LeNet-5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LeNet-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 우선, MNIST dataset에 적용할 transformation 객체를 생성합니다.\n",
    "from torchvision import transforms\n",
    "import torch\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Training 데이터셋 준비"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "변형 없는 transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformation 정의하기\n",
    "data_transform = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST training dataset 불러오기\n",
    "from torchvision import datasets\n",
    "\n",
    "# 데이터를 저장할 경로 설정\n",
    "path2data = './data'\n",
    "\n",
    "# training data 불러오기\n",
    "train_data = datasets.MNIST(path2data, train=True, download=True, transform=data_transform)\n",
    "\n",
    "# MNIST test dataset 불러오기\n",
    "test_data = datasets.MNIST(path2data, train=False, download=True, transform=data_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training dataset : 60000  \n",
    "Test dataset : 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data loader 를 생성합니다.\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_dl = DataLoader(train_data, batch_size=32, shuffle=True)\n",
    "test_dl = DataLoader(test_data, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample 이미지 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample images를 확인합니다.\n",
    "from torchvision import utils\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# training data를 추출합니다.\n",
    "x_train, y_train = train_data.data, train_data.targets\n",
    "\n",
    "# val data를 추출합니다.\n",
    "x_test, y_test = test_data.data, test_data.targets\n",
    "\n",
    "# 차원을 추가하여 B*C*H*W 가 되도록 합니다.\n",
    "if len(x_train.shape) == 3:\n",
    "    x_train = x_train.unsqueeze(1)\n",
    "\n",
    "if len(x_test.shape) == 3:\n",
    "    x_test = x_test.unsqueeze(1)\n",
    "\n",
    "# tensor를 image로 변경하는 함수를 정의합니다.\n",
    "def show(img):\n",
    "    # tensor를 numpy array로 변경합니다.\n",
    "    npimg = img.numpy()\n",
    "    # C*H*W를 H*W*C로 변경합니다.\n",
    "    npimg_tr = npimg.transpose((1,2,0))\n",
    "    plt.imshow(npimg_tr, interpolation='nearest')\n",
    "\n",
    "# images grid를 생성하고 출력합니다.\n",
    "# 총 40개 이미지, 행당 8개 이미지를 출력합니다.\n",
    "x_grid = utils.make_grid(x_train[:40], nrow=8, padding=2)\n",
    "\n",
    "show(x_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. LeNet-5 모델 정의"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LeNet_5(\n",
      "  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv3): Conv2d(16, 120, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=120, out_features=84, bias=True)\n",
      "  (fc2): Linear(in_features=84, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class LeNet_5(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet_5,self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)\n",
    "        self.conv3 = nn.Conv2d(16, 120, kernel_size=5, stride=1)\n",
    "        self.fc1 = nn.Linear(120, 84)\n",
    "        self.fc2 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.tanh(self.conv1(x))\n",
    "        x = F.avg_pool2d(x, 2, 2)\n",
    "        x = F.tanh(self.conv2(x))\n",
    "        x = F.avg_pool2d(x, 2, 2)\n",
    "        x = F.tanh(self.conv3(x))\n",
    "        x = x.view(-1, 120)\n",
    "        x = F.tanh(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "model = LeNet_5()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "model.to(device)\n",
    "print(next(model.parameters()).device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1            [-1, 6, 28, 28]             156\n",
      "            Conv2d-2           [-1, 16, 10, 10]           2,416\n",
      "            Conv2d-3            [-1, 120, 1, 1]          48,120\n",
      "            Linear-4                   [-1, 84]          10,164\n",
      "            Linear-5                   [-1, 10]             850\n",
      "================================================================\n",
      "Total params: 61,706\n",
      "Trainable params: 61,706\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.05\n",
      "Params size (MB): 0.24\n",
      "Estimated Total Size (MB): 0.29\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 모델 summary를 확인합니다.\n",
    "from torchsummary import summary\n",
    "summary(model, input_size=(1, 28, 28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 하이퍼파라미터 설정 및 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 배치당 performance metric 을 계산하는 함수 정의\n",
    "def metrics_batch(output, target):\n",
    "    pred = output.argmax(dim=1, keepdim=True)\n",
    "    corrects = pred.eq(target.view_as(pred)).sum().item()\n",
    "    return corrects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 배치당 loss를 계산하는 함수를 정의\n",
    "def loss_batch(loss_func, output, target, opt=None):\n",
    "    loss = loss_func(output, target)\n",
    "    metric_b = metrics_batch(output, target)\n",
    "    if opt is not None:\n",
    "        opt.zero_grad()\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "    return loss.item(), metric_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# epoch당 loss와 performance metric을 계산하는 함수 정의\n",
    "def loss_epoch(model, loss_func, dataset_dl, sanity_check=False, opt=None):\n",
    "    running_loss = 0.0\n",
    "    running_metric = 0.0\n",
    "    len_data = len(dataset_dl.dataset)\n",
    "\n",
    "    for xb, yb in dataset_dl:\n",
    "        xb = xb.type(torch.float).to(device)\n",
    "        yb = yb.to(device)\n",
    "        output = model(xb)\n",
    "        loss_b, metric_b = loss_batch(loss_func, output, yb, opt) # 배치당 loss를 계산\n",
    "        running_loss += loss_b\n",
    "\n",
    "        if metric_b is not None:\n",
    "            running_metric += metric_b\n",
    "        \n",
    "        if sanity_check is True: # sanity_check가 True이면 1epoch만 학습합니다.\n",
    "            break\n",
    "\n",
    "    loss = running_loss / float(len_data)\n",
    "    metric = running_metric / float(len_data)\n",
    "    return loss, metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss function 정의합니다.\n",
    "loss_func = nn.CrossEntropyLoss(reduction='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimizer 정의합니다.\n",
    "from torch import optim\n",
    "opt = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 현재 lr을 계산하는 함수를 정의합니다.\n",
    "def get_lr(opt):\n",
    "    for param_group in opt.param_groups:\n",
    "        return param_group['lr']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "# train_val 함수 정의\n",
    "def train_val(model, params):\n",
    "    num_epochs = params['num_epochs']\n",
    "    loss_func = params['loss_func']\n",
    "    opt = params['optimizer']\n",
    "    train_dl = params['train_dl']\n",
    "    sanity_check = params['sanity_check']\n",
    "\n",
    "    loss_history = {\n",
    "        'train': [],\n",
    "        'val': [],\n",
    "    }\n",
    "\n",
    "    metric_history = {\n",
    "        'train': [],\n",
    "        'val': [],\n",
    "    }\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        current_lr = get_lr(opt)\n",
    "        print('Epoch {}/{}, current lr={}'.format(epoch, num_epochs-1, current_lr))\n",
    "        epochStartTime = time.time()\n",
    "        model.train()\n",
    "        # epoch당 loss와 performance metric을 계산\n",
    "        train_loss, train_metric = loss_epoch(model, loss_func, train_dl, sanity_check, opt)\n",
    "\n",
    "        # training 도중 기록한 loss와 accuracy를 기록 (향후 시각화)\n",
    "        loss_history['train'].append(train_loss)\n",
    "        metric_history['train'].append(train_metric)\n",
    "\n",
    "        model.eval()\n",
    "        epoch_calculation_time = time.time() - epochStartTime\n",
    "\n",
    "        print('train loss: %.6f, calculation time: %0.3f sec'%(train_loss, epoch_calculation_time))\n",
    "        print('-'*10)\n",
    "    return model, loss_history, metric_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/29, current lr=0.001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss: 0.238310, calculation time: 16.159 sec\n",
      "----------\n",
      "Epoch 1/29, current lr=0.001\n",
      "train loss: 0.076883, calculation time: 15.384 sec\n",
      "----------\n",
      "Epoch 2/29, current lr=0.001\n",
      "train loss: 0.054729, calculation time: 16.448 sec\n",
      "----------\n",
      "Epoch 3/29, current lr=0.001\n",
      "train loss: 0.041404, calculation time: 16.063 sec\n",
      "----------\n",
      "Epoch 4/29, current lr=0.001\n",
      "train loss: 0.032387, calculation time: 16.147 sec\n",
      "----------\n",
      "Epoch 5/29, current lr=0.001\n",
      "train loss: 0.027765, calculation time: 16.306 sec\n",
      "----------\n",
      "Epoch 6/29, current lr=0.001\n",
      "train loss: 0.024746, calculation time: 16.290 sec\n",
      "----------\n",
      "Epoch 7/29, current lr=0.001\n",
      "train loss: 0.022053, calculation time: 16.277 sec\n",
      "----------\n",
      "Epoch 8/29, current lr=0.001\n",
      "train loss: 0.017460, calculation time: 16.585 sec\n",
      "----------\n",
      "Epoch 9/29, current lr=0.001\n",
      "train loss: 0.017097, calculation time: 16.416 sec\n",
      "----------\n",
      "Epoch 10/29, current lr=0.001\n",
      "train loss: 0.014561, calculation time: 16.339 sec\n",
      "----------\n",
      "Epoch 11/29, current lr=0.001\n",
      "train loss: 0.013726, calculation time: 16.430 sec\n",
      "----------\n",
      "Epoch 12/29, current lr=0.001\n",
      "train loss: 0.011881, calculation time: 16.448 sec\n",
      "----------\n",
      "Epoch 13/29, current lr=0.001\n",
      "train loss: 0.011633, calculation time: 16.420 sec\n",
      "----------\n",
      "Epoch 14/29, current lr=0.001\n",
      "train loss: 0.011214, calculation time: 16.258 sec\n",
      "----------\n",
      "Epoch 15/29, current lr=0.001\n",
      "train loss: 0.008989, calculation time: 15.970 sec\n",
      "----------\n",
      "Epoch 16/29, current lr=0.001\n",
      "train loss: 0.008869, calculation time: 16.185 sec\n",
      "----------\n",
      "Epoch 17/29, current lr=0.001\n",
      "train loss: 0.008563, calculation time: 16.439 sec\n",
      "----------\n",
      "Epoch 18/29, current lr=0.001\n",
      "train loss: 0.008516, calculation time: 16.387 sec\n",
      "----------\n",
      "Epoch 19/29, current lr=0.001\n",
      "train loss: 0.006193, calculation time: 16.433 sec\n",
      "----------\n",
      "Epoch 20/29, current lr=0.001\n",
      "train loss: 0.009101, calculation time: 16.424 sec\n",
      "----------\n",
      "Epoch 21/29, current lr=0.001\n",
      "train loss: 0.006818, calculation time: 16.407 sec\n",
      "----------\n",
      "Epoch 22/29, current lr=0.001\n",
      "train loss: 0.007525, calculation time: 16.299 sec\n",
      "----------\n",
      "Epoch 23/29, current lr=0.001\n",
      "train loss: 0.006584, calculation time: 16.483 sec\n",
      "----------\n",
      "Epoch 24/29, current lr=0.001\n",
      "train loss: 0.006065, calculation time: 16.901 sec\n",
      "----------\n",
      "Epoch 25/29, current lr=0.001\n",
      "train loss: 0.007012, calculation time: 16.944 sec\n",
      "----------\n",
      "Epoch 26/29, current lr=0.001\n",
      "train loss: 0.006389, calculation time: 16.961 sec\n",
      "----------\n",
      "Epoch 27/29, current lr=0.001\n",
      "train loss: 0.005735, calculation time: 16.977 sec\n",
      "----------\n",
      "Epoch 28/29, current lr=0.001\n",
      "train loss: 0.004949, calculation time: 16.932 sec\n",
      "----------\n",
      "Epoch 29/29, current lr=0.001\n",
      "train loss: 0.004800, calculation time: 16.150 sec\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "params_train = dict()\n",
    "\n",
    "params_train['num_epochs'] = 30\n",
    "params_train['loss_func'] = loss_func\n",
    "params_train['optimizer'] = opt\n",
    "params_train['train_dl'] = train_dl\n",
    "params_train['sanity_check'] = False\n",
    "#params_train['lr_scheduler'] = lr_scheduler\n",
    "params_train['lr'] = 0.001\n",
    "\n",
    "\n",
    "# 모델을 학습합니다.\n",
    "model,loss_hist,metric_hist=train_val(model,params_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loss와 Accuracy 그래프 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_epochs=params_train[\"num_epochs\"]\n",
    "\n",
    "plt.title(\"Train Loss\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"train\"],label=\"train\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# plot accuracy progress\n",
    "plt.title(\"Train Accuracy\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"train\"],label=\"train\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위에서 학습시킨 모델 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path\n",
    "if os.path.isfile(f'./model/epochs-{num_epochs}/LeNet-5_b{32}.pth') == False:\n",
    "    torch.save(model, f'./model/epochs-{num_epochs}/LeNet-5_b{32}.pth')\n",
    "else:\n",
    "    print('Cannot save into file. The file already exists..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Test 데이터셋 준비 및 테스트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "저장된 모델 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeNet_5(\n",
       "  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (conv3): Conv2d(16, 120, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (fc2): Linear(in_features=84, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "model = torch.load('./model/epochs-30/LeNet-5_b32.pth')\n",
    "model.to(device)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도 측정 함수 정의"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_accuracy(loader, model):\n",
    "    \n",
    "    correct_list=[]\n",
    "    wrong_list=[]\n",
    "\n",
    "    total = 0\n",
    "    correct = 0  # 정답 개수를 기록하기 위한 변수\n",
    "\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "            infer = model(inputs)\n",
    "            predicted = infer.argmax(dim=1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "            # 올바르게 분류된 샘플과 잘못 분류된 샘플을 구분하여 저장\n",
    "            for i in range(len(predicted)):\n",
    "                if predicted[i] == labels[i]:\n",
    "                    correct_list.append((inputs[i], labels[i], predicted[i]))\n",
    "                else:\n",
    "                    wrong_list.append((inputs[i], labels[i], predicted[i]))\n",
    "\n",
    "    # 정확도 출력\n",
    "    accuracy = 100 * correct / total\n",
    "    print(f'Accuracy on the test set: {accuracy:.3f}%')\n",
    "\n",
    "    # 옳게 분류된 샘플들과 잘못된 분류된 샘플들을 반환\n",
    "    return correct_list, wrong_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TEST1 : 변형이 없는 테스트 데이터셋을 사용하여 테스트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "테스트 데이터셋 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST training dataset 불러오기\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# 데이터를 저장할 경로 설정\n",
    "path2data = './data'\n",
    "\n",
    "# transformation 정의하기\n",
    "data_transform = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# MNIST test dataset 불러오기\n",
    "test_data = datasets.MNIST(path2data, train=False, download=True, transform=data_transform)\n",
    "test_dl = DataLoader(test_data, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test set: 98.750%\n"
     ]
    }
   ],
   "source": [
    "correct_samples, wrong_samples = check_accuracy(test_dl, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "틀리게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrongly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 잘못 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Wrongly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(wrong_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "맞게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correctly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 정확하게 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Correctly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(correct_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TEST1 : 표준편차 0.4 가우시안 노이즈를 적용한 테스트 데이터셋을 사용하여 테스트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가우시안 노이즈를 적용하기 위한 클래스를 정의 (랜덤 시드를 지정함)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 가우시안 노이즈를 적용하기 위한 클래스를 정의 (랜덤 시드를 지정함)\n",
    "class AddGaussianNoise(object):\n",
    "    def __init__(self, mean=0., std=1.):\n",
    "        self.std = std\n",
    "        self.mean = mean\n",
    "        \n",
    "    def __call__(self, tensor):\n",
    "        torch.manual_seed(0)\n",
    "        return tensor + torch.randn(tensor.size()) * self.std + self.mean\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가우시안 노이즈를 추가하고 노이즈를 추가한 Sample 이미지 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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aOELW5q+3aSQS+jVZvSNkbaRISa4IWRspUiFX0jdxoqFfk9U7EfCskXdA5cuXWMuKkLWJ753oWNkIWVvAipC14SNZvdlDywF7P1+9+OKLmDZtWoSszU8FRdbaiHh5y5TfeW/evDmhaiJ69+7d26lNtWrVkJaWFto7KyvLeq39anLGGWdIj/cLL7wgXT4lm5iP8847z7Xu7NmzrWBlWdhA1n711VfKfbWNw1XlTWR+H7GXN1He9do7d+605svLVh14v3ihdpn3okWLYo7vL7/8EpOHGCndxCqFHUCiDh8+jDlz5sSso3u1jhfrCpBfBmgDz8r//eyzz+KVV16x5i27jfD333+X1sn0GFx22WXS+QWJlTUdOzNmzHB5T506Vbl+oW9iFWGBV7Vq1ZxGAPIOWJs2bZQfX2TeumKY3FNPPTWQN5s2b9486f6aesvqwq4wEl8c6tev7+sdZuCK+xim4cQImrdOTmFr4rXvzNsLX1vom1j26slgAEGKyofXK7Pot27dupg8db29lhf9wqB2mZ9q+imnnBIaz8qLv/2RSbzn2tRbdstnUKws05EjR1zTq1SpYi1vv+MbIWsVwb57iNMZ5pRXkIMiu0soIyND+rgY04PM0K9Tp05V4nGJgn28Y3fVqGoSxlsExzdt2lR67IKgX71wuF418fNm4j/a8tMBoFatWtL6m+Bw+fWD5F3omxiIbRTZNB79yjR58mQQ6SNrieSYU977lltucfxnzJih5f3QQw8563z//fee+37++eeDyBv9qsobcDeRWBMez2py4z6PrPVS0aJFA3kzbK3sE0YYrCxT3bp1lU0sI4Wa5C02MZ83O5EWIWvhxrMy9nP79u2daTz6lReRGbJWhTnlsbKifxBvAJ4PD2My9QbyaJCyZyUB7o+MKm8/ZK24/0z876QmyFpZY6m2xY6nrre4PsPKAm5krWw7Xt6yF4u5c+c6f8seYBAhayVFW7dunfM3Q6Tw8gK8eYUMRcr+L74jhfVmGJ5//etfrucZBfG/8847fes6b968wDVhGF9Wk5ycHK1cddGvsn3/+++/nb8//PBDY+/XXnvN5adC1sr2QTfvs846S1prfnz65Z0STSxiZZ977jlfD92TVjrIWqI89GvDhg0df5FSaeL95ZdfKpG1Dz/8sGs//LwZEK5Pnz7OOv/9739d3jp+Xt6ymjRr1szx3Lp1q/YLgh8Ol3nLkLVBvJlUyNpt27YBiD35pZs37ymTTk1Soon54N9hS5QogVdffRUXXXSR01RA3nczhiI1CdW7d7t27Vz5ZWVlgcgcz0rkBsz17ds35sWA5f3ee+85y4XJe8KECY5PUKysbDrTfffd58o7qDd7fKy4zNChQ51tBfX+6KOPXMfvmmuuQa9evZy/H3/8cevIWv7Y+XmnXBN7IVRZ8VTrBsWcjh071smtUaNGzvyg6Fem9evX+w4CAKGwslOnTo2pSVhkrdczscJ4M8l+nvvggw+seIsKkrcYsjHI+/t5p1QT+33PZRKnB0XWqg5KWG9e4oUG4sdp2TZNgj3SRTzjHgZZ65dXUG9e//3vf53/f/bZZ6FqoNrG7NmzA3moxorXT4ZekVJN7BW///67sz4/Pewlhvv27ZP62oglS5Z41kHHwwsry18auXz5cuP8vLCyYeuhyps91hUA3nvvvZj5QZG1LIoWLer4HzhwIJQ3/zA4pnLlyknXi5C1wk7LkLX9+/d31reFfhVFZAdZy+dN5D6zfM8991j1ZhIHrEmoULs2cbh33HGHk/fkyZMT3lsl3lt3rBT6JlaF+MrGSzW4wxyosAe9oIIp6E9u+VWPRMfKirF161ajWkTIWo0DwsvGQPrmm29cfomElY2ntxf6VaxvImNlI2RtAStC1hact2kkEvo1Wb0jZG2kSEmuCFkbKVIhV9I3caKhX5PVOxHwrJF3QOXLl1jLipC1ie+d6FjZCFlbwIqQteEj8o6fd8Ija5cuXYqLL77Y+V3x9ddfd80/duwYxowZg0qVKqFEiRI4//zzXaxgANi1axeuuuoqlClTBllZWejbty/279+vnYMJshYABg0aZO1Ay1CkKtnwjlfeQfLz8/aSjbyZhg4dGrdjGc96B62DXxMbfyf+448/qFGjRjRt2jTp/AcffJAmT55Mjz/+OK1YsYJKly5NHTt2pEOHDjnL9OzZk7766itatGgR/d///R998MEH1L9/f9NUlPrzzz9df9eqVYuaNWvm/H366adb8a5Tpw6lp6tLCMMT/2Le6enpccm7fPnyREQub6K8fE1zFr1VCuKr8n7kkUcCecm8jxw54uw3H40aNQrt7acwx9Mlzxb3EZH7nfjYsWOoVKmS6/a2PXv2oHjx4pgxYwYA4OuvvwYR4bPPPnOWWbBgAdLS0vDLL79obdcUWdulSxdn2scff2zlldakRkEj3ghV1fSg3n66/PLLQ+XN3x5ooyZ+uu2226zU26vmOt5x/U5M5G7iH374AUSE1atXu5Y799xzMWTIEADAM888g+zsbNf8I0eOoEiRIi7SAq9Dhw5h7969TmzZssXZQRVh4bbbbgMABwXKrlmdPn26s0xQ9KuoP//807NGJt5eA4zdzB8GWVukSBHHT7xG2C9nnRDv1NGth260bt1a6hMEK+unTz/9NLC3GPfcc0/gvPO1iT/66CMQEX799VfXcj169MDll18OALj33ntx6qmnxniVL18ejz32mHQ7Y8eOVe6g6t0KAHbu3BkzfcCAASAKhn71qgMf/I3qsvmit0wiTHzXrl0AgIULF4ZG1gLH+diyeUEHqmqf/WoRZBv8tKDIWp19sO19/fXXu6YVOLKWKH+a2Oud2Ktg4rSMjAwAwPz5840PwCeffOJZBxmKlJ+vO3BUefP8J6JgH++uvfZaAMCyZctc3izvMN48VlasyZdffqncPxP0K18rG8ha3lt1XG14i7knFLJWbOJ4fZwW5YesveOOO2Ke7sDwrACQnp7uIinqoF9vuukmaS4jR450oV+Zd926dZ1lduzY4ekNAKNGjULv3r09cbh83YMia/l5vPf06dO1vGXBY2UBoG3btq75jRs3dtUsiLesBsw7KLJWhpVlysrKsuYtyz1hkLViE7MTWzxHee/evdITWytXrnSWeeeddwKf2ALceNYOHTo4ubEQkbVE5FAldZG1qv0XkbXMm9ekSZM8vYnMcLhsvi6y9pprrnHls3TpUixcuBBTpkxxvBcvXqzl7YesZXmz+TJUcBBkLYtVq1Y5Pny92XwTZK24jEz8fda63rLgcbiyp0/mK7J2//79WL16NVavXg0iwsSJE7F69Wps2rQJAHD//fcjOzsb8+bNwxdffIEuXbqgdu3aOHjwoOPRqVMnnHnmmVixYgWWLVuGOnXqxDyZwUs6yFpnB8mNlWXTiEhJWfDzBIAiRYrEeKsg6rrb0MXh6vqxqFatmnZtTb2J3NAClndOTo6Tt+yYsNBFv/I1YeKJoLII4t29e3etmgTx5jG+Jnlbb2IZEoXoOBKVXexRsWJFFC9eHOeffz6+/fZbl8euXbtw5ZVX4oQTTkBmZiauvfbaUBd7iOhXHs/qJ78ByrxFichalVRP8JPl7YXDZds4dOiQVmOJCFUd6XKnRe+7777b8eCRtTKsrF/NvdCvKqysbuh483nzXGtb3kFyT6nLLom88axsABCRMbKW/yRhIl1/v7xZ7jaxsnxNwnr77S9Tx44dQ+ctbstmTWSNY+rtFbI6RchaIeKFrP3hhx+M83zttde0vHXyFnMPi5WVIVTDeDMNHz5ceoMFk428xXxt12T79u2haiIG7y3LPULWcqHDierbt2/MNJ2zpTNnztTOz9RbJ+9TTjkFvXv3NvbWiaB5szjjjDNiajBu3Di88MILLiY3+8gZNm+mm266ycr+y7xlNdEN1fHkHwxg4pdSTcyiadOmMdOqVq0KIsLGjRtBlPckQtm6d955p9J34MCBvrkF9fbLO17eZ599tpN7UG/V84ZktTENWd5Me/bsQW5urjJvU29RCxYssOZNRHjwwQcd79zcXG2vQt/EushaouOXXgJ6A8oL/cqrU6dOuOaaa0AUH2TtxIkTAdhD7fLe4kPFgoQKWcsrrDePlQ3ryXvLFE8cLtPcuXMjZK3fjntddQQgptiJHEyJ7n/DDTe4rs4iMj+BGOR48mF6PHn5/WxkA4c7ePBgab0jZK3mwU4k9KuJN1Mi5F1YsLLMm13nbtPbKyJk7T9KJWQtu4SVvy2uIPM2jURCvyard4SsjRQpyRUhayNFKuRK+iZONPRrsnonAp418g6ouH+BjYMiZG3ieyc6VjZC1hawImRt+Ii84+ed8MjaRJAOslZU165drbCSRDzrs88+67tORkaGkXc8QoZ+9VJQ73jmzeceT28b21DVBAC2bdtm5JWSTewlIj20il/MmzfP8eQvEhC9GfxAx5PPkSiPEsJ7y/bFNETxrK2w3nzeNppMp0a2fW3VQFaTW265xeWvOw5Trol5XC4To1YAeagg2wddtUyFChV8l+FDxRhTKexAtdEkPBCAf2azalsFiX6VxUknnRSTp2wbYfMuXbp04JoU+iaWXSoHxB5kJv5OoDDoV6aZM2dKr7Vl3ky63j169NCug0neTZs2ddbr0KEDiEiat5+3ankdeSFowjZx0K9KTAxtzKaxG2XCeHvVatOmTdrefk1clJJc69evj5mWlpamXP6FF14gIqKKFStSkSJFjL1FNWzYkA4ePOiaJnrL8lF5z549W5k/uOtyvPZR9FatJ+Zt+tMU7+uXEwDat2+fkb9Mu3fvprJlyxIAZ3s6x1IlVc61atUK7e2lmjVrEhHZ8TZ6KU0Q6T4BgojQpEkT17oiLjRIXHHFFY4fmxYGWcuH7A4k5s0ryMc7kUIp5t22bVvtnPmPzn55T5o0yVkuLPqVoY9lx9IGVpYdN9veoky8C/3HaSB2wDGmkXirGZsfBFnLe4t+MmStXxOrvFXIWlFBkLVeOFxTrCy/bzJv1bJBcbjs77lz57r84oGV5Wtiy5vXmDFjEgdZW1DyQtaKDSTTmDFjjJG1RG5sLPsOpULWsvUPHz6s5a1C1orvwHPnznXm6+bth8Pt16+f4+/nrYN+5ZG1TDz5MQyytnbt2o7n3Xff7ao3O55BvfkahMHhyoIXD+/3806JJpZF165dY9bhGxBAoJMVl1xyibM+myZiZdn/L7roItdyOuGFZwWAWbNmGefs5S0OMJM7pfy8c3JyXMQP1bphkLU8m8qGN5+rbRwuL1PvlGhiXfTrjz/+6PJp3ry57wBl3m3atHGtW7FiRdSqVctZTvxIxJ7gp+PtlTcvk8ZSIVS9ULs2vXk8qw1vvrGC1MPPm+nxxx93oXb58Nqml/fs2bMDH8eUaWI+dNGvTLqFDCpdfzFvlY8NPGs8vf1qZsM7XjXhvwYQkQv2DgBNmjQxbkAi9wnDCRMmxMyPkLVC6KBfd+/e7Xjx071QpF7ivzOOGTPGNU/HW5Y3LxmozgShyvLwEr/8zJkzHdxuEPSr12/diYisBfJ+t5VhfIPkLUbfvn2VtdbJO6WaWAf9Kh4gIj2EKvsOrboTReUfBFnL65NPPlGup3uW10TsWUdLliwxGqh+2yM6/tgcW8jaMB6iHxGhUaNG0rxNQzYOw3imVBOzkOFC27VrhwsuuCDGS1wuDFaWxdq1a6U3Peh68xK9g2BlZTp48CCqVq0a89hRWV1Ma8KrQYMGVrGyvD8RhfZW6dZbb40bDpflHTXxPzuowrN+//33nh46xTNBv7JLGMNgZRs0aODKccOGDVa8GfKW7bcs7zAhq4lJnXW844Gsfe+992LGxPz58+OCrL3vvvti8o6QtT477iXVACzo8Moz2YLd5WXLT7yqCYD0t1kbWFlVFJR3yjVxsiNrmdi7cKLlrYuVZfLzK2hkbTy8vSJC1v6jVELWJpq3aSQS+jVZvSNkbaRISa4IWRspUiFX0jdxoqFfk9U7EfCskXdA5cuXWMuKkLWJ753oWNn8QtZu27bNNXaDeBf6E1t8JCtWNkLWFi5v/ngCwNtvv+0av6Z+KdXEYvznP/9x/n/55ZfHXOBORJ43s+t6E5HUO2jklzejL95zzz1WvEXZzJuPUqVKKb2DHk+munXrWvV+/PHHXccRkLPGvLxTuolZpKenAzj+CmgDWct7eyFrgw4mmTc/z1benTp1suIL5N1EwMtWjWX1kSnM7+nxzptRPM4880xnOxGy1qCADBeaH4OKnxeEg8X7lC5dWjovNzfXCkJVts0weav8wtaERVpaGoA8+HqDBg3Qp08fLF26FL169cKiRYtCHWPVMbSRt+pY6noX+ib2IyzIisbCBrJW1MqVK0N5A8Cbb77p/M1fx8vk99QJ3ZowbwDOrXk28aym9faLRx991KmBzPvSSy8Nla+Xt42aiNubM2eOFWRt0v/EpIOVZcrJyXH+HwRZO3bsWELeCx8VKVKE0tLSXDFq1Cj6/fffQ+Nw+/Xr5/yfYWXBXZMzYcKEwN68KlSo4PiWLVvWGp71/vvvj5lmw3vz5s1ERDR69Gip9xtvvBHYu169es7/+VrHC1lLRHTZZZdFyFqTAIC33norNLL2kUceARD7ap2RkeHkx6aF/VjKT2PatWtXYG/xmlymJ5980jU9rDeTDOMbBv3K9O2331r3Fut83333WfXmx8ntt9/uGisRshZqZK04YLt16wai8MhaFfr16NGjAODCnJp4M6kaGICzbRvIWn57Ip41DFZW3A9b3irFAyu7bNky6zhcFs8884yrRhGyFnp4VgCoU6dODIrUBFnrh34F4PImMsPKzpgxA8BxEqdIuwTU6FfTvIHjjxKR4VnDIGtlCous5c/qsmnjxo0DAEyZMsWZFgZZy+NwZccyLLJWrA8/P0LWCiHeGsaa4cYbb/TFhfqFCv3KFNab6dxzz43Zbx72Fybvl19+2cn11FNPxYcffojrrrsOv//+u7Ot/fv3h6qJ+HA41bq66FcmhsMlOg7y/+yzz0J5i+PEVt6XXnppjB97AN2BAwe0vVOiiXXRr0F+R8wPrCzv3bNnT+V+B/EWEaomUn3v00XWdu/e3fHSJUWqvCtXruxA5Jo1a+bCyurWxgSHa1pzmTdT2HGSEk3MhxfmlP+bYU5Nwg+hKsomnpXlHwTPqlLfvn2lNbGFrN25c6ezLfYkxiDe/fv3jzl+4r7ZzJtXkJqoxtzUqVNjjqeOd8o1sRfm1K/oYRGqzZo1w8MPP+z8bRPPKuZviqxlatmypRTjG9RblbcI6g/jzSTLGwCKFi1qLW+xXkHy5nOrVKkSli5dGuPH/x0ha7nwe3VVzQuClRW9+efZ2vZmCuLt5xGmJrI4/fTTY46XyI8y8WYntPi7i8aNGxfTCLaCnSxj+u2334w92PGUiS1ToUIFfPzxxwCAqVOnevqlVBOzUGFO+b+DoF8LypspqDeQdyeNOJ3H4XrhWXXz3rhxY8yx8vP2Cx4ry6Y9+OCDrm3YxOGK3uKxNfUGgBkzZoCIXM3KkLVML7/8stKr0DexCivLbge78cYb0atXLxDZwcry7ypM8fAW8axr16617s3nHSaYt6h4erO62/DmcbiTJ09G5cqV8f7774faB5U37xcha312PJ4YUhZMYQeTzjbivZ1ED3bb5OTJk60fzwhZWwBKFGQtUzy8xe0cPXo0Lt7xQtbqekfIWm9vohRo4ghZm7/eppFI6Ndk9Y6QtZEiJbkiZG2kSIVcSd/EiYZ+TVbvRMCzRt4BlS9fYi0rQtYmvneiY2XzC1lrw7vQn9jiI1mxsrreAHD99deH3p6tvAuTN4C4IGtt5J1STSyGDvo1GZC1PAni9ttvt+odr7y9sLJho1SpUq7xEPZ48j7xwOHq1CRC1iqiV69ezjoy9CtgfgGFuJ4MK7tw4UIrgzU9Pd11KWPHjh0D+QwdOhSvvfaasp62m4zhWfngX4hMt928efO45LxkyRKXnyxvG3H33XeHqn3KNnH9+vVd68gaUZyuE+J6KqwskR3MqSpXXW8TmXp75c3/Puq1rSD7oLrP2TRvMR/xWIbx5uO3336L2ZbJOCn0TeyFfuUlQ78C3oNJ5i1bT+b9//7f/zP29toPfpoJDtdEOt5+cckll/huwzQA+OZlmrdJXmFrItvO2rVrtfaLKAWaWPXqyUv8GMrLq3gyb7/1mFTIGL+8+XjooYditlWxYkVf0qKONxFh+fLlrlroeJs0Bn+JYRhvAHj11VeV84N464yBBx54AAsXLnSOZRCQhNe2dMYgUQo0sc5gUk3n55keEHE6Q6jy89977z1jfxHPesYZZ2jhWU28/eoU1Fv0soGVZY+GEX2GDBkCAK47gUy8vcaAl3Tzlh3LL774wlUTpghZC7ODIIrIDFnLr8eCIWv5+YyxFBQry8R786jdIMjaefPmaQ3SIFhZprVr18bMD4OsFest0y+//OJ4m/p6HWO/OvnVRDyWYk3Yo2fyFVl73333oVmzZjjhhBNQvnx5dOnSBd98841rmYMHD2LQoEEoW7YsSpcuje7du8c8o3XTpk248MILUbJkSZQvXx4jRozAkSNHtPPwQtaaiMgcWcuvS+RG1i5btizGPwhWltfw4cNdB/HgwYPOcibe69evN6qNKbIWAJ566inpMSBS43D58PJmPvxPNDwqGABat26tjZUV82PeXnmPGDEiZh2Zt9+LBcv7ySefdKbnG7K2Y8eOmD59Or788kusWbMGF154IWrUqIEDBw44ywwYMADVq1fH4sWLsXLlSrRq1QpnnXWWM//o0aOoX78+2rdvj9WrV2P+/PkoV66cFBeqktfHaR776ifdV22vg6JS9+7djb1FPGu88t6zZ4/Lp3r16njllVdcy7CGNPU2zTkIVpZH1oo56HrLcuPH4JYtWzzXM0XtAojJu1KlStLa5CuydseOHSAiBwS2Z88eFCtWDLNnz3aWWb9+PYiOn0SZP38+0tPTXe/Oubm5yMzMxOHDh7W264es1ZXOABW9xZ8LbHqLWFk2neFZmRYsWKDtzYBwDzzwAMqUKYOXX37ZdUaUR78OGzZMax9Eb1H9+vXD6NGjcd999wWuiRdWVkTWEpHzRtKyZUstbzEvXawscHzc6+TNe4l5Z2dna9Ulrk28YcMGEBHWrVsHAFi8eDGIyAUhB4AaNWpg4sSJAIAxY8agUaNGrvk//vgjiAiff/65dDuHDh3C3r17nRBfJWVM4+zsbLRo0ULZ3I8++qjWoJJ5P/3009I8hw4d6vw/qDfLs2HDhtLpYb3FmrBgH/H+/e9/+25H5g2of1azkXd2drZn3nfddZfvNnhvht8Rc6tTp450Or8vTz/9tNGxlPmcd955eO+997TqErcm/vvvv3HRRRfh7LPPdqa99NJLrgeLMTVv3hw333wzAOC6665Dhw4dXPP/+OMPEBHmz58v3dbYsWO1BgCRPooUiC1eGMyp7KAFRajKcmzfvj2A480dxPv5558HkHeyTXYTxIABA2K2nSg1UXmrmsUUhwvEAvfETxIAcOzYMVxzzTXa4xEAli5dKs1btyZxa+IBAwagZs2a2LJlizMtXk3s907Mwgv9ylMTnZ3/Z15YrKx4YNhZ4KDevPivJuyJiCbefMgufWzWrJm05mPGjDHyFmPcuHFo0KBBTK2D5C3zltVd3I5OsEfa8FqxYoV0uq4/fzwBYPv27TF5m/jFpYkHDx6MatWqxQDC4/VxWlRQZK0o2bpBsLI8+tWG9+TJk5X5hsXK6ipsTUSFxcryOT344IPO/9mVT6qa63gPHz7ctx6dOnXSxh7J6s3y7tKli7LO+dLEx44dw+DBg1GlShV89913MfPZia05c+Y407755hsQxZ7Y2r59u7PME088gczMTBw6dEgrD1NkrWxQ6RQvCPoVyHsOcjy8bSNr2bXZy5Yt8+Qee4WXBg0aFMhTlveECROU2wnr7YWVteEtU6tWrQoGWTtw4EBkZWXh/fffx9atW534888/nWUGDBiAGjVqYMmSJVi5ciVat26N1q1bO/PZT0wdOnTAmjVr8Pbbb6N8+fLWfmJioSJTAnAAceLgthFA3icPm56JHrfffjtKly6tPGlmIxIdK+vlLQLpTzvtNCNvq02s2sj06dOdZdjFHjk5OShVqhS6deuGrVu3unw2btyIzp07o2TJkihXrhyGDx8e+GIP3QOisx9hDnY8kbWJ5F1YsLIRsraAFSFrC87bNBIJ/Zqs3hGyNlKkJFeErI0UqZArZZq4WLFiynldu3aNvC17ly1btkC8wyopvfPlS6xlRcjaxPdOdKxshKwtYKUqstZmRN7x8xaPJ7vQ6eSTTw7kl1JNLEZhQdYmo3e8kbWJhpX1Cl5z5swx9k7ZJmYiisXKMvwJAPTo0SPUQWfeTGEYVSpvMe94eIvB7i8O6m0rT6+GEJWWlmbNV/y4HY+8/S5nZZGSTfzAAw84yxL5w/TCHBzmLfOygX7lbzDh/W14+w22jz76SLqMeAMLAMyYMcPXO2zeugri3bdvX+kFRzbr/fbbb0v9I2StD7KWEQrF62F37tzpLKMqni5WtmTJkjFeJlhZWXjB3mU3HATNW9Uk5557rvVmCoN+zcrKcnmzJ2FkZmYiPT3dc7t+oVrn8ssvD523LHiKSoSsJfmr/osvvugs6zcQve471UW/8pB3onBYWVGqeUG8ici5EV2mXr16BUK/8k9SUMkWDlc2r2LFitq1UXmr5oXNWxVFixYFANxwww2+yxb6JvZrBH46w4VeccUV0vm64YV+5aebfgQbN26c49OuXbuYvHm1bds2UN4yxQOHe/XVV8dsR+ati3795ZdfABynaYo4XNkx0PFm1/3LxolXTUyQtTJvk5oU+iYG1MhRfpoMK8uWCYqVlfkFxcr27dvXKG8gOA63c+fO6NSpk8s7DFZWVhNVzmG8ZfXhb3uVzdfx9hpDfE10fU1qouOdEk3shaxlRBEgD8S+YcMG13xTZK1YYIY5Xb58ucN7UuFZvbx5VatWDUSEHj16KGtg4s3yvvLKK9GmTRsnb5avSd4qrKxYE/b/8847DwsWLFDmzYeOt1c9iNToVy/v+fPno3Tp0k7e5513HoC8FxwRWavjrZPvrFmzXDdC5BuyNlHk9XFapIr4yeTVlUiOUGU+PELVNEzyPnLkSKC8GTqY5c3mhclbVRMez8pryJAhrnVNkLWMM8bL6wmUJt4A8MwzzyAnJ8fx5msUxltGavFaN1+RtQUlL2RttWrVjLz8BqgKK1uxYkUX5lREqOpEfqB2VQhV5hMmb9GbrwmPZ500aZJ2/jJv/qtGkFr45c3XpWXLlta9WU2uvfbaQN4p0cQqhOrOnTtBRGjTpg3KlSsXc8B03oH80K8rV64EEOwEmRfmVDX98OHDVrzFbbCP1GG8VWQPXibeKv3vf/9z/n/33XeHzpvxn0UdOnTIiresFsuXL9f2Tokm5sMPc8okA4AHQagC6oFpE88KuCmaYb3zK2+xGXW9RfE/G/KKR97szHpQb5Z/zZo1HW/2hMsgeadUE+tgZZkeeeQRZ1oYZC3g/e5iA4f7119/SbcTBv1qI2+vYHhWHqg4d+5cI28/hcnPK2/2s1PQSy9r1qzpyk9kbInoYb9IqSZmocLKqqj/fJjiWQG4kLVE4bGyYt58vra8xbxzc3Ot5s2wsn719grZ5Z2il1fefqHKm1dQb5XY9d25ubnaXoW+iXWRtUSEe+65x/GYOXOmb/H80K8Mo0oUP6wsLxvevPi8w4Qsb/GMbFjveGNlZXlPmjQprnkXCLI2URQEWcsCABo0aCAdgEECAIoWLRp6QHn5M9n0i1e+8co9P5G1TOJ9wQWFw025Jk4W9KuuNxP/iSKMN+BuqHgia3mplklEZK1fzn7eXhEha/9RhKwtOG/TSCT0a7J6R8jaSJGSXBGyNlKkQq6UaeJkRb8mq3eErM1H73z5EmtZqYCsrV69Otq3bx8X7whZGyFrC1ypgKzt3bs3gPj9FJQM6Ndk9ZYdzzDeKdXEYiQzspYpHt7xzJv9P0LWHo8777zTNX5l79gRstYn8gv9CthB1gLAq6++mu/I2jVr1jg1DpKzl2zkvH79esfv6aefRteuXTFixAh899136Nevn5Wa2KqvSW0Y9E8VKdXEOho6dChGjhyJBx54AN98842VAyT7P1FwzCnzUQHvmjRpEhdkLS+bg5T3izeyNkxNmjdvjvr160vnBc2bp6oCwP3332+cd6FvYv7dJKhUxfOjN/Tr1w8AnOthea+gyNqRI0fiww8/dP7mr7W1lbfMm/e///77AzXY33//HVMPpo8++igU+lWlRx55RLsuJnr66ae1j6Uq3nnnHZen+BgX3bwLfRPrYmW9DqhqGT/vqVOnutZn/w+LrPXKd/fu3cZ5d+rUyXedN998EwCwefPmwI3GomfPnli9erWrxlu3bjX+OlCvXj2t48TXG4DrRVAVPBAiMzPTuXvppZdeko451eNXdMaXV+46yxT6JjYJGfo1iA+7tpX3yMjIiPEL8hFMlpOYq2nu7CkSo0eP9q1J0LzFmohS1cSrsR9//HFn/YEDB0rzFuu9ePFiZ3tBsbKNGzf23Acdb9V6QVC7hb6JATmeVZwWD2QtE/MGgiNrVc3Ji3kz6XhfdtllAIA///wz5gwoy5u/h9Yvb1nIaiIqCA5XPE6yEFG7Mo60Tt4qb50cguTeuHFj7SdXFPomlgV7JZsxYwauu+4611nHkSNHOj5E5GJv6QT/sY1p+fLluOWWWxzPoMHnJU7j30Vly6miT58+vsvXrVsXQPD7Z2U14WWzHn7BBrxJ3rrNaDJWdPdfZ7mUbOKuXbsa+ZkMEoYi5VW7dm3nt8CgA1Y2aBkOF4CLrGmyHfEZRsxn1KhRDjM7SB38aqJbY6+TcADwyy+/eOJwvWpogpWVeXudS/DzZvrhhx8885bVKOWRtUTHcaEHDhzQ8vvggw+UB0PlzcSmsycxmgx+FbJWxOEyX55Xpeu9b98+7bqa5u2Fww3qz3uL68lwuLLm+eyzz4zylnnzz9eaMmWKcU14eeWtU5+UaGIZLjQ7O9tBqFauXNm1PlHemWXVpZR+3kRyPCvz1g3Rm4n3BoD58+e75g8ePNjIe9euXVp1DZq3qiYiON7Um19HRL+yYKjd119/3Xc7ulhZvtaffPJJqGOpyoflrVOflGhiPkQUKdP06dN9D4QNrCw/PwhClal58+YxT/vj/W3jcG15b9iwQerN/E28Zdq0aRMAxPyEJe5DmJqErbfoIcP48g/147cVIWsF9OugQYOcdbwKbgMr+9///le6HVNv9nutTDwbTNfbZLCF9ebFP+ERAJYtWxbIW0dLliwJjUfyq4dJyD75+cnLL6WamAVDkfJKJqysSvHMG1DjWcPm7eXtF02bNsW//vWvuHmrmrhevXqhcLgm8vMq9E2swrPyYtPjhZXlD1o8vPMjb75OpiHD4fJi3+nDeMcbWcvLBl9LrMlJJ50UU+cIWeux01u3bnWWVT1GUza4wwSTDa/8DOD440Zs1oHJ5JlDXhFvZC0v295h8065Ju7WrZuz3C233OKal4zI2kTzThSsrG1vpnLlyoX29ooIWfuPImRtwXmbRiKhX5PVO0LWRoqU5IqQtZEiFXKlTBMnK/o1Wb0jZG0+eufLl1jLSgVkbbJ7JzpWNkLWFrBSAVlry1sVkXf8vCNkrYb8fic+9dRTPdfnl01UZG0ye3fp0sWp9a+//mrNn0WyIWvDeqdcE7ds2TJm+f79+0uBeiVKlAg9oBhnS5SNwSoSN+KNrLURTJdccon1erC8becsen/88cf5krfu8Uy5JiY6ft/ta6+9BqJYcBwvW4NW1JVXXhkaK/vzzz8r87SNrB0+fDgAhMLhMtWpUwdE7gtvbOR95MgRx++ll16KW01k48OGd7FixZTjxWvdQt/EYfGsXuvo0htUCuPN+2/atMk1PSgOl0VWVhaI3B/hmCZOnBh4oDIShrgPunkHqXNYbzFq1arleB85ciSUN0P46qhDhw5Kn0LfxEGRteyWP69lvLx5nXbaadLpQb29vExxuE8//bR2XS+66CKrH9n53HXy1tl/PqZNmwYAWLx4cei8mzRpErOtWbNmATB/qsdrr72mXXOm888/P3Wb2CR4XGiRIkUABPs4feaZZ6J8+fJOPp9++mlMjrNmzQqFfhUHsQzPauo9cuRIdO/eHf369cOwYcMwY8YM1zbCePN58ye2ZsyY4Zm3V4MAwPPPPy+9KysjIwMDBw4EAHTr1s3YW8yb18knnxxTFxPvr776ynBEw9O70DcxENuIfshaHhVKFB5ZK9P1119vjKwVEaq8WN5BcbgyPOtNN93k2kYQrKzozT+Rgc0XsbKmyFqvM7cAcPToUcdbN2fVsRTrvmjRokDeTL1793ZghW3atJEeW9nxSqkmloUJijQospb/WyYTT5X3hAkTQvn51YQHCYa559cLWRsm5wkTJqB///6+x5H/2+R4srzF+89V+Qfxlk2Xycvbr4kL5WWXdevWdf09atQo5//Vq1entWvXOn/v3LnT2LtMmTLO3+IlhKeeeiqlpaUZeaq8R4wY4fx/wIABgTxFbwDUu3dvAkClS5d25nfu3Jl++eUXAkCDBw8O5J2WlkZpaWl02223UdmyZal69eqEvDcK5SWHzZo1U/qOHDmSduzY4arJqFGjKCcnx/EVxR9PL28+7wMHDlCdOnWIiGjWrFnOfPE4BvHm8+7bty99/fXXruWuueYaSktLM/KOkWeLJ6h0kbU8+pWIHFwok84N637en3/+uSs3Pz8T70aNGjm+aWlpgbxNsbIA8OKLLwby5uvN41nZI1PD5E3kxsqGqbeXdxBfU2/T8ZISH6dlKFIAuOGGG6RFYdIpoBee9cwzz3TlpcMn1vUmchMnTAeVyttLX331lZW8xWDPegrjzWNl+Zow9KsNb/bsJzbdprdMut4p0cQs2ONIAGDp0qWeHrL1TTCnOn5hsbLPPfec0t8Gsjae3uJybDthvHXr3bdvX2Pvhx9+2NqxlO2713jx806pJp44caKzDD/9t99+c60vrmeKlS1atKinXxhvPnJzcz23ERZZG0/vcePGgYjQo0ePmG0E9TaRqfdPP/0UeF2v4ynypXUeWCBGSjWxbGBmZmbGrN+gQQP89ttv0vV18Kyi4oXDZeIf62kTWcvXygvP6ud91VVXuWpO5L7uGwiPlWXP15o7dy4A4L777nM9+vTdd991bc/Em68Fv24YZG3Tpk0xZ86cmLHCe0dN/M8Oyi4E4B/pwUREMU+w37Nnj2fxZN4yX97bFlb2rbfecrYRL2StuA9Bw+snGt2cvbyJ8hdZ+/bbb4f2VonPu0CQtY899hgaNGiAMmXKoEyZMmjVqhXmz5/vzD948CAGDRqEsmXLonTp0ujevTu2bdvm8ti0aRMuvPBClCxZEuXLl8eIESNcF7brSOeKrXghTnnF844aNliB+GFwAeDvv/+O6z7YivxE1rZv3z60t0xB87baxG+88QbeeustfPfdd/j2229x2223oVixYvjyyy8BAAMGDED16tWxePFirFy5Eq1atcJZZ53lrH/06FHUr18f7du3x+rVqzF//nyUK1fO9QhPHXk1cbzRr0zVqlWz7h3PvL0GmolXYUfWArCGrOU1a9asQHkT5cPH6ZycHDz99NPYs2cPihUrhtmzZzvz1q9fDyLC8uXLAeRdFZSenu56d87NzUVmZiYOHz6svU2vJh4yZIh0Ov+7HR/smbEsqlSpYs1bjETzBvQaOIi3bnjhdYN488ezoLzFGuvU2cs7bk189OhRzJgxAxkZGfjqq6+wePFiEBF+//1313I1atRwzhqPGTPGdQEDAPz4448gopiLJngdOnQIe/fudWLLli2hBk4UUSRTWL/sct26dXTCCSdQ8eLFacCAAfT6669TvXr1aNu2bZSRkUHZ2dmu5StWrEjbtm0jIqJt27ZRxYoVY+azeSqNHz+esrKynKhevbpp2pEiFVoZN3HdunVpzZo1tGLFCho4cCD16dMn5npQ2xo1ahTt3bvXiS1bthh7JCv6NVm9I2RtPnoH+izN6fzzz0f//v3j+nFaVISsTXzvRMfKRshaTm3btkWfPn2cE1v8D9zffPMNiGJPbG3fvt1Z5oknnkBmZiYOHTqkvc1ERNauWLHCOQNpy7tdu3YgIowePRoXXHBB3gELuR82axJ56x/PhEHW3nrrrVi6dCl++uknfPHFF7j11luRlpaGhQsXAsj7ialGjRpYsmQJVq5cidatW6N169bO+uwnpg4dOmDNmjV4++23Ub58eas/MfGhg2e1gax1FdTCIJgwYQKmT5+u3P8w3vmFw/XCs4aNRMTKxtPbahP37dsXNWvWREZGBsqXL4/zzz/faWDg+MUeOTk5KFWqFLp16+Z6VjAAbNy4EZ07d0bJkiVRrlw5DB8+PC4Xe/Ah4lltcaRycnJiclPdyaMbMrEb9pkYxTNMxAtZy7z5fEePHu3an8mTJxvXRPRW1cxG3mHj0Ucf9Ry/puOw0F92qYqZM2cCyLsPt1WrVsjOzsavv/4KwN5HUlHi/CCsqgULFjh+//vf/5TbC+J97NgxPP/88yAiXH/99WjQoIF0uaB4VobgkdWEv2vqwIEDgWpMFAsY5G8SMT22jM4pExG5bs00qQlDJvP5eN0l5edd6JtY9W7iJbZMGPQrr0GDBsXMD4uVZcFfayvbBxNvP+nkrdtwYo66ufs1cdBlZMFfTegl01sPvfIUr9jSqXehb2KivNvdTAbsu+++68tu8vL2Gjht2rQJ7e03WJ9++ulA3joKmreoRo0agSj2iRym3rJ1GTFEnC8Dynt56+J8X3755dDHUrb/PDgv5ZtYp2gXXHABJk2aBAC46667jJpHDJ7owabxOFwWYdGvojdTsWLFAnnz+fLeTBdeeGGgvJnEx+LwUtXED1kr8zjxxBOl4yIMspaIcPHFFyu3G9RbfOrD6NGjfcdJhKylPKYRE5vGkLUA8NFHHznf34iCIWsBYPjw4S5vIvtYWeb94osvuvbJ1Fush1gTAHj22Wd9vcXo27ev1Jv3nTBhQihkLROPw5UpDLKWD/42UL7eJt69e/f2zHXIkCHOiTQ/70LfxLLgsaDiDftM7O8gyNpnn30WAHDFFVdoNYuJt9+A/vjjj419N2/e7JlXmJzFd0X2sTKMp6qJvRTkeKrqLfM19RZ1zz33KHP2807JJmYUCCCvUKNGjbIyYJl3mTJlYrxlCurN/pZ5h81b9LZVk/vvv195vLzW1T156CdTb1VNHnvsMa3cg3izO5XYfeKqfRC9U6KJVehXwP2Mm5NOOsnx0B2gXt4MRfrZZ59J8zT17tKlC8qVK6fc73r16hnn7YWVDVsT0ZtdVRakFjLvW265RWs8LFy4UAvpa4qVPeOMM0LXhPfmMb5VqlRxbUv21JKUamIZLjQ7O1t64QWT7sHxQr+Knmya7jZUqF0v6f4EZIKVtVkTsRam/l41YY9MZQ0hyq+R/bCyPCwPyPsOHrYmPGpXVSs/75RoYj78EKpMsnXDIGtFEZlhTr3ETqTJtmGat05NgmJld+3a5XiJ3zeZbOFwx48fH6reXjjcMWPGxCwfBlnL5x1knKRUE6vQr+LBEgF5QbCyfjL1Vqlx48bOcvfee69rngncXBX883hNayLGDTfc4HixB5GJ9Qrqzce4ceOktQ4TNv1U45BJ9hOlV6RUE7OQ4VnZWWog70l1NtCvMn399df4+eefjb1lWrx4cczZ9bVr1+KSSy4BAAwbNixQ3nxNeNmuCZv25JNPAnC/C+uGLG/bOFxV3mGRtfzfO3bscLxl20rpJlbhWdntYAxzSpR3CSOTTvF00a/Mm8g+Vjbe3ryCDFaZt3jPuA1vEVlr25tXPHG4vAoEWZsoCoOsZZINwFQL8Z3Ytr9Nby86JT/dBrJWtVxQb9mvFxkZGdreKdfEfphTJhsDSfS2OUjzy5tp/PjxWl6FHVnbpUsXa8haG3kTpWAT66BfgeNNnMrI2u7duzs1te2tG4mCrNWpQxBkrY28/Zo47Z/Ek0r79u2jrKysgk4jUqR80d69eykzM1M535h2GSlSpMRSyjRxsqJfk9U7Qtbmo3c8vrPGWybI2ldeeQUA8Oijjwb6rlLQ6Nf88GZIIJveiY6VjZC1BSwTZC0vr0L5RUHicOMRzNtWffI770T2TmhkbaJIl3Ypqm3btjHL2EDWEuWhXxctWgQgfDPkF1aWF4P42/JONmTttGnTnFoUamRtokinia+//npn+W3btsUFWUvkRr8y+oYN33bt2in334Z///79AQAjR460Vgu+JkTuoTV06NDAfl4SX/BsbMN2PVhNbrjhBudv3QtMiFK4iXmJmFObwbxLliyJdevWAVDzpIIOWkbzYGrRokVgb3EbsnlhvVlNAKBTp04Omqd+/foAEPPbvGk9RNnIW+Znsyait2ofZFHom9gPz8pA67LrYW1iZadMmeJsM6w3Lxmy1u9GAr+8mWQ5hkHWitvwG8gm8dprrynHg428RT+bNRG9Zfl7RaFvYiJvhCp7nhEf8cDK8rKBw/XaRti8VT62avLCCy/E+DOtXr06dN6ibOQtylZNunTp4njyUEGTY5kSTex1UM455xxnGkO/7tmzx5mvU0QxxGtbefHTbSBrefGPPwnqzfTZZ5+5amIrb74OvDeQR+YIg5VVSZV3WG+vmuh4y7xk2+BpqTLvQt/EQGwjeiFrxULWrVs3FFb29ttvd/nZQtY+//zz0n0P6w0Af/zxh6sm7GZ+BjM3wcqKeQPu77w8d9mGt19NdH1lx1JU48aNA3vzYtP4hwsCcD0dtFOnTkqvQt/EsuCRteIP7A899FCMX8uWLbUPjvgqOWLECJeXyYH28lZp7ty5obz5PDMzM2P8bXmL+xHPmvDLB0XWnnvuub61CIqsZdM6duzomr5lyxbn/8eOHYuamA8VspafFnSQiShSXjt27AjVxF5YWf7ThUm+ojeQ90LAi93RY8NbJq91TdGvAPDzzz9rbSOIt428dR8Po9pWhKylWHSqeIBMmliFrGXoV/b0RT+fIN5sOsOcmnyXVyFUef32228xzzXyekcw8R4wYEComvihXz///HPjunt5+8HdTbw7dOigHLsy+XmnRBP7oV95fjH/MDImrwL6oV95mRx0HW8xeKRqUG9Vvueddx4AoEiRIlby5rdhAvTTRb+KH01teIsKk7eudLxToon54FGk/LuBSvy6YTCnsuWDImuJYrGy/DLDhg0LjGfVqUcY9Gv58uWt1USFrC1durS09mG8L7744phanHvuuYG8WdSvX19Zc5OapFQTy1Ch//nPfzy9iOwga1Xr6XoXLVrU10ucHxT9etlllylrEdabnQEfN25cqJqo9tsv7zBhC/AnG4dh8k6pJmYhw5zyatCgAQB5EXXxrLy+/PJL1zJB0K98Y/HTeYQOADz33HOh8mZx2mmnxdQ1LLL21ltvdbwefPBBZ34Y9KuObGFlZduz5S2TjEoaNbFHqIDeNq5v/uKLLwJ5qDyvuuoqDB48GPPmzXNtJ4in1w0OOrB9E28+z6JFi1rxPnbsmO94MD2eXjWJl7f4xArA/WuGl3fKNrFucyYSsvbss8/GkSNHXPsqXk2VyAHE5w4g5s3L5vGM51hRUTXFh+NFyFrNA5KsWNlE8i6syFqb3l4RIWv/UVhkLR+pjKyNp7duJAqy1qZ3kIiQtZEiFWJFyNpIkQq5UqaJkxX9mqzeEbI2H73z5UusZZkga/3Qr36RKFjZZPJ+4oknAMjPICcKVjZC1hawTJC1RImPlfXzLlOmDO677z5n/8Nuz1bespg9e7bVPPMrb5veEbJWQ7oXe+igX20ia20NJhVWlpcNb9t533XXXU5+FStWTCpkbUF4X3DBBZgwYYKvd0o3Md8ENpG1wPEmOuecczBkyBD07t0bP/30k/RSvjDbAfLYTCzvK664AgDw5ptvBvYVLyhhMEFb+TJkbTxCduUTgMDXevORlZUFInL45CLwAUAMSkc3ZDVh8ls3auJ/ZLuxVOKXDYM5ZfdEn3XWWdL5QbwfeeQRrdzDXIr6wAMPeCKCbWFlZQryAhpEOr4MXMD+ltWEya8mhb6JdfGstpC1fqpVq1Zg73jmrZO7jrdfzo0aNVLOt4l+/fTTT52cmXTJpCY1qV+/Pr7//nvce++9APTf8Zm8PibzdU/pJibSR5GyaWGQtT179gQA52OtqBdeeMEKslbMOWzeovbv3x8zfcqUKaHzZv/niSFE4dGvrO6yPHmx75hBaiKrOZ/3ypUrA3mragLoMdNSoon9gpeNGwriiawdOXIkAODJJ590ptnAysryY95i/kHyvvfee/HAAw+4plWpUkV6/PhlgqJfZTURl/HyFsWTOXTq7Ze3an+Z98KFC5X1jpC1RC4+FRFh1apVzrolSpRwYWWJ1Depq7zFj0i8wiJrmdg0/kRKUO+qVavG+PLeooJgZWUnx3ixvAGgc+fO2t78g85k83msrNdyfk3MS+ZtA1krm6/jXeibWBayV0kmkUhpgiHlvWV6/fXXXcs2bdoUbdu21b5vl0ftmuQUZkDJZOp/+umnY8yYMVLv3r17u6alp6ejY8eO1pqBD/YbtY5vhQoVcPfdd6NmzZox21m7di0uvfTSmHV0xwq72MUrF3G+l3dKNrF4a9ioUaNc64dpCD88q0rs5ws/bz5HEVkbJm9x/1XiQYImNfnrr78cAgirtypv/nur7gk+vibsjh/Ru1u3bq7ja4qsZd46YyXISU+ZdLxTool10K+8TAaol7dK8+bNw9ixY30/NorezzzzjOPBD1CTj5+iNwPC6SqIN4+sZXn/+eefSr9HH31Uy7tEiRJOXsOGDXMha8WTRJ06dfLdB10c7kknnRSoJkGk450STSxDkWZnZ8egX5lMmsELzyo+QcG00bxQu+LfvCZNmhTImz83INvO9u3bQ9WE9+vVq5dDi+TRr3514r1POeUUJ8+//vpLujzz1jkOujjcIONF5X3KKaegdu3a6NixI/73v//h0ksvNR4zKdHEfKgwpzqDqCCRtcx7586dLt/3339fur2nnnoqcN4qHG6YvMX8WrdujZo1azoPgZPVStebSZZ3WG+/mjDPIMhalfekSZOMvFOqiXVOIAGxDRcEWUuUd5kek+rVPCwO12u6jUsNiY6/45nmLQse7rdu3TrXI2Nuv/12Y2/+aR5smojDldUqaIwbNw4tW7Z0PHV/39YZhyxvngqq45dSTcxCdvld1apVnZMWRMGwsqJ3bm6uk9OsWbOc6WG9/RQ2b74m/PZs1IQo78IHUboXksi8d+3aBQBYsmQJiMjB4fbu3dvxP3z4cCBvcdqDDz7oyluFCA7qTeT/s5kYKdnEsqhcubLzHZafHuZaXv4JEzNmzAjsI4tvv/02Zr+D+Ogga5m8Hq9p6m0LWcvCS+wdXvd4Mm/+ec+q7ZiOFd2aiP4RslYSqqIA7gMTBln7zTffODmFGbAFHUx9+vQp8Fx0jie7eGXevHkxy5kcT5VUyxcUDjflmji/0a9A3vemRMLKmnp/8MEHAOBLmCAqPFhZ5i0+JjUtLS1C1uaHImRt4nvrRoSs9feOkLWRIiW5ImRtpEiFXCnTxMmKfk1W7whZm4/e+fIl1rIiZG3ieyc6VjZC1hawUg1ZG4+IvOPnnVTI2vHjx4OIMHToUGfawYMHMWjQIJQtWxalS5dG9+7dsW3bNtd6mzZtwoUXXoiSJUuifPnyGDFiRAyB0Uupgqxdu3ats89//vmnVW9Z3kA4UiXv7YV+DRsA4oqV/fDDD/HVV185tbfpHSTvuDXxp59+ilq1aqFhw4auJh4wYACqV6+OxYsXY+XKlWjVqhXOOussZ/7Ro0dRv359tG/fHqtXr8b8+fNRrly5mHt+veTXxKVKlYrL4MkvbwBo06aNZw1s5z1w4EAA6mvAC7omfATZf5O886PeJhGXJt6/fz/q1KmDRYsW4bzzznOaeM+ePShWrJjrKQDr168HEWH58uUAgPnz5yM9Pd317pybm4vMzEwcPnxYa/s678ReslXc7t27e3qbXNI5ePDgGC8ZD8zWfjCEKs/YatiwoXHefPD3//LiCZhhLnPl91+cZtObJ4zaqLffOCwQZG3v3r0xbNgwAHA18eLFi0FE+P33313L16hRAxMnTgQAjBkzBo0aNXLN//HHH0FE+Pzzz6XbO3ToEPbu3evEli1bnB3UwbMSEWbOnOn6mygcVtZLH330kbH3qaee6qxfpEgRZ7qIrF2yZEnMfpjkzUfjxo2xZs0al19YZC0v8ZlMNpG14vR4envVOkhdTOttvYlnzJiB+vXr4+DBgwDcTfzSSy/F0BMBoHnz5rj55psBANdddx06dOjgmv/HH3+AiDB//nzpNseOHeu5kypcqKqgO3fuDIVQfeONN5xtXHXVVTjttNN8B4OO965duzwvoG/Tpg2WLl2q5a/DYBYhAWFq8q9//cvldfHFF4OIsGfPHhCFR9bytZWhX215szeUd955R/s46ng/9dRTMcSQZ599VsvbahNv3rwZFSpUcJ1wyY8m9nonVh0M2TwZitQ0Nm3aJPVn3vz2g3y8E6+bleVdp04d5T6aePPiQW2meR86dCim7iVLlnT5q7x1kLVnnnkmAOCRRx6R1just1gLUWG9VTVnL9j5iqxlNMciRYo4QURIS0tDkSJF8O6774LI/sdpUV7IWmfHhOKp0K9EZshamT/vzc8PgqwVz1KKefMK483D73Nzc2PwrCY37usoqLcM4ytuOyhWlq/JuHHjpHnXr18/tLeqVuyXgHxF1u7btw/r1q1zRbNmzdCrVy+sW7fOObE1Z84cZ51vvvkGRLEntrZv3+4s88QTTyAzM9P1iu4lrxNbzo5pFtsUWevnb7p9PoKC1IN4B/Xxy0mloN5eGN8DBw7ETDc5nnxNFi9eHJPzySefbMWbBU8MMck77hd78B+ngbyfmGrUqIElS5Zg5cqVaN26NVq3bu3MZz8xdejQAWvWrMHbb7+N8uXLW/uJSfwYxw60SmEG7HfffWfFkwVDqE6cONH5CMmu8uHfkYJsh8ezMr366quOd5gmZt5t2rQBUd67Wk5Ojut7smpdHaws78GwsqNHj3am8+PL1FusCS8eZxskb379F198UfvY5TuyVmxidrFHTk4OSpUqhW7dumHr1q2udTZu3IjOnTujZMmSKFeuHIYPHx7qYg8R/arS559/jl69ermW8Rugorffi43J4JfhcPlPMWG2o8KzPvbYYy4P8SSRSd6iN48IbtasWaC6yLx19fzzzwfKmz/jv3r1auf/n376aeiaEMW+AJvWOyUuu9RF1soa3a+AXnhWxonmp5kcHNGb19ixYwHAdS8vrzB5yzx4rGxQb7He7NOESV108hb166+/BvZmuu6665xp/MfesHmzi2fEupvUOyWamA8dFOnDDz/sePHTw2JOAWDDhg3O37qY0yFDhrj2j/cWOcVMX3zxRaC8xe3IIiiyVqyJbFs2vcPmzUt2LIPmLQZ/YVOQeqdUE+s+84j/PkUUHFkrBgB069bNNU3Hm9fDDz+snEdEWLBgAQDgmmuuCYSVFf1UERaHK57tjYd3GI9SpUo5uZ1xxhnO9CeffFK7RrpjJaxfSjUxCy88KxHFXKXER1j0K5E5spbXKaecggYNGmD8+PHOtIceesj5WP3QQw9hxIgRgfPmxdckNzfXGrKWKBb9qvL2Cy9vv7y9gn8nHjZsmHMdA69Zs2ZZyZsXX++oiQ2Lyr868gp7vS3vDQR7pfWS10OzdUK8Auz77793vHU/ueh681G0aFHXfsTDWzbPBCurErveP4y36viq1ouQtYZF4RUGWcvH/fffD8DezRWFIXiF9bKBfi0ob506RMhazQPCXxnDZOtgB/ELkndBe5tgZUeOHOlbk4JG1sbDW4z69esDAHbv3h0ha5kSEVk7efJkAN4DNtGwshGyNj7eQSJC1kaKVIgVIWsjRSrkSpkmTlb0a7J6R8jafPSO/zdY+4qQtYnvnehY2QhZW8CKkLXhI/KOn3dSIWsLSqmCrM0Pb6bhw4db9Y4nsjYo+jVZvVOyifnrYpm2bNkCQE3DMDkQ8RiYBeFdr149pz5Tpkyx4s1UEDUx3XZBHEteul4p2cRilC5d2rW+rQO1cOHCmNz4+UEv6ZRJXCbs5aJTpkyx7p2VleVbYxNvmQ9D7dpoDr9xwnuFqbefCgRZW9Dim1gXz8qLTYsXsjZe3jbyJiJcdtllMZ663l6xYsUKpe+gQYOMvWU+JsfXJGrWrCldv3PnzoFr0qNHD+UxJCLnoQp+PoW+iVmxTA5ymzZtQiNUmQ4fPoxOnTrh7rvvdqa9+OKLVrxF2cib9xen26rJ/fffbwUrq8pT5s3kdcOITk3E7QXJW3UcxUsuVfsnRko0sV8wffzxx6GRtarit23b1pUjkT1krWx7Qb2ZSpQoAaI89Cv7WmDLm03LyMjArFmzAORhb2TeXnBAJvEebebNjqVYdx1vPu8ZM2bE5H3llVdi/fr1ePzxx43z9spJlne+ImsTRV7IWiI3+I0XmxYGWct4Sfy0nj174oQTTojZli1kLcPF8HkH9QbgDBKGw2UKg5Vl3jfffLN0MLPfZU28ZcdOjPbt2zvLXH755do5i3mrtv/LL784eZt4++XOgynyFVmbKPJ7J2aDlJdqWVNkbbt27QDk3UiuOmg1atQw8hTzFiMoLF70ZlRI1YAL4y3z0am/biMAkJ7x5cGCpsfz9NNPx6JFi5T5idNNvP32W5wfBllbKC+7rFu3LhERpaWlUVpaGhERIe8FiyDc77Fz504jb3Yh+qRJk4iIaNSoUS7PtLQ02rx5c+C8y5Qp4/zN8v3uu+8890HXu1evXi4f9v+wYnkvXrw4xt9PzZo1U8576aWXXH//8ccfrhoAoMsuu4yIiB555BEich9PL2+Wd/v27Z2/R40aRTk5Ocr8Tby7devmumlh1KhR1KlTJ/r1119De8fIs8UTVH7IWhlClRcpXvFkofJWKZ7eTHXq1NH2FhGqNvOWIWt5se/DJv6it5/Yo3+aN28eKO++ffvGtSZEJB2DJttIiY/Tusjad999F4DZwTFFqCardzyQtWXKlDHOXebN4AKyepj4q/LeuHGj1L9v376ha5Kdne16dlmQY5oSTcyHihvVrl0752KEn3/+2Tk7axJe3kxE5HifeOKJgbxlYvPCehO5H2Zu25uPt956CwCwatUqK95PPPEEevfu7ZrG74OtvMMeS5Wf6rj6eadUE3sdmJ07dzrri/NsIGvj5Q3AxSU29Q6Ssw1vHlkbD2+dfQiT9ymnnBLYwwtZy+cKQOv68pQ6sbV161YiImratGnMvBNPPNH190MPPeT8f9u2bc7/77zzTmPvqlWrUr9+/eLiTUQ0ffr0wN5Move5557r+js3N9eaNxFRqVKltLz95OcdRjLvcuXKERHR999/H5O3rrZu3ao8luxEa25uLhER3Xbbbcb+MTJ5B0wU6V7swX6bnDp1qrPuypUrXcvYQta2atUKgL13BCJyHgEbxkOFfu3cuTMAoGHDhta9idzI2nh4s/8DwDvvvGN0PL28vXK2jazdsWOHMy1C1kqC/YjPpFrOFrJ2woQJntsJGvHwzK/QvTZYJ1SDXPQPi6xlWrhwoZWx4tf47OKeCFmrWbR4ol/ZZZffffedVe8nn3wyaZG1iYyV9XpROHjwYChvr4iQtf8oEZG1bAB4HZxEw8pGyNr4eAeJCFkbKVIhVoSsjRSpkCtlmjhZ0a/J6h0ha/PRO/7fYO2L/06sc81s0EglbxVxU4wg8LvIO7g3UYqd2EpWrGxBIWtLlCjh1LRPnz5WveOZd6J7R8haDSUaspaXjUGQX8jaeOadjMjaQ4cOoXv37hGyNj+kg6zl/77lllukPjfeeGOgAyFO4xX2IMdj0PvlXb58+aTKm+X+5JNPWvPmtWjRorjlzQfPZQOAli1bSpdLuSY2VdiD5eVjekmnTCVLlrTibTtvAPjwww+l81RYWZt5M/EvQEG8ea1duxb9+vUDAPzwww8u3E/YvPv27Ythw4Ypx2FKvxOLeFYvyZbxKrwf+pVJ1mgMc6q6KEDl7ZW36B3PvHUHv8lAfuONN4zXUW1z1apVAIDbb7/dKG8WV199tbLG4vQwGF+/MalzgqvQNzGRHHM6dOhQJbdoyJAhAIC6dev6FtAU/cpjZW+55RYHRmfizUPUeNlA1g4YMECaN5EZslblwcILKxum3sx7w4YNjl+QmvC64oorYvLWzVeVd+3atT3HMJH+5Zop0cR8dOzYEQDwySefxMzjcaFhQoVn5b2ZTJ/KmF/IWj5vXibeQcV76GJlxfVkfkFwuCtWrJCOk169ejl3GQXNm2nWrFmeYzBC1kJNbxQ1YsQIpKenh0bWsjukrrnmGhCR6902PT3dtU0byNpp06YBAE444QQryFreW6agWFldmeJwmfjpTz31lDOdr4lJzitXrvT8Ltq4cWNX3ro5ExEeffRRz/UaN26M9PR0rbwLfRMHGVj8sqbIWh7PKkPT7tmzR7ktXW/VPt1zzz1Gfqq8x44d61mnBx98UNt3xIgRSp/TTz/d9XeQmw54D9UxNj2efH66y5oezwcffNBZ59VXX/VdPgyytlA2cdeuXZ1l161bp/QJ0gxdu3Z1AfdGjRoFIsLhw4eteJcpU8b5m3mzwRS0gZn3n3/+6cqbid1UHzR3Wd45OTla9fA7CccfS1lTBfGeN28err76amXeRMcJleKLlEneLVu2xF133eVa/88//3Q93EAn75RoYhX69eyzz8bJJ5/sagReCxYs8B2gMu+GDRt65qc7+HVQu0R5J1sA4NZbbzX25hGqTCJCVaxPEG8x72bNmoWqCe/NA+KZHnvsscDeALBt2zYpVrZZs2aBTsp5IWvDjJGUaWJdZK3JQfHyVh0YHT8db1neNrzFHPm/e/XqBQBaT5nUzTtIXXTrLdsfXe9vv/3WtV52drZ0eYbxvffee3235YWsZf/nqagmeadEE/PhhZUNM7hk3gBw7Ngxx2vSpEkgsot+bdeunetRI2G82TOJRWTtm2++6aqHTfTrqlWrrNVEPF5MQbx1j32JEiUwe/bsQONFtV3RK0LWagwoBpyTiSgcVpb3kYUNHO7q1aul2wiCftVR06ZNQ2FlBw4c6HhNnjzZSt58MGQt+807TDP16NEDVatWBdHxZ17Vq1cPxYsXxzXXXCMdL0GPJ4/xNck7pZqYRdOmTbV89u/fH7PunXfe6estDoRevXq5lnnooYek65p4s6hataqzHRveMo0ePRq5ublW82YaOXKkp7dfyLz5s+cAAnmzh8vp6uWXX8b69euN8gaAGTNmONOef/55l2fUxD47riOicNfEMvm9k4aJypUru/I1DS88a9i8g6Jfw3qLyNqnn37a+dsUK9ukSRPfcfLMM88E8tYZfzp5p2wTV6hQAcWKFXOt99JLL8UsZwtZG89gKug8THN+9913rfl5DXK+PvHAyrII4t2mTZuY8VuzZk0j75Rr4oJC1sbTmykR8jZBv65bt87XL9GQtTa8vSJC1v6jREXW+kUqeetGhKz1946QtZEiJbkKJbI2CV93IkUKLL/xnpRNvGvXroJOIVKkfNP+/fs95xfNpzysivF7N2/eHH2s1tS+ffuoevXqtGXLFs+PZpGOq6BrBoD2799PVapU8VwuKZs4PT3vA0RWVlY0IA2VmZkZ1cxQBVkznTeppPw4HSlSpOOKmjhSpCRXUjZx8eLFaezYsVS8ePGCTiVpFNXMXMlSs6T8nThSpEjHlZTvxJEiRTquqIkjRUpyRU0cKVKSK2riSJGSXEnZxNOmTaNatWpRiRIlqGXLlvTpp58WdEoFpg8++IAuueQSqlKlCqWlpdHcuXNd8wHQHXfcQZUrV6aSJUtS+/btacOGDa5ldu/eTT179qTMzEzKzs6mfv360YEDB/JxL/JP48ePp+bNm1OZMmWoQoUK1LVrV/r2229dyxw6dIgGDx5MJ554Ip1wwgl02WWX0fbt213LbN68mS666CIqVaoUVahQgUaOHElHjx7Nz105rjjfNWhdM2fOREZGBp599ll89dVXuO6665CdnY3t27cXdGoFovnz52P06NF47bXXQER4/fXXXfPvv/9+ZGVlYe7cuVi7di0uvfRS1K5dGwcPHnSW6dSpExo1aoRPPvkEH374IU455RRceeWV+bwn+aOOHTti+vTp+PLLL7FmzRpceOGFqFGjBg4cOOAsM2DAAFSvXh2LFy/GypUr0apVK5x11lnO/KNHj6J+/fpo3749Vq9ejfnz56NcuXIulnd+KumauEWLFhg8eLDz999//40qVapg/PjxBZhVYkhs4mPHjqFSpUqYMGGCM23Pnj0oXrw4ZsyYAQD4+uuvQUT47LPPnGUWLFiAtLQ0/PLLL/mWe0Fpx44dICIsXboUQF59ihUr5iJcMrbW8uXLAeS9cKanp2Pbtm3OMrm5ucjMzIx5iEB+KKk+Tv/111+0atUqat++vTMtPT2d2rdvT8uXLy/AzBJTP/30E23bts1Vr6ysLGrZsqVTr+XLl1N2djY1a9bMWaZ9+/aUnp5OK1asyPec81t79+4louM31axatYqOHDniqtlpp51GNWrUcNWsQYMGVLFiRWeZjh070r59++irr77Kx+zzlFRNvHPnTvr7779dxSMiqlixIm3btq2AskpcsZp41Wvbtm1UoUIF1/yiRYtS2bJlC31Njx07RsOGDaOzzz6b6tevT0R59cjIyKDs7GzXsmLNZDVl8/JbSXkXU6RINjR48GD68ssvadmyZQWdSigl1TtxuXLlqEiRIjFnCrdv306VKlUqoKwSV6wmXvWqVKkS7dixwzX/6NGjtHv37kJd0+uvv57+7//+j9577z2qVq2aM71SpUr0119/0Z49e1zLizWT1ZTNy28lVRNnZGRQ06ZNafHixc60Y8eO0eLFi6l169YFmFliqnbt2lSpUiVXvfbt20crVqxw6tW6dWvas2cPrVq1yllmyZIldOzYMWrZsmW+5xxvAaDrr7+eXn/9dVqyZAnVrl3bNb9p06ZUrFgxV82+/fZb2rx5s6tm69atc734LVq0iDIzM6levXr5syO88v1UWkjNnDkTxYsXx3PPPYevv/4a/fv3R3Z2tutMYSpp//79WL16NVavXg0iwsSJE7F69Wps2rQJQN5PTNnZ2Zg3bx6++OILdOnSRfoT05lnnokVK1Zg2bJlqFOnTqH9iWngwIHIysrC+++/j61btzrBHvsK5P3EVKNGDSxZsgQrV65E69atXU+MYD8xdejQAWvWrMHbb7+N8uXLRz8xmWjKlCmoUaMGMjIy0KJFC3zyyScFnVKB6b333pMiUPv06QMg72emMWPGoGLFiihevDjOP/9856mATLt27cKVV16JE044AZmZmbj22muxf//+Atib+EtWKyLC9OnTnWUOHjyIQYMGIScnB6VKlUK3bt2wdetWl8/GjRvRuXNnlCxZEuXKlcPw4cNx5MiRfN6bPEW3IkaKlORKqu/EkSJFilXUxJEiJbmiJo4UKckVNXGkSEmuqIkjRUpyRU0cKVKSK2riSJGSXFETR4qU5IqaOFKkJFfUxJEiJbmiJo4UKckVNXGkSEmu/w8wqayAMokoqAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchvision import utils\n",
