0. EDA and Data Generation .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_ngfVeOrX7If"
   },
   "source": [
    "### Social network Graph Link Prediction - Facebook Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BIdTcywoX7Ih"
   },
   "source": [
    "### Problem statement: \n",
    "Given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EKXSOWj3X7Ii"
   },
   "source": [
    "### Data Overview\n",
    "Taken data from facebook's recruting challenge on kaggle https://www.kaggle.com/c/FacebookRecruiting  \n",
    "data contains two columns source and destination eac edge in graph \n",
    "    - Data columns (total 2 columns):  \n",
    "    - source_node         int64  \n",
    "    - destination_node    int64  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "frIBf5F2X7Ij"
   },
   "source": [
    "### Mapping the problem into supervised learning problem:\n",
    "- Generated training samples of good and bad links from given directed graph and for each link got some features like no of followers, is he followed back, page rank, katz score, adar index, some svd fetures of adj matrix, some weight features etc. and trained ml model based on these features to predict link. \n",
    "- Some reference papers and videos :  \n",
    "    - https://www.cs.cornell.edu/home/kleinber/link-pred.pdf\n",
    "    - https://www3.nd.edu/~dial/publications/lichtenwalter2010new.pdf\n",
    "    - https://kaggle2.blob.core.windows.net/forum-message-attachments/2594/supervised_link_prediction.pdf\n",
    "    - https://www.youtube.com/watch?v=2M77Hgy17cg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zej3Dqi7X7Ik"
   },
   "source": [
    "### Business objectives and constraints:  \n",
    "- No low-latency requirement.\n",
    "- Probability of prediction is useful to recommend ighest probability links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fSW6zbsHX7Il"
   },
   "source": [
    "### Performance metric for supervised learning:  \n",
    "- Both precision and recall is important so F1 score is good choice\n",
    "- Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jGe4BKx9X7Im"
   },
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "# please do go through this python notebook: \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import csv\n",
    "import pandas as pd#pandas to create small dataframes \n",
    "import datetime #Convert to unix time\n",
    "import time #Convert to unix time\n",
    "# if numpy is not installed already : pip3 install numpy\n",
    "import numpy as np#Do aritmetic operations on arrays\n",
    "# matplotlib: used to plot graphs\n",
    "import matplotlib\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns#Plots\n",
    "from matplotlib import rcParams#Size of plots  \n",
    "from sklearn.cluster import MiniBatchKMeans, KMeans#Clustering\n",
    "import math\n",
    "import pickle\n",
    "import os\n",
    "# to install xgboost: pip3 install xgboost\n",
    "import xgboost as xgb\n",
    "\n",
    "import warnings\n",
    "import networkx as nx\n",
    "import pdb\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OOi4aONAX7Iv",
    "outputId": "b413d19f-8322-43df-c9da-d88a60d5fe1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 1862220\n",
      "Number of edges: 9437519\n",
      "Average in degree:   5.0679\n",
      "Average out degree:   5.0679\n"
     ]
    }
   ],
   "source": [
    "#reading graph\n",
    "if not os.path.isfile('saved_after_eda/train_woheader.csv'):\n",
    "    traincsv = pd.read_csv('data/train.csv')\n",
    "    print(traincsv[traincsv.isna().any(1)])\n",
    "    print(traincsv.info())\n",
    "    print(\"Number of duplicate entries: \",sum(traincsv.duplicated()))\n",
    "    traincsv.to_csv('saved_after_eda/train_woheader.csv',header=False,index=False)\n",
    "    print(\"saved the graph into file\")\n",
    "else:\n",
    "    g=nx.read_edgelist('saved_after_eda/train_woheader.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
    "    print(nx.info(g))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ba1XVmouX7I2"
   },
   "source": [
    "> Displaying a sub graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rvhGErEXX7I3",
    "outputId": "3211434c-3826-4d98-af7e-949329c2906a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 66\n",
      "Number of edges: 50\n",
      "Average in degree:   0.7576\n",
      "Average out degree:   0.7576\n"
     ]
    },
    {
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PtUtPT6dnz55MmzZNd58AihcvTmpqKomJicyePVv3cvA2gre2wOdEEGGB/xmCgoKoU6cOLi4uumUNGzZkxowZtG/fnhIlSlCyZEksLCywcSgKgFMpN2zsiyCWSChT8f0CmB/Gpmakpb8ZteUKnqGhIarX4+yqbfpiVsIDEFG7VRdOHj/Kjdg0Yl/JeJCQkcd0aSgRa8XtneJlSS9iuRd2kdrN2+std34nwUYuUokYNzc3lEolDx5oR6XuDhZkPo+iWGnt6F0sFtN20FgW7r/A4oOXcSrlhpGJCd91qMd3HetjbVeYPp3b6OJvoxIzSYqLIfL6JWo30+/Hn0f2MrieJ4GNK5KenEiR4qXy9OnlsyfMHdoNiYGBbs5eoVBw8OBBli9f/tr7WnveL58+1l6P12bn1gNGYmhsTLEyHlRr0oqI0NOAVuTtnYojFosp5upOqwGjOH80WO+42dnZtGzZkho1avDdd9/l6ReAra0tvXv3pmnTpri7u+sSnQQfO8mvh08zd0hXhjX0YnjjSqyYOJjUxJe6tgtH9NJz7OtfszTfdWqkc96bMmUKXl5eGBgYMP21yV1AoKAIIizwP0NQUJDeKDiXYcOG8eDBA+Lj42nfvj1KpRI3D89895GfAL4Pp1JuREfe0X0OCwujcOHCZChFpKsNMJBKUarUuFauTqEijty5EopGreFe+BXuXbuIU6VaHIlMYOWWX0lJSUGj0fDsXgQnfttEZb/Gese6cPgPXCtUxcG5pN7yWgFteXgzjNuXzqFWqTi2YwMW1jZU8iqHmZkZ7dq1Y+rUqWRmZhIaGsqZo4eYMOwbilubkJ2eSuLzJ2g0Gp4/us+OxTPpNnoq60LusnRHMF07daCC85v8z6nZCkIO7MatUjXsnYrr9aOmfxvWnLnD3N1nKeHuhfSdOdvEuBjmDemCRGJA/1ETdaPRihUrkpSURK1atWhT3YMdS2YBMG9IFxJin1GsjPub+1IAJKI3NZFBW/CiTZs2ODk5sXbt2r9se+rUKdLT01m6dCmvXr0iJCSEbFN7XqWm4te2Gwv2XWBB8J+YmJqzfsa3unZjlwXpOfa5VqiKT8PmqNQabsal4+rqyk8//UTz5s0LdA4CAm8jiLDA/wQXLlzg+fPnuhFWLjKZjFu3bqHRaHj69CkDBw5k5MiRFCtij0QEYWePkZmeikaj4dHtcI6/I4BKhRx5jgyNRoNKqUCeI0Ot1iat8G3enkO7tnPnzh1SUlIYOHAgL1++ZN3yRVw8uh+lQsGWH79DkZNDn+9+JPL6RSIunGbTnAl8M30xjiVdUak1bNuxA5fSpbGwsGDG6CE07z2EOi30zyP04G5qN++Q57yLlizNwJlL2TJ3EkMbehF29hijF23Ao6g2J/SqVavIzs7GwcGBrl27snr1aur4VKK+qx21CxuwYmw/vvF1Y8HIHrTp0oMxw4fSqXIxRnRpQUp8HKtXr9YdS65Sc+HQburk0w8AlVKJbeGimFtZ8/BmGPIcGSqlkpT4F8wd3BmJREKF2vXpNHCErs3kyZMZM2YMxsbGSA0k1PJvC8C3K7ZTqLAjDs4lcatcjeCNy1HIc4h9/IDLx4OpVKchABGhp0lLSgAg9kkUe9cvo33bNoB2lN2hQwdMTEwICgrKk0Lzjz/+IDIyErVaTUJCAiNGjKB48eI0bdoUsViMrUMRckxsqFC7PtUatcDE3AIjYxMadupNVMTVfK9BQuwz7odfplazdgA8T8umc7ceBAQEYGEhZPQS+HhEGk0BhwUCAp+RQYMGkZWVxdat+o5Vqamp1K1bl4cPH+pCY2bPno1cDbsiYlk5KZBbl0JQyuXYOBSlYYeeNO7yJs73x0GdiAy7qLfPCWt+w6NqTSQieH7qNxYtmE92djbNW7ehybBpiF9nkMrOeMWmHyYSEXoKU3NL/Np0pdWAkfmO6iRiEf5lHbAzM+R0lNYB6FMpbm1CfdeCVTCKzIzD88/vMBIbkFJvNUZiqW7dgAEDMDMzY+nSpQCs2nmI0b07sPTINUzMzPPsa8/Pi9i3bonestbfjEKtVhO8YRliiQSpoRFikQixCF2o0NsMmjCNdQtms+HPRzrRTIl/wYZZ43hw4wqWNoVo1nsI9dv1AODXJbO5cPgPZFmZWNna4d+2ExsW/4gSMb8GH6Vf++YYGZvo9iUWweHDh/H19WX58uUsWrSI+Ph4LCwsiI+PZ9y4cezevRuZTIZv42Y0HjAOyTuj+qO/rOfS8WCmbsrrO7Bv3RLuXL3Ad2t3AtpReSUnK8oXsaRHjx64uroKJmmBj0LIHS3wP8H7TI3W1tZERETkWW4iAWcrY4bMWcGJnZs5f+B3YqIieXLvlm6bC4f38ORuBEYm2rSPGrUaeY4Mk9eOTU5WJng2bMDB4P2EhYVx9PBhxA6laNK1PwAZ6amkJyeiVqkwNDHFtUJVPa/tP9Ys4FzwTnKysihethzy2fPpF1AHr6KW9Gxah8S4GF1fFPIcvGrWY/TiTQBcDznOrpXzSIyLoZirB30nz8OplBsSsQivopYFvm6jIrejQYMqOYvANbNY3Gs8JiYmnDhxgh07dvDLL7/otj21fyc+DQLyCPDZvTuoXLcxbQeOoVqjFqycOASvmn50HT0VpVLB8nEDqezXhMC5azCUGuhECSA5OZmUlBRKlSrF3bt3CQneRZ/vftAbtdo4FOHb5fl7rXcZNZkuoyYD2heZGsWtOR+dSkyaDGkJLzZfearbNtdMrbQyJjEzh+HDh+uSusTGxuLk5MTJkyc5d+4cUqmU+k2bk7FhGR2GvvGYfvbgLvs3LGXEgvX59if00G5a9nsz0ldpICVL8cH7ICDwPgQRFvhq8SpqyfP0HKztCtOy3whuXTyLXPZmBForoC21AtrqPp8L/p39G5ZSwt0LiViEo1SOr78/ixcvpnnrtuwMiybp5ZtMUGsmD8fVqwpjlmzhxoVTrJg4hHl/nMXSphBXThzg3P7fmLRuN3ZFndm9ej4zRw+hW6Nw7MyMOPHnVZ1HrkajYXybOlRrpJ1TfPH0MWunjmTMks2ULl+Fw1vXsHRsf37afQbv4oWwMzMs0PlfTIsiJDUSDSBHRdDPG/l90jLUajUlSpRgyZIltG7dGtCa9U8c2MvgH1fn2c+DG1fZvXo+sqxMLGwK4dOwOe0Ga1NnRt24xo3zJzE0MmZoA23CEwOxSDcaTUxMpGXLljx79gx7e3tGjhxJsx7ffHTqSolYRDErYy4+Tf2kog8mJiYADB8+nKJFtU577foOYvPyRToRfvnsCQtH9qLb2OmUrVw9z/7vh18mLSkBn4bN9Jbn5twWEPgUBBEW+GrJDduhUTNUag1P7kaQLMubTjGX3NAcA4kYb2crtiz5kaZNm9K9e3duxqVjamaOkUsZAF5EPyL63i2+Xb4NQ2NjfBo04/iODVw9dYgG7XuSEPuMMpV8dNm5agW05diODUQlZVK+iKVOHK7GpHH7ykXSU5LwbqB9uN+6eBa3StVwq1QNgGa9h7Jvw1LUT2/iXq1lgc9/x4uLZKnliBBhYGOGfEFDzvlMpZpV3lrAxsbGpKam5msqHzBt4XuP4V61ht5o9F1TuZubmy55Ri7ZyhwupMZjmOFQICHOFeBnabKPDiMCrde4jY0Nzs7OelMFBm+NxhPjYvhpWDda9R+RxzM8l/MHdlG1vr8uBCyXt3NuCwh8LMJ/j8BXjbuDBd7OVkjEf+19m/g6NMe3RQfdCOrixYvY2tpSq1Yt6pYvzYJRfUl6oc09/PzRfeydiuuZbouV8eT5I224UPUmrYh/Fs2L6EcolQrOH9xF+Zp+eqZLdwcL/Ms6EH58Dz4Nm2H6uhqSRqMBjUbnCVzMyhgxkP780Ued+xK37sgbbKCBrQdB5b4hps5ifCzzhha9TW7Sj0/hQ6ZypVrFzzGnsQ8JZODT5fiXdaC4tUkej2d44wVd3NqEGsWtP0qAc3k7B/j8J4dwauXN5IWzWX3rIL8+OMuuzaup7NuQlPgXzBvShYYde9Ggfc989yWXybhy8lAehzqJCMylWkuCWq1GqVQik8lQqYQUlwIFQxgJC3z1uDtYYGdmxBEjA1JeP9zfzogkEmm4cGg3Fbxr0KuBt87cGxMTQ1hYGMePHyfRpAiLZk9l9feBTN6wB1l2PvV5zS1IiX8BgLWdA26VfJjYoR5iiQTbwo5MWLUjj+nSVKTk3LFgJNMb4VnElMwcaNiwEbtXzkMRHUH7pg1YumiBXoKQgiISiZCKDFCq1RQ2tMbJOG/e5nfJtR58irk4N+lHfvzx8irDI7fyYsRO1HfiERlIKCn+HScnJ27cusMv+w8ze9J44uOeI5FI8K5Rm59Xr6R0STtORyXw59H9HNuxkaf3b+NSrpLOMQog8volFo3UD13Lyc5i2Lw1+DRoRr8BAzm0bwcqjRoUKoZ6tQDAo2tDWvYL5LcVc0l4/pSdy35g57IfEEu04Wc/n3szgg87exRTcws8vGvlObdF349h29Yg3ec5c+awadOmPJW2BATyQxgJC/y/wM7MkJK2ppS0MaWSkxWlbE1xtjKmlK0pe2WnOHBgE8MG9tUTERMTE9q2bYuPjw/mpqa0GTCaqIhrZGWkY2xihixTP2NSdmYGxq9HxnvXLeHxnQgWHbjEuvMPaD1gFPOGdkUt1zf1Bu38hWxTEeleFiQYx+JbqhB9/WuxNWgLC6dOwKW4M4mJiXh6euLs7PxJ5y5TKzB+yyv6QxTUepBLrgDnmtjzY270ARIUr1BrNBBYnW6Xl5GRkUFkZCTGUgnN6/hw/vQJMtLTiH8RR1UvD0aPCNRl8TKztKZJ13407z00z77zq0RlbGpGhZr1AOj47Sz6X/4ZUXB3ONITUZPSFG3lzc3txyjlYM3L6MfUbt6Bn8/fZ8nhqziWLE3HYRP0jlGjaWsWBv+Zx/PdycqErUFb0Gg0ZMmVRMSmEfIwkWK1mwuFHwQKhDAS/srJVqiISswkNVuBXKXGUCLG2kRKGTszjKWS/3d9k4hFOs9dALVGTf3gnagS0/nDPYF+GjUSkfbdtEKFCrqHrrWJVCdKGo0Gp1JuxD9/RnZmhs4k/ezBHWo0ba37u1rjFtgW1joB+bbsyI5FM0h+9hDKFQO05tmJq39E08gFRCKOJ92iaSFtvuYOHTrQoYM2Xjc1NZWNGzfi46NfArCgyNQKTCQFF2F4Yz24GZdOTFo2IvStB7nmYycrE7yKWn7QWey892SqXZ7ODUCMiKoWJfXWFy5cWO9zbrGFqMRMREC56tqCHWf37vhg30MP7MK7QTOd17tMrcBB4YiFxIiszCzU55+x5cBaJCIxXkUtSYx9RsNOvTE0MsbQyJjyNevx/HXxir8i1/yemJnz+jrJ8r1ON2LTcLIyfn2d8hYKEfj/jSDCXylf8oPhv+6bUqlEqVSiUqlQqVTIZDIMDAwwMDDgSXYiHH8EdYpzNucR7W8sw8bAjEKG5hgHuLN9yFysOlRmWt3+7F2/FLdKPphZWGFmYUVxN0/2rVtCuyHfcvPCGZ49uEfgPG0olYtnRa6cPEj1Jq2wsCnEn0f2oFQq8KvqpetX4Pk1pF19DMO0yScOJUWwAG0ZxmvXrlGpUiWSk5MJDAykZcuW+eY9LggfOxLOxc7MEDdnMe2fzmO+U3/cpMV1L0s2plJcCxX8ZelE8m1SlFl4mjlxd2MYM7Z0ZZe7J3PmzKFevXqANjd4hQoVSE9PRyKRsG7dOlKzFXmKKfwVObJsrp46xMhFG3XLDEVSmlhVAYt01m/ZhFFhRxrVa/D6HI34ZugwThwPxr1qTbLS07h54TTtBn/7vkMAb0b/iZk5f2m6/yuPbQEBEET4q+RDCek/54Phc/Rt9uzZzJgxQ/d527ZtTJs2jenTp3Mt8SGqM49hqh8qjZp9idd124kcQNOnHEt6TWSzairulasxaNab4gFD5qxg/YyxDGvoRaHCTgTOXY2ljbbWcLNeQ0hPTmJqd39yZNkUdi7JzJWbKGJfSNc+7chtrCqUIN3REqlIQmRmHDKVHGOJISNHjuTGjRtIpVI6duzIokWLPvn8P1WEs1Q5NAibR4r6FTe5Tf9SlT/p+C9yUul/ZwM7vYZhuLgzHh5YKhfpAAAgAElEQVQeGBkZ8euvv9KyZUvCw8MpXbq0rthCcnIy69atw93dncyPDP/JrxIVaMOIpru2JfTCSur26q1nVu7coim/b9vCkHqeqFUqajfvQJV6Td97jFwBBj6p8AMgCLGADiFj1lfGx1aEgYLN6f0T/NN9+yfM2cueHuPbB79SzMiW4sa2jC7hz4A7G0lQaOd7jcVSLvpMpaJFcRIzczgSmfDRXrq555GbMettYnNSKP/nJMKqzSQy+wVNbMsXOI9yQSkSMoLw6jMpYmRd4DYajYZWN5ZwLOkmco2K8mbO3Kw556OPrdaoCbi+kOpWpZlZul2e9f7+/jRv3lyXVCOXFy9eULFiRX49F8HTdLlu+dm9O7hweI+eY9bb/DS0K2UqetN20Fi95aVsTSkpzcLFxYX79+9TqpTWS1ytVlOyZEkGDRpE136DGDhoMCHHDqJWqXAq5UaHoeOpULs+EhFcOh7M/nVLSIqPw8nJmYBvxlLZTyvW54J/Z+PscXp1kEct3oRH1ZqAthzj9gVTefbgHsZmZgz8ZiBzZ2tfDO/cuUOvXr14+PAhAFWrVmXZsmV4euaf/1zg60JwzPqK+JBp7H28HcrxKTx58oRmzZphY2NDkSJFCAwMRKlUAtoar1WrVsXU1JQAv9o8uvsmY9XRX9YzrnVtBtfzZFSAN78smoHqdTvQJomY2rMFlV2KUq68F+fPn9ed54Cx32NjZYlP6aI0Ll+cAK9i+Lnac/7O448qvj6sWCPifZfT19GXC2lRjIrcTiv7ypiIDTERG7LLK5CKFtpiBrmewx8bwvNXnsMX0x5S06oMJU3taVrI6x8XYPi0kfDKmJMcTLyBXKN1KorM0o7SP5bFT4+SoZIx1aV1vutFIhH5jQOUSiXx8fFIldl5wpfex/sqUUlEYGMqJSgoiFq1aukEGLQZvZ49e0ZgYCCFLU2oU6U8ixctpmTp0gwdN4nV3w/FJCuRouJM1k4dxZoVS3mVnk6fsVNY/f1w0pMTdfty9aqi5yCWK8AAa6cMx61ydVaejOC7tTtZ9/Ma9u/fD4CjoyO7du0iOTmZxMREWrVqRZcuXQp20gL/8wgi/AXz66+/4uHhoSu7du7cOS5evEjjxo2xtbXF3t6ejh07EhenTUBxMy5dJ8BKhZyJHeozunk13f4ir1/SK8k2qK47fXyKc+XUIVRqDQtW/kzVqlWxtLTE2dmZ8ePHo1QqyVaouPYkgeadelDYqRim5uZ4lK/AvuADAAwdOhQHBwfi4uIIDw/n7NmzrFq1CrlcTuvWrenRowd7rtyndvP2LPt2AMrXxeXfLRb/7MFdjv+mTduYkZbK0rH9Ceg5iFWnbtG671BatmzJpftPORKZgG/XwXoPvGa9huBetSamVrYFLr4enZ3I5Ie7cPtzAqGpD7A2MGNvxZGs9ehLUSMrfnDtQHP7Snpt/mnPYa0I502e8U/yKSLc3K4iM0u1xcrABEORBIVGxa3M5x+1j2vpj5n35CDbyw/GQCwhNTWVo0ePIpPJUCqVbN++nZCQEJo2bZqn2MKYMWOoXLkyVcsUQwOoVSptwQiVEo1Gm15UqdRPF/m+SlQAroXMCAoKyhM2ZGdnh4uLC6tXr8bIyIhRo0Zx4vhRqletwoQB3XAtVQpJwiPM5WlYW1sTEBCATKnGuYovRiamxMdEF+haJMbGUNO/DWKJBAfnkpSu4M2NiJuANvVqyZIldS8kuU5pAv8/EET4C+X48eNMmDCBTZs26cqulSpVSlfN58mTJ0RHR+uKFuSGcuRyaOtaLG0L6e3zQ6EcL1LSmbdgIYmJiVy6dImjx08w5LsZ7IqI5XpMMlJre8at/o2Vp27TbMBounTpwo4z14h6+IhOnTphbGxMkSJF8Pf35/bt25w5cwalUsmgYcOJl2lo3KUfGo2GO1cuAPkUixeJiH/2BICoiKtY2tpRrVELxBIJnvVaYGVbiHXbduYZ6Ws0Gi4c+iPPCCi/4usajYaTybdpe2MpVS5PRaZSEOozmcNVvqVjYR8OJUUgEYl5UOsnRhXPf14wN8lGQRJN+Jd1+Esz/8W0KGpYub53/d9Fo9EgVyv1CjcUBBcTeyaWbIFaoyHGdwl3a/5IRfNiBW6foZTR9dZqlpXtQUkTe0Bb9Wjy5MnY29tjZ2fH8uXL2bt3L2XLluX58+f4+/tjYWGBl5cXYrGYVTs2sfDZAW4rozh/aBcD67gRNPd77l+/zMA6bmyarR9G9L5KVE5WJly/epmYmJg8VbhAW23pyJEj2Nvb4+rqioGBAYsXL+bly5fcv3+fcuXK4e3tjYeHB/v37yfyZTrXzhzFwNCQYmU8dPuJjrxNYKOKTGjvx771S/WsOk269if04G6USgVxTx4SdTOMMu/EHFtbW2NsbMzw4cOZNGmSbnm2QsXNuHTOPUri5IMEIfTpK0NwzPpCmTZtGlOnTqVGDa2DiZOTk97vXAIDA/Hz89OFcgAkPH/Kn4f30HXUFDb9oP+gept3QzkadehJEScrDA0NeSW1pHz9Fly5eIE6nQciNTal7cAxurYV6jTCzrEYoZevUrt9b9Zu3kq9evVISUnh8OHDzJo1i9u3b1OhQgUeJmXp+lasjDvPH92nQq16gLZY/Ja5k5BlZmBhbUuXUVOA11mjeCO2GkCmUPEs6p5u2aC6Wm9htUqFQp7D+pnf8vjuTXqOm6nbJleITY01HEi7woqYE0gQM6xYQ7aWG4S5wZs5vBZ2lZj9eD8TS7ZALPrr91M7M0Pqu9ohU6iISsokJUvx0Z7DCrWSsFfRVLP66yxWf4cctQJDscEnmbnDM55SwtgOe0NL7A0LXjQCYOT97dS2KkOXIm8cpOzt7bly5Uq+2+cWW5CrlQQnXGdDbAjN4tbSqXA1urtUILp1Geq27PyXx5y763SeZblhRHauNcnMzMy3XaVKlThz5ozeMoVCQUBAAL1799Z5pffq1Ytu3bohk8mQGEgZOne17rtTtnI15vx6nEJFnXn+6D6rJw1FIpHQom8gABXrNGTd9NEc2f4zapWK1gNG4uRWQe+YqampZGZmsmXLFkqUKPFFRzgI/HMIIvwFolKpuHr1Kq1atcLV1RWZTEabNm2YP3++LhF9LiEhIZQrV04vlGPbgql0GDoeqbFxPnvXkl8oR25FmFwHqrthl3Aq5ZZv+7SkBF48faxdr9Hw854dWFpaolKp6N27N23atGH27NlYWVnp9c3EzBJZ1psSdzX921DTvw0vnj4m9OBuLG21eYfLVPAmJSGei0f34d2wGReP7CU+Jhq5LFvXdm2IVpA3zBqHUp5D2NljVGuYt7C6Uq1myZ1LPDW9x6qyvfCzcc9XlOrZuNPp5koS5a+wMyyYk5qxVKIXd/wxRGQ8w8XYHksDkw9v/Ilkf6JnNMD51PvUsS7z0e12vrzEudRIwqrN/PDGr7mbGcuG52fZ+uIC7qZF6e9Yl10VAjGVaMXFnk936itowYtc1Go1PXv2xNDQkBUrVgBw4sQJxo8fz5kzZ0ixKMb5i1dYOrYfY5YGUaJsOV2OcIBiru60GjCKw1vX0KJvIBlpqSwc2Yue42ZSo2kb0pISWDFxML8Hrcd3uv5LspmZGYMHD8bWzo45O09hbq1vzcrlv4pw+JLzDHwtCCL8BfLy5UsUCgW7du3SlV1r3bo1s2fPZs6cNx6qERERzJw5k3379unSIV47fQSVUsXP00ahVqtQyOUMquuOPEdGgw7aB8GFw3vYOHscSoWCJaP76kr4TQ86gIO3N1Hxqfw0rAeR1y8TaX6R54/u0+e7H7FxKALAsR0b+GPNAlRKBQc3r+Tu1T+p1647UzftobajKd3atcTOzo7MzEwMDAzwrBtAiWrauMzszFcoFQoWj+5DZNglDKSG+LbqROcR3+NU2o2t8yYzfP7PmFvbMHLBen5bOputP02mfA0/PKvVwcahqN610ub0PUiD9j2xtCmEW+VqvIsIEZWl7vzg0eAvHxzGEkMa2HpyJCmCHkVr/+37+CH+TIuixhc4H5zL+dT7tLWv+lFtnmQnEHhvK4crj9WzMuRHhlLGby8vsSE2hMfZCfQuWodzVb/HzaxInm1L25lwOe0RkleFClz04VOESaPR0L9/f16+fMmhQ4eQSrXXLjw8nLp16+Lt7c25R0mUKleRUuUrc+fyeUqULZdnPyLQGXISnkcjFot1pnLbwkWp3rglV0NOAnktVXdepJGVlUXSy7j3ivDb/BuhT8Io/L9DmBP+Anm37JqdnR1jxozh0KFDum2ioqIICAhg6dKl+Pr6YigRk5Odxc7lP9Bj3EzWhtxjzNIgbOwLs+xoGIZGxrpRYq2AtrhV9KF1/xGsDblHzwlzsHcqTgl3LzLkStbPmsD9G1cYv2oHSw5fxdTCkm3zpwLaUcLlEwcpXMyFuq27oJDLSX4ZS6NOvREbGBL+NJEbN25gbm7Ovn37MDY2Zs7YwTov0mcP7nI++Hc8vGuz9Mg1Fh28RK0AbeiKWqkk/vkbRxf3qjWYFnSAlSdvMnDGEl5EP6JUOX1HqaunD2Nmac2j2+HUat7+vWZXERCVlL858m1a2FXkQOKNAt6pv8fFtIdfrAhrNBrOpz6gjnX+lpD8UKpVdL+1hvElm1HV0uW9+/0zNYoBdzZQ7Pxo9idcZ2LJ5jyts4i5ZTrlEWClWsXm2HPYhwTSJ3oRTd3s/5G5+PcxZMgQ7t69S3BwsJ7VycfHh3PnzhEeHo61iZRnkbe4H35ZNyccEXqatKQEAGKfRPHL4pmkJcUzoJYrR7avR6PRTr2o1WrO/LGNXavmceX8GTw9PZk+fTrXr19HpVJx70kMzevXQalQMHdwZ4Lmfq9zQFPIc9gwaxxjW9ZksJ8HU7sHEBGqNcGr1Bp+PXyaeg0a5uu0mUtYWBh169bF3NycwoULs3Tp0jzX4F78K45EaqtpqTXkSZaier2sIM6PAh9GGAl/geRXdu1toqOjadSoEVOmTKFnT23VF2sTKQnPHpMYG8MP32jfuFVKOVkZrxgZ4IOZpbVulJgbytFn0o/AmxJ+EpGIS2dPceXEAao3aYWHtzbEonqTVvy6eBYajYaNs8ZhIJUyfuUvBG9ajlIux96xGKd2bSWgxyDCIm4iFoupU6cOhpWdSDdQoEZD3JMoLp84QE52Fo4urvh3/0ZXLL5YGQ+eP7rPgc0r8arp9+Y8I2/hVLosCpmMP9YuxMahiN763L5XqduYE79vof+U+e+9pgUtvt7crhLjHvyGQq1EKv53vx4X0x4yqWTBSxN+Cp8qwg+z45GKJBQ3/vBILJfZj/djJjFiTHH/POsS5OlsjbvAhtizyNUq+jvV5U7NHyn6F7HLv7+4zKj720lVZpGlllPMyBZ7cyPquxr9rbn49xEdHc3atWsxMjKiSJE3LwNr166le/fuTJ8+nQ4dOvDy5UtMrGxo0SeQ8jXqAnDnSijrZ45FlpWJla0dbhV98GncgrtXQpHLZAz/aS2/L/+RLT9+hywrE6+afpw+tJ+Loedo164d27dvJy4uDqVKjURqyMQ1OylasjRLxvQjeMMy2g4ai1qlwrZwUSau3UmhIk5EhJ5i1aShzNpxDHvHYrxKTcWvbTf2792DgYEBgYGB9O3blyNHjgCQmJiI/+v62B06dEAulxMTE6N3DT42ll9IQPL3EUT4C6Vv374sX74cf39/pFIpS5YsoUWLFjx//pwGDRowbNgwBg8erNve1c4Mp9JlWXjgom5ZVMQ1ts2fgr1TcTy8a+tE/e1QjtwSfv2nzOf2lVDWTh1B97EzOH/wd1ISXmBqYcXFI3vxqlWPLXMnEfskivErf8HwrfnmwJ9+5pdFMzgUtBqRSIyxmTmXy2SzK3whijZlYMN15g/vgZOLG26VfDA2M2fhiF7cvXqBoHnfIxJLsCpkr1csHuBQ0Brdm75XTT9GzF+nd41S4l9w9+oFnEq54VbRB3un4n95TQtSfL2okTWupoUJTX1APVuPD27/qcTL00lUZOBuVvTDG/8NZCoFJp8gwrnzwQV16DqXEsna56cJqz5T59Sm0qg5lnSTDbEhnEi+Qyu7Sqx2742vddkC7XdFzAkSFK9QvI5VrvmWF/nfmYt/HyVKlMg3ZjmXwMBAAgO1jlbv1l3uMmoyXUZNztPm2f07JMvi8PSpzbSgAzy8dZ0lY/qxNGgXhawtad68OZaWlgQFBVHJuxqeFSvTrNcQyr5+YW7cuS+/L/+RtoPGYmSi7xxZyVfrHPnk3k1unD/J+QO/ExMVyaMrIWwN2kJgYCC+vr6IRCLMzMyQy+VoNBoePXqEkZERRkZGFC5cmM6dO3Py5Ek0GihbzZdeE+boKoQ9uHGVXxbNIO5JFHaOxeg1YbauznUuKrWGgQP6cy74dx48eICrq/Y+3b17l2HDhnHt2jXs7e2ZP38+bdu2BWD79u0MGjRItw+1Wk12djZXr16latWPmwL5X0cQ4S+UKVOmkJiYiJubG8bGxnTq1Invv/+eefPm8ejRI2bMmKGXijEjI4MShcx5KnlzS82srNFoNETdDGPgjCW65aEHdxPQc5Dub7dK1bB3Ks7m2ePIynjFL4tnoFTIGd1M+2Ur7laOAb2GMLW7PwaGRoz0135JlAo5pctXoUTZcnoZjLbvWczxectBrgKpGPH0+kxvOgsnHFkQ2JOI0NOMXLiBkQvXc+zXjZzetZUfd53GQKrvQDNkzoq/vEY2DkXYePExE9r75Vtd510KWnxda5IO/1dF+FLaQ6pZunzQC/vvIlMrMP7I4g0A51Lv42tdtkDbpigy6XF7Les9+1HUyJrH2Qlsij3HpthzFDGyor9jXTZ49sfKwPSj+nCiynh8Lk/nVkYMIkT/uun+fag1ah5lJxD26gnnU+6zO/4qy136IxHbfnRiHBePCji6uPI87CwqlzYEBwdjZGREhQoViErMhNe1pHPRaDQkx8eRlZGOqbn+S8fbzpEiRLTsN4LbF8+SnqMNjQoJCcHNzY3w8HBSU1Np0qQJXl5eHD58mOXLl1O9enWsra1JSUnh0aNHnH2YwMRBvdm7bjFdR0/Vxer3mjgH7/oBXDy6jyVj+jF/73nMLN9YMO6HX+blO/HSSqWS1q1bM3jwYI4fP87Zs2dp2bIl169fx83Nje7du9O9e3fd9ps3b2bWrFlUqVLlo67n14AwJ/yFIpVKWbVqFampqbx48YJly5ZhbGzMtGnT0Gg0ZGRk6P1A3oLsHlVr0rBj7zyjxLm7TuPXWpuR58Kh3dRp3gGJWMT8rXvYePExVfyaUsm3EStORLAu9AFV6/uz+YeJbL7ylPWhD/QSZLw7+rx96RwXV21mWfBWqodOxWhRC9SLLmCfmoCBWIKhkTFlKvlQoXZ9DKSGBPQYREZaKrGP35+cQCx6/z/qgxtXSYl/gU8+XtFvk5s1qSC0sKv0r88La5N0/Hvxwbl8qjm6oJ7RGo2GgXc30cKuEunKbBqFzcPn8nRSlVkcqDSaK9WmM9i5wUcLMMC+hOu8Usn41Wso5gbGVP8MIqzRaHA8N4oKF7+n3+0NLI85wQt5GjXsi31S9jSp1IAePXoyuF9vjIyM6NatG2vXrsXMzIzUbAVetepz7NeNpKckkZoYz4nXyWvejgoAUCoVrJ0ygjrN2+NY0hXvBgFUrdcUMysb5Eq1zmnzu+++07WJiYlhy5YtLF26lKdPn+Li4sKBAwdo06YNUhMzUtWGVKnnr6sg9W6sfq1m7bCwKcTV00d0+1QplWybP42e42YBkPM6dvnevXvExsYyevRoJBIJDRo0oHbt2mzdujXf67JlyxZ69er1r2SM+9IRRPgrIr+0iqGHdlO7Rd4EBgAPblwhJeElVRo2xdleg7mhdhT97MEd6rToiLmVNVJDIxp17sOj2+G8Sk3+YB+e3r9DJZ+aDG/UhYs1pnOl13JcKnvw9Eo43s5WFC/j8VFfNIlYRCVHK3hPk9CDu6ha319XTvCvcC1kVqBjVrYoQboymwdZLwrcz4/lv/CMBpCp5R8twvHydF7K0yln/uH6xTMe7eV0yl1+fXGRjbHnGODoR0ydxSwr20OX7vNTeJj1kqH3tvCb1zA6FK5Gkt9Kan1CuNTfRSQSMcSpPmqNhkx1DgYiMSOLNcHR2OaTsqfJH4axePZUzpw5g1wu5+zZswwYMIDw8HDkKjUt+w6nRNnyTO3uz5z+bani1wSJgRRLGzvdftRqNT9PHYWB1JAe42flOU7Gq3Sd02a1alprVokSJXjy5An29va4uLjoXuhTU1PZu3cvYVExZKWncu30YbxeJ+95N1b/9UKeP4zUfTy6Yz1lK1fTOahFp2a91fbdphpu3bqVZ3l0dDQhISH06tWrQNfxa0MQ4a+Mtx8MHxolnj+wC5+GzdgvPk/9yMmseXkUNWpcPCsSenA3WRnpKJUKTu3airV9YSysbQHt2688R4ZapUKtfp1O8HV2oNLlKnLz6kXCw8MBUD5IJO36E+pWrYG7gwWD+vXm4c0wbl86h1ql4tiODZhb2+DokndUmBtm4lXUEmer/MNd+kyay6CZeT0838XJyqTADjtikZjmdhU5+C+NhlUaNVfTH/8nI7tPGQmHpt6nlpWrrq7yu6Qps1gTc4ryF75j1uN9dHTw4Wr1GRyvMp4uRWpgLPm4uNx3yVEr6HxzFZNdWuH92sv63zbbvw+5WkmC/BUikQgDkRipSMJElxa69R/KniYCRG95bCdH39eFOonFYnx8fKhevTonTpzAUCLG0NiYnuNnseTQFebvC8XcyoaSHl6IJdr/3VznyPTkRALnrcXAQP/eZmWkczX0jM5p087OjitXrhAdHU2rVq1QKBQ6M3Duy7BcLse3nAtDGlZELBbTsKNWDN+O1VcqFZw/8LterH7Si1jO/LGdtm/5caRla58D7u7uODg4MH/+fBQKBceOHePs2bNkZWXlucZBQUH4+vri4pK/R/3XjiDCXyG5D4Ybx/fiU98fc3P9UaJEBCq5jKsnDzJm8ACqFi2ECAjOOo9So6LLyMlIDY2Y0M6P4Y0rExF6Ss8pav/GZQys48bBLav48/AeBtZxY//GZQB4VK3B1GnT6NChAxYWFrRv355JkybRpEkTAAJqVWHtxi1s/+l7hjb04vrZY4xauEE3H/y+MJN3Te0fQ27WpI9Ba5IO/6TjfYjbGTE4GtlgK/3w6P3v8ikinF9okkaj4WzKPXrdWkuJ82M5lnSLbI2CFWV7stqjDy6vU1P+E4x/8BvFjG0ZXqzxP7bPT+GpLIm6V38gRp7C3Ro/UkhqTq+itXF4J3tYbva0DhUcqeRkRSlbU4qaGeBkJsbe1IBilkbUdDbH2kisF+oEcP36dc6dO0eFChWwNpGSlvCClIQXOl+O/RuW6Tlj5TpHjlq0Uc85ErSOin8e3kMpVzed06a5uTne3t4YGBgwdOhQUlJSOHbsGElJScyaNQsLCws8PT3Zf/0Rq8/cwd65BGunjtS2fR2rf3T7OkY2rcLNP8/qxer/smgGrQeM1JurVqi1zo9SqZS9e/dy8OBBihQpwsKFC+nUqRPOznmtK0FBQfTu3fvv3q7/WYRShl85BQnliMyMo/KlKWSrFQw37UYVQw9E77P/foDi1ibUd7X78IYF7Nvb/JdlGjOUMoqeG8lz3yX/eEartTGn+TMtis3lvvlH95sfm2JDCEmJZNNHHKva5eksLNMVX5uyxOWksiXuPBueh2AoltDf0Y+eRWvx45MDRGcnsatC4D86j7c3/hqj7v/C9eozsZEWbPrg3+Bo0k16317HmOJNGVeiGSKRiAR5OhYS4wKN9KdPn67nOAnoalivWLGCJUuW8PLlS+zt7Rk2bBhjx44lW6Fi9uY9rJ02mlfJidgWdqTVgJHUCtB6FCfGxfBtq1oYGBohkbz5fvT+7kdqBbRl77rF7P15MQYGBhgZvUmgkeszAjBv3jwmTpyIlZUVvr6+nDp1igsXLpBu4cyj5CyiI2/zwzftddno3kalVDK+rS99Js3Fq6YfQ+qXR2poSO5cUXpyAla2hVi1fBndunXL075WrVr07t1bzys6NDSUJk2a8OLFCyws/n+GOAkiLABA6dBveSZLprZpeXpI22LwCY7z76uZ+0/yMUL8d+skB1xfQD/HunQsnDcL19+h7+111LByZZBz/X90v/mx6tlJbmY8Y7VHnwJtn6nKweFsIFvKfUNQXCjnUu/T3sGbAU5+VLcsjUgk4khiBAPvbiK8xqx/dDT/JDuB6ldmsr/iqM/ihAVaT+hZj/fx8/Mz/FJ+CH427v/p8d8NfSooKqUSlUrJ6a0rkGQms27dOgwMDLh27RrW1taUKVOGlJQUhg4dSnx8PKdOnUIkElG/fn3Kly9Pr9GTuRmXzvbFs3j64A6TN+wB8sbqP75zQ7cuPTkRtfpN2N+oAG+2HThBuwa1MDExISIiAjc3N9RqNatWrWLlypXcu3dP7wVh4MCByGQygoKC/uaV+99FCFESAGCqSxueZCcwqWRLvo84SlmV50cJ8afm6f1Y3B0ssDMz4mZcOs/TtHNT76bUA+0csDal3qf3J9ck/U+L8MW0h++t0PRP8zHm6PuZL5j28A8UGhWLnx6lv6Mfv5Qfopd+8mVOGv3ubOCX8oP/UQGWq5V0ubma8SWafTYBTpS/osfttWSr5FytNuMvE4n8W3gVteR5es5Hhz7t37iMfevehCFu27aNadOmUbZsWSZNmkR8fDyWlpY0btyYCj90w+BkX4zFUgwHORO6LJg1lTdihBQXz0oMmLZIt5//Y+88o6K6ujD8DL0KKE2aiKgoYgWx914Se0nsfrHElqixxYa9R2NLjL3HlqiJvSt2EEUEpYh0EJA2zABTvh/ICNJhEJPwrOVS595z5k47+5599nnfgvbqZ+m8Z6ehnZVCaezAgQPs3LmTjIwMWh7qybEAACAASURBVLduzeXLl3MEYLFYzLFjxzh58mSxXuu/jYqZcAW5kMikzH92kZqSOkUKxKWdcZaUslBNys4bUSzODxcT1ebnfIuUikt8Rgq2d2YQ33YbaiplL4C/4vVZkqUiVtoPyvO4UJrGiehH7Iq4ycvUKOy1TXHQsWCX49hc58rkMnp6baCJvi3L7POuuC8pM18d4WVqFGcafFcu21QeJAYyyHsrg81cWVFjwCf5bLIjlcu4Fv8Ct6A/sZLY0EuzQ5ktu9x650fXJ+sQyzIV5ATAUrt+tJC1KNEsPIviLEVV8IGKmXAFuVBTUWV5g2589/w4xmmW2Amq5SniDsqZcZaUslBNyk41bWOqahrwMDGI5obK2dP7MDEI50rVP9kgn9dMWC6X8zjpNbsibnEs+iHNDez5zqYrvYwb0strA31M8xZM2BRyiQRJKovs+ij1Gv9668Wx6Ic8cV36yQOwXC5nW9hV3IL+ZEed0fQx/bRqTfEZKbgF/cn+SHfSZBJEsnT6mOjjXNVA6csuUrmMc7FP2RRyiXRZZhWztoo606y78KPdl8QK00o0C8+6huIWP1aQSUUQ/gdTljZjqgIVNtUbyDjfPdwW3mO1xRhS0yiTGefnTFZKWllB+FOYNmRHLMvA+H3aOC49hYNRmfrNKdI0xlRtzbNmy7DSytx6JpFJuZ8YyFGn3OpjT5LesDL4Lx40XaRUTe1QcRxjfXdxqv4UqmiUfbV4dlIkYsb57uGFMIK7Lguw1zH7pM8PEJ72ju1h1xTSnDoqGkyy6oRDFeUtuyRkCNkTcZstYVeorK7HVOvODDRzYaLffjpVrscK+4GIMqREJqVhqKVGfGrGx7uDC+RTLUX9W6kIwv9APpXNmIpAhR11RvOt334mR2zlQqOZJVI++ifTy7ghE/32sVxJ6dd7iQFMsu6olL6KgkiaTrAkliHe27gQ501P4wZsrPU17Ywccu29fZoSgo1WlVxrvUJpGkOfb2dT7a+VuhUpQyZhiPc2vrfpSstiuDUpA19hBP2fbaZZpRrcc1mAdin3NpcUJz1rdtQZxZgXu8iUxpDTxihTLjRr61NJl118hRFsDr3Mkaj7dK9Sn0P1JiiK60TSdF6LYpli1pMbgbF5jiVFobyWov5NVAThfxiFVQcr2+xbRaDCdoeRTHl5gM6ea7jU6AcMy3HryKfG1aAGEWkJhIjjiuUolBcyuYwHSUEcMBhf+MmlJEQcx96I2+yNvI2hmi6zbXuw3WFkgdt+8rMu/O7lIVwN7Bhq3lyp17gw6BSV1LSZVa2HUvstjKNR95ny8iCr7Acy1rJt4Q3KED9hBPMCTrLafhBur0/TztABjY8yDcVZdslKOW8OvcyzlDDGWbbDp/kKLDSNcpynrarBKIPuXA94V+z08+ewFPVvoiII/4MoL5sxgUDA5trD+f7VYTp6ruZy41mfRGjic0BVoEL3Kk78HevFRKvSzWBfpkZRRV03l9iDskiTZXDm7RN2RdziUVIQQ8ya0dbQgUFmTRlh0arQ9ncSXvGlSaMcj52IfsSNd354urrl06pkXIh9xoHIuzzJ5rpU1qTLJMz0P8rfsV5cavQDjSpV+yTPmx+vRW/p4rmWlfYDGWnRigFmTVEp4f787ClnIzVdptl0YZBZUzTzqYwvyZ57AGMddapV1vlPLEV9KioUs8qBYcOGUbVqVSpVqkStWrXYuXMnkCkfN2DAAGxtbREIBNy4cUPRJlaYxuOwRAJfPGPFuAGMb+PA1K6NuXRkF5C5Z2/7j5P5rrszE9s5smxsXwKfP1EE4lhhOps3b6Z69epUqlQJZ2dn7ty5o+i/e/fu6OnpKf5oaGjg5OSkOC4QCPip1ld0qFyXjh6riU3/7xh59zJpyF9vSy9heS8hgGZlYNrgkxLG9FeHsb79PdvCrjLMvDlhrTay1WEEOqoaRXJRksvl700bPsyEQ8RxTHq5n8P1JqCvRMGScHE8o17s5FC9CZiU0Q3Jx4S+V796I4rFo6lbuQfgiLR3dPJczRzbXox8f4NUXduEatrFqy72FUYwyW8/lkv7sLj9KCK6bOPdVwexDZDx5KEHnTt3pnLlypiYmDBw4EAiIyMVY4lUJifYzzvP8QTgbUQoqyYMZlyrWswZ0B6fB7d5J5Zgrq+VIwB36NABgUCA5L10LUBwcDDt27dHR0cHBwcHrly5Usp37N9LRRD+BIgypHhHJnE7KI6r/m/pMmwif917RkzcO86cOcP8+fPx8PAAoFWrVhw8eDCHqTiAd2QSCfFxrJ86gnZ9v2bLlaesPnVLYSouThVSvW4DFh/4m61Xn9Gq5wB++m4U4lQhUpmcY+evM2fOHE6cOEFiYiJjx46lb9++SKWZBSHnz5/P4crUokULBg4cmOMaBAIBa+wH083YiQ6eq4hJT/oE717506VyPW4nvEQoTStVP/eTAmiupKKsZImI38Jv0OzhEro8WYuWijp3XRZwvclchlVtqVjjLOo+4SBRDGoCVappZQYBqVzGsOe/MMOmGy4Gdkq5Zsgs/vrq+S9Msur4yYQwLsV54/LQjb6mTfijwdRyX055m55EJ881jLNsz7clqA+QyWX89daLLp5raO+xisQHgRjt9ePi4T8RJqdw69Yt7OzsePfuHePGjSM4OJg3b96gr6/P6NGj8Y5MQiqTk5wQn+94AvDL/ClUq+3IlivP6P/tD2yZM5F3cbF4R3743R86dChH8M1i6NChNGrUiLi4OJYvX86AAQN4+/Ztyd6wfzkV6egyJN8CKmMbfOPE+MZFII17hxwBgYGBNGnShO+++w4ghyydKENKWKKYi4d+o16zNgoZO3UNTbSrZzrLmFpVo9vXH6QJ2/X7mqM/LyfqTSC2derzwj+AOnXrKgyzR4wYoVDPqVo1p7F8cHAwt2/fZs+ePblek0AgYEWNgagL1GjvsZJrjedgpmmgrLfss8RQXRfnStW5Fv+C3h+la4vD/cRAxlmWXCVLLpdzN9GfXeG3+OOtB+2MHJhf/Qu6VXHKd8tTUYPw7ffWhVlbhJa/PoOGihozq3Uv8fXmhdvrP1FXUWVe9d5K7TcvZHIZy16f4Zew6xytN7FM/aGLSkKGkK5P1tHPpAmzbQu238yr7Z6I22wNu4qhmo4i5dx+TlvcFi2mWbNmAFhaWub4O4vJkyfTtm1bwhIz9wIXNJ5EvQnijd9zZm4+iIaWFi4denD5yC4eXzuH0YDhiDOkpKWm4Obmxv79+2ne/EO9wKtXr/D09OTSpUtoa2vTv39/Nm7cyMmTJxWa1hV8oCIIlxGFrbnsWfkjd/46TnqamGq161HDpXW+fQXEChEAgc+fYFWjNsvG9CU6LBg7x4aMmL2MKuaWudq8eemDJCMDU2tbABq0aM+1I7/x4MEDnJ2d2b17Nw0bNsw144bCXU0EAgFLavRDTaBCO4+VXGsyp1zUhT4lWVuVShqEkyQigkRvqa9nXey2MelJ7H+v3yxDzliLNvjar8S8CO95UYNw9lS0e8IrtoVdw9PVTanrtVfifNgdcQvPpkuUJn6SH1nqV6nSNDxcy0f96mOE0jR6em2gtWEtltboX+R2vsIItoRe4XDUPbpXqc8Bx/E0M8iscpZKpTx+/JgvvvgCe3t7xGIxffr0Ye3atQrlqixu3bpF9VoOilXngsaT8KBXmFja5LAIta5Zl/AgfwAC4oRsXzqPiRMn5hpDfHx8sLOzy6EF3aBBA3x8fIr5jv03qEhHlwFFKXoYMWc5v9z0Zd5vmX643jFi/GLyXmdNEGUglUN8TCR3/j7JVzMWs/7sPUwsrdn+4+Rc54tSkvlt0Xf0yeZwoq6jR6suvWjVqhWampq4ubmxY8eOPMUR9u/fz6hRowp9nQvt+jC8akvaeqwgXFy41/A/mawgXFKBuUdJQTTSr5ar8jU/JDIpf8d60e/pz9S6OxuflHB+qzsGv+armGXbs0gBGEBU5CCcWRmdkCHk6+e/8lud0bkqaktDVFoCI3x2sN9xXJlnTh4mBuL8cDFOelZcbTz7swjAYmk6Xz7diIOuBT/V+qpQUZKslHNXz7W091hFFXU9fJqv4LDTRJob2ivaR0dHk5GRwYkTJxTuTE+ePGHZsmU5+nv27BlLlixh3KxFioxcQeOJWCREWy9nMae2nj5iYQpSOdy7/xB3d3emTJmS69pTUlIwMMj5GRsYGJCc/N+pIykOFUFYyWQveigMFVVVajVsSnxMJFeO71cUUH1MujRTJF1DU4sm7bpi59gADU0t+vzvewKeeZCa8mGNJl0sZuP0MdjVa0Sv0R8C9M0/j/D38UP4+PiQnp7OwYMH6dWrFxERETme686dO0RFRTFgQNH2xc6r3pv/WbSlrcdKQsRxRWrzT6SWrjl6qlp4JYeUqH1RRToCU6P5MeAEtu4zWBJ0mm5VnAhptYE9jt/QyrBWsRWlxNLCg/Db9CSi0hNx1LVkgt8+ehs3LFXa/WOkchlfP/+V/1m2pWNlR6X1+zFyuZxtoVfp5fUTP9UaytqaQ5QqLFJSMmQSBnlvxVhdnx11RheYXUiUpLIx5CK17s5mcdAffG3enDet1rOkRr88b4qyZrtTpkyhatWqGBsbM336dM6dO6c4JyAggO7du7Np0ybqNHZVPF7QeKKlrYtYmDNoioQpaOnqIZPJWL9wFps2bUJNLff7q6enR1JSznqRpKSk/6xLUmFUBOFsbNmyBWdnZzQ1NXPMBO/fv59nlWF2PD09adOmDdamlZnUuVGOKsNVEwYzpXNDJrSry4KvuuJ585LiWEJsNM/cr3N862qGN7Hm8qPnOfqdOXMmIzu7MqFtHd5GhBIT9ubDwfcDslwu585fxxnlYsPikT0xMjVn1LxVOfoJ9felZceu1KpVCxUVFbp160bVqlW5e/dujvP27dtHv379cnkQF8Qs255MsupIO4+VBIv+vcUXpfEYvpcYQLNKeQdhkTSdQ5F36eCximaPlpIqTeNCo5k8aLqIcVbtS2WlmJmOLngfp3uCP80NanAgyh2flHDW1Bxc4ufLi+WvzyCVy5Qud5mdFImYYT6/8mv4ddxd5tPX1LnMnqs4SOUyRvjsQA4ccByXbxre732Vc/U7M3mQGMgBx/E8arqYERat8t1mBGBkZISVlVW+N2dv3ryhU6dOLFiwgOHDh6Oh+uH5rewdcrbLNp5Y2tUiJjwUkfCDDWKo/wss7WoiEibz6rkXgwcPxtzcHBcXl8z+rKy4ffs2jo6OBAUF5Zj5Pn36FEfHsrsB+ydTEYSzYWFhwfz58xkzZkyOx/OrMswiNjaWbt26MXrsN2zNo8rw6xmL2Xj+Mb/ceMHAyXPYPm8S0aGvkUml+HncJzH+LV/+L9NIOyw+iYRkIZC5ZUlTU5Ot+37n1xs+DPh2Nq+8HnL77HEkkgzO7NpErYYuIJdzds9WtHR0UVNT55vFP6GikvOjreFYn4c3rxAUFIRcLufy5cu8evWKevXqKc4RiUQcP368SKnoj/m+Wje+t+5KO49VBKXGFLv950r2yvamkmaI4irhHZmEOENa5D7kcjn3EwNzSV8+SXrDZL/9WN35jv2R7kywak9Y65/4qfbX1NPLbX5eEsSyjEK3KN1OeEUdHQtm+R/jqNNEpapH3Yj3ZXvYNQ47TSizdWA/YQSuj9zQEKhyz2UBNXVy1zmUB3K5nAm+e4lJT+a406Rcs3KZXMbfsZkp57YeK6mirsfz5ss54vRtjpRzYYwePZrNmzcTExPDu3fv2LhxI7169SI8PJwOHTowadIkRUGUoba6Qmyjde9BeNy4kFk/km080dU3wLyaHTa16nL6t42kp4nxuH6BUH8/nDv0QF+/Ete8XuLl5YWXl5di1u3h4YGrqyu1atWiYcOGuLm5IRaL+eOPP3j27Bn9+xd9Hfy/RIWLUh7Mnz+fsLAw9u7dm+dxT09P2rZtq7jTmzdvHqGhocxas5WnEYkFSr9537vJ+mkj0NTSQaAiwNjckk6DR9O69yDGNrfDyNScdzFROdr4vgrgUZIGMjnMH9KFuKhwBCoCajZwYcTs5ZzdsxkNDS0uHd2Fqpo6auofBt3pm/ZRu5ErIOfO7+v5++gJkhISsbC0ZN7ceYwaOVJx7pEjR5gzZw7BwcElFtLfHnaVla//4lqTOeWixassCpIGVRFkOs8UVRrUPzWKjh6rCWn9E+8yhByOuseuiFvEZaQwxqI1o6q2Lvb+0KJicnMyL5qvKHA/rsuDxSRJRXxn06XUgiTZiUlPovGDheyqO5auVZwKb1ACjkU/YJLfAVbaD2CsRdtycWDKC7lczvRXh7mfFMjlRrNy2EEmSlIzhTVCr2CopsNU684MMmuKVglvfjIyMpg2bRqHDx9GS0uLQYMGsWbNGlavXs3ixYvR1c25JWv7TV+yVsuunTjAmd0/ky4WKcaTKuYWQOY+4Z1uMwjyeUIVM0uGz1qKo2trVAUwoL6FYq9wcHAw1atXJyMjQ5GeDg4OZtSoUTx48AAbGxu2bt1Kp06dSvT6/u1UBOE8KCwIb9y4kaNHj3L//n0gc7O6k5MTV2/fIyQ4KM+q5Z++H4XPQ3ck6WnUa9aW6Zv25ZitSiUSxja3Y+1pd1zr1aa1XU6JxOsBbwmISuCHPi0Zu3A99Vu0AyDIx4v9q39k4d6zrJ44hBbd+9K2z9Bc1+wtecn6lANoq6gjQIBYlsFc215Kt6QD+C38BkuCTnOl8Sxq61YtvMFnRnHUhIqinbsv4g57Im5jpWXEX7FP6VqlHmMt2tCxsmOZVwnrXx9PeOuN+aa0hdI0DK9PoLtxfU4r0UZQJpfRw2sDjfRt8rVRLA3pMgk/+B/lbKwXJ5wm07iSrdKfozQsDDzF2bdPuNZkjkIq1E9R5XyfrlXqMcW6M80Nij7jVRbXA95WWBZ+RpR/1cI/jKwqw9OnTyseCwsLw9PTk+W7jqFhXp1jm1ew/cfJzN/1h+Kc73/ai0SSwYsHd4h8E5grXZydrEKs7DhVrcTC777FumZdnJpn6t3KpFL2r/6RYTOXFNifqoqAwTUc2PxMDdF7D1ENgRqjLPLfFlUavrFsh5pAhQ6eq7jcaBZ19XJvofpcUaY0aLg4nr2Rd1gT/Dc6qpr0NW3CxlpfY6zx6QpUxLIMtAtYU9waehUVgQp76n6j1GCwOvhvUiRiltopPwUZJo5noPcWTNT18WjqVqAednmwNvgcx6MfctN5HgZq2vwd68XPIZfxSglhnGU7vJstw/K9c1V54FS1UoVl4WdExZpwMcheZdi69YcApq2tTd++fanfqEm+VcsAamrq1G/ZHu97N3mSrTjrY7IXT2SxevF84kMDmLp6u2KwvHpiP9b2dbCvn78HatZMzcXEivU1h6CjooEqKqgKBMwLOM4rYVS+bUvDaIs2rLIfRCfPNTxPCSuT51A2xalsz052adAMmYQ/Yh7Ty2sDTvfnEyKOo6qmISfrT2GaTZdPGoAlMikyuQw1Qd5CHjHpSSx7fZq+pk2UaiN4J+EVm0Ivc8RpotJ9ky/HPcfl4WK+NGnMnw2mfXYBeHvYVbaHX+Nk/SkcibpP7btzWBh4KrPKueV6ltboX64BGMBYVxNnKwNUVYp301VhWVg2VMyEi8jHVYbZqV+/PgKB4EPRQ7Yqw7yQSSXEhOe91UVVAEY6OWcuixYt4vz589y6eZO3Ug1FoHjxyJ2Xnvd56n4dAGFSAiEvfQh59YLhs5bmSpVOsu7EybePuZvgz/NmK/g9+gEtHy+jv6kzi+z6KH0/5fCqLVEVqNDZcw0XGs2kgb6NUvtXNllyfvkRFfKa+UO74NKhB+OXbspxTCqTc9jfhxVJe6ipY8ZYizb87jQJANObk2ms/+m1itPkErRU1POc4crlckb77MRYXY+vleiOFJuezFfe29lVdwzWpXSdyo5MLmP567OZRV71JtL+M1C/+pgDke64BZ6mSxVHWj1eTpcq9djn+E25pJwLI2tMeByaUCT7wgrLwrKjIghnQyKRIJFIkEqlSKVSxGIxampqREdH56oyzM7o0aPp378/4yZOIkNmlKPKMCI4gNjwUByaNEdFTZWHl87y8slDBk2dp2ifniZGLstMQUvS07HS/TB7WLlyJYcPH+bWrVtUqVKFKqAw+x6/eD0ZaWmKH9GWWeNp2rEHbfsMxsYwt82YQCDghNMUvFNCsdMxZW713oyzbM+qN39R7/48Jlh2YJZtD6V6Bn9l3hw1gQpdn6zjfMMZ5S6cnx9Z0qAFcWDNfOzq1s/3uL6kMlcazKGegYXisVvv/HDSsy5x0U1pEEvzr4zeHHqZ2IxkYtNTaGFQUynPJ5PLGOnzG4PNXelp3FApfQLEpacw3OdXkqViHrsuVqqIiDKQyWUsCDzFujfn0VfTopq2cbmnnItCFUM52/0P00rNBSe1zO/Ax97kUGFZWNZUBOFsLFu2DDe3D5ZtBw8eZNGiRQgEAoKCgnBzc8txPCUlcw9dhw4dWLFiBf37fEFSipAa9Z0Zv3Rz5klyOX/+9hMR875FoKKKubUt367Yiq3Dh2rRca0+ONfMGtCeWUA3j7Xoqmlyct481DTUqVnzw0A5b9485s2bR/NqRjnMvvV0NKllZcKIFg752oxV0dDLoaFbRUOPtTWHMNW6M4sC/6Cm+yxmv9/3q6zAMcjMFVVU6Oa1jr8bTse5Ut5ymOVJljRofty/dAYd/UpY1G9CTOibPM9RF6iASA+yiQXdK6JIR1kglqXnKdTxNDmEZa/PsLPOGOYGHldaKnpDyAXiMlJYUUN5xX6PEoMY6L2VAabOrLQf+FmIb2SRKEllb0Tmmn9MRhILq/fhh2rdy+WGq7jcTfCnt9dPxEuECPWScavfNsdYoqGqgpGOeoVl4SegojpaycQK07jw8m2Jix6SDV/zbdDOD4+hwkSrDmx2GF5AS+XhkxLGj4EneZL8Bje7voqUsjI4HePJN767Odvwe1zLKTDlx+2gOILiU/M8JkpJZvHIXszaeoRbZ44SE/omVzo6C7vKOjkq2/s+3cRgM1eGmDcrk+suiMDUaLo8WUtgy3WKx1KlaTg/XMw8217EZwjxEYbza53RBfRSNO4nBvCF10YeNl2ErbZJqfuTy+X8En6NRYF/8EudUfT7TMQ3AF4KI9kSeoVDUfdopG+DZ9Ib/mr4PS2NahXe+DNga+gVfvA/qijSdK1Ug/tNF5bzVf13qSjMUjKlLXqYaNeaYeYt0HhfTCNHTjfjstljmReOelb82WAah+tNYFfELRrcn8+Zt54l1kzOzpemjdld93/09vqJuwn+Srha5ZFXRXoWp35ZR5svBiv2Txa1H4VIRxl4CBeFvHSjv391mCb6tgyr2jKXf3BJic9IYYj3dnbUGa2UACyUpjHc51e2h13D3WX+ZxGAZXIZ52Kf0u3JOtp4rMBQXYcDjuPwTgnjRIPJ/5gADBCTnkz2X7OM/L/7FZQ9FUG4DHAw1S9WIP646GG7w0iqqOuhLlCln0kTJvkdoM/TTfgKIwrpSXm0NKzFrSbzWGk/kHkBJ2j9eDnuCa9K3W8vk4bsdxzHl083cfvdSyVcqXLIqyIdMt2ofB7eoetX/yt2P2/EsQDYKLFAqTh87KB0KuYxV+NfsNVhBHK5nNsJr2hdyiCcVeDVx6QxfUzzr9IvKn7CCJo+XIyqQIX7LgvLXf0qSSLi55BL1L47hwWBJxlq1ow3LdczyKwpY313s6vu2DLVwy4L3Gr0ZaX9QKw0jdAQqKJSEQbKlc9ngeVfhoOpvqKAKjxRBBS96EFPTYuT9acy0W8v++uNRwBsDbtK28cr6GPamMV2fT9JcYpAIKC3SSN6GDfgYORdvnr+Cw31bFhhPwDHUsgqdjOuz+F6E+j3bDPHnSZ9Fj6veloqgIyP70v9PO4RGxnG9F6ZFcRpIiEymZSIYf64HTyX49yPK9uzZsHlVRmbPQiHiuOY6LePsw2+o5KaNoGp0agKVKimVTrRhU2hlwhPe8ex+pNKfb1Z6lcr7Afwv3JWv8qecv64yvmVMIpuT9axsdZXSjW6+FRIZFK2hl7lYL0JOOpaEptR4W5UnlSsCX8CElLTeRyWQIJYglQmR1VFgKGWGs7WhhhqF72I412GkFXBf7Ez4iYTLTswy7ZnqcT9i4tYms62sGusCv6LnsYNcKvRr1SzvGvxLxjsvY2jThPLbTbhlfyGXeG3OBv9jIW6k1AlZxFKmliEKOXDIHXh4A5iI0MZMWcFlYxyvvaP5fy+e3kIC01DZhXTvF1ZXInzYWXwX1xq/AMdPVbTzdiJOba9gEwVr/Nxzzjq9G2J+3+UGEQPrw08cFmInY5piftJl0mY5f87Z2KfcNxpEk3KqXBPJpdxIc6bzaGX8Ux+wzcWbZlg1QGrbFXOb0SxtPFYwcLqXzLWsm25XGdpORR5l1/Cr3OrybzPbuvUf5GKPEQZEitM43rAW876RhOVnIYwXYpYIkOYLiUqOY2/XkRzPeAtscK0IvVnpK7L6pqD8XJdSnjaO2rdnc3PIZdIl0nK+JVkoqWqwfRq3fBvuQZLTSMaPVjAzFdHiEtPKbxxHnSoXJeT9SczxHs7F+O8lXy1+ZOQIWRb6FWaPFjIF14bqayuy42ms6humFv4QVNLG0NjU8UfTR0d1DW0cgVgyMxqZK8kvZcYUG6V0fBeLUtVnZWvz6IiEPBDtR6KY5nrwSXfmpSQIWTw821sdxhZqgAcJo6nncdKAkTRPG66uFwCcFbK2eHeHOYHnmTI+5TzMvsBOQJwZFoCnTzXMN2m2z82AMvkMpYHn2VB9S8qAvBnQsVMuIxQtv5wXjxLDmFOwHFepUaxvMYABpk1/aQ/rMi0BJYE/cnxmEdMt+nGNJsu6KoWbGaQF+4Jr+j79Gf2On5DD+MGZXClmYPPzXcv2RVxk79in9KlsiNjLdvSKZt+c2krVdqV7QAAIABJREFU27vVNlUsK4il6VS5OYmYtltK9J4ogxPRj9gadgVfYSQeTRfn2Lda5+4cjjp9WyIBFblczkDvLZhpGLDVYUSJr+9KnA/DfX5lqnVnZtv2LNBntyx4JYxiS9gVDkbepXMVR6Zad6aFQc08f0Nx6Sm09VjBEDNX5tt9+UmvU5mciH7E2jfnuO+ysNhjhShDSkCskATRh21Mhtrq1DSu2MZUGiqCcBlQXP1hKJ0izfV4X2YF/A7AGvvBn1xNyD81ivmBJ7mT8IqF1fswxqJ1sfdzZm1x2VV3rFLX2bL0m3dH3EJHRZOxlm0YZt4iX/nIknx2AoGcptZGOT67ewkBTH65Hw/XJaV+DSVlR9h1Zvgf4aDjBL40bax4/G16EjXvziau7dYSbT/bGnqFneE3ueeyoER7YrOrXx2sN54OlesWu4+SIpPLuBjnzc8FpJw/JkkioqPnajoY1WGV/aB/7AxSLpfT6MFCltboV6zfWEGOYh9qWzIdxXQ11CoCdTGpCMJKRpmzqeIgk8s4Hv2IeYHHcdCxYFXNgTjpWRe7n9LwOOk1s/2PEZoWx/IaAxhg6lKsAetRYhC9nv7ELw4jS2XKni6T8FesF7vCb3EvMYCBZi6MtWiDSyW7Il1PcQKxDCmRWq+ZV699jsc3vLlAoCimVDPF0iCXy2n2aAnJEhEvWqzKcezPGA9+Db/O+UYzi92vZ1IwXZ+s467L/BJVLmepXyVJRPzu9O0nU5VKkojYG3GbLWFX0FPVYqp1Z4aYuRZ6E5EqTaPbk3U46Vmzpfbwf2wABjj79gkLA0/h6bqkyK+juDelAjKtPgsK1IVZf/7XqFgTLgL+/v5oaWkxbNgwAK5fv46TkxOGhoZUqVKFvn37Eh4eDmTqD9+7eIZlY/oyrlUtVo7PbeM2ysWGca1rM76NA+PbOLB72SwgU3/4xyUrqFevHvr6+lSvXp21a9cq2sXExDB06FAsLCwwMDCgZcuWPHjwAAAVgQqDzV3xbb6KrlXq0clzDaN9fiNUHFfWb48C50rVudJ4FltqD2dl8F+4PnLjWvyLIrd3MbDjfMMZTPDbx/Hoh8V+fl9hBDNfHcH6zvdsDLnIILOmhLb+iV/rjKapQY0iDzwOpvp0q22KjaE2qoIPg0gWWY/ZGGpjbyPnQNLFXH3cTwygeTmuB++PdCdUHEcbo9q5jpV0f3CSRMRg7238XPvrEgXgx0mvcX64iDq6FlxvMueTBOBXwiimvjyI7Z0ZuCf6s6fu//Bo6sYoi9aFBuA0WQZ9n/5MdW0TNtce9o8OwHK5nKWvTzO/GGvBJckKySGXFrVUnvknJEHMhZdv8YupqMbOTsUWpSIwadIkXFxcFP+vW7cuFy9exMLCgrS0NBYsWMDEiRP5/eQfhCWK0a1kSJehY4gMDuTF47t59rn08EXMrG1zPZ4oSue33XtwadyIwMBAunTpgrW1NUOGDCElJQUXFxc2bNiAqakpu3btomfPngQHB6Onlyk9qKGixlSbLoy0aMWa4HM0fLCA/1m0Za5tLww/geOMQCCgSxUnOlV25Fj0Q8b57qGGtimr7AcVSTe6cSVbLjX6gW5P1iGVywpVmkqWiDgW/ZBdEbd4LXrLyKqtuN3kR2rplm5/qbGuBu3tjRFnSAuU85PJK5Pgl0pAajT2OmaK9vcSA1lhP7BU11BSAlKjmel/lBFVWyDIQ4zzToI/q2sWz+NXLpcz3ncP7Ss7MLSYhg9Z6lcLA//gF4eR9DdzKbxRKcgr5fys2bICU84fkyGTMMR7G/pqWuyqM/aTr1crm0vxzxFK0+hbxL3cJXUUK4yCrD//q1QE4UI4evQohoaGtGjRgoCAAADMzMxynKOqqkpAQIBCf9jRNdPm8OafR4r9fL1GTkTf0gA1NTVq167Nl19+ibu7O0OGDMHOzo7p06crzh03bhwzZ87k5cuXNGmS88dloKbDcvsBfGvVgcVBf1Lr7mzm2PZiknVHNAvwl1UWKgIVhpg3o5+pM7+F36CH13raG9VhaY1+1NAxK7BtA30bLjX+gS6ea5HIpQyr2jLHcblczr3EAHZF3OJUzGPaGNZmjm1PelRpoHTrPC11VeqZ5++fqiJQoZdxQ/6Ofco0my5AZsWvWJZBDe2SVw2XlHSZhKHPt7PYrg/vMoSI30sTZiGUpvFcGEbTSnbF6ve38Bv4CMN54LKoWO2E0jQm+O7laUoI7s7zS31zVBB5pZz/qD+12OvWMrmM0S92IpZlcLrBd0r/Tn1q5HI5S4NO82P1L/K9mci6ic9CJBLRfsAIhv+whLvn/2DfyrmKvjLS05DLZOhWMqRZ1y+pbFqVu+dPERcVjp5hZToMGE6P4R+Mbk5uX8eTmxeJCA6g95gp9B03ncdhiRjralJFR50VK1bw66+/kpCQQI8ePdixYweVKuX8zcXHx1O7dm1q167NnTt3lPwOlS//7Nu7MiYpKYmFCxeyfv36XMdCQkIwNDREW1ubdevWMWvWLBJEGUWyBQNYOW4AU7s2YfMP43gbEap4XCqHd6mZA6dcLuf27ds4Oua9h9bLy4v09HTs7fOXRbTUqsxvdcdwo8lcbr7zo/bdORyMdEcm/zRSdRoqakyy7oR/izXU0bWg6SM3pvgdIDotscB2TnrWXG08m9kBx9gbcRvI9L9d9+Ycde/NZfSLndTWMedF85WcbvgdX5g0LrfBspdxQ/6K9VL8/8F704bySF8uCDxJVQ0DvrXqmEsxC+BhYhD19azRLkZgepYcwo+BJznmNKlY7V4KI3F96IaKQMB9l4VlFoBLk3L+GLlczrd++wkVx3Oy/hQ0PiPDiJJy850fMRlJDDZzzfeclJQUxZ/gsAjUNbVo2jFzf3uL7n359ZYfv97yw6FxM2rUa4yxhTXLjl7mped9/J895hu3n9h61ZsZm/Zz9dg+7l86o+jbzLoag6bMo0HLDorHpDI53pFJ7N+/nwMHDuDu7k5ERAQikYgpU6bkur7Zs2dTp075i/qUBRVBuAAWLFjA2LFjsbbOXeBkY2NDQkICsbGxLFu2DAcHhwL1h7Mz99fjrDtzl5UnrmFoYsbG70cjlXzY65vVz+wF84hLT+ZUw3cM9d6Wo4+kpCSGDx/OokWLMDAwoDDq6llyuuF3HHAcx5bQqzR5sIjLcc+LdL3KQE9NiwV2X+LXfBWqAhXq3p/LosBTJEtEBV7zxUYzmfnqKI0fLKDW3dn4pITzW90x+DVfxSzbnkr3QC4JHSvX5UFiIEnvX0t57Q++EufDoah77K77PwQCQWYQ/sjKsLjrwSkSMYO8t7Gh1lAcdAvXzs7iePRDWj1ezlTrzuyt+w06St6mJZPLOB/7lB5P1tPaYzn6qlo8a7aM350m0dKwVolugORyOT/4H8UzOZizDb9X+jWXF0tfn2Guba8iV8L/su8wlSpXoVajprmOvY0IRZyaQqteAzAyMaNe83YYGpti6+CEqpoaVW1r0KhtZ/yfPla0adVrIPVbtkdLJ+dsOzxRxJ+nzyjGWD09PWbPns3vv/9OauoHM5V79+7x/PlzRo8uvdHI50hFEM4HLy8vrly5wvfff1/geZUrV2bkyJF8+eWXqBRxdlm7sStq6hro6hvw9YzFvI0IJSI4QHE8KuMdht+1Y+2uLbxd2JTLKX7EZwgVx0UiEb1796ZZs2bMnTu3WK+rtVFt7rksYH71L5j0cj9dPNfwJClva76ywESjEhtrf41HUzdei95S873gSNpHadPA1Gh+DDhB1yfrsNIyIlgUy6LqX7LH8RtalXCQLSv01LRoaVhTcVNTHqYNb9OTGPXiN/Y5fqPYfpWXgUNxRDrkcjkT/fbRwsCe4R8tCeRHhkzC9y8PMcv/dy40msE4q/ZK/aySJCI2h1zG4d4cfgw8ySCzprxpuZ7lHwlrlIQlr//kUvxzLjSa+UmV6MqSuwn+BIliGGbeoshtTh09RIse/fP83Jp370NYgB8uHXvyLiYK77vXcWreTnFcLpfz6skjLO2K9h1LScvIYQ4jl8tJS0vD3z/T4EUqlTJp0iS2bNnyWf3mlck/P9dSRty4cYPg4GBsbDLFDFJSUpBKpbx48QJPT88c50okEmJiYlCXiFAVqBY5JZ2FQCCA919EVQE8OHuSpEOPEWzoirhK5iD6OOk1fZ5uopaaCecnbcLS1Iz1W/O20yvK8/U3c+ELk0bsjLhJD6/1dKxcl2U1+ivFBaco2GqbsL/eeJ4lhzA34AQ/hVxkQfUvUROosCfyNj4p4Qyr2oKLjX6gnp4VganRdPBcjbqKKpOtO3+SaywOWSnp3iaN8EoJweUTKj/J5XLGvtjFMPMWOeQ/P05HS2RS7icGcrjexCL1uzfyNp7JwTxsurhI54eJ4xnsvRUjdV08XN2orK4cn2LILayxu+7/aJmPsEZJ2PDmAoei7nG7yY9Kve7yZtnrM8yx7VXkffshISE8fXiXr+asyvN4Ulwsmto6LPiqKzKplJY9B9C4XVfF8T93bEAul9G6d+GFf1I5NG7VgZ07tzFo0CCMjIxYvXo1gGIm/PPPP+Pq6kqTJk3w9v50qnqfkoognA/jxo1jyJAhiv+vW7eO4OBgtm/fzqlTp3B0dKRmzZrExcUxffp0GjVqRJOa1rx+FoFMKkUiyUAqlSCXy0hPE6OiqoqamjrhgS+RSCRY2zuQnibm5Pa1GJmYU7V65szJ/fwf/LFlE4+uu7OBe/wZ44EMOXNte2GtZsTCkVMRqqSjMs0J8zvTMNHQp66uBXV0LRR/19G1KNJAoq6ixkSrjgwzb8H6Nxdo8nARo6q2Zp5tb6UZvReGk541bjX6suz1acb57UFHRYMp1p250HAmmtlSqTV0zLjRZC4dPFYhkcv4zqZrAb1+enoaN8At6E88k4Kx0zZB/xPOpLaGXSEqPZGT9XOupX0chJ+lhGKlZVSkz9YnJYxZ/se40WROkRS/stSvplh3Zo6S1K9kchmX4p7zc+hlHie95hvLdsWuci4KO8Ku83PoZW45z8NMs/ClnX8Kj5Ne8ywllD8aTC1ym/379+Pk7IqJZW4lNZlMxrWTB2jYpjMTl20mLTWVXUtncmzzCgZP/ZErx/bifu4U83acQF2jaKn8Tv2GoiV+R7