{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4eaa195a",
   "metadata": {},
   "source": [
    "# Working with the real data:\n",
    "\n",
    "When you are learning about Machine Learning it is best to actually experiment with real-world data, not just artificial datasets. Fortunately, there are thousands of open datasets to choose from, ranging across all sorts of domains. Here are a few places you can look to get data:\n",
    "- http://archive.ics.uci.edu/ml/\n",
    "- https://www.kaggle.com/datasets\n",
    "- https://registry.opendata.aws/\n",
    "- http://dataportals.org/\n",
    "- http://opendatamonitor.eu/\n",
    "- http://quandl.com/\n",
    "- https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public\n",
    "- https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research\n",
    "- https://www.reddit.com/r/datasets/\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "Schermata%202022-07-20%20alle%2015.45.04.png": {
     "image/png": 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4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpVc2VyQ29tbWVudD5TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KHGwWgAAAABxpRE9UAAAAAgAAAAAAAAEfAAAAKAAAAR8AAAEfAAJ+sMmCra4AAEAASURBVHgB7H0HYFzF1fXZqi733isGjBsYG7BxwwVCLylAQgIpJCG0VFLpNeULSSABEkI+/iRfAmmE7o4L2Lgbd7n3Lqtu/8+Zp5UlWWW1WtkrMwPW7r4y5b55M3Pm3nuuK8YEm6wErASsBKwErASsBKwErASsBKwErASsBFIuAZcFXCmXqc3QSsBKwErASsBKwErASsBKwErASsBKwEjAAi7bEawErASsBKwErASsBKwErASsBKwErASaSQIWcDWTYG22VgJWAlYCVgJWAlYCVgJWAlYCVgJWAhZw2T5gJWAlYCVgJWAlYCVgJWAlYCVgJWAl0EwSsICrmQRrs7USsBKwErASsBKwErASsBKwErASsBKwgMv2ASsBKwErASsBKwErASsBK4GPvQTixN3hcBg//elPMXjwYEyePBkZGRlwuVyV8jlw4AC2bt2Kffv2obi42Jzv0aMHevXqhVatWsHr9VZer7z27t2L9evX49ChQ2jbti0GDBiArl27wu/3mzyrlqu8N23aZO7xeDzo3LkzBg0ahDZt2sDtduPxxx/HP/7xDzz11FO4+OKLK8uprJz9kpYSsIArLR+LrZSVgJWAlYCVgJWAlYCVgJXAyZRANBrFsWPHcPToUfzmN7/BwIEDMW7cOAOS8vPzKwHSs88+i//3//6fuVagKBAIQJ9XXHEFbr31VvTp08eAIwGp5cuX45e//CWWLl1qwFEwGMSIESNw1113mU+fzwddJ2Cma5577jksWbLEHNNxnf/xj3+MKVOmICsrywKuk9khUlhW2gKuONrXjoK+x3cW6jsevyaF8rFZWQlYCVgJWAlYCVgJWAlYCZzmEtD6cvv27XjllVcwd+5crF69Gjk5OejWrRvOP/98fPazn0X//v3NmvS9995DYWEhevfubTRP0lz94Q9/wH/+8x/cfffduOWWW9C6dWscPHgQ3/72t7Fq1Sp8/etfx9ixY7F48WKjnTrvvPPwne98x+Qp0a5duxbf/e53UV5ejs9//vMYPXo0BADXrVuHvn37ol+/fkaTVpeGS9fqn9bC0oTZNXF6ddi0BFzq9NoBUGcW4lenFapXRyotLTXH1SH1Wy+DzmdnZ9vOlV59y9bGSsBKwErASsBKwErASqBFSEDry0ceecSY6wkMHTlyxJgIZmZmoqioCF/84hcxcuTIOteaK1euNKDqrLPOwve+9z2j5XrjjTfwgx/8ANdccw2++tWvokOHDgawSWP173//G48++iimTZtm5PPQQw/hrbfewk9+8hNjxijNVm2pNsAls8YtW7aYy2XamJeXZ0FXbcI7hcfSEnBFIhFs2LABv/3tb3H48GHcfvvtuOiii0znf+edd6AOrF0DAbOOHTvisssuM/9yc3NPoSht0VYCVgJWAlYCVgJWAlYCVgItUQLyyfrKV74CAZb77rvPAK8hQ4ZgwoQJZvNfflkCQXHNkdag2vjXp5QD0lx9//vfN+vVb3zjG0Yz9sQTT+BPf/oTHnzwQVx55ZXmfsnmX//6F374wx/iS1/6ktFmCTDJFFH+Wrq3Xbt2CIVCENiTz5eUCtJaKdUEXDKB/Oc//2lMHLVWvu2220zZ8evNTfbPKZdAWgIuOSG+/PLLxn5Wnezhhx/G1VdfbVS9f/3rX42WS+pdAbPXX3/d2LpqN+G6666rU6B6IcrKyrB//37zYjTUEeUaGaszN3vCSsBKwErASsBKwErASiD9JSBQIMIFLdzjYCEdaq16aVO9mNqjKNdozZlUllJ87ScNkKyjqmqRZE4osCNZfe1rXzOb+8OHD6/0napaP60p5be1ceNG0wYRXQj0yLTwm9/8pvH7EkDTd5kfioBDvmBx+b///vu48847jSZLZe7YsQN33HEHpB3r0qWLMUFUXtKICahJsdCpUydTfwEulfXkk08aUg+tl//yl79g0qRJRovWvXv3qlW139NEAmkHuGQqOH36dLMjIKfDefPmGftXAS7tIOifEH88LVu2zKhrZVcrp8R4Z46fj3/q5ZDj4hPsoMeKSpFJE0Udq5lisSjciMLnPn5O4Os4N03NO5L/rRLipTRXGYnUTsOQREGzX7a95SbJMqp28NPdHA+s5YqmzppLZnr2+pTMrNjqFFWDJ9T34nKMX/xxkqfaLhkofRz7UjgSNZuAPrGTUQAfp2fvPPWW+zc+B3rS8KFFOEBrXZPs3KxX8uChI7j5c7cYTUo6PSVpcH5L8ol33ngdCEc4btBnP8UVlOzKCYzKBTqpNZJ7itZ+IqAQwUX79u0r141yZfnVr36FP//5z8YcUG4tYgH8xCc+gd69e0OkGQJR8SSQJc2V/L10rcCcgNpNN91kwJGUAgJRK1asMIBrzJgx8VvNMZkYjho1yoAyMRh+61vfwp49ewzouuSSSwzboay65P8lP7DPfOYzBiTGAde9995r/LvefPNNXH/99cZvTADNpvSUQFoBLnVOOSnKlFAOgmeffTYeeOABo9oV4KqZdL1UuOrw55xzjlGzVgVc2tHQDoReaqXFixbjmed+jzFTrkKvfgM4OTrHnXw50sZcCAVDcAVL0DYjiExfFLn+CDQIN8c4rB2dIFcobpcbfoMQUj3UOC1r6G8ownrwX4bXDZ/HWYA3dE/Kz0vATWk+B1Ujz3DMPC8/H1pTskt5+9I1QwqpPEyTCMovw5v8pJ6uzTt59eKkzgUDXyNkUo4eyvPjltSHyoJaNAEZHjlsf3wkoHnn8KEiHD1Sgk6d6XOcLQrpj0/7W3JL9ZgCXCtwGEQO58B0mjcE24u5yaz1gZ+L+UbXjWuLKO97+tcv4KwRY7ho/1ZaPSoBnAfor7Tvw8UY3q07Mv21+yw1pdIetwdrtm/DZq61riWRxVCaCApwSdsnsFWTll3A6b///a/xr1q0aJHZRBF9++WXX24Aj9amYiNUHqq//KakpZNl1muvvQb5ccks8YYbboDcXKS9EvOgNFwCXPE1qpQFAmcXXngh7rnnHuNGI22YzAMF+gS4VDfRw8utRt8FtGTiKDp4abXEYSBTRJWh8qSxsyl9JZA2gEudVx1WNJtr1qyBnAe1K3D//fcbm9irrrqqmhR1vcwDZRsrx0M5JV566aXVrlHHVafevHmz2XnQ54o163HrN+7DgDPO5osUrHK9FuguatCi7MABxAjmsr0RdM0pRfuskAEiujg+h9Yc+OLHq2TY4FcDELhCI84zC5Rkd7AaLKiOC+JtCHChGCBQEeDy1rPFpzZqB6pmW5WPnoejsK+jsHoOc2PY3Eusx+dUz4UNnIpyQA0xMw1ofrYlVYlLR7OITFV+6ZSPnl1ZKGIm8kyibfP44x0jnSraAupSyrFDu9FZ7HseoQ7Ksea70gKakXQVo2xsCQGXh63OpAzMHlLSubWwGznmHNhfiEMHj6Fb9/bIzct0AJd9l1rEg4xvlmRzDEynd1bdpyhIqx4OzH4u8hudCLgC3Pz4yY8eQ4deZ+Kb1IikUxJgeYQb5oFVKzGmd29kEVRoLZHK5KXclm3ZjPUeL75w9z1GoxTPX2XFAZC+x3/rmICM/K+ktdLaVJZX8o2Sz5WAWtV6xvMQ+JLWSoBMZBhSBMjdRffK/E8mf/FrpRWThkpATsBr27ZtRhsms8HHHnsMZ555pqmmrhfr4Zw5c/D0008blkOBtxdeeMG4ych8UOtf+ZlJexfPP95G+5k+EkgLwKWOK02UVKei1VSHFnXmzJkzTacVbaZUv/GdCGmutAshp8MXX3zRnFM8AwWmq5qk7n377beNj5eO66U5VFiCr37rJ+g78KwaGi5dER9q9cnFe4i7XuVl6JRZhE7ZAXgNJNN1ejF5tS7jPwE1o08RcmpE0j1hgoRAhICLCxRujPPIyUuqbpiL7aLicgRCYYItLpKMDE6shVrmz/DRcTPDgK6qtRRwLCsLksq0KoCtekXd33XLgYMeHCv2oFV+FB3bRZDBOICSb6OkwYehxa5AlxZ6qdIwyDQoK8uPLFXqNE2lBNwStxbJRm4nPv70bbk6ZprUt4xy5NCEDB/fZe0cOJ04fWWX4ppFXTEDuLx8Fw3gckbFFJeSntlpkXPwYBG1XMcYzLQ9snMrHOvTpG+mp9TSo1YaQsq08cmXN4vv7vF1wKmvn+p2jJsYGcRaGdKqJFIl3aSki/k9zLHo/gd/is59zyHgusecSpc/AlyPEnAF163FxX37EXBlVAMyqainANfSzQVYy+d7C9eJo2nCV1vSOlRrS30KMOm7NEliK5S1lbRM8vsS0YWCEOs6/dO7r3/6LpcYAS75deleaa8Uy0tWW6J7l9lf3CVGGipprKSdkgmiGBEF6MRbIKbEOOBSvgJmAmhym9HaWEBQ9PO6b9asWUbDpjXwxIkTjVbNgq7anvCpP5Y2gEvsMELtsmP9whe+YDqvVLNyDLz22msNpaY6vnYbBLbU2f74xz9i/PjxRh2rF6FmJ4u/DHExy0nxd79/CZd/+jYMOPMcRMJVTQrjV1X55EsUpaYrUFqOrNARtIod4oLU0ePwHeAATVOEoJvAjeZYGfT78lYMh/EBr0pWdX1VHgIsbi7sWVxyKT4KJ3G/tEshghQjqzqq7xxWXAdQ03diPXWvzDEoKibBz8QSsR7WbXTj/cV+HC30oj3B1ujzghjUP9JoWaiOcbzLdV/Cdai9poLCFcJgTjKzNBqLRBtWe6b1Hz1eXP3Xpfosyw3z+al4gS2B1RaTjMxUd214nPqkzRP1f/UVuYDKxO7jlNR69SW1Wn3p49R6tbW8LIxAeQjZORnw+njkY/b8W3JflymwJnSPJrk0S5qfZXkg65LEkjOe6w00ayI26bGfP4MBQ87DvTRdS6cU13CV05ppTI9eyObGptYTqUxGw7V1C9b7vPj8PffWC7gEekScIVM9EWc888wzhpRCcbjEPqg4WAJOYjEUTbwINwSgtC6V60rcD0vr0biry8KFC41vlujkFYerV69eKCkpMVop8QoIXIlMQ/wEKkMEGz/60Y8MeJK/WJzIQ2UIoMXdZ/7xj3+YNbO0bXK92UPfL4GuyZMnG2BYcz2cSpnavJKTQNoArp07d+Kll16q1EZpd0EaKnXgwYMHm50B7TBIzStmQmnC5Mwo5C9HxprgKi6O+MurzifA9dsX/ogrPvPFxABXxVKupDSEsiOH0DnjELI8RAnMi+8Gdu/xYuXqbLImevgSleHsM8uRm2NWWvHi6/10Foq8XghBKabBvnGDjcYm+bLpPi9V5omuckwp/OPPykCbDm2QlZNJGdIcr47iVU7JsRIcOXDYaJFUXSVdrgVmfttWaMV/CdefK9Ki4hh3Z6IkM2E8tVw5tsYw7Bw3bZe9RsvVKFFwLopPM7ov0alJbagrCcBJtoXctS46UmQuS0W+NcuTDKPUjii5vell0mIqlY5/9GyI2MtLyg1TQ0aml7uSjX9/Utc0LW6cPhgR8OL/ZhMldQW0iJzia8IUr5laRts56oRpV+nmIOqqayBtES35+FWyst9q5kizzuvUzdGgNPRkuGxCKcdEWWbkcE7Xukffn/jlMzhj2PlpC7hCDPg7jr5R0nClMmlu9XFeWEIN1xqOy/VpuFSu/KUEarSpL62WfK8EnrQWlYWU1ptiw9Z5BUcW4JJPl0CXXFxkdSWCCxFlfOpTnzI+VfHYXoqvJdcYMWwr37///e/GOkvrWvmIKQmcKT6XwJ7idulT/mSzZ882GjZptHRMGi4pI/SpdbDCKAngielQzIciBKlNCWEKsX9OmQROOeASIKr6T5LQby10ZQ4oSnjZwIo0Qx1XYOsXv/iFAWFyNIwHPJZzYlW2mdokagDX8wRcNyYKuJxcygi4ooFSnNXThdZ5jjP4ocMuzJzhx5KlPvj8MeRlxzB5ahDDhhGJ6S1PMGls1+XSNGms93JwlH4locTLJCeZY0q9k52Tzdt4kPkkkhzA5ywU67+Feep//attIVFRpNlRMRnVn5upGy8pK4sRBIewYGFEBEU0WQQuGO3DmAu8ZAJipgk2RFca5QIrSfGZ+xKogS6sM0nrGOa2pyYwbXpqLR+flOu8qQknymjWKRlnapIUbrCpfglQSKXcANi9aSfVKhF06qLg56nfHa2/ErWfLaMpst6VTGo5Et+Vrj0ve7RlSaCEmwA+OulzM73OzauW1SJb25YiAc15gUAUmwt20+LGh959RSFOKx1OXj+6/0l06jM4bX24giuWY0zPXo7pviZ0/UtB0qapTyaFWzdjnddXr4ZLxWmNKV8pgaN169YZACa/KJn3CQBNnTrVxMdSHFitQ2fMmOGsv3ivlASicxd5hYCV4mgpaV0kIPS73/3O+HKpDGmu5M8lcg1pzXRNfB387rvvGv+sgoICs77T2lYg78YbbzTMh7pW5ogyMZT54rBhw8z9AosyOZSm7Mtf/rJZI1elvDeVsX9OqQROOeBS69VRzWKd3+Of6jTy4frZz35mHAZFiCG7WO0+LFiwAKKBl2ZLgEO+Wwr2JlPE+jpYcoCLfgll1HAVlaM32Tb7dCWxABfEe/a68OZbfuzc4UFrKnYY4gujLwhi3MUyU+RbnuCAwbUZ9he6se2Ax4CMvh0iaJenmxsCXmLli6K8tMy8YAJbXmpHnNQw3JA2iKJDGdf54ZBMIrlAzBRgqfveeJNqXlHX8YrK1Pmh+/bvj3K3J4xdO2PUErox4lwvByoKWCvWBJLaUHgsRhtm3eIi8xBIpSptnwNba9Y1gSwJfpVfhINkGCVFUbRt50H3Hl7uGIkIIJkc6y9VLS0vKTV9JjPHOr3WLy3nrMaJkqJi7BLgov9hl67U0tK/MMFuk0gRSV2jt7aU9dEGgPxBmqO/JFUxe1MKJODo0J2xufbsiklwoAWen0Ox+oJNVgLNJQFnhnRMeE1v42ZooDxMwLWP/u4+9OnX0aynouyPDuA6Oy0Bl3y4ItRwje9HDRcXIgIeqUxet5cark1YzUH5c/X4cFUtU3WQNZXcXBQXS7TwAj7xumn+0Ua3tFrSfOm7NEoisBBToMz/dG18Pau8dY3MAxVbS1qqnj17Gs1Y/JqqeYv1UCQaMrmUOaOINKrS0ctXTOcEBuPcBipDxwXotCaW1k2+aDaljwTSAnDVJg6BsL1792ItX0Q5KErlevToUaOuVQcXINM1SuqIvXv3hmxk1dHrSo0FXHrvyRKPw0fDcIXKMGIA0LE1J1H+X1xME8UP/Jg7z0M/Lhd3NiKYNjWEQQNYp0THC+YjsDV9JU0Tt5GYgZqyEf2CmHROGNysrwd08EYm+ZeVcqEejUWQR/AZf3HNyQb+cE3IFxoEO27K1cWXP4bhw6Po1FGVd/JvIIvUnGZRETIkhvjPR42AR8bqCcpPA9SOXTHMn+/Cxg1OnblZhAsvAsEbNVNJ1nDXnjDmzinD2nUBglEunLNcGD4iExddmMWBUmAwyYzruE3ZlQlwMWVlW8BlBNHAH/X1kqIS7CqoAFxdBLik4WrgxmY+rV5YSi2HRiaxFVrA1cwCP4nZC27pvyg3drjXVbn4qloFPXsBLnEvqC+c4u5YtWr2+2kkAY1/bprlq4/pu/mPP8rpQ1hQsBdZmdRw9enMc1wnpDngEkthaMVKjOOknRn34UrVQE4B+AS4tmzBGjpjf55WUXWRZtTsHtrMF+GFtFsy26tJylbzevvbSqAhCaQt4KpZ8bgWTINLsqkxgEvve2ExsHazG5t3RBiPK4jLRkcxfIC0J9xJIkDYsM2DGe/5sWO3G716R3D+sAjO6BlB61xq7MxQ2HBNN+/34L9LfDhSqoHBRUAXxTXnl6N9rib06vcLYJhdEH6KjU+gU4OCQGZmlmOvXf2OKr/iYlOelOHe/S68+7abgJZ0swR3Mj0YdX6EjpoR87tm2VVyaoavplKNy5ftKSmNUa3uwgcfMH5KrnaTeIxAePgwYNIlPEYzz0omjURy5/2hYIwa1DLMm19Gmbq5q+UmexCprrlRNGlSFvMmIEokr2rX1N8+nbWAq5rAGvyhccACrgbFZC9IoQQ09paQVbWEZlsGdCnvGgOliFM0HptFsDmnt9smK4EUSqBiDeRxM05ohpubsy0bcEnDFaX53njulhpa+BRvUwhwfUgN1ypu0H/uzrpZCms+Ia05ZVUlS6qOHTtabVFNAdnfjZZAiwFcjW5ZLTc0BnAFqNlYtcGNBcvJPiMHo0gYQ/vFcMPEELrR7G/TTjfeWZyBXQRMGRzwZLDv5Y7TOf1CGD88jDY0C9R8W+/inCcPFrkx9yMfVu7wcLABhvcJ4+IzGXQ5znhooJvDligVcoD/5EYlVbGHjgJi4PFQw1dfQTKRo6WLycnPS+liQNtkmkS+6aGGzE0VOE3opOXqEcWVV4Y5wHAdwXvqzbQW+Tb9UCPKpKwP0Yzw3XfdHBSlmWOdWYFDB100/4sxJlsMHdtrJ7reJ1C9yry0pCRGu+wSrFodQJu28sVgMFcCu1L6m40elYmxYzIrzRWr31zHL1O8ahbXjOl79aQjFnBVl8kJv+KPsUJ8FnCdICF7oBkloO5XSBbCowEvMrNzqEHIMsCqZpECZc56ON5ha15hf1sJNF0CAgNl9GMoKTmGjjkg8HJMx8q5cGmJGq6I0XD1pEmhz9nDOHGaTE5oXCwZDRdZsFdznXRLIzRcyRVo77ISqFsCFnDVIhst5o5RuzVnsQcLl3GhTMAVIe/5IJqrfXpyObp3iuE/c32Y+aHP0P+2a80Aye1JC0/NV5gU8ReTOOPCwSGaE9WSeY1DwjWHit3Yut9taOX7dowiP8vRZJmJm+YrhpqeQEvaLB9ts90kDHDTkcxDVkLjqcQ8zPhUS3kCjrt20pF2a5RAAejX10XzTAbppM/Y9OkeFGyW/5ZyoTnjsCgmjI+RvMLYy7CmtWRYo/6n7CerRmZVvDcvhg+XxKjNcmpC82XSprowaaJYmnisMQM38wxKw7WwHAvfLzOavixquYroIyZzxwnjszBsaEbFgqr+lgtsB5iXwJpuoKm1AebOYqz6vaqiBVzVZVLzl0hl9K7If1LvlQVcNSVkfzenBGjQgL1HI8hq3Rbt2rTi5pqzwK1ZZtwSQ8fVR22yEmguCciHex9Zg4OFR9ClHVlaWVBLBFzScMXoOjKhn1gKaRbeqEm7fulqbvUbDddmrCRI/WyCPlz152rPWgkkJwELuGqRm1jijhW58NYsL96cQ78ialOiXPH16xbDJycGjM/TH17LwLa9XrOob9MhigHUTA3uGzY+Gz07RXH1WDo0ZjharlqKqHbIUAibuZnAR/4BFaYo+ojSbDBERzI3NVk+DkbGR00qLuf/eiCRhi0Xtm6Lku0xhC1bZQoJDKQf2tSpPrTvQLBFLdeiRTSZK3TR5ymGUSMj6NyZVdMo1QxJ2WrRzHGPcpLpjVNIfFniFOv8MkDSnI6fPbFCks92uvDMew/YyvYp9egJjLnIg7599Ns5Zk4k+ofF7dwdxpy5ZXQ+DprdNslt8OAMjBmThbZt+KOBbHX6KBdnq1cHsWEjnx1/n3GGjzSzfhJ68P4aSddbwFVDKFV+lhK0bt/haBm7dnGRjdRFv02yFFofripSsl+bSwIai0hUiwPchOvUpasJhG7GAA1NNcYCAa76/Iibq44234+RBCr6nfxDS8sD2LV9B7qR2ElKrnKSZrQ0DZchzWAcrvH04TKAy6x/Uvc85VMpH65VtAj6XD1xuFJXos3JSqB2CVjAVYtcBLgKaWI3Y7YHcxfTOdUfQTZN8XI40PkZK2r1OhfWHqamg6x+GQRVufkxUntGceGQMNrSf6tz+wiuvTgIMnw76vFaykjkkIBWOc0GBLRMdHINtI1IYW7Lfrg0gukzQowP5jY05xrLJk/ykiDDSzY+shySUT7E4M0ktIHfp9VDot5njahIxaUiF9lLk78DpNTPpX9VNwLTXApVBB6lZawLzfkEEnPovyb/KxNsuB5IqWzVnsLCGHaTNVJArmuXGNq01u6yYE6N1VBFPRr6UJ4HyFK4aVPQAKfOHT3o389PU8sTwdKJedF8k4uz9xeV0b+sjMEQBaBJoED5TpiQhXPPzaQmtPqDVC0t4DpRkjoi2SxfGaU2NkKtLOhn6ML48R4GyfaYuHC7CnY4LIWWNKN2AdqjTZYARxYUc9w/Uu5FZ9I+i3Jb73RtyQKu2qRijzWHBKTpD3Dy3LFtBzqTKTmLVhgtFXDRmRwT+8mHS3G4OJGnKGk94aMl0OKCTVjBBYLVcKVIsDabpCRgAVctYpMlSEmJC4sJtpYt40CWRb+mTBc2rGU8h4VeHKJGKNjejSh9nUD3LjHr9e0VwaRRAfToHMGQ/hFMPjdkTASlsUo2BYiGAgRcWdnZ8Im9x8CRxPMTAPnoowjemR4yTIqqR6s8MCieBwMGEjxoXGN2gljHFxBaSSRehvI0ibdU+i/U0maGtML6LR7MXOjH7n0MlJwXxaihYZzRK0xg4yZbYgT7D9CcMo/EI2e4cN65HnSj6WMiLG/Gcsdoyyghtamy/CTbogYxP+1sO6lCPvGfFUdr/eAsKO3WW2+XYtPGIDpQk6iYXocORqjhymBQ5xzkMcizI3BHzsrWAq7apOnEQXtvfpREJlEcOQpS9LL/TlbfiAMuqji56OhiAVdtArTHUiCB44DLQ8DV1QKuFMjUZtF0CTiAK4LtpBvuwrVIlt/dYgFXbOUKTKjUcFWde5smJwdwefAhNVwr6cP1WevD1TSB2rubJAELuE4Qn7OqltZlc0EYSz90IUINkDtMNjyCraWLqBmiLVyY8Z7CeVwwC3BR+9W+XQQj6bd12ZggLr0ohF7U3lSu108oI7EDIq4IMnaD2AgzSRcus0JniZ7Y/bpY2p8PP4xg5Sr5f8Xog+Q1YCaTALKp9YvXQv410l6FQvJ1ipHaVUApfpaf/F5S7sIcym7RCi9acXIQCQWIwsLHIpgxPUbzxgjNHGMYMiSCXt2j9Cfz4JJJbprgCZtUzaxKvun4lUCtqDiKWTPLGMKgnLE5pHkjo14JGS5HZGD8xTnIJs181WQBV1Vp1PhOUe3YFcH7BFyHDit0gZs+eh5qQAW4GIfL0sLXEJj9mWoJyPy5uDyKw3ENF5mHtJCrLVkNV21SOTXHHCsHzR/ahUt9au78G6qxemCQ/uU7tm03Gq5sarjKWqhJoXv9OkxibNVsargcl4qGWp/Yec2tMimUhmspfeA/2wiWwsRKsFdZCSQuAQu4apGVNElRgoFjxwJc1NFMkDEtjpKN8MUXffSH8sOT6UYZTcRCJGpwc5DLoUlh+zZksRsUwp03l2IkTQsdMNNE8zyOFtFohLtWAbgI8gz1ey31behQkEDoWJGCGoNmcfJ/4R0CMRqNal83NJRl5XmO99izB1hDM8uDhyiH9tRCnEHTvq5xk0CnDAVYXrDcSxISP8kjGMeM5oNbNsSwdjkJPQpC9FVjAOssryHuGDYkhM4kJplMTdwZ1MQZ5VVlien/RRqt9RuCxqRw3371BRc1Ml5cPC6Lpok+iry60C3gquOZSu1q1JfUABKgi3I7ixsFnD+Nyaj14apDbvZwSiVwsgGX6fLs+hoX0iE5r2D1Mcupl45xtjxuHpEO1a2sw8HDR0mCFEbnjm2T9quLm6bX1sYDh47QfJza9Y7tks6/srJJfJH0TxfA5aKGa6LRcBFwpbjn+0hws5garmV0xrY+XEl0NHtLyiRgAVc9olSsqwgj37ahSWHpkRiefzYbf/0/Dggc6cppRhigJieHGpjevUmowcDHY84K4aYbytGpc7RxdOT11EGnBLhCZCnMzctNjvmK9a1Ytzomdw2Ul/Bp5rtnjwszZwhwOaaVmpgGMlbZ5CkwoMkZOx3jvJ17PZi/1Itte0i1T5+t3ZvDWL6UhBdbqOwisIzF3OhDWZ45KEwfLzfGjvUx4CCDTXdkHCyWlXDijvTxMbsxNyZcQv0XssgwWS13kXxjyxYGzebvfn19NHsjk5QaUmMlpZ8t2qQwLuIa7apfSI08yzIkR7O2YzmWpbCR8rOXJy2B5gZcVV8fTjnO8ECtjNhoXdwl4yRdc8hIui2NvVEl79y5G8XFJWaM1gsoEOLPyKD2Ph/t2ramVQN38MyL2djcm+d6jROq44MPPUoW3s34xc+eYsD61qxi4gOUrpS2sqikHJl+ElLQyVnPwUmSgQff/+GPsWPnDjz7m1/RFzmnUfmnouXqN6cL4PLQh2tyP4elMLV9SWRhtKxhP1jCl+tmy1KYiq5n80hSAhZw1Sc4s8DjznowitLCAJZ84MWfX8rCfhI0cMSFm6QZXbpLoxNFHwKuC0aGMGZsiCZ1qZt/NMQHystZhxCyc3O5k6ZhNj2SFgcrGKfs3ekyq3SZeF5H6WdD7ERigyhGjuQFxhzQqbMmPBLL4Rj94w7SX2vBvCjmvOchWPNg314FF47S5wnoQBa6Th286N3Lh3PPizHgcJBEGJz6E7AMUcwxUcMXF8Ew2eW3IrEJTSn5wE660Djnc7kiGTgAoa76q3YtFXCpD4TloMckkhPRtp+MZAHXyZCyLUMSaG7AFeG4WMYQEkcZ56uIJmFBmpDrXVJspVa0rsjN9J5AtHMynozeMVGP33XXtzF/wQKOp7Q2MC+4i3OcHwMHDsQVV1yBcePHIo9+xumSVG+Z30+Z+gmGDFmKlcuXoFuXTsaXNtE6uqgV2bd3H174w4sYMWIo4zpehhhjcSppJnEp/ymXYS1N4VYtX865L88ANHPBSfqjepwugMu9ciUm9WIcLhKEab5MZfKyzy7auhXLCLysD1cqJWvzaqwELOBKUGJhOioJFCz9wIdVS7nDRVAxiIGQ+1Ijk5sTZbDdCM48S6QPHC0UbDdF63uNPeFQCEFquRTs2Oy0mS28BCvejJdF2M7lK8BAwdJuwfgrFdF0UcBq7FjSzJ/Pwg3gqlIJap+0a3uMsa0WLoxiHuNo7dzjAc3QtaWIPLIptm7jx6BB2jXl91ZRTLssgD69pDWskk8tXyWr/QdiWLwoho0MWp2b6zaA7eyzBbpquSFNDqneLQpwsW/rGcvMb9++CA4eFLkFAXcbNwNQu9gPHPCVspegludkAVctQrGHmkUCzQm4yriZt+1wOTbsL8HewnKUhSImdIYa4qc2vF2OH307ZKN/x2y0psm1M/SnaHJpQFp6x7RhdOWV12PGzJm46abPoAvjhujd375jB8fu+fTdDeGnTz2G6669hpoETgJMuu/Ed9+ZF8wFFX/i18nHKv7dWW3r/tqud3ZzavPJqupPpbxEzX/5FVdjydJl+HDxQnRlvWXqrXS8LPOz4k/18jw+Pz5avQJDho3CV778RfzmmWcQo7WL6iUQKjKnl/70/3Do0GF87atfIcsvSa0q8leG8fpUZG7uq3o+fty5zim75j21tTN+nz4lpdMFcHnXrsHkvv2QTc1pbXKq2u7GfpdJ4QebN+FDPp+b77obo0eNamwW9norgZRIwAKuhMXI4Y1gQYvM3TsZbyscwNjhjAnUVv5e3AXVXMBLNOZqIIz/NV+b8EdThAIeB0rLCTii1HLlOPbiTiEN5xy/zplrGr4+4SucyXjnrhhNCl0GMGXSt42Wj+jWnSaFlwDdSX5hwGcteao6YiWcQ8C1YIkPJQE3BvWPoms7F3Zt83Mi5+RNYfZjbLNLJovtj5NSfYCL7QyRBv+DRS7Mns268doIiU6694hxdzKG7oyhFhMQTsMkWbQ0wHXkSJSLmSBWrCB1PolZJFuRgQwa5MH5o/yO+WQziluLphIbhysNe/PpV6VmAVx8N47RrHrp9qNYtfuY9pqo0fIiQ1okqsY5q5jxrJiR67Wo7tU+G6P6tELX1pn0aW3GF6vK49M7xjcbV1/zKcyaPQvvzZlJAHKu0WKHw0H88Y8v4dvf+T4Gn30m/vHq3xkQ2sTjMPPVkcIikgeVGK1PdlY24xfmMZC8tBfxiYgU5sEArRGCZM7NJr15iD7ADOLLYxkZWSShak1/zepxVYo4B4YpC11flb1WWR4roVkDU34Oz3FD70TA1cUAJYEuxa4q4thRTssRLe6zMrPQpm0+TQcr6sd2B2kSvmzpYpq0X4Jbb/08HnjwYZO/+kJbarPkU11cQkIrzs2t87IqQBxnfcmMeRYy/8JjRSSSCrE9GbTQyKeZfJY5ZzLiH83rR4tKeTyDjMZehiIpJLlSCTWJXmrMWqFVbna16+P3xT/VC04XwOVhHK7JvXqbZ6A+l8oebgDX1i1YynfrZhuHK9597OcpkIAFXAkL3RkCFKS4tCwAb6QM5/T2GCpWM4Xwjz5TOVCoahq8A2IqpHNuNicTDej613DisMUKccOUkwJ3S2ny5/VoKFMdE7m/4RJ0Ba1fGFwZWLoEnDAVlBZk45PPkkPSUadUKMfiMmD+Mh/mEHAFw260pfnf2b1DyGIlt29xI49kJCPODaNf74iZROutEZsUINibN9+F999XHC+CGOYvU8Sp08BAyKxJfYCt3syb96SeScsBXDEulKidXBAkVTt3fBkUvFUrsniyT5aQCKWoKIKhQ/00M/JzM0KguXlkp3fAAq7mka3NtboEUg64OFaVU7P13qbDWLm7GPk0Hcyj6aDeoeq7+7yQ42QgFMX+4gC6tvLjkjM7oCO1Xikcwqs3tsovAx74AgtwzZw1kxtrb5Ph9lxVCS4GHpd2Z8zYiThy+DDmzp6OAQP6s11BhlNZjNffeBPr12+k73GIBEpdGMrhEkyYNJFjPJ2emafL4+PG2CzMnDkbV15xOTduVpjv+w8cpKa8Na64/FJqqC5H6/x8p6mUzZ/+9DKD0u/G1792uzkuWfGwAWHPPPs75YrbqY3KoDnDCYCL8dPEdrtt+za89t83yCC7Gnv37uWmXASdOnXA+AnjuTE3zYBGl9eHJYsX4Ze/egZ/+cv/YeiQczBh/AQCJLIFU5P1nW9/E63btsXzz72AfQf2496772boGPkROMJbv34D/vnv/zDUyTICylK2pw3GXHQB23kFZUENIe2wZfK4jXTuzz3/B567kACrNf7291cYImUT65+JCy4Yhc985lNGM1e9Txx/QGz6aQO4/NRwTenblxouaQqPtzEV38RS+P7mAizm/H+T9eFKhUhtHklKwAKuRgpOg3Z5eRCuUBkG9aQZFckdkk8aWTRs1p2Mdos7ctr1FEthA5dXZqRBq/AYsKnATQ2Eo22SWZ5jWifTjxSNahXVF9ghLmT+UUML3yCVO2ftXfvdeHthBnYfZCBbmg4eZHyzbh1CpNXnricDInOjkpNPRT3NR/2ykj8R5yvMmUMt2R6CTK5Lhg6leeMY7nzSzC1VTa4Ucoq+qGktBXCxG9JRPII33ijngitKZ3SCLR5Tf9OnNF/yP5k8xY/Bg7mITLTDNlKWFnA1UmD28qQlkHLAxZos334Mswm48jIZ4oA083UN7BobvJxzglSB7T5WjnO65WPcgDbI4gaaQRtJt6rhG2sCrlkz3mXg9hF8o1krDgRHjhzBxeMvwf79+x3ANXAAx4U38d3vfp/sfSEMOeccAx5Wf7Qa+/ftx7e+dQ9u/+rtyOE85vFl4pGHH8LDjzxOa4hJ2EETxTMHnUkyDj/9rpZg167d+MmPf4Avf+VL1Pp5DYCadtmV5tyyJYtosdDVgFPVMUCQN+I8momxWh8snEeTZodcqppJIQGXy+PHSy+9iCee/Bm6M55a5y6dqUUMY+2atdw03IZ77rmTdf8uQVUm3nzjdXz/Bz9mOJXVJH/qhF59+hjn5Fzu5L30xz+gC8sfM2Y81qxZw5Am62n6nk/reRfWr9uAr339G1izdh3bP5gAqys2btxkrrvqqivxFM0v27dtR8BKAqm58zFu4iWYNGGCmZrkI9ehfQds3LQRq1Z/hNtuvQVPPvkE/feo8awFhWg2PF00XL4VK6nh6mkAl+bxVCX1VUfDtRWL2Y9utnG4UiVam08SErCAKwmhRUgSINbANplB9O/iBjf7zICZUFYaJTmgmAGUoIOwos7bxJKkOFzahVPwY8dhuc7Lj59gGbSWwJIlHkyf6TEkEj17RDF1agT9+1PLxZ0eVSOliZMN/3faVTk51FMK2773kBszFvtQsMOLXKq1SgM0IewextQLw2hHbZfRSNX0Aau30nwu1Ljt3EUN2Taa6ORQ09Yf3D1Uxeq98ZSeVNVOGeCKP6IE5SPAtXp1CO/OkHlPDPn0k1MvVtLzF+g+diyKi8dmYPRoPxnMeCLBvE0mCf6xgCtBQdnLmiyBVAIubSKJHOOfy/eRKCOMfJqT1T0DqOp6eZyxtZiEGvp3xdBO6N2ecRmb3LL6M6gKuGbNno133nodw0YM50YYtdyBKGbPnIEvfeV2Wg/0xb//9SrH/gi1YTcQLO3Cz3/xM3zi0qkEXBlYsXIV7rjjThQUbMErf/s/XHjRaHj9mXj80UfxwEOPEGS0x69//TQunTqZ4wW1S9QMffHLtxPIHcAbr/+HJotnm82cK6+61pxb9P48dDUaK2m4CEZDQVxw4TijOZo7d4ZhDDxBw0UfLk6gWE6Ci5KSMsZ7HIwcMQvyv02bCkgMci81cx8ylMdMnH3WWQaIffghTQrHTcIXb/sCHnv8SfpwcXCj0PNzcll/PzV2U7Fu3Tqs/WgFY0vmM2wFNWy3344/Uyv24x/eh69+7as0f8zFbpJv/OD7P8TLf/4rfv7UE/jG3Xea57pg3gJaAkyihUArPPTQ/fjcZ2825o3bt+/ALV+41YCuBfNm48wzzzRsyTWflkbd0wVwZRCgTumXeg2XZGQ0XAUF+IDrKavhqtmL7O+TKQELuJKUdoC29Rkox9k9SZGboOWUcEhZuRtF1DyJTS+fJnMyfdNCtWbSNBugZitIu7is7BwGLdbKlamWa50TVf7ymiKy9M2Z7cXiD92MbyVSixjNIiI4fyTRVjMsgKuUnvBXrh2wZjPV/QyGfLjIjQ5to7jwnBDO6iMtScLZVL+QbTfy5KKAKwD+zwONAm3VszsZv/Q4ThXgEkOa+oP8HhLqWnwua9eG8M675QRXUS4WPI6M2QbJvZRmnGUM8jx+XAZGnu+viPmWeilawJV6mdoca5dAKgGXtL9r9hTj7bX7GW6EvjuNGej4ru48WoJze9JEbUA7+PkuauxorhQHXNdc+ylMnzHLaJwG9O9HvyxaJ1AD9dJLf8I+ardkYveVr34Nr7/2L9x621dwyy03G4CSpQoyub2Z+N+X/oivEnR9nRqu+x98gCZ4WXiMgOtBAi6RUgjQZHid1rh9WXj6l/+D737vPjz6yEP4Ou+TEvCKK6/hJuIyLP5gHn1EZSIos3VquEIBjL5gPH+ThOm9mZxTHT/nqhquONmHh1qOCOeD4uJimpxzM5NkWBr33nrzNXz59jvw++efxec+fyvHsig+WrWSPmvnkxTjdjz9q1+RKCtIebOOBFYuahgnTJxCwLWeGjKxFLahieB2msCPIrtuLwK3WXQBoM8W/bRkPrlh3RpcOHYCBvTrR1PK6ZyTs8nSOx9jx03E1CmT8dpr/2ToE+pjCApingz86ulf4Dvf/QH+9teXcfW11yES5A5qjaR6ny6ASxquqZRbNtc5XKEwJTIbmQvr/eNouNx4nyyF0nDdZDVc9crLnmxeCVjAlaR8Q1SluDkA9yBIaJXrMhNCvUMETx4tdHOHzYPVH3mMk/SA/mGMPC9KB2FniKlaFU09AYKtckYMziXtm3b+HK1Y1avq/q5gx6tWu2le5+XkAvTqGeUEEUFPfiKaLJqpu7zjU3+9UjghA2mkDlEuhaSKb02Gx/bUbHnoF9T0pDwaV5eml5lcDqrpyQZcWjgUHo0wjhoXEaxA5y5+LhrksN+AzNh19pOZ8O23yo1pYT4Bl9FisQ0yBTl6OELzTZkUZtKnQ0GemydZwNU8crW5niiBVAIuvV8zNxzCql1FZCCkdivBF0QLR/X5/UXl6JCbgSuGdEYO40Dq3W2uFAdc1173abz9zrtkIO1IczuxyDkbNIptdc3VV+JLX/4S/ZTa44Gf/BhP/fQXePHF56npuhbcimHVOJ7TJ2rjhk0EHBfTB2wEfZX+j2Z/rQi4HjGxsl764wu45trr6Q7KyYBJDIGLFi3CRGqQPvWp6/Gb3zxLMgV3kwBXXCNWStOPj2iut2jRYuNDZdwDaAu9m75h/6Lf1SMPP0Cgcx+fSwSrGIx32IjRhoVQgCtGYKdxk0bUxoftOOBaQZ+u9phDP7fxBGFfp2br1795hiCJMVAqksg6xk+cTG3YGua7DF179CLgeo8mmZPwzXvvwZNPPYVwwLne7csg0PoLPnvLbXj+d8/g87fexry4k1UjqeucLoArkxquqdRw5VBz6LBJJvhi1JBJzZ/KRZsaCws2430Ccqvhqikh+/tkSsACriSlLfBTVhpCJFCGPl3d6CpflrrGCB4PkTFv1Uov3nyTO2wRXssdO+3IXTQ6jHHjQoZWvWZVxExYzl24nHxHw2UAV82L6vitqa6s3EXbePpy0YerW1fGDOvCibKuOtaRT0KHtUhngZqMksneJYAVz+NE7JlQFVryReZZVbBsZZHJSgudZk3Mv4RaqPnzjlIDWmTiaI0cmYcLL2xFX4QG1LWsGjdtuXEQJC100BBlZGU7Jk/SeGn3e+TIDIwanUmKf/ULtS71yQKu1MvU5li7BFIDuPgu6LXm6/DG6n3YcjiANtnycUws6Vb5TR0tDRgCpOuGd0Y+aeKVX3OlOOCShmvGzFl45KEH0bdfHzNU5zEmZJ8+vclE251zCt9/bwbuJXB49tnn8J9//Z3mdpPgorZGSeFM9tCs7vzzL0RP+um8+eZ/kN+qPQHXwwZw/fuff8PESybzeg4sup47OOvWraWG/CKawU/G//7vy8gig2PtGi4y49LUb9QF44x2qC4Nl3ypNDa98uqreOjhx6jdKqP/KX3h6K+l+hVykpTp44MP/IS+Wz9MAnB1xGv//geuvPp6agJ/iPsfeKga4HJRu3LNNdez7W9Tc7YM/QeeiYXzZmPs+Mn44fe/S9PKRyuvd3n9+Pv//QU3f+42/O6ZX+PWL33ptAdcGQS3cQ2XpoxUdWtNpfLhen/rFnxATaPVcJlXzP45RRKwgCtJwWsXJkibuAyUoh8BV16mFp1mWjwxRx4uLXNh/ns+zFvgoTaBl9CsQXTavXrQ7v3qIM0POMTQVKFqCpGJQoArQ6YnVLXXmX/Vm6p+Z3ZiVRStlKFEN6NY9TKqXt747wwKHaF5BrVTx0h4oWGyFZmBc9mWxljKNL7c9LlD/UAThFl0JClaPZaTpuFSZRnfZ8/uEP77+kFquGRWA3TpmoFp09rQHIbELA2BXvancvrbrVsTwfJlQcbhItBivrl5bpx9tg9Dh/kI3CiMlPe34889XQGXahigzyXdPGlqLP/OJDvF8abab6dYAqkBXGqEM0a+9dF+bDoowOU1ZmQJNY/vl/r8EdKoZ/rciAMuvc7NlVSeNtHitPDybxo6dIQxPzYvN1n75Ges5PFn4Uc//AF+9vNf4uU//QFXXHUVNVYV5wg2CjYVUMM1HkNJpPHqP/5GBtq2lYDr5f99EVdddTWnqTjg8tN0cDH9m6bg+uuuwW9/+xw1ax7GA7vWaKYWL1rADcTjJoVlNLc7//wxBjjVCbi6dcNhsirKZ6qUYOunTz5JApDhjNWYRX+sLEx/dzo+9ekbcf9PfpQ04Jr57tu4ZOpluPuuO+jD9j/VQJIkoUDMS0gIspoarh69+lrARZkE6Qv/6IMPImvtWkwjmM+hz5/p06kaNpmZANfCzZuxkID7RstSqNfVplMkAQu4khS85rkwOdfdkXJqt0gta8yx6siMg0cg6MKyJV5Mn+UlE5MY+Fwk3ojReTdElqYg/L5aRhgOFkGBrvIyAi6/YXySr41JdYG7OqqQ+sMx44e2i0GLF33ox5atbi4eGAi6T4RmkmE6NWuBwFJTOnqmvhVNyVEg48CBEA4fDpMO30fTUCcwqbOwSjxn9aWTBrhULT6XwqNhxtYpxJqPSoyG66yzs+kg3hod2Q4HKDVQf+YhjWsRA1gfPqy+QNPa1gpUTZAhBrVmXAiaJrBzpSMtvJEa5VIuxEUhyJxFvhk2tVwJpA5wEZhw/J636QgWbS9Eu2z6OMbH8wbEo97kZX/aVRRAT8biunRwB2T7HKO9Bm5N+nRVwOXQwr9jTAKdMb16tmIdfPlPf8LX77wL37jjDnzvvvuMVkpXyUTwn9QsyTfqC7fcgsfJvJdNP6u4husekkj88Ec/NkBS14uW/fcvvIA777rHaIvuufdbZJx14+abPov//vdNmuLNZby/M3glpUJzwAISIoy9eCLZBDviPZJm1ObD1bV7D+OTde7IC/G5m2/Es797nrJXEGOBPC9+/vOfsc4/wAP3/xj3ff8HRsP10erVGDr8fHzpi7fimWfjgY9ZPxYrWvzqJoVtsXHdRowcdQGGDx9G6vl/O0F8VUdqJrczdor8tTq0b8c6zqbVSj4B11yj4foRNVz3f8w1XJn04bq0gqWwQYZjdZKEUwXg2roF7xP432h9uBKWnL0w9RKwgCtpmWqwBv2jAsj1BDC0nwdeLqx4qM60/4AL7y3wkjpWNLdATwYGHjs2jB7UctV1Y1WmQjEvifVJMTySst2rs2ZJnOAa8ijJP+ZQa7dkqY9O0IoXBmNidv55ETJOhY2mq652NaZEyVm4TevWBNcnjck+uWtZlyBN6ObNO0aq4mKav8gkLx8ZtQHnBkpQnzl5gMtZ/GvnWv5b69aXGlObQWdk0eQ0wywIG6hu5Wnl4eID0X9xtZiek/PMnXIqL07xl3TWcKnlZQycKvlIy2UBV4of/knOLpWASxttmw+W4L+rDnA3n+MmtVWJJPWpCC0gdhcW46J+7TCSQZA13zRnOg64PskYWYyZNYOA6zwGWTQvefWS3VzMbinYQm3Y9VSQx/DMr3+Fs885m7EffYzPeBDfvOdbmL9wIf7wwnOYxnhXPn8GAZdDmjFgQD8885tf46yzyEbIuW3f3j1k+LuD5Dxr8c9/vsINPAZbppmdfL7uf+AR/OKnT+G6T36Sc46fpoDFePEPL+Cxx54kCBuEuXOmE3Ap+LEbn7j8aixdtgwfLl4IAa7NGzcQEI0he+r5BFC/Rdu2rci0GsVm0rB/7et3cBxfdtykECHs2L6T2vrzcO7Ic/FbxvnKoP+a5p+O7dpSK+Yl4JpM08cNFaQZrQxz44033oy3336LvlfPUtt1KU0WfQzPUoLnf/ssnnjqZ/jud77NIMr3U0JRAq55jknhfTQpfLimSeFfaVJ468fGpDCHbI+X9qMPF8MCRFNIcqUVmZd9aiFB+QISpHzGariqv7j210mVgAVcTRC3bMJLGW0+wx0gWyHjqciJuc78dIYMhTS/27PHZbRDXTrGSFRQ9x1Vs4rH49IUKz8fR31U9YqT/J3rhO07PHhrOifUA27GVoli0+Yotm5xoX+/GGOIhEjny6mXthTOgrzx9dO8Xkpf4QMH3QS2LpBhlzuEUWQrMrLZZlSekgjlzosd6TfvIqSyFSwmbIhJSrB+YxnOPCOH5nTZHNwrr0j4i3rAyQNc8WqxAZJhRZLsGr+zePx+J5uTJHsWZgFX/MnZz+aWQCoBlzBSGQMZv7ZiH4MZh8hUSD8ujRl1LTJ1A8+5Ob4dYfxHDaiXD+uKbvkVrLXN2HgDuPiKX3fDpzFr1iy88/abdQIuVcPl8eLZZ36LX/zP0wwm3BFXXPEJarJyyZY7Cws/WMzAv5/A4489SsKMHBOHK06acQ7NDNu0boVLLplI648MvPv2dG7iLcXnPncTfkKNUyZ9ulxcNIvS/aabPw8RVn2WWqoOHdozvtWZ6r+3AABAAElEQVRaA8zWrF1Pq4rOBhQqVpYA1xVXXY9ly5dh0fvzuZnUmRuCQXz+C18kAcg7xlRxxIhzyeZbRMbV6bQiCRiijvvpf/U9argQDbCcCBkXb8VMUuJPGD+Oc49IQ/y473vfQVtS2U+ePM2wFIoEo3UrTk5uugzMm48vk9I+GAzgxhs/zXhh3RlkeRWJQl41dPN/IKGIWAyF3BbOX0DftWn4HkHY/Q8e9/mSD9crf/8763obfvOrp/GF226leeLpzVKYTf+5S3szDhcteWrB803o5bIy8GLhli1YwD5kNVxNEKW9tckSsICrCSLUIj9QHjH07d3bMsZVV1Fk15choYchiHCuMSbu9V5fPa8AVUgh0tFn5zoTSvWzJ/kXAdfu3W68O9OPbTsYDJokDB+tpnnZURJ00EftC7fQVHJylPNKkotw3iZ2xWXLSW2/2MPYTh4yYcVw3sgQRgwLG9AVIuCRqaYWBtx8dIhHKE+V2AixNklwYe6a0WWHQMvllM/SG9ti1fXkA64mNfuk3Rx/n2p2Iwu4Ttoj+NgXlErAJWGq727cV4wZ6w7wOzfqMuvz5eJoQsBVRpAh/61Rvdvg/L6tSQnvjHDNOc6pntpUfPDhR7BixSpjAjhwYP+6xzdeH2LA4/99+c8EGK9QU7XP+HhlU+M0deoUw/YnpkORRcnnKw64Xvz981i0eDGp1N8j0VM5zQf9jOE1jbGxvkEtVBtDhmE06ZTVK6/+A889/3vmvVeSRN++fXD33d/AK6+8aur60ycfp+Yr08j4Rz95gDG2NuPXT/8P82ltAKHiZj3+xFMEbyuo3QpzozALEydOwJQpU6iB+iluufkmfPrGG0nnHjQgbx2B3Asv/B4fEdgJlOWQ6v33v/8dAWUXBkm+j5uOOwyToEhEZI0iTd87b7+L3z33AjZRqxJhGXIHOI/xy+78xh04Z+g5iIZDvM7H+XINvvmt7+CmGz9DRsJbEA05oEo08nPnzDH1uYv3TJ02jfcQbNdImmdaOkuhglY/Rh+uPAaQ/gSfZTY1XM6Y39hZtIZwKn8KcHmwYPNmzGPGn77rbowexSDZNlkJnAIJWMDVRKELNAW489jKX4YzuisifP0ZxheODuOgLtbA4gwuzl8e40UGqMQvrshSE4Ric2n3LrNiUqm/tGY8y8qKBfHDZR6aiviwexewuYCBmumXdtagCD55Q5QDm+KkJF+H9RvdNM/w4yhBnMg4jpE+vm3bGJ2PQ6QoDnN3MYZtW6PUfMVwxpkiIyHwC3FxEqAfEeOO5VHrxg3JCukmX4+G7tRjaui515eHeoEFXMclJHno/SgpidEciZvN9Idq25YBR/NpnlfRoSzgOi4v+615JZBqwKXxXUDmw21HsGRbITfhPMYfy8dNG2fI14BicJa5LsCNpWJqZ/p2yMa4Ae2RT4Im6fONVrrZRzdacTA0SZi7Sjm0rBCjX11J763mLWmjDuzfh507dxtQI5DVvYfDZhg1TITcnKoCuN6kv9NFY8diy+ZNOHLkKNq1bYfefXrxXXeT0Mch0tD7riRAIyC3c+dOYyrWp08f5FM7VnSM9u1M2WJ5rZBJKc0jdH9ubo4BYDqv+0vJCLuFC/CS0lK0a9eOfse9DVg6Rm2XTPYzM0gcJI0//3pkssBnVcTdP+Wlajgmix5zv+J45XIDVHWNzwFu+qwVk/Vw67Zt3DQsNmyIfXr3gp+7ghECZ2Wi1ii/YtYlkyAjo6JMHmZygKvAZxbv8TE/0yHMueN/lEdLB1xx0ozclStxGTV/ooV3JH+8nU39ZkgzGIdrHp/lp+++xwKupgrU3p+0BCzgSlp0zo0iaQpyYMxzF6NfJ1mvH1/g63tjkyZipSzZodP/o2rSgF5WUkKyjSAnGe7YafTXqHvKEtkJqYVau96DtetkKkl2QgaMHDokSmpwgJuTtc0TCdVWC25pt2ZQg6Y5vlUrJ46Zi/FRLrwgjMKiKF7+s4cTdBi9ugdx7nkx9DsnA3tLfNh7xE2HbeDMHhEM7hlC6xwFt6wuy4QqcZIu0hM3gItfsriDGl9cnKTiU1dMvC86XTj5fJlPUXEUH3wQwvIVEWp1+SzPcvO5+9Cxg7Pgk4zSlTRDYrA+XMk//nS7M+WAiw1U/5V2fMWuYqzYWYhS+oNmkGzGI9DF8wJUEc4FIanPGdFqYKcsnEvtVpus4wv7kyUn1VVTTXxuSqRcx8+YY665MWICAFcdFgS4Hn3kYUPR/tq/X6U/1CTOd2o5/3EXUwGDnU3JE0szeTt2mCZGhTRLtdWxtmPKzRwn8KpaVvy4yqxZbjwfXaMUl0OcwCr+2znr/NWmqGPy4DzNGDdLT8zXqYvm9RPPOTKv7Vy8HOXc0gFXXMOVTw3i5X37Ips7pNKAVu0r8fYm8ykZ+RiDZz4B9nsU5qetD1cyYrT3pEgCFnA1UZAO4AqgdUYZBnbzcEfPiVMkkGDGdJO/hg+9+g0n7fgEZbpAEwWPzwTrqrxJA7sW5jJTyGuVbyaOBLOtzCPlX9gsTRbhkNssIGQy6SNxhNFEGL+EZIZOR14bC9yYTh+xw4fd3EWkiSH93zp2iJLON4R577tptuLl8RjJR0rRqnMEno7ZyKBvQ6aP7I4hDrT8HHtWGKMGhkzsmhPaHn8kyVTxhMySP6Di9VzDrHRmdqYxQdEk35KSFgb0P2df0DpDwDv5+pPUCxs2hPH6GyE+cy1KnD42bYofw4fR/IrvluSTzoCrnMLQsiHOUpi8NFpSLzg969ocgEvjNsPnUnlC8prCANbvKyEhRhClHAMieon4EmTQx7JDrh8DOuejT7tMM4Y5C/OW35s8viw88cRjeOzxp/Dq3/+CcRMmVMbhOj17UepbpV5w2gAu+nAZDReJZPROpDIJcC0wGi631XClUrA2r0ZLwAKuRous+g2VgCuzHD3be7F1qxt7qemhFQX6kTyCln9MGkASmyTjgEsaLmPOEC+Og5CcheXH5aMTsZyLm7Cmjeeask9TFzWRTU3JeMm8iEGwjCaLS0inX0KwlZcPjDw3TDaqCN6b78Zf/+ahuUsE3boF4Gnrgb9zFnp0dtEsQYt/mqMdc6FnhwguOy+I9ry3ar103lh3cNnDEGc0hUntIN9YwYr+v4QmLT7a9ufk53K9lb4auZpt0yLwKOOwbd/hZUBR0L8hAvqo00xGMnWMn2reU99vgfY1ayN0Zg/Rb0L+cQTQdGGYNNGPEcO9oIj4NqUv4FLbpJ0IcnAQ8PTxWabTu1qf7O25EyXQLICrohgNmdo8EMgqopariONAkGQNHmpwchnsN5/xp2RqaMbVE6vWYo+4SQzxz3+8iv/726u477vfxuAhg8nhc2rH4JYmTPWd0wVwtVqzzmi4ZFKorapUJPUmzRMizVhQsBlzEcGnrIYrFaK1eSQpAQu4khRc/DYHcNGHK6McGREv/vuaB/SjJQiIYdplUbLXcZ/bjB8aHk9MGhTiJgnSCoXpdCw/LWm3PBwovLIh1zU0sQgSbOl6gTHlZqYns5Iz38wBDTDmpG5q6YlNCdAnbP9+N0kz3DQrjFLDRaptmgvS1B8rlsewaWMEWblcqDAOzNYjmSZWTW42QReDLxeRUKMryUwuPTeIzq35HCqYwATkdu6gCeRe+gRRC9ajR4wAIUZK91MkMLZTfUSAS6gwh3SMiQAurU/0T13gVC3oFVi7pJSxheaT3ORDnwHGPSnPiRNDZKukc3ijK+Y06GhhlE70IVIuR7nRwPhufd0YN87HgKfO+5DOGi71Ij4SlHDhrAeUyXc4bn50inqYLbYJEmhOwOVUi6O2OgyT81nxgyO83u+4yZxzxenxVy0Mc06TyaQ2mWQhaOau06N5J6UVkuFpA7jow/UJ48PlY59np0/ZIsZhKZxPDddcmkZ82sbhOil90xZSuwQs4KpdLgkfFeAKcVcy31eOWImHgRk99D9xGXAwYUKM5m9aTGuX/8SkXfCjhRHs3RsC/WZJa6t4VjTN40KtlA69MWaekUnGJQKxYFmQQIyTE1VmGX5N0M6gFJ+o47k7II2miLUVGL8oTT/VImewdSqoJjjsVPqis5IlXcX5Ve3WtaJmLyWwenthJp7/Wwb27HOhQ7sY+g+IokfvCM4fFMbYwWSj8gv1klSjXL5hLiyghuzIUbfRbAlwjRvHe/o3jeTDVDCZP2yLQHdJUbHZ5W0IcEkSAqIHD9CZnhgtn/5t7dpRU3cSCEJqNk/mf7v3uPHaa37sP+AhbbJCJQDjxoZwwYU05azd37tmNrX8Jg32UZKibI8YM90ePdwMLO3hzr9zadoDLj7T0iD9UPifAVw1X9RaWmwPpacEmh9wpWe7m7tWeoeVzPra2T5s7iJPq/wlvdMFcLWmD9cV/foglxOG5nVntk/N45KGSz5cs5mv1XClRqY2l+QkYAFXcnKrvCscZghDevW3z6GWy+fB+/QtIhMtevWM4aKLuPinRsYZPWoMIRwti4ojWLiwCPMXFDEOCSPXj2+FYUPorMQk88Fy2lNpdzNKkLGPLLjKV0QaHJfQuRNBnFtkEM6kpXtUgtiS/ERvXpodpjQdL6aiPSnN3YDLCHc746xUyl1gy8PdTw/bLP+gkqCbZAQyEYggj+Zqfp7Xgn/NJg+efi4T/yGjYcxLrVV2DJ26kSnxcsZCmRZA93a8WdotXrtrJ8HZ2y7s3KVn4yYBidgPY4boY+IlZLTK4aXmUVVtcGrbekJuLKoxgCtC1r6166OYPz+GffuonevqwtgxXgwYIN+5eH87oZRmOaA104HDLlIh+7Flq6N94mvAvhzE8OFhY/7X+Nmz4l1h3nq+SkY7yccYTxZwxSVhP5tbAtrcKi6PMg6Wl0yoXbjh5WWXrn18MNTgLcgcuLllZ/NvPgmoBxrAtXUbLThcZLokQ295GAUFe03A5d59OpuNySg1Oz+6/0l06nM2vnnvvc1XoSRyjpNmtKaG6/JqGq4kMqvjFgO4tmzFHFoafMpquOqQkj18MiRgAVcTpEylBMoYGyXLVU5KeA54GW5Gm6dvCWNSZTI2sUzb6kw8deBgmH4qR+mnVMLAiV5ccEEuJo6vIMMwN/IiTvaHSI09c6YbS5c5WozBg6OMaxIjTbaARMWKlNdrsg+QwlefmaLwbfLE7zA2BUm1XkoKeC1yFXTY79WCuJ621dno2k9EWF+ZS0aNeYmXi2ynTQJf2usSze7eYh+W7iL9PE0L27AOw7uG0Z/+WV4CjDdn+PHE/2RhyxYfcvMZWT4vhs49ovjCdeW4YWqAPkAst6LKBQUEB++4UUgWYTICk4CE5okEDH37RjFlWpTMiizRLOxT177aW13lKItKFHCZxV9pjG0gw9lKEoNQgyT/plEj3Rg/Xs+HmVXglSolNOtXkq1h/QYPPqSvnaj7B/SPULNLvzlqGlPXT6rnZQFXsz5Sm3kVCWgkKA6GcbiUgKtrF0Pj7WzKVLmo4qsFXCfKxB5pHglwvxHl7Jc7SD/fxQAubki2UMDV1mi4+iKHdv0pnb6YmeJwzaMP1yyr4WqejmhzTVgCFnAlLKoTLzSL5JIAcj3lGNqvInilZmfiBbNor2/k4HVlZVFGoS9l0MdimoV5cdGFuejXlw5KFb5GpkRet327C6+/7sa+/aCJFmMSkW79qqui6Nr1RHCgwJNljD8i36+cnFwOXvVV4sQ2VT/CnV2ah21gPKwNG6g5InnBGfRJGzCAZlI0HXNVAXvV70v8lxYoAlsCXYqB4qX631ia8I8BYtTyHS2NYsHOPCzbS6YuOpCHqeHp2zaMKWeG0CUvilnz/Pj5M5kMTullm4G89jEMILHGTVcFMGlUyLDaGbUVweuBAy7MnOHGxk0O86E0lGKWHDkyhjFj6MclooemiCzxph+/ks84ccBFUzUCrtmzaRq5oqKi/FAsxwupUc3KVOc7nnXzf2NhrL/6e3ExNZHUQMp/zsd+qopU1cCmsi4WcKVSmjav+iSgvlZKi4ODNBXv0LEL4zBl1vmKidLaFVfL1pepPWcl0EQJCHAVl5Zj7+6d6NrGgwwCi3LGBG2JGq62DKx9hTRcAlwERqmawigiAi4f5m3Zglm0frEariZ2Ont7kyRgAVcTxCewUCLA5Q1gSB8SXHAEbOxAEaQP0rEiml7RbK5VvoeTtXLQMFGR+PXwYWDePDeWr3SRUS+GwYNjGD8uZqjSqxXIhYGCI5cWlRjtVi7Z7sxObJXs4tkm8ikN3vr1LrxFjZCAihRPXRhrbOrUKH2keJKmjk1JGljFzieiEAWFFPuikxwZiB42TJu/XYejmLklB7tLMtGJNPCHy7io97EeZwRxBhnxdu7z4FX6EL1JCvkyauPyqKU6b0gYn7wsiEH9GKxS2VUkhbXZuNGDhe/TTHMfWeTo7tavbwwXjo4a07xq8ozf1NyfFGOigMupSgxbtgEfvB/D7t1RdGfA7dGjXehBLasBq81d3xPydwSshalAlvnFZ+fArab1kROKqjhgAVddkrHHm0MCYhHcVxhGZk4btG7Tips4HKsrCqoyvHC85c4DAVfFq9AcVbF5WgkYCWj9cejgEQRLj6BzG5res9OVl4daJOBqRw3XVX2l4aJbQIqfr5+MLO9t3ozpfDetD1eKhWuza5QELOBqlLiqXhyjvxEBAxkF22aGMKAbCRi05dTYxFsqF6rGlO3EDAR8DtPsbfMWB4r1Jd18u7Y8WDkyHS9XZnjScOlIttgMhZKOnz4x87qO8B7iICxa5MKs2R7k5TlU12KMGzc2xgV+1fLryqTu4yIEkTZOvmryN/OTm12MdpVN4q2iwFeg57KoHx/ub40Ve/yk2Ga9CJp6tYngEsbX6kKzSi3s9x6KYckqLxZ+6MGWzTHu+EUxeSxjcBFItaa5RaXWkOgrEnHh8BEXSR5ofkjAJQrzfLavWuF1Vz31Z1i9RgEuyYmCKmbwZ2m7snPcRrOXTPdLfWNOTo4WcJ0cOdtSHAloCD3GEBSFNK3OpOVAJjeIFNy26nilK/UeS8NlANcJZ5287F8rgaZKQERc8vEuI9FSW25C5jJ8gOaEQKCFAi76cF3Zu5chzTBxuJwXqKli4pxOFwjuqr63ZQtmcJPEariaLlKbQ/ISsIArSdk52pkIctxl6NcVDLYbn2STzLDe2zjd01fJjEEcQDSwOgBCX7QUOJ50RKyJ+idq+cwsOpMlOfETEzEekgszZrhQxDhYWtC3ahXDpEkRxsJito3VcLFy2pXTvwidpyJ0/vER8XhrAVtqkYCjtF+ZOdnYU+LH4u0+7GK8pzbZDH7cPYyB7eXDxfbSjC1I08Dt24C//hWY+x5BFc0Ee/YEbrxR9aW5RQVbXqXE2BbFe1IS82GSIjL3N/kPi28U4IoXSPAo6yWHUMJpS83+EL/0dPtMd8AleZdpZ4AdK0O08KlaQJxuD7IFtEdjvdkU42dxkFYNAWrepcjiMzVjcZXBI8TjXh53LBVaQONsFVucBJx+x81Cjv8CWjkZnEDMOiBGwNUySTPar1uHq/pTw2VYCiuak5InQ/N2Bj6eV1CA6VxvfNLG4UqJVG0myUnAAq7k5GYWyEE6qLbyl+PMXtzp5HpXw96pTlGqzQO065ZfVAbBlsgzKlYFja8aG6TQUCtpyvjRRy5qg1w4++wozhlCrQqZAJ34hIm1WnAgTG2WAJQWMNIGeslA6JWKqY4syhlFV6Asi9SB8gUqo8athAQRGSTtEEuhFrEChTt2xuiTFTX/5sxx4cAhmV4qplkUY6mN+9xnvejdi4sjXpuWie13AFeRMX9siBY+LdtwkiuVzoBLY4ECa4fZOWkpXBH4uI5OfpLlZotrmgQ45JgxJ1IxmMRj+8Vzla+Xnw9dMRX1n01WAqmWQBzMe6lhVX90QL82AEii0UJNCjusXIWrpOHiziiHTqZUvTtRvo8yKdyCd/n5SctSmOruaPNrhAQs4GqEsKpeKrW3AVyMvzWoJ1dVTI5BnDNQaMxI1ZBhMk/kDwsUtXopmS6iBCrZDKDrpZarafXgwpEb9QoWrAWESCk4bnFPl7u+Vck94vUzg2XFDxYscCXzhzBVTgJconrPYORi+UAoj/qSAVzcSs7KpWmkMdNhmbzB3FUh4CjNA1evjuK9eWHs2k3TS4KtwiKPMYfMyYlC5pdXfMKNEcOlCqqvtFN0zjwcR2tZfIyAi/9lE2DKXMmmuiWQroBLjzNAU+Ny9ls/35MMLr5VV5tOMwkYx1C+rTXGlGJqNQW45M9r3+DT7JmnSXOcaZcdr8b825IBV8c1a3BNnz5VfLhSNWZKw+U2gOsdrkVuuPtujBbDlE1WAqdAAhZwJSl0mcWFycfdITuE3p2PA64gzduOkNShmLb+WeSAaEvGtkzRqKdq/KiaT43J3jSFg4qAjfyj5L/l445Rk+nhWWZ8zXhcS6TCj1dGoCoaJY17jaaKaTBEOcVZCP1kIhSWkP9bKeUkeWWR8TCTZhEOYD3+QERxLxIQmRQKoJnMmb8BcRVwTXeVljKuFskjFi+OYNOmGIkwHI2jymzTmiaFE70YfLZz7Hjup+6b2ql/UU6YYZozSopumjeWFRcbTBgHXJJl6pLMopQb/zBjJ+vjzy915ZycnATcy0gOs7tgF9VJYXTp0gZZ2XS4TqnMGt8WSbSUi24FNZeZsXahbToNJSCkVUtfk8mhmFS9/Gef/Gn43NOlSRpoThPA9fiDD6LDqlW4psKHS2sJMymmSNaihZ/LOFzvcCy+wWq4UiRVm00yErCAKxmp8Z4g41/4o+UY2I2AgUzuGiBCZMgrOMDgxwUeHCgi6yDjRY3oFcGQHiFjBtfoUaTqephjkMYhcnTQTlv08CBQ4UK96jWmGg5sCZHdTz5QAipZNCvULntz7LQL/EibJmAk7ZoW9VXXISpTdZDGRp9aKEtWhTRVXLLSi517PRgyKIzBpJv3kna+agoHQ4Y4w00fGB8bbEg1KASZJToBkp3GGwDHW1evjmHefGnjVKYTY2vgwBjGXuQyVPqnejEeb5tkRHExYLAPew9KJlF0ah9Gfla5kYEIRKTRi0tDMlZK5vk5EnKeicpUH1JcspaOA9SPAiXl2L9jL9xsVOc0A1zSgGdRu1zz/Yz3Aft5ekqgOCgNlwAXSZROzybaVqWpBDSvtESTQgGuTmvX4pq+fQxphpnv4hNXk2XtIi28G3MLtuAtjsk3WB+uJkvUZpC8BCzgSkJ22r0Okw0oj+aEA0nJHaeD308Q8c5qP9bv8RFsaaebbILUcF0xLIheXFBLO5Toollr7HI6Z5eV0w+EE3hWpotBZV1YsyaGPXsZtLc9cNZZQOdODplF9WbE6eGLDDDJIDKTr5SH/0z5lSxajYaA1YrRojJE9CeCDjfz9hMUyYSxZoqPnXEAIcC1c48b/2XA4o3bPLh4ZJCMgiETVLnarh3zLyst5STC4MXMP35/XGPn5FuROz8ERgs2uwyVPQmcGKdMMoqhI+NyOTfHc6hZw5P4m7OiarFjtxvzFmVg8w6hdaB/ryDGnh9At84O0YJTX15LGQTVGK7cpR2saK2554Q/VU9WNpUH+T+tFbG5gGCUcdV69Xahc2c6XQvgVl53Qm5pfIANojzK6at4YMc+eNIRcFF6WdS0WrKMNO5GzVA1C7iaQag2y4Qk0KIBl9Fw9SRpRoZjpVB1Lkuo9XVfZEwKtxBwkR7earjqlpM90/wSsICrETIWCApTmxMge0OGO4Q+nQio8o6zE2495MZry/00J/SgU16MFMIEZgRZk88OY1gvMj5wYZgIxNFVR45GsGZ1CNu2Rxlvy4Xevb04sN+L+QtEi84dVMahGjrUhcmXuJArAgvdVGU57sTjKtZBxrjKZNG6RmQVrK+u5OgscOSWKijJJJO9cjp3RUlFn52bS0CXYF4CAGQ9XLnWgz373DirfwRnMF5WbbfHfb+kRRMNrhawefn5DtlGFbRgvLto5iMxhAi8RELhoxZQcbYE4mr6WiTZ5BTcJjNCYP5iBmPkvwwGCFbdqczD+FFBjB4RpgmoeZgsS6QgZEUjc4mem2TM5teadAcfg2k/N/SER0wyOfE7fZLx9tsuHDwAjLkIGDeBjJP5PJuuRCK1tjJ+kA1iA0toUrhr0w6plmlS2DatTAolVgu44s/r4/NpAdfH51mnW0s1N7RcDddHuLZfbwdwpViwPs4VczdvxVsclG+4y/pwpVi8NrtGSMACrkYIK8yVsgIdR2mu16u9Cz070UeK2qf4onY/zQinf+TBRprJ5VIjJf8kUZhPGxJEnw70b0qERp2jpjRby5YGMGN60CzEPdRE5OV5CB4ycfSIBx2p1TpaGEVbxuK6+mo3OnXiQpuLbY63JhmtiKjh6TslrYiCCivulYCRAg0HpApirbPJgKFzySS1OUSUYMqgCZyP5TTWgUbAg25fVUzclGu8FRW10k/KRICrlDFHlHIVFMwkI/mK77zQ3MpjRFfyjBLGrMzPfK96fcVtJ/uDbREJyfzFfixcSq0gA1mrVmH2lTHU9I0eTsAV1zxVtluAi6ahYmusIR5VX/cfY0yu3bsipu906uRBh/bsm2SVdMz8XVi+HJg5W6QiMZw/0oVx48D4ZLyzhQOu3QU72RHTz4fLAi71zI9fsoDr4/fM06XFmhtaKuDqvHolru3TE7ncJXWmJM1qqUgVJoWbtxkN1/V332NJM1IhVptHUhKwgKsRYgvRGb64KEBzwRDO6U2/IoItLfKlPWEYKBwppsnfdg+W7vAgEHWjNc0KpdmSH5evhn9SncUyv6OFMcyeFcDyZdy57+o2wW25mc/gvJn8Tv8e+vwITAwcAEyZ4kJ+PhfW1OjwVpMErMpJOCE/l0yCKj5kc1zn9TUcDdNMjfzqTMYMUKql2lby5ora/wjUqQxpYHIIBJJNKraievVmIW1aKYWgNhjadN2oHy0xscNsYR+Z874fW9hfyH6P7p3DmDohhIF9o8ebxfYZoEl7QAO48gS4qjdaP8uoSf1gURAffBCk/1qE/cKH8eMy0K1rhcaR1xwtBKn9Yzh2LIYzznCjZw/5mbRE4Tl1lhyMhssCrpb7EE/DmlvAdRo+1BbSJM0FLRZwrVuD6wb0Pe7DlSqZc70jy5u5BZvxFjc6r7/zLgu4UiVbm0+jJWABV6NERn8aBhbMQBnOIhW8tDPHiqjJCnpQTKXR+m1u7DssTUQM/XpG0KdrFO3pw+WtNBFLoDAOmiUlUXzwfojmg0FkUFOmIL6tWrswbKgfhw57TdypDh1cOG+EC717M0+OtI6eTcBK18vssYyAy40sMvzVRDTSesjkTtdEqW5RcGQFSa4r8fITkgFcJOUQ4BKrnlOHEy5LyQG1SkQZYi0U8UZmtmjiU5L1qcmEdZdpasFWD96dmYENmzzo1okxw0aHMWRImCakbLHzKBsGXDQf3H8gitffKMP2rVGaiNKUklqzyZdkYfgwkW8oK6d/KEvl68iuJQtQbbCA69R0XltqfRKwgKs+6dhzzSkBjestFXB1+WglruvLOFxiAzMTVWokpaxEYDN38xa8AS+shis1crW5JCcBC7gaKbcgyTJ8sTL07ejhApfarGUeHD3Gf6Q4LyQAy+RiWSQXFw0NYdJomvQRbMVijeOrkr/Vnj1RLFwYpA8X/aOy3fTX8jCWlBbQXGiShY9uWQ7JBOuvWFQkbONgK+ZCNzL9YZoNlhrAlW0AV+2NFH288YsiMHPL8aeWJIIKDx2hpC2rql0R4CojABJphiFzYL3kE2YCGdeST1MOCdRJmybgKLCloMktPrFfbN7sxrvvZpAEhbInCm7bJkqNZQiDziDCVmIzG9JwiQqtkOalM2eWY83aMAF0DO1pTjhxYiYGDmR/USZVZrDqv0wpLfKPBVwt8rGd9pW2gOu0f8Rp28AWC7geehBd16/Fdf2p4WIYG2e6StUcr/UYNVybCvAG58brrIYrbfvvx6FiFnA18ikHGMk9VFYKb9iDJQszsXc3wQi1CuvJthelNqtTnwgOFnlxRs8wbp4WQF+yznk0EiacnCWxAE1xSYzkGYzrwjGoXVuaMFYoobTYjGsr9HnoiBvLVnkY+NeD3j1IpjG4nDT0ZYaqPYsAxedjQDCTeLXqYm52johePUifLpFsnFBLXistWSWtO1XzItkwNWT9FOtLWif5csVIwShNl8pLVVI1BbYiFZTzqodfA/LJTlUFU0V2TamGi2aFq1Z7MH2mn356FUyTzHvi+BCG04/LdBmW2yDgYiUE0LdvD2PF8iCKi2kyeKYPZ/FfdjZtBk19U1TppBqsstmQuAxTVBULuJJ6GPamZpaABVzNLGCbfZ0S0JzRIjVcAlz04bq+X2/kcbHD6SyFyQFcc2hSKA3XddaHK4WytVk1VgIWcDVSYjIpLCwsw64djPu0OJN+Wl6yvQHL1ruxiox7mV1c1DwBvbtEMGZwEFPODaNTa7qBNmoQ0eqUhmD6oAZD9wqAHadMP56ZAueuXuvF6+/6Cc7c6NwxiksvKceAPmX0eSo18aty83LNolxkHOVl1BJRCyaaeZMx/yo5mhDne/yv6By04DcxtmjXKJLFDJJjeGs4/xhzP4I2LYIzSUEvRj2n8vGckvsUGAwo6BjbnkG6WNHam6Sqn6QksZNnhP9ihuAiM0OslNVll1RVmMWevS7MnutDQQHbxXJ60Qx1/LgwevSgqlSPmNckArhM+axTiEFXiU/5zAngKvpNUnVL8U1hBrkuPBahWWiU5C9eZGWpcscxWDLFWcCVjNTsPc0tAQu4mlvCNv+6JKBpqaUCru7UcN0gHy6j4dLkl5qknLRJPGcTARfD9FxrNVypEazNJSkJWMDVSLEJ+Mj/ad/eCD6YS1O3Iz7kk7Z9+/7/z955gNlxneX/u32beu+7KlaXLNmWZFm2ZTuOe2KMA//QIaElkEDywEOAAAkthRBSCDwPgQBJgEAMKe5dkotsy5LVe++9bL39/3vP7Kzurrbc3b2Sdq059uq2mXPOfDNz5rzn/b73C9uWU0zGB4RswpicTRyft8EDsnb3wqTdgOR5uwqFUttQ8aTkvPfd+jdvWQDX1h1Re+7lGK6NKBaOyNldt6dsyqSkNdQ1OPAjwCXgsHef2bbtIZs8OYTbGl+45pv70EW7zq1P6obM6JVIWYBKsMMDVprsp3ExhPljcJP6ofejfi+ouLim3A6ys9QUxW4liDFrC/IKar18b+k7iuy2bWuWuLmsjQVMz5wFwB7ED8Uei3/87Wyf49wdJh/Xjl3EAwKUpiGYMX488vgiJLU9+xYNuLQx15MeujrXRffv8lnPq5kOHT6Ssddeq0NhM2M33lhJbrQygLvf0Z51IABcPbNbsNfltYAAl8SUJEXNFd7r4tXBv7pdVNvFf3pd98UK6GspOnuxwuDdVbCAzqEPuMoSMauuGcXzn5huVt8+/Zkv2uia2fbJT3ziKvSs4yaTKCl/DoZr/JaN9oEpxHCVGHCp5RBzkpW799kTCJk98vFApbDjsxH8crktEACubltYk9qQNdTjOng0aakzMTt2NGxvbYnZqo1RYqlCNmKE2cRZGZsxO2s/ubTJlkwhJgdQFZZ4Bq9uPsw/KXyKWfzH/Y/4Jyes0bOn3nlAwaZtUTtCPyZNyNucGVnYmDQqbnUu9moAMupk4CJmCHC2LWxTpuZs5kzU8NSRbhRfBl6ufXpCyw6OzaKOlNwLYaMSyLoqbkwYUhN/Qt5Q7Q7xPSyV79nYUZtsLwvkcE/MIOYh5kz1K4+YHiZXvNDmtm0Ze+bplB2A0RTgeu89MZs712OkuuoPWBFAjEoSLqeK62vvEHRcDiBRWeF7Vze/FQ+4uurNVfodMmvtukZ7+qlaO3EibcuXV9kddwyEFeYH/8B70LUAcPXAaMEul90CAlzKFUjO65IUN0ZTH7q0rr72xpDeNpR3zyaY8XdDbGxvjdGP99fzwwdccR62EyeNcCEB+VDE/uTPv2hjJ/ddwDVhNwzXdKkUKr1MaU9CiJtxxc499kRjzh757UClsLTWDWrrjgUCwNUdazVvq/FA+azKwrjukYvrtTei9pkvVNrq9cQX8aCN8jIQULPw3oz97N1NNr8ya5UAjtHDiHOCHBJ7c4acXduRBJe64WQYsWpU6nr+kBZw4w9PNCnyaOBVfqwmVARFp4tx0iQgDcBTvqdojBXYqI5Cf8U/wh27RyNZwJD2lPx8hj+P9aP/uPxVSPEQl0Nwhp05H7JtKPCdOgsAHZN3kueV5CXrkNGjUjFpKfquV8WeqU5NrrvRTVouUaHZLVsy9uILKTt+ImfDhkdw+YsjYEKfungqoORuBw8gpHIOMYzhedwE8yb9km4V2u/fgMs7b4cOpey1V+vsrBium6ps9pwyc+nfhMp7WPo84OJaLosR/8i1q3vlyhS11D6wvzLtX9ut6GquJXVIKMQikQAXY3Jvz713h5CbUWMulSUYu0t5hlV/mop5NNiAmBefe22fxf579HpMOsC15xhu5R7gUjqRPNfjpz/7BRs3ZU6fZbgmbIPhmiaVwssAuFjsXCGGKx0wXP336n539DwAXD08j0mom/JQo00YErL//J+4/dnnB9hZxDJEGwlsVI0FYNydsVnzszaxIW8jKvK2eG7SbpwFSGGDt7dH7Zm3Y5YEAM2YkLGHlqRt+EApGhbZoebt/M3FpFgIWXAe9Fk+pMmzpQezYqqkMOie/LxIrEErmpqYeV8W2V7BZprsqp85dPEFuvRebYj5cuCIbXWMaxHyeG5V3M4DLscT03YPro4zcJvr6BgFLsSSCWx5sWKyZ0HDV/otbSsn2vp3MrZ3r2KrwrZgYRQ1QV+M4mKH/PPQRE6sk6fytmZN2NaS2DjZFLZx4w1mJ2Nz5+SsAzHIixUVvqP9/g24dDCszuOCe6GW655ZnRJ4J4gx623pq4BLx9XEpFvMtVzL/Puhp/dat+3Eva02dZ313srdbv2a3cG//xvkUsiCkwCXxtreF+8sNhCfqVyElQmEi0p4YlVVkou1iXuzSoCrhHX3/tiDGrpjgRbAtfsYC1oxq5k8xnnVZAFdn/6zLyDo1YcZrp1b7APX1eBSiPyygsVLWBzDtQuGC6HjIIarhIYNquq2BQLA1U2T6XkkVTjJw0cyKAEiVPEf34vbf3y/3C7UJSynUQ+1wgSs1fDqvE0lfmvOcEAJroaTxmbsg/enbPAgs9Wbo/byhqibGI0alrX3ISE/DiYEb7qOC+NQnoeu2lfRxCqH5PwpYsckLc64aqNHpW1AlYQmpG4Yd1LtHVfYxS/+w7eT8e/ihJImfSTFflLeWw3gWPlmDGbNiGfL2123pGz+zAzb+RW3bl8xW1I+LHcxW4AtUGKrpgt3a/UD7SFscY5zIeA5BJESyeb3tqgJnc40wbaeaAaJop1bpDpS2BmvpUbMvnFDzp58Om8rEcNobIxaTQ3nZGTI7ryD2Lr3ZDg2am3T9w77SRP9H3B5R8ciK0VAvRvH36FhvGu/LyY+1snNMGFIsuKg/GcXgc+l10snh9ejn9y1yuRZbHYZdHkpJ+Y96tA1tJNu6TS2FyiKYXsxm6U642GAWz3jW5YFrgoBrpLV7I1iKfqs67VKMril6vQ1dO77yqHq/vddCsvLFMM12j2/ciyE9nnAtb2Q4Sr2AVmk5bkfV+7aC8MVAXAFMVxFWi3Y7DJYIABc3TSqIECK1cY0SYOTp3P29mpPiGLN2oTtOVxhjczIQwCqyGBwVyUiCLgKThgESErmHKD6ybtSduvNGTuJu91rm2NWR/6u2dW4Wl2XIX9W553RZFUS7kkSbslVUK57p88kbOWqMmKNkI3HbXHm9KQtW9ZkI0bGHOPUeY0d/6p5MQv1PORJpAuQ6ygGqaMatP8BBCFeg8U7BiCcVp21JcidDx8qBb5Ln+pitZIgFiwFKKlgohrG0hcHXr1rAvg0AeTiMAflcU0svdb124GDDKqrADkwTMuWEj83XbEU+v3Stry9uvrXb1v7e3F77hv/67a709j+Azn79rfz9oMfRuzYsShgCXfCYWbz55n9/M9myY1FbJ3OcUd1tK2Tpn3AFQJNVwyodCC77WatP0vURUAc71biAvUQLt3Ur3VLV/OTgH7fBFyeVZLNbrdxEBeX6xUqIWvUigM3X4J2CxdDrlAHrt1mOMf1sFtRrst4s294qU57ALiu3cuqO0ferwHXri326HQxXHIpLPYBWZx1QoyFKxXDJYbrt3/HlixeXNyOwVaBBUpsgQBwddOgGguacNdLXWiwUwdCtn5dyIYMRYwCF8GnVpXbkSaoFVQLQ7yUDc4jDpC3sRFyY03K2vABAmBZe/iBlI0FiKHa7gQlBlbwkI5potR5Z8RsKe+V/uIEwYgpeuedsD37nEQleLxTRRkMyvseztns2QCWHlLzOsazF0K2l9xidXUhGztaqotZ3PzoXxd9LDwCTfzPs389IGhQFSuoxDB5k5DWUxFxASnAVgalwzgukPI/b70F/aGeLQfCdhjwNnSA2SzsOXqIBygEzDaT0+rxJ2NG2Jrd/Z60LQXURn1EVtipy/UeBmfDxrx9+ctmK1bimkPbGcDhAFQrb12Wtw99KGs3LsR43bCfjOAAl8RPOL/l5DnzRUo6OowGHipiO+vqwiRAzttI2FU/f1tH+/TH7/sy4NK12+gYLlIJOMDV9mq+PBYXsG50QM8DXGK6gnJlLKA1pEYAl5gtgd1LBrBedCMAXL0w3jW0q273fstw7dhoj143iTxcmmSodOdB6e3R/r+Miorh2kUMV5IYroDhat9MwbdXxAIB4OqmmbNMpLLppA2Mpm3/dlwD30QgY2DIjp4M2w/fKLddZ6qoESAwDGZostkgmJu5oazdCuMSg3HQBPiB96ZtzChgAgCpRSqwiPFFm8ilUCyXlPyiDE5btxInBeBqwmVR06shQ3N2730Zm6JYKdXf3aJBOxmyNe9EbcWrAjAhkiln7O7lKauZ1NzntnW6WAW1Tml7HHztxY35v2kD51+mrV2RImESxlBgIuHn8fJ/ZH+5JK7ZGbOX3olZPYBKbn1iBJfPT1tFmbOKnQaIbUAlMgngmDs3Y+PHOeP6tVz+V86tVCC/8tWQPfEk5wKbqJ+Ta1A2vNvswQdzVlNDX9th93QEsvNpxEWkbCiX00G4YEJiOtDsJ6YWyI6iGa8Ha3tFwHPbDnKOwPQdOx61WahV3n5rykaRm+2S89JeBf3ouwBwXXqydFn4QE+T/gBwXWqjy/VNALgul2WDeou1QL8GXHuI4fIZrhI/rMRwrdiBSyFzh0d+K1ApLPZ6CrYrvQUCwNUNmyro/+RxXPouNNq8KWHk4EP22P9GcGcL2QXidbbWJ+xotMxyTLRDowAGMDDDcCW8fkDaFo/L2OhBeVswJ2PzZsvPX9NsFf+1g1m0t1GrfzMgkCa5FeK60pSssLffjtuWzQRq4642d17abrgBX3+Yrp4CrvO1IXuJSfva9TGLJQAO8ZzdfVvKbpjXRvBCXab7cj0USFMpY3t3bO0AC7dBm3/kJikXSQl9VCDhGAJ0tSpU20DdLwMA3wR0DUJ8RCzWxJFZu29RyoYDTnwTNntTodTYqobSffBPkX/KCmtWPwFNTz0dsn//Ni6O+8M2DHfC669HLOSenC1ZJLbrYl9bdmW/ekDiemL61gEYFQc2dbLnfjkahkpFohONdfXORbSM+LaOVs/FKL6yOmKrXosiUoFYB0Il9yLcct1UuXG2tPiueBMArvZPYxMLQmLCxayJ4H2XnfZ2D1q3pVh5AUz9aeHhSpcAcF1piwfttbVAvwZcO4nhml5dkIerVPewGC7JwjczXL8dxHC1vW6Cz1fOAgHgKtLWeoifOJYh2XGdnT+RtGED4nbiZJW98GLCDuDqhvO+GbLhyYnc4GOYE5P7N8I8d/zArN0yJ223w8gsm5SyCbANvXZ1YywSSFHSwDJASiYbtzNnxKrgzViFciBPfynBlcP+aDLSYXE/amDjjT++8RbFe3sHhcFXV8cdAJiAO+Fy4qImjW8DuNjzQn3I9hyM2GHc2CSMMHEsTA7sUmV5Ab3W0gkace20fOFybUliP4oEvEQ+WnfY6xsEmL2zh+SFGxXzFrZyAO31U9POrhWFbo6OadPRFhyPPpagCMxJmENemsrN2C6oo9lzyMC/syFkO7bD1iVyNv06/maQBBtgyBm6tCfYTLFuT78Yt6Mkz44Qt668XXcuTdlN84hD4/diAZestXN3GNAVI+dV2GZMyzjXypECbvrxXVQCwNX+yUxzgWa4WDUWtHO1tb9TP/7WXdYcaAbBiihxjgnch3TsV7oEgOtKWzxor60F+jXg2gvDNVMxXDxctXpSssJ4wML0iu3EcDFXCRiukhk2qKgHFggAV5FGEwOzfVOjrXqu1rJNEVvzVpVt3lwFu9Os7KTRjlisclz5xsyHwYLdSJHZ/MbpaZtN4mMxM/dfl7ZpKBK6WXsvp0OKz3LMEMpVlQOqHNjBK8+2M9HfsSNqYwE98+dljLCfdifbAg5gNqesVV6mGLLC7fJWKyBFDNcFYrkEuMaTR6styFB81qsoEb7wWhwwErKBxGmNxKVx2U0Zux41wghudhmEI8RI6fcE7JeYt4iLrSLGKZshdquJCRK/ISsYZiXKFTdhah50HVOGrHhD2LYejNrBEyFYrbzNnpSxEby2lOZ99I0DNgU/tWzTwzdyaTxy1GwvLoNpjkM5tSZV51uUEFvN7/igSwHPT2d3T53Pe99u89ho38Ew8vkJ5OTJkcblJHB/2+K0LSKlgNwKiwVcql+51o4BtmqJ4Ro5PIeE/UVxkXbb76dfBoCr/ROny96br3Sx2NL+7v3wW91weatLMa7Q+zLyVF0NV8oAcPXDS+dd1uV+Dbh2wXDNIA8XgEtzm1Kx1JoLhJi4rBTDxWLtIwHD9S676vvX4QSAq6jzlUcaPGfHDtTb268mbdXL5fbGGwOsMU08TULiFExymJSL5Ro0MW8L70zbsOl5O8vk/CbcuYYADCJMCu6ZlrY5IyWLXlSjnW7kXPEAK0o87AAXo+3JU2bPPhu3N5Binz49Y+97XxKxC8VytYIErt6zqCRuRGhCzNgMWJhp07zJPSOdV9wubvrmIQjNKAp+Ewh5E1n7f/l+wnYBzCaNNauB3Urh0jQBsPeT96RsKMd96DDJg3F3PHs2bCNG5G3WrAyuboAAZkdNEgBB5UG5whLKhMsxyDb1uObV4hIXR3VxIAqPvvulQEyWYxHr0ywE1mJL9DYAh2EHdAYORBbej73t1IpF/MhhHz1m9tLLEeLlvPrHkWPtzjuydh3nuBkiXlKRM59M5tvski2av9BksSFkb+K+uX5LlHQD2HEirOiN2AlhFWd2HkDFuBS2NAGI08NX58tJ8HfVh5Yd+8+bFsC165ALfBszZoiVV/Cw7gPHKtP7sVRXUjSj/5y90vZU8ZK1AK4wJz/RnGy6tC10XVsAuLq2UbDF5bVAfwZc4/fBcM3yGC7NbUo1jAtwSdF5xY499mQdMVwfDWK4Lu9VGNTemQUCwNWZdZpve0mWZ8kPFc8nbcvaiP3VXyFWsYfJ3Qjob1TojEmynQUswBiNB7jc//9SNmxK3nafDsPoIA/P5H/2mIz9JImPR1YxmJRgNNGglMYVLwXSkNBELK64nZC99VbUiUfU1CDUsQzgN5TG2rSnjzt3hu0HP4ojrhC2pSRdvu/+tFMRbB33pS01fWyevfPOFT7KlfDxl2L2Xz9OwGCFHZM2GKBTiyLhQGKVPvxo0qaQd+xlttkEkBBIktrW3NlZu+uutA0mV1ayIWn1F+otTpLGciTPJXNfC2u2fn0U9jDiBEAW3ZS2SRNxkaJhrdnroaI+Km5MAEysm1537orYm2/FOE9mNyyEVSTJcBzp+LbH3nwExb/Q3iaA6bPPhcktxoQugRJcvdmSJdj3VikAqme9L7WAzCO4ZZL3GUGVnA1D4dLF4Ljj7R7g8s9a73vVd2vQdVCPzOfh3Yctz705ZvRgAFepUHbvjltXRAC4emfD7uwdAK7uWKv1trpWgzxcrW3SXz/1Z8A1YTcM18yJVklYQengFmeSlZCwGC4nmkEert8KYrj66/X9buh3ALg6O4sAhByxARmATVWchLyslDz5RBglurAdaopZcgJoShNuJvx2AUBxmtiiBWn76K822qCRZi/viNtxAEQFAhk34gL38PVJGzuEjbUcWoKSg91qqG9wQfIDhHIYcSXjrjiiKpIvDyYXmAbhtqhDwOXQYYQxXibW56TZwgUwKsioe7LvRUzXqbMBVcSX34jZv/9fwvYSw6aEvgNR1kvBQElG/mcfStroyqw9/xxxYDB9w1FtPHWafsm18r60TcbNMlmftAYmzTEAl3JMSQRkP2ITTz0ZB0CFUX80W0ay5NtvR+IdVzuBJ8VSnYEt248b3vlao92cc1tc+VrM3lpDHBguRXcuS9mjjyRtBC517tw4uNZDg3Osu3eH7PkXwnbypFz+5H6ZB8xmbeENuWb3yB7WXbgb15pYG68oVo73/LmX7jJchfW+K99zIWCrFOkZjh88bvXn6nCdrCBOrsIpXV7tQ9ZZDADXlTsLAeDqua11rQaAq+f260t76vHRL2Xh/+KzNmHfVhiuyS0xXJqFlKJ4LoWK4UKlsDYXMFylMGpQR48tEACuTkzn8l41pS1bl7JYJmaHEDd4aZXhspezE/moHassQ8ab4CfAQMRw7UNNbybiGHeh6DdoiMQnIjYY0NEI6yIXvCW4FN6Oq19Hbmjtd8UfevRobF3EvAlwZdESH4gqg5uws5nihhxT5XbVfn4dzfvzFaSAA0D1MFUjR+QcK+X9qm0vbat5z5YXsXTrt0btXx+L29so7GmwH4sgyMzpyODPTNtNc5B6BxA991wMt8UICn24/UDpjxyZt/e+N4lbYRogm3b9jJPxWeyWmj1yFBu/HLWdyJsPxCXxZti3RcSEKfGyjuOMEkbjMrnq9YiLqaqrQ5UNgZDTFyIko46Q9yxktwPSfvmDjVbT7E7pHY33b8sBFPuG3TCxbUAIY8P6sNXXh20CjNvixSSyHidX0WIr6tl2AeDqxG5cdOdPn7PDe486sYQxowexaBArCYPcSatd/qRLIgBcXZqpZBsEgKvnptS1GgCuntuvL+3ZbwHXnwO49iqGayKACy+FNtOV3tq4RRa+IWC4emvLYP/eWSAAXB3YT/d8KpWxC6eb7NT+qO2BrTpzLmx7kYBPI5sXRfxiRzZhR+rjThVwIMmNR6FKNw658nmAjXhliABus2EwOo2ALZTkbda4jN0/P2kxzdI7GFREp+u/dBbJ9wyMCvE45QA6pzPRpq9OTKG+3nL0p2rwwAKGpM2G7X2kCxqgBXJcgmTN7LtRxDS9jYz5syvjjnVKATYlinH/XUm7fraXdLgWF8c1a6K2dl2E2KSwVVbmbMFC7DOvgXiLJouhTCixDNePZoOkOOb9xITt3kU8HIBrxswcTJ04OcgqbLaZBNM/fCqGaEnetm7M4obHbySZHjCERL8jIzZhStTmI8V+55KUTR0HCOY3CZbEwHM9LjSuGDGlAdgO83bqfMRGk0dtFuB6JMxd9yzXvV4EgKtjeym59LnT5+3InqPIoJuNBnCVBYCrY4O9S38JAFfPT6zGrgBw9dx+fWnPfgu4YLjGHyCGa7ZiuHhgl7IwZwjhlbJSKoXnieH6SBDDVUrzBnV1zwIB4OrAXllm92my6J5HCn7jWxXE1uAaiJvelu0ITSDOMAc3vLNMtdcAxk4BxIbj2nYdan7zkeK+aW7W9sLq7CEZ70DGj1RaQg+4oU1L2WLkzEVzd1SEwy4Qx7PzdNQOoRA4gAWfGSRLHoMCYFvQ1SvA/d4ugwAAQABJREFU1VEHivxegGsdzNaLrxLHhq1y+QiS9Hl7z21NNmMyCDPv8XhitfbjcnjuDDbCxW/c+DRxV0lYuSwqf2XIwctX0C8aHXnPi4tz470eIrKJGLtT2PR52vu/J6P29uqsHd2b5hwhg4/LZoxYupGArZqFcRtfHbZJtCX3zTGDs7aAc1JNXFRP2Si1L9bwGAqJz6yI2yaYPSUmluvi4gUCl14f/aMo5evVAFyyPU6Njom96OZYyqMqTV0B4CqNHft7LQHg6vkZ1HAbAK6e268v7dlvARcM1/j9G+zRWTBccSZMeuCWsIQBXHIpfLIuikthEMNVQtMGVXXTAgHgasdgmnCmcHcrD8NGwbi88EIZcVGAJ3JcHTsZdoBrQg3MBlhhD583H4tYhHCum+ek7EN3J20yan3bj0bs9V0xO4fLHnoWNm1U1pYy8R8Cy9PZgJImBmrjcUQa9kStFlYoTk6mubBmd09J20BEIArHoqsJuGS244iCvE7c1K69Ydgqw50yCwBJ2yDAoXgfBywVmyRAglGVsDlN3I1+iyMjGJXmeQelGXd5v1KHkgq/vjZmT72MCiPCIO+8lbP600kUoaGekLXPDyCObHjIhs2K24jr4jZrDPnAOF9Z1CXn1WTsnhtSNsQJlqjmHhSOQaIWz6+K2469uDtS9zIEPRYDvH01+5ZaC5soPGEtGxT/5koDrjQy/seIATwPIzscEZThVRcVIYvv9ZXZMgBcV8bOfb2VAHD1/AwFgKvntutre/ZbwAXDNeHAJnt0thIfA7iYIpWyXGS4kIX/6O/YksWLS1l9UFdggaItEACudkyVgjXJwm7V4DZWBn3x5psRe3sdqImBQGIU103HhY98UweOEceFfPmmk1E7jfvcguqM/cIdTVaNaITky0/y20m+x8vJxsG0DMDlTmCjw8JPdbBhK/fH7LWDMRtZkbV68MRASKSHZ6RcEmUnptBcQTGAy833+UeDcY+Kv187wEEufudh4Y6dAnACDMcADAdUeHjS381vUzm3GmrrLQ81JkXCGPk2wFHFFSo7DQh4nCTTP3w2YVu24Nq5CwbyAmxhFjoQ8GMArqrREZt4Y8wG1cRs+AAYrjAxbth/6uiMffB2XAxHZ3se30MflBB6x+6IbdsNw0VC6+tnZZ1LYeFBQNw5WfsM51FCIuWc8x7bvrlixRI2EkgWRuqxvBxk39a4hR3oxXtNXI/iLvn8xrjtQr1yEWzsMvLIiaVthfR72AamsSYqinAA0hPs7WEEgKuHJ+JdthvLLS4PlxZ14qxmk4K9V0eo+zWMO0G3amHjhhSLL6wuKfly93buvLtSd61n7MmSc7EiEen18RW2pmMUw9VEOo8BWhkMSr+1QL8FXGK4DhDD5QCXn9qj2MlBV6dLsvBhXAoRzcAz6WowXBqX+rKnSFcWDH4vnQUCwNXGlgIxUj+rjDbZlDERU1Lg80z2NdE+S86qMWOyNoXkxicRhHhlY8y274nZO5tidvJE3sUMPXRHyu66BZc6nmRyuxtA/FAFE1Y92FxhHPF4qpZv/F/cK1jP1h6N2Yp9MbfQo21rhmTtXhiuobgtFiocdgW4JGN+6hSTkfo8KoEhGzKYSUQ3Zv+ME4AM1OCoR3mtpAB4sXgDoqsO9oduemDG+/riZs3vMgh7NJEBWdOYODL2ciUsrO2SHQq/YMOz+F9/57GYff3fErbveNQyKfKfncceGUDXEOzNsVWMidvYGUx4EGxsAvASVmdh+n4zzOJH72uyBRPZju96XOiHJOglgCL3zovekBw0nwWyDx0CgONqqYTRkyZlbdaMDOp5PW7R2TSDwkkqlUIhMUqcHCeCti5H0bk8gBvs0xsStpvkyYu45u6YlYaV1cntZYvsf4aJ41bEZQZzAFOAXcJxvSkB4OqN9d5d+yYBDGluzm7CpEuMoMtcw1kZ+bx0P2gMVOnq8tct2QibHmVgSIjyLuE9erkBVxol3kYeVpWM7/ovDGhs7UvhTBD808ctoOu136oUHtxsj86dzKK0D7hKZ+wQi+YrtxHDdVYxXMUxXAJJKrW1tS7WXOEPHc2dtK0EzLQg6hd/W/3mAy7/O38bvfq/FX7nf686VVRv233Vr4aGBuLciVumb0HpHxYIAFfBedItlkYoI46gw2RYqgFlrMUTe8Wz3E7BVh0jDkniC3JN23EgZI/BuGzEpW4TohByqRuJkMayJRm7971pJ5veiEvgVCb581EmLMMdsA63OD2HKzsRcdCD7jRqOutgz/bixjioLG/Xw9BMGyp1w9ZP8U4BF5PbI0fz9tJLOUBAjtxUIVu2jEluGYNCV7MHZ5OQXSD+atvWkJ0gdmny5DzJkZWEuMBgHb0t7GZzW862JJlqqm90ioRllRWwYh27FLaqmi7vP2T25X8K2ze/F8W2GDsH7QcYDCHRbkNZ40akJAoWKR+GYmQl55Gvo2xWNTBvd8xN28fvbrQbJpB0mnPZtnsaW/WwKvy+VfttPriJmL7TQbmiChic61BYXEF8GWxoBtGTIcSQ3Q0Al4hKsXX7NfqvTomyjsRfNFqJzdxkqKeV+ZV28ipFzR1Ho3aC1cAaknQrFo65ZwlK3siYZasNoRGssQBpz6oeW8XrTgC4SnBa3iVVuIlNCY5F40ZayeRRTc0yMCRZgGMo7fpK5f4U4BPYcoCrBH3xq7icgEtt6DnSBOgKh+g/x+09pfzWg9f+YgE9l/ol4BLDdRCGa45cCnmI64FcwhJSHq6tEs0onuHygdILL7xgY8eOtenTp7sFT79bei6fPHnSjhw5YqdPn7bGxkYHfLTtpEmTeFZXOpCkepqamuzgwYMswl5wwMyvQ68TJ04ktn14S92q9/jx4wiH7Xf1apshQ4ZYTU2NjRgxomW7l19+GUGyNfbQQw/Zdddddwkg035B6XsWCABXyzkJWZZVvkyqycYOzthYJvGeW0memBbyOMBi7TgUdfFYU0liPBi26f+eTNjj/xezA8RcsWBv4DObXp21RTBcg4fACgGyBgDObrshjStIzrbtihK3FLLp5KCaCvvhckvRftv5s9wOterYQPyYJrsVMS/xb9stOwNcGrL27M3aj3+Mkt9RJQM28l9Jnp1OtjOeFX7lHrh0as/ePPvHuPlDthB1wQcezMGSsaW38NJiOf+NX0eafjeSBJpxDnbvokudBpMkA5NiucoqKlz+LdcX3wB+BX6Fza+KY1qxLmyf+QYD5yrAFW5vlsQlcSDobzgbsTKbx4UvlMRdDTGL8okCWuom52Co2QOLUvbRWxttJgAiDwul5uSaWU8fT8Be1gOE5Qo5AoBUIRdAv/2WNzKZJl4FX/jbuFc6zk+nEfV49vmY7YQNjYEHVW5HWGPJjYDlnoAW6sxxTdZfqGX/iMtV1naly2ullP/qJGBPjlUuhqUsSWx4nj/OnHF6LllA6G5bAeDqrsXe3duX4mrNAD6S3HMVrIqneE0xDktV1lvs6uj+l13Vep4xL4xbYWfbdf8cXG7ApR4l8YVOZnheaZFRg1lQ+p0F+i3g+gtiuA6jUjhXKoWlBlxczdzLKwS4zubtkd/snOHSHEWlrq7OAZ6vfvWrDmwtX77cAZ6BJAeVurLA03e+8x0T8PGBVJaFmlGjRtlP/MRP2Hve8x7UlfHl4KQIbH3pS1+yHTt2uJAAnwnTbz//8z9vd955pwNoanf37t32H//xH/bGG284rxYBNm13yy232Ac/+EGbMgXfEBaqv/jFL7r29aq2/DpVR1D6rgUCwNV8bnjOIpSRsgRCGeNwT0smWeEEOAwclLODiEP8+I04QCFsFxpwt4AmuYXYlm3kZfrK35fbabYNgQFCPLAqAVlTZhLfg5hC9TjYAb6fiELe+QswTTAHMWIMJgDY7of5GEtOKvecbuf6cI+85udeRws+nQIu9j2PG97GjRk7jnT6jOlhJNbDDvC1bS6ZglU7k2WQ4YELIBs2FCYPwLB3b8ieeDJiRwFsixehyncnMVqIKHhuje1PbxoJqdqzL2K7AB0CZ7OxxbChOk7+GDjSuMalmpIWZ2CNEtyGFw6ch8NMBiF1aeE4kgg5/PebUfur70Rt11shyyFSYg0xCw3nXJDvzIhvcAU3T72vmJK30TNYsUW9MArb/tPLU/ZrtzbYaMQ8XN+psxaQtX5n1NYhM1/bELLBALUbZqRt3pSsi7sSE6aEzRByDkxVEY8lAN3hZARw0kQy6LXvRO2tt2Mud9f48Vm7dWnaaiZqEG8+ma6jRf7DLgJcDc2AS7Fvlx9weX1Tb1vOcPMHPYs4hU4E5eKPRR5L82aqSqWlbu9jj/4NAFePzBbs1IkFMlzkYnvEUqVwbRDYivPev2472dX9VIrrum0blxtwqc861iQD8UBc6Hs0VrXtdPD5iltAY3N/ZbgmHNpoj86ZBOBPMFUo5V3EE5v712O4yMP1kc5VCgW4Dh06ZC+++KKtXbvWVq9ebUOHDnVAZ9asWfbggw86Busok6Kvfe1rjtlauHChA1oCVv/5n//pvvvTP/1TE0iLxxHZAmj92q/9mgNqy5YtY45FzANFz/LbbrvN5syZ40IFkngAfeELX7D/+q//ciBMQErbPPnkk/b888/bRz/6UfvFX/xF15+//du/dcDsc5/7nNs2AFxX/HbrUYMB4Go2W4qA51yq0UbASJ1Ekv3tzfgSw4ZMg7EaglvVy8Rr7SV2SGxWCNeLBTVpi5zP22PfSdi2I1HLlXNj88AaXJYjFxTqbmOJvSIP1A2z0zZmGOwWk3s3jDAoCoQ9QL6qGTWIOGguXvTjvPU57gxwaUu1x6ILyZkVfwUAUeBRm5JhcrEXJuy111NQ5FkbTdzazUsS0OgR27rVbOXKEO5xIbvzjpxdPx9WwrEel9bjquW3Y8dD9uwLcVu3PmajSIR8951pW4h0ukeWeIBLA0sUwHUhkrB9uF3W4n43LApAjedsEOIbrWrnw2nA0TffStjXnyuzU7uo4yAMDKIOIQCQXAkNdiuvBi5gSVireDXAcTpgi8Uyyel/5P4m+7lbmwDTzQfPLjsORuyZ1eRWI9ZKcXa1AOnRuITeszhlk3AnPQnztRWlyIM637iV1ozN2IxJqExCzXQGuupQpTxwgGPCvVDxfmMA2wLZPSrs1lPAJUZO6Mi1zAOsE6jYedeoRkzgKdxplVctDioehmDMIFw1xWBezRIArqtp/Xdn2w5wsXAmoMVahxNNamfYvKIHf7kBlw5GTJ4YriqSyF+pRZ0rasRroLF+C7hguMYf3uwxXA5w6WRp9lKKAjsthmuLF8P1k10wXHL/+/rXv27f+973nCuh3AVHjx5tFXjkKIb6N3/zN+3GG290boLnWdEW46Xf/CKw9NnPftZ+4Rd+wYEsgbWdO3far/7qr9rtt9/uQNPIkSP9zR249O+3U6dOOUCl+dHf/M3f2PXXX++227Ztm33oQx8irGOyCchNnTrV2gNccmvcs2ePq1NujVVVOO3roghKn7FAALg4FVlWNTIIZQyvTFk4F7EXXi+zPUdRhEsgxIAr4LKbUk697b9XJew8E/PrmHxLSKAJivro1rC9sjpmR5hgK25nFAIXs1CvGzaamCcm6I/emyT5bt5WwpAdOCKGyRzzdReuZiOZuHrjSgc3ReHX7Yw/XQEud5VRh6ppd9GIH2rrsrZqZcpeX01+LFzzEMKyRYujNndOGfFfERQBEf6A/bnpJgN04Q4GcPPqKuxc8/UM6DlOvNdLK4lr2xqzEQDN229J2fx5HrBUgmUJQGRRLKyLldkbTeW2roF4J3pYFc7aTRU5W1yVtkrq0eG6Q+b9QeLnvrG23P5tdZmdP8lEiDYyu/hdAEu0mEAroCB/gYkSwLdqFmBggiYQuHhyPj75YJM9shSQJ4DgjIEwyfaYvQgTJbe5QbB2yq0Wj+bsPYvSVg1Qem191N7YHHNgUz2pAAwunZ+xRSR1lupkh4X63RhHvWLTnK38c1doMv+7DiviB7bvMeCi/aYm8p+dUwN5J9yBVkn3Cu0LZG3YErEN5B47h41isIY1EzlXJJceh51YPLxqJQBcV83079qG9Syol9ogKCuO645LUn+Vj/ZyAy4NS2AtWK4sC2pGHJcCdYsZoK6yYYLmW1mg3wIuxXAd2gDgAiRIAIJLr1RXn65tycKv2EoernPk4fpNMVyLWtmt8MPhw4ft13/91x1Y+YM/+AN76qmnbN68eXbzzTfjMXTexVNJqKIjIKOYr9///d93sVUf+chHTODKB1yzZ8+2D3zgAzZs2DAndqHYLQle+OzUmTNnHEiTIMZnPvMZB+z02+bNm12f1A/1qbq6+hLAJRfIFStW2A9/+EPnAvnTP/3TNn78+Ja6C48xeH/1LBAALm7tVBqUkU7aNJiNxqaoPbEybodhNspwq0sQ03Mv7IiS+v7vK2W2n+/HkYgY3ICiTs6mASpefSVqP3gxZvvIvVXF9qOQR1+AS+HPPJyyOxZl3KT94OEw+ZtYbYHBmY4AxUQmq97KqYaE1kWDjVipelzdpIgnkQ3FQnlo4eK2RQGui5tf+o6mL9Sp/2l75ZWkUzPMEDV+110JYr7KXLzUzp2AThZwFsxHfOIOABcroB0WfsJj0CU63o1L4WBcCmfielmFbHtGDCKTGQ2lUSi+ndlye7Y2gTpW2AYDdE7Doo2MZO3+wWkbT8yaio5Y7zYRG/XdrQl7fgMsI/U2nA1b1rFcbIN9FLslwKUEzBVI+Q+aShvEbmVgKCWt/3PLk/azd6Vs1CC2a0aeuw4BrNfEYbIA1ti2AXdAnVcBLoGwp0mwfJx2Rg6mWuo9BeM1ifxq996ccrFe3X0iyGW1gfOpcxoDtCq2T+Cl7Tnli4sFe/YUcClx97btEdwbYV8x4qIbszaDPGluLnWxhU7fqc/rN0fspVfkIgmQRYwkxXXPQpotQAjkNtwlXUxfC5rv5NrotKWe/RgArp7ZLdirYwuI4RLgijE4lzNO9QXFvssOuDRT5x5OcuxOMMQFoGpsCkp/skC/BVyK4ToCwzW/hsVp3FK8x3/JTC/AtWrrPvufQw229OH/x2LyHFe32CeBHrn9+eXAgQOOhZJAxW/91m/Z448/Tvz6QhcnJXDkuzu2B7ikGvj3f//39u///u/2u7/7uybQI/EMuRQKfOn3mpoaF4OldpcuXYqQ2TIHygSsFAP2rW99y7klCuDpd7FqAnHr1q1zLNl9993nwKDPcH3+85+3+fPn29NPP+1cDCXCITZt7ty5zoWxvX76xxq8XnkLXPOASyuaSZIcD4rDbDBZt3zUNmwP2xpEMjSPlEvhLQuQ5Ib9Wb+LRL9bYpbEha0cd7aFyI0vmpm2zZvC9sUvx+014ndCAJJcLmw3L0zbn/xek82diXQDE38VsUc0wA0n5kMPtEsnqPpWbn6SF98Ay6KJbg2uh3PnZLjRtPvFfUoBuNTWnj1Ze/yJRtwHU4CksP3Kr1TAaCVs+zbAzkaAJe0uWCBFHdrvqqh7HISONZ8HaMFmSSRDX8cY2AS2QsgOb2mM2rMXsCXHMwAQep54ObkV3jcobRMAsj6g0di74XTEfrwjZm+sQ4J/bdROAXqz57EhDFee/rsgsEaERVI5JOeRZgYklY1A4XEUIApgeyeiJR+AUZw1tjkPF51RIuX1iJi8swMbA7YEgG4AHM5F0OQYMXvPvA4YA3ANBzQKsJymvYnk8bpnacpGI4jSgjG6sIfOp8qZsyS0xi11H0miB+KON38+rouT6CvXQoeFn3oCuPTgbQSEPo9r56scRxaQdPutKVu+HPYQ0OTbtsN2+cHVAYh95iXynmGjgeQ1E7upnU9hl4GwnvcShziFa9Ndk/wkFyyJbYRxweT5cWk7hYfqG6azTnTxW18FXDq0JgUnAtwV/1NqEYUuzBL83AsL+DFcFZy39lywe1F1j3e93IBLHdOtKUXGDJO+MiZ53mJgj7sc7HgVLNBvAZcYrsOoFM4TwwX4cc+GEjwg3DnARZZ7+bWdB+xra/ZbxeRZNg4lQc2/Fi1aZLfeemuLuIU2FyiSO99zzz3nXPoUz7WYRMmKp5JghVwI26orq640K/ArV660v/zLv3QA6o/+6I9cbJYAj2K7/vmf/xlvk3OOIROD9c4777jPH/7wh+2nfuqnHOul9rWthDAUsyXQpyIlRAlxCLTJVVB1fvnLX3bA7BOf+ISdPXvW/vd//5c52gLHkMn10GfNXAXBP33GAtcs4Lpu5lxukhSr9RlL47c7dUwehgOfM+5ziSVosi2lwJFMriWYoKKJ+V5cDU8x+R5KbqUpTOarYJ82bcxzo8Xs1dUkAGaBJotb4i03Zez3PpmxiZPYH/dDLdzoodblMMIk7Rz1r1gRs7fejMGIkT9rKHm47gUwzBB4c11x//QOcDX3hE6JTRPoevPNpJXhkrdsWcLGkESYFFdObVATaOUj04BeTNEAlMM9JU0FUn4MMftOCGy10Cu4HiJAsqouattgFKnZohz3vIqMLatK4V7ot0IfaXM/cq7/viJh//dcws6RJ6opGbK60wibEH+Vp4+5JtqrRXq9gT/aVlbdOMIf1fNCduMS3BoBXHcDcCbCRracANpTjrETZyPEb5lzKxxJcmqBirO4h76CtPv6XTEPHIO4dOhyJ7x5XtqJahQCX7+3Hb3mACFr34nYiy/FrBa3PGW5uX5BmmBXgL7k+jq6KGi0J4BLndU5VZtvcg3leH/zEgRB5mdhbDtpr+AAdK5Jm2ZPvRi37dhh0EDAIUyk5cN2hutzANf0e0gmXT0h68RCagG/5wCVaRYjKgGvI4jfG0hsMM86V3RaBHK5LByjqFiw3pa+CLh0nSgeRsILUhiNc/+Gi71xemuQYP9eWUDjVgrQoTWcci7cvnLarhTgcjL4Gq8Zr8qIe3GDXq8sGux8JS2g67Vfimb8xZ/bhGOb7APXo1LoGC49EDt6KHbXoswuuJZXbdtv/7b1lE1behfpbaY6wCVgIkl1xWAVMkF79+61xx57zAGoLcRUKBZKcVN33HGHPfDAA07KvRDQKMeoJNoleCHFwk9+8pNuW5czk+4qJktATmyXvhOTJcAlUKbvxVItWbLEqRIKOKltMWAS6VC/3n77bfKpnnJxYffee68DfV/5ylccGyZG69ixY3b//fc7Zkuy9IXH0l1rBdtfXgtcw4BrHhP3JquvbUICOGVTx5I3yZdMZzLuPWzbxOBwLuRupsmjfveGhLDt2Zu3v/1S2B77X8UE8QP/L1yQs/e/X/FPIefKNRsXLC+HVRcDCfWfAky88HzcthA3Uw6gi8IA3XWXxCd8kQ3vougp4FKElI6hxaWR/upg0rgTyo0sUeA22HKcXXTbv0zlNphJwWoBtsIETcUTceeWowbUjAyntkX2HQF0bW6K2Dlex8AYzi5L23DnZufXpu1Z9SFu61tPlNsTuPlVAH5Vz+GD5EXDTTNDrrAM6orpWsAADJerHHtVDs7Zotvz9v5Hc7b8hqxNBxwnlAnZ64XXABXp+NxX9MkDs54NjpyMEOfliWbIThLNWEAS41Eu7k47FV/E/Kx+I2arXlFsBImkcbucNjVr731vipUtfmxmQC+pkWZ6BLhUEfvWI+Bx+KjX17G4y8olsDvF9XtN1F4n1k0ALkugRx3xdAJOUp9cAOvaUBfChZQFCs5RkgUJnWdds5NJDq7rdSwLGcLZp0kavnFTlNU44yGXI3k45+OiJ0d3utWybV8FXA24KGvSrkmrEuEGpe9bQMNbhgu+iWu8HKTcl87blQBcOkNaG2lkRUQLBgPi3LTBpSuz9JuiZ1m/BFwwXBOOolI4H5VCAIkWPkpZXB4uRDOeOhuzn/iN30FxueMYLr9dMVZim+S6l+KBLRZp+/bt9hu/8Rv2Mz/zM44VUz998KTtjqJcKBZKoMhXIvTra+/1r//6rx1o+vM//3PHYKk97a8YsT/5kz8xxXxpPql25d4odk3bCohJrv4f/uEfON9Njh371Kc+ZXI3LBTwaK/N4Lura4FrGnA1kiwqk2y0alQIRynvVk+eMNwQZ87m8J8N2z/+Ay5yqTAsESv8sGXVNcQAjTSbgevhwz+Rgk4G6AjRdFYYNBUjs2kjE903oo6Fqcat8bZb08SGsW/B7j0BXJKAJ68e9ZqNHU18WBUNNtepAdu9LWijpav85kp7v7VsxKQF98FGVm3CBJyXk6hXk2IwpJN/V1JduZyVwyLFm+OXFKyd4TsRHmK5LqmeTu2DVfyPp+P2yjsx58qJR6IpJu7IAaSbYbnqz8CeoBgJJQlC4TVM0mrEMm5/j9kv/WLGbr0hY8NgW7o791Vc13mXczgEAyZZeDFdviEKDrqTtxyaK/v3w5oBuA4eirDSJRESANz15CPrjHFiXw9w1TmWsKIbsvCyowP/sJNAXA9M+p3ppL+tfqL9k7hXrkIU5sWXI7Z5A+zWKST0AZ233YpNSZ+wgzixXXu45jmO2QCw0cQvNgHIdA3Pnc05WJ61EbjqbtgQIQCZ2MgjIVtyc9bufg/gukU0plWrRX/oy4BL0L4cwBWwW0Wfzqu6oe4X5d3ylPr61nm7UoBLQ1VTM+CqBHB1d7y8qicwaNwtHvZLwEUM1/jjW+0DC6Y4hqudWUCvzq7mIis377YnTuTskV//OKIZizusTyDKPTfZQu/l3ie1QLnyfexjH3PKhX/4h38ISzaNUIMcz7UNDpQp9kvS75KNF9gqrMN/37ZRsVTf+MY37NOf/rQ9+uijLveWXATf9773OTVE36VQ/VDdGzduNO0jNsx3KRSzpT6oDSkZSglRTFpQ+qYFrm3AxepADpZrCu5PI5g8cl13v/CUEnhZuSJi//TNKC54TParcC3kJh+I2+G4CYhkTM3Ye+9O2wDcsjpkM1padlNlxyIcRoSjsSGPex8y88M0ELRs5N50G3Cx//ETxPY8nzUEceye94ZRtMHBTbPDTopLBir2iFKeUCD5pRvrK/VH9Lko9gT0hWK2vIkMboGnIrbjRMThoSmIU0xhYl6mmKCuQAAVn0Ep71lUHp9YpbgqT665Fre2WpjABlT4zh7DxRDFyHyTEhsDikBvCQQyxk0LM7k3e5BYo9uJqRtJPrBuTYBp29lc3Wz+4x1Hqh8utUFn38iV7jj9PHYsbFWAvwnjSa4KE9RpoQ0BrsYLHuAqOg9Xc98EtLx+qiK11Nz3Thtt8yPgdtOWMG4PCVhXj6EbAlAaR4654cM8YCUmLU6Mo9Q5FW8oRkvqiLqubr8941wZJZW/clXMjgOeFwKAly4lObgTMWnTXjc+BoCrG8YKNu3UAjlu8CTMDg9EVPoEuOT428X92WmNpfvxSgEujRVpxhu50kc5/gRiA33DAqWz5bu5Jj2r+ivgmnBULoXVpQdcegTiabNyM7LwhCM8AsPVGeCSS6DAk5iiIUOGOIl4MU3V1dVOIVAufFIKrKmpIW3OVge2JNuuWKyHHnrIsVC6xjT/890OpSCo9+Xl5e57AbXjrHr/8R//sRPDUC6tu+66y7kZ/vZv/7ZjsH7v937PuTFqPykniuFSPQKAitUSo6acX3JjVKiGAJjmXRLMkOuj7ybZEdh7N98HffnYrm3ABcOVTRG/NYpVeMQiugIeHZ1IPZTEYDz+eNT2MbGsrCDGC4A0qTrHpDLn3KvGM0GVy0bRhcGz9WxfX7QuPQFcJ0/lYVqIE2NCfPttRt/oVRdP1VNnUrZpM6wVI/q8uZXQ6cyoC/fhvVZhRMPrL8YAkIDyEEjRQR9C4e8FVAZ3KacVhzEO9unuWWkT8HLbtD6sSz6JtFL+s9cQndiK6IRiiBTn1gAQOw/oknvhaVi7+lO4MhKTlyfeLDYI0YzhKAtOCduNyNI/cmcS8ZM0TAwdKOy7WpNpm7/Tcbiv1FH3hn8KfsuCnLIogjimq2093h7t/MuGVOeqdNV64L4129kOd8a2GpyTBFMJXCQYsNvZqqC95g7RkFwAa2HnUoqpqgiz6iZJd23afFwFe3X2VjnI3loTsb/+qzgrbCh3cm2niH2T0qJiE/VefRuCUMn1MHZyJQwzUZPb5KHD5EEjH938+XmbgGBJE6yX/sYDNkeOaBbW6KzxLn4LAFcXBgp+LtoCWlQSu1OBqI9EToq+tYtuoecbXinApWPW6CCmLwvoKo9f5UR7PTfZNbmnni/9FXCNP45KYTPgKunJ06OXBRQHuEgn0xXg2rdvn/3TP/1Ti6LgqlWrXDyVwI4PrKQ+KFdCCWR85zvfcYyT3Pnk8qf5g/4UGybXP7FNUhkUiJNEvNgv5ct69dVX7YknnnCM1Mc//nGrqalhEfyMY7tef/11e/jhh52oh4Cb5N5/9KMfue/k0qg4rS996Uv23e9+18V/Cay9+eabDoQpJkxM1/Lly51rog/6SmrToLIeW+AaBVwfsutmEsPVBBsD4JqIKMWoIVITkx31T/cft2K59gAGtm9TolziVKblbSquhDHiiVSlXOlc9d04VX4vOtqvQ8DldmDvS9gjfI5hHZRbSfFag2HgYjATXZVduxrtx4+fYyUlBN092KqrkUelHgGGHOBD/cjrIc173eBxwJa/sqJV4m3HIvY0iaSbiNVi7HP2uHNGym6cCDjyD7KrTvB7A0mS63BX230oZKsBX3L5S6LiuHUbkvskRD68lxiMU0mnFJmvQllvSIjJPjF0nIul89N2zy1pp64ntsfrO2/8w6cfWuV2DBnfSejD/aSnmPtNgiWoeIFkNNjKTdL9UES/W2+iWvXHuaAu2UcuD1pRb68+2SfvVgI8YOP61LrC1p/YQKBmL7bYsiOL1L/cWhM2Z3aMQT3qAc7We3TyiRV/ANfmLVH7O1Q4V6z0kndrMjaIhQSeL3aWhMhS0hw7Nme3LCV5dbVsp/xuZu9sjlqUeMCx4xFEmZWx5cjIy+VQ9tT90NsSAK7eWjDYXxbQPSZJdI1jZZKC72NmuVKAS/elhjvFcCmerQzA1fu7tI8Z813cHZ27/gu4UCm8nhguLdSW8hzpmnaAay8uhWK4lIerY5dCMVw/+MEPyEH6EnHJJ23Xrl0uV9a4ceMcCyXXvwkTJrDAvt8BHMm1i00Sy+SDLXVf4hradtSoUY6J+v73v+9AnEQzFHeluDC5Kv7SL/2Si9XyBcUkviFpeIE7X4a+vr7eSb+LvRKIEwgTu/XMM8+4mC8lYta8S/v+4z/+o2PmfvmXf9mBPr/eUpo0qKvnFrh2AdesuZZCDl4xR+URL0fTUAQuHCDoiT0Z7fTgTgFkNKPEm857WJVgYtlRd1oDLiXjwyUEZgPNCie0kegATGk7dU79LWZ0O30mY5s2NQIK8jZ3HlQ7DFc2g+sJg4ZkhHWz608DQRT6XmBLVfvlMAzXS8i67z3lxQWMGYRU+/SM1RTJcPn1qN8CKJvJxfUcSZCbkJKPA2i37wgzuY/Ykf0AwDNJ1OEAUrA6FQCuuXP5mxOyW1AqXDIvBQCW8Ajb0e+MYr4Ke0oDOg4NnGKWVGQqFQEw/aYBTK/+996vPfu3SQwr7SQqylDzo85Co3VUZWHDbbZ3p5Pfd+7M8sBIEi+Wol59G+EhEbc770ggbeuxjB1V3/p79qW+WlQbn3kmSlLFiB1jlVBgfdw43Acn5W3fvogdAAAnElmbPxflzikhrgPSGhwhaTay+xMmwvLiyinFwvtRZZyBWIjsW4oSAK5SWPHarkNXoudGx33YzG4V3mJ9wTpXDHBxsDr2FGOSQFcZY7m3CNkXrBD0oSsL6PnYXwHXhBO4FC70XAq9iUlXR1v873IpXCGXQtz5H/k1XAqXdAy4VKue/UpyLPU/CVMoXktCGAJaAkx6fgk0STJeEu/+XKGwR4q/Gj16tMvxJcGN3bt3O5Cm7VVHdXW1A08SyNB8wi+q+8SJEyZ1RIloqMiNcQ65w5Q3zN9WLolixAQEC2PGxKRJ0VAsmJQOA8DlW7ZvvF6jgOvDdh2AK4P7m+Z+yaYUCnZpm4woxSBcpnpTBDWEsVRv0ZyW32Q356GtAddAwA+Kcbg27t4b4mZXolviEZwSnN9AN45MuzT3Ry/gE3dQEVgusVlaodFAEwVkRWLIpzOotTtQYgiFf+07jQT8UZLw8vm60RmbOoK8Tp15rNCo2DOxSTJoOMKgBHVymhiutajdrVoXt1NIrDMvAAQg7Q5rlyd+LoxLYVko7Sb9Ofo2dGTIJf29944mEhenyEmlOC+PVRJAdCi12SwR2ohEkVHnuKSy6B+/ZiHRSNQdo86pQF/Lb837dvuFOlNcd5LOl5KjBsZOrxe212DcmNQEEZEKXPoSTlpdZ4cf+V9/AkMrViRJeJxBmESiHHL19PK5Lb05ZrfcEqc9ti16IUD1K0E2apy7USNENGPvPuLRjnOvwHCdB0xv3hpikA8BtsK4C3rS78kmzg2nrIptMJ1VT8zaHTCM48ZwMXhVunp7808AuHpjvWBf3TIZ7qkkCy8x7v0oEx/Fp5bo8iyZga8k4FKnNb41Ss2I13LGddkpKH3fAv0bcMFwLRDgIu6be7J0hacq17BzKTwO4Pr1zhkutav23aIxr0pkPHPmTJevK8Y8p7D4XjyF37V9r7mEtmtvW7Xjt+X/7n9uW08xn9vu2/ZzMXUE21xeC1yzgGsaebiymlRT0iQpdeIZgJQRgyNuQt6u2QufPCUaE1SNWKksYElKvBGp9xVZdyHgGjBkoJsUv/wS/sorIzZnbhb3P5TkYHm6O37J7VA3q8MjhY9b+iWXOoEtDRDKrRURld5Zh9nHuR1SXxr5cx2agrHF6LRXfPZJwCzbnDRZ/Y+x+lzbELPX1iRs3YaYnUD0Yw8ArgE3xcEkJx6D0uRARCgyScQcBuDfSezScaTdNdkfMRylwiWNMDAIdSQ8hkoATq58/kCnvuiY/VL4fdvf/G16+6rBuLG+ERCKgAaKjgJdhX0orF82Pnoy7MCmjmsCQipzYAlHcdxhlqG1n8Dn6fOGKErK9uzAfbKSeLoY4DEXRc49hBxuzJbdFm+OYyu8mAtb6uA9m+uBrsthxw6zF15EnfO0eoVs7e6oY7QqKonxas7XNgPp96WLSXjN9axrezoy8BMneHnOOgV7frcunooOOkRXmB2f44CP7DnqwPvo0YM4Ni9heYc7XeYf1H3JwosfDVQKL7OxS1B9ksFO92FCzLV/7ZWg3lJWcaUBl+5zAS55L1TwUNLnotj3Uh50UFe3LaDz1G8ZrlOb7dGF5OFi8bHT+UR3raLFWt+l8KgVxXD5TWhckJvesGHDnEphwBb5lglee2qBAHBhuYyEEFDXmzAsS6JjJeG99MkLKULeLg1oJIUsyxteYL0u3M92FoZgD4xUHQzCyBEhq67OIHJQxGyT1tsCLsU0bXgH97r1EZs8hQnv0ubcSwVAoqtOy3f/1Mkkx0q8zehyN4H195FiXhMBnxqIygkGFbvFTN+BqEJcpi+0jf4UfyTXPYEu36zhEK6HZL4VK+YDG8VOqf6WGCmtNjvGScyPmsnZrr15e+alcmyGKEciZ+u34dpIxMU8EhIPHuAJcDQ0hklKjXgGynlnTsecUuRZBEJmz8zYvUiRV1VRG/1TX4qzsn/0l+e1EUEMCY1IZMQxbuoUfZPrgIvr4r36qnP7o+fi9j+Px+0EUu2jhuTsfhIPv/fOlEUrpOSI6x/HfgAw9s76jJ3dT36OskauJfLqpFHMHBB17NbMWYrjAtj3dHbJzAtvCkRUSMi41mzzroidOA+NSn8lIR8HTE+BzRqOG+FdpDJYSOxcgjgu59rIcTjbd2BKBxq5J3RNcOq7LAHg6tJEwQadWEDDYj1jkxaAYh0sAHWy+xX76WoALomIpHAb130Ydzejbt6g9GUL6DnRXwHX+JNiuLwYLs/GpXo6wy5x/a7YTAyXXAqLYLj68jkO+ta/LRAALs6fJnppAAayDjaRvFlD/QTIzedWt/4FGIJNqLQdORImLiWLWl/WKss1f2yeRXb3OmDieoEcUm+Qa+uNt2JMulE2JE/XLbekXH6mwgmx2neiG+yjxMs+Q1AIuKoG47vFiKv8R7Uko62gbwPIHdWtwqEkk1nbs+c8LnopmzFzqA2sYjKNfRRrJGAgUCRqPSrJO6p3j2EmK84KgKIsvwtoOeAEG6bJs4t54tVnb/xXsWMOZNBJCUhoHx2D6o8ByBwYcw3wD/8fIofTCytjxAxJchXlQj6HYfmnTCbeABCRJscY2Nkm4raWRqFvL/FFyil8GqGNmSSefvCulI0lfsx5MtLOVS/YT8edcnZtjm1y3QL0A2Ylqy/7hGCwjiAp/4/fLrNVb8o2sI90fiaM0XvuIos9brDbD0cAXWHHrOQg+DJnG21o5oJVEJ9YNTBq1TXlKCZ5SahranL4eCuhdic20KXDeXcvbKYtm7vmzpE8PTdvD9uPX4wDBpG8hdnasCVCYDDy8IjFlNPHxUjx335z8wICFamu9ltkcgfZfOx4Ful83Ltick2MEvzbOfAKABcGDUqPLKBrUWOa8m4luN7aW2TrUcWXYacrDbh0CBoek7gnZ1goq8T323vOXYaDC6osmQV0zvor4JpwehMMVzWiGVrJ1t1ZwiLAtWmvPcl8oZgYrhK2HFQVWKCVBQLAhTncQAXQaKxrsGljkK0e0VoiXuzWLoQafviDuB3FD7h6Usbe//4Ur1Ku01J8JwOED5A00yzcjO8PHIwgDUpuKXJUKSkycZrEwVD3QynHcglkgV+QcM+zjSejPQpAOGggbVJfDh+yRhRscqAMAS4PoHigzKnAFR2nc/GaEIirbxD4oc4B5NIC2QgIScpUQKkcJBcJRzyAxKxbgMB3hVO8lYu5ojpJyDulP5gUASuxNTKBitiuNH6UXnwWRmFblx+LgVH1OZDhbXrxX+zVlESRcEfU1qFQKLZRCY4PYLtjpyPkgcrbgPKczcR17VYEMjJs+wIJe9ftJ3k04LN6Zs5unpm2myeQcJfE1A7AXqz96r3j8OVSKPdJuVG6E8uFkkH5RG6XCR5AUlk6fDhk3/pumb3wetQqqpBbH0ay7gE5GzM9Z4fLYLZQC4wCWnRsg2G8Jg4lTm7QeUtY0oaNKLPzFxL20ovYCuB2O8mIb7tN7CdnpPCabLaCc+tUn3SOmJSq6NxFFMfGORLQCcEK7D4QtadXArgAtBXEim3eEbG9B1iQGJWz2eSeW87iwWz6x+lvtx3V65W87dmXsVdWpbgn4CwBgvPnR+3mJXEWIcSi+tu1fg0AV2t7BJ+Kt4DutUbYrSjXs+K3NIr31XJ1AJcYLhaxGKvLcefmju+r5gn61WyB/gy4xp/aCODyGa4OBvwenGk95+VS6AAXOSAf+TViuLoQzehBM8EugQWKskAAuGQm7m+50OXTjTZ5FIITTPJ8Fsb9zMNZbMnTzySQCg3Z+HFZu+eetI2fIMDlTUbbs7bmqqRvIGcUoARAMBQ3MDExrgAgDh8OU2ec1yiTX9z1iD+awQT1vnthCBx7lrdTxMmsfiNr27cr/kWKe2HyM0SsEte4HKuzlwCulo74gxYPSj0r/Y8tv3fyxn+2ah/+MtAPtXX4rFHKkUB17n8AM/2nTR3Qc78iLiF2ij99p788xykc4apUfX7dvHU21ncUV4dzc+OL5u+8X1r/q3i3c4hlQArZCRjCJ1+L27otMdwWxWxl7dG7k6gRpl0zr+2M2dPb4g4cxAEiVbhq3jM1ZXNGUIlro6AzrZu58p/0tGwpTHbIM6A/2TJRHicGK2I/Is/bD5/kM2ySZNgTuO2FqkN2FNfM4+fDdh2ulAuHpm0w4GcKLN+4QfUWznPdcNHt3htFuTBsJ7h+b745hzRuDlBPg4WgnC7oXKVQTxTIdj/x+WLPdE75DBCMMgk7dT5qL75eZvsOxZwq5zkWDPDMtZk1GXKfAfh4vaSNlmP03ui8S2b+5ZebYHs5XvKkNTYBnrm+77+/jPQKUJgdXA8B4GpjzOBjURbQ9SyXuQYGE4EJiWX05XJ1AJfiipUM2lOhTQBKg9K3LaCxud8yXGeI4bqhxsnCu4dQiUytGYqThd+ESuFhYrh+tWuVwhI1HVQTWOASCwSAC5PowZJhpjgMkYGxw1itj+vh0nqWh7YBcttRxxBMIHHrddPk+iR7aruLU1J945czKOq9vTZqO1n5r8S9T4lhZ83CJc/l5oJJIs5o/YYIgZkxN1EdRrLkJUvSNnO6WCrP5WXz5qw9+VQGZToIHZoaNDBkDz5ETqXJsEyIfXQEuDymShPgELKhku1m59aH5Hez61cmJ41MwhsbGxygkjthFDW/GK+S1ZeroSa/sRiMGJN/lZ421WVnsItWp8+cQxzk7Zi9hFqh3AoVUzeSfGr3LklaDQxLHW6Vb+2L2VPr43aW/BvsZEMRmHj/kpQtm5lyAiUdnbcu+3AFNpBbZh3JrMT8VQ6opEVUAveE7ZVXo4CmiDXCcF0gyXBtFddWCgYRl9QpgzN2fzWxiFWKl2OyBA2oayRRnkCYBVfMAyGuOZIQs1BAHLDHKhYci86ZWMcmMiazlGBloKU451lXt2yeAtXKBVInVyxYFkS273DC3iGp9Sni6sDiNue6rM3BpXDwADbSjkVcCGJxX1mVBHCB1rh8UrwMGhyye+8pt2lTPdfVgm62vA0AV4spgjfdsIAuySQMPrcI45ViJXWh9t1yNQCXrCFWS6BULHeVJ4nad40U9MzNGfor4BqPS+EHbvBUCgW4inhsFHXGdQ0LcDmGiwXuwKWwKLMFG10mCwSAC8Mq7ijLLG94FapvTPQqCPL3nsG67f2HsReDJNYKrzdK4W/63LowH7WtW4lxwWWwsYGHOoBk0qQsrohpGzpYYhLekCJ3rMP4FkvWfMRws9FjSJaMu5Z+FmjatClrTz9Lzii+CPF54IAwK/9RXA8RjOgIcDGTqG8wW494xlEGmRtvytmkajrO/z0q9EUT8SSgVEBALJYELzzXP2zlmClqbj6mHrXRjZ3OwKSsBsiuejtuO49GDM1EG4dqn5Ib3zA1bScP5W0d4iFrYbfW8HfqBMIbsIFjYcDef0/KPvhIktwarNz21YkWl5xi5eqbAVcFgCuES6auGeXEOo0IyKZEzt7AtfDoGdzuTsVsBOdozrCMLRmVtiH8pjxjngIiAi9KzCi1sRYbcyHkqa/ls04diw6AKf3J+VPnOMw5dvvovMpW7gPXJteAXELztMH/dham8TwAtxzQOxwgV1nenMi50L7ufXOLhQ2rD