Lab3-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 3 : Classification\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Summary of the lab session : \n",
    "\n",
    "\n",
    "#### I) Basic application of classification models on toy dataset : \n",
    "    - Logistic Regression\n",
    "    - LDA\n",
    "    - SVM\n",
    "    - Kernels\n",
    "\n",
    "    \n",
    "#### II) Marketing project : \n",
    "    - Presentation of pandas\n",
    "    - Feature engineering \n",
    "    - Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the packages\n",
    "import pandas as pd \n",
    "pd.set_option('display.max_columns', 500)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x175b5babe50>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FnCY18bKyGFM0j896ZqW8YM+sjGuesULdpvfZwNjmtSHHv+ytPIvOF4gZvoTsMQzkuNtuK/+975z4hvq4hi/t7x8fOjSvyZrHZfNUf+pT0sc+dvR5d+6XF8z1+yp1PzOz58X12tGo/rTkXc8LjrwRiJG9COsvLJSdHen69eO/X131nxPXhB17e+4gMw0Q04DhMhuwqsbaTv9+//3Fz5/6lHuBBTMuL5jr91Vcn6XpRCXzZs+L67UPPdQsqLLABF7mqiqnfNA0jbqCE3IyS69OURxfP6ePq5l5ZSW8qbSqb7Oqybl2k2yCNtyy9yR0N5l9vDAgoo8YSyGzjre2xYk9iYWvP9O3vzrH5BuW5MvC9iYpdRT9CLJIiUCM5ZBZKmqb4jRJrKrabqzT4wtYTcch55J4RzBGKr5A3LqP2BjzWmPMbxtjnjDGfN0Y86HW7eVAEykmYW7BtduQxRV8iVVlfZWnTkk3bjSfs7nOJMplfZvTl1tbfWxlUibehR7azk6xgMTeXnEce3vFz03n0QaCuSJ06EPS35T0A7f+/ypJ35L0Rt9rqBEjiYHUiOfHypaps/TfaGTt6mp57XleaY0vsA29zhjkOg/XhBox1kOu0z2Q2ccHC0ZdNk1L+oykH/M9h0CMJDLsIw6dT3leSFJUtDV1AyJQk6bykMc0USwkmK+t1Q/OdYIr49WRUmeBWNKGpMuSXu17HoEYyUTo5IvZTxjUJ1qyw6qpGENqoMEBJCAC+QJajMUiQoN56GpNNQ4t6Bg9b1Ut9EEvr04CsaQzkh6X9M8cf78oaVfS7vr6eicHDtQVu1JdeXH37NB10Q4NWjFrxFVzQ7t+X1Vjnu6iTTD3HWedGnGKBSbqbB+LLXkglrQq6fOSfibk+dSIkavY/YSVF98GOwwJWvMXeG9NLCBC+Pq7t7aqA0ybYU9Vj6bTerqe7zpPbT8b9EEvt6SBWJKR9GuSfjn0NQRi5CpFP6E3CDbYYVXQml8SMSgYVbSZ+vq758cHT1cjmt+Mbxchze1N+9tjNQe3/WzQB73cUgfivy/JSvqqpK/cepz3vYZAjFx1XmtpsMOqoDUfZGMdU5PaapsJTOYTs8pq3mULRaRSJ4GuLOBTI15unWZNhzwIxMhV5/14DXcY2vdqbbGU4Px6vk1qYk2bj2MGmq2t6oStVAlRZW/V6upha0BIuegjXl4EYnRnAdJCkx9CxAGzlc2dk4l9zhxfz/eCJo1m1Go6XrjO6fAdfkitNGWwmx+/fepUvZuQBfh6oCECMbrR5Cq4bFemyJGisrnT8YQ9M248Qcbs2+WaO3r+psCXHFXndFTdeHTZ/BvSQkD/L6YIxOhG3avgMrbVRY4Uk8nxWtmpUzOn0BG5DhQnQoTWkl2HV/d0VD2/y4SokOx1+n8x5QvErEeMeOrO9eybUHlRJZgP21rPz45JnJuu5ztvfoF7l7qH7fp91drUrjmrU8xlXbXNZV4zG/UQiBFP3atgZos0dCJypLh0Sbp58+jvbt68dS+zs1OsBDFvLkLUWO+h1OwiEONx+XPqHrbr9/OBfzwuft7cLP5eFahjKtvX9GZkvlyAl6uqnPJB0/SCqtvUnKpDL+d+58jN8a7m0fvkaDOeG2Qcu3egyQQaVbNZ1XkrJ5Oj/dbzY6pjy/mjhryIPmJ0ps6VqWkUqDszRMt+5+gX24gbdN3LPK3yPzyzMj6y27ZjY2Mcnuv5MYL69EaFIIm+EYiRryZX7SZzJbZIhso5n2y+fBdujRk+cGQP7ctUJhhNA1jZmN2QwBbrPiNWIleO7xuWD4EY1YbSxtZx2uwQZkOavnX3aWJvyJ/C/JTGQYF4NKrOCi4LbDFvXOq+lWQxI2e+QGyKv3fr3Llzdnd3t/P9wmFnR7p48WgG89pantkmJ04U19R5xhTZQhsb0t7e8b+Px0VGUeTdZcV17Lc8pzX9lB7WI/K/p2tr0iteIV27Vr3L+dPqKsJoJJ05U+Thra8XiU5VH626b2XF4UvK9H3DUjDGPG6tPVf2N7KmMaxhRFVptpHTZrscDtOaI9vcSnpa48ogPJuF/N3vNtulK+H92rUiSFpb/HvxYnV2dtlbubpaJIKXZXiXPX9elu8b4Koqp3zQNJ2ZIS0LE9L2GaOZ/dY2DmTsnjmcnznrvkZHO3pIc/RoVH/GrLKm3jrzUYc0E08m1r7yleHN47MJaFVzPwNdEn3E8BpSR+h0kt9pJ2aK/uySYP+cWbP33ZqfOaeL+expeXA0sTdPlc8r7QuIq6vls3OtrvoD6ZEZvGbKEzofdch93tZW84A+lLSHLHCykiMQw29oqcGpyxf7xiTmRW5mW9dHY/vA6tEg+8DqxP7Z6eMrLZXVEKc1YVftd7aWPBodrjM8G8DLDiV0PuqQ07myEiegwyP37/+sAd8wEIhRLecPeNc19phN9TEvciXbKqvxhgSv2UAacrht3oKyUzBdPrDq4xZyHKNR/VOJGUNoEbN2WDcMJXyBmKxp5K/r1OWYmdcdbOtpjXWXam7rlmk2c1UR274FOztF7t/ly9Jtt0nXr0svvnj4d1eS/smT0v5+9TF85zvVZYDDUIYGRB4R0TWyptGfthMZS85U1xu3rb+86dtvLx5tdvMyT+Z17cOJOZ+24zXrOvr7lZXwTV67Jp0/X51o3jZ7fHY+6jNnjgZhyZ2kf/Fi9bZDM7zhkMvQgKov1yLPTe+qKqd80DS9JGI1JZVs5+aptWP9o1FbrEqa6hsdTkizX2i3QEBW9NpakeDkmurRVZSqIvQ5UcfWlr+5nabplnJo8g0pw1Ca0B1EHzF6EfOLMxcpHhz5M4FTfD8bJR2FrGoQehF03JA8OJqUztM8G1h92cehXd99TV055UsqQ0t954iE3rD2fcPQAoEYcYV+aROOTw6ZzjBmNu1k0mI/vvNVNyq1uGC2yV72qTuyrOn1dEjD3VFT6JvbxQ1Don0QiBGPa6DomTPHP7AJm5JCJo6IWSP27a/VfhJEl1irGYXuyzdu2Fe5r3utG3jLJHxyeXMT1roJxIjHF5HmZ3hI+KFuGgCacsXLC5rY66Nx87vnBKtFlU3O4WuybnPz3/aGqE5AHnjLJHzqdtFUJTQ0/VAnvCEgECOeqjbh+Q9swqakLifbKvt+XtDEPje/2lHdyBA5utTpR22y6/m3syoI+yr3MfZPEF4gIW9uzJyLMgn7PwjEiKfq6rugHXZl3+89Mw67GQnZeKTo4ntr5jXpnq6TjV21vVxaI9GRGJ/zqg9N2w8VNWIMgm8qpgW/is5fRw7kv3vuo/ZWJxD73sbHto4X3nWN8m3HVxkh+WqJxGr5qfrQtP1Q0UeMwXCNhSlbBWBBlAZVz91zX/2ZdZqmXcW/oIm9Mdfk/pzxLx7RpIuAGvESifVmp64RW0vWNAZkMjl61R+Nhh+EHV9AV1B9bMsdbfsKMpPJ8VWTfIszlCW8PaXywruWU2x6TCRftTSkDvOQmmoOfcQJEYgBa/1fdM8X2BtUHdusuu6kvIbWzUSeL+O+o8l9X+bYr2MMf0pxHoYUoxrJOOCUqrozzSVrOiECMVD1RfdcKJp0O/muO7ldQ+fLGlojzugad0Ru5zeJobXrt/j+LQoCMVD1RfdE2ybXCN91J7drznxZy/qI55dbzPn6mNv5TWKImW6+mmpOM2slQiAGqr7oCRKvXNeMHK+h82WdZk0fyNg9Mz4ShOe75HK7LuZ4fqPr6m6jqzc45HjqJEBkKHkglvQrkp6V9LWQ5xOI0bmWfVQxr0dDq7G5jj3XJuChnd9Gujj5Xb7BIfsa+KofXQTit0r6AQIxshXyRe/o7j/XAFZXrgEv9vnNsdZvrU1fsK7f4KrjKSvL9BFrHwl10jQtaYNAjCB9fRkyuqIONPHziDpNwF0fT6z9LcpNUyMx2/hjvCFtA3HPb2YWgVjSRUm7knbX19e7OG7kaKmvbGG2to5fA3M8RaEVpiG/5bnW+jvhOviVlfA3b36+gTYfgNCmaVfQ7/nNzCIQzz6oES8x15dh+oWIdHWeTKx9cDSxT2ls92WKFZIGcOWfTNwVkdwu/qEBNudgVlVRW4rEL5eyN7hOIPW9PuQDMP/mbG35lxZz7XNa1p7fTAIx8lG1SkCEqtJkYu0Dq8eH4Nw8lX81zHefUnq9aNnk17bFMOT1XVz/mhxHyI1EzjcRnZhMihpwk5Pg+zBXfQBcb07Vup2+N4waMYEYt1R9OWe/GA07Usdj96QUuV9Bg9fTiNDk11WTcerrX9PjCB0xM9Rm9Wia3knVXTJ1VtMPja+si95HLOkRSX8q6aakK5L+le/5BOIlVtVcFfql8fzdGPc0jcVHvqJ8XWUVlezLdf25T5Oied0Y+1dnRvYFrbqPLzDCtQmQdU5T6utfimv2fPlzT5xLqukJ9t10V30Amgb/kGGKi5w1XedBIF5ykyKoHPgCSdUXyvN3b41YKpq3HOXqe9zkY1uTY7++TxP7wsmKm5c6F6tbml7rmpymlNe/VNds3NL0e+G66T5xwv0dnGr65mTchEEgRlam35WyqRRf/tJUBRnP1XfaR+ysFU9r3PMafvkbBRnPvua3d33keK7vRqZdERq9bmWln1rjAl6z89P0TsrVhbK66l8vs82bk2kTBoEYWZm9cF7QYWbzMytjaycT+9iWJ4gG1IitLb57+3WDVYOqVePrRZ19VfW1NYwkTcseUpwuA9oCXrMXS0heSNkbtmBvDoEYWanKp9gzY/8TrG2X8uoKeA2qVlU3Fc5rSZ19BVzIDqRGa0I3udaFXFdDaqQxLdg1e7GE3kgueJ8AgRhZ8cWgB0cTZ9/xgXTkYjtdmMDbvFVnUG6DqtV082XN7DdPrdkHViflm6uzr5LnvqBT9lmN7L6MfUpj+1Ov7C7yhOTbzd43ESCXXOid24IPziYQIyuuGPTYVkmf8cxjfj3coObHutNU1Ywc02tM6Bq+R+4B6qYej8cvB97Z1ZD6uIbNFt01zHQ0og8WNvzOjRoxgRjdKo1Bnjvn+fVwZ5uBQ4NYimrZ9Brj6tPelym/8W9YJtcsf31ew1w3VjmWtQ1q9y3MnrzR6PgMWUtwh0YgRrUcrjKOZuQD6VgQdmZb93BIk4kt+oQDa8QPjpplF00mx5djlY7P8teHsnO9SNNDkmHd0vwHpGqGrAVEIIZfLlcZRxXqWY2O/Cpk1qxahxQjYpfs0NVH7ByOVFFVdDUY5Loc6yKN012kY2mszRCmHK4vZeXq8GaAQAy/XK4ygYHYOz647iGVVTNXV5sH47m7/uujw37dB0dFFnXTquLQapi5Xn+bGNq5j67Nm9nn9cUVbHv4cBKI4Zf6KhN65+kox3w/67Oq7nwMPqTQpdXqHp/vi+64MF0fjb27cV3PHhxl0K3gkEOPRwy53Kv2ps0J6OsupsF3MOUbSiBeVqFXwZQfyjp3np4ANT2MB0cT+9JKdUdp8CGVPWn6aHN8vkylyaRYCWruZuNgGowd71PZrh5YPb6twVY7M5Z17b6Lu502wbSvuxhfX47rO5/w5oBAvIxajlONcpWZTOotodZmko65GmzwIbUNxKFjJOe+6IdrJet4U7vn3M9fc139zc+sjPMIEnZxasVZHkdXdwhtgmlfdzF1ZqTr4OaAQLyM6n5xqq4yda9CVWMHXXeeVfupcWceVOQ6gXi2Sct1gxH4RZ8eRuvlGp3N+cqixpZ1TXIRdFXbbPtGxkqIrLONujfJ9BEjupj9Mk2+hFVfgqYXitgXntA+4tBJCWZf7zln08MISTxrcj72ZewFTapPS+Jq3tL3rabWZf9r289Kk9fP3vzWmZhn+to631myphFdzCtgk235moWaZiVbG7+KFZo1Xefueloez4Vnehita8SeAbtPaey/HndQXW0TJ7JsCs5NjO95Fye6yWctJJBWHef8sfU4ywyBeBnFvMg2uZr6AldVRnLVhWFr67BpeGWlem3TKmVNzvP7rehvOrhVC61zIZtMir5i1+QkwddHR5n2ZfzXlw6qq013QZN2oBhNxrn2MYfc/Nat+ff4wSIQL6tYd7pNvkSTSbMvT9UXJWVimW+7FReFytrn3K5m35ayxStqHaajbHumImGrZbNmyMer6dtFk3YNbb7nXZ3oJp+1kGSrJuXsqamFQIzmJpPy5pyQq6mrGWhlpflQqlQXjqrteprJpvNgx0wgrXWYJRt9zqwVAb7NMUc4julz6173ln4Cja50daJT1IgH1kRCIEYzruATuu6tr4/H9SWqujCkunCEbPdWRDmQ7E2t2H3p5ZWQYk8yNF+cykUu5qLdY1uT6uDXonUhdUWKGnFHcs66LnvN9IsxwKQBAjGaCaklhrRN1hhL7BoX+/KsU13XiB1f+qatW6H3EbPFCV3kYlquWg0YDQ+ki8nY6CPuQJcnuk3W9AJk7BGIcxfrwxb7Q+u72tb5Ate4apclL01nnbLjcZGY1VUfcYKLU+h9xGxxQjOrqw4hZiWni4rUAl2D88aJ7gSBOGex7khT3Nn6rrZ1rsQ1nmvMbDOsY9apVEuozWZPJ4oyTfpWQ8caV3Wpxez2o8YK1EMgzlmsqkWKKorvalunbbLGVXv2MFqPsW0qdrtrk/7bWYHvbVWSaYpuPypSQBgCcc5iXfR9V+EUzd11A3/gVXs2Zreedaqptjc1s8c6Gh2fMOTUqeL3baYLLbmR8dWIqa0C/SIQ5yxWTda3ooiKITYvr4cbgys4RGg2nsax3mrEbdpdqzpqm0bJgBuZtknuANIhEOcsVop+RSC2KobaRK0ZzQeHpolUriDTZ0dk03bXqo7axO3GNBcDeSIQ5242SajuxOZTARf7fSlthbIqCLnGv1bNpNVnZKm7/yZLr3XR3A6gVwTioajTTD0fIE6cqLzY39SK/5rfNuiFBKHQ9YWr7hbalDX0tU1q5LFqxDFvQEq21ff9DbBsCMRDEZq41aQfsqpGHKMZODQIzS760CRZLXYfruu1TW4SGr43R/Yfs0m+ZFs3T63ZB1YnUTZPRAfCEIiHIvTC37DW5e0jjpE0VicINc2+blvWOq913CQcqKIZuWo8su/GpO3xBR7vUxq33zyDiYFgyQOxpHdI+qakP5L081XPJxA7hF7YGvRDvqBT/qzpquFPdYbauKa0LLvqlx1z1fCeNkO+6rzWEcQqVzWq2lfZ+ztbs3Q9t0k/smN7+zL1Nz9f++1xbVdgaJIGYkkrkv5Y0usknZL0B5Le6HsNgdgjpKnPVWMajdx/K1vsPmSbTZLHQmrGJYspOMfdxupXrnptSRb4c+bocdRZacm5r5WVo+9vaEtCnzXiOq0dJJ4Bx6QOxG+R9PmZnz8s6cO+1xCIW6qqOcfq23TVzkICQtPpIkPK3mYMs++1Jce/r+MrLQXHmtAWjpBm7MR9xA+sToqFNVznrk5TOzVi4JjUgfhfSPr4zM/3S/poyfMuStqVtLu+vt7NkeesbZKL7/VNm27nt+mr8aTKPK6TsNZ0DHNZ2SsCzbQmXDvWhJwnZ1900YT8zIrjdaH7qMiafnA0sTdPVZy70O4Q+oiBUlkE4tnH0teIUye5xEr28TWB1yl/nZuOumVvWvOeFxBops250WNNRfOx9/4pZbb77Lmr6g4haxrwomk6NzGzYsvECvSu7aRM0qlT9rp90T4BTa/TGur10TjuuN6S2vxsDdx7WmN8lkJaIciQBlpJHYhPSvoTSXfNJGu9yfeapQ/EqVdVt7ZeLdT33LK/dbEq/Pw+GzQnBwWk2e3UyUZvEIQmk/L1lqd91NdHY7svc6QvunI3nqzox7YCPwOhwZwxw0BjXQxfOi/pW7eypy9VPX/pA3HqGnEdMWePSlV+VxmrgqUxRW2zznbrPMoyrR3nbbqrqoUsasc6x3vxrEblAb9OAltIdjyBGQjChB65yamZryqoumqnsZq+29TYQsYr+8pVJxPYt/2A/U13FX1px5L34obW7LOq2X1QN6jm9BkGBoBAnKNcahO+ZmbfkKbRqN6auvPqTOThazJuM/626QINVTcCJTcx0+bmJEs7TortzjZrJ1/LOadWHWAACMRw811Qq2qMbWpAdcbO+jJ2t7aqa8au4NOmRuy7AXDcxNzQmv3P2gpvMm5xOpOv5dxFngOwQHyB+ISQp50daWNDOnGi+HdnJ81+treltbWjv1tbK35/+bL/tc8/L1261Gy/Vdue3f72trS6evzvf/EX0sc/Lu3v+7ezvl7++7Jjr2KMNB5LDz9c/Ova36VLRflnvFLP6536rH5KD+tpjXUgoxujW9va3KxXjjnzh/IL2tZzcryvMbjOqev3ANxcETrlgxpxha7731zN5K5hSjFqQKG10en2z5xpXnMNzRgvm17TV5v0vU+ebOZUvRHzb2Nw1nTTndFHDAQTTdMDk0v/W0jwa1qmOvMrTyb1A3CbvuuyG5DV1fL+a9dNTC7vYUq55DkAA0AgHpoc+t9Cgl/bGlBVbbSqjzj2zYGvbKdO1Tv2shuN1dWjNzejEcELWBK+QEwfcY5y6H8L6ft9xSva7WNzU3r6aengQPrOd6Rf/dWi33W2H3Zz09+fPN93HKsfdLZsZ85IL7549O9V/eObm4f9yMZIo1ERfm/cOHzOtWvS+96Xrv8fwCAQiHMyTdDa2ysu3rNiJtqECEmmunZNunixXSCZTUqbJmYdHBRBcJrA5LoBGY3cwTsm17moOkfzwfyll44/5+bN5glvABYCgTgXOztFUNvbK3629jAYpwowvszs0Np3m8zp2WO2tvi3LLC7MrsfeuhosJsN3k3KUvdc1Gmh8AXt2b91lS0PIB+uNuuUD/qIS+QybeRsElLqheDrHHPKxKAm5yLWCkezx5syE5nEKqBXIllrALpO0AoJgvMXb9dwppWVZhf2HJLSrG12LppkY88nfE0TuFJnWjPUCOgdgThXsxf3qukSY2sSBH215CYX9qaBJ3btLsYNQUiZ5odGzWdNp7oxWYahVEDmCMQ5Cmn6TVlraRMEfVNK1gmMTWpqKWp3bQOVq0xbW/VuGFIFzFxaHoAlRiDOkW9FoS768doEtKrFEuoExrq12xTBqs65aLoucl83GdZSIwYyQCDOUQ61lKZNvCGBJ6cm9RChTctN1kWu2+IQO6mKPmKgdwTiHA25lhKaUZ3iQt/neWuzLnLIDUPqzHCypoHe+ALx4o0jHso4TN+qR7mbzhpVpe1kH2X6PG+uscD7++GrOLnGHoeOqW4q1nhrAPG5InTKR7Ia8dCa4IZeS2nTRD3btzqtUYaeg75qjr7aeEhfse+zOOQWEgCVtDRN01zMuhXSRF3WFBt7GFQsMSb2cB1b1QIPOeQMAEjGF4gXq2m66ZzAaGZ2YQOXsqbYS5eKqTHLtJkys62ycs2WZ34hh7KpR8ueM5kUi1r4moNzWOgDQC8WKxBzMevetO9xMgnvu626Mdrb66efP+RGLqSvtUl/7JBzBgC0sliBmItZf0Jqi1NVN0bGpEta8nGV67bb0u+7zvkDsFAWKxAv4sUsVRZ4iu2G1gTLbpimjCkC8Kyumqu3t4+vbyxJ1693cyNQtyY9lBECAPxcnccpH4wjDhQzC3w2G3g0Or4AQddJUq6s6Sbjb2NyLWyRW8Lf0EYIAEtOnmQtY+drHx04d+6c3d3d7Xy/g7Oxcbg+8azxuKgxhZqOUXUlSDXdbgqxjnnezk5Rq758uWiC3t4ur3GeOHG8Ri4VNfWDg+b7jy3VeQKQhDHmcWvtubK/LVbT9KKJlQXuy1Jus90UUvTz15ksYygJf4wQABYGgThnsYJC6MW57nZdfZRt+i5T9PO7hiX95E8W+zh5Uvrpny5+P5SEv6HcMACo5mqzTvmgjzhQrH7AkBmw6m7Xt/Rfbn2XVatFTR9bW4fHlvuMZ/QRA4OihZ1ZawgXzLZiHGPZRXt1tUhMarrdugsg9JnsFLpM4cpKf2VsYhk+/8CC8AXi4SZrlSUgra0Nd7hSaDJRLtt3JTW59JnsFJqsJtU7JgAIlCxZyxjzL40xXzfGHBhjSneQTNV0hEOSeuUdKf7qO66+yJWV8t+fOFG/zzjWONn5fmcXV9lTlg0AXFXlkIekeyS9XtLvSDoX+rooTdOLNEn+EBerqNNH3KQ/OmUf6NaWv4+46bHTNAzAQan7iHsJxDkHr7p9d33eVLTpZ3S9dvb3TfuMU7+/W1uHZVtZCQ/CMcpG3y6wdBYzEOdaK2lSrr5uKro4h01vMnJu8WhTtlw/twCS8gXiyj5iY8wXjTFfK3m8u04TuDHmojFm1xize/Xq1TovLZfrvNJN+q77GrvqKut73xuv77PpeNecx8m2Kdsi5TYAiMMVoes81EeNOFdNa0t1myubNG/OvyZkSE+d2lpZmZrWAHOuObYpW841fQDJaCGbpnPVRTNzk0BQ9prQiS6mZfcF/7Ltnzp1uIjC7MIObQJ7LpqWLefcBgDJJAvEkv6ppCuS/p+k/yvp8yGvW+hA3EVNrsnF3PWakGBsTPVxpZi9axHlXNMHkIwvELcaR2yt/Q1r7Vlr7fdYa/+6tfbtbba3ELrou24y4b/rb9YeltU1jnZ9vbpvM2Q+60XqC206jjjX3AYAvRnuzFrLrMkSeCGv8c1Wdv/9RdCeN50xy7V91/OHbNFmdQOQHMsgLpomWdYhr/HV1qoyhUMzvHPIem6LzGcAERGIh6hJ82boa1xTYVYF8s1NaTTyl7vsZmGIU0WyFjCAmFydxykfg03WyjmLtwtVWdPTDOmyZLCy8zXUxCUynwHUpIVcfalr9Au6uVY3Go2khx5yn58mfd054LMAoCZfHzGBONRQg0YXmp4b11KKQ0joSr1sJYCFQiCOYchBI7Wm54abGwBLgqzpGHKe+7hvTc9NX3NsA0BGCMShCBpuTc8Nk1sAAIE4GEHDrc25cQ2XAoAlQSCuY2hBwzdGN/b43aGdGwDIBIF4UU2H2OztFYlUe3vFzzs7/r+FbHdoE3AAQMbIml5UvoxkqVm2MuNnAaARsqYXTUit1DcNY9MpGpljuZkuWxFosQAG52TfBUBN87XSabOydLRWur5eXuudDiny/c2FOZbrC32/hrYvANHQND00oZNg+JqRpWZNzEzAUV+X54z3B8gWTdOLJLRW6htS1HS4EWOp6+uyFYEWC2CQqBEPTd+1HuZYrocaMQBRIx62+eSb8+f7rZUyXrieLlsRaLEABolAnLOy8b6f/KT03vcyw9dQdDkjG7O/AYNE03TOaGoEgIVA0/RQkXwDAAuPQJwzll4EgIVHIM4ZyTcAsPAIxDkj+QYAFh5TXOZuOgEHAGAhUSMGAKBHBGIAAHpEIAYAoEcEYgAAekQgxnDNz8O9s9N3iQCgtlZZ08aYX5L0TyS9KOmPJb3PWvvnEcoF+M2vt7y3V/wskWUOYFDa1oi/IOlea+2bJX1L0ofbFwkIcOnSYRCeev754vfUlAEMSKtAbK39LWvtS7d+/F1JZ9sXCQjgmm97WjOeXbHq4kWCMYBsxewjfr+kz7n+aIy5aIzZNcbsXr16NeJusZRc822vrLhrygCQocpAbIz5ojHmayWPd88855KklyQ5qx3W2oetteestefuuOOOOKXH8nLNw72/X/58VqwCkKnKZC1r7Y/6/m6MeUDSOyW9zfaxuDGW0zQh69KlIsiurxfB+dKl8jWcWbEKQKbaZk2/Q9LPSfoH1trnq54PROWah3s2m1pixSoAWWvbR/xRSa+S9AVjzFeMMf8tQpmA5lixCsDAtKoRW2vvjlUQIBpWrAIwIMysBQBAjwjEAAD0iEAMAECPCMQAAPSIQAwAQI8IxAAA9IhADABAjwjEQ8HSfgCwkFpN6IGO7OwcnbZxurSfxMQVADBw1IiH4NIllvYDgAVFIB4C1xJ+LO0HAINHIB4C1xJ+LO0HAINHIB6C7e1iKb9ZLO0HAAuBQDwELO0HAAuLrOmhYGk/AFhI1IgBAOgRgRgAgB4RiAEA6BGBGACAHhGIAQDoEYEYAIAeEYgBAOgRgRgAgB4RiAEA6BGBGACAHhGIAQDoEYEYAIAeEYgBAOgRgRgAgB4RiAEA6FGrQGyM+Q/GmK8aY75ijPktY8zfilUwAACWQdsa8S9Za99srf07kn5T0i+2LxIAAMujVSC21v7lzI+vlGTbFQcAgOXSuo/YGLNtjHlG0qY8NWJjzEVjzK4xZvfq1attdwuXnR1pY0M6caL4d2en7xIBADyMtf5KrDHmi5L+RsmfLllrPzPzvA9LOm2t/XdVOz137pzd3d2tW1ZU2dmRLl6Unn/+8Hdra9LDD0ubm/2VCwCWnDHmcWvtudK/VQXiGjtZl/RZa+29Vc8lECeysSHt7R3//XgsPf1016UBANziC8Rts6a/b+bHd0v6RpvtoaXLl+v9HgDQu5MtX/8RY8zrJR1I2pP0gfZFQmPr6+U14vX17ssCAAjSKhBba/95rIIggu3t8j7i7e3+ygQA8GJmrUWyuVkkZo3HkjHFvyRqAUDW2jZNIzebmwReABgQasQAAPSIQAwAQI8IxAAA9IhADABAjwjEAAD0iEAMAECPCMQAAPSIQAwAQI8IxAAA9IhADABAj6KtR1xrp8ZcVbFa0zK4XdJ3+i7EEuK894Pz3g/Oez/qnPextfaOsj/0EoiXiTFm17UYNNLhvPeD894Pzns/Yp13mqYBAOgRgRgAgB4RiNN7uO8CLCnOez847/3gvPcjynmnjxgAgB5RIwYAoEcE4sSMMb9kjPmGMearxpjfMMa8pu8yLTJjzDuMMd80xvyRMebn+y7PMjDGvNYY89vGmCeMMV83xnyo7zItE2PMijHm940xv9l3WZaFMeY1xphfv3Vtf9IY85Y22yMQp/cFSfdaa98s6VuSPtxzeRaWMWZF0n+R9I8lvVHSBWPMG/st1VJ4SdK/tda+UdIPS/og571TH5L0ZN+FWDIPSXrUWvsGSX9bLc8/gTgxa+1vWWtfuvXj70o622d5FtzflfRH1to/sda+KOnTkt7dc5kWnrX2T621v3fr/9dVXJTu7LdUy8EYc1bSj0v6eN9lWRbGmL8m6a2SPiFJ1toXrbV/3mabBOJuvV/S5/ouxAK7U9IzMz9fEQGhU8aYDUnfL+lLPRdlWfyypJ+TdNBzOZbJXZKuSvrVW10CHzfGvLLNBgnEERhjvmiM+VrJ490zz7mkoglvp7+SAukYY85I+u+S/o219i/7Ls+iM8a8U9Kz1trH+y7Lkjkp6Qck/Vdr7fdLek5Sq3yUkzFKteystT/q+7sx5gFJ75T0Nst4sZS+Lem1Mz+fvfU7JGaMWVURhHestf+j7/IsiR+R9C5jzHlJpyW92hgzsdb+ZM/lWnRXJF2x1k5bfX5dLQMxNeLEjDHvUNF09C5r7fN9l2fB/W9J32eMucsYc0rSeyT9z57LtPCMMUZFf9mT1tr/1Hd5loW19sPW2rPW2g0Vn/X/RRBOz1r7Z5KeMca8/tav3ibpiTbbpEac3kclfY+kLxTXK/2utfYD/RZpMVlrXzLG/GtJn5e0IulXrLVf77lYy+BHJN0v6f8YY75y63e/YK39bH9FApJ6UNLOrRv+P5H0vjYbY2YtAAB6RNM0AAA9IhADANAjAjEAAD0iEAMA0CMCMQAAPSIQAwDQIwIxAAA9IhADANCj/w9sbMT+4ukvPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the data \n",
    "plt.figure(figsize=(8,6))\n",
    "data = np.loadtxt('NLS_2.csv',delimiter=\",\")\n",
    "X = data[:,:2]\n",
    "y = data[:,2]\n",
    "\n",
    "color = ['b','r']\n",
    "labels = ['0','1']\n",
    "for i in range(len(color)) : \n",
    "    idx=np.where(data[:,2]==i)\n",
    "    plt.scatter(data[idx,0],data[idx,1], c=color[i], marker='o',label=labels[i])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recap Classification : \n",
    "Classification is the process of predicting the class of given data points. Classes are sometimes called as targets/ labels or categories. Classification predictive modeling is the task of approximating a mapping/decision function $(\\hat{f})$ from input variables ($X$) to discrete output variables $(y)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a data-science project the common practice is to split randomly your data between three datasets :\n",
    "\n",
    "- a train dataset for fitting the paramaters of the models\n",
    "- a test data set to compute the test-error on unseen data and deduce the hyperparameters (like regularisation value C) which minimise the test error \n",
    "- finally a generalisation dataset to evaluate the final error of the models i.e the error on unseen data with a tuned model. \n",
    "\n",
    "#### => In this Lab session we are going to use only 2 datasets : a train dataset for fitting the parameters and a test-generalisation dataset for finding the best parameters and evaluate the generalisation error.\n",
    "\n",
    "\n",
    "A more advanced advanced way to split the data is the use of the cross validation, you will see this later normally.\n",
    "\n",
    "\n",
    "### Task : split your dataset between a train dataset and a test dataset. (The size of the test dataset must be 33% of the size of the dataset)\n",
    "Use https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\"\"\"Output must be :\n",
    "X_train : features of the train dataset\n",
    "y_train : labels of the train dataset\n",
    "X_test = features of the test dataset \n",
    "y_test = labels of the test dataset \"\"\"\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression - Recap :\n",
    "\n",
    "The idea is to put a probabilistic model on the Random Variable $Y \\text{ (label) }$ given  $ X(\\text{features)}$ for an observed data. We suppose that : \n",
    "$$ P(Y=1|X=x)=\\frac{1}{1+e^{-\\langle X,\\beta \\rangle}}=\\frac{1}{1+e^{-(X^1\\beta_1 +...X^p\\beta_p )}}$$\n",
    "So the goal of the fitting part is to find the vector $\\beta$ maximising the likelihood of the train dataset\n",
    "\n",
    "### Question : Write the log-likelihood for the train dataset i.e $((x_1,y_1),...(x_n,y_n))$ :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer : \n",
    "\n",
    "We consider the labels $y$ to be 1 or 0\n",
    "\n",
    "The likelikehood function is equal to:\n",
    "\n",
    "$\\mathcal{l}(Y;\\beta,X) = \\prod_{i=1}^n p(x_i)^y_i \\cdot (1-p(x_i))^{1-y_i}$\n",
    "\n",
    "where $p(x_i)$ equals to probability that observation $x_i$ has label 1. \n",
    "\n",
    "$p(x_i) = \\frac{1}{1+e^{-(X^1\\beta_1 +...X^p\\beta_p )}} $\n",
    "\n",
    "Therefore the log-likelihood is equal to: \n",
    "\n",
    "$\\mathcal{L}(Y;\\beta,X)= \\sum_{i=1}^n y_i \\cdot \\log(p(x_i) + y_i \\cdot (1-y_i) \\cdot \\log(1-p(x_i)) \\iff$\n",
    "\n",
    "$\\mathcal{L}(Y;\\beta,X) = \\sum_{i=1}^n y_i \\cdot \\langle X^i,\\beta \\rangle + \\log(\\frac{1}{1+e^{\\langle X^i,\\beta \\rangle}}) $\n",
    "\n",
    "where \n",
    "- $X^i$ is a $i^{th}$ observation therefore vector of size $(1,p)$, $p$-number of features.\n",
    "- $\\beta$ is a vector of model parameters of size $(1,p)$\n",
    "- $n$ is a number of observations\n",
    "\n",
    "\n",
    "\n",
    "### After the training, $\\beta$ is set. How does the model assign the labels for the test dataset i.e $((x'_1,y_1),...(x'_k,y'_k))$. \n",
    "\n",
    "Answer : When $\\beta$ is set, model computes probability of $p(x_i)$. If it is bigger or equal to $0.5$ it assgins label $1$. Otherwise it assigns label $0$.\n",
    "\n",
    "### Task : Fit a Logistic Regression model on the train dataset with arbitrary regularisation\n",
    "use : https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#fitting model with default regularization 1\n",
    "model = LogisticRegression(penalty = \"l2\").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to evaluate the performances of our model. There are plenty of metrics for classification problem, we will work with two non neglible metrics.\n",
    "$$ \\text{Accuracy} = \\frac{\\text{Number of right classifications}}{\\text{Number of classifications}} $$\n",
    "\n",
    "\n",
    "$$Auc = P(\\hat{f(X)} >\\hat{f(X')}|Y=1 , Y'=0) $$\n",
    "\n",
    "Given a randomly chosen observation $X$ belonging to class 1, and a randomly chosen observation $X'$ belonging to class 0, the AUC is the probability that the evaluated classification algorithm will assign a higher score to X than to X'. For instance the score of our Logisitic model is $P(Y=1|X=x)$\n",
    "\n",
    "\n",
    "PS : The AUC could be considered as the most important metric in a classification problem, because it's insentitive to unbalanced dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK : Print the Accuracy and the AUC of our model on the test dataset\n",
    "use : \n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy: 0.8028169014084507\n",
      "Model auc score: 0.9035139964264444\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "#making predictions of test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "#calculating metrics on test set\n",
    "acu = accuracy_score(y_test,y_pred)\n",
    "auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "print(f\"Model accuracy: {acu}\")\n",
    "print(f\"Model auc score: {auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to tune the hyperparameters especially the regularisation parameter (C).\n",
    "\n",
    "### Task : Fill error_c_Logistic.\n",
    "For a list of predefined C values, the function plot_error_c_Logistic will fit a model and compute the error for each value of C in order to select the parameters with the highest accuracy or AUC. \n",
    "Return the best C and plot the test accuracy with respect to C, with a L2 regularisation. Try with different C beetwen 0.01 and 1\n",
    "\n",
    "We can use np.linspace(0.01,1,n) to divide equally [0.01,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error_c_Logistic(X_train,X_test,y_train,y_test,C):\n",
    "    test_acu = list()\n",
    "    test_auc = list()\n",
    "    for c in C:\n",
    "        #fitting model\n",
    "        model = LogisticRegression(penalty = \"l2\", \n",
    "                                   C = c).fit(X_train, y_train)\n",
    "        \n",
    "        #predicting values\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        #calculating performance score on test data\n",
    "        acu = accuracy_score(y_test,y_pred)\n",
    "        auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "        \n",
    "        #appending to list of all test metrics\n",
    "        test_acu.append(acu)\n",
    "        test_auc.append(auc)\n",
    "    \n",
    "    #plotting graphs\n",
    "    plt.plot(C, test_acu, label = \"Accuracy\")\n",
    "    plt.plot(C, test_auc, label = \"AUC\")\n",
    "    plt.xlabel('C')\n",
    "    plt.ylabel('Performance function')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    #finding C for maximum acu and acuracy \n",
    "    c_max_acu = C[test_acu.index(max(test_acu))]\n",
    "    c_max_auc = C[test_auc.index(max(test_auc))]\n",
    "    print(f\"Maximal test Accuracy - {max(test_acu)} - is obtained for c = {c_max_acu}\")\n",
    "    print(f\"Maximal test auc - {max(test_auc)} - is obtained for c = {c_max_auc}\")\n",
    "    return c_max_acu, c_max_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximal test Accuracy - 0.8098591549295775 - is obtained for c = 0.2705263157894737\n",
      "Maximal test auc - 0.905300774270399 - is obtained for c = 0.06210526315789474\n"
     ]
    }
   ],
   "source": [
    "C = np.linspace(0.01,1,20)\n",
    "c_max_acu, c_max_auc = error_c_Logistic(X_train,X_test,y_train,y_test,C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task : Train a model with the best value of C and return the AUC and the accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose c equal to  0.06210526315789474 as both AUC has highest value for this parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy: 0.795774647887324\n",
      "Model auc score: 0.9045066507841969\n"
     ]
    }
   ],
   "source": [
    "#fitting model\n",
    "model = LogisticRegression(penalty = \"l2\", \n",
    "                           C = c_max_auc).fit(X, y)\n",
    "\n",
    "#predicting values\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "#calculating performance score on test data\n",
    "acu = accuracy_score(y_test,y_pred)\n",
    "auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "\n",
    "#printing metrics\n",
    "print(f\"Model accuracy: {acu}\")\n",
    "print(f\"Model auc score: {auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDA \n",
    "\n",
    "As the Logistic Regression, LDA puts a probalistic model on the data. This time $P(Y)$ and $P(X |Y)$ are modelized. It assumes that X are sampled from a K gaussian mixture with K the number of classes.\n",
    "$$P(Y=k) = \\frac{\\text{Number of train samples labeled K}}{\\text{Number of train sample}}$$\n",
    "\n",
    "$$P(X=x |Y=k) = \\mathcal{N}(x,\\mu_k,\\Sigma_k) $$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question :  How does the model assign the labels for the test dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer : \n",
    "\n",
    "The LDA classifier assumes that the observations in the $k^{th}$ class are drawn from a multivariate Gaussian distribution \n",
    "$\\mathcal{N}(μ_k,Σ)$, where $μ_k$ is a class-specific mean vector, and $Σ$ is a covariance matrix that is common to all K classes. LDA classifier computes following:\n",
    "\n",
    "$\\hat \\delta (x) = x^T \\Sigma^{-1} \\hat \\mu_k - \\frac{1}{2} \\hat \\mu_k \\Sigma^{-1} \\hat \\mu_k + \\log(\\hat \\pi_k)$\n",
    "\n",
    "where:\n",
    "- $\\hat \\delta_k (x)$ is the estimated discriminant score\n",
    "- $\\hat \\mu_k$ is the average of all the training observations from the $k^{th}$ class.\n",
    "- $\\Sigma$ is estimated covariance matrix on training data.\n",
    "- $\\hat \\pi_k$ is the prior probability that an observation belongs to the $k^{th}$ class.\n",
    "\n",
    "A new observation will be assigned to class k where the discriminant score $\\hat \\delta_k(x)$ is largest.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task : Fit a LDA model on the train dataset and returns the test metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomLDA():\n",
    "    def __init__(self):\n",
    "        self.means = []\n",
    "        self.priors = []\n",
    "        self.covariance_inv = []\n",
    "        self.classes = []\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        num_observations, num_params = X.shape\n",
    "        num_classes = len(np.unique(y))\n",
    "        \n",
    "        self.classes = np.unique(y)\n",
    "        \n",
    "        #computing mean vector per class and priors:\n",
    "        for c in self.classes:\n",
    "            #means\n",
    "            self.means.append(np.mean(X[y==c], axis = 0))\n",
    "            #priors\n",
    "            self.priors.append(len(y[y==c])/len(y))\n",
    "        \n",
    "        #computing covariance\n",
    "        cov = np.zeros(shape=(X.shape[1], X.shape[1])) \n",
    "        for c in self.classes: \n",
    "            Xg = X[y == c, :] \n",
    "            cov += np.count_nonzero(y==c) / len(y) * np.cov(Xg.T, bias=1) \n",
    "        self.covariance_inv = np.linalg.inv(cov)\n",
    "    \n",
    "    def compute_score(self,x, k):\n",
    "        mean_k = self.means[k].reshape((x.size,1))\n",
    "        prior = self.priors[k]\n",
    "        cov_invers = self.covariance_inv\n",
    "        return x @ cov_invers @ mean_k - 1/2 * mean_k.T @ cov_invers @ mean_k + np.log10(prior)\n",
    "    \n",
    "    def predict(self,X):\n",
    "        result = []\n",
    "        for x in X:\n",
    "            #computing discriminant scores\n",
    "            scores = []\n",
    "            for c in range(len(self.classes)):\n",
    "                scores.append(self.compute_score(x,c))\n",
    "            #index of a class\n",
    "            idx = np.argmax(scores)\n",
    "            result.append(self.classes[idx])\n",
    "        return np.asarray(result)\n",
    "    \n",
    "    @staticmethod\n",
    "    def proba(x):\n",
    "        e_x = np.exp(x)\n",
    "        return e_x / e_x.sum()\n",
    "    \n",
    "    def predict_proba(self,X):\n",
    "        result = []\n",
    "        posterior = []\n",
    "        for x in X:\n",
    "            #computing discriminant scores\n",
    "            scores = []\n",
    "            for c in range(len(self.classes)):\n",
    "                scores.append(self.compute_score(x,c))\n",
    "            #index of a class\n",
    "            idx = np.argmax(scores)\n",
    "            result.append(self.classes[idx])\n",
    "            posterior.append(float(self.proba(scores)[idx]))\n",
    "               \n",
    "        return np.column_stack((result,posterior))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model test accuracy: 0.8028169014084507\n",
      "Model test auc score: 0.5239229700218384\n"
     ]
    }
   ],
   "source": [
    "#fitting model\n",
    "model = CustomLDA()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "#predicting values\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "#calculating performance score on test data\n",
    "acu = accuracy_score(y_test,y_pred)\n",
    "auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "\n",
    "#printing metrics \n",
    "print(f\"Model test accuracy: {acu}\")\n",
    "print(f\"Model test auc score: {auc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model test accuracy: 0.8098591549295775\n",
      "Model test auc score: 0.9009330950962874\n"
     ]
    }
   ],
   "source": [
    "#comparing to the sikit learn implementation\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "#fitting model\n",
    "model = LinearDiscriminantAnalysis()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "#predicting values\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "#calculating performance score on test data\n",
    "acu = accuracy_score(y_test,y_pred)\n",
    "auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "\n",
    "#printing metrics \n",
    "print(f\"Model test accuracy: {acu}\")\n",
    "print(f\"Model test auc score: {auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Difference in AUC comes from different ways of estimating posterior probaility. I use softmax, sikit learn uses different function when number of classes is equal to 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM\n",
    "A support vector machine constructs a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. \n",
    "\n",
    "### Question Bonus : Suppose that the labels $\\in \\{1,-1\\}$. Write the Loss fonction $\\mathcal{L}(y,x;\\theta)$ of the SVM for a train dataset ((x_1,y_1),...(x_n,y_n)). Then compare to the Logistic Regression with Ridge regularization and comment. "
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer :\n",
    "\n",
    "Let's define loss function for observations $x_i$ and paremters of model $\\theta$\n",
    "\n",
    "$L(x,y;\\theta)=\\sum_i^n f(x_i,y_i,\\theta)+R(\\theta)$\n",
    "\n",
    "In SVM: \n",
    "- $R(\\theta) = \\frac{1}{2}||\\theta||^2$\n",
    "- $f(x_i,y_i,\\theta) = \\text{ hinge loss } = \\max(0,1-y_i(\\theta^Tx_i))$\n",
    "\n",
    "In Logistic Regression with Ridge regularization there are two ways of writing loss depending on $y$ labels. When $y_i \\in \\{-1,1 \\}$:\n",
    "- $R(\\theta) = \\frac{1}{2}||\\theta||^2$\n",
    "- $f(x_i,y_i,\\theta) = \\text{ logistic loss } = \\ln(1+e^{-y_i\\theta^Tx_i})$\n",
    "\n",
    "As the regularization term is the same we can compare only loss term dependen on $x_i$. We can see they are similar but not the same, logistic loss is smooth. (0-1) is an indicator whether $y\\cdot\\theta^Tx_i>0$\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "x axis is $y \\cdot \\theta^Tx_i$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task : Fit a SVM model on the train dataset and return the test metrics\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model test accuracy: 0.8802816901408451\n",
      "Model test auc score: 0.9616835417907484\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "#fitting model on train data\n",
    "model = SVC(probability=True)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "#predicting values\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "#calculating performance score on test data\n",
    "acu = accuracy_score(y_test,y_pred)\n",
    "auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "\n",
    "#printing metrics \n",
    "print(f\"Model test accuracy: {acu}\")\n",
    "print(f\"Model test auc score: {auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Kernel function transforms the training data so that a non-linear decision surface is transformed to a linear equation in a higher number of dimensions.\n",
    "Linear discriminant functions can provide very efficient 2-class classifiers, provided that the class features can be separated by a linear decision surface.\n",
    "\n",
    "The SVM model from sklearn have kernels already implemented. \n",
    "### Task : Try SVM with different kernels to increase the test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_per_kernel_performance(X_train,X_test,y_train,y_test):\n",
    "    #all possible kernels implemented in sickit learn SVM\n",
    "    kernels = ['linear', 'poly', 'rbf', 'sigmoid'] \n",
    "    acu_list = list()\n",
    "    auc_list = list()\n",
    "    for kernel in kernels:\n",
    "        #fitting model on train data\n",
    "        model = SVC(kernel = kernel, probability=True)\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        #calculating test score\n",
    "        y_pred = model.predict(X_test)\n",
    "        acu = accuracy_score(y_pred,y_test)\n",
    "        auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "        \n",
    "        #append test metrics on this particular kernel\n",
    "        acu_list.append(acu)\n",
    "        auc_list.append(auc)\n",
    "    \n",
    "    #plotting accuracy and auc\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
    "    ax[0].bar(kernels,acu_list)\n",
    "    ax[0].set_title('Test accuracy', size=16)\n",
    "    ax[0].set_xlabel(\"Kernel\")\n",
    "    ax[0].set_ylabel(\"Metric value\")\n",
    "    \n",
    "    ax[1].bar(kernels,auc_list)\n",
    "    ax[1].set_title('Test auc', size=16)\n",
    "    ax[1].set_xlabel(\"Kernel\")\n",
    "    ax[1].set_ylabel(\"Metric value\")\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    #finding maximal accuracy and auc\n",
    "    k_max_acu = kernels[acu_list.index(max(acu_list))]\n",
    "    k_max_auc = kernels[auc_list.index(max(auc_list))]\n",
    "    \n",
    "    #printing kernels that give highest acu and auc \n",
    "    print(f\"Maximal test Accuracy - {max(acu_list)} - is obtained for kernel = {k_max_acu}\")\n",
    "    print(f\"Maximal test auc - {max(auc_list)} - is obtained for kernel = {k_max_auc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximal test Accuracy - 0.8802816901408451 - is obtained for kernel = rbf\n",
      "Maximal test auc - 0.9617828072265238 - is obtained for kernel = rbf\n"
     ]
    }
   ],
   "source": [
    "plot_per_kernel_performance(X_train,X_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot decision function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to visualize the impact of the kernels, we are going to plot the decision fonction for different models : Logistic Regression, LDA, Vanilla SVM, SVM with polynomial kernel, SVM with RBF kernel.\n",
    "### Just run the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WV1Kwl8CwfhhMEQBEJlzOlh8+QlGUC7aMryRJkvTXpng8rPv+BXZv/gxQ6NhzCoMufhihUhEUmsToiSeuGlhZepjCnO0YfYJJbjsclUp9bgNvQTWVuWh1gQSG9QTAP7gTRp8oqsoziTb/ub97/FYyCZOkv4ilc+7HajPS/aL5WOvzWLHwUQKC44mM7XzSbVZ/92/Sdy/FL6gjtRWv0bXfNfQeevu5C7qF+QVEUV87B4/bgUqto7ZyN2bfSJmASZIkSefNzo0zydi/ik4DPgIhOLLrScx+H9Jj4E0n3ebIgR/44ev/IzC8D42WbPZs/oIJ13/0p03EfPzCsTWWYWssxmCKxG6roNFS+Kcot/9HkUmYJP1F5GVtoOuQL9FoTPgGtCEkcij5RzefNAmrKj/K/u3f0HnATDQ6Xxy2SrauvpYOPSb+aUu9p6SNJH3Pd+zdcBNGn1gsVQcYN+3t8x2WJEmS9DeWnbGOqMTJ6I3ea2tk4hSyM344ZRK2fN6jtO72DH6B7VAUNwc2303mge9p3eGScxV2izL5hDBg1INsWH4HfoFpWGrS6TXkVvwCY853aOeNTMIk6S/CYAqk0ZKNf3AnFEXB2pCL0XTyR/yN9RUYzVFodL4A6AzB6AzBWBuq/rRJmFCpGDPldQpztmFtqCIittPf+i6bJEmSdP6ZfIJoqM8mmIEAWOtzMPkEnbS94vFgs1bj49cKACHUmHyTaLCUn5N4/yhd+l5DXHJvKssyCQxNIjSizfkO6bySSZgk/UUMG/sk33/9IMGRg7A1FKDVuZvnhJ1IcHhrbI2FVJZsICi8D+WFP6IodvyD439zDNaGaqyNVfgHxqLW6H5zP7+HEIKYxJ7nZd+SJEmS9Gv9LrqbWW9djq2xACFU1FXuYPKt35y0vVCpiIzrTv6RT4hrfT2N9TlUlW4gKn7Gb47B6WikrqYIX/8IdHqf39zP7xUc3prg8Nbnbf8XEpmESdJfREq7EVwVFEt+1iYMpkG07nAxGq3hpO2NpgDGX/s+i7+4m4wdj+MXmMDl13+E9hTbnMrmVW+xZdWb6PT+qFRw+Q0zCZEftJIkSdLfnH9QHNfe/R2ZB5ahoJCS9i98/MJPuc2YKa+x6PPb2fT9KLQ6M8PH/ZPw6Pa/af85R9ax+Is70Wh8cDhqGHn5c6R2/HMOa/wrkUmYJJ2FyrJMFn9xN1Vlh/ALjOeSSS9fUOXeQyPbEhrZ9ozbR8d3Z8bDG3C7najV2t+834Kcbexc/yldBn6CzhBMSd5ivv38Dq6/b9lv7lOSJEmSzpTdZmHpnPvJyViFVu/DoIsfon33iec7rGZm3zA69b76jNv7+IUz5ba5uN1OVCrNby4w5bA3sPiLO2nV+Sn8gztRX3uEZfMeIDqh+2kTQemPJdcJk6Qz5HY5mPvhtfiFXETPkd8RHn818z6+AVtj7fkO7Xf7PQkYQEVxOgGhPdAZggEIjxlFdcURFI+nJcKTJEmSpFNaNu9R6i0K3S+aT5vuz7FmyQsUHN1yvsP63dRq7e+q8GupKUSj9cU/2HvD2Me/FWbfeKrLj7ZUiNJvJJMwSTpDNVV5eDwQmTAOtVpPSNQQDOYoykvSz3do511AcDx1VXtxuRoBqCrbgo9/DEIlP2IkSZKkP15e5npiW9+IRmPC7JdCaPQI8rI2nu+wzjsf/wgc9moa6rxJl62xhAZL7t+6KuGFQg5HlKQmiqLg8bhO+lTIaArEaa/BYa9Gpw/E7WrE2lCC0XzyCkd/F/GtBpKSNog9a6/D6BON1ZLL+OveP99hSZIkSX8Rpxs2760QnIPeGPZzhWAfOS9Zb/DjovHP8OPCezH7JdJQl03/EffhHxR7vkP725NJmCQBWekr+P7rB7BbawiJaMu4aW/jHxR3TBuTTzDd+k9n7+Y7CQjpRV3VbpLaDEanN+PxuP+0Cyi2BCEEw8f/k869p9JYX0FIZFtMMjmVJEmSfqeq8qMs+vw2qsoyMJhDuHjiiyS0Hnhcu2Fjn2DxF3cRHDkYW2MxKmEhLqkvLqftlEWq/g7adhlPTFIvqsqzCAiKO+77jXR+yCRM+turqcxl6ez7aN31X/gGplF0dA7zZt7E9ff+cFzbfiP+QUxSDyqKD1FWBBl7v+XooZUYjP5cfsNMAoJb7oNN8XhwOhvR6sy/azz4uRQSkQqknu8wJEmSpL8AxeNh3sc3EBw5htTub1JXuYfFX9zFtf9YetwakAmtBzHp1q/Jy9yApTaMvVvn8NW7U1A8Di6Z/CqJqYNbNDaHvR6t1vSnGXbv6x8p1828wMgkTPrbK87fjX9IF/yCvKVfo5KuIv/IJ9htdegNfse1j0/pj0qo2brmQ7oM/gy9IYTCo7NZ/OXdXH3H/BaJKfPgcpbOuQ+X04ZvQDSXXfsewWGtWqRvSZIkSfozaKgvx9pYQ2TiBAD8Q7rgG9iW0oJ9J0woQsJb4+sfwfv/HUjrLk/hH9yZuqp9fPflPdz4wCqM5sDfHVNVeRYLPplBbXUeGo2ekVc+T+v2o393v9Lfz58jfZekP5DZJ4RGSzYetwMAa0MeQgi0OvNJtykt2k9AWB/0hhAAIuLGUl68H4Ccw2vYsPwl9m75ArfLcdbx1Fbl8f2cB2jT7b/0HvU9IVGXMe/jG/6wSoOKolBfV0p9XSmKovwh+5AkSZKks6U3+uNx27E1FAHgdttotORi9g096TY1lbnojMH4B3cGwC+oAwZzBNWV2VSUHmbTitfYtuYdGixlZx2PoijMmzmdwIhL6D3qe9r2eIFlcx+ipjL3Nx3fmbA11lJblYfH7frD9iGdH/JJmPS3F5vcl6j4juzfdBtm/9ZUl21h2Lh/nnKOl39gLPXVX+Nx21Gp9dRUbMfXP4Zta95j+7qZBEUOpbFuEwd3LWLiTZ+jUp/5f7WyogP4BbXDN7AtiuLxXnTqa5j/yU0MHP1g05C/luFy2lj4+W0U5WwHICq+G2Onvf2bF2yWJEmSpJai1RoYdPGjbFh+NwGhPaivOURCan8iYjufdBsfvwhsDaXYGoowmKOwNZZgrS+ioa6MeXNvICRqBG5XPdvXf8zVdyw4qyF6NmstDXVltO8zHgCnowahMrLo8zvoM+x2WrUf9TuP+FibfnyNrWveQavzQac3ccWNnxAQHN+i+5DOH5mESX97Qggunfwq2RmrqK8rJSLmDsKi2p1ym5S0EWTsW8qedTdg9ImmoTaTsde8wzcfTPMOUTSGoSgeDmy6nZwja0hqM+y0cSgeD1UVWbhcDuprj+J2NVKQ9SXVZVto1fkRbA0FfPXuJKbduajFqhptWvE69RYn3YbNBSBz9zNs+vFVBo7+v9/dt6Io7N36JdmH12E2B9F72B1yPLokSZJ0Vjr3uZrI2E6UFu3HL2AS8a0GnnKetNk3lIGjH2L9D7fjE9Ca+toj9BtxH9vXf0xcm9sIi7kIgJz0t9mx7kMGX/rYGcVRV1NEo6UcRfHQaMnGYavkyJ7nSEy7HSFULJv3OAhBq3YjW+S4c4+sZ/eW2XQZ/Bk6fRBFR+ew+Mt7WmzaQ37WJvZs+QqEoEufqUQn9GiRfqUzJ5MwScKbiCW1GXrm7VUqLpn0CiUFe7A1VhMW1R6d3owC6JqGKAqhQm+KwG6znLY/p6OReR/fSGV5NgKBSgV7N9yM3VpN5wHvYzBHAWBrzOfw/iX0GDjjNx3nr5UU7CckagQqlbfsb0j0SEoKvm+Rvjcuf4UDO78jImEiVdU5zHpjPNfcvQSTT3CL9C9JkiT9PYTHdCA8psMZt+/c52riW/WnuiKLgOBEgkKT2LdtDsFN11IAvTECm7XkjPpb/d2z7Nv6JXpjCBqtgf2b70aj9Sc+dTqh0T/fZN296YsWS8LKig8QENoHnd5baTgs7lK2//hRi/Sdm7meb2fdSXTKteDxMH/mTYy/9j1iEnu2SP/SmZFJmCT9RkIIIn81JCIipjM56W8SlTgJS81Bait3E53wn9P2tenH13E4jXQZ9DkgyNr3Ar6+grysjXiUn8eBK4obIVpyKqdCVekmgsL7AVBTvpXQ8HgURaG8+CB2ax1h0e1OWKDkdHZu+JCO/T9AbwwHwGEt4ciB7+nUa2oLxi9JkiRJxwsMSSAwJKH5dUracA7tfY+kDg/gctZTnDOHERP+ddp+cg6v5dCeJXQe9BlanR+l+UupKJiLwRSI8ovrs8fjatHrs0BQXbaFuNTpqNV6asq24hvgHQVTW5VHbVU+gaFJv2mEyY51M4lLnUFYjDdhFELNzg2fyCTsHJNJmPS3ZbdZWPX1/eRmbsBk9Kf/Zf/+3SVsx13zDj98/X/s33QLZt9wJlz3Idb6SpbMvg9LTTEupxW320FUXFdGXfkcJh/vU7PykgyCwgchhHceWnDEYGrLF9Fj4E3s2fo0UYlTsDUUUlO+mdSOj//eQ2/a5yFKCvag1vixZ/0MFI8Lp72C8Vev5rsv7yYvayt6YwgOWxlXTP+U0Ig2Z9W/x+NBqH5eWFOl0qF43C0SuyRJkvTXpSgKW398hf2bPgWgY78b6D70jt+1XEufYXfidFpJ33YfKrWOfhfdRVRcF76ddSelRQdwOW24HA34BcYy4vL/EBHTEYDK0sP4B/dAq/PejAyJGsrRfS8y8vJnWfDpDBQUVEJD/pGPuGTSS7//4AG3y8G2tR+i1UWwa/V16I1h1NdmMG7a2+za+Cnrl73YvPDysHFPk9Zl/Fn17/G4j7s+e5zy+nyuySRM+tv68cu7iDi6hwc8IeTVOXj+81u5/PYFv6vwhckcxGXXvd/8uq66gE9fG0Ns65sIiU4i7/BMVCodDlcw82fexNQ75uNy2VEUNxXFqwiK6A9AVek6wiNT6DXkdurryjiw41WESoVQ3FSWZuDrH/G7j78kfw9BYb1JbPcPLDXp3jlsWx7gaMYqSgoz6TxwJiq1ntK8JXw/50Gm3bXorPpv3/1Kjuz+F9FJU2m0ZFNTsZXktCd/d9ySJEnSX9uejTMpWPcxz7oDUIBn17yHwRxMh95TfnOfKrWGwZc8wuBLHgHA43bx2evj0JvSSEh7iMqSdVQU/oh/2MV88+G1XHfvD5h9w7BZ66gu20Rs6+vRaH2oLF6Df3ASMUm9GDbuKX5c+DSKx1tEq6Rwf4usR2apLUFRoH2fl2moPYzTWUfR0c+xNdawftmLdOj7NgZTJI2WbH5ccBdJbYZgMPqfcf+del3FsnmPoxIaFMVD/pEPGT3x+d8dt3R2ZBIm/W0dzVzH08RhFmpChJb+OMjL3HDaJMzjdmGpLUJv9D/th1724TUEhvYiPNa7hkjrLo+xbfll9Bq1hG3Lx9FgqWDRrNuxNjixNpSw7ccrUWt0+AeEM2DUs1gbqkjfvZC0ns/hG9iW2sq9LP7ibm56aO1vGiL4Sz5+4TTUZQIC/+DOWGoOodf7UluVj29gF1RqPQCB4b3JP/zecdtXlR/1luF3O2nbZRxRcV2PeX/IpY+zZfXbZB/+GpM5kEkzvpKFOSRJkqTTytu7hKtdZmJV3uvQVJeZ+fu+O20S9tOSK0IIzL5hp3xyVlOVS2N9Fa273YoQAh//VlSXbcboE4dPYFuKcndSWrCf/TsXodEFs33FVegNwYCdy2/4GIBtaz8gOvlqohIn4LBVsnvjnUTHdyUuue/vOn6jORCnw4LdWoJPQCoupwVbQxEejxuzbxwGk/daavJNRG8Ipr625JjvI3abhR3rP6K+rpTYpF606TT2mHPRqv0oPIqH3Zu+QAAjr3j2rObFSy1DJmHS35Lb5UCr0lLicZKMGkVRKFF7CDf4nnK7qvKjfPPRtTgddpwOC32G3UXPwbeetL1Go8flrGt+7XLWIVRanPYaFMVNQc5WqivyCY4YSmjMaNQaXzJ2Ps6kW9ag0eopzN2O0RyNb2BbAPyDO6IzBFFTmUd4dPvj9udy2Vn17b84sv97NFoj/YbfTbvuV5wwtuiEnqjVsG3FlWg0RtyuBkZNfAEhVOzf/l+ik65Co/OlvOB7Qn41FLGyLJMv376S0OiLUWtMzPv4Ri6d8hoJrQY0t1GpNfQZdid9ht15ynN6MoqiYGusQac3o9boflMfkiRJ0p+P0+OkGGfz62LhQmcKOPU2jkYWfDqDkoK9KIqHuKS+jJn6+kmvHxqNHpfLisfjQK3W4/G4cDvrEUKNvbEUoVKxfd27hMdfht4Yjm/gXRze9QSXXPW/5grKlaXpJHf+HwA6QzABob0pLzp40iRsz5ZZbF75Fm6Xg7adxzJw9EOoNdrj2un0PiSmDmbPuhmoNQYUxUWHHleQ0HogqxY/Q33tEXz8W1FbuRenoxa/wOhfnAcrX751BRp9PCa/VNYufZXKsiz6j7j3mH2kdriY1A4Xn/KcnorD3tAU68nXVJVOTSZh0gWpuiKHguwtGIz+JLUdhlp9/IfUb+V2O5nz/lSEIYxH6wsZiQ85ag+lfmEM7DTmuPaKonBw13zyszaTc3gt4fETiUy4DLutgu3r7iQqvttJJ7OmtBvJppVvkrXvBUw+yRQenYNPQBv2bbwdH79oNq94HSH0CKGiMGt2U7IlUBTvwsx+AdFY6wuwNRZjMEViqT5EY30RltoiQiPaHLf+2Nol/6Ug5zBpvV7HYa/kx4UPsW7Z/5hw3UeERaUd03bpnPvQGmJpl/Yg9TWHKDjyMVFxXTH7hlGYs52dqyej1fmh1Ru48sZPj9l25/qPCY8dT2zrawDQmyLYvPKtY5Kw36Oupoh5H99IbVUeiuJi4OiH6drvuhbpW5IkSfrtbNZasg+tREEhsfUQjObAFu1/7dLnKK0q5XOlhiK3E4+ADVo3V/4qifhJYe52Du5cQHH+HhAhdB86F0XxcHjXU2xd/TZ9ht99wu18A6JJaDWAjO0PERg+kMritQiVhuwDb+B01LJv22yESo9KpaO2YidlBd9j8onC43H9oo9Yasq2Ehw5AKeznuqyzdiS4rDb6o4brZJ5YBkbl79Bq85PotH6krHzafZu/YoRl/+Htp3HHdN296bPKM5Pp033Z/C4rWTte4H4lH74+IUzYsJ/+OGbe9HpAnA5LVw65XV0ep/mbY8eWomCmeSODyGEICRyENtXTaHv8LtPuf7pmXK7HCyZfR9Z6csAaNV+NKOufKFFv6f9XcgkTLrg5Gau59vP7yAgrCe2hkJ2rJ/JldM/bbGnIVkHl9PYYKPjwI+wVO9nQ+EqygqWcPsdi064SPHGH19l//ZFhMaMocFSSnjcpQDoDSEEhvWirOjASZMwvcGXqbfPZ8f6D6mvKyM0MpGS/H1ExI9Ho/UlP/Nzegz/GrXaQFTyVWz/cSIhkWlodUYAfP0j6T/yfjYsuw2DOQpLTRa+ga35ccG/EeJp2nQaQ4+B05sLfGSlryC549MYTBEYTBFEJ0+iumwLc96bxNhp7zTfnXO7nRxNX07PkYtRq/X4+LfGUr2H3CPraNftCgZf8ig9B83AbrPgFxhz3Ier02lDowttfq3V+eNy2n7/L6fJ4i/uxhzQm9Tub2K3lrB55T2ERbcjRq5jIkmSdN7U15Uy680JGEzxCKFm7ZLnmHzr3BZbu7KxoYpdmz6l6+BZuJ0N7C/8keLsr7l00msEhSYf1/6n7wuRiVdRX1dLYrtpCJUGAYREXURx/saT7ksIwSWTX2HP5s8pLUonNCKCo+kHMUe2JiZ5Chm7/knHfm9g9ktBURQObLkfS3XGMQtFXzzpJebPvJHSvG+w1GSiMwRzaO8qdq7/mOS0i+jW//rmUSuZB38kImEiPgHeKQ+J7e8ic8/zLPvmYawNlXTqPa35Wntw10Li2tyKf7C3OEh0yjQO7lpEYupgUjteQmLqICy1JfgFRKHVmY45LpfThkbn3zz8UKP1RVE83sJYLZCEbVr5BpXl5fS4aCGgcHjnE2xb/Q69f+Ool7+zlqx1LUktYvm8x0nu+BApHR+mXe/XsDY6ObirZRYnBO9dPIM5CiFU+AV1JLHd7SiKG80JkjzF42Hbmrdp0+M5ohLHYzBFUlO+DQC7tZyqsi0oihtFUU66P6MpgP4j7mPUFc+heCCl08PEt5mOX1BHdPpg1Gpv4qfRmNDqfBh88UPHbN+l7zVcfecidDoV8W1upn3v1+g04EM0ukgy9q3l8zfGY22sAUCn98XWWNS8rbWhgPraI5j82rH4i3tZvfjfAKiEGqFS43bWe49TUXA561Br9M3bmnxCCAxJPOHdrbadx1B0dBbVZVupq9pP7qG3SOsy7rh2v1Vp4W6iEq9ECIHBFElgWH9K8/e0WP+SJEnS2du4/BUCQgeQ2u3ftO76T4KjRrPuh/+1WP92ax1anS9anT8GcxRxra/BNyAZrVZ/wvZbVr1DfNvbiU6ehF9ge6rLNqMoCm63k/LCZWh0epynuEGoUqnp0vdaRl3xX8Ki0ohIGEtql8cIiugLige90Tv3SgiBwRhOp95TMZmDmrePiuvK9fetICquNQGhXek88GPa9XqVyISJ5BzZwdfvX01x3i4ADEY/7Nbi5m1tDUU47FX4BrRn+7ovmf3upOabmRqtAZfjF1MZHJZjzoFO70NwWMpxCRhAfKv+1FXtoSR3MfW1R8ja9xyJqUNb7EZ2Yc5OwmLHolbrUasNhMVcSmHurhbp++9GJmHSBcfaUN58p0gIFUbfFBrry1us/9ik3lSXbqG6fBtORw256W8Sk9D7uKF9QPPdI43GO+a5VeeHOLzrGfasv5mdq6eh1fqzdfVHLPr8NjxnUH5dqNQoHu84d5NvAi5HLUXZ32C3VVB4dDY6vY7IuC7HbRcQ7B3eEBDSDQCHrRK12kRjXbY3Gdv7LQCDLn6QrH3Pk33wLQ7veoaKolW07/0yaT3/Q8f+73Fg5wLKi9MRKhU9Bt5C+rYHKc6ZT9a+58FTS3LbYcft+0QSUwczbNwTlBfMoijzTbr1nUyXvtee0bZnwuwbQV3VXgA8HicNden4yKIekiRJ51V9XTlmv5+LV5n9Uqmva7nrs39gDDqdgcKjX+F01FJWsAxbYyFhUcfPgQZwuRxotN653HFtplNZvI7da69n+4oraLTkUlqYyaevXkKD5fQxqlSq5uszgF9QJ7L2/Q+7tYzqsi3UlG+ifbfj51h7kzKBX1A3hFDhdttQFBe2xiL8QnqxZY23sFW3AdOpLlvD4V3/JvvAm2Tte4nI+MtI6/UcHfq9g8OhY+/WLwHoPeRWctPfoCDzS/IyPqY09xu69r/+jM6hj184E2+ahb1+E7npzxMeGc4lk185o23PhF9gJJbqfc2vLTX78A2Q1+ffQg5HlC44UfE9KMj8jIQ2t2JrLKGyeCX9h7/ZYv0HhiQyZuobLJ//ONaGcqITejF64htYG6pZ9e2/KCs+RFBoIkPHPkFF6WEMphB2rrqawPB+mP2S0Gh1qFVuEtvdQUTcpXjcDg5uvY+tq98iteOlBAQnIITA2ljD6sXPUF6cQVBoEkPGPEb3ATewZPb9WGoO4XY3oiguLJWrKD46i6Cw1lw5fRYazYnv+IVHd6QkdwFhsZdwcMsD+AV3wjeoPXVV+7HWe0vbJ7QexMSbZrFi4RPUlB8GVFSXbaLRkkVYzEjMvvHU15USGtmWvhfdQ1BYInmZmwkLa023Ac+d8K7aybTpNIY2J5hD1xJGXfkciz67Db/gDljrCwiPakXr9qP/kH1JkiRJZyY2uRd7t87z3hAUKkpy59Km48AW61+l1nDl9E/57qv72LV6Fv5BsVxx4yfo9GY2LHuZzPQVGAx+DBh1HyafUJz2Bg7v+je+AW0Jix2FEApBIZE4nCmkdHoEEOSmv8PyeY/Sb8Q/CApNRq3R4fG42bzidTLTV2IwBTBw5H207XIZ29eNIWu/QCW02KwF+OnD2b/pNgymAC6d8hrB4a1PGHdkXCe2rZlFUHg/0rc9hFpjJiRyIJXFqxCKt7CWr38E19z1LWuXPEvGPu+NU4+7kcKs2YTFjsTs3xZLbQngfZo14foPObhrISq1hmFj5hIclnLG5zE0si1Xzfji9/0yTmLAyPv58u0rSN+agYKCx1XN2Mlz/5B9/dWJUw2j+q0iYjoqV9+5uMX7lf4eGhuqWPT57RTnbkWl1jH40sfo1GvqH7pPj8fNrDfGo9GnEBI1nOqyjdSUr8VurSOp/b3oDCFk7XsZtdrF+GveZs57U2jf7z30hhAc9irSV1+PztWIR6MlPLE3I6e9zVfvTEStTfT2V7qehrqdTLl9HnPenYylrso7j8pRzqRb5hAUmnRG5+Wz18ZgbaghLvUGohInAJC19yWMxgb0Rn9A0Ln3ZKITerBk9r1kpa8lLGYUddX7UDxu7I35XH/fMnz8wv/Q89kS6mqKKMnfhcEUSGxib4RKPrg/mRcfit+hKEr38x2HdOGT12fp9/B43Kxc9DT7tn0JikLbrpcz4rJ/n3AkSUtatfgZjmZsJ7bVDdgai8g99DYatZawuAn4h3SnOGceNeVbuOiyf3JozxLU+q6ERg9HUdwc3nQfjVV78dWZ8ZgDuOyWr9m+7kOOHtpKTKsbsDUUkn/4fabcPo+d62dycNcCTL7xNFqyGXXlC7TucPobgIrHw/xPppN9eDWBYb1o2/3fCCGoLF5L/pF3SW47FGtDDUltBtGu2xXs3z6bFQufJjzuUpyOWmordqHR6LjosidJaTfiDz2XLcFhrycvaxMCQWxyH1kh8RROdX2WT8KkC47JHMSkGV/idjlQqbWnXOejpdRU5mCpK6fLoFe964UEtKWyZB0BoX0JjvTe5WvV+WGO7nua0Mi2hEamUZb3HTGtriF/9wsMcQluViXg8ig8mbOLTctfob62nM6DXkEIgW9gO/ZtuImNy1/Go/jSdfArCKGm6Ohcls17lCtv/OS047VN5iD6DLuT1d89h4//z3fEzAGtyU1/l7jUG0BRmD/zJi6d8jqH931H18Gz0BmCUTwudqy6ml5DbjmjBOyn8vAanfGExUrOBb+AKPwCos7LviVJkqTjqVRqho//J0PHPOF9/QcnXz85uHM+7Xq/jsEUiV9QBxpqD1NdtpHo5MkApHR8gO0rJhCT2JOaylzS9ywnOGIgZQXf4199mHfVSejcgll1Naz++gHyig7SrvcbGEyR+Ad3otGSye5Nn5KxbyldBn+CVhdAfe1hvp97H3HJfTGYTr0mqFCpGDLmCfJe3oBvQJvm7y0mvyRsjdVUVSoYfdqzYflb1FvKOLJ/Ga06PUxwpLei8JE9z6PX1pxxAuaw16Mont+9XuhvpdP7kJJ20XnZ91+JTMKkC9YfuTZUXXUBNmsdQaFJaLQG1GodHpcdxeNAqPWguPG4bXg81uZtflm4YtTE5/ns1TEUZX+DztnAUHU0Qgi0CPq7tKwqz8LttqMoLoTQguLG7bbRaCnHN6gzQngrFAWEdmfv+o947Yk0OvaaytAxT57yiU/77leyfe2H5GZ8RNvu/8LtslKU9RVBEQOJTLgMALutnKWz70NRBFq9dwKxUGnwDUg8YXWpX2uwlDFv5k1UlWWiKC56D72T3kPv+M3nWpIkSfpr+SOTL2tDNXU1hfgHxjYnP2qNDldTISkAt6sej9uBonia52F53E7UGj09Bt5MVvoKtiwbh9rjYprwRy+819VBipnlJRmoVBrcrp+v7263FYddg09Aa7S6AAB8/Fvjcbl565luRMV1Z9y0t09Zjj8wJIG0bhM4tHs+wZGD0RtCycv4EJ0hlPg20wEwGKPYsuoRBCqikn+eR2X0iSMoMOK058bjdvHDNw+TsXcRIIhvNZBLp7x23m6WSr+PHN8jnReW2hIO7fmW7IxVeNyu02+Ad22K30tRFFYseJJPXrmY+Z/cxkcvDqe6Ihu/wBhik3uTseMxSnIXc3jXUwSFxlNfu5+cg29TdPRrMvf8hz5Dbwe863eFx7THJ6AtviHdWK80eisMKgqbNE7C4rsRHd+NjB2PUZq3hMO7/0lQaDzxrQdQVbISl9N7F6s4ZwEBYT3pcdE8sg9vZ/fmz04Zv0ql5pq7FxMaFs62Hy9n99rr0Rv0+Ad7i3k01udSkvstUcnXYTBFkJP+DnZbBeWFK6ivzThh0Y9fWzrnQXTGtvQc8S1dB89i96avyM5Y9bvPvSRJknThczoayTywjMP7vmuuvHs6brfzlFWCz9TBXQt4//kBLPzsbt5/fgCZB7xrUfUafCtHdj9Nce5CctLfxlK9m4CQOA7veprinPkc2vYgbTqPxWgKQK3R0aHHVajUeiJaXc1G4cTZtPbmBhoICkuh5+BbOLzrSUpyF5OT/g6W6l106DkJS9UBGi05AFQWr0WtMdJ71HcoIoIfvnnoZGE3GzHhWboPuI79G29j67Kx4CnHL8i7sLPHbefI3ucJixlNQGhPju5/HVtjMZbqA5TmziOp7dDT9r9j/UcU52fSY/g8el60EEutjQ3LXvqNZ1s63+STMOmcK87fzaL3p5KmMlGuONkZlsD4GXNO+uSrvOQQiz67jdqqHEw+oVwy+VVik3p7+8rbxdrv/4fNWkty26H0HXbXKe/QZR74nqxD6+kyeBYarQ9FR+eyZPZ9TL19HpdOeY1dG2ZSWpROaoc+dB8wncaGSnZu+ASHvZZLJr90zGLEETEdcLrLiU6ayI8b7mSDs4RGVyMG/1gG9b+BLv1vYMf6jygrSqd1u570GHgTao2e0oL97Fh5FQoCvSGU9n1eRqP1JSzmUvKzt5+2yqBGq2f8te81X/C2rH6LbWveQq3WU1GyhtDoYYTHjSYwrCeHd/2bnaumEhTaistv+ASzb+gp+wYoKdhDx353IIRAZwgmMGIwRXm7SEwdctptJUmSpD8va2MNc18fQ0hjA3qhYq3Kw5V3LMA/KO6E7W3WWr794i4KstajUuvoP/IBuvW/AfCOqli56F9UlmURGpnKkDGPH1Pe/dfq60pZseAJ2vV6FbNfEpbqdJZ+fT83J6+nS99rMPuGkpW+koBIPy6ZuAiDKYCd6z+kpjKfpIFT6NhjcnNfQaFJ6A1BxKZM5WhNBtdV7sbgcmJRq5h65Qv4BcY09bcK33B/Lpm4AF//SIaOfZIfF9yBEFo8HhdpvV5ApdIRmXgVB7fcdUbnsO9F99D3ontQFIXCnK3M+/gGin29a42heEhoeyuK4iLn4DvsWnMDRnMQgy5+8JjvFydTmLuT0JhLUGu8RbTC4sZSlPvVGcUlXXhkEiadc2vm3M+tLh8GqfzwKAqPl+awf/scOvW++ri2bpeDeR9dT0TiNNJ6j6SmfDsLP7uFG+77EZu1jm8+up7Y1JsJjIghY99HOGwWho598qT7rig9jH9ILzRa7+ryIdHD2LP2UwDUai3dB950THu/gGgGX/LICftq1X4UO9ZPIiCkKwk9/kXW3pdRq/V4tI7mhLLn4FuO2274+H/Sb8S9fPflPSiq5OahD/W16UTFnHnBDCEEB3bOY8faj/AJaEf2wTdxueoIDvd+kOsMwSS2u43Dux7j2nu+O+N+ff0jqK3aQ2jUUBSPi4baA/gFTDrj7SVJkqQ/p+0rXqWzpYE7lSCEEHylVLNx0dOMvu7DE7Zf9s2jOBy+9Bq1FIe1nC2r7yM4LIWYpF7MfncK5oAeRKfcTkXxCuZ+MI2r71h40hulNZW5mHzjMPt5C1X5BrZFpw+irrqQ0Eg/WncYfVyRjF5Dbj9hX2FR7XC76ijKnkdk2m0UHZ1NTcUO7NYyjOZg4MQVftO6Xkar9qPYvu59Du1dh4+/txqipeYgZt+wMz+RQFXZERZ8egsBob0ozl2Iw1aBRmsGxY1KpSWh7QyqSlczacZXBASfOMn9Nb/AKEoK9xISNRQhBHVVe/ELjD6ruKQLh0zCpHPOYikjTXjvhqmEIM2lIru2+IRt62oKcbs9mP1ScDpqCAzridk3kfLiQ5QW7Sc4cgjhsd4P5ZSOD7F/462nTMKCQpPZv/073K6rUWuMVJasJTDk9POkTiQ8ugNxKX3J2vsiQqUhMKw3Zv9UrLWrT7ut0RTA8PFP8+U7E2m0pONx2XA6yrGH9OOHbx4irfM4YpP7nLaftUueo3W3Z/ANaIOiKBzcej81FZvJPvgmBnMMJTlf02vwbWd1XCMuf5Z5H11PVclK7I0lBIZEk9Z1wln1IUmSJP35NFTl09+jRai8hSXSFD3rqgtP2j7/6GaS2j+I3VqGwRRFSNRICrK3oDf64XIpxKXe1FzsaveaqVRXZhMc1uqEffkHxdFoycNan4/RJ5b62iM4bJX4/oYCTTq9mX4j/sG671+iOOcbTH7JpHZ5ggOb70at1p5yW63OSI9BM8g5vI4Dm+9AbwynpmIXrdqNYOmcB4hJ7E777hNPWzRs04o3iUyYSHSy9yZm/pEvqShcQMbOJwgI60t16VpiEnviHxR7xsfVZ9hdfPX2lRzceg8qlQ6HrZjRt8ny8H9WMgmTzrmouK7MPbqPGUogVbhYqXHQN77bCdvWVORit1ZzeNe/cdgriEmeQmN9AWbfUDRqHW53Y3Nbl7MB1Uk+XMuKDlBfV0JkXFdik7qwe800dMZg3M46rrzp5POwnA4rxXk7UQD/4Hiy039E8XhIaTcCv8AYho55gllvXkZAaC/UGhP5Ge8w7pp3zug8BATHc90935OXtYEGSzkbf3yV2lodGq0PCz+/ndFXPk9y2vBT9uF01GMweSf3WmoOYqnOADyU5X9HVHx3ho59lNQOF59RPBUlGXz31T+oLs/ELyie9l2GEBLZhtikPqhU6jPqQ5IkSfrzCk/uy+KsbfRym9EiWKBuJDz5xFXwGixluF12sva9iMfjwD+4Mx6PDXObkajVOtxuKyhuEBoUjxO3245affy0g7rqAipKM/ANiGbImMdY9e0dGH2isdYXMuLy/2IwnrgyoaIolOTvxmatITg8lfysjTTWVxCd2JOouK506D6RAzvm4XIZMfmlkrXvP/QZfs8ZFRXRaPRMvPkLcg6vxdpQydbVBVSUW/Dxb8vmlR9SWZrJ4EsfPWUfdpsFva93HrbDVkl54ffYrTXYGrehEhbadxtL137Xn1EFaJu1lqWz7yc3cy16QwDtug0jKr4bsUm90Rt8T7u9dGGSSZh0zg256iWWfHw9lxftB6DPoHtIanP8hFTF42Hp1/eT2u1pgsJ7Y7eWsmvtjaSkDSckIhWTTzBb17xLTvrbGEzRFOfMoefgW4/rZ/Xif3Ng50LMfgnU1xzm4kkv0XPwzditdQSHtz7p+hYNlnK+fPtKrI0WHPYqBCrMfkn4BqSyacXrDLrkUSpLD9G5z1TcTgdCpWbIxbMIi2p3xufCaA4kteOlrFj4JGExY4lLvR4AvSmCTSvfprwkg/3b56JSaeg5+Gbad7/ymO0TU4eSc/B1olOuJX3bI7Tu8ghB4X2prdzLkV1PEjvp5TOKw+mwMvfDa4hMnEZK5yFUFK9h65r3mf7gKpmASZIk/U107nsdNaUZTN0xFwEkJw9mxOgTF6T4ccFThMVeTHybGSgeB/s33wtKLe17TEKj1hEclsThXU/hH9rb+9Qnoftxc8sy9i1h+TeP4BPQmoa6o3TqNYXr71tObXU+AUHxJ53H7PG4WfTZ7eQd3YzLYUHBg84QQnBEP7aueZ+eg27GbrcQl9y7qXqiky69njirsupqtZbktsM4sv97UPmS3OFBhBAERw5k+4qJxCR2Z/2yV3A6GkhpN4KBo//vmKdsKe2Gs3nVBxjNsRw98DqBoT1JSLsNh72Sg1vuISS81Wmfyv1k6ZwHaLTq6T5sLtb6PPZufZzWHS6WCdifnEzCpHPOZA7iijsW4rDXo9boT/ohZLXW4HLaCAr3FuHQG8MJDO1CStPTIZNPCFffuYhta9/D1ljA0DGPkNrxkmP6KMrdQfruxXQa8AEarS911Qf47qt7uOOJ3add/Hft0udxubX4BXUipdODuBy1HNh8H/4hPWioL2TFwqcx+6dQX5OBRqvj2nuW4usfeco+iwv2sHze4zTWVxAZ25kRE/6N0RyIy2VHo/15wrJG60tDXTH7ti0ksf1DuF021i59FoMp4JiLyMgr/svy+Y9xYPOdqFQ6gsL7AuAf3BGTbyyVZZmYfEK8+87bxZ4tX6Kg0KnXZKLiujb3U1WeiUrjQ3ic9/yFx46mLG8+FSWH8Q3wHpPZN+ycrNkmSZIknR9CpWLI5c8xYOzTKIqCVmc8aduKkgwS2j2EEAKh1hMaNQyjobS5XPqE6z5g+/oPqSzNon3Xi+ja/9inPi6XnR++fpC0Xi/i498ap6OGPetvpnWHkUTHn3rt+UN7FlKcvx+N1p9O/d9Do/PlyK5nQRFEJk5h/bKX8Alog60+H4/Hwdir3yQxddAp+2yor+T7OQ9QXpKOX0A0w8f/k7CoNNxuBxqNuTl2tdqEonj4/uuHSO74f+iNYeSkv4miPNu8fhpAx56TsTXWsGvTY1gbKknt8hhCCPSGEILCB1Oct7u54FV9XSlbV79LY0MViakDSety2THnKi9zPd2GfIVG64NvYBrBkUPJz9pIQFA8dlsdvgFR8obpn5AsUS+dNzq9zynvAhmN3lKzNRU7Ae/j/PqaDAJDEpvb+PiFM+TSxxk98YXjEjCA2up8fALaoNF67xb5BbbD43Zit1tOG191ZS4uh4WYlKmo1Xr0xjACwvuQe+gDGuqO0HXIZ7Tv/RId+72J09HImsXPnrK/9N2L+OqtK6ipLMBuraOqopr5n9yEoigktRlKQdbnVJasp7ZyNznpr6PS6IhNnYGPf2v8gzsSlTSFjL1Lf3UOzVwy6WWmP7gGxWPF1uidW+ewV9NoycfHz7vuSGHONuZ+dB0N1hCs1lDmfXQDBdlbKS85xNFDK3A57ditFbic3vPictZjs5azesl/+PilEXz84kUs/HRGiywTIEmSJF3YNFrDKRMwgOCwZCpL1gPg8TipqdhMaETrY/roPeR2Lpn0Ej0G3Xzc9d7WWINKpW0ufqHVBeAT0Ira6oLTxldbmY9KbSYyfix6YyhqtYGI+LGUF64m99B7tOv9Ih36vELXIZ+h0fqxZPa9p+yvpjKPD58fTEHODqwNVdgdRuZ+MI3G+gpiEnpSW7WPwqNfY6k+SObe/+IfGEdo7KUEhvXC5JtIQtrdHNn3/TF9CiHoNeQ2bnlkE4EhydRV7QXwFryqO9h8g7OxoYrP3xhPWYkFt9KK9T+8xtbVb1NXU8TRQysoKzqIwRhAY32Od3tFwdaQR0H2Nt59tg+fvzGBmS+NpO4Mzpt0YZFPwqQzUpS7g+3rZ+JxO+nQ40qS2w77w/cpVCrGTHmdRbPuwGCKwFpfRI/BMwiPbn/GfYRGplFb+TTW+gKMPjGUFSxHqzOfdJV5bwlZBaFSERnTgcqybBpqD3vL5dYcpixvMUERA7E3lqDVecepm3wTUGvM1NacfPKytbGG5fMeoV3vF/EL6kBd1V7Stz+O4nFRU5nLigVPokNH3q7/YFMU2nYZQ211EQ5bRXMfDnsF5kDTMf0eOfAD6394Cae9gYiYzuzfdDs+/m2x1BwkMXUgAcHxAGxY/iqxrW4kIt5bCUqlMXqfyDVUYvZLor7mMNEJPTiw+U78g3tQW7mdgOA4nE4z3YbOBcXD4V1PsWXVW/S96J4zPv+SJEnSH6u+rpSNy1+hvq6cuORedOt/42lHerSEYeOfZvZ7U9i7YT0uRwMRMe3ofJolVn7JZA5GrdFSWbyW4MiBNFqyqSnfc9LCHeAdhqhSqQmPaY993UdYajOIxDsn/MjuZwkK70V54XJ8A9IAUGtM+AS0pbL41GtdfjvrDqKSJhOTMhmHrZJ9G+9EZwynIHsLe7fNRY2G8oyZFChu/MOSadtlHNmHDzdv77BVoPlV0lpZephl8x6jtjof/8AYcg6+RkXRcqwNhRhNRlq1987XPrD9awymZBLSvEW0/II7snX1bWxb817TMM1souI7cXjXkwRHDMHWmIfHXU1JYRFdB89Co/OnMOsLlsy+j0m3zD7j8y+dfzIJk06rOH838z6+keiUa1FrDXz/9UNcdNnTtD7Dgg+/R1xKP258YBXV5Vn4+IXjFxhzVtuHhLcmKCyZ3WtvQK31RSBQcFKUt+OY4Q6KorDu++fZueEjFMVDm07jSG4zjD2bPydr38tUlqynvjaDmFbXEBo1hN1rb6K+JgOfgFQqitfgcduIO0U1w5rKHAymcPyCOgDgF9QRnT4EW2M+e7d8gauhgrH4Eo4PX2Ah+9Aqxkx9kwWfzsDWUIjbbaWq5EcuGjuvuc/C3O38MPdhUjo+jM4YSu7B1zH5hNBQl4l/SE+Kcg+yfP5jDB/3NKUF+0hI+3kohlptxFJbTJfBn6LV+VNfk8HBrfdx8aRXqK44SnDYaLasep+A8JGoVN6PiZCoEZQUbDir8y9JkiT9cWyNtU3FoQZh9uvHnq3zqK0uYNi4p//wffv6R3LdPUupKMlAozUQHN76rIasq9QaOvWawpbVz6Le/woetw2dIZgj+7+n15Bjq/pm7FvCj/MfxW6tJTyms7cKsuKhsngNexsKcbvtGHxiaNX5/7DUpFOSu4jIhHFY6/OpKd9KSETbU8ZSWXaI7h29o1l0hmACw/tSVbKGuppiyjI30FmY6KX4sFzYyCzNoPWU19i3dRpZ+15EZwijLH8hw8Y91dyfrbGWOe9fTUTCFCKSulOWvxitroDaqr34B3dDURzMfncyU26bS/ruRag1P3+3UauNOJ1WOvR5Fd/ANFxOC/s2zGDIpY9hbazCYOxDXXUB+bmlaPUBAITHXcLuNXK9sD8bmYRJp7V78ywik6YQmTAeALXWzI71n5yTJAy85dyNJ6meeCaqy3OIazsDh7UUH/9UGizZ5GSsOSYJ27ftK9J3LSUy4UpUaj1FeTvJzlhDqy5PojUEYKnaT31tBhqNGb0xnJSOD7B/8z+aF18UQk1sUg/AO869vrYUs28IWp33yZVaraWxvpCS3G8JixmJw1aBrbGQjj2nUFKwh8GYuFblXbskUdHzeGMxMUm9uPKmzzm051vUGn9GX77wmLVEsg6uICx2DAGh3uOISJxExo4n6D5sDhqtDy5XI7vXTCOtyzg8ipuc9PeaF3jMPvA6elN489M8n4BUhEpDZGyn5jlnh/cvp7piOwGhPQGordxBZMyZrWUiSZIk/fGyM1ZiMCcS3+ZmAAJCurFtxeUMGfPEOZkjpNEaiIjt9Ju3ryjLIirxcjweFzpDCEZzFJkH5x2ThFWWHmbZ3IcJiRmFRm3Cbitj4ae3EBZ7MZEJl1NVso6ywuVom6YdtOn2NOnbHiEn/W0UjwuVWkdsknduueLxYKktQqszYzQHAuB02jAYA8nL+JiYlCmoNSZqyrdh8vFeH30VhYdFCCqVYIDiyxTFgtNm4eo7F7F3yxfY7fX0HvRm8z4ASgr2YDBFE5kwDoC41Jspyl5Au14v4BfUHkVRyNjxKOm7F9BYX4Xdlkdx7kJMPgnkZXyIEGp8A9OazrEvPgGpqDVaug/wrmWavnshB3cvx+N2oFLrqC7bhl/gmZe6ly4MMgmTTk8BIX4e2iCE2vvDJrVV+ZSXHMIvIJqwqLSW373Hw44NH5F54Ef0Rj/6XXTPaffjdFjRaA1Nd+UUSnMXERTej8Kjs/G4bbRqc90x7Q/vXYrVWo6P04Jir6K+7igobsx+CRhMkfgFtKWhJoO8wx+i1QegUutRa/2JjB9HZMJYinMWkJ2xBo3OxKLPbkFRVLgc9XTpdx2t2o1k3swbCAjpQnHOfPIyPsTjcdKu6wSGjn2KZd/8H6acjOZYTKiaS+2HR7c/6fBLnd6M057X/NrWUIhW59+8ELVGY0JvDAEBAoXIxAkUHf0aj8eNR3HisJbSaMnB5JtARdFqtFpD8yKWAANH3c9X70ziwObbURQ3Op2GvsP/fTa/OkmSJOkPpKAcc31GqKBpWD14n8gU5e1AqzMRHd/9jMqzn63sjNVsX/cxisdNp96TSO146Snbu5w21GodQqXCbq2hJHc14bEXU1e5i+Lsb4iIaXNM+9zMjSgI7A1FYIqkqmQDTkcNEQkx6I2hRCZOQKMPImvv8xRlz8PHvxV6UyRm/xSSO9yLraGQ3Mw3qK8r5ZuPrsdSW4LL2Uhscj+GjXuKhZ/OQKMLpbEui52rpqJS6wkKjeeqm74kP3sTOgSqpid8GgQaITCagzH7htJn+N0nPEat3oTDXoXicSFUGtzOehTFhdHHmygJITCYo7FZa73TBtTJVJdtobxgGS5nPRqtkfLCFYRGD6OxPo/ayr2ERjzS3H+bjmM4sn8Ze9bfgMEUjtWSy+U3zvwdv0XpfJBJmHRaHXpOZMHMm1BrTKjVRvIy3mXIpd6StRl7vmX13AdJUZvJcTfSts80+l78cIvuf8OPr5C++wdiUq7H1ljEnPencvUdC5rnO/1SaeF+Fn52Kw11xRhMgQwZ8yRul52uQ2ai0frgdk1j+4qJRMQce+eutrqQuNY3EJXoXZRYmxFMWf635GW8T2K7e7BbS7DU7KXnoOlkZyymsiyL4PCBxKR4F2F02ErR6GJY+OkMkto/iI9/Kge3/h/b173P9rXvERjelzbd/+m9+7XzCRJbtWNQ03nqNmA6X+9eSLynljC0vC+qaNfnmtOel449J7Fn8xiy9v0PrT6U0ryFqNVqirLn4xfUgZqyLbgcNYRHteeiCf/hxwVP4BfUjobaTLr0uZbQiNb8uOAO1BojKrWay659/5g7pyafEKbdvZjivJ0IBJHxXdFo9L/59yhJkiS1rITWg1mz5Dnyj3yK2a81JblzSet6BSq1hsqyTOa9fQWxipo6xYkmvBVjb/6iRT/HczPX891X9xLf5jaESsOKhf8COGEiZm2oZtHnt1GUuw2hUtN/5INUlmXStse/8QvqgKIoHNhyP6GRrY/ZrrI0Ax//VNp0/ycAQWG9Sd/+KCW53+Af0hmV2kBZ/kLad7uC2uoDHN3/DUIVQFqP/6BS66it2IlO78MPcx/G4NOJVl1fJWvvi+QeXsZHL6xGbwyn08AP0WgMFGV/g61uM5Nv+xohBLFJfVnjE8BHDRX0Ema+V+oIDE897QLLUbFdCQ5L4ND2h/EN6kxV6WoCghLITX+LqKSp1NceobzwR4aMvpbkNkOZ/d5UTL4JCJwEBkcw+JJXWfjZreRlvIPL2ciwcU8RHP7zeREqFWOmvkFJwR7s1jrCozs0P9mT/jxkEiadVkxCD8ZOe4vtaz/C4XYydOyjtOk0BpfLzo9fP8DzSihJioE6xY/bN31OSuexZ7VW1uns2/oVbbr/r/kOUqMlh33bZjNg1IPHtHO57MybeSMxKTcREjWU2oodLJ//KDp9YPPTIbXGiEbrx+x3J5KcNpKLJ72MVmvAYArAaP55TLbRHItfQBSO8m3sWzYeNAYGjHmCjr0m02vw7eza9Anrl72MWmPC6aiipmwj5UVOUNQEhvUifdtj+AV1pGP/tykvXEHOwbfYu+FOYlIm4xvQHpfTRlV5FrVVeQSFJnPpDZ/y7fcv4LTXk9BxEt2H3H7S8+GwN7B68b8pzNmOf1A8oVEhaLUG+g17H7VGz9wPriUvwwZAREwHhFDRtvM4ImM7Nz2xjCI82js3rVX7UVgbKjH7hZ+wUqVWayAuue/v+v1JkiRJfwyTOYgpt85l3Q8vUlfxLW07Dmoeyrf26weY5NAxVvjjVhSeLj7Knk2f0W3A9Bbb/94ts4lJuY7QaG+xLoFg25oPTpiE/fDNQ7iVMHqPWordVs62NffhsNdgaLr2CiEw+SawY90HZGesZfw17xEQHIfZNxyTr6e5H4M5xttWOElfdS0IQVLXCQwd+yQqtYaC7K0s/HQG2QdfR6P1pTR3EVq9GZu1li6DPqYkZwG2xkJ6jliE3VpG+vZH2bvuZsLjLsEvqCO1ZctorC+nrOggJp8QLr9tARsWPcHWihxCYnsxfuyTJ537pigKO9Z9wP4d81GptUQnpKHTQfsud5HQejBfvnUFu9dNR6MxodMb0Rv9CQiO4/p7l1GUuwOtzkhMYi9Uag03P7SeekspRlPQCStVCiGIjO3cAr9F6XyRSZh0RuKS+x73ZdzaUI1OCJLwrgniJ9QkqE3UVReeMgmz2ywU5+1Co9ETFd/t9MMjhMDjcTa/VDxO9m//mn4X3XvMtrVV+Qi0zReDgNDumH3jaazPZ9/Gu9HqAlBpjLjddnoM/4bsAy+z7vvnGTrmCVq1v4g9W2ZiNMfg8TjJP/Ixalsl9yvBRKiDeE9VR2XBHjzdr2TBZ7dQWngIgzGU0rwFxKX0wVLlQ5suT7F/09001B3FUnOQpPZ3UVOxg5z0t0lIuxUhVGTufRG1WoNR35cv3roCs18StVWH8LhtCKGiS9/rCY5ux4pFT2Ew+tO137XN63z9ZPEXd9HQALGp92KpOUjG7llc+4/vMfuGsmrxM/gHdyal48MoKBzZ/U82r3yT/iPvw9c/Eo/HhcH0890yrc6IVnd2xU4kSZKkC4d/UCyXTn7luJ/XVhfQhaZ5yULQ2a1iT2XuKfvyuF0U5e3E5bQSGdflpJWEfyJUahTXz9dnj8dJZXkWtVX5xz0tKszZRvs+7yBUGgymSIIih9NQs5GDWx5Ab4xAo/OjsmQ9Hfq9SV3VbhZ+dgvX3rOExNRB7Nx4I0Hh/TCYosg++Bo6xc3Yeic91bH8KBpYl7MNgC2r32bbmvcwmmMoL1xObHJfhEpNQrv7KTjyGdVlW7HUHCQ87lI8bhsHttxHZMLlmP0SKTgyi/KCHwiPTuHjl0bg45dCXfVh3K5GAGKS+tK211Q2rXgdxeMmreuE46YL7Fj/ITs3ziYh7U5vxcb9LzJmymvEpfTj6KEV2O12egyfg1YXQGHWVyyZfS9TbpuLwRRASEQqQqVCNI1IUak1+AVEn/L8S39uMgmTfjOzbyhCa2C9zUJ/lS85ip0j7nq6RqSedJuayjxmv3sVWkM4LocFX/9grpj+afPijifSqt1FpG9/lPjUG7E1FFFVthm1WktNVR5FOVvZveINPB4XyV0uw26rwm4tQ28Mw+Ww0GDJQwiBb2AaJp948g5/QmjUULT6ACISrqDg6PsAdO59DTWVuRzYfAdCpSEsojU9cuz0UHmfoN3uDuC+/UsJiulATWUlHft9QFnh91QWraEgeweRiZPx8U8hueN97N90Nwg1dVX7qCxZT3ybmwiLGdF8PKW5X5CZvpJO/T+gKHsuCjpSuz6Bx23j4JYH2LvlK6KTp1BWksuBneOYdte3mMzehZydThu5mWvpNfI77/oqAalYqnaRf3QTbTqNpbTwICFRYxEqDQIIjhxOaeE6KsuOMPfD6/B4FBz2WnoOuoU+w+484fl2Oqz8uPAJcg6vxWDwZ/Clj5CYOvgs/3VIkiRJ51N4bCe+O7KLm5VAGvGwUuOkbXyXk7Z3uex88+F11FSXotX547AWc9WML49Zm/PXuvSeypwPrgYhECoteRkf4ePfitwj6wiNbMu6bx6iob6C6KTeGM0h1NccQm8MRVE8NNZloCigN4YTHDmQ8oLlGE1R+Pin4uOfyqZDH+By2giP6Ujf4Xeydc2zuByNhEd3IEBj4jKXt3DGNYqWlZYScjPXs23N+3Ts9z71tYfRFK0k78gqgsJ7ERjaA70hhANbHkBRFFQqPW5XIwGhPYhtNRUAs39rdq6aSmlhI8kdHkJRPDRYXqfTgPfQ6oPJ3v8yX39wDREJ41EJLV+/fzWXXf/BMUW+Du5cSHzb2/EL6giArWES6bu/JS6lH+XF6QSE9kGrCwAgLHY0u1bPwmFvYP7M6VSUHEZRPETGd2HctLdPOGz0pydtOzd+Cih06j2VnoNuOauqlNKFQy7WLP1mKpWaS2/4hLcNdqaIAu4XpQya8N8TztX6yYqFTxISPZa0ni/Tod97OBw6dm746JT7ad/9KhSPncritdhtZbTr9T88HjsFRzezY9G/uM8CjzdoKdk0i4TkPuzfdAdZe59l38ZbCI9OIyC0FwltZxAWO4q0Xs9RWrCU8qKVWKoP4OMfQVHuDj54fgBHD63H7XbQ96J7SEwdROUv/ndU4UKrNVBdkYNvUFeO7HmWyqLVBIb1xOMRNNR61wsJjRpKTKtrcbusZO55nvqadFB+HkahKB6M5iB8/JPQG0OxVO0jJnkSarUBrS6AiPjL8AlIIzr5KpLa/wOzXxrpu+Yfc84B3K6Gpv4UXM461E0f1kEh8dSUb0ZRFBRFobZiK4Gh8Xw76y7C4ybSZdAsugz8hN2bviA/a9MJz/eyeY9QXlJG2+4vEZF4A999eQ/lxemn/B1JkiRJF5ZBVzzPntBwpogCriGPkK7jaNv5spO2373xU6xW6NjvPdJ6vkRY7ASWz3/ilPuISuiOEIKail3UVuymdedHUKt1OB2NLPxgKpPKa3jW5kPIoU346gxkH3iJzD3PcHDLXahVDTjsNtp0/ydhMSNo2/NZ7NZS8o98Sl3VHrRaE3abhc9eG8Pmle/gtDeQ1HYYA0Y9gMXjxNlUfMSKgtXjxNpQhY9/CpUlazl64DXMvkn4BnbAUnMEj9uByTeRtj2exWmvoqJ4FQWZs/C47T8fjOJBrdbRWF9GQGh36qr2ERY7Cr0xHJVKQ3TyVBAq4lNvJLb1NcS2ns7mlW8fcz40WgMuh6X5tctpQdN0k9k/KB5L9W7cTfusLtuCf1As63/4Hw6Xma5DZ9N16Gzq6+xsWfnWCc/3gR1fs2PDLJI6PE5yxyfZvflr9myedcrfkXThkk/CpN8lIqYjNzy6jYb6coymwOYPm5Opqconvu1kwFtx0S+oCzUVeafcJjyqHVFxnairbcTkm0jOwVdJajOEkiPrmOw2k6byjpWe7vLwfk0Rl9/wIRUlhwgInk5JwR4OH9jf3JdarQfFTUHmLBy2Mqbe/g1z3ptCUPhwzP4pGM2xrP/hES679j2WGbS8Yq0kwiNYrLbS7+JnUVA4tOdlnI4Gug2ZhUqtIyR6GDtXTWP/5vvQ6gOpLtmIr38SPv7+xCb2YNdG79M2hIr8wx8wdOwTrFj4JPW1h9Hqg7BUH2q+a1ZXvR+9Maw5XpXGF5u1tvm1onjo2vdG0rc+QGjMxdTXpKNWO0lo7V0DbMCoB5n97iT2b7wFBTcGg5G+w59l98aZtO72KuBdA8U3qCM7N85k88o3cDjshEa0YsCoBzGaAzmavpxOAz9Fpw/EYI4iOGo42YfXEBp56nVWJEmSpAuHyRzElXd9R4OlDK3OeNqhhTVVefgFdWmqgAz+Id04um/JKbcRQtD3on+wc/3nhMWOoaJ4BW5XOWqtnq7CzCCVd593eQK5omg/1967gqK8negNvph9Q1k06x/89DxACO9QvNqKnRRlfcWwy/7J8vlPoBBEdPKlmP1TyE1/nZKCPQTGd+Xx3L10d6lZq3GR2mEs0QndsSx6mtrKg3To9wYmn3iikyexfcVV7F43Hd/A9lSVbsA3qD1uZzm9htzGxuUvk3/kE4w+CRRnf0XnPtPISl9Bad536PRB1FbuRFEUhBBYag+h1f58DrX6QOqra5pfu10Oeg2ZwdI5D2JrLMTtqqe8YAnDx34DQGqHS8g6uII9a6/DYArH1ljI5TfMZOWifxESdTlCqBFCTVD4YDIPzqeiNANLbRk+fmH0HX4XYVFpHN63jOjka/Dx9y5oHZNyPYf3/0DnPlef1b8N6cIgk7C/ieqKbLYs/S82SwWxacPpOnAGQtUyD0JVag2+/pFn1DY8uj1l+d9h9rsLt8tKZclKkgZOPeU2QqVi/DXvsXvTp1SWZ5PU70r0Bl92r3mHCtzN7SpxodX7EBHTkYgYb1Lj6x/B5pVvYfRJxOgTQ17GR0TEjSWuzY3sW38TxXm78TRUE5S3DA3LOaxyYw5sRX1dKVfds5R9W2ZxuLGWEWnDiU3qjaIoHNn/A/lHd6NS6wDQ6UPQ6gLxC+qIraEIhAqXy0ZFcQkDR95PXHIfdm76HBSFSye/gn9QHEltBrN/452oNUZqKrZTX7MPxWOnvvYIWkMk9bVHsDUUUpa/FDWdqSw9zMLPbqOmKhuDMYB23SZgaywjPLUd3Qe+3Dyc02gO5Oq7FlFSsBcBRMR0Qq3R4RcYR3XpJoIjB9JQl01ZwWpq9P74BLQlPHYMFeWbmf3eZK6+cyFanQ92ayk6vXfumMNWil7f+Sz/VUiSJElnwm6zsHnpc1QXHSAgsg19Ln74tAnTmRJC4OMXfkZtI2I6kL3yI8JiR6PWmCjLX0xETIfTbtdz0AwCgmLJzdxIRFQbImMnsW/rl6jcVhTFHyEE1bhRq9QEhCQQGOod3uh2OzEYTeQcfJ2g8AGUF61AbwijfZ+XOLr/JWor8yg5vJpARU1YRTq7lAb8ooZSVpzBxdd9xL5tX7G/LIvW0e1J6zoBIQTDxj7F91/fh04f3Hz8/iHdUAkVWn0AiseD01aFEFoa6kqZduciNv74Go2WdXTrN4m0rhNQqQS7Nn0Iigq328b+jbdj9ImiqmwrHo9CTcUuVCoN2QfeRPE0Ul9byg/fPERe5lqESkO7rlcgVI2o1XouGjeveTinUKm4eNLLlBUdwG6tJSyqPQaTP0GhiVSVbSIgpDsej4PcjPdAqGmobyCh7c3YGouZ8/4Upt6+AJ3Be33+id1ait7ge7b/LKQLhFAU5fStzlJETEfl6jsXt3i/0m9TX1fKFy9dxASnjnhFy5eaRgK7T2DA2Ceb2yiKwvZVb3J461eo1Fo6D7+btl3Gt3gs1sYa5s+8kcqyTDxuJ207j+Oiy/59Vgnhqm/+j6o9S2nvFPyo1HGRyh8TKr5TW7nkxk+JSehxTPuyooOsWfIcRbk7iEq6ipiUyQihJmPnY/gboc3Rfdyq9l6kvvBUs0BYmHDbNyddn8vptPHxi8MJihhFUER/ygqWUV26kdRu/2TfxjtIbHsrJr9kcg+9j5+/gSunf/aLWA4w5/2pBEcMweNxUlO2gXHXvktddQFqtY6a6nx2b5yHx+NArfEhJmUah7Y/io9/JGGxVxEedwl1VXs4sutprv3H9/j6R5zROSvO28W8mTdgNMdiqc0mNOoiKopX0WPYHIRKg6Io7N84g4snPUtNRS6rv3uW0OiLsVsLcNryuPrOBej0Pmf8O5LOrRcfit+hKEr307eU/u7k9fnC4vG4+ebN8SSXFTLQrWeD2s6h4DCuvHPxMYWnsjNWsfW7/+C0N5LU6RJ6j3ywxdf8UhSFFQue4MDOuajUegJDEplw/YfNc5LPxKHdC1n/zcMMd+lZqdSRIgykCQPfa+y0GXob3Qffdkx7a0M1q5f8h8wDP+Ib2JGUDveh0fl617R07YfMrbwqYtAIwQHFyhOeEvpc/NApKzwu+vx2qqvqiG11A42WbLL2v0KH3q9xePcz+AV3Ijx2FJXFaykvXMLND29ovolpt1mY9cZlaPQx6I0RlBf8wKBLH8Fg8MFhb8TsF863s+5Cqw8GxUN47MVUFC0jMDQKm1VHUof7cNqrObj1Pi4a/wQp7UacNMZfamyoYs67k3E6PTjsNWj1IThslaT1fA6TbwIA2QffIDElkZR2I/jqnasIjhwGCCqLljPx5i/+kDVapZZxquuzfBL2N5B54Ae6ubVcKQJBQLLbwIytXxyThO1a9x65q9/jAbcfVsXBi/MewWDyJzF1SIvGYjQFMPnWuTRYylBr9BhNAWe1fV1NERm7FvCxEo1JreZijz/3KkW07jaBy/ped8IPorCoNK64cSZv/rMbiseF222nrnIPVaXbMQTHkyZ+Lv3aBh0+pkDCo9tTWrCPqoosgkJTjknItFoDV938JcvnP8aRnUuwWetI7fZPait3Ehjai7DYUQCkdn2CbT9e3jyUAWD9speJTr6OyIRxAOQdDuLAjnmMvPy/ABzcOQ+dIYA23Z9DCEGjJRutzozTYSUi3lvy1z+4Mz6BqZQVHTjjJCwyrgs33L+KiuJ0Nix/Fb1PHCgKCvDTdF5FcSMQtOt2Of6BMeQcWYvB1IcOPV6VCZgkSdIfoLo8i8bybP7hiUClEnTzmLm5qpDykkPN152S/D0s//w27nEHECo0vLN5Nhs9bvpf+niLxiKEYPhl/6L/yPtxuWyYfcPOuuDDpsXP8JgniLZqI5OVIO5ViihLaEWf/tNJTht+XHujOZDRV77AV1WTqbdYUHDTaMmhIOtLouLak4IeTVMMrdHjUJx06j2N2qp8ivN3YTAFEp/c75gbuaMn/o9V3/6LrL1PYW2oIjL+MlCBy1lHYtrtCCEw+6VQU76R8uKDRMV1BeDAjrlo9LG07uKdBxcQ0pMtK19j+oOrAe/3DxQX7Xu/jEZrxu22U5T9FRXFtaR2ex6VSoveGEZo9Gjys7eccRJmMgdx9V2LKC3cR8bexZQW1VBZsgnll/PJPW6EgJDw1lx9xwLSdy1AQWHUhPmnLJwiXdhkEvZ3IAQeATQ99PQc89XbK2v7PG5x+ZKqMoKAyW4HG3bOPy4JKy3cR87hNegMvqR1mYDe4Etl6WFWfHkXlVV5hIYmM3zKG6cszvHL4RGKooCinPGTMIe9Hh+1DpPbO2Y9TqUnUuNLWo+Jp7wTZG2owu2yUlu5i8KsL9EZQ9Go9VSVZ7JQ0dJNmFEj+EZVT0rXqWxe+SY7NnyCX2A76qr20XPQTfQYNANLbQllRQcw+4Zy+Q0zEUJwcOd8flzwGIoCZr+fF1N0OerQaHTHxGGz1hEY/vPQTYMpGpt1L3XVBaTvXoDL6UDxVHN41xMYzIlUFH5P/xH3snrJf7A1FKE1BFFRtIq6yrMvlGE0BRCb3IdWxYfYseErTH5JZOx4ktDoi6gu24TRZCa8aRhnTFIvYpJ6nfU+JEmSpLMgBAoKvxyT5IFjkp/M/Uu5xG2kV1O13jvdgsd2LzouCauvK+XQnkV4PG5atx9NQHA8Nmstq2bfS+7RzZiM/gy8/NnmOcQnYzD5A97Kg4rHc1YjVeyORsLwxmkQKrpioqJV/xMmYMfEXluMWhvNjpVTUKn1aPUhFGVuoFpRyBP+xKDjK6Wa2KgOFOZu49vP78A/pDPW+nxCIhIZf827uJxWinJ3oFJpGDbuKdQaHWVFB5g380ZK87/F7bJ5R5mo9Xg8LtyuRjSan+ex26y16I0/39g0mKOx2yw4HVb2b59NQ30lMYk9SN/6D/xD+lBXtZ34Vn0pLTyApeYQBnMM1eVbqSxeTVBQ/zM+ZwAajZ7o+O5oNHoO7ryGoPAhZOx8mthW12BrLKa6bA1tJt4HQEBwPH2G331W/UsXJpmE/Ul53C5qqvLQG3ww+4adsm2rdiP54of/MctdTbyiYY6mkc69rjmmjUZvpIafC0BU4UHzq6cfmQeXs/LLuxjmMVKigtlr3+eyW+Yy/91JXG3X0ptwVpWUsODdq7j6wTUnLK/6SzvWf8SGZS/hdtlIansRoyf+D53efMptAoMTcBt8mVNfwxDMbKKRarWK0IjTFI0QAiFUtOv1Am63g0Prb8XjsJCMnlC0XO0+ioKCGoFmx3wcdgtdBn2CzhCM3VrOppU3YqkrZe+WLzH5xGFtLMbHL4w+w+4gretltGo/itqqPBZ8OoMje57D7JdCSc48eg2545iLaau0YezeMhODKQqPx0HR0S/o2ncyn70+lsCwgQiVhkZLOR17DUOt1tFn8KuUFaWjMwSzZ8PtCEBvisQvqCNL59zPlTd+SvgZjNn/pa59r6W+rpTdmz7F43HjcZWS1GYwfS968YQLNkuSJElnTlEULLVFeNwu/ANjT5nEBIUk4xvemudLchnk1rFB7cAQmkBIRJvmNhqdiZpfdFGtuI9b1qW2Kp85r19KT5canQKzV7zO+Bmz2br0OeJyD/CQJ4wci53nP7uFK+5cRHBYq1MeQ37WJpbMvo8GSzHB4W0Ze/WbZ/TEJbnNUN5KX8/NHn+KFAcr1I2MO03SByCEmpiUKbTp/k8ydzxNQ8k6ItDQR/hwvzsfBx60qPBUeVj02e2kdH6UwNAeeDwuDm65hw0/vMjuzZ+jM4ThsFejVqvp2Osq+gy9k5v/bz2WuhLWfPcs6VsfIDhiMFVlGwmJaEXoL85zYutB7NxwIwGhvTCYosg99BYJrQfy5dtXouCPwSeJyqJ9JKcNxezjR/su0wkMTeLrD6Zh2f8a+Uc+RVFc+Pq35eCuRQSHt6JTrymnPfZfCo/uwEWX/YsfFzyF3VpNYeZHRCd0Y/Ktc49bd03685Nzwv6E6mqKWPjuVXgaqmnwOEjrdiWDxj9zymEDtVV5bPvhRWyWcmLaXUSnvtcd0z4vcwNLP5nOeLeRRgHLNA4m3vntMR+6n/23H3fVQWeVd/HH/4gKbD3GU71zAa+7fl5Q+EZ1KaNum0tI+M9PhX4tK30Fy755nDY9nkOnD+bo/hcJCQ9i9JUvnPb4a6vyWfHV3VSUHSEoKJ6hk18lKDT5tNst+GQGNdUWnDWZdLY3MkYVwE5PA6sVCy+p4pjvqWKeqhFFpUat8aHH8DnN2+5acy22xhJadXqIowfeIDz2YgzmKAqzPqfX4Ol0638DHo+b2e9Noao8H60uEKe9gpS0IcQk9kBv9CMxdTAqoWbD8pfYu3U2QqWi+wDv2iANDb7EtJoGQOHRubisu7jsuvfQ6X2Y897V+ASPprpsKy5nHa27PI4QgrL872moWcXkW+ec7JClvwk5J0w6U/L6/Mdyuxws/XQGRUc3oRYq/MKSGXvTrFMW2nA6Gtmy7CWqC/cTEJVGrxH3HXNDssFSxpcvj6S/XRCqCOarrQy44jlSO41pbrNy7oO02rWCa4R3/tZ3nhpWJLYm6+gmZqsSMQhvFveqqhr36Dvp0ufak8ZTX1fKzJdGktzxYQJCulKcu4iq4kXccP/K5mVSTn4sVtbMe5icjFXo9b70Hfc0yW2Hnfa87dwwk61rPsJkiMFQto1bVWFU4uJ9TznPqKKpwc1znlKsag2Kx02vkYtQa7xTCbIPvEZp/vdExE/AUr0XnSGEwLDeVBQuJzA4kPHXvocQgo3LX2HHhk/QG6PwuBox+pjpOXA6CEFCq4EYzYEc3reUNUuexWGzkNR2GDGJPdm+fjZtur+AEIKGuqMc2HwX0x9cg8knhK2r3+bo4aP4BrYn++BbdBk8E7XagLWhgL3rb+L2J/ec9oa09Nf2m+eECSH8gFBFUbJ+9fOOiqLsbcEYpbOw8su7GV5nZTIRNOLhwV3fkpHUmza/+ED+Nf+gOIZPfvWk78el9GPszV9yaNcC1Bo9V/WactyQQpvNQpT4eYJujFuQ4XZS47ZjUzwYhIoGxU29237Kaj0ej5s9mz9Hb4rH5azHaI4hOmUambsfO6Pj9w+KZcJt886o7S9dOvU11i75L4c2buY+dRJqIWirNrLL1cg3ShWLVQ206/s6bredg1seoLxoDT5+SdRW7sXaUIKiuLA1lhAU3pf4NjcC4OPfiu1rn6Rb/xsoytlOXU05XQd/ihBqqko3cXDHU5SVlGG3lrBt7QdMnP4Z/UfeT/+R9zfHteDTWxHqcHIOvkNDfQ6Wqn2oNUbe+U9vRkx4FoMpAGt9PkKo8Qlo05w8m/1bU5b/10jAfjlvTpKk05PX5wvTjjXvYsrZzadKNGpF8FpZIRsW/ZOhE/930m20OhP9Lz359c/sG8ake75nz6ZPKbVZGNF+FHHJfY9p42ioIlrRNM80iEaLw1qLQWeixOUkAT2KolAs3MSdpvLigR3foNb5oSguEGqiEidQdHQWDZay01ZC1uqMDJ/0yinbnEiXvtei05tZP/9RnlBHkii8iUuB4uAbTxWHhJOItFsIihrC7nU3k7n3JeLb3oTVkkN54SrUGh8MpnCqSmto3+cVhFAREjmYnasmYakpxOwXztbVb9F1yBfeES62KnatuYZNqz5DrTGw5rv/MPnWr2ndYTStO4xujmv35s/Q6UMpzPqShtpM6qr3oSgqPnh+EB16TCIoNAmbtQCfwPaY/JJQq3+qVByDSqXDbq1D4xt61ufjQiKvz3+ckyZhQoiJwCtAmRBCC1ynKMq2prdnAl3/8OikEyovzeAigryTS1HTz6Uhq+ggnCIJOxORsZ2JjO18wvcqy45gNAXxWHUR14oggoSG79VWLu4yHsVax4OH1tDNpWazxkXbLlec9IPa43Hz7YfTEDl76K5o2Fz5DyLa34VQ6zGZQ064zU+cjkYaLBX4+Ief8Z0lh72emspczL5h5BxZR11NMW4Bzqahhx5FoQY3i2kgKOEyfAJSAdAZQ8nc819UKi3gQas1IlRGLNUH0Jt+HjOuUnnHlgM4HA3o9UHNa6zkHnqf1l0fJziiP4ri4dD2hziw8xs69px8TIxxyb1Ys+S/hMaMwlK1jzbdnyEgpAsNdZksn38/46a9w7ezbsdgTvAOoYgcjFbnT9HRWX/6eVvlJYf44dMZlFfnEewXyYhp7zQvL/BHstQWU5K/G6MpiOjEnvICI/2pyOvzhauqYA8jXTq0TUMQh3mMvF2473f36+MXTr+RD5zwPZu1FofHw8dKFWVuB4OFL59oGohrP4VU31AeW/Q0wz0GstUK1QFhDGs/+oT9AGxf9SYHV7xBf7eWAzufpS68FxFtpuN2NmIw+p90O7fbSX1tMQZT4BmXTHe7nVSXH0WjNdJgKSP/6FbUKg1Wz88FKepxs0nY0PnEk5x4OUIIgsL7U170I1Wla1FrfAA3Gm0wlcVrUal0/JSJCpUGoVJ7h927nSiAVuc9hsLMWYRFX0RSe+/cqoIjn7F26QuMmfr6MTHGJPZm9eJn8QvqiN1aSnjcJcS2uha3s54DW+5h4KgOCKWOsvyF1FUdorZyD35BHSjOWYDRJxiTOfiMzsWFyNZYy7JZt3L06GYMOhMDxjxBu+4T//D9OuwN5B/djBCC2KQ+aHXG02/0J3WqJ2GPAN0URSkWQvQEPhNCPKwoynx+XdVBOqcCg+LYWlLGJcIfp+Jhh8ZNQmjSH7a/yrIjzH1jPGNdBszCj1c9pagNvgy67HmiE7oTFdeVjH2LKSjLoktkG1LajTppXzmHV+MqOMCrRKJWCSYodu7c+z9UOl8m3PDhSbfL2PMtK+Y+gFGocQjBJdd+eNoEJP/oZpZ8Mh1/1FQ6GxA6P6JTp2M0HeDhhgJGq/zZjh2bORKN1hdrQwEA1vp8HLZKgsL70rrLIyiKQsaOJ6ir3o+l+gA1Fdsw+sRhNEWRk/42Rh/vh2xkXFca63MpyV2Ef3BX7NZSfAN+mqsmECojGXu+xWgKIKXdqOYv/rbGGoIjBhCVOJ7q0vUEhHQBwOyXgt4YCYqHaXd9y5F9S8nN2siuNdegeFwkthnOsLFPnOmv8YLjdNpY9P4UrrVpGaxKZrOlnrc+uJprH1rfYmvknEhe1kaWfDKdNiozRR4bgcm9GTXt3RZbN0+SzgF5fb5A+YW3ZkvmTgZ7FASwRdjxD+v8h+3P6bAy941xtKutY6QIYKFSw3xRT8de19Ft8K2oVGoCQpPIz9xIoE8w/bpdgeZX88l+YrfVsfnHV3iPGILVGmyKh+nFa9lXtYP+ox5EqzOdcLuK0sMsen8KwmGl0e2k76gH6DLgplPGXV9XyoJ3JkJDFY0uB07FQ0TqjehDevHv4jVMVQVTrrhZqXIREDWS6rLNgILbbae6bCM6nT+dBryHRutDYdZXFGR+jqK4sFtLyT7wOsERAygtWIrbbcfkE4pWZyI6vgdHD7xKVOJV1NdmEJlwWXM8aq0vxfnL2LvlC9p2Gd98rC5HIxqtmbY9nmHrsssIj70EIQQanS8BYf2oKD3MlNvmkr57AcW5Ozmy+2kctlqCwlpz+fUf/amvKytm30NC7iGeVSVS7HLy+MKnCAhJJPpXywC1pPq6Uua+MY5whxO3orDeZOKKOxad1TIJfyanSsLUiqIUAyiKslUIMQRYLISIBVp+Ipl0xoZMepkv3rmKZZ5Kaj0OQpN7k9Z1whlvrygK5cXpOJ2NhEWmnfSD9Sf7NsxkjNvAFJX3P0EYWmb6mLBZa1nz3TNo9T5ExXej70X/OO2+G+uriEWHuikBiUaHR/FwycT/UVORi9tpJza57zFPJiy1xaya+yDPecJIEnp2ehp4/tPp3PjotpNeTDxuF0s+vYn/c/rRRWWmVDi5y1GMb0BbwgZ/zLYfxvOZwYhvxAjapEzGbi1n74Zb2bP+VhotOQiVmvDY0U0r2ENI9HA0Gisuhw1LXRnlBcsRQkVQxCAKMj9D8XgwmgKYePMsls97jNK8ORhMARRmzSIh7XYy975Efe1hDMaBrPruJbIz1jLi8mcBaKivQKsPwO2y4XTU0lB3FLNfEnZrKQ11OWi0RvwCouk2YDrdBkxH8XhQFE+LrxFzrtVU5mB0ubhIeJPYAcKXr3FQUXqY6PiWmd7ksDdQXX4Uo08QfgHRAKz46h4edAXQTWXGqXi4N2srmQd/oNUp7g5L0gVGXp8vUD2G3sGCI+uYUZGLDhVWky8Txj51Vn3UVuVhqSkmKCwZk8+pR4jkHF5DcH0t/1CCESpBX8WXazw5+IYms2HZi94kLDieXkNuP+01w9ZYi1GlJVjxtjMIFREaI4H9r8fkE8yR/d+TkDr4uKIgS2fewDSrmhEiijKc3LvsZSITehIR2+mk+1rz9YMMqm1gGuE4UHiYEtwaI627PcG+TffySV0mgZGDSUuehN4UyZYfLmX32ptw2itxuRqITpqIRustHhYaM4LCo18QHBZLcV4FltrD1NceweyXjMEYRnnJQaLjuzN22lssn/c4h3c9hBCCktx5BIb1pLp0C7mH3icsdjS7Ni9m58bPmHr7N2h1JlwuOyqVASHUKIqH6rJNRMSPxeN2UF22maCgHmh1Rjr2nEzHnpMZifcJ31+hyFXe0c08qkRgECoS0TPUY6Age0uLJWEet4vK8kxUQkVQaApCpWLzd/9hcKOL6/F+33zbUsXWH15g8IRnW2SfF5pT/Y+0CCGSfxpv3nTHbTCwAGj3x4cmnUxwWCumPbiG8uJ0dHozoZFpJxxO5XLaUGv0x7zncbtY8sl0KnO246vSUqvVctktcwkIjjvp/txOGz6KaL6/akZFTWUuu7euwFJ9AK0+iD2bZ9N9wHX0GDTjlLFHxXfja089+xUdrTDwFbUE+0exdM79BIR2o6Eui/hWvRh1xXPNcVeWHiFebSZJ8Q5B7KoyY/DUYaktpihnO/vXvAtAu4HTad9jEgDWxipwOemi8k5uDhdaWgkz1vo8TL4J6H2i0PkmE9s0t8tuK0NnCCOu9fUYfWLZv/leKkvW4h/SDVCoKd9EfEofgsOS2b5+PqndnvFuZy2jOPsraIo1JLw1k26Z7V3jq6GKRZ/fzqYlIxEqDT2Gf41G64vb1cjuNdfQfeB0gkKTSesyjjnvX+396qTAvo13YvJNpNFyFKHSHFcxUqhUCP68d9d+YjQFUuO2U6u48BcaGhQ3FW4rRlPL3PEqLdzHog+uxl8RVLlsdOp3PX1G/x+19RW0V3kLzmiFiraK1rv+iyT9ecjr8wVKqzNx+W3zKS3aj8fjIjyq/QlvFrrdTgTiuMRo+6q32LHiNSI1JorcVkZMfYOkNicvbOF2OzAJdfP10ohAUdzs3LQYe2M5TkcVBlMYh/YuYcK1H5zyyYyvfyRqox8LG2oYjd//s3fWYXJUWR9+b1V797j7TCbu7q4EEtyCu9vCIru4LO7urgmQABHi7jLxZDIZd9fuaau63x89TBIiJMgu8M37PHmge0puVXfXueeec36HDOmiUPWTs+p9gsN6oPmbWL3wJaZdP7015VDze6mqK2K80h6AaGGkj7BTUbobiWTtdw/hdtWS1HkMQ07+V2spQXV5JqOwIYTAjGCktPJj/X4AQsJ7UNFcQnL3WxCKAZ+3ASk1kjteitkWR3H2dGrKV5PY4WJU1UJN2WrCI9OZfM6TfPTSFHoMfg5FNSN1P9tWXY6qBlrFWKwhTLmgpTZeShZ//xAbF54JQqXboKcJDu+JlJJ9W+5jd8ZMeg26kJiEHvh8teTteQ9d95K3523KC+fhba5E1z1HVIz8OzhgADZrKPlNHsKEASkluaok8ndKr3S76pn11rn4akvQ0AmJ78aUKz+mqTqfHrqJn6Y4PXUj+6vyf5dz/hk5lhN2PT9La5BSNgohTgL++KTQNo6J2RJEYtrAI/6tqaGcuR9cTknZHlSh0nvE1QyZcBsGg5ntG7/AmLeVd7RYjLrga18dy2f8k9OuO7rAQ8d+Z/PVjrlEa0bsKLyklxPRfhpJna9A87vIWH457brfxuoFD9F3+BXHfACFR7VjwoWv8uSMO2l0lRIX05HK8ix6j3gbe3A7NM3NjtXXUJy3sfX6QsKTKNScVMsgIoSBPOmhSfdRkreZzd89xK1aKAJ48ftHUQ0WuvQ5HastHKmq7PS76C5sVEkfWXo9cX4npfnf4XWXoflqyN7+DEZLFCU500nudCVh0QPR/C6krlFVshRn/S503U9waBSDx96ElBprF79Czs7nsQW1p7xgFv1GXM2y2Y9RkLOO5qZamp0VmCzBjJ5yH+df+wWlhVv5/tPbMBgDRks12DBbI3E3B1oCJKQOYMi4m1iz6BXCYgbRrtvNNDcVoBhs7Fx7KyHhR3eQ/8o4gmPoM/xK/rHmY/pIC9sVD536nk3475Ra++PH13Ctx8IoJZh6qXHb2k9I7DiS+NjOfFdRyTkyhAr8rBPNTPwVdWi6rrFr89fUVeYQGd+Fzr1Oa6sta+O/RZt9/hOjqIaj1ldrmo8lM+5k17bvQUrSOoxg4rSXsNrCqC7fR8aSV3hNxhHhN7BXNvPA5zdzzQNbUX/Wc/InktOHsVL4mCnr6IKZ6bKO4KD2dO73aCCVfstDOEI6UVW6kKK8DSS1G3zMcZ92zRfM/fha3qvMIiwoBp/PRGL6JcSlnoGUkv3bHmfzqvcZ2tKnSjWYcFhD2eZ20UfYcUmdPbjprZr47p0LuMYfRLIw8fGm71jubmDcuc8DEBaVzhrXPlIw45M6K2UTPgE15WuoLJ6HIyiSPZvuJiisD5VF8wkK605E3EiklOi6D7erjK3LL8FsjcLvrebsqz4hKDSBlPZD2bv5XsJjRlFftZ6wyGRKCzNYNOshXE2VuJoqEULQY8D5jD31QcZMvZ/XH+mLxR6QfxdCYLYlttpno8nKGZe+y8wPr0I1WOk7+mOcDTmoBht7Nt6HvaXn6d+RkWc9wZOfXs8Q4aNE+GkIj2NknzN+ecfjYPXsR+heXcVNMgYdeLw4i01LXiW63UBml8+gp25FB+Ya3ES3O/Jc95fI27eCouw1WIOi6Dlw2i9mff0vOKoTJqXcdpT3fcBnf9iI2vjNzP/kWgaWl6EQwly9nv3L32L3uk8456bvqC/PYoBfxagE7PdgbHxfmXPM4yWlD2HsBa/y6YLn8Xtd1DaY6N/pciDgUFgdyYHO7kLB72v+xVWgdp3H0e7+LUgp+eqtaSiKAXtwYOKtqhYUxc7mFW9TWbaHHv3PIywyjb5jbuKmpa+SotrJ05yMO/Np9m+azmWao1Uy/0pNY+bGr+jS53QU1cBJF73JY59cR5TSTIXfRXTaYBqqFmC2ODBbgnG7GqgoWoAjJJ6ouC4U7PuAxtqdNNVnoRps6Jobv6+Bdp3HMuH0x1CNASN4wY0z2bj8bRpqsug7/EKKsjfS2NCM12vEHtqLHsNuptlZxLLZ/yIsMpXouK6Aj9K8mQSH9yZn50s0O8vI2rmAmPjuqAYTg8fejMFoZffWVZit0Zit0XjcVaiq8agpl38Hhpx0FwkdhlNVlsnQqHRSOoz4XY6r6xrV9aUMVwOrsyFCpRdWqiv3M+nit/jh3YuYUZeHX+r0HnxZa3rFni3fsmn+s/j8Hjr2Pp2hJ//riCk8Ukp+/PgaZM5m+vsVVhn8lGWvZcxZT/0u42+jjWPRZp//umxc/DL6ziX8Q0TxpqygPms17z7aj5MufB2hqLRT7a3pgJ2FFYOUOJuqCA6NP+LxbI4IzrrhW1bMvI959WXUNUo69r4HCDgUjpCO+Lx1WGyxeJobfnF8YZFpnH/7AqSUbFj2BuuXvUNQWLfW4xnNkWTvWoCqqnTrdw6O4BgmXfg6T350Famql1J/M2m9T8PtqmOYtDJOCdT3/lMzcOX2Oa1O2KhznmbmG2ezwlOJU/dhi26HIsuoK/+O0IhEKkr2IgQ467NJ6zyKfTvms2fjffi8dXg9tYAAqREUHMLkc98lOCxwf6ZMe5mMNR9SUrid5HadsQdFs2HZBzhCe6PrDfQZ/QlCKGRlPIRjxdsMHH09KR1Gkb/nDZI7XU1B5vvUVqzBbBqMs7ECe1A0yemDmXbDDKa/fREGg53QyD5IqWMwmgg/jt5pf1XSOo3m7Ju/pzB7LSnWYDp0n/y7ye3XluzmXN2CUAQqMEIzMbd4FxMueZP5Fdmcv28ZUkoSEvowevQNQCC7Zfn0O2loKCMuqTdjznv+qLViW1d/yNYfn+Ekzcx+VfL1hi85++YfDkul/V/z189pauMQpJQUFm2jCybWyibeUdP42JDOhV4LCz65noiE7qwy+GmWOlJKFuMkKu4XGh0D6V3Gcfatczj3jkWY7RGUF8xGSkldVQZNdZnUV64nMrbLCQkqCCGoqynEZI6gNG8mUkrytj+PsS6LgZlbqJ33IrPeOhdN89F/7I2cfctsOk57lmm3L6Rzn9NQjVaaOKCi1ICGetAPLLXDCC65exWDLn+bC/65mDOv+YyLb54FUmIP6cOACbMYOGk2urQRHd8Fs9mG112BwejA72ug68Cn6Nz/GQpzd7B59QHREKs1FFdTNblZy9mw9E3yspaT0uUmnA1ZpHa+BtVgxRHSgYi48RTmrMNgtHDOVZ/QWL2U7auvxxHaifY97yI3M4N50w9I1XftcwbOhj0UZL5PVcky9m25nz5DLvvbR1eS04fSd9jlpHYc+auvNS9rJbPfvYgf3p7G/t0LURSViOA41sgmABqkxnaaiYhqT3BoAiERKcQYrIxWw8ja8CU7139OXtZK1s68j9ubBI+7HTRs/Jp1C44sK11ZupvKnPU8oUVwvhLB01okezNm0dRQ/qvvQxtttPH3p3TfSk7WrLylV/ComsgHhnY8rSSw8MtbsQdFsV9zUiK9AGzRneiqAfsvSJyHR6Vz6jVfMO3OpaR0nURp3tfomhdPcznlBXMBQVNdJnHJvY97nEIIaipysDlSKM7+Cl33UV26iqrsrxhZXopj8ft88cJEGupKSE4fysV3LqPztOc45foZjD7zcQwmC43iQHliAzqGgxZog0MTuOCfSxl55UdMvXEm594yhwtumEGH7uNprK9n4ISvGTRpDpEJE6ku309CSj/crgIU1YzPU0WHXnfTbcir+PUI5n9zT+txFdWAwWQnd+9i9m6by5pFLxCddDpedyWJ7S/EbInEZA4nLm0auftWA3DSOU8SEmomY/lleNwVtO91Dx5vGF+8cQ5ejxOAqJhORMd1Jmvbf6gqXU729qcICYsjKvaX509/ZSKiO9B7yCV06X36r3bA6qrzmf/p9Xz3+llsWvoauq4RFteFFYobKWVAgEP1EhrfFYPBTGzaQGyqmVFqKLJsP4u/+gdNDRV89/YFnF1Vy/PeUNrn7GDOe5dwpF7HUkpW//gkj+uRTFMiuE+PIKK+iv27fvytt+N3569d2d/GYQghsJnsbPa66CfsBLfIpY9Tgnm/Kptufc+mbP8aLts5D5tiQnGEc/ox+pf8HEVROeuK9/nuk+vJ2fkCimpGEQomUwOTz337hMcbEhZPQ4OT4uwvydn1Oor08b6aRqQwomuSf1QWsHnlu9RW5aOoBnoPvpDgsEQAeo+7iY+zp9Go6Qhgpupi6ribDzm+1R5Ggv1QkYeKkj107v84QghU1Ux04kkU7P+By/6xgMwds9m56Rtikq5AUc2U5X+Paghjy4p3yFz7MapqxhASS11dHf3GfImiWsne+QK5u1/HaArF2ZhLqLkPUkrczjystt5AIA1z6PibWbngLdK6Xh+49si+bFx4Ol6PE5PZjs0RwYU3fsuahS/jalpNv2Hn02fIJSd8T/+/UbB/NQs/voYrtWAMwLuFt8L5LzLpkrd4/d2L+AI31X43PQdfSlL6EPL2rcBdsI2XtRgMQlCse7n5+wfp1vcsztRsdG+JrF6rBfPk9jkMnXzPYef0epyEKCaMemAdy4qCTTHi9TQBf9/0lDbaaOO3YQmKJENmE42RDiKwaNheWIgQAQdl2JT7ufWHhwlRzTSpOqdc8t4J1RiNO/1hZn9xK+vmnwwIFMWIx7mDM6/4AHtQ9AmNNTQiidx9a/F5G1j348lYdY1blGhGtUS3LN5qNix8EcXswONupEO3CUTHdwWgU48pfLHoZV5triZFV5hlcDNg3C2HHN9otBwm3rF/10KiEiaiGgLP4ZikU9ixZi43P/Q9mdt/YP/uhZgsoYRE9qUkZzq65qcgfyVfPDMGn6+Z0Lgu5OZspOfwN7HaE6kqXU7OzhcJix6KqzEXGA2AqzGnNYpiMjuYdPbjvPHYQLoOeBxFNRMeM4TdGzIpyl1Hu87jEIrCGZe/x7rFL1NRspLE1HSGjn/+Ly+Q9UfjbKxkxquncqrXSJo0Mr3sbVbWlzF06gPMKj6Hq+rL0aROUFxnpo67GZ/XxZoFz/IWCUQJIx5N5/rM5eyKn0EHYWacCHz3rpFhnFueiae5AYvtZ60TpMTn9xKpBD4bIQRRUm11qP9MHNe3RwhhBZKllJl/8Hja+B0YOPluFs26j2iMXCR1rEJhrWwiIjQRoSiMP/9FBtbdhc/rIiwi9YQfIhHRHbjijkVofu9R89R1XcPracJsCW6NbtTXFLD867upq8olIq4L7fueRVV5JuExo/EYHHg9dQgpCGv5WipCEC4Faxe/QlKHy9B1L1+9NY1zr/kcRTVQWpBBr9HXsauuFEVROX3gNGISuv/i+FWDiZqKtThCOyF1P7XlazAYzVjtYfQefDFVZVmUlewhP/Md4tPOxussIsLVyB2qnUbp48maDThSprYaidjkqexe/08iEyawd9N9RMSNwOepxGSSdD04f1oIOHjVRsrDmiAGhyZw0jl/zZS2vdu+Z+Ws+3F5mkhLG8SEC1/Hagv9w8+7e/WHXKI5WtNeFE3w7cp3mHrNV3Ttfy57t3yD1RZKaEwHAJqd1SRiwtBy3+MxIqWOarJTIQ5EViukD6PpyOOPju9KtSr43lfPIGwsEk0o9rC/bf1eG39e2uzzX4uhUx7g4z0jUYBC6SVJmCiUXqqll5CwROKSepPe42ScjRWEhCWecB2L2RLEWZe/j6b5UIR6RCEOKSUedwNmc1Dr331eFyu/e4CirFXYHJH0nXg729Z/gT24E4rBRrOzCEVRiBYHHMIYqTB/2/dEpJ6ByZrAwpkP4myqokO3SWTt+pFuwy6joq6YMm8zg7qOPy4FWos1iKryNcSlnYWiGKgpX4MQCopqoEufMzAYLSyf+yo7Vt9EcEQvDKZgbAiurfUQJky8lLkaiy0Cqz2wWBsZN4qsjMcxmEIozZuJs2E/BqONxtqtTLv+64POHLAHh8RVpOTg0kuj0cKIk+467s/iz0Rl6R4WfHo9lTUFRIYlMfGiN1od5j+S7D2L6KkZOF+EgYBOmoUrNn7JyNMeoe/4W1k7+zH8Pg+RCT1QFBVXUx0WxUCUDHzPzEIhTrEESgxkQLZeFYJ6NDSpYzAeHp0TikL79sN5JW8nF+nB5Eg364WL89KH/eHXe6L84uxbCDEVeBYwAWlCiN7AI1LKU4+2T5zDxb3Dtv6mgf1nde/ftP//Z3oPvhhnXSkZy97gci2HMAzUmkycfuH7rdscLb/8RDiaA7Zvx1wWTr8dqfkJCopmypUfERyawLdvnM1Ul2QgVhZn7+DH/WtI6/1vIuNGApC19XFk4y7ectZylgxir2xmu9ZAUtcbiU/7yZlRWD73CcqLthMRNxq3qxRVcTLt+q+Pu6HfwDHXsnLe09SUrcLvd+L3NTL1gpdb/z5g5FV89OLJJLS7gMT209i79GJuVmNIFxYQMI1wPi9fi+x2E0IxUFe5gZDwRIKD/IT3OoXwqHY4QmLo2OOUQ8L3yelD0fyPkbf7dYLCelBR+D0de5xy3EbW2VjB7M9vpbRgM1Z7JBPPepy0TqOPa98/mvKiHaz8+m4e0iNJEpG8l5/Jos9vYupVn/7h5xZCHGI4dQKGc+OSV6jeMIP/aCE0eTSenvUAVkcEccn9WK43sV030UlYmEE9UZHt6DfqGr7M+BaPt5pQHeaqzUya8swRz2kyOzjjuuks+Op2vqrOJyq2E6ed98LfRhWrjb8G/yv7/N/m7zQfCIlI5pTL3mf2x9dwm5ZPLEYqFMmoMx5vlaO32kJ/8wLW0Z5F1eX7mP3+pTQ2VaEoKhPOe5EO3U9i0Re3Erp/Ew/qQeQ2VfL6x9cSnDCe9r0CTkdwWHdqCr7kXVcDt2kKTeh8JRqwhPcmpfM1ADiCO7J28aOsXfQS9pDuKIqRuqoNnH/dV0REdziucY846R4+fe0Mtiy9EIMpBLezmG79DixmpnUey7K5T2KyRNC+5x3k73qFs5VAyxGAW9VY7nEV4/PWYTSF0lCzE9VgIjjIS0j3CYSGJ2EPjia9y39wHCSqYbYEkd5lAvu2PEB04hQaa7ch9cZjCpkcjKb5WPzdQ+zJmImiGhgw6loGjb7hT1FO4PO6+O6dC7jcY2KEksaq+ibee+cCLr1n9WHqy38E8gj/X5S7gRUz7uQuLYxwEcRrm2ayVigMO+VezPZwvm2sYwrBbJMusvVmpvU7i9LMZTxQlkN3v8ISg4+Bw64/as38hAtfY/nXd/PPnDXYbOFMOfMlwiJT/+hLPWGOJwTyEDAQWAYgpdwqhDhmJaK3opKSF1//1YOSEi771Xu3AXBZONScMootVTXYDAZ6hIcQVPwyFP+x532h3b9YOv0OntKjSFcszGtq4Kv3LmHc+S8R6vNxjggYmculicVaLYp6wEmx2JMIT4ojt6aI2wu2EBQUjUOEY7HFtW5jMNopyt9Jeq97CYsa0CIn+wC7Ns+g93Gm7vUbdjkmo43Nqz9EVW0MGfsI6V0OSACHhCeTlD4MYQj0IBGqhVrZ1LogVo2ORCFjxRUtfcQ8DBl3EwJBfGp/ImM6HvG8JrOdadd/zeqFL9BQu5QuvUYxYPR1x31vZ318HQZzZwZMuI/GukzmfHEbF94060/xYCnMWctIaaNjS3rN5TKEi3PX/6pjedwN5Oxdgq5ppHUa9Yt9croNv5KPsi5DaGBE8L7axJhR17JxzuPcqgWRKgLfsXM0D+u3/sCYc54hIq4rDxZsxo/Eppg4/dz3cQTHcP5tP7Jr8wwavG5O7z6JmIQeRz1veFQ6Z9303a+6xjba+J14iP+yff5v83edD9wzZSRbq2tp9ut0Dw8hRi6CDYv+0HPqUnLKvBVc4A1hopJKlu7moS9u5Jpxg3l572q+UNKxCIVUYWaj7mSP7m3d1+pIIpRmRqSE8WhBCSah0Cc0mCxbSus2qtGO1lxHdMqpJHUO2LbSnOnkfnU5dww6NOry4cAjly9ExnbkvGs+Y8kPj+J1N9Kz/zUMOSiN0WAwM2Dk1WzbsBAAoVqpPmiaXyv9GIwOMpZfgdkag8dVxJDxt2Ay2wmLSCW5/dGjISef9xzrl71BSf4SIqISOfWCGce9SLpm4YsU5+2lz+hP0fwutq27j+CQeLr2/X3UBH8LNRXZBOuS8S2pfOMI5htZSU1lNrEnqAwsdZ2cvUtwNlWSkNKPiKPMd36ifdeJfPbjU3zqqSVNGphhaKZ3/4vI3b2A0zQrvVuc5+u1YB7aPpvhU++nfd+z+GzJK7xPBWYEQyc9QHBoAqdd8yU7Nn7J/roSBib3pX23iUc9r9kSxMSL/vzPueNxwnxSyvqfefPHbAYpDEbUqITfNLA2fjvRwElJ/z3lHn9FMV1W3EJvDIGoETBZBPNhYzYTdz/FSn8TfhmBQQg8SHxCpzL3SxwhHfG6q6jJ/4b7o9ozuFsEdAtIkn6TW8wLu19AUe5E0zyUZ72HqnmxOVKBQBTEak+i074PuUxddWIDHhaQpMX1NWz4+pA/tQ+r5M4tazFZIrDHj+X5ve+Qj5cGqbFQ8dCx/4tUFs7FW76ItCAHGUvfwxqUxuo5j/GfPh0Yn3D0uqAbE4FEI7ADNt94XEP1aBovFm1l0MnPIIRCaGQfoiJ60G7pjZyW8su/tfJmN25NI9Fua22UfTDTT/nmuMZxNEJik8lVJdIfSK/Mx4vdFoIt6sR6ijTVl/Plk5NIdvswAZ/PeYwL71tCREz7o+7TMepkTCFfs+zHl0FqnDz+etr3mMj2Za9SXVPa6jxXCYnBEkTGmo+oLcggDRMGBCW6j3VzHuP0a6fjCI5h0JibDjm+rmtUFO9E031H7fnTRhv/I9rs81+UYGBkfMovbvd7UuVqptHnZ6ISqKHpICx0VWzsl0aMikIdGrEoSClpFDpNVRtxNeajGu2U7XuH09slcfvA3tzRcrx9NbVc8N0crMHtMVtjKc18jRhHECZHeus5rUHtqKkRGKIPfOf8FcVctuGaY471X/0jgAhgN2w6dLGy3OPmzOqNlOXNxBHalcXyK6SmESkMfC0bSOh2J7rmoXjPq5yWFMP8pW8QEtmPxpptTIkL5p6e6Uc8J8CVwUAPE1ABu/913Pf2u20ZJHS5D5M5DMxhxKSchVz3Apf55/3ivvVeH9VuD/F2KxZVPe5zHi8lrmZ+8DbSJMJxCJUmqdHga+SirKdJLDn+dFdNSv6xegtFNU2kYGaWdPJAv25MSIxl523fszvLd9g+tqgILn1wNau/fZhtdWWk957MgPE3sOL7J6g6KFO2WvoxW4Nw+irZvvR1wlBJwEImbtbNf5ahU/+BohoYeuo/DjtHVWkmTfXlRCd2w+b4ffqYHQ1XZfXverzjccJ2CSEuAFQhRAfgFmDN7zqKNv5naLrOrH37ya6upX1EGKd36oDyK8PnhugE4jSV3Ix9NLfUouVJDwhB/87d6JhTyqPlpfTRraxRXYxPTUFTm1my/AIsBiP3DOrF8C6HpiycGxWPCMrii70vYlAEj4/qxw/7i9iV+RZJ3f6B21VCbfE8hkwcgiE6lll7s/hmfxFWg8rVPTsyID72V13L6OgE/uMI5a1tr2PRJRf06o7UNUxeL5FltWSuu4WOkVGc0b8nr++qotPQNxCKgfDa3Ty05S5O6tMXgK3lFWwpLSfCZuXk9HYY1V8nSKpIidFgoNlZiM2RgtT9NDuLCI9ud4hx+zm6lNy9dC2L8woxGMxEW418NGUsEdYDqZv+imLOnXPWrxrXT5yu6Vxp8XO/s4REaWQVTTzUrTvjTvC4T23dw7DGZq4WUdRLP9/oNWS+OIEXh/c94vZSSjLrG5i/NZPsunrCjSaGB1czsuAtUpIU7iiqJF/z4FR01ipOPgvJ4R8rvmGwsHOLEoMQgg+0Shbnb26dFGhSIgjUJDb7Na5fuYmKxmbMKOgmwXujBxJp+X1ken8LCf+4gfnypP/1MNr439Jmn//mrCkqZk1BMaFWC+d17UyQ+chlAMdDsNmED0mB9JAszLikTp7uIdZh58b+fXho804m6UHkKl4abSrXdO7Ahxtuwq9pTOnYjlv6Hxo16Rgextsnjea5jZ/R6PVxfrt4IqwRvLL9M0IieiIUIxX7P+DctIAgyLbySl7bshu3pnNKu3jO7dLxV6XrJQCfnBrGf9Z+T1Wzm8md2hNlMdLg8ZBWbSJ71/OEW+08OWYo9yxbR49Rn2KyROD3NTJr+UVc2L8XaaEhFDc2sSQvH1UoTEpPPcQunigRjn1UNeYSHB64R57GHKJCQ45pnwG+3J3JU2s3YzGHIDUnb0waTZ/YExNQ+SWSgTO7VnPn3mz6YGWr0szpnTqSmnZ8KaI/sSKvgOJaF8/IJHxI+kkbj2zZzcSUBLq/eCpHqsivdnt4acc+skrKUYVgnCjiXO8SxmlepilNvKb5CZcG5ol6Hk7vwfZPziFEwstqChahsFl38oS3hLEzTyPaamlVUzS01DI+t20v3+cVE6eYKJE+Xhjah36RYb/9pv2OPHeMv4kjyTsesoEQNuBe4Ke433zgMSml+2j7dI+OlF+fddqJjrON/zJSSu5asJTcokr6aVbWKk4Uh5mhSQlMSE87oQfBrsoqXtm0k5zKSvyaH+nX6Kha2a67uHfUUKZ0SMen6Xy5ew95NfV0jo7grM4df5XD1+T1cufSdawuyMNqtHD34D5M7ZDOxT8sYHdNM+2634rmd1Ga+Trvnjya3jG/7wPtYD7fuYcPC0NJ7nE3ALruY/28Sey45lJmZWbzxLpthMWNxdOYRaKpng9PGferHbEZe7J4esMOwmJH0Vy/l/Z2D+9OHo2qBFYuZ2XuZ2NZNQlBFi7r2Q270cj03Zm8srOa9gNfQFEtFO19nXR9K69P+n36cR2MV9P4MTuXOreHgfGxdI488RWp2+ctpkuhh3J8zNRrcaDgFJIZ551OWuihCkjNPj+3zF1IRmk5o0QQlyqRZEsPTyvlfHrWVNLDQtlVWcWC7DwMisBiMLC1uIydFVVcqoW1Knxl6E5eVauYf+l53Ld4BfNbjPKVvbsjpWTnjjzukDEowIdU40sJ4amJY36PW/ab8FccyC3u8e38zVLK/sfYvI2/IW32+e/NjN2ZvLZmIxP1IPKEj90GDxPbpdIvIY6T09OO24GpcjXz0sZtbCouwef10uD10NfgYL/0MCo9hftHB1L0FuXms76wmHCbjYt6dP1VDp+Ukpc2buPjHbvRpeTMzh3599B+vLZpK29v3UNKl2sxW6MpzXyTq7sncGWvbid8juNlf00tl8xdS9fRX7a+l7X2Gp4d1p5gs4mLf1hESMwIdM2Np3YzX58xmVjHr6uR2ltVzcU/LCI0Zji634WvYQffnHkSUbZApGljSRmzswuwqAoXdutIckgw2bV1nDdrIZ2GvonVnkBN+RrKdj7ByovPQj2CoMpvQUrJqsJismvrSA8LZXhSwgk7wNN372X12t0M0Ww8r5fhQKUKP89MGM2k9MOzrl5et4kPtu0kURq5T43Hh+RxpZyrhvbjzC4dqXS5+HpPJi6vn7ggB5sKS9hbU0NaE9yuBspQ/FJyhpZFxlWX8vmO3by6aQteXWd0YiLn9OjMIwtX8pyegEOobNKdvGmqYell036Xe/Z70eXN949qn48ZCRNCqMAcKeUYAg/6Nv5G5NU3sK6whLdkMo1CY7a/lsH1JnwNZdy0Zx//mTCaYJOJGTv3AHBujy70iQ2k2dU0N1PmdJEUFERBQwMXf78QqWmcTzAR0sYHoprw1Cg+69OTdmGhABhVhYt7/PYHrsNk4o1JI4GRre+9sWUHe+s8dOh9D2HRgwDw+5uYvmcZIWYzb2/KYHtZJUZFYUBCHLcNHYDdeHQRhUW5+byekYlX1zmnYzKX9OhyxAdW9+hIqjcuIzz5TOxBaRTve58eMQkI4D9rNtJpyOvYgtKQUidr7XUszS9gYrvU477WksYm/r18I9m1taSGhvD4iL4UN5YSlR7HpHaprQ/qJ9duYXZBA6GJp7KuZBsLchcy44xJ7KyqIyh2PKohsMIXnjCZzG1Lj/v8J4JJVTm149HTBo+HAUnxvF+0Bb+m87aaSqgwMFuv4875S/j6vENz61/bsBlR0YQbnWuUaAxC0FPYGCjsbCotIz0slG5RkXSLiuT1jVv4LmM3Z2uh1EiFObKOQcKBiuAHWcfItGReWLuR6sIaPlfScaHz0I59BAfbGatbUVuamw+QNj6vrf9N1/h78UsrrG38vWmzz39/Xl63iQdlHMnCxH16ESleFUtmNa9nFbC3oooLe3bjnc3bqG92MzwtidM6tkcIgdPno6C+gQirFavBwNnfzqPJrTFEN9CfEGbJOmqCDDw5fBj9Yg+kzo9PS2F82m9LkRRCcNvA3tw2sHfre1tKy3l/eyZxaWcSn3YmAGZrNJ9t+zfndenAW5u3sjK3EJ+m0yEijH8MG0hKyNF7jmbV1PLYmq2Uu5oZGBvJPUP6YDuCPU8IcoDuorJoPpEJ46mtWE+zs4QO4QO4c+k6otpfQVxqYDwFe97gzYxdPDRi4HFfq9vv5z9rNrO8oIRgs5n7h/Wj0VuPUVGY2O4UQlsyJpbmFXDnsg1EpV2I5qlj5szv+fqMyeyvqSU0vCtWe+BZHh4zlLxtOrVuD5G2Xx+VOxJCCEYkJzIiOfFXH6N3TDTP6xtYozfwoJpAF2Flv3Tz4NJVDIiPJfygSOLKgiJ+2LWPNGniQiWSyBZVzTO0EFbmFnBml45E2Wxc368PG4pLuW3eIi7Uw3BIAz/Keiqkj2hhZLasJdXuYHVRMZ9t3s7LJBOmqLxaWsk7nq10xYqjpRVTP2Gj0l2CV9Mw/QFpnX8Ex3TCpJSaEEIXQoRIKf8cM482fjdcPh/BigGzrjBdq2G4COIaNRA1aqebeXrlWmrdHs7VQwG4IX8BL588gdzaOp5ds4Eo1USN9NElNg5zcBfG1OZxthJOg9RQpeDLwhLajT3gKOlS8vrGDL7bsw+DonBFv16c07XTrx7/otx83t2QgVfXaUZFMYQgZUBi3NNcQX3hfNZ6S5i3dw82BMNFMAOFnYX7SripZgHvn37yER2rtUUl/Gv5JpJ63I3VYOfNnc+CEFza49CmjN/szeLRVRvQJexYfSNIP91i4nl5wnA0KXH7PFjsgbozIRTM9mTq3M3HfX1eTeOS2Ysxxp5KcodxVJcu4/G13zDvvKlYDAd+uh6/n8937abvuG8wmoKRKaeTtfZaVhcVkx7qYPn+Neipp6MoBuoq1hzTuP2vOa9bZ37cn0N4uZtQEbjG8SKY9+qyD9t2T3kV43UHGTgpwksqZnQpKRI+Qs2Hpgt+tn03T+rxxCsmhkgHN+n5XKjnoApB37ho/jViCBfM+I6rZQhWoWBF4WQtiFVorFRcjJRBqAiWiSY6RR27cWobbfw3aLPPf39cmp9IDOyQLlxS5wU1GUUIxssQLt+xix8y9zPCb6OjNPJW0UbKm5wMSUzgxjkLCEKhUvMxMiUJrzGaSHcFt4pIPEjiMXJ3TRFpoSGH2MDFufm8tGYjTT4f49JSuHP4oF89mc2qqeWpFeuodDoJspgx25Na7bOueSjP/ormpgYmfvQlFo1ASx0lnH1FTi75djYzzz/jkEn9T1S5mrn4h4VEpF9BSGpXVuV8xu2L1/DmSaMO2W5nRRXXzV+Gy+sje+eLZG19kihHCG+dNIpQi5katxdrXGrr9hZHGtVNW0/oGh9YsYENDWEk9r+b5qZCHln9OJ+fNpGO4Yemw728ZQ/JPe4hPGZoyzuCz3bt5PSOadTXZeD11GIyh9FQsxMFvdV5+7PRMSKcK/r15LMN2+kiAp9Ne2EhVjGRX994yOe1t7qagbqNUuGlEA+9CUQEi4SXsJ/19fpi2y4u0sOZ1FKrWK35uVbLw6aqhFjNvDX1JL7cuYcJehCxSsCZO0+G8VB9GQVSp0qGEimMLJONJNrtfxkHDI6vJqwJ2CGEWAi0djqTUt5y9F3a+CvQPiwUv1Ewo7mGavwkigOpB+HCQF2zh0tkeGsRr1lXeGdjBjsqq3ieROJ0E9t1F48WFWMJj0KR8J1ey2d6NcGo1Db72VhSyoD4QFj5g4wdLNy+j3tkFG50nluziVCLmQk/iwx9vy+bJ9dl4PJ6GJ6czFOjB2M3HbrKtaaomIcWr+QmPRKrUHhFVqBa7WTveAGPu5KyPW9xsmaihwhnFrXk4+FqJQohBD2kjcur8ihtchIf5Djsvny3v4Co9EtbH5iy6+3MzHruECcss7qGJ9Zto+vwd7A5kinc9zHV+TPw6fDq5p3cPbgPveISKNr7BvEdLqepPouairX0HzrpuD+f3Lp6XLqJrh0uBcDa/iL2li4gq6aWHtEHHAG/LhEIDC19y4QQGEzBeP0a07p1ZlnhcnavuBCT0Y7iq+XRU8cf9xj+2yhCcFnvHjyzeDXNeqCucJN0kmQ//HNKCgthS3UlV+tRPKAVMVQ4yJRu6lTYVFxKelgo7VuMoQSUFoUOgxD0UG106N2O87p1IsxiQQhBhM1GdpOHTi3GJUfxMiAhiTx7HVcU52MQgsTQYB4edvwrpW208QfTZp//xoxLSeaV/Ap6YSUItTV9P4hABkQnn4nLlEgQ0EVauXvrTqbv3Ms1/nCGKkHU4Oe2vEI0RyJC6uykmae0Umwo+JG8v2U7dw4LZI5sLa/ggcUruF2PJkoYeSermKdZz32jhh4ypt1V1dyxeC0lDbWkhUfywrihh6WKV7mauXzWXM71h9BJhPKNqxadQiqaKzCZw6gvWkS7plJOE1Fs1Z3MoZ4nlSQiFCN9sJOj+1hRUMTpnQ6vWVpXXII9tEdrBMve635WzZ+MVxveOvn2aTrX/LiU6M530Dl+NPU1O8jccBchFhtvbcvkHouFMUnRTM96D6sjGV3zUJn7GZf1TT2hz2dxXj7dRj2OyRyOzZFMU/VEVhYUHeaEefwaDtOBe6QaQ2j2a3SJjODSbu35cMUlOIISaGos5Pnxw1vrnf6MnNOlM+9u3t7a465UeinVPIGo40EkBgUxW3FznYzgEb2EbOmhGZ0MXIzzhrA8v5BRKYFFah04+Ir7CTveGDuPjR9FlN2GIgSRdhsbFW9rb9Vs6SbGbmdi+zRu3JRBmGLEZxK8Mfnoiol/Ro7HCfu25V8bfzPMBgM3DOzHY8vX41cMqFod7TETIgy8Livx6n5MyoFVMjOCJq+PdMVCnB5w2HoqNmxSoalhD99LF3ap8rqaQqQwslxv4PYfl7DyigsBWLQ/l0v1cFKVwCrP2Xooi/bnHuKEbSkt59E1W2nX/2l03c+WvW9x68KV9I6JZG1pFbE2C7cP7MnczGzO1kMYoAR++DfKaJ5qLsXqiKMo8z3aaTqXqQFHpbuwcq62H7fUsQoVHYmGPGo9mllV0HwNra/9vgbMP3so7q6sJiyyLzZHMprmprJ4IWGJp2CIHszqwh+45sflvDx+GP9csp6MxT8QarHzwrihramZx4PVYMDrc6JpblTVgq558HgbD0u7sJuM9IuLJ2/7k0SlnUtjzXaa6zMZGD8Vk6ry3slj2FNVjdvvp0tkxBHTNv5MjE5JYmlaIjfk5BOjmChRvLwx4XDn9dYh/Tmv8Du2upowo7BSNuFG41xfBP69FVy8L5uPzpxCx/AwzunaiWd2ZXO+HkoxPtapLv7Zqf0hK3d3jRjEZbPmsgcPTnQqzPBA356EmE2UNDnRdJ3E4KBfLVzTRht/AG32+W/MHcMGcnLeN2xTJH7dw1y9jp7CxveyDinh4Ce5GQW/1Kl0NzNEaUlvEwZ6KTbWukoo0zQepYl/K3H0VuwUSg937NzDtB5dSQwOYlleARP1oFbJ8GtkBA/m5h/ihDV4PFwxZwnRnW6hW0gnSnOnc9H3C7h7UB++3V+EUVG4umcHKlwuOmPhFCUUgNtlDGdr2djMQVTkz8TnquA+NR2jUOiLnW3+ZnbSzKiWK/Idwz6bVBW/r7F1Mq75XQjEIdtXOJ34pYHI+NEAlOf/gD20K+b2F1FQv5dp333Od2efQq17FzNXXIwqFC7v2YUzOp1YOr3ZYMTrrsJkDgdAc1dgMx4+rT69QzIf7XoWut6B39dAZe5nTJ04HICb+vdkaodUypqcpIf1+d3TEH9vwqwW/j1iCPesXEeKYiZf83DH0IFE2w9VWZyUnsb8rBwezi8mGgOrZSMSGImDuLwmHixYzi3DB3Jm546c26MLd5UsxagLFAQfKDU83HskMQfV503r1oU5e/fzgKuUCAxsVl28PmoivWOiOaNLR2rcbhKDHJgNx+PW/Hn4xdFKKT/6bwykjf8NdR4PYcmTSO5+O1Wly3luz9u4XYWAZCgOPtKrMLesUbytVxLkC6be52SPaKajsLAPNz4Bz40dzM3zl9BNWFtzf0eKIJ73luP2+7EYDNhNRqo4IGFaiR+76dDC37XFJYQlTqGxciOV+z8nVVjZXFPHjhofCV1vYlf9Xs6bNZMxCdE0obfu14hGelgIV/TvwLaKSjbszGkVav5pq1f0cgbhYCEN9E+II8Z+ZGnWS7t3ZO6sGSB1FKODyuxPeH7coQ0b44LsNNVvR/M301i3B9VgJ61LQEY3OKw7Wxadzo0LVuH0aVzYvSu3Deh1wiHypOAgRifHs279rTiiR+KsXM2Q+Eja/WzVEeDViSN4bM1mtuy8n1i7jZdPnUCYNSClrghBt6hj99r6MyGE4JGxI9jXqxu1bg+dIsKPmJ4RZrFwUe/uLFm/izMI5SutmnFKCKNbBDdMusLHGTt4bNxIbh3cnzCrhQU5BYRYrXw8aPhhUdCOEeHMPO8MVhUVY1QUxqWmtEZgf77K10Ybfwba7PPfm5pmNxZHLF1GfoqzIYcZ257hw4YspNRIx8wm6WSOXkciJj7UK7FZLeh6M4v0ekYowXjQ2Y2bx0cP4T/L1+L3yVYnK0mYSRVmcurqSAwOwm4yka1oreeulH6sP5vQZlbXYrbFYbJEsHfVtaQrNop8zTy8Yj2JPe9B193csOA1ru7ZkSaptTpKLnRUIXh+dG/KXS4eW1GJRsCJlFKiA5/pAenvTOmm2KoxuiVK8nNGJCVi27iDvO2PYwnpTm3hTC7t2f2Q6FGY1YLP34yrKR+LLYGqkqUMmvQ9qsFKSGQf3HXbuHnBcpr8MCIpmfuH9f1Vzs8dA3rx5Ia7CU8+C58zD8W5hyntTz5su6t6dwN2MWvvY5gNKk+PHniIcnNKSPCfukzg55zeuQMDE+LIr28gOTiIhOCgw7ZRhODGgX3ZWlzGpXoku2QzNWjcrAZqEDtKCy9vyODMzh0ZlpTAExNH8XnGLiSSh3uNZGxq8iHHs5uMfH72qSzJL8Dl83FPQnzrecOsltb5zl+NX3TChBC5HKHviJSy3R8yojaOi0qXi8zqGiKsVrr8ChW6n0gPC6Fxxyb8vkYi40bhcZXh3PMGURhQhGACwcyWdTRJDc0YDBHjcDd8w0OyOJDipSo8O2EMo1KS6BsdzZ6KapqkhkOobJUuQk0mzC3Ox42D+3HD7AUU6j48QrLa4OKLPoeqzIVazDTX78ZVvYc3RTzhGChWg7mluYTgsO6Exwwhp3EvZmMD38hafJrEIRS+FfU8NWg0I5OTGJwYz8KsXN50VdANK98JJ1ZjKBnARl85F/Xqxk0D+x1VGahdWChfnT6Jz3dtwaNLTjtpBP3jDpW6HxQfx+iEApauvBSjJQK/r6HV4OhSw6v5aAibSlB4d+bse5d6z0b+M2rwEc93NIQQPD1mCLMys8isWUeH7uGc0an9EcdtNxl5YvSRjy+l5O2tO5m+NxdVUbi6Z0fO6XLsBot/BjpGhP/iNn1jY3hbZKDo0IiOUR64N0EoVPv9wIE0x8t6H735MkCMw85Znf/896aNNqDNPv9Zafb52VZRgUFR6BUd/asVceODHDS7q3E27Mce3J6Env9k78qrSWrpcXiWEsYWvYml6OTjIz71Kuoz3+E9WcXrWiVGoXB57+5Mbt+OCqeL59ZuZL90015YqJI+ivCRFByY/J/dpSNf7djNS55yIqXKfKWJB4cOP2Q8IRYzza5K8rY8wr9lOH2kHbcaxs2yFNVgJSpmDJrPyY6qeRRID0/rpXQXNn5UGrisZzeGt4hCrC8o5rGCUibpwWzFS7mqoCh2XvdX0zU6jC8mnUSw+ch1UVajgemnT+K9bbsoc81ncN+AIMnB2IxG7h8+kMfX3ERIWBdAR9e9qAQcLZfHRbEhiYTOl7G3bBkX/7CI784++YQXSs/q0oH4IDsrCzcQFmri3AmTj6goqQjBNX26c02fI4m4w4qCQp5ev4Mmr49xqQncPbjPn76uKT7IccRyjoNJCglGUwS1mp9aNAwHPamCUPFoB5z+kclJjEw+suP9E1ajgVPa/70ebccTtztYVtECnAP88uyojT+M9S1KMmmKhSLdw4QOadw3cuiv6rcxLDGBqWnlfLPsAmzWcJzOcuII9BKRwCLZwABhZ6PQiEg7g+rsL3hVTSFRmFimN/CpsZ6RyYlsKCllT10TiimYK725RGGgWtV5ddIEhBDsqazmq12Z9EuIQ1rMpAY5uK1Th8N+xGd06sD7234gSArClcDXM0GYcGCgpmIdEXEj0Pwevt+bxXmEU4yXjdJDfIij9QdsNxr57Kwp3DhnIcvrmohMP5+e7c/H1ZhL5rpbuH3IL9f0tAsL5b7hR99OCMHjowaxtbyCsiYnr2XUkLftUeyRg6kunI3FGk1C+nkAWHs/yJyl552wEwaBh/eZv9Ep+HjnXj7aW0Fyz8fRNQ/PbHyYEJOJiempv+m4fzR7qqpZX1xKqMXM5PS0I6YZdI+OZFBSPPfl5hOKyvOyjBrdTzwmvlLqeLRzr199/oL6BrJqakkMDqJTRDj7a2p5b/M2XF4f4zukMfU3KkG20cbvQJt9/pNR4XRxybezsfkkHqnjCLbx/uknH1ON92iEmM38Z9Rg7lt+C3Z7DI2NpZiEoEL66S/srNQbMaNgRyEiZgQV+z/jNiIYbggiX3r4NyVM6dSeKlcz727PxGiL525XEfEYqRR+rh/Yh7TQEOrcbl7btIUuUZGYDCpBQQ5eTU1uVUP+iQ5hoYxNjuX7zD30bEn3twiFztLE3qpNOEI7oUud7SUlDNXtKMAq2Uil9HNp7wMOyH/GjeKZNet5ZXcWYYmT6N7lWhTFyJYl5/HQ6OGtsu5HI8hs4raBfY65zZmd2tMnJop91TUsCGrH+o13EJ5yDq66XTgb9tNv3AwMRjuO0C7sWbmarJraX5UxMiQxniGJ8Se830/sqqzi9sVrSe55H/G2WBbteRltzRYeGjHgVx/zv0GF08XC3DwAJqSlHpaOCGAxGLhxUD+eXLmWUFRq0fBokslKCB+KGk7pePSm2b9ErdvNtvJKbEYD/WJjcPp8vL4hg8K6errFRnFV3xPPPvpfcDzpiD9vD/2iEGIz8MAfM6Q2fkKXEk2Xh62i3bVwKXfo0fTBjkvq3LE/n3Xpacf9IPDrOq+u38yC/XnYjAZuHNyPS7p3pNbtZkleAZ9v2cm7aho2oVAr/Vyh5WK0JRBii6ML5lYBj9FKMK97K6n3eHlu4y4Su91BVMI4XE35FGa+z8lhlQxMiGNuVg7/XrycBExU4EMKeOnkCUdcRbEZjbw0YQSXzJxDpmymk7CyWXfSpDdj2f4SxTteQKg6vVQb54hwNCkpkl5uq82nyevF0ZLeGG61Eh8WwR5XA/V1u/FlfkBp/vc4dB9TPp3B0OQE7hg68DflDwshWo3UyOQk3szYQW7D16QE+9jpPhBK9/uaMPwPHwazs4uJ63wzjpCAMxfT/gpm53z3p3bCFubk8eCSlYzAQZHw8cW23Xx85imHfV6Z1TWsLyzhLTWVKGEMNHaUpbQPCeGefkNaC39PlO8y9/PkyrV0Uqxk625O6tiOOVk5nKYF0x4DL5VuoNHj5YIeXX+Py22jjV9Fm33+3yGlxKfrh030nlm1jkFuM5eICHQpeaG+gnc2b+O2wcffxm9uVjbvbNyGV9eY2rk9888/leLGJpDduGTWHJ5Rk0gXFjQpuVnLpwSN7p0vp37ltQxXAgrHKcJMF8XKvppa9tXUYYocTqced+J1V1NeOJeIiu+5ok9PihsbOeOLbwnWFXxI3EjO79X1MAcMAjbv0ZEDWZeXyxxfHaeKMMqlj016PY78uezI/x6pCIy6n2tFIooiqJQ+HpdlbCwpa+0lZVQVusdE8W1ePXX1+xC5X1NdthrF5+Tm2QtJCwvhvtHDfnXPrp9ICw0hLTSECe1S+XJ3JmtLvsVuhkUGI4oamCtIqaH53Rj/RzZ6aV4h4UmnEh4TWKRN6vZP5q+/7k/thBXUN3DhNz/QS7cgELy5IYNPz5p6WEqlpuu8sn4z9yrx9FXsVEgft2j5ZJl1Tu7UnhsGHNuRPhr7qmu48rt5pGCiRvqJCQ+mzu0h1QkDdStLKrLJrKzhpZP/vCJkP3E86Yh9D3qpEFh5+2tVvv0FeXfLdl7blIFf6oxISOCZiWOwm4x4NY1qt4deamDVwSYUOmOhsKGBIfyyE6bpOv+Yv4SdhWWcTxihzQbuW7ScV6dMpE9sDDXNbuYLAzYRcPzChAGHMOLUvZTmfEml1kiTGoFDqGTKZhQhCDIZafL6sNsCKXs2RwpB4b3waouQUvLA0pWtP8JGqXGDlseTK9byw4VnHza+CqeLW+YtIl1YuFcrQkGgI3lQSaCnYuMrvYYtNo3KJjcNup9H9BKq8WNE4cpZ8/jkrCmtRlFHEJd2FopQaW4qxOBv5ioRQXKzic/3FXNv8wqenTT2d/m87CYjdwwK/FTq3B5O+3oO+btexOxoR3nO55zf5cQ60/+e2I0q1e6q1tc+TyUO4x9jcOo9Hl5cu5Gc6jo6RIZz25D+rY7xifDkirX8S8bSVViRuuShxlLm7M85LCqYU1tHZ8VKlB5YZe6n2DFIhbdPm/yrc8RdPh+PrljDMySQrJupl34u351JGCpraeJkJYQ7ZDQvb93Z5oS18T+lzT7/b1hdWMw9C5dR6/WSFhTESyePbxVdKqxrYFqLHLciBL11K3tPoLfgh9t28ur6zZwqQxgggnhzWyYGReWqvj3xaxo+JCkEUvVUIUgTZqoc0ezf9jQ+3ct+EUg3bJAa+7RmEoIcbC6rxGgNzA9MlgjCY4ZRXvodAPcsWMYEGcwVhig0KXlUK+b9bTu5cWDfwxxMv65z/ewFBGuC6XoNn+rVeNG5QERwrhLBbtnMI7IUr67TqGh8qlezSjZiRPDgklV0j45qra/16zohIe0Iih2HsyEH1VPDBIIY4w5hbUkTl86cw/fTzvxdhBYUIbigW2cu6BZwnqt/XEbWpntwxIyhvnwZKUEW2p+AaNbvic1oQPNUtr72eqqwGv4Y8SxN13kvYztr8osIs1q5ZUj/w5Qtj4fXN2xhsj+I85RA0H26v4bX12/mqYmHlpfUeTz4NY2+LXWI0cJIT1MQ5w7re8TmzsfLQ0tXMc0fyiQlJLAQUZFPg9TwYcAhBHdo0VxVnE+Vq/lPL3RyPInKzx307wmgL3DuHzmo/+8szs3nqy07eV0kM0NJR5Q18tjy1UBAGSg1KIhFMqDeVyF9bJUuOkdEUNbk5NFlq7l93mK+2bMPKQ8tFdCl5LZ5i9lXUEZfaeUjvYp86eFkPZhFOfkA9IyJos4gWaM34pM68/Q63GgYpBt/Qy79hY2btHwe0Iq4XytmVFoyqqIwMTWe0r2v4moqoLF2F1W5nzExLQG3X6NZ1+gjAkYpSKh0FVYqmg/0y5q+ay+jPvicwW9/zJRPZzDQY+ZJJZEv1PacJcKIw0BPJbD/IGGn0eulWpHcrheRIky8p6bxmZqOUtfMh1t3tB73nE4pVGZ/jNEUhq77GIqVMUow6cLCbTKaH3PzmJOVc8TPoMLp4sfsXFYVFuHX9SNuczRCLWY+nDKWhtJFlGZ/gckSwVd79rOrsuqXd/4DuLVfV0r3vkpB5rvk736NmvwZXN27yy/veIL4NJ0rZ82lfn8Fp1Qbqcgq49rv56Od4P0DqPN5SSbgvAkhSNRN1Lk9h22XGhpCpt5MtfQjpWSz5sSkKoQcQchD03XWFhVz35KVjPngc0Z/8DmvrN+M/rPfSU2zG7tQSRaBY+yQzdhQuEmJ4WIlgs/1GrbpzsP2a6ON/wFt9vm/THmTk3/OX8Lt/ihmqu2Z5LRw3Q8HnnNdo6NYJBrRZCAdcbnaRLfYKDx+P69u2MJtcxfxyvrNuFvqVQ/m4207eWvdFkZJB+ukky9lDVdo4fy4L9An0aCq9IuK4hNZhVfq7JXNbMSJv7kYf30mo0QQD2jFPKAVcYOWh81kpFtUJGNTEqnOn0F99XaanUWU7HmJCakBBcX8unqGtKgMq0IwVAnCjMCnBa4no6yCqZ99zaC3P2Lou59QVFbDszKRj9V23K/EIYBzWibjXYUVFegRHcmNeiF7ZDPvqe342JDOFBnMA4tXtF7riOREnLVb8bkrsNjiMPmcXKFE0U6YuVCJwO10c+eSVYfNYyBQc7c0r4CFuXk0eA63C8dCCMGrE0YQrBVSvO99fN5GcuqbmLFn3wkd5/fijM4d0Go3krv9KQqzPiU34wHuGHDs2uVfy9Or17MwI5PJVUbiCp1c/O1sKpyuEz5OnctN0kHanMmYqHW5D9su1GxGVRS264FzVOg+MvVmUo/i+O2pqubVjVuY/Ml0hr33Kf+Yt/iIn29po5NeLa1kKvBRKX1cpkRygxpNDh4+kFUo8Jew0cezxHCllPKQmaoQ4te7sG38IhuLSxmvO4huaUp3rgzjsZKy1r+/MHkc1/8wny99dTRKP7cN7EdisIMzv5rFCJ+VztLEeyWbKW9q4oYBBxZKN5SUsr+sipeUFIxCcKYM51otl2E46NEiqxpmsfD6lIncvWAZTzpLSXUE8dlJUwg2mbh05hymekIZIhxUoxGJoVWp76Z+PfBoW/l+4y0YVZW7B3RtVTf6ST58pAiiSvrYJZuJswdUbRbl5vPymo14NY2rlWiWyHpSWia/RiHopliZo9XjljpmBEtppHNkBE4M7CipZIwMRhECBRgjg9hXWdN6vUMTE3hmzEDe2fYBTU2NNIkDzkA9GgYE9y5bzYD42EPymbeXV3Ld7Pl0EVYqpI/I8CDeOvWkE8ovnp2ViyNqCGm97gWgonAej635gi9OOzw8vjy/kPd37EeTkgu7pDG5/e/78+oTG8Nnp05gdtYODIrgjEEnkfwHKDHtq6mhsamZG2QSQhH0kTauqSsgr76B9KOsMpY2NfHlzr00+3xMbJ/WKoAyJD6eD0uquVxGUISXlaKRixPiDtu/S2QEl/btwXUbM9CkxIekszWU6ubmQ+oKvJrGdT/MJ7uiGnTJfWoCRgQv7MwiyGw6RLAj2m5DqoJ1viYGKw7m6XVcrUTRp2U17zIieUdWcnn33r/fzWujjV9Hm33+L7O7qpoOipWeLdGuySKUL7y1VLqaiXXYuX3YAG6sXcCl1Xn4kYxISODint24YfYC9Momhup21pXkcn1JGe+dfnKrtLrH7+eF9Zt4XUkhWhjxSZ2rtTxmyzqshgOr+c9PHsedPy7lnIpsQoxGHhk9ggHxcTy9ah3x+U5OFiFk4qa3tLLTEVhnH5QQx4NDe/HCpkdw+3xMapfMPwf1A8BuMrHYVU8nxYIfyQq9ASkC2R3FjU1cP3s+wq8zVYTgQlKBD7VlzF2xoQHF0kuiMLNRb8JoUJnYPo0XG7IZ5qU1q2acCObu2tLW64iy2fjitEk8uW4BRQ1O/ASifCYEPinxIlmVV8SivHwmpKW27lfn9nDxtz9gc+uYEDyh+vnkzKknpGC7raKSSq+BPmM/QVHNNDuLeHzlFZzZqcNh5R+5dfU8u2E7lS4PIxOjua5v99+1j1eYxcLMs07my917afAWM2b8EAYewdb9Hny9dx9vkUKYYqA/dgp0H4vz8pnW7ciLsm6/n8927qakvpEesdGc1jEgDDYsNYlvqnbQQbcggBlKHWemHS46oioKz580llvmLUL6dZzohKkmmo+wAPHq+s18vn03bk3jTiWWdsLM58W13L1gGW9MPbRFTbfoCOaUNHCFjGCF3shQEcT4ln62tyuxXKHlMigmjqg/eRQMjs8J+5rA6trP3+v3+w+nDYBIu40NBzWly5JuIg/qZ9QhPIx5F51LmbOJULOFILOJL3ftpYvfxGUi0Lixm27hjm27DnHCapvdROsqxpYHaAwGDAi2SBePdu3cul2n8HASgxy43B6cbi//Wb6Gt6aexLndO/PApm0ICSqCZnSuaXG0VEXhrsF9uWvwz78qgeja6+XlfKpXUYeGBUGd10Ojx8vi/bkkagb6KiGMbZEWn65X00NYsaDwpagjJtTBlY35WBWFUJuVV4YP4rXN2/FqblZjoJtiRQfWCxdJlhDOnrmACqeTvrHRPDKiP2NSkmn0eJnwyVe8qJWRJszM1us4SYSwRLrIq68/xAl7eOkqrtYiGKkEoUnJwzWlzMrcz7ldOx12bXuqqpm1LwdFCM7p3L41JaWi2Ys5+MA9tQW3pyr/8JWi1YXF3LF0PQldbkVRjDy45iWA390R6xQRTqfjUBv8LQgh0AlItYmW/+rH6PdS2tTEudO/Y7hmI1Sq3JqZzcPjRjA+LYXHx4/k3wuXc2lJLiEmE/cNH3bUoukhSQm8v3k7DxNPCiY+bqrmzh+X8uGZp7Ru883efXirmugozQxUHLRrcfQv1MKYvT/vECfMpKq8cvJ4bp67iNe1SrxCO6QdQhMa8aFBXNnnj1mtbKONE6DNPv+XibRZKdQ9uKWORSiUSS9uqRPSoubnMJn48IxTKHe6UBVBlM1GTm0d+ytreFsmoyqCkTKI62oK2F9b19rYt8nnw4AgqmVaZhQKqZhYSxOvDxjSev4om43ecdHsqqoiCJVnV2/g7VNP4sJe3bk6fx66rhOGgRr83NDuwFdjSod0pnQ4XAjhrO6deXd9Bpu0XHxI7CgIGRCMyKqpJQEjNiGYpkZSLL3cqRWyXXfRXlj4khpSHEH8s7mYMGHEadB5efI4dlVW4/M5WSPhLBmGSSislU0kOhzcMH8FOyqrSQhy8NiI/rx10iiklFwycw7/rihilBLEBumkExa26M1sK6s8xAl7e1MGHV0K18sYhBB8oVXz/Or1PHfSuMOuraa5mU937qXO42NsSjzDkxJb3ndjdyShqIHPzGJLQAiFJp+XMPVAGnuF08W07xYQnnoB1qj2TM/+mArXJh4Z+cviXidCmNXC9f16/67HPBICgXaQmKoGR7XPPk3nqu/mYap101238H5WIXsrqrlnxGAu7NmVKqeLW3fvRSI5r2tnLurZ7YjH6R0TjUU1cIkeyigRxHrNyY1zFjDvwnNaFTD319TyxY7dnK6HUCS8DGyJzF4nIzm3JBtdHjqPeHjsCG74YQEX1efiRKO/OFA72ISOUVF4afL4XyVW99/mqE6YEKIz0A0IEUKcedCfggmoMLXxBzGtexfmZmZzn7OUcFS2qs28OerQlQCjqrRKy0IgyoWmQ0uwxoSC9rNQbK+YaO7Tm9kinHQVVr7RD0SNfAeljL25KQOlysV7pIKEl2sqeGndRkItFjoqVu4lDgV4jUqm79jDw2NHHPN6njtpLFM+nUGd5kdHMlyE4vTqfLR9Jw6zGTeBVS+AMSKIbcLFjVoBOpJzO3fknhFDqGp24fZrJAQ5uHLOUopkInFdT2Lx3rfZoOehqApxoUHMyS0mrvNNxNjiWbfjWU77Yha3DO7D6Z06MKl9Ktv3FmJGcIUShRnBIr2RdUUlpIaEtDpiZU4nXUUoEEjP6KSZKGtqOuy6MsoquHreUiJSzkHqPr6eNYtPT51Ap4hwhiVEs3Dt14THDMNoCqZ8/weMiI8+7Bhf7s0jtsM1RCUEDIhE8tmeD393J+y/QafwMGJCg3i+toLBuo1VipP0yPCj9j+ZvmsvwzQbV4koEJCmm3lz/RbGp6UQbDbz6pSJx3XejLIKhgoH7VrqJC6U4Zxbmd26iAFQ0thEF91MFX7KD+pVV46PoCOkLvaKiWbJpedT7WqmsLGRm+csxKnrCOA7pZ7XR036Szzg2/h70maf/3d0j4pkZLsUbs8toBNWMnDyz8EDsB7UpFcIcYioxI6KKnyaxk8BFAUwIA5J1Q63WAgxm5nuqeE0wtgpm9lBMyrg0Q5EDtYUFTN71z7eEqmESJX53nrunL+E+0YPwy5UnlNTCBMGluoNfLMrk6v7HFsh9vJe3ZmflUNmTQ1mFMJROVWE8uSKtVzatycNUsMgAwNPECZuUKJ4WC/GD/SICOfjU6agKoJKVzOJQQ4+2L6HzzIrSO12C5X7P+dSVy6RBiMes4JFt1NFTxL7nELR3re54OvZnNe9MzcN7MNV/Xry73nLKJBeBgg7o0QQl+i5FNQ3sKuyqnURrqShie66BaEEnr/dpJUZDYfb51q3mzO/mYchYhhGWxKzl87grgHNnN2lAz2iIqmrmUt9VQZB4T0oy51OrCOI0J/J4i/LL8QR0Y/49GkAOEI6Mmvx2Tw8YsBf8vl/Yfcu/Gd3NmdoIRQIL7uNHv6TlnLEbTeVltFQ5+Q5mYCiCMbJYC7bs5ebB/fDbjTyj6ED+MfQXxYPKahvwCYFY1oW2YcIB99Sz/6aOvrGBcRfypxOUhQLkbqBzdLZarvL8WFTDfz8TkdYrXx5zqlUN7vxahqXzJzDm55KkqSRH5QGbuzf95Df45+ZY42yEzAFCAWmHvR+I3D1Hzimvw1byyvYWVFFrMPO2NTko644/ByrwcAXZ09laX4hLp+P+xLij9mPYW5WNpvzimmWfr7XakkWZj6nmkirhWHvfUq0zcoDY4bTJzaGaT278OS23bjRScPMa0oKy2jkptkLmDktYMt3l1cyXLejtjzk+mlWvikoJjU0hJHSgaHl/VG6g+lVNUcd109EWK1EWq1c5Aqis7BiRuFpvYQF+3IYm55KmcHPt/5aTFIQJFQ2Ci+20M70D/dz/+hhAMQ5Ate/r6aWffXNdB/1CEIxEJ04iR3LzueJkf3waBov7vESFjOEXcsuZ6JfIRUHb6/aRFmjk6v79uasrDy66wqV+PhMr0azJ/JdZSxffD2Hr884iYTgIHpFRzGrrI4rZCS1aKxQnDwQe7gD9VrGHmI7XU9s8hQAVKODd7Yt59mxQzkpPY38hibeWHEJfl1jbFo6/xpyuES9KkDKA06B1H2t9/2vhqoovHXqSby5MYMN1bV0i0rluv69j/q9b/b5CZEqPz1hQ4V6xDSFXyLCaiFXeNF0iSoE2XgIN5kPMZI9Y6J4VsniFj2Kx/QSqqQPEworVSfvDpp8xOMaFIUYh50Yh513T5vMt7szAXi369C/VPPrNv6WtNnn30hRQyMrC4uwGgxMSEttbcz+S0jgoTHDWNsxjeLGJq6NjDjm86CgvoEnVqwhSApe0ysYKYJYQSNeBa75/kfMqso1/XtzbrfO/GfcSG6cvYDPqCYUlQeUeHTggSWr6BkdTYzDzr7qWrrrFkKUwIrrYOy8Wl9ORlk5fRU7YTIwrRspgnihqQy/rh8zfU5VFHrFRtOrVjBRCSEclVmylpzaejJKy3AE2cita+A1rZxuwsosGrGHdsHgL+XDMw4o1oZZAr7/B9t303nou1js8UQnTSZr452MiXNzbpfOnDHzR3oPuYHsdXeSUp/HKCJYsyuP6ysqeX3KRCJD7NQ1aLRDcK9ejFG1sksM4NLZ83lmzCDGpCbTJyGG2aW7GaTbMSKYrTTQJ/5wFdwf9mWjhPQhtcedAARH9Oblzf/i7C4dSAgO4sXxw7hr6YPUNTfSISKWVyePOsyxUhWB1A/YZ133/SWdr5+4bXB/YoPsrMkrIswWzhcDxhFuPXLKntvvJ1iorfbbioJBKHj92gm1WwizWqjWfNTiJ0wYaJIaZZqXiINSBTuEhZGju4kgBI+UPKwV005YWKY2cfuQIzu8QohW0Y2vzjmVj7bupMLVzD9Tu/0m0Y//Nkd1wqSU3wHfCSGGSCnX/hfH9Lfgi527eWPdFgZiJ1N4mJeQzbOTxhzzB/xjdi6PLltNvc9Hn6gonjtp7BF7L/yc+ftyuEiG004185lezSK9nkpFY1RzEGeLKDIb3dw4ZyHfnHc6dwwewL6qWlwltTysBkLz58lwvm7MbpV4d+k6q/AwVDr4Ua/nfVlJcJOBdS4XFZgZKYNQgPXCSbvw40txG5AQz5LsUnroVh7Ti/EKyQSnnfW7cugeFUVyWDBL84upcXvxCMGocD+Pjxp0yDHqPR7WFZWg6Rq61FEB1WDFaLSSGhZKUUMjPk8ZVSXL6aUJrlQCTay7SSt3bNvJ9QP6MP2cU5k2cy7NfnAkTKBTn38BUJj5Lm9kZPDYqEE8On4kt8xZyHk12WgSbujf+4hNBJt9Gibzges3miNw1R9Y2by2Tw+u6d090NT6KJ/7pd07cNW890BKhGKkLOsd/jn2xPuJ/VmwG43cMfT4UjUmpqdx894s0nQTYcLA26KayR1OvPfWhHapfLMrk7urSkjCyEacPDZ65CHbjE9NYU+Pau7dtgNVEWwz+5naKZ0vOnc8apHwwXSLiqTbqDbHq40/B232+bexvbySa3/4kQHYaRA6b2/cyhfnnNqaUngk9tXUcse8xeQ0NhBvs/P0xDGc0+XwFPWfs7qomEGKgyuI4EO9ik/0KnLwkOq3cAexuDSdJ9duJtJuY2xqMncMH8gzqzbwseFA6mBH1cquqmpiHHZ8ms4W3YVLaBTg5WG9mEgMvLVxKyYEl4lwHEJlg3QSb7MfV/3SgIQ4nt+XxwQZzLd6LbNlHecRTuGuEhpNHi7q252lOfmsa6rFpeukm+p47qSxhygX+nWdjSVl+Pw+NC0gpiCEwGyJID6onnCrBU3z0lS/D099JveTgEERjJJBXFtVQEFDI5+ffSqXf/8j71c3gC2GPqM/QFFU6mKG8p+1jzMmNZmLenQjp6aei/btRwAjkxK4efDh2bduvx+DOaL1tckcjsd/wKEanpTImkvORtN11KPco7Gpyby4cQ4Fe97AGtyBqtzPuah717+sIyaEYFr3rkzr/suqvn1iYygUPn7Qa+khbMwV9XQOCyf0CJkjxyLKZuOK3j3457bd9BY2dopmzuzc8ZAMmRiHncfHjeRfi1cAkmIFUtITeLrjIAYdR31chNXK7UP+vJL+x+J44nUZQogbCaQ+tKY5SCmv+MNG9RfHq2k8s2Yjr4hkYoURn65za3ERm0rLGRAfe8R9MqtreGTpKh6QcaSpZj6uquKib37g6Ulj6B1zeBTmYGwmIzU0M1YEc68az496He/rVVyjRqEIQaQwskq42FBSxuT0NC7r051HK1bg0yVGISjGhyIE1pYHakp4BIsaqrnUW4JHNvOqmkKsMLFNd/GoLOF6pQCTUDDbzHwwbNAxx/YT/xw+kHual3FeQTZWFD5W0jEKwQQ9hKsr87l31FAeGDnssP3Kmpzk1tWjCLh74XLidQPhPi87Fp1FWOqZeJtyiLUotAsNITUkmKgtuynMn0mK1FsjLOaDUjOtRiNuVEwh7YlJnNB6HrM9mbrGDUDgB/3Z2afS6PFiNqhHFeQ4tX0iL2a8hsEYjJR+yrPe4YahhxanCiEOC6UfTJ/YaN6dPIqPd81H0+He8UN/U+PHvxJ942J4dPwo3li/GbfPz6SOHbi+f+8TPo5BUXhz6iSW5RdS0+zmltho2rfUWfyEEIJbBvXj6r69aPb7CLNY/rKGtI02DqLNPv8Knl65jiv0iEAdsoSX3BV8un03Nx6lb5FX07ju+x851xvMWDWGjW4nV38/j3tHDeX0FrGCo2E1GKhBw47CTWoMxdLLzVo+VxFJfEvPzTP0EJbsz2NoYjynd+rIM2s2Ui59xAgjbqlTpHsIb2m5kRwShNcaxuXuYnTp5Q4llsGKgyrp4zZZyNUynwTVTIXi4/VJx5fSPSk9jZyaOq7N2IaUkpfVlNZ+oE/5y4lx2Jl5/pmH7dfo8bKnqhqTqvDs6g3U1znphJE9K68iLPUMhNRoLF/FmBGnEGQ2cV7Xzny37VEMUkdtuWUKAREuTdexG41UenRCkiZhMAahtET7rPZEirwBx05VFB4eM5x/jRiMJuVRozKjU5N5e9s8bGE9sNoTKNn7GpPapR623dEcMAhE974+4yRe27KTyrotnNcjlmlHqA3/OxJqMfPB6Sfzn2VrWNBUTfeYKF4dNfRX2c3rB/RhYGIc+2vrmBYSckTHamxaCqsuv5A6j5twi+WYn8vfieNxwj4B9gKTgEeAC4E9f+Sg/uo0eb2oQhDTcnub0HFIhX01NUd1wraUldNHt9JBCTxoL1EimenK4qqZcwkJCuGRkQMZlpRwxH2v7NeLSwrm0KBpGBHMVxrxSUk1fqIwoktJkd/DfUtWcv/SlYxNTqJDbCR3lRWTjpmNOLl32BBURWFvVTXxDjM6PqypU0gomE9si1R4L8WGHQMPThxFmMVMp4jw41YMtBmNvHzyBLaVV3D37MUYdNCkxC01LELg1bTD9pmzL5tHV6whRTGz3+eiFzbuVePIE26e95VRk/UpEujbuX3rat9np47n7S3b+HhrIbN1E8mY+FKp5bSWCIvdaETTvERG9KRg3wdYbPHoupey/R9yxYBDozBB5mP3tzq3Swc8msZnux9FEYK7+nfipF8RBu8TG3PExpj/HxibmszY1ORf3vAXMCgK44+S234wVqPhL5Mr3kYbx0Gbff4V1DS7SRWBLAa31DHrkv3VNYcJAPxEYUMjuldjQosC22DhINZfzfNL1/LMqk1c0a8HV/bqdsQJ6sR2qby/eTvPuipI1Y0sUBqJt9spa/bRpUVmuxQf87Nz+D47m/YhIVzRuzv3bN9DP2EnU7gZlJJIr+goihub8Pg1fP5GojtfQfHetxjcImIQKYz0Vh3079uBvrHRpIeFtgofHA/XD+jDtf17M/DdT3CgIKXEiY5dHtk+Z9XUctV384iSBir8HhQJb4oUmoTOB1SyLvcbQoWR5BB7a6riv4b0o3tENs+v2cjrvkpG4mAtThx2K+3DAgtnDpMJny2OkpzphEUPxmKLI3/XS4xoEdX4Ccsv9A/rGB7GaxNH8uT6t6n0eJmUEt+qCHkixDjsPDLy+Bab/260CwvlvTNO/l2O1S8uln5xR57//oRRVQ5RNf7/wPHMRtpLKc8RQpwmpfxICPE5sPKPHthfmTCLhVi7jW+aakmUJl7Qy4jByKtrN9PY7OG6g1bbShqb2FtdQ7WrmWzdjSYCdS25eAhB5Qk1kVuairhl4So+mjKO7tEHUqJWFBSyv6aO1NAQPjtrCt9nBmTOP+rYnlX5hdy7eQcjdDt7hYca6eM9JY0gofB4QSl7ZTM2ixlzcjhv9RhBt6hInlixlnn7sklSzAi/i9rCWdT5fVSqYUQJI7tlM34hSQiys6OiiipXMyOSE4+Z7iClxK/LVtnXLpERmKwmHm8qZZvuQkOiSoFfO7SXVKPHy8PLV/MECaTqgVW9m7V8nvOXsgUXvYUNr3STJEwszcplauf29I6JZlVhMSEWC/eOHMLCrFzWNzczMrUD17ZEWOwmI5f06MaMrEUIUxRbV1yJIuDa3l2Z1C4wiS9tamJ/TR3xQY6jSqtDILpySY8uXNLj9++51UYbbbRxHLTZ51/BkKR4vtpfyjQ9lEf0Eswo+Aqbuf6H+bxyyoTWxcVGj5eM8nKafRq1uo9aEahrcUmNWvw8qCTwgL+Ut3YUYFQULu15IM1rb1U164pLCTabeP/0yczK3E+Nq5kHE/vgMBm5ac5CcqWHJnSW6w38W4mjr7DzdUMtH2zZgc1opCHKwh09+jI2NZkfs3N5ZNlq2qkWTLqf+pwPEFJns+6kn2KnTvrZrbu4OCKckiYnJY1NDE9OPGaKJQRU8H6yz4oQnJLejieyi1rreDSgt354v6X7F63gXH8wJ4lQvELnTr2Qp2UJe/DQBSupmGmQGkGNPj7avpPr+vVme0UldR4PNw3uR0ZxKV9W15EeEc57wwe1juHOgd25ffGnBIf2Zs/Gf6NrbkanHpDUb/R42VFZidVgpFdM1DFr7QclxDHzzD9G7r2NNn4PjscJ+ymJtk4I0R0oA46dH/f/HCEEb0yZxO3zFvNZbSmPq4l0EVZqpZ9/bNvFqLRkukRGsCy/kH8tXEZH1Uq214ULjdu0AtKEiS3SxXVKNPGY0KRGdNJpLMjd1eqEPbtmAwt276evtPG10syQ9ORWEQsIrAJ1jAxnS2kFFBRxYbWdCCXwcZ+nhPOiVkaD28OCrFzahYTi8vlZnJXLqzIJu66yQ7h4RlRw9aB+3LopgzjFTLnu5fI+Pbnom9n0UeyUSC+fRYbw5tRJR3TEfszO5eFlq2n0+egeEc4Lk8cR53Dw+PiRXDpzDo+qCXQSVpbqDVw/ZwFLLj2/9TgVLhchioFUGTAg0cJIHAZW08SzajLthBmflPxDyydSGChqaOTLHXvYVVBKV2lhAy4u7tuDK/v2PGxctw/sTc/ofHZWVBNj782mohLe3pzB25sz6BcTzbaaBkJC02loyOey7h25qf/hxziYNUXFfJaxC13qnNOz6+8S2WmjjTbaOA7a7POv4M7hg7jfs4I7cws4RwnjXCUCv5Q8WlHKl7v2cEnP7hQ1NHLpzDlEaypOqaEAt2r59BI2sqSbESKIdMVCpF/BGT2MmfvXtzphS/MK+Pei5YzAQYnw8/m2XXx61tRDojcfnzmFRbl5ZNXU0a9A0k8GIlpnE8anVBHh09leUg4Eel4+sHQVj5NAO81MLX5u1Qp5eMxwnl61nmhRR5nm5czOHbh/yUqS/AaEgOfVjXx21tRDlBp/Yl9NLbfPW0xuYwNRFitPTxzNwPg4/j1yMONyv+RsGcpUJYw86eHfG7YwJDGejge1OSlobGQAgeiUSSgMEHZ+lPWcLcI4XQ1s95pWTrnmo6iuga93Z/LSmo0Mxk628BITHcpn50w9LO1sZHISH55iZUleIbbUjvj8ft7K2M7kvBxSgoMp9/gx2ZPweOrpHGri7ZNGH9bb62AK6ht4df1mqp3NDElJ5PLe3f/fpLq18efneL6JbwshwoD7ge+B3cDTf+io/gYkBgfx2tSJ2FRDa8pBmDDQQbFQ2NCIlJJ7Fi3jXhnLA1os/xCxhKGShJGVsomLRQR9hY0P9EpMKEhfHRZDYHWuvMnJ9F17eVomcLWI5Ck9gR+zcnh6zXpe3biFfdUBxcLhSYncMrAvAxPiyFW9rWPbqDupwc8Q4WAUQbyxMYONpaV0EhbsInCO7lhp8PrIKCnjmn69eXDyaOZeeA7f7NrLHXo0d+jRPK0n0FDVyNz9OfycrJpaHl66iof0WL5V29OtDm6bu5iSxiY2lZbT2WCnU8t9GaME4/R4eXTVhtb94xx2mtBaO63nSg/l+NCQpLWkRxqFIEWYydbcrCosZnluAQ9rcVxLFE/JeF7blIHT6ztsbEIIJqSl8o9B/ahsclJVVMMXSjs+U9pRU15HWNwk2g18ha4jPuSjnVnsq6k9ZH8pJQtz83gnYztvbdnGHfMW06vUx4AynQcXrWBRbv6v+s600UYbbZwgbfb5V2AxGHhm0liibVYGiYDzYxCCvrqV/Np6AJ5YsZbxXhuP6XE8ryfgQ3IxEaySjfQQNi4QEazUGynGh0DBcpAj8OSKtdylx3AtUTykx2Jv8nPfslW8tH4zKwoKgUC/z+v79eGszh0pxYdPBrJB9kk3JgQxGJkkQtheUs7H23diFUprf8MwYSBRmvh6517O6NyBeyaO5JvzTqfB7WW418r9Mpb79FhGeK28sm7TYdcfaF7/I6c4rcxSO3CDN5zb5i0mt66ObRWVNPp8TGlp05IqzHTWLdy8YAWeg9RrO4WHsYgGpJQ0SY11sokgVDoqB6neCQv5eGnWNR5ZuYbb9CiuI4on9XiKKmpZXVR8xM+ne3QktwzsQ7eoSKZv28MbIoXpIp0u9QIpQmg/+A26jviYHE8Y0/fsPWz/7eWVvJuxnY+27+LCb34gNL+RcZUK87fs4alV64/7e9JGG380v+iESSnflVLWSimXSynbSSmjpZRv/jcG91cn3GLBZFDZoAd6WJRIL3v1ZtqHBSJPzX6Nzi211MnCRA0a45UQjAi+kDVcrOWwUTpRVAvNVas4u3NHIKASGK4aCWpxmGxCwaYJ9uzMpyyjgEtnzmFTaVnrOK7o05NMq8ZdeiEP+4uZK+s4U4RxixrLTWoMFysRbCwoYZvuoqxFLn2xbMCBQrdiLzO37GJlQRFhVgtVbjcdRWDMqhC0043M2J3JC+s2UdjQ0HrOreUVDBAO2gsLqhCcRzh7aqo548tv+WrTDnb5mtjR4mAVSS8+xcCcnBL2VFUHrslo5LlJY3lareBifzb3aIXcoMTSDjPT9UDu/n7pZo1sRBECb3YV7XQT9+pFuKRGpDBiU1QaWop5j0ZGcRlT9GDMQsEqFM4QYejOgJE0mcOx2uOYtTeLjLKK1n0eWrqal5asJXdTDl9u3E57zcQ4JYRRSjBX6RF8uW3Xr/zGtNFGG20cP232+bfROTKCxQQWRd1SZ7XqolNrP6pGehFwKIQQRGDAIARhGNgmXVys5/CRXokG1BfP5ea+B9LS67xekluELYQQxPsNbM0pon5rIQ8vWMF7W7a3bjs0MZ4uCTHcTCGPayXcpxeRjoUH1QQuV6N4Qk3ig4wdSEW0ziXypIcsrZneVZL9ewp5fcNmYh12KpqcdJQH0g87SjNbSyt4ctW6QC/RFkqbnEi/zkQlBEUI+il2QnSVc2d8z6M/LgfgSz1gi91SJ0+BRiWKr1radAD8Z/woVts8XKLncrmWQx9hp7+wMUOrwS116qSfGXo1bkXSmFfNcBw8o5eRLd2oQpAoTNS6j22ft5VXMFy3Ey2MCCE4Wwmj2V0VuK+KAWNwN5bnF7M8vxBfS0nD9/v2c/0P88nelM3c9TvBq3EmYQxSHNwjY5ixNxMpD0+vbKON/wW/mI4ohIgBHgfipZSThRBdgSFSyvf+8NH9BdF0ndc2ZvDjvmwsBgMX9+7Oaxk7eF/WUCf93DV0IO3CQpFSEmezsai5gQkihGYCvTyeooxgFOrQ0JA4UKnR3XxzxtmtcvUpIcF4VZjnr2OUCGatbKIOP8+LJGxCJU038ezK9YRazFQ5m+mfGMdnZ07h671ZvL5hC9HS0KrKBBCPkR3Sz42D+nLLuo1YdAWPrvGUmkSqMNNL2rhh+05uHtiXPtFRTK+s5RIZQRk+Fmv1DC13UFVRyHm79vL5WVNJDQ0h0molFw9+KTEIwUJZjwOV10ghWKosVxp4RC+mi7SRKTRSe/wDZ9kCSpucdIkMyMoOTUxgySXnc+uPizCUORmIHYei8KReyudaDTaDgTirnUs9IQxSHKDAU1oJs/U6jELBYTURfVCR596qah5Zupoyp5Me0VE8NGY4scEO9tTU049AusYO6cLfomdYlPkBzup97G8IYt7uLE7p0p4zu3ZicU4eb8pkrELhPCWMK7RcqqWfCGFAQ3JsPcQ22mijjd+HNvt84szbn8PbG7bi0TTGpCezpqGBq5oKaJYa41JSOLtLYLGze0wUP+ZW0V5a8CBxGAy8LasJ0gT1LfZZQWBE8NDw/ocIZw1NiOPD4mqukpGU4GOhrOchkUAX1cokGcK1m7aQU1PL3spqkkODuWv4YHZWVvHQslVEew3EtTgdAHEYcWl+PjxlMrfMW8wbehV1mo+bRQxj1WA0KbmxtpC91TX0S4rjh6p99JQB53GGrMHsAnZXcPve/dw7eiiT27cjzGKmQfNTKXxECSPVuo8S3c0TaiKddCtFqpdbtXy2+V2UKQJz3AgsjlQKGw90Q4gPcvDdtLN4e8s2Zmfs5XTCQErupIjztGxUIegQHsrAesnVIgpUmKfV8bFWxVlKOBk4uecg5edat5tHlq5iW3klsXY7948ZRqzDzlLV09oDco90Y1SMSClpqN5K1f4vSDDYeLFsDe+E2njvtMk8tWo9D8pY0oUFKSV3ikJWy0ZGi2ACrlebfW7jz8Px1IR9CHwA3Nvyeh/wFdD2kD8Cr23MYMWO/fxDRlArNV7avJ1nThpDjN1OpM3aWiQrhOCVUyZw/ez5fOINrBz9a/hgaj0elm/ayxNEESUCyoZn6PsPaWxnNhh459TJ3DV/Ke805BCkGpgsQ7C1RMaihZGc2kouEOG0Fw6+zSziMZeLZyeNZVRKEg8uW8UnldWkSTMqgi+UOs5s141pPbpyaucOfLJjF9u3ZJNKYKxmFPy6TqXTxX/GjeL2+Ys5syoLAYwTIdysBpT9TJrCp9t2cd+ooYxKSeKb2HDuLCsmFRMrZT3DlWCCW8Y4XATxLGXUSR8+qVG08xWk9NEh7LRD7qfVaOC5iWP518JlnFecjU01cG63LqzJL6LO7aHO4yHpIIcyWZj5VK/GioFvTz29Nfe7urmZq77/kQv9ofQQMXxfXM+Ncxbw3KSxXFwymyytFA2oMGp4mnazffEZNDfX8bqaQrw00SQ1bt6TRYfIcCIVI1Y9cFyHULGi8KNeR6ww8rFSy6O9D+1R1UYbbbTxB/Ehbfb5uFlbVMLjywJpcQ6h8sbuXCb26sgpHdtjUVViDqqdunvEYG5uWMhFVbn4pWRKejvO7t6Z67/7kacILFAC3KeUtsrH/8QjY0dy76LlXF6ch1lRSFTNB8oSMODXdRpzq7iCYDY3Obls1hxmnn8mcy44h2fXbmBuZjYjdCdpwswn1DA8Pp6+cbEsufR8NpeWc8+8JYwmCGiReJdQ7XJzQfeuFNc3csG+/ehIkjDxspqCKgQddQtvrN/C5PbtCDabuWlAX+7avI0+wsZW4SREqK0lAonCRDvMGBE06G4oWY5JXUXnYYdK+BsUhev79cbr17h+xy40JBPSUqhyusipraPW6SJZhrT6PcmKmQ+1Kh7RS7lvxJBDekXdPGchcTV+HiaGXbXNXPXdPL457wzmRmVzR2UxMcLILlyE2YPYvvh0dHcTNypRjCUYXZf8p7aM6bszafR5SVAORCETMLFUNmDSBTPVeqZ16tTWnqSNPw3H44RFSimnCyH+BSCl9AshDtcrbQOAH/flcJuMoJ2wgICpuocVeYXcM/zwBrwdwsOYf9G5VLmaCbGYsRgMZFbX8OHmbbiljpSSWbKOjiGhh0jBbykt556FyyhxuegYGsq5PTrz1totDJbNOFB5nyqihIGpSkDy9Q5pZlpeNn5dp0N4GJ+dMYV3t2znoe270KXknG6dubhXoL+V3WjkjI4d+GTrTr7z15KGmfdFNUahcNoX3yIVGBAXS1/VQa3mZbQS1DquUFSKW9ILFCF4+eQJLM8v5Lu9WWj59SzR66nUvfxLjWeLdBGOig68oiahAE+IMpbkFhBqs5BXV0/78DBOTk8jyGzi1SkT0XSdZ9Zv5vNtO7lDiSVNhPGILOE9WcltSiyV+JkjmonvcBnG6nkkH/SAzyirIB0zE1skhq+WkZxVlcUl386h2u1Gt1i4qFc3zuvWGaOisLW8gjvnLia+pf7MIVSSFDNWg5EaobFYr2eQcLBUb8BvFGx1SIKMksf7jzpiY+c22mijjT+ANvt8AizYn8Ppegi9lYCzdbWM4N2sXG4Y0PewbR0mE++ffjI1bjdGRSHYbMaraRgMKgU+L6nCzC7ZTJ7upsNBfQkrnC7u+HExGVVVRJjM3DKkP8+t3sBKrZFOwsK31GIRCjcTjSIEnbGS4StmZ0UVAxPieGzMCE5qn8bjy9dS63EzKC6OJ8eNAMCkqgyMjyUuNIg36isZKR0sk01UST93LliCV+qMT0slSjXSXTMTJYyoLQ5HqFBp8h2okb68Tw/6xscwb38O6/dk0Sj9XOnP4X41ASOCQrwI4CElgWRh4gO9iiXZ+SQGB7O+uIRwq5UzO3fAZjRy2+D+3DqoH8vzC7l9/lJOI4SrlHg+1av4SlbTXVixofARjYQmn0pVyTzGtzvQUqTB42F3dQ0PinYoQhAvTCz01XPejO9p8nkxqgoD2ifzaN+exNjtlDU5ueDr7+nqDzi/ihB01E1UOJ0Mjovjg/IqLpYR5OJhvXCRFBLED8LDaR27tc512mjjz8DxOGFOIUQEBCK5QojBQP0fOqq/MFaDSq3UWld+aoVG/DH6EqmKcsjqW6eIcO4aPpg7Vq5BSkmiI4jXTj7QVLimuZmb5i7kJi2Svmoc8xsaeG/TNrrGR/FwUSkS6BodSXNloGBWCEEzOkKIVilXIQRX9+vF1f16AaBLyfsZO5izNwuzwUCHqHC6R0SwqLERm+qjtNHLXcTST9jZpTfzSEExT6pJZChO3tUquUmNwSl1vtCrMZbUU+t2E2axoAiBEIIdRWW8paQSgYGX9DKu1fJwo6MCVyvRJLREsi7Uw3k/YwfBukJvv5m3DFlsLirlgdHDKG5oZMaeTL7ck8dQNZyhIuD8PS4SuUbP5RItB0Wo2EI7UFswg9cnHRqNshkN1Eg/mgykNdRKPwqCiR4rk5RYNnmcvL9lO2d27ojdaKRPTAzCoLLc28AoJZg9spkc3U2vmCjenjqJfy1czpuNVYSYjNg1hYQmyTZZR15tfZsT1kYbbfy3aLPPJ4DVaKSWAz5qjfRjOYZ9FkIQYT2QhWJSVV6fMpFb5i7iFU8FZlXlmYljWksFAG6du4hOdZJ/K+lk+T08sWo9p3bpwDu7s/DoOu3DQ5E1gVorm1DRW+rRDlb4G56UyNyLzml9vTy/kDc3bMHt89M5JooYm439vnpyRSMNHi8n+0K4iHBqhcbNOQWcJ8LopFp4VCuhvW4hUhh4TaugQfeSUVbe2pcyPsjBd3uyuFuPoYdqZZHewJ1aARoSHRgrgumpBK7tWqK5sDibHWUVjNMdbFP8fLsrk8/Pnorbr7GioJAHVqzHoZi5SATq6u5QYrlK5nGrXhBorGxPxF+5ghv79z5EOt+sqmhS0iA0QjGgS0mp0OnkNXGLkkqF7uOxfTlMbt+OOIeD+CAHfWKjmVlUzzUykno0litO7oyN4co+vfj3ouVcUZqHRVFRNEhokuTiYXNJWZsT1safiuNxwm7n/9i77/C4iquBw7+5u6tdSaveiy3LknvvvWFsMBhTTO8dQiCU0BIgQCgfkBAgAUIJvRdTbWNwwTZuuPcuWbJ6723Lne8PyXKT3C25nPd58sSrvXfuuVKys+fOzJn6qktJSqmFQARw8XGN6iR2x+B+/G32fCaagZQqk8XWGr7sVr9g1+X1UuPxEOjjc8Dh8PM7d2BixySq3Z79NgzeVFhMO2WvXwMFTFTBfFpbRGFWMdeYIcygnBW5eViBm1UaFxDMLEsl13fv1ux+Gv9buZapqzdxqxlGmfbyUnEKE1UwDouVbQ4vYRYbPU0/vjGLydQuTDRpZh0XGiGYBjzlzcKG4lYjgjWeGj7fsJk/9OsNwMqcXMaYTiKN+l3trzTCWexNYyyBFCsPmXp31cYsXFS4XPzbSMRuMbjQ9HLLthSinP68v3odEaYVpd1sV1409QlmpTKxWa0svv4KZu1Ip9LtZnBct72mOQAMjI0hNMTJo/mZ9DH8+YkaHMrGRUZ9Kd1RKpCfqWRzURGD42KxWQz+O3E8d02byat1BfhYDJ4/czRRTn+inP58e8VFbC0u4aZvpvGaboM/FvK1mzt/X8EFnTsc1iaZQghxhKR/PgxX9ejKZVu24/VqArTBVKOc5waNBuofRpbV1RFktx9w76luEeHMuvYyKlwunD4+ex3r8npZX1zEk0YyFqXohi9xpo3pG7ZxuQ5hta7m96IifJXBDeYOLtAh7LS4CQt20iMyosnrrczJ4y8z53KHGUGw8uPf23bSRvkwRNn50VKByzSZRBALdCWrdBU+GrZQyyQjhLuNKN4xCyjHZIIKYgJBvLJoOe9fdC4AW4tKSDQc9KQ+0RpnCeIDTyFhWBionGzQNY0Pc7NxYWh4XMfQzrCjteaJqlzeXrWWz9dtJMK04uP1Uq09VBte/AwLbjTaUHx+wXkU1NSws6yCzmGh9I2J2use7VYrN/Xuwf2rNzFeBbJWeajB5BYjgkBlIRAL40wnizOzGBBbv+Hv42NGcM/0WVxWmIKp4ba+PTkzsX507fWJ49FaM+Tdj/m7iiVZO3BrzQM5WczbmcGYBNlGRpwYmk3ClFJttdY7tdYrlVKjgE7Uj+9s0VrvX/dbADA2MYGQc8czM2UHcT42vuzamWinPx+sXsdLS1dgQZEYFMjrE8/a6+nZviyGsV8CBhDi6yDbdFGrTRzKIN90U6G9VHg9fEoxNZiMVgH8UUXyb/KZ5VfLDf36cVHnDs1ea9rmbdxuhtXPB1dwCaEUaw936QgeqMtmh6eOJ3UmVmUwSPmTpV28rvPY6K2mAA82FC9bEghQFvJMDxV7VDyK8PdjjuFq/CDfpmuxoPiDJZIc3DzkzSDH68ZmGCw2qghRPth1/RNBP2XBDyvvrFrLS8QTbfiQoeu425vBE0Y+nbWV6bqMe4YNwG61cm6HpGbv0WoYvH3+BIZ/9BU5QZ0JihpC5vpXKNMegpSVWm2SZ7r2ejrXJTyMmc10tgAFVdXEG3b8zd1r8QINKyW1dZKECSGOG+mfj0xcYABfXHI+X2/cTJ3Hy+vJQ+gdFcnSrBzu/XkOLo8Hu9XKyxPG0j8mutl2lFJNfsbbDAOHYSETFwnY8WpNtllHW3z4kPqqfu2w85QRzxJdyYdGMdf06s4NvXs0udcmwE/bUjnfDGJIw4PXeyxRvOrN56+WWKq9ml+tVfzPXcA2XZ94GSh+1eX83ZNFsLJQgpcnLXF0Vr5s0jVU1FU0th3u50uGWUeV9uKvLORpN1WYPGXEEad8WOnN5DFvJomGg/lGFV4gClvj7yBSW/l6/SZu84QxzAigxjC5y8zkTrI52/RjoVlJ34QYOoaF0kkpOMAkkbsG9mV5XhE/VPkREn8WjtSvyaxzEaXqr5dpeEhw7F57F+yw8/5F51LlcmOzGHst2QBwmyaVHg+JRv3fyaYUidgprK5pPgghWtiBRsK+A3ZNlP5Caz35+IdzaugbE7XXk57fs3J4b/la/qsSiMDKJ+XFPPTLr7x34bmH3XaXsFCGJ7bhgbQsumoHy1QVdhS9lB83GRHUoHnMm8lcVcG5Koh3LRVMbqj21Bwfi4UKbTZOoazQXmzUTyV0KIPOEeGk5hfzodEeq1KcqQK5wbuDAGUhS7uJxEYtJtnaxU9GOc8n9G9s+5IunZi6eTsPl2cT6FWsNqtQKFxoYrBxtgriB12K02Ljxl49+HzdJn50lTBEOZlLBaZNEWfaiTZ90FqTrl3YUKxRXtZ6K3lo+ACu6N6lmTvb/z5fHT+KO36ej13VYbX4cI8ng+EWJ+uNOoa0bUPnPTajhOY7W6ifOppm1rLarKKX8mOOrgAfg5gmNsYUQohj6Dukfz4icQFO7h60u48qq6vjnhmz+bM3kt6GHys8Vfxp+ixmXnMZ/j62w2pbKcWjI4bw2ILfGYI/qYYLl9bUmCbvW9rjg+ItM5//mQXca0TxhreA63t3x/cAUyLtVgulmI2vK7WJraGztqPoFR3BbzuzeNWS0Di1v8TjwVcZ6IZjbCjytJuPjGJGtU9ubKtTWCjndEzivm07SNY+rPRW4ovCo+o3YL7CCOUVM4805WFEQjzVLjf/zSngah1Kuq5jkaqgss7LAEt9n5eBC19tkmFY+dQsY0JyAv93xohDKoShlOLFM4dz2Xe/UJ03B22z8lxNFmeqIAoML4W+qnGbnj019zfysVjoGhzCV+UlXKpDSKOO5VRyZ5TsZS5OHAdKwvb8f0374x3IqarS5eLzDZuI9VpQRv0Hzfk6mJsLj2xDX6UUT50xgrk7M8gsr6CXhpcXLeMsIxilFH4ohhlOPjILUSiCvb54TLPZp2wAtw7qw5OzF3Ch6aJEe5iuy/ijiuQzXUSBxUtAnRsnBrueM1lR2FGMNAK5gjDuMXdyl84g0O7DfYMHMSQ+trFth9XKRxdNZN7ODIqqa2FrCivz8nnYm0Gs8mGLruUJSxxuj+aVVRu4aVBvft6Sytfl2SQHB/PqsDO5+ccZbNe1LNVVzDfLudQIYa23mk1oKg+yz8i+BsbGMPWSiazNLyDYMYI6j5ctRcWMDgpkXGLCYVVNCvfz5eUJZ/LAL79SXJdNG6c/b044a78nckIIcYxJ/3wMmFrz1cYt2Exwqvo+sp/hT4gqIb28nK4N26UcjvM7dyApNITVefkMtlp4fv4SzjSCcDS0f6YRxBPeLG7y7sBHKWrcHvxszSd7l3fvwmWbt2F4IAgLn5tFnKuCmW2WM80o52xn/forJ7v7HaeykKzsnGeE8C9vDo+YWdhtViZ2SOKOAXtXOPzLyCGMTW7HzrJywnLymLothSe8WYxVgczQZdxjRBGNjffTC0hOiqW6jZWHsrIJttt5adSZvPDbEmZVlBOjbfzDzGWiCqKb6WWmrqGoqorD2ZErws+PHy85l6XZOShiCXH0YmVuHv1tPpzboT3+B/g9NeXlc87knumz+Lx0O34WK4+PHkbHPYqoCNHaDpSE6Wb+LQ5RSW0tV3z1A+G14I/BPd6dPG2JJ1+7idqj5PyhKqqp4fE5v7GxoIhYp5O/nTGcL9ZtJAIry3QlbVQoHq1ZYlYyWDmZYATzVm0hryxZzp+HDmy23XGJ7djUs4iPVq0jSttwYvCuUcTA+Fg+GjGWh37+lVLgA7OQoUYAc81yyvAyjTLKDBPD187sSyY1O2LkY7HQOyqSP/80h5UFBTgtFrontWFpeha3uSPqS/cquNIMZlVmLh9ffN5e5z91xgj+OnseNaaX9y3tCVFWLlQh3O/N4H8r1zG2fTvC/Hx5c9lqsssq6BUXxXW9ujebeEY7/YneY7RqRNt4qt1uVuTm4bBY6RoRdsA1AXsaFBfDvOuvwOX1YrceyhJLIYQ4atI/HyVTa+79aTap2QV0Ne08obO4wQinl/Ij3+si4jD7aLfX5KXFy5iZsgNfq5U7h9SPtoUqKyt1FWfpICxKscSsJAwL91timU4Zd0z9hc8vmdTsA8D4wAD+MX40d02fRYS2EKIs/EQZncJC+feQcSzPyaWN8uFfZg5XGmGkm3X8pisot2h2UsBKSy3vTjqn2TVnAH2jo5i+JYWp21IBGJAQy9bKasaXBDG0oQLynTqCB3ek89uNV+117otnn8GtP86gorqOPxqRDGs43u41WJhbxNebtnJxl458sGY9a7LyiAlycvuAPoQ49i7rv4u/zbbXmq1eUZGYWrOxoIhar4eu4WEHTFr3FBvg5MvLLqDO48HHYpHS9OKEc6Bvjb2UUuXUP3Hzbfg3Da+11jqw+VMFwDsr19K11sofVSRYYLpZylNmNm6r4t9jzzystrTW/OHHn0kug78TxZqSam7+fjrjkxLprHyZZpay0KzftLkak/tVDJ+ZRXi0l282buHOgX0PmCRM2bCFJ404OilftNY8ZuQwJimBGKeTa/r24IlZv7HRW8MCbyWVystDI4ZQ5/Xia7VyTnIiTp/916/t6f4Zc2hb7OERSzJp1PFkajrJIcGUFu2uVFWCt7FSldaaktpaFIpx7dvROyqSMz/+ksCGp32GUoQqC16lSCkp5d4Zs+lUbdDbdDAzdzMpRSU8e+aoQ/rdZpVXcP1303F66qdito8I5dVzxx3yiJZSShIwIURLkv75KC3MyGJbTgH/0vHYLIoM7eJP3nQCLFb+OKAPEX7Nr9luystLlrFqczqP6AiK67w8OWcBN/bvicOwUOsx+aM3DV8MduLiX6oNc81ydupaUkpcbCwsoltEeLNtf7N+C5cSwsWW+unyn+kiaoICGRAbTYzTnw9XrcPpMfiXN5cqZXJ+5w50iwzH5fVyT9s2e23X0pQ3l69mS0o2HxqJuNE8k51LfFw4FWW7C22W4MG+R59Y5XJT6XLRLjiI6VddwuTPviG0Znc/GKqshJoW0kpKefzX30jZkcd408nGnFyu2TmVry694IDTMHdxeb38ceovpBWW4FQWKq3w/gXnEBcYcNBzd5H+WZyomv1fptZa5lQdoYLqau79aTYbCgq50YhonDiSpOw47D58MXniXiMxh9ZmfWWhZ2mHUooYfFika+gSEc6/tu9gmNuJG818XHTQdh4xMxmuAhhuBPC9t4RrvpnK8+PHkBgc1GT7pW4XbdXujaTjtY2Shql+4xLbYR1n8M26zXgrK/DW1PL28tVc3bMbl/ToeNCnS6bWrCgo4C9GElalSMbBEJxExETyQclWCk0PbjQzjAre6D6IWo+H+2bMYVl2fcn9QbHRJIYGE26380pdLpcYYWzWNWzStWgU5S4XjlqT23UUylAM0k6uSknlkZFDD2lO/9PzFnFGnS+XqlC8WvN0fg6frt/E9VLKVghxApL++ch5TJO/z13IN1u300f5YrPU91/x2FAKXjtvPL2jow7Syv5mpaTxoA6njbLTRsG5ZgD5FVU4An1xlrlIMO0sVdX4mQafU0wdmouMUNbqam79YQbPjRvFiGa2NympqaUfux90ttE+LKmurY87MICPJ5/HW8tWocvK8VZWMTc1Ha/HyyOjhh7SqNHvO7O4yAzCadT/z+o8byC/17nZbnPzuiufaG3lR1XOjb17A/DfZat4e9VaHMogzM+XcR0S8fWx8VpVPvcZUVRok2/MEvwsFtqHBvPsb0v42GiPr2EwUmv+WpfDkuzsQ6pS+On6TdQVVPKaboNFKb6sK+bpeYv473lnHfRcIU50zS8UEkfsgRm/kljs5Q8qkh/NUgq0m2pt8qUqZUz7hMNOwAB8rVZc2qSiYYGuV2uKtYe2QYF8cfEkonq2IbxLLM+NG026zUsgFq6zhNPP8OdRI5ZNxcVM/uJb5u/MaLL9ITExvKcKqdJeNukaFlLJoNjd1aHGtGvLkHbxeKpcPO6N5mFXBF+sWMe3m7cdNHZDKYJtPqRS1xh7mnLROyqS/50/gZKEAKZShgPFrT/+zD0zZuHKLecDEhmtnczLzOK9tRsIrNUs11X82buT98xCXAY8NHww4b6+jYVEACwN1zT1oc3SSS8tZwD1fxOLUvQxfUkvKT2kc4UQQpw83lqxmm2pObxotGGrrmWDrsGjNV9SQlJQ8BElYAB+NhtF2tP4ukh5CXQ4eO/CcxjQryN+naK4a+QgksJDWKyreNiIoZ/hzw2WCMLdBndPn83Li5c12fbwdm340lL/XSJfu5liKWNE4u6ELSkkmOv79iSjrJw73GE8742lIK2AJ39deEixh/n7NfbPADuUi9igAL689Hwiusbxg7UCt9a8vnQlf5j6M1+v3shbKoEbzFAyKyp4Y+UaSoorqMPkL95M/s/MpkqZDE5qy4Sk9ii1+4m/UvWFQkzzEPvnklL6mI7GTacH4E96aflBzhLi5HBKj9G6vSY/p+6guKaWAbHRdDmCRbaHS2vNioJ8HjKSsBmKAu3hNm8aJnBO+0TuHdL/oG00JcDuwxXdOvPo5lSGe/3YYNQRFx5M3+hILIbBn4cMaDy2xuPhnXnLGsvCm4AFhV3DPT/N4fNLJu23OPW5caP568y5XJ+TRpCPD0+OGkHnfX5fs7encaUZQruGkq+XmSHM2Z7GRQepvgjwt1FDefzXBQxWTtKVi9DwQM5MTEDr+uqRDxJNf/zJxMV9mTu5SUUwQ5WRquv42JKEATxr5jCGQOZYKvnkovOI9Pcj0G5nU2ERKZ4aLjO3E4WNIIuVYXGxTZb4b0qn8FDmZFZwo/ahDs0io5rJEckHP1EIIcQRW5SZxbbiUtoFBTKybXyLrNlZujObC80gkg0H9xLNM95sKvDSMyyMV88ed8Tt/mlIfx6ZNY8JZiDFystqWx0Pd+uEn83GrX17NR43pl0bRn7w2R71DsEHRSRW3l+znpigAC7r2nmvtq/r3Z2i6mru3rQVgCu7ddmvKvCCjCzGaCd9jfoHirfqcP7YzEPXfd0zdABXT/mRHdqNB02qzc3nA/oS5utLZkkZZ3idXKNCqUHzl+xMYrWVIsPDe7qQf1ra0gYfPjIL2apr0VaDx84cQfeIcCL9/ahyu4mw+3JVTSqBWOhoOMjz8TIwNuaQYusUEcaU7VmM10HYUcxRFXQKDz34iUKcBE7ZkTC31+Tm76fz4fwVrF26lZu+m85P21OP+3WVUoT62EmhDqUUl6hQkqy+PDd2JM+PH31Uc5PvHzqQO0cNwto9hnMG9+D1ieOxNFF8YlxiO7x+Nl4z81hgVvCMmc0wFcAZKog67eX6b6eRWb57rxBTa35N30mN6SXS15f2QYGENrFoNsDuQz67t6DJx43TcWiJzvikRF47dxzZAQZZ2kVeZRXLc3IpqqlBmyb9GzqOeOVDslFfen+9ruF8I4QAZcFfWbhQhZCq63CbJkkhwQTa7dR6PNw1fSaXEcLrlnaMNQLZoVw8NXbEIf9eHx09lC0BmpvVTm4kjeSEaC7dpxMUQghx7LyyZDl/mzGPDUu38uys33h2/uIWuW64vx8pqn7UZ4DhZLwliEs6duDzS84nNsB5xO2OadeW1887C/+esXTs256vLr2gyXVlwQ4H57Zvz9/MLBaYFbzpzacML1eqUPwweHnhUmbuSNvrnKXZOWRXVRHisJMYGEhME3E6fWzkG7vXWOfjxnmI3zcSggKZctmFWGIC2ahr0Rp+2pYCwJaiYs4goL76sjIYiZN0XKwzaxiqnLRTdixKcYURxkZq8FUG4b6+jfug/m32b7R3W3nNksAfjUhW6CqeHDPikB+SXtKlE0kJUdxIGjernWxxah4bPfSQzhXiRHfKjoT9krqD6pIqnjVjMZTiP32yMAABAABJREFUDO3k2fmLmZB8/Kv5Pj5mOI/MmscA5U+GchEeHsRZSYlH3a5SignJ7Q96D742Kx9NnsiEj74kw+uiv+HkAoJ5zMwiHh96mv7MSNnBzX16orXm4Zlz2ZSWwyD8yTfdVFdVcOe0mbx3wTl7jYbdPrAPN2RPJ1d7MIEFlio+7j/ykON/e/kaosu93KfjSa2q496fZvPBhefiAbbqWjoqB8XaQ6Zy4/Cx4a5zE62tDKd+Ae52XUuh8nB2YmLjE9PU0jJ8PJrzjfqRvfNVCLONKjLLKwlupvrSvsJ8ffny0vPJrKjEYbEQJft8CSHEcZNfVc3H6zbyJgkEKQvVppfbt6VwZa9uza5bPlbuHtqfq6dMJdV040WTbvPw2aB+x6Tt3lGR9D6EfaieGjuCK0p+4LPiInoqP56zxPOTWYYbzc1mOF+s2ci4xHYAfLVxC68sXMo4M4BorcmknLcWrUADl3Xb/bBwUodkPlm9gRdqcokxrcwyKnl42JBDjn36thQyc4p4Vsfidmv+sXwdIX6+tA0KZFl+FfHKB4/WrDZqCQvw57vyUoK9Bl6tsSjFdurwxQC7lU5hu2fazMvI5G0SCFQWIpSNsTqILUXFDGsTd0hxWQyD58aNJq+yilqvl/gAZ5MPn4U4GZ2ySVhJbR1ttU9jqfF22Cl1uxqn6B1PZ7Rry0cXTWRlbj6hvg7GJLRp8Q+NEIeDF88eyz0/zSJU1/JnnUExHv5ltOVrShp/ByklpSzZmcUbqi12ZXC+CuFm7w6Gefx5ev5igmw2EkOD+cPAvnQOD+OziyfxU0oqoPhThyTiD7FCkak1C7Kz+cJIIhc3NZh01HZW5ubz7NiRPDJ7PgmGnZ3eOm7s25Orenbl17SdPL/gd7K8OZhekw3UMCG5PY+PHtbYrtNmo9TroQYTX2VQo01KvW6ch7nJpsUwSDhIBSkhhBBHr7S2llDDRlBDfRE/ZSHS8KG0thY4vklYm8BAvr38QubvzEQpxeiENgQ1s73K8WI1DP5zzjgu/ep7Mutc/MvMZYOu4V4jGi97bwL30uJlPEUs7Sx2tNY8bmbRzuvDO8vXsHBHBoEOO7cM6E1CUCCfXzKJrzdtpbyujlfaxNEvJrq5EPYzZ3saV3lD8FcGG3UN/U1fZm3bwd/GDOfG76azyFtNmfbQMTKc9yecyZq8fP6x8HfuL88ixrSwzKyiU3goL589dq8ZP/5WK3keN4FY0FqTZ3jod5Bqyk2Rh6PiVHTKJmH9Y6J4nRWcoZ20w86HFDEoKrrF9olIDg0heZ91V1prvt28jdnbd+C027mlf6/9jtmX22uypaiYGo+H91euZUF2NkE2Gw8MG8R5HQ+8bmlYmzi+vPQCXly8lIqMXG7WEcylgiWWGv7cMDJX6XYTbFixm/VJop8ycKJYSTXtCk0GEsDSvExuzs7jo8kTaRccxB/69TnQZZukAF+LhW+9xUzVZXRTvmwza3Cm7eS1c8fT44rJpJaWEuN0NiZD53ZIYnjbeOalZ6CBf7eJJ8R379GttkGBnJnUjr/uyKKf18FKSy1j2iXQbo+nqVprPlyznqmbt+OwWrl9UN9DfgonhBDi2EoICsRlgZ9dZYxWASzRlRQaHpJDWmYj3VBfXy7o1GG/n6/KzeOjVevxmCYXduvEmHYHrt6ntWZHaRmltXXMSk3j601bMDVc3KUjDwwdeMCHr9FOf364YjLvrVnHR6vXc5UKowaTj4wSnuozsrH9Ko+HSKP+oaJSikhsbKAWT62X3tlucqjhqp0/8tWl5xPjdB5xVV+n3Yf1upqXzFw6Kwd52k1Nfg3R/n78cOVkNhYU4Wez0iW8fh/NAXExfHbxJOamZ1BSW8dD0ZG0Dwner90/DxvI0/OXMNZ0kmV4KPEzmNhh79k8CzOyeHPpSmrcHs7tnMx1vbrLnl7itKD0IVaQOxzdI8P115PPP+btHq5fUnbwzPzFlLjqGBgVzQtnjSbU9/A3ST5W3l+9js+Wr+NyM4QC3HxnKeeLSybRJrDpEZjS2jpu+m46VVW1lHpc9MOf241IsnHxd5XDa+eddUhTH7TWfL1pK3NT0gh02Ll1QJ/GKR/VbjfnfTqFc+v8GaKczDXL+U6X4AU+sdSXlDe15k4jgxfPG0/3yOb3Mtklq7yCmTvSsRiKs5MSG+fFf7h2A/9ctJRXLQnEKR8qtZc7VQbvXHgOHcOOfKGt1pqfUnawvbiUpJAgzkluv9cH+Lur1vLNig3crMMo017eNAp5beJ4+hxhFSwhTjRd3nh3hdb6yKr+iNPKidI/bysu4YGf57C9vJwEfycvnDXmgHtlHW9r8vK5/cefudIMwY7BR0Yxj50xnPHt2zV5vNaax2b/xry0nVhM8DHh75Y4DBTPG3mc06cLN/XteUjXXpady6dr1mOamot7dGFE2/jG9+6ePgtXVinXEkaaruNFMxcvmn9Z2jZuK/Ma+XTv34Ebe/c46LWq3G6mbUuhwuVmaHxsY8GyDQWFXDdlKrcZkZxhBGJqzVMqh/GDunN1j66HdB/NWZmTx6LMLIIdDi7s3AH/Pcrmr87L544ff+Y2M5wgZeF/qogL+nbj5kP83QlxojtQ/3zKjoRBfTGI8UmJLTIF8VB8smYDD+pI2hv1oznFppcft6ZwR/+mR5ZeXryMxErN7Tqey3UKN1nC8VMGyTgYrQNYmpVzwCRsZU4es1LT8Pfx4dJunRgYF8NLC5fy6Mx5DIiP4Y6BffGz2Xjn/Ak8Nns+X5Vk4udjpX9kHGuz8xqnRCjqqyt6tdnstXbZWlTM9d9NZ7D2x019SdsOoaEE+zq4sntngixW4lT9VASnstDWYie3quqokjClFOccYJ3c9xu3cbsOp4vyBQW5ppvpW1MlCRNCiFbSITSE766YfML0z1+u28QlZjDnGMEA+JkGn6xa12wSNiNlB+vSs3lDt+WfOofRRiARqj65uMQbxC9pGQdMwrIqKpmyaQsuj5cJHdrz9NhRvLR4Gf9dsoJpW7bz52EDifDz49kzR/H3uQt4ICMLhaJPVBQbCouwmLt/Zxat8B7CA/Uqt5srvvqBsBqTKNPG28tWkRgWQoCPD5d164yfj42u3vrvJ4ZSdDHt5FZUHuJvsHl9Y6LoG9N0fzt9awrnmUEMN+qXNtyhFW9u2ipJmDgtnHJJWJ3Hw/KcPEyt6RsThb/NdkJ8wANowNhjtrfR8LPmpBaVcJ7pjzIUgVhI1y56KitaazIMN70OUHhi5o40npi9gHPNAFKVl8nrN2FqzXmeAEbi4LuyNB6vrOb/xo2iXXAQH00+r/FcU2uu+2Yq/ynJZ6TpZKlRjcPffkgl/v+9eDmXeoM4r6FQxgeeQtbnlRGmark97RcCfHyY5ylnlBHIJl1DqreWTqHHt9yszWJQrc3GifZVmDgtsrBXCCFaktaa1XkFFNfU0D0inCin/4nTP+u9y0UbwIG2stpRVk4vrwOHYTT0z3WNRaTScRHi1/wapozyci7/+kdGeP3w1wY3bdhMfGAAURUml5gBrCgr5bq8aXxz+YX4+9h4fvyYvc7/9+8r+Nf6bVzuDSYXNwstVfwpqd1B7/HbzduIrNb8hRhQMMD048X8HCYYQdyfOYekkGC+LS/jVh1OOV7mGlU80EzydKzYLBbK9yjYX6VNbIbsRS5OD6dUElZWV8d130zDqHFjRVFm03zUsJ9Ua8goL+eZuYvILK+ga2Q4kzp34F/rtnKVGUI+bn41KvnsACM4HSJCWVCaR2/tx01GOE+b2YwigDyLF3eAD5M6JjV77muLV3CvGUGfhtLvj7gycRgGFxv1CU+C6cN1KSn0jYtmWJu4vUrzGkrxxnln8/LiZfxYUERiaDjvDh2Ij+XgH4xltXXEs3vRbVvlQy5u7rREob2aND+DT+rKea2uAKth8Py40XstuK1y1ZfA9z/MwhoHcvOA3jzz60Immy7KMZllqeSzffZYEUIIcfxorfnLrHksS88i3rCz1azh5QlnMiju0PaLOtaq3G7+seB3VmTlEOHvx3ldOvBC+k58TQMHBu8ZRTzQq/nqgskhwUyz1HCx6eUyI4x7vOns9LrwtVhZZa3ho8Gjmj33ozUbONPjz7VGOCgI9Fr4qKSQZy1JWAxFD+3L77U7eWXpCiZ2SNpvmuadA/sSZPfh59T0+g2hBw1rdlnDnkpra4kzrY3ZZryyoYErjXDisfNWWQH+4aFcVpiCqeHWvj05MzGh8XyX10uV202w3X7MkufLunXm8k1bsXoVQRhMMcr46wApQS9OD6dUEvbGslW0r4I7dCxKKT6sLeJfi5by3LjRx/xaWmu2FpdQVltH5/BQAveprlTpcnH9t9M5y+XPZEL4eWcpi4MquGpQT+ZsTyfA7st7A4fuVUBiX/cMHsBtBTO4pXQnXqXpEBJC9w7t6W0YJIcGU1Zbh8PZ9J+w2uMhVO1RoQiDmoZxt2pt8qiZSTIOfl28gZcWL+Ot887ea72Xv4+NW/r35sFffuWH7SksyszmiTHDD1rQYkRiGz4r20Kc6YMbzVdmMRc1jIqFYyXF9DDr2ssod7kI8NldvdLl9fLwzLn8ujMDrWF8YgLPjB2F7QAjVl7TPKSqk2cnJRLg48NPW1Nw2Kx80nO0VEIUQogWNHdnBut25vAf3Qa7abDSrOKvs+Yx+7rLj8v1sioqySgvJyEokBjn/vtqPfTLXLw55dylQ9hWU8s/Fy3liTHD+WHjNjyml4e7D+PsA2wtMy4xgaUZ2dy6dTtBhg2nj4M+3erXO10YGIDXNJudalntchOxx9evUGXB1OBFY2j4j5mHW3vJ3JjJbRu3cefgfly+x4NDQymu7NGNtNIyftyWyrLvpnNT357c1OfAU/iGtYnjrjUbGaT9icbG22YB/ZR/YwxeU/PBRROpdLnwsVj2evD63qp1/HvZCqwo2gYG8vrE8QesWHio/XPboEA+mXwen6zdQKHbw3MdpXCWOH2cUklYVmkFvU0Hyqj/0OuhHXxfVnGQsw6f1pq/zprPovQMIgwf8nDz1qSz95qutyavgDCvwWRVn4Dcpu1cV5rGGYntuLJHt0O6ToDdh48umkhaWTmGUrQLCuSn7ak8OXcR8RY7md5aHhg2iIu7dtrv3PFJ7XhrUzq36DCKtIeNRi0Om4233QUUmW5ilQ9/MWJQWjHHLOf/5i/ik4sn7dXG3dNn0bFU82eVyNbaWu7/eQ5fXnr+AZ+43dSnJ6W1ddy3eSt1Hi+BGCRhZ4VZxXe6lCvad0MptV9J4P8uW0VxZjGfqvaYCv5vZy7vrFrD7U2sl5u9I53Hf11AqctFj7BQ/jVhbJOd7J6GtYmTD3YhhGgl2RWVdNYO7Kr+i3lP5UdeTTam1o0P446VrzZu5sWFy0iw2En31vHwiMFc0Hl3NUSX18v8zEy+NJKwKYMkHKyhjjqvl9fOG39I11BK8eioodzUrycVdS7aBQeRXlbOzd//RIi2UGS6GdmuDU+PHblfIja+QyKP7ZhHW9MHpzL4SpWSEBTIs1V5dPTaWKGredPSDgcGOdrFnxYt5cLOHfYq/f7q0hVs3Z7NG7Sl2jR5ZsV6op3+nNuh+RkyfaKjeHjUEP65cCmlrjoMDferaFJ0LW9482kTXN+3O/cpIb8kK5sPV6zlDZVAGFY+qSjm4Zlzee/Cc/e7RmpJKff+NJvt5WVEOXx5bvxoBsYeeLQzMTiIR0fK6Jc4/ZxSSViPmEhm521hkOnEguJno4Ie0W2O+XV+Tk1jw84cXtdtcZgGc8xy/jpzHt9ecVHjMQ6rhUrtbdzIsBaNS5vY95nSl1ZaxswdaVgNg3OTk/abOmkxDHIrq5iTmobNYmHKpq08RxztTDu/6DKe/20xy7JyuKXf3uXu7x48gFeAF1LS8LPZuLlLH9xeLytz8sgsKWN09e7pBMnKztfVeyer1W43G4uL+buRhKEUvZU/vQ1/VubmHzAJsxgGDw4bxIPDBlHtdnPl1z/yYFkmChieEM99g5ou4LYmO4+zzQB8Gp6cnWUGsCg7b7/jUktKeXT2fB7V0XSwOPiytJi7p8/iy0svaDYmIYQQratbRDhvspI8HUyUsjFNl9I5JPiYJ2D5VdX8Y+FSXiSeWNOHDFw8+NtiRiW0adzixFAKhaISkxAMtNaUay8O695fiSrqXHy/dTuVLhcj2sY3Wb2x1uPlu83bcHtNfs/I4jJPEGepIFLNWv6eks4N5dO5rl8PxiTsLnc/sm0b/jxyMP9bthq3aTIqMZHYwACWZ+ewtqyC+DIf7A2LmGOUD3YMyupcRO4R38K0TK7XIYQoKyHAJDOQhWmZB0zCACZ2SGJiwzEPzprLP7enYXohJsDJuxec0+Q5a/MKGKb9CW8oPHK+DubmwvT9jvOYJrf9+DOTav35h6UDq13V3PPTbL6/4qLGKslCiN1OqSTs+t492FZUzDVpO7AA/WOi+dPgfsf8OjvLyulpOnA0PNEbqPx5o7Jgr2N6RUUSERLAc8W59DQd/Gap5pzE9nvtc7WhoJCbv/+JkdpJrdK8t3Idn108ibg91md9v2U7L/62hElmECnU4mtCO6udhWYFH5uFXGuEU76jjGvSp/LhRROZl5bBjC0pOGwW/jCoH/cPG8RXG7fw70XLGIQ/udRS6HXxvq5kpqec+41ovjfKGbDPvHyH1YrVUOTgJg4fvFqTpV0EH8amln42G99dcRFe00QpdcDONibQyYaiEgZQf+8bVB0xgRH7Hbcmr4C+hj+dzfqtBi7ToXxRsp06j2evp4RCCCFOHL2jIrl1YB/uXLIcOwbBfg7ePPusY36drIpKYi12Ys360Zw2yocIw4fsysrG/tdqGNzUuwd/W7+V8V4nWw0XtX5WRu5RGr68ro7LvvqBtrWKCG3hllXreGbcqL2SqZSSUq75ZirjvU7sGKSZFQy2hJOjXfzNzGKCCiK0UPPEzPmUjxhMsMPOW0tXUef1MrFzMt9fOZlNRcXc/P1P9Nd+VCqTHd4qqkwvV5DCHUYkZXgJ8rUT7rf39jrBvnYyKlz1VX+BDOUm1K/5Yl1NeeHM0eixGq/WWA+yp9kvRh0eU2NVig26hmj//bf7yausos7lbqww2U/5006Vs7mwmIi2koQJsa9T6lurzWLwwvgxlNXVYZqaYMexWzy6p45hoUwxNjDZ9BKoLMzW5XTcZ5NCq2Hw1qSz+WjdRjJKyrksugMXd9l72uC/Fy/najOEsxs+sD50F/LuyjU8NmpY4zFvLl3J/TqKboYvVdrLdaSyRdfwnVnCXUYUA4z6pMVrap6cu4DykipuMkMp014e+HkOr04cz/8tWMLLqg1xygeP1vxJp3O/EUWJ9vKwmcnQ2Dj+MnLvBciGUjw0dBCPLF7OMO3PdsNFTEQww49gSt+hzAu/pX9vrtjxA794yrAohcPhw9eD+u53XKivg3TtwqPrO4OduHAYlkMqGiKEEKL1XN2zG5O7dKLC5SLcz/eYj4IBJAQFkGu62K5rSVYONusaikw38YEBex1318C+tA8NZnlmDt0D/HmhZ7e9RsKmbN5Ku1qD+4kCBb1NP15csHSvJOyj1es51xvA5Ub9UoS5ZjlzdDkV2uQMFchVlvqRszjThzeWrqS8ro7bzXAClYO3l6/H1JqFaZlc7Q3hLCMINPxH5xKgLAw3AviLN4NIf3/+e95Z+/2u7h02kFt+mME2XUeV0my3ufm8z8H3CduXUgrrQf4OE5ISeXv5aq4pT8WKwmVo3jrj7P2OC3LYqTS9FCg3EcpGjTbJNl2E+bXe/qxCnMhOqSRsl33XGx1roxPasKpLB27dsIlAZcXqa+Ht8RP2O85utXLzARbKltfWEcPuD6cYbKTU1O11TK3XSxD1CYa/sjBA+fOYzsamwFftTm58MdhRXMqjOoaOygEKMk0X07dsx6s1sQ3TCKxKkaDsVGAyzhLEFFXGn4cN3GvzxF0u7daZDmEhrM7LZ4SfH2clJR5SQnUk/rlwKX2UH5dYgknRdbzjLqLW493vuBFt4/kqOowHc7Noj52lVPLYyKEnTJljIYQQzfO1WfG1Hb+vHqG+vjx1xggemTOfIGWlHC/Pnzlqv+8FSqm9pubtq7zWRbRpaawkGKN8qHC59jqmxu0hnN0PACerEN7WhVhQnMvuafu+yqCyzsX5ZhBDG/bDuk0r3tu8HVNr2qndxyYpR2MCOcoWzMC+nUlsooBXt4hwvrhkEnPTM7AZFp5LakfIAbatORqfr9+MWePmSUscLkz+pfIpqa3d7zinjw9/GtiXh5avoa/yZ5OqZVRiW7ocxT6gQpzKTskk7Fgytaasto5Au89eCci9QwdwbZ/ulNe5iA8IOGAVv+aMTkrgk9VbiDSt1KGZYpRxT1LnvY6Z0KE9r29K40YdRr52s85Sx3/PGc9v6Zn8d8M2bjbDqMDkW6OMQLudqhrvXvthhdl9iPR18ElNEZcYoWzRtazRVVxnhFGsPZSYbkIcdmrcHl5evIxV2blEBzh5YMQg2gQG0ic66rhvauz2msxrWCTtowzaKjsrVS2Ls7KZ3LnjXscaSvHvc87k17SdFFTXcGtUxCHtXyaEEOLUorWm3OXCYbHsNR19XPt2DI2PI7eqiminf5MPGQ9mRNt4/rRuE321P5FYeVcVMSph7zXm53RK4rGd84g2bTiUwXfWcv7Yry9RTn+e/HUhMaYPoVh51yiiXWgwVYWexnOrtBebYZAcHsoH27P4qxFLJV6+N0u40gjDqzVpysUEX1+01ny2fhNTN22rf7g7oDfD2sTRJjCQaw6x0NfRmLE1hRu8oXQ06pO8y8wQZmxNYewe5et3ub53D3pFR7G5qIjJgQEMi4+Th6RCNEOSsANYn1/IndNnUuV2YyjFC+NG7/UhHObrS5jvkQ+z39ynJ5V1Lv6yeRsWpbihT4/9FtXeO2QArxoGr6am47TZ+OeQMxgYG8OAmGhinP58s2kbPlYL/xhwBmV1dfzf3EVcZLooxctcazWfdu7I/LQMfqou5WtvMb4Y2FB8bBaxyeLilr69CPX15c5pM6nLKeNqHcTGihqu+WYa311+EcGO4zuqCGAxFDZlUIqXyIZF0qXKg28za7wMpZr88BdCCHF6KKmp5c5pv7CpuASv1tzapyd/HLh7Cru/j40kn+Ajbr9vTBR/HTWElxYto8rjYUxCG/66z7T9UQlteGjUEN5bvha3Nrm0a3eu69UdpRRhvr68sXQlNe4aLu3UgxFt47nqm6kYXkUABt8YZTw+cDgb8wqZrmu51puKBVAofjcr+d5aTnREMGPbteXjtRv5dNkabmp46Hr/z3N4feJZ9ImOPOL7Oxy+Nisl7J6ZUoIHv30qKO6pT3Rki8UmxMlMaX2ALeGPUPfIcP315POPebstyeX1Mu7DL7jRHcJwI4BNuoZnVC7fXzG51TZ/PhQLM7L4eVsqvjYrV/XsxrbiEl6ZvQiHB0CTgJ1ZlHN2xyQu6dKJvjFRVLndDH33E74wkrA1PLH6u5HLVaP7M759uxaJ++0Va/hy1QbGm05SDBd5/gafXjzpuE5bEeJU0eWNd1dorZsuPSrEHk6F/hngT9Nm4ptdyc2EU4aXR1Q2D44ddkI/oNtRWsanazdS5/FwTqckOoWFMu6jLxhrBrBcVzJAOVmoK4kMDeT2/r05o11bLIbB5M++4dpKJ91V/XePKWYxdZ3C9lo/fjwtz8nlrmkzOccMpA7NHEsln0w+r8lpkkKIvR2of5ZvuM3IraxCmZrhDfO3uyhfEi0OthWXnNBJ2L77YS3Oyqa9cnC7JZz5uoIS7cGlNU+PHt44vXLXolwXJjYsaK2pwcR2nNZ/NeWWfr1oFxrEsswc+jn9uLJbV0nAhBBCNGlNfgH/RwyGUoRgZYTpz5q8/BM6CUsMDuKRPUbTdpSWEWTYuFVF0s/0J406/CxWHhg6kMHxsY3HWQ2DWq0blxrUYrZoMar+MdH87/wJTN+WitMw+KxLRxKCmt+qRghxaORbbjPCfH2pNL1k4SJO+VCuvez01hHVQgnY1qJi5u/MxN/HxnkdknD6+JBVXsHqvHyCHQ6GxMceUmWpXpERvKKXMZFAxqgAPlfF9AwO32t9m91q5eJOHXhyeybjTCebjDpqHRYGx8UeoOVjb1xiO8YltmvRawohhDj5RPv7saG0hihlw6s1WywuJjidBz/xGCiqqWH69lQ8psnYdgm0DQqkos7F4qxsAIbGx+634XFT4gKceC2KX13ljFYBoKFCldMxLGSv427o34tnfl3IpaabCrzMsFTycbfOzbR6fHSLCG9ynzQhxJGTJKwZ/j42Hh42kIcXLaOr4cc2s4aLu3Xaa0Pk42VhRhb3/zyHUdpJoeHlo1XruW/YAB6b/Rs9DD+ytIuEqDD+c86Z+1Ur9JgmLyz4ne+2bsOqDK7r3Z2/jBzCX+cvosbrpUtICK9MGLvfNR8ZNZTPwzaxKjuXtgERPNWvl4xECSGEOCH9bcxwbv1hBotUNYXaQ1hoABd36XjwE49SXmUVl339A909Pji0wVvLVvP8uNH8fe5CYrwWTOCfVpNPLj6vyQ2Kv9uyjZcWLaPS42Z0mza8fPYZPDxzHi9X5RHmsPPy+LGE7rPW/OykRJw+NqZt3o7dauXDXiNpv8+2OEKIk4+sCTuI7cUlbC0uoU1gAD0i9988+GhtLSpmZ3kFSSHBjfOrL/h0CpdX+jfuAfYieaygmvvNSPoa/ni05i9GNjePGsA5ye33au/1pSuZt3Ybf9ZR1GHyrMrlthEDOL9jMi6v96Td0NjUmi82bGZdTj7xwYFc37s7fkdQ8Qogo7ycN5auoqy2jhGJbbm0ayep3iROarImTByqU6l/LqiuZlVuPn42K4PjYg+44fCRyK2sYkNBIaG+vvSOikApxbO/LaZqcx43qvpRoZ/NMr7zqWCI25drVX2l3nd1IdakcJ48Y/he7S3LzuW+6bN4VEcThY03VAEhCeH837jR1Hk8J23/DPUPj2dsTcHPx8bVvbrRJvDIpitWud28sWwVKUUldAgP5fb+feSBsDipyZqwo5AcGnLcRr/eWr6aD1evJ9lwsNWs4b6hA7m4ayfKXS7i1O5rxppWfjPddLHUPx2zKkUH7UN+VfV+bS5Iy+ByM4Qwo/5Pe4EZxIIdGVzQqcNJ/QH/5NyFrE/N4gyvP2uNAm7YsZOPJk887Hnx+VXVXPn1j5zldZKsffgodxWF1dX8ccD+G0MLIYQ4cUX4+R234lGLM7O5b8ZsOhm+ZOg6BiXE88zYkZTV1JKgbY3rs+KUjRq3m846uPFnnbWdhRWVTbSZxTjTSXJDqfdrdRgPZ9ZPYTyZ++dp21J4bt5iJptBlODl8m2pfHnxJOL22SD7YLymyW0/zCCgxMVg049FeTv5Q24+715wznHZ2FuI1tZylRfEXtLLynlv1Tpe1vE8ZkbznI7j+YW/U15Xx/C28XygiinVHrbpWn42KkgKCmYKJWitydEuFlFFr6j9R+aCfR1ksHtDyUzlJtj3+Gzg2FIq6lz8uC2FJ8xozjaCuV9HUVtRw4qcvMNua0bKDvqavlyhwhhpBPCwjuKTtRuPQ9RCCCFOVn+ZNY/7zSgeM6P5j9mG1enZzN+ZycjEtnxnlJGh6yjUbj5VJSSFhzLVKKdWm9Rok+mWCvrERe/XZpDDQaaxe6+wTO0i+BDWjp3o3lq2mnvNSM4zQrjWCGekx4+vN2057Ha2FZeQW1rBfTqS4UYAf9aRpBWVsqO07DhELUTrO3kfvZykfk3byZq8fGq9XtpY7ISY9X+COOVDkLJSWF3DX0YM4UnvAu5Iz8DXYuG+IQMZGh/HXdNncnFJCkopHhoysMlNlP80pD83fv8TO7SLOqXZaHXxeb9eLX2bx5TL9GJRCnvDMwOlFH7KgsvrPciZ+9Ps/eTBgkJz7KfkCiGEOLlsLCxidmo6dquFgtoaejTMPrErg044yKms4vJuncmvrOaxVWtxmyaTOiZz75ABPDF3AVempgIwsX0iN/XtuV/7F3fuyJT1m3mqJodIbeU3o5J/jBzTovd4PLi9XvzV7p7VH4M6z5H3z7vGvBRgqPrlCEKciiQJa0GvLV3J92s3M8L0Z51RwzZvLZuNGjorX5abVdRYNTFOJ742K8+NG73f+V9eegFVLjcOq2W/ghy7dA4P46tLzmd22k4shuLppMT9FvmebEIdDrqHh/FqUQFn6wDWUUOe4aFvE0nowYxLTODNZauI85YQj40vLKVc2qVlq0wJIYQ4sSzKzOLPM+ZwphlAufLiqwym6VImqRBytZsVVHFDeP2arxv69OCGPj32Ov/5caN50uNB0fzUQn8fG59dMonp21Opcrm5uU0cHVug2Nfxdl7nDry+Zgs36TBKtIdpRjlvJg897HY6hIYQEuDPa+UFDDH9WWBUER0UQHvZj0ycoiQJayG1Hg9vr17L/1Q7QgwrXq25zUjnCXKwaYVhNfjPhDMPugDV3+fgxSjiAgO4tme3YxV6q1NK8Z9zx/H8b0t4O6+A2MAAPhg5mgD74U/jiA1w8tFFE3l1yQo21tQyKbEb1/XufhyiFkIIcbL496Ll3GGGM7Rhb1CXMpliKeNLs5Q6bXL/4AH0bGIJwJ4ch7Cuy99m45IunY5JzCeK2/r3xmIoPty6A1+blX8OOuOgv6umWA2Dt88/m1cWL2d6UQnJ4dE8O6R/sw+dhTjZSRLWQmo8HmwogqgvJGFRigTDwaUj+9IvOoowP99jXtnpVOL08eGpsSOPSVtJIcG81ESZfiGEEKenSpeLKOXf+Lo9dmKSYrl9QB8CfHykQt8BGEpxa7/e3Nqv91G3FWi389joYUcflBAnAfnW30KC7XaSQoJ5jyLytZtfzXK26VoGxsYQ5fSXBEwIIYRoJWPbt+NdVUyWdrFJ1/CjUc7YpHZE+vtJAiaEOC7kk6WFKKV4beJ4Hpv9Gw/lZxPl789bZ5xNuN/JvV5LCCGEONndNbgfLtPL49t2YLda+PPAQQxrE9faYQkhTmGShLWgMF9fXp84vrXDEEIIIcQerIbBQ8MH89Dwwa0dihDiNCFJmDhspbV1/Of35WSUlNMlKpw7BvQ5qTeaFEIIIU4FHtPkvVXrWJ6ZTYS/P38c3JcYp7O1wxJCNEG+OYvD4vJ6ueHbaSRVwSjTj18L0/lDXgFWpViRn0+43cHfxgyXaRxCCCFEC3tm/iI2bs/iPDOIFFXClZk/MCqhLT+l7MCqFDf06clNfXqglDp4Y0KI40qqQYjDsiavAG+1i9t1BIMNJ/frKNbm5hOaX8u7tOPW2mDu/3kOabLDvRBCCNFiPKbJlC3b+KuOZojh5GoVht2lWb89k3/reJ41Y/lq5Xp+3JbS2qEKIZAkTBwmzd4713u0phqTGwnHqSz0Nvzpp/xZkZvXShEKIYQQp689x7iqTC9X6VDClY145cMFZhC/7djZarEJIXaTJEwclt5RkRh+PryhCvndrOQllYcNRTZuALxak4WbILu9lSMVQgghTh9Ww+Cijh14xsjld7OSj3URLkOThavxmCzlJthXqjILcSKQNWHisPhYLLx34Tm8umQFc0rK6B7VjjMD/fnbkpUM1/6kGm6CQ52MTmjT2qEKIYQQp5VHRw3lncC1/JqRQ4QzmH927MtDM+eRqt3UYbLB5uKLvj1bO0whBJKEiSMQ4nDst6N95/AwVuTkMdDPl3OTk2TzaSGEEKKFWQ2D2/r15rZ+vRt/9sUlk5iTloHVUPw9KZEwGQkT4oQgSZg4JvpER9EnOqq1wxBCCCHEHtoEBnJdz26tHYYQYh8yXCGEEEIIIYQQLUhGwlqBqTULM7Iorq2ld1QkCUGBrR2SEEIIIYD1+YVsLymhXXAQvaMiWzscIcQpSpKwFuY1Tf40fRbpecW0UT48Z1bx/PjRjGwrhSyEEEKI1vTuyrW8v3It3Q0/Npo1TO7eibsG92/tsIQQpyBJwlrYrB3pZOcV86IZh1Up1ulq/jZnAXOvv6K1QxNCCCFOW0U1Nfx3xWpepQ3hpo0y7eWP6zZxfpeOtJUZK0KIY0zWhLWwguoakrQdq6rfTrETDgrratFaH+RMIYQQQhwvRdU1hFlshCsbAEHKQozFh4Lq6laOTAhxKpIkrIX1iopgCVVkahdaa76mhN5h4SilDn6yEEIIIY6LNoGBVGGyyKwAYIVZRa52kxQS0sqRCSFORZKEtbAekRHcO2wg95HBZDOFdUGaf559RmuHJYQQQpzWfG1WXp84nvfspVzk3c5/bIW8MuFMgh321g5NCHEKkjVhrWByl45c2LkDtR4PfjZba4cjhBBCCOoflM6+9nKqPR78rFaZpSKEOG4kCWslhlKSgAkhhBAnGKUU/tI/CyGOM5mOKIQQQgghhBAtSJIwIYQQQgghhGhBkoQJIYQQQgghRAuSJEwIIYQQQgghWpAkYUIIIYQQQgjRgiQJE0IIIYQQQogWJEmYEEIIIYQQQrQg2SfsBFPldrO9uIRAu53E4KDWDkcIIYQQgNc02VpcggY6hIRgs8hzbCHEkZMk7ASyvbiEm3+YQYCpKDbdjG3fjifHDEcp1dqhCSGEEKetKreb236YQW5pBQYQ5PTjnQsmEGi3t3ZoQoiTlDzGOYH8deY8LnEH8LIZz5s6gZU7MvklNa21wxJCCCFOa/9dupLAEhf/NdvwutmGNhUmLy9e3tphCSFOYpKEnUDSKsoZQgAAvsqgj9eX1NKyVo5KCCGEOL2lFpcyyPTDUAqlFINNf1KKSlo7LCHESUySsBNI+8Ag5utyAKq0lxWWGpJDg1s3KCGEEOI01yE8lEVGFV6tMbVmgVFFh/DQ1g5LCHESkyTsBPJ/40cz1V7FH40MbiWdockJnNkuobXDEkIIIU5rt/fvgyvMl1vVTm5VOykMtnLPkP6tHZYQ4iQmhTlOIInBQUy96mLSSssIsNuJC3C2dkhCCCHEac/XZuWdC85hR2kZWmsSg4OwGPIcWwhx5CQJO8E4rFY6h4e1dhhCCCGE2IOhFEkhwa0dhhDiFCGPcYQQQgghhBCiBUkSJoQQQgghhBAtSJIwIYQQQgghhGhBkoQJIYQQQgghRAuSJEwIIYQQQgghWpAkYUIIIYQQQgjRgiQJE0IIIYQQQogWJEmYEEIIIYQQQrQgScKEEEIIIYQQogVJEiaEEEIIIYQQLUiSMCGEEEIIIYRoQZKECSGEEEIIIUQLkiRMCCGEEEIIIVqQJGFCCCGEEEII0YIkCRNCCCGEEEKIFiRJmBBCCCGEEEK0IEnChBBCCCGEEKIFSRImhBBCCCGEEC1Iaa2PfaNKFQDpx7xhIYQQTUnQWke0dhDixCf9sxBCtKhm++fjkoQJIYQQQgghhGiaTEcUQgghhBBCiBYkSZgQQgghhBBCtCBJwoQQQgghhBCiBUkSJk46SimvUmr1Hv9pdwRtXKCU6nocwtvV/gylVKlSaurxuoYQQghxIpH+WYhDZ23tAIQ4AjVa695H2cYFwFRg46GeoJSyaq09h3j4PwA/4LbDD00IIYQ4KUn/LMQhkpEwcUpQSvVTSs1TSq1QSv2slIpp+PktSqllSqk1SqkpSik/pdRQYBLwj4YndUlKqblKqf4N54QrpdIa/n29UuoHpdQcYLZSyl8p9a5SaqlSapVS6vym4tFazwYqWuTmhRBCiBOU9M9CNE2SMHEy8t1jqsO3Sikb8B/gYq11P+Bd4JmGY7/RWg/QWvcCNgE3aa0XAT8AD2ite2utUw5yvb4NbY8CHgHmaK0HAmOo7yj8j8M9CiGEECcb6Z+FOEQyHVGcjPaa7qCU6g50B2YqpQAsQE7D292VUk8DwYAT+PkIrjdTa13c8O/xwCSl1P0Nrx1AW+o7ECGEEOJ0Jv2zEIdIkjBxKlDABq31kCbeex+4QGu9Ril1PTC6mTY87B4ZduzzXtU+15qstd5yxNEKIYQQpwfpn4VohkxHFKeCLUCEUmoIgFLKppTq1vBeAJDTMCXiqj3OqWh4b5c0oF/Dvy8+wLV+Bu5SDY/0lFJ9jj58IYQQ4pQk/bMQzZAkTJz0tNYu6j+Yn1dKrQFWA0Mb3n4M+B1YCGze47TPgQcaFu8mAf8E/qCUWgWEH+ByTwE2YK1SakPD6/0opX4DvgLGKqUylVJnHen9CSGEECcj6Z+FaJ7SWrd2DEIIIYQQQghx2pCRMCGEEEIIIYRoQZKECSGEEEIIIUQLkiRMCCGEEEIIIVqQJGFCCCGEEEII0YIkCRNCCCGEEEKIFiRJmBBCCCGEEEK0IEnChBBCCCGEEKIFSRImhBBCCCGEEC1IkjAhhBBCCCGEaEGShAkhhBBCCCFEC5IkTAghhBBCCCFakCRhQgghhBBCCNGCJAkTQgghhBBCiBYkSZgQQgghhBBCtCBJwoQQQgghhBCiBUkSJoQQQgghhBAtSJIwIYQQQgghhGhBkoQJIYQQQgghRAuSJEwIIYQQQgghWpAkYUIIIYQQQgjRgiQJE0IIIYQQQogWJEmYEEIIIYQQQrQgScKEEEIIIYQQogVJEiaEEEIIIYQQLUiSMCGEEEIIIYRoQZKECSGEEEIIIUQLkiRMCCGEEEIIIVqQJGFCCCGEEEII0YIkCRNCCCGEEEKIFiRJmBBCCCGEEEK0IEnChBBCCCGEEKIFSRImhBBCCCGEEC1IkjAhhBBCCCGEaEGShAkhhBBCCCFEC5IkTAghhBBCCCFakCRhQgghhBBCCNGCJAkTQgghhBBCiBYkSZgQQgghhBBCtCBJwoQQQgghhBCiBUkSJoQQQgghhBAtSJIwIYQQQgghhGhBkoQJIYQQQgghRAuSJEwIIYQQQgghWpAkYUIIIYQQQgjRgiQJE0IIIYQQQogWJEmYEEIIIYQQQrQgScKEEEIIIYQQogVJEiaEEEIIIYQQLUiSMCGEEEIIIYRoQZKECSGEEEIIIUQLkiRMCCGEEEIIIVqQJGFCCCGEEEII0YIkCRNCCCGEEEKIFiRJmBBCCCGEEEK0IEnChBBCCCGEEKIFSRImhBBCCCGEEC1IkjAhhBBCCCGEaEGShAkhhBBCiGNCKdVWKVWplLIc4BitlEpuybgOhVLqeqXUghMgjveVUk+3dhzi+JIkTJzSlFLDlVKLlFJlSqlipdRCpdQIpVSVUsrZxPGrlFJ3KqXaNXQSq/Z5P1wp5VJKpbXYTQghhBB7aKZvG6CUGtza/ZvWeqfW2qm19ja0O1cpdfORtqeUekIp5W5I7Eob7nvIHu+PVkqZDe9XKqWylFJP7tOGbvi97Dqm9IhvUIhjRJIwccpSSgUCU4H/AKFAHPAkUAZkAhfvc3x3oCvw2R4/9mv4+S5XAjuOY9hCCCFEsw7Qt9VprZdwavZvX2itnUA48Cvw1T7vZzckfk5gOHCTUuqCfY7ptesYrXXw8QxWKWU9nu2LU4MkYeJU1hFAa/2Z1tqrta7RWv+itV4LfABcu8/x1wLTtdZFe/zsI+C6fY758HgGLYQQQhzAgfo2OE79m1LqSaXUfxr+bWsYWfpHw2tfpVStUip0j5E2q1LqGWAE8GrDCNSrezR5plJqW8Po1mtKKXWwG9dae4BPgDilVEQzx+wAFlGfdB4VpdQ/lFILlFJBDf95RymV0zDa9vSuKZcN0xgXKqVeUkoVAU80TCl8TSk1TSlVoZT6XSmVtEfbnZVSMxtGMrcopS492njFyUWSMHEq2wp4lVIfKKUmKKVC9njvI2CkUqoNgFLKoP4p4Af7tPExcLlSyqKU6go4gd9bIHYhhBCiKQfq2+D49W/zgNEN/x4A5AIjG14PAbZorYv3PEFr/QjwG3BnwwjUnXu8PbGhnZ7ApcBZB75tUEr5UJ8sFgElzRzTARgGLDlYewe4jqGUershtvFa6zLgfcADJAN9gPHAntMsBwGpQBTwTMPPLqd+lDIE2L7r50opf2Am8CkQ2XDc6w1/B3GakCRMnLK01uXUT0vQwNtAgVLqB6VUlNY6A5gLXNNw+FjADkzbp5lMYAtwJvUf/B+1QOhCCCFEkw7UtzW8f7z6t8VAB6VUGPXJ1zvUj0g5gVHUJ2mH4zmtdanWeif1Uwx7H+DYSxvWcdUAtwAXN4yK7RLbMKJWTn2S+juwb4GNlQ3HlCql/n2Aa9mon7YZCpynta5u+N2eA9yjta7SWucDL1GfPO2SrbX+j9bao7WuafjZt1rrpXuM4O26x4lAmtb6vYbjVwFTgEsOEJc4xUgSJk5pWutNWuvrtdbxQHcgFni54e0P2N1JXQN8rrV2N9HMh8D1wBVIEiaEEKKVHaRvg+PQvzUkFsupT7hGUp90LaJ+1OlIkrDcPf5dTf1IXHO+bFjHFQWsB/rt83621jpYax0IBFOfrO078te34ZhgrfWfDnCtZOB84EmttavhZwnUJ2c5uxI54E3qR7F2yWiirebuMQEYtEdSWApcBUQfIC5xipEkTJw2tNabqZ9OsGsh8jdAvFJqDHAR+39g7zIFOBdIbXhiJ4QQQpwQmujb4Pj1b/OAM6ifjres4fVZwEBgfnMhHkK7h0RrXQjcSv2aq5hmjimjfprfeUd4mU3ADcBPSqlODT/LAOqA8D0SuUCtdbc9L30Y18gA5u3RVnDDdM0/HGHM4iQkSZg4ZTUsev2zUiq+4XUb6p/2LQHQWlcBXwPvAela6+VNtdNw3BnsPfdbCCGEaHEH69vguPZv86ifurixYZRobsO5O7TWBc2ckwe0P8T2D0prvQX4GXiwqfcbpkdeDmw4imt8BvwVmKWUStJa5wC/AC8qpQIb1owlKaVGHeElpgIdlVLXNBQ5san6LQa6HGnM4uQjSZg4lVVQv1D2d6VUFfUd1Hrgz3sc8wH10wIOWPFQa71ca51yvAIVQgghDtGh9G1wfPq3RYAvu0e9NgK1ND8KBvAKcLFSquQga7EOxz+AW5VSu6YDxu7aAwxIp34911VHcwGt9QfA34E5Sql21CefPtTfcwn1SW6To3GH0HYF9YU9LgeyqZ+2+Dz1a/fEaUJpfcxGiYUQQgghhBBCHISMhAkhhBBCCCFEC5IkTAghhBBCCCFakCRhQgghhBBCCNGCJAkTQgghhBBCiBZkPR6N+vqH6qCQ+OPRtBBCiH3kZa0r1FpHtHYc4sQX4uvQcQEH2hNXCHE4tHufPbDjEigqbZVQTgsxzmpceQ27IShQVlvrBnQQGwqKmu2fj0sSFhQSz9V3TT0eTQshhNjHiw8npLd2DOLkEBfg5OvJ57d2GEKc9Dz5WY3/Xn/PDyxflNuK0Zx+rpsE6tFbAVAKLBFxrRxR07q88W6z/fNxScKEEEIIIYQ41eyZfL0/8K36f0gC1uI++AEY+BZ+EWFcOm1y49/FGnliJmNNkSRMCCGEEEKIZuyZeMWP7cvTFbe3YjRiT9UFRY3J8PVLb8WTn3XSJGKShAkhhBBCCLEHb0EWWu9+3TjqVdE68YiDe3/gW42J2Ik8RXEXScKEEEIIIYRg71Ev/fRb9dPexEnj/X2mKJ7Io2KShAkhhBBCiNPWnolX3L138NLWsVQXFIEkYCelXVMUT/RRMUnChBBCCCHEacFblAumd6+phrDHdMOFAEUtHZY4Dt4f+Bb9h0bT/eVJrR1KkyQJE0IIIYQQpyRvQf0o155JV/zYvszo8YSUlT8NLF+Uy1n33kHWS68DJ1b1REnChBBCCCHESW3PKYX70k+/xVeLw+qnGEJ9cQ1JwE4bzyzsDQ3TE70FWSfM1ERJwoQQQgghxEnhQMnW+nt2L+Laa5TrB5AphuLLc6dw6bTJJ0wiJkmYEEIIIYQ4oXiLctFeb5PvNa7f2peMbokDqC4oQj/9Fjx6a2uHAkgSJoQQQgghWlFz67YyxtwuJeLFMfXBD/Do2L5kzl7Z6uvDJAkTQgghhBAtat/NkOPuvYOf9dm7pxFWICXixXHxdMXtXM+trb6PmCRhQgghhBDiuNo36Yof25d/OR7ZXSxjIYBMJxQt4/0ToFCHJGFCCCGEEOKY23dd15fnTgHq1+ZQAVRIsQzRehoLdRTlYgmLbvHrSxImhBBCCCGOmT1HvfTTb+1e11UgSZc4cVQXFBF37x1kv/x6q1xfkjAhhBBCCHHUdpWP32szZFnXJU5gP+uz6a4lCRNCCCGEECeRPaccxt17R/3GuLIZsjhJLF+US3dolSIdkoQJIfZSW1PGwpkvUVKQRlRcN4aMvQurzdHaYQkhhDiBNDnlcGGrhnTKM00vKxa8S/q2RTgDIxk67m4Cg2NbO6yT3q61YS3NaPErCiFOWF6Piy/fupLcrEJ8g85gx9Z1fPfh7eg9S1oJIYQ4bXnys/Dk1ydgX547hfcHviV7ebWQX3/8O2uX/oBv4GjKK3z49PWLqKkqae2wTnq7KnTu2q+upchImBCiUW7WWmpra+nR9z6UUoREDmHlnEupKMsmMLh1NzVsitfj4rcZ/2D7xlnYHQGMmPAA7TqMaO2whBDilLPneq+nK26v/6EU2mgx2jRZt/Qz+o39EptPEGExI6mtTCN18xy69Wv5UZxDsX75Vyyb/w7a9NJz4KX0G3EzSqnWDqtJrVGgQ5IwIcRuWqP2GCBXAErBCToSNnfaM+xM2UBi90eorc7hx0/u5LJbPyUytltrhyaEEKeEPacdvj/wrfr1XqJVaPbuo1EGcGL2z9vWz2D+Ty+S1PMhDMOHFQtfxGK102fota0dWrNa+quOTEcUQjSKiu+JzcfKjg2vUJy3mG1rnyUqrhsBJ+AoGMCWtdNJ7H4f/oFJhEUPJyJuAikbZ7V2WEIIcUrYNe1w/T0/1CdgotUow6B7/8vYsupvFOctImPbB1SVbyax8xmtHVqTNq2ZRlzytQSF9SYgpCttO93K5jXTWjusZj2zsHeLX1OSMCFEI6vVzmW3fUZEVAAVRdNpm9iBC65964SdPmC1OXDVFTe+9rhKsPn4tmJEQghx8mtc9/X0W7w/8K36UvOi1Y2d9ATd+oyjsugn/HzLuOqOKfj5h7Z2WE2y+fji3qN/dtUVS/+8D5mOKMRpoLamjMzU3zEsVtokDcV2gGqHvn7BjLvw6RaM7sgNG3cPc6c9SVTbC6mrzaGqfB1d+z7b2mEJIcRJade6L2iYeigFN4470+shI3UJrroKYhP64R8Q2eyxhsXK4DP+yOAz/tiCER6ZgSNv5rM3LsPrqUIZdvJ3fscF17/d2mGdUCQJE+IUV16SyWdvXIrdEYvXW4vF8gKX3/4FDt+g1g7tqHXrNxlnYBQpm2Zj902kz5DH8XOGtXZYQghxUtlzry+ZdthyvB4XX797PWUl+dh9I6ksfYSLb/yAqPgerR3aUQuL6siVd0xh/fIvMb0eRp31MVFx3Vs7rBOKJGFCnOJ+nfosodFn0abDtWitSV3/T37/9b+MOufh1g7tmEjoMJyEDsNbOwwhhDgp7Rr9atzrS7SY9cu/pLrSRY+hb6CUhfzMX/jl20e55q7vWzu0YyI0oj0jJ5wa3zWOB1kTJsQprrw0m8DQXgAopQgI7kF5ScvuhSGEEOLEsysBk72+Wkd5aTb+wd1RygJAYGhPKstyWjkq0VJkJEyIU1xcQh8y078jIKQrpummIOsneg++oLXDOm7KS7NYPPs/VFUUkdhxBL2HXHPCFhYRQoiWtufUw7h772iVqnCiXkzbPmxY+RTRbSdis4eQmzaF6PherR3WceN21bBkzqsU5GwhLCqZIWPvwsfu39phtRpJwoQ4xY04+0F+/OROlv4yCdB06X0hfYZe19phHRfVlUV88tpFhEWPxzdgMMsXfEJFWQ4jJzzU2qEJIUSr22/q4cLWjed0l9x1HPlZG1j665Uow0Z4VEfOuvLULF6hTZNvP7iF2loLoVGj2bljIVnvXMvlt32BYTk905HT866FOEXt2DKXHVvn4+sXTO8h1+LrF4zNx5eLbniH2poyDMPa5FMnrfUpMVq0fcPPOIO607bTjQAEhvRg9W83MOLsB0+J+xNCiCO1KwH78twpVP9Q1MrRnH6K8rexYcUUtNZ063sR4dGdABg67h4GjL4dj7sWh2/Qfn3VqdI/lxSlUZCzhb6jP0UZVsJiRrLmt+spyN182hbskDVhQpwi1vz+KT99+RDFRVZStmzik1fPp7amrPF9h2/QfglYTsZq/vfCaF76axLvvTiewtwtLR32MaW1iTIsja+VYUFrsxUjEkKI1rfn2q/qAknAWlp+9gY+e/0ScrIqyM2p4vM3LiM3c23j+zabA1+/4L2SrerKQr5460peeiSZ15/qx5a1J+5Gx4eisX9Wu1IPVb8WTutWjas1SRImxCli4S8v0anf08QnX0Fyzwfx8W3H5jXNr7SurS7j2/duIrrddQyeMIPQmPP5+p3r8LhrWzDqYyup6zjKi1aQlfIZJfm/s23VE3QfcNkp8RRRCCEOl7egftPl+LF9pfR8K1ry6xvEtr+KhM43k9DpJuKSr+X3X/97wHN+/OQutIpn8FnT6NDnaWZ++yj52RtbKOJjLzS8PcGh8aSs+wcl+UvZseFlfP2chMd0bu3QWo0kYUKcIryeGmz20MbXNp9Q3K7qZo8vzNuC3S+a8NjRGIaNqLbngPKhtHhnS4R7XDgDo7j89q+wWbIpK/iB7v0mMGbiY60dlhBCtDhPfhZa108/fLri9tYO57TmcdVgc+zun33sYbjqmu+ftWmSlb6Uth1vwrDYCQjuTGjUcLLSl7VEuMeFMgwm3/geMbFRlOV/S3hkAJfe8gkWi621QwPgkWGrW/yasiZMiFNEcrcJpK5/kTYdbqKmKoOinF8Zf+EdzR7v6x9KTVUOHnclVpsTV10JrtpifP2CWy7o4yA0oj2Trn61tcMQQohWs+f0Q2T6Yavr3Otc5v/0InZHBCiDjG3vMHz8nc0erwwDuyOIqopUAoI7o7WXmso0/PzPasGojz27I4CxFzzZ2mE0q6UnzUgSJsQpYtyFTzF32v+Ruu4pHH7BnH/tG4RFdmj2+LDIZLr0nsT6xXcSFNab0oJl9BtxM/4BkUd0/ZLCHfw2459UVhTQLnkog8+487SteCSEEK1lrwRMnBC69r0Ql6uSlQtfBa0ZOOomuvW75IDnnHnB35n57V8JjRpOTWUazsAAOnQ7+4iu76qrZP5P/yA/eyOhEYmMPOdh/PxDD36iOK7kG5IQh0GbJutXfNX4QdZz0FUnzFC61ebgzMN8wnTGpMdJ6jKG4oJUwqMvo23S0CO6dlVFPp/99xKi2l5ESPQoNq/7nMqKfMZf9OwRtSeEEOLwefKzTuu9v9K3LyBl0xwcjgB6D7kWP2dYa4fUqPfga+g9+JpDPr5Tz4mEhLcnK30Zfv5nkdztrCN6sKlNkynv3ojXDCE89lJKChbz5ZtXcPWffsBqtR92e6civ4gwsl56HWWxHPzgY0iSMCEOw89THiYzbQMhUSPZmTKV1M3zuOj6d1DGybm8UilFu46jaNdx1FG1k7p5DgEhPYlLugIAZ1AnVsy+hHEXPH3S/m6EEOJksmsE7HRNwDasmMLcac8R1fZCXLWprFs+iWv+9ONJPeITGduVyNiuR9VGWUkGxQU76DvmWZSyEBTej3WLbiE/az2xCf2OUaSnBktYdIteT74dCXGIKspy2bZ+Bl0GvEBc+0vp1P//yM/ZSn7OhtYOrdUpZUFrT+NrbbrrJ1dLVUIhhDjuZAoiLJz5Mh37PE588hW0734f/oHd2LhySmuH1eqUMtDau8d2LRptevfazuV0d+m0ya1yXRkJE+IQedw1WGx+GBZfAAzDis0nEPdJXNL9WEnqOo6Fv/yL9M1v4ReQRG761/QZcr2UhhdCiONMErB6HnftXhWCrT6huF01rRjRiSEwJJ6YNr3YtupJQmPOoLRgCc6AEKJiT88NkptjjYxr8WvKSJgQQF1tBQtnvsRPXz3AumVfoJvYPDAotC1+/sHs3PI21RXpZKV8jumtIDLm6KYKnAp8/YK56s7vCAk18NYuo//wKxl5zsOtHZYQQpzSToc9wLRpsub3T/jpqwdYPOvfzW690qnHOezY8BJV5akU5S6gMGsGSV3GtnC0Jx6lFOdf+wbJXQfgqfmdtolJXHLLx1I4q0FrlKbfRTX1ZfNoRcf31FffNfWYtyvE8eBx1/LJaxdhWONwhnSjMPMnkruNYMzER/c7tqoin5nfPEpB7macgTHYfQPxeupI7DSSfsNulPVPolW8+HDCCq11/9aOQ5z4ukeG668nn9/aYYhj4HQZAZv57aOkb19JWOx4KorXYKgSrrj9SyxWn72O83pczJ/xAikbZ2Oz+xMcGkddbTXhUckMG38fdkdAK92BOJE9Mmw12S+/jiXi+IyEdXnj3Wb7Z/nGKE576dsW4PFaSe71F2ISzqfzgBdYvfgDPJ66/Y71