t27ZBIJMyYMYOzZ89iZWVFREQEP//8Mx4eHkW+/n8iFUE4H3R0dNDR0VH8X09PDy0tLUxMTAgPD2fGjBnExMSgr69Pu3bt+OOPP9BWV8XKQItDBw6wa8kMRdtxrWrRsucAvlm8gcT4WPav+pH4mEg0tXWwr9+E737ag5pa5mD556/riYuLo22zzPSfVC5D3rE6vY8sJerxK15de4y2tjYJ3TahAsQip92Rzag4GeKe4M/O8Fu8EIajo6pJHd2q1NW1zPa3BeYaBrlmD/pq2iyu0ZcJVu1Z8vo0DvfmMLNad6Zady5WIU5xiM9I4WDkXXZF3CJJImKMRRt+rjUMj+Rg5gWcwP39NhrXbGur1bVNMgOx5yokcikzs2khlze22iaYaRhwLPrhJ01FP0sOwS3oNPdcFuSa7YilGWipfPj8iroeLJSmMch7K6vtBxXqliWTy1gZ/BdbQq9wsN54pRhxJElE7Iu4w5awK+ioaDDVpjMn608pk+/ioci7uL3+k5tN5pWb5WRZsfz1GWZV61Gs3RD79+9n2IRpqApy7/f1vnsdmVTKsJluqGtooq6hSavegzi1fS1Vbe35e9825v56nMpmVYv8fFrqari5ueHm5gbApUuXsLS0xNLSkjNnzhAZGUndupmKaiKRCJFIhLm5OeHh4aiq/rOr1rOoUMxSMmWhmCWXy4uVdpPL5USkvcNXGMkLYTi+wgheCCPwFUaQLpfmOXOuplVFMXt5KYxkXsAJHiUF4VajLyOqtiqRvOHHyOQyrsa/YFfELS7EedOjSn3GWrahvVGdHDMniUzK3sjbLA76E9dKNVhu3z9HUVCYOJ72HqsYY9GaudV7l/q6lMXcgGOcfevFjGrdGG3RpsyfTyRNx/nhImZX68kIi1a5jvd8soGJVh3oZdIQgEHPttDbpFGh67tjfHaSIZey33Fcgd+7+IwUhj/fQaIkVSnqV/6pUWwJvcKByLt0qlyXqTZdlJpy/pg/YzyY4LePK41nUa8U1pyfI8+SQ+j6ZB1BLdcV+ebl7t27dO7cmdeh4Vx8nczHQ9ie5bO5f/E0vcdMofuw8YhFQnYtmUlSXCwx4SHM+eUoFtVzrwVLJBnIpFJ2L/0BE0sbeo+ZgpqaOupqqlTXkWKuIcXOzg5fX18GDRrE1KlTGTduHGlpabx7907Rz++//87hw4c5ffo05ubl6zOtTCqCcBEpjnj5p9aOLg6x6cn4vg/IL7L9HZ+RQm3dqjkCc5pUwpawKyRJRKyuOYjuVeqXaEAMEcexJ+IWeyLuYKSmw1jLNnxl3rzQlLlIms7m0MusfXOePiaNWWzXRzHQR6S9o73HKoaZt2DBZyKo757wig4eq/Bqtow6xagkLinf+u0jUSLioOP4PD+Xjh6rmVe9Fx0rOyKXy7G8/R3uLvOpXsC6/4FId5a/PsvjpovRU9PK97zHSa8Z+GwLfU2bsNp+ULG1wrPIK+U8wao91mU8K70c95yvn//CuUYzcC4n68SyZLD3Vpz1q/ODbdGzRePHjyc1NZUDBw5wPeAtIQlixbH0NDHTujkzaPIc7l86Q6j/C1RUVHFwbk7Qcy8SY2NQ0/gQ7Jt378uouSsB+G3xdNz/PpHjucYuXE+7LwbSQCuFAf36EBoaiomJCdOmTcuhhZCdvXv3snPnzn/dPuGKIFwIxREvz66J+ilclJRJskSEnzAyW2AOx1cYSVhaPCbq+iRIUqmsrsv/LNrSy6QhtXWqFniHnSbL4PRbT3aF3+Jx8muGmDVjrEUbGleyLfa1ZfdR/saiHbNte2KkrktUWgIdPFcz0NSFxXZ9y716MkL8Dqs73/G65TqqlXGB258xHkz3P8IT1yUYqOnkeU7LR0tZU3MwLQ1rEZgaTVuPlYS2+inf98lPGEHrxyu42ngW9fNxV5LL5ewIv8H8wJNsdxjJgBKqX+WVch5i1qzMlj+yc+e9a9ep+lNonYes5z8dP2EEbR6vIKjlugJvpAqiNBm9omJjqE17e+My6/+fQsWacAEUFkizAnJIgpjwpLQcgdTBVB9jXU28I5MITxTlOB+yB3Dt9wG87AefgtBX08bFwA4Xg5xKSmJpOq9So3ieEs6R6PssDz7L2jfnSZNnYKlppFhrzlp3lsll/B79kENR93DSs2KsRRv+bDCtVINrlo/yFOvOLA76g1p3ZzOrWg8mW3fiRpO5dPRYjUQuY1mNvLdVfCoeJb3GXMOQ83HeTLDqUHiDEhImjme8315ON5iWbwCGnIVZdxL8C9zaJZKmM8h7K8tr9M83AAulaUz03cuT5JKrX32cct5Vd2yZppw/xiPpNf2e/szBeuP/lQEYYMXrs0yz6VLiAAxgrKuJs5UBj0ITcqWllYGqigCnqvkr0f2XqAjC+VDclHJemqhF1R/+nNFS1aC+vg319W34qmpzhNI0NoZcZMObC7QwqEmnynUJEsXya9h1vFPCEMnS0VPVor6eNU56ViRLxTxMCqKurgUmGqX70VlpVWZn3bHMEEbwY8AJfg69jJtdXy41nkm3J+vJkEtZXY5Wc/eTAmhpWJO/Yr3KLAhL5TKFB2+zQgrAcgbhgvcHf/fqEHV1LfnGsl2ex18KIxnwbAuN9G2433RhsTyS80o5P222tMxTzh/jkxJGT6+f2FFnNF2rOH3S5/5UBKZGcy7uGZsdhiulv7LIk2Zl/sp74vG5UBGE86Ck4uVZgdhYVzPHF6ww/eF/ErqqmvxY/Qu+sWjLRL/9jPfd+960oR4L7frQubIjEWkJirVmj6RgDkS64yuMREUgyFYUllm1XUfXAivNysUKnHV0LTjVYCr3EgKYE3CMdSHnmVutFxtCLiCRS1lfc2i5BOL7iYFMturE6Bc7SZWmlYni0prgvwGYY9ur0HM/DsKTrDvmed7RqPtci/fFw9Utz/ftRPQjJvrtY3mN/nxj2a7I722yRMS+yDtsDi37KufCCEiNpsuTtayvOYQ+RTQx+CeyKvhvvrXqmGeGxN/fHycnJwYMGMDBgwdZsWIFK1asUByXSqWkpaURExMD2vrcC4phz4p5PLp2Dg1NbXqMmEC3r78BIMDbkz9+WUewnzcqKqrUbtKMYTPdMDTO1IUXJidyeP1int29AUCHAcPpO256rqW3mzdv0q5dO3788UeWLVtWtm/OZ8q/TjFry5YtODs7o6mpyahRoxSPv3jxAmdnZ4yMjDAyMqJTp068ePHBZm/t2rUKC0Enh1qc3bc9z/79PO4zysWGk9s/WAxmpKdxeIMb33V3Znw7R8aOm0BGxgcFqODgYHr06IGRkRHm5uZMnjwZSTaZynHjxlG7dm1UVFTYu3ev8t6MMiAyLYFVwX/RymM5vqkRfG/Tla5VnHic9JrwtHcIEFBN25huxvWZXq0bv9Udg7vLAuLabsWn2Qrc7PrRQM8G/9QoVgefo+lDNwxuTKDpw8WM8vmN1cF/c+atJwGp0UgLUSBrbmjPjSZzWVdzCGtDzqGuosq52KdMe3WIT13qIJFJeZwUTIfKdXGuVJ1r8b5Kf44HiYFsCr3MAcfxRapWzwrCb9OTiExPzNNf2j81iikvD3LMaVIulagMmYTprw7zg/9RzhdD/co/NYppLw9i6z6DW+9esrPOGDxdlzDaok25BOBQcRydPNewqHofvq5adOWofxoh4jhOvX3MNOsueR7/2A1u3rx5OTSjZ8+eTbt27TA2NsY7MomTv2wgOvQ168/cZc4vRzm3/xdFUE1NSqRt369Yd/ou687eQ1tHj51uH/ypj2xYQppYxLozd3Hbd4Z7507hc+UPutU2VQTgjIwMpk2bhqtr/prW/wX+dTNhCwsL5s+fz8WLFxGJRDkeP3HiBNWqVUMmk7F161aGDBnCs2fPgMyCk/3791OzjiPbz91jzeRhVDazoFmXLxR9SCQZHFq/GLt6jXI859/7thHs+4xlR68gk0nZNH0Mi92WsHzZUgC+/fZbTE1NiYyMJCEhgc6dO7Nt2zamTs3cRN+gQQMGDx7M7Nmzy/rtKREZMgnnYp+xK+ImtxNe0d/UmX11x+UwKXiUGMSsgN/Z8OYCq+wH8YVJoxwDtkAgwEzTADNNA9pXzinE/i5D+L4oLHM71c13fvgKI4hOT6KmjlmONec6uhbU1DFT7H0UCAT0MG5A1ypOHI66x48BJzgQ6U50eiJH6k38ZBZ0z4VhWGtVxkhdV6GelbU1SBkkSUR89Xw72x1GFFmsIjMIa3A74SXNDWrkCtxiaTqDnm1lsV2fXDaT4eJ4BhVD/Uoml3E53oefQy7zKCmI/1m2xcv106ecPyY6LZFOnmuYYt2JcVbty/Vaypo1wX/zP4u2eYqx5OUGlx25XM6BAwdYuHAhogwpYYli3M+d5H8L16NbyRDdSoa07TOUO38dp36LdtRvmfO97DhoJKuyead73b7Cip1HqGNRBSN7c5LHf8Plv49jPOeDcMj69evp0qVL5sz7P8y/Lgj369cPgMePHxMWFqZ43NDQEEPDTLF/uVyusB/MYtasWQB4RyZhkU2EPHsQvnBwB/WatSYpPi7Hc3rdvkKPERPRM8jsv8uQ0ezatloRhF+/fs3Q8aNQ1VDD3Nycbt264ePjo2g/adIkALS0Sl5IURa8FEayO+IW+yPdqaFtyljLNhyuNzHPgg8XAzuuNZ7D+bhnzPY/xto351hbcwjNDQsXrjBS16W5oX2uc4XSNF4KIxWp7SNR93khjOCNOBYbrSo5tlPV1bWgn6kzg8yasjHkIgsCT1H77hwuNpqJnU7ZWwreSwygWaVMYZFexg3p4Lmq2Pu7C+Jbv310rlyPvqbORW4jlmYaOOQn0jHT/yj2OmZ8a5UzTX013odhz4umfpU95aytosE0m86cqD+5XGa8HxOfkULnJ2sYataMGdW6l/fllCmRaQkcjrqPX4vccpNZbnBXr15l165deba/ffs20dHR9O/fn4BYIalJCSS8jca65ocbZptadXhy82Ke7V96PsDCLud3zFhXg9Z2mTdhp1UEPH/+wTDmzZs37N69G09PTyZPnlzs1/tv4l8XhAvD0NCQlJQUZDIZS5YsyXU8QZSBRJYpQt6u31eKx2Mjw7h99hhuB85xYM2CHG3kcnmOCgapTE50RDgJCQk8lIYi7VOLEVvmYtDYliYqlpw/f56lS5eW3YssBUJpGsejH7Ir4hb+qdGMqNqS603mFMlBJ/us9ECkO4O9t+JSyY4V9gOorVt0FZ0sdFU1aVzJNte2pnSZBP/UKHzfB+hzsU9Z/+YCr1KjMNWoRB3dqoyq2oqTMY9xuDeHMRatWVZjgMLYoCy4nxhIS4PMwqdauuboqGrglRySa4ZZEg5GuvMk+Q2PiqjhDJnfSbEsA02BGnfeq49l50T0I87HPcPTdYniRqE46lcBqdGZVc5R7nQwqsvOOmM+K2ONZImI7k/W07lyPRbZ9Snvyylz1r45x8iqLTHNo/ixIDe4LPbt28eAAQPQ09MjISYOoTBTu1lb78NvRltPH1GqMFfbUH9fzuzaxNR1OxWPOTVvx/aN6+nVypno6Gh2796dwxlp6tSpLF26FD09PaQyOTEpadwOiitUg+HfyH8uCCckJCAUCtm3bx/VquUeINOlsjxFyA+tW0S/8TPQ0tHN1aZ+i/ZcOrobB+cWyKRSrvy+B4BqV6eSbqSO2B4EJxPpWz3z+NDhX/PFl1/k6qe8kMvlPEwKYlfETU5EP6alYU1m2HSjp3GDEokwqApUGGXRmsFmrmwOvUyrx8sZYOrMIrs+mCvBelBDRQ1HPatckopSuYxg0Vshd1TAAAAgAElEQVSFUlgP4wb8EePBb+E32RF+E1stYzpVdqS+vlWBMp4l4X5iIDOzzbayUtKlDcKBqdFMf3WEK41nFavQK0MuRUWgQrpcwnNhGE0rfdh6FpQaw7d++/i74XRFAU98RgojfHbwLkPI46aL81S/+lxTzh+TKk2jt9dGGurbsK7mkM/mxqCseJuexN6IO3g3y13YlOUG9+TJk3zbi0Qijh8/zunTp4HMMVDrvWSvWJiChmZm5kskTEH7o/EvOjSY9dNG8NWMxdRu9GFt9+uZbpzatJiaNWtSpUoVhg4dypEjRwA4e/YsycnJdOzVh+sBbwl+l0pl9TSC4j8EaVUBPI1IzFOD4d/Gfy4IA+jq6jJhwgRMTEzw9fXF1PRDuvLsod25RMif3LqMOFWIa5e8A2fv0VNITU5i4dfdUFfXoG2fobx5+ZxkPZBLMmDuFehZG9tfhiNOFXF85QWODG2A/oSWGKjpYKCmjYGaNr7CcLaHXeW+L+8f+3DMQE2HStn+baCmTSVV7VIZ2cemJ3Mg0p1dEbdIk0kYY9Ga582XY6FpVOI+s6OtqsEs256MtWjLiuCzON6fx2SrTsys1r1MDA5UBSrU0DGjho6ZYj02VZrGF14bAZDIpfwe/QBfYQQAfqmRSOTSHCntrL9tssl4FkZcegpRaYnU1bVUPNbLuCFzA46XSs0rQybhq+e/sKD6l/nu3f2YLGW3t6lipul8zd+vwhmu1x2BTBVUM0VUBj/fxrzqvRV7wgtTv0qWiNgf6c7m0Ctoqagz1frzSTl/TLpMwoBnW7DUMmKbw8h/fQAG2BBygSHmrnneOBXFDe7UqVNUrlyZdu3aAaChqoJuJUMMjU0J8X9BPddMCdbQV745Us6xkWGsmfQVX4ydSsse/XM8r56BIYs37lCko+fNm0fTppn+xFevXuXho8fYV7NGLgeRMAkVFVVCA/yYtj4zXV6QBsO/jf9kEIZMR5DU1FTCw8MVQXj37t0c2fEzc7f/nkOE/MUjd177PmNq18ytDVlfmrD3XxoNLS2Gz1rK8Fn/Z++8w5o63zf+SQJh76EIyBBRREUUZ9W696rbWq2jrVtbR90WZ62jv2q1WldFa791VnFVbbXuDbgABRRkqGzCSAJJzu+PlCgbFa3a3tflFcx5z3vOSU7e+zzrfrQu5tO/7cDbx5fAlt/y2aV1HE/KQdKrFn82nYWrkR37VfuZM2cO51v8HxkqORmqHDJUckZJ91HPzAUfM2fd+3HK1AJjMlQ5yFSKv1/lGEmkhQi7IHFbSApuM9UzJCwrnsPJNzifEUkPWx/W1hxKS8sar2zBspGastJzEBOc2zEnai+eF6Yz160nnzq+/8Jyh+WFscSAg/W+4IObq7GWmHCi/jTmRu3jvjyJNTWG0NqqJndzHuuUwo6l3CIs+1ERGc/812pG9kXO+bIsioYWbgUSn5pbenIv5zFPlBkv3Jln3v192EnNGO/crsyxxSm71dH3JCcbGol82XMzAUcLQ07kXsDRwIpJzh0KqF/9UHMo/SoVbOJe2OW80Wv4G+VyLgyVRs2Ht9chFeuxtdYnFaJ3/qYjNS+LDfF/EdSoaGgNSu8Gl4+AgACGDh2q+14tjfSRiKBZlz4c3Pw9bl51yUhN5vT+Xxg5byUAaYmP+WbMQNr2G0qbPkVrkpPjo6lm7Ixabcnx48fZsGEDp0+fBmDIxC/x6DJUVwL6y0p/LO0q0WPkpGKvoTgNhncJ75xspUqlQqVSMX/+fOLi4ti4cSN6enqcOnUKW1tb6tatS3Z2NnPmzGHPnj3cv38fQ0NDduzYwZQpUzh6/A9u51kUUImRZ2ehlD91lTx705haWJKW+BhEYGlbiajbwfwwcwwBWzbTrYvWPVnF1RmrnvW4vGwnKnkuw4cPx9jYmB07dgCQm5uLRqOhbdu2DBs2jCFDhiCVShGLS19EBEEgS60oQtLa14LvxSvTuJMdT4w8BYlIjLFEilrQkKlWIBXpFU/gpZF7Iev8eTq1BMtimB65k2hFMkuq9aOPvd8rX9gV6lz63FyDgViPX+uM5Wz6XaZH7EJA4BuPAbSzKRj/lKnkhGcnFGmCEa9Mw93IrkCt86nUcKz0jPnGc0CBOfrfXENn27ov1MzhZGooQ+78SEjjhWWKnJRfWEYgV1DRwMmcmvZmjA7bSlBmDHvrTtDF7Au7nEc6tmSMU9s3vsOQRtAwInQzCco0Dtb74rnux7cZ/lG/EatMZXOtkeUb7+9PZGQkP//8MwDx8fG4uLgQHh6Oh4c2MVKep2bPzQSUSiXbls7+u07YEI+6DUh9kkBc5F0cXD2IjQjFwEjrts7LVaLRaEAQmL5+J9lpyexZvYiM9HSqVatGpUqVuHnzJoIALXp/RM9Pv9Cd00b/yVhXcsC7UQuWju5P9xET6DNmGgBnD+5my6JpOpe4RCzi8KFDOqt97ty57N+/n7CwMObMmYO/v79uXkEQWLJkCT/++CPp6el06dKFDRs2YG7+Zmk2vHMk7O/vr2uLlY+vvvoKb29v5s6dS1xcHEZGRjRs2JClS5dSt25dANzc3IiLi8PAwAC1ICAIBUXIn0X+TZN/o9wNuswG/y/ITE3GulIVRkycxuIpo3XjQ0JC+Pzzz7lx4wYSiYTWrVuz4rvVyMQmpMvzGD+oBzevXChwjFOnTulutBeFQp3LvqTrbI4/w42shwyu3JSRVVoWcG0KgkC2WlmEvGXqwkReHNE/3SYRiYuStKR0Ug/PTmBt3J8Yi6Usrd6f9ja1X+p6y4JSk0f/m2sB2FV3HPoiCXsSrzI7ci+uRrYs9ehHgzLE/PNlPJ9tfnE05SZ5GhVOhtYFZDyj5ckEZz4ksN7nz3WeybmZ+F6ex5ZaI8v8TF6kWYhYBL/nnUFimsU6r2GYSAyKdTkPqvx6tJxfFoIgMOHudm5kxfK779TnUvN6myFTyal2fhoXG87Fw7hShc5duIEDwLWTRxGJxdy+dJpchYJP/b8FQJWXy5+7t+FWqy5rZ4xh9OI1dGzbRqcLPXz4cLKysggICODAlTAmfdSbHiMm0qLH05wblSqP+UO7o29ggHej5gVI+MyB/zF70z6gqN50QEAA9vb2rF+/Hl9f3wIkHBAQwNdff82JEyewsrJi8ODBWFpaEhAQUKGf1cvinSPhisCraEf47Nwv0hDieRAsi2Fzwml+fXKZ+mYujKzyPj3tfDF8RQuqIAjINbllEnVxLvUMVQ5PcmVkqhWIEWGhZ4S1vulzWePPvhqJpaVa1bkaFQNv/YBSo2Jv3fEYSqTkaVRsTjjDgvsHaGHlyaJqfahuXD5dZLWgwfqvsYQ3XYpMLS/Qnepm5kNuZsdRRWpJLVPHInHn4ixcQRDodWMVNUwcWFZ9QDFHfIqXuU8FNHStWZkMcXoBl/NE5/ZvtMu5OMyM3MWJlDv82WB6qVra7xqWPDhIWHYC22uPqvC5S7u39q5bTuqTRzoSfhZfdG3EmIWr+WJwT90aaGtry9GjR6ldrz57biZwYMsabl04xayNe3X7Hdq6lmxZOrLUlAIGTmESloigb90qRbKmP/roIzw8PAqQcN++fWncuDHTpmnnunDhAm3atCE1NbVAr/h/Gv/amHBpyBcvf9F2hCUR8Ms0hCgLaXnZ/PL4IpsTzpCSl8VwhxZcbzQfF6NX36VEJBJhLDHAWGKAwwtmPyvUuax6eILlMYfxNavKMIcWGIj1i5B5nCKVO/mkri5K9GpBUzCZTWJUhLgbmbuzJ/EKja8uYIF7b+yl5rS0rMHpBjP4+fFFml5dSP9KjZjn1rPMbO7w7ATspeY4GFrigCU1TBzoxVNZxPeuLmSMUxts9E0Jy37ENdkDtj86T2h2AhKRuAgxX/1beWx33bJrJ289kqHWCPyxayvnDu0mLvIujTv00C2O8ffvsdH/CxLjYgBwrVmHwVPn4+juiQgRG+4GsSprByMdWxLceOEb73IuDkseHCQwKZjTDWb9qwg4W61kVexx/mows8Lnjlek4h+zHx+jWljKnZ77Ia+GnUmxPdEjk7MR/f13XNRd3bbSyj8BYu7eYXw7H0wsLGnepTe1Z8+inlPZgjWCIBRQzhMEAaVSSUREBD4+Ps91Ta8S/5FwCcgnv4pqR1gRDSEKQyNo+CstnM0JZzicfIOONrVZ6tGPttbeb11SiqFEynS3roxyasXS6MMMDd3AKMfWTHft+lyLq1KTh6xE97n276S8TOqaVuWP1DuMCN2Eu5Edmc+45JWaPLbEn+HHuFPY6pvhYWyPjb5psbHw67JonA2tOZ9+r2ACnMQAsUhMN9t6XJE9YHWNj+hs+/SHLwgCT3IzCsScf3l8kWuyaIzE+jS/tqiIxra7kb3ue81XNQJtLkL3ERN1bsJ8WNpVYtzS9dg6OCFoNPy5O4B1s8ez6H/HARGOQhXuNl6B1RsmElNerH54nC0JZzjrN/uV1oC/iVgfd5L3LWtUWN/qLJWCPYlXWRd3kuDMGPIENbNdzWln4/1ca6BUIqaqVcHfa6dOnVi6dCmj/L8l4WEsZwN3FrhPSyv/rOHbiMW/nsDGwYn4+/dYN2ss60yN+HF52ToLnTt3ZtmyZfTv3x8rKyu++eYbgAL1ym8C/iPhUlBR7QgruiFEnCKVrQln+enRWUwlhoys0pLVnh8VK1f3tsFS34Sl1fszzrkt86L24XlhOrNcuzPGqQ3ScmRSG4j1sZPql6tjk1rQMPzORuKUafzlN0sXS8zTqMhQyQnNimf5w6OcT7/HB3YNaGZRnRxNLhmqHJLzMomSP+HP1FAMxfpMjfi1AOHL1bmY6RlhJNYnJS+LIFk0lvrGxbrUrfRNaGNdi6MpN5nv3ouedvV5pEznoTKV8OxHnE4LJzQ7gSe5GVQ3rkQtE0cai+thLTgCYvzaaBMAo8Nukqp4pLs+EzMLTMy0mdkaQUAklpAYG63bricSE5+ei1Xlt4+Et8SfYeXD3znTYNYLe1/eVsjVuayM+Z3ffadU2JzLog+zKPogAto1Sl8kYbxzOyobFF0Dn0XhNXCGuGgYY/Xq1UyYMIFBbRthZGZJk449uXRMW5NcVvmnvdPTOntnj5r0+ORz/vxlA5SDhEeMGEFsbCytWrVCpVIxZcoUDh48iJOTU5n7vk78R8Jl4Nl2hKEJqcyd9gXXzp8mMyMNFzd3li5ZQusG3dixYwejRj2NzWg0GuRyOdeuXUNmURWFQsGOlf4E/XUMtSoPj7p+DJv5NVb2BWOPjx8+YM6gDjRs04VRC1eh1ghs++0IW76Zw4OHMeSJNFCnEgMWTmBn/XE0MHNl2rRpNDkwjsePH+Po6MisWbMYOnTo6/6oKhTOhjb85P0pt7JimRGxm1Wxx1lSrS/9KzWqMD1oiUjMT96f8knoZjoHr+BwvcmY6RmhL9bDVmpGS+uatLSuye2sOGZF7mZhdCAL3D9grHNbnUXqfXEm271HFVH1UmnUyNRy0vNyeO/aIj51bIWVvnEBok7NyyZankyGKocL6ZHkaJT8+vgyG+JPk6HKIVutxERioCPryqbOSERiYuTJuKLAuozs+XyMaV0bpTwbQaPhg1FPF261AGk5eaXs+WZi15PLzInay18NZr6WcMubhs0Jp2lo7lbu2vHyYLZ7D46n3uay7D4AHkb2ulBM4Zasxw31UUoluFsbl6slq7W1NTt27ODs/RTup+awZ+03uHtr6/jLKv8sDC3Fl8+YEYvFzJ8/X5eoe/z4cRwdHXF0dCxjz9eL/0i4nDDUl1DD1piGtTxYu2wRVatW5ciRIwwaNIhbt24xePBgBg8erBu/detWFi5ciFcdH/beesSJX7cQdSuIhb8cw8jUjJ8WT+fn5fOYsHxDgeNsXzYH91p1C7wntnNCNr8lDas6M9S2CbfWHCLq21P4BWrT/E1MTDh48CCenp5cvXqVTp064eHhQbNmb3/HmDqmzhz2ncxfqWF8GbmTFTFHWVZ9AG2sa1XI/BKRmM21RjIqbCudgldw1HdqkW5CtU2dCKz3BefS7zE9YifLY46ytHo/mpl7EKNIoW4x3Yn0xBKsxaZY65vS37Ypmen6uBu6YKLW4FpIli8wKYhLGVFENFuGpf5Tl5xa0JBZxLWu/VuRZAW55bvGdaduo5TncO7QHmwcCi5AuerSO1W9aTiUFMKEuz9zwncaniblS557l6DU5LEs+gj7fCaWPfg5EJnzhAfyZBqZu3NN9oAPKzctMkZPJOBhKcXRXIog06dhFRP09PTQ05OgVCp18dfc3FwUCgUGBgaIRCKioqKwtLTETCrm9oVT/PXbL8z8cRcAvUdPpevHY3XHKFwzfPP8KVxq1sbCxo6E6EgCN6+mZ++nwiB5eXmo1Wo0Gg0qlQqFQoG+vj4SiYTU1FTS0tJwd3cnLCyMyZMnM2/evDJLP183/iPh54CJiUmB7Ltu3brh5ubG9evXcXV1LTA2vwA+KiUHEZCUEEvtJi2xsLEDoHGHHvz6fwVdKpeOB2JsZk6Vug1IjI3RvW9hY8s2r9m0quqIUqnEX/94geYTz5ZkNW7cmBYtWnDx4sV3goTz0crai8sNv2J34hU+DduCp3FlvvHoXyHWgFgk5kevYYy/u50OQcs5Vn9qsXHo5paenPObw8HkYL6M2IkYMdWNKpWoWpafCV8/5z1UqLmfU1SWz9pUwoKk39lRe3QBAtaOEWOpb4KlvgkaQUOMIoWMrHjilGkIKhEOlD/8YGBkTOs+HzGhQz2+3nUSc2utBSmVvFkLUmk4mRrKiNBNHKr3RYVagW8TAhLO4W3qiF8ZpXTPg6icJ3QMXsFKz4EMqtyUrx8cZFiVFkXGLVq0qMBa8/PPP/PVV1/h7+9PjRo1iInRrlkdO3YEtI1rXF1duX79Op9//jnp6enYObsxauFqHKvVAMDIxBQjk6f3sb6BIQZGxrpmOKFXz7NpwRQUOdlYWNvyXpfeLJ0/Tzf+008/LVBytHjxYn766SeGDRtGcnIy3bt3JzY2Fjs7OyZNmsRnn31WYZ9bReG/EqWXwJMnT3BxcSEkJISaNWvq3o+JicHd3Z3IyEjiBHPup+bwIPQGO1b6M27pOozNLPhp0ZeYWdkweIo/APKsTPw/7saXa//HmcBfSYyNYdTCVbo5TRSpfNLtfWQyGRKJhI0bNxbol5wPuVyOu7s7P/30E506dXrVH8E/glyNih/jTrE4+iAdbWqzsFqfCsnsFQSBSfd2cDEjkuO+07DSL5ookg+1oKHvze/5MzWUNta1WFKtL7VMn1qZ5U3E06ABBJpWtaGmvRmCIBCrTOVOVhx3suO5kxXPnex4wrIfYaFnRG1TJ7xNHKkteCHJskYQnsbgSisdAVCrVIxu5cWczb/hUqM2GtTYWqvp4ub2xifyXUyPpOeN79hddzzvW9Use4d3EHkaFTUuzmC792e8V0xXrBdBrCKFlteWMMO1G6MquNWjSqPmcW4GccpUHspTOJkWSidRWzKyXrz8rXCd8LuA/yzhciJfkzddnkeuWoNYo2bmJwP5aMjQAgQMsG3bNlq0aIGbmxv3I5IAqFzVHZvKjnzRpRFiiQSnajX5ctpTS3jf+hW07DEAm8rFZztaVapCeno6KSkpLF67kluWMh4r04uU0IwePRofHx/d0+i7CKlYjwlV2/NxleYsjzmC7+W5jKzyPjNdu5VKnGVBJBKxynMwUyL+R9ugbzjh+2WJyW4SkRilRsUmrxHEKlNpdf1rutv54u/ei2yZtNyJeGK05HfhYQrLoo+wJ/skphJDvE0d8TZxpJlldT51bEUtkyoFLGV5nprdN+MBLbmq1So0ajUajZpcpQKJRI+w6xcws7TG2cMLpTyHveuXY2JmgYNrfstIEUsSf2VE7CO62vrQ086XDjZ13jixi5DMGHrdXEWA96f/WgIG+OXxJVwNbSuMgB8r02kb9A0TnNtXOAGrBQ3Wp8eSJ6jRF0nIUeeiRkPPWk2Q5Ji9sAZDHYc3S+2qIvCfJVwGihPX0Gg0rJ8zAUV2Fl98uwkXG7MC4hrVq1dn1qxZDB8+nFNRiTxMU7J+zkRylXJGzFmOgZERR7at58a5P5m3NZCYu3f4ce5EFuw4ip6+lN82fFvEEpYbpBGYd4KLskiUyTLkn+7jz3tXaWP3VFFp2rRpnDx5klOnTr1x0myvEgnKNPzv/8b+xCC+dO3CeKd2LyVMIggC0yN3cTzlNifqTytRVMP2zDhuN1mCg4El6XnZLIs5wpFH4UwyGsLJXduLrd0FuHLiIL9t+D/SEh9phQnGTqdBq46IRALNPcxxtyg92/e67AHzon6jdm49vCQe7N/wfxzY+F2BMT0//RxHd0/2rV9JWuIjpAaGuNXyod/4GThX90IjaMiRpvOxdw1S8rIITArmQHIQVzLu09KqBj3t6tPdtl6FdL16GYRnJ9D6+lLW1BhCn0oN/9Fz+SehFjTUujiTdTU/rpB8iJTcLFpd/5q+lfz4yv2DCjjDopgQvo0f4/8iT1AjQcxQh/fY4v3JC6m8lVUC+jbjPxIuBcXdLIIgsHnBVJIfxTH5uwCkhk81Tf2cLEiJuEmHDh14/PgxRibGTA06grfgzfyBnekz9kvqv98BgOzMDMa1qcP3J0K4cGQfe9ctx9BYa3Up5dloNGqquFZn/s9HkIjgoPIvduX8oT2JpGz4cC+rbgXS2b0h1Yzsme8/n71793L69GlsbN4+0YWKQFh2AjMjdxOS+ZCF1XozuHLTF86kFgSBOVF7CUwK5s8G04v0ab2b/YiOwSuIbr5S915iroxj9x6jkhsQdOpYsRJ/aYmPmdrzPSat2ESdZq24cf4kP8wYw4rAC5hb25bqbruZ+ZCv7v/GFdl9Zrl25wPLZpyMSH0hq0IsgvN6Zzkou8wC994Mq9ICiUhMel42R1NuciApmGMpt6hp4kAPW1962tXHy6TKa1XSeiBPouW1JSyq1oePqzR/bcd9E/Hr40t8H3uCc35zXvo7kKnktA36hlZWNVnmMeCVfKf3sh8zMnQzl2VR5AlqzCWGRDf/Vuepeh4ifpcJGP5zR5eIkm6SgKWzSIiO5Mu1v+gIGJ7W9Aau30SfPn0wNTVl6J0fOZ4ZxmJTb9xq+XD+8F5qNmiC1NCIk3u2Y2lXCTNLa1r1HlygTu73nzeQ/CiWoTOWAHD15FG+7NQUC4tctt89iWL9NcQeNpxWR/Nt0F88DjiH+FgUQ39ZwPG8e/jluFHNyL7CSnneFniZVGG/zyTOpd/jy4idrIz5nW+q96eDde3nXmhEIhGLqvVBTySm9fWv+bP+dJ1VmJKbRUDCOWz1TRkXvk0XtzXEAH+T8eiJSq7dTU18hLGZOXXf07r/6jVvi4GRMYlxMZhb2xKfIUeRpy5Q8hGWnYD//d84nXaX6S5d+KX2GJ2ms5+T+oWtiiH2HzIiozGT7/2P1bEnWFF9IO1tajOoclMGVW5KrkbFX2lhHEgKpmPwCgzF+vSw86WnnS/NLKq/VBvNshCvSKVd0DfMdO32rydgjaBh0YPACumNnKNW0i3kW/zMXF8JAas0alY+/J3lMUeY49aDee496RC8nOXVBxYIFVWUBsO7gHeOhNesWcPWrVu5desWgwYNYuvWrbptu3bt4quvviIuLg5nZ2eWLFlCr169AG1J0ciRIzEyMkJAS6qf/99PeDXQpupH3LjGtm9mExsRBiIRE9rXQyzRLkKte39E1O0g4qLuIs/Ool37jnx4ZTV7skJQCRqupASRnZnBrQt/Ma5NHcQSCS416zBx+UYADAyNOP3bLxz/dQuy1GSkhoZ41GmAuZXWotVkptC3Rw8SExMxMDFE6W1B29Xj2OszAQBRCxH6Un1+6jiFTYIGDQLSD31pOqYXDcxcaWDuip/5v4eYm1t6ct5vDvuTrjPx7s84G1qzzGNAkVrespCuyqG9TW2CM2PwujgTbxNHIuWJyNW5GEn0cTO0o7pxJXrZ1cfb1JGUVDE3EmQFFpPCcPOqSxVXD4JPH8eneVuCz/6BnlSKc3Uv3ZjIlGxqVzYnMucJC+7v5/eUW0x26cRmr5GY6hUU1qhpb4ZSk8eV2HSkIj3yKylLQmGropFFNc76zWZf4jXGhAfgaVyZ5dUH4G3qhFSsRwebOnSwqcOaGkMIzozhQFIQk+7uIE6Z9sriyEm5MtoFLeMzx9aMdW5bYfO+rdifFISRREpHmzovNY9Sk8cHN1bjamjL2ppDK5yAg2UxjAzbjI2+KVcb+eNmpK0EueA3l8Z/965+FoXrj9NytPk2Uom4XPXH7wreOXf0vn37EIvFHDt2DLlcriPh+Ph43NzcOHDgAJ06deLIkSP069eP6Oho7O3t2bp1K5s2beLcuXNFOohkZaQzo8/7DJ2xGL/Wnbl07AA/r5jH8v3nMDG35OLv+zExt6RG/caocpX85D+JIIsEhEmNMRDrY7z6BpUeGzBx6QYEBNZ8OQrn6l4M+kKban96//84sfMnxixeQxW36iTFx2BsZomphWWxDSHuZT9GJKLUJgPJuZlcz4zmukz771rmAzJUcnzNqv6riPnZ5gytrb1YVK2PbnHIR4Yqh9CsBO5kx+ms2jvZ8chUCmqZVMHbxJHHuRncyHrI/rqT8DN3w/fyPH70GkZji2q6efLFCJ5FcRnLpw/8yi8r/cnLVaKnp8/Ypeuo1/wp2VSykLAn7yj7k4KY6Nyez6t2LFK7/Cwm3f0ZIVefD4zavpRVkatRsTb2D5ZEH6KPvR/z3T8osRdyjDz5lcSR0/OyaX19KV1tfVjk0feF53lXIAgCDa58xVduvehpX/+F58nTqOh3ay16IjG/1h5boV4MuTqXBQ/2szn+DMuqD+Bjh+avLGxROEFWWqjm/m3EO2cJ9+7dG4Br164RFxenez8uLg5LS0s6d9a6Cbt27YqJiQlRUVHY29vrxj2ryZuPyJvXMJXKUCMAACAASURBVLe2pVG7bgA069KbA5tXce3U77zfcyBNO/XSjTUwNKJZ94HIA74npM0WkvMy6fN1L7y7vI+pmRlqAeq36kTI2ROANsnrwMbv+OSrlTi6a7Me7Z1cgZIbQpRHpMBWakZHmzoFnp6TcmUEZcZwXRbN7idXmRG5uwAx+5m70cDc9Z0iZn2xHqOd2vBR5WYsiT5IvUtzaGxeDQ/jStxXJHEnK57UvCy8TKroMpLb29TG28QRZ0Nr3ecgz1OzJeI6v4ZHkmChTzNNEwyybVAYP3Udl0f44s7ls+z6fgkz1u/EpWYdosNusWrKCCav2oZLDW1P45Mp4VS2tSCi2bIys70vZ0Sx68kVbjdZgo3U9KWsCqlYjy9cOvFxleYsehCI96VZTK7aiS+qdizS0tDFyJYJVdszoWr7AnHkLyN2UsPYgZ52zx9HzlIp6BLyLS2tarCwWp+yd/gX4EjKDdSChu529V54DrWgYVjoJnI1Knb5TKpQAj6TFs4nYVuoZ1qVm00WvbJEvrK6z91IyHjp7nP/FN45Ei4Jfn5+eHl5ERgYSNeuXTl48CAGBga6fsIAwcHBVKlkj6GZBU0796bbsHFI9PT+VoIp5DAQBOKf6QTyLO4GX8bZwxM9sYTKBpbMnDSVH374geoDRxKSIOP6qaPUa9EOgLTER6QmPiIu6i6b5k9BrKfHe11603vUZPycLCs0GcFOal4qMe96coXpkbveemLOVisJy054atX+/ZqYK8PDyJ4YZQrnMyLoY+/HSt8peJlUKfHanv3xW+BAXYkD6VnQWFqX248yuf0oU/fjL4/wxcN7odTwbYxbLW0zB3dvH9xr+xJ65ZyOhDvY1qKdR9kPWnkaFZ+GbeFbz0G6UipDfQm1K79cZry1vinfen7IWKe2zIjcRY0L01ni0ZcPS0h0s9Q3KRBHPp0WzoGkoOeKIyvUufS8sQovkyr8n+eHb1UrxVcFQRBYeD+Q2W7dXyrBcEz4VhKUaezz/oK7T3IqxIqUqeRMj9jJweQQ1tQYQi/7BmXv9IJ4ld3n3gT8a0hYIpEwdOhQPvzwQxQKBVKplN27d2NiorU0WrZsye3bt3moNuXstRDWzRqLRCKh2/DxVK/rR1pSIpeOHcCvbRcu/b6fxLgYchVFBc1vXz7D2UN72Pjbcd179evXJzc3l6Y1XbX/b9qSDv2HIhFB6pNHf+93liU7j5OTKePbiUN436cGNf3GvPLPpTRiviZ78EYTs1ydS3j2I60b+RmyTVCm42lcGW8TR7xNHfnU8X28TR0LdCGKyHnMrMg9dApeyXz3D/i4SvMighXP++OvYmaARKR9v6TaXbdaPhwO+IGYu3dwqeFNzN3b3Au5Qtu+Wq1viQgqm5ava9TymKM4GVgzsFKTF/wES4eHcSX21J3AufR7TLn3P757eJyVnoNKrdWVivVob1Ob9ja1+b5QHDlWmfp3HLk+Haxr6+LbeRoV/W+txU5qxgav4W/Nw96rxp+poWSocuhj/2KlWYIgMPneLzzKVDDTagSH7iRViBV5MCmYseHb6Gxbh9tNFhdReqtIvIruc28a3rmYcD7mzJlDXFycLib8xx9/MGDAAI4dO0b9+vW5fv06PXr04OjRo9Sr99TV82dEEnEZCi4dD+To9vXM334EgPDrl9i5ahGJ8THUbvI+WRlpeNZrRM9PJun2jbwVxKrJIxizeA0d2rejbXVt7PG9997Dx8eH5cuXIwgCU6dOJSk5hflrNnP56nU+6dGG1b8coHXrVnjYmLB29XecO3eO33777fV9YGUgKVemjS/nx5kzo18bMSs1edzNfsSd7HhuP2PdxilTqWZkr3Mj55OuRylSkoVxOSOKLyN2kpKXxTfV+9PFxgeRSPRCtYxiEQiC1mfy24Zvi63d/eCzyfyxayvH/7cZWWoyZpbWtOn3MZ0/0srpldS0vDAich7T9OrC19YzWiNo2PnkMjMj9+BrVpVvPAY8t3ZzcXHk7ra+HEm+gQaBfXUnoF+OTln/Frx/bQmfOr7PRw7vvdD+86L28SBZQUe9FqUmC+ajrFKgxFwZE+/+zDXZAzZ6jaC