28J3atiCTnjF2M5ZYsWAJrOA/rU1wl5B14Mwzy56AofX3K25L0tZqctrxsRebXltCgZ06SH8ckoefarS1z/OwYTUkefYUGBIfH1ixlZRX7/7/4+d9A7MaWNV2oSf/INtAMZuv9j8zx8BgNHWYXeJ9LHXtf24uYhHYyxT7DDKkSn8SYNQUWhH5LLZzEFVGQJPF5boX3Bemj3S4agM1KO+53UlwSdIjpwnxmjVG6PUw83MrQwIUZLFSxjZE8wOEm+g2F44fW9Ch/jonG383RTGh1yfbRl0tYLwsbmFT4796348vvT7CSvxiQ5Mcf2P6+eQNNM/Z//zuIlRUaLCy5pNbdFo55F6S/e0OHWuYpbxZSv35dFBODvIiZl4FLCFtHI97ApWLzL1mrsfHF99syJUYi5DxmLuJhZibekKuusY+y3wvedIWam2Af9TXDLs2mPlZ//uSSjHk5MVTx91WxtdglUe8WQgghhBBCiIajjZ0QQgghhBBCNBxt7IQQQgghhBCi4WhjJ4QQQgghhBAN562ap+QFEUl6US9RKPrs9SGEEJzQn53HlJN1aYXFJRFulvMJxPZP0LggXrCi6Af3fgJlFlMUQD96+K05fv4cDR3WV+5BLHLPXBHzlJI8sxewMuF0RNxMvI6XSTlrpjJluPuKiAC4JLHcva86l3nKmySlf+t5/btjbaquL2HGQUyCNvrr5rg7wf6YT4YQW7yGpgQL67aPHhHzlNUu3vvL8dQVwjLtxQWIBWdmQHTTIUrRVGL55h37c7eeQJndr7+BWDS25hCbfTSxOOniGJSQ/l5Bh2c9Hs+rXYzpycUVQuaswlW6N+gKgZvoJH5uIO83JgZAxdyaiwxfvoQysxEakCyvonFQu2vNktiqpESvgfDw1JqnHHaxzMf/9tf4e337e09/+zmUyVp4n6kzD2v38AeTGM2LssSNEzWOeS1mPpXg/J623BqA9GNvlBJCCKk3RsJfE1dITebROtj3yYzDmO8UW5td4rQQ+bfMrk3Oq8iaDtaaEXZS6mXixipWpiDjWe1/jxmsEGcWvzaJvPFb+CcMT9x5NTEXo/sJ6p3iTHKogQtZb/tjOv9eDv3HTgghhBBCCCEajjZ2QgghhBBCCNFwtLETQgghhBBCiIbzVjV2NPmig31NPJ7gt/qhbXUqMfmWtiDf4I6HVq9TE91fORlA7PwME51u3/zQHN999waU6ccoDmjP7LV+8wPq9y4GqCGKvY6J3LtP3B4C5peGb68Dr3efILGco1anJLoNdv06sU0tIZqCmH7xbW++JAnsxdVRl1OIRZFtHRH5aJx8Ag/tLiMJixd6SxDb7Lpyrw6gTES+8V+/ex9ia9fvmuOihYlOX3zxe4glLhHwQge1a5fJ8UslqERcEbl6WH/3p1DmeHcXYpMzm8i8225DmRsrKxA7I/0oj22dsoTyFdNXBq/7kcjuTcLG+MK9qiGZMxc72PYT13GZnGc8w7Yym9gEwssd1Ju1E7zY/tEJxIZOz4qjRAjnBT7PQ6cvHaysQZlkextiFwPbZ0ZzliwYx5fZ3D7zYIja31BjrCoubKAk55GBIo7wfeG4R+ZRshZKnU6yZr4F4spILuEh4MfNEHDO/HPMajKp9op5K5Sv1z7Hl7yW19jR09giALwVCOS8y/g7sLkVem3E7un1+jZaLyzbOs2A7q+P10rI4sH/ZnqJ/dI/hf5jJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGs5bNU+5jBQwjlDUWxVnEMuWbSLQIsNHiQoURc+H9lrjAYqyO21MIBrHGJtOrHHIdIZGIllSQGw4smLxmtRMjyQxnvWsgcPsBOvl/OQUYv55Ts7QmCXKSAJml+N2fIomFvkIDVWod0Jmk8JGKdZLKEnycXexyrsEiCtlMkOTIOhZLMkouVYSW/H/6vo6lHlw4x2ItV8+Nsfj4QWUWSLJyLceoOFIvGhNSYb7aFRU5Tjm7Kzv2HvqYMLnusCnLp1QOiG2RDkzLnBJi7tbt6DIxr13Ibb3xy/ttc+x/29u7UAs6mBy9ZFLMh0zQweSUD5yz8gSzoqrgyUQ9glvS9Y2c5wPM/c+0xTngZKYp5yc2nGiTYyR2l00/zjZw0Tmw2+tWdk6+p2Eo5NXEDvct9daWcUxYX1lFWLnsZ3z41U0Xfn8Ffb3eWrreEIGvZ1FPK83ODTHu0+/hzKzlJhBbd+E2JkbnytixAQma4G0j0skvRZXizeHYwYrSZusYzu2b1XEYSWq2brZGZ6QeadOXz+WhBBC7cZ4tmaFpOL/r6Q5Iss3ZoICScRJwvCaPLM3KmEJyn0Sc3p5Yp5SF+T3KhwEfO0l5N6zFo4Tkeu3P8aDTP+xE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDeetmqckRJDoBYNJgrdU5SjmnERW/F8voHi7nqAZRzWxxiEHuyjmfue96xDb2VyB2D8+/9YcfzY5hDLtgOYsx6f2HhavP4AyqxuoIC9jayLx/PgLKPOH3/43iHWdoHRKBJ/b9z6G2IYTdF+8xLqaEWF9FeE77DoR+zhHk445EfznuY2V5Y9QlIrXwoXTrydlfduFet0elOkF7KPTsxN7HTImLN+5B7HOGpogTKbn5nh3/wmUyRYWIVaVVtzczlDsPB6hCdHkom2OB2dHUOabQxwndla2zPG722h4snnvPYj5Pnm2h+Yw6RTHoJXFDYgdePE2lAihDvieWx37zG1isCKuEnwzkXsvMemPzHRlFtn+HpOXPhuiIc/xySNzvE4MSFodYgB2hu3z8MlvzfHSbTT2eXl4DLG1JWvG9OGHN7DMMoRCEdm5fI2YEn3/9QuI/cPn/8Mc3+23ocwn7WsQe/nZH83xoTOHCiGExY0tjK2jecrR178zx2VNHFyI60LtTSTkQfZGIf41wXtvRMRUo036TLtnDcDmyQmUCdUEQ27dXEzR6C7JsA1TXx0/dpD2E11izGHrxRkxLwuR/z2cU2pi2Va400qyd6hLjHmDEzYOzudYxxEzCnP1EKe4dmh1+hCLEluuJvV5WfQfOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRrO201QXpLvwd13pBX7Tpckrj66sEm+N9cw+XE4xcTGdW7PO3/+HZQZb21C7N6DTyEWtb4yx7v7+O3zmHw+fOPOB+b4zv2fQJl+m3xfu2mTFn/6KX53/PgFagPmpd2/r6xhUuibO6jpqw5s3RzvYYLykiXLbWNiWp+c+mC4B2Um5Ntn/932j/jsWFyCOMHvwf138kyHh2k5AwgNVrv4XXl9irrN6dBq1xa2SeLhe/chVkR4D69cQvKoxr9l/fT9X0Ds2YuH5nhlGXW3Syn2v71HX5vjP3z1Jyjz+AA1dtdv3DXHrV/+LZR5dx3vYeue1eeOT86gzOwUx6X+0hLEeu7dJ6Sv5eQ9l6XXTSj58ZskJQIelHqwBMJMD2LLFWQMHoyw/eyePjHHmxvYt1ubqBs7eYzJuf/w2X82x/F2F8p8+MnfQ+xfXrdzVrZ4uclhqWV1TJ9u49y3TTIpzzesNq91gfqks29/gNjuD1aPmES4nuktYn88GmC5V2c2mXtOkzTju/eJzEFzJ64USLAdQohdnVdknMxaqLHrLDh/gvYrKFMQ/Vft5u3JANfDfTInszWWs1sICZkceuTeZ23bR84HAyjD9HqLPXutFtOptYn3xoXVEZ6eoT44Jn2ms+R0vUT3V0ymEGO9yEsn0w7ee9rBMa4Odj2hBOVCCCGEEEII8c8YbeyEyXA9FwAAFMtJREFUEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoOG/VPIWpMisn6K5nKFBkSXHPB9aAYGMHE4NmS2hSMDu24s1qgIlP9777I8Ruf/QziL334a/M8Z33UexclCyhrzUXSYgJAzMgqFJ73soOmqB8unMLYt7rIqnwnmYHuxB7+r1NwD4fYaLjkiQj7xPRfJzZc4dDFPvOCqyHygldK4m+3yitFop6i8K+u6pCYXFF2lS3bQXQvRz79ngXheCxE1xvPvgAyvTX0eBoPEVh9uNX1qRnjST9vbGD7fXl0TNzTLTbYT5GMfoXX35mjp+R5ws5qqL3nlizls/aJJn7L38NsfVbNmn5xiH+3qvv0NBheoSJote69n0dt7FvnxIBOZjrMJMOcWXEl1DVV6SMn2tDwETmbL5iRhBPj87NcbpwBGV+dh0Tf9+4h/f64vk35vh//tf/AmVmOF2E9z/8uTlevYfzYbyK5hDLboj7ZBtNUD7u4Xpi2LJ18/X//i2UebX3OZ53Ys1nkgiTuXcX8B52h48gNpjbei+IyUsc4zxaundYk+TY4uqIicFRHfw4SdaGLTyvs2zngnQZjXaKCc5FUWFNrcoprk/nAzTb6i+vQqyztWGOkxjnhijCSTJZsYZD+Rznj4TUVeYM+KIEf2+pj/XnE42XBSZE76f4e62W7X+TiyGUCdT8EUOxS/q+sLgIZdI2Gs1Agvcf4Z6iVbIQQgghhBBCNBxt7IQQQgghhBCi4WhjJ4QQQgghhBANRxs7IYQQQgghhGg4b9k8BUNeH+iNPkKgnishzG2G+cMRqqu3t29ArBzbcsUYBaWjvccQe0qy1V978FNzvLSzAWW6Ge6dq9gZxhCjlFBdQjhZE9MVUllxaQWrFy9eQJlX36NhzODg1N0S+TvAIgp5V3ew3l8dfGeOTwZodDEnYuLaNYha5ilvlCTCtphHtu0TvX6oibh5Y3HBHLdGaFQ0m2C/Xbp+217nFpoipBkKtednY4glmb2v9c3rUCZuEQOgvm1n+Qz7zKOD5xA7H1oTol4GRUIZM+G0rfeLk4dQZm//DsTW3rntjtGw4vQlGqqMhyi273Xsfa0vYN8e5TgOzgobi4ihkrg6amZC5s0xyHkxGTtTZ7TB9PpFgb83mdr2un+IJgzfhwOI3d9cgVjvmjU0ev4YzX4GxBRo9ys7Z9248ysos33nJxBbXLHjUj7Duej8EM3Evv/KmqU8/OZ3UObiDOshL+y6YOkW3tNxD43Jnh1+CbF5YfttQlydSmJuVTsTibr8y40ZxCVg5jRu4ozJXBuRRfLykjXfGG9sQ5mcrKeKgV2/xcQ8aX5+DrGImItki85QhRj0sEEnc/NvK1uAMnHE2qKtBzAWCdzAZWnBxtjWISJz2OTMmSpOyVqCPWBC6mrBujP1l5ahTCtDc7TY/Z+NGfBcFq2ShRBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDhvVQzBkqbGiftGPGbJbfF73sR9PHt+gsnIu1uYjHj1pk3gffEE9XTVDL93H+4+gdjTgf0+eeU26luWr2Mi5f6K1a4kLRTiRERs6BPFlnO8z/kAv5k+37PJlo/JM8+G+E1xXdl9f93B76Ov330PYtMZvouXx1YbkBN9INMapk5DoNTHb5bWIn77PR9afUYxw7fQ7WPb2Oraa5V7T6FMElAjsrVpNXVtotnKz1HLks4wGem7a1bf0i5Q0zc5Rr3ZtksQPp1iwtJOH/VCy+9/bI7rCtt5XmIy4qi2yU+TBHUA2QVqFI8e2uepRlgvwY+xIYRqiuNEfW51Btsr+HwXXay/fTd2VEwoLa6MKME2FbmRsSqx/bBEwLHTjUSkDNNWz+b2+iekP/rE9SGEcD5HLdAHm1aDcvfmfSgzPTiB2NPfW43d7heoS41S7Gup0/2ECseN8Rjvc1rY+TbJcN5e7KL+qbu2Y44POvh7/+vx9xA7OGB6PVvvJdHK8d5nF0ztFo4J4upg/zHx3YHp6QLRm/W8Tnx7C8rMR7h+G/j14XSEv1fg702PSV+e2XaXLaH+2ifmDgEThlPVG5MjQhEsFDHNm5t7qin2tQnRFVZT60MRs17kNx0hhM4C6uVX19dtmSVM+B6luKap3TNWP8JPQv+xE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDeetmqdERAwIJiFkq8mSDFYuCWdOjEQOT/Yhlq5ZM5OFd1AkOXiBJg/llCTmPbeC7v2vUXR68hjNKBadmLLT70OZmAg1y8ImUx+PUAQ6PkfRdzmxdVOV+MwVS2zeswLZa3dQ1F7GWO8Pd/cgNsqt0NW/vxBCCCneQ5pZo4CYZdAVV4ZPNh1CCHlu20sSo2nAdhfNU7KRM8wZT6FMUmGHP/jBJrM/ek4MjkIBsVDjvVfueZiBU1EQoyLXPmuS3JW1YUgOTIxESnIPkU8wTcqUZCTcdyFvhhECT0zLrj+fWHF9hwjkt1bWITZz4/q8IO9GXBkxSUpdu/5QEvMU1vYT9+6YwUpSk7nIOUFMxsR8q5xDbD5HE6KQW0Oe2dYtKLJ8432IpWPbXqdjNBcaTTA2v/B18/p5J4QQOs6IKXTQxGIYo4HEfm7XBU8PMAH7MTGfmc/wffkhrq6xr9VkERVfIhG9uDpY/dbOGIUmzybvzi+LVpZwvVjexT5TV7b/DfdeYBmSiDsihir5wM3lEzyv1cP7ynp2/ZsS056KJDuH+YkYMVVzNEYppnYsKae45qjJGsfPv8y3JOtj317d3IHY8oaNxRkarNTkmf27Z+3jsug/dkIIIYQQQgjRcLSxE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouG8VfOUhGwjayfyriIUKDIDAm9ckERELD5Fofb+qb1WsbIBZdbvo9hxsIvC07kTlAYiyiwv0Mzk9MKanviM8yGEQPTqIQb9KFPoMlW0vVidoHCzvYz1cO3ePXM8nqNg9oc9NLYYzbAe4tiKZmvyvmLSQLy4n7UFcXXMiMFJWdt30G51ocx6hiZB5ekzc8wMT+oa3/no/KUtQ+4zZv2D9SMfIOdRobsryMyF2MUSf7GIGFawm3CmFaz/pxERmbv7ikjFRMQQIzDBemZNcVodNMlJIxLrWZF+laNphrg6qoBjYJLa91lV+H5LYmrjDVXSFM+LiXHBrLLX8u3wz9eGUBhP0fDgh4Mjc/x4gPPM9uIyxG6ldvlyfRUNnLbeQXOD0nUuZp40IW34fGqNwg4HB1DmeIxmYqPcmrwUOT7fdILjbp5jXSWJfeYWMdIpmQGH7++aR98oFRnjfRdhMwqb1yI3h2Skr22sYP+I7941x/tk3Tfcew6xaoLrZmguOTFZ8+vhEEI+tG2drfHYXIQ3wIyEmAlK5Y7xUt4oJYQQgjMr7CziGmdtaxNimzvXINZxY1VFntmPQSGQNQdd+VwO/cdOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhqONnRBCCCGEEEI0nLdqnlISYWjScmLgFpowVAUKiyMnLGRC1KJCsXgxt5npT8+OoExOhKjrzkgkhBCWLux9jQ93ocxkhILScu4MD4iYkz2PN3ComUg6wzNbi+vmuH/9BpRZXkax6KsDa2Lx4gDF4lMiomXGD3Xly9FCEJo4wXpFhO7i6mC64sopkHNS6Ixcq7+6Zo6zVSLwnpG271wXihYaBNREZR4TI4FOz40n5DzWhsvaCroTNpYQs6TYmRuAYj6AT8qfY/7eMzRwKolYvJjZ/hGl+Hura6t4rRn2o6Mza/T0fHACZY4n+KaHc2ekUaPpg7g6MmKCUJb2fabUqYzEXHPJSZvOUlwmRC6WtrBMi8RScq3SjSdTYt5wkBNDh56ds6Yt0u5GaF52cWHXACV55hkxLhlN7Xw/IWVKMq9Vfp5mRkwk1iLjXhzsu4+I8UTqx6AQwEUqoisMcVVQ0w5/TN55TQwzfMSvfUMIIU3wWutrK7YMmR93yXwxPXwFsfmFNUFhBlnUXsyv+yqyfiNGepeBtmA3llATwjb2j8yNJa0umjhmXVwjz4oRxNq1LZe1lqBMGcgY7u5d5ilCCCGEEEII8c8YbeyEEEIIIYQQouFoYyeEEEIIIYQQDeetauzaXdTPxbX9vjau8NvdosZvcOe11XXkoOEKoSzxWi0ncCkKTCg6PEMtWd5CzUtvuW+OV37yLpRZKfG75jC2915MMGFpRZLJ1k53ky5gfbYXiB7CvebDI9QdfPbdHyF24b6rrmtybfKnAfIqQuE0IEwbwPRzNXyTrb9FvEm2VvF78JOxbQezKWpeHx6/gNjKim2fy/0tKNNKTiF2fGS/W58SHV5VTyDW6S9C7J1tm6S1yPG8ZwePIZaXtp1d29qGMqPpOcSeP7MJX/M5jkE1EWC0M/tNf2txBcokTDc1t+8iI0nFH6yhtnE2Q83S84M9c3w+wOfz9xlCCFlix7g4xrFSXB1Mk+2loxUZmKMYx+/Kja95iWNwlLw+oW9E2maa4dyXEH1g6m4+C3geS8p+HtnnmUyI3ptoYydjOwbkc1wDVKQevMaVaV5johNP3FxXE5EtaHNDCK2IrB3cb/qkxiGEUBD9k9ev+/curpY6kEUQ6ByxLzBVFeghiXCsYgnnXbG0jb9X1NhWStLfs9UFdyL+XE3mOj//laQ/RqQt1u4ZWWLzmOjlE6dLjTOcD0vS/0aFvc/ZDLVzk0OSEP0EPTpubltfjRu3P8D7zMheyK1tmQbzsmiVLIQQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGs5bNU/JcxROtrzAE/WdoUUE10VuBZ5ZinvUggiL505Y3CHiyqyFsZg4gpy7BN7HROu41EXDiIUFa7pSdlEkPRmjoDR3z9OdoelKPsCE6Bfn1ihhMsP3MCfC16qydcq8G2KWZJNl54xfn1DeJ18NIYTKJ5xkImFxZVxbx+T1Sdea7RwdYwLT0RhNSQ72rSnBWYomBaTbBp8veD5DE4/ZHA1cuiWadmzktk0VU2yb8zHeV17ZMaCMcEzIS2Lq4g0VSqIyJxSVfZ6yRFOZ7gKaw3SdoVKXjF1sPCuYiYVLDB+TbsySJkfOqKNi7kniyqD5rd2IGjNzquoSYnxiutJqY7/yl2eJgAvSDgpmnuDPJaYIbMZoZXYezdI+lJmO0AQhd+ZPFbknb94QAiaPjklS6JQY1MSuTmtSx3XEjC1ebxjBkhjXzFTClWuRZNXiCiEGR2gYh++JJY6HGFtfkVDp5qchMcM6OUGjwPkFGmtFztynT0y02j2cZ0LPjh31jJi1zHHtELu2n/QWoEzJTBVLO5eXBa4TSmYQ5caqssb+mJc4liQpPvPR4bEtkz2FMpvX7uC12tZQJSHjy2XRf+yEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTDeavmKT6zegghxLEV8c4LIq4scryYMwRImXqUxLyguypQOD3NUbRY5WiwEDvBc0mMPYZTFFz2llbNcTLFa798joLLaWTrqtsjQu0anydN7HlU9M1cLJx5Slnh8xXkmdmb8GLiOMFSKbmvuftNcgviCmlffwdi/YNdc3xycgxl4gjbXe76Vh6IIVDEzDj8tchLJ8YCocb+4M13yooYnpBr+Zgfp0IIISEGFVnL3kNFjAwiZjjkBN05MV2ZjnEsmTtDqiLFazP7Fm/C8OeYMyoidVUw4bm7FB2vxZXB2k/i3ydxvoljbOfzuW0ddUBDgrzA+ckb8sTEjIfNBDUxlfB9kniZ0GfuuVCLPF/JjETcLRTe8CiEEBEzk8QZSGRtNE5opTgGla4v+zEphBACMV1hpnF1afvWZIbGE8x8JnXjV0onaXFV+DVXCCFE0SUWL8RYJ3L9qCIGK7MKzzs+sSYoR0dnUGZKjPSqAhtH4saFEWl34xkalbjhJZRkvR2TNWTmTMHYfU7HA4j52a7XJu+BrDMjbwqWYH9kYxd7p52+M0es8Fp5jvucKOvaABmXLov+YyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTDeasau5Lo5wqXjTgi39tGLIGoS2btk4eGEAKRiITKXZ99X1/PicZujroR/809+2y9ItqjtG+/pb22gK9h9zley+tpqoLoZFpML+SSLxZYxxXRzgR3Xk6+vY7I9/wsiXGn03ZliM6I3Nd87nQ/f3nORnEJohbRdXjtCmkqFelssdONpC4B559PJN/OT6ymx+vPQgghSogWgemKnOY0IsldSZ5xaNdUk0baor8rliyYJaH1F0uI7q8m3+UHpxmOCxxLohLHLqZrgBGMJIAN7F2krs1QvZW4MojeLHX9ls2jVNeR2HJMy1bV2H68BrXl20Bg6Ze5fgfmbjZxEw2an39jNibwbO7254hwOyHP08qcVp0k+WZrgMT1h5K8h4SNSxXW+0LP/mYvJvpHkoh+7iZOMpKIK4T5BfhxH+bVwJt+5LRdEfl/TEX03qdDq7GrptjOuxkm/s7JWrft1nQ5mT4mRGOX515kR+ZosuYoXE9K2BxG9G21q2My3FCvj+XehiuEPaTK8WJZB9/z1OkIF6ZHUKadbkLMT7e+Dv5/0H/shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDja2AkhhBBCCCFEw4lY0j0hhBBCCCGEEM1B/7ETQgghhBBCiIajjZ0QQgghhBBCNBxt7IQQQgghhBCi4WhjJ4QQQgghhBANRxs7IYQQQgghhGg42tgJIYQQQgghRMPRxk4IIYQQQgghGo42dkIIIYQQQgjRcLSxE0IIIYQQQoiGo42dEEIIIYQQQjQcbeyEEEIIIYQQouFoYyeEEEIIIYQQDUcbOyGEEEIIIYRoONrYCSGEEEIIIUTD0cZOCCGEEEIIIRqONnZCCCGEEEII0XC0sRNCCCGEEEKIhqONnRBCCCGEEEI0HG3shBBCCCGEEKLhaGMnhBBCCCGEEA1HGzshhBBCCCGEaDja2AkhhBBCCCFEw9HGTgghhBBCCCEazv8FAPSQkyz1IcUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32edf32710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "fig=plt.figure(figsize=(16, 16))\n",
    "columns = 4\n",
    "rows = 5\n",
    "for i in range(1, columns*rows +1):\n",
    "    # show random images from the dataset\n",
    "    img = X_train[randint(0, n_train-1)]\n",
