Pretrainned_Models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "\n",
    "from skimage import io, transform\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    " \n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchsummary import summary\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "from PIL import Image\n",
    "import torchvision\n",
    "from torchvision import models,transforms\n",
    "\n",
    "from Dataloaders.HAM10000Dataset import HAM10000Dataset\n",
    "from functions import normalization, Model_functions\n",
    "\n",
    "import time\n",
    "\n",
    "import random\n",
    "from PIL import Image\n",
    "import cv2\n",
    "import gc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load and normalize the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data_enum.csv')\n",
    "data = data.sample(frac=1)\n",
    "img_h, img_w = 224, 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_means, norm_std = compute_img_mean_std(data['image_path'],img_h,img_w)\n",
    "\n",
    "norm_means = [0.77148203, 0.55764165, 0.58345652]\n",
    "norm_std = [0.12655577, 0.14245141, 0.15189891]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the transformation of the train images.\n",
    "transform = transforms.Compose([transforms.Resize((img_h,img_w)),transforms.RandomHorizontalFlip(),\n",
    "                                      transforms.RandomVerticalFlip(),transforms.RandomRotation(20),\n",
    "                                      transforms.ColorJitter(brightness=0.1, contrast=0.1, hue=0.1),\n",
    "                                        transforms.ToTensor(), transforms.Normalize(norm_means, norm_std)])\n",
    "# define the transformation of the val and test images.\n",
    "val_test_transform = transforms.Compose([transforms.Resize((img_h,img_w)), transforms.ToTensor(),\n",
    "                                        transforms.Normalize(norm_means, norm_std)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_val_data, test_data = train_test_split(data, test_size=0.2, random_state=42)\n",
    "\n",
    "train_data, val_data = train_test_split(train_val_data, test_size=0.5, random_state=42)\n",
    "\n",
    "train_dataset = HAM10000Dataset(train_data, transform=transform)\n",
    "val_dataset = HAM10000Dataset(val_data, transform=transform)\n",
    "test_dataset = HAM10000Dataset(test_data, transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data len Train: 12362 Val: 12362 Test: 6181\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "num_workers = 4\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, num_workers=num_workers)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=num_workers)\n",
    "\n",
    "print('Data len Train:', len(train_dataset), 'Val:', len(val_dataset), 'Test:', len(test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f'Using device: {device}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Resnet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/spyros/miniconda3/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/spyros/miniconda3/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=7, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_temp = 'resnet_pret'\n",
    "feature_extract = False\n",
    "num_classes = 7\n",
    "model_res = Model_functions.initialise_model(model_temp, num_classes, feature_extract, use_pretrained=True)\n",
    "model_res.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Conv2d: 1-1                            [-1, 64, 112, 112]        9,408\n",
      "├─BatchNorm2d: 1-2                       [-1, 64, 112, 112]        128\n",
      "├─ReLU: 1-3                              [-1, 64, 112, 112]        --\n",
      "├─MaxPool2d: 1-4                         [-1, 64, 56, 56]          --\n",
      "├─Sequential: 1-5                        [-1, 256, 56, 56]         --\n",
      "|    └─Bottleneck: 2-1                   [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-1                  [-1, 64, 56, 56]          4,096\n",
      "|    |    └─BatchNorm2d: 3-2             [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-3                    [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-4                  [-1, 64, 56, 56]          36,864\n",
      "|    |    └─BatchNorm2d: 3-5             [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-6                    [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-7                  [-1, 256, 56, 56]         16,384\n",
      "|    |    └─BatchNorm2d: 3-8             [-1, 256, 56, 56]         512\n",
      "|    |    └─Sequential: 3-9              [-1, 256, 56, 56]         16,896\n",
      "|    |    └─ReLU: 3-10                   [-1, 256, 56, 56]         --\n",
      "|    └─Bottleneck: 2-2                   [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-11                 [-1, 64, 56, 56]          16,384\n",
      "|    |    └─BatchNorm2d: 3-12            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-13                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-14                 [-1, 64, 56, 56]          36,864\n",
      "|    |    └─BatchNorm2d: 3-15            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-16                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-17                 [-1, 256, 56, 56]         16,384\n",
      "|    |    └─BatchNorm2d: 3-18            [-1, 256, 56, 56]         512\n",
      "|    |    └─ReLU: 3-19                   [-1, 256, 56, 56]         --\n",
      "|    └─Bottleneck: 2-3                   [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-20                 [-1, 64, 56, 56]          16,384\n",
      "|    |    └─BatchNorm2d: 3-21            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-22                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-23                 [-1, 64, 56, 56]          36,864\n",
      "|    |    └─BatchNorm2d: 3-24            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-25                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-26                 [-1, 256, 56, 56]         16,384\n",
      "|    |    └─BatchNorm2d: 3-27            [-1, 256, 56, 56]         512\n",
      "|    |    └─ReLU: 3-28                   [-1, 256, 56, 56]         --\n",
      "├─Sequential: 1-6                        [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-4                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-29                 [-1, 128, 56, 56]         32,768\n",
      "|    |    └─BatchNorm2d: 3-30            [-1, 128, 56, 56]         256\n",
      "|    |    └─ReLU: 3-31                   [-1, 128, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-32                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-33            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-34                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-35                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-36            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─Sequential: 3-37             [-1, 512, 28, 28]         132,096\n",
      "|    |    └─ReLU: 3-38                   [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-5                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-39                 [-1, 128, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-40            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-41                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-42                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-43            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-44                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-45                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-46            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-47                   [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-6                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-48                 [-1, 128, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-49            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-50                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-51                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-52            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-53                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-54                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-55            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-56                   [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-7                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-57                 [-1, 128, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-58            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-59                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-60                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-61            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-62                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-63                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-64            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-65                   [-1, 512, 28, 28]         --\n",
      "├─Sequential: 1-7                        [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-8                   [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-66                 [-1, 256, 28, 28]         131,072\n",
      "|    |    └─BatchNorm2d: 3-67            [-1, 256, 28, 28]         512\n",
      "|    |    └─ReLU: 3-68                   [-1, 256, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-69                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-70            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-71                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-72                 [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-73            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─Sequential: 3-74             [-1, 1024, 14, 14]        526,336\n",
      "|    |    └─ReLU: 3-75                   [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-9                   [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-76                 [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-77            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-78                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-79                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-80            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-81                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-82                 [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-83            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-84                   [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-10                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-85                 [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-86            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-87                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-88                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-89            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-90                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-91                 [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-92            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-93                   [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-11                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-94                 [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-95            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-96                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-97                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-98            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-99                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-100                [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-101           [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-102                  [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-12                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-103                [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-104           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-105                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-106                [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-107           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-108                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-109                [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-110           [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-111                  [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-13                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-112                [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-113           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-114                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-115                [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-116           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-117                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-118                [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-119           [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-120                  [-1, 1024, 14, 14]        --\n",
      "├─Sequential: 1-8                        [-1, 2048, 7, 7]          --\n",
      "|    └─Bottleneck: 2-14                  [-1, 2048, 7, 7]          --\n",
      "|    |    └─Conv2d: 3-121                [-1, 512, 14, 14]         524,288\n",
      "|    |    └─BatchNorm2d: 3-122           [-1, 512, 14, 14]         1,024\n",
      "|    |    └─ReLU: 3-123                  [-1, 512, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-124                [-1, 512, 7, 7]           2,359,296\n",
      "|    |    └─BatchNorm2d: 3-125           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-126                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-127                [-1, 2048, 7, 7]          1,048,576\n",
      "|    |    └─BatchNorm2d: 3-128           [-1, 2048, 7, 7]          4,096\n",
      "|    |    └─Sequential: 3-129            [-1, 2048, 7, 7]          2,101,248\n",
      "|    |    └─ReLU: 3-130                  [-1, 2048, 7, 7]          --\n",
      "|    └─Bottleneck: 2-15                  [-1, 2048, 7, 7]          --\n",
      "|    |    └─Conv2d: 3-131                [-1, 512, 7, 7]           1,048,576\n",
      "|    |    └─BatchNorm2d: 3-132           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-133                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-134                [-1, 512, 7, 7]           2,359,296\n",
      "|    |    └─BatchNorm2d: 3-135           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-136                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-137                [-1, 2048, 7, 7]          1,048,576\n",
      "|    |    └─BatchNorm2d: 3-138           [-1, 2048, 7, 7]          4,096\n",
      "|    |    └─ReLU: 3-139                  [-1, 2048, 7, 7]          --\n",
      "|    └─Bottleneck: 2-16                  [-1, 2048, 7, 7]          --\n",
      "|    |    └─Conv2d: 3-140                [-1, 512, 7, 7]           1,048,576\n",
      "|    |    └─BatchNorm2d: 3-141           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-142                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-143                [-1, 512, 7, 7]           2,359,296\n",
      "|    |    └─BatchNorm2d: 3-144           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-145                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-146                [-1, 2048, 7, 7]          1,048,576\n",
      "|    |    └─BatchNorm2d: 3-147           [-1, 2048, 7, 7]          4,096\n",
      "|    |    └─ReLU: 3-148                  [-1, 2048, 7, 7]          --\n",
      "├─AdaptiveAvgPool2d: 1-9                 [-1, 2048, 1, 1]          --\n",
      "├─Linear: 1-10                           [-1, 7]                   14,343\n",
      "==========================================================================================\n",
      "Total params: 23,522,375\n",
      "Trainable params: 23,522,375\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (G): 4.14\n",
      "==========================================================================================\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 169.59\n",
      "Params size (MB): 89.73\n",
      "Estimated Total Size (MB): 259.89\n",
      "==========================================================================================\n",
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Conv2d: 1-1                            [-1, 64, 112, 112]        9,408\n",
      "├─BatchNorm2d: 1-2                       [-1, 64, 112, 112]        128\n",
      "├─ReLU: 1-3                              [-1, 64, 112, 112]        --\n",
      "├─MaxPool2d: 1-4                         [-1, 64, 56, 56]          --\n",
      "├─Sequential: 1-5                        [-1, 256, 56, 56]         --\n",
      "|    └─Bottleneck: 2-1                   [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-1                  [-1, 64, 56, 56]          4,096\n",
      "|    |    └─BatchNorm2d: 3-2             [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-3                    [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-4                  [-1, 64, 56, 56]          36,864\n",
      "|    |    └─BatchNorm2d: 3-5             [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-6                    [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-7                  [-1, 256, 56, 56]         16,384\n",
      "|    |    └─BatchNorm2d: 3-8             [-1, 256, 56, 56]         512\n",
      "|    |    └─Sequential: 3-9              [-1, 256, 56, 56]         16,896\n",
      "|    |    └─ReLU: 3-10                   [-1, 256, 56, 56]         --\n",
      "|    └─Bottleneck: 2-2                   [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-11                 [-1, 64, 56, 56]          16,384\n",
      "|    |    └─BatchNorm2d: 3-12            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-13                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-14                 [-1, 64, 56, 56]          36,864\n",
      "|    |    └─BatchNorm2d: 3-15            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-16                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-17                 [-1, 256, 56, 56]         16,384\n",
      "|    |    └─BatchNorm2d: 3-18            [-1, 256, 56, 56]         512\n",
      "|    |    └─ReLU: 3-19                   [-1, 256, 56, 56]         --\n",
      "|    └─Bottleneck: 2-3                   [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-20                 [-1, 64, 56, 56]          16,384\n",
      "|    |    └─BatchNorm2d: 3-21            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-22                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-23                 [-1, 64, 56, 56]          36,864\n",
      "|    |    └─BatchNorm2d: 3-24            [-1, 64, 56, 56]          128\n",
      "|    |    └─ReLU: 3-25                   [-1, 64, 56, 56]          --\n",
      "|    |    └─Conv2d: 3-26                 [-1, 256, 56, 56]         16,384\n",
      "|    |    └─BatchNorm2d: 3-27            [-1, 256, 56, 56]         512\n",
      "|    |    └─ReLU: 3-28                   [-1, 256, 56, 56]         --\n",
      "├─Sequential: 1-6                        [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-4                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-29                 [-1, 128, 56, 56]         32,768\n",
      "|    |    └─BatchNorm2d: 3-30            [-1, 128, 56, 56]         256\n",
      "|    |    └─ReLU: 3-31                   [-1, 128, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-32                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-33            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-34                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-35                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-36            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─Sequential: 3-37             [-1, 512, 28, 28]         132,096\n",
      "|    |    └─ReLU: 3-38                   [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-5                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-39                 [-1, 128, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-40            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-41                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-42                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-43            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-44                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-45                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-46            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-47                   [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-6                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-48                 [-1, 128, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-49            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-50                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-51                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-52            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-53                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-54                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-55            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-56                   [-1, 512, 28, 28]         --\n",
      "|    └─Bottleneck: 2-7                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-57                 [-1, 128, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-58            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-59                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-60                 [-1, 128, 28, 28]         147,456\n",
      "|    |    └─BatchNorm2d: 3-61            [-1, 128, 28, 28]         256\n",
      "|    |    └─ReLU: 3-62                   [-1, 128, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-63                 [-1, 512, 28, 28]         65,536\n",
      "|    |    └─BatchNorm2d: 3-64            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-65                   [-1, 512, 28, 28]         --\n",
      "├─Sequential: 1-7                        [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-8                   [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-66                 [-1, 256, 28, 28]         131,072\n",
      "|    |    └─BatchNorm2d: 3-67            [-1, 256, 28, 28]         512\n",
      "|    |    └─ReLU: 3-68                   [-1, 256, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-69                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-70            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-71                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-72                 [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-73            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─Sequential: 3-74             [-1, 1024, 14, 14]        526,336\n",
      "|    |    └─ReLU: 3-75                   [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-9                   [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-76                 [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-77            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-78                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-79                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-80            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-81                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-82                 [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-83            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-84                   [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-10                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-85                 [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-86            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-87                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-88                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-89            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-90                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-91                 [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-92            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-93                   [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-11                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-94                 [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-95            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-96                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-97                 [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-98            [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-99                   [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-100                [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-101           [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-102                  [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-12                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-103                [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-104           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-105                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-106                [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-107           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-108                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-109                [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-110           [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-111                  [-1, 1024, 14, 14]        --\n",
      "|    └─Bottleneck: 2-13                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-112                [-1, 256, 14, 14]         262,144\n",
      "|    |    └─BatchNorm2d: 3-113           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-114                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-115                [-1, 256, 14, 14]         589,824\n",
      "|    |    └─BatchNorm2d: 3-116           [-1, 256, 14, 14]         512\n",
      "|    |    └─ReLU: 3-117                  [-1, 256, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-118                [-1, 1024, 14, 14]        262,144\n",
      "|    |    └─BatchNorm2d: 3-119           [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-120                  [-1, 1024, 14, 14]        --\n",