    "\n",
    "noisy_transform=transforms.Compose([\n",
    "    #transforms.Resize((32, 32)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307,), (0.3081,)),\n",
    "    AddGaussianNoise(mean=0., std=0.4),\n",
    "])\n",
    "\n",
    "# MNIST test dataset 불러오기\n",
    "gau_04_test_data = datasets.MNIST(path2data, train=False, download=True, transform=noisy_transform)\n",
    "gau_04_test_dl = DataLoader(gau_04_test_data, batch_size=128)\n",
    "# test data를 추출합니다.\n",
    "x_test, y_test = gau_04_test_data.data, gau_04_test_data.targets\n",
    "\n",
    "if len(x_test.shape) == 3:\n",
    "    x_test = x_test.unsqueeze(1)\n",
    "\n",
    "# tensor를 image로 변경하는 함수를 정의합니다.\n",
    "def show(img):\n",
    "    # tensor를 numpy array로 변경합니다.\n",
    "    npimg = img.numpy()\n",
    "    # C*H*W를 H*W*C로 변경합니다.\n",
    "    npimg_tr = npimg.transpose((1,2,0))\n",
    "    plt.imshow(npimg_tr, interpolation='nearest')\n",
    "\n",
    "\n",
    "for batch in gau_04_test_dl:\n",
    "    inputs, targets = (b.to(device) for b in batch)\n",
    "    x_test_grid = utils.make_grid(inputs, nrow=8, padding=2)\n",
    "    show(x_test_grid.to('cpu'))\n",
    "    break\n",
    "\n",
    "\n",
    "# 아래처럼 x_test로 접근하면 transform을 거치기 전 원본 데이터가 시각화 되므로 위의 코드처럼 순환하여 시각화하는 방식을 사용\n",
    "#x_test_grid = utils.make_grid(x_test[:40], nrow=8, padding=2)\n",
    "#show(x_test_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도 출력 (표준편차 0.4 가우시안 노이즈 적용)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test set: 91.620%\n"
     ]
    }
   ],
   "source": [
    "correct_samples, wrong_samples = check_accuracy(gau_04_test_dl, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "틀리게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrongly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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5cmSYOHFiuOGGG0L79u1D06ZNS/2B/6FDh0K/fv1CzZo1w/e+970wceLEMH78+DBmzJhQr169kJ+ff2xtWdvxJk2aFDp27Bhuv/328NBDD4V777039OjRI5hZuPfee0ut/fQPyP/9mDzqQ80/6/P+IH3JkiWhUaNGoUmTJmHs2LHhkUceCePGjQsXXXRR6NWr17F1W7duDU2aNAmNGjUKd911V/jtb38bcnNzQ69evWL/wP/IkSNh6NChwczCqFGjwoMPPhh+85vfhOHDh4cXXnjh2Lpu3bqF7Ozs8OCDD4a//e1vYdGiRZV2jEna8X74wx+G66+/Pvzxj38Mv//978PAgQNDWlpaeOKJJ2K/9nirTjMRQghnn312uP3228Of/vSnMG7cuNC6devQqFGjsHDhwlLrmInUmolP9e/fP7Rs2TKUlJSU+WuON2aCmTieM1GWx7OqVaeZ4L1T9Z2J733ve8HMwjnnnBMmTJgQ7r333tCqVatQo0aNMH369NivP56q00yEwD5RXWciyeMZ57hdPJeUlITbb789NG3aNGRkZITzzz8/rF69OtKOF8InLYF33HFH6NSpU6hdu3Zo2rRpOOOMM8L48eNDUVHRsXVlPdnnzZsXvvrVr4acnJxQu3btkJmZGc4888zw7LPPRtYuWrQomFkYO3Zs7GPwRU/2EEJYs2ZNuPrqq0N2dnaoVatWyMnJCSNGjAjPP/98qXULFy4MQ4YMCXXq1Ak5OTlh3Lhx4dFHH4092UMI4fDhw+HnP/95aN++fahVq1bIzs4OV1xxRVizZs2xNbNmzQr9+/cPtWvXjjTlVfQxJjnZJ02aFHr37h3q1asX6tevH4YNGxbeeuut2K+rCtVpJkII4ZZbbgkdOnQI6enpoVmzZuGqq64qdU58iplIrZkIIYTly5cHMws//vGPy7S+qjATzMTxmol/dyJdPPPeiZn4VJKZKC4uDhMmTAh9+vQJmZmZITMzM5xzzjkp+f6pOs1ECOwT1XUmPuuL7BNpIfzb73TA/vjHP9ptt91ma9asSfyB7MCJiJkASmMmgNKYCaA0ZuLEddz+5rm6ePvtt+2HP/whJzrw/zATQGnMBFAaMwGUxkycuPjJMwAAAAAAMfjJMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABCjZnm/MDc3t8xrGzduLPMjR47I3Kt1P3r0aCTbtWuXXLtu3TqZ9+zZM5Jt2rRJrm3Tpo3MvYJydX+aNWsm16r7YmaWl5cXyXbs2CHXFhcXy7xbt24yr1kz+nSXlJTItcuWLZO5em66du0q1+bn58tc3Uczszp16kSy9PR0uXb69Okyr0rMRBQzUdqXbSZOOeWUMq9t1KiRzL2ZaNmypczV87dz5065ds2aNTLv06dPJFu/fr1c265dO5l7M6GOr3nz5mVea2a2ZcuWSOadW4WFhTLv0aOHzGvVqhXJvOdg6dKlMs/Ozo5k3gx6x71161aZJ5mJt956S+ZVqUuXLjJXr39NmjSRa73nQz3uZslmwtsnevXqFck2btwo13r7hEcdn7dPeDOhXkOTzkT37t1lXqNGjUjm7VfePqFmPOlMVMQ+MW3aNJlXJW+/VK+h3j7hPR8tWrSQeZKZWL16tcz79u0bybz5ad++vcwrc5/YvHlzJEs6E+r9oZneJ7z3X0n2CW9f8t7zJdknateuLdeWZ5/gJ88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEIOLZwAAAAAAYnDxDAAAAABAjLTgVb3FuPLKK2Wu2msPHjwo13qtdOo2zMwyMzMj2aFDh+TaBg0alPm2vZa5jIwMmXvNdupYvGZAr/FONSTWrVtXrvXa+1577TWZqzZC7/hat24tc9U6uXfvXrm2oKBA5t7jpxqod+/eLdfOmzdP5lWJmYhiJkr7ss3EVVddJXP1mHkz4TWdejORlZUVyQ4fPizX1q9fX+aqSbSoqEiu9c7FJDPhte97M6FaQ73Z9GbilVdekbmaCe/4vFblJDPx8ccfl/k4vGPxZmLOnDkyr0rf+MY3ZJ5kJtauXStzdd6a6X3Cmwk1P2Z63rzXs3r16sncmwl1P73XYe/TJNRMeLPZoUMHmf/rX/+SeZKZSPV94v3335d5VfL2CXU+HzhwQK5dtWqVzL2GZfXaX5nvnbyZ8Jrzk+wTXhO1eu/k7RMdO3aU+UsvvSTzJE3obdu2lbl6/Lzz1ntcvcdPfVKB99pRnn2CnzwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiKEbV8rAK6xo3rx5JPP+2P6SSy6R+VNPPSVzVSoxaNAguXbx4sUyV1q1aiVzVfBg5hfVqGKbvn37yrXbt2+XuSo3WL58uVy7c+dOmXt/tK94pSHefVTH7X0/r2DHW7958+ZI5p07qYiZiGImSvuyzYRX4pKdnR3JvPt1xRVXyHzSpEkyVzNx1llnybULFy6UuSpD8YqAvPPCy1euXBnJTj31VLk2Ly9P5qoAasmSJXKtVzrWtGlTmStecZVXULVt27ZI5pXdFBcXy9w7HzZs2BDJGjZsKNemojVr1sg8yT5x2WWXyfyJJ56QuXr+zjjjDLnWmwklSTmWmT8T6nViwIABcq23T6hCpmXLlsm13kyokiGPVy7l3Uc1E97388qRvJndtGlTJKtOM+HtEy1atIhk3kx8/etfl/ljjz0mczUTgwcPlmsrYiaS7hMrVqyIZN4+sXXrVpmrUjRvn/BKxypzn1D7mzcT3j6hXjfNzNavXx/JvD2oPPjJMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABAjLaha0TK46KKLZK4a23JycuTaAwcOyNxrMezdu3ckmzp1qlzbvn17me/ZsyeSpaWlybVe65tqsDPTjbmzZ89OdHyqpXLWrFll/n5m/uOdkZERybynv6SkROaqNe+kk5L9fzBeg2zbtm0j2bp16+Rar0WzKjETUcxE2ZyoM/G1r31N5qp5NulMeM2eqs19ypQpcm2nTp1krlrbvQbhpDOh1r/77rtyrTcTqqH1nXfekWu9hlGvUV+1Fh89elSu9fKKmAmvQVY9Jt4nHST5hIHjZcSIETJXLdLeTOzfv1/m3icF9OnTJ5K9+OKLcm2HDh1kvmvXrkjm7RNeQ683E6oZOuk+oWbCmytvn/BmIsk+ceTIEZmrmfBeUzzePtGmTZtIplrpzfy25ap06aWXylzdX6/N2psJb/9Qbe6TJ0+Wazt27CjzJPtE0plQ+8TMmTPlWm9m1bEk3SfUuWWWOu+dtmzZInP1mHj7xKJFixJ9TzN+8gwAAAAAQCwungEAAAAAiMHFMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADEiNYTlpFqXjQzW7lyZSRTjWpmfqta8+bNZb569epI5rUmeurUqRPJvHY8r2HPaytU7a+q5dLMb2RUx9euXTu5tm7dujL3mpkbNGgQyby2TK8dT7VRvvfee3Kt99x4rX6qXbW4uFiuTUXMRBQzUdqXbSZU07yZ2fLlyyNZ7dq1E912dna2zFetWhXJvEZsjzrnVJuvmVlubq7MP/roI5mrc1E1hJv5z7VqOvUaV9V9MfObmVXzsTcTXtu2asT1mmK9FlovV+2qhYWFcm0qUg29ZmYrVqyIZN5MeM+Ht0+oPch7zfEk2Se8Fnuv/TzJTHj7hHrt95q5vZmoiH3Ce51Q+4TXKO69Xnn7h2qlLioqkmtTUX5+vswrYp9o0aKFzNW8Jd0n1Ouw9x7O2ycWLlwoc3Uu9uvXT6719gk1E94+oe6Lmf+pFmomPN4+oZq8Z8yYIdcm3Sc2b94cyQoKCrxDTIyfPAMAAAAAEIOLZwAAAAAAYnDxDAAAAABADC6eAQAAAACIwcUzAAAAAAAxyt227TXKtWzZMpJ57Yj16tWTudfQqlr2Tj75ZLnWayXcu3dvmdd67XhXXXWVzFXD6O7du+Var11w2bJlkUw1KpuZZWVlydxrHFbPg/cceN9TNQ57j1+zZs1kvmnTJpmrc8drOE5FJ9JMeE2iqqHSLFl7uHdueTOh5tB7rOvXry9zZqJqeG286n4laZY2M9u2bZvMVZOu10Lstavu2bMnknnnnNc0n56eLnM1W6oN2cyf+1NOOSWSeS2/qj3bzH8NUueXNxPecavb9h7ripgJ7zUvFXmvrTk5OZHMa0zOzMyU+fbt22WeZCaS7BPeTKj3MWb+OaoeE9Wab+bvE126dIlk6j2ZmVmvXr1krhqxzfRr0NChQ+Va9QkJZrp939tTvCZ01SBsphulvYbjVOSdR+r5SPreSTWRm+mZ8F5HvOcjyT6xdOlSmSdpD/deb72579q1ayTzZtC7nvAe1yT7hPe+Ud2G156ddJ9Qr6cVuU/wk2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiMHFMwAAAAAAMcpdGKb+2N5M/0G2V3rh/WG9V3SgylNUUYKZ2cKFC2Xet2/fSHb77bfLtd4f4XvlAaqYxbsv3bt3L/PxnX766XLt/PnzZb527VqZqz/a90p6PF5hhVJQUCBzrxDAW19dpPpMLFiwQObZ2dmRzCvl6Natm8zPP/98mZ9xxhmR7ODBg3Lt4sWLZa4KYh555BG5dufOnTL3jls9fsxExfGK2tQ56s2EVzoWQpC5KhLzZuKDDz6QuSql8o7P2ye8chI1b9598YqN9u3bF8m8AjWvYCnJTBQWFsq1Hq/ERfn4449l7hVaJT2WVOPNhCqY8+6rVzrm7RPqHPVm4sMPP5R5kpnwiu6851TNhHd85513nszbtWsXybz91CvzUwVLZvrx84qXvFlW8+aVIW7cuFHmXmmS9zxUF0neO3n7s/dce9TrpSqZMvPfaycpvvRen9W5/3m5csUVV8hcldp5BWV/+9vfZO7tH2omkp6Hap/w5ifV9gl+8gwAAAAAQAwungEAAAAAiMHFMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADEKHfbttfqWL9+/UjmtZGqtWZ+q5riNeyNHj1a5ldffXUkW7NmjVzrNS967XNJ2v4OHTpU5tvwmo+/+c1vynzWrFkynzFjRiRr3769XOvdd/VYea2yXuvg4cOHZb5r165I1qhRI7k2FaX6THTu3Fnmqrm1bdu2cu2NN94oc+/4VIum17Z95plnylw9rt7xqXPczGzatGky3717dyTzWoiZieRKSkpknpWVFcmSzkSSJnKvGbdLly5lvg1vn+jRo4fMO3XqJPORI0dGMtXaaua3sC9fvjySTZ8+Xa71HqdXXnlF5uqc8+5jz549Za4+2cFrj/VmwnudUI36TZo0kWtTUZJ9wntdVfNjlqxh1mux92ZC7ROrV6+Wa5O0Vpvp9yFDhgyRa0899VSZq8dVnYdmZq1atZK5176v2u3Vp6uY+e/X1HzWq1dPrvVe4719Qs3EibBPNGjQIJJ5rdWZmZkyT/LeKelMqPdaq1atkmuTfLqBmf6UncGDB8u13qf3qMd16dKlcu1//ud/yvy6666T+fr16yOZtx94nxqh9jGvZd/bJw4cOCBzNRPefloe/OQZAAAAAIAYXDwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiFHutm2v9U+1hnptaF7uadeuXZmPw2vjVa2JXpvrxo0bZV6rVi2Z33333ZGsX79+cu3ZZ58t8yVLlkQy1VhsZjZ//nyZ79+/X+aqra5u3bpy7aZNm2SuWoS976faOc3MmjdvLnPVDpmXlyfXpqJUmQnVIP15+cCBAyPZFVdcIdd6zZVeW7BqO/Wef68Rec6cOZHMm0GvsdtrPv7DH/4QyZiJiuPNRE5OTiTzHjOvdTmEIHPV3Ltnzx651vvkBNUMPXz4cLl22LBhMj/rrLNkvnbt2kjmNTB7x61uw2vT9xp9vdbiO+64I5J5M+HtkaoxVe29Zv6nAzRr1kzmqll7y5Ytcm0q8j5pI8k+4c2Kdx6pfcI7t1RLrZl+jfIajr1Wba+N97zzzivzWm+feOaZZyLZ22+/Ldd6c+9R6/v06SPXbtiwQeaqId17HpPOhNontm7dKtemIm+fUK3o3uuIN1fePtGxY8dIpj5pwMyfCbX3e0343jmnPn3BTO8f3mu89/qnGrQvuugiudZ7DrzWdjWHGRkZcq03E2qfSPreyftkE9WsvXnzZrm2PPjJMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAYFV4Ypv4gu0OHDnKtV8DjlSWoP4r3/pi9R48eMld/zL9w4UK59rHHHpO594f/6g/avaKB559/XuaqNOTCCy+Ua6dNmyZz7w/oBw8eHMlUQZmZWePGjWVeUlISybxCM++2vZIR9VgVFRXJtakoVWbCK9NSJUhmZtdcc00k88oZ0tPTZe4VB7344ouRzCu18Yo2Lr744jLfhldU881vflPmubm5kWz8+PFybcOGDWXOTPi8Ehc1E6rAxcw/t7xzVM1E7dq15VpVPmVmdsMNN0Qyb2a923700UdlvmDBgkjmzbdX8qf2IO+c84rBfvvb38pcFa386Ec/kmu9x0/Np1c65u2/3kyo+15YWCjXpiKvdFGdt6r8zswvvqlRo0aZb9s7b729X70+e+9vvDKtrl27ylwVQ3nH570+T58+PZJ5+4Q3V4MGDZK52ieWLl0q11bEe6fFixfL3Cv/UzOhCmJTVVXsE+q9VtJ9Qs2E9x5OFTGamfXt21fmama9PWXGjBkyV+VbAwYMkGu9sjRV7mlmNmTIkEj20UcfybVqTzFLNhPePuEVPu/YsSOSVeQ+wU+eAQAAAACIwcUzAAAAAAAxuHgGAAAAACAGF88AAAAAAMTg4hkAAAAAgBjlbtv2mmdVq5rXPue1E3utb+p7ek1w+/btk7lqIp47d65c67Vqe82Aqh3Ua01U7Y1mun3u/fffl2tVG5+Z2axZs2TeqVOnSNauXTu5dsWKFTJv06ZNJEva0qcaAD1t27Yt89qqlioz4bVqe+etahH2GrvnzJkj80mTJsl87969ZcrM/PNFtW17j5PXxOnNoWrlv+WWW+TaW2+9VebMhC/JTHhtqQcOHJD5nj17yvw9W7RoIdc2b95c5l/5ylci2cGDB+XasWPHynzt2rUyV+2gXvtvs2bNZK54c7V+/XqZe/uKmgnVyG9m9tBDD8lctUR7+6k3E979Ue25Xit1KvJmQjX6eq3a3rmYZJ/wZsLb+1WLdM+ePeXa0aNHy9z7dJQnnngikr300ktyrdfce+TIkUjmfTKB1/qd5P1aKr13Um396vulKq+hPMk+kXQm1LnozUSSZvXf/e53cq13vnz44YcynzhxYiTzXkOTtFlv27ZNru3Vq5fMvU8IUee59/5r2bJlMlfvZbznq3Xr1jL3rvXUPuF9YkZ58JNnAAAAAABicPEMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADG4eAYAAAAAIEa527ZVu5+ZbqXzmvS8Nk1PgwYNIpnXBOe1Eqp2yI8//liu9RqHvSbEli1bRrIzzzxTru3Tp4/MVdufut9mZr/+9a9l7rV5qiZJr6nOa95TrbDe8+s1InvNe+edd14k++CDD+TaVJQqM+E9viNHjpS5ann1Gi29JlGvLVg9Jt752apVK5mr1lBvJrzj8FpeVRPx/Pnz5VqvqZGZ8NWoUUPm6rVInYdm/kyEEGSuzi/vvPBapFV76S9+8Qu51mvOr1u3rszVcXvnS+3atct8fEmazc3MunTpInM1E1u3bk10Gzt27CjzcXgtuV7D7fnnnx/JvObwVKRaYM30/fVet7x926PODe81R7VWm5mdfPLJkUy9PpmZ1atXT+YzZsyQ+cyZMyOZNxPp6ekyV830SfcJ75MM1q1bF8m858B7b7dz585IlnSf8GZi+PDhkczbx1KR995p0aJFkcx7HamIfcK7nlCt1WZmF1xwQSTzPknH+6SSJ598UuZqDr3XA28m1PnvfRpLRkaGzK+++mqZq5l44YUX5NoBAwbIXLWHe+8DvE+L8RryL7zwwkhWkfsEP3kGAAAAACAGF88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEIOLZwAAAAAAYpS7bdtrZhs9enQke/nll+Va1Sxt5rdR7tq1K5J5zZBew179+vUj2UUXXSTXqnZJM93qaKZbQDds2CDXnnLKKTKfPXt2JPMaIH/84x/LfMuWLTL/7W9/G8nWrl0r19apU0fmNWtGTxmv0dJrAOzevbvMDxw4UKbvl6pSZSbUOW5mdsUVV8hcPU8rV66Ua//yl7/I3Gu3HzhwYCQrLi6Wa//jP/5D5qph1JsJr6nRa9RXt+M1fzITyXkz8Z3vfCeSvfjii3Kt9+kG3vOkWm2/+c1vyrWqpdZMt/96TcGqMdTMb8o+44wzIlnSfUIdX/v27eXa66+/XuZeE7pqt1ft2Wb+fVSfVOHNhHcb6pMxzHQ7t3cbqchrolbvQ6ZOnSrXevtEkpnwXkf69u0rc9VO7B2H184+ZcoUmasGYO8THzp37izzWbNmRbJ27drJtV7LdUFBgczV7Wzfvl2u9T6lpTL3CbX/Vqd9wpuJ733ve5HMa3RWn3Zj5s+Eet32HrP+/fvL/Gtf+1ok895/33fffTL31g8ePDiSqU9CMDPr2rWrzNWetW3bNrm2W7duMvea39VriveJKd7rc5KZSLpPqJnwZrM8+MkzAAAAAAAxuHgGAAAAACAGF88AAAAAAMTg4hkAAAAAgBjlbhTYvHmzzL3SF8UrLPHKkVRxjPfH7F6Bkfrj/KysLLnWKxLzCplUgYR3H+fOnSvzv/71r5FsyJAhcu3pp58u86KiIpmroiavIGHv3r0yV49VYWFhouPwikA6dOgQybzCj1SUKjPhnZ8dO3aUuZqJffv2ybWq2MXMLDc3V+ZqJsaMGSPXesUeJSUlkUwVaX0e7/VAFQsuWrRIrmUmktu0aZPMJ0+eXObbSDoTqlRHFbuY+eecKjbyiky8mfDmUBW2eMexYMECmavztnfv3nJt69atZZ6Xlyfz1157LZKtWrVKru3Vq5fM1fmfdCa8cpxOnTpFMjUnqcqbib///e9lvo2kM6GKfLwSRe9cVM+TV7Dllfx572WmT59e5uNYuHChzNU+4b3ue5LsE14ZordPqNePiton1N7uFQimIq8w8fnnn49kXhmwVzrmzYR6XfSuJ7xzUc2yKtYz84vuvFlR+4Q39x9++KHM1dx77x9UAZiZXyq8fPnySJaZmSnXejOhHqukM+GdO5W9T/CTZwAAAAAAYnDxDAAAAABADC6eAQAAAACIwcUzAAAAAAAxuHgGAAAAACBGudu2e/ToIfO+fftGsvfee0+u/eCDD2TutZq2aNEiknnNdg8++KDMVYOd18R69OhRmXtN2eeee24kW7x4sVy7a9cumavHymuZUw2VZmbf//73Zf6d73wnkt16661yrdeiqZ53b63X0ue1wq5cuTKSnXrqqXJtKkr1mVixYoXM1XnuHYfXzu7NxLBhwyKZd+537dpV5vPnz49kXmuidxxeG3idOnUimdcg7LXhMhO+nj17ylzdh3fffVeunTdvnswbNWok88aNG0eyJk2ayLXLli0r8/f0PpXBM2fOHJmff/75kcxrYFbzY6bv40033STXzp49W+be/VH33WtiVQ3HZvrx9tqdV69eLfN+/frJXD1n3idPpCJvJtT99Z477/XZe+1XjfDeWtWia6bbjG+55Ra51nvv1KZNG5mPGDEikk2dOlWuTbJPeJ8w4c1mkn1izZo1cq3XOKyaj5PuE3369JG52ttPO+00uTYVefuf2idmzpwp177//vsy9/YJ9d7JW7tkyRKZq08s8NqsvXNr4sSJMlf7hPepB4MHD5b5OeecE8lUC7WZ/1qjPjHFTM+yer9i5s+Eei30ZsKbN/X+2kzvE4MGDZJry4OfPAMAAAAAEIOLZwAAAAAAYnDxDAAAAABADC6eAQAAAACIwcUzAAAAAAAxyt227bUBqrxp06Zyrdds57VLFxcXR7LDhw/Ltd26dZP5hx9+GMm8xtCLL75Y5kuXLpX5xo0bI9nmzZvl2hkzZshcNTKq+21mtmPHDpk//vjjMlftfSeffLJce+jQIZmrxrv8/Hy51mu49dr7VGue196XilJlJtavXy/Xem2F5513XiRr3769XLt3716Ze99T3R+vufJ//ud/ZP71r389knmPR9u2bWWuWvbNzGrXrh3JVAunmd80zkz41q1bV+Y86UwkeV386KOP5NqhQ4fKXDVX//Of/5RrvXPOa5AdOXJkJPPmbc+ePTJXj4k3E1776wsvvCBz1X7tPTc1atSQudojvZlQzeFmusnWzGzAgAGRzGuITkVJZsJ7vfBmorCwUObq3PD2eO+8VbftfQLBNddcI/MuXbrIXL03uf766+Xap556Sub169ePZN4nO3gz670Oq33CO2+PHDkiczVX3kx48+btY6qpvTrtE957E5U3a9ZMrk26T6j84MGDcm3//v1lrs5n7z2SNxMLFiyQuboWuPzyy+Xali1bynzfvn2RzNsnvD3o1Vdflbm6tvHO2yQz4V3XeLft7ROqbd77dI3y4CfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABCDi2cAAAAAAGKUuzDM+6P45s2bRzLvD7294gxVqGJm9sorr0Syk07S1//e91SlXt5xeCVI6o/wzfQfunt/yO8VrajSA6/0YtWqVTLv1KmTzHv06BHJMjIy5FrvPtaqVSuS1atXT67dsGGDzHNzc2U+b968SOYVmqWiVJ8Jr6hLnS9ekdIFF1wg8wkTJshcnQNnnnmmXOsVcqnSnJ07d8q13kx4xTtZWVmRzCsv8UovmAnf7t27Za5mwiuCUaUiZmajRo2S+dSpUyPZn//8Z7n29NNPl7kqpfJKY7zyobS0NJmrUiKvCOivf/2rzNPT0yPZD37wA7nWKw56/fXXZd6gQYNI5pVqejOheDPhveZ17dpV5u+//34kOxFmQt0Hb5/wyimvvPJKmb/00kuRLOl7J/V+yDtvTznlFJl7Ra6q8M1773TOOefI/Lvf/W4kmz17tlw7c+ZMmVfETBQUFMhc7ROZmZlyrffes3PnzjJXM5GdnS3XpiJvJtR98M5Pb++/6qqrZP7iiy9GMu81W+1XZmZ33XVXJLvjjjvkWm+P90r31PVETk6OXOuV6KmSS+812zsXvTJGNRNHjx6Va5PMRNJ9wishnDNnTiSryJngJ88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEIOLZwAAAAAAYnDxDAAAAABAjHK3bXtNiKohrk2bNnLtihUrZK4ahM10O6TXGDljxgyZt2rVKpJ59+XQoUMyf++998p8216LoNdsp5pRvaa6s846S+ZFRUUyV0IIMveaj1VbsNfY7TWgbtq0Seaqgdx7nFJRqs/E3/72N5mrWRk9erRc67W5fvWrX5W5up/169eXa73zdtmyZZGssLBQrvVmYv78+TJ/+umnI9n06dPlWmYiOa8dVM1EcXGxXOu1fapWbTN9/nuN3aeeeqrMhw0bFsm8ps46derIfNq0aWU+Pu813psJ1Ti7efNmubZFixYy9xqbW7duHcm8FtWDBw/KXLUFey2q3uum10yvGnG9205F3kzk5+dHMq8Z19snVKu2mX4d8ZqF33nnHZmr86Jx48Zy7XPPPSdz1Qptpj9NwmvC917/1Kx4r8M/+9nPZO7N+MsvvxzJvLbtJPuEd1+841CfFuN9zxNhn1Az4X26gbdPqFZtMz0T3nunt956S+bqWLxPdrjwwgtl7n1Pddve+4Rnn31W5kn22bPPPlvmDzzwgMwVb5/wZkI1aHvnrbePeTNR2e+d+MkzAAAAAAAxuHgGAAAAACAGF88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEKPcbdte2227du0i2ZtvvinXeq27DRo0kLlqTPUaGfPy8mR++PDhSOa1jnquuOIKmavb2bVrl1zrtS2r9bVr1y7zWjOzZs2ayVw1T6pWWTO/XVA9fl6DsNeC57XmrVq1KpK9++67cm0qOpFm4qGHHpJr09PTZX7eeefJfP/+/ZHMa+hVj5OZ2c6dOyOZNxOqrd7M7Pe//73MVdOldx8bNmwoc2bCl2QmXnvtNbk2KytL5t5MqMds8ODBcu2sWbNkPnPmzEjmtet6vPN57969Zb7t3NxcmV9wwQWRzJsJ73zxGr5Vc7W3tkuXLjJftGhRJPPOcdW4aua3cKsZV89XqvJmQrXrvvHGG3Jt0plQ+8SgQYPk2m3btslcffpI0vdO3bp1k/mcOXMimTebPXr0kHnPnj0jWZ8+feRataeY+e/LksyE10D+8ccfRzKvVdtrmvdmSD2/J8JMqNfQV199Va71ZsLL1T5x5plnyrVbtmyRuZqJefPmybVe7rWHq33Ca7Nu3769zPfs2VPmtYsXL5b5kiVLZN63b99IlnQm1HtB73VffYLD561Xn0jgfZJAefCTZwAAAAAAYnDxDAAAAABADC6eAQAAAACIwcUzAAAAAAAxuHgGAAAAACBGudu2a9bUX6paDL0Gw1q1aslctUKb6ZbFzp07y7XvvfeezJs2bRrJVKudmd/iNmbMGJkXFxdHsv/+7/+Wa5ctWyZz1VbnPR6qodLM7NJLL5W5anD0mrmbNGki802bNkUyr2HPa4bMycmRuWot9s6dVPRlmIl//OMfMvdceOGFkayoqEiuTTITXtO8N2/e46pmNi0tTa71mlFVYzcz8YkkM+G1U3st0l4zfe/evSNZ165d5Vqv0VfNxL59++TazMxMmXv3vXnz5pHMO19UI6yZ2TXXXBPJjhw5Itf+7ne/k7l3zm3evDmSefdl69atMq9Ro0YkU23DZmatWrVKlKuG1hNhJvLz8yOZ18TrzYR3Lqom6lNOOUWu9fYJ9V7BmwnVTm2mzwvvtr2ZmDt3rsxVM/l9990n13qfeuA1Eavz3Lsv3j6h9hX1+m7m7xPeTFT3fcLbn9VMeM+Rdxte27ZqYvfa4L1PLFDnrffeyduvvNcD9Ykd3kx4TdkhhEjmvb/xric6dOggc9VArt4Lmfnnc5J9wtuvvJlQTejee4zy4CfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABCDi2cAAAAAAGKUuzDM+6Pz9PT0SOb9wb73x++qJMBMl2dMmTJFrlVlAGa6aMgrMFJlQmZma9eulbkq5bjnnnvk2j//+c8yV0UWH374oVw7dOhQmauSADP/cVXWrFlT5rWNGzdO9P1UcZmZLh9Zt25dmY+jqn0ZZsIrWnn11VdlPmjQoEiWm5sr16qCBzNdYDRhwgS51ivfyMvLk7kqrPAKMtavXy9zhZn4hFcgospdGjVqJNd6M7Ft2zaZq0KZZ599Vq7t16+fzFWhmXccXlHXypUrZa7Kyzp16iTXDhgwQObq9cMrF1PlZ2a68MVMz5B3fEuXLpW5ei30Sih37Nghc+88P3DgQCRLsl9VNW8mkuwT3uvz9u3bZa6Kcp5//nm51tsnksxESUmJzFevXi1zVV7mvQ6rwjgzs4suuiiSeSWUF1xwgcz/8z//U+aqBCrpPqGed28mvH2CmTBr2LChXFsR+8Qzzzwj1/bv31/mSWbi6NGjMl+xYoXMVXmZ9zqsnn8zfU3izaZ6r2Zm9sQTT8h89+7dkcx7b+edt0n2Ce+1zbvt/fv3RzLv9ac8+MkzAAAAAAAxuHgGAAAAACAGF88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEKPcbdte46Fq6vSa4OrUqSPzzMxMmS9ZsiSSdezYUa6tX7++zFWzcO3ateVarzXPu++FhYWRrHv37nLtLbfcInPV5O01EaomPTOzp59+WuYjRoyIZA8++KBc67UTqzY9r+3Oaz722vRUG+GYMWPk2lT0ZZiJjIwMmV9zzTUyV03E3vnsNVT/61//KvPxqdZWM7P58+fLXB33okWL5Fr1OJkxE59HNV6a6efPmwnVuGrmn8/q+fNaQCtiJurVqydz77lW+4Q3E95cqcfKa9v2mmIfeeQRmX/ve9+LZB999JFcq9pmzcy6dOkSybz2X+9xatasmcxVW+x3v/tduTYVJZkJ7xMIks7E4sWLI5nX3OvdhmrXTToT6tMNzPRM9O3bV669+OKLZa72mtatW8u1qj3bzOztt9+W+bXXXhvJFi5cKNd6+0Tnzp0jmbdPeI+Tt0+o++69dqQibybUpzKk0j6hXv+84/DeO7Vs2VLmSfaJ5s2by1ydLy1atJBrvfdf3nun66+/PpJ5nwyUZJ/wGrG9fcK772om1N5WXvzkGQAAAACAGFw8AwAAAAAQg4tnAAAAAABicPEMAAAAAEAMLp4BAAAAAIhR7rZt1WRmptvgvBbi4uJimXst0qr92mtmO/nkk8t8Gx6vcXrKlCky79WrV5lvw2uOUy2LH3/8sVy7bNkymdetW1fm7777biRTbb5m/uOnjlu1cJr5zYVew2R2dnYk++CDD+TaVPRlmImvfe1rMvdawtVj4s3EzJkzZb5gwYJI5h3z4cOHZe41Xa5fvz6SqYZPM2aiPCpiJrzn2mvjVetXrFgh13rNo+o1N4Qg1+7YsUPmSV7jvfnx2nW3bNkSyV544YUyfz8zfybUJz4knQnVwuzNhGoh9o7DTD9n77//vlybiipiJoqKimTu7RPqfK6IfcJ7/+C1hJ933nky79ChQyRTnw5i5p+L6v3Q/fffL9d6e1CSfcJrGlev2d76JM3cZv4+oWbCa0lORd5MpKWlRTKvbVu1U5uZ9e7dW+ZqJlauXCnXJpkJb5/Iz8+Xuff6rPaPrVu3lnmtmVmrVq0i2bZt2+Ra73rCmwn16QneTHj7rGom37Nnj1zrzYT3KQ6VvU/wk2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiMHFMwAAAAAAMcpdGOb9AXjjxo0jmfcH4F6BiPcH4Ko8xSt+8IpWVKGQVzzjFWp07NhR5i+99FIk+8UvfiHXeuVI6rY3bNgg16rH2szskksukflPfvKTSOaVSiQpNfHKbrwSA2+94pUVpKIvw0yoUjwzszPOOEPm06dPj2R9+vSRa73CI1V6oQpczJLPxMKFC2WeBDPha9mypcybNm0aybzyHG8mvNdndduqYMtba6Znol+/fnLtqlWrZN6pUyeZv/HGG5Hs61//uly7fPlymTdv3jySea/Zaq2Z2ciRI2X+4YcfyjyJJKVoXgGUKpPx1KtXr8xrq5o3E0n2CW9WvJI1dZ57xUHea2iDBg0i2Z133inXes9p9+7dZf72229HMq946/e//73M58yZE8m814icnByZe4WYSfYJrzBKlV95M+GVSCXZJ06EmVDn7c6dO+XapO+dmjVrFsm8fUKtNdOlYwMGDJBrvdJKb594/fXXy3zb06ZNk/k999wTybwCYm8fu/zyy2WuikvVeyEzv1gwyXsn7zWlqt478ZNnAAAAAABicPEMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADG4eAYAAAAAIEa527a9Jsx9+/ZFMq9l7tChQzL32qxV25rXMFpQUCBz1YTotfd5jYeqZc5Mtxyrxjwzs6uuukrm6nFVbcOf59lnn5X5Rx99FMlUA6SZfh7NzNq2bRvJVDOtmdmyZctk7jU2q/Okfv36cm0q+jLMhNcW7bW8qhm65ppr5NoDBw7IXB1369at5VqP126vGkm99kbvMWEmfN5juXfv3kjmPTbeTOTm5spcnaMVMRNew7G3T3hNv+r4Dh48KNf27t1b5q+99loka9OmjVzrUU34ZmaZmZmRzHsed+zYIfP27dtHMm8mli5dKnNvJtR50rBhQ7k2FVXEPuGdL15zrzrniouL5dqioiKZX3rppZHMO+f2798v80cffVTm6r2Jago281//1CwnnYkZM2bIPMlMJNknvOZj73nv2bNnmddnZWXJtamoIvYJ7/XF2yfUe6eSkhK51tsnVIt00usJb5+oXbt2JPOuJ7z7qN5Tee8lVVu9WbJ9wnt/6D0mHTp0iGTefu/tE94eqc6TipwJfvIMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADG4eAYAAAAAIAYXzwAAAAAAxCh323ajRo1k/uKLL0ay/v37y7VeO57XiKYa+U477TS59v3335e5auVctGiRXKsaQ810C56Z2cknnxzJvDbrv/zlLzJXzXbr1q2Ta7dt2ybzjIyMMudNmzaVa73WPNXq2LJlS7m2sLBQ5osXL5b5qaeeGsm8NtdU9GWYCe989hpkVUPi5s2b5VqvZVHNYUXNhGrc9J5HZiI577GcPHlyJBs4cKBc650XDRo0kLmaicGDB8u1s2fPlrmaCe9TFpK0fpuZtWjRIpJlZ2fLtV5LqXo98M4L9SkQZpU7E6rp1PvUCG8mFi5cKPNBgwZFsu3bt8u1qch7LF944YVIpubfzJ+JJPuEehzN/NbdDRs2RLL7779frlUtumZ+47Bqnfb2mvz8fJlX5j6hzvOkM7Fy5cpI5u0TXruzty+r187qtE80btxY5s8//3wkS7pPJJkJb5+YNWuWzNU54O0TXsu1995J7RPeTHj7RPPmzSOZd17861//krnaD8z0ee49j95tqPeH3iepeJ8CoJr6zSp/n+AnzwAAAAAAxODiGQAAAACAGFw8AwAAAAAQg4tnAAAAAABicPEMAAAAAECMtBBCKM8XNmvWTOZ9+vSJZCedpK/Rd+3aJXPVdGpmtnfv3kjWq1cvuVa1uJnptrp69erJtV5D3IEDB2SuGiOPHDki1/bs2VPm8+fPj2TeU+S1zXqPq2rAVMds5rfmqbZMr8HOa7r0njPVdOmdO6qFsaoxE1HMRGlftplQbZ9mum0+abuu93yomejbt69c6zV1quc0MzNTrvXadb2ZUI36AwYMkGsnTJgg89GjR0eyBQsWyLXea4fX0NqpU6dI5rX/Jmkc9lq/165dK3PvOUsyE08//bTMq5K3T6j7W5n7hNqXzJJ90oI3ExWxTxQXF8u13mvlvHnzIpm3TySdCdWUXBEz4bV+ezPRu3dvmdetWzeSec3mzz33nMyrkrdPeK+LijcT3vOxZ8+eSOZ9Coq3T6j5rKh9QrWHe++dvNfKuXPnRrKSkhK51ttPvXZu9SkT3kx4t51kn1i9erXM+/XrJ/Mk+8Tf/vY3mX8efvIMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADG4eAYAAAAAIIb+C/YyaNeunczr168fydasWSPXeiUBmzdvlrn6w/WsrCy51vvD+sLCwkjWvn17udYrqvGKLNQfxXtrvbxFixaRLDs7W671ChKSPH5eMZRXKqAKNbyinx49esjcK65Sj58qwkhVzEQUM1Hal20mVCGbmZ6JVatWybXec71p0yaZqxIer0jOmwlVYKRKg8z8QpWioiKZN2nSJJJ59/3qq6+WuTrnTj311DKvNfNnQj1+XgnO0aNHZa5KX7yyFq8EySuuatq0aSSrTjPh7RPqMfaKo7x9wpsJ9bqddCbUPpF0JlJln9i9e7fMvZlQBZDeTFTEPuGVZy5ZskTm6jWlTp06cm0qSrJPrFy5Uq5Vz7+Z2caNG2WunlOvSK4iZsIrEfXOZ/U656319pqWLVtGMu9x8t7bJXn8ks6E+p7eTCTdJ9RMqBKx8uInzwAAAAAAxODiGQAAAACAGFw8AwAAAAAQg4tnAAAAAABicPEMAAAAAECMtBBCqOqDAAAAAAAglfGTZwAAAAAAYnDxDAAAAABADC6eAQAAAACIwcUzAAAAAAAxuHgGAAAAACAGF88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEIOL52rsL3/5i6Wlpdn69euPZUOHDrWhQ4dW2TF9ljpGoLIwE0BpzARQGjMBlMZMJFPmi+e0tLQy/TNt2rRKPNzyO3jwoN18883WqlUrS09Pt65du9rEiRO/0G22a9eu1H1v3ry5nXXWWTZlypQKOurj4/Dhw3bXXXel7HP3qeLiYuvWrZulpaXZ+PHjq/pwmAmBmTi+mImKVVBQYL/+9a+tW7dulpGRYTk5OXbllVfakiVLyn2bzMTxsWzZMrvgggssMzPTGjdubKNHj7b8/PyqPixmQmAmKtfRo0ftL3/5i11yySXWunVrq1evnvXo0cPuueceKygoqOrDq/YzwXsnX6rOhJnZ3Llz7cYbb7T+/ftbrVq1LC0trdy3VbOsC//617+W+vcnnnjCXn/99UjetWvXch9MZSkpKbHzzz/f5s2bZzfddJPl5ubaq6++ajfeeKPt2bPHfvazn5X7tvv06WM/+clPzMxs69at9vDDD9vIkSNt4sSJdsMNN1TUXSiz1157LfHXHD582O6++24zs5T6f5k+a8KECbZx48aqPoxjmAmNmTh+mImK9a1vfcumTp1q1113nfXr18+2bt1qDz74oA0aNMgWLVpkbdu2LdftMhOVa/PmzXb22WdbgwYN7N5777WDBw/a+PHjbdGiRTZ37lyrXbt2lR0bM6ExE5Xn8OHDdu2119rpp59uN9xwgzVv3txmz55td955p7355pv21ltvfaELhy+qOs8E750+X6rOhJnZP//5T/vzn/9svXr1sg4dOtjKlSvLf2OhnG666aZQli8/dOhQeb9FhXn22WeDmYVHH320VH755ZeHOnXqhO3bt5frdtu2bRsuvvjiUlleXl6oV69e6Ny5s/t1xcXFobCwsFzf899NmjQpmFlYt27dF7qd/Pz8YGbhzjvv/MLH9FkVdYzbt28PDRo0CL/61a+CmYXf/va3FXOAFYiZYCbKgpmISoWZ2Lx5czCz8NOf/rRU/tZbbwUzC7/73e/KdbvMRLwveozf//73Q926dcOGDRuOZa+//nows/Dwww9X0FFWDGaCmSiLL3KMhYWF4d13343kd999dzCz8Prrr1fAEVac6jQTvHf6fKk6EyGEsG3btnD48OEQQtnPOU+F/s3z0KFDrUePHjZ//nw7++yzLSMj49j/C5OWlmZ33XVX5GvatWtn11xzTals7969dvPNN1vr1q0tPT3dOnXqZPfff78dPXq01Lq8vDxbvny5FRcXf+5xzZgxw8zMRo0aVSofNWqUFRQU2Isvvpjwnvqys7Ota9eutm7dOjMzW79+/bFfqXzggQesY8eOlp6ebkuXLjUzs+XLl9sVV1xhjRs3tjp16tiAAQNs6tSpkdtdsmSJfeUrX7G6detaq1at7J577ok8Hmb6bxQKCgrsrrvuss6dO1udOnWsRYsWNnLkSFuzZo2tX7/emjVrZmZmd99997FfGfn356qij3Hfvn22fPly27dvX5kf17Fjx1qXLl3s29/+dpm/JhUwE8wEM1Faqs7EgQMHzMzs5JNPLpW3aNHCzMzq1q2b5G5+LmaiYmdi8uTJNmLECGvTps2x7Nxzz7XOnTvbs88+G/v1VY2ZYCYqciZq165tZ5xxRiS/7LLLzOyTP3FIdak6E7x3qp4zYfbJ61hFvWaV+de2y2rXrl124YUX2qhRo+zb3/525EU3zuHDh23IkCG2ZcsW+4//+A9r06aNzZo1y+644w7Ly8uzBx544NjaO+64wx5//HFbt26dtWvXzr3NwsJCq1GjRuRXtzIyMszMbP78+XbdddclOk5PcXGxbdq0yZo0aVIqnzRpkhUUFNj1