    "    fig.add_subplot(rows, columns, i)\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')   \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "In this step, I am designing and implementing a deep learning model that learns to recognize traffic signs. Training and testing the model is done using the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "LeNet-5, a type of CNN, is being used here. LeNet is a deep-learning model developed by Yann LeCun, more information can be found [here](http://yann.lecun.com/exdb/lenet/).\n",
    "\n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture (is the network over or underfitting?)\n",
    "- Preprocessing techniques (normalization, rgb to grayscale, etc)\n",
    "- Number of examples per label (some have more than others).\n",
    "\n",
    "\n",
    "Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data Set (normalization, grayscale, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, `(pixel - 128)/ 128` is a quick way to approximately normalize the data and can be used in this project. \n",
    "\n",
    "Other pre-processing steps are optional. You can try different techniques to see if it improves performance. \n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.\n",
    "\n",
    "Here, I am using a simple normalization technique - (x-min)/(max-min) for all the pixels in the image.\n",
    "\n",
    "What this does is essentially normalizes the pixel values to between 0 and 1, thereby chaning contrast. Images before and after normalization are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def normalize(x):\n",
    "    \"\"\"\n",
    "        argument\n",
    "            - x: input image data in numpy array [length, width, color_depth]\n",
    "        return\n",
    "            - normalized x \n",
    "    \"\"\"\n",
    "    min_val = np.min(x)\n",
    "    max_val = np.max(x)\n",
    "    x = (x-min_val) / (max_val-min_val)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_train = np.zeros([n_train,32,32,3])\n",
    "normalized_validation = np.zeros([n_validation,32,32,3])\n",
    "normalized_test = np.zeros([n_test,32,32,3])\n",
    "for i in range(0,n_train):\n",
    "    normalized_train[i] = normalize(X_train[i])\n",
    "for i in range(0,n_validation):\n",
    "    normalized_validation[i] = normalize(X_valid[i])\n",
    "for i in range(0,n_test):\n",
    "    normalized_test[i] = normalize(X_test[i])\n",
    "\n",
    "assert(X_train.shape==normalized_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the difference normalization makes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Normalized Image')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32ea2a2400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(nrows=2,ncols=2,figsize = (16,16))\n",
    "randomindex = randint(0,n_train-1)\n",
    "axarr[0,0].imshow(X_train[randomindex])\n",
    "axarr[0,0].axis('off')\n",
    "axarr[0,0].set_title('Original Image')\n",
    "axarr[0,1].imshow(normalized_train[randomindex])\n",
    "axarr[0,1].axis('off')\n",
    "axarr[0,1].set_title('Normalized Image')\n",
    "randomindex = randint(0,n_train-1)\n",
    "axarr[1,0].imshow(X_train[randomindex])\n",
    "axarr[1,0].axis('off')\n",
    "axarr[1,0].set_title('Original Image')\n",
    "axarr[1,1].imshow(normalized_train[randomindex])\n",
    "axarr[1,1].axis('off')\n",
    "axarr[1,1].set_title('Normalized Image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the normalized dataset the set to be used in the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = normalized_train\n",
    "\n",
    "X_valid = normalized_validation\n",
    "\n",
    "X_test = normalized_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "# convolution function (defining now with Wx + b, so I can use conv layers multiple times)\n",
    "def convolve(X, W, b, strides=1):\n",
    "    X = tf.nn.conv2d(X, W, strides=[1, strides, strides, 1], padding='VALID')\n",
    "    X = tf.nn.bias_add(X, b)\n",