      "├─Sequential: 1-8                        [-1, 2048, 7, 7]          --\n",
      "|    └─Bottleneck: 2-14                  [-1, 2048, 7, 7]          --\n",
      "|    |    └─Conv2d: 3-121                [-1, 512, 14, 14]         524,288\n",
      "|    |    └─BatchNorm2d: 3-122           [-1, 512, 14, 14]         1,024\n",
      "|    |    └─ReLU: 3-123                  [-1, 512, 14, 14]         --\n",
      "|    |    └─Conv2d: 3-124                [-1, 512, 7, 7]           2,359,296\n",
      "|    |    └─BatchNorm2d: 3-125           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-126                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-127                [-1, 2048, 7, 7]          1,048,576\n",
      "|    |    └─BatchNorm2d: 3-128           [-1, 2048, 7, 7]          4,096\n",
      "|    |    └─Sequential: 3-129            [-1, 2048, 7, 7]          2,101,248\n",
      "|    |    └─ReLU: 3-130                  [-1, 2048, 7, 7]          --\n",
      "|    └─Bottleneck: 2-15                  [-1, 2048, 7, 7]          --\n",
      "|    |    └─Conv2d: 3-131                [-1, 512, 7, 7]           1,048,576\n",
      "|    |    └─BatchNorm2d: 3-132           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-133                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-134                [-1, 512, 7, 7]           2,359,296\n",
      "|    |    └─BatchNorm2d: 3-135           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-136                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-137                [-1, 2048, 7, 7]          1,048,576\n",
      "|    |    └─BatchNorm2d: 3-138           [-1, 2048, 7, 7]          4,096\n",
      "|    |    └─ReLU: 3-139                  [-1, 2048, 7, 7]          --\n",
      "|    └─Bottleneck: 2-16                  [-1, 2048, 7, 7]          --\n",
      "|    |    └─Conv2d: 3-140                [-1, 512, 7, 7]           1,048,576\n",
      "|    |    └─BatchNorm2d: 3-141           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-142                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-143                [-1, 512, 7, 7]           2,359,296\n",
      "|    |    └─BatchNorm2d: 3-144           [-1, 512, 7, 7]           1,024\n",
      "|    |    └─ReLU: 3-145                  [-1, 512, 7, 7]           --\n",
      "|    |    └─Conv2d: 3-146                [-1, 2048, 7, 7]          1,048,576\n",
      "|    |    └─BatchNorm2d: 3-147           [-1, 2048, 7, 7]          4,096\n",
      "|    |    └─ReLU: 3-148                  [-1, 2048, 7, 7]          --\n",
      "├─AdaptiveAvgPool2d: 1-9                 [-1, 2048, 1, 1]          --\n",
      "├─Linear: 1-10                           [-1, 7]                   14,343\n",
      "==========================================================================================\n",
      "Total params: 23,522,375\n",
      "Trainable params: 23,522,375\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (G): 4.14\n",
      "==========================================================================================\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 169.59\n",
      "Params size (MB): 89.73\n",
      "Estimated Total Size (MB): 259.89\n",
      "==========================================================================================\n"
     ]
    }
   ],
   "source": [
    "input_size = (3, 224, 224)\n",
    "print(summary(model_res, input_size, device=device.type))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss().to(device)\n",
    "optimizer = torch.optim.Adam(model_res.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 1/30 [01:59<57:39, 119.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training Loss: 1.15972318290432 \n",
      "Training Accuracy: 56.94062449441838%\n",
      "Validation Loss: 1.028638577122405 \n",
      "Validation Accuracy: 59.74761365474842%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(inf--->1.028639) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 2/30 [04:07<57:59, 124.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2\n",
      "Training Loss: 0.7851583068222963 \n",
      "Training Accuracy: 71.0726419673192%\n",
      "Validation Loss: 0.7193977991431875 \n",
      "Validation Accuracy: 75.2952596667206%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(1.028639--->0.719398) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 3/30 [06:19<57:39, 128.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3\n",
      "Training Loss: 0.6338727496545136 \n",
      "Training Accuracy: 76.7836919592299%\n",
      "Validation Loss: 0.5532690856499881 \n",
      "Validation Accuracy: 80.10839669956317%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.719398--->0.553269) \n",
      "Saving The Model\n",
      "=======================================================================\n",
      "Epoch 4\n",
      "Training Loss: 0.5421617780005901 \n",
      "Training Accuracy: 80.10030739362563%\n",
      "Validation Loss: 0.46308344893292247 \n",
      "Validation Accuracy: 82.21161624332632%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.553269--->0.463083) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 5/30 [11:06<57:40, 138.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5\n",
      "Training Loss: 0.4753365613693415 \n",
      "Training Accuracy: 82.5756350105161%\n",
      "Validation Loss: 0.681458515021228 \n",
      "Validation Accuracy: 77.05872836110662%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 6/30 [13:32<56:22, 140.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6\n",
      "Training Loss: 0.41692690870866606 \n",
      "Training Accuracy: 85.05905193334412%\n",
      "Validation Loss: 0.36998320738628543 \n",
      "Validation Accuracy: 86.8629671574179%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.463083--->0.369983) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|██▎       | 7/30 [15:55<54:19, 141.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7\n",
      "Training Loss: 0.37041839874453014 \n",
      "Training Accuracy: 86.66882381491668%\n",
      "Validation Loss: 0.4596676390223417 \n",
      "Validation Accuracy: 83.80520951302378%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██▋       | 8/30 [18:34<53:53, 146.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8\n",
      "Training Loss: 0.34436393971976076 \n",
      "Training Accuracy: 87.87413039961172%\n",
      "Validation Loss: 0.35431929658472694 \n",
      "Validation Accuracy: 87.51820093835948%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.369983--->0.354319) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 9/30 [20:54<50:41, 144.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9\n",
      "Training Loss: 0.30481484258975783 \n",
      "Training Accuracy: 89.1522407377447%\n",
      "Validation Loss: 0.28894481613500483 \n",
      "Validation Accuracy: 89.32211616243326%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.354319--->0.288945) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 10/30 [23:22<48:36, 145.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10\n",
      "Training Loss: 0.2628443400912199 \n",
      "Training Accuracy: 91.02087040931887%\n",
      "Validation Loss: 0.2044906113272682 \n",
      "Validation Accuracy: 92.8975893868306%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.288945--->0.204491) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███▋      | 11/30 [25:48<46:12, 145.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11\n",
      "Training Loss: 0.24153556961575837 \n",
      "Training Accuracy: 91.50622876557192%\n",
      "Validation Loss: 0.22046102955937386 \n",
      "Validation Accuracy: 92.50121339589063%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 12/30 [28:15<43:54, 146.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12\n",
      "Training Loss: 0.21629578817529888 \n",
      "Training Accuracy: 92.62255298495388%\n",
      "Validation Loss: 0.23056066284095747 \n",
      "Validation Accuracy: 91.94305128619965%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|████▎     | 13/30 [30:47<41:54, 147.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13\n",
      "Training Loss: 0.2099289468005877 \n",
      "Training Accuracy: 92.63873159682899%\n",
      "Validation Loss: 0.24089755886053854 \n",
      "Validation Accuracy: 92.00776573370005%\n",
      "=======================================================================\n",
      "Epoch 14\n",
      "Training Loss: 0.19962790874326336 \n",
      "Training Accuracy: 93.19689370651999%\n",
      "Validation Loss: 0.16332371903520837 \n",
      "Validation Accuracy: 94.42646820902766%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.204491--->0.163324) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 15/30 [35:52<37:34, 150.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15\n",
      "Training Loss: 0.15043874380469938 \n",
      "Training Accuracy: 94.68532599902929%\n",
      "Validation Loss: 0.2743468355021659 \n",
      "Validation Accuracy: 91.09367416275684%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|█████▎    | 16/30 [38:24<35:08, 150.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16\n",
      "Training Loss: 0.15452361588176214 \n",
      "Training Accuracy: 94.59634363371623%\n",
      "Validation Loss: 0.19549177796880712 \n",
      "Validation Accuracy: 93.23734023620773%\n",
      "=======================================================================\n",
      "Epoch 17\n",
      "Training Loss: 0.13736456416990098 \n",
      "Training Accuracy: 95.38100630965863%\n",
      "Validation Loss: 0.14641212873059764 \n",
      "Validation Accuracy: 95.26775602653292%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.163324--->0.146412) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 18/30 [42:02<25:50, 129.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18\n",
      "Training Loss: 0.13619535786246528 \n",
      "Training Accuracy: 95.46998867497169%\n",
      "Validation Loss: 0.11988785997206389 \n",
      "Validation Accuracy: 96.17375829153859%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.146412--->0.119888) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|██████▎   | 19/30 [43:48<22:23, 122.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19\n",
      "Training Loss: 0.13412369112183342 \n",
      "Training Accuracy: 95.38100630965863%\n",
      "Validation Loss: 0.12291357703073773 \n",
      "Validation Accuracy: 95.89063258372431%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 20/30 [45:34<19:32, 117.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20\n",
      "Training Loss: 0.10584340449085686 \n",
      "Training Accuracy: 96.3193657984145%\n",
      "Validation Loss: 0.14016165995479438 \n",
      "Validation Accuracy: 95.46189936903414%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 21/30 [47:20<17:04, 113.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21\n",
      "Training Loss: 0.12895015004098184 \n",
      "Training Accuracy: 95.65604271153535%\n",
      "Validation Loss: 0.1667664928159626 \n",
      "Validation Accuracy: 94.49927196246563%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████▎  | 22/30 [49:06<14:51, 111.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22\n",
      "Training Loss: 0.10837780180909413 \n",
      "Training Accuracy: 96.34363371622715%\n",
      "Validation Loss: 0.09551184986764927 \n",
      "Validation Accuracy: 97.17683222779486%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.119888--->0.095512) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 23/30 [50:51<12:48, 109.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23\n",
      "Training Loss: 0.10247501396292517 \n",
      "Training Accuracy: 96.60249150622876%\n",
      "Validation Loss: 0.24376342060087725 \n",
      "Validation Accuracy: 92.27471282963921%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 24/30 [52:37<10:51, 108.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24\n",
      "Training Loss: 0.09661333290234363 \n",
      "Training Accuracy: 96.61058081216632%\n",
      "Validation Loss: 0.11880962179524907 \n",
      "Validation Accuracy: 96.47306261122796%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 25/30 [54:23<08:58, 107.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25\n",
      "Training Loss: 0.08682382647736234 \n",
      "Training Accuracy: 97.06358194466915%\n",
      "Validation Loss: 0.1577770349868027 \n",
      "Validation Accuracy: 94.87138003559295%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|████████▋ | 26/30 [56:09<07:08, 107.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26\n",
      "Training Loss: 0.11359689033407362 \n",
      "Training Accuracy: 96.30318718653939%\n",
      "Validation Loss: 0.0879724062342998 \n",
      "Validation Accuracy: 97.35479695842096%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.095512--->0.087972) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 27/30 [57:55<05:20, 106.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27\n",
      "Training Loss: 0.07938220274504708 \n",
      "Training Accuracy: 97.15256430998221%\n",
      "Validation Loss: 0.12866935514524888 \n",
      "Validation Accuracy: 95.7288464649733%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 28/30 [59:41<03:33, 106.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28\n",
      "Training Loss: 0.07755554834734504 \n",
      "Training Accuracy: 97.52467238310953%\n",
      "Validation Loss: 0.07324862792634933 \n",
      "Validation Accuracy: 97.71881572561074%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.087972--->0.073249) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 29/30 [1:01:27<01:46, 106.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29\n",
      "Training Loss: 0.07332104093603338 \n",
      "Training Accuracy: 97.58129752467238%\n",
      "Validation Loss: 0.12671571178542262 \n",
      "Validation Accuracy: 96.02006147872513%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [1:03:13<00:00, 126.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30\n",
      "Training Loss: 0.07626833323688903 \n",
      "Training Accuracy: 97.50849377123443%\n",
      "Validation Loss: 0.10110202212384793 \n",
      "Validation Accuracy: 97.0554926387316%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 30\n",
    "patience = 5\n",
    "model_filename = 'resnet_pret.pth'\n",
    "resnet_model, total_loss_train, total_loss_val, total_acc_train, total_acc_val = Model_functions.train_and_validate_model(\n",
    "                        model_res, train_loader, val_loader,test_loader, criterion,patience, optimizer, device, epochs, model_filename,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training and validation time for the Resnet Pretrainned Model: 63.22 minutes\n"
     ]
    }
   ],
   "source": [
    "end_time = time.time()\n",
    "total_time = end_time - start_time\n",
    "print(f\"Total training and validation time for the Resnet Pretrainned Model: {total_time/60:.2f} minutes\")\n",
    "elapsed_time = end_time - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bb6V169bcc889TJ8+nccff5zRo0fTu3dv6tWrx4oVK5g9ezazZs1i8uTJ1KtXr0xrPOyww/jnP//J5ZdfziGHHMIJJ5xA69at2bx5M4sWLeLLL79k2LBhRfeCtGvXjkaNGvH6668THx9PkyZNCAQCXHnllUXHOZW1QYMGcccdd3DfffeRlpbGoEGDaN68OevXryc9PZ2JEydy//330759eyB0+XhiYiKPPfYYGzdupG7dugDFCgwaNmzIGWecwdtvv023bt3o378/q1ev5sMPP6R///4sXLiwTD5jfHw8b731Fscffzx9+vShX79+dOzYkUAgwJIlS5g4cSK1a9cuumQ+NTWVxx9/nGHDhnHkkUfyhz/8gdTUVD788EMSExOL7uCRJEmSIsVgRJIkSfofP//8My+88MIe3+vTpw8333wzhxxyCFOnTuWee+7hk08+YfTo0dStW5cLLriAu+66q+iv+yF0EXpGRgYTJkzgnXfeYcuWLTRu3JizzjqLG2+8kQ4dOgDQrVs3brrpJsaPH8/o0aPZtGkTDRo0YMCAAdxwww0cddRRpf5sCQkJ3HLLLVx55ZXcc889vPjii8THx/Pxxx/z3HPP8eKLL/L222+TnZ1N/fr16dChA5dddhkdO3YslxovueQSOnfuXLQ74YMPPiA1NZVmzZpx7bXXMnTo0KK+0dHRvPPOO9x000289tprbN68GYDzzjuv3IIRgHvvvZfevXvz+OOPM3bsWDZt2kTt2rVp2bIld999N+eee25R31q1avHWW29x99138+yzz7J9+3ageMEIwMiRI2nRogVvv/02TzzxBM2aNeO6667jpptu4q233iqTzwdw5JFHMmvWLP72t7/x0UcfMWnSJOLj42ncuDGnnnoqZ5999i79hw4dSmpqKvfffz8vvPACqampnHzyyfz1r3/liCOOKLM6JUmSpOIIBCN1e6EkSZIkSZIkSVI5844RSZIkSZIkSZJUZRiMSJIkSZIkSZKkKsNgRJIkSZIkSZIkVRmlDkYmTJjA4MGDadSoEYFAgPfee2+/Y8aPH0+XLl2Ij48nLS2NkSNHlrYMSZIkSZIkSZKk/Sp1MLJ161Y6derEU089Vaz+ixcv5sQTT6Rv377MnDmTa665hosvvphPP/20tKVIkiRJkiRJkiTtUyAYDAbDNlkgwLvvvsupp5661z433XQTo0eP5ocffih67Q9/+AObNm3ik08+CVcpkiRJkiRJkiRJu4kp7wUnT57MgAEDdnlt4MCBXHPNNXsdk52dTXZ2dtH3BQUFbNiwgdq1axMIBMqqVEmSJEmSJEmSVAkEg0E2b95Mo0aNiIra92FZ5R6MrFq1ivr16+/yWv369cnKymL79u0kJibuNuahhx7innvuKa8SJUmSJEmSJElSJbRs2TKaNGmyzz7lHowciFtuuYXrrruu6PvMzEyaNWvGsmXLSElJiWBllcCbF0L6Z9DnJjj6yhIP/78xC3h24mJO7tSIB0/vWAYFSpIkSZIkSZJUOllZWTRt2pTq1avvt2+5ByMNGjRg9erVu7y2evVqUlJS9rhbBCA+Pp74+PjdXk9JSTEY2Z/DB8Gyz2HlJEi5rcTDj+nQjOe+W80Pa3P8WUuSJEmSJEmSKrTiXL+x74O2ykDPnj0ZO3bsLq99/vnn9OzZs7xLqRpa9w89l30LOzJLPLxLs5oEApCxfhtrN2fvf4AkSZIkSZIkSRVYqYORLVu2MHPmTGbOnAnA4sWLmTlzJkuXLgVCx2ANGTKkqP9ll13GokWLuPHGG5k3bx7//Oc/+e9//8u1115b2lK0J7VaQu00KMiDxRNKPDw1KZa29UJbj6Yt2RDu6iRJkiRJkiRJKlelDkamTp3KEUccwRFHHAHAddddxxFHHMGdd94JwMqVK4tCEoCWLVsyevRoPv/8czp16sTf//53/vOf/zBw4MDSlqK9Kdw1kj7mgIZ3bVETgKkZG8NVkSRJkiRJkiRJEVHqO0aOO+44gsHgXt8fOXLkHsfMmDGjtEuruNIGwHf/gvQvIBiEYpyx9mtHtqjJq98uZeoSgxFJkiRJkiRJ+1ZQUEBOTk6ky9BBJjY2lujo6LDMVe6XrysCWvSC6HjIXArrfoK6bUs0vFvzWgD8sCKT7Tn5JMaF5x+fJEmSJEmSpINLTk4OixcvpqCgINKl6CBUo0YNGjRoUKwL1vfFYKQqiKsGzY+GReNCx2mVMBhpUjORetXjWbM5m1nLN3FUq9plVKgkSZIkSZKkyioYDLJy5Uqio6Np2rQpUVGlvslBAkL/trZt28aaNWsAaNiwYanmMxipKtIG/BKM9PxTiYYGAgGObFGL0bNXMm3JRoMRSZIkSZIkSbvJy8tj27ZtNGrUiKSkpEiXo4NMYmIiAGvWrKFevXqlOlbLyK6qSBsQei6ZBLnbSzy8a/PCC9g3hLMqSZIkSZIkSQeJ/Px8AOLi4iJciQ5WhYFbbm5uqeYxGKkq6h4CKY0hbwdkTCrx8G4tQsHItCUbKSgIhrs6SZIkSZIkSQeJ0t7/IO1NuP5tGYxUFYEApPUPtReOLfHw9g1TSIyNJmtHHj+t2RLm4iRJkiRJkiRJKh8GI1VJ4XFa6WNKPDQ2OoojmtUAYOoSj9OSJEmSJEmSpL1p0aIFjz32WLH7jx8/nkAgwKZNm8qsJv3CYKQqadkHAtGwbgFsXFLi4d2K7hnZGO7KJEmSJEmSJKncBQKBfX7dfffdBzTvlClTuPTSS4vd/+ijj2blypWkpqYe0HrFZQATEhPpAlSOEmtA0+6wdHLoOK1uF5ZoeNcWtQB3jEiSJEmSJEk6OKxcubKo/cYbb3DnnXcyf/78oteSk5OL2sFgkPz8fGJi9v9r9bp165aojri4OBo0aFCiMTpw7hipagrvGUkv+T0jXZrVICoAyzZsZ3XWjjAXJkmSJEmSJEnlq0GDBkVfqampBAKBou/nzZtH9erV+fjjj+natSvx8fF89dVXLFy4kFNOOYX69euTnJzMkUceyZgxu15f8L9HaQUCAf7zn/9w2mmnkZSURJs2bRg1alTR+/+7k2PkyJHUqFGDTz/9lPbt25OcnMygQYN2CXLy8vK46qqrqFGjBrVr1+amm25i6NChnHrqqQf889i4cSNDhgyhZs2aJCUlcfzxx/PTTz8Vvb9kyRIGDx5MzZo1qVatGoceeigfffRR0dhzzz2XunXrkpiYSJs2bRgxYsQB11KWDEaqmtY7g5FFX0J+bomGVk+I5ZAGKYDHaUmSJEmSJEnat2AwyLacvIh8BYPBsH2Om2++mb/85S/MnTuXww8/nC1btnDCCScwduxYZsyYwaBBgxg8eDBLly7d5zz33HMPZ555Jt9//z0nnHAC5557Lhs27P10nm3btvHII4/w0ksvMWHCBJYuXcr1119f9P7DDz/MK6+8wogRI5g0aRJZWVm89957pfqsw4YNY+rUqYwaNYrJkycTDAY54YQTyM0N/S55+PDhZGdnM2HCBGbPns3DDz9ctKvmjjvuYM6cOXz88cfMnTuXp59+mjp16pSqnrLiUVpVTcPOkFQbtq2HZd9Bi14lGt6teU3mrsxi6pINnHh4w7KpUZIkSZIkSVKltz03nw53fhqRtefcO5CkuPD8+vvee+/lN7/5TdH3tWrVolOnTkXf33fffbz77ruMGjWKK664Yq/zDBs2jLPPPhuABx98kMcff5zvvvuOQYMG7bF/bm4uzzzzDK1btwbgiiuu4N577y16/4knnuCWW27htNNOA+DJJ58s2r1xIH766SdGjRrFpEmTOProowF45ZVXaNq0Ke+99x6///3vWbp0KWeccQYdO3YEoFWrVkXjly5dyhFHHEG3bt2A0K6ZisodI1VNVNQvu0bSx+y77x50axG6gH3aEneMSJIkSZIkSTr4Ff6iv9CWLVu4/vrrad++PTVq1CA5OZm5c+fud8fI4YcfXtSuVq0aKSkprFmzZq/9k5KSikIRgIYNGxb1z8zMZPXq1XTv3r3o/ejoaLp27Vqiz/Zrc+fOJSYmhh49ehS9Vrt2bQ455BDmzp0LwFVXXcX9999Pr169uOuuu/j++++L+l5++eW8/vrrdO7cmRtvvJGvv/76gGspa+4YqYrSBsDs/4aCkQF3lWhot50XsP/4cxbbcvLClrpKkiRJkiRJOrgkxkYz596BEVs7XKpVq7bL99dffz2ff/45jzzyCGlpaSQmJvK73/2OnJycfc4TGxu7y/eBQICCgoIS9Q/nEWEH4uKLL2bgwIGMHj2azz77jIceeoi///3vXHnllRx//PEsWbKEjz76iM8//5z+/fszfPhwHnnkkYjWvCfuGKmKWvcLPVd9D5tXl2ho4xqJNExNIL8gyMylm8JfmyRJkiRJkqSDQiAQICkuJiJfgUCgzD7XpEmTGDZsGKeddhodO3akQYMGZGRklNl6e5Kamkr9+vWZMmVK0Wv5+flMnz79gOds3749eXl5fPvtt0WvrV+/nvnz59OhQ4ei15o2bcpll13GO++8w5///GeeffbZovfq1q3L0KFDefnll3nsscf497//fcD1lCX/3L8qSq4bumtk5UxY+AV0PrtEw7s2r8mH369k6pKNHJ1WMS/PkSRJkiRJkqSy0KZNG9555x0GDx5MIBDgjjvu2OfOj7Jy5ZVX8tBDD5GWlka7du144okn2LhxY7FCodmzZ1O9evWi7wOBAJ06deKUU07hkksu4V//+hfVq1fn5ptvpnHjxpxyyikAXHPNNRx//PG0bduWjRs3Mm7cONq3bw/AnXfeSdeuXTn00EPJzs7mww8/LHqvojEYqarS+oeCkfQxJQ5GjmxRqygYkSRJkiRJkqSq5NFHH+XCCy/k6KOPpk6dOtx0001kZWWVex033XQTq1atYsiQIURHR3PppZcycOBAoqP3f4xY7969d/k+OjqavLw8RowYwdVXX81JJ51ETk4OvXv35qOPPio61is/P5/hw4ezfPlyUlJSGDRoEP/4xz8AiIuL45ZbbiEjI4PExESOPfZYXn/99fB/8DAIBCN9KNkByMrKIjU1lczMTFJSUiJdTuW05GsYcTwk1oIb0iGq+Gfu/bAik5Oe+Irk+Bhm3fVboqPKbluaJEmSJEmSpMphx44dLF68mJYtW5KQkBDpcqqcgoIC2rdvz5lnnsl9990X6XLKxL7+jZUkN/COkaqqyZEQnwLbN4R2jpRAuwbVqRYXzZbsPOav2lw29UmSJEmSJEmS9mrJkiU8++yzLFiwgNmzZ3P55ZezePFizjnnnEiXVuEZjFRV0bHQqk+onT62RENjoqPo0rwmANOWbAh3ZZIkSZIkSZKk/YiKimLkyJEceeSR9OrVi9mzZzNmzJgKe69HRWIwUpWlDQg908eUeGjXncHIlAzvGZEkSZIkSZKk8ta0aVMmTZpEZmYmWVlZfP3117vdHaI9Mxipylr3Dz2XT4HtJQs4ujWvBcA0L2CXJEmSJEmSJFUiBiNVWY2mUOcQCBbAovElGtq5WQ2iowKs2LSdlZnby6Y+SZIkSZIkSZLCzGCkqis6Tqtk94wkx8fQvmF1AKZ6nJYkSZIkSZIkqZIwGKnq0nYep5U+FoLBEg0tPE5raoYXsEuSJEmSJEmSKgeDkaqueS+ISYTNP8OauSUaWngB+1TvGZEkSZIkSZIkVRIGI1VdbAK0OCbUTh9ToqHdWoSCkbkrs9iSnRfuyiRJkiRJkiRJCjuDEf3qOK2SBSMNUxNpXCORgiDMWOquEUmSJEmSJElV03HHHcc111xT9H2LFi147LHH9jkmEAjw3nvvlXrtcM1TlRiM6JcL2JdOhpytJRpauGvEC9glSZIkSZIkVTaDBw9m0KBBe3xv4sSJBAIBvv/++xLPO2XKFC699NLSlreLu+++m86dO+/2+sqVKzn++OPDutb/GjlyJDVq1CjTNcqTwYigdhrUaAb5OZDxVYmGdmsRuoB9mveMSJIkSZIkSapkLrroIj7//HOWL1++23sjRoygW7duHH744SWet27duiQlJYWjxP1q0KAB8fHx5bLWwcJgRBAI/LJrpKT3jOy8gH360o3k5ReEuzJJkiRJkiRJKjMnnXQSdevWZeTIkbu8vmXLFt58800uuugi1q9fz9lnn03jxo1JSkqiY8eOvPbaa/uc93+P0vrpp5/o3bs3CQkJdOjQgc8//3y3MTfddBNt27YlKSmJVq1acccdd5CbmwuEdmzcc889zJo1i0AgQCAQKKr5f4/Smj17Nv369SMxMZHatWtz6aWXsmXLlqL3hw0bxqmnnsojjzxCw4YNqV27NsOHDy9a60AsXbqUU045heTkZFJSUjjzzDNZvXp10fuzZs2ib9++VK9enZSUFLp27crUqVMBWLJkCYMHD6ZmzZpUq1aNQw89lI8++uiAaymOmDKdXZVH2gCY+nyJg5G29atTPT6Gzdl5zFu1mcMap5ZRgZIkSZIkSZIqlWAQcrdFZu3YpNAfhO9HTEwMQ4YMYeTIkdx2220Edo558803yc/P5+yzz2bLli107dqVm266iZSUFEaPHs35559P69at6d69+37XKCgo4PTTT6d+/fp8++23ZGZm7nIfSaHq1aszcuRIGjVqxOzZs7nkkkuoXr06N954I2eddRY//PADn3zyCWPGhH6Hm5q6++9it27dysCBA+nZsydTpkxhzZo1XHzxxVxxxRW7hD/jxo2jYcOGjBs3jvT0dM466yw6d+7MJZdcst/Ps6fPVxiKfPnll+Tl5TF8+HDOOussxo8fD8C5557LEUccwdNPP010dDQzZ84kNjYWgOHDh5OTk8OECROoVq0ac+bMITk5ucR1lITBiEJa9oaoGNiwCNYvhNqtizUsOipAl+Y1+XLBWqZmbDAYkSRJkiRJkhSSuw0ebBSZtW/9GeKqFavrhRdeyN/+9je+/PJLjjvuOCB0jNYZZ5xBamoqqampXH/99UX9r7zySj799FP++9//FisYGTNmDPPmzePTTz+lUaPQz+PBBx/c7V6Q22+/vajdokULrr/+el5//XVuvPFGEhMTSU5OJiYmhgYNGux1rVdffZUdO3bw4osvUq1a6PM/+eSTDB48mIcffpj69esDULNmTZ588kmio6Np164dJ554ImPHjj2gYGTs2LHMnj2bxYsX07RpUwBefPFFDj30UKZMmcKRRx7J0qVLueGGG2jXrh0Abdq0KRq/dOlSzjjjDDp27AhAq1atSlxDSXmUlkLiq0PTo0LthV+UaGjhcVpTvWdEkiRJkiRJUiXTrl07jj76aJ5//nkA0tPTmThxIhdddBEA+fn53HfffXTs2JFatWqRnJzMp59+ytKlS4s1/9y5c2natGlRKALQs2fP3fq98cYb9OrViwYNGpCcnMztt99e7DV+vVanTp2KQhGAXr16UVBQwPz584teO/TQQ4mOji76vmHDhqxZs6ZEa/16zaZNmxaFIgAdOnSgRo0azJ07F4DrrruOiy++mAEDBvCXv/yFhQsXFvW96qqruP/+++nVqxd33XXXAV12X1LuGNEv0vrDkq8gfSx0L34y2LXFzmAkYyPBYLBou5kkSZIkSZKkKiw2KbRzI1Jrl8BFF13ElVdeyVNPPcWIESNo3bo1ffr0AeBvf/sb//d//8djjz1Gx44dqVatGtdccw05OTlhK3fy5Mmce+653HPPPQwcOJDU1FRef/11/v73v4dtjV8rPMaqUCAQoKCg7O6QvvvuuznnnHMYPXo0H3/8MXfddRevv/46p512GhdffDEDBw5k9OjRfPbZZzz00EP8/e9/58orryyzetwxol8UXsC+eALkZRd7WOemNYiOCrAqawcrNm0vo+IkSZIkSZIkVSqBQOg4q0h8lfCPt88880yioqJ49dVXefHFF7nwwguL/gB80qRJnHLKKZx33nl06tSJVq1asWDBgmLP3b59e5YtW8bKlSuLXvvmm2926fP111/TvHlzbrvtNrp160abNm1YsmTJLn3i4uLIz8/f71qzZs1i69atRa9NmjSJqKgoDjnkkGLXXBKFn2/ZsmVFr82ZM4dNmzbRoUOHotfatm3Ltddey2effcbpp5/OiBEjit5r2rQpl112Ge+88w5//vOfefbZZ8uk1kIGI/pFg46QXB9yt8LSb/bff6ekuBgOa5QCwDSP05IkSZIkSZJUySQnJ3PWWWdxyy23sHLlSoYNG1b0Xps2bfj888/5+uuvmTt3Ln/84x9ZvXp1seceMGAAbdu2ZejQocyaNYuJEydy22237dKnTZs2LF26lNdff52FCxfy+OOP8+677+7Sp0WLFixevJiZM2eybt06srN3/+P2c889l4SEBIYOHcoPP/zAuHHjuPLKKzn//POL7hc5UPn5+cycOXOXr7lz5zJgwAA6duzIueeey/Tp0/nuu+8YMmQIffr0oVu3bmzfvp0rrriC8ePHs2TJEiZNmsSUKVNo3749ANdccw2ffvopixcvZvr06YwbN67ovbJiMKJfBALQun+onT6mREO7Nq8FwJSMDeGuSpIkSZIkSZLK3EUXXcTGjRsZOHDgLveB3H777XTp0oWBAwdy3HHH0aBBA0499dRizxsVFcW7777L9u3b6d69OxdffDEPPPDALn1OPvlkrr32Wq644go6d+7M119/zR133LFLnzPOOINBgwbRt29f6taty2uvvbbbWklJSXz66ads2LCBI488kt/97nf079+fJ598smQ/jD3YsmULRxxxxC5fgwcPJhAI8P7771OzZk169+7NgAEDaNWqFW+88QYA0dHRrF+/niFDhtC2bVvOPPNMjj/+eO655x4gFLgMHz6c9u3bM2jQINq2bcs///nPUte7L4FgMBgs0xXKQFZWFqmpqWRmZpKSkhLpcg4us9+Cty+CeofCn74u9rCPZq/kT69Mp12D6nxyTe8yLFCSJEmSJElSRbRjxw4WL15My5YtSUhIiHQ5Ogjt699YSXIDd4xoV637AQFY8yNkFf9ipG7NQxewz1+9mawduWVUnCRJkiRJkiRJpWMwol0l1YLGXULthV8Ue1i9lASa1UoiGITp3jMiSZIkSZIkSaqgDEa0u7QBoWcJ7xkp3DXiBeySJEmSJEmSpIrKYES7KwxGFo6D/LxiD+vWInQB+9QMgxFJkiRJkiRJUsVkMKLdNeoCCTVgxyb4eXqxh3VrEdoxMnPZJnLzC8qmNkmSJEmSJEmSSsFgRLuLjoHWfUPtEhynlVY3mZSEGLbn5jPn56wyKk6SJEmSJElSRRYMBiNdgg5SBQXh+YP8mLDMooNP6/7w47uhYKTvrcUaEhUVoGvzmoybv5apSzbSqWmNsq1RkiRJkiRJUoURGxtLIBBg7dq11K1bl0AgEOmSdJAIBoPk5OSwdu1aoqKiiIuLK9V8BiPas7T+oeeK6bBtAyTVKtawbi1qMW7+WqYt2cBFx7QswwIlSZIkSZIkVSTR0dE0adKE5cuXk5GREelydBBKSkqiWbNmREWV7jAsgxHtWUojqHcorPkRFn4BHX9XrGHdmofuGZmSsZFgMGgqLEmSJEmSJFUhycnJtGnThtzc3EiXooNMdHQ0MTExYfmds8GI9i6tfygYSR9b7GCkU9MaxEYHWLs5m2UbttOsdlIZFylJkiRJkiSpIomOjiY6OjrSZUh75eXr2ru0AaFn+hgo5qU2CbHRHNY4FYCpSzaUVWWSJEmSJEmSJB0QgxHtXbOjILYabF0Dq38o9rBfH6clSZIkSZIkSVJFYjCivYuJh5bHhtrpY4o9rGvz0EXt09wxIkmSJEmSJEmqYAxGtG+Fx2kt/KLYQ7q1CO0YWbB6C5nbvGRJkiRJkiRJklRxGIxo39L6h55LJ0P25mINqZMcT8s61QCYvtTjtCRJkiRJkiRJFYfBiPatVqvQV0EeLJ5Q7GFdi+4Z8TgtSZIkSZIkSVLFYTCi/Ss8TqsE94wUXsA+dYk7RiRJkiRJkiRJFYfBiPbv18FIMFisId1ahC5gn7VsEzl5BWVVmSRJkiRJkiRJJWIwov1rcQxEx8GmpbA+vVhDWtetRs2kWLLzCvjh58wyLlCSJEmSJEmSpOIxGNH+xVWDZj1D7fSxxRoSCASK7hmZluFxWpIkSZIkSZKkisFgRMVzIPeM7DxOa+oSL2CXJEmSJEmSJFUMBiMqnsJgJOMryN1erCFFF7BnbCRYzLtJJEmSJEmSJEkqSwYjKp567aF6I8jbDku+LtaQwxqnEhcdxfqtOWSs31bGBUqSJEmSJEmStH8GIyqeQADS+oXaxbxnJCE2msObpAIwNcPjtCRJkiRJkiRJkWcwouIrPE5rYfGCEYCuLXZewL7EC9glSZIkSZIkSZFnMKLia3UcBKJg7TzYtKxYQ7o1D13APsUdI5IkSZIkSZKkCsBgRMWXWBOaHBlqF3PXSNedF7AvXLuVjVtzyqoySZIkSZIkSZKKxWBEJVN4nFb6mGJ1r1UtjtZ1qwEepyVJkiRJkiRJijyDEZVMWv/Qc9GXkJ9brCFFx2kt8TgtSZIkSZIkSVJkGYyoZBp2hsRakJ0Fy6cUa0jRBewZ7hiRJEmSJEmSJEWWwYhKJioaWvcLtdOLd8/IkS1CO0a+X57Jjtz8sqpMkiRJkiRJkqT9MhhRyZXwnpEWtZOoXS2OnPwCfliRWYaFSZIkSZIkSZK0bwYjKrnCHSMrZ8KWtfvtHggE6No8dJzWVC9glyRJkiRJkiRFkMGISq56fWhweKi98ItiDSk8Tmuq94xIkiRJkiRJkiLIYEQHpoTHaRVdwL5kA8FgsKyqkiRJkiRJkiRpnwxGdGDS+oeeC8dCQcF+ux/WKJX4mCg2bstl4dqtZVycJEmSJEmSJEl7ZjCiA9OkO8RVh23rYdWs/XaPi4miU5MaQGjXiCRJkiRJkiRJkWAwogMTEwet+oTaxTxOq9vO47S8Z0SSJEmSJEmSFCkGIzpwhcdppY8tVveiYGSJwYgkSZIkSZIkKTIMRnTgWu8MRpZ9B9s37bd7l2ahYGTxuq2s25JdhoVJkiRJkiRJkrRnBiM6cDWbQ522EMyHxV/ut3uNpDja1k8GYJq7RiRJkiRJkiRJEWAwotIp3DVSzHtGujavBcDUDC9glyRJkiRJkiSVP4MRlU7agNAz/QsIBvfbvVtz7xmRJEmSJEmSJEWOwYhKp0UviEmArOWwdv5+ux/ZIrRj5IcVmezIzS/r6iRJkiRJkiRJ2oXBiEonNhGa9wq1i3GcVtNaidStHk9ufpDvl2eWcXGSJEmSJEmSJO3KYESlV3Sc1v6DkUAgUHSc1hTvGZEkSZIkSZIklTODEZVeYTCyZBLkbN1v9647g5Fp3jMiSZIkSZIkSSpnBiMqvTptILUp5OdAxqT9di+8Z2Tako0UFOz/wnZJkiRJkiRJksLFYESlFwhAWv9Qe+HY/Xbv0CiFxNhoMrfnkr52SxkXJ0mSJEmSJEnSLwxGFB4luGckNjqKTk1TAZia4XFakiRJkiRJkqTyYzCi8GjZG6JiYH06bFi83+6Fx2lNXeIF7JIkSZIkSZKk8mMwovBISIWmPULtYhynVXgBuztGJEmSJEmSJEnlyWBE4VN4z0j6/oORLs1rEgjA0g3bWLN5RxkXJkmSJEmSJElSiMGIwqf1zmBk8QTIy9ln15SEWA6pXx2Aae4akSRJkiRJkiSVE4MRhU+Dw6FaXcjZAsu+3W/3bi12Hqe1xGBEkiRJkiRJklQ+DEYUPlFRv+waSR+z3+7dmu+8gD3DC9glSZIkSZIkSeXDYEThlTYg9CzGPSOFO0Z+/DmL7Tn5ZVmVJEmSJEmSJEmAwYjCrXVfIACrZ0PWyn12bVwjkQYpCeQVBJm5bFO5lCdJkiRJkiRJqtoMRhRe1epAo86h9sIv9tk1EAjQtfCeEY/TkiRJkiRJkiSVA4MRhV/hcVoLi3GcVnMvYJckSZIkSZIklR+DEYVfUTDyBRTs++6QI1uELmCfvnQj+QXBsq5MkiRJkiRJklTFGYwo/Bp3g/hU2L4Rfp6xz67tGlQnKS6azTvyWLB6czkVKEmSJEmSJEmqqsISjDz11FO0aNGChIQEevTowXfffbfP/o899hiHHHIIiYmJNG3alGuvvZYdO3aEoxRVBNEx0Pq4UDt9zD67xkRHcUSzGoDHaUmSJEmSJEmSyl6pg5E33niD6667jrvuuovp06fTqVMnBg4cyJo1a/bY/9VXX+Xmm2/mrrvuYu7cuTz33HO88cYb3HrrraUtRRVJ4XFa+wlGALo1Dx2nNc0L2CVJkiRJkiRJZazUwcijjz7KJZdcwgUXXECHDh145plnSEpK4vnnn99j/6+//ppevXpxzjnn0KJFC377299y9tln73eXiSqZ1v1DzxXTYNu+A49uLbyAXZIkSZIkSZJUPkoVjOTk5DBt2jQGDBjwy4RRUQwYMIDJkyfvcczRRx/NtGnTioKQRYsW8dFHH3HCCSfsdZ3s7GyysrJ2+VIFl9oY6raHYAEsGr/Prkc0q0lUAJZv3M6qTI9UkyRJkiRJkiSVnVIFI+vWrSM/P5/69evv8nr9+vVZtWrVHsecc8453HvvvRxzzDHExsbSunVrjjvuuH0epfXQQw+Rmppa9NW0adPSlK3ykrZz10j62H12