1tVex44pDq1atuHTpEnp62u/X0dGRu3fvsmTJEpYsWaIbp1arUSqVJCYmgpEZQ0dPJOj0cTJSkrC0q0z34eN4r+vTXAGNWs1vG77lbOBOFDnZ2Du5Mn39r5hbWNKphj2yJ3FMnDiR06dPY2BgwIgRI1i2bBkAqampjBw5kuPHj2Nra8vXX3/Nhx9+WO5rqij8a+74kJAQWrZsiZ+fHwANGzakcePG/PHHHwVION+lKIICHuiaDZrw1bZDgNbK+fKDFnQa/FSHNObubVZNGcmIucup1ah5AdfkjRs3+OGHH3RW9+jRo2nevDl7K5vj3rYxY6VSfBwtX9qN+CphJzWnk21dOtk+dd8/S8yFLWY/czddAlh5iTlXo+JezuMibuRoRTJuhrZ4mzpS28SJwZWb4m3iSHXjSi+9aDe2qMZfDWZyKDmELyN2sjz6KPOdBxAbLy1CwAkPIti+bC7RYbcws7JmwMTZNGj9VBr02eEffDaZDz6bXOwx2/UfRrv+w4rd5mhhVCYBC4LAqLCtzHbt/tq6CIlFYgZVbsoHdg1YFXuCZtcW8mHlpnzl1qvcLTQLx5EPJ99gbtQ+YpUp+Jq5sCLm6D9Sj/wm4kxaOPHKtBf2ciyLPkxEUjZd9FuVi4ChZCtSEAR+fnyBqRG/8rFDc7bUGonxC2TDr1mzhsEfD9clVv0ZkUSLgaO4OHy8ziXu7+/PmTNnsLW15VRkElJDIyZ9u4XKVd15EHqDlROHYO/kSnUf7Tr+24Zvibx5nTlb9mNT2ZH4qHvoSw1QawSCYpIZ070948aNY+fOnUgkEu7du6c7n3HjxiGVSnny5AkhISF07doVHx8fvL29n/vaXgbvHAmrVCpUKhVqtRq1Wo1CoUBPT4+GDRuydOlSQkJCqFevHsHBwZw9e5axY7UdPI4ePUr9+vWxNDLiSYy2W0fDdl1188bcvY1jtRrkKRTs+3ElVvaVqdP0fQDiIu+ycuJQPpo6H9+W7ZGIwMpYX7dvw4YN2bRpk+4JbMOGDfj4aGOCxsbGDBgwgGXLluHr60tGRgYbN25k2rRpr+sje2G8KDHXNXVGQCAs35X8N9lGyZOoamhNbRMnvE0d6VepIf6mvfA0royBWL+UM3k5iEQiutv50tmmLgGPznE4OhovSTWdpCRoH7xWTf2E1r0/YtqaHYQHXeK7ySNY8PNRKrsULL8QUd5mawVRXlm+nxLOkqlWMLFqhxc4ysvBUCJlumtXRlRpgf/9/dS8OIPprl2Y4Nz+ub4jCz1jrsoeUNnAgmuN/LmeGc2BpCA6Ba9AKtajp13911KP/KZi0YNAZrp2e6Fr/yH2TwITQhllMJDn1XEp3MM8Rp7MqPCtPFKmc7je5BduHJGn1hCemMmemwklusSrmBsQsG0b/l99pUuQfbb1ZrXavnjWa0TkrSCq+/iRLUvn+P82s/CX37F10PYIdvKooRv/645tVHZwYPLkpw/D+TlA2dnZ7N27l9u3b2Nqakrz5s3p0aMH27dvZ+nSpS90jS+Kd84d7e/vX6DTB6Dr9LFmzRq+++47njx5gp2dHePGjWPKFO2XPHXqVLZv305WVhYmVjY07dSbHp9MRE9Pu7Csmz2em+dPAVCn6ft8NG2BrhPNpvlTOH94D1LDp2Uk7q6uhIbeAbTdRCZOnMiFCxcQBIFGjRrx/fffU716dQBkMhmfffYZhw8fxtLSkk8//ZS5c+e+9daAWtAQlZPIhYwITqWGEZwVQ4w8mUy1AhEirPRN8DCyx8/cTRtHtPLGWO+fzWyU56nZczOhyOIVF3mXhSN6sv50mO57WT5+MO7evvQZM7XQLAK5ggqpqPykJBFTrkS8J8oM6lyazYn6X+LzBnQSCstO4MuIndzJjmepRz/62Tcq1307N2ovh5JCONVgRoGYoiAIhGQ+5EBSEAeSgkqMI7/LuJwRRf9ba4lotgzpc3p6AhLOMSdqL+vsp5KSqeGPXVuLlU9V5eWyfs4EHoTdIuVRHNPX79RpIgA4WxpyRxrC/Af7mVy1E9NcOr+w1yk8MZMPunQgPuoegiBQ2cWdPmO/LHA8gLtBl/n28485fycSqaEJNxIyCpB1rkLBtF7vMXLeSuo2a8XdoMt8N2Uk3YaN5dgvmzEyNaX9gBG06/8xAFsWTMVcKkKVncHVq1epXbs233//PXXq1CE4OJhmzZohlz/N61mxYgWnT5/m4MGDL3SdL4p3joQrAoXrhJ8X72Knj9KgETQ8kCfpXMi3/369l/OYSlLzAjHb2qZO1DRxIFut1FnM12QPuC6LRqZWvLAru6Jw65GsyI8fIDYynEUjehUk4XEfYmBsohNdyUeukIdSX4ZBnjl6IkkBi7o4qFCRZBjDjNqtyjy/gbd+wNXQlqXV+z/Xdb1qnEwNZcq9/2ElMWeKbV/MBfMSM3CXRR/mp4SznPGbVawu97PIjyMHJgdzOSOKllY16GHrS3c7Xxz+YV3rV4VuId/SxcbnuYVKdj+5wqS7Ozjm8yUhkRo0Alw7ebRY+VRVXi5/7t6GW626rJ0xhtGL1xQgRZWg4n/sYU2twdQ0Kb6pTHmQn1tx72YQVdyqo6evz+Xjgfy8fB4LdhzVlWMCbF44DY1azWfzV2JtJCUlJ6/AXBv9vyAjJZkpq7chEom4+Pt+fpw7kRY9BjBk2kKexD5g2dhBjFq0mtqNW7J8/GDCr1/kYGAgbdu2ZdWqVaxbt47w8HAuX75Mv379ePz48dP5N25kx44d/PXXXy98vS+Cd84dXRGo42BOvEz5X6ePQtAIGh4qUp5JjtJmJYdnP8JG31QbszV1or21N587d8DLpEqJlouxxKBUV/bOJ5f5MmInMrWC+mYuNDB3fS3EnC7PKzaG5uBaDXMrG45uX0+HDz8h/NpFwoMu4+VXtPm5VKRPvCqTti4WqGQmZQpoONmKef/mbzROrVRqssvh5BCuyR6wpdbIl7rGV4G6BtVYaTuRh+k5JCSrSRYV7Z3saGHIXfFd1j86xdkGZRMwlFyPPD1yl64euYedL7VMHN96zxFoVaeCM2PYU2f8c+13ODmE8Xe3c9x3GmSZIUIb2y1JPlVPX0rHDz8B0Cn/PQuJSMz/OX5GTZOnYi1KTR4ylZxMlYJMtUL7t1pBpkqBTJ3/vhzZ36+iXAMa5TVDDz2q1fbVzdO8Wz8uHQvkxvlTtB8wHNBauVf/PMykFZsQBFERAv511WLiou4yY91O3fcsNdCuLT0/mYTU0BDn6l406tCDm+dPUbtxS6QGhtRu0FinDTF16lQWLVpEWFgYpqamyGSyAseQyWSYmb3+jOr/SLgY2JoY4Odk8cKavG+7zmlZPW3zs5Dft6rJWOe21DJxLFXRqbwoLsacmCsjSKa1ll8HMZckuKGnp8/EFZv4efk8Dm9bh5tXXRq164aetPjvuo5xVVrYu4A95RLQ2FJrJB/f2UBIk4VY6xdNdMpSKRgbvo3NXi+WFPMq8WwmuQgx0kLfw9NSLjkSoQrbXafgaGj93McpqR65c/DKdyaOvOhBINNcujxXF7CTqaEMv7OJg/W+wMesKmeTUsqdjFUSRIjZHnuV3VFHdQSrOXUftt9Ak5iFvo0pnvN6I06Sc2fxXkSIEIlA0AioFLnMPLyemlWbs2LMcO6FXNXNq8rLw8HFHSt7BxAEkhJi2bxgKhE3riEIAmqVqsB5HPtlE3vXrSBPqaBxh+4Ffm/bl2sVB2f1a4NILMajbgMquzxVsHPy8CIuNKjY6/P09ESlUhEREaELC964ceO1J2XBf+7oUvGud/oQBIEEZVoBor2THU9oVgLGEmmR0h9vE8dXWhNYXiTmyrj+tws7v2TqWWL2M3taLvW81lFx0pMlYdGID3ivWx9a9/6oyLbrebf5sFZ1mlh4FLNn8fj87g7ilWnsqjOuyHl/fncH6aoctnp/Wu75XgfehLZ070oc+XZWHO2ClnH/veXlftC6mB5JjxvfsbvOOFr97UXJL7N8FsXJp+bji66N+GzBqiIxWnMTAR8XQ8z1jLh86hxjPxvNzp07adSoEY8eaa1qR0fHAvts3bqVhQsXcivsLntvPUIjaDvG3b8dQo36jVkxYQjmltbcvPgX87cfYdOCKXjUqc/DiDBMzC24c/kc3+w7jbmVDbcunmbN9FGYWlgyZfV2ApbOplptX/pPmAnAlB7NMDI2waNuAwZPnU9S/EO+HtWfMYu+p1aj5iTGRDHvoy4cPBhI69atWb16NWvWrCEsLAypVMrAgdqGGJs2bSIkJIQuXbpw4cKF/7Kj3yTkd/oIic8gQaYokvEqBkSiN7/ThyAIPMnNKEC2t7PiCM1OQF8k0ZX++Jm78bFDc7xNHYu1xt4U2EvN6WzrQ2dbH917zxLzr08uMS3iVx0xF44xl0bMlkb6OsGNwoiNCKNSVTcEQeDk7m2kpyTSvFu/IuMkImhh706PkO/4zWdikSbsJenf+rv0oUXwQgIenWNYlRa68Vcyotj55DK3mywpfKh/FMnZyiIEXFIi0LMonIH7shCJRPiau+Br7oJ/tQ94qEghMCmIdXEnGXZnIy0sa9DT7s2PIy95cJAvqnYsNwEHy2LoeeM7tnl/qiNgoFzKbeWBrYEJ7sbaJjKL5y9k3rx5NGmiLZkqTL75CAgIYOjQoUSl5OhaiahVKvatW07CgwiUCjlVPWsxccVGRCIRMeG3+WTeSmYPbMfXu08hS5nCtZNHaNNnCOcP70Epz0GtUjH/4+5o1GruBl3CyNSM7sO17vpuw8dz7tAexrfzwdzKht6jp1CrkbbrloNrNX4K2Mbo0aNJTEykfv36BAYGIv3bmv7hhx8YMWIE9vb22NjYsG7dun/EEv6PhEtBvmTho0wF4kILc34ZioOZIXUczN4YAk7KlRUg2nzrFtCV/tQ1dWZQpSZ4mzqWKy73NuB5iDmfkIsjZg9bE0ISMoo9xvkj+zhz4H+oVSo86zVi2pod6EuLXzC7V/XA3HIUvW6sZm/d8bS0qlkO/VtYaDWaBVEBNLf0xMO40t/SlD+x8hlpyjcFtx7JiljAlraV6D5ioi4RqCSoNRpuPZK9kgTGqoY2jHduz3jnp3HkwGfiyPkymm9SHPle9mNOpN7hR69h5Roflp1Al5CVrPcaVuCeh9IfJMuLZ8ss1Wo1165do0ePHnh4eKBQKOjVqxfLly/HyOhpGComJoYzZ86wZcsW4p7JrTC3suGrbYc4sPE7Qq9dYOaPuwC4fup37Byr4uBajS2XHgDgXL0W8fcjAIi/f48xi9fo2rxmpqcyoX09Wn8wWHfMX76dj6DR4FG3AQMmzqaqZy3dNkcLI1r378vA/sU3ArG2tmb//v0v/iFVEP4j4RJQlpst/914mYLHWa9frzQlN6uIXOOdrHjyBHUB93HfSg3xNnGkktTijVlwXhfKQ8xTI34lS62g/jPEbGnqSGpm0e994KTZDJw0u9RjagQNWfrp5Ils6WhTh//VHkOfm2vYXHUSsjSjMiUwM7JgotEQFt8+xQa/vqx8+DsOBpYMekXSlC+K4hqdQMmJQEUh4kF6FlWzoJrpq6skKE8cuYedL+/9w3HkJdEHmeDcDrNy5FZE5TyhQ9ByllUfQG97vyLbn32QLEk+VaKnR16ukvxopDovj1ylAn2pwdMHUhtt6OnJkyfk5eWxZ88ezp49i76+Pj179mTRokUsXrxYd9xt27bRokUL3NzcuB+RVOS8zh/ZS/cRE3X/V8izMTItuGYamZqRlqjNWFbkZGNsal5gG4A8JwtTSytGL1yFS406CAic+N9mVkwYwtd7TmJiZvFWJcj+R8LF4E3SK03Pyy4Ss72TFU+OOpdaplV0Mdsedr7UNnXCQWr5ryPb50F5iPlRppyRBv2fq843H3piMWHiMDwvbGaOWw8+c2zFlqqTeJwsQSoq3/0kQkxzcWMW3/6LNWlHudbI/437TiOTs8voXFw2RMCXNw7jVcmE6a5dy0VAL4PCutb5ceTPS9G1fh14IE/iUHIIkc2WlTk2VpFCu6BlzHHrwZAS5CyN9CU4WRjyMF1B4JbVBeRTLx79TSefOqNva1IeaTvNrZigzWtYfuA8dlWcCyi35Vu7EyZMwMHBAYDJkycXS8KzZs0CirrE74VcISMliYZtu+jeMzQyQZGdWWCcPDsLQ5O/m5EYmyBRyZGIRag1AoqsLO35GGu3V/d5qqndbfh4zh3ey73gK/i16vBWJcj+R8KFUFycqzwoKc4Vnp3AI2VGmTqrMpWc0EJW7Z3seDJUcrxMHHSu5M42dfE2dcTJwPqNW5jfVhRHzHvvh5OWAvrPJbihTTgaYj+QTzObMT1yFztjQxhp0K9IxvCPcycRevUcSoUcCxs7ugwZzfu9Bum266FHFaUbI2zb4Vqo//GbgJJKuZ4HYiR8Vqk9PysO4nlhOvPdP2BElZavxSJ9k+LIS6MPMdqxdZlJj0+UGbQLWsZ453aMcmpd6tj8MsvS5FNXBl4o9v3CVqSVlRVOTk6lrjfnz58nISGBvn21rt/CLvFzh/bQoHUnDI2fXqOjuyeJ8bHIs7Mw+pt4YyNCadKxJwBO7p6kxdzDz2kQ1+IyeBgRirm1HaaWVsWeg0gkQizirUuQfWfblZiamhb4J5FImDBhAgChoaH4+flhZWWFlZUV7dq1IzQ0FNDGuW5fOc/S0QMY08qbKT2alXiM8OuXGNawKnvXLQe0RLx8zY9IJBJMTU0xNDHCy9aFj39ZqNvHxdUFQyNDDE2MkZoYYtu0BlXPfoHDmYmMD9/OmfS7VJZaMLFqe875zSaj1TquNPJni/cnTHHpTCfbujgb2vxHwK8I12UP6Br8LV/E/4jIIk1Xz1saNGjQoEZknoa+aQ6CIOBjVpXffacyzrI3mmKqnroNG8uKwAus/yuUSSs3s3fdCqLDbhYYo48eoiwL0vOyK+jqKgaPlOnEytMqZC49QZ/ttUdxsN4X7Hh8kXqX5/J78s2yd6xg5MeRT9T/ktgW/8cQh2acSguj1sWZNL4ynyUPDnInK46KLiaJVaSw+8lVPq/asdRxqXlZtA9exoeVmzDFpXOZ89qaGOBqL0aFqsyxz6KkMsvhw4fz/fffk5iYSFpaGt999x3dunXTbQ8ICKBPnz66OlsPWxNdyE5bA3ykSBJjZRd3qnrW4sDG78hVKrh+6ndiI8Lxa6O1lpt37UPgzp/RJMfSxF6f49vW0rJ7XyQiSHkcT8SNq6jyclHnKvh9+3pyMtIY3afzW0XA8A5bwll/uy5AqxNaqVIl+vXT3gRVqlRhz549uLi4oNFoWLt2LQMHDuTy9WDiMhQYGBnTokd/mnTowcGta4udX6XKY8dKf9yfKUIHSJPn4de4ETZr+3IqLQw0eSQgpmvwSkKzE4hVpOL+TR+atGmhi93WNnHC1cj2vxZu/yBuZcUyL2ofV2T3meXanX0+EzAQ65OcncutRzLup2UiEYkRPfPcKhFpcwOkRnk81HvAxexQZlyPJkutoIG5K41MqlMjxw9xMQ9MjtWeatzm11gmxsXg6vW0RlosElNT4s7n4TvZWmfEK73+kvBYmc41WTTXM7Xu+muyaJRCHhNMBuGG60vPn++29DN3468GMwlMCmbivZ9xe2jHCs+B1DF1fuljPC8s9IwZWLkJAys30cWRA5OCX0kceXnMUUY6tsRWWjJxyFRyOgWvoKNNHea59SpzTpVGzarY43wdfQj/Sh9hme300l2U5s6dS3JyMp6enhgaGtK/f39mz9bmRygUCnbt2sXevXt14591iQedPoaxqRlefkUNmjGL17Bp/hTGta2DTSVHxi9dh7mVNiO7c+fOVM5LonXr1sjlcvr06cN3368gNktFUNJD1iyfw6OHMRgYGuBbrx5/HP8dz6oOZV/oG4Z/RZ1wQEAA8+fPJyoqqogFqVKp+PHHH5k2bRqXox4XkCy8c/ksWxZPL9Ztc2jrWrJl6chSU7Cu5ECfMdqGC+cP7WbvvvWkfvu05k5fJGFNjSG0tvaifa0mbNq0iXbt2r26C/4P5UZ4dgL+9/fzV1o4X7p0YYxTG4wKCSXkalQ4nZ7M+1I/+to0w1ZiUazgRj6eKDO4nhnNvSc5mMkro1fCs+62pbM5d2g3uUoFLjVqM3PD7gLuOtAS/R+qC7Sv6sxgh5K9MhWBJ8oMrv1Nttdl0VzLfIBCk6fLIs+vv3YxtOX248xi5T3zE4EObPyO1MRHDJ/9jS4RqDByhTwuaa7jZCuhl10D6plVRSQSkatRsT7uJIseBNLTrj4Lq/Wm8htQWvRsPXJgcjAPFSl0tfWhh60vHW3qPHcc+bEynVoXZxHadEmJ15ejVtI5eCW1TKrwQ82Py/SA3cx8yMiwLZhJDNngNRwP40q6B8mylNtepswyIucxF9MjeaBI5k5WHOfTI+hq3oiWmvdfWHmwUw37tyau+zJ4Zy3hZ5Ffu1b4Bra0tCQrKwuNRsOCBQvKHedKfhTH2YO7mL/9CNuXzS2wTSNAdmQcxv33ozbVQ9XWFfXA2jgbWlPdWNt/dfDgwWg0Gnx9fVm+fLmuo9J/eH2IzHnCgvv7+T3lFpNdOrHJa0SJi+gfqXdI12TRw70aAxzKFt+oZGBBFwMfzGQp3JeXLPwxdMZiPpq2gMhb1wm/fqlY9S21AM1MajHu7los9YypZeqIhZ4R5hKjl7LC8h8UntXvzlHn6jLEhzi8x3c1BuNqaFvswl9SKVdpiUCFYSjW52N3Hw6mBtHv1hryBDW97OrTy64BY53aMsThPZZEH8T70iw+d+7IFJdO/6haWElx5PXxpxgWuomWzxlHXhFzlI8cmpVIwEpNHr1vfo+LoQ1raxZdvwqPXfQgkPVx+MvdxwAAIABJREFUp/jaoy8jq7yvG29rIqW1h225lNteFHOj9rIv8ToqQYPwtyO6TeVq1BP/e5UHy4t33hJ++PAhbm5uREZG4uZWtA1XdnY2AQEBuLi4YOjZqEDZRUmW8KopI2nSsSeNO/Rgo//kApZwYlwMDuaGfNiqPnfu3KFv/37U792G1V8txU5qzvnz56lfvz6CILBq1SpWrVpFeHg4lpb//JP+vwEx8mQWPjjA/qQgJjq35/OqHUuV3EzPy8b74iwcDa240sj/uY5VnHJRSdj69Uwc3arTfmBRt3OCKIGN8l08yZVho2+KTK0gUyXHSCLFQs8ISz1jLPSMsdAzKubVCDFiEnMziFOkEaVIJDz7ETnqXPzMXfEzd9OJmbgZ2T1XrkFFNjoRBIHQ7Hj2JwWxPymIB/Ikutr60MuuPtWNK7PwwQHOp0ewuFpfhjg0e+NCNxmqHI4m3+RAUhC/p9zC07iyTkazuHrk5NxMPC9M52aTRTgVI+GZp1HR/9ZaxIjZWWdsqQ9c59Pv8UnoFmqaOLC25lCqGBSfuPQq8SAnkRoXZ5AnqBEhoo2VF380mA68+8qDL4t33hLetm0bzZs3L5aAAUxMTBg9ejR2dnb8dPQ8iEvPUAw+cwJFTraugLww7J1ccLE2RiwWU6dOHeZ/5c/y5cuxW6zNNnzvvadlBTNnziQgIICzZ8/SvXv3F7zC/1AexCtSWRx9kJ1PLjPasQ33mn1TpiqYQp1LrxurMdUzZLRj6dmoxeF5lIs0ajWJcTHFbmtu5cH0+svpELyc9y1rMte9p7YeWa0kQ5VDhkque41VpHArK46QzIdEy5NIUKaTK6gw1zPCUKyPRCRC9Hdx0V9p4VyRPfibxI1KIHHjZ7YXHOdqJ62wRicikQhvUye8TZ2Y7daDWEUKgUnB/BB3kssZUbS28uJjh/f4PvYE3z08xkrPQbSxrlXKEV4vCseRz6TdLVCPrBUIqa+LI//fw2P0r9SoWALWCBqGh25CqVGx32dSiQScqZIzK3IPexOvsbrGR/Sx9/tHEjZvZj5kWOgmPI0rc1+eCMA6r4912/OVB1+1S/xtxb+ChGfMmFHqGI1GQ05ODor0JCQ2JqW6pEOvnudB2E0mdmwAgDxbhlgsIS4ynEkrNxdQmgHt4lKas6Gs7f/h5fBEmcHS6ENse3yeEVVaEt50ablUwjSChqF3NmAnNeOhIpmmltWf+9glKRfJUpMJvXaBes3bIjUw5M6Vc1w6doDRi74vMkf+/SQWiQmo9Sn1r3xFO2tvmlp6kKtREZqd8HfC1AOuZ0aToZLrpDpHVmlZqlSnRtCQqVYUIPH81/S8nALErt32zHaVdntDSV0GGHXCQFT+hVMkEqhiBzmSTGQqY0wlBkUsW2dDG8Y5t2OcczvS8rI5knyD/UlB3Mt+jJOhFf1urqGeWVXW1hz6Uq32XgWkYj3a2XjTzsab1TU+IiTzIYHJQXxx7xceKlJob+3NoeQQLjacV2RfQRAYEx5AvDKNI/WmlNhP+GjyDUaHB9DGyovbTRf/IzKzeRoVS6MPszr2BN949GeYQ3PaBS/Dx9RZF3rLx/+zd96BNZ1vHP/c7L33EhEhIglJjKqRGjVKbWq1lFZtNav23q1VSlGjitp7lNrESCI7MmWKLFk3487fH1duRYKEGPXz6T91xnvfc3Lvec7zPuP7JpbE/6u818vR169fp127dqSlpZWRqPr7778xMzPDw8MDoVDIjBkz2L9/P2H3ojkelY1EKkMiFhF55wbbl/zI4v0XUFFRQU1dgyJhASVPxPn+XDkHI3NLPh86Dj1DI0KvX2B0j7bUsLMhMjKSXr160bt3b2bPnk1iYiJJSUk0atQImUzG2rVrWbZsGZGRkZiamr6NW/TekinKZ3nCSTanXmKgVTOmOXaudHKPXC5nfNQugvKT2FH/Wzz8ZpDd6pcqL4EWiaXsD07laUcx71EWv0z9jsToCORyGWZWtrTtOwTf7v3LjaEqgF4eNgjlRfjnx7Mj9RqHMwIwUddVGFyD8r2x3+RSrVQuIzjtEaGphcjkcnhOCw/Z4/9uyG/hJwl6bMiLKJSWoK/0sJ/vhWurqBNfnMmtnDgu5kQikknw0LNjmXNf2pi6vfOle4nFWYyI2Mbd/ETypcVl6pGtNAyZGL2b6zkx/O01ucLmJZmifMZH7eJ6bgwb6w6mnWn9t3AVEJSfyJDwzVhqGPCb69dKj14mlz3O9n+9f4dn9V9/Urf6v8J77Qlv376dHj16lNOIzMnJYcyYMSQnJ6OtrU2jRo04ffo0xvq62BkWcub8BZZ+11d5/LfNXajj1ZRpG/9CW1dPWVgOoK6phaa2DnqGigd8QvBNGi+cTEFBAZaWlgwcOFDZRSY/P58RI0YQGxuLlpYWDRo04NSpUx8McDWSIxayMvE065PP09uiMXebzMdeq2r3d3nCSf7JjuCKz49cyYmiiYHTSxm2J8s0nsTA2JRpm/a98HyZXEaK4AGuN1eRLRbSUL8GPgaONDasiZGaLvs9Rr/12KiqQIWG1qbYG+i/cLnRwVAXd2sDhur2Bf79fUnlMvIq8MZzJYVKQ50lLiCuKP3f/dIiamqb8bAkl6CCJNrdVdTq66loYqFpgI2GEUbquhUspZddUn9yv66q5ms3HkZqOtzKi+eazwwsNQ2UceSpMX+ho6qBTC7ngMcY9FTLJgnK5XL2PPTj+6jd9LdqSkjThei+hSQ1sUzC4vvHWZt0jmW1+zDYukWZe/a6v48v7r+u0K1WLGu/W5Kfz+K99oRfhkxhCafvZXxIq/+PkS8pYnXiWVYlneVzs4bMdOpKzZfoNLXzwTVmxB7gms8M7LRMmB6zHzWBCnNr9XipeWUKSzgVmV7OG64MYrmY9cV/MsH5E/pbfaR8wBVIivG6NYv5Tj3pa9Xkpeb1unhby41h+cmMuLedoPwkDNQ0yREX4WNQE3d9exy0TCmWisiVPmngnzL24kJK5BIMVMt648824o/3q5dNitNR0XiuIV9y/zjB+Un86T6izPZF8UfZkHyBT03rcy47DHWBqjKO7KBlwth7u4gvymBLvaE0Maz1jNFfL0H5iQwO+w1rTSM2uQ6pMJ79OnlfE7w+GOEKeBc0Uj9QOYTSEn5JOsfKxNO0M3FjtlO3cvGoynI2K4RBYZv4x2sqbnp2ALTxX8qkGh3KKdVUZX4j/Q/gq9IMFSpvhEq/TzGCWL6N+J0B1h+xoFZPNFUU+QZ38uLpFLiSO03m4lBFT/995mJ2BBOjdyOTy2ltXI8QYRJ+ubH4Gtelm7k3XcwbPDMnQCyTkCcpfq5HXn5f2f1iuRQDNS2FYVYta6R1VDTZ8eAqI+xaU0fXWmm4z2WFsfuhH0c9x1FX1wYtgTrBwiQOp/uzNfUKySWP8NCzY5pjZz4za/DG9ZHFMgmL7h9jXdL5Cr3fN8H7/Ez+YISfwfv61vW+UCwV8WvKBZbeP0ELIxfmOHWnnl7FGqeVISDvPu0DV3DIcyzNH+v/SuUyjC+OIP7jlVWWEcwRC7mdF8/4qF0USUV4q7rTTq0F6gI1VF7QLfbp71OGKI/vIrYTVZjGH/WH46nvACi8qtOZIZz3norqO1ay8zaRyWXsSrvB9Jj9NDZ0YrpjFyIKH3A43Z+z2WF46NnRzdybruYNqaVjWa2fLZZJnmmkj6QHEFWURgdTD+X+CGEqUYVp2GuZIJSKyJUUIpHL0FPVpFgmRgUBtXWskCMnQ5RPpjifGlpmeOrb09SgFjW0zSr01rVU1KvFUJZ6vzaaxmx0HfzK3q+vry9+fn6oPW7eYmtry71797hw4QJjx44lKSkJVVVVWrZsybp167C1tSVTWMKfV4LZtng6UXdvoaGlTZevx9C65yBAIXG4euJQHiTEIpPKsKnpzBfjplPbsxGqKgKKgi+yYvF80tLS0NTUpGPHjqxduxYDg39fxvbs2cPcuXNJTEzEysqKbdu20aJFiwqvobr5YISfw/M6zZTqCeuoq2BvpE0DG8P/XELAf5ESmZgtKZdZdP8Y3vqOzK3VnQb6NV5pzLjCdJrfWci6uoPKSMOFFCTRK3gd95otfe75uZJCAvISlBnK/nn3SRPloq+qhapAhXlO3Wlq5Iyp3ISwtAJScouQQ5kl6heVacjlcnY+uMbE6D1MrNGByTUU/XXb+C+lg5k7Pzh25gNlKZSW8HPiGX5KPM1X1s2ZWbMr2irq/PMogiMZARzJCMRcXZ9uFl50M/fCS9/xtXl4xVIRta5P5rjnBBoaKL6v+x/eZsy9nVzw/kGZ4V2acfxz4hlG2bWhi3lDCqTFSkOe9rjJSmhBMvFFmeiqamKirouWijolMjF5j7PdZXLZU0vqVStD01ZR5+ekM/ySdJ5ltfvylXXzark3vr6+DBw4kGHDhpXZ/iDtAYE59+lYpykikYiZM2cSGRnJ0aNHuRCTwcgvumJf25W+46aTGhfN0hF9Gb10I64+zRCVFJP1IAVLh5oIBAICLp1l6/xJrDkTiKqaGlpF2bRxtcXMzIyCggKGDx+Oqakpa9asARSJusOGDWPv3r00btyYBw8UEpy2ti//Ul8V3uvErFflybT6oNRcEnOKKBQruvGXPj8LxTJiMoXEZAr/cwkB/yXEMgnbH1xlQfwxXHWtOeQxlkaGTq88boYojw53VzCj5ufltFlv5MTQ9Kn4W56kiIDHLR1Lu02lluTgqWePj0FNOpl6MKtmV85lh/FrygWu+8woo45j4axFsVjKvNBzWKiY4a7jUKm4qUAg4Eub5rQyrsuQ8M0cz7jLdrdv2Fn/W7xvKsqWfAwqroX/f0VHVZPpNT9nqE1LZscdos71qfxYswsj7drQycyTDXW/4mZuHIcz/OkXuoFiqZiu5l50s/CipVEd1J9RHvQybE29gpe+o9IAn8i8y6h7OzjTcJLSAPvnxTMsfCuWmoYENJn7QvWsJ+uRj2YGoiZQYYDVR3Q198JH3xGhTFQu7v10TXloqbcu/bc0LUucT46kEBBgrKbDwvhj/JJ8XmGkVSs25opl9/IGvjR88iIe6or4LPRXmuT9zfpaA1BVVSUmJkaRBf0gi0j/G4xcvB41NXUcXOrh07oTl4/uxdWnGRqaWlg7Kn6nMpkMFRUVhHm5CPNyMDAxQ6xjgp7hvw1MSscuZfbs2cyaNYumTRWa3W/K+JbywROuBB+Wpt8eUrmMP9NuMDfuMDW0zJhfqwfNXqJmtyKE0hI+8V/Mpyb1WeDcq9z+QaEbMdfQx1bTWGlwU0oe4fHY4HrrO+Jj4EhdXZsyy8EnMu/yTfjvXGs0o8LksFOZQYy6t/OlM1xlchmrE8+y6P5xFjn3wkBVm5lxBwhsMv+tZMz+VwgtSGZy9B5iCtNZWrsP3c29ld6dXC4n8vGS9eGMAGKKHtLJVNGxqyp9oSsqndHXUqVf3HK2uX9NE8NaXMiOoG/ILxxtMJ6mhs4USUXMiTvEtgdXWVH7CwZaNauy1ymXywkqeNzXOiOQhOIsOpl60LUK8xfJJCyKP8b65PMsr/0FfSwaKT3rp+vDnxUfz5EIyyzDqwhUyiS4RY/cSWHcQwRyASY1rfl00iAaNG9Maskj1tw5Qsk3B6BQjEBVhZXrV9O285f4Rafwbat6rDkTgIGJosPa1gVTuB8Rwrxdp5Tzn9HvUx7cj0UqEdOy6xd8PUOhz6wqAFlyOGO+7ENeXh46OjocOnSITz/9FKlUira2NvPmzWPz5s0UFxfTrVs3li9frtRRft18MMIv4H1OCHiXkcll7Ht4mzlxhzDT0Ge+Uw98X6DJXBXEMgndgtZgqWHAlnpDKZAWE5if8FgxSNH8IrowDXc9e1oa1VEIGBjUpK6O9XNbCN7NT+DTgOXKB+zTCKUl1L/xIxtdB/OpqfsrXUNYQTKDwjZhq2mMjooGhurabHJ9O2pL/yXOZoUwKXoPRmo6rKzdr8IVlZTibI5mBnI4PYAbuTG0Mq5LN3Mvupg3xKKCxK7nlc7IkSFDTk0jXdDLo++9n/nLfRS+Jq5czI7gm4jf8TZwZLXLACw1DavlGks7jh3JCMAvN5bmRi50Nfeii3mDCtta3s1PYHDYZmw1jdnkOhjbash8lsvlFMvEZYy1300/jGrZIBSIuXToDIdmr+eLQwtIMhFz5dE9JMggrwRORuPezJtffBcTl13IwmE9cKhTn75jfyQ1Pprlo/qjb2TKkgMXy3ymqKSYgItnkIhFZaQTnUx0aOFkSkpKCr/99hv9+/fHxcWF1NRUbG1t8fb25tixY6irq9O1a1d8fX1ZuHDhK9+DyvB/YYT19Mom1RQVFTFy5EjWrlV0KDp//jyjRo0iMTGRJk2asG3bNmrUqEGmsIT9t6L4fdGPhN++BkD9pq346oeFaOs9TppJTWLLvEnEhQZiYmXLoMnzcGvSQlmutHPTLyxdulQpxbVhwwY0NRXeyvXr1xk/fjwRERHUrFmT9evX07x58zd4Z9495HI5hzP8mR13CC0VdebX6smnJvWrNVaXLy7ii9ANxBWl01DfgYD8RJKKs/DQt1c2vXDRsaJD4AoetdpQaaGElOJsPro9n5Uu/eht2bjCYyZG7SZdlMfO+sOr5VpEMgnz4g6zKeUSKgLYUPcruj+1rP6B8kjlMn5Pvcys2EN8YuLKolq9qKFtVuGxOWIhp7KCOZwRwJmsUNz17Ohm7kVXcy+cdSyr8KIuRySXYGIqoq2DA1Oi93IyM5hf6g7ic3Ov6r/Ix+RKCjmdGcKRjABOZQXjomOlLH+qrW3J4vvHld7vl9Yfv9HM5w4dOvDZZ59h0LMhwyK2oiZQoaVRHYbrNGVEq17svHiXB0IJmQ+S2blsBnGhdzG3daCmWwNS46KYumFPheNO692aEQvX4eCiaG1qZ6hFm9qKVSk/Pz9GjhxJQEAAjx49wsTEhG3btvHVV4pWmwcOHGDBggUEBga+kXvwfxETfp62cGZmJj169GDz5s106dKFmTNn0rdvX/z8/Ah5kMe+X5YjzM9l+eGryJGzbspwDv/2M/2+V7Sc+3XGGJzdvZiwajtB1/9h3Q8jWHrwEgbGpmz96zArlyzhn3/+wcbGhu7duzN79myWLFlCdnY2n3/+ORs2bKBHjx7s3r2bLl26EBcXh7Hxm2/A/raRy+WczApiVuxBZHI5C2v1orNZg1d+IBRIirlbkKhImnrc3jGm8CGaKur0t/qIpoa1+MGxM666NmVigGezQvDWr1lpA1wgKabz3Z8ZZd/2mQb4Tl48u9JuENJ0wStd05NoqKixwLkXn5k1oE/IOvqH/kpAk3m4vmOtHN81VAUqDLP15QvLpixLOInXrVl8a+vLNMcu5QQ9jNR16Wf1Ef2sPqJEJuaf7HAOZwTQ4s5CWms0pq1a80qWnwnQEKhT8EiNIWl7sDBWIfSjhRiq6byei3yMoZoOfa2a0NeqiTKOfDQzkHYBy8gWC7HVNOKXOl/S3cL7jZcelbbt9dCzZ5JDB76za00NbTOSk5NJT09HXCQENDGztuP7n7cpz/t1xhic3Bo8c1ypRExGSqLSCD/Zx10ikRAbGwuAsbExdnZ2b7XT2v9dXcP+/fuxsLBQpp8fPHgQNzc3evfujZaWFnPmzCEoKIi7IWEk5xaTkZqEV6v2aOvpo6NngJdvB1LiogBIS4gjITKUbt9OQENLi0atO2HvXIc7/5wE4PDeP/lq8BDc3NwwNjZm5syZbNu2DVB4waUvA6qqqgwcOBBzc3MOHjz4Vu7L20Iul/N3VijN7sxnavRf/OjYBf8mc+li3rDKPwyhtISrOVGsTjzLl6EbqXdjGhaXxzAxajdRhWn4Gtelh4UPjtpmxDdfwaZ6Q/jatiUe+g7lknD8cmPLJWU9C6lcRr/QDfgY1GTK46zlpxHLJHwTvpXltftWqnd1VfnIyJnIZktx17Oj4c1ZnMsKq/bPeB/RU9NiXq0eBDVZQFpJLi7Xp7Ih+TwSmbTC4zVV1Olo5slG1yEEeS2jvXrLMgZYLCphy/zJTOzyEd+1cmXWgI4EX7tQdhC5gG6abVng0P+1G+Cn0VBRo6VxHYzVdJDIpMyo+TlfWjdnScJxrK6M5cvQjRx4eJt8SVG1f3ZOTg5nzpyhuLgYiUTCrl27uHz5Mu3btyf+nD+DZZ7Ya5qQkZHBhAkTaNiwIQ7WFqgKIDU+miJhARKxiOsnDxLqd5n2A74BICYkgKi7t5CIRYiKizmxfT152Zk41VcYab/ThyjOTkMul5OQkMD06dNp06aNcl5Dhgxh7dq1pKen8+jRI1atWkXnzm+u2uD/whN+kqe1hcPCwsro+erq6lKrVi3+uRmARUNf2vT+kn/276Rpe4Vqkv+FUzRo0RaAlLgozG0dyrSxtK9dj5S4aACS46Kw6N5Vuc/T05OHDx+SlZWFXC4vJ9wgl8sJDQ19PRf+DnL5USQzYw+SJspljlM3+lg2qXS9q1Bawt38hDJ6uPFFmdTXs8Vb35GWxnWYUKMDbrq2SgN7ON2fmbEHueoz/YWG0C83lm9tfSs1lwlRf1IsE7P+OZqvqxLPYq6hz0CrZpUa82XQVdXkus9MPG/OoGfwWr62acEi595oq37o4PYi7LRM+N3tGwLzEpgUvYe1SedYXrsvnUw9n/k3DUvLRy4vu08mlWJiac0PG//C1MqW4Gv/sP7HkczffRZzG3vlcXK5gJAHeUopxzdFYF4Cg8N/w17LhKCPFijjw3NqdVfGkTelXGRI+OYXxpGrilgsZsaMGURGRqKqqopDrdrM37CDZBUTLgdFMWb89+RkZaKvr4+vry+HDh3C4rFudciNSxz/fR0lxUXUqOPGxDU7MDBWNKmRiETsWjmbjJREVNXUsKtVl+9/3oaxuaJpT2pcNIO7LSPn0SOMjY3p1KkTixcvVs5r5syZZGZm4uLigpaWFn369GH69OmvfL2V5f/KCCcmJnLp0iW2bNmi3FZQUIC5edkMVkNDQzKzczGVQ4269ZGIRYxuqzDU9Rp9TJveXwJQXCRUxoZL0dbT51F6mmJ/oRA0dMuMC4oe0s2aNSM1NZXdu3fTq1cv/vzzT2JjYyksfLYI/PuCX24MM2MPEluUzqyaXRlo1ey5y76F0hLu5ieWMbhxRRm4PTa4zY1cGG/fHjc922eqzlzLieLbiN851XAiTjoWz52fXC7nZl4sW+oNfeG1rEk8y7nsMK75zHhmSUtcYTpLE05wq9Hs177spaaiyokGE2h0aw5hwhR8bs1mp9twvAwcX+vnvi80NKjBOa8pHM+8y8SoPfysdYaVtfspG6SUUiSWVqgVramtQ/dvJyj/3aBFW8xs7LkfGVLGCAOk5BZRLJa+kf4CIpmEBfFH+TX5H1a69KswA/tJ5aon48jTYvZRW8dSGUd2q0AfuTKYm5tz6uLVcglsybnFNOwyAJ/PBwCUK/W0M9Siff9htO8/rMJx63o3Zf6fZ575ueN/nM0nW8srlJWirq7O+vXrWb9+fZWvqTr4vzLCFWkL6+npkZeXV+a4vLw81HUUy0S//DACh9r1GLdyi6KJ+uoFbJw1jlGLN6ClrUuxML/MuUXCArQee8ZaOrrkPjF26efo6+tjamrKkSNHmDRpEqNGjaJ9+/a0bdsWOzu713Lt7wL+efHMij1ESEESM2p+zhCbFuUMV6G0hKD8JPzzS2O494ktSqeerg3eBo58bFSbsfbtqK9n90yD+zThBSn0CF7LH/WH412JWtrowofoq2pj/QLVpWMZgSy5f4LrjcrWAj+JXC5neOQ2ptb47IXGv7pw1DZnlcsAFsYfY7JjRzoErmCsfTt+cOxc6Rj3/zMCgYAu5g3pYOrObymXaB+4gk6mHixw7qn0CGMyhc/Ri/qX3KwM0hLjsXVyKbdPBsRkCalvVf3hiScJyLvP4PDfqKFlxt2m8yvl1T4ZRxbLJFzOuceRjEA+u/sTagIVPjdTGOTmRi5lvlM/JZyiiWEtPjYqf70vSmArzShPzCkmJa9EWWHibm1QbbrV7yL/d0b4aW1hNzc3tm/frvy3UCgkNjaW2nXqIQWSoiP4cupCNLUVRvmTHgNZ9E1PAGydXEhPSaJIWKBckk6KDqdp+67K/Qn3wimRiQkrSGHX+YNomxog1AVToFWrVty+fRtQJAvUqlWLiRMnvua78OYJKUhiVuxBbuXFMc2xMwc9x6Cpok6RVIR/bozCw33cACOmMB1XXWu8DRz5yNCZ0fZtqa9nV+mi/6dJKc6m092fWFH7i0qXBPnllm/S8TSBeQkMDd/C8QbfP7epws4H18gSF/C9Q/sqzftVGWDdjJNZwdzJu09Ak3kMCd/MCf8gdrh9+9K9tf/fUFdRY6R9GwZYf8Ti+OO4+01njF07Jjt2IqdI/FzdcQCJRMzGmWNp/llPbBzLl6vJ5bA94RYaBTl0M/fC28CxWlWIKuP9VgZ1FTXamLjRxsSN1S4DCCpI5GhGIBOjdyvrkT8398LXuA7TYw+ggoDDnuPKyCxWtdRTKpNzJzkXgLoW+vjYGb50qei7Lqjzf5OYdf36dVJSUpRZ0aV0796d0NBQDhw4QHFxMfPmzcPDwwPP+vVQFUDNep5cOrIHUXExouJiLh36E/vainpVqxpOOLjU48hvqxCVFON/4TRJ0ZH4tFYk57T4rCd/7d2C9u+9aX5hFqsWr6CorQPFUjEAgYGBiMVi8vLymDRpEnZ2drRv/2Yf1q+TSGEqX4Ssp13Acpoa1OLP+iNQQYUREdvx9JuB6aVRjIzcwd38RJoY1mJrvWE88l2Pf5N5bHL9mm/tPsHboOZLG+AcsZCOd1cywq41g6w/rvR5N15ghJOLs/k8aBUb6n5F4+cclyHKY3LMXn5zHfJWPNBf6gziRGYQd/MTOdNwEv0tP+Kj2/PZkHy+XD7CB56NoZoOS2r3wb/xXO4VPsDl+hRihBnPPUdeGVQhAAAgAElEQVQmk7Fp1njU1DUYOGX+M49rou+MDBlfhm3C4eoERkXu4O+sUEQyySvNOSDvPj63ZnM3P4GgpgsY9JKlR+vWrcPHxwdNTU0GDx6MQCCggX4NPk23wmTGDSTd/+RQq6mMHjAU+yPfIZZJKZSJ+DxoFUfTAxCJRNR2qUMzd5cKDejV4/sY3MiBS4d3l9tXUlJC68YNsbG1UxriwtxHLBjanVFtPRjxSX3mf92N6KDbynOuHNvHkCaODG9Zl+Et6uLjZM3FixerfN1vkv+LOmGA4cOHU1hYyM6dO8vtO3fuHKNHjyYhIUFZJ2xpa8/+4FQeJifyx4rZxIT4g1xOzXoNGDh5HlYOimXNjNQkNs+dSFxYIKaWtgyaMh+3JorMa1UBrN8zlRubD4FICs0dUB/fnPXug+lg5sHkIaM4eVKRSd2hQwfWrl2LhcWbWbJ8nYQXJDMpei9Xcu5RR8eaEpmE2KJ06uhYKZteeOs74q5nh9ZrShoqkYnpELgCdz17VrsMqNIDqKHfTH51HVyhZFy+pIgWdxYxwOojJjtWnAldyqDQjVhoGLDSpV+V519dXHl0jz4hvxDYZB5WmkZEClP5MmwTJup6bK03tFoSbv7fuJkby7GoJGoLKn4Bk8vlbJk3icwHyUxYtR0NrWd3qyptIgGKl9YjGYEczvDnnjCNjmYedDP3ooOpO/pqleveVCITsyD+KBuTL/CTSz8GvKT3W8rBgwdRUVHhzJkzFBUVKas7Tp06RUFBAe3bt0dNTY3Ro0dz+t5NHswtW6Pe+YwKCRcjSU68z88nbpXZJ8zLYf7X3VFVVeXTfkNp1a3s7+To1rWE+l0iOzWJjLRUAJKz8jjvH47A2BqBQMDti//2idZQV+PysX3cOL6XC5euvPMecCn/N0b4ZbgQk1FOkL0qOBhp84mzGZuTLzI2ahdFMhEfG9bGTsuEc9lhWGoY0N7Unfam7rQ0qvOfzGItkYkJzk/CP/8+F7IjOJcdRrZEiKWGIe1M3Ghm5Iy3viMeevavzeA+jexxyZBMLmeP+8gqKQwJpSVYXBpNtu/6ch64RCala9Bq7LSM+bXu4Oc+3M5khfBdxDZCP1r01ltJzow9wJ28eE42mIhAIFBK061P/oc1LgPfOU3i/wIhD3IJSMmhosXEbYunkRgVzpRf/kRLp+JcAVC8pDewNawwJvygJIejjw3ytZxoWhjVoZuFF13MGmD1jFwF/7x4Bodtpqa2GRtdh7wwp6EqzJgxg+TkZKURfpqAgAB8mjdF5egA7LVMcNO1QTNdhP+YzfQaPZutC6eWM8LbFk/D3tmVW+eO06xj9zJGOCMlkZ/GD6bf+Jn8vmgqqcnJZRLYisVSojLyOXniBNO+HciRmxE4Odhw48R+tv++latXr1bbtb9u/q9iwlWluhIChtn5YqlpSK/gtUxx7MTn5l5I5TL88+I5kxXK/LgjBBUk8bFRbdqb1Ke9qTuuujZvtYC8IkpkYkIKkpVNL/zz7xMpfICjlilyFK3yelk2ZmGtXthqvR0PSy6XMyFqNw9L8jjdcGKVJf7u5MXjoW9fzgDL5XLGRe1CIpeyrs6g5/5thNISRkRu51fXwW/dAAPMqtmVFncWsS7pHGMc2qGuosZsp+50MvVkUNgmjmQE8EvdLzF+RnLZB8rjZKqDf0pOueSszAfJXDy4CzUNTcZ18FZu/2raYpp17F5uHGfTiu+5taYRw+0+YbjdJ+RJijiVGczhDH+mRO/FVdeGbuZedDP3xkXXihKZmPlxR/gt9RI/1e5Hf6uP3viz4/Lly3i7N+BG683K0Evnzp0Z/8M8MsTlw0lxYXe5HxHMl1MXcuvc8XL7/1gxi14jp6D+eBXh6QS2xt4NiYyMRCwWM2zYMD5vXBeAO6oqBAYGYmZmhomJCYMGDWLatGlK6cR3kXd3Zu8AZrqa1ZYQ0MW8IQnNf8JSQ1GmpCpQobFhLRob1mKmU1dyxELOZ4dzJjuEVUlnkcvlSi+5jUm9N/6AFMkkhBQkKROm/PPuEyF8gLOOxWPhgpp0t/DiVGYIu9JuMNS2JVNqdHotjSiqwoqEU5zPDueKz48v5Xn75cbS1KD8MuPqpLNcehTJtUbPLkUqZXbsQZoZOtP+FXtDVxfqKmrsqj+cprfn84mJK/X1FBn4jQydCGgylx9i9uHhN4Ot9YaWSab5QMX458UzPGIbXVQ/xZGypUtm1nZsu51YqXFsDbUrVZ5koKatzFQukYm5+CiSw+n++PovRlNFDaG0hLq61gQ0nlstPZ+rSnBwMPPmzePIkSNKA3zo0CEkEgnevh048ff5MsfLpFJ2LJ3OwEnzUFEp/5Lsf+E0UokU7086EOF/A4BHheJyn1lcXMyhQ4cQiUTK7S1btiQ0NJQaNWoQFhZG3759UVNTY9q0adV92dXGByP8AkpFGF5GRakiRZUM7Xxqm5WXrDNS16WnZSN6WjZSKrqcyQphS+olhoRvxl3Pjvam9elg6oGPQc1qFXEXySSEFiQra3D98+4TLkyllrbF4xiuI0OsW+Kpb4+OqiZZogKWJZxgZuxBBlo3I+yjRdW69PWy/PHgGuuSz3HN59klQy/CLzeGLyybltl2JD2A5Qknue4z84UdjgLy7rMz7TqhTd9M8/fKUkvHkuW1+9IvZAO3G89WvqDoqGqyps5Aupg14OvwLXQz92Jp7T7ovAMe/LtGvqSImbEH2f3Qj6XOffjMwIczURkvtVImQ4qbld6LD3wKTRV12pu642tcFxN1XTYkX6C5UW2iCx/S+PZchRSjuRe+xq6VLuF7FWJiYujYsSOrV69WdiEUCoVMmTKFkydPkiiVlTvn/P4d2Du74uzhXW5fSVEhf61dxPertpfZLqpgHC0tLfr164erqysNGjTA09MTJ6d/xTjc3d2ZNWsWy5cv/2CE/+vUtdDHTFeTkAd5pOQq2rlJnyPIDnIuxGRUqKiiKoCg1Nznag8LBAJcdW1w1bVhvEN7iqUiruREcSYrhKHhW3ggyqGtiRvtTdxpb1q/Sm+/IpmEsIIU/PPjlYpBYQUpOGmbKwyufk2+sm6Op75DuaXUHLGQJfdP8EvyOXpbNCao6XzstUwr/dmvk7+zQpkYvYd/vKZi95LegFwuxy83llUuA5Tb/PPiGRaxlZMNJjyzwX8pEpmUYRFbWeb8elpTvipfWTfnZGYQP8TsY1WdAWX2tTOtT3DTBYy+txOvm7PZ6fZtteg1vw/I5XIOZfgz7t4u2prUI6zpIsw0FC/nL7VSJgA/uT9/Rcezu/6IKr8w3smLZ3DYbzjrWBL+0SJljPie8AFHMgKYE3eYCOF6Opp60M3Ciw6mHuX6YVcHCQkJtG3blpkzZzJo0CDl9ujoaO7fv0+LFi0QS+WUiEooLMhnbHtvZv5+mPDb17gX4EfQ43aewrwcEu+FkRgVTsuuX5CZmsyibxTSolKJiMKCfPo0q4//7Zs4OjqWm4dYLCYuLq5M58NSSntTv8t8SMyqIsViKTFZQh4V/uvdPinI/ia0h5OLszmbFcqZ7BDOZYdho2FMe1NFLLmFkYvSyxHLJIQJU8rEcEMLUqipbaZcUvY2cKSBfo3nxi7zJUWsTjzL6qS/6WLWgJlOXSvUyX1bBOTdp0PgCg56jqV5BU0CKktCUSZNb88jtcVqBAIBicVZNLs9n7V1BlZKmWhFwknOZIVytuHkdy6eX0q2uIAGfjPZ5DqEDmYeFR7z18ObjLn3ByNsWzO9ZpdqFbf/r5FQlMmYezuJKnzIr3W/qlBO82V+885mOkyM3s3prBCOeI6jbiUEN0pkYubFHWFz6iV+dulPP8umz/yePSjJ4VhGIIczAriaE8XHRrXpZu7N5+YNq7RqJZFIkEgkzJ07l+TkZH777TfU1NR4+PAhLVu25LvvvmPy5MnlzsnMzAQg4mE+h89eYMeymczZeRIDY1OKCgsQl5Qoj183ZTg+bTrRsmtfNLV0yM/JVu6LCfbnj+UzOXT+Ki3cnLh9+zYSiYTGjRsjlUpZs2YNCxYs4N69e9jY2HDq1Cm8vLywtLQkMjKSXr160bt3b2bPnl3pa37TfDDC1cjb0B6WymXczovjZGYQRzICuVeYhoW6PgIEpIvycNQ2o5FBTbxLDa6eQ6UFyoXSEn5JOsfKxNO0M3FjtlO3d67RQ1xhOi38F7K2ziB6vKKE3960m+x56Mchz3HkSYpofmcBX1k3Z2KNjpWaR+Pbc7nZaBa1dCxfaR6vmwvZEQwM+5W7TeY/02NPLXnE0PAtZIoL2On2baWMxPuEWCZhddJZltw/wTj7T5ni2Om59eqZQtHjdoxFFMvEaAj+PVZVABK5jBBJNBoGBUxybouphmIpemvKZX6I+Yttbt/Qyay8J1dKqfdbW8eSDXW/emaGdEXkSYo4nRXM4XSFlGFdXWu6mXvTzdyLOrrWzz13zpw5zJ07t8y22bMV7VfnzJmDrm5ZL/5JxTpQtPecv/UAv84aVy47upTFw/uUy44uJcL/BptmjVNmR1+6dImxY8cSFxeHuro67u7uzJ8/n5YtWwIwadIkdu7cSUFBAZaWlgwcOJCZM2eirv5yvQbeBB+McDWRKSzh9L2Xiw+Vag+XJnLJ5XLWJv2Nk7Y5nc0bljteIpMSLkx5HMNVdJsKKUjCQcsUdz17dFQ1yBTlc7cgEVWBisJLNlEkeFVm6atYKuLXlAssvX+C5kYuzHHqhpveu9dOM0OUx8d3FjDevj0j7du8+IQXMP7eLqw1jZjo0IEuQT/jqGXG+rpfvdCrlcvltA9cTlsTN6Y4fvbK83gT/BD9F+HCFI54jn/m9cnlcjamXGBG7AFm1ezKaPu21drR6V3FLzeG4RHbsNAwYH3dL6v04hmQk8Sae9cZat623EpZnlzInLhD7Ht4m2mOnRll3wZNFXWu50TTO2Qd4+w/ZXKNTmX+HiUyMXPjDrMl9TKrXPrzxXO838ogkkm4+CiCwxkBHMkIwEBVm24WikzrRgY1X8vft7pKPd9XPhjhChg4cCDnz59HKBRiZWXFlClTGDZsGCKRiP79+3Pnzh0SEhK4cOECvr6+gOKLtnrZIo5vXYeaxr9ZufP/PIOFXQ0Awm9fY+/qhTxMvo++oTGffTUS3x6K2Jy9oRY3/trErxt/JS07A2kja9rMG8qpj6cRUZjK5EmT8Tt1kfzMR8hNtbH8qjlt+nZWitA31K9RrqBfLpcTIUzlTFYIZ7JDuZYTjaee/eOs6/p4P5XgJZJJ2JJ6iYXxx/DWd2Rure400K/xmu/2yyGUltDafwltTdxY6NyrWsZsemseS5x7s/fhTe4XZ3LM8/tKdbr648E1ViSc5nbj2f+ZpVuRTEKz2/MZZtuK7+xaP/fY6MI0vgzbhK6qJr/XG/bO5AFUNzliIT/G7udQuj8ran/xUqU+h9P92Zx6ieMNJjzzmAhhKpOj9xAhTGWJcx96WTQipeQR3YJWU0fXms2uX6OtqsHt3DgGh/+Gi45Vlb3fyiCTy7iTF69sEPJIXEhX84Z0s/Dmk2pM7KpOB+V95IMRroCwsDCcnZ3R1NQkMjISX19fTpw4gbu7O+vXr8fHx4fevXuze/dufH19KRJL2R+cyoGNP5GelMDw+avLjSmRiBnTtgF9xk7Dt/sA4sODWTqiL9M3H8TBpR7XT+zjzK71FC1qQZaWBOmiiwj0NNH+4RPstEzQ2BlKp17d6NDgY+SRGfTs3I0TJ07QrFnlpfGKpCIu59xTGOWsEB6K8mj3uCdsrqSQtUnncNW1Zp5Tj3c6KUcik9IteDXm6vpsrTesWuKvJTIxJhdHMqNmV3Y/vMFVnxmVSmbJFOVT3286xxt8j08lxCHeJe4JH9D8zkIu+/yI6wuWmyUyKUsTTrA68Sw/u/R/K7Worwu5XM5fD28xIfpPPjPzZIlzH0zUq565DPBTwmkSijNZXWfgC489nx3GxKg96KpqsNKlH556DgyL2EpEQSrNjJzZl367WrzfyhIlTONIRgCHM/wJF6bSwdSdbubedDR79cSutxGq+6/w33htf8O4ubkp/18gECAQCIiNjcXb25vx48cDoKr6r4dUGUUVYW4ORcJ8mnXqiUAgwMnNExtHZ1Ljo3FwqUfg5XNot21IgpEMUIG+9ZFPPsud3edxNa0BT9raj6FFixZcvnoN/Zr1y5RAGWmrV1gCBaCtqkGJVEy+pJiwjxZzvyiD+fFHGB+1ixKZGActU+rr2ZEnLaJEJn7pns2vk1JVIplczibXIdX2cArMT8BKw5B1yX9zo9GsSj90JkT9SX+rpv85AwxQR9eaRc696B+yAb/Gs57791ZTUWV6zc/paOrBoLCNHMkIYEPdwcrY5n+VuMJ0Rt3bQXLxI/5yH1Wh+k9VKG3PWhnamLjh32QuOx5cpVfwOpobudDXognfZW9jS+pl9rmPqjAc9bpw0bVism4nJjt2Iq0kh2OZd9nx4BrfRGzlY6PadDX34nPzhi/V6vRVSj3fd97/AM9LMnLkSHR0dKhbty7W1tZ06vTsPsFPKqrcvXKOUW3c+bFPG/7Z/2+fakNTc5q278qVo38hk0qJCfYnMy2F2p6NAJDJobleXfa6j6SfZVP0VbVALONmeFC5z0vKzOGa302ytK0ISs0lLruQ5Nxi4rILCUrNZX9wKhdiMsgUlpQ5L6EokwFhv7LzwTUWxR+jY+BK7gnTOOb5PUWfbOYPt+/QVdFkeswBzC+N5rPAn1iTeJYoYdo7k+Y/O+4QwQVJ/OU+qlqXfv96eItUUQ5HPcfjUMnl1rNZIVzJiWKeU49qm8ebZphNKxy1zZgZe7BSx3sZOOLfeC52WiZ43JzOqczy38//AiKZhMXxx2h0ew6+xq4ENJn7ygYYFEbYUcOSkAd5XInL4nx0Blfisgh5kEexWFrueFWBCkNsWhLUZD7Jxdn0DFlLEwMnttUbxtfhW9iacvmV5/QyWGka8Y2tLycaTiClxSq+tmnJlZwo3G78qAjb3D9OpDC1SmPWtdCnQx0LHIy0URX8W9pZiqoAVARgqKWGlZ4GKbnFz7137wsflqOfg1Qq5caNG1y8eJGpU6eWybCzs7Pjjz/+wNfXl/PRiprglLgodPQNMDQxJzY0kHVTh9Pv+1lKacPAy3/z+8KpFOQ+AuDLqQvx7d4fgEuHd3N210auXjiPsbExgwYN4tixY1y+eoUWHzdXfm5kej7ffP01OVkZTFyz47me4JNvk2KZBJ+bswkVpiBDjp6qJvs9xvCpSf0Kx8gWF3AuO4wzWaGcyQpBXaCqjCW3Nqn3wqYVr4ONyRdYkXCKa41mYFGNdbgJRZnUvfEDw20/KVc/+ywKpSXU95vOL3UG0fE5Wa3/BTJF+TS4OZPtbt/QxsTtxSc85kJ2BEPCN9PR1J3ltb+odNb92+ZqThTfRWzDXsuE9XW/qrZyu0xhCStDb+CqVgsVntVLoHx/gFu5sQwO34yrjg2zanZlTfLfHM+8y7c2rdj78BYdzTxYWbvfO6EFLZJJuPQoUpnYpaeqpWihaeFFYwOnSid2PV3qKZdDgUhMXrEUFUHl7937wAcjXAm+++476tWrx9ixY5XbnjTCV+KyiMsuLHfe8W2/EB8ezJhlG0m9H8OcgZ0Ys/w33Jq04GFiPKsmDKHfhNk0aN4GmUzG5T/WceHoPiQSCRMnTmTChAkkJiZib28PKAzwxEmTCbt9jR827EVb78VLNaWGeGbadvan/yv5pYoKl31+pJlR7ReOIZfLCROmKGPJN3JjaaDvQIfHbTW99Gu89qzZw+n+jIzcwRWfH6u1BChXUkjz2wtJKXmEX6NZuOhWbilxSvRekouz+dN9RLXN5W3yd1YoX4dv4W6T+VVaYs6VFDLu3i6u5USzw+1bPjIqr5v7rpAtLmBq9F+czAriZ5f+9LZoXG3hjNKYp1gqfeFvofQ36WiqyZy4w2x7cJXVLgPoY/nvfILyE5kUvYeEokz01bQwVNVhn8fod2r5XyaX4Z93X2mQs8QFyo5dn5i4KsMbEpmUgWEbmV+rR4WZ5m+it8K7zAcjXAmGDRuGrq4uq1f/m3D1pBEOeZBHUGpuOZHvE9vXExd6lzHLN3H7/AmObV3HvF2n2DhzHOG3r1KQm4OGljZfjJtO6+79EKRFsW3VEvz9/ZHJZJSUlBATE4OtrS2ZwhK+HjuJ8/t2oqqqikwmxdnDh8HTFmNs8e8X2+/sUY78toqstBQMTc0ZNnsl9bybsvDoVO4t2/c4di0AmRxZiZg7d+7g7e1NSUkJ48aN49ChQ4jFYj7++GN+/fVXbG1ty92PQmkJlx/d4/Rjo5wpLqCdiRvtTd351LR+tbewvJYTRfegNZxsOLFaY69imYTOd3/GWtOIYxmBZLb6pVIP5cC8BDrcXUFI04XV6pG/bSZG7Sa+KIMDHmOqbJwOpt9hZOQOhtq0ZLZTtzfSMrGyyOVydqVdZ3L0Xnpa+LDQuVe1ruS8TNKRQCDntPgSIp0c1tf9qsLvkVwu51RWMJOi9iCUliBFyumGk5W9v981ogsfJ3alBxAqTKGDqTtdzRtioKZN96A16KlqcdVnOvX0/n2mfEjY+hATLkd6ejp79uyhoKAAqVTKmTNn2L17N61bK8o4SkpKKC5W1LyJRCKKi4upZaqDHAi4dBZhXg5yuZy4sLv8vfd3GrZqB0CNOm48TIon/PY1PvtqJFM37MbUyob2/YdxYMMKwm5fpyAljm+++YbTp09jY2ODl5cXQ4cOBWDa7PlcOLALIzMLFuz5m59P3kZH34A/ls9Szj305mX2rV3M0Fkr+PVSBD9u2o+FbQ2kMjnrB65GVihCWihCWljCll834eTkhJeXFwCrV6/mxo0bBAcHk5qaipGREWPGjKnwHumoatLBzINVdQYQ0WwJ/k3m8omJK8cyA6l3YxoN/GYyNXov/2SHUyITVzhGZYkQptIjeC076w+vVgMsl8sZc+8PVAUqdDFrQBPDWpUyPIrWlFtY6tznvTLAAIucexFblM7W1KrHIXtY+HC3yTyCC5JoenseYQXJr2GGVSdKmEa7wGWsSDjNEc9xrKv7ZbUa4ExhSYVGZPHwPgz7uLZCXL5lXX7o6Vtmv1wuoL16SzY4ffvM75FAIKCTmSfBTRcwrWZnCqQl+NyczZbkS9U2/+rE/+hFtnSYSKDvYoy+Po1DtIRdaTfoumg04i/386jjb3i0bsrZKEXTjkxhCbeTcti9eiGj2nowqq0He9csLJN/MriRA9+2qKO8j1sXTEEqk3MnOZdMoYiAgABatmyJnp4elpaWZRyl69ev07hxY/T19fHw8Hhn5Q0/eMJPkZGRQa9evQgKCkImk1GjRg3Gjh3LN998A4CjoyMJCQllzomPjydeosu0UcMIvXkZiUiEsYU1bXoNot0XXyuPu/X3MY5sXk1WWgraevp81KEbH3fuzbIRXzDgu3Gc27eDpKQkzM3NGTduHL6+vrRq1Yr07Bx0NNQQqKigoqKCmrqiZs77k47EhQay5MBFABZ83Z0WXfvSqusX5a5LVQC9PGyUWdOffPIJvr6+ynZuI0aMQF9fn2XLlgFw4sQJJkyYwL1796p0/yQyKTfzYpWx5AhhKq2M6yrbajprW1bay0oteUSz2wuY59SdL22av/iEKrAi4SQ7H1znqs90FsYfQ0dVg1lO3V543sqEU5zMDOac15T3pkTnScILUmjlv5hrPjMqvTT/JHK5nC2pl5gWs59pjp0Z7/DpW2nwUSITs/T+CdYk/c2Pjl0Ya9/utcRUn9WI4nldoJ6kKo0o8iRFjLv3BzseXKOFkQvHPL9HX736e0K/DH///TfDhg1j7969NG7cmAcPHgCKPtKtu3dCvrwd2OrD+tuQkEvI9TtkpGmyfetmzuzazJT1uxEIBCwf3Z+2fYfQuqeiF/XgRg4sPXgZS3vHcp9pKCvk284t+Pnnn+nVqxcikYjk5GRcXV3Jzs7GxcWFDRs20KNHD3bv3s2YMWOIi4vD2PjtyKw+iw9GuJqoakH6jiXTuXp8H6KSYmrUqc/FS5dwtCwrPLBq1Sr27NnDb4fOEpSaS0xYELtWzmHUkg3o6Bvy+4Ip6BubMmDiHGRSKd80d6H78AlcPrIHsagEr1bt6Tt2OhpaWmUExBMSEnByciImJoaaNRXe5Z07dxg3bhz79u3DyMiIYcOGYWFhwapVq17tvojyHyd4hXAmKxQtVXXamyjUoFqbuJZrMFJKrqSQlncW0c+qKT84dn6lOTzNgYe3GRe1C79Gs7DTMqHVnUVMr9mFT18gPRhflEGjW3PwazQL53e8NeWrsD7pPL8/uMJ1nxfLNj6LuMJ0vgzbhLqKKtvqffNC8Yvq5GJ2BN9FbqeOjhVr6w6qdLZ7VSntD1DRT76yRvjpl+PKcDs3jk53V1IgLWF57S8YYde6WlXVXoZmzZoxdOhQ5cpdKSO/H8PGuHNYTGyHq641tYUGbGo1mjuhoYQXGzBvSHead+6lbFp06cgeLh3ezazfjwDPN8IH1i9FrySHP3f9UW7f8ePHmTp1KmFhYcptLi4uTJ06tdwc3zYflqOriVLtYVWVynlHX/6wkF8vRTBj8wG6duuGrUnZ+EapRufy5cuVJVBWDk6YWtnyfafGjPCtR2p8DF2HKeqWc7MzkErE3PnnJD/+tp95u06TcC+Uo1vXAIpsw1JNzh07dtCiRQulAQbFF9TBwQFbW1sMDAyIiIhg1qxZvCpmGvp8YdWU392+IaXFKo54jqOWjgVrk//G5sp4Wt1ZxKL4YwTk3UcmV8iVlcjEdAtaTUujOkytUb1tIG/lxvJd5HaOeo7HTssEiUyKf/59Ghs8vzmJXC5nROR2JtXo+F4bYIARdq2x1DBgTtzhlx7DSceCSz4/KqQ3b81he+rV117mlinKZ3DYbwwK28QS594caVD5crOX4UimI+MAACAASURBVEX9Afb/spTRbT1ZMLS7Uhe3wnGyhFX63EaGTiQ1/5m2xvX4Ifov3G5M40xWSJXGqE6kUil37twhIyMDZ2dn7OzsGD16NEVFRWiraPCtjS8PWq7hH+9pzKqlWG26disYAZASF4W9Sz3lWA61XUmNiyoz/uJvezG2vTdrJ39LRmqScntMSCAq2vo0a9YMCwsLunTpQmKiQstZLpeX+77J5XJCQ0Nf0114eT4Y4WqkroV+lQyxuroaA7q0oyQngw0bNii3P63RWaqluX3JdMSiEtadC2bj5Ui8P+nAT+O+BEBDU1Ee0rbPYIzMLNE3MqH9gG8IfiwXBv9qcu7YsYOvvvqqzFxGjBhBcXExWVlZCIVCevToQceOLxYuqAoCgQB3PXsm1ejEOa+ppLVcw1THz3goyqV/6K9YXxnHgJANtLyzCD1VLVbVGVCtS773izLoFrSGLfW+xsvAEYCQgmRqaJm+sKf2n2k3SCvJZaJDh2qbz7uKQCBga71h/J56hcuPIl96HFWBClMdP+Oc1xRWJp6iZ/BaMkR51ThTBXK5nN9TL+Pm9yPGajqEf7SIbhbltWqrmyf7AzxNnzHTWHb4Kj+fvIVv9/6snvA16cn3yx335MtxVdBS1eBog++ZX6sHD0V5DA3fQofAFYS+hVj8w4cPEYvF7N+/nytXrnD37l0CAwNZsGABnTp1Yt++fQQHB1NUVMS8efMQCARk5eYjlUNxkbBMlYe2ngHFhUKlAZ22cR8rjl5n8f5/MDK3ZNX3Q5BKJABkpz/gyF9/snr1ahITE6lZsyb9+ilWHpo1a0Zqaiq7d+9GLBazfft2YmNjKSwsX8XytvlghKuZyhSkqwoUsaAOdSyoa6GPRCIhNjYWqFijU0NV8WdKig6neefe6Bkaoa6hSdu+g4kLu0t+Tja6BkaYWFg/12hpqKpw7do1UlNT6dWrbL/loKAgBg8ejImJCZqamowZM4Zbt24pJcleB7qqmnQy82R1nYFENluCn89M0kR5xBalc/nRPXxuzmZazF9czI5AJJO80mfliIV8dvcnfnD8jM/NvZTb/fJiaGr4/LKaTFE+E6P38JvrkP9Mb+hXxULDgC31vmZQ2CZyxFXz1J7GU9+B243n4KxjiaffTI5lBFbTLBWJe77+i1mf/A+nGkzk5zoDnhniqG4qEpovpVb9hmjr6qGuoUnzzr1x9vBR6udWZZznIRAI+L5GB/5yH4VIJsFAVZvW/kv4NmIraSU5LzXmy6CtrbjfY8aMwdraGjMzMyZMmMDJkydp06YNc+fOpWfPntSoUQNHR0f09fUxtlCoN2lp61Is/Fd5qViYj5aOrvI5VserCWrqGujqGzJg4hwyUpNIvR8DKByPj9t1olGjRmhpaTF79myuX79Obm4upqamHDlyhJ9++glLS0tOnz5N27ZtsbN79zLLPxjh14CZrgafOJvRy8OGBraGOJnoYGeohZOJDvaaImQRV2hkpYWxlmqZ7OuUlBRat27NqFGj+O6775TjGWmroyqAmvU8uXbiAIUFeUgkYv7ZvxMjc4XXC9C8Sx/O7d1GXnYmwrwc/t69Bc/mCnUhVQEY66izfft2evbsib5+2eXvRo0asWPHDnJzcxGLxaxfvx4bGxvMzN5cLO9Axh0einKJbraMzFbrWFNnIKqoMDlmL+aXRvP53Z9Zn3Se2MKHVRpXLJPQO+QXWhvXY6zDp2X2+eXG0tSw1nPPnxS9h36WTd7pftqvg45mnnQ19+K7yO2vvJSsqaLOstp92es+knFRuxgWvoV8SdFLj1ckFTEz9gAt7iykl0Uj/BrNUq5uvClKX44rg0AggGfcw6qMUxHtTOtzvdHM/7F3nlFRXV0YfoahdxEQFCwIAorYsfdesZtEY2+xxhJL7LGLGktiib1HxdhLjL2CFSkCgoBI7yBlmPr9GBlFUAGxfjxruZYz994zZ+4ws/fZZ+9345sRQRfTGuipaeHkMYtFIcfJlGW/f4APpFSpUlhZWb11ATB27FiCgoKIi4ujV69eSKVSqjgqQ9DlbKoQ/uSx6tzwIH/K2rxduez1+2hl64BQTS33MVD9rTZv3py7d++SlJTEnj17CAwMxMXF5cPe7EegxAh/RLQ1hDhZGNLUpjSt7cxoalOaqmUM2bZlM1ZWVpQqVYqpU6eyZs0aXF1d2bp1KyEhISxYsAB9fX3VP1tTPRTAdxNno6GpxfSezRnfthbeNy8xwW2L6vW6DZ9Apao1mN6rBTP7tKa8fTW6Dh2nOm6lJ+TQoUN5QtEAK1euRFtbGzs7O8zMzDhz5gxHjx79FLcJgH3Rt1gX/h/nak2llIYeGmrqNC1lzyLb3tx1mc/Txm58b9GAO2khNLm3GNubvzA2YDcn4x+SLn17mzSFQsGYgN1oqanze5Uf8hx/nxG+kOjHleQAFlbuVSzv82tjuW1ffNMj2BN9s1jGa1rKnkf1FwJQw3MO15MLl30PSmGR6h6z8M+I4lH9hYwv3/azJCblOMdvkvEiFZ/bVxFni5BJpdw6e5TAh544NWie59wc5/hDsdUtg0e9uSRI0nnw4hlna07BO/059rdmsDvqhirf4mMxZMgQ1q9fT1xcHMnJyaxZs4YuXbogEonw9fVFoVAQHh7OyJEjmThxItYWZggF0KhzT/7dv5XkuBiS42M4t/cvmnRRRukinwbyLNAPuUyGKDODA2sWUsrMAstKyshV8259uf7faby8vJBIJCxcuJAmTZpgbKzUKXj48CESiYS0tDSmTp2KlZUV7du3/6j3oSiUZEd/JXzLPTkvJPrR328Tl2pPL1DfYoVCgXf6c1XG9Z20EOoaVlTKappUp4aBtaosZkXYaQ7EeHC97qw8soqJ4nQq3ZxCcouN+f6IZ8qyqe4xi/X2P76z4fq3jveLcFo/WIFHvTnFqlZ2Iv4Bo/138aNlI36r3PO9DUNis1OZHHSAmylB/OnwI51NaxbbXIrC27Kj05IT+X3iIKKfPUWgJsSyYmV6jp6CU/1mecYoSnb0u5Ar5Mx5+g/7Ym5zrMYEMmVipgQdQCyXssrue1qYOBbL67yJRCJh4sSJ7N+/H21tbfr27cuKFSsQiUQ0a9aMp0+fYmBgwJAhQ1i0aBFiObh7RyGTKzi0fgnXjv8NQDPX7+g7/lcEAgGP795k97JZJMVFo6Wji61zHfpNmIVFeWVCqVAASbdPsHzpEjIzM2nSpAkbNmxQKQx+//33nDlzBoAOHTqwfv16zM3NP8r7/xBKjPBXwrfak/Nh2jPaP3TjiPN4mpayL9IY6VIRV5IDXvZN9iFNKqKdSTVKqetxOO4ud1zmYaVtkue6swmPWPnsHBfrTM933OlBBwkXJXKg+pgizetbYk34vxyM9eR6nVnFWm8bL05jpP8OQrLi2VNtJM4G5fOcI1fI2Rp5ldlPjzCkbFPm2nRHT/j59YPvp4VyNCCcymoVUStiUPFjOccHYzwZF7iHDQ4D6W1ej0Oxd5gRfAhnfWtW2PXDXs+y2F+zsHzLC4vCIJw/f/78zz2JEt6PrqY6mkIBMS+y37a1lC85Em/lS336hgvvIzQrnjYPl7PBYRAdTJ2LPI6mmjpV9CzoZFqD8dZt6WVel4DMaP6MuIgMOf8l+RIuSkRDoEZZLWPVqndP9C3KaZWitUnVPGM+THvGlKADnKjx81fTmOBj4mJog3vsXUKz4ot1NaUn1KJfmfroCDUZ4PcXoKCBkS1qL/f3fNKf08t7PQ/Tn3G4+lgGWDb+7JKYYrmU+SFHmRx0gP7l6iEUGRbqO5mDUE1A44om6GoWv4iIk74VbU2qMcx/O0nSdMZat+Ynq1bEiFMZ5r+d56JEXAxt0P2MzoyBljohSZlf3L371JSshL8yvhWx83hxGo3vLWKidTvGWrcp1rFDs+JpdHchWxyH0r60E7dTg1UKXsFZcbQo5UCH0tXZF32LaRU70/WNnq0yhZz6dxYwxqo1Q8vlDSH+vxKdnUJtz7kccR5foMYfhSUsK57BfluRIWeTw2B2R99ge9R1FlbuychyLT6L8tabPEx7xuDHW6igXZrNjkOw1DImIO4FN8IT0ChEe/ZP9d2ME6fRy3s9pTX02VNtJAbqOiSIX7Ag5Bh/x3oyrUInxlu3QVv4eaJkJdrRJUb4qyQhQ4xPdBqRqcrs0vzbfum8bPv15YWgM2TZtLq/jNYmVVli26dYx06RZNDo3iLGWLVinHXbPMfjxGn8l+jLuUQf9sXcopKOGZ1K16B96eq0KOWAvro2vz87x8kELy7Wnv5NSlN+CCfiH/Dzk/141V+I4UcoBZIr5Izy38n2qGvUNazEMecJWGp/fplBsVzKktCTbIi4yEq77/jRsnGuv42et3bQQ6tNvupZb/KpjYhYLmV84B5upgS9FMtR7usHZkQzLegg3unPWWbbN1cXp0/Jt7KwKColRvgr5s2enJpCNUrpamBbWq/YEj2KG6lcRnfvtZhqGLCj6vBi/dKL5VI6PlyFk3451toPeOe5j9Mj6er1O0dqjFd1g7qXFkZ1PSsepYdzoPoYuprWLDHC+fCT/04yZNnsdhpVrONGZSczMXAfD16EMb1CJzZGXMZa24QtjkMpo2VUrK9VGB69CGew3xbKapXiL8fBlHsjv0AkE2N05SfCXf7gcWzGF+kcKxQKNkZcYkHoMfY7jaa1STVWPTtLPcNKyBQKpgYdQFNNndV2P3yWdpRf+8LiQygxwiV8MhQKBcP9txOdncLxGhOLVfgiZ+x4cRpHa0x8b8nK9shrXEp+zF6nV/XYaZJMWj5YhqZAnXjJCzJk2cqM69JOtDVxwlTz43nfWRIZwQkZpGS9cqiMdTSwM/3yHKpMWTZ1POcx18aV7y0afvB4MoWcjREXmR9yjNHlWjKrUjd0hJqI5VIWhBxjW9Q1NjoMpId53WKYfcGRyKUse9kEws2uH4Msm+TrlAVkRNHVaw1BjZXNT75k5/hKkj/f+W6knUk19sd64KhbFp+Gi5Er5OyNvsWsp0doaFSZZbZ9sdH99JnEX/K9+1iUGOESPhlzn/7D2URvLteeUezJTktDT3I47i7X6vxaoLFH+m/HWd86V8j6QMxtload4r7LAjTU1HmaGavaS76SHEAVPQtl8wlTZ+ob2hSLE5GQkY1PdBoRqSIEvG0FoP1yBfD5M4JzeJAWRoeHK7nrMv+DmjM8THvGqIAdaKtpsMlhcK5eszncSglioN9fNDGuwlr7/sXaivBt+KQ/Z7DfFsw0DdnqODTf7PocTid4sf75Bc7VmvrR51UcnIx/iOujtShQoKumyX+1p6n2+DNl2ax+do4tz6/zk0k36urYI1CofdFO4ddOiREu4ZOwOeIyK5+d5Wa92cXeh/dgjCe/BP2Nh8tcymoVbP/Q2WMW26sOV/UoThSn4+TxK8dqTKR+PuIdYrmUWylB/JukrE0OzYqnZSlH1Uq5oo5Zoef9te+FuYWd4UTCQ67UmVlosYx0qYi5If+wN/oWy2z7Mrhsk3cmXqVLRUwN+ptziT7srDr8o9W7SuUylj87zZrw8yy17c2wss3fuyWxLvw8AZnRbHDIK4IDX1aUI1suwfr6JOIlL1TPtTB24HLdmUBup1CikCHk1fy+ZKfwa6bECJfw0TkWd58xAbu5XvfXYhV7ALidEky3R2u4UHsaNfKpMc2PF9IsLK9PJKn5BlW5yxC/LRiq67x3LzmH2OxU/ktSJnidT/TFRENf1TO5RSmH95Z+fAtZoXKFnLYP3Ghl4sisSt0KfN3xuAeMD9xDSxNHVtp9h1khnLIzCY8Y4b+d78o0YHHlXsWa1euXHsHgx1sxVtdlW9VhBe7A9HPgPqy1TZhSIXfDky8xyiFTyNkWeZUzid7cSgkiUZKOHAXe9RehkWVccKdQAKVNJXSs8P8l5foxKDHCJXwUfvTdzCirlgiAHo/WcabWFNWqs7gIyYyj8b1FbKs6rFCKVheT/Jj39Cg36s1WPR76eBu+DRYXSfxfrpDj9SJcpeB1/0UYDYwqq4yyk15uXd0c4ZV//97BjVOHiQgOpH67boyYvxqAYJ8HHN20krAAH9TUhNjXacCAqQswNi3zxQmvRIiSqHNnHidr/IzLezS4n4sSGR+4F/+MKDY6DKJVPvXZBSFB/IKfAnbhnxHFnmqjqGVYoUjj5CCVy1gZfpaVz86ypHJvRpRrUaiEvK5evzOsbLNcnZu+lihHTHYK7rF3aapZB5+ozEI5hdkKMdbmgrca4i8pAvAl8/kL70r45ggXJXIw1pO2D1bQ6eEq9jiNKnYDnCzJoJPXauZUci20pOTretFZMjGj/HeywWFgkbvvqAnUqG1YkZmVunKl7kwim65hnFUbQrLi6f5oLVY3fmaI3xYOxniSKE7HJzoNmVyBsWkZug6dQNNufXONl5mWSvMeP7Dy+C1WnryNjq4+Wxco9xtlcgU+0cXfDrCoWGmbsMF+IP19N79Vw1sql/H7s3PU8pxLLYPyeDdYVGQDDMoe1Yeqj2VGxc60f+jGktCTSOWyIo3lnxFF43uL+C/Rj3su8xlp1bLQGfFPs+KweW07orBRDplcwb2IVALiXrz/5Pfg7+9Pq1atMDIywtbWNpf+e2ZmJmPGjMHU1BQjIyOaNWuGhZYx35VuxsHjF1g8si8/tajGlG6N8oz7LNCPJSN68VOLakzq7MLxLWvQEmgSEQcRL9I5dOgQjo6OGBgYYO/gyMKNu3D3juJRVCohSZk88Atk/KC+uNiWxczMjO9GjCMh4+M3l/gaKDHCJRQ75xN90BAIEcklZMklBGXEFOv4YrmUnt7r6GTqzBjr1oW+3iP1KQ1fti/8LfQYdQwrFqsOsaG6Dq7mtdngMIinjVdytc6v1DGsyN6YWzjfmktIsrJ1W91WHanToj36Rrn3sZ0