4-1_CIFAR_MLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 1. Module Import '''\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using PyTorch version: 1.6.0+cu101  Device: cuda\n"
     ]
    }
   ],
   "source": [
    "''' 2. 딥러닝 모델을 설계할 때 활용하는 장비 확인 '''\n",
    "if torch.cuda.is_available():\n",
    "    DEVICE = torch.device('cuda')\n",
    "else:\n",
    "    DEVICE = torch.device('cpu')\n",
    "print('Using PyTorch version:', torch.__version__, ' Device:', DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "EPOCHS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ../data/CIFAR_10\\cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../data/CIFAR_10\\cifar-10-python.tar.gz to ../data/CIFAR_10\n"
     ]
    }
   ],
   "source": [
    "''' 3. CIFAR10 데이터 다운로드 (Train set, Test set 분리하기) '''\n",
    "train_dataset = datasets.CIFAR10(root = \"../data/CIFAR_10\",\n",
    "                                  train = True,\n",
    "                                  download = True,\n",
    "                                  transform = transforms.ToTensor())\n",
    "\n",
    "test_dataset = datasets.CIFAR10(root = \"../data/CIFAR_10\",\n",
    "                                train = False,\n",
    "                                transform = transforms.ToTensor())\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset = train_dataset,\n",
    "                                            batch_size = BATCH_SIZE,\n",
    "                                            shuffle = True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset = test_dataset,\n",
    "                                          batch_size = BATCH_SIZE,\n",
    "                                          shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: torch.Size([32, 3, 32, 32]) type: torch.FloatTensor\n",
      "y_train: torch.Size([32]) type: torch.LongTensor\n"
     ]
    }
   ],
   "source": [
    "''' 4. 데이터 확인하기 (1) '''\n",
    "for (X_train, y_train) in train_loader:\n",
    "    print('X_train:', X_train.size(), 'type:', X_train.type())\n",
    "    print('y_train:', y_train.size(), 'type:', y_train.type())\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kzLoskSkwG5X8+J3biJ05fjLC+kARE8o5znfnjOc1Ozd3eP1whyg1LgmkEEQtsIXgdH8HmRx1KTnYmcB0RBiVV/KxKjRCSBAR5xQpAjEhU6JH0ItAEhCdJ208dajYMRXzYk5Px9K2xN4RU0CVGlVoKglBRIJI2OE0+pQIcrivhEfGABikTAgliEmQYgIBQoq84ACkFCQlc3A/PLTWSCm5+pn8+PXyt9NedgzPTqy/6jp9JtC5+Fekl+/8YicKIRVCSqQSaCRJauqyYFQq6kLhfI/rO0IEZxukNChVUJdjhNJIoZBKI6VCSQkInOvo2pbedoxGY4qqwhQFMQzBseuJSoHSlMUYFTTSaWToIa8N6aUkIolJEEQi+ezjx4bWq7j4ivduCOFizrTWXjz8MC5uH1u7PH5tH9u/L7/+vLHq13JtvTDg+f/8hZ8hhEDwnpOzMz784fcoi4KUIs5bnLd4F1G6QJsCFwMhBmIIiBSwnWPpG2R0+aaSksmkoCoFRnnwLa5d0TRrVBqhJyO0rvEkpAwYE/A+IIWgMJqmtSTyxba3PwUEgZ4nx/extqVvVhRFSXRrxlcbf4gh0PcdIXlOT05pu5Zms8FZS/T5JG5PJjH/2K5zAKhSgYRqVNKvuhyFAz7Ei5tHJFAIdBKEJPDWk0Lg7Zuvc7i3z3Q64fHxE1yMCBK9s0gtGU3G9G1AkYOyGzf3MZVCFtA2FgLEK7Z1+7f/xP8ZHyFE2JnP2N2dM52MaHuoCxAq4ruWKMAnODtd0XmHD4K+87jSE4InuA5vW3zf4ENLSIIQBWUZkUIjBITgUUqgtCAJhTIgNSSR1xe5j90QxKTLN9U2sNkGRHH47JXElpmPbqGEQitDoXfRSqElFGpEjCGjb+Hp99c60sWA9xFtdlBKIiWslwtSlEhhSEKghUalwHiySwgO7x2+s+jhhkQMu/QJpTRCGoTQSDNCSglS0jtL6zsinuVmQSEkpdT0qx4hrj7OXraPiWk95+bfDihSyo8NNM/u45OeXw5cts8vIzbw/NXWsxPOq9jX3rzJl9+8yZfevIlWCaQFEUj7FeN2TGmn3KzG1GVJWRTYIIgaRLL4zRpCQCFoHt+lfXyXECNRSnTwSOc5tg1r29P7wN0PfohD0KWIi4FSSHQS3Dt6QtFWjLoN94+P2TSOTWORRlEVCq0l945OEQKMVpzG91jZ77NoPb/77/uHXurjqEhIleN4qxICgUAhUkGZNH2KRJsQPqI3jtQGEAGl4e29O5xvVpxulhyfrZCVQFeS6AJJQhLgi0hUIJRG1wahBUkHhPCI1KOEQBcKHwOIiCk0pkiYIiFEBBJSJWL0JCJCJowyJMSnuk4/Kyal4GnD98v3TUZYhRBXDnoSioQCoQhpCJpSwMiEkYlCJ5Qkj49JYISia3qa9YpztRwCXokux0/3JTUiekotENJQGoVWkqIokCJBioSoST4QfGCzWUHXIlZL1HpNqmp0UVPXOwhVIKUmIRAiL+KSSMiUEZ6/XbYNcLz3fPjhh5ydnXF+fs75+Tnr9ZrmUnYmeI8fFmxSSIR8OnYolTM7WmtG9Yi6rqmqit3dXSaTCbPZjLfffpuiKD4W+Hwae2HAs1lvcNZhbU+3abGyp1N5ReqcxTlLSgJTBrQP+OiHAS9HfKoAMawWlJQorZjPphSFoTA5ui1MDbXIK7Wk8B6izIOAEpLSjEg6kWLCGAhekKKkQhN8xIeIUALrO5brc4qiyKiFDFf6AUIIQ7oqsFots1/W5mFI559HSplPWggUuoQk8M6japVX/TF8bFKJISKFQAlBqQ2TomJUFLRFQ2t7+uAQQPAe2/V4a0lGIYyi3TREG/DOIcgBQQwR2zsCARkFUiiMMcQrBnUf3n2IDeCDoDAnjMcjRnWJjC1aK6pKYWJPDiDFBZKQJ8uAs5aua+iaDX3X0VtLiI7eB6xLKJ0DHikzbBtiQvhE0pO8kkxigHElKT1FeIQg/30xyGyfiyG9NaA8V7D5/pTkA9FGTk6OGI1KJpOa8awgpkSIiRAjAplTSvMdemfpnSeqMVIoBLA80xSloag0XiWEzOmwcpRIURC8xPUSJSRSgPAa5WsKX2TkJyoICigQQZACuJBX1UJpqnJEUQjKQlFoc+UA4Xloy/Og4ssrok+Cf5+Xfnr2/ecd18uO8zLs/En7eJEtFgueHGtqBX1vIeUV+aiQHB2dcnK+oaoESjcIKVh3nnFhqAvN8fEJbUhEIThR+Rp2zoNSjJSkkrDZbGg6i3UBO+lwUtJLiagrlJAUMSFCRKaIFolpobFtjw+O3VlFoSVKgHMe5zzbJunr3rMcFkEvM6X6jLLIRKFDRviGpZKMAh0k1lqUVxgqimJMQmNtoDKGShTM1AinUkYaos7oogAhEyo4ZPIIPKkFtCRpTTmVRBQ+QNFGlBJgNGJANaUU5CyUQMSMQEkJWgsSkhAFIVztXD7vunuVFXmCARL/VTv+5P1tP/7My9vP+RAI0YMQw++dsNZefKYoiiuHAmmbkkqCvLbdXufpYrkmc54RkQRIgVaCqCRG52+JKRJ8j3VgbaJzHogkmRBaQoyIBLYaoSQIEUkCpFTowoDO35aEINmWFCP4gNETkjAIKfEip7+FhChEPs+vGPBcvqef99piseDx0RF/9a/+Vc5OT1muVmzWa2w/IDzBX4wDZVXm4yGPTzmFmM+DuHQejDEURcF0OmU8HjObzXDOcefOHQ4PDy8CrU+D8rw4pRUSzjq6rif4SBQxT+ZK4V3E2Zjh+zisy9PT6C2FgFGKsigQZIjKGMPuzu4AkQq0SEhpKMoRUiakzBeRQOZ1uBSYqtpOj0QUISRigJGssNbRW4eLiUSktQ0+2gz5X6nHa161kjLkt9msiSFDb2VRfix1QMqBjBI6X6whUEkgMqQ54sWNnkJAKIUSiqoomE0mzMcTWl2yWK+g3ZBCwHY9Iia89SBAhki3aYg+Emy+gWKK+JBo2xYVFCpIxvVkgPKvdsKfnC7oHfQuYa1HSUFhJF98e0Rda0a9pEoOKXOQEWI+oVIKUop4b+n7lrZt6AaoMiRH0znazqNKkEqhpKGuaog5PWWUJMb8SEmSUZwMtz69VrdP8mo3kcc6KcQQ7Fwt4JnuV7imo1tbThePCGJCMZ6zO9vNAXhMxKQRUqCkYFwYnHM475HljJQk0UNZSmIKRBmRhUcahTICU+abMwWBcTnFSRIIrzDeEIPAdoLQiwwfe00ccvKBiDISbRRVWVOMBOVIUpQ657vj1YLzT+LOxBg/lgu/nLaC5w9WwMeC9Oe9/uxg92mRm6tud3Z2Ri0TWMv5Yo0IHkniYHfC40XDyfkGUyUSCZcCi03P3qhipy45PzmlC4kgBEslsd7RWYdWmlQXyNLguh7b9vQ2EJ0jlhXJFJTzXWohqJ1HA0YIKiXZn45orUe1PXu7M4zIi5QUI31vcdahEzTW09orBjzSDqvuiNRPg42YJCoITEqk1qOdoKSgGFCAtrdIpZBBUsuKuRFEKQlJQgwEkZAxYYLE+5QneAUoCVpTDved9GDahK4EYqSQ+Jy6kpkjQspEtQhIBWpYpIT46c//r74GnjduXSHoHxDGjMaIX7WXEMJFouoiJZNyyq7rLU3fIYS84MQslksYFuPj8QijFFopCmNeeBxS5pR2jAnnQv69Bt5CShGSzOmuwVclBFpJklEURuUFWEjY4LGdY7NxnC1WSA3KCKpxCSEgItjRBCUTUkZQGm1KjNEo5MVCLnk7jLkRM87bSSGxIuXhM0EcjueKSYFPXOx8DOGVgpPTU777ve/xsz/7sywWC5qmwTuX06UxEUggBFJJdnfnyCFPqqQixnydLhfLfO5CQKrht1OSuqqoyorJZMJkMsEYw8HBwXPR7e3zlwVBLwwLfuzrP0HbtbRtS9/3OWjRiqKoM9nKhyHHm9EbITOJUimF956iMBmJSH64UCSz2TQfVAIpcv5SZsJFvoBFJv5mLy6tFgQkNSAASWCCom07uq5Hj8ohhxnzKv4Kjl+cNCEyvhMCKimUVhhpkGmYOJSiLEtSJAd5vSOpCAbKqiTEgFQCv3IYbVBG0/b9cONoRmWFiAnbtOzO5lhnWayWrFYrxDC7ixBJ1hPo6b3L124AbxMpeFIMeN8wmo4ZMSUWYLSm1FeL6qazKdompE3Q9GilKUsNRtNGz1lrmahAoUDJhBADVJkSRaGpRwXjSYUPAR/AR03bWZpOsbECIUYoWaC0IZoJWku0ltRmBsxw1tC2GUzVRme4N0Z8CEghkcSLVa64GMhebXC1Ox/i60CoPfvBUY5WiGnPZmeVIeQkkEpdBFVL0rC61vQbS7sItIuIX4F3AR8DoopUY005GfgzW55RNKSQZ4XYG3wPvofT4wW27fC9w5jygrznXP8Uti019a5itKeZvaFAJRCv0n/04/Y8rsxlhOVl2z4vdfVJg90noUXPBlTP7vOqE2WJZyQcM+UoRhItS0otOJwUxL6j1Yl5TmojpOHN1/c4nE3ZHdeMRxVd2xNd4De98Rpnfc9J1zGb7zIzhomU3Do94Wi54qxtub0/J966hbt5k/kbb1P3FrlYsvuD71HVBTvjiv3bb7DWp6xUxRvvfonQdTSLBbW5SzXPKPVrezus2pZFs7mSj0JEpMypojz2O4SIGFHhLfSryOPvPmQmKkw5pdobsV6vOT854uTJgp3ZDns7e+zuz3DBYa2jUvXAg/MUvmdKSZAFi6bHO0EMisqVOJvTsWwsciJROwo9L9FVRIsA0qOkxAhFCAaRByicz//+nbRtoLId85+1pmtZNxvarqWuR9RlngzlgIL74Pn+B++x6Vqst+zMJnRdS9d2zOeH3H1wj7sP7pEkaG2QQvLB3Q9IKc8X4/GI/d1d9nZ2+QO/5/e/8Fjv3LmDcx1t27A4e4hQCqkN09pAqJACTFGBECREDkrIZHRETg4KGSmFJhUF0Uvurx6QpEeZhFZTDIkkFdF2hNgTfUcfE1KVSF1QFAVVVVFVJaNRQRKGKAuiKbHaoKTGEodrTJDIPDH1KU/j88aEpun4W7/8y/zMf/bn+NZ3vp15qwzp9AHd6mMAJUBJVu0G7xzBe4wxmLJCFwXBurzKTaDSNg7IBSExnCHIxUEAb7zxBuPx+OIYLo9NVyHKv3DG3N3dZRpmQzrH550KgTEVMSZCiBn6HFYI6tLKMsbwFB0RTxlTpSkGaFJAUkNuFWJ0JHJAqgTDSmMYMAfYMMY0QJ0J4QLRZz5MGjg2W1gxvQL/7AKKzMgj2miM1tjeZj5SjPRdT3ABmQRKCUxlMCOFUYIYIQXykQuJFJJSa4ohIPGux8eYK0G0IoRI23aMZoZEwqdAEpKQBMlHvPOkJIbqje2x5ZMZfKJre4gwGVeMRtWVfJTJIhKImBDJZ+TNZMQNsSU4qpzfjQlnPZ4Mg15M1MYQhcZHsCHSeImnRBcV5eQ23gucT5yvBEJEhIy0pqdrHvDkUct6AdPpDtPpnHe++AV2dneYzicMrMkh+BMf5zS/ggnTIUkYkZjf0igjUEUgyW5YDQpQmXOVdy+xG0W/tpzetfSriG0SMuTquwTIThB7iW8SQuU8eooBYsjnJyaCS0Sfg+HNYknftrjeUtWjnO4Mkd52KK3R2mCcwQVB2whEWVLNBeX01Rx+lqC8tZdBvZ9UibX996opq8t/PxtsfZr9bm00nTLZO2B285A6RETKDIdSOIqipRAJJaASikIa1O4uo1s3qff3qHVBenJCXKyod3ZxAmxMGcUpS4RWVG1P2TuKENGjEb3W9DFwcnrCDKiCRaq8yEFqGqlY+8Sisfzw/mPGWqEjFMag6jGj2YyDwz3M+QlXLUoypcn3h4hAQAzpCuUjzcaxfNITW0kxKZiOayazgnUHa2t57/iU3ShZmRoxHzEqJdNRSe8UIiUkAl0XJCCkRKEC1kl8SCSXCBaSFZgkST2wCLgjSFogao2UAanSkHzOqegoE0FkTkq6Imp+2Z5Hbk8psW43nK9WLNdrfHQXc0yMiaooKIuCwuR/tVI8eHzEvYf3efTkMTs7M/Z2dtiZzREkpFTECN/+/vc4W52x3iyphgpZax3z3YNcuXt+ggvxgstzvjgnxFysoIxkNp0wnUxeGvDs7O3SNWuaDfStx/sEwZKCwztH33WYwua0slLg1XCvMHB/0lCVOiBrIqfYBAkpEkomlEgoIoqYQQCVcN5m+oS3RCdIviS5ElFXFKMdTDUl1RVRGZyUA60CDBAGZEW+QsDzSQug7WtPjp/w8NFDHj58gLU2jz3DOS5MQVEWvHXnTXRVIo3GNg2r5ZL1coVUktnODtOdXUZVRfAe1/c8un8f2/cXlI4Yc/bk3r17PHz4kOPjY0aj0a8aA68KcLzwEq5HNWJIUQ1ekkjU5ThD9T7iQ09KAZHiBds8BzDx6YStMq42/JnzmkIMKFwCkcvQt9Go2s56KZNg4zBoejzDTIN04K3HeZ/Z6CqBjENUffVBdnshZnRKURQaY4ohuoykmOjbDmKeNLUSjMYFo3mBUNDbiHfhKVclCUpjKIyhlJKua9FFIiqNEhLvA23XI3ZmuYw0epJUhBRzKisMP1LK6SQpcsSslSFFQd85XO8wWjIaXy3gMSJgiNiU0CJQqERlJEZphMjl9EkqIhGix/lIzFg7UirkUJHgAvRB0DtB6xSqmFJWe9STL9I0gdZ1bNYbfLDEaNnIDY/DCpHu8cMfPGK+s8v+3iG/tfd84d13MFVJXYLcRiE5eT8EyNuc+BV5A8qhBEgtKMd6uAYiSdin5SZKINWQQugN7cpx/iBy77sLQpdIXlJUI7TSQ4pOEVuJN4KIIMWQH5cC7IvqhBRp1mu6tsH2HTFHwUP6o0Nqg9aG6Au6JsJZwtQjpNRUk08xkzxjl4nF27+vOii8SlDy7KrqRft50YD5PBvPdpjs32R2+42crvGe6Hr05hhjzjEyZ2gqqRmbEuZzils3UXduY3qP6QO+6THTGfWoZlIYTpzHjUbEoqC4/xhTNGjvkXVFVALrLP3xEaI0efLREqEVSSoaBE2EjfW8f++Imztj9ic12hjK6ZTR4Q3mN/cRRETfXslHUxggkkRGDIXMk5z0iX7ds3qyQQfDyNTMJyPGE4M8l7Qx8NH5ijNZsa6mjPoRr4/HjOcVYhNRMRdGzKoCIQSBjB71IZclLxYNKUAKgkJqvPMEH3BHglBrxFwhsBcLPyEEOkGIAikCWvF0HniJfXIqNA3pscjx4pyPHtzn3qNHtK7F2g7XW3wMTEYjpuMxk3rEdDyhLiu+/8P3+c73v8sPPvghBwdzbt28yY2DAyQBpQtA8+3v/4Dj08ecn58QbT/4GJnt5oIDZ/vMhUxDFiENSHOKuOQoS0NZFS/1bz6bofOqnLZxpOAJMdD1Fu8cXSNR2l0QcbXQKKUxWuG9vBQcp4ushhyy90oJjJJomYMVSUTKRBICSx57YnL0vSc5Q7QGwhhVTajLEuoKh848mSjRAUiJIMWAnl3pFH7sXH4Sj+fo8WOOjh5zcnJCDOFiLJRSok2BLgve/MI71NMJRVWyOjvn+OgJx+UxAAc3Djm4dZPDgwP6tmO9XHL8+DFd22Z+VeIi4NlsNjx+/JjHjx/z5ptvfgzFvqCSPINyP89eONL+0rd+frv5sBMoTMFv+22/E2sD3XLNN775CzjXQ/RUyqBV1p+59NWZ7rBFXS4hMUTyv8Sh+nhbs59hR601fgiEgFwmHHO5UerDgAhcWrlLkStxxNNjfrkliqqgHhXs39olpoTzgbjIE4cUEt/7HKwJwbQo2N0bc3B7zsn5gpPjhuPHC1QqSFWuyJrVI0Jw2M2G3nVMioKqLLFdh/eOKEAblclkKeIQeHIpsynrnPtN0PmGuqyoyzIHX1IQRSY1np4tWKzOruThb/7S65yvLefrns2mG4LRDtGAMRW1nhB9jzSSqhJMxwGfBD7BZrPg/Lxjs+rYbBxBjEhyhBjvcHDjKxze+jIfPQzY6PHJ0fRnbDZL2qZHdk+4c3OPN+7c5Mtf+jJCKryP/Ad/6k/xxpuv85Uvv8s/+o/8A0yqCqPUkN/9dPD5uM6rvBQTPtdiAtuJPhM6i5K8YhSG7/zVjg++eczd754Sekc1GlGNR6AgRo0KGlXmNIDt4wVJNcWIdd3wrYIY09PS0nwApBTYrM4u0M7eOlRwhGAoCoOzFmt77n5rSQwTymryyv5u7SrkvS1C+EkVXdvPvIik/CLy87PPt3+/Kudjfus1dt/5Cgdf/ir1qM5VH6fHiKNIUS8YlxUjU7A3nrI3mWGVxjYtj05O6RE4IJILBkajMYwnfPjee1QIktJoo1BKIkhU2jCejjjYGyHCklp7NIFbXxgRpEbWMN5XvDt/k9tffJ37799lpMhcitJi1Yp1Uqxjoqot4/2rLT50kXLVYPQgczpcSoHtPc71xK7n63de53BWsT8qMFpQTEaYw31O5Hvce3LCd06XnPZLfurHv8jenUOkPafrHL7voazYm1bsjwum40jnofGCR1Kw3jjWxrFYrYdaRYVeSdyjxDIF5rMZamIRtUWoBinzJB0TqKTIPLxXs7T9L+WigbPFiqPTU/6rn/sbfHD3A+4+vM/y/JwUcgm985nUraRAK824rqlHFdE7NuuGpmlYLR5x/94PKUuN0gyEamiaMNxblm6zyrQJBJumGUQkJEpuJUeyBEcg4NPAtess3fIKXKzUMR1XzKYTvvDFH8MOmnHLxQnNZkO7aVltWsLGEUKHDH7IdghOl4qyKqmqnJIKIVf1Sh1RWlEUhrLapa7GVEWV0WEVUTJm7qvIfCoJmQMbPTH0CAm6qJBFSRg4poGcMfcKiCATr5TS2gYSYSiL995fUFZCjHz729/m8eMjYkp473HWEkJgOpshhMj8nNWSajZhMp8xHk24cfsOKWTqyVbWpTIFm9Waxfkil98P48Z6s86UByFwzvHRRx/xi7/4i/yG3/Ab0FrjnGOz2WCMoSxLjDG/Ng5PZ5uLiSPXxCuUlpnEicSUCmUyeSulgJIKIT2QMulU5HSWC+kiveWsz+9JgVZPB0qpQAh5cWNok9A6R745A5ZX2SIqRFIkKbHW4Zyjqif5+ITMFVavAHFJBWWlGc9GzOZjeutIbY82GlRGbGK/ZbtLZGGQxiCUxg7ok1SS2HmCkwQlEZVhS3I2piAl6K1lZEYZdpYSJQQxeLxzeOczqUxKjNEZ7IjxguyltMbalhgYxPHEK/n4pduHLBrHcmNZNy1uWN15pZFGI7zEho5Kaoqq5OaNMb2PtNayWbf0XUcbLS4qkhBgDLdvvM1oepMkRsS0Ig7ITNzWvKVI8JGdnV3effdLfPFLXyCEQNO0PDo+pu873v/wQ777K9/lrTde5/aNGxf8mqcBz9WDH2kSYkAML1MOBTnYkRIUmuYksjnp+eCXH/Pk/oJunW8qb3usyinJKDO6Q/AXYmRhGJBTivRdk6s9RE49igE+hm38reiap/eOT5EQFNI7jNbEEIjB0ax61gvJ+uzlE8kWMd0SlJ8l6V1e5TwrFnh5pf0qSM7z/n3Z55733lVNFQaEGHh4khBTDgRCIpGJjqYyTA5H7N+Y05UVy0rhUi5+0EUBVYkqCmRRYsqKQhdURcVoNMLsTqlcQ506hLQYNmgC1ShidJ5Edl4rWVpPm1pW7gwbJS0RX6xxKqe0xSwQygYL9E7jYgc0V/IxE/j1BWdtO/kmLSlGBbN5olaC2kBdSOqyYDabcWAVs9EYv+7oXWC17Dg9XfPkZMWezos+rzXn1lOFxEwJJiONtInYw2w6QiiHUA7PU2G4qqyQPuDOA49/0DI5CIz3A9MDiUhZC0xk6nK+96/mJT4kfIg8OTul6Vt6a6mrEefLNU9Ozvjo7j0ePT7i/PSUZr3OmmMxEbGIlHmEWij6pqVYaiBzRjOSkCUmgssaaGEIXlyfSbAxeJIgk2bTUDGaNUXwyQ2aMPm9kHJRQfCZR/irqdC/2rxrMabGqIKyHqFjpCg9hdG4ucM5T9v1uVKp72g2K5y1OOdoncdGT9tH1LonpTxmJQTG1IzHE3b3bqGlQiIQKZPcEUOKkacjYubLZFRIXWjYCEwUlDJhETnVmcRThOcV+QIxRh49ekTTZD28/f196roG4NGjR6xWK1KMOdMyzFllWWKKAqM17aZhs1xR1TVVWefCGKVQCWIKJB9YrM9YnJ+zXq1QSlGVJVopmubj91TW97tH27aEEFiv11hrqaqKuq6Zz+cfE1N9nr0w4AnRXcw5Um5JpflHNoWmHlWMxjW98EQbKPSWgxHRSg9kY0HCo1Q+IdF7xFCiXhuV+RApDAFP/joX7ECEzux2MeCsEYlIApkE6ERMAesCZanRyqCEQsIAy1494Ckqw3hSMZ6OYNPS2wxHEgUiSayzCCGRWiGMISlNSILeeRKZm2NjTwwqaxAN+e+UyMJRCbquh3FOTxmphoAnYG1WNs2SM5lXk2LMkLQUF4x1Hzw+RhKCsqwwWmFejr4C8NbhLsvWsWgcm6bFWYeznqWDLiZaF3E2QanQasTOzh6b3iI3DUo8oQ+Wvo/IoiSKAiUrDm++gyx2sFETyaquiEASnogn4iBFprMZr7/xOjdvHdJ1LaaQ3L5zk7PzBSenp3zz29+hLAw3Dg5QKk/826BHXKS3rnAedSLGi6zmxy76QQ6HsBGcP/A8/H7PB996RN92BO8oqxrvHXR54AgDFys4m7VchuqPbcDTtQ0ghnsiH6kQUBQVkNBK4vp+2C6RZBZklFLRyq3QYKRtWpqVZH1+tYDnsnjXZUTmcnXWZcTneZyd7fsvSm9dfv5skPSiweTXUsUDIJTMwbJ3eB9x1mN7N4imZf90qRntVuy8NqFJmojGRkFZFMi6QkzHqLKEosAUBaOqZFxXjMYj9N6Mid/Q0yG0Q4uGKlqmZZ3Lr0VieqNgs7Z0TQv9KX2MtM7RqxVSa4RRMPckAh5P60qi73DxaimtFLdyCxpB5oRFEUlaUE4q9J7CbAJaRYxOFKVmRsVhqjjcmZOiZLnp8a1nedbw5GjBzu0pSWmiTixszzREXIJpZXDJo1ygrksCmoTBk3DeEUNgVE6w0WKXPUftgr4NpATTnSqj8XFA0pGIK57fGCO9dTRdxw/v3+N0ccayWXG4e5P1puPsbMn9h484PT1ms1xie5urelJCyKE4ICWSUPje0kkGrk6+L5FAjFkvy2c6QIiB4HKKKguAStIQ2BCHoDIlvH0a8PgBfYop4W0chpqXzxvBd8P8ViO0QqNRpmBU14iB1uFDzAvFtuX05Jj1es2m2dC2TaY12EDcIsUChNCYoqaqZ8znh8QY8K4HHxgYrBfBTuJp8KIkaCXz/LpdSMsMDhRkVEdEcsDzCgTJzMPNGjsPHz7k/PycpmkylzQEhJQ8PjpitVpdfC6lhJJyQFsKlNK06w2r8wVKafYPD9HGPOX5+oDre548OuL87JT1apX106qKGDOiFbcaeEKwXC558OAB6/Warus4OTkBoKoqxuPxBbfnRVo9Lwx4RlWRCblBUOsZJImyms2i5dad13jzjX1oHefHD1idPWLVntN7jw0Rrcssg60VhXb4rqftEs7rHOEZyY13biOJJO/YbNa4kIUGvWvoUp9Jn0KjjEFrRR/6PAADwSV632OxbBbHOWcmoChyyfsVBXopR1lzRZeKJCMuWNq+xccIMefiXPBoU2S146pk1XQ0rqHrAikJCmPQZaIsDGVhCD6glKaqx/gU8CHQk5GgUVWyMx4zLopc4h0TikFkeHtTDhVMRVUhlMTF3PYgpZwCm0wn3Li5x8HB/Eo+Hp+uePjklEfHZ/TW5clXSCw1Xmq8VAhVo9QIoypOj5acr1oWm547t79MiB3WtSx6h6n3Kcc3qaa32XSw3FgiDdCSUktwx/j+DNcu8JslP/jerxD6JW23YjnoHP3+f+gP8s4X36YaTfjP/tx/ghuCjq+9+2WkGmr9XzGtVRTb32cIeMgDglSJFCS+FXz3r6354Jsn3P3uCV2zupigMyooSSoHrJF883adH5CdNJTs50m9tx1bQloc1LNjjFRVjdZZA2O9WhNjrlqoJuM8YHnHytkcyCLYrNZMF4F29XJfLyuWbqsRtsHHRXHA8Hx7nM/+ezlYulzRcBkdel6Q87yg5+9E4LNpN7gQicKw3GRhy3oy5XCmWZ2f4+M97KanW23olueokeFwYrg5mfPmrTn9DYPf7DPa3SEZKNWKX/9GzaiGUd2i3pkze83wjj3InMPkUTj8akWUjoDl6PwxR33LqXPs2TnLdsGiOSO4ROsNutfYokcNKffTdE4bWlp/NYSnNNNLvyOElNuXSGWp9kvQibPvPSTFDcFW0I4xcsprkxH/+O/53Tx6suDx8YKHdx8wCZr+tGMxEqjSoIqC2XiETZ6H5w3zt25wujzio8dPODnpKMoxVTVhPKmIsSClhO2AaMAK7Krk1G5oFj07O3NSGQjK4b1FqoTQV5ssH54sODo55sHRY3725/46D5/c52RxzMH+AbUZoYThwUfvsVosaTcNSJmLAmTK3JgIhIQfFhVSZN0zBj6jkDnLoPRAY4iZ+xhCBCmykjQZYU8+IsLTa9rbcBHwRCUu5hMhGEqpX14xKQh419EksByhTUVhcjsaNSx+tAQ1rqjHNbODg4xexUjXdzSblnbTsD47xzubuWqDHs3x8Ya9/Q1agRKRUVlmSmcIJJMDT5LC24DRCa0SSkS0BKMyDzVTRXJZeqYGDT4mMlhwRYsx4pzj/v37NE1DHLg0cUB0PvrwQ46Pjtis1vRdlwtcyizn4n3W7wvB8eTRQ5z3/Ma/67cwnc2oh8BkvVhwfnrK3/xvfi4jO1XF3s7OhbL34eEhi8WC1WpFiIGj4yck4N79+6SUWK1XHB8f51SbUvw9v+vvYT6fMxqNPtGnFwY81vUEm4g24ZB4l6tOvvmtb6GKksPD2xzs3aQUUGtJ99jS+DWt6zCASw4dQBGpqxmz8YQHj5bcuPEat994gzfefhORMuu8axusj7jgaVaPWS7PWa6WnK/WWUAsOHyy26p0fIh4PF4mSPbpBR39EDhcrdQ3IfAx0juP94EkBMoYpLKZvZdzSEitMKUBKeisI7R91r+wHm8tpRRoIZApCwVmxfhI57oc0EhNIlegGSkojR70CvJqRgyzte16oki5qktlrQXrslL1Ft266FFyxaBgpeYssZz6htNFmysBhGC5WVBNJtTTKd45pgPJb1yU2FLifElRTuicIoZEMZoxmb/GZP4aTS/ZWEfn7UDk9STvEK5HOJsfMXB6ckLyHYiADw6lFG3TEKLHlJrXv/gFVtby89/4Fm+89hZ1ZYYIPX0sTfQyy5pBCXFBzEsDEVqwOU2cPwh89J1TTh4u6Lsmo2hDmsgURUYPtEZJiR+qLWzfXVxXRpsB0k+kQZgRASmGof+Qp08JbwqkVFmUM+ay0OD9xaompUj0KcPAfU/wJfEKktmX+9KEEJ4bdGxXZZcDoe3flwOebUrs8nbPq956XrrqWWTpefY8PtBVTFhHd3bC+f33KYwhOIf0Dm883lt8jHStZXm24bjW7Ly2A82a6CILmyB4REoELPhIsI7m+BGnvcW5QDWqmE8r5pOKuioR3oGTuG5JjD197Dg6XXLiOpbJo9Qjlk3Dcr0hJVADgT+KHhU1OjpckrjkcfpqOjy68ENBwuBzypMSUkCtAYm7NcbGwEp4ynZJqQWFUXzhjQNuHuxxvur5m32HSBHnLNbXaJnxG50FzAiyRO6+iekKijXI5gShS9AGQo8QiSQi0ShSzIT+StX0rWV11PPo/Z7RDUm5qyG5fD7D1Xz8uW9+h+V6xeninLPFmrZxhB6OHz9BiZzOW69W2G1hSAg5MBGQpBjYNkP7C5GrRS8QypR1Zoi5bQJyKH4JCe/8IPApiCSCC0QfSeKiNDMrSwz3kvc5jZa2BNMEV7lsBYG+29C5NatHZ5hiRFnUjEc15aACXpjcT1Fqg9CDppuSlKZATRSjsmJaj3O/RpdViPuuwzpLiFmTTknBIO+PIGX+2SDIKtGZ26NyxZccMi9a5lL4lARFyiktkTLCkwVgrx7wbNGSGzdusF6v6fueRKJpWzabDefn57RtFj2s65qiKCjLMqtDDwuqqqooiyIjb95j+24gLgiCc2ipeO3OnZz50JqiyISEGBPTaZaw2fJ1cnuTxHK5RGmFc46u6yjLkul0SlmWF2PeJ5WovzDg6W2HawO2DUjnaRtL31kWbsGt197g61+X7O3eoFIyq51uliytp282+OCQISBEpFKKvd0xhzt3eHjccXj7db72638jN27dQJJyZZDtcT43UVue3efhwwekh/c5XffY0OJdTxSOJMUQ8AyVMjJzfuLQjNIFmWHiq4q5kYMnax3WBxASXRRI1Q2Rcv6MVBJtDCCwvaNrW0qjCdYTrcXoGk0u+wsDKcuHSNf3aCHA5AlYCUGhBJUxdPT5htxOLjHSdx4xVIqgVM61hzCk6XI/G+8z98e5qw1AG7PLxjga3XHUr5DJI5Pj8ePH7KfIQV3SuY5ZY+jHJbvTMa4q8Qm0qcAHPD2m2mM0vcV05w73z1Y01tE5O1SzbcWDHMI5hPMoYL1c0qwWjMYlpjCMxjXLxTmz9R71fMabX3qXh3cf8PPf+BZ/z2//XQg1o1YGkeK2huFKtp17hUjbOIdEIljD4rHj/nd77n//hLZpiNEOufN4ERA87eMiwGexxa5rn/b3KiJb2PAiLTSUTQaflbGjDyjvkUpngboh6L5M9ktDimxL8vPekdLLr9XLHJ5nA43ta5dv9Mt8n8vbbt97lsB8GTV6XrDzSeXF2+fP++xV9YAu9ml72pPHnLqG3XENIqcC+yLibEeIid46FucNRgtGN+d4v6HzK06XK4qyoh6PCLFHJE+0HeuThzx6fMLx2ZLZ4S3efOMWVXmTyTS3S4ghEfpI7x1r2/PkbM1Z6NgIj1aJ1dqxWrtcwTWkl4XyWQTUdBlRTIKrQsra+G2h6XB959L7hIBC5WrOWxN807JuO8R6wawUzIXm9TfeROgxvZfc/+B9zs+X9F2HCwLhI5KALCRCVSQ9Qs5fo+w14yZRb/LYpITAhy5zWogkk/t5iSioqOnXLZul4NGHPYe6pJyWSLUdo642pv7id76D9Y7eWbreIpOilBWr1ZNMpg0Rb7NURq5cskPQkYCMVmqZ6QsX14bObVyy7vMgdZISIkm28LeIGQ2SYrgGQyL5SJTb1hI56BiYFgSbF8cipkERQ1wp4yNSrrxcrBruHW3QuqYsR8ymE0Z1zaiumIxqiqqiKEtMNUbIIYxLgqow6LqG6TyrdluHD56+63NQkS4tqFIcUokpq2MP3FjJICMg4xAc5cW0HjRiEhKz/V1SIsgEaSvmejXbLgJfe+01FosF6/U6I999x2K5yArJQgx6QNUFl2YymeTqNK2pqupiQVUWBVKILCBJ7tE2nUz48pe+dPGdfd/hXCZIj0YjxuMx8/n8grdTFAVN21AUxcXCbzKZcHh4SFVX6Jdo072YtBw7Fus1i5MVrhGslw1N0/OmeZ3T8zMW6zVv3LqD39ul717j4N2v8b0Pvst33/823//BN0jJoRWMxjvceOcLfOXdn+A81sxv38ZpxV/4L/8Kt24c8Nqdm4yqmtZ62t4z3n+bQ7ODHB/y0dkZ7aplYxuk9k+DkItIPQz6PEOm027VNq9mRTlBKkVMiq73GdEUks72eOtzQBLBOofsO0QjcL0jdLlXmBGCejSmiDnCFiRa6zJz34XM7zC51F0qyXw2Y1IWHOzMWa8amk2Dtw5psjBeSLkUPoWhean3RB8wUhF8JIWEalrOzwQhdC/1D6Cc7fP6eJfDt97l9eMTTh99xOmjD1ifP6ZWCZMcH92/S/NE8eRuzU9+9ceR9Q5mNEUWJb5VdE6wu7OL85LTszWnJ0s63+JCSxk9VVExLkqO7z7Ix5gE+4c3kSIRk+f4+Amq93S952f/yl/j4Acfcvjaa9z54he5+/4DHt17xF/9az/PT/7E1/jql9/JGkufJjOSnibDYoCPvmn5/n99wnu/kPkCQgiUNHR9mwmZAowxefWUIrZr6W2P7Tps2w5rkSyPwLA20UqSx+eUNSO8u9AQuWiKJ+UguxDxfUZ7ktZ4n0mVwW9RJE/bXE3/9EWBw7Plo5eVli8KAy6lvraf+6TPXw6unpcWu9wFOQwlqdtqjsto1Pb5u++++1L//MO7NEdZWuDwcE4vDC2aZCRhsaIWkT4ENm2PWWhaL1lYz+m6ZbXacGNXM59qpqXBmAJSgVvs0LlAC+hK4l3L+vQYmmO0zkKjqtA0UXGeEn0M9H3CuoQtIn1nM1wvc5WXSIqm6TNnLd/dhAs5iZebwJKvozz5bCcD1yd87PHRIqcVXZA0K8nPf+MBN2dL3jxYMr8xYzLawciag72K1eKMxWLFYbNLPa8YV5oUe4QaocsS66Aez7nzuuL24Wuszxeszs45P27pe5/b45gqB8UqU5M7MUJ7y/JRz85BhfYF5aTA43DJXslH+JDJuGavrLmxv4/rZ7jeYrt9DAZDQdt0dN2avt9gZXexoNNGo7TMqa0BCUsMfMYhUDEqk5hTSoQUB44RORggV/mGYV5IMUt/sCUkC3VRhBAG2REGqsDVLWKMYjIac7Cff2frIycnxzx2luAsVaEoyqwjNJ5MqKqaqhpR1xPqekxZj0jSXGQsylFBNTaQpljrcX2Ltw0+BFTyyBSxIeRqZCVzgZDIcr1h+JEUiUKJzGMceDsagRugK5WydMGrmFKKO3fusL+/T9u2fOtb36LtOoQQ/ME/+AcvStG36I4x5uKxbRNxsdjbFuHIHLxKhgD10tjjnLsYV5xzF9Vh1tqLfe7u7tI0DV3X8Xt/7+9lMs6B5rPp/OfZCwMelzxBeKKMCKOZ7c/ZPTRMZhN0YXJgLVVuVmZARMPejdf4ohYkFTg5fsji/ISIZrnueHR8iqlGtM7z4PETfv6XvsEX3nqTEHOk/+TkjOOzBQc3biBVJi93UeFlQTIlNllE3NK2RIZkRcyN84YVQhr6wVyVnFUUJVpnQmkMuSTdWjfQ2Z7WCbngoAdtFaREYQqU99SFoS4MIsSMUDlP31v8UEGfUiKI3NenMJqd+YxxYbBdk9tDiFy1BSnLiZP76+SemVvRQTHcnHkCunX7JtqAVFe7Sc2QWzYpR9VjZZmqlvXRXWR0NItz7KZhYzUywOPzFWXQqJFCRIcNEaUrehcRJpMpR1WB37SDZHuDGlUXUTcJtDLs7x/mbtUpcHp2TowB5wKrVUd8dMq6S5xtHMuzJc1yw9/6W9/AaCgN7O9NqcoMh17F8kScb/y8epP0DTx+r+H00YZmvRlWBOTGqyELY265GEAOXPyANg5Q87D2JqanVVEu5L9jyCmFy4F3HNTHsxjaU6KxHJCdLWK3RcWCDdj25QHP9mb+pHYQ2+Biq4MRQvhYgOMGRPCTGolu01zPBjDbap7Lzy8/spK0u3j0ff+r3g8h8Dt/5+98+TlMER+gz3RerLNsXE/Qmta6LFOfEkVhGE9GuBCQpNxPa2fGWAnYrFg9+OCClzceG+7c2mU6H9F3LSY0tIsGGSrKMq/AlVFIo9ChoDIlpc8yC11nc7PfQQtkiy5zEdwFkgfXR6y9GvohyQN/XvHnijTvAqulxdoh0OokJw83PHmw5t57Kx6PO06PW0bVhNdv3+Zw75AbN3dYLFYsliuUSJkrWZas24bQNUSpCd2Gdr1ieX6Oby2lMhzs7aFpWS3PWK1XNL3HdY7QOwohkSRKbYjJolEUskASAJ8rMa9go3FCaotSWWBRyoApIqNSYpzA+MSUkk5DX2nE3hxUQqisvp/F+ADEliqXuTlsK5NkHutJQ6CyDXh4uvCNT+H5jP7nAAohhzRWJIanqSxk3u5KLiaBEopCK+rSoDRoD6kw2E5kheMUIDhCSHRt7j3Zti3LxYrxZMp4MkPVY3zIhOtRXTMajamqGhly5edW6DeDWYF104KKSA2VMagh7aUG1EaKhBYMJEaZnw8rNi+GdhyfQlpgm1baln1vx4nd3d0huCnQWl0ENtvHFuV5KkD88bY3GSV/fkqdoajnchq/GNJlDGPctpv6th1Fyjv49FVankiUCVkIjC7YnR+wM9+nTy1FVZJE5r9YH+h6TxMT48k+b87GJJk7QbdNi9IVy3XLvYdH6GJK01lO10d88zvfI0bBeDIjhsAHH93j7oOHvPbWO8x3Z4wmNX3SRF0hxBjXrhEp5FYEQoDIKa3thZq27DOuDt2ZwuQ+R0pc5IGttfkGGfaVxFDu2EcKZyj0cJITjMuKaV3jnWfddoTe0fUuQ9wiB1GePCloozk82Of23i4//OF7udWBlJgh+hUktJQkKYkD2x4EQiiic/l9Jbh56wbOd7T9+ko+aqXwMafU5vMZI/aYsebBpGLT9jTrnug8HkmrEiebjpFqKaQBb/LNY0Y4HzEhUQCTuqLvVmyCp20aSiUppRqUokEqzXS6Q1kZMhxrcD4RAvSdx4YlZ6ueRycLRJJoJN/4xrepSkFdwte+8kX29nZf2tfmwrYDFwmkxPfQnCWO3m9YHrdY2wODhtMQfCitMcUgCBgjzjtcn8tHtxLoW1KjGm6yLJ1wKQC4JEIYt6THlFAmw9H5vTh0aocY/KUbOeJ9wHYvD3guBx7PVmpd1tn52HkfBhzIjTM3m81F0PMsOXmbEtsSFbdozeVV1nbbbZCz/UzXddit/knXfSzg2W57FRNSEITESkXQht47Gu9xEboYiQikSBR1wWRngg8BLSXT2lCZmtRuCOsl56tjynFJNSmZ7O9xazzjllCcPnqQU6zrDUrN8ipTScpKoQtNQcnIFGxcT+MSfdtn/aWQq2SShzhwuHwfsH0gdImu9fT9FbsUJTmkZPJAHTqL7RyLs552E2ibwPI8ce/DJfc+WnJ0r+VJ1XB6tGZkDLZPKKnZ2Z0y3x0xOSkwCgqT9V3O14HQNjgXCe2a5vyM08dHNKuG127d4catm5ihn5dzWdvGdx226bPkBonSKCwCTa5AYkBNYrpawFOPxUD8t0BAKYEGCpkwKaB9QqiCqiiwhaa8VZLKlFv2RDJ8v52pL2KQLftEPL3PGe6xi1TUU1bjVtYjx01D1Wy+yHKKKGX0Z7s4llpe3K8vs1wdldvnlEVu+moKgaSkG8jG+B6txdBGx+H7QNt2OBtp257eesp5xLrcD3IyGSO1ZjSeZHFUpRBy6EQwIMXrTQsqIY1E1Aqtc3PXYkBtJFmJfIDLMNtuBTLzRklXlzJ51i5QXp4iwNuU03g0zgrll4KZZ/999rXncWye/fzHXxcURaZFNJvmIgiLMfOKL8axl/jx4oSXgfHuhOl8xo2d1/jyuz/GW29+gb/wX/55iroEKfjo/gP+m7/xX/MLP/+LfOErX+e1N29xeHuf8yc9b938Cr/uCz9BjIFHj885OTrjzXdu0jlP37ZM5/vMdvYZz3Y5eXIMqqAcTRGjAiuB4Jnv3caFHfqwoXnQQGpB2Iy4DMTULagjksxS3lckn0Hm8JWVoiwNIToa73L5dFkg8LjkEDGLJGVtHogiEFOiUIoiKZQXnDaWdWtpOodPuX8KBKqyQBCIIhFcT3IdyXYcn56w7lqiVIxKzWwyYmc24e3XX+fh6Qn3njzhSdgghEEJTVWQe5LJhA0tfbT0XA3habuG733/Pd57/wPuvPYaNyaKHZMoygpVTJipknp+B1OMKOoJs8N9ilGJqUuq0QyBhqQIqkTrEVoVSO/Zm06oTOJh1/Lk0RHvnZ2xPDvPSFVK/NIvfYOtkGBCUxS5eWdCk0JWLu5Dg0KhheThgwf85f/qCb/8S3+DH/vqF/mpn/opfvNv+s289to7VzuZ5BWM8Irzez33vtPw8IdP6DYdxkj6bghkhmCnKEtGkwnamEwY7Htcb3NwMnS89xc8n2EQRTxFOobyWVKeEPKCaoBmbf/0mITDD8jPsKOB35Myh8S+/GL9i3/xL14EG5fFuS4HP9tApO972rZlf3+f/f39C8Jh27Y8efKE1Wp1UXWx9eUyhyfGeLF6uvydzw5il1Nfz5bMCyEwxlwEhlex2c3b+CRIUrHc2aH3IF1kqjXy9JgYjomHiur1OeO3dgnO58oUJYj9mk27ZrPeEGKkspa622D7ltHODqPZLntvfRmzXKLOz/Fdg08aG6FKktLU7KqSsVU8OutZnC2IRhAyFQRTJXyf6JvIam1xNuFdwvceH9KVU1oxBpRKKJ0QwtK7JWeLJT/4lYZH9zuOHlrOTwRtk7A9iDhis5E87OCv/NwxP/how+vf+YivffUtYvDMb1bsziv2DibszKc8eOTpOkeix3U9bdOwXC548ugEJTVlVXHz5g1EoVGlYdO8j7cRr11eKWdCD03bslwVnC9KylLQJkcTr5bSarbXOgmRBNqDcaBPLMJDDIJVe0aQNZiaqlH0rqeV3cA52S70tmPH9rcdJu2hEoqBpycu/tgG8Tmdve3npAdBUlIuhEiZ0kySkaTUVswm38pXGlKHdOTwkGIQi0u596CiYlKMMWVWG6as6frMgX348JjUeZzsqNiw2TRsmobppKEsJ+wfGCbTMa7QuFKTNnmUDz5wdr7OaUVtSQf7lIWiKCSleVqdqgcuj0QQ0pAyCvk3kTxFg17FBILVas3Rkyc8OTm9IFD3XQspEr1lvrN38fln5TKeF+RcTj9dRnvywm07fz9NpWttMnAVY27+PBoxquvhg/HKbM8XBjxxyLlJqRBDrtuUhrKuKKucwnj/hx+xWi0J3nL/o/dp2wXHR3MWZyd88Z03eW33NuNpiQg1Mp6zO93h+Owc2/YDRy3vv7OWtuvzwzZE6XHRUBUTkhX0LlCVOwSb6/efrgLy5bu9SYYw/so8Huc7lI9oPUCqikEv6CnEKaVEDYqPaTvZIZmMd9ibzpnWY+6fneVVb7ycCkv5NxQMMLbAGE1dFjibU19db5FoRlXJjb09Xr95g95ZnpydIqLY6rwjBOzs7DLbnTKdT4nrJevuaqWw9+7d46OPPuSDD96ntxZ/MCXMK4KqUWaErqbs79ToYoQuRphxiTA5B9z2cegdlXDJk2JLDILNZkXXb2jbNafHJ7mtQtPkTvHDFdv3LgekcuheHzOKJUUkBZ/TkSKXpEphgID3ia5LnJyecnp6ytnZ6dUCnjhk6ZOgXQROHjQ8+OGCvu0utJEuUkkhIk2+RmKMWGtxzg7EY//MSm9I8/iPT9oXKdQtyeDyijMMqhnbrvADJL1dYT69owUpJLx7+Sj74Ycf0nUdXddd5M3hKWH5cupq29cmDxSa+/fvA9vmvbk3XtM0rFYriiI3IhyNRhfbbtGci5/2mYAGMnr0bDk7POUFXfxG8NzV3PNsdnBAFwQ2SfxkFy0VYxQzo3DRk5oFrpb4WhNLnStQZF7dO5v772QNFkVMAutgvdpknkNIjA/q3Ax4NCaF3KYm9D2+1OhB8HOsC6aqYCIK1psO6yLWR4oZWS7Ch1zaHAQpCILLyPBVKSBxS0KNWYPH9p6u9Zyfd5yedJweO7pNTfAyk4kJBAE9kqVNcLphbRs21jIqNKPSsFOPQQqKuqIoarouYK3l8aOHrNZrgnOYwrDebPjo7n2SCGgN4+k897MLDhH8BbqSUrrwzfeCmpKULMFf7TyG6FHbaqsuoPqE7BK+TRdFAFVpEFqjjKKwPV44pMlJMyGe3nu52ealFX/Mvb62f6aQ8r0fIdm8UCFkIrJKApUEJjydEIUQuY0PEa8TsczXUpTpgi/0MruoHBtI9Urk6tmMNEmE0pRV1oFSpiCqkpQivQtsmo7eJnqX2/Rsmoa27diZ7UIE2/YEnfv1SSlxKGLSxGTwQdJ0Hb1v0STqumBUFRhq7KAJJwdez7aPVkx57lGDNs9V58XLSFffdZwcn3D33n3WTUNhDJUsqOsaQSJ4l4nTQ6ywTWk9D+3ZPi6Tiy8HPfKC/H95TMnVXttxJAxjYAwB7yyF0ZRF1v7ZnuNPshcGPEPskCFBIkkkkhSUVUVVV5RlydGTI/q+oyo1x48fsFqc8bge4TvHzdkh+u2S/ekevhVEq5lP55yfr7BtLkkU5Hx23/d0fZe7s9sNIVmMNUzmM2QswVvqYpfOO7qwAXITPkiEQe9EiG2e9qqnFazvkD6hQmJUVRijMEZhnd8CSBfVIiKRSyjJgci4HrE732FnMsO6b18KeLZBz9Oyygv4vCioh2Zptnf01lIVkrqq2N+dc7i7w9HpCUblHmZRZDkghGC+u8Nrb75GUrBuG7y/2ij70Ucf8uDhA46OjkBIChxG7hLNGDOaU4x3MeN9lKqRqiCqiCcTKG3rSN4TPbS+w/YO21nOz04y6bBraJuWGHJgtK0ogCwCJmWuq0hpCEwjQ6ATh/y8zmRFnchdpHMvtK5rWa9XnC/Or+RjJiDn878+sxzf3/DwgwWutxejWEYzwoDU5NW2846UYu7j49xwfi+1P0m5hPwCzr8Mt15Oo8HTbbbNAS8i36fpo+3cv91djBCuEPAcHx+zXC5ZrVYfQ3guc3WklEMTxlzNMJ1Omc1mPH78mLIsGY/H3L59mxgjbdtyfn5+QQLc399ns9lcoD/bVBZwMWBd5vFsc+j5J3kacD372lWDHYB6tkP0EILE13OKoqQwBXWhMOszOC9JY0GsDbFQKJerayIJby0x+JxO1Fk22YdE9JbEhhgixXQPITVFVeO7jmQborUEa/K6Qilm9Zj98YyudYTG45qe0DpcUvgQsD4QJeQmMpLoc+BzUc33UhsQsQTOepqNZ7MKLM46lueW1dIjQm6qmzPaliDACWgQ2E3P2abj/HzB3nTEwXzCG7f26YMnCYkyJVJ1pGQ5PTnC+QBEqqpkvd5werYAlbhxc5/d3SnT+ZjQd/i+o2/cBTctRUmwAteCiAUEQwxXO5cpBYhDK4cuoDYR2UZ6O/AsJOxOqoHQKgmuw6mINQkvt5TuvJTdJrLUwNtB5PYIYrivoo/gAJ9IjUO4AC5iUkIngQHM0PhayG338tw/yxpBGMf8+5qt+tfLz2MUiSghpEFQ9CL7JvICXilMkWkPUhcEaYiho7eeddOhZcRasFHStB29tZRFDUnQtx1CiSy8KyFESUyakArA0Pctq80GGS2T8YgwHlNqibVhCJCHoEeCTClXCF8EPFfz75mTSds0HB8fc/fuPboYmIxyeqmsSrztCd4NgWoeE7Zl6c+msz7Gu7n03pZ3eCEsmX/OC2RdiNzX012qprZ9j7c9KQaM1tRVTVGUL03ZvRjhSdv/hdxGAYE2BXt7h+zMdqirksXqHFNIbt0+5Pz0FIJHxcTrd95GOMH73/2A1cljFpuOVeu4eectSBFve5SAQikqYxAx5Pb1QLM8w2mDEYbvf3jOZLbDeD5Hm11cWOE2mqJUpOSJyRN8blCayUu5fPKqOjyrdkUQDlmO+fqXvsLJyQIenrA8P0IiMWVJuxkqcVJiIgp253N2Z3NGZUnynma1IthuKK0PIPTFJBvRSBJRSIqipCorxlWdydcxq3u+8cbrHOzNqZRgfXKE3SwQMbecwBRIkweKpDIj//HDJ5yenrJarK7k43d/5VcwZcXXvv4V9vYPmc9mmPGE+eQ2UZQEUbDsEtH3pNBRFOBij/Ud5ydr2nVHt+lYdSeZzGtd1twRESkiAvWUYEe60IQY6eIit76lRAkJSuXgWUjY2Z8P9RO5O3BZFUwmI95443Vm8ylXW3Nl3QZk5lfc/f45D98/5+ThOUbn1id+KB3PPXugWTf0bY/SS3Shh9gmbWs5YKgS2AZJQny8iukp2S7D5zFkefrpfsn8oGI6N3SNp1k5zo56CBmBMoVAGYlQguVxyCkt//KUz0//9E9zcnLC8fExjx8/ZrPZ0LYtbdt+DH3ZBkLbctDz83PG4zHeezabDcvlkqIouDNoX9y8eZM7d+7wla98ha7rLtJfpwPC5pzLKrGbDYvF4gI5KoriYvDi4rcQHyMpbsXEyrK80jn89nc/pItgUdzqXO6npxTLsaYqDW9+5V0O5rmp5rSuON+cEGLAp4AoDJWMVLXI/Z+GabOsJphqhK5qrBcXnD9dliQRSC6iRKKqSqrpnN/yzhf5jcnTuY7vfePb/Mq3PuS7377HD95/TE8gaBgfFLmQg4RwefK9avGLMYq+8zSN5f5HK+7fXXD/oyUf/bCj3ZDTx3JIh6Y89no8IWVehzaGoii5sTtjWhoqo/il737Eh48X7Mw/oDKaWzf3eecrX+bGjR1c52jWHb/yKx9wenbOk+NTHh4f86Uvvc0Xv/gmznmatmNxvoag6Gyg6yO2T6yOLMd6TTGucJVC6vGVfKwcKA/GJcpl5rhIqSjGEqENyhhm0xHJdQTbQN9QDQFSNy1yQJESOmmMB+MTqreokJAxUibAR5ILBGtzJW2IRDdwgGIOvLdK9ULIXDAQHc52GXlNglpLQlfgNwF3UEMZybXcL7ZEHLircQgyhsIGGJSgNVIVSGVQyiCVySDBaITzHmlMbkyMQCiNKRV7h4eYQtE0a1BkaRXvsU2LERItp7z1zlcpHt9HPLrPevkEg2NSKLQ6yAh2DCiRVfohYmRuz2GkIPgsf/AqtWiQeVt373/EBx99wAcffUBUBTdv3aIaz7j38BitBIXRnJ2dM5vNmE6nv2pRtB03QwgXis0xRiaTycVj+1m1DYTYLqLyCW17z3sf3ef9uw/QSrFenLFenOG7DT/5Ez/BzqWU2ovsxQhPhG2+civNnZIY+qtk+KlrG9abNX3TZFJrKVBGgQjUo5L9g11u3dmju/eQo8UmN8BMMcPPziJSxChB8lm4LnlHt3YEmXs3ndw947U3YXc25fj4nGbVk5zmqz/24yQ8znacnB3T9h2d7YnBwjDpXsWSBFMVjKcT6skYvWwR5KoxkeTw4AJKrXVBpQyVLrh1cIPDnV2m9Zj3Hz9g1Xb0zhGSZLlas1xvED6ii9wgLYWQb8wQ6JsOLRXz2ZxRXUEMdOsl5bhkYhSzqiAtc6+EGMGTOF0ssDGwXCxpmhbbX02Hp21bpBlKA1Oi6Rw2tvRO4EKPjxJUhUwgQ2S1WNN0G5quYbVs8b0nWIeN6yFIy2kpIeJFgCOeZt+Qkot+aVkWQiCVGNqLSMrK5MoJkdjfnQ0kYM/y7AxSgTGa6WwywJhXO5FSClKI2DZy9qilWVlSSASx5aDktJwyuQfcbL+kbzzt2uG6PiM3Uj5F9VJWbs2lrWTy38fYBEOZrIKilJiyYHZYcfjmhMM3Jsx3KpqlZXHc8cs/+4hu4wg+Mj+s0UaSBKxOMsHX2ZcTXg8PDxmNRuzu7nLjxg26rqPve5qmuUB1rM0cixgjy+WSs7Mz7t27R13XF4+zs7OLAaiua2KMrFYrTk9PL7g64/GY6XTK22+/fRH8nJ2dfQzm3gY2l4nT24DncpXGVojsKjaazhiZEoqSqqooioqqLNibasbSMJaaqRYU0ZO6Fi1F5s8QUWUNRZGrY2K4gNCSLkjKkKTO1Zcp5k72ziJTQCuNqSp0WaKrgqKqiASqQvGld9+mLqfs7d1g81/8DU43G9ahp6pNbvroIjEJiqKiKK/mo1aaNnjaTeDBR2sefNTy8K6nWRUMABUMU1YuvMik/dxYMyvrVgL2b8y5sz/jYDbm8ckZ3gWenG2YjioOb2nG8ymjccnCZj2p4D1KKcqqZrXesFo3NG3PuBoxHk/pJh2LkxW29/SdJwaFbRKbU8uDHx6jdgVyerV7MfYS7RTCKYTMVAGhRG4rYHIqywqdS8OtRfmAsJFCKLywRJHnmiokjA2o3hOb9qIhh1IyE8kHDZ0wpKqJWyHBAW0RmiTURUFBiGlA67OWjdYCoRRpaEmUYm5r8jITSRB9xFlL10aMllnheEDykxSsNi1C9Aipme6XSGMoRyPqyYS6HDEeTWlDpJqM0EWFrkpiCvTBohM5zd73+N4RjUGWJbdffwNdFxR1wcOPfO4oPxkzqgq0guB7umZJay1t31OOSrSuUKpAi4iIIfe7or7SeRwuPGKKmMIwnk7R9ZTRZIYwJRRVHjO1ZLXeXCx0tgKAz1aLhhBoB9HCLU+wKIqP6aGpAeXZpr1C8DifePTkmMVqgwuJajyiCplHG7SkKMtc1XVJo+yT2ku8OODJOBUMTT0vOJoDfUakRN+1rFYrNqsVLsShokfjY0dVKw4Od7h95zaPzxb0PgsHCpn7UhmlKLWi1BotEpochfatI0YBNvL47j325jNkuMlmscJ1Aa1G/PhXfwMQaNs1H957n5OzE04X57TdkjwdXY0oiVKYqqSejFGFyWmklHuB5eAyg6uSrKQ8MgWFVOgkuHl4g7du3+FgZ4+7jx+yahs66+ms5979B3SblhgTpdSMTEm0ftDVySmUQhfszDO0G5yj8x2jO4fMSsPOqEYMWhEiJnyKnC+XLFYr7ECs/aQS42fN+SwOmFELS+8hbDw2SHyAkATjiUSlCM5y+uQRy9WK1XpD1/lBDTYh1dDjTOXOv9tlrZKXAgC5Vf1MQ8Segx1tFMZojNGMxhU+5cac00lFTAFn4fhxQ4gjlJJMxmOqsshQ9pVOo8DZhGsD50ct3TrntEPIrT18yH+XtWa6W/DGV+acPd7w5H5gcTwEPCoLgwkyUnLB/heX+mYN3JstT9IYwWiume5WvP3je9z50g633pkx3xmxPus5ebjhvV8+zY0PY2S+X6OL3G0Ymb/jKhye2WzGZDLh4OAAO3Ql3iqN2m1jwjb3cwoh8P3vf58HDx7w3nvvcfv2bW7cuMF0OmW5XF6spra8nbOzMx4+fHjx+nw+5/DwkP39fbqu48mTjChulU8vQ9TbVNf29ctlqUVRfKxS7GW2e3BAPZ1RTqas1xvKIvfBOpwrJklQx4DqO5J1hARaqpwSjAlZFEPyI+H6NqcuU8IzpKJ9zN3IvSMFh04eVRh0WWDqGl2V6MJgTObOJGF444077Ozuc+vObX7wzfcQR4mw8dSVwfURYQdeVD1mNrlax3slJMFBt44cPdjw+F7P8cNA344zX0VGoOcpDzGX4SaRy/SNyujHbKfmtTcPeev2AfxwxNHRKacn54AlICiqClNIgu/ZbFbEGIZy/jHHi3M2Tct60zLfmzCZzAg7nvPjNd7lEvskFLZNbM4sC7dk3FaMb1xtooxWkLwEp4hGZsDbCHRVgtGgNH0bcCERbKAKIctmxITeppSJVC6gOotsO7r1BpRCaAODCCFJXMg/MIxvKQ09p4RCCg1CElIuqY8JpNRZC2ZoBIvWRDOgEDES01UCnowYuT5XXMXSIFRGbXI+SbBcb/AhcxSr3QOEVhRVzXg+Y1SNGY8m9MsV1XjCdDpHlQXRdjgbkOTu4G3XZZkLKShkxY07r1GMa4q6oF2fMK1KptMxdWWQIuFdx3p5wmK9Zrlas7O/Q11PKKtRpqX4TE9gfjU0ZIuuSyWoRhU7OztUswOKaoTQWVBRyUwuX58tLjR14Gka/HK63TlH0+SO9ymliy7nW86hUiqDC5qLscjHSO8DD4+OWW26nJKuJ7m9iFKkUlFV9cXCC34NHJ4YIJB1FLaN2GIQ7O7sIRIsTs/wtuN8seDJk1OUmiHKGkrN3aMfsrNvuLEYUxwpFps1XkSChHe//hXe/dqX+amf+q3cPNhjf28H1645ODjgwaMjnixXPHnwmOPHj3CLJYtH9zmZl/xdv+k35PRVkvxdP/nbMYUiJcemW3GyOOXJyRP+5i/+dY6O7nF6/vhKp7SezrBRcHy24Afv3+X8+JzVcjMQWBPJJ0RITEdjdsZjDiZjNusVj89XHP7dv5svvPk2b7/2OlpJpNZIrfmFX/4mP9d7zh48Zm/ngHpcUY9KVsentDduwC042N+nCNAKxYf3fsjNUcn+zoQbszFCgI+J+r17rPuADR3lyDCZzhiPJyzOF3RNQ99eTXhwOp1SlAUxJR4+fEhnEz4q3nrnXX7sq1/m3S9+AWd7vvOtX+Y73/gmDz+8S0iaJApKLcltW0MuM40Qk8AUxcVKyYhtQ8xM+o6DbsmoymiN0gpBpCw1RVngoqPZbFhvNjx59ABtcvnl0ZMjpIDpeIQ2JnMxrojU1dLkqpQTz+qJpW+GCc9lFVdJoh5p3vn6Hl/+jQd8/bff4uijJXe/e8pf/rMf0G4cofcok4NeKXMn9GqkGE01b3x5DkkQfR7TtFGYUjE/LNm9OWL31oib70wpqtwnDg9GS4pScfD6FOcifRdzr7DL/J5A5iC8xC5XVJVl+bF00mXbrmxu3LhBjJHz83OOjo4uUJi9vT26oalhXdcXn3/vvfeALMK4s7Nz8V7TNEynU27dusVsNsNaS9/3F2qqSqmPpdOeTa29itLyb/+p/xYHN28z37/BL33nOyweP6A7e8LuuMCvnnB+fkRzvqAaTxjN5tS7e6TQEeyadrPK7WZCpG/zgLolNef2LYEYc6PfolDMpxNUMctdnKfT3Gl9KPkUpUEITfPwCElkbwy/4zd+kZs/1Lx3N7Lxgd4JfKy4+e67jCc1o9HVEJ7myYr+2OGOHbqpqKJmpDxWhkErHgRPgzdwbDsiEQv6xnLeO775K++hTWQ0L3jnS69z83CXxcmCux/dp19vOHn4mLpNrB49YXl8wo2DQ1brDrNsWE8nNMs13/vO99nc2eP1w0O+/NUvgfPcffCE9skZqIK+t7QbSzwLBBtJV6gmBAijNZaSKEqiVplRLAMh5c7ayScKOUFXEp128YuHqJDQwMxLoncEb4nNJhcTBJfpFELgQgDvh6BFZeTGB7zP6IwYiiSKYYEiCRgtCTIRZA6QtDFoo3HRI4oCXZWkMqvju/DyxUcfoPeJPgSa4DJyHBOmHLONvc6WLdY5QHDbORAalQSv33qNuhpRVjVFVTOZzZnOd4eFlkTrgnFlECn36OuFJWmJFxCVpBiNme4cYMrRIJaaCLZjszojEfjWt7/B8ekpZ4sFk/mU0XjCeDxhZ76D6zr6puPtf/ifudJ5BEDAZDZmvjNn03u8LEBpotDs37yNtz1ds2GxWOFsT993F4rMW1mKy5pcW9Na0zQNR0dHTxFhlZWXt9zD119/nSeLFQ+PT/jh3fvszne4ebjH+aal3azp2zUjmYtBLloLPVPS/qy9uFs6QEqIGAlp0CNRiulohlEFMSRu3bxNioq6nGK9ZDYbMxmPmFZvcnDjJqPplKOTM1brDdFHtJTMJxPGoxF74xmFyUjP62+8we7eAe98oeV83XL3/Q/5cPeH2MU5dV1Sloaf/PW/DiULBIrJeELmsRVZPM5UjMdzvHP84L3RlcvUlC6JKecIl6sNm02boe+w1YTIN/lkNObmwSE7VUG0Dt9bikH5saoqDuY7jCYTqnrEermhWzUUQnPj5i2khkTg/PQBpSkw2jCf77A6W7BYLujaFlEb6qKgkoJpYdgdVVRS04fcvVhuOTJaMZnOLlatV/NRUdcVo+mE5WoF0aMSvPvGbX7iq+/w41//CrbbsFt27FUdf+H0Ppsu4AZ1YfBkEZJtf2GBEynrImhD8ls9mtyoL8ZATBHbg7O56iIljzYarRWdC3TWYa3Lx6IlSkvKsiKmxHq9Yb1esVwsBlXVl1tKgb7xrE4sto2DSvYgXZAyoW7vZsWdL85482s7jHcK9v2Y4AP1RGeBuQC7NyqCS1ljxeUApag1X/j1B1S1QWmJiGQ1WKOop5p6UlBPDHVtkCrfcFFGlBKYQlGUOTedQmJx3A4aPUOVVuJKZdvn5+eDn+mi+mqbMrpcBr5dVe3t7fGVr3yFEALf/OY3L5AcY8zF4LNery+Clm5QTw0h5KC461itVuzv71+sxLYrtLZtMYM+0mXBQniqwHz5WK8a8Hz0/R+wPD1nvvuY0wcPcctjaM9wq5J+eUazWhDTtiAgE5JJ4WK1HmNEJI9IAZEyP04iEApADb9b1k/RZYEuCrTRyKFHlpAqyxHkTozonX2S7aBrmM9GTEpNnSKbtUN7jUmSaVFQaJW1V65gpZbUhWJal9zYn9J3LU3bct52DNqzPO21lUDEC36QRJKSIvjEk+OWH374BKEUX3pLgfUEn1v/rFcbzs+W3CwMMiWqokAQCd5i+5a6Kuis4/S0Y9OuIUBtSvYP9uhjIMjE2ke81QRriCmgksRtruajlIkkHEEm7MCJEylhgxvGhtzqoJAlqcicpDSQ/bXM6V4tBb1zJCHRwWRFZQGBLKCnpUZIhZA69waUEWkyr0wbndXvqxFFNaaoK7qmoW1ykYVQkqQlNnqiiUTj82+cJCK+fLyxSRCGHELvHXSOFDtc7wBFiBLnA1pl9eFKlRRljVKGSTFDqdxna1KNmMx3mM53h8o4S/KWYBu8cVSlz2lAJTFGP03XJXA+kTRDu5rE8nzJ6WLJoydHNE1L23f0tmW9WlJWFe1ygQhx6L7+CiYEpamYjMfMJz33Hj7Bk3lK3aoi+NxzcL1ekmKNAI6Ojui6lr7vPiZWmkVp87intLr0FU9R46osmUym7Ozs8Dt+x9/N+WrDyckpdrOiERGVHJvW5YVscEz354P8Re4rtt3fJ9lLqrREVjcl4fOsgVKKyXiHwlQIFG+89hajasr+7oKzRUNZFdR1xd7ObW7fuc1ktsfjo7t0nUUKgRGCSV2zt7OL3NnLF2LbcHhwyGuvlZiipO0c79+4ycF8xv0P3mN3b8Z8PuPHvvY16moCQiFlGmBrhdR6iJZ3qcua4DzL5fJq51NpQnA461mtG7rO4ryHEBHxqbrneDzm8PCQmc7NL7uuzTLoKg+W88mE+c4us51dNpsOGQUH8z1u3riFdR1Nu+Z7sWU6GlEaw2QyxR9nYqi1HUrMqIuCksREK3arkloqmphwKattZlg2UY9HuS1BdzVdjG1Pk8k4kw4lucngl99+jV/3pbf49V99C9ssORg7bs4TP/9z/1847dm0WWsmJUsc0B2GVahzWfFWaYWzljC0V0BmjkSKkV5GYvTE4IhxaOwnBK2NxJT7vSgRM79HS3b2d1BS0bUt69WKRT26VKb4YovR064siyc9rstNXf9/7f3Hs23Znd+JfZbb7rjrn82XiUwgC1UsQ7DFDjY1kIKh1p+p0KCHHT1SBDlQSIoetIIk2FVFFquAAtLnc9ff47ZZToO19rn3PQCZB+pRV7wf4sZLXHv22Wuv9TNfE7xFKpUYBEhOnk54/JMZTz6bo2vF7Lgihkg90bSrZI57+rSm23hWt5Z+k9gd2iie/+yAg9OGZlaAi7kLJJEm64Jk0OCoFUK+LqMkRZE6WMFH7i63KC0Q2dsmxgR4/rG4uLjYtYlHmfXRNO/h7HxMYJqm4ZNPPqFpGrqu45tvvuHNmzfvSL1773eJk8sYjxACr169Yr1es16vOT4+3v3dkcq+Xq/fASI/HK2OydvDZGff0euv/+7vmM3mTGYzttZSxy2N6OjXBe36jnazoWpSOztG8LaH6NFKoo0kBEFwESV2HEmUlCitUUqnTp9KdhKmbtBViSrMO213fJJIlyiKgyP8dk0MkWZSU2mFcR5WFiUkxggqpZAiEvf07qtKha8FYaZ49EiwaQN3qw55ZYlBg8hmnpmRlNK78f1TEBUhwvXNFvnNFW03UKiaqdaYEGn7geVqy831Hf1sBjF5GLkY8N7ibE9Z6ARUXi5ZnrfURcXRbM6L4wUOT1SBy9WaoTe4vsRbjxepA7pPSAFIRxDJqkdmD6nBdzvBzUga6UoDpqpS5ycGtFbJR8+HRADRHhkiwqU9JIRAEJIgTeqoK4PUCe0kRWZHFYbJbE49XVBPFlSzOavba+T1BcPNOVEEnIj0EoIOBO2IqJzw/LjQae8jNkGGEsvTe4KL9KFHqgKkwftIWRZMmimVKphVE+p6ylBHfEzjtaoumB0cM10cJhmI6Ijecn7+MiWBMZFxIKaEPEYIMVkW+gDRoKUmuMByveZ2s86O4mnttN0WpTW6MPjNllIqzB/BmsxABYqiYtZMsXPLN199RddbbAhs73T2BbS0203y85KCb7/9lru7W9br5W60PKolp+5/3OFJR6XsnYSFEjTNhMODIz797DPabmB1d4d0PXYbWPse5xJWp9SKw8Wcsix+R0/sD8UPJjy7GVwGYymtqMuag8dHHM0POZgfcLI4o+u2tN2WbdcTo4QoKQrJZFJR1SUxNpycrmi7jnlZoX3AtS0+Rl6+esnLly8Zup6PP/6Yjz/+mEml+fzzT/jo+SMWhxOOjo44e3QGIUl0a62RRZG0D0LEi8R4UjFwsnjC2fEzHp+82euW2pBMyYJzLG8dKkqquma7WSF8AqhJKZnMZhw/esqjwwPqkyPOVte4Am67Nee3V6hSI+oSPan5/E//jI8//Rn9YBEh0nUbunbFL/78cwoZCK7nzfU1ry4vOb+94mhaUmlDEQV6GJiJiDdwVGg2zrEeLMtlx9YN6G7NbL5Ibd49RYhnsxnBB66vb9huthRScTCp+Kt/9jMen8wRbkPoznl+Zjg9+IQ///lH/Oo3l3z7cgVa4mzSO5gsDhPmxDvabkCKiFGS1na7Dk+MGZ1vkk5LDBFnfeYZJv8hrSqESJu7FAFlJGVV8PM/+Tl26JJRbPD0fZdYC3vEZuN5/f2S3/79OevVGh88ujAkh+CA0vDizxccfVShJqQxUpRIpSmqAl0MFCHyV//nF9jOc3fR8b/8P75kaB03b3te/XZFWRkOT2v6LrWqo4g4F3aSDcqoHcDdk7pDsoSiljvl5W6dXe5HzYeYxqY/Fv/23/5bICUS1lpms1QFnZ6evsOKGhOYh5vM6ekpq9WK29tbvvzyy11n6NGjR5RlufudI+38+vqak5MTvPdcXl4yDAPr9RohRMLrbTZMJpNd1fZQYAze7fKMCdI+8R9/9Zukbh0CP3nxhGfPDnn6ZM5dbEEGtNEUdZ2S2/UtqBlSJ3B8ygsCxICpKoJ3RO9QWqSERxtkZggZY5gcnlI3FUVVJN/PjLYPwUNrif0WcQR6ViHrGl18gRoE6taibyyhKQgzWN4tqfpy5/L8Y7E4LmkaxXwh6MMNaxdYDj0Xt4K+k1grGEZJHARE9Z7MhiIicW7C5ZVnubplvf47np0c8OhwTh8lr6+W3C1XHOonyQR3GGhmhxwsFswXB7x6fc7tKjDYge0geHWxojSvOF1Mmc0nVI0gvmq5XfYsXU9wkrhnBwugFEXqBIeIDTY9H4Bh9IxIAN8gejrpKRZHRNEgRJWKqDAgXI8XiqHrcH2f13TqHHsk2hTooiRESVE1lFVDXU9St1gptC4QUjMgGdYbXBSoyRy/PMf6xOzb1BKrkxK+9gXaS0T4/WDXh/Hq1QXRQbRgXKCuDE1l0KJAqIIoFJvtXerCh4jvLbKGCk3Xt1jv8TGgokfNA5OixMzmBOewQ8vlxWuaqmLaNBweHCQdMyGZGoO0DmsKJvWcpi4oi4qLN6+53qy4aze0fZf3AklvPaYAITSFKpABwvDH8bSEEDT1hKooOTs54dNPP+Hi4pLz83POz89ZrzdstxGzWKCUwlrH1dUVy7tbNpsVQryr+H6PPc0WNg9G4DHGdLZsW7q2Z7lccXR0xOOzs9SdNZqiKPJ0JzFktNZ4l8DQf2jM/zB+MOHRGcCpEOAiRmqaqmYxOaIuawSSpplQViXTMGPTJVG6GCRKR4yWKC1SW7wwrNcbIO4OxrEyfkhfe1gZCil49OgRi8WC2Wye531JIffs8SOkyM638R4kNbQ9203L3Z6U7aIwDKMMvk+UchklznuEi8gACI0PEReSCJiuS2oxZTV0vL29xvnIrClxSmKFwJgaIRSyNCigMVDVmkklcf2W9d0NXW/p+wE7OEQsUqWPSGA7JSiKSFkUqHYg+pCYDgKEjGzaNdGGJMC4R0ihGAZL1w9Y6ykKjVYKoQxRabxUIGGwWzabK9p2myT1oyKGIc9Xq+T9pSMmqqS8G0MCwmUUe8qLk0dQDILUKJOJpr/jOAVG248YAz56KlMwm014+vgRq+Utd7fXfPftt7TrDdvDw72u0VnP0Hm6rSc4v5vneh8xpWR6oDl51lBNU/UlYtJwSSB6hTIKTaQ5SGq0h09qvv6HGZevtrQry8X3G06eTHDPR7aWT3P3XAwgkt/ZiM1JeiNJr6OoNKZIWKYYY7K3GOfNsBfz/urq6p0kpus6ttstm81mBw7eUTszLmdsJW+3W/q+3+n0jKyuvu93LKrRdsJ7T1mW9H3P3d3drtvTNM3OnqJtW7bbLU3TUNc1xph3NDbG3w9/XMLz4sULvHfE6Dk8PkCUhuXgEb6j8YK6rBL8KeTx6fheipDGUtnt3jufqMJKootEC5ZSIzKt21R1ZmZVqLLIe4eH0GfT80RbjrZD6gJZpu/VUqM9CAdKFqh6Tj09QBmdJG73iCgiIftSzQ8Lnn28QNaCrr/l+sJxe+Ow1iGiTF0HkV5P/ulMgJYEYbBeEgbPzcqi5RrnPN5LVPCUMvLybY2WWYdnMs9rVTJfTLExIIwhXG7wMXBxfc0/fvU1J4cNi0XJixdPqa5WSLPk4s1tuqdhvw5PUiBPrzf4xI6MGYmU2JyJxB1JYGwvOmxO2gtdU4oCbRpk3YOPeJtG30Im40kpJMqk7jJCg1Z4KbEx4nxERp+SCZVsPIKAwQd6F0CXIJPpaCs9lojzUIic6Oyh4fbq6+8pMBg0QgRUbZB1gdYeaQJCGQg+aa0NPb0d8ocFKdGZzFFWGqlVGv9nMd0oRNa0EQkXGqEQmkJrdIgo75E+MKkapk3FpK640UXuLFlMkbpl3gcEGqVKClPjnUh4mj0Yobu1Op7DYrS6SISk6aShm03ptpuEZ8wMvBgjzrldp3mcLI2F0UMJi/d9AMdOsPceYwrKMgmiGm1ybkDCdSqVz5EE8fCj/Y29/7s/FD/4VUUapcgIwidOf2lKmqrB28CyW1MXNcZoqsqgioIQSAwr4YnBEYPn4HDB/WQi7g5IEPlnK7RUv1OZeu85PDxkMplQFAVXV1dIqSirKrXajSEJvKTXGoWg7/pEs7252eumlkVBtA4XwQ0WZEQJnSo9Hwg+dbacd/R2YPAWtMQ0Jauhh+WStnMcH0wYgM57ppODZExoKkqt0KWmqBRNLdkuBdt2nQQKY0oQpFAIkeiRXmWcSBRJ60TIbHMgd5n7utsiXETsO46NMAyW7bZLjusGEJJNN7AeLLX1RGfZLG+5On/JcnlH3ztC7qAZoymLpCmhtUSKSN92SALBDqmy5sH8NGvauBCJURIx7GSvsnJq8qgKxOgwWjGbTnl0doYWgW6z5LtvvqHbtthhv7Gd7QNDn2jpYZT5lxIfLJPaMD8pOXrcUNYGt9OkyDRzKZOUgoJyKjk6ayhKzfM/WdC3jtV1x+X3a+4+XdBtPapiR1eUSjMyarz3eVWL3fseiRSlwhQqbW47h+Z7tdh9Mp7NZvNOMTAmLJvNhrqudyOmhwnPQwPQEaMD7H7eObfD/vR9v2NLjMyJ5XLJy5cvWa1WTCYT3rx5Q9u2DMPAdrulqiqqqqKu610VZ4zZKTXHGHdJ0j7x6aefpBEqkaLU9K5j1W1Rg6UoEu4mJvXPXbcwKRbb7DuUNuUY+l13WhuTtAOETBIK8t6RW2qFVDpRp8mccCkQKECD6wGPMCrR1qVGeYH0IE1FMV0wOzwlKIHfU+EkEPHR42JgdligmwXzk5rNEkq9JLiB9aYnep26f+gdc3B8ftITpACB95Ll1iJI90VJgwwBQ+T1xR2TEqpSsvAur1LJfDFBlwX1bEbHJd1mw81yyW+/6vH+jGbyiOefPEGWE7w0XJzf4qzD7kHZhnv1b4hJdTwb646GmOksSM9PxONUi8jFh5ELhCqRwiCqNQwDoevw3mJEIoYIlTp2yhRIXYIyeCnpfUzrQoAKyRlcComT0DpHbx1aV2nNEOjkgI2pyxClREaxl5zJ269fMtEVE1NRFhpqg68MurLowiJNQRwGXIz0IrJtW6qiRauSoNLr1oWhqDRCiaRxF2Jiu4q0JyXMZtr7lQYjJMp7hPMI75nUEyZVTVNVaF2i1IDSDmlg6HuCGxDSoHWJMRXWhmSD0u/reP9uPOzgGmOoq5Jm0pDGbQKk3klj1HXNujBIqRhV68eE5/1kB353/G20pmnSniaVTOrkiF1C1LbtO4SJ5HuYWKsjpvEPxQ8mPPduIYkf7wbH7fUt/8v/+z/wD//wK3772y84mB7y8U9e8Onnn/Lf/1//L2iTuhVtu2G1vKPdbvjpTz5lGHq22w3GmJTw5APv6PCYg8UhMYbdXC/GNOOr63oncKak5E//9E+BRGufTqdpvikgOpdM2Lqet2/ecnl5zd1yvw5PU9TQB4JyCdCmk51GVVZ4HCFXNV3XcnN3hTEDqlTIQhMKA9MGUc/Z+J5hecPtdk3d3KBVMqWcNw2FlhglUNHhh542en72s88pmjmPH11ijOPR4QHToyPs9Ih133PTOYpmQVM7mi5VV9KDsBGRMcRxz4SnbbdoZThYHDIMkeAd55fX/N/+7/8DT58/4cmTU+pww9XbL3j76ku+/foNq64ihAqhYrYeqDg4OOXocMFsWvOf//Y/026S9xSCDKiLu0MeSEncrkrIrQ+RE50RnxAjVVlxuDjk7PQMEQObzZZ//M2XNJMFiP1GBZffddy9GdjeJFwKWXAsBlic1Dz6yYz6oAYl6NuAMoGu96y2A8PgMZVi2hiqaUHZGOq65C//T8+IwHZt+f7LOw6fTjh82vDRn06JDnwauydskhK5a5Q2U4nM5BSRkxwSy+uBLYQQD5OeH46nT5++A1geNw+l1A6XM9o9jBoW46hr/Lnlcrnz1zo5OaHruh1g2Tn3TtJT1zXWWoQQ3N3dobXm4uKCvu/x3nN3d7fbBEfWmNZ656A8bkb7ApYhVZFV2WCKAoiozjBEhSkHpEp+dEqA0JqkcjzgbYcfOlTZ5HGqQJPEJYUPuLbLXbdIsH2yEagMyt7iZwvidIaaV6gmuauLwkCUGTYjIHpE7EGlNRuiICrN/PiY088+46f/zf8BF0Oq3ve5RjmjrCKFEpSTkiADLjimVcE3X9zw1Rc3/IdfvubqyrFcOWCSJBN2HkieKHx6HSJpzayH5K3ggyViic6jQ+T8oOHsoKQoBK9fv6aqa+pJw/xoxsHRgo+KgsPTI7775iUvv39FHwWvrpZsrUVPpsyPzviLx5/w6rsb7O0F2+1+iasP7DA1hWzSMyFT2RNFIIiAJxvzhoClxUfHEDoG5yjUlFI2aFXjmkOiNLS35/REjIdZUVEvjpgeHKEWj+ldoBtsUv0NIXVvgseLiA8Dy/48mRr7yJN6gZINhegR/Su8tQyuBy1G278fDXF9gypqjKnow0AnBVEJkAVKK3RWyUdrhDEMlxc0kznNdEa1OKKcTqimU06PjtgMHdV2g64a6qZJUhwqd0eUQpRlKoRtANvD0GOiZzqZJIHNwdPdbPHOIYSmnjUIuSaEDUEoglT0PrK+vsX1STT2j4v4jrfedrtls+24W3e0g2cIgiA0QlpEPrcnkwnLZUpWnPW7M2GIw24/GPeHMYkZk50YA82k4eTkFGsHbm9vd4XZmCz1ecRZFAUvXrzADskSpyyTftcPxQ8mPNb5vHCT8GAeQPHN19/yzVff8t0339Me9QxuYN1u+O/+j/8d06lIN0zKpGsik7FauiBPjLkyy9RcKRO1WewEou4NCMd/xwxRZYM37z1/8zd/k6pIIh9/9BFVWSKEZLVaMQzDgw3ih8O2A956oo+UpqQsK8qyYj2skuMvpIfSDvR9S6RBCJmkumNStfQZ6+iy4FPn7K7SHvqa0ujE5MATvMX2Pc+fP2N6cMjHbQdiYFGXHDY1s6NDKufRRwO/0HPqr79BfvMtX7/8CtcNeJEPES/Ys6hEivTe+xiZTWfEbAFxe7ticJ7z8wvYvqVdnbNdXtB2Lgn14TFKUhYVTT0FItPphCePH/H2zVvevjpns7reVXQjZueemTP+mw8gMbJ30usSpBZ7VdXMZnOIAqkKqnrCweEx0+kiaUjsEavLnmETERiaSYlUaQbowi1VY5jOy1RZxoSLEBFwgWBjkjpAJFsRrbKwmGdyaDh62vDoxZSv/ssttxctF682PP28GYdyxJBFFkVacaOGSMgjqyTlEBAiYgwYo7FD8mMak519koLj4+MdMO9hMvNQ+2YcLY0Jz/g8bbfbxICoKp4/f87x8TGHh4f0fb8zGx39tcZO0OhEvN1uU+cgFyNVlXSjnjx58s7fGu9/VVW76uuPSXYATFkmWQClkyxETOMbpUxS0Y5upwuWgI4hdw8cYegyQD5Vy+ROkY/3a1PqNOIyVRJGEzESvSP6AZwDJ4lSJruzzNoSwcPQIUVIeKEijU8G79n2HZc3N3hEGofvEVpX6RqSHA1SJkPLs0c1SgimsxovKr79bsnLl2surmyyr3AJGxlJLKaYhTBTsSGwDtodjiyplitdUNcTDuY1niGZWRrJ3d0tXd9TlCW+3yBxlIWm6xyqSwSCN+dXeGGYLSRFWWAKhbb7ZefedkmfQkh8iNmWJeZOtkhss9QfTp3R4HAhgLAMItDLjl7W1HqWGHZVhZweEH3PEC1CqSRgWBTc9EtW3ZZNt0m4rbz/+BHvEz0bfw1RoKKG4igJReoJtVsyOEuIHb23aV/aY802tqeWMjFqmyQqKI1O+jzOEdxAsANRCpCKOFj66hpX1mymM3RdY5qazfyAoplQ1hPK6ZxmMqVuatbLW3RZUNbJwknrZKgrjaKgoBIRtdGEGOmdJ1iHlILSFChVIGWSVUjjsoTPs87Stx3Dtt3rHqbIgo25OxNC4Fe/+hW//fIrvv3ue+YHB0wnE5qmQatUcBXG0DQNVZn2gbFDv4Or5P5k/mQic4xixiKxaauyZjqdsVqt2W62LFdLyrLcdZC+/vpriqJgPp9zenrK6Ic4UuB/KH4w4Rl8SniUyOOJRBvg1ctXnL855/b6lkJVdH3Hapvk58uqROkk7mS0wZflA4p4xu/sOgAPDr8H7fb3DQhDSDPhIlNjN5sN//E//IfE9w+B+K//NY/PzpjP5yzvlgz9gJL70Zn7bVKzjC5S6pKmbqjrhs3NmvFECiFg3cCQ7TCMlGglU2s0BkYehXU2C/vdt9SHbkNVGEqjk4mod0TvefT4jDOZgLsh9mgZMQKauiYiaaLgL88+IlQVa2v56rsvcF2ihmNSFSrifkmdlHnRW6ibg4wbiXS9Y729xrqB7cU3CL9Bxh4nCjwJrKC1oa4qppMFAc9sNuP07JTHjx5xd7NM6r4P8BuBxG57P+l5OL6JGbCb3iNFWVZMJtNELZSaup5yeHhCM5mhzH62BOvrgaGNSDRVU+ekQLBpV1S1ZjovUkcFkdkYArwn2kiwAaVzwqNUUm+1gWoqOXpc8/gnc377v16zuu65fLXFu5CT+YTnSI2rLLyYmQfJ6kImLSuX1KjLSqK0ol1HvPV7q4EDHB4e7sZTDxOedH/vE4+Hxn193++ECWXuiv785z/n8PCQ2Wy2s5sYi4TNZrPr9jysvh7KxDdNw2w24+OPP94lWA+f3fF7H7oh7xtFVSNlcq8O1mVzRoXSBSKm1xSFymtH5na5xwdPcO0OGxhiNryNCcQ80s5VYdBVRdE0aV2JlNhGN6SOqRMgKjACkUH3OEd0LtOeJaIq8NrTecd6s+HN27epAt+3+JBlKvKAIIZstQIHR4amLjg+naPLOfPDC4rmgt6f020Ctg8Eq/ExedON7YgRz2B9ugelUuhRi6YomE0mHB3M6f0WaQQowfXdHaZtKYqCoe/BDxgtuektQiaNqYvLa6QuiSiKUlNWmmJf81DXpwNaKqwPuz1fSIPGoKXK9HsJMeCCJ5DkNyIDUrb0sgQBhawxukKzoLerVJyKnEhIxcXmkpvNDcvt7Y5RO3b0YjZo7VijhKZUFUiJMTW6KCi3NUqsk3ZOdBlH+ePReEvlJJWWLJoZRVWjiwrlod+u6W3H0G3SPicEbtvilaLXCl9WiKJAFCW39QRT1uiqpp4f0kyn1JMJwkiK6YR6PqVpKmRRo3SBzp1kVWiQIhWlzhJDYreVRUEQEjLjTMpEWbd55DP0He2e4+VUrLHD5YwY21/96tf88q//mn/49T/yF3/xl7x48YKqbhIbLGNtmqahrJJkSUu6KUKK+2Qk3ygpBCFkscIY8YCSyY5mOp3StlvOL855+/Ytk8lkR5749//+31PXNWdnZ/ybf/NvAOiHHuvs/7aEJ8HdEghtM3S8ubjgyy+/4uryEu890+k0t7CmHB0csVouqasSoxVaCBbzOQfzRapyQ3owY8xkinzlKau75+G/L2Q2Sug75zg5PuHv/st/4Ze//CX/7t/9O6IPGGP4m1/+kl/84hf8s3/25/zDr37Nul8xm872urF3V7d4mxSQp80U11u2fkvfdclVOwvQjWDUx0en1KVBK8EQIjNgphVCGKK32CEw9EMGVnoGISi0xmids+XEQplPD1ByBJpCPzi64BhciylrVFHhpWPwA23fJgBjCJBR9mNnbJ9QGuym5W7Zsly2NE3DZNKwODikKJML7RfrW1wnCVbjbMQLT5QtRDg6OuLzn/0pn33+E5qqQEk4OTnh9ewtZVEyZDPLdw83cf+m5Y05M23J430iAq00hUkANYRktjjEVBP+9u/+nuHNBbfL/R7Qvk3mnz54+r5FytRy1sZQT0rmi4pSpu6ND6BCxkA5gRt6qknJ/LikqhQm6yaVwvD0ozlNYfjil5cIAlffLRk2AT2TaQSi4449MsrGJS0OCy4SOtjeOYra8OjjGUoKzr9fsln2FIXZO+mZzWbvjHt/n/GeyQwkrTVXV1e8fJkYkMYYHj9+zJ/92Z/x7NmzdxKmqqpomobtdst8Psd7z7Nnz3aiYcvlcmckend3t+sknZyc7EZZ4+hqTHjG1/bw8/vEyZOnjF5mdrBJFdkOqM0r2AwMQweka5RGIVRaQyEkM8GQleADIIRJuJ7CELJSgDIG0Ryg5gdEIen7lmF9SxElkhppGmJM+GOpI269Ign+CVRhKI7mlE9OkWHFICV3tzes7UAgHQ77hPN2N8qUMne8nUd4hzYwm8Of/6Lh2adP+av/9pjf/PqIy7cbLt9s+PXf33B3F1itJZGC5GWXMJYxKPqoiENkUcB8ojk9m3FyMufoaIEqajrXshlaCmuwQ6RbdfjNhvXastw4vn5zw6OzOfWiYTKb0HVb3rz8nslcISYzmj3xrn1HwnCKhHsU2Uy3E8POX6pQ2TIDmajrUaYPGQg+MPiWjb+gmDxiUh0gTMnq7prb/oKZmCM3JYMUvL3+jnW7pB02COF3xSeiQMsCJTSdBS0sqECYbBm0pBeO1bChtT2DDxip0/fz4yP0GWCsRYaIvdWIakBUPYUpKdyACBYVPSZGighlCMSgCF4m1ftWEaRkkBovBEEKemXoleJGK9pKI4sSVVZ8OTukqidU9YTp8QFFVWOKio3zlEZTKkUzn6EF9AIurm/pNltsO2CESsbPg8X1HXa7YVjvJ9cyRhohWYwpMUaz3mw5f/OGb778gidnp5wcHoA/TV03KXYjrclkymSadN92bG+VOrNw3/ERUqIEmYQgmdRTDg6OOD4+5fHjJ7Rtx+XlJev1eqcsP3acTNbBa7uWTdcy7busHfeH44dp6VKNGp84H3j96hXDjePm+oq23eC9S7YANuFztusNw2JO9D61g3noliyIUdD3FjvY7B8kxy/9zghrjHFTHUGar1+94te/+hW2GxhNxm6uR8+g1J0J0kO3X4s5ZuO56CP9tku4BS1x3iWGVgCffVskkqP5EdNSYySstlsqIsYNBJKLrxYJwBhI1WfCWN631smHgAsWIVObN4SQtGq8w/mB0G9xAb57e82333/N1c0l2hRgI97bLKMu2TPfQYg044eAcwNtG/BhQBWGKDxKagZn83XK1GIksWWMSVl7CDCdziAmrJPW+l4NOXcEfldo7v6eJsBizL8rJQYJ4a8IwdF3bXqPtURKTYySwXridj81aSkTANJ7SxhcMqEj4dAgObeLzBwRMY3TRIhJYBKoJ5qDkwptJFKJnY9cWRlmx5GnP51xd9WzXQ1sboYkqT4VCJ9hyjGpkoeYSbhREDzYIbBZehCCZlGkUZaSO6hA6gr9eNbT9/0DPNS7NPBxQxkZWEoprq6uuLi44OLigufPn1OWJWVZ7kZWD39mVE8emVrW2l3xkTytil0iMyZco2/X+8KDD12Q/QO80j5RVfWuSjdKE0NJDA4hNli3wnUaFyL49H4KKROrUZfQD7kjlZzIR0ZjmioIpASCxPUt3VpRaJ0McL2jswUMitgZVKEQ0gHtvYt9TPgMPT2kevSUeVwTtEEUBmE0Pttb7BNS5Rb/+ImY8F0+5NcsQCjHZCpQpqIwj1l/1LK82TKdKr75as1337bc3AQIisyhTc0SAc5aZKkoC0PdlAx+4Or2mnoqiCogtQLh6PqB9WogWsHVyvPmuud2E5G3FmU2HM43FFpmzJTFqQ4n99tTlZxkgHRESA0Z1+ZixLk0IhE6MTeDSM+PjgkrGv19oRRD2ptMUeO2tzg/4LyjnE0Q2mBDILqkmWRUgcdmJ/Q0aibEZCYdZFLQjhqtCwKCwblk0WMjOoznXPII/LEwpINThohrW6L3+KEnaA0+JekyBsToxYRDoFAjIjYmPaVJTN2qGJIcQnCCaKF0IjELlaFYtYiiZihLrq+blHiokp5EptFaELYDVkmslETbYaxHhSQy2G5atpsNvW2xQ0vYR9Z9XJoxmTKv1+tdMdXUNVon8dKE53Pv7EcjCSIRJ8aiPGbQOmlN8D5wWWTyTvpvKdUumRlHVm/evGG5XO7ygNGawntPm0VSjw4O/7d1eEYHbIkg2MDrV695077l9voK69OIS8rk+7Ferbm9uWY6rSkLjdElWmmUNGitcNZjrYc40Ncp6YmZYQX8DmVt3JCLothtuNdX17x584avvvwy1dFSojKQ6vztWwpj+JO/+OdE4bB2v4OSnAuIAHboiEMGoIWYoUYiM2okRhkOJnNmpcYIjxj6lMnbnkFqFJFCgBWRkIF6MiQvLGJAakPMSVrCIySjzWAHgnd473DBsx161m3HF19+w6vXr7i7u6EqS4YYsL3L2gVjO/vHQ0qBkgIlSeDUYWBwW+pZjY8WgaIbBqILiCBJzCqPIGC0ggjWOgpj6DtL1/eoPLPdMet4N9kZncRHFp3WagdcTodnamlqmXxp+r5LGixCZQabwHuH9/tdo9IpsQvREWzM1NfRRHYUuGLX7hYxmY1G6xFA3ZiU8OhsKxEh+CQ6WM8Nz342w1rP22+3LC96ikJhKgU+ZiK+zJiB3OcRyaG47wKblaOZS5q5YX3bJdxFFBnALfa6j23bvpNQvr/JPAQwSym5urri+vqau7s7Pvnkk11XaLlcvpMsjfi4tm1ZLpe0bcvjx493VPcRjDw+j2MSM3Zd39e9GP8+7Kcg/TDKokgHPoKQN0xiIIQ5sW3wyuB8v+sZInUCh+sijThI9gDeZyNbmRR9BTI9aNHjupY+eGJZIUjrsHcSegXaUCiJsKnz4nxidqWeU4maHFI98szVJq3RzKjxPrln7xNK9mSCNhKZFOyzQe3oQxejTWOkuuDkeJ6Me9uOqvTUVcC7Ld2mxw0lwWuiHCU+yAeQojAaXWi2fUe7bZnHkmpSYsoC5wObtudm2UJsuLhzvLrquWsFXgxYv2Ixa2hKRWkA2aOaiGr2fBbFBEk68I3UeOHxeJwbUrc/elRM2lVRhDTaIL+fMREZiJEoI0JotClxvsX5AUKkbuagC4YQkVFgpEaIijakQs17T/QwbuOjVIaQBq0KBgSdTQkPPmCCIsllBMQeSZ0iImNEEPB9j3cWqyReShQZPzXu0TEkTa7gEUiUkKio0TFQxrw2CVlQMQkS9sEjUaioEQz0xtBrzfpKooVGY3AinVNRg1YGpzVe64RTy/gz6xzrtmWzXLH1LdL1yL2pvTDu1avVirqud6bCdV2jldqNvt/v4qaERyNEGqul77kvPu+R4Sm/CHnsLMSISZQ7L77T01Nms9nOP3C0rTAZKxRCoOs61qs11mb83g/ED3tpbfpd2RGcor3d0F73LG9vOT4+5ezxYz792ee8efOWN2/e8j/9j/8TUgWkhMdnz5jPj1jMDzk+PcHaZHD46NETvE9Fmi7UO4JpDytWYNeuh7R5fv/991xeXLBerTHaMJ/Pmc/nfPPt15xfXTJ4x/zkSVq0ex6UUgikNqA0RoQ0fjI66Q4EECEilObTZ8/46Ucf4Tdr7m5bwrBhMSsw0aF8h7SKkkjQkdZuU4KhBEPfpVFOlDTFHCENSEWhwCjQ0tPGgSDTgvcyJ0Re8GhREU8WTEMgWEnb96y7lu/u7tgM92aRPxbPnz5Fq0us9ZSlwroeHy2TieTi4i3nb6/AhVSDCAkiP5hSURiTtE28Y7Ve411P8J75IplZlmUJ6xWjntLDA1BkUJqQAmNSFeO9y8zRVE2FYBExIIks727YdJbb1Zo2+yEptV8b6+TxhNU13LwJrFZtAuQOA/XUUNaKZlYmYJwUCcsgYLuyXL/uiFFR1gWzgxqX7TpCCASXKlAfI8//7IDl9cDFd1v+6//3nJ/94oTPJkViygWPwCFUYPQ9MqVmvew4f7VlfTcwO2pYnNbc3QyEmDZgP1Y9e1C1xgf9IcZtTFYeJkJjYrNarYgx0jQNMUaur693SdN4b+C+0Li5ueHu7o7NZsP5+fk7mhYjLmikoWutd2Ouh8/sqAI9xsPx1j5RFoZRm8hnsgMxEFxFrCtiWXFzcQdSIo2m7CxaJwxM1BVJd8ShYtzp8hitk5WEEhRFOggCktb6/HnJtvOgHbocqKs6AWKVwfUBY0qULum2EGtJedzwqElsoBBSwuNCwhHtF0sEJn8UGYojQJg8jklKOwnrP6ROMJGqivzVX53y9GnDX/7VKf/+f37Ft19vefndmtuNJESFCBLtFcJKhlbwzTdXyNhD6JivGupJjSkLvv3+gtXGst54Xl1fc7MauNtYgjB0K8/dZqAfvuP4sOT4sOL4pOR0Pufk8X4wAesso/mCizYrC4eEfUOghcL2Q5IDkMlQNUTLNnraoc1K14LD5oh+SCr167trhHNMy4b68BHbrqXdriiiQKJxCFq3xkeLix4tzKhGQNQCowumRZ3wYTF1uF0cIFvAhJDMLTu/T6GcevggQSokERVDwvNxTyuPAqySDEru2M5aSTRphFNat5tuyFyQCimYK0+FJ/WiNFsEWwHrvqcWhkZohigYhGeQAVkW9FIySElRaASKIBS3vqEkUk8rVm1HkAL9xwAHSddzc3PDdDrl+PiYzz77jM8+/ZRvvvma6XSKtZarqysmkwnWWoqiyPvOhJOTM4bhHkis1H3Cc7+HjbjdBFieTKbM5wu01jx69IimaTDG8NOf/pTz83Nev37N2dkZBwcHPHnyhNlsxvnlBZvtZpcU/VD8YMLz5PgMrSRGKiQld3rFtbvl8tUFVV1ycLBgOptQXBti9Jy/eU3Xbxlsy9uX5zTNgqaZMZ3PgXQzX7z4eOf8rIpxDmd2FPRRMn/sHoyVJhG2mw3ttsVZy+c/+5znz59zdHTEm7evsM6yXK+4ur5CKxK4d4+QQjKZTphOp5wWEyZFSV0Wyc1da4xKY5uToyNODg9Z3t0wbJe4fo13NWWVpMy7kJu4MeLckCqUGLB9TkqE5G65Tn4vuqBUGi0FSgg62+fOQMQFl6qyrkN4y9liwmlVIQaBRdCGiPjma+7anu2eUu+nJ8cM1tF1A3fLbXogyVR5ESBayJ0JxoooP4gqM3aKskhiVkJQlCWHZwd8/eW3D1g64+F5D/SGmLpRuWMzYl3YMZlSguCDxbkBpWC7XXH+9g0xOJqmYTqd7HWN1bSiqBzKDAjRY21ylC+moAtB3RhiIP/NtPD7rWN1YxFSo4xGFSLpFOURHqRRro+eamKYHBRMDgxvv12xOK04elKzOCpT2zyGxEgMCdgvBPStZ3nl8E6ASB5O3iZWjRCJJSSFQOkf34Qmk8k7Cc9DPZ6H8ZCWPpvN6Puek5OT3bP1foIyRgiB6XRK3/ccHR3t2tIPxQx3EhH58++P1R4mYu93+/YJkZN9kUGOY9EYs9M9Molyeu+w3YAXOpm4mjQ+90HgPambGlJ3Nb2e5EGVcvncsREWRbImqCcN9aygmhXoOml7xeAxRQWyZogVy65niCWqgkoWCSgdknil8/snPN71aRwqFYwCfRGiUKmXFAVQIHPvLy1aSYhJrfvwaEJZasIAj5+s+e7Zmt9+ObBaObZrR2wDjZLUStNvBwod0bpAmQmb1tPerXl9uWbTOdrB00aLKGGiDUpVFEpSaMnhQcHJSc3pccOTp1OOHzccnO7nlu6EZUSAut1aEIkBF+/3hzT2j7jdmgm4AESJEgKlCoSQhOjv3yskQRX0cc1mWDP4YWzapnaOj+ADHosNniAt0QsqU3FQTelcx8YObIYt4/RKCBA+JL25Pdi9MrPjYHxN4yhunFiIxNKUiRwhhLxf1358TgRu7M7K/HMi3XMhPDJ38IWwdAS6YBFYlEjJnI2gRKCQMY/XBCpAIzVRKByKi9XA2kVuXaB1LcINyB/BuLwfo5ZW2yZHguPjYz7/kz9hvd3sSAzOud3XH/63Vob5fLHzAPNh2LGUpJK7js7DvaSua7z3XF1dcXl5yfHxMfP5nKqqODo62k0UklRKkzTG2o7NZrOzsfih+MGE58WjZxRaYZSmNA1v5AV0EaUEdV2yWMxpmpqiMAgit9c3LFc3rNd3XJgrTNFgTI0yBSonL7c3S6o64QKUEZRVSV2XTGczmrrJh9w0V5Mlk8l0h+NZr1Z026Q58MnHn/Czn/2Ms7NT/l//n/8n6+0Gt9lweXlJZRTmj7AMmUwmPDo749PDxyzqmmlVIY2izq9Ba4PRac775a++Y7O+wfZrrKupKkNRaPowPrTpd3rn8M4RbJtN7hTLzqOKpNpaqQKdx4W9d3iREh5r+wSmHHomdc3pbM7J4zmFFcSipleGa99zve1Yd/vNY4+ODmn7gbbt2Wy3CeiZLRckASUCngS+9ZDczxlVsAWF0VRVQQiJpq6LgqdPnzKfJ/XWsRMgZXqQx8ree58ZSx7nRwBzAloSR40WjXOZ8h89282S66sLtBIsZhNOTo/3ukZTl+hySBYCMmnf9P0A0qBLQVXrDJYP6XAKku3asbpxyYDQJF+spLadDhiIuMxUKsuCZp6Snu9+fc3hy5rT5w3zo5EynboK5Ic7xEC7dtxdWrxPytMxgOsD0YOQEikCWgvK6sfl7KfT6TtJxPjvQ2sHuE88FovF7nse0tUfAp7Hnwkh0DTNTml5MpnsEpv3k56H+J+HicxDEPX4+vb10HrwS+67XXkkGWMkZEdqhERqiXM9dujx3mDKAh8FWicvLR+SN5kc8WIxErTOIxOZR7tp9CCNQEvDdF5TLyrKeYGqDaH3BOsp6oo+VHS2ZNlZbDToQiZ18sxiQwhU8HvT0kNwSOEzvuO9hCczsJRI4p5SBGK0+KgJQaFkpJlIZjPDYlbz6PGKJ0+XSHPL21dbrs8dQ4RZKZkWGt8PCGUoioqymrG6vePt1Zrz2y2dc9gYULVgNi1RqkSLhrooqYuSg3nJ6UnD6cmE5x8tmJ8YZgf7MV+dsBkzGHnH3/hBVe9DGvU4EYghJxsRQDMKJBpdJ4xjvO9ihgBeajrvWHUrOt8jk0RuWiMjJhPPIJIkR6E0jdQsyinrvmXVbVh2G/CZPSpSkiTHbtuPhBIZULBrxSUcUmonpeQlIJA52ZGoDA3Jo1YhQIIT40GfXsP4b8zSI04kqv4WxzZAKUPGQUa8SGJHWglKIZEhoEJkEgwRxYBi6B2rwXMzeHqZgPFy33UaU6cqhMgy28l0XcdkOuUnn/4EoSS3Nzc7aYtRv2tkUrVth/eBqqx352LXZcZejCj1roTGQ+JF13VcXFzw6tWr3ZQniZoWHBwcUucky9nElLxbLVmtVjsdn/eZ3g/jB1fwv/7Fv0oVqMj+WP63LC83CClo6prjoyNOj465fHtOaQzbjHMpTYUSyXnYhZ5hyBVajNxcXTOdTplOG6JwGScEUiuUzJVkWSYJeKOpq5r5YsF8PkMCFxevITi87Wk3G1a3JTJIwhDo+o6/+U+/5POf/oSfffaTvW6s6wPXl7f024HhYs2kKqhLg8oChFII7u6WWOuwztF2LcElBgkhJRwRsDIvkrz2o09qsGfThulsSlnV/Orbl1gh8SqrhYrU6oyZtiaIGOGROLQKfPL0EY9/8YSf/+Wf8+LwFOsF685yObTc9D3bvbU/FI/OTphMGvqh5e7ujtVqw7dffsPQD2iSDrJQ6ZqNNMiYDgatNNNpw9HRAUPfI0pNUZQcHR7R1M2us+O9T++RvWdsxdERSMRskJkSHussQoScTDVsN3d89+3AYLe0vUMQ+Bf//C959OgRxyf7JTybrcBZlWntBUoI6qrk8UczJosCJxzWQYwpET1/E/nuNyu+/+KO4JM6s7UBQqrYrXXY3PkiRiQDsohUc0XXWV59fQvas3iuqKoCLRW965PuiI9s3ji+/c2S73+zwlrHm29WrK623Fxs6FqLLgXHz2uOTioOjn5YLAvuR08PE4pxM3iYwPw+I9Hx3/c3l/eTlh/qGr3fEXq/wzN+38N4PznbJ9J+IEGRu46R3jls39N3HTYIPAqEot0usa7C+ZrDk2NQBUFafL/F566Ad+BjQEew3lKVBdoYDg/nLE4OWZweMj07BJUOMKEaorII6fCuYrWWXK0trUuGlbrU4CXSJ7xQIKKjQu55jYWcINCImA7pQifn7rVL+I0YJZo6jdMJEHtAEASEmEwShXfoKvL4o5rDU8npk4Kbtx03bzq++6/X1LKk0RLheo5OZxydHOGU5mLTc2vv8DNYzCcsjmv+5S+eUZsaFQvefremkA2lnqSkxLc4m7ySJkONiD++TgGc65PwqjRoKXHBYYNNY6R8sHslCCQBwuATI7IUhkaWSd1fCBbTA4zWWNtxtblGeEcpNcMwcL2549ubNwxDh1GpKHek9SLkmIekEdpnpx9zMD+DcsZvXv8ty+0dbd8yKcs8uoeQNd348XyHuS6yYnYcodmJKp+xWQndk8Uio0TthBgjUmZ8UkxSaiIKZBQpkRnRXUERhMAJmXE9ARkjjS7QUeBioCNgkBQBlLUgU3FaB0+QqTAhRPwwMLQ96mFHcY9YbZKf4XbT8cVXX3F1c813r15meYw06j04PKLr+51gadt3bFYr/v7v/57b6yuWt7fJFJV0vW5w911fmW1ClEyaaaRnvyxK6qZm0kx4/eoNx6enHB0f7b4HoM9WEnaw3C2XvHr5HTfXV/yr//Zf0feW9aZjNv393cgfTHgqM7m//wHm8wOePH3Co0ePExsqeA7mCwptsEOfJZ6THUMUieUkGLUz7n9R326SfoCwJGO0+41aZr8bZe6l6u9ubyiMoes2XF1eAg479Nzd3DB0AyJTGvGR6CyLacOL50/3urHOetrY42xA9ZHSKEojKeokoqSU4vz8gsE5nA+4KPKCjZD1ZgLgJLBr0QmICiUig6ywskapmkFVdAi8TNL2hVIUWnE0mWOyk21TGrzv8L5jdrSgnEyRhaGe1BQuPRKNUWyGyNbvJxOevIWgLA1Pnz6iKAqkMJyvzzMGReQxVgLQhugQGdhLJCczPWWVWD62b7m8umS1SppHY3Z/r5Y5wnBHhWXSeLBI+KimSh2juio4OT1Jnjcy6SwdHU9omhnPnz1hMp1Q7NmqG7qBvu3oti19l6i/1cRw+nxCPU8VuXUe7wND53n525bL71vWNz1CkbpQztN2luDA2YDngdNvcPSdxw0gpcJZaNeObpt8j4IaWVqJ1ffmi5brlx2b2x6Cw/WSTkSU1FQNlFPByU8aDo9rFgc/fpC8n+j8oZHRw2RmFOsMIezo6g8xP+PPvp+o/L7///s6Og9f18Pf8/sSqb1CjCKkiZ5MzIeGSCOCZCzY4eyAswPbdkB7iUezCBHvQy5MII5SGNFSlYZaK6aTKbWR1IXEKMBbhs2G9VuHKjWq0JjFlBAULsAweLZdpO0lHshNP0TuKORCPcGP971EqSHKDK70iFzRp+ct4XfS8Jq0zxDItML0Xo4sFwJSR8oaTk41taqYFZLtmxWxS+7i9aSgqhWmgqhannxUUp0+odOSelowmZd88tECjQan0D7gB0Owgc2mwxQWU8F0oSgbgTL7dexEHitGlbomMgZUjAn3khshQsQsP5g89+T4eaUw0lCokqZeYINn3a5Zbu+olKGq0lhV5mJRhrEXnZKHh/uWUAapFEOUXG9WxO2W1m5SMiwSY3hsscmdbP2PZzxGmhHBA4ik6CwSHd7lnxdIXEzfYzN5QuSOU7p20JH8/sT8b7rVOsjkEykFHrBCEITEOrUT4+yUwoeUWLkATgi8kMQoAIUVBq9BFVBHicl+hvsmPF999RUxRtq25fLykjdv3vDbL77g+PiY1MW/xw9CYox1XWJrr9drhsHmYvi+q1eUxe/+fTGSlNLLc94zDBYpOq5vbun6gcurK6yzOxaqizFL3QS22w2b1ZLgHN22ZbVcc1Pf/v+X8GhR5BebFutsvuDJ02c8e/qMqqrp2o7SFBAjfdfish6LlGOCkyp6EUXezESSw3aOdugJZPqrvL/BUkpkNoaTGa0d85t7df0WJZOJ4NB33N7csLxdpY0xgPARQ+RgNuX5s8d73VjvIs7b1IXqPFpGlIo0s5I6q8pe3t0yuKSqHHSJyC1KgSbE1NnxkEwopUzCi4ARglhUUDTEoiGWUwICrySqFCijqMqCp8+e05iSxhQsZlM22yWb7ZJFYyiaJqH/jUYKQWUCldZoAsLtl/A454hEtBI8enyKlAa84ubtHY6Ai4kpkRgSPrEoIO3wMWLtQNtuaSYTurZl6FvevHnL7d0d3ZDAwb/DGYuJGDPqL0wmJU1TM5k0nBwvmM+nzGdTTs+Oc7Ix0HU9J6ePODl9zHSalJ33HYu4oWfoO7quo+sG6sZQTTTHz2qqaUp4BgvOebo2cP5Ny+15R7dxTA4TU8J7T9c7oouEgSSDnxle1nuGPhCcyAljwuPYPqClJ2qB846YN/Cr7zuWVwND69A6sYFilNR1QdQSWSuOP2k4OKiZzvYTVxwTyoeWDaPezfj1h8nGwzHTQ52ch3pX73dq3k9Oxt/5fpv4/aRr/D0Pf98fKz5onSdIkUDIIRu8hoiPAhsivfWs1y1uGLDOsdoMFC4lPN4HXPbTcc7jXLqfzg5EITGloGkmVDpSSk/0ln69wQ8D8jpQTRuq2RQ9Be8lzsF2O7DtNN2Qn3Py8CKrNIsod+fj/lBQlTEn2UBZjODslACkGPJoPKbsIevYxJj+LuPrEBGlI4upohKSRileLTSbfGiUzRRdaYTxqGLL00c1P5kfE5oSUymKUjEziQrte1C2YnMXWS8dzq2pJpLFkWFxbKimElnsd4UygwEDPh3iIbGWXNapSuvIJ8o7giAEIp/4USXsTllMKIoJ2+0ty+2KVbdC1TNEVlBPHDeFRqOjRAaZ9YjS34tipHYXbKyjb7d0Q0cfEstPS5NwYD7ZBymdZEf2kYhIx+aoIZe6MFEErAhjQ5idOOTuOc3PIyElZ0JQxIgKqZmpRD7DBJiQz5cocYik2CwEW6+yqaygVQqLwEaBJOKExMlkXi1kgdcFPmqM0Ex1QSGy7+Se8cUXX4IgddNurjk/P2e5XHJ0dJSuOStol0VBWSZIypCFTttNmxThM+FlHGnprL1EzBiueM+efbgHjYrKq+WK1XqddAC3W9q2peu6ZPicsU/OOSSRaVXTtomebkzJi4+e/ME79wejLMt3NquiKDmYL1gtl/zDP/ya//Sf/iO//eI3vH37lvOLcyZNw4sXCUhMTBXXOCpIIkOCxeKA8/O3vHnzhvVmmy3i00M9HpqRkZefPUl24Si0JmjP7e0tREVZ1tze3LDdrrF24OAgWcf33X60dCVMVu4UWCSORCO3nWUQmkpozHROdMlM1EcFUhGkzoDbdOOijKntpiRoPU51EcakBM5ohM5dqBgwBCYaDiv45NGC4/mCg8mMyWTO64sLXp9L2s0ty03P7abntndUqkQ1DZ9//jmLuxuuVnuKSEUSPicm5sTx4ZxFM2daVlxeXXF9fcWrNxe4mGl9QoBIRowCxc3VNcMQ2Gy7JKgYHF988SVv3p6z2W7zczQ+3B5lUifncLHgYD7n4GDBi4+fM1/Mmc2nTCdTtNF5bKly6znsqnshVRKd+yNGIWbu0sfUYa1CVxIzFaiDSKw8fUgrKzqJ94rFwYTwPFBNBdUxHDxVlBNPkA5RaJSRCQBLrvyCYT7VqCcThj8TFAuYnEn0DNCk6tUI8GkrfvrimMm05OSpJtQ11URT1gaiSDRiFZgeJ6l/uwc04iE+5v1uyhijz9X4PSPuZkyS3hcFBN5JZN73vhpxWGO8r7nzEEQ96nSMOJ/3R237xH/8298kifqs2CpISY9rV2yue9aXnpff3tG1W/q+Y7XumEwaDg8dTz46Q4Qe6bfYzYbeBnob2fSe1kaGoDg9U6y2ls2m5+V3b6lix1RaXjyacfLiGUcffURxEuhbz2Y9cLMWrG1JZ0u63u58mtJGnT5kttDYyR78SIzY2hgTPiLGBCKVQuYuQCTGISc0WZNEJVq9xCScrCcdhl7hBkV36/CDwPeRrq+5Wbasbi0HZwt6mZTDZ4vIo48aTp8e0wbP4Aac64m+ACGQBppDjYuWzjmE2fDo2Qmf/fyM6UGDjY7e74cZLIxOe3lM5sAhehweN9hsi0F6ZjIVufcO7xOmx/tIqwbuXMftm4GuX7HZ3qVeUNFAPeWLN19xfnvBenBUpkEIjSJ5WNngccHRugEfI/QD36y+TaazAqqmwkSFioqKCh9sxq4NCJmgBj8Wf9v2yWRWyCQoS8Tn/6UOSxo7jX8zZNB8wlS7XcJWYNE6GUJro7IBrkCTJh1KaFRZ5MpRUEaNkukjVMXOw09pQ9SKqJIcgdIGpQ211pwFOIwx65I9wMj9SLx69QoA7y1Db9luExQiaeEkvSut9b2BcHXv0u5DyBjNsAMSxxiJ/l5aYzRaBkDcd6aFuIe2aL3NiU3ed5LgFZeXlyAFSieRw7ooKauSEEZ5jbs/eF0/qsPzDiASQSw0T5894fLykrfnb1hvlhijePTolM8+/QlPnz7l7OwsJTzZR8i67BBNGlu8eX3C8cnh/UjEDliXAJPOOwbncrIUdhuJlJKjwzntdkPf92y3a4qiJIRI33dEImVZMF/MqesapX8cCJp+r0kPoEiZdBQSROpKdTbiosU5CEGmSlFlY0opsTEBuBOjQGbQWRLB01JSKElZCqpS0FSSs4MpTiqC0kzLyLTUzOqSUoLwDtt13HUD/XqNdJ7oIttNz+X1kreTFYuZoKka5odHBGMo6v18pkLwjIovgtRW1RqOT+cUlWAyM6A86/WWzbZl6BxRkUZ0Y+Uukk5KURQoKfj+5Wtu7+6SyJ7wKCUxRnN0dMJsNmU6nXB6uGA2nTCdTDg4OqCsC4qyoChLQpQMHu6Wdwk8LeBgsUCNJpxx//YrwOSo4uwTjxSC9jZQTwomBwXzeY0qgDxykoVCCcmTzxumj+FwK1Ezz/ykQEwU6JgBoxB9EikMSKI1VPPE0PhMH0LtEdOIKBRCSZRQaJMqzBAiRz/VNNuaw1bgzYAyyZE9upi7BFCWI57mxzeh0eLh93W8xudTPZhzj0DlEbuT1kHY/a6HPzd2fkbriofdmve97cbvH//WmPz0ff8OLughSPrh6/qh+Pr7q2wUrHbviQCi7xg2grafcM0hPQUDLSsP2xZaOr56vWRaRCohiFLT+4G71vH95RZlesrLlmUv6LqB9brl8uKGmfIcViCLknDoYetZfXvB9cUdl+c3rNvIEAqGWNBSEEyVPrjvmkk9JpR7Cp0GlcdtmdaendCFSLiNEQc7dn6EkkSfMIHbzQa8RkSDCBo3RIYusno74IZkPzF0Ju2ZWrEZBnwbsdpzPKnQpUmWKDHej0wyaSfBND3CWHRlefLRgsOTCVWt01g6PvBA+tFr9Akknj30xiRxh/HN3ZGYxVkVMeEZpSYoCHFgsJZh3ROyQ29jCgieTbti3XdshjVBJs0am0pPCJId+1Pk7SNCYcqcfES88ym5lIKyrohFOqiD8AihEOLH1+qVuZ9GaDVmcCkhFSppz6Qz4p6ZNb57UhSkzVWhVWIYaqMxZTIeVUolMUWlUFpR1NXuvNFolEoJTyx19uoS2UE+JUx6JBdIRaUUGij443B0jN+fYECM86Z7k89Uzs/n86STZwoIDzus2ZomJkB6+vfeZWBHaMjn7rt7hk4TH6koijSGV2jKosRoTVUUXNxcJAunIa0fPV88YAuLjBf9/fGj6ezDpGfURXn06JQnTx9xeXXBd9+/ZDJZMJ1M+Rf/4p/z7PkzHp092r1pIYBz+cJzG+zs7JizRyeslqs8gkg0bDskPMim3dLbtOidyxWQlHzy8Ue8ef2Kt2/esN1u0brEuYDzlsLoNC45OWY6m2LMfi7bUuic8EBQMs/MZVKRcBHhEuA6PSTJH0woSZSC4G3KsJWkKsxu4xMCCi2pC0NdSZpaMm00z04PEKZEFRV1AZVRVEYl0LC1DAHabYu1nkIICm3wLrJad1yvtsiiRjeS6eIQWZZUzX6U7QyrS9eLIAqPkIHFYUM90cwWFQjL5eUN8hKuu1UG4mXriyxcOGkaJpNkJ/LXf/O3KVnJYmlVnd7/T37ynJOTI46ODnh0cEBVlknLp1A7UGCUkmHwtK3l4vwKmQUOm3pKUYxgvz/uIZ0uJqiPNYt5SbcOCSM0KZhNCqJIFdgQE6ZFFYrpZ5pFL2kHRawcUqfDmdyJVDIBNMbKLHiDnICpIscnh1hpGbQj6oCWGiM1qhLZcDZQHkq8r/DO4Bh24xDf+x2YUfqMRdij6nqY8LxP/35oIjo+q++Dk4F3Oj0P8VajjcS4WT0EOY8dnIcdJmBHcf99rKyH1Pix87NPfPvyPOnn5A0vVaRpfXkXcLbklgOcNFhZshWObT+wtgNfvLzldFFwNDEYoVnZgaut5euLDS7rL725aWnbgfWmp920HNaK01nB7MgSbgf8TU9//pLX37zm9bevGbwnYoiyoK+OiNMjmBwQhMr4GpBKJX2pvXV4VOp85MQ4hUCgGHP8uEtIZDocgyc4z93VBnyVsJGhwPUS2ymuXnqGPlXifSsJMclqrPsOZyyhDJSTQ1SRGG0hSiIKIRJtfByxeCxoh2k8x4sDDg4bjEnXF0Lc+3kMweauR5J3CCEdeCPWZuxeRTwhupzwiPReqoh3edTkNmgh0UIy0SUxeNpuSR8jg49EKeidI0leiJwo5oJOyl3GU5TJlysQaPtN0vaSejealqQ9TkSVVJB/JDZVMseWY9ITJYok+KezQ7oqdP66AJvH/TImOf5YQCwojKYoDKY0FHWVlesVdrAJx6Yk9aTZdXiUSAmPUBqMSoBmkaAUWirU2JXPWFKEQAnwRNyI3dtzlaafJ+8laoebciETjZTi4OAgFVZC0m022BgJ3uOsTVINec8akyTnhrz27yc+4sEelc6aSJAJw5e0iRRSC8osTxOJFEZj/YDzlq6HGGcUxmBMsduz/lD8YMLz0JZdSgEiorRgNmv4l//yv+Ff/OKf0/d2d+Pr5h4oJDIAUYp7bRZI+J5nz86I8c/xLiVAMUSGIbUWBzvQdt0u4bHW7kBNs8mEL377W37zj//Ib7/4muVqRQyCTz/9mM8++5RPP/0JJ6dHLBYzZrP9uh9FpZLjOWSp+uziK1PFLJVCqbR4lRA0VYEukgT9ECwqZ+izpkqHhkuLdTqdMJ82PCkCB03NfDLh8+PHVM2Usm4QImD7gaHrWd7cJAPNquZxWdKUFXVZUk4mlEVFVVTMmwna6MxmE0zEwV7Xl+8GcA/ukwlNlu+HoCwNP/3pZzx90rNabfn7//qPbDZbrLUYkzyHnPNMm4qu23J9c81v/vFXOGeZzRqePT3h8ZPHPHnymOfPn1CWBq0lMoyVm0Qamdv5gdV6y9uLa96eX7G6vWE+m3J4uEgrJMZ3DvV94+nqBTZY7MTSTbskOqYFhTUoUut8cD1KJjH+obXUlBywIDqRkxoIfQ8iMT1qZUg4osi2dQQvIEhKWdLIBF0tpEELiQK6uxU9PYPoAUshDEI0OCpsCLgQKWLy8yIIXOvQUmL26IC8j8N5+PmHo6qxqzI+uyN4+WHhMnZyxkps/O+HG8X4M+9jch62pB8mYO8bjY4+XaOe1j7x3X/+n+83P53wcCK34uNYsmdwa6VAzWqsVVjb83ffXFBoQaGSuNqmG9hsO5abPosqRc5vVrtrMcBlG7kdAud//YbZr2+Z1P9I6HqGwaeutC6JSoMukIuIngR0Y9FFxailMgL99z9KMjtFgHgA+pQi4ULiOAvP99b7SPQRaz3Xl3esrq/Z3Ea6taAuptTFlH6puL655frumvU2iU1EKdjenvPR0SHHH51w9vwRUisGB85JfJT4KEhaeWn81IWOYqaYHU84nlcYBSH2IDPgdc9Yb1dECSGDbUdcjFIJL+hiQPQiWzmkCx5iws5FHzBSUeoCPKiYbCemusaJiBVJcsCIiJORXjh8niY4mwUOY0AJjQ9pnSP65FsoVGL4eItVLRu7QgiFlJrT6SlNUVGbHz83fv7JT7KsRGZXKYPQhig10qhESKkVhdIUSqOCQkiPUB5TlRCnEKfpe41KWlJliVCpqzcMw24iInXGfOUePUIShcCRVN19DAQXEgQhJzyMI1fAxoiNgcDvsip/cJXmhEcrxfNnz5FCYnSRDTpj7pwVDzCCoJVGlAKtTcbRpUJg1FiyVu469ztMk5S7zpZSOrkzjP+d3deNNtmMNH3tL/7iL5MOFh4pFWfHpzx99JTJZIrMCtN/KH5wJ3oHGJlBdSl/iclZWmvKsrpHaqv7i5djO088eIIRkOXchZDIQiamXK4ilZaYQlNUZfJE8T5XF5m7LwQff/wxs+mMp08/YujTm39yfMrp2SmnZyc0TUVZ7r/Jnjw6YDtYtoPFRpI+BoIoAiiB0En7AxI4uQ8W60LShxAR6dODFwd2D5jSmuB7XA9TIVF9WjwX8Yqi2lCUJVI6okuLVcaA0QIqg2pKiqqkruukMqkLjC4QRuJlxGPpXUjskD0Xb9qUyQttPMTu763Mlcl0qinKis9++hO2my19P3BwsGC13rDerPhf/+aXDINlu91S1Yb57IjDo0NefPSExcGc+XxG3VRolfyoNEmXIkbBYB0+pnt6dXPHcrXGDpbjk2MOFnMOF0mc0ntPDL+LF/mxUJ2BIJExzb5jAJxA9gGZfXoKVTEqmqpQgEpVVAxq54Hl7JaABxEoiwYlNFJIStETsh9ZETTSaVTUFIhsXxFgCAgMQhiE7VDCoERBUBLrPdZ7YhB5lh/wdxGtQO+hJj0m/g+TnfdZUe+vh4fVzvsA4rELFEL4nbHT+Ow87BqN8TDpephEPUyOxu7QQ/HCfcL6DhEyazOI+y4H7AgmO90SktaVcy4nbJ7BjSynBE4fPLvqmAhB5C6DELjcgbAe/NayGQJm1SOCz+zdZOoYpSNKC/YcebdE6AptkiCeyGaJu1n2HiHEfXI0esuNXaz8HbsDYgdclokFc3h0iFGe0gRuaBNYlZ5YReRUYKKmNKnrpArN7PiEZx8fcvrsKIHqY0wq90HlsZxA6pxMBpC6QBUKXSiCGDW0ZAIK/xEMn+g86DSOSySI9ONCgYgRHQUiqIzbCziV7CZC8Il/pgJBgpYalbsnPt+/QCKZOB/xMdkLiZjey+AT7ToxJe9FTqWU+OgZvMUFmzuqCqVkwhiFQG83pBLwxzt1zWy6awJUdZF15gocMtlhKCiKSFMW1EVBdBGUQ2iPKSvsUGAHTVQF0miEUVAUyVcr3/Kxg8hI2Sah5sPuNiSgt4ySKD2gCLnBMI4SA/lsjSIXfakJsU9MJml6EGPk4xefcHR4zMcvPtkVRjGyU1WOMeCyonLIZ+CoRP7QesJ7+w7uT+aR7ZjkSKV246xxTxq/Zsx919mPbD+RGLNNXTPNyc6PnRn7ZQX5JmRNpRQiJylC53mdZ/RI2jUQxh/M6PzUUrsX5xOY+8qVuNssldFZ1yDFWDnavufx4yc8e/qMn37WY63DOc9sOqOsCorScJ9Q7XdZx48OkOsNdr0l2rhrTXtiAoXp9BFD9ukJHuEcIkiEURnZH2iDJdtGomWkGyKbaDkQJTIGvHO8WnZok4QKtbSoKNFCcTRfUEuIhUJUGlVrdK0pKpMeeimJIrEefAgMQ79bMPuEEOwqx3HDf3ho7USfakPdKMqiyoj4HoBu6Gmvtvz1X/9yJ7B2dnbG02ePefHiI168eEpRmCwdPjpBC7Qqk1CYj/T9gPWOwVpu7u7YZMD66ekpRwcHzGdTgkvqyIG49yG5u8ZB7pRStSiSAWqIEHtExuI0kyJhhHKzQMnk3IyVOxDq4AQOSyDQqBmlqjDCYGRLDAPg0hnkS0Q0FDGCSEar0QmiMIBBbCNG6MR0LAp65xicJfjIgMcHcEtPqRTK7D/S+kN08ocJzUNA8sOR1O/7/p0X2oNN4mGSY8yDZ/TB+GpMNN5nZI309zGBep+i/kMRss9PSLCwVDTl7kAM969vFBcLWWQwZAr6CJiMD0ZFyuh8Vj/YBCNJQyVXm8MQ2PZJukHJVAAIoQhYgkj4C7/tGH/taFshpcxMFLn3QZJAyelFCBF2VS6we57H69kdbVIghebo5JCqjDSVx7lrhs5jhwFqiUZQaU0oI6ZI9O1nn0559HTB8eM5QY7GnQlzsmOcaQUhsc2UKVEmWa94PIqRuRkYwbj73ch0WIzYn/F9DjFzq6JABk1McqcEHXHBE3wgw50JIl2zlImN5XLnJhB2RschgKnrXYIgVSR6gYwJt5jIEGk9OzvQ2wEfbOrEINEq7U0+OHq7JaGKflyJuGyS/phSgvlittON6wP4BL2m1I7ZpGJalwyDhdxxNoVhu1E4SO+w1kit8VIlhfcYiUrfs5dUFlXcJcHjKZOFYVOzGEjTiSiSqGwYV3zW+UnrKxlc7BOTJnW6hBDMZ7N3Orv3RU98R934933Pw8+/j0F8X9T04Z4xfu19XGBieudCSsl3CuP3SRe/L8QfC2b6EB/iQ3yID/EhPsSH+N9b/HFl9If4EB/iQ3yID/EhPsT/DuNDwvMhPsSH+BAf4kN8iH/y8SHh+RAf4kN8iA/xIT7EP/n4kPB8iA/xIT7Eh/gQH+KffHxIeD7Eh/gQH+JDfIgP8U8+PiQ8H+JDfIgP8SE+xIf4Jx//P8KWo9Mq1m6EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' 5. 데이터 확인하기 (2) '''\n",
    "pltsize = 1\n",
    "plt.figure(figsize=(10 * pltsize, pltsize))\n",
    "\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i + 1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(np.transpose(X_train[i], (1, 2, 0)))\n",
    "    plt.title('Class: ' + str(y_train[i].item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 6. Multi Layer Perceptron (MLP) 모델 설계하기 '''\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.fc1 = nn.Linear(32 * 32 * 3, 512)\n",
    "        self.fc2 = nn.Linear(512, 256)\n",