    "    print('Shape of X after bias, before activation is',X.shape,'\\n')\n",
    "    return tf.nn.relu(X)\n",
    "\n",
    "# start LeNet \n",
    "def LeNet(X):\n",
    "    # mean = 0, std. deviation = 0.1\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "    \n",
    "    W_1 = tf.Variable(tf.truncated_normal(shape=(5, 5, image_depth_dimension, 6), mean = mu, stddev = sigma))\n",
    "    b_1 = tf.Variable(tf.zeros(6))\n",
    "    # perform convolution\n",
    "    layer_1 = convolve(X, W_1, b_1, 1)\n",
    "    \n",
    "    layer_1 = tf.nn.max_pool(layer_1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    print('Shape of layer 1:',layer_1.shape,'\\n')\n",
    "\n",
    "    \n",
    "    W_2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n",
    "    b_2 = tf.Variable(tf.zeros(16))\n",
    "    layer_2 = convolve(layer_1, W_2, b_2, 1)\n",
    "\n",
    "    layer_2 = tf.nn.max_pool(layer_2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    print('Shape of layer 2:',layer_2.shape,'\\n')\n",
    "    \n",
    "    \n",
    "    W_2_1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 412), mean = mu, stddev = sigma))\n",
    "    b_2_1 = tf.Variable(tf.zeros(412))\n",
    "    layer_2_1 = convolve(layer_2, W_2_1, b_2_1, 1)\n",
    "\n",
    "    \n",
    "    print('Shape of layer 2.1 =',layer_2_1.shape,'\\n')\n",
    "  \n",
    "    \n",
    "    flat = flatten(layer_2_1)\n",
    "\n",
    "    W_3 = tf.Variable(tf.truncated_normal(shape=(412, 122), mean = mu, stddev = sigma))\n",
    "    b_3 = tf.Variable(tf.zeros(122))\n",
    "    layer_3 = tf.nn.relu(tf.nn.bias_add(tf.matmul(flat, W_3), b_3))\n",
    "    layer_3 = tf.nn.dropout(layer_3, keep_prob)\n",
    "    \n",
    "    W_4 = tf.Variable(tf.truncated_normal(shape=(122, 84), mean = mu, stddev = sigma))\n",
    "    b_4 = tf.Variable(tf.zeros(84))\n",
    "    layer_4 = tf.nn.relu(tf.nn.bias_add(tf.matmul(layer_3, W_4), b_4))\n",
    "    layer_4 = tf.nn.dropout(layer_4, keep_prob)\n",
    "    \n",
    "    W_5 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\n",
    "    b_5 = tf.Variable(tf.zeros(43))\n",
    "    layer_5 = tf.nn.bias_add(tf.matmul(layer_4, W_5), b_5)\n",
    "    \n",
    "    return layer_5\n",
    "\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, image_depth_dimension))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y_labels = tf.one_hot(y, n_classes)\n",
    "\n",
    "keep_prob = tf.placeholder(tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X after bias, before activation is (?, 28, 28, 6) \n",
      "\n",
      "Shape of layer 1: (?, 14, 14, 6) \n",
      "\n",
      "Shape of X after bias, before activation is (?, 10, 10, 16) \n",
      "\n",
      "Shape of layer 2: (?, 5, 5, 16) \n",
      "\n",
      "Shape of X after bias, before activation is (?, 1, 1, 412) \n",
      "\n",
      "Shape of layer 2.1 = (?, 1, 1, 412) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 25\n",
    "batch_size = 156\n",
    "\n",
    "learn_rate = 0.004\n",
    "\n",
    "logits = LeNet(x)\n",
    "\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=one_hot_y_labels)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = learn_rate)\n",
    "training_operation = optimizer.minimize(loss_operation)\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y_labels, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "def evaluate(X_data, y_data):\n",
    "    n_examples = len(X_data)\n",
    "    final_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "    for i in range(0, n_examples, batch_size):\n",
    "        batch_x, batch_y = X_data[i:i+batch_size], y_data[i:i+batch_size]\n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1})\n",
    "        final_accuracy += (accuracy * len(batch_x))\n",
    "    \n",