1yvBw5k7dVXU+QO47ygbDhvbswW3ZQgOTdgue32zf0OAFfBCQzeFmcBriWX2BfXBOWZ8oeu4na+OltdNcDF/d7AM0bPxwqpu16dww9aLdICGmL7LeA6u9k+cBMqhXhj+HOjIg+7y830HFuxcY89eTBguLo0VrDBZbVAALhkXh7CWsXLALoGl2VtPJPGCty23Pyyjfk1qHWJK9hGE9HNm8OwU3HYAW/SOArm5f3vA9gNp8HmSWSeCqX6Rq46Jsgh4qbyjn2INzNSJ0/m7bXXs7ZjF65hCBHMmhGxpTdH2A5BCFSk2mW4mMCSXw/GIGL7D4bt1mVZGLmLbbY5pKI/itWQJLxjtOi33MEUv6U8UleqyGxbYAyfRTyjDiCbwbR7SLI7AsbnEQQkkuey9u1/g/1aZXaEeLtMDuEJmLdEAqW+KmK8rsvYL/9ikz1MDB5Ci84l9Er1veh2uH4EuBou1Dkw6wMu7Q/ucm6ae7kI3+YaSyYjNgx3w6FgqEkDibeAPT3ZgOBGPm0DrJ7EpTBVICGxkimMdRpmsAmXvSGDyKNFMmLnKYRRJdyRAlDrfCoxYwuI7rDTzXeHViBoQ3F5ujbEFivGT4BNdfk3USMqixBnuMWSm6tSgh0FFbO/7pcTJ3N29Kiuc7MJiGYMhJl1rqYFmxa+DQBXoTWC911ZQFesLtf6JDnqEILw1PeaB+Kudr6Kv18twKVDTmtxRfc0cZVxXC+5VVvu6atokqDpdiyguUl/BFyf/4vP2vgzxHCJ4cIbo+sJVjsH38lXUilciWjGE2K4fjWI4erEVMFPl9kCAeAqMLBAV46J56AE+asQJBhYTqxKy5zRfzA3TzQL9mv3LZudOBFi1T6G4EbYuVstmE9sy4162GsP6mOExFMPJgy3wreiTl2QBOVsk7bp0xGkYPlVE++z5/OoIzJJpS/jxoaZiGp/fgMAtQu4+JXdnAgHquM2bCixVyWQsW8BibxJE9fVROViuioHQLVcoSJngw1bY/Y8Yg0cvpu8nzobtjEjs3bLDSl7+fmQfeVrZnv3KqmwOiWmBsBVVk6+rpBNrk47wZMHH8B9lDinKtzg+koIh3+F6dqQq2YDDJdASyHg0hFpOx0a+MWtOlcxC5KXqq6nfedDtmp/3EYAahaPabLykBJU8yO/7T6QsDUbcbEEoM1AqXARLq7DiCvMEh3fiPthlHMpefpLQI4u+Yud44NftCjBD97/uiQdQ6ZrQ0V9d0CPXGmbN0ds67aIy5m2EPXC0SQZ1wTBK82Vc727+vjSLWoUxpf5mxa8BoCrwBjB26IskFFcEgtVCdyMJJjRwYVdVF1XaqOrCbjkmtwklotbtCIm7tu/Q6/U0QftFGsBjaf9FnCd22qPLprsGK6S3pNcty7x8cbd9sSBvD3y4Y8HohnFXlDBdiW3QAC42phUdLbcpaJoaw9ByKISpmswTIjyC3W3CPScI8/WseNKEuwJHVQox1bz/FIT1CNHQ044Yy+iBgNgt8R2TUWp8IEHUg5YaRB1E1P9Q9+8iajXk84Al7bw9nO7XWyzuwfRwfayUxK0KBfD8ipc3jrYruRf09Bh8mm8/FrM9sLeQaY4luZ6cnHNwU3we/8Ztr/9uwx2RRvdiaezAXm/4okK3DWjNmNGxuZdn7bJxD9NmZRzyZBHyh/PV4goeYeLq1D2lDKk8o2pyLZSCSxDFTIBWr4EBLGNb3P/ctIXJ2BJt5yMsmiQs+nErJWh5Cj2qbExZ8+/ErW3NsUdKzYWtcv770zbjMlpa1DyZeqTAqXLraYONBe65KT2xUh5MvItrfmbXPKqLRobGlz/y8mdcL62zB5/PObiFQcNzNuDXNuKZ3Tz3Uv2Lv6LAHAVb6tgS28IVFLqGMDeKRMyQOq+6+vl6gIuJvHYTPGalYg2SYQgKH3TAnre91/ABcOFS2FlGYmPvUdgSYysqzWMMM5KXAqfYL7wyIcD0YySGDaopEcWCABXO2YTi5LmIdPYkMSFImOTkIkfPZikrrhDdfdxo0eU5vJuZbDNs10D5N79koaP2XlcvYbiF3YGtbkKXN+W3JIhaS/uhYPzNly5o1zDrVvvCnC1c2gl+0oTlSZAgeJ5rijg4ggkV3zgUAgZ8qhdIHZoDGzJ3BlZXOTy9qMfhuxzX8gwuUd1Mg8lCAekBMqDhyVgDSNWXZO3MeOIhYOUw8vObllM3BN/kpVvAcJFWknEkeZrigNx4LbI/drbTKyQ2EpdIoqNU73KRSZ3TeUxK6ZoX127WZKPqT+6ZuQCJLCWIqfO6+si9vrbYXLKIZxBPNsdNydt0lhPnj+CC6BzJSw4EIGt/YfCdgiAOwo32OoJUtmkQjXUWaHdFKqfaVwUIxi5sSlhq1YlbPMWgODAnN1xR8qmX5frNbMYAK7OTkLwW1sL6H5twIOhTMyru6e6upDb1nB1Pl9VwMUhNylWk0mwGC49yYLSNy2gobvfAq7z5OFapBguAFdJF0GUqxPAtQHAtZ88XAHg6psX7zXSqwBwdXKiU4CupsY00sGAHlwMhwKEBuBm2L1ntR7q/kOq8L339ekzIVu5Moo4hvJZhQnoZj6LJHw5cWQRxAOqx2Zt2fyUjUYC3Jvo+nUpHqFjl8JODqskP11NwEUwvlkAAEAASURBVKUD0JhMyJCLL1AIWUyrryDbHTvM/uEfcva9/07bWdhF5XQq45zVTAnb1KlK4BmxUaPCNnYczONJEuxOyNp770zZFMBEFXYvrqgt2CTc984B+EYAinVt9LSoriZUThRDJYASjye8xM9CciUsJ3G93LyDPgPqx49OA7YaiVXM0Z4SWTcnmvbbQxLzPGIYz65AeGRDzGZOJRXC8qSNIQ7RZZP2t+vkVQIcivfTeTl9Oo5bbNwGkJ97UjXJVCuoh+97UwLA1RvrXVv7couxUJN1aptx5for8b11Oa15NQGXjisD5ZDGDVN5Ab24t8t5tEHdPbVAvwZcF1oDrt49GS5aUPd9mAWWAHBdtEnw7upZoM8CLk3o23OjKjRVMdsUbr969Wr7x3/6V3voZz5s02bOReLaizUp3OaS99z5SjCsXFTlZJYdw+R6EAljo0xI/f7ppr44QPBJI5++LKJkUM87QkK+DRtha5jAZ3HdOlQXsSRCG4r1IlLL7iSR72Jc5twsv6ClaxlwOdNC37TEFGBvmZz5va1dm7P/+q+srX0HIQc+i/EZNwaQMR5p9f1ha0whMjGaGDAkz0fD9MxA2XDpAtwLZwA+ALnFlBxgYQvs5D7Yn1moT06mnt6URgCX4rbEaCn2SbFx7jrqTaVt9lVONDFdJ1BtPHkcYY1ozsaOz9oQrulLFBu5vi/UEmy8OmHrt4RtCiBp+VJir3BTLPbaVvOKQRR7p+TOAkiKE9OxSXXRK/6d03wCm78t5iUAXMVYKdjGjQsSfwBwxbn2xG75V11/sM7VBFyynR5njawEphnzBriUKf3Bav2vj/KG0RO/p0XnKdmUJmb8ODHbUbw5Rrtzl2OB4dN/9gUbVTPbPvmJT/S0+suyX5JVUyeaUUseLjFciGZ4DJdsUYriJT52gGsfKoUfCkQzSmHVoI6eWaDPAq563Kv27NljDcSCTJkyBeW+YQweSpKatTNnzpAIeDuiEOeJexrgfh87dqz7vTMzdB9wafDzbnwNAikeOjFLIx+Pa1UMVzqStCZgVmLN7mhaya9lgl+bClslE/dBuF9pEOx8CJUTmNga1ArJP7T7cNSeeQMXw3qJY+RZUczZ7QsydtMsAa7WR3fNA67W5vA+YWyB2AMH8wg1kDB6W87WrCWB9Kk8ACMNiMhaQ6bMYvgUzpidsDnkRUti+8kwXffeyrkFfIhx6rzonIXsDAmYL6BcMRQ3uUFKlVVEUdXe9cDjlTdeEmmpTQpwpR3YCvOAjIO2lefMXUBF1FvUJgCus7iuriKWa/MmQB2YZ/HiFCItME7qf5vjlguWWMDjJEseOgRZ/ZHkkHPXeudXdNu+yJ5i77Ro4UAXjJpzk9TN0VI8y+j7S8Bfyzat3wSAq7U9gk/tW0BXVgN+yDHuq5jnm93FmNx+PVfr26sOuDjwJhiuFANCGV4YDhjIqG2KvvLuYn+Ma7NB8LFdCzibMRa6RB0aE9uxbbs7tv2SXdO4cu/Zc4K5CYBrcj8CXGK4Fl8EXIVPhraH2Z3Pek6HuWZXrkc0Y59cCgPA1R37BduW1gJ9EnDJDWnt2rX21a9+lWD/RvvYxz5my5cvdysfR48etccee8xeeeUVF3Mic8yYMcN+7ud+ziZOnNipdboPuFpXp4mjp2SY4TZG6SpC3FC5Yqzwbwd8nUf6+i3yQu05G0GimyTG49PIzCuFrHiYzgu8gR1mcrtyXcze3By107BdE3DfWjw76/JLjSR3V9uBuC8ALolmVFxJ0YzOzej9irFFoDQ05O3VV7L2wx/nyEmWQnK8EWVCYr4mlCEuUWlDRsRt3myYJCb5U6szds8tHuAqpgm3jTupzU9HoacuCqaCXfL6NQwZewmoKJGw/jJZJoTI18eRZE+inCKAX15e7uK5urx4umjX/zkEa7VnX8SefDJuJ5DMp0mbOTNj770nbSMkHKKLsG1xh6V//ONsu0Fxn3U8+tOCiYCX7iNXfLOpeb6LNceSKe6uqxIArq4sFPwuC2QBCgIMTpmwH7kS+mfvagIuvw9SKWy5Z/0vL3mVAiRquoynRdy+l+x9LX+hxWQll9e12tMirJYh5cHB/QJcsX4DuD4nWXgYrg8s8QEXFui5GS4xX5jYwxXvkIdrnxiuQDTjEgMFX1wxC/QpwOVPyg4ePGj/8i//Yj/+8Y/dJOzP/uzP7OGHH3Zs1xNPPGF/93d/Z/fddx/B93fYunXr7Ac/+IE99NBD9tGPftQxBB1Zr7eAy9XbPEFUvqEUq6aRPKCrLGPjiLk63RSxH2xHBv4sq0uDyLk1I2VTh0p5rgvIBfNQD1h7nViZlevIGwUGOA9zUo1s+aPEF43BjYtNKK1HoQBwdXSm+R6DJYnxWvVKzp56KmtbNvMgOtiEmwUy+QCtnQcTJEwut+EjYzZ+bMgeRLHvPcsyyMxfaueOW/HYya7htFdDYxPJilebHT+etwXXJ20sghVhFBRdAmkmghG5EiK7KPCVbCSeC/enBC4Wrer3AYquBXc5tHzRcTebf5HE84lTIXvppbhtQ3AkBnt6A2kKlizJIiLi13dpNWqm+FYu3d//RphU17Fiu7JS5Cgs3CNiwVSisHsCXM61UjdDByUAXB0YJvjaWUD3jZa75A4n1lSAS5PS/lb6AuDSCCDTOW7fexhdakZu8DoYljiLJnEWdzxja/QIStcWCJEzUvkMtRDVw4uU3VIArv17cSnsd4Brk31g8WXKw+UYLvJw7UOlMABcXV+KwRaXzQJ9DnBduHDBAa1nn30WVbnpKPg9Yb//+7/vAJeA2Je//GXbtm2bfetb30L8YBST6IP2la98BbGEHfbNb37TRo5E77qD4gDXN4nh+mA3Yrg6qEtfa1VKcQFKmDyyKoN7IxnN9wlwhW3SYFTgiHsZXSXA5T+uOqiMgbK2kfxJa2P2xqaYS0jbRKzN5LE5e9+ypFWUMwi3w6BcbcCVAtFo8lxWVubFHHVweFfray0Wvr3W7LvflXuhck2lHPjNECgXT+D2ObjMzl6I2bBBAK57zB64NwNLKvayhw+8Dg7UZ3aamjK2D4apsUH+9RkbTA4sYIUHuLiWPPjmVdJQ1+Ceu5JV96c66lYDyZ7PnZdIiNlgVCzldVhs0WQT4U17bU3M1qyPWs24rN26OG2jcBX0Fv5Le9yd9Uv3TmHRPSL2K8dfEuCla6uCY9e11dEsOQBchRYM3re1gO6bLBdWA4ArAYB37oStL7u2u/TJz30DcHVtGtm7DvY6FiF5utZJNKbpxg5K0Rbo1eXJzl4M1zFiuGC4+kkMlxiuCXVbYLgEuAhcb2euU7QBL9mQxW4A14r1AK49AcN1iXmCL66oBfoU4EoRQPnmm286MHXLLbfY6NGj7TOf+Yx96lOfcoBr48aNJrZLQOvrX/+6iwOpra1lQv1d+/a3v23f+MY3bP78+S0GFBhQnJfiwFTEhn3vsR/ZT/7Cb9jUYkUzWmpr/40m9Skm8oMTSZuGKMNJmKkTxF8NxdVwDDFY0sXzSidDKT+hl2A7EGFY+U4c5Tvit8gBdhPuXjfMSHfonnE1AZeOKYsggtzftHosYNDXnq1idDZsjNiPfhSzcxdwK0pm7a23srj15a16otmQoQIvIVitsM2eHbX778N9cwmsk1ZnS1UwivK6pRFoUXySEgGHYLW0kCnQIdcbd2U0Xx5a4ZTQhECHVMEu5uDS6idCHSTJfuPNqIupWro0w70gMF5cZ+VmeQrlxqdWx20rq33LF6RRwMxYeZw6XC86uUaLa6J3W9G8riHl8GqoqwdwVeB2SWbqNuDMbyQAXL4lgtf2LRDCxc1b8FLeLd1r/bH0J8CVZPzSc6GsOUlyf7R3f+2zhsn+KAvvAS65FHqAq9TzCAGulQ5wEcMVMFz99fJ+V/S7zwAurYTt27fP/vVf/9UBJLFaa9ascQDrj/7oj+x973uf+6z3CxYssM997nPuBDQx4f/+979vX/va1+yLX/yi3XbbbS0n5uzZs/b666/b1q1b3eR2//4Dtmv/Ifvwx//QpkyfjVtTESqFLbW1/0Z+1ynULspD5DQaBXOCWEYZ7InHGDB7LGa1pnmeixeZHTwRMcl3D0GGfhIuhZUs8LsZddtJNaOrBBcamZjKPatq8EB3jO338vJ8K+W5poYmN1WvqKroI4BLhpI/PJOtVJ5zH7FXX407xb2zZ/L21hoA18kMgCULQ6QtwzZseMxmzwnZXXdmcPWT+1FbY/fQflwbSmYsV7kw7oFSIMxlQ3bkWMQOHcJdkOTE1ZNyNgRBCl0vetAIwGrhQe50Es7QqzMwXRAof+21qD3/fMxGwEo9+AAJnKcAloSXiim0cb6OOjaSNBp1xRunZ23+dRlcrWi4mOu0mDZKsE2S+6murs7FsFWQjLk/A66I7tMS2CSoovsWkN0llpHg3ot6A3L3K+kDe/QXwOWZCuEo3AorUTMMtyiR9gEjXgNd6NeAq36LPXqzYrhKnYdLsvBayCYP1x4Yrl8JYriugVuhzx5inwFcciVULJbA00c+8hHHVL300kv2pS99yX73d3/XAS65DQpwXX/99fb5z3/eGVWA63/+538c49UWcJ07d87kRqj9VATodu49aL/ysU+VDHAJKErkI4srRXmcnE/RrA1DxXDowO5JD/uTMg2aXvEmwfq+5Sv/p+ZXsSFN9Y0OeF0NwKUg6iSiJnIlqUDmrtAlrk1Xu/ex5YA9G3RvZ/AHux0/FbEdu0naeySMT3vI9u3O28EDKFyiViiDjv7/7L15dF1Xdt6534x5ICaSAAgSHMVB4iSKpFSaxypVuaqiSsp2KvGQdNpZq9srTv+VtbziuNppJ6n8k9Xu1V52206n3F62E8exhtJQokSWRFKUxHmeCZIgOIGY3/xe/77z8EiAxMwHElDdIxEPeO/ec8/Z575793e/vb89N8t5lCaczk/YWogaXRRPXpNy4KcQHrJC45I4e2nyscRkhYoijt2SaMZ26q4dPRpyRYQ3b07apsfTgAusx8AHUOcUwyV2J0BoztDF17wuXfKj0OknfDWL4AXqiIQVTmS8bOWazterN/12WbLwMHn15AfOQZmRkP8Z0xRa2Nvd4wRE8iGVAqPuuyEwPAgOZzLDJZEBKbpJFC9v+xlj4J+LgZATw0kjtkW1o2ZT3a27l2d2AS7C48kj8gDX3as4/X/PWsD1ewopRKVwCwxXyXTIwg8CLvwBD3BN/3noHWF0C8wIwJVnt370ox+Zwga//vWvuxFL+v3DDz+0l19+2d544w3qKDU5xquurs7+4A/+4HZI4Y9//GPTv7tDCu+etsDXH/7xf7bXf/HXJ16H6+5ORvobj0rJ2QIgsQFyT3z9toRQr0gw93R74g6XtryNNvg9v+fQ9+4MQAyX2BCJLlRUOrrmzofT/RtXdwG+eBRFPV6LCf2SA3xfjT7TqFxFYzm1JjAKLE8uDmjCPbNhHyGdn34etD17gwbmtgsX0tbelrCem0nAld/mzQvYxvVm3/5O1pYuxcqE6qFUTl5U3t73MQu6yBf8FehSGKGUB/WUTQ98z5z127vvhqzjikILzYG8l14iJBWRFQEJhdKJ7iqG2cnlbg0fi0aYTvEJwGMMPYnhO+X/4niy7fGTATt0hNIDFDauRv1y3WNpW9JKjSJsMMzO+T8KYJb8ECbymsJu/b19jt0LhoutE4B4q9MIrzRCKLOA0ZxldL513+y29rNXcKoFoisBsaGHyrRqZDGxmqBjFy6qRVbD+dd/t9c0/37uU+/nNFhAxXqLWASFE85mc3uAaxpOjq9glzrHZ2VIIYCraYCQwq2DKoVubQp301GZlR37kIU/TUihx3B9Bc/82TOlGQO4rl+/jlz1O04QQ4yAnHnV4dq5c6cLExTg2rRpk1MoVIjgH/3RH1ljY6O1tbU50Yxz587ZH/7hHyJvXTeq9QuiUjhq70oQBgAlyRlIRG1OUcyqkf0OU6dretiDnI0EuNTKysoGKYBRB1jYDwb9SEl8q7BtiHC5omIX/zi149AfZJldaMvYuXMoDMazNrfBByCiBhS5VoOHG79vQM11HPT3PgrZ0eMBu9qRtp99mrHrVxIWyiYYo99WLPPbP/gHfvuFb/utroYLe+5/9zr+AXJb6HbgxgTwUf0111h/hXcqZ0tNNnEKhO4vfjC2Gzf8touwwOOEOkaKsrYRdms9uVRF1GzT0/gYRtA+LndrlFnrxupuR+5HvvMJvMJonWvz2/sfhglrJD8MQBvH5ksWp+3FZynqDZjJdZx7Afe4lgMOE+i/QJvo4UVKgizJDAxlkFDiEMXBYY8BXBs3iomkDINL7Zp5gEsmkLSz2JW7lyeBQRViGAR550+ZApnM62YECyShGYnutiAn8ERru43QzUN/ywNcD30JZsUAZjPgah44ZG9sVQ5XnuEqnMmleLtjP6IZp8VweXW4CmdZr6fJWmBGAC4NWiyXQJZe1cQSvPvuu/bDH/7Q/tW/+lf2ne98h6c3MQfKlK/16quv2tatWwnNOmpvvfWW+1uy8K6gquvh3h/TDbjy3qoc5wzjj/iTVkPo13wAw/2SP/fORnWLclLaycGcn5G2me73tF4KARNIFkhwOUdTOChLbydPZezj7Wm70sE5QH/Cbxs3+BCyQFEQYYvBU2Ps3gEjvX0+2/1lyD7c4aeeW9oOHYQ1602YP8M/FuLFFwKEqfrt8ce1LnmQMQiaxu598NOcM80yo3Los+udSKOwFtXlcQogAz4RxlA9KR/sVo7VYDcdBwAl4Ytr16m5RmigAE/zAiTZOUc0N4GtDICtCHZr0nbMD1+HGa0BuPahTvjhDqgsthOrduNmwEp5MPDKi3FbpnwwkIDGfJM8wgvkmekm3tKUtjlsqzV+YI1DRQfS9sEHGdu7j3OrCHXHmN8WNGf5rmetqVEsH6InN7tmFMM11D45a+VtRukHrgkCWwpxu810Dd3B+72gFnAhhRmULznXZXeXx5VfjoIeaXo78wDX9Nr3q9K7Ls+zleFqjpHD9WS+DtdYN7HJrhZZ2gi47NhLDtfJrH33V70crsla0Nu+cBaYMYDr7inJiRe7Jan3X/3VX7Vnn33WbaLCx3/xF3/hxDCqqqqcmtvixYvt137t16y5ufnubob9Pb2A686hFO4ldiCh5OFg0hbWZ62iRAzYnW0K9ZsDqHQ8DV1PeIgCfvG41Ar9hHtNQa2QG4WKFO/YkbY9qAhWVivEz2ediFzU1vjs1dcCqAoCXsADE2kSzLgMg/PWT/z2znsZO3MqZbeuJ7kZpenP7Nd/LWT/9J8ErIbfJ0w15B01GVqsFi9XqWm151DYTp6jdhYCLK2NCdu6DjBAyOLtxn560k5ag5Oljigti/dcN/x05wTjlfhKP4qbwmWl5WUOrN3uY5xfRKglkjAn9J0LC1TvIzSe9p8+E7APPqawdqcfQJu1vgEfgCpjLz6XYNwCXBovNeG+DNonn+eA2bNbE7aB/LYIOYpu4CN0Pewt2Ur/8sPIvw7baJw/2F911N5/N2t79yPrHSa/DUn8+Y1+avAx5gUCXL4ZDbiGz5Dxw9gplygHuIZ/6v01PRZQLt0ADHyAa1OYsF7H1k7PoaatVw9wTZtpv1IdDwVcCq1e1DrX3WsyREz89u/8e2tYtMr+5W/91oyac5yHxb//e4QURgkpfLIlJ5oxwfv8RCfiDymkEMB1CobLA1wTNZu33TRYYMYCLgGJzs5OO4/QRUtLi9XW1t6evgQ2lOvV3t7uQgjXEGNU47zn25uM+MuDAlzu4HjOGe72KS4oFZGk1VXgSBcRFgWQuKcNvuXceJzvEba4Z5e732C3h9J0WBcKNxB1YTtTUisECPT2Zu3jj9I41xmrJSo0SP6b8nbKy3z2yqt+W7p4goCLAQmAnKPe1fafBWzXbgpBnid/q1v5VFl7YpPf/tEPAoTxBSbIOlJrjXOxFxAS5Z8KelYAbCTs9/nhgH1MuFsyQSihnDou7C9s4RiPUhBbmIttOmHbDgHILlxFDREmaw212eYjVKHPBbZ0k9Q/gVblLfGFnATgYh/qt50+H7R2+pf4xbJFaatWPthIjXOyH4C1F5br0NEAAMaH+EbGNiIesgoBDoU1aj2pcmDbPg3ZLnLglN/27BaAJAWSXcQoYx6r6eMoRbwRGXTzKqegspg89TvZplkcPYaq4qc+QkNVHy1l6zeYbdkaIveMkwbDzbQcrtHn6AGu0W0zPZ/kT9WkIg4U7stFUnm1YrtmU5tNgEs27+PpUglJlQqf9dqDs4DMnWe4IgCu1tYGLpE8zOMpw2//zn+Y0YCrGcD1Rh5w5b+4BTEdDBcPWlwOlwe4CmJRr5OpW2DGAq6pT2n0PR8o4Bochpi6OB5sMAvTRU5SQ/Vw4IBGBKFGJNrDiJWFUKfDEZ8a5Boy7/x9bqIXrvz26mKi+wweTsBYKo0uj4swOkmfT7px/CSFnvfvzwKSVPg2S96Fwkp9tmK5z557DjBRg5M0Co4Ydjz6uglQ27Y9bIePBukrjUAFRYYrk/b4BsAFIYqLW5GJxvEaSjnmmULXl+5cg3aI4qSdg1k53hewriSsUCBrSyhmvRhJ94MHA7ZzP3MOoEaYTFg8HbKnNpo9s4HQMcihrl6fvf9ZyP5qW9jOXQ5YLYqCr25K2Asbk+T1EeYYRf4fSfiGOpTUWPv+nskxXFkA1PHTAfvok7BTHawE0D27JWnrYKNCTup9mGUG/8DxJ2/rCiqF4DuAC8eHgZVASX7OAoLnLvns4DHCIjHFY9SDWzBfgJU/xmyEIsKcKXfuYluOTVhEgeflyM9XlOd2VH8TbmyrUgltbT7raEf1L0ye2dwBq2+IEL4acSItHuCasDV/bjfUKQfksh6uBcUoR4bd05DZY47ZArh035KdJRqjhNUItp5d0Hb2nBMjjVTXVge4zna4kPYFC+tykRJ88Ns//JE1tq6euQxX7DAMl3K4JAs/0uym+h5nJefhzySacVIMl5fDNVVLevvdvwU8wHX/Nhy1B93ode2II6SRphZT85y0NdZxE3J0VC4s7RoMxd4bIbsBK9BaTl2kWmpqyVmedNM+qPzBlMTiyhXCQSVkLCRd6jEaxIzdupVzwstxionSHAy7GXs/dTkUbKmgr+pG3U+ez60uQNeBjMvlUok0iWasW+ejVhU2G9fZH5wkw5bAwns/DdtFQE4FbE83SoUrqDf16ssAL5hGd0EfYmLlC0r8wy0WICanJZd7PTYQsXevFdkp8rTiMEJSUZwHa/PSvJhVDaRsHyGFqpumrRtq/fbcprQ9ggiFcrU+o97V//mXRfbpQSkSIg0PiFzenLaNS1JWxtqkYUAFdp4EgLUuSFm0HwTEuhWXlcKAje+qCHB9Dlv1MWxUP6BQu2ylr6eekAiHFmjQJiO8+GDqXOPF8VrDtgUMJqgXxrwUHjsXJcMSh6NHOyfYmWMn2GfX7hClGELWQ6HpFGGOVQDKLVuTVotqZxqbzIXdq+E97oFjNPrjUBqSnAg3PuySZp0GQIlSfVSe20yUhR9jUhbFEZV4Q5jcPs3Law/GAjK1B7im39Y5O2Nr2ETHcsGyhLwTffoNP3gEmfoO4Apa04J6p3jLhdL+9f/+I2taPHMBV3OMkMKnBkMKC2oxABc3RhdSeBKVwl/xAFdBzet1NikLeIBrUuaa2sYSAxELVGQJq0O9va5S+QTKlTHbdyNob18M44z5rakkba8vjFtrhcRDJnksLrYcwi63++wEst/4poRiSu4b9kLigSP0l8Lhv3DBbM+eDMxE1hqbfNSE8tn8eXpWqdvn2G1o4eNi6kbdb9MQ0UVxAFBhgZWVPpgRQgJw+ifatOWlyz575ych2w/QKSllXo1Z+9pTSVu7Jg1zhvuu/rKDHr9uUkjbxyngLKEKAY1rqBwqfyga8dkH3cX27lmkyS8GLXkLp5/xQeTZ2qa4vbp0wBqQme8fUIFiny1BAGMZNpcprnX67M/fKrI//R8RB1yC5D+VhDM2H/BSA9haSL7UPIoXg8Nty/qkPU09Lj/nhxQOpaqksYwnnCHAdZE6YzsJa7x4mZDF6oxt2cgYCCucAF4b1aQC0ievB+zzC0yUE2fzQgBhba448/CdgJk8PJB4CCZEgMNvH8HmnTwFYwur29cv9jNgza1+q6j3o9zns0cBvlvXJ6hTp5Ua+RxTPtutbgqoEo6pkNJKthVYTXGC93X3w26hiFlS5J7eds0wWfjh9hn+V5LrgL5zkilXLtfIsx++j/fX/VtAdvYA1/3bcaI96PvbwwW0jAulFCK99mAscBtwnekg/DtkLQvn8qCSe5afHK5/8+9t7kzO4YrDcD21kHsnT/YmfrufgGEVUjgomnHCA1wTMJi3yTRawANc02jcoV2n8Er7exGWILSwGSGIhjkKazM73Bm0Dy6FAFwAndK0vdKctBbyaqYCuK4jOf7x9pAdIrdI+UrzCQN76QWc5UU4xjjFwxpeCDV27ZNPMvbJpzjoMGFhQMHXvua3p7biDE7AGxwKuKaUuzVsQINXWQ6cP7Z7h3HnGadhm4/0BwCkt9ePjHgAKfgQEvPU4AEzPP9cyrZsTlAjjXwsgNzcuRlC6XLsSS7kM+4YtIyv2I6eCto+7HeVcL8LpYCOgaCdPgHYugiwAECwfO6GUEZx67VL0vZt2KSX18WtpYFwJcIEZUcBEIUa/qf/UmQf7A6SZ8VdDwe7hOLG82tTtpAQwpUtGaubgxsII7llPSAEeXgJQ0hWPiG0xwmQk5XP1esaabp6T9E7Aog3COWrYExzFR5IP1Ntsj3li+yTMyH74Hju5vf66rhtbCFMcojvpCP0cP60c85dJzyx8xr5VLd8dobikqfPpmG4JOuOWAgAtraZm/+yEDe+HCh97ZmYNbMGt0Hv0MFy/FvdZrv3BO0MuWmLF2VsEyGatYBJFZJWnlsE+m42Mlw6cfoSgH7OhQhhbfnzfOj0vd+nxwJioFWQtwjkHnZs+QQucNMzlEn3OltCCvMT8wBX3hIP9lXXk3wOlwDXwkWzSDQjAcP1tRYAF6EZU799jWhwn3K49p6xd455gGtEA3lvPjALeIDrAZla15AUnmwsSqHibMrqYboaUcmLwrQcvBkkpNBgttK2EscSBexJNnrnYnseoYi33wkh6R1w4YQCWS88T+4SYWYSPxjW+FM1dj/dmbbduyTiIMDlR2qff1t4KjQk5GzYfkP+GA64SsEIBb5SDjnWhH5lzGcRqHj/PcL8bgQcu6UovUcQhGioS9thgJTCLR9F1OKJTSknxx4loSkWTRL2WYz4RNh27AnbJex3pTxg+2Mh67wOW3PFD9giTDOB20Z+mQFyxJKFCMFcDuj6p6/E7ftPx6yWUEO1y8i+//FfF9uf/Nciu3wlgHgKb2qJYNfmEFr3PGzWpqUCMITrAb42r1OOlJg3tzsgTOAi4cBYnulyoZp3LWFu6xxwdHQJ9nfhd3cvQ36/u9/Pd3DXqzY7A5D68kIuFHITrF3LHEQrBvvRSwzguecYNbIooHz8UMCSXTBRJRlk8s1OUEtNQLa6iBA6EOqKNbBcSwFvnGOrCLd84rEkoZ0cxYXWDj+49AzOtfnszZ+E7dSZIGIpsL6vJWxRM2wgLKRyBZW/FVL4Ko7zbGG4ZDN9Pxzgwg4e4Bq+7tP9l5RCkzzcCPPUQOmbs6l5gGs2rdbDG+tsBVz/7vdQKQRwvQHgKgdwFdSN4Dbjy8vCe4Dr4Z2c3pGdBTzA9YBPBDmzScUSpuI2tzLlRDSUn6S3Ijjk+GK0yXkEcrORt7PrhIHt+BgHmJBCOff1OPNid5ZQX+kehoujKN/rfFvW9nyGIuRNsyZCCh9HxW9ug56UoQnHmCQd7gQYRhiTnmQmiAHMQOmIjcldKSc3ds22ME02MIplI3v+0yJXR0q1oAcAlU2NFKPmw2PHgwABs6VLkvaN1+KOAezricHKmB06UmYnzgbt7OWgdRUBgBeE7FqU2k/nYXK6AqgQIuvfxzHI4fLRB5JnyE6ieFaXtacAcP/ym1F74VGFBWbtwz0B+3d/XGKf7I6Qu8eg9D+2zrJfNRLsv/KLMfulF+JWUZyxKoQu5hA6GSBvL3+j0QN4MV8KL1TekvJ+glB1Kog8wjKMaT5GST+5TUizuw2axtyJOYjl6uEcEKgpZ2mDft7g4OpPYLOrD9n9T8N2VDY7RrggIYAt9bBzfHaMHLquW7BtRXFb3Jywl14iP3EpB+fkngurJ7A1aiFaAFcnuWM7P6Pf835rRdVxM+B4TmWaWmoDFkT3PjIoezibAFfe3lHWVMGETlCAdZU9vTbNFuCL1Y8kf4Qn3WIX+Xph99lj+dkKuEr5roYU0+a1B2KB2Q24jtr3ns6LZhTyu8n33TFcyMKjePvdX/HqcD2Qk9E7yIgW8ADXiGaZrjd1q8896VaNrkAmZgtqpVw46HhN+TqT21GqfhcvohKHA6y6TwJay5fgaIySw6WxCIB0deOQwOBUIChRgTPc1eW3w0eCSKmjErgiZYtcTpDGnnNVtF++Ka9GLJFYmYCkBd02+U+Hv+Zmn9skDy6Gb3E/f+UYou5uQgq/jNiRI2HCB31Wg4O/elWCulwwX9tKXIjbU5uj9q2v9ztBjZMn/PbmWxE7cEhhaijrFfvsHJLG7bU4CwCE/jaf3WBfAS6DhRRy9WFbQ+nPx3SzAK75C7P2D7bG7dub4lZKCOO7u4LkbhXZGUIRs4iiOJSjfcjXqiKU8Df+p6j95htRayCfS36f8qB6sf8VwvISbFN3W1gC0IuDnoDtEsMlUZKcjSdoJ8BPEuGKcxdg6FjnBYC90VLt8qfe7TXSwHQHdwOEDWX+Haguxsg1rCN0UWqNnx0O2YGT1PS6Rl4bUZAtsIg1hApeA4x13sxYZTBhiwFMjz0WsPp65eJxnutA+YONMg1tcwvGrJvjVZLPWAUbnAWtRvsGnGBGXglzNgIuPeRIANYFqnV+jQo8R7GN9/bELaCzV48JUlybZPMI+USu+PHEu5gRW84WwDXU3jGe2BQTvhl015AZYcav/CBk6tkYUugYriQ5XF/L53DpBqGzqTBNKoU7vjwH4EKl0ANchTGq18uULOABrimZ7f53ipPLEe+Pks+TtQUNARxKFwx23x3rUoV/DgDiKTrh0C40cBwHV2Fc7uiMQc7uSZzo//F3KCcSVvbM0wl78cWEU70TMLi7ZfDk+3v72T/r1PXG8qbVdwJp5jQOUIQKwDklvnEGd/cB3d+5ceTv5cNCGRmjnPXzhBb2AWIaGqhrRajfhztC9ua7EY7JU65vxuybr8bJ9zJ7++2IvfVW0P1eKoGGGnI9YPrOVpDwHaKvUwG7DGsmewpkWQzAxUsWG/uiafMBUGsW+20N4HbdgqTVAxCOX4JpRKHwIvsmKI6sEEQbzP2qezRjv/nPYvYbL0etmpwutRgg6wDg5QtC8/oBdctbyTkjr6u+lg/xGNPplAulEwASyyVRDT9Pjl2YoethlB8cWiGUh48od81njyzPsUsO8PBZlnWQ7cRQiq0UgFG/+b7zK6NQrEOERn6OcEhPzG9LUNLcspCQSOqSnUGsQwItAo9zmHtxMVPlWMrfCvj4m9pzIRgyjVV2m3ADLGp9dc7o5qtabwPEwEZgUqVS6N5lvLMlpNANePBHHGc0znwEAFwR5EkZZmhP3u9jWUBm1VdvgNy/IOA2jHiAgO5sa7MHcEkWHsVb7B0iQTmEsT0O98GdbbpezkbA9fu/90NrTh22N56G4VIOl56SFKhJWMqJZghwHSWH6x97DFeBTOt1MwULeIBrCkYrxC6qD5WI5gBXc/0UAJccB5xRAZ3hrix/8ZkLmcl7zBMcsNuHbS8KMOwIUnjYT+2qlG2g6K3EJ0ZqytUZUJVbRlFGsSWf0JsGpkHcbrm/+/pSdvx4J2xawlatqqWYNd55zqO+veVEf8FfdTloUvkTayNBijsAhJkM9tsN27Jnb8i2AbhOI+hQDeP1938hbs8+mbKzZwL23/82ZHv3QT6RmwX+cKF9mcWEtbWGLBEJWAcFgjuOiiFiXoChbD8bya4AKB/5RL5q1BSbArYQsYr1gJAFhNWd7fDbEUITr19DwbADVqjH51QPQ4C5lmcz9ovfjNsPVsZtQRHgjTyuGygavvthxE6Rg6fQ0AoA4ivPJm019avcsThc1oUYJgE3osqQVafQl8IMHUMy1NR3GZDdKEKcY5ZKUWwU4FQT2FKemEQ6ZDf1I9Alu4lFU+ieHwdVXfdg43eoyXXieohtsDXiIK+uSNpj81O3l+9Ozt/gYJiHcrS0DPzv+tFxR2za1p0vvN7ONdReauqDEEfmHacoV6Qo7PK33CezFHDJn3AggPELdN05bzUrrxXMAoOnk8QyVH9L6pBjfFUKdthCdzRbAJfmrW+tJOFLiRLw2NtCnwlj96dL6OwFXIfse88MAi6dRPnL/9hTHv9TfeHzDBcPHj3ANb7JvC2mzwIe4Jo+247ZcwKGK0Eo3gLkthc0OIppzO2Hfqhr0QD5NR03ceZhWmoJuaql3lTemR667WR/V9/K+bkBMxMlHK6+zqzcqSYOBTR3elUoYXRgAEYDlT3U40Kj5Rpx4bvZGbMd29thoOKIeTTbwoXlDkjc6W3iv3UQxrb/QMCxcFJhXL1aIhjsP+RCLex36pzf3v0oZFevBRHHIE8OBb9vfyNuj69NkbsVtJ+8G7KDhwgbvGHWTQhlUSRpc+gruy5iN2BTTlDEt/0wLA6gKIPNbQCQhHgGyifmq+H3+exT7rdFgKTnWxJWD4g6hrpeF9tmOL6kzfthmW4R2lc2N2srn0S2fVnaXqxJ2uZyBCDoRxLoH+0M25EThC6CaObCbL2AjP1SQjkFWIY2VzOMImVipQKBwdpn4z22Z9i677i++EX5YXF03AWWBa5CAm88kXZ9w6apb7FdQfoXCzkA2PwY1cJDV0LkGqI6CHB7cWnSltUrH2nQ5HeNc+iYR/1dO7Ng3QhtSFK+tJTzGAAbHKxDN/gxjFkS1pb8ODFChFWKgVPz8ZpjuNphiqQ+Wcn6he6xmdt4Bv2QqfKAS0V4HdacQeP7qgxF5w9nsg1wQfNzMRC4He+rMhPnPusAFwxXKUIFsrnXHpwFZi/g+l1rTqNS+MyiHMM1lXvJqGbmPs09brsYrsNEt/zj37TNm58YdWvvA88C02kBD3BNp3XH6FshUgnQUlVxyppxsEth0if0pBsvQmDr8GlCvI4CInDkW+YSgrYmSVHlMQ444Y84AKFgeSfQhRGOcQHMh6SpzlgSuimA814iysl5y8MPqjDK9vZ+2KQ0Ah1lVlaWF9oYvt2Yf9Gv8pG+/JKcrA+DFDX2Uxg5Y6++miBnDYZG9EG+se2FS9TS2h62dvKwdP+vq0nbC0+nHJjpBeh88UXQ9vBPxZKj0SwiDUl7ZEPCbtQGbF9fxPZfiFBXCvEMWKvULfJBJA2PcIYhSGLzceLI+SqX3DuAd2UItqhDwM1vC+YySHQiLlIwOUtu3AB5dMUAlTVreNpOPcp1JSl7viphKM87cZJzFwN26DjgBun6Za30BSgrLeE4Q2wvdmmAAscJQtLCAfK6oNkUuuNymvILlp/7aK+yH/uLlRSAKQbluCfROo76gG0S66XcsXzIoYoaX8YWRykA3RsPWGtNyh6pT1p5ZPj4RjvkaO/rcHHA68cIvXy8PWitzPvrr3Eeo9iYn7fGEIUJFgAMU1DOgS2Nlab3ejp77Mq5KwgiED46GwEXzoDcUp22qtFF5Btz5IfXCmaBFF+cGNfbIs53L4erYGYdsSPlyqW5CBfBKgyPvBhxc+/NAlpA19PZyXABuDLkcD2jwsc4QoPX90KZRiqF278Q4CKHywspLJRZvX6mYAEPcE3BaIXaRexQiif3VUUJFN4ILyJnaNyLDZtcgdl6b2fIzl4KuhwZuWtPrUs6tbyJ+t2FmkO+nwxOunJs0migl5SXOaYr/9nQ1zyoBLaMP9ehO+Z/Z/4CXPv3B+wjnHSxUi0LMvYC9cYWwXQ5z3XItpBvdhiW6hAS5hlC3FYgx752ddrKAT9ifLrJ9zqHGt5NwifLUQxcQPHiaiTQz3albeeNiH1xrdT2E+onBiYKW9Wj8EDG7oNNC+IllxBmuLQpYy2Ik/h6CH07jeOPPPqyZmpRAYz3nQ0ZpbzM5qD2CPBqJUywmaLHT1YkbT1110IKp6Php9gAjCJ+oQuPVGHsYecC877VRa7XAcbK+q9aST9NMZwa+iBJSsArB5hcd6P/oB8xXLH+AQfwi4pzBYRH2kHnpwQ7tLZK30tkFVKI04rUu/KztJb3k6MhANyDMMaf/VmJ/c3fFtvKR5L2z//nPkROcqqSboFYJIF5P0yQEwzJmSs3XBiu/u4+u3HxOt+dAICrinIICJUM3WakiT3k9zQ8MVwqfizlPI1XeXIC0nJUJabhtcJZgG8roi8UnuccCT2sC+R9TGe2MFw6a3t5qKaHH5464X0s+BR3ndWAK4tK4XOtAC4ewurpU4GavvtOFv7LM/b2wax99x95OVwFMq3XzRQs4AGuKRitkLskkCu2VNRaCE+rrfDjhI3TO3c1Aa73d4XsHHlCRRHl32Rt69qkPYks+cNy1ZRjlIDhiqMAIYn4ktLiaXV8OwFIh49QdJdQQAGtFSsAeqSEDQMpg6ZU2KXC9sTWSZJcUve37YTt5PBKFEORaiIXNI8oeQjX08V2uLfY9mLns1cJCyQf7Cqhlh38HmfDIgByKUhp1TIAFsxVWQIADeBCnM9qKlF+JPfpxGUENwgbbACUlc43q2/K2rKqtK2G4arm/WFNg9I/Dch9dHuUjp0732b21ps+a7sQsJdezNhW8tCCfuVhSSEySE5X8Ha43bB+83/QrwRLFDooFsuFEoLs8iA4v9mwV+7i7sEAoCcN+HL7irwjxyvgnmIP23rSfyRgaHfujNjO3WGAc4pQU8olUETatbvMkzfJbaswtr6uPrsuwMVYZgvgEriOAgCCnHBBvvCqZ8fXxxL8KAKA5QrzTtqU3g6jWMADXKMYpsBv63vZCzOu0M2Qnqbc/qIW+EBedyNaYFYDLhiuN54jpFCAS/e/AjX15OdByw4xXKQOeICrQIb1upmSBTzANSWzFW4n52iBCIp9UWudC8tCbaeRCsLePiIAIRdSSNHZYwFCshS+RvFcwFYjcuIPsylMUrlBCokqnmbApXmCAVw4XhgRBxVuHrXxkWpHqY0WIpnfW9d6hbBlcYLDRcV2+VbQ9rcFraOH8DtCBweQ0N9LCOJpaKtoPc4y/dbAXi2cl7aN1FUrgYU63041LvoRW0XKka1EdfAR8rFKYcXChOGVIZRBjelBUOWGdfuHRpkfy+039QtvdnZy7L0CmRRvXgPIWwZrppBEnJwkIErAKSQgNEIyn0I/VdNL26oJnCn8UzfpcZu2YWDJeIJw0LjL94oovG/cpwPj9uzmJRayu5uQS+xbjo1y3d47MNlG7fYnnGfdN7vt8pnZlcMlyewUJ4cYLv533xeFdUq9UMyAx3Dl1rlQPz3AVShLjtMP57LObV38ihDccbGy4+zifVw4C8xmwNWUBXA9L8CFOlf+Ql8Q06BSyL1OIYXvHPAAV0FM6nUyZQt4gGvKpivQjtykogOwB/EBe6QJQYJKOeu3XcpRDzKAEINEMxBus/pqnH4YFTlvuavV+PuP2vF9fJADXFHHsjwIwJUHC+76PNJFOm8GvQ79fOjvQ+fL+1LuUxidGJz+RNg+PxiywydR7GO71YQjPrI4bcdPU5gX1b6LFOK6ziAgjaye+MI1AK6VTcj9Iyxx7SZKfzBYiwktXM0+KnDsml74J0Cm5ghO2LUgIIxQ81Gb243jDBDCCG7KhR1yb8oH9SVhTMTMSblQOV3DWCt2djl2yYQDYwpBdLlQDoTmjTTqoYd9ICbThRKy2+AUhn0+lT8kJR0l/BLSzYoBpC6c8g6sGrVL5XDNJll4rblCaQcIu1KDzHIsV0DUKk3vSybeA1zOHAX74QGugplynI5yAcZ91Jgs5UmQnvtM5F42TqfexxO0wKwGXHYEwJUvfDzBCU9oMwEuGK7PYbgAXH/PCymckNW8jabHAh7gmh67TrxXHNdolDyZeNRWNPoBTvJkJ+IEy/HlMNpc2xfK+534yO/Z8kEDrnsGMOQNObZoc1g/IhQ9iFj0kSsEKWMNiF2Uoyg4ooUBEwP9/Tj+sFGVZXYBVb53d4QJI8zJSTfAZL36dJJivEi/XyCXCtbr1NWgXbzqtxLAFWlN9vhq1Acfg3HidzEY5eRzhfhMhaVvIfBBipsrLl0GALtFMeFzPeShAZorYOkW0m8d22u/XBt8zaMzvclbelf1tXpQ45evXoG6XzCQdrW6FDIoZUEBL50WaRgt9w9GJSg6LFCEXH3A1R2rJYy1poa3eHtSTefa4NAmtd8IG2tql5HPP4Zkfx+CIIsArEsplgxBOu45PdMBl2T20/y743TmvqQSx9B3Nwwrq7XGJaA4L+vH+wJcs1HYYYSlnTFv6dvu5XA9oOXgFO8hP7EMCt+J8RTqQvGAhj+bDzNrAde//V1rEuB6bhoBlxiu/R7DNZvP76/C2D3A9ZBXURfJODeoVDxmjdUZa6ohj0vhcTnfrECjU2cF8pDHGNFtwIXTWFxSgqN5n5NwQ6aPCQHQOwNTjsw1BC6OXArYcWptXaCQc3c7EvcoCT5LIefNm3AIEFK8x8aMV8IfecB19WbIPtoVtjMXBbhQMGzK2PNbUja/Ael0wFkaJuvA8ZD9bG/YugFUxRHyqpCbX4/4Q/8ArE0UlguJ8+6ujB3k6drJUzjW5Cw1NZu1rEAKHYnCMwNIrXPcIgDXI6j/bZ6btGL67UIqXcVaq2EvS1Sv6/b6ISJBYeGTzOvwccm2m61dlbJFiH2oFpZCBnNhg4KcTJHQSEmqK8ywtLzUrnSEqLHmt4sXCUt8NItELiIhVbLxHfvdz29a8lz45vjnm859an/bh+RvHUDYRCCysSFjLz+ZgBkEsY7THibgcua68+P2SDUnRVUpVDgJcNcs5HjqfbQEEBsBIfOHMDX/uyabRVk3sY65ulyDH3gvBbGAB7gKYsYJd6K6Z6UOcI1B2U+4N2/DiVpA15hZqVII4GoGcH0PhquUHC5dDwvZ/ISOOIbLA1yFNKvX1xQs4AGuKRhtOnaJc5MKUVm3tYG6VyU8+S7oQXQFy7t3Be14WGeq6xQjh0sX/qJiFTUe9nHuDz7LhaSNPx7lQClcT6AiR/qwj/PoR+g3/xabdMJqbScM8G1qW+3fF7RrbUiKwwbVIZbx0nMx+7V/HAOgAEhuF9kd3Jnx5gAXDkNFGY4zgOhC0I4iwa+K9SsJDVzaQtiXikAPjqMT9uzw6aBdguVqQHJ+1eKUUzPc+2UO1AxEM3b+fMr27c9Q60sgiHpTyOHXLQxZ8eKglVHHq54cvGKA1RwKIz+KcqH/Wpbi0zAdgLVHlhKS+Ej6do0xgZkumLX3PgrbvkNSJjR7alOCQs5JByI1rDR1qwb6BhwoLAZZSiBDa1NUWmSnToXsJz8JWhsgct26rL38ctLmUR/sfpShNCuFBLar0DNKiw3kEtby8AAdC9ro6yzs0YXq499tC9s55qvTRXXSvgGLuAZZ/PHawwBcmo2YqyQnplQv1YbNkPURoyUpcm2XDxHUNu68d3vkfmi+el8PJvoJJ1Rh3jBGG9bfkO29X6dmAQ9wTc1uU9nL2ZqQwhKc3Hyo7FT68faZvAVmNeDy5XO4Cg24BkMKYbje3gfD9QNPpXDyZ5a3R6Es4AGuQlnyPvqRg6Uwoy5ECWKEhtWTx9VKseE5sB73OF/5N+StjdfYVoAlBWMihkYO/B2mZLydJ/95Bqc+SjFnhbVFyCNyHvSQbqR2J+dSOUZBBBvundydjXtgqC4QticZ9nkAksZG+qTLcRtzPnXdb3+9K2Lvv4eSI/W6VG9LYVrVOPOvvhCz/+WfR23pYsZyF+ASQzfQ2+fGWEJIoVghpuTy5DRYFNTpB8PnPWW3FoQuokoYJzxQ4h3sbJ/8LGTbtgXs6DGzI0eNostx6mfFnANuAeqM+CMUYwRwVQasptVvzavMmpZS8HgRuXgoHfous050lYLJqq7K2IsAkEcAIDqcj+N3Ex75IaGOB4+gTAhtsnljwrY+ThHlcAbASw0t6oT5fSpqTNgacZQpBpgifyuCjGNXd9DVMOugrtjy5RkKRqetjJDEu9dqXDsP2QDoASgN2s79IevE1quQyN9KmYIaGMUx2UkmRLqc7TkQsr3I9vfBdi1qStnTj6essX7QzkOOc/evDwpw6XwWY5VvApgCVO67JC/nrqa3BLZU+FVhg+M952dTABcPXDzAdZclC/OnVsoLKSyMLcfshfNe34s48dTFKLj6OP/v/XaM2YP34X1YYHTA5bff/p3/YA2LVtm//K3fuo8jFH7XODeA34fhagJwfe+FVhiuwheuD7gcrrMe4Cr88nk9TtICHuCapMEKvbm7IYm1SCHEgPrcvlsUBSZX6MV5KXuulvCyQapL/p5y7fsISZMDURpSIc/RMYucwj7Yhovk61y7RSI+zvf82rTNI2+nSMBliAM56Tlp0Pn9B++ochrzgEugIyJ0km+D2zpBCpx/CWpI5W7ERn9SHzx4OGCffBJyYEm1sZ4GdCxcOI4Drw6x5RkEK/7ioyL7rz8O2wVU/RxTxhhqyZv61V+O2T//jZjVw8Lcya3JjSRJWFd/Ty99+KwMwOVH1YAvCNbWBHLKg25LxqjcMOVkCcTOqYY9Ur1GalShW2G7dvnt794M2K6dfjtxKmu9fXFYJtCEH9c7RCyjXxQZDgkOdnFd0Oat9Ftda9ZWozzY6AcoUWC5BsYLhX0H5p7ZkrRN61W3Sccg54kizTt3Be3CxaAtWZK2JzYmrY61vdaRtpMnOEcIb2xZGLDFizlPyggzAewpj0trEgCFUYKLcfIZhZUjTiJ/cBFzZpj4z8Hd0jws2P552D7ZS+gmQHnpgoy9xnq1zOeEHVz70TvNkosGuL4csP6Y3xop4j23Nqe+OPo+uU+mC3A5RUcMLZCl4ede70xEAEoAXrLuuXbnM61rDEZR+0w0H0tOaoKTVIBL/U5xNQbH4r3cWRN9c1kIGMd+lGZU88yrwzWN5wfXzSgOtN9PMXZ33/LO5Wm09j1d3wZcZ6/y8C1oTS31jlXP8tDnX//uj2x+6+qZCbh+74fWFBDgysnC51IRCnUVHGS4JJqx1zyG656zxnvjQVrAA1wP0tojHEvs0w18/L0IM3xwPWAXyf2ph916uTVpv7E8anNxiBW6dAsH+QSqhG2ILGS5ss4vy9hKCvTWIqV9T+NaJbbgAKzDF+T59OHI4sehZoh8/CrYEsLi9LdrI+x+T3+Db+j2mSG5X0/8cxfF/JbqTDlNsCs49iEEG1RQN+9t5w6B7DXhhnEYsEhJUQ6QjXRsANMAYObj7SHb/Rmy5cy9tFSFjeO2YUPKgZr80PNHH/bK/j3M98N9YfvjPwnbHpgmVN7NR2LNKlikf/G/Juy736VWmGOjhvckBb4B0IhqTQXQc0+lAtgJRo5tlYfjnDcOls4QZngqYvsOBsmvQihjQ9KWIPvuBBCY06V2s//+NwF7E9B1+ixAyDFcoEj68gVBZoFBqo4nwEU1EatcFLJmJN6/92rcllSm7TRsD2lXjp2UsMVzTyVsWSsMF4sW7c/Y9h0BaleFYBHNnn8+YevWJiianHXvHTwY4QlzwObNz5KvlrLWRXGKa8fd0+YIqNCF+Wja/BN4kz86keYsxbbKb1PdKDWFyDlwAlg4dYEi0UeL7Ba5ZysY6xOPppxy5kT6vo3GnhiEAABAAElEQVQw6F9OgxvXBHYsJOASQBIr5f7Lgy03nhw7evv7MjiunCDAvYOUSEYCwKXPBbhko9GaPtHxopybYsOkTpiHcKPt470/EQtQDpxz1K1l7lS1FOsSgbG+ex0n0tvD3ma2FD7Wd0i5iGEYhdkIbB/2Ot/v8XWpcTlcAK4QT1ibFtTycI83eTD0r3/4H61p8cwFXM0ArjdeBHCVcG/UiXT7pnCfVtE9l/Nx+x5EM770ANd9WtPb/T4t4AGu+zTg/eyOD2AdFPD95Dj/LvrtKL/Hkggo4MQ/vzph/9sz1OYiL8hH6NthwNjOSzjihJnJKw2jfLehIWlPNcKCwV7dfYE6e8Vv7+4J2w1YGCm+3STU6xbheasWpexbiBLUAb70FFL5URNpYq/yuUACVnc3l5/ChV0KeSrCe3ctKF1CE1A2MQBX0ZiAKxdmduAg8/00ZH0UG1Y44dPPJAAPYkzkpo7d5GjdIM9p+6dBe/ftkJ1GrCLkRzDjayn7/i+mbEmrAOPIfWiO0VgKUQm/nT0bANRQqJhwvnrUDWWDLIBzIBGxj3eW267PQwCurD2PEMfXtlLIFluKNVPXR4/67J23AvbZ51k7ejxhF9sSAD+BLlxqMSM+ihQHw1bdVGxzl4bt6a0p+7XvIpzCeh84HLIL5DTJ+W5BqGPlcsBLtUQV0tZ5M2Nv/wQg+UWR1aEw+HVA2jrC9252mr33finsV9ABMbGETzPfdWtAQNkENi/mJjwI9Eae+tjvMikBUbGUCg3NN4FU/UtmQ6g2Rjg/g+Rwmc2pAEQo/LJQN878AYe8FhJwxZISuABY46AoEArs40CTe0bvTrm7T5h7z0P5CWKq9IlCCccCW3emAcgnBEsFkEMc+95e72zp/Ta6BfJ2E+DVOghgSTDGCZZoPfkyOcGS0buYsZ/MFsClsga6vKkIuRdM+OBPpzuAqwOGK2SLFjW4B1gZTv7f/jc/ImR9BocUiuEaBFzTwXDlABc5XP/Qy+F68Gemd8S8BTzAlbfEA37VxZGHgXbkPI75voAdAiCdxskXm4Uvbkuak/bGSwkU6MixwYO4iCBBOyCigTAwuX5ivKpQr/vW4rg1ESo3NDxOYGL/maC9ByDQjU85P6fP++0K4YVF1It6ek3SHlsB+0Ho18JmJLgBDWM5xroAOnYqFqOwrvKvHMIbZjE5lw5oDRaTGiRBbm8jVi6RZ7hgv4aFHN7eKveLZtgNODyLEl9XF8p1TVlTOKEEFdzk79p+pD8lLhHFRpcALhfacgzfYvK2mgBvYz7lxmG4Cdv40UchO4QohULutmxG6v0Jko0yAw6oZfzFsFth+5KcpWLW4Anypxrq0uScYSfEI1oWmFXPMTuH1PmRY6zr6bS9/8GAfbk3CmAR2OMgvrCVVZVZ6+oSW7UuYK89m7CXYbLqCCXsj1LcWGwm58PVazAsgYy1tsRsXkOC0Luwbd9dYidOh60JFus7X4/bGkQ1+hAFESt4+EgAYGRWRV22Z59J2eKWXoAigKu01BVFnqj9htpU51OSHDCxlw6QyKsa1hCJQKgD5OXyxFRU+e71H7Z5gf4oBOASPNZ3TqIVmlaxnEV9OUdo+paojfSp3osTWpkCjE40lNB1xvEFuHLhhOp7pN5zW3o/x7IA7DsnnQCvrlcCV1pPsc65/8bad2Z/NvMBF+c9X44Y3yGBrVwdOe88ftBnlS5bs1WlsCmokEJk4WG4ch5Ogc4frgWuDhcM19sIWXmA60Gfld7xhlrAA1xDrfGAf8c3sHMdPtu2XyyXz46Sx+KPyAUkjA5Hfj2sxxNPpm0OBZ6C3QIQfqunTpMc9i7AhMDT60sStqgK5ucO6cD+ZsfbAo7hugpr1gboOAHL03ERAYlOn9WhhLeBnKCntqbtediuVeQBjXV5yzBQJ4aBU11SXkY+2L2AS6bTfJQ/BEmEqh4iHUPYMwEuiTcopDDA0zcBrvEcW01Ezr4cJ+cDa2KTaYAuNzEcajX1of6cgdw7I/yAlblyJWDvIbhxAjn5COGEGwgZfPZZwvLSyu8KYINSQCz2RHgiBAgMh9JIrWftw23kXVGYeON6n33n2z5b0EIIIHl0Uif8f/40an/yJ1HAXMYJcQQILaxprLB1TxbbY2vNmhvSTplvzXKKJFcwBs6LnwKgTp71U9A4bSuWpGztmrQdOR1Bpp5QS2yiiTzJ2Lawlgp7FLg8ipCGwOpCalmtBIiF/AOOlSpGGSMkeUXNX+bQPxlDf99efffHkL85rQAQadifGBNT8nFRUbFzYrXXnQaYhvkSKA+w6GGFLoo6dYt2Z6uJ/zY4yMEXjVOshbobyhrp9+6b3Xb5zGXU/Xw2d24luXSTS7qWGdS3AJdYpgj96L2JNmdGNnYPJVSPgCbANSaod1vlf9wBXLlnFZM5er6Pn+NXzCWQK4CvkFCdI7quKL8uvzaz3TozGXDptqMQ8zRfIoHbALbX99I7ix/8Wadzf/YCLupwvZQDXHduUgWwoa4J3JN2KKTwC4/hKoBFvS7uwwIe4LoP4xVi1yjCCCfazH7yWca2HQ1bVxLlOQCCRBA2w1BsfDxtLajP+XozdvwGOUODICZO7tcSxBpeXhi36mJue4Ogwo2JC+9NIsk+JTRtG7lMX5LHdZJ8ri5qUaVu8BSfJ+pLV6RtyzNpex0W7WXC4UKAt6Es2dC5SeEuAVgK4rArLM05vTnfMreZ7q5ilGB3Tp0MuByslTBzlY55y21yG3CRUBWAAckBLocahh7q9u/q/qHctJnHAKISB8nPkgpgMQzXRgDN4sVJwGIfcydEE9ApdkU3OAk+bP+Z2Z/+aRpGC6YCda7qah+hiwH75uswTdS4ShIm+vbbKfuD/ytme/elsaVZSVWJ1bZU2oZNAWTfcyCslHy8resSyM+nrONqwN7fFrQrVwEbLO+CZp89tgo1wLO8dyNg5eS1dfdmbd1KxFW2qK4YzwXZTrWspG5YxLgxM3MBcEmmubTEsZO6mbGU1gPrKUxUTi6gtsudP/lFzbEFSaiyhFRAcKbEakpdUqIbozUHugBmfjpWHp9sNJkmewoDqt1x2QgPRfSlnQLTAvHz6gGXeaYTp7q3s8eunieEhifrDfOqAFywa/lp5Loa86dGKJw0gEqg6mC5MFs5jBrMBJrGqTC2JE8bNF2F1I5+Vo/UoQe4RrLKRN/TKkV5wqPviCskjf0ntnITPcLD326mAi49Gozx5XGMIt8dhcSq6ev3VVsDN7EZ/kOXrFkLuELkcL2kHC49FJzEBXwCayLxKxdS+LnHcE3AXN4m02gBD3BNo3En1DUXl3gsaRevpGz7kSL74mzEenGYGxC2WL8RVqMFie05ScvitH9Ofk4HOU26HM0tzdp6CvAugd0aya+VE3gBefS/2V1kf70jAvOBChzslg8wEerKWgOha1ufTdqv/P2EvQjLFVC+zV3XOf2Jr243OhOWTRM2V1/imIQR58XFXr55G8yagNdi8qQkdpHvc1hI4Vg5XCN2Xrg3XbiCLuj5uTLunHMtx0HHkbONAw4zdYtwRuELFQb2+2Bw+sXgBAnPK3b7JJCCP4Bd/9vf+ey9d5J2/QosCQxJBhZsw6aQ/cK3fPYUYGjhgpRd7cjYBx+k7IMP0zCOQYv5Sq1qXsTWr6Wg8oKMYwWvkYdVGkjaqoXax+wsQPzgYRTxOOb6tX5bsSwFwxWwg8dC5EqJRczAcCVMrJgYHjcnXlwbnF+cvDkBoTChoPqnduZcwD5HUVCgbOP6uM0FxAxlSLWNE0ABcClvS+CpmBywgF/1yPTpyE1sWBIWNIMIRL4EwNAth+46xPxYHFA56KIJrKQ5iBjcARQ5S3gQcIIHETs/L7GWZsDlkwPks5FLRwdat/7ufrt56QZ5awFrmDt5wKXxqa8YY9Z3RjZULSw57+M1bSF2LB/GFmI/PeGfXPMA1+TsddfW2H+A3ErBrK9q0egZC7h4ONUP++5YbW5Ckz3z71pJ78/7tMDsBVw/tOYwDNfLi3OASxfVAjYfTxR3fIYsPPnU3/3l37TNm58oYO9eV54FJm4BD3BN3FYF31I3qAThWr5UzObOiVtPMmZfXgjbgc5KS1CPoo48nHmwVy/MT9qCsrTdAgRcRcFPl6N62JAa/o3qF9L5ZQQz3jtCzs9+1Ao/heU6wdN/ZMxRCremEsIJke7+Z/8wZqtUZDZ/jRt8VaFf5UAduxCwfSfJzaE+2KZVAVvSqNCR0U2B70NXOfYkN9LctupWohkKKZSAQ1gqhoMO7ui9FegT5uK8Ae5IAhLJGMIPMBJy2NMIkkh+XWuhHLFQGGlugImYirzvrKfnMZi5LPtIfVEMjmiMm4RrKuzv08989vnOuO3/ghwSFAJ1ga9tCNgja/z2ra+n7DvfSFhLY9r6+7N2/oIPwBO0AyfDdo2wxKZGBCYqeUoP63Qd9rHvRsLmV6XspRcoHlyL2MYlHElC9Ba2+MnLSgN+/XaS/bvJ2ZrfkKUQcwq2S2Ax5+7kx+zWk2kLYMZhnZLQWj6eQiezQdu9t8g++rjIKjkHXn81ZsuXKf9quK3F9sg++qcnhPp7Qo1hSERD4iPKqcmNSntyNnAMScjrTZlQLO1NFCV7OXwnvwP7rBSAGwVotcFq9RKeOpcQ2hBAtO1E2JrJv9vyeNzmKIRW42VsvV19MFzIIMMsThVwqS96dKFRAl4CXEXui3Vn9Hw8YhOzlRQDiMOpPKzx97i7Gw9w3W2RifztHpxgbdUvTHGe5WX6J2//iRzt4W4z0wFXkOuDwmE9yPVwzxNd+2ctwwXg+p4YrlIYLgGusZ7sTcbMuiAMAq53Pkel8Jc90YzJmM/btrAW8ABXYe056d4S8RRsQI81NF2xdGm7XY8Gbd+VFuvsWWARmJIEXuoT8+L2ODW5CGSzIP+cr6lX/TJa40JzGmXDdwFcUio8f9JvP9sZtF7ywGpQkFs/N2mvI9TwjZdQ1wP7XBvQHZNQxiJEOmAV5PyeaA/YX/2syA6eyVolOWUvPpZF4TBpFQC9MY89wpg0VKdSiOx6CAARlqa5YuBGu7DqQslOEr/QrVxNoSvu19H2cVvlf+S21Z6S3ld9qAHmGIBBKg4D+oqwH+okly4it39c3YqVS5AHxOclGp9CJ7EJ3RBs6UBLEJRQXFbiivVev+m3S+TcHSDs8PCxrO3ZmbB9yM6mMrBI1KEJhrHlfIoSb0nbr34/bs9/LeFC97q7cyGXHbCN+wj1vHYrwBNinEY8/jkwVs01SP1XpK11YcbKS/oZAwWXy0tuj0UDUsig2BiIL0ZNPh+y99cBJwKPCi+tjsBKkfcn22YBAol4zHp7o9aeCNnlVJGd7Siy6+dC9khlyp5Fan9eg2w2aHBnvhzQ0rF1PK3AZJvb9fZOsH783oEYyYXL1B7j/FkIeLqS8NvH7SE72BuwC2lqmKEIWcF6J6lHF+g3a2S7WljSr7Emizj/KsuyVs1DiCAKna6xPl2d3dZ+pt2FAk4lhys/RM1UOUB9krUGOIktydkkv8XwV22vNYgrlBDb5STgh28zsb88wDUxO3Em6vtP03mp3wS2tAbCxkF+fFUd/pkMuPpguEIALp5TaWX0w2sPyQK65s5WwNUcJqTwlUFZeH3PBy/x921KbOLjRrkdhuudPeRweYDrvk3qdTB1C3iAa+q2K8iecWLDBjLt5ms8bH0lnU5W+1pnnXVe3UheSAVMV9xa5vXbkjLyiLIo01nI4FDGPzabtCMF/wHhWOfI+VFI4nHyq66QB9YCQ/DSCqTMUdernZexL6+FbEd72DnFa+sIYwTccR+1bcfC9t5nYbt5E2GIYNpeWJWyf/pK0uoRdRBWmkzT9VOAa6CvH4YIFokwNYfaRrqwDpleErZDqnvanAdVOfd/pH3uHsxgH9q37WIA9b4gIYIBwhxTtnJl2latDuKwEWqw3Wd7uBBLfW/t6pg9vrGPp2wwMMpBGkQNcsQVWhcBJBaXlNi5Nr99tido7R0BB3K6epK2+9OUffE5IW5x0GuAfXEA6+dlbe2jGfs+db++9Qqy8NTQ+uILvyuYvGQZNi3z2znCCwUGFX65cnEadco0gJB5wsT19sYsmkaanhDMMroNAcx0I3JTGxxbByzZl/RxFuZLoK2+GPGN+gFrqiTnDidIzpr6ujgQtF39ZdaeLmKdfQhpmC1DPOXx2pQ1wyKV0nfOjcWQArRD1uBu0076b/qKEX65/fOgfXYwZBXM9UXyBs/Ctv0ZTN9n/UjKA1uyFUmLAGCCnKdVsLkrCHVcMT9jb6zhgUMjQEj4d8jaF0KlMD8XTVfOe/8EAZec/4QcfvYr4nyW0z9kaPluJ/BKHgysms415cCMJiQzgY6+0ptofRS6KYGMEA8+ZH89CJc4S4BzPHfuaquvXvMA11dvTadjRrolzE7ARUhhBJXClwcZrqEX+UIYCsDlQgqJRPEAVyEM6vUxVQt4gGuqlivIfhQDBnDdyF6w3sb9FitG6YKiusF4lRXf2kiIYbVdrehBFS9uxeRYtVjYnsyWWulEvGEuvir7dOQyIWSEoPXgwJLKA2tm9tj8lK1Hxa6qKmPHYL/+/ESR7QZ0KQ2otTxtq6kF1Q3QOYZaX5LQrmKKMScAHI8hHPErT6ZsAflfkwVcyulRPlGafpQHJdA1apPnihPV00343KkgKn/U4sLxXoJSXzHiEBNtcmKvXzfbvt1vx44T9gLrpPpUCxeavfQyrB1sybZtftu7V4ArB46eeTrthCR0zc8fSSFyEo8QSIwUl9ruPQH7iFDCLmT65zVkbNnShJ07l7S/+RuKTZOH1xcT6PJbbU3WHiHvSiGF3/lWCuXBrL37HuFzSL5v2Wz2/AuAKMbUD0OlfKoK5hZkHDpeZ3fSTlyCRevOhTC2zk3bMsBxGdu5xlohrGc7zwf5FwL4Aa6yqhFGaB3rt4iyAnPorwkFxVrA1BfUyNrVTS2uQbMfg1VSTbc15Ul7fA6MGuteCnNUwvGL+Keb9/gtb6FxNubjAfL6tlHI+rNDsmHGXiFv8Czhlz8+EbGTAK8uQmUD1DgLMfc0JRKKNQ+YvhdXJu2XNyVsierR3TWghwW4hEddKCGAS2GEYRdumbfFXYMc50/tpdBLqbzp9yCsndTevDbcArKRZPf1nEcsv85P2UoPSu58U4fv81X5ywNcX5WVnN556DsxWwFXU+SQfe+VRYTHIws/9OZ7vybDJmK4duxGFv4zL6Twfs3p7X9/FvAA1/3Z7773TqAY2J7tsOtzj5HXdB1HAjGDbJ1tyC7nwlNsu/191usjMRkvbx7FZZ8FblWIDZjAkeXCRXFc2zphtgAvCsVqxLluBGgg5kY+mN/evxC0j6jtdJNQQ13nIvzQPu0xPTmmHlh5yh6BkUhQP6q2LGHfWp6wxXV6wjyBAQxuom1VMFe1nELhXLjeeE6SHNDDh4MAIjFsAWuG+XnhhYQtRcJ+YsfWlTZrp8/47P33AtYDlq2jePEtAKb84xeeh+ValbEzfH7oIJuy7ZpHFVaosENNLj9BQvWgjpTDpafpkeJimEK/fboLIMi4lrSmkddPkJuUsr/+a7O/fZMwxY4IeWEhcrMAY0sQwUCefc0GpMdxEPdyrBD9vfwUku5PcAxymW6Sj6WbTBVAJII4RwrG4+hFgNQp5Ofjqkti2D5rz6xOWjOgq4cQSRVyzvL6IWGJp68SjkgoqNa0bSBsl2HYSqjRJXC6mNy7NeT/nQHwnO4LWC1z72Z9v7wltxU5ekL1KpCUn49aYW0kY42EI66ETa2BZdPnozWBvWsATgGlSkL/xHpKQn+0lqGUQRsy+nuRub/MAqwHvPcoB+5kyK5k/E7KPnOLnC7O05v0WYMi4SKUOn/hsYS9uiZhczgHxbyl+McZ4ICjgEn3jR5CCi+72j9z501eFj4/Xs3VMVxMTOIXo+ZwMUXVHErwAEHfwpwE/FiWyh/h3te8tbS3vqtJAAUvLkfS5dDdu8vP7TtJ7J0S++nYxFx+5eCzA5nsK908wPWVXt6CTW72Aq7f5T4Ew/Vqa64O18Ru8BO2mxPN2I1oxm4A1y95OVwTNpy3YcEt4AGugpt0ch3iR9jVbI+dL71swcpuK6LW0ZxspT1utVbEs9xDlrDz/EMXz5ZlI7aCsEIxUWO7w0PHIFcOYQjYDIkTCFhUShyCq/NRgNi7iHT0JcmfwYG+iCpfbz/OL+CrHae3mO0eIeRsOUyYGLal5QP2ZGMUhcNidTT0ILnfh76V94J4VTiehBt8OEuRoiIcyryrdG8X+XeIgrPPvwgAuFBtJD9JMvkvkW/2KLWoJsyuMeZLiE789AOc/Ta/lQEKJPleM8fsxReYzzIEDwi11HtqpGfBvjGJ/Nh5T0AoRn0piW34SXbr6Q1Tf8uHbDv5dGwrADh/fhpGym9/+5bfPvgpgEbhfQiOVHK8VSsJFyMc8gZsTsVCihJjtwpYp1c3xmzxPFQHCXc83q5kLL8tqU/aynkDVkwu06cny21/WxGsV86ovbCMqwB3pc1ZOwlg84Xi5idn6/CpIsJP/bYAcFVb7LdTsbAdI6fCmCOkpvlx4hsBYyrBJY6okfWP88FZQhHnRWDzYL86EgFCETNWD/iKYLOnqpO2EeGOoiHLpFHoPijTyCanYet2UWqggzDNOnICNy9O2EJCUVnioeZja+2QO10k7HGVc+uLWNDmhNNW3Ze1Q7Bzl3vIjQPkxrHZ7raIXaBPMX+rEIv5wYYB29iSgBGVDQGX/WG7hVJnOV+cJli5iswt622/AthEpXAKsvC5AeZ+pgD5CcCuWKtc8dbc+/k5K7RU39ecFIjYLVi5kb4HQzudxO86jkCF7Cv2TrWktAR6/+e9idlKKGYW20R4Yl1As89403qAa8Yv0YwYoL4Ts5PhAnAVK4drIQxXxN1nCmdQrqX4VNt3UYdrNyGFHuAqnGm9niZtAQ9wTdpkhd1Bid49yTi5NZ0Wnh+zagptNWWLbAEQS4RBL+5dJ//ICrIa6JAIr2q5nxMbSxxv5Ww3ynjkcinXYTXCDMvI4zpyM2jvt4VxHMVA+Ww/zn8ngEtAohffpgqHfAOgoBXAVQ/DsLwyajWhGIIS1ONCyS+f4ySXUM70AGp//TApRbAjZbAechTESigcLwpDJLBVBEM0URfyEgILewhD67jmQ6UvZY8/noKlyvU5oZljJA5rhw/57AvCBnthknT4NasJH1yHSh5j6wFszYE9Kme8YvQEGJxxB71chUIOsGN/NGOXrpTTV8huAahUC2rRopQ9+mjKSmCHvjgasf/xQdj2fknYYjeCFwCKBhi1FUi298AWnu0K2tLHYNnmUQwZYLFpcZSQvxQFrwFM5FdJWrkC0PP86oQ90kQJgFNIoR+niO/gRP2IYCxYlLX2uoQd9PXZDYtZZy+5T22VlrpeZs3ECtYDWq4w+AvlfouR8CQmJsVaGABvDrXcGslNW8o2zYAK5XDx5bfrrDslnRHQAAAxp2uoBq6A4XoR8FQzyFgJACjMMUU8puyh8MXtx8m9OhN2OWxyhrcujdmWxTEeGPBHftCDY7/zQh9AiE4EMtDLhKvNOvB0E6ZVLFl3ym8/OVNs5zpD7gHB6oakPdcatTLOw6vss3eA0NezRXb5BADtFozk3JS9vqbDmoPtFAoHNE5RFl7jU4ig5qW8NzFn+XPb5e8xd73mctsEhACy/NN3dzLfwzt2GOs3Qipla8aiMUh9j/+9hi3iAC4B4pIw35efI5t4gMs7/SdigdkLuH5oTcWEFL6aE83QpbaQzS/ABcP1zi4PcBXSrl5fk7eAB7gmb7OC7iGXbYBkq2SijzpDGZTqcCazAZenoAMNTQbXdUj/JuVrsPEtWIWPLoVsz1WAEo7iqpqkfaMl7pQJ34Ph6gQA9BAetvsIYAKWqwzd+DmAhVWow72Mct96JM2LYUKKcPoHUBlUPkVZRbl7Cp9zrrPWRSHdgzAeF64g5U3u0lrYozrqJclJFTsUV5EummpBCVw4V3WciYjl0nj6kFMXW1SJfPpURAVQorfL7eROIS9eXu6zJgANUXe2+wQKeedhRgBMVSgzprFTNeBrETXQ5pKbRQShk48f6O+3c+dhnXaVI9MeoriumAjJgGdt8xMxawR4bT9QBNsUtvaLsEfHcNwJFZ1P3tV8AKuc5zgFHRtXUFeKUDlN+2uEGYZYjE8ZgxxrOflxWMhnViXtCXLVpGK4Gxn/8yhN6hxoraevhRnbXdxrn6R77Vx/AKat1OI34T7jpVadoh9EUm5g715YMdWyIgLVsqyFAZYFAJsJcXyM9zZnEWAJMT/YrLPRgF1m3qWAqwjnhnL31pLX9SygvIr3JJ+vtctoMdTcmgVs78US230mgkgIsvUQdF8j1HTjoiRM2vh3Sw7jThtZwp3f9JkBnN+IsSaIt5wGcGF6W07eFqWmYbXIwYMhPAszdoFQzm4AdBB2bA5z+fUXOuylZedQdsxaXYPqcAFSxx+CZnK7KZQwRnkGMa8qoKzRgXncP32W5XyXEqE+1/n3IABQhgFIoVTmzoGu6QB3t00wK34REFUelx6KOFXIWTHq+x+kB7ju34Y/Dz3MXsAFw1UCwzUIuHIXcHejKciyieHasYuQQg9wFcSeXidTt4AHuKZuuwLtKTGLNCFoUVtA2Nz8ObncqUn5jFybBsmknCc7dGR81o1T/OkVABfCGCGc7FVzqPPUTC0qnPLPOsjhuhiyfefCtp96XV2AkhLqOq1YnLJvrk/Yd1fGbGkdzjZ5NqpDJcAlAFVWRSaZrvCD7Tj1ut7dST4OAhc1FAp+DrnxjQgeaAuNLQ0zogK8YktUyFf/FDY1btPO+ckx3ik17a+m/d2AcmzclxQR3vZlyK4Sdnidgs3XEAmR0MRzm8lV+1YcVo0n6lR+TiSSFCAutk93Is/Oo/UKnPs04Ogmc5WQx1rUHr8EbJ684EeJEMB1kv7acZZx1EMAueVL0/bo44R1FgXtCuyYxCmeXJGioLLZPnLoLpO7pIHVE5r31PKkLZ6roDXYH/q6Qnidfp8PeA2gKrgtfcs+6EvY3o4qFA7LEJoAT5WFrLK3yLpOkx/FOqUVhkj4aJZ1tDp6Zk5FgMpyANdqQPMG8vEeBYRthsUSsNgN+9YOsyXzzIHl2lqVtOWoOVLF2IWCpshPCyIR6cJBXTgXzBghiYcuEA7I+BrICVzbQmFsxu/s64w9yg8tpz7SD37nf0v6UnbNBqwzO4DgCOqRfZVW44+4MMK/aY/YJ51Bu8hDCAHXnm1Yg7y7Sibuh019Y8t1e/XRMzZvDnlk9dWMEe5MnU6wadM4HStHSIAq6M7pXAccjmEOMlqDg3YvE+z7fjfT8aXKJyPpu6bwxSFfufvtftbtL9tLhl+hn8U4UQ9yLR6msTzA9TCtP3uOrWvDbA0pbCpVDldeFr6wNvfxYHPHLkQzBLh+0cvhKqx1vd4mYwEPcE3GWtOyLQ6knmbj1Bf7yeuZiwNN8sztp//jHDMKcXQVJcF+8nKqYE/qCP+TfPid5+EKUTK7BCNyhJpPclIewXlfgAOuC/RFhBR+TFHZP/+y2C6dABT04NwhnrB0adL+yXMD9ssrEVCASaCgk3vkL7YjSWiZnO+QEoP0Eb2ebCMc7JOwtVG7qwHg+OLmpK0HVOh4+icXVk/tU6mkE6HgTydCIeB1u6iuNhypaefRPhtp+zHey7nSuf66ATQ/A3C99X7IPid0sR3ARfSdy636JWpnvf56DAnzKI5/1o6drKBYcE7AQuqGUju8hdrgUopGP/ts0jq6zb48FrQ2+uggp0oFjjs7yccB7K1dk7FnNqXtOvY/dBqVPvpb1AQII9xwTi1hggh53OwhZ451qyaPTMxZKflUc+dkyJfjdxg1WTCOit/+RKe9ez1jH56rsWMwXGkAYHV1wGr6ALtHAVwwVlaFsaRmKMClUElk30sAzSHOj8WEEKLjYXNhojYgiFKF+MZVALnyzOZxTiwCiDUSwhdKAzQplixVSa1zhILPKpycazkrSlxFRYqL2Vf5flNbIvLhfD12wq5bD2GSYraas3OsJVtrl/sj9kcXsX3XVcYXs5KSBsvurbLuLwTKzKqpW/bapmu2vumClZagbFhZa/5QGPaD3C7mEFZn97T8GSAYq1DCQVl2/hKTpHelfBcgp04PBMRsqSkTUm1qc3S7TvGH2ErON4CXnhsob2zog44pdjord5PtBUCVbyqmXrW3cpB4Vk5nwoP2ANeETfVzveHsBVyEFJYSUviaZOGJgdeTpgK2HOCC4drp9wBXAe3qdTV5C3iAa/I2m5Y9lCyfpkBtA/kz82rIEQrdgUyjHTBGHs4xQuI+x9HvA3A1VGdsE7WyFlNU9m7ySNcwKcvJdYzgqLje8Vc62O8/H4vYn31RZDfOI1EO4PLhmC9ZOWD/8Nlu+26zzxakcpljiraSGp/k3eV8F5coHyvX2tp99uZ7Rbb3cMCWw459+7WELaOuVBZhh2FOKk5jEtAl1UK1QCAHuPLbCHwp5DCru4ec3Jyf67ad6g8BTrVcfpbEGRReiMIe4YrnLgfs/Z+F7IOPInYVsBiGlapBjGHzE+QxoYq47rFep5DY2VVmHwO4zp7LrQ1TQMo9Y1u2wuRtZJ4wRydYi227yeMiFPDYecAPdbrEIFUBfBrIoYrCHgrkVdWSj9XCvoCw1yg+reHtA4hdhelKkMekfYIAIYU3PgLLtppQwqoS2LYEYaDJmL3fHrKfnCu1k11hux6LECbptwbARUcb4iDnYIIIdxSzZQpfJC8s3JCycupYBVj3kt6gNXJyzIF5qyHJq1qMI578InL6nmuE1StLsmaci+TLuHppg4B4JDbSLZEMS5sMq5TbI//TZ6d8N/h3HVl7FBvJd+vrqbTqgQa7Ei+xfbF2q0/8FIGPa4RobrH5iTV2+XCJnWnDjoDR5Y03rTZ7kWoKEUuUNQJAyX1E6XFe8YDVFVPUO48RBw/nhC/0O8fSmAVmVEBX54bAjOakEM/cf/kxPrxXjYNhko/HmnAiu7wuxpeTQ39443qYR1bdMgGvElCXH2D8VW8e4Pqqr3Bh5qdr1+xkuKjDBeB64+t5hktXPN3/C9G4B7qQQhiuTz2GqxAW9fqYugU8wDV12xV8z2g0YQEc6uWAnEqK4o4JNrgeXSXP54M9YdilgJXAZCQJc3sUkPPKE3GXZ5TfP/90Pokj3qdQM2LHSgB0ReTxdAAA/u8viu0PP45YH8yMcFCA0LAVj/faN5/vtMfJiam6Wm5EalkrRZHLYBrExhXBeIRUjZgmQHPkSMDeez9iFwFeIRiyp59K2gvPpWAeuHTq+jmkKaxQzEJaqg7kE2UBcWK/1AJ4vgJczvPVG7wtx1+S7HI2J9PkUF8nN+0C89KuC8mpKoHZOXmCcLjDOcBFxKCdbQvawaMh64EplErfIsDQEsIAS1D3W7K4357YnLLaWvKzrrDdwYALJYzwIG4xtl6NamI14WxSfzwJ4Pp/3yy29ymKfJ5aUp0wWkzPfOSgZW8ylX6BGNggmJk66oo9jzT9669T+JpJnaVvgexixiciKYltJECC+e0JmMJ1qABaspfO/HaWAsYfoe63/zo5TYTgpWFE6wBWSZiqM2cIjWQN+sDCiWIcc0BXiJy1EtgyP3OtAwBvoDQAlQbsAmIkFYmslWGbeoDWd1v7bX1N3AEzhRAKDDuby+z5RZxqWOeoC+ezyzBcJ+2qdWcTdrSj0o62zbVbvZXW66c2XPCSbQi+bUvD7bZs/lO2tPYxS6MUeRV2MU0iXjjVY4HOc+SrFVt6zkLzhUstypOIikyPLawYQGAhJ0LhgJZON8ahkDQ1CVKq2DCnopN5Vx6damoJzOS2uDNtt8ND/KERaQ76runVx3mg88SRPG5cWqSvftO6xPhuZKk5V8R14ueB7fMA11f/vC7EDGcv4CKHq4wcLgEuMVy6cRewOcC1E4brE4/hKqBZva6mYAEPcE3BaNO1S3QgZQk0ylvnmTXW4kzo4e1o1x78q3ZyiN7dFbaLAIpK2BCxXMua0/aNp+KEpOX2FdiSk3aTkMKTN/3WQe4N7prVwbosA0Cp/cG2YvuPbxU5h11+dYC4uqWP99mrr3bZgp4i6z5dbmEYkXXNcdvQ0kffgDXYLcd6sL36P3AgYG++E7Fj5DJdukr9LuTSf+l7cdsK+1NGDlFuIgyai2kfQOIMUuhdyHsvQmlublWct+mEJpEG5YjJfdRe+ULJJSC326GH2nC8RgdSA/wMpb+dR8kZA+g8sQLlPebx6SfIk18F3ABKpNZ38mTQbgHM5lO7qgL0MQehiUpAZ/tlRBt6UXRcnoXFQkZ+OeNJU9OL8MEwjJVysIiszD2LA6jtpUjz7/9Zib0JeIWszIVGyMRdzKSTf4qF0qwEugjdWwNYe+J5wFcrkvUIlQgcuEmzlQBcAoDcToHiWnKvXnsMcY7SLqTpUR8MldkF6mi1dQeor+W36+RttbD+FQDdPYhsfEZ9sEuo+vXDliUBSGnRPHLOcVRXwMo9i9x6AuC1CzYs3Us+DABkYVncfv3RftuMQIoL85RiiEbLkPoIHexlzQQGywlVvJs1chvex4842oUKK7ySjtq2S1X24YU6O4MiIcSqNYYHEPo4ZUtDvVZV2WyRSLXNQ8q/GiMGpJQ50Gm+a2ctGy62bO0i80VKrb8/aWXpHmut6GfM5B3CiAikgKMcmBK41/kl4QWFERL4aVEYPc1VxXTvxvVimVy4oXaaAU2MXB40ipnLjW2GDG4a7aN1S2qRWA8BTacmOY3Hmylde4BrpqzEzB7HbAVc/+7f/i4lPgS4FjrApW94wRqd+ZXDtROGi/v+d7//L2zz5icK1r3XkWeByVjAA1yTsdY0b5tAKW2gj7whmJXF86g1BCAY9QkuDocA1hcwM18cCxCKZg50PUFx3LXkFTmnmIuNLl43+nCukX8/AjBTAVo1gbkl1NhaiBz4j7cX2R/8NGIZhVTxmQ+HbvnSqP3ia302L8F+SIB39yBKgNLd1zfEbP1KCTUA3OhcT96liKfcrb/6bxH787+K2A1C9eaSo/Ti0yn7wd+LudBCikDBXEkwg3wyQMFPYObarvvt+bVJe2pNknAuNyzAm5TxAFzOscLBQrhBUuRO3RBGbVR7sLvmquZcTwBLX9RvHx8I26fYSHlR61s5Tl/aDu5HuAKAIvZNtZ8O7Jf0vN8eW0uoX3NOoU6TO3MqZadOA6yoabX1yay98oqEGXJ2DyHaUALYcllxQqk47j8F2P3OH5faTkBwTpMdx17hfUiY200cehfTyXvsW1lNqCH/6hb6bOMzPicfLzYrN4ccIOjiDip1vgx9/P1lUdsyt9MGfAEEJCqsi/cbButmdaAyiFK21QECDwPA3owiZkFtNdUCu4oNrgPakgDtasBsM8duAkxWkNd19Rr5S4DBAOBrzZyE/WBtgtpXjHeIIXupx7a/LWSnsU8tOV4bUGScT55gYRvnEB1egQ78q66Q/RUKiGe7iqwEu369Nm5PVyatHkB7hnphF8jdWoPk/VLYO7Gh2T4A141zhI0Q9lq7AIarBNXPhJVk+shTJCfMTygm57PCKR17xfTy/FWeMdV0xbpy6uXOPzf/nBH0nn6TeqHYsJnQNB7lfeq7wlnlxDS+yqBLYFkAU5cEPUAK8TAgz+zJFvezKupbfczkpvlFUT3VWJ1YyP1MuIAT1TAGuPGoLpryJXNXrQIewOtqUhaYvYCLHK7yI/bGNyh8LIbLfSMLdJLznXE5XGK4fkbh4+97ohmTOqm8jQtqAQ9wFdSc99eZHIoUYCNFyF5VJGkt9YT+SavbtXvdAr1zC0f6zCVYFxzRepiZ1vmE8QHU8l4E92nbDxjadjpXN6tSzBcNoTt3A2+sSNu+M0H7ww8JKcRxl/sSxrl5dEHK/smLUVuNYt5nR322DynuQDxsTyCi8Y3nE9bM+zpGFKB3AYf+KGIRH1KH6s2/Dlk/gKsKBmfJYsQitiRsIdLopCRZa2vGVq4EVMK8fIoEvXKWNi1L2WOt5BcNdWa51iq08cYNxoncfAUy5aWlMUIVEW+I5CuRuWnwg9A7QEE7cxTT1ghDUwZ7J+gIsWGnydHaf1bhcTB6sGkXTwKwDgA+YKcUEYkOhWO4ThKKt2yZ8S8nJhJHya/tXBK5fIQr5putW2uO5bqJyMjJ80GrJCzw0aXKt+MgGi+g6C8/j9j/8V+K7QRzcyqBUsKHjfHd4iBdAlz0zfoqnq8UYYuiMkJBEbx4ZAOg63EUKrGpBDrUdLu5Crg6AECOAZy+vyBqzzV12nEKG++PVVEnzW9N1PFajQBKF6zMKaTTifbEtmZfYMtOwFgomLZbgKnrN8KWQM5+GQBmIWISSdjE5bCKczJJa0cB0OcL2kYA8paFhEfq3BlsuoGfuQY4PhBy8vSVhC2+/GjSNizUeuW3KsyrVuwAdvmvLNpBxmtXio3Txd6AVV2PqmaUOmifXA5ZW1/YllQGbC6sqQNM/TnAFQBxhubMhfUilwsnMJyNW00RhZjJrVOIoNisoafYaKPWd3CoC65zKAb7VSTARR9al5nUwJKc5wySV4GuYd+jmTTQKYxFtta3Ia9MGOIpkR4kjfXQZbTDKFdPgCXX7qyiQOvQ9R5t/4f7Piw1a6zRh/iuS4hnZjTGxfVMADhMfG7ucd3MGNnP4yhmNeCqgOH6hkIKVdajgOc3XfkJj9/hAJcYLg9w/Tx+N2bKnD3ANVNWIj8OfIGBAZJtklEK4AIMyvkABmmshs8AC0S+Dk8ZFX7i7szagd0UDvazc0H7/GLYhRGq9pJ6k6PWjYNeX5Z29Zf++LOI7Uc2XjlH1Xy4CqXB55B1f3lV1NLUCdv5JQINN0psGU75c1viKBHqOCjMUbT27bMo/RG6d5iwwuu7CWO7CCMArqgDjJQDvNLkCUmZbzOFi3/lBwnynijmDFCU6MccWJMyKeoNc2WzdgkQ+alk5jv8MGRRW/tYD7XBAFxFhDJq83zjj/NI0v8UsAcZZi+9nADocXDsoQZpSHFjXAH+LIYB2r+P8IIdudykcsL4pFhxiZynywDGmhpyoSisrG0TjLkoNEBfSVuwMGhzYRxRJrdte0J2iNDBKqThn9+UtE2ryUGjj1MX/faXuyL2/yG+cQYAa4CkLPW/qK5MfSwGRv5WVjSk/nHQII+EI2VBq1sQsMZlflu9OmOPAD6LYZ4k1685RnGuLgHk+unn5UVxQNEt29UbsaOJKkszyHLCI+vI0brOdscpat0Og0lEqkXrE5YEqASKURhkzIZQRvJ8sS1grcQUWSZlzzTGbFN9kifnkucP2jxYryryAIfiKNnhImGoHwIgz8FKVgNkn+ecWNUEgM6Zl84L07SkxwFHn5Gbk4K9a+4JWwsncjPjCmDgvefMdlD3qytRZCsBwMsbAEFhbDwIuPwoKfprGs1PaKESx/9/9t4DPq7zvPJ+pg96IYgOEmARe5VE9W7Jciw5LpGd5Msm3jhx7JR17PSyccpv1ym7cXpvm2STz3ZcY8mSZRWKFClRhaTYK1gBEL1OL/s/72BAsIogAIuU5krDGczc8t733vve59xznvOEMxFrKE64PEUBMwXpU22y9j/vYhiEUrkWAZd63zFAXLO6sh0g4eBMdV9n5ijO7Fq0D5IQJpDCil2ULPj8PdN546ivSZvWeOi+H/9OPaPxTnFcLpbL/ar1C4yfv85Jq3rrP44PdhPHk7Fh8r69dQ3MwVSNF+pbd33RSP2d+6xWTrT6rWvmO2jLGq+uO9MMnHB/73MwXBVnARcD2oyd4zoDPTyMe+FFGK4Xpga4BPxUykb541eTQ34lp562kVdaaP7z/77UOuQWncJBWA+glQJQmK6PHigArmvuOElGJ5ASt/qKlDUDAnL2xxdvaP62psE2F0xMmo/vxgA1L2Kw8BKSwjkEzKixmCTREeDy2EKKyy6GFfvaqZC90ceFCyNjsErNBL5rcbe7fcGIC1x7h0usdzhgtZgvtDQAGBTIs9Fugvgv7A7aX78RssNn+PJ1brj7MWJArleKfC2CWUQE0OeFObkBJutnfyxmH8Qowk8+kMCABhgBFg2M+X813O5Fnvf4ExhVwFwtWzyGnG/M5rXBbsH65OZ1C7hFTp7y2iZAFESE3Y2MsXU+K1TH5GcZX0Dr7ezy2JYtPjt0EKmeA2WAQQBfA0G8TCsGsGhPY3VeTqC/EKC3sA2JJ4yK2DXZugtw7aF+VxWA6+4bsb6ngHE/BYefRSK5DSD2KuDvtX0Bi2Bo4gAX+MqDjJGKwnQEDoCyN6RtHgwfiit91rjEZw3kcN2ASUdbi6STHCPaA35wgcsQslH10d0r4ra4dtAOREK2M15hwzBc83EurITp2Y/U7tVT5HQBTkcwQUk3R81D27MAEheAY6aShjEqOROy8lja5iANXYmByH0Ap5WVKSvWccj36qR+U/fFCXaPcFxPcm4IHN/QkLYKgNlsTBFa0c8rxHlVxrEJEGxKOtdF/tvWo0Hb3F5kPRE/uYTUEFuA9Tv1xvKSQoPZsjnzKWlQDOOZtGJyuOaXRV1enFp7/rWhU0I3unPOpfN2Sr8peBfDoulKWTI383fxH4XgMsKR7E5MjkCEkxh+F9swG5tS/yfYpxjjocYKyTrdMdHlzQd91j7r6Eyckdp/ftF4mJt01ecBN19OPhH4M1/b7PJnQn5db8X7xJ6x8dy+aM8ndu+taNJ4S/KbFsHKYyc3TulAqL8l4Vbf5o5Sfs7C+2z2gLr7egRcv/s5TDMqkRQ+Mu5SqEF3xibORgEuMVzPTx1wvfjii1ZXV4c6Z4EDXWqW7seaRkdHrbOz073GxsYoW1Js8+fPJxe80QEhN9Okf3p6engYewSH5H6rqKig9M5iHvLOuWC9+r29vR2FT69bj7bd0NAwYVKmVapd+/btswcffNBtczJom7TJwsdrrAcKgOsaOyD55iTigJJMzBbUGbWgFDDkf5nau4DVXmRhzyAplNysQhJp1jUmEAT4umM+BXhxrft2XxCTBb5jMBklaF9G7suNFMatLRoxoIyVlJc7kwwFdxO3ew3wrPMrr4Xst54O22GYFGtHKncC5gXLbj+SupEegiaBPKSMzViTP/pgyu7ESENW6aWYPKTQJHqRpcytofBvLbItgIaiqU5MLV55xW9nsFZvmz9ma9YkrKY2p3c7vyvkNNgPwFFSPePXOQ6N5/eWYudu8pEOHCS3qVt5ZeQ0tWCDvzAnMRxADolpIlbrgB4YsU6MM7p70q5t9Y20h0K/7bBZ5bR/SVvGqqnJdRxHwqe2hKwbl8M+ANJGCkgfPuqzmBguGltMX/r7EzZ2KmHxiMJDtksib3VLwOpvAMS2UANrLmCLeZMcl2Kke5XkSZVW0Bb6rqUO18dVMZsbHraEBazXV2qjHKt6cv385MXtJG9r8xm/HaWIdWcUUDk3YTFknAYG0To5fOaFnSxBqrcQreMSWEcQnzXBpr27MW7zKagc5yCpb0IAYQemXSv1j6RYBPMwbWJHfQ6wT/w4Yx/yt1idX33II49gCKPzs6mcosgDHAdsF4/0h/mN2me11BOjrlhQDNfoADlcRwHwgOI5uM1QhysFi1jiiVlFSAyfgtTzJ84VznOBUdXbutSUP89SzCumSwyXnA0vXN+5a9AT/qu9Xs9d05X/lW9rVBc8U0gSL0Vgb9bYK9/Ed2FONTa/J7lPApBj5D6KRZENvHZpfBeZd3wsOrsIX+m44mrqDsDZndeYlRu3zn6nHTr3L31TmKbeAw7yu86UvFXHTJOKiOsc1HWcO0STD9TUt1JY4vI9oGvjegVczZUUPn5UgAuVzbh89vJ7e2W/uvMPSeHGF9vtieevHHCJPRoYGLA/+ZM/sSVLltj999/vwJFyyTWNjIzYf/zHf/DwdosDXgJhktfWkuT93ve+1+666y4e2pZPNFJA64tf/CIux2+4Oqaaf82aNfbhD38Yg7FFjqXSd0ePHrWvfOUrtmPHjon5mpqa7AMf+IDdfPPNGH2h8OFA/6//9b/sq1/9qn3uc59z23Jj/cTWCh+u1R4oAK5r9MjIrU+FZ8uDSZz8cIgLMZpeVXSQtUGYrFfJf9kFAxInf0qARiYLN8xN2q3kaiUITp4hGG+naC5G5tZSlLb7q5PWREAfRaOWJIAtrSgjiDkvOKVJCtL/k9pTv/4PRbb3JCtFplhOdNSM5EugpAw2RA+sRgFfc5AhllEfqgaQQoqNRYZ4H4lZbRgjjlVmd95FjajG3H6mCfCHKAYcAcCEg1H2H7kdtJkGFua4YPJIKsekJ/1v2k+sQzliMeR+istwuCdgzwUNelrr1sXqDh5A1rjZyxMszDLqknbHHeSlLdLWBQZk2kDAx7p6YcXEfB04jkU7g2Y7rFy/ChmTR+VhHuU+DXcm7ZWNGTt1XIEIBYZbsZ9fBePUFLBywGk/zoK9mIhEMTiJj8otMWO1zRQ/Xoy5CXllrRyr2tAI1vz0XxUNphljPNs/5YnbUYoU7yW37DB1uQ7DDB0K+WyQm4zcEIVfAxhohIfYFq6G87FZaKOv4vGc8cB7kBYuIdfrGG3vQj5YB+Op3LRarO5dzKpOZWI3x5/s8WGWJh1BBdYvkXP3IjJVhdN3L4xbeSBJ0B2wRDbI7x7nwIki0zUqGwFw9bSj08dCvqbJYrhI+lJRayhJWAmAmcNxwaR9UWCo3y53o8qfaYJsapeMGrT3F1nlxDb0e65osvqPc0RL6MsZmNxN3W384i3QZiAwmcRkK9idgY3O5ipc+/iH3ckdJz7QaF2J+UmfZHqicUYAWcdL56WUdvotB67yc1/6XZs6u9ZLz1f4ZRo9oA52h5MHFBwv5Z2p08W2vp1NXabRYzO6qK736x5wucLHF3tIdnVdpet+QlL43JUBrq6uLtu0aZNt377dNm7cSEmYGsdGLVu2DKXNu625udk0z5/92Z9ZX18fed83WFVVFQ9yu+2JJ55wTNcv//Iv22233eZYqeHhYfv85z9vTz31lPtO6zl48KA9++yz9uijj9qP//iPO1ZM8/3RH/2RPf7447ZhwwYHyAYHB906xXD94i/+IqkHKx0j9ru/+7sOmP3BH/wBqp673bio+8P50+Xub+fPW/h79nugALhmv4+veguyt/YQPK5qFaNCGML9a6qTLNjlBniaQr+jLO8LIdkicpmLS18rZgRVgAEZPhyL+u0g1vGKfBaXUDwZFkSSLtXcUpHiABq33NMdDWFnJ+GbF3f57XN/X2QvvhZgMABwse4aijBXw5ytJJ8qzEOhdpgqzetxMresnYRlOt1FYNiHbK8karfelLH3PuKz1atZgfaTuwcPmQBc3Ljxbg/4EkjtgrQD84+zm7/qT7o55VekcUrFkDs6yEmjNpXYrUbIkp24GW4GcCWRGAaQ4d1B7poKIgeJ9lWnKr8C1Tc7fMJrr1OAugegJTDbRp2tFpmFsB05PL603WzzS347fRIAhDnEu+/FZv9GAHCGvDf6YvPL5EkB1FAmALjIo6P4cjmsV/NajzUtIngHKBV743YHUrobFxDGY3d+0KK2F9jVQ0B6CLB8lH47CT11OllG4O2nG9k4QCUMaPXzu2fUb+UwRPWATTCWlcFmrYZBKu3BIIS2t7MPklL+wMMJu/9mjEqUW3fhGH7Vff6mC3K+iUl7jhy475zifGF/HuX8WVKVsKEhDhDyjUA8Yum5DZYqrwQY47oZGbJkbweFj0uxhV9gI8gr/fEhu6Eq6s7tSzVfDJdAVO73S831pi2+YAYHF1hdkmBToDzEayZuemfztNik0MZFJm07p8YRVGVy/1xkxln+Sps9v4XnNyXXTs0n9pS5uzqjNAAAQABJREFU8wsAqiCydPm7SW/uo75gHvWlQFYelOUXy81d+Pfa6QHYeQ6yHGbd2agDxSGUPF5jYmGa+R7QJXK9Aq6WqnGGC8B1NXHOpXuTs4/7tcvheo46XB+5vGmGcqP+z//5P/aFL3yBOKDMgSixVmKrJBn8+Mc/buvWrSNXOmLHjx9HiVLpWC3lUWnZf/7nf7Y//MM/dPP91//6X93vYsF+/ud/3tauXeve29ranFzw137t10id2Gu/8zu/40CTAN5/+2//zQTIfumXfskxX2LatM4//dM/tU984hP2gz/4g06OeDHAlUwmXZuUdyZQWEqtmSmV0rl0JxZ+maEeKACuGerI2VhNFLMKTwpb9SYPATjh1BSjC1SJthfmQrboowCX2krqPlFEd3GT5FEMRLrxja9TMU98PKcpTMAj+ZiCoiHyuU52pth2yuY1BQFT/Dh5Yh2qB/bl7wTtOzBdKcCJANMIxX6HWVbmEi04BwZUhJefQuQojXLn3Utg3B1B/nMwak3DMbtpdQY9stduuZn8qEo90fYwePhs916KEQN2liwaQ0udoeBy0YwPIgpm29sBPS8CiABdZSgBblqfdn3w8jYVMPbwlCtrd91BTa5FcSSH2MsDQAMkrGpSNybpazFdA+xzGGarFit51dIawLBj/1GzVzAUiccll4DBIk/uXbcm7Pa1KTt1mpvBCz7b9jo5YjBQuJuTd4S8kFpo8RJYQRiuZvovg51iHzWzljVk7T2rCVwASluyw9aNyDCUDtheANU28u6OI3vrT5YS8FPHjXYFwSkV0TFACCzaSA0FmEutie23wF4uwDBFviEnD3hs8BDOhoBNFdD+4UcS1HKDXSU/LH9+MNtVT3rypsDLsRMEy5ec1JH05oFhWFNy3sCU9p5yr7XA8saGRi2+cZNQsSVuvtPibUstliFXbmjEsgMAMW6Ggbr5NhL3mzc+7ABXJcdh/PS+6Ca1ucv9ftGFrvDLCWmfCzDdjl3hkheZjUYm6D8xPZIKOrncbDX8Ipuf6lcOG+Xbx67ro3v6mv+Ob1zulZ7AMJ39V2OS8n8EHXOT3t04NfFNbv6JVY3PV3i7xnqAA5S/v3AZI8k9ey1KlpyT3eaP8jXW9uu0Oerv6xpwvU8uhbMAuHg4qhyuJwBcH/iwANeGiSOcZ4XyD8U6eaj30z/90+5+pffnn3/esUqS/w0NDVlbWxvGWnPdODWxEj5oPVrHM88845goMVc/+ZM/6cDYn//5n9tf/MVfmFivxx57jPggZxX9L//yL/Z7v/d7DmQJSH3rW9+y3/iN33DL/ciP/IgDfFqngNgnP/lJHkavtl/5lV9xbcgDrt///d+3e+65x4E9sXFax4oVK+yRRx5xuWeXAlz5/RZIlIxRL0ko6+vrHbumHLMO7rXKJ5s3b9458sjJ+134PLUeKACuqfXXd3Fuyb4IuiMxq4ctaq2n1hLkj6OJrqQVDL6DyNOepQ7VdooRFxN86onjrUvS9gCBvhdAxRhxzqTbn75KkB8km/k+zCAOn5ANuqRRSVu3NGM3r1GOFHNOWlZOgCq+fAA3xCGW2b/fa09vDNpBGBswld0wn5pct2OhDmASCDtMjs5riN1OYUPv3x230r3ICjHZaG312Z23Z+19mGrUY5G+aVPAnn0uyMCTsfvuHbO1KwEODJ7hkCR1tEENnvZE8AdY2rKVQXkTjn30sYohL2Nfb7stZT09uSLJ9ZhMLIZpKgGE6JhkeAwXovizD0ZJTVF/5JqT+zAAk7TzeABbda91dGHxfhrgE8YGHuAZAkncQU5aM+DplTcAxJt9NoK5SC3SwrXUAptTFbde8pYOUU8rVoSTIYWPdbDOAERasYG/F2v+3sqkPe+LW4T8sCryvk5QRPqlFAWPOUki2QC5cUHzsx+l2ZhV24CFWX5sqNGy/ZXWBCJeVZZyuVsKbcXORWhfJbb1rUhB714PKG/JGRXkmIi8vHDqHZ7GyURP3twNScfKddZlDhqbwGjRDiG79LPxFq6BYpaRTG94+z4bOtlvyaVrrae81fZ1UGT7RNSSfb1WCiBetKLa5uIAUpoeuSLAdZlWTOsn9ZIAl4QxMtrImQdc3Sq1LoETAS7dfCUVVLB6PUyuThjnXX6oyLNSanvuuxywkgNhPuC5Hvar0Map94A73pwLSfJcsu78zT2Ay5m7XB/n89T3+ru7hLr1ugVc1TBcE4ArP2JMv/90ZnnI731hyzH7yrcSdu+DPwJDtcbdj8Rg6RVAjp4ff06cOGE/8RM/4UwqJOH72te+5pgp5XDl87cu1Srd5/7qr/7K/vqv/9p+5md+xrFRJSUlDiQ9/fTTDly9613vmtjWCy+8YJ/+9KedrFDgTH8LcInJEuAS6NH02muvORAmNk25W6tWrbI84JKkcP369fbkk0/av//7vztp4w/90A9RwuYmSumUXPLhtO7HApCSQAqkCXBJJnnrrbfab/7mbzoJ5XPPPeeYvu///u+3e++917Wl8M/0eqAAuKbXf7O4dBY5XcrieIK3YFUuwKWbUy5S0TCSmxTOJHlSPAZzom/LKHjlAjz+GEFKtgV266V9OVAQhj25E1vv25bCnF0iaJNVu0DWbkDaSXK+TnT6ADhYpAdTtCNuj94PcJor5He2DWqUAvMkQC0GoyLp3B//dbG9jEwtDTZaMi9jD9+VtAVtaZz+yCc74be9MRguQEJ0H0BwL0E5+Vxq04I2s5/5qbj94EcSduCAHyAUgBrPAsQStnDBGOwSEkWeEAUAXlMbljX35Dbn+k9tJwbgKZLfnodpUk0vMQhr16TtnnuTJKkiaQSQKV8LbOVAapantQkGVyXJJlIh9htAy3wCY+pWPdF9gz58bm/QFV+Ox7HBB1DNhTpsJDeqrZGaWwCbg7Bbb6ifTyKTYp771iWoWxa3ivKEgens5VNFtu0kSbJwVZgkMmVsZVPSylqi9io5bQf8KRsDcCkfbnjA7CiW6b3MmCaPKTkQMi/7IvlimHmV9hUaK7YQdayW4Nm/jDw9H/vBKWZDAN/F1PS6jTyxRsBdEYySB0ZtAInpKXLLJE2cj3lLLbXDdApevB/1/YVTFE2oJKnBcTZQfaY+z52tF86fO0psBF2mgEWGE9LHsZaUdHgwbgdPpqwrU0aZgyLbvAf3RJjJUYpKB8uTtuZGCnOvTdsa5IRLeVW8CcN14dan+83ZcyzHcMFIAbimA5B0YxSzpTXLmn4665ru3r3Z8s4lUSe/O7656yD/JNVJyfSTLhD2SXLQy3CdmrMwvd16gOMumQbPDhBI5x4kON9JTgkZ0rjzQeOLTvbCNOUe0KV13QKuOZMA14wef8Ya7h9btp6wP//HY1ZZs8ZaWpodg3XjjTc6gCFgk4+HZIbxP//n/ySVYLPdcsstTqJ3++232/d8z/c4N8A8O3X+wdE499JLL9lv/dZvOWfBX//1X3dASMoOyQRff/11B5ZkppGf9N1P/dRPkRt+hwNeYpQ+9alPObDzC7/wC05aGIvF7J/+6Z/cssrjkrRQ7Rbg+vrXv+6kh2KhZLQhN8OPfvSjjpGTxFH7lN+v/Dbz77oPf+lLX3LrEzATgyd2TgyXQN3y5csdsyYJ5UMPPWT/43/8j/yihfdp9EABcE2j82ZzUV3A0WgcSWHcmqr9BNxeChpnYKpywaruS7ozSQZ4FPCyC7vsEDez1eRftRJoavDVE+bT1GfaTlHffsBNUw15Q+QAzSVP51LTaRiZp5EGtpNDE6WW1LHT5PUAeOaRj7SoKW733RyxGgJvH25wylGZPElCp7GyA+v1r38zaF//dsg6qLfVOj9rN60hJ4gHNo7hAsgdG/RbRydFebGQjx4DNMRoEyyGArOHHkrZr/1SHKt03OmYJ4ChhWR14TAANAoiZCoC4VxqMHEznPNP7kafAwpqM3+Py5nys/X1UyD6DZ+dACjJeXA1gfv8+YDX8ahQsYLbOS1Kv8biUUBUwvYfKKaOV7EtxthizRqOD0xdmgB5K0BqM7XJwgAlcA3Oj5hRNMIStmVZP6AWsPUdQF4MVok0OWohZ+3BddQbwyRCAGMgFraXqTv10pGAdQ15AUG4Gi1KWuvKpL1RlbI9NMaPzDPOf6dZ/wDMYv9wERldyBYxyUgPUZcKS/csJin+bMjqymHWiHJIA7OlAUAzEsMRQK5KA2Q4WR5ui9j7Fo4BzCRfhKHh2G87RG2249Q3Y7l1bUm7fXkGaev5YDvfgxd5p69kl5uGQiwuLXHJvvkA/CJzn/OVAxa0S/O7Gwfn2igOjM/vzdoTsIIbD4SsvTNkcZwhM+yDx4fdfVPU3n9v3D64XDXGIgAuzquLguxzNjUjf2RhPHWK6BzTw4c4DwZ8nMsCXJcCl2+2YZ1zCW6MWq8C0ukwZW+2ren+rqsqAquncx3Vo+t1yVnoAv4YP4b6yCvXT3woTO/gHsgZocj9U1eOHnKJ6dQJoutF50lhmloP6J5/vQKuZge4WpEUhtxDzant+aXnFqsuhmvz1uP29/92htIydwJM2tx9RblSkt8p10n3mPy9affu3fblL3/ZOQUeOHDAsUZLly510j2BD4GS82MPuQ/+7//9v4kfTjj533ve8x7Hnon1kjRx586dDsjceeedE42VVFDMlsw1PvOZz7j5lf8l1kkMVV7GuHXrVgfmFi5c6FwTBbgkJfy3f/s3U7tkNy9m6od/+Icd6MqDrYkNXeSDDD7EpOn+LEAo0PezP/uzTlYowKl+EfgU06b1y0SkME2/BwqAa/p9OCtrSEmKxdONBJq87p4QAbDX6nGQW4WDXD3yKQ0kmvoIqJ8dCNqrGCKUIBNcT17Og9VxkvVz4Ie0HhshII0ShKvAMOPZ+M3s4mHPQfKmntgUsggBd5ggv/2UcqiydsvqJO51gJD6OHVzAQW4Y4SkRb7InVE5Ub1I8XZQS2v7QYDVMOCNyGsI6/FKrOBrCISPYaV+EDe6M0fMug5i+0zOFxDCDWStSBA/+RNx+9GPJm0OjJADO/yqATGFRbQSSb0EoBpY1I4rmcZi9CO5ZjK4qKUgcwX5UbpB5SdtPQIjNIrcUfvrwW59hM9itaoAqDmGiWCRNsSjMWRyCWp2+e25jaX2xq6Q3XxTyh56N/I+tZeg8yD7t5F6XN3DQmzkgBVF7K5FFFJGRiiwt/tEyJ57I+xy63QkKrFmv29NzBbNjVoXxY23nyi3IxiNnAIw7+OYxBIUo16WsqZ74tYNU3iG9skGP4rne4fyuyLIEqm1lRjkhiUAwguy0zL4ylewE2th1OYA2HuZLwo483ezj+MW9SWs68a5MXugNeoki1VlFALI+u3J10O2+2TA5ZwtA8w8tDpmdeTX+XywT/R7/gya1I357nTvKRLbpBGXu2UQenVCT36pBfJL51c8/ncS2hQYQ+3ogP3NUyH7+28BtgbDLk8Npat5VDuO66VoTsJuvT9iH1o9Zu9tGLNGJahdxvY9v7npvOtc13HIwy0da9d8vlcQ6Vjpi10kb7JRnY8ykxBrFFTeljtZ36zj3mSls/izWjYGMhdQdkxcfp+v3SbPYm8UVn2lPcBl4q4duYbqVJE9kEZMjbtnT52zn650ve/E+TREXLeAqwaG63vzdbhm9uh5cO19YRN1uJ7BNOPDnwKc3DKxgTzIygMu/a3Pch8U2Pqbv/kbp2QRMBHwEOPz/ve//5ycJhlfiHlSXaz/8l/+i7Nwr64miZtJDJcMMyQXFEi67777JsCagJQYLYE4SRBV7+vQoUMTYE/LygZeQOsb3/iGyx1TzpdyucRC/f3f/70z75BD4m//9m+79eRljxP32ok9PfeD2DW5I37oQx9y+6R6YB/72MfcfucBl1gwgTJt+8yZM+euoPDXVfVAAXBdVbfN5kK4O3GipwhUM+SvHD0WtEPkAnkBLBAfduOypN1zM0YJKgfBzaofwPX8YMBepQaWANc6ANf9AK4wT5Xzt6x8rKabWy4avHT7OwElz1HEV3k9ilWLkROuI2doHcF+OeyNzDRkV+9kYsi8/JhHnP+0R0+0YwTCpyiYe/CYzwGH18m3OUHh4PWtSVs7J2avv4IzXlcAQECuF8YYHcfFuCiI9+AQmEFSmLRPALpa5xHKAhwmJnYizWP0fF6QcroEvISeLnpb5ssUUsc9hzCVgBlRvak19OGNK2FdJrnwuT7SRtg/GYXsRA55CEAYBCCuIu9t6QJAF0BMdv1jw9h+0Ddef5ntlbvfMaSQCwBEMEAqoqw+jkiayf4foT4Wh87mI3FrqYjCeBCUAhaHE4Cqo0UwXeo/csaw518HmE4mI7YZoHagA6dBQNMuiiy/TkHlEfYhXIPr4wNxW3Z/zAYrktjBZ4yyYa5WdRQGc+RU0GLHipBnBmknMlL9B8BuIgfwoZa4LafIcRd5fVtxARwl566ZXLI63CjnME8M+WklroXzMVZZSnHjRXUpO8Qxex17dhmCrKNo9dr5MYB8wgEMDejqewGvc2Ru2n13jJBbIiXUuRFCTqh9vppJ10LvvkOWSUQtUrXIPveVOvviEyEbyxKRybkTBtGDCyQOGla3LG6r7x21G5uH7HvKh2xVjQ8mNHzh+Xk1DbnIMjpuCZ2LMDtiZpVnRktcf+g3/r+qScsJaMUAkWK2BGByU/79qlY7qwupZQJcAoZq7wVjwqxuvbDy67cHdMXk2AUN8zJLkp28rh89rNBDC40cmqswXb4H1GfXLeASw/X+Bc40gxsMOzpzY51zKXSAC1t4Z5pxLuDKj1W6b+UBl74T4BFIEhMmVutXf/VXnXOgHAQl39O0f/9+Zw+/a9cu+4Ef+AH7vu/7Pmcj734c/0d5VnIa/OxnP2vf+73f63LG9JNkgPpOwGdyzpZyq44dO+YMKwSmFOuIAZMlvLbd2trq2qU6YGLMZDEvJ8Uf/dEfdXJDyR7z+zS5HZM/v/zyyw5MaRkxY3Jh1GfJE/OAS3LGH/uxH3PslhwZC9P0e6AAuKbfhzO+hjj2gsloFHMGr+06ELaxqBeWhXpaSMtU3+r9D1DUtYybEOMSxIcdR1K4F0mhMNgKJIXNMEgarq5myJK87RhmF/uQIcYwY2gB/CxfmLJKrNLzdz1J6pLMqGBYphEu6VQBtUN02jBSRqzon3klBOjyWmszOTawDR0wK4sxn6jxJGzX9hTSOZ/NqQ3aHkDL9u0Uuz0MCwboumFR2n7wsaS95z3kHlEAOL/dyR2tQSgG06S2+Nm2D9DlpS3n77NuQuo/gcit20O02WzlooS9++6Y1cBG8Xj1gukAIPGZF4MYV+Qesy4E9D14B8YeSDLTBNexsYgDGiXlZcgBcR1EngemoC0erO4xrcARsh6DkEqkiXF+V5vCOA+mE2IHCaKxt/fS3iEYtG4KKRs0ZDlAeYj92A7ztPUggCnGC5bwBfLhjqgdsGxZGKrmmyK27D1DlqJO2ilAdtTYZ/o93gNz1ePHIh1QOVwMk0eD2HIQ+eK8Wtiz+pitxGhjiLZtOhC0AcDgata5BLZPgGkneXUewEMD8tEmQNeDKxPIEJF0Uk5AhY8bAG0V5Khl6UCBbd2MBLo8Coj0ro5mUo6dq7/DZ0lOA0hPHQt5/oFxc7/5P1pf9579lgVwHfEttz/4z3r7zsYccLZSVgoI9sNeltdlbSV5gm2AroXFw3ZbutPasGgsK6Pgc6ly/ujDWZji9Jn2N8S5p0KvmqYbHGotklpFQbph2i3Qda1PanMBcF3rR+nabp9GSl07Kqkgdldji65a3hwAO+fBzrW9K29J6zT8XLeAyzFcAK5SJPH5OGIGelHjknK4HMP1nQsB1/mbUC0ssTkCOsrtEpMkO/e2tjaXZ6U8KjkFqljx4cOH7S//8i9dfpaA1kc+8hHHQp2/TplSKKdLBZGVsyUmS2BGrJTMLpT39fDDD7s4SvdV3Uvz91PN94//+I/2d3/3d06aqO3I6ENMl3K4ZCk/Ojpqf/u3f+ss6AXeVMMrn2uWX8/5bZJJxkc/+lGXqyX3xPnz508wXCqmnM/hUnv1mww5CtP0e6AAuKbfhzO+BjE48TjB8QAsAzk8vX0BF9B7QFirb8Dx7zbMHBTgu8gOxocWxDICPDBSMDQaePWTBpurmdg8kkLlImETDmMTRHIm5umc9bEBSftSAB/9kA+6xXj4/F47Tp7Wk1vDznjj1lVJWw9L1gkI64VByyABKyGhaFErMrc5OPiRN3YS6WIn4GwAyqahNmPrcVKsr88Vj2UMYso55Wm/nPOdAB/bF+gT6PIRlHqRup2d8v0AIAI47j4UtNf2hN3nVYtjdjPyOOooO9B6dhl2hX3ZrzpQtH0YQKRCyvMAnQ/dEbG6uSn38E37rJuCjCDCMCh+9lntOnKM/LdNQTuC0cjSJSm775akzWdf6A7XfxnXVoAKbfXAhqjfZPcu8HoI5mkLOVgHh2DWzmDjjxFHphe3vt1eansxK2yUD3v8yvWjVn7LCH1IYNIdRp2ITJLzIolsMzmGkYjwadZriWgI44ug1QGU5tenbDmgqw7AdJqcusMUqE4geaxhH+YBrvQU+QxguBo2rwpGcw6s10Mcs6WNlANgv/KBkNtJ/s49Ccw4tk+MnwNZWOVLXqd5ZIigvgmiw9RnrUP9czWTtiVprY7x43vK7fNfL6XemSz2Cc7owgCs3FyA7ZJ1yG2pkdZEf68pTtiiZLclkYCkkaBWVBTxKqY9gErXmqtpyYXL6DpRjpUDlrA6uT29cL6pfqP+ut4Al/YxLmdGDrRs3XVOTee4T7XPCvO/vXpAY4nGDAEvfRbYCiCt5ZLnARPjslNwvL32ebp7o3vXdQu45u61xz6wEIYLyYIGkRmbGId4aOUA17ezF0gKz9+McrBUh0t1tlpbW50tvGzgBXzkFig2SC6AineUbyVZn/KdZAMvlik/CZApv0qGFAJwYrLEhskWXpJAgTWBKH2W5FDz614n4w1JF+VIqEkyRZlbSFYoR0MVWda2xUKJIVPemHLAvvOd7ziHRNUKU/0vGX3kc9PybZr8HuWBvtokowy16cEHH3QgTnlbP/dzP+ccGWVbr/aoXpicCgvT9HugALim34ezsgZdfBHqcJ3GEeFMh5cgxo87YBprdgwPAAAjAKxh8nNCJOpU4aBXpBtQ7v8ZaY8Gb0VMbuybNP7JERFCxz15DPN7Jp20ODk2SWSGAj/K1QmXFMHs+O3oacwfCOTFkmW5cb60PUBNL1zvWKks0DdgpLGMvDDp9RVQ5+QkACp+H0MqGRV7hFvfIMWE9ZC/hf2vItdJOVR6EhSS7aJr6HgDJ7VTbdfyJ2Fo+gEZJcJiyAnVTQ21KYwrYAG1j+dN+q6Pelqvkpd1qN3HwEMAvxTDC/o9LNcJJm0mJZaHiNuPpax002Lr9iFb/L+PhzCbCFg9Jh/vuzuBBX/SqmGN1I8TwEVrGN92jIFvFP3l1rFS2x4psiggd28vMksYSzZhY4PULRsDWCKZC2CGUrYwYp5iJKeAMh/MXbAyCuAGCMJ2pehzI63Oj+V7bAhTkWiRrSTv7y7kis1zcbLkqO2BvUwDaj24RR4GdPk5d+pKM1bPq4mX7neL6B9JP9Vu5ZtdpJtcP+gHd9wAvpIPCniJ7VSfCITq6RqLT3sSNlVx7i9uDdpfPwEopd1RNi4zjzpKBqyel7FWZJArkb1K+riIotElJHf1d/Xy6sdEhHpjpSHy60qtyJ0z026S6xOBItm1B0HUeXZr+mvOnRrXI+DiEiZABojTCWdNPviyMBV64Cp7QPcKjbrKk9QYKrYrwICgMaEwndsDunddr4CrpfaspDBnQDQDNw66R2txksIXyOH69pszXJLUSaonoCHmSHlcYosEum6++WYHPASOTp8+bWKCXn31VcdYCVgpZstPyst63/veNyEv1PrEVMl6XXlZkg2KLZOML89IaXmBHFnI62G2Yhzljkm+qDpdKrisWEP3Va1LYElGFzLR0PyycdfybW1tLpdMy10qx13b2rFjh33+8583GX6IxRK403q0ncHBQbePkkCK5VItrsI0/R4oAK7p9+GsrUE3mziBdnwMK3CkU03Yw1diWnCKorY7Y37rI08nDOBajOvcSsBAGezWrEyMWsSW1o3xxj6AwDGMF0KEVauRwd0AAJJbXkKgC+YnZ2ahgJvIneeRWe6QSYDh1texZqW4rxgz5YGNYFCxGHOMd9+TdKYU44jE4gTWhzGK2AUw60ZeOAjLkwF0lYGtlgMcNixNWLE/xsCQhvULugBfLIqb8mP0eDcchU17HnneaZzsFsFO3X1D0snlcsPwpXtKgeMgZhc9fZgeAAbrAHpiw84ul8vlcuwOnvFilaKJgJ0mX+sF6p692g4Yor13whI9fBPGH5UJG81GXHI4Ib9jngSsFDxYbNTGYGG2xKtsG4Crl+jiBHbsJ7Xv5FV5fay/GHkpTE5aT3i9DMQwgh7aVhyihlnDoIVLRwFR5PmxbJA8r2wIt8K+SivG6XAZUs67ePC2FungKLWtNuM62d+HFBH54SnaOxc2qwHpYBns1o1N1N9iX+cAvMqxuXfte7NTSvvAPDpXdaP0KhjSnV+Tls0fE/fF1f8jx82NuD7+8zNhe+OYj3LPrJon3QuR2K7jPBLjt2J+yh7AWr9G8lemkaFhGxkYschIFMYvDiscsHLYrmKcY/wO5V99A7Vbswm4EuxvFKBYBJjLSQpz++R2bKY6NbeyGf1XN3L1C6e3C4z1oETTDJ0GuZUV/n1H9MDE1cnJw2nl8iU1DjAMuqf86gSlN06MN++IXrn0Tl7fgGsXDNdkSeHMjBh6wCqGa6NyuAS4Hvv0OaYZ5/emxi+BnFOnTjnAIQAj4HL//fc7xkpmGGKYxATJzVDmGgJGeuUnrUPLLF682IErfa/fBd4EcvIMlupnaZ68/E/zKR9LjoYCZpoaGxudvXxzc7OLdbRune9HjhzBwbnT5ZepTfpOYEm5ZGqTJIHKO1NbLzZpPXrJKVGmGHrXNsXkCRBqu8oPk0GIwJjLk7/YigrfTakHCoBrSt311sycRXaWxRmwqgiWBYv4F2Ih2x+HfWE0wc/Aygmk7yZ3aw31ljToapCZiSnLICFgo8i5B3C3eTRkG/tgX/phU5DpbcAV7v+bR04RNZx08eYHHRW3jKZggAjAS8NFsFvFtnFbyF7fE7DaauX+4GIIy1JPTtQjD5ArBBgQaNF2BDSeORy0Y4CkAdipI8c81gQgaCKHTUDgoZuox9WQtCiFqsSqSbomUwZNafpJA4+e6mig2XnaZ0/tCVoX4KkVpufhFQlbQoAumeCbTerH/OS68/w+Hf9d8s+e7jSDJDLKU5iABGF2ymHUyrFfb0HKNy9tY6EBO2GnHRNVnWyw4f56Oz6ATBRCan542OqKkrY3U2H/f3+J7QHMqvD0IIBUxZP9QepQlQMYkAPGvOQuka+X6SMIx5Wj3D9MDlwP+UvDOBJmAWAUOa6Ss+UcSwzMt/p0kdUBSsL+gM2bk7Qw6zkKiDsx4Lf+42ELdlGTC0BWDNgibdDuX5y0m+YncShU4V7lw8Gcqv6YO6HGd3hSv0DAOhYxzH4UAQg1Tfo5333TfteaR2hfJ0znbuSeB3Bt1Imv4yj3TUp2ce6Z3UT77wLkVgKOdS5GuHHqybg4uuFBgNcw7GBW4DkE0yWZ7pW7XJ6/E9rP2QRcSdqtnKgQqFeGHLo21Ls6vyWvmo1+Pn8fr/ZvhgKL8SBCD1xkoHEt29lf7T4Wlvvu94C7x7BZnV8yldFDHgH6IOoPhZWEkN/9Rl1DW9Q967pluOp22/c5wEUmug7wTE2sakJS+JQA189eFnBN3qzuIX/8x3/szCruuecexy5N/v1qP+s81jh+uelK5rnc8lP5Taxee3v7OYBLAE+gUczdm7V1Ktt6p89bAFzXwRmg4SdK/akAFW7TcwL2InWVgC1ICZGOYdQwjLxwJWDr4RIAyMXQllZw+ev7gl5QvlGSPLI0gVOKwHZ3ImjfHsVVj2D9UEfQujFyaMG44SdWjNgj1RGeZut2x1MWWtbnGaM21BA1wqj55Su1Bk+1HTlYYZtepUgwMj8xXLpZSqZ3N3k35YAJTf2Ygjy/H6v1Yyp2TFDJk/KX9uI0R9tvgLlZUk8B5ZtxLuQ9gQwvAuWvgSms6sS8C/xoHJN7ngrldpPT9Moxv51GIriA5W+CAamG0ZkJnRt+BnYGU4sO2Kb2o17bu5taW8MYZVR5bcNtMErrUljPk+cFEDllXXbIcwzARX/2t+I8ucB6RzDOoK1txSN2T3PUzoTL7C+6ymwb7JNi1Qj9FCOvK81+ZnwJS2Fjn5Jde4DQAnonANicG+u21tJ2KwVwZcsxIKlK2PISmLTRYhsevsFK47UAsYCd8ZDbhUtifSWmHSzeST+N0Cfl7UU2p7/I5f/56cL7kRG2lmPZz3HoiPqsmLYvRHopqWExoEbn1ijOiX0AwT7y2/Z1+6wTd8Q2APMdrQkHjGcjsV3s6k7KHgwD8FrJY4shUx0FOCq3sIPCzB39PmfosW5h0ubNlWMg+UQwrinyynQe6KU8uThFlMeGRlme2mDY+pcAvMrJ7QoIJE/xAtHlJFAkl0IxUHJU03czNbFa51Io0CvzAE0613WCq7bXtQ1iOG95gqJ6dPrPz8MP5blpX6Y8ELk9L/xT6IFcD+TPIDFdkp7z5h5AuCuY+8r4pZIb42fygrwODsB1D7g+2GZlpUEnU5/J7vbwRO4FSQqfnDrg2rJli8vNEvgosDwzeVTemesqAK7r5LjraYsc4joANK95kOyFMT3wk8/EKDsG6BK79VBJHOBz4V1GGEwAR4zTlUyK6xJQyzKHEJhJerFUjwRs42iQoBXJXxe1tZD91WC48IHFUQBX3Jl1SOox7Inbfk+PdXuVMcTTeSL8tlSllfVX2d7DRXa8A7BIe6sI7Oco1waJYUszOV115izQv4Gb4F7YrSAsURCm4jhFlL1YMTYy/8OwF4+uSzqAphuucrkS2OdrIHROiey7+ilFPwl0qTjzEIzQKGxcOWYQFbj8uRuy7trTnE6QV7fllYDpvQvg1dfNNmCjmmDQ7rsvi947VzRZLMyAjViH5wyAK22nOxpsZzs7C2CWbKzSRu3eeVE76Cu3vz1dakcBtIoilBs0zHqjQ8QOFHzOULg4U0GPAuJ8gbQVp8ZsbfdOuzH+mhXXIkucV2rl8yphG8MUZUaaMNxE8eR5gPFSG+UJcLAyZnNLyQXzJG1Q1buySauBrfQfq7T2jkoYCVgwmK4goOY07phx2j0XaV4zLOR82MEGgGoYs5NT5E8dxInxEK+DAK4IfbsA58mP4+L43uUJJ/10cfU0+3fy4mnasg+ZpazxiykUfQK56Rms+yWFXAr4LiLProgHo+VFBFxsXNIKlS4IcA4EOQccCtdlwSuZSNkogGuwu98SY1Fkhn6rqCpGRjGpTtjkjV/ms4I9mWZoCgKCZhJs6uGFy1uZ6EzV5NKTffYVRClTimt50lNRuTfqOtXYI9mvzGMQnF7LzS607TrrAQe6OMfckM555sxakDWL8cpJPd4555tu/dctw1UPw/UhAFcJ47UG1pkcJ3ig9sLGqQMunUIuTYKx61K5UJrnepxUH1MmIJI3ugd55+1EntVSPLVkyRInUTxvlsKfV9EDBcB1FZ32Viyi24aAUD9R13aC4hMMAmEYiGKMFOoZo+5BlrY0mJPxnG2fwrYcCNEFJgB1flCYH9o0WGv9mvSmoFX5OLI+zxJM7iYI/w4FeaPElxkYln7+9sMwLEVO2MKTRUnPFgCg/MWjtsfTRUgPWANyRcnvakqX2eJopUVHAWQRAlt/0EZw3tv2MuwEOUUrV2SsbTF6YupNbW8HKAEqQnOyNgybo7F3LvuoYHNdS9LeCysm8KSn5zKcEOASKHS1J2i3grs4Thv5HK8geV4avC82qPDD+MRG2H+ZdsicwQdCDbI/Fw76mo/+pA9efI3CwFtD5GbBMAJwumG66mAY71kFgLolY/NaVMSZ8F9BMjK2BEBH2zg9ELbNyPlOYeQRYBut4VErxnjka/2V9u2+Yqz4CagBR77iGKwmrNmYAC9WuUEWhuVS7SkvwKLFe8we6fqmrY/ssPLwoCVqi6xryVrLVLYgdUvbiWiVnUk24WBI33hh04oCNhaALfMMU1467oBnRRRr+kMVtmfPXNi6UopPs28AKNV8K2EblZhmBHEAVPHpRgBXDPA3AghUUeujOEqOxjgH6e9K5J6fuDdmP3471vkzxCDmj4zedSTiHJskr60U0n4N1lISSKT59uCKuK2bJybW65hBYCx9zrkLKBHYUgDmzmu6T5PWpS8ioxHr7ei10f4RLPt9Vj2nlNwuzljO9dx0seM//tOkN9nCyygiJOMMrsmZnHKc2XgwSctz5yfXGNsJCHCN79NMbnM21qX+SdHnGk/Ubo01hanQAzPRA+56divigQTnmQC+nFuDnGdOievOtdwJd2VX9Ey06q1Zx9sBcJXCcOUA18z1oWO4BLi+NTWGa+ZacO2tSaYdsqrftm0b987cQ8N8K93DfXfTJEeS9AwZe/zkT/5k/ufC+zR6oAC4ptF5b8Wi4qh6uD4OEb8PcFFUE0SvLU3a6mIADgYaPE/mJaB11gJbF5QrQsu3QQoVT540b4pAVsGcHOvEUuUn5UHlCxv3wS5sGwvYXsw6lOOjm5mPNiQBUXLTU57PUuRnq+pH7HSoCyZulLpgADbaschqrCVJzSqsVhXsFuFAcfJ00J56ym/HT3itkqLs/iIs48m1kpRuJYWJl69K2W4s0jtGfVZJitYo21hSk7J3L4LhgtFIoulTgqeaW4QV6sQTKO46CepdRSmGFQqSq1NMDpkGj0n7ld8/9y76jwnfCvLGfHZ0ECOJYpiTmqRz7BsjB6rLzZK1euJpTACtA4e/L74Qsm+9hlSN7UEgEdybtTVyHNqiFN5N2LIGQEsIy3baqdpbfvKoxLpRy9qOsZ/HyVWjtJT1HU/aMzv99ky01HpKMCOpHLWyxiELkJOHhhTwiD19J8UPsYn3VrNwGQErEsy20iP26ODXbcPoqxQsHrShqmrbtXCDDVU2WhBEHM3MAaA0IUujjluSgsiZYhsNRDDUGCT3K0MtNBrcE7Z9O8qt/WQROU4lFhvmZsdx9gAiVUvah8wwgDttLZLBMmYfHgT8giADo1k7fMpnvbCdYB7y4lL2mQei9oE1YrjoLJaf8YlVEk/ZM/swJQFwJZ3pSMbetTxu82DiDvLdwGDa5tXHbXFrFmkK/UXgNTHlm5Q73A6ERamn1t/VB+gChHLiS15YVlrE57zxR36hibVc8EF5XEkapjyrANfL7IIJzm3OJfWDAB6bu24mMXN6aOLGDRouvFiYCj0wsz0g0KU7IDE7wCsnY+WccyCfE07/j1//M7vda2Nt1zPgam7ApfBD46YZjuGauT71ct/diKTwCQGu77vyHK6Za8G1tyYZg6i21qFDh5wqaHILxerJkENgTO6Mv/iLv+gKOk+ep/D56nqgALiurt/e8qUS3D3GiGAy8ajN9UaQffFUPzxiY57TtA35V7ae11zmkg07uU18K/AUIPB30SZ/Ky4epJ7RQYrl9sJuyJxiMblOsgbXPMIpbkHedBOjLJi1Y9YxgGV7MZH2CMtsx+0OzKb/AR0pW9U8YLHyXuvIjsmPz5YimlsB4CrJBiyFzEtGFx7Yh1QmZCeOA7hOemzXQezKMcqookhvNW3YsD5pd9yesj09Adt6IoAxAoE+eUQbADIrVQsLAxGBSBU79oN2zoKtXHsdwHRgDICJoYZqg11qUhCo+mAHTvhse2/AjgE41mN28QiOhrU4MO6HOXmB4F436rtg9Eow+/jqjqD9+6aQ7TuMxI8+0UO5ha1pu5laUFVVFDH2xe2WpqgtAyAG2bZAbSaN+QVtzaKT7EYed7iD7e0CaD3rt5eptTZYSrA+nwK+qwesuGmY+lECzICMBJLIk5VIOamrRU0tsVxewFhlTZ/dmHnNbowgKSyNWXdjnfU01FuwRHIaADbHPpNosp6xcjvDOiS9CYZGzR+inhiAdSFBSOR0sb2yowJ5KOzXQLFFBmRwQjvYV1SkgDOOKUxjtcqLcBOMw0rOx6ClmqCmEwllL3lUzYCxx9bE7LF15NZhiEJPXaqrZ+T7dqSs2wBXPUgKG5G03oRz5dEjHnuJvsQXwxa2ZOzdd6atBamhgKMmnZtRmDsda5l7KA0uPynXa3RohP7FhAUnQxGbAWYoge0KqY6YW8Wl90nXiAAXUNjJCgUoZmPKNUOASwYnXN9i1GZrY7O0Ay7vhmOgByAKgl0gPBvbKqzzHdwDutp1TeaAl/uD880Pc61r6KzCQ/PN0sXqNvrd/+f6Bly7COrbyN2e+RwunwDXRjFcXvtQAXC5E1MslmpxScl0/qTvuru7XWHlrVu32ic/+UlXmPn8+Qp/T70HCoBr6n12TSyhW4UCYxVIzkCV1FSMWNGcgxbxH+EpMkYT1mDV2XWQJNSHYEYFwk6XqwXH7zXck2w31uDPAnaGYCuUE3P/woQDXeegrYk95ibGMoi2IEIydhL2aWMHUjaYmowHAFQ6Zs2NA5gRYPIAyMsyT1u21NZYtZWJC2FZZ16ADNAH+PNhZtAD2PniN0L2n08HMX2g4PGKlN1/Z9JuXEu+U3/CvrOTPLWM325f4cOynLpj2HKIvRL7VlxSnNunifahYSew7sIyfWQ0ZVUAkRpMLORimNckT5rVdeAxJI3a9iu7AjZAEFvemrXvvTVh71mexGExbTsAYE+KXaLxdwI7Txz2258+HbbXXw1YAvDFjhuKSquvy9hdtyVt5RKqYhGAtwB+72iNWkMNeUSjHZbqPmp9Vm97Uitt89Fye/0Nrx3aDoPXTr6UAB22/x5qk5XcNmKl6wYBaIjjcIVMjpE3112CKyTAFnmfBwBk5KIFixJWW9lp9aEO5JTUwCpFQleBc2H1mHkASpFMJQCk0QaiFcgZiXLpt7CfnCUs9WthuBpFU/aHYBiLYbhKrOdkOYArhBwvx9h5AJFZXpWUIphTk2M+4wAazwhtBF1wuF0u3SM4P354VcxaKnKg/pz+nYU/BJpk2jGKjLOC/L8wNvnPbvXa6weQu2LNr5pv33NX0hY00UB2UTFVP+YjO0/gzIjRxzLqdclKPgR4dr8T+MvOXiYxIwOjrm5XXLlduBhWVZfAkMrcZBJCO2+fdDkluXmJIVUeF6fQrE00FbCFlBaWK4ye0skKZ21rs7PiDCBYbJceAsloxCdWcHY2VVjrO7IH9HhLl33OokUXeZJBw5Em/KCHKWefU7y9zrzrGXA1NZLDNQG4ZvbEVY1Ml8P1hKcAuKbQtWK5PvGJTziWS0WaC9P0e6AAuKbfh2/tGriJqOCsefrNW7YdudlR3OX8VuqrsZrsrRbKljvAdUEjuddosVexMv82tapiBP1VSOnevShhq5HGubvWRUIhbBfsjA1bt2eEgrIB6xmmwPFwzCLeuHkqVFPLb7VevMaBKAPMm8r6bU22wpaDAmTeLuv2PgDXSQBACj/xMnK6jrwStm2vUSMJFuvmNSlbvSyNzHDUjvcdt5d2R1yOze2rS63K34S7XC6idU6E0r1NmnSjPQqA2kSB5T7yjVa2jtmNS+JWWYE5BMV4z5+I++zbL/jtH74Qtt5+gj8qObcuythH3gPorBewA09R96yvEmYBZiRGVP1Viht/7bkiG8DmPoubn9NG0XfisRYAth64g1pfzvo+jq3qkNXV8jr9mgUPH7Ito7fYk0N3YWsOQ3MkZX1HSyxCgWKtIx8e+OdTZ+z2UcdmJciZS46FYLnI4ZLWk1wqfN6J7GGr/AmrguUKBcY4xgnA4ShSzYiV1MTMS+5VNB227uEGpJiVwESCECIOLnacBsmBw92yDMvxWkBGKRLO7vYqO7h/DkWqld1FW9iWGC4fgGYpAEXMlerA9eAOuB8poRd5p1wLVzak7GM3xWDzkudIUc/v5xn/mwcIAkgp9kH5dEc7ArbjENJLmMMlrRRupAByJXmAmhSE7KbNT+wMIQX12h0wl/evoKizyzU7t2WqqzY2EqH22yDgawg3QOSsGGqUIjM8m9t17jL6S/2bwFBE25JjoYDRbEwe9hvPj3HApXyoWUR3s7EDWieHRcSjDDWcmQbnm3N4VOcVpkIPzEIPiIXWQzBdp/lbmvJrg+76yY0T2uzZT7PQiFlepa4eXUIyzTh6pIuHcAFrbat332UYx/77b/6+1bWtsJ/7zGdmuSVTW30cxcvv/u5vW1OjGC4khZhm6HjN3CSTKR4Mb0RS+AQM14cKksIr7VuxYB/72Mfs2WeftePHj1/pYoX5LtMDBcB1mc65Hn7SQEuoR/BC4WFfu2VCBwiqkzY32GKNRYt5mge7c4lbica1k9iDv4RsTzK3+cjhbm1mWYBP7vajtZ87jVjM9pGhdQqAF8IQoxLpYBDTh2G2coJAfdQqLJSus9FMufUAukZ4LSLz6W6MG5ZBBcmQYm8mbttSUYvxuYkizktHKEYbCVu42GPVMCUhDCJi1K06k9huPYOwYei8aqgHURJbSV2iaitC7iWG69wp92RzJ8Yb33oxaH3s1403jNndq0estgYpZRHZV5MHcnZN9uuPPxO0f/xiCLAByGCVq5an7Q6cEOUOOMA65s2DdduQtkhTwp4hAevLx8gh2lRk8RdxW6QeVlYLKe8JQDO3OWN33pq0NfMxmAjGLdTab3Mbeqy285hFdyftyfZ1dipRas3evZbtH7Udu1ba4SOLLIV9vTRpWe6YXoCCdyn9j3OjjmuWHLKsfPFlmCFzwznMx7ZKSsasem6veaH/A1T9rSgfcoArWIG0lBwv/iUnrtb6o0gLFeGyTCxe7JiyEtpWguyxDpC8AqfL8sFSJI4ldrw/gBFGAKZGOUJI78Coq2GDbiB3TBbxB88gg6QGFgaSVsKxXAfg+sjqmLUpt2xcvjdxTDi2gpGiYd0N9PzfJ2a8sg+qCZdAW66HCzkDFDkSAjowRcl4YK/Ii4sDHqvKAIcYfuQYTb2bHcHQ5Lm9QTsDQN6wIGEbFgE6wbnnnA/jzdApoiLeg70DOBkOuNIEZeVhq6gsQTIrIxQWm3wejS+nukCyiRfYEvOUA138MYPT2wJwTfSHbL09HDNkt0iMc8zgzPbXxKYKHwo9QA/oss3lEubGpty1mruP6Lq+6ICgr6+TSWNePCaJdaeTQ7cuuJ4AFwzXY0gKKUp/eYOrqR8MGSiJ4XpCDFcBcLkOVB9fVPUz3r3K49q7d699+tOfdk6GKqhcmKbfAwXANf0+vDbWQGCL4Ajb7yF0udi0e4qxUg9RCyr3BPlSjZTzWx+Og6NYr1fCaFTLWvsycc8Y2zjk6aaQbw+r7Md8YcTq2VbMAraTp4g7kN91xRrtNA55o76AVSBfm+8L2hpkjg8TxDdi7PEK1vGvARD11HEOusY70kFrY3k/9TJkJa/gPZLpZO2vWcLby3Ywk0g3WXlqNUCgilykHFt1btyb+6uLYsnbDwRsiPpQixvHrLVuDHAStBCA6/zd0hL7Yay++mTQdh3AqQ42a8N6ijjHsKM/wtx0BG7hdvvdSctuiNpGAurNx0P28iu4E75MkWNyibLJnFTSCwNU05ax1avTtpz8oeJSmLV5vVZTB/MIs7L95Rp7ZluNNRfttA0NG2GuqDG26xZ77aWbbOwUaIp1Z9G6eKqRWC0kCMB0I8s60Yaah2Mid8JsM10D4PLAMJWWDll1fY+FPVGs99OAgYQVlWEUUk2uGK6OwjfDY9UwkA3kwAVhFLw2Fi3BUITi06Xk/YWx04fIbIApqwPQjuA4eAbA5aMuVy0gmJJfyOrw6IAtQ1XnHBPbcSbswzBkLvbxC2C97qL21oZm2DJRl5Mm9esY+V19FCrWVAOIxq3+gv53P17BP0qAj0tvDqulSW6bQeSocqb0cTN1KCgXN+ViJjUgP9EEym/ZSeqbjSFDbKqiFhsmG5etY8Xy0rELdA2c6QfksY9lYdwYqdkF6LoQ7GuzMp/JBXSSLOUMNHL7n2/KdN/fXoBLfZYzAVHfCaRKXliYCj0wWz2QGxbyD4B4sCJ6nIFSeV1isC/1YHK22jPT69VoE8O+9Vh7J6qQoF1PgKsRSeFjj1FolxwuMd8zOSnP+4WNRwqAa1KnKn9r06ZN1tHRcQHAFRgbHBy0l19+2RlnfPSjH7XPfvazk5YufLzaHigArqvtuWtqufwARcDCR41XekKBUBB5G8xLZc6ePPcU7yINd3Hh+DoUqV9mgl+wIUzfe2C4xjztwKR+K+e/eNZnOwhsn4oFbXe8xU4nGtk6jn8E7k0E5CsI2n8whMkBTTwC/7Id0EWJXpuP5HA5OWDhMWzQkT6c8YdYv9cqvORf+U+R7HzatSbgrWJ9VTaYrjZfpoKCyir8rLpak9sr62wcDQEPkksWU6/KS70pV39MN1UCOtXrkntVvscUjJ+iltZp6kuVYW8+lxypHa/57PXXAVNsuRwsdOe9mH2sj9lWaKidPUHbfAS2ZB9ujW8AZJCziWsJUK+qaiF28DBTc2GYVlSn7N6F/VYP83QIQPWv36q1TTBvS+dstzuWboQ5Sdm2fRts+4vrLHJC7Btb0760ASZXAiKou8VqoZr4HjbJg9wxu5i/YdF82MxXlAxYXUOHVYf6ucsGyI0rsRC2/KE5yBFDFDjmOAZSZRaP1FhnpBSDi2JAUJjj5cHhEJBF34CF6XiKR/thvMgR85M3VjsStqIzmGckvIBNmLQyziWWOdbldYWYwwB79cwtSPfetSRh9bChjsliVW6i2aRA2XYKWO9rR2hJO1YuxL3yBkCLClyrU6cwaXYZrcjUQuDKh+mLSgGEYLZkiOLWN/kUuOS6mcn1cb4Bl15INxyXa0idN71Luqi8rgyfZVRRTMHkYhCk72wyCFvVemFmQV0CECpOfO65ecmGXfEPbzfApR13YxXspT7oiasC3wLsuuJTojDjNHpAgb1cDXW9p7h2Bfp17Z4znk1j/d/9RZEcYxl84lg3dQmvL8DV0LTHPiSGqxTlCPmdMzkFHOCC4XrcqPVVkBSqbwcGBuxXfuVXTKYYMhk7n+2SnLAEVdGdd95pH//4x23p0qUzeUjesesqAK634aHXDUPsURw0URlO2ry5gI8Q3+ZlXhfscy5YvODry3xBaVnr9xzBCOIQgaXfYrgQ7oHVeHx0rm2PLLXeZLWNIIcLEj2V4154E7b1P10Rt7sCyO0IwoEJznwD+IRUMGGdI1HbjPTweX/YOgm8GsEc76aY871YmRd5+uxApsteQjJ2KoHzImBuMSDvzuK0LYDNUXrTOZPYPn0He8f9k36I29jYqHMrLC0tdaBLP2ivNd/E4nzQDfgYQGG7DC2QFFbMS9v6dciecEfcDuDaGsva1p6MnQKApI/6LU0+ViLFU7nyoBVX+K2RPLgqJHs3zk3YDy9O2yLqlH3lpYD9wX+U2R6MHap8/ba4ci9mDCPWPrrITnc1WwrHPYcQyRnzLAZwrQbS6Gk//ckm+ZKWCngtANy0IJnzjlhj3WlrXHAag5Ix7ParcKustlKYsFLyuLxeqKuMalCFCSRwoEyEsHAPAxhg+lIBLEz8Vkn/NnJuJIrGrJcjUZmikDXBbrC7yBLHS3FfpAYam1ZB4TDAtpu+CHFcymFB+8YA0BQ7fmBpwqrZ38lyQvlLHAPAPrk5AGPGOmDWGnC+fPcdCWttzD1RVrdPTOwi/zPpaBDwuIPivmC1qnmWthhgx6u2ST7IDDFZ/gO2cjXWcvPO2L/qctD62DBAlG0Vl5U4N8wouV39ZyhhPTDsZLHV1WVWjq2QwlAAAEAASURBVC5xcm6X9iPGdSfIFeL4FQDXlR8V5XQlOZV04w8AZAug68r7rjDn9HpApjdR8nNLYK/zBlPTW+Nbt3Sch0LH28nhuq4A1+9YffM+JIULHcOlcXcmp4BXLoVH7FuPpwFcn7Jbb71lJld/Xa5LtvDf/OY3XW6W+jsPuPKfFSc1Nzfb2rVrrbGxkVIpPDkvTNPugQLgmnYXXqsroLYWwV82leBGkrDGKlzdsB7XWJYLcK+23bkVaEiMwXKdtt3Wxb8xkMHpeIk93b/SNg2utQFs35NBucHhqAZHMi+Im11ln91fPGSlsGEVAKYqXsACi8BebB5L2z/4Sm0rwVaMgDUAKLsVZudTAKo230n7Wvy0bUmgrwPYRRMNFoo32LvAJo9UJq1BErpzxugcmMo/qVQArZpdepKjgSMUDvEu9kWgL0PemXg7Vk2NsRGKAUOo2DDA6ghApx+ZYTl5VYtoSwDDic3WaS/F+qxrsMhGVah4xE8R3RIbotCwnzYvKz5lKytO2LKqqN1fX2YN6fn2V//ZZH/yzRLrwgre25tEHjlCfau0JWvpmWo90aO9UcAUY5qniWNUzY0/DSKK0ipyy+g+jDKIRgFUfnSAdeWdtnDFYVu2/oxVN4QpvlyPPKbIypTrBjBpSPutHBnhMZBoO2BXwClM27STccBhMBm2aiSeVTCOoxRT097XcVZUs83OIyXW24V8DvJoGKnpKRwfxeEVIQetwgulGJljL65/S8ntemBpktw6AS42MD4JcLVjXPLEpoD1AtLktlhLjtfDdyZsQbMAl2bMnYEa3EfjXusmt0o5UDVI/SoBcLIL13wpJD8pAgg9bZOEUGUNJPVT/azZA1xcN2xjZHCIbYYsXEJxA4Ff2qoHGAM9gC4cWcRQlpaGXe0usW35SdbnCdrroyP0xBz8MGPT25Hhmtw5Ok9lsa/rVueATgPXfTpnZrAfJ2+z8Pmd3QM6rSQtjIH2y0Pi7q/fSUHz9ZrD1QDg+lAecLk71kwdB+5f3Fgd4HoCwPXBdybDlQdS+V7VPVXx0Pnf53+XbF6vPBDLf194n14PFADX9Prvml5aF0sCTXcUhqC2HDaoEYtyPai4yrsKsaSNYLt+mvyoGG6BAaBRn+ewdRftJ5+GPCd/kb06tMq+3LfS2mFIfORrhdh+iJyyIpiUVfWddl9Vj91A9FRtxdZMmF9PUtIgtcS+HIvYvzIwdoIzgCQU6i2yFkDCZ0IJW+Edsn+PD9ipDEYRPPseSM5nmTpbhlnF92MSsQ53xHMn7aBeup3qBaDhT8nDVBRZjoUK2FncTpIDdTgbwTQjYJFT5eTtFGOljvkHwCLZiJtdcwo2KoPJvofaVf12wLMb05AeiwAuO0er7Ax1roZ5jUTKYOVO2/riHdYSGrCmopStx92wpH+x/emX7rJ/eqGZQB3xYXfCwuVQWcg841VFlmoGziDBM1gjzzwaiTV+NgIYJI/Kubl30XpAD1SVeSgyXVHUZ60rDlnLkg67a33GblvcYGOBGjsMGFTAuhigu4hl/cjuXkO2eYQgohzDEjgrG2YeyQOxxcj1C/1bRn8uRtZZL4DAsd22H4v8EyHzAbiUD9eNMUgZYG8O0sIKgKcf4FXC37cvSNlaDFZYPQ1UH49PfIQMslf3+G3vUWrAwTKuxKRiPc6TFeSDTT73RsiVe/1owPadogg07OU8mLANi5NWX8nNANCTSiUJurFalwwUIClnsRRAJw+4QuGZdrRiH7hmHOAaGuacBnAVyV2DnRrfxbFI0ob6x2y0t8/S2FiWkORdWSX7eOq9jaOrCBJEnX2qlZX/brx3pvX2dgdc6nsZwgh86yGE8uwmanVNOsWm1YmFhQs9MKkHdFo5wEW5hfJQ7iGcRIXX26QWa/iJ41J45Dp0KRTg+uCHxxmuGQVcPKt0gOsoDFcGwPXOZLgEsBQPFgDUW3tlFwDXW9v/s7p1DcKSFkYiCZ7ypK2hkgLCFBcmNmRSSDiFGwtszxhB8Y4uv73aEbTeEUDXSBJnvJOWrj6DwyAuh3UEo4E6++rQQts9ytNCCuXGB6nPRfBcVDxqzQtP2ZrmXruFtswjD0wsywIK9I6AFL4BMPsGDn7dPFUZAx7E0mV2E2K3TweOWS2B9peTFEr2DbsCyoOpNvKEmmxBPGgfqEzZLbBc+Un29mdgZqIAlFon7xNdnttVOd3JhlaMl1iuODVZdpAx9ioStUNHSrFqJ08M1qpz2Gd9oLHwnLTdRu7WAnKVqmGD6u2otXv2IXkkpwvY0hUvt66RahvMlCF7jNhaz06rtW40aRXWRg2vFWUjVjIQsP/7rTvtX7evt34KOVt3xgJlCDJBvrHSYssAgsGthmoSK3i01NjRc7ByNvAwRNl9sESYVXgiPPGPpW3uwi5bePMxa6xN2sPLKu3R5dUAXq+9Qn+OcUwXkt/V5BmGpRuygzzBOhMstwpfJRArgN1J1ubzaY43gKsk2wMM1SP7bEJiWAzwOnXSY9/e6rdtJ+Ad53gsiTNhgsLYlZhkLCd3rIkix17khXXUa5sHQ4c5ohvABaQckB8/CAK3w7S3G8Cms0wMF34TjrEYn8UdkwNID7+9A4dI6mOBp1ywfTd1vdbNi8B7ArbI6QsGgjmGyR3DHBgS4JI7YQDQnDu0UzyX84242Dsr1PkxAuDyIMkMhYtgurBt4UGAnBD3HgWEooctDwxbbVEPxiO4dKKblalGMeArjKFLlDwugd+iAuC6WA+/6Xc6mmLnU5xIAtwzzRS+aQMKM7xjekDjRw5wcU2HpADRN9fnpPucbOEd4GIcamtDtwBNnGbw+o1r2hYeSWHLXhiuRZhN8RBtRgGXxwEumWY4SeE7FHD19vbali1brL9fyRxTm1avXm3r16+f2kKFuS/aAwXAddFueRt9SdCS5pVM8IQjnQB0pa25ZjzAVWRzhZNEekcGfPb1/SHbcYb8HILPDhibylICU4CFiuS2tWSsoqXYDnjL7RgysWEC6rFhSeWQhFF0t6aty+oWDthSX9TWZbus1dMDy4V7HAvvTRbZtkSd7adm12i2jBpRHnvMs93e498Dixawpzxz7A1esXQ5d8haKx6ZT32vcntkTsIWUYlXu6JX+5DPNmNk0Uch53W1KdsACCwCOKQBF7qVZjOAHaRhPgpNZagDdtAbtedgnV7eVWZ9p8pob8gGYzAuqN9k6rFoecIeuDNqG6pilL/aZ1uQTx5Jl9oYTNIgta5GEmGLA2BaPCdsbWYHUkC5Qs6xFSUxaw7GrGwUO/n9S+0fDtxux/dWk38Gg0U+VvR4EcckbNlyInmYK08nLwJ1TxtGH+RqZXElTPfyxJV8MsMZMAvr5oUxrACMrFg5Yovm4vq4PGAPrU3bsaIogCtG3bM4rvF9Nh/nyDBgawDsdhL9ehrAVeGpgqUL22L6uSgiBJHFPj1D4WDly6isgNmLL/rseeqSdXOca1qowbWO9pCzhZrSVrWkrQqGa5h2FAPAxpAVvr4XAMl065qktZDTpfnYC/0z8Zb7g3/dbxN/OcC185jfnt6pvCxYN3L7+oayduviqN2+KEY+mpgtga1zAyCBoVEY2zP0XT/SSJl2NPIC88zYJKnF4fakHaJcggeq74a2rCuovHO/z7bsAKzSB2XkD961atgaK3AFTcQsgemLjw4oLyOvC4TmA8wXGK7pHRLl1vC/A+qO6dIT2umtsrB0oQfO6QGdT287wHX0DOOp3+a11jB+cg/h9dnf/gNyfq/VOly/Y3UtMFxOUojE/vybxTlHbOp/BHmwK8D1JDlcj71DAde+ffuc06DepzKJGZNpxqc+9ampLFaY9xI9UABcl+iYt9PXevKVxgxCzoVhit7WlFLwFmO8Eow0/HKccMGw/rl0ONMLa/R8e8AePxS0PQCBM8gKI7AfAcAU4i9AFUnHWJmXL4eRqSE3B038cAea8n5YG4J7X5hcsnmDVlwftRJkYa3AgxW+LqukJtcgIb+XulAyl09kkPtlFyEjrLKbM1sJtvbZNn/Qtnhq7FC2DRBRZb7YXFsba7JHw8W2vhz2jPVrErOyi7Y93g4Dh1PhbY046c1LUATY7AhFffV7K9LKakAWNIaFcJqj/Jht6sjYN7cX27HTQQAUkjjkkEns3qMwOBuWJexj98VsbSV2+Il2+7fEgB2ixliaPLQ4T9+pEubeGzIddrPnFcplUYQ4MMcWIrGci1yxij5/eugG+9vjt9rxTTVIB3mKCnCK7QxbehCwwfJGzhO0GWANNg6DE+9C4G0jvhcqTgSAg+wBlPGkjrdmChHffAPyUFwR74ENuhNA2B9I2nZs9k97esmNE4iFxfQEkemlrDcdsVQQeaZ3ntVhrNFxjELTJwM4EGZs7UKAUi3HhiYoN+q1Vzz2wqagDcJMLVmasjvvSVpZVe680I17L/1zqNPvlg3Q1gMH/RgcmD16T9yWITF04XDuUNCCs5NjGM/7Xg+ST/Vk7Zmdfjve4+P85AlzScruW4mEdB4eIVqxW/DsevRJJhq9QzHb2FliuwY5/uSS3dNCEWiZiszApE0Oj2bt2y/6bc8h5RRRTHmBx+7bkLJdh2ES91AOAHQqZ8qHb4/bmhvifEojRR3CQh6akj4PUSeuvKIYUBbk/GWFMzS97SWFF+knPQhQTpykhqEZZgwvsrnCV++wHtDV+bYEXOS7CnCJIRbg+o1rGHB9jsLHdS377QMwXKWyhZ/RcxD1BZLCFzYetqceTwG43pk5XF1dXdjiP2FnzpyZUu8qx+uOO+6we+65Z0rLFWa+eA8UANfF++Vt+a0uHjEEKeUxkc/TUEVNpQrc3wS65ISn6XxJBT+pVteODr89c8RvJ5Hbvcrn/RTB1byo37hhsSxkh78O8LMqbqXNJPFgOx6JAMaIQVOYOGSV/yPpn6RjgKEggGwOZZFDfuzgYXMqiwfsPu82eyC1D6v4VVYXvB1r7R5rz75qT6I3exUYMZCppKBykYUSrfZ93kb7ICYSFUjcJmvuJSfcTgA/CBiUNXs9crdXkfLt7Mvp85dRP+qWqhHymhK43IWdeUZ7v8++tDNk3zlMsI1OjpJkNjJG3hM3qx9BUvjDN0UBVWZfGkzYV+Lk7wBwQpiAeMhlSmPykeQJWql30Fb5dtly7ymrx5Cjmu6pBYgWZebYF2Lr7F/7F1jvrhJLwgolATzJM34Cd8DpIH1zEBlFDxsQYVTLoRBiwp5eRY6NPLAsneyHBauGHVtJH69oytiyppTduypp8wGVxwj4d4HKej2nmb3HWoBmJaxM4GQ0NYbJCA6KIzfYaFetbduLa+GgxyqwaL9nVcJuXw4IDwKZAQrDsIPt7djyUz+rsTFuTY3km5HLJhv9oz0he3pPMVJSJJD83Ybcspb+L0e2uaAJEEutLckKNS+DCseE/9gFtUEv/rwAQEWiWBh3Y+zRGwLABpCkZm1xE+tUoeeLTXwt9qkfwPVKD0zqSImtJO/rFtpZDos5ExOH3M6QM/fVp4PWSam5eDxrczBO+d53cd0Afl/ciRkIjGMDDxXuuTFl8xooMc0+q/8GegZtCL2hXDGLAVvVuIwUYfM4U6DrnQi4dExVUFpmGsLgPq419UOB65qJs72wDo00bz/AhUshkutWJIUaezLcP//7NSwpFOCqbd5PDteicZfCmTwvZXblt00vCHC9cxkuMVV6qKoYcCqT5pfRWMGlcCq9dul5C4Dr0n3ztvxF11scvZwCxLA/ZRXkJs2h5lMJQS4qBPDQecEuA/YI8rwnCdSfOwgiILjcCcuxixckilVIrkc8HYfhKm4eser13VbWNEQOEla7BEdjSL9GMyUWR36XigYsnWQjtCFDDlgWB74A4MU7FyfD2ojdFt5mP5R5DmYraOWZuy0QXGxd4XZ7nOLHm2GVBgFb8VSlraIC8I8GK2wtMjTgiFufhhG99M0ouA48Z2WSqcX99hQ5SUcAitqzptKUvbspavU+mB/Ap6SFcVi1TZ0Z+7e9yMi6MdNIKckNl8QGsx9bn7TlgJxNAI4/7yiiuHMCkDhIbg+lnotwPaT/qkuGbD6yynmhJAYUp605S30s+q0OaWEss8z+Kd5mX4j6bAADivge3BtfClqKXLgE4DCFQYWdAnCNEE3i0offvXlhbYKVSBgxpTBAcSxLrtJwwOZixrCiOmm3zE/bfcswosCyfhiHxi0Au04nxOjl8JxCUojlO/JBsZqncbA4cZp6XF1LbLS/2g4e43iOsFokcTfB/q1gG421yEzrVeQYmSU5XTK6EGsDvIFZILRlfUe7ybd6I2QdyA05BewWjDA2LE5R3NhrrwPEYxhurIMxW9rMsdQ5IWAv4wsYPk2u/pmoNE2chO5YSS/GylOALTFoKjKtXC6X3Jub85x/de6KpY0g3xtj/yK8VKy7ij5gMXd8z1ngav5g31S0efNrAdtFUewI7pELmyJ2/23IZyt8dgqgLEBag7lJw1xYlzzQ035wTUVHo9YP6IoMjroHGWK6JDP0c3FN9WZ3fvPfqYBLZ4ue6SgfVUdZtdAuW7j6/I4r/F3ogUv0AJf72w9wHemiaH0AwFXvxu4M4+61DbhyksIPPLY4B7gucayu7msAFyqSTS8cGWe43pmmGVfXd4WlZroHCoBrpnv0OlqfEmzjFHctEXCCZZpLelQF1t8KqCeSh/ncCTD6wpaQfXNH0KIE4P0E1/3YeVdVZG0ZtZWooGMnEtRKaumzyhWdZkgWs8j8vMgXk+QQncnWWtdAvaWG0GfL7py4KYshRBJDCS8MSqAMtqklYm21R+0R//N2q52y6vhSOK3bYQpKbCfufy9mx6wzXWzVmRq7w1NhqwCLqjIVyBbxKkY2IEc4GWToFqoXERoyuyEkgK92J+2N/goC+2pbCoC5oxbXRKR2o7Q5Ta5NlrpVJ1Kd9lRPxF7qqLPBsSqbW+azD84L2aP15OMAQv7lUNj+8ljYOoj6PZkYNamoBVVGfhg5caurB+391N1aXlxGseGoFSW6rQqWqzxYY2PpGvtSNGj/0I9jIMyJmK00Nb6ysGkpPsfbi2C5OAD0qQd2y5aQVwVDWDYfl7wmSkcDEKNx7OOHym0hRZgX00+LW+K2bm7GlpAYPUwO3GuwlYJH2ICQ7tWJ9+MgLBcFqgF0h04BEE7UWSjSCPMUtAGAZ1e/33A9t1pAcVNZxprrkCfejBEGzFkPkscxMFINjGQVDJbOBfUmGMd2n/TaYQpEhwHXq5qjzr3wP7aU2tdeLjFwhj2wOmY/dO+otWLooTu9OxYsLHtZAS65DOYnB7ncuplPG9EkFKbgmug6Q3Cdg2X6bvyI8rsKeyZ5FZcUO6dJt0hulhn7V+vsG/BSTwzrjmTW6udEbA5OmP+PvfeAkus8zzTfyqFzTugEoJEDCRIEmDMpWpLtlSmPbcmW7JHPOqw9Z+yRj4/O2N61nGYsz4zXSSOPZyTLnt0jj7yyKFIScwRB5BwbjW50zqmqK1ft891CI5AACZCNXD9Q3dVVt+797//fuvd77/t+72eOhG6KaFo/Dfzke3XuZvOvxeaQyY5MKjI1y5hjGGKGGqBJHxSgZx505nf23A9/4PNbFXDND4xZ7hvbZUeLl2PKAec8t1EvtMIIfJgRsGPppmO4bkDAlWe48oDLHEoXslkO11mG69cLdbgYXLv5Z9fnWCzm1OLq6+ujVmmUay/X3dPv2RzY38uXL9eaNWsWckpu2XUVANctO/X5L1MS1sSIBgR3uDSlMUZIO0WSw0FMC07TBiPc0f/+TiRl+5FToa2b43xobNiGVnJuVgOqAhPakexSXwANVkVESUDWtKvUeWRgLqazZeqbWKTkBKxLHJYszaCzzmwE8IVxgxfgEm6dVXt9lx72bdHqzDGVzRXjuHen2opuVxIWbBAjiLlcMTbm1JFyTcNuTGmCGk/DFEJWtk6L3SEto0ZVWQ76xuAH0VjO1YNZxmHkhXPqB0Slcqu0qHgJYIjyVskZDfsimvUDOpE2DmVHcfXzqTdVDovmpbhwWMs8JdroCaoRoPdPx0v01WMUZSZ3Kw2Y9CI99MNwlZVk9Fh5Ur/SQnHnSgBVMo2kDECGpCOAZNGc1raS7/afO716dZCAMYERQD/7fhQOiXy4dA/sFu87jhMwau7VrHPNpCraJuUrSsKMZGAHffTHp82t5Dk10V+YtEWAl1bu3VUppAFC0FHmzy5TIfLgVrA/Zamk9vb6dbK7WOEYDx/1pCxChQuKU+T4wFHmBellO86DFcgCH7wTC3zyw3YhFx3DYXCZWbS3WJ0tWyvlBcBQg4Bkq8EVQEpZGqIm1YSLGmPF+sHOELXUXNqwOKnf/lez2ryKzDbm3YMUYR5MWWBjJ3L+v6dlqINjJ3bbTgJgF4dV8lHDzYw0nHb6M3kwDctlrpdBHARxKXTeOo3X3rPij/hCvkvMFaymU2yZbfrMsvCDGheyFG6YcSST0dmI5qYxlYH9KsIetLQUsSfaOOe+wAet513v3+qAy4bDYHgK5taOI8PpXn4slGTzXcNd+PMWGIEC4Lq2k2yuwX/8J19WTfNRcrjmGa4LXCQ+Qjd93JQ0wPXCsylyuAqAy4bSrsWjo6P67ne/q1deecUBXXNz2IQ5F738YM9fb3/hF35Bv/Zrv/YRZqDw0fkRKACu+ZG4VX+fDlYtKT2LDAyRG45vBOE4/1UjNbT8rgg1So6O5XRqFBCThlUxpwkCn3XNGa0if2fC168duTfUlewBXPk1R92nceR0xzLLNURR3hSSsempcsWGw8okCcLJ/cohRctMYfsdTFDraoQT7ojaKBh8p/Zq7eghVVNctsyzROW1d8tbVQFLQl0jhHJznDwHkM3NcGLYgwnE2wAk7Di0GKjxyfSA7knsg/Eiag8ijwsDAN1RglukjDncMFxUTPc8pCifO5Ee0kAIcIY8aYq1HsrEKdxcpVi8FCYOxggWKBv3q578qc0EyUNz5fr7niIdozCy4Tl3gtUxDJUAogcBbZ9bEtf9yPyCgENjDX2wT15/AAMOlzoBVN+AIftOjw/2LKMMnuw5HBzTuN5lzDDDri+wSY5hxvKMyleNq6ppHNBi0k/6AcO2tHFGdyLZi8OyJLNe9teLeb60hP0uZ2T6WUmScV3EDC5hlXOzOb1wACv9IfK1AEh+HBndsG7WDHZ1nfLoKJK5YsCj5U09vjmhGXbtHfLLZgHVi8oyenwZphrkvCXYh+4htw72IqmzeWMFVZUkI7Oe51/26c1tWKEDuFYtSutLPxvXk3eTI8h6z7tTyT6avNBkhuehLpDHfIFFy5Haf4D5hVlas4bSAUgnmZ4zbZ4hi0YizmfC4bAD6hzK48xSl/Hk3Ov6Odt59xqsfpsDokMALpi6S24GvNjnWeSFltuVxF3RcivydbvI7TLEQB/OduPsswttwxanegIPk22aXbrN5K3W7LiClYABTQK8/NxECHCQ5AHs+0zirTZMhf29pBGwI6bAcF3SUF2Rhc4CrmP68U8vOy0pzN98W5gNns7hes0AV1I/WQBczrDadfh//I//ob/5m79RdXW1mpqa9Pzzz6ulpUVr166V2ci/8847zuu/9Vu/paeffnphpuMWX0sBcN3iB0B+9y3QywcrJtsxkwN3NqkqmI9SJHNjwTn1IekTcsJG5HsVyTCyOqtbAi/mTVKbqlNdrjdwxOtWb7yO6lkVjlRvINuog5lV5NsUKTaBlfoI5XfZTICCvF6sA9OzPnJwJrSqdb9qqkZUGoxqebxT604eVNX4BDWYKuRrWK3sog5stinX66ZmF7bxM66oJsjpei5eq71ZjC+QBHqwVH8sckCfjVMLK0cml3dC2XKC43ArexYmqJ2C8SIw0/2aTrTpYK5P07A0pTBZnVjFv8adtuMjNRo/0qA4QCQ3C6oqhckiV2dpMa59yMK60n5tm/RiqIGzH4DLj+lFE4Hf6nhaj7Sm9CMPULQXm/okgMuDS9QQOWd7AZVDFDLeP+7VbvK2JsZcivbC0lDQeA6pZiwKg4JbZK4OIIS0z43RR1HljCqrxgEtVgDYQ42zhDraJwFd5ER5SnFx9AM93QAtj27LATZdGF0AlM3+vxJwWwS7NAQw+j45V6fGkuTpmckHcjgAl820MQKD5JPtOYIMEynjMmpsbcbxMMj+HjW5IVLEVkwxFmH1PgOTNohrYfcJt8aRRI7GcKcEgC0BaBeTP7VrK8ANiSSqRi1j+V99Oq6nHkkA1BPI/+bBlR1bbNnG35mDsyDD/vbBhHnoGySQtrztw7gDNm9zCikD4+FgCuu1NazyOTYj1MiyHDE/tdQCxjrx+dOHb36xS/l5epXOUc+P+S2c91FetCLIcRhLpyYY2/NcLshh/0wiGY/MaXRgVLMTM+Sq+Zjf4rxE8cz6LtiD87pjfU0AMhIc67cq4LI7s2luttixnhcX5m8g+ABeBabrvMOl8McljIB9pwqA6xIG6gotMg+4qpuPO4DLXAr5ai9cQ/rtmGa8dhzAlSgArtMjOzg4qF/8xV90JIVm+W5Ay+zfzY3w53/+5x3p/re//W3t3LlTv/7rv66PfexjCzcnt/CaCoDrFp78i+26BTUZgrpsmiLBJTMar51WlGK/liOFKBAWhXpQBJLFSAWThD2nXCcoJYV0L3tcfaliDWbJ1+Ku82i2VrsyGxC4FSs2WqToYBkgCgDgnwIgZZBd+bDzntSq+gOqLx3CTj2htvSIVvQfURV3WMKBsKK1t2u4eIPDJpUBABNenArZ/kTGrxdS1TqcLgN2ZLjbndLq2SN6ODas9iwFlZP7cc6LyFvbDotFchqfytEPlx7QTLxBR7GZGAvHAQABbYt79fy0X10H6jTxVq2ysE8m33JjiuAirymM7HEldNJqJHdTyC67TV9HzaU68tGWjGBmgcPgatiY++5Mq5x8tFQMsOHx621c9HYC0MyIZIDcqFGATCgO9DuJEzwW4xOYOU7Q1xgFNzPYwOfq2Z4ng819VLVVw0jQrBIY44xksW1RRA2lWP3mqpETYuvO3tTBci3OMSMAWOAjmXQUPUZ0We8qIaeKfTro1+F+6m0x5kE/ZheOxI9tAZwPwXANAP7aYbCqqGNlNaRWIRGthLlKw6h5MbA4nHRr74hHJ/o8GjmFWQHEoZURCHphN9uNrXJp+xavRinOjGmjbu9I6uc+NatNGxMAIfYHxs3G0YIa++lBamrACgx3XjPGk8PJYS5G6VME2WI1UseSEgejnVnWrsN2bCZxATTGyS7MjhkHoMVyo7zkSBlb9oGNzxnbZmDK1mkd9PnI1aNv5zXeTCUSaNvnFCJnzACe7cuHacbKxGG4psenNDs2wzGR5W5uQEUlYWSj+e06feHHhbbBENnOAzYAgawsxHxaQeBbphlw5eaGSXRtnIz5tDyuDOArb9NvKuLTbOktMyiFHf2oI2DfoALg+qij+OE/fxZwHdOPwXAVc07Mf8MX6txmgIscLgDXiwWG68xEGZAywPXjP/7j+uVf/mVHiWEs1kMPPeQArPr6enV1delXfuVX1NbWpq9+9atnPlt48uFHoAC4PvzY3eSfJLAlv2vIPamJ6lGK5BLvQWNMUVi3ITutxsSMwuQWZf01GsOpL6NhgNVeTefIrYKFGsrV6DDs1v70OnKkqAlF/lZkoJz6UBhC+Ge4sxLGnt6rcDCixvIBLSs/rkWlg2r1zGhFrFeNsxNI1+q0zfWwtsWWYz6RUV1plMLNsEe+GKwZzFqmSEfTTRpNlbE+8q6mp9WUHddi2K01seNa7zmh5XUR8FYZrFsj/cduXhvkBRQOpCZ0yjeuk5ga/DBaqtcHqjXxZpWS+50dNRooH/k2ABWoeVUH2LtneVJLOiLk8eAgCEAJEhp3YHjROuNXXTkmFFiF58jtSgEIouzzy1PFOopJRZkfRgnnuzGMRurMJIQcpQGAUD/gopsixMPY1sYBdHhjCNKMYDKp2pIR1VYMYcwRd8BHRxXFdv2VWqpKbQaxVHFZChG2m/fiMfauk8cc+1JBnbKVmJTUIes82M+FhlpZs3NpFVN3y5yrDJBMAGj2nACAIeFbAYgsZ9+mydvqwADloXXUD8Pi/RSs3P/XG9DrVgqAfsZh4zzYoUMGqgVXw9va0xrqdevAO7gtDuCKSB7eJx6d0ZOPMEdNHgAXbBAAJpFyY/phdbVQeZrF/AddR533CakNYTgIxHAGrCt/mDzPZK8GPOwtYzRsf0x3bkG3B5bDcqzeA5zsm2rr4EMGssxNMAcLmn85v95QmHIDsGXnNZZPArhiaNuDZtLBPs336bzlLuGP07vC9mENqdk1BttloC9IXldJKduGEbV8t7xufn7psyu2V2xo8mIb8ugAmHYD5FZpTvF2AJehdT9z7jm9786RwDCYmYYtYyDM8roKrTAClzICdqQUANeljNSVWeYs4DquHz0tKVzoLeUBV6deejZeYLhOD+7bb7/tAK1f+qVf0mc+8xlK+MzpJ3/yJ3Xbbbfpi1/8ohYtWuTI/w2Uvf766zpx4sRCT8stub4C4Lolp/0SdpqAxmLSScV0yj+sbElEwRDFbbOAotlONUz3IDsk+g61a7RisSYDceR+h1EdjsG0uNWTq9KBzDKATTMmDaWO097YZCV5LJi0YugQnwvh/gezgttgIJDU0iqkhNUHdWfoqDZ4+zG/KFFX7C59c+RxvRat1RRSqjAM2Oqqfq2o7Icpi4gSSRrOVOj4XAOyt0phPKiO8DGtKTqA3G5a9ZlpbQxRDLg4rP2u2+jbRm1SCyyRhzylfvWRc/bidLH+Z6RdB4arlNiCY+Ap5GlI/JxmaV/1yP0AUrWwXGspyrtsdUQrwlGKJ+cAmNKqnFd3YdseIPi3f0nYLwvSsx5qf80Vaeu4T3MAGyvOHGBM52CXjPNyA8omZnI6MhPSAUDXjIEHdJppfkcZdx8wtS48rrrWSa3A/n1xCGOKXKkaYLeehBBpYzlraULwE4DggwDNk7BnsUi1WtI12lTqVjW5ePt7cTE8iUkITidmTW6W2sZMHYfhmprECp5ErGJqchWRQ3bn8rTupS5XPXWnzI3yf1GT6xv7/RqadakKIFgTQeKHa19jgxlt5HRyHwYiQ/SBfLGW+oQ+86mo1q8xwwvmGPAzC8jcj1lIN86GdTBna7GRNzBn43RJjfk1eas5ExqTYTSYfdJAie2L4/jnjMNpG3oADO/CVjHCBkgIvg2sWX6igTUnf+z05w2UOaYe/D0Hg2XrCwK48oCHF2kGjmwuDWT5yd/ymIsMzz9So78G+mYnZzHVAJhPR+SiCLePdZfAdoUAYA4DyHYuOkq8wdFCNz5qZz7Snly1D6dt/gBblvNmrJ4Vc33vvnPMcmza4RDg2HMOi6vWw8KGbtQRsO9YAXBdu9k7C7hOwHCtUNgkhQt8XjNJ4ZuvHQNwxQBcv1ZwKWS6Dx06pM9//vNObtYXvvAF+blR+bnPfc6Rz//hH/6h1q1bJyuWbIBs37596u7uvnYHyU205QLguokm80rsigX0Y+lZJYNTasSKPJDqVnDsRQWjA9xpLlGRq1KRqts0UF5K2H+SYAiRIcChH+OKYWLkBDW45jKlgAg/5hGtOjC2FOc2AnLc+nwwZknyotyAkcW1PbqndreeKN4CQ8X2XKu0a+ZefW14k3ZA/xiHFvTE1Vw8pNsru9RC7Su7kT0BeOicqlL/dA3sB7lH7l61+HtUEpyBlYtoaSCmdoLvkUwrMdpy3eGth/3BICPZrTf7svrO4VXakmvRpBe27iA5TiexbAdssDlDPQLhKEfx5BZMJO7cQKHbJTA4mG0UYXlfDHS6g3yqFbBZ82I0C+xN8ublBBZ1B3WcemNjSPNqYJjKkQvuQIJ4eNqnMElgYdYxnApoB6Brmv0wcwtjOaZ4HgXsVbmokdUUVQW5WznysmYw0FhGl56GkVpptacsWqARuuvtGJb21MjKYPrhSoV1O/19qJrizIzJsYGMuvEPSdJXI3GsdNSOgz7tPA6zxrYMy7TUZrURx8nqSqsrlqO4clpHqA321T1BHSb3DPymZsDPspK0VrTDluEgeHCHS6c6PRrFQr0GFvCnfoxCyhuYJ3PT4Lg5Dth7/h1qd2G2UoZs8dG7krptKUYYNlgAIUMU9svau0GYve7I/timgabzQJbzgXM+YePA8pbf5bBXRmU5y+Rfs424AF8WhFsBx7x7ogXteVCTJH/PgJUf10Mfpha2vQzzmCHIt+3ma4jll3c+tAA/HNaO7USno5oxmeHENHlvXpWXF6m4BHbQGaSLbcj27/TkX2yRG/51vneMj4EtDk/2Ns9e5Vm99+67vTLPctmuO7bx713M3iq0wgicGQE7RAqA68xwXPUn84Crqrkzz3Bx7jM1w0I2uzq/iWmGMVz/qgC4nKGdmJjQ5wFc5eXl+p3f+R0tWbJEX/nKV/TNb35TDz30kMN0GSizPK6nnnrKMddYyDm5VddVAFy36sxf8n4ba2MSrKgaK3KqCg4pNfED5aInAEzFCrlLlau6V+NlzZhPdGoqR96UsT+ecbgxmAPYnxjAKkWuUl9kkV4d7dCh0UYlogjinCDZSzCOcUbTUW2qOayNwX5VuKthydq0Ndasf5xsUXeU/BmkeiZFDPmjagsO6PbSU9S5QvIYL9GRoTr1ztZwmvaoKjOpUs8sgXVKFcVjWlVxQptSfWoC/RUly1VRU6npyqCeHWjQP2zHE/FEu6IgBMvVglRSDhleborwjnwl/OedwsMu6l+1L4L9WZPSPY1xtYWoOYaDYB3sVpvjFmiX7XwIbIArAeCywN3PIwV4SyGPA8ZpYNilFzCY2I4LnzAjaWiB9SryqpPizEOAsjDkmoGoKWR4pCiRI0YxaWpl9QPExkBJZby/msfTNWndVw0ziKlGfqs57YeFemnUT400FqDdUZ4GcGGTD/iZi8KkzIEgfQHykDC/wM79B28HdJR+mJHIJBLDcupxVdYxIeyzDwD8IPlYBrBfPuXTNpwLp/kMGFYtWOG31qa1rJE8tUhOP3wjqFMUiy6HFbt7fUpPPsDnsJS3fnUCuH7wjl/9FE0up6jzE5uSWg/LZVjCgukIMkZzP8zAALaw7XJAmY2kMVIOq8X+O1XujbECMH1QM5WZOSOaxDANaxTHvMTYJJMK2sNYt3MZLGd9fMb6EpuLMUO43oVhuXgtFok62w7AbF3Ktj+obxd6n82wcQ47jpfJsUmcDKeQeWJUUxZSGcDL7ONv1WZHgrFaXKAcoxKm7gObjSeL50EaT4yQNJOT/LfzAz9eWOAWHAE7NgqA69pN/Dzgqmw+AeBaTk5r/obXQvbIhyrEGK6XYbgKgCs/snZz8h//8R/V39/vSAnb29t17Ngx/Zf/8l9kckN7P8FNSKu/ZRLDe+65ZyGn5JZdVwFw3bJTf6k7bhIoAthETGVBGJ4qiv2mjyg3vRf6IKl0uFHJkttgtKq1bW4CO/gBlQTmyBkizwmGCHGd4tTNCuOe50dKeHQmqReHVmnv6G24rYVgXGIE6L1qqu8nYTalWqLxpRQ2XgR42pPx6rvxCo1GsP9GP1jiiTj4otw9pbtK9lOjaxIHwGbtOtWhg5P49hFg1XpGMN+IILXD8hwmamPldj04sxMjjilVIblzVRVpW+0K/eXhj+uH2+5TFHYpB8pxAYDcuPRZgGdBW9YcGC3K478P+dxS8ps+3p7Up3EhXIH1u0VxZqFgJNi5zViZM4DrdM6PwaJuHP6+t92vHYco+IzhYwngddE6pIa1LuzopR7yu0zG5wEA5Vh9gL76pjEkwcDjBIYb8TIYr6qcliDJ+2kMKz7Vgp0+rJkF7GZMMQlI2z3lUyfGHMWYWtwBw7WYmmoWdJpNfRbg4capcDhCblc3oO+wD3MKFKEAMspgqRaGK4mUcgrgZ4DrgaUp3dGW1jh27yfGWPdhWEIs7Itgtsy0oY5+mATxKI6CBzr9DtBZuTitJ++lRli9BcouR1J4sIv+9xur5FIDQK0E9gwc6kgLB4bdemMPkku28dCGFOwXJQnAi2aIkUbOZ9LEfMHkywyZWdxAlM2D/Ta5xPsyRjbfgLQkc5dOpxzAZ1s0sOWD+bzSETubd+SOM+MzmhyewHQlTrHkEDW7wnw/8rld5x5jN/vzvAthftg/TJ0tG08Da/Y9tnOCs47Tk2jvFVphBOZHwL7nBcA1PxpX//c84KpwANcKB3AtPMNlksLjeuXZuVsWcNl18N03GycnJ4kBIsQVVPMkZcGWMVZr+/btjpywoqJCd9xxh1avXi0rwVJoH30ECoDro4/hTb+GNIArl4zJi8QtBAPSgGteODeMPfiMBmCHxr1BDcyU6/BUKblLOLoBtlbCaHTg2DfkGsZoYwSjeGzmkRzOZIa0O1Kj7VPLNZyulhvGxF2c0WSwAtuNek4Kfi3Bde8ezxjjGtVb6UqdnKM21pw58wHieA9jeG0qOoRV+5DGUvXa0b9COwcXay4FM+CdAhhOISFDwlc0pU3Fu3T/yF4tHRlWZRbaqKRU3wk/qP9734/r0IEVSs1R54q8Jqup5SFHLVCMKQfsUKo6oDgBt9mhVQBsHqun1taihO4tSiGvu3jYZoDLiuSahM0LYLAL+hhW6i93+fTcbupljfkZO4pGYzzxCNbnpeRDjQO2xpH+We4W8aGGYZMmsG3vpDjyVupSTeCGmAOYeXAUbId1+pS5AS6jwDGW9aStkBcGYOBz5j5oRBbYS2mAURFjUGNrJFcoA5AYgVV8uzOEvNCjMWSAboBTI8CsmRy1ZYCrGSzo9yCpRFypzcuwp4dV6wMUjU/ldLgHoAgQrGb5BEA4kfLoodtTjHEOwOWlDhf5bACu1UgGi/AdMZmiFcweYF/GsMY/yTaPs65RmDgfwG5dW0b1wYw6T3oUBXA9uTGpu6hj5uE4MYmfmV8YS2jj92GbkwvA+Lz7QnPB9QF0ndyqqRkuPIwdFonGUF61xvYtX22WnK6JoQklZua4YcGxV4F9PODLw3FoAOJmbjZf4GzUvLBaTLyXMbmkubvAoNhQWQ5qCiMZE5/OSwxv8iG8wEgUXnq/ESgArvcbnSv/3jzgKm/uguEih+uKSArzOVyvArh+6lP/xy2Zw2VgyvKwpjEWM0MMA1MXuwlpuc6mMLGbnaYw+bDn4Ct/9Nx4WygArhtvzq56j7MYVpg8KxFP4hDoFSQR7EoMO/MpHQ1Pa5zcqkysWiPjLZqOU1uL3KQHsPW+rSKjcUw0+tRNCN9FztNxmKco5hopdWHrfoDcqTF3JUV7m3QyuxgwVgojhuwNxmaTZ0B3efs0QJ2tgxQqHpsLU2wYFz7XlBYH+rQhfFJtPvqE7+DOmSV6qaedgL6RaC2n0tCMvNTYKi6PYB5xTB+PvqPbJw6qkvpdiaoGfTPyY/qrtx5T78EqgBxgJozsKGDyvhh3eqLyAapSpX5F/GXKQbncWZnSr3bE9IkmLO3BL/NB24XAgFHxVofLTB6cvCGWPjru0Q8xoOgbC2hqOqgZANbHllETZGMc63ryePi7nzynEVwA3YCcEzGPDg/79PYOmKgjXqWLCLYrmHbqXtXDLG2kBtanlya0EYAz6nerF4SVgVFqoghzRwD3QIxIdmJo4ucu/12AsEabNzq7f6hErx0pcgJRA0hhtnXvyqSWN2PzD3Dq7vXoVYoYz8B8NQDsZmHZRjH2iFMIeXjSp2KWaaoxC35cCCF+Ht9IrazmjKatlhjBbRm5biHGkTiZAs+wV4e9OtwHyKIm2MkJj45hMT80zQDCKC6HMfwM+7+UYwTyi1pgaVWVJByGyYuhhQGuKyXlu9AXyKSV5oAYmZrGQANwj9HK1dy+0yfrAwPp2MePTSk5h/U9fTKWKxjy4jLpx/TExKnzR+CF9uTGfc3koDFu7lgx44WwvLfvp+WA2cOaH7qXo4/Rs3duzjG0/Sy0Sx8BOxIKDNelj9dCLzkPuMocwLUSwHUlJIVevYWk8FYGXDZv3/rWt/TMM884hY7vvPNOrV+/XosXL3bYrQKoWugj+8LrKwCuC49L4dVzRsDurCcSsD4Ef34AiIu7xj4sAb3hiKYo0hsNIm8DEbhxExwFLJSSgbU0CKNFvlAWidgkoGtSB8gX2k3AM2YiQ5z1qFOldp1Sg7ozS9SXbnJioCSAK0Jx5TXIBp8KHCdPbIZaVWEcyJCtJZBaaVT1gUEt8sbUigV6GCOM12Mt+uaJah0ZrOEOOUiA/Co37EkpBhBP1h7Tz5U8r+W5QfLNqOMVLNe3+35Ef/bSRnXu9ilH/lIOQGO0U4C6XAEkjh4+my7xKekNqzLo0tOtSf3q6riWsz7HUOOcsXnv03wOkeUhOY1Vd47l9PwJnwbmggAKq58kAFdSd7ekuPMOwMIx8O29PgArASHW6REAbR9OituQ/R3FlMLAFEPggMIWzCk2YuW+DKBS1oZ7Wz0YEwmgxY/GWC3Hsp5iXzpA7pcpDh+BMeiIsx0Q18nJEr1yKKTeYRgu8qrKMN54bENSm9Za/TCKDm9H677drWlcB6NICz04F9bDxLkw7LB8LGO4amDZagDT6zuQFK7AZAMA6GzcUJbtMg97OgzIehYJ5W7s549R9Lkb8xCzozfnQlusFQnnFz8W04/DbFFTGmaLu2rIVl1ISgOYV1xtsHMu4HJyvqDp2I1r1hxXxSRgFkONyCSsG0C+uCiAzLCI8cnn6V2zzl2BDZulu1N0HVrKzzGwkEWMDcilALL2nQwAWB2TwyuwD4VV3ngjYN/xAuC6dvM2D7jKm0/qE59eSW1CcylcOFGhsdvmUpgHXNFbluGyGX7xxRf1ta99Tbt373ZAlhllmGRww4YNTq5WQ0ODw2pdu6Ph5t9yAXDd/HO8IHtolLQ1C1as5pHldeUyOO1RG6sSEIAACwImQGFSAn+CJhfgLEUx4oQ7pVnqZqU9Uwq6ujCPOAArdQquy6e3tBiRYZ36ss3qSi/BRS/AA7laJqjbfZN63N+JgcYsWAj3OLaPd59KcLMoxQWxEr6sPtWgyUS1vj1erO/1V2g85nfkSMmkuczBClDM9q6ak3q6+WUs5ydVAaPjxi1x3/jD+puta/T9N72aGeSSa94EABo3IMWNEYcb2VyuAue9EpfWAwyeXpzSj/GoAohZMd/3NBg559X5H/abccgvm9PgTFrfx27+ncki3A1durcmpQeaqHeF22Act8aX3kY6uM9HbhOyPVwDE9Tk6sdBY3e/T0NI+eIwTmlwZBmgcCOM0trmNAAWs4sqaTH5XI3IMi0PawrziRJvBjlnEoYPJ0Hg7XIepQkr8stcZAJ6B0nhc28iAyUvqxZ2zR4b16a0YVVSh49Jb2xjTABHLgrr5pC0hQGj04CnU/08R6ZYBjBbi9zwsXtSaq5nPB34dO6IMBYcIzFYt8MAyRODHj3f6dOrPEzGaMyaYdG1OC/+zo/F9CO3UVCbWUshI7QjzGSEXgLuq90McBmomZuNOlKLAOYZVxv0nbfPhlppJq+M4GQ4OTyuxOycioqCKq8oQlNPBmF+kfM+diP+YQyUgS0n34qduhJ3Wx32jLqCXm4eWJ6gBWKFVhgBOwoKgOvaHQdnAVe3PgngMlt4g1t2LViIZvPrPwO4Ircs4LL4zeSER44c0d69ex3QtWfPHo2MUO+ztlbLly+XsV4GvlasWOFIDq/p9W8hJv86XEcBcF2Hk3L9donTIGcww16pJGwRuUH1pWktaYSZmY/+Tscxdjd5Cjv57uwQRhq4BiITqnX71AhjlXUdAnL1aSco5zjQaSiLrDDTwPLlimdLkA0GdRcuh2s1COACyHnJ4eI0zBbJSQqT6VWC+6EHIBbWMQry/gOg6TA5ZJZbNIIhxeyEW8lZ7pRheFBeO6W1S49ofVOf2gKzALYyChNvUM9gg97e7dEucqSGAAIo7wAK5EnBMAUwtKipw3odk4fbcQR8siWtu+qR6MGQOQNw3gThqAfYSZqVO+xNP657KMEcMGIuf0igdWgqqxeQ03VT2TjMODyCXft9OAgCT3HHS+vNXdyB28tfoFUrZFzdgsMfErI3h7KKpii8y2aj2MeXURW5oxhHwSJkesCUZKlLrWVJFQEULFPFW+ZRQ7X0cYw9TFpoxYOLmBcPI2d5ZaYlPNpbrB+8RTYcfaxCpjhFnlVdRUqPbrbcu7Q6e3zsB8V4yzDQwIDjOEDxwFHAF+zWYqSM5qJoFu+P3YeccLFt9dzGvUk7sfO5sYgbtpHtQ7NZ/tr/3B5UL7LJTIpQF1bvvuUp/drDMa2DrTOHQNMkBkncvWDR4nM3cQWf20XJbOXN1t+P4YmZdlzzxgDbd2l2KuKArjlyuwJI48ori2G8AIWmFb1Bm+XKpTlGTX5rZjHGwl5JIDQvL7Rj2MMP+30lt3eDTsst1W379hQA17Wb8nnAVXaG4Zq3hV+Y85rpKfIM11G99uyty3DZtW3+Rpa5Dw4ODjquhMZ2GfA6cOCAU4OrtbVVa9eudYCXFUE2FswMNeY/e+2OlJtjywXAdXPM41XdC+du8RzljXEpXFyHw13Fe8MWu0fVT2WuYwCrDPRLFhki2VlI++pV6Ulq1NOpI9hk9AIV4li5ZzNjXPgMGlRRtLhCdaCWqomIitMlSlW2KFpRryjue4SYyAr9Zq0A64XV/Hi5/ldXpY5EQrjMeTClIAeJRxbw4w4DkpoSqlg6qarGCZVTYLnGFVZDpka11KqqAGyM9Hi0BdnbKYwd7K5akDypClicBgwpWpHt3dmABTtsVD1slC1guNIuBQY6rY3BPh3Gha8PU4huDCBOnIChoobWuo6MfvRhgA81q7aMufQKjFKGYsiW13VHaUr3l6U0M50C7EmjU1inD3phMsg/w54915zTFsblUGKKsUtoaqBIkyfLlJ1gBAGV4RD29830gXGPRmEGrKgxiVDmeHhHe5pcswROhYApQGAVrN7SoqyKk1Gl4zF19wf08vZizZBjZY6Bk9MeNdVl9AR27k24C45GkP+RrzWFkUUiCegadenYIQ/sJDlitRRuBhvNkdv1yN0p3YkU0VwQ86iLGWdQesnV2gnA6gP0GghfDGj1wMy90k0dtjGkYiweJM/r4SVJPUUeWrk3iVSRwr/kTfkN4NgAX6TZMfXeI+0iC3/Il+3YniOJzU++lNXlui4aQ2Kgy1wXp0anNMPDBUgx6/iS0hBjd2YSrovuXkonjNEyN0K7kNtxwn2IKz631i+nZADfXZtnx0gD1HXxI+5S9qSwzI08Ajb3BcB17WbQGPw/+pMvqxTA9XFHUhhwrsML2SMfjlhbyOF6/dlbl+G60Hja9dpcCk+dOuUwX7t27XLAV1dXl3M9tvwuY7w+8YlPOL8vtI7Ca5c3AgXAdXnjVViaEeB7SpAMrZMmSK9Iq6nKAr5zm91Xou4U+VrHXP0ON5XlbraX/KUliXpYKswxMLzocQ+q392lQG6HQrljBD4sw8kxnQ1RO4rir5NelYxhHe+uV6JtncZr6zRDPbAgHaBKEayPV/sHavXWiWadivg0GcWUgcLC6QQ8EYYRcCzyVyRVunpaxcsicoVgh5AUtfPZxlSxbqc+xwaWOQUY2klx3z5AyARugT6kdCvK0roNdms99a4WwegYkWAYCwMfRxLn2JczBNtw6HvzBGBi2Kt9O91KYK5YjktfVWlWn/l4TB9/MKaTsZzemPRTCJp6Vbg8bgZwFUcxtujKwcgBwsgrMYC0DGmmH1OK5wE6W5MzmnZPauxQica3VSneT/7XNI6K9BEsoJbNBKuLXRrCsCIAM1YEuCoGLK5uTGllCzl2gEZedgwp1hentZH8tDCgaxSA9/LeUu3Eyh1iTDWArgduz2jTbcjpYNC2k6t1HGAUh50yBiBNVH8qAABAAElEQVSHPHPiFM9xF7ScLwOT5djnPwHDtQLTjizU4AzgjPjZyUfbetynXd0+xp5jgNfMsn49y3nYTifgzgot1zOed9alVY99vwswHoBN8mLG8p67aOdGwqyL/0479+XTLy3YL5PLRmatjpsXxu0aywrfvVcAk2QSiS728RODYxzn5L+RAFdRSV4XB4WNi4359d5sjJM8bL4NK55hx69Kx+3MxPkLcx3+O06IdtOArtCu5JF1VXausJHLHAGb8QLgusxBW8DF5wFXSXM3OVyryOEyhuvsuf6jbsrm109M8dZrR/WGA7h+9ZZ0KfygcbQbUVYM2cDW/v379f3vf1+vvvqq87Hf/u3f1r/7d//ug1ZReP8SRqAAuC5hkAqLvHcE7O6IFUQu9iZguXCnI/9ovpkGO80/PAHVh2HGOHW4PDm3qrPlakpVOixNkvyvIWp2DXn3AH92wlb1cKJNK0N+1rQ7jJV8KXlHsAwU1S3rpQ5XaKWml62Wi/ykEKYYAep6nSQnandvDUV5q9U9FdIRnP3GMZ4wKZ3lZblgYjzkkZUsm1bZPeMKNCSRBXpUyxZbEiW6D0nix2HBiunvEDK4E+QuvTUBQ4YL32O1CW2i5pgVDnZUW6xyhsK/h5HXWU7TiuUZlZRLPzjg08vkWp2YwZEPwwnXELWSWV8VNbA+/2MRferRiGM7P0Re2ijOjOVIFsth+7YdcekgzFiIXC2TMhp+vR23wGBbWt+CbTsQn9XkTFy9L1cpCkCSSfEsUoRhEusoXZNTeAl/8tkAwWsVUsoG8uqqYauqeb0ZOSPKQ00CcMopkPxEVUKLmJEj9O+tnlIdHfIrxrqWUqT4k7cntQR26x3LM6PQsfFIYbZhLYL0kPOwigGN5ZyUQ5A+q5dltI79D5EndqTPo72wV2waVjCjLmSD/VNYy9v40+G5uFubl6R013KkevyNfwdMoxVx5giJzyGJgz27ELBhvKOAye4BkyayP9jnG9A7e5Q53VvwH3Zcz0XnHCbED+vmuCUa/XK9NMbFXEOnx6Y1NT5JKTy+D6D/cNiPmQZW+gz8e4Dr9dJ3+mHHiUkIDWSZzNhuzVyLZludlxiaS6bV6rqex+1ajNGtsE2m/qYGXBzWKCs8+p3/8z+qrn21fvM3fuO6mtazgKsHwLUad9jTksIFOi3YjRQDXHmGa0Y//akC4LrYAWB1uY4ePerU4TLAtWXLFieP+Utf+lIBcF1s0C7z9esOcFnAc6UufFu3btVX//br+uTPfEEdK9c6RgKXOV6Fxc8ZgRTRkyudUFN5Sg1VRNicJDFUh9eaADBNI/zzw0QVA6RgSPhXSv5VGGGh3YWPICMcQnA4SxaXJ70fS+6DgK2Upn3FmnSXaQ7xYJYTZXYOxNMV1OzwMs2U3q3VzWG1hYewLh/TtrEGdUY6VO0HQEwE9WqX38lJEjbm1gxwuSLkZLXMqeLxEZUsn8Vm26sAToarc0X6GX9IjyIVDJ6uqxWB1TkKSzaXcWM8kVaNH+gIeLP+2om7GwOLZ3/gh0Vz64lHUloPK/R6l1ffOB7QnjGvpk6xl30UaJ7IqJ28qF/4iZg++UgCCR2BJUAvy0rcgI7e0ay+vx/jCkw+asiVsjaKNHERBhZtK1J6NunRs4C7scG0Zl9EKnkElOMsxnoAQjnMMkKNGF4sMpMPAnBAUUMkqzXYwlchYXQ3kQeG02Ep7oVjMHYBtmmAqwqQ+zy1vY7DtoWtsDMg2Nzh7saEYznA61WKGPdMeFWBO2Fe4sV8EiCbbLIdqeNa7PErkCdWV8KoweJ1D7n0rVf92oshhhfmbjG5bmxOJ3ElnAMgWgshafw0ToRPrKV2G0DLBjOJhjwDinLDInkBCx7G5vy4m+Vo+4969QbOiZE5l+7Dgv721WkH8J2/rLPogv6wvtkdP6uLZXyIsV1Ws8TkoM485LuX73N+Nxd0+x+4Mo4j66PVSkkhM5ydmFFsBrMP+meMV4lTt+v6AIk2VDZE9h0ygGO/LQi0HCr7Tl3rZse/gUDrmAFAyyV718F4rbtY2P4VHAGb7ZuR4QpQt6Otvc65mZflvPC7/9efqv46BlzFzaeQFFJg13EpnD9rLMzEz0sK33x2ugC43jWkMe66njx5Uvv27dO2bducXK6enh5SK4JauXKl42L45JNPat26de/6ZOHPDzMC1yXgsh0x4PV+Lin2vrXLAWcFwOUM2QL9cME8EKxg1d4A69RWbwE88jP887pcPcCtadwKKayL7XsbboTcfyfwys+ZhWAzrhH1uw4rk+iVa24vxM0hJcjRGvNXKJYjSdMkP26zIvcrMR7Q2Fibpofv0ppIWHenD1DrakivpDp0sHSDqusr1RfH8rzXrwEMMNIE/LYNF/lNOZgZLyCm9N5JhRujylB3S+RBtcM4/WyxTz/RRP0n6kdZ16x3Zls+TYBvGCCERM+YHnPhtiBxBKC1lTpVY9TL2rghrRUwPT3kKv233QE914MsjhU0RFKqnUoBNF26Z0NWH3s4pcpy+mFBHetIEyQPjGf0cmexTk0jMXQKuOM4OCetrE/qjg4cDWcD+q/U7OqaAvC8Q3C9n84Ys2WmAtX8hukJkE/VhDV7KX2vYKzuIS/ubgo2F9dIR3BlGiCfjU+RCSatCGf0IMWqLd/rmQMwdIxJXblZb3s0jFX7kkrMQTCv2N7nVT+yxQqzoD89V8TIGoM9W8/7D5F3VYKs0AUIxfhQL2z16R9eCGgQVtFeW7Qoo3KA4DBs4SzFnm0dJQDan7gzpY8BuEr5bA4gM4fbnrFocRjGaNLH6zB2Jts8jWeMyrO52LrLrxffsLpgLj12f0r3sp4iwODprrHElW1ZZKkGaqwQpJ2LDHjNn5PsvGMAx9hJm9fTp6Mr26F3r/00OIhFYpocmdQ0uV3mEFpRWYJ9fBiZph0BNpLXtlm+VOo02LL6Wga2rpdmPUkzgea6as36Zsdhod0aI2Dzf9MBrq4hmG6vFrVwMeAcZRev3/vyV7RoydrrluG6soDLC8N1VG9yF/OnP/Urt7yk0EqODAwMOOBqx44dDtgyZsvMNJYuXSozy7DcLQNcZqJRVFR05rp3a5wVrtxeXjeAy4L1sbExh8bs7e11Ap1wOHzGrrKkhKQUWhx7NTs47EAxm0t7fc2aNbr99tsdVP5+Q1UAXO83Opf/Xh5wJdRQCuCqg8FhDofxzzvmOkbtrVGlcjBLmGCs1jrV5RoIa+YDLZcirglyvA4rHT0EyjmgSf+gojAyM55ico+oVWWW8v4AuURFSuGaN5esVObwIlVtgW2B3aoo96rT36jtoSXK1jdqKlCrHYCFEwCuuT7YMepICYBhxag8a8hpWgTTVBLDDCFBCAoTNVWs+3FN/PWVcd2N+56LXKT+EZfePuLT3kGfZglmS6kx1VJFMeHyrBbB8JQQ7NMtJ4mrHDYKkzhRU1jP7fDruQOwRgCVeldaYV6cniVPrNmthx5MqAX2yM/6zTwkwfFr1u9HsLLfdopCyIATC4orizK6F+ldJQ6J3zwR1N8PBHWKfUj18m4Xi8wS1+NQ6CoBjMDalcBuddyZVSNqw3rs7J8iV+1BJJBBGKUuZHyH57B3h26qxMp9DYCrFev4IYDL9w4CxqaxJimxu/k+TQD0VpFPZSzXbiSO+4Z8OAvmiyIb7JmFLYyncrq/LaVNzUg5DVUyjRMYjnznZb+efRtgeMqNmYNbba0ZrdiYVgVg0ACYxdWz5IBV0+e7lwJQa+JIUFPUWbLCyOR6dQbVPeLFap4i2VjNN1Lfa5I+zpDDVgMAy5IbdvS4B0bMpVVIEpsbyfG7ysGwY7KAqYf9zjcDMOwYx/r8TR8DYWZn7yJSByt+9MZKbD15jD7/nXn/1aZAwNMT3OYAdGU4/kLYxpt9fCh0LZ0W832PW+IjzUwqTLp3/TXrE8wroMvMPAIkll3dvLLrb0RulR7ZzN+MgMsMiFpba1DXc9uL89Lv/f5X1Lh4zXUKuP5Axc19MFxrYLjMNGMhTqJnj+C8LfwRANcUgOuXb1nAZfGyuRFu377dsYU/fPgwKRjjqqurc5wJN27cqNWrVzvOhFVVVY6q4+woFp4txAhcN4DLAppjx47p7//+7x2wZcVPTVM6NTWlRx99VJ/5zGcclG2059/+7d86AVBTU5OT6Dc3N6enn35aP/qjP/q+SLwAuBbikMmvg3jTsdCGf1IzZg+VxflIeJY8oeOuI+pGLmghY6nK1Kw2teeWwXHlT6Z2Qk24pjTmOg7bsU+u8d2azg4CwsBI3hBAyw87k1HEX6wpdzVGC2HqbdXIta9U9W9OyY/5RlVdqdyhIu10N6mrpE2Z0jr1EjAdIb9qGrCSnQCc0EcPOVEepG45QI4/kFBJHVo9uhqZKFNN3K9PLcHYop08LxiZ17cG9O23g5hG+BQjyDcpXgWArQGWphEpXS1Ff1csyWj9yiSgIqkYNbtYs3owzjiwIyjvLEWfyS+bBYR1G7+0Jq3FFBZeTf2uDQCuKpOoAbp8HNtxKkL3wMT1k69lZiGLKKq8CKnemwNe/Rm27e/0MLKsJwtgYYgdAAN6JTogGJ/Mqm5NSg/endEqAFeSda8uzmgjFv0xAOE0nwtj/FGGhK8cwFWCZNLy0GLEvdtgsd7uZv8BNT4Ym0ocBO9rTWsVBY57AKuvIcscZD8M2Ng82Y3/pTBgD1CHbBEA1AIUI1amsX1/9g2vvv3DgPbu88KwuLSUsdnwGMBoCQ56gNEsoGtPnxtjELeayCnrKJ+jZlhKdZU+DSJdfOsQeXgANWPLFpO/tqQxoygg0QDXYuqf3QvIa2HMQ7CMljvmtQLPTg/4dZWbgStj5+y4ty447Bd3BBNWWIwWLEIsa7loDM7lsO7Oh9/1I2FzzD+qGjhjbWN+Kc1yo2IzMY32DyuKfXw45Fc12tIQv11WHO0qNuuz7UWSY9765QNseQyQXsU+XM6m7Ji2Y91AlzFc1l97rdBu7hGwKb7pANeJIW4++9TeXu8cw9kbIIcr3NyrH7mCgGvLa0e0xZEU3roM1w9/+EOn8LHZwBuZYeDKam+ZXHDZsmVqbGzEKZgchUK7YiNw3QAuR45Gdv7x48cd1so0pFYr4Fvf+pYDxP7qr/7Ked3A1rPPPqt//+//vTo6OrDhPqGvf/3rjtznr//6r5HSEIFepBUA10UG5kO8bIFnIpbEAAEampyh+TvXZpgx4hrUCeSCcRwFQ9iw1+aa1Jpb6uR0cQ8Z/4cRYNlRhwWL4cYX6j0g9ewGWAGyysLkahUr7oPVAnxNhys1kqrnWGhR8QGC9n1IEL1hpZvqFC0r1zFvow54WzRS1ACIS2sC+VsSWaAfO/Usd9NzdTAtsFFpHA8RKKqkYRqdHSzKcLly8aAWVSe0HIYnQN7U0YMBdR6h8DK1tEhEc1glAztGNXi48x0k4K9blNayzdiZL09SoBgAQq6UH4YpA8AT+VyBYZdiAZeGl2Fcvy6uYDWySgLOT1K37BEASLkZG+DKZ8Fchr+tfpe1AMBoGBDzlVdD+ru3yFmjYLAjIwTsUZaM6JXljPKYRSIZTqrynqTuvyOn5cVIBCkyvAzzjyb60W85aKyzHjZuMwWWl8CcGdhCdeiAw9mEW8eo79WLW2EwiLSyUuSbYRsPqIkDyLqxdT8y6tU4skrrYx37uAojjmYAkOM+bl3hQRytg9jp/9NzyP5guiaxvr9/c0obHzWgDGhCAmkOijthzSYYWx/5cDWBlFZhrGFGGceQZ3ZTm2yWOmrgFsAK5QWQXi7BHMMs+S21rZ1lH12X1FK2z5/XXcvA3FjSt90ssrGy74TlepmdvNuidhsoa5faeZYbGHTrwGHqwjHfa1Zl1FCbBwD5FX3ATzph9vFWvHkCiWF0Gnt7Jr+M71RJcdCpcebcPZ7v1wes7sO+bVJSy300GaHlSBl4t9yojwpEP2x/LudzjvyRL4sNkZ85NELOpu8KD9nldLGw7AKOgN3YyAMuZM3k2l6wsP0Cbu9KrsrOQfF4iphoCDm85XDdKIDrDzQPuIpKFtoWnvMP/952AJcxXLcu4Pqnf/onPffcc6qvr3+PZNDOzTfC+flKfn+uxrqvG8BlOzsv0ZmfeJMYfu1rX9Mzzzyjv/u7v1N5ebn+4i/+Qjt37tQ//MM/OBWyTX74n/7Tf3KAmrFjlZVEkBdpBrj+6999Q5/4qX9dMM24yBhdyssWfKQdW/iEGssyaqy2y5aFJda4gIFSxl3DGGeMOblbVa56ivZWOstQghe53l4Nah/AzILVWZUNdqqo6yhggoifYCeLfXuCE28G1iwZBlDEyzVyskGpg8WqPBpVMaYW4+0d2tW8Wvv9rep0YQtfUk2wG0NBGFNZ6RSFe2cUiRZrOl2qFOAtkwAM0a/i0hkkeUjgjlfh8MapGCDh4kKb5v0kNahyyNjonPDyUA7Whp3hdjcPbM0FKHKZvT3W7e4WgjLLg0IuF2IdRVivBzCu8MDYuIphY9bTz8VxbgQQtBF4Pgqq+Bn63VZUTHHl98q8rBjw7h6vfu+7YX0PeSJJbuZAwnZ5IONzASJzBuoSsAU1SQXJZ2pdTdHiUuzLYbMMcJnaL4e1ezmyQrtbby6L99emwGo5nQD09LH9MrbTgjmJD2bG9qW0yKcA/Zn/zpG25DBMBsws2CyF4TPjC2PEZnjND+gsx50QrKY4LOCJU27t3u9VFMnjupUAs7acBmG0tmK1/wLM316MOKbjZvVPwWQYtyX1GVwgXRhruBUdZwPYzbsBqG7Ll2M7D8M23t1M/wjYLZ/uwVVJbV6KDNHS8q7DNn/OynKzwHK9HCaMflq+lz2ccWU3L6UZiH3pda+27fYROLn0ENb7d2/k2AJ0X07Eb99PK0oei8YVI18uhQIgSE6HHyAchO2y/I4r2UyWZ8DF+mEyQrcdSIZGb5CWN9KwL1NeFmt5Zzw9c4a7QXbjKnbz7Nxe4qF+Ffv2/psyFjZlUm9uUpVwHioArvcfr4V+d96lMIyk8ClMM4ow/Zk/py7Etux4tFIz8wzXz9yigMvG1OptmWKsoaFBBcngQhxdl7+O6w5wzeC9bQXYrBbAyMiIU5DNKl//m3/zbwgUfHrxxRcdsGV6U0vws4PIANjdd9+tL3zhC+dRopbwbusw7aoFPnv37tO/PPdD/eTP/6qWrii4FF7+4ZL/hAVTc9Gkiihauxx2C0MkJxghtmKcuUvIvzRoIZlLWrYUtqzc8Scvy5oBrgnXHgSHe5AUwoAle1Q01Kug3Y1PxVQ1NKFwGnqElL0czFQG5iUZp+bWTKl6hto1fLhVZSM5RRva9L2OJ/SGe7nGUqWKUYcoEJxD9jeo5pZTBJZz2LiXaTJWoaTPrxygKkug74KJyQ1i+DGMZG82qEwR/cJ4Ipn0K+VFt1bFJThJ5LuL5XZQQBmwoCYCijYeyO+MYXJhOqES3ufhAijkwEfekoyKy5IAPsztAT3BxoRqls7Jz36UstdPkbv1v3FRXxQuuSjg2tuN1v6fw3rmEHf5yO3KMQz4X+QHd4ZtkntllvDuShjFlozql1B3awlGHeRMWUA4Su4WNZG12AARIGtzdUr3IX3sAmi9GfVrjN+YCeqOUFrrslMKphI4BWJ9bqwb36150DUfWdrFKsFQWMHiA8MeTSC7NB8GM9lYA3CqYjwsjo7DULHbTn0wMIai/P1DCkH/ty1B7dgTwPQCN0RW5kNWGMZ8I0U/5gBlJvs0HanLVAz01wcYfBgG8bFWABbH0sRcDsCVAnABZg2jsq3rtlnfGAy7sCVJSjb2y8uYOuNqgOMDmi1hoPSFV73aut3vAK5HH8II5S6MUOy4u8x9n188Tf6Z1e2aGhnH0TAOeAuS21XK9+PKgK58fau8hNVrJi/2j3PvQgZRHzCUC/a2YxvPDSA3x6XPkUN+8Dwu2MZvkBXZcZbiLs38/M4fdzdI9+kmgm5OXlkcW0uCdpzeuHN8YzNc/QCuNU4droU+hvIuhSYpnNCtDLjsO3nmGn/jfEFvqp5ed4DL8rZeeuklvfHGGzL2ygDYL/7iL+rTn/60M/BHjhzRn/3ZnzkywyVLljiAyvK9vvjFL+ree+89b3KMIXv55ZcBWnudA62/v1+DVH79pd/8XS1evpqgyCiEQrucEbCTYYZb8WZHXeKjThWSNspxObI1y7UhxoQZsbpcLgJwonOaBeVnI8Ys5M2AjsBwDbm6KcZ7XOGhboWoeF6SmFLtCKwYzFcOlihThDwPNDMXARiR8zQcqNf24fsVO1QDm1Ct75b/iHbPtcBOGUOCoUNtTHWLB9TScZI7ZRHuWgYVzRYBcDA9oLjwdKxcc7BkqaMAMNiTFMYSBrhyFRTkdYWUKgJQ1CXkL03IOwbSwCEwdqxEieqQcou4ECPZo2sUtyKwJm/KBVAzwMWr8lDoOFSZxOI+qxRMmZucqqZ1s1rSnNRtXAkfJblqDSxROdpps0I/v7EexmxoxqP/8FxAf/sKhZ8BOHiOgEMYvBiR+CT9MQoExstVyrYBujVNWf3cAzgMrsnpICYUR8m9msWOvQ03wDux6r8XSWEbQPDNmE9bYzAcbHSOgKIded8D8I+1SDA9sB1WiNZYCAMHbrNnnG/06SQ5Zq90kXNFbpyXt0wG6SUANRbqDhwejZXKNyt+DLsFc3WMel4vU0D5e4f8Oo7MMgYQdAEC/atAYq3IL7Glz9BXAbrohsPA8WEbXq0nV+4BWK5is5+HPX0cd8MOZIaOicH8pub7d739tv5xmFjwGYvGHFt5y+u6YFHnC/adkgEDHh2i1ptN++oVGInU5y36L7j4B77ISrgBkoWNnpmkbhfnvjlyuyynqxx7zDDGGiaBPPvd/MAVXnQB2/W0E7haiqQdI2zaIsAbtOV7zo0Z5wYKLDj74mUszcXQ9rXQzo7ALHdlbKodJvDsyzfOMybUzi8BCrLdyHNrc3BjSgq/rFBzvz5mgMsYrgWdBa5r5FO//dphvf3s5C0LuG6cL+PN3dPrDnAZK2VAydxTjJ165ZVXnFyu3/qt3yIBvFqmQ33hhRf0xBNPqLm5WcPDw3rrrbccpxWrhn2upDAajTrAzICWNXM33LJtlz7zv/8GDNeaAuD6EMe2XZDi5K24YKMMVMFFEDADMOwkmQXAUtTXbuiXmNU3rn3FITfW6rxgHzz9K42kbVgjOuU6icnDXgV6tqpi4KQqU5MqTczKjateBhe9GHfjZ5AETsdBX3ZRB2i8mX1I+w7cpenDNdrSv0mD2Vo2xsqRXXlx6qvomFDbhi5VNw8phQuhrOCyJyJ3skSDY5VKD1LU9pRH0akiZWGo5gg8x1J1yNfKlGXxUHlEwUUYDpTDvk0ifzxcrompWiU9MEDc7TZgpGke1KaCRuIDXKS50nmo2+ULgjyR1rlhyMJlcW1aO6Wn20Z1p2ZUOwf7xb4ES1oBXBfIM2S9SXKx/nanT//xuaD69lGAGJbLFQdkDQJUZnggeXEiWYCtayW5QoDAf70pqc8/klE3joO7yVMzC44mZICVgKoiNIZ1zMMkMfUe6o/NsCrr/wYA2V3ZaZUDnPzkSmaRFqaTSA/ZJQNdHpCVBZhgIL3e7dc7fYA1pIRFIDZ7bRSg1IaM8rEOHCrJ8XLoNbo2Tc7Wa7twOjzh0v5Br7ZjlT/Ixlk1gAsQvjEq9/KsEjMhct4YbPLkckPsC0DUDhE/zGEjOX4PYrm/YV1at7Vj5mGgzqnfxQI3SLOxi0Uo+81NCT+Aywo7Xyr4MEAbs/px/Auy35YK9pEbc2ZgOD4X1+TwBODL7OORhpYVw3YhcTV0ZAjvQzZbt+VrsZK8hJD9v3Fm64N32vLiUnxHLK/OmC7LiSy0syMwTfKnjUvIKkjfoM16Pq/OuEF3gf7fuIAreC7g+vCnogtOnTFcBri2FgDXBcen8OLVG4HrDnDZ3WELTuxh9QLMWeUP//AP9W//7b917N9///d/37F//8u//EsVF+Nihyb1G9/4hmOuYfldVj/gYs0xzfhv39Anf7qQw3WxMXq/121uktwtj8zGnXpSAQpUBcIhEnSNO0FGiLQkw8MwCbaAgLA0LnjYlpNW54AuZynbAneOCaynzUR++jWNbX1WwaOH1VIzgywP1zcAVxZwFQuEsYMvoohuSG4Ai8+X1HbvPXp+4hM6tGuVDm9ZoUiwTK5F3BOzoBwgUlw3q7aNXapb1yd/OKUm14xWImP0TZXo5FEAxdAx1U4Patxdo5NrF+tUXZsGJpo1PlhLoBuSrwLZVeuMyiqnKBxMQdmpsEaGGwhSy7DbNoEkgIoxcBsQAsy4TIaSANHARtkjgGyrtnRYVTWTuP8N66fqutWGdDIHSPWnfQoVLZMntB7iwQpqndO4WGbBa98+5NUfbfFr/06ALKAlNwFa6ZmigDNIjnEm08mGWu7VsGStHt1D7avfeCqj1WsMBzJmyBb7AGoD9DUNC4eSUi2AwRAfQ1irYk9GawIZVccplMvmbf7M1jyTweEwMueYPQR5zWpMmcTtZditndjkFzG2lrdlbQSJYD0M3hMdSTXjXGj94ea/Tg649N3X3RrFRfHUpE9bjheRz+WBabScHvrSgS08zo1uDwzXDMzaKPl+owSz/C7G7KQkkVM7Msif+8mkHrmXwtOl2OmbLI2xMdb0RmkGN+a42WNMcJ7hAqxfTudP76vJm+yfM8CX8/n3WTaJZfzk2KTGB8YdxrSyKg+6vEZfXibosp5ZvpNTY4vnPibqeqqx9T7DcFlv2X6aGVCcL4TNR9BQ8GVN6GVt7oZZ2PggK4yeYFwMcOUFDYWBuVYTaOfJG5Ph+gMFHMC19hxb+IU7jvKA64jeQVL4mVvYFv5aHZeF7Z4dgesGcJmO2tgtA1omEbRmdu/f/va39ed//uf60pe+5FhX/u7v/q4DtMyR0ACXSRD/+3//7w7zZSDMbC4v1gouhRcbmUt53fJT0OvD3thJ3RLjLVAPkQviP0ciZ/NnwMysoA18uUERFViBN1WZxJCTKOtIE1BOYsE3lj6p9PQ7iu3fpUxPt+rLplThGQHQRB1GRJhSpEkKymRhXHD5S1Fna9/0bXqm4pN6/dT96vrhMs15AFx19M2kbeS6hIqiWr7mkG6/a4dai0fVRDBQ0xtT+tC45g5MENiPy1+f1dEVK7V/zXoNl9aLShQan62mMHCRchQVDlXNqcw/rSKYMdt2AACTjlSoL9qoKNK8HHWpBGhxY8Pugy1yw/hEjiID7AyormiIelG93O1Nal1dv+5p7cFaHqBGIeKaRFSt0H9FwXXkLd0FioC5swE5p+3o8ugrFBR+bo+X9SEtO4kRQz+Wgsgt5cGuMM1nAFWuWnu41dLs1RP3efSj92d0J7WsesiPegNbe7OKDwOQzGK93CSEGG00IdPzMvbFMFvJKOiQTfuLQuTUUT8NgBzCn70a0Bey3CNyu1zM7+4hJIUn/Q6QKwXUJknGmsXQ4fYGbOkXp1XO/tt6DHD1DmX13VelPuzuByYD2t1N/hZ3F61ms7mn17altWljCnMP6mv1UeuMcUN5pwjmGSFkmouI3h6/LanPfjqhDizmB3i9e9hLfa6cmqmH5qHf132ji06OIxJZ+34EGV/7/a5pvoTdsH1duKDjzAb5fqagHKfHyOsCeDnSYKjLsrIibmT56Wv++3tm+fd5YtKfJKyv/XNs3y3iu0mb7VkMAG0g2ADXTbyrlzyDNgZz3Hyyc76fY9yUDYV27UbA5uOGBVwtA3ry0+uuDODi1uLW1w/rne+NFwDXtTs8C1tmBK4bwGXgygqymTywpaXFMb8wQ4wtW7Y4Vu+/93u/51S8NnD1ne98R5/4xCcc04y+vj5Hdmif+fKXv+zIDi82swXAdbGRudTXCa0I5A1QWSxogZZdbC9YJJT37fqbJMHLlYmrgXycWnKlTOc/nonqUHpAo5kTCmV7VTM7rhrm35vDG/zwAXn3v60Sf0yeJTAgi5AWkqMVG+Zi0lmkntlVeqn1Ab08eI86t9yhmVADkTyghxwuTzk5L6W9emzVC3pq+ffVkTqlVL9fI1uTSu0fU9loFKlfQKMb2rVr0yadaF+mqB/r+RJyu7JYtc8mlILdSZWQ3+IBISDJ85G0db86tSRbr22pzToQy0vkTO7lRbqXYP/jFCgePBbW6M6QGoK9uAD1Ix+kWG/jgFZ39KsKJtCX8ctPoLs8Oa4WQKI7/AiGGO2M0FkQYTjuyLhH/3O3X/+81aveHYzfMfLPpkAlMGdiOy7TPTIH1reyZo8WrQ+qucOrNUszumd1SmkMNTqpU2YSQAwMNQXgMpfCxzDPuK0MNHU6iI/BwNg0uqnevMed1G4MThA7aiMsXImZhtD8mGnMpHDN6/fqIMBnjo/bRX0REs57WlKObbvd7LddsLmOxjLafiClfbBi/ZNFFJLmMwSoEd4NgBM33Z7SI6uT6kOi+P/uDugEzpBGDrpgwNpcGT1cldbja5Ja0QH4Q4J4CNfGXSc9WtVM8WZMQnzs03Xf6GKKG0dRJIWWqxcqJoeQvDiHub9OOm+CxSzObOZgOIHEMAX7anXvSopD3OzCfIbj1b7X79cctpubLqYktO+0qRLz7f0/N7/Ujfbb9qoAuM6fNZMnR5B6+2BHrWSEsX+Fdu1GwL6yNybg+rL8BrieBnCZLfwCn+aN4dr6+hFtKwCuMwenE8Px1/x5PsINQkvBMWfdsrIy57dzzfqA68CZFRaeXNIIXDeAK05AaW6D//zP/+wwXfOTbTUDHn/8cd1xxx1O0GJ1t/7lX/7Fqb9l+Sa2nC3z1FNPOXJC527yRXa9ALguMjCX/bJdWC/trGjzk0YaajWwagjUS5HX9Qwe1a54N9bvcZVRa6kdZmqNuxap2TLNvvqq0v/8FypNj0jLMLoATw315jTbg1X8EPW3vE06Vrtar83dpT29D2kguwqQARtTm1ZV05gebXtJn1r1Ha2r2K/6mSFNd2Z0cisY5VhKFZBExaTQzrTUacdDm3Rs7QpFMbEYq6lWJONR6NSQODcrWkPOljcI6JdWlgzo4+pTR2ytTno3ais26qPYsFOjWAHAxCCf68GVsHuKIsbHAyqemAX0nVJb2Yg6aka5gKAETBWr1JfBQTCiFalxdTB83tB9sFy3OSNvI2mPExG05hQEPoor4K6DHh18FXB6CF/HOQRN46CdMdgkJHZu8tuKSANrXu7WouVetSyiXhagsxQGq5jx6sdsZBTAZwyXj/HvIM/qcQBXG3luth2bPQNc9oebPLmDnrT243/fytjcnmMfYAWNbTZ0ZU57kZRPfdjUT2A0EgDoNWEQUodNvoG6/Nr4xdM0DMDUVBKZqAWnAFlkg3sxgDg55NVK2LdH7k4iGcxqz2GvvnkgqCPk/gUwHgnDXN0BW7i2Ik3eEuOK/X5TCU6LjO8U3azkuKliuRvhDrqNrTG7Cc5nxnT5sNz3YNhizNH8xY1Frn2zjoKW4uSZJbkRMDM2pTgAzF4uLQ1iWhJEwntaQ/qu3tp+mYMfRyOMaV5GaJ+7mZvtXwFwnT/D5wIucxU9cy44f7HCX1dpBCw2vlEBV6B58DTDdQVMMwxwvXYISWGB4Zo/FPO1I13q6enR66+/rt27dzveCeYI/tnPftYpgmwFkg8cOKD77rtPbW1t8x8t/P4II3DdAC4LzA1hG2M1QQFkq2lTUlLiTHxtbe2ZYMXyIkxGaA6GhsqtYrZVyLZl8o5bFx+NAuC6+NhcyXdsbm3ePMjWcv3HNXn4TQ0Uz2i6Pqzymird1rJEi3P5QsTZPa8r98x/Vma8R+kSvyb4TG9fBnc1FzlUAfkSvFbdquOb7lNn9XodH16q/ukWzVWEtXR1p35m2f+jh+teU4k7qrIIkf9gRCOHCZaOYvpwJKuKk0gdYbGObl6hg3etUd/iRToJOzqeLVaoZ1iLTh5QaHxCEcweyrwJbeqIaHl5JbW7VlPPq0ETrTUaWdmqeEWJk9dhbu1jAK4ZHhMzQcVx4GtStzYW73QcBvdG2zSdKIaxS2hdcb8ecp9Qi5uxKHmQ+lO3nxn2ONHrq6PkS5H7ZHlXPeRDvfWmR727YYhg91IDgKUxNgaYcnW4VdkhtZO71lEjLQdsVQCCprFYz4AEx5Eb9qXd2DW71AgL9xP1Cd1PTa6gI8nLh8aWr2UgyXK4zDBwGoYjDMAp4UV87UANsCBI/Aw8ePGC9yAxRD3msJnmUphv+XXZeizf0r6zHqO83DBXANGRCbdeehP3ROpxrUR++PADSVEuTTNTSAopsLwNq3pmSHaJrWGbXpg7sxXncEGqmNNGLOSXVQNUkYtyM/2Dm3XnUpb74DW9dwk6ZUDDbuhYYGN9vFDLj4gZjmBEwniYLbvV6DIjkjMFkS/0wWv1GjtjTHUCQ43ZyVlNUjA5Q804K5RshhoB/PjngaLtsiMVZufNYMDMI+xxKwTatpsFwGVzfbYVANfZsbgentl56UYFXH4HcK1HUnjlANe2Z8cKksLTB6rFZMeOHZOl5piqzMiL7u5uh9j40z/9U61cudJRjpl/wuc+9zn97M/+7PVwiN/wfbhuANfVGMkC4Loao3zxbdhdldkTxzWz5XkKsw4qUlWm8NL1Wrx4hUr6TgAqulXC61W9Lyg+dRyWgGLEg3ENwe5MUXQ35w8rEc2pP1iuyONrpTVVmiqp1wk3wMu9WFU4Av6run/S3YmtDtixO/iu8bjcR3Ae7IrKtwsThh0AF4LFyboKnepo154712rX8hWaoGBWcDam5fuQtG55Ezt4TCUIlJetgkFa3w6LtgHr+Aalja2g+JVr820O6EoZUIAXKKLOWBZZ4mw6oNLsAKYUW7RzNKCdc0v4DCxVBvYoOKBPFG9XY7hYrpLH5fK1nQEIBrheGfFrB06DYVikPsDKth1Y4W+j4DCuf9MYS4BZyVfjI3cg91vuwpI/p1akd3eQI9cBKzRDEeGJcnJM2mGHyJebwyLezCbuL0npzqKMSjG+MIZqAjDVOZ3SLL9rYOxaAWXFsFwR1yQu7dRr4h+iSpVnauRLUTT6NJDyQ/kZ4DgPa/CHA7YAXGjnMFMMqHPYhxEJdbtAUweO8HvYrc0rU1p3b0a9IY+q6MNqCkUPkhvXg6nG4BTzAaA2E0azmTcwY7lnTTBzjy6mwDNyVAt4L9bAhOqe9sDEudSC0UYFYG1BGqvJAZwMdBpLa/tpFyZj/ezmjusDKDfrhY1dhjwXk/BZTpyPMXRA6YJ0cGFXYt/PaWp2jQ+O8T3DqRP7+MrKEkC5OZGaDNFIMY539sMeBtVulWbHXwFwnT/bBcB1/nhc679ubMA1pCc+nQdcC3lasTNUXlJ4SNueHdVnC6YZzmEai8X0x3/8x3rmmWccBdn999+vr371q8517U/+5E+0atUqp8bt5z//eSfF5+tf//q1Prxviu0vGOAyxDx/J/R6HZkC4LrGM8MVIT0Ls3Vwn+a6jslbXqHiVbc7FtXRN3+gzKmjqqot1pKyMRil3RS+JWLvT2huNK1RD6YUWJhPn4rwOq5YK8oUaAwq2VKjsTUbtLf8PvUlqnVPydt6pPwlbOmnyTkK4KhIsHgioZaDvWrfO6jQwSR1wzwaL63WQH2Tji5q1R4eUxVFSodCqhrp0dIDW+U/MSCdmnJMIZqWw1AtXSVXw0akVCVyEzQ3PLBZ5betVtaCaELPAPvmhiEiY418mJgi0aN6vn9WhyIAOZRZJstbGejWk9Xd1JdaA61zJwE4RbW4IljgSjq+OqnftWXcBwhx6RSFgyd7Mag47lLXCY+GABRxtuGyIscrmcd6niN5DAK4lnkxo0hgTpIA0NYB/loEqEOGiQSwi7pYxdTxamD9reVZtWAYMogk8kQkozT9DZFntIJCyMuLhpBRnlSUf3yTnX5VYSbSkGuRN4kJRzLhnIwtr2tetuvYZQNCMqcZnLG5gN457lfnECYjgDljTqaYwolJl9a3ZbXqtoxOwJbVAPweqEhhSpLVBG6KL3f5dWrarTKAnx/2zAKHWfLVcJvWo+1JbYDpuqjjNMvGAFovniRvbNathymavKzSEvk/+rFu+2fSSgNbBroMNLkBWubg6EHrbs+9p4HX+577mGNjeB2ZJt3Kgy6z3s/PvfOEZSw4uLbNQH1W0dmoJgBd0alZh+EqKS/CbdHPvubrUBnYMpLTcgNvlWaHUwFwnT/bBcB1/nhc67/sfHKjMlw+yrgY4AqRR2qxpF2CFqSxKr+Tw3VI22G4PvupX9LmzZsWZNU38ko6Ozv1hS98wTGi+83f/E3HD8Hq3Vo5pj/6oz/S6tWrlUDpYMvs2LFDhw8jEyq0jzwCCwK47AtiD5PQmNRv3759zsS1trbqwQcfdAINM8AwV0Ezt7hWrQC4rtXIn7NdToDp2BzAa1Yegnc/CZqp6SlNbdsCuDiqUGOtlq7BSS/2qvoGOjU+ElcMl8E4tYmSFJKKH5mWj+C/qoFgt8KnqbJSja5fpZ1ln9brbz9K0D6h+37kNbUtPY5ENaDxmSrFZ7hTPzii+w++o82dezCeSKurul1dvnb1Zit1vLJM041FitZXqyo+qNbeAwr0D6m485TKcBasRMbnK0JetWSjQi13is2rfu1KtT/8AHWWzKadW/9cICz8tIddK5KZiPZOTGrbaBx2Djc/X1x3FI9oBY5wwdAK+UJ1mGbYuGQBhlipA2rGABmkQGkEwHWEWmGJSbcjy3vjpFd9vJcGKDnIwxLIynhOvlOOzZcBYNbjMLiaQsbCNGOmjKAe98BJGK8hXAZzIwTSyPisnlXz4iwmGzBgHowdAGTjKebAhSlF+XEtqehWFXXTgtQci+Nx783BfuU6VJGrdr7byUTcYXd8uIjmABDmdmd0lBX2TWLm8dYRv3ZhmOEmGi9iW/bb2KdIzJgRQNfSNN//nCoBidU4JwZ4PwqT9czxoLroH0pLzUTIe4LlCtEPvDT0SFtSGxvT55gy2Jid0xgGk04eIO9tGvfEFbgZ1pHLZnPw4RvnM8CWMVpnZITza7QVs89WLNrOeQak/n/23gNMjuu68r+de3IOGMwAM8iZIEiAAHMUo7RLSpQcZEVbDvLaWnt3P3sty7JN+fM6rC1bDvp7JVmWZCtTIglKJMUoJpDIOQ4wGEzOeXo6/X/n9TQ4AwIgQoNDyP2Ank5V7726VV11T517zxXYCnAsK2xQvM+ZmtiuEfKktGwoB+aQpVuYsyoMVLO/CmH+zrb+mfrN9OfarnHkI4f6Bqnb1ee2M5+CpCUCXuSkucYyZ9nUTE9pxvvTXs0Crum7IQu4pttjpt9dvoDrIQvWddodD64GcCmkMLONM7NtemEPgKuDkMIs4JJ15Qv/+q//un384x934YKFhYXu9VTApWublpGugmrjZtvFWyBjgEsqg4oF/frXv+6oSL2XRLviQVUr6xvf+IZzTlScWGE5M9GygGsmrH6aMbkyOEZAThunV+e89/XaOADFmzdqJbMPWHD0NRvcv9NaDzbDaoFC2sdtGHYq2h5zNaFKS71WWBtCVKPY9sxbac8Of8ReffRuFLMidsWHt1nt1cesq7PUeocqLE7+URS1vfmDh+0Dbd+2q3peQ+bch5pfg20pWGqHZpMjiGx9MDlm5d2NVtjfZXlDw1Y7hIKif0DlvQhxZM6lC6xg+a04yoVWuWSBNdx+iyXyqBMG4JJDhmi7C4tz+eO8HiJ/p4X6Yf0Ak2JyuCpRAwzCuiW9FP4NR2wwOGTatF0DZbZ3oBQWLGhVuRErCqMe14MjPxC07t48+yk5UMfHBLhAaORGSczRo2LPMFx6DRZ0LNXyMOPkxA1VeApCw3bBLI0Q2jfBI0GI4higzVeMWuDKOAWLJ6wHVqkLRq2d3LDiwjZbPbfZrq7rs4ZKASvC5+i83hZZuVU7kDHBHS/lJEkAQidjL+p7CjNUjlczUvA/Rl2xB5BXjKCGQhm1d2UXPXfxeW1pwm6noLEk3t2u53OBqp8eD9hPm/zWhBJiP8v5EeMoKzFbWBWzuxdM2CKWT9tUKwoQiBVyIX16zygRQBfYzoHHlGKeRr6wpm0bHoQJBU/mEnKZmweQYjw3Z43LCwEuheDFYmn2K+EYwJRIhtir04zPegPEWfqxme7kHoDRfILwy0HmfktFzK4qIpSTfLp3ShNzqZyu7rYui2MMAS7ldakMhLbP7Yd3ymQv8Ty0N7OAa7qRs4Bruj1m+p1OOZcvw9XxBuByJ9rMWFPXBjFcrznApZDCLOCSZSWSIfbqfe97n33iE5+wsrIy+9jHPjaN4ZJWwkcIKZSuggTtsu3iLZARwCXHQ2omDz30kGOzpGqiHSrlwa997WuuntaXv/xle+aZZ0yxoFIVnImWBVwzYfVzHFNXC060gcB+anA9A519yJL7NtvI1oMWHSL8rYmcn58mKUSMr41TWlAZsKrluda/eI49v+p2e77zPjv2tVWWbPLa3PuO2qzbj+O8k+sVK7aoN4jDD6hivQ05L9i9iR/YkvE9lgS8HRyfY81zqq2ofMAqBpoR6yDnBgn32dFeqz/Rar6RiPXBXAyNoaBXOdtCDddZ0j/HSimwnbxuvTUFgtQU47QOBihm/g28mMumQNAohcw6CXPrhrGqIB+pJIgTT5jkuKfDOsOdtiUatp8OVOJ451vrSCn5T8VWCeCcX9pt4QlqfMHQjTbl294jYTue9Nt4gnAL6oJZAQPAbHlgpkACCMeg1FlKoWCKBhcVwDwFYQKHsXur3/p6yf+CRUkMwlqRH0YJNasitLCc5YkMs94jflQgCYssHbSGJc127coDtg4p+1LyvqqSc22BXeFCJkeSvQDPXBQXCy1BgeIIbFcOgjUq7Cvgs6fZbz/ZGSSUMEk4p9QQmeNkU6pTHzYgKs3upM7WohooHWzjGt+1AbKePBKwjbuC1tovcGO2si5h968ct2tmRxEc0cLqj+0ADEUo6iWYJUZJN29ciKOOHy3iLtZ6cQ4tPYfJRdXnBBLpJ1ritncfConklpWU+AmvQAqf8gSBM8Q1OuAFEE1C5+mfwg01r1NBlwDKYB8AHsDlDefad6lv9p0TIan924fmT9g9syYotn3KpM5hMy7ZIthUAHuwd9BGuQERh0X1EFYplitEnGxubihl+0s2gXdOxzqisoBr+v7IAq7p9pjpd5cv4HrIAo7hutKJZqS0dDN1HiT6gCzr117Y7RiuLOBKHaWdnZ2mEEJFcfzBH/yBrV271rFZEqyTUMb8+fMds6VSSw888IDz7Wf6+P5ZGD8jgEvqgpJq/7u/+zv77Gc/aytWrLCvfOUrJllJAS45RI899pj9/d//vXusWrVqRmyXBVwzYvbzGJT8p8Bhi+T92Ea82y3QvM/yUM4JDo5a/5a49exIMRmjOO+hCqSrryq346tX2XOr77Lj0fmW++SEhTfFbLQKSfL6Qmpr5dl4INdGork2MFCCeEXA5pQ32l3zH7P1s1+ByRo1X2fY/BKUQNCieLANkEKB5UjYYnnE6x2HUOL9sdJCm6Ai8LyiMcsJ1ViiYIX1L3uXHa5eaaMgmAlAVm84x2IwPrO4TlyLA72az7xE+L18hDA7AM3K6jFbXzdEvZp9NuDbYTv4TTw6uMq2DswlB4k5j+ZZ13A1oYYxW11zyKpjYwgYlNpYY5H1dudbE9s7EIcdQzoe5XZiBnEBwS1+UF1Q4XsAOi/AK1iEqAES8Lk4xoETfhvpQuaeAsPj7UGLUuMrQTkvL6AoRNhdsAJ7kqo2Bkj15yLbv6TLrrtmh12/6ihMBvLvnnpbaQst6mm3LmtEUr+c7VtGjbF8ABdxgjSBBx/g4kCLz54CcOGTW6Hk5wkhTDcBqN5hcskQs7jzyglbUD0FcLGQ1A9PDFKwGdC1i34EzK5F1fDqupiVAt5cTywjhTzFlY9R7ytOgWeGJscoJV2vWldvJWKRno+eBZAFgNIsDW+5+EQIwRy3l17227ETdI4QShwKbsECs1tu9ls1+XFnaupP4YdRGC+FWgoMqoD7VNDlABcMl/LgEgjAfHtv2DayjwphBD+2PGJrKwAz6mgG2uT9jjePrO3C7mL9xkcjHJMDNsQ2cCRxV7QAJdk8ctk44E8i6Dd38bPwifZ8FnBN35NZwDXdHjP97vIFXH9qvjcBrkxZE4bLhRTuti0bO7MM16RZlf4j31w+u1J/brvtNlffViTJgw8+6EgSCWrkkNcu0HXddddlaof8p+4nI4BL1KN23iOPPOJCB2XRL37xiw5wKcRQxdQef/xx++u//mv7m7/5GxdqOBNWzwKumbD6+YyJAp6vywbzNwJgnrPQWKfVDzZaKXLtjT/xWcvhsEWLcmwEdbtkab5Fb5lvxwDveytW25gvz5a27bdljQcIIyyz53tvsv2tS60vWeYYroSYoYmkVQKsrlv3gi1ds8+SYfLA4jjSyS6rjyDX3tpoJXu7rD9RZLsXUt+LIsAl/b3WUl5hXRWlNme83+rJOYqU19r+0vnW1VVupYf6LOEL2pGFq6yzDDDG5i7EQX03J7T5FAXdiuO+rzNkq2B1rprbQ9jZs9afRFBjosEeHkSWHmYrSY7XcG+BtffWIFnvsVUL9yCw0WQD7bNs8GCDjXcXW1MDLB3hgDFEMJIUX0atgzDFpJWQBzUMsOsZ4iPYPy/1ubzLJgBUUfOeIAyxE3n4Ab/FqImVYO7UF6bhRRPKSFE0AAGgYz+vYa0qV/XbTdcfsiVXDFiUItUFngK70Vtus70oSnoaUaSvwFaLLYeQyng8Bhv0hhR871jYfrI7B3VFj6HQT1igAI0j4BAaoS7YgMcWYIPbV01YZSFf8H9q07L9FLjuBpgRsWaVAEjJwSdgjfQdXr+7G9eOWuOBxjAKiAGrBAQvnjcGq0eOF06/Y7sQsTjJeLkBJsdy4/FH6M8xZYA3mDLlVTmwwad+GKyDhz321NPYC0BXRA21QdmVCdx3tx/lJsQihC3O0jTMKDegxHblILmvc58bms+1LUN0GIQh8gPQ96BKuQtxlEoUG6/GNqXEpEpq5O1rGovwUIVH8lKhmFMBoptHahF94cDp+DihqK38Rjv7XTphITu7mFBDv3b4z3DLAq4379ws4HqzTWbyk8sXcD0E4OoipHDNFIYrc5ZMM1ybN7bbLz3wq1nRjEnTiuX6j//4D/vud7/rQgk7OjrcOV4llgTIxHIp7PDOO++kNiOORbZdtAUyAriGEED41re+5VgtAS3tsKmAS6j5q1/9qj388MP2pS99ySmiXPTML6CDLOC6AKO9rat4SVHqtd78H1tH3kuwEEM2Z6zJKo512tHX8+zw/hBskN/iUCB5iwknvHGpHZ69zE546wAAfquPHrOFg4esYGTYOgZn2f6mpbardZW1dtaSx4Q3O0q9J9SQ5r3rsJUt7yCPhsrqPvJmkuNWHhmwJR0HbfmeXdZrxXZo0QKr9nVYVaLLugvLbF/hIvMhU18/MW59+aUUP6ZO19EBy39tEJn1cttx9e3WP2+Z0qq4n0boHM71vRLTIF5shDmX5PmtIKcfhcSnkGPfbxsnFtu3B5Za11iuTfRRW6ydfLNhwBfChYsX77crivZYTjMA7sAKG+vMt8Zq8qTqwUWAkQhhd4WwfGths0jhslcJHTx2ApcQcOD8Y3KexNp5CCtMoG6YgNnyIBvvKULkAUCSFCOhkEMW9kmAAxbMN5GwuUu77JqrWyyfcQaodxaHddtQWGD3lo1YRWiQ7cqBtePEC/MTZCM9kyAId90JV2w/FrRNhD9G2F7q5mJbcpzITxpCiTAfBcIbl0Vt5ZyYA1SM/kZjCgrn00leQC7dBLSQ0uAtAYoAw/GIz17cnGO7D8DWUdjZR/jdhjXjtnpZhILJsDDkHImNSUm2vCzVawAAQABJREFUA0yxvwBPgBw+l4NFTzH61zhim7S8PpfaoFog5LcjjR5uDkmdMGnouTiVRaHo++4V4BJAdYue8Y/k08fJX42J5aJfKRL6GV+hkNHYhNNXCVDbSgBlHFA3AiMYZjtUpPqt+j7joBf8RdK6YRb3NfusnxpucyoSDhRTCxu7n76JuIyMTVCnbgRBjW7Y4HFqdgHBC3NciOGbANvpu7nsPpU9sgzX9N2WBVzT7THT73T+uBxzuD7352K4uuw2AFceohm6+ZPJphyuTYQUbkU0Iwu4pltWYhhK/9GjpaWF6BGieGC16uvrHTGyevVqVw93+lrZdxdqgYwALjkwEsz49Kc/zd2D9Xb//fc7RmvXrl32t3/7ty7hTkCroaHBiWjkITQwEy0LuDJl9fQZUW5IJhtsTKDZuvN/ZD3hPYTqTaBIOGyFLcM22J5jzWMFNjSCZ1pIras5KOzNnm1Hwgutg3pR1SPttih6yDnmBTGUBeO9iEYU2DPHb7eXj26wkaPQFTAtBasGrXRtt1XNbrHqvBZC/GCC2IRk1GfFqCVe3bXZFWjuKSy1xYnDhNB1Mp8S21+wyIagj0pjg4Q75tqYP8fK+3osvH/EOsYr7fV5N1hn1RVW6Q/jxCftamp9fZCO6zCRrCXgQMAZoGGPjcZ32vNWZv86uNz295Fn1k3x4a4CmCBYpcIhW9Rw0GqHe2HbFvBcY0Ec8+6g19rKycdCnbAPAY8AgGtDLTlt1Nt6FGXAxqMAKUCUZ5AHwhk+CgYnASWJMexFpGSAcLlADaIj5N1MeFGCQmnRA6gIVgAsipNWT/2quaX9VlLRax0Jv3W151p8LM+WFATsI0smCIeMWgRWbRfCFh2MvZT+55cQtsi2igGamIjjtMdsX0vQ9rflWD/jKlTQx/dl1AtbVR+z5XVxF1aYOmpSVhEoUghejFpVKUuBUcFcTcd91g37o9ypujpCB1E2HAA8/vDJsLV2eHHyKTgN27dqScxuvQFBB2p1qZ+UcqLYJMCXAJfqpsFqpXLK0sctRBdgSLlWCv1LgwQ5kAPI2D/3XMz27JVKoWaatKVLvHYzIYWVFJl+q6aYeMm/+9j/agqDBN1hbEIfGU+KjmlZfbcAY/Kl++fe62hhWA/I5o3Zpr7J6F/GnYh67dkdftuJCqZy+wpA73esIceulvxBVTQ+wwT0sURDhpGN7+3osdH+YfK6/FZWXoASLcc/m65NdhuS0UnPXGc6ErKAa7r9s4Bruj1m+t3lC7hSDJcAV6rwcWYtSdVEl8O1Nctwndawukk5PDzMtW/AXbsUCl9cXMwNtNzp16rTrp398HwskBHApbvFXV1dLpxQ9KTk30VXSp3wmmuuIS+i3crLy+13fud3bMOGDScdnPOZaCaWzQKuTFjx0vQhlzPuidpw6IAN5D1jI8FGG/QSgpVA0Q1peD+qfe3hSuvIr7AId9Q9YY8N+/KtizC3kUieXdO/2W6MvEQtKUQckl7LSYwiOZ5rjw3fYxv77ySXKd+BqlDtmBXO7beakharDR8nZybCSSXhZM2jgyFbMnHYKnPaEcootKp4p82NN9u4P2Qt5G7FvH7Lh3Ub88DioDRYirBGEYIC46M51hSaaztyN9h47nILUTNsLVe/X+IxF8dWvqccNv0dHe3Dke+ygzkB+3Kk1J7rzUXYImjjQzAxyLpXlfTYgrIe824tsNzt1TBdfpfHpPpeEGwWwRFu66HAccBrNYthSMCRr/X57Ai5T4PUv4KQM28OTj55VAnC8wS4PD0ITPQTdkduV7yCcDkV8FIuF8ROYVnS5lGbaw31rkoLo9bcjgDOIWqAUb8sjOc8m0XfjxP+wPoIUYcee+xgyA7BsN0yd8JupuZVHmF/xwldPHLcY6VFMUDJhBPp6B2BhUJWPoxiYhVMXHVJKkTQmYE/qmnlGCmYQAE2GUmsjw/msLXNa08/E7CjgMiFCxJ2y61xm11DfahRs5+8ELAd+wFzgEnZ9Pp11CC7Kmr5bLPrZrIvhQrK8glA9hh1yCRR7/eL8VLtLNXQQoBdyOCUptXbO5J28FAcpUjAKGGXCxd5rXoWoNXlKp2ywuRbXbQEtpTrpHyytGjGODlnIwhOiO1SiKGAoI4HgS45SAKCCZg2zUvMl+YEN+kOGFTiL1mTs9xP0elvPosSJixXDvl1Uq28jRy7DYSkKo+OQltnHp+vEiDq8dExQFefk5DnkHThhfn5Ibc96aP+zJ1cPt/IElnANX1/ZQHXdHvM9LvLGXB567rt1gdTohnpvNqM2JMfbsCDaMbzu227A1yfcKRARvq+zDsRu6XoMykUhqlxqhuPilZ77bXXrLERtWbCCOW/19bWcj7H+ci2i7ZARgCXnA09FAP69NNP24svvujqcelOb0lJiatarThQycQryT59R/miZ3+eHWQB13ka7AyLqziqTopyEjPV5D5HfAPWF95lwzxGA63W64sQ+UY4GDW4/NTgGkwWWUtBjQ3nFADOAA7cuUJrj1A/2J6BXXbbxIuwKgFK91LEGIdyFFnzZ+M32g+id1nnmHKlqDFVOGbh8lGK7o5aDWOUBNAYVL0oWJDQcNQWxFtsbh4Kh4Ewlai405NAxjuJk8zJqCrZQd4Kaom+amYLcxMDGAECouQv+eMTdjCwzLbm322+YJ29m+XfR5+lcp6diy3vGcBFfaMYv4sxnNLHAIbfRwCiDaYpFoVtYrkqRCLqCIEY2wxAfNVvi6hXVYGAhcMGdDE8CICAjShawslxjt92E7LXRKhk6xCMEPlTkSYpEgK0EMpIIgsPOiQRiQfLeAoAYrOZhmp4ETbmw6MvBrDV5MZtHgDKU5YgpM5v3btgYlgnCFgh88vuXTdhv/ruiJUyj1ebA3aCsa6sJjwQ2XawKvXP/Pbcq0FbgNDFu25iOXKqBvr5gpN0YSHiEbBwjq9h/hw51sn8jrRJOCOBBL1ZOaDGj628sEIeAOXx4xLZCdjBg14KM8btnnujNndOClA1t3hs1wG/9VCjrJL5rlpG3S1C4VjdWfrk8Shzs1+bAKI7D/qsripOvhdKk24u2hWscIamVVX3LcJ+0dwlzCHnMgXx3rySzn1SN0z9JrAd250S8EjJ54+BFAXEOEm6ZYLhkANk6TlEuOjpmPDk59kRzo8HYDQDEz5bAehpCCCk8eYhL/oTbT4aGPbYJnIMjwNg2dcB7HX3uoitnBdPhX3KEG/RBFAFNIf6hlxuVww7FEKVlZYVIg7CRVoL/Aw0HS1ZwDV9R2YB13R7zPQ7/aYvz5BCMVw9hBRenarD5c4ZmTlv6Kwd4Hr6OiGF2za2ElL4K1nAxYEqoPX5z3/ehdf/0i/9ElEkSsuIu7JO0l2QFLx8ddXR/e3f/u0ZSwOa6d9UpsfPCOCaekdCO7Ktrc0VSlOooWjJWbNmWUVFhburq2WzgCvTu/Ht60/7bwyPP0IMUi5FfENyqqa7uhc4GYCEv9N6crbbQIiwQt8wDNYgYCiCzwaIINxuOFHA5+WwS6j14SBq3EJfnxUhBlE/km9rR/ehoNdtY4TryU1Nkmt0JFFrP/Cstte8NYQC4sESNpXEsU9AF+V7hy2fcQqohzUnp5HwwRarSfRYrbcVABazfl+hAxzF8QErQZEiLzli3f5Sa/dVWZi8r8LxIQBUvg2R11TGej3+anul4H6rCCy1X+DqdzWy7X5yhjRTqBt33I/ifMu5DuXnWhOM2Q8jCdvEMrjcKL0jQx4LW0E0hEQ9eU97EdM4qHA9QCXIBn/dAYvFSxC4WJO011Aq3CrJd9ieEOFvBxHI2L05iOogMEniGqodhlNterAuOBS6CavN4bmS36HUCsHMyiGqzCe0EpZoGODnA7gFmwKsTw2v8YTduiJqn/rAuC1bSNigCvUyXhHKiGK3wN62a5/Ptu3xWx0s1NorozZKaOOrrwCifHG7ei3sVpXYnBSj09qXtJcO+u0wYYESa1hZl7RrF6fCDsE0TI5iyIQs7tntN4GrOXPihPQlYM35kv8KZRtCNGUcgY1cwgzzeKTBKGtPa3JA9jZ67eWtAVs0F9GSldj4XNGL22mT3Z3m2p/6Gtl0znG6saTmFBthsk79OUgsQ4yeQJmPyarwcTpvTOuJ6WsbG7KtgKunOeL27A/bxJE8u26W1z6yKmoLpUB5aqda8aKaIHDSjnUASAkpHBgh/LWcAtqA0mKA+Zu5v7MMhqG1jf3d/TYyOEzpg3ELcTNG54Y81D6DSvjL+PzPMp9L8JX2dxZwTTdsFnBNt8dMv9P57nIEXH/25w+ZF8B1y4NXufqEU/3Ji7WpTt3K4RLg2u4Alxiuay6228t+/aamJvvwhz9sV1xxhak2rgDX4cOH7Td/8zdNInh33HGHi1p74YUX7Ld+67ecZPxlv9HvgA24KMAl+vGt2lSApbAaUZfTchjeqoMMfp9luDJjzAlU9SIo1OkOdogwKOcJZ8ChGvd3W3fONjsRPkEJKRJ5vH2AEBXewmHlpDnOu6GxfPM2E/LUMoAMullJ7ZCV58ctL05R4gRiBACfXPKBigilyyUva9wTsj2+UvtReJ69mD/X2v0lgAjoHQoJQ1Y5Z70q2GpX5b5u9eGjVuQdmGS1kD0H5CB3YEXwbGK6osyhz1diUULSlG81Pgx6g0nyAQKChEO2hxdbX97dtt5fb9cDoorbjyMt3+jAhtUvMG91LTLyFA/G8c7Ny7VkIGRHAEqbAA9HYK2iMBt55EnNAgCtQh6+ajRhLY1mzcdhxgAxIaY9q4Y6V+RcbaFG1MsAoiisU+28pK2jttYRQvG+/EyOHd4OWCLML4kQAj9wIUziKjCjhIZ4eFjfUApkKPPA4MiOhexTIKr5Z8cJJYQ73BawYAsrAcDWLozaJ38RcYoVLthNuCe1y/VMG4FFG8JhDxOWVggwOnrMaz96HDAeIL/qtlGrnU2/AM4ETOGWxqQ9A6AgiA7ABaikZtjdqwFVLHPSyReoh41EEA8xjKR7pAGOxptMj3Jz0OaduVH3Cht0Aj41r1K2OxOkrObCLk+JfCADrzmcCWxNnVuSldx2iI47pW1LDNr3OdZf7iV/7ccUwd5XYHMrQvZH90ftNsDi2XCiekubIQWj3tz/KcOdfCtma5DfwgTy9/mwq2HuSSgH8Uxs3skVT30h0AVwjAG8xjgYBrr7CJOFIya3S4IaSobX+T+TztSpU7iU72XRLOCabuEs4Jpuj5l+dzkDLk9dLyGFYrgIu86oIXVt9tlmQgq3bTwBw5UFXDLv5s2bXcHjD33oQ/aRj3zE5WxJ+O4zn/mMifH66Ec/6vK6/tt/+2/uvP2jH/0oo3vlP2tnFwW4Pv7xjzu7pRkrXVD10F1fhdmkwZY+U5jNkiVLTOuI9ZqJlgVcmbG68+EJB9N+10keFzgjHcdgnHpz9tih3P3W4h8hvwrABQDShV1NTJe3CbDy027LPTFoBUWjVl3Pcw0hf7nFNpRXY+25VeadiFtDS4fVNlP3Kp5nfRVX2pM1t9n3C+cCvkLIqCM9jsBCHDW9CQL5KgJdtiJnl9XkNFuhf8DCnnGACsAjDlc22muV/d1WMkFoIYqGYBEc0hwbrKy2lhzYNhT4cilulSR3LBm62ur919tyAF5JV7slt7xEgakmzIOd5s4zz1XXI3KRmwJcXFhQw6BW2ID1A7QGoiWusHGQbS0l16gcVUHS1AhrI4xwSIWGwUyAowhqgq9v89pz5FIdQZlxHAd51RUx++DV5DGxY77wYo5987mQ9e4FvpDP5ZpyjyTFTr0nD2GEPhxrD6GKcZzhBDL0fkLzihiTCE1LUkjMx74N7eDmSKOXMMukrZ4XtTsQprhiOXadk0CwIu3eT/avp5OHACCHcMSjR1UoGNakFlXCYNSdD6ITETvQEbDnjhQ6Jz/OfijLHbd7rozbklrEK97oJNWf+tRQU4fDPnHCKGNMVzLmZ0mrmpzT5Mru6eQkNeMLbPSBnVUPTOc55Wo5IQyhwIvofj+87KOebnuxL2JHf1JiA3uLbMHsXPuDu+N2A8c30Pc0DVuwXW2oN7bAPAYBcnMRTCnSPj6nucgoLOh+xGk7TT5fyMakTgaO7RqH5erv6rWBrj5FdlppKTW7KOng1067kL5Ps/Vv50cyZxZwTbd4FnBNt8dMv9PP73JkuD4HwyXAdQuAK8yNmUzflAmSfrD5hV0wXC32oWxIoTtMX375ZfuN3/gN+7Vf+zX7xV/8Rae7oALIqqcrobtbbrnFLafvN27c6KLWZvr4/lkY/6IA1wc+8AFnAzneiv9UKOH+/futpqbGFVPTXV9Vrj5y5IhjtYSa/9f/+l9OdnImjJcFXDNh9XMdU/fm4zYWaLYTOa/akdAxJNBhssi1ovwsLhpOJPJ1+ft6rGBTp+WOkYOV24OcOwn7OLuDaFmP15VZ35J5Ng5lU3u4zRZvbraiE+SqVKy356/9iD02/0o74I9YK8BpAvAWjZE1hTNYHW4jpPCYFfsBeJ5RGJ4xCyUmCAckP6in1Vbv22kN3e2ESQUsMEox21FUCpfBZq2ZbaPhEYiwJPkv9VbsWWeVnvmWQ8ijNR2yxEvPmMexwARvlZSa97o7bKy43AGuAOBnNHiMfLMW537mWp3lJxYQb67AwrTTm/KFo3jV7d0wNX3gt+ak7dsXBKj5bCTfYy3kMi3GIf/5WyZsPuIXj24J2j88HbbXYbmGj3EFHk4BKZvFa8ZUDKAYLQ9Ay3KQVq9CCRKFwhDhjTG0yWOwQOI3fK0UQz5BqGE3oXhIzKvvstIEhZGjLm8KnHHWBtnhtssHCEgCXiMwe2PsszFsfqCnmPBHwB4LLakYtSsaqCeGGqLCDt+qqd7Y3hOoKJIHVgOrt6Q2bsXarlMbm9c/5EEuH+VL2MM5hDuWwwaeFaCd2sdp3it/UZLvChGUymGAmlrpG06nWfycPtLsR7mve8gzZi9EBm37cdjHrhLbUJFr9xFyWcPhxOactjXBTj3fG7AOGEEttYz8uWuLY+QmZvY+8WkHf4sPo1CU3Z09Ntg9YAmKexcWhp2ohq4LKZudZr+9RZ8z9bXsnwVc062fBVzT7THT704HuDj9ulIgf/jZv7CqhuX2uwiXvZOawqwFuLx1AzBca10OF/EsGZwiN6IEuJ7fBcN1HIbrl7M5XFhXCuLyx++66y4Ti6Xz8cc+9jEXHq96uStXrnSvP/GJT9hTTz3lJOMzuFP+03Z1UYBrx44dznDaWc3Nza7WliQl3/e+97m8Ld39HRwctK1bt9pPfvITu/766128qD6fiZYFXDNh9XMdUy6NHgMW8z1vbeFX7XhuzEa5W0+wEicEnGVCBQv2dFvxK63kTA1YDUWSo/uGrYnQvh6S9OM4qIGrSs3fUGoV+/pt0bNHrPQorqyvxvZf+3577Pafs4OVERsItllbohjQFYJFGrXicJ8VBgfAH2MwCVGEMeKWQ45WIIGTODRgi48ctFUHm6yOEL6cKNWHhymYW1lr8VvvsOSCCjdrf7KKdWHXFPzF7yHZ2wXD9bIljx5KbdaCpea96loKNIdSDnterw2G9pK5NQrAAbCRYFWWXA0mkqrFG46oQtcOIiLxynYvgAtJ+F6fDfGoJt+mAMDRSb5VA4DrnluiVjsLhcHDfvvGkwhyvBSyw8d9FkHd0UM9rORs6CvqSjlKBOrMU0AR30LYp+oxgAOMdHcY4Q4cYXTs/cjPF8CqlbDebKikBZ0AT+YUA/htQBXwpuvI3xNJ/cY0eZNq+kgXfmGnJCy3lOzi7LeIqDpWEMAlg82G46gi8b44TDgoYYMp1b5JxpR9rb7FaE5tE+SOvYhC4dYjfoRDyOEih+wGantd0UA4pFIJ043VNNwr2/y2fV+Au75myxbE7Lo1MSsDdF1QY7Uo4YMxgIPOd2Lsp0rKX1CfkyulZyRdxbbYuDVRODnoy4WtovQAVNXUTZs6jn4ZT3ejwNXPjQBoJN1ECGH393CMLwAkvzV8ndrbpXktQY2xoTHrau20CPmLuQiGSFAjB5VO2fFyaZppFnBN31tZwDXdHjP9Tj8nx3A1cnOQG0H1KBLpN5Ygb/QP/+SvbNY7FHClcriGAFzrUqIZGTakVApff34ngOsYgOvjWcCFfUWEfPKTn3RqhPfdd59TJ1RI4Qc/+EEHwKqrq526uMIL5a8//vjjGd4r/zm7uyjAlTaZhDKefPJJV+z493//9530u8II1RRaKPnJf//3fzfRmP/3//5fJzOZXvftfM4CrrfT2hcyFiGDSZzn5G5EBZ60/pwj1kjCfacvj3A98qn4rnh3u1X8EOn2zi7Lyx2xrmNRa6E+Vt+SSkLyuJs1J9fCN82y2qZ+m/fdI+Y/4KFOVr11LLnFmj7wLmtdRx2twEFrj5Uh4EdYH067HqrHFUBhQk6ERDWkRpiD81s8MmAV3T3W0NRpq5tGrBa5dM/YMGCC52veZ75rbzWv8tgQ6Ei1lPuchB1LKqywrTnlWM6eY57yKlge5XDBkORz5x/AhTuKm52wYDIXqQ8KP04BXHLyBkeS9pNNXtt5KIhIhNf6YXa6O1OiFZJCl3DEWkLyriCssAAp+GGk4J/fFLCvPR625w8FrBe1PakwCgcmBbQQ2gDdmb8oaqFiwJYXxzyKpHkXwKQbO5f5LQcRi3m1FEJGsCIPSXV0TKx0MGmzyhMObK1arlA6JpdGCpNb7pClu+grdwqQhw3CQRg0EINEIhR6pzpZqr0V5MaM3msbBcLUxgiLOwEjJVKwqjJhlVVMFcCtJmdiCDbnB5so8kx9rny2pQ8Gaw1A6uaVE1YkKfw0QGMfDvDdD58K2VHYMDFcVRVxuw8WsIHtOu/GFKIIY0zQkZ/tUDFjycpfkgbCHiNcUbL2KkApmfw32zk1sgDXU+y31wYClotAyRg5cToKH6iesIV5k2Itl2SS59Ep+025a6PDo9bT1m3DUJQh9ntJeT4FNSmkLWD+puPoPPp/mxbVcZoFXNONnQVc0+0x0++mAi45ybPnlHPiJVcZmuszf/rXVjd/xTuW4fLUDRFSuJY6XDmc7nRmy0zTqSUNuLZvbCKkMAu4ZFn55T/+8Y/tH/7hH1AFPg5QH7fFixfb7/3e79m6detcTrJ8ekWk/dzP/Zz7PDN75D93LxkBXKq39bWvfc0efvhhV4tLqoRq6Vhc5Ts89thj9jd/8zf293//97ZmzZoZsXoWcM2I2c97UC9qgGE7bL7Qa4hUHLFuPDIvgCsPxqXwaI/5vrfL4luayD9CSRBAMUgR4sHFldaPCqG/FEnq9aVWHR+ykp/2WvyVgB3uWmL7Cm+3iXuWWd6722GnDlpXnHDXRKmNhnIsBqjzSVEQ4QsJzXOV4kGybXLCKge7rIZQ2byecWvojNrqtogVoWCRGMW1XXqn+W+/17ylUEecwKY250MCLJI4zu7iQTiiaiyNDo84hitAHpRCCoeTzQ6n5CfnQEAthM2QlKCYIGAY6/YPxe2p13LsQDPhWAUIHCACEcQOi2C1igilCxfRT5nHWgGJQYQPGvguHEnaky8G7PtItTdGUKADHET5Lknuk5CLl3ytgipUFymyLAXDyEiujfTn2gR1yFQA2geJN2sl4XorYjYXR95PWGYD69xIkeGl82H9TpfDxXwFpLpg3Hbs9lrjMcZhOxYtiNuK5RKs4MLPHBV6PIY0vooRhwEUaeAi8+3c5bNNr/utnzDJWoDRDddHUU+C3cQizpmA1Xp2V8A2H/FZ75DXegc8tqIubveujdj82YA4oQ3XJOmOXP3rQdu+12+jiHqI4bppPfuTumMngVl68bM8i6UTSNS8XS4qd44lX58xj+A0Y4tF03EiYBfKIYSVjT8TG9TIcfhcT9BaEV5RW0FI4fWlUXIA31koRsdzZIwQw9Yu8rr6LQjoKi3Js7yCEHbVsZGa/2nM8Y74SLPLAq7puyILuKbbY6bfuXMk6sFHjrRzQytAWgc1NzjPJrnu/NGf/KXNmvfOBVyJuj67CZXCNODKpC2JRbAtqBTuQjTjw+eRwyVQonpUBQUFJ1W2zzQv5fPqHKdrxNnO11p/dHTU5QArGuxUETn1ofXVn5bTa2keqN/0d+pDioIqwyRdBNXRupA2QiTFzp07XRqQ+l62bJmtWLHC5XOpP0WmqSaXItP0ebZdvAUyArhUpVoFjwWo/uiP/shEUSpOX02OihD0F77wBXv99dftS1/6ki1cuPDiZ34BPWQB1wUYbYZW8eC8B3yN5Ew9bL6eF8wbERvDnfxQrkXahu3ES43WtwtA1UeyEJ52pDzHxirzrbCuwMrmhS3aELQhy7f+Q8XW2DmfvKErKag8xwquGLHyhY0WLuu2CM5zX6DYhoMwOygZqeRsCAQSAHQp3E2hjKVDPTafHMRwz7Dl9iftSpizuQq9i5UjhLHG/HfcgfogVMwpgCttNpEuipBLNdXhSgGu3Dzu5MH+TEiFkZGCVsy4ubxiVPqaEBPEs+a1rylsr+wKWTfKhOFg0tYsidqapRQdRrVw94jfvtkasq1dKEbqpImqx2rqavmou7UT4YwjMCAjeV5rQjq/FSAzMSrBCcBrwaDl5w7x+yREcajYxoZzec1MAGXgXSujbtW69TFbhHx8kLDAa+qiduuCqBXDop0ELGxXgv2kYr7RKNtC3aotO3Nt83YKQzMe07EC8OPNN8RsJfWynEIgn0lGPcpNGAEKP7J4Xi4qvLUf/Thgu5CDT1KfzI9S4x23T9iaK1FMVEwd6/Hf2pG6fw7Q9cIO9hLMVWVB0haTx3UzcvR1VSlwpiXHAWeHCKnc0+izHtiuOQCytYtRfgSgqgiybK75na25/UDumZofwOxCCHFgLmWLkdemulxqAiLKGdOIaUbQvTk5b4URktc37rN2HgEOtDr2vcCW/9JO083vQv6Mw/CODiAdj2M4poLQ7Cs/ky0qykOFM3XNuJB+L/U6MmcWcE23chZwTbfHTL+bCrjCVJxvaKgW3roscriS5HDdPKlSmDl+K7VH/Nwg20IO1y5yuD50HjlcAiFf+cpXnL+qAsBpnza9n1tbW01pNQcPEjHT3u4iEt773vc6EJSO8Eovq+djx47ZM88840COFL4VwXDVVVfZPffcY1VVBO1PXpD27NljTzzxhB06RDoCTUBIPvXcuXO5dqVuTv2///f/XM3b3/3d33X1bd2CF/gnXdZErOjUG19jY9QMBfhpntnCxxdo3FNWywjgEqjSgfenf/qn1tnZabfddpsrlCYEr3BCAa29e/e6g0aa/1mVwlP2QvbtaSwAixDpseCxL1tR57+BqQaAQoCwilLLmbXIju/02sF/3m6eXe1WRmRgISxPYEGOBeblWWRRoR3eMM8OVc3HEa0ipLDKWnvqrKOpGqYHiWqEGupmN9mcqqMgAuoH+YvoG6YLfsnLX+CCY7rE0BSOkSvWdMxye1Ev7KBe195uqw/NtsrAbCuoXmT+2241T0UZyvUnveDTbMsbH6kOl8LT8vLzCctTqWNAFV/LodNDDvYE6EPOvgMkMAGjqNAdbyeHqxcZ9dykza0hF6kI+W1AxdcOhe0fD4etGXl69J1sBfO/IkSRX0LoasiPGgZETaBu+FJz0F44HKAgMaF5PGIjMHoxQtcAdBFP2BIU2nXJQki7JynmXFcdtw/ePm6rpJDHVXt5dczmleo1k2TCmqfC3gS0dJ1QEeyRMZ/9GFn6Y8cDVkxxZX1ONKatu0p5XzFCQPlgkggc4+4dI1mQMhFaV+DpBVi5TZuot0YdrnKYqHe9K0oNLkIeJ8dkbbfOq7BWP6HemBgtPSLY4Y61E3blIvYgQE2hh9vJ8zoA4DrWRYgipGVJadKu5/u18xBeIWdMcug+gNcZ8dPkXcZx9pfAjrvYagddwiZ7Kolck0qJcXA0EsaoGle6GAfIG/NiK4Vnaiqyn/sLqo/z0GeylV5h/Us40wvvWs5UEsGReIzwV2p29bX1OBl5ycaXoWQYzuFmxiW284XMXlPKAq7plssCrun2mOl3UwFXDoCrHsClzxKcL97pohmJun670YlmcDHPYNNZUIWPt5DDtXtjMwzXW4tm6NorsCP2R6kws2fPNgGupUuXurA7MV5qjzzyiP3rv/6rq1cltkmg5K/+6q+4br3LvXYLTf45cOCA/fM//7MDZ6p5JVZKkWElJSWuHpZICJ3jtZzSbgTOFOIn31rz2LBhg1MVrK+vd6Doz/7sz+x73/ueSeBCxYmnAqWp457ptfqVry5QpXHPtL5sUVpa6h5n6iv7+blbICOAS8OJ/nzllVfsm9/8Jipq+3DEoid3onbYzTffbA8++CB3XRrOfXYZXjLLcGXYoJeyO64U0eF+i2//lhW0fM1ywp02Qn2f/Jo6q7nh/YhHzLXd//ADG3ryBSsZH6BoKzk/8/A2K/3WtL7OXrh7g+0rXgjTgVofAhknhsjj6qyywc5Ci6GlXlA2aovn7bf5NYfMH8TRhWOiXCsPakRxR8yLsl5IxY3H+q0Qeet8wqCKDh+3nN3dVlQ+1+rr6m1O9RorXX2DeaipJXSRcnnPbhQ51AJUOsEpxE7MiZxrVsa5nnBFcPWdFPBOijLwHedHHoTpASjEFLGIHQRM/Nnrufa9lpBNqAtqZs2hdtcKpMHnwvYsoH5WAMAkMYV9MGQ7KDg8CJjqArz1NRNm2InLLsRRzEM0nHTopV5I+GF9Xdw+ccOYvZs+xCIVAMQkJqLwOjFaPs2fdSG/3LYEkZgfB7Q98UzA9lAEWeAqwbhDgKfrN0ygbsidMvLI0jhAd850svd5ESLJQUADW3SwPXth5XoRB6mrTXKnMG6FjJ1ueqXt3t/ks6deD9C3OEmP5SBxf9e6CVvWwLwAUXuP++3pLSGK+ZITBqt3rMdjpRUJu++qqK2vj1kXOXDtfDaLzxazToGAoPrWTkg3BhLYUfhjmPm5fZT+LmPPjIuNBJxSIFshKRwTutMIctJs3Mw4tpwyooCX/vFFUMWTsd2UGWdsVm9HR+ntGhlUza4BhGD62XcwXcV5VlSYCqE5ebC8HRN6izFk5yzgmm6kLOCabo+Zfqdz4+UqC5+oHSSkENEM8jp1Lc3Mbz9128kP4Nr6wk7HcH34LXK4BDCef/55JwAnBkvK2wJF+dwgVaqMCgNfeeWVbldv27bNqf3pO8mpK/zu//yf/2P33nuvCwFMHw9is/7yL//SXnzxRecDyxcuKioyhfQpd0rMVWEhfgnXRAG273znO/aRj3zE7r77bgeGvvrVrzrRit9BYfL++++nmHye/fmf/7l9//vfd/3eeOON7hqs8QSe1I98CLFhZ6p5KzG7v/iLv3BgTuucrqkPfScRPBVJzraLt0DGAJemolwthQ+KYhV6FujSgTRnzhxHy5aXk8RJ046ciZYFXDNh9QsfM8HxM7Bvs0V2PGKB4cMWyA/YvPXrrObqe2wU8YrGhx+15kcfs8jRRgofD1j9rKiVUUC38foGe+LOO+1wfr0FI6MwHGXW0ltnQ+NFFkfRzYMTPkLeVXllj62q20YCP7LV1AfycqISARMhFyxOoV4/4U8lJ7qsrKXHStt7rOTYcYv0j1tkXp0VLp9rs+qvsobqm63YixqUXOOzHdfuGoKzrBMiRZGVp6PXeqTCBLywWgq7S4kyCMykm7plMSGCVNNQYMtnmv322Rdz7bXWgFPpAydZgBizWvKtamB0ShYCcqihVQqjM3wgYC1HkX1nmT7yrI7soYjzYTpR9JrUC5WXhcw8twPdZpTBZl1ZRtFdFADvWhOxeQBUD6IlmkSCkL8YNcA6OoLWfAKnn7nOA7jMAtw1HvXZKwhbdHQqWIwQN/bH9RuiVj8HOfr0/OmF0RzIkICImLwAOQeCrKNjAp4qoixQkcqmY9E3Gn0MERb54g6/CyvsGvDZynlxe/d15Nih0qgxXt1PrS8KN0MO2gihjfsAl/PmxOw966M2Tljm9j3UPQOs5RMeeTOqi8sJldSyHiGZdMPo2kdjMFyXDnAxGMzoOOdN3XF0dmAipzs/6jiRM+Bk6fldKNxUd1S1Tnp59zxlE9Kb8sazvpTl3zlN2xWl3ENvZ6/1IqqhA72sJN/Jx/tcza53xlxluSzgmr4vsoBruj1m+p2uE5cr4IrXDsBwSaWQUHt3scucNf3cmNpKHa49iGa8FcMl9T7VoxLTJHAj+fQFCxbYvHnzHKmgfKaGhgZ3zpW/q7kq+uGhhx5yQOlzn/vcmwDXpk2bTKF/1113nf3Wb/2WY8xOt3UtLS32q7/6qy6v64//+I9dKKHO6du3b3fsloCeRCwE0E4HuOR7q0CxAJmizBSmeDbApRQgbd+p9tZ2SXVc+Wva9k996lNZwHW6HXYBn2UEcKV3WPrCr/dC2XIQnHwyTqTaqctdwHwvapUs4Loo883IypHBIRs8fsTGVAeLwqlLVs2xOXWVsFQRa3/pVTv47R9Y1/4DNtHfZ6GhLivMG7XR6yps1/tvs0MLrjQ/jFKkT/lPs2woUEa4HKIHSJ6P5BVZVU6HrSzdZuH6iE2UEI5IUn94fMQSaGtHoWPy2wet9vVjVtA8AIMUtDHCnoYLwjZaVWzh2WGrrgjbktAVtiB+lYWThfLYz2wj95X+pBgN91vgre5wKcRQJ+2QC6/D4Z5EJlq6G2EI1Zwqp3BxRSH5PJMOtQcm59m2gH32pzm26TDrAlLi5F4lCJUrJUxyIQzXrIXUqILRqSXEsOeY3/YR6qfRF1CnqauF4sk/DVp7E4GTGghw44Elc4CLvKscRETKEeCYQy7XAzeM2HvXD1sBoOTosbAdPASLCIt0jD77mV8YZmwB4hjLkGj3A9g6u5SHRu2rOhgkwviqlQemvk9pssEYCEubpG13Ahpu+zQh2lQAlPpE5nMhhC9s99vz5HGNoGxYDAu2YTnsFeOTCmSNbV77CfXI2rvZjzCDIiBvWB21FYCu518L2o79gES2hbRAu2Ud65H/lQsolR3c8M7IHhfeFxmNALhQVDzdBqTndIHPChVUvpa2VvtfrGZ6/56pS4FYrScwGCffSyDdNToRC6d5uvwB15F6nsq8utsCZ+raLZuygKygdd+mxnDRSAyWa8C627osDgAr5LemYskBp4Siubg98zZN6M3DaPQs4Jpulyzgmm6PmX6nn/zlCrhiqBTe+D4xXLkn/cRM2VMhhWK49j5+3D58/8fsmvXXnLHro0ePumLAAlgCN2KbJBihQsC6XinPSY9T25/8yZ+cEXAplPCf/umfnAy7yAcBGQGh5cuXoy58hSMl1J+AmWpiCSwJmEmaXT71wMCAq5ElRkwMmtY5FXAJIH7xi190oYhSFbyTG85i0dI++anzlX8ugCb9hVObiJKenh6nyyAQ+D//5/80sXLZdvEWyAjg0t1Z7Zhnn33WJcSnp5Xe2ak7+B5XEFk7TrldM9GygGsmrH6RY+JrybmMwITEiV8rgYmZW5FEFpxyWC1t1vjYE3b0iWes71izRfv6zTuKEAT5SwP/Zak133m9jYRLLNEZt5F4ufUlZ1mEsLUgIYKF1msL5rbZ3IU9NjK7xPpKKUhM8V9Xh8sH44DzW9xCztbrTYA7r+1buMRaZteYBzAWg/3Sxa3W02ZXA2au8qyxytgywhCV9H9+jqrYDYXo6a6U+12oY3l3kz3tJQ9pD0zWUoQhls8hn2kS1Gmxw8M++/yOsH17Z8j6AU6JRhghVBtLyhJWB8NVU8/cEL6opV5XC4xTK3XE2snfEuCal5ewZwAsYoIGegjNQ8kPhXpy0eiDEHXwiOWCmhQaeNWSiN17LSIXUa9tIs9qD8IWHYAqhTiWMY7mEuditBRhjEXzJYLAZwDCBfMR3lgbtVlVU51+t2knzaQT/zihhboxE3KhhZMbP7nYtCeZFlKuC5C3EQXGVsnDI4s/MCpAmrC7r4nAcrHvCSV8cXfAtgIMla+1Hhn7lTBwAlXb98GMbfE7IFtShH1QY6xkfoX0k0+xYAmT9MOgFeWgaFgYRaREIX7kTnGBzFjDVhJF0XlT50htux7p/X4u4+jir/UFugTCtLJCIJOAMaeURWiiLDkGG9kWA5iyXwWgy6m55j/DjQG3hcxHoN3rTeUWMszb1pLQr8MDQ9bX1WcqmlyAKkxhITmX/Ngzav8L2CJnS+yt2nBh2fYsh+kFdH9ZrpIFXO+s3aZj8rIFXIQU3pBmuDJs1gDhIDte2mtP/cvLtnjWChiqegeeBHhWr17twgXTvmpXV5djdJSTpbA+KfWJ1VKB4LNpD5wNcElI7tvf/rbLydI1XudugScBm/e85z32/ve/3yorK13YoGTZP/ShD9nHP/5xF8ooU+g8/2u/9muE6u9xwnTKJxPgkiq4QhXlOwjUae7qSzlkyhFLb5P6ON+mOYr9Etu3du1a+8xnPnO+XWSXP