    "        self.fc3 = nn.Linear(256, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 32 * 32 * 3)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.log_softmax(x, dim = 1)\n",
    "        return x "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=3072, out_features=512, bias=True)\n",
      "  (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
      "  (fc3): Linear(in_features=256, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "''' 7. Optimizer, Objective Function 설정하기 '''\n",
    "model = Net().to(DEVICE)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 8. MLP 모델 학습을 진행하며 학습 데이터에 대한 모델 성능을 확인하는 함수 정의 '''\n",
    "def train(model, train_loader, optimizer, log_interval):\n",
    "    model.train()\n",
    "    for batch_idx, (image, label) in enumerate(train_loader):\n",
    "        image = image.to(DEVICE)\n",
    "        label = label.to(DEVICE)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(image)\n",
    "        loss = criterion(output, label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch_idx % log_interval == 0:\n",
    "            print(\"Train Epoch: {} [{}/{} ({:.0f}%)]\\tTrain Loss: {:.6f}\".format(\n",
    "                epoch, batch_idx * len(image), \n",
    "                len(train_loader.dataset), 100. * batch_idx / len(train_loader), \n",
    "                loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 9. 학습되는 과정 속에서 검증 데이터에 대한 모델 성능을 확인하는 함수 정의 '''\n",
    "def evaluate(model, test_loader):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for image, label in test_loader:\n",
    "            image = image.to(DEVICE)\n",
    "            label = label.to(DEVICE)\n",
    "            output = model(image)\n",
    "            test_loss += criterion(output, label).item()\n",
    "            prediction = output.max(1, keepdim = True)[1]\n",
    "            correct += prediction.eq(label.view_as(prediction)).sum().item()\n",
    "    \n",
    "    test_loss /= (len(test_loader.dataset) / BATCH_SIZE)\n",
    "    test_accuracy = 100. * correct / len(test_loader.dataset)\n",
    "    return test_loss, test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/50000 (0%)]\tTrain Loss: 2.292372\n",
      "Train Epoch: 1 [6400/50000 (13%)]\tTrain Loss: 2.135060\n",
      "Train Epoch: 1 [12800/50000 (26%)]\tTrain Loss: 1.747329\n",
      "Train Epoch: 1 [19200/50000 (38%)]\tTrain Loss: 2.003584\n",
      "Train Epoch: 1 [25600/50000 (51%)]\tTrain Loss: 2.111284\n",
      "Train Epoch: 1 [32000/50000 (64%)]\tTrain Loss: 1.761167\n",
      "Train Epoch: 1 [38400/50000 (77%)]\tTrain Loss: 2.067584\n",
      "Train Epoch: 1 [44800/50000 (90%)]\tTrain Loss: 2.144316\n",
      "\n",
      "[EPOCH: 1], \tTest Loss: 0.0549, \tTest Accuracy: 37.01 % \n",
      "\n",
      "Train Epoch: 2 [0/50000 (0%)]\tTrain Loss: 1.973657\n",
      "Train Epoch: 2 [6400/50000 (13%)]\tTrain Loss: 1.594304\n",
      "Train Epoch: 2 [12800/50000 (26%)]\tTrain Loss: 1.373497\n",
      "Train Epoch: 2 [19200/50000 (38%)]\tTrain Loss: 1.841484\n",
      "Train Epoch: 2 [25600/50000 (51%)]\tTrain Loss: 1.726713\n",
      "Train Epoch: 2 [32000/50000 (64%)]\tTrain Loss: 1.883440\n",
      "Train Epoch: 2 [38400/50000 (77%)]\tTrain Loss: 1.489735\n",
      "Train Epoch: 2 [44800/50000 (90%)]\tTrain Loss: 1.554924\n",
      "\n",
      "[EPOCH: 2], \tTest Loss: 0.0504, \tTest Accuracy: 42.50 % \n",
      "\n",
      "Train Epoch: 3 [0/50000 (0%)]\tTrain Loss: 1.568125\n",
      "Train Epoch: 3 [6400/50000 (13%)]\tTrain Loss: 1.855520\n",
      "Train Epoch: 3 [12800/50000 (26%)]\tTrain Loss: 1.264208\n",
      "Train Epoch: 3 [19200/50000 (38%)]\tTrain Loss: 1.716537\n",
      "Train Epoch: 3 [25600/50000 (51%)]\tTrain Loss: 1.350208\n",
      "Train Epoch: 3 [32000/50000 (64%)]\tTrain Loss: 1.647580\n",
      "Train Epoch: 3 [38400/50000 (77%)]\tTrain Loss: 1.681061\n",
      "Train Epoch: 3 [44800/50000 (90%)]\tTrain Loss: 1.561477\n",
      "\n",
      "[EPOCH: 3], \tTest Loss: 0.0482, \tTest Accuracy: 44.88 % \n",
      "\n",
      "Train Epoch: 4 [0/50000 (0%)]\tTrain Loss: 1.307033\n",
      "Train Epoch: 4 [6400/50000 (13%)]\tTrain Loss: 1.481202\n",
      "Train Epoch: 4 [12800/50000 (26%)]\tTrain Loss: 1.427039\n",
      "Train Epoch: 4 [19200/50000 (38%)]\tTrain Loss: 1.324464\n",
      "Train Epoch: 4 [25600/50000 (51%)]\tTrain Loss: 1.336774\n",
      "Train Epoch: 4 [32000/50000 (64%)]\tTrain Loss: 1.628810\n",
      "Train Epoch: 4 [38400/50000 (77%)]\tTrain Loss: 1.560683\n",
      "Train Epoch: 4 [44800/50000 (90%)]\tTrain Loss: 1.074077\n",
      "\n",
      "[EPOCH: 4], \tTest Loss: 0.0484, \tTest Accuracy: 44.89 % \n",
      "\n",
      "Train Epoch: 5 [0/50000 (0%)]\tTrain Loss: 1.760947\n",
      "Train Epoch: 5 [6400/50000 (13%)]\tTrain Loss: 1.117796\n",
      "Train Epoch: 5 [12800/50000 (26%)]\tTrain Loss: 1.579174\n",
      "Train Epoch: 5 [19200/50000 (38%)]\tTrain Loss: 1.245046\n",
      "Train Epoch: 5 [25600/50000 (51%)]\tTrain Loss: 1.362765\n",
      "Train Epoch: 5 [32000/50000 (64%)]\tTrain Loss: 1.528075\n",
      "Train Epoch: 5 [38400/50000 (77%)]\tTrain Loss: 1.468138\n",
      "Train Epoch: 5 [44800/50000 (90%)]\tTrain Loss: 1.518390\n",
      "\n",
      "[EPOCH: 5], \tTest Loss: 0.0471, \tTest Accuracy: 46.65 % \n",
      "\n",
      "Train Epoch: 6 [0/50000 (0%)]\tTrain Loss: 1.512002\n",
      "Train Epoch: 6 [6400/50000 (13%)]\tTrain Loss: 1.350190\n",
      "Train Epoch: 6 [12800/50000 (26%)]\tTrain Loss: 1.655404\n",
      "Train Epoch: 6 [19200/50000 (38%)]\tTrain Loss: 1.275682\n",
      "Train Epoch: 6 [25600/50000 (51%)]\tTrain Loss: 1.298011\n",
      "Train Epoch: 6 [32000/50000 (64%)]\tTrain Loss: 1.283646\n",
      "Train Epoch: 6 [38400/50000 (77%)]\tTrain Loss: 1.749762\n",
      "Train Epoch: 6 [44800/50000 (90%)]\tTrain Loss: 1.508679\n",
      "\n",
      "[EPOCH: 6], \tTest Loss: 0.0459, \tTest Accuracy: 47.81 % \n",
      "\n",
      "Train Epoch: 7 [0/50000 (0%)]\tTrain Loss: 1.099644\n",
      "Train Epoch: 7 [6400/50000 (13%)]\tTrain Loss: 1.526307\n",
      "Train Epoch: 7 [12800/50000 (26%)]\tTrain Loss: 1.403745\n",
      "Train Epoch: 7 [19200/50000 (38%)]\tTrain Loss: 1.570230\n",
      "Train Epoch: 7 [25600/50000 (51%)]\tTrain Loss: 1.552853\n",
      "Train Epoch: 7 [32000/50000 (64%)]\tTrain Loss: 1.778810\n",
      "Train Epoch: 7 [38400/50000 (77%)]\tTrain Loss: 1.515645\n",
      "Train Epoch: 7 [44800/50000 (90%)]\tTrain Loss: 1.370822\n",
      "\n",
      "[EPOCH: 7], \tTest Loss: 0.0469, \tTest Accuracy: 47.16 % \n",
      "\n",
      "Train Epoch: 8 [0/50000 (0%)]\tTrain Loss: 1.366061\n",
      "Train Epoch: 8 [6400/50000 (13%)]\tTrain Loss: 1.514001\n",
      "Train Epoch: 8 [12800/50000 (26%)]\tTrain Loss: 1.817281\n",
      "Train Epoch: 8 [19200/50000 (38%)]\tTrain Loss: 1.500162\n",
      "Train Epoch: 8 [25600/50000 (51%)]\tTrain Loss: 1.507653\n",
      "Train Epoch: 8 [32000/50000 (64%)]\tTrain Loss: 1.124266\n",
      "Train Epoch: 8 [38400/50000 (77%)]\tTrain Loss: 1.588721\n",
      "Train Epoch: 8 [44800/50000 (90%)]\tTrain Loss: 1.486453\n",
      "\n",
      "[EPOCH: 8], \tTest Loss: 0.0467, \tTest Accuracy: 46.89 % \n",
      "\n",
      "Train Epoch: 9 [0/50000 (0%)]\tTrain Loss: 1.397540\n",
      "Train Epoch: 9 [6400/50000 (13%)]\tTrain Loss: 1.192298\n",
      "Train Epoch: 9 [12800/50000 (26%)]\tTrain Loss: 1.631072\n",
      "Train Epoch: 9 [19200/50000 (38%)]\tTrain Loss: 1.515226\n",
      "Train Epoch: 9 [25600/50000 (51%)]\tTrain Loss: 1.618453\n",
      "Train Epoch: 9 [32000/50000 (64%)]\tTrain Loss: 1.333039\n",
      "Train Epoch: 9 [38400/50000 (77%)]\tTrain Loss: 1.480763\n",
      "Train Epoch: 9 [44800/50000 (90%)]\tTrain Loss: 1.485399\n",
      "\n",
      "[EPOCH: 9], \tTest Loss: 0.0459, \tTest Accuracy: 47.65 % \n",
      "\n",
      "Train Epoch: 10 [0/50000 (0%)]\tTrain Loss: 1.629312\n",
      "Train Epoch: 10 [6400/50000 (13%)]\tTrain Loss: 1.333044\n",
      "Train Epoch: 10 [12800/50000 (26%)]\tTrain Loss: 1.130229\n",
      "Train Epoch: 10 [19200/50000 (38%)]\tTrain Loss: 1.336096\n",
      "Train Epoch: 10 [25600/50000 (51%)]\tTrain Loss: 1.388097\n",
      "Train Epoch: 10 [32000/50000 (64%)]\tTrain Loss: 1.126451\n",
      "Train Epoch: 10 [38400/50000 (77%)]\tTrain Loss: 1.638735\n",
      "Train Epoch: 10 [44800/50000 (90%)]\tTrain Loss: 1.271647\n",
      "\n",
      "[EPOCH: 10], \tTest Loss: 0.0462, \tTest Accuracy: 48.30 % \n",
      "\n"
     ]
    }
   ],
   "source": [
    "''' 10. MLP 학습 실행하며 Train, Test set의 Loss 및 Test set Accuracy 확인하기 '''\n",
    "for epoch in range(1, EPOCHS + 1):\n",
    "    train(model, train_loader, optimizer, log_interval = 200)\n",
    "    test_loss, test_accuracy = evaluate(model, test_loader)\n",
    "    print(\"\\n[EPOCH: {}], \\tTest Loss: {:.4f}, \\tTest Accuracy: {:.2f} % \\n\".format(\n",
    "        epoch, test_loss, test_accuracy))"
   ]
  }
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