119v6enp1rhxY1uyZIkNHjzYcnJybOzYsVavXj179tln7dJLL7XJkycfe6Hbtm2bnXPOOXbkyJFj6x555JEynQQlJSU2YsQIe/PNN23UqFH2ox/9yA4cOGCvv/66LV682M4991ybOHGiff/737fLLrvMRo4caWZmvXr1MjOrlGOcMmWKXXvttTZp0qTIC50yd+5ce/zxx23mzJlV+qtG5cVMMBPMRGmpOBMdO3a0Vq1a2X/9139Zly5drG/fvrZ161a77bbbrH379pE3S18EM1FxM7FlyxbbsWOHDRgwIPLfTjvtNPvnP/8Ze/9TATPBTFT0PvFZ27ZtMzOzpk2bJv7aqpCKM8F7pxNrJsqtvD+yVj/yHjJkSDCz8NBDD0XWm/Nj/LZt24YxY8Yc+/dx48aFevXqhZUrV5ZaN3bs2FCjRo2wcePGY9mYMWPK9CP8//qv/wpmFmbMmBG5TTMLI0aM+Nyv97Rt2zYMHz485Ofnh/z8/PDRRx+FUaNGBTMLP/jBD0IIIaxbty6YWcjKygo7duwo9fXDhg0LPXv2DAUFBceyo0ePhjPOOCPk5uYey26++eZgZmHOnDnHsh07doQGDRpE7v+QIUPCkCFDjv37Y4895v561dGjR0MIn/9rFpVxjJ/+6sWkSZMi308d42mnnRa++c1vhhD+9/GsLr+iykwwE8xE9ZiJEEKYM2dO6NixYzCzY//0798/5OXlxX6th5mo3Jl4//33g5mFJ554IvLfbr311mBmpY6rqjETzMTx2CeUc889N2RlZYU9e/aU6+srS3WaCd47nRgz8UV/bbvCL57T09Pl79+X9WTv1atXuOCCC46dQJ/+88YbbwQzC08++WTiY83LywsNGjQIubm54bXXXgvr1q0LDz/8cMjKygpmFoYNG5b4Nj899n/fUMws1KhRI4wePfrY79V/erJfe+21pb52165dIS0tLYwbNy5yXz/9u5TNmzeHEELo3LlzOP300yPf/8Ybb4w92S+++OLQtGnTUFxc7N4P72SvrGNM4rHHHgt169Y99iJXHS8UmAlmIu4Yk2AmKmcmQghh5cqV4fLLLw9jx44NL7zwQhg/fnxo0qRJOPPMM8PHH39crttkJip3Jt55551gZuGZZ56J/Ldf/vKXwcxS6mKBmWAmjsc+8Vn/5//8n2Bm4Y9//GOF3F5Fqk4zwXunE2MmvujFc4X/2nZOTs4XarZctWqVLVy48NjvzX/Wjh07Et9mdna2TZ061UaPHm3Dhw83M7OsrCybMGGCjRkzxjIzM8t9vAMHDrR77rnH0tLSLCMjw7p27WoNGzaMrGvfvn2pf1+9erWFEOyXv/yl/fKXv5S3vWPHDsvJybENGzbYwIEDI/+9S5cusce3Zs0a69Kli9WsmfypPl7H6Nm/f7/dcccdduutt1rr1q3LfTtVjZlgJpIeo4eZ+ERlzMS+ffvsrLPOsltvvfVY46mZ2YABA2zo0KE2adIk+/73v1+u42UmKm8mPv1VvsLCwsh/+/RjeSryb3MrCzPBTCQ9xrJ65pln7Be/+IV997vfLffzVRVScSZ473RizMQXVeEXz0k3qZKSklL/fvToUTvvvPPstttuk+s7d+5cruM6++yzbe3atbZo0SI7dOiQ9e7d27Zu3fqFbtPsk78dOffcc2PXffZx+fQP33/605/a+eefL7+mU6dO5T6uilDVxzh+/HgrKiqyb3zjG8c+FH3z5s1mZrZnzx5bv369tWzZsko/hqQsmAmNmUiOmfhEZczE5MmTbfv27XbJJZeUyocMGWJZWVn27rvvlvuNJzNRecf4aXlVXl5e5L/l5eVZ48aNLT09vdK+f0VhJjRm4ot5/fXX7eqrr7aLL77YHnrooePyPStKKs6EGe+dyqM6HGMSFX7x7GnUqJHt3bu3VFZUVBTZ8Dp27GgHDx4s0wmUVI0aNaxPnz7H/v2NN94wM6uU7xWnQ4cOZmZWq1at2O/ftm1bW7VqVSRfsWJF7Pfp2LGjzZkzx4qLi61WrVpyjVc4dLyO0bNx40bbs2ePde/ePfLf7r33Xrv33nvtww8/LPWcVifMRGnMRDxm4hOVMRPbt283s+gbsBCClZSU2JEjRyrse5UVMxEvJyfHmjVrZvPmzYv8t7lz51bbWfgUM1EaM1F2c+bMscsuu8wGDBhgzz77bLl+YpiKeO9UGjNx/FXoR1V9no4dO9o777xTKnvkkUciL8pf//rXbfbs2fbqq69GbmPv3r2lXqzLWi2v5Ofn2/3332+9evWqkpO9efPmNnToUHv44Yfl/2Oen59/7H9fdNFF9t5779ncuXNL/fennnoq9vtcfvnltnPnTvvDH/4Q+W8hBDP735bAz74YVdYxlrVa/oc//KFNmTKl1D8PP/ywmZldc801NmXKlMivr1QnzERpzAQzUZUz8elPDJ5++ulS+dSpU+3QoUPWt2/fRPelIjATZfsIkssvv9xeeukl27Rp07HszTfftJUrV9qVV14Z+/WpjJkojZko20wsW7bMLr74YmvXrp299NJL1eJPF8qK906lMRPJPuazQlTkH1sPGTIkdO/eXa5/6KGHgpmFkSNHhokTJ4YbbrghtG/fPjRt2rTUH/gfOnQo9OvXL9SsWTN873vfCxMnTgzjx48PY8aMCfXq1Qv5+fnH1iZpjDz77LPD7bffHv70pz+FcePGhdatW4dGjRqFhQsXllr36R/k//sxedSHmn/W55X5LFmyJDRq1Cg0adIkjB07NjzyyCNh3Lhx4aKLLgq9evU6tm7r1q2hSZMmoVGjRuGuu+4Kv/3tb0Nubm7o1atX7B/4HzlyJAwdOjSYWRg1alR48MEHw29+85swfPjw8MILLxxb161bt5CdnR0efPDB8Le//S0sWrSo0o7xizRGVsdyJGaiNGaCmUjFmSgsLAzdu3cPaWlp4ZprrgkPPfRQ+OlPfxrq1KkTWrRoUeo2mYnUmomNGzeGJk2ahI4dO4b/+Z//Cffee29o1KhRpNk1FTATzERlz8T+/ftD69atw0knnRTuu+++8Ne//rXUP7Nmzfrcrz/eqtNMhMB7p+o4EyGEsH79+jBu3Lgwbty4MHDgwGBmx/5dfVrD5zluF88lJSXh9ttvD02bNg0ZGRnh/PPPD6tXr46044UQwoEDB8Idd9wROnXqFGrXrh2aNm0azjjjjDB+/PhQVFR0bF2Sk/2WW24JHTp0COnp6aFZs2bhqquuCmvWrImsW7RoUTCzMHbs2Njb/KInewghrFmzJlx99dUhOzs71KpVK+Tk5IQRI0aE559/vtS6hQsXhiFDhoQ6deqEnJycMG7cuPDoo4/GnuwhhHD48OHw85//PLRv3z7UqlUrZGdnhyuuuKLU/Z81a1bo379/qF27dqQpr6KPkQuFTzATzMSnmIlPVPVM7N69O9xyyy2hc+fOIT09PTRt2jSMGjUqrF27ttQ6ZiL1ZmLx4sVh+PDhISMjIzRs2DB861vfCtu2bSvT1x5PzAQzUdkz8elj5/1Tlou546m6zQTvnarfTIQQwttvv+3OxGfvZ5y0EP7fz9phZmZ//OMf7bbbbrM1a9Yk/kB24ETETAClMRNAacwEUBozceI6bn/zXF28/fbb9sMf/pATHfh/mAmgNGYCKI2ZAEpjJk5c/OQZAAAAAIAY/OQZAAAAAIAYXDwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiFGzvF/YpUuXMq9t1KiRzI8cOSLzFi1ayLykpCSS5efny7Xr1q2Tea9evSLZxo0b5do2bdrI3KOOr1mzZnLt0aNHZb5169ZI5t3HwsJCmXfv3l3mtWrVimTec7Bs2TKZN2/evMzfb8eOHTLPy8uTeZ06dSJZenq6XDtt2jSZV6Xc3Nwyr23cuLHMvefD+6gDdR7t2rVLrvVmomfPnpFs06ZNcq03E15pv7o/SWdCnS/euVVcXCzzbt26ybxmzehLoJpjM38m1HPTtWtXudab5YqYienTp8u8KrFPRLFPlPZl2ye81wb1GurNhHdeJJmJnTt3yrWrV6+Wed++fSOZNz/t27eXuUedX+ocMvPvu9qzvJkoKiqSeY8ePWSuZsLba5YuXSpz9dwknQk192Z6JmrXri3XvvXWWzKvSuwTUewTpaXaPsFPngEAAAAAiMHFMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAYacGryY3xjW98Q+Y1atSIZAcPHpRr165dK3PV4mZmlpmZGckOHz4s12ZlZclctet6zYsZGRky9xrl1LF4zYBe451qSKxbt65c26FDB5m/+uqrMlftfV7rc+vWrWWuHr89e/bItQUFBWU+Du9Ydu/eLde+//77Mq9KV155pczVY+bNhNfqqG7DTM/EoUOH5NoGDRqU+ba95kVvJrznVB1L0plQDYneTHgtr6+99prMVUuld3zeTKjXvL1798q1lTkT8+bNk3lVYp+IYp8o7cu2T1x11VUyV+fzgQMH5No1a9bI3Nsn6tevH8kqYp+oqJlQx+Kdc17rrtonvOPo2LGjzF966SWZJ2lCb9eunczVa5533nr7b5KZ8D51Y86cOTKvSuwTUep+NmnSRK5ln4iq7H2CnzwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiKHbJcrAK6xo3rx5JPMKKC677DKZP/HEEzJXf0B/xhlnyLULFy6UuZLkj9nNdImBmdmqVasiWb9+/eTavLw8mderVy+SLVu2TK71SgK8P9pXvNIQr2RBHbdXYlBcXCxz7/g2bdoUybxzJxV5hRVJZuKSSy6R+VNPPSVzNRODBg2SaxcvXixzpVWrVjL3zn1vVlavXh3J+vbtK9du375d5qr0Yvny5XLtzp07Ze6VuyheaYh3H9Vxe9/PKxPx1m/evDmSVaeZYJ+IYp8o7cu2T6jn38ysRYsWkaxhw4Zy7RVXXCHzxx57TOZqJs466yy5dsGCBTJX2rRpI/OkM7FixYpIduqpp8q1W7dulbkqRVuyZIlc65WONW3aVOaKV1yVZCa8czzpTGzcuDGSeedOKmKfiFKvEwMGDJBr2SeiKnuf4CfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQIy0EEIozxeOGDFC5qp5NicnR67dv3+/zL222z59+kSyF198Ua7t2LGjzHft2iVzxWteVK2OZrr1bfbs2XJt27ZtZa4a+d5991251mvo9ZqSMzIyIpn39B85ckTmqjXPawv0eM2Aqrlzw4YNcq3XolmVLrroIpmrZk9vJg4cOCBzr8Wwd+/ekWzq1Klybfv27WW+Z8+eSJaWlibXek2I3kyoc9SbCe/41Pk1a9asMn8/M//xTjITJSUlMlczcdJJyf5/SW8m1OvEunXr5FqvRbMqsU9EsU+UzYm6T1x66aUyV/fXa+719glvVlRL7+TJk+VabyZ2794dybx9wpuJrKwsmauZmDlzplzboUMHmauZmD59ulzr7WPe450q+8SWLVtkrh4T79M/Fi1alOh7Hg+pvk9455yaCQ/7RFR13if4yTMAAAAAADG4eAYAAAAAIAYXzwAAAAAAxODiGQAAAACAGFw8AwAAAAAQI1rFVkY7d+6U+YoVKyJZ7dq15VqvqfHkk0+W+cqVKyOZaqQz81vf6tSpE8m8drdOnTrJfPHixTJXjciqDdnMb5+rW7duJPNaiNVa7zjMzBo0aBDJvOdAtfSZ6TZKr/nYa7T0ctWaV1RUJNemIq+hV523qmXQzG/fbN68ucxXr14dybzWRE+SmfCaWL22woMHD0Yy1XJp5s+EOr527drJtZU5E16LqmqjfO+99+Ra77nxXse2bt0ayYqLi+XaVMQ+EcU+UdqXbZ/Iz8+X+fLlyyOZNxPePtGiRQuZq3nzHl+PmgnvODp37izzjz76SOZqn+jfv79c673+qePzWpJVU7B3HGZ6Jrz77u0TaiZmzJgh13rNzM2aNZO5auGuTjOR6vuE50TaJ9R98Y7DjH2CnzwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiMHFMwAAAAAAMcrdtu01yuXk5EQyrwnOazzcvn27zNPT0yOZ10Lstdru3bs3knmtiar90sxvjlNNc6tWrZJrvcbNLl26RDLvsa5fv77MvcdVtUDWq1dPrlVNhN5teI+11xi5efNmmaum0KNHj8q1qcg7j1q2bBnJvJnwno8dO3bIXDVPeu2SSWbCa01U7ZdmydrDVUO4mT8Tubm5ZbpdM38mvMZh9Tx4z4F33ElmwmtL3bRpk8zVueO1uaYi9oko9onS2Cc+oVr7k+4TqmHWTL/+efuE1167e/fuSObdF+/TF7ymZMXba1RDr5nZbbfdFsm8c3z//v0yf+aZZ2SuznOvndibCfVcJm3V9vYJ9XrKPvEJ9onSvMc6KytL5t559GXfJ/jJMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAY5S4MU39sb2aWnZ0dyQoKCuRar5QohCBz9Yf/6o/Czcw+/PBDmasCnqKiIrnWK2nySgXUfffuS8+ePWWuCgi8woNdu3bJvGvXrjJX98e77x6vsELxnnfv8Ut6LKnGKxBRxSzeffXKJryiA/WcejOxYMECmavz1ivl8M5Fr3xG5d596dOnj8y7d+8eyQYOHCjXesVl3bp1k/kjjzwSyaZNmybXerzCCsWbCa8Mw1tfXbBPRKn77j1OP//5z2XeqVOnSHb48GG51itpUmUyZvoxeeihh+RaD/uEzysvVOdoYWGhXOuV/nhUkZgqYzIzmz9/vszV8RUXF8u127Ztk7k69728bdu2cu2kSZNkvnjx4kj2q1/9Sq49ePCgzH/2s5/JfN68eZHszTfflGs9SWbi448/lrk3EyfqPqHeP3gz4e0TXuFVddwnkl5PHDhwIJJ5rx1XXHGFzM8880yZb9y4MZK1a9dOrr3yyitlXp33CX7yDAAAAABADC6eAQAAAACIwcUzAAAAAAAxuHgGAAAAACAGF88AAAAAAMQod9u215hbv379SOa1gGZlZcnca9NTvOY4r0lUNfKtXr1arvUa7LyGSdX6NmzYMLlWtaWamfXq1SuS3XTTTXKt9xysWrVK5vv27YtkqsnYzKxHjx5lvm2vadl7nA4dOiTznTt3RrJGjRrJtakoyUx4rdVqrZnfvql4jd2dO3eWuZqJNWvWyLVJmty921YNlWZmo0ePlvkpp5wSyVasWCHXem2PS5YskXm/fv0imdfqmJGRIXP1WCWdCe81UjXqn6gzUV33CW8mvBlv3759JPPaSL3Xg5kzZ0ayF154Qa71muZXrlwp86FDh0ayn/70p3Kt18L9wQcfRDL2iU94n2TQoEGDSOY9Bt554c2Q4s2Et0+o9d455J1z3j6h9qyvfe1rcu3s2bNl/t///d+RzGtJ9t7b/eQnP5H56aefHskuvfRSufaNN96Q+fLlyyOZ1+6cdCbU4+p9gkMqSrJPeO+FTrR9Qt229zh57/nOP//8Mh9Hw4YNZX7nnXfK/LTTTotkvXv3lmu/9a1vyfz555+PZNVln+AnzwAAAAAAxODiGQAAAACAGFw8AwAAAAAQg4tnAAAAAABicPEMAAAAAECMcrdtew1nqkn3wIEDcu3Bgwdl7jXKqZbS3bt3y7Wqac1MN2Jffvnlcq1qvjbTbaRmZmvXro1kHTp0kGtr164t8/feey+SqcY8M7Nx48bJ3Pue6jmrW7euXLthwwaZqya8/fv3y7VeA2CzZs1k3rhx40i2ZcsWuTYVeU2nSWbCyz2qXdqbCS9XM5GZmSnXem2pOTk5Mlfn+S9/+Uu5Vj3/ZmbPPPNMJFu0aJFc67Wr/vrXvy7z8XmtnXl5eTJPMhOqQdNMPwdmuh3SO45UdCLtE95M5Ofny3zAgAEyv+CCCyJZUVGRXHvvvffKfPr06ZGsVq1acq03K16rcs2a0bcFXttskyZNZM4+4fPaglu3bh3J1CdkmPmzEkKQeceOHSOZavI382ciOzs7knmvld5MqPvo3bZ3XqhWbTOzWbNmRTJvJubOnSvz3Nxcmb/55puR7Lrrrkt026pZO+k+4TURq5nYvHmzXJuKvgz7hNeq3apVK5mrJvs+ffrItT/+8Y9lvnXr1kjmPdbeXHmt7WqWvU8kadOmjcxVK/+6devk2lTbJ/jJMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAY5S4M80ov1B9kewVWmzZtknmNGjVkrgoQvOItr3xo8ODBkWz48OFyrfeH6F55wKpVqyLZ3r175VqvYEkVZ9SvX1+urVOnjsxViZSZLn1ZvHixXOs9fqp8wSsd84pqkpRRFRcXy7WpyCsMU+dt0pnwyhLUvHklKd5zqs4v7xz3yiO8/JZbbolkXsnMk08+KfO//e1vkUyVGpn5BSxeYYV6Hu677z65tmHDhjIvKSmJZN5sLlmyRObeTKjnwSuXSkXVdZ9o0KBBJEs6E95rvCrBefzxx+Var8AmScHSkSNHZH7nnXfKXO0fDz30kFyr9lMz/drPPvEJr9hIneeq6MvMbOPGjTL3ZkLdtjcTXgmc2ie8YjDvvZNXUqfOo8mTJ8u13uOnCrm8x8MrS/PeO51yyimR7K233pJrvYLXP//5z5HMm4mPPvpI5t57QfU8FBQUyLWp6Mu8T3jr1XuW2267Ta713lOtX78+kr3yyity7bJly2TurVfPw9///ne59umnn5a5eu9UXfYJfvIMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADG4eAYAAAAAIAYXzwAAAAAAxCh327bXPKuaGr0WvEOHDsnca59T31M1LJqZrVixQubDhg2LZF4DpNcy98gjj8g8PT09knmtjnfddZfMVbtmYWGhXNurVy+Ze610ubm5kcxrl/QeP9UA6D1frVq1kvm+fftkrtpivZbkVOTNhGpZ9GbCa+z2ziP1PVUTr5nZ8uXLZd6oUaNIps4VM90UbGY2cuRImasZf+yxx+TaDz74QOaK1855//33y9w75x5++OFIph4Ps2Qz4T1fXgOz1xKutG3btsxrq1p13SdUu6r6tAIz/7lWba6eadOmydxrbVe84xs7dqzMvfs+ZcqUSOa1Pr/zzjsyV3sh+8QnvOZe1VDttWp7jdPeuahe07yZ8Fp3k8zEgQMHZH7mmWfKXJ1z8+fPl2u9Jm/F2ye6d+8u84ULF8pcvUds3769XLtnzx6Zq1n2nq/WrVvL3JsJxWulTkUn0j7hvXfyzgvvPV+PHj0imffpPd6njzz33HORTDVwm/mf+OA9fuqTel5++WW5Ni8vT+ZJ3jul2j7BT54BAAAAAIjBxTMAAAAAADG4eAYAAAAAIAYXzwAAAAAAxODiGQAAAACAGOVu265Ro4bMly5dGsm89lqvOc6jbsdrhjxy5IjMVWue12zotcx5972kpCSSec2atWvXlrlq0vVatb0GwDfffFPm69ati2ReU12fPn1krh4T1Yho5rcfqnPEzGz48OGRzGvcTEWq3c9M31/vvEg6Ew0aNIhk3kyo89M7Fm9+rrzySpmrRnEzs6lTp0ayuXPnyrV169aVuZoJr53Ta1GtX7++zNWseOen16K5e/fuSOY9v16zpvecnXfeeZEsSSt5Vauu+4R6TfOaj73b2LZtW5nXDxw4UK5dtWqVzC+66KJI9oMf/ECuXbBggcwnT54s85UrV0ayJE39Zro9nn3iE95MqKZn73XVm4kQgszV6+WSJUvkWm+fUMeyYcMGubZWrVoy9xqq1fsK79xSTe5muoXb2ye893beJxmo5nzvvZN6n2VWMTOhGo7N9OuBt8+morS0NJmfSPtEcXGxzL33PWqWvfcV06dPl7malaefflqu9T7ZwWv2V586NGvWLLn2RLye4CfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQIxyt21nZGTIfMyYMZFMNe6ambVo0ULmXmtxfn5+JKtZU9+Fvn37ylw1x23ZskWuLSwslPmaNWtkPmjQoEiWtI1SNcd5DZBdunSR+VtvvSXzdu3aRTL1mH7e8anHOysrS671GsW9RmTVRuk9v6nIm4nRo0dHspdfflmu9WbCa6PctWtXJPOeO28mMjMzI1mPHj3K/P3MzKZNmybzRYsWRTKvvdE7n2fPnh3J1OuMmW5cNTMrKiqS+XXXXRfJvBZV73VCnaOqBd3Mb4r1ZuLAgQNl+n6pqrruE+p58tqzvebe9evXyzzJcVxyySUyV68HTz75pFw7c+ZMmX/88ccyT7JPeM8B+4TPmwn1WjRlyhS5tmXLljJPMhPePtG/f3+ZqzberVu3yrUFBQUy37Rpk8xbtWpV5rVdu3aV+YwZMyKZ1+7ttZh7jcjjx4+PZN5+4LVtq8c76T7hffKKmgnv+U1F6j2ImW4RP9H2Ce95Gjp0aCTz3jupx8lMN4ofPXpUrvXarH/zm9/IXB2L97p0Il5P8JNnAAAAAABicPEMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADHK/dfTXpnD3//+9zLfRo0aNWTulSOpUgmv9MQrCXjnnXci2eDBg+Xayy+/XOYNGzaU+dNPP13m41BrzcyuvfbaSOaVcvzP//yPzL1SpxBCJPPKS9Qf25vp++4Vq3klTRs3bpR5x44dI5lX+JGKNm/eLPMXX3yxzLeRdCZycnIi2eHDh+Va71zs06dPJBswYIBc6xUHrVixQubbt2+PZF5pw0cffSRzVXpx4YUXyrV79+6VuXc+q5nwngPvtlXBRUXNRIcOHSKZKnRKVdV1n1BlQPXr15drs7OzZe6tf//99yOZmkEzv2TmgQceiGTeOe4VsCSZCfaJiuOViD733HNlvo2kM9G6detI5u0T3m2rWfbeC3nnizq3zHRRl3ccH374ocyPHDkSybzzU5WfmZmNHTtW5gsWLIhk3vPl3fckM+HlXglhbm5uJFN7R6pin4hSxcReoZ33vkwVqK5du1auffDBB2Wel5cn84rYJ1RhXnXZJ/jJMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABCj3G3bPXr0kHm/fv0i2XvvvSfXzp8/X+Zeg6Nqq/PWLl++XOYlJSWRbOXKlXJty5YtZa4ajs3M7rnnnkjmNWKfddZZMq9Vq1Yk81oTvYZj1bxophsmVaOfmd94px6TgoICudZr9fOaZdX9Oe200+TaVOTNRN++fSOZNxMffPCBzFUroZlZixYtIpk3E15To3o+0tPT5dpJkybJfNq0aTIfNmxYJFu9erVce91118lcnXOqSdHMbO7cuTL3ZmLJkiWR7OWXX5ZrMzMzZa6e96Qz0bt3b5mr16ZTTz1Vrk1FJ9I+0bRpU7nWaxBWrdpmZuPGjYtkXiOs9xqvGka9hnzvcWWfqBrerKu5fvfdd+Va73VOfTKBmd4nvLVLly6VeZKZOHr0qMy9TxkZPnx4JFMN12b+a4p6TK688kq5tk2bNjL3GrTVvHlN+N4+od43ejPhzZt6L2FmtmzZskg2aNAguTYVfZn3iTlz5sj8vPPOi2Te+8Pvfve7Zb5tr61+0aJFMq/MfaJ79+6RrLrsE/zkGQAAAACAGFw8AwAAAAAQg4tnAAAAAABicPEMAAAAAEAMLp4BAAAAAIhR7rbtdevWlTlv0qSJXOu1PXrNbKot+NChQ3Kt18C2f//+SPbII4/ItVlZWTJv3LixzM8999xIptoCzfzmOHXbXnOldx+9265du3Yk854b1SJoppuS8/Pz5VqvdXD79u0yV4+V14SeirzHXeXeY+PNRFFRkczVTBw+fFiu/cY3viFz1abozYTX2F2/fn2Z9+zZM5J5DaitWrWSuXqsvMfDa1HNy8uTuWpX9Z6bI0eOyFw1THoz4c2bNxOqXbU6zcSJtE9498U750aPHi1z1Yq9detWubZu3boyV7PiPR7t2rWTuXfOsU9ULq+RVuXNmjWTa73noyJmwmt0PnDgQCTz7os3EzVq1JC5ahb+5z//Kddu2rRJ5nfddVcka9++vVz7+9//XuZeq7L69AnvvPVmQj1W3jnu3ba3j6kWYdXAnaq+zPuEd52hPlHkq1/9qlzrnbeLFy+OZF5TP/tEMvzkGQAAAACAGFw8AwAAAAAQg4tnAAAAAABicPEMAAAAAECMcheG7d69W+Ynn3xyJPNKL7yCJa9Q6KWXXopkJ52kr/+976kKF7zbmDp1qsyHDRsm84kTJ0ayoUOHyrXe91SFYTt37pRrc3JyZK7+CN/MrEGDBpHM+0N+r2RBFX5kZmbKtevXr5d5ly5dZP7+++9HsuzsbLk2Fe3du1fmzZs3j2Re+YFXNjFy5EiZv/LKK5HMO7e2bNkic1VWpMrvzMxGjBgh87S0NJmrUgn1eJiZ/eMf/5C5KvW64YYb5NpVq1bJ3CuV2LVrVyTzSm0KCgpkXqtWrUhWr149uXbDhg0yV6VtZmbz5s2LZOo1NlWdSPtEzZp6u+zWrZvMb7rpJpnfeuutkWz48OGJblvtE17RircfeOUu7BOVy5sJdR+8fcJ7nbvqqqtk/uKLL0Yy7zXbm4nNmzdHMvXaZ6YLtsz818WDBw9GMq9wr06dOjJXrxO/+MUv5FqvTMu776rUySty9fYJ9frhzYT3PsCbiTlz5kSyE2EmTqR9Qr0XMjMbNGiQzFu2bFnm49ixY4fM1Xs773qCfSIZfvIMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADG4eAYAAAAAIAYXzwAAAAAAxCh323arVq1krlrfiouL5doVK1bIXLXgmelmNq8xcvr06TJv3bp1JPNaq73bvvvuu2Xepk2bSKZaLr3jMDO79tprI5nX4qwaKs38x1vx2vG81sGNGzdGMq9B02u28xqHk7RRpiLVjmimZ0KdK2b+TKhWbTP9PHnP3Z/+9CeZd+7cOZINGTJErvVaVGfMmCFz1cio2rPNzN5++22Zq8ZNrzn8rLPOkrnXuKl4Lapei6Y6nzMyMuRaryl706ZNMk/SRpmKvgz7xIUXXijzxx57TOazZs2KZF5D73e+8x2ZL1++PJJ5jaZnn322zBcsWCBzhX2i4nh7v/pEgLZt28q16vk3899vqJnwnjvvdVjtWS1atJBrvWbhl19+WebqHB0wYIBce/vtt8tctQV7c/WVr3xF5urTDTwhBJl7+4Rq0PbOW+9xVXNlxj5hlvr7hNdM7z1P6pN6VOO9mb/XqBb2iton1Pn/Zdon+MkzAAAAAAAxuHgGAAAAACAGF88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEKPcbdtei6Fqh3zjjTfk2qysLJk3bNhQ5qtWrYpkgwYNkmu3bdsm80OHDkUy1dL4efr16yfzffv2len7mfmPnzpur3nRy702PdUA7LXgNW7cWOYff/xxJEvagucdt3oeZs6cKdemIu9xb9euXSR788035dr69evLXLVWm+nH7PTTT5dr8/LyZP7rX/86kj355JNyrffcHT58WOaqTdFrZOzVq5fMr7rqKpkrkyZNkvnatWtl3rNnz0jmzab3uqTuu9eq7bWleo+res1799135dpU9GXYJ7yWV+9TElTrqtdoesopp8j8tNNOi2TvvfeeXLtz506Zs09UDW8m2rdvH8leffVVuTbpTKxcuTKSDR48WK71PslAzYSatc9zxx13yPzUU0+NZN752alTJ5mr88L7dAjVbG7mPzeqpddb26hRI5mrfcI7x1Uzt5nf5Kza19955x25NhV9GfYJ7xNW7rzzTpkrXpu+er31eK3fSfcJ1YrtPY8n4j7BT54BAAAAAIjBxTMAAAAAADG4eAYAAAAAIAYXzwAAAAAAxODiGQAAAACAGOVu265ZU39pfn5+JFONeWZmtWvXlrlqNjTTzbheG6nXPNqsWbNIplqyzXSbnJlZjRo1ynzbXvvc0qVLZX7++edHMq/p1LuPXludalv2btu7jbS0tEjmNS23bNlS5l5jpGre8xoKU5E3E6rF0LtfXhOiasA1M+vRo0ck69y5s1zrnS9NmzaNZF4z98GDB2XutYEnmYkmTZrIXK332i/vvfdemXfp0kXm6n6qc9zMnwk1Q9599JohvZlQs3UizMSJtE94TaJdu3aV+aZNmyKZd758+OGHZb7tuXPnyrXefVSPkxn7RGVLMhOqgdvM3ye8mejdu3ck69atm1zrtfk3b948ku3Zs0eu7d+/v8wvuugimT/wwAORzNvHvO+pzou6devKtbNnz5Z5nz59ZK4ayL2Z8F7j1XpvJrxzv3Xr1jJX+7L6lI9UVZn7hPc+/njvE9/5zndk7u0fBw4ciGRLliyRa73rCXV+ee8lT7R9orLfO/GTZwAAAAAAYnDxDAAAAABADC6eAQAAAACIwcUzAAAAAAAxyl0Y5v1heJ06dSKZVybklaR4ZUCqAOH555+Xa73ih927d0eywsJCufbo0aMyX716tcxV2UDHjh3lWq94KSsrK5IVFxfLtd4f8v/jH/+QuSo669Chg1y7fv16mavn3St6UkVZn3fb+/fvj2Rr166Va1ORVzSVnp4eydTzbObPhCrOMNPlGVOmTJFrvZnYtWtXJCsqKpJr1XNkZrZq1SqZq5lQ5TVmfpmMelyfeuopudYrmfEK0CpiJhSvBMR7Hjds2CBzVT6ybt26Mh9HVfsy7BPeuXXo0CGZq+fU2yfef/99mavzvKSkRK719gnv8WOfqFzeTKjCo4YNG8q1SWdCFY8988wzcq1X9qWeJ+84vFKiJ598UuaqCMkrx7r66qtlvnz58kim9jYzs169esl88+bNMleFVrm5uXJtktdnbya8fcI7z9VMeO9TU1Fl7hPbt2+XeUXsE2omvH3CK6vyXuPVc+3Nlfd+Ul3DJL2eSJV9wpsJ77ZV4dqaNWvk2vLgJ88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEIOLZwAAAAAAYnDxDAAAAABAjHK3bXutu6oxUrWemekWYjOz+vXry3zx4sWRrFOnToluQ7Wo1qpVS66tV6+ezFu0aCFz1bLnNTA3a9ZM5upYvO/nNQBOnDhR5tdee20kW7RokVzrNaCqllevXVI1VJqZNW3aVOZHjhyJZNdcc41cm4q8BnX1OHgzodolzcwyMzNlrlpKveZebyZUI6maYzO/7TE7O1vmqgHz9NNPl2u9Wf7ggw8imddo6bWozp8/X+bq/PJmwmtuVa2rSWfCa5hUbZljxoyRa1PRl2Gf8NpIR44cKfN58+ZFMm+f8B6/nJycSOa11X/88ccy//Of/yxz9onK5T2n6vxKuk9457N6/ry2aK+5Vz3X3myeccYZMveacdXruXe+XHDBBTLv169fJFP7o5lZ9+7dZe41H19//fWRbMGCBXJtkpnw2rO99unmzZvLXM3EddddJ9emouq6T6hjufHGGxPdxrRp02T+4osvRjLvvPWuJw4fPhzJGjVqJNd6t50q+4Rq9zbz77uaCXXM5cVPngEAAAAAiMHFMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAY5W7bVk1mZrol0GshLioqkrnXmKtaQ1evXi3XnnzyyTJX7b+q0dbMLD8/X+ZeE6JqDc3LyyvzWjOzEEIk27Fjh1zrNQh7jciq6dJrkPXak1X7oddCrJr0zMzWrFkjc9UqrpppU1VFzERxcbHMe/bsKXN1PlfETHi8c9E7n9u2bRvJTjvttDJ/PzOzV199NZJ5s6naJc0qZia8x0/dd9XWbOY33HoNk2oOVft4qvoy7BOvvPKKzM8++2yZX3XVVZHsueeeK/NxmOnHzzs+7zWUfaJqeDOhGte9ZmHv0wb69u0rc3UerVy5Uq5NMhPq/YqZ2UsvvSTzbt26yVzte1u2bJFr9+7dK3O1R7711lty7aFDh2TuzYRqxU46E6oNujJnYu7cuXJtKqqu+4T65JAuXbrItd61gLefq8ck6fWE4r13Yp9Ihp88AwAAAAAQg4tnAAAAAABicPEMAAAAAEAMLp4BAAAAAIhR7sKwli1byrxJkyaRzCvP8f4w3PsD8KZNm0aybdu2ybWNGzeWuSoJ8Eo2vEKNTp06yfztt98u823PmDFD5qo4wysDaNasmcy/9rWvyXzhwoUyV7wiEFVqUqdOHbnWKw/w1iuZmZllXlvVVEGBmT4X9+zZI9d6s+LNhJo3r1RCrTXTJVveeesVanTo0EHmqhCiTZs2cu2//vUvmW/cuDGSeTPhzf0ll1wi8yQz4VGlJt457h13kpnwCjxS0Zd5n/COr1WrVpHs17/+tVx78803y1w9rt7rrTf37BNVw5sJdd7u3LlTrvX2j1WrVpX5tjdv3izXeu8r1EwMGDBArvUKEO+55x6Zq33FK8n0StRq1KgRybzXW/V4mJldfvnlMl+wYIHMFa+4L8k+wUx8IlX2ie9973syV9cCXiHqfffdJ3NvP1flrEmvJzp27BjJvNlkn0iGnzwDAAAAABCDi2cAAAAAAGJw8QwAAAAAQAwungEAAAAAiMHFMwAAAAAAMcrdtu01nO3duzeSrVixQq49dOiQzFVDnJluTlTt1GZmRUVFMldNiF5Ln9fU6DUOq/VvvvmmXJubmyvzjz76KJINHDhQrh02bJjMH3/8cZmrpjnvefRaPtu2bRvJVAunmdmyZctk7rVoqvMkKytLrk1F6enpMt+3b18kq6iZUA2ER44ckWsLCgpkrpoQvedfNZqama1du1bmY8aMiWTTpk2Ta/Pz82WuHr/WrVvLtZ6ZM2fKvF69epGsImZCNZib+TPRq1cvmavzpH79+nJtKvoy7xPPPfeczFUD/Y033ijXTpw4UeYHDx6MZN7j5M2s19DKPlG5ksyE99h4ry/e+wq1T5SUlMi1hYWFMk+yT3iN0//f//f/ybx3796RzGv/9dqCX3zxxUjmfbKDZ/r06TJPMhPePqY+kcKb2aVLl8pcPU5m+jw5UWeiKvaJk08+WeZqrrx94q233pK5936tIq4n1D6RdCbYJzR+8gwAAAAAQAwungEAAAAAiMHFMwAAAAAAMbh4BgAAAAAgBhfPAAAAAADEKHfbdqNGjWQ+ZcqUSHbqqafKtV47XoMGDWSumtwGDRok186ZM6fMt71gwQK5VrUjesdhZpadnR3J0tLS5Fqvka958+aRzGtvXLRokcxVm6uZbu/znkevQXblypWRLCcnR6712p2941at4jt27JBrU5H3WKoW0P79+8u1Xouq1xKozsXTTjtNrn3//fdl3rBhw0jmPUft27eXuXfOqeZWr/33sccek3mLFi0imde4um3bNplnZGTIXLVlJp0J1erYsmVLudZrsl28eLHM1WvniTATX+Z9QrWJ/t//+3/lWm//UDP0wQcfyLVbtmyRuTcT7BOVq3HjxjJ//vnnI5n3SRveTCTZJwYPHizXzpo1S+bqHPDOOa/h2Hv9Uy3X77zzjlzrnS9qDr1PgcjLy5N53bp1ZV4RM6EatL1PjfA+BUB9GouZfn2rTjOR6vvEwoULZd6uXbtINn78eLnWey2vzOsJ1WZdUe+dvuz7BD95BgAAAAAgBhfPAAAAAADE4OIZAAAAAIAYXDwDAAAAABCDi2cAAAAAAGKUu2375ZdflrlqODvpJH2NfuTIEZmXlJTIXLWG9unTR671GtvUsag2XzO/FfjAgQMyVy2q3n3s1auXzB9++OFIlpubK9d6zZVeU2OrVq0imddQ2KRJE5mffPLJkcxr6du+fbvMvedMtel5zaSp6JVXXpG5ar9OOhNHjx6VuZoJ79zyZkI1OKrn2Sz5TKjz2buPPXv2lPn8+fMjWQhBrlVt9WZ+G6V6TA4ePCjXejOhvqd37nu595ypWa5OM8E+EaX2iT179si1zz33nMzVTHiPhzfL3kywT1SuqVOnyvyMM84o820UFxfL3NsnNm/eHMn69esn16rn3+z4z4T3PqZv374yV58m4b12eDPhtfGqVmxvJpo2bSpz1Z7stX5v3bpV5t5zpt4LVqeZSPV9QjXhe8dSr149udZrnK6I6wnvvZNqw/8y7xNeG3h58JNnAAAAAABicPEMAAAAAEAMLp4BAAAAAIjBxTMAAAAAADHKXRjWrl07mWdmZkaytWvXyrVeuc+mTZtknp+fH8kaNGgg13p/WK/+iLx9+/ZyrVce4ZV1qIIG7zi821B/0P7oo4/Ktbt375a5Khow0/fHKzfwikfUH/OrwikzvwRp8eLFMlelAnXq1JFrU5E3E/Xr149ka9askWu9mVCFL2b6Oc3KypJrvXNRlY14M6Fm0CzZTHhrvVyV0qjyFTO/3CLJ4+fNhFe0sXPnzkjmzUSPHj1kvnTpUpmrx69u3bpybSpin4iqiH1CFa14M+HtExUxE+wTyXXo0EHmap9YuXKlXOsVdW3cuFHmqmwn6UyoAq+OHTvKtVUxE+oxadmyZaLjS/L4qdcwM3+fUK9L3kz07t1b5osWLZK5Kiljn/jEl2GfSFKMVxX7REW8d/JK0apqn+AnzwAAAAAAxODiGQAAAACAGFw8AwAAAAAQg4tnAAAAAABicPEMAAAAAECMtBBCqOqDAAAAAAAglfGTZwAAAAAAYnDxDAAAAABADC6eAQAAAACIwcUzAAAAAAAxuHgGAAAAACAGF88AAAAAAMTg4hkAAAAAgBhcPAMAAAAAEIOLZwAAAAAAYvz/0Z54nreLSVkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 잘못 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Wrongly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(wrong_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "맞게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correctly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 정확하게 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Correctly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(correct_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TEST2 : 표준편차 0.6 가우시안 노이즈를 적용한 테스트 데이터셋을 사용하여 테스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchvision import utils\n",
    "\n",
    "noisy_transform=transforms.Compose([\n",
    "    #transforms.Resize((32, 32)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307,), (0.3081,)),\n",
    "    AddGaussianNoise(mean=0., std=0.6),\n",
    "])\n",
    "\n",
    "# MNIST test dataset 불러오기\n",
    "gau_06_test_data = datasets.MNIST(path2data, train=False, download=True, transform=noisy_transform)\n",
    "gau_06_test_dl = DataLoader(gau_06_test_data, batch_size=128)\n",
    "# test data를 추출합니다.\n",
    "x_test, y_test = gau_06_test_data.data, gau_06_test_data.targets\n",
    "\n",
    "if len(x_test.shape) == 3:\n",
    "    x_test = x_test.unsqueeze(1)\n",
    "\n",
    "# tensor를 image로 변경하는 함수를 정의합니다.\n",
    "def show(img):\n",
    "    # tensor를 numpy array로 변경합니다.\n",
    "    npimg = img.numpy()\n",
    "    # C*H*W를 H*W*C로 변경합니다.\n",
    "    npimg_tr = npimg.transpose((1,2,0))\n",
    "    plt.imshow(npimg_tr, interpolation='nearest')\n",
    "\n",
    "\n",
    "for batch in gau_06_test_dl:\n",
    "    inputs, targets = (b.to(device) for b in batch)\n",
    "    x_test_grid = utils.make_grid(inputs, nrow=8, padding=2)\n",
    "    show(x_test_grid.to('cpu'))\n",
    "    break\n",
    "\n",
    "\n",
    "# 아래처럼 x_test로 접근하면 transform을 거치기 전 원본 데이터가 시각화 되므로 위의 코드처럼 순환하여 시각화하는 방식을 사용\n",
    "#x_test_grid = utils.make_grid(x_test[:40], nrow=8, padding=2)\n",
    "#show(x_test_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도 출력 (표준편차 0.6 가우시안 노이즈 적용)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test set: 89.040%\n"
     ]
    }
   ],
   "source": [
    "correct_samples, wrong_samples = check_accuracy(gau_06_test_dl, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "틀리게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrongly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 잘못 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Wrongly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(wrong_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "맞게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correctly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 정확하게 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Correctly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(correct_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TEST3 : 표준편차 0.8 가우시안 노이즈를 적용한 테스트 데이터셋을 사용하여 테스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchvision import utils\n",
    "\n",
    "noisy_transform=transforms.Compose([\n",
    "    #transforms.Resize((32, 32)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307,), (0.3081,)),\n",
    "    AddGaussianNoise(mean=0., std=0.8),\n",
    "])\n",
    "\n",
    "# MNIST test dataset 불러오기\n",
    "gau_08_test_data = datasets.MNIST(path2data, train=False, download=True, transform=noisy_transform)\n",
    "gau_08_test_dl = DataLoader(gau_08_test_data, batch_size=128)\n",
    "# test data를 추출합니다.\n",
    "x_test, y_test = gau_08_test_data.data, gau_08_test_data.targets\n",
    "\n",
    "if len(x_test.shape) == 3:\n",
    "    x_test = x_test.unsqueeze(1)\n",
    "\n",
    "# tensor를 image로 변경하는 함수를 정의합니다.\n",
    "def show(img):\n",
    "    # tensor를 numpy array로 변경합니다.\n",
    "    npimg = img.numpy()\n",
    "    # C*H*W를 H*W*C로 변경합니다.\n",
    "    npimg_tr = npimg.transpose((1,2,0))\n",