S46PoX3DFACmLvE4LUmSJEmSJElS2QnL5eslMX78eB588EH++c9/Mn36dN555x1Gjx7Nfffdt9cxt9xyC5mZmUVfy5YtK8eKdcB+fc9IMLjPrt2a7zxOK8PjtCRJkiRJkiRJZSemNIPr1KlDdHQ0q1ev3uX11atX06BBgz2OueOOOzj//PO5+OKLAejYsSNbt27l0ksv5bbbbiMqavesJj4+nvj4+NKUqkho1hNik2DLKlj9IzQ4bK9du7aoxQuTl7hjRJIkSZIkSZJUpkq1YyQuLo6uXbsyduwvRyUVFBQwduxYevbsuccx27Zt2y38iI6OBiC4n10FqmRiE6DFsaF2+ph9di3cMTJ35Wa2ZueVdWWSJEmSJEmSpCqq1EdpXXfddTz77LO88MILzJ07l8svv5ytW7dywQUXADBkyBBuueWWov6DBw/m6aef5vXXX2fx4sV8/vnn3HHHHQwePLgoINFBpOiekX0HI41qJNK4RiL5BUFmLttU9nVJkiRJkiRJkqqkUh2lBXDWWWexdu1a7rzzTlatWkXnzp355JNPii5kX7p06S47RG6//XYCgQC33347K1asoG7dugwePJgHHnigtKWoIiq8Z2TpN5C9BeKT99q1a/OarNi0nSkZG+iVVqecCpQkSZIkSZIkVSWBYCU8vyorK4vU1FQyMzNJSUmJdDnal2AQHu8MGzPg7NfhkOP32vXFyRnc+f6PHNumDi9d1KPcSpQkSZIkSZIkVW4lyQ1KfZSWtE+BwC+7RvZ7z0gtAGYs3UR+QaXL6yRJkiRJkiRJlYDBiMpeYTDy0+ehHSR7cUiD6lSPj2FLdh7j5q0pp+IkSZIkSZIkSVWJwYjKXotjISoWNi2BDYv22i06KsA5RzUD4MGP5pKbX1BeFUqSJEmSJEmSqgiDEZW9+GRodlSovZ/jtK7om0ad5DgWrdvKi5OXlENxkiRJkiRJkqSqxGBE5aPonpGx++xWPSGW6397CAD/N2YBG7bmlHVlkiRJkiRJkqQqxGBE5aMwGMmYCLk79tn1992a0r5hClk78nj08/nlUJwkSZIkSZIkqaowGFH5qH8oJDeA3G2wdPI+u0ZHBbhrcAcAXv12KfNWZZVHhZIkSZIkSZKkKsBgROUjEPjVcVr7vmcE4KhWtTn+sAYUBOG+D+cQDAbLuEBJkiRJkiRJUlVgMKLyk9Y/9NzPPSOFbj2hPXExUUxKX8+YuWvKsDBJkiRJkiRJUlVhMKLy0+o4CETB2rmQuXy/3ZvWSuLiY1oC8MDoOWTn5ZdxgZIkSZIkSZKkg53BiMpPUi1o3DXUXvhFsYb8qW8adavHk7F+Gy98nVF2tUmSJEmSJEmSqgSDEZWvEtwzApAcH8MNAw8B4Imx6azbkl1WlUmSJEmSJEmSqgCDEZWvwmBk4XjIzyvWkN91aULHxqlszs7j75/NL7vaJEmSJEmSJEkHPYMRla9GR0BiTcjOhBVTizUkKirAnYM7APD6lGX8+HNmWVYoSZIkSZIkSTqIGYyofEVFQ+t+oXYxj9MCOLJFLU46vCHBINz34RyCwWAZFShJkiRJkiRJOpgZjKj8te4feqaPLdGwm49vR3xMFN8s2sCnP64qg8IkSZIkSZIkSQc7gxGVv7SdwcjPM2DrumIPa1IziUt7twLggY/msiM3vyyqkyRJkiRJkiQdxAxGVP6qN4D6HYEgLBxXoqGX9WlN/ZR4lm3YzvOTFpdNfZIkSZIkSZKkg5bBiCKjcNdICe4ZAagWH8NNg9oB8NQX6azJ2hHuyiRJkiRJkiRJBzGDEUVG2oDQc+FYKCgo0dBTOzemU9MabM3J55HP5pdBcZIkSZIkSZKkg5XBiCKjaQ+IS4ata2HV9yUaGhUV4K7BHQB4c9pyfliRWRYVSpIkSZIkSZIOQgYjioyYOGjZO9ReOLbEw7s0q8mpnRsRDMI9H/xIMBgMc4GSJEmSJEmSpIORwYgip+iekZIHIwA3DmpHQmwUUzI2Mnr2yjAWJkmSJEmSJEk6WBmMKHJa7wxGln0LO0p+HFajGolc1qc1AA99NI8dufnhrE6SJEmSJEmSdBAyGFHk1GoJtdOgIA8WTzigKf7YuzWNUhNYsWk7z05YFOYCJUmSJEmSJEkHG4MRRVbagNAzfcwBDU+Mi+am49sB8M/xC1mdtSNclUmSJEmSJEmSDkIGI4qs1r+6Z+QAL1A/uVMjujavyfbcfB7+ZF4Yi5MkSZIkSZIkHWwMRhRZLXpBdDxkLoN1Px3QFIFAgDtP6gDAO9NXMHPZpjAWKEmSJEmSJEk6mBiMKLLiqkHzo0PtAzxOC6BT0xqc3qUxAPd+8CPBA9x9IkmSJEmSJEk6uBmMKPJKec9IoZsGtSMpLprpSzcxatbPYShMkiRJkiRJknSwMRhR5BUGI0smQe72A56mfkoCfzquNQB/+Xge23Pyw1GdJEmSJEmSJOkgYjCiyKt7CKQ0gbwdkDGpVFNdfGwrGtdIZGXmDv41YWGYCpQkSZIkSZIkHSwMRhR5gQCk9Qu1S3mcVkJsNLee0B6AZ75cyM+bDnwHiiRJkiRJkiTp4GMwooqh8DithWNLPdUJHRvQvUUtduQW8PAn80o9nyRJkiRJkiTp4GEwooqhZR8IRMO6BbBxSammCgQC3Dm4A4EAvD/zZ6Yt2RimIiVJkiRJkiRJlZ3BiCqGxBrQtHuoHYZdI4c1TuX3XZsAcO8HP1JQECz1nJIkSZIkSZKkys9gRBVHWv/QM730wQjA9QMPITk+hlnLM3lv5oqwzClJkiRJkiRJqtwMRlRxFN4zsuhLyMsp9XT1qicwvG8aAA9/Mo+t2XmlnlOSJEmSJEmSVLkZjKjiaNAJqtWDnM3w4zthmfLCY1rQrFYSq7OyeebLhWGZU5IkSZIkSZJUeRmMqOKIioKjLg+1xz8E+bmlnjI+JppbT2gHwL8nLGL5xm2lnlOSJEmSJEmSVHkZjKhi6fHH0K6RjRkw46WwTDnw0AYc1aoW2XkFPPTxvLDMKUmSJEmSJEmqnAxGVLHEVYPe14faX/4VcreXespAIMCdJx1KVABGf7+S7xZvKPWckiRJkiRJkqTKyWBEFU/XYZDaFDavhCn/CcuUHRqlcNaRzQC498MfKSgIhmVeSZIkSZIkSVLlYjCiiicmHo67OdSe+CjsyArLtH/+bVuqx8fww4os3pq+PCxzSpIkSZIkSZIqF4MRVUyH/wFqt4HtG+Cbp8MyZZ3keK7snwbA3z6dz5bsvLDMK0mSJEmSJEmqPAxGVDFFx0DfW0Ptr5+AbeG5F2TY0S1pUTuJtZuzeWpceljmlCRJkiRJkiRVHgYjqrg6nAoNOkLOZvjqH2GZMi4mittO7ADAcxMXs3T9trDMK0mSJEmSJEmqHAxGVHFFRUG/O0Lt756FrJVhmXZA+3ock1aHnPwCHvxobljmlCRJkiRJkiRVDgYjqtja/Baa9oC87TDxkbBMGQgEuOOkDkQF4JMfVzF54fqwzCtJkiRJkiRJqvgMRlSxBQLQ/85Qe9pI2JgRlmkPaVCdc3s0B+DeD+eQXxAMy7ySJEmSJEmSpIrNYEQVX4tjoHU/KMiD8Q+Hbdprf9OWlIQY5q7M4r9Tl4VtXkmSJEmSJElSxWUwosqh3+2h5/evw5p5YZmyVrU4rh7QFoBHPp1P1o7csMwrSZIkSZIkSaq4DEZUOTTuCu1OgmABjHsgbNMO6dmcVnWrsX5rDk9+kR62eSVJkiRJkiRJFZPBiCqPfrcDAZg7Cn6eEZYpY6OjuOPEDgCMmLSYjHVbwzKvJEmSJEmSJKliMhhR5VGvPRx+Zqj9xf1hm7Zvu3r0aVuX3PwgD3w0N2zzSpIkSZIkSZIqHoMRVS7H3QxRMZA+BjImhW3a209sT3RUgM/nrGZS+rqwzStJkiRJkiRJqlgMRlS51GoFXYaE2l/cB8FgWKZtU7865x/VHIB7P5hDXn5BWOaVJEmSJEmSJFUsBiOqfHrfADEJsHQypI8N27TXDGhDjaRY5q/ezGtTloVtXkmSJEmSJElSxWEwosonpREceXGoPfYeKAjP7o4aSXFcO6AtAI9+Np/MbblhmVcRsHIWbFgU6SokSZIkSZIkVUAGI6qcjrkO4pJh1fcwd1TYpj23RzPa1Etm47ZcHv/ip7DNq3K07id4tj+MOAHy8yJdjSRJkiRJkqQKxmBElVO12tDzilB73ANQkB+WaWOio7jjpA4AvPB1BgvXbgnLvCpH00ZCQS5sXgkrpkW6GkmSJEmSJEkVjMGIKq+ewyGxJqxbAN+/EbZpe7etS7929cgrCPLA6Llhm1flIC8bZr76y/cLw3cHjSRJkiRJkqSDg8GIKq+EFDjm2lB73EOhX4qHyW0nticmKsAX89bw5YK1YZtXZWzeh7B9wy/fpxuMSJIkSZIkSdqVwYgqtyMvgeQGkLkUpr8Ytmlb101m6NEtALjvwznk5ofngneVsWkjQ88jzg89f54O2zbstbskSZIkSZKkqsdgRJVbXBL0vj7UnvA3yNkWtqmv6t+GWtXiSF+zhVe+WRK2eVVG1i+ExROAAPS5Ceq2h2ABLBoX6cokSZIkSZIkVSAGI6r8ugyFGs1gy2r47t9hmzY1MZbrftMWgH+M+YlN23LCNrfKwPQXQs82v4EaTSGtf+j79C8iV5MkSZIkSZKkCsdgRJVfTBwcd2uo/dU/YEdm2Kb+w5FNadegOpnbc3lszE9hm1dhlpfzy6XrXYeFnoXByMKxEAxGpCxJkiRJkiRJFY/BiA4Oh58JdQ6BHZtg8lNhmzYmOoo7TuoAwEvfLOGn1ZvDNrfCaP5HsHVt6L6ZNgNDrzU7GmISYfNKWDMnsvVJkiRJkiRJqjAMRnRwiIqGfreF2pOfgq3rwjZ1r7Q6/KZDffILgtw3ei5Bdx9UPEWXrp8H0TGhdmwCtOgVaqePjUhZkiRJkiRJkioegxEdPNqfDA07Q86W0JFaYXTbCe2JjQ4wYcFaxs1fE9a5VUobM3ZesB6ALufv+l7agNBzocGIJEmSJEmSpBCDER08AgHof0eo/d2zkLkibFO3qFONC3u1BOD+D+eSk1cQtrlVStNfDD1b94OaLXZ9r/XOe0aWfA05W8u1LEmSJEmSJEkVk8GIDi6t+4fulsjPhgl/C+vUV/RLo05yHIvWbeWlb5aEdW4doPxcmPFyqN116O7v12kDqU0hPwcyJpVvbZIkSZIkSZIqJIMRHVx+vWtkxkuwfmHYpq6eEMuff3sIAP83ZgEbtuaEbW4doAWfwJbVUK0eHHLC7u8HApC2c9eIx2lJkiRJkiRJwmBEB6PmR0Pab6AgD8b/JaxTn9mtKe0bppC1I49HP58f1rl1AKa9EHoecS5Ex+65T+FxWuljyqcmSZIkSZIkSRWawYgOTv1uDz1nvwmr54Rt2uioAHcN7gDAq98uZd6qrLDNrRLatPSXsKPLkL33a9UHAtGwPh02egSaJEmSJEmSVNUZjOjg1KgzdDgFCMK4B8I69VGtanP8YQ0oCMJ9H84hGAyGdX4V0/SXgCC07AO1Wu29X0IqNDky1PY4LUmSJEmSJKnKMxjRwavvbRCIgnkfwvJpYZ361hPaExcTxaT09Xw+Z3VY51Yx5Of96tL1YfvvnzYg9Ew3GJEkSZIkSZKqOoMRHbzqHgKdzg61v7g3rFM3rZXExce0BOCBj+aSnZcf1vm1H+mfw+afIak2tDtx//3T+oWei76E/NyyrU2SJEmSJElShWYwooNbn5sgKhYWjYfFE8I69Z/6plG3ejxL1m/jha8zwjq39mPayNCz8zkQE7///g07Q2ItyNkMy6eUZWWSJEmSJEmSKjiDER3cajb/5ailsfdBGO8DSY6P4YaBhwDwxNh01m3JDtvc2ofMFfDTZ6F2l2HFGxMVDa137hrxOC1JkiRJkiSpSjMY0cGv9/UQkwjLv4MFn4Z16t91aULHxqlszs7j75/ND+vc2osZL0OwAFocC3XSij8urX/omT6mbOqSJEmSJEmSVCkYjOjgV70B9PhjqP3F/VBQELapo6IC3Dm4AwCvT1nGjz9nhm1u7UFBPkx/MdTuMrRkYwt3jKycBVvXhbcuSZIkSZIkSZWGwYiqhl5XQ3wKrJ4Nc94N69RHtqjFSYc3JBiEez+YQzCMx3Xpf6SPhazlkFgT2g8u2djqDaB+RyAIC8eVSXmSJEmSJEmSKj6DEVUNSbXg6CtD7S8egPy8sE5/8/HtiI+J4tvFG/j0x1VhnVu/UnjpeqdzIDah5OPTCu8Z8TgtSZIkSZIkqaoyGFHVcdTlkFQbNiyEWa+GdeomNZO4tHcrAB74aC47cvPDOr+ArJWw4JNQu2sJj9Eq1HrnPSMLvwjrkWqSJEmSJEmSKg+DEVUd8dXhmOtC7fEPQ152WKe/rE9r6qfEs2zDdv49YVFY5xYw82UI5kOznlD3kAObo9lREFsNtq6B1T+Etz5JkiRJkiRJlYLBiKqWIy+C6o1C91RMHRHWqavFx3Dz8e0A+MeYBbw3Y0VY56/SCgp+uXS967ADnycmHloeG2p7nJYkSZIkSZJUJRmMqGqJTYQ+N4baEx+BnK1hnf7Uzo0Z0rM5wSD8+c1Z3jcSLovGwaalkJAKHU4p3Vy/Pk5LkiRJkiRJUpVjMKKq54jzoGZL2LoWvn0mrFMHAgHuHnwop3dpTH5BkCtfncHEn9aGdY0qqfDS9cP/EAq3SiNtZzCy9BvI3ly6uSRJkiRJkiRVOgYjqnqiY6HvraH2pP+D7RvDOn1UVIC/nnE4gw5tQE5+AZe+OI2pGRvCukaVsnk1zP8o1D7QS9d/rXZrqNkCCnJh8cTSzydJkiRJkiSpUjEYUdV02BlQrwPsyISvnwj79DHRUfzf2Z3p07Yu23PzuWDEFH5YkRn2daqEma9AQR406Q71Dw3PnEXHaY0Nz3ySJEmSJEmSKg2DEVVNUdHQ7/ZQ+5unYcuasC8RHxPNM+d1pXuLWmzOzmPI89+Rvsajm0pkl0vXw7BbpFDagNAz3WBEkiRJkiRJqmoMRlR1HXICNO4Kudtg4qNlskRiXDTPDevG4U1S2bA1h3P/8y3LNmwrk7UOShkTYONiiE+BQ08L37wtj4WomNDc6xeGb15JkiRJkiRJFZ7BiKquQAD63RFqT30ONi0rk2WqJ8TywgXdaVs/mdVZ2Zzzn29YlbmjTNY66BRdun4mxFUL37zx1aHpUaH2wi/CN68kSZIkSZKkCs9gRFVbq+OgxbGQnwNfPlxmy9SsFsfLF/Wgee0klm3YznnPfcv6Ldlltt5BYes6mPthqN11WPjnT+sXenqcliRJkiRJklSlGIyoagsEoP+dofbMV2FdepktVS8lgZcv6kHD1ATS12xhyPPfkbUjt8zWq/RmvgoFudCoCzToGP75C+8ZWTwB8nLCP78kSZIkSZKkCslgRGraHdoOgmA+jH+wbJeqlcTLF/egdrU4fvw5iwtHTGFbTl6ZrlkpBYO/HKNVFrtFAOp3hGp1IXcrLPumbNaQJEmSJEmSVOEYjEgA/W4PPX94G1bNLtOlWtdN5qWLepCSEMPUJRv540vTyM7LL9M1K52Mr2DDQohLhsPOKJs1oqKgtcdpSZIkSZIkSVWNwYgEoaOaCn8B/8UDZb5ch0YpjLigO0lx0Uz8aR1XvjqD3PyCMl+30pj+QujZ8XcQn1x26xQep7XQYESSJEmSJEmqKgxGpELH3QqBaFjwMSz7rsyX69q8Js8O6UZcTBSfzVnNDW/OoqAgWObrVnjbNsCc90PtsjpGq1CrvqHnqtmweXXZriVJkiRJkiSpQjAYkQrVSYPO54TaY+8N3XNRxnql1eGf53QhJirAezN/5o73fyBYDutWaLNeg/wcaNgJGh1Rtmsl1w2tA7Dwi7JdS5IkSZIkSVKFEJZg5KmnnqJFixYkJCTQo0cPvvtu339tv2nTJoYPH07Dhg2Jj4+nbdu2fPTRR+EoRSqdPjdBdBxkTIRF48tlyQEd6vPoWZ0JBOCVb5fyl4/nVd1wJBiEaTuP0Srr3SKFPE5LkiRJkiRJqlJKHYy88cYbXHfdddx1111Mnz6dTp06MXDgQNasWbPH/jk5OfzmN78hIyODt956i/nz5/Pss8/SuHHj0pYilV6NptDtwlD7i/vKZdcIwMmdGvHQaR0B+NeERTw1Lr1c1q1wln4D6+ZDbBIc9rvyWbN1/9Bz4RdQ4D0vkiRJkiRJ0sGu1MHIo48+yiWXXMIFF1xAhw4deOaZZ0hKSuL555/fY//nn3+eDRs28N5779GrVy9atGhBnz596NSpU2lLkcLj2D+HfjG/YhrML7+dTH/o3ozbT2wPwCOfLeD5rxaX29oVxrSRoedhZ0BCSvms2bQ7xFWHbeth5czyWVOSJEmSJElSxJQqGMnJyWHatGkMGDDglwmjohgwYACTJ0/e45hRo0bRs2dPhg8fTv369TnssMN48MEHyc/P3+s62dnZZGVl7fIllZnkenDU5aH2F/dDwd7/bYbbxce24poBbQC498M5/HfKsnJbO+K2b4Q574XaXS8ov3WjY6FVn1Db47QkSZIkSZKkg16pgpF169aRn59P/fr1d3m9fv36rFq1ao9jFi1axFtvvUV+fj4fffQRd9xxB3//+9+5//7797rOQw89RGpqatFX06ZNS1O2tH9HXwnxqbBmDvzwTrkufXX/Nlx8TEsAbn7nez78/udyXT9ivv8v5O2A+odB4y7lu3brfqFnusGIJEmSJEmSdLALy+XrJVFQUEC9evX497//TdeuXTnrrLO47bbbeOaZZ/Y65pZbbiEzM7Poa9myKvRX9IqMxJrQ66pQe9wDkJ9bbksHAgFuO7E9Z3dvSkEQrnl9JuPm7fnOnoNGMPjLMVpdh0EgUL7rp+28Z2TZd7Ajs3zXliRJkiRJklSuShWM1KlTh+joaFavXr3L66tXr6ZBgwZ7HNOwYUPatm1LdHR00Wvt27dn1apV5OTk7HFMfHw8KSkpu3xJZa7HZVCtLmxcDDNeLtelA4EA95/akZM7NSKvIMhlL09j8sL15VpDuVo+JbQ7JyYROv6+/Nev2QJqp0EwHxZPKP/1JUmSJEmSJJWbUgUjcXFxdO3albFjfzl+pqCggLFjx9KzZ889junVqxfp6ekUFBQUvbZgwQIaNmxIXFxcacqRwis+GY69PtT+8q+Qu6Ncl4+OCvD3MzsxoH09svMKuPiFKcxYurFcayg3014IPQ87HRJrRKaG1jt3jaSPicz6kiRJkiRJkspFqY/Suu6663j22Wd54YUXmDt3Lpdffjlbt27lggtClycPGTKEW265paj/5ZdfzoYNG7j66qtZsGABo0eP5sEHH2T48OGlLUUKv24XQEoT2PwzTH2u3JePjY7iyXO6cHTr2mzNyWfYiCnMXZlV7nWUqR2Z8MPboXaXoZGro/A4rfQvQkd7SZIkSZIkSToolToYOeuss3jkkUe488476dy5MzNnzuSTTz4pupB96dKlrFy5sqh/06ZN+fTTT5kyZQqHH344V111FVdffTU333xzaUuRwi8mHo67KdSe+HfI3lzuJSTERvPskG50aVaDzO25nP/cdyxau6Xc6ygz3/8X8rZD3fbQtHvk6mhxDETHQeZSWJ8euTokSZIkSZIklalAMFj5/jQ6KyuL1NRUMjMzvW9EZS8/D57qDhsWQt/boM+NESkjc3suZ//7G+aszKJRagJvXn40jWskRqSWsAkG4ZljYfVsGPQwHHVZZOt54WRY/CUM+gscdXlka5EkSZIkSZJUbCXJDUq9Y0Q66EXHQN9bQ+2vn4BtGyJSRmpiLC9e1J1Wdavxc+YOzn32G9ZsLt97T8Lu5+mhUCQ6Hg4/M9LV/Oo4rbH77idJkiRJkiSp0jIYkYrj0NOh/mGQnQWT/i9iZdRJjueVi3vQuEYiGeu3MeS579i0LSdi9ZTatJGh56GnQlKtSFYSUngBe8ZXkFvJQydJkiRJkiRJe2QwIhVHVBT0uyPU/vZfsHlVxEppmJrIq5f0oF71eOat2szQ579jS3ZexOo5YNmbYfbOS9e7DotoKUXqHwrJDUJ3niydHOlqJEmSJEmSJJUBgxGpuNoOhCbdQ780n/BIREtpXrsaL1/cgxpJscxanslFI6ewIzc/ojWV2Oy3IHcr1GkLzXpGupqQQOBXx2mNiWwtkiRJkiRJksqEwYhUXIEA9N+5a2TaSNi4JKLltK1fnRcv7E5yfAzfLt7A5S9PIyevIKI1lUjhMVpdhoZ+thVF636h58IvIluHJEmSJEmSpDJhMCKVRMve0Oo4KMiFLx+OdDUc3qQGzw87koTYKMbNX8u1b8wkvyAY6bL27+cZsHImRMdBp7MjXc2uWvcDArBmDmT9HOlqJEmSJEmSJIWZwYhUUv3uDD1nvQZr50e2FqB7y1r86/xuxEYHGD17JTe//T0FFT0cmfZC6Nn+ZKhWO7K1/K+kWtC4S6idPjaytUiSJEmSJEkKO4MRqaSadIVDToRgAYx7MNLVANCnbV2eOPsIogLw5rTl3PvhHILBChqOZG8J3S8C0HVoZGvZm9Y77xlZaDAiSZIkSZIkHWwMRqQD0e82IABz3oOfZ0a4mJBBhzXkb7/rBMDIrzN49PMFEa5oL358B3I2Q61W0OLYSFezZ2kDQs+F46Cgkl1qL0mSJEmSJGmfDEakA1H/UOj4+1D7i/sjW8uvnNG1CfedcigAT3yRzjNfLoxwRXtQeOl612EV69L1X2vcFeJTYccmWDE90tVIkiRJkiRJCiODEelAHXczRMVA+uewZHKkqylyfs8W3DSoHQB/+XgeL3+zJMIV/cqq2bBiGkTFQqdzIl3N3kXHQKs+obbHaUmSJEmSJEkHFYMR6UDVbg1HnBdqj70XKtCdHpcf15rhfVsDcMf7P/DujOURrminwkvX250IyXUjW8v+FB6n5QXskiRJkiRJ0kHFYEQqjd43QnQ8LP26wu0suP63hzC0Z3OCQbj+ze/55IdVkS0oZxt8/0ao3XVYREsplrSdF7CvmArbN0a2FkmSJEmSJElhYzAilUZqY+h+Sag99r4KtWskEAhw1+BDOaNLE/ILglz12gwmLFgbuYJ+fBeys6BmC2jZJ3J1FFdqE6hzCAQLYNH4SFcjSZIkSZIkKUwMRqTSOuZaiEuGlTNh7geRrmYXUVEBHj6jI8cf1oCc/AIufWkqUzI2RKaY6TuP0eoyBKIqyf/o8TgtSZIkSZIk6aBTSX47KVVg1erAUX8Ktb+4HwryI1vP/4iJjuL//nAEfdrWZUduAReOmMIPKzLLt4jVc2DZt6HL6jufV75rl0Zav9AzfWyF2g0kSZIkSZIk6cAZjEjhcPQVkFAD1s2H7/8b6Wp2ExcTxTPndaV7y1pszs7j/Oe+5afVm8uvgMLdIoccD9Xrl9+6pdW8F8QkwOafYe28SFcjSZIkSZIkKQwMRqRwSEiFY64Jtcc/CHk5ES1nTxLjonluaDcOb5LKxm25nPfctyxdv63sF87dDrNeC7W7DCv79cIpNhGaHx1qe5yWJEmSJEmSdFAwGJHCpfulkFwfNi39ZYdEBVM9IZYXLuhO2/rJrM7K5pz/fMOqzB1lu+icUbAjE1KbQeu+ZbtWWSi6Z2RMZOuQJEmSJEmSFBYGI1K4xFWD3jeE2p/eBovGR7ScvalZLY6XL+pBi9pJLN+4nXP/8w3rt2SX3YLTRoaeXYZAVHTZrVNWWvcPPZd8DTnlsMNGkiRJkiRJUpkyGJHCqesFcMiJkJ8Nr50NSyZHuqI9qpeSwMsX96BhagIL125lyPPfkbk9N/wLrZ0PS7+GQDQccW745y8PdQ+BlMah/6ZLvo50NZIkSZIkSZJKyWBECqfoGPj9iNDxS7nb4JXfw/Jpka5qj5rUTOKVi3tQJzmOH3/O4sKRU9iWkxfeRaa/GHq2HQgpjcI7d3kJBCBt566Rhd4zIkmSJEmSJFV2BiNSuMXEw1kvQ4tjIWczvHwarPw+0lXtUau6ybx4YQ9SEmKYtmQjl744jR25+eGZPHcHzHw11O46LDxzRkrhcVreMyJJkiRJkiRVegYjUlmITYSzX4cm3UMXj790KqyZF+mq9qhDoxRGXtidpLhovkpfx5WvzSA3v6D0E8/7ELZvCB1DVXiBeWXVqg8EomDdAti0LNLVSJIkSZIkSSoFgxGprMQnw3lvQcPOsG09vHgKrF8Y6ar2qEuzmvxnSDfiYqL4fM5qrn9zFvkFwdJNWnjp+hHnV85L138tsSY0OTLU9jgtSZIkSZIkqVIzGJHKUkIqnP8u1DsUtqyCF06GTUsjXdUeHZ1Wh6fP7UJMVID3Z/7M0Oe/Y/2W7AObbF06ZEwM7bI44rzwFhopHqclSZIkSZIkHRQMRqSyllQLhrwHtdtA1nJ4YTBk/Rzpqvaof/v6PHnOESTGho7VOvHxr5i2ZGPJJ5r+QuiZ9huo0TS8RUZK4QXsiyZAfpgvqZckSZIkSZJUbgxGpPKQXA+GjoKaLWBjRuhYrS1rI13VHg06rCHvX9GLVnWrsSprB2f9azIjJi0mGCzm0Vp5OQfPpeu/1uiI0JFa2ZmwYmqkq5EkSZIkSZJ0gAxGpPKS0giGjIKUJqFLvF86FbZtiHRVe9S2fnVGXXEMJx7ekLyCIPd8MIcrXpvBluxi7JSYPxq2rYPqDaHNb8u+2PISFQ2t+obaHqclSZIkSZIkVVoGI1J5qtk8tHMkuT6s/gFePh12ZEa6qj1Kjo/hybOP4M6TOhATFWD09ys55cmvWLB6874HFl26fh5Ex5R5neWq8DitdC9glyRJkiRJkiorgxGpvNVuHdo5klQbfp4Br/wesrdEuqo9CgQCXHhMS97441E0SElg4dqtnPLkJN6fuWLPAzYsgkXjgQAccX55llo+Ci9g/3kGbF0f2VokSZIkSZIkHRCDESkS6rWD89+DhFRY9i289gfI3R7pqvaqa/NafHjVMfRKq8323Hyufn0md7z3A9l5+bt2nP5i6Nm6X2h3zMEmpSHUOxQIwqJxka5GkiRJkiRJ0gEwGJEipeHhcN67EFcdMibCG+dBXnakq9qrOsnxvHhhD67slwbAS98s4cx/fcOKTTsDnfxcmPFKqH0wXbr+v9L6hZ4epyVJkiRJkiRVSgYjUiQ16Qrn/hdiEkMXer91YShgqKCiowL8+beH8PywbqQmxjJr2SZOenwiXy5YC/M/hq1roFo9OOT4SJdadtIGhJ4Lx0IwGNlaJEmSJEmSJJWYwYgUac2PhrNfg+h4mPchvPtHKMjf/7gI6teuPh9eeQwdG6eycVsuw0Z8R8ZnT4XePOJciI6NbIFlqVlPiE2CLath9Q+RrkaSJEmSJElSCRmMSBVB675w1ksQFQs/vA2jroSCgkhXtU9NayXx5mU9OadHMxqzlmYbvwVgU7uzI1xZGYuJhxbHhNoepyVJkiRJkiRVOgYjUkXRdiD87jkIRMHMV+DjGyr8UU0JsdE8eFpHnj1sDlGBIBPzD+OEl5YzY+nGSJdWtlr3Dz0XGoxIkiRJkiRJlY3BiFSRdDgFTn0GCMCU/8Bnt1f4cIT8PNqvfB+AsdVO4OfMHZz5r8m8ODmDYEWv/UAV3jOyZDJkb4lsLZIkSZIkSZJKxGBEqmg6nQWD/y/UnvwkjHswsvXsz0+fwpZVkFSHP195DYMObUBufpA73/+Rq1+fydbsvEhXGH61W0ONZlCQCxlfRboaSZIkSZIkSSVgMCJVRF2HwvF/DbUn/BUm/j2y9ezLtBdCz87nUL1aNZ4+rwu3n9ie6KgAo2b9zClPTSJ9zebI1hhugYDHaUmSJEmSJEmVlMGIVFH1+CMMuCfUHnsvfPN0ZOvZk03LIP3zULvLUAACgQAXH9uK1y89inrV40lfs4WTn5zEB7N+jmChZaDwOC0vYJckSZIkSZIqFYMRqSI75hroc3Oo/cnNMHVERMvZzYyXIVgALY6FOmm7vHVki1qMvupYeraqzbacfK58bQZ3j/qRnLyCCBUbZi17Q1QMbFgIGxZHuhpJkiRJkiRJxWQwIlV0x90Mva4OtT+8Fma+Ftl6ChXkw4yXQu2uw/bYpW71eF66qDt/Oq41ACO/zuCsf09mZeb2ciqyDCWkQJPuobbHaUmSJEmSJEmVhsGIVNEFAqEjtbpfCgTh/T/Bj+9GuipIHwNZKyCxFrQ7aa/dYqKjuHFQO/4zpBvVE2KYsXQTJz7+FRN/WluOxZaRtJ33jKR/Edk6JEmSJEmSJBWbwYhUGQQCMOhh6DIkdHTV2xfD/I8jW9O0kaFn53MgNmG/3Qd0qM/oK4/l0EYpbNiaw5Dnv+PxsT9RUBAs2zrLUmEwsvhLyMuJbC2SJEmSJEmSisVgRKosoqLgpMeg45lQkAf/HRK5i7+zfoYFn4TaXYYUe1iz2km8ffnR/OHIpgSD8OjnC7jwhSls3FpJQ4UGnSCpDuRsgeXfRboaSZIkSZIkScVgMCJVJlHRcOrT0P5kyM+B18+FjK/Kv44Zr4R2rjQ7GuoeUqKhCbHR/OWMw/nb7w4nPiaK8fPXctITX/H98k1lU2tZioqC1v1C7UiFVJIkSZIkSZJKxGBEqmyiY+CM56DNbyFvO7x6FiybUn7rF+TD9BdD7b1cul4cv+/WlHf/1IvmtZNYsWk7v3t6Mi9/s4RgsJIdrVV0z8iYyNYhSZIkSZIkqVgMRqTKKCYOznwJWvYJHeP08hnw88zyWXvhOMhcCgmp0OHkUk3VoVEKo644ht92qE9OfgG3v/cD1/13Ftty8sJUbDko3DGy6nvYsiaytUiSJEmSJEnaL4MRqbKKTYCzX4NmPSE7E146DVbPKft1p40IPTudDbGJpZ4uNTGWf53flVuOb0d0VIB3Z6zg1KcmsXDtllLPXS6S60GDw0PtheMiW4skSZIkSZKk/TIYkSqzuGpwzn+hURfYvgFePAXWpZfdeptX/erS9aFhmzYQCPDHPq159eIe1K0ez4LVWzjlyUl8NHtl2NYoUx6nJUmSJEmSJFUaBiNSZZeQAue9DfU7wtY18OLJsDGjbNaa+QoU5EHTHlC/Q9in79GqNqOvPIbuLWuxJTuPP70ynfs+nENufkHY1wqr1juDkYVfQEEFr1WSJEmSJEmq4gxGpINBUi0Y8h7UOQSyVsALJ0PmivCuUVAA014ItcO4W+R/1UtJ4NWLe/DHPq0AeO6rxZz9729YlbmjzNYstaY9IC4Ztq0L3TUiSZIkSZIkqcIyGJEOFtXqwNBRUKsVbFoS2jmyeXX45l/8ZWje+FQ49LTwzbsHMdFR3HJ8e/51fleqx8cwdclGTnpiIl+nryvTdQ9YTBy07B1qe5yWJEmSJEmSVKEZjEgHk+oNYMgoSG0G69PhpVNh6/rwzD1tZOh5+JkQlxSeOfdj4KEN+ODKY2jfMIV1W3I477lveWpcOgUFwXJZv0Ra9ws9F34R2TokSZIkSZIk7ZPBiHSwqdEUhr4P1RvCmjmhcGT7ptLNuWUNzPsw1O5adsdo7UmLOtV4909H8/uuTSgIwt8+nc8lL04lc1tuudaxX4UXsC/7FnZkRbYWSZIkSZIkSXtlMCIdjGq1Cu0cSaoTuvPild9B9uYDn2/mq6FL1xt3hQYdw1dnMSXERvO333fi4TM6EhcTxdh5azjxiYnMXp5Z7rXsVa1Woa+CPMiYGOlqJEmSJEmSJO2FwYh0sKrbFoa8Dwk1YPkUePUPkLOt5PMEgzB956XrXYeFs8ISO+vIZrxz+dE0q5XE8o3bOeOZr3ntu6UEgxXkaK3WO3eNeM+IJEmSJEmSVGEZjEgHswaHwfnvQnwKLPkK3jgX8rJLNkfGRNiwCOKqw6Gnl02dJXBY41Q+uOIYBrSvT05eAbe8M5vr3/ye7Tn5kS7tl+O00seGAiVJkiRJkiRJFY7BiHSwa9wFzn0LYquFLgZ/cxjkl+B+jsJL1zv+DuKTy6LCEktNiuXf53flpkHtiArA29OXc9o/J7F43dbIFtbiWIiKhU1LQmGSJEmSJEmSpArHYESqCpr1gLNfg5gEmP8RvH0x5Oftf9zW9TD3g1A7wsdo/a+oqACXH9ealy/uQZ3kOOat2szJT3zFJz+sjFxR8cnQ7KhQ2+O0JEmSJEmSpArJYESqKlr1gbNeCe1omPMejLoCCgr2PWbWa5CfAw07Q6PO5VBkyR3dug6jrzqWI1vUZHN2Hpe9PJ0HRs8hN38/n62s/Po4LUmSJEmSJEkVjsGIVJW0GQC/HwmB6FDoMfq6vd+FEQz+coxW16HlVeEBqZ+SwKuXHMUlx7YE4NmJi/nDv7+JzNFaaQNCz4yJJb/PRZIkSZIkSVKZMxiRqpr2J8Hp/wYCMG0EfHLLnsORJV/D+p9Cd5Mc9rtyL7OkYqOjuO3EDjxzXheS42OYtmQjx//fBEZOWkxBQTlehF7/MEiuD7nbYOnk8ltXkiRJkiRJUrEYjEhVUcffwSlPhdrfPg1f3Ld7n+kv7Ox7BiSklF9tpTTosIZ8cs2xHN26NjtyC7j7gzmc859vWLZhW/kUEAhA636htsdpSZIkSZIkSRWOwYhUVR1xLpz491B74t/hy7/98t62DfDje6F2l2HlXVmpNamZxMsX9eC+Uw4lMTaabxZtYOBjE3j5myUE93Z0WDgVHqe18IuyX0uSJEmSJElSiRiMSFXZkRfDbx8ItcfdD18/EWp//wbkZ0P9jtC4S+TqK4WoqADn92zBJ9ccS/eWtdiWk8/t7/3A+c99x4pN28t28VZ9gQCs/gGyVpbtWpIkSZIkSZJKxGBEquqOvgL63h5qf3Y7fPcsTNt5jFbXoaGjoSqx5rWr8folR3HnSR1IiI3iq/R1DPzHBN6YsrTsdo9Uqw2NOofa7hqRJEmSJEmSKhSDEUnQ+3o45rpQ+6PrYe1ciEmEw8+MbF1hEhUV4MJjWvLRVcfSpVkNtmTncdPbs7lg5BRWZe4om0WLjtPynhFJkiRJkiSpIjEYkRTaFdL/TjjqT7+8dtjpkJAauZrKQKu6ybx52dHcekI74mKiGD9/Lb/5x5e8PW15+HePtO4fei78Agrywzu3JEmSJEmSpANmMCIpJBCAgQ9CzysguQEcfWWkKyoT0VEBLu3dmo+uOoZOTVLZvCOPP785i0tenMaazWHcPdKkG8SnwPaN8PPM8M1bWeTnQvbmSFchSZIkSZIk7cZgRNIvAgEY+ABcPx/qtY90NWUqrV513r78aG4YeAix0QHGzF3Nb/8xgfdnrgjP7pHoWGjVJ9SuasdpbcyAJ7rAPw6DVbMjXY0kSZIkSZK0C4MRSVVWTHQUw/um8cGVx3BooxQ2bcvl6tdn8qdXprNuS3bpFyg8Tit9TOnnqiwyl8MLJ8OmpbBjE7x2NmxZG+mqJEmSJEmSpCIGI5KqvHYNUnhveC+uHdCWmKgAH/+wit/+YwIfzV5ZuonTdgYjy6fC9k2lrrPC27xqZyiyBGq1glqtIXMZ/HcI5OVEujpJkiRJkiQJMBiRJABio6O4ekAb3hvei3YNqrNhaw5/emU6V742g41bD/CX+jWaQe02EMyHxV+Gt+CKZus6ePEU2LAw9LmHfgBnvx66Z2Xp1/DRnyHcF9xLkiRJkiRJB8BgRJJ+5bDGqYy64hiu7JdGdFSAD2b9zG/+MYHPflx1YBOmDQg90w/ie0a2bYAXT4W18yClcSgUSW0CddvC756HQBRMfxG+/VekK5UkSZIkSZIMRiTpf8XFRPHn3x7CO5cfTZt6yazbks2lL03jujdmkrktt2STFR6nlT724NwxsSMTXj4dVs+G5PowZBTUbPHL+21+A7+5N9T+9BZY+EVEypQkSZIkSZIKGYxI0l50alqDD648hsv6tCYqAO/MWMFvH/uScfPWFH+S5r0gOh6ylsO6BWVXbCRkb4FXfg8/z4Ck2qFQpE7a7v16XgGdz4VgAbw5DNall3upkiRJkiRJUiGDEUnah4TYaG4+vh1vXnY0repUY3VWNheMnMKNb80ia0cxdo/EJUHzo0Ptg+k4rZxt8NofYNm3kFADhrwP9drtuW8gACf9A5p0D+0wee0PVeMyekmSJEmSJFVIBiOSVAxdm9fko6uP5eJjWhIIwH+nLmfQPyYw8ae1+x9cdJzWmLItsrzk7oA3zoWMiaHL1c9/Bxp03PeYmHg46+XQHSTrf4K3L4KC/PKpV5IkSZIkSfoVgxFJKqaE2GhuP6kDb1zak+a1k/g5cwfnP/cdt747my3ZeXsf2HpnMLJkEuRuL59iy0peDrw5NHRXSGw1OPctaNy1eGOr14ezX4OYxFBI9PmdZVurJEmSJEmStAcGI5JUQt1b1uLjq49laM/mALz67VIGPTaBrxeu2/OAeu2heiPI2wFLvi7HSsMsPy+002PBJxCTAOe8Ac16lGyOhp3gtKdD7clPwsxXw1+nJEmSJEmStA8GI5J0AJLiYrjnlMN49ZIeNKmZyPKN2znn2W+56/0f2JbzP7tHAgFI6xdqV9Z7Rgry4b3LYO4oiI6DP7wKLY89sLkOPQ363BRqf3A1LP02fHVKkiRJkiRJ+2EwIkmlcHTrOnxyTW/O7dEMgBcmL+H4/5vId4s37Nqx8DithZUwGCkogFFXwew3ISoGznzxl3tTDlSfm6HdSZCfA2+cB5nLw1OrJEmSJEmStB8GI5JUSsnxMTxwWkdeuqg7jVITWLJ+G2f9ezL3fTiHHbk7LxhvdRwEomDtvMoVAgSD8NH1MPNlCETD756HQ44v/bxRUXDav6D+YbB1Dbx2NuRsLf28kiRJkiRJ0n4YjEhSmBzbpi6fXNubM7s1IRiE575azAn/N5HpSzdCUq1fLimvLMdpBYPw6a0w9TkgEAoyOpwSvvnjk0OXsSfVgVXfw3t/Cq0pSZIkSZIklSGDEUkKo5SEWP76u06MGHYk9VPiWbRuK797+mse+ngueS37hjpVhuO0gkEYew9888/Q96c8CYf/Pvzr1GgGZ70MUbEw5z2Y8LfwryFJkiRJkiT9isGIJJWBvu3q8dk1fTi9S2MKgvCvLxdx3bQ6oTcXjYf8vH2Oj7gv/wpf/SPUPvHvcMR5ZbdW855w0qOh9rgHYM6osltLkiRJkiRJVZ7BiCSVkdSkWB49szP/Pr8rdZLj+XBDIzYFq8GOTHKWfhfp8vbuq8dg/IOh9sAH4ciLy37NLkOgx+Wh9rt/hFWzy35NSZIkSZIkVUkGI5JUxn57aAM+v7Y3J3ZqwlcFhwHw39df4IcVmRGubA++eQbG3BVq978Teg4vv7V/ez+06gu520KXsW9ZW35rS5IkSZIkqcowGJGkclCzWhxPnH0EzbqfDMCh26dy6lOTeGzMAnLzCyJc3U5TR8AnN4XafW6CY/9cvutHx8DvR0Ct1pC5DP57PuTllG8NkiRJkiRJOugZjEhSOTq892kAdIpaRHJBFo+N+YlTn5rEvFVZkS1s5qvw4bWhdq+r4bhbIlNHYk04+3WIT4Wlk2H0daGL4CVJkiRJkqQwMRiRpPKU2hjqtieKAv5z7BZqJMXy489ZDH7iK54al05eJHaPzH4L3h8OBKHHZTDgHggEyr+OQnXbwu+eh0AUzHgJvv1X5GqRJEmSJEnSQcdgRJLKW1p/ALrlzuCza3szoH19cvOD/O3T+Zzx9Nekr9lcfrXM/QDeuRSCBdB1GAz6S2RDkUJtBsBv7gu1P70FFn4R2XokSZIkSZJ00DAYkaTytjMYYeFY6iXH8+yQrjx6ZidSEmKYtTyTEx7/in99ubDsd48s+AzevACC+dDpHDjxHxUjFCnUczh0PjcU2rw5DNalR7oiSZIkSZIkHQQMRiSpvDU7GmISYfNKWDOHQCDA6V2a8Nm1fTjukLrk5BXw0MfzGPR/E/n0x1UEy+KOjYXj4I3zoCAXDj0dTnkSoirY/0oIBOCkf0DTHrAjE177A2zfFOmqJEmSJEmSVMlVsN+CSVIVEJsALXqF2ulji15ukJrAiGFH8tczDqdGUizpa7bwx5emccbTX/PtovXhWz9jErx2NuRnQ7uT4PR/Q1R0+OYPp5h4OOtlSGkC63+Cty+CgvxIVyVJkiRJkqRKzGBEkiIhbUDouXDsLi8HAgHOPLIpE27sy/C+rUmIjWL60k2c9e9vuHDkFOatyirdusumwKtnQt52SPtN6JLz6NjSzVnWkuvB2a+Gdtmkj4HP74x0RZIkSZIkSarEDEYkKRJa77xnZMnXkLN1t7dTEmK5YWA7vryhL+f0aEZ0VIAv5q3h+P+byHVvzGTZhm0lX/PnGfDyGZCzBVr2gbNeCu3IqAwadoLTng61Jz8JM16JbD2SJEmSJEmqtAxGJCkS6rSB1KaQnxM62mov6qck8OBpHfn82t6c2LEhwSC8M2MF/f/+Jfd+MIcNW3OKt96qH+Cl0yA7M3THydmvQWximD5MOTn0NOhzU6j94TWw9NuIliNJkiRJkqTKyWBEkiIhEIC0nbtG/uc4rT1pVTeZp87twqgrenF069rk5Bfw/KTF9P7rOB4f+xNbs/P2PnjtfHjxFNi+ERp3g3P/C3HVwvRBylmfm6H9yaFA6Y3zIHN5pCuSJEmSJElSJROWYOSpp56iRYsWJCQk0KNHD7777rtijXv99dcJBAKceuqp4ShDkiqXwuO00scUe8jhTWrwysU9ePHC7hzaKIUt2Xk8+vkC+vxtPC9NziA3v2DXAesXwgsnw7Z1oeOoznsb4quH8UOUs6goOO0ZqN8Rtq4JXSK/h6PIJEmSJEmSpL0pdTDyxhtvcN1113HXXXcxffp0OnXqxMCBA1mzZs0+x2VkZHD99ddz7LHHlrYESaqcWvWBQDSsT4eNS4o9LBAI0LttXT644hgeP/sImtdOYt2WbO54/0cGPPolo2b9TEFBMDTnCyfDllVQ71A4/z1IrFFmH6fcxFULXcaeVAdWfQ/v/QmCwUhXJUmSJEmSpEqi1MHIo48+yiWXXMIFF1xAhw4deOaZZ0hKSuL555/f65j8/HzOPfdc7rnnHlq1alXaEiSpckpIhabdQ+1iHKf1v6KiApzcqRGfX9uH+045lDrJ8SxZv42rXpvBBY+/x/bnToSs5VCnLQx5D5Jqhbf+SKrRDP7wCkTFwpz34Mu/RroiSZIkSZIkVRKlCkZycnKYNm0aAwYM+GXCqCgGDBjA5MmT9zru3nvvpV69elx00UXFWic7O5usrKxdviTpoFB0nFbJg5FCcTFRnN+zBV/ecBzX/aYtzeO3cOeGW0jcsoxV0Q2Z85uXILlemAquQJodBSf9I9Qe/yDMeT+y9UiSJEmSJKlSKFUwsm7dOvLz86lfv/4ur9evX59Vq1btccxXX33Fc889x7PPPlvsdR566CFSU1OLvpo2bVqasiWp4kjrF3ou+hLyc0s1VbX4GK46qhZj6zxK66iVLA/W4fStt3DCiIUMf2U6i9cdhHdxdDkfelwear97Gaz8PrL1SJIkSZIkqcILy+XrxbV582bOP/98nn32WerUqVPscbfccguZmZlFX8uWLSvDKiWpHDU8ApJqQ85mWD6ldHNt3wgvnULM+nlQvSFRQz/gqC6dCARg9OyVDHj0S257dzZrsnaEp/aK4rf3Q6u+kLsNXj8HtqyNdEWSJEmSJEmqwEoVjNSpU4fo6GhWr169y+urV6+mQYMGu/VfuHAhGRkZDB48mJiYGGJiYnjxxRcZNWoUMTExLFy4cI/rxMfHk5KSssuXJB0UoqJCv9QHSB9z4PPsyIKXTodVs6FaPRj6AY1adeDRMzvz0VXH0q9dPfILgrzy7VL6/G08f/t0Hlk7SrdDpcKIjoHfj4DaaZC5DP57PuTlRLoqSZIkSZIkVVClCkbi4uLo2rUrY8f+cjZ+QUEBY8eOpWfPnrv1b9euHbNnz2bmzJlFXyeffDJ9+/Zl5syZHpElqWpKK+U9I9lb4JXfw8/TIbEWDHkf6rQpert9wxSeH3Yk//1jT7o0q8H23HyeGreQ3n8dx7MTFrEjNz8MHyLCEmvC2a9DfCosnQyjr4NgMNJVSZIkSZIkqQIq9VFa1113Hc8++ywvvPACc+fO5fLLL2fr1q1ccMEFAAwZMoRbbrkFgISEBA477LBdvmrUqEH16tU57LDDiIuLK205klT5tN55z8jKWbB1XcnG5m6H1/4Ay76BhFQY8h7U77DHrt1b1uLty4/mX+d3Ja1eMpu25fLAR3Pp98h43py6jPyCSh4k1GkDv38eAlEw4yX49plIVyRJkiRJkqQKqNTByFlnncUjjzzCnXfeSefOnZk5cyaffPJJ0YXsS5cuZeXKlaUuVJIOWtUbQP2OQBAWjiv+uLxseP1cyJj4/+zdd3iV9f3/8efJToAEwt57CoIiS0DFhbhXHdW6R63aVltrbW1tf3Zr+7V171G31r0VBMQBOFCZsvcOSSA755zfH3dICENWkpPxfFxXrnPuce77fZIcTrhf5/N5Q1ITOP8laDvwex8SCoUYe0Ab3vnZaP5xxoG0zUhhVU4hN7z4DeP+PZn3Z68lWpdHWvQ4Oug5AvDub/Z9FI4kSZIkSZLqrVC0Dl4By83NJSMjg5ycHPuNSKof3v89fPxvOPAcOP3+3e9fWgzPXwDfvQ2JaUEo0nnHKQx3p7AkzBOfLuHuDxeSUxD0HDmkczNuHNeHIV0y9/p4tUI0Cq9eAzOeDEbRXDYBWvSIdVWSJEmSJEmqRnuTG+z3iBFJUhXoXtZnZOEEiES+f99wKbx0WRCKJKQEvTX2IRQBSEmM54rDujP5V2O46ojupCTG8fnSTfzgvk+57PHpzFuzeZ+OG1OhEJz4L+g4DApz4JmzoSA71lVJkiRJkiSpljAYkaTaoNNwSGwEeetg7cxd7xcJwytXwexXIT4Jzn4Kuh2+36fPSE3kxuP6MOmGMZw7tBPxcSE+mLOO4/49mV88/zUrNuXv9zlqVEIynP0kpHeAjQvgxUuC750kSZIkSZIaPIMRSaoNEpKh6+jg/oIPdr5PJAKv/wy+fR7iEuAHj0HPo6u0jNbpKfz19AG8d91hjOvfhmgU/vflCo68fRJ/emM2m/KKq/R81apxKzj36WCqsYXjg+nKJEmSJEmS1OAZjEhSbbHtdFrbi0bh7Rvgq/9CKA7OeAj6nFB9pbRszL3nD+aVq0cyoltzisMRHpqymMP+8SF3TZhPfnFptZ27SrUdCKfeG9z/9C746qnY1iNJkiRJkqSYMxiRpNqiR1kwsuwzKNqmt0c0Cu/dDNMfAkJw6n1wwGk1UtKgjk15+vJhPH7JUPq1TWdzUSm3v/cdh982kf9+tpSS8G76odQGB5wKh/86uP/Gz2HZ1FhWI0mSJEmSpBgzGJGk2qJ5d2jWBSIlsPijivUT/hSMdgA46d8w8OwaLSsUCnF4r5a8ce0o/n3OIDpmprJ+cxG/e2Umx/xrEm98s4pIJFqjNe21w2+EvidDuBieOw+yl8e6IkmSJEmSJMWIwYgk1Sbl02mND24n3QYf3R7cP/52GHxhbOoC4uJCnDKoPeOvP4I/nnwALRonsWRjPtc8/RWn3P0xU+ZviFltuxUXB6fdB20GQN56ePZcKM6LdVWSJEmSJEmKAYMRSapNepQ1U18wHj7+D3z4p2D52D/B0MtjV9c2khLiuPDQLky8YQzXHd2LRknxfLsyh/Mfnsr5D03l2xU5sS5x55IawTnPQFoLWPMtvHJV0NBekiRJkiRJDUooGo3W8vlPdpSbm0tGRgY5OTmkp6fHuhxJqjpFm+HvXSCyTXPzI2+Gw26IWUm7s2FLEXdNWMBTU5dSEg7eUk48sC3XHdOL7i0bx7i6nVj2GTx2YjBl2RG/gSNujHVFkiRJkiRJ2k97kxsYjEhSbfPoCbB0SnB/9C/hqN/Ftp49tDwrn3+9/x2vzFjJ1neW3q2bcFTfVhzVtzWDOjYlPi4U2yK3+vK/8No1wf2znoB+p8S2HkmSJEmSJO0XgxFJqsu+ehJevRpG/gyO/iOEakmYsIdmr8rln+/NY+J36wlv05S9eaMkxvRpxVF9WjG6V0saJyfEsErgnZvgs3sgMQ0ueRfaHhjbeiRJkiRJkrTPDEYkqa4r2gLJtXAaqr2QnV/MxHnrGT93HRPnrWNzYcX0YEnxcQzrlsnRfVtzVN9WdGiWVvMFhkvh6bOCRvcZHeHyD6Fxy5qvQ5IkSZIkSfvNYESSVKuUhCNMX5LF+DnrGD9nLUs25lfa3qdNxZRbAzvU4JRbBdnw0FGwcQF0HA4XvgYJyTVzbkmSJEmSJFUZgxFJUq0VjUZZuD6P8XPWMn7OOj5fmsU2M26VT7l1dN9WjO7ZkkbVPeXWhvnw4FFQlAMHnQ8n31Xnpi+TJEmSJElq6AxGJEl1xqa8YiZ9t54P5qxl0nfrd5hya3j35hxdNpqkfdPU6iliwQfw1A8gGoGxf4URP6me80iSJEmSJKlaGIxIkuqkknCE6Yuz+GDOOsbPXcvSnUy5dXTf1hzZtxWDOjQlriqn3Pr0bnj3NxCKg/NehB5HVd2xJUmSJEmSVK0MRiRJdV4w5dYWPpizjgk7mXKrReMkxvQORpKM7tli/6fcikbh1WtgxpOQkgGXTYAWPfbvmJIkSZIkSaoRBiOSpHpnU14xE79bxwdz1jF53no2F20z5VZCHCO6BVNuHbk/U26VFsHjJ8HyqdC8B1w2HlKbVs0TkCRJkiRJUrUxGJEk1WvFpRGmL8nig7IG7suydj7l1lF9WzFwb6fc2rIOHhgDuSug7UD4wWOQ2a1qn4AkSZIkSZKqlMGIJKnB2HbKrfFz1vLF0k3bTbmVzJF9WpZPuZWWtAdTbq3+Bp44BQqyIDkdTrkL+p1SfU9CkiRJkiRJ+8VgRJLUYGXlFTNx3jrGz1nHpO/Ws2W7KbcO7d6co/q25qg+rWj3fVNu5ayAFy8JptUCGPZjOOZWSEiq5mcgSZIkSZKkvWUwIkkSwZRb0xZnMX7uzqfc6ts2naP7Bg3cD2yfseOUW+ESGP//4JP/BMvtDg6m1mrWuWaegCRJkiRJkvaIwYgkSduJRqMsWFcx5daXyypPudWySTJH9m7FUX1bMWr7KbfmvQMvXwmF2ZCSAafeC31OqPHnIEmSJEmSpJ0zGJEkaTey8or5cO46xs9dy+TvNux0yq0zDu7AuP5tSIiPg+zl8OLFsGJ6sNPwq+HoPzi1liRJkiRJUi1gMCJJ0l7YOuXWB3PWMn7uWpZnFZRv69AslUtHdeWsQzrSKD4C4/8In94VbGx/SDC1VtOOsSlckiRJkiRJgMGIJEn7LBqNMn/dFt74ZjVPfraUrLxiADJSE/nR8M5ccGhnWq0cD69cBYU5kNIUTrsfeh8X28IlSZIkSZIaMIMRSZKqQEFxmBe/XMFDHy1i6cagcXtSQhynH9SeHw9MoMuEq2HVl8HOh/4Ujvo9xCfGsGJJkiRJkqSGyWBEkqQqFI5EeX/2Gu6fvIivlmWXrx/bpxl/TH2ONnMeC1Z0HAZnPgIZHWJSpyRJkiRJUkNlMCJJUjWIRqN8sXQT909exAdz1rL1HfSqVjO5Pv8/JJZugdRMOP0B6HlMbIuVJEmSJElqQAxGJEmqZgvXb+Ghjxbxvy9XUlwaoVNoLQ+m3knvyKJgh1HXwZibIT4htoVKkiRJkiQ1AAYjkiTVkPWbi3ji0yX897Ol5Ofn89uEJ7kw4X0ASjoMJ/GsRyG9XYyrlCRJkiRJqt8MRiRJqmH5xaW88PkKHpqyiAOzP+RviQ/SJFTAloSmbDn+HtocfEKsS5QkSZIkSaq3DEYkSYqR0nCEd2at4fUPP+KnG//MAXFLiURDvJ15Hm1O/iODu7aIdYmSJEmSJEn1jsGIJEkxFo1GmbZgNfmv3sCYLW8A8Gm4Hw+1/i1njTmEY/q2Ji4uFOMqJUmSJEmS6geDEUmSapHVU/5L5oQbSI4UsD6azs9KrmF15jAuG92VMw7uQEpifKxLlCRJkiRJqtMMRiRJqm02zKf02R+RsGEOEUL8u+R07gyfRrNGKVx4aBd+NLwzzRolxbpKSZIkSZKkOslgRJKk2qg4H97+FXz1XwCmxx3IVflXsYEMUhLjOOuQjlw2qhudmqfFuFBJkiRJkqS6xWBEkqTa7Otn4Y3roCSfwuQW/CHpep5d3wWAuBAc178NVxzWnUEdm8a0TEmSJEmSpLrCYESSpNpu/Tx4/kJYP4doKI5lB/6MW7KOY+L8jeW7DO2ayZWHdWNM71Y2apckSZIkSfoeBiOSJNUFxXnw1g0w46lgufuRzB/1L+6dnsNrM1ZRGgneonu0aszlo7tyyqD2NmqXJEmSJEnaCYMRSZLqkq+egjd/AaUF0KQtnPkIq5sexGMfL+HpqcvYXFQKQIvGyVw8sgvnD+tMRlpijIuWJEmSJEmqPQxGJEmqa9bOhhcuhA3fQSgejvwtjLyOzcVhnp22nEc+XszqnEIA0pLiOXtIRy4Z2ZWOmTZqlyRJkiRJMhiRJKkuKtoCb14P3zwXLPc4Bk67Hxo1p7g0whvfrOKByYuYu2YzAPFxIY4f0JYrD+tG//YZMSxckiRJkiQptgxGJEmqq6JR+Oq/Qe+R0kJIbw9nPgKdhpdtjvLR/A08MHkRUxZsKH/YiG7NueLwbhzRqyWhkI3aJUmSJElSw2IwIklSXbdmZjC11sYFwdRaR98CI66FuLjyXWatyuHByYt4/ZvVhMsatfdu3YTLyhq1JyXE7erokiRJkiRJ9YrBiCRJ9UHRZnj9ZzDzf8Fyr+Pg1HshLbPSbiuzC3h0ymKembaMvOIwAK3Tk7l4ZFfOG9aJJik2apckSZIkSfWbwYgkSfVFNApfPApv/xrCRZDREc58FDoO2WHXnIISnp66jEc/Xsy6zUUApKckcPHIrlw8sgtN05JqunpJkiRJkqQaYTAiSVJ9s/preOEiyFoEcQlwzP+D4T+BnfQTKS6N8OqMldw7aSGL1ucB0Cgpnh+N6MJlo7vSonFyDRcvSZIkSZJUvQxGJEmqjwpz4bVrYfYrwXLvE+DUuyG12U53D0eivDNzDXdOmM/cNZsBSEmM49yhnbjisG60zUitocIlSZIkSZKql8GIJEn1VTQK0x+Cd38D4WJo2gl+8Bi0H/w9D4kyfs467vxwAV8vzwYgKT6OMwZ34KrDu9OpeVrN1C5JkiRJklRNDEYkSarvVn0Fz18I2UshLhGO/RMMu3KnU2ttFY1GmbJgA3dOWMC0xVkAxMeFOGVQO35yRA96tGpcU9VLkiRJkiRVKYMRSZIagoJseO0amPN6sNz3JDj5LkhtutuHTlucxV0fLmDyd+uBIE85fkBbrj6iB/3a+d4qSZIkSZLqFoMRSZIaimgUpt4P790MkRJo1gV+8Di0G7RHD/96eTZ3fbiA92evLV93dN9WXHNkTwZ1bFotJUuSJEmSJFU1gxFJkhqaFV/ACxdBzjKIT4Kxf4Ehl33v1Frbmrsml7s/XMgb36xi618Go3u24JoxPRjWrXn11S1JkiRJklQFDEYkSWqICjbBK1fDvDeD5QNOg1PugaQ9b66+cP0W7p24kJe/Wkk4EvyJMKRLM645sieH9WxBaA+DFkmSJEmSpJpkMCJJUkMVjcKnd8MHt0CkFLqMhnOfheS9a6y+PCuf+yYt5IXPV1AcjgBwYIcMrhnTg6P7tiYuzoBEkiRJkiTVHgYjkiQ1dEs/gafOguLN0HE4nPcCpOz9e+aanEIemLyIp6ctpbAkCEj6tGnC1WN6cPyAtsQbkEiSJEmSpFrAYESSJAV9R548DQpzoP0hcP7/ILXpPh1qw5YiHpmymCc+XcqWolIAurVoxE/G9OCUQe1IjI+rwsIlSZIkSZL2jsGIJEkKrJoB/z016D/SdiD86BVIy9znw+Xkl/DYJ0t45OPF5BSUANChWSo/Prw7Zw7uQEpifJWULUmSJEmStDcMRiRJUoW1s+DxkyF