bt8SlTRd09A3Q0tahdd9BBHvfUx2PTM1CJCma0fkY9CpTj2al7Jn4ZF+eY3dTQ3C5u4CTCV7crDubeTY93qsRXRAEAgEDLBtzv/4CLiY9ptn9JQQXoruWTCHHLewMTe8tZnDZJvxX+5ci7evLFXJCs+Kx0VFmEidkZHMvIpV//97B/IGdGd7Ili3zJ6vOj496zuB65RnVzEH17/jWtSpDnJAhLvQccpBKpbi6utKlSxeSkpL466+/GDBgAE+ePAFg5MiRJCUl4e/vT1JSEr///jsAPtFpaGjr0LRbX/pN+DXfsTfPGU+VWvX586I3Mzcf4tKRPTy8eh51hGy+eZUBAwawevVq7gRH0vWnGSyaNJqUxARkCpBKxLiN649j3casPXef1ac9cWrVjXOB8cXieHzt/H80Ry2h2ChIiOlQ7B0y5cofE3WBkAWhxxlp1bJY5AYVCgWj/HdgKNTBze67Il3vkfqUTQ6DePQinG2R1/BusOiD5/UubHXLME63LeOs2/IwKhnv6BdQiE2gwAeeedq7BSdm4GRRvAluH8LaKv2p5TkX99i79C5Tj1RpJrODj3A47i5udv0YYNHoo9RcW2uX5r/av/DH8ws0vLuQhZV7Mqrcu1ezgRnRDH68BW01De66zKdSEYxvDtHZKRip66oy8t+Mcvh6XEUsyhsh2HDJF6F67u9DTpSjqHrIAQEBREVFMWnSJAQCAa1ataJx48bs2bOHAQMGcOLECSIiIjA0VP7d1KlThyyJjIhUETbVamJTrSZ+ntfzHTshKoKGHbqjJhRiblURu5r1iAx5Qq3m7ZBGZ6NjqE+lOk24F5FK9cat0NLRJS7iGYYmptw4eZhSZmXo0H+EajxrO0eV4wF8MXkOn4MSI1xCgXhfksmjqFTKGWnjZGHIpSR/BAioZ1iJKRU64GpWu9j0fpeEncQ7/TnX6s4qUvu6kKw4tNU0sNAypof3Opba9sFCy7hY5lYQ0kVyUBTcGD0P8ufEtrVMWLlV9ZxMAcmZko8xvSKjr67NPqdRdPVaQ6IknYWhx+lQujp+DZZQWlP/o762mkCNCeXb0ba0Ez/6buZE/EO2VR2G5Rufq0whZ034vywNO8V8m+6MsWr9wVKYT7PiqPzSiOcYNFBGOQDC/L1JEkUXeLycKEdR9kzzS+9RKBT4+vri6elJhQoVmDdvHnv27MHS0pL58+dTpVFbLhzcybWXuQn2teurrr119ii7liqzpmUyKXN+6IBcJmXcsk089XlAclwMJ3f8AYAkW0S1sqWwrGhLz9FTUdfU5MHV8+xcMoOIkCdYVbZn1YSBhD5+RLnK9gz45TcC7t/m3/1bSU9Nxr5KFf5Yt5YmTZrkmr9YLMbZ2Zn09HQiIiIAePLkCb/88gu3bt1CJpNRr1491q1bh7190RrAfG5KwtElvJeAuBecC1R2PJErchtgUD6WKSA8RcS/T+KZbtqPgIbL8HSZR98y9Ysl/AjKOt6/Iq9wsuakInfRydkPXv/8P/SEWgwt+2m1ocWygvd1jX0exqqJA/lhynzsa9XPdSxb+uWEo3Mw1zTEWF2XyU/2s7faKLZWHfbRDfDrOOqV5Xa9OdQzrERNzzkcjr2jOvYkI4Zm9xZzPP4BnvXmMs66bbFoUYe8FooOTsigoO7VlG4NmdTZha0LpvAiJSnXseDEjCLNxcHBAXNzc9zc3JBIJJw/f56rV6+SmZlJREQEvr6+GBkZERUVxR9//MGgQYPw8vHD8C25CY069mDztQA2XwtgxqZD6BkqVcv+mDGaZt36MWf7MdXxkXOWo1AoiAp5wubZ4xk0cyllK9nSd/yvGBiVIvJpIG37DWHN2bvUaNKKVeMHcPiPZYxbtpGNl/1o4fodPXr0QCbL/Xft5uaWp/1gSkoK3bp1IzAwkNjYWFxcXHB1dS3SPfsSKDHCJbyToiSZOMqdkGfoFes8bqY8YULgPk7W+LnAtcD54ZH6lCq6FiwKPcFmhyGfXJZSU1iwr1xCdAQrxv5At2ETaNypV57jRxLuUNtzLoP8/mLlszP8m+hDVHZyvquhj41ELmVF2Gnq3pnPj5aNqW1QgTtpIZ98HgAaauosqNyTkzV+ZvbTI/T32cjS0JM0ureQfmXqc6XOzGItk1OuhJVGIiVLksdBfRMDYxPm7TrJqhO3mb/7NKLMdDbPmaA6/iFRDg0NDY4dO8bp06exsLBg1apV9O3bFysrK3R0dNDQ0GD27NloamrSvHlzWrZsice1S2/NTcghPTWFVRMHom9kTLfhE1l9yhMfj2tcPLwbAD/P6+xa/RsoYNk/15ix+TA7Fk3D2q4qzo1boq6piYlFOeX/NTTpOGAUGS/SMCtrTUVHZwQCAU6tu5GQkEBcXJzqdUNDQ9m7dy8zZ87MNR8XFxeGDRuGiYkJGhoaTJo0icDAQBITE4t03z43JeHoEt5KTpJJYXsY5+z1mOppFUspTXBmLL28/3hrv9nCcDs1GE01IZPKt6eKnsUHz62wGOtoIBS8jB5IpchkUuQyGXK5DHG2CKFQnbSkBJb/9B2t+wykVa8f84whFMCIio0YoueIT3oEPukRnEnwxic9AjlynPWtqa5vTXV9K6rrW+GkZ/XR2jHeTglmVMAOLDWN8aw3l8q6ZfjRshH17syntUlV6hRzVnxBcTGqjHv1cbR/uBL3uHtsdhjM4HJNi/11nmbG0fFlG86CRDm0dfWoVFWZzW9U2owBvyzk5451yUp/gY6+QYHHeRvOzs5cvXpV9bhRo0YMGjQIW9v89aCFBXBC4yOfARATHkrTLn0wKWNJ/bZd8b51idZ9BhL+5DHGpc0xtDOjjFUFsKqAjVMtHt+5QQX7augaGJGdmXt1LxSqI5fLeer7kEqOzlw7fhAHp+pYWLz6To4fP54lS5ago/PuqoVr165hYWFB6dIFq+v+0ihZCf+f0KJFC7S1tdHX10dfX1+1f7JkyRLVc/r6+ujo6KCmpkZCQgI+0WmkJiezYeYYxrWpwbg2Ndg0ewJZ6a8yGoMe3WPBoK6Mbu7I7O/b8cRLGQKUyRXsPnqG6tWrY2xsTOnSpenRoweRkZGqa6dNm4a1tTWGhoZUqFCBxYsX55l3kiSdzl6rmWfj+kE9h0FZjuST/pw0qYhfKnT6oLGKiq2pnion68T2dYxsUoXTuzZw++xRRjapwont67h6/ADxkeEc37ImVxbt61Q1M8LFqDLDyjVnjX1/LtWZQVyz9fg1WMKvFbtSUduUmylBjA3Yjfm18VS+OZXuj9Yy5+kRDsV64p8RVeSyHlCWiI3230kv7/X8WrEr52pNVa0wK+iYss5+AP19N5Mh+/RlKHKFnPXh/9HywTKmVujIsRoTmBPyDxMC95JZTPPJ+b4crP8zo+zaIRQK2bAwb2Zx4ENPbp52z5Pw5Od5nXkDOjGtu3IP9N7lswDEPAth3piBmJmZYWJiQvv27QkMDCzwvLy9vRGJRGRmZrJy5Uqio6MZPHgwzZo1o3z58ixduhSpVMrNmze5cuUKbdq2QygAuVyOTCpFIVeAQoE4W4RUokyutChvg1QiwaK8DaUtrUhJiOPOhVNY2ynLzCpVrUFMeCiOdZWlTc8CfXnidQdrO6WqmVlZa5LiovHzvI5cJuP8gW3oG5eiQftuLBnei+GNbTm6ZQ2TFq5SRaaOHj2KVCqlR48e73y/ERERjB07ltWrVxf4Hn1plIh1fMO8nsk87vtudOjehwGDh76zWH7+/Plcu3aN0//+h7t3FDuXziI2IoxxyzahQMEf00ZhbefI95Pmkp6awoxezRk4YzF1W3bE49/j7F05F7djN9AzNCY9KZ6O9mbYVLAmOzubOXPmEBAQwIkTJwAIDAzEysoKPT09IiMjadeuHQsXLqRnz56AUmKv3QM36hlWYmWV7z/4fpxJeET3R2u5XndWvtKUn4rLwcr99aJS3linUBm0MoWc4MzYl6vm53inP8cnPYKo7BQc9CxVK+ac1bOlpvFbw/QKhYIDsR5MffI3rma1T9162wAAIABJREFUWGrbB2ON/LceBvpuRleoxSbHwUV5m0UiJDOOoY+3IVZI2VF1OPZ6loDSmRsbsBuvF+HFWrdudnUcntVn4lzenjU7D6JRUekoyqRSosNDWD6qL6KsTMYu20j1Bs0JC/DhRXIS2xZOZcDU37h38TQpiXGMmL8ac6uKhPh5QXwYE4f2x8DAgN9++43Dhw8TEBBQoPn88ssvbN26FYlEQtOmTVm/fr1qFezn58fw4cPx9vZWOb0dunTD3TsKv3u3WT66X66x7Gs3YObmQwD83MkFdXV1MtJS0dDSpmbT1vSfugAtbR2eeN1hxZgfMDYrQ3pKEgbGJrTqM4iOA0YCsHnORKRSCc8CfEhLTqSCvROOdRpy6+w/TF6zC3Privh6XGP7b5Px836EkZERNWvW5MyZM9jZ2XHlyhUGDBigSszKIT4+nubNm9O/f39mzZr1QZ/j56QkHP0Nkl8mc7ZUTnyGmEdRqapM5jfl8hQKBXv27GHu3LmqJJP4qOfUbt5eFSqr3aIDXtf/AyDY+x6GJqa4tOkCQKNOPTm+bS33Lp+juet3GJU2I/O1FnRCoZDg4GDV4zezGdXU1FTHFQoFIx5vx0RDj+V2uX8cCoNCoaDF/aWMLNeCNeH/4qhX9rMaYIDqloZEpmUXOswPSnWl6paFK00SCtSw17PEXs+S3mXqqZ7PkGXj9zKcXZCQdow4lTEBu4gVp/FPjfE0MHp3y7s/HAZS03MOx+Me4Gpeu9DvtTDIFXI2RlxiXshRZlTszKTyHXJlz5to6HOg+hj+jvGgs9dqxlq1ZmbFLh/UhCNNmkWmLJtrJ/7D3NycWg0a4RujrAE/sX0dx7esUZ27ZtIQXEf8jGWFyuxYPB2JWMz+1fOpVr8pY5dtwthUua9sU60mzpZNMTFRZnZPmjSJRYsWkZiYWKBwq5ubG25ubvkeq1atGrdv387zvJWRNvI6Dek6dDxJsdEq5bYcgh7dJfNFKmvP3UdHL2+inceZf3Bp24WRC9bkOZZD2Uq2jF26QfV4z4o51GzaBouXalvOjVpgam7BrVu3sLW1JSwsjKZNldsHYrGY1NRULCws8PDwoGLFiiQnJ9OuXTu6dev2VRtgKDHC3xzvSqRy/3M5h/9YhkUFG3qNmUZkvUa55PKuX79ObGwsvXr14mFcNjIFtO4zkEvue2jQXqkNfP/yWWo2bQPklES88ToKBZFPleEzmQICgkNp4tCctLQ0hEIhW7ZsyXX6smXLWLRoERkZGVSqVIkffvgBgEWhJ/DPjC5Sc4DXSZFmcis1iDtpIcrG7JV7F3ms4sJUT4u6VkZF1o4uLslKPaEWLkaVc8lNKhQKYsWpKsN8MyWIjRGX8E2PQKaQ46hXlm5mtQgXJWKkroudThnU1fKPqhiq67C32ih6eq/HxcgmT8lQcRGWFc/Qx9vIlIm5UXcWDnpl33rudxYNaGpchaGPt9H43iL2VBulWi0XlpCsOGx0zNm9ezcDBw4kU/xqL7ecTRVqNWvLxFXbmNKtEUNnLadafaVRObJhBQ069ODh1X/x87zOQfli+k9dgL6R8v6kZ7/aJvgU+52OZrqExqfmyU3IqWO+ccqduq065muAZWIRdy6cYtyKzXmOSaUS5DIZCoUcuUw5rrq6BmpCIZWqOnNyxx+06TsYs3Ll8b9znYjQpzg5OWFra8vz589V49y6dYtx48bx4MEDzMzMSEtLo3379jRu3Jhly5Z9tPvyqSgxwt8Q7zLAfcfPpGwlO9Q1NPA8f4K1k4fy276z3EMZlnMwN2DXrl307t0bfX19xNHK5toVHJyQSsSMa6NMJqlarzGt+wwEwM65LsnxcXj8e5y6rTvhce4YcRHPEIuyVK9bqkxZUlJSSEpKYsuWLTg45N7bnDFjBtOnT8fLy4tjx45hZGTEvuhbbIu6hke9OUUuRcohKjsZHTVNXsiU4d85If/Q0sTxvTrHH5scx+dL0xcWCARYaBljoWVM29JOXEsOYHTALtqaVGNK+Y4kSTPwSX/OgRgPfk13f29Iu5GxHaPLtWSw3xbO1ppSLGVBOSgUCjZHXmb20yNMq9CJyeU7vNUheJ1y2iacqzWVjRGXaHJvMfNsXItUM/w0Mw7LVA0uXr3Ktm3bCJEqjbAoM4MjG1Ywdf3efK9Liovh1tl/+GX9XozNyrBl/mT2us1l9KJ1wKvErE+137npdzcWLFigenz77FFcR/xMj5GTEWeLuHPhNOOXb8pznVBNQIL3dUqVMsaxbqM8+jM7Fk3n5ml31eOT29czbO4qmnbtQ+POvYmLCGfZ6H5kvkjFxNySPzdsVP0+vJ6gZWJigpqamuq5o0ePcvfuXfz8/Ni5c6fqvMePH1O+/Iclbn4OSvaEvxFymgIUdGW1cvyP1GjSirb9hiBUE9C8vAHVbKw5fvw4LVu25HpIIiFJmSwa1oPydlXpN3EWCoWCv9cuIiMthbFLNwIQcN+Dg2sXERf5DKcGzUlPTaZKTRdch08EwMZEl6Y2Si8+JiaGGjVqEBkZibp6Xv9v2bJlPAwP4HI/fS7VmYGTvtUH35f/En3p6vU72QopAAMsGvGnw0AMi6gTXdwkZIjxiU4jMlXpuLxe4qImAAFQzkjn5dbBp2vakChO55egvzmf5MvaKv3paV43333iN0PaOfvNr4e0q+qV5c/nF+hv0YgZlboUy/yeZSUw3H87qdJMdlYdkW8f4oLwJCOGH/02Y6Suw46qwyn3jr7Bb7Ii7DTH1+xC/WEsV69eVX1nDvy+EF19A1xH/AyQZyX8U4tqtO8/nO4jJgFKQY8VY39gwyVfQPmdcTCQf/L9zg9pOvGp8xy+JUpWwt8IPtFpb+3KA/D4zg12r5hDUkwkNk61lKHkl/6XTK5gw879mJiY0KJFC+BVKc3zIH8GTl+Mlo6ykXrLngNYMuJV3apDnQbM231KOY5UyrQeTenQX5mQIRRAKd1XQh1SqZS4uDjS0tIwMcn7Yxeblcxx72ucWHi8UAb4XVKad1JDyFZIsdA0wlbHnD1Oowo87qfAVE+TlramiCQyghMzSM5UvoeLKT40Ll2J1lblP2nHGYVCwe7om0wPPkS/Mi48brj0nQ5LQULaHqlPUaBg5tPD/BFxgbqGlXKtnN8V0s5vflujrvJrsDuTy7fnlwqdCnxtflTRs+Bm3dksDTtFLc+5rLXvz/cWDQt0bUhWPMEnbrF09m/Aq+/M47s3SY6L5qL7HgBepCSy4dcxdBr4E50HjcHazgHBW2Q9hAJQE6fTrl33T77f6WBugKme1ludwlftF/M6hZ86z+FbosQIfwPkyOW9Ta82NiKMNZOHMXjWMmo1a8vGWePwuXWFwTOXqs456X6AJp17kZgpxlRPC1tTPbyiUqlUtQZXj/9Nv/HK8ourR/erSg9AWY5QrrI9EpGIfzavopS5BdUbNgfg7qWzOHdsgtzcgcTERCZPnkytWrUwMTFBLpezZcsW+vbti7GxMRduXWX9n3/Se8IQ2pWuXqD3XRApTRvtqswuJyBD7QWmml+uPq22hjCXFvRO3yDitTTQ1ijeOtt3OSzPxHGMDthJmjSLUzUnFTmD+M2Qdg67om6wKPQ4fcxdeJIZXeCQdg7PRYkMf7ydREk6l4spUgKgriZkjo0rnUydGeC7mePxD9ngMBATjXerfd33uENqbCJ9+vQBUH1npm84gFT6SnDjt0Fd+W7SHJwbtQSgSde+nNi2joYde2Bkas7p3Rup0UTZiCQr/QUTRw/6bPudb3MKNYVqlNLVwLZ0/pUVX0qew9dIiRH+BsjJZH6bXu3DK/8iUBOwe9mv7FkxB4vylVBTF6J4uYuTHBeD/71bDJy+mHOB8aowk5WRNsPmuLF35TwmdXEBhYJKVWsyfN6rFfaZ3ZvwvnkZgOoNmzPB7VXilfxFIq5dOxMXF4eBgQEtWrTI1Vrt6NGjzJw5E7FYjLy0No0GduHAnHUFes/vC53lGGRZlhb2arW4IrtFl8qfdx+4MFTRteBJZvF1n3qfw3IvMhlfaRC9zBozulLjD1pdvo0+pg0IiE0nJkadZvpNaWOghrG5BuVKaRCcHfXWLG0nPSvSpFkcj3/IeOs2zLVx/aCs5rdRx7ASD+r/xq/Bh3H2mM1Wx6HvrE0PPHaT9t06Y2CgdO50NIRYGWkTrsitPCUQCtEzMEJbV1nK1axbPxKjI1g4RCm16NSwOQOmKvdkn3pe4v69e/g/fvxZ9zvfdAoLwpea5/ClU7In/A2QsxeVw5GNbrlKDfatnIdUKmHQjCWqc2b1a0P3UZOp1yqvaMXr3mlh9pnfHKMgzecVCgU/+m0mSybhsPPYAiXHFFZKE5QNyOtbl6KGRcH3/D4nB2M8ORx3B3fn8R881uduMP+6AwAgzzfEmbtkLiekfSnJn99CjxEnfoGFphFhogQstIxe7jcXLaRdEC4lPWbI4610Nq2Bm913eRIEJXIp+pdH8aLl5lzNSQqbm/E6Bf3OfOm8K8/hXSHt/1dKVsLfAO+TuRNlZWJgnNv46OgbIMrIXyg+R3ayg735Rw8xLQg5xpPMGK7UmVkgA1xUKU0tgSY+UZmUM9D/Kr749nrFsxIuaoN5KJ72cgWNWISniIhMy87lAJxL9OGXoIOMt26jqul9U3iksCHtgtLKpCqP6i9kfOBeannOYU+1Ubnqy8NFiVhqGefpDlYSli16SPv/lRIj/JUyYMAALl68SEZGBkalzWjbfxTNu3+PVCLm3qUzJMVGc/O0O9M3HURbRxdRhlJEwP/eLY5vXctT34fEPg+ladc+ucZ9FujHvpVzeR4UgJ6+PuPHjKbv6EmqH5VsURYH1yzizoVTyKRSrKs48utfr8oQCrOS2ht9kz/3bMX472DMnrthYWHBzp07adq0KYcOHWLevHlERERgbW3NkiVL6N69u6pf67/7t3J610Yk2SLqturIwBmL0dBUrlaWje5H5NNAJBIxZmWt6TFqCrWbt0MmV3Dl0RN2L/uVe/fuER0dTWhoKBUrVlTNadq0aRw4cIDU1FRKlSrFyJEjP4sYgK1OGYIz45Ar5EUu63m9wfzbEvZyOLbld4799Tu//LGPavWbFov2d1EdgFRpJgvj9vFclMR/tX+hpkEF1TlFER6prmeNs0HhtbSNNfTY4zQK99i7uD5ay4hyzZlbSRkKf71xw5uUhGWVFCWk/f9IiRH+Spk5cybbtm1DS0uLE9fuMbBnJyrYV8PK1gGTMmUxtbQm/IkfoBQOuPGyXk9LR5eGHboT9OguQvW8LQY3zxlP7RYdmLHpEEkxz1k5ui81a9akQ+sO+ESnMXvCRGRSKUsOX0Lf0JjwJ35FCjFdSw5g3P4V6G5/xJ5D7ri4uBAdrdzHjoyMZMCAARw/fpwOHTpw5swZ+vTpg3/QUyJSpfjcvsrpXRuYvuEAxmZlWPfLSI5uXk3f8cpuK/2nzKdsJTuE6uo89X2I29gfWHbkCsamZYhNF9O6bTtmzpxJo0aN8sxr2LBhzJs3L5eUpqOjo0pK81Ohr66NiYYe4aJEKhax6XxBG8zHRYRx7+IZlWoTfHiD+XdFLDzOn+D4ljUkxkRiVNqM4fNWqVo1yuQKvCMzaGhYlX+c2xS4D3VBsrRvpQSxKeIS/hnRWGgZUV3P6jXj/PaQdu8y9WhsbMfwx9tpcPc39lQbRUhWPJV13/65fEimcQn/X5QY4a+UatWqqf5fwURZPhTzLJSyNlWo6FCdpLhoBGpqyGUyarfswMF1S7h76Qw1Grfi3qUzmFlVQJKdV8w+ISqChh26oyYUUsaqItXr1sfPz49u3bpRVpaIz40LnL//GIlQB7FMjm3jBgUOMf3y5G801YT8aNGYPj5/Uu7gMybPW0CDBg0AKFdOWevp6emJsbExHTsqE806d+6Mnp4e1x/4IShrz83T7jTr1o9ylZWyl67DJrBpzkSVEX49e1sgECCVSkmKjcbYtAxGpc2o2WcgDqa6+c7xXVKan5oquhYEZcYWyQgXpsH8nhVz6TN+JruXz871/Ic0mM9xAN7E1/Mah9cv5aclf2JTrSapCXF5ztEUqNNIWLfABvhtvC1L+82Q9t8xnqqQtr2uBdX1c4zzq5C2pZYxp2pOYlPEJZrfX0J1fSvamDi949VLwrIlFIwSI/wVM2bMGHbu3ElWVha2Vavz/Kk/m+bkTuS5deYII+b/zrjlm9jjNpe/5k7Eplotug4Zzz+bVuYZs933w7h5+gg9f5pKdEQ4XvfusHiuMhzr6elJhQoVOLhhFXv27MHS0pL58+fTtFfefrdvolAo2BF9nXSZiDXh51lu04fJXpuJj4/H1tYWkUhE9+7dcXNzo27dujg6OnLixAk6d+7MyZMn0dLSwrxSFaJFEBnyhFrN2qrGtq5SlbSkeNJTktE3Vmam/j5pMH53biIVZ+PUoDkVHV8K6xegX+vbpDQ/NTkZ0q8bkIJS0Abzdy6cQl1DgxqNW+U/TmJGoUOKrzsAb3Js8+90Gz4R2+pKLelS5vm1kxR8kAPwPgoa0j6b4IN3+nNVSLu6vhWPXoSTIEnncnIAt1Ofcv9FGJscBmGm+fZ7VBKWLeFdlBjhr5gNGzawfv16bt++zenzF7DvPIg+Y2eojk/q7EKTrn0BqFa/KcvcL6uOvdlaLYcaTVqzZf4kzu37C7lMxo9jp1CvnvKHKiIiAl9fX3r16kVUVBS3b9+mc+fOVK1aFUdHx3zHyyFMlECmLJtsuRR1gRqbfc8ikUhwd3fn+vXraGho4OrqyqJFi1i8eDEDBw7khx9+QCQSoampyeHDhxFq6YBIhCgzAx39Vz9qOc0lsjLTVUZ40u87kUolPPa8QfSzp6ipvdpXfV8iW35Smp8D+w8oU4pOz3hvg/n3ySsWtcH82xwAuUxGqL83NZu1YVqPpkjE2dRu3p5+E2ahqZ13j7YoDsCHUJCQtk9GBGoIkKNAJJfwT9w9MqXZDC7bFGeD4s/SLk7eVSNesiL/fJT0E/7KEQqFNGnShOS4GIIuHEaoVvhM0BzSU1NYNXEgrsMnsuVGEKtPeXL/5hU2bFB2P9HR0UFDQ4PZs2ejqalJ8+bNadmyJafPnsMnOo3rIYlcDIrnekgiPtFpiCSvhOgvJT1G/LJ/rbaaBuEKZQbu+PHjsbS0xNTUlMmTJ3PmzBkuXLjAtGnTuHLlCmKxmKtXrzJ8+HCeBSr3uLV19RBlvOppLEpXJp3p6OYWV1BX18C5cUt8bl/l4dXzquc1he//sxcIBNSqVQsdHR3mzZtXlNv5wVTRsyCwkEZY2U3oIhcS39/67ujm1TTq2BOzcm+vPy1Kg/mULEm+DkBqUjwyqYR7l87w6xZ3ftt3jmeBvpzYnrc2vKgOQHGTE9JuW9qJyRU6cLrmZFWYXFugQf8yDbn/IozJT/bT+eFqDK+MppbHHAb6bsYt7Az/JvoQlZ3M56wETcjI5nJwPO7eUTyKSiUkKZOIVBEhSZk8ikrF3TuKy8HxJGR8+t7PJZSshL8ZpFIp6XGRRSqPyCE+8hlqamo07qzsNGRmYUmXHr05c+YMY8aMwdk5t3BBQkY2CRlivKJSsYhKzVexKqf+c134f8heii/MrNiFnuZ1qWz1X77lI15eXjRr1oy6desCUK9ePerXr4/fnRvUcq1MOZsqhAf549K2KwDhQY8xNDFTrYLfRC6TEhcZrprX61Ka70MqlfL06dMCn1+cFFaw49GLcEb570RdoMZ0k0Ekpb37/PfJK0LBHJY3eZvh1tRSrnbb9B2MsWkZANr3H8HJbevpPWZagcf5nOgJtWhmbM/5JF/am1Znb/XRiGRi5oT8w/6Y2+ypNhJrbRPVyvlcog8+6c+RvRbSzkkEc9Ivh8FH1jD/kBKxEj4NJUb4KyQuLo5Lly7RpUsXdHR0uHDhAgcOHGD//v04mBtgoA6PIlMAkEkkiLNFaGhqIRAIkMvlSCViZFIpKBSIs0WoqamhrqGJRXkbFAq4fe4Y9dt140VyAlfOHKNNa6WkXrNmzShfvjxLly6lx7BxHDp3BS/Pm3QbMyPPyufNL/cEkx7Y2uvR3ORVF6UhQ4awfv16OnTogIaGBmvWrKFLly7Uq1ePZcuW4eXlRc2aNXn48CHXr19n2MhRpACNOvVi64IpNOzQHWNTc05uX0+TLkrHISosmITI5zjUaYiaupA7508S+PAOfSf8qnpdKz0h2S+T0rKzsxGJRGhra+eR0rx79y5//vknM2fO/Eif5LupqG1KVHYK2XIJWmpvdxzSpSLmhxxld/RNFtv2ZljZZvjFpJP6QukYyaRSZDJpnlZ175NXfJfD8q7Q5tsMt56hMSbmlgWu2y2KA/ApcDWrxaVkf7ZXHQaAtlATN7vv6GJak8F+W2hbuhqr7X5gWLnmqmtis1NVzS1upwbzV+QV/DOiKPMyS/v12uYquhbFEtL+3DXiJRSMEsWsr5D4+Hh69+7No0ePkMvlVKhQgQkTJjBixAgAKlasyLNnz3Jd43b8JmZlrfG/f5vlo/vlOmZfuwEzNx8ClKujw+uXEhMeiraONr26u7J27Vp0dZXZxH5+fvQfNAR/P19MLcvR66dp1GnZ4b1zzq8WUiKRMHHiRPbv34+2tjZ9+/ZlxYoVaGtr88cff7BmzRpiY2MxMzNj7NixTJkyRdWt5dy+LZzZvRFxtoi6LTsyaOYSNDS1iAoNYuuCKUSFBiFQE2JhXZEuQ8ap5ljeWIdWdnmzjRUKBXK5nE6dOnHnzh3EYjFly5Zl8ODBzJw5s0iCD8WB/a3pHHWe8NYuQSfjHzI+cC9Njauwqsr3mL9MEMqSyHD3jkKugKN/rc7VYB5Qtap7nTe7/QgF0Nu5bK79wvfJXwLoawl5kS0jv9/+fzatwufWZSat2YlQXZ21U4ZhX7shvX6amus8oQBqljP6YhKa3nQ6FAIZZfR08+ynpkmz+DlwH1dTAthdbSSNjatwKMaTWHEa48u3zTWmTCHnaWbcS+P8XLV6jsxOVmVpV39NGSwjPAFnZ2d69+7N3r17uXLlCq1atUKgrYG2mgYC4M8//2TQoEEq5a6b547nWw4W7POAo5tWEhbgg5qaEPs6DRgwdQHGpmUQqgnwdt/M727L0NJ6pRTm7e2NjY0NCQkJuLq6EhAQgEwmw9HRkZUrV9K4ceNP9XF8U5QY4W+YjyGh97ll+T73639qunn9ztCyzehuXifX8xGiJCYG7sM7/TkbHQbRpnS1PNcWd3u5osiFvolUKmH/yvnc/vc4GppauLTtTN/xv6pC1TkIBND3DQfgc1AQp+NNyU2A43EPGB2wk+5mtdkdfRMFENx4BWW18m6ZvGng1dQUiNQyiRZG4J31TFVKlTz1OLpSIRbW5ZiyYRESrygmDxuDdF8PjNR1+cd5PC1MlAmSl4PjOfPveXYsmp6nHKyUuQXeNy8jysqgeoPmqKmrs3fFHJLjY5m6Xrk1cWn3emRJUezdmzdpTyQS8ezZM+zs7BAIBBw/fpyhQ4cSFxeXb4vSEt7NlxnvKaFYyJHQe1+yVkx4KMMb27F5jrIH8IvEOLbPHImzXUUEAgFhYWGqc32i09i3eiHTezZjdHNHZvRumatx9+vcOHWYwfXKc/XYAeCVAASAWCzGwcEBK6tXnXCuX7+Ovr5+rn8CgYAjR44U+j29ydcqC1hFN3dylkwhZ234eWp6zsFJvxw+DRbla4BB2V6uqIl6b7aXKw4DDMpkuYEzFrPxsi/r/r3PgKm/5THAChQEyUJRE37e9UFA3AvOBSodGbmCfLdcZArllsu5wHgC4l4lC7qa1+ZB/d/YH+NBplyMRC5lWtDBXNe/LWEqPDmbxCQhmvEVcBW246DDz6yLbkLHCnVxbdsJfaEWt1ODWRd+HrFcihwFydIM2j5cwXfeG8gUS4lIFeUqB1NTU6OUuYWqJMy5cUtc2nRBR98ALW0dWvcdRLD3PdXcXogkb/2stbW1sbe3R01NDYVCgVAoJDk5maSkpGK68/9flLgt3zgFkdDbs2I2NlWVSVdCNQE1yxmj5tqFWvPn5FKVyqn/1NLRYeLq7ViUtyH08SNWTfgRc6uK2NWoqzo3Iy2FUzs3UM6mSq7Xyqn/XOXmhrm5OekvM5sBmjZtmuvxlStX6Nq1Kx065A53F0YWUI5c2WHKyuSr3OeqomuBZ1oIAPfTQhnpvwMDoQ436s7CQa/sO6/9e8cWNm3dTuBjvzxyle+SH81xWAw1wMHBgbQX6aw46aG613KZjKN/reb6iYOIMjMwt6rI9E1/o2dghEKh4J9NK7l+8hDZmZmUt6/GwGkLVcIqv/ZtTWJMpGoeEnE21Ru2YNLvO1RjH9uymv+O72F5lohqdg5cvnwZY2Pj4rupBaA49lP3xdwiU67MPZAi52CsJ9MqdMbZwLpQCVPBUQksnjWb61cus23bNuR6aeyoNoJTMdZ0TdkLfQ4h0FZH1siaqyN1CDJNR1HIcrDAB56UfeO7eurUKUxMTLC0tGTcuHH89NNPuY47OzsTEBCARCJh+PDhmJvnL+NZwrspMcL/B7xLQs/j/An0DAwp51yHF7ERqnBtE6cxSKXSXOPk1H/2GDVF9Vxlp1pUqelCsM+DXEb48J/LadtvCHcunMoznysP/di7dy+rV69W7WPnx65du+jduzd6enqFek8A2QoJAsBb8gRP+QMGms3IM8aXjlQuQ0+oxa2UIJrdXYxXejjr7X9koGXjAu1Rly1blkXz5/L3sVNEJqbmOrZz8Qxkstzyo5B7737x4sWYm5uTmJJb/eroX6sJ9r7P7O3HKG1RjsinT1S63XcvnOL6iYP8uuUIppZWHNnoxl/zJrFg7xkAlhy6qBpHoVAwrXsTXNp0Vj13bMtqYgMecePWdTpH/cU49dZo52M0PiZFbRKSY4hzNLftdS3pZlqbgMxonmUlkCHPpr/vRg5XnlnJL4MhAAAgAElEQVSo8Q9vcKNB575kaOV2RMrYWGO67Xta1ahPC3F59k52w+kfGWl2MpISc5eDCdU1WDtlGCe2r8uTif48yJ8T29YyYeVW1XN123ThxyHD6ObiiKenJ7169cLY2Jjvv/9edY63tzcikYijR48iFosLda9KeEWJEf4/IT8JveTUVE5tWc0O95NcOfY3YZnvFuzPr/5TLBIR+vgRrXoPVD0X4udFmL83A6cvzmOEZQr4bcZUlixZgo7O28szMjMzcXd35+TJk4V6T2KZnARZKv8kXONy9l1kAimeLvOK3AThcyGWSyl9dQzZcikShYxAomlsZMegsk0KPEaO3vW9e/dAKqa8sQ6RqVlEhz3l4fX/+P2UJzr6BggFULmqcy4d49DQUPbu3cvSFW4MHf7KUcpIS+H8gW0s3H8OU0vlVoKV7Supz/io59jVrIe5lbLpQqOOPfj3wLZ85xf4wJO05ETqvmynKUpP5cKB7Xh7P6Jy5crsKDWcEf476CFsTVHNcFhYGGPGjOH27dtoaWnRu3dv1qxZQ0pKyluTi96U3Fz+03f437vFttshCF/ueb6rSciW/e6c3rUBX19ftLW16dq1Kx6rVyPVFhCfLuaSVxg7lvzK47s3AXBq0JxBMxarRGde51mgH353bvDbvrPci0glU/yq9r5epWrEV3q1Z1vLrQydO3em79RFBS4Hi30exqqJA/lhynyVfjco9eYNjbQRCoU0atSIiRMn4u7unssIgzI0/f333+Po6EjNmjWpUaNGET+p/1++rl+mEj6YHAm9pjalubhzLeNGj6RNnaqoF6AcJL+6zV3LZmJtV5XqDZXlGHKZjN3LZzFg6m+5VKpyuH/5HBKplB49erzztY4cOYKpqSnNmzd/53lvvqfWdmZ0trXilOgaWYjYU20UTvpW7x3jSyM6OwVTDX2kCuWPrq5QixHlWhR5PG11NVramtLbuSySyEBMLC04tHUpE9vVZMGADgiDPWhpa6pywsaPH8+SJUtIEudecUcEB6ImVOfuxTNMaF+H6b2ac+HQLtXx+u26Eff8GTHPQpBKJdw47Y5zw+YYa6sjFLxKZgK4edqdOq06oqmjTXljHSyzY9HQUMfd3R0LCwsmNOqN7X/JjPTfUWSxizFjxmBubk50dDReXl5cvXqVDRs2oK+vz/bt24mPjyc5OZnp06fTtWtXXmRl55LcvHX2KDKZNM+4/afMp+vQCViUr0RUaBB/TB9NSkIsABfPn+P+/fvK2v30dLZv346hoSEh3oFEJso4/KcbCTGRWFSwQZIt4u7F06ydMkw1dtCjeywY1JXRzR1Z/fMg4iPDmdylIWPb1mb92tUcPHgQTU1NDA0N+e6770hLU+ZZCAQC0tPT6VCtHJO7NACBgJ1LZiKXyfLMHyAhOoIVY3+g27AJNO6UV3r29RIxgUDwzs9AIpEQEhLynk+jhPwoMcL/p3h5eXHhwgUmTZpU4GverNv8e+1iIp4GMnbpBlV49KL7bqxtHbF1rpPn+uysTA6tX8Kkecve+1q7du1i4MCBRSoN0lfXppSGHj3N6tCzTN33X/AFIZFLWfnsDHXuzGOQZRMqaiuzk8VyKV3Nan3w+NoaQkhPJCo4GImJiLiYaLZu2sCIYUPw9/cH4OjRo0hfOkrp2bkNUFJcNFnpacSEh7Ly+E3GLdvE8S2/4+t5DQBjU3Oq1KzHjN4tGNmkCncvnuH7SXMx0dWkt3NZrM2E3Mx+yL0Xj/C4eBJZu0qEGT2ipa0p6YmxpKam8uTJE0JDQ3F3d8d342nuX7nNzuj8ZVbfR2hoKH379kVbWxsLCws6dOiAn5/fW5OL7gdHqCQ3M9PTOL51DX3H/5pnXGs7R0zKWNJ16ARqNm2DQiEnKVbZHGPwjMXcDY0lPT2d9PR0fvrpJzQ0NHCsXoOIVBHRz54SGfKEVr0H8sdFb3qPnY70paFPT01h7ZRhdPxxFBsu+dJr1FTUNTSYsfkgv+07S5Ua9dDW1ubOnTscPHiQxMRExo0bx/Pnz5kxYwbW1tYMGTORrdcD6DZ0Ata2DqSnJpORlsJ/B7ZRo4my5j85LoblP31H6z4DadXrxzzvz+vqedTE6SgUCu7cucO6detwdXUFwMPDgxs3biAWi8nKymL58uXExsZSv379POOU8H5KwtH/p1y5coWwsDDKl1dKFqanpyOTyXj8+DEPHjzI9xpjHQ2EAmVI+ehmZb3nzM2Hc4XRHt+9SeADDx7dVOpUZ6SlEB7oR/iTxzRz/Y6EqAgmfN+VyWoCxGIxqampWFhY4OHhoerr+/z5c65cucLmzZsL/H7eLPM4bDUPU12tj9YE4GPgkRrMKP+dmGsa4lFvLra6ZehVph41PGZTSduUUhp598aLgo6ODkINdRqN6ZlLfvT8+fOUL1+eadOmceaMcg9X8sa+ZU6Y03X4RDS1tbG2c8SlXTe8b17GqX4z/tfencfVmL4PHP+cTovTdtoo2VX2LbsZJWPJYAxjZ5AtUcoyGDOGqDHIMkbG15JpxBeDGVuhMQmTZTCShCJFQmhT2s7y++NwcoSxfacxv/v9evV66SzP85wndT3PfV/3de1c+y3XEuJYuvckcuuKHNv3CwsnDCZ031EqGFjTobo9/82/z+oNP1Jipse5hpmEVWmtPS6A2bNnI5PJaNKkCYMGDSI7sYDpST/hYlEXR2PbV/qsfn5+bNmyBTc3N7Kysti3bx8BAQHa559OLpKaWKAsegjA9pWL+KDvMOTWz+5idXTPVm2TEAsb2+c2Cdm1axd16tTh6v2HSABTuSUyE1Oavt+R4sIC4mKiaPVoSP5K3GnMrWxo3bknAK69BxEetoqkc6fp8PEg7t26SdWatWnWrBlRUVHai+moqCj69OmDvb09ljID1ECvMb7kZWcyo6+bdjnYR6N8ADi8azN3b15n19pvddaQrz6iKXd6InI3G+ZPp6ioiKpVqzJjxgxGjBgBaArc+Pr6kpycjIGBAY0bNyY8PBx7+xcnCgrPJoLw/1Oenp4MGjRI+/3ixYtJSUlh1apVgGYtoPLRMNbjqlKONibEpuew94dgThzYxcw128qUihwzZ4lOi8Tg6eNo2ak7rh8PxKiCMd+Gn6BnAzsqGEg5duwYPj4+/Pnnn1SsWPqHLiwsjPfeew8HBwf+yovWcd7MLuJceu4z13H+k2SX5PPF1e38knGGJXUGM9i2rXYEoLFpNSZXc6eGzPqt7e9x+dFnjS4mJSWRkpKCi4umYMfDwiLyHuTi696Cr37YSTUnTcWz541Q3EhKoHWXnljZVgbA5aP+/HfpXNKTk6CJZp54aZ0hhP46Bzo7UF1mjZOxnc5xPb1tGwMzZtfqzpD4VcS0nIXBK7Q47NChA2vXrsXc3BylUsmIESPo3bu39vmnk4seT7lcSzhH0rnTDJ3qT2ZG2faPUNokZO3sSWTcvP7MJiEbN24kLS2N0NBQbU5F1t3b6En08O6k+bymFpZ4fKEZHdIM+T71g1GruXn1MgCVazlpG6pMmTKFNm3a0L59e8LDw2natCkeHh6sXf0fFKtWYVW5Kj1H+jD886/LHHvvsZPpPfb5o2DzV4Y8t5d0hw4dOHfu3HPfK7waMRz9/5SxsTF2dnbaL1NTUypUqKANhjKZDFNTTUOEevXqIZPJkBlIqSqvwPbvF3H/djozPunAONd6jHOtx54fggEwMZNjYVNJ+yU1MEBmYoqxqTlSfX0aOtSgZrUq2NnZYWVlhZ6eHnZ2dkilpXerGzZs0F51v8ibrOP8J1Cr1Wy9fZIGx79ApVaT0O4bhti10wlCBSVKPMx74qxo+dzmGM+jUCi0F1NKpZLCwkIUCgWurq7I7StyfLVm