    "    plt.imshow(npimg_tr, interpolation='nearest')\n",
    "\n",
    "\n",
    "for batch in gau_06_test_dl:\n",
    "    inputs, targets = (b.to(device) for b in batch)\n",
    "    x_test_grid = utils.make_grid(inputs, nrow=8, padding=2)\n",
    "    show(x_test_grid.to('cpu'))\n",
    "    break\n",
    "\n",
    "\n",
    "# 아래처럼 x_test로 접근하면 transform을 거치기 전 원본 데이터가 시각화 되므로 위의 코드처럼 순환하여 시각화하는 방식을 사용\n",
    "#x_test_grid = utils.make_grid(x_test[:40], nrow=8, padding=2)\n",
    "#show(x_test_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도 출력 (표준편차 0.8 가우시안 노이즈 적용)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the test set: 86.430%\n"
     ]
    }
   ],
   "source": [
    "correct_samples, wrong_samples = check_accuracy(gau_08_test_dl, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "틀리게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrongly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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5554z48aNM6+99poJCwszxYsXN9u3b8+07uYfkP/2PmnQh5r/3q3+IH3Xrl2mePHiJiQkxAwfPtyMHTvWvPbaa6Zbt24mKioqY92pU6dMSEiIKV68uHn55ZfNO++8Y6pUqWKioqIc/8A/NTXVxMTEGBEx/fv3N59++qn573//azp16mRmzpyZsa5mzZqmdOnS5tNPPzXffvut2bFjR47dxz/SLJwXSy+8cSaSkpJMrVq1jI+Pj3nwwQfN6NGjzTPPPGMKFSpkypQpk+k2ORPeMxNXr141YWFhJl++fGbkyJFm0qRJmf5Zs2bNLb/+dstLMzFhwgQTGRlpnnvuOTN69Gjz5ptvmtq1axsRMW+++WamtZwJ75mJS5cumYCAAPOPf/zDvPfee2b06NHmySefNP7+/iY4ONhq2s1teWkmjOG5U16cCbfPZ27jTHAm8tpM3LaL57S0NPPcc8+Z0NBQ4+/vbzp37mwOHjxoteMZY8y1a9fMiBEjTOXKlY2vr68JDQ01LVq0MO+++65JTk7OWOfmYB86dKipVKmS8fPzMyVKlDB///vfzaFDh6x1O3bsMCJihg8f7nibf/RgN8aYQ4cOmQceeMCULl3aFCxY0JQrV8706NHDTJs2LdO67du3m+joaFOoUCFTrlw589prr5kvvvjC8WA3xpiEhATzn//8x0RERJiCBQua0qVLmzvvvDPT41+zZo1p2LCh8fX1tZryPH0fuQH8KrdnIi4uzgwdOtRUrVrV+Pn5mdDQUNO/f39z+PDhTOs4E94zEzefO+2frGzSt1NemolNmzaZO+64w5QrV874+vqawMBA06pVKzN16lRrLWfCe2YiKSnJDBkyxERFRZmgoCBTsGBBEx4ebh566CGPNdp7Ul6aCWN47pQXZwLhuRNn4ibOxK/+yEz4GPP/ftZOIiLy2WefybBhw+TQoUOuP5Cd6M+IM0GUGWeCKDPOBFFmnIk/r9v2N895xbJly2Tw4ME80In+H84EUWacCaLMOBNEmXEm/rz4k2ciIiIiIiIiB/zJMxEREREREZEDXjwTEREREREROeDFMxEREREREZEDXjwTEREREREROeDFMxEREREREZGDAtn9wqZNm8I8PT3dysqUKQPXpqSkwDwiIgLmaWlpVnby5Em49pdffoF527ZtrWz37t1wba1atWCuFZSjxxMWFgbXosciInLw4EErO3bsGFx748YNmLdu3Rrmvr6+VpacnAzXrl27Fubh4eFW1qpVK7hWu9+HDh2CeUBAgJUVLlwYrv32229hnps8MROpqakwr1SpUpbXazOxdetWmLdr187Kdu3aBdfWrl0b5ugxiuCZqFChAlyrPXZ0vLidCe0YRTOhvS+tWbMG5uj9qmXLlnCtdr/R3IuIBAYGWpk2E9988w3Mc1Pjxo2zvJb7hC0n9wntGPXz87My7TXgPuGem5koXbo0zLXXw80+cerUKbhWm4mYmBgr27NnD1xbs2ZNmLuZCbf7BJqJ48ePw7XeMhMtWrSAa0+cOAFzbZ9AM+Hv7w/XeuNM8HrCdrtnIiEhAeZuric8ce6kzYR2vz0xE9k5d+JPnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc+Bit6s3BiBEjYI4a2C5dugTXau2/qNlQRKR48eJWdvXqVbg2JCQky/dPa14sWrQozLWGanRftLZMrTkOtYaixl0RkXr16sF83LhxMEeNfFpzodaWWbBgQSs7c+YMXBsfH5/l+yGCnyvttufOnQvz3PT888/D3M1MaK2O6DZE3M1EaGgozNFrqs1EUFAQzLWWxStXrliZ25lADYmoSVFEpH79+jAfO3YszFGbZ6lSpeBarS3TEzOhPX9ly5a1Mq0l1xtn4q+wT7idCXRftGNOa9319n2iQAH7gzzOnj0L1/7V9gltJtD7iNt9Iq/OhCf2CTQTRYoUgWvr1q0L888//xzmbmaievXqMEfPX07OxOnTp+HaefPmwTw3aedObmbCW/aJxMREuFabCV5PZJZXrif4k2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInJgt3pkkVZYER4ebmUlSpSAa//973/D/MUXX4T55cuXrax3795w7YoVK2CO+tG0ggf0x+y3yjdv3mxlnTt3hmsPHz4M82LFilnZ2rVr4VqtTKZcuXIwR48dFXWI6AVVsbGxVqaVBCQlJcFcW797924r0woSvJFWWFGxYkUrczsT//nPf2COyjP69u0L1y5fvhzmiDYTqAhIRD9eNm3aZGVdunSBa7WZQIUfnpoJRJsJbe7dzIRWJqKt37Nnj5WVLFkSrvVGOTkTf4V9Ah1bItwnfk8rXPNGbmZCK3kcOnQozLWZQPtEnz594Nqff/4Z5ki1atVgnhvnTqgASpsJrWAJFTRqtJnQCqo8sU9o50Non/gzzISb64mnn34a5i+88ALM/0z7hCdm4q+wT3jyeoI/eSYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJy4GNQZVoWPPHEEzA/cuSIlVWtWhWujYuLgzlqwRMR6dChg5X973//g2vr168P89OnT1tZvnz4vyFozYvBwcEwR61vM2fOhGujoqJgjtqMp0+fDtdqzXFaA2ZQUJCVpaenw7VpaWkwR615WgOz5uDBgzCvXbu2le3YsQOuXbVqlavveTsMGjQI5qgJUXuNPDETH3zwAVyrzcTJkyetTJsJrXnREzNRp04dmLuZCa150c1MaG+JKSkpMEftqvnz54drNdpMoOdk+/btcK03zgT3CdufaZ9ITU2FuZuZ8PHxgfmfdZ/QZgI1z+bkTLjdJ06dOmVl2mvqdibQMTpr1iy4VtsnUGuxp2YiMDAQ5ognZkJz6NAhmKOZ0PaJ1atXu/qet8NfYZ/wxLnTjBkz4Nqc3Ce09vAiRYpYmdvriZw8d8rpfYI/eSYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJy4K4m+TdQQ6+IyIYNG6ysUKFCcK3WSlexYkWYb9q0ycq0BjtNQECAlWlt0Q0bNoT5ihUrYH7lyhUra9euHVyrNfeiVketSU9rgET3Q0SkZMmSVqY1nWqNkaiNUmuK1Ro3tdcMtebduHEDrvVGx48fh/ntngmtcVqDZgI1l4roM/Hzzz/DHB2L/fr1g2tLlSoF8+joaCvr0qULXLt//36YL168GOYlSpSwMu010GYCtVFqrZjaa6PNxIEDB6wsMTERrvVGntgnNN6yTzRo0ADmK1euhLm37xOhoaFWpjWg5uQ+oeV5fZ84ceIEzDdu3GhlhQsXdnXbOTkT/v7+VoY+fUNEnwnt3Ak1ImszkZycDHPU/puTM+GJfcJTM8F9wvuvJzwxE6ghXEQkKSkJ5p6YCa2tHJ07ub2ecHPu5G3XE/zJMxEREREREZEDXjwTEREREREROeDFMxEREREREZEDXjwTEREREREROeDFMxEREREREZGDbLdta+2bVapUsTKtHTEoKAjmR44cgTlqe6xQoQJcW7p0aZifO3fOyrTHsnbtWpj7+fnBHLX9bdmyBa49evQozJs2bWplWvMxaoAU0ZvtUHNr0aJF4VrU/KndttaCFxYWBvO9e/fCvHLlyln6ft7KzUxoLbra6xEbGwtzNBNau6Q2E2fPnrUytzOhzXKtWrWyfP/Onz8PczSz2kxoDa27du2COTq+tNcAtXOK4NdSa9XW3q+0mUDHzp91JtzuE9pMoAbUnNwn1q1bB/OQkBCYV6pUKcu3rTXINmnSxMq05uPDhw/DXDuO0tLSrExrYtX2CTQT3Cd+9Wc6d9KOT20m3DQluz13QjOhNR9rs6m996Pjq1ixYnCtm3MnbZ8oX748zPft2wdzzkTOzoT23nXmzBkry8nrCe0chNcTtpyeCf7kmYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHGS7MAyVsoiIREREWNmNGzfwN1fKHNLT02GO/vAfla+IiCxZsgTmkZGRVpaYmAjXan+EHx4eDnNUhKQ9ljZt2sAclSZphQcnTpyAecuWLWGObkd77Brtj/OR69evw1wrw0hISHB1X7yNVqqDZkJ73rWyCWMMzNFriooSREQWL14MczRDWikHKsgQERk8eDDMW7dubWUnT56Ea7t06QJzNzOh3Xb9+vVhvmPHDivTXhttlj0xE1pRSV6fiZzcJ9zMhNt9onr16lamFXJVq1YN5qisRUSkbt26Vnb69Gm4tmrVqjBHM6GVMWn7RPPmzWF+7NgxK3O7T2jFRsi1a9dgrhXEcCY8s0+gcyGRv8a5k7ZPaDPhLedO2kyg40Q7FryRt19PLFq0COa3eya0Qi7t/AuVqGnHoVZepxV4TZkyxcrcvjfn5esJ/uSZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyEG227ZTU1NhHhISYmVay1xwcDDMtVY11B5YsGBBuLZx48Ywz5fP/u8FW7ZsgWtbtGgBc+3xoAZM7TFq7YLoezZp0gSubdWqFcy1BjvUJKi1uZ46dQrmX331lZWVLl0artVaB69evQpz1AqrNQB6IzczobWAaseL1kiLaK2TnpiJt99+G+ZasyG67Q0bNsC106dPh3mdOnWydLsiIs2aNYN5WloazGfMmGFl2rxFR0fDHD0erbUzKSkJ5pcvX4Y5aoX9s85EbuwTMTExMO/du7eVaTOrNblrjwe9Lx4+fBiu3bVrF8zRY1+6dClcGxUVBfPNmzfD/Ny5c1aGWvNvlW/atMnKUHusiN7sr80E2pv+DDOBjnNtr9RmIj4+Psv3w+0+4ePjY2Vbt26Fa7XWam0m0G2j9wgR/b0VzaH2WLRPJFmwYAHM582bZ2Xa+Rdnwj1v3ydy8nriypUrMEefPqLdP61xGj2vx48fh2vReZaI/skJNWvWtDJtj9Qa8jdu3Ghl2nx74npCaw7PDv7kmYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMhBttu2tYa4KlWqWFlcXBxce+nSJZijFjwR3Bp65swZuBY1rYngdkOtpQ813YqIdOvWDeaovVJrmStSpAjMZ8+ebWWFChWCa7///nuYFy9eHObo8WgNgForcM+ePa1s7ty5cK3WiFyhQgWYo3bIAwcOwLXeSGv9QzNx8eJFuFabFU/MhNay6GYmtNbvGjVqwHz+/PlWprVlak2SU6ZMsbJ+/frBtagVVURvRC5cuLCVabO5e/dumEdGRlrZhQsX4FrUKisiEh4eDnPUDrl//3641hu52SdyYya04xm1s2szobWrpqSkwPzDDz+0surVq8O12nv/nDlzsrzWbUs4es0CAwPh2j179sAcNaZqM+F2n0AzcfDgQbjWG2n7BPrkC+0cScvT09Nhjmbi9OnTcK123oPeo7SZcNvoi25ba8LXjkU0E35+fnCttk/Uq1cP5qg9XNsntJlA+6z2noc+uUVEJCwsDOZoJvLSuZO2T6CZ8NT1RN26da1MmwlPXE9or+mLL74Ic/RJC9onJ2i3/fXXX1uZdh4zadIkmD/77LMwR+dObdu2hWtHjRoF85zcJ3L6eoI/eSYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJywItnIiIiIiIiIgfZLgzTilZQmQ36w3wRkb1798JcK9VBt62VpKA/FhcRCQkJsTJfX1+4tnPnzjDXShvQH+1rhRUrVqyA+Y4dO7J0uyIimzZtgnmvXr1gXrRoUSt7/vnn4VrtD//RH9xrBR7aYyxWrBjMUSnDjRs34FpvpJVeeGImtGPAEzMRGhpqZb/88gtc6+/vD3OtIAYdc0FBQXCtVuqESiW0kohTp07BvFatWjC/6667rAyVnInozx8qlwoICIBrtZlwU7yTkJAA13ojb98ntOcdvUdpRUpawcnq1auzfNtoTkREzp8/D3NUUqcVqmglONpMNG7c2MpWrlwJ17qZCe29Y9WqVTD/s+4TbmZCKwjSZkI7BjyxT6BZ0QqvunbtCvOGDRvCHO1v2mxqhVzovrgt5Lr33nthjkpix44dC9dqzx8qENT2CW3e/mozsW/fPivz1LkTum1PXE9oxWXaebl23lOyZEkr056nL7/8Eubbtm2zMu09QrvfWhnjww8/bGUzZsyAa0uUKAFztE/klesJ/uSZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyEG227ZLlSoF8/Lly1uZ1oJ3+fJlmGuNueh7oiZeEZG1a9fCvF+/flbWtGlTuFZrKdUaHFF736JFi+DasmXLwhw1NicmJsK12v3WWulQ02X16tXh2lmzZsEcNbRqz0fVqlVhrq1H7bm1a9eGa71R6dKlYe6JmdCaftH3jIiIgGu1mUDHImrcFRE5fPgwzFEzpHb/Nm/eDNfGx8fDHDURX79+Ha5t0aIFzLWZQG3GderUgWvXr18PczQT2uulzYTWqoxmQrt/3shb9gltJtatWwdz1IyrNQVPnjwZ5levXoV58eLFrWz58uVwrfbJDsYYK9Pmp3nz5jDXGn3R49Tehzds2ABzT8zEn3Wf0Jpn0fuwNhPaJzvk5Eyg9/KBAwfCtWi+RfRjAJ1rvf/++3Ct1kwfFxdnZdonE2jnTgsWLIA5asV2OxM1a9a0Mu0cs0qVKjDXmv3z+kx4yz7h9noC3b9XX30Vrk1KSoK5Nsvo2Jg2bRpcqz1/iNt9Ys6cOTBv1aqVlbVp0wau1VrC0T4xfvx4uNbtPoFaxT157sSfPBMRERERERE54MUzERERERERkQNePBMRERERERE54MUzERERERERkQNePBMRERERERE5yHbbdv78+WG+atUqKytTpgxce+7cOVffE7VUrlmzxtVtdO/e3cr27NkD1+7atQvm+/fvhzlqx9NaiAsXLgxz1N6n3YbW1FivXj2Y37hxw8q09mStWfb48eNWpr2+Wtvs6tWrYf7QQw9Z2fz58+Fab6TNBGq11drWz5496+p7upmJ1NRUmJcrV87KtGN/7ty5MP/73/8O8/T0dCtDMyji7v2gW7duMNdmQmsenTRpkpUdOXIEru3QoQPMT5w4YWVa87rWOoneN0VEHn74YSubN28eXOuNvGWf0NpStZlAr2loaChcq31igXYcoU9P0B57Tu4TqOlURGT79u1Wpr0G2kzk5D6BGp4XLlwI13qjggULwhy9b7udCdTCLuJun0hLS4N5y5YtraxixYpw7datW2GuNXmjOSxUqBBc6+vrC3N0PGvN5tpta58yMWLECCs7evQoXNu+fXuYo/cUzsSv3OwT2rlTTl5PaPtEVFSUlWnzo7Vta+3X6LGHhITAtf7+/jD3xD6hnTtt27bNyrT3n65du8IczYT2/bzteoI/eSYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJykO227aJFi8L8jTfesLJPPvkEro2MjIS51rx38uRJK9OaK1977TWYBwYGWpnWtv3+++/DXGuU6927t5VprcVNmzaF+Q8//GBlWisqakkWEYmOjoZ5lSpVrGzKlClwbUJCAsz9/PysTGu01BoAW7duDfOLFy9m6ft5q6CgIJi/9dZbVvbxxx/DtZUrV4Z5vnz4v3OhtkJtJjp27Ahz9DrFxsbCtahFV0Tk2LFjMJ8wYYKVaS3EPXr0gDmaCa3xXpsJrVW+atWqVlagAH5b1JqPUfur1mgZEBAA8zZt2sD8zzoT3rJPaG3R6HjeuXMnXKvNptakm5KSYmXaPtGkSROYoyZWrSlY2w82bNgAc3Q8a/OttRbn5D5x6dKlLH0/b6XNxOuvv25ln376KVyrzYSbfUJrrdaOlzp16ljZ5s2b4VptlrXW4l69elnZ7t274VptJmbMmGFlWsvv/fffD3PUhC8icscdd1iZ1tzrZp/QGvy1udJmIi4uzsq09zxv5O3XE9q5E2qbd3vutHjxYpijT83JyeuJatWqwVzbV9Dzre0T2kyg8zLt2kP7BJPcup7gT56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMiBj9Harxxof6St/eE/ohWI+Pj4wBwVi1y7dg2uvfPOO2FetmxZK9PKh6ZPnw7z4OBgmJ86dcrKkpOT4Vqt2OP8+fNWFhYWBtdqj1ErK0KWLFkCc+15RYVrN27cgGvj4+NhrhXV1K9f38q0gqXRo0fDPDdpM1GsWDEr08bO7Uyg50d77cqXLw9zdNyiMg0RkSJFisA8JCQE5ujY0EpctNu+cOGClWkzob3/vP322zBH86kV+mj3D+XaTGhlGOvWrYN5gwYNrEybiVGjRsE8N7nZJ9LT0+Fa7f1Mew91s09oBXOnT5+2Mu3YQmVHIvrrhMoCtUIarTjoypUrVobKa0T02UR7jQguTTp48CBcq5XjoNfA7T6xfv16mLuZCW/cJ1q1agVztE+4nQltn0CFPdevX4drtX0CfU/tffjHH3+EuVbGiOYtKSkJrtVmApVv9evXD67VyvzOnj0L8/Hjx2f5fmj7BDp30grK3M4EOndC30/EO2fC268ntOMcXQtopa/acaGV7i1fvtzK3F5PoBnXCvc6deoEc61wDZ3DasVg2vnkL7/8YmXaOZL2fpVb1xP8yTMRERERERGRA148ExERERERETngxTMRERERERGRA148ExERERERETngxTMRERERERGRA1x9mAVaO16XLl2sbObMmXDtggULYF6qVCmYV6pUycq01kmt7bFDhw5Whhq4RfRGxhkzZsD8/vvvt7ItW7bAtW3atIE5ajUdNGgQXIua6kT0x5OSkpLl29Ba6dDrrjVDarfdvn17mKMmyR49esC13ig6OhrmnTt3tjLtGHI7E5GRkVamzYTW6JyWlmZlWgux1v46e/ZsmD/88MNWhpqCRfB7h4jIvHnzrAw17oqI/PDDDzDXmjjRca61X6I2XBGRqlWrWpnWDLlt2zaYazOBmiS7d+8O13ojbZ9AMzFr1iy4Nif3CW0m0HGO9g4RkYIFC8J84cKFMH/rrbesTNsnWrRoAfNhw4ZZWUREBFz73Xffwbxly5Ywj4qKsrLU1FS4VmtRRXunp/aJvD4T2t6P2m6191X0nigiUrp0aZijY0ObH20mqlWrZmWNGjWCaxs2bAjzyZMnw3zgwIFWdvHiRbi2f//+MF+1apWVNW3aFK6dM2cOzLVPwUBN9to+Ubx4cZhXqVLFyrSZ4D7xq9t9PeF2JtB5RbNmzeBa7dxJ+xSCkSNHWtnWrVvhWm2fWLp0qZX17dsXrtVmQptx1Db/5ptvwrWXL1+GuZvrCfSpMCK5dz3BnzwTEREREREROeDFMxEREREREZEDXjwTEREREREROeDFMxEREREREZEDXjwTEREREREROch22/aOHTuynGvNvVqzXVJSEswTExOtTGvuRe2IIrgZNzY2Fq7917/+BXPtfqMWSK1B+Pjx4zBHjZtaUyxqRRUROXToEMznz59vZVoLMWrmFsFtf8eOHYNrtdZv7flGDbyoRdJbbd++Pcu5NhNawzs69kVwU2O+fPi/iWmthHFxcVamNeDWrFkT5toxihroCxcuDNeuXr0a5tWrV7cyraGydu3aMJ86dSrM0X3RXhtPzIT2+rqZiY0bN8K13sjb9wmtQRvNxNGjR+Fa7dgPDQ2FOWpE1h77I488AvOiRYta2eLFi+Hac+fOwfzzzz+H+V133WVlaN8U0fe3r7/+2src7hNHjhyBOdoj/2ozob2PeGIm2rVrB/Nr165Z2bJly+BadAyJiOTPnx/mDz74oJXt3bsXrtValdFta/tE+fLlYf7+++/D3M/Pz8q01wB9eoUI3ie080C3505oJvLSuZObmdBeu5zcJ7SZQC3S2if9oHMhEf0cHJ33FClSBK7Vzj3r169vZW7PnQ4cOABz1M6dnJwM1/r6+sLczUx44tzJkzPBnzwTEREREREROeDFMxEREREREZEDXjwTEREREREROeDFMxEREREREZGDbBeGnTlzBubh4eFWpv2Bv1ZK9Nxzz8H8s88+szKtgCIsLAzmr7zyipU1adIErq1cuTLMUbmFCC44qVSpUpbXioikp6db2cmTJ+FarSRFK0hAhUcFCuBDICEhAeboD/9ReY2IXgLRtGlTmM+dO9fKtOI3b3T69GmYo5nQjk9tJoYPHw7zTz/91Mq0wjDte6JCiEKFCsG1WmHFV199BfOLFy9aWZUqVeBa7X6HhIRYmTYTmzdvhnm1atVgjsozUlNT4dr4+HiYu5mJXbt2wVx7D/rpp5+sLCIiAq71RtpMoLn2pn0CzYR27Guz0q1bN5iPGjXKylAZk4j+Xo7KU06cOAHXbtq0Ceba833hwgUrq1GjBlyrFcQYY6zMU/vEvHnzrOzPsE9UqFDBytzOxLBhw2COjjn03nyr74kKvLT3bO211t6Hx44da2WXLl2Ca9euXQvzgQMHWtmpU6fg2vXr18Nce+9Hz4m2Viudyslzp7w+E26uJ7QSPVQ+JeKZfUKbiX379lmZVhj33XffwVw7H/rhhx+sTNuDtFlp2LChlWn7xM8//wxzrYwM7XturycKFixoZW7PnRo3bgxzdD3hyXMn/uSZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyEG227a1hrhjx45ZmdbIqTUeomZIEdzkprXjac12qDVUa52cPXs2zFGzoYhIjx49rGzRokVwrdYCiZ4rrc21Z8+eMH/sscdgXrZsWStD7d4iuAVPRGTnzp1WVrx4cbg2MjIS5nv27IE5en212/ZG2kwcP37cyrQW3XXr1sEctWqL4OdMa0B1MxNaq2ffvn1hvmTJEpjXrFnTyrS2R9RwLIJbE5s1awbX/u1vf4P5uHHjsvw9tZlAbakiuBlVO2619v3du3fDPK/PRNWqVWHuiX0CtaWK4OfMx8cHrnUzE6jhWkR/P/v6669hjlrbUaO8iEhwcDDM0XOitcH3798f5osXL4Y5asrmPuE5bmZC2yc8ce6kzcSUKVNgjmZCa3RGje23un/otrWZKFWqFMzR8a81BYeGhsJce77R8e+JmShWrBhcq82E1jiMXl/ttr3Rn+l6QptvzRtvvPGHb7t9+/YwR+eTWht86dKlYa5dB6Hm/JzcJ7SmbDf7hCdngj95JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInKQ7bZtrdmzdu3aVvbll1/CtVqbotaEuHnzZivr3bs3XHvw4EGYX7161cq2bt0K12rq168P823btllZSkoKXHv//ffDHK3XGuy+/fZbmJ89exbm1atXt7KEhAS4VmveQ8+f1oKnNUNqTZIbN260sunTp8O1Y8eOhXlu0p7LWrVqWdnEiRPhWrczsWXLFivTZuLQoUMwT0xMtDKtRXXv3r0w79SpE8zPnz9vZVqbq7+/P8xRA2bhwoXhWtRsLoKfJxGR8uXLW5n23qbNBGqvdDsTlStXhvmGDRusbNq0aXDtmDFjYJ6btOcSzYTbfaJEiRIwd7NPHD58GOZu9gn0/UTwXigikpaWZmWXL1+Ga+vUqQPzkydPWpnbmdDer4oWLWpl2myitSJ4VrhP/MrNTHz11VdwbW7MBHqf095XNR07doQ52ie0mdA+TSQuLs7KtHMh7ZMdtJlALb3aWq0NHL2naJ++gD7BQUTfJzZt2mRlP/zwA1yrffJEbtKey9t9PdGnTx+4NievJ7RzJ3Q8t2rVCq5t2LAhzNGnoKxYsQKuRe3UIvprExQUlOW1f8brCf7kmYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMhBttu2fX19YX7s2DErQy2SIiKFChWCOWo2FBGJiYmxsmbNmsG1s2bNgjlq10VNj7e6H9pjDwsLszLtMaampsL8+vXrVnbt2jW49sMPP4R569atYY7allGTsYjeAonWa/dPa8GrVq0azFGbp3bseCPtuDhx4oSVaY9La7UNDg6GuZuZmDlzJsybNGliZVpzZfHixWGuNTXu3LnTyrS22X379sEcPcZ8+fB/99Ma6LX1qEVTeyxuZgLNsYhIlSpVYF61alWY/1lnAjVA/5X3CW0mVq9eDXN0fBUpUgSunT17Nsy7dOkC84IFC1qZ9tijoqJg7uPjY2XcJ36VGzMRHR1tZU2bNoVrPTETbvcJNBNacy9qlhYRKVmypJVpc6XNBHrvEHF37qS1BaM9SJsJT+wTNWrUgGu9kbdcT2gzoZ07eWIm/Pz8YD5kyBArCwwMhGu1feLHH3+0Mu3TF9C5mohI27ZtYf5Xv57gT56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMhBtgvDtD8MDwgIsLISJUrAtVqZw5EjR2Bep04dK3v33Xfh2g4dOsD81KlTWb4faWlpMNcKK5o3b25lzz77LFx76dIlmBtjrGzSpElwbcuWLWGOSpBEcBFM/fr14dodO3bAHBXBlC1bFq7Vigm027548aKV/fLLL3CtN9JmonDhwlamzYRWknL48GGYo8Ked955B67t2LEjzNH91u6HNhOvvfYazENDQ62se/fucK1WGoLcuHED5ikpKTDXysg8MROoCEabCVSAIiKyfft2mF+4cMHK/gwzgfYJdKyI6Meit+wTWvnjli1bYP6vf/3Lyho0aADXfvDBBzBH+4Q2E23atIG5tgeh4xmVMYmIfPnllzBHrzv3iV9p5YWoLNLtPuFmJrR9ol27djA/ffp0lu+H23MnVOinlTfVrFkT5uix//zzz3CtNhOoBEkEF51p+4RWvIRmokyZMnAtKhkV0fcJNBPbtm2Da72RNhM5eT2Bzp1y43oCFVuJ4OdEK4zT5urcuXNWpp0joVJBEV5PaPiTZyIiIiIiIiIHvHgmIiIiIiIicsCLZyIiIiIiIiIHvHgmIiIiIiIicsCLZyIiIiIiIiIH2W7bRk1mIiKFChWysri4OLgWtUuKiAQHB8N8xYoVVtawYUNXt4Ha8dB9FhEpVqwYzCtXrgxz1BJXo0YNuFZrjluzZk2WbldEpEWLFjCfP38+zN9++20rW7ZsGVyrtT02adLEyrQGSNTGJyJSvnx5mKMWwLfeeguu9UZuZkJrukXN3CIiISEhMF+5cqWVac292kygxkNtJooWLQpzrQEVHbv9+vWDa7Vjbs+ePVamNUZWqVIF5mPGjIH5f//7XytbunSpq/uHZkJrOvXETIwcORKu9UaemIm8uk8EBgbCvFatWlamtahWrFgR5teuXbOy0qVLw7V33HEHzNHzJCJy//33W9msWbPg2p9++gnmtWvXtjJtn0BNxiIiYWFhMEcz8eabb8K13kg7H/LEPqEdi+i11ppxtb3mzJkzVuZ2n4iMjIQ5aorXPh1Cew9dtWqVlYWHh8O16JNRRESGDRsGc3Qesnz5crj25MmTMG/cuLGVeercCTX+/xlmws/PL8tr3e4TqIk9N/aJbt26wRw12Wut5MnJyTBHx1FERARcy+sJd/iTZyIiIiIiIiIHvHgmIiIiIiIicsCLZyIiIiIiIiIHvHgmIiIiIiIicsCLZyIiIiIiIiIH2W7b1tpu8+fPb2VaYyRqWBQRiYmJgXl8fLyVbd68Ga7VWkrRbaSnp8O1x44dg7nWphcVFWVlhw4dgmu1FlrUnnz58mW49urVqzAPCgqCOWqxQ22GInojH3rsp0+fhmu11metiRg1cc6bNw+u/de//gXz3KTNBGpI1NoRtZlo27YtzNHxvGnTJrhWe03R/a5WrRpce+XKFZij1kQRkeLFi1tZbGwsXJuWlgbzRYsWWdkvv/wC1wYEBMDczUxo8609f6j5VpuJRo0aZfl+iOCZ0NovBw0aBPPc9FfeJ7S2aDT7qLVVRG/hvn79upWVLFkSrvX19YV5mTJlYD537lwr0445reH7du8T2v0bPHgwzHNTTu4T2kyg5t6tW7fCtdpMoGPOGAPXap8mojWro6bkCxcuwLXacYtm6OjRo3Ctto8VKVIE5jt27LAyba48ce6ktT5r+0SlSpWsLC/NhHaco5lwu0+4OXfyxD6hzQRqqxfR3w+SkpKsTGuzPnz4MMzRfdH2K15PuMOfPBMRERERERE54MUzERERERERkQNePBMRERERERE54MUzERERERERkYNsF4ahggIRkfLly1uZVoai5VoZULly5azs4MGDWV4rgksvOnbsCNdu3LgR5v/85z9hfuDAASsrVaoUXDt06FCYV65c2cp27twJ15YtWxbmQ4YMgblWNoBopQeo6EcradKKl1A5iKZYsWJZXpvbUEGBCD4WtWNfK0vQyl3QbaPjUFsrIrJ3714rS0xMhGu1udeKsL7++msr08rInnzySZhrzyuilck89dRTMF+6dKmV+fj4wLVaYRQqwdFmQiuZcTMTWoGHN9KOF3Qsasd+buwT165dszJtn9AK+ho3bgzzVatWWdlLL70E1+7Zswfm6HhGpUYi+vxUqFAB5iNHjrQyrUzGzT6BivVE/nr7RG7MBDov08pMtfdQT8xEvXr1YL5hwwYra9q0KVw7ceJEmKP3Re39Vpv7Fi1awBydO7ndJ9BMaMe4Vrz0V5sJdNxqM+GJc6ecvJ7QzqnuvfdemE+ePDnL92PNmjUwR/PG6wnP4E+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBxku207MDAQ5ufOnbOy9evXw7VXrlyBeYMGDWCOmhNTUlLg2oSEBJinpaVZ2cmTJ+HaokWLwjxfPvzfHFBz3ODBg+Ha8PBwmF+6dMnKatSoAddqvv/+e5ijpjmt2e7EiRMwr127tpVpTaxr166FeXR0NMzRcRIaGgrXeiPtuUQzsW7dOrg2J2ciPj4e5mgmtm3bBtcuXrwY5qNGjYL5/v37rWzcuHFwbcOGDWEeFxdnZTVr1oRrNVOnToW5J2aiTp06VuZ2JmJiYmCOjpO8NBPaPnH+/Hkry8l9Ijk5Ga7V9gnUmKvtEwULFoS51jiM2vC1Vu27774b5mifKFGiBFy7fPlymE+aNAnmqLVYazTlPuGem33C7UzUr18f5ugY1WZCawX2xExobeCoWRt9UoOISHBwMMxR27Lbc6dp06bBHJ0Lut0natWqZWXaTGjnB25mIiQkBK71Rm6uJ3Lj3MkT1xNdu3aFudYGjmbo22+/hWvRsSUicvnyZSv7s11P5Na5E3/yTEREREREROSAF89EREREREREDnjxTEREREREROSAF89EREREREREDnjxTEREREREROQg223bpUuXhvn//vc/K+vWrRtcq7XjaY1oqMmtV69ecO3s2bNhXqpUKStbsmQJXPv444/DvEiRIjBHLXFaoyVqwRPBzb3bt2+Haw8fPuzq/qH2PvR8aGtFcKtjlSpV4NobN27A/Oeff4b5HXfcYWVHjx6Fa72RNhPvv/++lXXv3h2u9cRM9O7dG66dNWsWzNH91mZCa3NFzZUiIpGRkVbm4+MD12qNjFFRUVbmqZnw8/OzMu111B4janWsVq0aXKvNhNaI3LNnTyvLSzNRsmRJmH/44YdWlpP7hDYTbvaJpUuXwrX16tWDeeHChWEeERFhZdonOGiNw7d7n3A7Ezm5T/To0cPKjh07Btd6I23PdTMT2vmD1rCM2oy1c6c5c+bA3M1MoPdsEf0TTNAMFSpUCK798ccfYV6hQgUr89RMoOPc7Uxs3LjRyjgTv8qN6wk3M+GJ64nKlSvDvFmzZjBHn56gvXegTzURwXOofZKKt19PaJ8CoJ07oesJT84Ef/JMRERERERE5IAXz0REREREREQOePFMRERERERE5IAXz0REREREREQOePFMRERERERE5CDbbdujR4+GOWo405pEtSbqtLQ0mKNGufbt28O1VatWhXn+/PmtDLWfiujNcVu2bIH5ggULrCw+Ph6ubdu2Lcznz59vZdrzER4eDvOTJ0/CHD0nqCFcRKRcuXIwr1ixopXFxsbCtVp7n/aaJSQkWFmZMmXgWm80atQomKOZQMehiEhSUhLMU1NTYb5v3z4r69ixI1yrNUCj+dRmQrvfcXFxML927ZqVaY+xXbt2MPfETGhN3ug50Vo7ORPujR07Fuae2Ce0mbjd+4Q2E5cuXYK5m5ngPmFDTcRaW683ysmZSE9Ph7mbmdDabt3MhNauq723olk5cuQIXBscHAzzGTNmWFlOzoT2WMqWLZvl76k9Rm0mtD0yr8+Em+sJ7f3W7fUEOnfq0KEDXOtmn6hUqRJcu2nTJpgvWrQI5n/VfUKbiUOHDsE8t/YJ/uSZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgcZLswrHbt2jAPCQmxss2bN8O16I/FRfAf8ovgPyQvUaIEXJuSkgJz9AftdevWhWt/+OEHmF+4cAHm6L4ULVoUrtX+8D8yMtLKtAICrQRJe/6OHTtmZcWKFYNrtTIe9D21UpOYmBiYr1ixAubly5e3ssDAQLjWG0VFRcE8NDTUyrTSuQoVKsAcFb6IuJsJrVADFSvUq1cvy99PRD+eUbmVdj9ycib27t0Lc/R4ihcvDte6mQkfHx+4VpuJlStXwhyVzxQpUgSu9UbaPoFKf7SZyMl9QjsWExMTrUybb/S+qt2GCJ4JbW1OzsSePXtgzn0iZ3nLTKB9ScQz507aTGglWz/++KOVace+9tjR8a/NhFaCpM0Eev604jKtkAl9T22faNOmDcy1fQIVMv0Z9ok/0/XE0aNHYe5mn8iNc6c/0/WEJ2eCP3kmIiIiIiIicsCLZyIiIiIiIiIHvHgmIiIiIiIicsCLZyIiIiIiIiIHvHgmIiIiIiIicuBjjDG5fSeIiIiIiIiIvBl/8kxERERERETkgBfPRERERERERA548UxERERERETkgBfPRERERERERA548UxERERERETkgBfPRERERERERA548UxERERERETkgBfPRERERERERA548ZyHLV++XHx8fGT58uUZ2YMPPigVK1bMtfv0e+g+EuWUiRMnio+Pjxw5ciQji4mJkZiYmFy7T7+H7iNRTuE+QZQZ9wmizDgT7mT54tnHxydL/3jr5jd06FBp0KCBBAcHi7+/v9SoUUNefvlluX79erZvMyYmJtNjDw4OlsaNG8v48eMlPT3dg/c+57355psyc+bM3L4blh9++EHuueceqVSpkvj7+0u1atXk6aeflsuXL+f2XcvzM1GxYkV4fx977DGP3WbJkiWldevWMmPGDA/e85yXkJAgL7/8sle+duPGjZPo6GgpVaqU+Pn5SUREhAwcONArNpS8PhPcJ27NW/cJEZHFixdL27ZtJTQ0VIoVKyZNmjSRSZMm5fbdytMzcfM/amj/vPHGG9m6Xe4TOSs9PV0mTpwoPXv2lLCwMAkICJDatWvL66+/LomJibl99/L0TIjw3OlWvHUmbpo6dao0a9ZMihUrJiEhIRIdHS0//fST69spkNWFv9+EvvrqK1m0aJGV16hRw/WduB02btworVu3loEDB0qhQoVk69atMnLkSFm8eLGsWLFC8uXL3g/hy5cvL2+99ZaIiJw/f16++uoreeihh2T//v0ycuRITz6ELBk3bly2TsjefPNNufPOO6V3796ev1N/wKOPPiply5aV++67TypUqCA7duyQTz75RObOnStbtmyRwoUL59p9y+szISJSr149efrppzNlVatW9dhtnjp1SsaMGSN9+/aVUaNG/aHNJbsWLlzo+msSEhLklVdeERHxqv/yKiKydetWiYiIkJ49e0rx4sUlNjZWxo0bJz/++KNs27ZNypYtm2v3La/PBPeJW/PWfWL27NnSu3dvad68ubz88svi4+MjU6dOlQceeEAuXLggQ4cOzbX7lpdnokaNGvA/QEyaNEkWLlwonTp1yvZtc5/IOQkJCTJw4EBp1qyZPPbYY1KyZElZu3atvPTSS7JkyRJZunSp+Pj45Nr9y8szcRPPnTBvnQkRkY8//lgGDx4s3bt3l5EjR0piYqJMnDhRevToIdOnT5e+fftm/cZMNj355JMmK18eHx+f3W+R4959910jImbt2rXZ+vro6GhTq1atTFl8fLwpX768CQgIMMnJyfDr0tLSzI0bN7L1PX9r2bJlRkTMsmXL/vBtBQQEmAEDBvzh2/m9P3of0dd9+eWXRkTMuHHj/tid87C8NhPh4eGme/fuOX6bp0+fNgEBAaZq1arq16WkpJikpKQ//P0nTJhgRMTExsb+ods5f/68ERHz0ksv/eH79Hueuo+/tWnTJiMi5q233vLYbXpCXpsJhPvE//HWfaJjx46mbNmyJjExMSNLSUkxkZGRJioqykP30jP+DDNRuXJlU6VKlWx/PfcJZ3/kPiYlJZnVq1db+SuvvGJExCxatMgD99Bz8tpM8NxJ560zYYwxVapUMY0bNzbp6ekZ2ZUrV0xgYKDp2bOnq9vy6N88x8TESO3atWXz5s3Spk0b8ff3l+eff15Efv01jZdfftn6mooVK8qDDz6YKbt8+bI89dRTEhYWJn5+flK5cmV5++23rf9Sfvr0adm7d6+kpKRk6/7e/JsvT/4KsL+/vzRr1kzi4+Pl/PnzIvLrYx80aJBMnjxZatWqJX5+fjJ//nwRETl58qT84x//yPgVzFq1asn48eOt2z1x4oT07t1bAgICpGTJkjJ06FBJSkqy1qG/ZUtPT5cPP/xQ6tSpI4UKFZISJUpIly5dZNOmTRn3Lz4+Xr788suMXxn57Wvi6fuYkJAge/fulQsXLjg+n+i/XPXp00dERPbs2eP49bktL8xEcnKyxMfHu35sWVW6dGmpUaOGxMbGiojIkSNHxMfHR95991354IMPJDIyUvz8/GT37t0iIrJ371658847JTg4WAoVKiSNGjWS2bNnW7e7a9cuadeunRQuXFjKly8vr7/+OvxpGvq7ncTERHn55ZelatWqUqhQISlTpoz07dtXDh06JEeOHJESJUqIiMgrr7ySMRO/fa08fR+vXLkie/fulStXrmT5ef2tnHgvyyl5YSZ+/71vfj9P4T7h2X3i6tWrUrx4cfHz88vIChQoIKGhobn620lZlZdmYsOGDXLw4EG59957XX/trXCf8Nw+4evrKy1atLBynjvx3EnkrzkTIr/uEyVLlsz0WxdBQUESGBjoep/I8q9tZ9XFixela9eu0r9/f7nvvvukVKlSrr4+ISFBoqOj5eTJk/LPf/5TKlSoIGvWrJERI0bI6dOn5YMPPshYO2LECPnyyy8lNjY2S+UnqampcvnyZUlOTpadO3fKCy+8IEWKFJEmTZq4fJS3dvjwYcmfP78UK1YsI1u6dKlMnTpVBg0aJKGhoVKxYkU5e/asNGvWLOOkqUSJEjJv3jx56KGH5OrVq/LUU0+JiMiNGzekffv2cuzYMRk8eLCULVtWJk2aJEuXLs3S/XnooYdk4sSJ0rVrV3n44YclNTVVVq5cKevWrZNGjRrJpEmT5OGHH5YmTZrIo48+KiIikZGRIiI5ch83bNggbdu2lZdeegm+ATo5c+aMiIiEhoa6/trc4M0zsXTpUvH395e0tDQJDw+XoUOHypAhQ1w+wltLSUmR48ePS0hISKZ8woQJkpiYKI8++qj4+flJcHCw7Nq1S1q2bCnlypWT4cOHS0BAgEydOlV69+4t06dPz9j8z5w5I23btpXU1NSMdWPHjs3SG2BaWpr06NFDlixZIv3795chQ4bItWvXZNGiRbJz507p0KGDjBo1Sh5//HHp06dPxq/yREVFiYjkyH2cMWOGDBw4UCZMmGBt/pqLFy9KWlqaHDt2TF599VUREWnfvn2Wvja3efNMcJ/Ie/tETEyMvP322/Liiy/KgAEDxMfHR7755hvZtGmTTJ06NUuPP7d580z81uTJk0VEPH7xzH0iZ/aJ3+K5E8+d/sozERMTI9OmTZOPP/5Y7rjjDklMTJSPP/5Yrly54v61y9bPvg3+NYvo6GgjImb06NHWelF+jB8eHp7p18Bee+01ExAQYPbv359p3fDhw03+/PnNsWPHMrIBAwa4+hH+2rVrjYhk/FOtWrU/9Kts0dHRpnr16ub8+fPm/PnzZs+ePWbw4MFGRMwdd9yRsU5ETL58+cyuXbsyff1DDz1kypQpYy5cuJAp79+/vylatKhJSEgwxhjzwQcfGBExU6dOzVgTHx9vKleubP2q24ABA0x4eHjG/1+6dKkRETN48GDr/v/2Vxe0X8fLift481f0svtrHQ899JDJnz+/dYzktrw2E3fccYd5++23zcyZM80XX3xhWrdubUTEDBs2zPFrNeHh4aZTp04ZM7Ft2zbTv39/IyLmX//6lzHGmNjYWCMiJigoyJw7dy7T17dv397UqVMn069fpqenmxYtWmT6NcGnnnrKiIhZv359Rnbu3DlTtGhR6/FHR0eb6OjojP8/fvx4IyLm/ffft+7/zZm41a8e5cR9vPnrSBMmTLC+n8bPzy/jvSwkJMR89NFHWf7a2yWvzYQx3Cd+Ly/sE9evXzd333238fHxyXjd/P39zcyZMx2/9nbLizNxU2pqqilVqpRp0qSJq6/7Pe4Tt2+f+K0OHTqYoKAgc+nSpWx9fU7JazPBc6e8ORNnz5417du3z7S/h4aGmjVr1jh+7e95/OLZz88P/v59Vg/2qKgo06VLl4wD6OY/ixcvNiJivv766+zeZXPlyhWzaNEiM3PmTDNs2DDToEEDM2fOnGzf3s3h/u0/Pj4+pnv37ub8+fMZ60TEtG3bNtPXpqenm2LFiplHH33Ueqw3D4ZVq1YZY4zp1KmTKVOmTKaTGGOM+e9//+t4UvTkk08aHx8fc/HixVs+FnRSlFP38Y+YPHnyH36Tyil5cSZ+Kz093XTu3NkUKFDAHD9+PFu3ER4ebs1E/vz5zf33359xAn1zAxg4cGCmr7148aLx8fExr732mvVYb/6t1okTJ4wxxlStWtU0a9bM+v5PPPGE4wbQvXt3ExoaalJSUtTHoW0AOXUfs2Pp0qVm7ty55r333jP169f3ur93NiZvzgT3CZ237hMpKSnmhRdeMHfddZf59ttvzddff23atGljAgMDs/236jklL87ETQsWLDAiYj788MM/dDvcJ27fPnHTG2+8YUTEfPbZZx65PU/KyzNhDM+dfsubZ+LatWvmiSeeMAMGDDDff/+9GT9+vKlTp44pXbq0OXDggKvb8vivbZcrV058fX2z/fUHDhyQ7du3Z/ze/O+dO3cu27cdFBQkHTp0EBGRXr16yTfffCO9evWSLVu2SN26dbN1mxUrVpRx48aJj4+PFCpUSKpUqSIlS5a01kVERGT6/+fPn5fLly/L2LFjZezYsfC2bz7Wo0ePSuXKla12xGrVqjnev0OHDknZsmUlODg4qw/ptt/HrFq5cqU89NBD0rlz52x/REZu8OaZ+C0fHx8ZOnSoLFiwQJYvXy733Xdftm6nadOm8vrrr4uPj0/Gx/389ldTb/r9TBw8eFCMMfLiiy/Kiy++CG/73LlzUq5cOTl69Kg0bdrU+vdZnYlq1apJgQLu3/5u133MirZt24qISNeuXaVXr15Su3ZtCQwMlEGDBnnk9nOSN88E9wl3vGGfGDRokKxbt062bNmS0Yh+9913S61atWTIkCGyfv36P3T7t4M3z8RNkydPlvz588s999