    "    return final_accuracy/n_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training LeNet on dataset, please wait\n",
      "\n",
      "Epoch 1 ...\n",
      "Training Accuracy = 0.899\n",
      "Validation Accuracy = 0.827\n",
      "----------------------------\n",
      "Epoch 2 ...\n",
      "Training Accuracy = 0.975\n",
      "Validation Accuracy = 0.916\n",
      "----------------------------\n",
      "Epoch 3 ...\n",
      "Training Accuracy = 0.984\n",
      "Validation Accuracy = 0.914\n",
      "----------------------------\n",
      "Epoch 4 ...\n",
      "Training Accuracy = 0.982\n",
      "Validation Accuracy = 0.926\n",
      "----------------------------\n",
      "Epoch 5 ...\n",
      "Training Accuracy = 0.993\n",
      "Validation Accuracy = 0.934\n",
      "----------------------------\n",
      "Epoch 6 ...\n",
      "Training Accuracy = 0.994\n",
      "Validation Accuracy = 0.926\n",
      "----------------------------\n",
      "Epoch 7 ...\n",
      "Training Accuracy = 0.993\n",
      "Validation Accuracy = 0.930\n",
      "----------------------------\n",
      "Epoch 8 ...\n",
      "Training Accuracy = 0.975\n",
      "Validation Accuracy = 0.907\n",
      "----------------------------\n",
      "Epoch 9 ...\n",
      "Training Accuracy = 0.992\n",
      "Validation Accuracy = 0.941\n",
      "----------------------------\n",
      "Epoch 10 ...\n",
      "Training Accuracy = 0.992\n",
      "Validation Accuracy = 0.934\n",
      "----------------------------\n",
      "Epoch 11 ...\n",
      "Training Accuracy = 0.994\n",
      "Validation Accuracy = 0.936\n",
      "----------------------------\n",
      "Epoch 12 ...\n",
      "Training Accuracy = 0.996\n",
      "Validation Accuracy = 0.942\n",
      "----------------------------\n",
      "Epoch 13 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.940\n",
      "----------------------------\n",
      "Epoch 14 ...\n",
      "Training Accuracy = 0.994\n",
      "Validation Accuracy = 0.936\n",
      "----------------------------\n",
      "Epoch 15 ...\n",
      "Training Accuracy = 0.996\n",
      "Validation Accuracy = 0.938\n",
      "----------------------------\n",
      "Epoch 16 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.938\n",
      "----------------------------\n",
      "Epoch 17 ...\n",
      "Training Accuracy = 0.995\n",
      "Validation Accuracy = 0.941\n",
      "----------------------------\n",
      "Epoch 18 ...\n",
      "Training Accuracy = 0.998\n",
      "Validation Accuracy = 0.944\n",
      "----------------------------\n",
      "Epoch 19 ...\n",
      "Training Accuracy = 0.996\n",
      "Validation Accuracy = 0.939\n",
      "----------------------------\n",
      "Epoch 20 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.949\n",
      "----------------------------\n",
      "Epoch 21 ...\n",
      "Training Accuracy = 0.995\n",
      "Validation Accuracy = 0.929\n",
      "----------------------------\n",
      "Epoch 22 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.947\n",
      "----------------------------\n",
      "Epoch 23 ...\n",
      "Training Accuracy = 0.992\n",
      "Validation Accuracy = 0.926\n",
      "----------------------------\n",
      "Epoch 24 ...\n",
      "Training Accuracy = 0.996\n",
      "Validation Accuracy = 0.934\n",
      "----------------------------\n",
      "Epoch 25 ...\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.942\n",
      "----------------------------\n",
      "Training completed, model saved\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    n_examples = len(X_train)\n",
    "    \n",
    "    print(\"Training LeNet on dataset, please wait\\n\")\n",
    "    \n",
    "    train_accuracy_list = []\n",
    "    validation_accuracy_list = []\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        for ii in range(0, n_examples, batch_size):\n",