2DkmJobo6Gjc3d1p1KgRN27cIDY2ltjYWOYuWYHcyoZ5m/ZhbWtPpao1qePcmj3rV1BSXET6tST++HUPzR4Nc9Zq0JRTv4WTc/8uKpWKmIgdKBUlNKxfuvwl69ZdlLHp0NWBj21Kpy0cHBxwcXHh66+/pqioiIsXL7J161Z69uyJT7XOWBuYMjd550ufX5VKhbu7O5988gn5+fncu3dPO//7pMfJRQsWLOB6YgIqlYoNC2cxdKq/NhHrefT1DahUrSZZGbfLNAk5ceIE48aNo0mTJnTq1EkbmFMvxZObdY+Zq7exIjIWuXUlAkZqhnqdmrQk624GJw7s0syp791GRloqxYWaFQBN3nNjz08bSUlJIScnh4ULFwKaREYAX19fkpKSSExNo9/4aYTMnUrSuVMvfc6g7Bpx4X9L3AkLAPj7++t8/7wkjMaVzQk7c+Oll1c8LocJZX+53dzcSEtLK/OeS5f+ugPQP6Eu7qu2htt/L47pSVv5o/Uc0ouymXB5AzcLs9jexIf3LJx0XvtXlZqebI7xvDv8wMBA5s6dq/1+48aNzJkzB39/f0asmcWWGd8il8upUaMGGzZsoF49zV2unV1pj98GNSoj0dPDwqZ0Dej4wGBCAqbh07kp5pbWfOI1lQatNVnb3YePJzfzPrOHdqOosADbqjXxW7Sa5rVLy26GhYXRrl072rcbxISqnXSOefPmzYwePRpra2sqVapEQEAAnR7VLv+hwRicT86mq3UjilVKTude4/NaPZ/788nMzOTGjRv4+PhgZGSEkZERI0eOZNasWSxatKjM60tKSsi5k4bMxJqUi3F8/4U3AOpH3b8m92iD94LvdbKIH1Or1TpNQm5fTcB7aF/kcjmTJk0CSnMqFCXFNHPpTN3mmu18+tk8Fo4fyMO8XEwtLPFbvI6tywMJWzSLRm070KB1eywrPRpZ6DUQde5d3NzcUCgUTJ06lT179miL3jRv3lx7TGMG9eHskV85HbUfp6alfZNf5F0tavMuE2UrhVf2OtWTnlU7+nWVd+nK1yllWKAsplbMVDJL8nlP7kh8/k2m1ejOlOruZYZXX+X8vu55DUqJIKMklyCnQX/52rdd/hJev7ftL3dOMyJhLcUqBTKpIVluq16479q1a+Pp6clnn31GXl4eI0eOxNjYmIkTJ6JQKGjdujVKpZLvvvuOwMBAYuMTiLmrJuveXe02Mu/cYp7HRywL/wMzSysybl7XaRKycoYXsUcPMmfDXmrWa0z61ct86zsUb29vFi5cyO3btzEzM+P8rVzOpefg3bU5JuYWzA3TzLuHLZrF0T0/sTLqPCZmur2rlQoF0/u44PHFAhq361CmpnZkZCSjRo3i+vXrz1yNMMhjDDkKCYMmzX7xD4m3+zsqvDwxHC28snqVzF6pdOTzfrmLiooYPXo0NWrUwMzMDGdnZ/bt26d9ft26dTg6OmJqakq3bt1IT08H0K7jTLl0nvme/RjnWg9f9+ZEPrEedYHXQCZ2aYaXWwO+GuLOn4+GCpUqNZ/PnqtTGlMmk6Gnp8e9e/cAaNiwoc7z+vr6fPSRpm3i6w6B+yf/QrbiISVqJUezE9naaAIzavZ4owD8+POcTst55aF2iQRUL3n9/TbLX75Jb9uUgrvMuPITBcpiitQK8pRF5CoKXrj/n3/+mf3791OxYkUcHR3R19dn2bJlFBUV4e3tjbW1NVWqVCEiIoLw8HAcalSjmoVMZ0rFzNIKAHMrG/QNDEGtZufaZUzs2gyfzs1IvhBLHefW2NdyQqlQcPinEO7evUtAQADFxcVUrlyZhg0b4mhjghoYPOkrMm6k4PthcyZ1b0nCqRhq1m+sDcCpl+NRKEooyHvAluWBWFay0y4BzMvJRi/7Nmq1moSEBKZMmcLs2bO1AXj79u3k5eWhUqmIjIwkfMdWvIYOoLqFrMwSMUD7WHULGd3qVhIBuByIO2Hhtd3LL35uxarSO8LSAhBPy8/PJygoCA8PD6pXr05ERASDBw/m/PnzpKam0r9/fw4dOoSTkxN+fn4kJCSw/2AU2+PSycnK5IsBnRg8eTatOnVHUVJCVsYt7GtphnVvJF3EvpYTUn19rsafJch7CAt2RGu6xDzVHcjf358jR44QFRVV5hjVajUODg74+/vTuluf5wbIgz+F8vvebaRduaxTIvLahbPsXreIs+dOgZ4EmtiBdytcHZw53LJ06UtxcTENGzXmfk4uy8L/KLP93/duY93cqYz8ciEdemsKJvyyZil71wejb6g5t1I9CecfdboBiIqK4rPPPuPKlSvY2Njw+eef4+npCUD36R4cXLkFQ4nmIkCpVFJUVERGRgY2NmUTct7G6Meb3uEfz77Ch7GLKVYpKFCVINMz4HCLL2glr/3Sx/QyXnak5Vndqfp4TmbN0gWYSlXY2dmxY8cO7XA6lI4qRG0PY/f67yguLMCpaSuGz/gaaztNdvGqL32Ie7S6oHG7Dnw6bR7mVpqfiSQrna99hnPjxg0qVqyIn58fU6aUdsNycXEhLi4OtVpNrVq1mDlzpjYB8+miNoZSPSyNDXC0fvHog/C/JYKw8Mbe5i93kyZNmDNnDsePH6egoICVK1cCkJ6eTpUqVQg/dpZMQ2u2Bi/k/p10xs1b/pfbTL4Qy3zP/nyxZhu1GzbTGdJTq9U4Ojoye/bsZyaDHT58mB49enAh+TpH0x4+9w/z6ah9SPT0iD9xmOLCQm0Qjos5REHBA440vYnczICMJQdR33/I0h0h9KpYOn/39ddfs3XnXtKup5QJwvm52QSM6oNUKqXr4NE6QTjjRirjAjTn4Mlh35KSEmxsbFi0aBGenp6cPn2ajh07EhMTQ9OmTVmaup8bhfdZVnco8OILkcfeJIi+rSmMfGURS1P38U3KXgpUJSxxGsyUGpra4q87xP2mn/VFx/u08p5KEf55RGKW8MYeV6x6U3fu3CExMZGGDRty7NgxneSwx/+OjTtPlRZuXI0/S1WHugSO6sOdtBRqN2zG8BmBWNuVJgAtm+zB+eNHUCkVSCQSDv70I55zm2nXcT58+JAhQ4aQnJzMxIkTCQkJ4cgRTdGJQ4cOMW/ePGJiYjA0NCQ5V6nzh3Nqr/fIzbyL3qPm645NWjAteBMpF+O49zCN/y6dyx+/7qG4qJC2XT9m2ccL6VK/Mif0P8TFxYWJLfswNDMTR0dHfH19CQvbSM8JM/nPV76Mc61X+rlVKoqLCuk+fDxX48++8Pw9bo5RwUBKZmYmubm5DBs2DIlEQqtWrahfvz6HY0+yX57G0azLZCnymX9tDx9aNyYsLIzZs8vOGwYHBxMaGsr58+cZPHgwi1eu4fytXGIvJjKl1/sYyYy1r+05Yjy9x/hRRS5j/EeupN3QJCqp0Sx5e7pRw/OaQIR+M5Pj+34pPQdKBYaGhjx48AATqRHeFq6cWB7CgcgDBFrvxyRgEXVcupOWU8jF08cIC5rD/Tu30JPqUc+5DcOnB9DYqeYrLVN7lSYh8PLzqY+7gL1ugBcB+N9HBGHhH6GkpIShQ4cyYsQI6tWrR/fu3Rk4cCBeXl44OTkxb948JBIJ+Y+WYmRm3CLlUjzTgjdR1bEuP62Yz6ovfZgVUvrHe/KyUE7+upe0q5e4eCpGZ+lPsVKFp6cncXFxDBw4kE2bNhEbG6t93sTEhKFDh3LixAlMTE11Shk+NmnJehq2cSnzeNrVy8iMTQjcchCVSsm3U0bx/bKFuKxcwpEjR7C1teXw4cPaIfjevXszc/5iVEYVMKwgY+Vv57Xb+vk/S4jYsIq+E6azaMLgMvuKPXoQ706NkVtXostAD5pV8aGRnTm2trYMHjyYH374AS8vL/744w9SU1Op3NyJgVdCAU1wPJmTTO7ZFO7cuUPfvmVLF9rb2zNr1iwOHDhAQUGBtl63vTqHKcC+2GRUEr0yox+XLiZotxGVlMEgt5Y6jRpe1ATCY+Y3eMz8RvvazfOnYW9RGuy9vb0xrWBM9t1Mdh06xtjB/ZgVUoUqDnWxq+nE1BVhWFa0o6S4iJ//s5gfvvmCSUvXczO36JUSj/6qScjLTLk8b7vw9gO88G4SQVgodyqVimHDhmFoaEhwsGa9cadOnZg7dy59+/YlJyeHyZMnY2ZmRmV7zZ2uoVEFWri5U7uhpmB87zGT8enSlId5uRiblt6Vt+nSkzZdejJt/070DUvvgm6nXmXXrl1IJBLGjRuHVCqlRYvSNautW7cmMTERuVyOnr4hr5KWlHknnRGfz8dUrul602XgSLYFf0P4oAEsWLCAXbt2aauDlZSUIJPJMDS1IP/p86JUErl5He9176Ozjlp7jJ174tZnCHKrilyNP0vwjHHUsLWh0STNvO/gwYMZM2YMfn6aPtGrVq2iXyM3muZHEZunuUu1MjDhzt4/6devn3Zd+JOebALx5HIyo0fDuy61rV/YyL2gRMlvhw7rNGr4qyYQTyoqeMjhA3vZvXu35r35+ezYsYP4+HjSHqpRV2lAM9fOxET8zICJM8tUt9LTk5KRlgK83jK15zUJedP51P9VgBfePSI7WihXarWa0aNHc+fOHXbs2IGBgYH2OW9vb5KSksjIyKBv374oFAqcmzRGKoGqjvV0Kys9+vfzUhzUKhWF+ZpexVIJpCac0/ao7devH40bN2bHjh067/nxxx/p3LkzaspmQQOsnu3HxC7NCPIZyvXE0js/1OiUo1Kr1WTeucW4QZ+wfPlybTWq/Px8pk6dqikN+FQvV4Dd67+j8GE+H42c+MzPVKV2HSwr2qEnleLUtCVdBo0iap8mWF26dImBAweyYcMGiouLuXDhAosWLSIiIoKldYZgKJGiL9Fjln0Pdmzf8VLFUZ6lRo0aVK1alZEjR2qzy5905V4+v+/dTssPumuHrv+qCcSTTkdFYGZphV0DzQVSYmIiUqkUqyo1tHeS1Z0acDM5Ufue+7dvMr5jIzzb12H/xjV8OKy0D+7jQHwv/9Va7z3dJMSltjWN7MzfKKHpcYDv18SeZlXk1LYypqq8ArWtjGlWRU6/JvY6TTWEfycRhIVyNX78eC5evMiePXt0WhsWFhYSHx+PWq3m+vXreHp64ufnR3PHqqgBl48GcCZ6P6mXL6BQlLA7ZDl1mrXCxExOesoV4mIOUVxYiEJRwrGIn7l/J13nLkmRc5e0tDScnZ25desWwcHBjBgxQtvEIC0tjUOHDtG1a9eniwgC4BWwnMW7jrF4z3Hqt2jH4onDyH+gucuyrGRH5Jb15GbdJ/teBvvCNDWw+4zwZNiw0mL5CQkJpKSkoK+vz2fD+hA83ZPsexn4urfgbvoNTkbuRk8qJWBUH3zdW3Al7gxbvg0kbNFXzzyXEolEe8ceHx9P3bp1cXd3R09Pj7p169KjRw/27dtHR6v62BrK0ZdIkZ+4i5WVFW5ubq/0c7OxseHUqVOkpqZy5swZHjx4wNChQ8u87nZmDn9ERdD+o/7ax57VBGL7ygVM6dkWT5e6TOvdnstnT6IoKWbL8kDycrJpYm9BdHQ0eXl5yOVyzt/KJTcnm7X+k9m17lviTxzmlzWaZDhruyqsOhTPil/P8cn4zyguKsCjVXV2rAoCNIE4KHg1UqlUZyladHS0zrEvX76cWrVqYWJiQv369UlMTOR/4X8R4IV3hxiOFspNamoqq1evxsjISKdK0+rVq+nRowdDhgzh6tWrmJmZMXLkSAICApBKNaUzVa3ep9+EGSyb7KFd5jEuYIVmA4/WcaZ/MQGJnhS7ajVp4dZNeydWRS4jSVGi3dfTTQzq16+vrepkb2//zKHoJysQ9Rzpw9G920n4IwaVUkmV2nWQmZrz1RB3pFIpRQUFSPT0GOrpo32PSqUiKCiIjh07EhoaypXMQnZGHmLDoq/wD4vA3NIaRUkxA32/pE3XXoBuHW6APw9HUte5NcZmcq4lnOPg1h+YOssfAGdnZ5KSkoiKiqJjx44kJyezd+9ebcnGtQ1Gce1hBpsWLHytblWmpqa0bKnpUGVra0twcDCVK1cmNzcXc/PS6YCofXswMbegXvO22seebgKRk6kpjOHYpAVegSvIuZcBwP3bt8jLzsIrMJit387V7jc3V1MoZfPSeRQVFtDDw4eEP45yLOJnbOyq4tJrgOa1cgvauvdmWu/3qd2wmc7xZxWU0KZtW47FxDzz861bt46QkBDCw8OpX78+ycnJWFpaPvO1gvAmRBAWyk2NGjVe2KM0Li7umY83rmzOzdwiPug3jA/6lW3DZl/Lidmhu3Ue27EqiMw7t7TFI1SurhgYGODo6PjMfcycOZOZM2dy8OBB9CQSbfeo58nPyWLl5146j308dhISiYSda5Yh0dOje7PaPK550b9/f+7evUtERAQymQwbOyW/njirLRGZdO4UOffv4frxIGQmmrnaJ+twA5yM3E1IwGcoiouxrFSZHiPGM3X8WEBTh3n9+vX4+vqSmpqKXC5n6NChDBnuwflbuRgX2GN5U01UVBR+/gvfuNuU5DnTAQd3/sT73fvqBPmnm0DsXL2MWg2aYmFTCT09PSwraS7Idq9fgVOzVrTp+hHbVwQCUKdOHUoUCu5cv0bs0YNMWb6Bgz/9QK0GTWnQuj1H92zVBmGAqO0bUCmVVKxSvcwxF5aU7Y8NmgukuXPnEhoaSoMGDQBeqpmIILwOEYSFd86rLPNQKhQolQpNY3O1isY2RlgY6eHq6kr16tX55ptvmDlzJidPniQ6OpqgIM2QpUqlori4mJKSEqR6UFRUCBI99A0MuX/7Jpl30qnVoCkqlYqDP4UikUgI/vUcphaWZGXcBglY2NhyNf4sR3ZuYczsIOZ6DqKCgRQvLy9tC7rHQ/AyAyldOnWkbgvNGmHNPOqH2gAMunW4AcZ/HazzfXULmU4gHTBgAAMGaALS41KbP8ffLi21KbMk5MQ1siSwPS79mbWoFQoFCoVCpwmEvr4+Z86cwcLCAicnJ7KysvD19cXNzQ25vLTsYlpaGmdP/M6gaaWtAwGdJhCDJ8/mWsI5pIaG3Eq5yqnfwmnewZ2Bvl9yLHwH3UeMRyopDdgmJia4du3B9v8sQfh3XXUAAApQSURBVI2a64kXOHv4V2aF/MyZw5GkXr7ArZSr2FavxfXECxzc9iPVnOprKl09QaWGi/Fx2NjYYGVlxbBhw5g5cyb6+vqkpaWRlpZGfHw8Hh4e6OvrM3z4cObMmfPM0pCC8CbE/yjhnfSypTN3r/8Oz/Z1CP/xe2IifqZZjUoEBgZiYGDArl27iIiIQC6XM3bsWJ0mBkeOHEEmk9G9e3duXL/OmPfrEOTzKQCF+Xn8uOBLJnzQmMk9WnP+WDRTlv+obeuYkZZK4OhPGOdSl3X+U+jn8znd3N2pYCDVDsHHxsZqu1eZmpqyadMmbXnI4qJC/jgYTvse/V76fLyo882bdJsKDAxEJpOxYMECNm7ciEwmIzAwkOTkZLp164aZmRmNGjXCyMiIzZs362w3LCyMNm3bUqlqzTLHND4wmHu3bjKxazOUSgVmckv8w/Yyb9N+Ui/H8+OCmWRm3KJVJ82yJv0nfs4+cxZSXFRIwYMHbFriz6DJX6FvaMTR3VspKS5iid9wvDrUJ3BUH6o61MVvSUiZ/dd1bs3avYfJyMhgx44dbN68WXsB9jgLPDIykvPnz3Po0CE2b95MSEjZ7QjCmxIVs4R32puWznz5/fw9lY7edqWm8m62AS9uAJGfm413pyaMmbOE9j01yVunoiLYE7KCeZs0dcSrW8gY5taMjRs34ubmxtHk+yRnPiQvJ5uNQbNJOBWDqdyCFh27ceLALoJ2/s7ZI78SuTmEGau2ALDWfwpWtpXpO36adt+1rYxxqa3p07xlyxaCgoI4c+YMZ8+epXnz5kRHR9Ohg6Zm85IlS/j999/55ZdfEIS3SQxHC++0/9U6zrL7+XsqHT2vkMPqr/xIOPU7RYUFyK0r0n2YFx16D0aqJ+Humd/oM3QBaWlpVKtWjfnz59O7d2/u5RexbNU6DmxZz50bKchMTGnr/jH9JszQ6ZN7InI3u9Z+y/3bN5FbV2TMnCVAWx7eu0WLhnUxMTHRvnbGjBl89ZUmOzszM5Px48fz22+/AeDu7s6qVat0ErOgdA7/WefNxNwCq0qVn5sY9qw7fAuZAVKJJvHKK/A77ePbVy7UJmAlnIrh2sU4fN01S5sK8nPR05OSduUSfktCkErA0rh0OZxEItHOZ9etWxdDQ8NXTlYThNchgrDwr/C2Sme+yN9V6ehZhRx6ekxg1FeLMDA04k7qFeaPG0ibli14r2FtWniPZdeuXXTr1o2IiAj69+9PSkoKF3IlFBYUMGTKHBwaOfMgK5Nvp45i38bV9PTQ9MqNP3mEbSu+Yfz8ldRu2EybmaxUqbmcoVlXnZ2d/cyCHLNmzSIrK4vk5GTUajV9+/bF39+fpUuX6rzury5g2n80gINbQ2nczg2pvj6/bg6haftOSPUkNKlYAVOpJoGquLiYwsJCHKyNiU3PISMtBWNTOcZm5sSfOEL0L//Vzpt/4vUZPUZM0O7jv0v8sahoS6/RmsIl52IO4fJJZ8CcS5cuERAQQP/+mjtxY2NjBg4cyKJFi3B2diYnJ4e1a9cybdo0BOFtE0FYEF7B31Xp6Ok7/BqWzto7fKtCc2QGUiyL7/Mw0xQLCws+/PBDAHr06IGJiQkJlxNJM66hkz1uWcmOdt36cPH0Me1jO1cvo9cYPxwbN9e+5rHbD17cQ/jatWv07t1be+fbp08fbWWrp73oAqbXGF/ysjOZ0dcNA0MjWnfpQe8xE2lZVU631o1JTU0FNHfaj/dbVW7Cicjz/HfpXB4+yMW2em3GBXxHFQdN5S2ZialOUpuBUQWMZMbaKmapcSdp/fU08vLysLW15dNPP+WLL0q7WgUHB+Pp6Ym9vT0WFhaMHTuWUaNGvfB8CMLrEHPCgvCa/u7WcBMmTCA0NJSCggKcnZ21yWMffPABU6dOpUePHuzZswcfHx92HD5FUraiTBLW8s/GULmGAwMmzkSlVDK2fR36jJvCkV1bKCku0mYmG1aoQOatG0zp9b5mrbREQpcuXQgKCtK2Oty7dy/ff/89mzZtAqBv37706tWLSZMmPfczvK05fNGNSPi3EEFYEN4hSqWS48ePEx0dzYwZMzAwMCAkJAQ/Pz8KCwsxNDRk27ZtmNdvS3LmQ533Ht39Ez+vXsy8Tfsxs7Ai6+5tJndvTc36jZm0dD1SfQOWTx1NvRbt6DdhOoUP89HLTGNkTzfu37+Pt7c3Dx484MCBA4CmveTw4cO17Q87depEeHg4hoZ/HdzexgXMPyHpTBDelFiiJAjvEKlUSvv27UlLS2PVqlUcPHiQ6dOnEx0dTXFxMYcPH2bMmDEknD+n874z0QfYtnIBU5ZvwMzCCiitXNV5gAcWNraYWVjhPnSstqF8BWMTajdsir6+vrYqVmRkJLm5uYCm4EidOnV48OABubm5ODg48Omnn77U53gbpRpfdpnaYyIAC/9EYk5YEN5BCoWCq1evUlxcjKurq7aEZKtWrWjTpg3nThylTV8nAOKORRP69QwmfxtKNcfSXsV/lZkMYCgtvU5/uirWuXPn+P7777XZ015eXrRv3/6tfs6/IroRCe86cScsCP9wGRkZbNmyhby8PJRKJQcOHGDz5s188MEHtGrViqNHj2p7IZ89e5ajR4/StEkTpBLNUp3Vs33xWbi6TP1kKM1Mzs28R35utjYzGeDahbNkp6egUqm4f/9+mapYrVq1Yt26dRQUFFBQUMCaNWto2rTp33diHhHdiIR3mloQhH+0jIwMtaurq1oul6vNzMzUjRo1Uq9Zs0b7/IoVK9QODg5qU1NTda1atdSLFy9WPyxWqDecvq6u16KdWk8qVRvJjLVfjdu5qUNPXVeHnrquXnf8qvqDvsPUMlNztblVRXXngR7qNb8nqkNPXVdPCFyhrlGzptrY2FhtZ2enHjZsmPrWrVva/SYnJ6t79uyptrKyUltaWqrd3d3ViYmJ5XGKBOGdJRKzBOFf6kWVql5GdQsZHR1t3uIRCYLwNDEcLQj/Uo9rUb+OF9WiFgTh7RFBWBD+pR5XqnrVQPyqpTYFQXh9IggLwr+YWMYjCP9sYk5YEP4f+Lu6TQmC8GpEEBaE/0f+7lKbgiC8mAjCgiAIglBOxJywIAiCIJQTEYQFQRAEoZyIICwIgiAI5UQEYUEQBEEoJyIIC4IgCEI5EUFYEARBEMqJCMKCIAiCUE5EEBYEQRCEciKCsCAIgiCUExGEBUEQBKGciCAsCIIgCOVEBGFBEARBKCciCAuCIAhCORFBWBAEQRDKiQjCgiAIglBORBAWBEEQhHIigrAgCIIglBMRhAVBEAShnIggLAiCIAjlRARhQRAEQSgnIggLgiAIQjkRQVgQBEEQyokIwoIgCIJQTkQQFgRBEIRyIoKwIAiCIJQTEYQFQRAEoZyIICwIgiAI5UQEYUEQBEEoJyIIC4IgCEI5EUFYEARBEMqJCMKCIAiCUE5EEBYEQRCEciKCsCAIgiCUExGEBUEQBKGciCAsCIIgCOVEBGFBEARBKCciCAuCIAhCORFBWBAEQRDKyf8BuCJ93McXPq0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not os.path.isfile('saved_after_eda/train_woheader_sample.csv'):\n",
    "    pd.read_csv('data/train.csv', nrows=50).to_csv('saved_after_eda/train_woheader_sample.csv',header=False,index=False)\n",
    "    \n",
    "subgraph=nx.read_edgelist('saved_after_eda/train_woheader_sample.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
    "# https://stackoverflow.com/questions/9402255/drawing-a-huge-graph-with-networkx-and-matplotlib\n",
    "\n",
    "pos=nx.spring_layout(subgraph)\n",
    "nx.draw(subgraph,pos,node_color='#A0CBE2',edge_color='#00bb5e',width=1,edge_cmap=plt.cm.Blues,with_labels=True)\n",
    "plt.savefig(\"saved_after_eda/graph_sample.pdf\")\n",
    "print(nx.info(subgraph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QirDnZ-FX7I8"
   },
   "source": [
    "# 1. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gdlTIj97X7I9",
    "outputId": "2e956d78-2d25-45be-e390-fa64bd98225c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of unique persons 1862220\n"
     ]
    }
   ],
   "source": [
    "# No of Unique persons \n",
    "print(\"The number of unique persons\",len(g.nodes()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BdvJPYMHX7JC"
   },
   "source": [
    "## 1.1 No of followers for each person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4fFEbqiSX7JD",
    "outputId": "d4aab961-ab7b-44c8-a6b0-de6c9ed04691"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the indegree on each node and sort in ascending\n",
    "indegree_dist = list(dict(g.in_degree()).values())\n",
    "indegree_dist.sort()\n",
    "\n",
    "#create a visualization to observe the range and parity of indegree\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(indegree_dist)\n",
    "plt.xlabel('Index No')\n",
    "plt.ylabel('No Of Followers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CZPczewbX7JL",
    "outputId": "ef79e2c1-ccc2-4d11-915d-01bee576f0a2"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indegree_dist = list(dict(g.in_degree()).values())\n",
    "indegree_dist.sort()\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "#zooming in to get a better view\n",
    "plt.plot(indegree_dist[0:1500000])\n",
    "plt.xlabel('Index No')\n",
    "plt.ylabel('No Of Followers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wqf9qkr_X7JS",
    "outputId": "6e880aa0-6fe3-4d22-e813-85141dab0678"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot to visualize high level stats of indegree\n",
    "plt.boxplot(indegree_dist)\n",
    "plt.ylabel('No Of Followers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5q9wSyvGX7JY",
    "outputId": "54a2deaa-d1f0-487d-d6ab-84d1645be6ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90 percentile value is 12.0\n",
      "91 percentile value is 13.0\n",
      "92 percentile value is 14.0\n",
      "93 percentile value is 15.0\n",
      "94 percentile value is 17.0\n",
      "95 percentile value is 19.0\n",
      "96 percentile value is 21.0\n",
      "97 percentile value is 24.0\n",
      "98 percentile value is 29.0\n",
      "99 percentile value is 40.0\n",
      "100 percentile value is 552.0\n"
     ]
    }
   ],
   "source": [
    "#90-100 percentile\n",
    "for i in range(0,11):\n",
    "    print(90+i,'percentile value is',np.percentile(indegree_dist,90+i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9HvreJYlX7Jf"
   },
   "source": [
    "99% of data having followers <= 40 only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mt8DA-MmX7Jg",
    "outputId": "0ac13b00-ca01-4baf-cb93-fc50efc2959b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.1 percentile value is 42.0\n",
      "99.2 percentile value is 44.0\n",
      "99.3 percentile value is 47.0\n",
      "99.4 percentile value is 50.0\n",
      "99.5 percentile value is 55.0\n",
      "99.6 percentile value is 61.0\n",
      "99.7 percentile value is 70.0\n",
      "99.8 percentile value is 84.0\n",
      "99.9 percentile value is 112.0\n",
      "100.0 percentile value is 552.0\n"
     ]
    }
   ],
   "source": [
    "#99-100 percentile\n",
    "for i in range(10,110,10):\n",
    "    print(99+(i/100),'percentile value is',np.percentile(indegree_dist,99+(i/100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hZQed_RqX7Jk",
    "outputId": "bb5921e4-c9f5-48b8-c0d7-963d86cc9f99"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\installed\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.set_style('ticks')\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.7, 8.27)\n",
    "sns.distplot(indegree_dist, color='#16A085')\n",
    "plt.xlabel('PDF of Indegree')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F_rfNRYCX7Jx"
   },
   "source": [
    "## 1.2 No of people each person is following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L-2plpMuX7Jy",
    "outputId": "1355b363-1079-44e5-d062-ee352036d126"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the outdegree on each node and sort in ascending\n",
    "outdegree_dist = list(dict(g.out_degree()).values())\n",
    "outdegree_dist.sort()\n",
    "\n",
    "#create a visualization to observe the range and parity of outdegree\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(outdegree_dist)\n",
    "plt.xlabel('Index No')\n",
    "plt.ylabel('No Of people each person is following')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aNaQeE5cX7J2",
    "outputId": "c59a155f-c894-4600-9daf-35a45f7c8d4d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indegree_dist = list(dict(g.in_degree()).values())\n",
    "indegree_dist.sort()\n",
    "\n",
    "#zooming in to get a better view\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(outdegree_dist[0:1500000])\n",
    "plt.xlabel('Index No')\n",
    "plt.ylabel('No Of people each person is following')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HVYdHvLAX7J7",
    "outputId": "da8978d6-76fe-400c-ff7d-ff60d2303efb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot to visualize high level stats of outdegree\n",
    "plt.boxplot(indegree_dist)\n",
    "plt.ylabel('No Of people each person is following')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FVGmz09xX7KB",
    "outputId": "e2058803-7251-4836-df99-28075908ae99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90 percentile value is 12.0\n",
      "91 percentile value is 13.0\n",
      "92 percentile value is 14.0\n",
      "93 percentile value is 15.0\n",
      "94 percentile value is 17.0\n",
      "95 percentile value is 19.0\n",
      "96 percentile value is 21.0\n",
      "97 percentile value is 24.0\n",
      "98 percentile value is 29.0\n",
      "99 percentile value is 40.0\n",
      "100 percentile value is 1566.0\n"
     ]
    }
   ],
   "source": [
    "#90-100 percentile\n",
    "for i in range(0,11):\n",
    "    print(90+i,'percentile value is',np.percentile(outdegree_dist,90+i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zcNoTvuzX7KF",
    "outputId": "b74b3bc9-146e-4106-b3df-af5f2a8938a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.1 percentile value is 42.0\n",
      "99.2 percentile value is 45.0\n",
      "99.3 percentile value is 48.0\n",
      "99.4 percentile value is 52.0\n",
      "99.5 percentile value is 56.0\n",
      "99.6 percentile value is 63.0\n",
      "99.7 percentile value is 73.0\n",
      "99.8 percentile value is 90.0\n",
      "99.9 percentile value is 123.0\n",
      "100.0 percentile value is 1566.0\n"
     ]
    }
   ],
   "source": [
    "#99-100 percentile\n",
    "for i in range(10,110,10):\n",
    "    print(99+(i/100),'percentile value is',np.percentile(outdegree_dist,99+(i/100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "27t51jOjX7KK",
    "outputId": "9655f989-f453-47db-be7b-732351ee3db3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\installed\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.7, 8.27)\n",
    "sns.distplot(outdegree_dist, color='#16A085')\n",
    "plt.xlabel('PDF of Outdegree')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9LOGY_CyX7KT",
    "outputId": "eb5a2706-7829-4f99-c46d-84b0f7d03328"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of persons those are not following anyone are 274512 and % is 14.741115442858524\n"