zzh2+L+8Tt2SdERKZMmSIvvPCCPPTQQ/L444977HZzWl6YCRGeO2WFN8zEXXfdJQUKFJA5c+ZkZL169ZIqVarIf/7zH5kyZUqWb8vjF89u/+g6LS0t0/9PT0+Xjh07yrBhw+D6P1oF/1t9+/aV+++/X7777rtsnxQFBARknGjdyu+fl5t/+H7ffffJgAED4Nfc/DuB3OJN93Hbtm3Ss2dPqV27tkybNi1bw5tb8tJMhIWFiYhIXFxctm8jNDT0D83EM888I507d4ZfU7ly5WzfL0/w1vsYGRkp9evXl8mTJ+eJi+e8NBPcJ24tt+9jcnKyfPHFFzJs2LBMHyVWsGBB6dq1q3zyySeSnJz8h07Cbwdvn4kbN27IjBkzpEOHDq7/9hThPnF77uOiRYvkgQcekO7du8vo0aNvy/f0FG+fid/iudOt5fZ9PHz4sMyfP9/6D7zBwcHSqlUrWb16tavbu21XIMWLF7faSpOTk+X06dOZssjISLl+/XqWDqA/KikpSdLT07PdcPtHlChRQooUKSJpaWmOjzU8PFx27twpxphM/8V+3759jt8nMjJSFixYIHFxcbf8qQL6zL/bdR+dHDp0SLp06SIlS5aUuXPnSmBg4B++TW/gjTNx+PBhERH1v9TmpEqVKonIrye9WTneDhw4YOVZnYn169dLSkqKFCxYEK7RPgPzdt3H7Lhx4wZsLc5LvHEmuE/8H2/cJy5evCipqanWibPIr4U76enp8N/lFd4yE7Nnz5Zr1655vCjMLe4TWbd+/Xrp06ePNGrUSKZOnZqnfuhwK94yE7/Fc6dfeetMnD17VkTs/8Ai8us+kZqa6ur2PPpRVbcSGRkpK1asyJSNHTvWeiB33323rF27VhYsWGDdxuXLlzM9wKxWy1++fBmu+fzzz0VEpFGjRll+HJ6SP39+6devn0yfPl127txp/fubH18iItKtWzc5deqUTJs2LSNLSEhQf0Xut/r16yfGmIwPLf8tY0zG/w4ICLDejHLqPrr5CJIzZ85Ip06dJF++fLJgwYJceWPKKbk5E3Fxcdb3SUlJkZEjR4qvr2/GrwTfTiVLlpSYmBgZM2aMtQmK2MfbunXrZMOGDZn+/c0m2Fvp16+fXLhwQT755BPr392cCX9/fxGxP54op+5jVj9uITU1VS5dumTlGzZskB07duTKe5kncZ/IjPuE8z5RsmRJKVasmMyYMUOSk5Mz8uvXr8ucOXOkevXqeeLjqjS5ORO/9c0334i/v39GI25u4T6RtY/l2bNnj3Tv3l0qVqwoP/74Y56egd/juVNmnAnnmahcubLky5dPpkyZkmlPO3HihKxcuVLq169/y6//vdv2n6Eefvhheeyxx6Rfv37SsWNH2bZtmyxYsMCqzH/22Wdl9uzZ0qNHD3nwwQelYcOGEh8fLzt27JBp06bJkSNHMr4mq9Xyy5cvl8GDB8udd94pVapUkeTkZFm5cqX88MMP0qhRI+vvE3x8fCQ6OlqWL1/u6achk5EjR8qyZcukadOm8sgjj0jNmjUlLi5OtmzZIosXL8749Y9HHnlEPvnkE3nggQdk8+bNUqZMGZk0aVLGQXorbdu2lfvvv18++ugjOXDggHTp0kXS09Nl5cqV0rZt24xf8WzYsKEsXrxY3n//fSlbtqxERERI06ZNc+Q+uvkIki5dusjhw4dl2LBhsmrVKlm1alXGvytVqpR07Ngxq0+318nNmZg9e7a8/vrrcuedd0pERITExcXJN998Izt37pQ333xTSpcunbH2yJEjEhERIQMGDJCJEyfmxFOR4dNPP5VWrVpJnTp15JFHHpFKlSrJ2bNnZe3atXLixAnZtm2biIgMGzZMJk2aJF26dJEhQ4ZkfJRBeHi4bN++/Zbf44EHHpCvvvpK/v3vf8uGDRukdevWEh8fL4sXL5YnnnhCevXqJYULF5aaNWvKlClTpGrVqhIcHCy1a9eW2rVr58h9zOrHLVy/fl3CwsLknnvukVq1aklAQIDs2LFDJkyYIEWLFlX/liiv4D5h4z5x630if/788swzz8gLL7wgzZo1kwceeEDS0tLkiy++kBMnTsjXX3+djWfde+TmTNwUFxcn8+bNk379+qm/+cV9wnv2iWvXrknnzp3l0qVL8uyzz8pPP/2U6d9HRkZK8+bNs/HMeweeO9k4E7eeiRIlSsg//vEP+fzzz6V9+/bSt29fuXbtmnz22Wdy48YNGTFihLsnPFuVZUZvx6tVqxZcn5aWZp577jkTGhpq/P39TefOnc3Bgwetdjxjfm1EGzFihKlcubLx9fU1oaGhpkWLFubdd981ycnJGeuyWi1/8OBB88ADD5hKlSqZwoULm0KFCplatWqZl156yVy/ft363iJi+vfv7/gc3Orx/paImCeffBL+u7Nnz5onn3zShIWFmYIFC5rSpUub9u3bm7Fjx2Zad/ToUdOzZ0/j7+9vQkNDzZAhQ8z8+fMdW1SN+fXjJd555x1TvXp14+vra0qUKGG6du1qNm/enLFm7969pk2bNqZw4cJGRDK9Jp6+j24+gkR+1z74239+2wLoDfLSTGzatMnccccdply5csbX19cEBgaaVq1aZfoImZt27NhhRMQMHz7c8TkIDw833bt3v+Wam42R77zzDvz3hw4dMg888IApXbq0KViwoClXrpzp0aOHmTZtWqZ127dvN9HR0aZQoUKmXLly5rXXXjNffPGFY2OkMcYkJCSY//znPyYiIiLjmL7zzjvNoUOHMtasWbPGNGzY0Pj6+lrHq6fvY1Y/biEpKckMGTLEREVFmaCgIFOwYEETHh5uHnroIY+1snpSXpoJ7hN5d58w5tdPYmjSpIkpVqyYKVy4sGnatKk1j94gL83ETaNHjzYiYmbPnq2u4T7hPfvEzedO+wd93FxuykszwXOnvDkTxvz6qQwff/yxqVevngkMDDSBgYGmbdu2ZunSpY5f+3s+xvzm59ckc+fOlR49esi2bdukTp06uX13iHLdZ599JsOGDZNDhw55pCiGKK/jPkGUGfcJosw4E39et+1vnvOKZcuWSf/+/XlCRPT/LFu2TAYPHsw3f6L/h/sEUWbcJ4gy40z8efEnz0REREREREQO+JNnIiIiIiIiIge8eCYiIiIiIiJywItnIiIiIiIiIge8eCYiIiIiIiJywItnIiIiIiIiIgcFsvuF9evXz/LakiVLwjwlJQXm4eHhME9LS7Oy06dPw7W7du2CecuWLa1s7969cG316tVhrhWUp6amWlnZsmXh2vT0dJjHxsZa2cmTJ+HaxMREmDdp0gTmvr6+VpacnAzXbt68GeYVKlSwssaNG8O12v0+cuQIzP39/a2sUKFCcO2MGTNgnpu05wEpXbo0zLWZqFSpEszRMXfq1Cm49pdffoF5TEyMle3ZsweurVmzJsy1mUCPBx1DIvixiIgcPHjQyo4fPw7X3rhxA+Zo7kVE/Pz8rEx7DdauXQtz9H7VokULuPbEiRMwR49RRCQgIMDK0JyIiHz77bcwz03eMhPa8759+3aYo5nQ9pTatWvD3M1MhIWFwbVozxPJ2Zlws0+sX78e5m5mQrvfhw4dgjmaicKFC8O13333HcxzU/PmzWGOjpcyZcrAtdpMREZGZnm9tj9v2bIF5h06dLCynTt3wrVuZwIdXxUrVoRrtZnYv3+/lWnnGtq5U5s2bWCO9gltJlavXg3ziIgIK2vdujVce+zYMZgfOHAA5mhPQHMiIjJp0iSY56amTZtmea3bfcITM6GdO7Vt29bKdu/eDdfWqlUL5p44d9JmAh0v2rGl7RPaMVqwYEEr087h1qxZA3M0461atYJrtfvt5txJ2ye++eYbmN8Kf/JMRERERERE5IAXz0REREREREQOePFMRERERERE5IAXz0REREREREQOePFMRERERERE5MDHaFVvDgYPHgzzAgXsAu+rV6/CtVpTI2pxExEpXry4lV27di3La0Vwk6jWvBgYGAhzrVEOPU6taVxrREbt0tr90JqPJ0+eDHN0v7XmwipVqsAcvb7nz5+HaxMSEmCutSKi+3LmzBm4dunSpTDPTSNGjIA5Op4vXboE12qtjqjtUwQf59q8hYSEwBzNhNa8GBQUBHPtNb1y5YqVacec1rqLGhKLFCkC19atWxfmn3/+OczRTGgNt1r7Pnr+zp49C9fGx8dn+X6I4OdK+4SBefPmwTw3DR8+HOZuZmLbtm1Zvg0RkeDgYCvT9gltJtBte2om0HyWKlUKrtVawtFMaPuENhNffPEFzD0xE+j589RMoPuSl2bi//v//j+Yo/eRuLg4uFZrxNb2CTQT6L1ZRKREiRIwR6+ptscXLVoU5m72Ce2Y01p33ewT2qfFjB49GuaozVi7f1qrMnr+tPOb69evw1ybCfSpLto55pw5c2Cem55//nmYo5lwe+6EbkPE3blTaGgozNFrql1PuN0nLl++bGXauZObfUKbiXr16sF8zJgxMEefGKTtY56YCW2f0J4/NzMxd+5cmN8Kf/JMRERERERE5IAXz0REREREREQOePFMRERERERE5IAXz0REREREREQO7PanLNLKvsLCwqxMK2X55z//CfO3334b5uiP+bt16wbXrl27FuaoHy0yMhKu1YoGUGmWCC62adu2LVx79OhRmKOijY0bN8K12h+/a6UCiFaQoD12dL+1koCkpCSYayVqBw8etDKtqMEbbd26FeYVK1a0Mu1xDR06FOYvvvgizFF5Rp8+feDan3/+GeZItWrVYK6VNGn55s2braxz585w7eHDh2GOij20+dZKx1B5hEYr0tHKeGJjY61MK5PRykS0md2zZ4+VafPmjbQSl/DwcCvTioqeeuopmGvFS+j16927N1y7YsUKmCNuZ0LbJ1DZU6dOneBadGyJiBQrVszK1q1bB9dqZTLaTKA9MidnQtsntPV5fSY2bdoE80qVKlmZtlcOGzYM5s899xzMUfHYXXfdBde6KeOsUaMGzN3OBDrH0c7t0HmCCN4nVq9eDddq+0S5cuVgjmjFVdpMHDp0yMq0GdT2iT/rTGj7BDp30vaJp59+GuZaGRl6/fr16wfXLl++HObovdJNsaiIu5no2rUrXKvtE56YifLly8PcE/sEOufTzoW8bSb4k2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiBz4GVaZlwcCBA2GO2pgrV64M116+fBnmWmNb69atrWzcuHFwbZ06dWB+5swZK/Px8YFrtRY31HQqgpvc5s2bB9dqLZWoee+nn36Ca7UmTq09vEiRIlaWnp4O16alpcEcNQbmz58frtUcOXIE5qilcNeuXXCt1kCem5544gmYoybEqlWrwrWoFVVEn5UOHTpY2QcffADX1q9fH+aotV17TbV20ODgYJij5sRZs2bBtdrMoubW6dOnZ/n7iehNyYGBgTBHUlNTYY6aJN3OBGpiFRGpXbu2lW3fvh2u1Vo0c1NuzET79u2t7MMPP4Rr3cyEtk9oDb3aTKB9xe1MoH3ihx9+gGtzYybc7BPa86rNRK1ataxsx44dcK03zsSQIUNgjppntebeixcvwlxrgEZt7u+99x5c26BBA5ifPHnSyvLlwz9/0Rp6UfuvCN5XtPf4evXqwRztE1OmTMny9xPRn2/0KSjaqXNKSgrMPbFPaE3j6H1Ca7BeuXKlq+95OwwaNAjmnpgJbZ/o2LGjlf3vf/+Da7V9As2E9pp6Yp+YMWMGXBsVFQVzNBPff/99lr+fiL5PBAUFWZk2Ezl57nTgwAGYo5nQ9onszAR/8kxERERERETkgBfPRERERERERA548UxERERERETkgBfPRERERERERA548UxERERERETkwK7szKLTp0/DHDX8FSpUCK7VmhrDwsJgjhpQmzdvDteGhobCPCQkxMq0dreWLVvCXGtGvXr1qpWhhnARkaSkJJijplOtmTsgICDL90MEP3btNdDa8VBzutYojtrHRfT2V9SumpycDNd6oxMnTsAcNYMXLlzY1W1XrFgR5ps2bbIyrdVR4+/vb2WoLVdEb2JdsWIFzFHTZbt27eBa7bVGLfFau6TWFKw1+KP3Cbczgdo/Z86cCddqLa9ajpokExMT4VpvpM3Ehg0brMxTM7F582Yr055fDZoJ1Fwqos+E1uCJjkW3M4GOc62ZG82PiN7M7ImZQA2t2r6ptbxqrxlqHM5LM3H8+HGYr1271sq0mdAayiMiImCO5k173jXomNPOnRo1agTz5cuXwxztE507d4ZrtXMndJxrzdzaTGjNzOiTTdzOBDqP05qPtXNgbSb2799vZXlpJtzsE+i9WcT9TKDzMrf7BJoJ9EkIIvo+8fPPP8McHYvo01VE9H0CXSNo507aTGjnTmgmtNfAzbmT1iiunddqzf5on7hx4wZcmx38yTMRERERERGRA148ExERERERETngxTMRERERERGRA148ExERERERETngxTMRERERERGRg2y3bWtNg5UqVbKylJQUuFZrd0ONziIiFSpUsLKgoCC4FjXBiYicO3fOyrTGSK21ukuXLjBHDeSLFy+Ga1FzuIhI/fr1rUxreQ0ODoZ5WloazFHjnfb8bdu2Lcu3obVqa+14qAVPBB872mPxRtpxVKVKFSvT2hG11+PIkSMwR82TaE5E9JZz1CqvNRhqzYvaMXDx4kUr04591BgqIlK8eHEr0xotUaO8iEjRokVhjo7nYsWKwbWonVO7Da3JVnte9+3bB3P0Xqg1V3ojNzOhtcNqr4ebmQgPD4drtZk4c+aMlWnH3Lp162Du5+cH8xIlSliZtkdeuHAB5nfddZeVacec9l6ekJAAc3R8afOD2v5F8Pubdv+0ZuG9e/fC/M86E6ihXGuW1mYiNjYW5qi1G+23IvrrhGZCOw9cvXo1zLWZQM8JaloWETl69CjMmzZtamXauZP2aSza84rmU1ur3W90G9p+oOXaTFStWtXK8tJMaMcR2ie0mdDeo7SZcLNPuJkJbb5Rm76I/mlEqLlae7/V9sJmzZpZmduZ0I4jNzPhiXMnbZ/Ys2cPzHN6n+BPnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyEG2C8PQH9uL4LIirQhGK2AxxsAc/bG89ofyWgEPKgTQyptQWYGISIMGDWDeo0cPK2vUqBFcqxU/oD9+P378OFyLCspERBo3bgzzEydOWJlWvqDRyr6Q+Ph4mGtFYug40Y4FbxQQEADziIgIK7tx4wZcq5VNaM8DOv4jIyPh2gMHDsC8a9euVnb+/Pksfz8RkYoVK8K8devWVqYdt88++yzMT548aWVbt26Fa1Exjoheovb5559bmfZ+pdFmGbl+/TrMtTIMdJxwJn6llQm6mYklS5bAHK3Xjgvt9bj33nthHhUVZWWXLl2Ca1ERkAieT62sBT3XIvrzjZ4/tzOhlf8hbmdCKzrLK7SZQMec9li1mUhPT4c5Kk3Szm8WLFgAczclf1pJk3YsuikLbdu2LczR8VyjRg24Vpurw4cPwxy9x7udCa3YCLl27RrMtUIr7VwrrwgMDIQ5Ol60590TM6GVFS9atAjmaL2nZgKdU2l7TXR0NMxRQbJ2P9D1gQgulBVxt0+g8jMRfE6qXRdq+5VWrpfT5078yTMRERERERGRA148ExERERERETngxTMRERERERGRA148ExERERERETngxTMRERERERGRg2y3bWtNiMWLF7cyrcVNawdduXIlzFFLr9YYWbduXZijRr4dO3bAtaVLl4b5woULYV6vXj0r09rdtMbuuLg4K9OahWvWrAnzbdu2wfzChQtW1rRpU7hWy9Fta62oWov51atXYY5e31KlSsG13ig1NRXmwcHBVqY9B2itiLs2Ta2tUHtNS5QoYWXaMac1Lx49ehTm+fLZ/31OmwntOUEN2lqzZpMmTWCuNWKj5tuCBQvCtag5XAR/CoDWPq7NxOXLl2F+6tQpKytTpgxc643czITWMIv2FBHPzETDhg1hjo6vixcvwrX//ve/Ya411qNPqkDv+yJ6CzdqL9We6xYtWsD8vffegzl6H27VqhVcq+WbN2+2Mm0mtIbWP+tMaOdOISEhVuZ2n9CayxG3+wR6L9+4cSNcqx0X2qc1oNtGz4eISPv27WFesmRJK1u3bh1cq83EmjVrYL5r1y4ra9OmDVyr5ei5Qi3jIvqnoGgzcezYMSvTWoi9kfbehY4B7VwjJ8+dtPMK9D68ZcsWuNbtuRPag7RzE+1+o1x7rrVrEq2dG72PoU8cEhGpXbs2zNFjRNdRIiLr16+H+erVq2GOrjs9uU/wJ89EREREREREDnjxTEREREREROSAF89EREREREREDnjxTEREREREROSAF89EREREREREDrLdtq21OkZERFiZ1hiqNQdqbby1atWysnPnzsG1qDFUBDcQaq3fWku41pD46quvWtmTTz4J12rfEzXvaQ29ixcvhnlUVBTMUXNnYGAgXLt//36Yo2ZtN42wInoLJGrL1Jr+vJHWjFq1alUr054zLU9PT4c5eq21Yx814IrgdlWt4Ri1joroDYkbNmywsjp16sC1WpPk/PnzrczPzw+unTdvnqv7V7RoUSvTmrz37NkDc9QirDUza7etNdaXLVvWyg4cOADXeiNtJtCnJLjdJ7SZQMfXmTNn4NqTJ0/C/N5777Uy7TXSWrW1T4LwxEwsWLDAygoVKgTXbt++HebVq1eHOTp2tX1Ca7HPyZlAn4Jx6NAhuNYbXblyBeZon9CeM62dXZsJ9P6n7RPHjx+HOTq3Cw0NhWu1Vu1q1arBPCEhwcoGDRoE1wYFBcF85syZVpaSkgLX/vOf/4S51vCN5lC7H9oeGRkZaWXa+avWnlyhQgWYo31i3759cK03cnPupM2E23Mn9Ik8npgJrfVba9VGj1EEN8WjfUlEpGvXrjAfNWpUltdqnwLw2GOPwRzNeOPGjeFa9KkmIvi5Qp8KJCJSuXJlmKNjX0Rk1apVVubJcyf+5JmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBxkuzDs2rVrMD98+LCV1axZE649ePAgzLUCEVQKopWklCpVCuaoCEkrk0HFJCIip06dgnm+fPZ/i7hx4wZcqxUkdOvWzcq0P6A/e/YszLVSDlQagkoJRPTnLzU11coKFy4M165fvx7mWlkael4TExPhWm+kzQQqX9NK3bQCHnRsabetzYRWNoJKUrRiBVR2JKKXkaFZ1soGteIlVARSv359uFYr/NDKh9C8vffee3BtmTJlYI5KabSCjJUrV8LcTWmh9p7ijdzMBCpwEXE/E6goR3uPuuuuu2Detm1bK9OKt9wWTpYoUcLKUHGdiD5vaIa0gjJtv9KKYNC+px232kygfcJTM4HKe/LSPqHt/ei41YoOd+/eDXPt3AndtrZPlCtXDuaoHEw7xrX3Z+3xoO/p6+sL1x47dizL9097jH/7299grr1fNWrUyMqWL18O12ozgYqrihQpAtdqt60VeaLnJC/tE1qJnpt9Qiv0zMmZQOdOWrmYdg6inbN07tzZytD5ioheqtmqVSsr08rotH2idu3aMG/SpImV/fzzz3CtNhOopEwrp9TOMbXnFb2+qJgwu/iTZyIiIiIiIiIHvHgmIiIiIiIicsCLZyIiIiIiIiIHvHgmIiIiIiIicsCLZyIiIiIiIiIH2W7bRs2GIrhVDbVki+itk1qLNPqe4eHhcK3WzFayZEkr09r7tBZurZERNaZqbdYtWrSAORIfHw9z1AApIrJ27VqYo8dZo0YNuFZ7/qpXr25l2vNUqVIlmMfFxcHcx8cnS9/PW6EWXRGRsmXLWpnWIKy1TmpNiKgVPSIiAq7V5soYY2VNmzaFa7VGS20m6tSpY2WxsbFwLXqeRPBxoTUfa627jzzyCMzRDGntklrzMfo0Aa1BU2tE1hr10WPX7p830mYCtZdqM6E1iWqt7eiY69GjB1yrNaOimWjYsCFcq82mtr+httOPPvoIrj158mSW7592bDVv3hzmv/zyC8xRgyx6TkX0/Q0do9rz5HYmUMt6rVq14FpvpH2SRfny5a1Ma9XWZsLNPhEZGQnXrlmzJsv3T2ts197LtffFqVOnWpn2foDuhwjeO9GciIh8/PHHMF+4cCHM0SdBaOeN2vkXOka1tnLtvEf7RAq0T2if6OGNtE+2QfuEdg6izYT2Hoq+p3bOqr3Poeudxo0bw7Xasd+3b1+YoxlavXo1XKvNBKLtm1pD/pw5c2COmvPR3iEi8tlnn8EcfarFpk2b4Frtdffz84M5mgltH8sO/uSZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyEG227bz588Pc9RIq7VLam2aWkMianLbuHEjXJuWlgZz1LC3f/9+uDY5ORnmgYGBMEdN3loLqNYQV61aNStr3749XKu1J2tNjai58+LFi3Bt69atYY7aPLWmRK2BWWst/tvf/mZly5Ytg2u9UcGCBWGO2ktRS6OIyLlz52CuzQRqM9baUrXjtkiRIlamtYB26tQJ5tpjR+8TWlu0dv/Q3KNZE9Hv95gxY2COWru1RlNtDtH31F5frYFZa9EcOHCglWmNsN4INUuL4MerPWfa66G1yj/wwANWtmvXLu0uQqjpVGs+vnTpEsy1vally5ZWhhrbRUSaNGkC88qVK1uZ1hSrfRqC1rY8cuRIKzt8+DBc265dO5ijhlvt9dU+YUBrLUYzsWDBArjWG2nnTj///LOVaS26bvcJ9H65cuVKuDY1NRXm6P0vJSUFrl26dCnMtXMt1KCuHS+FCxeGeYUKFaxMOzfR3pe0eXv55ZetTDv/6tixI8zRTGit5NpMaK/Zo48+amU//fQTXOuNtNdj1apVVqYdF9rroUHnTto+rF1PoNdPO8ZRO7WIfi6DZtnNJ5KIiCQkJFjZkSNH4FrtsWu+/fZbK9Pelzp06ADzWbNmWVmhQoXgWu2aCR0jIiIPP/ywlc2fPx+uzQ7+5JmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIQbbbtlFDr4jIiBEjrGz8+PFwbcWKFWGOmhdFcGuor68vXNusWTOY+/v7W9nRo0fhWq0ZNywsDOao3W3fvn1wbbly5WD+7LPPWllwcDBcqzVJxsfHwxy1cKP2bBG98Q4939r9026jadOmML98+XKWvp+3CgoKgvnrr79uZZ9++ilcqzXgajOBmp6156x58+YwT09PtzKtXVdrs9Ze6169elmZ1nzcuHFjmM+YMcPKtMZurY3yjjvugDlqKNbayrWWV/R8h4aGwrXa86S128fFxVmZ1mzujTwxE1qrttbQunfvXivTZkJrAUX7RGxsLFyrzYoGfaLCnj174Fqt3R7NBJpjEX0mtKZk9D6hvf+4mQnUbnur22jVqhXM0UzkpX2iWLFiMH/nnXes7IMPPoBrUdu6iLt9Qmuv7dKlC8zRedI333wD1x4/fhzm2vtf3759rWznzp1wrXZu9/3331vZgAED4FrtnGXHjh0wj4qKsjKttRi9d4jgY1RrWtZuIyYmBuboU1O059obafvEW2+9ZWUfffQRXFulShWYazOB2s+1mYiOjoZ5QECAlWnN11q+ZcsWmKMZX7x4MVyrfcIOmk/t3Em7JtE+NadOnTpWduzYMbg2J/eJNm3awBzNhCf3Cf7kmYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHGS7MOzAgQMwHzNmTNa/uVL4ov1hPSr3uX79OlybP39+mB88eNDKQkJC4Nonn3wS5loxASrJ0O7H6tWrYZ6ammplly5dgms1qFBFRMQYY2Va8duFCxdgjoo2EhMT4Vot379/P8xRkUHNmjXhWm+klf6g0het3Kdo0aIw12YCHYvaTGjFGaiEQiu10coZtAIWVLKkzf3y5cthjmYClUHcivZ8o9vWCrnOnTsHc1RwcePGDbhWmwmtRK1+/fpWVrduXbjWG6HyLhGR//3vf1m+DW0mtOM5MDDQysqXLw/Xau/PqDTu9OnTcK1WgBgeHg5zVKriiZnQ3vc12gyhUhXtsZw/fx7maCYSEhLgWm0mdu/eDXM0E6jQyVtps44KwzTa8aLNBCqp0wpRtZlA+5tW7qMVX2rnWqh0T7sfWmkSmokKFSrAtagsSkRk6tSpMEd7qva+5Gaf0GZC2z+0QrOGDRtaWV7aJ7RZf++997J8G9pMaKpWrWplWjmWdiyi/Q1dp4jopWNovxLB7wfae7xWZolKIbX3fe0cU7sWQOdU2kycPXsW5qgwz+1MaO+nDRo0sDJPzgR/8kxERERERETkgBfPRERERERERA548UxERERERETkgBfPRERERERERA548UxERERERETkINtt282bN4d5TEyMlc2fPx+uXbZsGcy1BkfUnBgaGgrXbt68GeaokbFMmTJwbbly5WC+aNEimDdr1szKtGbpu+++G+Znzpyxsv79+8O1CxYsgLl2v9evX29la9euhWuDgoJgjh6j1o6nteC1bt0a5lu2bLGyjh07wrXeSGui7tSpk5XNnj0brp03bx7MS5cuDfOIiAgrK1WqFFy7bt06mKPWxOrVq8O1qLVVRG8af/DBB60Mvc4ierM6aqZHjbsiIj/++CPM+/TpA3PUwrx161a4VmuMRI3n8fHxcO22bdtg3r59e5hv2LDByrp37w7XeiM3MzFr1iy4Vts/tJlA789a27Z2LKJmT61RXmtXnTNnDszdzAR6vxXB7/316tVzdT8aNWoE88qVK1uZtk8sXLgQ5m5mQpu3du3awTyvz0Tbtm1h3rVrVyv74Ycf4NqffvoJ5tpMoNdUW4ua5kXwuZM2V9qnG8ycORPmDz/8sJVt2rQJrm3VqhXM0XGEzkdFRMaPHw9z7XwSPc6NGzfCtdr7BHov1GZCez/QzofQfPbu3Ruu9Uba69S5c2cr044h7dxJOx9CjfDaWu39Ly0tzcr69u0L12qf4qG9Pw8cONDKpk2bBtdqrfJz5861MtRCLaKfkzZu3BjmqCVcm1ltJtD5pNt9okOHDjBH1zue3Cf4k2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB9lu2969e3eWc63NumTJkjBPTEzMcn7t2jW4Vmt0vnz5spVprdBaM+4vv/wC84CAACvTGnr/+9//wtzHx8fKUGOeiN5KrrVfh4eHW5m/vz9cqz32nTt3WtnJkyfhWq258NixYzCPjo62Mq1hzxvt2LEjy7nWiK7NSlJSEszRTFy5cgWu1dpr4+LirGz79u1wrdYYqTW0ogbtEydOwLVa6yR6TqZOnQrXnjt3Dubaa4PaVVEbsojedImO0ePHj8O1ZcuWhXlsbCzMUSs1ahv2VtpxhHK3M4EasUXwnqC95zRs2BDmbmYCtY6K6A2j6H2xTp06cK32yQ6oKVmbCW0Punr1KsxRe3jFihXhWj8/P5h7YiaOHDkCczQTWvOxN9LOH1CutVlrz5k2EyhH50IieqMzmgmtFbpWrVowDwkJgTk6nrX2eNSiKyLy73//28q05+P8+fMw37dvH8wLFSpkZdr7VUpKCszRc6Ud49rrru0T3bp1szLt0zW8kXa+iXLtudH2CTfXE9q5k9bojGYCnSOLiNx5550w12YZvc9pa7/99luY33vvvVZ26tQpuLZ27dowP3z4MMwLFy5sZdpMoKZ+ETwT2l6t3bY2E6ip3ZP7BH/yTEREREREROSAF89EREREREREDnjxTEREREREROSAF89EREREREREDrJdGKYV86A/5tf+kF/7w/pBgwbBfOLEiVZ26dIluFb7w3r0x+++vr5w7ZkzZ2BepUoVmKOSFK1oZf/+/TBHRRtr166Fa7XiEe2xd+nSxcq0x4KKy0Tw664Vz+zZswfm9evXh/myZcusLCwsDK71RqdPn4Z5hQoVrEwrvdBe02HDhsF81KhRVnbx4kW4Vvuehw4dsjJUkCKCSyJERHr06AFzVO6iHXP58+eHOXr/0ErHNm3aBPP4+HiYP/PMM1ZmjIFrtfcJdNtFixaFa7XisqZNm8J83rx5Vqa9p3gj7T3UzUxoZTLPPvsszEePHm1lbmfi4MGDVqa9/toxV716dZjPnDnTyrT329mzZ8MclXppxY3a/dMKWO677z4rQ4VOIvh1FMHFS25nonHjxjCfP3++laEyTG+l7RNorrXnd/PmzTB/8cUXYf7RRx9ZWb58+Gcnbl5TbZ/Qikh79+4Nc3SOU61aNbhWK4NFpXva8/TDDz/AXJsJVG6rlSBpha2oXK9YsWJwrVZO2KxZM5j/9NNPVhYREQHXeiM3M6G9Z2vlssOHD4f5p59+amXaOYj2PdF5vPaaovetW902Oh/WSr3atGkDc1S6N2XKFLh2xowZMNfOwVFhsVaWp51/oT1Ve/7cnjuhmUD7ZnbxJ89EREREREREDnjxTEREREREROSAF89EREREREREDnjxTEREREREROSAF89EREREREREDrLdtq21lqHGz+TkZLh2y5YtMEet2iK4CU9rx0ONpiIiVatWtTLtsWjtqnPmzIE5ahHWWsmDgoJgjtohtaa6Pn36wHzFihUwR211qEFTRKRFixYwL1DAPmTcNCWKiBw4cADm6LXUGlq9ETq2RESOHTtmZUlJSXAtaqcWwa3aIvj10Jp7tZbFGjVqWJnWiK21q37//fdZvm3teNFaFtFzos3EPffcA/MlS5bAHLUqay2lnTp1gvn7779vZVpjd2RkJMx37doFc/T6as+TN9KOIzQT2j6hzQRq1RbB7yNas/DUqVNhjpp+tfnWZkJrykYtvcHBwXAtev1F8HOitfxqM7F48WKYnzp1ysq05nCt+Rg1fGufyqDtv9qnNaDXt3jx4nCtN9JapGNjY61Ma9ddt24dzFGrtgg+jrRzp8mTJ8O8Zs2aVqYdF9pMfPvttzBHTdmVK1eGa7Xnb+XKlVb24YcfwrV/+9vfYL5gwQKYI+np6TAvWLAgzNH5l/Zers2Etk/k9ZnQ9onjx49bmbZPaDOBWrVF3M3Ed999B3N0flOqVCm4VvvEB+3+oU/e0d5vtU8mQC322jn/3//+d5gvXLgQ5ugcR5sJ7VoKtcprx63bmcjpcyf+5JmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIQbbbtrVmT9SEqDWaag2jISEhMEfNbI8//jhc+9VXX8H82rVrVoZaEG8lOjoa5nFxcVamNYxqLZVnz561Mq258sSJEzC/ceMGzFFztfY6ao3IqC04LS0Nrp0/fz7MtTbjrVu3WtmPP/4I13ojrQEatSZqx6d27JcoUQLmqJ29d+/ecO3hw4dhfuXKFSvTmvA1HTt2hPn58+et7PLly3Ct1iyLjkW3M6Ed52hW/P39Xd0/1MKstTtr7zVasyxqLf7hhx/g2nHjxsE8N2nPuydmIjQ0FObo2O3Vqxdcq80E2ie0mdAaPN944w2Yz50718pQK6oIbiEWwY3Y2kygZnMRfZ+4cOFClteGh4fDHO1v2l64c+dOmLuZienTp8O13jgT169fh3ndunWt7IsvvoBrtWO/ZMmSMN+4caOV9evXD67dv38/zNHrh16LW+ncuTPMixQpYmXap3Vozcwff/xxlu+HNhPa+xU6d9L2+9KlS8Mc7XvaMa7tE9pjR+372qdrfPbZZzDPTdrzjvZc7dN43M4EOna1T7BBn8ohgmdC2ye0T+DQZgJ9Us/q1avh2h49esD8zJkzVqbtE6jZXER/70f7ntuZQM+f1qrtdp/YsGGDlU2bNg2uHTNmDMxvhT95JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInKQ7bZtX19fmKNmXK1ZWrsN1GwoItK8eXMre/rpp+FarTkOtbz+/PPPcG1SUhLMCxYsCPOyZctamdYiiNovRXBbXWBgIFy7cOFCmLds2RLmsbGxVqa1JGsNgKjxXGtc1Vq1UWO3CG7eQ+3t3ko7ntGxiI5DEb0JUWv0Rc3vTZs2hWtnzZoF8/Lly1tZ8eLF4Vrtfu/atQvmYWFhVqbNxJo1a2CO2mlRO6uIyOzZs2EeExOT5dtGDeEiegskatZGbc0ielsqauwWwU3oNWrUgGu9kfZeiVrRa9asCdcWLlwY5tox6mYmtOMFzYR2XKD3fRH9sXfq1MnKtJnQWrjd7BPaJxZoM4Heg1ADt4hIw4YNYY5awrVPX9BmQsvz+kz4+fnBHDVAa23rbmeibdu2VtaiRQu4VmsuR+/lqBH4VvdD2yPReYjW0Dtq1CiYoz0oKCgIrp0xYwbM27dvD/MDBw5YWYEC+PRZO79B67V9QtsPtPMh1OStfTqEN9JmAp07afuE9ikZ2rkTev9r1qwZXDtz5kyYlytXzsq090ptJrR9At0X7fXXmqjRe4rWpq9dk6D3DhF3M6GdO+XPn9/KtE9l0Fq1tecE7ROenAn+5JmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBxkuzAMleSI4LKRkJAQuPbGjRsw18q+UCnIc889B9dqRSaoaKNdu3ZwrZsCKBGRRo0aWZn2B+qbN2+G+cWLF61s3rx5cO39998Pc+15RX/MrxWSoKIB7ba1QobExESYa+VS6LHv3r0brvVG2kygIosSJUrAtVpx0JEjR2COXr933nkHrtWOc1TypxXwlCxZEuZa2QQq9tKKgOLi4mBujLEy7Ri/8847Ya59T1ROqL02O3bsgDl63cuUKQPXoqIsEVzEJ4JnYtu2bXCtN0KFICJ4JkJDQ+FatzOB3nPfffdduNbNTGj3QyuImTt3LsxR8aW2j02cOBHm06ZNszJtJtq0aQPzkydPwhyVq2kFRlpRDZoJrVhN20+1ecvrM+Hj4wNzNBPa+y0qOhTBpaAiIlFRUVb21ltvwbVaWSg6XlBxnYhIamoqzLVypODgYCsLCAiAaw8dOpTl29Bmwk0Jkgg+d0LneyL6ezmaCVQ4JaKff2nHOSoz3Lp1K1zrjdycO2n7hPZae+LcqUOHDjBHM6HNpjYTWhHlPffcY2XaMbdu3TqYf/3111aGirREcNGmiMjBgwdhjmaiQYMGcK02E+i9UJuJo0ePurpttE94cib4k2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB7x4JiIiIiIiInLAi2ciIiIiIiIiB9lu27506RLM/fz8rOzy5ctwLWq+FtHbm1GjnNYWrbWqoQbo5ORkuLZXr14w1xpkUduf1qypNY9eu3bNygYMGADXas17r776KsxfeOEFK1uzZg1cu2jRIpij10Zr+tOaNbUmYtRGOGLECLjWG2lt0aiBXpsftzOxYsUKK6tfvz5cq7XenzlzJsv3o3jx4jDXZgW1aKakpMC12qygtkytGVJrin3ppZdg/tRTT1nZ8uXL4dqVK1fCHD1XWgOkNhPly5eHOZqJN998E671RtpMoH1CmwnUuCqizwR6ndzOBGrbRnN8q/unzQraJ7Q9pVOnTjAPCwvL8vdr1qwZzLWG76FDh1rZzz//DNdq7fGo3f6rr76Ca93OBNqv33jjDbjWG7nZJ7S1bo859Pppn0iizQRqFtb2Ca11t3Xr1jBHLdfauYnWtoz2Me1co1WrVjAfMmQIzN9//30rW7JkCVyrzUSTJk2s7JdffoFrtZlAcy+C9wntEwa8EWpGFvHMuZM2E+jcqV+/fnCtdp1x9uxZK9P2fm3eUEu8CH6f0x77woULYZ6UlGRllSpVgmtbtmwJ86effhrmqJl86dKlcK32yQ6NGze2Mq1RXpsJrZ0bnWeOHDkSrs0O/uSZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyAEvnomIiIiIiIgc8OKZiIiIiIiIyEG227a1hmrUrnvlyhW4FjVfi4i0aNEC5gkJCVamNdtprYSorc4YA9e+/vrrMC9QAD9tRYsWtbK5c+fCtVpDHGpdvX79OlyrPfbvv/8e5uh1QM+piEh6ejrM0euLGgdFROrWrQvzXbt2wTwiIsLKtOZj1Bye27QWafScafODmnhFRGJiYmCOXj+tab5ixYowR8eXdhuBgYGuctR0GRkZCddqjZa7d++2MtRKL4IbNEVEihQpAvMdO3ZYmdZoGRsbC/MSJUpYGWprFtEbN7VZRs2Y8+fPh2sHDx4M89zkZp/QnvecnInw8HCYx8fHW5m2T+zbtw/mp06dgvnAgQOt7NChQ3Ctr68vzNPS0qzs6NGjcK22/2ozgY5FrelUe09Br4GnZqJy5cpWtmDBArhWa0/OTW5mQmvb1vbtdu3awRwdz5s2bYJr0T6s3Ubp0qXh2qioKJhfvXoV5vPmzbMyrU1fmwkEfVKDiP4JMOgcTgQ3AGvt+1qbMVqvtRCjZm4R/X0MzYR27vnYY4/BPDdp507ofNjtPtG2bVuYo+NZO7a08xvUZq01ymszq70/o/dLbe/Xnj9E2ye02QwKCoI5mgn0KRoi+nsKmglt39Q+XcgT506DBg2C+a3wJ89EREREREREDnjxTEREREREROSAF89EREREREREDnjxTEREREREROQg24Vh2h+AowKJM2fOwLXnz5+HuVYohW5bK/EpU6YMzFFJQJs2beBa9AfxInqx0fTp060sOjoarp0zZ06Wb3vPnj1wrfYaPPLIIzBfvXo1zBGtHAcVOPj7+8O1WvmCth7Rygq8kVYUgsrhtPIcrSzhl19+gXn58uWtTCsf0mYClW9pZW9aOZJW4DZlyhQr69ChA1w7bdo0mNevX9/KUNGXiEjZsmVhrhUHTZ482cq0Ih3ttUlNTbUy7RjXCjXczIRWpOONtHI49Dppz6+2f2gzgW5bmwnteEEletpxqx0v6LgVERk1apSVffvtt3DtsmXLYN64cWMr27lzJ1yrPUatYE4raUS0shs0n25nonDhwlm+H1rRkzeqUqUKzNE+oc2Elm/ZsgXmaJ84ePBglteK4KKz8ePHu7of586dgzkqwtKKrbRZQYVC2jmc9hi1sqclS5bAHNHKVtG5k1ZEpc1EQEBAlu/Hn2GfuN3nTlqZn1bwi86dtOuDGTNmwBwVjoqIfPTRR1amnduj8ywRXMaoFWxpj3Ho0KEwX7p0KcwR7XoClSRqx7i3XU/wJ89EREREREREDnjxTEREREREROSAF89EREREREREDnjxTEREREREROSAF89EREREREREDrLdtq01nF28eNHKUJOiiMjVq1dhHhUVBfOCBQtaWUpKClx748YNmKNmXK3NtUAB/PRoTb/o/mkNwloj3+XLl61Ma+fU/PjjjzBHTXNao6nWaFijRg0rQ42DInoLbYsWLWCOGjqDg4PhWm+ktQSihtH169fDtVp7rdbci445rTEyMTER5qgd9OTJk3DthQsXYP7qq6/CHLWJfv3113AtaoYUEYmLi7Oy6tWrw7Vaq+PTTz8Nc/R+gN7DRPTHXqtWLSvT3tvWrVsHc62VHx0nISEhcK030mYCfdLCxo0b4Vq3M4FaObWZ0PaJtLQ0K9NmAs2giN7yiuawb9++cO1LL70E80uXLlmZNhMabW9CLb1ae7LWQty8eXMr01pl165dC3M3MxEaGgrXeiM3+4T23KDzBBH9PRS1NyclJcG1Wkvz3XffbWXaecKiRYtgvnfvXpijmZ04cSJci1q1RfD7NjpfuRWttRg9J9o58PHjx2GOPsFCe2/TPhmlXbt2MF+zZo2VafPmjTwxE9pzqc0Eet/WPmlA+6SSefPmWVmFChXgWq0NHO012v2bNGkSXNugQQOY5+RMFC9e3Mq011GbCXQdpJ07aa9727Zts7zekzPBnzwTEREREREROeDFMxEREREREZEDXjwTEREREREROeDFMxEREREREZEDXjwTEREREREROfAxWj2tg+effx7mY8aMsbIOHTrAtVqrGmpxE8HNvailUURkwYIFMEetnAkJCXAtatEV0Vu4r1+/bmX58uH/PqF9z5o1a1rZ7t274dqjR4/CHD1PIiJFihSxsrJly8K1Wpsnaj+sVKkSXKu1C2ptxh07drQyreFWawDMTa+//jrMP/zwQyvr1q0bXKs971rDMmpQ12Zizpw5MC9VqpSVoWNZRG/CdzMTGq35GH3P7du3w7WHDx+GOTr2RfDzFxYWBtei1m8R3NavNeSfOHEC5qh9WkSkR48eVnbs2DG49ssvv4R5btJm4oMPPrAybSa0FlVtJtBrrc2E9skEJUuWtDLtWEYtuiI5OxOopVSbidjYWJi7mYny5cvDtdpMnD171srczgTaa0TczcRXX30F89z0zjvvwPy///2vld1xxx1wrbZPoONWBL/W2nmWdlz4+PhY2fTp0+HaatWqwVw7H4qPj4c5op07oTnUGu8PHToEc3Tsi4gULVrUyrSZQE34IriZXHuetONZm4nevXtbmTb348ePh3lueuutt2D+/vvvWxmafxF9JrQmfjf7xKxZs2BeunRpK9Pe3+vVqwfz/PnzwxzdDppBEX1+0Exs27YNrnV77oRmQjt30s753ewTWmO3NhM9e/a0Mm2uJkyYAPNb4U+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBzw4pmIiIiIiIjIAS+eiYiIiIiIiBzgOtAs0JpdO3funOXbSEpKgnl6ejrMDx48aGXR0dFwbWRkJMxR22PFihXhWq0tVWv1Q+14ycnJcG3Lli1hvnTpUivTng+t7VFruUbPybVr1+Ba1CKofU+tBU9rA2/dujXMExMTrUxrD/VGY8eOhTlqTNVaR7XjRTsG9u/fb2Xt27eHa7UWQ9T2GBERAdcWLFgQ5lrrLjq+tMcYExMDc9Scn5aWBteGh4fDXGttr1q1qpVp7c5aMz36nkeOHIFrtUbLdu3awRy1LWuz6Y3Qpy+I4JnQmkS140U7Bm73TGj7hNa662YmtP1t4cKFVqY9HxUqVIC51nKNZkL7ZIxy5crBHO2pnpoJ1Lacl2bik08+gXmfPn2szO0+kZqaCvO9e/daWadOneDaZcuWwRzNROXKleFa7X67mQnt/FCb5Z9++snKtOdDO+fTZqJ69epW5nYm0PuHduyjc10R/IkkIngmtP3KG40aNQrmvXr1sjLt2NKOF+19Ec2E9vy6aY93u0+4OXdC58gi+nvl/PnzrcxT505oJrRzJzczobXEa7OivR/k9LkTf/JMRERERERE5IAXz0REREREREQOePFMRERERERE5IAXz0REREREREQOsl0YVqNGDZgXK1bMynbs2AHXhoWFwfzAgQMwR2UOoaGhcK1WqIFKBWrVqpXl76fdhohIqVKlsrxWu3+oyEL7Q36tGEwrm0DFXuj1EtGLNk6fPm1lWtFP8+bNYb5+/XqYoz/mDwgIgGu9Ue3atWEeHBxsZVu2bIFrtSKTffv2wRyV8GgzkZKSAnNUfFK3bl249tixYzDXjvMyZcpkea1WhlGpUqUsZSJ6ucWePXtgjp4/9HqJ6EUb6HtqM9GmTRuYr1y5EuaoaKNIkSJwrTeqU6cOzIsXL25lv/zyC1yrvf+hYjAR/JqWKFECrnUzE1FRUXCtNhPa8exmJrQcHf9aUY22j6HCHBH8/Gn7hPb8ob0GFU6J/PVmQntvDQkJsbLNmzfDtdo+ob3PoRIebSa0cxNUSlW/fn24ViuH02YClVu5nQlU/qftE9pMuNkn0HuYiL5PoMIjbZ/QSpBWrFgBczQTQUFBcK030t5bPXHu5OZ9Tiuo1WYCHc/16tWDa93uE+h8WHu/1WYClQS7PXfyxD7hZia0QjitUNbNPhEYGAjXZgd/8kxERERERETkgBfPRERERERERA548UxERERERETkgBfPRERERERERA548UxERERERETkwMcYY3L7ThARERERERF5M/7kmYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMgBL56JiIiIiIiIHPDimYiIiIiIiMjB/w87cYyWmMijGAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 잘못 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Wrongly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(wrong_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "맞게 분류된 샘플 랜덤 선택 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correctly classified samples:\n"
     ]
    },
    {
     "data": {