/A7TuH4QjjVvu1yG3FJXy5GdLeeijRWzYUgxA6/RkrjisO+cO7UhaUkIVFC5JkiRJkrRnDEYkSVJl6+bCEyfDlrXQojdc+Bo0abPfhy0oDvPc9GXcN2kRa3ILAWjeKIlLRnXlghGdaZKSuN/nkCRJkiRJ2h2DEUmStKMNC+Dxk2DzKsjsDhe+Dhntq+TQRaVhXvpyJfdMXMDyrAIA0lMSuGhkVy4Z2YWmaUlVch5JkiRJkqSdMRiRJEk7l7U4mFYrZxk06xKEI007VdnhS8MRXvt6FXd/uICF6/MAaJQUz/kjOnPZqG60bJJcZeeSVAdEo7D04+DfmSr8t0aSJEmStmcwIkmSdi17WTByZNMSyOgYTKuV2a1KTxGORHl31hrunLCAOatzAUhOiOPcoZ244rButGuaWqXnk1RLTX8I3vwFJDWBc5+GrofFuiJJkiRJ9ZTBiCRJ+n65q4JwZOMCaNIuCEda9Kzy00SjUSbMXcedExYwY3k2AInxIc4c3IGrDu9Bp+ZpVX5OSbXE8unw6DiIlATL8Ulw+oNwwKkxLUuSJElS/WQwIkmSdm/z2qAh+/q50KhVMK1Wqz7VcqpoNMrHCzZy54T5TF2cBUB8XIhTBrbjJ2O606NVk2o5r6QY2bIe7j8s6GnU96Rg3ZzXgRCc8E8YcmlMy5MkSZJU/xiMSJKkPZO3AZ44BdbOhLQWcMGr0KZ/tZ5y+pIs7pqwgEnfrQcgFILj+7flJ2O6c0C7jGo9t6QaEC6F/54KSz6CFr3g8gmQmBZMqfXFo8E+h/8ajvh18A+AJEmSJFUBgxFJkrTn8rOCi5irv4bUZvCjV6DdoGo/7TcrsrlrwgLem722fN1RfVpx9ZE9OLhTs2o/v6Rq8v7v4eN/Q1LjIBRp2TtYH43CxL/BpL8Fy4dcAsffDnHxsatVkiRJUr1hMCJJkvZOQTY8eQas/BySM+BHL0GHQ2rk1HPX5HL3hwt585tVRMr+KhnVowXXHNmDYV0zCfmJcqnumP0qPH9BcP8Hj8EBp+24z7QH4a0bgCj0PTnoO5KYUpNVSpIkSaqHDEYkSdLeK8yFp8+CZZ9CUhM47wXoPKLGTr9o/RbunbiQl79aSWlZQtK9ZSOGd2vOsG7NGd41k1bpXjyVaq3138GDR0LxZhhxDYz98673nfUKvHQ5hIuhy2g45ylIcSo9SZIkSfvOYESSJO2boi3wzDlBb4DERvDD56Dr6BotYXlWPvdPXsjz01dQHI5U2ta1RSOGdc1kWLdMhnVtTrumqTVam6RdKNoShCIb5kHnUUG/oviE73/Moknw7HlBkNJmAJz3P2jSumbqlSRJklTvGIxIkqR9V5wPz/4QFn0ICalw7tPQ/cgaLyM7v5jPFmUxdfFGpi7KYs6aXLb/q6VDs1SGdW3OsG6ZDO/anI6ZqU69JdW0aBRevBhmvQxN2sKVk6Fxqz177KoZ8NSZkLcemnWBH70Mmd2qs1pJkiRJ9ZTBiCRJ2j8lhUGfgPnvQnwynP0k9Do2piXl5JcwfUkQlExbnMXMVbmEI5X/jGmbkVI2oqQ5w7pm0rVFI4MSqbp9eje8+xuIS4CL3oJOw/bu8RsXwpOnw6Yl0KglnP8/aDuwWkqVJEmSVH8ZjEiSpP1XWhx8CnzuGxCXGDRS7ntirKsqt6WolM+XZDF1cRZTF23kmxU55b1JtmrZJJmhXTMZXhaW9GzV2KBEqkpLpsDjJ0M0DONug2FX7NtxNq+Fp86ANd8GPY7OeQq6HV61tUqSJEmq1wxGJElS1QiXBA2SZ70cfBr8jIfggNNiXdVO5ReX8uXSbKYt3shni7OYsSx7hx4lmY2SGNqlokdJnzZNiIszKJH2Se4quP+wYBqsA8+G0+6H/QkeC3OCniNLPoL4JDj9QTjg1CorV5IkSVL9ZjAiSZKqTrgUXv0JfPMchOKCi58HnhXrqnarsCTMjOXZTC3rU/Llsk0UllQOStJTEhjaNbO8T0m/tukkxMfFqGKpDikthsdPhOVToXV/uPR9SErb/+OWFMJLl8Gc14EQnHA7DLls/48rSZIkqd4zGJEkSVUrEobXfgozngRCcMpdcND5sa5qrxSXRvh2ZXZZQ/csvliSRV5xuNI+jZMTOKRLs/KgZED7DBINSqQdvfUrmHY/JGfAFR9C8+5Vd+xIGN78BXzxaLB8+K/hiF/v32gUSZIkSfWewYgkSap6kQi89Qv4/JFg+cT/g0MuiW1N+6E0HGHmqlymLtrI1MVZTF+SxebC0kr7pCXFM7hzs7Lpt5ozsGMGyQnxMapYqiW+eT6YYg/g3Geh97iqP0c0ChP/BpP+FiwfcgkcfzvE+fqTJEmStHMGI5IkqXpEo/DOr2HqfcHyuH/AsCtjW1MVCUeizFmdW97MfdqSLLLzSyrtk5wQx0GdmpaPKDm4UzNSEr1QqwZkzUx46GgoLYDDboAjb67e801/CN78JRCFvifB6Q9BYkr1nlOSJElSnWQwIkmSqk80Cu//Hj75T7B8zK0w8qexrakaRCJRvlu3mamLspi2OOhTsmFLcaV9EuNDDOzQtLyZ++DOzWiUnBCjiqVqVpAND46BrEXQ/Ug478WaGcEx65VghEq4GLqMhnOegpSM6j+vJKlqRSKw5htocyDEOVWpJKnqGYxIkqTqFY3Ch3+GybcFy0feHHx6vB6LRqMsXJ/H1MUbyxu6r80tqrRPQlyI/u0zGNYtk+Fdm3NIl2Y0SUmMUcVSFYpE4NkfwndvQ0YnuHISpGXW3PkXTYJnz4PizdBmAJz3P2jSuubOL0naf+P/H3z0TzjkUjjxX7GuRpJUDxmMSJKkmjHpH0FAAnD4jXDETQ2mQXI0GmXpxnymLc7is7KwZGV2QaV94kIwuHMzjujdiiP7tKJPmyaEGsj3p8ZFo1CYDdnLIWcF5CwPvgqyYdB50HlErCus2ybfDhNuhfhkuOQdaH9wzdew+mt48gzIWw/NusD5L1Vt03dJUvXZsADuGQ6RsmlKf/g89Bob25okSfWOwYgkSao5U+6AD24J7o/8ORz9hwYTjmxvxab88tEkUxdnsXRjfqXtbTNSOKJ3K8b0bsnIHi2cdmtvhEthy5ptgo9lwe22QUjxlp0/Ni4RTr0XDvxBzdZcXywYHwQSROHkO+HgC2JXy8aF8OTpsGkJNGoZTOfVblDs6pEk7ZmnfgDz34OkJsHov0at4CefQqMWsa5MklSPGIxIkqSa9ek98O5Nwf3hP4Gxf2mw4ci2VmzKZ+K89Xw4dx0fL9xAYUmkfFtSfBzDumUypncrxvRpRdcWjWJYaS1QnLdN0LH1a5vgI3clRMO7P05aC8joAE07QkZHyFocTP8EcOyf4NBrq/d51DfZy+D+w6EgKwhETr4z1hXB5rXw1Bmw5tvgAts5T0G3w2NdlSRpV757F54+K/igwhUT4X+Xwfo50OdEOPtJ/2aUJFUZgxFJklTzpj8Eb/4iuD/kMhh3m401t1FYEuazRRuZOG89E+auY1lW5dEkXVs04ojeLTmyTyuGds0kOaEGmlrXlGg0mP4oZ/l2U12tCC6856wILrzvTlwCpLcPAo+mHYMAJKPstmmnYFtSWuXHRCLw3s3w2d3B8vCrg4DE383dKymER4+DVV9B20FwybuQmBLrqgKFOUHPkSUfQXwSnP4AHHBarKuSJG2vtCiYQitrERz6Uzj2Vlj9DTx4ZDCt1qn3wqAfxrpKSVI9YTAiSZJi48sn4LWfAtHg0+Un/tsL0DsRjUZZtCGPD+eu48N565i2OIuScMWfZGlJ8Yzs0aJsNElL2makxrDaPVBaDLkrtpvaaruprsJFuz9Ocvo2Qce2wUdZENK4NcTtY2D0yZ1BQAJwwOlw2n2QkLxvx2ooXvspfPk4pDaDKycH4VNtUlIIL10Oc14DQnD8bTD08lhXJUna1tYpVxu1gmu/gJSyazhT/g8++EMw8u+qj6FZ51hWKUmqJwxGJElS7Hz9LLxyFUQjMPBcOOXufb+Y3UBsLizh4wUby4OSdZsrhwh92jRhTJ+ggftBHZuSEF+DYVM0Gnw6v9LUVttNdbVlLbC7PylD0KTNdsFHx8rLKRnV+1y+eSH43YyUQJfRwRRM1X3OuurLJ+C1a4EQnP8/6HFUrCvauUgY3volfP5IsHz4jXDETU7LIkm1Qe5quOuQoAfY9iNDImF47ARY9il0OhQuesO/FyVJ+81gRJIkxdbM/8H/Lg96QvQ/A067H+ITY11VnRCNRpm1KpeJ89YxYe46vlqezbZ/rWWkJnJYr5aM6d2Sw3u1pHnj/Rz1EC6B3FVlozy2meIqd2VF8FG8effHSUjZcWqrbZfT20NC0v7VWhUWTYRnzw+eU+v+QfPu9Laxrqp2WfklPHJcMMrnyJvhsBtiXdH3i0Zh0t9h4l+D5cEXwwn/9AKbJMXaS1fCN89C+0Pg0vd3HEWctRjuGxUEJ8f8Pxj5s9jUKUmqNwxGJElS7M15HV64OPh0ft+T4IxHaseF8TomK6+Yyd+t58N565j03Xqy80vKt4VCMLBDU8b0DkaTHNAunbi4bT4pH41CflZF2JGzomLKq61fm9ew+9EeQFrzHae22na5UYu68yn91d/AU2cGI10yOgYjIlr2jnVVtUPeRnjg8OB3ptc4OOfpujMd3vSH4M1fAtHg35zTH6o9PVEkqaFZNhUeOTa4f/kEaD945/t9+V947ZqgX9TlH0Kb/jVXoySp3jEYkSRJtcO8d+D5H0G4OLjIetbj9nXYD+FIlBnLN/Hh3KCB+8LVG2gX2ki7UHDbIzmHg9I30y0pm2ala4nPXQWlBbs/cHxSWcjRAdI7VNzf9iupUfU/wZq0aQk8eQZsXAApTeGHz0Gn4bGuKrYi4SAwWjgBMrsFF6hSm8a6qr0z65Wg70i42OnSJClWIhF4cAysngEHnR9Mq7or0Sg8ex7MexNaHQBXfOjfipKkfWYwIkmSao8F4+HZH0JpIXQ/KrhQmVjLm4nXBpFIMKJhZ6M8cpZDzkrI37BHhypNa0V8s46E0ttXTG2V0QEyypbTWtSdUQFVKW8jPHM2rJgeTAV2xsPQ98RYVxU7E/4Ek2+DhFS4fDy0PiDWFe2bxZPhmR+WTZc2AM5/MehvI0mqGVv7VCWnBw3XG7f6/v23rId7R0Deejj0p3DsrTVTpySp3jEYkSRJtcuiSfDMOVCSD10Pg3OfrX8jEPZWYW5FH49tp7rKWRks564KpiHbncRGkNGBSHp71sW1ZE5+Bp9lpfJNbmNWRluwJppJMYl0aJZaPuXW8G7NSU2y/wIAxfnw4sXw3TsQioPjb4chl8a6qpo37+3gNQpw+oNw4FmxrWd/rf4anjwT8tZB087wo5ehefdYVyVJ9V9BNtw5OPjwxrF/hkOv2bPHzX0Lnj0XCAWN2LuMqs4qJUn1lMGIJEmqfZZ+Ak/9IGiw2elQOO95SG4S66qqT34WrJ+3XeixzVdRzu6PEYqDJu12HOGx7RRXKU132ttj6cY8Ppy7jgnz1vPZoo0Ul0bKtyUnxHFo9+aM6dOKMb1b0TEzrQqfeB0ULoU3r4cvHw+WD7sBxvy27vRM2V8bF8IDY4LfyaFXwvH/iHVFVSNrEfz3tGDatLQWQS+ZdoNiXZUk1W/v3ASf3QMtesGPP967/nKvXRuMNsnoBFd9DCle75Ek7R2DEUmSVDstnx70dSjKgQ5Dgylu6vr8/5EwZC2Gtd/Cmm9hzUxYOzMYDbI7KU3Lgo7224QdZcFHento0hbiE/a7xPziUj5ZsJEP563jw7nrWJVTWGl7j1aNObJPK47o3ZJDOmeSlNAAp9WKRmHS32HiX4Plg86HE++A+MSYllXtivPhoaNh3SzoOAwufGPvLmLVdlvWwZOnB6/NpMbBVH7djoh1VZJUP62bC/ceCtEwnP8S9Dhq7x5ftBnuHQnZS2HQeXDqPdVTpySp3jIYkSRJtdeqr+CJU6EwG9odFPzHOS0z1lXtmaLNsHZWcJF17cwgBFk3O5gibGcyOkKzLpVHeKRvM/ojBiNmotEo363dwofz1jFh7jq+WLqJcKTiz8HGyQmM7tmCMb2DoKRVekqN1xhTXzwGb1wH0Qj0PBZ+8Fj9nfYtGoWXr4RvnoNGreDKyZDeNtZVVb3C3KDP0ZKPID4JTrsf+p8e66okqX6JRuGJU2DxJOh9Apz79L4dZ9ln8Oi44H34rP9Cv5Ortk5JUr1mMCJJkmq3Nd8G/3nO3whtBsCPXoVGzWNdVYVoFLKXVYQfW0eDbFqy8/0TUqBVP2jTP2j23KZ/0Li6DoyGySko4aP565kwdx2T5q1nY15xpe0dM1PJTEuiaVoSzdISaZqWRNO0RJrt4rZxcgKhuj4F1by34YWLobQA2h0M570AjVrEuqqqN+1BeOuXEIqHC1+r3/O5lxTCy1fA7FeBEBx/Gwy9PNZVSVL9Med1eO58iE+Gq6dCZtd9P9YHf4Qp/4LUTPjJZ9CkddXVKUmq1wxGJElS7bduDjx+ctAcuWXf4MJs41Y1X0dJQVDLtqNA1s7adQ+QJm2hdf+y8KM/tDkwaOocV/ebmUciUb5ZmcOHc9fx4bx1fLNiD/qgbCcxPkRG6tawJLE8UGmWlkRG2e3OApbkhFr2/Vs+HZ4+CwqyILNbMLJpfy7y1DbLpsJjx0OkdO+a49ZlkTC8dQN8/nCwfNivYMxvGk4vGUmqLiUFcPfQ4EMlo38JR/1u/45XWgwPHRn8bdZzLPzwOf+tliTtEYMRSZJUN2yYD4+fBJtXB006L3it+qbyiUZh85qy8GObEGTj/GC6hu3FJULLPtsEIGWjQWrTyJZqtmFLEUs25LEpv4RN+cXklN1uyi8hO7+YTfnFZOeXkF22vqh0J9/HPZSWFB+EJ6mJNGtUEag0Ta0IULaub5oaLKenJhIfV40XSjbMh/+eDjnLoFHLYORIu4Oq73w1Zcs6uP+w4HXX79RgurCGcsFp+14ygy+CE/5VL4JNSYqZSbfBh3+CJu3g2s+rZgrKdXPg/sMhXBT0/Drk4v0/piSp3jMYkSRJdcfGhcHIkdwVwSfzL3w96MGxP0qLYcN3FSHI1iAkf+PO909rXhZ+DKgIQVr0rl9NqGtAQXGY7IJiNuVtDU7KApWCEjblbReoFJSUhSrFRPbxr9FQCDJSE2maWnlkSsVolESaNUqiTXoK7Zqm0jo9Ze+DlM1r4KkzK5p3n/XE3jeTrU3CpcE0dkunBL/jl4+PSa+bmJv+MLz5CyAKfU6EMx6GxAbWT0eSqkLOCrjzkGD6yTMehgFnVt2xP70b3v0NJKbBj6cEI3QlSfoeBiOSJKlu2bQ0GDmSvRSadgrCkWZd9uyx+VmVw481M2H9XIiU7LhvKA6a96g8AqTNAGjSpuF8Yr6WiUSibC4sDQKVsiAlO78sXCmoCFiyy0anbB2lsqWodK/PFR8Xok16Cu2bpdK+aSrtmqbQvmka7Zqm0KFZKu2appKWlLDjAwtzg3nTF0+CuAQ45W4YeE4VPPsYeO9m+OTOIOS5/ENo2SvWFcXO7Ffhf5dBuBg6jwoaBdeBvkCSVKu8cDHMegk6jYCL367av6ciEXjiZFjyEXQYAhe/A/E7eZ+WJKmMwYgkSap7clYEI0eyFkJ6h6DnyLafDIyEg9Ela78t6wNSFoJsXrXz4yWnb9cLpH/QyyQprWaej6pVcWmEnILKI1MqwpOKkSmb8kpYnVvA6uxCSvdgaErTtMSy0CQIT7be75AeT+/PbiRl7kvBjkf/AUb+vG4FarNegRcuDO6f9QT0OyWm5dQKiyfDMz+E4s1BUHr+i0FQKknavSVT4LETgg+eXDEJ2h5Y9efIXg73jgx6v425GQ6/oerPIUmqN2o8GLn77ru57bbbWLNmDQMHDuTOO+9k6NChO933wQcf5IknnmDmzJkADB48mL/85S+73H9nDEYkSaqnNq8JRo5s+A4at4GRP4X184LRIOvmBNM07EyzLjtOhdW0c926aK1qFY5EWb+5iJXZ+azMLmRVdgErNxUEt2Vfmwu/fxRKiAi/TXqWy+LeAGBK5hl83ucG2mU2Lg9Q2makkJJYC/tVrJ8HDx4JxVvg0J/CsbfGuqLaY/XX8OSZkLcu+HfjRy87XYsk7U64FB44PPigyiGXwIn/V33n+vo5ePmKYNTmZR/Uj35fkqRqUaPByHPPPccFF1zAfffdx7Bhw7jjjjt44YUXmDdvHq1atdph//POO4+RI0dy6KGHkpKSwt///ndefvllZs2aRfv27ffonAYjkiTVY1vWBT0Q1s3ecVtCKrTuVxGCtBkArfpBin8PaP/lFpawKrugPDQpD1DK1q3NLSQShUvi3+b3if8F4M3wUK4v+QlFVPSjadE4uWy6rhTaZaTSvlnlEShN0xIJ1WRoV5gbhCIb50OX0fCjV5yKZHtZi+C/p8OmxZDWIhg54oU3Sdq16Q8FvZpSmsK1X0Kj5tV3rmgUXrgIZr8CLXrBlZMhMbX6zidJqrNqNBgZNmwYQ4YM4a677gIgEonQsWNHrr32Wn7961/v9vHhcJhmzZpx1113ccEFF+zROQ1GJEmq5/I2whs/h9KibabCGhA0Z4+rhZ/GV4NQEo6wJicIS5j1Eod8eRPx0RLmJg/gxqSb+C47noKS8G6Pk5YUT7tK03UFfU/aZQTr2mSkkBgfVzVFR6Pw/AUw5zVo0i64mNS4ZdUcu77Zsg6ePAPWfBP0YDn7Seg+JtZVSVLtk58Fdx4MBZtg3G0w7IqaOec9I2DLGhj2Yxj39+o/pySpzqmxYKS4uJi0tDRefPFFTj311PL1F154IdnZ2bz66qu7PcbmzZtp1aoVL7zwAieeeOJO9ykqKqKoqKh8OTc3l44dOxqMSJIkKXYWT4Znz4OiXGjZl+j5L5Kd0Kp8aq7y6bpyKkagbNhStNvDxoWgdXpKeXDSu00Tzh/WmYy0xL2v8eP/wPu/g7jEoCluxyH78EQbkMJceO684GcblwinPwD9T491VZJUu7z5i2DESKt+cOVHNTcKccEHQYANwbSH3Y+smfNKkuqMvQlG9uvda8OGDYTDYVq3bl1pfevWrZk7d+4eHePGG2+kXbt2HH300bvc569//St//OMf96dUSZIkqWp1PSwIG548A9bPIfTwsTQ7/380a9+X/u0zdvqQwpIwq3MKt5muq6DSdF2rsgspDkdYnVPI6pxCvli6Cb6Ghz5axHXH9OKHQzuRsKejSRZPhg9uCe6P+5uhyJ5ISYfzXoSXLofZr8KLl0D+Rhh6eawrk6TaYc238Pkjwf1xf6/ZqRl7HA1DLgtCmVeuhp98AqnNau78kqR6Zb9GjKxatYr27dvzySefMGLEiPL1v/rVr5g0aRJTp0793sf/7W9/4x//+AcTJ07kwAMP3OV+jhiRJElSrZW9LAhHNnwHKRlw7rPQ+dB9OlQkEmVDXhGrsgtZuamAFZvyefGLFcxftwWAHq0a89sT+jKm9469/CrJWQn3Hwb5G2DguXDqvVCTfU3qukgY3roBPn84WD7sBhjzW7+Hkhq2aBQeOwGWfgz9ToWzHq/5Gorzgve3jQug/5lw5sM1X4MkqdbamxEj+zV5cYsWLYiPj2ft2rWV1q9du5Y2bdp872Nvv/12/va3v/Hee+99bygCkJycTHp6eqUvSZIkqVZo2gkueRc6DoPCHHji1GC0wT6IiwvRqkkKgzo25YQD23Ll4d15+2ejufWUA2iWlsiCdVu4+NHpXPDINL5bu3nnBykthhcuDEKR1gPghH95QX9vxcXDCf+EI34TLE++DR4ZCyu+iG1dkhRLs14OQpGEVDj21tjUkNQITnsAQvEw80X49sXY1CFJqvP2KxhJSkpi8ODBjB8/vnxdJBJh/PjxlUaQbO8f//gHt956K++88w6HHHLI/pQgSZIkxV5aJlzwKvQ5EcJF8PyFMPWBKjl0QnwcPxrRhYk3jOHy0V1JjA8x+bv1jPv3R9z8yrds3L5vybu/gRXTg9ErZz8BSWlVUkeDEwrBETfCSf+GxDRYPhUeOhL+dznkrIh1dZJUs4rz4L3fBfdHXRd8KCBWOgyGw38V3H/z+mCUpCRJe2m/ghGA66+/ngcffJDHH3+cOXPmcNVVV5GXl8fFF18MwAUXXMBNN91Uvv/f//53fve73/HII4/QpUsX1qxZw5o1a9iyZcv+liJJkiTFTmIqnPUEHHIJEIW3b4AP/hhMPVIFMlIT+e0J/Xj/usMZe0BrwpEoT362jCNun8iDkxdRVBqGr5+F6Q8GDzj9QcjsViXnbtAGXwTXfhFMSQbw7fNw5yEw4c9Q5P9hJDUQU+6A3BWQ0QlG/jTW1cDoX0C7g4ORmq9eDZFIrCuSJNUx+9VjZKu77rqL2267jTVr1jBo0CD+85//MGzYMACOOOIIunTpwmOPPQZAly5dWLp06Q7HuOWWW/jDH/6wR+fbm7nCJEmSpBoVjcLk2+HDPwXLA8+Fk++E+MQqPc2nCzdy6xuzmb06F4Ajm67lwZKbiA8XwuE3wpjfVOn5BKz8MhiRs+zTYLlxGzjq98HPOG6/P3MmSbXTpiVw19BgRORZT0C/U2JdUWDDfLhvNJQWwLh/wLArY12RJCnG9iY3qJJgpKYZjEiSJKnW+/K/8PrPIBqG7kcFF5OSG1fpKcKRKP/7YgX3vfsFjxbfQOe4dcxIPoSE81+gf8fMKj2XykSjMOe1YEqZ7LIPfLU5EI77K3QZFdvaJKk6PHsezH0Duh4GF7xWu/pWTXsQ3volJKTAlZOhZe9YVyRJiqEaa74uSZIkaRcO/hGc+0zQn2LheHjsBNiyrkpPER8X4qzB7Xmvy9N0jlvHimhLLsy5gpPu+ZQbXviadbmFVXo+EVwQ7HcKXDMdjvl/kJwOa74Jfr7PngcbF8a6QkmqOgs/DEKRUDwc9/faFYoADLks+PBBaSG8dAWES2JdkSSpjjAYkSRJkqpLr7Fw4RuQ1hxWz4CHj6n6C+cf/ZOEBe9CfDJJ5z3F4QN7EY3CC1+s4IjbJ3LXhPkUloSr9pyChGQY+TO49sugr0woLrh4ePcwePe3UJAd6wolaf+ES+CdXwf3h14OrfvFtp6dCYXglLshtVnwPjvpH7GuSJJURziVliRJklTdNi6E/54WTL2U1gLOex7aD97/4y74AJ48E4gGF4YOOh+AL5Zu4tY3ZjNjeTYA7Zum8qvjenPywHaEatunfeuLdXOCQGTh+GA5NTPo8zL4YohPiG1tkrQvPrs3CEZSM+GnXwbhQ20162V44aIgpL7kPeg4JNYVSZJiwB4jkiRJUm2zZR08dSas/jqYXuusJ6DnMft+vE1L4YHDoWATDL4ITvp3pc3RaJTXvl7F39+ey6qcYEqtgzo15Xcn9uPgTrX44lZdN//9ICDZMC9YbtEbxv55/37WklTTtqyHOwdDUQ6ceAcccnGsK9q9l66Ab56DzG5w5UdV3tdLklT7GYxIkiRJtVHRZnj+Alg4IZiv/eQ74aDz9v44JQXwyNggZGl3MFzyTjC1004UFId56KNF3DtpIfnFwZRaJw9sx43j+tC+aer+PBvtSrgEvngMPvwLFGQF67ofFQQkrfrGtDRJ2iOvXQtfPgFtDoQrJkJcfKwr2r2CbLh3JOSuCEbrnXRHrCuSJNUwgxFJkiSptiotDi44ffNssHzkzTD6l3ve0DYahVevgRlPBr1LrpgETTvu9mFrcwu5/d15vPjlCqJRSE6I44rDuvHjw7vTKNmpnqpFQTZMvg2m3g+RkmCKl8EXwZjfQqMWsa5OknZu5Zfw4JFAFC55FzoNj3VFe27RJHji5OD+D58Pen1JkhqMvckNbL4uSZIk1aSEJDjtPhj582B5wp/grV9CZA8bpH/5eBCKhOLgzEf2KBQBaJ2ewm0/GMjr14xiaNdMikoj3DlhAWNun8jzny8nEqlzn5eq/VKbBqNErp4KfU6EaAQ+fwT+cxB8/G8oLYp1hZJUWTQKb98IRGHAWXUrFAHodjgMvzq4/+o1kLcxtvVIkmotR4xIkiRJsTL1/ooLUH1OhDMegsTvmd5q5RfwyHEQLoajboHR1+/TaaPRKO/OWsNf3prLsqx8AA5ol87vTuzH8G7N9+mY2gOLP4J3fwNrvgmWm3WBY/4f9D15z0cMSVJ1+vo5ePkKSGwE134O6e1iXdHeKykMenCtnxu8t579pP/GSlID4VRakiRJUl0x6xV46fIg7Og4HM59BtIyd9wvbyPcf1gwd3oVXegpKg3z+CdLuHP8AjYXlQIw9oDW/Ob4vnRu3mi/jq1diETg62dg/P+DLWuCdZ0ODUaWtD84trVJatiKNsOdhwT/Nh31exj9i1hXtO9Wfw0PHhVMY3jqvTDoh7GuSJJUAwxGJEmSpLpkyRR45odQlAMtesP5/6s8RVYkDE+eDosmQmZ3uOJDSMmostNv3FLE/33wHU9PXUYkConxIS4e2ZVrjuxBekpilZ1H2yjaEkyn9cmdUFoQrBt4bnAxsi5+QltS3ff+LfDxHdCsK/zkM0hMiXVF++ejfwYhdFITuOpjaNY51hVJkqqZwYgkSZJU16ydDU+eAZtXQZO2QTjS+oBg2/j/F1zgSUyDy8ZD637VUsJ3azdz6xuz+Wj+BgAyGyVx3TG9OHdIRxLibU9YLXJWBD/fb54LlhPTYOTP4NBrIclRO1KVCZdCwSZo3DLWldROGxfC3cOCERbnPgu9x8W6ov0XCcOj42D5VOg8Ei58HeLiY12VJKkaGYxIkiRJdVHOiiAcWT8XktPhnKehKBeeLZsC5IyHYcCZ1VpCNBpl4rz1/OnN2SxcnwdAr9aN+e0J/Ti8lxcUq82KL+Ddm4ILeABN2gWjRw48G+IMpaR9UrQZFnwA896G796Fwmw47FdwxE2+rrb31Fkw/13ocTSc92L96cmRtRjuGwXFW+CYW2HkT2NdkSSpGhmMSJIkSXVVwaZgWq1ln0B8UvBVvAWGXQXj/lZjZZSEIzw9dRn/98F3ZOeXAHBE75bcfEJferRqUmN1NCjRKMx6GT64BbKXBevaHQRj/wKdD41tbVJdkbMSvnsb5r4FSz4K+jdtr8+JcNr9kNy45uurjb57D57+AcQlBFNotegZ64qq1pdPwGvXBu+nl38IbfrHuiJJUjUxGJEkSZLqspJCeOkymPN6sNxxOFz0BsTXfL+PnPwS/jNhPo9/soTSSJT4uBDnDevEz4/uRWajpBqvp0EoKYTP7oGP/gXFm4N1/U6Bo/8ImV1jW5tU20SjsHZmEITMewtWz6i8vXkP6H188JW1CN74eRCWtDoAzn3GvhOlxXDPcMhaGEzhd+yfYl1R1YtG4Zlzg8Cs1QFBn66E5FhXJUmqBgYjkiRJUl0XCcPEv8Lqr+HkO6FJm5iWs3hDHn95aw7vz14LQHpKAj89qicXjOhCUoJT0lSLLevgwz8Hn3aORoJPOw/7MRz2S0jJiHV1UuyES2DJlGCKrHlvQ86ybTaGoOMw6FMWhmw/+mH59GB6wrx1kNYcznoCuoyq0fJrlY//De//Hhq1gmu/gJR6eo1lyzq4ZwTkbwj6OB3z/2JdkSSpGhiMSJIkSaoWnyzYwK1vzmHO6lwAurZoxE3j+nBMv9aE6suc9LXN2lnw7m9g0cRgOa0FjPkNHHwhxCfEtDSpxhRkl/ULeQvmfwBFORXbElKhx1FBw/CeY3ffYD1nZRCOrJ4RTB91/G1wyCXVWX3ttHkN3Dk4mK7xlHvgoPNiXVH1mvtmWc+uEFz0JnQZGeuKYqMwF5Kb1J8+MpK0DYMRSZIkSdUmHInywufLuf2979iwpQiAEd2a87sT+9GvnX+fV4toNGge/d7NsHF+sK5lXxj75+CCsFQfZS8rGxXyVjBCJFJasa1RK+h9HPQ+AbodDompe3fs4nx47RqY+b9gecjlcNxfYzJlYcy8/GP4+hloPxgu/aBhNKR/9Rr46r+Q0Qmu+rj+jpDZXjQKyz6FT+4KXk9dRsGZj+4+RJSkOsZgRJIkSVK121JUyj0fLuChKYspLo0QCsFZgzvyi7G9aNUkJdbl7bXi0gj5xaXkFYfJL6q4DUej9GrdhNbpteA5hUvg80eCadYKNgXrehwTBCQte8e2Nml/RaPBKI65bwWByNpvK29v2aeiX0j7wft/IT8ahY/+CRNuDZa7jA6m1krL3L/j1gXLp8PDRwf3L5sAHQbHtp6aUrQZ7h0J2Uth0Hlw6j2xrqh6hUthzqtBILLqy8rb0jvAOU9Cu4NiU5skVQODEUmSJEk1ZnlWPn9/Zy5vfLMagEZJ8fxkTA8uHdWVlMT4Kj9fNBqlqDRCXlEp+cVh8opLySsKB6HG1tut4cbWgOP79inbXhL+/v8atWySTP926Qxon0H/sq+2GSmxmUKsYBNM+gdMeyD4FH0oPpgK6IiboFHzmq9H2lelRbD4o+BT7PPehs2rKraF4qDTocEUWb3HQfPu1VPD3DfhpSuCKaWadoZzn4XW/arnXLVBJAIPHQmrvoJB58Opd8e6opq19FN4dBwQhbOfhL4nxbqiqle0OehP9dl9FT14ElJg4DnQ50R4+0bIWhisO/lOOPCs2NYrSVXEYESSJElSjftiaRb/7405fL08G4D2TVP59bg+HNmnFXnFpeQXBSFGfnG4ItTYJtzYuj2vaMfQovyxZbeRavxfTFJCHI2S4klLSqBRcjylkShLNuTt9JzNGyWVhSRBYHJAuww6NEutubBkw4KgcfK8N4Pl5Aw4/Fcw9ApISKqZGqS9lZ8F898Pfm8XjA8Cia0SGwXTw/U5AXoeW3OjN9bOhmfOCUYSJDWG0x8MGrjXR1/+N5hGLDk9aLjeuFWsK6p5H/wBpvwfpDWHqz6FJq1jXVHVyFkBU++DLx6HoqAXGGktYOjlMOQyaNQiWFeQHYSB898NlkdcA0f/0b5Vkuo8gxFJkiRJMRGJRHnt61X8/Z25rM4prPbzpSbG0yg5CDHSkuJplFx2m5RAWvJ2t7vYvvXxjZISSE2KJylhx+l58otLmbM6l5krc/l2ZQ4zV+Ywf90WwjtJS5qlJdK/LCQZ0D746phZzWHJoknw7m8rph5q1hWOvTX4ZLANdlUbZC2uGBWy9BOIhiu2NWlbNirk+GA6q8QYTVuXnwXPXwBLPgJCcNTvYNT19es1VJAdNFzP3wDH/gkOvTbWFcVGaTE8eGTwb2bPsfDD5+r2z3nVDPj0Lpj1ckUvnha9YMTVcODZO+/BE4nAh3+Gj24PlrseDj94rGFMJSep3jIYkSRJkhRTBcVhHpi8iPsnLyS/OEwoBGmJ8aQlJ9B4Z+HFtqHFHm5PS0ogPi52F7IKS8JBWLIql5krcvh2ZQ7frd1M6U7CkvSUhPLpt/qXhSWdM9OIq8r6I2GY8RSMvxXy1gXruowO+o+0HVh155H2RCQS9DSY91bQM2T9nMrbW/evCEPaDqo9jb/DJfDOr2H6Q8Fy/zPg5LsgKS22dVWVd34Dn90NzXvCVZ807JFla2fDA4dDuBhOvAMOuTjWFe2dSATmvxcEIks+qljfZXQQePU4Zs9eV7NegVd+AiV50LQTnPM0tBlQbWVLUnUyGJEkSZJUKxSXRiiNREhJiK/aEKCWKioNM2/N5kojS+at2UxxOLLDvk2SE+jXLr08KOnfPoOuLRrtf9hTtBmm3BFcLCstBELQ72Todwr0OBpSMvbv+NKulBTA4slBz47v3oEtayu2heKhy0jofQL0Pg6adYlZmXtk+sPw9q+CT9+3HRRcLM5oH+uq9s+6uXDfyOA5nfc/6Hl0rCuKvU/ugvd+C4lp8OMp1dfHpiqVFMDXz8Jn98CG74J1cQlwwOnBCJF2g/b+mGtnwbM/hE1Lgu/FKXdD/9OrsmpJqhEGI5IkSZJUSxSXRvhu7WZmrQpGlXy7Mpc5q3MpLt0xLElLiueAsrCkf7sMBnTIoFuLRiTE78On6bOXB/Poz3yxYl1cInQZFXxKv/dxwaeDpf2RtzEIQea9BQsnQEl+xbbk9CCM6318cBE+tVns6twXS6bAcz+Cgixo3BrOfgo6Dol1VfsmGoX/ngaLPgx+Huc+E+uKaodIBJ44ORhx0WEoXPx27e2zsWV9MJJp+kPBVGgQvMYGXwTDroSMDvt3/PwsePGS4HcEYNR1cOTvIC5+/44rSTXIYESSJEmSarGScIQF67Yws2xUybcrc5i9OpfCkh3DkpTEOPq1LWvuXja6pGerxnselqz+Gma+FFy43vrp4q1aDwgaTPceF3wqvi7Psa+as2FBWb+Qt2D5VIhu83ub3iH4fepzPHQeVfenatq0BJ75IaybBfFJcNK/YdAPY13V3pv7ZjAiID4Jrp4Kmd1iXVHtkb0c7j00aFZ+5M1w2A2xrqiy9d8FIwC/fhbCRcG6jE4w/Co4+EeQ3KTqzhUuhfF/hE/+Eyz3OBrOeKjuhZqSGiyDEUmSJEmqY8KRKAvXb+HbFTnMXBUEJrNW5ZJfHN5h3+SEOPq0TWdA+3T6twum4erVuslOG8dXsmEBfPd20AB72aeVL2g3aReMIul9AnQdDQnJVfwMVWcV58PqGWUjQ97eMWBrcyD0OSEIRNocWP8CtqLN8PKPYe4bwfKIa+CY/1d3PklfUgh3D4XspTD6F3DU72NdUe3z9bPw8pXBlFSXfQDtDoptPdFoMIrlk7tg/rsV69sPDn7/+p5cvSNbvn0RXr0GSgugWddghFGrvtV3PkmqIgYjkiRJklQPhCNRFm/IqzSyZNaqXLYUle6wb1J8HL3bNNmmZ0k6vds0ITlhFxdv8zYGjXvnvQULxgeNd8sP1hi6HxlMudNrLKRlVtMzVK1TtAXWfBsEIatmBCOONsyrHKLFJQbhWe+y0Ub7O4VPXRCJwMS/wuR/BMs9joYzHobUpjEta49Mvg0m/AmatIVrPofkxrGuqPaJRuGFC2H2q9CiF1w5GRJTa76OcAnMehk+uRPWfFO2MhQEjyOugU7Day54XP0NPHse5CwL3hNOuw/6nlQz55akfWQwIkmSJEn1VCQSZWlWfhCSlIUlM1fmkFu4Y1iSEBeiV+smdGiWSnxcqPJXqOJ+MsV0z/uKPjlT6JU9hSYl6yvORxyrMwaxrOXhLG95BHmNO5MQFyIuLhTchkIkxJfdxsURHwfx296GQsTFUXlbKLRDPTscs2x567bE+Lj9b0yvyoo2Bxc/V39dEYRs+A7YyWWCxm0qwpAeR0FKRg0XW0vMfAle+UnwSfrmPeHcZ6FFj1hXtWs5K+CuIUHvl9MfggN/EOuKaq/8LLhnBGxZA8OugnF/q7lzF2TDl4/D1Pshd2WwLiEVDjoPhv8kdk3h8zYGgdGSj4Llw2+Ew38NcfvQ90qSaoDBiCRJkiQ1INFolOVZBUFIsqpidEl2fsm+HI0BocUcHf8Fx8R9Sb+4pZW2zo+054PIwbwfHsyMaA8i1NwFsrgQJMbHkRQfR2LC1ttQxbr4OBLjy5YT4irtmxgf2maf4HFblyv2DW2zvfJy5ePteM5tt8fVxgCnMDf4BPqqGRUhyMYF7DQEadIO2g2CtgOD3jPtBkGTNjVYbC23akbQryN3JSRnwA8eCUaQ1EYvXgIz/wcdh8Ml79S/ac6q2vwP4Kkzgvs/egW6j6ne821aClPvgy+fgOItwbpGrWDYFXDIpbVjtF64FN7/HXx2T7Dcaxycfn/DDUcl1WoGI5IkSZLUwEWjUVZmFzBzZQ4b84qJRKKURqKEI1Ei0eD+1nXl26JRwuGy27J904tW0zt7Cn03T6FH3gziqeh5khPXlK9Th/NF6nC+TjyIfJKJRCo/vtJXNEppuPL5tz3ntrXUZfFxoYqApiw8adkkmaP7tub4AW3o2boKmyXvTGFOMApkawiy+uuyEGQn0ttXhB9bbxu3qt766oMt6+C584Pm86E4OPZPwSf7a1PwsORjeOx4IARXTgqCLu3eG9fD5w8HAeFPPqmexuMrvoBP7wym7to6TV2rfjDiahjwg9rZ42nGM/D6z4IG8M17Bn1HWvSMdVWSVInBiCRJkiSp6hXmwIIPYO5bMP99KMqp2JaQAt3GBD0neh0HTVrv16nKQ5uywKQ0HKE4HKG4NEJJOEpJ+f1tlsMRSkrLbsMRSkqjFffL9tv6mPLbssdu/SoujW5zfyf7lFYsb92+t0FO95aNGNe/LeMGtKFf23RC+3MxvWBT2VRY2wQhWYt2vm9Gx8qjQNoOgsYt9/3cDV1pUXARfcaTwfKg8+HEf9WOi9qRMNx/OKz9FgZfDCfdEeuK6o7iPLhvNGQthP5nwpkPV81xI2GY9zZ8ehcs+7RifbcxcOg10P2o2hWs7czKL4NAMHclJKfD6Q9C7+NiXZUklTMYkSRJkiRVr3AJLP0kuNA3703IXlZ5e/tDoM/xQU+Kln1q/wW//RCJRCmJlAU05WFKRRBTXBph7ppc3p65hinzN1Acrmhk3ikzjXED2jCuf1sGdsj4/pAkP6tyP5DVM2DTkp3vm9EJ2m0XgjRqUVVPWVtFo/DZvfDeb4NP/ncYCmc/ud/B4H6b/jC8eX0w3dG1X0Gj5rGtp65Z8Tk8fCxEw3DGwzDgzH0/VnEezHg6mIpqa2gZlxiMDBlxNbTpXzU115Qt6+D5C8rCnRCM+S2M/oV9RyTVCgYjkiRJkqSaE43Cutkw761gNMmqLytvb9YlCEh6Hw+dRkB8QkzKrA1yC0uYMGcdb89czcR56ykqrQhJ2jdNZewBbTh+QBsObhElbs2MbUKQryF76c4P2rRz5Z4gbQd5IRwoLAnzxdJNTFmwgY8XbGBZVj4HtEtnSJdMhnbJZFCnpqQlVdHv4oLx8OLFwaiq9PZwztPBzyQW8rPgzoOD0UTj/gHDroxNHXXdh3+FSX8LwqWffAbp7fbu8ZvXwLQH4PNHgp8FQEpTOOQSGHoFpLet8pJrTGkxvHsTTH8oWO57Epx6LyRX8zSBdV00Goy6nPZAMILvyJtrRx8ZqR4xGJEkSZIkxU7uavjunWA0yaKJwZz0W6U0hZ7HBlNu9TgaUhru/+nyikqZOG89H82Yw8YF0+gZXsiAuMUMiFtMh9CGnT+oWZfKo0DaDvTCWplIJMrs1bnlQci0xVmVgqftJcSFOKB9BkO7NOOQLpkM6ZJJZqOkfS9gwwJ45hzYOB8SUuHUu6H/Gft+vH315i9h+oPQsi/8eEqDDiL3S7gEHj4GVn0VTHd1/kt7Nipi7exguqxvX4BwcbCuWRcYfjUcdB4kNarWsmvUF4/DW78MnmfLvnDOU9C8e6yrqn3CpTD7FZhyRzC93VaNWgbh5QGn1etRlbVOYS58cidsXhW8tnscVT29hBQTBiOSJEmSpNqhaAss+jAISb57B/I3VmyLS4Suo8tGk4yDjA6xq7OmbFlfeSqsVTMgd8VOd10cac3MaFcWJvSkUZeD6Tf4MIb27UZivFPWbLU8K58pCzYwZcEGPlmwgU35JZW2t2qSzKgeLRjZowU9WjXmmxXZTF+yielLslidU7jD8Xq0asyQLpkM6dKMIV0y6dAsde96wBTmwIuXBJ8KBzjsBjjiNzU3zdCamXD/6GBarwteg26H18x566sN84N+I6UF3z/6JhqFhROCQGThhIr1HYfBiGugzwkQF18zNde05dPguR/BljXB6JozHwlCb0FJAcx4Cj7+T8WIv8RGQUC2aBJsmBes63UcnPDPhvEeGEvRKMx6Gd65Kfh93SoUD52GBx/a6HUctOxtUFWHGYxIkiRJkmqfSDi4iDbvrSAo2Ti/8vY2B1aEJG0H1p4LE+GS4AJXaeHub0sLoaQwuJC67W3uqiAIyV2583Nkdi8fBVLSeiCfFbTn9Xl5vDd7LdnbXOxPT0ngmH5tGNe/DaN6tiAlsZ5ebN2FTXnFfLJwY6XpsbbVKCmeEd2bM7JHC0aVhSE7Czai0SgrNhUwfUlWeVCyYN2WHfZrm5HCIV0yGdqlGUO6ZtKrVRPi4nbzexkJwwe3BJ9IBuh9Apx+f/VPMxSNwmMnwtIp0O8UOOuJ6j1fQzHtwWBUREIKXDk5uGi6VWkRfPsifHo3rJsVrAvFBVNLjbgWOg6JTc01LXc1PP8jWDE9eP5H3QIjf1Z7/g2vaYU5QZ+fz+6FvHXBurTmMOwqGHJpMMqvtAim/B9Mvh0iJZDUOPi+Dbm0/oZosZS1KBhNt3B8sJzZLQhBFk6A9XMr79u0M/QaG3x1HgWJKTVfr/aZwYgkSZIkqfbbML8iJFk+NfiU+1bp7YOApPc46DIaEpKD9ZHIjqHDtrelRXseYuxwW7hNuLHNtmi4Cp90CJr3qDwVVtsDg09a70RJOMLURVm8PXM1785aw4YtxeXbGicncGSfVozr34YjerciNan+XUwrLAnz+ZKKPiEzV+Ww7VWMhLgQB3VqWh6EDOzYdJ9H1GTlFfP5kiymL8li2pJNzFqZQ2mk8iWT9JSE8mm3hnZtRv/2GSQn7OL7PuMZeP2nwTRDrfrBuc8EUypVl1kvwwsXBRfwr5kOTTtV37kakmgUnjwjuKDadhBc9gEUbQ56h0x7sOKT54mN4OAfwbAfQ2bXmJYcE6VF8OYv4Kv/BssHnA6n3FW/pg7bnc1r4bN7gt+NotxgXUZHOPSncND5kJS242PWzYXXroUV04LlDkPg5DuhVd+aq7s+Ky2Cj/8dBFDhIohPgtG/gJE/rwg8Ni2B796D+e/C4skVU+ABJKYF0231OhZ6jq3bvYEaCIMRSZIkSVLdkrcBvns3CEoWToCSbUYCJKQGFzNKCypfsIiFhJTgKzG18m1CSnCRJSF1u9uyfdKaByNi2h64zyMHwpEony/J4u2Za3hn5hrW5FZMBZWaGM8RvVsybkBbjuzTisbJdbOvRDgSZfaqXD5asJ6PF2xg+pJNFG/XJ6RX68aM7NGC0T1bMLRr82p7rvnFpcxYls20srDky6XZFJRUDsmSE+IY2LEpQ7tkMqRrJgd3akqTlMSKHZZPh+fOgy1rITUzGMXRdXTVF1ucD3cNCaZlO/zXMOamqj9HQ5a7Gu4ZDoXZ0HE4rPmm4t+oJm2DKbYGX2SfgmgUPn8Y3r4RIqXQegCc82T1BoK1wcaFwQixGU9X9NRq2RdGXQf9T4f4xO9/fCQSfN8++CMUbw6mmRx1HRz2y4oPBWjvLZoEb14PGxcEy92OgBP+9f19cIq2wOJJwd8j89+Dzasrb29zYDDSpNdx0O6gmpsmUXvMYESSJEmSVHeVFAaf2pz3Jsx7p/Jc4NuKTwoCiITkXYQSO7lNSN4x1Ci/3YN9a8nUMJFIlBkrsnn729W8PXMNKzYVlG9LSojjsJ4tGde/DUf3bU1G2m4uysXY0o155SNCPlm4sdLUYQCt05MZ1aMlo3o2Z2T3FrRKj820JiXhCLNX5ZZNvxVMwZWVVzmoiwtB37bpZX1KMhnStRmtIhvh2R8GU6nFJQS9KoZcWrXFffgXmPT34NPpV0/b+SfTtX9mvgQvXlyx3HoAHHpNMDIiISl2ddVGSz+B5y+AvPVBWPSDx4KL0vXN6q+DhuqzX6kY8dhxGIy6PuhXsbcXzXNWBqNuvns7WG7RC076D3QeUZVV139b1sG7v4Vvnw+WG7eGsX+B/mfs3Xt4NBqEoN+9G3yt/ALY5jJ6o5bBz7nnsdD9SEjxGnVtYDAiSZIkSaofIpFgbnDYcSSG87ADQb+MmStzeXtmEJIs3pBXvi0hLsTIHi0Y178Nxx7QhsxGsb+Am5VXzCcLgyBkyoINLM8qqLS9cXICw7s1Z1SP5ozq2YLuLXfeJyTWotEoC9fn8fmSrPJRJds/F4AuzdMY0akRP875PzqveitYecilMO7vu/8k+Z7YtATuGhp8Uv0Hj8MBp+7/MbVzk24LeokMvgi6Hl5rgtJaKWcFPHc+rPoqaG597J9g+FV1/3sWjcKSKUF/kK39KiC4OD7qOuh86P4ff/Yr8NavKvqTHHIJHP2HXU65qDKRCHzxaDDypigHCMGQy+DImyG16f4ff8t6WPA+fPcOLJgQjO7ZKi4x+Nn3GhuMJvm+USmqVgYjkiRJkiQ1QNFolHlrN/P2t2t4e+Zqvltb0VA8Pi7EsK6ZjBvQlrEHtKZVk5oZeVFYEmb6kiymzA+CkFmrcittT4gLcXCnZozq2YKRPVowsEMGCfvYJyTW1uQUlo8ombY4i3lrN2/TEyXKVfGvc0Pic8QRZXWzQ8g+8SF6de1C/O4aun+f586HOa8HvXgufL3uX3hW/VFSAG9cB18/EywfeA6cdEcwAm8fRKNRZq/OZW1uISO6tajZvkqRSDDV45T/g5WfB+tCccEohJE/gzYDqvZ8BZvgvd9V9Gxp0haOvx36nli156kvVn8d/K6t/CJYbjsQTvw/aD+4es5XWgzLPi2bcuvdium6tsrsXjbl1rHQ6VBHldUggxFJkiRJksSCdVt4p2wkybaBRCgEQzpnclz/NhzXvw3tmu7bhcqdCUeizFyZUz491udLd+wT0qdNk/KG6UO7ZtKojvZE2Z2cghK+XLqJaUuy+HxJFl8vz2F0dDr/TrybxqFClkVa8tPQjTTpdGB5n5JBHZuSkriHF3wXTYQnTgk+kf/jj6D1AdX6fKS9Fo3C1PuCqY2i4aCB/TlPQUaHPXp4STjCtMVZvD97Le/PXsvK7GBUVpPkBE45qB3nDOlE//bVOJKitBi+fQE+vgM2fBesS0gJmqmPuAYyu1bfuSGYVvL1n1WMnOx7Eoy7zSbgWxVtDqYSnHpfMJ1ZUhM46nfBSJGaHFW6cWHZlFvvBFPJRbaZEjKpCfQ4Mmje3vNYaNyy5upqgAxGJEmSJElSJcs25pdPtzVjeXalbYM6NmVc/zaM69+WTs33rj9FNBpl6cb8Sn1Ccgoq9wlpm5FSHoQc2qN5jY1WqW0KS8J8syKHhbOmc9SMn9GqdDVboilcV/IT3o8cAkBifIgB7TMY0jWToV0yGdy5GU3TdvJp43AJ3Dca1s+BoVfC8f+o4Wcj7YVFk+CFi6AgC9JawFlPQJeRO911c2EJk75bz/uz1/Lh3HXkFpaWb0tJjCMzLYlVOYXl6wa0z+CcoR05eWA7mqRUUU+l4jz48gn45C7IXRGsS86AoZfBsB9D41ZVc549UVIAk/4BH/87CJeSM+CYP8LBFzbc5t/RKMx+Fd65CTavCtYdcBqM/WvsQ6PCXFj0YUUD97z122wMQfuDy0aTjA2auTvKr0oZjEiSJEmSpF1alV3AOzOD6bY+X7qJba8MHNAuneMHtOW4/m3o3rLxTh+/cUsRHy/cyMdl02Nt/RT3Vk2SExjRvXn59FjdWjSqlX1CYio/i+jzFxBa8hEArzW/lD/ljGPdluIddu3duglDujajZ6smNE1LJD01kZ6LnqTD1D8SSc0kcvUXJDTOrOlnIO2dTUvhufNgzbcQlwDH/S34ZH8oxOqcAj6YvZb356zj04UbKAlX/KPUvFESR/VtxbH92jCqZwuS4uP4dNFGnpm2jPdmraU4HIxIS02M56SBbTlnaCcO6th03/7Nyc+CaQ8EIxAKNgXrGreGEVfD4Itj22B7zbfw2rVB3xaAziPhpH9Di56xqykWshbDWzcE/T4AmnWFE26HHkfHtq6diUSCn9f8stEkq7+uvL1J22AUSa+x0O0ISGoUkzLrE4MRSZIkSZK0R9blFvLurDW8PXMNny3aSGSbqwS9WjdmXP+2HNOvNRvzioOG6fM3MHt15T4hifFlfUJ6tGBkzxYc2L7u9gmpUeGS4BPP0x8EIHrA6awYfRtTVxYyfXEW05dmsWh93g4PyySXicnXkx7K5zcll/J0+CgaJyeQkZpY/tU0rex+2jbrUpMqbUtPTaRJcgJx+9PjRNobxfnBxf2ZLwIwq/XJ3FxyCV+tyq+0W7cWjTimX2uO6deagzo122Ufnqy8Yl76cgXPTFvGwm1eK71bN+GcoR057aD2Ox9xtb2cFfDp3fDFY1BSVkuzrkH/kIHnQmItGeUWCQehzYQ/BXXGJ8PhN8ChP6v/fSxKi+CT/8Dk26G0MGh4Puo6GH39PvetqXG5q4NRJPPfg4UfQsk2/77HJ0PX0cGUW72OhWZdYlZmXWYwIkmSJEmS9trGLUW8P3stb89cw8cLNlAa2fUlgz5tmjCqRwtG9Qz6hKQl1c8+ITXi80eCT0BHSoOmwec8Xd6DYcOWIj5fksX0JZtYnVNATkEJP1r/L44repc50S6cUPQnIux7CBUXgvTURJqmVoQlTdOSyEhNKA9Stg1Ymm4TtKQmxjsSSHusNBxh2pIsPpi1lubf3s+Pi58gPhTlq0gPflxyHR06dSsPQ3Y1Wm1XotEony/dxDPTlvHmN6spKutrlJQQx/H923D2kE4M75a54+/r+nnBFFXfPBe8/iCY3mjUddDvlJrtU7E3Ni0Nmo0vHB8stzoATv4PdDgktnVVl8UfwZvXV/R56XoYnPCvuj1apqQQlk6B794LRpNkL628vWWfYCRJz7HQcRjE+x67JwxGJEmSJEnSfsnJL+GDOWt5e+ZqJs/fQItGSeVTYx3avQUtmyTHusT6ZcnH8PyPIH8jNGoVNKjuOHTH/VZ9BQ+MAaJw8TuUdhhGbmEpOQUl5BSUkJ1fXH4/J79s3XbLwbpiCksi+1VyUnxcWZCy7aiUxB3Xld+vCFqSEhxR1BDkFZUyuaxfyPi56yr1Hzoy8VvuTLyLRpHNhBu1Iv7sJ6HTsP0+Z05BCa/OWMkz05YzZ5vRbV1bNOLsIR05c3AHWmR/C1P+D+a+UfHALqODQKT7kXWj70M0GjSGf/vGoHcLoaD/yZE3Q/LeBUu11pb18P7v4OtnguVGLWHsX2DAD+rGz2hPRaNB6PPdO0FQsuzToJ/MVikZwVRhvY4LbtOcOnFXDEYkSZIkSVKV2XrpwNEB1WzTUnjmXFg3C+KT4MQ74KDzKrZHo/DIWFg+NbgweMZD+3W6wpIwuQXbhCc7BCnF2wQplcOV7xtNtCfSkuLLQ5JtR6xsP9VXMHqlYlt6SoLTtNVy63IL+WDOOt6fvYaPF26kuLQigGuWlshRfYNRIaN7tiBtyzJ49jxYNzuYGumE22HwRVVSRzQa5ZsVOTw7fRmvzVhFXnEph8V9w08SXmd43OyKHfucGAQidXW0Rd4GePc3wagXgIyOwWiKXsfGtq79EYnAl4/DB3+AwmwgBIdcDEf9HlKbxbi4GlCwCRZOqGjgvrXfDUAoDjoMDUaT9DoOWveLXZ21kMGIJEmSJElSXVS0BV6+suKT7COugaP/GEyj8s3z8NLlkNgIrv0c0tvFpMRoNEp+cbg8TMkuKK4IWL5vlEp+MZuLStnfK1FNkhN2OiolfbteKttvs59K9YhGoyxYt4X3Zq/l/dlrmbE8u9L2zs3TOLZfa47p14bBnXfSL6RoC7xyFcx5LVg+5BI47u9V1zMjEqbw65co+PB2muXOBaAkGs/L4VG8knYGw4cdylmHdKRNRi3pI7KvFnwQTK+VvSxY7n9m0OC+ccvY1rW31nwLb1wPK6YFy20GBCFxXQ2u9lckDCs+D0aTzH8P1s6s2NZhCFz2Qexqq4UMRiRJkiRJkuqqSAQm/Q0m/T1Y7n4UnHwnPHgkbFkDR/4ODvtlbGvcR+FIlC2FpWQXbDMaZZvwJHe75eyydTkFJWwpKt2vc2/tp5Kxi69tg5byab/K1jVKsp/KtsKRKF8s3cT7s9fw/uy1LNlYuXn6oI5Ny/uF9GzVePffu2gUPvpn0FScKHQcDmc9AU1a73uRJYXBFEwf/xs2LQ7WJaaxsc8PeTx6Ao/NLCG3MPidigvBmN6tOGdoJ8b0bll3RyUV58GHf4HP7oFoJBhdMfYvQQP52v77W7QFJv4VPrs3mEYqqTGM+S0MvcL+GtvKXh4EJN+9GzRrP/TaWFdUqxiMSJIkSZIk1XWzXoaXr4LSAkhIDW6bdYGfTIXEOv7p9n1QEo6UhyTbhyaVwpT8ku322/9+KglxITJSE8lslETbpqm0y0ihTUYK7TJSads0hbYZqbRrmkJaUv29gJtfXMpH8zfw/uy1TJi7jqy84vJtSfFxHNqjOcf2a8PRfVvRKn0ffz+/exf+dxkU5UKTdnDOk9B+8N4dozAXPn8kCAe2rA3WpTYL+m8MvaK8P0NhSZi3Z67mmWnLmbY4q/zhrdOT+cHgjpw9pCMdM9P27XnE2sov4bWfwtpvg+VuRwSjLjK7xrKqnYtGgxFyb98IuSuDdf1OCUa7xGhUnOougxFJkiRJkqT6YPXX8MwPIXdFsHzOM9Dn+NjWVAcVlYZ3mNpr+0Cl8vpicgpKyS0ooTi856FKRmoibTNSaNc0lbYZKWVfQXjSLiOVNhkppCTGV+MzrVrrNxcxYW4wRdZH8zdQtE2/kIzURI7s04pj+rXmsF4taZxcRaHQhvnw7A+DZtTxyXDi/1XutbMrW9YFow2mPwxFOcG69A5w6DVw8AWQ1GiXD124fgvPTV/Oi1+sqBT4jO7ZgnOGdOKYfq1JSqhjo0jCJfDpXTDxb1BaGISrY34Dw39Se0ZgbFoKb/8qmCYKoGlnOP72ut0fRTFlMCJJkiRJklRfbFkH7/4WMtrDUbfU/ilx6pFoNEphSaR86q8Nm4tZlVPA6uxCVucUsCqnkDVly5v3cKqv5o2SykeZbA1O2m2z3CYjhcQYTuW0YN0WPpgThCFfLttUqSdMh2ap5VNkDe2SWX1TThXmBr125r0VLA/7MRz7J4hP3HHfrMXwyZ3w1ZMQLgrWtegNo34e9NnYi14lxaUR3p+9lmenL+Oj+RvK1zdvlMQZgztw9pCOdG/ZeD+eWAxsXAiv/wyWfBQstx0IJ/0H2g2KXU2lxUFoM+kfwUi4uEQY+TMY/QtIqqOjdFQrGIxIkiRJkiRJNWhzYQmrcwpZlV3A6pxCVmcHwcnqsuBkVU7BHk3pFQpBy8bJ3ztlV6smKTs2Md9H4UiUGcs3Bc3TZ61l0Ya8StsHtM8oD0P6tGlSc71WIpGgz86kvwXLXUbDDx6DRi2C5TXfwpQ7YNZLQT8NgPaHwOjrodc4iNu/0GZ5Vj7PTV/OC18sZ21uUfn6oV0yOWdoR44f0LbujP6JRoPg6L3fQmEOhOJhxNVwxE01H0Qs+RjevB7Wzw2WO4+CE/8FLXvXbB2qlwxGJEmSJEmSpFokGo2SU1DCqm1Gm6wuC1G2hilrcgr3aOqu+LgQrZsE4cm2U3aVjzxpmkKLRsnE7SI8KSwJM6WsX8j4uWvZsKVi+qjE+BDDuzXn2H6tObpfa9pmpFbZ92CfzHkjGD1SvAUyOsKRN8O3L8KC9yv26XE0jLoOOo+s8hFVpeEIH85bz7PTlvHhvHVEyq6kpqckcNpB7TlnaCf6tq0j1yc3r4V3bgz6F0HQs+ikfwc9SKpb3kZ4//cw48lgOa1FMApo4DmOglOVMRiRJEmSJEmS6phoNMrGvOLyESblwck2Icqa3ELCkd1fzkuMD9Emo/KUXc0bJTF9SRYfzd9AQUm4fN8mKQmM6R30Czm8d0vSU3YyZVUsrZsLz54LWYsq1oXi4IDTgimY2g6skTJW5xTwwucreG76clZmF5SvH9ixKecO6chJA9vRqKp6rVSneW/Dm7+oaHY+6LwgpChrTF+lIpEgDHn/91CwKVg3+KJgWsDqOJ8aNIMRSZIkSZIkqR4KR6Js2FJUPsqkfOqunILy0SjrNhexuyt+7TJSyqbIasOwbpkx7WuyRwqy4eUfw6IPYeC5MPKnkNktJqVEIlE+WrCBZ6ct4/3ZayktC6oaJcVz8qB2nDOkEwd2yKi5acf2RWEuTLgVpj0IRIMRHOP+Dv3PqLoRHGtnwRvXw/LPguXW/eGEf0GnYVVzfGk7BiOSJEmSJElSA1USjrBuc1FFn5Oy8GT95iJ6tGrMMf1ac0C79Np94X5XImGIqz29PdZvLuJ/XwajSBZv05+lb9t0zh3akVMGtScjtZaNwNnWsqnw+k8ren70PDYIL5p23PdjFufBxL/BZ/dApBQSG8GY38CwH0N8HRhRozrLYESSJEmSJEmSakg0GmXq4iyenbaMt2auobg06BWTkhjH8QPacu7QThzSuVntDKNKi4JG9h/dDuHiIMg4+hYYctneh1Bz34K3fwU5y4PlPicGI1EyOlR52dL2DEYkSZIkSZIkKQay84t5+auVPDttOfPWbi5f371lI84Z0okhXTNJjA+RGB9HYnwcCXFb74dIiI8jKT6OhPgQCXGhmg1S1s+D135aMfVV+0Pg5Duhdb/dPzZ7Gbx9I8x7K1jO6ATH3wa9j6u+eqXtGIxIkiRJkiRJUgxFo1G+Wp7Ns9OW8frXqys1vN9TifEhEuLiyoOUhF0EKjvdlhBHYlwQtuywX1xc+f3yICY+jqS4KD2X/48Bc/5JYmkekVACy/pdwaoDryE+MYXEhCC4SUuKp1FyAo0SoqR9+QBxk/4GJfkQlwCHXguH3QBJjarhuyrtmsGIJEmSJEmSJNUSmwtLeO3rVbz05UrW5BRSEo5QGolSEo4E98PR8ibutUFrsrg18VGOjf8CgIWRttxUchnTon3L9xkcmsefEx+hT1wwbdaX9OU/qVexLqUbjZMTSEsuC0+2hihJCcFtcnzZ/WB9WlJCsH9SfPnjkhNqTx+Z6hSNRikqjVBUEqGoNExhSYTC0jBFZbeFJdve32afkjBdWzTi+AFtY/0UahWDEUmSJEmSJEmqQ6LRKCXhKKWRCCWlUUoiFaFJEKBsE6REKtaVlq3b4bGlW/eLlh0nQnHZ/qWRKMVl60rDW++XHTNSdszSMIcUTOHyzfeSGd0EwKsJx/JY6BTOL/kfZzABgKxoY/5Seh4vhg8Dqmbqr8T4UKXApHKgUvl+RaCSQOPk+J0ELQmkJcYTF/f9tYUjUQpLysKI0kjZ/R0Di6LS79lnu+Wi7UOO7R5TVBphX6/OH9uvNQ9ccMi+PbieMhiRJEmSJEmSJO2/gk3w/i3w5eM7bCodeD45o24mLy6DLUWl5BeXlt2Gg9uiUvKKw+QVlQZfW+9XWldKflGwf1FZ0/rqUB6wJAUhSdF2AUZJOLaXyeNCkJIYT3JCHCmJ8eX3kxPjSdnmNtgWR//2GVwwoktMa65tDEYkSZIkSZIkSVVnyZSgOXvWQmjVD078P+g0vEpPURqOkFccJr+4LDQp2i5I2cX6HQOZiv33ZYaypPi4ilAiMa5SWBEsB7cpCfEkly9vG2rE7bC89TGVbyvuJ8aHCIWqZsRNQ7U3uUFCDdUkSZIkSZIkSaqruoyCqz6B1TOg/WCIT6zyUyTEx5GRGkdGatUcOxqNUlgSqRyoFJcSjkR3DDK2CTnidzPtluo+gxFJkiRJkiRJ0u4lplT5KJHqFAqFSE2KJzUpnhaNk2NdjmqRuFgXIEmSJEmSJEmSVFMMRiRJkiRJkiRJUoNhMCJJkiRJkiRJkhoMgxFJkiRJkiRJktRgGIxIkiRJkiRJkqQGw2BEkiRJkiRJkiQ1GAYjkiRJkiRJkiSpwTAYkSRJkiRJkiRJDYbBiCRJkiRJkiRJajAMRiRJkiRJkiRJUoNhMCJJkiRJkiRJkhoMgxFJkiRJkiRJktRgGIxIkiRJkiRJkqQGw2BEkiRJkiRJkiQ1GAYjkiRJkiRJkiSpwTAYkSRJkiRJkiRJDYbBiCRJkiRJkiRJajAMRiRJkiRJkiRJUoNhMCJJkiRJkiRJkhoMgxFJkiRJkiRJktRgGIxIkiRJkiRJkqQGw2BEkiRJkiRJkiQ1GAYjkiRJkiRJkiSpwTAYkSRJkiRJkiRJDYbBiCRJkiRJkiRJajAMRiRJkiRJkiRJUoNhMCJJkiRJkiRJkhoMgxFJkiRJkiRJktRgGIxIkiRJkiRJkqQGw2BEkiRJkiRJkiQ1GAYjkiRJkiRJkiSpwTAYkSRJkiRJkiRJDYbBiCRJkiRJkiRJajAMRiRJkiRJkiRJUoNRJcHI3XffTZcuXUhJSWHYsGFMmzbte/d/4YUX6NOnDykpKQwYMIC33nqrKsqQJEmSJEmSJEn6XvsdjDz33HNcf/313HLLLXz55ZcMHDiQsWPHsm7dup3u/8knn3Duuedy6aWX8tVXX3Hqqady6qmnMnPmzP0tRZIkSZIkSZIk6XuFotFodH8OMGzYMIYMGcJdd90FQCQSoWPHjlx77bX8+te/3mH/s88+m7y8PN54443ydcOHD2fQoEHcd999e3TO3NxcMjIyyMnJIT09fX/KlyRJkiRJkiRJddze5AYJ+3Oi4uJivvjiC2666abydXFxcRx99NF8+umnO33Mp59+yvXXX19p3dixY3nllVd2eZ6ioiKKiorKl3NycoDgiUqSJEmSJEmSpIZta16wJ2NB9isY2bBhA+FwmNatW1da37p1a+bOnbvTx6xZs2an+69Zs2aX5/nrX//KH//4xx3Wd+zYcR+qliRJkiRJkiRJ9dHmzZvJyMj43n32KxipKTfddFOlUSaRSISsrCyaN29OKBSKYWW1T25uLh07dmT58uVOMybVUb6OpfrB17JUP/haluoHX8tS/eBrWaofquu1HI1G2bx5M+3atdvtvvsVjLRo0YL4+HjWrl1baf3atWtp06bNTh/Tpk2bvdofIDk5meTk5ErrmjZtum9FNxDp6em+QUh1nK9jqX7wtSzVD76WpfrB17JUP/haluqH6ngt726kyFZx+3OSpKQkBg8ezPjx48vXRSIRxo8fz4gRI3b6mBEjRlTaH+D999/f5f6SJEmSJEmSJElVZb+n0rr++uu58MILOeSQQxg6dCh33HEHeXl5XHzxxQBccMEFtG/fnr/+9a8A/OxnP+Pwww/nn//8JyeccALPPvssn3/+OQ888MD+liJJkiRJkiRJkvS99jsYOfvss1m/fj2///3vWbNmDYMGDeKdd94pb7C+bNky4uIqBqYceuihPP3009x888385je/oWfPnrzyyiv0799/f0sRwbRjt9xyyw5Tj0mqO3wdS/WDr2WpfvC1LNUPvpal+sHXslQ/1IbXcigajUZjdnZJkiRJkiRJkqQatF89RiRJkiRJkiRJkuoSgxFJkiRJkiRJktRgGIxIkiRJkiRJkqQGw2BEkiRJkiRJkiQ1GAYj9cjdd99Nly5dSElJYdiwYUybNi3WJUnaC3/4wx8IhUKVvvr06RPrsiTtxuTJkznppJNo164doVCIV155pdL2aDTK73//e9q2bUtqaipHH3008+fPj02xknZpd6/liy66aIf36eOOOy42xUrapb/+9a8MGTKEJk2a0KpVK0499VTmzZtXaZ/CwkKuvvpqmjdvTuPGjTnjjDNYu3ZtjCqWtDN78lo+4ogjdnhv/vGPfxyjiiXtzL333suBBx5Ieno66enpjBgxgrfffrt8eyzfkw1G6onnnnuO66+/nltuuYUvv/ySgQMHMnbsWNatWxfr0iTthQMOOIDVq1eXf02ZMiXWJUnajby8PAYOHMjdd9+90+3/+Mc/+M9//sN9993H1KlTadSoEWPHjqWwsLCGK5X0fXb3WgY47rjjKr1PP/PMMzVYoaQ9MWnSJK6++mo+++wz3n//fUpKSjj22GPJy8sr3+e6667j9ddf54UXXmDSpEmsWrWK008/PYZVS9renryWAS6//PJK783/+Mc/YlSxpJ3p0KEDf/vb3/jiiy/4/PPPOfLIIznllFOYNWsWENv35FA0Go3WyJlUrYYNG8aQIUO46667AIhEInTs2JFrr72WX//61zGuTtKe+MMf/sArr7zCjBkzYl2KpH0UCoV4+eWXOfXUU4FgtEi7du34xS9+wS9/+UsAcnJyaN26NY899hjnnHNODKuVtCvbv5YhGDGSnZ29w0gSSbXb+vXradWqFZMmTeKwww4jJyeHli1b8vTTT3PmmWcCMHfuXPr27cunn37K8OHDY1yxpJ3Z/rUMwYiRQYMGcccdd8S2OEl7JTMzk9tuu40zzzwzpu/JjhipB4qLi/niiy84+uijy9fFxcVx9NFH8+mnn8awMkl7a/78+bRr145u3bpx3nnnsWzZsliXJGk/LF68mDVr1lR6j87IyGDYsGG+R0t10MSJE2nVqhW9e/fmqquuYuPGjbEuSdJu5OTkAMFFGIAvvviCkpKSSu/Nffr0oVOnTr43S7XY9q/lrZ566ilatGhB//79uemmm8jPz49FeZL2QDgc5tlnnyUvL48RI0bE/D05odrPoGq3YcMGwuEwrVu3rrS+devWzJ07N0ZVSdpbw4YN47HHHqN3796sXr2aP/7xj4wePZqZM2fSpEmTWJcnaR+sWbMGYKfv0Vu3SaobjjvuOE4//XS6du3KwoUL+c1vfsO4ceP49NNPiY+Pj3V5knYiEonw85//nJEjR9K/f38geG9OSkqiadOmlfb1vVmqvXb2Wgb44Q9/SOfOnWnXrh3ffPMNN954I/PmzeOll16KYbWStvftt98yYsQICgsLady4MS+//DL9+vVjxowZMX1PNhiRpFpi3Lhx5fcPPPBAhg0bRufOnXn++ee59NJLY1iZJEnaduq7AQMGcOCBB9K9e3cmTpzIUUcdFcPKJO3K1VdfzcyZM+3bJ9Vxu3otX3HFFeX3BwwYQNu2bTnqqKNYuHAh3bt3r+kyJe1C7969mTFjBjk5Obz44otceOGFTJo0KdZlOZVWfdCiRQvi4+NZu3ZtpfVr166lTZs2MapK0v5q2rQpvXr1YsGCBbEuRdI+2vo+7Hu0VP9069aNFi1a+D4t1VLXXHMNb7zxBh9++CEdOnQoX9+mTRuKi4vJzs6utL/vzVLttKvX8s4MGzYMwPdmqZZJSkqiR48eDB48mL/+9a8MHDiQf//73zF/TzYYqQeSkpIYPHgw48ePL18XiUQYP348I0aMiGFlkvbHli1bWLhwIW3bto11KZL2UdeuXWnTpk2l9+jc3FymTp3qe7RUx61YsYKNGzf6Pi3VMtFolGuuuYaXX36ZCRMm0LVr10rbBw8eTGJiYqX35nnz5rFs2TLfm6VaZHev5Z2ZMWMGgO/NUi0XiUQoKiqK+XuyU2nVE9dffz0XXnghhxxyCEOHDuWOO+4gLy+Piy++ONalSdpDv/zlLznppJPo3Lkzq1at4pZbbiE+Pp5zzz031qVJ+h5btmyp9Km0xYsXM2PGDDIzM+nUqRM///nP+dOf/kTPnj3p2rUrv/vd72jXrh2nnnpq7IqWtIPvey1nZmbyxz/+kTPOOIM2bdqwcOFCfvWrX9GjRw/Gjh0bw6olbe/qq6/m6aef5tVXX6VJkyblc5RnZGSQmppKRkYGl156Kddffz2ZmZmkp6dz7bXXMmLECIYPHx7j6iVttbvX8sKFC3n66ac5/vjjad68Od988w3XXXcdhx12GAceeGCMq5e01U033cS4cePo1KkTmzdv5umnn2bixIm8++67MX9PDkWj0Wi1n0U14q677uK2225jzZo1DBo0iP/85z/lwwgl1X7nnHMOkydPZuPGjbRs2ZJRo0bx5z//2blRpVpu4sSJjBkzZof1F154IY899hjRaJRbbrmFBx54gOzsbEaNGsU999xDr169YlCtpF35vtfyvffey6mnnspXX31FdnY27dq149hjj+XWW2+ldevWMahW0q6EQqGdrn/00Ue56KKLACgsLOQXv/gFzzzzDEVFRYwdO5Z77rnHqbSkWmR3r+Xly5dz/vnnM3PmTPLy8ujYsSOnnXYaN998M+np6TVcraRdufTSSxk/fjyrV68mIyODAw88kBtvvJFjjjkGiO17ssGIJEmSJEmSJElqMOwxIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmSJEmSJDUYBiOSJEmSJEmSJKnBMBiRJEmSJEmSJEkNhsGIJEmSJEmSJElqMAxGJEmSJEmSJElSg2EwIkmSJEmSJEmSGgyDEUmSJEmSJEmS1GAYjEiSJEmSJEmSpAbDYESSJEmStNcee+wxQqEQjz32WKxLKXfRRRcRCoVYsmRJrEuRJElSLWYwIkmSpDrtkksuIRQK0bx5c4qKimJdTr3XpUsXQqFQ+Vd8fDzNmzfnqKOO4oUXXoh1eVUqFApxxBFH7NPjtv1KSEigbdu2nHrqqUyePLnqCy2zZMkSQqEQF110UbWdQ5IkSaoPEmJdgCRJkrSvNm/ezPPPP08oFCIrK4tXXnmFs88+O9Zl1Xvx8fHcfPPNAJSUlLBgwQJefvllJkyYwF/+8hduuummGFcYe82bN+eaa64BoLCwkBkzZvDqq6/y2muv8dxzz/GDH/wgxhXuv9NOO43hw4fTtm3bWJciSZIk7RWDEUmSJNVZzz33HHl5eVx//fXccccdPPzwwwYjNSAhIYE//OEPldZ9/PHHHHbYYdx666387Gc/Iy0tLTbF1RItWrTY4Xv00EMPcfnll/OrX/2qXgQjGRkZZGRkxLoMSZIkaa85lZYkSZLqrIcffpiEhAR+9atfMWbMGMaPH8/SpUt3uf/kyZM59dRTad26NcnJyXTs2JHTTz+dKVOmVNovGo3y6KOPMnr0aJo2bUpaWho9e/bkyiuvZNmyZeX7denShS5duuz0XEcccQShUKjSuj/84Q+EQiEmTpzIY489xsEHH0xaWlr5dE05OTn8/e9/5/DDD6ddu3YkJSXRrl07LrjgAhYuXLjT8+xJraNGjSIhIYHVq1fv9BgXXHABoVCITz/9dJffu90ZOXIkffr0oaCggNmzZ++w/dVXX+Woo46iWbNmpKSk0L9/f26//XbC4XCl/SKRCA899BBDhw4lMzOT1NRUOnTowEknncTEiRPL95s4cSKhUIg//OEPfP755xxzzDE0adKEjIwMTjvttF32mFi8eDGXXXYZnTp1Ijk5mbZt23LRRRdV+r3ZemyASZMmVZoWa3/6aVxyySU0atSIJUuWsH79eqDidyg7O5trrrmGjh07kpCQUOk833zzDeeccw5t27YlKSmJzp07c+2117Jx48byfR577DG6du0KwOOPP16p5q3ft6r+/dtVj5GtU5CtXbuWCy+8kBYtWpCamsrw4cMr/Qy3tXnzZm655RYOOOAAUlNTadq0KWPHjt3htbnVrFmzOPHEE8t/5scffzwzZ87cg5+CJEmS5IgRSZIk1VGzZ8/ms88+4/jjj6d169ZccMEFjB8/nkcffXSHT+oD/Pvf/+a6664jNTWV0047jU6dOrFy5UqmTJnCiy++yKhRo4DgwvzZZ5/Niy++SPv27Tn33HNJT09nyZIlPP/88/x/9u47Poo6/+P4a3eTbHrvQEILvQoWpBfFLnY8Cyq2ux+W81DPjt2znf2KItjr2U49EQuKiHSk9xYgIY30ZLNlfn/MZpMlARJIA97Px2MemZ2ZnfnuZkGZ936+n9NPP520tLTDGvtTTz3Fjz/+yLnnnsupp56KzWYDYO3atdx///2MHj2a8847j7CwMNatW8e7777LV199xdKlS0lPT/edp6FjveGGG5g3bx4zZszg7rvv9htLYWEhH3/8Mb1792bIkCGH9bqqBQT4/zPjrrvu4oknnqBdu3acf/75REVFMXfuXG6//XYWLFjg15vkrrvu4sknn6RLly784Q9/ICIiwvd7+u677+r0/Fi0aBFPPvkko0eP5oYbbmDZsmV89tlnrFy5klWrVhEcHOw7dsGCBYwfP56ysjLOOussMjIy2LZtG++88w7/+9//mD9/Pp07d6Zjx4488MADPPjgg6Snp/v17BgwYECTvEe1QzOHw8GYMWMoLS3lnHPOISAggKSkJAC++OILLr74YqxWK+eeey4dOnRgzZo1vPTSS8yaNYsFCxYQExPDgAEDuOWWW3j++efp378/EyZM8J1/3/CuqT5/B1JYWMiwYcOIioriiiuuICcnhw8++IDx48ezZMkS+vTp4zu2oKCAESNGsHr1aoYOHcqNN95IcXExn3/+OaNHj+ajjz7yez2rVq1i6NChlJaWcv7555ORkcHChQsZOnQo/fv3b+RvQkRERESOSYaIiIiIyBHotttuMwDjvffeMwzDMEpKSoywsDAjLS3NcLvdfscuX77csFqtRmpqqrF161a/fR6Px9i1a5fv8YsvvmgAxtixY43y8nK/Y8vLy438/Hzf4/T0dCM9Pb3e8Y0cOdLY93+3H3jgAQMwwsLCjBUrVtR5TmFhod/5q/3www+G1Wo1rr32Wr/tDR1rRUWFERsba3Tu3NnweDx+x7300ksGYDz33HP1vo59paenG3a7vc72X375xbBarUZcXJxRUVHh2/7tt98agDF+/HijtLTUt93j8Rg33nijARgff/yxb3tsbKyRmppqlJWV1blG7ffmxx9/NAADMN5//32/46644gq/z4ZhGEZVVZXRsWNHIyIiwli6dKnf8XPnzjVsNptx1lln+W0HjJEjRx7kHakLMLp3715n++uvv24ARqdOnXzb0tPTfe/Pvr/DvLw8IzIy0mjXrp2xbds2v33vvfeeARhTpkzxbdu6dasBGJMmTap3XE39+ZsxY4YBGDNmzKjz+gHjT3/6k9+fxddee80AjBtuuMHv+D/84Q8GYLz66qt+2/fs2WN06NDBSEhI8PtMVf/Zevvtt/2Ov+uuu3zX3vfPuYiIiIhIbZpKS0RERESOOE6nk7feeovIyEjfN8nDw8M577zz2LFjB999953f8f/617/weDw88sgjdb49b7FYSE1N9T1+5ZVXsNls/OMf/yAkJMTv2JCQEGJjYw97/Ndffz19+/atsz0qKqre848ePZrevXvXeV0NHWtwcDCTJk1iy5Yt/PDDD37HTZ8+HbvdzhVXXNHg8btcLqZNm8a0adO45557uOSSSxg9ejRWq5VXXnnFr0rjpZdeAuDf//43YWFhvu0Wi4UnnngCi8XCe++953f+oKAgXxVDbfW9NyNGjKjTV+aaa64BzGqSal9++SXbtm3j9ttvZ+DAgX7HDxs2jHPPPZevv/6a4uLihr4NB5SXl+d7j/76179y+umnc80112C1WnnqqafqHP/kk0/W+R2++eabFBcX8/jjj9ep1Jg4cSLHHXcc77//fqPH1lSfvwMJCwvjb3/7G1ZrzT85J02aREBAgN/vJS8vjw8++IAxY8Zw7bXX+p0jMTGR22+/ndzcXN+1d+zYwU8//US/fv247LLL/I6/++67iY6ObvAYRUREROTYpam0REREROSI8/nnn5Obm8vkyZP9bsJfeeWVvP3220yfPp1TTz3Vt33hwoUAftvqU1paytq1a+natSsZGRnNM3jghBNO2O++OXPm8Nxzz7FgwQLy8vJwuVy+fUFBQYc81uuvv56///3vvPrqq4wdOxaAJUuWsGzZMv7whz80KvBxu908+OCDftsCAgLqTHkE8NtvvxEWFsbrr79e77lCQkJYt26d7/HEiRN55ZVX6NOnDxMnTmT06NEMGTKkTmhQbdCgQXW2tW/fHjCnc6o9DoD169fXO9VadnY2Ho+HDRs2MHjw4Hqv1Rj5+fm+98hmsxEfH8+5557LX/7yF4YPH+53bHBwcL1BRfWYFyxYUG+Pj8rKSvLy8sjLyyM+Pr7BY2uKz9/BdOvWjfDwcL9t1VOE1f69LFq0CLfbjcPhqPf3snHjRgDWrVvHWWedxe+//w7gm/qutvDwcAYMGLDfPiYiIiIiItUUjIiIiIjIEWf69OmAGYTUNnbsWNq1a8fnn39OQUGB72Z/UVERFouFlJSUA563qKgIgHbt2jXDqGtU94/Y10cffcQll1xCeHg448ePp2PHjoSGhvoaXNduEN7Ysfbo0YORI0fy2WefkZ+fT1xcHK+99hoA1113XaPGb7fbqaysBMyA5ocffuCaa67hiiuu4JdffvHr81BQUIDL5aoTpNRWVlbmW3/++efp1KkTM2bM4JFHHuGRRx4hODiYiy++mGeeeaZOABAZGVnnfNU9Tmo3di8oKADgnXfeOeBrqz2Ww9G9e3e/wOdAEhMT/XqOVKse88svv3zA55eVlTUqGGmKz9/B1Pd7AfN3U9/vZd68ecybN2+/56v+vVR/7hMTE+s9bn+vTURERESkNgUjIiIiInJEyczM5NtvvwVg5MiR+z3u7bff5uabbwYgOjoawzDIyso6YJAQFRUFwK5duxo0FqvVSlVVVb37qm/g1qe+m+AA06ZNIzg4mCVLltSpAtl3yqTGjhXgxhtv5KeffuLNN9/khhtu4L333iMjI6NOQ/PGCA8P55xzzuGDDz5g3LhxXH311SxZssT3GiMjI7FYLOTl5TXofAEBAUydOpWpU6eye/dufvrpJ2bMmMGbb75JdnY2s2bNOqRxVt+o/+9//8tZZ511SOdoLvv7PFSPeeXKlX7Nypvreo35/DWV6tf4l7/8haeffvqgx1d/7nNycurdv2fPnqYbnIiIiIgctdRjRERERESOKDNnzsTj8TBs2DAmT55cZ5k0aRJQU1UCNVMHVQcq+xMeHk6vXr3YunWrbwqfA4mJiSEnJ8dvuiEwv93ekOfva/PmzfTs2bPOTemsrCy2bNlyWGMFOP/880lISOC1117jo48+oqioqE5fh0M1duxYJkyYwLJly/x6hpx44onk5+cf0vuRmprKpZdeyjfffEPXrl357rvvqKioOKTxnXjiiQDMnz+/wc+xWq1+1Q0trbFjru7Lcqhjbsznr6kcf/zxWCyWBr/G6mqkX375pc6+0tJSli9f3pTDExEREZGjlIIRERERETliGIbBjBkzsFgsvPHGG7z22mt1lpkzZzJkyBBWrFjB4sWLAbNSwmazce+999aZDsgwDHbv3u17/H//93+43W7+9Kc/1bkJX1lZ6Zv6B8ybuk6n0296JsMwuOuuuw5pSqb09HQ2bdrk9633yspK/vjHP+J0Ousc35ixgtkj4qqrrmLNmjXcfffdBAYGctVVVzV6nPszbdo0LBYLDz74oO/mfHXVzjXXXEN+fn6d52RnZ7N27VoAHA4Hv/76a51jysrKKC0tJTAw0K+Zd2Oce+65pKWl8eyzz/Lzzz/X2e90OuvcbI+NjWXnzp2HdL2mcPXVVxMREcE999zD6tWr6+wvLy/39SEBM6izWCxkZmYe0vUa+/lrCsnJyVx88cX8+uuvPPXUUxiGUeeYBQsWUF5eDkBaWhojRoxgxYoVdaZFe+yxx/z6l4iIiIiI7I+m0hIRERGRI8YPP/zA1q1bGTlyJJ07d97vcVdffTXz589n+vTpDB48mL59+/Lcc89x880307t3byZMmEB6ejrZ2dn8/PPPnHnmmTz33HMA/PGPf+Snn37iww8/JCMjg3POOYfIyEh27NjBrFmzmD59uq/B+JQpU5gxYwbXXnsts2fPJiEhgblz51JYWEj//v19jaIb6qabbuKmm25i4MCBXHjhhbhcLmbPno1hGPWerzFjrXbDDTfw9NNPs3v3bi644IL99mo4FP379+e8887jk08+4e2332bSpEmcdtpp3HfffTz88MN07dqV0047jfT0dPLz89m0aRNz587lkUceoWfPnlRUVDB06FC6devGoEGDSEtLo7S0lC+//JLs7GymTp2K3W4/pLHZ7XY+/vhjTj/9dEaOHMmYMWPo27cvFouF7du3M3fuXOLi4vz6gowZM4YPP/yQCRMmMHDgQGw2G+eccw79+vVrqrfsgBISEnjvvfe46KKL6N+/P6eddho9evTA4XCwbds2fvrpJ04++WS++eYbwKwiOv744/n555+54ooryMjIwGq1csUVV5Cenn7Q6zX289dUXnnlFdavX88dd9zBW2+9xZAhQ4iOjiYzM5PFixezceNGsrKyCA0NBcyeK0OHDuXKK6/ks88+IyMjg4ULF7Jo0SKGDx/O3Llzm2WcIiIiInIUMUREREREjhCXXnqpARgzZsw44HFFRUVGSEiIERUVZZSXl/u2//jjj8ZZZ51lxMbGGkFBQUb79u2NCy64wJg3b57f8z0ej/Haa68ZJ510khEWFmaEhoYaGRkZxo033mjs2LHD79gffvjBOPHEEw273W7ExcUZV1xxhbFnzx5j5MiRxr7/u/3AAw8YgPHjjz/WO26Px2P885//NHr37m0EBwcbycnJxuTJk42cnJx6z9fYsVYbNmyYARjffPPNAd/H+qSnpxt2u32/+3///XfDYrEYnTt3NpxOp2/77NmzjbPPPttISEgwAgMDjeTkZGPIkCHGww8/7BtnVVWV8be//c049dRTjfbt2xtBQUFGUlKSMWLECOPdd981PB6P73w//vijARgPPPBAnTFs3brVAIxJkybV2bdz507jlltuMTIyMgy73W5ERkYaPXv2NK699lrj+++/9zs2KyvLuPjii434+HjDarU26LNnGIYBGN27dz/ocYZhvp/p6ekHPGbdunXG5MmTjfT0dCMoKMiIiYkx+vbta9x8883GwoUL/Y5dv369ccYZZxjR0dGGxWLx+7w19edvxowZ9b4ngDFy5MhGvd7y8nLjySefNAYNGmSEhYUZISEhRqdOnYwJEyYYb775pt9nyTAMY+XKlcYZZ5xhhIeHGxEREcbpp59urFy50pg0aZIBGFu3bt3f2ykiIiIiYlgMo55aZREREREROSpVVlbSvn17wsPD2bJlyyFPTSUiIiIiInKk0r+CRERERESOITNmzCA/P58bbrhBoYiIiIiIiByTVDEiIiIiInIMeOKJJ8jNzeVf//oXYWFhbNiwgaioqNYeloiIiIiISItTMCIiIiIicgywWCwEBgbSv39/XnzxRU466aTWHpKIiIiIiEiraHTt/M8//8zZZ59NamoqFouFzz77zG+/YRjcf//9pKSkEBISwrhx49i4caPfMQUFBVx22WVERkYSHR3N5MmTKS0tPawXIiIiIiIi+2cYBlVVVSxatEihiIiIiIiIHNMaHYyUlZXRv39/Xn755Xr3P/nkk7zwwgv885//ZMGCBYSFhTF+/HgqKyt9x1x22WWsXr2a2bNn8+WXX/Lzzz9z/fXXH/qrEBERERERERERERERaYDDmkrLYrHw6aefMmHCBMD8Flpqaip/EusfJwAAlDlJREFU+ctfmDp1KgBFRUUkJSUxc+ZMJk6cyNq1a+nVqxeLFi1i8ODBAHzzzTecccYZ7Ny5k9TU1MN/VSIiIiIiIiIiIiIiIvUIaMqTbd26lezsbMaNG+fbFhUVxYknnsj8+fOZOHEi8+fPJzo62heKAIwbNw6r1cqCBQs477zz6pzX4XDgcDh8jz0eDwUFBcTFxWGxWJryJYiIiIiIiIiIiIiIyBHGMAxKSkpITU3Faj3wZFlNGoxkZ2cDkJSU5Lc9KSnJty87O5vExET/QQQEEBsb6ztmX48//jgPPvhgUw5VRERERERERERERESOMpmZmbRv3/6AxzRpMNJc7rrrLm677Tbf46KiItLS0sjMzCQyMrIVRyYiIiIiIiIiIiIiIq2tuLiYDh06EBERcdBjmzQYSU5OBmDPnj2kpKT4tu/Zs4cBAwb4jsnJyfF7nsvloqCgwPf8fdntdux2e53tkZGRCkZERERERERERERERASgQe03DjzRViN16tSJ5ORkvv/+e9+24uJiFixYwJAhQwAYMmQIhYWFLFmyxHfMDz/8gMfj4cQTT2zK4YiIiIiIiIiIiIiIiPhpdMVIaWkpmzZt8j3eunUry5cvJzY2lrS0NG699VYeeeQRMjIy6NSpE/fddx+pqalMmDABgJ49e3Laaadx3XXX8c9//hOn08mUKVOYOHEiqampTfbCRERERERERERERERE9tXoYGTx4sWMHj3a97i698ekSZOYOXMmd9xxB2VlZVx//fUUFhYybNgwvvnmG4KDg33Peeedd5gyZQpjx47FarVywQUX8MILLzTByxEREREREREREREREdk/i2EYRmsPorGKi4uJioqiqKjogD1G3G43TqezBUcm0jICAwOx2WytPQwRERERERERERGRNqGhuQE0cfP1tsIwDLKzsyksLGztoYg0m+joaJKTkxvUTEhERERERERERERETEdlMFIdiiQmJhIaGqobx3JUMQyD8vJycnJyAEhJSWnlEYmIiIiIiIiIiIgcOY66YMTtdvtCkbi4uNYejkizCAkJASAnJ4fExERNqyUiIiIiIiIiIiLSQNbWHkBTq+4pEhoa2sojEWle1Z9x9dERERERERERERERabijLhippumz5Ginz7iIiIiIiIiIiIhI4x11U2mJiIiIiIiIiIiISBMp2gk7foPMhZD5G+RtgrjOkNIfUgaYS1JvCDr4DD6GYWAY4DEMPAYY1DyuvR3fuoFBzX6j9vZaj2uOqT6P/zkN77WqtwPYLBasFgsWC1gtFqxW78/qx7X3W83tNosFyz7HWLzPs9U+l/enxaIvN7dVCkaOYh07duTWW2/l1ltvbdDxc+bMYfTo0ezdu5fo6OhmHZuIiIiIiIiIiIg0L7fHoMLpprzKRUWVm3LvYq67vPuqt7mocDiILNpActFy2peupGP5SuLcuXVPnL3SXJa9bV4HC1uM9qyhI2uMTqzydGIN6ZQaIWYAUh14HIP8QpTawYnVsk+IUrNus9YfstisFt+xJ3eJ476zerX2yztiKRhpAw6WGj7wwANMmzat0eddtGgRYWFhDT7+5JNPJisri6ioqEZf61D16NGDrVu3sn37dpKTk1vsuiIiIiIiIiIicgTweCD7d9j4HWyaDQVbIaE7JPeD5L7mEt8NAoJae6SHrMrlMYMKp6tWaOGmwmmGFf5hhnlcxT4BR/Xx+25zuDwHvHYE5Qy0bmSQdQODLRsYYN1EmMXhd4zLsLLWSGOxpztLPN3YYLSnkyWb3tat9LFso691KwmWIjIsmWSQybmWub4GDps9Kaw2OrLK05FVRidWezpSRHij3yOrt/Ki+qeFmtDAUitwqB0mgH+oUFPBYZ7T46kJbHwVLIaB2zDwePwrWKqrVGpXojRUTZVK0yZDabEhTXq+Y42CkTYgKyvLt/7BBx9w//33s379et+28PCavywMw8DtdhMQcPBfXUJCQqPGERQU1KLhxC+//EJFRQUXXnghb7zxBnfeeWeLXbs+TqeTwMDAVh2DiIiIiIiIiI/bBTt+hXVfw+bvITwJxtwHaSe29sikjfN4DMqqXJQ53JQ6nJQ63JRWuiivchFuDyA+wk5cWBAxoUFYrW1wmp+KvbD5B28Y8h2U5fjvL8uBbXNrHtuCIKGHNyzpY4YlSX0gJLpFhw3gcnvIK60ip6SSnGIHOSUOc73EQU6xg9ySSoq9v4vqEMPVAqUUFguEBFrpGpjP8bYNDGQDfdxrSXNvx7rPDXuHLYzsyH7kxQykKP44yhP7ExQSSUZQAP2CbIQE2ryVC+Z5iy0WKspzsOeuxJ67kqDcFQTlrCSgdDddrFl0IYtzbPN953dHpeFJ6oc7qR+e5H4YKf2xhCfWBBe0/amoak8J5jbqhiiGp2bfvtOEeTz7hiy1whdPfSGMue72GBiuSoJKdxNUupOwyLLWfhuOaApG2oDaYURUVBQWi8W3rXp6q6+//pp7772XlStX8u2339KhQwduu+02fvvtN8rKyujZsyePP/4448aN851r36m0LBYLr776Kl999RWzZs2iXbt2PPPMM5xzzjl+16qeSmvmzJnceuutfPDBB9x6661kZmYybNgwZsyYQUpKCgAul4vbbruNN998E5vNxrXXXkt2djZFRUV89tlnB3zd06dP5w9/+AMjR47klltuqROM7Ny5k9tvv51Zs2bhcDjo2bMnL7/8MieeaP4P4H//+18eeughVq5cSXh4OMOHD+fTTz/1vdZPP/2UCRMm+M4XHR3Nc889x1VXXcW2bdvo1KkT77//Pq+88goLFizgn//8J2effTZTpkzh559/Zu/evXTp0oW7776bSy+91Hcej8fD008/zb///W8yMzNJSkrihhtu4J577mHMmDH06tWLl156yXd8bm4u7dq143//+x9jx45tyEdCREREREREjlWOEvNG8LqvYeO3UFlYsy9vA7x+KvQ+D8Y9CDHprTZMaXpuj0Gpw0WZdynx/iytdPm2lzpcZsjhcHpDD3N/WZWrZt3hoqzK3aBrWi0QG2YnPjyI+HA7cfv8rNluBinBgbbmefEeD2SvMCtCNn4HOxeCUavSISgcOo+CruPMwCNvQ81UTtkrwVFkPj97hf95o9P8K0uS+0JUh5qSgUaodLrJLfEGHcWVNYGHL/wwQ4/8sqpGVRPUFmC1EBJkIzTIRmhQACGB5rrftiAbob7tAfvstxESGFCzbnMTsXctoXsWE7BrIZbMBVC6B/b9eMR0hA4nmkvaSdgTepButdG4v2HCIb0zcG7NprI8yPrdf9m7FVvRDmxFOwjc8GXNsRGp3p4ltZbI1EP6XbUEX5UKlqa7wW4YZihYuAOKd0JRptnfpXCH+bMoE8pqTWvW+zwYMLKprn7MOSaCEcMw59JraSGBtiZLNP/617/y9NNP07lzZ2JiYsjMzOSMM87g0UcfxW638+abb3L22Wezfv160tLS9nueBx98kCeffJKnnnqKF198kcsuu4zt27cTGxtb7/Hl5eU8/fTTvPXWW1itVi6//HKmTp3KO++8A8Df/vY33nnnHWbMmEHPnj15/vnn+eyzzxg9evQBX09JSQkfffQRCxYsoEePHhQVFTF37lyGDx8OQGlpKSNHjqRdu3Z88cUXJCcns3TpUjwe8z+KX331Feeddx733HMPb775JlVVVXz99deH9L4+88wzDBw4kODgYCorKxk0aBB33nknkZGRfPXVV1xxxRV06dKFE044AYC77rqLV199lb///e8MGzaMrKws1q1bB8C1117LlClTeOaZZ7Db7QC8/fbbtGvXjjFjxjR6fCIiIiIiInIMKM6C9V+by9afwV1Vsy80DrqdDhmnmIHJsrdh9admcDLkTzDsNgiObL2xH+Ncbg9lDjclvqDCrM6oP9Co+7jM4abEG2Y0x70rm9VCuD2AcHsAYXbzRnqZw0VeqYPCciceA/JKHeSVOoCSg54vwh5QJzyJqx2ghAURH2EnPsxOZEjAge+LVeyFzT+an+uNs+tWhST0hIxx0PUUSBviP1VWh+Nr1g3DvHFcOyjJXglFO8zthTtgXa0b8MFRfmFJeWwvsoPSyCk3fKGHLwCpFXwUVTgb9qZjvu/x4UEkRgSTFGknISKYxAg7iZF2EiOCiQoJ9A80As3AIyjA2uBr1Ku8wGyQvnUBZC6AXUvAVel/jDXQDB3SToIOJ5hhSEQzzSATFg9dx5pLtYpC8/eTtbwmLMnbCCW7zWXD/2qODY03x5o6oCYsiU5vs2HJQbld5mss2gmFmd7gI7PW453gbEAVSGAYRHcwwyQ5ZMdEMFLhdNPr/lktft01D40nNKhp3uKHHnqIU045xfc4NjaW/v37+x4//PDDfPrpp3zxxRdMmTJlv+e56qqrfNUPjz32GC+88AILFy7ktNNOq/d4p9PJP//5T7p06QLAlClTeOihh3z7X3zxRe666y7OO+88AF566aUGBRTvv/8+GRkZ9O7dG4CJEycyffp0XzDy7rvvkpuby6JFi3yhTdeuXX3Pf/TRR5k4cSIPPvigb1vt96Ohbr31Vs4//3y/bVOnTvWt33TTTcyaNYsPP/yQE044gZKSEp5//nleeuklJk2aBECXLl0YNmwYAOeffz5Tpkzh888/5+KLLwZg5syZXHXVVW2y7E9ERERERERagWFAzlpY/5UZcOxe6r8/tgv0OAO6n2neuLR6v6XfewKceAPMutsMUH75uxmUjL4Hjruy5rhDGpKB023gcLmpcnlweBdz3e2/7vRQ5fbgcNbsq9nvqeccNc+v/U16o5759vf9pn29X7yvc4z/hvq+rb/vJqOeg+oeU/c8TrfHF26UVLoO2r/hUATaLN4gI6BWqBFAeHAA4UG11u02v2P8nhNs/rQHWPd7P8Lp9lBQVkVeqYP8Uv+fedWPyxzklVSRX+bA6TYo8VaybMsvb9DriAuzEx8RRFyYnbiwQPpYt9OnYiGdC38lbu8KLEatMCgwzKwKqQ5Dojs07A2zWMzqqZh06HkWYP5+i/fmUrxtGc5dK7DlrCRs71piSjdjqywyp+HyTsUVCrQ3bFQY7cn0pLPLSGeNpyNrjTSK8e/dGxRgNQOOCDPgMIMOcz2h1npsWBC25p6izDAgfzNk/maGIDsWQN76useFxPhVg5A6EAJbsTdFSDR0Gm4u1RylsGeVf2VJzloozzOnEdz8fc2xwVG1qkoGmD9ju4D1MEOlpuAoransKMqsFX54g4+S3f6VUPsTlghR7c0/A1HVS63HITFHbjjUhhwTwcjRYPDgwX6PS0tLmTZtGl999RVZWVm4XC4qKirYsWPHAc/Tr18/33pYWBiRkZHk5OTs9/jQ0FBfKAKQkpLiO76oqIg9e/b4KikAbDYbgwYN8lV27M/rr7/O5Zdf7nt8+eWXM3LkSF588UUiIiJYvnw5AwcO3G8ly/Lly7nuuusOeI2G2Pd9dbvdPPbYY3z44Yfs2rWLqqoqHA4HoaGhAKxduxaHw7HfKbGCg4O54ooreP3117n44otZunQpq1at4osvvjjssYqIiIiIiLSG6uljggKsRIcGYg9opql0jnZul3kDc523MmTv1lo7LdB+MHQ/A3qcaTaStliocnnYW1pFQVkZe8uqKKty43DF4ej9CvGxPzJg7dNElW2HL28l94eXmN3+JtaHDa431PAtTrcv1DB/ekMLt+eQp/852tlwc5Z1PjcGfEl7Sy4bjXas9aSzxkhnrSWNdUYaroBQIrzBRJg9wLtuIzw40AwwgmrCCr8wIzjA3Fe9bre12J+xQJuVpMhgkiKDD3qsYRgUV7r2CVEc5JZWke+tOMkvrSK/rIq8EgclDhdOt0F5cT5ppSsZZf2dUbbfSbQU+p13g6cdczwDmOPpz0ZLHyJ3hxFfbCd+fS7x4UXmFF77TOsVEmjzTlu171RWtfp4lDqo8oVWvbwLBOGkq2UXvazb6WXZ7vsZaSmnt2U7va3b/cZXFtqOyrhekNSX4A4DCE0bgCU6rXVuSjsrzSqLHd4gJHMBlOfXPS4uwxuCnAgdToK4rm0jNDgQe7gZ2qSdVLPNWQF71vhXluSsgcoiMxje+nPNsUHhZgVQdVCS0t/8e9TWhLe+DcOcxqp2pUd1lUeRd6qrir0HP481EKLa1QQe0d7QI6qDOfVbZGrrBlfHkGMiGAkJtLHmofGtct2mEhbmn1BPnTqV2bNn8/TTT9O1a1dCQkK48MILqaqq2s8ZTPs2F7dYLAcMMeo7vr5vVTTGmjVr+O2331i4cKFfXxG3283777/PddddR0jIgf8CONj++sbpdNYtedz3fX3qqad4/vnnee655+jbty9hYWHceuutvvf1YNcFczqtAQMGsHPnTmbMmMGYMWNIT9e8ryIiIiIijeKshNJsc4qhshywBkCAHQJCICDYXA8M8W4Lrlma8ibIMaDU4SK7qIKsokqyvUtWcSV7iirNbcWVFJT5/zsz3B5AdGggsWFBRIcGERsaaP4MCyImLIiY0EBiQ4N826JDA5uvL0FbV1UGm76H9V9jbJiFpaLAt8ttDWJP/ElsiB7B8tCTyKyKZO/mKgpW5FFQtpu9ZVWUOFwHOHkcgTzM5bbZ3BLwCQnlm/jDhlv4wT2AR12Xsdlod1hDDwqwYrdZsQdasQeYU/zYA6y+n7W31Wy3+a379gdaCbKZ26373FCu7/6yhYYcw0GP2feofY+p7ym1qyss7ipSt39Ox3X/IrS05ouox1k2cZx1k++xgQVLbCdvw+++Nc2/I9sdNd/qtlgsRIUEEhUSSJeEAxxoGJC9Euf6b/Fs+JagrMV+VSFV1hDWhx7HooBBzPH0Z015NAVlDjwGUAG5FWVszm26htLRoYE11R0Rdm9VR/+aqo/IYGzhQVCxu96puMLKdxFWvgsyZ8Ni70n3mYqL5L4Q391/qq+mUJrrDUB+M6tBspb7T7MHYLNDu+O8U2KdZAYiYXFNO47WEhgC7QeZSzVXFeSu868syV4JVaWwY765VAsINvvR1O5Zkthr/78nV5XZ18MXduwbfuwEt+Pg47ZH+YcdvkqPNHM9PKntB1XHiGPi/xgtFkuTTWnVVsybN4+rrrrKN4VVaWkp27Zta9ExREVFkZSUxKJFixgxYgRghhtLly5lwIAB+33e9OnTGTFiBC+//LLf9hkzZjB9+nSuu+46+vXrx2uvvUZBQUG9VSP9+vXj+++/5+qrr673GgkJCWRlZfkeb9y4kfLyg5d5zps3j3PPPddXzeLxeNiwYQO9epnfLMjIyCAkJITvv/+ea6+9tt5z9O3bl8GDB/Pqq6/y7rvv+jViFxERERE55nk85jdcS3aboUeJdyne7V3PNtdr3UBuFIut/sDEL0iptT+w1v6DbT/QeW32NnWjwzAM9pY7ySqqYE9xpV/wkV3rcekBb7zXCAqw4vYYvubQpQ4XO/dWNHg8oUE2YkKDiAkLNH/WCk1qApaa/bHN2eS5iVRUuckvc7C3zElBeRUFZQ4Kypw49u4mec8cuhb8TI/yJQRhfknPAuw1wvnBM5Bv3YOY6+lH+Y5g2AFQ7F3qslktxISa70t4cECdUKIo4Fpe4FJOzZ3J8Xn/YYxtOaNsK1nb/kJWZfwRQuMOHFrUDjWqAwybFWtzTwPUljkrYOlbMO9580YpQEgsDPk/6HaaeWN2zyrIXgXZK7GUZkPBFnNZ83nNeYKjvWGJNyhJ7gMJPcy/N44mFYWwZY7ZJ2TTd1Cajd/XbOO7mz1yuo4jKP1k+gbY6Qtc493t8RjsLa+pNskr869CqT21V16pA4fLQ1yYvVbPDv9prRJ8fT3sDa++Cfafist8XXt9v2Pz970CctaZFQu1puICzCqAxB7+gUlSH3PaqIbweMxpsKqnxMr8zfw87SssoWZKrA4nmjf7j7bP04EEBEFKP3PhCnOb2wX5G/dp8r4Cqkpg12JzqWYNhKRe5vsWHO0/zVXpHvYzeV8tFohI2Weaq/ZmpUdUe3MJjmqe1y5N7uhKC44hGRkZfPLJJ5x99tlYLBbuu+++g05f1RxuuukmHn/8cbp27UqPHj148cUX2bt37/7nr3Q6eeutt3jooYfo06eP375rr72WZ599ltWrV3PppZfy2GOPMWHCBB5//HFSUlJYtmwZqampDBkyhAceeICxY8fSpUsXJk6ciMvl4uuvv/ZVoIwZM4aXXnqJIUOG4Ha7ufPOO+tUv9QnIyODjz/+mF9//ZWYmBieffZZ9uzZ4wtGgoODufPOO7njjjsICgpi6NCh5Obmsnr1aiZPnuz3WqZMmUJYWJgvvBIREREROepVldUTdmTvE4Jkg6eBDWxtdohMMefaxjAbyDorweUAV4X3Z6X/N2gNt/nN0arSZnmJBx5vUD3hSvV6CARFQFCYOWVIkHexh5vbgsLBHlH/elCY37fO3R6D3BJH3dBjn/WqBvY+iAwOIDkqmOSoEFIig73r5pISFUxypNko2DCgpNLF3vIqCsqrKCyvoqDMyd6yKvaWexdvSFC9r7C8CpfHoLzKTXlVBbsKGx6mBAda/SpPqqtRzGAl0PvYP2AJCbQdUn9Hl9vD3nKneXO21HwtBWU1S+3He8vM11/prH5/DbpYdnOqdQmn2BYzwLIZq6Xm5tp2TyKzPYOY7R7MYqMbocF2YiOD6OYde/USExpEnPd1xnoDorgwOxHBAQ0MKYZC3h0w+36s67+m984P6J33DYy4AwZe3/TfZj8aOUph8evw64s1jcDDk2HozTDoKvPPIpgBR98La55Xllfr5vlK82Z63nqoLKznBnqAOcWPLzDpY1aZhB+oDKONMQzztW78FjZ+Z97M9+sVEgqdRtb0Cok58CwaVqvFO2WWnW5JEQe9vMdjtExwFxJTtxeGq8r83fpVl6www5Lqx7VFp9WtLonqYIZvu5bUTImVudD8vOwroWfNlFgdToDYzkdNFVKTsQVAYk9z6T/R3ObxmNMVZi2H3ctrApPKwpr1+gQE15rWqp7+HhGp+rv0KKJg5Aj17LPPcs0113DyyScTHx/PnXfeSXFx/d8uaU533nkn2dnZXHnlldhsNq6//nrGjx+PzVZ/Iv/FF1+Qn59fb1jQs2dPevbsyfTp03n22Wf59ttv+ctf/sIZZ5yBy+WiV69eviqTUaNG8dFHH/Hwww/zxBNPEBkZ6ataAXjmmWe4+uqrGT58OKmpqTz//PMsWbLkoK/n3nvvZcuWLYwfP57Q0FCuv/56JkyYQFFRke+Y++67j4CAAO6//352795NSkoKN954o995Lr30Um699VYuvfRSgoMPPleniIiIiEib5nFDaU49VR5Z5rYS75RXjqKDn6taWIL5rcvIVIhINm82RKaYPyOSze0NbS7qcdeEJL7FYd54auh2Z639tUOXA26v8G+i6q4ylwbMtNEYBhYclmDKLSGUeIIp8dgpJZhSI5gqgrEbwSQQTCghJBrBZHj3lVlDCAiOICQikrDwGKKioomOiSE2JpaU6DCSvKFHmL1htwYsFogKDSQqNJCO+zQk3u/YDbNZsxme1IQoBb4wpVawUub0BSxOt0Gl08Puokp2F1U2+L2yB1i9lSm1wxNzaq+QoAAKK6ooKK09Bif5pQ6KKxtWOVPNiofBlg2cFrCUU21LSCPLb392eC+yksdQnH4Kgcm9GRZh5xxvyBMU0IyVRfEZcOl75rf3Z91j3rz+9h5YPB1OeQh6nKWbqvWpKISFr8JvL9f0CIjqAMNuhQGXmyHngYTFQ5fR5lLN5TArS7JX1QpMVpo3ZnPWmAsf1BwfnuwNSfrU3ECP7dJ2pgesLILNP8Km2eb0cCX+n3niu5khSMY4SB/arFUMrVrNFBBU8/upZhhm5cG+YUnhjppl3Zc1x9ujwFkGnn3+3gkMhXaDaipC2g82/zsojWe1QlwXc+lzgbnNMMzfRXUwUlW2z5RXHcw/y/o78phhMQ63YUQrKC4uJioqiqKiIiIjI/32VVZWsnXrVjp16qQb0q3A4/HQs2dPLr74Yh5++OHWHk6r2bZtG126dGHRokUcd9xxzXINfdZFRERE5LAZBjiKoSQbo3h3rSUbS4kZelhKsrCU5WAxGlZ94AkIxRmWjDM0karQZKpCknCEJlEVkkhFSBKO4GQq7XG4rIF4PAYew6yAMAwDt2F4181tHqN6AavFnCbZZrFgtYLVYsFmtWC1WGqtmzfMrPUedyjPMffbrBbz2lbzGIt3m9ViwWq4sPiCk/qCFO82ZwWV5cWUFu+lrKSQyrJinOXFuCtLMBxlWJylBLrKsHsqCLNUEkol4VT6VR40qYCQA1Su7Lvdu4QnQGR7M7QKjjz4NQ6RYZjTdRWWO2sFKDXBSUFZVb37qtyHN4uCxYKvGsVXwRFeU5WSYHfTuWQR7bJ/IGrnD9gqajU9tgVBpxFm8/Tup5vvUWvzuGH5O/D9wzXVDx2Hw/hHzWlkBMry4bdXYOG/zb8LwfxG/vC/QL9LwHbwmScaxTCgeJc3LFlZU11SsIV6p/AJCDa/BZ/Ux1t10AeSerfMVD2GAXtWm1Uhm7xVIbVv5AeGmp/5ruPMabJiOjb/mI40FXvN97B2WJKzrqZqMiLVWw3iXZL7Nv1nTuQYc6DcYF8KRuSwbN++nW+//ZaRI0ficDh46aWXmDFjBr///js9e/Zs7eG1OKfTSX5+PlOnTmXr1q3Mmzev2a6lz7qIiIjIsc0wDMqq3OZ87N5513NLzfnZc0sd5JU4KCgpJ7xkM0lVO4kz8kkwCkggn0SjgAT2kkgBYZaGlTe4DQu5RJNtxLDHiPX+9K4TQ7YRS44RQwkh1N/S+OhVHbpYvcGJX9ji/VnpdDe4n4c9wOqdwspOWgS0DzdoH+omOdhFot1FfGAVkTYHVmeZOfVP9dRhvvUycJSYP/22l9b9hvKhCoqAqHZmABDZzlz2fdyM4cm+DMOcrssXmvim86qpSimvcvum3IqtNQ1X9RIVEoht32+il+bChm9g/dfmt+VdtaYCC46CjPHQ4wzoMrZFX2+jOErgl7/Dry95GwdbYMAfYMx9ZpXWsagk25wua/Hr4PT2I03oCSOmQu/zwNrC/W0cpWYFiW86rlXmDXXnfpqQV0/P5JuKq48ZTBzuN90ri729Qr71VoXs9t8fl2GGIBmnQNrJB6+kkbpcVWY/DHuEWaGg6gSRJqVgRDeLW0xmZiYTJ05k1apVGIZBnz59eOKJJ/ymtTqWzJkzh9GjR9OtWzc+/vhj+vbte/AnHSJ91kVERESOPtXflPc1mfWGHrkl3tCjtCYEySuposLp9nu+nSoGWDYzyLqe463rGWTdSKSl/KDXLTZCyTa84QYxvvU93vAj24gljygMi9VXWVFdNVGngmKfcKB25YVfJYYV73PrVmL4KjS8zzF7RpjVJG7DfJ98FSUecFdXlnjMqhOPBzy1jzFqPa6uUjGq16uPqznG8O53e5r+n8sRwQGkRAWTFOnt3xEV4uvjUd3TIyok8JD6ZByUYZiVLFVlZlNaR2k96/sJWRwl5nRqxTvNKXUawi88Sa2pNonyBieRqW2zSW3eJlj/Faz72vyWfO1v8kelmUFI9zMg/eQj69vdhTvguwdh1cfm48Awc6qoIVMgKLRVh9ZiCneYDdWXvuUNiYCUATDidvN3am3Gac4aq7pHwr69S6qbwe/LHmlWk9Ru9J7Yy+xvtD/VVSGbZnt7hfzmH54GhJhVId7G6cR2atrXKCLSxBSM6GaxHAP0WRcRERE5MlT3WDBDjipya1V4mKFHlS/8yCt14Ghgw2yAGIo5OXATw+ybOI51dHFtIgD/igB3QBiOuB64w5JxhyfjDkvBiEjGE56CJyIFS0QylqCwWoHGgaeTOtZUhzBuY58pvuoLZAyzKfC+04C5PQZBAdZG9fNo0xylULzbnBLI93MXFNV6XF8T4foERdQKS2pVm9SuQGnu8MTjgV2LYd1XZmVI3gb//Sn9ofuZZiCS1OfI/4Z35iKYdTfsXGg+jmwHYx+Avhe1rWCgKeVvhl+ehd/fr7nx3+FEszF917FH1u+0vKBWVYk3MMldZ/Y32pfFCnFdazV672v2oclaUROG1KkK6VqrV8gwVYWIyBFFwYhuFssxQJ91ERERkdZjGAbFlS7/kMMbfNQEHt4gpNRBVSPCDoCwIBvxEXbiw+3EhwcRH24nITyIjrZcOpevJKV4GdG5Swjcu6nuk8OTIX0IdDjJbN6a1KftNO+VY4ej1GzOXLTTG5bsNr/pXrzbG6AcQnjiV21Sq+okqp35bfnG3Nx2VsCWn8zKkPXf1PTgALAGmL04epxp9guJat+ol35EMAxY/QnMngZFO8xtqcfB+MfMvz+OFjlrYe4zsOo/UN0nqdNIs0Kk47AjKxA5ELfTDPT27V1Snnfw5waEQKfhNWFIbOfmH6+ISDNRMKKbxXIM0GddRERE2hLDMMgqqmTDnhI27ClhfXYpG/aUsDGnhEqnx6/59b5Nr23e6Zesln2mVrJ6p1ay1JqiyVrPOWo/rnOeWo2zLQc/p9X7nNrNuN2GQX6pY5/praoa3eg53B5QE3L4Qg878RFBvvVE7/aQIBu4XWaj1h2/wY755s/aN2+rJfQwA5C0IebP6PSj52afHN2qymoqTIr2qT4p3m2GKg0OT8JrghK/fifta0IVw2P2C1n3FWz+oaa3BJjBSsYp5nRKGae0zSm+moOzwmw+PvdZc+o0gF4T4JQHj+xm2ruXwc9Pw7ova7ZljDd7iHQ4ofXG1ZIMA0r3mAFJ9oqaKpP8jRDTqaZXSPrQA0+3JSJyBFEwopvFcgzQZ11EROQoV7jDbHxaWQg9z4G4Lq09Ip/8Ugfr95SwIbuE9XvMAGRDdgklDWwsfTSJCA4goZ6Aoyb8qAlCggMP0szXUQo7F9UEITsX1228awsyv9WddqIZhHQ4EUJjm+8FirS2qjIozqpbbVI7RKnYe2jnjmxnBiE9zjCnDAoIatqxH0lKc+CHR2DZW2aAZAuCk/4Iw/9yZIVEOxbAz0+Z00QBYIFe55ivI6V/qw6tzfB4jt4p00TkmKdgRDeL5Rigz7qIiMhRpqoctv8Km76Dzd/XneM+fRgMvNy8wRMU1iJDKql0ssEbfKzPLvFVg+SV1jOPOWZlRuf4MLolR9A9KYJuSRF0SwonMiSwQT0Yaje99vVt2Kcptu85ntrNs+s20q7dPLumUTf1Ntr27Pc85jEWC8SF1Qo9agUeBw07DvgGZ3srQRaYP7NXguHfTJ3gqJopsdKGQOpAzfcusi9feLKrnn4n3im8qsOTpL41zdNT+qu6al/Zq8z+I1t/Mh+HxsPou+G4SW13Sj7DgK0/m4HItrnmNovV7Jky7DZI7NG64xMRkRajYEQ3i+UYoM+6iIjIEc4wzGapm743w5Dtv4LbUbPfYoX2x0NgKGyZA3j/tz0oAvpeAAOvgHaDmuSmXqXTzaacUl/4sX5PCRv3lLKrsGK/z0mLDaVbUgTdk8O9PyPoFB+GPeAwgoKjmWGYYVf1lFg75sPebXWPi07zD0ISeuibvSJNoaocXJWqsGoIw4ANs+Dbe81plwASesL4R81G5W2FYcDGb80ps6obyVsDYcClMPTWNlVpKSIiLaMxwUgbjftFRERERI5CFXvNkGPT9+b89sW7/PdHtoeuY6DrOLM5bEi0ub1oJyx/z5zipHA7LJlpLgk9zSqSfpdAeMJBL+90e9iWV1ZrGqwSNuwpZXt+GZ79fF0qOTLYWwES7q0AiSAjKZzQIP1T4oBcDsj6vVYQ8htUFOxzkAWS+9T0BulwktkXQUSaXlCoucjBWSzQ/TQzBFn8Osx5HHLXwtvnmw26xz8KCd1bb3weD6z7rxmIZK8wt9nsMGgSnHwzRHdovbGJiMgRQxUjR5FRo0YxYMAAnnvuOQA6duzIrbfeyq233rrf51gsFj799FMmTJhwWNduqvNIwx3Ln3UREZEjhsdtNoDd9J0ZhuxabM7dXi0g2Gx62nUsdBlr3mg6UAWIxwPb55kByZrPzW8/A1gDoNtpcNyV0GUsHouNzL3ltaa/MqfD2pxbitNd///+x4QG+io/fD8TI4gKDWzCN+QoVlEImQtrgpBdS/wrgAACQqD9YG81yElmRdCRNHe/iBybygvMEGLhv8DjAosNBl8Do+6CsLiWG4fbBas/gbnPmBWXAIFhcPxkGDIFIpJabiwiItImqWLkCHP22WfjdDr55ptv6uybO3cuI0aM4Pfff6dfv36NOu+iRYsIC2va+aenTZvGZ599xvLly/22Z2VlERMT06TX2p+KigratWuH1Wpl165d2O32FrmuiIiISIMU7/ZWhHxvVofs2xQ4vrtZEdJ1jBmKBIY0/NxWK3Qabi5nPIWx8j84F79B0J7lsO5LWPcl+dZY/uMazrvOEWwzUuqcIizI5tcDpHuyWQGSEG7Horn2G64ws2ZKrB2/Qc4afNOdVQuNr5kSK+0kSO53bDd3FpEjU2gsnPaYGUB8ex+s/woWvQorPoSRt8MJ10NAM/673FUFK96Huc/C3q3mNnsUnHiD2SBe06OJiMghUDDSBkyePJkLLriAnTt30r59e799M2bMYPDgwY0ORQASEg4+nUJTSU5ObrFr/ec//6F3794YhsFnn33GJZdc0mLX3pdhGLjdbgIC9EdJRETkmOWsNG+Ob/7eDERy1vjvt0dB55FmGNJlzCFP8VFQVuVrfu7rBZLdjuLKO+hu2cFFtp84z/YLcZ4Crrd+zvX2z1kV0JsVCWdT1vVsurRLoFtSBO2iQxSANJbHbf5eq6fE2vGb2cx5X7FdakKQtCHm/PZ6