     ]
    }
   ],
   "source": [
    "print('No of persons those are not following anyone are' ,sum(np.array(outdegree_dist)==0),'and % is',\n",
    "                                sum(np.array(outdegree_dist)==0)*100/len(outdegree_dist) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hi30e3RmX7KX",
    "outputId": "f2feffac-585d-42ed-937f-5b61e1023040"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of persons having zero followers are 188043 and % is 10.097786512871734\n"
     ]
    }
   ],
   "source": [
    "print('No of persons having zero followers are' ,sum(np.array(indegree_dist)==0),'and % is',\n",
    "                                sum(np.array(indegree_dist)==0)*100/len(indegree_dist) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rzX8vbh5X7Kc",
    "outputId": "0fa6651f-4449-478f-a480-e289b9706afd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of persons those are not not following anyone and also not having any followers are 0\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "for i in g.nodes():\n",
    "    if len(list(g.predecessors(i)))==0 :\n",
    "        if len(list(g.successors(i)))==0:\n",
    "            count+=1\n",
    "print('No of persons those are not not following anyone and also not having any followers are',count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J7tiBqS3X7Kh"
   },
   "source": [
    "## 1.3 both followers + following "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'a': 3, 'b': 32})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MNu7k3-FX7Kj"
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "#Counter is an unordered collection where elements are stored as Dict keys and their count as dict value\n",
    "#we use counter to add outdegree and indegree for each node \n",
    "dict_in = dict(g.in_degree())\n",
    "dict_out = dict(g.out_degree())\n",
    "\n",
    "#if x = {'a':1, 'b':20}, y = {'a':2, 'b':12} then Counter(x) + Counter(y)-> Counter({'a': 3, 'b': 32})\n",
    "d = Counter(dict_in) + Counter(dict_out)\n",
    "in_out_degree = np.array(list(d.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ARCqfS9TX7Kn",
    "outputId": "44fe1bc9-a591-4cff-89aa-e36c0f7165e5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the in_out_degree on each node and sort in ascending\n",
    "in_out_degree_sort = sorted(in_out_degree)\n",
    "\n",
    "#create a visualization to observe the range and parity of in_out_degree\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(in_out_degree_sort)\n",
    "plt.xlabel('Index No')\n",
    "plt.ylabel('No Of people each person is following + followers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jVlgsM8hX7Ku",
    "outputId": "28758bec-8303-4a93-f4a9-b637cb14efe0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "in_out_degree_sort = sorted(in_out_degree)\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "#zooming in to get a better view\n",
    "plt.plot(in_out_degree_sort[0:1500000])\n",
    "plt.xlabel('Index No')\n",
    "plt.ylabel('No Of people each person is following + followers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4YpW20YEX7Kz",
    "outputId": "ba104e55-fae4-4a81-b2b6-887c804e6c6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90 percentile value is 24.0\n",
      "91 percentile value is 26.0\n",
      "92 percentile value is 28.0\n",
      "93 percentile value is 31.0\n",
      "94 percentile value is 33.0\n",
      "95 percentile value is 37.0\n",
      "96 percentile value is 41.0\n",
      "97 percentile value is 48.0\n",
      "98 percentile value is 58.0\n",
      "99 percentile value is 79.0\n",
      "100 percentile value is 1579.0\n"
     ]
    }
   ],
   "source": [
    "#90-100 percentile\n",
    "for i in range(0,11):\n",
    "    print(90+i,'percentile value is',np.percentile(in_out_degree_sort,90+i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LclmrhGbX7K3",
    "outputId": "43440486-b332-476b-a769-43e43e23600d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.1 percentile value is 83.0\n",
      "99.2 percentile value is 87.0\n",
      "99.3 percentile value is 93.0\n",
      "99.4 percentile value is 99.0\n",
      "99.5 percentile value is 108.0\n",
      "99.6 percentile value is 120.0\n",
      "99.7 percentile value is 138.0\n",
      "99.8 percentile value is 168.0\n",
      "99.9 percentile value is 221.0\n",
      "100.0 percentile value is 1579.0\n"
     ]
    }
   ],
   "source": [
    "#99-100 percentile\n",
    "for i in range(10,110,10):\n",
    "    print(99+(i/100),'percentile value is',np.percentile(in_out_degree_sort,99+(i/100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XqErF9SRX7K8",
    "outputId": "11746c24-4d22-4466-f2bf-8dae306ee408"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min of no of followers + following is 1\n",
      "334291  persons having minimum no of followers + following\n"
     ]
    }
   ],
   "source": [
    "print('Min of no of followers + following is',in_out_degree.min())\n",
    "print(np.sum(in_out_degree==in_out_degree.min()),' persons having minimum no of followers + following')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4rjy0p8-X7LA",
    "outputId": "e3d20325-27e8-44f3-f0ea-18aa395e687c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max of no of followers + following is 1579\n",
      "1  persons having maximum no of followers + following\n"
     ]
    }
   ],
   "source": [
    "print('Max of no of followers + following is',in_out_degree.max())\n",
    "print(np.sum(in_out_degree==in_out_degree.max()),' persons having maximum no of followers + following')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2vkQHuVgX7LE",
    "outputId": "c4bd961f-a5a9-45b7-d700-f8f368d6772d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of persons having followers + following less than 10 are 1320326\n"
     ]
    }
   ],
   "source": [
    "print('No of persons having followers + following less than 10 are',np.sum(in_out_degree<10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9YXunWI-X7LI",
    "outputId": "00c2e979-fcfc-412f-8a02-26bac9c912ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of weakly connected components 45558\n",
      "weakly connected components wit 2 nodes 32195\n"
     ]
    }
   ],
   "source": [
    "print('No of weakly connected components',len(list(nx.weakly_connected_components(g))))\n",
    "count=0\n",
    "for i in list(nx.weakly_connected_components(g)):\n",
    "    if len(i)==2:\n",
    "        count+=1\n",
    "print('weakly connected components wit 2 nodes',count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RiPVIlFUX7LQ"
   },
   "source": [
    "# 2. Posing the problem as classification problem "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TpDCmJroX7LS"
   },
   "source": [
    "## 2.1 Generating some edges which are not present in graph for supervised learning  \n",
    "Generated Bad links from graph which are not in graph and whose shortest path is greater than 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3K2YQD6ZX7LT",
    "outputId": "03656e72-53e4-4239-8227-c0ffe9f535d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5.08 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#generating bad edges from given graph\n",
    "import random\n",
    "if not os.path.isfile('saved_after_eda/missing_edges_final.p'):\n",
    "    #getting all set of edges\n",
    "    r = csv.reader(open('saved_after_eda/train_woheader.csv','r'))\n",
    "    \n",
    "    #creating a dict with keys as pair of linked users(i.e. directed edge present) and value as 1 indicating that link is present\n",
    "    edges = dict()\n",
    "    for edge in r:\n",
    "        edges[(edge[0], edge[1])] = 1\n",
    "        \n",
    "    #creating a set for storing user pairs with missing links    \n",
    "    missing_edges = set([])\n",
    "    while (len(missing_edges)<9437519):\n",
    "        #select 2 random users \n",
    "        a=random.randint(1, 1862220)\n",
    "        b=random.randint(1, 1862220)\n",
    "        \n",
    "        #check if an edge is present between them using edges dict\n",
    "        tmp = edges.get((a,b),-1)\n",
    "        \n",
    "        #if edge not present and user id are not the same then check the shortest path between them\n",
    "        if tmp == -1 and a!=b:\n",
    "            \n",
    "            #if shortest path>2 or no path detected the add the pair to missing_edges set \n",
    "            #reason to choose 2 as the threshold is because there are high chances that if the shortest path length is \n",
    "            #less than equal to 2 (i.e. 1 or 2)then there are anyways high chances to be an edge present in reality \n",
    "            #whereas we want to have pairs where predictng an edge is a bit more complex in the sense that we dont want user pairs where a link is very obvious \n",
    "            try:\n",
    "                if nx.shortest_path_length(g,source=a,target=b) > 2: \n",
    "\n",
    "                    missing_edges.add((a,b))\n",
    "                else:\n",
    "                    continue  \n",
    "            except:  \n",
    "                    missing_edges.add((a,b))              \n",
    "        else:\n",
    "            continue\n",
    "    pickle.dump(missing_edges,open('saved_after_eda/missing_edges_final.p','wb'))\n",
    "else:\n",
    "    missing_edges = pickle.load(open('saved_after_eda/missing_edges_final.p','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vrjL2UAmX7LX",
    "outputId": "8596ad2d-6c45-4c14-e377-3c06850b661d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9437519"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LA2GGc3lX7Lb"
   },
   "source": [
    "## 2.2 Training and Test data split:  \n",
    "Removed edges from Graph and used as test data and after removing used that graph for creating features for Train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hmcOk-nkX7Lb",
    "outputId": "891d417c-069b-4236-e3ba-35b2bbfc252d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes in the graph with edges 9437519\n",
      "Number of nodes in the graph without edges 9437519\n",
      "============================================================\n",
      "Number of nodes in the train data graph with edges 7550015 = 7550015\n",
      "Number of nodes in the train data graph without edges 7550015 = 7550015\n",
      "============================================================\n",
      "Number of nodes in the test data graph with edges 1887504 = 1887504\n",
      "Number of nodes in the test data graph without edges 1887504 = 1887504\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "if (not os.path.isfile('saved_after_eda/train_pos_after_eda.csv')) and (not os.path.isfile('saved_after_eda/test_pos_after_eda.csv')):\n",
    "    #reading total data df\n",
    "    df_pos = pd.read_csv('data/train.csv')\n",
    "    df_neg = pd.DataFrame(list(missing_edges), columns=['source_node', 'destination_node'])\n",
    "    \n",
    "    print(\"Number of nodes in the graph with edges\", df_pos.shape[0])\n",
    "    print(\"Number of nodes in the graph without edges\", df_neg.shape[0])\n",
    "    \n",
    "    #Trian test split \n",
    "    #Spiltted data into 80-20 \n",
    "    #positive links and negative links seperatly because we need positive training data only for creating graph \n",
    "    #and for feature generation\n",
    "    X_train_pos, X_test_pos, y_train_pos, y_test_pos  = train_test_split(df_pos,np.ones(len(df_pos)),test_size=0.2, random_state=9)\n",
    "    X_train_neg, X_test_neg, y_train_neg, y_test_neg  = train_test_split(df_neg,np.zeros(len(df_neg)),test_size=0.2, random_state=9)\n",
    "    \n",
    "    print('='*60)\n",
    "    print(\"Number of nodes in the train data graph with edges\", X_train_pos.shape[0],\"=\",y_train_pos.shape[0])\n",
    "    print(\"Number of nodes in the train data graph without edges\", X_train_neg.shape[0],\"=\", y_train_neg.shape[0])\n",
    "    print('='*60)\n",
    "    print(\"Number of nodes in the test data graph with edges\", X_test_pos.shape[0],\"=\",y_test_pos.shape[0])\n",
    "    print(\"Number of nodes in the test data graph without edges\", X_test_neg.shape[0],\"=\",y_test_neg.shape[0])\n",
    "\n",
    "    #removing header and saving\n",
    "    X_train_pos.to_csv('saved_after_eda/train_pos_after_eda.csv',header=False, index=False)\n",
    "    X_test_pos.to_csv('saved_after_eda/test_pos_after_eda.csv',header=False, index=False)\n",
    "    X_train_neg.to_csv('saved_after_eda/train_neg_after_eda.csv',header=False, index=False)\n",
    "    X_test_neg.to_csv('saved_after_eda/test_neg_after_eda.csv',header=False, index=False)\n",
    "else:\n",
    "    #Graph from Traing data only \n",
    "    del missing_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wj9OwIX6X7Lh",
    "outputId": "8a11e605-e133-4b3d-c2d2-585021be9cdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 1780722\n",
      "Number of edges: 7550015\n",
      "Average in degree:   4.2399\n",
      "Average out degree:   4.2399\n",
      "Name: \n",
      "Type: DiGraph\n",
      "Number of nodes: 1144623\n",
      "Number of edges: 1887504\n",
      "Average in degree:   1.6490\n",
      "Average out degree:   1.6490\n",
      "no of people common in train and test --  1063125\n",
      "no of people present in train but not present in test --  717597\n",
      "no of people present in test but not present in train --  81498\n",
      " % of people not there in Train but exist in Test in total Test data are 7.1200735962845405 %\n"
     ]
    }
   ],
   "source": [
    "if (os.path.isfile('saved_after_eda/train_pos_after_eda.csv')) and (os.path.isfile('saved_after_eda/test_pos_after_eda.csv')):        \n",
    "    train_graph=nx.read_edgelist('saved_after_eda/train_pos_after_eda.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
    "    test_graph=nx.read_edgelist('saved_after_eda/test_pos_after_eda.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int)\n",
    "    print(nx.info(train_graph))\n",
    "    print(nx.info(test_graph))\n",
    "\n",
    "    # finding the unique nodes in the both train and test graphs\n",
    "    train_nodes_pos = set(train_graph.nodes())\n",
    "    test_nodes_pos = set(test_graph.nodes())\n",
    "\n",
    "    trY_teY = len(train_nodes_pos.intersection(test_nodes_pos))\n",
    "    trY_teN = len(train_nodes_pos - test_nodes_pos)\n",
    "    teY_trN = len(test_nodes_pos - train_nodes_pos)\n",
    "\n",
    "    print('no of people common in train and test -- ',trY_teY)\n",
    "    print('no of people present in train but not present in test -- ',trY_teN)\n",
    "\n",
    "    print('no of people present in test but not present in train -- ',teY_trN)\n",
    "    print(' % of people not there in Train but exist in Test in total Test data are {} %'.format(teY_trN/len(test_nodes_pos)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OhpyrSuiX7Ll"
   },
   "source": [
    "> we have a cold start problem here\n",
    "\n",
    "> there are users which are present in train but not in test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "onultUqjX7Lm",
    "outputId": "795b2bac-18da-4540-9343-bb69f30a6f00"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "Number of nodes in the train data graph with edges 7550015\n",
      "Number of nodes in the train data graph without edges 7550015\n",
      "============================================================\n",
      "Number of nodes in the test data graph with edges 1887504\n",
      "Number of nodes in the test data graph without edges 1887504\n"
     ]
    }
   ],
   "source": [
    "#final train and test data sets\n",
    "if (not os.path.isfile('saved_after_eda/train_after_eda.csv')) and \\\n",
    "(not os.path.isfile('saved_after_eda/test_after_eda.csv')) and \\\n",
    "(not os.path.isfile('saved_after_eda/train_y.csv')) and \\\n",
    "(not os.path.isfile('saved_after_eda/test_y.csv')) and \\\n",
    "(os.path.isfile('saved_after_eda/train_pos_after_eda.csv')) and \\\n",
    "(os.path.isfile('saved_after_eda/test_pos_after_eda.csv')) and \\\n",
    "(os.path.isfile('saved_after_eda/train_neg_after_eda.csv')) and \\\n",
    "(os.path.isfile('saved_after_eda/test_neg_after_eda.csv')):\n",
    "    \n",
    "    X_train_pos = pd.read_csv('saved_after_eda/train_pos_after_eda.csv', names=['source_node', 'destination_node'])\n",
    "    X_test_pos = pd.read_csv('saved_after_eda/test_pos_after_eda.csv', names=['source_node', 'destination_node'])\n",
    "    X_train_neg = pd.read_csv('saved_after_eda/train_neg_after_eda.csv', names=['source_node', 'destination_node'])\n",
    "    X_test_neg = pd.read_csv('saved_after_eda/test_neg_after_eda.csv', names=['source_node', 'destination_node'])\n",
    "\n",
    "    print('='*60)\n",
    "    print(\"Number of nodes in the train data graph with edges\", X_train_pos.shape[0])\n",
    "    print(\"Number of nodes in the train data graph without edges\", X_train_neg.shape[0])\n",
    "    print('='*60)\n",
    "    print(\"Number of nodes in the test data graph with edges\", X_test_pos.shape[0])\n",
    "    print(\"Number of nodes in the test data graph without edges\", X_test_neg.shape[0])\n",
    "\n",
    "    X_train = X_train_pos.append(X_train_neg,ignore_index=True)\n",
    "    y_train = np.concatenate((y_train_pos,y_train_neg))\n",
    "    X_test = X_test_pos.append(X_test_neg,ignore_index=True)\n",
    "    y_test = np.concatenate((y_test_pos,y_test_neg)) \n",
    "    \n",
    "    X_train.to_csv('saved_after_eda/train_after_eda.csv',header=False,index=False)\n",
    "    X_test.to_csv('saved_after_eda/test_after_eda.csv',header=False,index=False)\n",
    "    pd.DataFrame(y_train.astype(int)).to_csv('saved_after_eda/train_y.csv',header=False,index=False)\n",
    "    pd.DataFrame(y_test.astype(int)).to_csv('saved_after_eda/test_y.csv',header=False,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DB_sXaqZX7Lt",
    "outputId": "6c5b1dcf-a7ff-420d-80fc-84a4fb6566fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points in train data (15100030, 2)\n",
      "Data points in test data (3775008, 2)\n",
      "Shape of traget variable in train (15100030,)\n",
      "Shape of traget variable in test (3775008,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Data points in train data\",X_train.shape)\n",
    "print(\"Data points in test data\",X_test.shape)\n",
    "print(\"Shape of traget variable in train\",y_train.shape)\n",
    "print(\"Shape of traget variable in test\", y_test.shape)"
   ]
  }
 ],
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