      "image/png": 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Nm4Zjx45d8l+L2K9cXK5t9LN9+3b07dsXcXFxmDx5MsqVK/eTHzMUhMJMhBLNhBTlTLRq1QoAsGzZslw3Bfv378fevXvz9GttBa1+/foAgNKlS+dpf9u6dauT53UmFi9ejMzMTJQuXZqusX4t73Jto6VmzZqIjY3NaXv9oSVLluS8r8WVZiI3zYQ/zcR3dO3k0rVTwSj8rvX/k5CQkOvvCwBg1KhRzr8U3XDDDVi4cCGmTZvmPMaJEydw8eLFnP8dpFr+448/xvDhw/HrX/8aL774Yj6fRcEpWbIkrrvuOkyYMAHr1q1z/vv3tfQA0K9fP+zfvz/Xx0JkZGSYv/rwQ9dddx08z8Pf//535795npfz/0dFRTkHo8LaxiDV8mlpaejduzdKlCiBadOmmb9+UxwV9UyEGs1E3mbi56woZyIpKQlNmjRxvt+bb76JsLCwXH9/dbnExcUhJSUFI0eOpD9F+vH+tmjRIixZsiTXf3///fd9v891112HI0eO4LXXXnP+2/czERkZCQDOTBTWNgb5WJ7rrrsOX375Jfbs2ZOTffPNN9iyZQt+8Ytf+H59KNNM5KaZ0Ezo2ik3XTtd/muny/aT59tvvx2/+93vcN1116FXr15YvXo1pk2b5nzc0EMPPYRJkyZhwIABuOWWW9C2bVukp6dj7dq1+OSTT5CamprzNXmtll+yZAluvvlmVKlSBT169HAOSldeeWXOvxQC3/2rSXJyMmbPnl1gz5955plnMGvWLHTs2BF33HEHmjZtimPHjmHFihWYMWMGjh07BgC444478Nprr+Hmm2/G8uXLUb16dYwdOzbnwH0p3bp1w0033YRXXnkFW7duRd++fZGdnY25c+eiW7duuOeeewAAbdu2xYwZM/Diiy+iRo0aqFevHjp27Fgo2xikWr5v377YsWMHHn74YcybNw/z5s3L+W9Vq1ZFr1698vpyh5yinAkAmDNnTs4J6PDhw0hPT8eIESMAAF27dkXXrl1z1momQmcmTp48mVN0OH/+fADAa6+9hkqVKqFSpUo5218cFfVMPPfcc7jmmmvQu3dvDB06FOvWrcNrr72G22+/HYmJiTnrUlNTUa9ePQwbNgyjR48u6Jchl9dffx1XXXUVmjdvjjvuuAP169fHwYMHsXDhQuzduxerV68GADz88MMYO3Ys+vbti/vuuy/nI2/i4+OxZs2aS36Pm2++GWPGjMEf/vAHLFmyBF26dEF6ejpmzJiBu+66C9deey3Kli2Lpk2b4uOPP0ajRo0QHR2NZs2aoVmzZoWyjXn9WB4A+POf/4zx48ejW7duuO+++3DmzBk899xzaN68+U8qHAoFmgmXZkIzoWun3HTtdJmvnfLV0e3Z1fJJSUl0fVZWlvenP/3Ji4mJ8SIjI70+ffp427Ztc6rlPc/zTp8+7T366KNegwYNvDJlyngxMTHelVde6T3//PPehQsXctbltVr++3p/6/9+WPt/+vRpD4A3dOhQ39fgUs/3hwB4d999N/1vBw8e9O6++26vdu3aXunSpb1q1ap5PXr08EaNGpVr3a5du7xrrrnGi4yM9GJiYrz77rvPmzp1qm+1vOd53sWLF73nnnvOa9KkiVemTBkvNjbWu/rqq73ly5fnrNm0aZPXtWtXr2zZsh6AXO9JQW9jkGr5S71vBfHxAAWpOM2E53ne448/br62P3xvNBOhNRPff4QL+78fP8+iVtxmwvM8b+LEiV6rVq288PBwr1atWt5f/vKXXI/neZ63du1aD4D3yCOP+D5efHy8179//0uu+f49fe655+h/3759u3fzzTd71apV80qXLu3VrFnTGzBggPfJJ5/kWrdmzRovOTnZi4iI8GrWrOk9+eST3ttvv+37sTye53kZGRneY4895tWrVy9nn77++uu97du356xZsGCB17ZtW69MmTLO/lrQ25jXj+X53rp167zevXt7kZGRXqVKlbxf//rXXlpaWp6+9nLSTGgmNBO5FbeZ0LWTrp3CPO8HP2sXTJ48GQMGDMDq1avRvHnzot4ckSKnmRDJ7Y033sDDDz+M7du3o2rVqkW9OSJFTjMhkpuunX6+LtvfPBcXs2bNwtChQ7Wji/wfzYRIbrNmzcLw4cN1kyDyfzQTIrnp2unnSz95FhEREREREfGhnzyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+CiV3y/s1KkTzVl5d7Vq1ejazMxMmickJND84sWLTrZv3z66duXKlTTv3r27k61fv56ubdasGc2tgvILFy44WXx8PF2blZVF8y1btjjZ7t276dqzZ8/SvGvXrjQvU6aMk7FtBoAFCxbQvF69ek521VVX0bW7du2i+bZt22herlw5J4uIiKBrP/jgA5oXpY4dO9I8OzvbyWrUqEHXWjNRv359mof6TLDnU6dOnTyvBYDt27c7WdCZ6NKlC81Lly6d5+0IMhOdO3ema/fs2UNzayaioqKcrGzZsnRtKM5E+/bt87w26HkiyEzs3buXrl2zZg3NU1JSnKwwZ6J27dp0rXWeYPuLtW9ZM2Hto0HOE4sXL6Y5O+9deeWVdK213WzugWAz8dFHH9G8KGkmXKE+E+Hh4U52/vx5ujZUZiIyMpKu/fDDD2lelK644oo8r61evTrN2T4OAA0aNMjzemsmli1bRvPevXs72erVq+naVq1a0dzan9n21a1bN89rAX4/kZqaStemp6fTvFu3bjRnM2Edl7799luas/fGun+x7ifYcwT4/YQ1E6NHj6b5pegnzyIiIiIiIiI+dPMsIiIiIiIi4kM3zyIiIiIiIiI+dPMsIiIiIiIi4kM3zyIiIiIiIiI+wjyr/tDHX/7yF5qz9trjx4/TtVb7L2txA4DKlSs72alTp+jamJiYPG+f1bxYsWJFmlvNoydOnHAyq1XZagtmbXCsNQ6w2/tGjhxJc9b6bDUXNm3alOalSrkF7WlpaXRtRkYGza1mQLYtVnP05MmTaV6U/vznP9OctddaM7Fq1ao8PwYQ+jNx8uRJJ7MaZK2mS9aky9pFAaB169Y0HzVqFM3ZTFStWpWuTUpKojl7/ayZsBotrZZKdvzYv38/XRuKM/HII4/QPMh5wmovZY8BANHR0U52+vRpurZKlSp5fmxrJipUqEBz6z1l82ntc0FmwjpPtGzZkuZvv/02zdnx2TpPNGnShObs9Tt48CBda81EkPPEgQMH6NopU6bQvChZM1EQ5wlrJth+bp0ngszEuXPn6FprJqzzBNsW6zxhNVH/nGbCunayjinFfSb++7//m+ZsJo4dO0bXWo3YQe4nrPNEbGwszS/3tZO1z1lN1Ox+wprNNm3a0Pz111+nOZsJ637HunZi74213545c4bm1kzUrFnTyaz7ic8//5zml6KfPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA+3/SmPVqxYQfO6des6WVxcHF37xz/+keZW8RIrz7juuuvo2lmzZtGcCVLwcKl86dKlTtavXz+6dvv27TRnf0A/f/58utYqzqhVqxbNGVZyZm0HwLfbKgmwihCs0oMNGzY4mVWkE4qsAjw2E1YBxR/+8AeaP/bYYzRnMzFkyBC6dvbs2TRnrJlghXEAL98AeIlH37596dqdO3fSvFKlSk62cOFCutYqhGDlERZW1AHYc5+amupkVtmNVbBjzcTGjRudzDqehiKr2Cg+Pt7JrJm4//77af63v/2N5uz9GzRoEF07Z84cmjONGzemubVfWLPCzp29e/ema4PMxKJFi+haq3TMOm6zDlFrJqzzBNtuax8/f/48zYPMRHE6TxTETDzwwAM0/+tf/0pz9v4NHjyYrv32229pzgSdCSsPMhM7duygOTsHFeZMWIVrhTkT1nmluM+EVfZVv359J7POf9Z9w4MPPkhzVjw2dOhQuvbrr7+mOROkWPRS+eLFi51swIABdO22bdtoHhER4WTWOY9dxwD2/QSbCet+gm0HwO8nrGs169rJmtl169Y5mTU/+aGfPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj4CPNYZVoe3HvvvTRn7WlWcy9ruwPsxraePXs62f/+7//StW3atKE5a1ksWbIkXWu1vlWpUoXmrDnx008/pWtbtmxJc7bdVhNhp06daJ6VlUVz1mh58eJFujYzM5PmrEnSev0sVtM4aylcu3YtXTt37txA3/NyuOeee2jO2kGtltKCmImXXnqJrm3dujXNWUO19Z5azYbR0dF5Xj9x4kS6tkWLFjRn2zJhwgS61movtV7vChUqOJl1SAwyE1bTsmXr1q00b968uZOtWbOGrp03b16g73k53HXXXTRnzbONGjWia4PORI8ePZzs5Zdfpmutmdi/f7+ThYWF0bXWecKaCbaPfv7553Qte/8Bvn9Z5xqrYdSaiXLlytGcsc4fZcqUcTLrmGK9rgVxnrA+qaIo/SfMhNXQa30qw3/CTITKtVMozoT1KSPs+SYmJtK1R48epTn7RBKAf+rHs88+S9e2a9eO5ux+okQJ/jPJ2rVr09y6n2DnlXHjxtG1rVq1ojlr8v7www/pWuvayWoPZ9dOlsK8dtqyZQvN2T2W9Yk4QT6J5nv6ybOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiIj2C1Zj+wZ88emi9ZssTJIiMj6VqrqbFu3bo0Z63TVvuvhbUmskY6AGjbti3NrWY21nR5880307VWgx1riLNa8MqXL0/z9PR0mrNWZauJ0GrHY02HVqOl1UJrvWfbtm1zsrNnz9K1oSjITERERNC1VlPj5Z4Jq/HQmolvv/2W5idPnnSygQMH0rXdu3enOWu0tNqprZlg2wEAcXFxTmYdl6wWVfZpAlajuDXL1qyw53nu3Dm6NhSx9w7gM1G2bNlAj23NxPLly50s6Eywc5Z1nrA+2WHhwoU0j4mJcbK33nqLro2KiqI5m1mr4XjTpk00t9pp2fZZxyVrJlhrsdWebM1EkPOEZuI7oTITVmO39SkZ7PhsnQ8uXLhAczYTVjO3dZ6wrocu90xY783PdSZ2795N8wULFjiZNRPW+1G/fn2aL1682Mms87AlyLVT+/btaT5z5kyas32RNYQDwPnz52nO9nNrNq32bKvBn107BZ2Jpk2bOpnVKG6d36x88+bNTlaQ9xP6ybOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiIj3y3bVutag0bNnQyqx2xYsWKNN+5cyfNWduj1S5pNXimpaU5WcmSJela1mQM2O19Xbp0yfNjb9iwgeasqY+1xgFAjx49aG69rp7nOZnVhM6aPwHemme91rVr16a51f7aoEEDJ8vKyqJrQ5H1XrOZsNrMg84Ea+ONj4+nawtiJqwGYas9nDVXr1mzhq61GlDZdlstr6wVFbDbHtn7YL0H1vGAPYb1WtepU4fm1kywfcd6LqEoyExY7bCVKlWieWpqKs3ZMc2aiWrVqtGczYTVorpy5Uqa33DDDTRn56wdO3bQtdZ+wRpTrefYrVs3mh87dozm69evdzKrrd46HrBzfmGeJzQT3wmVmVi0aBHNg3zKhHWeiI2NpXmvXr2czPp0CPbJI4C93Wz/CnqeCHLtFKRBGCj+M2HtR+yTLKz7CWsmWBM5wJuyrWt7q+V8//79TmY9l3nz5tE8yEywhnDAvj686aabnOzRRx+la63jLWvqB/j+Zb0HQc4T1r5vHa/Y+QoAGjVq5GQFORP6ybOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjId2GYVe7DiikyMjLoWqs4gxVbAbwMgxUlAMD06dNpztZbpRxdu3aleatWrWjeuHFjJ1u7di1da5WkfPLJJ05mFdJYxQS33XYbzWvWrOlkVnGZZePGjXlem56eTnOrNOns2bOBtiXUsAIKAKhXr56TWftc0JlgRRHWTHz99dc0DzITVjEFe44AL0c6cOAAXWsVGx06dMjJrGPK3r17ad65c2eas2OK9dwtVtEGc+bMGZpbM2E9z+KCFdoBfH+x5t+aCatMkL2nCQkJdO0333xDc7be2i+sc2HlypVpzgqPrCKYMmXK0Hzy5MlOZp0nrFKiO++8k+asiPLIkSN07YoVK2i+ZcsWmjPWTFjnyP+kmQh6ngiVmQh6nmDXVEOHDqVrO3ToQHNWMGZdr1jlQz179qT5zJkznawozhM/15mwrp3YtUnQ+wkLK2lkpX0AMGXKFJqzUirrPLZ9+3aaW3PIysuys7PpWqs8mJXo7d69m66tWrUqza3XhL1+QWeCzad1rXvq1CmaW6XRhT0T+smziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiI98t21nZmbSPCYmxsmslrTo6GiaW02DjNU4bTUylijh/nvB8uXL6dqOHTvS/PTp0zRnr8n48ePpWquJlTUGWq+11TK3dOlSmn/55ZdO1rRpU7rWahpnj81aAQG7ee/EiRM0Z03JVlNsKLp48SLNq1Sp4mS7du2ia62ZsPY5xpqJ9u3b05zNhNWia7VWW8+H7c9Wm+Lx48dpXqNGDScrW7YsXduiRQuaWzNx+PBhJ7vqqqvo2i5dutB8yZIlTmY1aAadiX379jnZz2Em2H5u7ePWsdJq82esmWjbti3N2X5rtVnfddddNLeO26y5mu1DgN2AymbWeq2tJveXX36Z5uwTMx577DG69vHHH6f5s88+62RsjoHgM7F//34n+7nOhNWMa50nQmUmrPOE1RZ84403Opn1yQnW9dqCBQucbNq0aXTtk08+SXO27wO8FTjoeWLZsmVOZl3D/afNhHWsZJ9McPLkSbqWXWcB9nmFXYeULl2arr3iiitoHhYW5mTWtUZycjLNWWs1EOza6dixYzRn+4DV+t2kSROajx07luYHDx50MusTU7p3705z9ikTBXU/wY6d7BOH8ks/eRYRERERERHxoZtnERERERERER+6eRYRERERERHxoZtnERERERERER+6eRYRERERERHxke+2bavBrnHjxk7G2kUBu13XamRs2bKlkx04cICutVoq69Wr52RWS9/TTz9N8759+9J8xowZTtasWTO6lrX8AsDEiROdLCIigq6dPHkyzX/3u9/RnLVrli9fnq5dv349zVkTnvX+skZYwG5/ZW2sW7ZsoWtDkdUq36hRIyc7evQoXWvNhNWyyNql09LS6No9e/bQnM2E1eZqtWo3bNiQ5osWLXIyq/XbalWeO3euk1nHiK+++ormbdq0oTlrarRmYsOGDTRnzdrWTLAGTQCoXbs2zdlMbN26la4NRdZMsP3F2vetNk1rH2jevLmTWTPB2swBID4+3slatWpF11qfDmF9ksGDDz7oZNb8WO2vU6dOdTLrPDFlyhSat27dmuZsxq25t2blV7/6lZN9+umndG3QmahWrZqTWQ2yoaggZiLotVNhzYR1nrCuvx566CGas9Zd65MTWKs2wD/ZxJqJf/zjHzQfMWIEzVnrvbXvs2ZugDdrW9cBOk98hzVAW+dW67W0ZoJdE1j7vnXtxK6HrZlITU2ludVyPX/+fCezjtlWcz67RwgPD6drrQZ667qMnZcrVKhA165du5bmQe4nrJlgxyWAz8TmzZvp2vzQT55FREREREREfOjmWURERERERMSHbp5FREREREREfOjmWURERERERMRHvgvDrD/wZ3+QbRWtWMUK1h+Gb9q0ycnKli1L19asWZPmMTExTrZ69epAj/Htt9/SnG13uXLl6FqrBKdBgwZ5elyAl4AAQFJSEs1/+ctfOhkrKAOA6tWr0zwrK8vJrOc4e/ZsmlsFBKyU4ezZs3RtKDp58iTNC3MmWKGaVZJi7c+sMM8qyKhVqxbN9+7dS/PY2Fgni4qKomutAhtWaGaVzlnHJau4r0OHDk42Z84curYgZsI6dlSqVInm7H3IyMiga0ORVSzJ9ltWCAnw4z5gFxKyebNe30GDBtE8JSXFyZYtW0bXWu81K/Wy1ltFK4cOHaI5K1qxjhFWqaZVaNauXTsnGzNmDF07ePBgmrMSHGt+rJK/IDNx7tw5ujYUFcVMsPIo6zxhvU+sCMkqWOrVqxfNrfKmuLg4J9u2bRtda5XXBZmJixcv0tw6T9xzzz1ONnLkSLrWev3Y97TOhawkE7Bngp1/fw7XTmw/t8o/rZJbax9g11oFce1kleVZZW/WtRYrqbPef+s8we4nLNaxwyruu+KKK5xs5syZdK31+mVmZjqZdT61Htu6n2AFbQV5ntBPnkVERERERER86OZZRERERERExIdunkVERERERER86OZZRERERERExIdunkVERERERER85LttmzXBAbyN12oQthqn9+/fn+fvyRoWAWDhwoU0Z02I7du3p2utJkmrGZC1UVqN01ZrMVOmTBmaX3311TSfNWsWzQ8ePOhkVpOe9fqxNkqrablx48Y0P3z4MM3DwsKczGoUD0XVqlWjeUHMhLUvsu9pzcSCBQvy/BjWTFizabVcs3ZIqzWxUaNGNGdtiuXLl6drWQMkYLdcs2ZhayYWLVpEc9Zub71f1kwcOXKE5sV9JljbOsDbN60GYWsmrBZp9j1vvvlmuvbMmTM0Z824Fy5coGs9z6P5unXraM5Y+2eQ84TV4typUyeaW63ybCasZm5rljt27Ohk8fHxdG3QmWCtsNYnTISiUJkJ6zyxePFimrPzBNtXALsVOMh5wppNqxWYsWbCOk9MmzaN5uwTFazjsHWeYNdO1vm0YcOGNA9ynihOM2FdO7GGaqtV+/jx4zS3rk/ZvUBCQgJdO2/ePJrXqFHDyazjrbUdQe4nvvnmG7o2yDWBdf1tnWus78mep/UpMvPnz6c5u9ayXqfExESaW03jrGXd+vSC/NBPnkVERERERER86OZZRERERERExIdunkVERERERER86OZZRERERERExIdunkVERERERER85LttmzWZAcDcuXOdjLXaAXZLWnZ2Ns1jYmKczGpxY22pAG+0tNr7zp8/T3Or6Zdtt/Xcy5YtS3PWLti7d2+6NioqiuYRERE0Z83f1nvQq1cvmrMmPPaaAnaLINtHAOCOO+5wsilTptC1ociaCdbUaO0XrBH9UliLamHOhNU4XK5cOWsT8/T9LvUYrKXUaqz98MMP8/wYALBmzRons2aiZ8+eNGczwVo4AXsmrDbP22+/3cmmTp1K14aiUqX4KYbto9ZMWO2gVss1+1SGZcuW0bV169alOWvD37p1K11rNeNaTb9su4OeJ1gzqjUTaWlpNA8yE9Z7cO2119I8NTXVyaz5sRqlp0+fTvNbb73VyayW5FBUunRpmrOZsI4jQWciLi7OyaxP1GDN0ta2bNiwga61Pq3BunZi2/3pp5/StdZ5gjXCs+cN2G3lTZo0ofkHH3zgZFZDfo8ePWge5DxhbZ/1nrGZsOYnFFnnCXbNal0/WMe5IDNhfQKBde3ErtfXrl1L1wa9dmLbbe0v1rGVtVnv3LmTrrXOkVZD9YoVK5zMun7t27cvzffs2eNkVuu3NRPWHP7+9793si+//JKuzQ/95FlERERERETEh26eRURERERERHzo5llERERERETEh26eRURERERERHzo5llERERERETER77btitWrEjzZ555xslefvllujYhIYHmVmvxvn37nKxMmTJ0rdUWzRqqd+zYQdeyJjjAbrMePHiwk1nNex06dKD5hAkTnKx79+50bXh4OM2txkDWvLdr1y661mp5ZU2hVsur9RjJyck0P3LkiJNZzzEUWTPx1FNPOdmrr75K1zZo0IDmQWbCes2CtLZbjYxBZ2LQoEFOduDAAbrWamRkM7F69Wq61noPrObj5s2bO1nQmWDHIPbJAJd6jK5du9L86NGjefp+oapChQo0HzFihJO9/vrrdG29evVobjW0sv3Zapa22nVnzJjhZCtXrqRrMzIyaM7aSAGgf//+TsbavQG7tZg1EbN9GbAbWs+cOUNz9lpZc2+10LLcOk9YTbHW63rs2DEn00x8x5qJINdO1rEoyHli/PjxgR574MCBTnb8+HG61pqJzz77zMmSkpLoWmsmzp07R3M2W+w1BexzYZDzhPUYV111Fc3ZTFit7qHIOm//7//+r5M9//zzdK31qQfWTOzevdvJrJno168fzdlMWJ/KwL4fYL/XN9xwg5OxT0IA+HUWwM8TbF8BgEqVKtHcunZq1aqVk7FPWQCAyMhImrPX22rItx6jW7duNGfXTgV5P6GfPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiI98F4atX7+e5i+88IKTeZ7Hv7nxh/wlSvB7+saNGzuZ9cfs1mOzYharyKRRo0Y0t8o6tm/f7mRWacPMmTNpnpiY6GRWocDixYtpbpXPsPfBKi85dOgQzatWrepkVrHL2bNnab5u3Tqat23b1slYyVmo2rBhA81ffPFFJ7NmwiptsLB91JoJq3Rs06ZNTmYVmVj7fpUqVWjOCmVYyQYAfPPNNzRnxUYHDx6ka63CD1ZGBwSbCet7suNH0Jmwjqdt2rRxsuI0E2zfAngRjMUqk7HOEz169HCy6tWr07XWTGzevNnJrJmwysjKly9Pc1aqYp2vZs+eTXNWyMUKUi7FWp+dne1k1nM5fPgwzdkxyJoJa96s42nr1q2drDjNhHV+DjIT1nkiLCyM5qxMySqMCzIT1rWTVep19dVX05zt50Fn4sKFC05m7ePWNVVaWhrN2XO3XidrJoKcJ6zisp/rTFjXhKxs1VK5cmWaWzPRtGlTJzt16hRda73X7P2wZsIqNLOundj9BHufAWDWrFk0Z/uXde9hHVOs/TnItZM1V+x+Ij09na61ZsUqZWb3E9brlx/6ybOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiIj3y3bXfr1o3mffv2dbKJEyfStVOmTKF5XFwczRs0aOBkrK0NABYuXEhz1lKalJRE17K2NsBujvvHP/7hZKNHj6ZrrVY6hjXuAsDdd99Nc6tZljWmLl26lK6Njo6mOWt3ttrxVq5cSfNevXrRfNGiRU42cOBAujYUJScn05zNxKeffkrXTps2jebWfp6QkJDntdZMZGVlOVnNmjXpWtbECwCTJk2i+a233upkrG0YADp37kzzN99808ms5kprO6z213LlyjnZsmXL6FprJoJ8CsDq1atp3rNnT5qzRv3+/fvTtaGoa9euNO/du7eTff7553Tt1KlTaV6tWjWa16lTx8msJlbrsVlLvDUT1v781Vdf0XzYsGFOtmLFCrr2iiuuoDk7TlhNol988QXNrZmIjIx0MutYbrUCs8ewjkuTJ0+meffu3Wm+ZMkSJ9NMfMeaCfYpCdZ1FjsPA/zYX6NGDbrW+pQRa19k+4vVklwQMzF//nya33777TRnLczWp51YM8HOWUGvndgnCQB8Jvr160fXhiLreQ0YMMDJxo0bR9da+5a1j7JrJ2t+5s2bR3N27VSrVi261rp2sq4F77zzTidj9y+Afe356KOPOpk1ExMmTKB5p06daM7uJ6yZsD5lhH26kPUpANZ1Gbu+BviMDx48mK7ND/3kWURERERERMSHbp5FREREREREfOjmWURERERERMSHbp5FREREREREfOjmWURERERERMRHvtu2rdZYllstpVYrtNXMxloMT5w4Qdda7X0nT550MtYgDdgNdjNmzKA5awP/3e9+R9dabZnt2rVzsvXr19O1ZcqUofmOHTtoXrZsWSezmgEzMzNpzlphd+/eTddaLYfW9rHWPNYiGarWrFmT5zzoTFgNngUxE8eOHXOyVatW0bVNmzaleUxMDM1Ze6XVlmo1oLI26+XLl9O1zZo1o7m1z0VERDiZ9d4UxExY76+1fX369HEyqyE/FBXmTFjnifPnzzsZO+4DdqPztm3bnMx6LqwxFACqVKlCc9Y63aJFC7rWashn5yzr9Qg6E+w8Yb0HVvsrawtm7wsAtGrViubffvstzVkrtWbiO9ZrzM4TQWeCnSes52I1iluNw+zTR4LOBDtPWDNh7XPWcXvOnDlOZr0HrIEZ4DOxZ88euta6dmKfAgDwTzApTjNhfdoAy9mnKQD2rBTE/QQ7DwN8JoJem1jXTmz/sj6xgO2fAJ9x1mwPAC1btqQ5OxcCwe4nrPME20etT6+wHnvr1q00Z03t1rEjP/STZxEREREREREfunkWERERERER8aGbZxEREREREREfunkWERERERER8ZHvwrD9+/fTvF69ek5Wu3ZtupYVKADAn//8Z5q/9tprTlaiBL//r1u3Ls1ZqYD1h+hWoUbp0qVpPnLkSCf7xS9+Qddarwkrihg9ejRdu3jxYppbxQlxcXFOZpUgZWRk0JyVlFWsWJGuXbt2Lc07duxI88mTJzuZ9T6GogMHDtCcPQfr/beKuh555BGav/76605WsmRJutb6nqxwgRVpAbwkAgAGDhxIc1ae0bNnT7rWKmthxUuTJk2ia62COeu5x8bGOplVbpGenk7zIDNhlf916NCB5l999ZWTsWNsqEpLS6M5K32xjsNWOeVDDz1E84MHDzqZdZ5ISEigOStJsQoarZno378/zdk+ysomAXu7WZnMvn376Nply5bR3DpPsHmzSpCClJSVL1+erl2wYAHN2WwCvGwzPj6erg1FRTET//znP53s6NGjdK11rCyImUhJSaH5Cy+84GTWMdQ6v7FySmsmTp8+TXOrzLJUKfdS2fM8utaaCXbdGPTaqX379jRnM1Gcrp2s+4n69es7mVUYZh3nnnjiCZq/+OKLTmbtW9b33LRpk5NZ105WUdeQIUNozs4Tzz//PF1rFfex+wnrOsu6n7COB6y8rCDuJypVqkTXWtfG1sx+/vnnTmad7/NDP3kWERERERER8aGbZxEREREREREfunkWERERERER8aGbZxEREREREREfunkWERERERER8ZHvtu3GjRvTfNeuXU6WlJRE1y5atIjmrFUb4E14VjvehAkTaM4a9qz2WtZgCPBmSIA3IXbu3JmujY6OpvnChQudbPv27XTtr371K5pPmzaN5qwdMjs7m661GsXXrVvnZFZjpNVst2HDBpqzZtnKlSvTtaGoYcOGNN+zZ4+TnT9/nq61ZoK1agO8BdRq6P3oo49onpiY6GSNGjWia60myY8//pjmbCas5l7rvWZtvJs3b6Zr/+u//ovm06dPpzljzYTVLMuaUa3nwtpDAXsm2Pv7c5gJ1vh54cIFutZqAWUNwgDQpUsXJ2vTpg1dO378eJqz192aifDwcJp/8sknNGfzZjX1W+81e02sRtNf/vKXNJ8xYwbNGWsmrHPn8ePHncxqJ7bmauPGjTRn5/yfw0yw80RBzQR7zazzhHUsZ9d8QWfC+pQEdj60ZsJq42XtxNYnJIwdO5bm7NMhAL7/B712Yp+0YD0X6zwRZCasxw5F7JgIADt37nQy69pp/vz5NGfX/AA/xlv3E++99x7Nmzdv7mTWc7Gunf7973/TfOjQoU7GjqsAEBUVRXP2mpw5c4auvfnmm2nOPgUHKJiZYJ8aYB3LreOm9QkmhX3tpJ88i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjId9u21WLI2ufeffddujYmJobmsbGxNF+2bJmTDRkyxNpE6uTJk0725Zdf0rVWY/c111xD806dOjnZiRMn6NoWLVrQfP/+/Xl6XIC3/ALA2bNnac5asa33sVq1ajRnr5/VDMmauQGgQYMGNGcNolYbrtUqWpSstttmzZo5mdXeaM2Ela9YscLJBg8eTNdare2nTp3K0+NeSp8+fWjO9i9rJtixA+CNkWXLlqVrWYszcPlnwmohtpohrZlgDbJWi/PIkSNpXpSsmWCfwDBmzBi6tkqVKjS3ZmL27NlOZjWJWu8Ha10NOhM9e/ak+ZEjR5ws6Eyw84TV5hp0JlhLr9WebH3iQ2pqqpPVqlWLrv30009pbs0Euw6wztVvvfUWzYtSUcwE23evvfZaunbHjh00P336dJ4eF7Bbwu+66y6ab9261cnYPg7YM8HauatXr07XWo3D+/btozmbiaDnCXaetc4TQa+divtMWO9Hy5YtnWzUqFF0rXXfULVqVZqzTzaxPplgy5YtNGfH7aVLl9K11qcN9OvXj+ZsPz927Bhda7Vws+9pXTuxT0oC7OMVmwlrrTWH7NrJ2sdXrVpFc6vxn31y0YcffkjXvvrqqzS/FP3kWURERERERMSHbp5FREREREREfOjmWURERERERMSHbp5FREREREREfOjmWURERERERMRHvtu2y5QpQ/M9e/Y4GWsbBuzWt8qVK9O8W7duTmY1UVtNjbVr13ayqVOn0rU1atSgedu2bWnesWNHJ2PNqgDwwgsv0Jw1BrJtvhSrvY+1q7LGPAD4r//6L5rPmzfPyVirHQA0bNiQ5o0bN6Y5a96z9p1QFGQmmjZtStdGRkbSPDo6muYpKSlOZs3ExIkTaV6zZk0ns/Zba38pXbo0zdm+a7Vfsn0L4E2cYWFhdO2kSZNozo4dALBt2zYnK1WKHxatVnnWzGy1h1ozYTVGFveZsPaLvXv3Opk1E0HPE6xB3Wp6th57wIABTmbNT7ly5WgeZCas/WXBggU0Z02/1nZYnyZhzUT58uWdrFWrVnRt3bp1ac7myvrkBOs9sGaFzURiYiJdG4qCnCdYAzdgN6tbM5GcnOxk7HoFsI+hbIYOHz5M11rtutZMdOjQwcmsfd/K2QxZr98XX3xBc2uGCuI8UaKE+7Mq1mAOFMx5ojjNhNXmzz4pgDVwA8HPE7169XKyq666iq61jl3x8fFOdvDgQbrWuoazjgdt2rRxMmufGzFiBM3Z/lWhQgW61voUD/Y6Abwh39o+q0GbXTuxVnoAaNKkCc2t/Zw1oVufcpQf+smziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj4yHdhGPtDb4D/0X5cXBxdy0pPAGDnzp00Z3/s/T//8z907RNPPEHz48ePO5lVYtG1a1eaHzp0iOY7duxwsk2bNtG127dvpzkrG7Be68GDB+d5OwD+PFkpwaUeY8OGDU7GCqcAXvYAAGvWrKE5K6lauXIlXRuKgswEK4YDgIyMDJqnpqbSvHnz5k5mzYRV/LBv3z4nswqMLl68SPOlS5fSvGfPnk5mlZ4cO3aM5qzwIzMzk661ZpaVWwDBZmLt2rU0Z0UwVtmgNRPWY7OZWLVqFV0biqyZYMV4MTExdG3QmWCFajfccANde++999Kc7edWUSQ7JgLAlClTaM4K/axCLlZ6AgCe5zmZVQTzq1/9iuZW+R8rPLJKkKwysunTpzuZdR3AiiwBeyaOHj3qZKtXr6ZrQxE7XgB8JqzzhHXtFGQmnn/+ebq2e/fuND9w4ICTWbPJ3iMAmDx5Ms1ZSZ1VLBpkJqyyVXbeBIBnn32W5ufOnXOy1q1b07UFcZ5g5XGAfe30c50Jdu1kFY5aM2Fdy7Jj7pNPPknXXn311TRnxZfWdmRlZdF80aJFNB80aJCT1alTh661juVsv2UZYM/9li1baM6undq1a0fXWtcsrPjVKvfctWsXza17BFZmuHz5cro2P/STZxEREREREREfunkWERERERER8aGbZxEREREREREfunkWERERERER8aGbZxEREREREREf+W7bttoUIyIinMxq0WXtkgAQHR1N8zlz5jiZ1YxrtdKxxkirSa9ixYo0t9rnPvroIydjLZeX2r7169c7mdWO17RpU5rPnDmT5s8995yTvfTSS3TtkiVLaM5aKq0mPavF3GrAZA3KVnN0KAoyE6z1HeDtkgBvnAaCzUSVKlVoztq2rdm0Gn0bNmxIc/aaWE2i1kywhlGr/bdz5840f/DBB2nO9q9Zs2bRtex1AoD27ds7mdV0as2E1TDJWp+feeYZujYUWcf+8PBwJ7NmwtoXrYbRuXPnOpnVjMvmBwCioqKcLCkpia7t2LEjza39hTW/W/NjHQ/YY1tt+ldddRXN//SnP9GcnQ+t88SCBQtozs4TVlNw0Jm4cOGCk/3jH/+ga0NRkJkIeu1UEDNhnSfS0tKcjJ3bAKBMmTI0b9CgAc3ZvFnHUOv6gZ0TrE9fYN8PAFasWEHzp59+2slmz55N11pzz5qINRPfYZ8qARTu/QQ7z7NzOWDPBGvbtrbDup+wGqrZ9bD1yQTWtRNrprc+Hcealbvvvpvmr7zyipOxT1kA7Gu+K6+80smsGSxVit+uxsfH05y9ftZ5LD/0k2cRERERERERH7p5FhEREREREfGhm2cRERERERERH7p5FhEREREREfGhm2cRERERERERH/lu22btfgBQooR7P26ttZrjunXrRvMzZ8442fLly+naF198keasMdVqEP76669pvnnzZpqzhsmgbZSe5znZrl276NqXX36Z5osXL6b5E0884WTsNQWAatWq0Zw9H9ZgDtgtglY7N2vinDJlCl1711130bwosXY/AChZsqSTWc3CQWciPT3dyZYtW0bX1qtXj+YZGRlOlp2dTddarYlWEyLL161bR9eyhl4g2EycOnWK5taMs7ZT1noL2K2OBTETVusqa5CdOnUqXXvPPffQvCgFOU8EnYmUlBSas/155cqVdK31nrJGV6uh19q3rEZ4Nst169ala61PVNiwYYOTWS2/r776Ks2tT1R48sknncz6JIEaNWrQPMhMtG3blubWTLDzxLRp0+ja++67j+ZFKchMsLZcoGhmgl0rsGMzAHz77beBHpvNivUJE9a1U2xsrJNZjd1WU3b58uVpvnbt2jxvh3WeZc35monvBLl2so5FbB8H7E8hYNdO1rVzQkICzdlMWNdOqampNLc+kYddO1mf9BPEzp07aX7y5EmaW+c3dvyw7nesln223jqPWZ9qYR3H2CdYfPnll3TtbbfdRvNL0U+eRURERERERHzo5llERERERETEh26eRURERERERHzo5llERERERETER74Lw6w/AK9Zs6aTWaUI+/fvp7n1B+C1atVysu3bt9O1QUrAevfuTdceOnSI5j169KD52LFj8/zYH330Ec1ZUYRVEmGVtfzhD3+g+TfffENzxioCYQUOVpGOVbxkrWcqVqyY57VFzSqVYDNh7fvWrFgzwR5769ateV4LAKdPn3Yya79dunQpzdu0aUPzf//7305mFa3MmDGD5mwfsPat6tWr0/z++++n+axZs2jOWDPBij2sfdx67pGRkXneDuvYFoqsmWDHLmsm0tLSaG4VD7LHts4T1jGUFcEkJyfTtVZBX7ly5Wj+t7/9zckee+wxuva1116jeevWrZ2MlRoBQJ06dWjep08fmltlSow1E6z8ytrHrVlmBUuWn8N5ojjORM+ePelaayasc9Du3budzCrxGT9+PM1btmzpZO+++y5da5WRDR8+nOahMhNBzhPFaSZYuRMA1K5d28msQikrt/ZF9thWIRe79wB4QWm/fv3o2kWLFtG8VatWNGez3LdvX7r2ueeeozkrKLWOEdZzfOSRR2g+ffp0mjNB7ies82ZBzESlSpXyvNaPfvIsIiIiIiIi4kM3zyIiIiIiIiI+dPMsIiIiIiIi4kM3zyIiIiIiIiI+dPMsIiIiIiIi4iPfbdtWmyxrqLZa5k6cOEFzq7mXNdWeP3+ers3IyKB5dna2k1ktfaVLl6a51XzM1o8ZM4auZa3aAHD8+HEna9q0KV1rGTduHM1Z+6L1Pu7du5fmzZs3dzLWOAgA8+fPp3m3bt1ovnDhQieLjY2la0NRQczEyZMnaW7tL2wmMjMz6dr09HSaZ2VlOZk1E6xZGgg2E48//jhda809a3lNTEykay3WTLDWVau90ZqJZs2aOZk1E2wfB/7zZuLw4cNOZjW5WzPBGqcBPhMXLlyga8+ePUvzIDNhnSesVlPWPPqPf/yDrrXmnrXy16tXj64NCwuj+YQJE2heEOeJpKQkJws6E1a7+eLFi50sJiaGrg1F/8kzYe1z7LGtdl3rPMGO8dZ1YNDtK6yZYJ90AQSfiSVLljhZlSpV6NpQZDUsHzx40Mms60p27QwAHTp0oDmbiXPnztG11n7E9ts9e/bQtdY+x45nAD+ef/XVV3StNRPsNWHX8Jfy/vvv05xdO1kzwdr0Ad6Qbx3b5s6dS/NevXrRfN68eU5WkNdO+smziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiI8wj1V/5sEzzzxD8xdeeMHJBgwYQNdarWpWcyZr5LMaGT/77DOaV6tWzclYmy9gN1eWLFmS5uxxrKZTq72vRYsWTma1tu7YsYPm5cuXp3mlSpWcrFatWnTtsWPHaJ6WluZkjRo1omut1kHWoAgA1157rZPt2rWLrn333XdpXpSefvppmr/44otO1r9/f7o26Eyw95q1SALA559/TnM2E1YLaGHOhNUGzhoZV69eTdcGnQnWohp0JljzcePGjelaayZYIzsAXHPNNU5mzcTo0aNpXpRGjBhB85deesnJ+vXrR9daM2G1yQaZiS+//JLmcXFxTmadJ9j+CdjN9NbjMFbzMWtMXbNmDV27c+dOmlszUaFCBScLOhPsGN+wYUO61montmaCXU9Yba7Wp10UJWsmXn75ZSe7+uqr6dqCmAnr2umLL76guWYit6KYCdbIDvDrieI0E//7v/9Lc3ZNNWjQILrW+vQe69qJnfut84TVwl69enUns66drEZsq4XbehzGup9g12srVqyga7dt20Zztu8D/H6iTp06dK2137L7iSZNmtC11v5s3U8MGTLEyay5HzVqFM0vRT95FhEREREREfGhm2cRERERERERH7p5FhEREREREfGhm2cRERERERERH7p5FhEREREREfHBqw/z4I033qA5a8IrUYLfo587d47mFy9epPmWLVucrGfPnnSt1djGWoHr1auX57WA3abI2vEuXLhA16akpNB82rRpTma9HnXr1qW51dTIXhOrtbNmzZo0Z6+V1XC8fft2mlvvGWvRZG2GoerNN9+k+cCBA53M2rfOnz9P86ysLJpv2rTJyXr16kXXWg3QbD4LaiZYi6o19927d6f51KlTncx6PeLj42m+b98+mrPX5NSpU3StNRNsDq1WR2tWevToQfPiPhMjR46kOZsJq4XdOoZa+wA7T1ivr9V2G+Q8YTUIHz9+nOZBzhPJyck0nz59upNZr4fVgGqdJ9inJxTETKSmptK11kxYxwPWLMs+MSBUBZkJi7W/ZGdn0/znNBNBrp0K6jyhmShcrGkeAK6//nons+4nrGsnayY2bNjgZFa7fWJiIs3ZTCQkJNC1Vqv20aNHac72L+s5Wtd87NMkrPsJa5atTwhhr4nVeG6dgxo0aOBkVuv31q1bad6nTx+as5moUaMGXZsf+smziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj4yHdhWMuWLWkeHR3tZCtWrKBrrcKrjRs30pyVK8TGxtK11h/Ws7Ii67ns2rUr0GOzP0a3Si+snJUN1K9fn661Cl+CvH6VK1ema62iDVZkYRX9dOvWjeZz5syhOSvaKFeuHF0bilq0aEHzmJgYJ7NmwipWYIUvAH9P4+Li6Fprn2OlVK1atcrz9wPsmWClJZmZmYEeoyBmghWrAXzGK1WqRNdaRRvse1qlJlbZzdy5c2le3GeiefPmNGfHnVWrVtG1VrlPkJmwzhPWvsjKWqz53r17N82tYjxW+Gbt+1bO9n+r8CXoTLDXz5oJ6/VjJTNW2WDXrl1pHmQmypcvT9eGImsmCuLaafPmzTQPlZmw9ucgM2HNFZsJ6zxhlSAFuXZi7xegmciP1q1b07xKlSpOtnTpUrrWeq/Xr19Pc3YtW7VqVbrWunZipVRt2rSha60SUWt/Zu9p0Jlg5X+spAuwZ8J6/djzse4nrGsndpywrp2sgsNZs2bRvHbt2k5WkDOhnzyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+AjzPM8r6o0QERERERERCWX6ybOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzXMxNnv2bISFhWH27Nk52S233IK6desW2Tb9GNtGkcKimRDJbfTo0QgLC0NqampOlpKSgpSUlCLbph9j2yhSWHSeEMlNMxFMnm+ew8LC8vR/ofCkmAceeABt2rRBdHQ0IiMjkZiYiCeeeAJnzpzJ92OmpKTkeu7R0dFo37493nnnHWRnZxfg1he+p556Cp999llRb4Zj4sSJ6NOnD2rUqIHw8HDUqlUL119/PdatW1fUm6aZIDQThU8zUXjOnTuHp59+Gk2bNkVkZCRq1qyJX/ziF1i/fn2+H7Nu3bq5nntcXBy6dOmCiRMnFuCWF76MjAw88cQTIfvejRs3Dp06dUKlSpVQpUoVJCcn46uvvirqzSr2M/Hxxx/jxhtvRMOGDREWFlYg/+ii80Th27x5Mx544AFceeWViIiICKl/nCruM6Frp0sL1ZkoyGunUnldOHbs2Fz/e8yYMfj666+dPDExMfBGXA5Lly5Fly5dcOuttyIiIgIrV67EM888gxkzZmDOnDkoUSJ/P4SvVasWnn76aQDA4cOHMWbMGNx2223YsmULnnnmmYJ8Cnny1ltv5WvQnnrqKVx//fUYNGhQwW/UT7B27VpUrlwZ9913H2JiYpCWloZ33nkHHTp0wMKFC9GyZcsi2zbNBKeZKFyaicLz61//GpMmTcIdd9yBNm3aYP/+/Xj99ddxxRVXYO3atYiPj8/X47Zq1QoPPvggAGD//v0YOXIkhgwZgjfffBO/+93vCvIp5Mn06dMDf01GRgb+/ve/A0BI/dQaAF599VUMHz4c/fv3xzPPPINz585h9OjRGDBgACZMmIAhQ4YU2bYV95l48803sXz5crRv3x5Hjx4tsMfVeaJwLVy4EK+88gqaNm2KxMRErFq1qqg3KUdxnwldO11aqM5EgV47efl09913e3n58vT09Px+i0L3/PPPewC8hQsX5uvrk5OTvaSkpFxZenq6V6tWLS8qKsq7cOEC/bqsrCzv7Nmz+fqePzRr1iwPgDdr1qyf/FhRUVHesGHDfvLj/FhBbuP30tLSvFKlSnl33nlngT1mQdBMaCbyQjPhCoWZ2Lt3rwfA++Mf/5grnzlzpgfAe/HFF/P1uPHx8V7//v1zZQcOHPCioqK8Ro0amV+XmZnpnT9/Pl/f84feffddD4C3c+fOn/Q4hw8f9gB4jz/++E/eph/7qdvYsGFDr3379l52dnZOdvLkSa9cuXLeNddcU0BbWTCK00x4nuft3r3by8rK8jzP85KSkrzk5OSf/Jg6T/j7qdt49OhR79SpU57ned5zzz1XIMeAwlLcZoLRtdP/F6ozweT32qlA/+Y5JSUFzZo1w/Lly9G1a1dERkbiz3/+M4Dvfk3jiSeecL6mbt26uOWWW3JlJ06cwP3334/atWsjPDwcDRo0wLPPPuv8C8iBAwewadMmZGZm5mt7v/9d/hMnTuTr65nIyEh06tQJ6enpOHz4MIDvnvs999yD999/H0lJSQgPD8fUqVMBAPv27cNvfvMbVK1aFeHh4UhKSsI777zjPO7evXsxaNAgREVFIS4uDg888ADOnz/vrGN/o5CdnY2XX34ZzZs3R0REBGJjY9G3b18sW7YsZ/vS09Px3nvv5fzKyA/fk4LexoyMDGzatAlHjhzJ8+v6Q3FxcYiMjCzQ962waCY0E5qJ3EJ1Jk6fPg0AqFq1aq68evXqAICyZcsGeZqXVK1aNSQmJmLnzp0AgNTUVISFheH555/HSy+9hISEBISHh2PDhg0AgE2bNuH6669HdHQ0IiIi0K5dO0yaNMl53PXr16N79+4oW7YsatWqhREjRtCfHLC/eT537hyeeOIJNGrUCBEREahevTqGDBmC7du3IzU1FbGxsQCAv//97zkz8cP3qqC38eTJk9i0aRNOnjzp+3qeOnUKcXFxCAsLy8kqVKiAcuXKFej7VlhCdSYAoHbt2vn+SVoQOk8U7HkiOjoa5cuX910XqkJ5JhhdO4X+TDD5vXbK869t59XRo0dx9dVXY+jQobjxxhudCxE/GRkZSE5Oxr59+3DnnXeiTp06WLBgAR599FEcOHAAL730Us7aRx99FO+99x527tyZpz9qv3jxIk6cOIELFy5g3bp1+Mtf/oLy5cujQ4cOAZ/lpe3YsQMlS5ZEpUqVcrKZM2di3LhxuOeeexATE4O6devi4MGD6NSpU84wxMbGYsqUKbjttttw6tQp3H///QCAs2fPokePHti9ezeGDx+OGjVqYOzYsZg5c2aetue2227D6NGjcfXVV+P222/HxYsXMXfuXCxatAjt2rXD2LFjcfvtt6NDhw747W9/CwBISEgAgELZxiVLlqBbt254/PHH6QGQOXHiBDIzM5GWloaXXnoJp06dQo8ePfL0tUVNM6GZ0EzkFoozkZCQgFq1auGFF15A48aN0bp1a+zfvx8PP/ww6tWrh6FDh+bz2boyMzOxZ88eVKlSJVf+7rvv4ty5c/jtb3+L8PBwREdHY/369ejcuTNq1qyJRx55BFFRURg3bhwGDRqECRMmYPDgwQCAtLQ0dOvWDRcvXsxZN2rUqDzdPGZlZWHAgAH45ptvMHToUNx33304ffo0vv76a6xbtw49e/bEm2++id///vcYPHhwzq9Bt2jRAgAKZRsnTpyIW2+9Fe+++65zQfxjKSkp+OSTT/Dqq69i4MCBOHfuHF599VWcPHkS9913n+/zDwWhOBOXm84TBX+eKM5CeSZ07VR8Z6JArp3y+6Nu9msWycnJHgDvn//8p7Mexq97xcfH5/rx/pNPPulFRUV5W7ZsybXukUce8UqWLOnt3r07Jxs2bFigX0VZuHChByDn/xo3bvyTfvyfnJzsNWnSxDt8+LB3+PBhb+PGjd7w4cM9AN7AgQNz1gHwSpQo4a1fvz7X1992221e9erVvSNHjuTKhw4d6lWsWNHLyMjwPM/zXnrpJQ+AN27cuJw16enpXoMGDZxfYRg2bJgXHx+f87+//5XD4cOHO9v/w19xs37NojC28ftfvQjy63+NGzfOed/KlSvn/eUvf8n5VbJQoZnQTGgmcituM7F48WIvISEh10y0bdvWO3DggO/XWuLj473evXvnzMTq1au9oUOHegC8e++91/M8z9u5c6cHwKtQoYJ36NChXF/fo0cPr3nz5t65c+dysuzsbO/KK6/0GjZsmJPdf//9HgBv8eLFOdmhQ4e8ihUrOs8/OTk516/fvvPOO+avpn8/E5f6te3C2Mbvf5X73Xffdb7fjx08eNDr0aNHrvctJibGW7Bgge/XXm7FbSZ+qCB/bVvnictznvC84vlr26E+E7p2yq04zURBXDsV+O/ihIeH49Zbb833148fPx5dunRB5cqVceTIkZz/69mzJ7KysjBnzpyctaNHj4bneXn+l9OmTZvi66+/xmeffYaHH34YUVFRP6kdD/juV9ViY2MRGxuLxMREvPrqq+jfv7/zawjJyclo2rRpzv/2PA8TJkzAwIED4Xlerufap08fnDx5EitWrAAATJ48GdWrV8f111+f8/WRkZE5/6pzKRMmTEBYWBgef/xx57/98FfcmMLaxpSUFHieF+hfTt99911MnToVb7zxBhITE3H27FlkZWXl+euLkmZCM+G3jZqJYAprJipXroxWrVrhkUcewWeffYbnn38eqamp+MUvfoFz587le3unT5+eMxMtW7bE+PHjcdNNN+HZZ5/Nte66667L+fVoADh27BhmzpyJG264AadPn855nkePHkWfPn2wdetW7Nu3D8B3+1unTp1y/eQjNjYWv/71r323b8KECYiJicG9997r/De/mSisbbzlllvgeZ7vT52B7+aqcePGGDZsGMaPH4933nkn59fOt23b5vv1oSBUZ6Kw6Dxxec4TxVkoz4SunXIrTjNRENdOBf5r2zVr1kSZMmXy/fVbt27FmjVrcl1A/NChQ4fy/dgVKlRAz549AQDXXnstPvjgA1x77bVYsWJFvhtq69ati7feegthYWGIiIhAw4YNERcX56yrV69erv99+PBhnDhxAqNGjcKoUaPoY3//XHft2oUGDRo4O2fjxo19t2/79u2oUaMGoqOj8/qULvs25sUVV1yR8/8PHTo0p4Xx+eefL5DHL0yaCc1E0G3MC81Ewc7EyZMn0aVLFzz00EM5zdgA0K5dO6SkpODdd9/F73//+3xtb8eOHTFixAiEhYXlfLTJD38N73s/nolt27bB8zz89a9/xV//+lf62IcOHULNmjWxa9cudOzY0fnveZ2Jxo0bo1Sp4JcEl2sbL+UXv/gFSpUqhS+++CInu/baa9GwYUM89thj+Pjjj3/S418OoTgThUnnictznijOQnkmdO0UTCjNREFcOxX4zXPQco4f3+1nZ2ejV69eePjhh+n6Ro0a5XvbfmzIkCG46aab8NFHH+V7Z4+KisoZoEv58evyfVnBjTfeiGHDhtGv+f7vyYpKqG5j5cqV0b17d7z//vvF4kZBM8FpJgqOZiK3/MzEhAkTcPDgQVxzzTW58uTkZFSoUAHz58/P981zTEzMT5qJP/7xj+jTpw/9mgYNGuRrmwpKUW/jjh07MHXqVOeCLDo6GldddRXmz59faN+7IIXiTBQmnSeKdhuLg+I0E7p2urRQ3cb8XjsV+M2zpXLlyk6b2YULF3DgwIFcWUJCAs6cOZOnHeinOn/+PLKzs/PU5lnQYmNjUb58eWRlZfk+1/j4eKxbtw6e5+X6l5jNmzf7fp+EhARMmzYNx44du+S/FrFfubhc25gfZ8+eLZL3rSBpJnLTTPw0momf5uDBgwDcCzDP85CVlYWLFy8W2PfKq/r16wMASpcunaf9bevWrU6e15lYvHgxMjMzUbp0abrG+rW8y7WNFut9A74rZiuK960gheJ5oijpPCGhOBO6dvr/ittM5OfaqfA/f+D/JCQk5Pr7AgAYNWqUc8K74YYbsHDhQkybNs15jBMnTuQ6Eea1Wv77ZrUf+9e//gXgu1/Lu9xKliyJ6667DhMmTMC6deuc//59LT0A9OvXD/v378cnn3ySk2VkZJi/+vBD1113HTzPw9///nfnv3mel/P/R0VFOQejwtrGINXy7NdqUlNT8c033xTJ+1aQNBO5aSY0E0U5E9//FOKjjz7KlU+aNAnp6elo3bp1oOdSEOLi4pCSkoKRI0c6F4aAu78tWrQIS5YsyfXf33//fd/vc9111+HIkSN47bXXnP/2/UxERkYCcD+KpbC2Ma8fVdWgQQOUKFECH3/8ca753bt3L+bOnVsk71tBKsqZCEU6T+T/Y3l+LnTtlJtm4vJfO122nzzffvvt+N3vfofrrrsOvXr1wurVqzFt2jTExMTkWvfQQw9h0qRJGDBgAG655Ra0bdsW6enpWLt2LT755BOkpqbmfE1eq+Vnz56N4cOH4/rrr0fDhg1x4cIFzJ07F59++inatWuHG2+8Mdf6sLAwJCcnY/bs2QX9MuTyzDPPYNasWejYsSPuuOMONG3aFMeOHcOKFSswY8YMHDt2DABwxx134LXXXsPNN9+M5cuXo3r16hg7dmzOxcyldOvWDTfddBNeeeUVbN26FX379kV2djbmzp2Lbt264Z577gEAtG3bFjNmzMCLL76IGjVqoF69eujYsWOhbGOQavnmzZujR48eaNWqFSpXroytW7fi7bffRmZmJp555pmAr3ho0Uy4NBOaiaKaiYEDByIpKQn//d//jV27dqFTp07Ytm0bXnvtNVSvXh233XZbztrU1FTUq1cPw4YNw+jRowvjpcjx+uuv46qrrkLz5s1xxx13oH79+jh48CAWLlyIvXv3YvXq1QCAhx9+GGPHjkXfvn1x33335XwMVHx8PNasWXPJ73HzzTdjzJgx+MMf/oAlS5agS5cuSE9Px4wZM3DXXXfh2muvRdmyZdG0aVN8/PHHaNSoEaKjo9GsWTM0a9asULYxrx9VFRsbi9/85jf417/+hR49emDIkCE4ffo03njjDZw9exaPPvroT3sDilhRzgQAzJkzJ+dG5fDhw0hPT8eIESMAAF27dkXXrl1z1uo8ETrniZMnT+LVV18FgJw/XXjttddQqVIlVKpUKWf7iyNdO7k0E5f52ilQN/cPWNXySUlJdH1WVpb3pz/9yYuJifEiIyO9Pn36eNu2bXOq5T3P806fPu09+uijXoMGDbwyZcp4MTEx3pVXXuk9//zz3oULF3LW5bVaftu2bd7NN9/s1a9f3ytbtqwXERHhJSUleY8//rh35swZ53sD8IYOHer7Glzq+f4QAO/uu++m/+3gwYPe3Xff7dWuXdsrXbq0V61aNa9Hjx7eqFGjcq3btWuXd80113iRkZFeTEyMd99993lTp071rZb3PM+7ePGi99xzz3lNmjTxypQp48XGxnpXX321t3z58pw1mzZt8rp27eqVLVvWA5DrPSnobQxSLf/444977dq18ypXruyVKlXKq1Gjhjd06FBvzZo1vl97uWkmNBOaidyK00x4nucdO3bMe+CBB7xGjRp54eHhXkxMjDd06FBvx44dudatXbvWA+A98sgjvo8ZHx/v9e/f/5Jrvv+oqueee47+9+3bt3s333yzV61aNa906dJezZo1vQEDBniffPJJrnVr1qzxkpOTvYiICK9mzZrek08+6b399tu+H1XleZ6XkZHhPfbYY169evVy9unrr7/e2759e86aBQsWeG3btvXKlCnj7K8FvY1BPqoqMzPTe/XVV71WrVp55cqV88qVK+d169bNmzlzpu/XXm7FbSYef/zxXB/J88P/++H7r/NEaJ0nvj+msP/78fMsasVpJnTtVHxnoiCvncI87wc/axdMnjwZAwYMwOrVq9G8efOi3hyRIqeZEMntjTfewMMPP4zt27ejatWqRb05IkVO5wmR3DQTP1+X7W+ei4tZs2Zh6NCh2tFF/o9mQiS3WbNmYfjw4bpxFvk/Ok+I5KaZ+PnST55FREREREREfOgnzyIiIiIiIiI+dPMsIiIiIiIi4kM3zyIiIiIiIiI+dPMsIiIiIiIi4kM3zyIiIiIiIiI+SuX3C9u3b5/ntdWqVaN5ZmYmzevXr0/zixcvOtnevXvp2jVr1tA8JSXFydavX0/XNmvWjOZWQTl7PrVr16Zrs7KyaL5t2zYn27NnD1179uxZmnfu3JnmZcqUcbILFy7QtYsXL6Z5fHy8k1155ZV0rbXd27dvp3lUVJSTlS1blq796KOPaF6U/pNnwtqfWa6ZcP1cZ6J169Z5XhsXF0dzaybY6w7wmUhLS6Nrrf2c7S+bNm2ia5s0aUJz6zzBtq9GjRp0bXZ2Ns137tzpZPv27aNrz507R/MOHTrQPMhMLF++nOZsxq3vZ213amoqzSMjI50sIiKCrp04cSLNi1KnTp1ozvaX6tWr07XW+9GgQQOaBzlPrFy5kuY9evRwsnXr1tG11kfzWPszm3Frvq3zxJYtW5xs165ddK11nujatSvNw8PDncx6D+bPn09zdg6/6qqr6Nrdu3fTfOvWrTRn5wk2JwDw73//m+ZFKVSunfbv30/Xrlq1iubs2mnjxo10bdOmTWke5H6iTp06dC17LkDhXjuxmbDeg4ULF9I8yLWTdbxizxEINhMffvghzS9FP3kWERERERER8aGbZxEREREREREfunkWERERERER8aGbZxEREREREREfunkWERERERER8RHmWVVvPh555BGaly5d2smOHz9O165evTrPjwEA0dHRTnb69Gm6tkqVKnl+bKtlrkKFCjS3GuVOnTrlZFWrVqVrreY41qRbrlw5urZly5Y0f/vtt2nOGvmsNk+rQZa9fgcPHqRr09PT87wd1rYcOHCArp0yZQrNi1KozATbDwEgJiYmz4+tmXBpJoIbPnw4zUuVcj/owdpvrUZfayYqV67sZNZ5gq21HttqrS5fvjzNrZlg22I1jVvtr6xdmrWLAkBSUhLN33//fZqzfdGa2UaNGtGcvb+HDx+mazMyMmhuvX6sbddqU585cybNi9Jf//pXmgc5T1iN2KwpHeDniZMnT9K1sbGxNGfvqTUTFStWpPn58+dpzmbfOg5bTdTsPGHNpvUpACNHjqR5kIZ8q1WZvb/WfmudJ6yZYNtiHTu+/PJLmhelRx99lOZBZsJqxGat0AA/9lvnIOt+gs1bQV07sfm0msatBu0gM2FdO/3rX/+ieUFcO7HXr6CundhrVZDXTvrJs4iIiIiIiIgP3TyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+HAbIPLI+uP8+Ph4J7MKKO6//36a/+1vf6M5+wP6QYMG0bVz5syhOdO4cWOaW4U0rDgDAFasWOFkvXv3pmt37txJ80qVKjnZokWL6FqrYMkqsmDdcFZpiFWywLbbKgmwykGs9Rs3bnQyq6gmFAWZCauAoihmgu0XQcqxAM3EjwWdCasIZNOmTU5mHU9DkVX2Vbt2bSezZuLOO++k+bPPPktzVvrSr18/unbBggU0ZxISEmhulTRZM7FmzRon69atG127a9cumrNCpmXLltG1VnGQtc8xVpGO9dzZdlvHcmsmrBK1rVu3Ollxmonly5fTvG7duk5mvWZ//OMfaW4VL7GSpeuvv56unTVrFs3ZsTIxMZGuDVLmBwBLly51squvvpqu3b59e54fe968eXStVbBUs2ZNmrPnfuLECbrWOk9s27Ytz9/PKmKzzisbNmxwsuJ07WQV4LGZsIpPH3jgAZpbBX1sJgYPHkzXfvvttzRngt5PWDk7TvTp04eu3bFjB83ZTCxcuJCutWbCunZiCvPayZoJ6zxW2PcT+smziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiI8wj9UI5sFdd91Fc9ae1qhRI7r22LFjNLdaDHv06OFkL7/8Ml3bunVrmrPm0bCwMLrWakKMjo6mOWuJ+/zzz+na5s2b05w1tH766ad0rdUyZ7X9lStXjubMxYsXac7aVUuWLEnXWq+r1ZaZlJTkZGvXrqVr58+fT/OipJlwBZmJZs2a0Zy1UQadCev1Ll++PM2ZzMxMmrMmSWsmLFZbZnGfiVtvvZXmrI25QYMGdC1rRQXsBuguXbo42VtvvUXXWsfhAwcOOFmJEvzfmq12UNYSD/DGzylTptC1VpsxO0989dVXdK3VWm21h7OZyM7OpmuzsrJoHuQ8YUlNTaU5+ySA9evX07WsxbmoDR8+nObsGGAdt6yZsHL2CQcvvPACXdu2bVuaszZeq1E+6HmCNfpOmDCBrm3ZsiXN2Xli3LhxdK01s9a8VahQwcmsS+cg5wnrmGKxrp3YcWz16tV07dy5cwN9z8uhKK6devbs6WQvvfQSXRvk2sk6zlmt1dZMsGuZoPcTbCasuSqK+4mCuHayZoJdT7JPugDyd+2knzyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+OBViXmwd+9emi9ZssTJypYtG+ix69atS/Ply5c7mdVgZ4mMjHQy1kgHAG3atKG51VaYkZHhZH379qVrW7RoQXPWFJucnEzXbty4keaLFy+meUxMjJNZbY9WOx5r3rMaAK1GS+s927Ztm5OdO3eOrg1FQWYiIiKCrrVarovrTJw8edLJunfvTtdeuHCB5qzV0WqXtBogrcbN2NhYmjOsQRjgM/HZZ5/RtdZ7Y81KcZ8J1kYKAKtWrXIyayasY1Tt2rVpzlpmrcZpq0U6yExYLfGLFi2iOWsJZ8d9ADh//jzN2X5uNQVHRUXleTsAoEqVKjRnrNePNadbjeKsfRyw219Zu6p17AhFrLUa4PuLde1knSfq1atHc9Y6bh2LrBZp1sJutW23a9eO5rNnz6Y5Oz6zhnDA3udYs3/nzp3p2smTJ9P88OHDNGfniaDXTqwl3mo+rlWrFs2tFvOtW7c6WXE6T1jXTmy/Laj7iWXLljmZ9fpa2HnCuk6wrp3mzJlDczYTQa+d2Mxa9x7WtRO7hgMK5n6CzUTQaycrL+yZ0E+eRURERERERHzo5llERERERETEh26eRURERERERHzo5llERERERETEh26eRURERERERHzku227ZMmSNG/YsKGTWQ1nlSpVonlqairNWbNdfHw8XWs1daalpTmZ1Ri5e/dumvfs2ZPmrVu3djKrvdFq3NyxY4eTWW2p1157Lc2tFlrWLmg9NlsL8FY/qynY2o5NmzbRnDW0Wi19oSjITFgtuqE+E6wlGbAbetk+YDXFHjp0iOaNGjVysrvuuouutZouDxw4QHPWoHzllVfStazZHADWr1/vZFYDZNCZSEhIcLKsrCy6NhRZM1G/fn0ny8zMpGtZYyhgH5/ZTFivu9X0zI7bVpPoihUraB6kUd+aK6utnJ1rrO9nNcpbDa2s6bdfv3507YIFC2i+a9cuJ7Nea6vhljXNA3zf+TnMBGvtD3qe2LlzJ83ZTFjN3Nb5nJ0nrOcyf/58mgdp1GdNy4A9E127dnWy6OhoutZqs7bOe+w6pGLFinSttd3s+Gbt+9bxyvqEFXaN8XO9drKOWxUqVKB5kGunOnXq0LXWtRO7ZrGuqa391vpEHjYr1r2U9V6za2rr2sRqMbeue9LT053MOi5ZM8G22zr+WDO7efNmmhf2/YR+8iwiIiIiIiLiQzfPIiIiIiIiIj508ywiIiIiIiLiQzfPIiIiIiIiIj7yXRhm/VE8K6E4e/YsXWuVBFjlH+wP/1mhDgDMmjWL5q1atXIyq2DJKqphxR4A/4P2oM9x5cqVTmaVBFgGDRpEc/b6nTx5MtBjb9myJc9rz5w5Q3OrDCMjIyPQtoSaUJ8Jq8TlxhtvdLLs7Gy61trupKQkmrOyL1Y8A/ByEICXN7E5AezCF2smWLGRVSphPTYrpZk9ezZda82EVYZhvd7FBStlAXgxi3Wcs2bC8zyaszJG6xg/Z84cmrP1VlGNVf5oHedYbpW13H333TRnx0rrWG49trU/s9IX67W2CmJYAZRVQsmKZwC7TIntJ9b2haJy5crRnBWhBT1PWMdtViRmHW+nT59Oc1bAY82sVVxmlZSx3Houv/jFL2jO5soq2Nq3bx/NmzRpQnN2ng16XbZhw4Y8r7XOE1ahla6dgp8nglw7ffPNNzTv3bu3k7Vt25auPXLkCM3r1q1Lc3afYRWfsussgF87BT1PPPLIIzT/9NNPncyaN4tVlMoEvZ9g+0lBnif0k2cRERERERERH7p5FhEREREREfGhm2cRERERERERH7p5FhEREREREfGhm2cRERERERERH/lu27YaaVnz7OnTp+naypUr09xq32Ssts/77ruP5i1btnQyq7k3MTGR5uvWraP5/v37ncxqnbSa41gbnPUcmzdvTnOr1ZS1PVrtgldddRXNly9f7mRWW6DVRnnixAmas9evevXqdG0oKsyZsPYXxmrR/eMf/0hztg+sWrWKrrX2C9ZaDfAGTKvx0NovWHOv9VpbM8H2W4C3UXbu3JmutZ77V1995WQtWrSga62ZsBowWStscZoJqyWe7ed79+6la63jn3WeYC291kyw8wHA99u1a9fStVa7KjueWY9t7S/WsZXNkNWmz1qSAWDevHk0Z9tttfx27NiR5gsXLnSypk2b0rVWe+6pU6dozhpnq1atSteGIuvYVaVKFSdjjcBA4Z4nOnToQPMgDerWsdJ6PuyxLdZ7zVrMrdfamvu5c+fSnJ0nunTpQtd27dqV5kuXLnUyq338/PnzNLfOkazx32qrD0VBrp2s4wJbCwS7n7Bmon379jRn+6J1PLOuK6xrp6NHjzrZoUOH6NqwsDCas/Ov9Vpb56DVq1fTnH0aUZs2beha6xzOjh/WOc/6tIuiup/QT55FREREREREfOjmWURERERERMSHbp5FREREREREfOjmWURERERERMSHbp5FREREREREfOS7bdtqvGvYsKGTHT9+nK61WtJYWyrAm3StJtHMzEyas9ZE6/u99dZbNI+IiKD5mjVrnMxqsLMegzXsJSUl0bVTpkyheXx8PM1ZwzNrqASATZs20Zw14bFtBnirLADUrl2b5tWqVXOy7du307WhKMhMHDt2jK61ZsVqqGYzkZaWRtda72mFChWczGpz3b17N83ZcwSARYsWOZnVdGrN7Isvvuhk1vxYLeatWrWiOTsGsRZJwG6dZK2OmonvWO8Ha5m19n2ridyaCXa8tFpKWXMzANSqVcvJrMZQq1Xb+rSGIMdh65MdPvnkEyc7cuQIXTty5Eiad+vWjeYVK1Z0stKlS9O1S5YsoTl7va3ts5pi2XsAAHFxcU5mfapFKLLOE40aNXIy6zhizYrVbs+Ouda+b7Xes+sK1hAO2A3CjRs3pnmQ84S1L06dOtXJrPPEF198QXOr6TfIeYJ9qgkA1K9f38nY9SgQ/DxRo0YNJ9uyZQtdG4qCzIS171u5dX3Prs2tmWCfegHw/aJMmTJ0rfWJHzExMTSfMWOGkwW9n2D7+W9+8xu61rqfsK6d2OttbcfWrVtpXpj3E2wmrO3ID/3kWURERERERMSHbp5FREREREREfOjmWURERERERMSHbp5FREREREREfOS7MMwqgmElBVbxg1VgVKIEv6ffvHmzk0VHR9O11h/tr1q1ysk+//xzujY2NpbmVgEBW8/KmAC77GbIkCFOZv2hvFVucPHiRZoPHDjQyebOnUvXVq9ePc+PHRUVRddaj20V7+zZs8fJzp07R9eGoiAzwYq+rLVAsJkoW7YsXbtx40aaX3HFFU5mFWSwMiEAGD9+fJ63zypBskqdWKlKv3796Nrw8HCaW6V77du3dzJrv922bRvNWUFb0JmwXldW3lOcZuLMmTM037Fjh5M1bdqUrrVed+u4yArVrCKTqlWr0pwV5lnH7MjISJpb5z1WMmOV/LHXCeDFUFbxjFVclpKSQvOuXbs62eLFi+laVsoCAAsXLnQy67hkPXaQgrbiNBNWAR67HmrdujVdax3LS5Xil3TsOGzNhPWesusbdj0FADVr1qQ5O8cDvATOOiZarx8rj7XOydaxwzpPdOzY0cm+/fZbuta6dmIzaxUFWo8dpMjz7NmzdG0oCnLtZJVmBb2fYI9tzYT1nrLr+7Vr19K1hTkT1rH1wQcfdDKriNG6n2jWrBnN2TXsH/7wB7qWPReAl8QW1P0Eu3YqyJnQT55FREREREREfOjmWURERERERMSHbp5FREREREREfOjmWURERERERMSHbp5FREREREREfOS7bdtqomaNclYLHmsdBezWN/Y9MzIy6Nr33nuP5qwhzmo+trbj1KlTNGdNiFZr4h133EFzxmqstZpi33zzTZoHee6LFi2iOWveY+2nANCwYUOaHzlyhOasFdFqvwxFQWbCatUuiJmoX78+XWu12n7wwQdOZrXVW9thNaAGmYkOHTrQnDWdWi32VjPknDlzaN62bVsnO3r0KF07adIkmrN9NOhMWN+zuM+E1QDN2kutVm3reHvw4ME8f8/atWvTtStXrqQ5a+791a9+RdcuXbqU5l9++SXN2ac1sEZ5wG55Ze2qx48fp2tvueUWmlvHmmPHjjmZ1dj9yiuv0Jy13rP2U8A+XrHtAICwsDAna9KkCV0biqzzNttHrVZt672zPiWBfc+EhAS6ljWlA3xftI7Z1nZY212lShUnmzVrFl1br149mrM2fGs7rBZz6zzRrl07J7Nan63Xj52brKblxo0b09w6TrDzhLV9oci6dmLN79b9hHUNYp2L2UxY+5Z1PVytWjUnY/vKpbbDOr+x87x13cM+MQXgr0l6enqgx5g6dSrNz58/72RWo/iSJUtozj5hw7rGDHo/wc4T1vVhfugnzyIiIiIiIiI+dPMsIiIiIiIi4kM3zyIiIiIiIiI+dPMsIiIiIiIi4kM3zyIiIiIiIiI+8t22XaoU/9L58+c7mdUYajUHstZEAIiLi3Myq9nw4sWLNGftfRs2bKBrL1y4QPPy5cvTnG13r1696FqrMTc6OtrJKlWqRNeOHDmS5lYr3Zo1a5zMeg+6d+9Oc9Zeab2/Vvuh9Z7deuutTjZt2jS6NhT9J8wEa1gEgs2E1XQ6ePDgPG8fe94AkJaWRnOrWfZ//ud/nGzZsmV0bUpKCs01E7aSJUvSnLVvWi3EVpumNROsuXf58uV0bVZWFs3ZcdhqPrYaRq12VZZbbbORkZE0Z/uitc+x53Kp9atXr3ayjz76iK5t1KgRzdlMsGZawG6btVrMWeu51cwciqzzBGt6tvYtq2meNbkD/Hg5b948utY6T7BtWb9+PV177tw5mlttwWwOu3btSte2atWK5lFRUU7GGncBYPfu3TS32nhXrVrlZNa52rrmY23ztWrVomut88TcuXNpzj69ZfLkyXRtKCpdujTNFyxY4GTWcevQoUM0t84T7JjLvh9gnyeCzIR1P1GuXDmas+t4a/+0zhNs/7Kundh1KmB/CgZrILdmokePHjRnM2G9v9Z5wtpudu00ffp0ujY/9JNnERERERERER+6eRYRERERERHxoZtnERERERERER+6eRYRERERERHxoZtnERERERERER/5btu2WhNHjBjhZK+//jpdW69ePb5RRhsla/AsU6YMXduzZ0+as0bGnTt35vn7AUB4eDjNBw4c6GRWk15iYiLNx40b52S7du2ia60WwRIl+L+JsKa+PXv20LVly5alOXu9raZY6zGuuuoqmh87dixP3y9UFeZMWK3FoT4Tjz76qJNZ7fFWi+pnn33mZFZbfWZmJs2/+OILmrPnbr0HmongrBZ2tl+88847dG3dunVpbh3nDhw44GTWa9apU6c8P/b48ePp2nbt2tE8IiKC5qyh1Wry/vOf/0xz1v7L9mUAOHPmDM2t9awB1dpvrblnr7fV+m29Th07dqT58ePH8/T9QlXFihVpzpr/X375Zbq2QYMGNLdmgr2n1nvXp08fmrP9ZceOHXStdV1hfc8hQ4Y4mdVwbB1bv/nmGydjzxuw3wNrVlq2bOlk1nVZkPNETExMoMdITk6m+dGjR53Meq1DUUFcO1mfqBFkJqzjiPW6s5brgrp2GjZsmJOxYx8AtG/fnuYTJ050Mquxe+vWrTS3mt+bN2/uZFaLfUHMhHWe6NKlC83ZtZPV6p4f+smziIiIiIiIiA/dPIuIiIiIiIj40M2ziIiIiIiIiA/dPIuIiIiIiIj4CPOs1ikfVsGN9Yf/jFX8YP2BP/ujc6vgoVatWjTfv3+/k1l/yG+V3VSpUoXmTZs2dbKgZTeHDx92MusP9tevX09zVh4BANnZ2U5mPUerUIMVoJ09e5auzcjIoPnixYtp3rp1ayezSm1GjhxJ86L0nzAT1v7cr18/mrOCBqvUK8hMpKWl0bVWgU1BzIT1PrL1monvWKVPQWbCOt6GhYXRPMhM1KhRg+Zs/7L2OWuuGjduTPODBw86mfVcrBIcxnoup06dovmXX35Jc7bvWjNhFWKyIp1z587RtVa+YsUKmrPCG2sm3nvvPZoXJat8yDrnMlapjlUsyd6P06dP07W1a9emOTsnWIVc1nxbs8we+7/+67/oWqtw8tChQ05m7UPWcfjIkSM0Z5fJ1nMsiPNEeno6zRcuXEjztm3bOhl7zwHgjTfeoHlRsq6d2HvNztkAEBcXR/OCOE9Yx3hWTmldO1nHSmsm2GNb23HFFVfQ/OLFi05mzbd1n/Hvf/+b5kFmIsj5wzofWDMR5NrJeg/++c9/0vxS9JNnERERERERER+6eRYRERERERHxoZtnERERERERER+6eRYRERERERHxoZtnERERERERER+l8vuFXbt2pXnv3r2d7PPPP6drp06dSvNq1arRvF69ek5mNewtWrSI5llZWU5Ws2ZNutZq9bNaSn/5y186mdUQ1759e5qz1+TGG2+ka8eMGUPzXbt20bxMmTJOtnLlSrrWarxr2LChk1nP0Xrs7t2703zJkiVO1r9/f7o2FFkz0atXLyebNGkSXRsqMzFw4EC6ljXKA8CyZctoPmzYMCez9otOnTrR/M0333Qyq1V29erVNLfmjTWSFuZMrFq1iuY/15mwWkBTUlKczNr3Z82aRXOrmb5OnTpOZrUTL1++nOaspbR69ep0rfVejxo1iuYjRoxwMqtpvl27djT/+uuvncxqlLda2Fu2bElz1tq9Zs0autZqV2WzbDUcr1u3juZWAy+bT3aMDVVs3weAvn37OtnEiRPp2smTJ9PcOk+w1vaqVavStQsWLKA5O09Y7b/WtZN1LXjnnXc6WXh4OF1r7RejR492Mmvure2wPh2AtfSyYzNgtyc3atTIyYJeO1n7OWvhvvbaa+naUBTkfsK6dpoyZQrNg1w7WTNhXTux/dz61IOg9xO33HKLk1nt8ewaBODHZ9ZCDQBffPEFza15Y58aYZ1PK1euTPMg107WtV2PHj1oXtjXTvrJs4iIiIiIiIgP3TyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+NDNs4iIiIiIiIiPfLdtW+2bLLfarK320rNnz9Kctd2ePHmSrrXaa48dO+Zk1nNJTEykudWmuHbtWierVKkSXXvrrbfSnDUDWi2lVsOe9brOnDkzz2tZ2yzAWyD37NlD11qtg6mpqTRnzYpLly6la0NRcZ0J1ox76tQpurZu3bo037FjB81Ze2WLFi3oWtYYCvAG1OjoaLrW8zyaWy3XrEE26Eywx7Zmwnrsn+tMbNiwIc+59dpY7fFW+znLT58+TddaTaJshtjxHbCb3GvXrk1z1g6alpZG17JWbQDYuXOnk1n7Z+PGjWlufSpDRESEk1nvAZsfgL9W+/fvp2uthltrhpKTk53MaicORdaxiOVBzxPWTLDzx4kTJ+haq9GZXTtZr7v1qQzWtRObQ+sx5s2bR3N2brJakps3b07z7du307xs2bJOZs13ZmYmzVlTsjWD1vtunWevvvpqJ7Oeeyiyjq0sDzoT58+fp3mo30+w46K1306fPp3mnTt3djLrGNGsWTOaf/jhhzRn+6L1HljniYK4n2DnQoBfO1kN+fmhnzyLiIiIiIiI+NDNs4iIiIiIiIgP3TyLiIiIiIiI+NDNs4iIiIiIiIiPfBeGWQUnderUcbJatWrRtatXr6b5Qw89RPN//vOfTnb06FG61vqe27Ztc7IyZcrQtawkAgD69+9Pc/bH6A0aNKBrS5Tg/27B/uB+8eLFdO3HH39M8wceeIDmt9xyi5ONGzeOrt23bx/N2WtVsWJFujZowc7UqVOdLD4+nq4NRUUxE2+++aaTlSxZkq61vicrjmGlQYA9EwMGDKA5mwmr6M7ablYcY+2flStXpjkrVAH4e8aOEYBd2sZmokKFCnTtunXraP5znYlDhw7RnO2LVtmI9Zrdc889NB89erSTHT9+nK61SkgOHz7sZP369aNrrTIZ67zCSlLq1atH11r7XFhYmJNZxx+roMp67qyMzyp8scpn2HMvX748Xbtx40aat27dmuazZs1yMqu8KRRZxWlsH2DnDsAu6nrsscdo/uqrrzqZdby1vufmzZudLDw8nK61zhPXXnstzdevX+9k1jHUwl7XvXv30rVW6aJ1joyNjXUyqxgsPT2d5mwmrEJZ69qJlWcCwFdffeVk1jElFB04cIDmQa6drOPcww8/THN27RT0foIVzBXUtRO77g967cTOH9a107Jly2ienZ1NczYTVmmlVcRWEPcT1kxMmTLFyazC2/zQT55FREREREREfOjmWURERERERMSHbp5FREREREREfOjmWURERERERMSHbp5FREREREREfOS7bdtqfdu9e7eTXbhwga61WqRZqzbAG+Ws1mqrRbpx48ZO1qhRI7rWapL85JNPaJ6YmOhkVoug1QrMXpOMjAy6dtCgQTS3GiZZSy97PQC7JZe1YlqNkfXr16e51a7K3l/rdQpFQWbi/PnzdG3QmShVyh3hoDPxl7/8xcnatGlD11pNkuPHj6c5a8oOOhOssduaiV/+8pc0nzFjBs1ZC/fbb79N11rNx5d7JqzHDkXW82WNn9Z5YsWKFTRnrdoAf83KlStH1+7Zs4fmgwcPdjLrPGHNxAcffEDznTt3OllycjJdm5SURPOtW7c6mdUSb50n5syZQ3PG8zyaly5dmuabNm1yMqtF1WpAZc8R4O9v0GbmotSkSROap6amOpk1E4sWLaL5a6+9RnP2mrFzB2Dvt+xYbs231SxsPfbTTz/tZNZ5LCoqiubs/GY1X//qV7+i+ddff01zJuhMsLZg65xnva7sXAPw97I4XTtZx9aCuHZirdoAf83YpxgA9ifbsGt+6zow6LVTkPsJ65qAvSbscQH72unDDz+kOWs3t5q5rZlgn6RhPZeEhASaB5mJgrx20k+eRURERERERHzo5llERERERETEh26eRURERERERHzo5llERERERETEh26eRURERERERHzku23bartl7aBjxoyha6tUqULzmJgYmrPW1WuvvZau3bFjB81Pnz6dp8e9lJ49e9L8yJEjTnbixAm6tnnz5jTfv3+/k1ktfYcPH6Z5+fLlac7aTmvUqEHXsmZuADh16pST1atXj65lTXoA0KBBA5ovW7bMySZMmEDXvvXWWzQvSlazJ5uJsWPH0rVFMRPvvPOOk82aNYuutbAmVgCYPXu2k2VmZtK1zZo1o/nKlSudzNo/rfZk63jFZsJ6ra2W18s9E59++ild+69//YvmRcl63VnLv9UGb7XGWrOyZs0aJ3viiSfoWtbmCvB9lD0uAHz77bc0L1OmDM1ZW6z1CQkpKSk0j4uLczLrPMGazQHg3LlzNGfN1VbTvDUr7DxrzSxr5gbsFm7W8vrVV1/RtaHIOk+wawJ2bAbs1z02NpbmS5cudbIhQ4bQtVbL+cmTJ51s+fLldK2FfboBwD/ZxGpVthrIWdOv1fpdEOeJM2fO0LXVqlWjOXv9rAZh6zxhNTmzT6SwjqdW+3RRCnLtFPR+wpoJtu9an0xgXTux9zTo/USvXr1ozq7vrfsJ69qJnROs84Q1E9a+yJqrrfmpWrUqzdm1k9U0z9rqgYK5dsrP/YR+8iwiIiIiIiLiQzfPIiIiIiIiIj508ywiIiIiIiLiQzfPIiIiIiIiIj508ywiIiIiIiLiI99t26VLl6Y5aw21mnitJkSrXTU5OdnJOnbsSNdOmTKF5p07d3Yyq03OaqWznnvt2rWdzGpkXLBgAc1Z66D1eqSlpdH8mmuuofm2bduczGrstto8S5Rw/72FNasCdjOklbPmwsTERLo2FFntuoU5E127dnUyayYmTZpE81q1ajkZa30HeMMiYLcWlyxZ0smsZs1///vfND927JiTsQZuAFi8eDHN77jjDpqzmWDNqoDdth1kJqxmyEaNGtH85zoTbP9q0qRJoMew9sWbb77ZyW688Ua6dsSIETQPCwtzspkzZ9K1VitwZGQkzdknHARpSQb4sZ/NGgBMnz6d5uxcCACpqalOxvZxAKhTpw7N2bZY50Krmd5qXWWzxdrbQ1WQ84TVohv0PNG9e3cnu/LKK+naiRMn0pxd3xw6dIiutY5n9913H83ZNZV1nrjnnntoztZbnzzy2Wef0bxHjx40Zw3kpUrxy2frGM/WB712svZz1sLMmqpDlTUTrAHael7W9bp1nghyP/H555/TnF07WdfU1mxa+xGbN6vN2mqiZvuzdZ744osvaG61mG/YsCHPj20d4wvifqKorp30k2cRERERERERH7p5FhEREREREfGhm2cRERERERERH7p5FhEREREREfGR78Iw6w/DWUmKVT5l/fE7KywBeHnG888/T9eyggwASEhIcDL2h/kAsHz5cppbZWSdOnVyslatWtG1rOAB4H9Ab/2x/fXXX09zVoIE8FIOq4Bi7Nixed4+VoAD8LIHwC43OHr0qJOtXr2arg1FxXUmWKGGtR3sPQLsWWEz0bJlS7r2+PHjNM/OznYyqwjm6quvpvn27dtpzso6rPKmr7/+muaaCZtVNMUKj6xikrNnz9L8wIEDNGeFQm+88QZdW716dZp/9NFHTnbq1Cm61iq7WbVqFc3btWvnZAMGDKBrL168SPOaNWs6GStwAYArrriC5jt27KA5ez5WcdWmTZtozgrXqlatStda5YTW82EzYa0NRUFmIi4ujq61yrR27txJ8+bNmzvZs88+S9f27t2b5vv27cvzdlilSV999RXNWXmZVbBkHSfYecI6dqSkpNB8y5YtNGfXTm3btqVr16xZQ3M2E0HPE9ax/8iRI05mlWqGImsm2LWTdX4Oeu3EZuK5556ja61rJ3YOsrYjKyuL5suWLaM5u3aytsOaFXb+sGbCOsY/9dRTNGfXTq1bt6ZrrVJmds1snZNZoSJgz1thXzvpJ88iIiIiIiIiPnTzLCIiIiIiIuJDN88iIiIiIiIiPnTzLCIiIiIiIuJDN88iIiIiIiIiPvLdtn3s2DGah4eHO5nVosua9ADe/gsAc+fOdTKr3a1ChQo0Z+14TZs2pWutNuvo6Gias/a5pKQkujYxMZHm7LWqU6cOXWu1qE6bNo3mjz32mJM99NBDdO3GjRtpzppirbY71lAJALVq1aL5hQsXnOwf//gHXRuKQmUmkpOT6drbb7+d5qyhtX379nQta1wFgNOnT9OcNU927dqVrrXaHtl+YbVLWjPxpz/9ieasSfI3v/kNXWs1dhfETLD2ZKD4z4S1n7OZsD6BgLUQA0DFihVpvnjx4jx9PwBYv349zZmIiAiaW+ca671u0KCBk1n7vtVazJpErfME2z8BYNasWTT/y1/+4mTz58+na9PS0mjOGvWtRmx23gTsJmLWIPvoo4/StaHI+sQCtn9Za6OiomhuXZvMmTPHyay2aKvNmrWiW7NpHZ+tWTl58qSTsXZqwP50FHYOspp7r7rqKprff//9NH/hhRec7JtvvqFrrXNkhw4dnMxq5Lca/K3nzmbCao4ORda1E5sJ65xi7YvWtRObCet+wpqJ+Ph4J7PO5UuXLqW5dS/AGuGtT2WwmqjZ+c36pBfrMawG+qefftrJZs+eTddaM8GuMwvqfoLNhNUcnh/6ybOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiIj3y3bbMWWAAoUcK9H7fa8c6ePUtz1jIH8ObelStX0rWsBQ/grY7nzp2ja+vXr0/zFi1a0Jw1TFrbYTXHsUbS3bt307WnTp2iudXQydrxrJbkevXq0Zy1H7IGc8Bu87Ta9FgLrdUcft9999G8KIXKTGzatImutRoPz5w542Se59G1VnNlbGwszdlMlCtXLs9rAd4YuWvXLrrWmony5cvTnDVgWrNZt25dmhfETKxevZrm/0kzwY7NgH187ty5M83ZDFnHRNY0D/C5smbCasRu2LAhzVkTddAW+9TUVCezjinWY1hzyM5BVlu51f7LZuLgwYN0LXs9ALsJnZ2brJZX1hxe1DIzM2lesmRJJ7PeU2smunfvTnO2Dyxbtoyutc797DyRnZ1N11rHZ2ufY8358+bNo2stbD7ZnAD2scZqA2et2Fb7fkFcO1mfdmG1c7PzxOTJk+na3//+9zQvStZMsPOEdU4pzPsJq4WbfZKBNRMdO3akufVpA+yTV6xP/LDa2dl5b+TIkXStdX6zrp3Wrl2b5+0oivsJdv82depUunb48OE0vxT95FlERERERETEh26eRURERERERHzo5llERERERETEh26eRURERERERHzkuzAsISGB5jVq1HCy/fv307VpaWk0t0oR2GNbf0DP1gK89MIqQVqxYgXN//a3v9F8woQJTtazZ0+69pVXXqE5K4rYsmULXcuKXQCgUaNGNJ85cybNGas8gBU4sGIDwC6ZKVu2bJ63gxWJhKpQmQlW5ADwQhqAF2T17t2brl2yZAnN27RpQ/MPPvjAyXr16kXXjh8/nuasrMN6jtbc9+/fn+bPPPOMk1llMtZMsNfVmgmrUCMqKormTHGaCasopFq1ak5m7ftWIde6detoHh0d7WTW/nLNNdfQPD093cluvPFGutYqtmrVqhXN//nPfzqZVWj24Ycf0jwxMdHJ2DYDQNWqVWl+xx130Hz+/Pk0ZwpzJqz1jFX0FIpYuRMA1KxZ08ms84RVqmNds7BiN+vaqVatWjRnpWN9+vSha60ivieffJLmL730kpNVr16drp0xYwbN2TnIKhNirzUAPPjgg4G+ZxBsJqzjvnXtFGQmrOvaUGSV87L3ydr3rVmxrp3Yfm7NhFUOx8rBBgwYQNcuX76c5tZ5gl07WfcTo0ePpjm7d7DOE1bB5ZVXXknzWbNmOVlYWBhda5WoBTlPhNpM6CfPIiIiIiIiIj508ywiIiIiIiLiQzfPIiIiIiIiIj508ywiIiIiIiLiQzfPIiIiIiIiIj7y3bZttQSyZtSlS5fStVarLWvXBXgr54ULF+jas2fP0jwrK8vJ9uzZQ9ceP36c5vfeey/Nt27d6mT/+te/6Nq2bdvSfOXKlU5Wvnx5utZqtps8eTLNWUuv9T7u3buX5klJSU526tQpunbhwoU0T05OpvnixYudLCYmhq4NRVbr3+WeiUOHDtG1H3/8Mc1ZO/HFixfpWqv52Johtn1WM6TVpshaHa3903pdZ8+eTXO2/xfmTCxatIjm/2kzceTIESdjxz7Afi2bN29Oc9Yef/DgQbrWei1ZbjV2W22u1nrWZv3NN9/QtdZzPHHihJNZn7Jg+fLLL2nOmqutT0iwmm+bNGniZKytGQCWLVtGc6vllTVKs2NYqLKOL2wftY4X7P0H7OuKINdOVhsvu3ayjonW9t166600Z63A7JM9APs5Hjt2zMmaNm1K11qscyRr6bXeR+tcyGbZOrbNmzeP5j169KA5u9YqTucJ67Vk1zLsnAgEv3Zi5wlrJs6dO0fzb7/91snYJ0kA/JN+APu8xx570qRJdK11nmDHZ/ZJDZfyySef0Pxy308UxLVTlSpV6Nr80E+eRURERERERHzo5llERERERETEh26eRURERERERHzo5llERERERETEh26eRURERERERHzku227atWqNH/ppZecrF+/fnSt1Y5nNaKxJrdrrrmGrrWaROPi4pxs5syZdG3Lli1pbjWP1q9fn+bMvn37aM5a89asWUPX7ty5k+ZWOzdr3LTeR9ZECPA244YNG9K1QRoKAWDAgAFOtnv3bro2FFkti8VxJqy11kyUKsUPJVbDJGM15GsmcitOMxEbG0vzUaNGOVnPnj3pWqt9s3LlyjRnDd99+vSha59++mmas6bajIwMupY1hgL2TFivCZOWlkZz1iK8YcMGunbXrl00L1euHM3Zfs6OEdZagDfIWudHayasT2vo1auXk1nn01BUvXp1mj/33HNONnDgQLrWarO29i32Xg8ePJiu/fzzz2nOjotff/01XdumTRuaW9dOCQkJTmZ9mojV3MvOTatXr6Zrt2/fTnNrJth5wjrfs7UAb/+1GvKtc6HVyj9o0CAns+Y+FFnn3JdfftnJrGsnayasayf2Xl977bV07RdffEFztt1WU3qLFi1obp0nMjMzncz69Ip169bl+Xta1047duygeZBrp6AzEeTayZqJorp20k+eRURERERERHzo5llERERERETEh26eRURERERERHzo5llERERERETEh26eRURERERERHzku2175MiRNGftkFZr4oULF2ielZVF8y1btjhZjx496Fqrsa1kyZJOVq9ePbrWasE7fvw4zU+fPu1k1nNMTk6m+fTp053Mej3q1KlDc6uNkjU7Wk22NWvWpHndunWdLDU1la612vu6d+9Oc9Zma7X3hSLNhKuwZuLixYt0bWHORI0aNWjOZsLa9//TZmLMmDE07927t5NZM3H+/HmaZ2dn03zbtm1O1rVrV7qWtfwCQIkS7r8rx8fH07XWTFjtr6yB3pqJzp0705x9QoT1etSqVYvm+/fvpzl7TdgcA3ZzdO3atZ1sz549dK3VCnzVVVfRnLVzW23goej111+nOWtMZvshYO8vrKEXADZt2uRkbAYBuwGanSesBnW2FgCOHTtGc3bMtZ6jdX6bPHmyk1nnCXbMBuzzBNvPrfm2zhPsnGqdD9gxDOBN8wBvIi5O5wn26QsAv3YKOhPWcfFyXztZn0xgzUSQa6eUlBSaT5s2zcmsa0nr/GZ9kgE7TlifFmPNBPueBXU/UdgzoZ88i4iIiIiIiPjQzbOIiIiIiIiID908i4iIiIiIiPjQzbOIiIiIiIiIj3wXhjVv3pzmlStXdrJVq1bRtdYfqLM/5Af4H5LHxsbStVZxBiumaNGiBV27e/dumrPCEoCXSlhlN1bOCjisAgKr3IKVgwD89atUqRJda71+rPTFKgexSnrmzp1Lc1ZSVr58ebo2FBXmTFjvKdtHY2Ji6NqimAlW0GCVXoT6TFjlM+x7FuZMlCtXjq4NRYmJiTRnM7F27Vq61iq8skp12PthzUSQfTEpKSnP3896DACoWrVqntdaOSs8so4dVjGY9fqxY3zQmThw4ICTWYVwV1xxBc0XLVpEc1Y+ExUVRdeGIuvYWqVKFSdbvnw5XWsVXlnHuZ07dzpZ0GsnVgbUqlUrutYqgbP2Z3acs9Za55oGDRo4mVUIaJXXbdy4kebsPMGOYYBdUMUKj6yZsEqQ5syZQ/Pifu3UrFkzmkdHRzvZihUr6FprJjZv3kxz9p4WxLVTy5Yt6Vrr2snaz4PcT1gzwa6drJI/qxgsyEyw9wuwS8rY97RmItTuJ/STZxEREREREREfunkWERERERER8aGbZxEREREREREfunkWERERERER8aGbZxEREREREREfYZ7neUW9ESIiIiIiIiKhTD95FhEREREREfGhm2cRERERERERH7p5FhEREREREfGhm2cRERERERERH7p5FhEREREREfGhm2cRERERERERH7p5FhEREREREfGhm2cRERERERERH7p5FhEREREREfHx/wD+9UMiLdp7VAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 정확하게 분류된 샘플 중 10개를 랜덤하게 선택하여 출력\n",
    "print(\"Correctly classified samples:\")\n",
    "plt.figure(figsize=(10, 4))  # 전체 그림의 크기 설정\n",
    "for i in range(10):\n",
    "    sample = random.choice(correct_samples)\n",
    "    input_img, true_label, predicted_label = sample\n",
    "    plt.subplot(2, 5, i + 1)  # 2행 5열의 subplot 중 i+1 번째에 그림을 출력\n",
    "    plt.imshow(input_img.squeeze().cpu().numpy(), cmap='gray')\n",
    "    plt.title(f'True: {true_label}, Predicted: {predicted_label}')\n",
    "    plt.axis('off')  # 축 제거\n",
    "plt.tight_layout()  # subplot 간격 자동 조절\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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