40FMgK45DRJGOOzn/2sK6CW3tHaaVJAUbyoDlah7k9/+tNOZvM0c7nkH2UB1yU38SUbQC67Eu3j0XGrwRGuq/A5Z3Oo+YQdevQJO/DIj6gj1QPgiNgYRYfHS5FDXznXeuvmWDA3bOWo90WHqEvFIwzYqgq1WeXsAau6IWCRhbV2xLfA+gBn/hBhc34AWiIfeslvRc2DNpxTYHvmLrX+YKHlJcac0MT4SMAqUSZam3PQbppdZMv8t8JyNbD9eLanBV3y0NJemrxYLYdDDMMjxkIiGj4c+0nf2X2nP12DkwwXLE4FQhnpHvSd6o5t6vHbD48Gbesetm2LxwrxrMEtNkooXUlZ3N53b8TKawAa7QFXv+oEuUvlONNzwglrBrBtBbT0o/w31kw4XwfONiwZNXdhm5hhF8/khVXXEKLIYxwg034IEQqpHbIJqpFVqPwnwuJEtKy9OkZyMbW3CMMfR6Y8CUBevTJqGwB9JQpbPJ3zzmfjkXEHqnUxcncPp26kNnRq47tO5vvoS2FrZx4l+XHrHfRZCaze3esjNpf6XTuP+exVQijbyAXLQ9Xx5pUIiSCWgR6DNXd47UnA2uETMIHk/eUhthKFUQyxLVJjHIZNBP9Zjm/UrluRsOXzKWUNa5exNnlO1E0EB7RgpC5UZn6qOTVDnWsl8sEQDiROIP2yeThoe8hZHAV4lfAbuCGfnAHEVAS6Tq7PC5Gqyu871MJxhD3LAaMCr8pvO7n1AmrplXgtB08AxIEPnjVuauH0QudhNVahJ+eMiPUd7h+14d5BU1ShmK7CIuV1CRKKiTw50HkMcHGLygZZhmu6DbOAa7o9Zvrd5Qy44nXDdsP7rnGy8Jm0o84UyuHa8dJu+9E/PW9zSxeQ4lLHTaWEy8USeBETlG4CQQrLU5kjpcXosWjRIsfwiHmSkMXpCIMzAS6dq1Sb9t/+7d9c/VkVEl65cqULT1Ru1rFjx5xPLJCkcQVwPvGJTziwN3Vev/Ebv+F87M9//vN27bXXOqZLOVzvfve7XfijQJmYLTFxaVGP9DZd6LNs8Su/8iu2ZcsWB74utJ/sem9YICOASwev7giIEtVOmgq4RFnqLoEeQt86cE5Hyb4xpUv3Kgu4Lp1t346e4zj2EwCq6nwK2FbjfHHnSsV1G597yTb/45dsrL3T8ivLbbSt3XoPHUZrEMGIwjzLLw7akpIJCyEI0RRCAh4GrLhE8XfkGC72WXhJofUX1Fq0sMhiObAfeeUURq6izlOZReI5NlKQb205lTaGwmFOnLhtHPdx/uRTxHVR/LDdXH4CR3aNVXhvxcUVe5sCU6faJEKdrzGSpgj6spwkd+35N8rvI47zncsYCik7tcm3VG9yNZ1jO20BanABulph4Fr7fDbYSu4ToYWjMDQHqVWVxCmuQQ59iGUOU6+pEYClzgS2emCojgFY2pkLIogWbafGVxMOL6qCPjBnDLG8JDL4IepwgTPZZoBIzIeQCa+V9wU4EdBiF7i55SLmccXauC1cS90qTABxBwXpsfmlMXv/PRFqcU2GQ+oKSHPb4hx2dcN2IIii/DXVJXHef2qxN/7KADQ5vhwC9ioA83VA1TDgKMDYa5B837ACxURms/G1kB1n+8LMYwTmroHwytuvnLAy2NGXd/ltM+upoHSzoilyEOgApI2yvR2Az9kIjpQASMSSXbEgabehsKhwTufjawIX0XRjSkBLF+E0q5UOH72Ibt2qwkJxUK/sKOOGYIfaKV/wSA/sJ0xkiM8GYJCu4bdzfTHH/ZRDjd1ox1GY3LjZT24cYiQcS3WFcbsHIZKbCMVUQW3173ba5P6LALCPO/ER1DFDHquvihHKyrEzuZ/Oe3sm+9UOFhSMMsDwAHeByaOc4G5wEOn4vDwVyybHkRsT6WvMeY9zgStkAdebDZcFXG+2yUx+onPq5cpwTQhwPZgCXJk4107dDz4A1/af7rRt39tn77n5fsdq6RwsfQGBmrQ/mrqRw2WLa7KE3+SzCtToBpB83JqaGvv1X/91B3hOBV1nA1x/+Id/6ML9PvjBD5qEL6RSqP4effRRxxwpVywNqMRwiVkTw1VcXOw2Q2BK40qt8G//9m9t/fr1TlxDJZZ0TRGbpfDHt2LhptrkXF6LMVNO2ebNm1344bmsk13m7BbICOBKH6h6nnoh1HsBMKF4UaragTow0wfS2aeW+W+zgCvzNn07e0wBrnGrLpiwedWgBO6qC4y0bd1hW/6/r1rv4aNWNLfWxvv6rJPPkmO91JtKFcStIJysfE7YWksLYbKSlpMLUzVBkWDYnPJZsEHl5L/MyrfO+tnWWFlv7d4q6xkvt3GKKk8EQtaWrLZuQ/QFh9KDKp4HJz+ErHpJvN/WjO2yG7gjvyD3NquoXAMAUGjh1AY7h1x7q6fDWoE4BdTbmpOso0pYvguni8FMuPwt2I6pv5+TPcjbSzuk7kPn/U5+nfpCdhDLpEVVA6uP0Lg9sFFbD/htCLYpAYMTATR5cFwFqPYfA5S1IcGeIBQxj8/wukcBXTm9hNYBaHp4HRlOoBiHTL3PYyMwJFEuXPjuIBzGxAZsEogGXgJQoy0unQ9jNB8mgGUmmIs09CqZ1Aeuj9gH7puwOkQt6MqBlyEWGgW4UY6MB0B6jIsaTF8IlCR1xqlNF+AewiE7u5UvliB3juUZu5H598B2FRIe2QADV07oZA8FkX/4Ssg6YKpKAVjdA16rLkHFkOLIhQCwH74QtBMd2Ih5/fQYtdrYriWzQVsAkHYEOBYCRhaWJK1/MOhYvfUArvoSmB7mncISKXtPnd8ZX2tR2Yum0D8lIys0Thf4VJ5V6ruL/js5Jak+jsOYSuQjDOA6HgnY97pCDhsXcLy2cByshhG8jcLIJQiriJ3SgdWU9Nm3DoXs2z8K2AlCTz3YtmAwaXctm7DffGDCFsyOpjDw5DhSn9xOfbNN5MINIsGvfubPitnNqyi4DDiV4zf9eD3PLaQDXTsSHJMSBxnuG7RBVEK1bUEAV2kZv6BcdubFjnMe09JQWYZrusGygGu6PWb63eUMuKJ1IwCu9W+IZugHl4nGOctPwu62F3bYvo2NiGZ8zAGW03Wtc07ah9V1WMBLIXsSn1CO7b/8y784BklKhbW1tdO6OBPg0kJSHhQzJVAkMJVmoFQqSWzWqlWr3HdSRfzt3/5tB5w0hsZV0zw+9rGPWR9+jZQFJZ6heUmyXsqJYuEeeOABt4zyvs6lCagpl0wKitrmqU1gUGMpRUiq47fffrup7le2XbwFMgK43moaukOwceNG+8d//Ed34M1U1eos4HqrPfXO/l534qPkcuV4Izab3KGiPK+Tkh7j5NC6Zbu179pjkYFBG2nrsO79h2yio9HKSwYsH4ZmrA+QVYCDXV9mnfkhKwgNW6l31EoRhK8CVKjm6vCcCmtdNd86yitsIFBgSeo7FXkGHONyNN5guzwrrSNUCYPjAzhQJwsY5YMBmj1+3NbtfNVWjpXawtUPWnnp0kn9jNSJTKFSSHfYUc9xa7JmShwX2SJbaEUIbUj0IMJdfM56FkScIV1jadqecBcf+sKxVY+nvxalv0mxMYcQtPjGI0F7cXPAAZoF5DBdvToCAI3ZAb57FZn4fa3kb0WpgQWSqECVcD7AYj5sxkSf2WuvwogdYjzYMZATqhZeGy/wUmCa8aVoyH/NI4kTDxI1D7lhXsAryXFO6yKBP5wspJ4U666uiNov3Dxh//WmCKArTg6W1zbv8+Hce60WafarlyasjHmNU1pCwg+qe5Vq9M3/Tvp/ibpbR48T3hdK2LrV5GQhGa+CyNzPcTL5isZ0DgfY6cW9AdtMzbExgCcY09YuQhhjKQWU2V+PvxSwnYc9rm7Zwb4gNch8liDEEtFFWCFAI8dCHTnRpTwKFnEXtB7VR1iv9cGo1QBStM3n1LQ7aI6t4eImFjMNtpw4SOrrjP0VoFMunC6eIQpVigUaBPQ+hWDMPopjS50yh+28rXjCVgK6QuTYAY0M89lTALOvbwvbCz/2Y094VwBsnJDLa8uj9scfohYbKo4psMnCGEAKj997CVESwlOFeybiHBvEmN67LkKuXIzaaOdspbfefnaqchxHh8YQ1Oi2EXK8wihalqCOotwuXybHOststEVZwDXdQFnANd0eM/3ucgZcYriuB3CFdQdOF5cMtjTg2g/g+shZZOF17hQQUXM5tTwL4Kg4sfK9lEclVkxhf0uWLHHLpf8IcH33u9811cdSIeOpOac/+MEPTCyXGC6F6KlMkppk5CWSoRwxMV9istLfK+8rPcb+/fsd06TQxv/9v/+3NTQ0uBwu9av1JfTx5JNPupSdj370ow6EnfbGbXqyPKsGmJQQVT/3VMCl98oxUw1dbbeAoqTns+3iLfC2AC7tXMWwCpFLSUUqKzPRsoBrJqye2TElnDE2Mm5Bz4TVV3vIa8Kj5/wcgz0YQzRjtKfXJiioOoigxmFioge2Po3i2WReC953rL7Y2lA5zC+AyYh0Whl3j3Jx4j3k6fTPIZTw6nnWV4WaIdMusj4r8gK4YGM6JqrtOc8ttimwzoZC+RSgRegiNGo5gXGWG7CV+162q9obbenyu2zO7PsslCC+akpoYYLX/SzXxyOHLLIy2DIylph7SvxANyXE7ChUYdrJkliuXkL/umAeiovjVgG7Myli+CbDas666J7o8Nm/PRqy7z8ftA5YoSDe9i1rovbL7x+k9lTEntuSY1v25yKV77dOwgzljK8nHO9e6mXVkCe2+XW/vfxKwBqPkD9GzpP6zCmj31y/tQZgxKLYi1A+ZyTCycCSlmzm/SDPLOwR3QV4gcqj4jBPsB5rq6P2/g0RWwNQaiJXbDP1tkYjXqtie+65DgVBAGFkdIQQRUAVLJfLa2J1efpbd/vsWRx8iEATNl2K+uLtN1AfjOLImC/V0s9MSizXoVYvz+R25SHegVx9SQEcI7liR1sJL2nMsZ8eQblynPw15nAAewmcVbLt1XVxqyXEcH4te6wGZx9wiRSF3YmIxvog+WnyvM+xibGTAqUuYF7CRQPsX9UbO3fUdo4DsZjGkoS9+g4jy6j6ZpK9aAdAHR71UwDbY9WBmC0APEptMt0UHfrwWNCeQunyFQBXb5sUOummO2m/sHLC/seDYzaXIthpyXw52Z0A5m88R+03bFaMfaMArlZCUe+8asLWLaFw+JT+0+Nc1DPblIRQjRBLOoCSYV87dwRA2sWSjy/JdWGG7li8qEHOvrJ2exZwTbdRFnBNt8dMv7t8AdfnLFo36gCXk4XPsCGpaDnJcB0GcJ2Z4dKwJ06csCeeeMIBKxU6/vrXv+4k4fWdCIM77rjDiWqoGLJyZqUgqCguSaw/9thjppBA5XpJyEJMloBbU1OTA1QCc5KHlwiF6tZ+4QtfsG9961vusw984AMu8kGy8go1FJBSqKGAm3xm5ZRJnl1pOep3qmjG/PnzncVGCYMAAD4QSURBVH8tUkNs1C//8i876flpfoQ2YEoTsyXGTiWcTgVcWk+aC5KfV46bGLizCYZM6Tb78i0skBHAJWTe1tbmCsbpIEzvaO1IHYyiLl944QWHzEWtputxvcXcMv51FnBl3KQz0uE4EtICWHWwMnOrUoDLoQJmkxQNpuOO75uQjd/7re/awP6dFhvutRACCqHqYuufVWi5cyK2NNxrOZ1DNtyN2ACOfKwkZCPXzraBG+abL99nNbEWK0yieojj3pyca9/33G8vJTYQYkj4IaFtOfljVhwcsILEoFW3HLcNg1vsmrpiqy97APZqCb6veIGTSAAHGOaAhxgvODL5xq5pCRcnzgk5FAqn7q6lv+T5eDMAAsW9OpQKF8wXU5L+crKDKU9KrXqCosB/950cO9jCKJgnSlrPLTA8n/rFEasqH7HHnw9Zc2cuTBKgB6dZxYmvWRG1u64ntBGlvh8/GbI9qPlxrseJB0cp14m84t3E3W2BFRsh3DBJuCEbg3fPQgJYx3nfyrwUpiagJZKKr5KEofuIcqgkL2plXtzmkdPV0pawpmaYEIDu6is99is/n7CrliF1zvYrbCzkJPLVAX0x/92ERT75PGwc6oQQObYGEYxbAGmlRdrXLHaaxiFC2CFgk/W9xMhFKSrNyY5QUPK3xgP26pGg/Whn0F4in6sZlcYATEkuILoUADh7bsLqsXWkgrBKhEfGcyfsgbyo3R0kfPHkXjvNoFM+EgCaWqflXCTfp6x+fi8nbTAyRD4gyCQ3Pw/WT6hHLBbHFvskziMEK0tE6bQtkMbhixG/PdEXti3UZms8gMHY3+sJ2/0YypTrl8YpWM2+1X5V4wmlfKcSuQ/1TEV/RgBzuRw37wGwL8RuGuOSNPqNcQD0dfQDurotTsmH4hIKJRdzLFN4e/qWZXYG2qQs4Jpu0yzgmm6PmX53OQOuSO2wXTcZUpjSR82cNQOTIYUHHcN1dsClsD5JuCt/S2BJ7JHLK2Y6yr365Cc/6epp6caoQgIFhLSMXgusKXpLYX3KsxI4UuihrmuPPPKIAzgKJW9oaHChfFIdXLdunSumLDEOgSvlaUn6XX3pM4X3iYXScpKKX7hwoVtOy0ilUM9in9oR8BI4FOi7+eabTUyXJOinMm1TLSqfXaGICkM/HeBSCKXy2wQcz9TH1P6yr8/NAhkBXDqgpFIoGlTIOQ240lNQzoLoUFGqok9d/kL6y7fxOQu43kZjX8KhlMsVhRGaVUh+TdVkHSk3Hm6Rc/bwynkeHxyzrr0HrWXLNusg3HC0tZ0QuDFYkl7LL+uxJYSa+UZi1nYo6liMZDHKhgvKbOQ+TmpLym3WcJfNiTbh8yfslcC19h+RnyM8a7FzPv25UQsScpUXHrGiWL+F2iZsxdgBu6uWk27xequwDcATlA7PhAim2oe56gQ4NjIGOCBPKTdn2kluGNAzQE5SPmxCIWqFurCetvH5CM7wN2Ef/u1xGJxehDPwuEcBVDcsjtp//8Vha+BO4gsAsp0HJdLBlmkBHjdeHbV1VxCmR9jZy6/67RWU/FTDStMPAjTqEaXYNuG3x/cFrfMEAhowZxHmleA7H/lh8YMApmagpGTlyIFKyv/V+rmEqKGSWEaoXiFhnf7BGLLfMULElM8Ea7TW7Pd+h/ldn2LSxoZhaZhPmLA4V/iWjR1AfGPHHr8dOeZ14ZFXrmC/1xHSeTYmRTaiHznoYpkEGFydKlaCoLMXD/vtB9TqenZbEDVDgg0FDvknzY4iwlWXzUZYBbGNZCVqfQuG7S4+W8WFm68nm6DMaXYEH+t86MZk7gICUp8UyJapL0mjX51zhzn36jiSAIsESM6lyWnuJY9vK2GFe4a9Ngh7VUtu4xqUQBeUC2ydupUpEHccVnALNmzHdn7yw5bPTSlB5kEBpqxyiTaW7YxxYI5wF6C7pRNBjXGcgqCVlOaz794oAn0u234+y2ibsoBrusWygGu6PWb6HT+Ny1Q043M2wXXpugc3uBwud03KkDH1u02FFG63AxvfmuHSTbLDhw+bwvhU6khsl3KpBHjWrFnjihHnoyisJuZKeU4SjBNo0SPt+wosydcVuaDPpNT9yiuv2Msvv+y0DOQTa5mbbrrJFRcWgBO40vlboOvpp58+KVQhkCXWTEWItZya+tIcb731VsdEaQzNQ+uJ9BAIE+BKh0a6lc7yR2Prpq/G1zoaJwu0zmKwC/wqI4BLO0toWSqFokq189MHnsCVkLJ2fkNDg6NN099d4JwveLUs4Lpg072jVnSAC5GFWUVTAZdOreTKWIfFPE1ApHHzJykcGK2xkb6o9R1vteETrTZ4cI91vP6Mjbfss4pyCg7DbAx0EPJFPth4Ta51w4AN37TIRm9YTuhcyOqizTbL32qvxNfbIwPvsc6hSktMoJRGLkxoNgIF4XHLHxo1b3PCVvgO2x31BxEmWGJz7HpIHsVqn7vjKSd9grv2Kl4bDOE8nuK6ni2DSztIDtAIjNUjqPR9/9mgHTvqtY5mn/WhJldfHrNffnDY3vtfOKkCBbftDVkTYXf8VG0erMRV1KqqKk+BORUu3r7Db40AHE1hwTxqbMGQHSUE7+FD5FLxfRznvJfCwj2ALv8AYWYvk5t1xG9JOfoSV1I+l+g2HPDAbLMqgGLZeNyGTkxYdytANz5BGFzMlixO2v/47z7iz32Wi3iHVCfHUdMQGAziQCu0UBaU2qCYOCkSFhHmx/XqrE1M5wQXzzjnJj99+f0IksAMKqJP4hiP70Y8o5dwQmy0q9FvXfQ9BlNTDlgMEw65hDC6BhjUIkRXbruKPCbe50LZOXJRF1cMIwClo+7UJqVAXbhyyEdgdDd3difgJQ2WT7fWqb2c//tYLAqrNsGFEttxDCms0B1/2slvarIqwiXkKR4jl66V7S8o9lgVwiPlsFW55Hjx/zQt9WGc3L5+1hmkBprKA0igRExY+pg9zYqZ+4jtcQ4CEpXdLV2udpfENIqQji8sTMvHZ2449SQLZgHXdJtmAdd0e8z0O/3ML0+Vws/ZGKIZDnAh9nPa086FGpeTtN8bIKRwux3aeMQ++hYhhelhdNNMvqzEKcRaqaSR8remghBFb4kh0rJqab9WwEsMUY5uGk658SUgpILCAl8CXAJjAm/pPtOATec2iVb09va6PrWcxk73r2eNq/E1hvpKN32uhz4/G2hKj6V0H/nFkn5XIWSBLrFaDQ0NTo1Rdcr0PtsyY4GMAC4dcDpAlGQn2lUUbPrg0DS1c/WdDjTtyKkHSGY249x6yQKuc7PTO32p0wMuESotiCBsQUmvPeVnAnkCtgLgtQJGjNA6HPnx7mbr2/6UdWx53Tp3HkPZsJnCxuSoEBoXr8+z+NUVNnL3Fda3dpX1JCoJDRyw+b5Dtr93se1sWmX57aPUayJ2u6HAumDDgoSaFQ8RVohYx+o8crjKBmyhf6UtTaK4pDi887h86HeiPBwBA7FcF+K8qgDx7uMBe2RT0J58FtbiNb9FyK0KwlytWT5hv/+pcbvpeoAPrFEfbIacphJC81DPx0FPXerAKigj4VCTviZPs5jNKOD7MZihPYCUbd0+60CIYZAaYR1HACEnErb3hRh35vyE8YEskN+3fB74+z5ENIqrk4CXhN1xBdIhPaP2o0cj1nwcLMbNuiVLPXbbrWLAPbZwEe8hEIMBQjzZV7pguDt6dKV5nGxvcUWeDrbInQJ86KIm/OfBDodhZx7fHQaMAdEhv46cQMADENmOGEQZrJbCC0sRyJiFeMQVc6J22xVRA4fbKKCvbyhlp2Ly0gSiMN+0JqdnuH+Ycx795ObZngOEZx6gX4DcWsQ+yilKfSH7ddogZ3ijqQjojYsp1THERfdsAh26k7z5WMBeQ2BkmN/HLHLYbl0edYI06QLbZxgq9bH2iQZN75tTbHHWdTP0pY6Tvs5ewgx7HUCUgmEhB7OfXMPz+e291XS0iVnANd1KWcA13R4z/e7yBVwP2XjduF374HWuDpeug2+cVC7WqgAujwDXNju48SCA66NnVCk8dSQBn+985ztWX1/vlAEFon5WmmysMESpDz7++OOOMNH2yTcXMBTTpzy1D3/4w/be9773pNDHz8r2z9R2ZARwCVE/99xzLtlPCi7aUVObEgu1U6Xn/9nPfvakLObUZd6O11nA9XZY+dKPIUdZMuIqgFyvHC48PrT1bMKzjb/beCdPGLbFhnDCKi2UvNF8PI9xEolEUMKL9NjI4d22/SsP286Hn8dBxdvEQfXVUFvr/hpL3r8ENcJaxCqKrBC58QrCHSIn8i2500tIHBIKOOTR/IAdX1BrQ4sKrCjcZ9XxDtTt+qzAV2rzba0tT3JnyNE8sgcXkHNwRtOASw5sDsIHF+SYs65qSm2HtfniN0L2OOIZCcIMRceUFift078zZr/wQIS6Ru6jyeua5ieXcko75a3mPwR71tjjsyaAmpd8uDIUDZsQtNi/hcKSW5P26mvcWOH7hGwPwMlFMGP+CrOVK1D5IwzwlmsiyM9H7YkfJ2wLy3Nep1sYLF+Q0Ebk26uS9r7/igrh1YCShGTiUdPj5o3/rLGDU+bMHF04H3f+NH0xhQpLHKFOWZNAIlL5ISTtvWz7luOEjyJproLHTNh6YerE9iybG7cq2JrkOBL5LLtgFkW2yePqRpZ+615y+dp9HCpJW8hyVyIQUQKgnNoc4BpIAa6EN882PpVj2wiHLKau1f13x2wxgh+YRht+yZpq1U1gA3cBxQZTb35NHVTS/Q+/HrKDAFDlXUVQT/kva1AxnAMvN5XeYqPkXIMh35EtOhGldtig9Xb0cfNkgsLfQWrT5HPsqF6XpnzqwXz+m6EesoBrut2ygGu6PWb6nY71y5PheshG68YAXNemZOEzbEgf16Mdz2+zw4QUng/g0vVYYXpiiyRUkWaiMjy9GelO10mJgChfbd68eU5sQyGLIkukUKg6ZMoR03IPPfSQU16ckYn+jA2aEcAl6lU7R2oqStxT+ODUJnbry1/+8kldf4UYzkTLAq6ZsHrmx4xBVcRRnKsqGkVRTi674poRObDXyMXaBbEiLxgn3rjrzes4gKvVZlvThPK3CEWMDlnN0det7eGNtv0HW6zraDc5N+QvkW+Uf3OFhW6qoj5Tjo2NUmi1MmilC8NWRa6Xv418l0CJjeTkWTg2bslcAMaV5RZVHa+2FqsYH7Highqbm3ed1Rdw8QikvHEP0uNq7s6du3vn3r7pTyYAVwzw0AE7ta85aT94ImiPb8yxHhicJDZbdUXMPv2pMXvX9ZN1ld40gzN8wIV8EACyqSlg+9pgzNASL0Ltbl0D9ZzYnq0qmLvJZ69s8tqRIzCJAKla7rnccnvS1l+bsIUNCZtTgyAF4Wpy0hCS5JG0vXvMHn/CZ61diC8QFTEynLSbbjT78M+zPEBHOW3KfwpRV8qBhrPYTgAmBsgQ0NCFMRAIOrA1BJbehODInuYA0uXS8SD0jTDHXsLhDqPAOIKsvZg21fC6ZmHMrl8YtQrJohNeKAcmDLCMAcSegSnceRAgyWcJcp6U13fzuqitRnVRYixpAKV1hgFcEs3whnLtVRQht+32UyoAZcUbYzZ7lhiuS9t0HEUBq1GAl0IL/dy5PN2YYvyeBQxuOpKS0Jei43tQGpyPVL93EnAJF4rZQ9Ueu6GFQrhhJgBMpi2gu9EjhPaOk8Q4MkhtGdC8mMpCwgxzSEQ7E+g813n8/+ydeXBc15XeT+9o7AuxEgAJkiIJ7hQlkrIkitRmaewoI5WdeMoTK5E9sV3lxEuqMmVXjT1TzlRsl13lqTgVV/7wVk7Jrrgcy5IXidqphRT3RdxAgguIfd8avXd+32s0CXCRCAMkQKkv2ejtvfvuPe/1e+d73znfkf2ygGuytbKAa7I9ZvvdrQu4/tnCteOAqyBX9wZnrCkMX4DrAAxX0x+aUCm8foZrxgYxBzuSKIfUDBXyKDEOiXxkQKWiJBRqKBJFcvcKK/z5z38+B2dx6w1pWoBL8Z4tLS0OHamdI9ClHTSx+JouhNq5Tz/9tOMI/fSnP3VQ9GyYKgu4ZsPqM7tNFUMdQxvc6++w0srz1K4ij8pVSujffJzgTjK33oKwQLglRRgZIAzdNDthG+0oAhajxI/FyHFxRbqtsm+f1Vw4Zu49HL/bT1nLwTZLhMesoB6QNZ8EpGJYsgLodXJw5pX4bFFe3JKEuA1QQ2MUFTh+OJZHKGH/kkJrry2ynMNtVt/da1U1FTBi6827YJMNl8wzL2PNGYatctVQD6zI8qXql/HOLzPNJcAFu0L+z1QZLl2oTiIf/9YpcnL6EQ3s8VrTca+FkPquJhfpka0R+9i2qFUhpz4Vp1kX8qMwOy+i6DcMKyTmR8WUl1Um7L4l5AzBoDWdhulq8ljrBbQZPUlrXJ609esQyuDeipiRIMWNcyk47eRA0Z+ctX37zf7n/0Y2l3VUfgV8ZctWuOxzf5uwtajjibkQgErnYCE+AZCSbPzlzQEYLKuYdiX8+vyArfENHTzntpff9QO02QY5RrJRN2xVK/lrOpaSlAMQEKsj5PFvtkVsHSqQyhPL2EeOeje2/N2LPoooeykxwHeMv4MQxA3LY7Z1U9zJKcvsUgdwIegglkny7GNR8gIJw8xl/hXkyIF/aOr//ZqWyRwrl8bzfmtN/D6E2orCUx2ZfSefa+K36dddw4DzVq+Tj1UPk7cURjdXAJLGqdtOYr+jJ9nvgPgKRDTWKdcPAZF0+CmGmENN+1ZhkmKse9q6yN0cBnC6yZVAbSs3h+vP9Vn+alPSTLOAa7JlsoBrsj1m+92tDLhCRJHc5TBcAlzp889M2dPLDc8D5HCdniLDNVPbn4v9SOTu85//vEmOXsCrrIy6L+NN9td1TwJ4KswsoQ8JhGTb9C0wLcClnfDDH/7QCRVUMqDoVymqZGQ0NTztPAEzxYZKqlKP2aJms4Br+gfMbPcgJ3AsOmCJ3MNWVN7CHXeJr/usNLWERxX5W2dguZpxaQECqXJrcd1mb1P1asBFnhbqZl043t3kZfnjzbY2dNLu7Gy33IMha91xEtGHZhzlsEWV0IJz5kVT3A1jVFXgsgWAFHclhZNrimy4vBAVPraKPnYnSfothQWW1ztoDQjHVBaT7LR0sZ1ets7aCyosOBCyaGuRxXsa7M76Cgr2lpmX/KwrfG6uMfqtjMEWC5UEp5LDxbq62A6EUrb9qNeOtHkBNxIW8FgveVYNFBUWG7VoftKKAQx0P6Wmvnef89qrCGZAGlghkvjt1LqqQXnw4RVRq4UdihBuOMr2I9hXvr3UFAdY5iCAr5WQtSJC6tbDBi2A6ULDwgFcbZ1mP3uaml+wY4IWAUQzVq502b/9VzFbvoR4NwBRDGpFRX2FlHTzRoP3+wHFdCJA6oQQwmboEi2wpYcjjsF7MTgvHPLZgTMUf/aruDXsEuufgfE7gdT8YoQwBIJIA6LINYV7qe21DjVGJ89ZHdI0935AyTMv+wkn9CKioS8QCelz2V1rYnbvHXErgOnM7E8tL4Yrzrhz83Md8CeANuXGOupLWwObavpTatqkbKPwED9s30WW8LJe1O9YHAYLEwtIq1C1I85CBy3tHnt+B6qUAEblhMWw0Rr24ZY7kWMHwE/1hsBlm75hbzUnlRcYHhhGPr6X3zAphQUBK6FuVzo8dYrGZKSyZxZwTd5lWcA12R6z/U7ni1szpPCfCSmMEFJ4jxNSmDmXzow9LzFcp/5wgpDCf3/dOVwzs/252YsAl8DUpz/9afvsZz9Ljc9irjc6y11qShXSdyrpJNIk26ZvgWkBLoUKSjazqanJVFNAOVqZBLsMStYQBcBUnE20ZUVFxRU7dvrTuL4esoDr+uw0p5cipivq6rOQfzdOVJcVU6AvlqL4MHWyKpMrcU6RJ08OkB+EelAixw55g7bTFwKI9dsI4KkjVkodY68l3Z22OHrctoU6bUlPiXn2HbeBs29bbzRsA+e5Q84jPkJxXZzW6mTU5uVELa8QpxPmK1ZFDFohxWQ9KP1FgtbhDpgHBbqayDCFiX0WWl5nXaUVNurPt0gwH5BXa+7WAnsQw25YtdI8C5aNO6uXHD/9XqSqpzA6HzkouSgD6QQ4+RSYcbwvrefsK94mk9TC64/bn47mWMdIjoEJEYUgdI6aShsQfrgPWfg8hu047s6fTM/qK/P66nte5+FmalVtRxK+lfwtD+goSd931EftniWIhsDepPsYHxcrMBV7bQ9gh3peGkccp34ly96Hsz6P3C5BCTn5O/dRA+oVj/WRX1Ve7rbNt8dt/QrCN6kZpdkXABC91McSGxVjXyivS8WDnQRmxiVVRzW9dxShnIsG4wClaJ2XUCPcTchcPmDCUdEDODRTn+w4QHApDN2CqqQRiUYIodmj95K/RI6VQOXEpsLQ+6lR9RYS8n2ASI1dLOG2TVFbUg+AdMLv0itp88PK4SKk0KmHBYIZt8rELq/xWkvSgfoIuQCqFCEmfLOK2mCliHRkRE2usfKkjzUa7eYINxAEWsVy+a5Vq0oLZ5ozWP4wzTf3+uy1d3ywc9wA4Pjuow5aDizhEw9RAw8mDL4xs9bcenbmoH2asMHeAeu+0IU4SoRjKZc7uUWATyUwTm3ImmkWcE22WRZwTbbHbL/TuedWBVwjtWEYrrstWHCVm5HTMqwAF9eh1/dZM6IZT2VDCh1rKjJN5Ieum9JdUDFm55o6bmtFi7zwwgvOd6tXr3bqjU1rN2RXdiwwLcClC5ryt4SEVbH6wIED9rGPfcyRgZ9oX911FujSDp0IxCYuczNeZwHXzbDyjd9G0jVmo553LZVzglAh5Lo9eVZB6GBwrMqSeMcOy8HVBx/a9nqi9rpXoYb9NgRY60yWUNe1zKKeUatLnbDbE21W0Vdq5cfbrajrbVixTvOSgD/YjwxHMtfycO49zcPmOz9KjLPPgkVIlZPrEudY7o35rTtEQd4oLEx40IrJ0wkug/3iouELk/TCBbC/ttwuLFmMYH2JbTw7ZKuLFpln1VYAWyVOreozjXt+PDkys/yevLDBkoUXuNDv5VJzOXOTZHqm6WvlLiUAI2MUk9rRnGcHW2GAABZSc1R43NalUbudOkmOv82KTp8ZkQzGKGdSzdnSxM2lP3b+ajonCFc8Sg6Xan1VAQDWzk9YTZFqd2mlTC/pl9TgtWcpsNxMuFoJrFAfiodVhKw9AqgpQvGvG+e9tydle99OIUFPDtw8jz34ACIbC1J27gwFeJtVXy1FrbSEraHQcQEAUnOVTaRM5xQyJkYsgJ3SIYQONXNpxKKFeJxs89j2gwGYPsL6AAtx5j2MGmEKQOOjIJdGjalsBaGE924AYBNamJ7JxPkgtw8oO3PBS74ZbA+IbAG5WHXVCYc108DEs2rFBLRaGFZJzk+AmwFTY/PTdhTB+vZ+nx1HZl+MYT123nJHNC3bf2mG1/VKPY6Q06QIA4Guy+9iXr0T1mI6OxnDS28DuFCdzAsmrRvQnYdQyhMPA7gAqlfeDrh6b7P3Kb8B2NGRwWEbIC50mBDD3KCfu7lBpI4z8vGy0Ps3HQ1ZwDXZTlnANdkes/3uVgVc/+07aYbrrhvCcBH54IQUSjTjBIAry3DpOJUS4Y9+9COnGPPChQvt/vvvd2rlSiBEoYQq5CwyZWRkxEkTeuyxx2b78P5AbH9agCtjATlGkpFU+IqoyYm1BzLLzIXnLOCaC3thJsaggMEBG7MWRAyGrK6g2GqD5RYZFvvhgh2So4sDjtN4Cqj1vKvTzqTSDFdbKs96KRSVcEWt3tVkddFO87X5rOZ8t5VGu63MT0Hk0BEr7O0h16vIYqXlNoSDnjiCsxYJWLCkxhKBIouGkjbW3u+wNx4Kr6Z6ms2duGAR5OLDlQVI0VNrCiCWBBx1N1ZZd8N8azwzaqtCueatW2lWs8pclcvNFaCIopAE7muKkLkQvyGFggl0pdslh1C/MznwfnLJBCoVYqecNNWa8vHezaN10Gf7Wrx2nqLHAkKLybtZXx+3CkL6oog/qK6wxASUM+UBjKGIb30IYkhso5QcKzFBGQx4+Z6KsGwvQCWOoEQhjEc+jJkKJcsZnQy4OB+wrR0wXO8cRqwClkbhgbeviNlqcrOaOn129KzHDr2TsAM74jbYlXBqfX3hP8attMRre/b5HYAjs+Tj4G+9L2KNTqifikumQZdu9sgWQUDNlSqGKebpNhWM9pKPdBZbHCYkUuyUxCAWIAqxeB77j+/F2mA2WwioqQdE5GTM7swp8ye9DzRvzQsMznT0mjA8f4LQRbe1kBeW64lYEZL2gzHCTQGC86jtFnDAaKaf939W36db3PYcddRGx9LqgWGA4UcROllHLTQnByw9nPfvTEvweximZMGUARfrtVPc+vnX/dRrU4Hs9B7euC5m98BCFnLT4VrHyfUN7CYs5dhJCqaEvBL+29fZC+PVjwImjGFJAcWSpTx2fePIAq4r7ZQFXFfaZDY/4Sd7SzJcAlwjtdFxhms8hyt9UZm+OTkHZADXmSzgumhP+RIKE/zFL35hv/3tbx2RDNUE03VCvrxAl0o8feYzn7HHH3/cqRl2ceXsi7/YAtMCXBLIKC8vt0cffdQZgGoWiO26/C5qxlFUWKF23sQcr7945H/BilnA9RcYbc6uopC0CAwrQhc4zPWlABEbxfnGSc3Lt27YnjFARNATsxbPkL3j6rXm5KBdSMURi5d0edL8qYjFBgJW3DJka3uPWJDPguhlrB89bI1tJy0fViThL7ZwsMASo7ADPYS2wThEvJUWC9RYEibMA4sSKC+z1MBZS556y4aKYtazssqRTQ8QzhUYwQEHgHWvqrClAMKlkWpz5VWBekA89Xeaq3q1uTyXvHyFyCl0TixWJk/J2QWcIAU2BMbiFAxOsypy/qXIhxIdan763RHJZoMAKKnwtZN709kPKwEwmg/71kOB27MXxFIAfhA/yKX+1hvkMu1DDENgYtXihFOHqb4E4Oo4q5d2fhRbNgFYmoa9lg9QW1WMeAKsx7Wvi8pzctuRU7BCOO7F4MrVS7ENQOjNE35ra3XZq3+M2ZF9CFmMJq0sP2H/+Usxq0QV8gyKgmJS1LdCDe++a8w2E4oIhnHmKLCp+lKar+Y/6XzDSgOAqHf2wJKdddvCOra7lvkAvIawiUAofrYVYQOlhDW1uezdMzBJ5KAtR+pdsvB5gMmJTeBODKIHO3sAq92EVe4lJPFCJ4WSC6JWAnN3rNdnDSUxq6bO1u5BsSdeu78WJgjFQzlC19VYTss2nXPb718MOMqAyicbpsj0I1uihKMKcDG2ycN7767pT4ArGdexHQSAqgD0uM10QL1HE0OqPK6T5z2EOLqtguNiWQO1xGAoxQreSs357cAE91Gzq6eVmykpiloX5QG6kI/HJrpGvVfTdLMM12QLZQHXZHvM9judO27FkEIBrtG6GIBrS7oO15ROcO9v9bQs/D5EM45mGa5xc+l8p+tme3s79TMPOQ/V5ZLeglguKY2vWbPG1q5de9X8rve3enaJq1lgWoBr69attmDBAvvKV77i9P3Nb37TQcaTHCC+0c4V67Vx40b7xje+ka3DdbU9kf1s6hbAR4og5y5nOA92oSwwhKiB11r8RbYr4rcBQMQSf9zWBGGB3M3WnDhk+whHvICKnofnkVShnRq8zQwVv7s6d9n8WAeFjN3WOHrOVnd3WX6Yu22EFbpyiGfLm8ddfp8lB6j1UzDfkgs2WqSJoslnzpuntATRBfJ2jr5uIQQ7ulZXWhhVNB+qiDmDYQuVBC22PGCLwyGbn1xGMeBG6jyFLFVca67FW6BxKgEXeP/8lduXSiocUeIWIEldRcd9QYEgsVph7kDpLpRU+3LQ6hbgmujVuwAVQzBRrxyh8DH1phKwMT5C3ZIwOmBKB5RVU4y4B7nvPxMydrYZrx5AVVObsif/esw+AaNUoryszHYZQgsO94swgS2jYl0QjKiI2WYeOWJwxpe7fAeKBYJYQCFQxYylEhi3vRRKfu0o+wYg+MaLSdu/k3DMwSSCHnH7+/+atAry4/buo3YYJhCrIoD00ANRW7cG1g9zMAOH2dI5Bgianra8YT00Dp5PoZj4hz9TJw31w/kUMv74X8XtNolwZMbJMlp/FGC6fa/Xjp13O4BrHnXKHgHYLZZ0u/pjAFFoLF2EdA5TAWXRpjsZ/86jAYcxlJ++AqVGiFCbj9BTAkD2YluAfeeyjy+M2EKERa6TRKFvGjt5CID1+m6k7AGrSlFbUBO3+zcjKU+dsqkCHU05xrEiEQkBLc84WNX4BArVMuDVeXPZH+2DEDcZlG+XA9iTBP6czd26bOxXvGWfqvzDUN8QIYZ9NkbyXkE+ZR9KC3EyQPPv0XQ4ZAHXZANlAddke8z2O52zblXANQzg2vzJey0nPzh+80O/uOk3XSVU+PggOVxnAFyfzYYUXmFU3VAUo6XwQd3QlT8hiXgxXlMLib+i6+wHl1lgWoDr5ZdfJhY+z4n9VL9Hjhxx8lAu24bzVg5OKZLaK1asSDuIV1voBn+WZbhusIFnoXs5lHHytiLEjwWSQ0ivI3MdLLZ33bmEycFmeBL2UD4had69Nhh/2w6Su3XCy4mFql0RC9r52AI7MXKbVXZ129bOHXZb5IzNT/RYRYywvXiVpXzkWgURvVASU2SIEEYc/sWbzHXbfTZ24ryNvrrDEv2DTn0mT7yb+IUmWC5ywBC+SMRh3dAcDzf4rX9xieXA1hRGqqy4aIVVU8cr6M+x1JJt5i5dBE5IAy7oHrzC44gu9FK7eTHbX8B3AKIJLSUgQAy2ZNOV66XHxKYLrxiuP+71U38KNg8wlURy3YvKXCG1lEYBWt1OrS5yso55qH9FThOoQGIYjz8Ysb9/KmSrGvCwNSSa+muG3XruQsA6WFfM0Opg3O6rhJECpExIKUuvcPEvjBsn8zjAU7lN3V1x238MAYoLQfLocqy302unDiSttyVpK5GR/+LfJW3pIhd323zU80JsAjTTSHHhDRviVlaaHodAk0BOpul1H4xTV6/CJFMwZEnrx84vvuK108jjL4Kxun8bYKVGK2bW4pk5SZjimbf8dgrArbpb+az/IHWoiDazEeZZkJOgMsAIoaJhJ4zTQ5iqixtHrx4M2q5jsF30EYoA1lfHrH5FylqlmImtCyIwdrCAdflJyxMjNcWmOfXC0rUixU9UqlXNg02UpLyXDf4FTXbUfsgYTseN6oSpxeMxJwdOKoYXQ8E1AO30THNeah68mPp0Mr3MiWcNX2IqYcRpetq7bbQvLR1fSgXr/ALZIA2PZbOJTSbIAq6JFtEhQuguccY+TgCo79MmHDOTF82+uwkW0E/2VgVco3VxGK77HMCVOU/NlMnSgGsvDNeRDy3DlTmfXU6GzJSNs/1cnwWmBbgym9DFXLLwXchiK2xQlOTEJtSs71RMTdWsJ6qhTFzuRr/OAq4bbeHZ6F8AQKUHIpYAhKBDYecAICfJc4rCjiwkx+ahPCTR3Ye5S7/dznnO2WEYru6UCiN7yPAK2BFrtOio37a1v2GNw6esBuGMMvpKIrCRzC2ngG2N+anB5OIuUCzos1j9Uhur2mx9sXJLnOq0kuZOI4jMfNWoHSI3Hzv5rIV7TtmY32WDVQV2dGmjHSpeg9x8juWApxKuelsf6rYHvdQSa9xqVrEGV0XOCo/4GUuNvcxCragu3E6O1xYoCBi2CZ6ultVvKkz4riOwITGEy0wfwVE/Aru1HwEKrVqF89/X47b2bsLDEGXoJFzyIOGEJ6idFYGJIh6TsEmXbVsbsX/60qjdtU4UU7pXXciHCLnb00f4ISCprRlw0kVxYoofr1uRsMYVKPIRipkZonxVsY4ao/LSUihHtrUnbOeuuJ1rIeQx6rWahlyrrQvasaOwZi30B8hZgULgts2oHiLIMdDPLNmuGJXzhEGeIy+tELZozcqEVVenFfs0ujbCJN/a7XGk3qW2/xHA2ZJFCetDtr2v18jVEQgTM3OZgdj7RNnZgVN+ewc7iO2qor6UD2GNPgBoGFbH51Wu15itWxC2ijKF4iFfD0vUTJjd6wcVKuk2ItLs3vUxa0X0460ooioccx8Phm25T+qFl29zau+FiWRLhRWm98TU1r/W0kmEJFSvSiItAlw6nsR86c6mmC/XeLjmtdb/IHwuByTKzZAByjkIfMVgMQvyc6izFuTGA8B5HHhl5ir7ZwFXxhrp5yzgmmyP2X53KwOuIRiuTYQUBi8yXDNnTakUHnp9j537w7EPNcN14sQJR3lQ6uKXAy+9z4CyiZbXZ1u2bLEHHnhg4sfZ13+hBWYEcClvS2zXM888YworrK2tvbjztCMHBwft2WefNWn/f/vb38ZxkgN581sWcN18m9+MLcoplVOvArtioPqJf2pFJj2vCCly8pQafOSvWB+hdG9Yt71GWGHYzqTKcC7J7aFq1+GRlTbSU2Afie602sAFq7U2q4p2WcybY335FZbwlVhZPN/yo4UWwjEdAlx0+8usw7cEpmC+LQuV2vJUifkjoKnz+yx54k821rPfOuf5bPey9fb2vI3W4yq3gtSw5blHCHXkNWp3j4Vb7O7aKgvW3Q2yKHcc3xSqiRZ+3VLxVsDWenKPNgK4qO2VQTO8kvMXx2m+NuBSsB3sCyBpACZLLR9BjFHEP3pgf1oBCvtRwHvrMAIV+5FkhyFyQdckCF97fGvY/uGLIVsD+MkALiGfFHez+2F9XtrptddeAtVSc4troy1EFv3hhwC0DdoiyznsmxgU1bOSoh+sEA7s7neitmNHul5WOJqy+fN9tnBJPuIZQSPaDXBDlzxv3RQzCTMoV2kIUPgCBYdfeNFv7QArsTIrkbd/6AGEQNYlrBRp9r0An1d3+hw2UeGLq2HE7kdgQjLqHAKOKALDv6I54Yh8OgqwUp6bBDDOdnntsBhB6D6Fz4XCLIVRHlqftHWLxhkm7CDxkY4+mDVqnBWh4FdJ3tbhlMd2hAnFAHB9NDdqCwBrV9nsFeN43w/UiUw7k228P4Eu/W7Uv26aiTXVazGmCp+8/KI8k0OY7b5kAplWNwXinC9GUXIc7B6wFJRiTo7XikvyUMBUCGl6L+pvFnBhhAktC7gmGGMOvNR57lZluIZhuDbBcOWi5nQ1x3865vW6JQu/x87+4d0PNeDavn27ozh46tQpx5yZ87vO/bK5IhwuB176/Mtf/rJ9/etfn84uyK47boFpAS7tDO0gScP/7ne/sx//+Mf2y1/+0km4m2hhIeqf/OQn9sorr9ivfvWrK2TjJy57I19nAdeNtO7s9+24RhyPYeidMMkvFcUJa5hH7oliv+RcAre6AV2740ftLHJwud5RCxG69fbRzdbdU2WN1UesdsF5xCA6bGHqLOAoZCFfnvX7Sy0Ph7qCsDh3Mh+skWdt9HnBXWGdrkYr9Cy1O0YLbdmJQ1Z24TiOfid5IgetIxmyF5Zvtd01t1sZdcAWBM6DfMghcVMIeTDPbsNpfxDWpH7+nWYlK3DtdJcJpzfZxSMEUCEpyKOiVc7MLhpY794bcF1cVD3Kh053AZOFew1YIHdpT8DeJMfrAGF3TUiwj8Ju1dUm7Ct/M2Z/+0DMSqh/pRW1rvzw3r6U7dzpsv/7mwDlH/w4pdSiqjZbg5DDE4+jPrg6AXOdtITDmvAMaGkj56sLRq2iAhDcF7a33iRvbkzMCnLvt/lsyfJcxCeAwoNijpgyg3vg7ojdiQy8ANj+/V77yc+Dduio2CpY8g5J0AO6YNUefNDssccARRGPvUJY4PCIB5GJlN25Lmrb7o5TmHd88Jr7+zTNUblSf0Qd8SSy9/nUnQp4xZrGsVXKNjem7D7mV8icM02vBC51/pOjMwgb2IFQS4AkuQoY1KA+zyx8CzxrLgIfkt3Xsxiv65eRvwUmeK0haiexM6X0OdQ7bH0dPU5uVy5M9rzyApjbHGf/apks4JpsxCzgmmyP2X6n89CtCrgGYbg23rCQQi8M114YrqP2uQ9hDlcGwJ45c4Zr+E4n0kzHagZwPf3000SE9NkTTzxB9Ej1FYB3w4YNtmnTptk+vD8Q258W4BKzdf78eSdc8NVXX3UYrm9961vOTlNyv3aoniU/KaAlBP2zn/0sq1L4gTh05u4kwFYwHoR0IR5fmht3igAHSTJwwWidG+22V4ePW2fwDNLm3UhF++10V52NIiWfXzFEflW/VVG3q9LaCRMMWZENsR7OtyvH8lNjVpKMkVdTasdcDbbbvQoubCFOdp6taB+0TYeP2m2IYRQRvtc/0kTOUqvtKV9lB5atsZqCdivJ67fenBLk7OmLcKbqoZR9JFJqjRXkhJUgEe/Am3EP8OLrK+0spicaI+SROYqNkHqe1rpa00VYTTZxmhgaihBLme8NJNsPnvVYCwxPQV7CHrg9Zn9FSF5dCUIPWo9HN7lRew54bec7KdvxqsuOAtJGUCqUuIOI6g23J23LvQkYqwSALWxLFyO4UUb+VLPf3nzTY51gR1I3bdUqWIRRmMUzMZgDree32nq/HWnyO6GNEcIcF8OWbSY8r5pCvyqU/P+e8dtP/0/AjjWhPAhgCw0RNqoivgC7ujq3/ZtPuu3xJ9w2MOqzM+c8TpHkDYhrLKhTXokAEXMAZGZs4JhUH43bVp/LLjpPRQCBz+322/E2L+IerO8R88H8metm8su2oOpYMEFIxLHlhD8yb9rEgDDnFcfbhO/n+kuNVbbQxVkqmMrzcnNTIkAuoluxkc53etL8ZmZmstfM9DR962osCk0eGyG3q7XTRgeGKfbsg+mC2aaunsIM04BLN3B0J3j627zVe8gCrrm1B3VMTgRcDYtQxOU8neB4/ea3vmcVDSvtv3zta3Nq0FLllUrhUH3SNn1ia1o0wzl/zswwdS5WDteh18RwHYHhetI2b948M53fQr3IDxeTpTDyDADLDF+id/LRpToujYXLm0LNZysN6PKx3OrvpwW4zp07Zz/4wQ+cUEEh5EwO10TZd+1cKaopr+upp56yJ598ctaUT7IM161+uE5t/AImKbzmXK/EHZJIk7vsHGzKc61hO5vXZHnlxxHbGLEwjlYKUJAiRM5DueR5VOqqJI8r1xWyqlQH4YhjBCSWAKwiVp3qss5Ejf0p8Yi9mbwbOFZCGFnYVnU225Yje61hbJhiwIwz1mMtvSHbUbTaDjSusfqyc1Yc6LfBVLG5cOSL49S5IGRts3uNLSveiBKiVpLbpyZvLvPa+eDiH+VERchXE5ZQ2Jd3PAzg4gITXkQALYOE6am3QiTcAxNEF8ZgdNp6PU4eEhoPVobkt+pTURP2oqqe1A6PN3nt17/3E9bnsuZjBGASVig2Kkr4npQ2li4DNN5F3hX2zc2JA77GbM1aQgjFoL3phnXikkf/99yTsFWrGTtJ9l7YwZISwvZyqBGFcIUELxIsV6Z9RCig1CUPdXvtN78N2PbfU1vsLOeQMS4UgFnodHORfMW0bdkyt/2nL3nto4/6YQMJl4St7EfcQxOuq0hYLQqFEuJTPps8ZOpfkldFPpfwA5+JEYPIAVhQqYljYO9JQ9nRb2MxHwWNcb6pp4WvbQ9Td2oFIS++dNfX2DMTDP8BeClHaAxbu2F4RwmV5FCzwoK0rL6Op+k3Hd96qLeZ6XH6Y2JEul4hqzlCyYfe9l6H8ZN8fCnJehyWMGEpCwJAM3eHZ2Kbt2ofWcA1t/bcRcDV3GEBnOT6ugrOeTjaRFH847e/b9WLV89ZwDXATbI7xxmu8ZiMGTOuF7Xfw6/tsxYA1+c+pIDrvYz5hS98gTzqFvvOd75DpMrq91o0+900LTAtwCWhjJdeeglFsdN28OBB27Vrl33qU5+ysjLqEul26XgT2Fq0aJHdcccdhBdVZD6+6c9ZwHXTTT6rG5QbFyM/Y4zknjwfzE05anx44C82e60pftoKF75pw7ndNsyd/JgfVUG8eN2/z6ee1/xUq1W5YKUosKwWRWRDn/vQK38jfrc9F/+YtaVqLJTMA1B4rG6kwx5s3mGb2961xZ5hQNuoNeWU2dPVW2xf6TJbUthkDcFm7jIBkgAXBWi017rn2aqc9VYdqAfckTPCP7EHaQZBo9el59LvSBdUJ5SQZCcVOZYkvJa6onFXU7lJh84jC3+BAskgnlW1CVvNRa1gQlicQBvRY84WfITQOayWBDTGm7bX1eu2l3d4YJsStneX1wb70wIdcUCPnM6VqPPdR42okjLlarpt0x0IbmwOcwL322s7/NbR6XYUBu+/n1DAlQI44/1nzg+8FdiR0+1m3DFC896gptXzZ332+g6fHXvJYyOnAURS+qAodAoqzMWduhTjFGh7Aobri1/0WaDQby/BUO2jPlYfuV/lJXFbD6tWipR6L2qRg4w1QIRmeWmSfYsT0gxrRh2tinLA1KIQuQNxOzWUY7vOBZkjAih414XU0FqFzVbCvBXmKs9JDvm4iAVjnbBrMib7QDxnjrOOjpAderfAzl1ACRMBmI0bEsjri2FkmpcOyynPWUdACLDbNuSxIlhDFdx2jr0p93SDVmCACY5v1ezqA3QluGGoQskBRDW8fg83LgiBvUGbvpW6zQKuubW39Lt1GC4Al4ubYQqL5dLmJLJ+71/+ly1bv9G+9tWvzqlBX2S46lKEFI4zXJlrwwyN1GG4nJDCQwCuz3woGa73MqUAlyLVvvvd72YB13sZaga+mxbgEkWpsELVqWlqanKKpz3yyCNX5GgplFCsl6hJAbHZujuYBVwzcMTcYl3oeBM4EDMUIP8nj7pc7aNeO8LNgnjZDovWH7eBHFTpoLdihB4IcPkoippPeeQyVzfAqcfyCC0MpsJWjOhFT7zcfhN/3HYlYaVw3IdDRRTnLbMcgMCycLPdEzpkH6Xe1wpk04erNtgLhQvszyGK2CbG7LbgaavydVNwmaLD7qBVBaqQrS9FINBnRTBf890LKOKcZ3GYtSRiHp4UDh78WpoBwMPliqrfXBjmwesFZPCbUtOFdmLT+1OAiZeOBKwbp1a+cVkhIYMro3ZbpYRCMiuoT63Jn2s40KStkb8F8/NK1H79a6NGVpCaHX6nePKiBlT8FlPrqsaFWqILp9zs/m0RW9U4Sg5d0lrbkX5H2XAe7OHChhQhfxNHmdn2+GcO0CPcEUfhxU6fPdvssz37vHbmNY8NHSMspgt2a3TYEbFQfSyFChaXeAB7LvsPTwHKKAPwwjsBO9XqIZyT/Qk4KoLhWoiCYhIw2ANbFYelKUFePTDK4xzqhcPIzftj9vC9UatkfG8OBq1pCDEPFAbXFsYdZcfSPJZFQEOqjkfb6BvGcD5pdY1VFM5FpfGSLSfO7QPwGkC5/4DZ8y+g5gk1GIu6rYFSAY8+HEOxkd/TNKao4/MCgiPbmwO2HMn7dVUIVcA+zqnGGCWmESYMVnldowMUOQdolZQVUusv59LNCef3M6dGftMGkwVcN83U17Uh/a4ygMvLb1bCL05uLH/++/f/hy1atWHOAq5+QgrvIKTQUSmc1tnlSlNJpfAwohkXnjtsf5dluK4wUBZwXWGSG/bBtABXZlROGAZ3AQW+pECYqeeSYbkEsDJAK/OcWfdmPmcB18209tzalhxE5XUpMSdM8dP20QTKgR02WHrURsvPARj6LMB3cZffQp5cCwNoJJqxNAUzlTgD6Bolp2vMTqWW2M/j/84OJVebj3jo/q5y6j6VWgEArdA9ZLX5XXZf4TG7PR+lvNwGG3AH7GB42E6j6ucm/6iGPhdy46GMO+VBlOxyPTBGClUDaJTHSwB6UYu4OwlsFOAiaT9eZwFUDV2pdN6UgGMc0CX5bie2WjcyFBc3wfHThffdVhT+DhEeh1Kh5i7BtYfXRGG64vSlfTNhBb29VnMWTllXZ9Le2Z203XsJ3etyWfm8pN2+wWXBXL+1tiKVT4jiEpQN165FsKIo4YQ9xiUywvz8JFQpp+piY9Nii6RKqGHkElqIL+ucI8S6tYy57Z0Or730ps92oFJ47oTHxggDTfUNQjXCdAFHNf+CfJ8tW27214/D4hH2tbspYG19bguhIjgIixgBj1YgeOGvEqMiMRUXTBb2Y6wxuqpEka4EYHj3RxK2rDEJ2PJaFwWeN82L2oPVcZuHRLysJJD18jG/NaFiCDHjyL3fvThmt9cTropdP3iN2w5QTrsovvzcnxT6SbFtwgqrK1Gx/NcRq6LW2bQaRlWo69Ee+iwg/w+5f/D6HG0cN2Nh62zpQFRjiFpobpjVPG4q5nOdm7ODvim2zAKum2Lm696IzvsZwJXDNaZ+ISVN+B0nUIv91j9936oWrZq7IYX1CB59YhuAi1CCGQdcEs3YYy3PZRmuqx1M2ZDCq1nlxnw2Y4BLQEry7wI1+/fvv1i1Wp9n5CYVVqiQQ1Wxno2WBVyzYfW5s02F5yUBAQoXkpJhXyhmfYT2jSFoUZxEEr7rDRiUUTtXXWMddbWAogFbGX7XKpPt5vaPARoidiq5zH6afNJ2JdZbIuyheC+1uKjhVWr9TqhbflHIVlRfsGWESZUTPufzRc2b8lHzKc26lKcKrYFQwoR12lgcCXgUD0MwXAl31IoAU7mpEQv6xiwfDxRoYP7EPCtJrLQAsvPjSMmpoaQ6VwJfDnMsammC7ydHqHvYZTuO++1oq0IKqXE1P273IKleiUz+1K9n2I3VINaojxWz0CgMXZ4HWXbQBndSB4cc8okbLkSv4ISWlwuUhMjziloBlZZz88YlxulDTRLsx6h99S5AStLkK5clrXEJyn65Ghs5NIQ57keZcPsrbtuJsMfpPp8NEdkZaaXPXsIKAcYSdPAQHjp/vtdWr+K50WdnhxBAQWVwBGAVAsDFKQvgXYjDwZxjMGIS4ggAHjwAMYEyG0tZPezVvWtiNr86aeXkd1XAgjYWxqwBIKBcL9n1NPlkzwJew6wjef0u2K5llXF7aEXUysfVHJ2JfYD+yHlr7XDbn5932ZmzspvXPrIpaRvvjFk+rN/Uj6HJxhFHphxDH/tJuIX/c7qFyesaQDa+t62bnyGFrcuLrIg4U+90i63N6Vm/9+CygOu97XOzv70IuE53WA5xvw2LKh2GS6Hy//CP37PKhXNXNKOPkMI7PrltxkUztA8UUnj49d3WCuD6sDJc8sOHqSPa0dHhaCpMjDJTqab29nb7KuGmqpOrlvle6ykNqLKSu23ZNm0L/H8AAAD//1OfLiQAAEAASURBVOy9B5xdZ3Xu/ZxepvfRSKPerW7Zlns3xmAMXByMkxB+kEJubhoQ8su9kOSXm3svueEmwHfzJR8kwYRAaEmAIGxsbMmyiiXZliWrd81oep8zM6ef8/3XPjP2aKwy0owsjXRee3TaLu9e+917r+d91nqWK0vTJLT29nZ94xvf0L//+7/LNtnW1qaSkhIFAgE1NDQoGAzqiSee0Gc/+1mVlpZOwh4vfhMvv/yy/u5rT+nRJ39VC5YsVzqVvPiN5NeY8hbIiv8yGdnpT2ZdSruTKhjYp8qGn8idOKTmuTXqnrVYJe5Tqk68LL+iktcltyulLk3Xes9DWp9Yp4a+GvX3F8sXS6soMSiP0qqu7ta8ac0q8blU4Emr2Duk6dkyVaSL1OVKq99TrEy2UtFMl/zJU/JFMxpMBdQWK1BXX6GScbemhWK6fVq3lpZ3qMDlUXl2uQqz9djdJZfLpWQqpdhQVB63W6FQSC5e+WlUyyrNVd3R79bpHn7jx+mlGVUVZ+T18PESrni7ppOJBNe2bcMjt9dLX9zsO6vePpdeecWnAwfcisawk9tsG1csGtdMun3HnV7NmeMVqzjtxCm3nt/kV1unW5wGVVemdf9dSc2bnWFdqaFRev75rE43cVB80THgVmu3W4l++jCQUF9vRqdZJpt1a8Z0N9v1aNlyl+qXuXSoP6Gm2KCG3C61xsvU6QkrWiilA7Zr+ubPyhtiv+wnmXKp3JvVLTNTKirO6obCpNYV8Z7f/BzXtGBGFaGM2rDhj14PqDvqVtgvdQ9Kq2akdN/ihMoLMOYl2DNniav73xTnpqkpq4ZTae7jWc2f5+Xebed3svpthjtj4E7Whi/LdlLJlPq6+tTb0ask118o5Fd5RZHzymXJMLB/rtHBcBaLulxZDcTT8nH9+Z0xMXXO5VkOZ8p/ZWMwFkvq2LFWBQM+zZlbyz1aynC//vyf/m/VzLlBn/7Up66q40zEE/rzL/wP9fCcuOnx+xUqDHMdceOZxOZx+fTGizvVuP51/doHP6p169ZN4tanzqY2b96sv/u7v3P88ZFemz9x5MgRxeNxntFzFA6HR3568/XJJ5/UJz/5yTc/599cugVckwG4ksmkXnrpJf3xH/+xbrnlFi1ZskRPPfWU7rrrLt17773aunWrNmzYoPe///36rd/6Lfl8vkvv8QTWzAOuCRjvGl3VHkiOI54ckn/wFOBrrwbLe5UKFsrrPqxQeqf85nm6gvIk8dr7p+lAdIH+ObNGz0cXK9YfUDg9pIqCTpXXdqu6plulhQNyZ30KuTIq8cQ1WxUqTZbrWDatFrcP+FamQbcXh79fwUhCAwNFOhovUk/G0FBaIZyYNUVNeu/0/Vob8mlG5kYVAPRyzQVIySgeiwMaszjCfnkMyTjHMbwILw6oxPlLA0rsRw8v7ktEBul0RgluyAa6/H72xwPcbtTOPnG6du/2auOLXg0BSPBD9fouqbGFxybAq7Q4qUffndITjwOIZgDQWP71N7x67kU/23M5/cxwHO+6N6WVN6TYtrR9h/TiJnPssyoucnGsWbV3ulQ3zaVCwNPefdL+/S4NDYGDWd7rdWv6HLcWPtSvvtpmNQ0MKMI2T3VU6XDjTEUUUjaOGeIsXEAfyul6CBtxWsFlqgBsFRdJNQCshcGUZgcyCrBoOa/rKlKq5/X1Bg9/PvWznarCrO6Yl9CCmpT8njGGd2xs39kfzjcvOR/c3PGxy7LIVd4wowYjMQdAFxYXYGsD2ld5py9b9xjTGCQ6ENVQZEhD/QNKJ5I8z9wqLg6pIBRgAmLkfF/7RsoDrss20C5pw3ZdjgCuUNCn2XOmDuDqBnCtffw+BQu5V0/ypIXXANemnWpav/u6Blzbtm3T1772NZ0+ffqM8eU8y/nmbHa37z7ykY/o4x//+Bnr5D9cmgUmBXD19/fru9/9rgOy/vZv/1bpdFqf+9zn9Nhjj+kTn/iEc4K//e1v6+DBg/riF7+oqqqqS+vtBNfKA64JGvAaXx0oo5T3tBKh3cp4G3GeTiuY7FcolsHJBEnAVHnaV+nkwUI9nSrR5rpFaskWKcV4r6jqUvWMDhUX9ivgiikAmCiFESt0JVWTLZAvXaImeQFaYcVdpUoAmrriKXVGStUbKVGXN6VMOC6f4vIzSznD26IHK1/Ru4NlWpq9U2FVYv3hmT+cYLsRJmCcbLLDWGSf/2yTGDbbfomOn60KYLDtp1JpZyteJkrM4R7ZpD3gzSHftMmnl7d7memX2tqzemGDSw0tXvkLWA/QtXxpSp/+L2ndcasAVBk1NLn17Aa/TgJiMmlpzqy0Hrg7qdkzsDMO644dLm3cCBhiVyXFUhRg1R/J6s47s1q8xKXNm13atcsLQw07mcrKw35PF3mVWtYsT2UzTKFLiYKkEjjCR44vUEcr95sWj7JddDjNDsDN7mLAXgFHCGByebIKV0iF1YCvwozuKk1oDsxWNOPSzFBaD9YkVARQbO3zaDCRVVk4q5qirII+x0hmqDGN721X6Zzt3YbqsGXu05hFr/aPnOQYyDYejSlcVCSvD8B1tff5svXPzitHz2WYYRIixqRHb2evIt19TLBkVMTsfElpGAAGWr8OWh5wXV0neSoDrp56lwO4HIbLANdk3WTYlNfldQDX9cxwmb9gfnpTU5PDZo135Np6tbW1qqurG+8q+eXOY4FJAVw9PT365je/qfXr1+tb3/qWc1L/23/7b7r//vv1+7//+84Jtt++8pWv6Mtf/rJWrlx5ni5dvp/ygOvy2fba2DJOOGAp7W1V2n9Q8h2RG2DkS/gBAl24zLVy99+uphMBbYKZ2j8jrJ6SrCL+pNxlg/KHkgoqpqJMhHBEmBGc9EINyedKYJ4C9WanKZCpUzDeq1Bkj/qGYnolsVaHskvUG04o7UsqnIkrnBhSabBXayve0COEwN2cXadyzWIb9hTiCeK8cwGECC2MxfABXQqEYOAmL9aL3QDoCPewfRiDZqx0DjjQh9EPQ97v3uPR8y/4NTRIGF9bVtsATO19bsL0UvTUHNGMPvjepO6/O6PZs2GpYJkMdB07gWMKqJk7J61Z9TB7wdyDtplQwmefc+n4CcAUgAhcqcIS6Z67pSWLs9r3hksvbfbCUHHchLqdTnm0E6iaLG9SUdFpDflh9GZDRfkyOnpkvtpPT1f2sE/Z43Snmw73A7KCHKLN+9hzBKzqBXCVLofpqs7qtrKE1hJaGAPQDRF2+si0pFaXpeXhfBrAdPN6PrYqwzlqAuAdOuFVLJnVAgDl7OmwkecEaPTham2MrehgDnAVlhQ5Y2H06b9au315+4UFuD6sWXhvf09EHY1tSnG9lJYWqLKy2GFdL28frvzW84Dryp+D0T2YyoCrE4brRofhspDC3LU1+tgu/T0h5DBcewkpbIbh+vXrNKTQgJO1ETbr0u2ZX3MiFpgUwDVACM/3v/99/fM//7P+8R//0XEC//RP/5Twn0J96UtfcmbiLb/rO9/5jr761a9qxYoVE+nzJa+bB1yXbLrraEVu94QCyt2njH8/TNdxblJ4/IQIupNL5Y4v12DUp1e6YtoYi6o5G1MMoOWePqCiyj4V+qNAq5hC3OBqYG9KowkVEGoX9PjVFa6GUanV3P49Ko28pnjar72JJfq59yYdCRcrYblkhDYWZABcxb1aV3pcD3l7tDJ7g6q0DH7MEpFGPYx4m4FdS8BCWZihhfvlGIhcqN4lnTS2aTdnY89s27Y9Y7bOeaM2/AKAeQ3Gaf8+L4BLOnoyo+PNOOpgnthAUuUlca1dkdL0OgNXHt12q0c1tTAnmNWASxDQRPfV3ct3rFME89TdldXBQ1J7u0stXW7Fsx4Fyb2yUD8PqCdi+Vxxlzr6pJMpr45inWhyQFXTG1S8pEfexUn1wCo2nJyh/j0lyrwMwjoNu9XLAUbpdCF/MGPQj4QYYq9a9ntjRtWzs7qxPKWF3rTaIm61ce7Wlab0vplxzQd0+dn/8LPr7OZlF509Lv18i18nmjw45OyiPKMHbzMGL+2EMJ59xav32yRhc3GAvY2FALm45xwLV+8hXL6eMYyM7err7ldXSwfjfchhuioqCslbtuvGxgv/XINtBHBZiHHgbaG1dsDcS+1eQkKpTVRwqV8Tze6ufo6ZwGqeFVfXIcXjSR0/3qoQOVxTK6TQDcOVy+GyCavJbDnAtUOn1+8ipPCXr9scrsm0aX5bl2aBSQFcNgv+4osv6i/+4i/06U9/WmvXrnXCC//+7//eyeOy2fHt27dr8eLFDsNVXk4SxRVoecB1BYw+5XbJzX74IZp19wOQmgFfA3hVxXKl6uTi1RyJvS1p/duxIR1KIZZRHlXh7C6VIHQRKowyowbY6CyVu7lYlf1plZjmBhtNkpBaXFGiVcHtmpvZRZiaRwOZSm0L36GXCufoGEArle5RobdPC4KNesj9hlaqVxWem1XsuZ9tQPOMhBXyzukm3U0Tl2ehf5Zf4obl8pCwZayUvR+Z2RrvaUgTPmjbsvUsfNABcOY1nqfZ43FgwKUugBGRZ+rpS+uNgy5t2w7j1QQQ9BD6SPhkIoYgyDQ34jke3XufG6YLh4WVjcHafYAcKQBbZFCaMS2jmwBopYh8GFP00g6f2hHNOHHSo+NH3SopyGjWjBQTN4hnRGDC+G3XCZ9ONLvlzwzCUkXk4rfBuSFFEbxI/syt9A7Q1QCsWJIcLou+rAAZkSMGhSkVu+WeB/C7CzZutgGurEoIVWznmAoIN5zjS2tleUIPzEmpDrbufNYwEZEDx71av9FPSCHnHYAWGcrq4dtTWnMDAgPGcpnBpkCz47R7e9ROKs3PfdxHzmAecDnmeOsfDGV5jkORqLpaOzXQ06tw0A/bVaiCwklmnt/a6xV/Z4ArQr6pxQETvWvI8m19sm+c24chk/NdOG9b82r/4uq8jhOJtBpPthMt4J1CgOt/qp2QwtWXgeGyIWeiGfte3KE2ANdvwHDdcp2KZlztV9T10L9JAVzmnJlKoYGquXPnOsDq2LFjTpihfWc5XSak8dGPflQ33XST4wheCePmAdeVsPpU3KfjJgx3HIoCdUFu2zgUqPMZmEoS0nbEpR8DuDp8/Sqe3quS+e3yFBLehwNvuUedzTXqOThLoR6Pqr39qvXHVegLyu0r0SzC3u6p2qYbitrkC85SX8nNaghU60A2qoZsEzzWHi3N7NHCTJuKEdoocC0lX+hRco0W0I+3OzXWUbsGTUUtpyQIawPYygEmZtkBYPhGTrPZ2bNvAecaoJWAzTDAZqyWAbYR59qcq5HmZCM5U7tvfWe/jXxKQ1f1wjz9/OdZPfdcRnv2pJ1QwxR28/tc+uVfduk3PunTTBQGba3GFnK6yANrafegeAYrlszothvTunVNUp1dANun/Xp9b0D793rVBEtVXpTRNOjDW+8AGJJ3dbgNlcSdATU2+5SBGXMP0BNAkwBuivYrcyKmTDeAK8vGs7CElu9WBrcWynmAnFb555DH9UhWodlSPUJNQXK1SmA6lxIWGQR8uZilf2xxSsvZr4UUnqsZ4GoAJK4nR60DsGfMXcCf0XvIUVs6n7BEO2THUsMO6Lk3da5dvGPf27k3cRYLWzWVWZs4c8bDNeU4T545jcUxcRkDXX3tvUZ9qay8UOXl5L7BiFzs5Mfk9ezybOlNhov7hG84T/HMPdlAQfzGuV6urUHDZc1lbBfv1XMBO9crE1snjrdNOcDVWe/WmscfQDRjskMKczlcezftUMtPXiOkMM9w2dDNtytjgUkBXNZ1A1UWhmQXvYU22cxoR0eHI5jhhCfheVhy/5o1a5zfr8Th5gHXlbD6VN9nzlGwf1M2sUBez0FkyjcfcmlnY1qpwKCq6lAnXNIob9EgTA5iDjggXZ3Vato3V7GWQoURzqgOJlSF0EI4TN6RG4W76g49vLBfa8tLkI4vUwq2axD2imwQpZPPKJjeJT9Sev5sGAGOgDy+O+Ty3kwvjJ45+0PeHDqbac/g6GXTKcfBM3DkdhP+YiEw/J3NYbZwxDTXq4ljGMgyAQ57HWngH7UCHloJlQsTmTezMqNC8q3O6UI5DlZWWzZn9H//34w2vUS/8EY94J1BGKl3PZTVH342qxXLYOFAIMcavXp6QxAmhUy3cEatiFusXBTTPesASqz3jR8U6jvfD+vEETegECENFAWra9OqnJ5R7VJpKODWnt0+nYb9SqBmmLU/lnN5jJls4cQRp5gtJPwHTXdE/uUB+KJ26KnCVoCIzJBP/gqX5v1CRnVLOQcNGfUcSasKG9YD3OI+QjXDbv3iuoTuX44QCoIajq81YqBRrzabPwghtPkVv7bv8WJz5I6XJ7V2WVKl9NvxS81wdgqHX8+1rVGbvTJvORgDEHFiQ4PhEGCYE3jOk35lung17jWZSKkHMY3ulk5lmMQoKUFMo6TACTG8Gvt7qX0aAVx5WfhLteDkrmf3nqmpUmgMlxuG6/5hwDV5QYUWsu4wXACuNgDXJwFceYZrcsddfmvjt8CkAa7RuxyZyXOcO37o6+tz6nNtRR7eVAqLiy0s651vecD1ztv8mtgjDzJIDp0iJO6VIbf2w9QcbSUl6AQTDQCVomJA16wWlU1vx59Pw4AVINRQq/bD0xTvoU4Wzn+AmlxlhWnNKEupPAyLhKDDogVJvW8+IWwkBlk5J/Nms+iXZ1MblUntBCCE5IF+MVZKvtsBXDeyDB78OQCXbcEcYtJIRDqJfITDVZF35EUt0cIE7Xo05soDjeQGCRhb50yUwGLYNWtAKyf7/ZZXbd06AmOz5aBPbd0wUITE3TgXALEA4GEiF06/nT2f+Q947fjxjP7PX6X0b/+egXkDbIA2wHR67yNu6sFIC+cn2X+KMEK/Nm4Pa99hcrXigDlyuO63EDzAzSA2/79fLdDXvx5QO1LzHm/GAX0lVVmVVvOQvhWQNoM8LliuPTs9gFzyptrYedz61kWfUJu0TrqgrSyWkGN2GdMIu+VHgCNTV6BkJCwPQHAJoh5BAF/DS0n1HULum/Acf4jQyko/QMyve++B5bonoZV1HPsw9jBpfsfow0efwY77CSncAgBsaCbnCXvdfRP2WkouH0Ctn7yw46gzdvW6CZnMai41wAxAOkDsTAte/k9mFtvL8D9n4z7teGKIZhgoDxWEnXHz1ui4/F2cknvAQDZ5EUM+vrOpXYO9gw7YqqwsUrjAQjLfmsyYksc33Ok84Lq6zt5UBlydMz268UNvMVx2V52MZvcqJ4cLwNX8k1dhuH4pn8M1GYbNb+OSLHBZANfYnnR1dTliGi+88IL+5V/+JV/4eKyB8p+vagvYg6w1QU7SAGp4gKdGiuVagd+hBpciYKwU4Wc+HyGDVX3yl8UAN34NdBYq1hFWqgd1PMsbwnENEVpWW57WHFTvwj5qOJXHdfvyhN5XQZFKB0hhBgvVy5xioy/z2ghUACB45qDZsQ5Hrc4WuICtXNp33K3ndvhVgnz5A7ckNL0qBTBMD7NYhEji3Xugm1y8WsK/PZQsXMxDwtHIJMnITkz04YU3AnoNEOEHPAxBFtWWZfTQ6qRmUbD4XN1xHv4s++P/SOsf/jGjgwdyLFdtrVsfIYfrIx92USQ6DbCkxheMwOlWl040BhQhHHD6NNQIFyKrT330VsIM//KvQ/rWP5PvhjS8EW8mshFGHbK4irj/O1yatpA8K0DQnu1u/fA7FJE+ZblaHAHFpXOAixlT2C0XkvQukk1cXvLxAh5518FuoTSZjkLbAbjKagDQJwCAr1J3KsKBw2baAXphw0qXBLT6Ua/WPUg+VxWqg4QblgH+qgBNBdTvsiXNnp0If/z0Jb9Ot2Mv+hlFoKMKe7379riKAXOWj3YEWyZsTLCNhYQZ3nljUpUWAnmeNva8nGfR8f/ELg1oWyjpSM6fs7IdzEjjvQGuGHlcBRQrs+VG/zyyWP71TAvkIm5zNbvamzs00B2hODCFtisKYLsKnbFy5hpT71MecF1d52wqA662mUyeGeAqmuTCx9zjcgzXTrUDuPIM19U1Zq+33rxjgOupp56SAa5vIRtfap7UFWh5husKGH2K7tIBOgZ2+M9CCd8g3O3FQdgtcnR6YY7wsZVGDCGOyMMgBXX7yNVK9uCcBwi/Kotyk4dV6iK0tsOvJAxOagBAAxtSUprR/DlJ1ZYmCVNLauGSqN5fl9UaNwWFcchy+wUtZDr5A82Ze+sGCbjRLUc243zNHGFz2w+eQqZ9pw8HH6bopoTqAAgG5DLUCjKmIgP4stwcy/fyEf4bhrkwR9p2NbqZQ5UAdLywx6+dxwxwEb4H0JyO6t7Da+KqdwDXmJVGbcAcgObWrDZuyJLfybrYcCFA6qEHYZOWWBFlumV2xC5pcqTiMIdWuwq9AfKeWBlwdeSoV1/6fwL6wQ+wMWkxXkBTMSCnCMA1k22tuier5cszWjEzqW0bvPqX7/rVAIPUz7kY6B9ie20cF5QfjahNiC5T2QM4WEjhzUjJ1xQqHQ+qFDBcDAju2kwh6sOALTMkfRFvRY208rl+PfCrPs2/E8l7Tk0JwLAWyf7lM1K6CbavjGLIRlw0kI/2w40UB4DRK+G7CGMmhTT8o3cnKJhLfbGXPc4xFgQB7ADSJNTpQ3fBmi0xQQ32xVhzGmPB7Jc2QIrymGMoZzTmfp6sf208xAkbdAC3gXDbsPNPbg9WNztJSKGNFavDFbDivrYAY8PFmLLeDveYb+3d8L+2iLMdeDPk+6/H5tiG85nk/PW0dfPXhc0ygK4SJ8zQSh7kLDY1rZMHXFfXeZvKgKu93ksO14M5wMU1M3JPmaiF7c6TA1zb1fqTVxDNyDNcZlObaBuZxLPolyjx/Jb6MxKdNtruYcS+CgoIO8m3CVsgD7gmbML8Bq45C9hdGsfI7RqEUYlrKBXSy5EibYp6dBiHf5DHQRgn0gole2G7BqBdWhrdip1E1a40oqK6fiW7/Yo2hZ1lMkmPYs0FSvf5FEblbv68uGbVwnAVpjRvcVTvq4prVQZpZZCGiV3k3FN75JhYh33C6x3+ljcXbIOAog7yraxvVaVW/2nMKmzaWA3Ls7ScL2O2LO/S8qnGNuvFiTYLKfSqodMtIqJ0MwBjFWGF4TEq9WPXtc8ZnPLevqw6KIichNUpg8kpQJ1wEKDqBryWl9E/A1fWhl9yHyg0TGjgxo0+bdnm1auveXTsGA4qIiJVFQAsVAgffX9GK9dmHHYIHlE/e9qrn7/g1alGDyI+tk8c3a5BHiQgPdu8G9GHILlsBbBb9CNbBODywTYEvJo5LaVwKqbTryTUdxoUaGDC0AZvkabU/JsCeuz3vCqcQ+7eQY9qARwzYPwK/Sm9F1A7hz51AMRON0tb9sHUxX3ko7mc8M6airQeuT0BePToVYQ/CmEeg4A1C69s6XDrjptTum0tJQEsRJMdDhK2erLBBDdcqquJw30OcH4I93QQmR3JJDYevBY2aA/fkQfwGVsH+CVgt5LUmAoXFcpvY9T+c84VABn70NPcEMVmTsM2Q1wr/f0cJ8dUBPC0yYTrsdkZNVulCE8d7B90QgyT1N8LhbyqrCp1rvm3IOvUslAecF1d58vG2VTM4frvX/ifap3p1coPAbgug2iGAa79m7arE8D1mwCufA5XbtwOUdB+w4YNPGM36uTJkw7osgm40c0A2BNPPKFf+ZVfGf11/v0lWuAdAVzd3d36p3/6JxTLnsszXJd4ovKrvXMWcLniqAkeVjqwS0lPrwaTddrWtlY/bZ6vw7BMsQC5PQCyMIqEhSRfxQEM7dRcSjUgZV7brQBhhZETxYq3IQmNQ+4G+MRaws5nC4dbRO7SzDpqdxUldcsNcT1aGtNsmKQg7MtosYq3EIi5beNtuWUN6Fg7p5/LYlkcYwNcCSt+RTNRGwNfY1sSpqeDQsbdaFBQWoiQQpQTcaTN8T5/YyfmBdAXc+qtgfO0a5dPu16HTQHIrFuX0lLYHfDeKL8zN8O5Y7tXL23xAUwQw4AxOkyOVwB7rllNYeK12HExYYWW/wQuMgn2vfvc2rLFi1APdc9w8vcddunFjRl1tqJmaCQftdSY7pSbyTo3UYRKWi4bYvu15HMVsINMQkOoIkZaAKNRW8F6zPLYZOWjIT38G7BsCbd2AP6q+LEMNREfNnwQwJXsjWvXayn19vLAgi2sQOqwappP5QDeNUsSWjovrT37PXphm5/cOpToGTeWazcwBON3V1I3kq9mDKLt1ApJv7jZR703j+5cN6AFc3oBqgXyOkayZSa5jZzG3Ck6Y+MGwmJQk3FAV6GFFPpzLFiS8Xr4tEenWt3k1KHmOCulSqoWeGB/uygu/fI2ZPpPmvS/OMdJ1Gux1fkJ2jP2e819sMuAoTEE6Oo43Q4Ag11mLJahYGjgy6774Utkyhx6HnBdXadqqgKuPwdwtc30E1L4kAIArtws1+TZNpfDtZ2Qwp0wXL+Yz+HCtJa7/c1vflNWuskUxisqKhD0YoLYBtGoZgDsl37pl/Txj3981Lf5t5dqgQkBriNHjiD9/HPn5I09UaM7ZEh6x44dstdvf/vbhFNYPaF3vuVDCt95m0/FPbp8pxQvWK/m0EG1E+oXp/bWsfYbCKtbrSaK6fZTWyeawQFOe1SG+EUQwNWDoESsMa7yyg7yuOLqPVmqWHsI4GZCDyTQtwQVbw05KnxLF8VxtNKqrkzpcRzt2wNR+Qnb8odQJQwGcn6+Gc5A05n3v3Ga0zzn8a9o+V05tguBDcQ0LMzQ8pFGN9uiOYT27Zh78ujFzvveQu66ccZ/st6vo8cIwQR8rb0xpXvvTaoMYPLWgRu7RL7TSz5tfdmHQ0qNYv4MSM2fl9Td9yQpomx1wt46SuublYvqQKHQ8qa6Cd/73o8CeuaniH00G+MCvgIsuQC/AdQirS8QWpThMjEIWB7CCdOomyDercwgtaf6AF3k5gG3HKBTszSgGSs8ACqPWk/DHvJ9OWGNMxDvAEqr4cigWppRaUUhsqSIYsl3+fXER/xavpSSXxwbp1WdjJGNAJGDxyzM0HK4pEXzUrrn1rhq2Y5zDwXAbd/u0zM/95HP5tZD90W0YkmvKisNcBml+A43OmnlBuKEnDhMaIBJAYDCvpNebQR49g15ANPScmqU3b0qgRJjVttgJDeRx+aCIeSWT5mQpN718Nhz/A4fx1WyO5sxtnDedDKt/u4+BDUiTGJQPw6W1ZQMTUl0qrQ84Lq6zpTdT6Ysw1Xv08rHc4Br8gIKc88HA1z7HIZrR57hGh6yjY2N+uQnP6menh6nXNPKlSud0h9j/Xi7X9XU1KiuzvLH822iFpgQ4Hr66af12c9+1on9PFdH7AQaSrY40Ztvvllf+9rX8iqF5zJW/vsrbAE4myzy6JmNagyu17ESgJI3rAFA1snB+eoYqJYHVb0UhbaaUjPU3D9TvoGwKlEgdHkiirYP4bAnFKxFZGAooL6WYqVjXmUisGKNKLwNErqGKt3sWQmVwwrcD+vx6MK0AjAlJzvTIvoNMY2sZlGUtzyQMjKGQrPkzIwBP5fDSKa2l0Rz3Zxrc/oMdJmq4KU1Ww8vfEwzh2CAY9ywwafXXsMuLHL3nWlCPAhPJMzujFUAHscAZT8HeDRT0NigTwjFwHsBW6tXpZ1QNVvBQJv95oAvduCoBrKfQ7Ar3/xBUJspQNzcRFFmZO1T5Ev5AQTFhP/F4y4cXgAXgh0ifywFeEul4xS3TsnLGEhih7RtO420O+yDH1lCF2xYIkYOln0PYCuq8Wg6csaDnQOEikWsI5wzBDTARfXLgtQbc+sDdwOmGEdmEbfXr96IX6dghnr7XSoC6M2pR96+AtEVmCGncdwdnR7tPcBIimVht4ZUO83KCQQAOhfnjNs+rQ1vOffhIv+lO876VlzbgIJt00tY4c92hrWb/Dpy3IUYH4Ayow/dFVN9TZZz5tdWmLwScu2sGLaN+Q+8PwFohOKZSGcusu9X8+Jmhjhhmga6epq6lIbxKi0ryNXsMgp0CrQ84Lq6TtJUBVzGcLXXB7Tq8XddtpDCfS++rPb12/MM1/CQ3bkTtu83fkPve9/7nFcDVWdG11xdY/ta6c2EANepU6eYzdx2QYbLjGVOnKHkW265xckXuRIGzDNcV8LqU2ifbhyd7hb17X9KBwp3qWdFjSOycCIxV6dSswgdDJDnE1d1vJM8rqB2966k1twCzQomdUPdISpkNepUQ7E6khXKwmokEoSFNZUoeoxcjd6wFpDLs3B6mqKUGS2mgO7DNyRVhHO+s9GrAx1exU17nsmJOsLbbqweUH1xUkFECgLGKljc3IgHfblMymyWMV3mCNr+/AHL6zJZ+gs1ljDPHG/aWdae/HzMOmGN9v1bzUBWW7sbO7kBMtKc2WlVkE9lq4xtdANpebeOHIE9BCDNnJnV4kUwSCU2gZMDZIcOAUxQkJw3F9suIsTTxCvYlqkFPrPJr+fJATsJoxQg/NM0R7oAwF6AWz+Ap5E+pGI5pcR0ikykIcIIkaV3IXji9UeRE0bZkbBDICg3MABikkyx1HBCnLFhQXLaquG60v1KwFR4vHaOYITCABFENm67261ff3dSt8HwWM5WElbDBEvA7E7ukx2/DTmz3ejDZwgozjFZDTITEClA/WQ8NbCchGf2b9tKACKHCAUNgtoD5Mqdzb5j7X2hz7kSApwU+rvtULG2oF5pMv0m3T8dtvYDdyQ0rZLzQq7az571qx2pfju+O+8kdPYWcuSwex5wvWVluzJMJbSPml2dTW1Kxig3UBhQVVUJ1x5jjetxMs7bW3uc3Hd5wDW59pzo1mysTF2Gy68VoxiuM58al24Zuxd6eTLv3/SyutbvhOH6xXwOFzbZvXu3PvGJT+hjH/sYE4O//CYJMpbhunTL59c8mwUmBLjsAWzMlbXxnChbxtSwxrPs2To70e/ygGuiFrzG1wdcZA6/rvYf/rWai44pcc8cRUoqdCC0TKdD05UigakoEdPCyEEcyYRe716szuZlun1WQHcseRVm4zWdavdpz7ElauifoVi6WJ7BsEojZapyl6iMvKEiwNYcwNZt1OCqh2nZ1erV1lPIkxOiFoLdAu+gepjRksqE7pqN1DyiHSbhHrCYtMvtfdlTDifPua5BBcYWjdTmOt+ZtzKVHe0pGKk4YhhpTZ/h1+zZfupOWV7KaChhWyFkL+OGTeNwABxemB23A9bGLpdb1nKFhggRNCXDMMp+YEDWy+oQ+VwmqNGFkId1O4hd77ozhWIhjCGmspyxZoDd/sNenTgBaATMNiDC8RL1sboBWwmETLoJd0twnAlTC+wCGRDumE2AdtiixxdTIEz9KeqgJVN++mxhjuw8A+DimFzoztuxGejyeqMAsYhYFGDmV5acrwLk7xculx69Na0PISU/bzbgygAcqoP9/Zxj1AuN1SsqQrQEO2THnNuRjwafPKAW5555NhOxy9HNbDHILXlnm0/H+lyqZMzdiiBIFTXALpmwHLUDqy+ViEXVAWv72tFCxrvVGiM0FFC5DPDM/ICTb9fY6FEb9g4jhT9rloFkO/5RG8q/fdMClkcZHYyqu7VLkc4+JmS8hCaXOODLmch42zX05qpX9E0ecF1R879t53bPmIqAyxiuVhiulcMMl91/JxNwOSqFLyKasf5lZOHzgMsGTm9vrz71qU85YYSf+cxnyLGd+7bxlP9i8i0wIcCVm4Eb/1P0Ypef7MPNA67Jtug1tj0AV+roG+r6xhc1eHKDUvPL1TFrug6tXqdjC1c5gGvG0GktGdgv872PH69UYF+1Hrq5TrNvO6Ye1ytK9cfJ5yrS8d7Fau1fo6HBMidHo6TApwoU8eYXl2gJzngt8vAxwMSzR3061OlVKSIU1D92/KsOhBTKcVQfmk8NrYIYDi4J9kxU+EAb79RkhQEWkwI3NUMvag6OiiHO/9sehICFzs6UNm2KIGoRA6wBKslhuv32Qi1fFmY97g9vW4lxM3zbYDe5t7bMWW8lIwu89WMG9ubnL/hRLvTinGI3n0m1u3TD0pTuuy9JAjDFixtc2oyAxv4D1EqLeAhvc+kUAGDPCWgur0n9wyLRt5rqpJpPwXbtg8liG450IB1xudOoGRImGOA7anhl0JTPiJMOUHMR6ifCQLOcP+uz22OMGMwPYioGzDIAHh+M1tIlGd28Wrr3jgz9EuFiSPbD1r2CYIiFOJrgxzrqmS0gj8vRwzAbTKCZX25n6DAlCp4+7lc//Uvz5btmxrWmOqPASNjiBPZhqw4RF5okFDPpLtIAaox+8uLKAI7GxtEFp4GPAaoGFgGvNq6Hv8+/nN0Cdurj5HH2dvSoi7pdXkC2CaWUlRGKDNts18nV1vKA6/KdEbOttbdPWJ17n1MVcJlKYTOAawWAy0QzJg9u5WxlOVwHCCnsJqQwz3DlbGKRLD/+8Y/11a9+VUuXLtXDDz+sGTNmOH7G2BFWVlZGOHjl2K/zny/BAhMCXJewvyu6Sh5wXVHzX/07h3LJ9HWp8/t/r/iP/j+FvSgUVhdqaO0MDdwOgKqrVxqWpTjbp0AqIf/2btXu8mjF8loV39avbjeFivsG5cEZT/gq1F92kzoK5mrI45Mbj7rCW6oFvjmq8ZU5rEY/YXI/OejXZmpc2fO1CDaiBiEGotwAXNRnWgDgCkeRkx7gRki9rKKCdwxw2cmy3EtHTIObswvb+AF9ptg3utlDfveeKDX2+glFtFwjN4AzpSVLg7rv/mLCBQE453EW7SfbhjmUxmLhW5IHNQzszrKeLWug7ufP+/XKq0iskwflQ/mxlfpXy5el2Wfcyef61r949Rz5Xw2NJlBBva6VHAss4pEmLyF6hGrBxFDH2glFbCBs8fUtHg010xlCFQ0euAyc+IcIKUX6v8SvQDGADKGLeD/OLzW+NMifwzzwSp8stM4TjMOIkfsF9rL6XbXV0m23ZnUngOv9HyA/r8Ct9eQ3NZKTZkWkB2HuZhFi+q57qZVWzXbGtBG7jPn6nB9tC7bOUYQ9fnjMp84oYZt8fnRuXKupxUat5wk32/5QBMBFbamCEiuEfJ6NsqzT3n5oE+7HtbgBM5PlDvZ39cGcdxDumqRsQtgR07CSEee9kK6AQfKA6/IY3ZjgIe4fWSIBwv70uEGXXZtTleFqqw+Sw/XwMOCyKIPJayOAK5fD9WQ+pBDTGsP1+c9/Xt/73vcYX1lNmzbNqbV1tjwuUyk0gY18m7gF8oBr4jbMb+FasgBPrci2TRr4h/+lwMktsBxS2TKfKu8KKVJfpZayGsCUT2W9PSo9QCZX/3yF6kvlrTiG/93Lb6gMIp3tsVydioWKzX5AveEy9WtIpa4Kzc4uBLCV4RgThgeT9Z1X/VpP3SYr+BuCKagEDFRTWHgdhXzvng2oS1NHimSmsKNgCCIbcWIvxeaOR55z28a7GZttNBVDq8NkzT9GOh4cptd2Den554mTAzAVFFooWUqLlwT1wINFCEIAuGB9ztf6IrAyh7OoECK1jxrhokWIUUwn/JhVz9rY3FHyhJ5/HvGSZpgntl9GXtc9sFvLUX1sI3for7/k06u7vDBfsEkwV4vI/aqbRv2vuFulMGCmTOjG1nEU4hpbXDpxUGo9Si21Xg4ojYIgzIwVPBbAKFQPe7YqoxllGR1Asv34a+Tn9eXC/Ow8muQ8MYuAjxiANEWeFowYTJgxOytWpfRrn0jqgwCufhDej58JKAlDV1KcoT4ZOU6Mt0ffFddCctByVrJ/CS0dtpmFG9o+cuDurNZ425fmrO3u8OhEP/L0MICrYfEqeZ2MkD5z6gYNcCWSKhwGXE73hsfW2zqT/+KiLGBmtIkOE9Poau1RlMmWcMin6upSRGMMdF09zcblQJyC3YBuP+N0Yjenq+e4rmRP7PrqJc/0RDfXLmHAs8qjjIfzTGqM6qytO1UB12kYruVvAq4zc1pHHeJFv7Xryevy6iAhhT2EFOYZrpwJI5GI/uZv/kZ79+517jcWOWPAa2yz7x577DGnFtfY3/KfL94CecB18TbLr3EtW4BQnnR7i2L/+nW5Nn5TRd5WVcxIqXghD4GZPiVQEnMR2ufH0XD1l6gvvFQ9ZT4FYseRB++XN0zon58woCR/oZVyz/+AoqW1ECLkZqhQpapwkni7cdBfasvoP6hJ1YhcvDfNtilYbHWu7qdu0/uXJVSPul2KBKNkHMl4BDsM7EwEcNnt1P7MpTcnedzbYllzAq0SfQZ6yRQMLcTR1jenq5X8rQ0vRHTwUMzJQSsuduvuuwu1cmUI9TqcBdvX2RrrR2MZFO3IZdsFiCF3zQQjptV5dN89HtQcYZqss2Mb31ne1SnYK8vPSiF4UT8jrTlz0uS9IEIA9vv61wP613+jCPJpziehbWVlLq1endFNNyVJEIZtCsP4kFe0+6QPlT3CAGHnNu8gBHG/V0MANNstFQEccYvC2qxuh4WqKE5o3y7A3nYKGgNorK6XLWn1xMKW75WJ0xcTOwhgJ2xDWOKKFS595jNJvecRCt8iLvGjn/od0RBKnjjCHdNq03rvg5xrlAjNUPbMa0A+/g2EVBL0e0V9UrNQ93PA57nsONY+fI4CuoZY38JUCwDykwG2crtxOaGmccakSfd7YW+D5Bc6YW9nO1dn6Vv+qwtYADva9RYdjKvtVIsGenoZX0HYrmIVlSCg4zhHF9jGO/BzHnBNrpGN1++Pe3V0oEi9lFuYVTCg+ZUGuBw0e8GdTWXA1VwfIofLGC5mOM/5wLigCc66gOVwHXhxGzlc2wBceYbLjGR52q2trU6pphGjnQ1w2W/lxMLnQwpHrDSx1zzgmpj98mtfaxawpxZCCplDzPxs+pECpzaq2H1SxTj0vno8XvKT/EXF8pVUa8g9S83k9wy4BlQcHVAo2Y28NzP/vhqIkVq5S1fLNe8hZYuqnFAzNyFt9p/JzOxxR/VsU0av7aaK05BP9UEYkQj5MOz+vatiup3iyH5yjSyhfjAygGNLeEkB8e1GKTlwgJezzEid63QggaEWV0xNigEMeZhnwyoG+l2Mj2xOoDEbpqxmoWReCzHEk0/xubU1SaHbJHL4WWTMAUuIZhTCduWgy9l7ZSzSqYa0nn46TY0qQjUBQkRqamAAgHObV7fe6nHqb539+QsLBKiJw1gZDgkQVmhq2khyqBspciuY/G8/9OoYgKyCGleFhW5C+5J66P6EI1fu8bBcxK1ntvkVwbkJwGTtgL3aAQBuOc4xDcI+cWwmAb98RUo33TaoVmpztbVT44s8sHaWGYShMuZpRn2G0MFBnTwxpD17ClBUDHHcaZXCVC5dSj2uD2f1wQ9Sh6osqzf2w7y9jtiHk8MFAFyDwuUiFPzA0tZ7fGw9syegI60oMxIKuQBAdv/yhGrJ98q18aIuO7PjXXZk2+N7tWFnwDtGbS7L8wvCvgZCQYDnxYym8e3rul3KhhZ2jlId20IMe9p7qPGWwfkpJEcyxKTHuejfd85iecA1MVvbvdGC59xM2Fizq6ehL6ATsRLeu1Xn7daCyqQz6eMscIF/pirgshyu0wCuZQCu4HAO12TdSewOaIDrIICrF4brP+cBlzOKRoOr/H37AhfWJP6cB1yTaMz8pq4RC/DkysRiGmpoUGrvFgUatqm4sFeFlRHA1oBK5s5RaPatGiQ/qzF5UtHIPhUNwUL1tisY6VWJZ5b8JUjU1a+Tq3op+UAjoUC5UIkYjvB2ANe2SFqH94TU2uhXHWEPVbBc5mA/sDKhGRRGbscpP0aR3Y4OCiXjcC+a51NpgQEe6Av6aCyTFSq+ULOHVxv1wV539fEalw/QtjRbqMX8Ifx+odXP+N1u1Fary9RJrT5YAObNAxi0/KsE+U9gL76jLpYlD12gGeA6ciSjnz5tdbEI3SwzoQUhJ57V2rVu3XU38uoXmvB8czf2aLWwRI+2bPGjYujSSWTf+ygePG0GYZpVgJ+FGa0k5NDYMHN1TjW69NJr5Hl1wlRhxjBCF4cPe/TSZr9OnwTIAXgqCf176P4hAFunXqWvUU8Y1s6vIBLeXQDmtDeo+Yvd+vD7YCIHrbC7W6++SliphTmWe1Ra6nPyx554IoU0OkqB2MUA4QDhpEFAotWmsqLOLlOZ4FgiQ259b0dAjbBccexZzzh4H6DMxoP97gBYFrWjvdLNgHfUKhvTgkwGWL2yfJtcC9j1ZpMuPUjH96BimByMEbYbVFV1McyisamTu7+L2VoecF2Mtc62bA5y9SYoOUGYcVVhSse7qNMXr2BCixxQV9cw4HrzJne2jbz53VQGXM31YUQz3n2ZRTO2wnB9JJ/DxYgZDbgsT7sf6dwoE2g2qWptBITZcsXMhJaWljrf5/+ZmAXygGti9suvfY1awO47A/1RhQZb5DuxQ5FDb8B4UINrcZ1m3LpGwZp5CNYF1ZI5rM7BHcRvtcqHkEBlslaV3hvkK5oNG1ZDDpCxHWc2k1E/7kro1UyCByx5Qw1BlfUHNSvs0rL6lGaiKHf0tFs/fMmv/TA0fgor15SltXhmWuuWxDW9AjVAbpJBEp6CMAsXarb/UzBbO1096ieU0WDfomyB1mRLCHO8OCfZtmVOXgqGI5UkxNCELnjSm4qhB4rpTf/vzTfn7p2RdZ1dWT0Dw3X4KCp3IeTaAV4WrfjgAx6AivvceVxjN0too4G1Fyiq/OMfex0gB/5xJLZvuiXLAwPiktpWJsO+BAlzy3VoaMioA2bNR1Hj5exr3qysNm0lpw5BjsZTMJH0ZRF5dPOmd6urI6I3jhIGmA5q9gK/FtbECcdAMr6gWCXlAT12T1zL5iWo+SX9dL1PL73kpT/UpwLsTa9LacH8FGGFFLWeTY4GzNjGjeS+vUL/EEe56x5pFaGHYc5/kly+VxBReeUkxbI5nhtnwa7NTSGoQrhRgrFCmGChD/l1GLnzsYdjzXNZPnOOjeGygshetOEDhLyOPKgvy/6umo1ieYvJzf3/jvQqx3QPqqOxXTFy6ArI5ypBUKMQRsBlkxvO9Wb/jL3bXL7u5QHXxGxrIYRZxlEPEQ7dCNxYGb/uqFcDqH96OKfT3DnANZLPeaG9TWXA1QjDtXQYcE32LIIxXIc2bVMfIYW/lWe4nGGUm8hJMzm5RevXr9fBgwcdwOWIZPFst7q59mcTq08++aRTHPlC4y//+4UtkAdcF7ZRfonr0AIGJLoHYhosHCSXp0vFTa2qx4GupHh3QU0l4gigAh6YUUXUkzmNsmCXAlmAk6tOYXcF7I/lOJ0bzKBnpy7+IuzHxwxnKEH4HLOahdST6oP9+Jfn/HpmO2IaACIfgKsOhqOanK4bF6Z0x3JqRMGQub2sBz1iTNP5mv3aRw2ovfT1FCWbw4QS3kBA4ewsuWPnW/ECv42EGJrT7SePxwol5xy/C6w46mdjxo4fz+qV1zJqaTF2TFp6g6kKIotNjtV4mmNmHBe6oe9816unvo4ABvlx1lw8bJcu9VBnBLYKZdsoIY9RkEwaKUHwIoARsRKUCB9+2KW58wkxfCmok+SGGePU3YekOXlZyKVgp7iOn0jo6CmPKqtQRFycAaSFwNTFHLdXC8jz89OHKPlgrW3S08+S5xXBaYJZy2L7GJ2850HyyJCK3/BMRn/7NTcS8RRNZgzcujahT/2+yC+jRCfDZpBcvg7CHemCKslJKwBsHUXqfSc123pwzGrJP7ulLqnpsG+m6HhFmuPgm6peUpG+PmcsFjETaqGvwz9dkW5dzp3aRIOBDKYYGGtp1DCRvccxNuluc54vdzMnKR5NwHZRr6srQh5mhtnnEMxwIeyi9eNy9+DM7ecB15n2uJRPNppMVOhUn18N0SLuf0QL2HjiOirP9GhhRdzJvxzPqZ2agCuuP//C/1JTPWVEHn/kMjJc2xDN2JxnuIYHqd1LfvrTn+rLX/4yz90WJ0/rwIEDTq5WTU2NGhsb1dTUpCVLluh3fud39LGPfexShnd+nTEWyAOuMQbJf8xbwLEA3u6pVFRHCgeUKU9qLk+8dZmQZnhNuMKc3Nwj0JkpQgM8jVNt+VlW1d7i70d+P581DRLYVmzpQf6O8+Bt45tO8oR+/pOQGg8TQliIkwUwmF5hgCurmRRNfgAHva4iARuUdPKMnBpZzEadr9m+ImSP9dNXMq8Q74CR4HUi7rqZIUU9JgtFuFTAZX1OUhSrt4/+UfbKamqVInARDhlzZtbJ9fBNXMFXOcvbT4AsGJ9OGKMuhDLs/eaNHn3r62n1dGFXxySwhsvcmjvbwvtgiGLMIncn1d2DCAgJ6sZkFhYm9OQTaa2+yaNnYbgakJgP+QkZ7CXMELCb7COHzp2kWHHcqe81d65P997r1w3LAqgdIm4BE7af/K8DB1BEBCj1s80TbWn1twHquhPqI9/NNc2vteSlrZqT1os/yOrZn/nI18JJJlKwPBjTf/10Qr/4pFdlFrlhxzh8kAYmuwFg648GKKYNG8fnGPL0q2qTugsly1LCIK9Us9Nj9dqGCCs0xjVUUAAjOQzhJzKwrtQBXXC/BKICcvYd6NG+fd0AnaBuWlutUsoGjB6rF9zMBBdIWEhvPKUhwoB62pgMIsSwtJjwYPK6LIdxZOxMcDcXXD0PuC5oonEtYGOnfQBWPlJIQETYUX20e9sg953FJRFVFlDfDxB2oTZVAdd/B3Cdqi8YZrgKnDF8oWO9mN9NFv4QOVyR9VthuPIhhWa77u5uB0gdOnTIYa/mzJmjP/iDPyBv+lb9wi/8AiH+cT311FPU2OzU5z73Od11110XY/L8suewQB5wncMw+a+vbwuYM9nqTmiXr18Rig+vQJr5LldIdVlzakY7uWMfhKN/G48NqbvFKq8Ctl5FjjzOTHUvwOP1LajnveIXu2bWM6tF5B0VwnRMr0zrwZvJ8aJuk+VSJWBr7EFryoEmZDG2N2N7YADRFsotd6Glx6595mcHcNGHWDTmFGW+FIYrt8Wczd4MkrNu8ZV9a8yBvcYIp0sBgk3F0ecU8EUdEdZnF+F3x5CGHwK8WOvscGnLhowaD8JA0Le5M6XH3g+ooo5WS4tPVSYJH0jq2efcajqNwiGOTBCH5r77MxROlrqQez/SLDUOAAQBsWthFCuSETUdi6mnF6A7LYD4RkjT603Qw63CIpeOHnHr+//qQ6DDqw7ENjoBXH1IzvcQthhrMi7T44RLLlogLbgBieLNLu3fzEw2fabKjgoDcf3xZxL6pY+4c6xezhwcTS507SS1v/79YMDpa4nVHBtwqa4orfcQwliLvP2VblnOiyOgYWGuwaCTW/hmaOHEhtiVPrQz9m/jvbs3pu9+75g6OhEMod7eex6ZpdWrKhG4Of+ExxkbmpQPOUGdnvZu9bZ1cI+A7YZpDaFmGCC8851oecA1SVZmXFnu6yFySTsSFJmtCgKas+rqGtLiol7VFVNiZByX+VQFXMZwNV5GhisnmrEV0YwtiGbkAZeN2j179uhXf/VXURO+W7/7u7/LxK1bH/rQh5hIvNcBYqZM+Prrr+uP/uiPdM899ziga5JG+3W9mTzguq5Pf/7gz2UB8xPNVT6ZGCDvCcCF9PvyQp9CFwjfO9f2zv096oE4/c8TrtjMQ7WEHQ8AvnZTZ6pzK0Ia5HjVlGZUQq6PMThV1WnyihB+QGBiBnldoSAMDMqBxjBYcWQH9Jx7Z5P6y+QBrnN3y4DUgUaPDgKqooms6lD6W0VOE0SDXnrDr+Oo+XkR3zDRNkcxEdudOOXSwX1pVQGs3n17WnMXurTvlF8nyYtbe0NMqxakyKEifPBnfh044SOE0w1QIDcK8DKdQsSFtYhAUHurnBS8wsK0llcBbDSAcmEU5cEws4PUyTlptbKkitlu7WkI65kNiJ/w3VA7rFYAxrOeHLByCg+Tl+XhvLl1p4VpAABAAElEQVS7AG6wdjeivJhsdmvnc4C7Vhwtzvf8uRl94b8m9PB9GSek0kGYmMTGoNwZChh79KPD5JUBBk2MZAjlxLU1Sd07G4YLAHZF2/DuTWbY4v8txNBi/y2fyymi6RzEFe3hpO3cRF56ehL65jcPqqkFVo9j/tAHF2j1mirGH9TjO9xsgsJq5EVJVjTgNUiYofWjkjDXMLKXb4Ley9SvPOCaHMPapBJEtw50BAFcRaqqREWHSZoOzueS6wBwGcN1EsCVy+G6kErSxdncbk85hmurBgBceYYrZ79t25DI/83fdNgtK2w8MDCgxx9/HGGnW/TpT39adaROWBkYA2UGzgx85dvELZAHXBO3YX4L16gFLIdmCEEAvzum2ZXkACFzPtl4yx62p7NuPYdT3oEDX4nfBrmiVkLTsvtwsAFeGcQTLKLkdMqj00GYHhivGXTuXdMTemhFwhHUSMLmmMNrioHGdjnOrj1tLqPDOzHAZcyV5b7kujjst58xktLM8r56zKetB/yALUALzBaHqdnkRaUJQ2zpRFCEGlNh8t5y7q5LfbCDx09S9BdwNb06pQ/dGyOnyqufbA6qGfA6ry6hG1dEsVNK23cGtGljAbWOCC0kLNA2UgxzOH1hVrfcm9Iyihb3AYRnoyR4z8x+FXBmDhz0afsrLkeOvhcHfE9PQIcbStTD+XMVDSrdnVGshe2RK5aF/fLUE8BZzLghhDEEGHzoVoAyeWqv7XTBuJE/hn8xm3IDTz6a0oN3JFRUiD04bgvV6+OYLZQojEDGyV6vdrV51I0Mfg0FUW+ahnIhOVykEV35Nnzy7IwaCLFC3ebsO0IqI6GuV0M/J2opjsHEK954o5txgBppiU8331RD/gPg0i6Gd7zl2F+7jhJDMbU1tmmgm3xDyLby8gKVlBY5kxA5rtg6N7l9zAOuiZ1w57JBEt4I+7ZBv45FUJ4stpBCK7VhZScihBT2qZIcU3sWXajZEJx6hY9zOVyN9UXIwr+HHC4DXJPXzGy5wsfGcL0Ew/VEXqUQm+zatcsBUx/96Ef1K7/yK47BP/KRjzhqhH/2Z3+mBQsWMJZi+sQnPqFnn30WpeSOyTsp1/GW8oDrOj75+UO/sAVShA0luPHUlqQ0u5osrUlGXOYCGcDazBTnazxkwRPwai5N50mxKkr4HCBhECGGN9oJW2sK6gQAwOpNQb9pOQDrE2tiumd1klA7GBdmu1MwDPaQMWfXi7awqWBdrjZRwGV5Ckmk163DpkHizMgPd9e23Yt4yE8JqzzR7hHaAIgUYCtCLDt6sBN5bTUliAbAStmyZscE+WSHjrh0utkH++RWlG1XoejXDDt2oIOcKQBPAJasIhwH2AwRskM9rZMFivdZ3h3dANy4YAwr6tO66b60Vt3OmSCM80Y+3z0/Jn8mSs2wDLlaFDpG7XB/1Kdnt5epvQ/xjEV9lAvolj+cVNeeSvW9UogqImcTpsvNst7GuIoRVZkzI6ASQFWQ7dZUUrOsBpDCMc+BWXvk3rjq+C5JP/d0ES5J/6wW15LSlFaSw2eOdYxxYoWMiwHdYGun35fr/F7qdk0uPkJCnuVzhajwbNfM5RuFl9rLS18vzj1hYJA6eVSVLqB+3mTfEy6pZxg4DtAdIO+no6GNSZqUCooCqigvIpz14sVsxtOHPOAaj5XOv4zlb3UMetQQCSsdLCZiAal//osMJFQuRDPKr33RjBGGa4kjmmE5XHa3sDvyZDTuk04O11YNIprxX/IhhY5RreixqQ+aKMYf/uEfOoyWvf7sZz/Tr//6rzs5WwbKvvjFL2r+/Pn60Y9+NBkn47rfRh5wXfdDIG+A81nAZrQHIkOqIWdmwXSv4+TmZown64GQ21oHjMZBHO1mnH78cS2CPSGqzZn9TMDm/OCNgL58lJATwFYJy6QIM6vqTOuXF8T0obsTqkBQw3JpTPXPVAMtB+BNhmHyunqmqdiu5ZFZDpeFMo47h4v1EgCfoyfJZyIHK0W9qfmzCZWcjyIfoZP2vLXwrU5yl360ww+gQfCCiU+buY8CNPeeIFSPZRbUpZHH5rhtebYZI59tz1632jsAm6DSE02IkDS6nNpWMcQ4MqzvQire0zMkd7xbKWT903FUKmJWAHoYFPhSyLwndeu7EKV4N4WHZ2W1GsA1k/BNU9h4fkNSL29L0I+0Xm4p0qt7S5WAYSue3S8PQhulcyMUf6bPL1Qr1sP+Sqn5haiEqykqspsUgsLKUrusgJyyRTBpN91IeCTAacm8pN51Z0rVZeSfRTxa3+BXl4lqcGyWmffIrISW0AfvMIDOcYP8eJU2G4Mx2OGRnK5J85+uhuO164lr0DkmO0FXuo10gS458vG9g2pvbtNg/4CKCC2srikDdDFb4SxnnZ+clgdcE7Oj2c9OyeFOv1qixSqvKnDY7ehQQr7UoOaXDjkTK1ZGZDwtdw9M6tixVge4zZ5T69wXM7DMn//T/62aOTfo05/61Hg29Y4tk0CcwXK4TtUX64bH3wvDZeGU4zve8XbS46Iu44tbkIV/iZDCPMNldrPQ7y984QuIQDXo937v91DyXaqtW7c6uVrNzc2UTykifLrHef2TP/kTfeADHxivufPLnccCecB1HuPkf8pbwKTPh1CnKw+hVDgN8QOTiXOaPRQm78FANCGVsvhjk6bzRuqP82oskIWV/ds+v/5yn08nkd4rQbrOT/2oOsLXfnllTI8RilZcxIrD3bE+W1iXhXcZ6LICyU6bFF/L9oO7z7ay/JMDXKgUjhtwsSLHdIhQyY3b/OqkuLO5HSYIcve6hJYtTjvS6LZ9CyN8bjehdIhRBJmkR7dEvYhSNHW6YHncmlVFDltgONyG5a0vjaddOkEh6cEBABe5Uo0NMF0IbmSo8WXFjV2oGaq9X9mhTmXZaNZFMlyCWmZpfnQDjmDEysnZWnNTUu9/zKUHbs04ypDoETj9bmrO6oXnE3p+Y1rbj5XoZGex0oXkUi3qJeSRHCxqpCWiAfVsrVCiD4bMBU2JA5ztj8kNsvL4wsp6gdTkOBWS47ViZUb33ZnUo/cntXQeoiCBtA71evTjkwHTm0P+n+T5mEcPzyAUsjrJ59ypvPr/JSSKvAArhuyB6TJBFyfM9erv+JTvoU22DA1E1dLQomgfNbvCflVVWaFky+uavMPLA65Lt6WdBohSdQx5dXoIWf8QkvBc3EMwW8FUv+YAtgr8FutgF/zwjf0Cu7NzOxVDCo3hOk5I4RJCCv3UlRsvwLyAOZyfzc6Ww3X4xa0aAnDlGa6c1eweYbLvVvB4xowZ5CoXOsqEL7/8sjZs2KBTp045EvEmmHHHHXegyMpzMt8mbIE84JqwCfMbuJYt4MheA7isaNOcWvKCyizojz+XycO/Q94vT40jHW79w3a/fnbUTy0et0pgiG5FOOEX74prJUp6jhq3zboPNwvrSqctrwsQwZPYgJfbYtAmodm2bWbSAF3G/mDVQjwox8Vw0cUUjN3zm/3a9QbS9AAtCyfsAkCuWprSPbcBXlD4Mx/DJK4bOt3adoiwQmpQxcmTqiQ8L+ijYDGhlgbCRhguYxsMcMWjAOS4D6l6j3Yf9OiV15FqJzzPisMiMEnlYhijrpi86U7OYgyh/BD7YVbVstYpCh2u8Kh2BowTQhs3LPXrzpvSum11XPishCa6Hfl6Y9J+skXatLdAB4+H1dsLfqqIyluUdPYT7woo0eLHNkDo6IDcMKRCwj/rUGzMYntDAC8k+XGwVtzo1uc+E9f9t7NvbGFObBdy9U83eHWUvK0k8YbVsH6PIAE/rzj1thxCc8XsrNvfW27ZyLd8eQVbrlgv9aIYf6ECqr8R4up09Ar26XrZteX8mLRzX2evulu64FYJQy0Jq6y8mPxFY07fGi2XapM84LpUyzG3w3V+us+r45GQiik46KFuY4xJslQsonnkglahnJo7RXZlj69NZcDVWF9KDtejDuDK8X7jO+bxLJUTzTCGa2M+h2vYYCPX/8jriB2N+erlgWalPkz4yICWRSk44f4jC+VfL9kCecB1yabLr3g9WMAeenFq3sQJm5tZeFpzwm9QO4msq/ASuQoX8eQEeI1ydS+XTZIAguMdXu084lET+VwVYWk1an2LZqYcdT1zfnIoBcZo+K31xRTMLK/LcmlMTMOWsZus5XuYCpEXpDYCxpwCyiPrjn7O23eOV09YHgArTqhYFhbN5QBOq5flkpdtG4tx/mYbAtsQOvnCFr92wF5ZvS0ffngHrNWaFQa4EuQ4sZwDHslL49VCC9t7kVFn3QoKAScBXi+8BpCi/lYpZJHV27L8piQ5U1lyV3xIFkaTKAmecuuN3QAvxEf6ECFx0EqEHC6AVUXxAP2PIsGfUW9/kllQQFUJMu+1RZo526vVy70qKfOrnIjDB9YhCY9gxauveBQZpB/04Tge7TGA1ZFjsHTsJ0PxLSco0bqOimAW1UOKJSmLs+sywIW9slmzf5EzZqxortfv0v3vlv7sc3GtvsE4Tv44XpMAaWI/RwmlpL6uZhWl+AMWWhzlm83eD58kXuycm8NlQ3HUL28u/U6+Ge6Gc80YCPYa++knT87p4DvZk/y+TEint71X7QhqZLl2DXRVVhZzjTgXzYQMlAdcl24+u0b6EMA51R/QkLuMAtY2MRRTONOn1XVRrmxbwv7G36Yy4DpWX6LFFlJYxIPNJGgnqdm90EtIoTFc0fUvwnDlQwpHQNbI/Xjks5l85LtJMn9+M2MskAdcYwyS/5i3wFgL4CuTM9WnisQm1bh3y+8hYb5gOt7/PaYfzuKT94AYu+/Rn01cwWTSLdTOalFR7xTAlFEztade3eNTW6tLs2dktOqGtCqpN+UGEEUHBgEiAC4YKB+5Qwa+bBbLucnaExpP3d7bjdaAkxP+Rcy/A77syAAWcQobO8sQHjYi+22zXo5YAH1ybtK2rfH6Byx7osGjDVu8SLWjwoj5qqkvdi+hkYvnZXKiIGwrCYt3GID52usUNu4GeNRnUIXLII3v0itHvNq8x4vgBEWcTZSCWfsUqNQc/FTGqz4YrpmoCxYBPzZs9WkbwCtFAeH5qBCuXhJTfX0C8AiY6wLUwQROgy2MwkC9fgwBjCIPyoEuBDoQ60CU46alSe19DdAH6PIBpHYDAKPIxXfDQO3b51J3FyAJMRNRR82M4EGq3Y+IiZscu8TpCPxZBLl3WMGk5ScEAbqmcojDy/lZtDiBtHhCH/4Q8vDzOK/DsoMG30wgw4CUhRWa9P2ZBs59NlzaSH8OdnpVAFO4tIo8MiNfHdhly1yBxm5tTAwQrmLjyR9irNj4yLcrYgG7JgYJLeykSPIQRf6KrFByWRETNc5AueQ+5QHXJZvOWdGYnO5BmOyBEp4nQYRmUirK9urG6TEnjPzM6/3C+7JL7MyQwhrnOswQ2fD5P/3LqzaHKyeaUYos/KMO4DK7TNbdwu6AXvhdy+EagOG6XnO42traHLXBvr4+vfvd71Ztba0jkGGhg6MB19hRZr+ZVLyFFebbxC2QB1wTt2F+C9e4BbI4xq7kgMJ9Lyg4+DJMAzlTlTPlr31ArtAsHg/mHr+DzdmZgSTpZItb3/jXAHWgAuTMuFRbntHjD8f12CMJ8jYyikaiiDgQ7gbD4DHVQmYP3VBCxji4AVb2cDOv3tihKGEEPr638C/Lt7FZVssHs++tsK3jALCs/R6EMRufDz3a6X/LSqZO2NTuUkOTB4l3VBlr06qvS5HsbXYELrDo67vc+s533dq33+TGUV4ExKy7OaMPfpAwu+lu7T7udYBXP3ldPidMCrCFmIXb69dcVP9uuyHpMGLGnnWQK+ZCubAcZcMyhCmCCF2YKqH1wxxH8CPOirQRVcS9xwBngDFTNL9leVoLKTr9/HMUNe409ce0Xu2hiDIsXRPFlZv3kLMFa5YlJMiSxFzs218K2JuNCiFkVorwS3+WkNQe8rEAHwaQjxxLqeEUUt6A5wDgrKIihWJURr/1n8lLM6WUN0028uYtu50xyvialA89fRSHAkVDs9tDcxNaWZNW4Aw27Iy1Lv8Hum2AKwLg8kJf+g2cj2+wXP6+Xad7sOs4NhSn9luH+gkzJKtOFdWFSMcXOtf5qEE3bgvlAde4TXWOBXNPjiNdfrXGipz7c2G2R2vqJgi4jrcSbh3QvDlVTohzCsD1uT/5S9VepaIZBriOElK4yAAXE4OT+Ty1Oyh3IB3etEUxANdvX6cM1969e/UHf/AHTm7WV77yFa1cuVKf+cxntGnTJqecjDNpepZRaveN3/7t33aUDM/yc/6ri7RAHnBdpMHyi1+PFjAnGAGKxCl5+1+hZlOPSqoXqHrGcmbwCYEwf9ioiHeqARAcFxxC5T9e9OuvvhZCCt2tAARKjOi1W5ck9Tsfj+rG1WmnIHLC8of4zwCUU6eLpCknn2uUE5wFGMQJMUylTO46wJ/fAVR2WMZqmQCCMWYWSugn+T4I6HL6cM5jHrGHY5wcOBvZHz/ZzBm7pJizbYAcM8CNARzH8WOVQULqnvqGGxn23PcGiCIASh+iFu95b0qPvQ/hDJioBsIrjyP73tEL0zSYVm88q0UzPbpjEcWHQSP79pKphbz8kqUezV/gJrQS4JXrUi6Cz9l77h/rU3uPR0ca3OohXLGcXLIFs9IqIuxx+8subdtGDlc/naxAth3wteNnCfUcJowxAdCyjVJTB9UNuX0BzV7q0pwlUuk86QZy7DzNgCP6evRUWjtfSailEaCZHAa8ZJLdvC6p//OXbt26Dhl5M8M4mh1GBLbsXw9SwJmaZJZv+OjCJDW6Ugoa/rpSjfP7JuDKM1xX6iy8fb/D13Jna6e6TrfDmnphukLkdZmzbxffyDX79lXP9k0ecJ3NKhfxHRdwhEmXI72FSvkKmIDJqDDbB+CKTozhAnAFuH9Pn1Wtpr4gz6is/vbLX9D0uUuvSpXCHMNVTkihAS5k4bmP2b1tMpqNaEc0A8A1sP4FQgo/fF3W4eru7ub5tc0pcHzbbbcxyVeh7du3UwuyxbH3uWxt52LZsmVatWrVuRbJf38RFsgDroswVn7R69cC9gBwZUEH5G+lENAQceE+AIiF9ZVR4LYAFTwnHOxNn8XWcPijSXx42DYz6oOJaez3agB2ZuNWr77/3aDigJEwAKEbKfIVCCx8+lfjug0n3nAA90ynL44/xRfnYhscEETOh4UgmcNsOV/miKX5zpJoXbBeBrps1stAl8+Sr87acrWlTrZ61EgNrRmEC9bXWLFil8NUBVEW9CHT/paDZ5186zO7UEur9JWvECr5KuqEAEnTW0gmLT8ty+xcQk9+BBC1mL6BcSzEMkb44X7W+TmKhnOrPbp7RlK7N8d0AEAEjiRE0K37H/CRK+GBrUN6n9yv8HANL8sJM0XDCEzZTJi2Ar5Pw3BZvTM/jBSHTX/SenYzEs6nyYEhpHHvG0n99HtptTXYuKBzsFsEJ/LKXzasaYv9ql+WUQXH/bH/lNIMihU/96xfz7yA/P/+lPoR8siaMqJjhaiWU4z5S39Fv++yWm/scJQ9nIVG/fPmEOM7E0c4yLb2Ui8gjGO1FrBVQ/8nuVzcqL2P/62Fslo4qo/kaxNUcUDp+FfPLznZFrCBw2VmbHakp0/drd2KDQzBcoUolIyKYSBXA8pZaBz7zgOucRjpPIuYcEYzuaWHI1U4wEF1d8dURA7XmhkALtj4i212r7eQwuMALrl5MFXNVdQVcp4Bf/OFz2nVsoX6/atQFt4A15H6Mi18/H05wDXJIfomC3/kxc1KrN8Aw3X9AS57ZtozeySVIDeZatEdSef7C40zy/N+U+n4Qgvnfz+vBfKA67zmyf+Yt8BYC+SyY9IpQnSi1LtKkx9FKkQFogY1pQCvAL9zg8PldfBElvwgAxAGVsbryIzd48hn22oTD+itjQhCENYG1lAjIYUvv0D+1gGYG4BFAWDmg/fH9Wu/FNNcalvlHHcDM+NsOGV2czYVQgNqARxlCz1MwnyZWIYThkh8n4UomorRWRuORARW6Zmdfr122Kv50wAtiFPsBjzFSUxaQZjenXeQTzXfZNDpIvt5q4fwcJiqvT2tv/076ZlnyAUaNKHgLAIbHs2ZS40yGKMPvh+Fv+WWa5brgTkbjd0p7Wh0qbrUp5kUMH5+fVyNTYAnVBFLAMXzFvhRYPKqp4uiydOkdeuQfJ9JtB91r/7jRST3seXDqCTeuJSQPNO3MDSDkS1g9LXDWT23A2CXsnpj5F0Aml74QVKnj6HSmDXgZB1BLYNj9/pCqA8ivLEOVpTcqw88QuFi8uq2I/TxtW/7tP0l2LhmA6C5ceL2DGr2nJh+45MePflEQHUAOhtldkwjzWxkzb4zOX7HYPYl/ycAh9ThdWrEWVFkoivP2+zn4c2dd7mJ/mjXQaSb8DWMaWGoecA1UYtO3vqWmzkE2Go/3aaB3j6k44PkfZYw2XCOa/osu84DrrMY5SK+8ripuYcwzqFIhWoJ7+zoGKDG4gCAC6Ej8klzT5rxb9BuC04O13FYCw+sWd0KJqoQ+eEe9hef/yxqq/OvWsB1or4cWfj3oVI4Uvh4/Md9oSVzsvBbKHz8/HXJcNl92Fr+/nuhkXL5f88Drstv4/werkEL2C3MCg3bzcyKDaOaQG2ohIqYWCwthEExIEGdi8ze/Y4n7F62VO66OsdPvlRzDEGebDpJXapWnyOaYbWhIjELU3Nr4LhLxUMZLZmd0v13JJE1pyaUf8StvoAHPqpDI864Sb8nYfLs1bx7gwbWTIkwBXNxfsDFgx/WaTNCHruPe1BIy2rLM1kd2I2ZAGuzZif12KNZWCq3FiwAqhhWGemq7YROWMrY97+f1j8+lQZ8mQldKiL/asECl25c49J73+PR3DmsOLIe3kaUqsiRQfLASED3AxJ37khox05y02IZzZjuU1+fj5AKt6OKOARLuGZ1RnfeBUCBynpxp08thAmuXoTgCJLMfhQEKyqRuADAxmES/2OLR3uOZlRaYnlYPjUQIvjqc1Ht2UoYo4FfxoALdsvj8WtaXUD33UM4IsAwSVHnOXNNFh1mkfNxstujLTvd2rU1q5bTHCsS90FKDYTIDZszzaUPf8Cr//TuFDltWBzTMwnpCIIYw2fI1MIGm2Dj2tpcqkIUpG4aKo/01UGt5xldZqYciCMvj22Yc2a2y31vHy5Pi+DMW+5gsCCUY+4uz27yW70EC9g0RoJJo/bmDvV1dDM5QDH1KkAXNy8veT8jl9a5Nn29A67cPfHt145ZbnRQXO4aY9pmZHYIg3IJqmvIo+aBgKK+UhWGvRqMMjkXG9C8soH/n733gI/juq6Hz3b03gmQIEFSFEmxiGpUJdW71WW5xI4tl8Q9jmOnOHacv205TvnFThy3uH12YkmOmtVFFZKSqEZR7B0EC4jesYvt+53zFkOCEEC0BQmK80hgF7Mzb97cmZ255517z0URJ4yk0Kr27j2Yxcf80qrab4AzL3V1TYix9ESkYinPI1ejsNB3/v6vcNGSmikLuHYRcM1lSOFkAa7dFM0IM6Tw86chw6ULRSGFqrGl18HAy/rbAmZaX8v0t37skEJZJDXNBlypsaPdy+lqAT0N+fAUKyRBCuVCpbliKHFztnLjOjg3bECcYMFx/jlwX7icKvL9lM4Y7SUHuYFhg0+zDtdhMjIF6RSH4DKp9DUTRFRlxHB+SRiluay3Q1l1KqNPuOmYBLiSEvBinjhbyvDCENUlJITgEz01TJPD0U2Wq4F1tH71awd+84sEWhvJwpHxSU8PM9wxji983okVKyQcwU4GeXfafstWsVxhvLmeN36G/WVThGLJ2cBNN3qoVuhCdubR7QQiNC7Z3ydVPIZB9PTG0diowsgJspFOrH6JcvFhgSYHGslm1cyK46qrKcBRzPJcfmBfHUM0X4jh7be4DUN6LiILdzOBYUm5E0++6sSbOyhDT8DlcLgRYIXqeJsfu9YH0Uq5/u5Oj6klxh2jqtKN+WfqOKlSGA2hm/uWcmL5NJcBeGHqa/xxDYHX2ji6WRw5TKGNhFg4cmkXLHXio3dFMI+S/3sIrusbyJoSrC2aF6UCZRQtFBpZtYqhmizwXFSU4PhjqJ5O0KiL4biNAY8EcLsOMO+Nx15VFsfsSgp5UIhkspp6lix5yJ8sjK3QQgP0JmuHdr/jskCEIcQdTR1o44yDyipkc9aoiIWShw8ZTu7mdAVcuq6dmg3hvYxZsebW1f8YMIYRP21NU2mB1opxfb+USdn0Ve2NebG3nSGcLh9yCrIMWJKIT4A1H/MS7ZiVFyJY0pYjfa/Vo549TnQzwqGLoigBf5A1/jLQnj4TYfg4cZSJf/vW32DpvCoCri9rgynTFEmhkMLaqkIyXO/rDylM7T1JIYW7GFIYeGIVGa67Tsscrq1bt+Kv//qvqaq7dUznXoDrz//8z43Axpg2tFce0gI24BrSLPZC2wJjtYD4Az5m5fizeJLb34Xpu9chf+vriHNm03npRXBdcD5ZDsaqcZ2xNjmqjcwxenq3F/WUAS/MlOy7VPAomc6cpLNKo7h8ZgQ5ZFH04B5f4wNenWrz5KGYsWq4mvESAJNiYYRJURmZmSaP67j7YXidChr/23+68d8/j6GDDBIn0QkOojiHwOmvvsK8qiuk/McdHjGJ3iTHEGIS+dsb4njhxQSBRhwzZlA18HwX5lMAIzdHM3Bak//ozAQJtmJxMlN06sVAyV5yVwybw3fd3bQdwxO370j2L3/p4osTuPAiVsZiPpck6H/7uyj+40cJ7N7Fw6cNKwi0vvxlJ+68C2jqSmAVQwoPd6YZR3QBwzVrCoN4540+7NrJnCyCYJ/PhbPOYrHIPAKyPtbq2hdEW0cfGprT0NjqRVm5C5+9N4G584D/XePDb/7gwt6dLuY5eRAnfemLhrnPGN53IwgGCVh7ZHMJizhRQpn/61aE0dkWx1NPajZcJ8lJQBjBksWxZE6cDm2YJqDbyFwvhU7ua3BjOq+Xmy6JoqKYYaeT1MxwOMzezm4jwiKQbgOuSTL2hLplSQUyz72UjG/a38B80D7mdWUwryiHIjOcDBjmujp9ABfvJLxwk8yVDJ18L4aKNdDN7cqYn+vo+tanafFuvrLpXsz7YIACOfvbOQvGZ4HM6WSon9OXLCjrYRH0ZOMTxJnOCZoYytzdOKM4xPuQ1jc99a/z7hc5xS2Ulz/UQpDW14KYKw01lRmIuDJQ18kcrsw8/Nc/fxOL5kzDX0xRwLWTgGvOHQwpHDiL9u5DHdcSFvPA7jUvI8qQwtOV4WpmmMgLL7zAsNUWY0Ndz2K7HnzwQZOffdFFF2HevHnIoPpwN9VlN27ciJdfftmwW1/96ldx9dVXj8v29kbHWmBKAi6LytRFoR81LdN7OX3Wcms9k2R+7HEN+Zco1R//7Fe46QP3Ys6ZZ3G2nvE6drMtkEIL6HINR+Ks+RRCUd9hlHfWGUfcWTMLzqJC80Ae1+7Yrx7uLx9w47WDVMLjMzqdTEkPFcfTCGJWzoxiabkK+I6nd7kATKIly9LexRpTVM7Kz6E6HxmkwQIbEkKQRLzYLYGb4zU5ZAHWvrr//1z40Y/i2LGdNcDIVGWRgbvrNgc++1nWoTpDe9bPEAPn9qzPjI5OFZ+myAUFIXPIcvkI0NqpIrhhB5kjMlZnVgdRTDl8SdpL5MO6Zxwdm0LxHDjI/K4tmxnK055AOXO4Fi1iXjnZLYXrdXDZP3wrhl//JsE8r6SHqXy1e+4G/vZvE2StInh9mxurd2Qx38WFG84NoaYsglYCwbr9YtJiVFYkS0UgWF2t/DYQKPaiq5tqiZxlbmKdrN6eCJYupDNV5sRz2/Lw5OpcNPN8Jlg/LNHDQsx9vQx3jKG80oWFSzKwZKmH9cHETCVVE6+8JIJKAqQ1q6nMyFDNMubGXXN1lHloI4tkKJqpmfXCHnrBhx3c52xK3d+6MogKsmST2XRedb3E6NCL4XIzfHPIcz2Zg7D7HpUFNGHU1d6D3rYehMUW8/lYVJTN3EnV3DsKN6zOTgvAxXtQnCUgunlP7CY17TTok0w9FUnbw2kIxDmBJp+EttOUG/wdFCuNIsfRQzPxe82ZHeVUcnrMiPuQi+I9lV9GLpNibLL1QypOrji8rNqXVk4FSTLQ+X4zgTbEndE6BXzl/YE5pBsOODnJ12jG0emtwAWcEEqj4E+T34ttbbn43c+/i8VzK6cs4NpTVURZeIvhSj4TBhzkuN8quNNF5ViJZgTJcH3uNGW4dP/t678Py5jynSUPL1D14Q9/GFIuzM3N5WSl24hpqF7Xww8/jMceewxf+tKXWIrltnGfA3vDoxaYMoBLF0A9c14effRR7Nq1y8xYZzOG6Nxzz8VVV11FFaUCc5FoPV0Mqh+wdu1aI3NZUlLCmfIrcNlllx09siHe2YBrCKPYi1JuAd3kld8VJzjxxftQlp9g7SeCATot5qE8zj2q3xayXG8d9lAGnCwKmY9cXwxLqKyn2ks5zDc6/sN5qB3L4WZNKOYpbdruwlssJtxDZqWqMo7lyyKokuBFf6cCk6orpbpeAiM+qoXoiIZvcuEcqKMz8PgTwOrVcYa7xLF0KXALZd0XLUrWoeJhca2h+9E+raaRWm3TzgQeedHNcTtxy4oQFs9jLpNJWLDWGPTKfmKsHM2hG9XCNBYwltS8nCH5P90MP7zvvgh+/nPW7GqRI5Tc8dVXRfCd/wfmxMXw8s4MPL+NzB7D8O66KIwzGeInp7OdYO2lF8PYvSfKEEyKe2Q6MZcS9Hv2sgYaWbq8PC9BXox1xQLMu2JuWLEbL24pxNv8CXYR7YV6kAh0stYb0SV3q4LSecUenH1eBmXivVzfSabMiZUS9FgUQQ/BZnu7g+CO4Y4lVIwUQzhS4yqMHMNeCq40MCyxjDXaZrJWmfL8hrP9SF2O5nOaxzidnczl8vJ6SU+Tatooxjuazu11UmsBnhblo8YZe6oC6d1tnehq7UAmJ1eKyXa5yMToHqT/aqcD4NKlWk9hi4YgcxB9DP3jrUE+iO4QCc3U0F5oP8CbSg/twbshWXaJ2jhyKgiouNjPnKyCsLFViJNNtZRp96Rn8V5lOlIv5juvWo/O3nZEezvZL4FdRjaC7lzMyQsgnwqn3OUQLfndDXLCZnddN7/gjD7Ir+YzwodLqwPIYH5oJ3N8NzZl45c//T5DCqcTcP3FEP2cvEVWSOEOMlyzCbiSOVypHE8ScO0h4IqR4frCaQq4Blt03759+MQnPmGA1qc//WlTCNkiLnR96x7d2NiIP/uzPzOTmH/4wx8Gd2H/PQ4LTBnAJeZqx44d+J//+R/mPqRzFjwLhw4dwubNmyG688tf/rKhO1Ux+3//939N1WzVBijmFLUkq+fOnYs77rjDXCjD2cEGXMNZxl6eWgvo6ahwNj5wQ8yJ8IQxnQIH2WRojqoGDvkEHXEYCpHrDTvRzrwgMV6ZdJgLM+JI57N/vG6sAEd9kxPPvsRwxQYVRSYGYIjd8rMjuPBcqjASmFhOlt4YuW9STy7OhhkVQ3khwzQdpZy4TtbJamUBYgHR/AKgIJ+y62To1N+Q45bTom6NmYybx/cCssoLinC8Cby5nXrxzKe6YCGZHzJBco6GbuqEH8r7N61/RetPLpNdV62K4r7vR7GO+Vohzmi73VF86EMJfO0rLlOUubY1E2/sUxFfUAlRTmkC03heY90hvLImSLtIwp5sVGcMpVQaVHHlgwfDvAY4mdQQ4URRDAvnewjA3Hji5QKs21QAfzcH4W8nfckCajy25FnkrDhtUz49E3MWZqCkwoMz57Cg9Q0hzK0WANa1lWS1hj9mc6ADfiVtQN/MhKEKmyZzRAasMllvuevunm46qy5zbz86sz9ZO7T7nZAFdFHx+opGYyyU3Iou5ndlMbQwKzudrLaL1w0vHrbTAXDpOIMESod7vGiJ5XOSycPj531M4CpCVrp1PxJ9XSy/R0rb6UU4swThhIfRM8DCMuZSUcU2mxNh+l7rO9tIxulwDycdsrKRJgCb/FoavyVCGj/S22WAlzcWQI8rj0XXPShhRIBEeZL3Bo2ov5nZDObJNnagoyOAuDcHoZwZiIX7OAkXZKF1nsMYnxVk5v76m/+CxQtnT1nAtbeqmAzXLQRcWbzlD/NMsI57jK9Jhmst+p549rRluAabTL6wgNa9995rGK6cnByzioCWBbiUf/vJT34STz75pAFfg/uw/x67BaYM4NJJFnBqbW2lE6c8DDfa2trw+9//3sSe/td//Rdqamrw3HPP4Yc//CFuv/12XHfddXRq0gwFqjoBAl/HazbgOp517M8mwwJBAi5vIoQZxQQaDDcTgBgenoxuBIILev6aRzD/0N8TaQI2+w5RGGIVZdNZ2DeLdZxUz+scsikrL4owhI69a2em8Q39B4UoqI5HjBMlqrHkZd7UcVPHNMj+Pvr9uf7+Br9wJa7bzYicXbvC2F8nwOfEvDMpl17BLmKMLeQKDiKSYJQCFrRmZhql2JlgPqFG56Wnh2qKVB18/PEIc71izKeK4wMf8OGi5U4yYZTJp/JXkMnutRSdeH2XFx0UMcmmomEBnaPDO/3IIGuWTlsdqo+SfXLi8pWsj8bzf/hwFLtrQ2TOIphW4aYogQtr16XjqZcK0NpGxy3UxeLJlE08cggUPWF5gaxCDypmZ6Nqtg+XLY/hnutCBJYxHf2EDtVsfmRfE+tqNFtrV7pO+nr95t6u0E+b5RqN5Sa+zpGcTJ4ETXaMpWlt5XYFe0MsY9CMQHcvGVEnn7P5vM7JbjOsrifEUFGidy5mm+B1OZbBneB1BVxag1408ifuyYUv2IhEbxvDgLsRyy2DM7cUgZ4wCtMZ1k1bsEIgKnLJbJl/1mClaEtFWWYyNPRloieRw0lkzTrxu8HbmjfSjbKMPjRTSKejvRd5aEN2TjpKKHqRQXbY4CurK/NKIEaTH6olKCaTnig7kxE/nNQp7kU2VU+TkzHMz+MN/m++8X1Uzp6ahY+/RdGM7QwpnH3nrRTNyKI1xhOpcYxhBvwhWRMP9jIiKvbEczbD1W+ZLVu24E//9E9Njtbf//3fY+bMmQNslnyrdSSY0dvbi7fffvtdn9sLxm6BKQO4NHSBLgtd64GsRL8f/ehHhs36xS9+waKlORDweueddxj+c5+5EESDzpo1y1TOtrYdzgwCXD/5+a9x4z0ft3O4hjOSvTylFjAhOhE+TalWxzx0MiIsUKtaXdyL9TulOxxHZ3owdxM8rHnNg/WbqeTHvKviwpiRl19ImXQzoW35atYr9yNWWtLxMc6Ea8LDzR/jSE/E7+K2zS1RPP1sEE88HiDgomQ6629deKEPd9zmYs4XWTU67E46eZZDcRSojOPgB20ix6etneqBZKkY/YbSUjqTzKtSAV8fF3jTPHh7jxvPvOMl08iHOcUoFnM2uWl7N3buCKOxOZkPVlXlxvU3+LD8fA9DDDlDToZr1XM9aGWtsDSe/61bXXhncz7VErOoVBgiW9bLkXBaXM6GkzLqdGhLCTAXX5iOilk+zK+hmMZK5XBpnYkYeNABn6A/ddn0UDxDuX+mftupdwgnyFKp2Y2Ec8RANTZHODsd4uQBJySLxld8Wueuj0qTHSyU3EElQx+TRguKcjmpwBwmsrpifLyG9HrvnlRNStW2st5hmOCHiaTuaA/iLbVwBgi4Ks+iMAUnT5p6cE5JO3NrxdFwEoi+iWx31CrJv8x56fVgV3eeEScJBsPwRP2oyQkwLDyKbRTZaWBYYLnjEBVnOhDLKmOZiEwq0zKEmT6S4IhYdjFme1o8CLU1ctqJ96hpixDq8mPZNOaCHtkpmTWen69/859ROnPqAq49lcWYd9dtyZBChjEcGf4Evw7iylSHazcBV3AMDNdIvuTAYWndwW2oCSWrT2v9wetYy9WXPrP+HrjeUMsG73s0fwtEfe1rX6OQ1NMmZUc5WmcwoVrRZT09PSay7Ne//jXeeOMNfOUrX8EXvvCF0XRrrzOCBaYc4Oro6DAslnK0pKLS2dmJm2++GR/96Eexf/9+fOtb38KePXugcEIBsjAV08rKygw1qsS/gU3KZQcPHjRMmZyzLVu24pnn1+D9934es+cttEUzBhrLfj9pFlA4SE9PEBms0zV3GvOusvggJkuUqofKhAdOx0yJ4e2dTuw7yBlYvxPlzA2qYo6P2KPjNT0AIgwvDEnVgl8yMc4exiRaxybHY/RNM8DAq68GKSXvx+uso8WoBpMXlZ/n4mxbJm69LYPCGfTuxtLt6Adw9KSw/ySgUy2sKCP+AgwnSgKuJuZTvbTZg1rmek0roNLh3DDeebWHghsB1O5LMGTQSeVAL0qZh7VihcuIX6ie1u7dQezYFWD4T5wS9G7K2GczFy4N6zc4+BnDiSi04qazlsbwrfwCL264kfW45nBmm+zhknnM3zozytAuDmyyjn0sdhrLuv0Xg58MiURNfFIrlMdot0myAG3L/x3tETz8aCsZ1iCmlXvxvvcVMhea8cJHvp2j370uuUg4itZDzUY+3kUnvrQ0l3lNXpYWcBvAJef2vXpWdblub/agNZbDmnKZPM4o3C074PG3IVa12ACuxsOduLC8ixNqVs7V0NbQ0ijvt/u60tHY50VxGidSsoPI8ignFNjTmoaDwSzkO9rh7jhAhsqFcFoZygiYyylmpAjuAMPKI8wXrT/UjniwD3EqHnZ5qjG3yI/SzEj/eUjuP05xjq9/8/sEXAvw5SmYw2UYLgKuGgEu2tY8HEd/aY6wpibFPNizei3iBFxfvP3OUcnC67mmH0VZaYJIKS5WjtPxdmhtJ5A0ECgdb5uxfKb+1TQuASPpF2TKZmNs6kcS8f/yL/+Cl156yRyjRDM0cSq/WToJimK5++67TVjhzJkzx7gHe/WhLDDlAJdO9KuvvmqKtAlgNTQ0mBjTD3zgA9i2bZtB2wJcH/vYx3DllVcaoQ3lfcnR+/73v2/Al3Wgyvd6/PHHDUrXxa+/e5j48rmvfgsz5863AZdlKPt10i0QplR8IhJEJh+qBQRcRQwvVK7USM5P8vY6ua7MEVDEWXHl+MT46iFzI2Yr+cg+vnl0844RKamosW7S+lFIsFQMJQAxaJp3+M4I/ARMnnoqgF/9ppeMkdg1FmdlPplYn3s/noM/+Ui6EZ7gNO+JabxvSEBAQiGq7xVnfsamzREKjMQobOHGsiVeMpdx/OGBXjz6GEETHSFJsM+b5zbAsISKhAX5Tj4caUs6SnNnA1UznGQdqDj5spt5q1Rb3MQE5WYnH3pxHhuVDpnjtfgsFz54T4S1u3h2eKyqrZbBXDo5W6cc4NKZoh3jBK5+v9/MoooNHdXFdWLO8ntuL7oG9+0P45e/qKewDMPVmOP58Y9VoGq6JkN0EY218fzxwlPR8+62brQ3UX6cdLCboW5lpQUs3K2bBe9WPM+n5PU5gjl0Hwwwl6sz5OGrmxak+mbbQST83YhWLmJdrWzkoB3zCvpMSOEI3fFjbs+SHow4ho/3OC/zrayzokLrnZSRP9ztQw8lurMibWRpuKInE4UMVfa4qJBIhizE7R3hDiiaOlwyB60U07mwqo+Aj4CLcvJWi/NG/vVv/tOUBly7Kksw947bknW4OEGZjP6wjmD8rxIwcTH8fM/qNSx8/Cw+PwLgsgCN/EW9/9WvfmX0Ac477zwDRrTMAlJiieSj7ty5E3V1debza6+91kRcaWJJTesrEkSCcPJbJQgnEkH96UdAzmoK0Vdfb775pnlV/wJUl1xyCc455xyzrrVvRXxJ5v2LX/wili1bdmRMVl8jvWpcek6LkJBSoaK/JKShiVMBOGkiXHzxxWa/paWl5thG6tP+fGQLTCnApeHqItBDWRebLkwl7Int+sY3vmFAlWoCaPnPf/5zQ4EK5Qtw/fKXv8T3vvc9ziivMBe5LkyxX6o7IBCntmnTJjzKL92df/rnZLhsWXhjFPvXCbEA728EJFEyNhQ9oJKVyxlBPiemyvIpJGCA17HDENA6TM9lJ/Mk1ObxcVyuMCHz1yT+snagAYy1cRsl2lo/EncQmyHwZbUj4M5aMOhVu33t9RB+9rNuvM5X1eJSweWy8nTc+7Fs3HKLl/K1XOlkAC6G+R1gweGnnwmRNaB8O8MNb7iBORYlTtx/fwAPPxI04NBDbf75CygNTye0l6GayuvSmEUClpU6mXvqQSZZuj886MQDD0Swdy8ZtIiPQUFu8/lVlwO33hLD0sUEWQRz8mPfKy3YJxuFDejSbKpp1jX3XjnIk34cvGCI7rt7GZr7dCv2MX9w1qw0lhAoYFg+v4sTuZ64rRzIAPPx6vfVI0jmPjszjUqaWXQIGYMrV9n0P5GdnHQDDjsApcHFeICSdj90oJmAqA+BvFmcQGOdvUxFMei4J37ssmFH0MMcORZQjwcYWdhm7oVOMosKVBRDpqL3vc48hBky58vOQTmZrcrsECdluLFmd/qHcSoArq3TSlDDHC5vJgFI8gIa9hyM9QMnAVctfcjECAyXQIgFqORj7t69Gz/5yU8wc+ZMrFy58kjqijc5U2pk03/6058asCU/NC8vzzBGIgIswKWxCmx985vfNKraejZKnl1iFJ/61KeOIQi2b99uIrgE3maw6KRUuvVeY/nQhz7EfOIPHNEpUErNQw89ZEiGSy+9dMyAa6AN5W9rH9JQsCZLBQR1PGL3LJA3cBv7/fgsMOUA18DD0MkXihfYkjyl1Aq/853v4MCBA0apsLCw0CTuq1aAwNbf/u3fMmzifQO7MF8gLdBFY0Qzfs46XPfYdbiOMZL9xwmzgJnxYiiIru04FaxKs6OoLOKspQpoDXhO+zmiVx0xbNG8Mq/dJXy4ns+fsQcPnLBDIzDQvGTyoSWRhBgfLnLO9N2TTy3g5aKTbXwBPVQHHO+RUXLFluYQVj3fRyn5EGcQQXU0L845N405XF4sXECVNOG3obY90kkK33DsRxguUkyH6p1ksgJ8EMYwY7oLt7wvnQ9HN7ZsC+OB+/vw9nrmaGU4ML1KaoRUkqQWhnK4SgodTIxPUCbewWLFLBFAOzzysJNsHpPkm1mwmaoDPb0uU2fsL//SiRtvZOhptuw28ED1XpZUG/g+ueRU+C0W1E9nXdeCwLjzSC2iU2H0p9IYxUixrlZnlKCAJSPIjGQTFMgZn9i0Da+/fsc00BtAF6Xj2xvJwPD+VViQw/1k8j0hQYqd5qlkeX0ndU+rq9NkbgBFlWUoymcotUilFB635pQE8ISf+sgmxhVvbTVzGyBLxkLJyYgETuow/NvD+l2m8L2ZpUneK6Y+4LoPuypKMedOMlwEXElxF93fUtB4nlwMyd6zejUiTz49KoZr/fr1EJBS8V9FWAn4qCDwnDlzTITVUtU1YZM/KT0BheKJGBAz9U//9E+chLvhGMAlIThFWmmiSUTCz372M5Mmo7yoCilB9TeBq1deeYWhuqWoqqoyrJK2VdhfbW0tvvvd7xpCQffOVAIuAUCBQkWACWjNnj3bsFxiu/Q9FsAcTTildRz26/AWmDKASw6oTrBOrMID1TRrIAl4zTL8wz/8g6E4f/Ob3+B3v/sd/vVf/9UAMF2Q+lwX9H/8x38YenW4wzWA62d24ePh7GMvP3EW0HM5zBArTzyEckqMF+eycGUyCsG41KzMhNVkt94h76F2DrWWLmaBy6kKuOQY+B2URmduQyYL/fqoDKWbdZTH2Nc/c+bhg08Oth6lYje8/Wp11qNVD1rdA1QvK0Qp4wP1CRw6yNlkhiXOmuVmgV+HCaszBjlRvwYCLoYURuMebNse5gMqylplFPGY4zV1t0Isdt3AZWtWR/Dq63Gy9E6CQydBF0VAKGHvpAMUZGhkKdmwm26i/P5hL156yYV1rwWwt5bKZpwZ9zPssKzcgf/3j15ceqmLD22CV/pYloPsoDNlAJeALX9OHOpMobF5sgXEAwotpPylAeAp7N7uajgL6FuWdMCHW2Psy/n9JmPf3tTGvK4W6gJFkUfavriYuSC8manob+r3OfZRTsYWKny8v66Nqq6sd1U8DdWlXgpVKOhycpomsgafPS0x++Mv3qbe9bk1klMBcG0pL8GsO27vz+HSUaXGkrKR7jG1DCl0PPkMvsjSQedfcIFlmne9djOE8+/+7u8g0KXJe4X4KbxOAEgskOq96m8BbjFCAlFigSwA9O1vf/tdgEusloCWWC+VOVIhYTFmAlzl5eVHxqC+9PwzedAcs5mc5YSloreULvOXf/mXuOeeewwosvan5RbDJX951apVpg8tk7bBcEBJfetH4YS//e1vDbjU9suXL8ff/M3fGEXwZ555Bk888YRh1xT+aLeJW2DKAC6d7Oeff94UPtYFrYtYqF5hgGeffbZhr1T8WBesLjIlDSq2VcIZGzZsMPlcKupnJRDqCzG42YBrsEXsv0+mBfQQ7WVITq4ngpoKBzJZw8l6zghmHeAf201IYRwLCLYqGUzSj8lO5rCH2DdzDuDHbkcDuhFANUpQlSiCV6Plc9OSj9cfYry0LMFXj4dy8r6kclqczlmIdJBywbRMgEwTuso/0QZeTh8rHUwKXye09QOuMGcBVbjXzXo7qlEWZg0uHyPixEyaWw3HVVsbZxmLMF4k6OrojKCQuVvnnOPFrGoXwVac9zQHiz67MX++m/H8Ljz9lNtIyG/bHuBrjPW6XGTx3Pjzz1B6u5QHS1CVxnBTzkHRbsD+A06CN7JnM1TwmGGp5hZnfp1Qk0xoZzr3fND3MMxbgMut83+CT+mExm9v/C4L6Dvb3dGNxn0NvIZDKMjLQgkFNZJM17tWfw8sSF6w3V19aDjcDr8zE/NnUSqf31Ve2lOunQqAa2d5KWbffocBXALqqbqrWYBrLxmuOAHE52+7HecdB3Apj0lS6CpBJED04IMPGul0ARg9u6TiJ0A02L+UmJvWHQpwDbwgpPr3+c9/3jBVgwHXwPX0XvdJ7UfK3GK5FMElEQuxbRbgEqN22WWXmdSZ//zP/zT6BwJlEpqTvzwc4FL/KrGkyDD50osWLTIgU6GM8q/nz59vGLvPfe5zpgSTlArtNnELTBnAJXSvwsdiqgSiNCsgGXideAErzTDo4pGCisQzdLGoMLIuvrPOOsvMPAjRD/4iDDSRDbgGWsN+PxUs0MWiudnuEM6oZLjgEcCVfGpLALxP6ISNpTKZ4TM1m7IKWhxd2Ir9BnjNTVSgBuUEXMkRmyPo90T0Xg/TEHN5pN7o6QdcCtvjI4ZOGuXlRfUZFMO1rSev6cT648TZQcMQS9fYyDDCAw6G/TlROc2FmbPIbKWLe+I/AiLmHuN3/xPF/Q/4Ubc/aMJiXDz8WTNd+NxnM8nOexmawrpdWcprk4iGAy+scuHtDRTQaCJL0BEjg5fAXXdS4CDbia2UnpcAx1mU5Z8/JykH/dijXs5Eujg7yoLUF1FB8lRULNSp47kMhciGUg47LTPdlBM4cWfU3lPKLcDzSUkgBLp60d7cAX9rF8+r1wCvLErHJ1HIif/upvw4j3SomxFDNnn/2ru3kRMwUZwxt4ITRe7+cLgjK06JN1MecH33Pmwm4JpJMOTJyjQ2PDaUeiJmJMNFxZi6l19Bx29/h2sXLMBcyp8LzAhcCFgJRFl+YyPVjFQQWP6nGCXpB4jdufzyy80koAWCrPWtkaUScGkf1n4U0ihGTOIcAkJS4hZTJsD18MMPm2XTp083EV8iI5TnpZBGiW0MzJ22xmm9iq0TmBLAFADUMYrZEhOnvuV3SyX8Ix/5iJkwlXy83SZugSkDuHSBCXRJ4EJUrWYTxHIpPnYga6X19GXQero4dFFpHcWeDv4SDDaPDbgGW8T++2RboK8vSh4ohGqq0xVSAU689gAAQABJREFUuTDJWpzsUY11/1TbQhjNhFt9LBBcnGCNHlBG9whaend/+n77e/xktJJ5XpKSz6A6UjLZmA4N/TOBElaJ4Pc7YXKbDAZ7d1eTukRgqqsriuee7aSke9wkqwtorbgsA4vOSuNsJ+WZqSr2wvNO/Or/CzHntIvjZl0dJXQw+cLnTeAzf5bJJOlMlJe5EIkmTF7a4cMEmyyWEyKr1drmQponhjlzye5lOvHca240t7J0KhPj87ITuP6yEKaXxvHaOg927iJrdi5B2PwI++ahm9DCpAmMffr9WhPNNamWmUDnHKPCCnvaKaGdnWUYzSTAnkCf9qYn3QJ6Nks6vuVQI/O6Ogz7W8R6XTm56eZ7rc/fS02Hs3v3YfotUdY5y6e6aBb9kamXv3YqAK4dZeVkuG43OVxEXCm7TAzDRcC1n7lR23/4A1QzcqKUE/N6/kiFTyGCYoLU5D8qGuP+++83eVYSk5Aveu655zIM/CazvoDMUKxRqgGXxiP/VszWAw88YMoe/cmf/ImpN6txipl69NFHoWVizQTMPvrRj5qaWhK7GGqM6tNqihy79957IVVFMXoiK6T8LbZLOgkLCExlC4HPp556iiJRh61N7dcJWGDKAK7hjsG6SVtgyvpb61vL9F7LB/6tZYObDbgGW8T++2RbQA/tQCCEoowwZpZQKv5oxcqTPbRR7z+ZTkQhEM5y6x9LEhNsEXAcp8n1CrPuVJSTLMp8cDpdZgZREyiq0SQ1rsYmhlWyLlhFGejQSHzjOB1O0kcay766IBWh2il4QVGLXIKhlggWzvfxYZ2NokKGCwaAP/7RhSeeChNwUUSgg7LMQs48uRlkoT75iQx8+lOZmFbpxt49MTy/CmigLHwhQfYVK10or2D+HnO4lLpae4iiHC/46HOI5YtznwncvDKEBbNVfJkMP5UOMzK5rk+uRNIgsqVsE2fuW6+ftqPpM9JlU+scaI0p0jgUjVU5u13tnQRczPjjxNpJOblTxCTvrWFo4jTKAslt6GhpR4zvCwpYU4o/hrl+bx0sHdQe8yMHfsYM1kSikqkKEk+ldioArs10+KtvU0hhFu0nwJUaG+pJZBguMlWRRx/Bh1ZchkWLF5vTo4l6gRPzzOl/uMiP1GS+2KJ169ZBgmyqDSvfUmIZCgPU60AVQnWWCsBl+bbal4QsFEr4K8rSX3/99UbVUIyctV+xXVLqFvmgPDCxcVJHVMSXth/JF1bpJQEtqSVKAVHCIB//+MffBbikpGhFnRmj2b8mZIEpD7gmdHSDNrYB1yCD2H+efAvQ+ewLhJHlIuAqBbKpcGceNal53gx5fEk3PVWPtCF3MaqF5gHT75woPEfhwmK50zMUJ69cKSYm94HgIvnT/0wcVd+pWknA6eCBIOtstaHb70J2rgdtzVHmlfoYZiLpXCdnSx149RXJxcf500MmzM+xE0PwYZ9N+fcbb8zEpz+ZhrOXObF5U4yJyAkCLqn1AVdf5cDVVzsZAiKg5EBbF5UL15LJqvWQLQBqZkRx7SVhVLAQ9bCMIU9olHk0G1mMecMmF8cDXEAW7IzZjBIgwzaVmsnX4onU7KlmcDOZx2Xk4U/GyZ1KhnkPjUVXXDzK65sMdsOBBkQ4oZROlqG4NIfnO13zEO+ZpmM5QHn4bubi1swqRVamANfUOrxTAXBtJ8NVcytFMxipZAGPVFhRzzonC3PXrlmNxDNUKaQwx/FEM6x96v4kXQGJs4n90QSRxNrECEkxe6DYhbZJFeASUNJz8L//+78NoFIoo0CRQh8Hhggqd8uqP6txKjzw/e9/v4n2GglsabySoFe4oPq3cskGM1x1VE3UMuWsSYXRbhO3gA24Jm5DuwfbAuO0AB8H9EAl96uCotnesKnNlUPQlUGBhclpDMklJaV/LiPIob0Mta+jXoPC6sRAGLneASFs7x5fchv1lmS9hur33VtZSyLMlerlw8PHWLmMDMbyqx/+qJeT5Y9rv/5ABK+s7cKGjaoRCIakOLFiZRbm1FBEgyFEKjJLRV2yXA4mTkexfkMQFLsikHBTKttFZsuDC5c78KEPalkMv/olCydviiMrJ46Z1QmcvVR5WV4+xKXsBjS2OlB7kCGFVE6pnhY3YIupbcOKS2j/zdzm4cd9DE9kmCKZsNmzCNSujJhCypZ9h3ztN7DESLTvJG9mWX3ILSa2kDsJE41qBley8DrXqrM2dLPGoVe1sV1PyW3s3yfDAjpjYn1CzNNrPtCE7pYOpKd5UFScxzIPLAfAU67r7VRvcm731zWjq6fPBlzjOJkSWfkWc7g2llSg+tY7Cbh430/xheHyuLCPDJf72SfxpTtHB7h0KGKP/vmf/xmLyYjp57Of/axJb5F4xbx58445WgGuP/zhD0Y0Q4yUmCjrOAYCoJFEMzThKFbrxz/+sVHhVkifROSO1Czs36tCCpXD9ZnPfMaIxklOXiGFEtXIz88fkeFSuKRAmmTtv/zlLxuRDRVRVt6WhD9UcumHP/yhAZlf+9rXTM2wYw7Y/mNcFrAB17jMZm9kWyC1FhDDEw5FEKEzWlmQwHQyHm6yK6l89rRHnNjW5cJ+MjU+OunzcqOoyYwhnfLjg51Zs4ROeCDoQFOr0zj/JQVxFjk9fuiugBxF/AjqmKNEZ8RtKI1R2IrrahbR393D3A+3UbA7aShrwHAFuCSa0dYWoAQ0s+0iLsNqlRS5Te0sfS4lxTfeTDCc0MnaLAmGozjRyhws2TSLIhlFhQlUkam66w6+VoTw+hsRhpEmkMfUAYUSBvxxXHqZj4nLPiPEIYYqLJlK2pHEgFFnHDCkd70VIG4h4HrgER8amhl22OfAAgpt3HAtAVcxO9PJHKIp/FN8qraPxcWf8Z9A+ABQreOz2oSvxf5z3NPRZRyCLOZvuQlKudshr3OzbwuLaVhmAAMGZA3Mfp26FuBJDLGGVOvhFoKuTk4aMFe1KNvkO4nRJSzjz6l7Tm3ANbFLzwJc20qnkeEi4GIebypzuHRtOQm4ateK4XoSXxgBcClXSaCosrLSsEo/+tGPcOaZZ5qw53/8x380+VwK31N4n+5HmlTQNaDP/u///s+8WoBrKMuobzFKUha0mCVtrx9JwqsMktQGly1bZkL+BOwE3vS5mhUmbolmiOmaNWuWAUcvvviiCQ/84Ac/eCTXa6gxaJnGrbDCr3/966YGl8Ikt27dap7BEtBQTpjyvMToSUxDoM9uE7eADbgmbkO7B9sCKbCAwBWTziP0tKMhlGTFUJRHWXDmdAl46X47jN88qn33Uqzh5RYP3ulQfQ+CG26VQ7blsuIIFuTG4SYAO+L4mLeMZe9x4bW3WTtkJxXzCCpmz4zi4nMYM17KFY4ZjP7g2BlatyfgwpYeN7qilDZnntGiLNarSouyfw3z+I6Vjl9Mnx7CEtGQPPxJaf0PN2vEEYZHBf0BeBjb6KKUvfxE/Rgz0W579wBUHKaSIfDWegfeeMtD2XN+rocxV0zLcGJWdZyJ1wS31RHU91DW3RVFMe1PXTMmJMexcJGH4R1pyGeIYrJj7v0YGx/PEhIScuCdzS5s2Mweacfzz45h3twYw0HEXAlKWd0lO00uiVHEw4ld+93YVedi/mACZxGoVZQwD08pZBwAuzKhnapP7ObnyXb883i8karwcVd7B4FlBnwsJK0LWyFYytmTM55k2NRDAh1dCQJdFd4ESopZKNkn51zt6P6Pvkt+ot/WKI8usd+dDAuYc8OTEaUwjtiCno4eNO9vQoLv8ygwUViUw++57ntDncWTMeKx71OOcN0+MnhkuGbPLmcOl9cOKRyDGS3Atam4AjNusUIKre/5GDoadlXV4XJi39q1ZLiewpfuOj7DdZBysyowrFJDyu9SQWAV/hUjJNbnq1/9qhGmUJidwvIUZqjQO60vJUGVMJo2bRrOP/983MGaXwJuAlJirZTSov7efvttToQVG5ZMRY4lXiHAo0LL6l9ASDLtVs6W7ue6zu666y4TAqh8KwvgKeRxxYoVRtRCOV8SuLjzzjtNuKDCHi2gNtg8hn3muFSsWXW4xJBJ9VsMm0TqFEZ53XXX4cMf/rAZp2xgt4lbwAZcE7eh3YNtgZRaQHVtxHRFwyGUEQxVFvKhIT9c3u84msLFav1uPNfsQxelxnPIqsTotHby/YLcCFYUB5HnoXRFv+Nj3B/ub8tOL15a5yXLBTrgDjpNCVx8bhjnLgrTOdd4LNeXjjL3scPvxepOH9rJAnlYpDdEAFaVFsfKvBCqfBK5P07jTl1kttT6BG64A4WcnahmLMsDkmOYoP2tJuwlwKUaYQp/M6EdXNmsbwzlwJq1Drz1poQggNVrCFLf8DBkTiCZM6CyacKJaWVxXHNPDLFFwD6qL5Y5Yljs8CMjGGU+VwLLL3BTBcvDHBA5oNbex/bKZyVl67lfnrssMpE6Rxr/kcaxCNKoCXA5XXFs2+fB8+t4XVDunkQeZk2L4eqLwigpYF0wqi9u3OHB9r0ehrgmeN55HstY/4sgc5xDNDOrChsVu9XpT8cm9t/R5UB1BaW1Z4WN2IdGuWt3Aq+SLWxmqKZCJs84w8UQG6AgN3nNHbN/Gsw4FjrW/g+O+dwcsf3rZFhA9yw5nDoxEnUJMimzu6WbryEjMFFWlsPvOZM01cZ74Se3Pim/dd01HG5lKJYfFRVFLPycznEM/NKdlGEds9NTIoerhAzXLXf0qxSm7turngS49q5dg/hzx2e4dK0qd2vXrl2G3dmzZw/EGhUVFRlmS6qGKkFkKWLrcwlKCKgoOkMgRmyUWCitd80110CASiDmkUceMeF7+i5YrJjWU18CSGLRVHdW66m/wSGEOqECQBewhpiEMVTgWABN9bbmzJljzrdYKSkXSllRLNvs2bOH7Mes3P9LY1MZJgFNba9Qb8nkS2pepZgEMgfmjg3c1n4/dgvYgGvsNrO3sC0w6RaI0TkJMcTQEw+iIi+MguykkzyeR7kc/+1knZ5vSUckRuEHMit9iTAaQg6ckRXHdSVRlFBcwfJ3jN/KX29t8mDNG2mG7ZDz3k7H+IIlIVx0Tsio5PUnatEWVFViSNqqdi+293mQSXbEy9A0LeuLOXFxThDn5oa4bHiz6cGoh5Zk4mNkuaRaqGRn01L1/KUdjDM0ZH/Jz1ysAebig9BqWqoHsX7kXDkHIhgej0Aq67Vj4yYnZ0ITWPNKnADMx4RrH9dVHD+BbDxCwJXATZ9m2N75TmxvYK9UOjxDwKMvgspyN668yo3qau77ODayxjT8K/s122tgA9YS8CP4dRCJJbuPm6LSfRQlWf2mlzW/fMiimqLIVZZHoypiH+aRzaw94MbTa3wE3G4jAnJmTRhXLic4zxnY+YD9jOKtnA0/1UL6Ij4CvRzsbyCoNqA0ihXLw1h6ZpgOQ5yOB0NZW6jySP+VeNeUCLh8BXDuOWQM5Z/3D8G88Nwo0Vznx+uT3SdkxFEchb3KaCxgXSX6Th3J0+PCKJULmxua0d5AptOnEN1MApVsTgAcPa+j6X8qrJN00qM4UNfC4ThQPauETm4yF3MqjE9jOBUA16Yi5nARcEmlUDZNZUsyXGvgWjUyw6V96x6l+4mAy7//+78biXTJxys3aqCiocBTN5N1BdLMhA8HbY1dDJjAlAVWtJ7AjD4fuK5Al5g0KbUK/EgAQ/1Z/Qy0g5gtgS2BOgkOaf/ah1F55Yoat/aj5VpXY7BCEAf2M9R7PXu1X/WhbcRojXbbofqzlw1tARtwDW0Xe6ltgZNvAT53JCSBaB9BVxxlFGAwaQ9jHRn9z6agE882eLGLDEjUwwKlBHJ9vLmeXezHtUUuzHLmwZsQJdLfOV8PNjjx/Bo39h2UA0FWRjLmF4cwt4b8mFkvyZiILemOUha9xY0DfS7kMvTMRSdf4hxtZNHOzw3j4oIIVNd5uKZHrGozSUBEYWchPpz0sPLwQXRkTMNtPMLy5OObv/uPzSEHf3DTxzwoF0He6B803IjHtJ5hhC8QdLkJuPbujeHppxM4dNBLoCC7kRFiYevK8jR84ONkFG9xol3Ga6JISmsfigmk5811MwyFIX2TEbXB8bW2Ezxv9CCP7NDCM5gnyPDDN7fxvB52oe4Qc74CDpRSBTFCCXofL4FbrwpTHTGGDVs9eOYlLx/crEVGVq6iNIZbrgqhlDlp421yJGKxCPbsd+Hh5xhWyCS1LIKqQ80E/xT6uJ4S+M1UcHzo4Tj3G6ODkbxoVAZmbo0TN9xIB53HYQEu80pzdjMvzEkaOINJ96M/f+M9Cnu7sVjAcjCPbMPzFWJ9g87WbjQfbISDk0tFDC8sKOS5U2gp/6XY5z6y68l4o2t6zx6qMXLGYvacCl7TNuAarZ2tkMJtRcyZeh8Bl1QKUyzzKMBV+/IaJFaR4brrtlGpFGr8AiFSC1T+kgoOn8phdbpGBegEJIcCc8c7XwJu+rHbxC1gA66J29DuwbbApFhAsCBKUNTbTcCVG8Msqtgpt+aIszmGvSqgbzdzsp5uC2ELw3qcDPmryQ1iVmEnSpgbMxdlKE9Q3cjqk6AgSkdo3yEH1u70YB8FI8qnJbBkdgSzCP4KGZYoxkqOkbaJ8HV1uwevdzL3imPMcMXQwfwguh64qiCMJTkEHkc6t3Zy7GsyUK1fPKOr2zzg0igb3o/ujl15VH9xNlHrieHh+IjneNwEVf02TBJeyUEZCMGDeZdzONJ+iAfaOxx49pkEdu4EH2hxvLQ6RMaLrAsl7SVCISA1bVom4++BD36U4XFnOYxoiYN5cSqQnEZDKkxvMhpJNuytc+Lhp70oo4DGNStC2FbnwzrlevFfW6fLKC8WZseQm+3AeQsiOJs/2TR7Y4sLT77kMbXBWM4Vl54XwQVLY8hkjS/+Of5Gmx067MT9f+SsLtmrTKpyNja7sGxhFFddGkZbawQP3B9lWCnLJGQnAdfBQ3EsXeQkE8haaBynrjszBr7qnHWpVg5PbCZDFY0Yw0TGN/4js7ccpQU056HJlfbGdiOoobOcTrarkIWSXQTOSZbo1DiJNuAa5UkfYjULcG0srMQMAS4jmsHznqJTryeAy8scLgIuz/NUKRwD4NJ5baL8rELsxBidqhM5FsBSDtlPf/pTEz4oMGktH+K0HFkkxkt5XJ/4xCeOLLPfjN8CNuAav+3sLW0LTLoFYrzh9XQxrJCAazYL5CaVvcb3NPInKFkebcK+iB+ZDi/yvDEqAkYRdIQwI1GImkQ5PIRIlifbzRye5+q9eKzOiwPMAVNR5rL8OOYVh3F+fhRLM6PIdzPfScOhA9XIwsCvdHixjcIZQSIcqR+eTUf+AuaJFTBHzKw0ksXoPIvp8gtwMWcqjSEU4wVcZlgcVxdDIbeI0dlPcMNcpIXzKcc+k4ISfG8eOkMxXiONs/9z7UM/EszYsMFBBcIEnnwijL21ZOv4gXLftEI5izcvO9uBG25w4aqrXSjI13ICB74Y4DfK/Y15NfbPlDjsO+AyeV0lRXE88Woadh10glFcaOtWCYIYli9ksjRLEahuVzHPsVgk8ZeHG11Gpj6dn1WWRZGdxYLKYugm0rh5mOGML7/lxhtbGB5D1cyyoihzx6KYNT1KBi6Oxx6N45V1xl2Ck+C+tdPNJHIPzj07wRpjcRSXxOCU/QxIdqKbQhwOhq5lspCyU0lsdpvyFtD3RvW6FMokhVZJx/vbu/mdcfH85hj5+GReqdacuk3XoM1wje/8WIBre2EVam4Ww0WVwqMptOPrdNBWTuVwieF64YkxMVzqxgIlY56IGzSGk/ln8h7pYNmSPxrFQeVrSdhD4YgCVAPB18Dj1Hb6XKIekpC328QtYAOuidvQ7sG2wKRZQICrt5viGTl0RivIiBgHfniHV58M5Z5oeZBCDdtwCE2OLjCYC26KOcRAcEOqZUaiCLMTpf2Aiw4xndknOpz4RYMbOzsItliLKgdBZDLkbjoTw2dQ0lyhghdQhTDXyMrrOUkhDrJaDZSS95NNymFoYRmdedb+HZHdOmLAFAIuQYYg89TWvOLEhnfcfLCIPXMwFp+hkQxdq6yg005rpTGkzSUZxaEMd2Rgw7/RQyrGh1OgT9LwMfzgBxHU7SfgTFdwFPOPWLtLY5k5042bb5LalJPhefxknPsbfiTDf0IMo+hHI0CxbrMXL27wEvTwYcvztHB2lGCLBVwZYtgTcJJximNRTRQuAu56hhxmZMaxZAnVC1mY28lzrWOaaBO7FmCYa0ML8/8YhlqQFzNAT2xgmHUFHnnci+eeZ2jmoQTqmiisQTEWXr5U0Irjkgui+OTHIli2JEb2kLmODE/r7eyGLz0NGWK4JgoIJ3pw9vZjtoCcu4hyu/Y3oLW5g6GtHtalI+hSvOkUbxq7DbjGd5IswLWxgAzXzf2y8CkGXC5OFO57hTlcLzyJv7h79CGF4zuiqbeVBbheeOEFo4K4efNmzJ8/34h6SAhE4hiWSIfWHdj0t0RDJP5ht4lbwAZcE7eh3YNtgUmzgO5/Uc4COxNkiTIobkHmIVOxfEN4vbo50kc2jrV78ApcrufYwUQrdjsaEaX3Ss09hCiekcbXMzANZYlcbpvs+B06tz9oc+GZRg+c0R5UZNQTcPnhDXqQ01eGTJSiijllN08LYjFB10BOQTW4BL7ElZm6Tnrt/3esoaybu/apH/6dQsAlSfaDhxN4/Ik0dHU6kU3FxziPq4X57dmZfRRk8NM5Zz2yeW4sXeJDbq41jmNHeby/RKYwLJ59MgeKKo47dsbwy19EsWlznACAkufM42rvFJPG/ROo3nGrA5/6eBzTK3ms1uEfbwcp/yyBXioovrnVhZdfd5NJZChXFq8xxh5mZzOHi4CnjyC1gQyYi8xYPm0W40U1n2GGV14eMWA1eYVMbGA6dPUjMQ9dlwpxNYCQEwoHue8HH/VRahtYu96FN9d50NtBcxnKMFkL7oYbYvjiZ0JYsphqioFeSo0z54sKG27mhCVh7sTGZ299EizA736Y4LmbLFfL4TYkImHk5Kab/C7V5hvsDJ6EEQ65S41rr8nhYpj2nPL+HC7rCh9ykxO68FQQzdjGkMKam5KiGfyip+zemOA1pcLHJodLDNdpCrh0wUmQQ5L0Ul58+eWXTWihhECWL19uwJfqb+nvUzV08oR+qca5MxtwjdNw9ma2BU6kBQS6enuDyPFFGFpIYYoMKzE7yTh0EDjsQAQHyVhl0Lk/kyCqipCHGgjHtCDXOYx21Ds7yVdFuK4Xlcgn2KJSUv/aYfb1B+YX/edhhgeS9SjPOIjSvEZEqEDoDsaQ05uJRH01ndxc3LKwD7fPCyGfTEPShR7JHdd6HLNYCLMqf3GRgKVpKQVcwP6DDjz6BKXtydwU5MnBTxAMRVlsuBfVVZS37x/3ypVpBF2q9US33xpL/5CGexHYqq2L4+GHgddf1zExt60wjpbmGHbvJbChQEmIoiEhhs9J/KGoNJ0PNhfu/UiE+06dUzHc+IZcznNLPxYvvOTC62+6qUrowNYDHjg4C7zs7CjFMyRvzHC/1QwuJft19kIqdjEXzeuN447bwpwNVUGBkc7xkHsefqG667e5g4Crdj+LOD/kZjFnNx5f5cHe7WRipSyu9QjMQDtXVCTwJx+O4nOfYr6Zj4IZPBnpBFwC2XY7tS0Qo8Pd2+lHQ+0hCroEWJuOghqUaU1PH3w3mxrHKcC1Z9dhMzE254xpZAuorqp7mnVRn+RhngqAa2M+GS4CLk+GVApTazAxXHVkuNwvkuF6/+nHcA20poQzpGTYwllHFTZevXq1KfQsMCYp+JUrV+LKK680DJjqcQ0MMRzYj/1+fBawAdf47GZvZVvghFpAD/UgazbFGQOWwbyrTDrAxSyMnEcNdvrPeMsZxmYGCFKE3IS3ldIBvYgc1rSEeCY1PcWUlUP1QK4lsMVypCy86zYMF7X5+h1phuFxzd+x0580eLGX4gYV2XtRlNOKvp50eKIRlEQITBpnsUZSEc6k+uAnzu/DWRVRbi+OYqRGJog+c30vFQ1Z+0nDqsqmA015emGdlDJc9L17/A4887ybIIsAgqbg8FG7t5dOXABzZsEwOg1NMYbMebBihY+5SxrTyE67wFZ7RwI//AHw+987KJnPRH/ms5UWx1BRGaZzqKK9ANWvmUPlYU5KGmbM8uL6q1kA+bowiqX0l2LHYiTL63ONu57n9NFHyLwxZPRwI/POKIrSyfNxRk0c57CwdUVFHOsJxjoaqKA4K25sVjMrxnBIFuQuntxBq4RBL5m1Z19g7uDjPjz+jBvtZCQTBIFm8KogxxDY0vIEli5z4jt/T+GXaZ0sDs78PAEuAXm7TdACsqHOs34se1rnnRfQJF+4+vrpftfb2YvW+hb0tHWbgsJl5XkE1b6UO+Q8oAm3znaOtZWh2plp/K7nc4KCwcqWySbc+8Q6mPqA63vYTsBVc9OdrMPFyTwx2SlrEs3gJA5zuGIvkuE6zQGXZVZ9vyRT39nZaZiu9evXYy2LQ2/bts2sohpiyttSuKHdUmcBG3ClzpZ2T7YFJtkCSmIlYCJiSTA2zsvZfofbjy5fC3ZmkbFK96HQWUTAlEEoRXGBhBtnkbWywgSPNzjLtYpQxrzLEcCTFK74bUcGNtbnIj3RgWk5dfDF++BhUeO8jlL4WivR1pOJfOaYfWRpEJeeGYGvv2zW8fYTZg7V280US6AYg4owa7+ZZJkuLI9gCYUTNIkdE5vX1WNEM9IHqxRqA3kylh94vJ1pVf40NjnxzkY32S4nGawE/L1+tDT1MT+E8vUcc3t7ApdclGYKD2ey8PCoABeZmLUvJ/D1rzuwub8GF0UdTf7TvDNYr+oKByWieSwECp3dDIkj6CoqcuCcpVHMIEvEyLeT0hzMwardR9XChzzYzzpb9cyRaiDYaqCCZZ6RqI9izlzWaQsyr8pLcE9z5DGs8NxzEpjNcgAeAv1J9reNXToo5vHAgz782w98FCBhKKgBXGR1qa7l8TD/roYA8QwHvv13QcyeLsDlIuBKswFXSq4qTcyQTOS5V9K8CcvS18JMROiTE9MUlhxmgeSW+iaGGfIccwz5VHrJy88yYjRTBdDIGgIJDQ2dDF3updhHBiqrCqbMtTj1Add92JhTheobb4dHdbh09aXqMuM142Z+7r5X1sK95gl8yQZc5ssrwKWmCSqJZvj9flOY+Sc/+QmeeuophtnPw49//GMsWrTIrGf/So0FbMCVGjvavdgWOHEW0M2Szk+EwKjbtwedngYc9vgR8oWQ584ndzWT3FY+Lkz4MI8MFp85x23Ws62HUO2AowVt6EQ90cOrfZnY0JGPptZsRLviyGFcV2nchaJANhKhTAQiDJMjOLrrrDAuW0BWh/k/x2siH2rJBD2z34d2hrJlU1RDOVQdZNPyqYJ31YwwZlONUcplvV1dfBgkCzAOPAA9IJTg62JOx8Dlx9tvnLbqo6BFH3OXBLB6e6N4bV2I+VZSxANmzXRhxWU+TK9SDa7j9ZT8TPaS3PpDDznwj9924EA9C/RSdCLMsDfqNrCuVhh3vx+45lo3HUTVc6HEPo9R+05LSxAcpDwob+RB96/hoPx8N/O2Hn7Eg8ef9FLBUcIpVHKk2Iknh3lbPCdnzY9hxfkRnEflwgz+7eEMMQWtOG6Fr5qjH/X+xr0iz8PmTbTvdzLwLGvB9fD6o8gmDUx2t5g1wVg77tZrkyGFuRmdFGWxGa5x23rQhjrLCo89dMhFsRkn1TaT4abTygWB2PRFPgHN3Jf4K0pWv7utE037mxAPxZBfwIke1uzyMeHQchxPwHBG2AVDdTkR1trShd6eIIpLc1nQlgqro71JjdD7RD4+FQDX9lwyXDf0i2ZM5GCH2NZJwFXLkMI4AZfNcB1rIMnev/7663j22WfNaxvDMqqrq3Hrrbfi7rvvRklJybEb2H9NyAI24JqQ+eyNbQucPAv4vfVkt3Yj5OxDGxUO2t2scOuMUMmwnEBrPs6j6mDhu7K4Bo836UAHGZi429GAJnQQookTc2NPyG0k3lsp835oTwmCh4tQQqSRRn8rwDwxD4tv1VDu/VYyXMuoakdV7hHdi9ca3Vh7mIGM7COTAESz530sxEsRRKysiuCcUqrjkWUL9QUZPqnwsaPOnWbbVQg6g6yXFOnG3pLHqoiVTjrwzU1JEY3iEkqkMzzTrUGNohmCjccqhuvv/s2LDQd5PBRtcDQTDDhjKCsLY8VKFum9wUlRBydycrhD/k/CrOQYRrGbSVtFI6jb58Kvfu3BG29SzCOHYZ3VdGzpWIcItG++PoLzl1HynyIaR1OiZBuyWyOeYa6SikYg7g8k8Jv/S8dPH/Dh4F6yHRxb2owEcqsduOXMMD58bRhnzuE10ScZSIdhuFT8OGWz46k4jlOwD33levwJ3H+/F4fqncxNYmH06ijuuCPMsgAn+oDId3A8CZ77TobsdbBul+4NGWmq2cUQQ1Li5h5hvmK6sk9WS34/Iiwsd2B/q2FVpleXMApWjPAoZnEmcdinAuDamF2J6dffwZDC1F5guiIEuPa9yjpca5/AX9xzeuZwaWLCepb29vaa/K3nnnvOCGgcOHCAuceFuOCCCxhWvwILFiwwqoQ5OTmmRMMkXpqnXdc24DrtTrl9wO8VC3T7atGRthsBF7kpJ7OymGweRQhZbh9qXNMpnFGDAv4bTTvsaDeAK0awQ8F4OtpOBOIR7An24J2ADxubq5n7Mx1ufxry/cxVYj5ZFX2Mi2dGcMUiytZTkEIux0je7htNbrzE2l7CNtmmNhdznMhcBBiqeEVVFMtKwnCThUnG8RsvKjl8eoFRgq0ANdZ9pJOUrzP+xp3zf0zIiw8isVpj5Zz08Ko9CHz718w1esuDvtY4PIeC8EZ6WMTYT3U1B0PesnDr+9Jw/XUOFBdZzmDSSuMf+0S3VMAOw0h47Du2u/HoYxSnaKP0O2MHYwS9S5aGccWVlGgv0FpDN4OB+aHExCa1cR/rat347Ss+hKgy6Utn8WzaNU71x48s6sPicoVmMmeRYDfAkBgXEb/qttmy8BM7K2JvmxmG++OfeFgygUw6rwvOfeBTn2AOnya8NeNwohv3GedPOBQ2DHhHawe6WjtRmJtNdc00hh+Tydc1eRKGJlNo3zGCwsP1bRToCbHOUTEys5RvxokY84U50QY7ur9TAXBtJ+CqufFo4WPOt6SkqRuX14W9YrjWPnlaMlwW2GpubsaTTz6Jp59+Glu3buWkgBuLFy82IGvJkiWcKCyjUm8uozDs0OyUXHxDdGIDriGMYi+yLXAqWKCPoYRNGe+gy9PI4rlJOWyF32TEslGSloM5numo4D+WSz7u4UjEfQ/ZrUMMJnTR2/JIaIPCBOHEIbzDme4nO+dhV6gMXb056OvMQhrl4s+jF3bn9C4smVuPzLwuFlLOIJtWzOwxKhuZvSV/D9yx/I4DPQ48vc+Hej+ZH4bhMWCJ4YUuI5px7YwQqnMU4Na/7YCHrpwWFUj1d/fS8Z4o4OoflTXEAfsZON7h3gun7drtYFiiC2/sdmN7sxO9LfRKW4M4VNeBjnYVPU6wUHQ6xThy8Ndfc+KKyxVOyB6P2VcS/Egowvhk+qUJcf3wrYZnhsht9Pd4GnEzO9FOGQyoPCjjkSb/loz9vn1u7NjhonIVi1ozZGzhgiiKKYwhomhg00g1GIUUBgL8zQFJGMTkB45zbAP7H+p98npx4v4dPvQyXDOb+WPtBOZF3O/dc0MozyQD2m+YbiZ/O5nHlWEXPh7KlGNbRsMHgwk8+KDXXBtRMtCLFkVxyy0sjs26tMdew2PrOjVr99fsYm5XT2s3vzNRFFGoIjcnI6mrosv7hDfl1zJkencDJzNilIefZliuEz6MIXY4pQEX47C/9Z37sCmrCjPIcCVVCnkCU3QOdQ9xeZIMl/sV5nCdhgyXBbhWrVqFb3zjG9i7dy+WLl2KCy+8EHPmzKECaIEJ09cz1lrXuoz0d3V1NWbOnGktsl8nYAEbcE3AePamtgVOpgVizhDa0rajMX0TomS43MzZckWz4QlnIYv5VPN8fIi5KikOP8h7HjBo+coxOuR7KBZ/kIBL4YQUf6dn3Y6e2AE80zUdz/gXoqk3G6G2DIR6vYgH3ZhFQHZbzT6srNmDnPQ+48cXJAopRV9NyKWwEPX87kYfHzvbXVjH0MLDPYJbzMeh47ycohln5MfBycghmxioCPXMBbi8RC5pmVSkG3LNyV0oANPWCtb2clPdiaCDIhstHQ7sZD5YS1OUrFEnRTik88ico0Q2c59y8JUvO/HJT0nBkIt5/HIC1AyI4W/leEnlUEWacxl+mJWZZJek+9jGjLwQs6yKeE4yjyhOJrcf/re8FQqS8Lyq5tjBQypmnGCOGnPNCFYGthidxGCIYWOcnfcyV8tn8uoGrpF8r6003q1bE3jrzWRB6WVnO3DWQhV4fvf6KVlCO0U4wA0tbrzOenAdzPvLY67fRdMiWFAQQ5oKbvO/HIWu9g4KyLiQaQOuCZteV58u0eZmB3bvkYmddMyoilrEq4q5XVOi8bwL2CjMsPFAIxIM5SsqyEJ+UaZhOnVdnOimkOfaPU38PkcJuCrgZv2nkzGOwcc9pQFXiIDru/dhR3YVZjOHyydZ+FReYryQnZSF3/vqasQYUviF0xhwPfbYY6bw8e7du8kKixnO5j0/mXgtYKU2FOD69Kc/jb/6q78afFnZf4/DAjbgGofR7E1sC0wFC8gtCru60Zy+ncIZhwi4WMI4lkX3PAIH86+mh6djbk4xcrO0pm6ocqOSzlTS2e8/Cjrm9azNtRPUAed66ZTcSDha0Rk7iEe65uLZ9sUEE5mI97ooFkGVROZ0+MhQnZPbjLvmHcCysm46uyFuSac+MRMliTK6aMOBPAInPlA7yFh0BJMqhflpcYpm0OEfbhONmk51lAxXL6vhyiFUzSU3692oSKM+m9yWtJ35zTHu2e0k4FJuSwL5FJoQO/Tq63Hsq6MCYG2IQhR+jonWSFBNjeGdX/x8Al/4QgyV0zRKqUIRjhFkORgO10XG75XX3HiNYYkt7Q6CsgSuXRHG2YujOJARwDvoQy/DPGt4bpcmMll8mudgxIOlhTjOpiYXnn7Wg30HKBbCvy+7hCGbZ7P2mgCSeb4O6Ml6a5YPsQNu384UwYceEohLhpnlsQj3Lbcw/6sqeWUNsdWEFul60rBCMSfaCUYDFB7JJCBk/WheK8nPdBw24JqQmYfYOHm3kOcrVVGdXa/ALd9ad48hNjqhi6zLVyAn0NOHhr31iASC8KW5jWBF5pGLXGtaF/dkDjHJcKkel5zW2WeUJ/NfDANrRjuZOz9u31MacPUzXJvJcFVfd2dSpTCF5tKZdzF+fd+61XCepgyXdXFs2bIFjzzyCCdSms090wJZ1udDvWqdq6++GjfddNNQH9vLxmgBG3CN0WD26rYFpooF9DDRs6nP3YlO736+tpOtIksUz0JOcBrS/QXIoqNUTllv1etSHSqBMUpFwJmgXDy9cvWhHz8VCneR5WpydMHjYJ0trheM15HdyscjB89BV0caitKayZR0E2h4EGjJR0E4DVdN78YV1W3IzpDGIQvjJqpRkag8DuDiztg0boXmqUmYQWMYqenmLwlb/YToXKlAahbl8zRLdwyAHKmjcXxuMJ1+0b6NDQ488ijrWDG0MjePoKnLwfolcTQ0kSkkGGtqZMgT84oUdlfGnJdvfINKjneDYh8Uq6h3YXutE4eYk9TMUMrdW1zY+pqT0tdSMmT+HMHEgnlBXHdjEL2XUgiFzF+2L44ZdBquIHNYGffStiNbS8D4rfUuPPecFx6GbnZ3O1EzM4rrrouMqwaYCgo38vh+/7/MtwsyV5BhZsxFx513gf3SDR95SOOw+tFNdKnoh7s8tnGhDbiONUlK/7LOa/93NaV9p6gz5XuGgiE0HqR8fEsHr3c3yksLGP7o44SMwqS0o8k+AE2w8Ht9sJn5hCxbML2Q8vCqKTUgRDpFxzvWbqY+4PoetmUxh+t6qhTyJqn7vHXZjfVYB6+vs+7iTF7tq2uQIOA6nVUKQ2QTJZgRZcmXsTQVQNZz1m4Tt4ANuCZuQ7sH2wIn2QJknRwhsl0BxB1ReOLkqGIZxgGQ0p8q1xZlR5BV0IoeMmEhsiY5iWLkk4kKkzEJ8amUJYjlCGM/ma0mRwcFOKJ0bv1oiLqxqrUAB/2sf8TPY3QqEhEHMnnTzoykY3amBxdVdiI/qxvp8WzMTMxGHoU6UvXAfJdh+/0msV2hcNg420ryFdM1WTsVmPCzgHI7QwedBDJBzvq/+jpzn7YQwDJ1K5NhetNYLPgwgdaaNS6GFzInisIiOelxXHIVcOkHmb9VzvDAw6zJRbDl97vwdqcL+3sj6NoURzelt6NdPBY6Z05Gc/qor589gyDmogjci2IoXEgANj2Oe4vTUK0wJTYSaMdtkn/ftduFR//oRQcLHId5zpZfEMblKyPIM6qJYzlD2hkVDHmsa9bE8dZbZJ3ITi5dQtbsMtbpomDKSWscmg24Tpr1T/6Ore8BL2cJavS096CRuV0JCuzkZqejtCyfE00W6JrM4Sa/T6oh2ENZ+I4OFmvOymDtvRwjyjOZex6p71MBcG3KqMQMMVyalUoi5JEOa9SfK4erbt0auNadnjlcozaUveKkW8AGXJNuYnsHtgVOnAWSYVgDnGl65n2BKAUc2uEt2YEIWSpmQhGYUYAiXo7NkSrsZ97XdEcWrvZkYJY7yk8D/Ccoxhwrbr821IBn2v1oDKSThXEzTwyo8AVZGDeO2RSwOCs/hCLOKhcnygm1ChnayOrFk93oaEmYItTXZxivNCYSeVSba2AzZlB4lOWVDfxwNO8VmuegOqMDL7/sYQFeF1rITMWZt+XLT6rjnVEaw3kUmqiqJHBifav16x3YyCLLAUqaF1ZTyrqGBYYJSrsp9tBa60BBD0EVwc/uWBiesnqE3o6h4ZE8hBj+p7wvayY+4aP1q8gizSFDtSzO/DYXPrMkgVso117Ags1GAGPY4+KBk4kTKNq6zYmdu5jbxG2WLomiooyCGEnMNhoDvGud3t4EmS6pHDL8sYSgksWSxSKctGYDrpNm+qm4Y4UYdnf0oL62HvFwlPmQXgO6fD5Pvx8/3nvB6I9WaoV7dtWbSaBZs8uMeEaKMcToB8M1pz7gug/bCLhqrpNKIZmUFBvLyUmqWgKuxKunZw7XmC4We+VJtYANuCbVvHbntgVOsgXoCyuqpS/BcBvfBoTSDsHpY/gf2bD1kRq8Ej4HDfF8pJEgupw46WMMv1jICr0KN2TSFnpjddgS341Xe4HtPaXwkzlLp5JhHj8uzwjh4pwCLEgr4jICA/4zuVsMpZMLPpmuTdLFV0HjPgLKAHM3KA3NgshH4tL7Y9zEfKk203jyvJSHJWZo1fNeA6J6BGAImkLM3Vp6Xhw+5jCVslDztSwSPIMKf9q3hC84JHQz3+ilbh8eb/FSXp/hgr0MNzpMdot2DLKPQGuEEuZdiNWzEPM7HsQ7Yqw1xMQ2Kq6ZxgN08KQ4KylzXkOh/rgH8wuBP70+jBsvi2BaaZzAiRYe0sjJHBz9DjO0MRQieOP5SmNooUt1gawd9L8b2wuBpokF5djIomn/47HtwH2aU8Xxqa8xJ8z3798WzRho0dP7fUzfQ38QDXWHyXh1I5uhheUVhZS4Z+gxAdlktzi/H/v3NSFMwDeDtbjSM9J4b5j8/Q53XKcC4NqYRoGnaymawfC1I/fw4Q5ojMtdqsP12hq4XyPD9YHTsw7XGE1mrz5JFrAB1yQZ1u7WtsDUsQAdfnJWPWlb0JW+nrleHah3ZOO1+BzsjVfQgZZKnQsFLNr7Pm8Id7kLMT3CYrJ9bxFwHUCtN4pW8l0tfWloCOUwT6wE2en5KKa64CJvGaodldTQo+hD2IkdFNboCjtQmRbD7KwYa4Il+aUkQEq9RVSbS6FEaoPziLRctZnSWSjZNWZah+wWAVcvj+eRR1kAtsEFP3ezZa8LgV4Hlp0XQy5BlnDp9eezAO8M5sURgFhs2voOF55qZv2yNjeitG8sQLENyq8fOkBAVkuUQCl5B2fCEyECtVYiYjKITEwb4JgJcXEdD1Wk8jIgDX1F25w7h+foyhBuvS6MyvKBhYkH29ayuBBJ/2d8m5pmdWR1PIFeaWOeQnR0inljUeNsJwtoj6FfHR5PvA24JnAO3mObWldnsC+EzuYOtDW0UvTDweLmVDEsICVrlBbHcI2N2T4O3jf6cHB/MyeCfAy5TUdeAUMLJ3OXxxnjqQC4tmdUoeba25nDxfNjTmBqjKX7sYsTibXr1iLOkMIv2IDrOFeK/dFkW8AGXJNtYbt/2wJTxAJRKhr2EnTVs2DyHqrnvR2vwuFEOp38KBmZPGZxhXFB+kHcg25c0NuAjHAXwu4M7KSDUhfroSPcQynuIENUvAj7KpHlmYszsIjVt4oN2HqB8t27ez0U5kiA6t1YXhDBuQUM6zFAZJKMwIfzcDOikpDXA1e1mcYOuDReAlWCgedf8ODNN13o6XMScDEfiozVkuUUJ8lOoKo4huuXUyyEr+L1JMUeJsB6qpE1jMhqtfS5UEeA1tbGMMI33Ggl4IpSfCLB0ETTuD66uJOGHjj8kte33EU5HARchLIJDwtRUpovu9iJBTNiWDw/hntuC+JchgimSdXX2iTZ4ynzWwA5SAGOta9EsHkTE8R0zSz3YMliL9k4Hf8oGo/dBlyjsNNpuIq++2L3Oymk0VDHED8y7/mcvCguIfgh662v2tHvW2oNJJars6MXrS2sE8Yd5RexNmJxnrkfpXZPI/d2KgCujXyezLiGohliuGi7UX77Rzx43RpdDCmsE8P1OhmuD9oM14hGs1eYNAvYgGvSTGt3bFtgallAD7EEhS8Oph3GVgKrvc4oix170MQcri4CLhflxytYTPl9zjdxZ6gRVfEiNHd4sWpHFFu9ZKtYmLiipA8FGb3wOF0o8cxDjeNSCnBU4o0OD9a0MTSOXrSUEdvJcpWT5bqqOILpFI+Y9DYIdMiZ7xHgYghRelYm8yiYuKR1xvAkl0OmfppbnXiDYGkXc6HaKZ4RpZKgj0IRpYVxXLgwinnTCXwoV55slOrn4f6RdaN2BzxIYwcKL9yy1YWNm104TDXCvoPMAyPj5aDyoBlUZxiJ+v44RImcHEFQlICnYmTCwVjADB9Kpjkwb3YMZ5Dl+sDtEZx/dtiECR5ZvX8Ep8oLI1Oxe2eEDCIrjdHJElGZT7vefmsGKipGmWhmzpHNcJ0q5/zEjjP5BY4xrDbQ60eMIX4dTa1IUNgiLy+b9wSK/2Qq3M/67qZ2dOq3syPAHNB27seDmppp3BenoyZnd8MO/lQAXNsYUlhzDXO4VIeL/zQPlYqmbiSaUUvAlSDgshmuVFjV7mO8FrAB13gtZ29nW+CUtEACrd5D2JWxD3u9fhwkIDgczkFfNIPQi/W7+ISa6ziAj2EbLor1YgsZmYcO5GOfj8nMLMibkxVETW4Hzs/uQWVOCXI9lxBwLcRrbVlY2+41tbSyPXE0s5huAUHINSVhzMwQiDixTVCmp6cXSqKXrK3bEtSgt2Oe5SMAL/PQJzozdccY+hcIsDBxXzI3SsWbVZ8og/XDcrNUrFncltVhcstXaIs1DCdM42c+gomNb7mxkwWId+13opXS8fEI1yPJ5WDOF9rphDWS5aISJFg09Wgik8pCcybewSLPTPrPLUqwoGoUNzOP64O3h1FdebyQwhNr77HuTedAio/btkZZ20v1kxjB2pcwzNbdd6ezSPMgAZThdsCObIZrOOPYywdaQCI7yu1qOdxC+fhOU5i4rDSPwi9UdDWCNQPXnvh7XZcBhjXu3dvA/FI3RWYKkJvL2RrrVjHxXYyqh6kPuL6Hjd5pmH61QgolmjGqwxr1Sk4C67rX18Lz5pM2wzVqq9krToYFbMA1GVa1+7QtMEUtIDjQ6duHVl8dDrrD2MSHfx3rdsnfSHdQgIKS8unxCD7i2IhLUYt1BGOrqGTY7MpBwJlBEQY3ckIduMJTjxUMGSxPW4rcxDLU9+Xh2SYv9jPsTrkKmt09Lz+CSwspz3xEtDDFT9Lj2Fg+TS8BVygYZDhhskCyjt3r8xqBDTlDx238WIWN6/ZRLGRHjIBLBX6dOHM+6DRp24EQK+lDJXtU7hfQRobv2WYvtvcyh4vCG7veZg5cqwMHDpHl2uJEhJ+zvjRRIW3Vwu27eAICEc6+M7TOTIFbjJx6pTKk14HiihgWLvDjxmsiuOMWByrK+RHDpE7NRvsx1LSTx/3s0yFsI4sqsYPl5/tw8cWUxWex7lE5XjbgOjVP/0kYte4++omGI2g+0EDg1Yp0rwcFlG4vUG6X+VTfJ62VmqaagW1tDC1s7eZkghczZ5aa6/5Eoq6pD7juww5fFWaR4fIJcDFkO1VN93zdO8VwxcRw2SGFqTKt3c84LGADrnEYzd7EtsCpbIGQq4v1uPZjn7cdzzhysSlRiHSWLS5wtCFC8YzCRICAaztK3U1Y40zHlkQF2j1FSDAsL0rnPxx1Yla0FTd7mnGxew7XX4pgwoddgTC2d3tZrykNVZzIXZgTQYmPLMxJMpYENWIDijxKzdBFJcMMhhi6mMMxnF9lXC7+2rwlhpdelKBDko1RtN/ZZztwycVUaaRC4XDb63CVhdDCELnN3S7s7XHj7Xfc6Gly/v/svXeYXOd15vlWrg7VuRsd0cgkQQLMJJhJkTQpUbICSUlWsDzBnvXM7sry2PN4/ezsrnctj+SZP+Z5xrN+vLOzXs+MvbYsWRLFJIk5AiQYACKHDuicY+W0v3OrG2g00UB3owAC5P2A7q66de93v+/cr6rOe99z3qPpMemtnQiMAL48ANE8RZM907gFMU5I4WNPGuDFeaxocqH/DA5aGjYopYY1iGYgEV8G02jj+MLnPFrXjkNhBl7gI9r459uCzfObLqm/Nr7JyawGB011kULRTT6kvLH3uQDx/CzowGW45o3h/l2WBXiDWKHkmfFZh+1KJ1KqrSl3QFcQlVbnfseyOlreTqZWePz4IGUrfIQVNvJ+XvgOXV4f57PX5QC49vhbtfaBQkihXQA+Fc9nyiePdQCX1eF661X5d5PD9Q03h+ukcdwHF90CLuC66CZ3T+ha4KO2ANLe3oQmceSf5svoaZzbMQomBymIXEtY4Rfy+/Sl/AH1hFJ6ldpcx1AhnPZXK8Qx8DXoHVJYOZ/SvYC0L+U3qtRDYWSNaTpPTaycT3W5Gq331KnCGxQ+9AVu5rLbSeb/njqdbbGf+TY7Pe18kZ8TcNEdKR964smsOjvzyEp7ZGKBU1N5BQiT/MzDPl1xhUnNz/d85r8WwpRiAAnu2HYgCf/s6yG9cySoN/f5NDQCSgKQ5RHTsMrTngR/IbegeSjaCugiDJF4SM5B8eNAErXFWUBITg0NpeJGORLXeX3qXo++9U3pqqsYn0XgcYiNycAa4oc8hdHjND7iRB0Ad+ZhfsRbCdNEMtts5TFVRiZQCNxc5rCcObs5XMu01gXezd4QLDzn/XiBT3We3RsDz9LR7FRUwz1Dik1Oq7KiTLX1EZVQW7BYeV32fkyns+R/9jllKzZtasY8duaL1y4HwHUoQA7XfEihLaEirSELFTXRjI5dMFxvw3C5gOviLTz3TB+ygAu4PmQSd4NrgU+OBU4gBf8G4KnTRDFKCQNExv3+9H7VxV/UK+GYXihpUYe3VXFPKcAKhghFw1mV48RndHM+qIfUJL8nAdiKAtgMjsHS4GRsgBVrzdezBY//AjZzm5y7ofNOzFIhdvg4s9PE7zE2R0QD0Q+8qrnvdXPxTzlBJvU+Ni796B9y/M2pqgLQAnKMkWMURXkx7/sAAEAASURBVHHw4Ye8una7BxDExE4dtvQsAUr9E1799QshvbI3qCNdfvUM+pQw4QzCCYUQB7n0xDrx3Ngtkvzx0nicI/eMAslcB7N3CmCWz/tVWuIFcHm0aUNOD/9KVo897tfGDcipc+d8kPDFvTBq3agjGn5rojj19ZGcWkJwk+YPf9yaXUIWnCsL/xFdWFv/Tm03A8we5z6Bx5ITL5Nmvn0qlnBCDCcJ+yspCaAmWEkpiRDMsbFdy3mDn32ylkc6NDhFYfQYRZgrVFVt4YsXr136gOv7eh+Gq/2BRxVwcrjO3+YLrWuFj7sBXP53ntLvuoBroWncxxfZAi7gusgGd0/nWuBSsIB9pRkwisYTKgmnyGPwobQH6AIwlQLAcvH3tTP/np4pCeuwf62iQniC10zGIYlqXgTgdUu+TDflfQp4jH0JK0RYYQ7gMwvzVa8qbUa9sEyoISwLlazSKszBcEQaoAE55DBQTsTOwu9sHts+01PTjohGiEKk5kzZsMxJtF19iGp450J9zC6kfunJp7LafxCGK+LBEYMFHCV/qyKvRz7r1foNwDwOPGc9U/qyUMQXDwT01nFUHAFVI2NevfZOQMdQK0zQZ2aQMSCe4aEzk642hiuXimNXrkVpTEFqo1keWCyeI5yTsXpxBqkrtnlTDuCX0pe+GNCn7qc+WMinX6AUeSJOVTTyo2w6saxX9bByv1KT0oaSzEnG0WqMOVYxw2CBUyGMc9ttc9HafJ/zf4vW8clr6AKuItp0BV3Ze6Wnz6v33mM9bs5ryxZCYDneWVYr6Oej2tVWpN14yfLhMdw3rAnyujwApMqaMtjkSucz4Xwxl9kiGkuqq2NI5eVhrV1fzzkvnoUuB8B1KNCqjQ/OqRQWURbeFqKpFB53Ga6P6i3mnneBBVzAtcAY7kPXAp8YC+BppGFSorNJtVajeNfQKV/uAyKCiKXzbSDPYCOS8QN6Q4e1my+tE4QNjnmqlYTHKsmxPwqGD2bGtDE/rkmfOfKEHALMcr5KdCAAXPlKbdFaANy8Apjj2hTfvACHXoQo3n4roPFxj67eltK2a8hzolbwyTZ36ngsDkuUnMMZcw4P3pSBLj/J86FQSF7yLJxGv8c78nrxhZxO9CDlnkC2PJnVlVdIO3YE1NjoV3VlDqbp7M6lAZsZ2KYfvY38/pRXFYY/ATjHe/x6/V2vjsF2zRwHdMF05VEutPrMPmBtNkVOXWVcJQCuLExXnByvmRkrEgwo8wcVIHGrqiqnJoovf/3X/Pq1rwW1x0fIIjljJbBxJQ7rQMFlzjZMQeprytK6HxGTGr8VawY088IMfcYBegFYzapS1BSDGMrxLs1gxXAIrT/6wb5mB+cyOL/MBks34yzj1ILLEk5Zyjry8W/JRn8uw7WkdS74Cx7W2osv+vSDH4TJbczo0ccSrKeCuuYFP3mRT2Dvs5nxGfUf71UqlVJdfYXqyO0yoZ0sBb0cJn0V5zRQGgNwdR4bVll5QOs2NBbeZqvoazWHXA6Aa6+vwHA5svAWTl3E5gtShwvA5YPh+s433RyuIprW7WqFFnAB1woN5u7uWuDjYAHzq5PJNPVpKNpb3aN1tS/DqAzhIOPcmofgv0VJ/zadyPfqcPwddSGk0R+oJX8rotpEXNfHDutGH/ArXKtjhGxMAbICPjLAgms5vgwghsyvzFm2O94hfk5KFRbNfObEEw2kX/wyQJ2rAGDKQ/J7npC/NOwPIXk2D5rzm/nmHCan4PgXXmG6vG61ulIUgbLwoXAY9mhOQt5UCscn8tr7QUbPP5/lHADUKMwYMu2trYRTPig9+GBa9ci1z/fnnHDBLxvCFHLyP3orqJFpjyoAgsakIQSpkQnC/zoQ45/MKTKbUxJxjiBhijXV1O3aO6WOzhlHLt2PwxClfurwSMYpxBygEHI5Kn7lhAqa5/bPfjOgL/9GSC9kSxDq4Bw+8hY4r83NxjWRhr1k2xfqkmqE7Xp/wKvnjgV1nDyyMHf214TzWl+b162b09pQn+E62gSWmpG9tvxm88+SwzYDjvdyvUzww8dGB9ct1Q0vxjzTrJwMYayV8udt7dhsztDY7AKuM9jlIm0ypckTlDvY/XaQvEZqxF2Rdq7vRTp90U9jq2xydEJj/WNOHb8K6nStWVPDjRVq4jmfHyt/X9h7IBZNqgPAVR4JaD3CGXzkXLR2OQCuAwCuDYhmmEqhfULzUVGUZoXo7Saa5XAJwPVtF3AVxa5uJ6uzgAu4Vmc39yjXApe9BczptaKgJfm31Fj6KjWlvDAspYTUjIC7NgG67lAq3ou63k81mRpQPNxKHtBawt4IzfN2KhAxifVSTVPBdhSnOAUQCVMMucl3tRphwnzar7Snm1DEFsINr+d5DTYr0jcpPZkjEyUP6mdPhnTokFcZamOV49A/8pm0tm7NOCF1iy/SUmc30BVFKSMYZE4wXbafkTNmo5//PKn/+H+m9O67yMPHw4QFlhHq59XDiGf87u8WGDUL31uqpQEcv9jr13tdAYfhKg2ZGAZjByAOTfl0y4aUrmvFUWU/bqQ7rNnhQ0n95/8c08630oXcLTofJ68slYRhLPUjTW/zzzFWr/7gX4X04OcDejIW1lTaS+FpWDDHYzFmCQeSbSac8SUAV++IR3+1O6guQg9NUMOSypsR4Gikdtqaipwe3A5YXZMlH2yp2Sx/uwGhGMzgC68H9CrKjEmUPHbcmNWn7szCHBhIdaxMhx82nlMXiZdNSOOszdln5TlcdmYD7AXFOM6GA+xxwrxsLPaq25ZnAQvl5WYBLKrdLPBRW+3DV3N5PV0qe9l7Pk0OZX93v6aGqddF+HFtbblq6wgxZHIrzety3gfcGTrRNcLnpU+tbQ3c1EEg5iJZ6nIAXHsRZlp7/5xKYZEXglOH6y1yuN5zGa4im9btboUWcAHXCg3m7u5a4ONlAeOeDsFbPaNIgFoxARQIAzPyha4lr6hK2an3NBE9qAli3pLomB8LXKc3vdvUma+gvkleWyNHdGvJPjVlZlSWqVF16HOKhG7BZe1UwvMmIW0TyGaUA+puB4IRj+c4GfNOmXPP+LzMadEnBw769PrrfgCJV9u3ZXXbbRlVV1tYk+NWL6t/c6Ki1O2y0MIwVXjNGcoDXEaRcf/jf5PS3/z1hMbHZnC2uNPtqeXVat13X0Df/eOMbgJEnOtcPeM+/fwDv7pGfI5janaIo1Jo4ObB7RbWaeOdc1X5g6K9uroz2rkzpcOHM8zEqyEIyHfesbvlCHaAQ1Lket1yc1D/8jsBbb3Wq6cotnw4RoFkWKwgYMxpmKCPItQbKdJ8Z2VKf/dOSC8fCagM0sgPuzXYB3vWT0ghSmql5PI/8mBKj92DTDblcM4Xd1jR6Fd2+vQf/zKskRErOiaFyBN85P60vvS5pNbApnkB6+fVmN9qGC4Doib339ubdVjE1lZYQEoYGMq219y2QguYzezt9jFpNhUrKzE+PKGJkTEnr7KqogTpeCui7js7Q3sGG9jnS4yiy70949ysCap1bf1cftiFN9rlALgO+lq03pGFpyZEkdk/H3eWjOHKv/O0y3CdYW26my6eBVzAdfFs7Z7JtcAlaAEPTu8sRY/fARS9w13+qBKZNpiWzarJH1Uie0w9npRGKYoczSb0fmyrnpz9nDoz7XjshK+VTeum2r16tOIXusE/o7rQI6oN7AAejBEW9gpBYd0Auhb6vhOmq+3k/JGF4F+W/cjQAcQ45MIqPDYLP8lQF2ySelakXagC0FBWCsNzDmLk5EDmHuRBbtGZGSTYA84dbHO2fOQ27dod0h99N6M3Xh+BDYRiIq8IcXjGXacH7m/WH/9xSDfeYGBpqWYOVUHQY3DSp309PvUgmmHbWmspZNyWUWPlPKN0ei/mdyRQJjRmiIA8RD/yevbZLDkzwNiJnNat9+oLX/Dpztv9CHtIR6M+PTUW1jhgLQLYsbvx09gmRNjXp2uTamDkf/piiY4RSmh1hYePedV30KuRLkQDCHc0UuxW8nC+9wcxXXcl1+b04TCG5TYDLTnNkiP2/X9fph89Te0zcteqITh95Pe0N2X16CNp3XFbCsVF+jwfn5NjVwq4DFCNT+b11FNpdZCnZ8ond9zhB6j7HXGUVU97ueZx97ssLGDL0goX22dDAnGhoa4BxGzSam6uZp0EVwS6bM0lkxnCCqnHRQ2HDZsanc8oY9MudLscANcHguH61GNzKoXFtYgxXN1vk8P1PgzXr7s5XMW1rtvbSizgAq6VWMvd17XAx9ACxs54yMHyerjlj0JhKl2h5MyEKuM/V8rfq/5yn8aCKQ1Fw3pr9Aa9lrhbQ541OCLmWJOXUz6ju+vf01eaDunm8nat9VyD2EGQcMIhZKLHgFRV/KzhHAWWY1aTGqJyVxQ9wzJVaA3hJGUwZvR2XtY132W1PZhTNQvgylAo2UIKHdVCyJefPOXXn3x/Vh+8j7OVnWG+SEUDIwO+En3lyxv1h38YQUiDOMACYjzr+HOcI0XYY4L8LQ/MTgjwEQTUnRXYnJwQ0BLvzOqDDQ3DjsXyqqzOq76WPKygXQfuyGOA4wh07J5Gdj4JiGU0lYhnXBHOajtFqM3633uhTO8NQm8B2IYPejTc4UGZzSuqAwg8rQhCHH/6R3E9+pmUysntOhcYsuHZeRY2W02muv/O215999+V6FBngDlz9WHZbr4WgNkAM4rNPvcwTFr18my3sP/THnPyFQMuxvbuuxlUKDNOLlwUm0bK8/rql0NqaMSWiyd02gndJ59IC7DQY7NxjfaMKDE9S10+j2pqK1g/iAIB2G3JnHyrnsFA9v5MUdi849iAw5AZ4LJtZz/qDB2tYtOlD7i+p4OeNm24//FCDhefk2ez5YpMQEfegEfHCSnME1Lo5nCtyHruzkW2gAu4imxQtzvXApefBezr7ZSX6XzdpRBsmPo5NaHe1XAkq/GSGR2daNCL4/dqH4BqPIdARtIKhOJcl2W0sbpfX2vdo4dxoNd71iOXUa8AMvEG5pwkaB5ZfZ6kJ6kTOqYJwJg5HFn+rUFgozm/DkBgdMe527mcm3P3cIY96NTuZudMmh2pQMvtMZbs+RcBDd+f1huv9SiVoI4XTJONurGhXN/5znp961vlhMbhcDnmW46bYNawhj2cv8v7deqYwvH23I4v9FJ41a6hBTdGIcSisFzDhBfuRXLemLUywj9vaco4Ih2/OObXiW6AVodXccIU7a8yHmWo0hwuy+n/+J/j+vqXUqokH25usAsGaWeF0eOQQjFlVouZxHYFgFuz0Xhh2H75XEB/9ufklc36NQCrZmzdPbdjOxiubeTYfeYhyhFUnjqHHXeqB+upMMNCj/Z8vtme1nidhysHXHnt25fWD3+URTKaPDMua0tzDgAdQpkONDbffeEk7m/XAo4F7J2XATQNdg5ofIh8LMKPm5prHeXBwhtiCUOxTA3EzyKc0dM9AuDyaOPmFtatLbTCil/iyKJsvuQB13e/p735NrXfV2C47OZXMZvJwnfBcPn3wnB9y2W4imlbt6+VWcAFXCuzl7u3a4FPgAXMkc0rlO5USeJVmJODGgsM6IXEFfrh5K/qRLZdsUwpITIlyhOuVl0zrrvXvKnP1r2llkBIsXQ71bdqtClImIi/USGPVQg21wIwAKvV6TkInxZlnxJq/s6SP1al9twWBOSJizuDtzvvkhgQdODGnAPjdHrBfhmQ8CAJn9ef/0VSf/ejEfWcGFYGWflQyE/9q0bdc1ettm/3EYoW0Nq1FB62ysLF9RWWnp0ZZalz8RoK13oeZml3v99J+k9AJFXDMG0qy+rto351oCzXZ0Ijs9JAp0f9hDoaa3bzjqy++wdR3XFTtpBrtugc9tTAZTc1xPYf9ONEetQCgLr6KsubsyvMFWIHA6vHjvv1H/4srNd2+jU06pXVNG1qy1GrKa1vPprUzTekYecMuXEc66hwy5+/jL1QF4y+7FrjmBqochwx28822o8157AVimbQX5Rwx9deS+uDfRnm6dFdd5EHdzXhlyaIyCzc5lpgKQukKYY3OTqp0f4RJ8SworKgZOi1xMrCnZeTh9pNJSt8PDI0o6lJ3mzcnaijsHI1cvN24+ZirLVLHnD9yfd1gJqNGwkpDJaWLTbhSVuu5oG9k328v4/vflV5Qgq/7YYUrsaM7jFFsoALuIpkSLcb1wIfNwuY8+zNotKV69N4oFtveAjDGtmsD8bWaTYdQRXQr7KSad3d+op+rf4ZbfEPaiZVoQOJ69SfbVZrOKD7I+u0OdRMSKGBLo/Q3CMn7KhG1W+uOX4zCnkIyLfkNziMmONBLzKkfWk6tajicRLZ04TgEUZH8k8Q73iRf7PoyPN8yomTxP8d3J8ANFDnCnn46GzMkWZvbg6pcY1fDElbNnuRhw9SKNUcrvM8ZxEON4ASQzXu7w8E1RelXhiqiGkEMiYIM3x4Q1JxGJ3XDoS0d69PgycAK8jTm+z8lvU5/dOvxvWp2zIFdusMYzE263inV0//grpiowDSE371Ufh287qUfu0rad0CUAuFCVHEEGliHH/8s4D+8q9DOgz48uCQ1m7MOCqS/+SzCTWTw2asp93+Hx2jVk6XrThpfXsOcYL5UEuT5vcgyZ8mlNKHzaVtV3tUVcnFsYWxGsDFYUbGJRPUXZoqOGSRCJl53AkvjMA6LkZbuBiK1WcxxuX2cT4WsKtqnzuzUzPqOdqjNFXSqyKlqm8gtwtBDANYtjgNbFku6MDABOHKSYRuPGpqqXbCEB3K63wGsYJjL3nA9d3va0+OOlz3ArhKDHA5Fl7BDJfe1W4BmSx85zswXB88pd91Ga6ljeW+csEt4AKuC25i9wSuBS5nCxScxqwnQYHjUe2cndJzg7U6klyjVGlWW+sP66v1z+uO4H4kyQOazKbVmWvTgcw18ua8eqC0SveVbVa1twojFPqapajtqKefml7kcOUjyE80OeyWfTl+qHGIFSG13CovtIntYc+N8fAR+me5VgGELuz5mQ7/UH8r2GDjsULJMzhW6XRQqYxf/UM5vfRShtBDD9Lmfk3gsAdgaT73SECbNhKOdom0DON7llpb7w/6nVyxODlUpcGcPn9lSq2wXD2jfu0/AciB2UqQw7S2Pqsbt2bV3pJFqfLMpnTu1nM9niVUcM++gGZnPdr5pg+2i1BGQhjvuCWp3/+9tK7emnfYvlkYsx/8OKCuXqqxId4xMEwB5huyeuiBlLagzgi+cZitXti2Z54NqbePsEZCG9etzekz5JBZUeeZ6ZyTa3Wsw9YO14P8t+u3efTAgwVpfGO67N/U+IQ8OFZlkTIHkC/rMthicpw7BuK0wvqce3Ief+gHYzlrkoeFmwIFJ/w8OnUPvcQsYNc1Fo1rfGCUn3GnXEMteV0VVDf3wupbrufERExDA5OqqCxVVTVl4EvszXWGz7kLOLdLHnDBcB2cY7hCJTB/RQ4p9PJBc3z3K8rtcRmuC7jM3K6XYQEXcC3DSO4urgU+8RYwJ8GX0Zi3U0fTg+qPlSKKkUZpr1M3Vg6rIdehodSw+tIhHUxuV0fuCmVQYrijxKtfLb1SLb5GQsLMisYhWK5Rxvkx5gvYdNK85jw7e/EnByuT4O6x3TEOhoI48QArXrQ7oAbA0gCvLK/5AV0hK1gMAJtvhbuk889W9xfiBRYvi0JZnDGgJAiwiyeC+ulPk4TLkeNUFeTOdV5trV595pGwWlpw3Dnmo28MAkP1zfj0U5isdwFVZUief3ZrSretTakMstF8GtJRsKM9RtACPzDkCHhw4BJzsCVgoYo/RXXwMGGJUyhDvviCX2OAzjxiIG1NCf2r30vq3nvzqBJ6NA2T9tc/DAFKfdzVz3Kn36fbb8nq7juTFIDmSnMdObWe+XkAufsARWHJ/cLoI0M+3X9/SreiYnjkUF7PPINICQIeoRCCI3FUGxMePfolWLXNaFzCGti/VQGuC3ShPAiVpAC4s0SQ+bFpeZmVEsBwNlm3fawsYG+VLCzWCIWSxwaHkI/Pq66uQvVrInx+ZXX86Ah7UFh8Y5NzE+KjmPwlD7iM4coSfn7Po3MMV/GsZO84y9PsJKTQv/9pl+EqnmndnlZhARdwrcJo7iGuBT4ZFjB3wr6yDCJJY6M+He/mji1FkH11Q+gspFVWOaWNDSlqb82qF+3BQ6lS9SIrPzFZpTi1p1pxMu+patcDa2vUhgKej+5O8+fnnyz0RW0ftpsUcx6nJQCYsto382DMsb056zzIZrIO8DIAdrLxWiAQRBUP0Q5zyM/njjInKZzJLEFhYn4OHkjp5ZeihNT5cK78uvOOgK7aanWcFk+ucKyd/7RQNafPuUkunPfJCZzvAysb7NF73T79Yn9QQ6gWYgZd15bUQ9vSaog4yJc9rPHb7GPGPP3K2IYPN3Ko3qOI87O/DHCNPXprl09Hj/sUKckSThjX//DPM7r2Oqu3hRQ+qO71XUG9sgtwNu1VQ11On/uVlDatQ6wCUQ2zi4G9H8GCHT6E6lsdzhFhh12IeNx5d1r33J3RBx+k9cTPss7ai8Vh6UpRZ0TE5Ztf8+sqQgv9LCj7V1zA5RiDuRcs9GEjLL3FQi6HR/J69ZWsOjsZHzXRbrjOqxtvIodteZowS3fuvnJJWsA+H+xm0sTIuPo6euWhmHx9QwV5WhU6dmTQYXE3bW7i5oAF2l78dskDLmO4CCnciGiGMVz22b+a996ZLGufu6ZSeAyGK49ohpvDdSYrudsulgVcwHWxLO2ex7XAZWeBgntgjrHl6ezahYz4EE4oTNdEDJn42ayqN3aoZUMvYAOnGlYjWkuuULRUPQfqlJ4IKJckzypVr9sRl/jKPdTsqhdyGgH5cbTt37yTn6HwrrFIPmrUmHy65WolYbdC4TBfwlaIeOmWh3ZJE9N2ktVi2MaK5YyOsS9cmC8LO7TwQ8MW59PMFvFYEqc6AfsVIHyIQsMV1v+ZOzYL2itJkoYGYtTSQgmwDramvoSwO14481HnM0L6pNMJRCH+YXcBbEVgtzIwUJPIvj+0Lakb2pG+P0UGrvhkpjb4/n4UED/wahAVxFg0i9pgmgLQoiYZtdkiXFmjB/mJwUb1sHamp31qIGyxqQG20hGmKJzWxDLeec+vZ2DNkik/uXqANUIfv/jFpK66Kqv+/pz+7b/Na/9+CnQjxW2M4o4dOX37f/Sqrc1OQZgpEy4G4HLWhiFTU+2wK8OfgvO3PBPZ8SkYjp88kdX+fdSDKyNnkfsAyURen/+CV9u2eSknQF+Ft9XyOnX3ujwswDW1z58ZhDHGBoYV468VOJ6dJeeU9b55kwu4znQhU6kkdQ5huDIwXHfPAa4ihhTau9kAV+c7rypwgByu33BVCs90HdxtF8cCLuC6OHZ2z+Ja4LK0QMGJlN6GyTh02CPwD+xFXkeOS7v2II5QN6pr7tqrusZZpQOlipX61TtcranRUvmDaSTBa3FCIqpum9Ed907p4S1IgwcrKIUcVg5v1JLKzau10D3LzfIDjgwsmaPrB3yVlJTIhziG+e9navaFas1eXriLw0rNfXFHLbYLB7q0rAzQZfWpCns6xy48yDo6RzPn3vK6DAwGAYJO/phzzPxIFnXA5ijhZa8NBfQ+eVNRgE8E+93dmNL2moxKzgP4LDrTyac2xn5Kqv39W+SdZb1CRM2x54kJr+69MqPbN1NwGJn40wx28uilHtj+3C9mPvaTIZcqHjMxE8AVc/ABnCAUuWZmafvn3PfnCSwW1yEHMPLx2ELrFlsqQWHn/TBxBw7YtffommuyuurKNDkxVvRZSMt79TKMkQcaIQnL9ehjHn35y3PCGfRttdHOH3AxXibW108oZp+HGmc5rWtnToC8Qpv7y58CmOSBA8hspoU5GZs6PJzXX/0VqNGXU3mpj7zDnHoI6bwO1u/hT3sRmaE3M6BjBfb7kDUKZ3N/X4YWcN5ShEsTq9vX0afJ4XEuNTdjKIDnAq4zX08DXP/7n3xPh7Jt2njv4wqfFM048/6r2Wqy8MZwZfe5DNdq7OceUzwLuICreLZ0e3It8DGzQKEWlRWGffVVj0zcoII8G3Og331P2n8YwEXdpvVX96jl6iMqa5lUlILHXT1NKOJVajpXoalklbIo5dU3D+q6OwZ065UIaZTUaRsS8NlpFP9gsiw3KgDtYbLfBrzMHQ1SfNhys8yJPd+WxGs3YGfYykITjemy8B4PP15AwOJ2kilb/ALPC4ArVWDfSguA62zAxbrfO+bXL/qCyjC/MgDJRNLjMFyfaUtpLQIWxW52Tkr+6Ce7UQcc8pNPBcsC85IDxX7+hqS2tZD3Nq8TsdyTs/+8pQwMOwiOLXYue3o2GzinsIOdHZ1nH/pFFBY2LZwhCCNnTBDcJP1n9OZOH8IZPo2O5lWLeuFjj6FUuC0n8LgDeorBcNk89h3I6+e/gC0z5UIc5bvvymrHbYXcMWedMPEEDOX0rA2OcggVCKYgte80kz1krY6NZvWXf0l1OUBVVaWtFxL2O/K6bYdHD/4KYYVcC4Ojspwu/hbyGgtdfJJ+O+vmLOvhcraFrcdkIkle14hG+8b5zMm7gGuJC+oALhiuvek2rbvHVAotpNAWRuGzYInDlr3Zbod4CSXofPdV+fY/qe+4DNeybefuWHwLuICr+DZ1e3Qt8LGwANyBI2Jg8t5vv+1DmtunUlgMnz+nt96SOjukME6nF9lxL/LwkQ3DytfENTxdo/FEreKeEnlxSHOoFVasG9Mtdx3QXRtTuj+0Udfka5RDYt0KDZvEe4F5wmx819pXrekROkRAMSxJnw5rBuCxL3gT27Av9XlQZ6jh5Nc7D0x8w2rqON/7i84/D7hMzCO8TMD1Un9Qb47A1uGnl+J8Tae9MEOIWKxN6koKRa8U+ywa0oefMl+zXQ85Zq8d8atzjPpSnPeGdYT9rc+ogtDPszVzhq2ZjRzLMO7hMY/2HkIRkpypbVtSqqowsGWOUfFagXl0Vl2hU+uec5j0/jHyxEbQH2hqzKt9vVRCWKYzTPY5X8Bl86W0kn74Q6mDNW31xAYIlQwB/H7j16kzRu2wsTFpN2t+F6zhCRirbMZHjo5g43K64caktl6BuiNjMjXGXz6X0WtvGOtFnTOAbnm5R4896tGmTeR0ARJnCS0d59pY/7VI43MP4IxrrXiWvbR6Mnvb+vexluaLZ19aIzx9NPP+v3Pvx96sDrhmtZ7tXglzTCZSOvruIWedbnZzuE436tyzecB1gJDCDYhmOCqFGLxYnyz2GeHl7tJxQgrzhBT+jisLf8br4G68OBZwAdfFsbN7FtcCl60FzEEaHvEAurzqoAZTOpVTJp7RKDLfg9RispC5YDW5SevJYWDfThySGUMXCdMgRPSiMamGGwZ08/Xv6rM1XbrPe6WaPdfLmy9xmCJTGgwGTYUQb9S+IS9gMxDhFNDlrzFfaRi2wh3V+ZOaU8y4GYuFC1oNl4Vt5YArr73jQT3bE4DhksoJJxzFLg0lCEisTWhtuTEdF6aBL2G68pqKexVkGhWE6JFqt6SJHXgFwEkSLngYQHCMEMgkIYONZRn1HKaWTYcPwEoe2J0prmUaEQi7WPZjczC2hseOd1r8+eCfO/ldVlvWfk62IgEuUgj1d/8fBVI7YSeoV/bBPp8mEQW5HaXEW27OqH8AsZD3QwCuMEyvHyBl4YIG2lGg25zV134toW98JaX6WqTsKQa9b18Oe+UcZcWtiHts2ADoJTetCyGT518KIZEP00rY4Q3bsrrzdsRnuDZLNXv/XSCzLnXKC7Ld5oHGjXbujOrosbhuviVC6Gio+Dccijx6s70HkZcYTP9u8iIH+r1atzGr66+1zy0mdKY7Q8w1TQ2Dw7sNcMFwuYDrjFdlHnDtTVKH666FdbjOuPuKN3IZuJnnVed7MFyHnnIZrhVb0D2gmBZwAVcxren25VrgY2oBczpmqLvUh4hBjCKeV6/jzj31p452ICJB+NpQDgcV1yKa8ujVUXJy8IpTWZQF8ZSDm5NacwWAq/m4vhzerztheCLZOwA16x0myXKsLLyvtKzUqbV1MUxo5IzNKWcPFjQDXynCgbKoHhrTZUDQQhAdXMF+KwVcdpwB0jcJ7Xt3lNpVPK4J4WQ3Uq+qJnvWHK7iONrm7hmUsnma+7F0s/PNAqhePEa+WT9Q2fKuADeYQycOIQJBXS1LQfrMfWlduzWtY9T4Ogjz5AeU3UjO1eYN2YIgxukmXfqEK33Fhr+4b56fL8PlDIN1YMIcP/4p+XavhSmyDAPDyUrLYLxqkppEhGRwskw93UEAqSE+LErumQgP9SOAsoncuD/6n2L67KfT5LIZ02WCGdgdo4LbWUsFpu4HPwqrGxGRqgqk4wF209G8vvi5hLZfTbgrAG5hs+thl4z7Ec71s/Ba52bBOa7jwj4upcem4Dg1kdGf/6dRmMSsbr+jVF/7anVBSORSGuhpYymwLcbm/vyZUr30mo+8UkJL49S0+zwhpzu4YcP6/9DCZJMLuE4z5BmfzAOuA+lWrb/7cUSSeMM5bwOzaTFaQaWw411UCg+6DFcxLOr2sXoLuIBr9bZzj3Qt8ImxgOP8MdtUOqcszND6hozqq3AqnfA46ZXDAX3Anf+cN6NdAKg3USecyvLlaQ7nuqQChBpubejSv6h7Sw/lkyrL3onTUlDuSpNfZSGFZQAuA14feQN02Xe+sV/mNPnJL7McMyePh/mkkuRwIVlvoZDGgi10kw2/8aG64KZ34dUETlln1KcTCa9qYbm2lGcQz3D86UXTNSe9cIyFYpqT6kAmZ9NqnRA7rsBCLTrZ6U85764Tfr1wFLaR61BGiJwJWRu7dIQiybnxnG5fj6N8bVq7j/n1X54K6mAHkwBEbm9K6198I67bbia0DpGAi0bJMLaiAC7mmYGhO3TIq7/4TyHk6HOELTINzOanwPfBLnLIJsLcUEB5E7bQLOoBVOUmyCVjTUQa8vra4wn969+PocZooi/OHmYIZ32YGuPgkFf/739jzQDIyktt3Dl19Hh1x805PfCppEocu81fkkIo7kGA7p692J41df210pYtMMYmb3mZNt5SeuqZWR08GNc995Zqxy3cZJn/cLlU58T7IgGw/rM/K4XZ9KhhTUad3dSBW5/Tr3+LuxFObOHpTLgtEBdwnfuCzgOuPQlUCu9YKAt/7mOXtwc3TiyH6/1XFDj8tL7zj1yVwuXZzd3rQljABVwXwqpun64FPnYWwHHEiUwmrO5VXC01ObXUWaia5fJ49C6hUi8cQKEQdbyDuTHtM8YrVqkgwCJE6FymktyfuoP63fqdeiDQqnD+dsWjOK+IZJgIh6NICICxvi6VZmyXOU0WemjqiT5fgekqALHUqRwwc6kdB9vyUthvjhGzqTg4iX6OEUb4OvWwumE1DMZsCWd0d2VKzagFLpyxga0JgNkHJ7zqGfOplppT29tzaoARwdQXrNlYkzAyP9pXou5xGEhAwXw0pb02Qd6R+ZWPXptQBN/y38PUPPk8189EJgGFfhigL9+f1G8ButbUmz0u2FBP75jzFAdwFXLSLOTtKSTq/6+/8JJr5VFzE8weTOSrFGYeHEaWPhuC2cIW2MSDI5dnvYfzGVWSW/bgpzL63v8aU0tTQWVz4UANOI8TovhX/yWoWAqGi/cPlQzIB/PqkQdThC7CDrK85u1mxZOPEMb5ox8T1glrbDlPIRQTv/x4Rhs3kjN5AdfCwnEX+7HBzyjqlrFYlnIKfH6gIHcJveXPMF270FwrwPh/49q9/X7QYcWHhnz60ucT+vpXAVxMwMNnWAFkz3XBYS7gOoM5F22aB1wHUuRw3UUOVxjRDLs3VMRmsvAd78FwEVL4O7/xqG7dsaOIvbtduRZYvgVcwLV8W7l7uhb4RFvAgFEahitJko8vl9bGJvJ7asxRJVQI5/T1o349+V5Qb43F1B9KKV4WVADmw0v9pRy5SrfXntC/BGTs8LWR21XpkCCmHmjHW60s70Vjtxwv/aRze7aL6uR8Uc/LCiub8MZ8MzyRBSxa6KEf3Wcr2Ms05rZlmZudw8Q/CCXzleiZaEQnyPspJ7TMto7BjtxUTnFf6ldVOdscv00JGIDnPgjqPRiVLAxXHFW8a9Zm9dD2tOrnChbPj6GYf+0axDj3374f1gihoxFjt9hoDrLNzEINk4z5sWtTqkUI5d/9TYmeeD7E+HD+AROVgIeHdiT127+eUmuz5bUUc3Rn6cvMzDjPXxberh6dAXRmZ7z6W/K5Xn9TamzMahaQvOsAajEoFx4fhLklt83k64k5lG+aXDbfJKIxKf3a4yH90R9SY40Czh+eP2wYjvsbOwN64eUA9clMOEVaTxHoL34mqcY1JrJhYyg0A2g/Q5nx7d1eNQFgvZyrkzVxP+Gcd91LbpiBs8uw2Xpy3hjO7/n5XqzFslqDcVOEux273/Xqu39armlEXMq5EbKxNaurN07oCpRXb92Bqio5ecob02WLEsCVzJDDdZD3j7SRHC6/AfSPYKqXfOHjP0alMN6mdofhIiri1Mfsai/Yacd5rA4XDJf/KDlcLsN1mm3cJxfXAi7gurj2ds/mWuCytIABCHNszblOAQImJxKKTUUVRrFwc3tArY1+pXE23u3w62/f8enVWELjbXEl1zDdEq82RWb0DYQ1vhSu1Bqk483B/yiac17zgOz8OD/O3dRVOkGmdmjyzyELLTRWi2YgLANNUrCXnYbcIBz4X84U6olFHBbDoxFCMav9WT1SgzQ8Ygo2HBtb/6RX/7ArhJMPiMApP4xaXi3S8f897NE2gNeFspudH4JLTx4kNBRQUR2GccHJBE45bMoQaigVCH186ZqkmiuzeuGdkP7iByU60k19Mw5uAVB/8aEUbE3MUTAsGNgscoEbYy4O4CqM0wCBAZ/R8bzeeD2v48e5lgCtqXRQo1MBvXMoyJwBw4DTMgv9nBqHeUwhDZPRb3xd+r3vhFVTZU736c2WmPVLNKo6T+TVT5mAMAlxG1CNrEep0LeIsjLA9fzzPr34oldrGjAwz7u78/rVz+Z06+2ns2Gnn8l9diEs4EHl88VXgvrJM0GtbbaPjqyefjaj9qYon30x/dZvVqltLe92QLQ1W5MppC+PvHcARpQC8TVlamyuLdyIKexy0X5f8oALWfhDiGZsuNNUCsuKDkpNNOM4DFfuMHW4XIbroq0790QftoALuD5sE3eLawHXAo4FCm6iPTRH39it3u5ZHT0whVJXTDMzyKvD8LQ0+HTVllJde11ErW2l6kIc4pkjAe2c8SlRaQxNSg83JXR9mCK8IDbzUy8UcDjrhePEprI30J/X+DhAph6GjlAwJ5QLZ3glzXGoCDU06WcHcFlRKECpWWzeabf+LETwg/G0niL/x3LBKsnTsbkPkAvU4M/okeqkWp2wwoJTPTzl0Y92hjQ+61XXlFf7EK9orMjqDz8b043kTy3yy8865DPdTT85y5MPFnZB6CM1w57cF9AkrE4VAMsP4zNNOGSMyKn7KJi8ox1GhzmYkuGbsJk7dweUjue1cV3OUdtrWQN4oO+T3RfjQptR59vJjgsbbI4nARdrqyxSho0MUa+y0V/eFqiFzwJ6Z2PYAGc7hdz7ERimA+S37aFAc1+/T7OTFDU+NoY4RlptLWl986tefeXxMkXKPgy4CqOxieCqw1xaXpyZxs8vWyOLm+V8DRO29pOfogzaZSGFPkIJM/rCr2bV0HA6G7b4WPd58S3g4e39xk6vfvhEqTbyPjS294kn02qumdRVXJd/9t9FyO2ysFu7loVFaiqhs5NT6jvWDxmaV1tbHWHI/osOui4HwLU3ikrh7Y9S146QwiJePntrWeFjh+E6BsP1j90criKa1+1qhRZwAdcKDebu7lrgk2YBU0eLEVO2950xHdg74aivlZhTibeb4S5uBaICfqiiAHcSb725StffVIkwQABH3ascYTRlRGNFYHOI7DjvZs61eapOyN6ZEMUSZ7DDLGF/J7WU3niTWlgwSZWVOd17L7ll28nDYYwr+aZfCnAtPr2ddxon7CdDCDLEQig7ov7HthhO990VKd1RkVQ5ByUoNpWBNsniyL15LKg3joXViZz8eNyj+7bE9OiNcfK4lh+qN+/2nRQkcGxF8eNTUGjxUJ3XLGryCCFzO5Gx75sJOUISFTBw2wHMt7QS/kioYRIwciga0msjJRomL+m68oTua46pLlI4a95oQyZOJGZRnEsHVNnlsUu/aNTzzlmSumihcFhlFeXnB7gW9A+xxZKA8eLENqU0YYSmPDjB2jlyzKu+AS/rKKXZqVkHuN91R1hr2wKny9Yv6G+lD23e4xN59ZDnZfNsa6PYchV5graoFhtipZ27+6/IAmbyCW6G/N0TQUoHFAqyt65Jacv6Wa1vRUBjC9vOdE24iNHZhDo/OOoI0dTWVai6lne8835c0RBWvfOlD7i+p4PJNm28A5VCCh+jELOSj+Kz2sWuieVwnWK4XMB1VoO5L15QC7iA64Ka1+3ctcDlbQH7wrIQwvd2jWjfe6MIQiDfTlycFxCWprBUlkLC9bBYEeLKprnjz81+bb8ronW34PyG/aonfLCcjK0z+SIrsgwdmNOZRR0wB8jzl1C/B8Zoud/MRnpYMdsnnvA4RWwrAAejFLNdv076lYfy5NDQ+bz3zsNztcWAy0Q/TjlRhbC0Qh84DxixG8d85wThZOmCLa4IpnU9eT9VAFE7cQZxjlAoBFANihJnCGYENUwIW1lpTutq06osyTg2P9e4Tr6OQ5fmp2uWwsdRmB/AwxYAWw1Fqh2H/eSOhWnb9TG33rghuzM/BbsznqDeFMAwEsgCtAhnpI+jMcDYTECvjoQ0mDSJdPK3fBk9DFPXxHUZAIDFuK28hZyk21oyqoUlc8QEzrkAsBPAxtgeh8Wz/Z3nBkZT5MOkCMGjkLYpeSzoywHeNhfGbLXTfADaQjadbVxNs0VggJ6aWUMBx8le18T8yZ+zNW/nsaubAURnzDEEhFluo5dxm6jF4tDA1YzAjrFR2C97/2XnnHN7bOvO7tq77aOxwPg0YjY9XANo3LUtWZXazSaYVWfNLjGkHG/xkb5B9Xf0K4I8Zfv6emctnVy7SxxXrM2XPOD64+9pDwzX2tseV9AYriKCUa6UA7i6LIer4yn9rstwFWtZuf2swgIu4FqF0dxDXAt8MiyAa4mD13F0SjtfHSJ8DjnrioDjBJqARA5GxvJ6KgFbaEbgIOY1AnAY3ZRXxc1hNdQEdQ3Fja/3lKnKgmrORFEsx5DmdJIXNbL/sPreflfx8UlVtDap9bZbVLW2Befl3CFkBhb6eqmz9BPqic3kVVMtZLrJPWr26NOfzquZvIyCl7ucARUc39TJkELCBQFcfJjOdcKAT0MFgBjsFWMOUYf8AQCxCxp/5to7JzQnw9QQDVQE6At4w4+TugNAWgjgnN3P8avAZO1E5fA1wjujhMNZpNP6spwepv5Xc4n1XmhzZ3fmPpYiVC6GoRhXC/tWEupoljUgZmN5a4r+JkOayfi0F5and9aUKGHwCM/08tc3jXw2xbBNtbC6nXNtS+mfXx3XlShqWOjcaTbh2eJm5jPANTDu0yFYnRigbz0hn82V04SuJhWprABU2fgubDNG9wSM5I+eC2pozKMbrmQut3N+pNwvbrOrM3+lLu6Z3bMtbQF7L1krfObZ9Znb4Gxd9MtZ1IjfxOLqPX5CcfJeI2UlqquvpMZb0D4yL3i75AGX5XAhmrHxdpOFvxA5XAWGK0tI4bdd0YwLvt7cEyxtARdwLW0b9xXXAp9oC5iPnEjktPOVQR0/MqXSclQHiYc3B8MU1swJroM1iZSyDc9hNofgQk1AR9Zx17cuperShJriEd2GcsY99UG14ngXGIBzOCmLrG61uUaPd+r4sy8Atibkp9BTcjaqui2btOGBe1TeUO+cf9Fhpz0tzEV64SVp1y5YGABCGLW9e+/N69ZbCHtEHMuJWTvtqKWfnM5whZG3RjQEpiMN2oDoYJ54UnPOlN1lLTwGCNnU519gu8NWYDvbNQHgygPMjOkyefnV+to211HAyg/7wppAUjxC3a8scYzjMJX3r0nrZmTOwzY+mg3H4Nx+mKmXhgMaSpoCX14N1KB6EHC2OZJ2CtMeAYg9ORrUFGBrCFZrz0RAhye8ivZ6lTnC+MdAkqBIj9WXsq4tjHRdXp/fktIfbI9pM7L2tnKWamYBY4m6GMNTu4IaBHSZUfwoON55xTR5gLOqqSvlOazmBW4Gzo91e/U3z4Y0POHTtZszehzp9kpAqNtcC6zIAoW3mS1lQppTsFx9mhzis7QkoKbmGpWVh52bMSvqc4U7X/KAC4br/RkYrh0FhuvU5+MKJ3qm3TG8k8O1hzpcnTBc/8QNKTyTmdxtF8cCLuC6OHZ2z+Ja4LKzgIVQjY0k9Orz/Robpf5SZchht6wmkDFOZSFC1Mrz5G7xHPZqX7pKr9eXKXvVCGGGQwoCIHITFSrr2aAH22b0qWs61YCiX3m+HX88sgx7FBg2c1a6X92p3p1vy0fIXbC0TPGJKXkIH9v80P2qv2LTMvoq7DI1gzN93OblQf0tr/UbcopEHEi07D5sx4WAK1wW1hRg8/WRgE7EvdoGCL2xOqNy7HJ6s+cO4jp989wzUziMUTQ6XFLqFFo+407L2GgsUX/cpx/0BAGBPlUCuOzMvYDMu+vTuq0+Q4ghW/hv4GwMcPYP7DvEMZE5UY8RZP7XoTz4SEvSCUN8aiygPTNBdbFvN8Vfu6c96v/ApxQ/+T7sh1S2MVtebKoafkCd4ba81lCT6n+5Nq4vUfw68iF7nJqMjQOcp6d2BvX+cb8qWFchxj0Mk1YeiOuRG6e1YW2JEzZ46qjlPTL20K5XAV1ykrNcA+vRrlCCWlm7EMcYGMlr+5a8NrUBPJfBpNrxbnMtcCYL2LoykZ3RgTGN9A5RIDyAcE+5qqsti5P3j0N32V7FbZc84ILhOhhrnWO4LKSwuPO3wsfHCSnMuQxXcQ3r9rZiC7iAa8Umcw9wLfDJsIABrr6eWb3x4qBmZwEQhBOal55JW85RSnWITlSVFxiv2VxQb2Zq9UZ1tUrXDWhdxVFyt6YUIJxtpnOdNpXF9fkbOrSuLkoFrivxya8kswsaxYECS9gTJ5msMFihjPrf3qeuF3fBcHgVRhghOjKiICFmWz79oKrXt5u3skQnCzbP7ZKxvBtydMx/NsZtNS7OKcBFGCAhQrunS/TysE/TyL3XUTT4c00pbSq3QLzlNxuehRWmkJovMan5wOrYHJtPlFM/2R/S/mkf0v1IVJMXZvP9QktKV1UQ4jdnd8MhBwgVfKIXlg7WqxRQZGGO04AqRCn16NqkGko8+uvBoF6YlfbDdI3BVc2O+JSmLlG+kw4GOWGCv8Z6khvngK5yANfavMrW5PX718T1j69MqIYQxaUut9nT8qJ+8FJQ3bBclfQTpOTAOJL6KfL2fvXWKV21YXWAy0IECxfZLMxjG4T9PwdhlbYbC+wTIF/HwlZtNbrNtcCqLWCfUc46z2lieFyjJ4YoL0Bdu7qIqqoqEH0hyNhUa4rcLnnABcP1AQxX+w4LKTRBEed/UaxgbLuXz7/Ova/I2/m0vuMyXEWxq9vJ6izgAq7V2c09yrXAx94CBrgGeuN6/YUB8p5Q0zNNc5rJw2fx4OsBXGAfJ8cnBsPziqdOr+TrYb+iaq/tUlXtBIArq/RkUJuqcvrKDeNav2ZYJWpGTGO7gqqgN74RFzR75kAgHJO0qOXlPYaiX6+8PWOaerFLk0fjSiE84UWRbu2dt6ptx80KlRMPuBzA9aHzFM62YPOyHjojtvEBjpJxwBGA60AsrKcHCIVDmXFzJOMArrXkt52tOf3wywCOCWvYP6vtNTszQ/hmGSIMVmz3bD0s9ZodlFM/Y3kTmffjUUAXoOHG6rSuBWyVo+8x3zitjgDKfgzDZWIa5WA8yxkbRwbexvdlAFcEtupPBgBlk2CrRIlS9JU/yoFdAC6AnCfJnjBBOcILHZqLQr0iR87bQv4VDNef7ojqc+utQLD1uLixjfM6ghm8/vqegJ57L+CoRoYBrsOELa6vjeqB7dNqay0l1HLB4Bd39aHnWJQ+J8YzOnosRjkAyhhAo9WvCWjLpnJCFE1gw9g/s7yN7XRjF0ZbePVDXbsbXAuchwXs42p6fErDPYOKTc86YhprGqtgt4uf13U5AK79iGasJ6QwFI4478TzMO3ph2Jn+8joIKRQiGb8jgu4TreP++yiWsAFXBfV3O7JXAtcPhYwZb+piZRefW5AwwOEFFYhmMG2DOqEGdQJa2EhqiJe5DDYhrP6S9Xp+eFGJXDyw6goeP1pR0iisiqqR249rMdvOqYWFA2r8teoOr+R4woAziziuLv8MuBhsuneVAzg8TYMx+vyTR5TMDGuHPWgEtPNSM3fovK1N6hq3VoFSb4yd/miNsaYSWcID0qQd+TFSQgpiet+CDZmhPpV6yhUvJafMPudrc278oXxsy+USw5JszgS8WaPEirjelGFXE2zWlIWopTCmAmUBg1EhWGvDK4stJcBjRmk6H/WG4DpCjpqhjn2naJe2U3ket3bNK2YP6V/0x/WEwMRjeXCypBf5u2hDMBMWv7apDwBrj7zzh3zK3nApORZJ9Qk8tTm1E4f37kRWfstaa1BadEk1p3wvsIVd8boQHbm3j/pQw7fr+f3+NU74icqkXC+dgoq3zSh1uqYKmEB/Mu0h+VhZZBw3/1OXC+9NKlJaqGFYepspaVhaKurgrr33oi2bSsndNFsjMULLzsjW43N3WNcC6zUAnEK3PUfPaHZCUoLNFWoobGGfMuz36hZ6TkufcD1fe2dhuG6ZZ7hsk+/4jX7DDWGy9dNHS4XcBXPsG5PK7aAC7hWbDL3ANcCnwwLGF5wnNY3hnVgzwShc37ABY41LEGW0BdfDrnyUpQLUSm0/J1fxGv1wkCjojBafoCD10NdKZz9qkaPfvuBEf36Tf1qpGpuKFeH42+Fr05vGVzdLpKBjmVn5Bk4quru11XT/6bqokcVREY9VVunZEWrgq2Pq7zx0zjfEXxkc07MU754zQBDgjC3BMpjpWWWb2WJT9iEIVgum4EaE4A417gMcE16pjXOPwIIVQc7SIYaDGEGVbOYE1IYREDDCYlbYnqFs8xDqAJbUwAOllcn9QOEBghzDPtyWhvOUHjaRrXYXnmYK4/eAuQcJU/LejGRi/V1vUqHuzXsmdFL0zV65uh2HYvXKAVwCfnjKklF5ScnJZsFaAHEsqgcipyr9ECIkE0fOXboaGCMWsRJbmpL6bGNaT3QjLw7YxiY8aiGiNIKiyrlGr7fFdDLBwKOHL0fBs1qpk2TL2Z5XLe3z3L8jOobsNIyRDNs3VrNpJ8+EdcTP5nSBPlmVVVebdqYU+vatBNGOj5WAH+f+1yN2q8p1ajFWxJq2kih5zJ+EPp21nnhMi621xIXw93sWmAFFrD3md3BGhsY1VDHoCoJ2a5fUw2Ly1osYrv0Adf3dICQwvW3Wh0ucntXGK1wLlNZSGEHgEuIZnzbBVznMpf7+gW0gAu4LqBx3a5dC1zuFjBGawC1hddeGCQ0K6GqakLPuGNo34lWh4sYOHK58ii44bDPVOjpwWYNThJniCKeB9YiT0jX+i1Z/W+fTerRK1IqMeEE/i9u5tgOeZJ6KzemnmMHNdGzn76HdH3uiK6LH1YEOWVPsFTJpjrFrnhUVc2ALl9j0b+cF4/rTM/NoU/EDHAlqUm2OoELAz1xwOVhHSMnahyQ5tOW/CY1iBo9PDbJ+Wg0inpimYIIhSzVHEDAi1EvcvyKazoPNcXGcuT4j09X6p3psGYMk2LftYicPFSTVOuc3PuXe8PJAABAAElEQVTCPsGJsGHSLLlbcRivgD+pgdBBUrOi9JsGBMf16onterl7i6ZKfKpoGlMkOavkWJjQShgtAHgyWaJMIKhMNKDsEIAr5pU/ngOUoVhYl9eGdunXr4jr6pK0XukKUqcrqR3tGfUhX//Eu7CEAMNK6hrN59XZGpshLHJiJqX7N0/qtq1hlYSMTz292fUogEz7m0eB0qsf/9Srv/3bcQ0OmkR+ifJZBEEiFG++OaZNG7gZwDocGIChW48M9W0N6qAotRXDbsrD6tVmtYMaYmtgcOfZOBdynW5z91mRLMAa51NS4whp9B3rRi6+AvXCOkpuFI/luhwA195JVApvsZDCQg5XkazrdFPI4XpZ/h4Yrn/6qG7dsaOY3bt9uRZYtgVcwLVsU7k7uhb4BFoATzMHVXL04JTeem0IZxaWpDqEdDmiCeRnWcHeetiQ0kBOx0eknfEm7U83aQanm9gy+VGs+437kvr9O+LaRM7XPED4sCUR6CBna1e0T8c/2KWx2U6FxoZ1S+aotodOqIY8Bx/jiDa3avqGb6m28YuqIGfso2jFAlwxxbTfcwiWa9KxyxX5zWrin49/pgKZILTQJPHDVuTZ+2GgYXM3Z63HMwtwI2zOA7Jx7g7DnBFK2DlTrpJkvepyZezn0RDKe7eUp3VPZUoVC7ozsGXXxQrsvosa4e4Rii8ztvrmPYifxDXrzaqH/qdim/Rc55V6NwODFZlRWSYmDwCNXQn/S2h2skIzyWqlgz5l+mDKhhCbQDTFB/iOAMqb11qIYUZXwAXOjHq0vTGrX92e1K7jAR3q96m6pCBQcQqRMyi67x3Jqa48qS/fLjVWsMFpvMaLdi1sytEoD0z0g0LRhw4F9X//P4C6V2OanAgToon0NmAyFMxq85WzuvraFIDLT25gRgdYy7kWhF5I9AhgE6uTVsPPDRHA6da0tremFYRxO3crjMcZMNa2MMnCPOa3n7uHC72Hw6iYzeZGtpxZXegxXdz+F87crtElYAGWhxVRHh+eUOeB44pQh6+Z9Wjv+YJq4flb6HIAXAcIKdxw61xIocmVFrF5eWN37HlZeUIKv/1PXMBVRNO6Xa3QAi7gWqHB3N1dC3zSLGBuSYY7rt0dUb3/1ogG+mCb8HQt1C2XSasMR7Ys5NG69jK1b6vVwXSlXuoNaYLvzR2EkX0dZmsrYOvsfithejiqB9ITeuPEm+rte1+R0UFdnR3W9nyHqpNTeONlSlZT6Pjqb8FwfQZVPe6Gnokuu8AXqBiAy4ZIxhbheiMagUMqRbGxKd/E31PVdTPE1VnB1BKcML8pFi7yDy1Pqzs/o72eMRHYx5F+/hEMxwCPIdN+OGXcjl8NyQaVZCo0DIPUHMroszUprSFUb6HpbE49szBDXTA9aStSjXtefkJbWztUVjqjXpKiQmpDJKRGP2e/N2AxvZkZledAW4T/eXCSxobrKbJcqUyIQsuDKK4NgmAAZCHEL0oAQo0gGRPCqGFcWwHopAHqns0pJ19LhJ5GQsDCRXM0gcGxGUIskzl94868Nq4x6GjN1h+5Zsj8v0ndrgNHDOB4dP12pFZm/frBD5LauStDaGFQMWPcAKB5mwNiHo0m4IGgyRSCH0O1QTVe7VfLVRTrDjNnOrc6apsYYwVhmF+8IaktnPPsa9e8ZgN/HGjIFWbXOsrNgd8CxHEG/ZH9MrDl9TAuo6znL7zFATNkG+4no9ntCfIPrQYd8zYQbjmMzpOP1ADkhHJzqr+jRyN9I2pCPKO+oYJxoiXqrCEb4+rb5QC49kyQw3XzYwoaw0U7vxmfshWXWV5CNDs/oA6XMVy/6dbhOmUd99HFtoALuC62xd3zuRa4LC2Ay8a34PRUWv09UZ3oimpqNEodrqzaW4Nav75E69eVqAIxggzOZgJH28QXQuTwQHjQYLtwbwg0O+OX6fwXrIlvdKW69WbsZQ3ke1SO4uH248e0fhTFQ1+FAuVbVLaFL+aGa3FwF9A0Z7Wp9c5Xr+MQ21cwP45TPH/Wsx78oRetm+WGFNrZznYWcwHznix+OmyQw4rYERzDSXLkciWiccAWAhKLcrnMiZ+2EEw02ac8KZXmC2DLjjWQ0kfu1nuoE04j55GLl6MU2UROVKk2l6b11TUJXY2SYonjb5sDbgd5dZSQuh93EjLqs/ymGQDJtO5vH9SmmogGPOXaD7LKEAZ5MJXQz3rWOixmaR6teFQro9NlmpqsUq4csAZgyyCckhspqBgGyfmy1CsTwTCp9xBrowEHs4Jw03XUBIuTY3U1IYf11N6yGmILm81lipIEk1BPXwdwbUH10LbZNTSc8NyLQb2xGxaQXDC7pKSUEeIqvf1GWm+9ldbEpF/ReFCpnB8Gj9VXQ7gk5yJuUlnCCrOlKCI2SWvuJ/yRcMYsuD5MDbINsHpBTHP/9Sl97a60qshDO1szsDUx5VVHh0n7S+3t9LmGQt9zYz11LHO0Cdg8yRlz/OlTL16ARwYEDdojJBn1q2+6kBtnCqQNZXm1EDZZzg0THAFWoq2F0+1/AQb0kXZpLG6vCb5wcZvILXWWm3ONPrphsSJ4vxNWODiqga4BlD9zqq8rZ/3UkEfIXQHnuqx+fJcD4Do41aYNFlJosvBFbl7ehMf3vlxguP6pC7iKbF63uxVYwAVcKzCWu6trgU+2Bcx5w7+GeYnOJlRNTlAbUX2lpTir5HU5fuScgQo+jEdRnPQOwtEGCXsrRxhiU75atXAxS/s4plKYUS+5TcfzB3DwU9o4Waqm/mkFEhl5665CAe8a8rlMnfDszqHzqv2yQRuAiUWVn5wQg5W3CqoDsQsDLs5YHM/37P3NX3vrbinANe+WewFQ5rzaH+ccdG0ubeFk8z2d+6/lcsWiMUcmPmjiHHPN+uz0TGkP/JixWiGg7HwzwY6BhF8vjgfVS2iiMUGpiTbC/0JqKplWY/mkNlbFtR6xkzZPWI25kMLZhCZ5/Zn+anWMT1C3a5B6Xb0UPj6ohvIWpTwb1JPtIOyzS4cypfqv/Y/oaKpFGZi01GRAmRjI2gAcwCczEVQuiUohzn0eMQ5vwIwA6GKIAUCWH4dyIwDmHsL1alBz3HkgyLhy2tZkAB0rzaEQx24IWAxNwGoh0PL1u/Jqn4siNaKGKFP9zd+XaGbWowpy7X04Vn2DKNJXcFOgK6+nn05piBDJqXgJqYaIvQBcrUZYnhBYZ1nAyvpTcQoaI6RxZYlyZQh+UF/MR2RmGQCxklpiG1rS+v0vxnTXtgwCHoVrOm/n+b9m34FBlB6fDKrnBGwa+WwRgMxnPp3WVVdlmLMdhy1YDHnAzwj12uJxBDrWZB32z7OQdZrvtEh/DQiOcY1e6wlo/zDvR0I8DdpbsxzLeux/R3tO2ymI7TfhkDnbF+n0l1Q3Fro30J/Wn/95UlWVHv3mb4WRYy+wuZfCQO3zYRQBjZ7DPQCuCrW01gO4oI9X+qGxaDKXA+DaM9am9psIKYThsvem/RSj2TvPyeEyhqvPZbiKYVO3j9VbwAVcq7ede6RrgU+cBQxsJJMF5mVjg9RSZ172h81gm0y174hnlJ8xHttddu6o5yO6Ot+gCBBhqVY4llpfBMqZE+KDvcHzcO5Ge/wWWgdTcU7HEHYFR9YJjUMIQYN90ltvKH/iBACEvKJrtil3/c3KhsLOHeWA5Un5cb4W5w8wYRuP4wDMAwE2LAW4zBgZAM8QQPGEJ4ZgRYqZetVCcF8rQhYlhHE5WGypyS/abmFpBrqyyNCHKYbsM9RCs1F9gF0NzBrYMvbQ7pQbQzQDg/QBbNVgyk8uVl5xH3ZMlag0PK0g4YFpxmZ2jQAgSnxZVacm1T7RQ9gmTJK/QVO5JCDEqy2RtNYHZ8hp6hckCLaMIKLhVX9+TH9x4h49kbxXU75yJRIBhDOYF0WRcxMA5jwiGgCrIAA5m4TpQrDCLgEClTj1FETG0W+BTVpXnVVlCbYaQBGQ0MA712VUg+qlMT/ObLBzlsfHBjO6tnlGn73Zr+oy5m8XhOsCVtJf/TCsvb3kExp4wqGmeoDuJwxwI/lhf/Ffs/rZawFNj4YcJcUsYh/5Cvo33Eo3JXUxbVhHqYLWbg3mW9VxeIvi/YhoIBriJQTRyqA1Ek741ZsS+u3Ho2pmvdupFzZ7zhD19DMhvQ3TVkPYZBBVtAHqlrW0ZvT4YylVk79mzZitY8dRT/yZX+Ojfu24La1P3ZshvLHwurPTol/2fpufLzGKK8JDhuMGCf/8h8NB9cG+VTIfy7UsKGgaMelzVCGT5EZ+CvGSO9ssX23psSwa2uX3FHtMsj7+5u9mAOhePfpoOe+Jldn0Qk86zfu8F5n46NiM2ri7EEHGk4++82qXA+A6OEUO182PKzhX+Pi8JrzoYB/vx+N7X+Gznxwul+FaZB336cW0gAu4Lqa13XO5FrjsLQAAoOhxDhCwriGnBuS2ndwV5rXQGbXHBLNpj2eQcDQEFnDCje2yPKNr841o8VGs+FzNfD88TofxcMBTFuCQwJkHcKGG50AhnFA71zyIsaBFYtpw8Ptx4ruU7xriixZRiqPdAmfIU89d49S4RgdH1Oep1UwwQihZmeq2bFTLrTeofA1etZ0XUGCUXQ42L02cmhd2xGdhfbxmYCDpyMKfUil0hsph+OkwP+M6PtWjOFWBc5VVSlKnywDORua8LVcB2DR4ZM0ZufNoyV/sYjW/TILeGK5A+BQrZ7lbBrgIAoQ7xJu0hiEOzpAkjqONgj8ALaup8LiSgRkUI2OkSgGCGAtcodJck0g6qgCCFyWco31ySBuSx+UDIEyWtaMCGaZeGnLyyXdVn62kjhfMoge2iOv5ykCJ/sPAw+rwbECgAxaJGmRphCs8MFIZY4IAQ96E5T4BnAFkWcIMIzi3xoIGMFIpoYVt1eRNATYmYarCSCleWUL9MsIBy8nlsuseR2xjhHnUhON6+IppXUHYqoHtgSjKhagXGjh+6rWgXnwfkQ9qa1keGJdJX/tUXHdendHfvBnQE69SPPkgYYkjFGwmrDBP7pqFb1orr59QxU3TCl+XUGk1aozd9Rp5tVHZ4QDrjFMRzVVJyOy9GzL6178Z07YrswBG59CTvwwQQSLqv/51CJbLq1qIUy+gZQLgmcNT/uY3kmpuLOzO0PXSi1794peliKIAOpvz+sY3Uf6sspVgK2hhM1tmlUx5CNU0ezCWCi9rgAeLd1142PxjdjOb/P3BUh0dL4QPhrgWhQLbC7pgvynYtmnO85WtSW2F6TLGbdE053v9GPzNa5b1ZmReCaGuDqC9hGaVZ2BWDHmkc0A1tRFk4q30BRfJ+VndQC8HwLVntE1rbwRwOSqFy1ngy7eFMVxd+2C4+mG4fssNKVy+5dw9i20BF3AV26Juf64FPsYWsOTuXDKhtlpkswFblgtScN/s76lmz+zGrLFbBx1hcaps4ei2eypguNYQXmg0w3K/WHGu0wnlD+9V7ugeeUKEJF5zK/TaOoetcpxDvNkkjNioZ5j8sb2qHHtekaPvKtA1ptwkYW847pO1rZppWaPprnENPHNMUyeAhKWMpW2TQvV1qr9mi5puvl41G9bhgFdrZnhUva/v0mRHp4KVlWq77SbVXrFJfmTak+RWLZaFR41ch8dgS3a/qJLO46qC+si1tWtyM6AEZiGG+MX2qk26IlSvkAdk4FhoaRs4Fp172QktNAGNCDLxCGiYE9atGb3HfA2+Gctl7FYMdmb3RMBSlGCvEBAMTWiyFNBJTTTDGSmceANaSQ+iFkCvpsSwQgDDuC+kOvq/efwD6qrNajzSrKyvGpmMGbUDuNKeavqrAjCHVOppQEExq7/rvFM/n7ldA94y9RM6OIrjnqYvP/ryPvKYDJ16EJ5IRVEChG3zA0RC/KQBKHVgp62soTD7DBHut50CydeH0jqC0MaAgUVs2RDO6mpytm5qmVJ9aVTJUJVe6Q3r0LgfdsajnjGUF1E7bAK8BQF4PpzVCKyZ4RMvxYy7xn2a4PXuw4Rq9ZLDZSycIWZs5AeUB+qSSm8hh+vGBHLcY4AsQgN3tSq9n7Vpgo+M0yTtr2rI6vu/E9N9tyQIecSIiy8Zm575eUBvvBFQbR1jART19nq0cUNWj32JWnWEMJrDbKxfd5dXT/wsrLER6ovdmdVddyYQEnFWMPssaLyv+vpzevGFJPmSVveO8grr/brvPp+am+xaL9j3DA8tzPH9Ab9+iGJjPUA3BDG8VGOVqnfGr1bA5VeuSaoCYZOCk7/UEZf39nnbnZMk/wimaZY3oD7aO6zB7l6tW9dE2GOJE6Y6P+6VDutyAFwHEc3YgGhGKExsMGu9mM1CmjsAXDlEM77tysIX07RuXyu0gAu4Vmgwd3fXAp9kC1iIWzqZVgmCDTWIHMzEqXPE92NLHXk05AQZALN/885MHFbrBLlGw/kYIMuv9nylqj2Ww3UOj3GBke2ub677qPK7nqe2E7enUUb0rN0s3XQPGt51MFdpJUePqTO5W4ci/UqWjKlhvFvrPuhA/SugvkaKMZfh9ANAMrM5zZ5AFOLAmHzvDWuyByYlUUrejgk6hFR75SY13nitNtx/tyYJQxzuPkS+GE7QRFoVlWu0+dMPqnrd2tMYrmAwQKhdXi9kJ9T32lNqf++XgJKoMuF6VU4iIEG+2MTGVg3Wh1XeVKnrUVps8W9SIG/UBiCAuTrWsF88Kbi7OcALTA5sEFBTAQBnkGLIFeVYEe/ZWMU4lN0uGMSxfEKRHOwXfY1QLPiN4bBGY4RKUpzYV9OB0AgMH4pnGcQ5ohw3lSuHiUOxj+PLYbhMmMTqpVUQSnjl9IgyJRUIKQxTiHmKQEjCIdNjjG9KOX+FkC1BoCOoiiyMTvJh/aTrXh1Cft4bMCDnVW88pTHk4lP0bSFr3kS5tleElIZZGgKREsmlQebEElITIYARwFKAULfrG9JaRzjeu1M+jZKTZQWz72bbp5uTzC2m6ZmEfjlcq6NTIVWxv595vN3hU/eETyXgo22At3VVSOln8nq+I6gSAEaStdnfzZj6/Zoe9zr5ZgZ9zMZeUJ+/jVDNKo6/KqGKLVO8HtL4njpl9zBIwjI9ALA866I6ktNjv5LS73wxrs3NVtC7sHqd62aPYfVGRqVnfx7UkSOA2WReDQhzPPKZhDZvguWbL2QL4rWwWmO/UjBKNYBMp2a2jWnB+4HLqBkYP1NaPHGCsMsq3ldsHBpC1n6LX1/8YtBhu06NwungtF8Gpn9woIQbAH6tgUy2VWb/ztiYQ5zrMcL1+UfXxrWh2vhPBuG2VViANzGfBfb55zCpsIoLr+25O+Szk2NShONaLleO0hCNLTXU41u9TPzlALj2DLeq7YZCDpct0yVW6rnNt2gP+0j1QEt3739ZgUEYrn+GLPytOxbtdeanFlmRzZIDy/vDy3fQfCTH/N4nc015fXErhLLzSW6fD2d4fX5/22/+dXts+dF2Lgsdn98+v6/79/K3gAu4Lv9r6M7AtcBFtYABrCwKespaLhB5MTiDniyiArAZFeTgVBCqQz1gJ7zLCIUsvyyDy753fDjlBUdx+UPOU4Mqc/yAPLuek4/QtzwMm5rbKFj7AKpza9Az36f+/me1t7xHE8bgQI94CE8rmU7pWMlmnQg1qCY/om2Zg6qYGIUVgWGobFB+OinPK91KPDek9JFp+aYozFxarrI19VrzwDYF76yUby2goQzlRZT0QtSLuvraT6t56zal+GJMRAlFY6IW6teFet8P40cUefkfdFXXHoAKQu1WO2scYfZgk0Zv3aredTwPZ7Q5ElBjsFV1ulHAz0KAoX1n45AbC2jucX/Gr1cJ0duFStlAgPOQh3UzOtb3Yb8NpaUUKfZqFkX2qVBUB8LDmgHg1HDciYkyPdVVozFyp9J+wgVLp7S2eUBlkWkAU1wj2VqN56pQkoQX40J6kfuHr4NRiql6dkTVh4d4rUGZ9fVqqufq+sp0Tf6QtiafV4jixxWA5RDKhKXe6wBKX9ZgYoPe7AlpH4qEVjCZIEpNMOYojkoVjs5tDR7dR37Q8YxPL4xQ3JhzziLckGBu6Gswr4Lk+vWoFfYBAnMA9gjbTLLdVsqX1yTVlJvV3r60nh2qVwTwWw6YSgHInjvg0zFyoaI8rocNawcYmeJ3F6BtIwBskoLK+w5jpykA3gzAFSCUBehYLhVEnHwANNVy/q0oaFK+ID0YUmYPYYcD7GOCH4QVmvPcDMvWQC7XF25L6rceTsDsGiTmJX6lUWg8wrroAPxNc44Qsvvr1ua1ti0HoDLW7f9n7z3gJDura99VOXXOuacn5yCNcg5ISAQBQiCLYMBcwGAbcHoY42vCfTZgY//8fLExBpMxiKCAMMJCYZSlkUaTc+ecQ3VXV67336emxSBppJE0Mj1QR+qpdOqc7+zzVdVeZ629Nis+c7E323KCrNLMHQ4eSOn730/SaJwpEyThY/W5+Sy1X9Lb3h7S8hUkZDzHkdiWfmWxcRnD+dUdIRpZC4fFZ69z/BtsO1nmVdeMW29cFdeZDbCUx69QuH/SEcghwXV65gHCHQYbJjvHRYAXu5j005ohD7b3Aa5DqsUq3uzNX8qy2AHXZ/7PZ3VgAobrmGnGc0zpl3LYT7/HzfdQB6YZWUwz/uhF2MIb0/jtb39by5Yt01lnneV8zz+9Ue4YGDNwZA3qd+3apb179zrPtWJRunnzZlUjX/di0JRMJtXV1aVDhw6pv7/fYTBtna1bt6qmpuZpMDc+Pq5PfvKTamlp0Tve8Q7VcaGwsPxmRaAAuH6zzmfhaAoReIUjQPJgGR03dsX8ePCUpphlenqe+py0Vrd4VYbL2wLVZSmjrZu/yv7iEhBLPnLRaeV2PUSB1F7qcKil2nK+3Cs2kpVz7X7/L7Q/8aB2VCGNc2p5qAsqL0WCFgB4lCszlcJsIap1gUPAw4SO+ldoKNiIuQNJ/+iEvD/cLe/N++U9MAojA+tQVazi17UqfGOzMmuqNR8sYbs++aNhnVNygTaXrXXWM+MMA1wev1ePuo7qJ5mj8nceVu3wsKrGJlQ/OqoArES6uBXA1abeloDmwqXUBYVg02BOEOxVZZGwpagrywWxIsdUBIu8dGZY/x0N6hbqpg6S+RqjWD/frRrvrM4urtTa2Qa1d5RpAGldBmCZWDWtRMO4Kr0xZGgl2j1cTHIWx/p6DsbHpaqaQbVWHnSYwcNaoTF3FWwXG+b4MUznrNCva2ZSFbsOquSJTvmmkkrUNch77SXyLm3WKm8n/dAeVGmyF+A6T21XlSq8b1LATfwtouxjcDannhkvzBoAnGcrcB1sQUrXAAgKcaxzYJudvN4T96gZyVoEtu3ojMcBBMtLMliUSz9FCmnujsZ6zWK0McN73lqTRIY6q3s7cnpyshxTDeqYAGQz9BS7c69PfWMADzsUpK4pJI3GjGVIei9eAdsFaHoQADVHzVeS+3aNIAsD6EgdbU6xL7eXnWALT1khiIaEtps5PQ6jGgOMUl8WwGRjSSM9xHAUbEVa+J4r4zp7VZoxwJiRSN9zn1ePPGLOhDbVbZs5ZIIpXcyfASWCa7t7UQu4Uzt3pnTLj1JYg1OViIzRemgliXN/Tw7AFdCqNVwIyG/+Wds2aeksDJ0BLjPEKLG6r+cbBC8bPmjnfFy3IqWzGhMOg/isDReeeMEI2Ffj4DDnbw+fTZjcLZv4DNTbxDOwdPITgWnkzKWOfUcx0JhV2/JanC9x0gQE2BxzJtYLjia/wmIHXJ8GcO0ZpQ/XGdcjKcQW/uTDdFIRMDOkThguz+BP9ZEXYLiOZ5wmJib0d3/3d1q1apWuueYaVVVVOczT8Tvdt2+fs86DDz6IWy+/BTBT9r4/+qM/0rve9S5VVlbKXjMg1dFBP0mk6nYOh/mNMCD38Y9/XJdeeqnTa3FoaEjvfOc7tWLFCv35n/85rSVanV0dP6bj9124f/pFoAC4Tr9zVhhxIQKLNgJW45Xmil51UUpNlVLAmnA5P6An+hXNJ4P26kJOaEmLA+oWjpIEg1zWrAFhpSaRvyGpKy4n0fZjnEACcvgBHZj+hZ6siJOUctUR+dpEbbnmfSGNzXE7jTmEf1arwkep2fKoPbAchs6lSDKqmDukuU6kdf/3AYVv3SXv1LzKlpXK/67Vit6wTiPNjUq4eX9yjvoljyp8a3UhQGNJDOMD5HPhSESTgYTucB3Qo1i0e7BUbxroV3NPvxqODMgTojfVyjM1Xl+k/bA9vpKwmjCuCLKtUVebnkpVqodG0aIx8WaKht7leUB16R26J16p73kv0OH5VlXOjWplYi/gKKZQabVc3asVmmhQadCnvTF6YMHULNsUV1FJUtGBgI4MBpEh0pS4qJPjnFFZRb/qyrs1Ml+l9uBSgCjOZ0gME2k/TAYAE0liWXeXPABO/xh1Sqmk/IPICbeuV/qKc2GO3FrlQtKpLjVQ/7U8txE2bRVHYBxVPvWz6/nk9kC3/GLpJUSN4Y2nF7vQD7x5+nnriWS5owfkYOD3Vizcd8/6nGR/HgSzMpzWddVI6pA9PtSZ1f3jVZoPeTXJRBmnZqyHeq/kcI7ecNwi/fMf244XgLe2lnPKvu7ZDuCiRiwDy8VmnP1B5uQHZgwW88BBGw3cB5i4kZxG6BxQCSikNE1RBlyLlfwFG2jwzXOvPjOhC9fTw4xDP3LIo5t/4IPpQypZZO5/uOAZmwYB+9a34pS4jFo2ZzKz7RexGOAaHszoa98EfJOrV1bkayUHhwCvuDG+A8BVV79wqeP4COd3Ys8kqXH7xu6gRgCRFTBc1ovrRIvVtZkbZB8M1zvWJ7UattFCVFheXARMCsrHX9/4tl9DWP8b+1HOxYR3vj3pAHaHvbYvupNZWM9WnZ6cwURjmCbjfGfQ1LASu/gAxjmWtJ/sstgBlzFce0eb1XYGfbgcwHXyx3YyMXAYLmq4hKTwI+/HNOMFJIXd3d36yle+QluJ/3KYKVMwGFA677zz9NGPflTr1693djswMOAAo76+Pr3vfe/TRRddJB/1tb29vUhAI1qyZAkXXYIOq9XZ2QnjXeEwWnbuHn/8cX3mM5/hgkqt/uZv/kYbN250QNjb3/72XwFcC2CrIC88mTO9+NcpAK7Ff44KIyxE4PSJANlejmQ5jotfkP5GzdR2VZCw5801juGo43NEsopZkmVLVoMkk3Y7S+1NzEwXkJ/lF5JLEkZSWiO0nFoxfCvyGYkltNExdfTdpx2ZnZrEXEH02EkWU9PkjrOdsPp7K9Dvj2tFVa8SrZU6Gl4jD3KQCFK1GVexRoeprfr3B1T5/Ye1EmC2bHNEQ6/boj0XnKnp6hIAXBYr8yT9w+hV5Fqmpa4ztClWqSKMI4L8sD7qH9V/uQ5RpxbVzHwpSXtSVTNjqpofV21Vmcor12uMK59TnnE14SpoNUD7cg16Mt2knlgE23dyfoBMCcf4e+6HdKmrX/cl6/QzzzJebwZwzGpNYo8i6UlFi5thYdZoKUCyPuLVgZgLYxKpmQa9ZwJOyqlperA3pCPxSRUF9wB4okhTujRZUaSpZKmWuTtUgmMhPJAm5yt1wLVWMR9AEnYruz+qMLVNHgBrsGdcJctbFX/NRcjzKtSC6QaVVDqTGryV2UpAGrQS+31Zyy9Pr3MyR5EaPgUL1k3T5irGsBVb+nr6dVkdS89oSl8aqNKuTEjkm4BF5JOU8wX4w6fesYO3BLcZ8FkGM2V26Mamth/AOIPapAwxzk7B5QFgnElm6A+0x4wDzIOqKjkYQJSdm9XIUldQT9YzAVMEM1FKrWJNRU7rl8X1zivTWtmUobcW7NY9fq5e+ykjxIDDMdMwBheTjh63rr4qpXPPpSUA8kcnc+bmxSyWcj6+PaW770o4rnqoSQHQbl1zbUCbNtEGgP0ZM/l8J+HuLnqxdQbVULLQgPm5RpBnz0bm6aXH9t69OaEa5MCF5cVHwJjWJ57w6Dv0hlvRRp0o31WHO7y67jUJXXoR8msD9y9msc8H83EeM5uRniHqEOlFCFtTWVGi8grqYNmcXbB4oWWxAy6H4RqmD5cBroD14TrFgAs2umvf/XKPWA3X8wOuGHWyxkb99Kc/1VVXXeWAoOXLl6sU0yRjoG688UadccYZxN6lf/u3f9OXv/xlfeQjH9Gb3vQmh+Gy501quLAsACUDWQv3F9b5sz/7M75D7tE//uM/6vLLL9fIyIieCbhsXWPMjhw5AsNZrCVLMHSCSSssp2cECoDr9DxvhVEXIrBoI2BpRTyRRl5IvUtpVisa3YqEyD5IEWdiuMhhCOD8JvFjgvpLQ5Mk+SCtSmyveUojNLqN4WpnCfQvlzzLZU53lSTAEb+l0yQcXJ23/XVmp7XfdxTmazvArRehHKYQPB9OTGkOZiA1HFNzqE/Bao92lJ+nAX8j+4IdwTUxO5nWhjsf0qrdo1rrHlO4LKrtZ6/RI2vP0jySQS9NqLywUpWAriKYnWrXOp0BW1SRIEmlmP0u35h+kTuiYUBmNFZMXyZqlFIRFYXiqgHMhDJVKkJmuSKYwDAkq+0YXGzLFqsvGXaAA1/CmGJwPNTbnKFhvc+zUz3QRbd51upovIXng9iyo4kT0qLyiNb0lmn+UBDXv3z9UnlzRmthYM6giW0579sxEdS2sVGNJneptLhHXnpYdXmaVZ2ijk17HEdCKpdUlp7V49qCdf86aduAyh86IB9gxWNyx5kZha48T5nzz9AWb0p1M50wimFtKVqrajcmGU7UGdIpXOx8GtNlWMjDuTEMYwAoSePn3pm4vjJdpyc5NhfHaPvvhb2ZIW5rSWYSA0SHurBNyLeKoNbmch71A9xn6PHV34tDJa/FYcVSANSs7WBhIeY5myhIB10Afuu9dWlZSjU09Z5FElZdm9YwxieQTnr/G5K6cC21iswHm6/b7qcu7V5cAKutXjFfo2jSwp4uj177mqS2no30kCT8pQAue5OxTv39OZopU7fIZlpavaqvx/reBuMsxx3HwlPHnjcgaezWN3fhWMl2qsOMj9ecBN0+ZCwWQ7trssNOWMKr2lK6tDWBXDL/urNS4Z+TjoAxk/uQEn75mz61tSAFBbh3dHv01rcAvs9KALiO/z476c06K5pZ0fjQmIbamejcr6ophTGJOIyKsSD2d6JlsQMuY7j2OQwXphkGuOxH4VRNQb5PjOHq3H+/XMP/pQ//rzfq7OMYroW4LYAhY6I++MEPOnK/D3/4w/rBD37gsE8XX3yxIxd0WnPAYs3SW8DWMzD0qU99ypH/zfCdaaDI2DCTFh7PTj1zP/F4XB/60Ie0f/9+/f3f/70uuOCCZzFcS5YscWSIn/70pzHP6dEf//Ef64orrnDkhyc614XnF3cECoBrcZ+fwugKETgNI5BP5GJYn+Wgb0owNMDIz0ka56mhmTWJl2OekQdRcjReljDkwZOZGgSRy3m5mpuHVfkQWHJoP1yJOAYHmBVYjmG/y/Y3g0vewZKoAkWPq8X/OH26qLHBUa8Gdzs95dZ056xqluxS3fIO9ZY2anvwHHULyQc9ts7o7dCmcViMcrT0XUeUOHyfDuEyt23rFnUsaZMbSVYZvafqgFsBrVObq02rxtj+0QHqpDwagjX7PmzMjjmc7pD+5ABUo7M1GGLkdCZGFLWMYbLcBdOEgK+4VHdRvXXYhVWFXfGGxXPh4GBmD1nkdI2phD7sY/zuGX07c64OuaqRBXKAJM0V9Kd6X4lL15C4tbdDdCHBqy/3awtX06tKaVprsSAoaRih6fk9GnH/TKOYajwcWgLjVaflyXacAblS7ilR3I3TYCKqh8cu1L1Tl9OnCrMQJDJFU4MKz08q0hBU7eVN2twS1nnDj6hm6BHVuuhXtuQGZJHL2dFLTx4Z5vMudj4X0kdLhJJc4T+MwcltcerpMN8Yh7Gapa+XzZZxLiavAalS6iUUovTOcqkBwLQGFmobDE8K85GhbhwNd3jUN0yDZMxGohhnQOTl5w/OmlCdSEMB8SVY1Tcm9eELkmqhR9jdO6lJo1HuHMD6nLUpvfb8BI2aGRyg2ZpnD/S79d3/DCgGkKuAAXNzEkeGsbRnvrztxriamg3k2NG81MUYMxg++3ywGQcgOaLMF95m/pME+B7y6nYaH9tnqjbEJwJQYK9ZfAktckuOA0v41ZUJvWl1EpkqzIwDL1/qmH9732fxjMHW33KbX0/tAbnzeA0mJDe8IYPk9NiMXpjYLyFMJkmcHBnX2CCtL+J8r4aDABTkzlwUCpktp53X