D4jkguve4oo/fEVJYQombfELHsfqxd8wf8YLxyymutpy5v/0PD9+chcrF76HaXqPWdtCCCFODad6AlZXW8GGFV/Tuf8LxCScT4fej1JTXUNm2tL9jrVYfRgz8VFuvH8WPj6+VJRrnKFnk52Zw1f/uxbT62niCodPmyarFn3Aj5/+iXnTn6O2puyYtCtaR9ZLr0MrrY+TJEyc9jyeOqw2Z+P6JYvVD1DoAyQ+2zf8TGBoP+KTryYsejgd+jzOmiUfA/Uf0FUVBU0mcYcUj7uWz9+4nJ07dqCNrqxa/C0zv3nkiNoSQghx6tk1Cnaq83pcGIYVi9UB1BeZsNqceD2uZs8pKUihtCiTpJ4PEho1lKQeD1BZXkBh3ha01tRUleCqqzzimGZ+9xgrFn6FqTqTmb6Tz/57iawNP0n1H1pfDbGlqyLuIhNCxWmvTfvBzP7+CbJ3TCEgpBu5aV/TNmkYNh+/5k9SCtD7/agobytT3ruJupoyTNPD2POfpHv/A2/IuK+M1CW43YqO/R5EKYUzpDtr5t9Ah+5nkdhx1DGf8pi6eQ6rFn0CaPoMvYr2nWUOvRBCnOhO9VEwAF//UKLiepC67kUi206ivGg1ddVZBy6trtT+67o1uOqq+ertq8nJWI3WHrr2vZhxFzx1WH2q21XDxhVf0f/MKVhtTsJiRrN+0R0sm/s6/Ufeio/deYR32rTighQW/vIyNVUlJHYeRf/hN8myh2Oo+8uTWnxvsD3JX1Kc9vycYVx+++eYrvVkbn2FqNhozrvq1QOek9x1POXFK8nc9hFFOb+xddUT9Bh4Fd9+cCuRbS9lwLjv6THsdeZOe5bCvK2HFY/p9WCx+qKUoqJkA+sW3YkzuCszv32Sbz+45ZhOTdyx5Vd++vIBbH6D8PEfwk9fPkjq5jnHrP3a6jIyUhZTmLvlmLUphBCns9NlFAzqi0pccN2bhEUEkLn1FZS5nctv/wKHb1Cz54SGJxES3pbta5+jKHcBKWtfICAogvXLv8LldjJw3Hf0H/s1O1NWsXbpp4cVj9ZeUArDYsftKmXdwj+AsrFxza988PI5VJbnHe0tN6ooy+Wz/15CnScWZ9gE1vz+LfN+ev6Yte/1uMhOX0H2zpV4ve5j1u7JYldBjtYaBQMZCRMCgNCIJC6+6f1DPt4ZGMWVd0xh0cxXqC6fQ9+hl9Ct72RWL36fbm3OBcDPmUBweB/ys9YTHtXxoG1uWTuVFQs+wDQ9VJXvIGPbh+Rn/kJSj3sJjxmFaXrYtPTPbF79PV37XnSkt7qX1Us+o03HW4iMHweA1iarF39K+85nHHXbuZlr+ebdG3D4x1JTlUuH7uMYd+EzUrZeCCGO0ukwCraL3RHI2Zcc+pprZRhMvvE9Fs18mYLcmcS17cCw8S/y6WsXE9fxbpRhxWo4CY89i+z01fQafPVB28zP3sj8n56nurKEgKB4tq1+Gq0VgaG9aN/9HpRSpG9+m99m/IMJl/7zaG630fYNMwgOH0R80uUA+Acms3bBzYw+9y9H3XZNdSlfvnUldXV1oDW+/k4uveVj7I7Ao277ZJH10uutvpWpJGFCHKHgsATOufxfja+1aWKx+FBZupmAkC54PNVUlG4lMPjgFXe2b5zJrO+eJLHbPShlobzkJeqqVuKqLSAorBdQvy+ZM7gr5aXH7imo6fVgNG7gCGjzmCVJ0z67l7ad/0h47Bi8nmo2LLmL1M1zpGSwEEIcIW/B6TMKdjR87P6Mnrj3WurAkDjKi1YTENwZrTUVJWuJ7NTjoG2Vl2bx1dtXEZt8HdERiWRu/wjtLaCqsoiETn9o7DMDQ3tSlv/9MbsHrTVer2uP1ybHKmv4bcY/sDk60LHf3QCkrvsHi2a+wpjzHjsm7Z/o/CLCAI5bRcRDJUmYOK1l7ljKzu0L8PUPpXv/Sw+8DuwglGEw4bJ/MePLBwgI7UZ1eSrJ3c4kpm1ftq3/icryfGprytDaJLZtH9p1HNV47vplX9Om482ERg0FwOu5nZryucQlDCAn7RvadLgBV10RxbnzGTTq2ExHcNVVUpCzgbrapdCQiKVvfpNJ17xO+vYFzJ/+AnW1FSR1OYOREx7arxzwwZSXpNN54BCgvthJQEhvSgt3HJPYhRDidBV37x2wsLWjOP5Ki3ayZe0PoAw69zyPoNA2R9XeGZP+xudvXEZ58Qq87irsvjb6j7yFzB1LKczbQl1NOR6Pi6CQOLr2uahxH62UTbMIjhxCTEL91g4dej/Cyl+vYNCYO9m0ehohUYNRKPIzfiSxY++jve1GO7cvpjhvETu3vo+fsx0ZW9+j16CrKSveycxvH6OkMI2I6E6ceeFTOAOjDqvtksI0giMmNiaQwRGDKS6Yd8xiP9FdOm1yq4+CgSRh4jS2YflXLPn+ccZ7HaRZNF8t/ohL7pqKzcf3iNtM7jqOa/70I/nZG3AGRhEV14Mp791IWUkhPo5YivMWEhzenzW/f0XfoVcxaMwdACjDgtezewNKr7cGi9XGuAufZ8q71/P7z19jmi6i43sT06b30d46UF+Qw+HfjnbdLiFv51RMrxuPpxpfvxC+evsqErv/GYdfLGlb3sTz41OMu/Cpw2o/LKoT+Rk/E9PufNx1pZQW/k54zLGZRimEEOLUVZC7mW9en8worx2t4PNfX+fiP35HWGTyEbcZHJbA9ff9TFbaciwWG23aD2bpvDdZtfhTAkN6UVKwHLtvFFarjc1rpnPRDe9gGBYMw4rXU9XYjtdTg2HYGDDqNgpzt7H0l0mgwS8gis69j81IUlVFPjtTF9Nj2H/JTv2CqrJteM0a2iYN4Yu3riIs5mySel5PYfZMvv7ftVx797TD2nw5MrYLmWmzCInojwaKcmaT2PHgo4KngusmAUtbfxQMJAkTp7GFU5/iGTOM9oYDbWoeKytmy9qph13NcF9BoW0an9htWTuNspIiug3+N0pZqCjZyKblj9Fr+Bssnn0N/UbcxObVP7Bj82w0szG9taAs5KR+yvnXvoGfMwzDsBIWPZzQ6JGU5C9kyrvXc/ntX2Ic5b4WpteDxeIgOLwfweH98HpqWPrLJFI2zSI87mzCokcA0L77n1m/+I/7JWFaa8qKM/B6XYSEJ+4Xz7lXvMKUd68nN/0r3HWl9Bt+MwnJw48qZiGEEKe+5TP+yeUePy4wggGI8JSwYuZLjL/qtaNq1+Eb1DglvqqigGXz3qD3qA/xsYficVeycu61dB/0ItvXPk1m6hLsjkAWzXyZ2ppyUta9hH9QB/LSv2HAyFuxWGz4BYQRENSBqISLcdXm8vX/ruLau6cf9sjUvrxeD4ay4ueMp0OvBwHYsOROigu2Yxj+xCVdCUCbjjezet6vlBanExqRtFcb1ZWFVFUWEhyasN/D5eHj7+Ob929m5a+Xo9FEx/dg8Ni7jirmk4V69NYTYhQMJAkTp7E6dzVRhAP1FZiitUFdbcUBzzFNL8vmv0na1kX4O8MYftZ9BIclNHt8dVUhfgHtUao+QfEPTMbjKsVmD0UpK7mZa/l16rN0Gfg8yrCSvvl/1FWnc9EN7xCb0I+8rHXUVFfQc/i/UUoRGjWUVfOuoqQwlbDIDk1eMy9rPambZmOz+9Gt78X4+oc0eVxChxHM+fHvbF7xOP6BHags20iH7udidwTgdm1oPM5VW4zVtvcHuNfj4odP7iQrbTmGxYeAoEguvulDfP2CG48Ji0zmpvvnUFq8E1+/EPycYQf83QohhDgIw1K/uewpXpjDXVNG9B5fUWO0FVd16UHP277hF1Yv+QzDsNB3+HW06zCi2WNrqkvwcYThYw8FwGpz4vCLwe2uwO4XQ011CVM/v5fYxGsJjujHzi3vk77pTYaNu5t+I24GYN3Sz+kz6kN8HPX9W01VOts3/kLvwdc0ec2qinw2rvwGj8dFh+5nN1u0KyAohuDwdqxffA+Bob3QphttVhEd3xO36y1M041h2DC9tXg81fv10Uvn/pclc17F7huB11vF5OvfJSp+90iXzcePS2/5hLLiDJRSBIbEnxZFs65feitwYoyCgZSoFyew/fb5OIbWL/8Sw9T8x8yjULtZYVaxgCradmh6pMbtqiYvaz2/TPkr65f/REDYBGpdEXz6+mSqKvKbvU5cQn+K8xZQWbYV0/Swc8u7BIR0ZeeWt3EGRbNt3U943DVsWfE4m5c/SpsO1+J2VREe3bmhBVVfEnfXnmTabFi/1fSHZdrWeXz19tWk78hh4+ol/O+FUSyZ8yqm17PfsTtTFqE1GBYfCrNnYbrzOOvi5+ja5yJqKjaRsu5FMrd/xrbVTzBs3N17nbt8wTuUlZTRd8zn9Bn1CRZbInOnPrPfNSxWH8Iikw87AdNas+b3T/nuw9v55Zu/Ul6afVjnCyHEqag1y2nvSWt93Pro3My1ZORu5l2zgJ26jjRdx8fWKtr1PLfJ402vh8K8raxa9AE/T3kEu3MYVt8BTP30bnZub37xXHBoW9B15Gf+jNZeinJ/o7Yqi9qqTMqL1uFy1eCqrSB981usmncDzqAOBIV3IySifWPCopSqL5ixi/aimumfK8py+PDf57F1w1rSUnbyyasX8NMX91HTRHJZXLCdsuJ0bPYQKks3UZA1g0lXv05Mmz5ExXVly/K/kJX6JZuWPUhSlzMJDI5tPDdn5yqW//YevUa+T68R79G24x/4/uM79ruGUorgsLYEhbY57AQsI2UxP376J6Z+ejeZO5Ye1rmt5bpJ9f9tjTwxEjCQkTBxAqqtKWP65/eRvn0+dnsgoyY+QrdjVJId6keK5k57jk7DXiVl26fcXvA7FgVnX/N2k0+l8rLWMeXdG7FYA6gqT2PAmVOw2YMJjRpKbVUaKRtn0XPQlU1eKzK2G2de8CSzvnsIV20ZNp9A3O5q6mry8HGEsHrJJ3Qd+DxBYb0oLVjOlhWPY7HaGwuERMR0ITA4ipS1zxEUPojM7R/jdlfz7fs302Pg5XTtcwEBQbs75XnTX6B9jwcaC3xsW/M8KxZ8QkHOFiZe+e/GD1qtNbO+fZSug/6Ff2Aypulhw+I/sjNlEUldxnL1nd+zeslH1NaU03/Yi/s9TSzI3kxI1CgMwwZAeOxYcne8ffR/nAaLZr7MhpXTiEm8jJLiND557UKuu3safs7wY3YNIYQ4WV2/9NZWKVOvTZPfZvyD1Us+RGuTHgMuZ/TER496evwuHk8d375/M2273ourMp0HdnyN11NNnxG3023gFfsdX1NdytfvXEtleRHuukrad7+H8NjRAJheF6uXfEbb5GFNXstqczD5xvf58ZO72LbmeWw2J15vHWmb38AZmMjs754gLukK4pOvxlWbz9qFd4F2ERAU09hG7yHXsmXVY8S0u5yCrJmUFa9h8ez15GZtoN/wGwiLSG7cXHnFb+8QEjmKdl3+AIB/YEdSt7xHVvoFXPOnH7E7AhrbnTf9BWLaXUFs+4sB2LHxP2xYMYUx5z3G+de+wdrfP6G4YAcdOl9Fj/6X7XVfhXlbCQrrg91R31+GxYxm6+pncLtrsdkcR/iX2S19+wJ+/OQu4pKvAw3ffXArF1z7JvHtBx1128fTiTQNcRdJwsQJZ8ZXD1JT62DguO+oqcpi7tS/EBKWQGxCv2PSfl7mWkIjBxMQ3JmAAX9Hay+Lp4+nTdLQJo//8ZM7adPpdsJjxrBkxoSGkal6uzZuPJDOvSbRudckTNPL1E/vps4dTpsO11FevJ6UdS82lqAPjuiPxerLkDNua0yWDMPCxTd9yKJZ/2b7hk+x+QTQud/fcdWVsGT2IyyZ/W96DbqKUef+FaUUrroKHH67n4j5+sdTWrCCbeun8+4/N3Du5S8R3aYXpteN212NX0Biw3Ws+AW2p7qyAKjfwHromfc0e09hkUls27iEyPjxgEFJ3kJCI9sf9Hd/qFYufJcew97C4VefYLpqc9m2fsYh7ecihBCnMmtkHJ78LPoPjWb5otwWvfaqxR+xZf0ceo14D6UMtq1+Ev95bzYWmTpa5SVZoKxExI0BIK7T9Wxcei9xycOaHK2ZN/05LLZEeo98mY1LH967f8Z70BGeiJgu3Hj/LEzTy/plX7Bi4RQ6D3gepSwsmnYmse0vQSmF3TeKkMiB+NhKiIzt2nj+iLMfJCAohg0rvqGyNJ2uA57D6hPA1lXPsGXtNCJiujD5hnfxsftTW1OO3Xd3Aufwj8U03VSUFfH28yMYfe4jjWvSqyoKiGy7e+TPLyCZyorNAFgsNvoMvb7ZewqJaE958cu4XWXYfIIoyf8dP//wY5KAAaxY8D5tO95KZJuzAVDKYMXCD07oJOxEm4a4i0xHFCecjJSFtO14MxarH86gDoTFnElG6pJj1r4zKJrKsi2Y3joAKko2YvcNxmKx7Xes1+umvGQn4TGjUUoR0+5CNi59iILMWaxdeCdFeUtZueAD0rYevLSrYVgoL80mIKQnAHbfSOpq8qmrqU98aqtzML1VdO49aa/zfOz+DZszahK73Y2vsw1BYT2JbX8Zvv7t2bz2Z3Zs+RWApC5jSdv0GrXVOZQXrycr5QtCIgYycPy3RCZcxZT3bqC6qhiL1YfI2J5kbPsIbXqoKNlESd4SYtv2PaTfYf9Rt2K3u1kz/3rWLbqFqvKVjJn46CGdeyi0NhtH2YD6ue97dK5CCHE6Uwq6hbdsAgaQtvU3Ytpdjt03Ah9HGDGJV5K27djVy/cPCMddV0ZtVf0UdLerjOqKnTgDm56GWZi7hbDG/vkCdmz4D3kZP7F11bOkb36b7J2rWDrvzYNOndzVP/sHdWtcw+3jiKCscDVQP6pWXb6VPkP2XuullKLP0GuJjO1CXPJVBIZ2x8+ZQPtudwM+uNy+LJr5MgDJ3caRm/YlFaWbqanKYseG17DaAuh3xud07vcc86a/0Dh9MiF5CDmpn+FxV+GqLSJv57ckJDf9oHhf8e0G0K3fhayZfwMbltzJjvUvMPGqVw/p3EOhTRO1T/+szRO3fz4RpyHuIiNh4oRj9w2hqiKVYHtftNbUVqXh69/7mLWf2GkMsW2/Y92i2/ALSKSscDXnXP4iUD+XujBvCyFhicS3H4RperH7hpC67mXikq8gtv2lFGTOICv1Paw+kfQc+h9qq3OY+undXHLLx0TFdd/rWrkZayjM20pIRCJxCf2Ja9eXjNTvCAzphsXmxMcRypoFtxAc1p3ykk2MOPsh/AMimozbx+6kriYPX/82pKx7kYKsWfU/d0RSkLOZ9p3PYOQ5D+P5/klWz78Rw7Bjml7adroBw+IgIvYMCrOmkZ+1jnYdR3H+Na/zw8d/ZPFPZ2H3DWL8xf9HWDOLhPdlszm45JaPKcjZhOl1ExHbFavVfhR/lb11638J21b/ndikq6mpSKO0cCnJXR8/Zu0LIcRJzbCgHr0Vv3OnUF1Q1GKX9XWGUlGeCowGoKYyFT//0GPWvt0RyMgJf2HBL3cRFNaLipKN9Bp0JWGRyZQW7SRzR33FwvZdxmIYVuwOJxlb30d1uoWQiAH4OduQu+NjXK46ug54FmX4sHrxP7FaHfQddt1e1yorziBzx1IcvkEkdhpNbEIf1i9/nOiESdjsIQQEd2Lrqr8TEtmHmspMYhN6ktz1rCbj9nEEUFeUB0D2jimkb34b0+uirHANOdYaoH4Lm6qKfOZPfxiv1w0oug58HovFgTO4E5HxE0nb9httk4cxbNy9VFX8lWWzLkQpg37Db6LHgMuavHZTRk54kB4DLqWqsoCwyA57Fc06Wj0HXcYvUx5FGVbQJhnb3mHCpS8cs/aPJb+IMNSjk1GWYzNd9lhTx2NhZXR8T331XVOPebvi9JC6eTbTP/8zYTGjqK3OxGb1cPntX2A9RkPpUL8mKjN1CVWVBUTH9yY4rC1L5/6XZfPfJTiiL+VF6+jc6xx2pizC67VjWAMpyV+CYbHSZ8h1rF/+FV0HvYLDLwatNRuX3I9RnUpwWAJ9xt9Hu46jWDr3DZb99i5BYb0pL1pLz4GXMOiMP/L5G5dSlLcdrU1CI9pz1uTnqCjLIjQi6YBJ0I4tc/nhkzvx9U/AYvGlc/8n8XpqWLf4bjp1H0O3fpNBKSJju1FdWcjUT/9EbuYaDMOB1h7ad7+brO0fcOF1/yW6Ta/Gdk3Te8zm8x8rpunl97n/ZceW+fj5hzLi7D83Ww1SwIsPJ6zQWvdv7TjEia97ZLj+evL5rR2GOAa8BVloTYuuDSsvyeST1y7CGdwDZVgoL1rBFX/4mpDwxGN6nYLczRTmbiE4LIGYNr3JSFnM9x/9geCIAdTVZOPn709wWALp25fiF9CRkvzfMQyDyNguOPxCwNqdqDYTAMjc/hnF2z8iJLQt7ftdRN+Rt5K543e+//B2giL6UledQ0BgMBff9AG/Tnua9cu+RmsTuyOAc694BVddBb5+IcS1G9Ds9MaKshw+fOVc7H6J1FSm02PIK/g4wti25nnctelccN0b1FaXEhHTGYvFhzk//p2NK78DNNr0Et3uAtyuIrr0GMCAUbc3tqtNE5Q64SoXbls/g1WLPgYFfYddR3LXca0d0n76D42m+8uTUKp1pyF2eePdZvtnScLECakwdws7Uxbh8A2iY49zjmkC1pTqykL+98Ioeo18H7sjHLernFVzr8YZ0oUu/Z9DKUVh9q8UZn3N9fdO5+3nR5LQ5QECQ7uTm/o1lZve5i4VTiUmr1nKOOOKfzP983voNeI97L4RuF2lrJl/A2PPf5I5P/6d5J6PYHOEkrbhFZK6DGbUOQ8dUpwzv/krm1ZPp3P/vxMYWl9uNm/nNLJSPsBqC0BrEz9nIJfc9CEfvDyBmPY3EBk/jqry7axbeBdtk4dx4fX/O+E+0MXRkSRMHCpJwk4tnvwsoGUTserKQrZt+Bm0JqnruKPeE+tQvPvPM4lOvIHQqKFobbJp2YNUlW2j75jPsVh9qavJY9W86/nDI8v4derTVFQ6adPhaipLt7B10V3/z955R1dVZn34Oef2m5vee0gCCYEAofcqgoIUEVBERayMvdfRsYxldBx77zQLgvTee2gJIYFU0nvP7eWc74+LQaTFOjrffdbStch92zk3Oe/Z7977t7mDAAJQ8qGyjZgRt3D0yPeEd5rbPt6JAw/To//l7N/yLpGJc/ENSqO6ZClOWwHX3rGsQ3tmfvZ6Vi++l8iEa4ju7A5btJgqyNo9D4VSi1bvTkG46qYvSN/+AS3NLuK734vTYSJrz10IWJn74CY0Wp/f+3b+z3PDpNNCHP/tPLALGWGecEQPf0qCwpIICkv6w+YzmxpRawPb1YRUah80+lDUmuD2h6+XT2cqClsAGDL2PjYv/zvhna6iseArHheDSRXcioa1Lgd7Dy5BowtGows+NZ4fOkMEBTkbCY25Et+gNABiu/6NrAOPYDU30WvQ7LPCGX9K/5HzyMlYgbE5t90Ia2s+jkLpR+qQDwAoPPoyKxbegc3aRkjU2FNrT8QvuCfd+0y76GYiyzI5h78jP3sTGp0PA0fN+81POT148ODBw6/nB5GOJ73f5/m22y/e4TdAbwii54Br/5C5fsBsqsfg634nEAQRL58uWM3VKJTu+lgaXSgqtQGbrZV+I25m0TtX4nS0Ymo8xhTZwEiF27C51yXyQvpizDYjBr+u7ePpfTpTU56F3jeR0JjLAYhNnsf+9RNZPv9vdO01kaTzSOT/QHzSKHRefrQ1H0eWZQRBwNSSj4xAr+FfoFDqqC1fz/L583A6rCT1eRFBUKBS+xAaMxGDV1OHDLDKkkMc3v0lkuSiR/8ZxHUZ/ovv6/8ic9JvhXQQFIo/TUmH8+ER5vDw/47qsky+/fh65r81mT2b3kCSXPgGxCBLVuoqNiPLMo01+7BbamltPIzVVInLZaO88Eui4wcCkNJ7KgEhcVQUfo0gKrH+qE6IGRmtIQCX00R95TZkWaapdj9WUxXefuHYrXXtbW2WWgRBS5vJl28/mk1NRdYF1+4bEM0Vs96kLP8zThx8iuPpD9NYvYPwTjPc9UokB20t+TTUVuFy2jG1FgLgdLRhbivGJyDqovfn4I6P2Ln+LRSaPhiNXix6d5qnTpcHDx48/ElRhkRSvvlwuwDBXxlTWx2rFt/Dl29cwdpvHmqvoRUR25eKwgXIkhOLqYL6ys047c001R1All1UFS9HrdFh8AkjMKQz/UfeTtXJZThdViw/Onc0yxIKhYqImD5UFC5Cll1YTZU0VG0jJLIbdkt9u8Ki096KJDmRhES2rHyBjH3zL7h2hVLNtXd8j818kqw9d5J35DkKs17FL6hvu7Foai3AYmpBkgSaT4l+yLKEsekoAUFxF70/lSWH+O6zuTikWCQxidVf3UfRic0/+z7/r/KDCqIyJPJPb4CBxxPm4b+Aw25h67cPUnBiC1q1jkETnqTrBeqASZKLjL1fUlORg39QLH2G3dwutepyOSg6sQWbpYWoTgPxC4y54NyNdUUs+eQGorvcjE9wJMczPsVuNTJy4hNceeMnrJj/N/IzX0TvFcTUGz+huiyT3RtuQXLZiEsawyVTnmkfKzAkEaWmGwbfLryW8SKzJSetsosVSjMzhs4lpf81LJ9/O/mZL6DV+TP5+g/wC4xlwVuTKMxyolT7U138PZ17PU5AqNu4O7xnPpdNv3CCa3zyaG56aCvFedsRFUoqSzKoKt9LcMRIasvXoVDqSR30Bg1V2zm29z68fBOwW6pISZtEWFSPi34/h3Z/Rudez+Pl45acd9jqOZGxnP4j5120rwcPHjx4+ONRhkTi/A2EOmRZ5tD29ziy7X1ckouUvtMZOvHvF8wbLjy+mfzsjWi1BnoPnXtG4eDy4gM01RUSFJpEeEzaBed2Om188+EsdD69Cet0C/WVm/nukxuYdcdSxk//FysX3MG+deMRFSqGX/Y4gaGJrPnqfo4bawgISWLa3M/b1xkY2gWddwzJfZ5j/c5b0EgNBKJgIS0MHvMicUkjWT5/HvvWjkcQFQy/7HF6DZpN0fFtnDj4GAa/VGrL1xMZP4OITlfi7Z9C+rYX6DXwugteg8EnlFse3kZBzkbsNiOCMI7dG9/BYW/FaW+ltmwdvUctxGFr4ti++6iv3AzYMXj70GvwDRccG+DI3gVEJlxPeNxUABRKPQd3fkZ88piL9v1fRh8cyIzV04A/pwri+fAYYR7+cLZ/9yiGE3v4SAqjxurguWVP4h0QTVRcv3O2X/vNA1RXFBMYNorcY/s5mbeTmbcsRJYlvvn4OkxtRrT6CLateoFJs985b2FGgILs9QSGjyL0VP0Nje4xsvfdyciJTxAamcotj+7E6bC256BFxfWjz9CbTkmmn7kJRccPomTjh0R3uQFF3+f45sSnmEyl+IV2bReRuPXR3TgdFpQqXXsY4HV3ryTrwDdk7ltEVOJ17QaYQqFDctXREQw+oXTvO4PW5gr2b32flsZyGmqmgOQkLHYKgiASFDEKtS6E4+kPMfPWr84Q47gQsuRyqx6dQhBUZ9Re8eDBgwcPfz4EAWasnvar8sOOH15K/pb3eNkVgAaBlw8u46DOj/5j7z1n+6wDX7Nz3X8Ij5tOc1MtC992Fx42+ISyc90rHDu4FJ/AHrTUv0bvoXMYeIF6YnWVOTgcEsnJ7lqZ3v7dOLL9WprqTxIYksjM2xbjdNpQiKr2Asi3P7HvnOJSwWFJOKy1CKKSrkM/YHvuxzRX78ImQHLaFERRwTXzvsHhsKJUqNvHmzb3M7IOfMXxjBXoDbHEJN0EuPfnjsqwK1VakntegctpZ9Xie7Gaazm4aToKpQ61NhCV2geV2oe0EZ9xdNctDB1/H937TD9nmZyf4r7W0/uzKKguKr//v86T3u9TvvrwnyL/6+fiCUf08IdTnLeNmyVf/AQlSYKOcS4tJbnnrrNlbK2h8Phmkvu+SFjsJLqkPU1rUzXVFUfJObIMq9lJt4FvktjzceJTH2Hjsr9fcG5RoURyWdv/7XJaEH/y4PupCIggCOc8BeyaNgVRsHJ463WUFyyk1VpDVPe7cLocZ/RVqfVn5GF5eYcwcPSdDLn0PmrLl9NUu5/6qu2UF3xBTOJAaitzTsnXXpzVi+/D4D+E/peupGvfFxCVGuqrNmMxlSNLThoqNxMdP7jDBhhAj/7XUJj5Ak21+6kqXkZD9Ra6pE7scH8PHjx48PDH88ML6A8hWb+Esuz1zHDqiBbUhAgqrncaKM1ef972+7a8S+defye805XEdb0d36ABZB/+juaGEjL3LaT74PdISH2U7oPeJX3ru5jaas87lnt/tgHu8H5ZciK57CgU6vY2SqWm3WBq73eO/dk3IIaElDEc2T6XomOvU99whKgeDyIIClxOW3s7lUp7xngKpZpeg67n0itfwNSSQ235OprrD1OY9TKdu4+jpjwLm7XtovfRfW/eoamhkf5jvydt5OdoDVHYbfU01uxFlmWa6w8higIpvaZ2yAAD6Nl/JuUFX1BXsYmGqh2UnHiHngOu7lDf/zWe9H6fOem3Ur75sDv88C9mgIHHE+bhv4BW602FzU6o4H7olCtA6+V/zrY2SyuyBMf23o9GF0Jc19tQKL1wOe2YjXXovDsjCO4HqLdfMoXGC3uSuvaazIHtH1Fy4kM0+giqTn5N/xG3dXjtLqcdWZZQqrSIooJh4x9i0/JnCIoYjbd/N0pOvEOXlJEdGqt73+mAzNH0bxFEkYDgTuxY8yoKpQatzosZtyxAbwi64Bi1lUfpM/rviKKIt18SOq8YJFczGTtuRpYcRMQNYPyMdzq0HklycezgN9isrYRFJdJc8z1avQ8zblmIfwdi1T148ODBw3+XH4Q65qTf+os8YmqvQMqF0x6fChxozlMHTJZlrJZmirLfRKk0EJl4jXt/dtgwGevQekWgUvu6x9UGotEFYTY24OUdcs7xgsNT8A+KJv/Is/gGD6SpZjuRsX3wDYju0NplScLhsKDWeAEw9NIHKMzZgrd/KrHJt9JQtYXQiFRUav1FxwoM7cKVcz9l94bXaWswEhAUyrFDSyg8vhO7rZEpN3xIZOyFBWkrSzMIjpqAqNCg0YVh8EnGaauj4OhLOOxGvH0jufLGT1CpdR26vuK8HZzM205iymhaGrciCCKjJz1Jcs8rOtT/f4UfQg/L+WuFHp4LjxHm4Q9n8ORn+dfCOxgl26gRZYq9vJjRd8Y5225f8xI+galExs+kpeEombtuR6fzbs9tOrTrS0JjJqDVh1NeuICIc4Q01pRnsXHZ32lrrSIipg/TbvqSrPSvsJqLGDXxYZJ7njubWZZl0re/z+Fdn+F0OlBr9JjaahAEgc7dLmP8jFdI7jkRY2sN6dveQ3I5SO41mUFj7+nwvejedwbd+87gwI4POXZoE2kjFyAISkpOvM+WFc8xbPyDFB7fhKhQ0iV1wllFMQ0+EbQ0ZBAQOoQTh55GVOgIi5tKc+0+BOqYcfN8RMXF/8xlWWbNV/dTU1mEb/BgWurKCAmPYeKstzxy9h48ePDwF+LXGGJ9xtzJ19lraXA0opFhl9LK1AmPn7PtoV2folB6E5t0Mw57C7mH/oEgwOgJ3+LtG4HNUkVjzV78QwbSULUNl8uEX2DcGWOYjfWs/+5xqssz8fGLZPSkpziZu52G2hN07TmCvsNuPu8edDJ3G1tWPIvZWI9a643ZWA/IBAQnMnXOR/gGxDDlhg/Y8N0TVJd8S3hMHyZe/36H70VETG+m3/wl1eVH+e7Tm+k17DPU2kAaa/awYsEdzH1gM7lHV+KwmYjrMvysOp++AZE01GcSGDaUyqJvaKrdQ3SXm7FZqqgtW8HVt399Rv7chTiavphd618nJPoKrKY2rKZKrrt7xf8rOfsf6n4Bf8nQw3PhqRPm4b9CbWUOxfk70GgMdE2bglpjOKuNzdrGe8/3pf+lKxFFt9fs2N576Df8anoOmAXAkb3z2bHmBSSXnfDY/kya/c4ZhoqxtYYv/jOe6KTb8QlIpbLoa0RquPr2ry66xiN7vmTPpndwuexEJczCbmuktmwt3Qa9TlHWv7GaSnE6LYiiim69r2TM5GfOCpP4KZLkIi9rDW3NFYRF92pXW1z79YPYnDGExbpPtNqaT1CS/TJWawv+IYORXFbMrTlce+f3Z9RkqSg+wLIvbkFv6ERrcx79x36PKCqRZYmju25i0uzXCY/u5Z7b5aSu+gQgExzW9QzjrKn+JIveuYpeIxeiUGiQXDaObJ/NNfO+JiA44aL3ysN/F0+dMA8dxVMn7P8Pv7SGmKmtjtyjq5AkJ4kp484rePX5a+OJSPxbe7mU8oJFqBRlTL7ObehUFB9g5aK7MBvrMPhEMHn2u4RGpbb3l2WZhW9PQanpQljcVFrqj1BR+Blz7t9w1oHjT6mtzObr969GkiEsdhIqtS/lhYuJ734fppYT1JStxuW0IggiIRHduHLOJ2j1vhe99vKi/VSVHcHbL4IuqRMQRQU5h5dyeO9qEns+2d5u79px+PrHoFCFoNaG0FC1jSuufZvYzkPb25iN9Sx+bwaIBtqaCkkd/Ga74FXRsddI7NqN/j8qzNxYV4TV0kxQaJez3onefa4fSX1ewMsnEYC8w0/Tc8C4P7xUwH+DH2p+wV/T+PLUCfPwpyMkIoWQiJQLthFFJcgyksuOKLqTTwURvH1PGyFpg66j18DZuFx2lErNWWOUn0zHO6A7IVGXAtCp213sXz8Bu814TsPvx+RmrUMQ1XRJfRC/YPffjyQ5qchfgKm1kE4pd+AX0p/q4uVkH/4e34Bo+o04fyy+1dzCgrenYDG3otYG4dz+CQNG3UbfYTcTGJpI9pGthERfhiAoaKzejktyEpl4A+Gx7hem4uPvkb7tA0ZPeqp9zMi4fsy5bz0FORvYtf71H50YCgiioj2R2GZt49uPr8fYWg8IGHwCmH7zl+2naA67GaXaG4XCfQ9FhQaV2oeiE1vZuvKfgECfoTd46pF48ODBw1+EX+oR8/IOpveQGy/aTlSqcf04x9plISjodBmUyLh+3P74vjPErn6M2VhPU/1J+l7yHwRBQBsTTlPtdqpKj5DQ9cJqfydzt6LWR+IX1IfY5JsB0HpFUZr7GbLsIiBkKNFdbsDYfJz8zJdYvuB2Zt66+LzjyZLE8gW3U1qwH5UmEFEhcPzISqZc/wEBwQm0NGZhtzWi1gTQWLMPhUKFUhNNlzT3fuwb1I+tq/7JnPvWto+pNwRx/T2rKD+5nzVfPdB+mAynBK9O7c+yLLN5+dOcyFyFVh+Cw9bItLmfn/GO5HRYUGtOpyeoNAG0NJSwYsGdWM3NJKSMpveQG/9nIlf0wYHcb/0n5ZsP/2Vqfv0SPEaYhw7hcjmoKstAcjkIj07rcAzzr0Gl1pHSZzq5hx4jKPIyjM3HUAg2ohPOVD8UBOGcBhiAWuOF3VKHLEsIgojD1owsyyjO0/7Mvnp3UrDKbay5XDaaa/fhsLeg84pqL+gYkzSX6tKV5GVvPK8RJrmcLH5/BiptJyISLqGhahtmo8DOdf+i54BraWuto7kug/QNk1EovdAbfNDp/dAb4trH0HnFYjbmnTGuzdpG4fFNOGxmfP0jKTz6CkERl9BUuxe1SkXIqeLPu9b/G4lAeg57FYDCrFfZuOzvNDeU0tZSSWhEKqIoU5b/JUHho2io3o7kMrNvy3vEJs9Dll2sWnwvV8x684yTPg8ePHjw8N8nt6GRerOFpMAAgvSn9+ffs5hz/xG3sHn5s0QkXIvT3kJd+WrGTlpyVrtzGWAAKrUel8uG09GCSu2HLDmxWWpRdyBnS6X2QnKZUapOH6bWV23DYipDkuz0Gv4hgqBAowumvmorFSd3XHC87WtepKosn4QeD2ExllBZ9C3VFXbKivZitbTisLZwcPPVqLWByJKFxG6XYjSd9qzpDXFYzI1njClLEkUnttDSWEZc0ggKjr5AVOIcrOYqGqq3MH7a3QAUHd9MfvY2eg3/EqXKi9rydaxafA+BIYlUlh7G2yecqE4DKTr2b6K73IzFWEp95RbqqyCi0yy8AgZyePd8LKYmho574KL37s/Mj0VlKoS/fs7XxfAYYR4uit1m4puPZmM2tiIqNCCbufr2r88Ii/u9GDvlOTL2zae8+BDRcbH0H/lie42wjhDbeRii4kWy9z2Ib1AaNaWrUakNOGxmFD8JTWiozSdz32IkyUFK2hT6DL2RkvwbKcj8Fwmp91F1chlarwgSez5IfuYrSJIDUVThdLQgOS0XDJ9oqM3HYmohbeS7CIKIf8gADm2djSgoOJ7xPXnpX2EQBLxlBY32VvRenYhOGEjesS/RGZ7A5bRQXfItQ8fd3T6m1dLCwrenotREotYE0dxQQnC4jrwjzyFJDlRqLdWlGUTFD6AgZxNRnW9rFzEJCB1KQebLxHa9naguvaguWYpaY0CQTpJ3ZCOBIQn4Bcbh5X8JwZHuE0lJspO5f7HHCPPgwYOHPwmyLPPs7oOsKizHYIjE2LqLty8dzoDI8PY2PxRz7ntvGAf3VP9mcyf3vAKN1kDOkZV4+WoZPe+bnxW+rtZ40a33VWTumkdo9OU01x/CYq5DVKrPamu1tHBo5ycYW2uJThhIcs8p7Fr/b8oKFqD1isRqrqW57iB9Ri/k0JZZ2Cx1aPVhyLKE1VSFSu11wbVkHfiKHkM/RqMLAYZgMZZibsunqeEk21c+j0qWCBF11FjqkVUauvaaxJqvHyYwbAgabSileR8T1/l0pIgsy6z+6l6qyvPw9u9BY81+AkNiKcx6BafDgiw7KDqxlT5DbyT78BJ8AtJQqtxrDAgbTkHmq+gM3eja7z+0NmZRmvse8cljKMj8O1qdH8m9JlJX00ZkwkwA9N6xHN1/71/OCPtxfa8f+F83vH6MxwjzcFHSt72HTCCpQ15FEERKcz9m26oXmDjrjd99bkEUSRt8A2kdKGJ4LhQKFS6XHb13LA5bE5263UFj9Q6OHfqGvsNuaW9XX5PHV+/NICjyMkSFlqWf3URASCL+IYNwOtrIO/I8LqeZTt3uxtu/O3rvOI7tvQ/fwDTqKzcjA/1HXkAWWBDc/7UjI0t2vHzDKMjeQKgk8aoQiUYQWSY3s7jsCDNvXYTV1ETG9usRFSr6DruZlLSp7SMc3b8ItS6ezr2eAMDLrxtFWa/SJe1pAkIH0lR3gOXzb+fau5ZjMdZTX7mVgNBBANSWb0CpMhAafRkAscnzOLBxMtNvmd9uTC755EbgRzmjsgz8b4Q6ePDgwcP/Avsrq1hXXEe34QtQqrxorjvI/ZufZff1V53RThCg++uTGD+m92/qEeuUNIpOSaN+cX9JlvDy6YLD1kxg2HCCwkexd9PbXHXTZ+1tHHYzi969CpW2EzpDHDvWvsHxI8vR6iPQ6KMoznkfu62JwLAhqDUBxCbdxLG99xEcOYa2pmzMxlL6Db/pwgsRBH6838myE6upEqfdisJp521FDGGoyBOtPGIvx8s7jOGXPcjOdY/hsJuIT76ES6Y8096/ujyTspOH6Dn0E0SFhohOMzi87TrC464kJukmbJYa9m+9l5DwZKpKj+J0CTjtbSjV3tRVbEIU1cSl3IUgiGj14TTX7iQxZTSXz/w3AAd3fkRtdSY/WvBP3jH+nPxYXOPH/H8yvH6MxwjzcFGa6kvwDezT7kXxDepPbemnf+gajK01VJYcQqPzITp+0DnrgpwPm6UFi3EbkmSnvnIrfsF9zqrzcWDHRyjVQVSd/A6Q8Q5Ipa4ql57DPkSrd8chH9t7Pw1VWwiKGEVS2lNk73+Q2vIN+AX1QZKslOTvJCSiG3s2vk51eRb+QbEMHfcgarWegzs+wWZpYP/6SQRHjsFua0QQZK666Uu2LHuCwejQnLq/QwQvFkqNiAoll0x9jkumPnfO67KYm9F6nZbuVYgqVGq/9uLP/sH90OhCaK4vBkHEbmvg4OZrEAQBl8uKRuuPLLsQBAVOe6vbe/YjL2PvIdex5usHkSQHsuSiPP9TJv8MZSkPHjx48PD7Utbahrd/93Yvim9Qb45bjNhdLtSK0/ukIjgSV0M15ZsPo58QiLmu4Tdbg8NuoaxoD5IkER0/EI3Wu8N9zW31mNuKaK5LB1kiOGocImfuz4XHN2Gz2mhp2AaCiN4QQ3H+DhJTHyA0xn2QWHnyOyoKFuGwNRMRPx27rYnKk0vxC+5DZKfplBUdQJZljqYvIi9rPWqNgUFj7iAkohuHd3+OLMkc3nY9PgG9MPh2pqF6FxOueR2bpZVIQUXYqZI6XQQtegRAIrXfTFL7zTzndVnNzWj1Ye7oIdw5XJLLTmT8THf+mz6MgLARVJVloFDp0HnHc2jrtag0AThsTbgkOw5bkzv8UZawWevO8OYl9ZjI/q3vUV4QilYfRWXRQtIGXf8zvrU/hieGZABQ8Z933T9I/2uKa/xeeIwwDxclNKo72Yc2EBgxClFUUlexltBTuUamtjq2fH0fNRVZ+PhGMHLGq4REdPtN568qPcLSz+Zi8E/BZq4mIDiKK+d8ck7pdZfLweHdn1NTkU1gcDw9B16Ly+mgc68nCQgdSHP9IY4feJzh4+ec0a+mLAu1NpAeQ95Gkl0cT38UZAlj8/H2kAaFUgVyExnbr8XpdKBS+5M2/GOUam/KC7/CYm5h+Ze3YzJJBEdOpKTwG3JeGo6XdxCiwp8+oxfjsDWSs/9hYhIHcO28nag1XiSlTWZn4X6myP7oBJFtcivBYUkXvS9xXYZx7OB9+IcMQqMNprZ8DQ57CzZLHRpdMHZrAxZTFQEhCcR2HkpLs4mg8OswtRzH3JaNr38EJw4+hsGvO43V2+gz9OYz6qfEJ49m4jX/IWPfYhAEJl/3XruaowcPHjx4+O+THBhAU/ouQi01aHSh1JWvI9I3ALVCgcMl8e896WwoPIlWoeCOgX0Y56tlxuppyM9/yBcrfv38FlMTi9+fAegRRCUO2zNcM+9bvH3Dz9k+L2stBTmb0Oh86Df8ZlqbKwgMG0Js8m3YrbVk7ppHcs/LzuhTXXYUp8NE71ELUGsDKT7+AWZjKcaWHEJxt5UlOwafII7suB6lygeHrZXUwa9j8O1CW/MJKvLSObDjQ47s+YqozjfS2nCURe9Mw+AXjtXcSvdBb6BU+ZB35FksbYe58f6N+AXG0NJYxi5Boly2EyWoyZEt2JVK/IPiL3hfQiNTMbcVUV+5Db/gPtSUrkSh1NLalEVA6GBkyYmp5QQG34GkDZrNgR2fE9v1VqzmaurKVtFzwLXkpD9AYPhoTC3HMXj7EpN4Oh/e2zecWX/7jr2b3sJqKmHAqBvp0X/Wr/syfwOe9H7fLaZxiopTRtf/qrDGr8VjhHm4KH2GzqWm/BiHtkxHFJQEhnVhxOWvIMsyKz+eTf+GBh6V/cmsa+TTD69m9oPb0BsCf7P513/3ODFd7yQ4YjSy5CQn/UGyD3931gmULMusXnwPDbW1BISNIi9nPwU5m9Dqg9u9Q35BfdDqwzm081NEQTz9UBMEIuKuQlRoEIGw2MnUli6mMOsVKgoXAuDt68/0m7+nraWSvZvforaqCpfLgrW5kuriJSjpTUVxOv3GLqe6ZAV2ax3x3e7E3FZKVckymusOEBg2nPD4GXj7OtoLSqakXUl1/m5uPLoKL0GF08vA1Osu7HGqqThGc0MJyT0nkHfkCRx2CwkpY+naczjp225CoTTgcppI7D4WH79Irpj1Jns2vUFV6X4CQyKZev0StDo/sg5+TUtjOb36PUxit/FnzRPXZQRxXUb8ym/QgwcPHjz8HqSGBHNHWhKvb78etdoLrSjx8eXu8MDX9x4gM7eUf8ihNDmcvLR9L8GXjaGPVoAnb0U/4btf7RHbu/kttF5d6dTtXgRBoCT3E7atfpErZr15VtuMffPZu+k9wjvNpK22mgVvT8FhM9G7xwsIgoBGF0pw5CVUlR4hc/9CevSf1a72FxI5Do0uGIDI+BlUlyyntSGdw9tmo1L74rTXc828b1EoNeQeXU36tg8BAZulhtLcDzB4+5G+7X2S+76MLLuordhATNJNCKKakhMfUFexhfC4KcR2nUdF3hvtsvy+AdEMn/o8937/dwIENc2Ck8tmf3hBcbK2lmqK87aRNvh6jh9ZQGHWywSFdWPctBfYsPRJlCpvXC4rXt4BJHWfiKhUoVLrOJG5Gq1Gz1U3f0F4dC/iOm9wC3MkX0Zqv6tRKFRnzOMf1InLr37tV31/vwU/FtP4Xyig/EfiMcI8XBSFQsUV176Fqa0Wl8uJt284giBgaquluaGYuXI0giAwFhVbcVFVlnFRedmfg7G1ivjUngAIohKDXzdyjiw7ywhra6mipGA3fUZ9jajQEBI9jsydc7GYammuO4SoUKFS+2Oz1OFiFCsW3sVl0/9FQsolBIV1obXxaLsUfXP9YSRTHT3QEmZsZDsm+k9+DJVaR1N9MXVVJzC11JKxYy4qjR5ZdmA0qd1pU8hUnVxCUp9n0OhCqCj8Cp0+kqqTyygvWIiXb2cC/GP56v2raW4swdsnFN+AWKJSL6dL9/HEJA7B2FqNsbXmnOIn2YeWsHXVC/iHDMTcmk9EbF8mXfsOgiiSn70eUaEhNvlWZNlF0fF3Kc7bTlyXEQy/7JGzxuo18Lrf7Hvy4MGDBw9/PHN6pDAtKZEmq5VwgwGVwh3avrmomIfkICIFNZGomSD5sPVkCf2HDsRVV8GM1dN+dg2xn9LaVIG3f792Y8k3oCdFx15BcjnPilbZv/UDOqc9jcHXXdTY6WijpW4PDdW70HlFodGF0tqYhd47nvTtX9JUV8zIiU8QGNqZwtzF7eHzLQ1H0Ss1BNit9HQ52W9tJLbfTPwCY2msK6QwZzOSy07O/vsRRAUqtR6rMgFZFgGZ6pIVRCXOJrzTleQdeQGlyofWxkxqSlcSGjMRjc6HNV/dT1nRPjRaHwJCE4jpNp6I6J5063sVNksLTfXF+AXEnFUbtK76BN98eA2+gX1wOk3ISNz66F50Xv60NJYhKhSExU5BZ4ihouALdm/6D8Mve4Qe/a+mR/+rzxgrsdulJHa79Fd9P781npyu3xaPEeahw3h5h5zxb5XaC4fkolVw4YsSlyzTKNtJ1pxfhUiSXOxc9yonMleiVKoZNOYuUnpPPW97gMCQLpQXfkWnlL9htzVQX7kVh70Rs9Fd8yovazWSy0lwRAqiqEY4VYtDEBSICi3+QXHkHv4Ham0QVnMVodETiOlyA17e8aRv/4iElEsYftkjLH5vOua2E0guB1ZzGT1cCv4uB4EAAyUdb6/4B16GQNZ+8yAJqY8QofKmJOdtdHoVCk0ScV3n4bA1c+LgU7hcNkRRTVneF/gG9qJTt7sRBIGi7LdprNpMXms2ITHTCfMZSsnxD9D5DMHlsrBuycOo1V5IsojD1kJqv5mMnPhk+wYnSxKblj9F6qB30XvHIkkOsvbcTknhbuI6DyNz32Jiu84jKGLkqfttJ2PfV8QkDGHf1ncozt+NlyGQYeMfvKCKVVVZBqUFu9HqfEnpfeUZYYoePHjw4OHPhbdGjbfmTFVBg1pNrdVBnODOS6oTnHQ61UYRfO4aYsczlrNn05s4HVaSekxg+PiHzxn6/wMRcb05svc7AkIHIQhKqoqXI8kCBTnrSUi5lNyjKzG11REZ29dd8kVxei9RKPQEhCRyMvstdF6RWEwVaL2iSUy9D6fTyMHNMxh++aOk9L6SE5mryNozD60+jOb6IxhcLl6TI9AKIlfLLm5KX0TqkBv45sNZhMTMoGv/m6g6+S0WYzYIerr0foaqk0vJO/w8am0QBt8kGqt3YTGV0nvkF4gKNY01e8g78jyhkd1pbDAS1+1hThx8Cq13TzS6cPZv+5Csg99gbK1DEBQEBHdi2txPz6g5um3VC0QknK7vWZj1bw7u+JBhlz1C/rG1BIQOJyJ+OgA6QwxZe+9g+GWPkHt0NZn7FiOIIn2G3kB88vkPslubK8nLWg2yTJfUy/Hxjzpv29+CHxdL9uR0/bZ4jDAPvxi1xos+Q2/i4X2LGOlUcVQpo4noSmRcv/P22bPpDQpydtG51/M4HS1sXfU8ekPgBYsA9x12E2u/eZjasjXIkpPoLtdTX7me+pp8Niy8g55OAa0MawQrXn5RnMx+g6DIS2mu3YvL0YQdP/qO+QaFUkdN6VqqipedqhWmw+Vy4HBYObz7c7y8g1EooHu/a2iuLyZ2x9dw6pArWlBjtTaSn72BkKgrkCQHeemPoFB6YWxtIrrzAARBIKn33yk69haS00Lu4adRKA1Exs9oN6L8gvrgtObhcEhEdJrGsX33k9DjAYLCT4f8Ndel03PwWzjtbWTvv5eo+P507jYOAIfDguRyojO4QyVEUYWXdyfMxjrAbXjKkrN9LFlyIooiW1Y+S0nhUSLiZ2FuLeKr92Zw/b1rzulpyz26io3LniIoYiw2cxkZ+xYx62/f/SG14Tx48ODBw2/D3YP78siGbYyTrDQJLrLUdh7r1rX9858Wcy4t2M2WFc+R2PMJVBp/CrNfRxT/fc4oih/oM/hGdm/4N+kbpoIg4B/cH/+g3hjb6lj+wdVoqwtJlBSsFd4gNK4fRcf+RVTnm7FZqqirWIcsS/Qa9hE6QzTmtmKO7rkLh70FhcoLZBnJ5ST78BKcDjs6nY7Ern0JCLmavKVPoXWeEgsTFHiLGspPpqPWheMX3Jfj6Y+2C1r4BfZAEETCO01DodJTlPUmxpZcAkIH4+2XgqhwG6a+gWnIkoPqssP0H7eayqKvCY4cTVxXt5qkl08CJw4+Sd9LliAIAoVZr7Bz3SuMmXxaFdHUVk9kaOf2f3v5dMbYVg6AICqQ5TP3Z0EQyc1aw+blzxCbfAeS7GTN1w8x8ZrXz/le1FR/kkXvTsMveDCCILB/23tcffs3BIZ0Pqvtr8FjeP0xiBdv4sHD+Rl02aP0mfEKBcNmEjbhIa64ecEFlQvzs9YTkzQPvXcsPgE9CIubTn72hgvOER0/EIVCQadud9Jv7FJUan8EnBRkLGe0TeBhKYC75QCud3rhr/fDP0BDVdH7aDUtdO97FT4BvVEo3QZEQNgQzG1F7F8/kYKjL5PabxqrF91NSeFxgqJmo9B0Ze+mt4jqNIANCisFspU22cWnYguxnYeh1uixWqooyPwXKQNeoe+Yr4jrejvlBQtpqt2P2VhCW/NxJFwYvH1wOeqpLlmO5LIhuezUla8mJKIrdmvTKdVBB0rlac+hUmVArXV7HJVqb/yCB1JfdaL9c7XGi4DgRCoKFyFLTlobj9Fcd4jw6DQA+gy9gdLcD6guWUVVyXLK8z8lbfB1HDv4NV3SnsY/uB+RCTPxCexNfvZ66qtzyT+2ltrKnPY5tq16gS69nyWu6+106f08suDL8SPLfv4vhwcPHjx4+K8xPCaajyZdRkCvaLr37cy3M6acUcgZToeRPTEkg/zsjYTGXIlvYC/0hlhik/9G/rEL788KlZrwmD6Exk6kz6jFRCZcQ1PdPmTJhVhTyMuuIG4jgBelYKpO7qNrz0uoKfkEa+suRlz+KHrvaHQGt8qv3jsOpVLPgc0zObrrdhJSxnE0fTF7N3+Ib8hkfILHc2jXZ+j0AVTjYLvUill2sVxuRtLq8QuIwWFrIu/I80TEz6DvmK9IG/YJrU3HqTy5FIuxmNaGowiiiF9AFDZzHg3V27FZapFlmeqS7wmOSEVUaLBb6921QH/kuVOqvBBEJaKoRBAUBIVfQk3F8TPuR2ziICqLFuFymrFZ6qgp/Z6YRHdpmKQeE2mpT6cs73PqKjaTl/EPeg+9kaP7vyImaR6B4cMJjhhNdOe5ZO7/ipbmCgqyN1BauAdJcgGwd9NbhMZcSULqA8R3v5+w2Jns3vDblQt6YkgGc9JvRXjyVgSFAmVIJMqQSI8B9jvh8YT9P0GWZSpLDmIxNREW3fM3K7QsCAKdu4+nc/ezRR3OhVrjhc1aizfu0zi7tRZ10IVFPLR6X66c+ymrFt1D4dFX8Q/uzNQ5n3Bg3cvEyIr20lVRqLBbWpg845X2voXHN5Nz5Fkc9lmo1D7UlW/A4JdMp5Q7OH7gYUKjerLt+6dQiEpaq/cTGn05SnUILpeDQVOe4+lVz2F1mEnoPJIxV/0Lh93MoV2fodYG4e2XDEB43FTK8hdwMuddnHYjMi4i4qZRU7aSG+5dw441L3Ng05WAQEziEMZMeoYVC+ZxfP+DKNV+FBx9lcSeD+FyminN/YTgKPe9dLlsNFTvJiBgMJLkInPfAqrKjhKTMJCyon3sXfspWr0/l838N/5BnQCI6zKcK659k8z9XyMAU274kMi4fgiCAsnlAECWXbQ2nWDrii2IogqfgG5YTCX0HnIDg8bchc3ais4rsv371eoisVpbO/T9evDgwYOHn09hUzMnm1uI9fWhc4D/bzZu95AguocEXbCNoFBQ8Z936ddYwCp7j/af2yy1qC6QXvADV8x6k1WL7ubglulodX5cMvU5jM2VRKFEPBUFEoEKh8vBgFHzGDz2HgDMxnq2r3kJY0s+Bt/OtDUdx+U0kzbiU04cfIKErqPZteE1ZHMLeQf/TqB/d4IiL6Pg+EYm3bKQLxfeyRstpYQEJTD5uvfxDYghIDiG0sJdhJyqgan3icM/ZCBVxd9TUfgVTkcbYTGTaKrdySVTnqS1qZLdG25AVGrxMgQxbe6nnDi6hkM7H8AnaCD1FRvQe8eh0YVSnPMWSrUvsiwBAg1VO7CaG5FlmZO5W8nLWodSqcHbV0f6hskIooJ+I+a11/c0+IQy629L2bflHazmAwwafSup/WZSfvIgkuRov58tDRnUV22j6PgmvP2TcTnbCAyJYeqcj7GYW9AaUtvbar2iMDVld+yX4QL8oGpYke7J7/oj8Rhh/w+QJYn1C+bRULCHMFHDZsnChBs/I6pT/zPatTZXUHRiCwqFis7dLkOr9/3N1zJ03P2sXHgnppZ8nM5WWuv3M3Hm8ov2C4/uxS2PbEeWZcpP7mfZhzNx2a0slmRSBB1aRBYoTUSnnJlfFp88mqQe+zmy7VoQ1CgUGlL6/wudIQq/4DQyd39GiCzwPOEoBYHnyrfQqNEhiiIpfaaR0ufMSu5qjRdX3fQlX70/A6u5Cq0+HLOxBEmy0X3gh+RnvIjd1ojT3oIsucg++B2TZr+DxdwMsoxSrWP1orspO7kPWXbhF5RAfNch1JUtcN/37mPJy1pLW1MOdms9Bt/OnDi6FpdLoqIkh8DwS2koOYKoUHHXszln1PX6gdjEocQmDj3jZ72HzOHE4b8TFncVNWVrEEU1okJNz6EfojNEYbc1cmjXzST3nEhclxEU57xLbNfbsRjLqK/azKgJv6xYtgcPHjx4uDCLsnJ4Z/8hOos68iULt/ZN44Ze3c9oY3Y42FBUjMnhYHBUJJ38frv9+Qfp8GssNr5L30thlhOl2p+6spVMuOb1i/b38g5m5m2LkWUZY2sNKz+aRXNTGZLLQYaoobOgYaHQQnRkDxTK03lrekMQl171EhuWPIAgaHG6zHTp9Th6QwxhMZMpOrEFV2sVz4gRxImhfN5Sxi5zFWHhkwiNTGX2w9vPWsuVcz7hw5eG0ly7n8Dw4bicFizGUjql3I6ppYCqkuW4nEYkSeLgjk+YdcdSeg6cjc3aipchmH3b3mX/lrcAEWNjOsk9JtDUsJnmWgudu48gc99Cjmyfg3CqbpjL2UL6tvc4tOtzwjtdjb2lgYbKI9z4wBZ8A6LbUxF+wC8whvHTXz7jZ32H3ciqxfchuaxYTOU01uxF5xVDZMLVhESNRZacHD/4CDmHvyMxZRT7t32Ol08CCAKVhfPpPeRMQY+fww/Khh5Vw/8OHiPs/wH52eswF+7jPVcIKkkkXVLx7uK7ueHxfe1t6qqOs/T96fSTtRiRWbzhNWbcvRov7+DfdC2xnYcy/Zb55B9bj1IVTvdZT/4sr5zDbmb1FzfxiMOXNCGU94Ra7nKVoFBpSUmbRv9L7j2jvSAIjJzwOL0H38Anr44kpf/L6AxRuJwW2ppyMQitXCsGEnLqgTpbMvBvexPR8YPPu4bQyO4Mv/wxdq2fh94QQ1tTPp26301r41FcLgu9hn+EICgIjZlAxr6/M+TS+9Dp/QDYtvoFWlus9B+7HFl2knvoSYJDk7l8hltm9siez2lssBAceTlKlQGNPpz09VeQe3Qlfcd8i0KpJzTmco7tuZ2q0sPEJJx/nT9m6LiH8PYLpzhvD7KrmZDoCVSdXIrO4E7oVWsC3NfSXMn46S+zfsljZO68EY3Wl3HTXiQ0MvUiM3jw4MGDh59LvdnCf/Yd5A2iCZVU1MsO7j5wmHGJnQgzuL1QJruDWd+twNcsESQreZtDvHH5JfSPOHctrl9KiF7DdwOS2Rzlx8Z8X0aM+5KwqB4X73gKQRDYtPAOhje1MptYdohtvCBV4hBFYqP7cNnsd8/qk5R6OXGdh7L4vRn4BE5319CSZVqbsvD2Fhms8CUFdwjlzbI/66wFdO8347xrUCjVTL7+fZZ9fjM1pUtpay7CP2QgXj5J5B15gd6j5qPW+ON0tHF422wa64oICI5HpdZxMncrGXsX03vkQlSaAEpzP8TYVs01874GoL46l9yj60ns8Rgg4eXbhZz993A0/SsSejyGb2AvACTJSvahbxly6QMdum9xXUZwxbVvcjT9GxzWIkKix1Jfua19PEFU4uXbjZamCoaMvR+zsYEje+8HWabHgFn0HjKnw9/RD+iDA5mx2n3I7DG+/nt4jLD/B7Q2V5AiqVAJ7hTAHoKeZmPxGW32rniG6x16Lhfdp2vvWxo4tPVdhk96+qzxrJYWlCotSqVbcUmWJI5nfE9TXSGBYckk9Zh41unPjwmNTP3FL/UtjaX4oqS36N6c/qYIJVeppO+cD4i6gCCI3hCIgIKc/Q/iF9yXtuZcHA4TtdW5nJS1/CCLUYKdsLh+tDaVsfrrB2iuL8I/OJHLZ/6bwJDE9vHSBl1HfNIIGutPsnfTmzRWb0WWRXRe0QiCOyfOyycBm7UFWZbb70dlyWFCYq4+lQisJjjqcipKDtPv1H0NiezO7g2vExg+BpU6kpKct4hKGEx1aQaiwn2/BUFEVOrbwws7giAI9Bp4Hb0GXsf6JY/S1FSLy2WhsWYPAaGDaWvKxtRWTGBoZ9QaA1dc+1bHvxQPHjx48PCLqDGZCFGoCZXcB4FBgopwUUO1ydRuhH1z/ARhJpmH5DAEQSBN0vLyjr18d/WVZ41ncTiRZBkv9emaUvsrqthfUUmATse05C7oVOd+9VMERxIGzKytoNszH3NwT/XPvp6qqhwm4V7nCMGHIsFB9eg5DBxz93n7aLQ+KBRKyvK/pLXpGA5bM+a2YpytaoqdJiRFIKIgUIodndoLg08o3395G2WFu9Ho/Bg96WkSU8a2jxcR05u5D2ymriqH3Ky1FB7fQXnhApRKPWqNO9RTqfLGyzsas6mBgGB34eXK0iMEhI1CrXWnSITFTePYbrenyOGwotLoUSigrSmLoMhLqC1bi9PeiKhQo1SdVkgURT2un7E/w+kIFrc65ScYfJOoKl5KbPKtOOxNNNVsp++gpxAEgUGX3MOgS+75WeP/mB+8Xx6xjf8+HiPs/wFhkT3YiIWrZANBKFlJK+GhSWe0sRrriRNOhwl0kpSUttWe0cZsrGfVJzdQW5uHJMsMHH0X/cbczYZFd2LN200/p4I9SidVBbsZNe2li66rtbmSjL1fYreZ6dJ93BnV4M+Hl08oTS4r1bKdMEFNveyg2mnG2+fCldjNpkZUGi+S+/4LU0seSnUA9Se/Iw0dW2mlzuVWLNovWOge0ZVvPppNeKfr6NR9OPWVW1jyyfXMuGURG5Y+Tl3VCXR6P/oMvZGU3lOZeetisg5+TVVpBvnHNmBszkXvE09p3mdExvY/wyD18Yt01yML6oMsy7Q1ZREWHsaqxfdQmLMBEAkKS6Ii/12slhZi4gfRa/DfWPr5TeRnvIxS7UNN6WpkyU5m+tdEdhpwzpDECzF47L0sevdK9IZI8o78E5BRKJRMuOb1s8oQePDgwYOH348YHx+aZCcZkpleop5jspkayU6c7+lww0azhVhJhSC695I4QUOTtfmMcVySxDPbdrM8vxABGBYVySvjRrEyr5C39hxgjGQgU3SyLCeXhdOuQKs8/+ufIEDivyfwrq0vzY1lhEX1ILXvjLNqYp0LX59QMpssDBe8ccoyxxQS8R2QUDebmuna9wWslmrs1npMLfn4msxoUfCEq5xoQc022Uhk8lhWzL8Dh8uHXsPnYzYWs+6bh7jq5i/J2DufotztiKKS5J6X03fYLYyd+jydumyg/GQ6TdV2asvWEhw5lsaaPdjM1QSFdmlfg49fBKasnW7VQlFJa+NRDL7hHN79OTvXvoSo1KDV+WJs2kVl0SL8Ajsx+boPWPr5TeQdeZGQ6MuoKFyMw9aIuTWJngNm4RsQc9Fr/zFJqRM4dnAJTfVV1FUUUFW8DJDoP/IOElIu+VljnYsfDDCP9+vPgSC7q8v+poRF9ZBn37XqNx/XgxtZljmy8yOKM1eh1vnQd/zDFw0ZOLz9A3ZveAW1oETvE8KkmxfiGxDd/vnu1f/Esv9bHnEFYEHiKUUDPSb/44ycqJUfzya5+Dg3y/404eJhRR3dL3uEw2te5GMpHI0gYpZd3ChUMuvBLfj4RZx3PW0tVcx/axL+ISNRawKpLlnCmMlPk9zziote/9G989m35gU6KwwUuIz0HnMXfUb+7YJ9XC4HH744mJiku7C0FlGX/wU9BB3Fsp1ego54Qcth2cRB2Y6s1KJUedF3zFft/TN33ojV0oB/8EBcLgttTTlupSSszLh1Ubs87KbvnyIrfTGS5ESp8mLirDfOKFzd1lLF4vemo9KEIkkOBCx0Sb2M/GN76NLnOQRBQX7G88QmJDFywhMALJ8/D6crmqb6w1jaTtJ90Otu+eCsfxERHcPYqc9f9J79FKu5hZN5W5FdLkKje+IXGItCobp4Rw9/Sv79aOwhWZb7/rfX4eHPT/eQIHnJtMn/7WX8T7OrrJwvDmXhlCSu7J7EFV0SL9h+f0UV963bDJKMLAq8eukohkSffkneXVbBE+u38bQcRhAq3hFqCYwL5p+XnC5t8kVGFqsP5fB3KRwV8KpQS5fkaJbl5vNPKZxYQYMsyzwtVjNzWBqTLrAmpyQx67s11Kri0Qf2p7FqM7Gde3dor6kqy2DFx7NJELTUyXa8Y9O4fM6nF1RNBvj+y9ux2gz4hw4nb8899BB0tMpOBATGCb6UYGO93IZT5YXTYWTAuJUoVW5P4cnsN2iq3Ykg+hAYNoyq4qWotcE4bHWMu+olknpMBNzlV9Z+8zAupxlRoaX3kBsYcflj7WtwuRws/fxmmurK0OjDMDYdZ9jlj7Bz7Wt0G/g6Wn04FUXfYmreyfV3rwQgY998MtM3oNFFUVW8lOQ+z+Ib2Iuq4u9oqd/CjfdvvGBk0LmQJBdFJ7ZgMTUSEJJISHjKry4R84PwBngMsD+aru9/et792eMJ+wuSvvE/VOz6nDlOAzVU88WHVzP9zhVnhMv9lN4jbiN10PXtyac/PdEaOO4htpkamJO5AoWopM+w2+na+8xQh6qyTB6X3WEBgSgZ6VRzrDwTP4UGjeweTy8oMCjU2G3GC15D5v5F+AUNplOK23jy8u3M3s3vdMgI6zHoOqISB9NQW0BqYBxBYUkX7aNQqJhy/Ycs++JmHKYG3lfEEiaoMcsu/uYqYYzgy3qMBMdNwulso7F6Nw6HEZXKgNNhxGysQZZs+Ab1obrke/qMXoRCoaGqZDnrvnmYa+9cRnNDKcczltNz2AfoDLHUV21n9eJ7kSQ7ao0PIyY8RrfeV3LDvWspK9qHKIpExw9m1eJ7CIq6DIetCbutAcklkX3oe6zmVoZf9jA2qxEv/2j01nr8g/u253FFJd7AyWM/3wADt+Jk115TflFfDx48ePBwbtIrqnhk/VZukgLRoOA/O/YjyTA56fz784DIcLbPmUWjxUKATodKceb+PCQ6ktsG9ubx/Ycwu5yMjorm8RFn5gMfqazhUpc3+lN7++WSD0sqqzE7nYSK7gM2QRAIlZW02e0XvIbDVTWUO7R0HfgCgiASEnUph7bMYNj4h9DqLiwIEh7di9kPbqW6LJNknQ+RsX075EG79Mrn+e6zmyjcu4y7xBBGiT7Issw/XZWYcSEBBr+ueIUNpiz/C1qbjhEQMgBZlt2hi04XcSnXcvLYG/Qc9iFafTim1gLWf3cfcV2Go9Z4s33Ny8R3v5fgyDFYTVVk7L6NzP0LQYbu/WYwcsITTLvxU8qK9mK3thEe05v8Y2vxDx2IQqGjrTkXyWWlsbaIpZ/fwpCx92C3mVBrQ/H264o5MA3/ELfgWUT81VQWfYXF1IDecGF1yp8iioozwit/LXPSb/UIb/xJuaARJgiCDxAsy3LhT37eQ5blo7/ryjycl+z9C3ne5Ues6M4RqnQ5yctcxaCx916wn0qtO+9pikKpZsyM1xg9/d/nPLWRZRmt3o8tza1MFQMQgRylTEhkd46c2MpKRwuD0LMVEy6tF/6BceddR17WGjK2v4/D5cBaf5T4/i+gUvvidFg7egsICE4gIDihw+0BAkISGT35GXZ+fT9hp0Iv9YKCQJQ8KVXg5ZdMcrc7ADhQdxWHt8xGpfbG6TQhKhRIshJTSx5+wX1RnMrPCgwdSkXBFwDU1+Ti498VvbdbLr6pZjc+gb1J7PEAVnMV21Y9jl9ANJFx/c54wPr4R5J3bAE2SwMC4BvUi4TUh2mq28fXH15Dr4HXkr79Mwx+3TG1nv5TNLcVoTdcWN7/z44kuchKX0RjxTF8Q5PoOeg6j0fOg4cO4Nmf/5wszcllhuTPCNEHAFGCJVnHL2iEAagUIqGG88vBX9O9K9d073pGjvGP8dVp2Cs00l/S4yUqyREshPsYCPDS835VHdfKARTLNvaKRu6MPH+USn5jE49t2k6zxUzGhql06vM0voG9EEUVLqetQ/fAyzv4Z4fOaXV+jJ36HCs/upYkpzvEXhAEuohaFklNoNTRrd9zqDX+mFoLOXHg72i9wpFcFiTZhlIVgLm1AL13LFq9W7DEyycRtcYfY0s1Bl8Bs7GWkCj33tvSeASVJoCU/i8jiCoKMp5F7/U+A0ffcYa6sI9fFE0171FbvgVR1KBU6umS9iQ2SzXffjSby2a8SvrW91GpA7EYS5FcNkSFBpulGpfLhlpjOPti/yCeGJJBxX/cYii/1ADbUVrG9pNl+Oo0zE5NIUD36zxyHs7kvEaYIAgzgNeBWsGtxTlHluUDpz7+HOj9u6/OwzkRBREHp8NIHbjrfPwWnOvh7nLaWf35XBxtdayWHSyXWvBW6VGHJzNmwLXEdR7GusX3sKihhKCQRKZc88YZMrQ/pqE2n63fPMDLhBGv0PC1uYlVe+8HfSBde53fC2Y2NbJvzQu01hYSGJPGwHEPXdQ9b7eZ2LPqeWqLD6L28qeithC1Ngiby8FGoYVLRB+ysVAsOLGhJDAgDUEQkSWnu0ijoCQuZR6S5KAo699o9RHUVW5BpfYhMn4mSpWBmrK1GE7lo/kFxGBsycdua0Kt8aep9gC9RnyCUuWNwdebwPBLKTq+hYjYvmfc56DQzuRmbiJ10H/I3vcASb2fQhAU+Ab14ejumwkOTyFt8Ewy9szHYm4kJ/1hNLpgmmr2MG3uZx35Wv+UyLLMhkV3IuXtYahTxX7letac2MrEuV906OT0l2KztrL1mwcpKdyDXufLsCtfIK7LiIt39ODhT4Jnf/7zohCEn+zPMgrx54WjXYhz7dEfHMpgbcFJDJLAHLmYGFFLmxq+HDIGX42aZ7bu4qGKSvw1Gl4bMYZ4f79zju2UJOatXM9VNm/GKiLJdFl4Pv1x/CKG0CWpC3rDudWSJZeTg1vepjJ/B3rfcAZc9ugZ6Q7nQpZlMnZ9QuGh7xCVapqtbdjtTmSXi8VSI/eIoTTjZI1sxKbUodGFt4tquJxmBFEgvNM0dF6RlOZ+hNVcR3XJKsCFqbUIL594WhuzsFnq8faLRKXWo1LpaWk4im9gD5pq9xPd+fp2gy0i4ToKj39NvxG3nnEQGBSejMPWQo9hH5C97wGS+z6H3jsWAIupjLrq40yc9SZbV72Ay2Ukc9dt+AX1orFmD8Mvexzlz8zZ/q3oOziMiv+8+6u8X9/m5PLOnoNMlHwoERzMOJHPtzOm4K/9/a5JlmXePXiEb4+dQBAEru2Rwk1pPX52SOdfhQt5wh4H+siyXCUIQn9gviAIj8myvIz28rge/hv0HHk7L234D7OcXlQLTrYrHVyTNvXiHX8hR3Z9iqE0m3/JUSgV8DGNHA2PZ9KtixEArd6faXet7NAfSVVpBmmigUTZ/Ud8Nf4sthTQd8A0Bp/Hk+d0WFn27pX0bmljkqRmY/UyVldmM/nWr847pyzLrP3iZsLLcrlT0pPZ0MD3oo2uw17FbCzl/d138rarFrXSi4T+LyK57BRmvYa3fxdqyzcAkJB6PwGh7pAPl8NIRdF8ZMmOzVzNgU1XodYEIssuwH06GBSWRNrg6zm862a8fDohy04sbcVotEG0Nh6jumQ51cUujh74mknXvkN0wiAAaiuzCYwYiSzLSJINWZZOKSzKOGwmJJeD/iNuo/+I27DbjOQfW4fDYSGu86P4Bf68pN8/E61N5ZSe2MrnciQaUeRyl8wtpUeor8klOLzr7zbvxkV3EXEyi4ekIEpa7bwy/3auunM5gT9K0Pbg4U+OZ3/+k3JNj27cUrwWUQINAovFZp7tPfx3my+rto6FR47xLjH4K5SkS0beEupZM2MavlotzVYbL44dibIDB1s1RhN2h5NLT6kk9xL0xLgEvG1ZvBHTHd8Dt/F5/w/P6rdt6aM4sjYx26kjv6KUbwsnM+uBTei9As4716Ft71G89X1ucXrTjJN35Ho6D34LrVck+zdfzZWufERBSXTnG+gUczkHt1xNTdk6rKZymuvSCY29gvDYSQBodGFk7b4dcOJyOsncdTsaXQhOeyuCoMDYWk1AcAITrnmdVYvuwcs3gdamPPQG9/5ptzZQlPU6dmstbz3dnaHjHqbvsJsAaGkoQesVid4Qg8tlQZad7dcguewYW2uI6zKcG+8fjixJFBzfSFtzJaHjryMy9r+Xptv99Un8WrvlvfTDPCaHkii639detdewKr+Q61K7/QYrPDcLjuawPjOPf8hhSMi8cjgbP62Wq1IunnbyV+RCRphCluUqAFmW0wVBGAWsEgQhGvjt1Tw8dJi0oTeh1Qew7ugq1DpfZoy5C58OqA/9wMncrRxY8xIOu5n4XpMYMPb+CybNNlXlMNypRHXqNG+4rGdLeRZvPNkFUVSD4A4/uHLOJxd9kfXyDiZHtuGQZVSCwEnsqJVayk8e4M2nUvDyDmP8VS+1GygA1WWZaI3NzJODEESBNMmL68qP0tZShVKl5eSJLciyTHzXMe0PfaulhfKSg7xKLEpBoJug4zD1tDYeJSB0MCq/zgQHDyCmy40IgoDFWIYsu6gpW4NK5YcgKJGk06EXLpeNmIRBBIbEU3Aii5ikW3E5TKh1waSvvwJZkhBEkcGX3ENSj8tpa67E2FbHtpXP4x86jLqKjXRJ+zsBoYNprjvIigV/46aHtqHV++If1ImCnPl4+3cHRI6nP0ZozAQaqnfidBrR6Hza16HWGOjW56oOf9d/ZpwOCzpRhdrl/r1SCQJ6UfmzwlIvOL7Txt41L1KRtxO9dzCDJz9DUFgSRQW7eE6IQy+IBAkqhmCnpGC3xwjz8FfCsz//SekeEsSHk8azMDMbp0vi5W5pZ4hsXIzi5hZe2L6H8lYjqaFBPDZ8MH5azXnbFzW30E3U4y+5X+f6iwaMjiqGfvkNsqBAFARUosCLIwczLiHugnP7abUYJRe1goMQQYVZlmhWuIj0MjB2/R5Uosgjws009vu4vY/kcpJ1ZBkLhVi8RAX9gAJXI8W520jqeQWFORuxmpuJ7NSvXcAKIDd9EQ+5fEgS3REt1ZKDbZWbiO12FwHxV9Fcl06PIe8gCApkWUah0FFXsQmFUofOEIfksv5oDe6wv4mz3mDFwntJHfw+dms9Gn0YuQcfwdRWR0BwAnFdRjDn/vXUV59ARmDdtw9ht9XS2pBNQNhQ4rrOw26tJX37vYREdCUmYTB+gbFYTOWYjeVITivHDzxGbPKtWEwV1FVsIr7zbe3rEESRzt3Gdfi7/j3oOziM7q+7jdNfKz9vdbnw5fS7oa8sYnW6ftWYP2ZJTi5Ljp1AIQjc0KcHl8bHsa2ohKslP6JFdzTVDMmfbUUl/y+NsDZBEBJ+iDc/deI2Evge+P3MYA8domvvqXTtfW7vlyzLnDjyPeUnNqPxDqHvqL+1J4ZWlh5m44K/ca/LjwBByXu7F7Bfkhh02SPnncsvvCu7j+9kjEtGCWyX2lAaIhk49F2a6w9RkPkv/MMmsPTzm7n54e0X9IjFdR5OTnw/7j15kE6oOSQZkRVaBFUCfcf8ndbGLJbPn8f19645ra4oikg/eq+QT12jsbWGNZ/fRLJLQEBg0ZoXmHHXSnz8oxBFBZIsYxdklAjIsoxFtuMlqnDYW3Ham6krX0Ng2FDU2mCKst8iMGwoiT0eBODI9rkUZv0Hp8OILDmoLFrEtLmfISpUHNr1Oc5Orei94yjL+5yw6N401hdSU55Fa0slptY6dHo/eg2+gdDI7mQfXEJrY3C7V80vuC8afTgNdflExval9+AbObJnPrkHn0KlDcLg15X6yq0giEguJ96+F5bf/6viHxSP6B3I5y3NjJL17BHMmDU6gn4jL9iWbx5AfXw390peFDYWseC9acy6fxMatZ5qh4N43GphVQqJCK3PxQc8B06HFbOpAS9D8HlDcD14+B3w7M9/YlJDgnlp7Mjzfp5RU8uy7DwEAaZ3T6ZbsHt/brXZmPP9Gq6wG5ghBLC2tIk7V21g/rTz196M9/MlWzLTJDvxF5QckIwoBCVpY5fhsDeQs/8RwuJn8PiOT0gJDiDa5/zPOi+1ivsG9OWRA0foJeg5IVjRabQcafOl+8h/47S38vLaB3l2xHrKzKeMjVPr+rHlLwGS7GL5BzNR1RQRJatYIhsZe+3bxCe71YJFUYVVtrf7bc2yhCAokWUXVmMJVlM5DVU78AvqS1XJ9yhV3nQb8DKCoODk8fepPrkMjTYYjT6CqpNf0Xf4zYRF9UQQJJrr0gmKGENzXTpWUwVehiCOZyzHYmygpakcQVS4RbLuWU1e1hq2rd5FVOK1CIKARhdKQOhIqkoPE5MwGB//KLp0H0/GjpsAgfC4q2io3oUgKEGW/nR52T94wH6L+l/jEzrxdkEFN8gBVMoOtotGboq9cJhpR1l6PI/39xzidikQOzLPbdmFVqnAR6OmGkt7u2oceGv0v2gOlyRRazbjo9Hgpfpz5ppfyAibx0/CGmRZbhMEYTxw/nLlHv7rHNj8JoXbP2S4U80uuY35uz+n6+DrGTrhSQqOrmaSS8cA0Z0sepdL4Nkjyy5ohPUeehOrC3ZzU8kR1IJIs2QmdeCniAoNAaGD8fLtjN4QQ4WxHpulFa3+/OpJgihy+fUfcTJvG8bWGqJPHuJ45nJik25GEAQCQgdSW5ZC0YnNdOt9FSq1jvConjh9Q3izqZF+LhUbFTbCY/uSveszxttErhPcceILnU3sX/syY2e9hUbrTUqvSTx1bDPjnBoyRQe1sg3x+DvYbY1ERPemsa6Q7H33ASAq1AiiipaGDFobjuKwN6NUqjE2bcPHL4JpN31OeHQvAEZPeorN39+D02EhOKIH3ftMZfG701HrwrCaqolMmEl1ZQHHDk3m+rtX0W/ErRxN/wqbpRaNLoSmugOYWouRJfnU3ArmPrCJpZ/fgM3mR2yyOwxCctnYt+4ydF5/rof8b4WoUDLl9m/YvuRRdlYfxz+4K1Oveuln1z07F5LkIidrDYvFTugFka6CjmxZojhvG0MmPMlTK55hrEvLSaVMvW8wI3tMAMBiauJE5nIcDisJXceccXL7UwpzNrJ+8d1oEHCKIpff8DHR8QN/9do9eOgAnv35L8qBymruXrORCS5vDskmVucWkBoWwmvjx5BZU0u4pGSK6N7TbpeDua7pJPUWC8H6c7+IpoYEc22v7txx5Cghopoyp5WYtCdQqQ2o1AbCYq/AZqnBz78rJxoaL2iEAVzXsxtp4aEcb2igl83Oawdz6N7rdtQaf9QafwLiZrLhledIeegyZLOEKCro2XcmT2es5kqnjnzRSaFKwM9pQ1tdxMuuIERBIEtW88qSR4h/8iAAvS65h38vfZxrXHaakFhNG1Rtor5mGwafEHwCoik69jqSy4pGH4AkyzTXH8Zha6amdC2S5MBuOQZSMcMvu5+UtCkATLvxU1Ys+Bv5mS+h9wpi1KS/89UHV6PVR9PSeJyITtMQBAXHPriGaXM/o9eg6zm0+wtaG48RGDYEi7GM5rp9SJ1OH3JPuOYN/IPiOLL3KyITTv95ZeyYS2AH1Jn/KJ70fp9yfrsCzI8OG8jryoO8UVyKj0bNG0MuoXOA/28y9vKcPG6SAkgT3WI0zZKLlTn5zBvQmzlVa6iWnLiQ2ac0s6CfO2fbKUmsLiii2miiR0gwg6LOLzBzsrmFeSvXY7TZsUgu7hnQh+t7dv9N1v5bcl4jTJblzPP83AEs/N1W5OFXIcsyB7a9xzNSIC/KVcwUA4hGwxd7F7PT5ULl5Ufzj7buFtl50cRRhVLNFTfNp7E2H6ullW8/uhZZcsdFu1w2rKYKHPZWBEFErb24EpAgisQnjwZg35b3ERCwWarR6sNpa87DWLOf9FWH2LXqOUZOepbu/a9m6rwl7F//KktqCwiM6cXll9zL2o+vJ1FWtb+KJMoqDrZUt88z6qpXyIz4gs0n92MIjOOGYbdgNtaRm7mKjP3fEt/9HpwOI0XHXqfvsLkc2P4xRdk1KJXeSE4LfuFDUaoMlBVuY+Co03XIUtKm4OMXyf6t7+Fy2tm26gW6D36L4wceJ2XAvzD4ul/c8zOe43jm96QNuoHBY+9l/9Y7EJUG7NZGDH7JfP/FLYyb/jKdu41DoVQx+JL7WD7/DqzmajS6UCqLlxIS0fN/NiEVwMs7hMtv/PRXj+N0WCkvPoAsuYiM64dKrUchKrAgocedC2ESZAwKNSm9r8TUVsf3m9/AaXcQKvtjbqtFpdbz9RuX083qxFcW+HbTG0yc+wVRnfqfNZ+prZaNi+/meSmQJEHHYYeJf31xM3OfOHBewRh98K8zpmesnnbez/79q0b28FfDsz//dfnsUCbXufxZJ7fQRdByvRDMtppWbly2moeHDqBNdiHJMqIgYEHCLstoFReuJnRb315MSk6k3mzhse0HEMXT7U2tJ9F5RdHcepIwr8EXGOU03UOC6B4SxOPb9qHWBmM2lmDwS8LpMFJfsJBtjha23hNLUtqVjL7qFYZPeY4jgTEszd2Bzi+c6eMeIjdzBQmSOxwSIAENRktN+xxd06ag0Xmz8/AylBovZg27BRloqClgzVf30an7XYTG+HMy+00SU8aQc3gZxTnvoVB6o1Co8PLuhlKTTF3VZuzW0yVxQiK6MfO2r9m+5iWMLdXs2fgWoTHTMbUWEJM0l8j46QCotYHs3/Y+U67/gHHTXuT7L26lsmgRxpYivP2TObxnAXa7ub2OWN/ht3Bk75c01R3AP7gfLQ1HcdqbLnhQ90fSd3AY5a8f/k1l6NUKBQ8PHcDDQwf8qnFkWSajpo4mq5XuwUGEeOlRKUTMSO1tTLhQKRV0CQzglUtH8cjGbTTabfiJairbjMT6+nDn6o001DaTJGn4WjjG7N6pzO197hq596/dzHiLjivECGoFB48cyKBHWAi9QkN+1bX81njqhP0P4pIcZMsWBggGrjh1ohYtR3D7oW+47qFtfLX3SxT2BoJkge8UFoaPe+WiYwqC0J4zM+TS+zmw4x58g/rSXH8EBIGSE+8y7qqXL1qQ8aeo1FqCo8aRtfceAkOH01SynHlCEJfgQ4Vs56GVzxAclYqAQLdB1xEYnNiunBeePIIllR/QzaVDAJYoLYQnnVa5E0UFaUPnwtC57T/z8g5iySdz6NzzUXyD0gB3Um5BzibGTPkH+7a8i9lYTFjcZHfct7EMUenDlpXPkNRjAkqVFr+AGJYv+BsxXW5Bq/ZFUXmCtqZsJJcVleb0KZFS7Y/T7nar9x12Mzq9P9tWv0SfUQtRqgy0NZ9g3TcPkPD0JYiigsi4fgwYNY+d6+agUGjw8g5h2txfb6D8r2MxN7P0nakYjM0oBIEdGi3Pfb6HK665j6eXfMBEu5pCpUSVj447bpxNS2MNR7e/y0uE0lmhZWljC5sW3UTvkVMZYHVyh+w2llJcKjZ+dTNPjjnbu3WgrpFdQJLgNrh6i174ueoZveMmEnx+P0liT50XDx7+2lgdTsxImJC4XQxBEARSZR23Gkvx0Wrw8/PixaZquktadihMTE1MxFtz8VDncIOBcIOBfwztze3r/kVb7R6MpmqsxmJsTYeZ1Dma1JBzqxueD61SxNsviZM579DWlI2xZh/9HC7uFROwIfPM0TU0jxgGip5EJQyhx+A57ZEMkXH9WC28znjJRhRqFggtxESdKdoZnzymPTzxB7aveYHw+KsIjb4MAJXal+MHH+Pq275i4/dP0dJQjEoTQPdBb2C31uPtl8LWVc8hKpQ4HRYiYvuwfP7t+IeMxjdkKibjNzTXHUCh1LWrKwKoNP6Ym937c1Sn/sy+awVfvD6O1MFvYvDtjNPextHdN5PcYyKhUamoNQYmz36f5QvmIUkSAjITZ711QfGRP5LfQojj90CWZR7duI1DZVVECGoKZCuvXzaGuX178uC6LTRLLuzIrBBb+KTXEFySxDNbdzHT6ct4hS/HXBYeXL+Fp0YOobS2kdelKBSCwBWyL7cdPMzsnt1Q/0QdXJJl8lqaeVnhNpBDBBV9BS9y6ho8RpiH3xdBEIiNH8TGgn10EU57uOy4wwa8fcOZec9aju75ggqbkUt7TCAmoWOnYz/Qb8RthMekUVORhcvZDR//aEIju+MfFHfBfrIsczJ3K831xQSFJRES0R1BEKgtW40sQ0P1TiTZySUKd7hEpKCmm+jF8i9uRZLd0vGBoQlcOedjlCotPQbPwdhYzvUHvwagR8/p9Bk576Lrd7kcSJKj/d+SZMNmaaNb7yvp1vtKVi2+F4crnJz0R7EYS5AkJ4KtiZTKk7TILnY4WwhOuIbQGHcIm1LlTUHmywSGj6Qg81/Edb0Ni7GMhsrNXDr5dNKuQqnGJyAFpcr9ku7tl4wkydisbej0fgD0GXojPQZcg93aht4Q9JfygiV3V5Ox7hVaGhsYNHwUw4aP+tljCE/e+rP7vJxxnN7NrcwjGEEQ+NxRz7q7BvJcv1Q+Swhmc3kNBrWSD3vFEPfRbFaWVpImKdoNqGmyL4srChC2zyfKJXLKcUakoKLV4Tin4ROl9aZsTwYNOAkUlFTIdppxER4dh1Jz/iR6Dx48/P/mipTOvLhtDxpEJECBO4fKiYRaoeDjyZexICuHypY2rg9LYmrSz/O09A0PY8nU8ewur8BkVxKiT6WTvx89O/DymV1Xz+HqGgJ0OsbGxWK0O6ir2HRqf96NytbEJEUkCkFAj8AIycDaD56gCi0qjR/INqbfvAD/oDhCI1MZOOlpHlrxD2xOK7HRaYyf/e5F12CztCIoT69VkuzIkpPQqFRm37mMYwe/ISN9E2V5X1BVvAyVNgCVy07TypcIkBUscTYiGmKISXKH9fsG9mTf+onEd7ubktxPUWuDEQQFZXkfM/TSO9vnUal1iKKqPZJFqfbG4NuZ1uZyQqNSAYiKH8C8J9IxmxrQeQX8aWpadiQM0SVJLDmRR0F9E4lB/lyV3AXF71gK5ge2lpSRXVbDm1IUGkHkkGTi8Y3b2XTD1bx86Sg+OpiBBDzbaxgpQYFUG02Y7HYuF93X0kPQ01nUkVPfQJigQnHqnSgQJUoETA7HWUaYKAiEanVk2M30EbywyhInBCtTfscD0l9Kh4wwQRB0QIwsy7m/83o8/AZcNvt9vnx5KPsszXzqqiVW0PIVzfQe7jZQfPwiGHrKxf5LierU/5xhWgBWcwsHNr+OsaGUsIRB9BxyI6KoYNt3j1JzdC09ZDXbBCuCbzhqr2QGXf42DlszWXvuRFSqOSFbSBZ0mGUXuY5W1AH9Ser9LMgu8jKeZffG/1BdnkVl8X5EhYqhlz9B78FzOlxbKjSyK/mZLxKbdAsOeyuVhV/Tve9pxcGErqPY9P0/MPim0GfUQvL33MO1DiWXyO5ctzdFmf2Np2uhyrITyWWhtnwdClFF/pG/Y/ANZfL175+huBcc3pWW+kzMbSXovWOpLd+AVu+HVndmDp1KpT1vXlT+sbWUnTyAwSeEtEHX80zglx265gtRvvnwrx6jzeHgmmf20sWuJkpW8dzXi7klNYGZCT9TQv8XJBRX2bPpK+sQTql3pso6VtscnBA0fJZbzGAMmEwubtt1hK+nTyY0DEoyC3DIEipBpBQ7ClFgTLcUnq3aSaqswwcFXwqNDI/vdM45o318uLVPT+47nEmCQke+y8KjQwbi4zHAPPzBePbnvxZTk7uwOq+AjMpannNVMEL0YYfcRkJIAAn+foiCwM1p5w6x6ihxfr7E+Z07N9slSSw4mk1GZQ2Rfu7nmI9Gw+r8Ql7YvodBGCgR7HyQfpgGMZh+Y5ehUGjJP/IM6pYMMuxmOqNFkmUyBQu1LhW9Rs1HodRRWfQta795mLCobmTuXwCyTJceV3Dp1H+i6qC4QlKPy9m+5mUUSgMabSAluZ9g8Alt/zyq0wC2rHgGUeFF75FfUl2ygk6mpTwku718aaKKty217e3dZWRkirLfBFmmKOtFtDpfBoy8mW59pre303sFotLoqavYTHDkGEytRbQ2ZhMUlnzG+kSF8oz1/Jia8ixOHF2JKKro0X8mvgG/fwmZjoQhyrLMQxu2Ul5eTz9Jx1KxlP2lFfx73Ojf/aC3ss1IsqxBI7jfz3oIOqotlTRbrbywYy+RNoEAWcFTW3biq9GQGhKMVZKoFuyECWosskSZZOOm8FC+y85lr2QkRdCxgmbifH3wO8+e+9LYkdy7bjMJYhsVLhuDY6MZFt1xFfE/iosaYYIgXAG8CqiBToIg9AKelWV50u+8Ng+/ELXWwA2P7WHvxtfYnbOJAyo9PYbcR7e+M3/3uR12C0venkTPViNDJSVrig7SWHWCXiNupShzJR9K4egFkWbJydy6ApI634MgKFBrAwmNmYhKUcbTeTtIVHhR5rLgUvsQHTfN/aAQlPiHDOVE5ny8/fswcPw6bJZa9m99gKCwLmdUub8Qk2a/x8K3p1KS+yGyJBES2Z2RE59s/7xrr8kc2PExfiHDEUQlLnszMcLpP/R41GxvyaWi6FtUal+qcj/gqe6RXBIRgk6pbI+Bp/5T938/IrFbFM/tvgWlQoNOhM8HdSfpwG10hPdPFLO9pAW/mClUFe2keu+HXD8iDa3616nyCQoFisBfp8C4MecEUU4V9wihIEAf2Yunc4qYNXDQxTufA1l2i5Z0ZINIDQthU00e/SUvRAQ2iG10D4vmtV3pzJECuORUzZsP7HV8npHFA4P6ER7oy501pcSjIVOw8PSIwYyKi6FmYG/+eeAINsnF+IRO3Dvo/HVe5vbuwYhOMZS2tpLg50eM7y9TWPTg4Zfi2Z//mnx0xWV8k5PLkuwTrHFZGRYXz7x+aaf3jt+Rp7fuIv9kFWMlb45VlnNdSRlfT5/MP3fs5Wk5nERBi0uSecBYhioqBZXa/VwLT5hFS04+G2gjw2XFJEtYlOAXORmF0h1VEBg+goydX2I2Gek7ZgmiqCTvyDPs3/ouQ8c/2KH1pQ2+kcrSDApyFiOKalQqBdPmft7+uV9gLKn9ZlJWXIJK44dkayL+R6+yMYIaWXJQdOxNfAJSqS5dTtdeUxkz5TkUCuV5vVeCKDL1ho9Y9vktlJx4F8llY+zUf+IfdO6DuJ9SVriX5fPnERIzGcnZxNH0Kcz629KLRgj9ERS3tHKgvIoP5RjUoshEWeLW8lKKW1rpdB5j/WLIstyh/blbcCAfYWKa7EeIoGK13EJXfz++yj5BolXkXtwGbaqk5ZVd+/l25hRu6d2T+w5l0gs9+YKNwfExDI+J5u3Lx/LUlp3UmmvpHhzEu2NHnncN/SPD+f7qK8mpbyBQp6V78J8zsqgjnrB/AP2BbQCyLGcIgtCx30oP/zVUaj3DJzwJE568eOPfkNLCPfiZWrlLDkQQBQa6vLg2Yxkpw68lWKlD73CfhvgJSgyCktbGTHwCuiHLEqbW4/QefgWXz3mdmrIs+vmFk77pQ5qqt+IT0BNZdtFatRmHqY7IfrMRRCVarwiCwkcTvOsp5jQmdnidtwyLp9QYhkIQiPLSIRy5+4zPjT4WlldvJThiNF7BA/i8bAOPEkobLpYKJsI6z6W1MYvWhkPM7BLDKznFPHU4h57hUbx5yRCC9OcWZ5gWEsnEtDSarTaC9LoOhwO4JIkPczfTc+RiNLpgZFmmYN8d7LZIXBr+35ewNzuc+MmnQwL8UWJ1/fx6IrIs83b6Yb44mo1LlpjapTOPDx90wUKjc9N6kFffyHWlJxGBfuHh3DWgD7O+XUGU4N3eLlpWUWO2klPfQGZNLWpZ4ChmFIJAjdEMwNXdu3J1945L5Cf4+5Hg7/ezr9ODh9+If+DZn/9yCILAzG7JzOyWfPHGvyEmu4PVhUXMF+LRiSIjZW8esVSyv6KKVoeDWIX7sFEhCMSh4UBbQfvLtqnpGDE+3vxn6qVk1NSiFhVUthl5JWMvLucsFEo9DZWb0Wh9CI2Zetp4i5tOadE3HV6jIAhMvOZN2lqqsVtb8QuMO6v8R2K3SzmReT8OewveIf35vmwt/WUv/FHymdyMX/hIJFniZM47xCT0pao0k7efTsHbL4aJ17xOeEzaOecOjUzl1kd3YTLWodP7X1S07Mfs3vQmsV3vJDjylAS/QsuhXZ9yyZRnOzzGL6EjuWAWpxMvQYH6VKy9WhAxCAosTueFO56DzSdLeHbbbhrtNvqEhPDKuFHnVe4ESAsL5aZ+vbhj/yG0iPjptbw/fhyLj+UQKanaw/+jBTUt1laMdjsLs7JRywJHMKGXRSpb2wDoHR7Kqms7Xic1xEtPiNcvk7f/o+iIEeaQZbnlJxakpxjknxR9cCDTBzX8rD5Wq5X6+lqCg0PQaNwPnV+SmwOwubKGcsnefuKgQkCUJeYcf431zhZ2SSr6C15skVtRKKGhaD6O5kzstgZCxVb+admIdu8W92Dl0BJg58aco2Rv34ckOekaYEDj7Y2xORdNWBCyLGFvPU5ocijKkEjabHYOV9egUoj0DQ87K1b4xySeO6IAgNsDQjm0dhvHtl2NICgwq1zc5ChGAGRRg6otC1tzFjeldmZBdiEJfV/ByyeB8ryPuXvTbhZNuqR9LLPDgfZHHjKNUkmo4eelY7pkGUmW3XH3uDcqlTagQw/RQ1XVvHPkBGaHiymJUcxM6fKbnwiNiI3mgwNHSJW0RAtqFghNjI2N/dnjfHc8j/VZebxNNGpB5JWCCt7XHeHOAX3O20elELmia2f8dFr89Vpu7JWKVqlkaGwUC3OKuU9WYcLFcrGVBzt14839h/CWRR5TRKBE4N+uauZnHuOWPj3POf7KvAI+O3QUpyQxJaULN/ZK/VOeqHn4f4lnf/4fxyVJVJvM+KjVHRLouOBYsoSAgPKUpLAgCKdy02T6hoTwZUM918qBnMTGQcx4uaop2H8HSqUXltZc3p40FoNazdBTYV2yLLOvqp6NW2ei1figcrUxMtCXjJZcgiPde6CxJReDjzvHS5JcVJxMx243Ex6TdkFRC2/fMDhPjczo+IGk9r2SI9tvQOsVilWh5AHqcLjsKBDxFuxYmvOJ7TyIypJDhMddS3L/cTTV7GHp5zdx04Nb20vpOJ02BEFs95CJCiXevuE/+9467BbUmtPXo9IE4rCVX7RfW0sV29e8RGtTBRExaQwZ90CHy7T8oLh7sRD+RH8/RI2SxZYGhmJgF0bQKkn8mQeIBY1NPLl5B0/IYcSLGhbVN3L/2i3Mnzbxgv0GREVwjcmMU5KYlZpCjK8PQ2KiePJ4AX1kLwJPhf8PjYliTUERJpuDB8Qw4gUNC6R69tXU0Wyz4a89+75k19Xz8o591JvN9IkI49Hhg/60NcHORUfeBLMFQZgFKARB6AzcDez5fZf11+OJIRm/eoyK/1w8abUjCGs63nZndR2P7juKBgEbMk/2TiE10JcIvQ5VSMfjZ12SRHZdAzWihmLBwVdSIymyltViK0OjIonulMh7V/jyyIZtvGKqprOPL5+Nn4i3Ws2Bqmr0qjiGREWeZTQFAktnxFLU1IxCFOnk58uhqhrmrX8BY3U/bOZKwtUmpiZdwvrCkzy6PR2NVwyiZCNElcnCSWPxUv/8P0itUskXE8dQ2NSMS5JI8PdHpXAf2WTU1FLW0kZS4GgOVtUQEDYEb3+39yQq6Rb2r12CJMtUGU3cvm47xU0NKBUK/jF0AJOTOu6t+zFqhYIBUTEUZ71MaPx1GFuO09qQwYCICe1tTA4HZS2tBHvpCdS5PXHZdfXctm474cl3olL780bG29hcLm7okfKL1nE+Ovn58taEsby6cz/N1laGxEbx8JCfL2u7p6ScSZIvVkHiE6kOo+xg+Yl85vVLO6fXcEtxKa/tTqfFaGaC4EeWaOe6wmIWXTWJuwb05UW7gzsLilCKAtf16IaPRk1pYwvXikEknhKuuUEM4k2nO4fgYFU1a3ILUSsVXN29KydbWnh1xz7ulkLQCQLvHMpGrVAwu4enHq6HPwWe/fl/mNKWVm5buR6T1YZRcjGnZ3cmJSUSbvBCo/x5B3knm1vIqq2js58fr7XWcLnswzEs1Cpc9A0Lo8dlwTy0fivX1BTir9bw3MjhDImKZHd5BQ6Xi4GRE/HXnfkSLAgCL40axN9aWjE6HCT4+dJUWco129dzwlKOKKowteRyzbxvaW4s5ZsPZuF0ymj1IVjNZUy/ZQHBYb/MGzh0/IP0GHA1ZmMDASEJqDVu0YWm+mKqyzPw8g5B7xXEkk9vahfRCgwfTnXJt9TX5BIW1YO13zxEQfZaEAR69J/F6Cue7nBu+U9J7jmBjL3v00l5Py6nmaqTixg//cX2zyWXk6b6IhRKLb4B0QiCgM3axuL3puMXMoqAiGGUFq2gaeFdTJ3zUYfmvN/6Tyo6cB6oVij4ZMplPLt1Ny801ZPg78eno8Zc8JD6XByurqGf4EWMrOZTqY4S2U5+nZUGs4XAc0T/FDQ28fz2PRytreMK/HAIMtfkFrBw2hUMjY7ijsH9eG7/QSxOF6NiYrikcxxfZZ+gv+DFwFO1bO8QQ9nmKkBEoKLNyMKj2VgcDi5J6ESCvx+3rFjHDS5/EoVAlpys41HrNt6aMPZnXdd/k478Fd8FPAHYgEXAeuD533NRHWFO+i/z1PxeVB749WP8VlXOO0qz1cajy7fwJOEkoeUFqZInD2RhUKoI8/biw0mBeKs1nGhwe9aSAwPbDZEfY3E4mbN6M4V19WglCZ0ssEJs4oi3N32jotrzalJDglkze/pZscQTEuMvuE6lKNIl8PQJU7+IML6fdjkHKqsxaKIYGRNDZm0tD21LJzx+JtGdr0eWZYoynuWjjGPc068X+U3NZNfWoxAFUoICSbxIwUGT3cGW4lIckouh0VFnXHev0NO1Jk42t2BpK0KWXQiCAlNrIQaNHlEQuGPDTpxBE+g/8DosxmKe23sPSYH+JAd1vF6ULMtsKy2jsLGZqZ1j2FZWzsHD9xPspeOzCaMJM7gLHR6pruH2ddtRqn0xWxq5u19PbuyRwor8YgJjZ7TL/SrV3izOefY3N8LArcr11YzJv2qMAL2OE7TwmauOaWIAlwq+LLQ28PKu/Tw+/Mz8si3FpTy1cTtGl5N3FLGECWpkWeYpUxWbT5YwoXMCT40cwlMjh7iN0ZXriTyaT4PDQg2nc/xqsBPp6832kjIe37iNKZIvJmRm5RaQFhrCDMmPXqI7pGGuFMDS3EKPEebhz8Kfcn/28Nvw8IatjDZruVIM53u5iY+PHOW7o8eRFAJvTxhLz9AQCpuaabHa6Bzgf15P2Ut7D/PNseMILidhKCkUHFR6yXQJDODLoWPa+3065fKz9udLOl08ouHH+bBhMfEsG61gR3Udrmk3UmYaCbLMJ6+MQGdIJG3oqwiCguqSVWxc+iSz/raE1uYKqsoysFuN+Ad1IjKu3wWjDWRJoih3K6bWGsJj0toNMAD/oLj2PCyzsR67tRG7rRG1JgCnw4TVXIXOK4BdG/5DY30j/cetQpbsnDj4GBn75pM2+IaLXu+PqanIorRwDxqtD936TOD4kX8hKtSMmPBou/y+qa2Obz++Hou5FZfTQkzCQCZe8yblJ9NRqUOI6eIuo+Pjn8qBTVOwWlrOEuw6Lx0sCxRuMPDeFeN+1rX9FH+dllLZzj9cFUQLaq4WA9gut3HrinV8NX3yGe9JFW1Grl+2Gj8H3CoGM/ZUbrbBJfLp4UyeGz2c6SlJTE9JotVmY86yNby4YSdtLgf+sqL997AWJ2pBxOx0cPW3yxnu1BOIkkcLihnTuRM90bXnfd8tBzOzvBCHSzrnu+qfkQsaYYIgKIDVsiyPwv2g7xCBppI/xEjy1Mz5dZS2tBIiqugq6dggtdAku5ivSEAnC3zS2sBTW3ZRazLR2uaupeHrrefjyZdhcjh4eec+ylta6RYajJdOR7FFSyxaXhBDUAoCK6QmMjUCjw4beNacm4tLUIoilyfGt3tsfi5RPt74aTVsKS5lRX4Ba4oqUaj98Q1yh60JgoBCF8H+ylyuW7qKvPoGBFkgQdBQLDq4b0g/pqec+wSuyWpl+tJ1uHSdUCi9eHnfKhZMGntWpfg6s5ldFbU4rbXk7L4dg28iTdW7eG5YP5ySRH59DQP7X4sgCOi9OxEQMoCjtXU/ywh7Ye9hVp+sxxA8EOPxdC6N9mPbtWcaOpIsc8f6HUSmPk5A6GBsllre3X0LgyJCEQWQJfvpti77H5L8/Uu5tW8vrij4jl6ynqmnatx1kjXclpt3lhH27dHj3CAF8CY1+J96lAmCQABKTI4zwzSf2LidG53+jBR9KBasPCCV0ehyokFgs2jks9GX88K2PcyTghl06gROdMkcMRqJ4PQm14QL7c88gfbg4ffgl+7PHv46nGhq4gkhnnLZzhK5kbcVsUQKavY5jdy1eiMj42LYWlRCsKiiXnDy4RXj6eTny+v7DnKovIpggxfjOsfzXV4pClnkPUUs/oKSQtnKk5ZKlowdccbzzGi3syq/CKPdzrCYKJICzx8ueCH8ImO5TKVg+/IvKUkUaSjPR6kOxC+4L+5fW9Dow6goKGTTd49w4tASBEkiSdCSoRAI7TqKsbPePqchJksSKxbcQW1VAV4+ndmx7lVGT3qKlLQpZ7RzOe1k7luEwTeCo7tuIyB0EG1NR0nuOZHAkEQqTh4kNPZaFAoNKDSERE+irOjAzzLC8o+tY/13jxEUPhqruRxRNHH9vWvOCifcvPwfaA09SOp7m9vgO/Q4Gfvm4x8UhyQ52g0OWXYiy1KH6q3qgwMpX30Y4Wd6s34No2Jj+MzvKMX1TbwsRiMKAj1lPXeYyshrbKRbcFB7200nSxgg66kUbAT8yNQIQEm+zXHGuG/vP0ysUeYOORKbLHEbJTwjVZKIho1CGw8N7s+3OXkMcemZK7pVMDtJGt4tLiMYsf3+teJCFAQU4p/3HeenXPBtQpZllyAIkiAIvrIst3R0UEGl8hhIfwHCDF5Uu+xU46BItjFc9EZ/Skb0Etmbp6oq6SvreQF3WOJbrXW8tvcAu0vLGWrTMhoDG4w1bFU4UHp3o5/cQr3gYL6rgRrZQVmdHbvL1e7yzq6r55YV6xgse2FF4uNDmXx91eR2b84PNFmtfJ2dS6vdwciYSPpHnh2f3WSxcs13Kwi1CegROeI0ovRNprp4OQafLpRmvYapfBPBoopsyYICgQ8VcfgKCiplO/ftTmd8Qvw5Tw4/zsgB/8EkpLrVnKqKl/LC3pV8NuF03Suj3c70ZetRB48hOGkq1fmfoW9L56qenUnw90Upihg0OozNJ/D274YkOTC15BPs1fFwxMo2I9+dKKDHqK9QqrxxOkys2n4NN/ZIIvZHJ4+tNjtmp4OAUHe9N40uBN+AbhQ2NTM9OZEl33+PQqlHqQmgJv8THhvQceGJP5oQLz239+3F3vTTatsOZETOfqgKgvt//THwtlTD1WIghbKNA4KR2T7eZ5zolptM9BHcG0ScqGUU3rSE6+kdHsbXifF08vPF5nLhLZw+PTOgIM7Pm9WmKqwuGS0Cq8RW3uh/yVlr8eDhj+aX7s8e/jpE6r04YjYBkISWSMG9Xw0UDbzmrOZAURnvyTHoJJEtUiuPbtxGvL8fLeVNzJZ9yW218kL1XtS+SYQ5q9BJIh+5aimUbUhIHKupp2+kO++qzWbn6iXLCbcKBEkKPjmUySvjRrXnf/2AU5L47kQeRc1tdA30Y1KXxLMO9iRZ5sGDJyiqrCXhwFOcwAzacOoqNhMaM4Hm2gOUZb5MkqDl5IFvUSLztCKSFEGHTZa4J3cnxXnb6ZQ08qx7UlKwk5rKPLoPfg9RVBHaWsSmZXfTtdfkM4y2VYvvoamhiZDoa6ir2EBz3R5Sek+lU9IoZFnG4BuCsTkHvyC3SIep5ThhERdIFD8HW1Y+R5e0f+AT0ANZlsk99DjHjyyjR/9rzmhXX51HTNeHEAQBQaHBP2QotVW5pPa/BlGwcjL7P3j796SuYg1dUiee4dm7GL9W2fjnoBRFnhk9jBu/W9WeeOqucSef9TsgnPpskGDgC6n+/9g76yi5quxtP+fe8qp2d4m7u7sT3AcYGGAGJ9jgDD64Du7ukkBCiLu7J51Ou3t51b3n+6OaTpoICT/8q2etrJWuvnLqVvXdd5+z9/sSLVT8SD4QdVyU0qPVs2FRfQOD9ZDNjEWoXCbjed/upE+7dB5JS2FwehqPrliDQyr88CjgECqKEHhsBh53VdBWNzNXaeKfvXr8oSeaf8zxTOk6ga1CiO8B1w8vSimvOfouYf4MJNptXNyrG1ev20yEMJKs+5gqolGFYIN0E9R0Bij2Fv+lAbqNryuqiAwKzhOh1ZwO0sJ5/v3gLWeu3sAs6pgsohmnRPKRXssVs+by6vRQKdzTK9ZynhbNRCUagDcD1by2YQu3H7LCUe/1ccpns1Gj+2OwpfPJvM+4a1AP+qYmsaOqhnible6JCby+aQtdPAauEKGywFnCzDuNe3DrAVbPnUqkFuRlNQebUNgnvNysFeFoluFJFSYihEqNx3PEJKzc5cUSdXCVzB7VgaryT1tts7SwGGHLIbNzyOzRWbeT/NL5fFJi45UtC7ixfzceHDGQmxfdQmxCb1yN+fSKMzMiM+O4P58Gnw+rJRqDMaTwZzDasVpiqfd6WyVhkWYTZlWlvnoD0fG98ftqaajbSXb0MHJjonlv+nhe3bwMj1PnmqHdGZebfdxj+D2Y3r4tb23cxtuBajKliS/VBv7W9fDyv/N6duXG8gWcJaNZLpu4TitEARQEN81ZQOfEeJ6dMg6TqtIxJobv6ho4nVgaZJBtqp87ewxg+CGfx7SObXl53TYulwKX1PhMqefxbn24dmBfPtu5m6Cm80r7IXRNjD9sLGHC/E6E4/NfmPvGDueSr2aTiJlafDTIIFHCwG7pwadLukgz1uayq4HCzv+aKtnf2MiHShvMQqGLsLJD+NjQkEde0M/duIkXBs5W4lgrnVw7Zx7zLjgbq9HAJzt3k+lRuJGQzUhv3cajS1cz9NyDSZiUkqvmLmWH04YtfhDfbFrAmrIabhvcm/VlFRgUhX6pyawpLWNfWRWPywyMQnCS9HGtsxB7XB/Wfn8mqgzwlJpJtjDjFBqXafmoEhBgFgptMdHUUHbEa+JqqsIWmYuihHq9bRE5aJqfYMCL0RSqrHE7qynYuzQkk6+aQRjYt+lBCvP2sHvL96Rn92L4pFv48KWzcDXuRNd8aMEaBpz/2Ql9Pj5PA1ZHyAtMCIHFloHXXX/YdrGJudRWLMMR1Q5dD9JQvZqM3mMxGi2c/a9PWDX/WRrq1tG1zwT6Dr3khMbwW9MmJpoOCXE8Ul3BYN3GGsVNakwU7X9UKTSxTQ6vrNtErCbIFCZu10IiJX4kH6zfyodbd/Da9MmkR0bQKSmBhZX59JMOBLBcdTMmJ4vrBhy0iJnYNofLduwmSzcRKwy8ptQwrUM7/t6rO+9s3U6V082M9K5MaPPnEoc9niTs8+Z/Yf6CRFosRCYPIiZ9MiV73+UfziJswdDqWDQGFstG+srQStV8GjEYbTRJT8sqQwCJFNAtSrDWGaArNs5SQwlaR2Hl7LKD9bmNXh9p4qBcaKo0st/jaTWeL3fvRY3qRVKbcyna+BC638+9i5ajGYzExHTG6dzCmIwEZMBLrjS1zIq0EWYSzCZiHD6qsZPuDmBrTrp+EGDYJF30EQ5W6E1oRkFqxJFnmwanJbBy4yfEJg5CNVipzHuL8akJrbbRpUSI0J+Pq3E/NeWL6T3qHQzGCDyuEh5eejEPjhzEZT06oCpuOsV1ZlB66gnN0ORER6FoTsoLviI+dQy15UvQ/bWH9bMpQvD0uGFcPfdOKm3JuNwVXNy9U0tpQPvYGB4Z9fP8un4PYq1WPjh9Gi+t3cQWt4fzs3txZucOh203JCONaZ3a8e32feQKMzapEitUrlCS0CU8VFnOaxu28K9+vXh4/Egu+3oO33oLcMogF3bp0ioBA7igR1ck8NbOvRhVlf/0G87AtFQAbhh0ZGPyMGF+Z8Lx+S9MpMmE0RqDvevNBEsXcXnpIpJ1SZH0YUCwSbppkhoRQmWBbCTZaqPE7cKHbOl69SMZm53G/H155OHlQaUtqhD0xMaWYAGbKysZmJZKg9dHqm5okQxPE0YafL5W49lZU8vGqkY6DH6c4q1P4G2q4Ls6NwsOFGCJyEYLeolRN3Nep1yyhBljc7zLxIQQ0MVYyp7kDrgrdpHd7L3pECrZmJktG+iAlRLpZ4N0cVJatyNek5TMXiyceT9NdTtwRHegJO8D4hLbtyRgEFJhFEIBEeotytvyKF0GPUlEdEd0zcfWlVewf9d8+o+4HJ+3kfikdmS3H4nJbD/iOY9GZtuhFO56mayO/8LjKqS6bD4jJ//tsO3GTL+Hj14+l63LVxAMuEhM7dBS9mi1RTNq2p0ndN7fE0UInp86npfXb2ZjVQ0d4rK4vG/Pw4SzEu02Hh43khu+nUcXrAwWDvKkl4fUTGxS4VNvLXfMW8Kbp07h8j49uL6qhovK8hEIuibEc1X/1orI3RITeGzCaP63aj3ugJvx7Tpwed+eKELwzz49f8Mr8Mvyk0mYlPKt32IgYX4fjKqCokBc8hBikwZSXbaE/A334UDhAmKZSxMXa/noSLxIbMEs3IFqLuMAOcKEU4Ex2Vk8PG4EI958H3HIPTuIBEGLh8WwnEze37KX66URHzpfKg1cldP64doTDCKM0exZcS1/C5roL5KYJxuZKSCrz3+ReoCFyy/h5OxoZooS+kkHNhQ+FfWMzM3ilqEDya9v4NxPv6ZA+sgSZubqDQgE9+mlKLrAYTTy4pQJR1UGOrVDWwoaXby58Gx0qTMuty039G+t9DckI52HVs2kZM+b6KiYLYktK1ZWexpSsXDfmv04IttQV7WGJ8cMOeElcovBwJtTx3D9/A/ZuOM5MqJjeWPK6CPKrw5MS+X7c04mv76eRFtv0iIjWv2+pMnJwgOFqIpgQm42sT+zF++3IsXh4J5RP22+3TM5kU27C7lRT+YWrYhTlBgUIVCAIbqdHVUhUZn0yAi+Pvc0ypxOIkxmoi3mw44lhOCint24qOeRg3+YMH80wvH5r41RVdH1INHxvYlNHIAr9wx2LvsnQsI4IjAh+IeWTxQqNQSxmjqhuvdwlVZABkbiVSP1Vnh5xCDQgizIL0BDoiKQUqIJWuLSkMw0bty2i77STgIG3miWDD8UTyCA2RRJ4cYH6FyXx9+IZ7/i4zFfFW0G3YrFlsaBLQ+wvbqYdZqTXUTQDgufUke7qGheGtwNXUrGLqpkYUMjo5RI9ksve/CyR/pYEtyLLgRjTnqQpLSuR7wmsQltmHD6w8z97FZ83gYSUrtx8oUvt9rGHpFISmYv9m15kLiUsWhBF46o0LOGopqx2rNYPvdp4lIG0VCzhe79zqB9tylHOt0xmXTmI8z++CbWLzwLsyWKcafcS3J698O2i4hK4aLrZlNVvguDwUx8UodWKowBv5tdm7/G73WS1W4Y8cmHTzr+kbAYDFxzDNuYH2gfF4OmCK4miS/1OgYc0vIygghm1pcCIdue56eMo9LlRgJJdtsR+wGHZKQxJOOv1er0k0mYECKfI/iOSCmPLWkX5lfD6fdz/+IVrC0pJ95m4dYRg1vU+k6U8bnZPL/+Gwq2P4U5oi1le18nGQP1BHmMCtKkkStFAvNlE3mpg3DVbmKCEkM3zHxJPURZeHDscJz+ANGOSHb6qnlBq6CTsPIV9ZzVsUOL0e6pndtT1uTkhgOFqELw917dmNa+dY/UiKwMXt40myTdwNTmssVzlFjmBMvwucuwOjKwR3VkVeF6fLrOP2Q+GpJucXFcNzC0dJ0THcVtwwcxY+GykCeKKYY2PR5GMdjZvuJqll583jETIiEEM/r35Lp+PZBSHlEaPdpi5qPpE3h0zUqKGp2Uu2pprN1KZGw3qkrmoekBOg55BVU1E129kduX/Icl5x+/5P8PtImJ5uvTJx3XttEWM72SD69p31NTy/kz5xGZOBSp+Xh+wzd8esqkw3rx/oh4g0FMqnrUz2tsThavr9/CJfX5+JG8olXyXyXkMbZGcdM97uD1MCgKGZGRRzzO8aBLyfaqatyBIJ3j4/7Pnj1hwvxfCcfnPx5SSl5ev5nPd+zGoCj8vXd3Tj/CSv7xkBkZQZ+kOHav+zcRyaOpL5sPeoAEDMyiAQcKF4l4vOh8bBJYjDFkKw6mSRubcLNccfPpSadgMxhw2OwIBHdpJUxSolgrXdijrC3PDp3i4rigT3ce2bwddzDIqKwMbh8+uNV4OsbHIf3VVLn2c5UaKnlMFEYGSC/F1RuxZqVji+3DlqJNGCTcpZfgQSfOaOLDydMxREag1ZTzQq9crli2nuf8lUjFSEa3G3BEd2D3+rs4+bynyGgz8EiXo4V2XSfSrutENC3Q4u91KEIIpv/tRZbNfZzyoq8xW2Mpzf+E1Jwz8DgPUFuxio79HiA6rhcBfz0bl1xEt35nEBV7/O0CAGZLJCdf8NJxbWswWkjJ6HnY636fi/f/dxpCicFkSWbF/GeZdu4zZLcfcUJj+T0IaDrAUZUIE2w2Tu3Yjit27EVIiZQwFAeZipmV0kn2IfFYCEHS//GZJL++gdImJ21iov8UzzdwfOWIfQ/5vwU4A/h5kjlhfhFunrsQpdzJ3TKRffVe/jXzOz496xTSjlJedyQO1DewvLgEq8HA29PG8v72PVR5dtE2JZKl++u5SUmmj7AzX2/kUVkOipW0hL7EVK7jH83qND2kjXPr9uPTNO5fsQG3vTedu57Hmp0vsqR2M0Mzkrlt+CAavT7O+fRryp0uVCFIjnDw9qlTj7ga0Tk+jpsG9OTZZWvwq3pImhQdl+Ymb9UNRCQNobp8JSbgJSUbBSiTAa6uK2yVLE1r35bVpZV8sbeAnO43YovIIX/H/4i02Phw+y76piYfVsP8YxRxyDLeEUiLjOCpsUOAUI/YjPm34NOCWAwm4hP7h1SXIBRcPM7j/mx+aR5bs5WENn8nJec0AAp3vsALG7fzn2F/3BK7Kreb676dx9aaGgxC4ZYhAziry+Fqlp5gkBqPh0kimq6Klc/1Ov4hD2BXDaTHRB3VgPmnqHC6uHP+EnbW1JIe4eCukUN4dvUG9lRUEy0MVCsar0+fRO4Jml2GCfMLE47PfzDe3ryNWZt2cqNMwIvkiRXriLKYT6gXt8HnY+7+A/g1jX8P6sW8/EJ21M4kNklhZpXCYBHBuUocB/Bxq1aMH0n7no+yb82t/E/JwqQoDMDBAVHKntpa1pWVM6/YTZfRH1O6721erlhOjFnlk1OnYlQUrpk9jxUFxagIUBVeP3lSK6W7H7AbjbwzbSzTP/qCaoKkEbIFKdPdlO56BXflWny+cpwNNbyi5BBnMODVda6TRVS43KRFRqDGJdMlLpl7vF6uWr2T9HYXEJs0iOqyxfi9NVSUbkXXg2S1++lKiCMlYD9gNFkZNfUOAOprCvjirUsp2vM6QigYTRFEx/Vq3i4aqy0Zt7P6hJOwX4Jt6z5GqIm073UPQgiiEwaw4Ov7uPjGYydh17efT8k3v9Egf0RQ17l30XK+3LsPgOlt23D3qKEtE+6HcqCugW6KjWlEsUW6maEXkqpa8JoFr449vgnmH+PXNP67dBXz8wuwGQxcM7gfxQ2NvLZ+C9mqhXzdy39GDf1T9IcdTzlizY9eekoIsR6469cZUhiA4sYm3tm8Dbc/wPh2uQxrLgsIaDpLS0r5RGmDUSikCRPrhJfVJaWc2rH9cR17bWkZV3/zPYOFgzqh8bplCx+cfhIRZhOf7NjFnv1l9GuW6R6nRvFWsIYm1YDHWYSqeeBHVXwCweaqWuI6X4MtIpP2/R+k7MBXmILfoQjBhV/MItUlmaFmsEv38FpjFXcvWMrTkw9XmZNSUlhXjxfJjVoRgxQHi/RGemHlzICFpwu+oW1sBNFNAVQE8/UG5umNGIDPd+7hzEMe1Ov8OslZ0ynY9SJ+Xz1mPUiyFmTt6l08K9dx35jhx+WBcjwMy0xn9UVn4fIHyKur49I5y3A3FWB1pFOy9y26J6X+Iuf5OdR6/VhTDt6MLBG51DRt/NXOt6emlgMNjeRERx0m63+8/HvuInLqdO5R2lJOgNtXrqNdbAy9U1qv9K0tLSddN3CGEnruvFmxcI6ex/OTx9AzKfGIq5g/hS4ll8/8jl5OlYtJZWOdm799+Q2xUuV0GcMAxcHiYCP3LFjG26dN/VnvL0yYX4JwfP59cAcCvLlpG8X1jXRLSeSsLh1bVuvn7s3nIj2WXCXUi3yGHs33e/OPOwmr9Xg48+OvyA6oOFB5XmzglZMmcmmvbrj8fj7YtoNzlThUIWiDhX7CzlIRxOsqQ8rW1hyh3mXYXFlDZOoEzLZ4crrPwN10GqUbZuAwmXhsxWq2F5Rxr5qGR+o8opVz6dezWX7x+UcsCdtRVY0C3KQVMUWJYrf00iQ1HtSimVm5kW2mIFahEovKLunhI1mLOxjkxXUbeXHqhJbrVGmwkpTYh/qqNZTt/xijMBKla5i/f4ll+CjqezpDT7r7539IhxAdl8XfZ8zF521CSsnrj42mumwxccnDqa1Yjs9bRWxim1/kXCeKx1WLxZ7Vcq1tEdl4PfU/uV/Jk//7WSrk5U4XWyqriLFY6JuSdEw/tqPx2sYt7NlfxntKLiB48ECo//ryvj1bbdfo87G2vIIPRC4GIegkrGxVfYzp05Fzu3TCdoTWiuPhkWWr2buvhAdkMlXBIHfPX0IQydkylv448KFzx4KlDM/MwGr8Y1vKHE85Yu9DflQIzbz9sd/Vn5ySJidnf/o1o4N24lC5PX8RNw0fyLT2bTEoAqNQqEMjsdkfoVYEsTZ7feyrraPe56NDbOwRy6UWHijk+jkLCKKzGw//VlP4wF3HB9t3clnvHvRKTuIJRcMlNexCpUYGcaEREdOFmrz38KDwulZFZ2HlS1lHl7g4rEYD6Q4bpdXriYjuiJQ6ntp1ZKZZ8WsaexsaeEhth1EIslUzKzUna8vKW8ZU7fbw1Z595NXW4Q4E2F9UyatKNiulk8/1OgzA7WoqQgiuV5N5yldLftDDK1SyRrq4TEnAh+TRZavJjI5sEVPoHBfB9qL99Bj2CtWli5Cbn+QJJQNFCsZIB7d+v5jefzvjF+uPUoQgwmyiZ3IS/x7QnXuW/YOgpqGqZnLSU/AEgr/LDWFkRhIf730NmyMLTfNSvf89Luz9yySfP+a1DZt5Y8NW2isWduserujfm/N+hrHxpsoqrhHZKEKQiokh0sHGisrDkjCDouBBbxGK8aOjA53j44+agJU7Xczam4cuJRPa5LRSmvzh99UuN+cRCoy9sKFpOklYWCNcvK/VcKOSzFeNP37+DRPmtyUcn397AprOxV9+S1RDgO66hc8Ky9lZWc29o4cBYDUaqEFr2b6GIDZT6GGzwumioLGRzMjII5ZL5dc3cOHns6jx+3Chcq2SRAfMPLFsDa+dMhm7yUSkwch+6aMdFjQp2S99RMR0pnD7sySg8qBWykQlms3STYnip29KMvl1jXj2r0fmnoEQKo01G0iNCJ1/9u79XKUk0UFYQcB5xPFmoIomv59Isxm/pvH1nn1srqhCAHP37udRJYN6GeRjvZZ9eHlHbYNFKFwrkzk3sJ8Ik4kXfJUsk01cpCQwXcTwcmkl/1u7oUVwoW1MNM6G1XQc8gpS19i24FyeU7OJ0lWcUuPvK98iue1g2nYe94t9dmZLqFf61Ite44u3LmX3+v+gqEYiY9LxuOoxW35+ufrPJavtUDatuoq45KFYrCkU7n6FrHbDfpVzrSkp49rZ8+ioWCmVfrqkJvHYxNEn3K++obiMqXoktmY/s2laJAuLyw5LwlRFQZcSv9AxEBJJ8QlJh9jYoyZg3mCQL3bvpcbtoX9aCv1TD7coWphfyN0ykWRhIl4aMeuCWFQKhJ9PtSJmKMlYlZACdrox4ghn+eNwPDfrxw/5fxDIB878dYYTBuCznXsYqtm4UAmVA2TpZl5du4lp7dsihOCqfr24c/1WxuoR5Cl+Ag4jo7IyuGfhMubnHSBBMVJFkBenTWhVUlDU2Mht8xbzgJpGByx8pddxi1bEVKKoa1YpbBsbw/RO7bhhTz6dsbAJN9M7tCHC4uKzGgN3ymS+k418JxswAL2bPUbuHtqbc7/6mLyalQQCLpJNPi7qPga1+Y+7CY1YDEgpcUkdQ3MNcbnTxRmffInuCxKDgUY0zlXiiFYMTCKaDGniKa28ZbamXmrYjUbS4xzMr67hRiWRvkoomNTrGl/v2NuShF3asysbKpawecHpBLUAo4Sx5WaTjRm3HuSML+Yw+6yTWol0eAJB7lm4jMWFRdiNRmYM7seUdic2S2Y3GbBZ42nT9xHMljj2bH6Ah1Zu4N7hh5cAugMBVpeWoeuSAWkpOEy/bK/RP3t3pc67ni+WnI8qFC7q3olTOxy/X9nxUtrk5JX1m3mGTOJ0A5UywDWr1zOx3bFNucudLjzBIBmRES3lDPEWC3u8XnoLO5qU5Cl+BtkOP8bAtBSethp51FmON6iTL/z0TUk6YrL77b79PLZsNbVeLyOUCCxC5ZyNW3l1+iQ6H2KgbTcZ8egaTqETgcp7eg1TRTQXqaEy3Pe0al6RVXSMD0vVh/ndCcfn35gN5RW4Gj3cJ9NQFMFwPZIL9+3nhiH9iTKbuWJAH678Zi6luh+fkCxWXbzfayRf7drLQ8tWkaGaKQr6uHnoAE7tdLB6RZeSf838jlODkUxVo9mJl7u1Yi4TCdR5Dype/WfUUO5ZuIx+wk6B8JMaH8OEVMkyzcb4RhNVBJkjGzACqQ47NqORc7p2ZG7BAnYt+zsmcxQ+ZwFPTwtVomhSp/6QpLFeBhEIHCYTAU3n0q9mU1hVhyYlVhS6CAs5qhmEmR7CxslaqN8HAW50gkgmtsvlg+15TCaK8UoUALfIFB7cubclCeuTksyFXXJ5ddF5mIwOIiVENRs5O4RKjFSY9d6VXHj9XGLis1vGJ6Vk/eIX2LT4JXRdp3PfMxk85bbjMjj+Aas9Fi3op3P/h4mO701p/qd88eYlXDRj7mErQ1JKivNX43bWkJLRg8iYE+/tPhbpuQMYOeUWlsy+E7+viZwOYxh3yv2/6Dl+4Pb5S7heT6QPdgJS55bSUublFzD+GKu0DT4fVS43aRERLXE1wWFnb2UdgwhVTO0RPhId0YftazcaObl9W+7JKyZeUynAh9eo0Csp4bBtd9fUcvv3i8mvb6C9sNBeWLhx8w6uHtyPM37UU+kwGajwB0gTJpbKJmIw8KiagSoEW3Q3j+tlSIOBRLvtsPP80TieJOwSKeX+Q18QQvzxCy3/xPiDQeytTOkU/Lre8vuLe3UnJyaatSVlDHbYOKtzR1aUlLHmEOPGRXoj/567iJnnnd6y39bKajphpaMIPcyerMbyTrCGL6nnsYyDSjf/HjaICKuZWbv2EavaGJCextR2bcivruGTsjrcUsOPpFLR+HvzH1NGZCTfnjmNjRUVmFSVPsnJLc2acSYzt/mLmKhEs1d6qSRATlSofOyNjVtI8iskCBs3KMm8qVezW3oZR+jGvUd6cQnJa7KKKKnytdLIfwYMY11ZFZ/XNeCTB6+LDx1FEczam0ely03PpERenTSSosYmdtXUcsf3i5koo8jCxLt6NdmYqXK52VFd00rY5L7Fy6krrOY5mUG5P8CDi1eQGuE4ouiFLiUlTU0ohCTvf7iBLy+pJC7rdGyOUI15UtuLWLn19sP2r/N6OfvLuXgNCSjCiFy+gQ9PHv+LNpUaFIU7h/bjzqH9frFjHokKl4sU1UycHrqtJAojCYqRSpf7iEmYLiV3zF/CgvwCLEIlxm7l5ekTSbDZuHv0UG6Ys4Ceip1S6SchPorJbQ/XGrAYDPxv6nhO//hL+koLpxHDt5V1vLJ+c6t+sA1lFTywaDntNROjRAznNk9wpGlGnl+1nuenjm/ZNsps5pwuHbl1134GazY24+YycfD70VZYmCuaeG30rzNbGSbMCRCOz78xfk3DLpSWCT0zAqMQLSIFvVOSeOPkyczel4+qKHzcsR02o4EHlq7kEdLI0MyU4OfGZasYlpVOgi30oFjr8VLn9TJNaa7kwEpHrLwqqzkz/WCZ/YQ2OaiKwnOr1+MPKPRMTuTKvr2IM5v4cu12ojWBEw2fkPRPDpWrmVSVt6aOYUNZBe5gkJ5J3Vt6srunJvH8gWLK8OOROnNkA2aDil/TWFFcSnlNI4qE59RstkkPb+hVBKTEKAT78WEUCo+LSrroFhYpLs7u2JFBGam8u30v3kPyGR8SVQhWlZSyvaqGtAgH/+rdlTM7taG8yck/Z81lfrCBEUokK6UTNzpoQXZt+ZpBow/a3u3c8Bl7F7zAw1oMRhQeWfsp623R9Btz9RE/L1dTJT5vE1GxmS09ZOXFW4iM60ZMYmhSNDX3LEry3sXrrsdqP1hCL3WdmR9cQ1nhVqyOTBprbmfqec+S/QuvVHXpczpd+pz+0xv+Hyn3uOmqhFaWjEKhgzRT5nQddfuPt+/i0RVriFEMuITk2Ulj6Z2SxNUD+nBe8UwOaKGKpmI1yHsDD28vAbhzxGDOqfqamnoPU4hmbdDDjO8W8uK0g6WpDT4fl349mz5+Cwpm7lPSEEIwUkbw7xVrOL1T+1bJ8XWD+3P7vMWM1yNYj5v2wtwy4d9WmKlH481JR1fA/iNxPEnYp0DvI7z20/qUYX4WE9vmcumO3WQ0m9K9rtQw9Uf9XqOyMxmVndny846qarroFqzN8p8DhINnXJWt9kmw2dive/AJHbNQKJI+dEAoCsMyDs7uLCwo5MNNO7hcj0cFHlu8CkUIpnVqx90lS7hGScKEwvNU4vL7W/aLMJsO814CuHv0UGbMWcBSvQkdiQBKqhuYu/8ADR4fioTOihUhBKcpsczQCrlRK8KCoMSk8+z48SwvKsbjD/JU2/4UNTr5eNc+MEXytKeCBjQ86HyuNJDVaGTxxgqsUZ14fuN8zuyQyRV9ejI+N5u3E7Zxe2UxXnS6CxuXKgncr5cT1PRW411aWMwjMpUYYSAGA+P0CJYVlRyWhLn8AS6dvYi99U1IqdMjMY7/jR+G2WAg0WrCV7m7ZVt34z7irZbDrs1z67aiRw+mXdcZABTvfpVHVq/liTFDjvDN+GOTHR1Fue5nq3TTTdjYoLuoFxqZR1Ek/GzXHnYXlPMa2Zil4C1nDfctXM4zU8YxOD2Nj8+czvqyCqItFoZlpB21vHBlSSkdsXCNElqV7SttXL+xdRK2pLCICXoExQRIEQdXGpMxss7rO+yYNwzuT/eUJHZUVdOn3sFXxdV01a0I4Eulgb/17PqnmGUL85cnHJ9/Y3olJ1Kl6nys1dIDK9+JJjrGxhJ3yP29Y3wcHQ9ZXd9UXkmsMJDR7N6VJkykKGZKm5wtSVik2YRf6pTgJ02Y8EqdIvx40Tmne+eWY5U5ndy1YClnalFkCwcfbc+n3uvj8n49eW7NBoaKGNopFt7Xa3H5Ay37GRSF/mmHl3bdMmQgJxd9xnLNiRkBSFJ1I48uX0235ETsKMQLIxah0Bsb84WJf2oHyBFmdipe7hs1lAqnm5LGRi5O6Ui72Bgu+XYhNnsS85tKiJCCZGHkE6WeTonJXLtgPVGJw3DuWM972/fy6OjBdEtK5B99e/DCyvU8rVWQiom71TRu1Uton/8pF63ZRtr1V/DA8p4UbpvDWUEbGUroWl4QtPPqtjmHJWFSShZ/8yBb1nyA0RyJ0Wji9EveJjouE5sjDndTIbrmQ1HNeN0lSKlhsrQWN8vbOY+K4t10G/wyimqivnoDcz6+iX/evupnfnt+X7rGxvJ1fT2nyxiqCLJauDj7CAIsECqNfWrlWp4gnVRpYq3m5NrZ81h00TkkOex8fvYpLCsqQQLDMtKINB8utAZQ5nRR0tDEqyILk1AYL6P4V2Uhe2pqW/5GtldVkyKN5Apz6BmxOaFKxohbC9Ls493CqOxMXpw2kcUFRQzw+Zi9ax9TpI90TLxPLf2TkuibkvwLXrlfj6MmYUKIjkAXIEoIceohv4okpMIU5jj4waj4ROiaGM8TE8fwwur1uP1eJrXvyKW9j67yVtLk5N0t2zFpknPUWKKEykLZSJrNznNrN5Bot3Nyh7b0TUkiNtLBlfUFtBFmNks3Q4WDvdLHEyvXcsPg0KzQl9t2c74ew6BmcQ6/Lvls6y5ibRYuVOIZrIRqbP+lJ/Dl9j2c8hOCIKOzs0i0Wcn0GEgRJsaLSL7XG5i1ex/DsjJYcqCQ7/QGhosIbCjkCCvb7XEY9CpmnjGdWKuVAc3Bo8rt5p9zFtFx8IvYHJlUFH7Lm1ufYHR2BleltuGFbRV0GPwsRdueQg34mbdlD9/s2suLJ03k/B5deHjeMp4VmUQLA4/qlQSBi2bN5aT27blveH9URSHSZKLUHSBRhGbNypQguUe4wTy+ZhMVanu6j74NKXX2b7iTFzdu49p+Pbmwe2dmffEdeWtnYDDFUF+5ijenjjnsGEVOD7bYni0/22N7UFK49JjX849KjMXC4xNHc8N3C5G6jmpQeXriWOymI9d+76qsYbBmw9KcXI2WETxcU93y+4zIyOOSlPdpWmjluBkHKn79YJ8YgMNkYq+i0U+387FeS44wY0TwvlrH9DaH96wJIRifm8343Gw0XefBJSu5cPdeIOQj949j/D2GCfNrE47PvwxBXUcV4oTECRwmE2+dMoWHlqxkTWMjXZISuH/owKMe4wclt/Kgjz2ql/bCwj7ppUzzMT+/kPVlFUxr34YEm41zunRixtad9BY2dksvSRjIwsRNcxfyzqnTMKoKiwqK6KlbmKaEVmwypZlL9u6ja3ICfVUHp8hQlckdioXzD+znYV0/pkBRWoSDcbnZFOwrox0W/qUkEpSSx/ILmdK+LcX4KZY6B6SPbGGmK1bWGVXWaS7emDr+sIfdiR/NIrHDNSSmj8PZmMc3K6+la4yRa7v0566lq+k+8n0aqzdQVfAtlU2Skz74nNuGDWJ8bg7/W72B+0mjq7AxU9YjpeTdvFLW1Lj5n/dJLrKYKXLvpEwcTC5LCGA+ZPXqB/J2fM/urfPoPfJ9DKYISvI+ZPbHN3LOvz4mPWcA6dk92LbqahxRHairXMXIqXcdprbY1FCKPboTihqauIuM7YbbWdkqtvyZeGzCaP41ay6fu/IJSJ3r+/c9rM/6B/bW1tFRsZGqh957P8VBUKuixuMl0W4j0mw+YnXKj/FpGmZFwaiHrpdBCGyKiv+QyW+H0US1DNAZC+/KGgbpTnKFhfepZUhq6hF71ronJdC9uRKrS0IctyxdhUfX6JOQwBMTR5/wtfm9ONZKWAdgKhANTDvk9Sbg0l9xTH8JChoaue7beexpqCfGZOahsSNaFA6PRoPPx9ubt1Hj8jAgI5W3T5t2zO1/4KW1G5mkRSKF5DItHwcKTiGxeRSqNxayUvXz7e59vDp9Ei9OG8+UDz5jb9DLg0o6uYqFBqnx9607uHpAH0yqSo3Xi6953qFSBvhKr6O0Mkiiw0aMPHiT8iKPKEl6JFLsDrp6FcYokSzTm/hI1hJf7GR1SRmj22QyP6+Qc7U8FARWUyS6v4L/TRp5mGhGaZMTuz0JmyO0CpiUOZm6gg+5qHc39tc1YHWkUV+9Fr1sCa+JdGwGhYXBUGnm1+eeRl5tHZet34yOxKxa6Tr8XQzmSJauvZG3tu7k4h5duHHoAG6bt5hRMoJKJUi5BU7t2O6w97S9ppHozAsQQkUIlajUCWyreQ+ASLOZz0+bxPz8ArxBL0OGTzmihUDfpFjezfuC2MSBCKFSU/AZk5N/nqLgH4HB6Wks/fu51Ht9xFjMxwz+2bFRzFGKmSqjMQjBGlxkRUWd8DmHZ6Tz7Kr1zNUayBFmPhR1TMzObhUkz+jcgU+37ULzuokVKjdphViMRs7q0pELex7ZFPQHVEXhzpFDuG34oJafw4T5nQnH5/8D7kCAW79fzIKiIgxC4Z+9exwmKvBjNF3nox272F1ZS1ZsFAyjAlsAAQAASURBVE9OGnNc5U7f5xegNXq5UUnmbq2YCFSqCWJQFKq3FlEgJG9t2srHp0/nhkH9WFdSxoraWv6tpDBQOJDAjMYSNpRXMCAthSafnyZdAxV8UudtvQpVwidbdqLIg71dPnRCLis/nSykR0XiVKr5m4inUPq4Sy/B4lO4YtZ3DMtMZ11JGdf5C1AQGBQTQhiZMbDvEVcbShtr6ZMcquRwRLYhNn0cI5KLGZiWikE1I1Ap3PIET4gkMhUzRdLPLUtX8s15Z/D4hNHcMHchLi2IGZWsXncSnzqCop3Pc9u2jbw2eST/6OLnvNLl1OpBjAiWK06mTb71sHFUle8kOmEQBlNo0jghfQKbl34AhK7JlHOeZv/O+TQ1lpGcdinJGYdPrCVn9GTFvOfwZJ+JxZZKWf4nJKZ2/1MmYBCy1fnqnFOp83qxG42YDUdPAbKiItmje6iTQWKEgZ3Sgy5Ck60nQlZUJNF2K687axghHazChWZS6RB38DmnW2I8XVOSeKGsht7SxuN6OVIRDE1P45ExP136eUrH9pzcoR1BXZ7wosfvzVE/ASnlV8BXQohBUsqVv+GY/vTI5gbbsW4Lj6jt2KF5uHnuwmN6ebn8Ac799GvauBVypYnH9xWwv7aeK/v/uNLkcOrdXnpiZIQayVQZzSrdyZuyiv+SSaJiRNclN9WWsKyohJFZGdw8ZABfL99MbvOEqZ3Qlzao65hUlSi7gzeqi3FpGjNlPeNFFBcpdma7G/icOgwamIXKx0odD/UZeVzX5MZhA7js6zlskm5W6k08omaSK8zsl17uyC/iuwvOxKyqrCouw69r9ExKbNUXFdR1nly5lq9378PlD1C461UyO/6DpvpdeDzVpEVEEGU2U7/8W4JqDH2lucWZfaBw8KwzVJp5Uc9uvLRpOzZHO7I6/QOLPRREYjJOYV35J1zcI7TU/drJk1leVExXo4np7dsecTUnN8rO+splRMf3BSRNlcsYFHdwzHajkZPaH1sA4+IencmrX823309HIBiWmcm1fQcfc58/OgZFIf4IIho/5uzOnVh+oJgrKguJEAZcBnhj1KgTPl9aZASvnDSRx5auZo6ngUGZGVw/qG+rbaLMZj46czpf7t6H0+/njswMuiaemLBGOPkK80chHJ//bzyybDXe0no+UtrQhMZdm3aQHRN1TF+h2+YtYX9hBUN0G/NFCUvyC3lt+qSfvC/Ue72kSyODlAh6CBsl0s8MvYirZDyjm0UrXgtU89bmbdw8ZADPTB7H1Pc+pb8I9RgLIEKoeIMh+fl4m5XtIsBLsoa9mgsHKneqqexu8PKB7uZZpYJOWJilNnJh5y7HpXx3XrfOnLNrHw/4ytkTdHOeEsckJRqn1Li5uIR7xw5nSEYaG8orqHK5aRsbQ4e41nZ0X+/Zx/Or1mPQJXtX30L7QU+gBd00Vq2kXefuJNhtxFvNlOx7i1jFTGZzaWaGMJGsmClpamJ4VgYpMQnUGNpji8giIS0UD5Jyzmbb8u9C22fm8OnZSXy7bz+alFwZaSGj+GkoPjiWj6d8RnRcNtvWz0bTfKiqmbqKlUTHZrdsI4SgTecj9zH9QEpGT4aMu47F3/4DUImMSePUi177yev5a3P7kE2UrPl5+wohjksRukNcLH/r2ZWrN24lXTFTrPt4ZNzIE05yDIrCK9Mn8dDilTxfU0tOTDRvDh/bKgEUQvDExNHM3JtHYUMTpyXEMSY784SSXSEERvXPlxwfT0/YRiHElYRKH1pSYCnlxb/aqP7k1Hm9VHs8TG9ugOyKjU6Kle1V1UdNwuYdKCDarXOtSAEBA6SDyzZs4vMdu7mgV3fO7NzhqPLmQ3IyeKd8Ix2kFQOwUHUSCEJ888erCEE8Bt7atJV5e/MZkZvJASXAN8F6OgoLX4oGBqYkt0iGdkuMY5Mzilk6xNZ7+FuziEFHaeE84aIyKwKjovBk5z5HrDE/Et0SE/j4zOl8sH0X+dv3kdt8A84VFhIVIyWNTromxjM652CfW5Xbzb0Ll7O7phajEBi9Gg+SglPVuXffB6zZ9z5GIbhiQG+iLWaiLWaeHTeMG+YvYpXm5Tw1isjm0sy2kaFg5/IHMBjMWOypNNZuJTq+d0ixsXYT6dEHZ3g6x8e1Usw7EjcP6Mn5M+exe9lGdKmRbNG5qs+xb+o/xqAo/HfUIO4e2hcpOWrp3l8Ro6rwv6nj2VldgzcYpFN83M/2DemSEM8bp0455jZRZjMX/gy5/DBh/sCE4/PPYE1xGTNkLGahYEZhgh7BmqLSoyZhZU4niw4U8rrIxqIoTJbRXFSxn/6vvsPpXTry9x5djyqm1C81hedYzwhpJxcz84WTaNVICof0pkoDi4tKuWXuQvqkpdApPpYXaquYJCPZIj2UGoL0Sg6JA+VER6OaHayP7U9FyRw+UdtgEgpdhY2dqh9PkoO9BiMXZLbl9E7H5x0aZTbz8ZnT+W7/AdYtXs4IESoDdwiVXrqNvLp6RmRltJIL92saT65cy6L8QlQhqHJ7uFOkEKMYeLJ+Hxu/nRDyTMhMZ2hzz/nrk0dx1ffL2R9sZK8aRTthIU96Kdf9ZESGVqya/H7ssTk01m5FSi0kqV+3lQT7wWenRLuNi3ocuYJBqyrhzG9O43QpucXuYsXS81Ctqfg8FZz+j7eP63ocSs9B59Ot/1n4fS4s1qg/xCqY2LIK8RuITlzetyeT2uVS7nSRGxN9XJOrRyLOauWxnygRVBWFkzscXnH0V+d4krB3gF3ABOBe4Dxg5685qD87DpOJoJSU4CcKlRf0SrYEXTjXbiI3Ooq2RzCwdQcCWHXRYoQcgYpAMMZr5dk1m/lkzwE+Onk89kMeUqWUeIJBzujUniqXixlbdiCRnNqxPZbySl6rq+FUGc1u6WGd3sSUcoV4oXHn/iW0S4xjgcfLd5qH3ilJ/HvYILZWVnH/ouVUuT2gS4LmWHzyYG9NAIkO3DFiSIuy0omQERnJJT278dn23ezSPaySTg7go1jzkvQjkQNN17nsqzl0dSrcRjyrpYuZej26kLxJFU1oWFEYTySvrd/MtHZtSLTbGJiWwqLzT+P5NRu4bNtOYoQRzSx4ecJEIDSLmGK34zNFUlH4DQ01m9ADTqJFLVeMnnhC7yfGauGL0yaxvaoaRQg6x8f/7KXwn5t8/NlRhGhloxAmTJgTIhyffwbxNit5Hi+ZmPhIr2G2bMB6wMng/HTG5BzuoegLaihSYm5+AFeFIA4Do2Ukb23dxWd79vP+9Im0/1Fs9waDtImO4oGxI7h/0XLq/D76Jydzclwqb+88wLUyASc6H+o15DRayG5081bBBiyRVkqjVZ701pIZFclbI6bg13SumjWXHdU1RCBoqF0NgB+JiebnAaFzbpcOjMvJPuFr4jCZOK1je97duI0VjY14hGSb9LADL0Pshz98P7JsNTv3FnOjjKNCBnhCdyGE5DNq2Se96MAoEcn6kjKWFBYxPDODtMgIPjp5HIsLCrljwTJiFAO1eoD7Rg1rWZ0ZlZnG/MotSF1j05JLMZqi8Tft4o0px9fnoyYcNDB+YnIaO/N20+gP0CGqCxGFT5B2Wkjg40RQVSNWW/QJ7fNXITMqksyo394/7f8XhJTy2BsIsVFK2UsIsUVK2V0IYQSWSikHHm2fronx8tPTpv/SY/1T8cmO3Ty1fA0GXdIDG6coMWyVHj41NvDVOacRY7FQ5/Vyz4JlbCyvwG40Uupy8U+RQBvFwod6LWYEM5Rkpmt7SUwZzIXZAf7eoxsAWyuruG72fKq9XqJNJh6fOLpVfXatx8Md85eyqaISA4LeQTNXq6Hfr9CbeFVvTmSMRt4//STMqsqpH33BJVos7YWFz6inOFLg9Ado41PppltYqLpIT4vHEwiwoqyMKKOJW4YNPGpzZoPPx1ubtlHpdNEvPZWT2rdBCMEXu/Zw36IVDBB2+gg7c0Uj2VlJPDL+YCnagfoGLvp0Fq/Kg0vS/wzmU0OQKSKas5U49uLlIa2MtkYr543oS7TFzC1zF1Hn95MV4eDukUOJtlrIioxotfRd7nRx44JV7KyqJMJsYlRmMk1uHwZVYVL7Nnywcz/bq2pIjYjg/uF9aXeEpPlQfMEg68oq0KWkd0pSq0Q5TJjfgk4vvr5eStn3p7cM81ciHJ9/Hturqrn06znEaApCl1ytJtEgNZ5Wqnhq8lj6pSaj6TrPrFrPzD37MAhBjcfLaBHBeCWK1dLFQr2RZ9QsbtGKaEofRa7YzRtTQjGsxuPhum/ns7m6ClUo3DioL+d1O7gKH9R1Hl+xhm/25CEBWxD+p4SSvwapcYGWhxFQhMLD40YwJiebMz/+ko6NgolEslG6+VCtp0N8HLVVDUzUI9ij+Nhj1RialcHnu/YgJZzVuQMzBvc/Ykmiput8uH0XOyuqyIiJ4sIeXbEYDOyuqeWCz2aRoKucrMSwAw+7bDqfnHVyq9g2/PX3eEhLJblZwOo1rZJZsp4OWLlVTcWDzr1aCW2EmYR2KVzWtwfXfDuP3Q31OAwGbh06kI7xcaQ6HESYD64K+jWN/yxby9y8AyiKYER6MqoMxdkh2RnsqWvku/xiLAYjN/Tv8pMJp5SSTRVV1Hm9dDLoJPyo2uTN/i+fyFfnD8EdES9SsmgzatyfQwHw/3eOFZ+PZyXsBxmaeiFEV6AcSDzG9mEICQFkREZwxTdzuUpJQhWCTGFmnfSysbyS0dmZXD97PgnVfv5LKov9jXyJh09kHU5NY7iI4GIlgX34MCAw2ttS5d4EhFbNrpg1l0uDsQxW0lgfcHPNt/P4+IyTsBqNxFosxFqt/K/Z++iO+UuIy2toGZtZCnQkQaAxEODiL77hyoF96CasjFRCMx5XyAROr9/H0+NHs7a0nDyniynJWawuKsVS4eIdkUNR0M8Di5aTGRl5WH+NKxDqcWvrVmgrzbx8oITC+gauHtCHZIeDDKOFG/RkhBAMkg7O3Z/HqcWlDEwPeaRYDQbcUsOLxIogKCVBJH4kFyjxKELQDRu9hY1dug9PwM+9C5dxi0ymm2plrquR2+YvZs75Zx4mHpLssPPuSSGlwhXFJdw4ZwFn6zHoSK7Pm09E6nAy+/+Hhur1XDDzVWafddJhK39SSlyBAEFd58IvvkHxhJqEG43w9qlTw/LlYcKE+S0Ix+efQZeEeD4762TO+2QmtwQTaSssIOAk3ce8vAP0S03mpXWbWL5jP3fLJFxo3EwRxdLPv7ViOmHhfjWdIJJKgkQYo6huOmh1cdv3i0mvDXCn0pYqAty+eiNZUVG0jwtJ2RsUhVuGDuSWoQOZtTePL5dugmaxOAuiWSg+FPdumbuY1062Utbk5GFCgkMpwsQy4WZIVhrB9BT2VdeSGpFAR7OJbzfu4lkZsop5eFc+iQ47FxyhdO+2+UvIL6hgmG5njVLF8oJi3jh5MjnRUXikzgNqFg6hMopIbnAV8fTq9dw29GBubzEYqNOCJBNKahrQiMfI39R4ooRKFCrTlRi+kfVkmY1c+c1cBjUZeVhpS57u4z9LV/H+6Se1SsAg5Gn2wIiBPDBiIJUuN6d//CWjA3ZyMPJ80Rqcllja9nsCv7eaWxffS5zFekSFP1cggFlRuH3BUtYXlpIqTOzVPTw9aWxLG0WwsoSL1lzWss/HUz7DXVXzs75TvxW2hDiKv9mAITHtpzcO84fneJKwl4UQMcCdwNeAA7jrVx3VX4TuSQnozS7yEajoUlIvg1gMKp5AkI2VVXyitEEVgn6qg5laPZeIeJ6TlWyTHp7RK9ggXUihUl/yDYNGhaxfihqbsKMwpFkqvq+wE6XVMOWDzzEpCl3j43h2yjgcptDNbVrHdszIn0+8bsCGwlOygjRMvKimoQD3+0pZnF9IjQy2lB7WEUSXkmcWrKRKaLwwdTzdEhN4evV6XhfZ2IRKB6wMkxHMzc8n3mYlyW5rWbVaVFBEjBeuEUkgYKDu4JJNW9haXklJYxNuLUBQSIwIDAiEULlm3lLmnHUSsVYrSQ4743OzuetAKYM0Gxt0NxmYaCDko5KBGU1K9kovXuCexSuRSPYoXroLGxNEFB/466h0uUk9Sh8ewDsbtvF3PY7RzcmniuBLTcNqT8dqT8dVPo8tlZWt/M/WlJYxY84CXMEAJqHQBQu3kYoQgje91Ty5Yg0PjRv5y3+hwoQJE6Y14fj8M0lxOIi1Wqhv1FpMiOqFRlJz7/WCvAIulLGkN/sKmhBcoiRyo17Ifny8rVezU3rREXgbtjAu42Duu6myihfIQBWCZEwM0e1cMft7HIoBm9nIC1MntFRYDEpP5b+s4ku9jvbCwttaNTZUnlAzSMLIR3ot9y1agUfXcQkdByqalJT5vXy6bht1UuOGIf04o3NHLv9qNqfpUcQrocTodC2aeXkFjMjOJC3C0TIhWelys/BAIW8Q6nGbICVX1hZy1TffU9HkxCChSWo4RKg/wqxY+Hh3AWNyslrsYq4c0If/Ll3FVD2SMulnu/SQiokC3UsXNVRauF/3UE6A97btQJOSROEgICTthIVeip3NFZXkRB9dEXd2Xj69NAt/U0L92e2lmRv8NdgisrFFZBOXdTrzDqxrlYSVOZ1c/c337KsPTTxHCZUXRBZmobBed3HbvMXMu/BsgFaJTLAy1EsGkD6mN3O63cO6FeUn9qX6DThjUA1883uPIswvxU8mYVLKV5v/uxj4aVOAMByob2BZUQkWg4EzOrbnzr0FjNQcbFe8REbb6ZeSghCAgFqCJGAkXRrRBcwXTaRIA5UEKZV+gkgUqXN17/YtEvdxVis1WoAagsQJAw1So1L387iSTiZmnq2p5JFlqxjXNpdqt4fuSQk8MG4Ejy5ZSZ3LQywGpijRWJrVA6eLGGa6PKiRFh5sKKedbuJbvYGzRCznyniWaU3cPHchs88/kyiTicKAn85YkVKyT/ewYPMOPt6yi8EZqTwyfhQGRcEf1HBwcAXKI3WEhK4VGieJGN7Rq7mBQv6hJDJLuImM6YaqBtlSWc3IrFDCc+/oYXyxey9PLV9DX2nln0oiC/VGbtaKGKJEsA8fXoNggLRxhZpAHUFu04rJwESWMOOWGlGHrGBpus6cvHxKnS66JyYwIC0FTdcxHWIDaESga97QmF0lOGv3cN2cHWQ4HDwwdgTpkRFcN3s+M7QEeil2VuhNPKdX4FMlFgTdpZWZDU2/9lcsTJgwYcLx+WfQ6PPx3f4D+DWNM7t34ukVa5mmR1EvNFYYPHzUtSMQEkmqlAE6i1BCkSvMPEI5Q4WDVdJFsfTjQyeIztCYIDP69285R7zFwh63l37CgSYl26WHc4jlTBHHXG8DV3/zPY9PHM3umloyIiN5+9Sp3L1gKR9VlpCMkSHCQXJz8neaEsMHDXmc37kTt+/JZ4hmZ53uJAkjD8h0Kghw0/K1DMvMINpqpUg0tozjAD62VtVx4SczibRZeHX6JBLtNgKahkkomOTB2NekBbGWu7iESFYIhRl6ITfIFPYKP3kGSXzmKSws2NKShE3v0JZEu43Hlq9Gr/fzuJpJLRq3akXs0Lx4hWSL4iFVMXOvTMWC4BGtjHf0Gi5U4jggfS1G1T+wpqSMzZVVpDjsTGqTg6brGKVoSZKNKMhmGX5N89FQ9B0fecuZvXsv1w7qy8kd2nHTnIX0aFR4WGlDKQFu1Ao5oProgJXuwkq5p/SIPl+HJmTF8zfQdf5JdIUWk+g/CuKOy34TUY4wvw0/mYQJIZKAB4FUKeUkIURnYJCU8vfX6fwDsrG8gitmzWUQduqFTplJ58JBvdhbXcuQSAfnde3cIt5wdd/e3LFhGyN0O3sVPznRMZgsZhqLK+mKla7CRkdh4XpZxNmdD6ocxdusXN6nBzdu2EpXxcbmgJNuWMhRQuJYk2QkD+0vZH1+CdmYeUS6uGfUUGaefwaPLV/Ne9t2skm6GEZoJW2H8JAaGc1/Rg/lox27WXygkOxyM+c2qyIOEA4edZYjpeTWEYO4e/5Shkg7ebqPKhngDZGNQHBfSTnvbtnBRT27MiQjjcfEar7V6mkrLDxPJf0VB1OUaADuUFM5R8vjEb2MgCmSqOiONFXMJ8J0cMVJEYLTOrYnIyKCa2fPIyiqqFSCJFodyPgYOigKawpLOF2LRhWCeIyMUSJ5U9ZSpwe4cUi/lhp2XUqunT2fsvJaOuhm3hNbubBPd07t1pGHFy7HoAs0JG/KGjS3pGjvO1TvfZfTcDBZRLPB6eLyWXN4cMwIkoWRXkpICWuwEsGrehWF0kcOFr5TmuiacvA9hAkTJsyvRTg+nxh1Hi9nf/o1GX4Fh1RYjYsZg/uRV1tPitHAR106kuIIVU5cPbgvV3/zPQd0Py6hU2GUTO/Ujk+37KQXNtoJC+2EhQ8N9ZzZKbeVZ9jdo4Zy7ex59FRcFGl+mtA4VQlJuo8TkbzgquTyL2fTU7GzCy9j2+fy9qlT+WZvHnctXIZbdxOQOkahsF16SDRbuGXoALqlJLKsoIjy/Q28TjaqEKRiIk0xU9zUxJUDenNe8UxK9FCp/BrdySNKBjnSzLuuWu5esJQXpk0gJcJBRlQkLzVUMVZGskg2IiX8iwQUIeioWlkbdPGsLMeDSmTyRLyNu4mMaf3IOCg9lRenTeCcT2fyXKAaMwKTYsCWFY8hqNPZ5WZwjUKkEro2p6qxPKKVs0pz0iU1icHN7QcAb2/exutrNzNY2pij+Jm9O4+bhg7g1fWbydKMpGLkHVEL6BTteZOGkvm09dRznZJFdSDIA0tXk2y3s6mmmtuVUP95GiYGCAfrdRcdVCvfyAY6xkT/pMLhDwmZVlNOyZP/46Lm13/vcsW+g5NhDeFesL8Qx1OO+CbwBnB78897gI+A8E3+CDyyZBWX6nGMUCJBwlO+CurcHu4cfrj30z96d6ddXAwbyyuYaLdzeqf2LC4s4sWKem7QU1CEoFj6MQkF4yF9TX5Nw6Cq9EpLxm4xM1ImULO/omV2Z7P0oOk6jytZGIVgn/Ry96JljM/N5uahA7m0T0/O+2wmt/pKMQpBhUHjvcH9sBgMXNi9Cx3jYrlt9gIaZJAoYeB7vYEYk4krZs4lJy6aS/r25Pk16zFKuE1JxdJ8gx2m2dlZWQWEJGTfOHkKjyxZxRxnHT5dYb/Hx2rdyQDFgRsdA4IgOqMCEjV/DnNpwGo0EtB06rxeYptr5/unpfDRGdNZVVKGJxDgrY1bKT1QSRthxqMF2KN4SREmpJTsEEGa4nqgNm3nnG6dW67ZutJy9pVX87SejkEITpJR/GP1WmYM7EenlAQ+qmsizmbh/l7D8Wk6K4qXsQCNs5rLIEaISObhotbroUzz00Do2lTLAI1C5zZZggFB35RkrhnQ59f8ioUJEybMD7xJOD4fN29t3kYXr4ErRah0sLPewDe79vHmqVMP27ZvSjJvnTKFufsPYFRV/t2hHUZV4YOtO/mXkkiUMOCTOpV6gCjzwYoLKSU1Hg/Ds9Lx6joTY2P4YvNO/FJiQLCbUKXFIzKNJGnCJTWu3L2PU7t0YEq7Nkxqm8uM2fO5trSYdMXMDtw8PnZ0yGC4bS4jMtNZmF/IPumlI1b2614O6B5eWr2RBIeNu0YM5raFS7HqME3EkNs8OTtGRnB3TQUQmuB8YdoEHl66kucrqgmgo7skX+p1TFdCZZIaEjMKbTDSpng53+j12HP7oEtJtdtDhMmE1WggwWbj87NOZmFBEZ5AgMade9m4v4SB2Nkgm4gRNsYTKjncJb1o9jScgSr+PXRgSzLk1zSeWL2OF0QWicJIUJdcUVrAu1t3MDIng3nl1VjVIJPbdqJPagoLCjbwha+cy5Q0YoSBGAxM0CNYXlRCgtnCLn+oLSEoJfsVP0t0J7NlE9E2Cy9OnHDc35dDk50fpO9/4PcQ9Oj61En8ARTyw/yCHE8SFi+l/FgIcSuAlDIohNB+aqf/X6nz+sgSB3uQMnUjtW7vUbcfkZXBiKyDKycjMzN5N2Ybd9WVkaubWCKc/HvwwZuVputc/vUc9Bo3PXQLy9RqOmWnUuVQudZZjEOoFKt+OksbxuY1/FzMOINBAs1mzDFWC5+edTKrS8vQdcmAtBSa/H4+3bkHi0GlV1IiA3LSuTTvALHCSAMBums2BpXrrKks5kvp5iaSWSSa2CjddMWGJiUr9SaES7Ykg+1jY7h/7HDO+PhLRgftxAsDz+sVLJdN7JBerCicLGI4XQ3NECbpBv67ZCW76+pQdBCqwpMTR9M/NaVFIvXkT2eSqRl4UIR6sNooJp7RK1gom6hVFGot0ahaI1M7tGl1net9PlKEEUPzdYzHgBGFD1dvZrSIpF7RsUabGJ2dhSIEwzLSmL1nX4tbfEDqVEg/baJjOK97Z2Zs3UUnxcJGzUWE0UhdwE92RCQ3DxmA5Rgu9GHChAnzCxKOzydArdtDpjS2lLdlCjPfeOqPun37uFja/8iU+MLuXbhl+x4G6Da2KV76ZaS28pR8Yd1Gvt6yi/FaBHsVH9/X1DMoJ4Mr9xeQKczslV7sqkpSc7mhXaikq2aqXG7ax8agCMGTk8awtqycOo+XbokJRFvMzNqbhzeo0Sc5iUv79OCedZuIFQaqpJ8ExcjgSiirbODf+5cwgUhSFSNLZRMBKTEKwRrdSVDTcfkD2E1Goi1m7h8znAs//wZHvY/pIoG5egOrNCdWFBrRaIuZO5VQrB0sHNy1fhMfb99JjduLX+pcN6APF/ToSqTZzKQ2OZz62WyKa+t4S83GIVRO0qO5Qi+gSBZiE2Z2KkEio7uT5PeR2uwLBuAOBDEgSGh+JDUIgVE38OX23ZyixJAtYLvJyxmdOxJjtdAnJYnl+wsodfpJa76OpUqQ3hYz944exs1zF9JDsZOve/EpIHWJ0aBy09ABP1tu/VDp+x8LevwWCVnLKlhCWJDjr8TxPC26hBBxgAQQQgwEGo69y/+/DM5M4/19ZVwjE6hDY7bSxJ2ZPY97f6Machf/Zu9+qj1uTk1Oos8h0vNbKqspq23gGZmBqgjG6VH8bd9+zIpCimIiX/MQaTKx1efiC1HHySKaT6mjU3RMq3IJm9HIqKyQMfIPcr29sFEng9wVdJNltGIUgv456SzJL+RmmYxT6CToBlbrjcSpBi4W8dyhFbM66MSDThwGnNWNzMnLZ1KzbP0Xu/cyULNyQXNpY44wc69WAoADhURxUC42ESM7qyq4Q0mli7CyIejiutnzefuUKTy2bDVbK6tRhZVEobQkpYNFBE9RQa8euczOLyVS+JiQncA1fbu3uq49EhO4R3pZrTvpIqx8KOvRgceVDGxC5RQpubKiiB1VNXRNjCfSbObiHl25detuBkgr2xUfPdKS6ZYYT/ekBIZlZ7C9qoaNq9ZxaTCWPoqduU0NXD7zO74574yf7RcWJkyYMCdAOD6fAEOy0nlsfxG9pZ1IVD4QdQzKPLHy8WsG9qVnahK7qmsZEhnBhDY5rSZJX964hZdFNnGKASklN7tLWFxQRIwwUKL7MQiBpuv8Vy/lBpHMNrwUCB8dDkn2hBAtxshNPj9nffI1UR6dSBTuDywn0WDGIVRSYqNw19Zxq55CgjBQjJ+FWiNxioHxIooN0s3lWj5WFDzo5OoWHl2+mntGDQVgS0UVdQ1O7pHpKIpguIjgfC0PiSQVI/HC2PLeEjHQFAhwihbFqUoKlQT499pNtI+LZe7efBYeKMAZAKtqbhH0SFCMZKgWBnbKYUVJFdEBI92txdw9dlQr2fwos4nMyEjebajhFCWGHdJDpfRyt5pKVxHqG3sqUMEXu/dycc+QTc9NwwZy43cLGCE91AiNYovk4c4diDSb+eiM6WyuqOK19Zvo51Q5R0lnX9DLbfMW895p08iNif4Z356D/FjQ44eETN7/Mm99/X869BG5fcgmSp78X3gV7C/I8SRhMwipLrURQiwHEoDTf9VR/Ym5eegA7vYv46ID+VhUA1f068Wo7ExKmpy8vG4TDR4vw3MyOaVju6PWJZtUlVM6Htk53BsMEiEMqM37WiRIqTNaj2S35kUi6e+z0EZE8YpexRtU0SkmhmcmjzvqmB9ZsooLtBjGKaGSgcdFGamaiQkiiev2FwCSpXoTL8pKEjDgBx7SSrlDTeMSkcgTsozzRTwTlSi+kHVsLq9sScICmo5VKi0zjzYUgkheUnPYKF28r9eQJowIBO+IWhyodGluhO6t2HFQw+Uzv2OM38ZZpLBAdzJLr2OjsNFGsfCGXs2Q1FRmDOzHjKM640CSw86zk8dy6VdzCAoVhyMLq9uDtVk8xCAE0cKAKxBo2eeqAX3omZrEjqoaBkVGMOmQYNsjKRFXIEC2amFwc2/dNBHDl4FGypzOsLlhmDBhfgvC8fkEmNAmh9JGJzev34Rf15mUk82Mwf3wBIK8vH4T+2vqaBsfy2V9erTylvwxwzMzWinm/oAmJbqURDaLXgkhaNQC9NCsNMggVWi0wcwEJYrPqeMsPY8Is4knxo8m3na4ITLAO1u3k+WG6wklZd2FhYVaI/9RMrivpgyPrlOGn7v0Yuwo1BDkfb2aHGHiHBHLY7KcdsLCZUoiRfh4payi5dgBXccqlJaEyIhARXCpSKSDYuEWrZjVupMsYeZdUYsKTBWh54REYaQfdp5cuZaIhgB3yCQK8PGMVsk7oppTlBhW6k7qTDpX9u3FLYNNh723HxBC8MLU8Zz+8dd8HmjEao5DCdqIkwc/gzhdxek/GJ+HZKTx1ilTWFJYTA+TkWnt2rQoQmdGRZLssHPrwiU8orRFFYKu2Ogr7Gwor/w/J2GH0tI/VlUCd1zGRfyy6oqHJmDhVbC/Hke9ywghMqWUhVLKDUKIEUAHQo/Su6WUgaPt9/87FoOB/44fyX8Pea3K7ebcT79mVMBOV4y8UrqeKpeby/v2POHjd0uMp0bV+DxYR29hYw4NBIG10sUlSmj17S29mtFKOtcryXwR5eHDs04+5jGrPR7aiIOGxO2EhRLpJ1Yx0EGxUWQI8Jy7gsfUTLKFmTzp5RatiAe0EtxIhuBgihqNLiW7FB8jDpGEn9Amm4u2bCdTN5EgDLyiVWFCECsMjCaSChHgZq0Ik0Fleod2fL1zD9UyQLwwUi4D1OgBUjBxlgjNEp4rDcwTTTygNKBpZeRERfLWISbPx6JPSjKnduvKd6UBEnIvoGjzf3kjUM1EJYp10kWNQaNLQlyrfYZmpDM0I/2Ix4uxWCjX/HiljkUo1MkgTXqQSLP5iNuHCRMmzC9BOD7/fP7eqxt/79Wt5WdN17l05mwstV4G6DZWlOVzRVkFr0yfdEST42NhUlWGp6fxdFkVp8to9kovtTLIKpxcpiQwiWhe06toRONeJZ3z9Dzm/e2sYyZ81U43ubqJH8SG2yoWvtDqMApBb91KINrMk7Xl/F3EM16Nxik1rtEKeEZWEpA6kahcJRIxCIXtupfkQ+Jzt8R4nEZ431tDL2zM0RuQQE9hI14YuV4k84ReRkDAkPRU4qoDbPV56CPs+KXOLuGlsNbLu0oudqGSiYmlwsVXBo3PAwdwGFTePGlyS3J0LJIcdv4zegi3LF5PSqd/UVMwk6drd3ClkkilDDJXaeLl7MxW+xypXPQHjIqCTVUpln6ymi1tioWfGMuvE58PTZBKFhxUV4SfX6540ZrLKFlD2BPsL8yxVsK+BHo3//8jKeVpx9g2zDGYvW8/3YLmQ7wuLNy6edvPSsIcJhNvnjyF+xctZ0FjDW1iorEWNzJDTaZD8wpSlQzwplZFojDi0386iAxIS+XjvDKuk4k0oTFLr+cCJZ5y6We37ibNGgHuANkidPNqIywkYmSGmoyK4FatiApZgkuRRETbOadLx5Zjd4iL5dkp43h+5Xqq3Q2UuQIIKfleb6A9FmbJevoKO4pQmJd3gHO7d2HGtl20Uyzs0T1c0L0LX2zdTUBKCvBxn16KRKJpLqJNJp6cOPowI+Vjceug3sRv3s6iwhfpkRxDqc/N3Q0VZEZG8Pro4wsWP9AxLpah2RncXFBCF2lhnXBzaa/uJzSeMGHChPkZfEk4Pv8i7Kurp6i2nhdlFqoiGCojuLS6gAP1DT9rxeS/40fx8JKVPF5aTpzVirnOwMkyijHNlSZWFF7QK2gUGvoRpNJ/zICMVJ7YV8Bg6SAClff0aroKK16ps0pxkx2bzI7aGoY3e106hEo/4SBZGJkgorhRK+Q6WUS8YqLQEOCtYVNajm0zGnnrlKn8d+lKXq1roMLlwaEpvCtr+Icez9uymjRhIku1sLqsgn/06cGT6zfTXrFSKv30TE+mqqiEeqmhS7hXK6EYPzIgsQiF20cMOmqSdCRGZ2fykJS8vf0tIiySqIw4Hqiqwm408uCQkXRNjD/uYwkhuG3oIO5ctprB2MlX/MTFR7Xqwf+1OFb/GIRUFo/GoeIfEE7A/uocKwk79M4Q9h/5GUgp+d+6jby8YQua1JFCcpWSGPKiCpXwnzArikvYXlXDyV06MLFNDs+v3cDG4jKCh2wTQFJGgBgMFDe5WFtaTr/Uo0ua3jR0ADe4FnBW8T4gZFj8tqilEY0bBvbjk227qEOjUPrIFGbypY8KAniljh+JQVUZ3rsDXRPjGZCaelg/VN+UZN44dQrf5eUzd28+lV4PH9U3Uu+t5BwljtOUWJDwVqCaBq+PN0+dQkFDI7nRUeRER7Gvuo57Sks5oHn5p5LIMCWCculnhr+Qv30+i9nnn0Gk2Ux+fQMVLhdtYqIP8x/5AVVR+Gevbvyz18+6/K0QQnDfmGEsLCiisKGR0+LjWjxUwoQJE+ZXJByffwEWFRRx27zF1Af93EIRN6spxGMI9W3JE4/R+2rrWFpUTJfkRG4eOpCd1TVcO2suwUPifQBJIxpVzQq772zexiW9exz1mBPa5LC/tp7LNmxCkxIzAqtQWYGb4VkZJDlsxGBguXQyRkTikhprpZNJRFEuAphVlc65qYzNzaZvSvJhk4TJDjtPThrLtspqPtm+i8LGRvKdbs5p2k8PbNynpCEQDNZdvLV9N1+dcxrbqqqJsZjpmZTIu1t2cM/aTUQGIUdY+K+SgR/JnVoxdy1cSlpkBD2TEql2e9hXV0eS3X5Mg+axOVmMzck64Wt/JE7u2I42sdFsKK9kpM3KhNzsFsPq34ofJ1E/Vlk8nn3C/HU5VhImj/L/MMfJzL15zNq8m5eVLGyoPKKV8YRWTq1RcmrH9j99gB/x0vpNfLxxOwOknW8VL9/vzSfKYqarsPKEVs65Shx1MsAsWc8zahZudCJ1hfsWLuPr847eJmA1GnD5/ZyqxHI+sRTg5y5KeWLCaIZlprO/tg7ZGCpBTMRICX4yoyL5X6AOq8HAvYOGMS4n+5hjf2X9Zj7ZuJ3JegRB4afaaqBTbCy59QcDQpY0scXlpl1sDO1iD5ZHPjlpDG9v2c7Tq9YxTAn1XyULEz2EjUI9yOaKKraWV/Lu5u1kqGYKdR//HT/yiDX7R0LTdV5Yt4nZe/KwqCr/HNj7J9/PDwghGP2jEokwYcKE+ZUJx+f/IwUNjdz6/SJuk8l0UC18Imu5Syuhm8FGfISD3GMkCkdidUkZ182ex3DpoFbReGvjFv7RtyfZqpWvg/WYdYUIFF7TqzhfxNFHsdNGmnl27UbO7toZu8l41GPrUtJVsXGLTEYiuVspY0SXtswY1I/5+QXMNOzmrWAVX+l1VBLAajKywuRnsaxhSocOXNGv1zFLK9eVlXP1N99zkh5FLpJdioczOrWH3dUtK3XZwkydz0ei3cZo+8GY97ceXUiPiuCe+Uv5lx6FIgQWBKOUSObrDczLO0CTz8/NcxeSqZgp0nyc16MLV/bvfbThHMb3+Qd4cdUGvJrG5A5t+FffY7+fQ+mWmEC3xITjPtevTbivK8yhHCsJ6yGEaCQ042Zt/j/NP0spZVh54Bh8uzePV9ZsYrIeQbwSurmeo8Zyvyzj7z17cPEhNenHg8sf4KX1m3lJZBErDAR0natLizmtZ2dWqEFO0WNYLZ3kSx/pmPhOb2CpbCIbM0VNbmbv298ilnEkNlVVc7OSiyoUcrEwnAjy6uoZlpnOdYP6cX19IwVl5RRKPznRUUzt0Jap7XJbjC2PhZSSlzZs5hkySGq+Fvf5yoiMs/NJYw250kIAna+URs7NCs0ILiks4pMtO1GE4JyeXZjcNpcX121ku/TQRVhpkhq7pRcVlXKXiw+37OBZMojWDeyQHm6au4jlF593XLNeL63fxIKte7hGj6dBatwzfxlRk80t6lRhwoQJ8wcjHJ//D2ytrOLJVevoKC10bi7jP4tYPqSWATnxvDJ0AOoJrpg8tnQVV+jxDFYiQh6h3gryaus4gI8LRBw7pIci6ScIRAsDM7QiOgkLFil4eOlK7hsz/KjH3lBcxkkyCkezJ+fpejQrmk2Dx+Rksb1bNa9v2oKvOQE7qVN7xuVk0SMp8bjG/traTVykxzK2uWTSogt2NzSxW3EyXEaQjJF3RS0DmmNifn0DL67ZSIPHy4g2mUxum0tubDRrqly0FRY0KdkgXShCYDYYuOn7hdwuk+msW2kgyHVbdjAyJ5MuCT9dXrimtIx75i/lWj2RKKHy0pY9COCKfsefxIUJ80flqEmYlFI92u/CHJtX1m/m043bSQgq7BMHPcL2Sx+dExO4tM/RSw+OhtPvxyIUYgh9LEahkKyY6BAfyxm9uvDShs0EpE6f5ER2VVazRG/iOTULh1DZL33MmLeID7bs4OnJY4mxWA47frzFwm6flx4i5Pm1T/Ezwh4q6bMZjbw4bQLryyq4ZvY82jUJdq7bx5sbt/LuqVOPWVrwAwFdJ+KQoBaJysCMNPKsVi7ZsxcpIcFiYWNpOTajkUeWruICPYZyGeCKorlIJFZF5S69mAxholIGiRIqKXFR2I1G2ilWovXQ17mzsIKUNPh8xFmPrDh1KHP27OdfejxthQUETNd9fL/vQDgJCxMmzB+ScHz++fyw6tNfs7JdegmoIR+tYgKYVIX/jBp6wgkYQIPPT4Y4mPum6UaklDw2YTR3zF9ClddLx+hoDE1NPKWV85CaQVthwSU1Lt+bz+LCYl6cNqGV59gPJETY2V3TQD9Ck567hY9Ex8EE5poBfTinWyf+/sW3JHokDVtKuGLbbu4YOfiYk68/4AtqRHDwKxWFisNg5Loh/blzxRqaAgHijCba+63Mzy/groXLmBaMIEcqPFe+jseWrcGiKGzUgyzTmwggkYBmUpjQJoe3N29rSXajhIH2ipXChsbjSsLm7stnuh5FH8UOwKV6PC/u2R9OwsL8JfjLu8pKKVtMin+r872ycTNPygxsqsLNWiG3a8VEKQa2ql7eGDb5Zx03wW4jzm7lE1cdk2UUm6WbPOGlW0ICo7Iy+Ufv7khAEYK7Fi1j9+6iFq+OXGHGhMKOyiou/2oOH545/bCl/P+MHspN3y2kp2KnWPpJTYhmQm52y++FELy3eRuna1FMV0KlggnBWl5eu5GHxo085tiFEEzIyeLJwirOktEckD7WqW5uyUznnC4dKW5sQlY0McEXyeaCBh44UMRFMpZMxcxrejVniJCB5ed6HWOJZJXRy7D0DLokJXBul04UNTaxOejkJQkdhRWTFJhNhiMmm0fCYjBQL4MtXRb1QiPe9Jf/0wgTJkyY3x2/pv1m8Rng9XWbuVCPZYwSySN6GddpBbRRLGxSvNwxdNDPSsAAhmam83ZeKVfKBGoJMkdp5IHMPgzJSGPhReegS4kiBIsLirh29rzQpB8hs+Z2mNni83DRF98w69zTSbS37mm+dlBfziudSb4WQEdSatB5f0DrJGR+fiGJHridFFBggLTz2PI1x5WETe7YlpdXrMcuFYJS8r5Sx+0dhzIuN5tGr4+P12/lQi2W2gqNf3+/iB7YmCSiuF4vZKCwk62YmanXM0hEsUA4GZiWQru4GM7t2pkEuw2DovBIsIxOwkJXrOzETdtDWg6OhdVopOYQ//F6GcRyDDXJMGH+TPylv8lf7d7H/UtW4NE1usfG8dTksYfd3H4N/LqOQyg4hMoTaib3yjKichP4fEBfkh32n3VMRQhemjaRW+Yu5NPaA6TY7fxv7PgWbxEhREun9iW9unP2vnzypY8cYWah3kgECh2xsKSulqu++Z5nJo9tVarXPzWFe0cNY1lxMZ1sNi7t2e2wYNTo8ZHCwbr1FIwUe33HNf77Rg/jseVrePRAIQjB6e07EmezUuf1sqG8gndFLgZF0BM7G3U3pQTYqns4W4nlpOakLxKV1bqT+oCfR8eNaqlVf2fTdhKFEatU+VCvpV7ReHny8csL/2tgb+74fjFTdT+NQmOZ6uajLp2Oa98wYcKECXPibK+q5vrZ8yl1u0mwWHhi4mh6JSf96uf1BTUcqChCcLOSwqt6FXujBC+PnnhcKzNH4+ZhA7hXW86/DhRgVQ1cPaAfQzIO9v/8EI+GZ6YTb7My39vAGCWKA9LHHnycIWKZpdVz5idf8vEZJ7d6Vkmy23l60li+3L0PRRHc37XTYc8yTX4fydLQMpmYghFn4PjcCk7r1J6ArvPKpm00BYL0SU9r6aP6ZNsurpWJtFNCSWOlFmAdLlbgJFuYuUINfWa9hY3rtEKyjVb+0bsHvVNCr8/csw+hSxIwsE66eFtWc/2Afq36vo/FOV07cdbOPUitikipMktp4P6BI45r3zBh/uj8ZZOw7VXVPLJ0FQ+TRoZi4r36Wm6cs4C3T5v6q55XCMGknByeLKjkLBnDAemjRA3yVL/ePzsB+4HUCAfvnDbtJ7fLiorkzhGDuX7+EqwIbCjcrqbxnV6PVSpUVdQxa28eJ3cIGUIvLSzmhu8WoGs6OpComlhxoIg3T5nSasZpZJtsPqjbSppuQkfysVrPxW2OryTAbDDQPiGO7/buZ5TuYPP2fC4sLOG5KePQAQ2JAYGUElTBXL2JNN1AL3HwmjlQqCVItiOiJQEramzk+7x8XiYLq6pwhozlMgqIPgGvrlFZmTw7ZTxz8/JJMhj4qEtHUiN+utctTJgwYcKcOJ5AkCtmzeXCQDTD1VTW+l1c+c33zD7/DKJ+ZZ/FaZ3b8dzSNdh0BQ3JatXNHf2H/Z8SMAhVVDw4dgQP/sR2Qgj+N2U85302k5eDVejQopqsSUkfv4UnVqzh4eYKk6LGRi77eg6VTjcBJBmKie/25PHWKa1bAQanp/HG+i30lzbSMPG6qGF4xvGJQAgh6JGcyDNeL8Olg8CBOk4v+pL3T5uGqggCh6o7ipD68hrpxM7BiVoHKgEkxUEv6ZERLa8/vnwNd5JCe9WClJL7lHLMxqOLkPyY1AgHH50xnY+378IbDPJsmwEtCV6YMH92/rJJ2MbySgZhb/G2OlvGcGZ1HvI4fDn+r/xn9FCeWLGWl4pKiLVaeGXoRNIOuSn9Fkxu14ZVxaVs3l3EZUoCBdLHPNnIRSKeWk2jtMkJQJ3Hy01zF3InKXQ2WNmku3hUKyep3sfLG7fQJzmJzKgIMiIjOb97Zxp8Xu7YtguE4LxunTmt0/GpPEopeWzFGh6WqWQqZqQuuaOpjDWlZYzMzODB4nJG6w62Kl4MdjP3DRjIK2s380Z9NVG6igq8pFfhV+GN8SNbjtvkDxCtGrDqoWBgFQqxihGn/8T8SnunJIVv7GHChAnzG1DU2IhVCkY0e1v1Fw6SRQN5tfW/+n345A7tCGg6H2zZgSIEN/Qa+ItJoh8v7eNiuW/0MO6dv5RrSMKMwuN6Oe2xMBA7MxuaWra9Yc4CRrktnGZIpUoGuFkrYoDfwYOLV3BJ3x5EmEx0jo+jS0I8940ZziNLV9EY8DM0PY17Rg877jE9v2o95+gxTFGiAYjRanh1/Wb+1rMrT65cz9l6DLUEWai6eGrcWD7YsoMVJaV00uvJEWbe1qtRheDW4YNardI5g0GSmytohBCk6Aacfv8JXa/UCAfXDex7QvuECfNn4C+bhMXbrOwXfjRdogrBXnzEmsy/egIGYFJV/j1s4BF/1+Dzsa2yGofJSLfEhOMumQNYXlTC0oIioixmzu7a6Sd7nu4cPoSnTGu5b/tuDLrkXyKRPoqdW0UpdzabHubXN5CqmOish8oaeyp2onSVgKbz1oatrDTmka95uWZgH87t1pmr+vfhqv59jnvMPyABlxZEEXCbXkSe9GHWFHbX1PHwuJG8vmkLm8qqSIuO5T/9ehFpNjM+N4dZe/N4d8NW/JrOabmdubR391aGyrnRUfgNgq+8dQwTESyXTXiN0OZHJpsVThcrS0oxqyojszKxGv+yX/0wYcKE+UMTa7VSowWoJUisMNAkNco1f0t5/a/NGZ07cEbnDoe9HtB0NldWEtR1uicmYDuBFZv9dfV8uTskMjWtQ1va/0S53cS2ufg0nSeXr8Hj9zNURPAPkcDjSiXdUkL2KlJKdtTV8R+lDQAJwkh/YccpNdaXV9Dw3TKq9QC90lN4dMIoxuRkMeZnJpSNXh9x0sgzWjmrpBMVQVq15N7Rw4gwm/huz35sJjtv9x5G29gYhmWms6O6hseXrmauu4F2Cck8NrBvq1UwgOEZabxcXM1FMpZi6Wex4uSi9NRW23gCQRYXFuENBhmUlkrS/7FqKEyYPwtC/gxDwp+ia2K8/PS06b/4cU+EoK5z5ay5lFfVk4mJddLFg2NHMOp39HTaU1vHpV/NJlkaqJVB2ifF8/SPerMORUrJxzt288mWndT7fXh9AaYRRakIsNMc5JOzTj6u0o1Kl5t/zvyO0qYmfFLn0l7dW5SFSpucnPrh5zxNBvHCSLn0c41WQBB4Ws0kQ5iplAGup5jPzz7luMv0vMEgihCtGq4v/3oOW0orOElEM1GJZqN08Ypay7fnnk6M9fhENI7EgfoG7pi3hP0NDeRERXL/2BGtyjR2Vddw8Vez6S6sNKDhtqq8d9q0VslcmDB/Zjq9+Pp6KWV4qjjMT/JHiM8AL6/fxPsbt9Nd2Ngu3Uzp3J4Zg/v9buNxBQJc/OW3uBo9mITAbRS8ferUY/aRry8r56nla6lye6j2ephMFArwndLEyydNPC5/qqCuc9u8xcw7UIhCqD/7iYmjW1oBxr39EZd4o+inOPBLnRu1ImoIcpESzzglCr/UuV0p4+/D+jKtfZvjeq8BTceva9gPSTLf2LiVN9ZsJFeauUpNooog98kynpoylv5pP18p2OUP8J9Fy1heVEKU2czNwwYyMuugh6fT7+f8z2Zh9QSJkipb8PDa9El0OoJKZJgwf0aOFZ//sssBBkXhf1PHs6SwmFqvl+uTEsn90erIb80985dyZjCSiSKagJTcXVHKl7v3cfpRSvq+2LWXV1du4Ao9nkf1Ru5W01oUlR7xVzBrbx7nde181PNpuk6504XVaOCzs06m2u1hZ00NTf4AhQ2NZEZFkhrh4PK+Pbl+3WbSdSP7dA8BJAmqiYzmUs5EYSRdMVPS5PzJJMyvhdST5hcUAXBK+zZc1LM7DrORGUP6ccGnszhDiUUIwQgRyTzhYnt1NUMz0n/OJQUgOzqKd08/eq/cw0tWcb4WwwQlCiklT7gqeXvLdq7o2+tnnzNMmDBhwvx8LuvTk35pqeyrq+NvUVH0S03+XcfzyvrNxDUEuV+moQjB294aHl22ikcnjD7i9vtq67jqm++5VI9jkdQZI2I4VYkFIE438PLaTTw7Zdwxz1nlduPXdB4eNxKn38/e2jrKnS721NTRPSmUwD04dgTXzp5Hhl5PkebDgw5C0Le5X9okFLppZgobG491qhZe37CFZ9ZtAAld4+K4a9QQbEYDf+vRhZfXbeJiEogRBmIwMFlGsrSw+P+UhNlNRh4ZP+qov39nyw5SXZIZMgUhBHP1Bh5evJK3fuX+/TBh/gj8ZZMwAFVRfteVrx9T3NREb0LL8EYh6KZZKGw4+o3zm517uUiPpbtiQ9MlsYd8XDFSwR0IHnXf0OrXHKqdbty6xknt2+D0B9hcVEaWMHO/7m5ZGfx7r+4My8qgoKGRVIeD1AgHE9/7mG26m67Cxj7ppVj3kR390/6fz65eT21xLR8ouWhIbttdyFe796ED7WOiCQhoQCMaAwGpU6H7iTT9uo3YVW437UQoOAohaKebqHK6ftVzhgkTJkyYY9MrOZFeycdnKPxrU1TXQE/diqKEWgR6Y+X9+qPH54UFhYyQDkYokSzXnMQdEp9jMeA+Rl/yD6tfiwqKMApBdnQ049tl8/LaTXRRbOzRPUzv0oHrB/WjX2oyX559KturqokwmeiaEMflM79jXk0jZxBLo9RYrXq4+ThWjpYVFfPuhq28KLKIEwZerqni7E++QkFgMxqJt1kpdflJE6EqkVIRpLflV47PThdtdROi+bq3ExZmumt/1XOGCfNH4S+VhEkpeWfrdt7ZuA1NSk7v0oF/9e31m/SBHW08S4uKKW500ik+jo7xccytaOQ8GYsTnRWqm2sTjn7jNBsNNBFKtAaLCJ7SyrlETaCkua764kOW9H/MfxYuo2uTwgVk4RY61+8tQAWeJgOTUNglPdw2bzHLLz4PRVFoGxvTyrfjsfGjuXHuQmwoNBHkgdHDSbD9tLz/xpJyTtGjMDeXWJ6sxLBKOrlGSeLGuiLaxEXz78ZSBug2titeeqQm0y3x/6ZK9VP0TU3ms/0VXCcTaUJnruLkqvQuv+o5w4QJEyZMa1aXlPHQ4hXUer30T03h7lFDiTD/fmXhO6pr2FxRSYLNRqekeBaU7mao7sCAYJ5ookvi0VfnTKoBp9ABGCIcvKPXkCCMqMDbSi2Xdjh67/T723ZSWFjFG2RjkoJn6ip5ctU6nlOySNFNNEmNq7bvZmr7NrSLiyXRbiPRfnBC+cGxI7hs5hzmeApwyiBnd+rEqGM8D/zAxvJKhut2EpRQGeIZSixLtSbeUXN5NljBlkCAp5UqRkoP1UKj2Cp5+Ai9c78kfdNTeGZfAUNlBJEofCbq6Zv2+66KhgnzW/GXSsJm7s3j3TVbuEmG5F6f3Lwbh8nEBT26/uZjkVJy98JlrM0vphMWXpIuzujWiXmuAua5CnDrGmd26MD4QwyRf8zFfXtw9TffU69rRKOyTDh50FhFjNXCU0PHHrPxd1d1LXeTiBACOyrZmhGTomASoeRol+7BJYP0euVtxmZlcv+Y4a3EKoZkpLHggrP5fPce8mvrKWpswhMI/qSgRVKEnV31LnoSSth2Sg9xGLAJldFKJMsCfu4aN5wd1TUMinAwqW1uS5K8uqSMT7buRABn9ehM35Rf5kZ8y7CB3OxZxJnFeQghuLRHdya3yflFjh0mTJgwYX6a/PoGrps9j6v1BHJELO8X13Lr94t4bur432U8X+3exyNLV9JfONiPj/SkWJJT47iw+AAGBO1jY3hoyNF71Ka1y+WtjVt4yV9FCgY8iuRJQzU2o4Hzunc/pnLwzooqhuk2LM2Tlf10K+twktK8ApUvfXi1ICd/8iVdYmJ5fNJoMiIPVqKkRUbw1TmnsbigiFXFJZgMBkqdLtJ+ol0gyW5nlXpQsGyX9BCPAUUIJipRrPKV8s7p01hSWEw3o5Fp7dq0JMnFjU28vG4TDR4vw3IyOa1T+19kgntSmxzy6+q5dOMWpJQMT0/nlqMIm4UJ81fjL5WELdh3gDP1aNo0mwqep8fy7b4Dv0oStqa0jCeWraHR52NoVjo3DhnQSoRiR3UNS/YX8rzMwCIUqmQ0V2zexsILzsIZCGIzGn5S3bBvSjIvnzSRL3ftxSgE73UZQazFwgOLV3DvwmXkREdxx8ghR/Qfy4h0sKHaRZowEZSSGlWjTPdShJ9S6ecrWc9zahYxqDxTXMV/l63inlFDWx3jrc1b+XzTTsboDlYopXy3Zz/vnDa11fv8MTMG9+f8z2exR/dTH/BTIwM8o2YTlJK1uos4exTDMtMZltm6B2x5UQk3fbeAc/UYJHB18fc8M3ncEfsEmnx+3t6yjcomF33SU5jWrs0xg4HNaOS5KePwaxqqEIeZUIcJEyZMmF+X1SVlDBAOBiihROFfMoGzSvLQpTwhleDjobTJyX2LlpNfV0/b2BjuHDmkleKeLiX3LVnBI6SRhZmglNxQUcJNY4dw64hBaLokyW47ZlyJtVr56IzpvLN5Ow1eHw/l9GBoRjpPr17Hp1t38d2e/Vw/pP8RJxMzo6PYoFQzvvm978dPQMAyvYmOwsKDeik3Kyl0F1ZmNjTwz5nfMfPc01tdp/VlFdwxfwmTtAhKFcmZ23by4enTWiVrP+bkDm35dvc+bqotIVITbNfd3KOEvMTW6i5MqkqHuFg6xMW22q/S5ebcz2YyNmCnO0ZeL99AjdvD5X17HnYOXUo+3rGLHRXVZERH8rfuXVr5jf4YIQRX9uvNv/r2Iqjrx3y+CBPmr8ZfKglzmE1U4G75uZIAjl+h1CGvrp5rv53HP/V40oSDd/aUcF9wOfeNHt6yTY3HQ5piwtLsX5UgjDiEitMfOMwzrMnnR1XEEeVwuyUm0DEujsLGRiyqyqVfzaaLU+E6YljhdnLOp1/zzOSxdEmIb3WDvnv0MP7+5bcs1z3UyyBtEuM4JyeLGStWIXXJuUpcS933OTKGR4pKWp03qOu8sH4zryjZxCkGpJT8u6mUpYXFx5TATYuM4MtzTmVVcSk7q2t4c+MW7tKKaUDDr8BXY47sdP/epm1cpMcxptk3RtHh/U3bDkvCPIEg538+kwwXtNVNvJRfTH5tPdceh4dI+OYeJkyYML8PDpORSgItXp2VBLGphl88AfMFg1z85bcM85o5g3iWljm55KvZfH72KS0xwK9p+HWNDCUUAw1CkCVM1Hq8h5Xd+4JBPEGNKLPpsKQswWbj+oF9KWlyEtB1Hlq6kt37SrhSxlLi9HPFrLncM3IIo3OyWiUiF/XsxvLCYq6pL8YqFFxmeGToSO5dtJx6v5+2wkwfJZQ0nipi+MydT43H02ps/1u1nkv1OEaooZhpCVbzzubt3DZs0FGvjUlVeXX6JFYUl1DmdLFn2Wqe0Cuw6oJygjw0cvgR95uTl09PzcJ5Sqh9op1u4dbN246YhN29cCk78ssYrtlZqZSz5EARb5w8+agq0D/wYzXlMGH+f+AvlYT9o08PziuYRb0WxIhgoeLk1QGTfvHzLCoo+n/s3Xd4VFX6wPHvmT6T3jtJ6L2D9KIUFUQEZa2IvZdVV13F+lNXXd21r2IDG4oNK7333nsgAdJ7ncxkyv39MSHSQg0J5f08Tx5N5t5z37kJ98x77znvoa/mTx+dL5l6QIvgvj17+b+Diii1Dg8nVXOy2ltBJ2VjulaCxWI85G5cpcvNYzPmsjQzE68GVzZrwnMDeh/ypCazrJzbf5mG0+mixOPCio5xKolCPCz1loND4/5fZtAqOoJ3Lh9EQWUly9IzsRgMfH/NlewuLqbc6eJ/K9fyf0uW4W8wEBXkx+ziUkxeHZepINI0J6GHlYh3e31j3YPwXRSVUoQqwzGLgRwQZDYztEkyQ5skc0Wzpny/fQdWg4Gx7dvU+vTP69UO+WM0oPAcZfmEhfv2Y6v08LAWg9Ip+ngDuG3DZu7v3lmecAkhxFlqcHIiE9du5JWyHJK8BuboynmkZ92XpN9ZWIS+ysO1ypcwNNJMLHHsJ624hObVT3gsBgMtQ0L4tqSQMVoou3Cw1lvBY4cVCflg1VomrNuIHkXzkBDeHz6YUOtfa5lVeTw8Mn0O6zJzMCodJR4X/9MlEoqB7ylE79F4a/5y3lm+hk9HXk6Ixcy8vfupcnt4bcgAcivsVFS5+H7zdh6aORcdirYRYezOLWSSJ4/RulDseKnSvAQctqSK3eUiTP31eSIUA7nO2ouBHGDQ6ejXyDd/7PImjZm4cTPlVVWMbNGs1rLwXu3I/tnLkf1zUaWDaSmpTFTJWHU6LtOCeLgonQ05uXSpo+kFQpxPzqskLCk4iCnXXMlvO1PwahrfNG18yHpRdcVi0FNSPSEXoAgPlsPu4ITbrLx92SD+OWs+WZUZNA8K5qPLLj3kbtBby1bhyi5lsmqCS3l5MTWTr8O3Mrb9X8Mnn52zkN6VZq5VMaTh5Anvfqr0Gp94c+mh/LhJF44HeDknm/8sX8Wv21PogG89rAk2A1+NHs6NP/5OzzIDr+qassvrYHxRBiNVMPO8pfyhiinTa3zQd+hh79HARTHRfJCbxygtmB2ag82qkv87yTLCTUKDebLXRcfd7ur2rXhm9gLWeuxY0bFUV8G/2g04Yjunx0MA+po7kv7o0NDwaBpyD00IIc5OZoOBL0ddwY/bd1Jgr+TVuBh6HrZob12wGAyUax5cmhej0lGFRoXmOWJI3DuXD+aRaXMYXZBCiMnEKxcfur7k3LR9TN2wnU9UEkHo+aw4n2fnLDpkDtuk9ZspzSrmU5LQa3CDtptSPKzSKijDw+f6ZIxKx3eOQp6bs5Acu53ASo0AdLzBCj698jKm7dxNeWYx3+qaUImXJ3L300XZKMDD3Z40DHodf7+o6xHxD23emM/WbuN+TUcFXn7UFfNisw4nda4CzCYeqF4z9FgGJyfy4ap1lHoyCcXAdr2Tq1sdWbDD6fFgUjrM+PpnvVL4KT1Oj+ek4hLiQnFeJWEAcQH+3N2l4xk9xvBmTZi0dhPvOnOJ0wz8oSvjvu5HVkLqFhvN7JuvrXXM+4asHK7TAjEqhRE9gz0BbMjIgYOSsJ1FxdyGb42ORGUiGiPPaBkUaC5G633rbRnwTez9eecexnlCuKR6Paw3K3L5fMNm0kpLeUPnmzfVAitdlY1YZWK0LpRxWir/u3zoUcsEvzn0Yl5asISXsnKIsNn4eMClZ2wl+wpnFWi+0v3bNQcxgf70io87YrsecbG8zgr+9BbTXFn4SRXTPz5ehjEIIcRZzmo0cGO72te2rAtNQ4LpEBvFC5nZdPVaWaG30yMujoTDpgFE+/vxzTUjau+fqysJhuh8H5NGaME8kZt5yDY78gro4/XDWF1efZAK5HlPBvGY6K7zx1hdCKsXfvxZkEUnzcpDWhRKKaZ7i3l90XKKKh3coQVhUTos6BipCyFFc/CgPppntQwu6tCUmzocWc331k7tqfJ4+M/2FIw6HY907UH/E6iQeCoq3L4RMEalI09zk4ebG9ofGVOUn42kkCA+Ks5jiBbIOuwU6r10iDw7liEQ4mxz3iVh9SHIbOa7MVfyzaZtFFc6eDGpU83j/aOpbcx7TKA/W0rttMWGpmls1TmIDzq0XHtiYADLC8oZoUJwoWE26GmSFI0jK4/5lWU0wYwLjWV6O06vlybVizkrpWjiNVFkr0Qp2EcViZhxaV72alUMVQbMKPx0BsJtvuEVKYVFbM7LJ9rfj4tiYwgwm3jtGIss1hVN03h58TL+peJIUmY8msYT5ZnM27uPwclJh2wb6Wfjs5GX89qCZcyyF9MlNpp/9Dn+kzYhhBDnP6UU/7n0EqZs3cHugiJGRzTj6pa1V/KrtX8O8Od3ndM3ykIptmiVRPkdehMyKTSYVemFDNAC0QFKp6NZeDiagsW5pVymBWFGsYBy/I1Gmjj+Wg+rqbIww15MqNXCrjIHLfTV/bDmIKT6o1mIzki0v6+QSb69khUZmZgMevomxGMxGLi3W2fuPYEnWafrrSUrudYbzBU6X0Xmz7R8Plm7gfH9eh2ynVKKD4YP5V8Ll/Fubj4JQQFM6j8QP9OR892FEJKEHVOVx8Nn6zaxNTuPRiGB3N2tE/7V47JDLBbu69bptNp/rPdF3PTz72z1OHHgxWU18GKXQ4cTvHhJP26b+icLvHaKvW46x0fzr4v7UeRwcvsv07jbvp9KzUPXmBiSTUZ+SMvnQS2CUjzM0pXxaEI70kpKeCJzP92VH9s0BzZ0+KH4hHzCA2wkBAbw284U/rVwGR11fuzWHHRLjOf/LulbL2usub0ads9fk6T1SpGAiWKH86jbNw8N4dOrLj/jcQkhhDh7/bFrN7N2peJvMnFb1w41wwkNOh3Xt211Wm2PatmMGTt382hhBuHKwA7l4OOLLz1km9s6tefu9EzuK9qPSenQWQx8PnQoIRYzT86azx379uKv02OymBjbvh1frthALy0A/+r1sLrFxdAsIpRXc5ezxVNJoeZhP06e1MUyw1vCWr2dpxLi2FVYxC1T/6QlFso0Dx/Y1vLl6OE1n0fOtKJKB4n8NRcuUTOy0+446rbBFjOvDRlQL3EJca6TJKwWmqbx2Iy5FGcWMcDrz7rsdG5Jz+Lrq6+os6FvcYEBTL12FKsys9HrdPSIizli3HdycBC/3XA1O/IL8TcZaRYaglKKcJuV78eMJK2kBJNeT6PAAOxuN086feth6ZXink4daRwcxOacfDxoGNExRAUyXSvm/1Q2PRPimNDfV0np+QVLeJ14krxmHJqXh/emszor56gl4uuaUa+jU3gEXxQWcJ0WSgoOVlHOw9FRZ/zYQgghzj1fb9rC5ys28DdvMHlUcMPe35hyzZXEHzbs8FSZ9Ho+vvIyVmRkUeFy0Sk68ojKiVajgc9GXs72gkJcXi+tw8NqPh+8PmQg6WVlVLo9JAUFYtTpKKx0cOf6jXg0jUsaJfBgz66M+vYn/NHh1Lx0Uza8msYrWhatI8L4pN9lxAb4c/vUaYxxBzFMF4ymafy3IpdJGzZzXz08BQPonRjPd6W7aaSZcKIxVV/K7YnN6uXYQpzPJAmrRZ69khUZWUwkCZNORz8tgIfLM9iYm1dniwgDBJrNxyz5DuBnNNI55siExKjX0eygBZv9jEbeHTYYl8eLXqfQKcV3W7fTU+fPYAL4xJPHBuwU42XR2GsJMJsBKHU60TRI0vm+tygdycpMrt1+xDHPlP9edgmPz5zH9Tl7CDOb+dfAATQOCa634wshhDh3fLFuM49pkTSrXhe0xOvl150p3Nv19EaoHMyg09E74ci5yQfT63S0iQg/4udKqSPW7Lq/e2fu7dYJj1fDqNeRWlwCbo3/6BP5nzeHaVopLqXx7MDeXNG8ac1+eRUVtFDBNe0295rJKas4/Td4gu7u1omiSgd37krBoHTc3L4NV7WUJEyI0yVJWC28moYOhZ6/huMZlcLrPbIs65lS4XJh0ukx6k+u9PrB24dYLOzHRXMsvGloxB7NyTO6zEOGMQSYTMT42fi9ophhKojdONnktfNULeVqz4Rwm5XPRsoQQyGEEMfn1TSMB/fPmsJTj/1zlceDx6thNZ7cxyidUuj0vriDzCZKPC70Csbr46jUvNyj9tHisL63U2w0U3fn8JBmxl491eCOuCOrE54pBp2OZwb05pkBvevtmEJcCCQJq0WUn42W4aG8VZDLJd4A1io7VWYd7aMizvixix1OHp42m/W5eWjAPV06cH271jw9eyHLM7MIMpn4Z7+etT5B255fwJ8pezDo9Ixo3oTgsACeLsgkSTOxVFXwVN+eh8z1Ukrx3rAhPPjHLD4rz8ei1/PSxX1JOgPl/YUQQojTNbpNC97asJ2bPKHk4WKOroyvmjU+48f1ahr/WrScKdt2ANAvPo5XBw/g47UbmLJlOwDXt2vFfd06H3VOdWFlJd9t3UG5s4oBSY24pWM7Ht+4jS7Y2KxzcHFyEs0PGuEC8I8+F/F4xTz+lrEbgHHt2jDioCdlQohzk9KOsiDu6WobGa79MPrKOm+3vlW4XLy9bBVbc/JpFBzEI72711QSPJP+Pm02+owy7tTCKcHD0yqTkCA/oks8jNPC2IeTV1UOn4+8nJaH3TFbm5XDfX/MZKg3ACcaC/QVTBo5jJ2FhRRUOugcHUXbyCOHThxgd7mwGgz1UpDjZJU5q9hXWkqkn+2IsfknQ9M0tuYXUOJ00io8rNYFpIU4V7T68LM1mqZ1beg4xNnvfOmfNU3jq41bmZ2Sip/JyD0XdaZd5Jm/STp58zamrNjA894YzCjeVLm4wqwUFZTxuBYFaLyqcri+Rweua3toOf6iSgfXTJlK2yoTkZqeP3VlPDWgFyEWCzsKCkkMCmRAYkKt/a/T7Uav0x2y3ujZosrjYU9RMRaDgcSgwNP6DLG/tJR9JWUkBgXW2Rw/IRrKsfpneRJ2DH5GI08dVoK1Lu0uKuaZ2QvZV1ZG85AQXhrUj9gAf9Zl5/KyFoNeKUIxMMDrx7cFBYzXN8Gm9LTFRl/8WZ6RdUQS9r8Va7nFG8olOt9TLD+Pjq83buH5gX1OKCab8ewsJbssPZNHZ8wlVBnI9VTx0EVdjrpOyfF4NY0nZ81n1b5MInVGMnDx0RVDjzqmXwghxNlJKcVNHdocdQ2tulDmrOK5eYtYlZlNqMXC0/170T0uhrUZWQz1BOCv8xXgGKEF8Xp+NvdoEUTrfP3nNd5gFu7Zd0QS9tOOXbRyGXlARYKCNpqV95at5s+bxpzQwtVmw9n5kS2rvJzbpk7D43RToXnokRDLa4MHoD+FZPHbzdt4a9lqGhsspHocPNqrO1e3rr+hl0LUp7PvdsoForyqitt+mUbPIh1vemJpmu/mjl+n4fZ6ibTZ2KZVAr6kYYeuCrPBQDouwHcHMEO5CbaYj2i30uUi9KDcOgwDFVVV9fOmzhCXx8ujM+byD08kb3vjeZsEPli5lt1FxSfd1ozdqezYn8P7WgKveGO51R3C07MW1H3QQgghzlmPz5yHJ72E1z2x/K3cj4emzWZvSSnRgf5s1zk5MIpom+bAYjKSzl/9bAYugqxHjrCwV7kI9f5VXTkMA/bqhZDPZS/OW0KvShMfaAlM0BqxLz2PH7fvPOl2civs/HfZKt4kjv/zxPCaFsfrS1ZQUFl5BqIWouGdnbdVzmMer5d8eyW7i4oJ8+q4XBcMwBgthFmOfewvLePZgX2487fpLFeVFGhu/IJtPN2mIy8tXk5/zZ/9OjfuABOXNUk+ov3BzZKZtGYzgV49TjSm6Ip5qtmZuVNYXworK1GaRnudbwhipDLSTGcltbiEJidZQTG9rIy2Xgtm5bv/0EX58X5FXl2HLIQQ4hyjaRoFlQ6MOsXizEym6JpgUjoilZHlys6KjExu79yBG1P387QjCys6UvVVvHJxfx6fNZ9MrxsNWGOo5OvuA45of0BSAndt3EJrr5UoZeATVcCgxkn1/Tbr3O7CYv6GbyioWeno5rGyK7/opNvJLC8nRm8m2usrHBanTEToTGSXVxBmPfNTQYSob5KE1aOt+QXc9/tMqtxu7F4vAUpHFV5MSocdL+VeD/4mI8nBQUy9dhSrs7KxGY30jo/DqNfROCSYlZlZdLZYuKJZ46MOTRjboS0Oj4f/bNmBXqfjvs5dGXyOX+RDrVa8SrHJa6edspGnudjlrSQpKPD4Ox+mZVgYU9QWRmlugpWBmVopLQ+bBC2EEOLCUuxwcv8fM9lZWITL60WPIg83cZjQNI1c5cbPZCLIbGbKNVeyND0Dl9fLRXExhFgs/DDmSmbu2YsCnmqcRJS/3xHHaBcZwSuDB/D20lVUVFVxceNEHunVvf7fbB1rHBLE0pxy/kYoVZqX1bpKRoWffL+aGBhItreKHVolLZSVrVolhV4XCTIvTJynpDBHPfFqGoO++JYbnUH01wWS5nXwhJZOI72Fjh4LKw2V9GyayNP9z9wctHPZkv0ZPDZjLhE6IzmeKu7r1pmxHdueUlvvrljDxA2b8dcZsFlMTBgx9Ij1XIQ4l0hhDnGipH8+usdnzMO7v