W2 - Breast Cancer Diagnosis LogReg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d608756f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function load_breast_cancer at 0x14d23a158>"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pd = import '/pandas'\n",
    "plt = import '/matplotlib/pyplot'\n",
    "mpatches = import '/matplotlib/patches'\n",
    "load_breast_cancer = import '/sklearn/datasets/load_breast_cancer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d712e6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "# Veri setini yükleme:\n",
    "cancer = load_breast_cancer!\n",
    "\n",
    "# Ortalama Textile\n",
    "x = (map (!! 1) cancer.data) |> list\n",
    "# 0: Kötü Huylu,\n",
    "# 1: İyi Huylu:\n",
    "y = cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3cb60d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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HoEX733zzLJs2LcuuuCK979Ury9q3z7Ly8ixr2XLZl1uhkIZbuNuIESt/fF9e112XaunSJa2rhddXcTtq2TLLunbNsmeeaRiutjbLjj8%2Bfb7ttqn2I45If1fKyxvmeYstsqxnz2XffjbYIMs%2B%2BujT6/75zxu2pdNOy7KTT07rvnXrLLv77rQcv/71hn1i0W2yrKyhziuvbHoa11%2Bfls0GG2TZ8OFpfF26pO3kf/932ZZvXV2WnXdems6mm6a/hccem2UdOmRZZWWWPfLIso1nbTd9epZttVVaL5/7XNqe9t47Lbc998yyjz9e8zV98skn2X/%2B85/sk08%2Baej4xhtZ1qbNmv2D0KZNmu46bs6cOdmbb76Z7b333tmRRx65WqbR5Dr/f6XOBiwuSl0AybIE9G9/%2B9vZFlts0ajbsGHDsqFDhy7zdEq5E86Zk77w7bRTlr37bkP3urosu/badKweNWrFx79gQZZtskmWbbll%2BtJQVtYQBIpfVi66KPWzcHjfeOP0peSeexqP79VXs6x//ywbNKjxF8tbbknD/fznqfubb6a/MZ/7XJq3Hj2y7I47Gv8N2njj9IW1UFj8S/3BBzd%2B37Ztw5f14pfdVf03sXv3lRu%2BRYv0ZXHR2ouv4vJe2dcGG6zZ7wpeab2uqnEVQ9C226Z/F96W27bNskcfTfvYosONGJFlc%2BemMLpw9%2BefTz%2BaLfxDVp8%2By/Zj1qLBvkePhv%2BfdlrD/w8/vOH/LVpk2fbbp2k29SPCzTc3hPEWLVJobts2HRNuvLGhv5/%2BNH3%2B1a9m2SefpONE//5Z9uKLKXyVlWXZxIlZ9qc/pcB44YWNj0WffNL42PnKKw1hsLivXXddls2f3/jYEZFlf/hDCgDrrZemUyikGv/5z3T8uvzy1F9ZWQpWO%2B2U3p9zTgpdW2yRZW%2B9lWXf/nbj4BeRwv8rryy%2Bng47LNX95JONv%2B//7ncrfnxfXo8/nub1hBNSDQMHphquuSbLvvzltJ0XQ%2B6mm6a/CXPnpmGvvjoN%2B9vfNj72f/azDfvIBx%2BkbgcemGWdOjWe//LyNJ327bNs//3TuiouhwMOWHrdjz7asGwXLGjoPnt2ln3xi2k8I0emfg45JI27Vav0w87kyVm23XZZ1rt3lm24YcNx/qGHGk/jiSfS/J1%2Betpmij75JG3/rVpl2Wuvffoy/sMfGrbvhZfThx9m2e67px8VZs789PGs7Q46KK2L555r3H3cuLSNnHLKmq9piWHtjTeybMKENfcSzrMsy7JRo0ZlZWVl2XbbbZe99dZbq2UaAnrzEqUugGRZAvpuu%2B2WnXHGGY263XTTTVlFRcUyT6eUO2Ex2L74YtOff%2B5zWbbNNis%2B/r/8JY3/4YfTl9lieCwUsuwnP0lnnfr3z7IxY1L3IUMavlB97WtNj/Pee9Pn48c3dNtuu/SlrOiCC7KsY8csq6nJsv/%2BN/X/mc%2BkGjp0aPgCWF7eEH7Ky9MX5g02SF8ct9uuIbx07do4yBQKWda374oFpCWd0Vn4bNiKhOn%2B/dOX8tXx48HCr5Ud/6InAxZ9v2iw22ijpU97dZ69XdF18WnLbHnHeeihy1fXomdKF66jGOgKhRT8Fh7P1Ven/WebbRq6FbfLtm2z7O23Fx/nrrumf088cfH1WCik9bvw/O%2B8c8P/Fz5rv%2BiyOu%2B8NM3iGe6F%2B/nb31JQi0g/1hXnd599Gm8vEel40aFD%2BiEwy7LsS19K/XftmoL8J5%2Bks4oRWXb//amf%2BfPTcaB4DPrmN1O/c%2BY0HGMWPXaefXY6llRUpHnYZ590HKmtTT9etG%2BflkXv3mmZ/fKXjbf1o45qfJzbZ5%2BGs%2BXF/eCee9L/H3ss1d25czrTWlxmgwalaU2fvvh%2BVF6eZVOnpnH/4hepW48eWbbDDst7VF9xX/1q%2BmHloovSsWrzzdPVTVmWfrCorEw/OmyxRcPfgttuS8twwIDFl9GLLzY%2BZvzpT1n20kvp/9/5TuP5v%2BqqNMz3v5%2B2q699LYXV4ufvvLPkur/ylfQjcm3t4p/NnJn%2B1qy/fvoRpFu3LNtxx7TNFf%2BkP/tsmsYFF6R/%2B/ZN2%2BGiy2bDDZuexsLL5tPsumuW7bVX05%2B98UZaVjfc8OnjWZsVt5Hf/rbpz3/wg7SNFH/wWVOWFtZYOwnozYtG4pqRadOmLXZPSvfu3aOmpiY%2B%2BeSTJoeZO3du1NTUNHqVyoMPRmyzTcSmmzb9%2BbBhEf/%2Bd8P9jisy/r590z2S77yT7u%2BMSF%2BJDj88jX/y5IgNN0z3Gy58f9yS7pXbf/90P9%2B4cen9xx%2Bn27QOP7zxdA84IN3nufHG6d7Tl15K9w326pWm9frrEZWVEX36RGy0UbpfvUuXdD/kxInpftEsS%2BN7992G/xfrP%2Bigpc97U/ewlTWxdxfvhZ0/v%2Bn7ixftL6Lpe/AnT47YfPPGdX7atJfFovfeLmn8y6p4f3NRhw6N3y8678V7nCPSvaWLWng3a9Vq5WprypZbrvw4Fl1mi85H375Lb%2Bvh9dcX77a0dgpmzUrrbdFxduuWlm%2B/fqmmL36x8XheeGHx6X3pS2l7%2B%2BST1FbCoiZMSPvvv//duHuWpf2q2C5C0ZQp6d%2BysnRfdPEe9IWHi4j485/TNBcsSPcVFwoNy%2B3VVyNeeSX9f7PNGobdYYfUvaIi3YNcHM8BB6RjQkQ6Tsyalfbpgw5K92Q/%2BGA6Fuy3X%2BqnRYu0bIrDDBuWap00qWFaDz4YMXBgw7HzwQfTuHr0SPc%2BH3dcOo5MmpSW5377NRwTHnkktZdRVdVw//Oi90EPG5aOmbNmpfef%2BUzEQw%2Bl49VOO6V19cEHEYcd1rDMzjgjTeu%2B%2Bxr2ow02SMeF2tp073REusc7Ii3fp55qvI%2BtTuPGpe3p4YfTvdX/%2BU/Dcbt9%2B3Sf9EMPpW4TJ0ZssUVaru%2B8k9b5wsf4iNRvRMQee0T075/6LXZr0SKt06LiPA8bluZ7881T2yjFY2lx2Syp7i9/ueljaIcO6d7y999P633GjHQsL/6dikjtAGy2WcSHH6a/gRtu2LBtFT34YFo2TU2juGwWHWZRCxakbWvYsKY/79MntRNR/Nu5ripuI1/%2BctOfH354w74BUCSgr%2BVGjhwZlZWV9a%2BqqqpSlwQAAEATBPRmpEePHjF9%2BvRG3aZPnx4VFRXRtm3bJoc5//zzo7q6uv715ptvrolSm7T77unM10svNf356NHpDPuKNiC5%2B%2B6pFeX3309nrj/4IHUvFCLuuCONv3//1Mru9OkRb7/dMOzC/1/YAw%2BkM/p77JHed%2BiQGhu9447G073//tRy78svRzzzTDrTVVubzsZMn57OWlZXpzN6r7ySflV/9910FnbgwNSqc/EMZLdujc9GFgqpBeulmT9/8W5NnRkvnv1q2bLh86bOoix8FrKpKxr6909npJZ0JrapaS%2BLRc/Urmyr/os0VFrf0nXRovPerl3D/5tq1Xzh3WzevJWrrSmr4rGriy6zRefjjTeWfmVCv36Ld1tSq%2BIR6Yxbbe3i45wxIy3f119PNf3v/zYezxZbLD69P/4xbW9t2zZ9xmn77dP%2Bu802jbsXCmm/atOm8fz36ZP%2BratLV6xkWdMt0n/%2B82maLVqk/S/LGpbbhhums/MRjc9qP/VU6l5TE/Hssw3juf/%2BdEyISMeJ9u0junZN%2B/CcOemz6urUAndEms7//m/DMKNHp1oXPlu/%2B%2B7pLG/x2Ln77mlc06alFrhvvjkdRzbbLC3Pv/2t4Ziw666pBfU330wti0c0/Fs0enQ6ZhZbcJ80KV3lM2VKaiF8iy3S2dg772xYZlddlY4DBx7YsB%2B99VY6LpSXp7OnERG33pr%2Bbds2XXWw8D62Ou2xR9qePvvZiH/%2BM53FLh63Z82KuOeeNI933JGW3QsvpOXaq1dqUX/hY3xE6jcinRGePDn1W%2By2YEFap0XFeR49Os33f/6TruwqHkuX8njj2GOPdPVIU8fQjz%2BO%2BMc/0rgefTRtqy1bNvydikhPFZk0KT3p4IMP0v5S3LaKdt89LZumplFcNosOs6gWLdK2NXp0059PmZKuFCj%2B7VxXFbeRpq4IikjbWXHfAKhX6mvsSSKWrZG4LbfcslG3I444QiNx/29lGolr2XLxRuJeeWX5Gok76KCGRuKKjecsPA2NxC3/SyNxa/61oo3ELe2e/VXdSNzCbTLkqZG4Aw9cexqJO/vsZWsk7uWXm0cjccW2Dpankbjf/Ka0jcQt3IBbU43Eff7z%2BW0kbrfdNBJXVGwk7tlnG3fPZSNxrLXcg968RKkLWJfNnDkze%2BaZZ7Jnnnkmi4jsiiuuyJ555pnsjf9v1fK8887Ljj766Pr%2Bi49Z%2B9a3vpVNmjQpu/baa5v1Y9a%2B%2BtXGj1n75jdX7WPW%2BvT59C/tiz5mbfDglX/M2qGHesyal9eSXnl%2BzNrSHiXW1KupfXSDDVbsMWvFBu2W5TFrX/1q049ZW/SHsR490vFpeR%2Bz9rOfpfer8jFrZ5%2B95h%2BzVnyU2Mo%2BZq34qLpSPWbtpJNW/WPWbrghLZvevVPjfyefvGKPWTv//DQdj1lbsoUfs3bQQek7xl57peW2555ZNmvWmq9JQF/3COjNSyHLsqyUZ/DXZWPHjo299tprse7HHnts3HzzzXHcccfF66%2B/HmPHjm00zFlnnRX/%2Bc9/YoMNNogLL7wwjjvuuGWeZk1NTVRWVkZ1dXVUrOi15Ctp%2BvSIX/4y4u67U4NBW24ZccopqSGflb2kOSJd4v7rX6dLyqZOTZciz5qVLgmtq0vTWH/9iHPOifjmN9PlZbW1qZ5f/zpdErjeehFHHJEaX2pqMWVZanjphhvSJYVt26ZLKefOTZf1LViQLmPcddeIu%2B5Kl7TPmpWGKytL/9bVpX/btk0NMZ17bmrc6a67Ir73vYj//rfhUtSV3UtbtYr43OdSA0KjR6fLXefNSzUsrQGwhbVtmy5zbd8%2BDV9skKusLC2vefPSZf4rq1BY%2BfltLtq0Seu41PPbokXTl/Q31ZDgwvvokuouK0uvhcdZXp4aUOzcOV1mP3t22leLDe%2B1a5e2sffeazyuvn0jBg1KjZEVGzHr3DntX6%2B9ll7LugyLjcQVX7W1qa7y8vT/QiE19tW5c6rvo4/SNIv9dewY0alTuj1lzpzUf9u2qSHITp3SfFVXp8uOt9024pJL0uXlxWX24osR116bjh0LFqTj0MyZad/ZcMOIE0%2BMOPTQJTeyOG1axI03Nhw7%2B/ZNl7JPmNBw%2B0a7dg2XtT/ySLosvmPHdMvAgQdG/O53Ebfckuahri7V2rdvOhYee2zDZe4TJqRaH3441Zhladzrr5%2BGmzYtzeu8eWn%2BevZMjeU9%2BWTDuNu0SY2Zfec7qZ5SmDgxzce4cemS7%2BJxr0WLVPNRR0WcdFJahwvLsnTp%2BC9%2BkdZbhw5pGR55ZFqG112X5rO8PM33rrumy85feSUtr4W3yQ4dIoYPj/judxdvoG9Jnnkm1f3oo2kaQ4akcRRvt8iyiL//PeL669NtCDU1DbeaVFSk/keMSLeFLMm//x1xzTVpGmVl6W/w8OERm2yyfMv4wQdTHRMnpnV%2B8MERX//6khtfXRfNnt2w782YkRptPOGEtE011cjr6jZnzpyYPHly9O/fP9osei8Ya6WlrfM8ZAMaE9DXMXZCAIB1V3MN6KecckpUV1fHbbfdVupSmh0BvXlp4kFCAAAA%2BfG9730v6la0FVpoRrTiDgAA5Fq3bt2iR48ey9z/JZdcEgMHDlx9BS2nfv36xZVXXlnqMmgGnEEHAIB13JQpi7cDsjp16dLwOMxlcdxxx8VHH30Ud9999zL1f84558Tpp5%2B%2BxM9ff/316N%2B/fzzzzDOLBfk999wzBg4cKFBTEgI6AACsw6ZMidh009QA5prSpk1qyHJ5Qvry6NChQ3To0GH1jBxWI5e4AwDAOuy999ZsOI9I01vRM/a//e1vY/3114%2B5xcfd/L9DDz00jj766IhYtZe4FwqFxc7cd%2BrUKW6%2B%2BeaIiNh7773jtNNOa/T5u%2B%2B%2BG61atYoxY8YsNr7XX389CoVCTJw4sb7bRx99FIVCodHTm1g3CegAAECz8eUvfzlqa2vjz3/%2Bc323GTNmxL333hsnnHDCGq/nxBNPjFtvvbXRDwa/%2B93vonfv3rH33nuv8Xpo3gR0AACg2Wjbtm0ceeSRMWrUqPpuv/vd76JPnz6x5557Lte4dtlll/rL4Yuvhx56aLnGcdhhh0VExJ/%2B9Kf6bjfffHMcd9xxUSgUlmtc4B50AACgWTnppJNixx13jLfffjt69%2B69woF49OjRsdlmmzXqdtRRRy3XONq0aRNHH3103HTTTXH44YfH008/Hc8//3yjM/ywrAR0AACgWdl2221jm222id/%2B9rex3377xQsvvBD33nvvco%2BnqqoqNtpoo0bd2rZt2%2Bh9oVCILMsadZs/f36j9yeeeGIMHDgw3nrrrRg1alTsvffe0bdv3yanWVaWLmJeeJyLjo91l0vcAQCAZufEE0%2BMm2%2B%2BOUaNGhVDhgyJqqqq1TKdrl27xtSpU%2Bvfv/zyyzF79uxG/Wy11Vaxww47xI033hi33nrrUu%2BF79q1a0REo3Eu3GAc6zYBHQAAaHaOPPLIeOutt%2BLGG29crY3D7b333nHNNdfEM888E0899VSccsop0bJly8X6O/HEE%2BPSSy%2BNLMviC1/4whLH17Zt29h5553j0ksvjUmTJsW4cePiggsuWG3107wI6AAAQLNTWVkZX/ziF6NDhw5x6KGHrrbpXH755VFVVRW77bZbHHnkkXHOOedEu3btFuvviCOOiBYtWsQRRxwRbdq0Weo4b7rppliwYEFsv/32ceaZZ8YPfvCD1VU%2BzUwhW/SGCtZqNTU1UVlZGdXV1VFRUVHqcgAAWIPmzJkTkydPjv79%2B9eHyClTIjbddM0%2BC71Nm4iXXoro02flxrPPPvvEFltsEVdfffWqKWwlvP766zFgwIB48sknY7vttit1OfWaWudFskH%2BaCQOAADWYX36pLD83ntrbppduqxcOP/www9j7NixMXbs2LjuuutWXWErYP78%2BfH%2B%2B%2B/HBRdcEDvvvHOuwjnNj4AOAADruD59Vv5s9pq07bbbxocffhg//vGPY9NNNy1pLY888kjstddesckmm8Qf//jHktZC8yegAwAAzcrrr79e6hLq7bnnnos9hg1WlEbiAAAAIAcEdAAAAMgBAR0AANYxLsled1jXzYuADgAA64jy8vKIiJg3b16JK2FNmT17dkREtGzZssSVsCw0EgcAAOuIFi1aRLt27eLdd9%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%2BMTTfdNNq2bRtVVVVx1llnxZw5c9ZQtQAAAKwuAnoJjR49OkaMGBEXX3xxPP3007HNNtvE0KFDY8aMGU32f%2Butt8Z5550XF198cUyaNCl%2B/etfx%2BjRo%2BN//ud/1nDlAAAArGoCegldccUVcdJJJ8Xxxx8fm2%2B%2Bedxwww3Rrl27uOmmm5rs/9FHH41dd901jjzyyOjXr1/st99%2BccQRR3zqWXcAAADyT0AvkXnz5sWECRNiyJAh9d3KyspiyJAhMX78%2BCaH2WWXXWLChAn1gfy1116L%2B%2B67Lw488MAlTmfu3LlRU1PT6AUAAED%2BtCh1Aeuq9957L2pra6N79%2B6Nunfv3j1efPHFJoc58sgj47333ovPfvazkWVZLFiwIE455ZSlXuI%2BcuTI%2BO53v7tKawcAAGDVcwa9GRk7dmz86Ec/iuuuuy6efvrpuPPOO%2BPee%2B%2BN73//%2B0sc5vzzz4/q6ur615tvvrkGKwYAAGBZOYNeIl26dIny8vKYPn16o%2B7Tp0%2BPHj16NDnMhRdeGEcffXSceOKJERGx1VZbxaxZs%2BLkk0%2BO73znO1FWtvjvLa1bt47WrVuv%2BhkAAABglXIGvURatWoV22%2B/fYwZM6a%2BW11dXYwZMyYGDx7c5DCzZ89eLISXl5dHRESWZauvWAAAAFY7Z9BLaMSIEXHsscfGDjvsEDvttFNceeWVMWvWrDj%2B%2BOMjIuKYY46J3r17x8iRIyMi4uCDD44rrrgitt122xg0aFC88sorceGFF8bBBx9cH9QBAABongT0Eho2bFi8%2B%2B67cdFFF8W0adNi4MCB8cADD9Q3HDdlypRGZ8wvuOCCKBQKccEFF8Tbb78dXbt2jYMPPjh%2B%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%2B/aJNmzYxaNCgeOKJJ5ba/0cffRTDhw%2BPnj17RuvWrWOTTTaJ%2B%2B67bw1VCwAAwOrSotQFrMtGjx4dI0aMiBtuuCEGDRoUV155ZQwdOjReeuml6Nat22L9z5s3L/bdd9/o1q1b/PGPf4zevXvHG2%2B8EZ06dVrzxQMAALBKFbIsy0pdxLpq0KBBseOOO8Y111wTERF1dXVRVVUVp59%2Bepx33nmL9X/DDTfET37yk3jxxRejZcuWKzTNmpqaqKysjOrq6qioqFip%2BgEAgOZLNsgfl7iXyLx582LChAkxZMiQ%2Bm5lZWUxZMiQGD9%2BfJPD/PnPf47BgwfH8OHDo3v37rHlllvGj370o6itrV1TZQMAALCauMS9RN57772ora2N7t27N%2BrevXv3ePHFF5sc5rXXXot//vOfcdRRR8V9990Xr7zySpx66qkxf/78uPjii5scZu7cuTF37tz69zU1NatuJgAAAFhlnEFvRurq6qJbt27xy1/%2BMrbffvsYNmxYfOc734kbbrhhicOMHDkyKisr619VVVVrsGIAAACWlYBeIl26dIny8vKYPn16o%2B7Tp0%2BPHj16NDlMz549Y5NNNony8vL6bptttllMmzYt5s2b1%2BQw559/flRXV9e/3nzzzVU3EwAAAKwyAnqJtGrVKrbffvsYM2ZMfbe6uroYM2ZMDB48uMlhdt1113jllVeirq6uvtt///vf6NmzZ7Rq1arJYVq3bh0VFRWNXgAAAOSPgF5CI0aMiBtvvDF%2B85vfxKRJk%2BIb3/hGzJo1K44//viIiDjmmGPi/PPPr%2B//G9/4RnzwwQdxxhlnxH//%2B9%2B4995740c/%2BlEMHz68VLMAAADAKqKRuBIaNmxYvPvuu3HRRRfFtGnTYuDAgfHAAw/UNxw3ZcqUKCtr%2BA2lqqoq/vrXv8ZZZ50VW2%2B9dfTu3TvOOOOMOPfcc0s1CwAAAKwinoO%2BjvGsQwAAIEI2yCOXuAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICAvgLuu%2B%2B%2B%2BOtf/7pY97/%2B9a9x//33l6AiAAAAmjsBfQWcd955UVtbu1j3LMvivPPOi//%2B979x1VVXxXPPPVeC6gAAAGiOBPQV8PLLL8fmm2%2B%2BWPfPfOYz8dxzz8U555wTDz74YBx88MElqA4AAIDmqEWpC2iOKisr47XXXot%2B/fo16v7KK69E586d489//nNMnjw5ttxyy9IUCAAAQLPjDPoKOOSQQ%2BLMM8%2BMV199tb7bK6%2B8EmeffXYceuihERHRoUOH%2BNOf/lSiCgEAAGhuClmWZaUuormprq6O/fffP5566qnYYIMNIiLirbfeit122y3uvPPO6NSpU2kLXIqampqorKyM6urqqKioKHU5AABAicgG%2BeMS9xVQWVkZjz76aPz973%2BPf//739G2bdvYeuutY/fddy91aQAAADRTzqCvY/xKBgAARMgGeeQM%2Bgr43ve%2Bt9TPL7roojVUCQAAAGsLAX0F3HXXXY3ez58/PyZPnhwtWrSIAQMGCOgAAAAsNwF9BTzzzDOLdaupqYnjjjsuvvCFL5SgIgAAAJo7j1lbRSoqKuK73/1uXHjhhaUuBQAAgGZIQF%2BFqquro7q6utRlAAAA0Ay5xH0FXH311Y3eZ1kWU6dOjVtuuSUOOOCAElUFAABAcyagr4Cf/exnjd6XlZVF165d49hjj43zzz%2B/RFUBAADQnAnoK2Dy5MmlLgEAAIC1jHvQAQAAIAecQV8Bs2bNiksvvTTGjBkTM2bMiLq6ukafv/baayWqDAAAgOZKQF8BJ554YowbNy6OPvro6NmzZxQKhVKXBAAAQDMnoK%2BA%2B%2B%2B/P%2B69997YddddS10KAAAAawn3oK%2BA9dZbLzp37lzqMgAAAFiLCOgr4Pvf/35cdNFFMXv27FKXAgAAwFrCJe4r4PLLL49XX301unfvHv369YuWLVs2%2Bvzpp58uUWUAAAA0VwL6Cjj00ENLXQIAAABrmUKWZVmpi2DNqampicrKyqiuro6KiopSlwMAAJSIbJA/zqCvhAkTJsSkSZMiImKLLbaIbbfdtsQVAQAA0FwJ6CtgxowZ8ZWvfCXGjh0bnTp1ioiIjz76KPbaa6%2B4/fbbo2vXrqUtEAAAgGZHK%2B4r4PTTT4%2BZM2fGCy%2B8EB988EF88MEH8fzzz0dNTU1885vfLHV5AAAANEPuQV8BlZWV8Y9//CN23HHHRt2feOKJ2G%2B//eKjjz4qTWHLwH0mAABAhGyQR86gr4C6urrFHq0WEdGyZcuoq6srQUUAAAA0dwL6Cth7773jjDPOiHfeeae%2B29tvvx1nnXVW7LPPPiWsDAAAgOZKQF8B11xzTdTU1ES/fv1iwIABMWDAgOjXr1/U1NTEz3/%2B81KXBwAAQDOkFfcVUFVVFU8//XT84x//iBdffDEiIjbffHNnzwEAAFhhzqAvh/Hjx8df/vKXiIgoFAqx7777RkVFRVx%2B%2BeVxxBFHxMknnxxz584tcZUAAAA0RwL6cvje974XL7zwQv375557Lk466aTYd99947zzzot77rknRo4cWcIKAQAAaK4E9OUwceLERpex33777bHTTjvFjTfeGCNGjIirr7467rjjjhJWCAAAQHMloC%2BHDz/8MLp3717/fty4cXHAAQfUv99xxx3jzTffLEVpAAAANHMC%2BnLo3r17TJ48OSIi5s2bF08//XTsvPPO9Z/PnDmzyeejAwAAwKcR0JfDgQceGOedd1489NBDcf7550e7du1it912q//82WefjQEDBpSwQgAAAJorj1lbDt///vfjsMMOiz322CM6dOgQv/nNb6JVq1b1n990002x3377lbBCAAAAmqtClmVZqYtobqqrq6NDhw5RXl7eqPsHH3wQHTp0aBTa86ampiYqKyujuro6KioqSl0OAABQIrJB/jiDvgIqKyub7N65c%2Bc1XAkAAABrC/egAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgl9i1114b/fr1izZt2sSgQYPiiSeeWKbhbr/99igUCnHooYeu3gIBAABYIwT0Eho9enSMGDEiLr744nj66adjm222iaFDh8aMGTOWOtzrr78e55xzTuy2225rqFIAAABWNwG9hK644oo46aST4vjjj4/NN988brjhhmjXrl3cdNNNSxymtrY2jjrqqPjud78bG2644RqsFgAAgNVJQC%2BRefPmxYQJE2LIkCH13crKymLIkCExfvz4JQ73ve99L7p16xZf%2B9rX1kSZAAAArCEtSl3Auuq9996L2tra6N69e6Pu3bt3jxdffLHJYR5%2B%2BOH49a9/HRMnTlzm6cydOzfmzp1b/76mpmaF6gUAAGD1cga9mZg5c2YcffTRceONN0aXLl2WebiRI0dGZWVl/auqqmo1VgkAAMCKcga9RLp06RLl5eUxffr0Rt2nT58ePXr0WKz/V199NV5//fU4%2BOCD67vV1dVFRESLFi3ipZdeigEDBiw23Pnnnx8jRoyof19TUyOkAwAA5JCAXiKtWrWK7bffPsaMGVP/qLS6uroYM2ZMnHbaaYv1/5nPfCaee%2B65Rt0uuOCCmDlzZlx11VVLDN2tW7eO1q1br/L6AQAAWLUE9BIaMWJEHHvssbHDDjvETjvtFFdeeWXMmjUrjj/%2B%2BIiIOOaYY6J3794xcuTIaNOmTWy55ZaNhu/UqVNExGLdAQAAaH4E9BIaNmxYvPvuu3HRRRfFtGnTYuDAgfHAAw/UNxw3ZcqUKCvTTAAAAMC6oJBlWVbqIlhzampqorKyMqqrq6OioqLU5QAAACUiG%2BSP07MAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICADgAAADkgoAMAAEAOCOgAAACQAwI6AAAA5ICAXmLXXntt9OvXL9q0aRODBg2KJ554Yon93njjjbHbbrvFeuutF%2Butt14MGTJkqf0DAADQfAjoJTR69OgYMWJEXHzxxfH000/HNttsE0OHDo0ZM2Y02f/YsWPjiCOOiH/9618xfvz4qKqqiv322y/efvvtNVw5AAAAq1ohy7Ks1EWsqwYNGhQ77rhjXHPNNRERUVdXF1VVVXH66afHeeed96nD19bWxnrrrRfXXHNNHHPMMcs0zZqamqisrIzq6uqoqKhYqfoBAIDmSzbIH2fQS2TevHkxYcKEGDJkSH23srKyGDJkSIwfP36ZxjF79uyYP39%2BdO7ceYn9zJ07N2pqahq9AAAAyB8BvUTee%2B%2B9qK2tje7duzfq3r1795g2bdoyjePcc8%2BNXr16NQr5ixo5cmRUVlbWv6qqqlaqbgAAAFYPAb2ZuvTSS%2BP222%2BPu%2B66K9q0abPE/s4///yorq6uf7355ptrsEoAAACWVYtSF7Cu6tKlS5SXl8f06dMbdZ8%2BfXr06NFjqcP%2B9Kc/jUsvvTT%2B8Y9/xNZbb73Uflu3bh2tW7de6XoBAABYvZxBL5FWrVrF9ttvH2PGjKnvVldXF2PGjInBgwcvcbjLLrssvv/978cDDzwQO%2Byww5ooFQAAgDXAGfQSGjFiRBx77LGxww47xE477RRXXnllzJo1K44//viIiDjmmGOid%2B/eMXLkyIiI%2BPGPfxwXXXRR3HrrrdGvX7/6e9U7dOgQHTp0KNl8AAAAsPIE9BIaNmxYvPvuu3HRRRfFtGnTYuDAgfHAAw/UNxw3ZcqUKCtruMjh%2Buuvj3nz5sWXvvSlRuO5%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%2BGzF7dkTr1hEDBkQcf3zEV78a0a7dso1r9uyI3/424uqrI954IyLLInr1ivj61yNOPjmisrKh348/jrj55ohrrol4883Ubf31I3r3jpgxI6K6OnWrrIzo1y9i3rzUvVOniC9/OaJnz4jbbot4%2BeXUz7BhEcceGzF2bMSvfhUxeXJE584Ru%2B4aMW1axCOPpOHnzo0oK4vo3j1ixx0jXn89TX/evIi2bSNatIh4//30PiLNe/v2ER9%2BmJZVeXlEhw4RbdpEzJqV5rm2Ns1ry5YRn/lM2kaefz5Nd%2BbMtB2XlaVlseGGEa%2B%2Bmj6bOzcN16JFxA47RFx3XcS220ZMnBhx6aURf/tbWg51dcu2/Nu1S8tv6tQ07nbtInr0SP9///2GWguFiFat0v%2BLtS9N2f//1luso1BY%2BjBt20Z06xbx7rtpmp%2BmRYtUT7HfQiEt44ED03KaPHnZ6lyW2ppSXp7mbWnDtWoVUVUVsdFG6Rg1b15atnV1abv66KOIDz6ImD9/6dNq3z5iyJCITz6JmDAhzXO7dhE77xzx2c%2Bm7Xf8%2BLTdLKmeqqqIM86I2G%2B/iFtvjbjvvlTP9ttHHHBAxPXXRzz%2BeMSCBWl5lJenf4vrr6wsLc%2BysrTss6yh7ixrmG7x85Yt07606aaprg8/TDW3aZPmuU2btJ%2BVlUU8/HDa3rbdNh0nP/vZpuchy9Jx56KL0j4cEdG3b8Q%2B%2B0S89FLqVlOTllNxm62oiNhiizSdf/87Lbvi/lgoNF72LVum/e2ww9Kxp3v3hs/%2B%2B9%2BIa6%2BN%2BOc/0zLZZZeI4cPTvvv736dj2LRpafhjj03zfsUVEc88k5ZzZWX6e1QoRDz3XPp3r70axtGU6uqIUaMibr89Lb8BAyJOPDHikEPSPCxpGd17bzo2//e/ER07pmPf176WjpWLeuONtO7vv79hexg%2BPGLw4KbHvzSzZkXcckvE736X9uOqqogTTkjTb9my6fm76aaI0aMb5u%2Bkk9L8la2Bc0Vz56Zle/PNaf/s0SPimGMijjoqbZ%2Brypw5TW8jRxyR/m6ybsuyiDFj0nfG559Px8lDD03fgRY%2BBkFGSV1zzTVZ3759s9atW2c77bRT9vjjjy%2B1/zvuuCPbdNNNs9atW2dbbrlldu%2B99y7X9Kqrq7OIyKqrq1em7NJ59NEs69Qpyzp0yLJjj82yM8/Mss98Jn1l/Pa3s6yubs3W8%2BCDWVZRkWUdO2bZccdl2RlnZNkmm6R6vvOdNVvL6jB3bpYdckian44d079du2ZZWVmWtWuX3g8cmGXvvvvp43r33Szbeuvi1/ss69kzy/r0Sf8vFLKsX78se%2B211O/UqVm22WYN/fbundZ7QzzIslat0mvhbnvskWVf%2BlIaX0SWbbNNlo0YkWVf/nLqt23b1H3XXVP3Yj3F/iPSvC08zkIhy1q0SOt54e7l5U33u/D7pb0WHV%2BxtqZeC4/3yCPT%2B%2BWZ1qKvsrIs69ZtxYf3%2BvR1u%2BmmDe9btlyx8ZSXZ9mGG6btb0XWd6GQ9tsTTsiyb34zy7p3X/3zXig0HJMjsqxLlyzbd9%2BG90OHpuPkxhun9xdeuPixoq4uy44%2BumF8m27aeHkWjz2LTnfh9y1aLL5PtWjRsFyLy6ZNm3RMe%2BaZNO3//d90rOjSJcu%2B/vUsGz48yzbYIA3Xt28abv/9s%2Bzss7NsyJDF1/smmzSu5aCDsuyUU9I0WrbMstGjF5/fyZOzrH//9PkXv5iOTYMHp%2BEPPTTL5s1bfJgFC7Lsq19N/Wy/fRpm2LAsa906HS9ffLFx/2PGZFn79llWWdmwPQwYkIb/wQ8%2B/fi9sOnTs2zLLdNx5OCD07T32iuNa6%2B9smzWrMb9v/ZaOr63bJmOzyNGZNnOO6f%2BDzus6flblaqrG5bnvvumdbf//mk97bBDln3wwaqZzgcfpPEtvI0Ut/2dd051sO6qq8uy009P28MWW2TZWWel41y7dlm2/vpZ9tRTJSut2WeDtVCUuoB12e233561atUqu%2Bmmm7IXXnghO%2Bmkk7JOnTpl06dPb7L/Rx55JCsvL88uu%2Byy7D//%2BU92wQUXZC1btsyee%2B65ZZ5ms94JP/44fcn57Gcb/0Gtq8uyyy9PB72mvvysLtXVWda5c5btuWeWffRRQ/fa2iy79NJUz513rrl6VocLL0xfqvbbL/0o8tBDqfurr6YvlFtumb7Ifv7znz6uQw5Jf4hatMiyu%2B9u6P7ww%2BmLY0VF%2BqJZV5e%2B1HTokKZ9//1Z9vjjDV96il%2Bwd901yy65JHUvL09fpiOy7MQT0xfHnj3TjwfFH22GD0%2Bf9%2B%2Bfuj34YHp/%2BOEN43zllbSdVVambsUfAI46Kn2RL37p3nLLhv8v/CNBMXQXg0Dx9YMfNNRenFZE2p4/%2BijLLrggvd9xx4Z%2Btt02fVb8cWGffRoHjWJo22mnTw9N663XdPcf/rBxmPjpT5ccKrfaasVDW4cO6d/ijzxLC3fF/6%2B/ftP9tGqV9vOmuq9ofZ9Wy6e9Fl3fxWD4q181BMtifU3V2bFjCs%2BLTnODDVJ4ueKKxt3btFn8x6Hia4stGte19dZpe//gg8WHOfXU9O/CgbdNmzRccRstK2uouTh8cX67dWtYp8UfjYqh77TTsuzpp9PxoWXLLNt997QNd%2ByYfqyrrc2ykSNTvwsfD7Isy37zm9S9V68se%2Bed1O2UU9Jy7dq1odaqqhT8unTJsr33bqivuB%2B2b59%2ByC3OS0T6caBjxxQM%2B/TJskGDUqiqqsqyl15K8zpsWJZ98klDPfPnZ9lGG6Xhf/Obhu4//3nDeDfbLIXmp59Oday3Xlqubdtm2dtvZ9mcOenHtZYts%2BzllxvGUVeX9vsNN0zH1YX96U%2Bp/4suWvx4evnlaTq33tq4%2B1tvZdnmm6cfSWprU7f330/LZL/9sqympqHf2tp0DI3Isvvua/q43ZQDDsiyHj2y7PnnG3cfOzbN86mnNp6/7bdP20XxB9iiu%2B9O29Illyz7tFfE0UenY/pjjzXu/tRT6W/44Yevmukcfnga36JB67HH0vSPPnrVTIfmadSotK9dd13jk0nvvpuOAb16peNECTTrbLCWilIXsC7baaedsuHDh9e/r62tzXr16pWNHDmyyf4PP/zw7KCDDmrUbdCgQdnXv/71ZZ5ms94Jf/nL9AVw8uSmP99nn/Qr%2BZpyzTXpi99bbzX9%2BW67pS%2BlzdUnn6SQ9PWvp7Myl17a%2BPN7701/bM4/P62XV15Z8rhefbXhi/Nppy3%2B%2BaWXNgTC3/62IeSce276/KtfTV/wLrkk9VcMy%2BuvnwJ5ZWW6gmLzzdNnp5ySzhhFZNm//pVls2enL05f/nLq9s9/pjNVm2%2BezugUv2Q//nhDcC/W07Zt%2Bn9ZWfrC37dvCibF0Ps//9M49Cx6lnLo0BTSbr45vd9884Ygsdlmaf6OPDKF2IUD7EYbpc8%2B/jgtt/POWzw0F%2BcnIoWMRcPannsuPWh%2B5zuNw%2BhxxzXUtt9%2BjfstniErBrU99mh4/5WvNA5BTb3atVt6LR07NoSgiBS8Fh5nWVnD8MUzmqvztaQAvHAdhUK6SmPRfooB9vHHG8bTqlXa5hYetvj/9u3Tv126LD6uO%2B5I28nmmzd0W/jHi2IYLZ61bteu4Qem4n4yblw6W1OsrU2btA0XryB5%2B%2B2GWrbbLv1b/BGqU6eGHxyKy33hq0d%2B8YuG%2BTnyyDTu8vI0veK2HZFljzySvoi2bp1lP/5xw77/2c%2Bm7XRhxZD/9NPp/QcfpPH%2B8IfpjHRxeRbnrRjoF72ypLIy1X/OOQ3L4sors%2BwnP0nDF4cr/uBz8MGp/48/blzPG2%2Bk%2BV1//XQsyrIUbjfaKAX77t3Tcp06NcuOPz4F/8cea1gHF1/c%2BJg6YkTDuB95JPX3wANNHzu/%2Bc30o8TCX9wXLEjHoeOOa3qYRx9N4yyG7p/%2BNM1vUz/%2BF38g2G%2B/pse1qBdfTOO%2B5ZamP//e99I2%2BOGH6f1DD6X%2B//a3pvs/7bS0Lc6du2zTX15Tp6Z187OfNf35ddeldTtlyspNZ8qUNJ7rr2/685/9LB2/p05duenQPNXVpb8VBx/c9OeTJqX95He/W6NlFTXrbLCW0khcicybNy8mTJgQQ4YMqe9WVlYWQ4YMifHjxzc5zPjx4xv1HxExdOjQJfYfETF37tyoqalp9Gq2Hnww3YfZr1/Tnw8blu7NLN4bvCbq2XXXdE/vkup56KFlvz84byZNSvclb7JJun/v8MMbf77//umez7Zt09fhhx9e8riKn9XUpOWyqGHD0v2hHTpE/PGP6Z7LTz5pmOa4cRFf%2BlJa5nvske7za9ky1XfUUREHH5w%2B23vv9Nnhh6d7Prt2Td1feCHdC3v22Q3dHnww3S85fnzDfeTF7p07p3sq27WL%2BNznUm11dem%2By69%2BNd1TXNyXZsyI6NKl4T7RsrI0fNG%2B%2B6Z7Zh9%2BOI2vXbuGbWLSpHSv/bhx6X7hmTPT/aqVlRGvvZb6ad8%2B1fDQQw33dhbv2dxxx4bpNNUOwDbbpHVTtOi9nv/7v%2BnfYj9//WtDbcX7fiPS/dVPPtkwj3V1ETvt1PB5ly6ffn/lsGGNa1nUoEERb7%2Bd/l8opHv/F9aqVbpnNiLdQ9q1a8NnxbpatWo8TItPaWZl4XtlF73Pd9H9tm3bhu4DBjQMXyik/7dv39BtwYK0rG%2B7LfXfqlU6Lu28c8MyKMbIiLRd9e0b8d57jWtr2TK1MzBpUsR666XuhULa7iPS/lJTk4Yt3r84e3a6vz8i7QvrrZe2r7/9LXXbb7/U/cMPI158MbV50K5dQy0vvJCm8dZb6f1HH6V9MSLda9y9e7rne6ONUreXXmqYn%2B22S%2BPebLO0vUY0DPvKK2k72XvvtI8VDRuW3heX9%2BzZqQ2G7t3TfeoREU891bBfP/VU6jZvXprP3XZrOE5kWWqXoLidV1en%2Bg84IA1fKKRpDRvWcK94u3Zpu9tii4gnnkj7WnFdFj36aKrvyCMbap82Lc1TTU06Bi1YEPHYY%2BnzL30pbc8DBqTjyLhxaZg2bSI%2B//mG9xGp/06d0nGiKcOGpeU%2BaVJDtylT0v3kix6Ti3beOU23WOuDD0bsuWfDdrGwQqFhHSyLhx5Kw3zpS0uud/bshvU0blzaBvfZZ8n9z5iRtsXV4bHH0rpp6u9Ocfp1dakNkpXxyCNpPEtaJ8W/cY89tnLToXmaOTO1i7Gk7fAzn0l/rxc%2BNrBOE9BL5L333ova2trovkijEN27d49p06Y1Ocy0adOWq/%2BIiJEjR0ZlZWX9q6qqauWLB2DttrQfU8g/6w%2Bg2RLQ13Lnn39%2BVFdX17/eLLaG3Rztvnv69XnRM2tFo0en1mgXPYu2Out55JGGM01N1bPbbmumhdrVYbPN0hmv//43nR29447Gnz/wQEMryoXCkltjjmj4rKIiLZdFjR6dzhZ%2B/HE6M1Nbm85sFae5xx7pzPruu6dfmNu0SWcjunRJLebec0/67J//TJ/dcUfEv/6Vzjztvns6O9a5c8Tllzd023331N/gwemMz6xZDd0/%2BCC13D57dmqdv2XLtB7bt0%2BtFnfrluYlIv3/vfdSzRHpLMoHHzTM29//nn4d/%2Bxn0/hmz27YJjbbLJ0F3WOPdIazY8f0xbq6Op3ZjEjL5C9/SdtSsRXq4tnGJ59smE5TraH/%2B98NZ3gXHq7oi19M/xb7GTq0obaNN27ob968dLa%2BOI9lZelsY9F776WrLJZm9OjGtSzq8ccbrkbJssWvlJk3L7VqHpFaRn733YbPinUtevXMggVLr2nhVr2L4yhadL8tngkuK0tneIvDL3wWvNitRYu0rI84IvU/b146Lj32WMMyKBQan31/4420PS9c2/z56Yz3ZpulM94RaXrFFro//jhth2%2B8ETF9eurWrl06IxmR9oUPP0zb1377pW5/%2B1vqvt56abt87bVUe7GWLbZI0yj%2BmNupU8PVA127pumUl6ezxxGp5fbi/Dz9dBr3pEkNT7EoDrvRRmmdjRmT9rGi0aPT%2B%2BLybtcunXmePj21ih6RnmDQtm3aX4tXUbRqlebzoYcajhOFQnrCQXE7r6xM9d9/f6ory9K0Ro9Ow1dXp/1mgw3SlQM77ZT2teK6LCq2QH/rrQ219%2BiR5qmiIh2DWrRIZ6533z0dqx5/PG0nb77Z8ISRTz6J%2BNOfGj9xZPfd01n%2Bv/89mjR6dFrum23W0K1Pn3TVxKLH5KLHHkt/l4q17r57av2/uF0sLMsa1sGy2G23NMwf/7jketu1S%2BssIs3rhx%2Bm9b6k/rt1W3Lr9itr553Tumnq705x%2BmVlaR2vjOI2sqR1Uvwbt/POKzcdmqeOHdMZ8iVthy%2B%2BmP5el%2BppRORPqa%2BxX1fNnTs3Ky8vz%2B66665G3Y855pjs80tocKuqqir72SL3UV100UXZ1ltvvczTbdb3mWgkbs1bUiNxr7yyahuJa9du3W0k7sMPNRJXfGkkrnH3YiNxxeNb8dWcGolbf/20Le22W2rIbVU2Etenz6ppJG777Rs3Enf44au3kbiF2%2BtYFY3ELdzg1NIaidt331XbSNyiDdTmvZG48eMbd9dIHGtSsZG4a69tupG43r01Eke9KHUB67KddtopO22hBrNqa2uz3r17L7WRuM997nONug0ePHjdaSQuy9If2OJj1o45Jj1mrfgF%2BNxz1/xj1h56aPHHrBUfH3TBBWu2ltVhVT9mbeEGtXr2TF%2BKi1/ul/aYtV69lu0xa3vumRriKn5R33rrhsestWzZEJx22SV1L4bOUjxmbdGw6jFra89rVT5mrX//lX/M2vHHr9nHrC087126NH4c2X77Ldtj1o45pvH4SvGYtZNPTj/s9e6dhlv4MWsjRiz%2BmLWOHRd/zNqBB6YfDz/tMWvFx%2BkVH7NWfAzZsjxmbbvtlu0xax06NH7M2oYbpuFX5jFrn/tcmvaee6ZxLekxa4s%2BRq6Uj1kbMWL1P2Zt6NA0HY9Zo6ipx6x99avpeNali8es0Ughy7KstOfw112jR4%2BOY489Nn7xi1/ETjvtFFdeeWXccccd8eKLL0b37t3jmGOOid69e8fIkSMjIuLRRx%2BNPfbYIy699NI46KCD4vbbb48f/ehH8fTTT8eWW265TNOsqamJysrKqK6ujoriJbrNzTvvRPziFxF//nNq%2BGebbSK%2B8Y3SXRr01lupnnvuSZf5brttxKmnLv2S7%2BaktjZdlvnLX0Y8%2B2y6/LN163QZ6gknpAaSmmqgrCmzZ0fcckvEVVelho6yLKJnz4hTTok46aR0SWrRrFkRN98c8fOfp8tEI9IlwL16NTTSlmVpmH790mXE776b3h9%2BeLoE9bbb0qW4lZWpcZZjj02Xvv/61%2BnS3s6d03qaNi014jZjRlqHZWWpkaodd0yXD0%2BZksbftm26XPL99xsupy42MPfhh2lZlZeny9bbtEnzMHt26p5l6RLHzTZLDVk991ya7syZaTsuK0uXeG%2B4Yap52rRUS5alae6wQ8T116dhJ06M%2BPGP0%2BXKH3207A0RtmuXpjF1asP89OyZpv/BB6ne2tp0qXCrVun/xdqXpnh5crGOQmHpw7Rt23BrwKKXEzelRYu0zRX7LRTSMh44MC2n119Pl7Mvy5%2BzT6utKeXlad6WNlyrVunS4402So2OzZuXlm1tbdquPvoobSOf1ohlhw4RQ4ak7ebpp9M8t2%2BfGh3bbbe0/Y4fn7abpuopFNIl22eemRoeu%2B22iHvvTdPdYYfUuOMNNzQ0nlUopPkrK2t8C0Ntbfq3Zcs078XbAYrRs9hfixapn86d02XKM2embaldu7QPfPBB%2BnfXXdN0Hnqo4Tg5fPiSLy3Osoi774646KK0P2RZ2s/32SddDvryyw232BRr7dgxYsst0/8nTkzLsEWLtPwKhYZ5KBRSzT17pts8TjqpoZG9iHRbz7XXpltm6uoidtkl4rTT0uX8t94a8ZvfpO2uV6%2BI445L837FFWl9zZuXLqsvXrb/7LNpenvtleZ3SZdyV1en491tt6VtZcMNI048MeLQQ5d8m1SWRdx3Xzo2v/RSutz%2By1%2BO%2BNrXGjdUWTRlSjqG3Hdfw/Zw6qnpNp/lNWtWut3nllvScbdPn/T34Etfatz44sLzN2pUxO23p/kbMCDN3yGHrJnbwObOTZcXjxqVvkP06JH%2BHhx5ZNo%2BV5U5c9I28tvfpuNscRv5ylc%2BvSFN1n5Zlo4rN9wQ8fzz6Th56KERJ5/c%2BBi0hq0V2WAtI6CX2DXXXBM/%2BclPYtq0aTFw4MC4%2BuqrY9CgQRERseeee0a/fv3i5ptvru//D3/4Q1xwwQXx%2Buuvx8YbbxyXXXZZHHjggcs8PTshAAAQIRvkkYC%2BjrETAgAAEbJBHjXT5qUBAABg7SKgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkgIAOAAAAOSCgAwAAQA4I6AAAAJADAjoAAADkQItSF8CalWVZRETU1NSUuBIAAKCUipmgmBEoPQF9HTNz5syIiKiqqipxJQAAQB7MnDkzKisrS10GEVHI/FyyTqmrq4t33nknOnbsGIVCodTlLFFNTU1UVVXFm2%2B%2BGRUVFaUuh1XIul17WbdrN%2Bt37WXdrr2s27XXqlq3WZbFzJkzo1evXlFW5u7nPHAGfR1TVlYWG2ywQanLWGYVFRX%2BoKylrNu1l3W7drN%2B117W7drLul17rYp168x5vviZBAAAAHJAQAcAAIAcENDJpdatW8fFF18crVu3LnUprGLW7drLul27Wb9rL%2Bt27WXdrr2s27WXRuIAAAAgB5xBBwAAgBwQ0AEAACAHBHQAAADIAQEdAAAAckBAp6QefPDBOPjgg6NXr15RKBTi7rvvbvR5lmVx0UUXRc%2BePaNt27YxZMiQePnll0tTLMvl09btcccdF4VCodFr//33L02xLJeRI0fGjjvuGB07doxu3brFoYceGi%2B99FKjfubMmRPDhw%2BP9ddfPzp06BBf/OIXY/r06SWqmGW1LOt2zz33XGzfPeWUU0pUMcvq%2Buuvj6233joqKiqioqIiBg8eHPfff3/95/bZ5uvT1q19du1x6aWXRqFQiDPPPLO%2Bm3137SOgU1KzZs2KbbbZJq699tomP7/sssvi6quvjhtuuCEef/zxaN%2B%2BfQwdOjTmzJmzhitleX3auo2I2H///WPq1Kn1r9tuu20NVsiKGjduXAwfPjwee%2Byx%2BPvf/x7z58%2BP/fbbL2bNmlXfz1lnnRX33HNP/OEPf4hx48bFO%2B%2B8E4cddlgJq2ZZLMu6jYg46aSTGu27l112WYkqZlltsMEGcemll8aECRPiqaeeir333jsOOeSQeOGFFyLCPtucfdq6jbDPrg2efPLJ%2BMUvfhFbb711o%2B723bVQBjkREdldd91V/76uri7r0aNH9pOf/KS%2B20cffZS1bt06u%2B2220pQIStq0XWbZVl27LHHZoccckhJ6mHVmjFjRhYR2bhx47IsS/tpy5Ytsz/84Q/1/UyaNCmLiGz8%2BPGlKpMVsOi6zbIs22OPPbIzzjijdEWxyqy33nrZr371K/vsWqi4brPMPrs2mDlzZrbxxhtnf//73xutT/vu2skZdHJr8uTJMW3atBgyZEh9t8rKyhg0aFCMHz%2B%2BhJWxqowdOza6desWm266aXzjG9%2BI999/v9QlsQKqq6sjIqJz584RETFhwoSYP39%2Bo333M5/5TPTp08e%2B28wsum6Lfv/730eXLl1iyy23jPPPPz9mz55divJYQbW1tXH77bfHrFmzYvDgwfbZtcii67bIPtu8DR8%2BPA466KBG%2B2iEv7drqxalLgCWZNq0aRER0b1790bdu3fvXv8Zzdf%2B%2B%2B8fhx12WPTv3z9effXV%2BJ//%2BZ844IADYvz48VFeXl7q8lhGdXV1ceaZZ8auu%2B4aW265ZUSkfbdVq1bRqVOnRv3ad5uXptZtRMSRRx4Zffv2jV69esWzzz4b5557brz00ktx5513lrBalsVzzz0XgwcPjjlz5kSHDh3irrvuis033zwmTpxon23mlrRuI%2Byzzd3tt98eTz/9dDz55JOLfebv7dpJQAdK4itf%2BUr9/7faaqvYeuutY8CAATF27NjYZ599SlgZy2P48OHx/PPPx8MPP1zqUljFlrRuTz755Pr/b7XVVtGzZ8/YZ5994tVXX40BAwas6TJZDptuumlMnDgxqqur449//GMce%2ByxMW7cuFKXxSqwpHW7%2Beab22ebsTfffDPOOOOM%2BPvf/x5t2rQpdTmsIS5xJ7d69OgREbFYS5TTp0%2Bv/4y1x4YbbhhdunSJV155pdSlsIxOO%2B20%2BMtf/hL/%2Bte/YoMNNqjv3qNHj5g3b1589NFHjfq37zYfS1q3TRk0aFBEhH23GWjVqlVstNFGsf3228fIkSNjm222iauuuso%2BuxZY0rptin22%2BZgwYULMmDEjtttuu2jRokW0aNEixo0bF1dffXW0aNEiunfvbt9dCwno5Fb//v2jR48eMWbMmPpuNTU18fjjjze6r4q1w1tvvRXvv/9%2B9OzZs9Sl8CmyLIvTTjst7rrrrvjnP/8Z/fv3b/T59ttvHy1btmy077700ksxZcoU%2B27Ofdq6bcrEiRMjIuy7zVBdXV3MnTvXPrsWKq7bpthnm4999tknnnvuuZg4cWL9a4cddoijjjqq/v/23bWPS9wpqY8//rjRL7iTJ0%2BOiRMnRufOnaNPnz5x5plnxg9%2B8IPYeOONo3///nHhhRdGr1694tBDDy1d0SyTpa3bzp07x3e/%2B9344he/GD169IhXX301vv3tb8dGG20UQ4cOLWHVLIvhw4fHrbfeGn/605%2BiY8eO9fe5VVZWRtu2baOysjK%2B9rWvxYgRI6Jz585RUVERp59%2BegwePDh23nnnElfP0nzaun311Vfj1ltvjQMPPDDWX3/9ePbZZ%2BOss86K3XfffbFH/5Av559/fhxwwAHRp0%2BfmDlzZtx6660xduzY%2BOtf/2qfbeaWtm7ts81bx44dG7UBEhHRvn37WH/99eu723fXQqVuRp5127/%2B9a8sIhZ7HXvssVmWpUetXXjhhVn37t2z1q1bZ/vss0/20ksvlbZolsnS1u3s2bOz/fbbL%2BvatWvWsmXLrG/fvtlJJ52UTZs2rdRlswyaWq8RkY0aNaq%2Bn08%2B%2BSQ79dRTs/XWWy9r165d9oUvfCGbOnVq6YpmmXzaup0yZUq2%2B%2B67Z507d85at26dbbTRRtm3vvWtrLq6urSF86lOOOGErG/fvlmrVq2yrl27Zvvss0/2t7/9rf5z%2B2zztbR1a59d%2Byz62Dz77tqnkGVZtiZ/EAAAAAAW5x50AAAAyAEBHQAAAHJAQAcAAIAcENABAAAgBwR0AAAAyAEBHQAAAHJAQAcAAIAcENABIAcKhULcfffdpS4DACghAR0AFvLmm2/GCSecEL169YpWrVpF375944wzzoj333//U4d9/fXXo1AoxMSJE1d/oWvA2LFjo1AoLPU1duzYFR7/zTffHJ06dVpl9QJAc9ei1AUAQF689tprMXjw4Nhkk03itttui/79%2B8cLL7wQ3/rWt%2BL%2B%2B%2B%2BPxx57LDp37tzksPPmzVvD1a5%2Bu%2ByyS0ydOrX%2B/RlnnBE1NTUxatSo%2Bm5LWh5r2vz586Nly5alLgMAVooz6ADw/4YPHx6tWrWKv/3tb7HHHntEnz594oADDoh//OMf8fbbb8d3vvOd%2Bn779esX3//%2B9%2BOYY46JioqKOPnkk6N///4REbHttttGoVCIPffcMyIinnzyydh3332jS5cuUVlZGXvssUc8/fTTS63l3HPPjU022STatWsXG264YVx44YUxf/78%2Bs8vueSSGDhwYNx0003Rp0%2Bf6NChQ5x66qlRW1sbl112WfTo0SO6desWP/zhDxuN94orroitttoq2rdvH1VVVXHqqafGxx9/3GQNrVq1ih49etS/2rZtG61bt65/v95668X//M//RO/evaN9%2B/YxaNCg%2BjPqc%2BbMiS222CJOPvnk%2BvG9%2Buqr0bFjx7jpppti7Nixcfzxx0d1dXX92fhLLrkkIpq%2B3L9Tp05x8803R0TDlQqjR4%2BOPfbYI9q0aRO///3vIyLiV7/6VWy22WbRpk2b%2BMxnPhPXXXfdUpczAOSJgA4AEfHBBx/EX//61zj11FOjbdu2jT7r0aNHHHXUUTF69OjIsqy%2B%2B09/%2BtPYZptt4plnnokLL7wwnnjiiYiI%2BMc//hFTp06NO%2B%2B8MyIiZs6cGccee2w8/PDD8dhjj8XGG28cBx54YMycOXOJ9XTs2DFuvvnm%2BM9//hNXXXVV3HjjjfGzn/2sUT%2Bvvvpq3H///fHAAw/EbbfdFr/%2B9a/joIMOirfeeivGjRsXP/7xj%2BOCCy6Ixx9/vH6YsrKyuPrqq%2BOFF16I3/zmN/HPf/4zvv3tb6/QMjvttNNi/Pjxcfvtt8ezzz4bX/7yl2P//fePl19%2BuT40/%2BY3v4k//elPUVtbG1/96ldj3333jRNOOCF22WWXuPLKK6OioiKmTp0aU6dOjXPOOWe5pn/eeefFGWecEZMmTYqhQ4fG73//%2B7jooovihz/8YUyaNCl%2B9KMfxYUXXhi/%2Bc1vVmj%2BAGCNywCA7LHHHssiIrvrrrua/PyKK67IIiKbPn16lmVZ1rdv3%2BzQQw9t1M/kyZOziMieeeaZpU6rtrY269ixY3bPPffUd1vatLMsy37yk59k22%2B/ff37iy%2B%2BOGvXrl1WU1NT323o0KFZv379stra2vpum266aTZy5MgljvcPf/hDtv766y%2B13qJjjz02O%2BSQQ7Isy7I33ngjKy8vz95%2B%2B%2B1G/eyzzz7Z%2BeefX//%2Bsssuy7p06ZKddtppWc%2BePbP33nuv/rNRo0ZllZWVi02nqWVRWVmZjRo1KsuyhuV85ZVXNupnwIAB2a233tqo2/e///1s8ODByzR/AFBq7kEHgIVkC50h/zQ77LDDMvU3ffr0uOCCC2Ls2LExY8aMqK2tjdmzZ8eUKVOWOMzo0aPj6quvjldffTU%2B/vjjWLBgQVRUVDTqp1%2B/ftGxY8f69927d4/y8vIoKytr1G3GjBn17//xj3/EyJEj48UXX4yamppYsGBBzJkzJ2bPnh3t2rVb1lmP5557Lmpra2OTTTZp1H3u3Lmx/vrr178/%2B%2Byz4%2B67745rrrkm7r///kafrayFl/%2BsWbPi1Vdfja997Wtx0kkn1XdfsGBBVFZWrrJpAsDqJKADQERstNFGUSgUYtKkSfGFL3xhsc8nTZoU6623XnTt2rW%2BW/v27Zdp3Mcee2y8//77cdVVV0Xfvn2jdevWMXjw4CU2LDd%2B/Pg46qij4rvf/W4MHTo0Kisr4/bbb4/LL7%2B8UX%2BLNopWKBSa7FZXVxcR6d7tz33uc/GNb3wjfvjDH0bnzp3j4Ycfjq997Wsxb9685QroH3/8cZSXl8eECROivLy80WcdOnSo//%2BMGTPiv//9b5SXl8fLL78c%2B%2B%2B//6eOu1AoLPZDycL33xctvPyL99HfeOONMWjQoEb9LVofAOSVgA4AEbH%2B%2BuvHvvvuG9ddd12cddZZje5DnzZtWvz%2B97%2BPY445JgqFwhLH0apVq4iIqK2tbdT9kUceieuuuy4OPPDAiEiPcnvvvfeWOJ5HH300%2Bvbt26hRujfeeGOF5mthEyZMiLq6urj88svrz7LfcccdKzSubbfdNmpra2PGjBmx2267LbG/E044Ibbaaqv6M9tDhgyJzTbbLCLS8lp0WUVEdO3atVHr8S%2B//HLMnj17qfV07949evXqFa%2B99locddRRKzRPAFBqAjoA/L9rrrkmdtlllxg6dGj84Ac/aPSYtd69ey/WIvqiunXrFm3bto0HHnggNthgg2jTpk1UVlbGxhtvHLfcckvssMMOUVNTE9/61rcWa4huYRtvvHFMmTIlbr/99thxxx3j3nvvjbvuumul52%2BjjTaK%2BfPnx89//vM4%2BOCD45FHHokbbrhhhca1ySabxFFHHRXHHHNMXH755bHtttvGu%2B%2B%2BG2PGjImtt946DjrooLj22mtj/Pjx8eyzz0ZVVVXce%2B%2B9cdRRR8Vjjz0WrVq1in79%2BsXHH38cY8aMiW222SbatWsX7dq1i7333juuueaaGDx4cNTW1sa55567TI9Q%2B%2B53vxvf/OY3o7KyMvbff/%2BYO3duPPXUU/Hhhx/GiBEjVmg%2BAWBN0oo7APy/jTfeOJ566qnYcMMN4/DDD48BAwbEySefHHvttVeMHz/%2BU5/53aJFi7j66qvjF7/4RfTq1SsOOeSQiIj49a9/HR9%2B%2BGFst912cfTRR8c3v/nN6Nat2xLH8/nPfz7OOuusOO2002LgwIHx6KOPxoUXXrjS87fNNtvEFVdcET/%2B8Y9jyy23jN///vcxcuTIFR7fqFGj4phjjomzzz47Nt100zj00EPjySefjD59%2BsSLL74Y3/rWt%2BK6666LqqqqiIi47rrr4r333qufl1122SVOOeWUGDZsWHTt2jUuu%2ByyiIi4/PLLo6qqKnbbbbc48sgj45xzzlmmy%2B9PPPHE%2BNWvfhWjRo2KrbbaKvbYY4%2B4%2Beab6x9/BwB5V8iWpzUcAAAAYLVwBh0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAcEdAAAAMgBAR0AAAByQEAHAACAHBDQAQAAIAf%2BD5AhhelNhZBEAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 1000
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1000x800)"
     ]
    }
   ],
   "source": [
    "#CHN {Python gerçeklemesinde önerdiğim şekilde,\n",
    "#     edgecolors için daha öz bir hesaplama yapıyoruz.}\n",
    "\n",
    "#NEW {Figür, idg (bu jupyter not defterinin çözümleyici arka-ucu)'nin \n",
    "#     grafik çizdirmesi için gerekli.}\n",
    "\n",
    "# Matplotlib temel ayarları:\n",
    "# Figür:\n",
    "fig = plt.figure figsize: [10, 8]\n",
    "# ___________________???\n",
    "ax = fig.add_subplot 111\n",
    "\n",
    "colors = {(0, \"red\"), (1, \"blue\")}\n",
    "plt.scatter x y facecolors: 'none' edgecolors: ((map (x -> colors !! x) cancer.target) |> list) cmap: colors\n",
    "plt.xlabel \"Ortalama Texture\"\n",
    "plt.ylabel \"Sonuç\"\n",
    "\n",
    "red = mpatches.Patch color: \"red\" label: \"Kötü Huylu\"\n",
    "blue = mpatches.Patch color: \"blue\" label: \"İyi Huylu\"\n",
    "plt.legend handles: [red, blue] loc: 1\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ba1fad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'numpy' from '/Users/sckn/.pyenv/versions/3.6.15/lib/python3.6/site-packages/numpy/__init__.py'>"
     ]
    }
   ],
   "source": [
    "#CHN {linear_model nesnesinin bir kez kullanımı üzerine, kullanımı elemine etmek için çağrımı ekleme\n",
    "#    kütüphanenin eklenmesi ifadesinde çözümledik}\n",
    "#ADD{Numpy kütüphanesinin çağırımsız kullanımını düzeltmek için, deklarasyon yapıldı}\n",
    "\n",
    "LogisticRegression = import '/sklearn/linear_model/LogisticRegression'\n",
    "np = import '/numpy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6c153e55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression()"
     ]
    }
   ],
   "source": [
    "#RNM {log_regress:lojistik_regresyon}\n",
    "\n",
    "lojistik_regresyon = LogisticRegression!\n",
    "#np.array(x).reshape(len(x), 1),\n",
    "lojistik_regresyon.fit X: ((np.array x) |>.reshape (len x) 1) y: y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e1b9c66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.12260029]\n",
      "[[-0.23448037]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print lojistik_regresyon.intercept_\n",
    "print lojistik_regresyon.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11320e1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function sigmoid at 0x15dc89598>"
     ]
    }
   ],
   "source": [
    "sigmoid = x ->\n",
    "        1 / (1 + (np.exp (- ((lojistik_regresyon.intercept_ !! 0) + ((lojistik_regresyon.coef_ |> (!! 0) |> (!! 0)) * x)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be010da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "x1 = np.arange 0 30 0.01\n",
    "y1 = (map sigmoid x1) |> list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "115cd978",
   "metadata": {},
   "outputs": [
    {
     "data": {
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WyWmnSQ0amO0BgHPw1cfPTZo0SXFxcXu61NRUu0sCAMAvVfzwBgDA0SKg%2B5Dk5GRlZmZW6ZeZmanY2FhFRERUO8348eOVm5u7p0tPT6%2BLUgG/cfLJ5qjV559XP3zaNNO6c5cuB59P377mSF5ZmbR9e%2BVRvGnTKh%2BDgiqPVknmqFCvXlXn89575jElpXLak082Rwq9XnNkLijI9C8sNEeyJDPsm2/MNBWta0vmaN/e9n1ut72PUKLu7H23gIPZvNlsfxs2mOdZWdL48VXHOdTRuYqjqvuq2I4r7N4tbdpk/l63zjxu314ZiuPjpQ8/NGeqNGpktvELLjDPy8rMUdL33qsaoqdNk%2BrVM626z5wpDRlijthOmyaddJIZJz9f%2BvRTc6S/4iS0/Hwzzt5HfXfvlh54wJyF0qeP6bd8edXXsGqVWV8//2yO/v74o3kNTZpUjvPhh6Z%2Br7dyPi%2B9ZB4jIqQePQ66Og/LySeb5bZta46w/vqrtH595fD33pOSk83R5Yqzej77zDzWq2eOmmdnmyP8Gzea/k2aVK6PbdvMEfrU1Mr1tGRJ5dkSp59euawBA6QPPth/Xz/%2BePNeFBaa519/XfUo9scfm/d1%2BXIz7YAB5gh5xefivtasMUfYBwyo2n/AAHOEfMWK6qebNs1sO3sfvfcVrVubMyQOtE5mzZJ27DDbAwAHsfscexjS4TUS16lTpyr9LrroIp9tJG78R39Yo1/%2Bxbr%2BzYXWbe8ttiZ%2B8qf1yIwV1pTZa6zXfkyz3v8t3fryj63W3FVZ1m8bdlortuVam3YUWjsKSqzisnLL64sXhMEnOb2RuB9%2BMOOPG2ca/jlYI3GRkTQSR3fknT80Evfyy%2BZvpzUSFxpqll1dI3EzZljWzTdXLiPQG4kbOtQ0Erd8OY3EHS4aicOhOCkbwHBZlmXZ/BtBwCooKNDatWslSccff7yeeOIJDRw4UPXr11fTpk01fvx4bdmyRW%2B88YYkKS0tTZ06ddLYsWN1xRVX6LvvvtNNN92kL774QoMHDz6sZebl5SkuLk65ubmKrbiYziZnPDlXqzMPclPUQwh2uxQVFqyo0CDzGBas2IgQxYQHKzY8RLERfz2Gm/4V/WLCK/%2BOCAmSa%2B8LEoFqbN5srtVbt85ch9mypbRggblGdehQ6aOPzH2XDyU311xH%2Bcsv5ijbwY5WR0SYI3IDB5ojSatWSV9%2BaY7Uf/ONuU/x5Zebe6X36GGuja24Z3FkpJm/L14zCf8RHGyOXtcFl8vEwgoREeZ5cbE5Ql%2B/vrR1qzlivGyZ2V9LSswR3vx88/f06Wb/3L3bXHP%2B7bfmzJfly82%2Bu/e892725ZRTzNHhXbsq%2B%2B195FcyRzEzM82%2BWVRk9tdDNQkTGmqODFesw5NPlr766vDbBziUdevM59rmzZXX%2B1esR7e78oj2bbdJd9xhjnr/8Yf5zGvTRnrrLXPGhGQ%2BE7durXoGUHWioqS1a83R%2Bb29%2BKJ03XXmzKBzzzVHxj/6yKyjdu2qtvFRUdve7/njj0vjxpnn48ZJTz1lrk0/4wxzhPjDD837NmOG1LPn/nX9/rsZt7BQGjHCtKXwzTfSn39KY8dKzz5bte0CX1JeLl10kTlLoU8f0%2B7Bpk3SJ5%2BYM0dmzara/gACj5OyAQwCuo3mzJmjgQMH7tf/sssu02uvvabLL79cGzZs0Jw5c6pMc%2Butt2r58uVq0qSJJkyYoMsvv/ywl%2BmknXD2qixl55eoqNSjgpJyFZWWq7DEo8KSchVW%2Bds8FpWWq6CkXMVlNXfOa7DbtV%2Boj48MVf3IUNWLDFG9qFDViwz96zFkz99RoQT7QJOfL73%2BuvT22%2BYLX4sW0pVXmi9z%2B56KezAlJdI770gvvyytXGm%2BgJaVmS%2BWLpc5jfKOO6QbbzSncL7yivkCnZgojR4tXXJJ5Rd0y5K%2B%2BMKc/rp6takjKsqEiZAQ8yV49WpzumnFF/CKT/zgYPMl%2B4YbzA8Nn39%2B6MBwIG63CTq7d0s7dx5eKHO7TQApKamsad%2BQBWeJijLb544dle9bde9ZWJjZpitCVEyMeX6AtkwPqOJ06YptxeUyAdDrNcsMCTH9Kra30NDK4O31mhB80UXShAmmf8X%2Bu3WrmcblMg1kDR5swmGLFpXLLi83gebll83p%2B2VlJoAXFJhlh4WZhupCQ02/lBQT/mfPltLSzPTBweYU47vvNqfaf/ml9MILJgwWFZnhYWFmvRQWmpqDgipDfHm5ee1Nmkj33y9dfLF5zTUpJ8d8xrz6qjlVveJ9jY42l8vccYf5AUIyNb31lvTGGyaYN2kidepkAt66dWa6%2BvXNKfGZmeZ1VWwbISGm4bkXXjjwJQ1LlkhTppjT/t1u6dRTTThu2dIE7KefNoG5YjuKijKnp99zT9VLgSzLvA8vvGBOXY%2BIMKH/6qv3/2Fgb5mZ0tSp5oeaoiKpY0fp2mvNjxi%2B/u/e6zWXaUydat6ruDjpb3%2BTxozxzVP3UbOclA1gENADjD/shB6vpcLSchWVVAb7gpJyFRSXK7%2B4XPnFZcorLlfe7jLlFZcpb3e58kvMo3luhnu8R7/phwa5FR8ZovpRoXs9hiohKlQJMWFKiA5Tw70eCfQAAABwGn/IBv4m2O4CgCMV5Hb9der60R9KsCxLu8s8%2B4T2MuXuLlNOUZl2FZZqV1GZdhaVKqeoVDsLy/56LFVJuVelHq%2By8kuUlV9yWMsLD3HvF9orHhtGhyoxNlyN4sLVMDpMwUG03QgAAAAEIgI6ApLL5VJkaLAiQ4OVHBd%2B6An2srvUo51FpX%2BFeBPkdxWa8L6zsFTb80uUXVCi7QUlys4vUWGpR8VlXm3etVubdx38HE%2B3S2oYE6bkuAg1ig1XcpwJ7uYxQsmx4UqKC1NY8BGcUw0AAADAJxDQgSMUERqkxqERahxf/a3t9lVUWq7s/FJtLyjW9vzSPcE9u6BE2/NNkM/KK1FmXrHKvZYy80qUmVeiJQeZZ4OoUKXERyi1foRS60WqSb0INakfuefv8BACPAAAAOBrCOhALYsMDVbTBsFq2uDgTe96vJZ2FJQoI69Y23KLlZFb8bhb23KLlflX/5Jyr3YUlmpHYamWbsmtdl4NY8KUWi9CTepF7gnxTRtEqmVCtJJiw7geHgAAAHAgAjrgEEFulxJjw5UYG64uTaofx7Is5RSVaWvubm3ZtVvpu3Zr864ipe80j5t37VZBSbk5Mp9fokWbcvabR0RIkJonRKllQpRaJESp%2BV%2BPLROiVC8qtHZfJAAAAIADIqADPsTlcplbvkWFqmNK3H7DKwL85l27lb6raE94T99VpI07irRpZ5F2l3m0YlueVmzb/55acREhapEQpeMaRqtNUrTaJMWodVK0GsdHcNQdAAAAqGXcZi3AcCuFwFbm8Sp9Z5E27CjU%2Bu2FSss23YbsQm3NLT7gdFGhQWqVFKM2iZWhvU1SjBrFhRPcAQAAfBTZwHkI6AGGnRAHsrvUsye4r80q0OqsfK3JzFdadqHKPNV/TMSEB6t9o1h1TIlVx5Q4dUyJVavEaIVwqzgAAADHIxs4DwE9wLAT4kiVebzakF2o1ZkFWp2ZrzVZ%2BVqdWaC07EJ5vPt/fIQGu9U2Keav0B6rDilxat8oRpGhXFEDAADgJGQD5yGgBxh2QtSU0nKv1mYVaPm2PC3bmqtlW/O0Ymue8kvK9xs3yO1Sm6QYdUuNU7fUeHVLradWidEKcnN6PAAAgF3IBs5DQA8w7ISoTV6vpfRdRVq2NU/Lt1YG96z8kv3GjQ4LVufGcerWNF7dUuN1fGq8EmPDbagaAAAgMJENnIeAHmDYCWGHzLxi/b4pR4vTc7Q4fZf%2B2JyrolLPfuM1rR%2Bp3i3qm655fTVrEEkjdAAAALWEbOA8BPQAw04IJ/B4La3JytfiPaE9R6sz87XvJe2JMWHq1aK%2B%2BvwV2tskxsjNafEAAAA1gmzgPAT0AMNOCKfKLy7Two27tCBtp37dsFNL0nNV6vFWGScuIkR9WzZQv9YJ6t8qgSPsAAAAx4Bs4DwE9ADDTghfUVzm0eL0HP2atlMLNuzUwo279jstvnF8hE5qlaCTWifoxOMaqEF0mE3VAgAA%2BB6ygfMQ0AMMOyF8VZnHq6VbcvXT2mz9sDZbCzfu2u/%2B7B0axerkNg11artEdW8ar2Duxw4AAHBAZAPnIaAHGHZC%2BIui0nItSNupH9dm6/s12VqZkV9leFxEiAa0aajT2idqQJuGio8MtalSAAAAZyIbOA8BPcCwE8Jfbc8v0U/rsjV7ZZbmrN6unKKyPcPcLql703o6tX2iBrVPUuvEaK5dBwAAAY9s4DwE9ADDTohA4PFa%2Bn3TLs1amaXZK7P2O7resmGUzuyUrDM7NVLHlFjCOgAACEhkA%2BchoAcYdkIEos27ijR71XbNWpGpn9buqNI6fGr9CA3pmKwzOzdStybx3MYNAAAEDLKB8xDQAww7IQJdfnGZvluZpa%2BWZmjO6iwVl1WG9eTYcJ3ZOVnndmusLk3iOLIOAAD8GtnAeQjoAYadEKhUVFquuau266s/M/TdyiwVlJTvGdYiIUrndEvRud0aq3lClI1VAgAA1A6ygfMQ0AMMOyFQveIyj35Yk61Pl2zVzOUZVY6sd02N17ndUnRWlxQ1jOFe6wAAwD%2BQDZyHgB5g2AmBQyssKdfM5Rn6%2BPet%2BmFttjxe8zEZ5HZpYNuGurBnqga2S1QI91kHAAA%2BjGzgPAT0AMNOCByZ7fkl%2BuKPrZq%2BeKuWpOfs6Z8QHaaRPRprVM9UtWwYbV%2BBAAAAR4ls4DwE9ADDTggcvbVZBXrvt3R9tGizsgtK9/Tv1byeRvVqqmGdGykiNMjGCgEAAA4f2cB5COgBhp0QOHZlHq9mrcjSe7%2Bla86qLP11Brxiw4N1Yc9UXdq3mZo1oGE5AADgbGQD5yGgBxh2QqBmZeQW68NFm/Xur5uUvnO3JMnlkk5p01CjT2yuAa0bcm91AADgSGQD5yGgBxh2QqB2eL2W5q7ertfnb9CcVdv39G/WIFKXntBMF/RMVVxEiI0VAgAAVEU2cB4CeoBhJwRq34bsQr3580a991u68orNvdWjQoM0qldTXXFSczWpF2lzhQAAAGQDJyKgBxh2QqDuFJWW65PFW/X6Txu0MiNfkrlV27DOjXT1yS3VqXGczRUCAIBARjZwHgJ6gGEnBOqeZVn6fk22Xpq3Xj%2Bszd7Tv1%2BrBrqqf0sNaNNQLhfXqQMAgLpFNnAeAnqAYScE7PXnllz99/v1%2BuyPbfL81fx7x5RY3Xhqa53RIYkG5QAAQJ0hGzgPAT3AsBMCzrAlZ7de%2BSFN7yzYpKJSjySpXXKMbji1lc7s1EhBBHUAAFDLyAbOQ0APMOyEgLPsKizVKz%2Bm6bUfNyi/xDQo1yoxWjee2kpndUkhqAMAgFpDNnAeAnqAYScEnCm3qEyv/pSmV35I29Pye8uEKN16ehsN69yIU98BAECNIxs4DwE9wLATAs6WV1ym/83fqKnfr1dOUZkkc4367YPb0pgcAACoUWQD5yGgBxh2QsA3FJSU65Uf0vTSvPUq%2BOvU994t6uvOIW3Vo1l9m6sDAAD%2BgGzgPAT0AMNOCPiWnYWlen7OWr0%2Bf6NKy72SpNPaJeqOIe3UNjnG5uoAAIAvIxs4DwE9wLATAr5pa85uPTNrjd5fuFkeryW3S/pb76Yad3obJUSH2V0eAADwQWQD5yGgBxh2QsC3rdteoMe%2BXqWv/syQJMWEBeuGU1vp8n7NFRYcZHN1AADAl5ANnIeAHmDYCQH/8Mv6HXrwi%2BX6c0ueJKlp/UiNP7OdhnRKpiE5AABwWMgGzkNADzDshID/8HotffT7Fj0yY6Wy8kskmYbk7j%2B7o9o3Yv8GAAAHRzZwHgJ6gGEnBPxPYUm5Xpy7Ti/OW6%2BScq%2BC3C6NObG5bjm9jaLDgu0uDwAAOBTZwHkI6AGGnRDwX1tydutfny/fc316UmyY7j2ro4Z25rR3AACwP7KB87jtLgAAUDMax0fo%2Bb/30Ktjeqlp/Uhl5pVo7NuLNPqVBVq/vcDu8gAAAHAIBHQA8DMD2yZq5q0n6%2BbTWis02K3v12RryFPf6%2Blv1%2By5lzoAAACch4AOAH4oPCRIt57eRl/fcrJObtNQpR6vnvx2tc6e/IOWpOfYXR4AAACqQUAHAD/WIiFKr4/ppaf/1k31o0K1MiNf5z33ox76coV2l3rsLg8AAAB7IaADgJ9zuVw6p1tjfTtugM7pliKvJb00b73OfHqe5q/bYXd5AAAA%2BAsBHQACRP2oUD39t%2BP18mU9lRwbrg07inTR1J91z8dLVVRabnd5AAAAAY%2BADgAB5rT2SZo57mRd3KepJOnNnzdp6NPfa9GmXTZXBgAAENgI6AAQgGLDQ/TQeZ311j/6qFGcOZp%2B/vM/6fGZq1TmoaV3AAAAOxDQASCA9WuVoBm3nKzzjm8sryU9%2B91anffcj1qblW93aQAAAAGHgA4AAS4uIkRPjuqmyRcfr7iIEP25JU/DnvlBr/6YJsuy7C4PAAAgYBDQAQCSpLO6pGjmrea%2B6SXlXt3/2XJd9cZv2lVYandpAAAAAYGADgDYIyk2XK%2BP6aX7hndQaJBb367I0tBnvtevG3baXRoAAIDfI6ADAKpwuVy6vF8LfXT9iWqREKVtucX620s/a/J3a%2BTxcso7AABAbSGgAwCq1alxnD678SSdd3xjebyWHpu5WqNf%2BUVZecV2lwYAAOCXCOgAgAOKDgvWExd21aPnd1FESJB%2BXLtDQ5/5Xj%2Bv32F3aQAAAH6HgA4AOCiXy6ULeqbqsxv7qV1yjLILSnXJf3/Rf79fTyvvAAAANYiADgA4LK0SYzT9%2Bn57Tnn/1xcrdMM7v6uwpNzu0gAAAPwCAR0AcNgiQoP0xIVddf/ZHRXsdumLP7bp3Ck/at32ArtLAwAA8HkEdADAEXG5XLrsxOZ69%2BoTlBgTpjVZBTpn8o/6elmG3aUBAAD4NAI6AOCo9GxeX5/fdJJ6N6%2BvgpJyXfO/hXr62zVclw4AAHCUCOgAgKOWGBOut67qozH9mkuSnvx2tW5453ftLvXYWxgAAIAPIqADAI5JSJBbE4d31MMjOu%2B5Ln3US/OVkcv90gEAAI4EAR0AUCP%2B1rup3vxHH9WLDNEfm3N19uQftCQ9x%2B6yAAAAfAYBHQBQY05o2UCfjD1JrROjlZVfogtfnK/Plmy1uywAAACfQEAHANSopg0i9dH1J2pg24YqKffqxnd%2B13Nz1tJ4HAAAwCEQ0AEANS4mPET/vayXrujXQpL0yIxVmvDJnyr3eG2uDAAAwLkI6ACAWhHkdune4R1071kd5HJJb/68Sde%2BuVBFpeV2lwYAAOBIBHQAQK264qQWeu7i7goLduvbFVm6aOovyi4osbssAAAAxyGgAwBq3ZmdG%2Bntq/ooPjJES9JzNOK5n5SWXWh3WQAAAI5CQAcA1Ikezerrw%2BtOVGr9CG3aWaSRz/%2BkpZtz7S4LAADAMQjoAIA6c1zDaH10XT91aRKnnYWlumjqz5q/bofdZQEAADgCAR0AUKcaxoTp7atOUN%2BWDVRQUq7LXl2gb5dn2l0WAACA7QjoAIA6Fx0WrFfH9NLpHZJUWu7VNW8u1PTfN9tdFgAAgK0I6AAAW4SHBOn5S7prZPcm8ngt3TptiV77Mc3usgAAAGxDQAcA2CY4yK1Hz%2B%2BiMf2aS5Lu%2B2y5nv52jSzLsrcwAAAAGxDQAQC2crtduvesDhp3ehtJ0pPfrtZjM1cR0gEAQMAhoAMAbOdyuXTTaa014awOkqQps9fp4a9WEtIBAEBAIaADABzjypNa6IFzOkqSXpy3Xg9%2BvoKQDgAAAgYBHQDgKKP7Nte/z%2BskSXrlxzTd9%2BkyQjoAAAgIBHQAgONc0qeZ/jOys1wu6fX5G3X3x3/K6yWkAwAA/0ZABwA40qheTfXo%2BV3lcklv/7JJd01fSkgHAAB%2BjYAOAHCs83s00VOjusntkt79NV0TOd0dAAD4MQI6AMDRzunWWI9faI6k/%2B/njfrXFzQcBwAA/BMBHQDgeOcd30QPj%2BgsSXr5hzQ9%2BjX3SQcAAP6HgA4A8AmjejXdcwu25%2Bas07PfrbW5IgAAgJpFQAcA%2BIzRfZvrnmHtJUlPfLNaL85dZ3NFAAAANYeADgDwKf/o31K3D24rSZr01Uq9/tMGewsCAACoIQR0AIDPGTuwlW46tZUkaeKny/Tx71tsrggAAODYEdABAD7p1tPbaEy/5pKk295fou9WZtpbEAAAwDEioAMAfJLL5dKEYR103vGNVe61dP1bi/Tbhp12lwUAAHDUCOgAAJ/ldrv0yPlddGq7RBWXeXXFa79qxbY8u8sCAAA4KgR0AIBPCwlya8rF3dWzWT3lFZdr9CsLtGlHkd1lAQAAHDECOgDA50WEBunly3upXXKMtueX6O8v/6Ks/GK7ywIAADgiBHQAgF%2BIiwjRG1f0VtP6kdq0s0hjXv1VBSXldpcFAABw2AjoAAC/kRgbrv9d2VsJ0aFatjVPY99apDKP1%2B6yAAAADgsBHQDgV5o1iNLLl/VSeIhbc1dv14SP/5RlWXaXBQAAcEgEdACA3%2BmaGq9nL%2Bout0t699d0TZm91u6SAAAADomAbrMpU6aoefPmCg8PV58%2BfbRgwYKDjv/UU0%2Bpbdu2ioiIUGpqqm699VYVF9MQEgDs6/QOSbrv7I6SpMdmrtb03zfbXBEAAMDBEdBtNG3aNI0bN04TJ07UokWL1LVrVw0ePFhZWVnVjv/222/rn//8pyZOnKgVK1bo5Zdf1rRp03TXXXfVceUA4BtG922ua05uKUm644M/9NPabJsrAgAAODACuo2eeOIJXXXVVRozZow6dOigF154QZGRkXrllVeqHf%2Bnn35Sv379dPHFF6t58%2BY644wzdNFFFx3yqDsABLI7h7TTWV0aqcxj6Zr/LdSqjHy7SwIAAKgWAd0mpaWlWrhwoQYNGrSnn9vt1qBBgzR//vxqpznxxBO1cOHCPYF8/fr1%2BvLLLzV06NADLqekpER5eXlVOgAIJG63S49d0FW9m9dXfkm5rnjtV23PL7G7LAAAgP0Q0G2SnZ0tj8ejpKSkKv2TkpKUkZFR7TQXX3yxHnjgAZ100kkKCQnRcccdp1NOOeWgp7hPmjRJcXFxe7rU1NQafR0A4AvCQ4L00ugeapkQpS05u3XN/35TcZnH7rIAAACqIKD7kDlz5uihhx7Sc889p0WLFumjjz7SF198oQcffPCA04wfP165ubl7uvT09DqsGACcIz4yVP%2B9rKdiw4O1aFOOxn%2B0lNuvAQAARwm2u4BAlZCQoKCgIGVmZlbpn5mZqeTk5GqnmTBhgi699FL94x//kCR17txZhYWFuvrqq3X33XfL7d7/95awsDCFhYXV/AsAAB/UsmG0nv97D41%2BZYGm/75FrRKjNXZgK7vLAgAAkMQRdNuEhoaqR48emjVr1p5%2BXq9Xs2bNUt%2B%2BfaudpqioaL8QHhQUJEkcBQKAw9SvVYLu/%2Bv2a49%2BvUoz/qz%2BsiIAAIC6RkC30bhx4zR16lS9/vrrWrFiha677joVFhZqzJgxkqTRo0dr/Pjxe8YfPny4nn/%2Beb377rtKS0vTN998owkTJmj48OF7gjoA4ND%2BfkIzXda3mSTp1mmL9eeWXJsrAgAA4BR3W40aNUrbt2/Xvffeq4yMDHXr1k0zZszY03Dcpk2bqhwxv%2Beee%2BRyuXTPPfdoy5YtatiwoYYPH65///vfdr0EAPBZE87qoPXZhfp%2BTbaueuM3fXJDPyXGhNtdFgAACGAui3OjA0peXp7i4uKUm5ur2NhYu8sBAFvl7i7Tec/9qPXbC9UtNV7TrjlBYcGckQQACAxkA%2BfhFHcAQMCKiwjRK5f1UlxEiBan52jiJ8to0wMAANiGgA4ACGjNE6L07EXHy%2B2S3v01XW8v2GR3SQAAIEAR0AEAAe/kNg11%2B%2BB2kqT7Pl2mhRt32lwRAAAIRAR0AAAkXTugpYZ1bqQyj6Vr31ykzLxiu0sCAAABhoAOAIAkl8ulR87vorZJMdqeX6Lr3lyoknKP3WUBAIAAQkAHAOAvUWHBevHSHooND9aiTTm6/7PldpcEAAACCAEdAIC9NE%2BI0jMXHS%2BXS3r7l016h0bjAABAHSGgAwCwj1PaJuq2M9pKkiZ%2Bskx/bM6xtyAAABAQCOgAAFTj%2BlOO0%2BCOSSr1eHXdm4uUU1Rqd0kAAMDPEdABAKiGaTSuq5o1iNSWnN36v/eWyOu17C4LAAD4MQI6AAAHEBcRoucu6a7QYLdmrczSi/PW210SAADwYwR0AAAOomNKnB44u6Mk6dGvV%2Brn9TtsrggAAPgrAjoAAIcwqleqRnRvLK8l3fjO78rKL7a7JAAA4IcI6AAAHILL5dK/zu2ktkkx2p5fopve%2BV3lHq/dZQEAAD9DQAcA4DBEhgbrub93V1RokH5ev1NPfrva7pIAAICfIaADAHCYjmsYrYdHdpEkTZm9TrNXZdlcEQAA8CcEdAAAjsDwrika3beZJOm295YoK4/r0QEAQM0goAMAcITuGtpe7RvFakdhqcZxf3QAAFBDCOgAAByh8JAgPXvR8YoICdIPa7P1wrx1dpcEAAD8AAEdAICj0CoxWvf/dX/0x2eu1sKNu2yuCAAA%2BDoCOgAAR%2BmCnk00vGuKPF5LN73zu3J3l9ldEgAA8GEEdAAAjpLL5dK/z%2Buk1PoR2pKzW3d9tFSWxfXoAADg6BDQAQA4BrHhIXr2ou4Kdrv0xdJtevfXdLtLAgAAPoqADgDAMeqWGq/bBreVJN3/2TKtycy3uSIAAOCLCOgAANSAq/u3VP/WCSou8%2BrGd35XcZnH7pIAAICPIaADAFAD3G6XnriwmxpEhWplRr4en7nK7pIAAICPIaADAFBDGsaE6eGRXSRJ//0hTT%2Btzba5IgAA4EsI6AAA1KDTOyTpot6psizp/95fotwibr0GAAAODwEdAIAads%2BwDmreIFLbcos14ZM/7S4HAAD4CAI6AAA1LCosWE%2BO6qYgt0ufLtmqTxZvsbskAADgAwjoAADUguOb1tONp7aSJN3z8Z/akrPb5ooAAIDTEdABAKglNwxspW6p8covLte4aYvl8Vp2lwQAAByMgA4AQC0JDnLryVHdFBkapF/Sduq/36%2B3uyQAAOBgBHQAAGpRi4Qo3XtWB0nSYzNXafnWPJsrAgAATkVABwCglo3qlapB7ZNU5rH0f%2B8vUWm51%2B6SAACAAxHQAQCoZS6XS5NGdFa9yBCt2Janyd%2BtsbskAADgQAR0AADqQMOYMD14bidJ0pQ567R0c67NFQEAAKchoAMAUEfO6pKiYV0ayeO19H/vL1ZJucfukgAAgIMQ0AEAqEMPntNJCdGhWp1ZoKe%2B5VR3AABQiYAOAEAdqh8Vqn%2Bf11mS9OLcdVq0aZfNFQEAAKcgoAMAUMcGd0zWecc3lteSbnt/iYrLONUdAAAQ0AEAsMV9wzsqMSZM67cX6rGvV9ldDgAAcAACOgAANoiLDNF/RnaRJL38Y5p%2B3bDT5ooAAIDdCOgAANhkYLtEXdiziay/TnUvKi23uyQAAGAjAjoAADa656wOSokL18YdRXpi5mq7ywEAADYioAMAYKPY8BD9e4Rp1f2VH9P0O626AwAQsAjoAADYbGDbRI34q1X3Oz/8QyXltOoOAEAgIqADAOAAE87qoIToUK3OLNBzs9fZXQ4AALABAR0AAAeoFxWq%2B8/uJEl6bs5arczIs7kiAABQ1wjoAAA4xNDOyTqjQ5LKPJbu/OAPebyW3SUBAIA6REAHAMAhXC6XHjy3k2LCg7Vkc65e%2BSHN7pIAAEAdIqADAOAgSbHhumdYe0nS49%2Bs0obsQpsrAgAAdYWADgCAw1zYM1UnHtdAxWVe/fOjP2RZnOoOAEAgIKADAOAwLpdLD4/oovAQt35ev1PvLEi3uyQAAFAHCOgAADhQ0waRuu2MtpKkSV%2BuUEZusc0VAQCA2kZABwDAocb0a6FuqfHKLynX/Z8ts7scAABQywjoAAA4VJDbpUkjOivI7dJXf2bo2%2BWZdpcEAABqEQEdAAAHa98oVv/o30KSNPHTZSosKbe5IgAAUFsI6AAAONzNp7VWk3oR2pKzW09%2Bs9rucgAAQC0hoAMA4HCRocF68NxOkqRXfkzTn1tyba4IAADUBgI6AAA%2BYGDbRJ3VpZG8ljT%2Bo6XyeLk3OgAA/oaADgCAj7h3eAfFhAdr6ZZcvTF/g93lAACAGkZABwDARyTGhOufZ7aTJD329Sptzdltc0UAAKAmEdABAPAhF/Vqqu5N41VY6tF9n3JvdAAA/AkBHQAAH%2BJ2uzRpRBcFu12auTxTXy/LsLskAABQQwjoAAD4mLbJMbr65JaSpPs%2BXaYC7o0OAIBfIKADAOCDbjqttZrWj9S23GI9MZN7owMA4A8I6AAA%2BKDwkKA990Z/ff4GrdiWZ3NFAADgWBHQAQDwUQPaNNTQzsnyeC1N%2BPhPebk3OgAAPo2ADgCAD5twVgdFhgbpt4279OGizXaXAwAAjgEBHQAAH9YoLkI3n9ZakjTpq5XKKSq1uSIAAHC0COgAAPi4K05qodaJ0dpZWKpHv15ldzkAAOAoEdABAPBxIUFuPXCOaTDu7QWbtCQ9x96CAADAUSGgAwDgB/oe10DndkuRZUkTPvlTHhqMAwDA5xDQAQDwE3cNa6%2BYsGD9sTlX7yzYZHc5AADgCBHQAQDwE4kx4fq/M9pIkh79epWyC0psrggAABwJAjoAAH7k7yc0U4dGscrdXaaHv1ppdzkAAOAIENABAPAjwUFuPXiuaTDug4Wb9duGnTZXBAAADhcBHQAAP9OjWT2N6pkqSbrn4z9V7vHaXBEAADgcBHQAAPzQnWe2U3xkiFZm5OuN%2BRvtLgcAABwGAjoAAH6oflSobh/cVpL05LeraTAOAAAfQEAHAMBP/a1XU3VqHKv84nI9OmOV3eUAAIBDIKADAOCngtwu3Te8oyTpvYXpWpKeY29BAADgoAjoAAD4sZ7N6%2Bu84xvLsqSJny6T12vZXRIAADgAAjoAAH7un2e2U1RokBan5%2Bij37fYXQ4AADgAAjoAAH4uKTZcN57WWpL08FcrlVdcZnNFAACgOgR0AAACwBX9WqhlQpSyC0r07Kw1dpcDAACqQUAHACAAhAa7NWF4B0nSqz9u0NqsApsrAgAA%2ByKgAwAQIAa2TdRp7RJV7rV0/2fLZFk0GAcAgJMQ0AEACCATzuqg0CC3vl%2BTrW%2BWZ9pdDgAA2AsBHQCAANI8IUr/6N9CkvTgF8tVXOaxuSIAAFCBgA4AQIAZO7CVkmPDlb5zt6bOW293OQAA4C8EdAAAAkxUWLDGD20nSZoyZ6225uy2uSIAACAR0AEACEhnd01R7%2Bb1VVzm1b%2B/XGF3OQAAQAR0AAACksvl0n1nd5TbJX3xxzYtSNtpd0kAAAQ8AjoAAAGqQ0qsRvVqKkl64PNl8nq57RoAAHYioAMAEMD%2B74w2igkL1p9b8vThos12lwMAQEAjoAMAEMASosN042mtJEmPfL1KBSXlNlcEAEDgIqADABDgLjuxuZo1iNT2/BI9P2et3eUAABCwCOgAAAS4sOAg3TW0vSRp6vdpSt9ZZHNFAAAEJgK6zaZMmaLmzZsrPDxcffr00YIFCw46fk5OjsaOHatGjRopLCxMbdq00ZdffllH1QIA/NUZHZJ04nENVFru1cNfrbS7HAAAAhIB3UbTpk3TuHHjNHHiRC1atEhdu3bV4MGDlZWVVe34paWlOv3007VhwwZ98MEHWrVqlaZOnarGjRvXceUAAH/jcrk04awO5rZrS7ntGgAAdnBZlsU9VWzSp08f9erVS5MnT5Ykeb1epaam6sYbb9Q///nP/cZ/4YUX9Oijj2rlypUKCQk5qmXm5eUpLi5Oubm5io2NPab6AQD%2BZ/xHS/XOgk3q1DhWn449SW63y%2B6SAAC1hGzgPBxBt0lpaakWLlyoQYMG7enndrs1aNAgzZ8/v9ppPv30U/Xt21djx45VUlKSOnXqpIceekgej6euygYA%2BLm9b7v2AbddAwCgThHQbZKdnS2Px6OkpKQq/ZOSkpSRkVHtNOvXr9cHH3wgj8ejL7/8UhMmTNDjjz%2Buf/3rXwdcTklJifLy8qp0AAAcyN63XXuU264BAFCnCOg%2BxOv1KjExUS%2B99JJ69OihUaNG6e6779YLL7xwwGkmTZqkuLi4PV1qamodVgwA8EXcdg0AAHsQ0G2SkJCgoKAgZWZmVumfmZmp5OTkaqdp1KiR2rRpo6CgoD392rdvr4yMDJWWllY7zfjx45Wbm7unS09Pr7kXAQDwS2HBQbqb264BAFDnCOg2CQ0NVY8ePTRr1qw9/bxer2bNmqW%2BfftWO02/fv20du1aeb3ePf1Wr16tRo0aKTQ0tNppwsLCFBsbW6UDAOBQTue2awAA1DkCuo3GjRunqVOn6vXXX9eKFSt03XXXqbCwUGPGjJEkjR49WuPHj98z/nXXXaedO3fq5ptv1urVq/XFF1/ooYce0tixY%2B16CQAAP8Vt1wAAqHvBdhcQyEaNGqXt27fr3nvvVUZGhrp166YZM2bsaThu06ZNcrsrf0NJTU3V119/rVtvvVVdunRR48aNdfPNN%2BvOO%2B%2B06yUAAPxY%2B0ax%2Blvvpnr7l0164PNl3HYNAIBaxn3QAwz3OgQAHInsghINfHSO8kvK9cj5XXRhTxobBQB/QTZwHk5xBwAAB5QQHaYbTjW3XXt85ioVlXLbNQAAagsBHQAAHNRlJzZXk3oRyswr0dR5aXaXAwCA3yKgAwCAgwoPCdKdQ9pJkl6ct05ZecU2VwQAgH8ioAMAgEM6q0sjHd80XkWlHj0%2Bc7Xd5QAA4JcI6AAA4JBcLpfuGdZekvTewnSt2JZnc0UAAPgfAjoAADgsPZrV17DOjWRZ0kNfrhA3ggEAoGYR0AEAwGG7c0g7hQS59P2abM1Zvd3ucgAA8CsEdAAAcNiaNojUZX2bS5Ie%2BmKFyj1eewsCAMCPENABAMARufHU1oqPDNGarAJN%2By3d7nIAAPAbBHQAAHBE4iJDdNOprSVJT36zWgUl5TZXBACAfyCgAwCAI/b3E5qpeYNIZReU6oU56%2BwuBwAAv0BABwAARyw02K1/nmluuzb1%2B/XamrPb5ooAAPB9BHQAAHBUBndMUu/m9VVS7tVjX6%2ByuxwAAHweAR0AABwVl8ule84yR9E/%2Bn2Llm7OtbkiAAB8GwEdAAActS5N4nVutxRJ0r%2B%2BWC7LsmyuCAAA30VABwAAx%2BS2wW0VGuzWL2k79c3yTLvLAQDAZxHQAQDAMWlSL1JXntRCkvTwVytV5vHaXBEAAL6JgA4AAI7Z9accpwZRoVqfXai3ft5odzkAAPgkAjoAADhmMeEhuuX0NpKkp2etUe7uMpsrAgDA9xDQAQBAjbioV6paJUZrV1GZnpu91u5yAADwOQR0AABQI4KD3Bp/ZjtJ0qs/bdDmXUU2VwQAgG8hoAMAgBpzartEndCyvkrLvXpi5mq7ywEAwKcQ0AEAQI1xuVy6a2h7SdL0xVv055ZcmysCAMB3ENABAECN6tIkXmd3TZFlSZO%2BWiHLsuwuCQAAn0BABwAANe72wW0VGuTWj2t3aO7q7XaXAwCATyCgAwCAGpdaP1Kj%2BzaTJD381Up5vBxFBwDgUAjoNYhT%2BAAAqHTDqa0UGx6slRn5%2BnDRZrvLAQDA8QjoR%2BjRRx%2Bttr/H49HFF19cx9UAAOBc8ZGhGjuwlSTpiZmrtbvUY3NFAAA4GwH9CD366KN6%2BeWXq/TzeDz629/%2BpsWLF9tTFAAADnXZic3VOD5CGXnFeuXHNLvLAQDA0QjoR%2BiLL77Qbbfdpg8%2B%2BECSVF5ergsuuEDLli3T7Nmzba4OAABnCQ8J0m2D20iSnp%2BzTjsKSmyuCAAA5yKgH6FevXrpww8/1BVXXKFPP/1UI0eO1KpVqzR79mwlJyfbXR4AAI5zTtfG6pgSq4KScj373Vq7ywEAwLEI6Efh1FNP1RtvvKGRI0cqLS1Nc%2BfOVVJSkt1lAQDgSG63S3cNbS9JevPnjUrLLrS5IgAAnCnY7gJ8wYgRI6rt37BhQ8XHx%2Bvqq6/e0%2B%2Bjjz6qq7IAAPAZ/Vol6JS2DTVn1XY9%2BvVKPXdJD7tLAgDAcQjohyEuLq7a/oMHD67jSgAA8F3/PLOd5q3eri%2BXZmjhxl3q0aye3SUBAOAoLoubdweUvLw8xcXFKTc3V7GxsXaXAwAIMHd8sETv/bZZPZvV0/vX9pXL5bK7JAAIWGQD5%2BEadAAAUGduPb2NwkPc%2Bm3jLs1cnml3OQAAOAqnuB%2Bh448//qC/9i9atKgOqwEAwLc0iovQlSe10JTZ6/Sfr1bq1HaJCgnieAEAABIB/Yide%2B65dpcAAIBPu3bAcXpnQbrWZxfq3V/TdekJzewuCQAAR%2BAa9ADDdSYAACd4/acNmvjpMiVEh2rO7QMVHcYxAwCoa2QD5%2BGcMgAAUOcu7tNULRKilF1QqpfmrrO7HAAAHIGfqw9DvXr1DruV2Z07d9ZyNQAA%2BL6QILfuGNxW1721SFO/T9MlJzRTUmy43WUBAGArAvpheOqpp%2BwuAQAAvzOkU7K6N43Xok05evKb1Xp4ZBe7SwIAwFZcgx5guM4EAOAkv23YqfNfmC%2B3S5pxy8lqkxRjd0kAEDDIBs7DNehHKC8v76AdAAA4fD2b19fgjknyWtLDX620uxwAAGzFKe5HKD4%2Bvtrr0S3LksvlksfjsaEqAAB8151D2unbFVn6bmWWflqXrROPS7C7JAAAbEFAP0KzZ8%2B2uwQAAPxKy4bRurh3U/3v542a9OVKfTK2n9zuw2ucFQAAf0JAP0IDBgywuwQAAPzOzYNa66NFm7V0S64%2B%2B2OrzunW2O6SAACoc1yDfoRmzJihH374Yc/zKVOmqFu3brr44ou1a9cuGysDAMB3JUSH6doBx0mSHv16lUrKuWQMABB4COhH6Pbbb9/TGNzSpUs1btw4DR06VGlpaRo3bpzN1QEA4Lv%2B0b%2BlkmLDtHnXbr3x00a7ywEAoM4R0I9QWlqaOnToIEn68MMPNXz4cD300EOaMmWKvvrqK5urAwDAd0WEBmnc6W0kSZNnr1VuUZnNFQEAULcI6EcoNDRURUVFkqRvv/1WZ5xxhiSpfv363GYNAIBjdH6PVLVJilbu7jJNmbPW7nIAAKhTBPQjdNJJJ2ncuHF68MEHtWDBAg0bNkyStHr1ajVp0sTm6gAA8G1BbpfGn9lekvTajxuUvrPI5ooAAKg7BPQjNHnyZAUHB%2BuDDz7Q888/r8aNTSuzX331lYYMGWJzdQAA%2BL5T2jbUicc1UKnHq8dmrrK7HAAA6ozLsizL7iJQd/Ly8hQXF6fc3FzFxsbaXQ4AANX6c0uuznrW3DXl0xv6qUuTeHsLAgA/RDZwHo6gH4Pi4mLl5eVV6QAAwLHr1DhO53ZLkSQ99OUKcTwBABAICOhHqLCwUDfccIMSExMVFRWlevXqVekAAEDNuG1wW4UGu/Xz%2Bp2avSrL7nIAAKh1BPQjdMcdd%2Bi7777T888/r7CwMP33v//V/fffr5SUFL3xxht2lwcAgN9oUi9SY05sLkma9OVKlXu89hYEAEAtI6Afoc8%2B%2B0zPPfecRo4cqeDgYPXv31/33HOPHnroIb311lt2lwcAgF%2B5fmArxUeGaE1WgT5YuNnucgAAqFUE9CO0c%2BdOtWzZUpIUGxurnTt3SjK3X5s3b56dpQEA4HfiIkJ0w8BWkqQnvlmtotJymysCAKD2ENCPUMuWLZWWliZJateund577z1J5sh6fHy8jZUBAOCfLu3bTKn1I5SVX6Kp89LsLgcAgFpDQD9CY8aM0ZIlSyRJ//znPzVlyhSFh4fr1ltv1e23325zdQAA%2BJ%2Bw4CDdMbidJOnFeeu0Pb/E5ooAAKgd3Af9GG3cuFELFy5Uq1at1KVLF7vLOSTudQgA8EWWZencKT9qyeZcXdKnqf59Xme7SwIAn0c2cB6OoB%2BjZs2aacSIET4RzgEA8FUul0t3DW0vSXr313StzSqwuSIAAGpesN0F%2BIJnnnnmsMe96aabarESAAACV5%2BWDTSofZK%2BXZGp/8xYqamje9pdEgAANYpT3A9DixYtDms8l8ul9evX13I1x4bTWAAAvmxtVoEGPzVPHq%2Bl967pq94t6ttdEgD4LLKB83AE/TBUtNq%2Bt%2B3bt8vlcikhIcGGigAACEytEqM1qleq3v5lk/795Qp9fP2JcrlcdpcFAECN4Br0I5CTk6OxY8cqISFBycnJSkpKUkJCgm644Qbl5ubaXR4AAAHhlkGtFRkapCXpOfr8j212lwMAQI3hCPph2rlzp/r27astW7bokksuUfv2pqGa5cuX67XXXtOsWbP0008/qV69ejZXCgCAf0uMCdc1Jx%2BnJ79drUe%2BXqkzOiYpLDjI7rIAADhmBPTD9MADDyg0NFTr1q1TUlLSfsPOOOMMPfDAA3ryySdtqhAAgMDxj/4t9OYvG5W%2Bc7fe/HmTrjzp8NqLAQDAyTjF/TB9/PHHeuyxx/YL55KUnJysRx55RNOnT7ehMgAAAk9UWLDGnd5GkvTsd2uUu7vM5ooAADh2BPTDtG3bNnXs2PGAwzt16qSMjIw6rAgAgMB2QY8map0YrZyiMj03Z63d5QAAcMwI6IcpISFBGzZsOODwtLQ01a/PrV4AAKgrwUFujR/aTpL06o8btHlXkc0VAQBwbAjoh2nw4MG6%2B%2B67VVpaut%2BwkpISTZgwQUOGDLGhMgAAAtfAtok6oWV9lZZ79fjM1XaXAwDAMXFZlmXZXYQv2Lx5s3r27KmwsDCNHTtW7dq1k2VZWrFihZ577jmVlJTot99%2BU2pqqt2lHlReXp7i4uKUm5ur2NhYu8sBAOCYLd2cq%2BGTf5AkfX7jSerUOM7migDAN5ANnIeAfgTS0tJ0/fXXa%2BbMmapYbS6XS6effromT56sVq1a2VzhobETAgD80c3v/q5PFm9Vv1YN9OaVfeRyuewuCQAcj2zgPAT0o7Br1y6tWbNGktSqVSufuvacnRAA4I/SdxbptMfnqtTj1atjemlg20S7SwIAxyMbOA/XoB%2BFevXqqXfv3urdu7dPhXMAAPxVav1IXXZiM0nSw1%2BulMfL8QcAgO8hoAMAAL9ww8DWiosI0arMfH24cLPd5QAAcMQI6AAAwC/ERYboxlNNezCPf7NKRaXlNlcEAMCRIaADAAC/cWnfZmpSL0KZeSV6%2Bfs0u8sBAOCIENABAIDfCAsO0u2D20qSXpi7TtkFJTZXBADA4SOgAwAAvzK8S4q6NIlTYalHT3%2B7xu5yAAA4bAR0AADgV9xul8af2V6S9PaCTVq3vcDmigAAODwEdAAA4Hf6HtdAp7VLlMdr6ZEZK%2B0uBwCAw0JABwAAfumfZ7aT2yV9vSxTv27YaXc5AAAcEgEdAAD4pdZJMRrVq6kk6aEvV8iyLJsrAgDg4AjoAADAb906qLUiQ4P0%2B6YcffVnht3lAABwUAR0AADgtxJjw3VV/5aSpIe/WqmSco/NFQEAcGAEdAAA4NeuPrmlGsaEadPOIv1v/ka7ywEA4IAI6AAAwK9FhQXr9jPaSpKenrVGOwtLba4IAIDqEdABAIDfG9mjiTo0ilV%2BcbmembXG7nIAAKgWAR0AAPi9ILdL9wxrL0n6388btTarwOaKAADYHwEdAAAEhBNbJWhQ%2ByR5vJYmfbnC7nIAANgPAR0AAASM8UPbKdjt0qyVWfphTbbd5QAAUAUBHQAABIzjGkbr7yc0kyT964vl8ngtmysCAKASAR0AAASUm09rrdjwYK3MyNcHC9PtLgcAgD0I6AAAIKDUiwrVTae1liQ9NnO1CkrKba4IAACDgA4AAALO6L7N1bxBpLbnl%2BjFuevsLgcAAEkEdAAAEIBCg90aP9Tcdu2leeu1NWe3zRUBAEBABwAAAeqMDknq06K%2BSsq9emTGSrvLAQCAgA4AAAKTy%2BXShLM6yOWSPl68VYvTc%2BwuCQAQ4AjoNpsyZYqaN2%2Bu8PBw9enTRwsWLDis6d599125XC6de%2B65tVsgAAB%2BrFPjOI04vokk6V%2BfL5dlcds1AIB9COg2mjZtmsaNG6eJEydq0aJF6tq1qwYPHqysrKyDTrdhwwbddttt6t%2B/fx1VCgCA/7p9cFuFh7j128Zd%2BurPDLvLAQAEMAK6jZ544gldddVVGjNmjDp06KAXXnhBkZGReuWVVw44jcfj0SWXXKL7779fLVu2rMNqAQDwT8lx4brm5OMkSZO%2BWqGSco/NFQEAAhUB3SalpaVauHChBg0atKef2%2B3WoEGDNH/%2B/ANO98ADDygxMVFXXnllXZQJAEBAuGZASyXFhil95269/tMGu8sBAAQoArpNsrOz5fF4lJSUVKV/UlKSMjKqP73uhx9%2B0Msvv6ypU6ce9nJKSkqUl5dXpQMAAFVFhgbr9sHtJEnPzlqrHQUlNlcEAAhEBHQfkZ%2Bfr0svvVRTp05VQkLCYU83adIkxcXF7elSU1NrsUoAAHzXiOMbq1PjWOWXlOupb9fYXQ4AIAAR0G2SkJCgoKAgZWZmVumfmZmp5OTk/cZft26dNmzYoOHDhys4OFjBwcF644039Omnnyo4OFjr1q2rdjnjx49Xbm7uni49Pb1WXg8AAL7O7Xbp7qEdJElvL9ikVRn5NlcEAAg0BHSbhIaGqkePHpo1a9aefl6vV7NmzVLfvn33G79du3ZaunSpFi9evKc7%2B%2ByzNXDgQC1evPiAR8bDwsIUGxtbpQMAANXre1wDDemYLI/X0gOfL%2BO2awCAOhVsdwGBbNy4cbrsssvUs2dP9e7dW0899ZQKCws1ZswYSdLo0aPVuHFjTZo0SeHh4erUqVOV6ePj4yVpv/4AAODo3TW0vb5blaUf1%2B7QN8szdUbH/c9sAwCgNhDQbTRq1Cht375d9957rzIyMtStWzfNmDFjT8NxmzZtktvNSQ4AANSlpg0idVX/Fpoye53%2B9cUKDWjbUGHBQXaXBQAIAC6Lc7cCSl5enuLi4pSbm8vp7gAAHEBhSbkGPjZHWfklunNIO113ynF2lwQANY5s4DwcngUAANhHVFiw/nmmue3a5O/WKCuv2OaKAACBgIAOAABQjXO7NVa31HgVlnr0yNer7C4HABAACOgAAADVcLtdmjjc3Hbtg4WbtSQ9x96CAAB%2Bj4AOAABwAMc3racR3RtLku77jNuuAQBqFwEdAADgIO4c0k6RoUH6fVOOPlm81e5yAAB%2BjIAOAABwEEmx4Ro7sJUkadJXK1RYUm5zRQAAf0VABwAAOIQrT2qh1PoRyswr0Qtz19ldDgDATxHQAQAADiE8JEh3DzUNxr04b73SdxbZXBEAwB8R0AEAAA7D4I5JOvG4Biot92rSVyvsLgcA4IcI6AAAAIfB5XLp3uEd5HZJXy7N0Px1O%2BwuCQDgZwjoAAAAh6ldcqwu6dNMknT/Z8vk8XLbNQBAzSGgAwAAHIFxp7dRXESIVmbk6%2B1fNtpdDgDAjxDQAQAAjkC9qFD93xltJEmPfr1KOwpKbK4IAOAvCOgAAABH6OLeTdW%2BUazyisv16Ner7C4HAOAnCOgAAABHKDjIrQfP6ShJmvZbuhan59hbEADALxDQAQAAjkLP5vU1ontjWZY08ZM/5aXBOADAMSKgAwAAHKV/ntlOMWHBWrI5V%2B/9lm53OQAAH0dABwAAOEqJMeG65XTTYNx/ZqxUTlGpzRUBAHwZAR0AAOAYjO7bTG2SorWrqEyPzaTBOADA0SOgAwAAHIOQILfuP7uTJOmtXzbpzy25NlcEAPBVBHQAAIBj1Pe4BhreNUWWJd1Lg3EAgKNEQAcAAKgBdw9tr6jQIC3alKOPft9idzkAAB9EQAcAAKgByXHhuum01pKkh79aodzdZTZXBADwNQR0AACAGjKmXwsd1zBK2QWleurb1XaXAwDwMQR0AACAGhIa7NZ9Z3eUJL0xf6NWZuTZXBEAwJcQ0AEAAGpQ/9YNdWanZHm8liZ8TINxAIDDR0AHAACoYRPO6qDI0CD9umGXPli02e5yAAA%2BgoAOAABQw1LiI3TLINNg3KQvV2hXYanNFQEAfAEBHQAAoBaM6ddC7ZJjtKuoTA9/tdLucgAAPoCADgAAUAtCgtz693mdJEnTfkvXrxt22lwRAMDpCOgAAAC1pEez%2Bvpbr1RJ0j3T/1SZx2tzRQAAJyOgAwAA1KI7h7RT/ahQrcrM1ys/pNldDgDAwQjoAAAAtaheVKjuGtpekvTUt2u0eVeRzRUBAJyKgA4AAFDLRnZvrN4t6mt3mUf3fbrc7nIAAA5FQAcAAKhlLpdL/z63k4LdLn27IlMzl2XYXRIAwIEI6AAAAHWgdVKMrj65pSTpvk%2BXqbCk3OaKAABOQ0AHAACoIzee2lpN6kVoa26xnpm1xu5yAAAOQ0AHAACoIxGhQXrgnI6SpP/%2BkKaVGXk2VwQAcBICOgAAQB06tV2ShnRMlsdrafxHS%2BXxWnaXBABwCAI6AABAHZt4dgdFhwXr9005evPnjXaXAwBwCAI6AABAHWsUF6E7h7SVJD0yY6W25uy2uSIAgBMQ0AEAAGxwSZ9m6tGsngpLPZrw8Z%2ByLE51B4BAR0AHAACwgdvt0sMjOiskyKVZK7P0xdJtdpcEALAZAR0AAMAmrZNidP0prSSZe6PnFJXaXBEAwE4EdAAAABtdP/A4tUqMVnZBqR76coXd5QAAbERABwAAsFFYcJAeHtFZkvTeb5v109psmysCANiFgA4AAGCzns3r69ITmkmSxk9fquIyj80VAQDsQEAHAABwgDuGtFVybLg27ijS07PW2F0OAMAGBHQAAAAHiAkP0YPndpIkvTRvvZZtzbW5IgBAXSOgAwAAOMTpHZI0tHOyPF5L//xwqco9XrtLAgDUIQI6AACAg9x3dkfFhgdr6ZZcvfT9ervLAQDUIQI6AACAgyTGhGvi8I6SpKe%2BWaM1mfk2VwQAqCsEdAAAAIcZ0b2xTm2XqFKPV7d98AenugNAgCCgAwAAOIzL5dJD53VWTHiwlqTn6L8/pNldEgCgDhDQAQAAHCg5LlwTzuogSXrim9Vam1Vgc0UAgNpGQAcAAHCoC3o00YA2DVVa7tXtHyyRx2vZXRIAoBYR0AEAABzK5XJp0ojOigkL1u%2BbcvQKp7oDgF8joAMAADhYSnyE7jmrvSTpsZmrtH47p7oDgL8ioAMAADjchT1T1b91gkrKvbrjgz841R0A/BQBHQAAwOFcLpceHtlF0WHB%2Bm3jLr36I6e6A4A/IqADAAD4gMbxEbprqDnV/dGvV9GqOwD4IQI6AACAj7iod%2BWp7uPeW6wyj9fukgAANYiADgAA4CNcLpcePb%2BrYsOD9cfmXE2ZvdbukgAANYiADgAA4EOS48L14LmdJEnPfrdWf2zOsbcgAECNIaADAAD4mHO6NdZZXRrJ47V067TFKi7z2F0SAKAGENABAAB80L/O7aTEmDCt216o/8xYaXc5AIAaQEAHAADwQfGRoXrk/C6SpFd/3KAf12bbXBEA4FgR0AEAAHzUKW0TdUmfppKk295fotzdZTZXBAA4FgR0AAAAH3b3sPZq1iBS23KLdf%2Bny%2BwuBwBwDAjoAAAAPiwyNFhPXNhNbpf00e9b9NXSbXaXBAA4SgR0AAAAH9ejWT1dd8pxkqTx05dqW%2B5umysCABwNAjoAAIAfuPm0NurcOE45RWUaN22JPF7L7pIAAEeIgA4AAOAHQoPdevpv3RQZGqT563fopXnr7S4JAHCECOgAAAB%2BomXDaN13dkdJ0uMzV2lJeo69BQEAjggBHQAAwI9c0KOJhnVppHKvpZvf/V0FJeV2lwQAOEwEdAAAAD/icrn00Lmd1Tg%2BQht2FOk%2Bbr0GAD6DgA4AAOBn4iJD9OQoc%2Bu1DxZu1qdLttpdEgDgMBDQAQAA/FDvFvV1w6mtJUl3T1%2Bq9J1FNlcEADgUAjoAAICfuunUVureNF75xeW6ddpilXu8dpcEADgIAjoAAICfCg5y6%2Bm/Ha%2BYsGD9tnGXnp61xu6SAAAHQUAHAADwY6n1I/XQiM6SpMmz1%2Br7NdttrggAcCAEdAAAAD83vGuKLunTVJYl3fLuYmXmFdtdEgCgGgR0AACAADDhrA5q3yhWOwpLddM7v3M9OgA4EAEdAAAgAISHBOm5S7orKjRIv6Tt5Hp0AHAgAjoAAECAaJEQpUkju0gy16PPW8316ADgJAR0AACAAHL2Xtej3zqN69EBwEkI6AAAAAGG69EBwJkI6AAAAAFm3%2BvRn/x2td0lAQBEQAcAAAhILRKi9PBf16NPmb1O3yzPtLkiAAABHQAAIEAN75qiy09sLkkaN22x0rIL7S0IAAIcAR0AACCA3TW0vXo2q6f8knJd%2B7%2BFKiott7skAAhYBHQAAIAAFhrs1nOXdFfDmDCtyszXnR8ulWVZdpcFAAGJgA4AABDgEmPD9dwl3RXsdumzJVv1yo8b7C4JAAISAR0AAADq1by%2B7h7WXpL00Jcr9Mv6HTZXBACBh4AOAAAASdLlJzbXOd1S5PFaGvv278rMK7a7JAAIKAR0AAAASJJcLpcmjeisdskxyi4o0XVvLlRJucfusgAgYBDQAQAAsEdkaLBe%2BHsPxYQHa9GmHN378TIajQOAOkJABwAAQBXNE6L07EXHy%2B2Spv2Wrtd%2B2mB3SQAQEAjoAAAA2M8pbRN111DTaNyDny/X92u221wRAPg/AjoAAACqdeVJLTSyexN5LWnsW4uUll1od0kA4NcI6AAAAKiWy%2BXSv8/rpOObxiuvuFz/eP1X5RWX2V0WAPgtAjoAAAAOKDwkSC9e2kON4sK1bnuhbnrnd3m8NBoHALWBgG6zKVOmqHnz5goPD1efPn20YMGCA447depU9e/fX/Xq1VO9evU0aNCgg44PAABQExJjwvXSpT0VHuLWnFXb9ciMlXaXBAB%2BiYBuo2nTpmncuHGaOHGiFi1apK5du2rw4MHKysqqdvw5c%2Bbooosu0uzZszV//nylpqbqjDPO0JYtW%2Bq4cgAAEGg6N4nTo%2Bd3lSS9OG%2B93vst3eaKAMD/uCxubGmbPn36qFevXpo8ebIkyev1KjU1VTfeeKP%2B%2Bc9/HnJ6j8ejevXqafLkyRo9evRhLTMvL09xcXHKzc1VbGzsMdUPAAACz%2BMzV%2BnZ79Yq2O3SG1f01omtEuwuCcBRIhs4D0fQbVJaWqqFCxdq0KBBe/q53W4NGjRI8%2BfPP6x5FBUVqaysTPXr1z/gOCUlJcrLy6vSAQAAHK1bB7XR8K4pKvdauubNhVqTmW93SQDgNwjoNsnOzpbH41FSUlKV/klJScrIyDisedx5551KSUmpEvL3NWnSJMXFxe3pUlNTj6luAAAQ2Nxulx49v4t6Nqun/OJyjXntV23PL7G7LADwCwR0H/Xwww/r3Xff1fTp0xUeHn7A8caPH6/c3Nw9XXo614sBAIBjEx4SpJdG91TzBpHavGu3/vHGb9pd6rG7LADweQR0myQkJCgoKEiZmZlV%2BmdmZio5Ofmg0z722GN6%2BOGHNXPmTHXp0uWg44aFhSk2NrZKBwAAcKzqR4Xq1TG9FR8ZoiXpObp12mJ5uf0aABwTArpNQkND1aNHD82aNWtPP6/Xq1mzZqlv374HnO6RRx7Rgw8%2BqBkzZqhnz551USoAAEC1WiRE6aVLeyo0yK0ZyzI06asVdpcEAD6NgG6jcePGaerUqXr99de1YsUKXXfddSosLNSYMWMkSaNHj9b48eP3jP%2Bf//xHEyZM0CuvvKLmzZsrIyNDGRkZKigosOslAACAANe7RX09eoE5o2/q92l6/acN9hYEAD4s2O4CAtmoUaO0fft23XvvvcrIyFC3bt00Y8aMPQ3Hbdq0SW535W8ozz//vEpLS3X%2B%2BedXmc/EiRN133331WXpAAAAe5zTrbHSdxbpsZmrdd9ny9QgOlRndUmxuywA8DncBz3AcK9DAABQGyzL0r2fLNP/ft6okCCXXhvTW/24RzrgaGQD5%2BEUdwAAABwzl8ul%2B87uqKGdk1XmsXTN/xbqzy25dpcFAD6FgA4AAIAaEeR26clR3dS3ZQMVlJTr8lcXaOOOQrvLAgCfQUAHAABAjQkLDtKLo3uoQ6NYZReU6tKXF2h7fondZQGATyCgAwAAoEbFhofotSt6KbV%2BhDbtLNLlry5QfnGZ3WUBgOMR0AEAAFDjEmPC9b8r%2BqhBVKiWbc3TP17/TbtLPXaXBQCORkAHAABArWieEKXXr%2BitmLBg/ZK2U9e%2BuVAl5YR0ADgQAjoAAABqTafGcXplTC9FhARp7urtuvmdxSr3eO0uCwAciYAOAACAWtWreX1NHd1ToUFuzViWods/%2BENer2V3WQDgOAR0AAAA1LqTWifouUu6K9jt0vTft%2BieT/6UZRHSAWBvBHQAAADUiUEdkvTkqG5yuaS3f9mkf3%2BxgpAOAHshoAMAAKDODO%2Baov%2BM6CJJ%2Bu8PaXrim9WEdAD4CwEdAAAAderCXqm6b3gHSdKz360lpAPAXwjoAAAAqHOX92uhe4a1l2RC%2BuMzCekAQEAHAACALf7Rv6UmnGWOpE%2BevVaPfr2KkA4goBHQAQAAYJsrT2qhe/8K6c/NWadHCOkAAhgBHQAAALa64qQWmvjXNenPz1mn/8wgpAMITAR0AAAA2G5Mvxa6/%2ByOkqQX5q7jFmwAAhIBHQAAAI5w2YnN9cA5JqT/94c03TV9qTxeQjqAwEFABwAAgGOM7ttcj4zsIrdLemdBum6ZtlhlHq/dZQFAnSCgAwAAwFEu7JWqZy46XsFulz5bslXXvblQxWUeu8sCgFpHQAcAAIDjnNUlRVNH91RYsFvfrsjSFa/9qsKScrvLAoBaRUAHAACAIw1sl6jXr%2BitqNAg/bRuhy757y/KKSq1uywAqDUEdAAAADjWCS0b6K2rTlBcRIgWp%2Bfoghfma2vObrvLAoBaQUAHAACAo3VLjdd71/RVUmyY1mQVaMRzP2llRp7dZQFAjSOgAwAAwPHaJsfoo%2Bv7qXVitDLyinXBC/M1f90Ou8sCgBpFQAcAAIBPaBwfofev7atezespv7hcl72yQF/8sc3usgCgxhDQAQAA4DPiI0P1vyv7aEjHZJV6vLrhnUV69cc0u8sCgBpBQAcAAIBPCQ8J0pRLuuvSE5rJsqT7P1uuBz5bLo/Xsrs0ADgmBHQAAAD4nCC3Sw%2Bc01G3D24rSXrlxzRd/cZvKuBe6QB8GAEdAAAAPsnlcmnswFaafPHxCgt2a9bKLJ3//E/awm3YAPgoAjoAAAB82lldUvTu1ScoITpMKzPydc7kH7U4PcfusgDgiBHQAQAA4POOb1pPH489Ue2SY5RdUKJRL86nhXcAPoeADgAAAL/QpF6kPrjuRJ3aLlEl5V6NfXsRIR2ATyGgAwAAwG9EhwVr6uieuqJfC7VvFKtT2ja0uyQAOGzBdhcAAAAA1KQgt0v3Du%2BgwpJyRYXxdReA7%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%2BX8vPN6zrpJOnBB6Xu3aXCQumFF6Rnn5UyMyXLkiIiJK9XKiuTQkOl6GgpKMj0b9nSzHfDBvN%2BnHKKVK%2BeNHOmtGOHFBkplZRI6enS7t1m3MhI0xUXS6WlZhkVjy6X6STzPChIcrul8nJTg2ReW3KymSYnxwxzucw8PR6pqKhyXg0bSvfea/5%2B7DFp48bK%2BRyI222Wa1nmNbndptZDTXek3O4jm2fFaywuNq%2BztkVESPXrm3VgWWY979pllm9Z1U8TEmLe/6Iis73ExZl9dvVqs51WJzlZOvdcadUqacsWKT7e7PerV5tl7s3lqlz23n/vO05oqFl2WJhZx5Zl1ndsrFmHeXlmnDPOkK6/XjruuMrp8/Olu%2B82nwUV4yUlme13507zuiq2zehoM/%2BiIjOt2125/VYs78QTpRtvlAYOlLZulV58Ufr0UzO/rl2lYcOk336TZs8282rf3ixn0SIzTkSElJhohkVESIMGmZpbt66seft26aWXpI8%2BMvtw%2B/bStdea11dRj2Tm%2B9//Vu7/bdpIV19taqgYb%2BFCafJk6ZdfzGusbh1Vp6hI%2Bt//TLd9u5SaKo0ZI114YeXnUXWf51dcIZ1/fs18nns80vTp5jWmpZnt96KLzOd4bOyRz2/LFvN5%2BNln5nOsWzezLvr3P/ZacfQsS5o1y7w3f/5p9ulzz5Wuucbsq0AFC7aaPHmy1axZMyssLMzq3bu39csvvxx0/Pfee89q27atFRYWZnXq1Mn64osvjmh5ubm5liQrNzf3WMoGcDBbtlhWu3aWFRRkWeecY1n/93%2BWdfLJliVZ1hlnWNbu3TW3rJ07LatXL8tyuSxryBCzrDPOMMs64QTLysmpuWUdqzfeMOskONjU16yZZUVFmdolyxo40LIKC/ef7vvvLSsiwowTFmZZnTpZVny8eS5Z1r//bVmtWpm/XS7Lql%2B/cphkWSEhVZ/HxVX%2BfcIJlnXRRZbldpvnffpYVosWVccPC6v6vKa6itd9qH50R99VvK9H0qWkHNn49epZ1oUX1s57t%2B%2B2Fx1tllW/vmWFh1vW55%2BbfWTr1srtPibGspo3P/Zlh4aax4svNvtMdLRljR5tWbfcYlmNG5thERGWdfXVlnXqqZXThYdXXYdut2UNHWpZDRqY1/Pxx6bmJUssKzHRjH/xxZZ1662W1aWLmeaqqyzL4zHjrVxplhcWZlmjRlnWuHGW1aOHGe/vf7es8nLLevJJ87xZM8u68UYz/b7rqDqZmZbVubOp8ayzzLwrXsspp1hWQYFZt%2B3b197neUmJZQ0fbubXr5%2BpYcQI8znZqpVlbdp0ZPP74QfzfsXEWNbll5v3q00bM/%2B77jq2WnH0vF6zbUqW1bGj2d4vvdSyIiPNvvHbb7aVRjZwHtldQCB79913rdDQUOuVV16xli1bZl111VVWfHy8lZmZWe34P/74oxUUFGQ98sgj1vLly6177rnHCgkJsZYuXXrYy2QnBOrAqaeaL5QrVlTt/8035gvjrbfW3LJGjTIB4ddfq/b/5RfzJe2SS2puWcdi2TLzBbdxY/OlvOJzq6TEsq64wnxBDg%2B3rOuvrzpdbq55HS6XZZ19tmUVFZn%2BXq9lPfRQZQgICjLB%2BqWXzPN27cx6qQjnxx1nWSedVBn0u3a1rHvvrfyy1KSJed%2BCg00wCQ42XUiI%2BZIcE1MZSCqWmZhY%2BWPDvmFq77BW0X/vfkFBla957yD5%2BuvmS9vRBrja6ho0OPJpfPXHBre7%2BmC/93u/b1exnYWFVU57OCH5YOsoJKTyxzbJsp5/3rLatjXba36%2BCYsREeYHwY4dzTgTJlTua/37V53fxx9bVqNGVbcbt9vUEB1dWcvw4Wa7Tkys/OGrdWvL2rHD7Hvr1pnaunc3w/7zn8rXXNFPMmFk1Cgz38hIy9q40bJGjjTb/Lp1Zv0cf7xlZWRU7u9er2W9/HLl6/V4TDhu396y0tOrfja8/bZZ7nXXmfFvv92E9QqFhVXXUXWGDbOspKTKz6MKc%2BeaHw%2BvvdayTjvt4J/nt9xy8M%2B%2BQxk/3nzm7PtDwpo15geHvn0Pf155eWZfHTDAsnbtquzv9VrWI4%2BY9fTBB8dWL47Oq6%2Ba9f/cc%2Bb9qLB9u/mRPSXFsoqLbSmNbOA8sruAQNa7d29r7Nixe557PB4rJSXFmjRpUrXjX3jhhdawYcOq9OvTp491zTXXHPYy2QmBWvbHH%2Baf8PvvVz/87rtN2MvPP/ZlpaebL6hTplQ//KmnzBftrVuPfVnH6vrrLathQ7Nu/ve/qsNKSy0rOdl8SYmMrPrFcsoU8wX/QOts70Awd645stWrl3n%2B%2BuuWdfrp5u/ERMtKSzN/x8SYL9Y7dlhWhw6V4WXLFvN3s2bm8amnLOsf/zB/d%2Bmyf%2BD68ksThCqeVyxr31Bb8QNDxfOKMLP32QMjR5r%2BrVtb1oYN1Ye46OgjC5onnFA5bZMmRxdWK36AOFRAr3hv9%2B6ioo4%2BnB5JV3E0dd%2Ba9w66FT%2BwVHTJyZVHa6ubprru3XfNkeDYWPO84seVvZez97o66STzuPcZHRVHpff%2BAaBevcp5VmxjMTHmC7vLZUL5gAEmcM%2Bda4bPnGnOjomKMvuWZPYFy7KssWNN6Gzb1vSv2Eb//e/K9b7vWSIul2Xddpv5%2B9JLLSshwbzWiuFdulTuc7fdZl5TUZHZ5hMTTeDv1s2yyspMTbGxJlwXF5t5BQVZ1qRJZh%2BOiTHbu2SOolfnggvMj2xffmnG%2B/HH6se7/HLzmdG%2BfdXQU6FiHU2cuP%2BwVavMvN94o/p5/%2Btfle/xe%2B9VP07F53leXvXDD6WoyLz///d/1Q//9FOz/EOcXbnHc8%2BZbetAR90HDDDbJeqW12t%2BFB4%2BvPrhK1aY9/nNN%2Bu0rApkA%2BehkTiblJaWauHChRo0aNCefm63W4MGDdL8%2BfOrnWb%2B/PlVxpekwYMHH3B8SSopKVFeXl6VDkAtmjvXXPt5zjnVDx81ylxD%2Bfvvx76sn34y13deeOGBl1VeLh3kM6LOzJ0rdexorhc9//yqw0JCpBEjzHXZRUXmutYK8%2BZJMTHmWtPo6P3n26aNeXS7zTWz339f2e%2BCCyqvBc7KMtd%2BBweba4eLi81yWrUyw886y1xvLplr0F0uc81mkyam37p1pg2Bva%2BLXbu26jXVmzZVHX7RRea15eWZmJOTY/pXXL9dcY10cLCZtlMnaf36/bcNyzKPJSXVrdlK7n3%2BpaemVk5b8dm/d5sEsbGV9R6orYKK9bdjx4GX63JVvw0WFh683gYNDj68Qnz8wYeHhJhrdiXzmva9/tuyzHXde8vKkq66qvJ5w4b7zzc5ufJvl0s67zyzT1Wsy%2BLi/cdv3rzyfV6yxNQeE1M5TmlpZU0REebv886rfG8r2kaIiDDXfVuWdPzxZv3%2B8IPZxhs1MvtFXJw0ZIhpN0GSbrjBPM6bJw0fbq6Nj4io3Ebfe888tmtn2nSo2J9cLqlp08rp5841n19bt5ptqkED6Y8/KttimDvXzD8iwqyPrCyzTY8aZbblivYY3G6zXY0YYa61nTfPLHPYMPOZ1Lat1KXL/utdMvNauVKaMcPsg337Hni8oiLp9NOr7nsVKtbRvHn7D/v%2Be/N4wQUHnndxsbme/dxzDzxOfr60eHH1ww9l2TLTPsKBPsOHDjXrrLr6qzNvntlGUlOrHz5qlNmO6qItCFTKzzefB6NGVT%2B8XTvTrsPcuXVbFxyLgG6T7OxseTweJe3TKERSUpIyMjKqnSYjI%2BOIxpekSZMmKS4ubk%2BXeqAPbQAIZBVBFqgO2wcAoI4Q0P3c%2BPHjlZubu6dLrzhCBKB2DBhgjpJ98kn1w6dNM0fUjj/%2B2Jd14onm6E7FkbHqlhUcfOCjT3VpwABztMiypA8%2BqDqstNS04hwfb4609exZOezkk83Rhy%2B%2BMC0p72v1avPo9ZozCvr3r%2Bz3/vuVLSwnJZkzG8rLTUv34eFmOWvXmuFffFF5tLxBA1Pnc8%2BZI42SaQn65ZerBrVWraq24Ny0adXh77xjWs6uOFJdcSS44kh3RSvu5eVm2j//NEdQ9902DnWUu8K%2BLaunp1dOW9ES9N5H4SuO7O/bf28Vr%2B9gR7stq/ptMCrq4PUe7Kj83iqOSB9IWZlp5Vsyr2nfVrVdrv1bYk9MlKZOrXy%2Bffv%2B8933x%2B/p080%2BVbEuw8P3H3/Dhsr3uWtXU3t%2BfuU4oaGVNVUckZ4%2BvfK9Xb/ePO7eLaWkmPEWLTLr96STzDa%2BbZvZL3JzzRHmM84w00yebB5PPtm03t22rZlPUJDpX3GUduVKs61X7E%2BWZc7gePZZ83zAAPP5lZJitqkdO8yR7ooj/gMGmPnv3m3WR2Ki2aanTTPbcsWJ8V6v2a4%2B%2Bsgc5T75ZLPML74wn0mrVpkj89WZNs0cVRwyxOyDBzoLaNo085kxc2b1P6JUrKOTT95/WEWL5u%2B/f%2BB5h4ebo80ff3zgcWJiTCvpR6NjR3P3gAN9hn/5pVln1dVfnZNPNtvIgb7rTZtmtqOKbQJ1IybGfB5Mm1b98JUrzRH2AQPqti44l93n2AeqkpISKygoyJo%2BfXqV/qNHj7bOPvvsaqdJTU21nnzyySr97r33XqvL3teGHQLXmQB1gEbi9lfTjcR5PEfeSFy/fkfeSFxFS8rH0khcxXXHNBLnG92BGonb95rzvTunNBJ3992%2B1Uhct25H10ic11uzjcT98UfV/r7eSNzJJ1dty8PjMe%2BTRCNxdqloJG7KlOobiWvcmEbisIfsLiCQ9e7d27rhhhv2PPd4PFbjxo0P2kjcWWedVaVf3759aSQOcJp9b7M2blzt3WZt167K26wNHmyW5Wu3Wav4Qs9t1nw31Dq1q6vbrFUE0Zquv7rbrF1wgVnmgW6zFh1ds7dZu%2BQSs89ERVXeZq1iHUVGmtusnXZa5XTh4ZU/BFS8B9XdZu2PPw5%2Bm7WKELNypfkBLTT0wLdZe%2Bop87xp06O/zdqwYWbeAweaeZ1yivk82vs2a2efXfOf53vfZu3EE838zzvv2G%2BzFh1tWZddZlk338xt1pygutus/f3vZh9KSOA2a6jCZVmWZe8x/MA1bdo0XXbZZXrxxRfVu3dvPfXUU3rvvfe0cuVKJSUlafTo0WrcuLEmTZokSfrpp580YMAAPfzwwxo2bJjeffddPfTQQ1q0aJE6dep0WMvMy8tTXFyccnNzFVtxmh6AmldQIL3xhvTWW%2BYU0WbNpCuukEaO3P8U3GNVXGxOp379dXP6a0qKdPnl0t/%2BdujTouva8uXSU0%2BZ01xzckwDX%2B3aSTffbBqPCwmpfrotW6SHHjKno%2BblmdfVv7/04IPmlPDCQunFF81puhWnJoeHm4hQVmbmGxNjTsWNiDCnkrtcUlqaeT8GDjSnms6cKWVnm9NmS0rMqaIVpyJHRprTtnfvNqflW1blY0XDWJJ5HhRkllVeXnnqeUiIaeCrtNQ0DFVxintkpDmNtqiocl4NG0r33mvm8eij0saN%2B5/Cvi%2B32yzXssxrCgoytR5quiPldlfGuMNR8RqLi%2BumcaqICNNoXMV%2BVl5uToEvLj5wzSEh5v0vKjLjx8WZyw5Wr97/9PgKjRqZBt5WrDDbZ3y82Q5Xrdq/oTqXq3LZe/%2B97zihoWbZFadWV2xLMTFm28vNNdv%2BGWdI111nLr2oUFAg3X23%2BczJzzevKTnZvKadO81%2BUDG/isb0iorMtBWnPFdsx7GxUr9%2B0o03SqecYhqMe%2Bklc%2Bp7cbE55X3YMGnhQum778w21qGD2a5/%2B82MEx5uLi3xeMx7MmiQNHZsZcOMkrm0YOpUcxp8QYGZxzXXmNe3d6NvO3eaS0wq9v82baSrrzY1VIy3cKE0ZYr088/mvT/9dNPQ497rqDpFRdKbb0r/%2B59p8C411XxWX3BB5efR3p/n2dnm8/zKK2vu89zjMZc6vPyyucyhfn3p4oulyy6rvKTiSGzZYj4PP/vMvBfdupl1UXFaP%2BxhWWZ/eeEFc0lTZKRpgPDqq82%2BYhOygfMQ0G02efJkPfroo8rIyFC3bt30zDPPqE%2BfPpKkU045Rc2bN9drr722Z/z3339f99xzjzZs2KDWrVvrkUce0dChQw97eeyEAAAAACSygRMR0AMMOyEAAAAAiWzgRLTiDgAAAACAAxDQAQAAAABwAAI6AAAAAAAOQEAHAAAAAMABCOgAAAAAADgAAR0AAAAAAAcgoAMAAAAA4AAEdAAAAAAAHICADgAAAACAAxDQAQAAAABwAAI6AAAAAAAOQEAHAAAAAMABCOgAAAAAADgAAR0AAAAAAAcgoAMAAAAA4AAEdAAAAAAAHICADgAAAACAAxDQAQAAAABwAAI6AAAAAAAOQEAHAAAAAMABgu0uAHXLsixJUl5ens2VAAAAALBTRSaoyAiwHwE9wOTn50uSUlNTba4EAAAAgBPk5%2BcrLi7O7jIgyWXxc0lA8Xq92rp1q2JiYuRyuWytJS8vT6mpqUpPT1dsbKyttfgz1nPdYD3XPtZx3WA91w3Wc%2B1jHdcN1nPdqK31bFmW8vPzlZKSIrebq5%2BdgCPoAcbtdqtJkyZ2l1FFbGwsH%2Bh1gPVcN1jPtY91XDdYz3WD9Vz7WMd1g/VcN2pjPXPk3Fn4mQQAAAAAAAcgoAMAAAAA4AAEdNgmLCxMEydOVFhYmN2l%2BDXWc91gPdc%2B1nHdYD3XDdZz7WMd1w3Wc91gPQcOGokDAAAAAMABOIIOAAAAAIADENABAAAAAHAAAjoAAAAAAA5AQAcAAAAAwAEI6LDNlClT1Lx5c4WHh6tPnz5asGCB3SX5lfvuu08ul6tK165dO7vL8mnz5s3T8OHDlZKSIpfLpY8//rjKcMuydO%2B996pRo0aKiIjQoEGDtGbNGnuK9WGHWs%2BXX375ftv2kCFD7CnWR02aNEm9evVSTEyMEhMTde6552rVqlVVxikuLtbYsWPVoEEDRUdHa%2BTIkcrMzLSpYt90OOv5lFNO2W97vvbaa22q2Dc9//zz6tKli2JjYxUbG6u%2Bffvqq6%2B%2B2jOcbfnYHWodsx3Xjocfflgul0u33HLLnn5sz/6PgA5bTJs2TePGjdPEiRO1aNEide3aVYMHD1ZWVpbdpfmVjh07atu2bXu6H374we6SfFphYaG6du2qKVOmVDv8kUce0TPPPKMXXnhBv/zyi6KiojR48GAVFxfXcaW%2B7VDrWZKGDBlSZdt%2B55136rBC3zd37lyNHTtWP//8s7755huVlZXpjDPOUGFh4Z5xbr31Vn322Wd6//33NXfuXG3dulUjRoywsWrfczjrWZKuuuqqKtvzI488YlPFvqlJkyZ6%2BOGHtXDhQv3222869dRTdc4552jZsmWS2JZrwqHWscR2XNN%2B/fVXvfjii%2BrSpUuV/mzPAcACbNC7d29r7Nixe557PB4rJSXFmjRpko1V%2BZeJEydaXbt2tbsMvyXJmj59%2Bp7nXq/XSk5Oth599NE9/XJycqywsDDrnXfesaFC/7DverYsy7rsssusc845x5Z6/FVWVpYlyZo7d65lWWbbDQkJsd5///0946xYscKSZM2fP9%2BuMn3evuvZsixrwIAB1s0332xfUX6qXr161n//%2B1%2B25VpUsY4ti%2B24puXn51utW7e2vvnmmyrrlu05MHAEHXWutLRUCxcu1KBBg/b0c7vdGjRokObPn29jZf5nzZo1SklJUcuWLXXJJZdo06ZNdpfkt9LS0pSRkVFlu46Li1OfPn3YrmvBnDlzlJiYqLZt2%2Bq6667Tjh077C7Jp%2BXm5kqS6tevL0lauHChysrKqmzP7dq1U9OmTdmej8G%2B67nCW2%2B9pYSEBHXq1Enjx49XUVGRHeX5BY/Ho3fffVeFhYXq27cv23It2HcdV2A7rjljx47VsGHDqmy3Ep/NgSLY7gIQeLKzs%2BXxeJSUlFSlf1JSklauXGlTVf6nT58%2Beu2119S2bVtt27ZN999/v/r3768///xTMTExdpfndzIyMiSp2u26YhhqxpAhQzRixAi1aNFC69at01133aUzzzxT8%2BfPV1BQkN3l%2BRyv16tbbrlF/fr1U6dOnSSZ7Tk0NFTx8fFVxmV7PnrVrWdJuvjii9WsWTOlpKTojz/%2B0J133qlVq1bpo48%2BsrFa37N06VL17dtXxcXFio6O1vTp09WhQwctXryYbbmGHGgdS2zHNendd9/VokWL9Ouvv%2B43jM/mwEBAB/zUmWeeuefvLl26qE%2BfPmrWrJnee%2B89XXnllTZWBhybv/3tb3v%2B7ty5s7p06aLjjjtOc%2BbM0WmnnWZjZb5p7Nix%2BvPPP2mjopYdaD1fffXVe/7u3LmzGjVqpNNOO03r1q3TcccdV9dl%2Bqy2bdtq8eLFys3N1QcffKDLLrtMc%2BfOtbssv3KgddyhQwe24xqSnp6um2%2B%2BWd98843Cw8PtLgc24RR31LmEhAQFBQXt1%2BJkZmamkpOTbarK/8XHx6tNmzZau3at3aX4pYptl%2B267rVs2VIJCQls20fhhhtu0Oeff67Zs2erSZMme/onJyertLRUOTk5VcZnez46B1rP1enTp48ksT0fodDQULVq1Uo9evTQpEmT1LVrVz399NNsyzXoQOu4OmzHR2fhwoXKyspS9%2B7dFRwcrODgYM2dO1fPPPOMgoODlZSUxPYcAAjoqHOhoaHq0aOHZs2ataef1%2BvVrFmzqlzLhJpVUFCgdevWqVGjRnaX4pdatGih5OTkKtt1Xl6efvnlF7brWrZ582bt2LGDbfsIWJalG264QdOnT9d3332nFi1aVBneo0cPhYSEVNmeV61apU2bNrE9H4FDrefqLF68WJLYno%2BR1%2BtVSUkJ23ItqljH1WE7PjqnnXaali5dqsWLF%2B/pevbsqUsuuWTP32zP/o9T3GGLcePG6bL/b%2B/%2BY6qq/ziOv67oRUC4AqKIwQUFzJWpRRpsSmWGPzJxrVxRMDFdoUn%2BQLMkdWpOC1M0VvMHuAZKbalrzh/Tok2FUtPRDMvQtAwlzYlI/rp%2Bvn9U9%2BtVQdOUQz0f2904n/O5n/M%2Bn51/Xpx7PictTXFxcerZs6cWLFigM2fOaPjw4Y1d2r/GxIkTNXjwYDmdTv3yyy%2BaNm2avLy89OyzzzZ2aU1WbW2tx92AgwcPas%2BePQoKClJERIReffVVzZo1SzExMYqKilJ2drbCwsKUnJzceEU3QQ3Nc1BQkGbMmKGnnnpKoaGhqqys1KRJkxQdHa2kpKRGrLppGT16tIqKirR27Vr5%2B/u7n110OBzy8fGRw%2BHQiBEjNH78eAUFBSkgIECvvPKK4uPj9dBDDzVy9U3H9ea5srJSRUVFGjhwoIKDg1VeXq5x48apT58%2BV71aCfWbMmWKBgwYoIiICJ0%2BfVpFRUUqKSnRxo0buZb/IQ3NMdfxP8ff399jjQpJ8vPzU3BwsLud6/k/oLGXkcd/16JFi0xERISx2%2B2mZ8%2BepqysrLFL%2BlcZNmyYad%2B%2BvbHb7aZDhw5m2LBh5ocffmjsspq0zz//3Ei66pOWlmaM%2BeNVa9nZ2aZdu3bG29vb9O3b13z33XeNW3QT1NA819XVmccff9yEhISYFi1aGKfTaUaOHGmOHj3a2GU3KdeaX0kmPz/f3ef33383GRkZJjAw0Pj6%2BpqhQ4eaqqqqxiu6CbrePB8%2BfNj06dPHBAUFGW9vbxMdHW2ysrLMqVOnGrfwJiY9Pd04nU5jt9tNSEiI6du3r9m0aZN7P9fyrWtojrmOb68rX2HH9fzvZzPGmDv5DwEAAAAAAHA1nkEHAAAAAMACCOgAAAAAAFgAAR0AAAAAAAsgoAMAAAAAYAEEdAAAAAAALICADgAAAACABRDQAQAAAACwAAI6AAAWYLPZtGbNmsYuAwAANCICOgAAl/npp5%2BUnp6usLAw2e12OZ1OZWZm6sSJE9f97o8//iibzaY9e/bc/kLvgJKSEtlstgY/JSUlNz1%2BQUGBWrdu/Y/VCwBAU9e8sQsAAMAqDhw4oPj4eMXGxmrlypWKiorS3r17lZWVpfXr16usrExBQUHX/O758%2BfvcLW3X0JCgqqqqtzbmZmZqqmpUX5%2Bvrutvvm40y5cuKAWLVo0dhkAANwS7qADAPCn0aNHy263a9OmTUpMTFRERIQGDBigzZs368iRI3rjjTfcfSMjIzVz5kylpqYqICBAo0aNUlRUlCSpR48estlsevjhhyVJO3bsUL9%2B/dSmTRs5HA4lJibq66%2B/brCWyZMnKzY2Vr6%2BvurYsaOys7N14cIF9/7p06ere/fuWr58uSIiItSqVStlZGTI5XJp3rx5Cg0NVdu2bTV79myPcefPn6%2BuXbvKz89P4eHhysjIUG1t7TVrsNvtCg0NdX98fHzk7e3t3g4MDNTrr7%2BuDh06yM/PT7169XLfUT979qzuuecejRo1yj1eZWWl/P39tXz5cpWUlGj48OE6deqU%2B2789OnTJV375/6tW7dWQUGBpP//UqG4uFiJiYlq2bKlCgsLJUlLly5Vly5d1LJlS919993Ky8trcJ4BALASAjoAAJJ%2B%2B%2B03bdy4URkZGfLx8fHYFxoaqpSUFBUXF8sY425/55131K1bN%2B3evVvZ2dn66quvJEmbN29WVVWVPvnkE0nS6dOnlZaWpq1bt6qsrEwxMTEaOHCgTp8%2BXW89/v7%2BKigo0LfffquFCxdqyZIlevfddz36VFZWav369dqwYYNWrlypZcuWadCgQfr555/1xRdfaO7cuZo6daq%2B/PJL93eaNWum3Nxc7d27VytWrNBnn32mSZMm3dScjRkzRqWlpVq1apXKy8v19NNPq3///tq/f787NK9YsUJr166Vy%2BXS888/r379%2Bik9PV0JCQlasGCBAgICVFVVpaqqKk2cOPFvHf%2B1115TZmamKioqlJSUpMLCQr355puaPXu2Kioq9NZbbyk7O1srVqy4qfMDAOCOMwAAwJSVlRlJZvXq1dfcP3/%2BfCPJHDt2zBhjjNPpNMnJyR59Dh48aCSZ3bt3N3gsl8tl/P39zaeffupua%2BjYxhjz9ttvmwceeMC9PW3aNOPr62tqamrcbUlJSSYyMtK4XC53W%2BfOnc2cOXPqHffjjz82wcHBDdb7l7S0NDNkyBBjjDGHDh0yXl5e5siRIx59%2Bvbta6ZMmeLenjdvnmnTpo0ZM2aMad%2B%2BvTl%2B/Lh7X35%2BvnE4HFcd51pz4XA4TH5%2BvjHm//O8YMECjz6dOnUyRUVFHm0zZ8408fHxN3R%2BAAA0Np5BBwDgMuayO%2BTXExcXd0P9jh07pqlTp6qkpETV1dVyuVyqq6vT4cOH6/1OcXGxcnNzVVlZqdraWl28eFEBAQEefSIjI%2BXv7%2B/ebteunby8vNSsWTOPturqavf25s2bNWfOHO3bt081NTW6ePGizp49q7q6Ovn6%2Bt7oqeubb76Ry%2BVSbGysR/u5c%2BcUHBzs3p4wYYLWrFmjxYsXa/369R77btXl83/mzBlVVlZqxIgRGjlypLv94sWLcjgc/9gxAQC4nQjoAABIio6Ols1mU0VFhYYOHXrV/oqKCgUGBiokJMTd5ufnd0Njp6Wl6cSJE1q4cKGcTqe8vb0VHx9f78JypaWlSklJ0YwZM5SUlCSHw6FVq1YpJyfHo9%2BVi6LZbLZrtl26dEnSH89uP/HEE3r55Zc1e/ZsBQUFaevWrRoxYoTOnz//twJ6bW2tvLy8tGvXLnl5eXnsa9Wqlfvv6upqff/99/Ly8tL%2B/fvVv3//645ts9mu%2BkfJ5c/f/%2BXy%2Bf/rOfolS5aoV69eHv2urA8AAKsioAMAICk4OFj9%2BvVTXl6exo0b5/Ec%2BtGjR1VYWKjU1FTZbLZ6x7Db7ZIkl8vl0b5t2zbl5eVp4MCBkv54ldvx48frHWf79u1yOp0ei9IdOnTops7rcrt27dKlS5eUk5Pjvsv%2B0Ucf3dRYPXr0kMvlUnV1tXr37l1vv/T0dHXt2tV9Z/uxxx5Tly5dJP0xX1fOlSSFhIR4rB6/f/9%2B1dXVNVhPu3btFBYWpgMHDiglJeWmzgkAgMZGQAcA4E%2BLFy9WQkKCkpKSNGvWLI/XrHXo0OGqFdGv1LZtW/n4%2BGjDhg2666671LJlSzkcDsXExOjDDz9UXFycampqlJWVddVCdJeLiYnR4cOHtWrVKj344INat26dVq9efcvnFx0drQsXLmjRokUaPHiwtm3bpvfff/%2BmxoqNjVVKSopSU1OVk5OjHj166Ndff9WWLVt03333adCgQXrvvfdUWlqq8vJyhYeHa926dUpJSVFZWZnsdrsiIyNVW1urLVu2qFu3bvL19ZWvr68effRRLV68WPHx8XK5XJo8efINvUJtxowZGjt2rBwOh/r3769z585p586dOnnypMaPH39T5wkAwJ3EKu4AAPwpJiZGO3fuVMeOHfXMM8%2BoU6dOGjVqlB555BGVlpZe953fzZs3V25urj744AOFhYVpyJAhkqRly5bp5MmTuv/%2B%2B/XCCy9o7Nixatu2bb3jPPnkkxo3bpzGjBmj7t27a/v27crOzr7l8%2BvWrZvmz5%2BvuXPn6t5771VhYaHmzJlz0%2BPl5%2BcrNTVVEyZMUOfOnZWcnKwdO3YoIiJC%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%3D%3D"
     },
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 1000
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1000x800)"
     ]
    }
   ],
   "source": [
    "#NEW {Figür, idg (bu jupyter not defterinin çözümleyici arka-ucu)'nin \n",
    "#     grafik çizdirmesi için gerekli.}\n",
    "\n",
    "# Matplotlib temel ayarları:\n",
    "# Figür:\n",
    "fig = plt.figure figsize: [10, 8]\n",
    "# ___________________???\n",
    "ax = fig.add_subplot 111\n",
    "\n",
    "plt.scatter x y facecolors: 'none' edgecolors: ((map (x -> colors !! x) cancer.target) |> list) cmap: colors\n",
    "plt.plot x1 y1\n",
    "plt.xlabel \"Ortalama Texture\"\n",
    "plt.ylabel \"Olasılık\"\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65a9ba6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.87122245 0.12877755]]\n",
      "[0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#SUG{np.array([30]).reshape(1, 1):[[30]], double question-mark (??)}\n",
    "\n",
    "# ____$'dan sonrasını parantez içinde hayal edin.\n",
    "print $ lojistik_regresyon.predict_proba [[30]]\n",
    "print $ lojistik_regresyon.predict [[30]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a772caff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.05853587 0.94146413]]\n",
      "[1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#SUG{np.array([10]).reshape(1, 1):[[10]], double question-mark (??)}\n",
    "\n",
    "# Yukarıdaki gibi,\n",
    "# ____$'dan sonrasını parantez içinde hayal edin.\n",
    "print $ lojistik_regresyon.predict_proba [[10]]\n",
    "print $ lojistik_regresyon.predict [[10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2be4253",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'sklearn.metrics' from '/Users/sckn/.pyenv/versions/3.6.15/lib/python3.6/site-packages/sklearn/metrics/__init__.py'>"
     ]
    }
   ],
   "source": [
    "# Daha fazla özellik kullanarak modeli eğitmek için;\n",
    "# eğitim seti için, veri setinin %80'i hedef değişkene göre rastgele seçilirse:\n",
    "\n",
    "train_test_split = import '/sklearn/model_selection/train_test_split'\n",
    "LinearRegression = import '/sklearn/linear_model/LinearRegression'\n",
    "metrics = import '/sklearn/metrics'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e48de7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[1.422e+01, 2.312e+01, 9.437e+01, ..., 1.772e-01, 5.166e-01,\n",
      "        1.446e-01],\n",
      "       [1.846e+01, 1.852e+01, 1.211e+02, ..., 1.642e-01, 3.695e-01,\n",
      "        8.579e-02],\n",
      "       [1.340e+01, 2.052e+01, 8.864e+01, ..., 2.051e-01, 3.585e-01,\n",
      "        1.109e-01],\n",
      "       ...,\n",
      "       [1.152e+01, 1.493e+01, 7.387e+01, ..., 9.608e-02, 2.664e-01,\n",
      "        7.809e-02],\n",
      "       [1.218e+01, 1.408e+01, 7.725e+01, ..., 1.852e-02, 2.293e-01,\n",
      "        6.037e-02],\n",
      "       [1.919e+01, 1.594e+01, 1.263e+02, ..., 1.777e-01, 2.443e-01,\n",
      "        6.251e-02]]), array([[1.371e+01, 1.868e+01, 8.873e+01, ..., 1.284e-01, 2.849e-01,\n",
      "        9.031e-02],\n",
      "       [1.624e+01, 1.877e+01, 1.088e+02, ..., 1.732e-01, 2.770e-01,\n",
      "        1.063e-01],\n",
      "       [1.163e+01, 2.929e+01, 7.487e+01, ..., 6.835e-02, 2.884e-01,\n",
      "        7.220e-02],\n",
      "       ...,\n",
      "       [1.719e+01, 2.207e+01, 1.116e+02, ..., 1.984e-01, 3.216e-01,\n",
      "        7.570e-02],\n",
      "       [1.831e+01, 1.858e+01, 1.186e+02, ..., 1.571e-01, 3.206e-01,\n",
      "        6.938e-02],\n",
      "       [1.249e+01, 1.685e+01, 7.919e+01, ..., 2.784e-02, 1.917e-01,\n",
      "        6.174e-02]]), array([0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
      "       0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
      "       1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "       0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
      "       0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
      "       0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
      "       0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
      "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
      "       1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
      "       0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "       0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
      "       0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
      "       1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0,\n",
      "       1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0,\n",
      "       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
      "       1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1,\n",
      "       0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
      "       1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1,\n",
      "       1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0]), array([1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,\n",
      "       1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
      "       0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
      "       1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,\n",
      "       1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
      "       0, 0, 0, 1])]"
     ]
    }
   ],
   "source": [
    "#RNM{train_set:eğitim_seti}\n",
    "#RNM{test_set:test_seti}\n",
    "#RNM{train_labels:eğitim_etiketleri}\n",
    "#RNM{test_labels:test_etiketleri}\n",
    "\n",
    "[eğitim_seti, test_seti, eğitim_etiketleri, test_etiketleri] =\n",
    "    # ____________________________________________________________ hedef değişkene göre tesadüfilik:\n",
    "    train_test_split cancer.data cancer.target test_size: 0.2 random_state: 1 stratify: cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59e73f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(max_iter=2500)"
     ]
    }
   ],
   "source": [
    "#RNM{log_regress:lojistik_regresyon_hdg, lojist_regresyon önceden tanımlı,\n",
    "#    bir son-ek ile yeniden tanımlandı.}\n",
    "#NEW{Görünüşe göre, Lojistik Regresyon sürecindeki varsayılan çözümleyici (hesaplayıcı, algoritma)\n",
    "#    Limited-memory Broyden–Fletcher–Goldfarb–Shanno Algorithm (lbfgs), total iterasyon limitine\n",
    "#    takılıyor. Varsayılan maksimum iterasyon değerini (100), 2500 olarak tanımlandı.\n",
    "\n",
    "# İlk on (10) özellik seçimi:\n",
    "x = (map (i -> take 30 i) eğitim_seti) |> (bind map list) |> list\n",
    "# 0: Kötü Huylu,\n",
    "# 1: İyi Huylu:\n",
    "y = eğitim_etiketleri\n",
    "# Hedef değişkene göre (hdg), Lojistik Regresyon değişkeni:\n",
    "lojistik_regresyon_hdg = LogisticRegression max_iter: 2500\n",
    "lojistik_regresyon_hdg.fit X: x y: y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6a9aebd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35.75236948]\n",
      "[[ 0.89910365  0.19953176 -0.35301804  0.03193124 -0.13088869 -0.19637684\n",
      "  -0.44327392 -0.24317193 -0.23468656 -0.02464939 -0.12876275  0.89073982\n",
      "   0.5576907  -0.15531508 -0.01768398  0.02757701 -0.03937877 -0.03478448\n",
      "  -0.03674257  0.00164348 -0.04193034 -0.41835611 -0.15197106 -0.00991119\n",
      "  -0.27715402 -0.70447342 -1.21617963 -0.52331131 -0.71399133 -0.09778556]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print lojistik_regresyon_hdg.intercept_\n",
    "print lojistik_regresyon_hdg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8dec0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Malignant        Benign  Tahmin  Orijinal Sonuç\n",
      "0    0.063909  9.360908e-01       1               1\n",
      "1    0.955738  4.426206e-02       0               0\n",
      "2    0.068295  9.317053e-01       1               1\n",
      "3    0.000502  9.994983e-01       1               1\n",
      "4    0.999978  2.229047e-05       0               0\n",
      "5    0.004134  9.958661e-01       1               1\n",
      "6    0.005241  9.947593e-01       1               1\n",
      "7    0.005547  9.944527e-01       1               1\n",
      "8    1.000000  5.478683e-10       0               0\n",
      "9    0.000614  9.993858e-01       1               1\n",
      "10   0.092594  9.074059e-01       1               0\n",
      "11   0.015449  9.845513e-01       1               1\n",
      "12   0.000011  9.999888e-01       1               1\n",
      "13   0.135334  8.646659e-01       1               0\n",
      "14   0.008156  9.918443e-01       1               1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#CHN{Panda, Notdefterine önceden eklendiği için yeniden içerleme yapılmadı.}\n",
    "#RNM{preds_proba:tahmin_olasılıkları}\n",
    "#RNM{preds_class:tahmin_sınıfları}\n",
    "#RNM{original_result:orijinal_sonuçlar}\n",
    "#RNM{result:sonuç}\n",
    "\n",
    "tahmin_olasılıkları = pd.DataFrame $ lojistik_regresyon_hdg.predict_proba X: test_seti\n",
    "tahmin_olasılıkları.columns = [\"Malignant\", \"Benign\"]\n",
    "\n",
    "tahminler = lojistik_regresyon_hdg.predict X: test_seti\n",
    "tahmin_sınıfları = pd.DataFrame tahminler\n",
    "tahmin_sınıfları.columns = [\"Tahmin\"]\n",
    "\n",
    "orijinal_sonuçlar = pd.DataFrame test_etiketleri\n",
    "orijinal_sonuçlar.columns = [\"Orijinal Sonuç\"]\n",
    "\n",
    "#Üç veri çerçevesini 1 veri çerçevesine indirgeme:\n",
    "sonuç = pd.concat [tahmin_olasılıkları, tahmin_sınıfları, orijinal_sonuçlar] axis: 1\n",
    "print $ sonuç.head 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3874a73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Karışıklık Matrisi----\n",
      "col_0   0   1\n",
      "row_0        \n",
      "0      38   1\n",
      "1       4  71\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print \"----Karışıklık Matrisi----\"\n",
    "print $ pd.crosstab tahminler test_etiketleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a06a9736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38, 1, 4, 71]"
     ]
    }
   ],
   "source": [
    "#NEW {Değerler, ileri kullanımlar için değişkenlere açıldı.}\n",
    "#New {Ufak bir yardımcı fonksiyonla erişeç açılımı.}\n",
    "karışıklıkmatrisi_ = pd.crosstab tahminler test_etiketleri\n",
    "erişeç_ = a b -> karışıklıkmatrisi_ |> (!! a) |> (!! b)\n",
    "\n",
    "[gn_, yp_, yn_, gp_] = list <| map erişeç_ [0, 1, 0, 1] [0, 0, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a8d4a56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'IPython.core.display.Markdown'>"
     ]
    }
   ],
   "source": [
    "md = import '/IPython/display/Markdown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ac2fcd7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "<style>\n",
       "table {float:left}\n",
       ".tg  {border-collapse:collapse;border-spacing:0;}\n",
       ".tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
       "  overflow:hidden;padding:10px 5px;word-break:normal;}\n",
       ".tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
       "  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\n",
       ".tg .tg-rgo1{background-color:#ffce93;border-color:inherit;text-align:center;vertical-align:middle}\n",
       ".tg .tg-c4ww{background-color:#cbcefb;border-color:inherit;text-align:center;vertical-align:top}\n",
       ".tg .tg-zme7{background-color:#ffce93;border-color:inherit;text-align:center;vertical-align:top}\n",
       ".tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}\n",
       "</style>\n",
       "\n",
       "<table class=\"tg\">\n",
       "<thead>\n",
       "  <tr>\n",
       "    <th class=\"tg-c3ow\"></th>\n",
       "    <th class=\"tg-c4ww\" colspan=\"3\">Tahmin Değerleri</th>\n",
       "  </tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "  <tr>\n",
       "    <td class=\"tg-rgo1\" rowspan=\"3\">Gerçek<br>Değer</td>\n",
       "    <td class=\"tg-c4ww\"></td>\n",
       "    <td class=\"tg-c4ww\">0</td>\n",
       "    <td class=\"tg-c4ww\">1</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td class=\"tg-zme7\">0</td>\n",
       "    <td class=\"tg-c3ow\">GN: 38</td>\n",
       "    <td class=\"tg-c3ow\">YP: 1</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td class=\"tg-zme7\">1</td>\n",
       "    <td class=\"tg-c3ow\">YN: 4</td>\n",
       "    <td class=\"tg-c3ow\">GP: 71</td>\n",
       "  </tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<IPython.core.display.Markdown object>"
     ]
    }
   ],
   "source": [
    "md $ (\"\"\"\n",
    "<style>\n",
    "table {float:left}\n",
    ".tg  {border-collapse:collapse;border-spacing:0;}\n",
    ".tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
    "  overflow:hidden;padding:10px 5px;word-break:normal;}\n",
    ".tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\n",
    "  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\n",
    ".tg .tg-rgo1{background-color:#ffce93;border-color:inherit;text-align:center;vertical-align:middle}\n",
    ".tg .tg-c4ww{background-color:#cbcefb;border-color:inherit;text-align:center;vertical-align:top}\n",
    ".tg .tg-zme7{background-color:#ffce93;border-color:inherit;text-align:center;vertical-align:top}\n",
    ".tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}\n",
    "</style>\n",
    "\"\"\" + \"\"\"\n",
    "<table class=\"tg\">\n",
    "<thead>\n",
    "  <tr>\n",
    "    <th class=\"tg-c3ow\"></th>\n",
    "    <th class=\"tg-c4ww\" colspan=\"3\">Tahmin Değerleri</th>\n",
    "  </tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "  <tr>\n",
    "    <td class=\"tg-rgo1\" rowspan=\"3\">Gerçek<br>Değer</td>\n",
    "    <td class=\"tg-c4ww\"></td>\n",
    "    <td class=\"tg-c4ww\">0</td>\n",
    "    <td class=\"tg-c4ww\">1</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-zme7\">0</td>\n",
    "    <td class=\"tg-c3ow\">GN: {gn_}</td>\n",
    "    <td class=\"tg-c3ow\">YP: {yp_}</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-zme7\">1</td>\n",
    "    <td class=\"tg-c3ow\">YN: {yn_}</td>\n",
    "    <td class=\"tg-c3ow\">GP: {gp_}</td>\n",
    "  </tr>\n",
    "</tbody>\n",
    "</table>\"\"\".format gp_: gp_ yp_: yp_ yn_:yn_ gn_:gn_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d910965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Doğruluk (Accuracy)----\n",
      "0.956140350877193\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print '----Doğruluk (Accuracy)----'\n",
    "print $ lojistik_regresyon_hdg.score X: test_seti y: test_etiketleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b8bb2f97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ----------Metriksler----------         \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.90      0.94        42\n",
      "           1       0.95      0.99      0.97        72\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.96      0.95      0.95       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#NEW{Başlık ortalandı.}\n",
    "\n",
    "print \"         ----------Metriksler----------         \"\n",
    "print $\n",
    "    metrics.classification_report y_true: test_etiketleri y_pred: tahminler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0586f53c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0.        , 0.02380952, 0.88095238, 0.88095238, 0.9047619 ,\n",
      "       0.9047619 , 0.92857143, 0.92857143, 0.95238095, 0.95238095,\n",
      "       1.        , 1.        ]), array([0.        , 0.        , 0.        , 0.01388889, 0.01388889,\n",
      "       0.02777778, 0.02777778, 0.05555556, 0.05555556, 0.06944444,\n",
      "       0.06944444, 1.        ]), array([2.00000000e+00, 1.00000000e+00, 9.41799271e-01, 9.16193200e-01,\n",
      "       6.05801644e-01, 3.12054258e-01, 2.49873640e-01, 1.51929769e-01,\n",
      "       1.35334145e-01, 1.10255814e-01, 9.25940891e-02, 8.96126999e-08]))"
     ]
    }
   ],
   "source": [
    "#AUC ÖRNEĞİ***\n",
    "#NEW{Örneğe bağlı temel değişkenler bu örnekten çıkarılacak.\n",
    "#    Tahmin_olasılıkları_ ve tahminler_ değerleri örnek özelinde,\n",
    "#    önceden elde ettiğimiz tahmin nesnesi üzerinden oluşturulmuştur.\n",
    "\n",
    "tahmin_olasılıkları_ = lojistik_regresyon_hdg.predict_proba X: test_seti\n",
    "tahminler_ = (map (i -> take 1 i) tahmin_olasılıkları_) |> map list |> map np.array |> list\n",
    "\n",
    "[fpr, tpr, threshold] = metrics.roc_curve test_etiketleri tahminler_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "235f662a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005621693121693121"
     ]
    }
   ],
   "source": [
    "roc_auc = metrics.auc fpr tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "93cb9898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9sh6zK9%2BvLd%2BznT3nn8qVKweE29NJknLPS9wlKcZkZGQwduxYTjnlFBYuXJh1vkGDBgwdOpSpU6fmungCWXNQc9oabVfVrl2bbt260a9fP7p27corr7xCQkLCNo979tlnGTp0KHPmzGHz5s1Z52vWrLnNY3M6l9fXGDRoEGvXruWtt97K0/7S8%2BbNA%2BDUU0/N8f5//hKiSJEiHHDAATt93auvvpqXX36ZM844g2rVqtGqVSvOPfdcTj/99Fxny8n111/Pxo0b%2BeabbzjqqKN2%2Bvi99f62lLYtxW97tlcMDznkkGzHtWrVIj4%2BPmue9rx58wiCYJvHbfHPRfmqVauW42JsP/zwA7fddhvvv//%2BNr8kSElJ2WF2gF9%2B%2BYVDDjkk29ZmsHUqwi%2B//JLt/PZ%2Blvems846i2uuuYa0tDS%2B%2BuorBg4cyPr167Nl3p3v186e809BEAC7/ktASSqsLOiSFGPef/99/vjjD8aOHcvYsWO3uX/MmDF5Kuh7y0033cSff/7JkCFDuPzyyxk9enS2f4y/8MILdO/enQ4dOnDjjTdSqVIlEhISGDRo0DYLV0H2Uc5dfY3WrVvz9ttvM2TIEJo3b57jatc52bI12vPPP0%2BVKlW2ub9Ikez/u0xMTNymrOWkUqVKzJo1i3feeYe33nqLt956i6effppu3bpts8BYXpx11lmMHTuWe%2B%2B9l%2Beee26nWfbW%2B0tOTmb//ffn22%2B/3eHjvv32W6pVq7bdqy22%2BGeZy8zMJC4ujrfeeivHXwD9czQ5p5%2Bh1atX06xZM5KSkrj77rupVasWxYsX5%2Buvv6Zfv3553hYvN3LKsb2imtsrYvbbbz/S09NZs2ZNjqPZBxxwQNYCcm3atKFChQpcc801nHLKKVl7t%2B/q96tq1ap8//33ucq5xV9//QWwzdoUkqQds6BLUowZM2YMlSpV4pFHHtnmvgkTJjBx4kRGjhyZYwnIyZZFor7//vs9vo/64MGDWbVqFU8%2B%2BSTlypVj6NChWfeNHz%2Begw8%2BmAkTJmQrJ3nZKi6vr3HSSSdx5ZVX0q5dO8455xwmTpyYrXxuryRt%2BRpVqlQp16tk51axYsVo37497du3JzMzk6uvvprHH3%2Bc22%2B/ndq1a%2B/SCGOHDh1o1aoV3bt3p0yZMjz22GM7fPzefH/t2rVj1KhRfPrppzRp0mSb%2Bz/55BMWLVpEz549t7lv3rx52Uab58%2BfT2ZmZtaCdbVq1SIIAmrWrMmhhx66S/k%2B/PBD/vzzTyZMmEDTpk2zzv/96pQttve9OOigg/j222/JzMzM9ouLLZfHH3TQQTvNseWS79WrV2c7/8/R9%2B05/PDDs3Ifc8wxO318z549eeCBB7jtttvo2LFj1nvble9Xu3bteOKJJ5g%2BfToNGzbMVd4tX9/cLngoSQo5B12SYsiGDRuYMGEC7dq1o3PnztvcrrnmGtasWcMbb7yR69ds1aoVZcqUYdCgQdtsYbXlMtTd8fjjj9O5c2eGDRvGf/7zn6zzW0Y8//45vvjiC6ZPn57r196V12jRogVjx47l7bff5qKLLso2QrplL%2B9/lqTWrVuTlJTEwIEDs11Gv8WKFStynfnv/vzzz2zH8fHxWeVq06ZNO8y0M926dePBBx9k5MiR9OvXb4eP3VvvD%2BDGG2%2BkRIkS9OzZc5v3u2rVKq688kpKlizJjTfeuM1z//lLqIceegiAM844A4BOnTqRkJDAXXfdtc3PahAE23y%2BnOT0M5SWlsajjz66zWNLlSqV4yXvbdq0YenSpYwbNy7rXHp6Og899BClS5emWbNmO82x5ZckH3/8cda5jIwMnnjiiZ0%2BF8gqxjNmzMjV44sUKcK//vUvfvzxR15//fWs87vy/brpppsoVaoUl112GcuWLdvmcy1YsCBr68AtZs6cSVxcXK4LvSQp5Ai6JMWQN954gzVr1nDmmWfmeP9JJ51ExYoVGTNmDF26dMnVayYlJfHAAw9w2WWXUb9%2Bfc4//3zKlSvH//73P9avX79bl1pDWDrHjBlDSkoKt99%2BO%2BXLl%2Bfqq6%2BmXbt2TJgwgY4dO9K2bVsWLlzIyJEjOfLII1m7dm2uXntXX6NDhw5Zl5InJSXx%2BOOPA3DccceRkJDA4MGDSUlJITExkVNPPZVKlSrx2GOPcdFFF3HCCSdw3nnnUbFiRRYvXsykSZNo3LgxDz/8cJ6/NpdddhmrVq3i1FNP5YADDuCXX37hoYce4rjjjssaWdxRpp255pprSE1N5dZbbyU5OTlr3%2B9/SkpK2ivvD8J55M8%2B%2BywXXHABRx99NJdeeik1a9Zk0aJFjB49mpUrV/LSSy9ts90XhKOsZ555JqeffjrTp0/nhRde4Pzzz%2BfYY48FwlL7n//8h/79%2B7No0SI6dOhAmTJlWLhwIRMnTuSKK66gb9%2B%2BO8zXqFEjypUrx8UXX8x1111HXFwczz//fI6/nKpbty7jxo2jT58%2B1K9fn9KlS9O%2BfXuuuOIKHn/8cbp3787MmTOpUaMG48eP57PPPmP48OG5WkDtqKOO4qSTTqJ///6sWrWK8uXLM3bsWNLT03P1dT744IOpU6cO7733HpdcckmuntO9e3cGDBjA4MGD6dChA7Br369atWrx4osv0qVLF4444gi6detGnTp1SEtLY9q0aVlbzv3dlClTaNy4Mfvtt1%2BuskqS/l9Uy8dLkrbVvn37oHjx4sG6deu2%2B5ju3bsHRYsWDVauXBkEwc63WdvijTfeCBo1ahSUKFEiSEpKCk488cTgpZdeyro/L9usHXXUUducX7t2bXDSSScF8fHxwZgxY4LMzMxg4MCBwUEHHRQkJiYGxx9/fPDf//53m8/z9y3S/ml3X%2BPRRx8NgKBv375Z50aNGhUcfPDBQUJCwjbbW33wwQdB69atg%2BTk5KB48eJBrVq1gu7duwczZszI9nUqVapUjl%2Bbf%2BYaP3580KpVq6BSpUpBsWLFggMPPDDo2bNn8Mcff2R73vYy7Wibtb%2B76aabAiB4%2BOGHgyDY8ZZeu/P%2BduTbb78NunbtGuy///5B0aJFgypVqgRdu3YNvvvuu20eu2WbtdmzZwedO3cOypQpE5QrVy645pprttkKMAiC4NVXXw2aNGkSlCpVKihVqlRw%2BOGHB7169Qrmzp2b9Zjt/VwGQRB89tlnwUknnRSUKFEiqFq1anDTTTcF77zzzjbf/7Vr1wbnn39%2BULZs2QDI9r1ctmxZ0KNHj6BChQpBsWLFgqOPPjp4%2Bumns32eHf0sB0EQLFiwIGjRokWQmJgYVK5cObjllluCKVOm5GqbtSAIgmHDhgWlS5feZts4IOjVq1eOz7nzzjtzfP28fL%2B2%2BOmnn4LLL788qFGjRlCsWLGgTJkyQePGjYOHHnoo23Z1q1evDooVKxY8%2BeSTO31PkqTs4oJgD1zfKEmSlEt33nknd911FytWrHARsTxISUnh4IMPZsiQIVx66aVRx9mu4cOHM2TIEBYsWJDrtTIkSSHnoEuSJOUDycnJ3HTTTdx33317ZfX5PWHz5s0MGzaM2267zXIuSbvAOeiSJEn5RL9%2B/Xa6KGCUihYtyuLFi6OOIUn5liPokiRJkiTFAAt6hD7%2B%2BGPat29P1apViYuL47XXXtvpcz788ENOOOEEEhMTqV27Ns8888xezylJ0p505513EgSB888lSfoHC3qE1q1bx7HHHrvNPrDbs3DhQtq2bcspp5zCrFmzuP7667nssst455139nJSSZIkSdLe5iruMSIuLo6JEydm7VOak379%2BjFp0iS%2B//77rHPnnXceq1ev5u23394HKSVJkiRJe4uLxOUj06dPp0WLFtnOtW7dmuuvv367z9m0aRObNm3KOs7MzGTVqlXst99%2BxMXF7a2okiRJkmJcEASsWbOGqlWrEh/vxdWxwIKejyxdupTKlStnO1e5cmVSU1PZsGFDjtuZDBo0iLvuumtfRZQkSZKUzyxZsoQDDjgg6hjCgl7g9e/fnz59%2BmQdp6SkcOCBB7JkyRKSkpIiTCZJkiRpn0pPhwED4P/XwHqdZnTjI8qUKRNxMG1hQc9HqlSpwrJly7KdW7ZsGUlJSTmOngMkJiaSmJi4zfmkpCQLuiRJklRYrFgBXbrABx8AkNb3Fi6%2Bvy9Q3qmvMcSJBvlIw4YNmTp1arZzU6ZMoWHDhhElkiRJkhTzFi%2BGunXDcl66NLz6KpvvvIeAhKiT6R8s6BFau3Yts2bNYtasWUC4jdqsWbNYvHgxEF6e3q1bt6zHX3nllfz888/cdNNNzJkzh0cffZSXX36ZG264IYr4kiRJkvKDatXgyCPhkEPgiy%2BgU6eoE2k7vMQ9QjNmzOCUU07JOt4yV/ziiy/mmWee4Y8//sgq6wA1a9Zk0qRJ3HDDDYwYMYIDDjiAJ598ktatW%2B/z7JIkSZJiWFoaBAEkJkJCArz0EsTHQ3Jy1Mm0A%2B6DXsikpqaSnJxMSkqKc9AlSZKkgmjpUjjnHDjsMBg1CnKYY75uHZQunQrYDWKJl7hLkiRJUkExfTqccAJ8%2Bim88ko4/1z5hgVdkiRJkgqCJ56AZs3gjz/giCPgq6/goIOiTqU8sKBLkiRJUn62aRNcfjn07AmbN4eLwH3xBRx6aNTJlEcuEidJkiRJ%2BVnHjvDWW%2BFc83vugZtvznHeuWKfI%2BiSJEmSlJ9ddx3stx9Mngz9%2B1vO8zFH0CVJkiQpPwmCcPG3LfPLTz8dFi6EMmWizaXd5gi6JEmSJOUXGzZA9%2B5w3HGwYMHW85bzAsGCLkmSJEn5wS%2B/QJMm8NxzkJoK06ZFnUh7mJe4S5IkSVKse/99OPdc%2BPNPqFABxo2DU0%2BNOpX2MEfQJUmSJClWBQEMHQotW4blvG5dmDnTcl5AWdAlSZIkKVaNHg19%2B0JmJlx8MXzyCRx4YNSptJdY0CVJkiQpVl10UTjv/OGH4emnoUSJqBNpL3IOuiRJkiTFks8/h/r1ISEBEhPho48g3rHVwsDvsiRJkiTFgiCAgQOhUSO4/fat5y3nhYYj6JIkSZIUtTVrwjnmEyeGx6tWhYU9Li7aXNqnLOiSJEmSFKW5c6FjR/jxRyhWDB55BC67LOpUioAFXZIkSZKi8sYb4UJwqalQrRq8%2Bio0aBB1KkXEgi5JkiRJUVixArp2hfXr4eST4ZVXoHLlqFMpQhZ0SZIkSYpCxYowahRMnw7DhkHRolEnUsTigiAIog6hfSc1NZXk5GRSUlJISkqKOo4kSZJUuPzwA2zYAPXqRRpj3TooXToVsBvEEtfrlyRJkqR9Yfz4cH55hw6wdGnUaRSDLOiSJEmStDdlZED//nDOOeHQ9WGHQRFnG2tb/lRIkiRJ0t6yalW4ENy774bHffvCoEEWdOXInwpJkiRJ2hv%2B979wf/OFC6FkSRg9Gs47L%2BpUimEWdEmSJEnaGwYPDsv5wQfDxIlwzDFRJ1KMs6BLkiRJ0t4wciSULQv/%2BQ%2BULx91GuUDLhInSZIkSXvCihXhqPmWnayTkuDRRy3nyjVH0CVJkiRpd82YAZ06wZIlULw49O4ddSLlQ46gS5IkSdLueOYZaNIkLOeHHgotW0adSPmUBV2SJEmSdkVaGvTqBT16wKZN0L49fPklHHlk1MmUT1nQJUmSJCmvli6FU08N55gD3HUXvPYaJCdHGkv5m3PQJUmSJCmv5s2Dzz8PF4IbMwbatYs6kQoAC7okSZIk5dXJJ8Ozz0L9%2BuG8c2kP8BJ3SZIkSdqZTZvg2mth9uyt5y64wHKuPcoRdEmSJEnakV9/hc6d4YsvYOpU%2BPZbKGKV0p7nCLokSZIkbc/HH0PdumE5L1cOHnjAcq69xoIuSZIkSf8UBPDgg3DaabB8ORxzDMyYAa1bR51MBZgFXZIkSZL%2BbuNGuPhi6N0b0tOha1eYNg0OPjjqZCrgLOiSJEmS9HcJCbB4cfjnsGHhNmqlSkWdSoWAkyckSZIk6e%2BKFoWXXw5XbG/ePOo0KkQs6JIkSZIKtyCAoUPh99/DEXOASpXCm7QPWdAlSZIkFV7r1sGll8K4ceFxp07QpEm0mVRoWdAlSZIkFU4LFkDHjvDdd%2BHWacOHQ%2BPGUadSIWZBlyRJklT4vP12uDr76tVQuTK88gqcfHLUqVTIuYq7JEmSpMJl2DBo0yYs5w0awMyZlnPFBAu6JEmSpMKlZs1wYbgrroCPPoJq1aJOJAFe4i5JkiSpMEhPD%2BeZQzjv/KuvoF69aDNJ/%2BAIuiRJkqSC7Y034IgjYMmSrecs54pBFnRJkiRJBVNmJtxxB5x1FsyfD4MHR51I2iEvcZckSZJU8KxeDRddBP/9b3h87bUwdGikkaSdsaBLkiRJKlh%2B%2BCGcZz5vHhQvDo8/Dt26RZ1K2ikLuiRJkqSCY/p0aNkS1q2DAw%2BEiRPhhBOiTiXligVdkiRJUsFx7LFQuzZUqABjx4Z/SvmEBV2SJElS/paSAmXKQHw8lCwJ774L5ctv3VZNyidcxV2SJElS/jVrFhx3HAwcuPVcpUqWc%2BVLFnRJkiRJ%2BdOLL0KjRrBoETz7LKxfH3UiabdY0CVJkiTlL%2Bnp0KcPXHABbNgAp58OX3wRXt4u5WMWdEmSJEn5x/Ll4SrtDzwQHt9yS7jXefny0eaS9gAnZkiSJEnKH9LSoHFjmD8fSpeG554L9zuXCghH0CVJkiTlD8WKQb9%2BcNhh8OWXlnMVOBZ0SZIkSbErLS1cBG6Lyy6Db76BI46ILJK0t1jQJUmSJMWmpUvh1FPhlFPgzz%2B3ni9RIrpM0l5kQZckSZIUe6ZPhxNOgM8%2Bg7/%2Bgjlzok4k7XUWdEmSJEmx5YknoFkz%2BOMPOPJI%2BOqrcHE4qYCzoEuSJEmKDZs2weWXQ8%2BesHkzdO4c7m9%2ByCFRJ5P2CQu6JEmSpNhw223w5JMQHw/33gsvvxxupyYVEhZ0SZIkSbGhf3%2BoXx/eeivcTi0uLupE0j5VJOoAkiRJkgqpIICpU%2BG008IyXr58eEm7xVyFlCPokiRJkva9DRvg4ouhZctwUbgtLOcqxBxBlyRJkrRvLVoEnTrBN99AQgKkpUWdSIoJFnRJkiRJ%2B87UqdClC/z5J1SoEC4Ed8opUaeSYoKXuEuSJEna%2B4IA7r8fWrUKy3ndujBzpuVc%2BhsLuiRJkqS9b9YsuOkmyMyE7t3hk0/gwAOjTiXFFC9xlyRJkrT3HX88DB4MpUrBVVe5GJyUAwu6JEmSpL3j7behdu3wBnDjjdHmkWKcl7hLkiRJ2rMyM%2BGee6BNm3C19nXrok4k5QuOoEuSJEnac1JTw/3NX3stPG7UCIpYO6Tc8L8USZIkSXvG3LnQoQPMmQPFisEjj8Bll0WdSso3LOiSJEmSdt/rr8NFF8GaNVCtGrz6KjRoEHUqKV9xDrokSZKk3ZOZCffeG5bzk08O9ze3nEt5ZkGXJEmStHvi42H8eOjfH6ZOhcqVo04k5UsWdEmSJEl598MPMHz41uNq1WDgQChaNLJIUn7nHHRJkiRJeTN%2BPHTvHm6fVrMmnHVW1ImkAsERdEmSJEm5k5EBN98M55wTlvNTTw23UZO0RziCLkmSJGnn/vwTzj8f3n03PP7Xv8KF4dzjXNpj/K9JkiRJ0o7NmgUdO8KiRVCyJIweDeedF3UqqcCxoEuSJEnasR9/DMv5wQfDxIlwzDFRJ5IKJAu6JEmSpB3r2hXWrw9H0cuXjzqNVGC5SJwkSZKk7JYvD%2BebL1269dyll1rOpb3MEXRJkiRJW331FXTqBL/%2BCqtXw%2BTJUSeSCg1H0CVJkiSFnn4aTj45LOeHHgr33x91IqlQsaBLkiRJhV1aGvTqBZdcAps2wZlnwpdfwpFHRp1MKlS8xF2SJEkqzJYvDy9p/%2BwziIuDu%2B6CW2%2BFeMfypH3Ngi5JkiQVZsWLw59/QnIyjBkDbdtGnUgqtCzokiRJUmETBOGfcXGQlASvvx5%2BfMgh0eaSCjmvW5EkSZIKk40b4fLLYfjwrecOPdRyLsUAR9AlSZKkwuLXX%2BHss8MF4IoVg3POgQMOiDqVpP/nCLokSZJUGHz8MdStG5bz8uXhv/%2B1nEsxxoIuSZIkFWRBAA8%2BCKedFq7YfuyxMGMGtGwZdTJJ/2BBlyRJkgqqIIDLLoPevSE9HS64AKZNg5o1o04mKQcWdEmSJKmgiouDY46BhAR44AF4/nkoWTLqVJK2w0XiJEmSpIJm0yZITAw/vu46aNECjjoq2kySdsoRdEmSJKmgCAK4/3444QRISQnPxcVZzqV8woIuSZIkFQTr1kHXrnDjjTB7NrzwQtSJJOWRl7hLkiRJ%2Bd2CBdCxI3z3HRQpEq7afuWVUaeSlEcWdEmSJCk/e/vtcOR89WqoUgVeeQWaNIk6laRd4CXukiRJUn714ovQpk1Yzhs2hJkzLedSPuYIuiRJkpRftWgB1apB27YwYsTWldsl5UsWdEmSJCk/Wb4cKlUKP65UCb75BipUiDaTpD3CS9wlSZKk/OL116F2bXj%2B%2Ba3nLOdSgWFBlyRJkmJdZiYMGAAdOsCaNTBmTLjnuaQCxYIuSZIkxbLVq6F9e/j3v8Pj666DN9%2BEuLhIY0na8yzoEXvkkUeoUaMGxYsXp0GDBnz55Zc7fPzw4cM57LDDKFGiBNWrV%2BeGG25g48aN%2ByitJEmS9qnvv4f69WHyZCheHJ57LlwMrmjRqJNJ2gtcJC5C48aNo0%2BfPowcOZIGDRowfPhwWrduzdy5c6m0ZeGPv3nxxRe5%2Beabeeqpp2jUqBE//fQT3bt3Jy4ujmHDhkXwDiRJkrTXLFsWbp22di0ceCBMnAgnnBB1Kkl7kSPoERo2bBiXX345PXr04Mgjj2TkyJGULFmSp556KsfHT5s2jcaNG3P%2B%2BedTo0YNWrVqRdeuXXc66i5JkqR8qHJluP56OO20cH9zy7lU4FnQI5KWlsbMmTNp0aJF1rn4%2BHhatGjB9OnTc3xOo0aNmDlzZlYh//nnn5k8eTJt2rTZ7ufZtGkTqamp2W6SJEmKUX/%2BCb/9tvX4rrvg7bddqV0qJLzEPSIrV64kIyODypUrZztfuXJl5syZk%2BNzzj//fFauXEmTJk0IgoD09HSuvPJKbrnllu1%2BnkGDBnHXXXft0eySJEnaC2bNgo4dwzL%2BySfhnPP4%2BPAmqVDwv/Z85MMPP2TgwIE8%2BuijfP3110yYMIFJkybx7y0reuagf//%2BpKSkZN2WLFmyDxNLkiQpV8aMgUaNYNEiWLUK/vgj6kSSIuAIekQqVKhAQkICy5Yty3Z%2B2bJlVKlSJcfn3H777Vx00UVcdtllABx99NGsW7eOK664gltvvZX4HH67mpiYSGJi4p5/A5IkSdp9mzfDTTfB8OHh8emnh2W9fPlIY0mKhiPoESlWrBh169Zl6tSpWecyMzOZOnUqDRs2zPE569ev36aEJyQkABAEwd4LK0mSpD1v%2BXJo2XJrOb/lFvjvfy3nUiHmCHqE%2BvTpw8UXX0y9evU48cQTGT58OOvWraNHjx4AdOvWjWrVqjFo0CAA2rdvz7Bhwzj%2B%2BONp0KAB8%2BfP5/bbb6d9%2B/ZZRV2SJEn5xGWXwUcfQenS8Oyz0KlT1IkkRcyCHqEuXbqwYsUKBgwYwNKlSznuuON4%2B%2B23sxaOW7x4cbYR89tuu424uDhuu%2B02fvvtNypWrEj79u255557onoLkiRJ2lUjRsDKlfDkk3DkkVGnkRQD4gKvjS5UUlNTSU5OJiUlhaSkpKjjSJIkFR5paTB1KpxxxtZzQQBxcdFlUqG1bh2ULp0K2A1iiXPQJUmSpL3tjz/g1FOhbVuYPHnrecu5pL%2BxoEuSJEl707RpULcufPYZOEopaQcs6JIkSdLeEAQwciQ0bx6OoB95JHz1FbRpE3UySTHKgi5JkiTtaRs3hqu0X3VVuNd5587wxRdwyCFRJ5MUwyzokiRJ0p42aRI89RTEx8O998LLL4fbqUnSDrjNmiRJkrSnnX023HQTnHYatGoVdRpJ%2BYQj6JIkSdLuCoJwP/M//9x6bvBgy7mkPLGgS5IkSbtj/Xro1g0uvxzOOw8yMqJOJCmf8hJ3SZIkaVctWgSdOsE330BCQrjPebxjYJJ2jQVdkiRJ2hXvvReOmP/5J1SsGC4E17x51Kkk5WP%2Bek%2BSJEnKiyCA%2B%2B6D1q3Dcl6vHsycaTmXtNss6JIkSVJerF0LI0dCZib06AGffALVq0edSlIB4CXukiRJUl6UKQMTJ8K0adCzJ8TFRZ1IUgFhQZckSZJ25q23YOnScMQc4Jhjwpsk7UEWdEmSJGl7MjNh0CC4/XYoUgSOPRZOOCHqVJIKKAu6JEmSlJPUVLj4YnjttfD40kuhTp1II0kq2CzokiRJ0j/NnQsdOsCcOVCsGDz6aFjQJWkvsqBLkiRJf/fGG3DhhbBmDVSrBhMmwIknRp1KUiHgNmuSJEnS3/3wQ1jOmzYN9ze3nEvaRxxBlyRJkv7u5puhSpVwFL1o0ajTSCpEHEGXJElS4fbDD9CpE6xbFx7HxYXbqVnOJe1jFnRJkiQVXuPHQ4MGMHEi3HJL1GkkFXIWdEmSJBU%2BGRnhpeznnBOOnJ92WrjXuSRFyIIuSZKkwuXPP6FNGxg8ODy%2B8UZ4%2B22oUCHaXJIKPReJkyRJUuHxww/Qrh0sWgQlS8JTT0GXLlGnkiTAgi5JkqTCpGxZ2LABDj4YXnsNjj466kSSlMWCLkmSpIItMxPi/39mZ7Vq4eXsBx0E5cpFm0uS/sE56JIkSSq4li8PF4B75ZWt5447znIuKSY5gi5JkqSC6auvwv3Nf/0V5s4N556XKBF1KknaLkfQJUmSVPA89RScfHJYzg87DKZOtZxLinkWdEmSJBUcaWlw9dVw6aWwaROceSZ88QUccUTUySRpp7zEXZIkSQXDpk3hfPPPPoO4OLjrLrj11q0LxElSjLOgS5IkqWBITIQGDeD772HMGGjbNupEkpQn/jpRkiRJ%2BVcQwPr1W48HD4ZZsyznkvIlC7okSZLyp40b4bLLoHXrcO45QJEiUKNGpLEkaVd5ibskSZLynyVL4Oyzw63U4uPho4%2BgZcuoU0nSbnEEXZIkSfnLRx9B3bphOS9XDt56y3IuqUCwoEuSJCl/CAJ48MFwpfYVK%2BDYY2HmTGjVKupkkrRHWNAlSZKUPwwYAL17Q0YGXHABTJsGNWtGnUqS9hgLuiRJkvKHCy%2BE8uXhgQfg%2BeehZMmoE0nSHuUicZIkSYpdv/4KBxwQfnzYYfDzz5CcHG0mSdpLHEGXJElS7AkCGDIEatWC99/fet5yLqkAs6BLkiQptqxdC%2BedB/36hfubT5oUdSJJ2ie8xF2SJEmxY/586NgRvv8eihQJV22/8sqoU0nSPmFBlyRJUmyYPDlcnX31aqhSBV55BZo0iTqVJO0zFnRJkiRF76uvoF27cO55w4YwfjxUrRp1KknapyzokiRJil69euHoealSMGIEJCZGnUiS9jkLuiRJkqIxd254KXtyMsTFwdNPh/POJamQchV3SZIk7Xuvvw7168NFF0FmZnjOci6pkLOgS5Ikad/JzIQBA6BDB1izBlJSwm3VJEkWdEmSJO0jq1dD%2B/bw73%2BHx717w3vvQVJSpLEkKVZ4HZEkSZL2vu%2B/D0fNFyyA4sVh1Ci48MKoU0lSTLGgS5Ikae/KyIBzzgnL%2BUEHwcSJcPzxUaeSpJjjJe6SJEnauxIS4Lnnwn3OZ8ywnEvSdljQJUmStOf9%2BSe8%2B%2B7W4/r14c03oUKF6DJJUoyzoEuSJGnPmjUL6tWDs86Cr7%2BOOo0k5RsWdEmSJO05Y8ZAo0awaBFUqwbFikWdSJLyDQu6JEmSdt/mzXDDDeHK7Bs2wBlnwFdfQZ06USeTpHzDgi5JkqTds3w5tGwJw4eHx7fdFs43L1cu0liSlN%2B4zZokSZJ2zzPPwEcfQZky4WrtHTpEnUiS8iULuiRJknZP376wZAlcfTUccUTUaSQp3/ISd0mSJOVNWhoMHhzONQeIj4eHHrKcS9JucgRdkiRJuffHH3DOOfDZZzB3Ljz1VNSJJKnAsKBLkiQpd6ZPh7PPDkt6cnL4sSRpj/ESd0mSJO1YEMDjj0OzZmE5P%2BqocAu1tm2jTiZJBYoFXZIkSdu3cSNcfjlceWW413nnzvD553DIIVEnk6QCx4IuSZKk7Vu%2BHCZODBeCGzwYXn4ZSpeOOpUkFUjOQZckSdL2HXhgWMozMqBVq6jTSFKBZkGXJEnSVkEADz4ItWtvnWN%2B2mnRZpKkQsKCLkmSpND69XDFFTBmTLhK%2B%2BzZULVq1KkkqdCwoEuSJAkWLYKOHWHWLEhIgDvvhP33jziUJBUuFnRJkqTCbsoUOO88WLUKKlYM55w3bx51KkkqdFzFXZIkqbAKArjvPjj99LCc16sHM2daziUpIhZ0SZKkwiouDn7%2BGTIzoUcP%2BOQTqF496lSSVGh5ibskSVJhNmIEnHIKnHNOWNglSZFxBF2SJKkwmTw5LOPp6eFxsWJw7rmWc0mKARZ0SZKkwiAzE/79b2jXDsaPh5Ejo04kSfoHL3GXJEkq6FJToVs3eP318PjKK8P9ziVJMcWCLkmSVJDNmQMdOsDcueHl7I8%2BCpdeGnUqSVIOLOiSJEkF1dtvh/PL16yBatVgwgQ48cSoU0mStsM56JIkSQVV1aqQkQFNm4b7m1vOJSmmOYIuSZJUkGRkQEJC%2BPExx8BHH8Gxx0LRotHmkiTtlCPokiRJBcX334elfNq0refq1bOcS1I%2BYUGXJEkqCF5%2BGRo0gNmzoW9fCIKoE0mS8siCLkmSlJ%2Blp0O/ftClC6xfD6edBm%2B8AXFxUSeTJOWRc9AlSZLyqz//hPPOg/feC49vvBEGDoQi/hNPkvIj//aWJEnKj/74Axo1gkWLoGRJeOqpcBRdkpRvWdAlSZLyoypVoG7dcMX2iRPh6KOjTiRJ2k0W9DzIzMzko48%2B4pNPPuGXX35h/fr1VKxYkeOPP54WLVpQvXr1qCNKkqSCbPPm8FayZDjH/Omnwzno5cpFnUyStAe4SFwubNiwgf/85z9Ur16dNm3a8NZbb7F69WoSEhKYP38%2Bd9xxBzVr1qRNmzZ8/vnnUceVJEkF0fLl0LIl9OixdYX2MmUs55JUgDiCnguHHnooDRs2ZNSoUbRs2ZKiOewl%2Bssvv/Diiy9y3nnnceutt3L55ZdHkFSSJBVIX30FnTrBr7%2BGpXz%2BfDjkkKhTSZL2sLggcJPMnfnxxx854ogjcvXYzZs3s3jxYmrVqrWXU%2B2a1NRUkpOTSUlJISkpKeo4kiRpZ556Cq6%2BGjZtgsMOg9deg8MPjzqVpHxu3TooXToVsBvEEi9xz4XclnOAokWLxmw5lyRJ%2BUhaWljML700LOcdOsCXX1rOJakA8xL3PPrqq6946aWX%2BOmnn4Dw8vfzzz%2BfevXqRZxMkiQVKF27woQJ4WJwd98Nt9wC8Y6tSFJB5t/yeXDTTTfRoEEDnnzySX799Vd%2B/fVXRo0aRYMGDejXr1/U8SRJUkFyww1QsSL8979w222Wc0kqBPybPpeeffZZHnroIR588EH%2B/PNPZs2axaxZs1i1ahUPPPAADz74IM8991zUMSVJUn4VBPDzz1uPmzSBhQuhTZvoMkmS9ikXiculE088ka5du3LDDTfkeP%2BwYcMYO3YsX3755T5OljcuEidJUgzauBF69YJx4%2BDzz6FOnagTSSrgXCQuNjmCnks//PADZ5111nbv79ChAz/88MM%2BTCRJkgqEJUugadNwtfYNG%2BCLL6JOJEmKiIvE5VJCQgJpaWnbvX/z5s0kJCTsw0SSJCnf%2B%2BgjOOccWLECypeHsWOhZcuoU0mSIuIIei6dcMIJjBkzZrv3P//885xwwgn7MJEkScq3ggAefBBOOy0s58cdBzNmWM4lqZBzBD2X%2BvbtS4cOHdi0aRP/%2Bte/qFy5MgBLly5l6NChDB8%2BnIkTJ0acUpIk5QsvvQS9e4cfX3ABPPEElCwZbSZJUuRcJC4PHnroIfr27Ut6ejrJyckApKSkUKRIEYYMGULvLf%2BjjWEuEidJUgxIT4e2bcMV2q%2B7LtzrXJL2IReJi00W9DxasmQJ48ePZ968eQAceuihnH322VSvXj3iZLljQZckKSLTp0PdulCsWHicmene5pIiY0GPTRb0PNq0aRPp6emUKlUq6ii7xIIuSdI%2BFgRw333Qvz9cdRU8/HDUiSTJgh6j/LVtLq1YsYIzzjiD0qVLk5SUxEknncT8%2BfOjjiVJkmLZ2rVw3nnQr184Yr5%2BPWRkRJ1KkhSjLOi51K9fP2bNmsXdd9/N/fffz%2BrVq7n88sujjiVJkmLV/PnQsCG8/DIULQqPPgqjR4PbskqStsNV3HNpypQpPPPMM7Ru3RqAdu3accQRR7Bp0yYSExMjTidJkmLKpEnh6uwpKVClCowfD40bR51KkhTjHEHPpd9//51jjz026/iQQw4hMTGRP/74I8JUkiQp5qxeDRdeGJbzhg1h5kzLuSQpVxxBz4OEf1ySlpCQgGvsSZKkbMqWheeeg8mTYcSIrau2S5K0E67inkvx8fEkJycT97d9SlevXk1SUhLxf9siZdWqVVHEyzVXcZckaS%2BYMwdWrICTT446iSTliqu4xyZH0HPp6aefjjqCJEmKRa%2B9Bt26hSPlM2fCQQdFnUiSlE9Z0HPp4osvjjqCJEmKJRkZcOed8J//hMdNm0Lx4pFGkiTlby4St4vWrFlDampq1m3t2rW79DqPPPIINWrUoHjx4jRo0IAvv/xyh49fvXo1vXr1Yv/99ycxMZFDDz2UyZMn79LnliRJu%2Bivv6B9%2B63lvHdveO89qFw52lySpHzNgp5Ls2bNok2bNlnHVatWpVy5clm3smXL8tVXX%2BXpNceNG0efPn244447%2BPrrrzn22GNp3bo1y5cvz/HxaWlptGzZkkWLFjF%2B/Hjmzp3LqFGjqFat2m69N0mSlAfffQf168Nbb4Uj5s8/D8OHh3udS5K0G1wkLpcuvfRSatWqxS233AJAmTJlePzxx6lWrRpBEPDUU08RBAHPP/98rl%2BzQYMG1K9fn4cffhiAzMxMqlevzrXXXsvNN9%2B8zeNHjhzJfffdx5w5cyi6i/8IcJE4SZJ2U8%2Be8MQT4VzziRPh%2BOOjTiRJeeYicbHJOei5NG3aNK655pps50466SQOPvhgAEqUKMG5556b69dLS0tj5syZ9O/fP%2BtcfHw8LVq0YPr06Tk%2B54033qBhw4b06tWL119/nYoVK3L%2B%2BefTr1%2B/bbaA22LTpk1s2rQp6zg1NTXXGSVJUg6GD4fERBgwACpUiDqNJKkA8RL3XPrll1%2BoWLFi1vHdd99Nhb/9T3n//fdn2bJluX69lStXkpGRQeV/zFWrXLkyS5cuzfE5P//8M%2BPHjycjI4PJkydz%2B%2B23M3ToUP6zZf5bDgYNGkRycnLWrXr16rnOKEmSgJUr4e67ITMzPC5RAh580HIuSdrjLOi5VLx4cX755Zes4xtuuCHbZSBLliyhZMmSezVDZmYmlSpV4oknnqBu3bp06dKFW2%2B9lZEjR273Of379yclJSXrtmTJkr2aUZKkAuWbb6BePbjjDhg4MOo0kqQCzkvcc%2Bn444/ntddeo3HjxjneP2HCBI7Pwxy0ChUqkJCQsM2o%2B7Jly6hSpUqOz9l///0pWrRotsvZjzjiCJYuXUpaWhrFihXb5jmJiYkkJibmOpckSfp/zz8PV1wBGzdCrVpw1llRJ5IkFXCOoOfS1VdfzfDhw3nkkUfI3HKJG5CRkcFDDz3EQw89xFVXXZXr1ytWrBh169Zl6tSpWecyMzOZOnUqDRs2zPE5jRs3Zv78%2Bdk%2B/08//cT%2B%2B%2B%2BfYzmXJEm7YPPmcNu0bt3Ccn7GGfDVV3D00VEnkyQVcBb0XDr77LPp06cP1157LeXKleP444/n%2BOOPp3z58lx//fX07t2bzp075%2Bk1%2B/Tpw6hRo3j22Wf58ccfueqqq1i3bh09evQAoFu3btkWkbvqqqtYtWoVvXv35qeffmLSpEkMHDiQXr167dH3KklSobVsGbRoEc4xB7jtNnjzTShXLtpckqRCwUvc82Dw4MF07NiRl156iXnz5gHQtGlTunbtykknnZTn1%2BvSpQsrVqxgwIABLF26lOOOO4633347a%2BG4xYsXEx%2B/9Xco1atX55133uGGG27gmGOOoVq1avTu3Zt%2B/frtmTcoSVJht2QJfPEFlCkDzz0HHTpEnUiSVIi4D3oh4z7okiTtxKuvwpFHwhFHRJ1EkvYa90GPTV7inguLFy/O0%2BN/%2B%2B23vZREkiTtUWlpcN118PXXW8%2BdfbblXJIUCQt6LtSvX5%2BePXvy1VdfbfcxKSkpjBo1ijp16vDqq6/uw3SSJGmX/P47nHIKPPRQWMo3bYo6kSSpkHMOei7Mnj2be%2B65h5YtW1K8eHHq1q1L1apVKV68OH/99RezZ8/mhx9%2B4IQTTmDIkCG0adMm6siSJGlHPvsMOneGpUshORkeeQTcllSSFDHnoOfBhg0bmDRpEp9%2B%2Bim//PILGzZsoEKFChx//PG0bt2aOnXqRB1xp5yDLkkq1IIARo4Mt1HbvBnq1IGJE6F27aiTSdI%2B5Rz02GRBL2Qs6JKkQistDa66Cp56Kjw%2B91wYPRpKl442lyRFwIIem5yDLkmSCociRcJL2uPjYcgQGDvWci5JiinOQZckSQVbEEBcXFjMx4yBWbOgefOoU0mStA1H0CVJUsEUBDB8OPTsGX4MULas5VySFLMcQZckSQXP%2BvVw%2BeXw4ovhcefO0KpVtJkkSdoJC7okSSpYFi6ETp3CS9kTEmDYMGjZMupUkiTtlAU9j954440d3n/mmWfuoySSJGkbU6bAeefBqlVQsSK88go0axZ1KkmScsWCnkcdOnTY7n1xcXFkZGTsuzCSJGmrRx6B666DzEyoXx9efRWqV486lSRJueYicXmUmZm53ZvlXJKkCB12WPjnJZfAxx9bziVJ%2BY4j6JIkKf/avBmKFg0/btECvv4ajjkm3FZNkqR8xoK%2BC6ZOncrUqVNZvnw5mZmZ2e576qmnIkolSVIhM3ky9OoF774LhxwSnjv22GgzSZK0G7zEPY/uuusuWrVqxdSpU1m5ciV//fVXtpskSdrLMjPhP/%2BBdu1g0SIYNCjqRJIk7RGOoOfRyJEjeeaZZ7jooouijiJJUuGTmgrdusHrr4fHV10Fw4dHGkmSpD3Fgp5HaWlpNGrUKOoYkiQVPnPmQIcOMHcuFCsGjz0WLggnSVIB4SXueXTZZZfx4osvRh1DkqTC5euv4cQTw3J%2BwAHwySeWc0lSgeMIeh5t3LiRJ554gvfee49jjjmGoltWjv1/w4YNiyiZJEkFWJ06cPTR4YrtL78MlSpFnUiSpD3Ogp5H3377LccddxwA33//fbb74tzSRZKkPSclBUqVgiJFwkva33gDkpK2bqsmSVIBY0HPow8%2B%2BCDqCJIkFXzffx/ON%2B/UCYYMCc/tt1%2BkkSRJ2tucgy5JkmLLyy9DgwawYAG88kq4crskSYWAI%2Bi7YMaMGbz88sssXryYtLS0bPdNmDAholSSJOVz6elwyy1w333hcYsW8NJL4WXtkiQVAo6g59HYsWNp1KgRP/74IxMnTmTz5s388MMPvP/%2B%2ByQnJ0cdT5Kk/GnlSjj99K3l/Kab4K23oEKFaHNJkrQPWdDzaODAgTzwwAO8%2BeabFCtWjBEjRjBnzhzOPfdcDjzwwKjjSZKU/2RkQPPmMHVquCjcuHEweHC4OJwkSYWIBT2PFixYQNu2bQEoVqwY69atIy4ujhtuuIEnnngi4nSSJOVDCQkwYADUrg2ffw7nnht1IkmSImFBz6Ny5cqxZs0aAKpVq5a11drq1atZv359lNEkSco/Nm%2BGefO2Hp97Lnz3XbjfuSRJhZQFPY%2BaNm3KlClTADjnnHPo3bs3l19%2BOV27duW0006LOJ0kSfnAsmXhAnBNm8Lvv289X7x4dJkkSYoBTu7Ko4cffpiNGzcCcOutt1K0aFGmTZvG2WefzW233RZxOkmSYtyXX4Z7m//2G5QpA3PnQtWqUaeSJCkmWNDzID09nf/%2B97%2B0bt0agPj4eG6%2B%2BeaIU0mSlE%2BMHg1XXw1paXDYYfDaa3D44VGnkiQpZniJex4UKVKEK6%2B8MmsEXZIk5cKmTXDllXDZZWE579AhHEm3nEuSlI0FPY9OPPFEZs2aFXUMSZLyj4ED4fHHIS4O/v1vePVVSEqKOpUkSTHHS9zz6Oqrr6ZPnz4sWbKEunXrUqpUqWz3H3PMMRElkyQpRvXtG%2B5xfsst0KZN1GkkSYpZcUEQBFGHyE/i47e96CAuLo4gCIiLiyMjIyOCVLmXmppKcnIyKSkpJDl6IUnaG4IA3nkHWrcOR823nNvysSQpcuvWQenSqYDdIJY4gp5HCxcujDqCJEmxa%2BPGcCG4p5%2BGIUPgxhvD85ZzSZJ2yoKeRwcddFDUESRJik1LloRbqM2YAfHxlnJJkvLIgp5Hr7zyCi%2B99BI//fQTAIceeijnn38%2BnTt3jjiZJEkR%2BvBDOPdcWLECypeHsWOhZcuoU0mSlK%2B4insuZWZm0qVLF7p06cLs2bOpXbs2tWvX5ocffqBLly6cd955OJ1fklToBAEMHw4tWoTl/LjjwhF0y7kkSXnmCHoujRgxgvfee4833niDdu3aZbvvjTfeoEePHowYMYLrr78%2BmoCSJEVh7txwnnlGBlx4YbidWsmSUaeSJClfchX3XDrmmGO4/vrrueSSS3K8f/To0YwYMYJvv/12HyfLG1dxlyTtcY89Bps3w7XXOu9ckvIJV3GPTRb0XCpRogRz587lwAMPzPH%2BX375hcMPP5wNGzbs42R5Y0GXJO22KVNg//2hTp2ok0iSdpEFPTY5Bz2XSpQowerVq7d7f2pqKsWLF993gSRJ2teCINw67fTToWNH2MH/FyVJUt5Z0HOpYcOGPPbYY9u9/5FHHqFhw4b7MJEkSfvQ2rXQpQv06weZmdC0KfiLaUmS9igXiculW2%2B9lebNm/Pnn3/St29fDj/8cIIg4Mcff2To0KG8/vrrfPDBB1HHlCRpz5s/Hzp0gB9%2BgKJF4cEHoWdP55tLkrSHWdBzqVGjRowbN44rrriCV199Ndt95cqV46WXXqJx48YRpZMkaS%2BZNAkuuABSUsJ55%2BPHQ6NGUaeSJKlAcpG4PFq/fj3vvPMO8%2BbNA%2BDQQw%2BlVatWlMwnW8q4SJwkKdeCAFq1gvfeC0v5%2BPFhSZck5XsuEhebLOiFjAVdkpQnK1bAiBEwYAAUKxZ1GknSHmJBj00uEidJkraaMwcGDdp6XLEi/Oc/lnNJkvYB56BLkqTQa69Bt26wZg0ceGA491ySJO0zjqBLklTYZWTA7beHe5uvWQPNmkHLllGnkiSp0LGg50KfPn1Yt24dAB9//DHp6ekRJ5IkaQ/56y9o3z68jB3g%2ButhyhSoVCnSWJIkFUYuEpcLRYsW5ddff6Vy5cokJCTwxx9/UCmf/sPFReIkSVm%2B/z7c33zBAihRAkaN8rJ2SSokXCQuNjkHPRdq1KjBgw8%2BSKtWrQiCgOnTp1OuXLkcH9u0adN9nE6SpF20aFFYzmvUgIkT4bjjIg4kSVLh5gh6Lrz22mtceeWVLF%2B%2BnLi4OLb3JYuLiyMjI2Mfp8sbR9AlSdmMGQOnnw777Rd1EknSPuQIemyyoOfB2rVrSUpKYu7cudu9xD05OXkfp8obC7okFWIrV0KvXjB4cDhqLkkqtCzosclL3POgdOnSfPDBB9SsWZMiRfzSSZLykW%2B%2BCVdp/%2BUX%2BOMP%2BOgjiIuLOpUkSfobW2YupKamZv1G6fjjj2f9%2BvXbfay/eZIkxZznn4crroCNG6FWLXj0Ucu5JEkxyIKeC%2BXKlctaub1s2bLE5fCPmiAI8sUcdElSIbJ5M/TtCw8%2BGB63aQMvvADbWehUkiRFy4KeC%2B%2B//z7ly5cH4IMPPog4jSRJufDnn9CpE3z8cXh8%2B%2B1w550QHx9pLEmStH0W9Fxo1qxZ1sc1a9akevXq24yiB0HAkiVL9nU0SZJyVrIkrF0LZcrAc8%2BF%2B51LkqSYZkHPo5o1a2Zd7v53q1atombNml7iLkmKVhCE88tLlIAJE2DDBjj88KhTSZKkXLCg59GWueb/tHbtWooXLx5BIkmSgLQ06N0bqlSBO%2B4Izx10ULSZJElSnljQc6lPnz4AxMXFcfvtt1OyZMms%2BzIyMvjiiy847rjjIkonSSrUfv8dOneG6dMhIQEuuABq1446lSRJyiMLei598803QDiC/t1331GsWLGs%2B4oVK8axxx5L3759o4onSSqsPvssLOdLl0LZsvDii5ZzSZLyKQt6Lm1Zvb1Hjx6MGDHC/c4lSdEKAnjssfCy9vR0qFMHJk60nEuSlI9Z0PPo6aefjjqCJEnQq1dY0AHOPRdGj4bSpaPNJEmSdosFPRc6derEM888Q1JSEp06ddrhYydMmLCPUkmSCrUTT4THH4d774W%2BfcOV2yVJUr5mQc%2BF5OTkrJXbk5KSclzFXZKkvW7jRtiyY0j37nDSSW6hJklSARIXBEEQdQjtO6mpqSQnJ5OSkuI8eknKL4IAhg%2BHhx%2BGzz%2BHihWjTiRJyufWrYPSpVMBu0EsiY86QH5z99138/77729zft26ddx9990RJJIkFWjr18OFF0KfPvDzz/DMM1EnkiRJe4kj6HkUHx9P0aJFGTRoUNbe6ADLli2jatWqZGRkRJhu5xxBl6R8ZOFC6NgR/ve/cH/zYcPg2mudby5J2m2OoMcmR9B3wXPPPcfAgQPp0aMHaWlpUceRJBVE774L9eqF5bxiRZg6Fa67znIuSVIBZkHfBaeccgpffPEFX3zxBc2bN2f58uVRR5IkFSSvvgpnnAGrVkH9%2BjBzJjRrFnUqSZK0l1nQ82jLCu61atXi888/Jykpibp16zJjxoyIk0mSCozTToODD4ZLLoGPP4bq1aNOJEmS9gG3Wcujv0/ZT0pKYvLkyVx//fV06NAhulCSpPxv6VKoXDm8hL1sWfjiCyhXzkvaJUkqRBxBz6Onn36a5OTkrOP4%2BHgefPBBnnjiCbp16xZhMklSvjVpUrif%2BaOPbj1XvrzlXJKkQsZV3AsZV3GXpBiSmQn/%2BQ/ceWe413nz5uFicPH%2B/lyStHe5ints8l8Au%2BCjjz6iffv21K5dm9q1a3PmmWfyySefRB1LkpSfpKSEW6jdcUdYzq%2B%2BGt55x3IuSVIh5r8C8uiFF16gRYsWlCxZkuuuu47rrruOEiVKcNppp/Hiiy9GHU%2BSlB/8%2BCM0aABvvAGJifDUU/DII1CsWNTJJElShLzEPY%2BOOOIIrrjiCm644YZs54cNG8aoUaP48ccfI0qWO17iLkkR%2B%2BuvcIX21avhgANgwoRwKzVJkvYhL3GPTY6g59HPP/9M%2B/bttzl/5plnsnDhwggSSZLylXLl4JZbwn3NZ860nEuSpCwW9DyqXr06U6dO3eb8e%2B%2B9R3X3qZUk5eSvv%2BDvv8Tt2xfeew8qVYoukyRJijnug55H//rXv7juuuuYNWsWjRo1AuCzzz7jmWeeYcSIERGnkyTFnO%2B%2BCxeDS0wM9zYvXTrcPq2I/wuWJEnZ%2Ba%2BDPLrqqquoUqUKQ4cO5eWXXwbCeenjxo3jrLPOijidJCmmvPwy9OgB69dDjRrw%2B%2B9w6KFRp5IkSTHKgp4HQRAwf/58Dj30UD788EOKOPohScpJeno4z/y%2B%2B8LjFi1g7FjYb79oc0mSpJjmHPRcWrhwIccccwyHH344xxxzDLVq1WLGjBlRx5IkxZqVK%2BH007eW83794O23LeeSJGmnLOi5dOONN5Kens4LL7zA%2BPHjOeCAA7jiiiuijiVJijXXXgtTp0KpUuEl7vfeCwkJUaeSJEn5gNdo59Knn37K%2BPHjadKkCQAnnXQSBxxwAOvWraNUqVIRp5MkxYxhw8K55o88AnXqRJ1GkiTlI46g59Ly5cs55JBDso73339/SpQowfLlyyNMJUmK3ObN8MYbW4/33x8%2B%2BshyLkmS8syCnktxcXGsXbuW1NTUrFt8fDxr1qzJdk6SVIgsWxYuAHfWWTBuXNRpJElSPucl7rkUBAGH/mNrnCAIOP7447M%2BjouLIyMjI4p4kqR97csvoVMn%2BO03KFMGSpSIOpEkScrnLOi59MEHH0QdQZIUK0aPhquvhrQ0OPxwmDgx/FOSJGk3WNBzqVmzZlFHkCRFbdMm6N0bHn88PO7QAZ59FpKSIo0lSZIKBuegS5KUWx9%2BGJbzuDj4z3/g1Vct55IkaY9xBF2SpNxq3Rruvhvq1YMzzog6jSRJKmAcQZckaXuCAJ58MtzXfIvbb7ecS5KkvcKCLklSTjZuhEsvhcsvh86dwwXhJEmS9iILeh7taDX3Rx55ZB8mkSTtNUuWwMknw9NPQ3w8dOwIRYtGnUqSJBVwFvQ86tSpEzNnztzm/IgRI%2Bjfv38EiSRJe9SHH0LdujBjBuy3H7zzDtx4Y7gwnCRJ0l5kQc%2Bj%2B%2B67jzPOOIM5c%2BZknRs6dCgDBgxg0qRJESaTJO2WIIDhw6FFC1ixAo4/PizpLVpEnUySJBUSruKeR5dddhmrVq2iRYsWfPrpp4wbN46BAwcyefJkGjduHHU8SdKu2rgxXBAuIwMuuijcTq1EiahTSZKkQsSCvgtuuukm/vzzT%2BrVq0dGRgbvvPMOJ510UtSxJEm7o0QJmDgR3n0Xrr7aS9olSdI%2BZ0HPhQcffHCbc9WqVaNkyZI0bdqUL7/8ki%2B//BKA6667bl/HkyTtqnffhXnzoFev8PiQQ8KbJElSBOKCIAiiDhHratasmavHxcXF8fPPP%2B/lNLsnNTWV5ORkUlJSSEpKijqOJEUjCGDIELjllvD4o4%2BgSZNoM0mStA%2BtWwelS6cCdoNY4gh6LixcuDDqCJKkPWXtWujRA8aPD48vvRTq1Ys2kyRJEq7ivsvS0tKYO3cu6enpUUeRJOXWvHnQoEFYzosWhZEjYdQoKF486mSSJEkW9Lxav349l156KSVLluSoo45i8eLFAFx77bXce%2B%2B9EaeTJG3XpElQvz7Mng377x9e1t6zp4vBSZKkmGFBz6P%2B/fvzv//9jw8//JDifxtxadGiBePGjYswmSRph375BVJSoFEjmDkTGjaMOpEkSVI2zkHPo9dee41x48Zx0kknEfe3UZejjjqKBQsWRJhMkrRDV10FSUlw7rlQrFjUaSRJkrbhCHoerVixgkqVKm1zft26ddkKuyQpYj/%2BCG3bwl9/hcdxcXDhhZZzSZIUsyzoeVSvXj0mTZqUdbyllD/55JM09HJJSYoNEybAiSfC5MnQt2/UaSRJknLFgp5HAwcO5JZbbuGqq64iPT2dESNG0KpVK55%2B%2BmnuueeePL/eI488Qo0aNShevDgNGjTgyy%2B/zNXzxo4dS1xcHB06dMjz55SkAisjA269Fc4%2BO9xOrVkzGDQo6lSSJEm5YkHPoyZNmjBr1izS09M5%2Buijeffdd6lUqRLTp0%2Bnbt26eXqtcePG0adPH%2B644w6%2B/vprjj32WFq3bs3y5ct3%2BLxFixbRt29fTj755N15K5JUsPz1F7RrBwMHhsfXXw9TpkAO05IkSZJiUVwQBEHUIQqrBg0aUL9%2BfR5%2B%2BGEAMjMzqV69Otdeey0333xzjs/JyMigadOmXHLJJXzyySesXr2a1157LdefMzU1leTkZFJSUkhKStoTb0OSojdnTjjf/OefoUQJePJJOP/8qFNJkhSz1q2D0qVTAbtBLHEEPY8mT57MO%2B%2B8s835d955h7feeivXr5OWlsbMmTNp0aJF1rn4%2BHhatGjB9OnTt/u8u%2B%2B%2Bm0qVKnHppZfmLbgkFWT77Qfp6VCjBkybZjmXJEn5kgU9j26%2B%2BWYyMjK2OR8EATfffDM//fQTI0aM4Lvvvtvh66xcuZKMjAwqV66c7XzlypVZunRpjs/59NNPGT16NKNGjcp13k2bNpGamprtJkkFQmbm1o8rVoS33oIZM%2BC44yKLJEmStDss6Hk0b948jjzyyG3OH3744Xz33Xf07duXjz/%2BmPbt2%2B/Rz7tmzRouuugiRo0aRYUKFXL9vEGDBpGcnJx1q169%2Bh7NJUmRWLkSWraEp5/eeu7II8ORdEmSpHyqSNQB8pvk5GR%2B/vlnatSoke38/PnzKV%2B%2BPG%2B88QYLFy6kTp06O3ydChUqkJCQwLJly7KdX7ZsGVWqVNnm8QsWLGDRokXZin/m/48eFSlShLlz51KrVq1tnte/f3/69OmTdZyammpJl5S/ff01dOwIixfD//4HnTtDmTJRp5IkSdptjqDn0VlnncX111/PggULss7Nnz%2Bff/3rX1lbnpUuXZrXX399h69TrFgx6taty9SpU7POZWZmMnXq1Bz3U98yQj9r1qys25lnnskpp5zCrFmztlu6ExMTSUpKynaTpHzr%2BeehceOwnNeuDR9%2BaDmXJEkFhiPoeTRkyBBOP/10Dj/8cA444AAAfv31V04%2B%2BWTuv/9%2BACpWrJht8bft6dOnDxdffDH16tXjxBNPZPjw4axbt44ePXoA0K1bN6pVq8agQYMoXrz4NqPyZcuWBdjpaL0k5XubN0PfvvDgg%2BFxmzYwZgz8/9%2BDkiRJBYEFPY%2BSk5OZNm0aU6ZM4X//%2Bx8lSpTgmGOOoWnTpnl%2BrS5durBixQoGDBjA0qVLOe6443j77bezFo5bvHgx8fFe5CCpkEtPh1atwtFygAED4I47wL8fJUlSAeM%2B6HmwefNmSpQowaxZs/LtqLX7oEvKl%2B66C4YODS9xP%2BusqNNIkpTvuQ96bHL4IQ%2BKFi3KgQcemOM2a5KkPWzt2q0f3347fPed5VySJBVoFvQ8uvXWW7nllltYtWpV1FEkqWDatAmuvBKaNoUNG8Jz8fFw0EHR5pIkSdrLnIOeRw8//DDz58%2BnatWqHHTQQZQqVSrb/V9//XVEySSpAPj993DbtOnTIS4O3nsP/ra9pCRJUkFmQc%2BjLVupSZL2sM8%2BC8v50qXh6uwvvghnnBF1KkmSpH3GReIKGReJkxRzggAeewx69w5XbD/6aJg4EWrVijqZJEkFlovExSbnoO%2BC1atX8%2BSTT9K/f/%2Bsuehff/01v/32W8TJJCkfGjQIevUKy3mXLuHl7ZZzSZJUCFnQ8%2Bjbb7/l0EMPZfDgwdx///2sXr0agAkTJtC/f/9ow0lSfnTBBVC5Mtx3H7z0EvxjbQ9JkqTCwoKeR3369KF79%2B7MmzeP4sWLZ51v06YNH3/8cYTJJCkfWbx468cHHQTz5kHfvuHCcJIkSYWUBT2PvvrqK3r27LnN%2BWrVqrF06dIIEklSPhIE8MAD4SXsb7yx9XyZMtFlkiRJihEW9DxKTEwkNTV1m/M//fQTFStWjCCRJOUT69fDhRdCnz7hfPN33ok6kSRJUkyxoOfRmWeeyd13383mzZsBiIuLY/HixfTr14%2Bzzz474nSSFKMWLoRGjcKt0xIS4MEH4eGHo04lSZIUUyzoeTR06FDWrl1LpUqV2LBhA82aNaN27dqUKVOGe%2B65J%2Bp4khR73n0X6tWD//0PKlWCqVPh2mudby5JkvQPRaIOkN8kJyczZcoUPv30U7799lvWrl3LCSecQIsWLaKOJkmx54cf4IwzIDMTTjwRXn0VDjgg6lSSJEkxKS4IgiDqENp3UlNTSU5OJiUlhaSkpKjjSCoMrrkGNm2Chx6Cv%2B1%2BIUmSorNuHZQunQrYDWKJI%2Bi5tGHDBqZOnUq7du0A6N%2B/P5s2bcq6PyEhgX//%2B9/Ztl6TpEJp3jwoWxa2LJw5YkQ471ySJEk7ZEHPpWeffZZJkyZlFfSHH36Yo446ihIlSgAwZ84cqlatyg033BBlTEmK1qRJcMEFcMIJ4dzzIkUs55IkSbnkInG5NGbMGK644ops51588UU%2B%2BOADPvjgA%2B677z5efvnliNJJUsQyM%2BHuu6F9e0hJgbQ0yGFLSkmSJG2fBT2X5s%2Bfz9FHH511XLx4ceLjt375TjzxRGbPnh1FNEmKVkoKdOwId9wBQQC9esH770P58lEnkyRJyle8xD2XVq9enW3O%2BYoVK7Ldn5mZme1%2BSSoUfvwROnSAn36CxEQYORK6d486lSRJUr7kCHouHXDAAXz//ffbvf/bb7/lALcOklSYBAFceGFYzqtXh08/tZxLkiTtBgt6LrVp04YBAwawcePGbe7bsGEDd911F23bto0gmSRFJC4OnnsO2rWDGTOgXr2oE0mSJOVr7oOeS8uWLeO4446jWLFiXHPNNRx66KEAzJ07l4cffpj09HS%2B%2BeYbKleuHHHSHXMfdEm75a%2B/wpHy9u2jTiJJknaD%2B6DHJueg51LlypWZNm0aV111FTfffDNbfq8RFxdHy5YtefTRR2O%2BnEvSbvn223AxuMWLw0XgTj456kSSJEkFigU9D2rWrMnbb7/NqlWrmD9/PgC1a9emvCsVSyroxo6FSy%2BF9euhRg0oUybqRJIkSQWOBX0XlC9fnhNPPDHqGJK096Wnw803w9Ch4XHLlvDSS7DfftHmkiRJKoBcJE6SlLOVK6F1663lvF8/eOsty7kkSdJe4gi6JClnL78czjUvVQqeeQY6d446kSRJUoFmQZck5eyqq2DRIrj4YjjqqKjTSJIkFXhe4i5JCm3eDAMHwpo14XFcHAwZYjmXJEnaRxxBlyTBsmVwzjnwyScwa1Z4ebskSZL2KQu6JBV2X3wBZ58Nv/0Wbp924YVRJ5IkSSqUvMRdkgqzJ5%2BEpk3Dcn744fDVV3DmmVGnkiRJKpQs6JJUGG3aBD17wuWXQ1oadOwYjqQfdljUySRJkgotC7okFUarV8Obb4YLwd1zD4wfD0lJUaeSJEkq1JyDLkmFUeXKYSlPSYEzzog6jSRJkrCgS1LhEATw2GOw337QpUt4rlGjaDNJkiQpGwu6JBV0GzfCVVfBM89AyZJw4olQs2bUqSRJkvQPFnRJKsgWLw63UJsxA%2BLj4a67oEaNqFNJkiQpBxZ0SSqoPvgAzj0XVq4ML20fOxZatIg6lSRJkrbDVdwlqSAaPhxatgzL%2BfHHhyPolnNJkqSYZkGXpILojz8gIwMuugg%2B%2B8zL2iVJkvIBL3GXpIJo4MBwMbhOncK9ziVJkhTzHEGXpILg3XehXTvYtCk8TkgIF4eznEuSJOUbFnRJys%2BCAO69F04/HSZNggceiDqRJEmSdpGXuEtSfrVmDVxyCYwfHx5feilcf32kkSRJkrTrLOiSlB/NmwcdOsDs2VC0KDz0EFxxhZe0S5Ik5WMWdEnKb95/P1z8LSUF9t8fXn0VGjaMOpUkSZJ2k3PQJSm/OfDAcKS8cWOYOdNyLkmSVEA4gi5J%2BUF6OhT5/7%2Bya9eGjz6Cww%2BHYsWizSVJkqQ9xhF0SYp1P/4IxxwDU6ZsPXfMMZZzSZKkAsaCLkmxbMIEOPHEsKTfdBNkZkadSJIkSXuJBV2SYlFGBtx6K5x9NqxdC82bw7vvQrx/bUuSJBVUzkGXpFjz119w/vnw9tvhcZ8%2BMHjw1jnokiRJKpD8154kxZI//wwvaf/5ZyhRAp58MizrkiRJKvAs6JIUS8qXh2bNIAhg4kQ49tioE0mSJGkfsaBLUtTS02HDBihTJtzf/NFHYf36sKxLkiSp0HC1IUmK0sqV0Lo1dOkSLgwHULy45VySJKkQcgRdkqLy9dfQsSMsXgylSsHs2XD00VGnkiRJUkQcQZekKDz3HDRuHJbzQw6BL76wnEuSJBVyFnRJ2pc2b4brroOLL4aNG6FtW/jySzjqqKiTSZIkKWIWdEnal3r0gIceCj8eMADeeAPKlo00kiRJkmKDBV2S9qU%2BfaByZXj9dbjrLoj3r2FJkiSFXCROkva2efPCeeYAJ5wACxdCiRLRZpIkSVLMcehGkvaWTZugZ89w8bevvtp63nIuSZKkHFjQJWlv%2BO03aN4cnngC0tKyF3RJkiQpB17iLkl72qefQufOsGxZuADcSy/B6adHnUqSJEkxzhF0SdpTggAefhhOOSUs50cfDTNmWM4lSZKUKxZ0SdpTXn8drr0W0tPhvPNg%2BnSoVSvqVJIkSconvMRdkvaUM8%2BEDh2gSZNwO7W4uKgTSZIkKR%2BxoEvS7pg2DY4/PlyZPT4eJkywmEuSJGmXeIm7JO2KIIBhw%2BDkk8Ot1IIgPG85lyRJ0i5yBF2S8mr9erjssnB1dghLeXo6FC0abS5JkiTlaxZ0ScqLn3%2BGjh3h22%2BhSJFwFP2aaxw5lyRJ0m6zoEtSbr37brg6%2B19/QaVK8Mor0LRp1KkkSZJUQFjQJSk31q2Dbt3Ccn7iifDqq3DAAVGnkiRJUgHiInGSlBulSoVzzq%2B4Aj7%2B2HIuSZKkPc4RdEnannnzYNEiaNkyPD7llPAmSZIk7QWOoEtSTv77X6hfH84%2BG%2BbOjTqNJEmSCgELuiT9XWYm3H03tG8PKSlwzDGQlBR1KkmSJBUCXuIuSVukpIQLwb3xRnjcq1e4jVqxYtHmkiRJUqFgQZckgNmzw/3Nf/oJEhNh5Ejo3j3qVJIkSSpELOiSBDB6dFjOq1eHCROgXr2oE0mSJKmQsaBLEsCgQeGf/fpBpUrRZpEkSVKh5CJxkgqnVavgttsgPT08LlYMhg61nEuSJCkyjqBLKny%2B/Tacb/7zz7B5MwweHHUiSZIkyRF0SYXM2LHQsGFYzmvUgK5do04kSZIkARZ0SYVFejr07RsW8vXroVUrmDEDjjsu6mSSJEkSYEGXVBisXAmtW4dzzAFuvhkmT4b99os2lyRJkvQ3zkGXVPAtXw5ffAGlSsEzz0DnzlEnkiRJkrZhQZdU8B15JLz8Mhx0EBx1VNRpJEmSpBx5ibukgmfzZrj%2Bevjkk63n2rSxnEuSJCmmWdAlFSzLlsFpp8GIEXDuubBuXdSJJEmSpFzxEndJBcfnn8PZZ8Pvv0NSEjz%2BeDjvXJIkScoHHEGXVDCMGgXNmoXl/Igj4Msv4cwzo04lSZIk5ZoFXVL%2Blp4OPXvCFVdAWhp06hSu2H7YYVEnkyRJkvLEgi4pf0tIgJQUiIuDgQNh/HgoUybqVJIkSVKeOQddUv4UBGEpj4uD0aPDUfRTTok6lSRJkrTLHEGXlL8EATzyCFxwQfgxhAvBWc4lSZKUzzmCLin/2LABrroKnn02PD7nHOjYMdpMkiRJ0h5iQZeUPyxeHC4AN3MmxMfDkCHQoUPUqSRJkqQ9xoIuKfZ98AGcey6sXAn77QfjxsFpp0WdSpIkSdqjnIMuKbaNGgUtW4bl/IQTwhF0y7kkSZIKIAu6pNh21FHhJe3dusGnn8JBB0WdSJIkSdorvMRdUuxJS4NixcKPGzWCr78Oi3pcXLS5JEmSpL3IEXRJseXdd6FWLfj2263n6tSxnEuSJKnAs6BLig1BAIMGwemnw6%2B/hh9LkiRJhYiXuEuK3po10KMHvPpqeHz55fDQQ9FmkiRJkvYxC7qkaP30E3TsCLNnQ9Gi8PDDcMUVUaeSJEmS9jkLuqTo/PBDuAhcaipUrQrjx0PDhlGnkiRJkiJhQZcUncMPhxNPhI0b4ZVXoEqVqBNJkiRJkbGgS9q3UlKgeHFITISEhHDUvESJrduqSZIkSYWUq7hL2ndmzw5HzHv33nouOdlyLkmSJGFBj9wjjzxCjRo1KF68OA0aNODLL7/c7mNHjRrFySefTLly5ShXrhwtWrTY4eOlmDJhAjRoEC4KN3kyrFwZdSJJkiQppljQIzRu3Dj69OnDHXfcwddff82xxx5L69atWb58eY6P//DDD%2BnatSsffPAB06dPp3r16rRq1YrffvttHyeX8iAjA265Bc4%2BG9auhebNYeZMqFAh6mSSJElSTIkLgiCIOkRh1aBBA%2BrXr8/DDz8MQGZmJtWrV%2Bfaa6/l5ptv3unzMzIyKFeuHA8//DDdunXL1edMTU0lOTmZlJQUkpKSdiu/tFOrVsH558M774THffrA4MFQxOUvJEmSorRuHZQunQrYDWKJI%2BgRSUtLY%2BbMmbRo0SLrXHx8PC1atGD69Om5eo3169ezefNmypcvv7diSrsuCKBVq7CclygBY8bA0KGWc0mSJGk7LOgRWblyJRkZGVSuXDnb%2BcqVK7N06dJcvUa/fv2oWrVqtpL/T5s2bSI1NTXbTdon4uLg7ruhVi2YPj0cSZckSZK0XRb0fOree%2B9l7NixTJw4keLFi2/3cYMGDSI5OTnrVr169X2YUoVOejr8%2BOPW4zZtwpXbjz02ukySJElSPmFBj0iFChVISEhg2bJl2c4vW7aMKlWq7PC5999/P/feey/vvvsuxxxzzA4f279/f1JSUrJuS5Ys2e3sUo5WrIDWraFxY1i4cOt5t1CTJEmScsWCHpFixYpRt25dpk6dmnUuMzOTqVOn0rBhw%2B0%2Bb8iQIfz73//m7bffpl69ejv9PImJiSQlJWW7SXvczJlQrx68/z6kpYVbqUmSJEnKE1drilCfPn24%2BOKLqVevHieeeCLDhw9n3bp19OjRA4Bu3bpRrVo1Bg0aBMDgwYMZMGAAL774IjVq1Miaq166dGlKly4d2ftQIffss9CzJ2zaBIccAhMnwlFHRZ1KkiRJyncs6BHq0qULK1asYMCAASxdupTjjjuOt99%2BO2vhuMWLFxMfv/Uih8cee4y0tDQ6d%2B6c7XXuuOMO7rzzzn0ZXYLNm8Nt0/5/m0DatoUXXoCyZSONJUmSJOVX7oNeyLgPuvaYIUOgX7/w4wED4I47IN5ZM5IkSfmB%2B6DHJkfQJe2a666Dd98N/zzzzKjTSJIkSfmeBV1S7k2aBKefDgkJULw4TJkS7ncuSZIkabd5Paqkndu0KVwIrl07%2BPt6B5ZzSZIkaY9xBF3Sjv32G3TuDJ9/HhbykiWjTiRJkiQVSBZ0Sdv36adhOV%2B2LFyd/aWXwkvcJUmSJO1xXuIuaVtBAI88AqecEpbzo4%2BGGTMs55IkSdJeZEGXtK1ffoG%2BfSE9Hc47D6ZPh1q1ok4lSZIkFWhe4i5pWzVqwKhRsHw53HCDi8FJkiRJ%2B4AFXVLo/fehTBmoXz88vvDCaPNIkiRJhYyXuEuFXRDAsGHQsiV07BiOmkuSJEna5xxBlwqzdevgsstg7Njw%2BLTTwlF0SZIkSfucBV0qrH7%2BORwx//ZbKFIEHngAevVyvrkkSZIUEQu6VBi98w507Qp//QWVKsH48XDyyVGnkiRJkgo1C7pUGI0cGZbzBg3g1VehWrWoE0mSJEmFnovESYXRM8/A7bfDRx9ZziVJkqQYYUGXCoOffgoLeRCEx8nJcPfdkJgYbS5JkiRJWbzEXSro/vtfuOACSE2FAw6Anj2jTiRJkiQpB46gSwVVZibcdRe0bx%2BW8yZN4Kyzok4lSZIkaTscQZcKopQUuOgiePPN8Piaa2DoUChWLNpckiRJkrbLgi4VNLNnh/ub//RTOMf88cfh4oujTiVJkiRpJyzoUkGzdCnMnw8HHggTJkDdulEnkiRJkpQLFnSpoDn1VBg3Dpo1g4oVo04jSZIkKZdcJE7K71atgnPPhblzt57r3NlyLkmSJOUzjqBL%2Bdn//hfON1%2B4EH7%2BGb76CuLiok4lSZIkaRc4gi7lVy%2B9BA0bhuW8Zk0YPdpyLkmSJOVjFnQpv0lPh3/9C84/HzZsgFatYMYMOPbYqJNJkiRJ2g0WdCk/Wb0aWreGYcPC4/79YfJkKF8%2B0liSJEmSdp9z0KX8pFQp2Lw5/PPZZ%2BHss6NOJEmSJGkPsaBL%2BUEQhPPLixaFV16BlSvhqKOiTiVJkiRpD7KgS7Fs82bo0ycs5lsua69cObxJkiRJKlAs6FKsWroUzjkHPv00HD2/5BKoUyfqVJIkSZL2EheJk2LR559D3bphOU9Kgtdes5xLkiRJBZwFXYo1TzwBTZvC77/DEUfAl1/CmWdGnUqSJEnSXmZBl2JJnz7Qs2c497xTJ/jiCzjssKhTSZIkSdoHLOhSLGncGOLjYeBAGD8eypSJOpEkSZKkfcRF4qSorV8PJUuGH599NsyZA4ccEm0mSZIkSfucI%2BhSVIIAHn4YDj0Ufv1163nLuSRJklQoWdClKGzYAN27w7XXwm%2B/wejRUSeSJEmSFDEvcZf2tV9%2BCReA%2B/rrcL75kCHh4nCSJEmSCjULurQvvf8%2BdOkCK1fCfvvBuHFw2mlRp5IkSZIUAyzo0r4yaVK4n3lmJhx/PEycCAcdFHUqSZIkSTHCgi7tK82bw1FHheV85EgoUSLqRJIkSZJiiAVd2pt%2B/x323x/i4qBUKfj4Y0hODo8lSZIk6W9cxV3aW955B%2BrUgXvv3XqubFnLuSRJkqQcWdClPS0IYNAgOOMM%2BOsvePNNSE%2BPOpUkSZKkGGdBl/akNWugc2e45ZawqF9%2BOXzwARRxNokkSZKkHbM1SHvK3LnQsSP8%2BCMUKwYPPxwWdEmSJEnKBQu6tCesXQtNmoT7m1etCq%2B%2BCiedFHUqSZIkSfmIl7hLe0Lp0vCf/4QlfeZMy7kkSZKkPLOgS7sqJQV%2B%2Bmnr8RVXhPPNq1SJLpMkSZKkfMuCLu2K2bOhfv1wpfZVq8JzcXEuBidJkiRpl1nQpbx69VVo0ADmzQu3T1u6NOpEkiRJkgoAC7qUWxkZ4fZpnTuHi8KdeirMmAFHHhl1MkmSJEkFgNfjSrmxahWcfz688054/K9/wb33ekm7JEmSpD3GdiHlxo03huW8RAl46ik477yoE0mSJEkqYCzoUm4MGQK//AJDh8Kxx0adRpIkSVIB5Bx0KSfp6TB%2B/Nbj/faD996znEuSJEnaayzo0j%2BtWAGtW8M558CTT0adRpIkSVIh4SXu0t/NnAmdOsHixVC6NJQvH3UiSZIkSYWEI%2BjSFs8%2BC40bh%2BX8kEPgiy/Csi5JkiRJ%2B4AFXdq8Ga69Frp3h02boH17%2BOor9zeXJEmStE9Z0KXPP4dHHgk/vvNOeO01SE6OMpEkSZKkQsg56NLJJ8N998Ghh4aj55IkSZIUAQu6Cqenn4bmzaFmzfD4X/%2BKNI4kSZIkeYm7CpdNm6BnT7jkEujYETZsiDqRJEmSJAGOoKsw%2Be036Nw5nHMeFwddukDx4lGnkiRJkiTAgq7C4pNP4JxzYNkyKFcOXnwRTj896lSSJEmSlMVL3FWwBQE89BCcempYzo85BmbMsJxLkiRJijkWdBVsmzfD889Dejqcdx5MmwYHHxx1KkmSJEnahpe4q2ArVgxefTXc2/yaa8K555IkSZIUgxxBV8Hz/vswZMjW4%2BrV4dprLeeSJEmSYpoj6Co4ggCGDYObboLMTDjuOGjVKupUkiRJkpQrFnQVDOvWwWWXwdix4XG3bnDyydFmkiRJkv5fEMD69VGn2GrduqgTKCcWdOV/CxZAx47w3XdQpAg88AD06uUl7ZIkSYoJQQBNmoTrFUs7YkFX/vbOO%2BHq7KtXQ6VKMH68I%2BeSJEmKKevXW86VOxZ05W/Ll4flvEGDcLX2atWiTiRJkiRt17JlUKpU1ClCqalQtWrUKfR3FnTlbxddBImJcNZZ4Z%2BSJElSDCtVKnYKekZG1An0T26zpvzlp5%2BgdevwV49bnHuu5VySJElSvmdBV/7x5ptQvz68%2By707h11GkmSJEnaoyzoin2ZmXDnnXDmmeFEmSZNYPjwqFNJkiRJ0h7lHHTFttWrw3nm//1veHzNNTB0KBQrFmksSZIkSdrTLOiKXQsWwBlnwLx54Rzzxx%2BHiy%2BOOpUkSZIKkSAIt0nbHevW7ZksKvgs6IpdFSpAXBwceCBMmAB160adSJIkSYVIEISzK93DXPuKBV2xJSMD4uPDYp6cHF7aXrYsVKwYdTJJkiQVMuvX79ly3rgxlCy5515PBY8FXbFj1Sro2hXatNm6Svshh0SbSZKkv8nMzCQtLS3qGJL2kU2b4KCDwo8/%2BwxKlNi91ytRInzNfaVo0aIkJCTsu0%2Bo3RYXBEEQdQjtO6mpqSQnJ5OSkkJSUlLUcbb63/%2BgY0dYuBCSksI/y5ePOpUkSVnS0tJYuHAhmZmZUUeRtI9kZsKSJeHH1auHF3rmN2XLlqVKlSrExcVtc1/MdoNCzBF0Re/FF%2BGyy2DDBqhZEyZOtJxLkmJKEAT88ccfJCQkUL16deLz47/SJeVZRkb4T1SAGjUgPw1GB0HA%2BvXrWb58OQD7779/xImUGxZ0RSc9Hfr1g2HDwuPWrcOybjmXJMWY9PR01q9fT9WqVSnpBFKp0MjI2Ppx8eL5q6ADlPj/a/KXL19OpUqVvNw9H7CgKxqZmeFc8ylTwuNbboG7785/f%2BtJkgqFjP//V3qxYsUiTiJJebPll4qbN2%2B2oOcDXp%2BlaMTHh3ucly4Nr74K99xjOZckxbyc5nBKUizz7638xYKufSs1devH118Ps2dDp06RxZEkSZKkWGFB176RlgbXXAMNGmwt6XFx4XKYkiQpUo0bN%2Ba7777jr7/%2B4qSTTuL777/f6XOeeeYZypYtu9ufu3v37nTo0GG3XyfWPteO7Kmv3Z7w4YcfEhcXx%2BrVq/f650pLS6N27dpM25Mbi%2B%2BC8847j6FDh0aaQdoeC7r2vqVL4bTT4JFHYM4ceOedqBNJklQodO/enbi4uG1up59%2BerbH9enTh4YNG1K%2BfHlq165NnTp1dvraXbp04aeffspVjp49e5KQkMArr7yy08c2b96c66%2B/PlevG4WcSv748eMpXrx4zJa%2B6dOnk5CQQNu2bSPNMXLkSGrWrEmjRo22uW9HPyPb%2B8VKTr9cSEtLY8iQIRx77LGULFmSChUq0LhxY55%2B%2Bmk2b94MwG233cY999xDSkrKHntvOdm4cSO9evViv/32o3Tp0px99tksW7Zsh88JgoABAwaw//77U6JECVq0aMG8efOyPeaee%2B6hUaNGlCxZMmZ%2B0aM9x4Kuvevzz6FuXfj003B/8zffhHPOiTqVJEmFxumnn84ff/yR7fbSSy9le8zZZ59NSkoKa9eu5YUXXtjpa27evJkSJUpQqVKlnT52/fr1jB07lptuuomnnnpql99HrHryySe54IILeOyxx/jXv/4VdZwcjR49mmuvvZaPP/6Y33//PZIMQRDw8MMPc%2Bmll25z3576GUlLS6N169bce%2B%2B9XHHFFUybNo0vv/ySXr168dBDD/HDDz8AUKdOHWrVqpWrn/XdccMNN/Dmm2/yyiuv8NFHH/H777/TaSdTO4cMGcKDDz7IyJEj%2BeKLLyhVqhStW7dm48aN2d7nOeecw1VXXbVX8ysaFnTtPU88AU2bwu%2B/w5FHwldfQbt2UaeSJKlQSUxMpEqVKtlu5cqVy7p/zpw5NGnShFKlSlG/fn3ee%2B894uLieO211wBYtGgRcXFxjBs3jmbNmlG8eHHGjBmT68u0X3nlFY488khuvvlmPv74Y5YsWbLdx3bv3p2PPvqIESNGZI32L1q0aJuR0i2f%2B5133uGII46gdOnSWb%2BI2CIjI4M%2BffpQtmxZ9ttvP2666SaCIMj2%2Bd5%2B%2B22aNGmS9Zh27dqxYMGCXH9thwwZwrXXXsvYsWPp0aNH1vlhw4Zx9NFHU6pUKapXr87VV1/N2rVrt/s6CxYs4KyzzqJy5cqULl066/vwdzVq1GDgwIH/1969x%2BV4/38Af92d7konSd1FKoqcpUTYjLXlMKv9%2BOrnkBAafjQhNn6L2ZyG5WxZaPuZ08j2wFhL5tSK1IYlUsm2chodFJ0%2Bvz%2Baa24dVqnuu7yej8f10PW5Pp/P9b7q0533/bmuz41JkybB0NAQbdq0QWho6L/GmJeXh71792LatGkYNmwYdu7cWWX9%2B/fvY/To0WjVqhX09fXRtWvXcm/ovPbaa5g1axaCgoJgamoKhUKBxYsXV9lvfHw8bty4UeEsfk3GSFVCQkJw6tQpREVFYcaMGejRowfatm2LMWPGIDY2Fg4ODlLd4cOHY8%2BePbU6T3VkZ2cjLCwMa9euxaBBg%2BDs7IwdO3bg3Llz%2BPnnnytsI4RASEgIFi1aBE9PT3Tr1g1ffvkl/vzzT%2Bn3EQCWLFmC2bNno2vXrvUWP6kOE3SqH599Bvj7A0VFwIgRZTPp7durOioiIqI6IQTw6JFqtudyzBdSUlICLy8v6OvrIzY2FqGhoVi4cGGFdRcsWICAgAAkJSXBw8Oj2ucICwvDuHHjYGxsjCFDhlSZIK5btw5ubm6YMmWKNNtvXcl6Nfn5%2BVi9ejW%2B%2BuornDp1ChkZGZg7d650fM2aNdi5cye2b9%2BOM2fO4K%2B//kJERIRSH48ePUJgYCAuXLiAqKgoaGho4J133kFpaem/Xtf8%2BfOxdOlSHD58GO%2B8847SMQ0NDaxfvx5XrlxBeHg4Tpw4gaCgoEr7ysvLw9ChQxEVFYWEhAQMHjwYw4cPR0ZGhlK9NWvWwMXFBQkJCZg%2BfTqmTZuG5OTkKuPct28fHB0d0aFDB4wbNw7bt28v90bFsx4/fgxnZ2ccOXIEly9fxtSpU%2BHj44O4uDileuHh4WjWrBliY2OxatUqfPTRR4h8%2BvG5FTh9%2BjTat28PQ0PDcsdqMkaqsmvXLri7u8PJyancMW1tbTRr1kzad3V1RVxcHJ48eVJpf0OGDIGxsQFefbVsMzY2gIHBP1vnzp0rbRsfH4%2BioiK4u7tLZY6OjmjTpg1iYmIqbJOWloasrCylNsbGxujdu3elbagJEvRSyc7OFgBEdnZ2/Z4oM1MIa2shli8XorS0fs9FRERUzwoKCsRvv/0mCgoKhBBC5OUJUZYqN/yWl1f9uH19fYWmpqZo1qyZ0vbJJ58IIYT4/vvvhZaWlsjMzJTaREZGCgAiIiJCCCFEWlqaACBCQkKU%2Bt6xY4cwNjau8vzXrl0T2tra4u7du0IIISIiIoSdnZ0ofeb/Br6%2BvsLT01PaHzBggAgICFDqJzo6WgAQDx48kM4NQKSkpEh1Nm3aJCwsLKR9S0tLsWrVKmm/qKhItG7dWulcz7t7964AIC5dulRpHV9fX6GjoyMAiKioqKouX7J//37RokULab8637vOnTuLDRs2SPs2NjZi3Lhx0n5paakwNzcXW7ZsqbKfvn37Sj%2B7oqIiYWZmJqKjo6Xjz39vKzJs2DAxZ84caX/AgAGif//%2BSnV69eol5s%2BfX2kfAQEBYtCgQeXKazNGKotdT09PzJo1q9IYnvXLL78IACI9Pb3SOr///ru4evW6OHiwbLt69bq4fv2fraq2u3btEjo6OuXKe/XqJYKCgipsc/bsWQFA/Pnnn0rl//nPf8SoUaPK1a/OOBKi/OvXsxosN6Bq01LN2wLUJKWlAXZ2ZV8rFEBSEvDMO5VERETU8AYOHIgtW7YolZmamgIAkpOTYW1tDYVCIR1zdXWtsB8XF5can3v79u3w8PCAmZkZAGDo0KHw8/PDiRMn8Prrr9e4v2fp6%2BujXbt20r6lpSXu3LkDoOz24szMTPTu3Vs6rqWlBRcXF6XZ4%2BvXr%2BPDDz9EbGws7t27J82cZ2RkVLlQXrdu3XDv3j0EBwfD1dUVBgYGSsd//PFHLF%2B%2BHFevXkVOTg6Ki4vx%2BPFj5OfnQ19fv1x/eXl5WLx4MY4cOYLMzEwUFxejoKCg3Ax6t27dpK9lMhkUCoV0zRVJTk5GXFycdOeAlpYWvL29ERYWhtdee63CNiUlJVi2bBn27duHP/74A4WFhXjy5Em5uJ%2BNBVD%2B/lekoKAAurq65crrcow8%2B7P9N3p6egDK7sSoTKtWrVBSAuTmlu3b2wOamjUKiajGmKDTixOibIX22bOB8HBgzJiycibnRETUROnrA1U8Ulzv566JZs2awd7e/oXP26yGf9dLSkoQHh6OrKwsaGlpKZVv3779hRN0bW1tpX2ZTFajBA0oew7ZxsYG27Ztg5WVFUpLS9GlSxcUFhZW2a5Vq1b45ptvMHDgQAwePBjff/%2B9dOt2eno63nrrLUybNg2ffPIJTE1NcebMGfj5%2BaGwsLDCBH3u3LmIjIzE6tWrYW9vDz09PYwcObJcHBVdc1W344eFhaG4uBhWVlZSmRACcrkcGzduhLGxcbk2n376KdatW4eQkBDpOfr33nvvhWMxMzPDpUuXlMqqO0aMjIxw8%2BbNcn0%2BfPgQmpqa0ths3749rl69%2Bvd1AlU9qXD37l8AAFPTligpqbjOsGFDcObMaakfjeceDraxsZEWnnueQqFAYWEhHj58qLRWw%2B3bt5XeEHu%2BzdM6lpaWSm169OhR%2BcVQk8IEnV5MQQHw7rvAl1%2BW7Z848U%2BCTkRE1ETJZE3jfegOHTrg1q1buH37NiwsLAAA58%2Bfr5O%2Bjx49itzcXCQkJEDzmWnHy5cvY%2BLEieUSl6d0dHRQUlnGVE3GxsawtLREbGwsXn31VQBAcXEx4uPj0bNnTwBli6ElJydj27ZteOWVVwAAZ86cqfY5bGxs8NNPP0lJ%2BrFjx2BoaIj4%2BHiUlpZizZo10Pg7o9u3b1%2BVfZ09exYTJkyQnmXPy8tDenp6TS9bSXFxMb788kusWbMGb775ptIxLy8v7N69G%2B%2B%2B%2B26FsXh6emLcuHEAgNLSUly7dg2dOnV6oXicnJywZcsWCCEgk8kAVH%2BMdOjQAXv27MGTJ08gl8ulehcvXoSdnZ30ZsGYMWPwwQcf4OLFBOjrOym9iVZcXISiokLo6ZX94n7//WWYm7fGrVtmqGxNulmzvoC/f4G036mT8gz6829SPMvZ2Rna2tqIiorCiBEjAJTd0ZCRkQE3N7cK29jZ2UGhUCAqKkpKyHNychAbG8sV218iXCSOau/mTaB//7LkXFMTWLsW2LZN1VERERHRM548eYKsrCyl7d69ewCAN954A%2B3atcOECRNw5coV/Pzzz9IicU%2BTqNoKCwvDsGHD0L17d3Tp0kXaRo0aBRMTE%2BzatavCdra2toiNjUV6errSbec1FRAQgBUrVuDQoUO4evUqpk%2BfrvR52c2bN0eLFi0QGhqKlJQUnDhxAoGBgTU6h7W1NU6ePIk7d%2B7Aw8MDOTk5sLe3R1FRETZs2IDU1FR89dVX2Lp1a5X9ODg44ODBg0hMTMQvv/yCMWPG1Pq6nzp8%2BDAePHgAPz8/pe9/ly5dMGLECISFhVUaS2RkJM6dO4ekpCT4%2B/v/62d3V8fAgQORl5enNONc3TEyduxYyGQyjB8/HvHx8UhJScH27dsREhKi9NF27733Hvr16wd399exffsmXLv2C37/PRWRkfswcWIfZGT883niCQmn0aeP8hsXzzM3bwVra3tYW9ujY0d7tG9vD3v7fzYbG5tK2xobG8PPzw%2BBgYGIjo5GfHw8Jk6cCDc3N/Tp00eq5%2BjoKD2CIJPJ8N577%2BHjjz/Gd999h0uXLmH8%2BPGwsrJS%2Bhz4jIwMJCYmIiMjAyUlJUhMTERiYmKVnxRAjQcTdKqdEycAFxfg4kXAzAyIjCy7xf0F/5gTERFR3Tp27BgsLS2Vtv79%2BwMANDU1cejQIeTm5qJnz56YOHGilKBX9Lxwdd2%2BfRtHjhyRZg6f9XSl9MoSxLlz50JTUxOdOnVCy5Ytyz2HXV1z5syBj48PfH194ebmBkNDQ6XV1jU0NLBnzx7Ex8ejS5cumD17Nj799NMan6d169Y4efIk7t27Bw8PD9jZ2WHt2rVYuXIlunTpgl27dmH58uVV9rF27Vo0b94cffv2xfDhw%2BHh4SHN9NdWWFgY3N3dK7yNfcSIEbhw4QJ%2B/fXXcscWLVqEnj17wsPDA6%2B99hoUCoVSclhbLVq0wDvvvCMl3TUZIyYmJjh9%2BjSKiorw9ttvo0ePHli/fj3Wrl0Lf39/qZ1cLkdkZCTmzg1CRMTnmDSpD/z8euHw4fUICpqFkSO7wMkJ6NjxMc6cOYSgoClwckK1tg4dav7f3M8%2B%2BwxvvfUWRowYgVdffRUKhQIHDx5UqpOcnIzs7GxpPygoCDNnzsTUqVPRq1cv5OXl4dixY0q/jx9%2B%2BCGcnJwQHByMvLw8ODk5wcnJCRcuXKhZgKSWZKKmD%2BtQo5aTkwNjY2NkZ2fDyMiodp2kppa9ShUXA87OwMGDQJs2dRsoERGRGnn8%2BDHS0tJgZ2f3QolrY3D27Fn0798fKSkpSouwEb2oX3/9FW%2B88QZu3LhRbmG9ulRSAiQklH3t5FR%2BYbctW7YgIiICP/zwQ73FoE6qev2qk9yA6hSfQaeaa9sWmDcP%2BPNPYMsW4O9VMImIiKjxiYiIgIGBARwcHJCSkoKAgAD069ePyTnVuW7dumHlypVIS0tD165dVRaHtrY2NmzYoLLzE1WFCTpVT2oqoKMDtG5dtv/xx2X3%2BfCWdiIiokYtNzcX8%2BfPR0ZGBszMzODu7o41a9aoOixqoiZMmKDqEDB58mRVh0BUKSbo9O%2BOHStbmd3BATh1CpDLy3/OBBERETVK48ePx/jx41UdBhERgYvEUVWEAJYtA4YOBR48KJstz8lRdVRERERE1EQJUfYM%2BYtsL7gAPpFKcQadKpabC0yYULYAHABMnQqsX182e05ERPSS4tq6RPVHCCA5GeCnhdUtvm41LkzQqbzkZOCdd4CkpLLnzjduBKZMUXVUREREKqP59zLQhYWF0OPiqET1orS0bpNzAwM%2BlQkA%2Bfn5AMoWxyP1xwSdlAlRlownJQFWVsCBA0CfPqqOioiISKW0tLSgr6%2BPu3fvQltbGxr8Xz9RnSsp%2BefrDh1ePLnW0ACePHmxPhozIQTy8/Nx584dmJiYSG80knpjgk7KZDJg505g9mzg888BhULVEREREamcTCaDpaUl0tLScPPmTVWHQ9QklZYC9%2B6Vfa2nx9nvumJiYgIF/0/faMgEH0p4qeTk5MDY2BjZ2dkwMjIqK3z4EPjxR2DkSJXGRkREpO5KS0tRWFio6jCImqT8fKBnz7KvL14E9PVVG09ToK2tXeXMeYW5AakUZ9BfdleulD1vnpICHD0KDB6s6oiIiIjUloaGBnR1dVUdBlGTVFICPL1BRS4H%2BKtGLyPeOKJimzZtgq2tLXR1ddG7d2/ExcVVWX///v1wdHSErq4uunbtiqNHj9b%2B5N98A/TuDVy/DrRuDbRsWfu%2BiIiIiIiI6IUwQVehvXv3IjAwEMHBwbh48SK6d%2B8ODw8P3Llzp8L6586dw%2BjRo%2BHn54eEhAR4eXnBy8sLly9frvnJFy8G/vMf4NEjYOBAID4ecHZ%2BsQsiIiIiIiKiWuMz6CrUu3dv9OrVCxs3bgRQ9lybtbU1Zs6ciQULFpSr7%2B3tjUePHuHw4cNSWZ8%2BfdCjRw9s3bq1WueUnjMBYASgaNYcFH60AtDi0w5EREREpDqPHgEWFmVf5%2BUBzZqpNp6XAZ9BVz/MylSksLAQ8fHxeP/996UyDQ0NuLu7IyYmpsI2MTExCAwMVCrz8PDAoUOHKj3PkydP8OSZz5fIzs4GANyGHJOwCQfW/wdYn/8CV0JEREREVLdycpQ/do3qR05ODoCyj2Qj9cAEXUXu3buHkpISWDx9m/BvFhYWuHr1aoVtsrKyKqyflZVV6XmWL1%2BOJUuWlCtvjycAJv%2B9ERERERGpDysrVUfwcrl//z6MjY1VHQaBCXqT9/777yvNuj98%2BBA2NjbIyMjgLyHVq5ycHFhbW%2BPWrVu8ZYrqFccaNRSONWooHGvUULKzs9GmTRuYmpqqOhT6GxN0FTEzM4OmpiZu376tVH779m0oFIoK2ygUihrVBwC5XA65XF6u3NjYmC/41CCMjIw41qhBcKxRQ%2BFYo4bCsUYNRUODa4erC/4kVERHRwfOzs6IioqSykpLSxEVFQU3N7cK27i5uSnVB4DIyMhK6xMREREREVHjwRl0FQoMDISvry9cXFzg6uqKkJAQPHr0CBMnTgQAjB8/Hq1atcLy5csBAAEBARgwYADWrFmDYcOGYc%2BePbhw4QJCQ0NVeRlERERERERUB5igq5C3tzfu3r2LDz/8EFlZWejRoweOHTsmLQSXkZGhdLtJ37598fXXX2PRokX44IMP4ODggEOHDqFLly7VPqdcLkdwcHCFt70T1SWONWooHGvUUDjWqKFwrFFD4VhTP/wcdCIiIiIiIiI1wGfQiYiIiIiIiNQAE3QiIiIiIiIiNcAEnYiIiIiIiEgNMEEnIiIiIiIiUgNM0JugTZs2wdbWFrq6uujduzfi4uKqrL9//344OjpCV1cXXbt2xdGjRxsoUmrsajLWtm3bhldeeQXNmzdH8%2BbN4e7u/q9jk%2Bipmr6uPbVnzx7IZDJ4eXnVb4DUZNR0rD18%2BBAzZsyApaUl5HI52rdvz7%2BjVC01HWshISHo0KED9PT0YG1tjdmzZ%2BPx48cNFC01VqdOncLw4cNhZWUFmUyGQ4cO/WubkydPomfPnpDL5bC3t8fOnTvrPU76BxP0Jmbv3r0IDAxEcHAwLl68iO7du8PDwwN37typsP65c%2BcwevRo%2BPn5ISEhAV5eXvDy8sLly5cbOHJqbGo61k6ePInRo0cjOjoaMTExsLa2xptvvok//vijgSOnxqamY%2B2p9PR0zJ07F6%2B88koDRUqNXU3HWmFhId544w2kp6fjm2%2B%2BQXJyMrZt24ZWrVo1cOTU2NR0rH399ddYsGABgoODkZSUhLCwMOzduxcffPBBA0dOjc2jR4/QvXt3bNq0qVr109LSMGzYMAwcOBCJiYl47733MHnyZBw/fryeIyWJoCbF1dVVzJgxQ9ovKSkRVlZWYvny5RXWHzVqlBg2bJhSWe/evYW/v3%2B9xkmNX03H2vOKi4uFoaGhCA8Pr68QqYmozVgrLi4Wffv2FV988YXw9fUVnp6eDRApNXY1HWtbtmwRbdu2FYWFhQ0VIjURNR1rM2bMEIMGDVIqCwwMFP369avXOKlpASAiIiKqrBMUFCQ6d%2B6sVObt7S08PDzqMTJ6FmfQm5DCwkLEx8fD3d1dKtPQ0IC7uztiYmIqbBMTE6NUHwA8PDwqrU8E1G6sPS8/Px9FRUUwNTWtrzCpCajtWPvoo49gbm4OPz%2B/hgiTmoDajLXvvvsObm5umDFjBiwsLNClSxcsW7YMJSUlDRU2NUK1GWt9%2B/ZFfHy8dBt8amoqjh49iqFDhzZIzPTyYG6gelqqDoDqzr1791BSUgILCwulcgsLC1y9erXCNllZWRXWz8rKqrc4qfGrzVh73vz582FlZVXujwDRs2oz1s6cOYOwsDAkJiY2QITUVNRmrKWmpuLEiRMYO3Ysjh49ipSUFEyfPh1FRUUIDg5uiLCpEarNWBszZgzu3buH/v37QwiB4uJivPvuu7zFnepcZblBTk4OCgoKoKenp6LIXh6cQSeiBrdixQrs2bMHERER0NXVVXU41ITk5ubCx8cH27Ztg5mZmarDoSautLQU5ubmCA0NhbOzM7y9vbFw4UJs3bpV1aFRE3Py5EksW7YMmzdvxsWLF3Hw4EEcOXIES5cuVXVoRFTHOIPehJiZmUFTUxO3b99WKr99%2BzYUCkWFbRQKRY3qEwG1G2tPrV69GitWrMCPP/6Ibt261WeY1ATUdKzduHED6enpGD58uFRWWloKANDS0kJycjLatWtXv0FTo1Sb1zVLS0toa2tDU1NTKuvYsSOysrJQWFgIHR2deo2ZGqfajLX//d//hY%2BPDyZPngwA6Nq1Kx49eoSpU6di4cKF0NDgnBvVjcpyAyMjI86eNxD%2BNjchOjo6cHZ2RlRUlFRWWlqKqKgouLm5VdjGzc1NqT4AREZGVlqfCKjdWAOAVatWYenSpTh27BhcXFwaIlRq5Go61hwdHXHp0iUkJiZK29tvvy2tRmttbd2Q4VMjUpvXtX79%2BiElJUV6EwgArl27BktLSybnVKnajLX8/PxySfjTN4aEEPUXLL10mBuoAVWvUkd1a8%2BePUIul4udO3eK3377TUydOlWYmJiIrKwsIYQQPj4%2BYsGCBVL9s2fPCi0tLbF69WqRlJQkgoODhba2trh06ZKqLoEaiZqOtRUrVggdHR3xzTffiMzMTGnLzc1V1SVQI1HTsfY8ruJO1VXTsZaRkSEMDQ3F//zP/4jk5GRx%2BPBhYW5uLj7%2B%2BGNVXQI1EjUda8HBwcLQ0FDs3r1bpKamih9%2B%2BEG0a9dOjBo1SlWXQI1Ebm6uSEhIEAkJCQKAWLt2rUhISBA3b94UQgixYMEC4ePjI9VPTU0V%2Bvr6Yt68eSIpKUls2rRJaGpqimPHjqnqEl46TNCboA0bNog2bdoIHR0d4erqKn7%2B%2BWfp2IABA4Svr69S/X379on27dsLHR0d0blzZ3HkyJEGjpgaq5qMNRsbGwGg3BYcHNzwgVOjU9PXtWcxQaeaqOlYO3funOjdu7eQy%2BWibdu24pNPPhHFxcUNHDU1RjUZa0VFRWLx4sWiXbt2QldXV1hbW4vp06eLBw8eNHzg1KhER0dX%2BP%2Bvp%2BPL19dXDBgwoFybHj16CB0dHdG2bVuxY8eOBo/7ZSYTgvfFEBEREREREakan0EnIiIiIiIiUgNM0ImIiIiIiIjUABN0IiIiIiIiIjXABJ2IiIiIiIhIDTBBJyIiIiIiIlIDTNCJiIiIiIiI1AATdCIiIiIiIiI1wASdiIgIgK2tLUJCQlQdRrVUJ9bFixejR48e5cosLCwgk8lw6NCheouvoURFRaFjx44oKSlRyfn79OmDAwcOqOTcRETUNDFBJyIitSeEgLu7Ozw8PMod27x5M0xMTPD777%2BrILKKLV68GDKZDDKZDFpaWrC1tcXs2bORl5dXJ/2fP38eU6dOlfYrSrjnzp2LqKgoaT8pKQlLlizB559/jszMTAwZMqTS/sPDw9GrVy/o6%2BvD0NAQAwYMwOHDh%2Bsk9roUFBSERYsWQVNTE0uXLoWlpSX%2B%2BusvpTq//PIL5HK5FP/Tn4tMJoOxsTH69euHEydOKLW5desWJk2aBCsrK%2Bjo6MDGxgYBAQG4f/%2B%2BUr1FixZhwYIFKC0trd8LJSKilwYTdCIiUnsymQw7duxAbGwsPv/8c6k8LS0NQUFB2LBhA1q3bq3CCMvr3LkzMjMzkZ6ejpUrVyI0NBRz5sypk75btmwJfX39KusYGBigRYsW0v6NGzcAAJ6enlAoFJDL5RW2mzt3Lvz9/eHt7Y1ff/0VcXFx6N%2B/Pzw9PbFx48ZKz1dSUtKgieqZM2dw48YNjBgxAgDw/vvvw9raGjNmzJDqFBUVwdfXF%2BPGjcNbb70lle/YsQOZmZk4e/YszMzM8NZbbyE1NRUAkJqaChcXF1y/fh27d%2B9GSkoKtm7diqioKLi5uSm9ATBkyBDk5ubi%2B%2B%2B/b6CrJiKiJk8QERE1Ejt37hQGBgYiNTVVlJaWioEDBwovLy8xadIkYWtrK3R1dUX79u1FSEiIUjtfX1/h6ekpPv30U6FQKISpqamYPn26KCwslOrY2NiIzz77TNoHICIiIoQQQjx58kTMmDFDKBQKIZfLRZs2bcSyZcsqjTM4OFh0795dqWzKlClCoVAIIYR4/PixmDlzpmjZsqWQy%2BWiX79%2BIi4uTileAOW26OjocrHa2Ngo1bGxsSkXQ3BwcLm%2BKhITEyMAiPXr15c7FhgYKLS1tUVGRoYQQogdO3YIY2Nj8e2334qOHTsKTU1NkZaWJuLi4oS7u7to0aKFMDIyEq%2B%2B%2BqqIj49X6guA2LZtm/Dy8hJ6enrC3t5efPvtt9Lx6OhoAUA8ePCg0u/xjBkzxMiRI5XKkpKShK6urti/f7903TY2NiI7O1vp3E9/rkII8ccffwgAYuvWrUIIIQYPHixat24t8vPzlfrOzMwU%2Bvr64t1331Uqnzhxohg3blylcRIREdUEZ9CJiKjR8PX1xeuvv45JkyZh48aNuHz5MkJDQ9G6dWvs378fv/32Gz788EN88MEH2Ldvn1Lb6Oho3LhxA9HR0QgPD8fOnTuxc%2BfOap13/fr1%2BO6777Bv3z4kJydj165dsLW1rVHsenp6KCwsBFB2a/aBAwcQHh6Oixcvwt7eHh4eHtLs7Lp165CZmSltAQEBMDc3h6OjY7l%2Bz58/D%2BCfWeGn%2B8%2BaO3cuduzYAQBSnxXZvXs3DAwM4O/vX%2B7YnDlzUFRUpPTMdX5%2BPlauXIkvvvgCV65cgbm5OXJzc%2BHr64szZ87g559/hoODA4YOHYrc3Fyl/pYsWYJRo0bh119/xdChQzF27Nhyt6dX5fTp03BxcVEqc3R0xPLlyzFt2jQcP34cy5cvx44dO2BkZFRpP3p6egCAwsJC/PXXXzh%2B/DimT58ulT%2BlUCgwduxY7N27F0IIqdzV1RWnT5%2BudtxERERV0VJ1AERERDURGhqKzp0749SpUzhw4ABatmyJJUuWSMft7OwQExODffv2YdSoUVJ58%2BbNsXHjRmhqasLR0RHDhg1DVFQUpkyZ8q/nzMjIgIODA/r37w%2BZTAYbG5saxRwfH4%2Bvv/4agwYNwqNHj7Blyxbs3LlTeg5827ZtiIyMRFhYGObNmwdjY2MYGxsDAA4ePIjPP/8cP/74IxQKRbm%2BW7ZsCQAwMTGp8DhQdru7iYkJAFRaBwCuXbuGdu3aQUdHp9wxKysrGBkZ4dq1a1JZUVERNm/ejO7du0tlgwYNUmoXGhoKExMT/PTTT0q3mU%2BYMAGjR48GACxbtgzr169HXFwcBg8eXGl8z7p58yasrKzKlQcEBODbb7/F0KFDMXPmTAwcOLDSPvLz86Vn2AcMGIDr169DCIGOHTtWWL9jx4548OAB7t69C3NzcwBl35dbt26htLQUGhqc9yAiohfDvyRERNSomJubw9/fHx07doSXlxcAYNOmTXB2dkbLli1hYGCA0NBQZGRkKLXr3LkzNDU1pX1LS0vcuXOnWuecMGECEhMT0aFDB8yaNQs//PDDv7a5dOkSDAwMoKenB1dXV7i5uWHjxo24ceMGioqK0K9fP6mutrY2XF1dkZSUpNRHQkICfHx8sHHjRqX69enZ2eF/o6Ojg27duimV3b59G1OmTIGDgwOMjY1hZGSEvLy8cj%2BPZ9s1a9YMRkZG1f55AEBBQQF0dXXLlctkMixcuBClpaVYtGhRhW1Hjx4NAwMDGBoa4sCBAwgLC1OKpybfAz09PZSWluLJkyfVbkNERFQZJuhERNToaGlpQUur7CawPXv2YO7cufDz88MPP/yAxMRETJw4Ubqd/CltbW2lfZlMVu1FzXr27Im0tDQsXboUBQUFGDVqFEaOHFllmw4dOiAxMRFJSUkoKCjAd999BwsLi2pfY1ZWFt5%2B%2B21MnjwZfn5%2B1W73Itq3b4/U1NRy3zsA%2BPPPP5GTk4P27dtLZXp6epDJZEr1fH19kZiYiHXr1uHcuXNITExEixYt6vTnAQBmZmZ48OBBhceejo2n/z7vs88%2BQ2JiIrKyspCVlQVfX18AgL29PWQyWbk3Sp5KSkpC8%2BbNpbsWAOCvv/5Cs2bNyt0ST0REVBtM0ImIqFE7e/Ys%2Bvbti%2BnTp8PJyQn29vbSiuV1ycjICN7e3ti2bRv27t2LAwcOVPnMtI6ODuzt7WFra6t0y/jTW8jPnj0rlRUVFeH8%2BfPo1KkTAODx48fw9PSEo6Mj1q5d%2B6%2BxaWtr18lngf/3f/838vLylFbKf2r16tXQ1taWVk2vzNmzZzFr1iwMHToUnTt3hlwux7179144tuc5OTnht99%2Bq1VbhUIBe3t7pUQbAFq0aIE33ngDmzdvRkFBgdKxrKws7Nq1C97e3kpvSly%2BfBlOTk61ioOIiOh5fAadiIgaNQcHB3z55Zc4fvw47Ozs8NVXX%2BH8%2BfOws7Ors3OsXbsWlpaWcHJygoaGBvbv3w%2BFQiE9110TzZo1w7Rp0zBv3jyYmpqiTZs2WLVqFfLz86WZcn9/f9y6dQtRUVG4e/eu1NbU1LTC58NtbW0RFRWFfv36QS6Xo3nz5rW6Tjc3NwQEBGDevHkoLCyEl5cXioqK8H//939Yt24dQkJCYG1tXWUfDg4O%2BOqrr%2BDi4oKcnBzMmzevXmaXPTw8EB4eXuf9bty4EX379oWHhwc%2B/vhj2NnZ4cqVK5g3bx5atWqFTz75RKn%2B6dOn8eabb9Z5HERE9HLiDDoRETVq/v7%2B%2BK//%2Bi94e3ujd%2B/euH//PqZPn16n5zA0NMSqVavg7OyMDh06ID09HUePHq31omArVqzAiBEj4OPjg549eyIlJQXHjx%2BXEuuffvoJmZmZ6NSpEywtLaXt3LlzFfa3Zs0aREZGwtra%2BoVnc0NCQrB582bs3r0bXbp0gYuLC06dOoVDhw5h5syZ/9o%2BLCwMDx48QM%2BePeHj44NZs2ZJC6rVpbFjx%2BLKlStITk6u034dHBxw4cIFtG3bFqNGjUK7du0wdepUDBw4EDExMTA1NZXq/vHHHzh37hwmTpxYpzEQEdHLSyZqshIKERHRS87T0xMrV66s8CPPqGHNmzcPOTk5Fd6S3xDmz5%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"
     },
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 1000
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1000x800)"
     ]
    }
   ],
   "source": [
    "#ADD {OZ_, sabiti tekrarlı sihirli değer kullanımını azaltmak için oluşturuldu.}\n",
    "#NEW {Figür, idg (bu jupyter not defterinin çözümleyici arka-ucu)'nin \n",
    "#     grafik çizdirmesi için gerekli.}\n",
    "\n",
    "# Matplotlib temel ayarları:\n",
    "# Figür:\n",
    "fig = plt.figure figsize: [10, 8]\n",
    "# ___________________???\n",
    "ax = fig.add_subplot 111\n",
    "\n",
    "OZ_ = [0, 1]\n",
    "\n",
    "plt.plot fpr tpr 'b' label: (\"Eğri Altında Kalan Alan (AUC) = {:.2f}\".format roc_auc)\n",
    "plt.plot OZ_ OZ_ 'r--'\n",
    "plt.xlim OZ_\n",
    "plt.ylim OZ_\n",
    "plt.xlabel \"Yanlış Pozitif Oranı (YPO)\"\n",
    "plt.ylabel \"Gerçek Pozitif Oranı (GPO)\"\n",
    "plt.title \"Alıcı Karakteristikler Operatörü (ROC)\"\n",
    "plt.legend loc: \"lower right\"\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b4c2517",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "# KAYNAK\n",
    "# https://stackoverflow.com/questions/38640109/logistic-regression-python-solvers-definitions/52388406#52388406"
   ]
  }
 ],
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