r0XkaBHXBS5912x0PutuyF5p9iFZNB1OeQh6nt20f+c5K2DpWzDv+Zq/c0NiYcj/wQnXqeJOREQOi7r4tQFnnXUWCQkJzJw50297aWkpH330EZMnTyY/P59LL72Udu3aERoaSt++fXnvvfcOeN6OHTv6ptUC2LhxIyNGjCA4OJhevXoxe/bsOs+588476datG6GhoXTu3Jn77rsPp9MJwMyZM3nwwQf5/fffsVgsWCwW35gtFgufffaZ7zwrV65kzJgxhISEEBcXx/XXX09paalv/1VXXcWECRN4+umnSUlJIS4ujv/7v//zXetApk+fzuWXX87ll1/O9OnT6+xfvXo1Z511FpGRkURERDB8+HA2b97s2//666/Tu3dv7HY7KSkpTJkyBYBt27ZhsVj8qmEKCwuxWCzMmTMHgDlz5mCxWPjf//7HoEGDsNvt/PLLL2zevJlzzz2XpKQkwsPDOf744/nuu+/8xuVwOLjzzjvp0KEDdrudrl27Mn36dAzDoGvXrjz99NN+xy9fvhyLxcKmTZsO+p6IiIhICzIMyNsIv/0T3rkI/tYR3poAv77oDUUsZlP0EXfANbPgji1wyVvm3OfeUMQwDMocLrKKKliXXczCrQXMXrOH/yzZyeu/bOW57zbw4H9X85cPf+ey137j+Ee/47iHZzPx379x/+ereWfBDhZt20txpQub1YI7oRfLet7BO0NnsfSk5ylLG4NhsdLHtZo/ZD3BdQvGM2bDI7QvXYVu0zeA2wU7l8Avz3l/x53gn8Pg66mw6mPzBp01wPw9D5kCl7wNUzfCzUthwstw3BUQ31WhiIgcnTqNgOt/gnNegvAks4rjwytg5lmwe/nhn99RCvNegOf6wf9uN//ODU+CUx+FP6+CEVMVioiIyGE7Nr7mbhjgLG/56waGNugfQwEBAVx55ZXMnDmTe+65x/cNvo8++gi3282ll15KaWkpgwYN4s477yQyMpKvvvqKK664gi5dunDCCScc9Boej4fzzz+fpKQkFixYQFFRUb29RyIiIpg5cyapqamsXLmS6667joiICO644w4uueQSVq1axTfffOO76R8VVfd/RsrKyhg/fjxDhgxh0aJF5OTkcO211zJlyhS/8OfHH38kJSWFH3/8kU2bNnHJJZcwYMAArrvuuv2+js2bNzN//nw++eQTDMPgz3/+M9u3byc9PR2AXbt2MWLECEaNGsUPP/xAZGQk8+bNw+VyAfCPf/yD2267jSeeeILTTz+doqIi5s2bd9D3b19//etfefrpp+ncuTMxMTFkZmZyxhln8Oijj2K323nzzTc5++yzWb9+PWlpaQBceeWVzJ8/nxdeeIH+/fuzdetW8vLysFgsXHPNNcyYMYOpU6f6rjFjxgxGjBhB165dGz0+ERERaWKVxbD1p5opsgp3+O12hiSQnzKCXXFD2Bp5PDnucIorXBQvcVL0ywqKK5zmUumiyLvu2l+38wPoEBviNwVWt6QIOieEYQ+w1TqqN3AVFO2C39+DZW+bN62Wvmku8d3Nhu39J0J44mG9LUcNt9PsBbNtLmybB5kLoKrU/5igcOhwQk1FSLtBENS009aKiBwxrDYzBO59Hsx7zvxywPZf4N+joP+lMPY+iExt3DkrCmHhq/DbyzXTUEZ1gKG3wMArIFC9NUVEpOkcG83Xq8rgsUb+B7kp3L27wf9YWrduHT179uTHH39k1KhRAIwYMYL09HTeeuutep9z1lln0aNHD1+lwYGar3/77beceeaZbN++ndRU87345ptvOP300w/YNP3pp5/m/fffZ/HixcD+e4zUbr7+6quvcuedd5KZmenrcfL1119z9tlns3v3bpKSkrjqqquYM2cOmzdvxmYz/yF/8cUXY7Vaef/99/f7Pt1zzz2sWbOGTz/9FIAJEyYwYMAApk2bBsDdd9/N+++/z/r16wkMrNsotF27dlx99dU88sgjdfZt27aNTp06sWzZMgYMGACYFSMxMTG+38ucOXMYPXo0n332Geeee+5+xwnQp08fbrzxRqZMmcKGDRvo3r07s2fPZty4cXWO3b17N2lpafz666+ccMIJOJ1OUlNTefrpp5k0aVK951fzdRE5ahXugCUzYfm75j+Kg8LMJTCsZt1vCTe/jFDfvvqeExhm9mmQY55hGJRVuX1BRVHt8KLcQXDeKpJz59GxcD7p5asJwO17bpURwCJPd37y9ONnT3/WGR3gEGoxAqwWokICiQoJJCIkkMjgAKJCAon0bosMDiQuPIhuSRFkJIYTZj+E7zUZhrdh+9uw+jNwVZjbqxu2D7wcup4CtmPjO1MAuBxmc/Rt88wbeZkL636RKjja7AHTcaj5M6nPsfUeiYg0RmEmfP8QrPzQfBwYagYaJ9908PsiZfnw2yuw8N/gKDa3xXaG4X+BvherN5OIiDSYmq8fgXr06MHJJ5/M66+/zqhRo9i0aRNz587loYceAsDtdvPYY4/x4YcfsmvXLqqqqnA4HISGhjbo/GvXrqVDhw6+UARgyJAhdY774IMPeOGFF9i8eTOlpaW4XK6Dfojqu1b//v39Gr8PHToUj8fD+vXrSUpKAqB3796+UAQgJSWFlStX7ve8brebN954g+eff9637fLLL2fq1Kncf//9WK1Wli9fzvDhw+sNRXJycti9ezdjx45t1Oupz+DBg/0el5aWMm3aNL766iuysrJwuVxUVFSwY4f5bdLly5djs9kYOXJkvedLTU3lzDPP5PXXX+eEE07gv//9Lw6Hg4suuuiwxyoickTweGDzD7DoNdg4CwxPzT5XJZTnN+31AkL8g5Wg0EMLWWo/JzD0qAhcnG4PuwsryCyoIHNvOSWV5jSX1V+lqf5GTc1jw+8xvv11tx/sub5Dq5+7n+MbMp7qHx7DoNThorjCW6lR6R+AuGtVbcRTxHDrCkbYVnCudSXxlmK/17TFk8zPnn787OnHb55elGN+MSHcHkC7kEAi6gk1zMcBNY9D/beHBNqav9+HxQIdh5nL6X+DVZ+YIcmuxb6G7YQnmd/wHXg5xGc073hag7MSdi4yA6Jtv5jrrkr/Y0LjIP1kSPe+V4m9joo/0yIiLSK6A1zwqtkQfdbdZuXdnMdhyRsw9n7od0ndv1NLss1Kk8Wv14TTCT3NqbJ6TVAYLSIizerY+K9MYKhZvdEa122EyZMnc9NNN/Hyyy8zY8YMunTp4ruR/tRTT/H888/z3HPP0bdvX8LCwrj11lupqqpqsuHOnz+fyy67jAcffJDx48cTFRXF+++/zzPPPNNk16ht3/DCYrHg8Xj2czTMmjWLXbt21Wm27na7+f777znllFMICQnZ7/MPtA/A6v2ftNpFVPvreVI79AGYOnUqs2fP5umnn6Zr166EhIRw4YUX+n4/B7s2wLXXXssVV1zB3//+d2bMmMEll1zS4OBLROSIVZYPy982/0G8d1vN9k4j4fjJkDLA/IdyVbk5rU1Vmbk4y2rW/ZZS7/He9apy/+dUBy6uCnMpz2va1xMY5h+yBIWZc2AHR5nfPg+OgpBo/8e1t9mjmv1GrMdjkFPiIHNvOZkF5b4AJLOgnJ17K8gqquAQZng6IgXi4njrBkbZVjDKtpIebPXb77CGsCNqMNkJw9ibOpyAuM50DQ5kUK2wI9weQIDtCLp5HhwFg682l5y1ZkDy+/tQusecCmXec9DhJHN6lF4TwB7eygM+RFXlsHOhtyJkHuxcDG6H/zFhCd6KEG8QEt9dQYiIyOFqP9jsr7X6U5j9ABTtgM9uhIX/gvGPmQF04Q6zofrSt2r+bk7pDyNuh+5n6u9iERFpEcdGMGKxHBHz/1588cXccsstvPvuu7z55pv88Y9/9H2DcN68eZx77rlcfvnlgNkzZMOGDfTq1atB5+7ZsyeZmZlkZWWRkpICwG+//eZ3zK+//kp6ejr33HOPb9v27dv9jgkKCsLtdnMgPXv2ZObMmZSVlfkChHnz5mG1WunevXuDxluf6dOnM3HiRL/xATz66KNMnz6dU045hX79+vHGG2/gdDrrBC8RERF07NiR77//ntGjR9c5f0JCAgBZWVkMHDgQoM6UYfszb948rrrqKs477zzArCDZtm2bb3/fvn3xeDz89NNP9U6lBXDGGWcQFhbGP/7xD7755ht+/vnnBl1bROSIYxjmt7UXvWZO61P9D+LgKBhwGQy+pnm+sW4Y5jfE64Qm+4YpZf5BjHOfUKa+fdWc3gCmLPcQB2kBeySE1BemRO8/XKl+HBiCYRjsLXeaocfeusHHrr0VVLn3/0UEAHuAlfYxIXSIDSU2NMg3Q5TFu2LxPWafx/vs9xVC1FRENPi5vv3+1RS1Hx50PBbziDB7gK9CI8mdRbu8+cRlzyV01zyszjL/F5/cD7qOha7jsLc/gYyAII7C+glTYk8Y/yiMfcCs1Fr6FmyaDZm/mcvXd0Cf82DglWZvjbbcSNxRan47efs8MwzZtQQ8+3zBJTy5ZlqsjsPNv2fa8msSETlSWSzQ53zofgYs+Af8/IzZx2nG6dD+BNi9FDxmH1A6nGgGIl3H6e9kERFpUcdGMHKECA8P55JLLuGuu+6iuLiYq666yrcvIyODjz/+mF9//ZWYmBieffZZ9uzZ0+BgZNy4cXTr1o1Jkybx1FNPUVxcXCdgyMjIYMeOHbz//vscf/zxfPXVV75eHtU6duzI1q1bWb58Oe3btyciIgK73e53zGWXXcYDDzzApEmTmDZtGrm5udx0001cccUVvmm0Gis3N5f//ve/fPHFF/Tp08dv35VXXsl5551HQUEBU6ZM4cUXX2TixIncddddREVF8dtvv3HCCSfQvXt3pk2bxo033khiYiKnn346JSUlzJs3j5tuuomQkBBOOukknnjiCTp16kROTg733ntvg8aXkZHBJ598wtlnn43FYuG+++7zq37p2LEjkyZN4pprrvE1X9++fTs5OTlcfPHFANhsNq666iruuusuMjIy6p3qTETkiOYohZUfwaLpsKfW1IkpA+D4a6HPBWa1RXOxWCAwxFzC4pvuvB6PWX1SX1WLo8Rs2l1ZCJVF5lJRvV7o/9hVARjgKDKXQ1BFIMVGKEVGKG7CCDfCSCWMcCOUVMLoaYRSRDiltjACw2MIi4wjMiaB2Lh4EhMTaR8bQYeYEBIi7M0/vVNLMAzzd7HtF7Nh+uLvoGCL/zGh8dBljBmGdBlzbDYjDwiCnmebS3FWTcP2gs3mz2VvQ1yGt2H7pRBxaP8/16Qqi80gZNsvZhiye1nNTbZqke1qeoR0HG7OV380fK5FRI4UgcEw7M/mF19+fAyWvmFW84FZHTzidrNiT383i4hIK1Aw0sZMnjyZ6dOnc8YZZ/j1A7n33nvZsmUL48ePJzQ0lOuvv54JEyZQVNSwGydWq5VPP/2UyZMnc8IJJ9CxY0deeOEFTjvtNN8x55xzDn/+85+ZMmUKDoeDM888k/vuu8/X2Bzgggsu4JNPPmH06NEUFhYyY8YMvwAHIDQ0lFmzZnHLLbdw/PHHExoaygUXXMCzzz57yO/Lm2++SVhYWL39QcaOHUtISAhvv/02N998Mz/88AO33347I0eOxGazMWDAAIYOHQrApEmTqKys5O9//ztTp04lPj6eCy+80Heu119/ncmTJzNo0CC6d+/Ok08+yamnnnrQ8T377LNcc801nHzyycTHx3PnnXdSXOw/L/k//vEP7r77bv70pz+Rn59PWload999t98xkydP5rHHHuPqq68+lLdJRKRtylkHi6eb0/VUN9QMCIY+F8Lx10C7Qa07vsNltdb0GyHh0M/jcviCkqqyAvLzctlbkEtJYR4Vxfk4y/biKS/E6igixF1CpKWcKMqItJQTSRk2i0EQTuItRcRbGvD/Bw4g17tUs0fWrUIJCDanIPNbjHq2eReMA++vc479HXuwaxzkmNp9avx+XwHmt1W7jjWX5P6asqO2yBQYfpt5I2vHfG/D9k8hfyN894DZWLfbeDMkyTgVbHX7ujWLikLY8ZvZKH3bPMhaXvd3HJVWqyJkGMR01M02EZG2IDwRzn4OTrgOVn4M3U83KxFFRERakcUw9m2V2fYdqLt8ZWUlW7dupVOnTgQHB7fSCEUOzdy5cxk7diyZmZkHra7RZ11E2jRXldnQedF080ZmtdguZu+Q/pdCaGzrja+VuT0GWUUV7Nxb4Z3yqoKdtaa+2lNSWaeZ+b6iQwPpEBNKh9gQOkSH0CnKID3USfsQB4mBDuzO4vorU+rbtu90Ukeb6DToYk6PRacREBx58OdIjcpiMxxZ9nbNN30BwhKh/0QYeAUkdGvaa5YXmMHMtnnm3yHZK+sGITEdvY3SvWFITHrTjkFERERERI4oB8oN9qWKEZE2wOFwkJuby7Rp07jooosOecoxEZFWV5gJS2bC0jehLMfcZrGZ3ww8/lpz2oSj+Nv5hmFQ6fRQ4nBSUumisNzJzr3ltQIQM/jYXViB6yAdzkODbL7go31MqK/nR/W2iOAm/Ka+q8qs5vGFJ3trQhRnJVhtYLGa3763WPe/YDn4Mb7zHOw4Sz3nPsgx9W232swqGFUOHLrgSBg0yVxy1sFyb8P2shz49QVz6XCiWUXS+zywRzT+GmX55pRY1T1C9qwC9vkzEtvFG4J4w5Co9k3y8kRERERE5NijYESkDXjvvfeYPHkyAwYM4M0332zt4YiINI7HA1t+MKtDNnxT863u8GTzRupxkyCqXeuO8SAMw8Dh8lBS6aLU4aK00kVJpZMS73qpw/9x7eOKK53muvfxwQKPaoE2C+2izbCjfXXlR0yoN/wIITYsqOX6fAQEQUB80/ZekaNTYg849RFvw/ZvzYbtG781+31kLoD//dUMRwZeDmkn7T+QKs2tmRZr+zzIWVP3mPhuNdNipQ81p/kSERERERFpAppKS+QIpc+6iLS68gJzap3Fr8PerTXbO42AwZOhx5kt0n/A4XLXCi9qhRbeqo2SWsFG9XHFlf6BR6nDhdPddP9LZLFAuD2AyOBA2sWE+FV+dPBWfiRFBmOzqopBjgIl2WYFybK3IH9Tzfa4rjUN26GmUfq2eZC3vu55EnrWTIuVPrRtNHkXEREREZEjhqbSEhERkeZhGLBzsdlMfdUn4HaY2+1RMOAPMPiaw+414HC5WbO7mBU7i9hTXFmrMsMMO6oDkNJKFyUOF1Wu/TTZPkTh9gAiggMItwcQHhxARHAgEfaAmu3efZHBgb71iODq5wQSERxAaJCt5ao9RFpbRDIMuxWG3mJWjSx7C1Z9aoYk302D7x6kzrRYAEl9vBUh3iBEFUsiIiIiItJCjtpg5AgshBFpFH3GRaRFVZXByo/M6bKyV9RsT+lv9g7pcwEEhTX6tIZhsD2/nOWZhSzPLGRZZiFrdxdT5W582BEaZKsVaAQSWb1uN8ON8OAAIvYJNyKCA33PiQgOICwoAKuqOEQOjcViTp+VdhKc9gSs/swMSTIXABZI7lszLVb6yRAa29ojFhERERGRY9RRF4wEBppTdpSXlxMSEtLKoxFpPuXl5UDNZ15EpFnkrjfDkN/fM5tzAwQEQ+/zzUCk3XGNampdVO5k+c5Clu8oZHnmXpZnFrK33FnnuNiwIAZ0iCYtNrRWcLH/cCPcHqBpqUTaEnsEHHeFuRTvhsBQCIlu7VGJiIiIiIgAR2EwYrPZiI6OJicnB4DQ0FBNZSFHFcMwKC8vJycnh+joaGw2W2sPSUSONm4nrPvSDES2za3ZHtvZnCprwGUN+qZ3lcvDuuxisxpkh1kRsiWvrM5xQTYrvVIjGdAhmoFp0QzsEEOH2BD991vkaBGZ2tojEBERERER8XPUBSMAycnJAL5wRORoFB0d7fusi4g0iaJdsGQmLH0DSveY2yxW6H4GHD8ZOo0Cq7XepxqGwc69Fb4psZZnFrJqVxGOevp/pMeFMrBDNAM6RDMgLYaeKRHYAxTyioiIiIiIiEjLOCqDEYvFQkpKComJiTiddafnEDnSBQYGqlJERJqGxwNbfoTFr8P6r8HwBhnhSXDcJBg0CaLa13laSaWTFTuLzL4g3mqQvFJHneOiQgLp7w1BBnaIpn+HaGLDgpr7VYmIiIiIiIiI7NdRGYxUs9lsunksIiJSn/ICWP6OGYgUbKnZ3nG4WR3S4yywmT2MXG4P6/eU+E2JtSm3FMPwP2WA1ULPlJopsQZ0iKZTfJimxBIRERERERGRNuWoDkZERESkFsOAXUth0Wuw+hNwVZrb7ZHQ/1Kzf0hiD7KKKli+Js+sBsksZOXOIiqc7jqnax8TYk6H5Q1CeqdGERyoLySIiIiIiIiISNumYERERORoV1UOqz42A5Gs32u2J/fDMfAafo8ex9LsKpbN2svyzO/YU1x3SqwIewD9OkR5p8SKoX+HaBIi7C34IkREREREREREmoaCERERkaNV7gZzqqzl74KjCACPzc725PH8L+QMvshNZcNnpXiMFX5Ps1ktdE+KYEBaTW+QLgnhWK2aEktEREREREREjnwKRkRERI4GLgdkr4LdS83psnYvhdx1vt17bCm85RrL22XDKdwc4d1aCkBKVLBvSqwBHaLp2z6K0CD9L4KIiIiIiIiIHJ1010NERORI43FD7nrYvRRX5hKcmYux563Fajj9DnMbFr73HMfb7nHM9fTFwEpokI0T20UxMC3G1xskKTK4lV6IiIiIiIiIiEjLUzAiIiLSlhkGzvytFGyYT+X2xQTtWU5s8VrsngrA/A959X/MC4xwVni68LvRhd89nfnd6Ep8YjsGdIjmMe+0WN2SIrBpSiwREREREREROYYpGBEREWkDPB6DrOJKdu7YRvnWBdiylxO9dxXpleuIooSkfY4vM+ysMjqx3NOFTQHdKI7rS1hCZzolhNMlIYyxcWF0ig8jzK7/1IuIiIiIiIiI1Ka7JSIiIi3EMAzyy6rYmlfG1twydu3ZA7uWEVmwgvYV6+hj2cyJlvw6z6sybKwnne3BPdgb3RdX8gAiO/SmY0IkF8eHERMW1AqvRkRERERERETkyKRgREREpIkVVzrZlldmBiDeZWfuXoLzVpPh2kA/6xYGWTZzsTWr5klW84cHC1mBaeRF9qYqaSD29MEkdh1En9hI+lo0BZaIiIiIiIiIyOFSMCIiInIIKp1utueXszWvlK151T/L2JpXzt7ScjIsu+hv3Ux/y2bGWbfQ3ZJJoMUNgf7nKQlOpSKhP4Fpg4jochIB7QbQzh5Bu9Z5WSIiIiIiIiIiRz0FIyIiIvvhcnvYubfCr/KjetldVIFhABikW/bQ37KF06yb6WfdTB/7NkIsVXXPFxKPpd1x2NoPgnaDIHUgEWHxRLT4KxMREREREREROXYpGBERkWOaw+Vm594KduSXsy2/jO355Wz3/txRUI7LY/gdn8he+ls3c6ltM8cFbKWvZQsRRmndEwdFQOoAaHccpB4H7QYRENUeNB2WiIiIiIiIiEirUjAiIiJHvfIql1/gsS2/nB0FZWzLKyerqIJ9sg+fSEoZHLiNEWE7GGDbSpeqDUQ4c/0PMgCbHZL71gpBjoO4DLBam/21iYiIiIiIiIhI4ygYERGRo0JheZU39CjzVn94w4/8cnJLHAd8bmiQjfS4MNJjQ+ke7WRU6Sy6Z31OaPFm84DKWgdbrJDQE9oNrAlBEntDQFDzvTgREREREREREWkyCkZEROSIYBgGuSUOtheUsy3PO+VVQU0VSFGF84DPjw4N9IUfHeNCSYsLo2NcKOlxYcSHB2HJWQML/gW/fwiuiponxnT09gPxhiAp/SEorHlfrIiIiIiIiIiINBsFIyIi0ma4PQa7Cyu8oYd/v4/t+eVUON0HfH5ihJ2OcWGkxe0TfsSGERUaWM8FXbDhf2Ygsm1uzfakvnDi9dDjLAiNbeJXKSIiIiIiIiIirUnBiIhIW2MYsHcbRKeB1dbao2ly1c3Oawce1euZe8txuvfT8AOwWiA1OsQv/EiPCyM9LpS02FBCgxr4n7XyAlj6Jix6DYoyzW0WG/Q8C068EdKGqEm6iIiIiIiIiMhRSsGIiEhbYhjw9e2w6FUIT4Y+F0DfCyF14BF1o77S6WZrXplfs/Pq9d1FFRj7zz4IsllpH2uGH+lxoaTHhpIeb06B1T4mlKCAw2honr0KFv4LVnwILm/jkJBYGHQVHD8Zotof+rlFREREREREROSIoGBERKQt+eVZMxQBKM2G3142l7iu0Pci6HMhxHdt3THuI6ekkjW7i1mbVcKarGLWZhWzJbcUzwHCj9rNztPjzamuzKmvQkmJCsFmbcIQyO2C9V+b02Vt/6Vme3JfszqkzwUQGNJ01xMRERERERERkTbNYhgH+t5u21RcXExUVBRFRUVERka29nBERJrG7+/DpzeY6+Mfg5hOsPIjWP8//2bgqQO9IckFEJHcYsNzuT1syStjbVYxa3YX+0KQvNKqeo+PCgmkY3zdZudpcaEkhNuxNHcFTHkBLH0DFr4GxTvNbRYb9DzbO13WSUdUFY6IiIiIiIiIiOxfY3IDBSMiIm3B5h/gnYvA44KTb4ZTH67Z5yiBdV/Dyg9h849gVDcgt0CnEWZI0vNsCIlusuEUVzpZl1XCmt1FvkqQ9XtKqHJ56hxrsUCn+DB6pUTSMyWSXqmR9EqJJDGiBcKP+mSvNKtDVn5UM11WaJw5XdbgyRDVruXHJCIiIiIiIiIizUrBiIjIkSRrBcw4A6pKzCqQ818D6376aJTmwprPzJv+mQtqttuCIONU6HcxZIyHwOAGXdowDHburfBVf6zZXcza7GIyCyrqPT4syEaPlEi/EKR7UgQhQa3cJN7tgnVfwsJ/w/Z5NduT+9WaLqth74mIiIiIiIiIiBx5FIyIiBwpCjPhtXFmP5GOw+Hy/0CAvWHP3bsNVv0HVnwEuWtrttsjzQqSvhdCp5FgNUOLSqebjXtKzQAkq2YqrJJKV72nT40KpleqNwDxBiFpsaFYm7L/x+Eqy4elM2HR6/7TZfU6F068ATqcqOmyRERERERERESOAQpGRESOBBV7Yfp4yFsPib3g6v8d2nRYhgF7VptVJCs/rgkIgPKgOBaFjeLjqiF8vTcVd92ZsAi0WchIjPBVgPRMiaBXSiTRoUGH/tqaW9YKWPgvMxRyO8xtofHe6bKu0XRZIiIiIiIiIiLHGAUjIiJtnbMS3joPdvwKEalw7WyIat/o07g9BlvzynzVH2t3FRK4exEjHT9ypm0BMZZS37HbPEl8ax3GhsTTiE7r4wtCuiSEExSwn6m72hK3C9b9Fxb823zfqqX0N6fL6n2+pssSERERERERETlGKRgREWnLPB74+GqzV4g9Eq75BpJ6H/RppQ4X66p7gWQVsyarhPXZxVQ662+I3jU2iPOiNjDW+TNdC37C5q7VNySlv9m0vc8FEJnahC+uGZTlwZKZsPh1KN5lbrMGmNNlnXADdDhB02WJiIiIiIiIiBzjFIyIiLRl39wNv70M1kC44hPoNKLOIbsLK1izu6YPyJqsYrbnl9d7upBAGz1SIvx6gfRIjiDMHlBzkKMU1v/PnG5r8/fgqe4rYoGOw8yQpNc5EBLTDC/4EGX9Dgv+ZU4PVnu6rMHXmEtkSuuOT0RERERERERE2gwFIyIibdX8l2HW3eb6BdPNBumYjdF/25LPnPW5zFmfw7b9hCDJkcFmD5BaTdHT48KwNaYhelm+Wa2y8mP/KamsgZBxqjmmbqdBUOghvsjD4HbC2v/Cwn/Djvk121MGwEl/hN7nNbw5vYiIiIiIiIiIHDMUjIiItEWrP4WPrgYMOOUhtve4lh/X5TBnQy7zN+fjcNVMiRVgtdA1MdxXAVIdhMSGNXFD9MIdsOo/ZkiyZ1XN9qBw6Hm2GZJ0GgW2gP2doWmU5cGSGbDodSjZbW6zBkCvCXDiDdD+eE2XJSIiIiIiIiIi+6VgRESkrdk2D+OtCVjcVSxIuJC/ll/O1n2qQlKighnVPYGR3RIZ2jWOiODAlh3jnjWw6mNzuq3CHTXbwxLMSo2+FzV9QLF7uTld1qr/1EyXFZZgTpU16GpNlyUiIiIiIiIiIg2iYEREpI3YkV/O0iXzOeW3KwnzlPKN+3j+5LwFD1YCrBYGd4xhVPdERndPpFtSOJa2UBVhGJC50AxIVn8C5fk1+6LTzYCk70WQ2OPQzu92wtovzEAkc0HN9tTj4MQbofcETZclIiIiIiIiIiKNomBERKSVVDrdLNhawJz1Ofy0PpfSvJ18Yn+A9pY8Fnu68Rf7gwzp0Z5R3VupKqSx3E7Y8hOs/BDWfgnOspp9SX2h30XQ5wKIan/wc5XmwpKZsHg6lGSZ26yBZhBy4o3QfnBzvAIRERERERERETkGKBgREWlBO/LLmbMhhznrc/l1cx6VTrNXSDjlfBj0ML2s2ykMSSfnkv+SkZ7WNqpCDkVVOWz4n9mPZONs8Dhr9qUPNfuR9JoAobH+z9u9rNZ0WVXmtrBEc7qswVdDRHKLvQQRERERERERETk6KRgREWlGlU43C7cWMGd9LnM25LAlt8xvf1KknbFdY7gt717ic341Q4BrZ0NMx9YZcHMoL4A1n5shyfZfarZbA6HrODMkATMQ2bmwZn+7wWYz9V4TIKCJG8mLiIiIiIiIiMgxS8GIiEgTyywoZ8766qqQfCqcbt8+m9XCoPQYRndPZFT3BHokhWP57I+w4n0IDIOrv4LUga04+mZWtBNWfWL2JMleUXe/NRD6nA8n3ADtB7X8+ERERERERERE5KjXmNwgoIXGJCJyRHG4alWFrM9h8z5VIYkRdl8QMjQjnsjavUK+f8gMRSw2uPjNozsUAbO/yNCbzSV3vRmQrPrE7E8y8DIYdDVEJLX2KEVERERERERERABVjIiI+GQWlDNnQy4/rc9h3qZ6qkLSYhjVI4FR3RLpmRJRf6+QRdPhq9vM9XNfhoGXt9DoRUREREREREREjl2qGBERaQCHy82irXuZsz6HH/dTFTKqewKjuicytGs8USGB+zmT17qv4eup5vqouxWKiIiIiIiIiIiItEEKRkTkmLJzb7lveqxfN+dTXuVfFXJcWjSjvFNk9UqJrL8qpD6Zi+Dja8DwwHFXwsg7mukViIiIiIiIiIiIyOFQMCIiRzWHy83ibdVVIblsyin1258QYWdUN7MqZFhGA6pC6pO/Gd67BFwVkHEqnPl3aGigIiIiIiIiIiIiIi1KwYiIHHV2FVaYQci6XH7dnOdXFWK1wKD0GEZ1T2RkN7MqxGo9jBCjNBfevgDK8yFlAFw4A2z6q1VERERERERERKSt0t07ETni1a4KmbM+l437VIXEh1f3CklgeNcEokIPoSqkPlVl8O7FsHcrxHSEyz4Ce3jTnFtERERERERERESahYIREfHnqjKnhAqOau2RHFBNr5D6q0IGpsUw2ts4/bCrQurjdsFHV8PupRASC5f9B8ITm/YaIiIiIiIiIiIi0uQUjIgc66rKYeci2P4rbJ8HOxebwciAy2DctDZzs9/hcrNoq7cqZEPdXiHx4XZGdvNWhWTEEx0a1HyDMQz46jbYOAsCguEPH0B81+a7noiIiIiIiIiIiDQZBSMix5rKIshcCNt+McOQ3cvA46x73PJ3YO2XMPpuOP7aVumbkVlQzpwNufy0PodfN+fXqQo5Li3GO0VWM1WF7M/PT8PSN8BihQtfhw4ntMx1RURERERERERE5LApGBE52pXlwY75NRUh2SvB8PgfE5EKHYdC+smQPhQqi+HrqZC1HL650wwBzngKOg5r1qE6XG4Wbi3wTpGVw+bcMr/9CRG1qkKasldIYyx7B358xFw//UnocWbLj0FEREREREREREQOmYIRkaNN8e6aEGT7r5C7ru4xsZ1rQpD0kyE6HSz7VFtc9wMsfRO+fxBy1sDMM6HPhXDqwxCZ2mTDrV0VMm9TPhXOmqoQm9XCcWnRjOqeyMhuCS1bFVKfTd/Bf28214f9GU64rvXGIiIiIiIiIiIiIofEYhiG0dqDaKzi4mKioqIoKioiMjKytYcj0noMA/Zu9QYh3jBk77a6xyX28gYhJ0PayRCZ0vBrlBfAD4/A4tcBAwLDYOQdcNKfIKDxfTwaUhUyqps5PdawrvGtUxVSn93LzXCoqhT6Xgzn/Qus1tYelYiIiIiIiIiIiNC43EDBiMiRxDAgd723GsRbEVKS5X+MxQrJ/WqqQdJPhtDYw7/27uXw9e2wc6H5OK6rOZVU17EHfWpmQbnZNH19Lr9urlsVMigthpHdzSmyeqVEYtm3eqW17d0O00+B0j3QaSRc9vEhhUIiIiIiIiIiIiLSPBSMiBwtPG6zJ0h1NciO+VCe73+MNRDaDaqZGqvDCRDcTH8uPB5Y8QHMvh/KcsxtPc6C8Y9BTLrvsEpnraqQDTls2acqJNHbK2R0j0SGdo0nKqSNVIXUp7wAXh8PeRsgqQ9c/TUER7X2qERERERERERERKQWBSMiRypXFexeVlMNkrkAHMX+xwSEmOFHdUVI+8EQGNKy46wsgjl/gwX/BMMNAcEUDrqJryIu5PtNJcw/EqtC6uOshDfPhczfILI9XDu7SfuriIiIiIiIiIiISNNoTG6g5usiramqHHYthm3eqbF2LgZXhf8x9khIG1JTEZLSv/WncQqOonLsw6yMPZP4uffSqWQp0QueYrhnJj+6rqTCcxyJEcGM6m72CmnzVSH18bjhk+vMUMQeBZd/rFBERERERERERETkKNDkwYjb7WbatGm8/fbbZGdnk5qaylVXXcW9997r+4a4YRg88MADvPrqqxQWFjJ06FD+8Y9/kJGR0dTDEWlbKosgc2FNRciupeBx+h8TGlcTgqSfbE7fZLW1znj3sSO/nDkbqnuF5FHp9AB/4UzrAu4NfJs0ay6vBT1DSYfRhJ/7NJb4rq095ENjGDDrblj7BdiC4NJ3IbFna49KREREREREREREmkCTByN/+9vf+Mc//sEbb7xB7969Wbx4MVdffTVRUVHcfPPNADz55JO88MILvPHGG3Tq1In77ruP8ePHs2bNGoKDg5t6SCKtpywfdvxa0yMkeyUYHv9jIlKh49CaMCS+G7SRaaYqnW4WbC1gzvocflqfy5Y8/14hSZF2RnVLZFT3QYSl3wYLX4BfXyQi80f4xxAYMgVGTIWgsFZ6BYdo/kvmNGEA5/0TOg5r3fGIiIiIiIiIiIhIk2nyHiNnnXUWSUlJTJ8+3bftggsuICQkhLfffhvDMEhNTeUvf/kLU6dOBaCoqIikpCRmzpzJxIkTD3oN9RiRNsvjhnVfwpY5ZhiSu67uMbGd/StCotPbTBACsKe4km9WZTNnfQ7zt+R7q0JMAVYLg9JjGNU9kVHdE+iRHFG3V0jeJvjmTtj0nfk4sh2c+gj0Pq9Nvc79Wvkx/GeyuX7qI3DyTa07HhERERERERERETmoVu0xcvLJJ/Pvf/+bDRs20K1bN37//Xd++eUXnn32WQC2bt1KdnY248aN8z0nKiqKE088kfnz59cbjDgcDhwOh+9xcXFxnWNEWpVhmEHA7PshZ43/vsRe3iDkZEg7GSJTWmeMB1Dl8vD92j18uDiTnzbk4qkVlyZHBjOyWwKjeyRwctd4IoMP0iskvitc9jGs/x9881co3A4fXw2LX4cznmrbU1JtnQuf/dFcP/GPZsWLiIiIiIiIiIiIHFWaPBj561//SnFxMT169MBms+F2u3n00Ue57LLLAMjOzgYgKSnJ73lJSUm+fft6/PHHefDBB5t6qCJNI3slfHuvWSUCEBID/S81p19KGwKhsa06vANZl13Mh4t28tnyXRSUVfm2D0qPYVzPpP1XhRyMxQI9zoAuo2HeC/DLs7BtLvxjKJx4I4y6E4KjmvjVHKY9a+D9y8BdBb3OhfGPHRkVLiIiIiIiIiIiItIoTR6MfPjhh7zzzju8++679O7dm+XLl3PrrbeSmprKpEmTDumcd911F7fddpvvcXFxMR06dGiqIYscmuLd8MOjsPwdwDCbdJ94Iwz/C4REt/bo9quo3MkXv+/ioyU7WbGzyLc9McLOBYPac9Gg9nROCG+aiwWGmCFI/4lmM/N1X8JvL8PKj+CUh6DfJWC1Ns21DkfxbnjnQnAUmWHWef9uG+MSERERERERERGRJtfkwcjtt9/OX//6V9+UWH379mX79u08/vjjTJo0ieTkZAD27NlDSkrNlEJ79uxhwIAB9Z7Tbrdjt9ubeqgih8ZRAvOeh19fAleFua3PhTD2Pojp2KpD2x+Px+DXzfl8uDiTb1ZnU+Uy+4YE2iyM65nExYM7MDwjngBbM4UBMekw8R1zurH/3Qn5m+CzG2HJDHN6rZT+zXPdhqgsgrcvhOJdZuP7ie9CYHDrjUdERERERERERESaVZMHI+Xl5Vj3+aa1zWbD4zFvxHbq1Ink5GS+//57XxBSXFzMggUL+OMf/9jUwxFpOm4XLHsTfnwcynLMbWlD4NRHof2g1h3bfmQWlPPxkp18vGQnuworfNt7JEdw0eAOTBiQSlx4C4aOXcfBH+fDb6/AT09C5gL49ygYdDWMubflpx1zVcEHl0POaghPMnujtOGpz0REREREREREROTwNXkwcvbZZ/Poo4+SlpZG7969WbZsGc8++yzXXHMNABaLhVtvvZVHHnmEjIwMOnXqxH333UdqaioTJkxo6uGIHD7DgI2zYfZ9kLvO3BbbxZwKqseZba4PRaXTzTersvlwcSa/bs73bY8IDmDCgHZcNLg9fdtFNb5vSFMJCIJht0K/i+Hb+2DVx7B4Oqz+FMbeD8ddCVZb84/D44HP/w+2/gxB4XDZR2Zli4iIiIiIiIiIiBzVLIZhGE15wpKSEu677z4+/fRTcnJySE1N5dJLL+X+++8nKCgIAMMweOCBB/j3v/9NYWEhw4YN45VXXqFbt24NukZxcTFRUVEUFRURGRnZlMMX8Zf1u3nzfutP5uOQWBh1Fwy+GmyBrTu2WgzDYMXOIj5cnMkXv++mpNIFmJnN0C7xXDS4PeN7JxMc2AKBQ2NtnQv/uwNy1piPUwfCGU9D+8HNe93ZD8C858AaAH/4ELqObd7riYiIiIiIiIiISLNpTG7Q5MFIS1AwIs2uaCf88Aj8/j5mY3U7nPRHGPbnNtVYPb/UwafLdvHR4p2s31Pi294uOoSLBrfnguPa0yE2tBVH2EBuJyx6DX58DBzF5raBl8PYaRCe0PTXW/gqfD3VXJ/wDxjwh6a/hoiIiIiIiIiIiLQYBSMih6qy2KwimP8yuCrNbX0vMqd4ik5r1aFVc7k9/LQhlw8XZ/L92hxcHvOPsD3Ayul9krl4cAdO6hyH1dq2pvhqkNIc+G4aLH/HfGyPgjH3wODJYGuimf/Wfmn2FcEw+5qMuL1pzisiIiIiIiIiIiKtRsGISGO5XbB0ptlYvTzP3JY+FE59BNod16pDq7Y5t5SPFu/kP0t3klvi8G3v3z6KiwZ34Oz+qUSFtJ3pvQ5L5kKzoiPrd/NxYm844ynoOPTwz/vG2WboNegqOOu5NtcjRkRERERERERERBpPwYhIQxkGbPgGZt8PeRvMbXFd4ZSHofvprX7TvNTh4qsVu/lw8U6WbN/r2x4XFsR5A9tx0eAOdE+OaMURNiOPG5a+Ad8/BBXe1973IvN3E5nS+PPlbYLpp0BFAXQ7DS55p+mqUERERERERERERKRVKRgRaYjdy8zG6tvmmo9D48zG6oOuatXG6oZhsGjbXj5cnMlXK7KocLoBsFpgdPdELhrcgTE9EgkKsLbaGFtUeQH88DAsngEYEBQOI++AE/8IAUENO0dpDrw2Dgq3Q7tBMOm/EBTWrMMWERERERERERGRlqNgRORACjPNG+0rPjAf2+ww5E9mY/XgqFYbVnZRJf9ZupOPFmeyLb/ct71zfBgXDe7A+ce1IykyuNXG1+p2Lzen19q5yHwclwFnPAldxhz4eY5SmHkmZC2HmE4weXbzNHQXERERERERERGRVqNgRKQ+lUXwy99h/ivg9vbo6DfRbMAd3aFVhuRwufl+bQ4fLs7k5w25ePuoExZk46x+qVx8fHuOS4vBoj4YJo8HVrxvTn1Wlmtu63k2jH8MotPqHu92wnuXwqbZZkXQ5NkQ16VlxywiIiIiIiIiIiLNTsGISG1uJyyZCXMeh/J8c1vH4XDqw5A6sFWGtDarmA8XZ/LZsl3sLXf6tp/QMZaLBrfnjL4phNnV/2K/KotgzhOw4F9guCEgBIbfBiffDIHeqhrDgC9ugmVvmfuv+hLaD27dcYuIiIiIiIiIiEizUDAiAuaN8fVfm9UF+ZvMbXEZZiDS7bQWb6xeVO7ki9938eHinazcVeTbnhRp58JB7blwUAc6xavvRaPsWQP/u6OmT0xMRzjtCeh+uhmczHkcLFaY+K65TURERERERERERI5KCkZEdi2Fb++F7fPMx6HxMPouOG5SizZW93gM5m3O48PFO5m1OpsqlweAQJuFU3olcdHgDgzvGk+A7RhppN4cDANWfwKz7oWS3ea2doNg1xJz/ay/w+BrWm98IiIiIiIiIiIi0uwakxtorh45uhTugO8fgpUfmY8DgmHI/8HQWyG45UK0zIJyPlqyk/8s2cmuwgrf9h7JEVw8uAMTBrYjNiyoxcZzVLNYoM8FkDEe5j4Nv75UE4oMn6pQRERERERERERERPwoGJGjQ2URzH0Gfvunt7G6Bfp7G6tHtW+xYazLLuaRL9fyy6Y837bI4AAmDGzHxYM70Ds1Uo3Um4s9HMZNgwGXw09/g5h0GH1Pa49KRERERERERERE2hgFI3Jkczth8etmP4mKAnNbx+Fw6iOQOqDFhlHl8vDKnE28/OMmnG4DiwWGdY3nosEdOLVXEsGBthYbyzEvvitc8Gprj0JERERERERERETaKAUjcmQyDFj3Jcx+AAo2m9viu5uN1TNObdHG6it3FnH7x7+zLrsEgFN6JXH/Wb3oEBvaYmMQERERERERERERkYZRMCJHnp1L4Nt7YMd883FYAoy+GwZeCbaW+0g7XG6e/24j//p5C26PQUxoIA+e24ez+6VouiwRERERERERERGRNkrBiBw59m6H7x+EVf8xHweEwMlTYOgtYI9o0aEs27GX2z9ewaacUgDO7JfCg+f0Jj7c3qLjEBEREREREREREZHGUTAibV/FXrOx+oJ/gbsKsMCAP5iNtaPatehQKp1unvl2PdN/2YrHgPhwO49M6M1pfVJadBwiIiIiIiIiIiIicmgUjEjb5aqCxdPhp7+Z4QhAp5FmY/WUfi0+nIVbC7jzPyvYmlcGwPkD23HfWb2ICQtq8bGIiIiIiIiIiIiIyKFRMCJtj2HA2i/gu2lQsMXcltDDDES6jmvRxuoAZQ4XT81azxvzt2EYkBRp57Hz+jK2Z1KLjkNEREREREREREREDp+CEWlbslfCV1Mh8zfzcVgijLkHBlzeoo3Vq/26KY87P1lBZkEFAJcM7sDdZ/YkKiSwxcciIiIiIiIiIiIiIodPwYi0HRV74Y1zoKLAbKw+9GY4+Wawh7f4UEoqnTz29TreW7gDgHbRITxxQV+GZyS0+FhEREREREREREREpOkoGJG2Y+4zZigS3x2u/AwiU1tlGHPW53DXJyvJKqoE4IqT0rnz9B6E2/XHRURERERERERERORIpzu90jbs3QYL/mWuj3+0VUKRonInD3+1ho+X7AQgLTaUv13QjyFd4lp8LCIiIiIiIiIiIiLSPBSMSNvw3TRwV0HnUWaD9RY2e80e7vl0JTklDiwWuPrkTkwd343QIP0RERERERERERERETma6K6vtL7MhbD6U8ACpz4CFkuLXbqgrIoH/7uaz5fvBqBzQhhPXdiPQemxLTYGEREREREREREREWk5CkakdRkGzLrbXB94GST3bbFLf70yi/s/X0VeaRVWC1w/ogu3jssgONDWYmMQERERERERERERkZalYERa1+pPYeciCAyF0fe2yCVzSxzc//kq/rcqG4BuSeE8dWF/+neIbpHri4iIiIiIiIiIiEjrUTAircflMHuLAAy9BSJTmvVyhmHw+fLdTPvvagrLnQRYLfxpVBf+b0xX7AGqEhERERERERERERE5FigYkdaz4F9QuB0iUuDkm5r1UtlFldz72Uq+W5sDQK+USJ66qB+9U6Oa9boiIiIiIiIiIiIi0rYoGJHWUV4APz9tro+5F4LCmuUyhmHw0ZKdPPzlGkoqXQTaLNwyNoMbRnYh0GZtlmuKiIiIiIiIiIiISNulYERax09/A0cRJPWB/pc2yyV2FVbw1/+sYO7GPAD6t4/iqYv60y0polmuJyIiIiIiIiIiIiJtn4IRaXl5m2DRa+b6qY+AtWn7e3g8Bu8u3MHjX6+lrMpNUICVv5zSjcnDOhGgKhERERERERERERGRY5qCEWl53z0AHhdknApdRjfpqXfkl3Pnf1Ywf0s+AIPTY/jbhf3okhDepNcRERERERERERERkSOTghFpWdt+gXVfgsUGpzzcZKf1eAzemL+NJ79ZT4XTTUigjTtO686VQzpis1qa7DoiIiIiIiIiIiIicmRTMCItx+OBWfeY64MmQWKPJjntltxS7vh4BYu37wXgpM6x/O2CfqTHNU9DdxERERERERERERE5cikYkZaz8iPIWg5BETDq7sM+ndtj8NrcLTw7ewMOl4ewIBt3ndGTP5yQhlVVIiIiIiIiIiIiIiJSDwUj0jKcFfD9Q+b68D9DeMJhnW7DnhJu/3gFv2cWmqfMiOeJC/rRLjrkMAcqIiIiIiIiIiIiIkczBSPSMn57BYp3QmR7OOlPh3wap9vDv37azAvfb6LK7SEiOID7zurFRYPaY7GoSkREREREREREREREDkzBiDS/0lyY+3dzfez9EHhoVR2rdxdxx8crWL272DxVj0QePa8vyVHBTTVSERERERERERERETnKKRiR5jfnMagqgdSB0PeiRj+9yuXhpR828sqczbg8BtGhgUw7uzfnDkhVlYiIiIiIiIiIiIiINIqCEWleOetgyUxz/dRHwWpt1NNX7Czk9o9WsH5PCQCn90nmoXP7kBBhb+KBioiIiIiIiIiIiMixQMGINK/Z94HhgR5nQcehDX5apdPNc99t5N8/b8ZjQFxYEA9P6MMZfVOacbAiIiIiIiIiIiIicrRTMCLNZ/OPsPFbsAbAuAcb/LQl2wu4/eMVbMktA+DcAak8cHZvYsOCmmukIiIiIiIiIiIiInKMUDAizcPjhm/vNdePvxbiuzboaS/9sJFnZm/AMCAxws6j5/XllF5JzThQERERERERERERETmWKBiR5vH7e7BnFdijYOSdDXrK4m0FPP3tBgAuGtSee8/sRVRoYHOOUkRERERERERERESOMQpGpOlVlcH3D5vrI6ZCaOxBn+J0e7jn01UATDy+A09c0K85RygiIiIiIiIiIiIixyhraw9AjkK/vgil2RCdDife0KCnzJi3lfV7SogNC+LO03o08wBFRERERERERERE5FilYESaVnEWzHveXB83DQLsB33K7sIKnvtuIwB/Pb0HMWqyLiIiIiIiIiIiIiLNRMGINK0fHwFnObQ/AXqf16CnPPjf1ZRXuTm+YwwXHte+mQcoIiIiIiIiIiIiIscyBSPSdLJXwrJ3zPXxj4LFctCnfL92D7NW7yHAauGRCX2xWg/+HBERERERERERERGRQ6VgRJqGYcC39wKGWSnS4YSDPqWiys0DX6wGYPLwTnRPjmjmQYqIiIiIiIiIiIjIsU7BiDSNjbNhyxywBZm9RRrgxR82snNvBe2iQ7hlbEazDk9EREREREREREREBBSMSFNwu2D2feb6iTdATMeDPmVTTgmvzt0CwANn9yI0KKAZBygiIiIiIiIiIiIiYlIwIodv2ZuQuw5CYmD4Xw56uGEY3PvZKpxug3E9Ezm1d3ILDFJERERERERERERERMGIHK7KYvjxMXN95F/NcOQgPl22i9+2FBAcaOWBs3s38wBFRERERERERERERGooGJHDM+85KMuF2C4w+JqDHl5YXsWjX60F4Jax3egQG9rMAxQRERERERERERERqaFgRA5d0U6Y/7K5fspDEBB00Kc8OWs9+WVVZCSGM3lYp2YeoIiIiIiIiIiIiIiIPwUjcui+fwhclZA+FHqcedDDl+3Yy3sLdwDwyIQ+BAXo4yciIiIiIiIiIiIiLUt3puXQ7FoKKz4w1099BCyWAx7ucnu459NVGAZccFx7Tuwc1wKDFBERERERERERERHxp2BEGs8w4Nv7zPV+l0C74w76lDfnb2dNVjFRIYHcfUaPZh6giIiIiIiIiIiIiEj9FIxI463/Grb/AgHBMOa+gx6eXVTJs7M3AHDnaT2IC7c39whFREREREREREREROqlYEQax+2E2feb6yf9CaI7HPQpD3+5hlKHi4Fp0Uw8/uDHi4iIiIiIiIiIiIg0FwUj0jiLX4f8TRCWAMP+fNDDf9qQy1crs7BZLTw6oS9W64F7kYiIiIiIiIiIiIiINCcFI9JwFYUw5wlzfdRdEBx5wMMrnW7u/3wVAFed3JFeqQc+XkRERERERERERESkuSkYkYab+zRUFEBCDzhu0kEPf2X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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(num=2, figsize=(20, 16))  \n",
    "fig1 = fig.add_subplot(2, 1, 1)\n",
    "fig2 = fig.add_subplot(2, 1, 2)\n",
    "\n",
    "fig1.plot(total_loss_train, label='Training Loss')\n",
    "fig1.plot(total_loss_val, label='Validation Loss')\n",
    "fig1.set_title('Loss Resnet Pretrained', fontsize=14)  \n",
    "fig1.set_ylim([0, 1])\n",
    "fig1.legend()\n",
    "fig2.plot(total_acc_train, label='Training Accuracy')\n",
    "fig2.plot(total_acc_val, label='Validation Accuracy')\n",
    "fig2.set_ylim([0, 100])\n",
    "fig2.set_title('Accuracy Resnet Pretrained', fontsize=14)  \n",
    "fig2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "Best model's accuracy: 98.09092379873807\n",
      "-------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "best_model_state = torch.load('resnet_pret.pth', map_location='cpu')\n",
    "model_res.load_state_dict(best_model_state)\n",
    "model_res.to('cpu')  \n",
    "device = torch.device('cpu')\n",
    "test_accuracy = Model_functions.test_model(model_res, test_loader, device)\n",
    "\n",
    "print('-------------------------------------------------------')\n",
    "print(f'Best model\\'s accuracy: {test_accuracy}')\n",
    "print('-------------------------------------------------------')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         bkl       0.99      0.95      0.97       945\n",
      "          nv       0.96      0.95      0.96       892\n",
      "          df       0.99      1.00      0.99       848\n",
      "         mel       0.97      0.99      0.98       840\n",
      "        vasc       1.00      1.00      1.00       899\n",
      "         bcc       0.99      1.00      1.00       855\n",
      "       akiec       0.99      1.00      0.99       902\n",
      "\n",
      "    accuracy                           0.98      6181\n",
      "   macro avg       0.98      0.98      0.98      6181\n",
      "weighted avg       0.98      0.98      0.98      6181\n",
      "\n",
      "-------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "Model_functions.conf_report(model_res, test_loader, 'resnet_conf_mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Empty the CUDA memory**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2381"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.empty_cache() \n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.cuda.memory_summary(device=None, abbreviated=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Densenet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f'Using device: {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/spyros/miniconda3/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/spyros/miniconda3/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=DenseNet121_Weights.IMAGENET1K_V1`. You can also use `weights=DenseNet121_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace=True)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace=True)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace=True)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Linear(in_features=1024, out_features=7, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_temp = 'densenet_pret'\n",
    "num_classes = 7\n",
    "feature_extract = False\n",
    "model_des = Model_functions.initialise_model(model_temp, num_classes, feature_extract, use_pretrained=True)\n",
    "model_des.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss().to(device)\n",
    "optimizer = torch.optim.Adam(model_des.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Sequential: 1-1                        [-1, 1024, 7, 7]          --\n",
      "|    └─Conv2d: 2-1                       [-1, 64, 112, 112]        9,408\n",
      "|    └─BatchNorm2d: 2-2                  [-1, 64, 112, 112]        128\n",
      "|    └─ReLU: 2-3                         [-1, 64, 112, 112]        --\n",
      "|    └─MaxPool2d: 2-4                    [-1, 64, 56, 56]          --\n",
      "|    └─_DenseBlock: 2-5                  [-1, 256, 56, 56]         --\n",
      "|    |    └─_DenseLayer: 3-1             [-1, 32, 56, 56]          45,440\n",
      "|    |    └─_DenseLayer: 3-2             [-1, 32, 56, 56]          49,600\n",
      "|    |    └─_DenseLayer: 3-3             [-1, 32, 56, 56]          53,760\n",
      "|    |    └─_DenseLayer: 3-4             [-1, 32, 56, 56]          57,920\n",
      "|    |    └─_DenseLayer: 3-5             [-1, 32, 56, 56]          62,080\n",
      "|    |    └─_DenseLayer: 3-6             [-1, 32, 56, 56]          66,240\n",
      "|    └─_Transition: 2-6                  [-1, 128, 28, 28]         --\n",
      "|    |    └─BatchNorm2d: 3-7             [-1, 256, 56, 56]         512\n",
      "|    |    └─ReLU: 3-8                    [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-9                  [-1, 128, 56, 56]         32,768\n",
      "|    |    └─AvgPool2d: 3-10              [-1, 128, 28, 28]         --\n",
      "|    └─_DenseBlock: 2-7                  [-1, 512, 28, 28]         --\n",
      "|    |    └─_DenseLayer: 3-11            [-1, 32, 28, 28]          53,760\n",
      "|    |    └─_DenseLayer: 3-12            [-1, 32, 28, 28]          57,920\n",