5wBeix1wOQzXEDVcm6nhcgDXSwjO87zFA+DqOvCAUr0/1rveepk2bd7ixKkIx9eSEnxXrc2GnTwWkwOaPNDkf3/1V3+l22+/3THAuOyyy5z1FnZjwOz973+/Ix1samrS9u3baQXR52zv+uuv13vf+17H/GJhu3Zeurq6nL/JyUk98MAD1H4+ore97W266aabnFovq+s6nuGamprSX//1XzumHMaGmWQxRGuRhW0ujKVwe/pEoAC4Tp9zVRhpIQKnUQTsx9+YrGOQ6VgiYKyVm6Qvn4jaj1w+NbQDezoXyT994mO1H0dn5fw78iyXS6OelHoDfbhJPaQS9xEFSazr0+tx31tDP6UcvbVulqvov5TOTsBe0Cx3KqISGvaWu1fKV3EdjocXKzc0otQjv9Bs12NqX5bT4xet1mBNFSYPEQBUC+xWk9ZhtZ248xGN7txPappVdNMm/df5F+tBH0AmOi8vNEcqG8RgIqk1qQltmsdqvWRG0Zo5DRdXa6e3RUNsLc0YLKHGosEBXmmkdHUzQf15BNMCAMA96aDak9RoJSYVTE3rUmqMbqiop0Ewlu/zSM0SScXCM9ShHcZZ45DDDs1hEjKBgUUWMFXj3Q1bN6btJcv0VGaFSrkau4p1M3REnkiVq31kue4Zv0JdqRbeG8TBj95cWKG7jWqD7WmoSOq1beO6Knmvtgw9qjp6cfna3ggbtBxm8YVO0olP34t+hdM8FEvpO8M+JX0RlZNbuplbk4ArU56+vjyp5UgPIT9xj8wi4wMy8OCWPX4dGPWqHMA/MuDRk495dWTAq0EAK2aX1GzxBtr+FBfnVAdgqsbg4wZqtG7cGlc5wKqHOpzDyAODbG/dElwgAbsO2OJt/M/9nPZh3PGLe9kmQCsLKK7C9fDKK6mDWs15RcoUxJSD1WzCA3rsjc4dzredcua+hfE5F3uTgVpWtDc7oMukSid8A68dv+R3av279owyxk6vY/se4LNnZZU8rRjjdfP53FCX0eVtsLHW/MyG98tP4vEbLNw/mQgQP64PkHzjTsjpampKk5RzcciByyezgROvkz+jfNRpPJ/hKsxo94gmkEr7Oan1DRXcYuzzNKX6y3my2AGXMVz7R/I1XP4AH0g70FO4UK6qvsMPa8ed/y/tOsZo71Hq1MBdd911uuGGG5w6KgMxBorsgtkXv/hF/cu//IsDgkwK+OpXv1pvectbZNJCY7hsXXMkNIZqx44d2rBhAzWbb3Vev+uuuxyQZtJDe72xsfHpI/n3f/93ffWrX3VAVzQa1Wte8xoH1K1bt85RcywArjVr1ujCCy+UrW+1YGagYY6GxwPDpzdauHNaRaAAuE6r01UYbCECp18Env795M6JE8yXeVzH8oskCWoKdshHw2UfsrLkfJliMR9X7kmoS/8bx7xvAZIOkmtHlaMOK5G6RBn/G1VasUHBMC4JZEk5fgxzc5NKBmc1BXM1hLEGYhHquIJqchUp+fg+dd11H5Iv2ICyUt1d3qSHlq5VWcuIKmCQrIatKjqu4vG4BgOt8lVSB1Q5rLgfi/dMHfK+Fg2qVrP04UrSgNh0lXB+9AjzaetUUB+vIUErntOdqSkdJbmaBmykM/QBg1lZEWrWulCxwtRhDWYGNZTqh9UZRF6IUyEJ17w7qHnGmchGFMHGfXPuKdVNdujw/hJNHqFUKTuu6qVZ9deeqQeSr9Kh9CpNYtoRTwVI4nzInkgUkZQFS+YUKJ3XEn9C11QM6dzMUbVQy1Zd2gDgqVeIsbtguSzsz3VV/WWezV95uyU4iVhSD9LR+AHGmqB+zECBwY/zitO6DMBVCoOT4T9uADj5ydCNFPBWJF7DUY8gAzRCf69dfT5MN8CMSCdjqDSnZz1q4rS3lSd0wdKUrl1P3RhGB8wCJwkyEGX7ypuwL0xgZInc7cCRcRggX4HdfJqN2l5r67BUj3h0N1bxfcj0zkWid0Yj6A554j7kZV0wTpVsfxOGGqWMKY/GfuVwT+kD++xZuj9BjZb15OqchOFjvHbho5oWDmtw9myj7i2IfjNLLZu1bSgsLy8C9h1nMTfkYJ8Qxyzj5W3yOd+d4qrBYNeAUvTsis1GqcETZipVKi6xifXLZbEDLmO4dg/CcG000wwDXBa7U7e4mNvdMFyZgVv03rddqc1bznC+s6wWygCNWbcvAC77LjOb9/vvv1+33nqr7r33Xo2NAdJgwoy5MhBl7oImBbT75lL4hS98gd54b3QMMgxI/emf/qn27Nmjz3/+87r00kuf3rYxW/Zn23/00Uf1pS99yXFANCbNQJbVcL373e+WmXZYLZm910w61q5d64zx1EWksKVfVwQKgOvXFfnCfgsRKETglEZgQb7l/F7nc262n098EgncExPTKo1sR4qzF2O6CCBjnSZiuPali7EbdylsjIeTXnNL1mTcG2QQjW/ztT32jNvlUc+Dj6rjvgep60LaVl+v7xU1a6ohpfUrjuBsl1YpJh6bj+5U2dS09oY36P615ymNQ2FyKqyxZKWiJBWzvmJFsyU0Ap5VlWdUpdlJlU3GtNZboepQOZbvw9QoRdVe1qKJcL5Xl8dqawBUPuR0Ac+M5nxJmB7aIgPE0sgvI9jCl7mm6AFGgy5ARyUGHXWjnar8yXacHKdZn9orao3C9Ft+cvkb9VT1lRrL1WtqthQWLZIHW0juTN8YqphGVjiFRfu8SgPA2GRAYRjDM2kq/WpeOyvUSL0YTaeNJVmQ5FjtxSuwOIALmWQM+We/t1RHAYcJigDbcA1cTd2a9RLbQcPhriGfltUntRkwE4ahM+ZzEOe9J/qotRrxAV5xE+Q8+zjPYCzYyqyWYau/FAv7DNlqN+cWPKJADLdG16BW13u1NoQbpIc2BEyqfCNbmxoudcx59aMBn4bpd7auNKk31CUxI7E6LpfuO+LV134R1GCfWy2VOf3+q2OKZxnDLiSNmHBATOr8NWlddQYul86cY74ZSGTfNm2z3H8hQ4yTDXOeXza4yDEyf6120qzzDRQYC+ghBgaybPf52X+yWy6s94IRcAL6ykXVtmzgxFiYQUw1xvtHqV/1UU9UAxvCZLKTauccScEnPvV3qmtbqz+hDmgxLUkk504fLkwzlm6+QX5cCp0LODb0U7EQJGur0EENV3b4p/rw+59dw2WfWfuz/S5cPDLjC+ufZfVVZphhYOhHP/qR0x/LpIQGiEw2aHVbZnpx7rnnOqO17dhjczk0IGVmGoEA8lKWhW3brTU4/qd/+idnvU984hMO02bbes973kPz9MMOMHvta18re80Anm23sJz+ESgArtP/HBaOoBCBQgROKgKWVVpSjGQOdiYHU2Iprv0AZrCzz0JbODmKPcsPtf3GZUjmPbmEmpCclUXcXIGld1R/rw7c8l+aOtql0fIq/Wj12SrZMK3W6i75sWFvGe7TSurCirMzOhxcoZ+uv0ozdUW47fk0k4BN8oZoSuzFWh3nRCR+pRlcAykqylJr4KEWw0Uz38ThNExMlfYuXafp0hIn4Tcr9yAJSvXMkKYCMD0hEmlA2Jw/DKsXBCRgUuKBZYPdq8sNqTnRJ/e2Trke6JOXnlDZioAal8woV1GMyccb1RHexD7KkSaWaJyas+h8CZb+xAQ2xk8j5IqmMZUvGYMto0h8GhlOzI/RxrwuDu/W9emdqtpNvKaDitCfpmbzBpW3tQC+MDshnqd04UQkKDJPUrcSLMbs3TRCAAiTYnoAvY8f8OrnO2gwmuJ5WJo3Umu1oQ0nQcs5AY8T9P76Zk8Qi/ecrDVcexfWzUdzODbmdN5ypILLs7o3GdQTs17NZJFppsawvx/TytpxbQ5V6VoYzBpIA8dp81jisxv27Gbs92PIGusx2LimOqUiwNNOJIz/eYefK9g+xWG9ghh0bGyhRcKKtBqbMK/A/XAKEGglcO+6gvYJpaZtZNg0wiK/Iz0m9gbCjGk6iTAu5GEvJuTOe2wXzp4L/5zuEbA5k+O7KzYzp55DfAdx8aWuoYx2BVjT818OaesnPvUF1S1ZsygBl8Nw9TerZeOb5XMA16k7IwZT3HxHdh24X74JXAp//8QuhfY7cDywMXmhsVRbtmxxJH2///u/77BYf/mXf6nm5mZ94AMfcOSBts7555/v/I7Y+//hH/7BkQP+7//9vx1WzGSIz7XcfPPNTo2Wbdd6ds1zUckaHi9dutRhtb75zW/K5Ia2P+vNdfzYnmt7hecWfwQKgGvxn6PCCAsRKETglETAfn7tR9WuZNoG7fHCsvDcsce85MiBeJgCDM3PxpHtZegtRuNdHAvnAF2ZgU5F6dH1r7UbNVV6RGcm71AF7Ejr/JAaZseQC/p1T+NlenTJ2UoEA7gV0lcJdiqVoq9Lckor6G0FV6M5AFjaYYkYFHl2GuAlDDFi0+XaX4HkD5ARovmvFwljJDqrhplB9fpbcS/MKFpUrFhJMe/xKYE8scI9qWZ3t1ZnDmv5+BHFf9Kt6T62Sb+usuJZNdOva7RhuR73XqbhoqWKBSLUtPk0Nl2prsFl2Ogj6QHEeN0JTCSiitTOIlnEMd07p1ofxwXzU5aa1OYn79KybZ0KhTfTiLpCLo5v2ZWXq/aMTfKaZz/A1urTHNYxH3YnwT8+4guRf/btwlrcGjrg/wQ9zxJxmi4XhR3XMZP02bZtzTu3+/TYIWz1ceIbwvr9NWcldfaaDPJR6utgcx6hofLNXZhb0DFgEpO39t0A3yhgiFq3HH23PGuxwd9M0oqTZo4mWhP0IStDTnnG8nas/YN6Q6RVFzTjckhtjsnDSqHHCKceoDbq8Bx2/xjBGLcX5dwe7CC5ezynAUxVkjgkepEt+pDz1dRmdekVOFUuzWgcc48mgNeNlyRUgZkCWF+7d1Jj9hQW4BSlbVgrnX0WLB11fPmJake5sNhzBth4E0+nYO2sZYKHOjNb18Zn/3HvGCPH+vbI3sO8N6C3EFOoLqfuzJ63jeX/XdjPs29/dS0bU37bzkCevfoLP2Pn1mFFbVu/fYudI4vdC0f+hWKTP98ZAELPwS7NTs4ijw6ouoFehWhsA9R1/fVn/gHAtXgZrv2YZizd9OZjDNcLHe/Jv26RcfpwwXDlRnEp/MDzA67x8XHH/MIAlTUtXgBc1i/LXAuNbfqLv/gLBxQZQ2U1WR/72Mf05jebwyKN7ZEL/uEf/qF27drlSA3N3dAYSKd2me/VhcUYtE9+8pP6zne+4/ThMkmimWSYacbKlSsdKaHJEs2hcPXq1TLwZrcF0LUQwdPztgC4Ts/zVhh1IQKFCPwPRsAAmv1wpqmb4AZcRL0LbEgZCfM2f5HuGD2kdfFvIkE7ohocDa2n01OBTdpeeo6GArWYUmAPz/reREru/ozOnn5SK8qPAtRKNBasVsLnhxnCfh7DihTgJY5XeWIuoq7cEqVgsioD1BFQSVaVGFElP8xdk206hNnHUHWNcsjkcvyYx92AOnqPrXId1DWJn2vV3FF1/iKpnl1ezQ9RW7Q8otBlNZqBiRr2tWgkQ1ewZLnD6lnKe5j+XqMz9cqESc6pcfK6kwr55pAUTqmpqE9bwjtU5Z6QK5FR1d49Cv7zdoAi43vDNcqE/Gr1JHXu2qWAi6Vyh1u5so6jFgmPyfGc5JwY5M027MQRRCfhtPvPtZit8jwyx0EA3zTsj9WxlSpU0gq4wOCD9xu48MBSdSPtu/UxXAjHDcjk9Ibzrf+b9TTO6LvDAX3vsF/7xz04DGK/f4QGvzQsngUkpTlNOeq6VEWftS2wnNBfbtilLAxoKce9fnk3MqQqneUq1Rlst7MfixRWv/bMpNY2G1Pq1v1IFe8bZtvIG81B8VYcMY/sSSsxxQyZoj5vnIQXpqsY8u3KqxnXchwlMed41RZzUmQ8WMzv3u3RLbegXaUKx5wOZ+gt9uqr01hFY0Fv5nMWvuMWM9+YnIbZ45jbOR57eQ129WdthcFj20+bc1iI3WzX2DIilmHidnZmMfqgHrDMpa1bfDTLNvCWfz1nclI29swr/bZrO4XOaoAkB8wR22zGxvzi4ZYzPgf5HQN8zzg+Z6O/wf/Y4boN5FrBodWM2qSyKy0vd2EzsZmY2vdwIQemeUlbnQOw8cFxJIXNyzcsTobrM5+ltrJZzRvytvDPnO8vNyxuJIUOwzUFw/XBEwMu24/1uzIAZXVU1uC4C2dBM6swB8JEIuEAoIWeW/ban/zJnzjrmmmGsVBmmvGTn/xEv/u7v+vYxtfV1TmywUOHDjl1WuZoaKDs7rvv1m233ebYvBuDZSDL+nyZa6Ftx8Zghht33nmns097n0kUN2HQZJbzheX0jEABcJ2e560w6kIEChH4NUTALspbVmryw7n5tOJz8/Jgpb1t4oD2zHaovqwfEDanyWCFhorrNFtSrpgHBgouK5nyKdg3rKr9Hbo8+4RaV0xqqKZOQ6FapXxWJ5RPdtNIgZIeJGmpsObSgCRfnPqsOept0iqhWbMHG3l/e0odk0v1ROOZipbT+4uEPoNMsdQ1rWtSP9dVKZwWvRF17w9o/PujmhlzaZhGzq5N2NU3ZJQqLaZRc4NGo7UwNwGaCSfVN9Ws/mizI3fEdQS5Ylal3ik1hvu0vniPmv09Khmc1MpHD2r5URwad43Rw2uJfv4H71HujDJqoga1BWbv1dQ31Bazr6KLiFUxpiQwXtQLZVwxxZA9GtsS5j8/qNSu2ALJ8mwMa5OCEwRs8uOHMTV5nHwUExKS0kzaElO35kpbNVhcrwkYHjMtiWRDqs7SCGumBXe4UtXQ960c1aH1EptMufXRnUW6p4tNVk8J5aVye0IauR9WjwbL1o7LbORVBnjbChMF2CovzSkKkMvSeqA4mVEFLQxWU5vV38MYMchY0Sy99eK4Lt+UcuqvbusJaNd0AMkhYwYI3dZJS7MHYSf7kIbigKl5l4qG2S4Omh/7g4SuvhyQTk1gsa3PfwYab/6RV4ePuHE4RE5Iw+ehIdbHlv6m30k7t8cnoAZW5uiv9eMfe3SIjtdFyFyNJJqYzui8czOALheSxSz20S41N+bU05tQ+9E4CV0YpkP69n/SvnrK3BwzOufcEFIp5hhNor0k/w0N9DCzfmScHwNdv7IQpumZjB55bFaT41mtXxckgbRecLaW86H4ldVP9MDOt7n4PfjgHA1cRa+yIOwp+zvRG34DnzfWaZJeg4NmoFKRVm2Nfe5PPobPCglvtXcnuZjTc7hL0Yk55kVArUtqHJCcBin/5Sf/Tg1t6xYl4HJquAathusY4DL6+hQt9t3i5rvMqeEao4brBRguq9sydsoAkTkQ2n3rrWXNjo2Fsn5ZZqBh89guwJl5hplfWANj681VTVM+czQ0q/eGhgbi79Ydd9yhr33ta866Vvtl7zUgZjVav/M7v6O2tjYHRFlPr7/927/F2bLJcT00Vs1kjWbc8a1vfcsBWwbI6qkbLiynZwQKgOv0PG+FURciUIjArzEC/GaSJFmNF0AiN6Pxzh9p3+OPaRRZX5paqQx6/PENbRrZslETgRpN4a43nwkpMD2jip5uvTq6TVubDmiyukLdYcCCPyLwBJkTzAtZbBoDBzM38AOygrBWllC5yKxrchPyZJKqHcFo4+iMtnvO1MNLzndqyYpo/tyQ69W1+oUapmeORwAAQABJREFUcEucdWMMgvPg2K6s+rYjpavfALALqqgFVmN1hZKRIsXTIa7c+mGPfOqebsOoo0lZknUPzEvQlVBbuFONvn5qwwZUnx1S5dCwWvd2qu5An3yPDWuUpsIPv+oKtb/zImWaSmFyotpycLe27MNe37tBT1Zt1kBtveqqJrQkMqBqmKAgQDDoDuOY2IrDX7UCWLnP+WMaBNyNcryZ9Jgq5vapOQXQdAHYUvTN4v5gZFoHAZ/D2YAGE1WKYpufAVgFaPIWdlcCNterxdus5YDVCIDigUGfPrc/rHaSlkDdnGPw4QL8RO+KKDOBJb+fOGAiYVbvWktiBjtUUoQ8D9/0BL4jFRitXN2cErvVQ3vdGqUn1xvOi+sDr8PZD4mg153RdrZze18Aq3W2BWzoJmE7MJLVnh0hRXfjftbvUhnNvi/cmNWn/nxe66jl8pr5ieWUx+bQD34AA3fQgz01dWeAon7MNqrpA/e236HvVxkrHkvGDQSZPGrvbre+d7Mnvx02xGl0EusjR3FfLGL7sGIGzJa0ZmimOqGJqTQ1J15tWB8hMUwA7Pzaw9h6AIseT5B13AohX7zo4qxeey1Neq1hs5mOsD9gpg3TkT3ecsu0ntgxB2nm4XgZ39vKkVjBzNqH4SQXqzns6cQw4J9hbMvc+sMPVQIqjYU7yQ2c1qsxz0jAu3vc+uHNftzvaAHHuXvd6zL0esoz546686SDQdwBbwnMZFKg2L72Xk4ULDnnpx45YRDW2b6jFr1LIQzXzh4YrvVvxvTDTDM4ySc/pZ5/RrAtcynsOni//NM/1R9/6PkZLtuYyf3M1MJuzYHQbN+tD5f17TJ2yQDTwmKgy5oYm/uggTUDZ/a3YB9v69m2jB0zUwyr07JtGGgzl0Rbz0CZLfZ5s23ZY2PVFp430GXvt/cdv77zpsI/p1UECoDrtDpdhcEWIlCIwKKKAD++GdeMknO3KnbXLYCbPo3AXPTXNmr31a/X0Mb1mlAVToghTBxgNcjwZ+fDWtN/RO9yfV1NpX1qp5aqgz9jtTy2DrVHXP+XHwanTNPOc/OwQbRKxgxj2GFlWsd7tX5wv6IzRbjrNQqxouJjSO+KfFq+elreCo/moXQSbHPCen7NYOgQX63pdJmacRqEdNOEr1Lj2TKnhsuNscSByXXqRWaYw7XPF0whS4qryjeqOs8wvcomtXp8v8pnJp10MPBAu9w/4q87rq7LztKRd1yp6PImuWDnKpJj2vLkEwpu60P6GNHw2loFrqxRkiu2lbmwlvtLyWpIEmdoJr2PPzMBaXNpoplkK0xzXsxGktTGVdBoegtmFpXxjDo0qSe4Uj2GxbsnOa/RdIX2J1drMltKk2uYuPJJFQM0phOrWGeVYtRs9SP1myTZytEUO1g+TwKDLHGCOHWEleU2N2nZGMkTIIC8VUIe6sOyHZ8M7N2l8FxODeEMMc9quB1GDKnf69YndNnWtJa1ppwmwvMAtoept3tq0utITTdW0h+pbEhPTtAa4FCtSgF9Zf6stm7MaNkSazxsCdvCDAbQsOMDB736wQ84X3FAGy+mYdZe97qUzj6H9QFYtrrloAZsxqk9+973PfrJbTBoJkXjfwNYNbQSaD+c1UaYt7VrMODAQKS9PYk8cZzELqulrR5dfU2Jbr8tpYcf8auj089YAg54imLiMZ8yZ0q3Wttyev/b47ruNXZ8gC3AqEkOZ6JZffFLo8xsksXyjI4eyujaa4t08cVF8v7ygBYO7MS3jJeck6av8woDbs/YEsLk4RTI6U68x0XzisFXEJFu/n5YT1Kzt6QtAdj102Q3rQ+8L0m/N2vm7VwBOKkxWx+3sYExjQ+Oc1EircqqsMpKIk5ibmYy+RqxxQ+4HIZroFnLHFt4EKhN9lO02KbMpbB9/zZlx3+qP3oe04xn7tKAjjkOmi37FVdc4RhmPHOdhccLjPDxYGzhtYXbhXXs8fOtt7B+4fY3LwIFwPWbd04LR1SIQCEC/2MRsKQXkJT8sbzD9yg6ntIh9v1A22Y9WnsZoIbCdX714b1I+LP0kPJpKl7uMCxXxH+htxZ9T7WBIXWWtmkgUo/bILbhgJIx1agM5qw2OwxThJSOxstluSn+JuXnSur6EWR9PUcVPzpPfUJAs0jr0rgpemoCaroSC3dMHsYiVdRqVWnWE6ZP1bS61cZVWIrpacYcj2HqgKxpdJq+Yv0p+WsiipY2qj27kvoxrr5XVihOoygvjXubfL2An0mtGNhPnRkuewEkNbceUvDIiBq6xtS/cbn6LtmgZB09z0rqlCiOaOVT92vp//cTlfWOa3/1Fu27+LXqXnuhEpWMhZ5XVa5hrfDvUbV/RMV+3Cxgqsbc9Zr2VCjCcRcjP7Qr061csV+JPf4hWK6Z2aR8XOl1IzmcJK4P6SINeWupq+JqsIeE04erItb/D7VfrN7+FqU4DmfB/c8NG+UCyGSRz2WxdHeZAcYIp47tupDxGVrKmoVhOevWZXVmcwaXSRK1AQAS520loKkGEFXNelXI7m68Lq7WRur4IGfSSPDmzPjSzjPbehRr/iHGf2m2kubXgBoG4TFQYtkf2zLJXn5xnoAhzVE74tHO3bAV1JatXZ1DspdWCGfJhezTSp7GpnK6+ecB/fjmoPY+ia09Bi7mcujHdbp/wKWhQVoTrI/p/AsATrCYfRAeoaK4Vq80EOZV2zIvQM2jb3+bdUe8qq0GJFK31dtHbImROWcWVbm0ZWVSn/vErBrrbZzABA4shf7yu9+fhBlLcpXdDZGS1TvfXo6sMGTCyGPHc7I3ZlFvzNkCg2bH+Zu/LACgb33Lr304a65YltUAjGsE1vdDH6KOMIQ7jRPK54+HgXRbbaR7SIOdA8xtk4NW8Lkyzeyz33s6MFx7epu1hBouv9OHi8/SKZoOzieM75HOA9vkoYbrIy9Qw3X8bg0gDQwMIM8NqaysjDgvfG6PX6twvxCBk49AAXCdfKwKaxYiUIhAIQLPigD8iLyTd8mTuA0ZYELdmEPcTm3VA5EL1J9pcIwiAt6kkyTNU5eVSISoT0Ky5hvXVizWzwjtVG1wSBNI7frUqOmMuYtlaI08rKJszLGXqMkMA75GMd6Iq2Z+WG1TXXIdiSp6hF5TR2nAO4jDHTU2davpw/NawBnStblIWCPeGgw5qB3zY6sOS5ZO+jXgAhQdmFMJvXuSgbBGd9B02RtW7YYiPdl8PiAGi/hwiQaq60nmsTP3Dqgu3qvQ9Kx6vcs0k6IJ9N3UcPVD+WBUkS4Jy4MBhFpx6WqsU7yxRsVd3Wp5+FHNDTVoR/pKpIIUi2OhH66Lq25Fv5ore1TvHSTx9tOUeFp1RcPIEaepuyrXMONLuv28kqFfFnURc/QXisUVSsZ4PqBpd16euT+zHmBThwQRZgDAEA7NqwTAdXD7OvUML1UqDPXDsNzIPrNY3dNpmvGS5I9yf4xbmm5lMc5wU5vlwlkgi/mIC6CaXU2vsoac2lbwPKsakFqK3K/+QEYluA66YX3eef08oEYapnGyn1q3GsCaz6SJJLyzsJQpkjW4H8ORx7JH0m0AXBL5pA+mzrGYf3om8Ro7yZhDvI3XXreU0957bHEh4Xtqv1df/FZQD9/lVV+XC0kj9Wr0VMsifRofxbQDJtAPeK0DMF51Nc2eYRuvvkq64GLYKxp/m8nFt77j0WOPmZzQjawJIEgOmQB0GsN1tJ/aM0BpTSSpD7w7qRvfyjFE8uMwieLwSEoPPjynKYDn+vVBakqCjjnDrwx0YcCF2+eIACeU87h/n1v/+Z8Y4wD6rQbuNUhUL7wwRb2iMYrE+wSLAS2bTHPTc46JzCifXxcN0Sur6edXFOE7g3l03JxZ2MzpALj29zapDUlhvg/XwshPza2XSd5xcBstLAxwXa9zzsn3zDqZrS+wUs93Xk5mO4V1ChGwCBQAV2EeFCJQiEAhAi8nAtRb+ccOyz/+Xdz6DmsiOasf1lyq+6ov00Cuid5O9OAiUTLThzRJrxXIB7BdrwhNa6Uvqq3I964EbMy4uvWECxkYrE42bZQJgCND7VP0qNYc2an6iVGVk/hHilI0Ik4pOUGPqYM5jfd4NTHi1hSOeVX1OS0/z61yWJJ0E/ujr5eZVIABRUonDx7kfakmTezGgGN8jEbMVTrYWYUpR1IrNrINT62eKl6rQ9XLNFtTooryWZXDrAXno+qKr9AwRhspWivPHYSN2Y/kbSRG0piSawmSx9XUEDVNKlCO+UPPlFIDMfUhU0wchWnCwGKidgkj8GIqMiVfNQ6IZTHAR0I5GLymkh6dVbVdAW9Cna4lGnFVwxx5FYB6ycDGtCWPqAp3xkEvjZppBB0D5I26KjSHpDKH7Xt2yot0blbRgVL17mhTbDaUZ6y4um0MVo5k1QV7pD64FQxEcmbdBsuXGwFUpUEdgBcK56CoOFNruG3OqbgNKd5W5IBNvMY2irqyqh/k8Yqszj4jpR3DPv3iqM8BXG/ekNSyChgvA10Glpw/bo4tlgxPwSgePAQz1oLJRi2OhLbasYURcg+wx40H8MWU4iEPjA0zsEiifuc2rz71+Yj2IkdjKjnHFPSB0hJpzY7QAJq2BW6cJd0wrm1tHv3OW91617uRR9YaSMwBrKSvf92nnTvzgKuMGiqPM4iM4omc9uwz9spAVk7NTWm9770u6ro8SAvzYwPaMX8tqed9xMNt84r/jjuMhcMp3D5PBKyxdW9/DiMWj6pqclq6hHpAB5lzUp15cOzNFvaFSUKQo/QTsPYUkyNjikdjMFoR1dZX8hmyt9l7F+besfcfuzkdANfeniYtWWeNj4ufEzT+6hG9iEfMe6uZ7KCGywXg+uhJ1HC9iK0XVi1E4EVFoAC4XlS4CisXIlCIQCECz4gAV6Xd9G4KtN+LBfid5Me7dXfrWn235c0wQks0jWFGNIv5gyWogCgP7FU4QNNbf1RnwUhcT0HVZTmcBl1DsEE71edqJ+mgEgz5mucgdVkPDMm7vxcDi1GtvALA1YqxBNbs40fdGudqeQKpWRTnuuikX5FqmhsvTau+MiH/EnL1ajJ3Eg7TtY1hjT42FaJWhyQ6GVd0Z0w9t8OQDeGiuKZSQUCAt3dSCazqBy89U7Fl1J614gBYEqIOrEa96SUAgQyGEiENd8OSDblV2tOusG9W8RX1SAlLVd40oeK6aUxDkKEhcZucqVH6UBAJY7HmI+WwWFNqqenAvJB9FSHbC08SFxorA0prS4dUUjyu0SwyyGil5hJBzU2ElQHUnF/2iOPUOOxvoNQqqizMS1ekiYbMJUr1AOhQJUaCUQ0/Ua/hg00OWMMgkbosjh0HRwMs9ucaAnh1A2xopGUOhagXyVPhk0hsc9YQyyz/AIwuHAMNdIU25NRUn1VRZU6NccAW4Oa8s2bVuiylh/ZHdKAvqBTyuhvob3ZmI/I+x+6cbT5zYY7EYaCGsOevRApYVIRU8Tmgis2PWdwNu7v9uJ4hGwRgL8GCfjbm0te/F9B/3xvQo3u8gpSiNgVZqLFiJOnxfppC05Q6GASggtrO3JzV3/+dl5ouwJEBN5sAgMFbb/HprrvF9pEPEhu/n7nL2EbHkMIepsE3ID0SBgS05bRydUbveacHq3n2YJsoLKcuApwOA9ecRgdXL2yYU8ET/OPEm4saXFjJAXKnAFljQxP03kupvCLk1BNFIkHAFrWIL3ByFjvg+sz/+Zz2Abja1l3/CjJc9+cZrgLgWphqhdtfQwQWJeBa+AI51TTuo48+qi/9+9f1upveqxVrNiDh4JJfYSlEoBCBQgReVgQscyJpR/IW6t2n8MH/1mRsl75+ziW6q+VSzSCBS2M2kAH4xL0BGB2XSgLzOsc7r5uQyF2SK6I2iwysfb/iO+8H3EwqfdYKBVqWyheNaPDuPRrffq8a/fTBWkYWvsKjGbZx9KdxZYaQ2hW5NUtd0uQALlnLS1VW41dtLi5fKKFECa515lEx69X+u7Pq2kOiXuNTAFvyTHtC810wPpb5IWOLYVSRxYK9NJhVYG21dpx9nXZvvlalq9OKBgCNoVJ5IlmNj1QhFSyFMSHZox+UGzc/TygpD7VNoZp5VS0fcuqB5uJFOHR5FN9TrNlu2LLIpDa27KJOaFpDyQZN0Vcr4qehM32vgjBs3sqkSmkInZjC1GEEhm26FIc1mCAMAVoDnUrN+53GzFXUfQUq4horqeIxjoP0vHJRROWdcKn7vjYav6KVIz4qAURhAGK6OSeRRfqHHlHqgJdBgofGksSWxyS0LlgHk/W5YHiwPMS8g1OKxbwAXqgbVbk5pzWrqSGbTkCCJXXeZaNIIelv1lGp5V6/rliWVGWI9zvg5rkmU76VgPObxmAsr37mYixYbx+GGHf41YfkD7wNa5XV+edn6MmT0x2/8Du9wO59HIDKNIgTW0NCSdbLkZiX0q/M7OZd0GMXX+zS5/42h9she+HYOBrAMjVpnVl96+t+PfWUF2krTGiFydpy9BlKqa8/DfPm1tJlyFmrMlq1CifCm+gfF3rmSAuPX5EIcJpSSDoTyGcX5sdAzwBS4TjSTcxiOH/VtXxmwn7OMfOVqXoyy2IHXJ/GpXBvV7Na1yIpXHApPJkDO8l1LG5dh++XO4qk8A9e2KXwJDdbWK0QgRcdgUUDuAxkdXZ26j/+4z+c3gdmn1laWqrLL7/c6Wtg/Q2OX8yy0zpxW6fv1tZWvfe973UKG49f55n3C4DrmREpPC5EoBCBUxUB0m3jEQAg8/L379ER91O6vaZSuyJtmvNSt4UFcMYXUQm24xfgRPhWEuNN9OcKuQy4TClzz61K9ffJC2PiWbtJ6fVnqW/PUfU9sUOp2QmVJQ6oOIV0sQTAdEmVRgFbfXcPO82YXX5onBC1ZJhmZLMp1VIr5MdKfXgCidkwDEkn36+9Wc1Qr+QFjIAVIHMAAdx6MWfAi0IJF3VT1IR4cTo8cN4NenDL+zReuVThGoBdkEa+sGVF1TOwTqWaGQVEYiWfhY1xs69gEdJCpJG+MmpK1gzjnAdTk4wokQ0quqtcia6g6soH1VA3oGB1DPDpBpSWaz5RpIhnXksrjsgN4PLR22zoQIPaO5bBimHIgFmFpwypXJT6JFwGvd40dWheeSqQYVUzaDN143iKIrOaeKJc/f/dqmScrtMms+K4XMQZ/WYec5hsEFAmZJgO8AKImCGGinmMFb4BLUeWZQCsBcADuHSZ1MsHSwTLVVXWKy+GILl0kc6qntUZG/er0XVYa6lpW1+9QWFq3xYSZTb0Ihb2B8OWiJsDoV9H2z308+H8YU4xi8nHxERa6zdirNHlwaXQo188DtCD8ZqmBiiVhOXit7OYeFy5Yoa5l8FcxK1XX4Oc8B30KSMUxy+Wo++BIbv1VrceetSnyQmOGQliCOONLKYrwUBGbUulRmzv33i9tG49if5LO6jjd1u4/wIRsPNijbQnRybUub+TmHORAJqriBPopRavsqKIiyR5m3fn4sELbO/4lxc74PrMZz4nkxS2ISkMBE+tS6HFwRofd2CaoRkAV4HhOn5qFO7/D0dg0QAu62dw4MABfe9733Mau5WXl8u6c//sZz/TmWeeqc9//vNOH4SF+HR0dOizn/2sbr75Zl100UVON29rFPd8SwFwPV90Cq8VIlCIwMuPgDElthVSKBiHEfpX7Q2k1IlscJZEvQ7QdRYyrvPoc1RHJus2ZoW1s9QnZbGVzw72ypOlye+azeqOedS37ygW7X7N079r+vAhFXkmYMemFaiOa64Y2/M+EjXMGNouxXJ9YysW0bNI6nrkmhlXRWlCc7jKDd8/r+Eh3AWp0/FTn1NfA3jCtGNsiqS+NJ9Nz8OYJKmzCpo1Oj26Zm+4RHfXvUNPRi+Q11wFm4aVLGYcnohioyVY0Efw98bZb46eXVinB0oAUQCGEMxT7WacvZC2TcZLNTFEv6yj5SpJzqgKuWACkFlZyTGUT6H0oxoMUFjin1LEN6d5pIuzk8Xa+/AmEk+oF8CUJwgDx5imesvobxNVMQ2M5zPUbZF1FlfMKIB0MliBiQBb63twuaZ/XoHcEhAB4MK8kFuiC4NjRJaxeBR+ybWX52a4b4wXUkEt5zV73RbAlSvGc2b4ZswYdVlq5T1c4PNPRZHrIXEs84HxsmoM9Ov89Y/p1VX7dVlzrWpDV7EZ2LWnF7ZzEhDMAeqwW30Dbn3za0GFOUchwHMOMGg1Yf29HjU002SbWrOOPq+exKJ+fBwJKU2VZwHQKDW1vDatjQ2wmRxWMTbw11+fxu0w5STuzlw8NiarubICsQmaFx89CuPV4YEdRUUJO9nUzEgAX3Yxs6WZXk6NZsyRnx9PH9IivGMXa+3iwWm7EGIb/dzUjEb6x2DKrQF1EAYLtpkG5QG+K5w6zJd4iIsdcBnDtbuzWS1rFgCXM0tPyem02es+xnB5ZqnhKjBcpySuhY28tAgsGsBlwzfQZU3izH7TKPNoNIqbz3/qG9/4htPNe8uWLXzx5JwGcj/+8Y8dcNbY2KiRkRGHGaupqXneKBQA1/OGp/BiIQKFCJzSCBjjRS0TxezkxpqeQ8MWTylMEt2G/XYZdUgRWJjUPO57WL2Hxvrk6TgAqxPQTGmdDj68U1ka+HqCNPrdtVeJCUBKKIhCLqfpHkBVMKHqrWTbiqvtVSvVeA6uFSQXyfi4JnoOa64jq+nH/Dryox2a6MYRkKQU40GYGNisOOMhWc8A+AI42Fm9PThPuYhb8boalb9jhZ5ovl4/mXyTPPScqqihfoRjcMNixTGkGO5sUGyEq9GYgLhLYabCGGdgSlFKDVfF+hG5kNdNz5drdG+tEn0h1Uf6VVcyrLl0WOHiuIoqptUU7tba9H4FcXb0UBs2TIPovdH12v3oJs2NFcNmwWKVw2jBok13lSsFWxaqmVMwjPqhfEYlzZMApqyiyWKNjDdgHFGiLA191clxwPzQMxmW61itFofown47N8Atf0KGmMMgIwfY8iCfyyU5OKSRAR+Njt0xrQ4c1gGt0hQ2gLklrDtHgGCXLAgutmlgzd2bUvXycb3/4m1675oO1QdeQ91dY34GAeByWK7nl+fPlBcAV2+/W9/4WghgmQVwwT7CcvgNiCEzXLMmqc1nZvTz+3y6f4dHR/rpC4Yivqoqq7M2ZfWaC+LKAjST/C1fnlJLkzXAtXTzBAsv2agMp5iJi0kh88yJPZtnap0VTvD2xfK05QmT4/SGw+LR+oWdnkv+osvM6CQXKCaQj1aruoaaTqaZnaV8icXznMsXOOjFDrg+A+Da3w3DtdZMM/hOOcUTz3qSdRy6XzkkhR8uMFwvMFsKL7+SEVhUgOuZBzo4OKh/+qd/0v333++ArhUrVjhX3x588EH9y7/8C40Xr8Xetocmio/gvvR1mj8+P+B67NHH9KWvfl2vvfH3CjVczwx24XEhAoUIvCIRsFTJ/ixxIsV3EtwYPaWshrS2LKcAHvEeit/9MFllJNrBgFcDT+5Sx30PICfyc4XWo4EndipJo2APgMuu2M70D6EnS+NGWK/S5UVa+vomBVuttgMHu1yF5vtDGryzXX3b9ql/+07NDCPz44p5QxPJHUDLbMBj/OH1oTAslyXblq8mYcty6+rku+ls/cT1e3oyuVWRVTMYNOBICOVUWj2ppM+v7o4VisJ00VmZI6OPlx+mChBUsXwM2WFUAezrR/rr1XuoFTyIC2F5j8rDE0pm/HxPj6i0yNpBD2iF9wiSQug2DCliNIfe496qXUNbSKLLYS089AoLyI9TYTn7HTxar/FJOhIDEIPlc/KsStPo2Y+kLkg/LSKLgUQ2BuA6wjHAFrkBWGaYkZvk4MheXcbmwebk67dYx0w0agAXmJhkMdDwEQ8f/bNqysZ1Xfktuhs7+8Mlq5SuIaYwYq5RbtmOu8xiSLw6AM9L5vSay+7RDRiFNLovVs9YDayftBpGajWNjv2cKwMFUJn5SYDjINPAZoP98/Riq8SJ03e/G1A3jFYtkj7CrJGxrJ7CGGXFmVldfXlKywBY04DF7iGPpubpw1WW1Zq2lOpwuzMnQnNgcC5Y2n6esY+nd/aMO+zaWX51RM9YaRE/nBqbUKS4WB4+Nyd5yIvsaPJzZLh7gL5a/QCuKlVh9Z49RazdYgdcDsPV3qTW1Qu28Kd2Jnr5/uw8TB+uGAzXH744W/hFNlEKwznNI7CoAJclJGNjY/rBD36g22+/He36hOPG85GPfERveMMbnB+u9vZ2ffGLX3Sa0X3wgx/Ul7/8ZT3xxBPPCbjm5uZk6w+TbNiP3oH9B7Ttocd00/s/quWr1xdMM07zyVsY/ukTAUsonQTQaBInKyLJsFteyCegp/JY7Ac7n3BazrJgiGBsk+3P+Y/E1JgFZyinctcn2FZ+RL980R7b912aInkUXDZcewKQgUQO6Zuvfbtmdj6GnJCaLG9QnbvbNXn4KOvkV85CUYWWrVbVRa9SMReiQpVFEDRwacQ3GAIIoaebQ3Y9ccd3NP7gvUpOTyvO92kNdU8ZQNYcPaloxezYxYeMJGOzySiMFwBv11Xv0e4z3qbO5HqlSGJrVo8gLUtQs8W2W+YVzZRoYqCWXlMB5IzI3nifCyYu4omqbWk7V+eHlYAF29+5QTNzZTj3YYYRpGkzgCscmVNLVY9a/B0qmphReXYEUg4nNq7mR8qSGso06bGDF2kYYDU6Wc2YQ2pb0qF1G5+SBxndQ9suoAnyOiWbAJerGDS1Ry7e7J4HNE3QyJfaJpdJCqc4/0goXUjzaOVlmBBGi6gbCQIT5w6wQ5wUc4AXXsn/sTlrFHxJ471qCXTr5/5rNVpbDmtE/7JZgC/bdqSJbCt7mP1y3JHlsxhMjKHPLKJWpBhwGlcDsLqh062rtszrkq1z5u6vXYdDxCqntcs5v8a62fk+brG54AaUdeO4+KPbirVrd1hRJJoHh5APwnT5kDZWtUpXr5/V7104q1Vt1teNYzj2eXLIEGcS2YbZvnP/uB38ht51PkNceCijUbfX6hjtVP4aF84i59HOBmcnf1JO4lwwr5gQg519GqGRcT2Aq5qWDL8tgMthuDDNWGYMF6YZp/ocetDZth/aRtPzAsP1a/xoFHZNBBYd4DIZoQGoHTt2OGDJjDRuvPFGvf3tb6eAeFbf//73dc899+gLX/gCkhOvIzXcvn27vvWtb6miAt3/cYt1Cbf1t23b5lz1MwDn8oX0kU/8LU0t1xQA13GxKtwtROCViwCJLYmhJZlZkuPZGcwOSEMiRQHHeevU7pckmx3F42lN4yqQY3/GMpgDHaURMDF+JFt+7JRxDcQcwTJ9W98a0drticCfvfbLTMDWe2Uyu5QxNWT1sSO7lXj0ZzBLsFn4m6dgXxIjA1it0+wUO3D0YirbfI6qr3qt/DQUdrI7hmROd5QeOccCFNLkwf0a+uFXaXSM8cbUNL2nhuVOzyIv5C1sswxziCJIo2rqd/wwNw81Xq3v1HxUh6a2KIasz2q0IoEo603IX5/E+h2jhbEqRIzUcFlNFCAmMI9zH/2fPDT8DVMclsDtMIYzY4ImzyVmA1/SiaPfrBpL+1QcmFKxe1Z1wUH5Y/m6Ly9GHRXuGRXhqjjhoe7rMBb0nUs0MNcoFy6LtS3DCpXMwvhgynEkq12Tm/VwwwWKYl/v9VC35AEMDnuV7A1zTIyJWjQhAXQBWGjn5TB6uWGet3NoAA02zV1DnDHEcEdZDzlhhnq5LLEOVCRV7xvSPMBztiQid4im0LgjJvcHiYVZuRPkUbYJKecqw6SEJsFC8ujCyc8NW+lFllg2wjYPAcZIvN//uwltOlO6Y5sfNjGn331zUmtWUHFmxKBNIf7sZmExlvEwNvif/myRfvaQT3Gkp6ESxguInAfoVTRndROg60PviKsRIw/nmPhs2eWD3+bF6/MtDojJFLPaSA+tDsrL7bNs0BfEfYLROcY1vDo9PqX+Iz2wtLR0aChXZVXZbw3gMoZr15FjDJff+nCdwrnM+fDwhdh55H554z/VH/9hwaXwt/l74td97IsKcFkw7MNmsg37M4D1wx/+0KnP+tznPkeS5NNf/dVf6bzzznMYr9HRUYcNM7ONv/mbv9H69evRtJsPbn5xrn7xBWZFwLY8/vh2ffUb39HrC7bw+QAV/i1E4BWMgHOF1n47SXTnaNhpV3zT/DOHnM4+38XFAZVQHO7zkWU6C7+Oz7PYqws/xZbGZO27AuRgdtfWS+jpxdgrVoxGExj3JdWK0rgYB7r+0bRG6FmV9RjQgw2hXjRKXVUK5iZSRH1QEPc/HMEs6V1IYRdAWAoGJ9/0NZ86oVKRH9DmvM6O8+s5B3tslAvjWRjx06N73jt2XPZ/fGZaPXfchrvgEVwCaxWIFNsRO8AqGZ1Rai6m2suuUMXGLVxEQnv29G7yYyAigEy+R7md4aLVzMP/rcyRpzS5ezf1UIcBB8gDl3iF07uKGlxaeznyvNZa/X3i/9ETsXMct8Ce7S0aP1yFGQYMC0mjq5jv0mYcFdsAGHXEHUCRRcLoQZdY4ptxLNyTMYBFir8Sv4IAofMqt2lT1Q6FwnHFYd1cyPjM6rzYC+hjbBlqrcpSs2qZI9kEkEz4KlQaB1x5Uw7Q7Eku0VwuRG8zRoC7o4+5UzsxrPuCl+nh8LnYuMMGwr7NddPn6yjJGv3IBICyRNYNoM6VEw+TFAKKcli+23nyYiHvKUpz3h1rPmo7CCtNkJ0gmrNhJUlaIyYZJfNy4QQYqqTJdDuM4UNY0ENm2XzLlXFrDFof93k/zcEwDyEe7Cs3zvOzVlOUVWtzRm97O33Xyj3MfZde/6qE1qwyMww+C4wvX2dl55xxMjYbRZbI/MlfhvUft8CsNcKYAOJSM14uEALqMBFZRSL/vhsSetuNCfqWMSeY7OZwxwb47+mJwOPfhsWOmGO28P2aD9e+gmYxXPm/3wjRwFt639u5LIFL5POeEd40OjCmgaPdfO/QCLmy2JET2jw9VctilxSaS+F+TDOWrnkz37/W9O1UHblth++BYwxXbg6Gq2CacSqDW9jWi4zAogNcx4/fAJPVb3384x/Xu9/9bgqH1+ijH/2oYlwZNZ16GjA1Pj4us5A/++yz9Qd/8Ae67rrrnE081xdWwTTj+OgW7hci8EpGAJZpPslFE9gHAytpA0d4HMCi1EFE+2FpDBRNUu8CLIK98Fq+eYIl/15jrbyAMz/rJgFLMzNkwzStLS2z95uhwTN/qV3sl/Q1ATgphSmp4L1c7ZyB/YjBcoQBVymS3gSJvi12XWY4yvVoX0BBv88BcTb2aWqI0KepkaQ5ApNh7x0cZ9egrpKSEEw7+7aE2Un5uOWqtnMwzxqPs5uT+wfGLTbQp+G7f65Ybzf1KQG5ueCUjseQqGVVceY5qjj3QvloNvzs4z5uFwY+AUXzOKDFjh5QYGi/Bu67UxOTT2C3Pi8OVU0XBNWyMaOe1Hr9c+LDOpJdKR9gabKjTKOPU5OUBlgCYNJpwFYVif1mDg+mxZogW6Lvns7gXTGqZDjgGFhYo90UPuPNxV26oHqbykonVREex6ADi/NMBRJDwI4bwAsbVwasapvuUOnMLLVYfpwXwyqi0KooN6fxTKUGcrUAax/yRRhJktcAzYNrZsbYTrl+HH6TEgFss+nhNdpTr7FdtUpPkukeW8wSPkeNk6iBgnaDLeMxdWEYvjtOcDnmUg4wmUN+6FwNsPnHvHThEijYI1+t9T/Cip5z3lSV0vL+oA48QP0a0sXEDG9BNigYQIrj8k2UDYBhOuKGispOI9gkDmXlGZ2zFcv2q9KasZ5lHPu69cwxwNcYNWGXXpLV8hV0aQOIJiF+x0bdGhpO608/7tETR4vkW8o5J27z48Btu27IOkWwsptXpfRe2LM3vxazFWrVnM+OaReJk7EqheXUR8DFBQxOFCyUnQgDuL+62DmYR656y520V/Cl9fqrYGSNbj7RwksxvsP6Dvbx5ZPEiZLm49av4RRDx8UOuByG6xB9uFZaHy5juE4UsJf2vKkEOo9skzcJw/VHBYbrpUWx8K5TEYFFA7iMhYrH43yfeZy6LTu4KaySv44ZhrkUmi38pZde6rBe9pqBMWO4vvKVrzh9u/71X/9VTU1NDgtmYKsAuCxKhaUQgV9fBIzhmoVlikZJXAFEJiGE0yBhoWaHpNGWeZJUyphUVBxSmIT9adDCPd7uXOw0Fmt+LqEkPZpM+kfWqlJ6JBVRCzMzDzAKhABhZC8O0MmzW7Zty1vs+vfkZFwVIRJmmJwQPZnsWadBrf2yO7mprWhJcE7RWZoDD2NDbtbgNlrGZtbcK7DIrkPeZVewD/disY5hQRGsWBBWzE1SFcd9MErylORgIlzWDkf8OAEC1Nh0iOPy2LhfxGISSNt/iv5cswf2Kdp1BGkbcSwtV+mqNYosW8Fxg0BOamFbAJypqRixT6hs6oAGDn1NY+N7kNIlVLkM04UqbNz3h3Wz523a13Sh5uqRDYb8iu4tVrwvwHqYuKdp2oxcL4uIwLWW81fEMXGI3hlkhe640vUAXBJGD7LIJOxRReko9VpHSaJoyuuaVnV2XOW5CbAPjY0DWLjTkbk2N6QrJu5VUQYTDPpspWjqWqQovcgy6k83aipVCkgCBPk599RdRWgCS6mYghPzerjuPHVUt8AUAfRmi9S1fZnme0pMJerE3Q3YyjG2SPcslufdKqmPOdb8/YNN2M9HkBACINmWa5pZYuAR44ycsZaYcuRg8DylGVwRY8wz6XVbx/WBOo+OPlGiz/6gSPseAvT3cPzIMvNzkvlKsm3ujC4uIrjoRmzzJkyMIuG0VmLRfsG5GRgpmhv3mtMuFxhgNF7/hoSuBoyFsaF/YJtXP/t5UDOTs7r7gbg6h6gJq6V5dKlbc5h25LC6DFF5FwEUbl6X0oZNKZ2/mYsHXCSwY15DTduGDVj0m1mITZ9FseTnsdPvzKhjmO0cYPN0WyyfmIVdHRlJqqUlYrjrBAsXG/jOsCP08r3A0T4HNON1YjE1NqW+I71c+HGrrr6S7zADyyfY7Mt4erEDLqeGq6PpGMP1CrgUcrLaMc3IOgxXwTTjZUylwltfZgQWDeCapqj7lltuceSDGzZscEDXwYMHZf223vKWt+hjH/uYY5Rx/PFajZa5GFoN13e/+13V1VGM8DxLgeF6nuAUXipE4BWIwIJE2GFCAB0GwuxvIa+wq8KWHTqSQB7Mx1IOSMtk06ootvohWCku+tr9sIEq1rf3+mAEZmGaDvXBUCkI04SJAi9YXZbHJIbHAI6JrMYn5lQdAXA1eQBcbODphG9hFLZNu8/asC4xJFwj9Cmaw83PgJiNMUCSa6ApRiI9Me+FUQuS2ObBm3MErDoPo5cAeNl2LLc01i0Q8PG+/LhtvRe9HHuryR8NpLo8HCeJuhOFF5WdASgBrmNjMVUVAQS8T6ln8A7N9x5VKNGr+QdHNLsDk6GSddp57Y3qf9VZmq7CMGKc+qXOsGI9YRgYa24F0wToctfAWga5T52Y6A9lJV3epYCiCIDaEk5OhjUtrqdX1cbcU1rtOkidFqYZ6XkNuJq0vXSrhr3VavN0amNyH42aR1QXH3LOwjQ6xzkaYcVN8ocboDE2mRByQsCNcIKPtpeqGMfG2HKfbk+/UT0DraDanOaCEaWGAIVTNIhFmZSNAdCOxLR++S6V1U8gSfQqUB7X+GiNDuxax/xBIhhn/ON5Bi8H4HL6diFDdDXw5wW8VCdVVZLQe84f1/+qiag06dfN9wb1j/8a1MGHkAkSC0diSAjExQP4MgA44+SWM+W4H5rDY2trWlu3pnT2GRkNDoCOiOOy5dIll+IcCbh78km3foyEsLPTo0sunFdvX0w3/yysKev7ReLuAcxZnM3Cv4S6svMvSCk2S08z4l1XbXvFeZLjvfiSlF6FPBSvlUWwGNwwiW5Go2M+en65VFGe0pIlHsAIc3kRjPBkh2B1kzu2T+j+B6Z0000NOG0yz+yK0EtZ+EJJJlJOzVYM5rmxqUolpVwAMM31K7AsdsD1aWt8fBRbeBgukxS+qK+1F4xX/vegnRouV8JcCgsM1wuGrLDCKxaBRQO4jOEyi/e77rrLaXhsbFdDQ4PT1NjkgsHgs6/mWo3XU0895bgQXnPNNVwlDPNDf+Kv8QLgesXmUWHDhQg8RwTss0hSYlIcR+Zkjxc+n8clK85TPGYd+7GNxZIkk3GtanKpuQYmwNJJh7awXeQBkN0zAmmcmqxJWKdJrv7HUBj6kPcVlwR+pS4sTnLjwqY8iJyrBqOD6jKSd1PuLCw2FABCioR7ABvuqTnAXMoHS0Mizr4NtFlCC1QE6FH3FDZW69ibFw7D0J5tZuFppF12397zchb7OrM925Kv8OHxS8pI8qOZR+4Wi6a0opnatciTuAE+qsT2+zXwxX2Kd8RwxvOo67ILdPSmN2hg02b6ZpXA1gBqJ/2aOlimdAyOZZrvYuqhPEUpZWBmUikeIxP1YE8eiMQBWsQbsPL/s/ceYHKc15Xo6ZzDdE/OCYNARCIQACkGURQlUszKlJVsy/az/UR71+/z7nv+vrW867Ba7/o9eyXbsiXRkkllUpRFMYABRCJBIk0CMDnn0Dl31zu3egYAQaQBekAArp+cmZ7u6r/+OlU9uKfOvecaibebf6M/lNyFLZ6DJA4kIELQSHhfMHwUo+ZqrLe1wsn8PGcggdLsFCr1o3RGNJMD+Umk6YLIOigT0630rPVSqCrGJu2IdNHBjQSrVVmHAwM7mMZIV0aSEX0dZ9/AdMVJbttuQq7NgGr/EFbd2s72ZwzkRCGromU9UxBb926i5Xox0xgZONMMIxciY0pzbSRb+lJ+0axDMfMY2F9stT+BP7sthrt9Nth4nk9y/c/uN+P7/9WOk29R+WPKn5lphXJaMryG5NoQy3mDuBmSMK5oidPQiWoZ68O2bCZmxPPBB3LYvE36bdGJsNOIp3+YD+DnZnT42GfiaE9m8O1nHRjuZR0Xa9JEtLJKBi1NPoq5xpYGmZ89uNiby8d6PLlOAgED1WQac3w+jYY6VsjJKb/AUFVeuY7lc0eSXPjBzw6V1Vl+pr73fQuGRw1MtQPrr1Mknzynpz4thd9zwWckwKEwFa6JJOrrHUxjvQi4F1oAT0xoJoSeY90koA7U1JVc5mf6Qjs5/dq1T7j+Em3drOFaIYSLTc0v6+/b6eN91yOeN7kBJ4RLT8L1hJZS+C54tF+uLgLXDOGSw168Gy4/5UvIk9Rqydf5htwVkm0lFfFiQyNcF0NIe11D4P1GgJElP89JBpxG1k3VMfj1OXmXXI1v5FuefJxaJf80SH1YiDbnE/Os6YyZYLbZmcJ4+m8G/4zkXf5oU6jk6BDHhk0L/EgNNiXwzM/MlDimu0m+kIVqiOT+nz3U7bi+09Tq7C0u9NrZ2y799yufnTVswSiaqFAV+1OYU1ox0vUv6PmrNzDzMtP9qNgkbmnE7Bduw+z2dZgxVyCadCI46cvbtE/bET3kRkZnhLGcjMJJRcvFv9H1VBdZI2e0sgmyWVwL07Aa2aA4HsCDyedQZ6NxR9rPdCsDLeLn8FziYRzPrcE231tY5TgOBwn28EgtqoyjWBduRdkIa764lmC5C5ESWt7T9TBM8jc7QnMMuhH2uhtwcG4rpoKs22JaZ0YcBLm9aVucxhLMdNxlRXbYiLr1fWi5pQMp2rqbTFnaq6dUa/ojr2zGWB8N3Fk3liNhytF10JMegVJGsl3J7tAk1kJCbPx2D8nL72xKY3NFFl6aVuxjGusw1ayyIeDV5yx4dR/JOZVTll+x0bMeyfE8lTBQjfIwQ+qDd5Dc8trLkSDd1JLF1lsUfPAupsQ6iRtTGY8cNeJHP7EwTVaHKUcQM41xHO23su9alM2cTZg7XoZUlLb0rDnzksiWk2ARdtTTuGP7liRJDK9fHoNclv0DBjz6SAI3k4id4/I9fcHxmk+SXB85yjWw1m3dTTne1BTV6fTn5vTGl/uItyo49zvvGPCjH1hR3yxNnBU0N+bwuc+yvokk9Xoa8ndE7NvVXOcrWLiOf8wCs0H0HethjZ8ddbUlVzrlBVdzPRCujq4a1LfQFt5MqVz9+3rBQ1rSi+JS2NdDhYs1XBrhWhJ02sYFRuCaIlwFPrb3TKcRrvdAoj2hIXDNIZBXtBQEw7Rly+SVrnKabZxNciT2SVAtCEazCNECfC7COgv+FFMNu6pCidSQ//dbtpUvSTkUgib/pstXjJbmBqYLWVi7I2mNRlqd5zP23ru/aw6oy1xQhkX/8XAMTWV6lIV2Yabr/8M7z3RgZFeCZiRGuBtZS7KjFtMPbUdXy0aqfVS5QllMOCvRP9KIqRcqkWRvMJCXWMtjMFazibOLLnrsMiw28HpatecYVFqEdJkS2JA7gtU4DnskBk82iKDNhx9GPonedANq7aPYUHoUVclRhMYdqBvsxwf69sGXDNAohOeMaZCRaidiq0ownK1hn6oKzA37cbR4LY7PrMbseAkyTLMUl8EsVSXT2jT0tSnoxrmeSVrnu4JY33KEGY8RmOlYaXHGaP2exNHDm3B890YkprkTqmM6pul5lH46PtLcoqSG6paNapweLqZFbqrMYVVxBmv8WayypHCyz4DpgBFRWtDPUFmdHMxhbJBEjS0H3CQ/Xq4jTFOVBK+ru27PYktJGm6Sr8r6LIZF1yF0N5O81bKei9oa6wL1eOlXJrQOcY7tIzg85MTka5NwzPSyGbQTo8GtCMdpXkLCJW6RTTQssfE6TvH47rszjUY2WRZyFWPa4eioDp97PEGDqRxVmPwFovB6f89nh2s8TmXtH/+JTaPJsX77KyThjVT25ENSoCG3MYRQDpAEfufbFtY3GlTDmXvvzeCeD3HNTN+9WkM9LLknK2L7lYnOV7zkPOEKkHD1aoSLTjFimtGuEq68acapm2FXjLT8+Zeb8TrVFl4jXAUAVJviihDQCNcVwae9WUNAQ2A5EJCwTyzkE0wv9FpZ91GmY5+mPEmS4FFUqQRVhqFpBeNBkiUG3TYb66X4j6v8M/veIf/0qu/kt3ySoGyj3kxVo7H86/JeaZScD1DPNY+86/oekhEQiWWo6EzBe+hvMHXsxxhqC2Cqm3VYVFn81UyvpHjl3eGF/24/Rl3NGJmvwGzGhxeq7kP76EbEbA6Y/XE4ioJsBkxrdrr5eU3zKDNNI02Ti8lMMUkskwL5vDgcVuYmUJ/phzMTxqH4NhwM3qKmlBXbp1BSMoaibIAkaw5bjxzEyv7jcCfm4eJ5d5JwS33W6LZ6DFkaMTZWjtlhL04UN6E1tBEj/bU0FiHhImnQN1O9rGUdGW3Tzbwu9NNshpxlCql7CqsaOqkohWlNTyJWZGQqZQU6X1qP6ABNNpiaKoG4kDteWmSRjMypHhlsdBD059g/TEEt697IFZEeA1YyPTBMovVvrUaMzJLcOzIo0mdorkEFii6FRayritGMY2xOjy8+FMcX7iLWtIz/t14LDkzQGZNph9UkW4+vjKOY17RcyxmSkePpMF6hqcjT33Uj+kYb7w4AoblyKoMVVPcq2DeM+XisL/RRidqwLoPhLj1q7DrcuYNEkvb4k+wPtooK2ic+ThxIZnp6peYLaCDRk9fPHGLPP8Gmyj97RmoMc3jk4QxKiuV6L/Q1L2mkOZzoYENt4uVn/7Ptt9DOXgxXCr6vM4/w3Y/TdCsdGuANAZp6FpcwBVM90e/e5mr9JoYZwbkAeo90w+u1o7a+jH+HCo376aO5HhSuthPVqF+xHI2PhXDlFS59mjVcX9VquE5fGdqjq42ARriuNuLa/jQENAQuGQGpgUqzBsugpOFg3ZCXhCBKohVjHJ5mLU1aJ6liZskCzJOnS5753/mGDN5n3jyAmW/9Z2QD78BJR7/5IQb/zKh0lTDdjLVAXipYqx+2Y7pmBfrmGxEc9+G7zi/gsHUzUj6mFDrZf8sTYlBP4uCeRo15QG0anKUSM5CqR0ghmaF8Inbv6RytsrNJBEJ+pgFWQE8DDCv7Zzl9c7SNn6GJRxz+7BzubN+N9R1tJEpJlGdnURGeY88wmpjc1ITe0haMhXyITZkxZi3HC7gXIxPV7E9FtsWmwwpJuc7GxzRkUE7S0ILpeCzDQok9ytTUYRQVh+i+yDon8paRuQoSzUqk2njAdADUsRdbfjDw9VNjdSskTgo2VWVotEKXTIqtIZo+mGI6rHAzDbDDiO4RkieqXDmmxulZRmahuuXjOljCBgOV17qyLP7kkxHcRMIzE9Djl+MW0NSf/aJ1wpvw62viqCTxUskeSeso7Q1/Fp3Bkz/wIvBKP2yBGRphFGM6vooqYwVy7CVnYH+y6vJRrNkwjXjQCeOMB5vKPCiiSciK5izuvDPFejEFz/7cgDffoisoydTDD2WweUuGaZ48rjPievlssVSaqhZrq6jACf9ZRKGwnw7OytOiiK+9lAhwcvGHWJ59vXflQi7b20z45j9YsbIlg9/8DVrpMzX0/bDPlzKJZCqF4ZODbEmQRmm5Z8Gd9b3rLtQz1zrhEpdCUbgamhdNM864SAsAgp434fp6mVJIwvWEZppRAES1KS4XAY1wXS5y2vs0BDQElhUB0Zzyd6LFsj3HVEA252WQIgTLSNcL6XMk/5hqYykIEC/J2aGRSfe3v4Xhb/0Nyc8YPCQr0VlF7Q/lq2BqHsmtbzut+h+pwZinhc2qRQlS8MvIQ9gzeyfCRrrniSU+zSXcJE3ryw5hdfwIPHq6B9LiOmDwol+pxbziVwkXz5S625nZMkTnXTQfScHK9L4c094MVMCcfF+peRprE62448AuVI6Ooi4yAb8SQHSVD8kyL/pKV2CaVvKm2TDasArPl9+DaaUYSR2NPFhDlczQYEKhqQnTRGumgvCy95bHGkEd11frGyWTBA5Et2Fm1I++Lh8m3mEvxymmT5JzUfjKkwIhHuVM3bOz/opNhQ0kZ/T+4NwkK2wHUEYMZmgjHxhjCiN7celYK6hQRQLfJ052YiYigqmZaZsP7Yjjjz6SxADJ396jTDekqUeWOIepoJaRzG2vTKGFvcEqmM6oJzFNckcvpqfxt+06dO2iE+bRcdXIYyywHinWsOVosOHwRuCnYudyRlT1cMOKUdzVvAqry/1sDm3EHNMKDdz3np8z5ZFGIEMjejQw5fATj2Swdk0aNt6wEJdQ+Vypny3140PqxcPmGeKBFDbYPX1lEr9Tn2fuQ1RkuQ6vwpAUvv6BHJ76Vysam4HHHk3T5OT9I1zRaBQnDnbR6dCF6iof2xrwAlNp6PKAca0TLkkpbD3OGq4mSSlk0WOBLwvJehDCZchqCtfyXGHarJeKgEa4LhUpbTsNAQ2BawABNULkOgr8r/I1cGRXawk6yoHJqXG0ff3PMfWrn9GcIcBAhw6PrF9LM/bzl+rQsE0P26NlCNQ3sYaoHrZIHPPuIvQUN8M2l8F4uAK7UvdgTudHcXYQW5r3o9HTB1c6hJyflu50ihzRV2FaV0oXP/bxYkAZ19kRTjupFllVspyOs1dZ1AGfYRYrHZ3s1RVlLVIaVWNdqOwZwLqjb9NEg2vbWM3aKyemvOXcXxRhiw8nSnbizcxG9GerqFjZEGetVZqBtY5OhuW0vv+c9y3s8MQwR7lpUB+jEhplzVU5RgIfhDtQjv2vmHB0j4FpqaQZVKZU1YXkSl9N9cXD+jOmMfpJ0IJsbJxgqqGIM7Yp9htjqmKANVcZEga1f5fUIUm6nsTMfL+QOjbLgimeRT1rvz68kT24qKZMDgO308iiuD6DF8bMmGYzafHftNEy/UPVGdziZ3MDErYAHRn/cmgKP5+gWcYLJQgec9NCnAYeDkq4VNNKyqZoCkKykDKi2DuJjTsPs4XBdjbnrkeNJ4LVFUUbxOoAAEAASURBVCnWcbnx5gtMdzxupELG5THgrKW9/UN3p/DbX6IRTYNcaSRdauqsPL6xR17V07OPVk611TeL5aPkoL4PQxSuRDKJkZND7KWeWlC4hLEv37j2CddfoeMk+3A1P8bUV+nDVbgh515NKSThUjKaaUbhkNVmuhwENMJ1Oahp79EQ0BB4HxDgv55yV5yObNq4PARUnYEpZFNvv4nh734DkWNvIzI5SUdAKk6M+4TOMrRH2SY7fJ8shbG4GFm9AyHmzO0p2YFosQdN8QF0T67CS5l7kCURsEfDWGFopanEEZS4Jtm7ik6IbNA8aGTdla6O/COBsM7J+ikLrLk40+msmEmU0NK/hHuia525GzXWYZVC69kehK+geegotu99GX7ObV9TAbvLgoinAXO+tTjsvQuDplqcmPKRIBqp/GQQoDqUoqNgJVMDi+NW3OHtwObiI+g1RFlPxhTUuBvxVBnG4s3sjdWIvjdMePsNqkCs31JyVLlIqNSswhr+ThAMtF430/Je1Co9ySgzHJHrJ+cZpNoqrgskp0pAXuRjcazgPCqBIeFS/KwbIUkrK8piZY0OX7ktgduqmT7G3/cwzfH1SSNKiLWVqYgzJIiiLv1aUwLVVJ90ujnsC+3Dnw+swhvPtSD+GpVFUXFJAOkOApcnSCxobW8PYMudR9m0Ooj+8SZM9HHhczZsMEzj8MEm9HR4kE6Q0lHJVNVFUb54kHfvSOOPfi+Jnaz7Epv6HCNSzn6DD2pr/JshZsdqm4b3mWgu1nD1He2Bx7NYw8XraJnG9UC4WjuqUdf4GG/8yAeosECoClffG3Sn1RSuwiKrzbZUBDTCtVTEtO01BDQENASuUwQk1oyG49B17sH0C89g/sRxzPUMIjYzz6CUOhSDUqkxMq50ovqTPtTXkUtYixEye/COdwsGS2sxFS7H2yd3YC5dApc/pBKAWnMvGnxdqHP3wmMMgV6BCJhL0WNfTUdDC6Z0JcxizMJNt0AJfiMxJwaDjYhlbVSAelTb+Cwt4w207K/WD+HRqWexrv8odPNppguWYKB8Aybrb0aNfyUcSg0OswHx61HWVWUcVJsq4SKJq2RaXhkVp2kaWqyib/om9xhei/egY9rAXo1VVNYMCNLifj5D1W3ShAibDafDTPGjysVMQRIb2qI38AHJibAgK5UuK8lTgkX32Qhfp2lGhrVt7FZAckOuNcOfrN1ieVpe4RKVi6mNujIhagrsJEn1lVn8P/cm8MBaGohw/u/1WDEWM6CYKkuWZGiWa5qI6vHrtIJfy9or6ew8qf8F2unetquvCi//604MHqcxCPvLcY/QZ+bh9ExizW09WLmuA+NKPa38nbCZ4xhqb0T3M01I00UxGaOqSCLJXcjb1PVJY2dJ3/woydavPZ7EffenuUYaSGg3MAjQVRy8vIKzIeQJlw1VNcWqU+pyreBaJ1x/xsbHHcer2Qg871Ion4KCDU4mhKu3ZzdvqlDh0kwzCgatNtHSEdAI19Ix096hIaAhoCFwXSIghCsWp417x37E3n4d0YlJBE6exGx7JxLhKMkQyRZJi63EjPrPuVC50cq6qWqSCgdiHi86y9ZgX2QHOsY3YD7oh5UmD9WGPjrED6Gi7yRWBvbBsTWHWFMpMjYbovWlSPp96Nc3IZj1MPOOZhtUoxyJCLpn1mAyVY5y3SRu8hyDFXF+JbBC14vbo6+heoyuhtEYEnQhfHO8GYGdq6Ff7UORVZQyK4mKH5NxFyzJDXDqGjkvU+goU42wQfBt3jTG2IfrtbEYRlJ0hmA/qCgt72f7ixFnE+cMe2Ilx6lSBUmmaHJBgS+vsK3ic6yvEvXKxGdsVXQQZMqhnw2Jg1NsO0A9SIkziiNRwywJWjFVIjG+YLqfpBXqmU6YI3HSMcgz0EFwJ5Wr//lIHBtoviEW8P82YsXBGSpc3K4rYUKnmrII/NGKGD5SRht9YhPBSUT0newPbcTwxEb89HU/Xj/qwEyEzZvnY7C5j6P+1j5KZGaEvFWgJQmcBqqI43YcevJmpLppac/zmCHhEg90tbcWVT6FRiUi2VW6mMK4MYs//IM0brklRZdCPn1dXs3X6aKZ+hoLxTF8fIAurFGmiXpRXu5jbd3yqFzXOuH62tf+CsfaqXDVLzQ+LuRpXSBc/aJw6alwPaG5FBYSXm2upSGgEa6l4aVtrSGgIaAhcN0iwLBbbvki3N+H8ed+ijhNSAxm9kc69hZC7xyg810YHobfIqgU32nFii8UIVfFQJ/0Y9pZhj3Ft6HDuhaxsRIM9pbRuVAPBw0umuKt+MDI8yifOIrEB8sR2ViHWIkPpjorilhX1G1ehVmlSE3fs+ZIgEgAjsxuwUCoCdZMEiXGaToVRtFgHsDd5l1ozpyEIxZDuTIF43AQB7mvtm13YWjtZsTpZMEqMXhSFsSjNhgTK+GdLUF6Lo24w4N1FRb4Ewp+0WrCUMAAhyvBY0yjrc2G0Q4r0qzBUpgumJOaK6YSSp8qurFT+iPxoB280kCVSlL4ZDCd0EFzjIoA+RW3iVANS6ckTY/b0vqdbEc12VBT1agg6STVkFlRRlrTVxZT3bonhk+zObGTdV58heqWCT+mPfmJiAFtasof306b99tKMvh8VQx0rFfnzulSqhLYqR9FfzqA2VE7BkZsiAUdGEhPIuSaJzErYq8zpoLSOj8VMCM44kTvK1QU50ntYkakZ/NNnZFgIC/qG3t3CbOy0LqxrBj47CMp/PEfxxcs2vm6Nq4aApJWOD06SfVyEGUVXlTSaObfK+EShauzkzVcNM2wiGlGAYfUcKkuhSRconB9VVO4CoiuNtVSEdAI11IR07bXENAQ0BC4jhFQqHKwAzQmXnsZc4cPwuTyIsW6rljnYQQP7oEvMImWKgYqbtqFf5hNh++oZjPgUvT76mlW0YxikxdbQlWs/ZrHicMDmN7XTwv3fjTO9iAQYCPfW6qhq6ci1lKB1Koy9neK0KPAgj5dLfkN64po5z+XLsKJ4Fr0sBbMkM7CrmdPLxKuh4t+igfMP0dZbIZuezQ5SLNRMQnFazTJeM13F2u2SHiCRgzl6jBv98NEkmNKkNQNRdBwcAA76Px214c24MV+H54/KfaDClwkVqNDOhx+xwSKePkSQKmLIjHSU/nS00o+FySJGmJ0lqDcw9oqOniApWuwsG+UjWpREdWHubSBTo5C0AhfTPpycds4yYyX7JQ1X2rqHomVNDhe15TBQ+sz+PTNcXjZg0sKw3jY3ETBbNKAt9mj6+dT1PQYEcr6drK314NlxIDqhwxRpUI0kd+v7yLnI2mk2pUia+qK5vBKN4P1UAKxlANOssQc1bjovI3mGgaMMHAVK349TSFCnTwHJxnAMl9SEQdF4okUUwhJ6tylBmxtSON//10MzU1aWqEK+hK+EeL8+Vbfw3MvdFpI7SUOcbScHtYIV0oaH3/tL9mjTRSuT6imGUKSCjkkpbB/YEHh+gNN4SokttpcS0NAI1xLw0vbWkNAQ0BD4LpHQMeIMROOYGr3ywh0HINYZ+tsdoSGhpDqO4415ROoMsVY05VGaKMPfbetwtCaelhcDtSkKlEZuYdO6HaqKD2Y2ncYgb5WZrgNwkry0lZegewGNpktTSFR5GYNlhUuQxhJvYk1R5WYU3yYy/oQjTswN12K6Vg5e3HNotY9gA3GVlQnRlCbGCPZirPxMWu+SFie8T2MmVwxapJDiIecOGZaj25nPbmEDboojTYCIXxq337c5/PAtXknnuytwoFhK+y0a4+zz1Z3mx4jwwbEmT4ozEdH+3axcqePPVArj0l0+ki6SGboZa+SQkuJDqV3pOGp0CPcnWOzWj2idCl0cpN6ErEsa72mmWaYJomTtEMTSVcFieq2lWw+vD2JW1Yw1U/2szDkkcTpMhQSwcG4AQdDRhHRcIs3S1t8idi5NjWaZ1omI3ghXOLoYSVLSjB18OSsHkemmaqZmsLsrIk96kzIzFpoNhBHlhb1XbtbkJqww2SLI8M6rvk3vHQ0pCkI9ydWizoBgIVdZrsFK6uy+Kd/jGHTzTT5kEVp4yII8OwRO6nPmyHxPtbKc9gnzp563LJDwcYNJNU0SxEt82KDPpMY7xvFxOA0ytiLq4oql9j1L8e41lMKpQ9XZ2cNmhqXg3Dx2qapTS8VrlxOU7iW4/rS5rx0BDTCdelYaVtqCGgIaAjcIAhInRFTzxJJhHpOItjZhtQMLceZ6mQur4bvJjeK7O0wh9qQds0gxML+uKuJgbsHxlQtrLFbWRZVzN+FIMjd/Wlg4knkZl9HkDVWM2VupEm44l4LMlTPsvwy55IwRdKs9qpCMkESwFS+rM3B3+sRZSqR3ZhApTLGBqUkPjST8GdmUJUcx4S5GHOOItTmRlTVS0mIVbsNzzvvwTB93FNRE58343doDf8lrwu5lB7f2G/B3iELnFYjZtiLanRKz3oxmmNQ3cmKFTyzGtmNGQot8JVGxtFO4sFb68osg+VRplSy3spexh5j69lomApQJQ0yMhNGTE2b2SCatV005jDQodDJJs9ldEn08ctJ8jbL7ViehU+z/9bWjUn2e2KQTnVLhqRzzlLjG6NW5SQetYqJ+8lTHXof8lVuJ/JTZhi5+JskwcXot1NVNMwRQ5pucHtnuAavD9rRZ+1Gghb8GdrPR6ap3I16aQ1filjaikAb69psaaYWGhEg4cqlKaFJM2j2saPDBoxUN01My6yhg+J3v5PA1m1SX5Zfo7pQ7dt5EZDaPN6TwFNPZbBnrwFHDtsxHzajriaNv/56DPd/THA+P5YiLiepOA71TSA0OcGmx3ZU17AWkqmoyzWudcIlfbjaWcPV0PDJvC18QXknCRfPWW//bl7hNM144jHWLW5fLqi1eTUELoiARrguCI/2ooaAhoCGwI2LgJAlUbfkznw2lRJPBejNZj5n4uMQU9FmETeNIa4fIQhxxu0uWBMbqT5V83exgSBNEPVEZ4al920YX/9bpOKTULxs3GuZQP9OP6ZX+tVaI9NcAvPdCQzZahEr8kE3EYODNSzGRAaza1ugX+tnLRcLphh8TlnKMZtiXQvJUQs6UWMfYxNiqkysMJvJ+eGMx3ECK/GmYRv7YjlgJpn6zy4nPuEx03hDh6cOW/DMcVpwkJDMThkxQeI1SzIpKoLCO95Zug1igMtmzZZ+HZ/n3OQhYFssNtVOwlUZgbUsBhNT/YzMTKxi/VMlCVk9e4ptTdHofsiI6QkDpvVJhEtCsPqAqX4/Tu6zoc6Sw+8+FMPW9UJs8tEjUcYMSdXL+hDGmRooNOsu5jSupsWhQUAnKSP1UwlXLv4GdKGfkntVIud9HEGDC4fHSFQHHdhQaoGH+2pLBxGwT1PZCqH7rWI8+/crEIvx2LdM0vY+jFC/FdEeOyKDTqQj4vcvvbuSahNnI4mig4YbK1fn8Hd/k8JG9ge7PhQuwVJUJv4vdvySpskfKm6qdpjHmhsty5D9kKPjySez+NlzRhw+ZMfcjAUOZ46pqnp88I4Ynn4qCb+fS+N1dK4hn7dYNIHjh7tQ5DSjoqKITcaZaLpAys/1nit97rogXG01qF9IKZSPQeFGnnD19fMzRdOMJzTTjMJBq820ZAQ0wrVkyLQ3aAhoCGgI3GAIMBCUe+xqrHOqiEKeYVBLQqXo2EGXspAuZ6cSZOGzp7bOAyHvp92f9cVnYXnu+5hfkWMvqwg8wQkM7SzHxNZKBBQDIrtnYBqdRWpbDRJHwvAe64FldA6ROao3q+yI1/nR++i96Gi8HcmwHR9O/BLN3l5kPDaSNqozTKub1BXTcdCNE+k1eA23IhW2YHXnAP4je4LddvMq2BxWHBs34VsHDOicNmKUBhIzZtZApUi0glTVmFKYZZpibkwHB23fjauzSFmoNjGYNrnDsFbOgU72SNPV0ETHQifTBb1sPrzKlMKK0jgqqWrdCQcq6AL4hhKiyhbDAM05ejoqYGJd1qO1Kdy5mk6MtIhfHILXCV0cryCMYpLTGab3NVDNulNxkiBScTu1IQlFZgxK7G0acNBK33oz67XsePJVM+34qYeZsvjSXQk0lvNMMarPktBF6Zj4d//qwC9fMyLI02RzJbG2fAo3V03gFwdW4sBBPxLz3AHPj4Hbu2maUV6mw90fzNA0g6oiUyYXzrxKuE+d/sU1XUM/dVTq0iTh4RAZDUFzuVjDx7q5PPE6heKyrFhH985ZppR+7b/l8BIbZ09PO5GMGwkr10B7/bqaGP7hGynsuI1onkflEsIVj8Zx8tAJ+PwOVFexzx1vdiznuB4IVwcJV0MDTTPMNOhZJsIFnUa4lvM60+a+OAIa4bo4RtoWGgIaAhoC/y4RYHzIIcG9PJDAnJoWn5SYSFLwTg8+ZpocWQqy+19HaGIv5urpPBhI0BAjgtmbDUyBiyD6/S4k903BXskasiSJXJimFJwzOpljSZWC+S0b0Pv792OiZBUSdAa8L/szuBoy0HvNdCakgQYrmSJ6D5sZl+DQ2AY2/W3B6skR7OzuoPpTg1UP3w9rkReRSBS7uk345YAbB0LsfUVjibQEwfI/o/Msg3R3JICPpPaihlJXoLgcXes3YLiB5hPsS6Vk9UxbNJCA0dCDBMXIwHojCd/ddBOMG9I0J9Thw7SsCCGJt0m6ZNQFS1FBMlXvorLF3yW4XhxCuKaoMv2KGt0Mrd/llTtzTqwl3aKNiPr74rYKlTjpAyYphpK2OMbasW+/YkOYqWg5Km1fuDOJVVUktCTCarNlwh6l8+LBdgO6BvVUTnTYsSFFIsW1tZvwD0+z5u2ICTNTbOZMg5Iqfw4rV6Xx2U9lsPPWDFU4Er4FiUthPZJOVDmSC1Fp5BTnz7as7vTxLK716vyU64wYcU1dLGl7dVcGI2NU7IhNXa0RH/6wjmTHTAdMqa+SFS3POmX/01M5/NEfA2/sN8NMEp8gmQ/TPKWsik6UTOP8+l8quOMuIVz5NZ+Jjzyj5wJPES6fA1VV4k4oryzfuOYJF00z2tvPULjyt30KBoiaUsgaLlG4NFv4gsGqTXQZCGiE6zJA096iIaAhoCGgIXAOBBhQCglIh6cQMBxH2D1DJ8A52CaPw/XCQcy9OoDxgShSiSxiktbH/812pv3wZyVd9gbvvQujn99EVz7Wf02l0FDUB/8qEhSbkfbnNHxwZDFlL0MkRMdA1lP5Z0zY3BdF+oQevs13ov7eu1kvJkSEwbfJjNYB4F9HXHhl2oHJadZ+MRC2sK7KYonintlduCPH2jWWNlmjQUw0ePDSzo9h2lfCND66NpL3GFkXJs2Cc2QejXzPDocJ66myzJM0NSlmfIBpgapNAmN8JwmpmmR5AcFinO8bZA1XEUldA+mjELcLDaFdCvf9dq8FR2nQ0FSew85VrC9j2uKZcelp17yF4J0qHiEg92DNGO3h9x0A2tsUBKf1qK3L4v6PAM3Nkm4lShENRUYMOHRYj4lxA6y0qV9zE50W12Zgo0onjaqFaC8XkbnQ8auv8XrSUX7s6WE633fiKvf3FbMGkC9OsZeag9fEl3/diqpKQsLjvjCiF93b+TfgxJm0Hv/962l853ustWMqrp0mMQmqika2EWiuy+Dvv8HUUyqGKvZnzaQiyEXHaJXZc6wbPo1wUXWmSyFruNpIuBqkD5fZWWCFK9/4WE0ppMKlEa6zLkrt16uKgEa4rirc2s40BDQENARubASkpksR8wfWLKUMIRIgGj6IO9480wt3vYQgGy3HuvoQOnES2SjJEglPMQlOLRWD4x/eAdcdRWixj8PI1KusV4GnOI5IVRFiDrooOlyIOq3ItY6TkHnRMEJXQtZTWf01aHn0AdZelWM+mkaAqY8T7N81+2Y7hs1+HKhejwmDE/NcRoKh+ubZw9g6c5C1TCRuGRNC2RhKor04sO1evL16MxLsTeahsmVgGmA2S8KnN6LaOs2fbtTRL/4mqlxCZj4SiqB6uk9VLlC6koSw7D0Bf77GjdG6qITyxf+FLai9u9RLYeEJPiP1dPKKEAcZonKJaqOjO142S7N4qV2SCVQStECu1C3P/U3Uqs42HX7ys5w4wrP5K3uJRfS4444c7vkQiSYJw9EjBjz7cxJMuibaSX7TJKVR2s9v36LQBIJE0pU+i8gs7je/xnPvuXDPyvFLyuB3vp9FX08CtbVUs1RkiASPp6uLJHS7EQ8+ZCDhXb41yTrkzsDxzhz+4s+zOHiQfeCY3mhl2mldXRpP/K4B9xEvmkuec4gynKZJzcTYBOaGZ1FczMbildLweBHPc77tip+85hUuEq4OMc2ofyxPuK74iN89gYF/W/r6dvNi0Uwz3o2M9tvVRkAjXFcbcW1/GgIaAhoCNzwC+cBXTXmTY2WwqTOR2JzsxPjLz9NJ7wiCTAO0eOl6yFQ93+wUymjxF91YBusH/EwhpFtiURR+5yScqQQCbhIt1ndkrXQCpB37SaZ0+YKVWN9wO/ylJSiprUKsuhnvTFvQMWfFLN36EoMjyBzvhsFhg7JhDWZI1qapSpFS4KOt34UjPcNGycUM2mmaYY/BFxrBr7Z+BK0t6+Ghz4RH0up0TIMkRaNmgTpTAFY6BwZybhIu1nVlZ/CB9lexbnoQpmwSSlEddOseYf+uEh7w6SC6n66K/TRVaHQANRaSN0KjFxVOnZU/ScIUEioxTgjGdCRG7PtFq0MT67UU1r3lkZTt8wRMnD3kvWp6p/Av9bH8fPeQ/SRZX/TkkzlMTOhQXi53+xXMMUUxQnXxN3+Tfc4sBvzzP5m4TyPKaHUv5FBIDLkuhgd0eODhNG7dIcRTjkcRUYeTLKyI6Yf5cfpYF54o6A9Zj6z37/5OGlgb4HYJ3ZKRN0SYmtTB5cjht3/Lynqugu56YS95YhcjCTXR0MXMFgID/Tns2Z3D0AidLzMpfPhDJuzcKUtibuZ54JBVpyinzo5OY3JkBn6e5OpqLaVQdSlsyxMuMxWu8+F3uWdWCJfqUqimFGouhZeLo/a+K0dAI1xXjqE2g4aAhoCGgIbAxRAQBsBoKjY5hkjXCcwfOoh4YJ4phwY4J8ZQdeBV2KojCD6wEiEGouUNY7BnAzBRTBJnvRjdAUfHFfR1sAlxxIsVJAu3ffH3UUoyEdjfgefK70Jn861wkS15mQpnTMaQHRuBzm6FpayUsbBFJSCzb++FfdeTcCthKDt2oKSCyYBZqg5GE35y+8MYZpNnC4Nmk6hIJEIZ2tmXm8KoZENki46thknaKDWhMdqFO4eO0LWQ22b5LTAKw9r7mBu5KR+hc44wVaXvjlnRETVgLUnB42UJKmc5JFkj9hb7aA1FddhQlEWzM4sDx43Yz68oCeXKyhzu25JBqTtf77UIrUAY4HveajOilG6FaxqlBktNalzc5NRPISrhMPCtf6Q6RjInBhNSQyTu8MPDYBqemEAY8YtfmlFZRedEIVILZMHIYx0bM6C8NINf+3waNqbMkRmy9xfrxE7wfLlyWLEyzfRMUTIXVLdTey7sAznmWFzBN7+RpEuggqKixTRHEkASyLERhWRRj99kWqGB6y7o4L6TrDXc9WqMSiDt/O3A3R+2Y906C3ejR+uRNL79nVncss2GT33aRSLMNyyCePZCWCg3NzGD/s5e1Q6+ptoLG/uhSZ3cco7rQ+E6wzSjoGAsuBSyhguqLbzW+Lig8GqTLQkBjXAtCS5tYw0BDQENAQ2By0VAwlFGyVRRDEgHqTCNDDINj3e1aR0R+Z//FTWtz8GzSYfJO2qg35hSVZ6I20Z+k0LMYMfsIGc4HsJ81o7iWAw7tt2OmcFN6N0/i4lNtyO3YTNVECODWCEIQpjyAbAYKqgqG7sUx59/ispXJ1yTg3A3V6K42oOUM4GX1mzH/jW3IOr2IMcUQqExaRIVM4nGSkuahIukhPOJy2GQwXZluB8f7DmE25UkHCRlSnQK/RWP4rBuI9jLFjezj5ek9P1w0oK2iBGbHBk8WErCRVv6jqABP2Rj5jiXVc6aqc2spdp10AAHzTws5HOj7Ad255oU7tlE50fhNAtD5uvqN+CbzzjQVJPEr30kTXVHVLLFLc74yUOXtf7oh1m0HVdQXcFeaHTzGJ0gcaTT4le+ksOhQ0bs3qdHVRnNMihfkcqokGWI1ziVIxMdGh//XBJu9hiLscHzT39kwvCgRTXzeODBFG69NUUyTJyXcUjTZjmNv/hFFm+8kUF9I+v+qDTJc0kqcb29OTz8sAEfuIMVcariV7jF6Hiu9u6J48c/DqKskmQ4xAbXzMv83d/3oKREj56uLL73dACbN9lw/312XhVyvb178BJSTTSC80GMdA1CoXGJv8SNiquQTigrueYJF00z2lRb+I+zzYEoXOe6mN+N6aX/RuMZ3kjoI+HSi8L1BxrhunTstC0LjYBGuAqNqDafhoCGgIbAdYOABIiFDHAu/cB1JER6pvvke3npEBkZRvwnf426vp/A7gkgd48VoS0+zJd4YGbN1FzWx8CV6X/jUfbnYqNhvw5lVHsm5+5Af/huhEyb2EOMJutnxbyKOBgo9AI0x6Ev6UKmZz9Sbw2wfmkji8fWYpzuhcO1cXTWF2GGDbV09IQ3s3mzOPXZGexbOaGBxKXakkWRgUoWI/2RiAGZoTnUPdeJW2ODuH/TKPMFW/D9xMMYSRcJbcHn1kawplRBmGuepCX9OBWzIfYLayDBsmeyeG7EQtWOZMeVQRUdGTt7jCj30UmQWWnMOEMZe4B99rYUXNbT50dquWJUwNr7jPC56YxYThIovPIcgb56JlQFSIef/jRLVUtPAmagqpLDgw+QEG7W4ehR1m89Y2Lwz7RBEgmxoiAfwEwwi55+qmhU2u7+KJUspjhShMQvfuRAfW0GWRLIVatT+OzjyWVt2ivHQB2LZDCLAC39f/B0At1dCjk6kzwJSzQCbNxkwsc/TrJqE+Xr0q+/S9lS3BuffjqIttYcmltE7TKi52Qcv/UV1hmuNrHnGRVbqm8WKqoGuZHAuj45G+8aDPij9OvvOtTJ4N+MYtrBl5axAdwyK4OLa7geCFdHRzUaGx5bBsIlabRMKTylcGkphYvXhfbz6iOgEa6rj7m2Rw0BDQENgcIiQGIgRgtqY9+FQE4vhEbEB5E/5Da7OhYJlmgZ8jSbHbPuhFuy31Z+04UNl/2HrEB6fMna1P9MVlhn9sLW/3UEDrbBY5pG7mNOBOpLENC5kIlxnXT5k2bGZqZ56f1UWhKstYqIecEmnIh+AhGF8sfZAS+PLZ3KQe8+AXPZCDFha+Qc0wkzddCHN2Jw1oahYBf6WhIYc/tgJYNx2Gyw8aeNAbSQkCjX6GTQX0cjDVlvO23sS0j81h+jTXxrAA/db0Tz9jr84GQRJtgXS1L4Hl+XxE2ltIjn+0bTBjw5byL5orLH+R5z0z6f3G2ctV0bi3hM7JP1471MfaQCZTMrGKfCdTtdAj+0QQjN2aeCdI5EQE5YnmDImVw8v+/eVl4nD0CQqYX9vTQMIW61tXqmCnIOXhvBgB7ffZKpbnMGphXS5IS289Mkf/NhM0K0O7/3QeCmdcSOpDMWU/DLn5nQ3Sm7S+PXPpXE3XdlmVb47n0ux29yhGIYQo8VpvZlVMdCUeRWrdIzvY/niXVVhSZbKqQk3e8cjOGppyIoLjciHuG1R3Xwd3/PyxosXhly7ZKAq4qqeuDvPReJZBpTgxMITE7TBr4YRT46W7IPm6z3zNYBl4qbKHvy4b1UMU8h4fiT//LfUdZwE/7DH/7hpe7mqmynuhRS4WptrUZ9HV0KqXCdxrIwS1BNM/p3k3iJwqURrsKgqs1yOQhohOtyUNPeoyGgIaAhcK0gwAhMzA5CwTiSsSTMZFkSlOmZP2ZgfpqRNVJGqc+RKE394mNunzB38asbxqwH1sQGGrB5+LRsc/WHrE4WbU7SvS30/yLW/xKSR4dgadJhbkMZQjYHLDSmiND9MEfzCStNLYwkUsq8Hkb2QnJm2TBZ+QJ7Xd3FQJRyjHqcp48jJz2aXCeRtnYjx1owgz1OslYKQ2g9UjkbTsyxtqZiCqPlFpRY06yLonqRK6IdfN64nc7fTC2klbopw25gwImxJNa3JbFu1ITxgQw+8lEXtu+0oi9owmFalVe5FWytSsNO3CkyoS9lwL8EpOOWgY6Ienzcm8BmphgaGbALiRSjjL0dJuw/aaSCZUBLdQb3bUqhmPOo2Jw+lMt6JOQKiw6HQtRYCyVnWk821kMiJi6FExMkXoEcAkzT83ot2LJdj607SRgtYnMuhhpAiC3HhvqIP2uZbI4wbqrNobaMys7VuGyE3JAAEUK1R5hqHML6NZphquTlsoA535t4rCmqV2/u12F6hjVwuTj6+1Kqinb3PTasXs0LkPvN5zCe+wwJmUrG4ug+1kXCn4bXbUd1rRiqnHv78y3l7OdVwZYwqDcrzn7xXL/zxsGf/OnXUdG49pokXH8mLoUdlWhopEthwQkXP3GicPW+QWSexxNf1QjXuS4R7bmrg4BGuK4OztpeNAQ0BDQElgUBNXxjxBuOpmBW0mgoBdPNmMI2l2W6He3Acya6iJvp4MZGvqx9kYg1ZwgiYt+DlD6oBo3O2FZYUyv4mkTmVyN6fi8Uki5HHzjY053wpb6FsZcPQImwhxcz/+LlVkR4HBFKO8VFTA10Sb0OHf1IcKwzGdZQ2dBl/j2MKR9lHCyE66z7/0zn05nIFrxHkbPOIpugWcFMCyYiVXiLBGeATYVBV0RTyxz8rgiKdJRS6EaYzlYTDdbu8GU3CUuZiY5/VFbqx1LwPhNHeoTNdxsseOwRF8rKaUvO104NkpqssBT5ySB7d9yMY2zuVU3Sdg/TCH0kY5JOKYqZuBZKIB6kipQm+fKQ0IgZxqUG57LXU8qAuoQz1nFqQULeuE+SalWVEYy4Wz2NJkbptvfNb5qwew/XENHhoU8C9zNNz0rbfFl//j8qXQze9ST08vvcdBxF9ixWVjHtknVnqtpzal+FesDjOONQ8lb6onTK2kkc1RcFu8IOHc/N4EAW/+t/5RCijf6XPq/D1luYcknF00SzFjIw7vtCnxUixCXO0iRjuHuQvcKsaKiv5CLFpVLed7mDqbf095cbCGficr7ZBDqpl/wv/+1/oK55Hf7wmlW4KmmtL324HLyOeGyFGvz8GViP2T/wBonX85rCVShctXkuCwGNcF0WbNqbNAQ0BDQEri0EJIhOZ3Ls9UOJIptWFYkGurdZSLJSabq5zYu7H9fMFEK7m3bW3sNIWfqpwDjgiO6AKVPx3hhOCMPVVL1kf1yFU38Uwbf+BZOvv4MkDRvmbq8CSozwYw4rrb3Qs64pTpnFRAMI3+QM5ryr0GH8UwSyNzMQZkDM72cPnbgOGuLIkXgZ2KdrMuzGjwd1OMEaLumRZDdH4aoNw7EySsITgluXoI13JSIZJ2KMAZ0kWw6mB25iLuAX2DzMNiX9qhSaJxjgYk3RmfsU8rNooGB3OTlPgipHFpksSZkhkw/V8xzrXctc9J+QePpSh7pnrl9/iuARQ1XNOscM3IHD6WS9ltQbCWERlSvHXlbAt78ttS583CtOgAp+6wkL1t6c73mV7wsm52ZxkOrwfexbSxUnxobMCkr9QtdZJ0bCuEggF7e+3J8JKkSJePy91+XlTnip7yOoAd6L+PEP2cqABiePPJbGimYSnfOpacRShuAp6mGWhXChmXlE52NUmKl4VpcSd7LSAowwreWtrLczLV4sF5nzWq/hUhWuznIqXI+ofbjO8dG9yBFe6GVxKTTyut7DP2MvUOH6OG65ZfuF3qC9piGwbAhohGvZoNUm1hDQENAQuMoIMO7LMhUqq96Bp1qSSDFIzKCBLnQepuJJQCh1XoMkKrMJBoOuGGtwXGBXKwa1DMIXlqtI/ZfcaSa5UHiHXOFd4quVbkjhguvgalInEOv8Nyiz3Yg0enHIbkfSlcXt8b1oMjHdkCmGTtZRBW3l2OX4DcznPkeFz79wDHKk5xpyhDw2Kkq/GjViF23Os444miu66Sw4QSLCmjY/dTaHCfMGF8bS1VS3nLBzUU7CsTFrwn90pLBSrNipZknMq6Z4LRCk/F7zSpIQ1UgkwnNBzc3uoLpIVY7KTJJBO4UiNc1TgvO8VHG+9Z7rGE4/J2RrhgTnMM0cMlT8NlizKKebIlfGV84xJ9cUjcTUa0IIlwxRrMZot/+P/+zEm2/RNp7r8zLl8d57MyQZZtX+nZxR5d2ibMmQ86P+5I90miSfxGtyOosSWtWXF4sCxm3ym6jbXc43UeIsFgvNTdhH7VzHcjmTLuU9XH+cNYL8+IAclde/ENRzYMo506zTUl/jdTDSO4xkJEURLEMCYWbNl4EqaMkCHlcICvd14xGuv2BKYQUaGh6F2odrKefoYtvydIkxj7gUAiRcT2iE62KQaa8vHwIa4Vo+bLWZNQQ0BDQErjoCaki3oF4IuZKvIO+0N5ZlUVPKtELJiGIQScEF3exhNMq0Q4+PJhHi2MAhao8+GYWj/yAsE51MwXMh3rQTSX89XxP1aLmHBLUM2rkOI2t2TPoIEhND6Hr+RUyyxirnmEF1ogP1xmFEDG4cqngE09YPokxfyWRC6Y90oSHpdHqSE+CpIQsOBQyoqu3Gisouus056BCYwoqabmz3tGGeZOtQ+GEmOdailKSziMQmwFqs+7xpbHUymFaLeCRdkIliNJsQXMU2XlLr1CGPKVWRN5DU6DDF9+6hOUVvzAQ3CdutvjRWch6pDbs8dsJGulTtfhgyojVpolEH0GTO4DM05PBJ4di5BhejrumM14R4ydehQxn84Ac0eJi2orYpCReJ0/CQFXffRxfDcjaipkAj/bnUND5ZM3fBt6mE69kfzuPVlyfQ0mTD//1HxahvyJP3hc3O2NvSHsq68imMS3vflW/Ng5PjE97KIeeQv8m39wxRM4d6eC3OhaHjjQ5pTSaGNQYqW+WVxWqaqZz/Qo0bjXBJ4+PW1jKaZjzCtGeaZhQKKHWevMLV37+Hf0teZEqhRrgKCq822ZIQ0AjXkuDSNtYQ0BDQELi+EJBgcWYmikY65onBgRADKRmS56WR7HSQNufzDGzNFqou7GHFF11dr8PVs5fZh1S2UjGki+oQXP8AUq7S84Sdy4QJ41SVIDFtbeSZHyFLb/JRTzP2G0pgSA5CRws/z5aNqC2qZo0V+3WpKsSF1yLNf5NMI3x6worDdAdsru6gHfso9FQG/d4Z1JYP4QPOLuiSdgyFfocVXI0Lik4O/Uk9VrvC2OFJoFJnxviUFQe6LZicZ/Ndbxa3rmK/KzYyljS9vAokB5BlWqIJT49a0Es7eY+Jv5O8iSH+Z6uZquZMn6EtXnjtZ74qIfwcDTj+ft5Go5S8qUWQKYtf9sZQy32IInOxIYSJfg4I0VnRRtv8TDKJjN4Fu1WvKnGHOhS8fcyEQVrWNzTr8IG7WdclTZD5vwyKB5iezODP/9NJmkuEkaQi9NU/rMEXP1Ok1nWJJiT7WNxeLp48eVHffsN8kwTS7qMnEKcdZBN7u9kdvDGgqpeFP8QbjXBJSmFbexkVrkdUW3j1+rj4pXtJwIoqLzVcfSRcOryoKVyXhJq20XIhoBGu5UJWm1dDQENAQ+B9RkAUgnA4jsB8EqtqdOzblCdcatTLgFzcDVmOhNbBLJKwM+AxkmBk4D3yDEzzw1STfCQ2NJBgztj8xkeQKm48343+ZTtSUVSEJEXoRz71yq8wQHL4pmk9DDSfcNRVobK5gYTJqqbp5YP5BTawsCIhHjmm8kmKpIHKw1zKiLaEgqNs6jtEFaLEOoKb/QdR4xhCmW0CPnMYDYZ5ZFJVODTzVTgN1ezFlScgQ7oJVHqHUGllH7CwD637qhAM2WBnTVSIhhhVJF2f2hlTGx+rRINrkFqnzpAJPxqxoshMD8UFkbCP2++gJfw9ZSnaz797zZcCprwjQ+L2bNyId2IWsNUX1rJB82MkcF7u81KGCJb795vw/AsmbLk5ijtvDSGjc9MQI0uCZaDql8Nf/g/2nho0MK3OgM98mb25yk6TJjnGFC33v/H1bry1dxY+WvX/H//XCtTXMFVTEYOIHFZXm+Bg7y8yefXLQklPJYOXccyXckzvxzZyjfZ1MPWVpNNBslVTxxsCrLNSvVsKRB4Wj+tGI1yicLUvKFwWVeG6tGt3EY8L/+SNDd40EsKlN2iE68JYaa8uNwIa4VpuhLX5NQQ0BDQE3jcE2BOKaU6zVLiay3OoWSRcp9YjKXCs64nrMDSdwVTQSEMNK/yDb8DRvY8Klwn6VBTJolqE1t2PjKv81Duv5gOVMFFFSM/Qvr17FK9MUUHx+eEq8tBEL8catBQ8ToVpkUIi3x3hqklwfE5qnAbYaHgv0/mG6BJoZL2TVT/Pmqdh7PTthssSZPqgggb9GJp0EWSUOrwW+hSG0rWw5qj+KRGYXK0otoXhpavh7GwM8QkXLPZKGFNlMMxWY2TMike20fK9UfpvyTpE4WHfrrABP6HCVUTjDatYxfP5nrgB27xJ3FuWYYPlpaMpYam49YWpap1IExv2+FrB5sx+ElFR+i5l6JgiuHu3Gb943oitN0dwzwcj2LffjhdfzuCuu/S4+4NGvPWWCa+yBMZdomDnh0wo8hKlM/ihqFxT4ym0HQmivMaCFauZFkZ1R1IrReTJplgHuOACIgSs0i1KK039L22Jl3IY7/s2kv6aiqXQ29rFtgMpNK+sglGk5DNwKtQib0jCJQpX/aOs2SuwSyFBl2bT0vhYr5Mark9ophmFuhC1eZaMgEa4lgyZ9gYNAQ2B6xaBd91VZ8S3DAHRtYVNXsWKx6TnU4I1Skx3O+OgJSjWs96njqmGdpKV0WkFw+wXZdVT5RpiDddkJ7JWN6KNt6k1XNz4fTg8icyFXsjdaj0ibCL8i2428SXJcBhZQSVkKsGfSgolHoW9kugPyOOS1Eh1kJDJUXdQCXqTva9GnAnk6AyhV7I0Dclgh38vdlj2I6kwJZGihEcfx2q+N6OzYya3EhOZNZgiYbAaBuE3TCLO/SRoOGKd0MHfHUOqqBFzpQyw46WYaVuJB9ezf9WKDAmX6B7q8hBk2t4Phi0YogW925hDgqmAKa7x0+zVtdolKYVLvxDlHTy7PHy2BGBzsFffsqoulHdsTlPJJOkiLhcd3CQl5JG1ZZJSaGIn6bmQE//87TR7F2XxpS+acdvtJqYKEmMqVj2TxFVvZ2DMVFPOLzbt+WuI6iGVBGnom2W6ptqfSlwh+bqOMpqQXvmw5XI8fzTtqGUdXE0Zz6cYsgh74zmSY7lehxxDltfIiUMnqQaTcK0gCVcJlxzT0s/thXC48QjXXzClsAT1DQ/lXQoLmYpJ+PW8aaQqXHgZf/DEJzXCdaGLS3ttWRHQCNeywqtNriGgIXCtICCBqQwJ+mSoTVTlQWHjIZnxmhsSHCeTbLBLRzk1wD1jhULADHRUy5FIGPVs+MvA2WxhLysqMfpsgioXSRaf16mB0PsHlppayHUL9XqlX48B2nW7LDQp4LGJWpKma0QuKUoXW2rxS5wIZbXyc4Qufi/RGr/PGEeKaXIGkkyzLw4jDSY2FLWyCfEhJEkMbLk0nCRxFr0bJYjSu9FG1cuNWbY77ifhKNL7Wf8VQzI6A8+kBeb2FELOJsyXr0CUDofG+To8tNKJ1V6zmoYoEo/wHqEmI3E9Xpm1YpC9tpidh53+DDZ60rDIBlcwJC3wYJsBP3rFgnhUjzu2xvHAHVTvaN5wKUPdPefI0UkkSuYmp/vkSTO+8c0MbrnNiMc/Y4bNquppJEsK2tl7jC2dea1QpRIcVdePS9kTceC5SKZ16GibQ3YuiZ1bxQXRApMchHq23r/r69KO4Dxbcdmy8kgwhNGeUd68MKCi0pc3/DhTDjzP25fy9I1IuNpVwvUwW1gs2GEuBZALbMt7GuqNADHN0JFwPaERrgugpb203AhohGu5Edbm1xDQEHjfEchIfyr2xmF2HV3hxMKZd+ypelilZomqido1SQL06zTeuzjAooRIYH/WMS6QgThtraMR9pZigZGdRhQSqIjgIL2k8q571w4w5IHommMj4QGeP6oIFqbFyZB0tSzTBnM8z3a69XntQgaY7saqtNcTObTH0kiw0ClHUpIj+dJTDTM3JqkG9aPB1Iu5eDkSihkN9ikaWfRhraEdPsVD5cuJAP9L69hwmeQrqPhgjmbgCbCGbNKJ3tw6TBqKWN82jZJiE+u7iuCnCrSRFvVlbMgsZDCvREnNVY49vXQ0udCR1HEt6pWoQq0ew1K+yVGrvb5IevrZuPgHL1sQCulx/20JbFsvKY1LmY1r4HoioQhS7OPmLvLhWFsOdl4PK1bQcU+dS8gj+59RERuZBr73dBTeYgtuv9vL1+WCOXvIc1yl+rnKX3+iuh3cE8LuV2fYwyuLUk8Oj3/Wi23bxPBk8f3nmmvxtWv4pxwqcTh55CSy8SSJZDXxyi58fgq37huOcH3tL9DeUYwGKlxmtfHxqQvhikGTK8kgClffXp6LXRrhumJEtQmuBAGNcF0Jetp7NQQ0BK55BIRohIJiHJGAiw5rq+sMDFQZtI/yjn6Gao7ZyEJ3E/skyV32f59D5WI8dDXUKVy8syxgyloTTFt7qU+H8YgJXgsVOpItRlT8kl0ybS0Ug99FcuDVY3dEj+MzKcSmqd4xrVBhvZqeqW86F8n3uij0dAr0GKhmWWf5fs6jmLDDewA77G/DoSuj8mUk3cqyAa8ZYabb9aUaqew4sMIaw3aHm8TGTtIVwqQpgHISL69SipCShF9nxU6lAm7OtzjU5ck3YlwImGNJpicyTdFOE48AiWSKNWoVPlrUn97l4q4v+lPgi4ajJFxUCf1eipp8QtYpvvdnDNlOarRefiOJWRp/NK5x8zPET5SaQvjeo9KTIcuNDEnXjJGo/ug70yTGVPk8BowORLBtoxlf/lIR1Q3e+GBansx/vQ45+pNHTiDDeq7SUjeKfE4eT2EP6IYjXH8mKYVsJVD/oJpSKGmqhRxSw9XPxsc63StaSmEhgdXmWjICGuFaMmTaGzQENASuJwREXUhR9QiF4ihx0TjCzya2rPORYDXC9DLpRxVKSE2Nhc5iJGMSGWrjmkZAVK4h2tm/2McmxvzPpZpl5EM1Od9Zpk7G2QC4gy6E7ayzCkxZaIFvQy4h55fBf3GKxUs5WBqSyDaSdLsi8FrDNN2IcjsTSkzzuNf7MknTHIIJB7qmV2NkpoppiybMGbw04LDDrjdjm4+mFxVTmLGOMs0uhypdDZPt7Kx3yiKgy+COXCUqmJb43pBbVkm1cfGF9/KUC+PP9/UM67DrLVrTz+jZbFjBfbemUFMhtVBUok5NfOFpznxVeEEswuNnWqaryHsRosD1M/WyeyiLqagDFlu+HmuB8S5MS2LLWsChfiO6OoGKGqC+OY3nfjjNvnBsJuxlO4LeCCr8wF132rF5o419q5i6qt4OWSogZx7J+/dYkljnZwMYOzEiuctYsaqCCkthb+TciISrvd2PhoYFwiX1fIufiys8laLO5xWuPOF64olPaTVcV4ip9vbLR0AjXJePnfZODQENgesEgYXwFrFYEnHeffa7FZQy4DOrpSP5upLhGSZ4mWywWs0M+rRxrSIgobhQYlEOjtHEYR/T6SRFz21hgp0oVPI6T+AIa5jemQpjrJN1VjovslQxVbUmwg38NMxgyqGzIoxcrQIjGxAbLBlVpFFyRs41j/WWNlSxVqt/vA4do2ugYxqixcTrx+aAjUYXbiuNNRIWrPMEsKquG1UWF8p0fqbGGZAg/YpyLbdT4SqnGceZ15OsX1LzErR0n6YiJc6IPtaiiaAk1+lFBzeJxAz4zi+M7P9Fhc+Rw+ScHk01WXz23gTcDu7hMviKBLnHT0QxOZrElu1e2O1qwuIFlyP1gG8dZ7qu0QKXi/mbZxA9OvBTFVTw0++ZMTamp3qRwSe/kKGKlsDrL85jNpCgwpVAdRkJbjFQV2OiSUcRPB6R5y7jAC640qv1Yj6ttettNgxnv4XmlSRcvIlTyMO54QgXUwpb23w4pXCRcBVyqISrfy/TXl9l42ONcBUSW22upSGgEa6l4aVtrSGgIXBdIyBRrQ7xOFPMSLwkNWrRTENPxzSHw8z0qMLekb6u4bpGFy/ERM4bRQQcndbj4KjEtAaqVEynE7bFeL2faW4nxseooiiYy7F5lJtP050wFyTFYW2+ozECexONM+xppMx8jn2TaKcBnSHNflnz8GSDcERjGDzZjNAs7aqZjhjNOKhuse6PBK22lrVe5WMo10WxhZbpbq8RTr2VhEuPILuaida1DWVwnJFSKHBK5VaYFu6/nDChjY6QwrE+UJzB7aWL/bj4xHkHj5s9tkYmDPjnn9vg4Y0DJw0tgkybjLK32G88kmBKobx/aUGrkK0ca8u++50wjh5L4Pf/TzeamkRtknH+9cj79renSbhscLvPJlz8nNEg5JmnjJgYF8KVw6OPZ1BVq6PClcHoaAy/+tkEqkoNKCtmeuFQEl/5SikaZb/XKd8SAjo3FcBk/xjPjQUlpUWqoqrCWKBvNxzhkpTCjiI0NDxA90sxzVjatXthWEVlZR8u1nDp8TpruDTCdWG8tFeXEwGNcC0nutrcGgIaAtcYAhI8CsmSZZ0dSJ5lKHGNrVxbzrsR4Fnkeczbifexf9jbowomSTzogwI3RZJJqwF9sSmMBGOYGC6BwlonRQxSfIBxNev56oPQUVYy0LxBb88gS8IttWBW1malE2Y6znkQD9iQnLVDl+C+Q1QvmHqqMOXQZQzilpY30VI/CC9t3ldQXSv1uKhs1dFO3kSaZcNN3FERydbZNTxipHBozoxnpC8XOUqKrplJEv/P18dRxwbBF01o5QbhqA5PPmfFLNMqPe4cpmYNqC9X8KmPJuGh9f2ZStO7UTv/b2L40N4axuhICjtuLYLTRXzPv/mpV8ZncximwqYz09FRrYNcZEt8zkBiNahHV4eOPboUtKxSMDOp4ERnnHVdaYTHIgixMbdCta+iTI8vftEPX5Hc8LiUPZ9awjX0gMd26ATdUdJY0VKl1q4VenE3IuFqbfWivi5PuJRCEy5pfEzCZTTs1hSuQl+M2nxLQkAjXEuCS9tYQ0BDQENAQ+DaQYDBPeUQITVxOhB2kXicoPfFHM0cgsxp67VlMMmUP0Ufg8KymrjdAR3riIqLZ+A3TtE8QwiPBRFQwbICFvahEnUsFKcV/FQFouNuZKmI0QNdrflyBUJwGQJYUdqFKt8oMg6qTCYrNliT2Fo6ixrjzfBS22JVmDqP0AbxwDxzCLE5MGXE85NMpWPtU5Y1hPMsKftcXQJNTukYdqFBVY/HZeAxn+w34OU3zZhgPVRZEXAva7gaq8V7czEV8N37vdCs6mtcbDImLoUpOIsoB4rad1HiQ+x5PCeH2DQ7bqWroZk1M3mTDBUAzmFgo2lR3PTcbmoiiyf/PoXWo9J7LIcP353BzTdzzdxmy812VFbRal7d7xLXftGDu1obKOg6Is2Pk2hqrnwP2S7EKm40wvVnVLg6OrxUuD620Pi4ECidnkP6w/X27ePlKArXp7UartPQaI+uMgIa4brKgGu70xDQENAQ0BAoDAKq8YRKMBjUk3SJcsl2YwjQiTDIZr0nqB69SMVrzDfGlLYAInQX9NIQo9nQi8rsOLkazVSMHkwZSmgfX0qFykJxiJOwV9TcZCkC0z6k6U5I1oBaUz8+qn8BtbkhVHmG0ZeuR292A6wkcVvdGaYVTsCJRkxFNmAoYUWa5K3YksZN7MlUyToe+iOq1EtMO2bYF+xHIyYMU5FL8o7+hqIsHq7MsCmyEI280ppXxoTynE0+ZB7SKrLFKI8xFqMJjB2wMmVPnpd021ytAABAAElEQVTBf9jlu0pG848WfldfPfc32V80HGaT4xS8/qJLJgsKUyhlRSeH0wgk6PZpt5Cf5ls5q2sXQsy6NiN5695X4/jmX6dQVMT1U02UHmn/+2+MWLE6b5WRZS818rJlGPlJxVZFNRQp8D5OocsHXcdIuJJxEq6qS8ZwKQd8IxKudhKuRpVwOXklFXYsphQCu/HEV7WUwsKiq822FAQ0wrUUtLRtNQQ0BDQENASuaQSEL8nXolL0VlqP7+lCGLIFSYL0uMnYjrtSu9GQHIYxm0GM9Ued7pVoS92E2ZwfWbYJMNGpbzpYylS5JiTSNrh0MTzo+AnqdCO0nHeixX0c5fYJHI7fjmlTC3aW0obePYI3JrZSvdqItCMMizMAmymFBtaH3cdG0pXWKHGjS6biRTGJ3VzKhD2RFE7Skr7OmcZtRgf8aTveGTNhmptuqcigmn2qxJHxfEOOUxWhGKWqHEt+5XPSb06UM6NK4GiBT7p3sSHvi4ZoCy/No2kLnyd8F3vXGa/z/XORHNstkCTa7FQLVUcadX1CvHQklq/tiuP731RQU6kgSlKcYyrlN//WiKYWrlG1lT9jvgI9VEkWrf2zC5bzotvlqW+BdsBpxCtEYa+/wZ4hhGejNFdR0NhYRgwXr8LC7etGI1xfo8LV2uphSuH9rJ+l0lzglEIxzejv38d7Jm8wpVBTuAp3JWozLRUBjXAtFTFtew0BDQENAQ2Bax4BNchnIJyjuvIG4vipIcIGxXHcq3sOdyT3I26gmsVUtpL0DEIs1j+KjTiZa0HUZIcjm8BYsBongquYXliMtUorPub6N5pveJn/p0eNuR+byw9jQNeCQcsm1PrnMJY1oiO8GclsBYxWGrJkzIhnTDDQ2dBuimGbPYUycxwVOhv7c9XCShLwkn4Gs2zBHSMZaKHhRtF0MX7a6mATZh1uoeL16JoYHGJfyCFa10UHN4lT9dpz0IihYT22rsthzeoUTJISeRHt4IoJl6yRkwSiWRwfpJJktVPV4n6FDMp/8QQq2Wz6H76hw2u7Rcegq+KnjPjSbzCVk+rXJR3fRQF47wYy7zB77h04oKC4TMHObUamrhVWR5GkSSFcJw61q4236xoq6PLIgyrsbtSDu9EIl6QUtnd4SFBZw0XCVWjQ9HppfLyPp0JTuN776dCeuZoIaITraqKt7UtDQENAQ0BD4KoiICoDhRS05jrxLJWuVbp92MGvjImqBx3MbFkqEnTF6HK1sG/XaijhDKy0Lh8JtOBEdg16Mw1Yb2rH3b5diLL/Vv3sEFYlu1HiGUfUUYJx/wZ06VZjz/R6TMfLkDLQL5H9v0rNWZpI0AHREqNqFkctpaoG1nq5mAr4WLYBVawb20PCNcgmyaRnWMMGyhVzRXjqmA1HWONV4s3hy2sT2FKapvPipSglpBY08DjWasDTz9ro26CDn3P8+ucSKPFL5H/h6L8QhEtOrMyTIPno6M9izx46RZ7QoamepiCfIibFdFOkLX9HB90k2TvtJqYSWswkYxde2mVfL7IWGlPiu08Cx9qSMJLk/vqXud/1xJPPF2pQv6PyGcVwRx8NQMQOvrLgdvCLa73RCFde4XLmFS6LKFyFvBikDxdNM6hwGfV7qXB9RqvhWryQtJ9XHQGNcF11yLUdaghoCGgIaAhcLQSkJktIV1HsJQwkDqLTFkCd5ThMVLcSegtbcs0ibHai116H4YMmGA/0wzkWQ6q4BuN3bsXxhnUwelJYbT5J18F52FJRNM4NYE2sh6mGRZiwb8Lf5r6AA+F66mgkcTywOK3jTeYUKsvG6PY3D2PGAh9ry+pdUThIvDYoftwN1klx205adsjPm+hV76XD4Y+7TXhxiA6G7K9VZAG+2EJVzHZptU06kr3jJwz4wc9sYNcDNkTO4fOfSarES/ZxoaESrjBTClnDdVkphQuTS7gsJhi7dxvwp3/KXmN0UkzS2fE//Yc0PvOJNGu0SHZYWyeNkw36fH3XIhkUNer/b+86AOOoru1ZtVWv7l0u2OBCMwZTjQ2EXj4QaoDQSwihJaQQAoQeCJCQHlogoQQICTVAMB1j3HuXq9zU+0qr+ee89QhZyCr2ypbRHVjvavbNm5nzdmbueffecxUCGFlaO+ItzVp4Uw8aj2f/EcL0Gax5xlC/Sy6IwfC9GO4YZcJVUsiCx4vXOMI1eGgvjj/DOKPJHXgeOp/SECcE6LFsGwkn1AztvPUX96Nn7kjceMMN7KHzLCGKi8jDNWduOnJzTyD5ZjJiw/hH4zglC08P14pPid1HJpoRDUitj+1GwAjXdkNnGxoChoAh0FkQoCkmcYIthxPVSeLOcorbeRyEhfZ9LJJrZqHXstuxILkMxTnxlMgoQNCrRmUq5dVTu2NTaRD5f9+MCkrMp1TWoa4EKDhtXxRPHIHNqRR6r6cQQm0eUuvLEE+jd+CnM9Bv3lpUjhyN3/S7BTMwnJLwzBCKYYhgOB61XhwyMzejW1YREhhemEtD8vDMGnSLTUABxTpGeCkY72UgSQfoZ5yRgHzE+lz/pWS8asQlkkCJcPWhXLzISKsLm9QxZ236rFiszAfGjKS3ZSA9bm1zkKGSdcdC1ZSWz9qOHK4tB+fOhp62R34dwIv/jEVCZhAzl8ThmMNCuOI7IXz6WQwKC1nouG89jjo6jD2GSzCDWzGHi44xGsiqqCbiFfGWtXrOrTXguW8uCGDmtFrkdIvDqFEsMk0PZIS+tLZxG7/fcrCV5VWs25aHIM+hT58eJPrMwdO+3NCxUHat+guQ+GlF5Jwje/BJZuSvbf8bQCkLKifz9xzrBlU7bnnp3IQrhDvuvBtzZtPDNeh4l8MVTXen0JGHawUJV0zMJ+bhavmnYt92MAJGuDoYYOveEDAEDIEORYC2W21tPQ1l1pJiLlCQqngJnF1vt+hBhx7krutcpm09PUfBilXosfBWrKG4RV6fDCSEayhpCKzs1RclmdmIm5OP+DcXYlXKQFTFpyGW9bvKDxiKgsNGoZpKhLFksan1lehftQbp69YiNLMIg96bQnGMbnjxtHsxI30MbelYhgfGMneLmoT08iQFq9AtnQWUY8PYl16qYyj7nkJXUilrcdWSdB2LHHQnMfPJlGhGMbf9cF0c1rHW1r7dwtg7pxYJDEOM6Pi1hKPMS3qOpHXvDHz+w1UqDu04nVvX/Pb6rYRqalgQvBopaakkaG1haM33pbUBinW883YAd98dg9LKWBaMjsHQPWOwdFMCVqyjFD5rmiVQYOOgfWrwq1/VYe+9GfK5IgZfLkhAZloYR+xdR9zq2nDO2z6Gr74huiRAERCEBzGK9oyEoCeG/J91x9Zg06oChi8CCbwWMzJS3fiG68MoLipz12VWNgktCXgs65YlJLMegXO3tTBAX50MVTj5G2I4bJwvStLou+Y+dm7CFfFwzZ2TitxcEi6GFDqm3dyJbMc65Q7GqvDxis+I+8f0cFlI4XbAaJtECQEjXFEC0roxBAwBQ2BXICBjuby8BgWbK5GWFEZSMiscxSSxgG8CZ3XbZsTtiuPemfv0SIQSylYjZ9GfUFLxJZblxiO/ZzfKd8egOCkNxfTolOXVIvWF6agL0tOVORhFifz+4P4oG9wPiRS+SAowYJAkaWQpQwuXrkAxC/hmfboYFZXd8MGZt2NO6p7MXUqQoyYSrRYMIYWEqzsVCzPj63AgicR4SrcHGVJXSwO9IFCLY7ws9EIiEkgAZKxr0bv4QD2l7+Tt0R/Ono+wqEijZv+NtNr6K63jooNyrCvy51b/son2WVNFwlXDgtCZGb6/batm7fqD/dXSXfXOO3GYNcNDUnoYz05Nw8ylQYQluxjm8azijtdX48c3VeGa6z38+e1EhFgAubKKwhb0zJ12aMiRlnbtt6XGOn/JCXb0wvHatLGARbLD2Lh2PWtyMXaRuxWpTkhJYLhmFjat2cDQQ455XBy69c5ARloSPZo8MP9H0MIxymsWzzIDqrbWlrPp3IQr4uGaOycFuYOOdYQr8mtvAYD2fMXxFuFaIcIV86kRrvZgZ22jjoARrqhDah0aAoaAIbDzEJCNFqZxW0MvFyXSUFVRQ0M9DukZyS5xP6oGzM47rejuiSAF6qpJlP4FTH+N8u/LsXxcT6ytSsaGykRUMsStpm8PJK8rRNzqjZjX+3CsHHYAUkdxu1R6E0ih0rxiR4CCpRSxoPJD3Ip8BD/NQ3jP4Vg3/nRsDudgY1Ei5d6DJLz1SE4tRbfkSvRNYhghZd8PSAxjCMlWHI3AKhKpKpK33lQ9XM/eD+RrqDweWwxu35DeQpeii0WT3mS219C7VVZSgrhgAtIy0p0x36TZdv2pPK2isgTc91wCfvMGcV69JXcqhWe2kWe5rBpnnFyL239Vi7+9l+jEPQqoAjmwl4fvHl1JL8527XbXbbRlwFQMWkItpfJoiT1z0XUYlxDLum1JrHdWgbLNJdi0ehPDHNMYfpjtWqhVayzKBQ5v2Y/ruJV/dgfCNWduMgYPOo6EK5kIiHlGaRHhoiz88uWfMXdQhOtcE82IErTWTfsRMMLVfsxsC0PAEDAEOhECMtJoWPBVXFKFTBbbTUv2UFQVjzA9O7GsONu2ufBOdEodcCjycsVXb0Di4nfhLXiSoYPlmEqPy8oeA1FfHIaXlY6aU1mLiz6nWaVjUVjSGz2S1lNZbwMCyVTTS65mLlYNwmUUxKD6YFVhIopKWEi5fzp6pnVjza0ajFGoYHkm5jAscFPSJiSz9lYm1fgGUj9hT45CKg1AeSQ3MPCwB2txVdZnYAa9T8eT4ownCWOJ5Z2/8HgqyytQxVdGdhbD3BTiuOOLCIY8rG9Oj8c9f0/ElyvjULWZfVcwPFKK6Wu4l3VVuPLyGtx3XxhvfhHE9OUxDIkF/u/gEEYPriVZicaR7Pi5bE8POnSJtbgz2HIaqjHlMcYzhnlFdQwNLFhbgA0r1iAtPQXduqchmEhgWlkU7NkOvtXJRTO25HDNTUJu7rEUzWDh4y0EtRUY2vy1Uykk4QoEPsP1RrjajJs1jD4CRriij6n1aAgYAobAzkeARl2oJoyY+mr0zaJngXkypeEkzhoHObu78w+ns+1ROTP1lAX3qIwWXvZ7LPCmYcaUGpSHGNJXEUJ4aDZqTxuNqTgaixfsgdD8OCRWVaLf0LXI7LMRCT3qqGxIhbj6GlTWBLFm00BsJG0KsDByBj0xAwaU4pCeNRgVmwRyXsxcUYfFCUXoyzyssekBysEzd4nmd3mgjkcSxgQvG2n1CXT0eOjHWmFptKSpkdEuYzoqGPO4RLhqKkjWc7IdIYwK42Ingbg6fLIgEQ/9MxEfz4pDYXEMwlU86s3MNsurQ1ZaLZ5+uhpHUzwjVEciWkRxE4Zd9sgUpdCPtj3UIipo7LxOSHSrK2uwdskqlBaUoldvEvdeOcwxi6KHh2ezW3i45gQxaNC3SLajm8OlwYzIwotwTSHhOq9dHi6NhSZI9NoZS10d8xa5rxjmUO6sfe6M87J9RBAwwmW/BEPAEDAEvhEIKA+IRlyoHnU1VchiPlctc2VqvAR6LViAt4uzroj5TgOepKuKZOvj+C+wcl4+auesJ5EiFTqEYhn9+uLdjcdh3bz+8PKIJ11OaRmlyMnaxDykcgT71mJzTTd6v3KYa5TC/BzKXFTF0mtBzwxFHsYOq6TIhYeSchppC+PRk/W9SrJLscceFUhLEd1i3pcXj/2RhgH0cMn3qKWOh1VNl4gm96VMyB6dd8d5STr6t9lhhEu/R/7+GO76wewg3poSjzkzgLw5sagrqEOvHvW49OIQzjuPeW1y7MiwpdiGYvE85nLp72804RIhJfYb16zHmkVr0KtPJnr2zCThIgZRXDo74brzDqoUknDl5kYIV3TPnoRLohnycMVMYUhh2wiXvGx6zZs3D1lZWejduzeJ29bxrSJj4XC44aV7b1CTW03IkvoJhUKOSDf13iUk8N7MfrWtwno//vhjJCUlYfTo0UhLS4vir8C66gwIGOHqDKNgx2AIGAKGQJQQ0MO7pLQSvdPq0C8ngPVFHjZUxJF0JSCO9Xto5kVpT7tnNzH0Ji1K3ohZGR+yVlUpNpQFkVpTiNTEKtQzaWjy6qOweNZIhDYlIja1CulJrE1FmffY6lr0PXAl1oUHonhFFjwWJ/YqyQtoIQYYNljPVyzrXvUZUIOU7FpkMXRuxGrKz9Nzdcr4UtRlVTJcUblgscgNJKIvvVsJ3LiSZG1KOckI1fxqSLSG0MNzGMcuJ57eyp0xVh1IuGpCHmbNUx0kEsmUOPQbVEc8SShZHDgxmSQ1KYCURG8L4WIuogxdkq045n4RzZ1x9rvwRyzC5WH9qo1Yt2Q1CVcGCVd2lyNckoWfPZtlEwYdw9plFA+J5oiwM+VwrVjxOYnXVMrCt41wiUzJ23TfffdhzJgxmDRpkiNCjclUXl4eC3t/hC+++AKLFi1CcnIybr75ZnrQmJFJMRR/Wb9+PR5//HHMnz/fkTN/ve7T5557Lo488kikpKSgoKAA55xzDvr27Yuf/exnGDJkiN/U3r8hCBjh+oYMpJ2GIWAIGAJCQA/yKpKDIFX1crsxlI1G7dqCMDaUMn8mmEghgogZH1XDZreBnl4X0pgPYldhTf3HDL/MR3WCR08Uc7tSma5PO+mTNYfhk6kTUJSXw9ytOqSkV6C+moWTA0XoMzYPi1eNQvEyKhjSu8UkOYD1ukDi5SWw5yz2kVuPuKEsHtyTBYsL6nFgTgn27l+OPJKrTeFkFkuOw4DYWhwWk4B9kIKPSoJ4n7W/kjgzLo9XIWfN96GoxElZVQwz1PG2kyCLAfIcnbusLePSQYQrQFn8T6ck4IVXgljPOlgbNwLjxrAWF71affoF8PKHcZi3LA579PNw2uE1Tiph8tw4rGfY4T65Yew3mCqFPDa38DcdURjsjL9aHVM7xyhyUiRcFGpctaHLE655cxMomnFMRDRDpDsy6jv8r64dP4cLgan0cJ3fakhhbW0t5s6di08++QSvvPIKunXrhgMOOAD7778/9t13X9bWy3TH9de//hUPP/wwSylUoaKiwnmqHnvsMZx44onus3/wixcvxmWXXcbac4XYc889HTGTp0v36bPOOguHH364I1z6/vzzz3eE68c//jEGDx7sd2Hv3xAEjHB9QwbSTsMQMAQMgQgCkYd5SUk1FfFqMbi3h+5MMiosq8PawhiEvESGblFMg8ZeLC2+aBk3uwP6MTzp6vx1eGP5u6iumYYxwTlI2iMJM7uPw9pgfxTU5qCQuVVr8wehaFYmqvKTkUBJ927pGzBi5FzUkbxO+ewwlDKkkNAC5USvXF4u4shivwpBBPO1PBbXTWJ4Ye6A9cgJFqFW9jgNrAyUubytzJgc7Mm2R4fT8d9N6ZSJj0GmQglJsEpJ4iiEiAu612AQyWCMI1BtQVc78ShTD8q7e6DYIEMlOcitjXBHES4ezlPPB/HqfxOwYFEMli1n6CTDXY8+sBI3/8TDu3ODzNdinS7KwF97ajWWbI7HG7NiKNVPTx/XXXN8FfYgeY2lmmOAhX51Hu0mn22BbbvbKOxMuTaMguR/MqA1Aq3B/dXu+NvheW1cuR5rl9LDRXn4nr26mocrUodr3rwgCcYxDMlLZkSp7kgOya+g2oFPcQwpXLb8c/bwBa67btseLpEgebaef/553H///SihaqfWpadTTIfhftnZ2bj33nsdQdJYr1mzBmVlZfRK9sQjjzyCl19+mXXn7sbxxx+/FeFasmSJI1xHHHEErrnmGvTo0aPZs2lKuHJzc93xKGxR3jZ51+IVGt4ktLHZzmxlp0TACFenHBY7KEPAEDAEdgABGum0UylSUEMFvTrkUmY7hSFvNSwAtXJDPZijT6W4eCQkJtLmk1H+zV9kDsuAWvH4HzB3xdsYvPcmDE1cg6JRg/F+zxPxEY7A8rpc1JA1JSWFKHxBIY01NKhZT2lo92Xo05dtC7Mw7bOxKM7vToESyulVs0+SA9mH/AQESbxYJwmcnE4YVYfhey1GfBJVIiiUMTZhLobGLHUEojjMcKHYfTHR64spG3IoCs/QOuaRqe5WVTgWBSHg/O7VGBLkujbangpPq6qKwSuv1mP5sgANP4+5IAzPa214O4pw0Vv477dicc9vUzB7JklgmcQgeDKVtbj80jAGHkAlxzKgD3PeLjmxFh8siMG/P0nEitn1mL8wiP361eC6q6oweI8AstPrkZ1GUqPtO8USOQ5RwM30Yq5dV4/UZAqn9I+oLDrO0NpxslGY+Vr5K9ZhM0sR9HSiGV2NcFGlkDlcsyioMmjg0dGXhef4xMZEQgrj4qbhhuvPx7gDD9rmyOTn5+PKK690BOenP/0pJk+e7EIK5eFSaGCfPn2cx0sdiHT5yx133IEXX3wRd911F0444YStSJFPuA455BBcccUV6N69u/teYYciUf7SlHDJw6XcrzfeeANvvfUWRNiOO+64Bg+bv5297z4IGOHafcbKjtQQMAQMgTYj4AgGiVdpcSWyk2vRj+GF6UmakVeNoABWbqxDQWU8YuJFulxr9v2VEdHmHe0mDWPoTajYtAHzb/sRajd+iiETapFwUBJqx/fAp3WH482Kk7G6ui8FLxgyRLKQwBpazhtYG4NB8SvQPXUjyvNSsGTKCNRUJ6EuNQF11ZS3KCCm9NwkxlYiPpWz0RR3D2WyA3q5ho1ZgtSsEmR4hTgi/lMaWJQDp4hJSqAY8YGxVCo8AB8XpmJpVRL60OuYSPg3cH+9mL91Vg5n1eNp0mvCvw2LwtOKioG//CWAZcs8/N/pARpp9UhQoeGWlo4iXDyeTaxvdstdqXjm7wmoreBBhEPwakIYMdTDpdfGopQxnJmcEDiRIYV1bH/9/Un46D0S2XIKa1R4GDiyHsecVIORQ+tx7rHVyJCIXRvxaOmUd+w7SrvzOpHk/bLF9XjxJcq78zxjY6hGSRJ54olxlDdvwx54HRZvLsaKecuQyNINPftmIZO187qcaAZzuOYzzy/i4WIOlxvgVn6zbYDXb6KQwmXLp7DfL3HVVWdi7NgD3FciPPIWNSY9S5cuxdVXX429994b1157LZ599lmMGjWKKppHs5B8ot/l195bI1yXX36522bEiBEufFDvEyZMwMCBA53XSl82Jly33HKLCy18+umnoTBFka3rrrsOubm5Wx3v1w7EVnRqBIxwderhsYMzBAwBQ2D7EZDXhSkJKCiqoOEdxvC+sehOPhFPY7E2TDGNYg95LEAbF6SUeVBqWdu/r86+ZYDGVTUJ19x7bkPN9HfpYSlF0k39ULDnQEyrOxCTN07C6sIBqKxLBZJoVNMjWFmZirpakoLEYqRVl6NqcTLKFyWjNoOyen0ZSlbPULcCSplvLEV3r4AsLYyUjApUpiciP70P+o1ei+79N1M4Ix/7xU5HVSCJBCoJWTGbMDxmDyQFhmNlKA7TijNJ4jKRg1T0J0E6OiuEYUGGEZFFOc9ZK+Bq2MRD6lnnackS5kttAkbuRUXELG3fytJBhEt7DTBM8sHfJeLWn6fS+1bHugX09tWH3DmNHpeIicfE83cXRnZmAGccW4Vf3hvES6+kIIXeohC9seV8//ZZIQzoyxDDMyrQqxs73YWES9dHLT3HYR5bDF2Hjz9eixV5YeQOikNFVRhrVnm47NJY7DWSgipst82FmJey8PG65WtRWxWi1yIVffvLu6WNWh2xbXbb3BedXaVQHq7Zs+XhOopEVYWPoznAEQ/XqtVfIm/lC9hrr+70UvV1pE7iFoceeqjzGPneKoUR3nTTTZgxYwa+853v8Fpa4kIIRbgUWritcL7WCNett96KlStXIicnB8XFxa5fkaef//znmDhxoiNzPuHq37+/I3vybD3zzDMuz+vSSy91oYj+cTY3zrau8yNghKvzj5EdoSFgCBgC242AzLdwmKpbtOVC1VXok6nwrADyGQq1uYw5P9UeVbVikZGZzNlW5cp8Qxday8oPyX/tFax85s+svTUbGXfnomjScOQH+uD91cdibSU9XHWZqEsgDvy/uiqZCoVxLGBciaTyatQVB1GVl4oaercCGQrbpEHHkMM+ZasQLKxBHCfBc/puoo+rEotY6rh+cBx6DcpHn+R8jI2binjKGnoMWRxOiIfEjMcKysOn8e8qRtutowjH3l4/jGQn6SQqEcNbo9e8Aeo8ATwnDi2ovE5j0KPXjAQrhn8wVMlj7ofLMWptOLcQruqKamR2y95C8rit26514z9ypI3N5C2Bf3TNhZnjdNkNifj7UySpMdUkm7U8JvZdGUJ8t0Qce0ICxu/toaykHpedH8Ir/2YI4q/SUMsctFAoBsP3DOOci6txAMMzD9lXddBaO5kO/J4euLLSMD79rIbGcy0NdxLlaazVlkhBFaqBxtBrvGBhHU49JR6HTyBNZl2xxovDk5h4nOiQMEPB2kJsWrkOPXplIbtb2hZjvvmxbtxPez93dsJ1Jz1ccymWMnjw0S6HizUW2nuK227PIYjh9ZW36kusWvky9tmnP/r16+cI13777Ydx48Y5IiUio+tJ7zNnznRiGO+++y5KS0udV0neqDPPPNOJZkiNsCnxaYlwVVZWOpIlsQ1tq7FX31IiVD7XPffcw+Pax3m4LrjgAkgqXtLwCxcudLlfEtbwhTqa7nfbJ27fdEYEjHB1xlGxYzIEDAFDIIoIBGToceY8XB9AGUMM6/jQT04N0sAhcaCRAeYd6X2LqRzFPXeerpgST+Mrnt6JauQt+ohqg28ibnw5sobXosJLx+R1R2KD1wMF4RyklFQgv64PSuMzaCAlIMzQwTgSheTqatRsTkA1Q78CdIRR4R3xDJMbUT0PyYXlLtwnIaMGSdWVWJPESlujYjEoqxxpyYX0aq1Bb6xDCkMKM7EXRnp7YGlMIZIYvxj26P3hGBzi9Ueml+bGIhJatW38PI5pCR1GUxcmYjk9Wv1YLPjAEZSTT1XxVLKDti4NHq4aJKSmIUyikJwSsyX3a2vS0HyXIgmxKGMIYDVzz7LTKf5BXHQI5VR3vPpniXj+WYZuUnkQJRS+Z/glVfGBgbHI6RXEuO71OHVMDc47pQr/XpGIx54JYunnsRiSUo+ffa8CE46k4maQRjjl8yMktPmj6Ki1HomUrp8Nm0L0OFTg8y+qMXQIw0lDYRbSTkEcWWCfnnEoL69HVSU9XJdxzHO/7uFSIGK4th4lhSXM21pLRcoYZGQkolvPdEdyOypUsrMTLufhmhOD3IETSTbo4Yoy4YqlGujyFV9wMmmWUykcN+7AZn8qut580lXN63zBggWOeEkcQy95t26//XacdNJJjhA17qQlwtW4XePP8m4999xzTohDfUqA45JLLsEHH3zglA+vv/56J7KhvDEjWo2R230/G+HafcfOjtwQMAQMgTYhILNZBroM1pqaMPNNSCBkFGtml+vd9+op+hPs6rWTLDpPhlLG1GFp2iKsSJ6KusBKBMMVnFSPx7T1B1KSPQvdl65BzOoQPhs8AcUpOQhXBREqTaA4Bgub1leilvlbNTSURbhYSov5b7UYUT8fvdbnM8wMVAcsQRU9YdX9B2DSvoXITdjE+ltlVCJMZ6njXvRo9aASYixG0+TOCFRgLXXlVX9qKLIxtL4bjfJYhpZRf4N9x1MIQ8yFD+pmMXxzWhAf0DsQpCR9OY39w/YK4dixtUhUIeG2LiRc1ZQ23LC+HB9/Sgn3DWHWBkphHguLsvK7ln8UEa+AvFEvvBGDlaw7du6pNRjYn+SWxywP15//lYBbH0hGYS8SpgJ6uBJ5cv3iELcpQCl+kFDV4aFTy9Gf4hmPzU9BGatAb6aM/GF963DaXlTaVCFkJdVJCbCt5xS1dpHzKy2rxROPV9ADV8aC1x723y8R3Xtw/FID6NkjlTlCHpJYT+ywCXEYdwDzgiKXGqHT+EUQrKmuwTr+tkqpWJOUHIe+/brRcOfvKooOneZOu9MTLnq45s0NIDf3KCeaEUErWiMt5U8VPp7K39B0XP+D72xTFt4nXMJQ90W9HnjgASeaIaGLG264AcOGDYPyq5rWyNoW4fInTdSXv/ik7o9//CN+/etfQxLw3/72tx3JuvDCCxtytETypGp42mmnkZhnNKz3+7H33Q8BI1y735jZERsChoAhsN0I6OHvGwLb3cluuiEDv1AeX4HlCVNQnPgZKkh2AoznU6HdFVVDsbpoEAbNXYrC2BxMSRmPfIYYxvA71itGaE0igqk1qGWyfSieyf2UNA+wiK88Z1nxm7BXxUJkJxaivioWm+r6Ysi4EE7vrfpaiVgNCj6w5lYcPSLyPm2sr0Z30q9JYJISZTboKqGnLB5z8xIwLS8OZTymgcxXGj8ihL70XEm9kMy4AXXZbzXMzfvLf4MoZsHknvQqrS8OsHhzGBcexZwg1vFqM3kmqQrRIzdtagn+/VosiVsA++0bj7PPTUcSiVzLiwhJDGoYDvksiVXeqgC+c3oNhlDOXWRDOWjrKeTx8N8S8SRrcm2m5LsnEid1kPXUJmEY5KhxYdx1QjmO2KcW/16eiA/zYhlC5eHcEbXYM4Mn6ThLa8fR8lHuyLcB5tQtX16D3/++hF4sD3PmVtETE8DIUUk49eQ0ih8koqgowPIBrHmXrjBO5nlxcOLiqT2pa40YlBWVI3/5GhLbKqSQZPXtm4NgIr1gbNvRS6cnXMzhmjMbJFwTEc9c0mjfm+KoUrh8xZe8hmay8HHLhEuqgAr58xUEH3zwQSegoRpcIj8SzpB6oeppafGP9c4772xQKfRl4f37rGTd9bmxOIf2cdttt7k6XyquLGVDhS