    "            \n",
    "            batch_x, batch_y = X_train[ii:ii+batch_size], y_train[ii:ii+batch_size]\n",
    "            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.7})\n",
    "            \n",
    "        train_accuracy = evaluate(X_train,y_train)\n",
    "        train_accuracy_list.append(train_accuracy)\n",
    "        \n",
    "        validation_accuracy = evaluate(X_valid, y_valid)\n",
    "        validation_accuracy_list.append(validation_accuracy)\n",
    "        \n",
    "      \n",
    "        print('Epoch {} ...'.format(i+1))\n",
    "        print('Training Accuracy = {:.3f}'.format(train_accuracy))\n",
    "        print('Validation Accuracy = {:.3f}'.format(validation_accuracy))\n",
    "        print('----------------------------')\n",
    "        \n",
    "        \n",
    "    saver.save(sess, './lenet')\n",
    "    print(\"Training completed, model saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the accuracies, make sense of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f8b53748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f8568048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_accuracy_list)\n",
    "plt.title(\"Training Accuracy\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(validation_accuracy_list)\n",
    "plt.title(\"Validation Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Training Accuracy = 0.997\n",
      "Validation Accuracy = 0.942\n",
      "Test Accuracy = 0.927\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
    "\n",
    "    train_accuracy = evaluate(X_train, y_train)\n",
    "    print(\"Training Accuracy = {:.3f}\".format(train_accuracy))\n",
    "    \n",
    "    valid_accuracy = evaluate(X_valid, y_valid)\n",
    "    print(\"Validation Accuracy = {:.3f}\".format(valid_accuracy))    \n",
    "    \n",
    "    test_accuracy = evaluate(X_test, y_test)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "To provide more insight into how the model is working, I downloaded 6 pictures of German traffic signs from the web and used the model to predict the traffic sign type.\n",
    "\n",
    "The `signnames.csv` is useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f8a76080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Load the images and plot them here.\n",
    "\n",
    "\n",
    "import glob\n",
    "import cv2\n",
    "\n",
    "my_images = sorted(glob.glob('./mysigns/*.png'))\n",
    "\n",
    "my_labels = np.array([18, 1, 38, 34, 25, 3])\n",
    "\n",
    "figures = {}\n",
    "labels = {}\n",
    "my_signs = []\n",
    "index = 0\n",
    "name_values = np.genfromtxt('signnames.csv', \n",
    "                            skip_header=1, dtype=[('myint','i8'), ('mystring','S55')],\n",
    "                            delimiter=',')\n",
    "#print(name_values)\n",
    "for my_image in my_images:\n",
    "    \n",
    "    img = cv2.cvtColor(cv2.imread(my_image), cv2.COLOR_BGR2RGB)\n",
    "    my_signs.append(img)\n",
    "    figures[index] = img\n",
    "    labels[index] = name_values[my_labels[index]][1].decode('ascii')\n",
    "    index += 1\n",
    "\n",
    "fig,ax = plt.subplots(nrows=2,ncols=3,figsize=(15,15))\n",
    "i=0\n",
    "j=0\n",
    "for idx in range(len(figures)):\n",
    "    \n",
    "    ax[i,j].imshow(figures[idx])\n",
    "    ax[i,j].axis('off')\n",
    "    ax[i,j].set_title(labels[idx])\n",
    "    j+=1\n",
    "    if j==3:\n",
    "        j=0\n",
    "        i=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize the images from the web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f8809470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_signs = np.array(my_signs)\n",
    "my_signs_norm=[]\n",
    "for i in range(len(my_signs)):\n",