4i7tAjseHlI24cbGEIAqToXFYFGvhw1/Kydl9WQMsvKuXXqn+hcHsq8HrrFx/DvIQNPaU7YvLR9PDl7ATalo1Lz8u8hA+nbKP4MRC1E/ZDCHGeBIoeDiioX/XW+D/tJOgvtlT/JzWMJMJm4LyyUS48yvFD49E6IY/pNY9hbXEqUn+2odxlP1AMXdeGmDm0pq3IS4+9/VlaaEkIIUX825+bxqBaGTin80TNCCyYlzkxQeBiD/KyMbtlcErBaxAb488t1o0gpKsZqMJAcHHTK1REHJjVi/s3XkWu3E+VnwyLnXJzH5K+7ngSazHiBFM1BU2WhXPOwBwd/b9GsXsrqng+CzOY6W6ct2GI+amETIYQQF57YAH82VNpJUma8msZmnYNesYnc3ql9Q4d2TjAbDHVWZdhq9JW5F+J8J0lYPTHqdbx8cV+em7eY5noraR4HV7SSBEwIIYRoaE8P6M24n/9gteagVPMQHOzHTe1aH39HIYQ4RZKE1aMhTZJpExHOjsIiov39aH3YGl9CCCGEqH/JwUH8ev1o1mfnYjEY6BwdhVEvQ9WFEGeOJGH1LC4wgLjzoNJPSmER6WXlNA0JlhXthRBCnPOCzGb6JyY0dBinLbfCzrb8AsJt1jobIiiEqHuShImT9tHq9XyxfjON9RZSPJU81a8n/mYTqzOyifCz8bfWLbEa5U9LCCGEqE9L0zN4dPpcmugspHuruLhpEmPatmT67lQMOh1XtWxOXIB/Q4cphECSMHGS9hQV88X6TbytJRDiMbBXc/LovMWE6o0M8vqzWOfij+0pfDVaSvkKIYQQ9UXTNJ6YOZ/HvVG0x0al5uW+Xan8vms3l2uBOJTGmE3b+Gb0FVL4QoizgAx4Ficls7ycRJ2FEOVLsBIw4dK8vKTFMkYXxpNaFLryKubv3d/AkQohhBAXDpfXS1FVFe2UFQCr0qH3atyuhXOTLpw7VARD3f5MWrepgSMVQoAkYeIkNQkJJtXrYLfmAGCltxwNCKl+qKqUIkzpsbvcDRilEEIIcWEx6fUkBQQwQysBIFtzUaF5CTto0FMYeiqqqhoqRCHEQSQJEyclxt+f5wf2YbzK5BbS+J+xkI7h4byvcsnUqljgLWW9Vkn3uJiGDlUIIYS4oLx12SX8bClnHGk8qO2jR2Ick/SF7NGcbNMq+UFXwqCmyQ0dphACmRMmTsHQJsn0T0ygoNJBhM1KlcfD/81fwnOZ2YRbrXwwYIhM/BVCCCHqWdPQEKbfOIbcCjtBFjNWg4GPVq/njW07Meh03Ne5K4MbJzV0mEIIJAkTp8hiMNQkWia9nteGDGzgiIQQQghh0OmIPehG6D3dOnFPt04NGJEQ4mhkOKIQQgghhBBC1CNJwoQQQgghhBCiHslwxAYwN20fLy9YSpHTSfeYaP41uD8hFktDhyWEEEJc0PYUFfPEzHmklJSQGBDAvwYPoFV4WEOHJYQ4D8mTsHq2s6CQp2cv4MGqUD5XSQRk23lixryGDksIIYS4oDndbu78dTr9Sox8oZIZVmblrt+mUy4l3YUQZ4AkYfVsVVY2vfCnrbLhr/TcQhjLsrPRNK2hQxNCCCEuWHtLy9C7NS7XBWNTei7WBRKmGdhZUNTQoQkhzkOShNWzILOZdOXCW5107aeKQKMJpVQDRyaEEEJcuILNZoq8Lso0DwB2zUue10WwxdzAkQkhzkcyJ6yeDWmcxLcbt/JscRaJXiOLVDn/7NurocMSQgghLmiRfjbGtGnJE9tS6Oy1sUFXyeDGSTQOCW7o0IQQ5yFJwuqZSa/ns5GX82fKHoocDm6IiaZdZERDhyWEEEJc8B7r1Z3u8THsKixmUFAgFyc1auiQhBDnKUnCGoBJr2dki2YNHYYQQgghDtOvUQL9GiU0dBhCiPOczAkTQgghhBBCiHokSZgQQgghhBBC1CNJwoQQQgghhBCiHkkSJoQQQgghhBD1SJIwIYQQQgghhKhHkoQJIYQQQgghRD2SJEwIIYQQQggh6pEkYUIIIYQQQghRj2Sx5rOIy+PlvZVrWJC6jyCzmYd7d6NTdFRDhyWEEEJc8H7bmcKX6zajaRp/a9+aq1u3aOiQhBDnMHkSdhZ5bfFyVm5N5c6KQPoW6Lj395nsKSpu6LCEEEKIC9qsPWm8uXA515RYua7Ujw+XruaXHbsaOiwhxDlMkrCzyJ8pe3jIG0ELZWWgLpB+mj9z0/Y1dFhCCCHEBe33bbu4wRtKZ50fHXQ2bvaG8ttWScKEEKdOkrCziEmnoxxvzfcVyotJr2/AiIQQQghhMugpx1PzfTleTAbpn4UQp07mhJ1F7uzakVdWrONKbxBZysVWUxUvNmvc0GEJIYQQF7Rxndtz+/5pVHq96FFM1ZXwXtfBDR2WEOIcJknYWeT6dq2J9PdjYeo+wi1mvu3QllCrtaHDEkIIIS5obSLC+Xzk5Xy/dQceTWNCq560i4xo6LCEEOcwScLOMoOSExmUnNjQYQghhBDiIC3Dw3imX6+GDkMIcZ6QOWFCCCGEEEIIUY8kCRNCCCGEEEKIeiRJmBBCCCGEEELUI0nChBBCCCGEEKIeSRImhBBCCCGEEPVIkjAhhBBCCCGEqEeShAkhhBBCCCFEPZIkTAghhBBCCCHqkSRhQgghhBBCCFGPJAkTQgghhBBCiHokSZgQQgghhBBC1CNJwoQQQgghhBCiHkkSJoQQQgghhBD1SJIwIYQQQgghhKhHkoQJIYQQQgghRD2SJEwIIYQQQggh6pEkYUIIIYQQQghRjyQJE0IIIYQQQoh6pDRNq/tGlcoD9tZ5w0IIIY4mUdO0iIYOQpz9pH8WQoh6VWv/fEaSMCGEEEIIIYQQRyfDEYUQQgghhBCiHkkSJoQQQgghhBD1SJIwIYQQQgghhKhHkoSJc45SyqOUWn/QV9IptDFSKdX6DIR3oP3pSqlipdTvZ+oYQgghxNlE+mchTpyhoQMQ4hRUaprW8TTbGAn8Dmw90R2UUgZN09wnuPm/ARtw18mHJoQQQpyTpH8W4gTJkzBxXlBKdVFKLVBKrVFKzVBKxVT//A6l1Cql1Aal1I9KKZtSqhcwAvh39Z26Jkqp+UqprtX7hCul0qr/f5xS6lel1FxgjlLKTyn1mVJqpVJqnVLqyqPFo2naHKCsXt68EEIIcZaS/lmIo5MkTJyLrAcNdfhZKWUE3gWu1jStC/AZ8HL1tj9pmtZN07QOwDbgNk3TlgK/Av/QNK2jpmm7j3O8ztVt9weeBuZqmtYdGIivo/A7A+9RCCGEONdI/yzECZLhiOJcdMhwB6VUW6AtMEspBaAHsqpfbquUegkIBvyBGadwvFmaphVW//8QYIRS6rHq7y1AI3wdiBBCCHEhk/5ZiBMkSZg4Hyhgi6ZpPY/y2kRgpKZpG5RS44ABtbTh5q8nw5bDXqs47FijNU3bccrRCiGEEBcG6Z+FqIUMRxTngx1AhFKqJ4BSyqiUalP9WgCQVT0k4oaD9imrfu2ANKBL9f9ffYxjzQAeUNW39JRSnU4/fCGEEOK8JP2zELWQJEyc8zRNq8J3YX5NKbUBWA/0qn75GWAFsATYftBu3wL/qJ682wR4A7hHKbUOCD/G4f4PMAIblVJbqr8/glJqEfA9cIlSKl0pNfRU358QQghxLpL+WYjaKU3TGjoGIYQQQgghhLhgyJMwIYQQQgghhKhHkoQJIYQQQgghRD2SJEwIIYQQQggh6pEkYUIIIYQQQghRjyQJE0IIIYQQQoh6JEmYEEIIIYQQQtQjScKEEEIIIYQQoh5JEiaEEEIIIYQQ9UiSMCGEEEIIIYSoR5KECSGEEEIIIUQ9kiRMCCGEEEIIIeqRJGFCCCGEEEIIUY8kCRNCCCGEEEKIeiRJmBBCCCGEEELUI0nChBBCCCGEEKIeSRImhBBCCCGEEPVIkjAhhBBCCCGEqEeShAkhhBBCCCFEPZIkTAghhBBCCCHqkSRhQgghhBBCCFGPJAkTQgghhBBCiHokSZgQQgghhBBC1CNJwoQQQgghhBCiHkkSJoQQQgghhBD1SJIwIYQQQgghhKhHkoQJIYQQQgghRD2SJEwIIYQQQggh6pEkYUIIIYQQQghRjyQJE0IIIYQQQoh6JEmYEEIIIYQQQtQjScKEEEIIIYQQoh5JEiaEEEIIIYQQ9UiSMCGEEEIIIYSoR5KECSGEEEIIIUQ9kiRMCCGEEEIIIeqRJGFCCCGEEEIIUY8kCRNCCCGEEEKIeiRJmBBCCCGEEELUI0nChBBCCCGEEKIeSRImhBBCCCGEEPVIkjAhhBBCCCGEqEeShAkhhBBCCCFEPZIkTAghhBBCCCHqkSRhQgghhBBCCFGPJAkTQgghhBBCiHokSZgQQgghhBBC1CNJwoQQQgghhBCiHkkSJoQQQgghhBD1SJIwIYQQQgghhKhHkoQJIYQQQgghRD2SJEwIIYQQQggh6pEkYUIIIYQQQghRjyQJE0IIIYQQQoh6JEmYEEIIIYQQQtQjScKEEEIIIYQQoh5JEiaEEEIIIYQQ9UiSMCGEEEIIIYSoR5KEnceUUo2UUuVKKf0xttGUUk3rIZaJSqmXzvRxToZS6iml1CcnuO0x41dKRSiltiulrLW8/rxS6qtTjfVsoJSKUkptU0qZj7HNWfF7VkqlKaUGNXQcQghxNNI/H5v0zydH+udzkyRhp0kp1UcptVQpVaKUKlRKLVFKdVNK9VBKVSil/I+yzzql1P1KqaTqi+y6w14PV0pVKaXSTic2TdP2aZrmr2map7rd+Uqp20+nzfOJpmmvaJpWV+fjSWCipmmVddReg1BKvaGU2qWUKqvutMYeeE3TtBxgHnBnw0UohBAnRvrnc5f0z0dSSr2ulNqvlCpVSu1VSj114DXpn89NkoSdBqVUIPA78C4QCsQBLwBOTdOWA+nA1Yft0xZoDUw+6Me26p8fcD2QegZDF3Wo+s7TzUC930lTShnquMkK4AogCN97elsp1eug178G7qrjYx7hWHeHhRDieKR/FnDe9c+fAi01TQsEegE3KKVGHfS69M/nGEnCTk9zAE3TJmua5tE0rVLTtJmapm2sfn0SMPawfcYCf2qaVnDQz77Ed5E4eJsvajuoUuoFpdS71f9vrL6j9+/q761KKYdSKvSgO3kGpdTLQF/gveohEO8d1OSg6qcfxUqp95VSqpbjPq+U+kEp9V31k5K1SqkOB73eqvpuXrFSaotSakQt7WxWSl1x0PdGpVS+UqrTQTHfrJTaV/3zpw/a1qyUeksplVn99daBx+9KqQFKqXSl1ONKqVylVJZSaqRS6nKl1M7qO6FPHdTWIUMQlFLfK6Wyq++aLlRKtantd3CYi4BiTdPSD2orWSm1oPo8zQLCDzsHParv0BYrpTYopQYctu/C6n1nV/9Ovqp+7cD5uU0ptQ+YW/3zW5VvKEKRUmqGUirxoPZaKqVmVb//HUqpMbW9EU3TntM0bbumaV5N01YAi4CeB22yAmh8cPu1UUoFKKXmKaXeUT61xqF8wyT+p5T6UylVAQxUviELjymlNlb/Tr5TSlkO2me4Ump99TlcqpRqf7yYhBAXDOmfpX+G86t/3qFpWsVBP/ICBw9Xlf75XKNpmnyd4hcQCBTgu5hfBoQc9noC4AYSqr/X4bv7NrL6+yRAq/7vfkCP7y7cdmAQkFbLcS8GNlX/fy9gN7DioNc2HNa+ofr7+cDth7Wl4btbGAw0AvKAS2s57vOAC9/dQyPwGL47gsbqrxTgKcBUHUcZ0KJ634nAS9X//zjw3UHtXnnQ+zkQ88eAFegAOIFW1a+/CCwHIoEIYCnwf9WvDag+389Wx3NH9fv5BggA2gCVQPJB7+erg+K4tXo7M/AWsP6g12riP8p5uQ/447CfLQP+U91Wv+pz8VX1a3H4/m4ur/6bGFz9fcRB+75RfR77AKUH7Xvg/HwB+FWfoyurz30rwACMB5ZWb++H72/rlurXOgH5QOsT+Pu2AlmH/z0AG4ERtewzEXgJCANWHvQ7P2Yc1fuVAL2rz4kFSKtuIxbfnextwN3V23cCcvF1sHp8H5LSAHP162nAoIa+RsiXfMlXw3wh/bP0z9r51z/jG1pZXn2cPUD8Ya9L/3wOfcmTsNOgaVopvn+EBy5KeUqpX5VSUdWv78d3Yb2pepdL8P2j/+OwptKBHfgu7GPx3Xk7lmVAM6VUGL4LyKdAnPKNb+8PLDjJt/KqpmnFmqbtwzemuOMxtl2jadoPmqa58F3ELECP6i//6raqNE2bi6/zuO4obXwFXK58w0XAd34Of88vaL47lxuADfgu9gA3AC9qmparaVoevuElNx20nwt4uTq+b/Hd4Xpb07QyTdO2AFsPausQmqZ9Vr2dE18H0EEpFXSMc3FAML6LOOCbcA10A57RNM2padpC4LeDtr8R393WPzXfE6dZwOrqc3Jg32erz+Ni4NejHPN5TdMqNN8Y97uBf2matk3TNDfwCtCx+m7YcHwfFj7XNM2tado64EfgmhN4Xx/iO/czDvt5WfV7rk0svr/B7zVNG1/9sxOJ4xdN05ZUnxNH9c/e0TQtU9O0QnznsGP1z+8EPtI0bYXmu8s9Cd+HgR4n8L6EEOc56Z+lf64WzHnUP2ua9iq+ZLQzvt9LyWGbSP98DpEk7DRV/8Map2laPNAW3x/4WwdtMom/LkI3Ad9WX4AO9wUwDt9F8ZgX+ep/2KvxXdD74fsHtRTfXYpTuchnH/T/dnwX69rsPygOL74OKrb6a3/1zw7Yi++u0uHxZwJLgNFKqWB8dym/PsGYYqvbPfgYsQd9X6BVT3TGd1cNIOeg1ys5yvtTSumVUq8qpXYrpUrx3amBw4Yp1KII30XxgFigSDt02MDBMScC11Q/pi9WShXj+7AQU71voaZp9oO238+RDv5ZIr65WwfaKgQUvnOfCFx02LFuAKKP9YaUb/hMW2CMpvluXR0kACg+xu7D8N0B/PCwGI8Xx9HeZ21/B4nAo4e1l8ChfwtCiAuY9M/SP3Me9s+azzp85+uFw16W/vkcUteTBi9omqZtV0pN5NCJkT8BHyilBgKj8D2SP5ofgffw3cnap5RqfpzDLcA3pKATsKr6+6FAd2BhbSGewNs4noQD/6OU0gHxQOaB15RSuoMu9I2AnbW0Mwm4Hd/f4DJN0zJO8PiZ+P6BbznoGJm1b37Crsc3bGAQvgt8EL6L91HH3x9mI/D3g77PAkKUUn4HXegb8df53w98qWnaHYc3VH13LFQpZTvoQp9w+HYc+rvcj+/u4uEd5YH2FmiaNvgE3seBfV7A1/H2r76bfPBrBnxj0Dcco4mPgRDgT6XUpdXnYP8JxHEyf58H3vPLJ7GPEOICJf2z9M/Vzun++TAGoMlB7Un/fI6RJ2GnoXoi46NKqfjq7xPw3SlbfmCb6j/wH4DPgb2apq0+WlvV212M78J3IhbgGxqxVdO0KqrHkwOp1cMAjiYHaHyC7demi1JqVPU/9ofxPWJejm9CqB14XPkm8g7AV2Xv21ramYrvcfpDHGOS81FMBsYr37of4fjGl9dF1aMAfO+lALDhGzJwolYCwUqpOABN0/biuxP6glLKpJTqg+9cHPAVcIVSamj1HT6L8k1ajj9o3+er9+152L5H8yHwT1U9UVkpFaSUOjCM4HeguVLqpurfi1H5SjS3OlpDSql/4uvwBmmHTk4/oDu+YQt7j/Lawe7HN4TnN+Vbm+Wk4jgBHwN3K6UuUj5+SqlhSqmA4+4phDjvSf8s/XO186J/VkrplFJ3KaVCqvu87vjmu805aDPpn88xkoSdnjJ8Ew9XKF/FmOXAZuDRw7abhO/u0DEvZpqmrdY0bfcJHnspvkfKB+6qbQUc1H6XDeBt4Grlq9Dzzgke53C/AH/DdxfqJmCUpmmu6o7mCnxPUPKBD4CxmqZtP1oj1UM2fgSS8d2NPFEv4bsIbgQ2AWurf3a6vsA3JCED37lcfuzN/1L93ifiG0t+wPX4/jYKgec46Hev+eYiXIlvknQevrtG/+Cvf4834KtIWIDvvX2HrwOq7fg/A68B3yrfUI3N+H4PaJpWBgwBrsV3RzK7etvaFnR8Bd9dwRTlq9JVrg6qWFUd24dH3/WQmDR848LT8f3NuE4yjuO1vxrfxO738P0tpuAbLiSEECD9s/TPnHf981X4Cr2U4UsW363+OkD653OMOnK6hxBHp5R6HmiqadqNx9v2BNt7FmheV+01JKVUBL5y7p20Ol4QUin1HbBd07Tn6rLdU4gjEt8d3k4HTcwVQgjRwKR/rp30z+JsJU/CRINQSoUCtwETGjqWuqBpWp6maS3r4gJfPQygSfXwg0vx3ZWbetpBnibNV/GqlVzghRDi/CX9c+2kfxZ1SZIwUe+UUnfge8Q/TfOVhxWHisY3h6AceAe4p7oSkhBCCHHGSP98XNI/izojwxGFEEIIIYQQoh7JkzAhhBBCCCGEqEdnZJ2wEKtFiws41nqC4nynuVxURjXFXn60dS+FEHUpJ2NTvqZpEQ0dhzj7Sf98btJcLoqCmuB1uxs6FCHESThW/3xGkrC4AH9+GH3lmWhanCPcuRlsfvhXVi/NPv7GQojT8uaTicdbF0YIQPrnc5UnL4PYh+/l5SUdGzoUIcRJOFb/LMMRhRBCCCGEEKIeSRImhBBCCHGWy/jvBw0dghCiDkkSJoQQQghxFtNHxAFgiwhr4EiEEHVFkjBxxrR9a0RDhyCEEEIIIcRZR5IwcUYYIuMaOgQhhBDivDLmj9ENHYIQoo5IEiaEEEIIcZaTm5tCnF8kCRNCCCGEEEKIeiRJmBBCCCHEOeLp3usbOgQhRB2QJEwIIYQQ4hygVENHIISoK5KECSGEEEKcI2S9MCHOD5KECSGEEEKcAw6sFyaEOPdJEibOqPEBHzZ0CEIIIYQQQpxVJAkTZ4zS6xs6BCGEEOK8M27lnQ0dghDiNEkSJoQQQghxjpD1woQ4P0gSJoQQQghxjrl5RENHIIQ4HZKECSGEEEKcQ6RUvRDnPknChBBCCCGEEKIeSRImzqj0OWsbOgRxCgpyU0jdMY+Swv0NHYoQQoijUOOlOMeFqKIsl9Qd88nJ2NzQoYjTZGjoAMT5Sx8WjTs3g669olm9NLuhwxEnaOX8D1m5YAL+QU0oK97JJSOep3Xnqxo6LCGEENX0EXG4czMaOgxRz/btXsqvX96Lf1BT7OX7adpmEIOvegkl41PPSZKECSFqFOWnsXL+h7Tr8zFmSzj2sjRmT32AJq0HYbYENHR4R1WQs5O9KUswWwJo3m4YRpO1oUMSQoh6MW7lnUzsPqGhwxD15M9v/07TDk8THNEVj9vO5mX3sjdlMUnN+jZ0aEdlrygkZfN0vJqHJq0GERAU09AhnVVkOKIQokZpcTq2wCTMlnAAbAFJGM1BVJTlNnBkR5e2axGT/zeG7ZvWsWrhFL754GpcVfaGDksIIc44KVV/YfF63NjLcwkK7wyA3mDDP7gNJYX7GjiyoysryeaLty9nw6pZbF67mC/eHkZBzs6GDuusIkmYEKJGaERTKkpTKS/xXSiL8lbh9dgJCD47O/u5v7xAk/ZP0rjt32nR9VVQwWxePaWhwxJCiHpjiwhr6BBEPdDpDYSENyVn3x8AOCtzKM5fTWRM6waO7OhWzH2f4MgBNOv4LE3bP0lM0rUsmv5GQ4d1VpHhiEKIGgFB0Qwd/SozfngUvcGGprm58sYPMRotDR3aUVXaC7EFJAOglMLin4y9orCBoxJCiPqhFFzTs4BJvzZ0JKI+jLjpA3787BYy93yFq6qMPkP/QUyjTg0d1lFVlBdg8+9Q870tsDGFmSsbMKKzjyRh4oxr+9YIVsuY9XNG83aXkdxiAPbyPPwCozAYzA0dUq0aNe3D/p2fktTmIZz2LPIzZtBr4LsNHZYQQtSb+IL1QMcGjkLUh7DIZtz+j/mUlWRh9QvBZPZv6JBqldyiL8vnfkpQWAd0OjMZu7+iVfsBDR3WWUWSMHFGGSKlglND83o9LJ7+BpvXfI9OZ6Bb/zvp0ufWY+5jNFkJCm1UTxGeuiGjXmbalMdYPesqjGY/+l32JAmNezR0WEIIUS/0EXFk/PcDkBud56ztG35j4bTXcTnLadJ6MJeMfPGYo090egNBoQn1GOGpadftWkqLs1i76HY0zU2bLmPocfH9DR3WWUWSMCHOcyvnf8jOLQtp1f0tvB4Hqxa8gM0/jFYdr2zo0E6b2RLAyLEfoWmalOgVQghxTslIW8Xsqc/RvNPzmK1RpG19l7m/PM/Qq19t6NBOm1KKPkMeoffgv9d8Lw4lhTmEOM+lbJ1DfLNbsfrF4RfYhJjka9m9dW5Dh1Wn5OIuhLiQjVspCzefi1J3LCAifhiBoe0wWyNJbHkPe3bMa+iw6pRSSvroWkgSJsR5zmINxGHPrPneYc/EYgtswIjOLE3TyM3cyt5di6VIhxDivCel6s9dFmsgVY6smu8d9kzM5rNzTc66UlqUTtquRRQXnJ2l9euTDEcU4jzXZ+gj/PDJWCrL0/B6HJQUrOSy0T83dFhnhKZpzPzxn+zeNheLXyyV5Xu5atynxDbq3NChCSHEGXXzCKRK4jmmbbe/sX7FZHaufxGTOYr8zBlcNub8LeO+efX3zPv9JfyDmlBesps+Qx+jU8+bGjqsBiNJmKgX4wM+5KWyuxs6jPOex+OitCgdsyUQm79v7Zjo+PZcf99P7NoyHZ1OT6uOz+IfGNXAkZ4Ze7bNYW/KSjr0nYjeYKUgaxF/fvsItz8+v6FDE0KIM0Yp0Bo6CHFMmqZRVpIJmkZAcJxvWRVrEDc98Atb1vxIlbOcgZd9QVR8u4YO9YywVxQy97cXaNvzfWz+jXDYs1g8/W6ath5EQFBMQ4fXICQJE2ec0usbOoQLQnHBXn74dCyuqiqqqkrp0usW+lz6GAChEY25aMC9R+zj8bhYv+wLCvNSiYhpQfvu16PTnbu/r+LCvQSEdEBvsAIQEtmdHetekMIdQojznhp/p1RJPEu5XA5++eJusvdvAKWIimvDyLETMJqsmC2BdO59y1H327V5OntTlmDzD6Nzr1uw2ILqOfK6U16ShcUaic3fV3nZYovBFhBPaVH6BZuEyZwwIc4Tf0x+mNDoy+k04Bs6D/iKzWt/IXXH/Fq317xepk66k42rplNWFsSaxd/z53eP1F/AZ0BkTGuK85ZT5SgAIGf/n4RHtZQETIhqmsvV0CGIM0AfIfPCzmbL57yDvcJL54un0GXgd1RWGlgy661j7rNqwQRm//ISJaUB7Nm5na8/GEWVs7x+Aj4DgkITqHIUUlq4EYCy4u3YyzMICU9u4MgajjwJE+I8kZ+znS5tXgDAaAomOKInuVlbSW4x4Kjb52VvIzdzOx36fYFOZyAqcThr511HaVE6gSHx9Rh53Ulo0pOOPa9n5fyxGM2BGAxGrr5tUkOHJcTZIy6xZu1GKehw/hm38k4mytOws05OxlbCYgeh0/k+dofHDiInY1qt22uaxrI579Cu94dY/Xz/TreveYpdm6fTpsvV9RJzXTNbAhl+/dv8/s2D6A023K5yLh3zBjb/8IYOrcFIEibqRfqctdC9oaM4vwWGNKIwZzmR8YPxeByUFa4nJKx/rdu7XJUYTAE1nYJOZ8ZgsOJyOeor5DOi5yUP0KnnWByVxQQEx6LXGxs6JCHOGgXFsPnhX2n71gg8eRnyBOU8YoiMq0mwxdklJDyR7MyVhEb1AaA4byURUUnH3MfjcWI0Bdd8bzQF4z7H++ek5v2566nllJdm4x8YhdFka+iQGpQMRxRnnD4sGgBbRFgDR3J+u/xvb5C+awJbVzzMhoXjiEtqS7O2l9e6fWRMG9Ac7N/1BRWle9i3YwJWv2BCwpJO6fiappG+ZwU7N/1BaVH6Kb6LumGxBREcligJmBBHsXppNlOG/YimIR/az0Nde0U3dAjiML2HPIKnKpVNS+9g89K7qKrcSZ+hj9W6vVKKZm0uY/em16goTSE3fSZFuctIat7vlGPIz9nJjo1/kJOx+ZTbqAtGk5WQ8OQLPgEDUJpW9/V02kaGaz+MvrLO2xXnLnduBlOG/Yg9r6ChQzltrqpKykoy8Q+MwmT2b+hwDuGwl5CbtQWLNZiImFbHnQtVWpzB7J+fpTBvDxExLRk08gX8AiJP+ria18sf3z5Metp6bAGJlBZsYvj175xWhyFO3JtPJq7RNK1rQ8chzn7R8e21Gx/4veb7A4v8ytDE84MnLwNN44Idkuj1uCkpSsds8T/rhrl53FVkp28AICq+PQaD+Zjbu1wO5v/+MvtSlmLzD2PA8KeISeh4Ssdet+xLlsz8L4GhbSgr2k7nXjfRc9CDp9SWODnH6p9lOKIQJ2HvrsX89s396A1+uKpKGDr6VVq0H97QYdWw2IJo1KTXCW8fGBzHqFs+Pe3j7tk+l8z922jf+2N0ehPF+euY/v0/uPvpFafdthDizJnYfQLjVt6JOzdDErHzgD7iwh2SWFqUzvef3ozTUYG7qox23a5lwPDxZ01hJr3BRFxStxPe3mi0MPiq/zvt41bai1k07VXa9/kYiy2GKmcRaxbdSsuOIwgJTzrt9sWpk+GIot5c0/PcfgrmqrLz2zf306zjc3Tq/xVtLvovM396mvLSnIYOrcGVlWTiH9QSnd4EQGBoO+zleWhebwNHJoQ4nondJxD393sv2A/v56PxAR82dAj1btr3jxMUPoDOAybTecBkdm2ZR8qW6Q0dVoOzl+VisoRisfnKwJvMIdgCEigvzW7gyIQkYaJeKL3et4bJOay0OAODMYCgsI4A+AU2xS8gicK83Q0b2FkgOr4DRbnLqKzIQNM0slJ/ICKmLUonlxghzgUvL+kI+IaOe/IkGTuXGSLjfMWwLjD5WduIiL8UAIMpgKCI3uRmbm3gqBpeYEgCXo+dwpylAJQUbMRetp+wyKYNHJmQT0iiXhwoznE2Ky/NISdjU63rcPgHRlHlLKKidA8ADns2FWVpBJ2j5dzrUnRCB3oP+TsbF9/OqlkjKMmfw4gb32vosIQQJ2Fi9wk1BTskETv3nU8FOpyOUnIyNlFRllfrNkGhiRTlLAfA63FSVrj2gl6D6gCjycrImz9m77a3WDlrBLvWPcvw69856+bMXYhkTpgQ+BZFXDb3XSy2SFzOIkaO/eiIsdtmSyCDr3qZ2VMfwS8wmYrSVNp2vYb8nF0YjJZTKmhxPunY4wbadb0Gp7Mcqy3krBmHL4Q4cfa8ArSXJsB4mScmzg77di/l16/uw2QJxVGRQ99LH6dTr7FHbHfpNa/x/Sc3UZg9E0dlPlGxLTGZ/SnITbngn/rENurM3U+toNJehMUWjE6nb+iQBJKECUFOxmZWLphAhz6fYrZGUJizjF+/upe7n155RCLRquOVxCd3pzAnhTVLJrF9w3T2pmykvGQXV437mLjEuitQV1qcQVlJFqHhTbD6hdRZu2eS3mDCZght6DCEEKdh0q+AFOw4pykFbd8awepzvEqix+Pit6/vo2mHpwkO74LDnsWSWffTqGmvIxKr8OgW3PrYbHIztpC6cyEbVkymvKySsuId9Br0EJ17j6uzuCrtxRTmpRAQGE3gOTIaRul02PxlqaCziQxHFPXq6d7rGzqEIxTmpRAY2g6zNQKA0KieVDnLcTpKj7p9QFAMVVXlFOTtp0OfT2jZ9TWS2zzCtO9qX/PjZK2c/yGT/ns506Y8y6f/HkjarkV11rYQQpyIA2XOpWDHuefAItzn+vqclRWFaF4IDu8CgMUWQ0BIy1rnYpstgYRGNmX9si9p1+sDWnT5F+16/Y8lM/9DWUndFKLYl7KET/89kGlTnmXSW8NYPvf9OmlXXHgkCRP1Run1qI3LGzqMI4SGN6G0aAtVDl/1xqK8VRhNNsyWwFr3KSlKxz+4DTq9b52P4PDOlJVk4PW4WTn/Q376/HZm/Tz+mOPXa5OXtY1VCz+mfd9PaNvzfZp1eo7fv3kAr8d9am/wOFxVlaTtXEDqjvm4qirPyDGEEOcmScTOXUrBI46XGzqM02L1CwWlUVLgW1/LWZlLWdF2Qo8x16u8NBuLX1RNNUCzNQqrXwxlJZns2jKDqV/cze/fPER2+saTjkfzevntmwdo2mE8bXu+T4e+n7Jm8cQztgCypmlkpK0iZessqcR8HpIkTFzwouLb0bn3WDYsuo3Ny+5hz8Z/ccUN7x9zTlNUbFuKcpfhrPQlWdl7fyUipi2zpo5n05rpmPx6U5Dn5JsPRuN0lJ1UPEX5qQQEt8JsCcfjriR3/3RcVXY+feNiUrbOOq33ejh7eQFfvDOcOb+9wdzf3uSLt4dhLz+3lxIQQtStgxMxT4GUtT5X6CN8VRLP5QIder2R4de/y651z7N52T1sXHwHPS6+l7Co5rXuExyWSJWjgJL8dQCUFm7EYc8hL3MbM396Bp2pI1WeBH74ZCy5mVtOKh5HZQket5Pg8M5omkZexizcrkp++GQsS2a9haZpp/V+D6Z5vfz29X389s2jLJn1KRP/O5SMtFV11r5oeDInTAig58X306bTSMrLcgkNb4LFFnTM7ROa9KRrn7Esmz0WvdGG1S+Eq8Z+xKS3L6PboJ8xGP0Ijx3AtlWp7N21kObthh03BqejlNQdCygryaSkcDMOexb7dk7E63bQecCXVFakM/37x7nm9i+IimtXJ+978cz/YAvsSHLr+wFI3fo+i2e8wZDR/zrttl0uB/N/f5m9uxZjtYUw4Iqn6nTOnBCi/kzsPoGbRwDn+FIj4tyT1Kwvtz0+n6L8PQQExRAQFHPM7c2WQK64/j1+++YBlNKhed0Mv/4dFs34L43bPEJI5EUAeNyVbFgxmcFXvXTcGLxeD2k752MvL0SnN1GYsxxXVRE5+6fRtufbKJ2erWtfwWINokufW+rkfe/cPI2czD207/0xOr2JguzFTJvyOLc/Pu+029Y0jfXLvmT9sq9BKbr0uZn23a+rg6jFyZAkTNQbfVg06XPWYhsWhj3v7HvaEhgSf1ITbLsPuIeOPW/C6SjDLyASr8cFmoY6qOqQTmfgRG6MlZfm8M0HozFZ41FKh1KKDYtux+tx0+XibzBZwjBbIwmPHUzazoV1loSVFKYTGDqo5vvA0I6UFM2sk7Zn/vhP8nPySG47HntZKj9/fjs33D+VkPCkOmlfCFG/Jv0K4y/pXLMGlRTsOPudLwU6rLZgrI06n/D2ic36cM/TK6goz8PmH47BYGbRjP+g1F/9s9IZ0DTHcdvyej38PPE2CvMysfo3wut1k7LxZZQykdTqXvwCGwMQ1/RmUrb+WWdJWGnxgWkPJgCCwjqxa31mnbS9Zc33rJj/CY3b/gNN87Jk1usYTTZadbyyTtoXJ0aGI4oLmqZpVJTl4XId/0J8NCazPwFBMeh0egxGC83bD2fn2ucoyl3Bvp2f47DvI7FZH9+xvF48Hhf28oIjhiwsnf02QWG9adnlX7To/DKRCSNIbjEAq18oDntWzXbOyizM1trnqp0st6uSrNSf8HqceD1Ocvf/RkxCB1wuB1vW/MjaJZ+Tn7PzlNretflPmrR7HL/AxkTEXUJodB/Sds6vs9iFEPXvpbK7a4YnytDEs9+BAh3jAz5s4EhOjaOypNYiWcejN5gIDI7DYPDN3e7Y43pSt75FQdZCcvb9SVbqt7Trdg3g6581rxd7eQEej+uQdnZtnkZRQR5te75P0w5P06LTC5hM/sQndTmkf3bYs7DUYf/scVeRlz4bZ2UumqaRlfYTkbHt0DSNPdvnsGbxp+xNWXxKbW9b/zsJzW4nMLQtQWHtiW96C9vX/1FnsYsTI0/CxAWrtCidXz+5gfKSbFyah95DH6dzv9MbanPp1a+zbO57pKf+SkBQFJfe8wN7dy1i9tRncFYWo9NbUTqF1RbCqHGfEh7dAoCy4hz8gvvWtOMf1JKyghQGDH+a2VOfIyJuKJUV+3E799O601WnFeMB6WmrKMrfhzWgKStmjgQ09AYTXfpOYPIHV+PxWjFbY1gy6y2GX/c2yS0GnFT7BqOVqqpCDKYAAFxVxRiM1jqJXQjRsDY//Ctt3xohJezPAUqv9z297N7QkZw4t9vJzK/uZc+uhaBpNGl1CUOufw+93njKbbbr9jd0eiNb105Frzdx1dgJGIwWPntzEEX5u9HrLaBAp3QMGf0qLTtcAfhGqvgFNkfpfB+Z/UNaUVmRT/9h/2Ty/67G5cwD9BRkzeFvd31bF28fV1UlqxZMIDx2MGvnj0WnM6FpbkbfOpHZU59lz/bFBIZ2ZOWCz2nXdRR9hj5yUu0bTb7+ueZ4zkJMJumf65s8CRP1bswfoxs6BABmfHEXQ4rtfKsl8CHxbJj1NumpK0+rTb3BRJ8hj3DtXd8w7Nr/4rAXM+vnZ2je+RV6XDaDqEbD8QtsTlTi9fw08fbqJ3G5OCqLyNz9LW5XOR63nZy9P5KQ3I2WHa6gVYdhZO+diqMig0p7KVvW/FQn778obzdB4R1o1fX/6HbJFLpc/B1ul51t635CI5CWXV+jcdu/06zDeOb++uJJt9/zkofYsfopMnZ/R8rG13E5M2je7vI6iV0I0bBWL82WyonnCH2YrzDHuVSgY+XM/2Lbs5bJNOIblYhp5wpWz3n3tNtt03kU19z+BaNu+YTohA78+Ok4wmJH0euymTTr8CQ6nZkWXV5m9s/PUJSfhsddRX72LnIzZmMv34emeclI+ZqYRl0JjWjMJVe+QG76TEoK1qA3+rF83gdoXu9px1lRlove6Efjtg/QfchUOvX/nMDQVhTl72bnxj9p0/M9kts8SNse77J2yWdUlOWeVPsXDbyHjF0T2bfjc/bu+JTMPd/Qrf8dpx23ODmShIl6dTbdMc3M2c6VBKKUIlIZ6YmV7PQNx92vrCSblC0zydi7+riVkDL2riI0qg8BwS3R6YwktriN0oINRMYPxWEvoqRoP998MBqdsTF6YwArZ17JihlXEB2fxEUX30dpcQZb1/1Kx36T6NjvM9r1ep9F018/Zul7TdOwVxRS5aw4Zmxhkc0oyV+PszIPgymA0oJ1+AfG4qwsxeKXVFMd0haQTKW96Ij9U3fM57sJN/DN/8awadV3R7zepc8tDBn1IsHBlTRp3pIb75uK2RJwzJiEEOcWScTODQfmhp0r8tJWMtxtwaR0mJWOyz0Wck/gJmmVs4LUHfNI27nwuNMMigv3gTIRlXAZSmcgLKYfFlssoAgMa0de1hb++PZhstLTCI8ZyPqFt7Psz8E47ZsYfv3bACyc9jotOj9Pp/6T6Nh3IjkZu0jZOuM4MZZTWVF0zM8PfoFReN2VFOevR6cz43HbqSjdjdkcgMUvBoPBBoDRHIzJEoLDXnzI/kX5qUz94i6+em8UC/58FbfbecjrMQkd+dtdk4mMMhEVbeW6e6YQFdf2mHGLuifDEcUFqaIsD4vBzCa3nW7KH5fmZZveRZvgYyeJ+3cv45cv7yEgpCX28nQSGndh2LVv1VrO3uYXTmV5KprmQSk9FaW7MZiCqCj1LTSZvmclLpcXR0Um/kHNadz+H6ybex2DR72MXm+krCQLq39czULSFlsMFlukb3hEQMQRx6usKOLnSXeQn70Nr8dFiw4jGDB8PFZb8BHbBgTHYTRZWD1nDDq9BYPBxJg7vsbtdrB26T2ExQzA4hfHvp2f0KhJr0PPw57l/PHtIyS1uh+d3sqSmb47lO26/e2Q7Zq0HkST1oM4FZX2YvKytmL1CyU8qsUxlwwQQjScid0nMG7lnTI08Symj4jDnZuBLeLsLIx1MI+7ikqXg/VU0g1/ADbqXPgfp6hTeWkOk/93DXpDCJrmRqdzce3dU47a/wFYbSFUOQqochZiMofidpXjsGeh05spL0nBYPRj97Y5BId3QdM8dOo/kZQN/0e/S/+Bf2AUABWlmQSGdQRApzfhH9Sa0qKj35DQvF5m//IsW9Z8j1J6wqNbcuk1/yYssskR2+qUjuiEjmxd8SgoHTqdgYtHPE9i837M+fUF8jLnEhrVm7z0mSjlISgssWZfe3kBkz8cQ1SjUUTEDyV15/eUlTzO8OvePuQYETGtGDB8/DHPaW3cbic5GZtQKKLi2qE3mE6pnQudJGGiQTzdez0vL+lY6+upO+azZ/t8rH7BdOw5FptfaJ0d2+ko5ev3R2EJ68ZrOcto6ikhR+chonE/mrW59Ijt3W4ni2e8wb6U5RQXpNGs43hCIrvj9TjZvPwBdm+bTdPWg496rGZthrJhxWS2LH8Isy2egqyFWPzi2LT0QfQGE4um/xtbQEvCYwaQn7WA3esPLQ0fGt4ER0UmJQUbCArrQPbe36isyGRfyhKCQhKOKKU/6+dnUPp4ug1+DberlI1L7mfHph5ccd17RyRDv355D0Hh/WjT8ybKS3ayc814DCYLUfHtGDDsSRb88U+qnGUkNh/A0KtfP2Tfzat/JLbxDYTHDgRAKR0bV0w5Igk7VdnpG/nps1uw+MVRWZFF09aXMGT0vyQRE+IsJYnY2U8p33SAiadZKbEgdxebV/+Apnlp03kUETGt6ihC30iO3755ELvLzGy9l82edDQ0ygLDufrSx4+6z6ZV37FxxRTKSjIIjhxAUqu70DSN1C1vsWz221w84rmj7ucXEEHXfneyftn9BIV2ojB3JQaDH9tWPYnmdbPgj1cwW6OIiBuEvSyNzcsexGKLQOOvJ1gRse3JTvuR2MbXUVGaQl7GLMIjbRTkphAW2fSQ421cNZm9KWvpeskP6PUWdq5/hS/fHUHXPrfSZ+ijh2y7ZNZblJXa6T5kKh63g+1r/olSCos1iFG3fMYfk/9OyvpXCI1swdW3TaopPgKQtmsB/kGtiGt8LQD+wS1ZOXMEnmv+XSfJUqW9mCkTrsfprAJNw2K18re7vsFsqbuiJBcKScJEvTve5+iNKyezeMZbRDYaSXbmLjavvpKxD/5+3LW7TtTubbMxWxNo0eV5nJV5FOevoWDTm9x444co3ZEjdGf88CR52dnEJN9GXtbfCQrvBIBObyYguDWlRem1HkunN3D1rRNJ2ToDe0Uh+3bryUjdSMuuL+Fxl5O65V1adH4WpXSERfdh1eyrSWoxsOaCavULYfgN7/L7Nw/gcVfh9XqIbjSMrRuWsnT2O0Q36sTAYf8kMrYNAFn719O80ysopcNoCiYqYRiFOUv57Zv76Tv0MTr2uhm93ojHXUVO5kZ6XvZvlNITGNKGkMiLyNy7hrDIZrTpcjVtulxd6/vS6w14PX8N9fB6nOj0+lq3P1l/fvsIjVreR3jsQDzuSrYcJ9kVQjQ8ScTObnXxNCw3cytTJlxPRPwwlNKz+aPrGH3r58Q06lQnMZaXZrN/zzK6DJyCprkpKVhP2tZ3GHb1a0e9Gbt59RSWzHyXxFYPUFb6MUHhvjL2SikCQztSXLjsmMfrNeghGjXuQV7OdspLklizZCJJLe8mJKona+feQOeBX2K2RgJgL0/DXrrlkPUur7j+HX76/FYydk/G46kiLLoPeTkVfPnOcELCkrno4nto2cE3DDQzbR3hsZdiMPqe7sUmX0N5yS7WLplIeVkO/S59Apt/GAB7U5YS23gcBmMABmMA0YmjSdu1hHbd/kZ0fHtu+8ecWt+TTmfE6/lr+KHXWwVKoVTdzEBaPOMNjJZmNO/yEAB7Nr/Jkpn/rTXZFbWTOWGiQaiNy2t9bemst2je+UXim1xLk3aPYfVvxrb1P9fZsTWvt2bdDbM1grDo6qqER0kOvV4POzf+RrOO4wkK64B/cAsyU38AoCh3Fbnps30l7qsqaz2eTm+gebthdOxxE2XFOSS3eYiQiC5YbLHodKa/Dqx06PQG+h52RyypWV/uHb+a4LBkmnd8iuQ2D9Ci8/OERvWlrKSSKR/fSEnhPgACg+MoKVjne5+al5L8tTjtWcQ3vZH1K/9g6qQ7qt+/EaPRhr1sb/U5cWMvT8PmF35C57Bjz5vITvue9N3fkrX3F9K2vV2nk3pLivYSEtkDAL3BSkBoB4oL0uqsfSHEmTGx+wTi/n6vzBE7i51OcayVCz4ipvF1JLa8nUYtbiG+2S0sn/e/OotN8/qG7iudHr3BRkhkT4zm4FqrIm5c9QONWt5HSGR3giO6kp02Fa/XhcOezf5dE1F4KS0+9t9ifOOL6NTzZnQ6PTGJVxGdeAVmSzhKpz9kXTGdzkDXfrdjPKiKYFBoAuMemUmbLqOIbjSM5p2eoXHbh2jc9hHsFXbm/voyOzdNq942nrKiDTVzwUoK1uGuKiY6cRR5OaV8+e4V2Mt9ybGff3jNtAUAe9lu/ANOrH9u3HIg7qocUre8TW76DLav/icde4xFp6+b5y6FeWkER1yEUgqlFMHhF1GYl1YnbV9oJAkT9U4fEedbtDki7Kivu10OjOa/7ngZTCGnvI7X0SQ1709Z0WYy9nxLcf5adq1/gZYdr0KnO/JJjkKB0uF2+YpctOj0DBm7v2X59MvZvuYZwmMvZvf2tXz9/ihcVfbjHttiDaLS7usQbAHJaJqX3Rtfpyh3Bbs3/ZvQiCTCopofsZ9Ob6DKWYHFz7eYdGnhJior0ikr2ozVvzEpW2cBMPiqF8nYPYmNS+5n/cLbKSvaQoc+HxHf9AZadXudvOw9ZO1fh1KKS0b+H9tW/YM9m//DlhUPEhoRR3LLgSd0DiNjWzPmzq/xtxVj0u1l2HVv1elTqrDIFuSmTwfA5SymOG8lEdF1N+RFCHHmHBhqLonY2efAE8qne68/pf1dVZWYDuqfjeaQY96EPFkBwXGERTVj98Z/U5y/jr3bP0Kv8xAV3/6o2+v1JtxuX/+c0Hwczsoclk8fzroFN2P1T8JuN/HlO8NPaL1LizUIZ+VfiyEHR3Rn66p/UpiznIzdkykv2kDrTqOO2E8phdtVhdnm65+dlbkU563E6cjDbEtk0yrfjduu/e4Aby7rF4xj07KH2b/rS5Lb3E+jFuNo1uEJ/IM6smnVZAD6X/44Walfk7LhZXaufYaywpVcNPDeEzqHJrM/19/3E9GxYeDZQre+1zFg2NMntO+JiIptRUHWbDSvG6/XTUHWbKLipH8+Fep41d1ORdvIcO2H0bLqtqidOzeDKcN+POqQiJk//pPMfXtIaHEnlRX7SdvyFtfeM4XwoyQnp6ooP5WF016nvDSXxCY96TnoQYoL0vh98sMU5u4gKDSZYdf+l/S01Sya/ioejwv/oOb4BTahsmwToIhvdi8hkd3RNI3tq54kKi6WZm2GktR8AEaTlYLcXfwx+e8U5u4kKCyZy//2JgBTJlyPxT8JzePE6ciicctLKCvOJiKmOX2GPlZrBcG5v75AWsomwmOHkLr1PeKbXIfbbSdzz3d0638nfYY8BoC9opCls96qrlioCA7vgtkWRUKzsaSsf4GBVzxCUjPf07+cjM1k7VuLX0AETVoPOWoi2hAK83bzw6fj8Hg8VDlL6NrndnoP+XtDh3XWevPJxDWapnU9/pbiQhcd31678YHf6+VY41b61l2UoYlnF09BNprHc0pzw7at/4X5v79G43ZPoHR69mx6nd6D76uz+cDgqx644M/XyM3YSkh4Iv2H/ROD0cyf3z1G2s55mMwBDBj2NIEhcfz2zQNUludhtkYTFtOXgsxZxCV3o8oVTmJL399fxu7vqChZTLe+t5HQpBf+gVE4HWVMm/IP9u6aj8kcSP9hT9Gk1cV8+e5IvF4TShmxl+2hebvLKC3OwmoLoc/QRwiNaHzUmFO2zGTmT8+S3PYRdq3/F+Gxl2CxRbN/5yRCwhIY+/CfgG+O+aaVk1k8401cLidBYR0xmYOJbTyGguxFxMT60bd67ltZSTapO+ah0xlo2mYIFmvdTMk4Xa4qOz9PvIPcrG0ARMe348qxH2E0Who4srPTsfpnScJEgzhwh/RonYDb7WThn6+yZ/t8zNYgBlz+BAlNep7ZeNxOPv33QKIaXUtE/BAKsxezd9t76PRmWnV/A5MlnJT1r6J5cxlz59d8/uZg2vb8ALM1EntZGtsW3UkrzYjTaKIgIJir7v6er967kqhGfyMibhAF2YtJ3/UJ19/3E999dC1mW2NM5lAKsuczcuxHJDTucUIxfvPB1RTmpdK03aOExw4AYN+OibidW6hylINSdO59M+27X8vUL+4iLzuTmKRRlBSsozBnGXq94tbHZp01F/Nj8birKCnaj8UaXDNOXhydJGHiRNVnEga+Jy4Z//1AErGzzLH64OPZuHIyaxZPRNM0Ova4nk69bj7jRZN+/eo+ykq9JLa+H0dFBttX/xOvx0mzjuMJDu9C1t5fyNz9FVff/iUr5n+IMrQnMn4IHnclWxfcSnhlIREmf7aoKkbeOZkV8z6ktMRNUuv7qKzIYMeap7lq3ARWzP0f+bnpBIS0pyhnEV36jD3hJ1BzfnmWDSsmExk/lKbtfdMKSgo2snvjywSFJuCwF5Pcsj/9LnuCjSu/ZenMd2nU8g5cziLSU75C6XRcfevnxCZ2OZOnsk5omlYzHz4wJF6KZh3DsfpnKcwhGoQhMq7WoSoGg5mLRzxXr5M8iwv2oml6ohOvACAi7hIy93yLX1BLrNVDABu1uI2d6/6JzS+UxGZ92Lv9Ixq3fZj96/7FWIIYoQ9B82j8p6SQ5bP+AxiJTvRNyI2MH0Lu/l9YMfdd/IM60KT9PwAIDOvE7KnPMeza/xAe3fKYT6IMBjO9Bj3In989hsH019MyozmI7H0ptOj8AqCxZNa/QdNI27mAboN/Rq+3EBbTn7LirfS8+JYTSsCcjlJyMjZjtgQQGdu2QS6weoOJ0IgjS/cKIc4dLy/pyDh8H/qV8g1HFw3vWH3w8bTvfh3tu19XxxEd277dS2jf+xMMBhv+Qc0Ijx1CYc5CQiIvAiA2eTRZqZOx+YfSuEU/ls7+kMDQduTs+5MWjjKe1TdCeRWztFKm/vgU2YX7aN/3MwzGAAKCWxIeO4Qtq78nLzuFdr0/RqczEtt4DMvm3EhMo07EJHTAaLIdM8aLBt7HplVTMJqCa35mNAVQ5SwnMOJyovwS2LfrM+ZMfY7Mfetp1sk31xzA7SrFYi44oQTM43GRk77RV6grvj2GBngCpZQiKDSh3o97vpEkTFxwvB43qTsX4HSUEJ/UncCQeKy2YKocRbiqijGagnG77VQ58jCaQ9E0DaUUFaW7sfn5nsgMGfUyk/57OStnjsSmQUu974OFUopWXj0LKopwOgpxVZVgNAXhdttxVubg8bTE7PfXhcvql0BpUQY/fX4XIeEJjLr182M+0m/aejDxiZ1J2fgGzTr8E4+7gn07Pyc6cSRBYb4x84GhXZj724t4vW5U9bRPpRQmsz8BQTHHPT952dv54ZOxmK3ROCvziEvqzPDr3zlrhioKIc4tE7tP4OYRwPg78RRkow+LbuiQRLXxAR/yUtndDR3GITL3rqEoP5XwqBZExbcDwGINwV6WiskShqZpOCpScbvK8Hgc6PUWnJW5uF12LNZg2nYdQ+r2BaydfzN6r4fBupCaG4ktMVNRloPZGoy9bC+m6j7eYd+LCm+MxRaDTucrAmIyh6FpimnfPw2agzF3fE1IeHKtcfsHRtHvssdZ8Ofr+AU2wWyNIHXLO9j8GxERezEAUY1GsW3tc4AirtlfH8GVMhAU2ui456bKWc6Uj2/EXl6K0hnQ671ce9e32PxPrGiHOLtIEiYaxNqsHOZt20lsxDPkRz6C1S/kmNs77CWUFO4jIDjmtC42HncVP3x6M2UlRZhtscz95UVGjv2I+MYX0bnXODYte4Dg8O6UFq6jebvLyNq/nu2rn8BkCacoZxlXjfsY8E18DYtuii2oAwavlymZS3hCM2HHyx8GB61aD8IalsyW5Q9Wt7eW5u0uo2nrQUyb8iTB4V0wmUNJ3foBEXFDSG5zLzvXvcjKef877tynq275jNULP2bL2g/RG8yERTbBZPYlh6WFm8nPWkDbnm+zb+dEtq95lpikqygt3IjHVUh88kXHPUfTv3+C2MZjiWo0DK/HydaVj7J9/S+07nzkhGQhhDgRk36Fm1+aAOPvbOhQRDVDZHWRrGGHlqwvLUpny5of8HrctOhwBeHRLY7ZjtvloDBvNyZzAMFhx08kjmXxjDfZuPJ7AsPaUZL/Gt3730G3/ndyyZXP8sfkvxMW0x+HPRO9rpImrQexedl9BAS3pThvOb2HPIbJ7AdAk9aXsC91LUkt72LahjcYqLkJRM8UVUpscl8adxrJtO8eJSzmYpyVGehUOd363cnX742kIGshgWEdyUz9AYstlva9PyEr7Semf/8E190z5Zjxd+59K6GRzVg84z+4XQ7Co+IpL/NWn6dydq1/hSbtHqXKWcCuda+Q1PpeXM4icvf/yoDLvjnu+Vk25z1QkbTr/Sag2Lv9fyz481UuG/PGaZ130TAkCRP1bsbuVF6ct4RLvQEsS/uNNeaZXPvw9FoTsT3b5/Lnt3/HbI3AYc9hwPDxNZOAt679mfl/vEyVo5TEZv257G9vHHO43dZ1P1FR4aRNz/dQSk9B9hJm/vQ0tz42mz6XPkZCkx7kZ28nJGIYyS0uxu1ykLJlOlVVdhKbPknwQavSxyZ2JmXbduLb3EeaI5+r81ai4SUqsi2tO49GKUWjJheRn7ODkPChNGk1CKUUvYc8yNJZ43FUFhMU3pWkVnehlJ6QyF4U5G4+7vlTStGt/510638nmqaxcNqrrFv6ES5nESWF64mMH4p/UDNadn6WtG0fsXP9yzRvO5RhY76v6aCOpaRwH4mtfHPUdHozAaGdKJLy8EKI0zTpVxh/SWfS56yVOWJnkYMXcC7KT2PKu1cwwG3ErMEPiz9jxB1fEduo81H3LS7Yx/ef3IDXq8flLKFpmyEMvfpVlFLkZGzij28foaQglZCIZgy//u1jFtgqLtjL+mVf0qHf5xhNwTgr81g25xbadBlFcouBXHv3d+zbvRSzpR/N2w/DYDCTumMeJUX7iYwde8j6XaERjdHp9ITF9MdZlsotKV+jaW6sBiu3jHoFsyWAv901mX0pSzBbe9CiwwiMRgtXjfuE6d8/wa4Nr2A0R9C2x5sopQiL7sOW5d+e0PlMata3pvhVytaZTPvuUXZteB2d3ozBFEhE3CVomobB4M/uTW8SFdeaUbd+RmRs6+O2XZSfRlB495o1v4IjLqIw88TiEmcfKVEv6t3bS1fxDy2S63VhPOSNoL3DxeY1R7+75Kqq5M9v/07zLi/TrvfHtO35AfP/eIWSwv1k7l3DvN9fonnnl+k6+Gcq7AZm/PDkMY9dUZqLX2CLmrU/AkJaU1GeW/N6YrM+dOl7O41bXoJSCqPJSqtOV9HhohsOScAAWnccSUHWXDLTfiIo4VJM/glENLqSKq9WM/QhucUAuvW7i6atB9f8rMNF13PP+JW06nQVFmsYSmfA63VRmL2AyNhj33E83JJZ/2XHprnEN72JkoI1lBdtw1GRhqZp6PRmQqN74+cfxmVj3sA/MOqE2gyPbkluum9dE1dVKcW5S4mMOX7nIIQQx3Ng6Js7NwNPQXYDRyMOL1m/bv4HXOE2cbcK4xZdGLd7/Fk97bVa95/+/eOExlxOh76f02nAV+xP3cD29VNxOsr48bNbiIgfQ/ehvxEcdTk/fnrzMZebKS/LxeofVzOnymyNwGyNoKI8H4Dw6BZ07n0LbbqMxmi0oJSiccuL6dTz5kMSMIDIuLaYTAZ2rf8XlsCmBEb2JCC8G3avpyaBiYhpRZe+t9O265iaaQCxiV249bHZDL36dUxmP/TVCyvnZcw+6vIxx7I3ZbFvZEmTG3E5i8lLn4HbVYzbVYFSitDoXqC5GHbtf2pNcg8XFdeagqw5eD1VaJqH/IyZRMa2Oam4xNlDkjBR7yrcbiIOeggb6QGXo/yo25aVZKF0JhwV6RTnr8PiF4d/UBOKCtLYv2cZYTGD8A9qjsFgo1GLO9i3e8kxjx2b2IWCrHk47FlompfMPd8Q2+jUKhEFhsTTutNI8tJnkpcxm9jG12D1iyMw+PjzrgAGXP4UXtc+1i+4kXXzr0evK2fP9oV8+e5I1i6ZxIlULl2/7Auad3qB+KbX0a7Xu4REdqeyfA9bVjxE6pa3SFn/EhePePaE35OrqpLegx+iOG8ua+dfx9r519Oi3SU0bTP0hNsQQohjmdh9AtpLE9A8noYORQBKryfjvx9giwjDVVlChPbX/N8IDLX2zwAFOTtRykRB1iJQeoLCe5Cfs4v8nB2YLBFExA1Cr7cQ3Wg4KDPFxxhVERbRlMqKTIryVgGQn7UAj7uC4NDEWvepjV5vZMDwpykpWE3O/j/wC0ikUfObMRotxy2wAdCywwgSm3Rm3bzrWL9wLAVZf6KUjklvD2fOL8+d0Npo65Z8SULzO4hrPIbW3V+hUcs7MZmsbFxyJ3u2vMOWZQ/Qude4E75B6vW4adHucvwDzayZN4Y1c8dg0JfS77J/nND+4uwjwxFFvRuUnMhHKZncroWRq7mYpUq4rNUlR902I20lrqpyinKWU1Gagl9Qc8qKUwgJS6S0cD+OisU1hTPsZalYrEcOafR63Gzf8CtlJVnENurMRQPvYtH0W9A0L1HxHbnsug9rjbW0KJ0dm/6ksqKAkIgmpO9ZDZqXtt2uJqFxD/pd/iQZe8fg9ZZTkr+MitJdXHvXdyd0Hiy2IK6/7yeK8lPJydjM3F9fJLn1Q+iN/qxa+B6g0bn3ONxuJ0rp0OuNRzaiaTV39fIy5lCcvwa/wGTspalERicw+O7vjjue/4ADwz71BhtudwX9Ln2CZm2HyoRfIUSdm/QrPP33e6V8/VlAHxaNJy+DMX+MZn2HK5m8czGJXhNmdHxqKCep481H3S8/ewcuVyXFeavwuO3s3zUJvcFA2453YbWF4rDn4HZVYDD64aoqwekowGo7so9O27WInPSNBIbEMeLGD/j9mwfY7ijF5hfGqHGfYDRZj3p8V5WdreumUlKQRnB4MvlZO6ioKCSxaS/adfsbTVoNpmnrmexNWYXRZGPn2mcYcvVrJ1TtVynFkNH/oscl91NamM6vX9+H3tyOmOg2ZKR9zx+TH2LkzRPwej14PK6jFtTS0NBV988VpXvYv/NzLNZINFcpHmcKQ695meTm/Y8bC/iGav7w6c1UOe24qspp1+1auvS5RcrDn+MkCRP17om+PXhdreD51L34G4280KYdexM6HrGd1+Nm7q8v0r73+9gCkvG4K1k770Y6dL+WoNBGtOo8ig0rv2X76sex2OIoyFrA5de+eWgbXg8/TbyNkuJi/ANbsmbxl1w08C4efHErHndVrRd38N3hm/y/a/BqCp3OhNtVjsUvnsi4wUz94h6atx1MUX4qoeFJJDTpic0/3Pdfv9Bjvn9N03BV2TGZ/Xxj1iObsmbRp8QmX0tYTD8AVKsH2LT6M9LTVrN7y3RQio49xjJg+PhDLrjtul3LrvUvEtXoalI2vkH73u/jF9gYhz2bTUvvZsCwp07od+Kwl/iGfXZ+icDQtpQVbWHh9Kdp3n7YCe0vhBAn6+Dy9ZKINSx9hK9k/cvjh/FoRRGvznsfr9dJq4tuoXPfO466z5xfXySx1V3EJF6JpmnsWPs8OvJp1XEkSqejZYcr2LL8fgJDO1OSv5KOPW464qnPinkfsHbJV4RE9aW8+E/Co+K5+6kVuN0OjCZbrQmGq8rOV++NoqK8CI+7Ep0yoAFxTa5l+dxPSNu5AKejFFB06nENgaHxRMU9U+tiywerclbUHDswOI7MvWvxD2pJXOMxAPgHNmPFzOEsm/seK+a+i+b1EJfcgxE3vI/F9tec9I49ruPP7x5DKQP7d00iscUdRCcOry549QiVFQW1hXCEPyY/TGj0ZcQ2HkOVs4ityx+kccsBUib+HCdJmKh3Jr2e8f17Mb5/L8DXAcf1Xs/LSzoesp3TUYZSCluArySs3mAlKKwN0Qm+UuxGo4Xr7p7Cjo1/4HSUknDlPUREtzykjX0piynMz6Rdzw9QOgNRiSNZNH0cnXredMwEDGDxzP9iMEUSEnURjZrfitdbxdYVj4POgMEUSurOtYREXsS+1Lmk7lzkKxN7nARs3dJJLJz2Kh6Pi6DQREaO/YiwyKboDSbcbnvNdm63ncqKQiCI7kN/x+txsH31k4RGfE2HHjfWbNfvsiewBYSxY+OvGM2B+AX6OhiLLRq/gERKCvcTGOJb52zz6u9Zu+RLQKNTrxtripsAFBfuxWyNJDC0LQABIW2w2KLIz9pW/SROEZd80THL5wshxMma2H0C41beKYnYWUApYPydtO85gfY9bzzu9uWlOSTGta7eVxEY2gF/WwFK53v6c8mVz9O45TyK8vYQFn1VTbGKA1xVdpbNeZtOA77CbAnH63WxackdZO5fS3xSt2Mee8vaH3E6nFissbTu/ipKZ2Tv9gmUF28lKLwHaTt/I6rRMEqLtpI570P6XvbYcROw9LRV/P71A9gr8jAYrFw25g2atb0Uvd6I211RM+rG43GgaV7WL5tMp/5fVFc6foeZPz3FiBvfr2kvucVALhvzOmuXfEWVI4/QqJ7AgYJXnSnKT6vZNmv/ehb88SqV9mKSm/elz6WPYTCYa17Pz9lGlzYvAmAyhxAc0ZOcjM0YjBYclSXEJHTELyDiOL8xcbaROWGiwdX2JN1iC8bmH0FW2lQ0TaOseDulhRuJimtXs43BaKFNl9F07n3LEQkYgKOyBKstFqXz3W8wW3134Vyu44/nrqwowu0qJSL2YpRS6PVmzLZo0nd9idtZTPs+H5DY8nY69P4Ar9fF4llvHbO9JTP/w4I/X8U/uC0Ggz8GUyI/T7wNTdNo1Kw3mXu+Y9/OSWSm/kTqlv9gNNmIThqDXm/GaAoiMmEE+/asPPTc6XR07n0LV938MWhVlBRsAKCiNIWKsr2EVHc62zf8xsJpbxKZOJbIxHEsnvE2W9b+xLZ1P7Ni3vuUFO7HYc+msiK9+r1nUFmeybTv/8G83//LnF9f56t3R1BpLz7ueRNCiJNxoDLfqS4eLOrGgYW0x608sWUE4pO6kpU2Ba/XRZWziPyMP0lo/FfydKBwRpe+tx+RgIFvzSu93lyzxIpOZ8Rii8FxAv2Mo6IIpYyExfRFpzehlMIvqDnFeevI2D2Z9n3+R2LLO2jb400stjgWTf/3MdtLT13F9x/fiMEUjdkajV9QK6Z//wTFBXuJSuhAZfk+dm34F9n7fmf76ieIjG1LeMwQzNZIlM5AbOPryEhbdUS7yS0u5oob3iE6viO56dMBcFeVHVLwqig/jR8+vRlb8ADimz9E2q6NzJn6HPt2L2XFvPfZuu4nAoITKMrz9f8ej5Oyog3s3Dyd3yf/g8WzPmHif4aQtX/9cc+bOLvIkzBxQn7cvouP1u/A7fVyTctk7u7Utu7GIet8k4Kp7ogPUEox6pZPmTrpLtK2/Q+Dwcyl1/z7iCqFxxKb2IXigmcozFlKQEhb0lO+wmoLx2wJOGJbTdMoLkjD63EREtGExq0GkJe1k/ysBTQKSKYgazFFOcuJTR5Ncf7qmgUdDaYgdHpr9dCHoysvzWH1ok/p1P9zzNYoHPYsNiy6E6Uge/96pn37CH46K0Up3+JS0KzzldjLiygv2VazCHNFyXZiEw5d5HTZnHd9wyGA0Iim7Fz3HDqdGZezhORWF2OuLte/etFnJDS/neBwXxGShOa3s2j6mxhNIfgFtaFoydc0ataHzcvuxz+oCeUluwmPbone2ITEVnejaRqpW95m6ey3uGTE8yd8/oUQ4kTIE7FTtzEnj+eXrCXfbqdbbBQv9OmKv8l0Sm0ZIn3DEsetvLMmOa7NwCue4fdvHmTFDN+w9a5976Blx5EnfCybXzj+QdHs3zWRmKTRlBSsoyhvQ61l7MtLc7CXFxASnkSjZn1YPu9DCrIXE9VoOFXOInZvfIOE5reyd9v/MFsjAVBKh8Uvhoqy3ceMZdqUx2jeaTxh0X3weqrYtPQBLH6xZO5by7I572HyKiqzFlOetRhzSCytOo5h05rZNU/Hyoq34Rdw6FDL1B3z+ePbh3G7KrHYQtHpMsjZ9wsuZymhkc2IjPONPNmy5nuCw3sQGe8rgNW4/ROsnXcdu7fNJzS6PxWlM7BaA9i3/QPy0n/Dac8hOCyeijI77ft8jE5nJD9zHjO+f5Jxj0w/4fMvGp4kYeK45qTu5bWV20jq+Cw6vZWvNr6CWb+VWzvUTVlUfVg07twMbh7hm6x9sNCIJtz62OxDxmifjMDgOELCk9m98U08Hgf+Qc3xapCyZSZN2wyp2c7tdvLLF3eTnb4Jnc6If1AELdsPw+t1kbH7O3L3T8fjriCx5V2ExfQla+9UcvbPICSiK9l7f0XzumndaUStcZSVZGH1i655EmexxWCyROCszGTTqu/Quyq5R/kTqWx8SAl7Nv7BtQ/8xuQPx1BRsgWPuxKvu5CLBrxQ0+auLTPYsOJ7Og38GpMpmD1b3sZgKMBkjSOuyU2UFKzip89uYdQtn1GQsxO/4H41+7pcpVQ5K2jfZyI6nYGYpNGsW3ATNz74O+XFWQSHJTH9+ycIDOsEHBhq0pGSgmNXnxRCiFM1ZdiPjPljtCRiJyGzrJzb/pxLbOu/kxDUgvW7J/Hw7CV8cvnAU27zRBMxk9mPUbd8isvlQKfTH7141DEonY6mrYewfvk3ZO75HrM1isCQNqxZ/DmXXPn8Idsum/MuqxZ8hMUWgdtdXj3fWaO8eDurZl8NmkZQWCfiGo+mKGcJe7a8R0LTGykr3kZhznIaNel9zFgqyrIJCveVidfpTQSEtCU/azblZXk4CvYySgXTXYUznQrmF6SR3KI/2zf8ztYVD2KyRFJSsJZRt3xW015ZSTZ/TH6Y5p1fIDC0PbnpM0nb9j5GYyCNWt6Jy1nIN++PYuxDf7Bz0zSU4a+qym5XKZrXS5seb2G2RqF53WxaejeDr3oJsyUAiy2IPdvnsS81s+ZmcFBYJ1K3/Pekzr9oeJKEieP6Y08GkU3GERjqeyIT0+p+/tjzVp0lYeArkcv4O494GnbAiSwyXJvign2Ex16M05GH1T8BzeslO33jIUnY6oUfU5iXg39wB5ROj8ddyYp5H9L6otfxC0jGXpbGznUv43aVYjQF0br7a+xY+yJ7Nv0HncGCXm/CZPY9XSvKTyM/ZwfBoY2IiGkFQJWjHHt5Brs2/JvEFrdSWb4Phz2DgcOfYefG3xlGIH11vv0f1fT8vSqdkPBkxj08nb0pi9DrjSQ1H3DIecjcu4awmMGYLb7qhaFRfcnPWkjHfq/55r8lXMr6hWPJ3LsGpdOxb+fnuF1lgCI95Wv8A5PRVQ/TNFnC0elN2GwhhEc2AyCmUQfSUv4gKKwTGhp5GX/Sqv1fiZwQQtQle15BzRMxT15GzfA4UbsVGVkER3QlIm4QAIltH2f5jMtwebwY9ac+4+RAIna0m6OHO525wgV5ewiJ7InHXYHBGEBgSAey9v95yDYZe1ezZvEkgiN6omku/IxBzJ76HPHNxhLX+G9Ulu9l385JuKuKAWjR5Vl2rvsXa+Zeh9KZMJgCsfn75kvZKwrJ2rcOk9mPuKRu6HR6ykqysdrC2LbqKZJa3YPJHEp+1lziEjuj1xmIRM/1Ol9Vx3s1M4u0IhyVpVx717ek7phPVVU58cn/R2DwX3+v+dnb8A9uVvO5KTJ+CLs3/Yd2PV/B6u8rpuFy5rFj4++4qpy4Knaxe9N/sfknkrFnChpgslQ/zdMZsPjFomkeEpv1AaCyopD1y78jJulqjOZQsvZOJfKgqRri3CBJmDguP6MetzOv5vsqRz7Bxr/+dBbvT2dzbj6xAf4Ma9oYve7kL/zHehrmqqpk1s/j2bN9LmZLAP0vf5Lm7S6vtS17RSElBXsJDImrufBW2jOIiL2EguxFlBZupHWHQ9fVSN25CHt5OhHxQ/F6XezfORFN86LXW9HpzfgHtyAgpDUZu7/B63Wh15txV5XSqvu/CA7vTMbu78jatx7N62Heb/+HX1AzSot2EBHdlBbthrN0zjvEJo/B6chlzbwb0On0XHLlC7Tvfh3FBXspSdtaE0sZHkzV65jY/MNoVcvwjoDAaNJ2zUHTvCilo7xkp++uWPVC1Cg9Or0JsyUAg9FCVKNrcNqzcbvK0On0OCozKchaSGBYB7LSfiYwOB6rX1hN+70GP0xh7gOsmj0K8NK45SC6Dbj7BH+jQghxajY//Ctt36p9ZIH4i9VooMqRWzMszlVVhF7pMOh8o0Z2FhaxYO9+rAYDVzRvQpDZfJwW/3KgUEdtN0fBN4x/9cKPWb34UzSvl3bdxtBnyKM1xTkO56qqpCB3FyazPyHhyTjshZTk7yOh+c1UVuxnz5a3SGx66FOrjNTVuF12LLYYLH5xpO/6CpezHJ0yVRfvSiIidiC7N73B7k1vYAtohtOeRVzTG2jU/GbKi3ewf8d/yMvaxvef3ITFrxGVFTkYDBo9Bz3Eoun/JiSiN3qDP5uXPQyamzZdr2HwVa+QnraSLXjxaBp6pahCw6MUgcGx6A2mQ27mHswvIIqK0r24XeUYjP5UVmSA5kWn/+v86/RmNK+HqPh2VNhNGAwBlJfsQun0BAbHsm/np8QmX0Np4WZKCzYRm/h6zb5JzfvTsed1rJh7A3qDhYCgWEbf+tnRQhFnMUnCxHHd1r4lM6b+gNtVjs5go3DfTzx7qW9tiw9Xr+OH9dvo6bUxW+9k5q5U3rl8ELpTmC9W2wV/1s/jyc/Np12vj3DYM5n503gCgmKIadTpiDZ2bZnB9Cn/wOoXQ2VFJl373YlCR8suL6LTGQiPHcDqOX/zJVFuZ031IXtZAY3bPkhE3IH1yhRZad+ye+NrxDe9GVdVESX5K7j82v+wL2UZKVt+IyrhcoLDO6NpXspLthAe3orZU5+hXa8PANiy4jGKC/NZPOttbAHJJDS/uXq9LzONkuNp3/06ALr2u4NvVk9hQlUe0Rj4XpXRY/iLxz1fbbtdy7YNf7Bl+YOYLOGUFmzAPzCS3Zv+jdkaS3nxFsxmGxGxrRk59iOmTroTlAF3VRmDRr5ISHgS0394kt2bXicqrj2jb/3skOGeRqOFq8Z9TGVFESiF1RZ80r9TIYQ4WauXZjNU1hA7IQMTE/hg3Tb2rH0Gc1AritN/476unVBKsTwjk79Pm0N/zZ8S5eWLdZv4bsyVhFhO7MnVgbL141beyeaHf2X10uwjttm69kfWLPma5p1eQaczsX3jK1hsQXTrd2Rxj8K8PXz/yY3odDacjkKatLqEguydtLroP9j8fXO9nRWZWP2CcFSWYKme01xatJ+w6H40anELAH6BTdm09H4y9nyF0RSA3hjI/p0f02foI9jLC0jbOQ+jOYD4pjf59i/chC0gnD+/e4y4JuMIj72YrSufwFmZw6Jpb+HxeIlvdjMmcwh+gU0pyfudIaP+BUBcYlesca14MWsPF3mtzFZ2mrS59LgLLEdEt6RVp+FsWnIXASGtKM5fR3zjHuxa/3+ERPahsnwfRXlLaDrmH7TqNJIfPh1HcW4WbpeDVp1G0vOSB5k25VHWzb8Rv8AoRt48gYCgmEOO0fPi++na51aqnOXY/CNkvbBzkCRh4riSgoP4adTl/Lh9Fy6vxvArBtEyPAy7y8VHazcwQSURqjPg9mo8lL2ftdk5dI2JPn7Dhzlwwe/aK/qQi33q9rm07fUhZmsEZmsEYTGD2bzmxyOSsCpnOdOnPEbLbq8TENySyop0Vi28F73eUrOgMehQSsfCaW+ycsFHjLnja/wCIvALjESn+2sis05nIsAWTFH+DnasehI/cwDDrnuH5JYDSWzaB6tfEGsWfYbDnoarqhh3VSHrV6wFZcAWkMimpQ8S3/RGohNHkLH7G9J3T2bV7GuITR6F0RxOVVUlOzf9SXHBXsKimjL4unfZueEX9qNjQPvLadzy4lrPU2lxBr99dT85GRuw2MLo0OM6QiOaEJ/8GlWOcib/72pMlmhQoFMOnI4yYht15s5/LqG8JBubf3jNsMZbH5113N+L1e/IxTWFEOJMkjXETozZYODbKwczect2cu3LuahPWy5OagTAm4tXcq83nF7VQ93fdeYyefM27u165A3M2hgi4/DkZdD2rRFsHfYj9rxD17batWUOsY2vr1keJb7pODav/uyoSdj0758gIn4Uscmj8bgr2bryETxuxyF9r9KbSdk6n5Stsxg5dgJxSd0ICIkjN6eiZhudzoROp8fodbJ7/SuYdEY6DLibzr1vQdM0wiKbsmj6v9my/H5M5iBKC7fi9brRNI3E1p1J3z0ZkyWctj3fojhvDSkbX2PtvJsIje5NZNwQXFV2MveuIT1tFTa/MPqNfJktq6ewuLKEhMQutO9Re/l+t9vJzB//yY6Nv6HTGWnZYTjxyRcRGfsYIRFN+PLt4WTv/QWLzZdQlZflEpfYlZse+JWykkwMRis2f9+IlDF3fHXc34/RZMNYPXJGnHskCRMnJD4wgIe6dz7kZ+VVLsxKTwi+4W8GpYhUJsqcVcdsa09RMfP37sds0HN508aH3JVTCtq+NYLVBz0NM5r9cdgza6odOezpbF+/gYHDx2MwWvB4XGheD2UlWRjNwQQE+0rVW/3i8Q9qjNuZz5q516OUHr3RD70hiPZ9PmD/zk+Y9/tLDL/ubTr2uJa5v74CCrxeF/t2fESQ18MEXSPCMDDBU8TWpZOITerK1++PQm+IICSqNwVZC0lq3oe8HANte73MugW3UJC9hMqKdEKjepKbPp3c9Fm06/kWoGPn2hepchagvF3YvXURtqBWLJvzHgZTAEpBRHQzbJlbWD73AyzWIHoP+TtR1RWUDpg66U6sgd3pcdnrlBVtZd2S57jh/qkEBEUzbcY/iIi/jMSWvg4wdeu7LJ31FoNGvoi9LI+cjM3Y/EKJb9zjmHfNNE3D6SjFaLSiN5xalS0hhDgdUjHxxNiMRm7reOR8oFKnk1j1VyXgGM1AqcN5zLZKnE5+37Ubh9vDgMQEmoQEo4/wJWJj/hh9RKEOqy2IkpL0mu8rK9IpLcokN3MrkbGt0bzemoWXi/JSaNv8ceDAup/dsZgNbFr6ADqdCb0xAIc9k459J2AvS+W3r+/n7qdX0Lzt5axaMAGrfxIWv1j27ZiAwePkKS2SDoY4FnrL+Gj5V3QfcC8zf3qKzL2bCQjtRkHWAsIiYigt0tG+zwTStn1IVurPVDnzCYnsQWX5Pnauf4lmHf6JLSCJvds/ZteGV2nUuCs/T7qL0KgBFOR8hsdVidUvAnAR1bgn339yM5rXQ4ce19Kq45WHnI9F0/9NblYW3Qb9jMddzvbV/yShSS8iYlqxdd1PeDXL/7N31uFxHEkffntmeQUrZsmSbJmZmTG2Qw4zMzPdhXOXSy6cXJjJIWMcMzPLMsmSxcy8NNPfH2vLVmw5di65OPn0Pk+eJNqemd7Z3amurqpf0XvUGyiKgYqi1Sz89iGuvmcxbncDpUV7EUIhPnnwLzpWHncTINscsD85bX3C2vjVhNmsRPnZ+ZJKaqSX1XodGdJJ9/DWGwZuKyrh/FkL+bIkmg+yLJz17Y9UNB3p2XW4EPuKo8oBevQ/n72bHyF779vs2/p3nA35GEyBVFdks/KHR3n9sU688bcubJj/HG5XNXVVvvqqxvpc6qszcTnrSOh8PR37Po7BYMcvsD2qaiQ4YjiVpQcBCI/uRseek6gs/I6mmuUkdx7FWN1KuDCiCsF50p+CnK3s3Pg5RnM8Sd0fQAgTZms0OQfWExw+EqMpgM79niIz9QV0zUVJ3gIqi9cSn3IF9oD22AOSiO90Lf6OaEoL99Jl0KvoXhdhsZPoM+oLeo34lLpaF1vXfEZw1AUIU3e+effSFg0d3a4GKkoPENv+MhTFQGBIDwLD+lCUtw2AmqoCAoJ7No/3D+pBTWUBOQfW8MkrU9m48it+nPkgcz+/Fanrx/2M6mtL+Oy1M3n72UG89nh3tqx+/9S+GG200UYbvxGHF/1aWVsPsVNlZLs4PhaVlEsPGdLJj0otwxPiWh1f1eTkrG9/5IMsM1+VxHDBrIVsLvRlpbTWQ2zAyBsoyPyKjNQXOJj2KnkHPsYR2pvCnC3s3fY9b/6tM2893p0vXhxLQHA8FYXLAdC8jVSXr6ehvpyIuCl06vcUjpBemExBmCxhBIUPprGhHI/Hidniz8BRN9FYs4qqom9ITOlPvDGAnorPARmh+GPWvBzct4zcjA10HvgyRnMoZlssOQfWEBDUBas9huTud1NXlUZF8VpK8xZQVbqRkMiRBEcMwmKLJLn7XXjd1eRkrKFTv39iD0jGZAql37iZ9Bj2PsFRk1k+71nsjrH4h05h+dzn2Ldzbov7kXNgHTHJl2Iw2jFbIwiPP5OcAz5V4brqQuyBXZoFsQJCelBfU0RdTTEfvzyZ1QvfZuWPr/HJq9NobKg87mekaR5+/PpuXn+iJ68/0Yt5X9yO5j3xxncbpy9tTlgbvxohBG9Nm8jBUAM3iTxm+TXyn6kTCbVZWz3mHxtTielyFwld7yKx9+MYQ4fzceren50XxKNHHvQpPaagqCpCqAQE96RT36fwuKrJ2ruM6u1z+UwkMFNJJODgdpISB7B/68PsWnsdu9ffSmLHEYRGjyUsegx+gR1I6f0o5UXLyd77LmUFPxEamcLeHbP54o1zKMzNor62zJfalzSYfQYdXUoA9ksn/v6hNDVUYbbFsmejT9gjofP1mKyRVJasQde9+Ad1IbLdWQihUHhwJrWVO3E2FjW/F1dTCXa/ECy2CFTVQmN9DqFRIxFCoCgGQqJGYfNvjyOsH1EJZxISNZoDaQuajzcYLSiKSlNDHuCL2jXVZWOz+xQSYxJ6U5I3B11zoWlOyvLnE9OuNz99cz/tez5CSu8n6T7kHUoKM8jYs+i4n9H8r+7CbO/BgAnz6TXiIzateJfczHWn8tVoo4022vjNmHnGd0gJWsWxNUlttM69QwfSLimSO5V8/mks4/ahAxga13pE8dO0vahBg0js/QQJXe8ktut9PLdhZ/Prh6ORV266nn5DfCUHQWGJmMx2hDBisoTQffCreNzleD1OVv/wCC/ICL4XiUyorEVx1lBeOJfUNVezfcWlhEcmoqh24jtejT3A15NS05ykb3+GouzvsftHUluZy0cvTWTPjhU0NTrRdQ89B15CkdZIndQAKJUeajUXIA9Fyj6gqnQjse0vISJ+KlXlqbhdlZjMQb6+l7pOQ10mOfvfp6k+F3nIzruaSjBZAvB6m7D6xdFYn0NQxCBU1ZetExo1CoRCaPQoQiKHkdDpJlI3ft3iHtr9Q2moPdKTrKkuE7u/zz5HxvaiqmQlrqYypJQUZX9HZGxPVi94HkfYKDr3/xddBr6Ezb8765e8ctzPaPOK/1BSkMOA8bMYMGEO5SUlrF/2+il8K9o4nWhLR2zjvyLSz86HZ59x0uNrXG6C7Ed24ky2BCqdB1uMOboY+KMB7xAclkz3/hewf+dP+Af3oDh7JgNG30Jl3g6mec34Kb50yHN0G29W5XPt/auoqczF3xHN/l3zSNu6svncHncVimrB1VRMTfkWzrhwPh//ewKhEaMwmvyJ6DGDA6nP0rHnFA6EJ3BXWQ7hwkiq3sC0817H63aya9PtCNVGUrc7D/XP6sHmxeeyddlFqKoVj7uGoIgheF1ZDJ94Dwu/fQCPqwKEQkXhEs664l3mfHojpfmLsdhiKCtcin9QN6TUKCtYgs0/sXm+Xk9DcxNoZ2MN1RXZDJtwDxuW301Q+BAaatMJj+5AQnufbO3gcbdTWXYnmxafBUjad51EvxHXs27xv5ulchXVhMkSyYblb7Fs7tO4XXUEhSYy6bznCY1IoThvB31HP4gQAostkuDIkRTl7SA+ecgpfTfaaKONNn4LGssqkE+/4xNuauOkMakqT44ZzpMMP6nxVS4PRnu75v+3+sVS6mqZvnhYuv7osoHx5z7Hom8fIihiMJm7/kFQSCRGsx/9FD/aSZ/41bkykE8rM7n5bzupqcrDbPHH43Ey853L0HUPimJE6m503YXJ7CBn3zucceErLPrhMaz2DlhMEYTGTKWicBHZ6SvpMuhSbtv4BZ2EjTS9gSET7icuaRCNPzxGdfku+o75EpM5mKDwgdRU7mTb8suw2KJoasgnPG4S1aWrmXbFO6yY+zTp2x/Ham9HWcECRkx+gD3bZ5Oz9y0stnhK838iOuk8VNVCWeFSTOYjCsK67sbt8tWqeT1OKkoz6D34Un765n7qa9LQPPV4XAX0H+ET2kroMIy+w69g3WJfJosjpB1nX/k+8764A0fEOUfd9yQO7v+OnBfG0VBfhtUWxMgpD9Ch22Tys7cRHncmqsG32R0efyYF2S0l/dv489DmhP0/YW1eAf9YtZ5ql4vBsdH8fdQw7KZjGyt6dR0Bv0pm/mQYHR/Jgv3/Ib7nI3jdNVTkfM2Y4T2PGXf4Qd983NRHSe40mqqKbBwhl5C/fyXF+ansxsVhPcO9uLEFRmGxBWKx+fLjO/U8k80r3yVz17+x2OMoyvqO+I5XERk/nT0bbyc9dT6Kx8mQwg0YpWBOzhzMwR1pqi/n7Ju+JTt9FW5nLRcnDmjuAdJr8MVsX/clIAGBIlQU1UJS19uoqdpFcfZsygoWYzQFgKJw6W1z2bdzNlJKJp0zi5rKXMKiu5KX/g5uZw0Go43aii1I3YPRZKWiKA+/wA401edTWbIOXSsgLnkI8z6/DbMtnKaGInoNvISAoGjsAVNp33l8sxywwWDmzMvewuWsQwiByewHQHhMTwqzviEm+WIqi1ZTUbweo8mfyITphMdNoapkPd+8exlX37sYv4BIaqt2ERwxBKl7aajZi3/gwN/l+9BGG220cTJ8PAceHduH/KXb/nL1YXm1tTy6ZBWZ1TW0Cwjg6XEjaOcIPGacLiWaLv+r/l8nYlRcFPNXfkNgaB9M5mCK9v+HsfHRx4w7uplz2p1zgMkEhyVTmL0Fi/1MmmpL2bv+U6SnFo8IwCgUDuDEarJhsvgTHu3rMSqlJCquB/u3PExg2CAqilbhCO1LYtfbUQ028rM2UZW7jT7Y6EA6c/MXIUJ7UVtdxPizn6Zdt4lUl+cwNTKFiEM9ss6+8j2+fvtCpO5tnq/NHkdo5AiMJgfZ+96hNG8eRlMgGbsXceltc0jb/DWNjVUMHvUqfoFR5GSsoyBrE0U58zEarWxbfhFmSxC61kRTQwWFB79FKEZy0z9ASI2SgjTmfn4Luq7gcdUSkziApE4jMRjMtO86EbPlSF3egJE30mfIlbjdjVhtQQghiGnXh6z0OQSG9MTjqiZ739tYrJGollB6DH0aZ2MRC797BL+ASAKCoqgs30VIlM+xrq9OI8DRUjWxjT+ewyU14tHrefEE48ThMOxvSbfwUPntuWf+8sA2/iccqKzi8u/ncYceRoIw86moxBwTyEuTxzWPcXm9PLJ0FYuycxAIruzelTsH9/vNJU/dmsZTa7ewICMLk8HALX26ckm3zscde9gJO7oQWOo6s96+gMjCDAZ4jXwoy4lUzPgZbRxQNc67ZTaOkIQW52msr2DzqrfZtvZjUno9SkiUL2q0Z9O9OGwKI3IzOU/x7W79pFfzAVVcePcSgkLbHXdeuq7x1X8uQNMdBIUPobRgMVL3EN/xOvZufoAuA57H5p9Izt638brSuez2I43PDu5byo9f30dsh6uQuof8jI85+/J3MFsDUFQjWftXsnPTfAxGBwajH1HtzmH7qqsxWwLo0OtxAkN64mwsIm39LVx887cEhyWf1H2vqczj+4+upa46H12XRCfOoDR/If3GftU8ZveG25hwzqMIBLM+vYGA4O44GwoJCY/j7CveQ1Hb9mxON2xhIdztfIagW57bKqXs90fPp43Tn8jYHvLS2+b90dP41RyuSfqrOGIur5dpX3zHeKeNEcKPtbKeH80NzL14Btaj+nF+uH0Xr23ehkfqDI2O5l8TRuNv/u1Fk77avZ9Xt+7C7fUyIakdjw/vj0lVjzv2sI0+Wr5+4+KXyV39Phd5bHxLFfVSJ9nsIFVvYMwFL9Gh26QW59A0Dzs3fMb6pW8QHDmGdp2uQygG8jM+B+9+ArK28SxRCCEokG5u1XIZe94LdO17bqvvYfVPz7Nv51IiE8+noeYAZQVL6DHkdVLX3UZ8x6sJjx1PZfE6Mnc9z/UPrsViOySFX13Ip69OJTzuTCy2aAoPfk7vwRfRodtEPO4GdM3L9x/dgH9QT6TUCY+bTEHG+1isZsy2XsR2uAxNc7Fv8330H3FxcxuaX8LrcTL/q7vI2r8MXfcSFD6Q+qr9dB/6OhabL+0zZ997xCWE0nPQpXz51gxUQwgIBY+rhItv/vYXJfPb+P141P8/AOQv3dbi74eXz92+W9iqfW5bVf0/YH1+IUPxo7/ii4rcKEO5PC+rxZiXN2yhIq+CL5VknOg8vjeDuKAAZnTueFLX8Gg6VU4nwVYLhhNE0UyqylMjBvLUiF+OrBzebXvU/z88XedrElxdmUNV4W5e0qNRVcFg3Y8blEJCR13NJQMuwmYPPuY8Nr8QRkx6kF2bv6WscDFGcyA1FTupLt+FOTCCEI5EBIMx4AiKwxEcT9qWb6gozSA0IoUuvc9ujjYpisqMaz5mw9LXKSlcQV1lKond7qSmfAtB4YPxC+wAQEKna9mw8IzmJpoAW1Z/RELnWwiL9knQSynZuekrzrjwJQAqSzPQvfV06PccimqiumwLNnsouq4TGOKLGFpsUfg7OlJVlnXSTlhgcBxX3vUTzsZqFsy8DwwOvJ46vO46DCZ/dM2F21mB+dAu5eV3/EhR7jYsVgdxyYNRlOMb4Tb+N9jCQjh//vEXHQVtrWHa+H/EYcXEvwpZ1TUoXp1zFF8rkDNFEMu1Bg5UVtEjwidytSInj8+2pvKGiCdIGHizpIynVq7l+QmjT+oaupSUNzbhbzK1cOyOx4VdO3Jh15Oz+4bwGLSKYrq9PJ1u+D6b1LUf8rwWTKxqYpj053FZTEWnQZw37m6Cw5KOOYeqGukz9CrS0xZSUbKOoPCBeFxV5B34jKi47gTrCkL1PeSCMaAL6Nz7bHIy1pBzYC02exA9Bl7cnPUBMGziffgFRpG9fzVVJTsIjhyKs6kERTESEedzAkOihlOU9SXlpenEtusPwL6dcwgKH0pch8sBsPknsW3dowwYdQPg29D1euqIS7kCiy0Kt7OCxvp8PC4r0cl3HXo/ZgJDB1NRcuCk7iH4ar3PvOwt3K56dm74nPQ9qahGG67GomYnzO0sxmxNwu4fxuV3/EhuxlokkvjkIS0ibW38fjzq/59jHC04ZIMVFaGqqCGn1p6pzQn7f4CfyUiZ8CJ1nzNQghc/Q8tUxM35RVwpHViEggWFKVoAm/MKj3HC5qRnsDwjG3+LmWv69iQhMIDVufnct3g5ig5CVXh50lj6R5/cF7Gwrp4mr5f4gIDjplgYwmPIX7qNK572paNIKREcWXXahIJNNdGu/bDjOmCHaagvQ9c1TOYQsva8ickSilk1oVUX8ZmURAgjBuA9pYbOQ25k/td3UZyfQWDoQA7sfp/czPVMOu9fFGRtoiBnMza/MIZOuAvVYKIwZys/ffsgtZU5WOxxSKkhhEpDbQYWa3CLaKKUOoo4cu8VxYCua+Qf3Mi2dZ+gaxp+gYGkrr0em18ctZVpTDrvX8z78jZqKnZisUWSn/ElVWWpuFx1J3WPDyOEwGoPolu/c1ky63EcoX1JXXcrIZEjqK3YSky7PoRFdQEgwBFNgOPYNJQ2fh9sYYf6wrTiaIFvV+2wQlkbbfx/568iW+9nMlGjeWlCxyoUnFKnSve2iHJtLihinO5HuOKzHedLB38rPFakZGdJKV+m7kHTdM7q2pGhcTHk1dZy49yFVDQ6cUuduwb247KeXU9qblVOJ2UNjcQE+GM3Hlu+ADQvOg9vmL6LbLbQQgjChAl7dLfjOmBHU1tVQEjkcHL3f4CimrHYY6jM3spWJCv1WpKEmY+pokPH0aRtmcmahf8mNGYKzoZ97Nr8DZfcOouGujIydv+Eohrp1HM6vQdfRn1tCT9+fQ/7tjyErnsPCXQE4/XU4WwqwWY/UuMldQ1xlH0WigGpa9RWF7Bu8as01JWT0GEEu9bdgiOkG7VVe+k/8gZ2b/2essKlxCRfSOHBbyjJnYOf36gWG7Ang8nsR0r3yWxc8RaO8BHs2/YEEXGTcTYW4nXn0bXvvw+Ns9O+64STPm8bJ8+JbHE+v30Evs0J+5NS2tDI9uIS/EwmBsZEnTD6NDk5iU+3p/GPhhLidANLlHruGjygxZgwu40DtU46C1+xZ4ZwEeXXUmr+k51pfLY5lfN0ByU0cHH2XN6bPpH7Fi3nYRlJV2Flq7eBOxYsYfHlF7T60AbfztyDKzawJDsPk8GGwyj5eOoYIvzsx4wVqgqPXo/tjO+Quk5AZAf+XZzLKM3MetUNgZGERXc54f0ymqzomou4lCvwuGtIX30jHq0RA0aGCn/e0kqoQ6caneIfn0NVTfQdOxNVNROdOIPtKy5h5rsXUZy/h8CQ3tRW7mLF/Ofo1u88Rky+n6vvWYyueZn57qWkrrkRq187qko3MPHcZ1vMo+fAC1k252lAoutu8jM+ZvDYW5j1yQ3EdrgaYTJQmf0+/UdcgyO0HZFxPdm+7lP8Atqzd8ujSN1LRNxkYttfwrI5T2EynfrDOKX7ZDSviy1rPsZkUjAZixk6/jo69zrrN08/beMI/YZE0jXUt3ASJxAY+CssLNto4/fkcDRMKys4LTcmmjxeNhQW4tV1BkZHEWA2tzo2NsCf8cnteDirgH5eC9sNTobFx9IuMKB5TJjdxmrF07yoPyBdhFhbqhCnlpRx49yFnK87MCG4P38Zz4wfxZsbtzKqwcI5ShSleHhw83a6RYTSO/LE6Wtf70nnn+u3YrEE4XXX8PqEEQyIab326PCG6cXxIfwju4JLvXYK8bLG6ObC7r8s3mUy+xEY0pO4lMvZu+p63A15WBBMEoHM06upwEs9Os70daQfWEuPoW82N4jev/VhFn5zLxl7lxEcMZT66n2sWfhvYpMGMfm8fzY3PV67+GW2r7mBgJA+1FbsIKX7pBbOYUr3M9i86iws9lgstmjyMz6kU6/pfPHGOQRHTsDmP4zinG9o32UsSZ1GExTajsb6crau+YSmvIUUZX2Hn6MT0YnnkZu5hBXznmb0tMd+8b0fTWBwPOdd+ykrf3weu38I0ptB197j6THgIsyWgF8+QRsnxWF7/Efb4jYn7E9IWmk518/9iU7CQpn0Ehrsz9vTJ7Wat201GvhsxjS+25tOtdPJizHRxzxM7x02kCt+mM8+6aYRnXILPNqnR4sxH+9I40EZQZLiMyhVmsbXe/YTrZjoqvsMQl/FToCopKC2jpSQ1iNTs/YfYF2pRs/R36CoFgrSP+DhVWt5f8qoY8aqIZEtGkVOu/ZzNiz4J58VpBEYmcLZUx5CVVt3+ADMlgB6D7mS3RvuQm8o4gJp5ww1mu2ygTf1Ul5XE/hKr2SJxYLX2wjCiKr63qdqsCIUCwXZW+jY5wkyUv9Jcre7sNijObj3LXT9GcZOfxxN81BfW4LJEolq8MPPkcLWtR9RlL8TizWQnoMupVPPaQgh2LnhK4SicsaF/yZt6/fEtL+SyIRpAHjctezdOZdJ5/2LAEcMxfm7iE6+hJrybUh0krreCoA9IJl1S179VTtinXufRefeZ53ycW38Mo8M3dH83wUvvXnkhU1HcsRpi2q10cZ/Rdqdc+j28vRfHvg/psbl4rLv5mFxapgRPKd6+fTsqcQEtJ4y9sToYfwYd5ADlVVcFeRgaofkFhti53fpyPx9GTxSX0QIBrYrDbw5cmKLc3yZupvzdAfTD6U12nWVT7buZE9VFU8qvrT1cGGkP3Z2l1Wc0AnLrq7h+Y076DzsXaz2WKrLtnDb4r+z+rIZra4zwLdovbmrxGE2sUYqlGqxzJh0H4HBrfcmO8zwSfeyYOa9WNQAUhoquFtNogIvT2gF3K5E4ELysqjGZHbgdlVhshyJYBlNoWTuX0ZCp+spyvqO8LjJBEcMpjRvAd+8dzmX3T4XRVGpqczFZAlFVS0EhPQgc88SVphsKKqBzr3PIiyyE+df9wVrF79MTelmeg8+D5PZjj2wC/EdrwbA6hdH6pob6dL7LEIjO7F55X8IiRxJQHBPcva/T5cB/0AIhbDYiWxddj7DJt5zyg2VI2K6c/51n57SMW0cnyumQ2zFDqAVe/wH2+ITOmFCiAAgTEqZ+bO/95BSpv6uM2ujVZ5YvpprtGBGKQFoUvJkRRHf7zvAhV07tXqM3Wjk8h6tpyAkBzn44cKzWZtXgFFVGRUfd4x6oi4lxqNSAU0IrAYDhZqLCryECAPF0k2F5iHM1vpDp8bl4qu0/eiag/LC5YTHTSYkZgIHtrReLP5z2foRZz3Z6tjWGDH5AQKDY9g4+3FmqD4HcYjwZ7ZezRtaKdtVja6D/wNItq+4kqzd/8Fij6SuKg23qxqk7pOEj51EaPQo333rcT97Nt7B2OmPU5C9GaHY6dzfF/0qyVtA9p43Mdv74Grax67NZ3LZ7XPp2GMqHXtMbZ5X2pbvcDUWs3vjfTQ1FOL11ODv6My3719F78GX4AiOo7JiKxKJyRzUfJzRHITH4zzl+3C64XbVU1tdiH9g5J9qp6/V/PCjnK22FMI2fi/+v9vnLeuKmXjXzRS+/OZp9Rt7Z8tOkhsEtxwSk/haq+SFtRtbCGH9HCEEZ3Rovb7XZjTy2blTWZGTR4PHwyMx0cT4+7UYox3HPusSIi1WUl1N9BV23FJnn+Jk8nEyTprPo+t8uGMXJkxUFCwlKvkCHGH9yMFARVMTUX5+rR4LYIyI5YqIWC45LKwVkXLC8YdJ7jyWc6/+kDlvX8i1agT+QsUflUlKIN/qlRwQHhL6PokjtB87Vl/P7o0PEpUwncb6bEoLFmMwOUBKDEY/4jpcCkC7LrewfeXFvpY1gVHsT53LgPGzUQ1WGuuy2blmDcVFdQjFSOrGCznn6g+Iju/D2VccEQNL3fQlmu5h/7anaajNwO0sxc/RhYXf/Z2g0Gi69j2H+ur5+AV2xmQOQohDSsVGO4piwOtxnbITdjqhed1UV+ZgtgSc9uIfx7XJm6DwsD3+FTVbvzetOmFCiPOBl4FS4UuSvVJKufnQyx8BfX732bVxXIobGukiHACoQtBRN1NcX/9fnzfMZuOsjh2O+9qKnDxMQvCQlscQ4UcCJlao9XzRpROhFgt3bdtFB8VCut7EPYP6E2S1HPc8jR4Pl343l3YNghGyhtm736KgPhfFHHBcSd6jyVStZOdlM3rZJdQ9urRZjelElBfvp6QgDbt/GFn7V3Fw33KcUqNSegkWBtxSpwA3+xQD0ckXNxfBGi2hFOfOwi8wBaSGoqiYrPFUl23FYj9SK+VxVaOqvtx9IRSklM0pI3kHPqXLgH/gH+Rzfg/sfIY9276j77BrWswxMWUYi2c9RrvON5G99216jXgPqz0Gj6uabauv5pyrP+CnmffhdntxNpZh80/GZA4ie+9rdO41lT8zB/cuY9GXt+IvDNTqbsbOeIGUnr//e8o/uJHczLVY7cF063d+q0bykaE7Wu6eHX0O2lIH2/hjaLPPPhbKSXSTx/99/lEU1dbRWVoQim/l1wULX9U1/NfnNRsMTExOPO5rWdU15NfWsUKvZJNezxgRwCdqFff0GES4zcYdC5bQUbFSIN30jotiTLv4Vq/z6NLVZOQUc6luZ3PmDxwoWU9U15uRuvuYFMijKaqvZ09ZBaE2Kz3Cw1rI2ENLlePD1NeWkHdwPQajlYa6MnZu/AqkJFe6iRE+u5opXaQJD9bAFIIjfL0qA4J7UF64jPzML7DaohBCQVEtlOYvQPM2InUvQjGga0687gYMRkvzzpjEpwhecHAmse0vaXbYzNYINix9g3Ouer/FHBPaD2PZnCeITDibhtoMkrvfQ1jMWKTU2LvpfrzuJsKjEslNfxu3s4bCg98RGNqbktxZhEV3w2JztHrPTneqK3KY9fYFqK4G6jQX3QdczLBpf//dyxaqK3LYnzoXhELnntMJCIpt8Xprwjx/Rpt8okjYw0BfKWWREGIA8KkQ4iEp5Q9AW+HIH0jP8DBmF9VwjQyhGo1VSgMPR4T/btfbVFjEI4tXcKMeip+i8posITNA8N64ySQ6ArmmT0+Gt4snp6aGZIeDpCBHq+dalZuPf5PkThEJAgZLPy4/+DWhfoG8Pb31ncI3N23jy9Q9JCsW9muN3FveF86ff8J57948k3Vz/k4PxY8tnlpcpgDa9X2CksyvuL14DUOEP7sUDWvkMIyqheqyzcSnXEF12RY8riqik84n4VAKwsHdr1CatwiT1KioTSdDMWH1i6Mg8yv8/H1Rteh2/VBVD1m7XyEgpDdedzUmSygAuubC2VBC2pbvcTbWMGDUTRhNPoNWWZZJRPw0AkJ6YjDasdp9DxGj2YHREobH3chld8yjMGcLRTnbSE/7HK/HRedeZzB47G0n/TmebricdSz68lYe14LpLKwclE4e+vY+YpIGYPf//b7PaZu/YtOcJxmvWcgxwbydX/N1r2gsx0mzKdj053uot/H/gjb7zJFoWMFLb542v9NeMRHMLdjNYGnHiGC+qKVXVOwvH/grqWxq4oof5nGmJ4BL1Bi+kVV8bKji3uGDmHooujbrwnNIKysn2GqhV0R4q4voyqYmlmRl85FIxKIoTJCS62sPkrH5fl4eN7zVVMS1eQXcu3AZHRUrebqLIYlxPDlmePNnctgZix3bp1npuLRwD7PevoAumKnUXeRrTtr1fYImRy4v7H2bEdKfckWQaQkgof315Ox9B03z4HVXUl64FJtfAt2HvoYQKhVFq8hIfR6TNQq3u5q0jfcSEjmc8sJlSCkxGm0YDGY69z6H9K2PEh5/JvXV+5sVh6WUNNXlUl2+h8U/PMrAUTc2L/zraosxmUNp1/l6inPmEBDsK9MQQsXu6EpVeRbTLnmd4rwdlBbtYc/2OWSlzScyrifjznznT11nveTzWzmzQeMcEUm91Lhny3dkth9K+y7jf7drlhXt5fv/nMdo3YwOfLfk33w0eiCJ/i2jt6fL7/2/5UROmCqlLAKQUm4SQowG5gkh4oDfvrlYGyfNk2OHc+v8xVxQeRBNSm7s05PRJ9jZ+jmNHg+Ls3Jo8ngZEhtNfOCJU8Dm7cvgXN3BEMWX036bHsE7nio+TztAds0m7CY7g2JCubJ7519s8uzWNOwcGWNDQRFwR79ufJa2n2g/Gxd26YjZcOSrmVVdw+epu3lVxuHQDeTh5t4de1kaczUp99/O02t8D9KjH3Zer4vlsx7lFaKIlSaaRAA3eAoRSDr0e4LNS85nnX8iEfFTCIkcgbOhgB2rr2PDT1Ob1Q0Dg4/UxPk7uqFSiqIqlJcYUBQjrsZiknvcy/6tf0PqOkajhQtv+Jp1S16mumIVoREdyUp7ibiON5CR+jwGg52gyAlk7l9P3sGrOP/6z1EUFU3zoHka0DyNeD31VBSvJSRyKLWVaTTWZWMy2TEaLSS0H0ZC+2EMGnv7SX/WpzO1VfkECmOzGEySsBCpWqmqyPnNnLAR3TLYuWMbwSEhDF/0FYoQjJizjCdlNImKGemRPF6Qy6IwhXP6DfjlE7bRxulBm30+xDNre3ElnDYiHZd060JmRTWXpmegCBgaHc2dg0++hZ+UkhW5eRTW1dMlNOQXBTTWFxTRXpo561At2IOYOd+TyeaiMmbuPYCimOkQ5M+t/boTeAKBEAC3pmMQCqZDfrwiBA6DgYv7dGZzUSm7SsuZ0TmFyKPSGaWUPLh4BQ/ICLrrNpxS557sAtblFzI0zvd5KGHRKEKQv3QbV3I98ul3OHPCQ1zptTJRBCKl5BlKKao7SGz7S6ivzWRVxU5iUy6jc/QYDAYbOfveZdOiM5HSAygEhPRCCJ9T6B/UFUU1kNJlMGnbZmMPSKapPo/w2ImUFy6ivHgfsUkDmXD2M2xe/Q75B1cTGhFFfsbHWGzRlBeuoLJkLfEpV1JZkc/nb5zN5XfMx+4fjqoY0DQnuu4FJAUHvyaxyy14XJWU5v9EWNgZCCGIiu9NVHxveg685KQ/69Od0rIMxuLLDvITKoM0E8VFe38zJyyli0rl3m9RVJURK2fhZzRw97rtXOyxNNc2hujwwcFCnhs/6je55unGiZywOiFE8uF880M7bqOAWcDJ6Zu28bsQbLXyxYzp1LpcWAyG4+5ONXg8HKyqJshiIfaoguB6t5uLv51LYJNOMCqvyM28OXXCCR/0JoNKA3rz/zeiU+HS2K6OoVrbjLu6gn31dWRWbeLZUYNOOPfBsdE8LzYyV6siRVj4XtQQ5+fPc1sOEhJ7Bs7MnczPXMrn08c3S9YX1tWToFpwaL6va5ww4a8YqLQ7uOOK2/gmKx9NqPTsfyHDz3wCRVFxNdViEIJYfCkNVqEQhwWXs8L3nsyBKIogJHI4Qghqq9KwByTRse8TmEwOtq28koLMr/AP7o6UGqX5c+nRfyoBjijWLHqbxK63IoRCfU06ZnNAcw8xk8WP3kMux2wJwGwNYOX85ziw4xFcznr6j/seRTEQFjOWHauuoLx4H+HRXek15Ep2bJiAx12DlJLM1H+RsfMf6LoHIVRMlhPn4P9Z8QuMokpzkiNdJAgzRdJNsbeRwKBT2zU+3LD4MIdzwpcWlHDFI2n0VezkSBffh/jz2llTadA0Ig5JPQshiMBIo/mveY/b+MvSZp+PQj79DpxA5ex/iaooPDF6GA8MG4guJX6mYxsqe3WdzKpqBL567MObl1JKHl6yitTcQjpJC+/QwDX9e3F5z26tXs+kKjSiN6fBNyHRpGSDNgS3sZzyohXkyETWzFrED+dOxmJofdkXYbeRHOzgraoyxkl/ttNEhaLx+ta9RCTOQPfU8Pl3P/LdOZOJPlST5tF1qtxuuqq+zTSLUEjBQmF9PXPTM3hu9QZqvR4GRkTwwsQxBNRXwKPXo5fvpyO+zTYhBF2lifzGEgDMljBqpIvQqFEYDDaa6vORuodeI97HZAkle/cblOb/RFS7szBZQinMmkl0fB8Gjb2d3du+J679ZRjNDrzeRgoyP8Vq9y3ohaLSqcc0OvWYSkBQHLs2f8XmVa9SV51Pj2H/weaX4Pt83JXs2zmXvsOuISKmO6oq2Lv5MXTdS1XJekrzFqDrbhRhxBF88hvgfzaCg2LZWFHLBAJwSp3tqpfOocdPiT0R/YZEMmnX48AR+5xX38gVj22kgzTjQfKGUeOL88+iQSpEHLVRH4mRApf7N3k/pyMncsJu4mdpDVLKOiHEJOD833VWbZwUrcne7iuv4Ia5C7F6JSW6h65hofxz/Chi/P34cvdeYhol98hIhBCs0uv456oNfHX+ma1e56LuXbg0fS5CBzsKn+kVxPZ+hLCYsUQnnsOOVdcR1v4K5m55hMeG9jthM8gwm42PzprC86s3srKhlniHg6zcfPqP/wiTORgpL2L/uuvYUFDI8HjfYrxDcBAHNSfp0kmKsLBRr8drgCWZ2WzNq+A9pR0CeDZ1Ltsc0fQbfTM2ewgWm4OfGmqYSAD7cbJPqyasNpOaii1I2Yju1dm17mYstggqijYQ3+k6zJZQnI1FeNx1aFo9mxZNByRd+pxL32FXI6XOjvWfs2fTXdj8EqkoXs3QCXfx9TsXU5S7BTBgMvvj9dTTc9BljD3zcXoNvoSZ71zeXLALCopiRNc1AIJCEph+6VvM/fxmQqNG0aHXA3jcNSjCwKbFZ+EIaXfqX44/AVabg9HnPMf9PzxCjGqjQGtg6BmP4R94fBnkEzVoPdwsEY6kKTw5bwWPEUVHacUrJfdXFrA8O5cRsTG8U1TGpTKYHOlmnVLPTUephbo1DY+mHyNM83NqXS5e27CV7KoaOoYFc8uAvr/YCLWNNn4j2uzzUXw8B678oyfxM2yttGipc7m5dvYCymrradI1rGYjz48fRb/oKFJLy9iUm89rehxmoVAqHdyycSszunRs9XzDYmN5zbKV1xrLSNFNzJE1hEYOb651Al9qe0NTPpsLi5vt6vEQQvDm1An8c/UG3iktJ9LfTm2pgaSeDxASNQKAHMXEZ7v3c/+gvgCYVJWkgAAW1NdwhnBQJN1so4GxisI/Vm3gCRlJnGLig7IKHli8gnem+5ol9w51MKuoiltlBHVozNOr8XrqyDvwKeUF84mI6cLO1dcQENyVypJN+Ad1w2KLRtddNNbn4HHXsn3FZSAEYVFdmX7FO9j8Quk77FpSN9yKI3QAtZU76NB1AlvXfsTe7bN971FRURUjoZEpnHPV+/QYcBFvPNEbRTmynhKK6VDkCxTVwKW3zeaLN2dgNPrTZ/RneD21qKqF7auuwT/oj4++/l6Mvfg1Pnr7QubLCio1J7GdxtLxBO0GrpjeSvuVTcc2NH516xLO0AKYofjKOd51l/H2lh2MTk7gy8pUonUjGvC1UsV1yUciyZquU+/xEGAynTDVU9N1Pt6ZxoacAkLsNm4d2OeECqV/FK2uGKSUO1v5uwf4/HebURv/NfctXM5F7gA20ICQEllaz/QvvuPNqROoaGiinW5qLhpOFGYqm2pPeL7kIAefnTuNL1J3U+h0482vIzR6NODLizaaApG6B4RoLno9Ee2Dg3jnTN+DePo3P6IoRoymQ6IcUsftauLVdZtZlHGQ2wb2I9xu4+mxw3lk6WpUCQajymtTxvPWhq2cpQXgUHxf43NcNpZteZdHpys8XXcj0679jK8/uJK3a7MwGiz0HH4TTQ1VIPwoL6pDMQTjdpbhdZeT3GUcB/d9QEnuXFxNZZgswbiaihFCJT55OGOm/R1FUQGVGdd+wr4dcyjK3UaHLjeza/NMzPae2PyrCYkcQUzyBXjcNezZeAexif1J7DgKf0cEWbtfxuqXTEHmF3g9daxb8iqTZvwTm18IyZ3HMP7sZ9i48ktAYDIH0VB7EJMlAFX96y7sO/c5h5QBU6kszWBA/w6M/PIW2LSy1fEnmweu6To1bjftVZ9AjEEIkjBT1tjEc+NH8uiSVdxWkIdJUTizcwrJQQ6klPx7/WY+2bUHAfSPjOClyWOPu5Pt0XSunvUjcXWS0bqNleV53FJWwftnTv5T1wC08eegzT4fS+zYPuQv3Xba14q8vGEz4TVe+uj+zJXVxDkVbp6ziOv696R9SDAxwoz50IZduDBiFQp1LnerTpjVaOCzc6fy/vZU8usaqM5vILHDlc2vG81B6JoTRTGiyV+2zwFmM8+MGwnACxu2saHC01zbDOBy1bDswEGKqqq4sk8PekaE88qUcdw0dyFfOqtwS537hwyg0ulkmLSTJHzP4MtkMJcXZzWf55EJY7nnp2WcX5QBwJkJMQg1i4zI9lSXOqgsK0I1+FNRtI5eQ69kx7pP2br8YnSvE6M5yLcDIVT8A6KYcsG/sfn55jh0wl3EJvXn4N5lxCZMorG+msLcDMJjJ+NsLKJj38cRQEbqP1mz8EVGT3uMrv3OI2PnM0QmnEt+xhc0NeTibkonPnkIETHdCXBEc/HN3/DxS5PRvI0YTYFomhOpOwn+FZGhPwthkZ24/IHVlBXvw2IJIK7bYIQQnDe4olVn62QVCEvrGxnOEQG39tJMWl0DDw0bRI3TxcOpe9B0nR4R4UxKbgfAwswsHl22Gl1KwqxWXj9jPO2Dg457/n+u2ci29FzO1gM5KKq5uGAuP1xwNsEnEJf5I/jrru7+nyKlJKuuDkVYqZUar6gJqEKwRW/gb0tX8eDIwTy1L5PB0o9gDHwpKhkY23oDxsMkBzl4bORQpJQc/GEhuXteISz+LKrKttBYn0NF7g+MTGjXqqFojRq3G3tgew6mvUp04nlk73iW2KZKznQGs7+ukkvy5/LDhWczLrEdI66Ko9rpIthqwaAohNhtZItqhhw610HhItThaM47T7tzDiHha3G7GjCabM2L449emkJM8qXEJJ2PlJJ9W/8GQGhkJ2prSjFZAlFVCwPGz0JRzWTsfJbVPz3PyCkPIRQVXfOwff1nNDU2YcxJp6osnZ7D7qMo64dmeXqjKZDA0MGUF+8lufNYZlz9MUtm/Y2MPf8hufs9BAR3pzDrW2Z9fD0X3/IdACk9zmDrmg9I3/YYFr9EygsWMnLKQ6d0P09Hjm5SDCdoVJzx6yTdpZR8mrqbT3ekoUnJeV07cWO/XnQPCeHr6koulMHk4mYT9VwfEYZBUciqqWGgsNNRN7NoXxb/AjqFhbB8TyYfKon4ofBaWSnPrVrfvCA5mj3l5TTWO7lFxiIUQT9p5+ryHPLr6ogL+PPI7LfRxl+Fp+tu5EpO3wbOh8kor2KYbuUdWcobajtChIFK6eXmrTv56OwzyJBOtuoN9BA2fpTVBNgshNlPLHEeYDZz16D+gM9xmr3nRWK63IXbWU5R1veERgxEuArpH3VqopmlTS7sgSlk7XmD5O73UJ6/BHfBUq5UwmjIc3JjwULeO3MyXcNC+fHS8yhvbCLQbMJsMDA7PYMcxYOuSxQhOIiLYNORRbefycTb0yfh9HoxKAoGRcFbWsC/Uhdx0BBBryG+fltFOXNI3/ktPQdeRNrW7zHbQmiqL6TXiPex+SdQePAbZn96E5ffMQ9d92IwmMnYvYT9uxbiF5BMZekWYpMvpa56D2GxE1AObdqGRo+jpHAO4GtfY7a8xZbVrxASNZbO/Z+mpmIH375/BVfetRC7fxgBjhg69ZrO3k13ERg6mNrKLSR2HEFw+PEVpf+sXHFU671jbHXuoX/PPzVhjA0FhTy3cj1VTicDY6L4++hh9IuNYlZNNh2lBQ+SH9Vazo3rjhCCkvoGooWRUdjZVVrPNbMX8My4ETy+fA3PEE2yYmFRUw03z1vEwsvOP2bjU0rJzH37+UAkEqioDMKPfM3L8pw8zu10ci0T/le0OWF/MYQQRFmtbHY2kCIsqIe+nF2EhVJnE6MT4snr35N7N2/DpemMiY/loRGDT+n8700Zxd9Xb2HnttVoEhL9zQyN1rij38mdx61pzXVscf5W9tRUUVOxg4ritWiuct5W22MVCoPxI9tbxPPrN5NaXo+qCG7omcKEpHYA3DKgDxfnzyVf8yKAvaqLzweOwRDgj1ZWQLeXp3M4k/5oedy6miJiOxwR83CE9qW0cC5X37OIwrztrF30Mhb/odSUb6cw61u8njr2b9rIrvUfgxD4+YcjLIl0G/wCQigUZM7kYNpLWOwxVJasIyJuMprmpK5qO46Q4QBYbIGkdJ9ARXk5YTE+Fch2nW9i06KpOJtqsFgDMRotXHTTt6Rt+ZrGhkqGjH6NuKQT19idDvQbEslE8RMAFdW17H/tbaJtVoyHRVqOblIMv3lzxDnpGXy+OZX7ZTgqgpdS9+FvNvHvyWO448clzKzKwKKo/G3EEDqHhrAkKwdLo8YdMgqhCIbq/lyZtpezOiQzVvcj8FBa43Tp4OXi0uNe8/+V8kEbbfxJkE+/g3js9KgNa42EoEBWlBUQjpEQ4VuCBQsDocKILiUvTxrLw0tWUtxUSKcgB/+ZNBHlFKLrdw7ohRA7WZD6MI1eLzF2E12tudw/asIvpliDzz4bFQUhBIkBVhZm7UY1B7Nn04MYXZU8oETSR/EJczToOm9v2UGZG+o8Xia2i+bmPj6rOzk5kW/T9vFwVSGxmNhAPc+OOnZD6+gaNUN4DDvqduII7ducvh8Y3IPcfW9zxtUv063fDNK2zKQgrxDQ2bPpITyuSppqM3j90Y5IqRMUGEV1UyN9Rn2KwWinvuYAu9bdTkT8GVSVbiAk0pdWWVW6ntBwXxRLUVR6DrqYzSvfIrHLLQghCI+dQFXJcopyt9O+6wQAxp31FOm75lNWvJ/ufa6jU88z/1SZD25XA/W1xTwxoRQ/my8idEwLlqPt9W9gq7Oqa7hrwVJu18NoJ4L5PK+Shxav5MVJY3isvoGLDmYiEFzYqSMXdetCjcvFvIyDfCwSsSkKE6Xk7toCfszIoqtiI1n3OfITlEA+cFVQ7XS12hLpz8BJOWFCCCsQL6Xc/zvPp43fgL+NHsatPy7Cgso0GUQEBmbqVfQI84XrL+/ZjcsONW7+NQ+QIIuFV8cPO+GYvNpaiusbaR/kaP6BbC0q5t6FyylzOom125nSqT3p1W6i21+Bq6mUgowvUREtCh28ms78rBISe/0NXXPyyOrnsRoMODUvi7OLGJGYSLtAPwLMJp5pF9/cx+ToB8fRvUpix/bhHYsfhVnf0L7ng2jeRopz5xLoCEJRDcS2609kbHcy9y2noeYgyT3upiJ7Du3qS3lQSaQBnftr8/Haux4xEqG9yc/4lKDwQWTtfo2irO/QtTrapQwnpfuU5nmYzP64nWXN6oseVxVS13w9TA5hNFnpPeTKVu+r1+uirroQqy0Yi+3EfdV+K66YDnHLj9+YGGhuhvh62gE+PZBDgKJiMBl5Z/qkX+z99luwLCOb83UHSYrvPl6iBbEwI4tzOqcQbrezr7IKl6axr6yCqR2SfQqdQmn+7lsPFQGH+9tJVUqYdqjAfZ9sIsJ+/KamXUJDsPlZeKOujP66jZVKAymhwcT6n3455238tWmzzy05iYy7P5R7hwxg3IGvcEudrXoDfRU7W/UGqhWNdoGB+JtNLLniwmahjVPFoCjcM7A39wzs3eqYyqYmMqtqiPKzNwt3ldQ3cOeCpeyqrMCmqlzXpyfvp+4nKukSVKOdnL3v4qcqiCMaXQhgTV4RcT0fwGaNZOa+13F6dzCtfQKf78kgJiiE+PgYHBYLN0VHktJK6tjRJATYWJI7j/DYiRhM/hQe/AajEJw//1wA1lLOg9VFpK3fSFzK5Xg9DTTVZvEvEUOgMPDvmmK2my0YjL5nt19gB5A6Xnc1NRXb2brsIoxmOxaLheGTvmi+rtFoRde9eNxVvvp03esrTTAfsQFCCDr2mErHHsfvZSl1ndrqAhTViH/gH9sU+OeNi1cUlvLI5l34CYXvXtZ5ZkA3RkdH/Krsk1NhfX4hg/BjgOITcrlJhnFRfiZGRSHBEQDS17sts7KaBrcHj6ZjFALzUQqddqHgbzKRpTtpkjpWoZAtXegcX/hGCMF5nTrybHM6oot9BhfPJcT9bu/z1/KLTpgQYhrwAmACEoUQvYAnpZTTT3hgG38Yw+NjeWniWO5fvIIbtCwUBEmBAbw94Yis6O+5e/PW5u18siONaNVEkfTwwsTRdA4N4bYfl3C7Fko/1c6Kpjre3LaLpIEv4Aj1GQvpbSCwejnP1hVzhh7AfuHigHQR1+U+HKG+NAq38xpe3vw5+Y06oUmX4GkoYGnqAmbPOKPVRpJHh83zl27jn11CuX3TOjb+dAZSaqgGC+OueqN5zMDRN5G2ZSxxKVcQHDGEkt1vcJESjEkomFA4Szj4uGwTXncdqtFGSc4PxCYOID65H2brGBzBCVjtwYRGdmpxn+OThxAQGMK+LQ9hD+xCZfEyBo65DYPhxLLBhykt3MP3H16FlApuVw3DJt5L32FXn/Ln83NOJHgB+IpqVfWE6Qdr8wqYl1nIOyKBQAzMcVZx/6LlzDz/rP96fr+En9lEKY3N/1+KFz+zmRfXbsJTVMNMNZkmJH/fm0VScBCj28XxHC5m61V0EhZmixqGx0ZzZa/uXHkwlwfqCwkUKgcUFx+MnHLca5pUlQ/OmsJrG7ayrLKaTuEGqlCuAACqpUlEQVSx3DKg759qV7SNPz9t9rklp6NAx88JsJj55oKzuXrOjzzTWIjUwWYw8PoZ4/E3H1lQ/l7PkjV5+dy3cDkxipl8zcXVvbtzfb9e3PPTMjpVS55Q2pMr3Ty8eQcB8dOIbX8xACZzCPXp/+bNxnIu03Ua0PiGKvyjxzRndxh7PMK3G27i670HCE28CKGaWLLrE14bP+ykHDCABwYPYOlXc9m8ZAZCqKiqkfsGdG+2PyPCoonbm0VBUBei2p1Nzq5XmCYCCBW+KN9FSjBbnPk01B7EHpBEad4CbH6hdOk1GFUdgSM0EbPZ7lM9NBy530aTlf4jbmTXxrsJjhxNQ/UugkKjiT3JbBRnUw3ff3g1VWXZ6LqHhJQRTL3wZZTfqabbFhbS7Jgej6MbF1c7XTw6Zxl/J4qOWNlPE49u3s2CS7sQZPl9o0h+JiOlwovUfZsKpXiwqQYWZ+Uwe+c+3lcTCUTljbIynl65ln+MH0XnkBBeqyhlsgxgJ02UqTrndOxAblUNd2Rkk6xY2KU38sTIYc0q2j/nwWED+djPzsrcAkLsgXwxcMxpVw8GJxcJexwYAKwAkFLuEEL8dSsR/yKMSUxgy/VX4PJ6cWt6i4f778nusnK+2Lmb14gjSDewSzZy78LlvDRpLDGKif7StxsyWgTwoVaO131EFERRLQxOiMWmKiwvKCHCP4ikOisuqTWP0Tx1ZNXUkzLw3/gF+nJ7s7y1zE3P5Ope3X9xfobwGEaHx/BlZCxf783A4GrkoqQYkvJegjzfmLQ757Bj3UQK8ht8x5hD2O8spoOwIKVkr/BisEWxeckMQBIW3ZWwqMFUV+aT0H4I8e2HHvfaimpgxjUfk7ZlJnU1RfQd9HeSu7TeoPrnzP70RqKTryY8dgKuphI2LLuN2Hb9Sew9ii4djk0z6fbyya/D/tti9n0VlfSTNgIPpdeME4F8XH3wV50rq7qG7/bsR9N1pnZsT9ew0BOOv7ZfLy7JmUuVrmMAliv1vD9wCo8sWsF1MhCjUDAC43U/thUUMbl9Iv5mE5+7K5D4Ui/e7zscu9HI5zOmsS6/EKfXy4DoyBM+tAPMZh4ZOaTV19to43/A47TZ5xb8GQQ6EhwBLL38QryaRoPX+4tKb78VXl3n3kXLeUhG0E23UYWXu3akMbxdHDvKy3lMbY8qBImYGSD82OGqbD5WVS34WWxc1787c/cewGSwMM2vPavqj9ger6cejxREJl9GdNIFABhNDt7e+RWDY6NPao4hNhs/XTiND3fuocrlYXJSLCPij0QwhBBc2qsbL6bV++ZlDWWP8HL2ochhunRhtkawa91tgMRq86Nr3/OpLMsiJCyR+KRBLZyvoxk64S4iYrpSlLeDgK7n0K3f+YcEuX6ZFfOeQYoo+ox5Hl33kL71Ebau/ZD+I6474XG2sJBj7PekXY+3nnlyFCcbxcqrrSVMMdJR99mzjsJKuGIkr6bulJ2werebz3btoay+kYFx0c3lIa0xIakdH23bxXP1JSToBpYq9dw9uD/bCosZo/sRfKhG72zp4JmiEoQQRPvb+am0lLXUAZJbevQlwGLmsZFDOKNjMsX1DTwUFkriCTJtVEXh6j49uLpPj1bHnA6cjBPmkVLW/OwBcZoH/Ns4jNlgwPw/rPzLqamlk2IlSPddtLuw4dE0TKpCoeaiXmr4CZUK6aURnZKM9zEY7bid5VTmzWLa9PF0DAkGX40xa/MKuGPJy7hdFehaExXZMzEoAtVwJEVAMfjh1spOaZ7dwkPpFn7s4t5b6qslu1Or5crMnUjpxRjUhQ+r0kgVTVRIL/mqgfgOl1NVupQg/ypqa2ooyMnCbEtm2ZznqK7IZcCoG497XdVgouegS1v87YqT8JVcD13DyzX5hMX4oplmawShQV1IWfcgZxe2/hA2hMfg1jQ2FBTi9Gr0i4r4XXaD4gP9+UFpwqnrWITCZllPnP3Ue29lVFZx+Q/zGaf5YUJw7b4DvDplPP2jW0/tSHQE8uW50/hy9z5MqsoXnTqQ6Agk3M/O/nonHYUVKSXpiov2/lG8uWUHJXUNDBN+GBGskHU8tXId311wNiZVZdTPUhaqnS6WZOXg1XVGJcS1aFTaRht/MG32+WccFuj4M2BQVQKP0+fz96La6QJd0k34hD6ChIEOioW82jocJhOZmpOOWNGkJFdxU1++ibLCZRgMdgr2vMxdfZKY0iGZKR2SAShtaGThdz+Ss8cPky2K8oOfkxgYgNtw5NlvMPrh1vTjzqc1wmw27j9Bg+sx7eJ5dcuPZO96AZM9iVTZyH0iH4duYLtsIib5dhzeGiqzvqSvn5FdO5fiHzWOkqx5OLe8x9tDuhN39y3kh/Q69uTTJwAT+HjOyc/3iukw6/29hMVcjxAKqmomOHIMFn13s31vVZAK2FFRTXFTEx0DA0j0t7eIYv0WRPn5UaK5KcBNjDBRIN0Ua26i/E/NljV6PFz87VxiGyBJmnjhQDbZVTVc37dnq8dYDAY+PecMPkpNo8bp5p+J8QyKieaDHbtYq7ib027TZRNhNhvbiopZnJlNZ6wkCTPLZC2vbtrKpd27YDYY6BvVci3g1XWWZOVQ3thE78jwX9y0Pd04meX5biHExYAqhOgA3A6s+32n1cb/ijqXm1c3bOFgRRXJocHcPqjvcXNsT5b2QQ726E2USg/hwsgmvR6r0UjPiHDO6pTC3fsy6Cps7KSBW/r3RlGN/HjwFYKMBh6fNMrngB3F0LgY3pk0gu/TV2IwCC6ePp7v07OYn/oMUR1vwtlYRFXBAsb3n4jT4+VvqzewKq8Ys8HATb06c2HXjqc0/8MPvm7hMXweEsFnu9fjNUnGThxFfWUZTq/G2rI68g++wjCHja4mK2+7A0nu9QAAwZFD2bzkSt6wbgXgu+xC1pXXEWFWua5jAsHHi0j+XLjiOFhUBX+zjZqKbThC++L11FFXvYd2ffthCG/dQWnyeLlk7mLKPHZMZgcNqzfz6bRxdDjJ1JCTZVy7BFYk5HBzdi4RqolCxcPbEyae8nk+3rGLaVoAM0QQabKJak3j9Q1b+Pic4+fgN3g8zE3P4M2N2xC6pF7XCDKbSezdnQeGD+KKH+azCxcNUsdpU3m2V3cu/34e04WDS1XfwzpWM/F1TRXge6BnVddgUlXiA/wpb2riom/mkOg1YEHh1Q1b+PjsM37z+9dGG7+SNvv8F0aXkk92prEmK48gm4WbB/Y94e7/LxFkMWNQVbZ4G+in2CmWHvbrTSQ5Anl89DAeW7qKfsJOrnATEx7MvT278PbOT3BrOvf0TWZGp5ZKgOF2G9+dM4kPU/dR605j4oheKEJw74r3MJqDUBQThXtf5f5+vuM+2LGLD9My8Ooa49vF8+jQvs0iXaeCn8nEN+dM4r0duylpzOSKof1xWEzUuz0klFWRWjqTcLuFJyeM5IZFq+k55n1U1YKecCa7V15EhmpFfflNtpRV8n1OGQYFLkmKorPDp2wr5SmmtW6Cdk1VZJStJyC4G1LqNJStI8lTgnJYKKaVqNUTazbzY1Yx/oHtqd65g8eH9WNqh6RTvicnItRm5YFhA7l/zSYSFDO5uov7hwwkzHZi1c2fszw7l4AmnbuJJEe4MWrw5pbtXNenx3EjubqUrMnL56V1mymqq0cCpXX19I2M5KKunVmYfpAH6goJFgZ2iybeGz2ZuekZxAgTTygxCCEYJQO4W8ulxuUmTFUpqKun0eulXWAAqhDcMm8R5WU1JEoTb7GN+4cP4syO7X+jO/f7I+QvVLEKIWzAI8CEQ39aCDwtpXS2dky38FD57bmtN/9t4/TAq+tc+t1cwms0hug2fhQ15ChekoMcTOnUnnM7p5x0isS8A5m8uGkXDQ11CCRuqRNuMNMgdF6bMp7ekeEAbCosIqemlpTgIHpGhP+qeWu6zpvb0liUXUiAyci9A7rTPsjBlG/m0aSE0rHP3/F6G8ja9hjPDO/O+MR2v+o6h38bJ7oHX+/Zx3tZNhJ6PgqA19vI1kXT2XndZby0aQffH6wmOOE8nLX70SpXM2fGlFN2cqWUNHq87Cot47bFq/Hzj6euvpBzUtrx8BBfw0yn18sLG3ewpbiCaD8rDw/uTWyAP+9u38UXuWaS+jyFEILi7B8IrJzF59PH/qp78kvzTK+sotrpomNIMA7LydW6Hc19Py0jMaeR7TSSK91EYGQXTfxn2kQGxrRspVDW2Mhl382jqqGJS5UQpigOyqWH+0UBL0+dQO/IcCqamthYUARINuYWsj6vkHq3m6tlCKMVn8HdqjfwjrGSmReexbWzF1DT4MQldXpHRRDpb6d+fwnXijAA5upVpEeZeHPaqTuYvyed//PBVill61vHbfwl+TX2OTK2h7z0tnn/i+n9YTwydAeFL795WkvVnwwvrtvE2r0HmaE52C+b+JFaUoId9I2J4pYBfTAbTi7NZU95BQ+v3ERRZQWa1JBSEmowUal7uXtQPy7q3gWAzKpqtheXEmK1MCI+FlU5fr3NL/FTZhbvph5Ak3BJ53bM6NSBm35aztqCcjr1fxazNYzcXf9ibISHvw/r/6uuAfyieElWdQ0XzllJt9HfNI/bv+YqXh3VhXq3h7uWbSA8+Uqk5qQs63M+mTaOLqEhpzyPRo+HaqeTy+cuxa0G4dWcxNngozPGYjUakFLyxe79/HAgD7NB5ZbeHRkSG8POklKuX7iBzsM/xmC001B7kH3rb2LTlRe1Wuv031BQV09uTS3xgQHE+J96psq3e9NZvi6N9pqRb/RKOmEhlSau79+L6/v2ajFW03XuWLCE7fnFdMXCvUoUGpJnlGJG9urIdX174tY01uQV0OTxUFBbz+y96VS6XPTymrlX9dl7t9SZoWWw/boreGzZalZn5+GnqBgtRq7u25NP1m7jX3oMqhBkSxcPiQI2XnPZaVWffSL7fMJfsBBCBeZLKUfje9C38Rcis6qa8toGnpJxZAsX+zUnV8hQHOUq767bikvzYlYNfLFzNwAX9+zKjC6+yNKWomLya+voFBJMUUMDf1u9DcXr5n7CCREGXqOULu2ieXTEkBa9wwZER9EtLLRZOv/XoCoKt/XrwW39eiClrz30s+u2UqeZ6ND9dqx+vnSy8OTLWJS1AItq4MW1Gymua8CDpEtICP8YP7LV7ulSSt7Ylsr7O9LQdJ1J7dvz9IgBx92xi/X3o6JkDda8n7AHtqf4wAeMTvTtYn2cmkav0V9hsoQAU8jcXMDy7DympSSf9HvdUVLKrYtWUetsws9k4Z+jB2ExGAi3dWrRpPDOJWvZ74oitP21ZFXt5MLZ3zD//Gnk1TViDRrQ/EDyD+5Jcd7v08tVCHFMJPNUmdQxmUdyVxKqq7yiJmAUgq16A48uXcXiyy9oMfaV9Vvo22RkFvVMFL7d4VBhpI+wsa+igt6R4YRYrUxpn8SjS1ZRkF3CwzKU+Vo1n1FOe2HGiOBjWc453brw3Kr1dKxTuIY4vEieLiqmsL6e8dLEYcnOdsLMhsam/+o9ttHGb0Gbff7rM3PPfl6RsdiFyvt6GRNFAD2qLCysyeW+6hqu6tOTF9dspMbpYmhCLHcP6Y9JVTlYVU1qaRnhdhuJgYFcPmcxugaXSwd9hI1ZsooMq+Sns84mxHYkPT05yEF8QABuTfvVDhjApOREJiX7ShN1KVmRk8fG4iqiks4jMMRXoxPb9U6WbrmDK7t35Mnla9hbVoFT14iw2fj7mGEMimm9hmxFTh6PrNxIdVM9XSOieXXc0OOmiZsUBT+DTt7eNwiJnUx1yVoMWjWdQkK47qdVxHS5k7DoMb7BQuHjXSv55+iTb9lT1tjITQtXs7+sBEUI7hzQm25hoRgUhe7hvn8DfJK2l//syiem853Uu2u4ffFLvDt5FKUNDfgFJjUrOdoDkhDCQK3b1arQ2H9DjL/fr3K+DjMkNpoX5EbW6xpvqAmECyOV0stt21KZmtKe6KPOvSAzi6LiKiKlgcmKA1UIVASjND92HWr9YlJVxrSLZ056BjO37+YuGUaZ9PKKLGab3kCSMPORXk6fsDDmpGeSkVvCuyRg1gVfNVby+Y7dxGNqXk/GYaJR0/Do+q+KsP4RnNAJk1JqQghdCBEopaz5X02qjf8NAp80KMAKvY6pIpCJim8xGyBV/r0lFaHp3Kr7ogCvr9uCxWhgV1EpS9IP0lGx8i+9keigICwBHZlcmUUfYWM3TQyTdubk5LdwwJxeL/ctXM6q/AIAZnRK4ZERg4/bA6XK6aTR7SHSz35cYyCl5K0t23l/xy68uiTEakc1RuJqKgZ6UFu1m5J971HorWdZ+n4U4FoRRj/FzqKKWq6f+xOzLzq3+SF5NHPSM/l8fyndRnyCarCxcfvjvLR5Jw8Matno8tl1W/k+PRubNZzsPa/jsJgZFRfFQ4MHIaVElzqK4ciDVDXY8Ojazy/XKg1uDzf+tIKorg/RKXIoVaUbeXDFUyy56EwCzEeiTA0eD+vycuk34S0U1URgSA8OVm1nY0ER/SJDWLJ1PmGxEzAY7JRmz6RXxOmbMz02MYEFSfFomRUYD30vugorJU2Fx4wtqqljPFbWUMcO2UhfYadJ6uwRTmb8rGHy4uwc3pBxBAkDN6jhZOv5PEABBkVhRreO3NivF+d8+T03EIgQAiOCwbqNDQbJXKWWntKGBYVvRDWDE37bVJE22vg1tNnn/x9IYLdsJFQYuEr12eIe0sqFeQfZVFjMdXoIccLBZ/vzeMrjZkh8LE+uWEtvxU6WdBEUaMfsl0RYXSHTpINc6aI7VlbVleHVW9Zqvb1lB29u3YEAeoSF8sqUcccVbmj0eChvbCLCbms1GrciJ4+/L1tNhctFjM2G0RKNq6kEALezgpytT9BQX8M5X36PPwqDhZ2LlBAympzctWApX513JgmBxza+z66u4d5l60js8xwdHZ0ozPiMmxYu5odzJ7cYtzAzm4dXbcRuj6Es7ydqi5bQLSyYJ6aPx2o04NElqtrSPrudp1ZOec+yDdT6DWVAvxtxNRbz1oZbeWN8aHPmz2Fm7sshtutDzQ6oq6mU2QfWcnWPTlRXbqa+5gB+gR0ozVtAgMn0uysW/lqi/f14ZMQQ/r18HeGH1CiDhYFIxUxJQ2MLJ6yovp5Oupka4WWbbKAHNqSU7FCaSAoOa3HeRekHuVh30Emx0klArnTxIiVIIRgQG8lrY0fw1ubtDNSsWA6t2UZIf5Y2FVMkNXbLJjpg5iuq6BkS8qdxwODkasLqgV1CiMVAw+E/Silv/91m1cb/hOQgB5GB/jxfWYwmdeLEkYW9F0mTx8MNhNFD8eUNX6YH803qXrKqqnlDxmHTVYqkm1vKc7EGRVIqPbwqS9gjm4jFRK3bzbd79zOjsy969vL6LTQV1fC1koQLyZMZeXwV7ODiQ6kQ4HOu/rVxO1+k7cNotBJiMfDhGaOJ8mu5ezMnPYO5O/fzBvHYFIV/OIupN1WRtecNait3UZ23gLtEGF2UUL7VK1kl6xin+hzM80UwPzXlUFhXT/xxHvKrC8oISbgAszUCgIj2V7Em/VkeOGrMlqJiZmcW0m3kZxiM/hRlzyJv37vMST/AvspqXhozmInJ7dm6/W+EJV1OY81+6iu3MSzu+LVNxyOnthaDKYiQSJ/aYlD4QEqtYRysrqHXUamcqhBIQNfdKKoJKSWatwmDEsy0Dsnsrajls6UzEELQMyKSx4eOOOk5/BFc3LUzd2YvpkR6CMfAbKrpHnJsikiP6Ah+qsziNiL4l15MpG6kCDdGofL2xm24NY3R7eIBsKoqFV4vQRgQQhCkGjl7UC8u7ta5+XyJQQ7WN9TRQZrRgM1KEyMSU3DFady2Mw1N6kxLTubm/n2OmUsbbfxBtNnnvzAXdevM06np9JVWmqTenH6nAV4pGaTbGHUorfoOGcaNmdksPJjN0zKaZN2CW+rcXpWH22KkWnOyUFbzqaygM1ZU4OlV63htik/saXl2Lt/u2MO7SjuCUHm3opzHl63hlSktFXwXH8zmwRXrMRhsSM3JqxOGHxO1yq6u4aHFK3hIRpKiWvjGWcmspmxq3PXs3/YMzrLNTPAKzlbbkSobeUUv4XwRTJAw0F/40Uc0srmw+LhO2I6SUoLC+jU7NDEpV7FpwRc0ebxYjb4lrdPr5aGV60kZ+Ap+gSnU12ayZ/3tbCks4LK51TwzYgDnd4zn+S0vI4SCrrkpOfA+D409OTn6w+wqKabH6EsRQsFij8YRNZYdJQePEZIyKAq6diSDQtcaMRgF8YEBPDOiPw+vuA0dgcNi4Z0po06pOff/mjHt4nnOsJ6tmq/HXareSKnwHFOr2C0sjC9FGg+IcF7US9jmbaAJSa3QKMnJ5x2TkWv79EQRApvRSCWu5mMdwsDAmCheOeNIW6WkEAffKzlMkzomobCOetoHB3Fxz648vnwNlW4XfcLCeGnimP/ZvfgtOBkn7PtD/7TxF0NVFM7v3pmn16VhsUSwrTYTP10lCJUPZTluqVOrHInc1KCBgDjFjE337TRECRP+ikpj3X5WSCehGHldTcAiFPbLJv62ZgPndvLVlm0rLOYSGdAsGT5B82N7QXELJ2xZdi4/ZJbRY/SX1JRvozR3HlfPX0pioINtJaUEW208ObwvG3ILmKr7E6b4dmMuJpjnvCV4FRPl+T/RHRPDFF+64bVKGAu0Gqp0D0GKkVqpUS+9+LdSmxVmNbGz5kDz/zfWZRJqbVnflFdTR0BQVwxGfzzuavIOfExi97sJChtAae5srvpxFrPPncyrW3exLuOfRNssvDp9AuH2ky+EDbVaaWyqwO2swGQJwe2qoqGhlDBbyxx6i8HA2R07snzzPQTFnU1j1U6sejlDYvsjhOCBwX24s3+P/2mrgv+GPlER3DCgN7ds2IwBQYy/P29OnHDMuJv69+aKvEKeqihEIHGiYERwux6Ou1Ln0SUr+dekMQyJjeG2Qf14du0mJukBFCoecs060zq0TAt9aMRgrp71I1uc+TRJnZTwEK7o2Q2TqnJLf18vu9Mpz7yNNmizz39pLu7emQ9T91IfGEN93UHekBX0kmbmU4NAUiO9zWNr0DCrKrUeN0mKz16ZhEIHYSVV1lGnu/kPZbyuJhAjTNRJjRvyskmvrCIlOIhtxSWM1O2EHJIMP0s6eKikqMV8ShsaeXDlRjoMfAXN66Q0fwE3/riU87p05MeDuRgUlet7dsJuVOml2Ol6SBL9IoKZqVeiuCupK1sLniauUtsjhGCkCGC+Xs1GGjgDB7qUlAgPAa3YqmCrhca6A+i6F0Ux0FSfi0FVMRuORD8qGpswqJYjbWx2v0ZozETiUq6kvnofty1+gu/Pmcz9feHLfa9jUgT/HNWfoXGnVkMYbLNTV7WH4IhBSKnhrNlNWMKxG4Y39OzA39f+A1f7q/C6a6jK/Z6Lz/LVFU9KTmR8YgK1bjcOs/m0tzF2k5FXp4zjzgVLcWslGFSFf08ac0wN+ODYaM7s3pF7tqdiQWBHpQ4v1xJOVK2JT3bso8nj5Y5B/bi2X0+uyJtPja6hAIuUOt4b0DKyOaNTCutz8rmxIJcAxYDbKPhg9GhiAvwZfdXFv7q5+R/NLzphUsqP/xcTaeOPocblJihqGPHd7qGhNoMF6Z9QXbwGHcm5IojP9Qpq0UBKvpVViCoT0uPmG6WCkfizCydms5GPpk3k7Jk/0EGYsQhfuDgFC05Nx6VpWAwGIv3s7KttotuhsPQ+4SImoGVYek95BX4RoyhIexVj6VbGSiOr9Vq2e/3pMOR96qv3c+NP/2BqYgzZwtN8XJZ00SUshL+NGcrmwmI+XLsNTZeoQlCJFx3J3/UCeks760QDl3TrTJD1+CH/63p14afvf+LglhJUox81ZRt4cVrL3cCUkCCqN64isqmExvpcLLZYwmJ8YhfR7S9lW9Y3jPzsexo9TvrHxPP86IGnnGIQbrdxfa9ufLD2WgKDu1NTlcbVPTofN6f78eH9SUnbx6biH4gNsnDD2AlYjkoTMRsMCKH5utH/DgW/vzWX9OjKeV070eDxtGqYTKrKWV1TcKxL4yYZxjN6IVeJIPopvvz6al1j9p50hsTGcG7nFKL87azJyaerxcxTXTsf45CG2218f+HZpFdUYVQVOgQHNe9I/hkf7m389Wmzz39tKpuc+PmF02HY63jcNeze9wFrCxbSpLnoj51s6eZ1rYQ4YeJbvZImVExC5RW9hOnCAUKwgwbemjiRBxauoLqxiRjhe+75C5UYYaKkvoGU4CAi/ewsVNzoUqIIwV7ZRPjP1POyq2vw94uloeYApbvfYJy0sl+q/LDnAO2HvgG6l1e3P8aFKZHkSRdeKTEIQT5uTKrCgovPpqyxkUt/mEc1GkEY0KSkAo1P9XJKpIf9OLEE+zE6If6492RYXCxdHZnsXX8j1oAUqkrW8tjQgS2iR2F2G0J6qCrdRGBIL+oq0+g26EWEUAkKH0B1aD8unrOQ6qZGYgODeWnsEDr/CkGOZ0cM4JZFT1EX2pumhgKS/XSmdhhwzLgp7ZPwMxmZfeBHLAaFK8+cQFKQo/l1VVEIMJma10qnO/2iIll11cVUO50Ems2t1g9O79iBWbv28S89hgWyBieSiaoDgDt0A0/uO8Adg/qREhLMFzOmM3v/AaSET1OSW9S8g+8evTRpLJlV1TR6vKSEBLW4V39WG/2Ln7YQIovj9B2RUrYVRvyB7CgpZWtRCaE2K1OSk371wrp3ZDivbVtFaLsZ2PyT0A1WBJJQDOyWTYwXAdTpXvLxYLXHYw/qSm3BIjbqDXxFJf5mE+9PnUz74CAGxcawNb+IPOkiTpiZK6tp5+/f/EO5d9hALvthHnt1N01Sp9Gq8PjP+kvEBfhTf2AtsqGUD4jGrCicJxxc2ZCHECohUcOpL1mKW89jiVZNPk78hEqa6uKj4WcQFxBAtJ8f8/Zl8GhZAZ00C8sUJwGOblQpKt9V7uSVcaMZf4IGg8FWK7NnTGFJVg4evZERI6cckw7ZNSyUW/t05qWVl2MwWPBIFV1zoahm3K4qnJ5GOvd/joCgLuTve4t7l63n/SmjT/nzubFPN4bFRpJRVUWiY0iripKKEFzavTOXHqdftVvTuG/5epYezEQgOK9LZx4d2u+0TnkAn5P1S7ndg2KieUluYpZeRYX0kof7cIs5nOgtav6GxMYwJPbEO50mVT1u/7g22jgdabPPpycFtXUszc5FVQSTkxN/dW/G+MAAdE8t5YUrCIkaiS20LyW5s+mIlUI8DBR+CCnJxEkdkNDldgrSXiFfunlI5iMFPDV2BD0jwrmqT3eeW7OB1XodwxV/9ssmCoSnud3GeZ078lP6Qe6rKiBUGNkjmnh7dEsV2Gh/P2rr8qne/Rr/JoIExYyUwdxDCU31OYRFjyEs+XLSyr+lXPdwm5ZDB2Fhm9LIo8MHE263EW63cV3vHjywYy9DNRu7FB2nNQyzLYZZ1bs4p0M8jw0f3OqaRhGCtyaOYGl2LmWNlfTsO/KYZ7ZJVXlj4ghuWfgk+Ypv89PZUITVLxYpNaprcggMG0f/zjdSWbSCa358g8UXnondZDzeJVtlYEwUs86dwrbiEgLN7RkWF9OqQzIiPq5F0+mj+WDnbl7etA1dSnpERvHGhOGnbV3YYRQhfvF7HRfgj91q4cuGSkqkBwXRHLFyorcQaEt0BHLnwBML/AohjnHO/uycjMt99F2xAOcB/50EWhv/Fd/tTefltZsYKv1YpLiYtXs/7545+bgiEyfCrWl0DQvlwUE9eHrt9Xg0jRCrDX9UYjDRT7GzQq8FIFt4iUiYRvX+D3lXScBPqOyXTTyhFZMc5GDW/gx2VjgxBvfgtsqdKPjS6d4/wxdS/mbPPj7duRt/k4nE2CiGxcUyJDamOYf7MNM6JDNz70Eq6jTMhx7CfkLFKhWKcuYQHjuRhvoclhTm8bgSQ5H0sI0GAs1mUg79OFVF4e1pk7hl4RJ+KKqjQ89HCY4cQlXpRrw7MxmbmPCLuyYBZjPndEo54Zgrunfm7JQkKhqbeGnLLrZtuBlbcB8qi1YSENiBoDDfTyem001sXvzrWza01lj6ZHlp80521jvoP3E+UvOwaPM9JKbt47LunX/54D8IKSXzMw6yJisPh9XC1X16HDeVMz7An95RESwtKCFZmPlSVpKtuUgQZn5Qani/19BfPYflObnsLa8kzt+fKe0TWZWXz2vrttDo8TAuuR13DOr/p4gqtvGXps0+n2bsK6/g6tkLGCRtOJG8u2UnX82YfspN3jVdx6iovDN5FLcuepmMHU/jZzZjEwZqpcY0xcEB6WQHjRilIDrpPPJ2v8bzShTthYVaqXGHyCMhMIDMqmr+tTkV/6hRvFK8hpe8xaiK4MWJY4j0s7OzpJRnV2+g1umkfVQI49sn8mxM9DHP3NgAf27t240X1m8kWvU5LEIIonSFtKLVWKwRuBsKSCsq4Ao9GH9FZa9swgsMOKpu7Ob+ffC3mHl27WYSOlxH16TzcLurqVlxGdf37vGLm2+qojDhBBup4IvWrLz0HIrqG1iVW8DrG28jKGosTTV78borSexyC0IxEBY7kcrsL8msqqZHRNgJz3k8YgP8iW1FaflkWJOXz9upWfQc9RkmSyi5u1/h4ZWbeGvi6V27nVpSxre79yGRzOja6bgbxAZFYUbXTryxcQvdhZUD0sU9Wh4TRADfqzVc2/fX11fvLa9gda5P/G16SjIVTU6eXL6G/No6uoSF8rfRQ38XhcnfmpNJR6z42Z9eFkJsBf72+0ypDfAtQnNr62j0eEgOcjQ/lKSU/GPNBv5FDHHCjK5LHqgsZHlO7kn3w6p3u7lv4XLWFBZiEAo39e3FtqsvxqVpfLwzjc83p/KIGo0qBGNEAJdqmWCNQTXYSZIqfopvLh2FFa+uU+f28FFaJvE9HsIR1g9d95KX/iFDTDto5wjktU1b+WjbLoYJPzKkkx/3ZNApJOQYBwx8D9d7BvbkhrkLWaRXM1D4sVjW4tHdJGbMYlPGZwRYrXQ32Omm24iXGmZdsL6hmNL6RiIOdYA3qgpWow2LXyDZe9+iOG8etWXbUHU3Pd/5mL7hYbw4aSyhtv/uRxpgNhNgNvPyuGEszMwiv+4g1X7hzCs40sOksS4Lf/OpNUX8LdlUVEFo4t2oqgVUC8EJM9hQ9C2XHSdqdrrwwY5dzNyaxnQ9gAJRxQWZWXx/wdnHpJBuKy4ho6SCN5V2WIRCnnRxh56LIzmc93sO/VU9XwBe2bCF+WnpDNJtLFVdfL9nH/vKKrhDDydMMfLu3hxeknD/sIG/xdtto41fRZt9/uMoa2ykpL6R+ED/Fmq1r6zbwkWagymKA4APPeW8t3Unj44cclLnlVLyysatfJiahpSSsfHxLLxwOlJCrcvJ2M9m8rKaSOghdboHvXnsxkWP2AmUZ31H+0ORnwChkqxYKKirJ7W0guC4s4jvdB1SSqpKN1J34EVGJ8SztaiYa2YvYKDwwyx11tf71Iunpxy/4e3ZHZP5ascu3nSVcgWhHJQuNuo19C7eQlrxGryqwCxhourAKyVGBFv0RpZn53Bp967N57EZjQQ6OlCYNZO6qjRqK9NQvS4mffktMTY7L0wc86ucoqOxGAwkOgJJdATSLSyYbcXZmCLtvLzZi9fbiNEUgNdTR2NTxa/qa/lbsLWolMDoSc1CYJHJl7Jt7TV/yFxOlu3FJdw8bxHn6IEIBDceXMjrZ4ynb1RLQRK3pvHa5q28qMQTJ0w4pc5NMoftESr3dRvS3MbgVFmdm8/9i5YzRvpTLjQ+2b4Ll6ZxltuPK5VwFhbWcuOchXx9/pmnfcbPyaQjHu2qKvh23k7/pNU/MZqu8+DiFazLK8BfGBBmA++fOZlofz+8uqRJ14hWfHndihDEYKTG6T6pc9e53Fz+wzwaahq5jjD6YePv23eTFOxgXGICvSIj+EGozWFiEwKjUNENVgr2vkOhXk++cBMrTCzXa/EzGgk0m1rkwyiKAdVgAyHQpeS97am8osYTJ8xoUnKzls17W3ZwQddOzcc4vV40XZJfV8et8xczRvrzjaziLcrwR+FlNZ5IYWK5bmWeoYkDziaycfG4XkA7TCRi5vxvZzP7onObH6ZWo4GQ8MH4B/egrmoP7tIt/EONIw4TH5WXc9v8RXx53m/TVFwRgsntfRlAbk1j89wlHNh4GwZbAhVFK7i+54mjar8nETYL2dW7cYT6xCWaqncT7f/7pDpkVdfwyOKVZNfWkuwI5JlxI4+rQPlLvL8tledkNLGHvudV3hIWZGa1UDMEKGtsIuGoOsQ4YcYkFO4deuIavAa3B03qLRZPh6lxufg4dTfviQQCFQMuTeOy4iy8SP5JEZN0B9eLYJ7NzGpzwtr4Q2mzz38MX+7aw0sbthChmiiXHl6cOKY51bna6SSWI5t7sdJIZlOrvbNboEvJw8tWsSYjhykEMEME81ZBOS+u3cQjI4dgUm1wSOTgMH5CxT+oB3s23IcivazR6xim+JMjXaR5ffVeqaVHfHUhBEaTP+JQ88OnV6zjKhHKVNWXSfKGVsKS3LwWaoNeXafe7cEgBBd/O5eOLgM5uourycKA4H4ligGKH8XSwy16Dk4JWcLJ23oZLnTCMPDius30CA9vdqzMqorVZCOp50vU1xygqWIHtyrhDBP+bHDWc8Pcn1hw6Xk4fqO0vL5Rkc1OQlGDm1nrrsce0p/qsk30DAsi7r+IZv03hNutuPN3I6WOEAp11XsJtZ9a1PRkqXe7+fuy1awvKCLIbOahkYMZFhd7yuf5eNsuLtGDmHxoo8GqCz7Ztou+Z7R0wurdblQEcYfqEC1Cob1q5fzunU8YyXRrGnVuN8EWy3Gzll5cs5E79fDm+u9bmrKplR4+wMlWvZG7RSS31eVR0tBwTCnJ6cbJPKxfPOq/vUAWcP7vM502AL7ff4DsvDLelQmYUfiqqYInlq/h7emTMKoKfcPC+KiinAtlMBnSxRalgbujItheXMoLazZQ7XQxND6Ge4YMaNHDw+X1cvn38wir9TJOOFig17CXJsbpfmwpKGJcYgI9wsOQNgNfOCvoj52l1GE0qNTVZ6PqGheIIO7ScrEi8CCZlOhTOLq8SxLPb3keb8eb8XrqKM/6gvOnjsWtaXilJAbfj1AVggRhZrfXJ0eqS8kzq9Yxc29685fxfCWY89UQbgDW6HV8qVcQeehHnCjMuLV6pNnEw00FnKUEcr4S4ts99JTw3rad3DvEVxh7dY+OXDL7S3TdRX3NAYZhJfGQDP+lhHB+RQYvbdrBXQN6HfMZbC8uYVVOPn5mEzM6pxB4nMV6a5hUlX+OHMQ538/DIo2ExE7hw7SFdAoJZmxiwil+G/57HhzUk4tmz8RVnYqmuzA487h55KTf/DqNHg/XzPqRs9z+3CViWVlRx7WzFzDvkhmn3LdDkxIrR1L9rCh4ftbTBqBrWAhP6I3sl00ES5X5soZgmxVHK87V9/sOMH9fBhnV1SgChsTE8MLE0S0KfOvdHuyKSoD0zXkBtURg4EnVZ6ye1QpxoWP9ExRQt/GXp80+H4eCl95E/E69grKqa3ht41ZeJo4I3Uiq3sg9C5ez8sqLMKkqw9rF8VXqAWKkCSc6s5VabkxMobShkadWrCWzsoqkIAePjjq2wfBTK9eyO7OAi0UIO2UjD+p53EYEHxb4VApVRWFSYgIv5pZwvgwiUzrZJZrQ6vZh9HqYJoJ4Ty/jHb2URnQi7XbaOQI5MyWJr2bPwmgJxWQOoSj9P9zcw6cOW97QSJIS0TyH9sLCSlnH4bXv7H0H+PuqtSg6uNHpKCzcqcaDAtXSyxXaQfoK3/uIwIBZKPSLi+LunHy6CAsvKvEoQrBSr+WpFWv55oKzABjdLp7Xti6g5ODnYAzFX3MzUvVt2A0V/nziLeeyOYv4/typx6R9F9TVMyc9A03XmZSceMp1QncP6MnS7FxqqnYTEDqQfTW7eHb9Vh4ZcuKapN+DczulMOvAUtLX34DFGkl1+TbemTzqd7nWQ4tXQlEtL8kYchp9GVGfnjP1lO+fR9OOsc9u7Vj7HGSxEGyx8GNTNcPxYxMN7JaNx81Q0XSduQcy+SnjIBvyizApCuE2G29Nm0Dcz/p+1rrdRB2KBB+QTiqkt3mD/UO9nBf1YpzofwqRk5MpaLhGSjn60D/jpZTXAycXdmnjV5FRXkV/3Yr50O7+cPzJrKpufv3fk8dSHm7hSpnNW5Yqnp8wGgHcPG8hYyoN3NkURHp6AU+sWNvivOsKClEaPdyvRDFJcfCMGstqWcdm2UDoodxvq9HAR2efQXGUlX+byikNMfLVjGmkXn8Z0X524jFzqwjnAhFCL8XWHOU4t3MHHh3UmaCyT4itm827k0fRNSwUi8GATah8rlfgljppspGdspF2h3pKfL17H6vSswjBwPNqPP2FHdtRu3xWFKrRKJJuGqXOl6KKQbEx9IyOBoONLsK34yiEoBtWSmrr0aWk0eMhJTiIr86ayGjrTjqZ8slW3GjSF7PLxIUVhU937CK3prbFffopM4tb5y2mZmce27akc/7M2dS4XJwKM/cdICz+bLoOfomkrrcQ3/0hXtu275hxUkre2Z7GkE++ZdDH3/Dixu3o8tQaRv4S8YEBzDtvKnd3s/FQz2BmzzijVWXI/4YDlVX46YIzFAfBwsDZShDCo5NV3Xof2a1Fxdww+ycu/3YuX6btQR5679M6JPNvUUqabOQnvZoNooEx7Y4tao4LCOCZcSP5m17AjXoOq6in0eMhvbKqxbgal4sLZs7m+42pyKpGPleS+EIk4Syq4dWNW1uMjbTbcFgtzKSKaulllV7LRUoIQcJAkDAwQwlmLfXcMqjvb3DX2mjjv6LNPreCGhL5y4N+BVnVNXRQrUQcWgT2UGyoEiqafH2gbujXi54p8dwm8nhQKeSs3p2ZlJzINbN+JKSwkXucIYQXNXH1rB9xa0dawDS4PcxKz+BZEcNEJZD7lEgUBB/qZYQdVZv1zNgRdOgYwyvmCpb5u3lh4mi2X3MxZ3b0pQ/eKsI5VwRzLkHE+PuiOx2Cg/h46lgSGn4koPQj7uuTzGXdfJkoIXYbn2vl1EuNUulhll6FqggsBgPpFZU8t3o9Jh3uUSK5ToThJ4/YZwsKEtiuN+CRku9lNeE2G6PbxWOwBNFVWJvTwToJK6WNjb736vFgMxj45uyJnBVeTi9lK/XoVB+S3K+TGjVo5FfVMjv9SLsY8Ck0nv/NbLK2ZVG0PYfLvp9HaknZKX2GGwoKcSrBdB/+Psk97qbDwFf4avdenF7vMWPX5hUw7ss59PvwK25euOqU1wK/hElV+WzaOJ4ckMBtHQWzZ5xB78iIXz7wV7CqoIDrZRihwkhfxc4Q/FhfUNjq+OL6Bu5fuJxLvpnDP1ZvoMnjuz9nde3Ip0olm/V6Nuv1fKJUcWbXY7N9hBC8NW0iP5jquELP4nO9Ak1KdhSXthgnpeSehct4Z9VmduQV84YSz5ciiZENZu5asPSY845MiOMjUUm59LBUr2G48Ke9sGAWCpcrIaTSyNkdO5z24iZwcpGwb4GfV899C7StQH4nkkOC+EbNYZrua0q3lnqSHI7m10OsVt45s2UPhU9SdzMYv+bGjbfLcK7LyuZZRjaP8Wg6Jv2IlKcFBRVBPq4WaV4GRSGjqopwTYVqJ1fO+pHPzp3Ktf178tTydSRixigE+3Und8QeKbad1iH5mN5LAOOSEliSmcu3WiWBhxysg1U1FNTWsb2gmGBNZYjiT5IwM1E4eEEvIhgVKwrvKRUMio/lrrx83LrO+HbxXNG7G4+t2YJTa+IbNFIUC24kPyl1dDUH0P/DL3F7vcQGBvGfSSN5aEg/PJrOxE++4h5XLonCzEbZwCVKCF9QQ0lDY4uUuVfWbeZ+GU63Q02qX3SV8MO+A1zZs9sx7+3HjIN8vicbRQiu7pHcLKvr1HQU05HPzGgKpOYog3uYH/Zn8uGeQhL7v4pQDPyw4wmCLHu4umfXY8b+NwRZLZzR/vcVTPM3majSvTiljkUoNEqNGulptR/bnvIKbp2/mCv1YByY+GjDTtxejSt6defB4YN4y7Kdz7NycVgtvDd08jG7YYexGgw4VBP/kjEECJWl3hruW7icORef2zzm273pJLtUXBKGKH7N6YuTdX9+KCxpcT5VUXh7+iQeXryS2ZV5mI0K2bqbwxUdB3HRIyriV+ezt9HGb0ibff4ZV266/nc9f7vAAA5oTZRKD+HCSJpsRFNoFgEwKAoPjxjMwyMGNx+zr7wCl9PNpUQghKAdZta78jhQWUXXMJ/okkfXEZLmzVchBA5UdtHE00OO9IY0KAoFtfUYPDrhXslDi1fyr4mjuaJ3N2bsn8VqWUe8MLODBm5JOBLZ6RoWypsThx/zfq7v34snl67mcu0gKoJAVHRdZ1l27qHUShMOoTBI8aNKevlaq2SWVkmKYuV7Uc3AyEjerq6i1FlEl6AgXpkwjk/T0vF6G1moe5ggAgjCwHeyig7BQYz9YjalDbVYDEZeHDuU2w71YLxbc3NbVg79hZ1dsonJIpC51JH9s028D7fvYrLXj4uUEBAQo5t4a+NW3pp+bHbH3vIKXtqcRrXLw/h2kVzTsyuKELg0DaMpoHktZDDYEELg0XQsR62KD1ZVc8eSNST0epxo/yTS09/jnqXreW/KqFP5yvwiRlVhdLvjS/L/lvgZjBRrHpIwI6WkRGndPje4PVz2/TyGuSycK638VJPPPTW1vDl1AuOT2uHWNL7csRsJ3NtrUKv2MNRqpcHri1SlCAvZ0sXDK9cxICaqWfglraycXYUlnKH7kyc8zZlP04WDj6oONLdNOMwDwwfxtL6Ou7JzQYEY3dA8Jgs3AUYTjxz1+zudadUJE0J0AroCgUKIc456KQCfClMbvxPndurAhtx8rs/3NaXTjArvj2m9C7im6yw5mI3mdTV/orV4MSsqFU1N+JtMmFSVAdFRPIqL77RKuipWvtMrsSBwSUivrKLXIXWbtzZtp4/TxLXCl7v9mbecl9dtIchqYbQawM3CN26mrOS9LTt4afK4487rMI+PHsbEvK8xuTVUBNcooeTrbr5I20tUoB+7hU6h9G3e9lJsjJL+vE41uvRw3+ABnNelY/O5PLrO1G/mo4RNpNOQoWRsf4bzGjNQhGBSUiILsgrpMPA1dM1N3rYnOe/rWZzTuQN3DxnAtM4prNmRTkdh4WwliEzpQtc1PtqVjllVm3PVGzwewjgiVRumq9S7j91cXpCRxePrdhHT+U6k9HL/8pd5aazCsLhYzkiOZ+7CL7D5xWM0BZK/+99clnJsJGdRThHh7a/E5t8OgIgO17I4583f3An7X5DoCGR4uzgeySmk1/+1d9/hUVXpA8e/594pmUx67wmh9957F1CwYK9r19/aXXvbFXet6+raFnuviKIiIAgivfdOQkjvPZl27/n9MTGAtIB0z+d59tmUmXvPDPGeee95z/saAazQ6xnbvDkJB+hrBvD91h2MM0MYoflXRYOlxusbt3J1l45YNI3benfntt6H/yy5o7ycLjgIEf4Af5AI5uXqon2aN1a73cSaOi40Nsh6BslghBBsEC4SDrBnLSE4iPfOHwf4y01fNuU7ck0fAthodfPJkIP/96gox5uanw+sR784WAaWmCNrunsk0sPDuLVXN+5cupJYzUax6eX5UcMOmXL96+4cyr0evLrEhsArTWrx922s9XpxNuytTg8L5fmKfM7Vwllr1rEdFxZgWU5+Y7A2OzOLvMIyXjCTsAjBOrOOx+b8yqND+hOj23hBJmFt6PP1r9XruL7roSswjW2RztebtrImrwANSU/hpDMOXluykrsH9KIYHy4pMKQkXFi4V4tjkpmPbuqMbJ7C40P6Y7dYGq+3981dzLKKINr1fZn87R9zXcE8dCHoHB1NZlUtYc1vokt0b3atfIK7f/yZbnExPDl8EJd1asfizGyaYWeIFkKssPK1UcGygjKmbdvJ+Fb+G7w1Ljet9vroGoPlgPPz7soqrvpuNrEtr8cel8T7W9+k0rWae/t0o0d8HK5fV5Cf+RXBEZ0p3vUFXWLj9+sduSQ3n4i4gYRH+4Pg5PZ3smTm2NO2MfA9fXvy5MJlDDWD2K15qQ+yHjR4Wp5fQJRP4wrhD3bbSQeX52VQ4XITFmBnXMvmjDvATfffy6upIVyz0Mr0X5bShJ1EzUZ2VVVjEFbt8RAlrMQLG7PNajzSvwCxUdYTHeDYr7hGgMXCpOH+6pEew+Dab6bzcEU+SdLKYlHDpGGDTpt/n0OthLUGzgbCgHP2+nk1cMNxHNMZwZSSKZu3sTavkPjQIK7p0hGn9fA9KIpq66hwuXhq+CAKauuo83ppER62z96u3/to3UZqiqupwMvLRgGpws43VACCsR99iQ/JYwP7MaFNS+4f0Ienf1nEVwb0Ek7e0dNZI+u488c5zL36EoQQ7CqvYLAMAOFfJg6QGr/k5ZMeFkqfhp8DtJEBrK+uPexrslsshAUE8DcjhjbCQaX0McuopGLLDrolxGEEWpldW0W54SMEnTmiHpszjgvSw7lor+Id4E95qzXttGl1PUII2g/9iE2/XMqbo3qSUVHJalciusXBtkW3c6sMJUUk8NHWHP7h9nLfwN78sHUH8101rJf1LJY1BMcPITOwA9dOf4e3xw6lc2wMQ9NSmZyRz/UykkLp5Setmv+l7h9AfbJlFwltbycy3n930fDV8dmWbxmQnESP+DieG9KLl1e9SpXP4MrWydzYZf+VtDC7lV212Y3fu2pziD7CXiWnCiEEk4YP4scdGWRUVPLXiHBGH2Lzra5pePYq6eJBHlUlo2ZhoXxIPTXSIEjoLJI1pDqD9rkID0xJ4o71W/irGcU7Zgl3GruxazqVAfBRv0PfREgMCebrS85jTuZuQPJkWuofrqqpKH+Qmp8PoH1UAU29hCzOyWPGtgwcNguXdWzXpAJCNR4PudU1nN2qBaOaN6OgppaU0JBDVtbbUFTCuyvX0UYE8ISRSx8tiAWyBs2mce230zGBC1q35OHB/Xh+9DAu/Hwqq4060rDxX92/h/hvK9cwpFkKzcJCya2uIcWwYtH8L9SJRoGrnsU5ebRoyFIBaEUAlR4PPtM8bPua5uHhtCjwcqEWgU9KXjcLya2s472V62gXH82q3ALuM7JpJxzMpQ57WCvibZU8OWxgY1+s3663s3Zm0HnY51htYbTs8QTWdU9zaUIJ41o05+yvfqRl0ig2zbuGES43g7UkFhTWcO230/n64vPokZzADzmFtJUBLDZrsAcm4Yq/kn8ue5cyl5trOrVjeIs0/p27hFRpx4bgA72c81vsf9NyVsYuwuJHEJd2HgABgQl8teRm7u3TjfCAAD4eP5InFnxPft4X9IiJ4tH++68ShthtuGtz9vS2qs3FYbWfNh/wf+/8tq1ICQthaW4+rQMCOK91y4Pum7JoAg9m42s3kEjYp7dXU8QFOSkzvGTgJl3YyZUecg33Ppkt7aOjyMNLhfSRIKzcYuwiTtjI0r28MOLQNzxtus47547lp4xdVLjd3BwfR6vI06dLx0E/2UspvwW+FUL0lVIuPoFjOiP869clrNiexTAjiA1aMddk7Objiecc8m7Zq8tW8d7aDURqVup1yeRzzmq8+3UoK3LyOUeG0kUP5DuznPVmHXXC4C9GFGdpYWRLNw8tWEK7mCguaNuKtYVF7Nqay116PAB9RBAveAqo8XgJttvwCcHXVNFNOnnfLGaJrKWFO4ANRSVkotFTOrEimKZV0i2xaUvoZ7VK5621W7nBiOTfZgFdRCBDjBAWZFcSEGThkaH9mJO5m/Xl1SRbIxnXPI7rO7fb5xg7ysr5dut2auvLcLuKCXDEIE0PXl89QTYb0YGB1FStQy9YQH8CGdqQmnmnjOGGzEwmjRjE1xefx7BPpuARgSQ2v5rE9In+g0vJBxtn80JsDA8N6sMzYimP7tqN02pl0oDBdIzZv1SuLgTS9DZ+b5oeLHtdoIampRw2xeDWru246Jsp+FxFoFmpzJ/DC+ccOig4lWlCNOnuGPgnhMs3bSXQ0AhD53Otgtu69Tz8E3+nf1Iiw1qnc8uW7cRoNsp0H6+P3rfJaPf4OB4a3JeXFq+g1gup0ZFc2L41/ZITCTpIOsbeIh0OLtprRVZRTiY1P+8vMDoS8cgF0ISCHLMydvGPnxdwvhlKBQaXbtvJZxPPOWjKM8D83dncN2seYZqFMsPLo4P7cc5ByrjvbW1REX1FEDdpUfwoK9kl3WyW9Qz2BnOXlowLkyd2ZvNF9BYuad+W50YP494ZP/O0Zc/c0UpzkFFeQbOwUDQhWGTWcIEIZ6fp4lVZRE/hZO6WnVQYPiaIEFKEnSmU0y48vEn9Q0e2SOOOrTtIN+3MkZWU4ONBLZ7sQjcf66U8MqQ/i/PyWFdURhgh9IjX+VvvYfs0Ji53uZi2bSdSmlSWrScqzh/UmN5Kgm12Qu12fD435UXLsHmquEbEIYSgGTaWuXLYUV7BS2NHcPm3M5lf7iEwtC/tej0FgDMknbdX3cs1ndoxtmVzKt0eXly1Dp8pOb99K646wFYBXRNIc88KmTQ9+9zkaxYWyvtnH/oD/shmqbyzfhs7V/wNW1BLyvNm8MBpvhe4R3wcPeIPv1+yR3wcPoeFV2uK6WAGMFurYVRa6n6rhYcTarfz96EDeGTeQuI1G/mGm/sH9NmnB12o3c5bE8bw6Oz5ZFe7SQwO4dwObRiUknTQTJq92XS9yZ87TjVN2RO2Wgjxf/hTHxrTHKSU1x63UZ3m6rxevtyylfdFM4I0nTFS8reaPJbl5R+0HOiyvHy+WreZN0ghTFqY7a7k6q+/5+5+vZjYthXaIS6kMUFOtokiBolgLtej+FSWstaoZ3RDmleysNNZc/Lzrt14DIPz2rTkzh27qJA+woSFNWYtgRYrQQ0rMC2iovixLoRLqnYQhOAtPY1AoZMrPdxmZnGFuRNNaAxPTW5SuhjAjd27YEjJCxu34XVLbtZiEELQRgZwa1027aKiOK/1/hs7F+fksbW0DJ9p8taKNQwjmH6mlUU/X0ZARCdMdxE946JIDQ0hNTSEfnG7mJfxGdGmwW/1PX5LzQSo8/lAs+EMSiMgcM+FSNNtGB7/qozdYuGxof15jEM3+r2+UwvunPMyhq8O0/RRuONtJh1hVaOU0BC+vWAcP+zIwJSS0f0OvvfpTNMsLJT3zxvH+6vWU+z18lDr/ow8TAPOAxFC8MDAPlzaqR3lLhctwsMOGFiNbdmcsafphVpRDkLNzw0u+uEChGhaQY7Jy1ZzuxlN94YS16YPvti4lXv6HvgmUK3Hy32z5vGIjKOt6SALN/f8vID1hSXc1qsrwYeonhsdGMhO4UZKOFsLY4Os4xezmvEiHF34y80PN4JYujuPtlGRdIyNxmLR2STraScclEgv200XqQ0rdSmhIehBcfxfbQ5Ig3/rKaQLO25pcruWzd0yB0xIDw3hv2NHNem96xEfx9+HD+T1JSvZXlnLO3ozwoWF9sLBDumhwuPmn0P3bx68o6ycxbl5CATvrFpHG6+VcTKIH1c8Tm5YG3QhsXvzOav5OBxWCw/268kzS/6Jbrjxav7UTB+SWmkQYLGgCUFerYuIhH2DI6HZMM09WROXdmjLpb9rV/J7Y5unM3nNdLK3RmF3JlO8832u7dTmkM/5PbvFwqfjR/Ltth2U1m+lV4e++/XCOlMFWCx8cP7Z/G/5atZVVjMioQ1XHyDYbYoxLdLpmRBPdlUVicHB+zUBB2gdGcEXDZUz/0yaEoR9CGwBRgP/AC4HNh/PQZ3uvKaJhmgs4bkZF7Wmj2W5+fRLSjxgytWOsnLaGXbCdP8/yVAthJeMQv736zL+uWApd/XtydWd2u33PIBbenXjst3TyPHmYxGCTM2LjsZ23LQigHppstFbw7qV65i6ehM2h41xbVrwf5u3k6DZKBBe/jN6OD5TMn3nTjQMXPXZxKSdR2TOnMZqhYnCRpBu4bMLJxDtDDyi8p+aEPxfz26Mat6MG76ejiGhRHopkB7qTQPrAYLMV5atYuq6zfQgkBVGDenSxnWWaBaIanabLqpK1xKp2fC6wzClRNc0nh/Wlzm7svjHvIX811dEirTyg1bFzT26ABAeYMcwPERF9yBz4yuAfwUrf9tbPDyyac00fzMgOYlXRvbj080/oAvBU2OG0jVu/67xhxMb5OTaLqdw5+TjqGVEOJNG7D+5H43fAnFF+RNR8zN7inHo0U3bC+YxDIIaCmDkSw850k1ecQm1Hi/OA6SD59fUYDehreZPQ04VdlKwsXxjBoM2bmFIixY8M7TvATNdhqel8G3sNu4pzCVF2Fht1tIiIpwN5fW0EgFIKVlLPSt2l5CRX0KJ9HJ99878c+Va4jUbeYabm7p3oUVEOAuzc9lUXIrpLScu/UIKMr6gWUP7F7vQaKkFcFv/3oxolnrA13EoI5qlMqJZKn3f/og600STBtm4KcWHVdv/dS3MzuXemT/TnyBypAeP4eH/9ASyhYcCPKyr2ExrEUiW3cTdUJTq4nat6BwTyeNzF/BERT59zUCWafV0io8lvaFicnyQk3JbCHmZU3A4kwlwJpCz5XUubXNkN9Big5x8ed5ZvL56JRVVS7m2R3MmtDrym3B2i4WL2h1Z8HamCLXbuW9An2NyrKhAh0rjP4CmfIpuIaW8UAgxQUr5vhDiE+DX4z2w01mo3U7XmGj+W1JEsKExT1bRUziZt3EnORVVvDB6WGNO8ZLcPNYUFFFU6y/d/tu+lqWyhnisPKsnc7WRyatrdpAWGszghr1JHsPgg7Ub2FpcSnpkOJ9OPIdleQWY+NOzVuTn8+icX2mrOdjhq8eGxqsyBd0UTK4u5pMNm9ERpCRH8eagfoQF2Lnpu5lUlVTTwrTixIeldA47jSo2a8G0IYDZsgqH3UpGRQUfr99EqCOAyzq0PWQPrcKaWkpd/rt4TquV5uFhtIqJ5M6C3ZSYXmKwUisMdldWkR4e1vi8svp63luznv+JVMKEhSu1SK43MrnLl0U9JqO0ULZIF4Wml/ziChbn5tEtLpZ316wnp6KKKzp3oM7jpdLl4sHUToxo6M9lt1h4fEAvJi36mEBHLBnrniPcbuX27m1wWq14DZMV+QVsLC4hMTiI0c2bHXKfUp/EBPokJhz094qiKMfRn35+fiT4DXI4smIc49q04I01WxhlBPG+WUIP4aSuqIYLv/iGTy8c3zinZVZUMjszC1NKKkwfGcJFugigQPoDjRe1FB6SeSwt1fj3sjU80HdPZsj0HRnMz9hNiMPOw4P7saO8nLJ6F/fF+rNArp76A2tNF1WmQZ7h4hUthVjTxjKzhqeXrkRHokcE887Q4bSLjuL15av5au0meksn8QJq878nQAi+kRWcSxgZuFlj1nGx3cb/Vq1BSpjQusUhe0BVud1kV1UTH+QkoqG647VdOvLoynXUmgZxWMnHS4cDtBn55y+LuMuMobvmRCL5O7nca+ymFB/jRRix2Jgnq+jmcfLB2g3c3acnU7ZsY1VOAb2T4glpYSe7vJqzotK5rEPbxs9Efx/Qnau//4Kw4GTydryPLg3ObZlE/6RYajweCmvr+HV3Dg6rhXEt0g+ZTp4UEsxTg49NEKEox0NTgrDfNr1UCCE6AAXAkd/u/5N5eexI/jl/EdN2ZDBZTyNGWPGaJrfn5rCyoJAe8XF8sHYD7yxfy0DTyQZRTy0G1xv+nllVGDyiJzT04ZCEJp7N0rxVDE5NRkrJXT/Oobqgkn5mIMuyS1mWncfb545tDBhGNEujzUWRbCop5csNm+mabzTmhg/Sglll1OJBsnRXDs9alnJWy3SKSyp5zkxEF4IxMpQ7arP59+hhPDRnPnU+H/GBgUxo3YrHf/qVMWYwmzQvl2zexhcXnnvAPOHXGva4Res2qjB4/ezRdIiJ4q5+Pbnm6+95VU8jUljYIuu5Z9Zcfv3LZQQ2FC+pcLkJ1a2ESf+fqENoRKOTgZt39HQihb8a04NGDj4EZfUurvvmR0IqPXQyA/hpdyGt0uJ4avjg/cZ1busWdIqJYmtpGSH2dF5YuIwvlq/nU9bjs2hUiQDC4oZQt20NP2bm8tKIAQfdiCul5MN1G/lwzQYMKbmwfRtu7tHltN24qyjKaeVPPT9fPR5yHll1xNUQb+jWGV1ovLNiDTdrMY2tXf7jKuTj9Zu4tUdX1hQWcct3Mxkkg6gTJiB5wMghEgsV+LhaiyJWs2H3gRbagYW5ixqP/8HaDXy4fB3nmiHkiwou3ZnJlIvO22cl4NtLz2dlfiHL8wrYtSmb2IYVrZ7CiYEkCRu5ZZXcM2Mun184njdXr2OySCVcWPBIk9u8OUwaPpBXl67iw+qd2HWNG7t34cHZvzDKDEYAV27cwlsTxhxwb/lve9wiNSvFhof7+vdmYrvWXNe1I2+tXsuDJNBFC6RSGty+cRt9kxMZmLJnO0WF201qQylxIQTpIoBMWcXNWgyDG95PhyHYaNYTWe/mhUXLmL85k9FmENu0UjICJV9cOAGHdd+Poa0jI/jhonNYmV9IgJ7E9G07mbVlO6u27aJQeqlHJzJxJD53MW+u/ZGvzz+LkEPcCF6Sm8e/fllMuctF78R4Hh86oEn7gBXlRGhKEDZZCBEOPApMA4KAx47rqM4ATpuVu/v3YnZmFtENb7NVaCQKG+UuF4Zp8u+lK3hNpBKrWdlk1vEShQwkiKlUcLGIwAc8a+RjRcNbvY2YKH/K/+6qatYWFPGWTMWqCYbJEG4q3c2N02YQoOuMb9uSUc2bkRQSTFJIMLsrKplftI1hMhQL8I1RTjkGV2lRWIB3d+wiMjCQBGFrrHwThxWPafDcr0sY27wZ/9e7GxEOBwPf+ZjHZRzNNP9F71/uAqbvzODi3y3Xr8ov5It1m3mdFMJMC7+a1dz142weHtKfhdm5pOoBRDYEWG2EA2HAzTN+4b2zh6MJQVJIMMKqMd1VwTARwgpZSwn+EuEhDemRQghChM4Ks46vN2xld1kFT4skEjQbg80Qrt6Zyb39ex+wYV96eBjp4WE8MXcB6TWCm2USEnjOVUBGUh9S2t2GaXhY9uuVrC0qbizfD+Dy+Xhr1VoySyswNcGG3fn8jVh0BP9Zt4Ugm40rT8MS84qinHb+tPPzb4U4jqYcvSYE13frxNcbt5Dm2vMBPtW0UVbnb7r80sLl/MWMZHhDQDGXSv5BPI+TRy+cpAg7HxollGIQbLqJDtwzz7y1ah2Py1jSGubJCl8ht/04mzCrla6J8VzbtSOhdjvD0lKIdARw+6ZtlEsf4cLCHLMKK4JuwkkzYefT2jKeX7yMQE0nrKFRsk1ohBiCp39dQruoSP4zdjjNQkO5Z8bPXGSGMV7zr36FmTpvrVjLi2OG7/P6670+7ps1j4dlHO1MB7l4uH/RMsIdARTW1uIyDLro/n07oUKnuXRw788LmHHxuYQ7/K+zX1ICH2SXcbOMogAfc2QVIWiE7/WxMlxYyJBuQmpq+G5bAXdrcfTTghktJY/U5/Nrdg6jDrAPONLhYFR6Gj/uyGDz7gJelykEmBqzzAret0Fqh7sByFgzic82buHGbp0bnyul5Ost21mclYPFojM3M4s7ZAzNRAQfZ5fxwE+/8Mq4kUf8N6Mox8Nhy+ZIKd+SUpZLKX+RUqZLKWOklG+ciMGdzjyGQajNRowzkCmyHI80WWnWssWsp2N0NG7DQEoaA7QUYacMg1Rhx45goazhDaOQHDzomo1g787Gflk+08QmROOlTgOkzyS2wEWXPB//nLuI77btaBzLVZ074IwL4Rozg2uNTLZQz9VaFGdrYZylhXGzFsOG/ELWmLWsNGupkQZvm8UkYuNOVwSZO/J45tclANQbBhHsyQ8PNTXeXbWOm76dwZK9Oq/vKK+gk3AQJvyj7IOT4vp6Xpi9kJytuWz31jHdrABglVmLaQlgW6WX1QX+xrk2XWfyOWcxL8TLRcYOPjBLeExPortw8qJZQJZ085NZyQpZS6jQ6VEiGU4I95vZFEkvNgTWhuaLezOlpMbjQUr/Jt9dZRX0Mv2NGjUh6C+CMOoKABCaFaHZeXfNej7ftAWvYWKYJjdNm8HadZm0znZTkFVEoAHp2EkTdi43wpmzI/MY/AUpiqIc2p91fr5m2Y2NhTiOhmGaeAyDvsmJfCLKqZUGudLDj1oV/RpWe6rcbhLEnn1VidhYI+pIx042Hl4zClkha/EhqSmYw4N99gQCPtMkcK+PVzYDfCU19CuQ/Lx6K3+fu6Dxd51jY7isSwdulllc78tksiyim3ByjR7NYC2ESXoi32/bSajDX/GwRhosMKvJMt3c5okkPr+eW7+bhdswqPd6idg7CMLCuvxCrpnyPR+v39g47xXW1hIoNNoJ/8pcorARZGo8Nns+i5ZuwSk1/unLBaBYetksfNjCOvPppi2Nx3586AAsCaFcZmTwkJHNlVokw7VQJptFbJMu1pp1vG+WYApBeoGXq7UoXjeLWGbWIIQgSOh4G/aK/UY2zM9mwzizKqvoZAQQ0LB/r7cIpt6zJzVS6oHMydzN5FVrKav3B8+vLl/FOwtX0nJ3PTKjFGmYtBMOYoSVW2Q083NyGt8HRTnZDrsSJoSIBf4JJEgpxwgh2gF9pZRvH/fRnYb8VZTmMj83F10ILm3XhtX5hXxUtpPYAAcvjRhBXJC/IlPHyEjeLS/hAhnOVulC0wSviRJchkkR/otpB+FgnlnNF+eOwtGQqpcWGkJkkJPJ1SUMkkEslDUYSK7UIrEKjRBTZ/KyNfxn8QpK3C66x8TwzMghZFRUcPeMn7F4Tazsmb2sCGy6zr/PGs4Tc3+lsK6eCHSe1ZMJFxbukjFckZnJ01IyMi2VV7KKuFJGkC09/GxWck1tFM46k7t+nMOr40bRLT6W9PBQ3pD1VEqDUKHzkSwlRlh53kzEIgSb9RAeNnL4zCyjTrOQ1vkRKnZ/TrVnT8n39PAwplxyHs8uXMqKzbuQUtIHJ29Tyk6rQawzkJg6B3d4Ihs3TNcZBp+bpXiBVtERRO+V/jE7cxcPz/kVt2EQ73TyyriRtI6JZF55Hl1kIBKYLavwyBh83hoyVk3CUZVPbE0I32SvY+7OLG7r053c8ipek8nommCQDOZqMijASzw2ivARdIQlXBVFUY7Gn3F+/q0Ix9GsgEkp+e+yVby9Zj0SycDEBMKTIrhqdyYBms6tPbsyrKGtyOD0VD5cv4M7ZQw1mLh1WGBzU1DvwoogDAvpwkaVMHlyeH9a7rX3anzrFry4NYvLzXDypJe5soqX9FQShY0u0sllO3ayubiU7ZWVJDqd/GvkYM5u1YK7ZszGU1a5z91xKxpSwP/OGc0Ds37h87Jd6KbkSS2R1pqDzgSy1JvDltIyRrdqzqtFy4mUFgTwlllMZ6+DAaUWPly6jjqPjxu6dybGGUitNNkq62ktHGw36yk0PbyppxElrdTokVxrZHKFbyd1AuKbX4apWalyr2gcV5DNxktjRzAvK5vHfvqFcNNCDFClmTxnKSLYZqNTUAwtC71cpPn7NkVi4TOzlDy8bNFc9Enas586o7yC26b/RG5NLXZdZ9KwgbSKjOAbfTMTTf9e+Z9lFXbNitddQXHuT1Rlfc8QEcbGsh18sX4zn184nvfWbuQVkonW/J+X8qWXRWY1o/UwivDi0C1qu4ByymhKOuJ7wLvAww3fbwM+B87Yi/wf8a/5i5EF1XypNacSg8e2ZHD30L6MSk/b7z/8l8aN4OGffuHWomyiAx28PnQ0WZWVzFy4nvtkLJoQeKXkZ7May17ViXRN499jhvHsr0t5t6oat2EwvDYEa8PdIgEU1tTwkJ5ACxHAZ8Vl3DVjDh9dcA4/XH4hryxfxbubtmE3BVYE72ilPNSxP32TEph55cVM3bKdaQvXEN6QLliJgVUTLMjO5S/dOvG+WMf9O3dhSsmlIpKzdf/kU2kaTN28lW7xsfSIj2NCh9bcun4zMcJKjnDTSTgwkFgQtCQAH5JwNKpNN5mrnsAiBK0j9y87e3ffnrym60zOyCLYZmNcVAu+3badUo8LixQ49qqsGIjO95oHHQ8/jxvf+J5nV1Xx6JxfeULG01KzM7Oukhun/cgXF07grwVFXFeZhQTSo8OIFyWs+ulcMA0+1NMJEjoXSMltRdlsKinFjmicJPWG9/tjs5RwzcJcrYa3eo85Fn9KiqIoh/Mef6L5+Y8EYADfbd/JjA3beFtLw4nGfwqKCGkez+obrt5vfr61Z1dqPR7u3bYTq6ZxQ4+uDEpJ5tIvvm3YCuCfBR7U8rBb9q0eeFuvbrxomHyYV4gU0KzGQWLDni8Nf0ZGn0qdv2vprKyr5dbvZ/H9ZRP5ZOJ4vt6yjWcXLGWKWUYz7HylVXB+y5YkhYTw0cRzyK2q5sLPvyENf6qjT0qqDR9bSkppHxXF1b068/Sy1dT7fLTGzp2avx9XhLTwwsYt3NC9M4FWK/8aPoiH5swnTrOx26gnWNOxNcxsQUInFRshaKyQdZRkfIEhfbQfOmC/93RIajIPDO7L+6vWY5iSMYktmbF9J9m1teTV1NJJ7GmcG4hGgQafUcm/RwwmsqEYiJSSW7+fxbj6QMZoceyUbh6ZM58PzhtH7/Qkrt+eSahmQQvQ6R4Ty5J5l6D7vDykxdOloc3Ay55Cvty8DUOa2MWezwQBCH4QVZSYPuZoNdx9kDYEinIyNCUIi5JSfiGEeBBASukTQhiHe9Kf1Yq8fO6TUViFRhQaI8wgVuTmM7p5s/0eG+lw8Mb4s/b5WUKQkxdYxkJZQ0vsfCEq6Bcfj1Xfc1H5dst2Jv26mHDNSg0Gf+3Tnf8uXkGMacWJxjuUkKwF0FH4c7qvkpGcX7IDj2EQFmDnkYF9GZiSxEerN2BKkwc79Wd082aNS/Sj0tOYvGINL9cXkiptTKECAbw+ezGZpovk4GCGaaHsMOpJEntWfTyY1O61knVHnx6c37YVk+YvoqSgmF2Gi1vYxSQtiTmyimgshGHhOT0FgeBZUcAHazYA/jTBllER3NKzGw6rhdv79ODWXt24fvocvt+ynZf0FBKw8qjM5Tkzn5u1GEqkj++1eiLSJhBZu3CfYiGbS8poqzloZfrz2c8SYfyvvphRH31JkNCplwZ39u3J5Q1VmvKqa7jw86k4pf99twhBhLAS7QhEd9h4p66U3jKQeVQTEGCjItiO5gzko95Dab5XlUdFUZTj6E8xP189HsQjfywAA1iRk89oI5hwzf/R53wZxqt5+QdcGbFoGg8O7MuDA/s2/swwTaKCnHxWU85IGcJKWUuRxaB91J7CF5tKSrnlu5nYTSg3ffylcwe+3ryNDz2ltJZ2vtOqcAqd83R/cNJfBDNdq2FzaSn9khK5qF0beibE8/Ki5Wyoq2dIaitu6N65cX5OCA6iX0oif8/2l3ifr9VQbRhMXbqeN00v4SFO4oSVtsKJD9n42rxIfOae9PxhzVL58fILeXvNOj7fsIUAQ3ADmdyrxaEh2I0HB4K39WZECStTzDI+WrOeinoXi7JyiAx0cEvvbiQGBzX2YHx7zQZeXrKc+7R4+mhBfG2U8aFZSiQWHGi8IioJTbuA0uyp+/TbqnC7Ka2vZ6zmXxlrQQBJPiuXT/meAF3HkCa905J4bHA/7A2tcYa//xlRnj3polHSQr3Xx/iWLXh+Zx4XyjB2STerNBctI8LIsFh4vPOgxpVORTkVNCUIqxVCRAISQAjRB9i/XqkCQFRgINtdLlKFHSklOzUPXRvSD5siMSSY184ezT/nLaSkvoIeCbFMGrLn7lNuVTVPL1jCcySSLO2sNet4dvEKWkaE835xCVZNo0VMJHlF5dSYBkGaTi4eHJq+Ty+uwanJjeXuXT4ff5v5M7N27caqaaSFhoCU7HCYyEgHruwyXhDJJJs2iqSXv5ZncbOeTFvNzmtmEZUY1EqDKbIcLaeSXRWVpDX0/FhZUEhJUSVvkkqAReNLo4w7zCw0BAYm12nR2BruWo0yg3l1yw4646CfGciCot3cWljMWxPGsDAnl883bWVtSS0D9QgSG4K/R0U8F5sZvKiVUGcY2BxReApn8PTZ+25Ejg4MJMt0Uy9NHEJjp1mPAB4gjm7CSQYuHl26khHNUokLchIX5CQ2yMkH1aWMlqGslrXk6166xsfw7nljefbXpXxcVk6Z20uSV6dLqWBhRQkfrNnA3w9wt1BRFOU4OOPn52uW3QjLQOh6kxoxH0p0UCDbtCKk9Acn26WLqMD9G8cejK5pTB5/Fo/Nmc/DpfkkhwTz9rAx+9zwu+vH2VztDWOwFkIJXu5du5EeyYnM2J3DTKBZeBju4loyTBfpWgAuaVJgeAjbq4BUs7BQXhw7ovH7t1ev440Va/CYJs1DQ/D4DHw22BRupapMcJMZzXAzBI80uak8iwtEON21QO41sgkxdCKFlY/NEuqlZPr2nY1N66s9Hr7asJX/kEScxcZmWc9DRg4aEjcwUIQS1bAvbpwI44OynbiXr+c8M5QsyrksZxpfX3weRbV1fLd9Jx9t2E6Y5qSPFgTA+XoEs6jmQ2sl5S4PWkA4Vdnf8MyQfo2VkAGCrDZMAVnSTaqw45Im2XgZSyhXyUhqNJOHduXyS7M9hTyGN0th8rYcbpJRFEsfM7QqXknrQ4foaF5zrOKz3bnUer2E1et0K4WdWh3/XbyCvokJ+1VkVJSTpSl/iXfjr7rUXAixEIgGJh7XUZ3GHhzclxumzWC1cFEhDTxOC1d0aIeUkm1l5VS63bSJjDhkSdWucTF8ecl5B/xdRkUl6VoAyab/+Z21QEzDJKTExaVEMNUoZ1d+CU6hc53MpK8IZrWo5+EBfQ+aB/3cwqWUZZfxkZZOFQYPlWVzjhaGjmCKp5AI3UqwqfGCkU+O9KADC8xqLtejsCJ40ywiDAtP6UnMN6v5Zut27uzdA4CdZRV0MwII2Ks8/hdGKddp0WRIN6tlHX1lEEII1op6fIbJHVoMmiboLYO4vjSLh3+ez5qsfNoZdpyynpWAVwvHKgRbcRMVEMDPV1/C9vIKaj1eWkWG47Tu26iyS2w0A5unctPWDFpawlhHNU4tgG4NqQzpIoA0LYCd5RXEBTnRhOCNc87i8Z9/5aGSfJKCg3hr2JjG/jHPjBrC2sIi7v1+No/JRHRNMNoM5drtO7mtd3fVlFBRlBPhjJ2ff0s9hD+2+rXPMTt35PIdu3isPp9gobNBq+fdQWMByKuuIbuqmpTQYOKDgg56jBhn4H4ZLL9x+3zk1tYxSPev6EQJKxGmzvbdBVxJBHPNKrYXl9JCC+BvZjZdcVKsGwxITaZtZMQBjzkrYxefrdzAf0giRLPwXGUeAWgMFaG8UliMS5p0wcGHRgmrZR0SyTxZxRg9lKf1JF4yCqiUBtdo0TgQfLh6Q2MQtruqinQ9gDjTH0S2FQ6caLQngP5aMF+Z/qJiNqGxVtZhBR6WcURoFgYA+YaPl5eu5KcdmfSRTtJMnRxZRz4e4jUbldJHDSZfnHcOutDIq6khLTR0v/nRqms8Prg/f5u7kI5aKBl48ArBWC0UIQTB6PQ1AtlYVNIYhN3bvzfPA09kZBFosfJ4v4F0jYsF/Fk4t/XuTvc3P+B1kUK0ZkVKyaN1+czL2s2YFulN+XNRlOPuoEGYECJFSrlbSrlKCDEYaI1/+8tWKaX3YM/7s2sfHcXXF5/Hktw8HBYLg1OTses6D8+Zz8Jd2URrNoqEl/+dcxZtoyKP+PjJIcFkGC6K8RItrGwx66mRBqtlLaV4CUAjGJ1/aIlMkeWsCTZ5behoOsVGH/SYS7LzuFOGEyg0AtGYoIWTJz3coseyAhdbfLXcL7PpJYIYq4cx16ziG1nOGl8tJRhEYuFfehJWobFY1mCYeyoPtYgM5z19J+eaJgFCY35D+d1RWijV0uBBI5v/M3bh0C3U2gVBHgtir8JFppT8kpnN/0jBqelcKQ3+YuziZvJJljobZQ0vDx+OEIJWh2hKKYTg8SH9WVhQQmZQV1qmnceWxXc23nkrlT6yDBdJIcGNz4lxBvL6OaMPesx6n49QYWks6+9A4NB03D5fU/4pFUVRjsqZPD8/3H8NuS++Bhy74Os3wXYbn184gXlZu3EbBk8mJRLjDOSrTVt5fuEyUix2dvvcPDCwD+e2aXnEx7fpOlEBAazy1NFdOKmRBgWmhzAszKASGwI7OneIWMp1H09SwNNDBzG8WepBb5Iu2Z3LGDOYOM0fKF2pRfO8kU8vPYizTQ+z7bU8XZ+PHY3r9Wh2mW7elsXcbOwiEI1SfDyvpxAnrKwx6zD2qgyYGhpChukiX3qIFzY2yXrqMLlGiyZWWFkuarnRyCROs5Or+5BSsPcoNWDGjkwel3G0Fg5MTXKPzOc2mUcngthiVHJVt44kh/jL/CcEHzy4Hd+qBYtyC5lXCEmtb8Dc9Dora2s4S4TilZINuouLwkL2ea8fGtSPhwb1O+DxDFPikyahYk9Lm3Ch4/Kdcdm6ymnsUCth3wDdGr7+XEp5wfEfzpkhLsjJua33XMBn7sxkQ1Y+rzX0uvjZrOKhn35h6qXnH/Gx08JCublnV+5cvppkzU6WWY8DjUtEJOP0MEwpecrI4wezgs7CwWpZc8gADCDSEUBmnZt04U+H2CXdRAurv5y7NGgdHUlOUTnXalEIIWijBbDKqGOECCMTN6tlDWtkHWXSYKZWzfutmjce+5yWzVm6O5cbM7Owm4Iq6cNAUi59BKOTho351KCbPiamtmJdfhGv1RTTywxkgVZLdHAQstaD09TxSsk0sxw7UGm6qLJovDluNN0TmpaiIoRg8pihXPvDz+xasxqJ5D6ZQ2s9kEzTxQ3dOpMaGnL4AzXoGB1NmW4w1VdODxHIT1QTHeQk/hATjaIoyjHwDWfY/PxI8BvkzFlF7rJjH3ztzWG17LMSUlRbx3MLl/IcSSQaNrLxcN+vixmcmtzYE6uphBA8P3oot0+fTaJWSZ7hRuoaiaaVh7QEhBBMNcp42yjmPi0OrzTpm5x4yGp9kU4HWzRP4/e7pLsxsKgSBh3jYpiTmcX7ejqhwkI73cFWw0UwGrENaYgbZT1Z0s27Whk3duzeeKzkkBDu6deTuxcuIwILBYYbJxo5+IOy1gSwgBrKzDp6RMXQLDyMp3fmcoEZShYe1ur11Bs+Uhv6oc2V1dSabjQhWWlUcm/f7lzTuUOT37/HB/Qmf+YvbFz7DwzDxzu4ma/VUiZ9tI6LYkKrFk0+llXXGJyUyH/zi7lQhrFDulkj6nk0Kb7Jx1CU4+1QQdjeVwW1dnuUdlVU8tG6jeAz2am5aY+DXsLJGzXFR3U8KSUXtGvF0GYpFNTUklFRyfO/LqFTQ4l2TQjaaw4+MkswJMR7Aql2e/bJWf+9+wb25cbvZrDWdFFietkpXUwknGe0QgJDAql3eZCAib8aoA9wYdJBczBMBnOHqOfjwGqSQ0N4vddoWu2VVqEJwVPDB5FdVU1xXR3fbt7GvF3Z3O7dTTQWrELwmdYcL/DkjmzGdWtLfmUNs8oqaBWdyKPdOjLx82/4xaxiqayhRprco8WxUdbznbeCj9dtolt8bJNLzjYLC2XWJRPIra4m1G6nzucjo7yCpJBgmjXsY2sqp83Ku+eO48m5C5hVVULbqEgmD+2PpsrfKopyfJ0x8/NvaYc5HN/g60BqPV5eWb4KmxRsxUUCVpKFjWjNRl5NzREHYQDtoiL59tLzyayoxDAlt/0wi67C2ThHddAC+dwo42JzJwFCI7+6hhaHyOK4slN7Lt22kyfq8wg0BUvNGkaJEN4xi1lgqeMsZzxWBHWY/DaD1WDQmgDGamHsxM3n1kpSQ0O4tV0PzmvTap/jX9iuDUPTUsmrqWFxdi5fb9rKM7UFtMVBpnTxqp5KDFbeLCum1F7PWd3aMjsrh4jAID7uM4RJ8xbyQVEpCYaFb2QF12nR1GAw2Szm43Ubmdi2FUG2prVtcVgtvDduGHk1tQgg0GphY3EpQTYrHWOij3hufWbUUP71y2KezssnKtDB5EFnHTLVVFFOtEMFYfIgXytNtKuiksumfMcII4hgEcS/jDzu0GLJw0uro6igt620jNum/0RRfT12XefpEUPYUlRCMlammRXcrPn7mfxoVnKtiOYsLYxXPUVM+mUhz4waetDjdoiJ4tw2LZm2cRtdZQBg5xutkss6deCGrp2444ef8FS5eMrMo48IYr5ZjQfJS1oxVRikR0fyyrhR+1Rw3JsQApfPxz0z52I3oN40GNE8hXX5RVxZH0pgw129c4wQ1uYV8dLvutm/cc5o/jZzLlk1NXypt8AuNLrjZLvPxZLsHNYVFRNotfLUvEXk19TQOTaGh4f0a9y/9XtWXWssHBIOJAYHkV1VxccbNuGwWBiVntbkSSM1NIS3zh3bpMcqiqIcI6f9/Hw89nwdCbfPx5Vff0dsteQCwvjaLCNbeOijOSk1vfukpjdFldvNXT/OYUVhEQA3dutEengY8Zqdn4xKBolgHGh8bZbRhgAe1xOZJau4+fuZzLjiIizagefPELud+wb24e4ZP9NNOuiDkzlUMyI9jc96j2RWRiaJmp1/GHlM0MLIkh7WyDpyLQbztFqqbfDheeccMhUwwKLzzPwl5FZUYSBpFx2JM8DGyJx6EhqKYF0sI7ijIIf/jhvJ9d06NT732VFDuX/WXObkF/KwlkAnzV/opAKDuXVVfLpxMxe1a8NTvyxibUER8UFOHh6yb1+1vQkhSNxrrP2TE6nxePhm63bqfT4GpSQ1pjcejtNqZdKIQU16rKKcDIcKwjoLIarw33FzNHxNw/dSStn0vK0/oXqvj1eXrWK0EcQVmr98bRxW/mMWEuoMYPKoI+sl5TNNbv1+Fhe5gxmuJbLFrOfBn+Yxvk1LYnUbuaaHS4yd+JA40WglAvibsZtCfLCrluK6OqIPUQVqyuZt/FskEWfxF7R4RhQSFxSIw2rhr317cOO0Hwk0NabJcoo1gzfOGY1hShwWS5PuUN0z42cu9YQwQgulEh9/25VLSmQYO+vddMVfHGOH8BAVFAb4KzauLihCE4KucTFMveQ8er79IXtnc5sCIoSV7Kpqnvl1CRf5Qukgovguu5zbf/iJ988/u0nv7drCIm7+bia9cVIpTN5csZbPLhx/0CBOURTlJDst5+ffUg5/czKCL/CXmv9y81a0Wi/3kIDQBINEMFcZGcygimdGDDni6/+keYsILnHxpdacKgweXruFCV3a4hYmnUQAVxkZjf0l/yNSmGTmsVW68NZJ5u/OZlha6kGP/cXazdxAFCN1/83DH8wKdnkNEoODOL9NKz5bu4lgl2CGWUme8HJn3x50ionBZ5q0j4nar1DV7724eDmRFV6ekCmYwHPlhYh4O5kWL9L0V5LcIV1ENlRwlFKyrqiYSrebjtHRTJ4whos+/wZf5Z77AQaSYKlTXufijumziSh184CMYqOrnmu/mc63l55PhOPwBawqXG4u+epbEtwaIVLjlSUr+d85Zx12m4WinA4OGoRJKfWD/U45tK2lZdw4bQY+r48L2HO3J0ToxAY7+eqS8w+6anQwJXX11Hu9DNf8F+E2wkFLzUGn2GgWZuUQ5rLQXVpZbdaSgI1/mHncoEXTQQQy1Sxj4mdTeWLYQIYepEeGzzRx7BVIOaTA29BTpFNsNB9PHM/07TuZm5GFr7KKG6bNZHR6GpOGDTpsAGZKSUZ1FUN0f+WiUGGhC4GkJsbzVcUWdphePJhkaB6eSkumtL6eq77+HpvbxDBN6m2C6MBAwnQrjxg5nC/C2STryZMevAg8hkEz7IzVwgC4WUZzaUkGlW53kybS539dynVmJEO1EJDwcn0hH67byF97djvscxVFUU60021+Ppkph79X4/Fw47QZZJZXkGbaELp//nKiownBj1dc2NhE+EisLijkERmDLgThWBhmBlFZ76JzcjybcgoZJENZbtYgpeRFWUg74eBWPZaNsp6/zZzHjT06c13XTgdcEfOZBg72/NyBhs/wz8+hdjtfXHwuX2/ZxoKsHHbnF/LK0lWkhYbw2tmjDxuAAWwrLmOC6UTTBBrQ33SyzJQYoXYerMwjFivLRA03te+C22fwwE/zWJ9XSIywkmG6aBURTrnLxQtmJdcSTY308a0sx6brdIqL5rONW/hMpKMLQYqwswIXKwsKGdks7bBj+2j9RlrX69wu/J8f2ptVPLdgCR9ecE7T/mEU5RR2ZJGA0iT3zviZy72h3EMcX5llLDdr2CzreVOUcl771kccgIH/QuuSJrnSv0G3ThrsNt2khYXy+UUTuGRQN0b378QXF06gxqGTLGwM1kKIFBau06Kp9Hi4f8bPfLRuwwGPf07L5jwvitgg65huVrBc1DEkdU/A1jw8DMMwCakx+FRL52MtnZysIt5Zve6wY9eEINkZxBJZA0CtNFgv6ukeH8vUS84jvnksG4w6mks7j/00nxu++ZHO9VYmGfEE+qC2zk1WSTlOH1Rj8LosYoasxOKw8d9xI4l1OqkwvZSaXgwpqcXEQGLTmvY5pczlInWvptOp0kZ5XX2TnqsoiqLs7+H+a7hm2Y2NAZglJvGkB2DgX/WJqvDxskwmS7r53ixnh3Txkiiif0LCUQVg4O9FuUX65w0pJds0D7HBQTw7aij3jRjAgH7tePu8sYxq0YwM3PxFiyJSWBikBdNC2nhz+RrumD67sSnz3ia0b827mv+zxFKzhg+1Mia037O3K9Rup01kJJlFZUzWUvlMpNOhSuOBWfOaNPZmEWEs0eqQUmJKyTKtjpZREbx33jjG9mjHErOGdC2AaSs3MfHzqWTmFvOKmURzrwWfz2B3URk19W5ShI23ZTEfyVKkrnFLn24MSU3BZ5rkSS8eaWJKSYX04bA0rVdXWW09qXLP/JwmbJTXu5r0XEU51Z3RHevK6ut5d816ymrr6ZuaxLgW6U0u4HC0pJRkVlczSI/BKjRuIYY3zGIsARYu69yeq4+gUtDeHFYLD/bvw4OLltFRC2S7dDGqZTPaR/tTHc9uuaci4SOD+zFp5nwMKdGFoBwDCYSi8+yiZSSEhOzXNf7hQf14zbGKT7JyCHME8E6/MfvkZQOszS9kjBmCreFO3WgzmMV5hU0a/3Ojh3LL97OYKisplB7ObtGCfkkJuA2D6dszeF4kkSLt1EiDm6p2MZgIvqCMEGHhPS0JAfzXLERDMF/UsPqGq9EaxrEoO5dsw83NMgsBhGoWrmjftskNGfsmJ/Lp9lzukDFUYjBdq+KBlE6Hf6KiKIrS6OrxIB7xB12Hq3LoNUw+Xr+JrcUlpEeGc3XnDtj047/At6OknHGmkwjNyiSRxItGAV/oFYxIT+O+gX2O+riPDO3P9d/+yHLholz6sIQEcGn7tgghGJyavOdxg/sxbUcG5Q3tXQwpqcGkJXaW5OQx6dfFPDJw376eY1ukY5gmn6/dhBCCB7r0Y3izfdMX1xUV0086G5srT5Bh3FiS1aSx392vF9cWTee22hy80iQqJIibe3TBpuv8vGMX18pIRstQTCn5R20+HgkbRD2LZQ1v6c0IFjpzzEqmmuVEWm08c84IOsb40wXza2oIt9u5y52FASRpNsJCg+mdkNCksfVNTeTZHVn0kE5C0flUlNM3+eQH84pyLJyxQViV280lX06jk9tGmrTyyq6l5FZWc1OPLsf1vEIIUoOCWFRXw2ARQicRiM2q8/jwQfT/gxeOie1a0ykuhq0lZSQEO+kef+DS7P2SEkiJCee+/Gy6iEAWyGou1iKokybfyHIemf0Lb04Y0xjA1Xg8vLRkBb9kZFHiduOstbChqIQ2v+tjFh8SzKbSCno07OHaJFwkhMQ0aewdY6J5a/xZ3DZ9Nj6XZMrW7aRHhjE0LRW7EKTgTxsMEjqpWgC/yBpCTI2hWkhjH64BIpjPzTJ0TTROUNVuD/fOmssjIp4uupO1Zh1PiwKu7965ye/rvf178bh7AVfvysSu6dzaowsjmh08P19RFEXx22ef1zIQAvToQ891UkrumTmH0rxy+pmBLMwqZsnuXN6cMOa4V5dNjwxjSXkx3WQgSdhIsgQwtH0Kd/ft+YeO2y4qkm8uOZ/l+QUEWiz0T048YFBpt1i4tUcX7lmxjuGEsAUXYULnbMJ4WRby69ZMPgwN4aqGG7amlHy0biNTNmwhr6YWqcHKnHxGNkvbJ6smLsjJbM2NYfpvvm6U9cQ6D74PfG9hAXbeO28st0+fzdqiYkrKy3ly3iKeHD6Qgppa2gv/PK8JQQcZwFQq2CpddBGBBDcU1hoognmZQmKkbZ+UyvtnzmOkz8klejIl+PibzOHBXt2anBE0slkauT2q+duKNbhNg9Fpadzbv1eTnqsop7ozNgj7KTOLZK/OLSIaBHSVTu5cvY4bu3c+7qthz44eyi3fz+RbWU2x4WFC65b0S2raXZ/DaRURfsimxAC6pvHa2aMZ+eHnZLhcXN+wN+wuM4t2OGglHfy6O4f20VH4TJPrv/2R8HIPVxHOfKoo9vh4eeEyEkKC6Je0ZzK9s29Prsj7ju2+fAwkVTbBx72bvm/q0TnzGeUK5HwtkQLp4YElq2gTGYnNYuEXTxWDtRC2SxdZwkPryHDWF5egSegrgxDAr7KaAuHlmi4dGv8NsyqriBQWumj+wLCzFkicZiOrsoqwgKaVFw6wWHhm1BCelvK4/20oiqKc7vZuqJwDCF1Hj2xav0aAnOpqVuYV8LZMxappjJSh3FK6m62lZbT93c2/Y+3Ovj25vvBHbq3JxgTiwoK5+RjdnI1xBjKuxeE7BtzUvQsbiopZlFXABBHOcBHCa7IIkFxlRjJ7x67GIOzfi5bxy6adXCTD2YWFH41K1u7I5rUAO3f06dF4zHEt0vlx607uLMohTljZIly8MmzkQUawv/8tX4OltJ5PtXR8wFNZBby/dgOdYqP5LqeCG6W/9Pw8rZbeyYl8sSubEKlxpTQIEjoLzGqC0EmN3PczyobSUu4VaQghiMbKQILYUlrGwJSkJo/tmi4duaZLR6Sao5UzzBkbhHkMA+deW96C0PBK84Scu310FNMvv5Cd5RWEBwSQcgQNgA/FZ5rkVtcQYrMdtn+JVdd4c/xZ/OWb6bzp8ZeSj8HC41oCL4ti2jSUYN9aWkZZVS1PimQ0IeglnVxnZNLNCOSpeYvwGgapYaE8NrQ/ySEhTL3kfJbm5SOA3onxBDZh0y/47+ZtKi/nSa0Fv5rV7JQuYjULG0pKeO3sUfzfD7N43VMCwD+HDWJEs1Syq6q5f9Y8bq7cjWGaSIvG//XqwaUd2jYeN8YZSKHhoRgv0cJKifRSaHiIdTqP+P1VF3dFUZT9BUb7A6OLfvD3hP6jDZU9holdaFik/5qrAQFCx2sc/zk61G7nk4nj2V5ejga0jAhHP0h5+CMhpaSgthYpIT7Iedj55OkRQ/jL1B/4tKyMz2QZBpJJWhKbcBHU0NdTSsmnm7bwBqlEaBb6EUyu4SXMpzN101amb9tJiM3GXQN60S8pkdfOHsXS3Hyq3G66xMYQG9T0eXBtfiHnmSHsFG6Wm7WEAMuz83h61FBun/4Tl5RkYEjJNZ3ac2efHlS7PbyweBk379hFiNQoFT7ObtOCB/r33uf9jAsMZENdPb1FED4p2aJ56H+IcvmHouZo5UxzxgZhg1OS+e+SlcwwK0gTdj4X5YxLb3bC/iMOstnoHLtvqp7HMHhh0TJ+zsjCabVyR/+eDE09cLXC35TW1/NLVjYVLhefr9+M2+2jxvRxeYd23NXv0OkTLSPCmXnlRXy4biPvrVpPTxnEf7VisgMk57be03lesG/nT5AspZZRdaEME5EsLarh2m9+5NtLz8dps+63n6wpNCGIDnDwoquA3XgYoAVjmCY/bNnB5R3aMfuqS6h0uQm270llSAkN4aMLzmZHeQVSSlpEhO9XOSrGGcj/9ezGPSvW0FoLYKvp4ubuXYnba/Jx+3w8MXchMzL96Ya39OjC1V06HvFrUBRF+bPo0S+ODv8Zv8/PmpJq2BSpoSFEBAXyVlUJg2QQS6kDu07ryENneRwrVl2j3QFW3L7atJV3V63DZ5qc164VN3XvcsjPDB7DYE5mFmUuFz9tz2RrSRka0Do6klfGjTzkTUqn1cqnE8czNyubh+fMZ4DpZLaoZp6o4e0++/aeFL/7ejW1xHmt3GBEUeDycs+Mn3nvvHG0joyg71Fm3SSEBDOzvIT1Rh2jRSg6glVFxUgp+eD8s6l0u7HrOgENBTVCAuz8fehAbujehXKXi/SwMJy2/V/vk8MHctv02cwSNRRIDy3iojkrPW2fx3ywdgOvLV/tTzdslsbfhw7A3sTCHYpyOhMHqsTzR3WIiZJfXTDhmB/3SG0uKeWFBcsor6+nT3Iid/TtcUI2/h7MU78sYtP2HG6QkRRJH//RinjjEP0usiqruPLr72lr2tnkreVsLYyJWgSV0uBBLZcHRwxkyF4bfg9ldUERC3bnEBJg5/zWLQluuNPmNUwunzKN+EqDvtLJz7Ka9bIOAXyg7ylkcreWyxNjh9A1Lvaw51qam8/UTVvRheDSTu3pEOPfezZzZyZ/+2keH+jpBAsdn5TcpmXz7Nkj6BLbtL1lB7OtrJxdFZWkhYXul675z/mL2L4tlzsbCm/8Q+Rz//D+TSqPqying7ZvvLNSStnj8I9U/uzikjrJK277/oC/2zvNEI5d0HUg5fUunv51MdtKykgLD+PBQX2JaeIepuPhp4xd/PPnhdwtYwhA8F9RwgU92h/0hp3b5+OaqdMxqlzU+7yEoPOologGvCiKSGuTyP0DmlboI7e6hu+378SUJmc1T6dZWGjj755esIRlW3Yx0QwjEzdTzXJ8SN7Q04huKMLxtiwhtXszbup2+L3QOVXVvLNqHdUuN0OapzKuoahXQU0t53z8FfeLOLo2pPe/JAvp0L0FNzbhuIdSVFvH2qIiQu12esTH7bPvb05mFv+as4BHZTxh6LwkimneMoGHB/f7Q+dUlFPFoebnM/pWQ9uoSN4698iaIh9PszOy+IeMJUHYSBZ2RpnBzMvafdAg7OXFyxnrDeJCLYKL2cEo4b8whwqdnmYgW0pKDxqESSn5YtNWZm3LINBm5aaeXemREMeT8xby2rJVdIuN4Z8jBhPuCOCtCWN4eclKvisqxmoJ5PLkFny4ej1uJAEIvFJSZTatpOyC7BzunzmXi81wvEiuyfiBYKsVp9XKFV06EKRbCGpIE7UIQYSwUuvxHuU7useh9sot2p3LHTKcIKEThM5YM4TFWbkqCFMU5U8tMDqyMcUQ/GmGxzPw2lu4I4BnRg097udpqp+2Z3KhGUY7zV+i/moznCnbMg4ahH27bSeWKjdPmvE8RR5DRQjWhuBiiBnEj0Ulhzzf8rwC3l25Fo9hMKFdK85ukc4DP83j3VXrSQwK4qmRg2kXFcnf+vXi/cBAZmXuxmXClakd+WbjNsp8PqLxB2HlwqBNE+bnwppaLv1qGsN9QTTHwgvZi3nm1yU4dAujW6cTbLcR7d2zmhUtLdR4PE16/w4lxhl40Pl20e4cxpohJGn+G8OXynD+k537h8+pKKeDMy4IK6ypZfrODAxTMrp5Gskhx2Y/1h9hmCa6puGw6JT6fCTgv9iUagZJh0hXKKmtp3dD1cA4rCw3axiuh+KWJhs0F/0OsdfsvTUb+GLlBq40wynFzbV50xHAPTKWFiKazwvKuXvGHN49bxwhdjuP/O6uU15FFY9l5dPHcLBKc9E+PobWkRGHfa3vrVzHdWYkgzX/2IQJK9y19PM6+NeCxcQFOvnQXcZoGcJqaskX3saVsuMl3BFAVp2HdOHfR7dbeGke2LSiHYqiKGeSyNqsxt5dcOKCLvCvHn27bQel9S56JsTR4yAVfk+k3+bnQJuVUnyNPy/FR+AB0usaf19fT5phRWiCOGyskLX0k/69TitFHalhB5/X1hQWcfv0n7jajMCJxr+LlyAtGmN8QdxFKquqa7lp2gy+u2wiYQF2ru3WiWu77WmbkhoawtO/LmWsGUKB8LHD7uNfrVoc9Hy/+W77TnoaDq7U/KmYzWUAj3tyuEKL4I01m4gJC+ZNXwk3yihKpI8ZWjWvHMX2gyMRHuhgu9gT6GVJN+FNLKqlKKe7MyoI211ZxWVTvqOn4cCC4J1V63hnwpj9Sq2fKMvy8nlg1jwKXfW0DA3lok5teX75WsaYwRQLg802L/9o0+qgz++dnMDU8h20lgFcq0UxycxnOtVUCINeyQmc1bzZQZ/75YbN3CGjaan5L2bLjFoCNY3uDWkG481QbircxZO/LOSsls3pmbDvZDhp+CCmbt3O5qISBgQ6uKyh38nhGKaJfa8/KzuCMKEzRgujUHpZa5MUhtl5oDSfpOAg3hw2hlC7P9D0GAYbikoQwl/c5Filjt47oDc3fzeTjbipEga5dpMnOh1dvzZFUZTTmbBaT0rTZI9hcNXUH7BXeUg1rNy1egN39OvJxHatT/hYwN+/6u4ff2Z9aSlhNht/7d2NlzN2UWea2KVgplbNK70PXl2wR3wc92gbGC5DmCjCucPczc0yC4fFguaw8t4h9mx/s2kb55uhjNT82S11hsm7nhIm6v4bnf0I4hNfGQ/P+YWRLZoxoVWLfebfc1u3JMYZyPxd2cRrgv9r04qwAPthX7PPNLHttcPMjkBH0E8LJgCNFyoLOKdNS57I9L+Ox/sNaNyCIKVkS2kZVW4PbaIiGuftP+qKju25dOsO/ukuIETqLNFqeH3g6GNybEU51Z1RQdhbK9cy2gjiMuEPuhINC68uXcV/xzW9TGtTeQyDGTszqXJ76JUYv18qXHFdHXf+OIc7jWi66IHMrK7k47WbeGHMMH7NyqG13cZj7VofssrhjT26UFhbx1XbdiAEXNC2JSOaNyOnugqPz2RJbt4+JeT3JTDZs99PB/KkFxNJKT7+ZmYzVoRi21rKndszeWL4gH3SBTQhaB8VyX+XrATD5J1V67inb08u69jukO/L+R3a8OL8pWgmeJB8ZJZyt+YP8Az8VbDeGH/Wfs8rd7m4dup0vHUeTCSBQQG8e+64xr1rv1dcV0dZvYvkkODDVmjsHBvD5xdO4NfsHOy6hdHpaQc9rqIoinLs/ZSxC1Hl5jEzHqEJhshgHl687LgEYVJKFmTnkllRSfPwsAP26Lzjh9l0qhL8XW/BNsPFU4tX8O+zhrM8rwDDNHm3RbND3sDtmRDHbf16ct+ipdSZBn3iYrmya0eqPR4qXG4W5+QxolnqAW8mCgF714G0IqiXJpXShxOdR40cEoSF5rlu3ilYyeaiEh4c2HefY3SIjuLVJavYVVHBlI1bGZKazL9GDD5kpcfRzZtx2er1JJtW4rDxnlnMcOHPWjEaPi88PLjffvuxTCl5aPYvLMnKJVqzUoCX/40/64DFTcDfezSnqppYp/OwlZzDAux8ceG5zMzYhdvwcVdy0jGrKK0op7ozKgirrHfRUVobSwklYGO1y33Mz+P2+bh66g+IKjcJ0sqrrOSfIwfvU+lwc0kpzYS9ceVprAjjS08FScHB3NuvaY0GLZrG34cO4PEh/QF/YPTqslVMWbeZLgTynqhnROv0A27+vbJrB15csppLzDBK8bFV95ASEsLj1fnU+3z0E0HcpPvvcDUz7by+ZNU+QZiUktunz+YKTyhDtRAKhZf7lq6iS3zsQS+8AOe0aoEpYcr6zeRUV2O4JZUYfGWUMl1W8FCnvgd83stLVtC8VnCz9E+W/60u5pVlK/ebeADeXLmWyavWEqlbqcXktbNH0THmwPvqfpMSGsLloYcOIBVFUZTjo8rjIR5L44pOAlZqfT5MKY95g+ZnFixl7rYMOkkHH4p6xrZtuU81YZfPx5aKcp7SWqAJQVscdNWcFNTUcnuvpve+vLBdaya2bYUpJbqmsTA7l6d+WURvEUQ+Xj5cvYH3zhu7X6W/Czu05bod07GZAic6H+tlDE5I4oG8PNJMK5UYPK3528YMN0O4ZvNWbu/dY5/qg8/+upTYCi9PyDQ8SJ7cXcDHGzZzVaf2Bx1vs7BQ3hw/hleWrGBGVSW7azwMIZg5ZiXvmCV0TT5weuisjF1s3l3AazIZu6nxs1nFwz/9wtRLz9/vsYtycrln5s+EYaHU9HJf/96HDbSD7TYmtj14VpCinKnOqCBsSPNU/pe/gnRpx4rgE62c8ekHvyAdre+2Z2Cp8vCEGY8QggGmk3/+soihV+0JwiIdDnJNDy5pEiA0iqWXGmkQ+ruUgQXZOUzdsBWLrnF55w77FekwpeSDtRuYvT0Ti66zoaSE/4lUQtH53ChjyoYtLMvO4699ezC0IXdbSsmlHdoSZLPy07ZMHDYHV0Ykk11eSWWQB29NHaLcoKahyWK40HH5jH3OW+fzUVhfzxDNX+42VljppAWyrbTskEEYwITWLZjQugVSSu6bPY/3d+UgNMFfu3fnonZtDvicrLJKRpkOhOafjHuYDhaWVe73uLWFRXy0ej2vkUKEaWGBWc1dM+Yw+6pLDjkmRVEU5eTpnRDPy3IFfc1a0oWdjymjf0LCMQ/AdldWMW3Ldl4nhSChU20a3LRxM5d0akt8kH/Pll3XsWs6u/GQhh2flOyWHiJ/t1c4s6KSt1eupdrlZnjLZow/wL6rBdm5vLdyLV7DZHd1NXebMXTTnKw0a3i5tIizP/qKiR3acEP3zmhCIKWkbWQEk8efxQer1+PxGVwS14H8ympaxEfhQxKY56Ucg0gsONCwCg234cPJniBsU3EJ18tgNCEIQDDIdLKpoBg67TfEfXSIiWrMRvlo/SbeXLYawzTpnZbECwcplLK7soqOZgB24V9l6ymcvFFTvN/j3D4f98ycy/1mLB1FILl4uG/RMnonxZ8S+/MV5VRzRgVh57ZuSVm9i7+v2YApJRe0a801XY5s749RfPiqPKWlhaSYe+7opQk7Fe59Kwi1i4pkUHoK92Tm0JYAVlHLHT27EWTbkwY3JzOLx+f8yuVmOC5Mbto9g8njz9pnVef1FauZtW4bVxrhbJb17JSCMIuFr80yFskaHtUTqa42eGT2LzwzeijTNm9nZmYWAbrO//Xsystnj+TZhUuZsXYro4wgKnGxw6ymEI25RjWXa5Es0OsY87vJJdBiIchqZb2vnk4ikBppsNWs57qQ4Ca/l0IInhvZtOpXrWMimVeWQwfTv59vnlZLx9i0/R63s7yCjiKQiIY/3f4iiOfrC3D5fI39SxRFUZRTS3p4GM+PHsZTvyyizOWid0I8zw4feMzPU+ZyEa3bCDL9aYDBQidSs1LhcjcGYUIIHhnUj8d+XUwvEUSGcJMWF8mA5KTG4+RWVXP5lO842wghDQuv5C+j0uXmyr1Wmpbl5vPArLlcb0bhQOdfppt03c5WWc+LZiG3ajGEey28vXYLhjSxWyxMXrkWt2kwplkzJg0fxKKcPB6ZPZ8LzFAsGHxtlhOIxi3sYqgIRuqCVuHh+xWrSAkNYWVNLa1xYErJGq2eruFH1iPsio7tuOIwWwwAWkVG8JXYyHjTR5jQmS2raB2+fzXi4rp6bFLQUfhbDSQKG831ADIrKlUQpigHcEZ9ajVL8rgmKZJrkgY3/kyW5GEAR9IOLfGuWw/5+7Ebt3LpHY8z2OciCRsfUkqvyHB8RfsGcI+2b8ai6BByauu4MiyEzpFhjUGeHp3Ih6vXc5MZST/NH9gYJny2bhMdR+wZ/9RN23jQjCZNs9NOOvjOqGCOWcV8s5qb9RjaCQcIyDO9PL9gKVG1kg+1ZlRIg78vX0tccBAfb9jMe1ozQjSds6Tkfjycp0UQJnQeMXK4qkN7bu3ZdZ+xCyF4btQQ7pnxM2laADmmm3PatKT7capmNbF9G67csp2LzZ3oCJqHhHFLjy77Pa5ZWCgbZT2V0iBU6KyQtUTaA7CfxP5viqIoyuH1T05k+hUXHtdztAwPp1z4mGtW0V8EMV9WU2eBtNDQfR43oXULWkWGs7awmLMDHQxNS9lnVW7a9p0MMJ1crPmLZSRLG8+vXr9PEPbNpm1caIYxqGEOb2kG8L5ZQiAa52hhjXP7zWYUz27ciuaTvCATCRY6L2YV8e9Fy1lfWMytZhR9NH+AKCXUYXKxFsFfjSzaREfz6lnD9yuMdf+gPlz99Q+s9uVSL00iw4K45iDl9P+ofkkJBDoD+EtlBgIIsVj5dOT4/R4XFejAhclWWU9r4aBIeskwXKSoAExRDuiUD8J+H9gcjpw0+YA//3JxJHXFpU07yMLDPaALvSfYeHTa49R7aklv1oeRl7/Ge4GhB3z01Q3Xqt824opHbsRXlIvh8WBhT3NKKwLzd9GiLgSehg2zdqHRTXPykaWcWo+Xamk07n+rwaCoto7bZAJOoeNE5ywzmMW7c5FIHA29uYQQhAgdL5K2wkGM1c74Ni0PuJm3X1Ii0y67gG2l5cQ4A2l5kD5cx8KDP81jnAzlIj2cXXh4vCaPrMoqWv2uLH7XuFjO69Ca/1u/mTjNRqHw8srokU2q3KgoiqKc2Zw2K2+ccxb3zZzLf2oKaR4cwuTRo3FY9/+40zYqkrYHSa83pcQiaZxj/fPzvo/RtT3zM8AoEcJHlgrKvG7GEtb48xpp4DNMzjNDiN+rH9Zr2blYNI0gsWeVK1joDTcZLfSxhtCjebMDViKMDwrim0vPZ0NRCVZdo2NMNJZDFOX4I15eupKwWoMv9BZ4kPydfBbl5JEatu9nngCLhadHDObB2b+QoNnINdzc1qs7aWEH/mykKH92JzUIa2qA9cW4Kfv97KAB1bSDHaWJAdjv1FQVUla0g5DwZMIi9+z5atvtPNp2Ow8p5WEDgPd/P6Ze/kAxxvI1r3/zGIYPXJh8Lsp4ISG18X0Rus413Trx/JJVXGiGUYyP9RYPX1w4gS0lpTw2Zz55ppcaDGbpNcQ7g9hV5SZN+C/YuzUv7YICSQ8J5dmqfCZq4Ww2XWyVLm7TYtkhXVTgIy7ISWFNLffPmsfakmJiHIE8OWwgvRLjiQ4MJDowkOPJa5hsKi9nUsMm6XTs9BRBrCks3i8IA7i9Tw/Oa9eKkrp60sPDjlmpXEVRFOX0UevxsrGkhCCbjbaREY1zcbuoSL6/fGKT5ueDGdsincvWbCDetBKDlY/1cia237fAxKWd23Nd5nSECQFofKaXM2n4YFpGhHPJV9Ow+0oIR2eqVkm3xDiysqsan5sl3YQ7AugYH8NLa7dyux5LrTT5wizjAT2eGmmwXtRzYVgoHsNg0i+L+HFnJjZd46buXbiqcwcCrVZ6JcYf/RvYRCty8rlEhhIgNAKAsWYIK7LzuLRD2/0eOzQthemXT2RXRRXxwc7GFFBFUfZ3XIIw6fM2OcB6r9eBV6720dQVrGNs27of+PnLe0nSA8n11dJz5F10G3zTPo/5IyswbbudD0Ljs8UfoGlWhg17jo2tBrER6NEvjg7/Gc+FMSGEdGvNzzlFBAdH8km3oSQGB5EYHETw2BHM2JZBgEXn047tKHe5uPW7mWwQLiowKAqAR9u05suNWyiQbl41vAj8Ad8DZg6VumTS0IEE2Wz8Zep0OlQJ7hHN2FTv4o4fZzPl4vNICD7+F1CLJgi2WMkw3bQkAK+UZAg35zodB31OckiIyjFXFEX5k8oor+C6b38kwtQoN310TIjl32cN2yer44/Mz83CQnlrwhheW7qKlW4PE1t02CcVEfzB3lsTxvDJ2o14TZNn23ZtLIf/2cTxfLJ+E9VeL8+06EbbqEgu/XIaT3kKCEZjuV7H5AFn8f7qdZTg5U2jCA2BD8krRiG1OpzfuhV9kxJ4bsFSMjMKeJ0UagyDScvXEh8StE9F4+MpxhnI9gpX416v7cJNTPD+N0h/E+FwEOE4+PytKIqfkEeyWaqJuqbGy7n3/4VJ1Tcf82OfKF5PHW8+2Y2nzWiaiwBKpJfbtEIuvGM64VEHb5J8PDzcfw25L77W+L0Q/j1lB7K7sooF2bk4LBZGpacxN2s3n/6ykjhDZ7msJQ4rW6jn/v59GN28GVGBDqrdHga//wlfiOaNk9a/tELOH9SVsS3ST8hrnLUzk8fnLqC7cJKFh2bxkfxnzIhjXjlLUc5Ebd94Z6WUssfJHody6usQEyW/umDCyR7GH3bllO/oUSo4WwvDK00e1fK5uF/XU7rUebXbw6zMXbh9BoNSkrDpOmd/8hV/MSN5T5bQDBtbcTEsPY3be3cntaFf1oSPp3BLXSgtG9IWvzPLKW8Rzt+HDTgh495VUclVU3+glbTjlibFNsmnE8cftgeYoiiHnp+Py0pYvhF9WgdgALXVxQQKC80bLnpRwkqKxUll2e4THoQ9tbBLYwrj1eOBhj1lAJaYfYOxlNAQLtur0WG9z0e4sHCrHkOGdFEkfWw267mkfZvGO4YBFgsSKMJHLFZ8UpIvPYQFnLg0v1HNm9E8Ipy1hUVEBToYkJykAjBFURTlgHZXVXOL8KfiWYVGFyOArMr925qcSoLtNi5osydIzKqsIlDojNRC6EIg2dLDB3oZl3Zs2xiAgb+hcXatpzEIyxE+kgJPXACUFhbK1EvOY1F2HromGJySvE/PMkVRjs4pX5jjZAkKicUlYK1ZR2ctkN3STZavln7RzU/I+betn07m6m+xBobSdcithEelkbV9AdffPJ+AoO5cfdtd9HrzCnxFufsFYlJKCmprsWga/ZISeZHlzDWraC7s/KrVMCwpZZ+UDauucXfvnjy0fA19pZNtmpvU2Ej6JB5Zuds/qnl4GM3Dw07oORVFUZTTT+uIcH4uquISGUEdJkv1em46wB7i42FTSSmfrNmI1zQY37YV/ZMTyayo5LttOxHAOa2aH7IYRbnLRZ3HS7wzkIggB+9VlzKCYLKEB5dF7Fcs5K4Bvbjlu5lsxUW1MNlpM3io87HvgXookQ4H57Q6MZ9/FOXP4rikI8YldZJX3Pb9MT/uibZ75yKmf3ADQehUGR6Gnv9P/z6u42zd4o9YM/1fXGo4KRYG31k8dB18MxvnvspYw8Eu3WR7cAgX3TGdf0R9SM6cVYB/VazW4+X/fpjFtpIyfEgGJCdyRecOPL9gKWX19fRMjOeBQX1xWve/i7Uiv4C1hcXEOQM5q3mzA1ZMVBTl1KPSEZWmOlPSEQtqarnh2x+pcbmpMQ3Gt2rOI4P6HfdKuZtLSrn2m+mcb4YSgMbnWgU39OrCG8tWM8z076Geq9fy3rlj9yssJaXkuUXL+HzjFpyaTnigg6dHDeH1pavYUlpGamgIjw7pT0ro/vudsyqrmJeVjV3XGdPiwBUTFUU59ZzwdMQzRUrzflz38AqqKnIIConFHnBsC0FI02TLuu+oKNlFdHxbmrfzl1pfM/dVHjDDaa35N7ZW+0qZNeclXiCOZpodJDxWU8rWdd8xqefN0Mufpuh75EaeX7mR4FIX75GGD8k/cwpYFVPIJxfu39Pj93rEx9HjOPUB+6NqPV6eW7iU9QVFJIYEc//APiQeQePovS3JzeP5X5dS5fYwIDWJ+wb0Vo2eFUVRTiNxQU6mXno+OVXVBFqtxDiPfRXf5XkFLM8vINIRwLmtWmC3WPhs/WbONUM5v6F/WJip886KtVxkhjFe87dxCTN03lyxhudGD9vneD9lZjFvSwZvizSCpMYntWW8uHAZkyeMOexYUkNDuLrTiV39aipTSt5bs56Z2zIIsFi4pU+3o86kya2q5u9zF5BRUUnz8DCeGDZAVVhUzlhqqeMwrDYHkTEtj30AJiWzPvkrW79+jMSf32fF5/ew4Lu/A2CaPux7/dPYJRiml+i9YuYYqeF11zV+//40f6XJzeVVDJfB6EJgFxqDzSA2FRYf07GfaFJKbp/+EyU7C/lLTTDxefVcNfUHqt2eIz7W9rJy7vpxDudWOXjIE8WuHfk8Oe+wjeEURVGUU4xF00gLCz0uAdiXm7Zyz/TZ5K/cxQ+L13PN1Ol4DAPDMLGzZ7XNjoZhSqL2mp+jsVLr8e53zE3FJfQ1AgkWOkIIRhHC5tKyYz72E+3NVWv5ZuVGLqkMZEipzl0/zmFDUckRH8fl83Htt9NpVuTlMU8MyYVurvvmRzyGcRxGrSgnnwrCTpLi/M3kbfuFZ40ortYiecGIZv3ST6itLqZt78t4Qa9gjVnLLLOSGbqLtGZ9eVkrJ096WGrWsIBaUlsN3O+4RsoAVuMPzkwpWaPVkxx+epdyL3e5WFdcwu0yhrbCwUUighhDZ1VB4REf69fdOQySQfTVgkgWdm6VUczetfs4jFpRFEU5HfnTBpfyDxnPNVoUj5lxGFUu5mRmMb5dS77UKphvVrPcrGGyVkK/tGQ+0SrYIV1sly4+1coZ2XL/Al7JoSGs1114G7aBrKKOpBPQBuZ4+3bTdv5PRtNJC2SQFsw4M4Tp23ce8XF2lFVg8UouEhEkChuXikgMt4/MilO74IqiHC2Vg3UCGYaXFT+/SlHGEkxbABGaDbvpj4ODhI5Ts+F2VdNr+B2ssgXy1pppWAOCGH/WfUTHt+WXrx/i/u2/4nCEMubc54mMabnfOfqPf4Ip2eexri4HDyZBThvPdu9ygl/psWXRNAwpcQtJIAIpJXXSwKof+T0Eh9VCmdhzV60MHwG6fiyHqyiKopyGfti+k+lbdhBgsVDnM4jT/XunhRDEYaHa42FMi3SeHjWU91auw2P4uKV9T85v05L31mzg3+s3IRBc3rkT57bef36e0KoFc3fu4raCbCKEhQLh483hh09FPNVZdY06afLbAmEdJkGWI59XHVYL1aYPjzSxCQ2XNKmRPhxqu4ByhlJ/2SfQT5/ejmXbYib6Algl3MwzK5klbPQSgcymBhwhhEYkIzSN7oNvovvvGkOPuOQ/hz1HUEgsl90zh/zs1bTrFMOEOU/jOEARjtNJiN3O2S3S+UdGHkPNIDZoLuzBAUe1f+3sFs15f/V6/uMqJMG0MkOr4rbePY/DqBVFUZTTxVebtvLaohVcYYZTiYcAIfgfJVwuI8iQbpZrtdyV4C+JPzAliYEpSfs8/y9dO/KXrh0PeQ6LpvHy2JGsLyqmxuOlQ0zUGVFg4/oenXlu/hImmmGUY/CLpZbP27Y+4uOkh4XSIymex3Pz6W44WK7XMyA5meSj3P+tKKc6FYSdIG5XNds3/cQnIpUATaMfwWyxGHzmtPBWbQExMS059/JX0PU/HjBZbQ5SmvejphbS7vkruS++tl8Z+9PN40P681n0FtbnF9E2NI5ru3XCdhQrWMF2G59fOIHPNm6hst7FP1O60z/59H5vFEVRlD/m4zUbuF1G01Hz7y8rNbyscRj81ZVNpCOAF4YMo9khys43lSYEnWNj/vBxTiXntGpBiN3OrO0ZOGxWPuk8lKSjCJyEEDw3aihTtmwno6ycSyIjOL9Ny+Ne8VJRThYVhJ1geyfQBWoWuoz/O83bjThu53tqYReugQP2Ezud6JrG5R3bQcd2f/hYoXY7N3XrfAxGpSiKopwJJLD3R/0AoTMgLYEHBvQ5WUM6rQxOTWZwavIfPo6uaVzU7shX0RTldKQKc5wg9oBgWrQeyiStjGVmDW9TTlGAneTmfY/7ud/rNRnwB2KKoiiKouzrkk7t+K8oYaFZzXSzgh+0Ks5ts/++LkVRlGNFBWEn0MjLXyGg3yV8kpRARseBTPzrt9jszhNy7t8CMaNYBWKKoiiKsrdLOrTlrwN7sjAWdiTaeeOc0bSJijzZw1IU5Qym0hFPIIvFTt8x95+0838xbgoX/XDBSTu/oiiKopyqzm3d8oBVDRVFUY4HFYT9idQVlyInTcb3yI1/aH/YjrJynvh5ATnV1bSJjOAfwwcR4QigsKaWsIAAnLbTuxqjoiiKopyOqtxu/jF3ISvzC4kKDOChwf3pEhtNUW0dFl0j0uE42UNUFKWBCsL+ZN6fBo8M70buz6vQo488EKtyu7nu2x+Z6A3hFhHPrMIqrpn6A27DwOv1UWMa3N2nB1d0an8cRq8oiqIoysHcO3MujqI6JslYtle6ufX7mTQLCyWjvBJDmgxJTeZfI4dg0dRuFEU52dR/hX9C2UNvRsqje+6G4hJipYWxWhhxwsqVRFBYU8tEdzDvksYrIoX/LVvNhqKSYztoRVEURVEOymMYLM7P5xYZTZywMVALJtTUCCvz8AFpvCeakZNTwofrNp7soSqKggrC/pTen+b//6Oplui0WimXPnwNUVylNKjHZKQIASBWWOkqnGwuLT1m41UURVEU5dAsmoZFaJTjA0BKSbVpMFqEoAtBgNAYajjZWFB8kkeqKAqoIOxP62irJXaMiaZlbBSPa/l8bpbyuJ6PU7ewVtYDUCdNNlOvOtwriqIoygmkCcEdvbrxsMjjM7OUf4lCdKvOGvzzsyklazQXyeEhJ3mkiqKA2hP2p5Z4163kvvjaET1HE4KXx47g6y3bya6s5NboKMID7Nw942fStQByTDcjW6TTOyH+OI1aURRFUZQDuaZLR5qFh7Eir4DBzkAeS0nmxu9+ZJ03F7eUOIMdXN+t88kepqIoqCDsT+2phV245iieZzlAR/tpl13AlpIyogIdtFW9VRRFURTlpBicmszg1OTG76decj7riorRhUaX2BisukqCUpRTgQrCFHxFuX+oZD1AdGAg0SmBx2hEiqIoiqIcC4FWK30SE072MBRF+R11O+RP7mj3himKoiiKoiiKcnRUEHYSuF3VLJv3OvO/+weZW+ee7OGQeNetJ3sIiqIoinLSmVIydct2nlm4hC82bcFnmid7SIqinKFUOuIJ5vXU8eV/z6FVVSVtDY3pyz6n8qx76dL/Lyd1XEfbN0xRFEVRzhSPzpnP5qx8ehuBTNGyWLgrh/+MGY4Q4mQPTVGUM4xaCTvBtq2fTkJNFfeZkVykRTDJjGLxzOdO6pieWtgFOLq+YYqiKIpyJsitrmFO5m6eNBOYqEXwdxnP2rxCtpeVn+yhKYpyBlJB2Anm9dQRKbXGu2oRWPD43MiTvBT1xbgpJ/X8iqIoinIy1Xu9BGk6dvzzs1VohGg6tV7fSR6ZoihnIhWEnWCpLQexmDoWmNXkSA8vaWW0aj3slEl1UAU6FEVRlD+j1NBQAgJsfEoZudLDFFlOvUXQJjLiZA9NUZQzkArCTrDwqDTOvvZ9PooO4WFHHbXthzDi0pdO9rCoKy5VBToURVGUPy2rrvHWhDHkxtr5u62IrdE675w7BodVbZ9XFOXYU1eWkyAprScX3zXrZA/jgFSBDkVRFOXPKi7IyRvjzzrZw1AU5U9ArYQpjX4r0KEoiqIoiqIoyvGjgjBFURRFURRFUZQTSKUjKvtIGt6NnDmrsMQknuyhKIqiKIryJ3OoAmF6tPpsopw5VBCm7OPfAQ9zERec7GEoiqIoinKGM0oLkIax38/lpMn7/Uw8cuNB+5mqG8fK6UgFYcoBGaUF6JFxJ3sYiqIoiqKcYYzi3MZCYHLSZL5cHEldcemeB0w7wJN67RuYBUZHAnDRDxfsE5ypgEw5XaggTNnHb6Xq817+38keiqIoiqIoZ5C9g68Nd05jxaKChoCr9FBPO6Dfgrb39grOHgl+g5w5qxq/F7qubigrpywVhCkHdKD0AEVRFEVRlKPx22rVF+Om+AOoRQXH/ByTqm+GXv6vfwvIfjuvCsiUU40KwpT9PLWwC9ec7EEoiqIoinJG+C0Qeq/XZCg+8lWvo7F3QHb1eGCvPWUqZVE5Fagg7BRTXpJJdsYSAhwhNG87Et1iO9lDUhRFUZQ/vUq3m3lZ2UgpGZSSRITDcbKHdFrYJwA7Sd6fBvSaTGB0JHe7nmpMWVTBmHIyqSDsFJK1fQEzPriBHiKILOFlXfT/OPfmL7FY7CdlPL6iXHWBUhRFUf70CmtquWzKd6T5LGgIXtSW8/EF55AUEnyyh3ZK+63c/MkMwPZWV1zKJPwrZA/3X0Pui68BKlVROTlUs+ZTyC9f3cffjHDuNcN43hdFaFE2m1dPPSlj+WLclJNyXkVRFEU51by2fDX9PQE8JON4QMYy2hfES4uXn+xhndJ8Rf4iHBvuPFCpw5PvqYVdeK/XZOSkyUjDwFeUe8geZYpyrKkg7BRSU1dOC+Ff9dKEoIUhqK0uPsmjUhRFUZQ/t5KaWprLPVkpzaWd4pq6kzii08MX46b4KyCewt6f5l+p23DnNKREBWPKCaOCsFNISmoPPhZV+KQkR3qYq7lIatb7pI7JKD21L56KoiiKcrz1Tklkml5FlTSokQbfaJX0Skk42cM6Zf22D6zuBBXhOBZWLCrgvV6T+WLclMZg7GDNoRXlWFBB2Clk+KUvszUxnfPNndwpCug57mGSmvU6KWOpKy4laXi3k3JuRVEURTmVXNGpPT1apXCNmclVZiYt0+O5sXuXkz2sU9qpsg/sSNUVl/Jer8mN41fBmHK8qMIcpxCHM5xzb/kS0/AhNB0hxMkekqIoiqL86WlC8ODAvtw/oA9SSnRN3cM+mDMpYPktELtmmb+8vSrgoRxL6ipyCtJ0yykTgKmmzYqiKIripwmhArBD+G0Lw5lW3Ou9XpNJvOvWPQU81FYN5RhQVxLloCZV33yyh6AoiqIoyunCNEi869bTai9YU6lqisqxpoIwRVEURVEU5Q+T8mSP4Pg7UDVFRTkaKghTFEVRFEVRjomnFnY52UM4IX6rpgiqrL1ydFQQphyWusujKIqiKMqh/Fk/K6geY8rRUkGYckinaqd7RVEURVFOLWdaQY6mOlCPMUU5HBWEKYqiKIqiKMof9FuPMVD9xZTDU0GYoiiKoiiKctSM0gKShnc7I6siHo3fVsWShndTKYrKQakgTDmkFYv8F1bVE0NRFEVRFKVp6opLmVR9s0pRVA5KBWGKoiiKoijK0TONkz2CU9bvUxTVqpjyGxWEKYqiKIqiKEdNSvh3wMMnexintN8X7lDBmKKCMKVJpKHucimKoiiKcmBqP9jh/bYqpho9K6CCMKUJJlXffLKHoCiKoiiKckZQjZ4VUEGYoiiKoiiKopxwqrfYn5sKwhRFURRFUZSjooKHP0YV7vjzUkGYoiiKoiiKctS+GDflZA/htKf2iv35qCBMaTJ1UVAURVEURTk+9tsrpnq0ntFUEKY0yYY7p53sISiKoiiKopzx3us1GTlpMtIwVIriGUwFYYqiKIqiKIpyCnl/mkpRPNMJKeWxP6gQxUDWMT+woiiKciCpUsrokz0I5dSn5mdFUZQT6qDz83EJwhRFURRFURRFUZQDU+mIiqIoiqIoiqIoJ5AKwhRFURRFURRFUU4gFYQpiqIoiqIoiqKcQCoIU047QghDCLFmr/+lHcUxzhVCtDsOw/vt+DOEEBVCiO+P1zkURVEU5VSi5mdFaTrLyR6AohyFeilllz94jHOB74FNTX2CEMIipfQ18eHPAYHATUc+NEVRFEU5Lan5WVGaSK2EKWcEIUR3IcQvQoiVQoiZQoj4hp/fIIRYLoRYK4SYIoQIFEL0A8YDzzXcqWsuhJgnhOjR8JwoIcSuhq+vEUJME0L8DMwRQjiFEO8IIZYJIVYLISYcaDxSyjlA9Ql58YqiKIpyilLzs6IcmArClNORY69Uh6lCCCvwX2CilLI78A7wVMNjv5ZS9pRSdgY2A9dJKRcB04C/SSm7SCl3HuZ83RqOPRh4GPhZStkLGIp/onAeh9eoKIqiKKcbNT8rShOpdETldLRPuoMQogPQAfhJCAGgA/kNv+4ghJgEhAFBwMyjON9PUsqyhq9HAeOFEPc2fB8ApOCfQBRFURTlz0zNz4rSRCoIU84EAtgopex7gN+9B5wrpVwrhLgGGHKQY/jYszIc8Lvf1f7uXBdIKbce9WgVRVEU5c9Bzc+KchAqHVE5E2wFooUQfQGEEFYhRPuG3wUD+Q0pEZfv9Zzqht/9ZhfQveHriYc410zgNtFwS08I0fWPD19RFEVRzkhqflaUg1BBmHLak1J68F+YnxFCrAXWAP0afv0osBRYCGzZ62mfAX9r2LzbHHgeuEUIsRqIOsTpngSswDohxMaG7/cjhPgV+BIYLoTIEUKMPtrXpyiKoiinIzU/K8rBCSnlyR6DoiiKoiiKoijKn4ZaCVMURVEURVEURTmBVBCmKIqiKIqiKIpyAqkgTFEURVEURVEU5QRSQZiiKIqiKIqiKMoJpIIwRVEURVEURVGUE0gFYYqiKIqiKIqiKCeQCsIURVEURVEURVFOoP8Hn+mfJOFmL3IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def make_meshgrid(x, y, h=.02):\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    return xx, yy\n",
    "\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "C = 1.0  # SVM regularization parameter\n",
    "\n",
    "models = (LogisticRegression(C=10),\n",
    "          LDA(),\n",
    "          svm.SVC(kernel='linear', C=C),\n",
    "          svm.SVC(kernel='rbf', gamma=1, C=C),\n",
    "          svm.SVC(kernel='poly', degree=2, gamma='auto', C=C),svm.SVC(kernel='poly', degree=3, gamma='auto', C=C))\n",
    "models = (clf.fit(X, y) for clf in models)\n",
    "# title for the plots\n",
    "titles = ('LogisticRegression',\n",
    "          'LDA',\n",
    "          'SVM',\n",
    "          'SVM with RBF kernel',\n",
    "          'SVM with polynomial (degree 2) kernel','SVM with polynomial (degree 3) kernel')\n",
    "\n",
    "# Set-up 2x2 grid for plotting.\n",
    "\n",
    "fig, sub = plt.subplots(3, 2,figsize=(15,15))\n",
    "\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "X0, X1 = X[:, 0], X[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "for clf, title, ax in zip(models, titles, sub.flatten()):\n",
    "    plot_contours(ax, clf, xx, yy,\n",
    "                  cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xlabel('Feature 1')\n",
    "    ax.set_ylabel('Feature 2')\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Kernelized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it has been done with the SVM function, we are going to improve the logistic regression with a Kernel in order to bring a bit more flexibility to the decision function\n",
    "\n",
    "### Task : Fill the kernel function below, it must transform your input data in a higher dimensionnal space. \n",
    "You can use https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html for a polynomial kernel for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "class Logit_Kernel():\n",
    "    # Class for Kernelized Logistic Regression :\n",
    "    model = None\n",
    "    C=None\n",
    "    def __init__(self,C):\n",
    "        self.C=C\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        self.model=LogisticRegression(C=self.C)\n",
    "        self.model.fit(self.kernel(X),y)\n",
    "        \n",
    "    def predict(self,X):\n",
    "        return self.model.predict(self.kernel(X))\n",
    "    \n",
    "    def predict_proba(self,X):\n",
    "        return self.model.predict_proba(self.kernel(X))\n",
    "\n",
    "    def kernel(self,X):\n",
    "        poly = PolynomialFeatures(2) #degree of kernel will be fixed to 2\n",
    "        X_0 = poly.fit_transform(X) #we use contant term, interaction, squares and identity values\n",
    "        return X_0      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model test accuracy: 0.8873239436619719\n",
      "Model test auc score: 0.9523525908278737\n"
     ]
    }
   ],
   "source": [
    "#Testing model on test data - better performance than rest of the models in this notebook\n",
    "\n",
    "#fitting model\n",
    "clf = Logit_Kernel(C=1)\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "#predicitng on test set\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "#calculating metrics on test set\n",
    "acu = accuracy_score(y_pred,y_test)\n",
    "auc = roc_auc_score(y_test,clf.predict_proba(X_test)[:,1])\n",
    "\n",
    "#printing metrics\n",
    "print(f\"Model test accuracy: {acu}\")\n",
    "print(f\"Model test auc score: {auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After filling the kernel function just run the following code. It must print the same decision graph as before.\n",
    "### Just run the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_contours(ax,X,y, **params):\n",
    "    clf = Logit_Kernel(C=1)\n",
    "    clf.fit(X,y)\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1,figsize=(15,15))\n",
    "\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "X0, X1 = X[:, 0], X[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "plot_contours(ax, X,y,\n",
    "              cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
    "ax.set_xlim(xx.min(), xx.max())\n",
    "ax.set_ylim(yy.min(), yy.max())\n",
    "ax.set_xlabel('Feature 1 ')\n",
    "ax.set_ylabel('Feature 2')\n",
    "ax.set_xticks(())\n",
    "ax.set_yticks(())\n",
    "ax.set_title('Logistic regresion with kernel')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercice 2 : Classification Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Now you are going to apply the methods you've seen on a real-like data science project.\n",
    " \n",
    " The data is related with direct marketing campaigns of a Portuguese banking institution. \n",
    " The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, \n",
    " in order to access if the product (bank term deposit) would be (or not) subscribed. \n",
    "\n",
    "\n",
    "   The classification goal is to predict if the client will subscribe a term deposit (variable y).\n",
    "   \n",
    "## Features - bank client data:\n",
    "   1 - age (numeric)\n",
    "   \n",
    "   2 - job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\",\n",
    "                                       \"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\") \n",
    "                                       \n",
    "   3 - marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n",
    "   \n",
    "   4 - education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n",
    "   \n",
    "   5 - default: has credit in default? (binary: \"yes\",\"no\")\n",
    "   \n",
    "   6 - balance: average yearly balance, in euros (numeric) \n",
    "   \n",
    "   7 - housing: has housing loan? (binary: \"yes\",\"no\")\n",
    "   \n",
    "   8 - loan: has personal loan? (binary: \"yes\",\"no\")\n",
    "    related with the last contact of the current campaign:\n",
    "    \n",
    "   9 - contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n",
    "   \n",
    "  10 - day: last contact day of the month (numeric)\n",
    "  \n",
    "  11 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "  \n",
    "  12 - duration: last contact duration, in seconds (numeric)\n",
    "   #other attributes:\n",
    "  13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "  14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n",
    "  15 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "  16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n",
    "\n",
    "  Output variable (desired target):\n",
    "  17 - y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas library "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab session we put aside the numpy's arrays, we will use the pandas library for the structure of our dataset. Pandas is preferred to numpy in this domain because of its simplicity and it suits wells for data science projects.\n",
    "\n",
    "Here is a sample of the most useful pandas commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the dataset\n",
    "bank = pd.read_csv('bank-full.csv',sep=';')\n",
    "bank = bank.drop(bank.loc[bank['y']=='no'].sample(25000).index)\n",
    "bank = bank.sample(10000)\n",
    "bank = bank.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task : Above each command, tell what it does"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>454</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>aug</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1631</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-197</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>-970</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>4</td>\n",
       "      <td>feb</td>\n",
       "      <td>489</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2816</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>20</td>\n",
       "      <td>jun</td>\n",
       "      <td>492</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job   marital  education default  balance housing loan  \\\n",
       "0   34    management    single   tertiary      no      454      no   no   \n",
       "1   44  entrepreneur   married   tertiary      no     1631     yes   no   \n",
       "2   28   blue-collar    single  secondary      no     -197     yes   no   \n",
       "3   52    management   married   tertiary      no     -970     yes   no   \n",
       "4   52    technician  divorced  secondary      no     2816      no   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome    y  \n",
       "0  cellular   14   aug        76         3     -1         0  unknown   no  \n",
       "1  cellular   17   nov        81         1    195         2  failure   no  \n",
       "2   unknown    9   may      2016         2     -1         0  unknown  yes  \n",
       "3  cellular    4   feb       489         1     -1         0  unknown  yes  \n",
       "4   unknown   20   jun       492         3     -1         0  unknown   no  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This method prints first n rows of pandas data frame. By default, so when not specified, n equals to 5.\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>job</th>\n",
       "      <th>balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>management</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>-197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>management</td>\n",
       "      <td>-970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>technician</td>\n",
       "      <td>2816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            job  balance\n",
       "0    management      454\n",
       "1  entrepreneur     1631\n",
       "2   blue-collar     -197\n",
       "3    management     -970\n",
       "4    technician     2816"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It shows first 5 rows from data frame limited to columns \"balance\" and \"age\" \n",
    "bank[['job','balance']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>balance</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-9</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>-22</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     balance  age\n",
       "13        -9   31\n",
       "97         9   32\n",
       "255       11   45\n",
       "265      -22   37\n",
       "305        0   54"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It shows 5 first rows from data frame limited to columns \"balance\" and \"age\" that have value \"yes\" in default column\n",
    "bank.loc[bank['default']=='yes',['balance','age']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>454</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>aug</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1631</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-197</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>-970</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>4</td>\n",
       "      <td>feb</td>\n",
       "      <td>489</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2816</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>20</td>\n",
       "      <td>jun</td>\n",
       "      <td>492</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job   marital  education default  balance housing loan  \\\n",
       "0   34    management    single   tertiary      no      454      no   no   \n",
       "1   44  entrepreneur   married   tertiary      no     1631     yes   no   \n",
       "2   28   blue-collar    single  secondary      no     -197     yes   no   \n",
       "3   52    management   married   tertiary      no     -970     yes   no   \n",
       "4   52    technician  divorced  secondary      no     2816      no   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome  y  \n",
       "0  cellular   14   aug        76         3     -1         0  unknown  0  \n",
       "1  cellular   17   nov        81         1    195         2  failure  0  \n",
       "2   unknown    9   may      2016         2     -1         0  unknown  1  \n",
       "3  cellular    4   feb       489         1     -1         0  unknown  1  \n",
       "4   unknown   20   jun       492         3     -1         0  unknown  0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Firstly it changes values of column \"y\" which have \"yes\" value to 1\n",
    "#Then it changes values of column \"y\" which have \"no\" value to 0\n",
    "#Shows 5 first rows of modified data frame\n",
    "bank.loc[bank['y']=='yes','y']=1\n",
    "bank.loc[bank['y']=='no','y']=0\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.876900</td>\n",
       "      <td>1409.598800</td>\n",
       "      <td>15.688900</td>\n",
       "      <td>302.210700</td>\n",
       "      <td>2.688800</td>\n",
       "      <td>45.136100</td>\n",
       "      <td>0.691200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.214407</td>\n",
       "      <td>3110.334769</td>\n",
       "      <td>8.384709</td>\n",
       "      <td>297.427531</td>\n",
       "      <td>3.053733</td>\n",
       "      <td>104.584504</td>\n",
       "      <td>3.400059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-3372.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1519.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>81204.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3881.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age       balance           day      duration      campaign  \\\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000   \n",
       "mean      40.876900   1409.598800     15.688900    302.210700      2.688800   \n",
       "std       11.214407   3110.334769      8.384709    297.427531      3.053733   \n",
       "min       18.000000  -3372.000000      1.000000      2.000000      1.000000   \n",
       "25%       32.000000     86.000000      8.000000    116.000000      1.000000   \n",
       "50%       39.000000    485.000000     16.000000    207.000000      2.000000   \n",
       "75%       48.000000   1519.000000     21.000000    376.000000      3.000000   \n",
       "max       95.000000  81204.000000     31.000000   3881.000000     55.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  10000.000000  10000.000000  \n",
       "mean      45.136100      0.691200  \n",
       "std      104.584504      3.400059  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      854.000000    275.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gives descriptive statistics (number of observations, mean, standard deviation, minimum, maximum, \n",
    "#25th percentile, 50th percentile, 75th percentile) about each numeric column of data frame\n",
    "bank.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you've got the minimum vital commands for interacting with your dataset. \n",
    "We are going to prepare our data for fitting our models.\n",
    "\n",
    "As you may have seen, some features are categorical (i.e not real number) and thus cannot be understood by our models. The most common way to fix this is the use of one hot encoding method. For each value of a categorical feature the one encoding create a new feature which will be equal to one or zero depending on the value of the feature for the rows.\n",
    "\n",
    "For instance the marital status takes its values in \\{single,married,divorced\\}.\n",
    "The one hot encoding value of divorced is [0,0,1], it also could be [0,Ø]\n",
    "\n",
    "use : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html\n",
    "\n",
    "### Task : Apply the one hot encoding on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list of categorical columns names\n",
    "cat_columns = [\"job\",\"marital\",\"education\",\"default\",\"housing\",\"loan\",\"contact\",\"month\",\"poutcome\"]\n",
    "\n",
    "bank_transformed = bank\n",
    "for col in cat_columns:\n",
    "    \n",
    "    # get the dummies and store it in a variable\n",
    "    dummies = pd.get_dummies(bank_transformed[[col]])\n",
    "\n",
    "    # Concatenate the dummies to original dataframe\n",
    "    bank_transformed = pd.concat([bank_transformed, dummies], axis='columns')\n",
    "    \n",
    "    # we dont need all one hot encoded columns as info about n possible values is encoded in n-1 columns\n",
    "    column_to_drop = dummies.columns[0]\n",
    "    # drop the values\n",
    "    bank_transformed.drop([col, column_to_drop], axis='columns',inplace = True)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting rid of label from feature data\n",
    "y = np.asarray(bank_transformed[[\"y\"]])\n",
    "bank_transformed.drop([\"y\"], axis='columns',inplace = True)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_housemaid</th>\n",
       "      <th>job_management</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_student</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_unemployed</th>\n",
       "      <th>job_unknown</th>\n",
       "      <th>marital_married</th>\n",
       "      <th>marital_single</th>\n",
       "      <th>education_secondary</th>\n",
       "      <th>education_tertiary</th>\n",
       "      <th>education_unknown</th>\n",
       "      <th>default_yes</th>\n",
       "      <th>housing_yes</th>\n",
       "      <th>loan_yes</th>\n",
       "      <th>contact_telephone</th>\n",
       "      <th>contact_unknown</th>\n",
       "      <th>month_aug</th>\n",
       "      <th>month_dec</th>\n",
       "      <th>month_feb</th>\n",
       "      <th>month_jan</th>\n",
       "      <th>month_jul</th>\n",
       "      <th>month_jun</th>\n",
       "      <th>month_mar</th>\n",
       "      <th>month_may</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>poutcome_other</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>poutcome_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>454</td>\n",
       "      <td>14</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>1631</td>\n",
       "      <td>17</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>-197</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>-970</td>\n",
       "      <td>4</td>\n",
       "      <td>489</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>2816</td>\n",
       "      <td>20</td>\n",
       "      <td>492</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  balance  day  duration  campaign  pdays  previous  job_blue-collar  \\\n",
       "0   34      454   14        76         3     -1         0                0   \n",
       "1   44     1631   17        81         1    195         2                0   \n",
       "2   28     -197    9      2016         2     -1         0                1   \n",
       "3   52     -970    4       489         1     -1         0                0   \n",
       "4   52     2816   20       492         3     -1         0                0   \n",
       "\n",
       "   job_entrepreneur  job_housemaid  job_management  job_retired  \\\n",
       "0                 0              0               1            0   \n",
       "1                 1              0               0            0   \n",
       "2                 0              0               0            0   \n",
       "3                 0              0               1            0   \n",
       "4                 0              0               0            0   \n",
       "\n",
       "   job_self-employed  job_services  job_student  job_technician  \\\n",
       "0                  0             0            0               0   \n",
       "1                  0             0            0               0   \n",
       "2                  0             0            0               0   \n",
       "3                  0             0            0               0   \n",
       "4                  0             0            0               1   \n",
       "\n",
       "   job_unemployed  job_unknown  marital_married  marital_single  \\\n",
       "0               0            0                0               1   \n",
       "1               0            0                1               0   \n",
       "2               0            0                0               1   \n",
       "3               0            0                1               0   \n",
       "4               0            0                0               0   \n",
       "\n",
       "   education_secondary  education_tertiary  education_unknown  default_yes  \\\n",
       "0                    0                   1                  0            0   \n",
       "1                    0                   1                  0            0   \n",
       "2                    1                   0                  0            0   \n",
       "3                    0                   1                  0            0   \n",
       "4                    1                   0                  0            0   \n",
       "\n",
       "   housing_yes  loan_yes  contact_telephone  contact_unknown  month_aug  \\\n",
       "0            0         0                  0                0          1   \n",
       "1            1         0                  0                0          0   \n",
       "2            1         0                  0                1          0   \n",
       "3            1         0                  0                0          0   \n",
       "4            0         0                  0                1          0   \n",
       "\n",
       "   month_dec  month_feb  month_jan  month_jul  month_jun  month_mar  \\\n",
       "0          0          0          0          0          0          0   \n",
       "1          0          0          0          0          0          0   \n",
       "2          0          0          0          0          0          0   \n",
       "3          0          1          0          0          0          0   \n",
       "4          0          0          0          0          1          0   \n",
       "\n",
       "   month_may  month_nov  month_oct  month_sep  poutcome_other  \\\n",
       "0          0          0          0          0               0   \n",
       "1          0          1          0          0               0   \n",
       "2          1          0          0          0               0   \n",
       "3          0          0          0          0               0   \n",
       "4          0          0          0          0               0   \n",
       "\n",
       "   poutcome_success  poutcome_unknown  \n",
       "0                 0                 1  \n",
       "1                 0                 0  \n",
       "2                 0                 1  \n",
       "3                 0                 1  \n",
       "4                 0                 1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visual check\n",
    "bank_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have to rescale our numerical data in a same range otherwise some high values features will be overrepresented compared to low value features. (Some models like Ensemble models are insensitive to scale difference) \n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task : Rescale the numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(bank_transformed)\n",
    "bank_rescaled = scaler.transform(bank_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task : split your dataset between a train dataset and a test dataset. (The size of the test dataset must be 33% of the size of the dataset)\n",
    "Use https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Output must be :\n",
    "X_train : features of the train dataset\n",
    "y_train : labels of the train dataset\n",
    "X_test = features of the test dataset \n",
    "y_test = labels of the test dataset \"\"\"\n",
    "X_train, X_test, y_train, y_test = train_test_split(bank_rescaled, y, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The goal is to find the best model maximising the AUC score. \n",
    "(Here we don't use the accuracy metric because the dataset is a bit unbalanced i.e label 0 are over-represented, and it could skew the accuracy metric, so it's preferable to use AUC which is insensitive to this issue.)\n",
    "\n",
    "\n",
    "\n",
    "### Task : Try different models and choose the one returning the best AUC\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting SVM model with different kernels and calculating test performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximal test Accuracy - 0.8448484848484848 - is obtained for kernel = rbf\n",
      "Maximal test auc - 0.9089903678814172 - is obtained for kernel = rbf\n"
     ]
    }
   ],
   "source": [
    "y_train=y_train.astype('int').flatten() #flattening list as it is column vector now\n",
    "y_test=y_test.astype('int').flatten() #flattening list as it is column vector now\n",
    "plot_per_kernel_performance(X_train,X_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting logistic regression and plotting performance on test dataset for different value for regularization parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximal test Accuracy - 0.8427272727272728 - is obtained for c = 0.9595918367346938\n",
      "Maximal test auc - 0.9015435505421393 - is obtained for c = 0.030204081632653063\n"
     ]
    }
   ],
   "source": [
    "C = np.linspace(0.01,1)\n",
    "_, _ = error_c_Logistic(X_train,X_test,y_train,y_test,C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting LDA model based on different shrinkage parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximal test auc - 0.9009850819567295 - is obtained for shrinkage = 0.061224489795918366\n"
     ]
    }
   ],
   "source": [
    "test_auc = list()\n",
    "shrinks = np.linspace(0,1)\n",
    "\n",
    "for s in shrinks:\n",
    "    #fitting model on train data\n",
    "    model = LinearDiscriminantAnalysis(shrinkage = s,\n",
    "                                      solver = \"lsqr\")\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    #calculating performance score on test data\n",
    "    auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "    \n",
    "    #storing metrics value in list\n",
    "    test_auc.append(auc)\n",
    "\n",
    "plt.plot(shrinks, test_auc, label = \"AUC\")\n",
    "plt.xlabel('Shrinkage parameter')\n",
    "plt.ylabel('Performance function')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "s_max_auc = shrinks[test_auc.index(max(test_auc))]\n",
    "print(f\"Maximal test auc - {max(test_auc)} - is obtained for shrinkage = {s_max_auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting Logistic Regression with polynomial kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximal test auc - 0.9085771304185244 - is obtained for c = 0.01\n"
     ]
    }
   ],
   "source": [
    "test_auc = list()\n",
    "C = np.linspace(0.01,1,20)\n",
    "for c in C:\n",
    "    #fitting model\n",
    "    model = Logit_Kernel(C = c)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    #calculating performance score on test data\n",
    "    auc = roc_auc_score(y_test,model.predict_proba(X_test)[:,1])\n",
    "\n",
    "    #storing model performance\n",
    "    test_auc.append(auc)\n",
    "\n",
    "plt.plot(C, test_auc, label = \"AUC\")\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Performance function')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "c_max_auc = C[test_auc.index(max(test_auc))]\n",
    "print(f\"Maximal test auc - {max(test_auc)} - is obtained for c = {c_max_auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of of trained models the highest test AUC is returnd for SVM model with rbf kernel: \n",
    "\n",
    "auc $\\approx$ 0.91\n",
    "\n",
    "However after finetuning (after choosing the best parameter out of considered) all of the models performance is similar."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}