+qDpeENCSy8eSTT7rQQ4l3KJwxKytrqz46+ndj/UcfASNc0cfUejQEDAFDwBDoZAiIr8jDVROoQV5wCkKBd1FcX4RKKuXFkDzFU2WutjARecsGYWHsCORX9UfJeubWcH0dBUWKVvRAfHYIiekMK/TiKR0di/icEGoSEhkmF0BWbRHS6ysQCDM8as98HLHHWkyKG0bS0RPTUEPPVgxzu8TdPGz0QtjTS8bBJFyU0eBaD58sTsBbM1hnh2IMwTgPRQzRG8xwuzMOCjHnjp40bsmUMUdAFFwnY/3fnydg+rIgUkn8iiixvjcLLp98QAjJlKd3Dfmvc7E49rVlY7eu0T8kQDXMM1mzqgz/m5yA9VSvPHJiCg46KMiwQtbFIndiGSAaoS41rNGGWz4qHJWerJJyFdxmDls2/VHcvbiEQvGKSmLw0ZfxeP2TeLw2OQ4b83gcGQxv7RGDWHqvEqheOLZPHa6+uAIbihKwcE0s9h5Zh+8cXY0kCpeoI9KWr+836muaxydAfAqL6/DXP5diA71/y1cxbDMDOOP0FBx6eCoSk3Qg/GVR8KJoQ7EjW6UURsjIyeL5Cz+qGK7dzDDeevTomUrDOZVjTOLJc5ch3tFLZydckRwuVlLIPTL6Hi6Rfufh+pJDNJMhhRds08OlcZBKoYQq5NESyZLU+4ABA0ioi1z43znnnEOlw6tc7pXCDgsKCpx0+6OPPurk2yW4ceSRRyItLc0RJBG3OXPm8DdQ7yTlRdhUU+u9995zNbtyc3MhsiYlRF80QyGEP/nJT1zelmTmJQkvqfqzzz7bSdLvjN9MR/8mu2r/Rri66sjbeRsChoAh0CURoIpfwmoUx7yO2sWTUUlyU8Ois5lxFdhr00KsLhmIj+sOwRJKW6xaOwBVMckoWZKNqqUUs9i3lCFxJCgUtqjvTXVH1sqqLkpB9apEzn5XoXtOKYYPX4YDBkzHSOZ/HSIfViATk1GKtYFaZjrFMLSwnnlcARwVSEcfj2SPRndVyMPj7yehqDKG8vMMxyPRqSHJWbM5gLMOrsKoAfUoqwGWszgwy1hhIIVP+mTWoqQiDpNnx2DFJopzZIUxce86J4oiYhlZxFaUm0JvnOMTzZAKEorK8kqSrgrEJWShmv1nZTKHy+VhxdC7U4/Jk2ud12vUaJIkFy/39R+O6oBpJ1+p84kmeXiJhY+nMeyxkEW3//lCPErWKF+QRCRVoXc8V7LIzO4xGLJPCGP3DqNv9zDKWN/smrOq0LsH+9wJXIvUxxEgHW8DdA2nGFm3kmqEU6dUIsj8toNZTgBeGUpKmUTHRXjHMw+vbP1m/sWi0xlpNNJZ7E5Jfvw7noS+R89sZJJsKTRyZ3i2uCO3dHbCdQdDCufM8ZA7iIQrQew1egMuD1RsbAJW0MMViJnFkMKWCdeGDRtc3tarr75Kb2Y5KioYbsw+gsGg80IprFDeLZGeKVOmOMGLuXPnuvwrFUcW0ZLoxbHHHktv2vUYOnQoHnroITz11FNuvVQK5cnKy8uDpOElnCGlRG0jwnXxxRc7YnbzzTcjNzfXFV+WB+z11193Xi8RPhVgtmX3RMAI1+45bnbUhoAhYAgYAtuBQIBkx6PBVBm3HDVznoD3yeuoSyTL6BaPAbEbEE6nlyU0FqtKh+DtjUdhyTqqGM7oi1775KPvBBK1mkwE6O2JzalFalo5yjamY03tAOw5Zi4O7LcAw1Pz0ZM2Yw+KvO/lTUI687aKaWQvDlQhHyGkk2TtEQiiN/PGRLbIPVDGML4//S9INUkKKdBbVU8SpJS7vI2xOG1sJfqT3L02Nx7LNrM+FWfL0+gqO35kLfbpFyKFIwGkhy0Yz9BIhr9Jqj4QYAzklqWWEoaVFbHMF6I3yYUINjFotxAuhbtldctkThq9L2JNImns74spdXj+hXpMZH7S0d8ieWhroWyeQD3P5y8vxWE9iWIeizG/8h/W42Loo9ThA3EkKXSFhQh9fDKl1Ud7OGR8Lb1HHnp2q8eFFNHITOdJNDrcRh/909vme5iynG1uz3HYuKkEmzaVkhT7ZHVL1xoIrmPao8NEnlJqQNJwZp02eq3Uuo5SkUG226NvOjGmhzIpkQIaZMjEUCegbVNSFS6nUEuu2onL7kC45s4l4aKHK0jCFQnTazIG242XsGdI4XKGFAZm08O17ZBCfxfyRqnI8bJly/DEE0844qMwQb+GltrpGDdv3oz58+c7UqQJEpEwrdf2vXv35sTLcKSmpmLdunVUs5xGJca5bhutGzNmjCNaPXv2bAgTFGGTQqK8YMoVk1Kh+hRBmzVrlju80aNHNygW+sdr77sPAka4dp+xsiM1BAwBQ8AQiAICpDPuv3BoFSpnvoHY5bOwjGp/cdVLse+Y6Qhkx2Bu5YH4x7zz8eGKw7HZ64VeB67GyEPnoZo5WVLyk0MnO2MTShMzsSY1F9/q8zEOTinCMHrE0j3K8AVK0Ns7BHHoj3Ja6fXcgJGBLPEVQ+PPpW05UqV/6pnD9fLUeMxZmYDuafUMOWMoHr1dYQpIXHAYFdMK4/DxUhbHJQEJUhAjnzlD2fSEnT+2BlnJ8s/IkBcN4LujAPyX+5DAw//er8fC+bEYOMjDUZNAgy1CAhpgdISrAjUkXAqDU96V21jMgOGCVTyO1WsZDteDnqj09hnCkqiftiAOb3+YQHW/eHzxEfPfyoEK5nJJ9U3hmjXVJC0kX0ceHcK1VzI/imR2xGAPvXvW8kx0rJGlrLQC1XS/teUIxGnyN5aglmxO5LrVheMTz3FQKGTzi98HfWAuhDIOPSicksJC4pLln7+qiOUDPBwwjEIYLCkQZrigyDTt78hw8IP77NiW31fze4r22t2CcM2px6DcCUikSmGEkUYHI3ksXUjhimlbCFfLHq6m2N9///2OHE2cKAVFBQS3fYkQR11K0TmXtu/ZWnZWBIxwddaRseMyBAwBQ8AQ6EAEIsWfi4oraPgDn7ybgs2zFmOf4JMYUvMOPow9Gk+UXIHVdUNRz3ytxNhqjJ40HZmjipGQLm9QMT1cJVhUN4IFenvh8HiGCcVsxn5xCUgNVFIgI5WhgwdhGt+XMo8nREN9D9KvQ5wsvPxSkUU2eQyJyZriOLxJ0rWqgNL1NOIl5DGRqoMHDa/DK3MSsXiDwg0Z0hdbD0WyhRiKd/G4GvSiUENzi7xwC+YF8PQzJGkMg2RNV5x3tod99mWYlb9zbdiIcGXmZEcMxC3fi8A5e9ExRJGGr467uX1qnWqd6eR0XhF+4bHwdgw+/Cgejz6aiAXzWSQ4RCVGEjuRlzo6glKy6nD5xRtxzYXFFJuIhPdFagCzjbrj+BRsLkVpGZPEuDQ+fLei6T9s0DMrieRNJLT1RYQ3m57NbNZXc5y1pU14zDqCSL8qtUD9u2WbXSHx/YZkUyFSHsKWOti533V6wnWH6nCpePQEhu5tIVxRIikiPRGVwmn8Dc1pNYer8chI7OLNN9+kwElfjBw5st2Eq3Ff9tkQEAJGuOx3YAgYAoaAIdAlEVD4T5ghd/UM83vjnxRHqEpEZvki5Ez/G2bE9sXbscdjc2l/umQUqsdQvkFlGDA+DwP2XYXee69CRUIKFlWOQiI9Yocyr6uHtxb7J+RjMAtNdWfh4zleDj4N1DGskIyBpKWAeT6HsRDr/gwnjKfRHiEOESIjJcIySs8v2xTDYtSx6JfNfCbmZcXF1eOTFRTUmJ+IDEqqSw1vfWkAQ7vV4ox9mDcSpHWvOLcmS4BhbEsXA88+x33Tq1bFcgDnfrsee41hsWY1pzGqzZRPVVVWjurKLR4uhkdtzWgUslhHcQCqrXGbbdnCjmOQkG0sKGHpARbkcqREIXf06MUyv6kkHs+/mIrX3+iOtfmJVFnkcRCTrJwwJh1ThHNPWUmPYYhS8pFj89HRadFPgZz0INKSqOzXJgpF72Na0GHVlubygrloP8eUWmZLaqsix1XEI8zfREFpNQrLQ+iWEUSf7CQOM/HrRMvuQrgGD55AUsOQQhLYqC38fcduCSkMBOa2i3CJrPlhgualitqIdOmOjHB16eG3kzcEDAFDoGsjIGOqjkbe/16XQATzi8LFSJ/1LObWd8eX8RNRltqTxIQGOXOLwJykQE09kntUYPgR81FPz1FlvzQMGViHg+P7oLIuDqcEq7APSVEC87ReQyVWUV0wk0Z4PDdfw9yqYSQlExlyKHXCpjRJqn7OneO+YQ4SPS8iLBW1Yby9KBFz11JMgw6t3tReOI45XIOyGWLHvn1/i0aywVxl5yKTn38RwOL5MRjIML2DDuKxMwxOSx1znEoplqGixbWUF6wO1SCFSf/OuGzoRN63ADZtLsbmjRQMEXF0B92ogest4hgiZ3QeHhU+/moRoZThy1y10jhMn53OnJQMlJQlYBDrdU04pAoHjy0jmXIckG23JizygvEouD1FSjQOX3Xc4qf6aBrujfdEslVGQrlifSUJcohS/HEY3i+DypD02ulE23qAjfvswM+dnXBJpXDevDBrW20hXI70Rg8QEa5lzOGCNxfXXde+kMLoHYX1ZAjw1kkW38luDx03LJ9//jn+8OcncdK5l2LYnqP5MGK2ri2GgCFgCBgCXRoBOSVW5wGv/ycGM9fHINGbykLAMVibOAwVaVlMjyJbIOGKLa1FTDlzrCh9npBZQ0JUj/6nFODIPSmCEcxkmFwA5wZDGBgbFjXBVNTSw1XLnC7+xf8LGK92KCXl9+MrnoY7m0cWn8ToaexIxRbRCuct4UoSsRA9QhvLSLhIJHqkgAa+vEd+B1v64eO8sLCc3iNKHPKz82CRASjnTLLu9dpnZBfMnQphzboiCj64Q3MdqL0WebJ84qBDSmXtqLRksShtrzVfX+R50nd9clKQGOTOGi3+d/JoiQeFqEIYJhZJlK9XiGSYxFK1yHyPQqNNI+fgdhnppfF3u+rzrOUFKK2sQ/fMJAzsmYJk5m0JG4f5rjqobey3sxOuO+64C7Nn1bL+1OGUhU+KkNZtnEt7V+vnrJDCFSumM1R1AZUDL2xRFr69/Vt7Q6A9CBjhag9a1tYQMAQMAUPgG4dAgF4UGfszF8XghXfp6UovxNrFiSxUm8QaXAmOSgSYP4XNDKurJLFgHSbSLqT0qsK4k8owfi+q79HDcXCwFpPiw8zdIjlgaF4549Q+Yt2v5exBBGMwE6sOYTghKzQRwwj5kSdGBOnrNEZtuJb/qxZUZUUNPUSUqWZ7tRVxabpQcBylxaUI0QvnL+rF7U7vke70ySkEdstIokGqjlwrt77pP/LapCcHWUyZhXz5XzO73WoT//i2WtnoD3fmW3anvsTtdrdlJglXeVUdxg7rTo8hPVtKumsBw115fp2fcN2N+QvqMGTwEY5wOSXHKAGmn5lk4eXh8urn0cNlhCtK0Fo324GAEa7tAM02MQQMAUPAEPjmIFBPD5JybySeMDkfmFy6Hl+8n4jChd0QTiTRSqbppsQnFveldrsjUwGq6/XeP4zxx9Zi+KAw9gvWYWIC87XYTlFwFVX0gLHIKTUhUMAV7MWFFga5Dzl0/OjByooQVq9mYVyG/23LZvfoeorhBlLSkx/JMaevwS8PkYf+vdJcmFvLzEj5KSzETFEJcjku7p+v9eivkCdPRZfFzXZDfuSfRtTefcK139BuVCpkXhnxaQ3DqO28nR11fsJFD9dseoW3eLh4EbbzDFtort9sTDxW5NHDFSsP10Xm4WoBLvuqYxEwwtWx+FrvhoAhYAgYArsJAgrxqyIber+4AK99WYr57/VCeVE6vUlUnhNjYs4WGAIHerniEjycelIhTmAO0vA0DwMojsHKWo4Q1VNiLz+/gLLsEo+g0cfNxFREtLYiLPxDYXapiZTQUJttLBJ0yEhlcePMZOeJ20Yzt9rtq4W+Gm+7O3qXGh//rvpshCs6yIdCIdxBlcL582sxeAhDChvqcEWnf/USEc2Yxgtvvnm4oger9bQdCBjh2g7QbBNDwBAwBAyBbx4CIkPxZD6rq8N4efUmTPs8jDWzM1C6OQO11fRkUDRDrqoEFhnef1gxrj9hOfrnUKOdi0L8lCMlSqXUq77dUpDColmeXFktLPEiXMyRaolwqVeFC+r/yB5a6NC+6nAEjHBFB+II4aKHa04NBg08zBGuJlMSO7wj1eFasWIGiddC83DtMJrWwY4g0GUJ1x4Uzagz0Ywd+e3YtoaAIWAIfKMQ8B1Dkrz4OL8eHyyrwoYldchflILCgiBqKa+eRGn2ccOqccMJhczhqaDkOQnWloiyCLWKUKIk1mOSsl5kXeTfZsHiV/I0tdBiy2ZGtZrFbxesFOGqYA6XCymkQIgjw7vgOFrbpfISw8wlvPX2+9Fz0EjceMMNrW2yU7/3Cde8+SHmcB2OID1c9XLnRmnRFeNyuJZN4/W1gLLwFlIYJWitm+1AoMsRrt//+QmcfA5VCvcaQ+WmSLjHduDWzk38x3jrj9R2dmzNo46AjVXUIe2wDs0A7TBoo97x7jJWkeOUoVpMRcKZ60MopYdqU3EM1f9ioZJXB/SqxjHDy1yNrFj5m1q6rbf0XdQxjkKHPB2HwO523FE49fZ0MSsvIpoxuHc6i1EnQjXP5KFsvSx0e/bSStu2jBXbhEi4fnH7A45w3XRj5yRcs2dXY9CgQ+nhCvJ6ih7h0q85NiYBy/Pk4VpED9d3LYerlZ+Vfd1xCHQpwvXZZ5/hwYd/g33HH4l+uUOpLETp3J26uEfZTt2j7ay9CGiMtJjFEcGh8/2rkYmMkl1PnW90vjoiN05bBqs5ue+vWna+T1Ljq6pm2ZCaSuSk1qKSyVcSq8hOYE2tBCkaUryC3q6IXmDnO/7tPyIJb/DuZ8ldLUJYUl7DemiU4C+tpLR9AoIUHxFuUn2MY4hoxz89uAdJ/HOnLf0G9TtWqYLHn/oH9jlwAm7slITrl/jii9Xo1XsU4uNFuKKLXgwLwK1ZswipKSW46aarSLgObHFs7UtDoKMQ6DKESw+QuXPn4pFHHmUycz5nO5gE3VGobulXN8Oamhps2LDe2e8DBw6ku7yj99rBJ/UN7V6me3V1tfttJCUloXef3pEwHxuvTjfiuq4qysuxbt065OTkIJsvMxA73TC5A4rhWJWWltLgWY0BAwYgJSW1w++70UBChqruCbpxx1Caz9Et3ro19x65JUS+dU2+Kf9wrEpKSrBy5UrsueeelI3fupbWN+U0o3EeEWESFcyuj+Tt6bFOEh5LDHfKL4P7KSosRF7eSuy77z4tnpIOrY7KmhdddBHOPPPMFtvu7C8VUvj000/jrbfeRhVVPf1rLnrHEelRIjajx4zCd7/7XYwYMSJ63VtPhkA7EOhShEv1QXTj0VXN+xXfIhdjO/Bqd9N1JHf//Oc/URuq5ezSjdzeCFe7QdxJGyxbvhzPPfcPDBwwEOeff/5O2qvtpt0I8OKdP28enn7qKRx9zDGYNGlSu7uwDToeAWeD8h47ffo0/PGPf8D3v38dRo4c2fE7jsIeIndperIaKgGr045/XkTh0Le7C53dlClT8MCvfsXx+iOys7O3u6+usmHkN86zbfiwc85cMvQffvQRfvXAr/Cf1/7Twk7p/+KxaUJKk8x6daZFx1UvGfjILIY7tMi1F72j9K9a9RvD8Eq9bDEEdgUCXYZw7QpwtU/NwotwaSbnpptu2lWHYfttAwLLli3D3//+d9YDGYgLLrigDVtYk12FwPz58/HEE0/gGBKuo48+elcdhu23DQhMnz4dv/vd75iw/gOMGjWqDVtYk12FgAjXfffdh7/85S9GuHbVILRhvyIpH374Ie6//3688cYbbdjCmhgChsCuRsAIVwePgAjXSy+9hNraWtzQyRSCOvjUd7vuRbiee+45R7jMw9W5h0+E6yl5uEi2jjrqqM59sF386ES45DH5/ve/v9t4uLrqkH3++ed48MEHzcPVyX8AIlwfycNFb+R//tOSh6uTn4gdniHQhRAwwtXBg11WVuZyx8IMZTz00EM7eG/W/Y4gUFBQwIr3s5GZmYl99onExStfyJbOh8CGDRswY8YMDB061L063xHaESlcSNePJp3kOTn44IPRs2dPA6YTI7Bq1Sp88sknOPXUU5GYSPU9u/91ytHStZWXl+dIl0VjdMohsoMyBL6GgBGur0HSMSt0g9RiD7COwTeavdpYRRNN66srI+CTLmHQ+HNXxqSznnvj8bF7YGcdpa8fV+Nx+/q3tsYQMAQ6CwJGuDrLSNhxGAKGgCFgCBgChoAhYAgYAobANw4BI1wdMKSKr/ZnnZoq4mi9vleIoZbWlIP89vKMNe2rAw69S3UpbP1Xc/j62PtjFUeZ5KZjoDb6Xi/10VybLgVqB5ysMK6rq3MvdR8fH+9w9nflfy9hGrXTGAWDQdeuqUdZuZSS/9c2apOQkGBeZx/IKLz7YyGchb0wbnrN+Pe/ltqoH5XU0Jj6Y6W+mo5nFA65y3ahcfCvK+HaXAhh4zb+ddXcGKhdVVWV688fd42XLdFDQNeBcNayLbXBtrTxj8h/rm2rL7+dvRsChkB0EDDCFR0cnVEgQ664uNi9Kisr0b17d/Tr188ZCXoI6eFWVFSEtWvXsjbXBrdN3759nUhDampqgzGhtv6NU3lFq1evdnlFgwYN2uaNNkqn0SW6EbYyDjQWesmo69OnD3r16uXOX9/LGCxknZM1a9Zg06ZNzsDv37+/G8/k5GQ3VtpO46M2mzdvdsalag1pTFXLqznDpEsAHMWTlIGxfv16SNBkxYoVzuA47LDDMHjwYIevxkp1nuZRJl75d6qxp/HZd9993UvS1r7Br2vyyy+/dPlEMuaVpzdu3Dh069bNrqsojJnGSteCxmo5Syxo8kEqksrb8sdA90DlCen7POag6L4n0RPVU/OvF3/MP/74YyxcuNDdC1UX6pBDDnHXqPry20bhsLtkF8JY15N/XemaOe2009x4+ICozdKlSxva6Hl28sknu/uc30bvug9qPKWap7FVX7qujjjiCDfp0bitfW4/AiJGek4pH1zPIhFjlVfwiZJvK+h5pTpqqT32LAAAHNRJREFUeialp6dj+PDhDddd472qvcZswYIFbqJwzJgxNk6NAbLPhkAHIWCEK0rA6gYmA+HXv/41lixZ4gz273znO/j5z3/ujAMZCHoo/fnPf8a7777rDHLd+HQzPfbYY3HFFVc4w0TrfGNCxOxPf/oT/vrXv+L000/HPffc4x5mUTrkLtuNxurVV191uMqgkGEoBcnLL7+8ARMZ78L+iy++cJhrG3lWvv3tb+Oss85yBqK+k/qajH0ZGSJxKSkproaXxksPPVt2DIFyFjh+6KGH8Morr7hJCs2aP/DAA+568K+Td955B3/4wx+cR0SEd+PGjc5Q1Bhccskl6N27t7seVWDzySefdARLhFjXqdqoGKZIgd/fjh1x191aY3XLLbe4RH6RZF0Lzz77LA488MAGw0/GoOoRTps2zZFjTUhJbVJGn4+/JqNuvvlmd12JFOueKPW8ww8/HD/5yU+giQ+/bddFe8fOXGN18cUXO4x1vaSlpTnCpMkiH1uN1Xnnnecm/PQsGjZsGN5+++2t5OJFyiZPnoxbb70VGRkZro0mHXXvu/vuu12/O3aktrVIlK4ZqX1KgEaTTf/73/8c8dVY6foQEZMK6KJFi9yErgivnnE+KWuMoiY9NGa67+k++MEHH7h7ZOM29tkQMASij4ARrihhKoN85syZTuFJ3hE9mPbff39X00S70I1Rs7+aYe/Rowf22msvF9qkuk96ydi/7LLLGo5G3rLXX3/d1a/RDfeAAw5whqcMe1t2DAF5N/zZcxl3mpn9v//7P/fAUs96gC1evNjNAMq4kxKevCiPPfYYPvvsM1x33XVuNlhES0b7kCFDMIjeRz30RA700PvhD3/oZvd37Ei79tYaB11XGgsZdlJPU027a665xo2XbxjKGBTZlYdSHhNdO5qckFEhA11elrlz5zqjZe+993b1oNT20UcfddeYjJlvfetbDaSga6O+/Wev62rOnDmOaMnY+9e//oWHH37YeTt8D5fGyVcCFdHS9fTII49g9OjRDYa+tv3Rj36E22+/3XlUZBRqokpy5b/5zW+c50ShoLZsPwK6rqZOneqeRRoHjdV///tf5+n3e9VYqY1ImOTHZ82ahddee20rwqX754knngh5nW+77TZHsDTWFRUVzhPTnMHv92/vbUPAf/ZoHDS5JBtCtoYfsqn7pCIsNPmn59Dzzz/vomz0XPPvkf6e1Fb3S9kb8vjLy/zpp59uNe5+W3s3BAyBKCPAC9CWKCPAG6J3xhlneCx03GrPNDg8PrBcW3q7WEC+3iNh83gT9GhYejQYvSuvvNLjTL3Hh1ir/VmD9iFAYuyddNJJHg3Dhg01Bno1XVjvxGPdJ9fWH6umbf7xj394kyZN8lg4tOlX9vcOIuBjS9LVYk8aOxZF9ugR8dRWY0Wjcqu/1YGuU40VvdIejZoW+7Qv24cAJyc8erY8eqaavZbU2y9/+UvvyCOP9GjIb9U5jX+Pk1UeC7p69MR4NPy9F1980dtvv/08Rgd4JAtbtbc/dgyBu+66y+MEoEdDfJsdXX/99d748eM9er22akMPpsdJKY/eF4/hiR7r43kM6/U46eGuu60a2x87hIB+9z/72c+8PfbYw+PkRrN9CXdO3HoMv/3adaf7IsMSPXojPdaZ9DiJ4XHiqcVxb3YnttIQMAS2CwHN5tuyDQR0g5KxRhf8Nl/NGd6cFfToMWmWcKlPvbSIWP373//2JkyY4HH21q3Xd4yD9zi769GT4jFszWO4ocfwDyNc2xgnrW7rWDXt4s033/ROOOGEVgmXHnAMMfSYb+I988wzDWPVuD8ZhhpHkTIZjbY0j4CuJ2GlCYTmXjKyhbd/nfi90BPsCNK2CJffnvkO7toRkaZXzG1+3333efR0OYPdb7dy5UqPOSluUoOhOv5u7L0RArpHcSbcEZ/mxkrrZAj6mPqbtoVw3Xnnne7e15Rw0VPs7ncyCv/2t795ItoaJxn9zGf92r78fXb1d43VtsZI63VdqU3TRcS3NcL1gx/8oFnCxVBsj/lEHj2S7tpkJIZHb7GnySld47Y0j0BrdoXukf415b/rOvvxj3/cLOHy24hwaXK2OcKlsX/rrbe8sWPHesyN9Bhi7TGU2ghX80Nkaw2BqCNgIYUteAzlclcSvkL6mrrm/c2UpyBXv0L9/DZy+SukSbHWyjdpunAUXU6JEsIV1qRYecW7KzFc+1R4B2d3Qc+WE95QOIe2UTiNwmts+ToCyknQWCmxWIs/Fn5L4aeCxhLHUNKxv/ABhN/+9rcgSXKhgv76xu+KeVfhVuXnSYRB4U4KI2y88GHoYuEVHqVcB3o33e+icRv7HAnXVGK9QmL0u2+6aJz00rWgvB7llvgLDW+Xd3fVVVe53Ct/vd61jRYaeSAhhvK1zj77bJdPp3Gnce/C1zR2ygXS70PhUApPVBgODUoniOI6sX8aEFDIrHJHdH01vabUSLgrz0oh0o3vTb/73e/cGOh6UD5Jc9vS0Md7773nQgo11v6ikFDl7Ol6o5Ho8lwVmqb8sOOPP96FLPohiv42Xfndv2b0PNFY6V7U3EKj3AmP6P6kvFV/oYfLhbUrXE33x+YWkl13D2waUqhrkaQY9BS7e55CevXMU36rnmP6XdhYbY2oxkH3Hj2vmrsu1FpjqvtSVlZWQxtdC7cxbPOll15yobvNhdUqrFf3NP0WPvroo4Zt1Z/uu8rbUx6y3nWfJIFzBeSV52qLIWAIdCwCRrhawFeCCjLGlaC6rUVqaLppjRgxoqFJa4SLs1suqV+J/sp5kBEog183Yhn2Wi+FIcVZS2jj97//vftOOQxKRm7uRtuw8y76YcaMGQ4n4adFDxh/8R9qUkP73ve+h9zcXP8rtEa4RLaUcyJiLKNTROqggw5q2F4f9CDUftVGcfVqo1whW5pHQDkDDPlzuVnNt4DLkRNh8pUj1a41wiWy9fLLL7t8H431pZde2rD9/fff73K6JMbAMDZniCin8tprr3XkTu9mdHx9NJTX8/jjjzvj8OvfRtYo+f7UU091Exp+mx0hXMp/lQjDmWee6YxDXVM6BuV2aSLr0EMPtXugDzTfda/TPU4ER7luuk9ta5E4kESa/PwftdsRwqV8Vok6aYLw4IMPdkROz03dI++9915IOKrxvrZ1XF1pvZ7zEu9RfuO2FhFiTcJqrPxlRwiX7o0ia3qWiWipL11PWqfcWBFtyw/3kbZ3Q6BjEDDC1QKummnVTJSSVn2jvXFzPeh0k9LNqvHsrgiXHjYy7Bt7uNReBrwSVaWAp9lIzRwq6VjfaWbyueeecwnKmhmUVLLkYHWT1E1aBqiMEH1ny9YIyDOosWpuJl7YapGKltToGhsAMu5EqmWgy3hovGisJIwhz6Ikd2WsyNhrvOjBJcU1kS3tRwIMDNlo3MQ+N0FAv2VNOvjj0uRr96c8GpoZb3zdNUe41IdeulZlQDB3zo2RJivkefYXGYVSzNMYM9TQ9a3rSuImPjmTJ8yWrRHY3rFqK+HSZJYMz8YeLnmyJKYhkZpRo0a5A2L4pyN1F154IS666CJ3LTf+bWx91F3zL42V7lktLTLkm3qcZNjr2miPh0vXnPDXJCDDdfH++++7iQv1Lc+1vJ5SzdNERuOIgpaOrSt9J4ElebiajoWPgfCVYJOeWf7vvDHhkgiQVHObLs15uNSXlCgl0y8VV3k49TuRGJFEUfyJDZE7f19N+7W/DQFDYMcRMMLVAob+Q6WFJlt9pfZ66MkA10NIhEvESzcxGZAyMn1peJEyGX/MH3J96MarbZl07MJsRCDUn9SHZHzoO0lcyzhUOJvdGLeCvt1/+GMlwiXjUCExMg40DnrpgaSxENnSGCjkTHWAtPht9ACUZ01kS+1FnqVM2biN+8P+2SEENBEhrKVQKEU1XQennHKK83LI6BDZYi6kG0eNkcJl5BnTNSJyLSNTHkgmnDujQ9/LU6zJDSl6XX311U4NzxTVdmiY3MYy+HQtiPjKiNeEk64JGd1+GJvGS230nSSpdY+USqE/VvK4aDzlldTkhcZRk1MaJ3mozznnnK1CTXf8qLtmD/44SFlVkxlSxZWCriYP/WtBnhE9txTFIeOc4iUurFrjqTaS61dpBRGv4447zpEArdPkoO6ruk6bIwZdE/HIWbfXrtBWGgPdA++44w53H1RkjPDXy7cF1EZjKqKrkEJdW/pOzytNROp61MShbAm1VR8i2QpBVKhu08nErjxGdu6GQEcgYIQrSqjqJqpcL83EKj9FOSTyfMmI18y5yJcMd4UHyshTHLWKSOrmpxuiHnSa0dLnxotyKJTLoBukPDF6eG3PDbtxn/YZTsJdIWWKc5chL9l91dfSOMgzomLTMvhkPDB53+X9CHcZjQo9k0Gvh5pmhzVTeBFn3dWHxlNtNJbqy5btR0B4axGp1YyupPwVjijDgIp1LjdI3l6NkUJjNEmhCQyNj64XGY4Kc1IupUibb+ArpFC5eMoTkqEvI15tbNlxBDRGumep1qBIrmbPhe2ECRPcPVAGoOSqNfGkfCCVUKCiKwYxX2XixInuutGElcJydf34RryuUeVnKp9V3hOfEOz4EXfdHhQuqucVhYMcmdJ1oJwh/xrSWClUUDLiIlqKzFBesdooKkOFkEUCNNGkZ568j7rmFP6p3GaFN/oTH10X5R0/cz1T9KxRtIs89fIKizxpgkK5roqyURtNEKoGmsivxkqpCX6bpl5GjZvsEHn4dW/V/dAWQ8AQ6FgEjHBFCV8Zh5oBlFEnY10GnsiTboYyEGVA6MGlm6HqmWiGXTdJvfRw0gNMCchNZwN143yS8d4yIDXLaHHW0RkwGRIK61RYhx4+Ikl6KJ122mnOQ6IxEsGVYeg/0DTGGjcRMBmSevAp7El9+G00nt26dXNeGBFqf/YxOkfdtXoR3nqJGGn2nUprztCWd0S4ahZdNbbkLVY4oUJKdZ3ope0UjiMjkHLWbnw14SHDXUINmrnXevWhyRBdqzZWO/b70m9fxrYIlX//01gJW93bVABZ15kmnUTGdN2JOOld155m5pX7qHugvPoyCJUPpLGUV1/5QPpeOaw2Vjs2VtpazyI9s7RojDQOwlX5c5rI0Gd5G+UJ0fd+G42VJjhEpLVOREDGvSZDdO1RtdCN98CBA40YO3R37B+Ni8LZNRGh60leZImTiNwKd9WJ1GSTJncXLFjgrj2Ng+wKESm1aSqGon7kRVZ0xgsvvGCTgzs2RLa1IdAmBIxwtQmm1hvJKNCNUF4u3ex8g0Dr9YCS8af1cu2rndb7ix5aunnKmNfib6vPujHK0NQixTa1tWXHEdADSjPmMhL9RWOih5QeZnrIaaz0rsUfL+GvNnppHNVGY+R/r7Zqo7FSG1u2HwFhqpeuh8bXhN9ja9+rXXNtRAa0NJ3caG4frqH90yYE/LFqrnFz49C0nd+m8T1OIVIaF59kqY0WG6um6EXv75bG0d9Lc23868oXddK9tfFY+tvae/sQENYKBdS1IDz12xe2eleetx/1ogklPYu03m+jCQ21aRx6qL2rT0066RmoCULzGLdvTKy1IbA9CBjh2h7UbBtDwBAwBAwBQ8AQMAQMAUPAEDAE2oCAEa42gGRNDAFDwBAwBAwBQ8AQMAQMAUPAENgeBIxwbQ9qto0hYAgYAoaAIWAIGAKGgCFgCBgCbUDACFcbQLImhoAhYAgYAoaAIWAIGAKGgCFgCGwPAka4tgc128YQMAQMAUPAEDAEDAFDwBAwBAyBNiBghKsNIFkTQ8AQMAR2FAGplEppTNLovurejva5re19FTKplakWj6nFbQspW28IGAKGgCFgCHQ8Aka4Oh5j24MhYAjsBAQklayaQJLyVxkGlVroTMv69evxzjvvuLo5Kt6rchEqLSApZ5EiETERpGgs6ld1doSBij+rWG20+o7G8VkfhoAhYAgYAoZAV0LACFdXGm07V0PgG4yASMZDDz2EefPm4Xvf+x4OOuigTnW2M2bMwI9+9CN861vfwne/+11Hgj744AOsWLECEydOxIABA6JGilQPUMW5e/TogV/84heucLARrk71c7CDMQQMAUPAEOhCCBjh6kKDbadqCHyTERDhuvHGG/HFF1/grrvuwtFHH73N01XInf8SEWlMRvRZ3zVe/LZ+4dHG3+mzvpeHTWGDauO303q///z8fLzxxhvYY489MHbsWOfh+vnPf453330X99xzD4444oim3W51XE2PyT9mf73e9dK+VdD0vPPOc0VNf/azn2Hw4MENffnt/Lb++fr9fe0gbIUhYAgYAoaAIWAI7BACRrh2CD7b2BAwBDoLAiJcP/zhDzF16lTceeedmDRpUrOHJmJUWlqKDRs2uHflU3Xr1g05OTkutE/EQ8SpsLAQsbGxro9NmzY5EpOamoqePXsiPT294TsRF/W3bNky16faDBo0yJEfhQuqfUpKCkKhkAt5VJif/lb/IoaffPIJrrvuOuy///4ut6tXr16ONMlLlZaWhuTkZHcM2o9ywLQv7UPrRa60Xm3XrVuHiooKZGZmuu8vu+wydO/eHSJcQ4YMcX0o3FJhlzqfyspKdxxqo22iGdLYLPC20hAwBAwBQ8AQ6KIIGOHqogNvp20IfNMQaAvhEjlZtWoVnn32Wbz99tsoKChwxGnMmDEuzG/8+PEu72njxo148MEHHQFTrtXnn3/uQv/0+YwzzsC5556Lfv36Oa+RiNMTTzyB559/3pEhESH1U15e7nK05HU74IADsHjxYjzyyCMup+qkk07CM888g8ceewxr1qxB//79HfkRUfvxj3/siNPjjz/u9qVwQ5FCLfLe/eEPf8Bpp53mPHgib8oNU9v//Oc/jhQqjPD444/Hyy+/jBEjRuCnP/2p83CJ/E2fPh1/+9vf8OWXX7rj0/kcfvjhuOSSSzB8+HBHur5pvws7H0PAEDAEDAFDYFcjYIRrV4+A7d8QMASigkBbCJc8Qffeey9effVVHHXUUTjyyCOdZ0jkR16eW2+91eV+rV27FldeeaUjKIceeqgjMNnZ2Y7ETJkyxZEYES8RoaeeegqPPvqo2+7ss89GfHw8XnzxRddW3q2HH34YhxxyiOvr2muvxQknnIArrrgCq1evxv3334+ZM2e6fKthw4Y5z5lyzz799FPnmbr++usdufMFQN566y3nxVOOmkIGtS8Rw6efftqdi85JnjsJZug4TznlFNePQgpnz57tcsjkaRNh0/6U76bjnzBhggvHHDhwYFTGwjoxBAwBQ8AQMAQMgUYIcMbXFkPAEDAEdnsEGG7nff/73/foXfKYF9Xs+bz//vseVfs8hvB5K1eubGjz97//3Rs9erRHMubRY+WRDHkkRh49Ux6FLTyGIbq29BB5hx12mEfxC48hfN7mzZu9s846yzv55JM9fecvy5cv94477jhv3LhxHkMGG7Yl8fLuvvtuj541t+7222/3SHa8Dz/80N/UvZMweVQy9Oi58hj61/AdvXIevXHeH//4R48eNG/hwoUeCaF38cUXewxpbGhHb5tHj5V34YUXuvX0bnn0nLnzUR9VVVUeiZd7kbB5++23n0cy5zHksKEP+2AIGAKGgCFgCBgC0UFA8f+2GAKGgCGw2yPQFsL1+9//3hEZeoQc2fBPeunSpd6xxx7rXXrppd6SJUs8hh06wsRQO/e3304ki+F6Hr1fnkgVPUQevWQNBMxvJ4JDL5QjZx999JFbLULWlHDddtttjjCJ1DVe2kq41E5E0Sdgfh8ijCJyF1xwgadzE8HTcTOvzdO5v/feew2vX//61x5zvLy//OUvjsT5fdi7IWAIGAKGgCFgCEQHASNc0cHRejEEDIFdjEBbCNd9993nUSHQe+WVVzwKYzQcMfOgvFNPPdVjSKA3d+5cR7hEUK655hqPsu0N7ag06Dxfl19+ufMc+R4vSq97zPtqaCePmLxgImMdSbiYj+UI13PPPecxpLJh//LSiUDKwyXCJQLJXDCPAhke87q8UaNGNbxGjhzpvGEiYhTkaOjDPhgChoAhYAgYAoZAdBAwwhUdHK0XQ8AQ2MUItIVwMdfKhc8phFAhdX6oYF5eniNSF110kbdo0aIGD9e2CBcVAB2RUUifvEY/+MEP3DZ+fwrZkxdM4Ycff/yxQ6Y1D5e/rRq/9tprzhP317/+1aPyYMNxUhjDE0HyPVr//Oc/HeFS6KHa+Ys8cToun3CJUDK/y3ntmL/msSZYw0vHpb9FGOWZs8UQMAQMAUPAEDAEoouAEa7o4mm9GQKGwC5CoDHhUp6S8pEav0QmRFiUl6VQPpEQkRy1YX0sj2IVbj1FJ9rs4SouLvZE0ljzy4XoKd9KZIsqgC6kT/tqKYfrl7/8pcs5U/6UCKCOUcekEEPKxHsPPPCAyykTpDo/5VtR0dD705/+5ML/pk2b5ra/+eabGwif+njnnXdcrpcfUqht5ZVjrS9PeWyNvWFqr3wwy9/aRT9c260hYAgYAobANx4BUylsJCBiHw0BQ2D3RYCkwqnwTZ48GRSRcMWFeQdvOCGpDEpVUEWG6ZlyUugU0HB1qX7729+COVlQIWIVTJbUupQEJdNOMuPe1ZHWM88Lffv2dWqBqm/10ksvgcTIqf5RPMNJyUty/s0333Q1uKQiqP3QkwSpFJ544omubx2P1BGlYsjwP6cUqHVDhw4FiRzoXXPHftVVV7liyQwNdLLyUjBUn+ecc477XjLyn332GVR3S4qKqsVFDxj+9a9/OUVEnZOOUwqHd9xxh5OKl8LhgAEDXB0v1eSigIg7Rl/q3nVs/xgChoAhYAgYAoZAVBAwwhUVGK0TQ8AQ2NUISBaeOVp44YUX5Ll3kun+Melv1Zu6+uqrHZkRyRHpkhS86mWp6K+I1Omnnw6RHhURVv0qESsRL9XJ0qL6XLfccgtU60pESKSFHi384x//cPWtRF4k4U5PkqvbJfLD/C5Xl2vOnDnus6TbRZYyMjJcDS7V5vrf//4HepgcaVOhYioUgmGPoMiHK3as4xRZ1HEwxwyShWfOmSt+LLl3nbfk5VW8WUWbVVNLhZiZr+ZIno5T6yVXT3EMh0FWVpY7J5E7naeKRasemQo/22IIGAKGgCFgCBgC0UPACFf0sLSeDAFDYBchIELFUDxX1Fh1qBgm50iXfzj6XmRExCMtLQ0Uv3B1quTV0t+Ub3deJBUCFuEQeRNhCQaDjoxovfpQDSutF/kRSdH3Wq+XSJo8YOnp6a5PETO1F4GiSIUjdtqfCF3v3r1dwWUdH8MQ3XEXFRW5fv16XDoHkampU6e681F9Lh2/jl2EjwIYzkMVExPj1jFXzNX2ys3NdQRPBZlFJEXSdJw6L70oAuL6VAFofa8aXSJaffr0cfv3MbN3Q8AQMAQMAUPAEIgOAka4ooOj9WIIGAJdEAERLRElkRh5tlJTUx05UjghBS9cQWN5zkSObDEEDAFDwBAwBAyBromAEa6uOe521oaAIbCDCIhsyWO0ePFi3HXXXc7zpdA/SqtDXjblbd14442gqqBrt4O7s80NAUPAEDAEDAFDYDdFwAjXbjpwdtiGgCGwaxEQ4dKiHC55tH73u9/h888/d6GLEu3QS+F8ImW2GAKGgCFgCBgChkDXRcAIV9cdeztzQ8AQMAQMAUPAEDAEDAFDwBDoYASMcHUwwNa9IWAIGAKGgCFgCBgChoAhYAh0XQSMcHXdsbczNwQMAUPAEDAEDAFDwBAwBAyBDkbACFcHA2zdGwKGgCFgCBgChoAhYAgYAoZA10XACFfXHXs7c0PAEDAEDAFDwBAwBAwBQ8AQ6GAEjHB1MMDWvSFgCBgChoAhYAgYAoaAIWAIdF0E/h91W0CDLlcjOAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "20a83bca",
   "metadata": {},
   "source": [
    "#  The California Housing Prices dataset \n",
    "from the StatLib repository. This dataset was based on data from the 1990 California census. It is not exactly recent (you could still afford a nice house in the Bay Area at the time), but it has many qualities for learning, so we will pretend it is recent data. We also added a categorical attribute and removed a few features for teaching purposes.\n",
    "\n",
    "![Schermata%202022-07-20%20alle%2015.45.04.png](attachment:Schermata%202022-07-20%20alle%2015.45.04.png)"
   ]
  },
  {
   "attachments": {
    "Schermata%202022-07-20%20alle%2015.52.47.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "ab42f7f9",
   "metadata": {},
   "source": [
    "\n",
    "So we have California home cost data available and we want to create a predictive model to predict the cost of a home based on its attributes, based on the data in this dataset.\n",
    "It is therefore a supervised learning task, to be exact Regression.\n",
    "\n",
    "# Pipeline : \n",
    "\n",
    "A sequence of data processing components is called a data pipeline. Pipelines are very common in Machine Learning systems, since there is a lot of data to manipulate and many data transformations to apply.\n",
    "\n",
    "Let's define a pipeline: what are the steps to take to create my predictive model?\n",
    "\n",
    "## 1. Performance Measures \n",
    "Select a performance measure, here we  present RMSE and MAE : both are ways to measure the distance between two vectors (the vector of predictions and the vector of target values).\n",
    "\n",
    "### Root Mean Square Error\n",
    "A typical performance measure for regression problems is the Root Mean Square Error (RMSE). It gives an idea of how much error the system typically makes in its predictions, with a higher weight for large errors. \n",
    "\n",
    "![Schermata%202022-07-20%20alle%2015.52.47.png](attachment:Schermata%202022-07-20%20alle%2015.52.47.png)\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "Schermata%202022-07-20%20alle%2015.55.08.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "cb9ca7e2",
   "metadata": {},
   "source": [
    "### Mean Absolute Error \n",
    "\n",
    "Even though the RMSE is generally the preferred performance measure for regression tasks, in some contexts you may prefer to use another function. For example, suppose that there are many outlier districts. In that case, you may consider using the Mean Absolute Error \n",
    "\n",
    "![Schermata%202022-07-20%20alle%2015.55.08.png](attachment:Schermata%202022-07-20%20alle%2015.55.08.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "801a8a93",
   "metadata": {},
   "source": [
    "##  2. Download the Data\n",
    "when you call fetch_housing_data(), it creates a datasets/housing directory in your workspace, downloads the housing.tgz file, and extracts the housing.csv from it in this directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "661ea608",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH): \n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fe3ceb",
   "metadata": {},
   "source": [
    "## 3. Load the Data\n",
    "Now let’s load the data using Pandas. Once again you should write a small function to load the data. This function returns a Pandas DataFrame object containing all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "c329ff88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "def load_housing_data(housing_path=HOUSING_PATH): \n",
    "        csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "        return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b433fa1",
   "metadata": {},
   "source": [
    "## Take a Quick Look at the Data Structure\n",
    "Let’s take a look at the top five rows using the DataFrame’s head() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "747f0e97",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetch_housing_data()\n",
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e0c244",
   "metadata": {},
   "source": [
    "Each row represents one district. There are 10 attributes: longitude, latitude, housing_median_age, total_rooms, total_bed rooms, population, households, median_income, median_house_value, and ocean_proximity.\n",
    "\n",
    "The info() method is useful to get a quick description of the data, in particular the total number of rows, and each attribute’s type and number of non-null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1517d05e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830bb002",
   "metadata": {},
   "source": [
    "There are 20,640 instances in the dataset, which means that it is fairly small by Machine Learning standards, but it’s perfect to get started. Notice that the total_bed rooms attribute has only 20,433 non-null values, meaning that 207 districts are missing this feature. We will need to take care of this later.\n",
    "\n",
    "All attributes are numerical, except the ocean_proximity field. Its type is object, so it could hold any kind of Python object, but since you loaded this data from a CSV file you know that it must be a text attribute. When you looked at the top five rows, you probably noticed that the values in the ocean_proximity column were repetitive, which means that it is probably a categorical attribute. You can find out what cate‐ gories exist and how many districts belong to each category by using the value_counts() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ca0fb0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613e050b",
   "metadata": {},
   "source": [
    "Let’s look at the other fields. The describe() method shows a summary of the numerical attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "4070323d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a567d7",
   "metadata": {},
   "source": [
    "The count, mean, min, and max rows are self-explanatory. Note that the null values are ignored (so, for example, count of total_bedrooms is 20,433, not 20,640). The std row shows the standard deviation, which measures how dispersed the values are. \n",
    "\n",
    "The 25%, 50%, and 75% rows show the corresponding percentiles: a percentile indicates the value below which a given percentage of observations in a group of observations falls. \n",
    "\n",
    "For example, 25% of the districts have a housing_median_age lower than 18, while 50% are lower than 29 and 75% are lower than 37. These are often called the 25th percentile (or 1st quartile), the median, and the 75th percentile (or 3rd quartile).\n",
    "\n",
    "Another quick way to get a feel of the type of data you are dealing with is to plot a histogram for each numerical attribute. A **histogram** shows the number of instances (on the vertical axis) that have a given value range (on the horizontal axis). You can either plot this one attribute at a time, or you can call the hist() method on the whole dataset, and it will plot a histogram for each numerical attribute (see Figure 2-8). For example, you can see that slightly over 800 districts have a median_house_value equal to about $100,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8b8bdd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "housing.hist(bins=50, figsize=(20,15)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6af6c7d",
   "metadata": {},
   "source": [
    "The hist() method relies on Matplotlib, which in turn relies on a user-specified graphical backend to draw on your screen. So before you can plot anything, you need to specify which backend Matplotlib should use. \n",
    "\n",
    "The simplest option is to use Jupyter’s magic command %matplotlib inline. This tells Jupyter to set up Matplotlib so it uses Jupyter’s own backend. Plots are then rendered within the notebook itself. \n",
    "\n",
    "Note that calling show() is optional in a Jupyter notebook, as Jupyter will automatically display plots when a cell is executed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0a6bb05",
   "metadata": {},
   "source": [
    "# 4. Create a test set "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8be7b26",
   "metadata": {},
   "source": [
    "Creating a test set is theoretically quite simple: just pick some instances randomly, typically 20% of the dataset (or less if your dataset is very large), and set them aside:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "1fee5787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12148,  5862,  4275, ...,  1132, 19227, 16961])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuf = np.random.permutation(len(data))\n",
    "shuf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "cee3197f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data)) # extract all the indexes\n",
    "    test_set_size = int(len(data) * test_ratio) \n",
    "    test_indices = shuffled_indices[:test_set_size]  # select the first 20% of indexes \n",
    "    train_indices = shuffled_indices[test_set_size:]   # select the remaining 80% of indexes \n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e47a3fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20640\n",
      "16512\n",
      "4128\n",
      "16512.0\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2) \n",
    "print(len(housing))\n",
    "print(len(train_set))\n",
    "print(len(test_set))\n",
    "\n",
    "print(len(housing) * 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "32193141",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20640\n"
     ]
    }
   ],
   "source": [
    "print(len(housing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "268d3595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "20640*20/100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc51b60",
   "metadata": {},
   "source": [
    "Well, this works, but it is not perfect: if you run the program again, it will generate a different test set! Over time, you (or your Machine Learning algorithms) will get to see the whole dataset, which is what you want to avoid. One solution is to save the test set on the first run and then load it in subsequent runs. Another option is to set the random number generator’s seed (e.g., np.random.seed(42)) before calling np.random.permutation(), so that it always generates the same shuffled indices.\n",
    "\n",
    "Scikit-Learn provides a few functions to split datasets into multiple subsets in various ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "ffe96027",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d8e257",
   "metadata": {},
   "source": [
    "# 5. Visualize the Data \n",
    "\n",
    "Since there is geographical information (latitude and longitude), it is a good idea to create a scatterplot of all districts to visualize the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "7639ad41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd811241310>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169e522f",
   "metadata": {},
   "source": [
    "This looks like California all right, but other than that it is hard to see any particular pattern. Setting the alpha option to 0.1 makes it much easier to visualize the places where there is a high density of data points:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b70773ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd810c91210>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UbY+sGTqBrQzeymciwfMlj+/Ph/LdK8JbG+UquuJG+/u6KsIyXMImv9mKxQtXFtBUhVRMw/ECLs5UObk/zzMPDPDi5QUuzVYANqzQ2ungoO0SDeGJiLh73BOOobULnyvbXF9sbHqt5bqkzBiOH9D0gg2nsFmuT9LUePq+XuYqNtfma1xfqPEX56d4dE+OS7NVJop1Rhfq7O9N8ZfnJ6nbPoPZ+KqTQGuXf3G6QrXpogqBqghuJGI8NJxBEQotQ732PVVsl9OX5smYOrommK3YXJ6r8plcnJLlkjK0dgVTNq53zFWklpPm33prat1Jo1wPZ17rqkIQSDRVwZcSxw0YyiZImiqP78u3Z0hsFsrZyeCg7RA1sUVE3D3uCcdguT6Vhssfnxnd8lpVUTncn0QRglLDxdQVTh3tI2Pq7fBKS8dIUwRWM1RG1YRCV8IgF9e5NFvl80cH+IvzUzx1sIe0qVOoNbk0W2RPPgGEJ4GlhsNzF2e5OFVhoeYwW7aQAo70p6jaLn96dpLd3XGODeVW7cZbp4Sq7XFxusyRgTTjiw0cP+DNm0XOT5ToThioqsIje7LtpPBGIZxWB/Xj+/MkDZV60+f05Tk+dyRMwlctB9sPcD1JQlf58rKeFAjyyxVcmsqGJ5u7FfaJmtgiIu4O94RjKDccfjC+RMXd/DoFUDWFB4ZyfO3kMJl4OD5yqd7km29OrgsBPX24l+++M0ux4dCVMDi2K0va1Jmr2tRcD0NTSC+HhFr5gbLlkk/GsByfQEocT3Kr2KDh+FhugBSSiaLFff0ZRnqSfOJgL7qqtHfYQDsHkTV1zt8q8R9/MEHCUBhdqGEvSwdNFi26kjr1pktPyuSLxwbYk092fN+tDmoFeH28iC8l9abHx0a6+aefO8S/euE6tuujqQq/9OkD3DeUwfGCbYdydhL2icpPIyI+fD7yjqE1me1QX5IXLtEx6QyQ0ODxAz0oiuDHTwyzuzvZvn+jZPBaldO0qbdzCildWz3fwZcc7ktRtTxqjoeK4JOHenj+8ixLdScMB6lhVVHF8mi6Afm0QSKmoinKqoRwey51IBEC6o7LrSV31XvzJSzWXISE10YLXJmp8FNP7OHafH2dg2uNFj13q9x2ZL4veX2syNdP7ubYUI7xYp2RriR92VAraiehnO1eu9Py06hcNSLi7vCRdgyOFzBXtZmv2JQtj56UxnzN63ht3FBZqDrs6orz364vEtOVTat5WmGQlsrpS1cXuDpXaecU/ubSLEcH0lyarVJpulQaDq4vaXou1+Zq7O1J8P2xAvf3Z0jFQuE+TRUoItwpB0HAwb4UmqK0d9iqENws1Dh/s4SuKQghKFlNFmsusuO7goW6y9tTJfJJg3/34g2+9OAQWTPUejp9eY6vP7oHgPsHMrw2uoSqhq9zYm8Xjh8wXqjx5q0SXiAZW6ivMr47CeVsde1Oy0+jctWIiLvHR9YxtHaTddvje1cWGMzE6E2bzNdqHa8PAomuKXzqvl6Shr6umqdque2y1LVhkIFsnGcfHOLP3pho5xRWzne4tVTn3794A4lgYqmGoigU6g6FqkO57vKFBwYZLdRx3IDZik0urnN0VwbLCWUzNEVwdCDNX741xdmbS0gkfhAQSHh7oryhU1j53hZrTVRVodhweKvQaIeL9uUTTBRtbDdAEYI9XXH25JO4nsTxfV4fK65KGp++PMcz9w+252XvRI9oZeJ+5det7611wEtWk7mqTX/ajGYuRER8gHwkHcPK3aSpqwxmTS7PVEjHNTSg05nBdgN2d8WJG9o6xdOjA2n+8NWbOH7YyPYzT+xdZ3x8KVflFFqGbaFm8913ZpgqWxiqwmTJIhfXGcwlMA2Vaws1fuET+8iMF3ltbIkDvSk+NtKFqWtUbJdnHhjA1FS+fWEaTRUkYxoJXaPheuzrSfKdt6e3/DyKDY/+lEEQBFyYLNObDsNBjhvwZ2en+OThXvLJGIqA8xMlYkY4OvSxkW5eGy+2ja+9iQTIdtgs9LM2DzFTsrgwWQYpMHWFJw/k2zmflcn/zRx2RETEnfGRdAwrd5NeEJAxdTJxgyODaaaLFvO19Vlo24fR+RqP78+jINpGxvECLs1WeepgmH8IAsml2SpHBrObSk23DFvd9nnp6iIxVWDoSpgvsH16gyDc9QeSvnScZx+K4/qS4Vy83R/RUiYtWg6259OfNlfJTtRsF8S6t7KOAKg6Dqf29lOxPRquhyoEx3ZlOXtzCUUJHzKYjRNI+MyRPvqXnce5W6V2j8NGEiDbnVi3WehnZR5iyWpyYbLMof4UrudTqjf5v//mKg/v6UJT4bGRbgazCfZ0mfzpG5P4ARs67IiIiJ3zkXQMa430fQMprsxVcbyAR/d288LlOZprBn+pCuQSOq+NL3FypKs9m6FsuXiBJJ+83XU8V7U7hixO7Mnx2vgSS5bkwmSZh3fnSBracj4gDOnEdJVivUnVcrk8W6UvbdJwPAaycdKmhutLNDWcOla3PZ57dwbPhwuTZZTh0Jifu1lcdhSxcLCQt36K2VoUofCZ+3qZqThoqiAb17Gafji3IQiDUZbjY+qhEutanaZq1cP2fJ480IOmKGjGxuWpnWg5a10VNByvLSGy8v5WHmKuanNxssQfnRmn5vi4fkB/OsZAzqRQdXjl6gKGptL0fLKmwfHdGTKm3tFhR0RE7JyPpGNYWwUTNzR+5ZnDvDlRxtAUhnImf/KDm4Sl+AqGJtCE4PMPDNL0A565f5CedAzHC3C9ANhc9G1197HgwaEMSMFgNo4XBOzrTjBWqNOTNNEUwYweagulTZ37BtK8Olrgq48Mr5thALKtTNoK8zy4O8uxXVk+tq+LdEzn+zcKnB1formFarWhCq4vNPjCsQFeHS1QqDtoiuBnntjLpdnqqlzGty9Mr9NpqtgucT0MMcHW4zJrdigF3hU3MDSFuu0xX7Y4f6uEoSu4bsCuLnOd8J6hKZRrDs+/O7/sSBSkDJgsWvzXt6boz5jYfsDh3jTzDZfhXJLRhTpPHMjTqPtRjiEi4n3gI+kYYHUVTLnhhDOcDY1ASv7Bx/fieD4vXlmk6vioAo4PZ0OpCz8gE9dXGfu67VO3GyRNnZbeT4tOIZJLM9X2rj9uqDy0O0fFdrE8n5imsbsryYm9XfRnTDRFaZ9AVq7Z9QOeuzjbju+vDfO0dJeeOtTDbNniVtHC3cQ5qIpKueGSTRjrqoOODGaxludCf/vCdMdwT08qxqmjfdsqT31jvMD/d+YmEonnBXSlDKqWx9hiHU0RDOZMluouJdvlL89PcupI/yq58ucvzxHTFYQnkUjcIOyxKNseltsI166peL7EC8KxpWXLXRX+i3ohIiLunI+sY4DbVS/fHS2Q0DXyydDYnb1Z5BefPshIb5pbhTqFmst9A2nKlsupo30Aq419zKdiuzyyO8ebt0qr9H7iy7ITcUPF84PQGMqAJ/flOXerRKXpoijw8O4c+VSMpKHxd9cXuTxTpT9trtt5t+LtnRrITF1ZVaFjaAo/+vAQ52+VqLs+xZqDG3Tu1RAEXJ4rowqxrpKo9XUrbLZRpc92ylNHF6r8+l9fxtDCfMx0yabh+BzoS6IpAkNVmKs0eXh3LjyVCGVVrsFyfVKGRtzQEHhoiobjhvIkXiBJxhQUISjUnbbkd4DE8yWnjvZtS/I8IiJicz7SjgE2LmvMxA1++vERLNdnsWrz8rVFhBCcuVHgxJ7c+ni4DPjB2BI9ydg62WpNEcyULK4v1LBdHz+QfPpw3+3dvxfw7Xem27mPljDfZMlqV/esNbJrk7EygFNH+9Zdl4kbPHmol735BH92bhJB6EwcL2Bl5mG25uHcKvLy1Tk+e//gptLdm4XNNitPdbyA5y7OIQBTU7k8U6Xe9PCRzJRsypaLrgpMTaXW46FrCp4fYAdB2/nEdZVMQufz9/fznXdmqVhNAinZlTPJJo2wF8T1CSRkEzqHB1I8NtLNof4MhqbwzTcno96GiIj3yEfeMWxm7FrG4uzN4iqD/9r4EuW6y/lbpbDqR8JgNkY8FnYnw20H40vJkwfy/PbfXkNZfu7BvlQ7b5CN69wq1LkwUUZVBKaucrA31VGYby0D2ThP7M9z+vI8yrLTelpVVu2A47qKpsJCNYzn68vNcdcX1osFLjV8fueF60wU7Y4SGS1ndPryHHPVgLihtJPw28FyfQxdQVcFU0UrbNhToOkG1GyPuKESBGC7Pu9MlTBjGlfnqkjg+K4sj47k22vwgoCvnxim4fk8OJDmuXfnmSpbZEyFwUwGhGAwF8PUNd6drXJ1vtZ26FFvQ0TEe+Mj/9ey1YzliuVStT107bbB9wJJseEwXqgzXqhzaabM2ZtF3pks8tLVBYr15ioHk4kbPDic46mDPXxspItsXMf2wkRoS5Lj4d05TD3scD4/WeKTh3voScW2nOL26miBnmSMXbl4x/nIhqbw2Eg3dcfFDQKW6k0q9eaGTW+zxRrnJ0r8zulrTCzVO18kxep/16ypbLkdZzTHdZWUoXFyb1d4EvDCprnuhIG33JC3vzfBQ8NZKk0fTYG0qbGnO84f/+AmNXtFh4kUxGMaw7kEJ/bl+V+euY9PHOqhOxnDk5L9PUlShk53wmjPjn5tfAlkmNuBrRPkERERnbnrJwYhhAqcBaaklF8WQnQD/wkYAcaBvy+lLN7NNWwUG58tW5y+PMfF6TI35muc2NuFqam4nmSu2uSh4SxV2+XczRJl26I7EaPR9DgzWlhV0gpg6gqVhrsunETCwAskg9k4vekYTS+g1AhDWVux3e7ewWyCZEznxO4uSrbL6EINGp1LWEtNmCzVGMomeO7iHD/zxEj7Wa1EesbU6c+YHae5bRa/v33iCDi6K0vddkmbOlNFi8W6zYk9XfSk4izUbPJJh5GeFLMlm6W6S9lyuTpX4diu3IZr+MefOtSeWwHw3Luzaz4bwcf2dfHmcm4nkuKOiLgzPoi/mG8Al1Z8/avAaSnlIeD08td3HUMLp46tM4Ixgyf35wE4c2ORiu3y5IFuVEUgpWC61EQs7zpzCZ10QufIQIZn7h9sG0VDCztzz0+WsFwfU1d5eHeOV0cLqwb3aIqCQtjJu51d7MowGGy8A/al5FB/ilzSYCgX56GB7KZ9b1dmarx1q8Rbt0qMF25LhHRyRK15Diurr/IJAyScvjy37uQwkI3zyUO9HOlPkU0aqIrg5N4uvvzgLhRFYanR5FBfmu6kwbmxInNVm+mShURyYapMpUMCvLUGQ1PoScXoScfIxPWOn81IPsVXHxnmyw8NtYUOIyIidsZdPTEIIYaBZ4FfB/7Z8rd/DPjU8n//AfAi8M/v5jo6sdIIxo2wkWuyZLXj/kcHMlyZrWK5HlJKuhIG6bhGoeYQ0xQya8ZstsJJuYROTFPaZai+lHc8UGYjVVII5btXykPkUzEGM/G2PITlObxwrfNBLPCh6QcIJC9fWWQomyBlapvmY1qfl+16nL1ZWSXNfbg/03624wWcuVFgX0+aw/2ZsNJJBu1xqEhQheDGQpU3b5Xwl/s1hrKx8DSwnNMp1JvtKXSdnOFWiq0rNwBR6WpExM6426Gk3wJ+BUiv+F6/lHIGQEo5I4To63SjEOIXgV8E2LNnz/u+sLVG0PUlaVNrJ4O/cnyI5y7O0vQCBjMmpq4yV2niegGfPNyzzsjEdRVTV5blNJRVRjUb1+94oMzaMNhmsyFeurqAt2xI/4+vPszwSzf4kx9MsDaoFCjguh5OEPD2VAn1DcEXjw2ses5SwyGQklNHbldCeYHPufEKuYRBTBFtae6RfGqVQN7KHX8+FWOqZFG0HPrTJkv1Jn/0g5u8emORhuOjCIhpCot1l7dulfjSsUHqTXfVNLmvHB/q+JltVT4byXJHRNwZd80xCCG+DMxLKd8QQnxqp/dLKX8X+F2AkydPbiUgumO22nEOZOP8xIndPLgry4WpMmUrlMs+3J/i9fEiCUPrGF/v9Lz3umtd2duw2WyIlUayYruMLtbWOQUAJQA1ptIVjxHTFXJxndOX5nnmWDix7ckDeU5fmkdR4JXrCzzm+ShCcHOhwduTJdJxlZHuJMf3dOPJYFXOY51mVNniwmQJRNDsigUAACAASURBVNizULaajC008APQFJAIPAnSD3DcUAm3N2XSczCG7QRIRdKdjK1a/8rPcyMiWe6IiDvnbp4YngK+IoT4EmACGSHEHwNzQojB5dPCIDB/F9ewKZvtOFfJXCCQUvKZI31tSe21RsbxAuKGxrPLYy9bz2sluC3ndvnnne5at0pGr+wxcL2AyZIdJpEEBCtcq1Qga+oEUrK/N0nT8/nBWIGJpTr1poeiCo4N5UBKzt0q8b1Lc7xza5HZFRWwxYaDooQDfj59uK89oW5V/0XD4cJkiYeHcwxm4xTqTd6drqGrgrih0nA8JKCrgrSpYeo6xbrDxFIoC64KQX8m1pbprtgu82Wb748VqDs+UkpMTSVpautOBJbr3x5o5AdR6WpExA64a45BSvlrwK8BLJ8YfllK+VNCiH8J/CzwL5b//cu7tYbt0Klha+1us1BvMl5ocN9yLH2tkekUssjGdRwv4FtvTTG22GjH/st1l7//2J5N+xfWrqXluHYyIrPYaKIrEgmhJtQKTuxJU2pI6rbLuzMVrsxUmStbzFSd9jU5/RaP7M/TFY/x0uVF1urRji3aDKTqfP7BAV64PM/XHt1Nygz/d1ophocIK7IgHG+qKBLPh3zKoG67OH7YSNidMHhgKMPNpQbZeDg7u9xwuDxTZabU4OWrC1ycrnBppozlBnQlDKq2x+H+FF89MbxO7bXccNb1jhjbTPpHRNzrfBgNbv8C+FMhxD8CbgFf/xDWsClrd+ar5jWnYqsmqi1Wmzz37gymGlbruP5tA1Wxw1h5byqUsSjWHZ6/NIeqKuQS+h2Nrtws/NVyIqoQvDlRYiiXZHzBXmfUr8w0UBSwHI9swuBWoUZljQpfyYXvXSmQMlh3f4sLk0X29qRouOHOv5WngNDh9qdNTE29ncfxJHu7k1ybq9J0A2K6SjauMNKb4rGRPJ+9v4/n351nrmIzXWowW7ZJxFT+9enraKogYWgUGx6qCJvkVAWuzdeoWB7dSYNK06VQb2I7Pi9fn+fh3Tmuz9favSPf+Oyh6LQQEbENPhDHIKV8kbD6CCllATj1QbzunbIuMe1Jjg5k8GSwToV0qtjglWuL7OtJkjZ1jg1l2uWVK7vMAhkwUWygKoLelIGuqnc8unKjnoyWE3G8gFKjScly0DSB691eiC7A8QMSmsbB/hRLdWedU1hJzdnwR9Q9qDUd8imTXFxf9X5aTuqJ/XleHS20FWPTpsYzxwaRhAqstuvzow8PkU+FeYR8qkRXUg+T2j1JAgn1psdspcm+HhXfl7hCIi0XgUAR4AQ+luMzU2zwG9+5jO0FzJYtvnRsgCcO5HfUOxIREXEPSGLcCZ0SyV85PkR3MrZKhdRQFJYaYdnoYs2hO2Fw7laJY8OZdujn6ECGscU6vpQ0XJ+RfJKEoaGpyqYx783yCSv7MWC9E6naLj8YLeC4ElURKMi2sJ4rQSVsyJMSNmyR3gYC0BSVI4NpVEVgN8Nu77VCdq3pay3F2NaUu4ypM1e1ScS09vs5OpDm914Z4+ZSnZSh8clDPUyWbGbLFn4AigDPlyiaIKYK3EBi2T5+YDO62KAvbTIYC0NJz1+a52ce30vgS+RyziIiImJrIsewARslplepkOqhoblvIMOl2QqVpofrBzw20t2+/ivHhzh9aZ6a7eH6Pg8P59BUZUu5hp3kE9Y6kbSpc2Qgzc3lOdJrzwOS0EhOFi10RaDQWZF1KzImDORiXJyqYC83wX18pJvzk2USuoauCsqWy8vXFvj6o3vWvaeq5eJ6Qdtgt6blffJwL+duhgnlmUqTA31JlupNqk2XbEKj6UqyCY2MafDgcJYvPDSI5wW8MV4iEVNwfcnB/hTnJ8pcmCpRqLvs703x7QvTUclqRMQ2iBzDJrRCInNVm6640U6utgyc74cGVko42Jvi/qEMUsLefKodSulOxvj6yd1Yrk/Fcjhzo9AOR23W6LZVOe1WSenBXJxnH+zjN2drq54bUyBrauSSMTRF0JXQGe4yef7yYud1KGFZaaPDoOySDX/0d2MM5kyy8RhDXXH+7fducLA/RW/K4O3JCiznA47vzjHcleTJA3nO3CgwUawzulBfZbBbEub96Rgn9nbxzlSZYsNhH0l+9YtHKTYcfu/lsdCZqYIjA2kycZ3+tInjBXi+5K3lQUCOG059606ZHBnMblhNFhERsR4h15as/HfIyZMn5dmzZz/w1/3+jQX+zQvXqNgu/SmTn/vEfk6OhPIZNwu1UIW14bYNXD5l8OSBPFXb5eXrC/gBxFSVZx7oZ3d3qGS6VU/D2p93ur5TUhpY9b3ju7P8T3/yBuML1qpehqQuODKY5QsPDJBPG8yWQ1lrQ1H45psTXJmrEwBxXZBLGJiKoOH5VCwPy+v8/4oCHOqL8/R9g8xVLCpWqH1kaAqaqpDQFJKmzvE9XZi6wsm9Xbx8bZFcXG8b7Ibr8eyDQ6sGBVVtl5Ll8vVHd7cltR0vaCeUg0Dyjc8eYnd3EscL+J2/vcJr48V29/RDwxl255Psyibaa52r2nz5oaF2QUFExEcZIcQbUsqTO70vOjFswPdvLPCPfv816u2dco03bhb4h588hKGKcJqbFHz2/n6GsolwiljD4b++Nc23zk+yVHMQIuz8PXdziV/94lH25JObzjPYqFN3s3zCRknpC1Ml5stNFAU0Aa4fhot8Cb1pg8mShaYIrs1V8WVA0tD5ycf38srVRS7OVsiaGmXbo+b6NF1/Q6cA4XNvFizOji+hqYLFWhPL8UiZOjKQmIbKfQmDXEJHQfDytUUUJQx5eUFAgMR2g7Z8yOlL88xVbeK6yhePDZAytXb4biMxQsv1GexK8DMDGWqOR8rQKDRcXDfYUl4jIiJiNZFj6EDN9vi//ubaCqcQUrAlZ8cWGcwlefpwL64vefNWiZFHUgD81fUFvj+2wPhCg2YQblyrTR/X9/nW+Wl+7sl9pEyt4ylgu526201Kx1QFsXybIgSqIiGAbEwjYWjMlm0KNYeP7+/G1FXO3Szy/dEipqHyyYN5vn1hFi+QNL0NRsKtwfZhvmrz4K4ss+XQqPelw3DV2GIDVQhiK96rKgQzZWvV7v8zR3rRVQXEshMSt53RukoxJKauoIowj6EKgedLbhUbOK5P05dYjouiCG6N2Zi6wkO7chvKa0RERNwmcgwdmK/ZzJbXD7oBGC00yCZiYcmnobWNMkC54XF1rs7K6k/LlUyVmrxxcwlVETyyO8s701U8GZCOae1O6O1KbK80kLoWGkUk63bBIz0p7h/McH6yTNMNG90MBY4MZVBbjxNhc5mhKTx1sCfUNKo3+faFGfwA/EDSYexCRxRCcbxbS3WSMQXLlcxVbKQEx/MZzMZYqDZ5a6KE6wcc6k3x+ngZU1eJaSoPDGV4+doCSEHGNOhPr3eOa3MurZJh2/OZXKzz8rU5bizYrDzcaEBP2qAnFWN3Lr5OXiMiImI9kWPogKmqJGMa0Fz3s8CDtyfLfGxvFwKxKjThBB6Npo8qwpDNSpu6VHf4wegiv//fRsmnDLLxGAOZGHXb4+ee2r/8DEmhthz28DdXFf3WW9Ncmq0AYYnnUr25qtomZWr8r1+6n9/47mWuzFXxg1AhVkqQUvAjh/K8MV7kv5yb4mBfGiklu7vjuL7E8wNAYm8SPlpJXIO0aaCqgorttUNhGVOn3AhzDW9OFLk6V0NXBPmkgQwCxhbrpMywdNcLAnrTMboTMfozZvjcFc4x/Pq25EirZNhxA67MVHnh6hzzZWfd4cYDilUHPI+/fMvmyUN5Pr4vmtEQEbEZkWPoQE86xlMHehgr1GmuCCeZGgx2mTh+wPfHijxxsHvVsJ5PH+7jj/9unLqkPQ9BAqoCDcfl6lwN2w2oNz2ODmos1R3emalQsV08P6Bu+1yaDaWyjw6k+crxXR0NWHcyRtJUeXxfvu1EOoWdjgxm+dyxfj51Xz8ZU2W+2uS5d+foy8Q5O1pksW7j+AFeEIZ2lupNZipNDE0lkJt0ti2jAZmERlcihut5oYH3JaYWzsCeKNrta2eKtzuwJ0pN3pqqogCH+5P0ZkzemiiRNDX29SRJxFQGs/F2iW654fDd0cKq3Evc0LA9n9GFetjU5wQbtmQ0gVkrACvg53//LM880Mf//uVjUdlqRMQGRI6hA4am8BMnh3n9VhE/CFisOGiqwNBU9vek0DWF3d0Jnrl/kExcb89GONCb4YFdXVyeLVNr+gRBACLcOV+bt9rPt90wFj7Sk8T3JY4b8Mr1BdKmypH+NB6hXEQr7LE2JxHuoEW7W1hT6Rh2CruvFXozOqoQzE5XUAS8fWuRqYqD4wUkDJWHdqU5NpTl5WuLJJaHDL14uclGyYWkBjFDQwJ9KYPGcq9ET1Ink0/w1kQJe42saydZjQC4OldnoWxjxjTu601xbCDD+YkSgQyb8FrlrWtzL88+OIRcnh8dNxS227vmB/DC5QWODU3x85/YH50cIiI6EDmGDTjYl+ELDwxwY6FOJetw7mYJhODmUp0fOdRDLqHTcDxOX55r72Qf2ZPj4/vz7Oo2KdU9JJKErvDnb86serYPLFVdVMXi+HAXhq5wbqzAy9cWqDZdFCHY35fi4d1ZksbyLOPlsNXTh3uXHcbGYaeWI1ms2lyYLKEoAkUIKraDFwRMVx3UZUPadH3+y7lpppZHbH72aC8Xp6pk4waWa+N28A11D+qeR29KR9FUumMKsyWbhKEy2GXy4pWtTxstAqDs+Fi+5PJCjYd353hwd5ZPHOohEwtLSjvlXnwpOXW0j3emyjScgIGsieXWabhbhL8EBFIwXbEipdWIiA2IHMMGGJrCj58Y5rmLs7w+VuTxA3lURRDTVMYXG3zpwUFeHV29k319rEg2oRHX01wJani+5ML0Qsfne0DWVJksNnjh3Rn+4vw0zfYuW/LWRJVf+pNzDHeZ9KQSPLY/B0Lj9OU5PnGwd8OwU6vk1fZ8LkyUGelJsFB1qDd9xhbqOF6AlKFzMjWBLwWBlMxXm/SnTV68vIiPRFEVEoZC2d44+7xQcxGEXeLpmMqlmQrji40dq2z4ASga9KQMXri6wCcP9fDq9cJyPE5St32s2PoO8Gw8yT85dYjTl+fpyxio1xYRvuT8THXD1wokKEiGMvGobDUiYgMix7AJA9k4zz44hOtLhrvCeHSrfl7X1I472Ud25/i9V0aBUO65Uu3QMkxo8x4a7sILJP/+5bEVTuE2E6Um06Umcb3Ma2MLPLgrRyBgumhxZCDLnnwirO/3w2E2q0pedRVVESxUHT420oXjBSzVbWYqNgLwgwBNVyGQxHSdvd0phIBC3UEAXXGdyULnyqyVzNdcHD8gF9fJxA0URSEX15irdX7fnYjrCroiAEHV9qjZLnvzqbYjqNsNKrbbsQN8Tz7JT358LxXbJZ+Mceb6IslCnbqzsUN76mCev3eic/4mIiIicgxbkonrpE0N15Or6ue74kZHLSPXk9iuJGko+L4klzWYqVvrnqsKwms9j8UNSmMh3NnXXKi5HnNXFlGA128s8gufOMjhwVCsr+g57cqdlrPy/ABzeVazL2Gx7lBsuBzqTdNsesxWHZpugK4qdMc1ZisWUkr25hPoqorvB7w7XcbfRrlqzQ5zHtWmjxcEmKpCXBObNsWteo9BgFQ1ZksWvpRcW6hxoD/TnsedNHWeeWAAXVU6dowbmkJPKsaPHOrhtbEl/A0WbQIPjWT4mSdGuDBVwvclu7oTHa+NiLiXibZMW9AqD224HlPlBoV6kycP5EmZGk8f7qViu4wX6lRsl4O9Sf7gzDgLVZv5io2iCNQNfK+hCSaKDWzXR2jbV/0MgKoLv/nCdf7z2Vu8fHWBN8aKLC53CreclaYqHOxNEQSSQrXJu5NlBrMmvWmTTx8d4MSeLMNdCfrSOm4A+YSOqijIAE7syeH6cts7ak9CrelhOR4KChXLRUhJbJv/d/kB2J6PUAQn9+SoWB5nri9iu37b4WZMfZ2q7EocL6A7GePh4Rx9qRhrP1Ed6M4avDFe4ad//yy/8Ifn+Nxvfo//8PK17S0yIuIeInIM22AgG+eJ/XnCIiPBmRsFZsvLp4Dl7lxP+vzd9QK6pnBsKAtCML5Ypz8d46GhFLEV4ey4JtiTT9CVNChZHk8f6L+jdf3tlUWuzVWRSP7dizeYLVttJzZXtTF0hW989hDH9+QIFDB1lUszZRZrTepOwJceHOD4njyP78+TTcT40YcGUdSwI1kRcHQwQ5e5tdPSVZDLDXG26+JJaPhsuzkuE1OIGxq9aYOEqeP4kivzVb51forJYn2deGDZcnFWPHy2bPHNNyd57uIsluuRTOiMdJsMJDWSSigz7gLTZWeVblTDg9/4zlWmlrYOmUVE3EtEoaRt4HgBr44W6EnG2mGj5y7O4voB+USM/rROodbk3EKJdFzH0FQeGMoyU7Y4tivLp+/r5a8vzPJ31+cpWx6P7u0mpitUmx4CSX/WpCsmKDZ3LmhoeS6D2QQly+H05Xl+8uN7V2kmOV7AcxfnMDWVXNygO2EwX20ynIvTndD5/ugSuqLgy4CDvUmODWewmgE/cqiHQq1JxXZxfAtNgXozYG3mIK4BCJrIdXmSDmmTdShA05ekdRhbqLNQaYKEXMrA1FSuzdXaTqCTllR3MrZKSiShq+ztqiGDgCsLNRwpsBobzaADR8JrYwt8tXvvDj71iIiPNpFj2AZr5Sps1+P1sSK+lPSlYxwbyizPMxbs6YozVbKx3TAE8vn7+9mTT/I/PGZgaAoTxQYxVUUBxheqjC1YvDVVxpOCpCbX6TNtxXSpyetjixzoS6EIsUozabZs8d13Znl7skRMVSjWm8QMFQT0pgz++p05JKEESNxQeWNiiSMDGW7M17k0DZOlBq4XEDdUBrMxypZHueFg6CqqkKiKSsPxiWkKVcvZ8doBMqaCDAIWqi4B4bS2mK6STegMZk3mqk3+6u0Zntqf58VrCwxlTfrTZruf4dSR/nWzKB7anSMIJCgKSw2b18fLm64hpkV/BhERK4n+IrbBKn0iVXDuVomEoWLoCgLBO9MVjg/nODqQJhnT2d+nIgM4dbSvLbedievkEjquF+PMjQLFusO1+RqpmEpc00jFdaqWQ09cULQDAhl2TS8rSG+I5wVMlizyaQNNZVUvw0tXF8iaOl0JA4HACwLuH8zg+pKlWhMvCI1+bypGLqHjS9AUhcVak5myTc32SJsqmbjGXMUlGVNIxHTyqRiqInA8D6Tk8GCam4Uao4vrJUS2omaHzXGtwFDTB8/3ubVYw5fgOA4vXpnlP7x8HV9KhrJxfuz4Lg72Z8JxocslrSt7OvIpg88fHeDP35zk1RsLm36GMUFbSj0iIiIkcgzbYKWAW7XqYXs+Tx7oAeCdqTKFWpPZqs2XHxqkPxPvqJxquT4fG+ni337vBgM5k5LloKsCTVEQLHcpC4FUBIf6kkigYrk0HB/pB1Q6NG7pQCKmkdAU5ioOj+zOtV+zYrlUbY/hrjjHhjK8Mx1KbzS9gMf2dfGt81MgJa4nUQWMF+pAKD/RaLrMLI/StFyfgYxJTBOkTZ2EIUmZKguVJjPFBg0fpiqFO/tcRRjKWYsPlB1JeUU/ggBiGkwUbf7T2Qn+wWN7yCQMGk2vY09HV8rg6cM9nB1fIqaJDXWffumzB+jLmlvOyYiIuJeIHMM2aY36rNih/IWphaWUI/kES3WHmKpw5kZh3ejIlXFxxwvYk0/QlzFxPcnNgkXd9bE8H78lPGdoFOou3cnwhDGQiTFfc3DrDk1XoimhQJ8vQdMEji+pOwHD3Sp96Xj7NU9fnuPidJkb8zVO7O3i+HCOsu3yiUM9fPvCDH/99iwCgaq6NJ0ATRXcP5jh+XfnVydo3YBCzcL2JI4foAhBse4wXdn56WAtnZzCRkjA9UA3JLYX8Pp4kf/5Mwd45doig9n4up4OCCfpndzXTanu8O5MmcoK2dt9eZOHd3fRlTKZWKpz5kZh3RyMiIh7lWhrtANa9fKnjva1y1cvTld4bF83e/JJErrGS1cX2snSlQ1n/WmTXFznVqGBCqRNjZShEAQBjhfg+hLLCRgrNlmoOVyZq3Njvs7oQoOYEk5T60poxHSVpBbuoG1PIgg1hRZqDs3lucsvXV0gEzN4bG83rh/wyrV5Gq7PqaN9fH+swK1Cg4FsnJiuUG54NJZ3yqNzlY4J43IzlPboT8eoWc33xSncCVKApirkEwa6qvDKtQJnby5hez6aopBPxkDQ7ukwNIXPH+1nuDvBZ4708eSBLvZ3x3h0T46nDvbx1IEePB9OX5pv/47W/g4jIu5FohPDHdA6PcxVbZCCweXd5doZCq2kta4KGo5HXFfZ35ui7vj0pGP0pGPkEwa3ShZISW1NuMgJwJCSajN8nhAKSJeaeztm7ro+xUBSslx++c/e5B9/6iBeIKk1Hd6eqqAoAt+F43uyZOIGlhMsS3soKAIShkqtGZa3Ws7GdUQ+MF+xqG5c4HPXCST0pDR25eJk4joj+QRTRYtzN4s8fV8vluO3hwC12JNP8o3PHuL0pXlcL+D8ZJkHBtPs603h+pKaE06522oORoso5BRxLxA5hjvE0BT60yamrqzrfm4lgOO6St12eWeyjKoKfF+yryfB1x7dje36KMD3xxbxC/V1aqQtak5AzQmIa6AL2oa5lVC1AyAIUIB3Z6r8n9+5xFMHe3l7soyhKShCkI1pvHp9iZ84kcD1wjkIpYbD2GIdLwi7sOO6QFUUNmp1rtgBle2McrsLaAIUBfIJg08e6mfJcjmxtwtT1zixJ8eZ0QKXZipMFS329yb59oXpVeGg3d1JTh3t4/Slee4bSHNjsY5cHrt66kgfr44WNvwdwm1nUG44vDoahZwiPvpEjuE90Gmq2MpmrJC1DWLhxLT5qsUrV+c5P1FZ1xvQCcuDlcIaa8PzAVBr+ixVnWUpC4mmChq2R63pISeLIMJy0J5UjKtzVfzl8aNxXeBL0D+kDbAqQMr1It+6AkJAylAxNJW9vUluFRs8vLsLTRHUnXAGxPHdOVw/4KkDPaTjOlXL5bvvzPK1R3e3R6meuVEgn4wx3JVguCtOyXJ59sEhUqaGoSkb/g7XihI+vDvXnhXRaQZGRMRHgcgxvEdaYaVO4QXL9UmaGk/f10vTC4hpCoW6w/W5Kr/9wlWuzte25RS2iy+haLnUbI+BXJyUoeH7EHg+CV0joau8XayzpzvOK1fDU4YHWJ5ESmjInTfYbYeUDiN9aXRVYb5s05eJcXmmguuHDsFQwV7+IFaWlrpBOI60YvsE0qdiueiaQuD73EiY+FJiqAo/fmKIsYJFOq5TrDd5Z7pCseEggS8eGyBuaOt6HRquj7/8fjf6HXYSJbw+X6M3Hdsy5BQR8cNM5BjeBwxN6WgcWv0PridJGtpyDF/y3LuzTC41sDsNO3iPOAG8PV3lwnSVmAqKopCN6/SlDIr1JjeXLN4YL1BdoT56N/OsuoB03MBqeniqSsbUCCTkkiaKlEghsZ2Apue1KyFWRtVWiqQ2fUnT83nl+hL/46cOMJJPYrk+1+braKqgarm8M11BIOhKGOTienuoTyfBw5Xhov+fvfeOkus87zSf76a6lau6OifkRAIEQIIQSQVKpIIlSrJlSQ5jWx7vnpHH3nHcmfGGOd6RjyfYM+Ndz4xn58jjPWs5S/JwJYtWsCgziBkgiUBkdDc6p6rqyrdu+vaP213oBrrRBbAbJKh6zsEBuqrurVvVuO/7fW/4vav9Dpc3Ni4XJay7Pg6rj15t0eKdQGups4ksF+CbKVlUHZfDA6lg1nFIv3Hn2ptEApYXlJtOFescG8nypy+PElJhqnDzGeT2yM0bwLgBmirIlm1yFZtsxWbBcqk7LpoiMTRB1NCImhpxQyBYa2ZcgL/4fXk+nBpb4NWxBc5Ol3h9bIHtmQgzlRoTC1Vs32V/X5K4qeP6Ek/K634P14f8rmctUcKFqtP0OVq0uBNp7Rg2mWvDFEXLQVUE/ekwJycWcNabOLZBzJZdBC7zhfpNpZA/sDuN68EH7+7hz18c4fxs84JzNRt0VSIE6JpKPKRRtl0qdY+D/SleH1tgtlQjbKiEdJWa664r8+0TrGZmSnW6UhF0VVCxXP7ypTEs12e6UGeh6rI9E8PU1GVDffQ1Q35rcW0OaUmUMBE2GudoVSm1eCfScgy3geVhioSps687zqnxIploiFLNplQPBtlvtouQrD57eS3aIyrpSBjL9YgYGjs74yxUHebLTlMCeS4gfIiaKroC0ZCG4/mkogZVO+j09iQYixVbmiqQUnIjX6kvyn7kqzavj2YZX7CwHR/Xh1hI4f5tbdRsyROnpviRw/185O6uN2XAb5RDWk3Ur1Wl1OKdQMsx3GYMTeGTh/pQNcFwtoKuCLa0a4zna1Qsl/pb3FfVbkJPWxx8CIVUPE/SHjWYLloMzVfwgWREQ1UEhUqw+6l5a1tyT0Ld9qjUPXwp0FWBkJIFyyUZ0dFUgeP6FG2blKmBCLSaHD+oSvL9YJegKRDWNTRNoArY2RVlaLZKzfaXlM9xPDg1UeSxA71MF2u8d3c73cnwmzbgq+UfViSmF/MWrSqlFu8UWo7hLaA7GeZn3rWNwVSE//zUZXRFkIwYHOxLMle1mVmoMldpZk2+8ezuTQVhHdtHFfDaeB5dDeZHVOsOqYhBWNfIli0UVZAwNdKKYLJor3o+H6j7wdiKouWQDhvkKg4hzcVyfAxNCaqFKiAUhS2ZKJmYzvnpMnFTRxCoCUohGGiLYLseuUod6S+OIRWgiKCs1XF9bM9jrlQjamikTYP5cp0nz86SMPUNNeDXKu62qpRavJNoOYa3CENTeHhPF5PFGtITqBocH84zW7FpT4TZ06MxXXCYL1RYWN3mbjjb2sPs7kpwfLRAd1zjWbqPzAAAIABJREFUtbESdTdwUCFNpeL4qMLHUT10NVhFdyVNitUbX6AvIawHpagxUycWUinXHcJA2fHxfImiCOq2w0S+hgAyUYPOhImqCMo1h7lynelijVrdxVBVyraLgsT1F2dC+EGfR6nqcX6mzE8d7edvT09RczzemCzw0I72xqjQjTDgyxPTa1U6tWhxp7JpSxshhCmEeFkIcUII8YYQ4guLjx8SQrwohHhdCHFMCHF0s67h7Y6hKXzkrh7CIRXHCYzcu7dnMDWVybyN5bh0JMMkQ82N/lQJuoRvleH5Gn/6wihnJgr8/fkshZqNqggMVWG6VKdW91BVgSoEni9Jmjrb22Nky+t7rroThF+KNRuhCA4OpCjbHjFDoz0WYndXHA+Fre0RupIhOpMmhq7wwb1d1FwPIQS+L6k5PuW6g+P6+Itht5oLtWUhuIlshf/8vcs8f3mOaCgQPHz1Sh7X9zfMgK9WcXajKqXVJs+1aPF2ZTN3DHXgESllWQihA98XQnwT+C3gC1LKbwohPgb8LvD+TbyOtzXdyTAP7cjwjZNT+NLn5EQB35eYhopwA9G4VMSgUF9fuM6DN53BbjTcLWbDF2pXW/A0AVJKUhGDvlSEwwNJTo4vUGqissonyDdUbJd9XTFyVZdkOBjjmQ5paIqgJ2nykf3dJE2dXMXm5eE82apN3Q3UXzVVRVc9HMfn4lSF1dyRRzBWtFp1OTFWIKSq3L+tjZdHcozna8QXZ3VvRLjnRonp5bSS1C3uNDZtxyADyos/6ot/lopvEouPJ4HJzbqGO4EluYbuhEkybOD6Ppbj05MM050IVs11V/J2CFAoEmxXogs4OJAkHQsxlas0fbyuBCGp46MLHBpIcqAvRWc8hC8lHYtyFYaiYOoaQggMVRDWVHRVENNVOuIGYU3gwKpO4VrmihaX58s4vs+RrWl+5FAfnzrcf1NGeb2VvqEpjYl5ax2/XGG3pd7a4k6gqR2DEGI38H8DXVLK/UKIe4BPSil/e53jVOA4sBP4AynlS0KIXwW+LYT49wSO6aE1jv088HmAwcHBZj/PHcdSEjMT1Tk4kGR4vkzNsanZLr2pMGemCpTrblPloZuNDYEKq+vRlQhxcbbMudnmHYMQAqGA5/sYqsLR7RlCYyrj+SoesLMzxkvDOQYzEUazVQ4PpulIhLgwU+S1sQWksHF9f92pdkvUfZgq1Hh5KM/n37eN9ngwp6HZ0tWNWOm3ktQt7kSa/Z/5h8D/ymIZvJTyJPAT6x0kpfSklIeAfuCoEGI/8AvAr0kpB4BfA/5ojWO/KKU8IqU80tHR0eRl3nksT2K2x0w+cnc3u7titEVDWG4QNpHy7bO6rHvB+M9L8zUe3tXBQMps/lhXoolA/O7JszM4jsee7iimrvLuXRkO9qe4dzCNIgT7+hL0pMJoisKH9/dwcCBFf1uEzkSEsN58ImVvd4KH93RwdrqE7fpMF2o8/to43zg5yeOvjTNdqK163Eas9G3Xx3F9QDYkzVtJ6hZ3As3mGCJSypeFWHFDNq3/JqVcEEI8BfwQ8LPAryw+9RXgvzV7nnci13bXRkIav/mJu4mENHLlOv/mm2exXEm9UGON6ZS3FU0EMheO53Fproxh3JyB86RP1fJ5cSjLyckC6bAO0uL40CyZhM6ergyelOzoiJLVNZJhHVNTeWRvF+/d3c43Tkzx+PFRLs6vbtCXowA7O6OkIwYzJYui5TTde7BCJ8n38ZFYjt/0Sn9pil7NDpxDxXKJmvoaCrwtWry9aNYxzAshdrC4gxdCfAaYutEBQogOwFl0CmHgg8DvEOQUHgaeAh4BLt7apb9zuFESMx7WGWgLo6qCuZJFxX5rdw+6CgtVm5kFixPjxZs61gdmi05DkqNedclXr64v5mo252am6E/oFGsOw9kKqqI05jh3J8O8Z2eG5y7NMpKrcSMNQgXoiOucniixrzsJEkqWg+X4ZKLrh3WWdnJThRqXZsvUHA/flzyyt4NkWL/h57Rdn6+/PsHwfLUxh2MgHeYjd3eTMNfOR7Ro8XahWcfwPwFfBPYKISaAYeCn1zmmB/jjxTyDAnxZSvkNIcQC8PtCCA2wWMwj/KCzWndtwtTZ35PgjCxStT0KNQdDEeTXmuqzQSisLmanK4AQWHX/pqQ1ltOMW5soOkCRn33PDiIhlVrdJxbSKVsux0cX+MQ9/SRMgxPjC9ieD1LiupKK7SGUYGxqXypM3ZWULJdLsyUysRDPXJzj1HgBRdCYqaApQfltoeascMqGpvDg9gz/8cmLqIuhn50dMZ6/nOVTifANjXvRcjg7XaIjZjbkOC7OlRvnvRVamkwtbidNOQYp5RDwQSFEFFCklKUmjjkJHF7l8e8D993shf4gsiSfETU19vYlKFcd8hWbP3tlfFPfdzXjnYmqdCUjeK5H2bKZKG7kJImVSKBgubx4eZ6edIRizaH6fReQXJqrkAjr3N2bIKSrXJot0ZeKsLMjRn+bwZdeHCMW0jEUgaZ62J7PVN6ivy1KTzKMIgUnxhbwZTAre193nCdOTV6XYLZdH6EEuY6OaAhDVdBUJUi+rxdOuibk5/mS+VKNrx4b5ej2Nvb3pm/KuLfKXVvcbm7oGIQQv77G4wBIKX9vE66pxTK6k2E+e99gY7X4+mhu0x3DalSrHgODYebKNhVr8wc/V2zJa2N5UpEQ0ZDG0FwFVRFoQjA0W+bSbJmBdJhH93XyyN5OTk8W8RG8f1c7F2bLzJXrmJrGB/Z0MJ63OD9dIhrS6EiEODCQ5JG9naTDBk+cmsRQlGCKnSd5+sIcD27P8MJQFsvxOTtRxOhP0ZMKN504ToR19nUnGJ6vYLkeT5+dJGfBs5fyKMADWxP83k8eaRj3G+0GWppMLd4K1tsxxBf/3gPcD3x98edPAM9s1kW1WMlag4BuJ1IBQ1cZSIdRhMd4cXXnEA9Baf1evHXxgVzV48zUAgPpCAuLI95SIQ1NVbBcDykhpKqcniySCBmEDZVESCduhijVHfZ0xTB1jStZi8vzJWzPRxWCbe1RuuImNccjW64zU6zjSYkqBO0xgyfPzdIeDZGJqiiCYIchJKamNpU4DnZ6vXz7jWmePDNNzgrkQHRV4HqSl0aK/NWxUX7h4V3kKvUb7gZa5a4t3gpu6BiklEsyFt8B7l0KIQkh/iVBRVGL24wvg8qg212htKUtwnt3ZUiYBqMLVYq1US7MrZzNkNBgZ2eSuutxabaMpkDlTWwuBGB7PjUn6Huouz4Llsue7jiVusd7drYzX7Gp2T5d8cBwWq7H+Zking8LVYeDA0kc10NZPJ/j+VieQ7Zcx3F9LkyVMHSVmKnhuJKLM2UODaQahrgnGcaX8MjeTrriZtPGuDsZ5rEDvbwylEUhqOQSixVdrie5MlehWFu/SqqlydTiraDZ5PMgK5tNbWDrhl9Ni3XpTYYJ64KSffs8Q8KATxzqoy1qUrM9DEXhE4f6GMlWuThTpFZ32dERZV9vmjPTBaQnKdQcIrrGlVyV+g1kuW+EBHIlm6ihM9gWAQGXZ8u8MVFga3uUl4ZzDLSFSUZ0araHrglevZInYmjc05fk5ESBZy/OoyqCR/Z0cCVXo+q4PHFimteuFBDA8FwFVwar9XTU4HB/mrrnU7Ic4mZwXlNXbsopNL63sM6unjji9Azu4rwJ15MIYEtHFATr7gauLWdulbu2uB006xj+BHhZCPE4wf36KeBLm3ZVLdakLRZie2eME+Pr5v/fFCqwvdPgH793B4e3dvDKSJ6ZkgVIjm5L89rYAv3pCO/b3YHvS+ZKFpqqoCCYqdp0xEzyVfuWnQJASIGQrpIr1+mMm0QMFelDeyyEqalIQFME79vVwfOXs5RKLpbr8dD2DOloiIejBiP5CkIKRuYrSCk5N10iX7VxPZ9k2KBYd0iaIfrbTEqWw7HRPO/aluaNiSI7O6MkIwaP7u28JUNsaAo/fmSQly7O8uJIEduTKMC7tib48SODJBb7Gq7dDVxbJbW0+5grWaiqQFOCSqeWc2ixWQgpm7txhRD3Au9d/PEZKeVrm3ZV13DkyBF57Nix2/V2b2sKNYcvPn2ZP3lhmOJtmOrzN//kIQ70p7Fdn5FsmVeG8yCgYgVVQlEzkMCqWB49yTBzZYuvn5hAIijXbC430YjWLBEVOhMhIqZGWyTE/dvSpKIhfuRQP6oI+jyeuTRLW9hsGNqq47KnK8YXnxliumiRrzgYmoJEUq171BwPRQRzJWq2RyykEQsH//al5AN7OulJhXlwR4atmdgtGWPb9Tk9mefMeJG7+hMrqpKurTja1x3n7HRpRc4B4OuvT/Ly0DzjhRqZiMGDO9r5zJGBphLYLX5wEUIcl1IeuenjmnEMQohVxYqklKM3+4a3QssxXMV2fb5yfJTvnZ3hyXPzt+U9v/jTh3n/3m4ef218RTy8aDl8ZH83SPj2mWkMRXBivMCF2RKO67O/J86fvLy5FVR72k1++UO7OT9TRQpJSFExNNHoMt7XHefURJHvX5xnslAlpCsUq4FTmy8Hg34y0RCJsM7F2TKJkEo4pDFfsqjUfbqTBslwiEwsMMYfubuLgbbohn6GJaOuCsETpyZXfsd1G9eDZ8/PcPxKnqrjAUHY61OHevm1D+9dN4Hd4geXW3UMzYaSnuBqdXYY2AacB+6+2Tds8eYwNIVH93ZRsVwm81XOz1TxCRKrCVNBVRRy1Y3tMfjVP3+Nb/36+1eNh+uqspgIlbw6WiBu6uzqiHNxtkSu6tKT0Jlao4JpIzg/b/FLf3ESQxOYmqArEebhXR386H0DmJrKE6cmSZg6D+9p57+/OoHleIR1hbrnoyiC9qhByNBwfYmuCHwJlu2SrwTChcNZi1iozpWsiirg3FSRX350F4OZ653Dra7al6rOCjXnuu94phSou56bKVF1fUxNxUNStlyeuTjPTxyt8eJwtlXO2mJDabbB7cDynxfDSj+/KVfUYl26k2H+4bu386P3DXBqLM83T00zW6mT0DWGchXqTulNVQNdS9WHV0fnAMiW6yTDOo4nG9UxhqZwdGsbx0byqI5AUwU/em8f5brHJw5285+evMx8qcrMKuNKNW5CdGsNfMCXEtsTjOer/M2pST58oJtdXYmGoQ0bKp863Mf3L80z0BZmdK5KZ8KkI2ZwV08SgO+dm+XCbJF8yVmhZms5EjSfct0lGtJ54tQUP/PAVmLm1dtnI5rQVqtAChsK0wsuxZqD70vqSDSFQKVWSgrW9c6kVc7a4s1yS/9zpJSvEvQ1tHiLMDSF9liID+zr5jc/uZ/DAynCpkZ/OoKmqsSMNzHKbRX+6NlhTo3neerCHH97eoqpQnVFdcyWTIwjW9o40JfkwR0ZooZO3NR4z64ufuzoIB/c37NiutzSP10gEVKIGYKUqRC5xSpMX4JE4kqo2B5/+MwQ8yWrYWgBYiGdo9sytMdMPnR3N4/t70FTVF4aznJyvMDurhiuJ3G8q9cnAN8HUw/GiU4s1HhjssBXjo81lFmbVWJtZrbDtVPh3rerg1Qk+C4BPM/H9UFToC8VJrWY42mpt7bYSJqdx7C8A1oB7gXmNuWKWtw0hqYQ1jUUIYiagSJpeyxEZyzEq6M55irumx3sxqmpKqemqvTFNQ4MZvB8SVs0tOIaHt3XydMX5shW7MaqOWYG3ccvXprH0ASuI1EWByooAkwN7t/aRtzU6U9HeHhvOy9eyjM0W+Sp89Pkm2yW832wfIkEIouy3N85M8OH7+ri2JV8o9TzwR1tvDKSJ74ohPeurWm++cYMh7ekiIU0XhzKYTkVkFD3gtkPSDA0KNZctvRFiZsGIU3h22/M8Ng9PTieT8lySZrBOVdbtTe7o7hWULHmeKRjIX7mXVt5/PVxZkp1pIQd7THu6knw7OV5KpZHxaq21FtbbBjN5hjiy/7tEuQc/nrjL6fFrVBzPKKmxsN7OqjaHtKXjOaqhEMa3akosVCdkZzVlIDdekyUXKzhLI7n89kjzorhN2FD47EDvXhSroizJyMGR7a3katavDJSQMgg/KNKMA2dHR0xFmo2w9kKD9POz71nGxdmijhSUHddzk0VqTses5XVg04qrAj95GsuLw1lURfzH+/b3Y6uqaTDBoam8MpIjmy5ji99jo0sMF20uDBdZntHlLipMZiOMFO0kFJi+5JdXRGS4RALNZvxhRoKFvlKnemixdBsifmyjeN5XI6Z3DuYwtS1Fav2m5W1uLbTXVMEfW1RfumR3UwsWJQdm3Q4REdssfoqtFgI0FJvbbFBNOsYzkgpV3Q6CyE+S6v7+W3BUmzacSUJU+fwYJpK3aVUd/F8H9eXG+IUlshWXU6M5hjOlmiPh1ZdDS+Xpg7rKqau0JOKsKvTZmiuBhJ0XSGsSr74/ZHGa791epoP7M6wv7+NkKaQjkRIhg1KNZtt7THKdY/vnxtnpuiztzdKZyLC0HyJi/PWimtcsDwEPnXH43f+9hx39yfJxAwO9CapWB6nJ7OM5Wr0JcPs6oyjKQpD8xUy0RAzxTq7u+K4viQeVrmnL40ZUjg3UWKmZGFoKpdmy0RDGs9enKMjYeJ6EkNTeX4oy5GtaR7d29Uw0G9G1mJ5g5vrS1JRnUcGO3h5JL9qIUDLKbTYCJotV31VSnnveo9tFq1y1fVZbpxBUqg6JCM6p8YK/P25aWbKG6+Gurczwh/89BGOXcmvWA1XHfe61fDFmSL/5anLIAUXZ4sMZiJULZenLmavO68AuhMh+lMhLsxVkBKEgA/u6eQXH9lF3fX4r08PMVmwMBTBZL7KSD5wDKqApZ66d29NMpyrMrlYFdVuwo7eBJ85vIWq7fHScJaYqbO3K8bQfJWi5dCbNDk/Hch0u56PvxhScjwPFcGl+QqqAMvx6IyHWLBcdnbEqdoOvckIOzpjfPre/sZOCoIdw7Wlvqt9RzdiecUTwFeOj6IJpVEI0Mz5Wr0OP3hsSrmqEOKjwMeAPiHEf1z2VII3X0zSYgNZHpt2XJ9vn5mmK27i9UpOjOc3xTFcnKvypy+NMJCKklnMN+iaoFQKqmiWG8ctmRi7OmJcmi0TCWksVBw872oAaPkcZwlYdZs3phw83wchUAW8OJzD/rvz3NWbYn9vgtmihRDg+FcXN8vXOa+MFlg+12jegvmhIpcmTrGtJ8VUrhpU9lTqhHSNeEilLRYiEbao1B2mi3VCuhLkL5xAGqMnEcL1JDNFC0UoGIqC7XkYqoovIaQpJK4Z5LMRshbLw0vThRoVy+PsdB6gMchos+dXt/jBYb1Q0iRwDPgkcHzZ4yWCec0t3kYsGQ/b9RvVOJ1xk71dScbmKpQ3eL6PJ+GlyznOhkvs6YrTkzIZmq/ieD5hI+i3WG58NFUhpKv0pcJM5GsYutIYCrR83yoA09AoVxw0VcHzgzCN7UmqtsfLw/Pct62N/rYIUUMjEzWwLkyTta7OkoiqsEp1LADZOqhzRUq2j+3AUxfmCekKAtjVGWVXZ9CHUay57OiM0REzODdVZK7kEA0FXdEd8RB1T9KVDGHZPh3xECB5dN/q8hk3mtJ3MyzlK3qSYQYzkaD3wVtZCLDWMa1ehxbNsp666gnghBDiz6SUrR3CHcKKuHRdsq0jwr3bMjxz6fqwzZvlzHQwmezF4WDWwJZMiH9wdCuJkLHC+CxPkNddH1XAdLHOvq4Y/+25sRWOoS9hEI8alOqBXIUigrp915cMz5eZLNR5dXSBsK6yqytGJKRx37ZOchWbqYJFIqzRGTN4+lJuzeueq/iN9/Q8SX1x93JstMix0WBkqaFCJmrQEQtRtDw6kyYH+5PUbI+5ksWe7gQj81XipmR7e5QP391FV2LtVfhGyKdfm6/IREPrDg9qSXe3uFnWCyV9WUr5Y8BrQojrkhFSyns27cpavCmWr1BVIcjEJgipPn93Pr9p7+kDw9k63zs7w8++eztuXTaMz/IEedQIVt1xU+NzH93PTz2wnacvzVCtOlgSyjWP77wxTUgVWG6QOLcdn2hIIVe2iegqEkmp7vLqaJ737Mjw8O4ukhGd752dZThboWT7a44oheuGrK2K7cGpiSKThRogyEQM6q4fOCtFYaAtwv7eJKPZCmdnihy7ssDpyeINwzRvNs5/KzLcLenuFjfLeqGkX1n8++ObfSEtNp7lK9RH93Viez7fO59ncydGw7ErCxzdsUBvKtowPoam8NCODE+enUXUWDH0ZmtHjK0dMQDKlstXj4/xuQe3cGK8wHC2jGX7dCdNHM/n4myFsB6I4JVLNo4P3z07z+nJEp++r599vQnOTBUoWB7tUW3NEtdm8YG5skMqpDA0X+LwYIqEqfPKlVxQeSVhorCYb9AEhqKsGabZiDj/reQrWtLdLW6W9UJJU4v//EUp5W8sf04I8TvAb1x/VIu3I8GI0AEEcPzyLN85O099cdkcEhALa9Q9n3ITiq2hxUFBazkYT8KJsQKfuW9wRcL0+ctZhBD4vuTB7ZlVjaInJbqmEA1ppCIGe/UkNdvlf3zPNl4azjG1MIq9OC/B8YN8hKEJCjWbr70+xX1bk9zdm0IIiUDwwtAc4wv2de9zsyzUfRZmq3zxmSF++GAP2zJRaraHqghy5TrZis2pcQVdVehKhChaV3WklvI+T56bQRMKmYiBszhG9Fbi/LeSr9ioHEeLHwya7WP4ENc7gY+u8liLtzExU+OH9ncTNlSO7OwiW7YoVh2eujDD5EKdqCFuGH5Zoi5BV0D6q782Zqoc6k+TCBvAyuRnJhqEMl4YytKdDF9noMK6ChJevZInbupEDI2SqnBupsTH7+llulDj/3t9kvriCLu4IVBVFc+XlC2H6YLFx+7uwQeeuzTP1AY4heXkqg6Pvz7Gh/b2cGG6RCKs8cZUkd3tMZJhg5rtcXqiQDQ0haaKxuq8aDkcG8kTDWmoQrC/N9BxutU4/7X5imtDVKuFrN4OI2Jb3Bmsl2P4BeAXge1CiJPLnooDz23mhbXYHJavHIs1m/f+7lON52yreeGMLW0mqqIyV6yRs666h4guODyQoiMZaoSRbib5aWgK929Lc+xKDtUJhtbcO5jC9iXJiMG/+Ph+Pryvmy98/Q3GCzVUTSOkKhRrdUAyX6zx5ePj9KZMhmaDxLgqlrSUNoaFmuQrr00SVaDiBxoxV+ar9M0UODzQhifB1BUy0RA12+PJczO4XhA+i+jBLffq6AL7+xPXxflvJQfRzEyHVmlqi5thvR3DnwPfBP4N8L8se7wkpVy75KPF25olg/PZP3jqls+xJRNje2cUz4eq5XJmqoiuKXQnwhwcSK7o/L3Z5OfWTIwjW9MrGrh8x228/sBgmt/+9H7+/bfOcWG2TMEJ+giObk0zV7G5kqsylq8hfR9VAc9f2SexUVQW/eGSWxzN10mYZXpTJlFDpWq7GKpCbbGZ4t4taU5PFPCkxHI9jm5tW2H8byUHcW0pasly+NILV3j3jnYyUb1Vmtrillgvx1AACsBPAgghOgETiAkhYrdrUE+LjWcsX+HCm5jz43ku3zw5Q1vMIKSrvH9vJ4mwzmMHesnEQiuM0GrJz4d2ZKg5XuP55SzNnHj6whzZ6lVBvmsH0vzbzxxitmzx7VPTlOoeSVNnJDeHpgliuo6uKhSqNlXboWIHs5ZNLdg9eH6QJ9lorsyVSEcMnrkwj6EreJ5koM0kGQ6hCcHBgWTwuaVgSybWOO5Wew2u3Y0pisD2fFQ1EBJslaa2uBWaVVf9BPB7QC8wC2wBztIa1HPHMpG79ZGbCvDaWIlwSCNXqWOogm+cnOaRvR2cmylyt0jSmTRXHLM8hFWo2jx/OXvDlfG1yVLgOlmJY1fyPHagl6G5CqfHi+SrdjBn2vXRhUd73MD1fO7qS5A2daaKddqiBq+O5rFdj4Wqu+EVWiUXPDzqtke17uL6kr5UmH09Mf78pTFsz8dQFT734JYVhvpWew2u3Y35vsRQA4cELRnuFrdGs8nn3wYeAL4rpTwshPgAi7uIFncmfW1hdOBm5/lEdLirJ8FkoY5EoAiB68NCucKfvFDhGyenMFSFX350B585smXFsUsG7ltDzU0cW54sXW26WbHu4EnJo3u7KFYdvv3GDCE9eL2pqxQqDvu6ExzemubTh/v5i5evcHGuRHssxPB8edPKdkdnS8ybFtMlBxW4PF9ioVbn3TvbUZSgKuvUZIHeVIREWF/R53GzvQar7cY+9+AWzk4Hgn9vRWlqS5PpzqdZx+BIKbNCCEUIoUgp/36xXLXFHcpAOsoje9v59jpzo5dLWusK3LclTaXuo6sKioC6K1moWNRcCOsKbVED1/X4d988RypscN+WDOmY0TjfRq2MlxvOZFjns0cGURQFQw3mTo/nq9iez56eGB++qwtPSoQQmJpGfzrMxZnim/0K12S85EHJI6QEoZ3JfJWXhvPs70kiEFiOy7GRPDXbJ25qjR3TrfYarFaKurcn+ZYY55Ym0zuDZh3DghAiBjwD/JkQYpaWiN4djaEp/M8f3cfluRe5lL1+3xDXQdVUKlbgFkxdIR02sFyJJyXv393OMxez1B0XXVXwgYihMl+0KNUdai786l+doD8V5p9/dA+P7OsGbr0Ld70mrURYJxbS0FTBB/d1Uaw5VF2PR/Z08MJQlpLlcmG2xJ7OGOW6S8zUsSvOhsqRX0vdB82XKCosVCyePDdH3NS4NFemJ2HSnw7juFf7Gd5Mr8G1pahvRWlqS5PpnUOzjuGHAYtAOO+ngCTwW5t1US1uD1szMX7ufbuZzNU4PVEIZgmrKu/f1cbfn59nNFdFyjqmrjLQFuGungSlustAOkxX0iQc0njm/Dx9aZPnL+dQhaRQc7EXR2OmIjrZap3f/94lDg+0NXYOhwdTvDKc35CV8RK5Sp1K3eHsdAmAfd0Jfmh/Ny8shq2SYZ0L0yW+d36OXYtd1ktlrM2+gv25AAAgAElEQVQSVqF2k/EnFxBesLPSFIErA4kPXQv6NcKGSq5WZ6Zk0RU37+heg5Ym0zuHphyDlLKy7Mc/3qRraXGbWV79YxoqF2fK7OqKo+s6/9tjd+FJyXfPzKAA4wsWluujCsFnjwzQlQhTtBx6khFMXaE3Gebx1yewvaAstCNmYOoqHpJy1eJvTo5zT1+K87PlYGaEkBzd2saWTOyWVsZL85OXdhqB4miEwbZokI+QPpGQRtl2kVISQ2N/X5LzMyWqTqCOWqk5ODfhGEK6iuN7N13NJIFt7VEe3NmG44EqBL6U2J7PXKnOqfECSIGpK+uGXt7O8fuWJtM7hxsO6hFClFi9/FsAUkqZ2KwLW05rUM/mUrZcvnJ8jFRYJ27qKwbJLJWIlusutuvz4bu62NF5ddLrdKHGk+dmqNl+MHjn0gzDczXiIQ2BZDJv4QJRQ8GX8JG7O3nsnv5bGlaz/D2X4tgzxRq+5yOFwuGBdOM1MyWLgZTJ//ndi/gyyH88uD3DSLbK9s4oz12cZ2qhxpW8dYN3uooKdCQ0qpZP0b75AFR3RPDwvj50XaFsOdiuz6EtaU6PFTi8JcVAOrrud7JZ8fuNdDatHMPbi00Z1COljN/o+RbvDDwpMTSF+CrD7LuTYR7cnuHJc7NEDI1jVwJZhxU3uwxq5jPxEP/k/bv4w2eGOD1ZoO74OEBEV0hFDIqWw3fOzPHA9g4ysdAthRmWx7H/4sXLvDBSbKxc7h+M8/Pv30PN9nA9yZ+/NIYiBFXbpWQ5fOfMNL/y6E6+dXqWjrhJ3faadgwdSY29nQlOjt2aOu10VXJ2Ksfe3jYMVaUnYWK7Pj6SkfkqMUMjHV35nVw7tW0z4vcbbchbmkzvDFq/tRYrQgCwsvbddn1eGMrSHg3RlwoT0TWevjCH7foNI50wdbZmoiRMnbmyze//xL383Lu38dg9PYQ1hbipYbkehqrgS5/JQm3VMMNSeMh2116RL8WxZ0pVXhgpBiJ6qkAT8MpoidfG8lQdl33dMSYKNZJhnW2ZCN2JEIWaw+tjRVwpObI1zU8+sIV7+uKIJr6j6YLLS0M5FLXZtNz1nJyscnm+gC99Ls+VaQsbZKIhBILTk0VKNafxnUwXajz+2jjfODnJ46+NcyVbvi5+v6S1dKssd7JdcXPF7/bNYGhBx3rLKdy5bNpvTghhCiFeFkKcEEK8IYT4wrLnfkkIcX7x8d/drGto0RxLFT9Vx2WmZFF13EZCeLWE4pJBWuu5uu/TlQzzwPb2RieuQCAJFE8NVVnxHsB1hnC6sHoD3pITOzdZQhKMEgUILU6DS5gqnzrcT0hXyZZqXJopMzJfYaZgoSkKPWkTXVF4Y6JIRFe5uzfF+3Zm2NIWZms6TH8qxN7uKG3m9e9d8yCkNuNG1ub4SIkXL81xdrqEJ3329yWRSLLlOtMli4d2ZACuM9gvj+RAsqrzvlVu9Lt9u9PMIqLFrXPry5/1qQOPSCnLQggd+L4Q4ptAmKDK6R4pZX1RZqPFW8xaIYD1EorLnyvVHBzXJ6ZraIogoms8dqCLb5ycpoaPqSn8yqM7+PSRQZA0ZiPfTJnjkhObWKgC4LgSU1caBuK9OzsA+OrLo1Rsn5pjgZSoCrxrWxvnp8tI4OJsie5kiExUJ1dVSUZ0NEXh0b0deL7kSy9cYan9b8U8aiHoS+pMFFZvDRQEN9WNGgfHiw57u3VeHy3wobu62JqJkKvYhFSF5y9nOTyYWqW6R3D/tjSvjS5s2EyFOzVZ3MpjbD6b5hhkkNUuL/6oL/6RwC8A/1ZKWV983exmXUOLm2O1Usn1+geWnhvLVxiaq7C9I8Z3zk43FD4PDraxvT1OR9Lgga3tqKrgybMzK27qsKE1XeZouz5hQ+Pz793J2FyJr52cxXICp/CZe7u5qy/FaK7CU5fm6YqbWJ5Pre5Ssl0UJVBrTUcMtM44UVPH1FR2dMYpWQ6nJ4tcmqtwaCDFo3s7+avjE9d9R6Yq0PQQXR7Mlh0kwU1k6ILaYonTekVLAuiMm1Rtl8vZMsOzFY5ua6MnGaZme7wynAchrzPYWzMxtmZiGxa/vxMH+LR6JW4Pm7ljQAihAseBncAfSClfEkLsBt4rhPhXBL0R/1RK+coqx34e+DzA4ODgZl5mi3W4UUKxOxnmsQO9fOX4GO/e2d6oajo7XeKxA714Uq6YEXCt3tHTF+Z47EBvUyvXa1eKv/HYfv7Rw3WOXclxZEsbd/WlAChUHKSEcEgjjCRharj5GuN5i7LtownBB/Z2IoTA8XziZlCN1ZkwGc/XeOxALx+5u5uXL88wvOA2DP09PSZSMdA1hfaogapUyJbr6Ap4vgxCWwLipkauduP+z7CqomsK0octmQg9iyveJad4dGsbr66xO9hIA3inJYtbvRK3h011DFJKDzgkhEgBjwsh9i++Z5pAe+l+4MtCiO3ymrpZKeUXgS9CUK66mdfZYn1u1Hi1VlWTJ2Uw/nKRtW5qT8p1V643WikuOYQl+tMRoobK1EINVRW4ro+uCu7qTZAMG+iqYGiuwt7eGJpQGw7JcSVxU2voF337n36IZy9M8crQAgf6E8xWXJ67OEfBcslEQ+SrdaaLdZaqVwUQ1gXtcZ1K3WWtYXi9SR3T0HjPznY0ReG5y/ONoUX+otPbkomxZQN3BzfiTmqqu1PDX3cam+oYlpBSLgghngJ+CBgH/vuiI3hZCOED7cDc7biWFhtPszfrta9bykmoQqyqprrUwLZWEnytlWLU1HhwZ4anzs3hS9B0QV9bmAe2Zzg7VcJyfSzX46HtwQ5nLYdkaAqP3tXHe3f38Phr46TCOn3pCD1SYjnBHOpLs5WggxqB40lcKdieSdCdMnlleAFNiCDUJUBXBJ97aJCZkkPU0IibOpqq0BEL8eyFORBcp7y69PfbubHtdnInhr/uRDbNMQghOgjE9xaEEGHgg8DvEOQdHgGeWgwrGcCbmAzQ4q2m2Zt1+euW5ySeODXZSCAamrJqcrEtGmo4FV0VFGoOIFddKdYcj11dCQ71t5Gv1YkbOs8NzSOE4MEdmaAz2pONruv1QilLTikT1dnfl+T0RIFy3cF2fExdpWp7eIsb3qrj892zMwy0R+iM6RQtn6SuoqkKn763jwP9KV64nMVyPGwvMPZz5Trv3dVOSFfxPMnZ6RLbO+J4UqIKwWiuzPOXc+iawNTUxvdxK46imRGgb3futPDXnchm7hh6gD9ezDMowJellN8QQhjA/yOEOA3YwM9eG0ZqcefR7M16bU4ibKgUag5Pnp3ls0cGgLUbuR7e3cHXX59YoYeUq9Svq0hZ2pmoimh0FO/rjuN6kmwlGPzz6L7OpmchL9/ppCMGhwZSZCs2dcfjxEQB75r/vS6QMDW6E3GyFYuoofHg9nb60hE0RWFnZ4wTYwvkaza+D9s7oqSjocbxF2ZKfPX4GFXb48R4nvFclYihsSUT4WB/mq+/PknUVAGxZlXOagb/nTQC9E4Kf92JbGZV0kng8CqP28BPb9b7tnjraPZmXcpJuL7khctZPCmp1F2ObkvTmQhjuR627XJxxiasK/iKoGg5tEVDREM6D2zPBCM/lymTrjcx7pOH+m64yl4ypKoQKxLma53v4/f0ULQc/vi5y5Tr13/G2YJFOmqgqSoHB9M8cldno9RUEYL/4T3b6IybmLrKE6cmr4bXLIehuTLv2tbGpdkyuYpDzfZIhHVGspVg8pzn88CODHFTw/Ou/w7W2nEtOVxdE+TKNl98dohD/SlqjsurV/L83clJfvHRXezvT7eM7g84tyXH0OIHG9v1KVpOo3chrAfKoq9eyTcS1p4neXkkxwPbMnz91QlOTRSw/WCrGQ8pzBYsfvz+ARCQWVxdawaNPAOwwuivtYO5kQZRtmwzNFdme0eUTCy0YgW92vly5TqDbSazlcp153R9h0zEQFUVuuJmo9T0SrbMyyM5XhtbaBjt5U7Hdn22d0QxdBXb87Edj9linemihetJLs+UgrCaqhA1NVQh6EqEVshorLbjenRvF64fzJo+diWYV3H8Sp6nz81Qsq9ueb57Mcsju9v4158+dMfsHlpsPC3H0GJTmS7U+PKxMU5PFDA0hXv6UnzyUC/3b0tz7EoO1Ql6C+4dTFF1Pf7m5BQXZwqNSh8fKNZ9vvb6JBN5i3sHUyRC+ookd6Fq862h60eFNrODWTKkhqowU7SIhXRminV6EuHrVuLLz/cvH3+dP3tlAmeNyiNNM7g4W+aj+7sbYSvb9Xl1dIFEyLguTLbkdFQheOLUJL4vQcJEoYaHj7eoWlu1fXTVZqpgcTiZpmZ7DM1VUEXQkb1Wkn6pS++V4RyqCiPzFYSQK5zCEn9/IcdfHxvnHz28Y9Xv707IS9wJ1/h2puUYWmwatuvzpedH+ObpaYQAVRE4rk80pPHDh/o4sjWNJgJdHceTTBVqHBvOU7mmbVgCclFjaa8dp1i3KdaD+PpDOzI8f7m5UaGrsWRITT0IIaXCOgs1e7HMVa5a9fTcxRm+9NIEigim2i13DnENknGTIwNpdvXE+ez9g7THQo33shwfU5e4vr+ismq5ttBDOzI8eXaWhKmhAEIoqKrE1FWEEKiqgu16ZCt1wrrKlvYI+Zp9wxGhCVNnW3uYrx4fw5OShZpDWFXIrzLgVAIjucqqn/1O6Dq+E67x7U7LMbTYNOZLdZ69NI+hKsTDOrbrc3G2zMH+NJbjcXggxfOXc1heYLx0VSFhatclcwEKlo9ZqnN5vsLHDvTQ1xYhrKtvuuFpyZD6flABVLbcIM/gyVVLbm3X589eGsMHNCGwr5n0U3KhLxRoDpUth6fPTnFmokg8ZnBPT4LjIzlCukLE0NjZGWsY8yWmCzWev5xFCEFnwmRvd4KRbBVPShQIjJ0q6E9HeNe2Noo1hzcmi3zv3EyjYmm1CjGA4fkau7riaEJQtVwq9tpNeN1xc9XPvpldxxuxym91Rm8MLcfQYtOwvGA1qqoCz5coisD1JIW6zVeOjXF2ukjd9mmLBwqoVdujNxVa83x1xyOiK7w2tsDOrnjjRn8zDU/LE8tdCbORY7B9f9Umu4mFKuW6gwCcNca/JQyNk+MLfOP09KrPt4dV7upLUaw5/NqHdq/oVVgyaplo8Fnu7kuQrdgsVB1qrkd7zKA3GWZfT5xy3eONySIH+1MNOY1rQ1NLRrZQC8JJ929t4/REgZ1dcU6ML6DAdeNNt2fC/NjRgesM6WZ2HW/UKr/VGb0xtBxDi02jM2YykA4zU6hTqru4nk/cVAlrCueni0wXLC7OFMkvm5fZlVjboAtFsKMz6AYuWg7tsdCGNDwtTyyvVpUEVw3XaLbC8HyVtCnIWSsdg7I4KvT4aGGVAM1V5msez1zKsrczQt29+srVjFp/W5R//akevn9pHl9KwiGND+/rojsZZqZkgZDXyWlcG5qCqzsjTQgODiSpOTHu25JmJFvhhctzzJUcFAG7OmP8zmcOMtAWve66N6vreCNX+a3O6I2h5RhabBoxU+MfP7yD//e5ESq2i64o/Nj9/ZydKjNZtJgt1lY4BYCZ4tomtStuMjRXaRjuR/d10p0Mb0jD040S1bbr8+TZWRCSXNWhM2ZS0lQ6Eh7nZ4NBP6qgEQJrVrT63GyVv3p5jH/+0X03zA/s602yrze5oiu85nikwwampjZlBA1NYV93nC+9cAXb8xsd1p842MvurgT5qk1YU/nogW4GM9c7haVzbEbX8Uau8lud0RtDyzG02FTuGUjzWz8SJ1+zSYcNDE3hwlwFy/EorCM0dy2qGgjVHd3aRiykr1hVbmbD05VsmWNXcuiqwmi2yo7OKCPZCr2pJL6X50quhkdQWhvToXgjze1r+M6ZaT5xqJeDA+l1jdpqXeFLTWrrGUHb9Tk7XeLdO4MZGb4fdFjv7Unyk0cHm3aqm9F1vNGr/BtdY6taqTlajqHFphMzNWLm1f9qH97XxQsX57iSvb7+/0YMzVXY253i3HSJ/X3JxlCZm73Bb8Y42K7PyyM5TE0lYmgYmsJorspAJsrBvhT3b0tzuD/Fl54foVR1qUuX757LNn0t2ZLF//Xd83zhkwcYzERpi4Z4dG8XCEiY+nVG7dqQy2oqtquxXNYDgia5mbLF+EKV/lRkhdjhemy0E96MVf5q19iqVmqelmNocdsZzET53z9+F//qiTO8cnm+6RV2zQ3KKPd2JXhlJMfurnijfr9ZbtY4BM1zQZ/Fc5fnyZYsZko1NFXhpFzgcw9u4a6+FL/64T18/fVJXhpq3iksfaY3Jop89dUxPnvfAM9fzl7XsVy0HApVm9lSnYLlNBr81lKxXU7ZcpkrWyiLjQw128NyXJ67NM+VXJUzE0X29yb45KG+po3kZqy6N1v/qFWtdHO0HEOLt4TtHXH+j0/s52snJvlP373QaGhbj4lslbrtoakK3YlQQ4CvGVG5WzEOS2GOv3tjku+eyzYqeE5cybL36JZGOKY7GeYfvGsLigIdcYO/PHb9kJ/ViBhQdTzOTBT5tjlDV9xsiAQ+/uoEEp+nzs9yYaaMriqoQuHj93TxwI7OdUMux0ey/JenLjG5YDWaCLtTJudmykwXLO7qTRI1NIbnqzx5bobP3je4rpHczFX3ZoYDW9VKN0fLMbR4yxjMRPn59+2gK6bzH75zEdvz0YTPXHVtLzFdtql7LvduybC9PYqqqE2JysGtGQdDU9jWHuZ7FwKnsLQ/KTnwrdNTdKbCjeM9KQkbGh+8q4e+ZIQ/eWGI2eqNU9GaoqIoCo7v40ofy3E5dqWI4/mcmSzSHtMZmqvi+5Ky6yFw+crxCcyQxkA62gi52K7P1EKNguUwkIqQr9X5za+dZnKhhusHn+N752f5xD19bG+PETN00hEDCMqJa7a/rpG8k1fdrWqlm6PlGFq8pRiawo/cO0jZ9rFdD11R+aNnLzFZWju+lK/5DM2W+MtXXN6/u4Pzs2Ue2JYhEwvd0FjdqnGYK9koIqg8WlSqAGC+bDOzUGscv/z8+/qS/LOP3c2ZqQVyxTrnZ8uM5Sp4vsDxJVIG59M0hbCmcnigjbCu8OroAhFDw3Z9JJKJBQvL8RFCQVMkuqbg+1B3fB470EvM1Jgu1PivT13iyXOzSAnJsMaujtiiAqxEUQWW4+FLOD9d4r5tbdRdj5rjogqFWt2jUncoVOym8hR34qq7Va10c7QcQ4u3HENT+NiBHp6+MEfZdknGTPKWQ+0GuYfhnMVwzuLkcJ6ju9qINmGsbtU43N2TREFgS8nyjIaUEDGuOhVDUxpyFqIGpqby8+/bSVs0RLZS529en0QImMxXeHEox4Ll0RbRed+udn7i6AAly+H5S1mG58v4ksUObA9fSpA+qqIi/aChLxEy8KTEdn2eODXF0xfmSZjB2NFizebFkRzVRY0kTVFwPB/H9XF8D9txKdcDFVddDbSmzk4XeOr8HO/Z2c5PPbBl1R3Xao4VJI7rY7vBLm+tWQ/XPvdW0Jrj0Dwtx9DibcHym3ZPR5R/9tWT1Bx73eOqwFMXc5Qtlx8/ugVT1264C7gV47C1I8Y/fGiALz472sgxhAQc3ZYmGbmqbLpczsL3JQ9uzzQMbE8yzCcP9QahmFCSrZ0x9nUnGEgHSq6GphAL6UR0jbb2EOmoznzJ5o3JBdojGtMlG/AJaSr7umO0xQxUIZgpWcyX6kHzm3F151Kpu6RMg5zlUHd9pIS4qaBrClszMfb1JJlcqPHXr02QioZIhg08T/LScI72uMlPHr0+33CtY61YDiB44vQkhYqNriqENJVs2UZTfWYKNgNtUXRNASRRU1+RVH8rDPRaeYzbWcZ6J5TMthxDi7cNSzfte3Z38e8+cw//4munGclaTR17bKxIxJzgI/t7eHRv1w1vuFtJcv76R+4mHArx/QszJCMhepKRRSG8oDFtNTmLF4ayDZVXWN8peVKyqyvOTNGiaLmEDZUf2t/Du7a38d03Zjk/UyJqqOzvT7K9PczXTozj+pKxfBXH88mV65hGsFJvixn0JU2mChaFqks8rNKbjHBwIEV8sYIpElIBiaYq6KqCrkKp7lC0ru64rjViS5+haDl8+/Q0vi85P13izFSRSt2lXLeZWLjq0DVgf1+Cfb1JHt79/7f33lF2Xfd972efcu+5fXoHMMCgDCobSElsIgmRapYsy3Jsxc+KVlyWlefIkeM49nLyVl7sFC/HyzFtR8vyenHsZ0UKxYiUREnUEyFSNEFQYEEnCGAADDAF02duL6fs98e5M5wZTMedAmJ/1roLM+eesve9mP3de/9aPbYrF7UJrfXAuZZurJPPKjgu0oNDuxvmjDJfb5QwKDYk+zfV8Iv3tfPihUFevzyOM21vfz56RgvsK3sIVZqAofHz924iZOqcG0iRKdnsbopNiVAyb0/tvzueh4dfF3r6ltZQskD3eJb26sic7qUhU6c2GqA5YU0FoZVcjzs31XDnphpSBZuhVJ7Xu8d56s0+LEPn7s1VJII6Y5kipbKdOxE2+P2PdfK+bXX84OwgRdclqOs81lnP693jU1tBuvATF7quh+16uOXQ7bjl18yYb8AMGL6QONLj8nCWXMlhPFtiIJkn78z8lhzgRF+KbXVRP+La0Dg3kJrXJrTWsQZraVCffFau6HDueppMwebVrhH+5eO72NUSr+izbhYlDIoNScjUMQ2NpniYTbVFhtNFio47NfjNhSk8Xr86zu6WBLCyPe2FZqtNiRCff2ArqXJCuukBaJP779eTebqGMuRtF8+TPNZZTyJk8vQbV3ny8KWpOs5fPNTBZw5umXH/6Vs1ji1vsIHELZPD5waxTJ1I0CCo6xzpGuVEzziRoE5r2N8O8qQkWXRojIf43AfaZ/QnHDBm2Fi++Nh2nn6zl76JApqAB7fX8eG9jcD8JVYn03dIz8+g2zWU9lcZzvzSfXUiQ0BvKtfqZkoYp9uEFnvmarCWBvW87TKYzvPSO8MMpYok8zZCSP7FUzn+8FP7uKe9tqLPuxmUMCg2JAFD49DuBs70JWmvDVOyXbIlKOXnVwYrqON6kq6hFKf7UjcEii0mFEuZrQYMjbrYuxlgpwvJB7bV8uThi+hlG8f2+iivXhrlQQRPHr5EOKATtUwyBZsnD1/i4R2NNCSsGfefrIk9mUJkesR43nYpOC6xoEGuYHMtm2M0U2QkXUBovtE5Z3vYJcmR88McaE1wR1vNVHryyfvPThh416YaxgslLF2nLha8YQUENw6YAUPjoR11fPN4Hznb9S3xC2BqOoOZAobwczbZrsTQmeEZth5eT2vpxqoLwYXrWcYyJT93mK4hy0GHf/NKN7uaEjO+7/VkY7RCoZiDTTURPnvfJv7Hq1fZ11ZFyXZ5+cII82VYqooGONuf5Pz1FHdtrqa5KkQ6b/PU69dIhAMYujbvgL+SLYXZQnLX5ir2b0r4OaF0DUPXGEwXuDCcxpWSaLmMadQySRcdusezNwjDQuKUzJU43ZMEBN1jeVx3sqSpxMUlPa329I8ujPLihVEe3VnD+zvqZxh+mxIhxrLFBUVwsvzqaLY4VV97csCcFMNEKMAHt9fx3Kl+qiMBkJLROYS7NWHyQEcdD+2op60qPPXsuTzD1jrWYC3dWF0paa8LcbJnAseVhEwNTdN8rzHPYzxfUsKgUCxGyfHoGs7yWGcDmiYYSOb40YWROc+NBwTVlsXdW6o5cW2C16+MsanOonsoy9uDabbVRniks55w0JpzwF/ubHUuIXn9yjiGJhAIDF2bcudsjlloQKZgT60YdCFor44ses/JtgIcvTzKHZuqeLs/RSZfYjhro+HNm81VAj+6MEZYN/nIHc1kSy4/ODvAE3ua+NH5wTlLjE72dSxbJFu0OTeQBmB3U5xP3tkyQ1DAj6vY0xInVXBIhAOY4znSBZuSIzGEoDkRZEdjnMujWY52jXJot75gPe71iDVYKzfWkKnTnAizrzXO693jaBoIAbGQQSRgUB0KrMpzV4ISBsWGZXbit2OXx+Y8TwPu21ZHLGSSydv85PKIH9w1bcTsGc1zqmecX36wg1gkcMOAv9wthfmE5L72Gt66NjHDnfPNngnev62GI12jpIvOlI1h9mph8p6mLsiVHAK6hlOUU9tAjiepjwVpqwrxg2wJEIwuElkNcKp/HFfzg/P6kzn6x/MMpArcs7kaU7duEMFJgWpOhNlcE6F/IsdwpsRwqsBPro6TCBrEggZF28VxPQxd0BgPoQl4ZEcduq7T2RDlRF+Sy8NZYpbB3VuqsQz9hoy4s1mvWIPVTMcx/RmHdjeQLfrV8y4PZ4gEDZoTFp9/oH3DrBZACYNiAzN7sD57be7Vgp+qQpIrORx+Z4KiI2eIAoAjYTTn8o3jPfzj92+5YcBf7mx1PiHZUhtlS210yp0zbpmEAjoP72xkZ2OcvW1xttfFbhCFkuMHoKVyJc70Jv2qd65kU42F7XhYpk624HDy2gTvDKZwPQ/PXVqCqd5kibH8IAFdB01wpn+cogN9E3k6m+LsaowRMN8tMTppywiZOqd6Jnju9HUm8jZffa2bgCEImQZBQ0MgiIdM2uvDaMIjV5JcHs2xuynOnrYEBzZX8+yJPpriQTwJpi5mCN18g/9aDNKrxWKutpMODJ++u41s0cFF0hC1NpQogBIGxQZm9mAdMHWYx/jcVhPmerJItujieXMPmB4wMFFgOFlkLFukKRGi5HiMpItkijZWQOfglmqSOZvaaJCayNxG5smBa7JtY/kitiO5v6OGkuNRcFzSBRtHejNWFGHLYG9L1Q2uqlO+7bbH6b4UpiYIWwbJfImRqwXyjkdA6IznCqTyJSayNq4nEMLPmLoYHpApgYZL3NI50ZPkwW31JEv+zPVk7wS/+aEdUwNZMlfixNUJiq7LsctjSOkBklTRgyLMLkX0Zk8SQ4MDrQl+5u42qkIBfh8P1hIAACAASURBVHxhmI/vb8HzPI50jU4J3da6MKl8iefPjL7n0l8v1dV20oFhuhPDRkMJg2JDM31rIW5I/vk3zt5wTrWlMZFz+I1HtvPfXrpEMl+Cwo0CogEBw8AyNZ492Ut9yOTrb17jTHea/BzPvm9zjF+6fys7GuOc7E3e8AfflAhxR1uCr7x0iQtDab57uoeoaRIKmOiahsTjgY56mqtCM7amZqeKOPzOIIbQiAZ1QgGdou2nxr48lKVnPMeL7wyjAboGrTUhopZOVSjM+cH0sj5LiT9LlxJ6kzk6mxK8b1sNedsjHgpQcjxSBZtvnein4LhcG8uRLTpomqC4SPpbx4O3epJkiw6/dH87QggKjp+yfOZ5kpcvDi9o31huvYyNEEV8KycYnAslDIoNz+QM/RP3tPOnL5zn8vi7fkkB4EtP7EYKyaa6CL/+yDb+0/cKDKUnbihyHw/qtFQFeeHtQa6MZMnaC8+2j11Lc+zaKaIBwcHNVbRUR2irDk2lqH7r6ihf+Ps3GM/PfFJbVZBNNREyBYcX3hlkT3Oc6qjJgx11HLk4wIvnR2hIhKgOBWiIBTh2eYxYyCRXdLgynGEkU8IydS4Ozypk5EHXSJ6gDgG97DlUnr3P3jqbC61cflQD+icKbKmNcvzaBFvrwiRzJZ7rGubKUIbnzw7SlLCImgYBQydVcJawLvHpHs3y9Bu9PLG3ESQEAxr3bq1GSogEDXrH8+RLHo2xuY38ywlw20iFd27W1XajCNwkShgUtxQ/+tcf5jtvdvPXr17jQHOMBzubsB1JznYImToHNlXz1//kffyPV7r4+2O95ItF8g7Uxyw214TxpOTc9dSiojCdTEnyUtc4MA7AroYI9WGD/+vbZ28QBYDBiSKRoMlopsjb11O8dWWEgA5/89JFBrMeQoChwbaaEEbAxNQF7SLMQKpAzva3oXom5k8FUnTBcaEmLInHQ1imYDhdYnyeUqmWDiUXDF2UPYUsSq4kFPQHMdeD5072c3k4y4XhNJmizdVRl5pIENdduiiA365cycF2PZJ5371W1wRWOa4jZOog5JxG/uXMujfaDP1m4iE2ksBNIuQigSkbgYMHD8o33nhjvZuh2EAs5Y9pKFmgayTNqWtJhAaXhrLlOIf0vLEQS2VrjcWVsaXlcVoIS4OICaPTYhCiJmSWUNVOBzbVhNA1gaELNCForw5xdiBNwXYpOR6hgOHv7zseaILaSICAodNeG+bBHfWEAzpXR3McuzxajswtMJgq4LgeLYkQo5kirvQH4qUUU0oEYUdTgkO7m/z+GRqXhrOkCzauB1963LdlHD43hND8DLST310yb/PcqX4aY+8a5gfTBX7qQMsNdpnlnLtWrGSALzkezxzvnSFwOdupmMAJId6UUh5c7nVqxaC4JVmKW2NDwqIhYbGtLsqPLwzT2aQxlivRM54lVVhiybh5yJZuVlp8Ch4UijOPLUUUwN9E6h7zrSNVQY3HdjVw7/Y6Ht3TRKrooAH3bK7mJ1fGSBVsro3laElY9Izl2d+WIG6ZU7EWI9kSNWETy9AxBJQ8KDgumiYwy6/SHHab2TTGLUKGxolrE5iGIKD5n/m1sSyZgsMffc9hb1sVwfIM+9GdtdREgiTzfmzHUmfdkzP0dN6eMmzfbDDczW7nrMTVdqPWuFDCoLhlWapb4/Q/2A/uquffPHuao5fHb+rZsaDBUKYy4lAJJooe3zw1wDdPDWDp0NEQ5Tc/tIOOhhitNWGQkMyXePniCJGgwdm+FPmSS200yEM76jh+bYJcyUXX/EhqXdcIGgYNsSBv96VZIA3SDLqGCwymbO5pr0YTOq9dHaE/+W621f/vnWFevDDMY50N1ESCPJUt0VxtMZlpdXdTjHMD6Rkuw8CUcLhSTg3+m6stnnqzF9cDQwg+c7BtzsF9KQN+pbZzlutqu9AW1HraHdRWkuK2o2csy3/+7tu8eGGY3CK2hkRQI1mcubpoigV5YHsN//v49dVsZsWwgLoQxKNhCq6HpnnsakzQXBPhZ+9qw5Eef/bDC1wcTFO0PUqeh6nr1EUD9CeLOLZN1uYGY/58RAMa2+tj5B2bK8O5Obeg2hIWH9zVwNvXk3zmnjYa46GpbZSP72+ZSjZ4dSzD0a4xxrIl3hlIsrUuQsQyMDWNyyNZNAT1MZPesQKZok08HKCzKU5tNDAlKosN+Ku9nbMYc4nSUtq9FFa6laSEQXFbUnI8esay9E/kqY8FGUoV+G8vdXG2d4KiA1saAvzeh/fR2VzF5aEU3z7dT77o0l4X5dHOet64Ns5//O759e7GhkQDLFMjEtQZzdhzCkrY1OhsjpEvufzqQ9uIl9NBTNoJ8iWHrx+7yvNnBsjkSwykbd/xVfilS5sSYVqrgoxlbbqGMuia7wrbWhViW12Uh3fWU/I8kGIqyHC+AX8j2CtmuzBXSqiUjUGhWAYBQ6OjIUZHQwyAzuYEd2+ppT+ZAwktVeGpaNSW6hD3ddTPWNbva62mWHT4kxcurWc3NiQeULA9NCCowRyOWwRMga5pGJqH6/mT03TexnY8PFfy31+5xDff7GU8584Ip9MkpAoO4UCJVL6EYWi4UmIKjVzR4dpYnoFkERBsrQ8TDwVojPsD/nz792uZYXU+pm9BLZbZdi1YtacIISwhxDEhxEkhxFkhxP896/3fFkJIIUTdarVBoVgOUctgZ2OcnU3xG1IUBAyNROjd+gsBQ+NzD3TwYEfVejR1w+MBGdubHd8G+N5UB1qr2NUY48Ed9WRLLhcGU7x8cZixXIm/+vElnj3eT6rgzukq67jguJJw0MBxJJ7nF0XSNIGu+W6519MFukdzmLooG9iZd8CfjGLP2Q6D6QI521mT5H3zMV2oYP52ryaruWIoAo9JKTNCCBN4RQjxfSnla0KITcDjwLVVfL5CsaqETJ3H9zXTN17kythcsdOKvAsBAU2JINmSg0CjPh6kPmbRXhdG1zQ+tq+Zr79+DUMXXBvJcapnnGTeRuJHe8/OcKIB8aCBqQvqokFils5Ayq977UlBJGCgAe11Ee7vqJ1KarhQ/qv1St43yWxD83pkmZ3OqgmD9I0XmfKvZvk1OQH4U+B3gG+t1vMVitUmYGh8eE8zA+MFnnqjh9HcjV5KCUunOmySypcYm2tP5TagJKF/ooiug6m5SBkkmSvx3VPXebSzgW+e7OEnl0eYyNtcGc4wx8c4hQBaEiYfv6OFg+1VHO9JcuLaBBM5m4Ch0ZIIsa0+ghCCxpg1ldRwKQP+WiTvm8vTaD6PqPUUqlW1MQghdOBNYDvwl1LKnwghPgn0SSlP+knA5r3214BfA9i8efNqNlOhWDFNiRBfeqKTj+xv5tnjvbx6cYj+ZJEdjTG+9KGdxEMBXr86jgZcT+W52J/i8liGQrZE0oaihIghqIkGGUgWWEZA9i2Fg78FVHTh0mCGbMkhEfbnim9dmeDNaxO4rpy3/3FLZ1tdmPu21vKpu1rZ3hAnYGjc015HKm/TPZLhh+cG6Rsv4HjSr8e9u2HG1t98rJVb6FwCUBMJLhjBvV7bWasqDFJKF7hTCFEFPCOEOAD8PvDEEq79CvAV8L2SVrOdCsXNEDA07thUze7mBKnCTpAQn2aP2N2SmCql+d3T/QynC7xycYRk3mYib9NRFyFuBdjR6JAqOPSP5+hPlRZ56q2LDXieR77o8g8XRkAIDCEpLPBXvqspwoc6W/jYgSbftbWcujtgaDiex6WRLC3VIepjFvd31EwJx2JUMh3FQgIzXwqPQ52N625onos18UqSUk4IIV4CfhrYCkyuFtqAt4QQ90kpB9aiLQrFahEwNOqiN6ZSnj7zm9w7fnxvEyXH49Fd9dRFLRCQKzo8f2aAU30TbE4Xee3KzQXhbWTGcjZVIb+UZ9QycBcJqu4by5Ir2XznZB9B0yBTcAiaGk/saeT17nHCpkFtJEi+5HK6L8X2hviibahkvqXFBGa+CGfE2pczXQqrJgxCiHrALotCCPgQ8EdSyoZp53QDB6WUc1dgUSjeYyy4dxwN8vkHtpLK+wPGsUvD/IfvnKIv895bMNeETCYKNtmiQ6Rg4CxifulPufzNy5ewQiYmHumCiyfg+TP9HOpsYndzAljejLtS6SiWIjDzucTGLXPdDc1zsZorhmbgb8t2Bg14Skr53Co+T6G4JVho73iyiAvAx+5oIxIw+eW/feOmk/5tJHTA9iBhmYQtEyE9lhJDnnIglZ6ZSOpET5rr4zl++yN7qQ4HljXjrlT8wlIEZj5PI/98Yyra+z2fdltKeQq4a5Fz2lfr+QrFe4Gt9VFiYZ3xJdR2vlWwTEFDIki24NIQC9BRH2U43cvEQkaGBRjMuPz5C+/wc/dtoSFmLXnGXSm30KUKzORqMVWwyRUdXusa4silcdpqwzTF/XavV2bY2ajIZ4ViA9NUFeJDnY18463+9W5KxXA9yWCygKlpeFJQsiWt1WEy17MrXhldmyjx2qVh/v1PH1iW8bgSbqHLEZixbJG/P9rN149dZaQs9rqAQ7tqATZMxTclDArFBiZgaPzLD3eSKzl878zQsormVIL6iEYq71GsYAhGPGSypyXB1dEcg6kCxZJDcyKMlDCWsRnIrMwj63qyxAvnhkiEAjO8whZjOW6h83keLUVgSo7HD96+zksXhqdEQeBX1Xvxwih7N9WsuzfSJEoYFIoNTlMixJ/+wj380tVR/vbIFb7/9vCaPbulKkimWACvMpIU0v1bFUoOB1ri9KcKZEoOuqbxM3dv4nTvBP0Tea5N5BhJzZ2Abz6yhSJHL42ga4KYZUx5BlUqTmExz6PFBCZvu4znbN+5oIzEFwfHg+vjuXX3RppECYNCscGZHNju3lLL3Vtq+eylYf7kh+c505vGBfY0hviL/+NebE/SO5zh6dd7+f75yohHqgj7WuMcv5Zcck2GdytR+wjAEBAOGuxvjfHOQIbryQLdo3laqixcV1K0XT9Lqq5RdCSG0GirtijYDrmiR8n2WGgdYQkImibpgkM8ZGJqgh9fGOYD22o5enn0puMUKuHaOpIucL4vTWFWkafJj/VQZ92GWC2AEgaFYkMz1yz14V2N3L2llqFMAUvXqYsFpwaUXY1xHups4p8Ppfjvr1zh6ZuwTcQ0iAQMOhri7GqK8Z0T13GlxPX8gXIuc7gADEMQNQQafjI9x4NEyKQuFmQgWSQS0Cm5Eld6jOdt4pZJzDIxdQ0JNMSD1EVN6uMhkLCjMYqL5L72anpGc7iOxzOnrjOeLdAzliVqGmiGQSRo4HoeR7tGCAU1asJB0gWHxph103EKN+va2jOW5csvXcIwBNURk6Hsu+KgAVtrAty1pX5ZbVpNlDAoFBuUhWapuaLDSKZIe3XkhoEpYGjsaaniP376Dn7r8U6eP93DH33vIsV5njMfaQ/qokHu2JTAcyVn6tOYuqB/Ikdfcu75uwYYQtJWHcEydUxDJ1900XUBUmJ7kr2tCd4ZSCGlTjxksLk6TCJscvfmKnRNYzRdxJMQCxkkQia2I0kVS2yujrKvpRrwk/MhJGf7UriuxHE9zg+mmcg59IzlGMkUMXXB5poIj+5uIBQIEQroDKcLdA2n2VwduSGD7kLcjGtryfE4fG4IXRNsqY1SGwliGmMUijZV4QCN0RB3tFcT3yAeSaCEQaHYsMw3S/1fx7r563/onqpy9sVDHXzm4JYbrg8YGi3VIf7pwzt5fF8rh88N4SJ57ngvx3vTS2rDlmqLN7vHKdgurucRDZhcn0cUwN8WsV0olRw0oZErFYgEA3zqrhbSBZcfnh1ASolEEA8ZBA0d6UlcVxI2DWxXErEM7u+o5dVLo4xmS2QLNq4Hz57oIxTQONTZyKHdDfz4wjBt1SEuD2eJhQxGs0WEgPMDRSQS2/FIFxwuDWf5hfs24boeh88P89a1cSxT53Mf2MKBTdVL+hxuxrU1b7sIDaxyuc6oZdJeGwEE+1sTRC1jRl6njYCq4KZQbFDmKjk5kMzzd0e7iQQNopZJpmCTK7k8/ev305CwFr3fpBH2+yeu8ptPv71oG/7ZB7eyozGOpgkGxvN87Vg33eMLrz0iJhiahhXw553b68Pc015LY8Kioy7CV3/Sgys9JrI2tdEgrifZ3xonHg7MsAOUHI9UweapY9foGc+j6wLXlWytC/P5B7YB/qDruZKvHuvmhXODpLIlBlIF8g5owk++Fy1/VrrQ2NkUpaUqTKbgkCna/LtP7ptaOSzFSL0SQ/bk91iyPbqGM2WRlXzhkQ7qYtaqBrWpCm4KxXuMuWapbbUhPCBq+dsOUcskXXToHs8uKgzTvWZ++uBW9m2u46tHr/HM8W7GCzeef3BTjMZEmJhl8lb3KF9++cqS3GWzNoAHwiVqGUzkHS4OZamNWtRGgxxsr6EqbKILgSslEzmbj+9vxjS0GYPk5L/nB9NUhQOEAwauJzk3kCZVsKmL+raVZN4mFjJpS4Q5OprDLTfSMjU86ds9bMfD0/1nJUIOUctgLFdkOF3AlUFS+RKvXvKN1Ei4d2s17bXRObfpljuIT/8etzVEkB4c2t3ApprIsu6zlihhUCg2MLP94yeyJXQhyBTsqRWDLgTt1csfZDoaYvzux3fz2fdv4jsn+nnuVC99o0VqowYfv7ONz93fzquXRvn6T67wwvnRZd8/WXApOi4aEAnoWKbGse4xDE2gIbBMnXTeRkqJZepz7vkPJQtcG8sxmi0R0DWaymU6pytUyNSxDJ1NNSGOXAZNEwhPopXT+ntAxDIImjquK7k6mqW1OoTjSl7uGgKpcbp3gr0t/srobF+KN66OcbC9mkOdjSvOtjqd9a6vsFyUMCgUG5zps9SGhMUXD3Xw5OFLpIvOlI1hsdXCQvfe0RjnNw5F+dz9W7E9D1PTpgLEtk3kVyQKk9gOjOdsciWHSEBnIOVwb3s1b12bID1q0z2cpa0mzLdO9vLwjnriocDUwFlyPI73TLClJsxYzqbkunQNZ3h0V8MMQ23A0Li/o5afXBqlOWExlC5SFRak8g7hoIFA8NF9zegCDr8zRDrrEDR0OurD1IQsPCRFx+N7Z677NoCgQWM8hCG0FXsxzfdZb3RBmEQJg0Jxi/GZg1t4eEcj3eNZ2qsjKxaF6UxP3jed66k59piWge885DGeKfK9k9cpei5n+5JsqY1wtm+CUMBgLFfi8lCGH5y6zr1ba6mJBTjU2UgoYICAB3bUc7JngpLrkSs5vH9bzQ3PiYcC3LG5mjs2V/HW1XH6J/LkIi73bqmmNmrRWhWm4Li0VoVJF232tyUAieu6DKSLdA0mGUzbCM1Pg53KOzzWWU+y4CzZJXWtCv6sBUoYFIpbkIaEVRFBWIytdTe/D1504Fj3BEHDD3K7s62aqyNZRrMloo6HrsGrl0co2B7dYzk6m2NkCw7/+H3tGJrAMnQe3FHH1dEs71xPc6o/yYWhzIxgtZDpb1WFTYOP7mtmNFskb7v8wr1byBRtDp8b4o2rY1iGzuPbGjF0jadev8r1iSKOlPROFDCAkAl60GA8V6RvokDU0pfkklrJgj8bgVtb1hQKxaqyqynBQx1Lc+mcj5L0Vw45B0ayDi9dGOZEr++KmirYvHl1Atv1CBo60aBJ93COU71JCo7LB3fWk7MdBlNFLg5muHtzNa2JMGHT4McXhimVCzlMGnhztsNoroSha/zUgRailkFTIsSH9zWxtyXBB3fWUx0JIqSkeyyPpgvG0/6qyAHSNgxlHEbSRd7oHiVbcBnLLuyFNT3epDFm3dC2WxG1YlAoFPMSMDT++B/dxX949jTfOXdzaTYEvs3YlZDMOTTELbrHckgPbFdimRAwNXK2Q8FxQb5rtB1MF0BImidXCHNEHs9l4J3c3rEMnZjlx0kYOlxPFdGFIByA7Bwh3AWXchrs4KJ2hkoV/NlIKGFQKBQL0pQI8a8+sZcjl19ibLnh09OQ0/51XA8BtCZCTORKCAGO49I/nsf1JBqCXMkBfJfUxpiFZfheTJPxDHNFHk838M7e3tndFPNdXYs2gYCfaO/yYGbOtgpgNFOiLmrhFOWCg3ylCv5sJG5NOVMoFGtKUzzERw60Vux+qZJkNFukOmKwvSFKJKBTcEEIuG9rDQ/trOfo5dEZW0W7m2IcuTTCC+cGOXJphN1NsQUD0WZv75wbSPPE7iYe3lnPp+/YxIf3NDBf/SMJHOka4u9evcKVoTS24827NTR9G2swXSBnOwtGRZccj2TeplS+5+TPGwm1YlAoFIsSMDQ+eUcr//P1vordcyjjkOtJsr+litaaMNFgkb2tVTzW2YBlGgymC1Mz9ZLjcW4gzQPb69A0gVcOdOtsTsw5AM+1vdMznuNbJ/tIFxyO94xxZWjhtCDHrqYAeO3KOOcHJvipOzfPa1ReapzC5Cqm4LgksyVMXSceNjecwVqtGBQKxZLIFCtbedoQUCh5OFIS0AVRK4AmBJ7khu2YVMEmXXAImTqRgEHMMnE8f4tnOpMzcF2Iqe0dgHTB5vJwBsf1ONI1zDvX01xZwr7YpF3ktStJ0oXSgkblgKGRWKBA0OQqJld0ONub4qULw7x4fggNNpzBWq0YFArFkhjP3YSBYQ4kYApoSVi0Voe4OpojW3QYzhaJBvxEennbzw/1DxdHONuf5NJQhrs3V2GZxg37+LNtCtvrI5zuSyHy4HmwuS5E13AWD7nkSngCf/bsejA4USDUYK7YqJy3XQbTeV7rGsOTkCm6xC042ZvkkZ31i9oy1pL1b4FCobglaIxVNm5CExALm1imQc9YnuvJAjubYmj4g/qrl0Z59kQvTx6+iCcl93fUAfDq5VHG8gXu3lw1da/ZNoWS7fG1Yz14nsTz4OEddcSDARxX+rWml1iRTsJUFblEJHBTRmVdCLoGfGGqCZtoQCrvIqUkmbc3lMFaCYNCoVgSjdXhit0rYmpsqbHY25JgS00IAXxifzP7W6uosky+dqyHgK5RHQqga4KuoYxfrnNXPZuqQ9gOHOse55njvQwk8zNsCo7rZzHVNEFtLEhdJMgbV8d5eGcdIKkKBQgaOrWBxdspy6/N1QGqI4Elp9qeC1dKdjRF0RCkig61kQChgEam6OBI76buXWnUVpJCoVgSrYkwd7fFeGuJtRzmwtRge32UfW0JpBTsb4vz0M56IiGD1oQvPLouKLkemiYI6BqWqZO3XYqOh+dK+icKbK2LErPMqeJFH9/fMmVTkEgKZSNw0NAwNI1U0aY2avHFQzs4/M4QOxoinBvI8FrXIPOVlxDAzsYIm2oifOnxHexoiN/UwB0ydRpiFo/vaeT8YAbX8wsM/crDW2/63pVGCYNCoVgSUcvg3/30fn7lb44ylFteHRcNv2RnbcSksylO0NTJFh3u76ilrSqMZehTcQCuKwno/naPEdDYXh/lZO8EEzk/E+u2el8U4N1gMlfKqdTWBcevd7C9IYqhaTMM2YnaCL/4vi1TdRyePdnHuZ4x/veJgalSpZMGZ0PAR/e38Om7Wtlce/OpQaan397dHMeTkkOdDRW5d6VRhXoUCsWyyBQcjl4Y5N8+c4KB/NKuua89QXUkyPu31tJcFSKZt3Fcyc8d3ETA0OYNRpv8/f6OWuKhALoQfPd0/4ziRTnbmYpMnox0TuZKHL08umjuosnnvnpxiOfPDlIsq0NQgy8e6uBXP7iz4jP5tUy2t9JCPUoYFArFiig5Hr/z9bd49szggud9eHcdH9jewEM76njj6vi8g/XsAXO+AXSpCeuWOgBPnpfJ25zsGafoONy3tZ7WmsrZVNYLVcFNoVCsKQFD43c/sZeLoxnOXs/Oec7upjAPdzZOFbzZVBOZd7CeXa9gvvoFSw0mW2r9g8nzEiHzPSEGlUAJg0KhWDFNiRDP/J8P8/qVEf7sh2c5fi2HDdzRFuGje9t4bE/DjBKZlSpWcysVvbkVUcKgUChuioCh8cCOBh7Y0QC8twrW3K4oYVAoFBVFzeZvfdS3p1AoFIoZKGFQKBQKxQyUMCgUCoViBkoYFAqFQjEDJQwKhUKhmMEtEfkshBgGrq7Bo+qAkTV4zkbiduvz7dZfuP36fLv1F+bv8xYpZf1yb3ZLCMNaIYR4YyXh47cyt1ufb7f+wu3X59utv1D5PqutJIVCoVDMQAmDQqFQKGaghGEmX1nvBqwDt1ufb7f+wu3X59utv1DhPisbg0KhUChmoFYMCoVCoZiBEgaFQqFQzOC2FAYhxM8JIc4KITwhxMFpxx8XQrwphDhd/vexOa79thDizNq2+OZZbp+FEGEhxHeFEO+Ur/vP69f65bOS71gIcU/5eJcQ4kkhhFif1q+MBfpcK4R4UQiREUL8xaxrPlvu8ykhxPNCiLq1b/nKWWGfA0KIrwghLpT/f//s2rd8Zaykv9POWfLYdVsKA3AG+DTw8qzjI8AnpJT7gX8C/L/T3xRCfBrIrEkLK89K+vxfpJSdwF3AA0KIj65JSyvDSvr7ZeDXgB3l10fWoJ2VZL4+F4B/C/z29INCCAP4M+BRKeUB4BTwG2vQzkqyrD6X+X1gSEq5E9gD/HhVW1hZVtLfZY9dt2U9BinlOYDZE0Ip5fFpv54FLCFEUEpZFEJEgd/CHzieWqu2VooV9DkHvFg+pySEeAtoW6Pm3jTL7S9QA8SllEfL1/0d8Cng+2vS4AqwQJ+zwCtCiO2zLhHlV0QIMQrEga41aGrFWEGfAf4p0Fk+z+MWipJeSX9XMnbdriuGpfCzwHEpZbH8+x8AfwLk1q9Jq87sPgMghKgCPgEcXpdWrR7T+9sK9E57r7d87D2LlNIGvgCcBvrxZ8//z7o2apUp/18G+AMhxFtCiG8IIRrXtVGrz7LHrvfsikEI8QLQNMdbvy+l/NYi1+4F/gh4ovz7ncB2KeWXhBDtFW5qxahkn6cdN4CvAU9KKS9Xqq2VoML9ncuesOF8FVCkhAAABCtJREFUuW+mz3Pcy8QXhruAy8CfA78H/OHNtrOSVLLP+GNeG3BESvlbQojfAv4L8Es32cyKUeHveEVj13tWGKSUH1rJdUKINuAZ4HNSykvlwx8A7hFCdON/Zg1CiJeklI9Uoq2VosJ9nuQrwEUp5X+92fZVmgr3t5eZW2Vt+LPoDcVK+zwPd5bveQlACPEU8LsVvH9FqHCfR/Fnzs+Uf/8G8MsVvP9NU+H+rmjsUltJ0ygvM78L/J6U8sjkcSnll6WULVLKduBB4MJGE4WVMl+fy+/9IZAA/sV6tG01WOA7vg6khRDvL3sjfQ5Y7mz0VqMP2COEmMy++Thwbh3bs+pIP6L3O8Aj5UOHgLfXrUGrzIrHLinlbfcCfgZ/hlgEBoEflI//GyALnJj2aph1bTtwZr37sNp9xp8xS/yBYvL4r6x3P1bzOwYO4nt9XAL+gnJmgFvlNV+fy+91A2P4nim9wJ7y8V8vf8en8AfM2vXuxxr0eQu+V88pfLvZ5vXux2r2d9r7Sx67VEoMhUKhUMxAbSUpFAqFYgZKGBQKhUIxAyUMCoVCoZiBEgaFQqFQzEAJg0KhUChmoIRBcVsghKh48kMhxCeFEL9b/vlTQog9K7jHS9OzZCoUGwElDArFCpFSfltKOZmO/FP4uYYUilseJQyK2wrh88dCiDPlOgQ/Xz7+SHn2/nQ5R/9XJ+sxCCE+Vj72SrlOw3Pl458XQvyFEOJ+4JPAHwshTgghOqavBIQQdeWUBAghQkKIr5frH/wvIDStbU8IIY5OS+4WXdtPR6Hwec/mSlIo5uHT+DmC7gDqgNeFEJO57e8C9uLnSDqCX4PiDeCvgIellFeEEF+bfUMp5atCiG8Dz0kpn4Yb0yJP4wtATkp5QAhxAHirfH4dflT2h6SUWSHEv8ZPlfzvK9FphWI5KGFQ3G48CHxNSukCg0KIHwP3AingmJSyF0AIcQI/hUAGuCylvFK+/mv4ee1XysPAkwBSylNCiFPl4+/H34o6UhaVAHD0Jp6jUKwYJQyK242FynVOr0Ph4v99rLS8p8O7W7XWrPfmykMjgB9KKT+7wucpFBVD2RgUtxsvAz8vhNDLWUUfBo4tcP47wLZpuex/fp7z0kBs2u/dwD3lnz8z6/m/CCCE2AccKB9/DX/ranv5vbAQYucS+qNQVBwlDIrbjWfws2qeBH4E/I6UcmC+k6WUeeCfAc8LIV7Bz2iZnOPUrwP/SghxXAjRgV/85QtCiFfxbRmTfBmIlreQfoeyKEkph4HPA18rv/ca5fKTCsVao7KrKhSLIISISikzZS+lv8QvXPSn690uhWK1UCsGhWJxfrVsjD6LX7jor9a5PQrFqqJWDAqFQqGYgVoxKBQKhWIGShgUCoVCMQMlDAqFQqGYgRIGhUKhUMxACYNCoVAoZvD/A+guu30GpBB1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa0c3bd",
   "metadata": {},
   "source": [
    "Now let’s look at the housing prices. The radius of each circle represents the district’s population (parameter s), and the color represents the price (parameter c). \n",
    "\n",
    "We will use a predefined color map (parameter cmap) called jet, which ranges from blue (low values) to red (high prices):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "82d78bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd810c91250>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "        s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "        c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    )\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce95ab7d",
   "metadata": {},
   "source": [
    "# Correlation matrix\n",
    "\n",
    "Since the dataset is not too large, you can easily compute the standard correlation coefficient (also called Pearson’s r) between every pair of attributes using the corr() method.\n",
    "\n",
    "The correlation coefficient ranges from –1 to 1. When it is close to 1, it means that there is a strong positive correlation; for example, the median house value tends to go up when the median income goes up. When the coefficient is close to –1, it means that there is a strong negative correlation; you can see a small negative correlation between the latitude and the median house value (i.e., prices have a slight tendency to go down when you go north). Finally, coefficients close to zero mean that there is no linear correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "9472ea67",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "            background-color:  #9ebeff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col4 {\n",
       "            background-color:  #9dbdff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col5 {\n",
       "            background-color:  #a1c0ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col6 {\n",
       "            background-color:  #96b7ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col7 {\n",
       "            background-color:  #5673e0;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col8 {\n",
       "            background-color:  #5470de;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col0 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col1 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col2 {\n",
       "            background-color:  #96b7ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col3 {\n",
       "            background-color:  #89acfd;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col4 {\n",
       "            background-color:  #799cf8;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col5 {\n",
       "            background-color:  #688aef;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col6 {\n",
       "            background-color:  #7396f5;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col7 {\n",
       "            background-color:  #445acc;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col8 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col0 {\n",
       "            background-color:  #c7d7f0;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col1 {\n",
       "            background-color:  #d9dce1;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col2 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col3 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col4 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col5 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col6 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col7 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col8 {\n",
       "            background-color:  #81a4fb;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col0 {\n",
       "            background-color:  #dddcdc;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col1 {\n",
       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col2 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col3 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col4 {\n",
       "            background-color:  #c73635;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col5 {\n",
       "            background-color:  #d95847;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col6 {\n",
       "            background-color:  #cb3e38;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col7 {\n",
       "            background-color:  #98b9ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col8 {\n",
       "            background-color:  #8badfd;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col0 {\n",
       "            background-color:  #e1dad6;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col1 {\n",
       "            background-color:  #cedaeb;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col2 {\n",
       "            background-color:  #4358cb;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col3 {\n",
       "            background-color:  #c73635;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col4 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col5 {\n",
       "            background-color:  #d55042;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col6 {\n",
       "            background-color:  #b8122a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col7 {\n",
       "            background-color:  #5977e3;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col8 {\n",
       "            background-color:  #7093f3;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col0 {\n",
       "            background-color:  #e5d8d1;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col1 {\n",
       "            background-color:  #c7d7f0;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col2 {\n",
       "            background-color:  #4961d2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col3 {\n",
       "            background-color:  #d75445;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col4 {\n",
       "            background-color:  #d44e41;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col5 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col6 {\n",
       "            background-color:  #cd423b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col7 {\n",
       "            background-color:  #5d7ce6;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col8 {\n",
       "            background-color:  #5a78e4;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col0 {\n",
       "            background-color:  #dfdbd9;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col1 {\n",
       "            background-color:  #cdd9ec;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col2 {\n",
       "            background-color:  #465ecf;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col3 {\n",
       "            background-color:  #ca3b37;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col4 {\n",
       "            background-color:  #b8122a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col5 {\n",
       "            background-color:  #cd423b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col6 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col7 {\n",
       "            background-color:  #5f7fe8;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col8 {\n",
       "            background-color:  #7597f6;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col0 {\n",
       "            background-color:  #d5dbe5;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col1 {\n",
       "            background-color:  #ccd9ed;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col2 {\n",
       "            background-color:  #7396f5;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col3 {\n",
       "            background-color:  #c4d5f3;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col4 {\n",
       "            background-color:  #88abfd;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col5 {\n",
       "            background-color:  #86a9fc;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col6 {\n",
       "            background-color:  #8badfd;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col7 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col8 {\n",
       "            background-color:  #f6a283;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col0 {\n",
       "            background-color:  #d1dae9;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col1 {\n",
       "            background-color:  #c1d4f4;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col2 {\n",
       "            background-color:  #adc9fd;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col3 {\n",
       "            background-color:  #b5cdfa;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col4 {\n",
       "            background-color:  #97b8ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col5 {\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col6 {\n",
       "            background-color:  #98b9ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col7 {\n",
       "            background-color:  #f6a586;\n",
       "            color:  #000000;\n",
       "        }    #T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col8 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >longitude</th>        <th class=\"col_heading level0 col1\" >latitude</th>        <th class=\"col_heading level0 col2\" >housing_median_age</th>        <th class=\"col_heading level0 col3\" >total_rooms</th>        <th class=\"col_heading level0 col4\" >total_bedrooms</th>        <th class=\"col_heading level0 col5\" >population</th>        <th class=\"col_heading level0 col6\" >households</th>        <th class=\"col_heading level0 col7\" >median_income</th>        <th class=\"col_heading level0 col8\" >median_house_value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row0\" class=\"row_heading level0 row0\" >longitude</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col1\" class=\"data row0 col1\" >-0.924664</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col2\" class=\"data row0 col2\" >-0.108197</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col3\" class=\"data row0 col3\" >0.044568</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col4\" class=\"data row0 col4\" >0.069608</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col5\" class=\"data row0 col5\" >0.099773</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col6\" class=\"data row0 col6\" >0.055310</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col7\" class=\"data row0 col7\" >-0.015176</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row0_col8\" class=\"data row0 col8\" >-0.045967</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row1\" class=\"row_heading level0 row1\" >latitude</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col0\" class=\"data row1 col0\" >-0.924664</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col2\" class=\"data row1 col2\" >0.011173</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col3\" class=\"data row1 col3\" >-0.036100</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col4\" class=\"data row1 col4\" >-0.066983</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col5\" class=\"data row1 col5\" >-0.108785</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col6\" class=\"data row1 col6\" >-0.071035</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col7\" class=\"data row1 col7\" >-0.079809</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row1_col8\" class=\"data row1 col8\" >-0.144160</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row2\" class=\"row_heading level0 row2\" >housing_median_age</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col0\" class=\"data row2 col0\" >-0.108197</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col1\" class=\"data row2 col1\" >0.011173</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col3\" class=\"data row2 col3\" >-0.361262</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col4\" class=\"data row2 col4\" >-0.320451</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col5\" class=\"data row2 col5\" >-0.296244</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col6\" class=\"data row2 col6\" >-0.302916</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col7\" class=\"data row2 col7\" >-0.119034</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row2_col8\" class=\"data row2 col8\" >0.105623</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row3\" class=\"row_heading level0 row3\" >total_rooms</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col0\" class=\"data row3 col0\" >0.044568</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col1\" class=\"data row3 col1\" >-0.036100</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col2\" class=\"data row3 col2\" >-0.361262</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col4\" class=\"data row3 col4\" >0.930380</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col5\" class=\"data row3 col5\" >0.857126</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col6\" class=\"data row3 col6\" >0.918484</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col7\" class=\"data row3 col7\" >0.198050</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row3_col8\" class=\"data row3 col8\" >0.134153</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row4\" class=\"row_heading level0 row4\" >total_bedrooms</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col0\" class=\"data row4 col0\" >0.069608</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col1\" class=\"data row4 col1\" >-0.066983</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col2\" class=\"data row4 col2\" >-0.320451</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col3\" class=\"data row4 col3\" >0.930380</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col5\" class=\"data row4 col5\" >0.877747</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col6\" class=\"data row4 col6\" >0.979728</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col7\" class=\"data row4 col7\" >-0.007723</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row4_col8\" class=\"data row4 col8\" >0.049686</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row5\" class=\"row_heading level0 row5\" >population</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col0\" class=\"data row5 col0\" >0.099773</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col1\" class=\"data row5 col1\" >-0.108785</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col2\" class=\"data row5 col2\" >-0.296244</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col3\" class=\"data row5 col3\" >0.857126</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col4\" class=\"data row5 col4\" >0.877747</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col6\" class=\"data row5 col6\" >0.907222</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col7\" class=\"data row5 col7\" >0.004834</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row5_col8\" class=\"data row5 col8\" >-0.024650</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row6\" class=\"row_heading level0 row6\" >households</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col0\" class=\"data row6 col0\" >0.055310</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col1\" class=\"data row6 col1\" >-0.071035</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col2\" class=\"data row6 col2\" >-0.302916</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col3\" class=\"data row6 col3\" >0.918484</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col4\" class=\"data row6 col4\" >0.979728</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col5\" class=\"data row6 col5\" >0.907222</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col7\" class=\"data row6 col7\" >0.013033</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row6_col8\" class=\"data row6 col8\" >0.065843</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row7\" class=\"row_heading level0 row7\" >median_income</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col0\" class=\"data row7 col0\" >-0.015176</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col1\" class=\"data row7 col1\" >-0.079809</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col2\" class=\"data row7 col2\" >-0.119034</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col3\" class=\"data row7 col3\" >0.198050</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col4\" class=\"data row7 col4\" >-0.007723</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col5\" class=\"data row7 col5\" >0.004834</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col6\" class=\"data row7 col6\" >0.013033</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row7_col8\" class=\"data row7 col8\" >0.688075</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04level0_row8\" class=\"row_heading level0 row8\" >median_house_value</th>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col0\" class=\"data row8 col0\" >-0.045967</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col1\" class=\"data row8 col1\" >-0.144160</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col2\" class=\"data row8 col2\" >0.105623</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col3\" class=\"data row8 col3\" >0.134153</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col4\" class=\"data row8 col4\" >0.049686</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col5\" class=\"data row8 col5\" >-0.024650</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col6\" class=\"data row8 col6\" >0.065843</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col7\" class=\"data row8 col7\" >0.688075</td>\n",
       "                        <td id=\"T_7e23446c_2d23_11ed_9710_3af9d3738a04row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd810cac4d0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "corr = housing.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')  # only lower/upper triangle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b13852d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "36a1409d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_rooms           1.000000\n",
       "total_bedrooms        0.930380\n",
       "households            0.918484\n",
       "population            0.857126\n",
       "median_income         0.198050\n",
       "median_house_value    0.134153\n",
       "longitude             0.044568\n",
       "latitude             -0.036100\n",
       "housing_median_age   -0.361262\n",
       "Name: total_rooms, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr[\"total_rooms\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986aa0a3",
   "metadata": {},
   "source": [
    "# Prepare the Data for Machine Learning Algorithms\n",
    "\n",
    "But first let’s revert to a clean training set (by copying strat_train_set once again), and let’s separate the predictors and the labels since we don’t necessarily want to apply the same transformations to the predictors and the target values (note that drop() creates a copy of the data and does not affect strat_train_set):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "03224abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = train_set.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36dfed7c",
   "metadata": {},
   "source": [
    "# Data Cleaning \n",
    "Most Machine Learning algorithms cannot work with missing features (null), so let’s create a few functions to take care of them. You noticed earlier that the total_bedrooms attribute has some missing values, so let’s fix this. \n",
    "\n",
    "You have three options: \n",
    "\n",
    "- Get rid of the corresponding districts.  (Drop only raws with zero values)\n",
    "- Get rid of the whole attribute. (Drop the column)\n",
    "- Set the values to some value (zero, the mean, the median, etc.).\n",
    "\n",
    "You can accomplish these easily using DataFrame’s dropna(), drop(), and fillna() methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f291989a",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing.dropna(subset=[\"total_bedrooms\"]) # option 1 \n",
    "housing.drop(\"total_bedrooms\", axis=1) # option 2 \n",
    "median = housing[\"total_bedrooms\"].median() # option 3 \n",
    "housing[\"total_bedrooms\"].fillna(median, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "84d944d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = housing.drop(\"total_bedrooms\", axis=1) # option 2 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e2158dc",
   "metadata": {},
   "source": [
    "If you choose option 3, you should compute the median value on the training set, and use it to fill the missing values in the training set, but also don’t forget to save the median value that you have computed. You will need it later to replace missing values in the test set when you want to evaluate your system, and also once the system goes live to replace missing values in new data.\n",
    "\n",
    "Scikit-Learn provides a handy class to take care of missing values: SimpleImputer. Here is how to use it. First, you need to create a SimpleImputer instance, specifying youu want the madian of each attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dede23ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer \n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9796ac63",
   "metadata": {},
   "source": [
    "Now you can fit the imputer instance to the training data using the fit() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6acde66d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(copy=True, fill_value=None, missing_values=nan,\n",
       "       strategy='median', verbose=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab41151c",
   "metadata": {},
   "source": [
    "The imputer has simply computed the median of each attribute and stored the result in its statistics_ instance variable. Only the total_bedrooms attribute had missing values, but we cannot be sure that there won’t be any missing values in new data after the system goes live, so it is safer to apply the imputer to all the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6d7dfb32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.1849e+02,  3.4260e+01,  2.9000e+01,  2.1270e+03,  4.3500e+02,\n",
       "        1.1660e+03,  4.0900e+02,  3.5348e+00,  1.7970e+05])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat = imputer.statistics_\n",
    "stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b6c52230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -118.49  ,     34.26  ,     29.    ,   2127.    ,    435.    ,\n",
       "         1166.    ,    409.    ,      3.5348, 179700.    ])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(suppress=True)   # suppress scientific annotation\n",
    "stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cc894ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eac277d9",
   "metadata": {},
   "source": [
    "# Handling Text and Categorical Attributes\n",
    "Earlier we left out the categorical attribute ocean_proximity because it is a text attribute so we cannot compute its median:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "6370ad2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8267</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17445</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11284</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "14196      NEAR OCEAN\n",
       "8267       NEAR OCEAN\n",
       "17445      NEAR OCEAN\n",
       "14265      NEAR OCEAN\n",
       "2271           INLAND\n",
       "...               ...\n",
       "11284       <1H OCEAN\n",
       "11964          INLAND\n",
       "5390        <1H OCEAN\n",
       "860         <1H OCEAN\n",
       "15795        NEAR BAY\n",
       "\n",
       "[16512 rows x 1 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[[\"ocean_proximity\"]]\n",
    "housing_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6417108f",
   "metadata": {},
   "source": [
    "Most Machine Learning algorithms prefer to work with numbers anyway, so let’s convert these categories from text to numbers. For this, we can use Scikit-Learn’s OrdinalEncoder class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ded52ab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.],\n",
       "       [3.],\n",
       "       [3.],\n",
       "       ...,\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder \n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "\n",
    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat) \n",
    "housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cd065c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e0da45ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0, 1.0, 2.0, 3.0, 4.0}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(housing_cat_encoded.flat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf7a725",
   "metadata": {},
   "source": [
    "One issue with this representation is that ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases (e.g., for ordered categories such as “bad”, “average”, “good”, “excellent”), but it is obviously not the case for the ocean_proximity column (for example, categories 0 and 4 are clearly more similar than categories 0 and 1). \n",
    "\n",
    "To fix this issue, a common solution is to create one binary attribute per category: one attribute equal to 1 when the category is “<1H OCEAN” (and 0 otherwise), another attribute equal to 1 when the category is “INLAND” (and 0 otherwise), and so on. \n",
    "\n",
    "This is called one-hot encoding, because only one attribute will be equal to 1 (hot), while the others will be 0 (cold). The new attributes are sometimes called dummy attributes. Scikit-Learn provides a OneHotEncoder class to convert categorical values into one-hot vectors:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "ffb2743d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 5)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "cat_encoder = OneHotEncoder()\n",
    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat) \n",
    "housing_cat_1hot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "092790a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 5)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5fe5b357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<20640x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 20640 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd64d160",
   "metadata": {},
   "source": [
    "Examples of quality feature as the color of the eyes! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159c5091",
   "metadata": {},
   "source": [
    "By default, the OneHotEncoder class returns a sparse array, but we can convert it to a dense array if needed by calling the toarray() method:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "a0674fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 1.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7da7d0eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "71329b32",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity_&lt;2H OCEAN</th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>ocean_proximity_ISLAND</th>\n",
       "      <th>ocean_proximity_NEAR BAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ocean_proximity_<2H OCEAN  ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n",
       "0                          0                       1                       0   \n",
       "1                          0                       0                       0   \n",
       "2                          1                       0                       0   \n",
       "3                          0                       0                       1   \n",
       "\n",
       "   ocean_proximity_NEAR BAY  \n",
       "0                         0  \n",
       "1                         1  \n",
       "2                         0  \n",
       "3                         0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.DataFrame({\"ocean_proximity\": [\"INLAND\", \"NEAR BAY\",\"<2H OCEAN\", \"ISLAND\"]})\n",
    "pd.get_dummies(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a54d3449",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "5301d93a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512,)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:, 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "ffc068f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16507</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16508</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16509</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16510</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16511</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       OCEAN  INLAND  ISLAND  NEAR BAY  NEAR OCEAN\n",
       "0        0.0     0.0     0.0       0.0         1.0\n",
       "1        0.0     0.0     0.0       0.0         1.0\n",
       "2        0.0     0.0     0.0       0.0         1.0\n",
       "3        0.0     0.0     0.0       0.0         1.0\n",
       "4        0.0     1.0     0.0       0.0         0.0\n",
       "...      ...     ...     ...       ...         ...\n",
       "16507    1.0     0.0     0.0       0.0         0.0\n",
       "16508    0.0     1.0     0.0       0.0         0.0\n",
       "16509    1.0     0.0     0.0       0.0         0.0\n",
       "16510    1.0     0.0     0.0       0.0         0.0\n",
       "16511    0.0     0.0     0.0       1.0         0.0\n",
       "\n",
       "[16512 rows x 5 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_data = pd.DataFrame({'OCEAN': data[:, 0], 'INLAND': data[:, 1], 'ISLAND': data[:, 2], 'NEAR BAY': data[:, 3], 'NEAR OCEAN': data[:, 4]})\n",
    "\n",
    "cat_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "3a3b2d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_cat = housing_num "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "d83949cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
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       "  </thead>\n",
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       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
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       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>565.0</td>\n",
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       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>356.0</td>\n",
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       "      <td>741.0</td>\n",
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       "<p>20640 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
       "0        -122.23     37.88                41.0        880.0       322.0   \n",
       "1        -122.22     37.86                21.0       7099.0      2401.0   \n",
       "2        -122.24     37.85                52.0       1467.0       496.0   \n",
       "3        -122.25     37.85                52.0       1274.0       558.0   \n",
       "4        -122.25     37.85                52.0       1627.0       565.0   \n",
       "...          ...       ...                 ...          ...         ...   \n",
       "20635    -121.09     39.48                25.0       1665.0       845.0   \n",
       "20636    -121.21     39.49                18.0        697.0       356.0   \n",
       "20637    -121.22     39.43                17.0       2254.0      1007.0   \n",
       "20638    -121.32     39.43                18.0       1860.0       741.0   \n",
       "20639    -121.24     39.37                16.0       2785.0      1387.0   \n",
       "\n",
       "       households  median_income  median_house_value  OCEAN  INLAND  ISLAND  \\\n",
       "0           126.0         8.3252            452600.0    0.0     0.0     0.0   \n",
       "1          1138.0         8.3014            358500.0    0.0     0.0     0.0   \n",
       "2           177.0         7.2574            352100.0    0.0     0.0     0.0   \n",
       "3           219.0         5.6431            341300.0    0.0     0.0     0.0   \n",
       "4           259.0         3.8462            342200.0    0.0     0.0     0.0   \n",
       "...           ...            ...                 ...    ...     ...     ...   \n",
       "20635       330.0         1.5603             78100.0    0.0     1.0     0.0   \n",
       "20636       114.0         2.5568             77100.0    0.0     1.0     0.0   \n",
       "20637       433.0         1.7000             92300.0    0.0     1.0     0.0   \n",
       "20638       349.0         1.8672             84700.0    0.0     1.0     0.0   \n",
       "20639       530.0         2.3886             89400.0    0.0     1.0     0.0   \n",
       "\n",
       "       NEAR BAY  NEAR OCEAN  \n",
       "0           1.0         0.0  \n",
       "1           1.0         0.0  \n",
       "2           1.0         0.0  \n",
       "3           1.0         0.0  \n",
       "4           1.0         0.0  \n",
       "...         ...         ...  \n",
       "20635       0.0         0.0  \n",
       "20636       0.0         0.0  \n",
       "20637       0.0         0.0  \n",
       "20638       0.0         0.0  \n",
       "20639       0.0         0.0  \n",
       "\n",
       "[20640 rows x 13 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e60b5d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_cat['OCEAN'] = data[:, 0]\n",
    "housing_num_cat['INLAND'] = data[:, 1]\n",
    "housing_num_cat['ISLAND'] = data[:, 2]\n",
    "housing_num_cat['NEAR BAY'] = data[:, 3]\n",
    "housing_num_cat['NEAR OCEAN'] = data[:, 4]\n",
    "\n",
    "housing_num_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "c1c9b6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
       "       'population', 'households', 'median_income', 'median_house_value',\n",
       "       'OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_cat.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a37e894",
   "metadata": {},
   "source": [
    "# Feature Scaling\n",
    "\n",
    "One of the most important transformations you need to apply to your data is feature scaling. With few exceptions, Machine Learning algorithms don’t perform well when the input numerical attributes have very different scales. This is the case for the housing data: the total number of rooms ranges from about 6 to 39,320, while the median incomes only range from 0 to 15. Note that scaling the target values is generally not required.\n",
    "There are two common ways to get all attributes to have the same scale: min-max scaling and standardization.\n",
    "\n",
    "**Min-max scaling** (many people call this normalization) is quite simple: values are shifted and rescaled so that they end up ranging from 0 to 1. We do this by subtracting the min value and dividing by the max minus the min. Scikit-Learn provides a transformer called MinMaxScaler for this. It has a feature_range hyperparameter that lets you change the range if you don’t want 0–1 for some reason.\n",
    "\n",
    "**Standardization** is quite different: first it subtracts the mean value (so standardized values always have a zero mean), and then it divides by the standard deviation so that the resulting distribution has unit variance. Unlike min-max scaling, standardization does not bound values to a specific range, which may be a problem for some algorithms (e.g., neural networks often expect an input value ranging from 0 to 1). However, standardization is much less affected by outliers. For example, suppose a district had a median income equal to 100 (by mistake). Min-max scaling would then crush all the other values from 0–15 down to 0–0.15, whereas standardization would not be much affected. Scikit-Learn provides a transformer called StandardScaler for standardization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35a0034",
   "metadata": {},
   "source": [
    "# Transformation Pipelines\n",
    "\n",
    "As you can see, there are many data transformation steps that need to be executed in the right order. Fortunately, Scikit-Learn provides the Pipeline class to help with such sequences of transformations. Here is a small pipeline for the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "f3c5cd18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 13)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "            ('std_scaler', StandardScaler()),\n",
    "        ])\n",
    "\n",
    "housing_prepared = num_pipeline.fit_transform(housing_num_cat)\n",
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "68e83fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.32783522,  1.05254828,  0.98214266, ..., -0.01556621,\n",
       "         2.83074203, -0.38446649],\n",
       "       [-1.32284391,  1.04318455, -0.60701891, ..., -0.01556621,\n",
       "         2.83074203, -0.38446649],\n",
       "       [-1.33282653,  1.03850269,  1.85618152, ..., -0.01556621,\n",
       "         2.83074203, -0.38446649],\n",
       "       ...,\n",
       "       [-0.8237132 ,  1.77823747, -0.92485123, ..., -0.01556621,\n",
       "        -0.35326426, -0.38446649],\n",
       "       [-0.87362627,  1.77823747, -0.84539315, ..., -0.01556621,\n",
       "        -0.35326426, -0.38446649],\n",
       "       [-0.83369581,  1.75014627, -1.00430931, ..., -0.01556621,\n",
       "        -0.35326426, -0.38446649]])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b916514e",
   "metadata": {},
   "source": [
    "\n",
    "# NAIVE BAYES "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "45e7d121",
   "metadata": {},
   "outputs": [],
   "source": [
    "h_data = housing_num_cat\n",
    "train_set, test_set = split_train_test(h_data, 0.2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "3c22c42a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 13)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "2a78b840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
       "       'population', 'households', 'median_income', 'median_house_value',\n",
       "       'OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "b24bdc68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
       "       'population', 'households', 'median_income', 'median_house_value',\n",
       "       'OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "59598f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_data = train_set.drop(\"median_house_value\", axis=1)\n",
    "train_set_label = train_set[[\"median_house_value\"]]  \n",
    "test_set_data = test_set.drop(\"median_house_value\", axis=1)\n",
    "test_set_label = test_set[[\"median_house_value\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "83c75b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4128, 1)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "1b678fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 1)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "43943a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 13)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "da6f851a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_data = num_pipeline.fit_transform(train_set_data)\n",
    "test_set_data = num_pipeline.fit_transform(test_set_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "3fe7cffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Gaussian Naive Bayes model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "gnb = GaussianNB()\n",
    "\n",
    "#Train the model using the training sets\n",
    "gnb.fit(train_set_data, train_set_label.values.ravel())\n",
    "\n",
    "#train_y.values.ravel()\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = gnb.predict(test_set_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "6376231a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.03948643410852713\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(test_set_label, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfa36fa1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d358a681",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e64e87e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb96e88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a5c948e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1895c6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "4e7a1db2",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-186-b671c8166a04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m#Train the model using the training sets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mgnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m#Predict the response for test dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "h_data = housing_num_cat\n",
    "train_set, test_set = split_train_test(h_data, 0.2) \n",
    "\n",
    "train_set_data = train_set.drop(\"median_house_value\", axis=1)\n",
    "train_set_label = train_set[[\"median_house_value\"]]  \n",
    "test_set_data = test_set.drop(\"median_house_value\", axis=1)\n",
    "test_set_label = test_set[[\"median_house_value\"]]\n",
    "\n",
    "\n",
    "train_set_data = num_pipeline.fit_transform(train_set_data)\n",
    "test_set_data = num_pipeline.fit_transform(test_set_data)\n",
    "\n",
    "\n",
    "#Import Gaussian Naive Bayes model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "gnb = GaussianNB()\n",
    "\n",
    "#Train the model using the training sets\n",
    "gnb.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9919422",
   "metadata": {},
   "source": [
    "## Evaluating Model\n",
    "After model generation, check the accuracy using actual and predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "feb2206d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9074074074074074\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad01e4fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723d47c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21782aeb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b397dd44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c86e996a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3699c12f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff7b244e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ef92f45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "715ee7ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58508830",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c83decb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83080d40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "19fed7c6",
   "metadata": {},
   "source": [
    "# Select and Train a Model\n",
    "\n",
    "Let’s train a DecisionTreeRegressor. This is a powerful model, capable of finding complex nonlinear relationships in the data. The code should look familiar by now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2fb3941f",
   "metadata": {},
   "outputs": [],
   "source": [
    "h_data = housing_num_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9ed7d6eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(h_data, 0.2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "73f7be60",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_data = train_set.drop(\"median_house_value\", axis=1)\n",
    "train_set_label = train_set[[\"median_house_value\"]]  \n",
    "test_set_data = test_set.drop(\"median_house_value\", axis=1)\n",
    "test_set_label = test_set[[\"median_house_value\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ff2167d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_data = num_pipeline.fit_transform(train_set_data)\n",
    "test_set_data = num_pipeline.fit_transform(test_set_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0dcfc2e3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(train_set_data, train_set_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "de39c549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70697.85765958912"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = tree_reg.predict(test_set_data)\n",
    "tree_mse = mean_squared_error(test_set_label, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "6cde8536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4998187077.655523"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ee48b8",
   "metadata": {},
   "source": [
    "The error is really high, but it also depends by the classifier, the  random seed, data training and so may features.\n",
    "\n",
    "The best result is near 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a049bc81",
   "metadata": {},
   "source": [
    "# Better Evaluation Using Cross-Validation\n",
    "One way to evaluate the Decision Tree model would be to use the train_test_split function to split the training set into a smaller training set and a validation set, then train your models against the smaller training set and evaluate them against the vali‐ dation set. It’s a bit of work, but nothing too difficult and it would work fairly well.\n",
    "\n",
    "A great alternative is to use Scikit-Learn’s K-fold cross-validation feature. The following code randomly splits the training set into 10 distinct subsets called folds, then it trains and evaluates the Decision Tree model 10 times, picking a different fold for evaluation every time and training on the other 9 folds. The result is an array con‐ taining the 10 evaluation scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "7bd4e7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "data = h_data.drop(\"median_house_value\", axis=1)\n",
    "label = h_data[[\"median_house_value\"]]  \n",
    "data_scale = num_pipeline.fit_transform(data)\n",
    "\n",
    "scores = cross_val_score(tree_reg, data_scale, label, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "744a9191",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [140737.53674672  70108.47236819 104066.3535419   78374.09498279\n",
      "  86268.27285416  81490.52917731  78853.6598763  107586.30154808\n",
      " 107099.52800012  73321.54515714]\n",
      "Mean: 92790.62942526951\n",
      "Standard deviation: 20773.87504880506\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a5f3d85",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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