      "|    |    └─_DenseLayer: 3-13            [-1, 32, 28, 28]          62,080\n",
      "|    |    └─_DenseLayer: 3-14            [-1, 32, 28, 28]          66,240\n",
      "|    |    └─_DenseLayer: 3-15            [-1, 32, 28, 28]          70,400\n",
      "|    |    └─_DenseLayer: 3-16            [-1, 32, 28, 28]          74,560\n",
      "|    |    └─_DenseLayer: 3-17            [-1, 32, 28, 28]          78,720\n",
      "|    |    └─_DenseLayer: 3-18            [-1, 32, 28, 28]          82,880\n",
      "|    |    └─_DenseLayer: 3-19            [-1, 32, 28, 28]          87,040\n",
      "|    |    └─_DenseLayer: 3-20            [-1, 32, 28, 28]          91,200\n",
      "|    |    └─_DenseLayer: 3-21            [-1, 32, 28, 28]          95,360\n",
      "|    |    └─_DenseLayer: 3-22            [-1, 32, 28, 28]          99,520\n",
      "|    └─_Transition: 2-8                  [-1, 256, 14, 14]         --\n",
      "|    |    └─BatchNorm2d: 3-23            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-24                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-25                 [-1, 256, 28, 28]         131,072\n",
      "|    |    └─AvgPool2d: 3-26              [-1, 256, 14, 14]         --\n",
      "|    └─_DenseBlock: 2-9                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─_DenseLayer: 3-27            [-1, 32, 14, 14]          70,400\n",
      "|    |    └─_DenseLayer: 3-28            [-1, 32, 14, 14]          74,560\n",
      "|    |    └─_DenseLayer: 3-29            [-1, 32, 14, 14]          78,720\n",
      "|    |    └─_DenseLayer: 3-30            [-1, 32, 14, 14]          82,880\n",
      "|    |    └─_DenseLayer: 3-31            [-1, 32, 14, 14]          87,040\n",
      "|    |    └─_DenseLayer: 3-32            [-1, 32, 14, 14]          91,200\n",
      "|    |    └─_DenseLayer: 3-33            [-1, 32, 14, 14]          95,360\n",
      "|    |    └─_DenseLayer: 3-34            [-1, 32, 14, 14]          99,520\n",
      "|    |    └─_DenseLayer: 3-35            [-1, 32, 14, 14]          103,680\n",
      "|    |    └─_DenseLayer: 3-36            [-1, 32, 14, 14]          107,840\n",
      "|    |    └─_DenseLayer: 3-37            [-1, 32, 14, 14]          112,000\n",
      "|    |    └─_DenseLayer: 3-38            [-1, 32, 14, 14]          116,160\n",
      "|    |    └─_DenseLayer: 3-39            [-1, 32, 14, 14]          120,320\n",
      "|    |    └─_DenseLayer: 3-40            [-1, 32, 14, 14]          124,480\n",
      "|    |    └─_DenseLayer: 3-41            [-1, 32, 14, 14]          128,640\n",
      "|    |    └─_DenseLayer: 3-42            [-1, 32, 14, 14]          132,800\n",
      "|    |    └─_DenseLayer: 3-43            [-1, 32, 14, 14]          136,960\n",
      "|    |    └─_DenseLayer: 3-44            [-1, 32, 14, 14]          141,120\n",
      "|    |    └─_DenseLayer: 3-45            [-1, 32, 14, 14]          145,280\n",
      "|    |    └─_DenseLayer: 3-46            [-1, 32, 14, 14]          149,440\n",
      "|    |    └─_DenseLayer: 3-47            [-1, 32, 14, 14]          153,600\n",
      "|    |    └─_DenseLayer: 3-48            [-1, 32, 14, 14]          157,760\n",
      "|    |    └─_DenseLayer: 3-49            [-1, 32, 14, 14]          161,920\n",
      "|    |    └─_DenseLayer: 3-50            [-1, 32, 14, 14]          166,080\n",
      "|    └─_Transition: 2-10                 [-1, 512, 7, 7]           --\n",
      "|    |    └─BatchNorm2d: 3-51            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-52                   [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-53                 [-1, 512, 14, 14]         524,288\n",
      "|    |    └─AvgPool2d: 3-54              [-1, 512, 7, 7]           --\n",
      "|    └─_DenseBlock: 2-11                 [-1, 1024, 7, 7]          --\n",
      "|    |    └─_DenseLayer: 3-55            [-1, 32, 7, 7]            103,680\n",
      "|    |    └─_DenseLayer: 3-56            [-1, 32, 7, 7]            107,840\n",
      "|    |    └─_DenseLayer: 3-57            [-1, 32, 7, 7]            112,000\n",
      "|    |    └─_DenseLayer: 3-58            [-1, 32, 7, 7]            116,160\n",
      "|    |    └─_DenseLayer: 3-59            [-1, 32, 7, 7]            120,320\n",
      "|    |    └─_DenseLayer: 3-60            [-1, 32, 7, 7]            124,480\n",
      "|    |    └─_DenseLayer: 3-61            [-1, 32, 7, 7]            128,640\n",
      "|    |    └─_DenseLayer: 3-62            [-1, 32, 7, 7]            132,800\n",
      "|    |    └─_DenseLayer: 3-63            [-1, 32, 7, 7]            136,960\n",
      "|    |    └─_DenseLayer: 3-64            [-1, 32, 7, 7]            141,120\n",
      "|    |    └─_DenseLayer: 3-65            [-1, 32, 7, 7]            145,280\n",
      "|    |    └─_DenseLayer: 3-66            [-1, 32, 7, 7]            149,440\n",
      "|    |    └─_DenseLayer: 3-67            [-1, 32, 7, 7]            153,600\n",
      "|    |    └─_DenseLayer: 3-68            [-1, 32, 7, 7]            157,760\n",
      "|    |    └─_DenseLayer: 3-69            [-1, 32, 7, 7]            161,920\n",
      "|    |    └─_DenseLayer: 3-70            [-1, 32, 7, 7]            166,080\n",
      "|    └─BatchNorm2d: 2-12                 [-1, 1024, 7, 7]          2,048\n",
      "├─Linear: 1-2                            [-1, 7]                   7,175\n",
      "==========================================================================================\n",
      "Total params: 6,961,031\n",
      "Trainable params: 6,961,031\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (G): 2.85\n",
      "==========================================================================================\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 172.17\n",
      "Params size (MB): 26.55\n",
      "Estimated Total Size (MB): 199.30\n",
      "==========================================================================================\n",
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─Sequential: 1-1                        [-1, 1024, 7, 7]          --\n",
      "|    └─Conv2d: 2-1                       [-1, 64, 112, 112]        9,408\n",
      "|    └─BatchNorm2d: 2-2                  [-1, 64, 112, 112]        128\n",
      "|    └─ReLU: 2-3                         [-1, 64, 112, 112]        --\n",
      "|    └─MaxPool2d: 2-4                    [-1, 64, 56, 56]          --\n",
      "|    └─_DenseBlock: 2-5                  [-1, 256, 56, 56]         --\n",
      "|    |    └─_DenseLayer: 3-1             [-1, 32, 56, 56]          45,440\n",
      "|    |    └─_DenseLayer: 3-2             [-1, 32, 56, 56]          49,600\n",
      "|    |    └─_DenseLayer: 3-3             [-1, 32, 56, 56]          53,760\n",
      "|    |    └─_DenseLayer: 3-4             [-1, 32, 56, 56]          57,920\n",
      "|    |    └─_DenseLayer: 3-5             [-1, 32, 56, 56]          62,080\n",
      "|    |    └─_DenseLayer: 3-6             [-1, 32, 56, 56]          66,240\n",
      "|    └─_Transition: 2-6                  [-1, 128, 28, 28]         --\n",
      "|    |    └─BatchNorm2d: 3-7             [-1, 256, 56, 56]         512\n",
      "|    |    └─ReLU: 3-8                    [-1, 256, 56, 56]         --\n",
      "|    |    └─Conv2d: 3-9                  [-1, 128, 56, 56]         32,768\n",
      "|    |    └─AvgPool2d: 3-10              [-1, 128, 28, 28]         --\n",
      "|    └─_DenseBlock: 2-7                  [-1, 512, 28, 28]         --\n",
      "|    |    └─_DenseLayer: 3-11            [-1, 32, 28, 28]          53,760\n",
      "|    |    └─_DenseLayer: 3-12            [-1, 32, 28, 28]          57,920\n",
      "|    |    └─_DenseLayer: 3-13            [-1, 32, 28, 28]          62,080\n",
      "|    |    └─_DenseLayer: 3-14            [-1, 32, 28, 28]          66,240\n",
      "|    |    └─_DenseLayer: 3-15            [-1, 32, 28, 28]          70,400\n",
      "|    |    └─_DenseLayer: 3-16            [-1, 32, 28, 28]          74,560\n",
      "|    |    └─_DenseLayer: 3-17            [-1, 32, 28, 28]          78,720\n",
      "|    |    └─_DenseLayer: 3-18            [-1, 32, 28, 28]          82,880\n",
      "|    |    └─_DenseLayer: 3-19            [-1, 32, 28, 28]          87,040\n",
      "|    |    └─_DenseLayer: 3-20            [-1, 32, 28, 28]          91,200\n",
      "|    |    └─_DenseLayer: 3-21            [-1, 32, 28, 28]          95,360\n",
      "|    |    └─_DenseLayer: 3-22            [-1, 32, 28, 28]          99,520\n",
      "|    └─_Transition: 2-8                  [-1, 256, 14, 14]         --\n",
      "|    |    └─BatchNorm2d: 3-23            [-1, 512, 28, 28]         1,024\n",
      "|    |    └─ReLU: 3-24                   [-1, 512, 28, 28]         --\n",
      "|    |    └─Conv2d: 3-25                 [-1, 256, 28, 28]         131,072\n",
      "|    |    └─AvgPool2d: 3-26              [-1, 256, 14, 14]         --\n",
      "|    └─_DenseBlock: 2-9                  [-1, 1024, 14, 14]        --\n",
      "|    |    └─_DenseLayer: 3-27            [-1, 32, 14, 14]          70,400\n",
      "|    |    └─_DenseLayer: 3-28            [-1, 32, 14, 14]          74,560\n",
      "|    |    └─_DenseLayer: 3-29            [-1, 32, 14, 14]          78,720\n",
      "|    |    └─_DenseLayer: 3-30            [-1, 32, 14, 14]          82,880\n",
      "|    |    └─_DenseLayer: 3-31            [-1, 32, 14, 14]          87,040\n",
      "|    |    └─_DenseLayer: 3-32            [-1, 32, 14, 14]          91,200\n",
      "|    |    └─_DenseLayer: 3-33            [-1, 32, 14, 14]          95,360\n",
      "|    |    └─_DenseLayer: 3-34            [-1, 32, 14, 14]          99,520\n",
      "|    |    └─_DenseLayer: 3-35            [-1, 32, 14, 14]          103,680\n",
      "|    |    └─_DenseLayer: 3-36            [-1, 32, 14, 14]          107,840\n",
      "|    |    └─_DenseLayer: 3-37            [-1, 32, 14, 14]          112,000\n",
      "|    |    └─_DenseLayer: 3-38            [-1, 32, 14, 14]          116,160\n",
      "|    |    └─_DenseLayer: 3-39            [-1, 32, 14, 14]          120,320\n",
      "|    |    └─_DenseLayer: 3-40            [-1, 32, 14, 14]          124,480\n",
      "|    |    └─_DenseLayer: 3-41            [-1, 32, 14, 14]          128,640\n",
      "|    |    └─_DenseLayer: 3-42            [-1, 32, 14, 14]          132,800\n",
      "|    |    └─_DenseLayer: 3-43            [-1, 32, 14, 14]          136,960\n",
      "|    |    └─_DenseLayer: 3-44            [-1, 32, 14, 14]          141,120\n",
      "|    |    └─_DenseLayer: 3-45            [-1, 32, 14, 14]          145,280\n",
      "|    |    └─_DenseLayer: 3-46            [-1, 32, 14, 14]          149,440\n",
      "|    |    └─_DenseLayer: 3-47            [-1, 32, 14, 14]          153,600\n",
      "|    |    └─_DenseLayer: 3-48            [-1, 32, 14, 14]          157,760\n",
      "|    |    └─_DenseLayer: 3-49            [-1, 32, 14, 14]          161,920\n",
      "|    |    └─_DenseLayer: 3-50            [-1, 32, 14, 14]          166,080\n",
      "|    └─_Transition: 2-10                 [-1, 512, 7, 7]           --\n",
      "|    |    └─BatchNorm2d: 3-51            [-1, 1024, 14, 14]        2,048\n",
      "|    |    └─ReLU: 3-52                   [-1, 1024, 14, 14]        --\n",
      "|    |    └─Conv2d: 3-53                 [-1, 512, 14, 14]         524,288\n",
      "|    |    └─AvgPool2d: 3-54              [-1, 512, 7, 7]           --\n",
      "|    └─_DenseBlock: 2-11                 [-1, 1024, 7, 7]          --\n",
      "|    |    └─_DenseLayer: 3-55            [-1, 32, 7, 7]            103,680\n",
      "|    |    └─_DenseLayer: 3-56            [-1, 32, 7, 7]            107,840\n",
      "|    |    └─_DenseLayer: 3-57            [-1, 32, 7, 7]            112,000\n",
      "|    |    └─_DenseLayer: 3-58            [-1, 32, 7, 7]            116,160\n",
      "|    |    └─_DenseLayer: 3-59            [-1, 32, 7, 7]            120,320\n",
      "|    |    └─_DenseLayer: 3-60            [-1, 32, 7, 7]            124,480\n",
      "|    |    └─_DenseLayer: 3-61            [-1, 32, 7, 7]            128,640\n",
      "|    |    └─_DenseLayer: 3-62            [-1, 32, 7, 7]            132,800\n",
      "|    |    └─_DenseLayer: 3-63            [-1, 32, 7, 7]            136,960\n",
      "|    |    └─_DenseLayer: 3-64            [-1, 32, 7, 7]            141,120\n",
      "|    |    └─_DenseLayer: 3-65            [-1, 32, 7, 7]            145,280\n",
      "|    |    └─_DenseLayer: 3-66            [-1, 32, 7, 7]            149,440\n",
      "|    |    └─_DenseLayer: 3-67            [-1, 32, 7, 7]            153,600\n",
      "|    |    └─_DenseLayer: 3-68            [-1, 32, 7, 7]            157,760\n",
      "|    |    └─_DenseLayer: 3-69            [-1, 32, 7, 7]            161,920\n",
      "|    |    └─_DenseLayer: 3-70            [-1, 32, 7, 7]            166,080\n",
      "|    └─BatchNorm2d: 2-12                 [-1, 1024, 7, 7]          2,048\n",
      "├─Linear: 1-2                            [-1, 7]                   7,175\n",
      "==========================================================================================\n",
      "Total params: 6,961,031\n",
      "Trainable params: 6,961,031\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (G): 2.85\n",
      "==========================================================================================\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 172.17\n",
      "Params size (MB): 26.55\n",
      "Estimated Total Size (MB): 199.30\n",
      "==========================================================================================\n"
     ]
    }
   ],
   "source": [
    "input_size = (3, 224, 224)\n",
    "print(summary(model_des, input_size, device=device.type))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 1/30 [01:55<55:37, 115.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training Loss: 0.8261477194091146 \n",
      "Training Accuracy: 69.62465620449765%\n",
      "Validation Loss: 0.7387107802453892 \n",
      "Validation Accuracy: 73.38618346545866%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(inf--->0.738711) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 2/30 [03:49<53:36, 114.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2\n",
      "Training Loss: 0.5516677750250474 \n",
      "Training Accuracy: 79.81718168581136%\n",
      "Validation Loss: 0.45847052482055445 \n",
      "Validation Accuracy: 83.30367254489565%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.738711--->0.458471) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 3/30 [05:44<51:42, 114.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3\n",
      "Training Loss: 0.43051664754555824 \n",
      "Training Accuracy: 84.6707652483417%\n",
      "Validation Loss: 0.39533784592798515 \n",
      "Validation Accuracy: 85.49587445397185%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.458471--->0.395338) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 4/30 [07:39<49:46, 114.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4\n",
      "Training Loss: 0.358678858630956 \n",
      "Training Accuracy: 87.42112926710888%\n",
      "Validation Loss: 0.3331285779982882 \n",
      "Validation Accuracy: 88.75586474680472%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.395338--->0.333129) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 5/30 [09:34<47:49, 114.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5\n",
      "Training Loss: 0.32545012278958807 \n",
      "Training Accuracy: 88.65879307555412%\n",
      "Validation Loss: 0.28737784574621716 \n",
      "Validation Accuracy: 89.8236531305614%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.333129--->0.287378) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 6/30 [11:28<45:51, 114.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6\n",
      "Training Loss: 0.26617743119173887 \n",
      "Training Accuracy: 90.48697621744054%\n",
      "Validation Loss: 0.28083337190770363 \n",
      "Validation Accuracy: 89.79938521274875%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.287378--->0.280833) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|██▎       | 7/30 [13:22<43:55, 114.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7\n",
      "Training Loss: 0.22882749899759774 \n",
      "Training Accuracy: 91.84597961494904%\n",
      "Validation Loss: 0.27155830569863704 \n",
      "Validation Accuracy: 91.43342501213395%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.280833--->0.271558) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██▋       | 8/30 [15:17<41:59, 114.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8\n",
      "Training Loss: 0.22190429074858203 \n",
      "Training Accuracy: 92.45267756026533%\n",
      "Validation Loss: 0.236189246923694 \n",
      "Validation Accuracy: 91.51431807150946%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.271558--->0.236189) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 9/30 [17:11<40:05, 114.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9\n",
      "Training Loss: 0.18984332203248983 \n",
      "Training Accuracy: 93.4072156608963%\n",
      "Validation Loss: 0.22644415471308146 \n",
      "Validation Accuracy: 91.4253357061964%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.236189--->0.226444) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 10/30 [19:06<38:10, 114.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10\n",
      "Training Loss: 0.17059026417642523 \n",
      "Training Accuracy: 94.08671735965054%\n",
      "Validation Loss: 0.1418726839731599 \n",
      "Validation Accuracy: 95.0412554602815%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.226444--->0.141873) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███▋      | 11/30 [21:00<36:14, 114.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11\n",
      "Training Loss: 0.15059652653202724 \n",
      "Training Accuracy: 94.75004044652968%\n",
      "Validation Loss: 0.13375652946762798 \n",
      "Validation Accuracy: 95.44572075715904%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.141873--->0.133757) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 12/30 [22:55<34:20, 114.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12\n",
      "Training Loss: 0.14759113188130296 \n",
      "Training Accuracy: 94.85520142371784%\n",
      "Validation Loss: 0.1660128860500971 \n",
      "Validation Accuracy: 94.240414172464%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|████▎     | 13/30 [24:49<32:26, 114.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13\n",
      "Training Loss: 0.12111504983099163 \n",
      "Training Accuracy: 95.79356091247371%\n",
      "Validation Loss: 0.11478010004444063 \n",
      "Validation Accuracy: 96.58631289435367%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.133757--->0.114780) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|████▋     | 14/30 [26:44<30:31, 114.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14\n",
      "Training Loss: 0.12189224241806786 \n",
      "Training Accuracy: 95.76929299466106%\n",
      "Validation Loss: 0.10033748351766678 \n",
      "Validation Accuracy: 96.81281346060508%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.114780--->0.100337) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 15/30 [28:38<28:36, 114.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15\n",
      "Training Loss: 0.12878750605046635 \n",
      "Training Accuracy: 95.68839993528555%\n",
      "Validation Loss: 0.10448945100955877 \n",
      "Validation Accuracy: 96.2870085746643%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|█████▎    | 16/30 [30:33<26:42, 114.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16\n",
      "Training Loss: 0.09298940319074582 \n",
      "Training Accuracy: 96.9260637437308%\n",
      "Validation Loss: 0.1414871282565097 \n",
      "Validation Accuracy: 95.6155961818476%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 57%|█████▋    | 17/30 [32:27<24:47, 114.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17\n",
      "Training Loss: 0.10701780053437988 \n",
      "Training Accuracy: 96.32745510435204%\n",
      "Validation Loss: 0.10155902878809672 \n",
      "Validation Accuracy: 96.60249150622876%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 18/30 [34:21<22:52, 114.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18\n",
      "Training Loss: 0.09516450006019045 \n",
      "Training Accuracy: 96.72383109529203%\n",
      "Validation Loss: 0.19266685638853495 \n",
      "Validation Accuracy: 94.83902281184274%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|██████▎   | 19/30 [36:16<20:58, 114.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19\n",
      "Training Loss: 0.08961643631093551 \n",
      "Training Accuracy: 96.99077819123119%\n",
      "Validation Loss: 0.09865076212003077 \n",
      "Validation Accuracy: 96.99077819123119%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.100337--->0.098651) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 20/30 [38:10<19:03, 114.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20\n",
      "Training Loss: 0.08382440775931267 \n",
      "Training Accuracy: 97.0393140268565%\n",
      "Validation Loss: 0.09013932396703489 \n",
      "Validation Accuracy: 97.120207086232%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.098651--->0.090139) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 21/30 [40:04<17:08, 114.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21\n",
      "Training Loss: 0.09954665608442984 \n",
      "Training Accuracy: 96.5296877527908%\n",
      "Validation Loss: 0.14634219781244148 \n",
      "Validation Accuracy: 95.32438116809578%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████▎  | 22/30 [41:58<15:13, 114.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22\n",
      "Training Loss: 0.07733391044879193 \n",
      "Training Accuracy: 97.40333279404626%\n",
      "Validation Loss: 0.2742250991193457 \n",
      "Validation Accuracy: 91.86215822682414%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 23/30 [43:52<13:19, 114.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23\n",
      "Training Loss: 0.0743620502284201 \n",
      "Training Accuracy: 97.54894030092218%\n",
      "Validation Loss: 0.08643695422344738 \n",
      "Validation Accuracy: 97.39524348810872%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.090139--->0.086437) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 24/30 [45:46<11:25, 114.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24\n",
      "Training Loss: 0.0545813854209518 \n",
      "Training Accuracy: 98.20417408186377%\n",
      "Validation Loss: 0.10853611325826472 \n",
      "Validation Accuracy: 96.55395567060346%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 25/30 [47:41<09:30, 114.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25\n",
      "Training Loss: 0.08036379410869632 \n",
      "Training Accuracy: 97.36288626435852%\n",
      "Validation Loss: 0.0850684615956061 \n",
      "Validation Accuracy: 97.38715418217117%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.086437--->0.085068) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|████████▋ | 26/30 [49:35<07:36, 114.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26\n",
      "Training Loss: 0.06276839776223318 \n",
      "Training Accuracy: 98.09901310467562%\n",
      "Validation Loss: 0.05782540044906725 \n",
      "Validation Accuracy: 98.32551367092704%\n",
      "=======================================================================\n",
      "Validation Loss Decreased(0.085068--->0.057825) \n",
      "Saving The Model\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 27/30 [51:29<05:42, 114.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27\n",
      "Training Loss: 0.06183996602025928 \n",
      "Training Accuracy: 97.8239767027989%\n",
      "Validation Loss: 0.14686114753858795 \n",
      "Validation Accuracy: 95.17877366121986%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 28/30 [53:23<03:48, 114.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28\n",
      "Training Loss: 0.06892703807472374 \n",
      "Training Accuracy: 97.71881572561074%\n",
      "Validation Loss: 0.05868526232870746 \n",
      "Validation Accuracy: 98.13945963436338%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 29/30 [55:17<01:54, 114.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29\n",
      "Training Loss: 0.052612463926213 \n",
      "Training Accuracy: 98.24462061155153%\n",
      "Validation Loss: 0.07212585435065114 \n",
      "Validation Accuracy: 97.7754408671736%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [57:11<00:00, 114.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30\n",
      "Training Loss: 0.053385695963267726 \n",
      "Training Accuracy: 98.14754894030092%\n",
      "Validation Loss: 0.11631534595535202 \n",
      "Validation Accuracy: 95.99579356091247%\n",
      "=======================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 30\n",
    "patience = 5\n",
    "model_filename = 'densenet_pret.pth'\n",
    "densenet_model, total_loss_train, total_loss_val, total_acc_train, total_acc_val = Model_functions.train_and_validate_model(\n",
    "                        model_des, train_loader, val_loader,test_loader, criterion,patience, optimizer, device, epochs, model_filename,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training and validation time for Pretrainned Densenet model: 57.34 minutes\n"
     ]
    }
   ],
   "source": [
    "end_time = time.time()\n",
    "total_time = end_time - start_time\n",