    "    my_signs_norm.append(normalize(my_signs[i]))\n",
    "\n",
    "fig,ax = plt.subplots(nrows=2,ncols=3,figsize=(15,15))\n",
    "i=0\n",
    "j=0\n",
    "for idx in range(len(my_signs_norm)):\n",
    "    \n",
    "    ax[i,j].imshow(my_signs_norm[idx])\n",
    "    ax[i,j].axis('off')\n",
    "    ax[i,j].set_title(labels[idx])\n",
    "    j+=1\n",
    "    if j==3:\n",
    "        j=0\n",
    "        i=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert to array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  1, 38, 34, 25,  3])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_signs_normalized = np.asarray(my_signs_norm)\n",
    "print(my_signs_normalized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "My Data Set Accuracy = 1.000\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    saver.restore(sess, \"./lenet\")\n",
    "    \n",
    "        \n",
    "    my_accuracy = evaluate(my_signs_normalized, my_labels)\n",
    "    print(\"My Data Set Accuracy = {:.3f}\".format(my_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 1\n",
      "Image Accuracy = 100.000 %\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 2\n",
      "Image Accuracy = 100.000 %\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 3\n",
      "Image Accuracy = 100.000 %\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 4\n",
      "Image Accuracy = 100.000 %\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 5\n",
      "Image Accuracy = 100.000 %\n",
      "INFO:tensorflow:Restoring parameters from ./lenet\n",
      "Image 6\n",
      "Image Accuracy = 100.000 %\n"
     ]
    }
   ],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "test_signs = []\n",
    "test_signs_label = []\n",
    "\n",
    "for i in range(6):\n",
    "    test_signs.append(my_signs_normalized[i])\n",
    "    test_signs_label.append(my_labels[i])\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        saver.restore(sess, \"./lenet\")\n",
    "        test_image_accuracy = evaluate(test_signs, test_signs_label)\n",
    "        print('Image {}'.format(i+1))\n",
    "        print(\"Image Accuracy = {:.3f}\".format(test_image_accuracy*100),\"%\")\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, we can confirm these are the 3 largest probabilities in `a`. Also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3199ddf860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "\n",
    "k = 5 # number of probabilities for class\n",
    "softmax_operation = tf.nn.softmax(logits)\n",
    "top_k = tf.nn.top_k(softmax_operation, k=k)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver.restore(sess, \"./lenet\")\n",
    "    my_softmax_logits = sess.run(softmax_operation, feed_dict={x: my_signs_normalized, keep_prob: 1.0})\n",
    "    my_signs_top_k = sess.run(top_k, feed_dict={x: my_signs_normalized, keep_prob: 1.0})\n",
    "    fig,ax = plt.subplots(ncols=6,nrows=6,figsize=(15,15))\n",
    "    for i in range(len(my_signs)):\n",
    "        test_im = {}\n",
    "        test_im_labels = {}\n",
    "        \n",
    "        test_im[0] = my_signs[i]\n",
    "        test_im_labels[0] = 'Original image'\n",
    "        \n",
    "        for j in range(k):\n",
    "            test_im[j+1] = X_valid[np.argwhere(y_valid == my_signs_top_k[1][i][j])[0]].squeeze()\n",
    "            test_im_labels[j+1] = 'Estimate {} : {:.0f}%'.format(j+1, 100*my_signs_top_k[0][i][j])\n",
    "            \n",
    "            #plt.figure()\n",
    "            ax[i,0].imshow(test_im[0])\n",
    "            ax[i,0].set_title(test_im_labels[0])\n",
    "            ax[i,0].axis('off')\n",
    "            ax[i,j+1].imshow(test_im[j+1])\n",
    "            ax[i,j+1].set_title(test_im_labels[j+1])\n",
    "            ax[i,j+1].axis('off')\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}