    "print(f\"Total training and validation time for Pretrainned Densenet model: {total_time/60:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ejH4KBt3u7opqxpd3wZp3odMlMHmOu6sRERE5K3UejNQ1BSMiIiLVS88r4qv1h/h0zQE2HMiqsj3E35teLcNLw5IIerUMJyzA5+QHdpTAF7fDho/AZocJb0CPq2rhGYiIiAD56TBrLKRshrCW5oTtpzfDka0Q2daEI8HR7q5SPIXTCe+MhYSlED8Epn4F9rPqTF5/pO6EV/oDFty+EqI7u7siERGRM6ZgRERERDiSU8jaxAzWJGayNjGDDQeyOFrsqLJfh+hgereqCEvaH29UidMBX94N694HbDD+Neg1pfafiIiIeJaCbHh3HBxaA8Expn1WVDvIPgRvj4bMRIjtAdPmg3+Yu6sVT7D8NVjwCPgEwW1LIbKNuyuqWR9ea9qm9roOxr/q7mpERETOmIIRERERqaLY4WR7Ug5rEjNYk5DB2v2ZJKTlV9mvbFRJ71YR9GkVTu+WEYQFlo4qcTrh6/th9UzABpe9CH2n1eXTEBGRxqwoH96fCInLICASbvzGdU6RtN0mHMk7AvGD4bpPwSfAffVK45e6E/4zBEoKYOy/oN90d1dU8/b/Dm+NMvPJ3bsBQuPcXZGIiMgZUTAiIiIipyQ1t5C1iZnlYcnxRpW0jw6mT+mokt4tw+m49m/YfnvDbLzkeTj35jquXEREGp2SQpgzGXYvBL9Q064orlfV/Q6vN222CrOh4xiY9D54edd5ueIBHCUmiDu4CtqNhOs+M5O4NUZvjzGB5Hl3w0V/c3c1IiIiZ0TBiIiIiJyREoeTbUk55S241iRmVD+qxM+Lp0M/4dKcuQAcHfE3AobfXdfliohIY+Eohk+mmXY+PoFw/TxoNeD4++9bCu9fYa7i7zkFxr3aeOZ8kPpjyT9h4V/BLwxuXw5hzd1dUe3ZsQBmXw2+IXDfJggId3dFIiIip03BiIiIiNSYtMqjShIzWL+/bFSJxYPeH3On9xcAvOl7PTs63Eyf+Ah6twqnQ3QIXtXNVSIiIlKZ0wGf3wIbPwEvP7j2Y2h7/snvt/1bMzeC5YCBd8DofzTeq/ml7iVtgjfOB2cxjP8P9Jrs7opql9MJr58HR7bCqCdgyH3urkhEROS0KRgRERGRWlPicLI9OcdM6r4vnW67/8v04jkAvFB8JS85rgAg2M/MVdKnVTi9400LrvBAX3eWLiIi9Y1lwVf3wJp3wO5t2mJ1GnPq9183B+bdapZH/gWGPVg7dYpnKSmC/42EpI3Q6VK45gPPCN3WzYZ5t0FwDNy7Ebz93F2RiIjIaVEwIiIiInUqf+GzBC75BwCfB0/m/7LHkV/krLJf26ZB9GkVYb7iNapERMSjWRYs+D9Y8SrY7DDxf9Bt4ukfZ/lrsOARs9xYJ8eWuvXTP+CXZyEgEu5YCcHR7q6obpQUwUu9IPsgXPYS9J3q7opEREROi4IRERERqXvLXoHv/w8A56A72dbtIdbsNy241iZmsjc1r8pdgv286dkyrDws6dUynIggjSoREfEIP/8/WPyMWR73KvS+7syP9dPf4ZfnABtcNRO6TqiREsUDHVwN/7vQtGi7apbn/S6V/Xsuqj3c8bvm7hERkQZFwYiIiIi4x8o34Ns/muUBt8LFT5e3nkjPK2Ld/gzWJGSWzlWSSV6Ro8oh2jYJonfpiJK+8RF0jA7BrlElIiKNy68vwo+Pm+Uxz8KAW87ueJYFX98Pq94Gu4+Zp6TdyLMuUzxMcQH8dxikbjejl658290V1b3CHHihKxRmwaQPoMtYd1ckIiJyyhSMiIiIiPusmgnz7zXL/abDJf+s9mpDh9NiR3KOmdQ9IZO1iRnsqWZUSYi/N31aRdA3PoJ+8RH0ahVOoK93LT8JERGpNb+9Cd+UzgVyweMw9P6aOa7TAZ/eBJs/A58gmPoltOhXM8cWz/D9o7DsZTPHxu0rIDDS3RW5x49Pwq8vQIv+MOMHz5hfRUREGgUFIyIiIuJea9+HL+4ELNMa5bKXwO510rtl5BWxrrT9VlkLrvxjRpV42W2c0yzUBCWtI+gXH0lsmH8tPREREalRZZM7Awx9EC74S80ev6QI5kyC3T9BQATc+C1Ed6nZx5DGKWE5zBwDWDD5Q+g0xt0VuU9OMrzYHRyF5j0Uf567KxIRETklCkZERETE/TZ8DJ/fApYTekyCca+B1+mN9ChxONmWlMOqfemsSshgdUIGh7MKquzXPDygNCSJoG98JJ1iNan7WbEsSFwBmz4Fbz84724IiXF3VSLS0G2eB3NvNJ8LA26Di5+qnSvRi/Lg3XFw4HcIaQbTF0BEfM0/jjQeRXnw+mDI2Au9roPxr7q7Ivf76h5YPQs6XgxTPnJ3NSIiIqdEwYiIiIjUD5s/h7kzzASmXa+AK94AL5+zOuTBzKOs2pfO6oQMVu3LYFtSNs5j/jUT4udNr1bh9IuPpH9rtd86ZZmJsP5DWD8H0vdUrPcNgfP/ZOYAOMufn4h4qB0L4MMp4CyB3tfD5S/Xbnue/HSYeQkc2QqR7Uw4Ety09h5PGravH4Tf34TQFnD7MvAPc3dF7pe6C17pB1imrZhGXomISAOgYERERETqj63z4ZNp4CyGzmPhypng7Vtjh88tLGFtoglJVidksDYxo8qk7mq/dQKFubDlCxOG7FtSsd4nCM4ZB0e2waE1Zl2TTjDmGWg3wj21ikjDtGcxfHCVacvTbSJc8eYptVc8a9mH4O3RJvSN7QHT5uuEt1S1+2d4b7xZvn6ePuMq++h62Pol9JwCE153dzUiIiInpWBERERE6pcdC8x/rh2FpiXD1e+aFk21oKz91uqEDNN+a186h47TfqssKOkbH0Hn2FDPab/ldJoQZP0cE4oU55dusEGbodDrWuhyGfgGmX3XvW8mYs1PNbt1uRxG/wPCW7ntKYhIA7H/N3h3PBTnQadLzN//uhx5lrbbhCN5RyB+MFz3KfgE1N3jS/1WkAWvnQfZB6D/TXDpP91dUf1yYBX87wKwe8M9GyCsubsrEhEROSEFIyIiIlL/7Fpo2qiUFEC7C+CaD+rs5NShzKPlIcmqhAy2Hq7afivYz5vercJNWBIfSe9W4QT5NbL2W2m7zcTHGz6CrP0V6yPbQa/J0OMaCG9Z/X2PZsKip+C3N01rNO8AGHIfDL5bJxlFpHqH18Osy6AwC9qOMBNa+7hhtN7h9TBrLBRml4Yz7532nFfSSH1xB6x9HyLawK2/gl+wuyuqf2ZeCgm/wqA7zUURIiIi9ZiCEREREamf9iyGOdeYEQpthpmTZL5BdV5GbmEJ6xIzS0eVpLM2MZPcwhKXfew26NIs1Ezo3jqSfvERxIU3wADgaKaZ62X9HNi/smK9Xxh0uwJ6TYEW/U+913/yZvjmIXOSBMyokdFPQedLa3e+ABFpWI5sh5ljID8NWg0yIzXc8Pe+3L6l8P4VJpzvOQXGvQp2u/vqEffb/h3MmQTY4MZvIX6Quyuqn3Z8D7OvAt9guG8zBIS7uyIREZHjUjAiIiIi9VfCMtNrvijXtDWZ8hH4hbi1JIfTYntSDqsTzIiSVfsyOJh5tMp+cWH+5SFJ3/gIujSrp+23HCWw52czOmTb16aFGYDNbkbr9JoMnS498yu3LQs2fwbf/wWyD5p17UbCmGehSYeaeQ4i0nCl74G3x0BuEjTrBVO/rB9ze2z/Fj681ox6G3iHufpdga5nyk+H1wZCbrJGQpyMZcHr50HKFrjgMRj6gLsrEhEROS4FIyIiIlK/7f/dXLlbmA0tzoXr5taPk2aVHM46akaUlE7qvuVwNo5j+m8F+XrRu1VE+VwlvVtFEOzO9lvJW2D9bNjwsTnZUyb6HOg5GXpcDSGxNfd4RXmw5J+w7GVwFIHdBwbeBsMfcnvYJSJuknXAhCJZieZvz7SvITDS3VVVWDcH5t1qlnWS13PNnQGb5kKTTnDLL+5p8daQrP8QPr8FgqLh3o16vUREpN5SMCIiIiL138E18N4EKMiEuD5w/WcQEOHuqo4rr7CE9fszzYiShAzWJmSQU037rc6xoeUTuvduGUFYgA9+Pnb8vO3YauPK5Lw0c3Jn3Ww4vK5ifUAkdL/KtMpq1rN2r4pO2w3fPQI7F5jbwbFw4V9NEKOrsUU8R26KaZ+Vtgsi28KN30FIjLurqmr5a7DgEbM89kXod6Nby5E6tvlz+GQa2Lzgph+geV93V1T/OYrh373MJPWX/Rv6TnN3RSIiItVSMCIiIiINw+EN8O44OJoOsT3g+nkQFOXuqk6Jw2mxIznHZVL3AxlV22+VsdnAz9uOv48X/t5e+PnY8ff2wt/Hjp+PV+n60u0+dvxKt/mXbiu/r48X/vYSWqT+SouEeUQeWoTdWQyAZfemqO2FlHSfjHfni/D19a+dMOZ4diyA7/5k2ugAtBwIlzxrghkRadzy0+GdyyB5E4S1NHM2hLd0d1XHt/BvsOR5wAZXzYSuE9xdkdSF3BR4dYD5d8ewP8LIR91dUcOx/FVY8GeIag93/AZ2L3dXJCIiUoWCEREREWk4krfAu5dD3hGI7go3fAHBTd1d1RlJzi5g1T4zofvqhAy2Hs6m2FFT/9Sy6Grbx5Vev3C51zKibDnlWzY6W/OpYxhfOs4jnYp/G9lslIcvlQMWv2NCmLKwpjyk8a4Ia5qHBzCkQ5NTbxFWUgjLX4FfnofifDOvSd9pMPIv9audjojUnIJseG88HFwNwTEmFIlq5+6qTsyyYP59sHqmaQN47cdmriRpvCwLProOts2HmO5w80/g7evuqhqOwlz4V1cz0vfq9+Ccy91dkUjDZVkaVS1SSxSMiIiISMNyZIe50jg3yfT7nvplzc6F4SaWZVHssCgscVBQ7KSg2OGyXLGu9Hbl/YodFJQ48c5PpmvqAnpnfEtc4d7yY6fbI/jJ53y+tp/PNmfLiuOVOKjpf935etkZ1C6KUefEMKpLNM3CAk5+p6yD8MNfYNOn5nZAhAlH+k7TVaYijUlRPnxwJSQsNS38bvwGoru4u6pT43TApzNMayWfIPPZ06Kfu6uS2mBZZsTD9/9ngrA/LILYbu6uquEpG2nVvC/ctFAndkXOxJEd8NG1ENEGrnwb/ILdXZFIo6JgRERERBqetN0mHMk+CJHtYOpXENbc3VW5R3EBbP8G1s+BXT+C5TTrvfyg8yXQc4q5stmr6igOy7IocjgpKHaacKXYWRHGlDhcApmC0vClsHI4U2n70WIHGw9mkZCW7/IY3ZuHMapLDKPOieacZqEnbte1dwl8+zCkbDa3Y3vAJc9Bq4E19WqJiLuUFMKcybB7IfiFmmAhrre7qzo9JUUwZxLs/skEuDd+B9Gd3V2V1KSMffDVvbDnZ3N75F9g2IPurKjhyk2Bf3UDRyFM+wZaD3Z3RSINS9pumHmJuRgMIH4wXPsJ+Aa5ty6RRkTBiIiIiDRMGftg1mWQlQgRreHKmWYCX/+wxn9VomXBgd/NJOqbP4OCrIptLc6FXpNND/w6nqDesix2peTyw9ZkftySzNr9mS4jUuLC/EtHksQwsG0Uvt72qgdxlMCqt+Dnf1Q8rx6TzATtjWBkkIhHcpTAJ1NNWyKfQLj+84YbeBbmmvmuDq6CkDiYsQDCW7m7KjlbTgf89gYs/Ktp7ejtD+c/AufdDfZqPqvk1My/D1a9DR0uMid0ReTUZOyDmZdC9gFo0hFykqAwG1oPhSkfg2+guysUaRQUjIiIiEjDlbnfjBzJqGgbhZef6VsfHF36vekxtyst+5xCm6f6JOsArP/QjA5J21WxPrQF9LwGek6GJu3dV98xjuQU8vO2FH7YmsySnUcoKHaWbwv282Z4p6Zc2CWG8zs1JTzwmN7teamw8ElY8x5ggW8wDH8IBtymPu9S/zidZiTEyv9C0gbodAmcd1f9nzujLjid8PktsPFj8/f52o+h7fnururs5KfDzDFwZJsZtTh9QYOd70qAlG3w5Z3mggMwV2Vf/rLevzUhbTe83Bew4LZlENPV3RWJ1H+Z+2HWJZCZaEKRaV9DRgK8NwGKcsxn6OQPG97/Y0TqIQUjIiIi0rBlH4LPb4XD61xHTpwKv9BKgcmx3ystBzapthVVnSjKg61fmdEhe38BSv855hMIXS43o0NaD6v3V7QWFDtYuiuVH7Yk8+PWFFJzC8u3edlt9G8dwaguMVx4TgzxUZVaBBxcDd88ZK7OBojqAGOehvaj6vgZiFSjIMu8N397E9J3H7PRBl3GwuB7PXcuCsuC+ffC6llg94ZJ70OnMe6uqmZkH4K3RptRi7E9zIkrf/1/s0EpKYJf/wW/PAfOYvANgYv+Cn2m1fvP1Abl4xtgyxfm4o0J/3F3NSL1W/Yh0z4rY68ZCT/tGwhtZrYlroD3roDiPPPv4EkfgI+/e+sVaeAUjIiIiEjjUVwAeSmmr3VuculXyjHfS5dLCk7jwDYIjDp5gBIcbdpXnW0rL6cTEpfBujmwZR4U5VZsaz3UnFw453LwCzm7x3ETp9Ni/YFMftyazI9bUtienOOyvUN0MKPOMSFJrxbh2LFg/Wz48QnIO2J26jwWRv/DtFETqWtHtpu2O+s/rHh/+oVB7+ug9RBY8w7s+K5i//jBMPgeaH+h55xwtSxY8H+w4lWw2WHi/6DbRHdXVbPSdsNbF0F+KsQPgevm6grehuLgavjiTkjZYm53vBgufcFz5yurTQdXw5sjTTh6z3oIa+HuikTqp5xkmHUppO2E8Hi48Zuq75d9S+GDK03Lvw6jYdJ74O3nnnpFGgEFIyIiIuJ5LMv06c1NqSY4OSZAyUupmND8VNh9jhOgVP5eunzs5InpeypaZWUmVqyPaG0mUe95DUTE18hLUJ8kpuWbkGRrMiv3puNwVvyTs0mwLxd0jmHUOTEMaeFLwLJnTbsiy2F6wA++x1yRr17LUtucDtj5vfn9K5uYGaBpZzj3D2YuHL/givUpW2HZy7DhY3M1OkDTLjD4buh2ZeNvCffz/4PFz5jly1+BPte7t57acng9zBprPlM6XQJXv+e+EYZyckX5Zg6rFa+Zz/bAKBjzrAntGvv8ZO40ayzsWwID74CL/5+7qxGpf/JSzfvkyFYIa2lGIR7v3/x7l8AHV0HJUfO5c9U7jf/fFCK1RMGIiIiIyIk4HaaffHUjUPKOCVOOZpzesX2DK0ISR3FFuygwbb66jjeBSKuBHnPCJiu/mEU7UvhhSzKLtx8hp7CkfJuft52hHZpwRYscRiW8gG/iErMhrKUZPdLlco95naQOHc2Ate+bdlmZCaUrbeZkxIA/QJvhJ/69yzoIK1+HVbNMb3Awk3YPvA36Tmuc7ZeW/ht+eMwsj3kWBtzi3npq276l8P4VZiRizykw7lXPGRnUkOz9Bb68y0xqDND9arj4aQiKcmtZHmHnj/DBRPAJgvs3m9G1ImLkp8M7l0PyRghpZkaKRLY98X32LILZk8znTuexcNUs8PKpi2pFGhUFIyIiIiI1paTQtHo60QiUsu/F+dUcwAbtRpgTa50v9fhREEUlTn7bm86PW5P5YUsyBzOPlm+z2Sxua7qZ2wrfIqQw2axsM9ychI3u7KaKpVFJ3mLaZW34qOL96h8OfW6A/jed/uitgixYNRNWvA65SWadXyj0mw4Dbq3oId7Q/fYmfPOgWb7gcRh6v3vrqSvbvoGPrjOj2QbdCRf9XUFtfXE0E374C6x519wObQ5j/wUdR7u1LI9iWfCfIZC8CUb+BYY96O6KROqHo5nw7jgzV2JQtAlFmnQ4tfvuWghzJoOjEM4ZBxPf1ohFkdOkYERERESkrlmWmZegciuvojwTioTGubu6esmyLLYl5fDjFtNya/2BLAD8KeQ27y+51Xs+fhTjtHljDbgFr/P/1DivxJfa5SiBHd+adln7llSsj+5qRod0v/rsA8uSQtNea9lLkLrDrLP7QM9JcN7d0LTT2R3fndbNhnm3meWhD8IFf3FvPXVt3RyYd6tZvuAxGPqAe+sR2PY1zL+/IozsNwNGPaHPB3fY8DF8djMENYV7N2nSaJGCbHhvghkxHtjEtM863Yt7dnwPH10LjiLTEnDCGwpHRE6DghERERERaXCSsgpYuC2ZH7cks3R3GjGOwzzm/T4Xeq0GINsrgj29HqLtqBmEBmhSSndyOi2yjhaTnl9ERl4RaXmu39Pzi0gvXc48Wkzn2BCuGxjP4HZNsNvr6Ir7/HQzYfrvb0HWfrPOZjftKQbcYiZPr+mr/51O2LnAtJ1KXF6xvtMlZu6cVgNr9vFq2+Z5MPdGM2/DgFtNiyJPHDGx/FVY8GezPPZF6HejW8vxWLkp8O1DsPlzczuyHVz+MrQe7N66PJmjGF7qbf7Gjv2XGS0n4qkKc80k6onLTWu5qfMhttuZHWv7t/DR9WY+s+5Xw4T/gN2rZusVaaQUjIiIiIhIg5ZXWMKSnan8uDWZgq0LuL/kLdrazdXBa5wd+LzZPbTvOZQLukTTIsKz25PVhKNFDtLyCsnIKy4NNQpJzys+btiRkV+E8wz+F9GmSRDXDmjFlX1bEB5YS5OKJm00o0M2fmL6dAMERELfqebK8vCWtfO4x0pcaUaQbPsaKH2xWg4wI0g6XVL/56vY8T18OMWclOl9PVz2Uv2vuTYt/BsseR6wmb7vXce7uSAPYlmm/d13fzLzA9m84Ly74Pw/gU+Au6uTFa+bn01kW7hzlU7eimcqyofZV5uRqf5hcMOXENfr7I65dT58MhWcJdBzculcV3p/iZyMghERERERaTQcTot1e5PIXvQSA/e/TQAFOC0bHzrO57mSScQ2a8GFXaIZdU4M3ZuHYfPEK9orKXE4yTxaTaiRW+Q6wqPSuoJi5xk9Voi/N5FBvkQE+hIV5EtEkC+RZV+B5naQrxffb0nm09UHyCksAcDP285lPeO4fmA8PVuGn/2TdhTDtvmw8g1IXFaxPraHGR3SbaL7TqAe2QHLX4b1H5q2GABRHcyJ3R6T6mfrmb2/wAdXmWCp20S44k2djLEsmH8frJ5p2qRd+zG0G+nuqhq/zETzuu/60dyO7Q6Xv3L2Jxyl5hTmwr+6QkEmXP2umRdBxJMUF8Cca2DPz+AbAjd8AS361syxt3wBn9xo5rrqfR1c9rJnX6QgcgoUjIiIiIhI45R9iJyv/kzITtNKJcsK5PmSq5ntuAAHXsSE+jGqSwyjzolhUNso/H0a5slcy7JwWuC0LAqKHac8kiM9v4iso8Wcyb/wfb3sRAT5EBnkR2SQzwnDjqggX8IDffH1PvX/nOcVlvDl+kO8tzyBLYezy9f3aBHGdQPiuaxnHAG+p/nzykuF1bNMu6ycQ2adzcucmBtwixmhUV+CspwkM5Ll97eg0MynQ3CMaVHVbzoEhLu1vHL7f4N3x0NxnhnZcvW74OXj7qrqB6cD5k6HLfPAJwimfgkt+rm7qsbJ6YTf/wc/PmF+F7384PyHzYgr/T7WPz/9A355FuL6wM0/1Z+/uyK1raQQProOdn5vPheu/6zm22Zu+gw+nWHaWvadBpf+S+GIyAkoGBERERGRxi1hGXzzECRvBOCAb1seKbieJUUVk1wH+noxpH0TooL9cDotnKVhg2VZOCoFD5W3uSxbpcvOSsvl6znO/U5y32OP7zxm/9LlmvgXenigD5GBJtCIKA01IoNdw43K64N8vepktI1lWaxJzOSDFQnM33CYIocZrRLq782VfVty3cBWtG0afOKDHFprRods+hQchWZdYBMz90O/6RAaV8vP4iwU5sDqd2DFa5B90KzzDTYnOwbeBmEt3Ffb4fUw6zIT3LQdAZM/rJ8jWtyppBBmTzJXBgdEwI3fnf7EunJiR3bAl3fB/hXmdsuBZi6Rph3dW5ccX+4ReLGbGWU2dT60GeruikRqn6MYPp4K278G7wC4bi60HlI7j7XhE/j8DyYc6X8TXPK8AkiR41AwIiIiIiKNn6PEtLX56e+mhQeQHD+WWUEz+HyXRVJ2gXvrq0EBPl6lAUfpiI5An6rhRlBFCBIe4IO3V/2/mjA9r4hPVu3n/ZUJ7E8/Wr5+SPsmXDewFaO6xFQ8j5Ii2PqlGXVx4LeKg8T1MaNDuk4Ab786fgZnoaQINn9mJmpP2WLW2b2h+1WmzVZM17qt58h2mDkG8tOg1SC47lPwDarbGhqKwlx4dxwcXAUhcTBjAYS3cndVDZ+j2LwfFj9j2s75BsOoJ8zcQLo6uv6bfz+segvaX2hOEIs0Zo4S+HS6aXXl5QdTPoJ2I2r3MdfNgXm3AZYZbXrx0wpHRKqhYEREREREPEdeGvz0V3MVPhb4BGENe5At8dfx694cCkuceNlt2Gxgt9nwslUs222UbrOZbXZclu02s82rdF9bpfvYS4/jsmyzYbdX7Otlq7qf3UbpPqXLle5jt1Vd7+tlP/0WUw2M02mxeOcRPliRwMJtKeUjZmJD/ZnRK5DJXgsJ3vge5CaZDXYfM/n1gFsbfisjyzLzJyz9t5m0tUz7C2HwPebq09o+8ZG+14QiOYehWS/TIso/rHYfs6HLTzev2ZFtENXejBwJburuqhquQ2vhi7vKRwHS/kIY+y8Ib+neuuTUpe+Bl/uaK9pvXQqx3dxdkUjtcDrg81tg4yfg5QvXzIYOF9bNY699H764wywPvANG/0PhiCcrzDU/f13I4kLBiIiIiIh4nkNrTXutstEEke3M1XQdL3JvXXJa9qfnM+e3RDb/tpDxxV9zqX0FvjYHAEUBTfE5dwa2ftMhJMbNldaCg6th6UtmZIxlWowR18cEJF0uq50J0LMOwsyLzSTX0efAtK8hMLLmH6cxyj4Eb42GrERo1tO0EPLX/09PS/FRWPQULHvFTC4cEGn+bve4Wif7GqJPpsHmz6HHJLjiDXdXI1LznE748k5Y94EZ5Xn1e9D5krqtYfUs+Ooes3ze3XDhX/X30tMU5Zt5uJa+CANug+F/dHdF9YqCERERERHxTE4nbPgIfngM8lLMusAmEN0FmnaCpp1LlztDUBP31ipVlRSak2or/wuH1pSvXu3swDslo/nWeS6tmoZx3cB4rujTgrCARjoJc9puWP6qOfFSUtoSLqKNabHVawr4BNTM4+SmmFEPabsgsq0Z9dAYA6falLoL3h4N+akQP8S0INO8LKdm369mLpH0PeZ2t4lw8TMaedOQHVwDb44Amxfcs04t5qRxcTph/r2w5h3zO37VTDhnnHtq+f0t+Pp+szzkfrjgMYUjnqC4wLQRXvJCxf9z4nrDzT/r51+JghERERER8WwF2aZP/cr/grO4+n0Cm5QGJZ3N97LQRIFJ3cs+DKveNv/Zyzti1nn5mhOl5/6BbV7teX9FAp+vOUhekRk9EuDjxbhecVw3MJ5uzRtp26fcI/D7m/DbG3A0w6wLbGLmVOl/09mN7MhPh3cug+RNENYSbvxWbYvO1OH1MGssFGZDp0vh6nfBy9vdVdVfBVnww+Pm/Q4Q0gwufaHur7qW2vHOZbD3Fxh4O1z8lLurEakZlgXf/NF8JtvscMWb0P1K99a08g34tnSkwPCHYcSf3VuP1J6SQljzLiz5p2l7CiZ4HvYQ9Jysf3McQ8GIiIiIiAhAUR6k7oCUbXBkq5lgOmUrZCYc/z6BUdC0iwKT2mZZsH+lCa+2fgnOErM+JA76T4c+06pcOZ5bWMLnaw/y/vIEtifnlK/v1TKc6wfGc2mPZvj7NML5WIryTF/xZa+Ytk0APoHQ5wZz8jEi/vSOV5hTOnn4agiOMaFIVLuar9uT7PsV3rsCHIXQ61q4/BVNGF6d7d/B/Psg55C53XeaaQOjOW0aj10L4f0rwCcI7tuk1nzS8FkWLPg/WPEqYIPxr0Ovye6uylj+Gix4xCyf/2c4/2H31iM1y1FsRg//8jxk7TfrQlvAsAfNvzW8fd1bXz2lYERERERE5ERcApPSrzMJTJp2VtuX01VcAJvmmkAkaUPF+laD4Nw/mLk0vE7cIsuyLFYlZPDe8gS+3XSYYof5L014oA9X92vJtQNaER/VCCeidJTAlnmmp3RS6STVNi/odoXpM96sx8mPUZQPH1wJCUvNfA7TvoaYc2qzas+x7Rv46DozV8agO+Giv6u1RZm8VPj2YfPeB9Ma7vKXoM0w99YlNc+y4D9DIXkjjHi08fS+tyzz74R9v0LJUWg9BJr1qp25n6T+sCxY+CT8+i9z+7KXoO9U99Z0rGUvw/ePmuWRfzEnzaVhc5TAhg9h8bMV/zcJjjU/2z43gLefe+ur5xSMiIiIiIicibMJTJp2qjSXSRcFJsfKOmB6Yq+eBUfTzTpvf9OK4txbTu2kfjWO5BTy8ar9zF6ZyMHMo+Xrh3VsyvUD4xnZORoveyM7OW1ZsOdnM1H7np8r1rcdAYPvoSR+GIkZR9mVksuuI7nsSskFC1qHe3P17oeJTfkVp28ItqlfYWve233PozFaNxvm3WaWL3gcht7v3nrczbJg4ycmFDmablrQDLrDXNnsG+ju6qS2bPgEPrvJtP67b1PNzYtUlyzLzL+0dzHsXWICkfxU130CIky4126k+ft7uqP3pP77+SlY/LRZvuR5OPdm99ZzPL/+C358wixf+FcYfI9by5Ez5HTAxrnmd65s/q2gpmYemX43Nsy/pW6gYEREREREpCaVBSZlrbjKQpOMBOA4/5wOjHJtxdXYAxPLMj2Qi/KgKLfie14qrJ8D2742V9KDaQNw7k3QZ2qNtVlxOC0WbU/hvRUJLN5xhLL/5cSF+TNlQCsm9W9F05DGdYVdYYmDQ9tW4rPiFeIOfIcd8/putlrzn+KxfOMcgANzNbMXDl71eYmLvX4n3/Lj+qI/sc3nHFpGBhIfFUh8VBCtypYjg4gL98fbS62gzsjyV2FBaa/3y/5t2kV5oqwDpm3Wzu/N7eiuMO4VaN7HvXVJ7XOUwEu9Teu/S1+A/jPcXdHJWRZk7C0NQZaY77lJrvt4B0CrgaaV4b4lZl6hyiLbmoCk3UhoM1Qt4hq6X56Hn/5mlkc/BYNud289J/PLc/DT383y6P9nQmhpGJxO2PI5LHra/H8DzKjeIfeaOeV8G+Eo6FqkYEREREREpC4U5ZcGJqUjS45sN3OZnGpgUj75e+kcJnXZdqekqDTAKAsxSoOMwtyq4Ubl74XV3Kfsdtk8IcfTeqhpl9XpklqdKDIxLZ8Pfkvg49/3k5FfDICPl43RXWO5fmA857aJxNaAWhzlFpawO8WM/NhZ+n1XSg6J6fk4S3/NWthSmOH1LZO8FhFoKwQgyR7Dr00mkdpuIiN2P0OnlG8oxocHfP7MV7mdONH/BL3tNppHBLiEJa2izHKryEACfTXR5wkt/KuZJNVmhytnQtfx7q6o7jidsPpt+OEJKMoBL18zQezge9QP3ZOs/C98+5Bpm3bX6vrZcipzf0UIsm9JRQ//Ml5+0PJcMyqk9VBo3rfid9hRAofWwO6fYPfPcOD3ivAfTJvD5n2hXWlQ0rzvSdtESj2y9CX44S9medST5gR1Q7DoaVj0lFke8ywMuMW99ciJOZ2wbb75maVsMev8w2Hw3ebfy34hbi2voVIwIiIiIiLiTpUDkyPbKiZ/P1FgEhDpOrKkbC6ToKYmcKgcRriEE8cEFYU5lUKLPHNisvLtsu3O4tp7/t7+4BtsrnDzDTYnls69GWK61t5jVqOg2MG3mw7z3vIE1iRmlq/vGBPMdQPjmdC7OSH+9edEVUZeUaXgI5edKTnsTsnlUFbBce8T4u9Nh+hg2pd+dQkrocfhTwjd8Da2/DSzk5efmRTc7g2T3odOYygscXAg4yiJafkkpOWRkJ5vltPzSUzPp6jEecJam4b4ER8ZaMKSyCATmEQFEh8ZSGSQb4MKnmqFZZnREqtngt0Hrv3YnBxt7FJ3wZd3QeIyc7vFuXD5y+bvmXiWojz4V1c4mgFXzYKuE9xdEeQkmRBk72IThGTsc91u94EW/UwI0mao+f318T+1Yxdkm3Zbu38yLQ7Tdrlu9w0xxywbURLVTnMQ1Vcr/gPflU5iPuL/YPhD7q3ndFiWGTWy5Hlzuz63//JklgU7voOf/1ExZ5xfqJmfbOCtGm12lhSMiIiIiIjUR9UGJttKT84c55/ldp/aDTG8/EyA4RdcKcwoDTQq3/YLOf423+DS+weBT1CtjgY5U5sPZfH+ikTmrT3I0WJzVW+grxfjezfnugHxnBNXN/+vsCyL5OzC8uBjV6UgJC2v6Lj3axLsR/voIDpEh9A+Org8DGka4ld9CFF8FNZ9AMteMe1hsMGVb0G3iSet0em0SM4pICGtLCzJM8vp+SSk5ZN19MS/j8F+3qZFV+lok8rhSbMwD2rR5XTA3OmwZZ55X0z9Clr0dXdVtcNRAstfNv34HYWm1dAFj5sTcm4eKVBQ7GBXSi5bD2eTlldEr5bh9G4Vjp93PRzB0Nj8/P9g8TNmkvI/LKr7ICAvtXREyC8mEEnb6brd5gVxvU1g0XqoaZNVUy1rMvebgGT3T7BnccXcWmXCWkLb882IkjbnQ1BUzTyunJ3f34KvS+eGGvZHGPmoe+s5E5Zl5htZ+qK5PfZFMz+FuJ9lwa4fTSByaK1Z5xsMA28zrc8CItxbXyOhYEREREREpCEpD0xKW3EdLzDx8i0NIiqHFMeGFsdurxRalIcalb57WGuP7IJiPlt9gPdXJppJyUv1i4/guoHxjOkeWyMnTB1OiwMZ+ZVGf5jvu1NyySk8fsux5uEBLsFH2Vd44Bm2IHI6YOcP4B8K8eed4bNxlZVffExYUrF8+ASjW8C06GoREUCrqKCK4CSyYo6TAN9GdrK6pBBmTzInSG1eEBILwTHme0gsBMdCSIzr96Cm9TJcPK7D6+GLOyFpg7ndbqQ5EVfHE1FblkVSdgHbDuew5XA225Jy2HY4mz2peTicrqc9/Lzt9GsdwaC2UQxq14QeLcLw8ZTAri7lpcK/ukHJURMMthlWu4+Xnw4JSytaY5W1pilng2Y9SkeEDINWg8zfxtrmdELSetNya/dPsH8lOCoH4TZo1tOEJG1HmIDGu3HNidUgrHkPvrzTLJ93t5nEvKGO6rEs+P5RWP6KuX35K9DnevfW5MksC/YsMmHxgd/MOp9A0y7rvLsVjNYwBSMiIiIiIo1BUb65yrRsJIb689cYy7JYsSed91cksGBzEiWlJ06jgny5ql9Lrh3QipaRgSc9TlGJk4S0vGNaYOWy50guhcdpR+VltxEfFUj7pib06BATTPumIbRtGkSQXwM6IV6NgmIHBzLMyBKX4CQ9nwPpRylynLhFV3SIX2lYElQ6KbwJTlpFBhIR6Ivd3gBPUhXmwpxrzInaU2GzQ2ATE5SENKsIUsq/l4UoMe49eVpcYEYDLP23mVvBPxwufgp6Tq71k4kFxQ52JOdUCkFMEJKZX/1opvBAH7rEhhIR5MPv+zI4klPosj3I14v+bSIZ1DaK89o14Zy4ULwa4u9affT1g/D7m9DuArj+s5o9dkEWJCyvGBWStJEqoy+ju5oRIW2GmXC4PlyRXZRn6i4bUXJsgOMdAK0Hl7bdGgHR5zTcE/QNxfqP4PNbAAsG3Gb+ljX019yy4LtHYOXrgA3Gvwa9pri7Ks+z71cTiCQsNbe9/c2E6oPvheCmbi2tsVIwIiIiIiIicopSsgv48Pf9zPktsXzEg80GIzpFc93AVgzvGE1RiZPdR3JdWmDtTMklIS2/ytXoZXy97bRtEkSHmBCXECQ+KtAj2/g4nOaK/oS0vIr5TCqFJ9kFxx9JA+ZnEuznTViAD6H+PoQGVF72KV32Jizw2HVm3wAfL/fNfWJZkH0QcpIhN8nMdZCbDDmHK61LhrwUsE4cHrkIiDhOeHLM95pqD1QmYbm5srpsHoVzxsGY50xgU4Msy+JwVgFbS0eAbDmczbbD2exNzaO6t52X3Ua7pkF0jg2lS7NQOjcLoUtsKDGhFS3nLMti95Fclu9OY9nuNJbvSasSqIT6ezOgbZQJStpH0TE6pGGGcvVB+l54uY/5vb71V4jtfubHKsqDxOWl84T8AofXVX2/NOlYMVl66yEQ1OSsyq8TOUnmavLdP5uwJDfZdXtwbEXbrbbnm/e01JxNn8GnM8zvUr8ZcOk/G34oUsay4Js/mnASG0z4L/Sc5O6qPEPiStMya+9ic9vLF/pNhyH36T1cyxSMiIiIiIiInKYSh5OF21J4f0UCS3amlq8P8fcm5wQn7YP9vGlXuf1VUxOAtIgI1FXnpyEzv8iMNEnPJ7G0PVdZeJKUfeIWXafCx8tGqL8JS0LKQpQAE6CUrT9e2BIa4FM3rZacDtN+qCwoyTlcGqAkVf1+OnMP+YWePDwJiTX7neiEYGEO/Phk6Uk2zH0v/Sd0uezsnjdwtMjB9mTT/qpyCHK8wCwyyJcuzUIqQpDYEDrEBJ926Oh0WmxLymHZ7lRW7Elj5Z70Ku3uooJ8Gdg2ikHtzFfbJkHuC9kaornTYdOn0P1qmPjmqd+v+Cjs/82EIPuWwMHV4Dzm9yGybUVrrNZDGv4JR8syI0jKQpJ9S00rssqizzEt69qOMKNgfE8+ulGOY+tX8PFUM+qt9/Vw2Utgb2Rt9SzLzJuy6m0zIvGKN6H7le6uqvE6sNoEIrsXmtt2H+hzAwx9AMKau7c2D6FgRERERERE5CzsTc3jgxUJfLL6QPlk41FBvrQrDT8qzwESG+qvk6S1rKjESdbRYrILisk+Wly6XFJp2azPPlpCdkHputJ9so4WH3dUz+kI9PVyCVAqlivCk2NHqZRtD/b1rtkRB5YFRzNKg5LSESjVhSe5yVCcf+rH9Q6oOudJ2XebDX76B2QfMPv2vh4u+ttptyayLIuDmUfZergiBNl6OJu9aXlUd3bC226jfXQwnWNDSkeBhNIlNoSmIX618r4rcTjZfCi7fDTJ73vTOVrscNknJtSvvO3WoHZRp9R2z6MdWgdvDDfz7Ny99vjzz5QUwoFVpa2xlphe/C5zcWAmLS8bEdJmKIS1qPXy3aq4wMxJsudnE5YcXk+VucdaDaxouxXbs/Gd2K8t27+Dj64zIXOPa2D86433tXM6Yf49sOZd8z688i3oOsHdVTUuh9bBoqdgx3fmts0Lel8LQx+s8zm3PJ2CERERERERkRpQUOxgZ3IuzSMCiAzSHC8NkWVZ5Bc5KgUoJZWCk7IQpaRK8JJTGrwcO3rgTNhtEFIapFT+Cg2oui480HV7iN9ZhCqWZUZ5VGnbdWyIkgyFWad2zPB4uOzf5iTsSeQXlbA9KceEIEnZpiXW4ZzjvqZNgv1KR4GUhiCxobSPDsbX230nK4tKnGw4kGmCkt1prE7MoOiY+YNaRASUt90a1LYJsWH+bqq2Hnt3nGkXNeBWGPOMWecohkNrK0aEJK6sOjoipFlFCNJ6KES0bjxtjs5EXhrsXVQ6omQRZO133R4QCW2HV4woCW/pjirrv10/wpzJJnjrNtGMorA38haXTqdpgbjuA7B7w1WzamS0n8dL2mQCkW3zzW2b3cy1NexBM6JN6pyCEREREREREZEaUOJwkltY4hKeVBesVF5feURLYclpzBlSjROFKpVDlOq2n1aoUpRfKSxJcp37JDcJ8tOgzXA4/09V5iyxLIsDGUfZejjbJQRJSM+vdhSIj5eN9tEhdCkfBWJaYjUNceNk8qeooNjBmoQMlu8xc5Ss359JyTEjkto2CWJguyjOaxfFwLZRNAmu/8+r1u3+Cd6bAD6BMOyPkLDMzBdSlOu6X2CTihCkzXCIaufZQciJWJaZ56es7dbeJVCU47pPVPvS0SQjTasxf51DY89imH01lBSYYODKmeDl4+6q6obTAfNuhw0fmnDk6veg8yXurqphStlmApEt80pX2KD7VTD8YWjS3p2VeTwFIyIiIiIiIiL1QEGxw2UkSvlXfjFZpWFL5tGiqtuPFlNQXLOhSnhg9aNUTidUyS00o0AqjwDZlpRD7nFGgTQN8aNLafurshCkbRP3jgKpSXmFJfy+L53le8yIkk0Hs6pMDN8pJqR8fpKBbaIIC/SQk7CVWRb8dxgkbXBdHxAB8YNNCNJmKDTtrCDkTDmKTSuysrZbB1ebuTPK2LygRX8z2qvDRRDX2/Ne64Rl8P5E02Kw4xi4+l3w9rDRoE4HfH4LbPzEzH9xzQfQcbS7q2o4UnfB4qdh41zK29p1nQDD/wTRnd1amhgKRkREREREREQauMISh8solLKvzHzX27UVqlQOUYL9vDmYeZSEtOrnLPH1stMhJrh0MnQTgnSKDfG40RJZR4v5bW86y3ansnx3GtuSXK/gt9mga1yomZ+kbRT920QS7Oftpmrr2N4lMP9eiOpQMSokplvjndfB3Y5mmhZlZSNK0ve4bg9tDp0vhc5jzSTujX3UxP7fzKilolxoPwqumQ3envX3qZyjBD67CTZ/buapmTzHvCZyfOl7YPFzZrSNVfr52nksnP8IxHZzb23iQsGIiIiIiIiIiAc7NlQ5Nkw5m1AlJtSvfA6QshCkTZMgfLx0gvtYabmFrKwUlOw+kuey3ctuo0eLMM5rZ+Yn6RsfQYBvI5/rQNwjY58JSXYvhF0/QXGl30X/cOh4MXQZa9puHdMur8E7uBreHQ+F2WZ00pSPwCfA3VW5l6MY5t4IW78CLz/zmpzC3FEeJzMRfnkO1n5QMQKr48UmEInr5dbSpHoKRkRERERERETkjBQUO6oEJtkFxcSE+tM5NpTIIA9rPVODkrMLWLEnjWW70li+J43EdNcROL5ednq1Ci8NSqLo1SocP28FJXJ2LMti95FcFm5NYcWeNKIDLC7030avvF+JOrAQ29G0ip29A0w40vlS6DQGAiPdV3hNOLwe3rkMCrJM27Zr54JvoLurqh9KiuCTabD9a/D2hykfQ9vh7q6qfsg6CEv+CWveBWexWdd+FJz/Z2jR1721yQkpGBERERERERERqef2p+ezfE8aK3abydyTsgtctvv72OkXH1k+R0mH6GD8fbzwttuwedr8EHJaCksc/LY3nYVbU/hpW0qVEK6Mj91iXGQil/utpU/+rwQfPVSx0WY3YULnS81XeKs6qr6GJG+GWWPhaDq0HADXfQp+Ie6uqn4pKYKPr4cd34FPIFz7CbQe4u6q3CcnCZa8AKtngqPIrGszHEb8GVoNdG9tckoUjIiIiIiIiIiINCCWZbEvLb+87dby3Wmk5RVVu6/dBv4+XubL246fjxd+3vbSdeZ7+W1vs86v0r7+x+7v7YWfj+v+Zfv4le7j62VXGFPPHckp5OftKfy0NYUlO4+QV1Qx+bqvl51B7aIY1rEp2UeL2Xgwiw0HMknNrfw7ZtHFlsjF3qsY67uWdo5j5iWJ7WHmVegyFqLPqd+Ttx/ZDrMuhbwjENcHbpgH/mHurqp+KimED6+FXT+AT5AJkOIHubuqupV7BJa+CL//D0pKA+pW58HI//PsoKgBUjAiIiIiIiIiItKAWZbFzpRclu1KNaNK9qSTdbTYbfXYbFQNWyqHLpWDl0qhi1+loCXQ15tz4kLpGheqOWlqgGVZbD6UzU/bUli4LYX1+zNdtjcN8eOCztGM7BzN4PZNCPLzrnL/pOwCNhzIYuOBrPKwJCPf/J61sKVwkX01o71+p59tO162ilOIRaGt8D7ncuxdxkLLc8Fej1q+pe2GmZdAbpIJc6Z+CQER7q6qfisugDnXwJ6fwTcYrv/c/Fwbu7w0WPYS/PYGFJeOqmpxrglE2gyv3+GfVEvBiIiIiIiIiIhII2JZFoUlTgqLnRSUOCgodlBY4qSg2EFBsfOY2w4KSpwUHrPu2P3L9in/fuw+JQ5q46yRv4+dHi3C6RsfQb/4CPq0iiBCc9eckqNFDpbuSmXhthR+3pZSpf1ajxZhjOwczQWdY+gaF4rdfnondi3L4mDmUTYeyGLDQROYbDiQiXdBOhd4reEi+yqG2TfiZ6sI6XK9IzgSNxLfbpcT22s0Xr5unNg8fa8ZKZJ9EKK7wrT5DX+elLpSfBRmXw17fwHfELjhi8Y5n4ZlQW6yGR2y4nUoyjXr43rDiEeh/QUKRBowBSMiIiIiIiIiInJWLMui2GFVBDHFTgpLKgUrxwYypdvK9jk2bMnIL2b9gUwy86uOfGnbNIh+8RH0Lf1q2yT4tE/qN1aHMo/y0zYzV8jSXakUljjLtwX4eDGkQxNGdYlmRKdookP9a/zxLcsiMT3fjCw5mMWOxCTCDy9hmHMlF9jXEGarmL8kz/JnfUB/kppdgF+Xi+ncpiVtooLq5meZmQgzL4WsRGjSCaZ9DcFNa/9xG5OifBOO7FsCfmEw9QsTGDREjmITlKXthNQdkLqz9GsHFGRW7BfbHUb8H3S8WIFII6BgRERERERERERE6h3LstiTmsfqfRmsTshgdWIGu1Jyq+wXHuhDn1YVQUnPFuEE+Najdk21yOG0WH8gk5+2mhZZWw9nu2xvHh7ABV1Mi6yBbaPw96n718XptNiXlsem/alkb1tEkwM/0itvKbG29PJ9iiwvVjjPYbF9AIdiR9Ayvh3dm4fRo0UYrSIDa3bOmuxDMHMMZOyDyHZw4zcQEltzx/ckhbnwwVWQuAz8w7Fu+IL8qG6k5xWRlldEel4h+UUO2jQJokN0CL7ebm6Ll59uAo9jA5CMveAsOc6dbNCsBwx90MybY1drv8ZCwYiIiIiIiIiIiDQIGXlFrN1vgpJV+zJYfyCTgmKnyz7edhtd40LpUxqU9IuPJDas5kdHuEtOQTFLdqaycGsKi7ankJZXMSm63QZ9WkUwsotpkdUxJrhmQ4Ua4nA4ObhlKfnrvyTqwPc0Ldjnsn2tsz3fO/qxwNmPVL9WdG8RRvfm4fRoEUb35mG0iAg4s+eVk2TaZ6XtgojWMO0bCGteI8+pMbIsi+yjJaTlFVYKO1y/8nIyuTfpT5xTspUMK5gpRf/HViu+yrF8vGy0jw7hnGZm7qBz4kLp0iyUsACfmi3aUQKZCdUHIPmpx7+fTyA06QBNOkJUh0rL7cDHjS3fpNYoGBERERERERERkQap2OFky6Hs8hElq/dlVJlLA8zIiT6l85T0jY+gc2wI3g1oUvd9qXks3JbCT9uS+W1vOsWOilN0If7eDO/YlAu6RDO8YzSRDXEOltSdOLbOp2jTlwQkr3HZtMsZx/fOfixw9GOD1RYLOxGBPnRvEU6P5mF0b2FGlsSG+p84LMk9Au+MhSPbIKylGSkS3qqWn1j94nBaZOSbQCMttyzcKCQtr4iMY4KPsnUlzpOfDg4mn/d8n6a3fRdpVghTHY+RHtSOyGBf/L292JmSS9bRqm3xAFpEBHBOMxOUnNMslK7Nw4gLO8nPEqAgC1J3meCjcgCSvgccRce/X2jz6gOQ0Di1x/IwCkZERERERERERKRRsCyLQ1kFJijZl87qxAy2Hs7BcczJ3UBfL3q1DC9vv9W7VUTNX7l+FoodTlYnZLBwazILt6Ww50iey/a2TYO4oHM0IzvH0K91BD4NKOQ5qZwk2P4NbJ2PtfcXbM6KE+qp9ii+K+7Dd45+rHB2oQTv8m1Ngv3o3jy0PDDp0SKsYh6V/HSYNRZSNkNInAlFItvU9TOrcYUlDjLyistHdFQOPMqCDbNstmceLeZMzu4G+3kTGeRLZJAvUaXfI4PLlv2IDPKhiU8hnRdch2/KeqzAJtimfQ3RnYGK9+WWQ9lsPpTFlkPZbDmczYGMo9U+XliAjwlLmgXTLzyPrr5JNHccwCt9V8XcH7nJxy/Y2x+i2lcNQKLag1/w6b8A0igpGBERERERERERkUYrr7CE9fszTfuthAzWJGaQU+A6n4DNBh2ig+kbH1kelrSOquG5LU4iI6+IxTuOsHBbCou3p5BdqUZvu41z20QysnM0F3SJoU2ToDqry60KsmDnD7BtvvleVDHHTJF3CJuCBvJtSV8+zOhEjtOvyt3DAnyItOfzuvMJOlt7SSWCu/3/wWHv5thtYLfZ8LLbKn0Hu92Gl82GvfS26/ayZbPeZjP7Vmwr3b/sGJWOV76/HbxsZcvV3PeYWvIKS8xIjsqBR765nVN4vHkxTiw80Mc15CgNNyKD/Cqt8yUq2JeIQN9Tn5vmaAa8czkkbYCgaDOpfdOOx90962gxWw9nsyMxibTEzRQlbycwew9tOEQ72yHa2A7jb6t+pAmAMzgGe5OOVQOQsJaaC0ROSsGIiIiIiIiIiIh4DKfTYteRXFaVTuq+JjGDval5VfaLCvKtNE9JBN2ah9Xo5OWWZbEzJZeFW02LrNUJGVQe2BIZ5Mv5nZpyQecYhnZsQqh//RnR4hYlhbBnsQlJtn8DeUfKN1lefmTHDWFTyBAWlPRlRTLsSskl0Mrnfd+n6GXfTaoVyqSiv7DbalxzinjZbUQEuo7kiAysCDYqRnr4ERnkS0SgT+22kctPN+FI8kYIjjWjc6LagWVB9sGK+T7KW2DtNOuPoxgf9hHLLkcsu604djvj2GM1Y48VRw6BtIoMdJm35Jy40JO3VRNBwYiIiIiIiIiIiHi41NxC1lSap2TDwSyKSlwndffxstGteVj5PCV94iOIDjm9Sd0LSxys2JPOT6Utso5tJdQ5NoQLupgWWb1ahuNl18ndajkdcOB3E5JsnQ8Zeyu22ezQciBFHcbg2PwFAUmrKPGLYM+lH5If0RmH08JpWeXfnU5wWBbOY9Y7nJjt5etMqOYovW2VfneUrndaVvlxjr2vw7KwLDPHR/njlj3Gsfe1Su9bum+Ar1el0R1+LoFHVJAvof4+2Ovb70leGrxzmWldFhQNoc3MfCDFVQPIcoFNzKiPJu1Lv3c0ra/C47HsXhzIOMrm0hZcWw5ls/VwNgczq2/FFRHoUz5nyTlxoXSNC6Ntk6AGNa+Q1D4FIyIiIiIiIiIiIpUUljjYfCib1aWjSlYlZJCaW1hlv1aRgeWtt/rGR9AxJqRKmJGSU8DP21JYuDWFX3elkl/kKN/m621ncLsoRnaJYWTnaJqHB9T6c2t0LAtStsK2r2HbV3B4vet2/zCY+hU06+me+jxV5cnuy9i9IaJN9QFIYORpP0RmflH5fCVl33em5FaZUwjMe61zbIjLRO+dm4US7OddzZHFEygYEREREREREREROQHLstiffpTVienlLbi2J+dUmcg6xM+bXq3MpO4AP29LYf2BLJd9okP8uKBLNBd0juG89lEE+urEbI3K3F86eftXZiL3K/4Lzfu6uyrPlJcGmz+D0DgTgES0Bq/abQlXUOxgV0quyyTvWw/nkHuc+VhaRwXSNS7MZYRJdIifWnF5AAUjIiIiIiIiIiIipymnoJi1iZnl85SsTcw87snXni3CGNk5hgu6RNM1LlQnXUXqkNNpsT8jvzwo2XzIjDBJyi6odv+oIF+XoCQuPKC05ZkJSR1WaWs1q6ylWtVlZ6X2aeXLVlkbNcpbsZUtO49zHKt0f8dxjuk85jhVjlm6T88WYfxhWLs6fuXrNwUjIiIiIiIiIiIiZ8nhtNielFM6T0k6xU6L4R2acn7npqc9F4mI1L603EK2Hs5hy2EzumTzoWx2H8mlmk5cDd6F58Tw5g393F1GvaJgREREREREREREREQ8XkGxg+1JOeXzlmw+lEVGfjF2G9htNuw2GzYbeNltpbfBXmnZZrPhZbNht1fsX3Zfm82Gl73649hsmPuV3rf8OKXHtFe6r+2YZXvZfe2ux7GVPq6X3UaryEBGdI5298tbr5xObqCGhyIiIiIiIiIiIiLSKPn7eNGzZTg9W4a7uxSpR+zuLkBERERERERERERERKSuKBgRERERERERERERERGPoWBEREREREREREREREQ8hoIRERERERERERERERHxGApGRERERERERERERETEYygYERERERERERERERERj6FgREREREREREREREREPIaCERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDyGghEREREREREREREREfEYCkZERERERERERERERMRjKBgRERERERERERERERGPoWBEREREREREREREREQ8hoIRERERERERERERERHxGApGRERERERERERERETEYygYERERERERERERERERj6FgREREREREREREREREPIaCERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDxGjQQjr776Kq1bt8bf358BAwbw22+/nXD/Tz75hM6dO+Pv70/37t355ptvaqIMERERERERERERERGREzrrYOSjjz7i/vvv5/HHH2fNmjX07NmT0aNHk5KSUu3+y5YtY/LkycyYMYO1a9cyfvx4xo8fz6ZNm862FBERERERERERERERkROyWZZlnc0BBgwYQP/+/XnllVcAcDqdtGzZkrvuuos//elPVfafNGkSeXl5zJ8/v3zdwIED6dWrF//5z39O6TGzs7MJCwsjKyuL0NDQsylfREREREREREREREQauNPJDbzP5oGKiopYvXo1jzzySPk6u93OqFGjWL58ebX3Wb58Offff7/LutGjRzNv3rzjPk5hYSGFhYXlt7OysgDzREVERERERERERERExLOV5QWnMhbkrIKR1NRUHA4HMTExLutjYmLYtm1btfdJSkqqdv+kpKTjPs5TTz3Fk08+WWV9y5Ytz6BqERERERERERERERFpjHJycggLCzvhPmcVjNSVRx55xGWUidPpJD09naioKGw2mxsrq3+ys7Np2bIl+/fvV5sxkQZK72ORxkHvZZHGQe9lkcZB72WRxkHvZZHGobbey5ZlkZOTQ1xc3En3PatgpEmTJnh5eZGcnOyyPjk5mdjY2GrvExsbe1r7A/j5+eHn5+eyLjw8/MyK9hChoaH6gBBp4PQ+Fmkc9F4WaRz0XhZpHPReFmkc9F4WaRxq4718spEiZexn8yC+vr707duXhQsXlq9zOp0sXLiQQYMGVXufQYMGuewP8MMPPxx3fxERERERERERERERkZpy1q207r//fqZOnUq/fv0499xzefHFF8nLy+PGG28E4IYbbqB58+Y89dRTANxzzz0MHz6cf/7zn1x66aV8+OGHrFq1ijfeeONsSxERERERERERERERETmhsw5GJk2axJEjR3jsscdISkqiV69efPfdd+UTrCcmJmK3VwxMOe+885g9ezaPPvoof/7zn+nQoQPz5s2jW7duZ1uKYNqOPf7441Vaj4lIw6H3sUjjoPeySOOg97JI46D3skjjoPeySONQH97LNsuyLLc9uoiIiIiIiIiIiIiISB06qzlGREREREREREREREREGhIFIyIiIiIiIiIiIiIi4jEUjIiIiIiIiIiIiIiIiMdQMCIiIiIiIiIiIiIiIh5DwUgj8uqrr9K6dWv8/f0ZMGAAv/32m7tLEpHT8MQTT2Cz2Vy+Onfu7O6yROQkfvnlFy677DLi4uKw2WzMmzfPZbtlWTz22GM0a9aMgIAARo0axc6dO91TrIgc18ney9OmTavyOX3xxRe7p1gROa6nnnqK/v37ExISQnR0NOPHj2f79u0u+xQUFHDHHXcQFRVFcHAwEydOJDk52U0Vi0h1TuW9fP7551f5bL711lvdVLGIVOf111+nR48ehIaGEhoayqBBg/j222/Lt7vzM1nBSCPx0Ucfcf/99/P444+zZs0aevbsyejRo0lJSXF3aSJyGrp27crhw4fLv3799Vd3lyQiJ5GXl0fPnj159dVXq93+7LPP8tJLL/Gf//yHlStXEhQUxOjRoykoKKjjSkXkRE72Xga4+OKLXT6n58yZU4cVisipWLx4MXfccQcrVqzghx9+oLi4mIsuuoi8vLzyfe677z6++uorPvnkExYvXsyhQ4e44oor3Fi1iBzrVN7LADfffLPLZ/Ozzz7rpopFpDotWrTg6aefZvXq1axatYqRI0cybtw4Nm/eDLj3M9lmWZZVJ48ktWrAgAH079+fV155BQCn00nLli256667+NOf/uTm6kTkVDzxxBPMmzePdevWubsUETlDNpuNzz//nPHjxwNmtEhcXBwPPPAADz74IABZWVnExMQwa9YsrrnmGjdWKyLHc+x7GcyIkczMzCojSUSkfjty5AjR0dEsXryYYcOGkZWVRdOmTZk9ezZXXnklANu2baNLly4sX76cgQMHurliEanOse9lMCNGevXqxYsvvuje4kTktERGRvLcc89x5ZVXuvUzWSNGGoGioiJWr17NqFGjytfZ7XZGjRrF8uXL3ViZiJyunTt3EhcXR9u2bbn22mtJTEx0d0kichb27t1LUlKSy2d0WFgYAwYM0Ge0SAO0aNEioqOj6dSpE7fddhtpaWnuLklETiIrKwswJ2EAVq9eTXFxsctnc+fOnWnVqpU+m0XqsWPfy2U++OADmjRpQrdu3XjkkUfIz893R3kicgocDgcffvgheXl5DBo0yO2fyd61/ghS61JTU3E4HMTExLisj4mJYdu2bW6qSkRO14ABA5g1axadOnXi8OHDPPnkkwwdOpRNmzYREhLi7vJE5AwkJSUBVPsZXbZNRBqGiy++mCuuuII2bdqwe/du/vznPzNmzBiWL1+Ol5eXu8sTkWo4nU7uvfdeBg8eTLdu3QDz2ezr60t4eLjLvvpsFqm/qnsvA0yZMoX4+Hji4uLYsGEDDz/8MNu3b+ezzz5zY7UicqyNGzcyaNAgCgoKCA4O5vPPP+ecc85h3bp1bv1MVjAiIlJPjBkzpny5R48eDBgwgPj4eD7++GNmzJjhxspERESkcuu77t2706NHD9q1a8eiRYu44IIL3FiZiBzPHXfcwaZNmzRvn0gDd7z38h/+8Ify5e7du9OsWTMuuOACdu/eTbt27eq6TBE5jk6dOrFu3TqysrKYO3cuU6dOZfHixe4uS620GoMmTZrg5eVFcnKyy/rk5GRiY2PdVJWInK3w8HA6duzIrl273F2KiJyhss9hfUaLND5t27alSZMm+pwWqafuvPNO5s+fz88//0yLFi3K18fGxlJUVERmZqbL/vpsFqmfjvders6AAQMA9NksUs/4+vrSvn17+vbty1NPPUXPnj3597//7fbPZAUjjYCvry99+/Zl4cKF5eucTicLFy5k0KBBbqxMRM5Gbm4uu3fvplmzZu4uRUTOUJs2bYiNjXX5jM7OzmblypX6jBZp4A4cOEBaWpo+p0XqGcuyuPPOO/n888/56aefaNOmjcv2vn374uPj4/LZvH37dhITE/XZLFKPnOy9XJ1169YB6LNZpJ5zOp0UFha6/TNZrbQaifvvv5+pU6fSr18/zj33XF588UXy8vK48cYb3V2aiJyiBx98kMsuu4z4+HgOHTrE448/jpeXF5MnT3Z3aSJyArm5uS5Xpe3du5d169YRGRlJq1atuPfee/n73/9Ohw4daNOmDX/5y1+Ii4tj/Pjx7itaRKo40Xs5MjKSJ598kokTJxIbG8vu3bt56KGHaN++PaNHj3Zj1SJyrDvuuIPZs2fzxRdfEBISUt6jPCwsjICAAMLCwpgxYwb3338/kZGRhIaGctdddzFo0CAGDhzo5upFpMzJ3su7d+9m9uzZXHLJJURFRbFhwwbuu+8+hg0bRo8ePdxcvYiUeeSRRxgzZgytWrUiJyeH2bNns2jRIhYsWOD2z2SbZVlWrT+K1IlXXnmF5557jqSkJHr16sVLL71UPoxQROq/a665hl9++YW0tDSaNm3KkCFD+Mc//qHeqCL13KJFixgxYkSV9VOnTmXWrFlYlsXjjz/OG2+8QWZmJkOGDOG1116jY8eObqhWRI7nRO/l119/nfHjx7N27VoyMzOJi4vjoosu4m9/+xsxMTFuqFZEjsdms1W7fubMmUybNg2AgoICHnjgAebMmUNhYSGjR4/mtddeUystkXrkZO/l/fv3c91117Fp0yby8vJo2bIlEyZM4NFHHyU0NLSOqxWR45kxYwYLFy7k8OHDhIWF0aNHDx5++GEuvPBCwL2fyQpGRERERERERERERETEY2iOERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDyGghEREREREREREREREfEYCkZERERERERERERERMRjKBgRERERERERERERERGPoWBEREREREREREREREQ8hoIRERERERERERERERHxGApGRERERERERERERETEYygYERERERERERERERERj6FgREREREREREREREREPIaCERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDyGghEREREREREREREREfEYCkZERERERERERERERMRjKBgRERERERERERERERGPoWBEREREREREREREREQ8hoIRERERERERERERERHxGApGRERERERERERERETEYygYERERERERERERERERj6FgREREREREREREREREPIaCERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDyGghEREREREREREREREfEYCkZERERERERERERERMRjKBgRERERERERERERERGPoWBEREREREREREREREQ8hoIRERERERERERERERHxGApGRERERERERERERETEYygYERERERERERERERERj6FgREREREREREREREREPIaCERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDyGghEREREREREREREREfEYCkZERERERERERERERMRjKBgRERERERERERERERGPoWBEREREREREREREREQ8hoIRERERERERERERERHxGApGRERERERERERERETEYygYERERERERERERERERj6FgREREREREREREREREPIaCERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDyGghEREREREREREREREfEYCkZERERERERERERERMRjKBgRERERERERERERERGPoWBEREREREREREREREQ8hoIRERERERERERERERHxGApGRERERERERERERETEYygYERERERERERERERERj6FgREREREREREREREREPIaCERERERERERERERER8RgKRkRERERERERERERExGMoGBEREREREREREREREY+hYERERERERERERERERDyGghEREREREREREREREfEYCkZERERERERERERERMRjKBgRERERERERERERERGPoWBEREREROQ45syZQ58+fQgJCcFms3Hvvfe6u6RGq3Xr1rRu3drdZZyVxvAcpH6x2Wycf/75Z3WMadOmYbPZ2LdvX43UJCIiItIYKBgRERERaSCmT5+OzWYjKiqKwsJCd5fT6C1fvpxrr72W7OxsbrvtNh5//HEuvvjiOq3hiSeewGazuXwFBQXRo0cPnnjiCfLy8mrtsXUytXYtWrSoys82ODiYli1bMmbMGJ5++mkOHTrk7jIbHIVTIiIiInIqvN1dgIiIiIicXE5ODh9//DE2m4309HTmzZvHpEmT3F1Wo/b1119jWRbvvvsu5513nltrmThxIt26dQPg8OHDfPnllzz55JN89dVXLF++HF9fX7fWVxMWLlzo7hLcom/fvowdOxaA/Px8kpKSWLZsGd999x1PPvkkzz77LHfddZebqxQRERERaVwUjIiIiIg0AB999BF5eXncf//9vPjii7z11lsKRmpZ2dX6cXFxbq4ErrzySq655pry288//zznnnsua9asYfbs2UybNs19xdWQdu3aubsEt+jXrx9PPPFElfVffPEFM2bM4O677yYoKIjp06fXfXEiIiIiIo2UWmmJiIiINABvvfUW3t7ePPTQQ4wYMYKFCxeSkJBw3P1/+eUXxo8fT0xMDH5+frRs2ZIrrriCX3/91WU/y7KYOXMmQ4cOJTw8nMDAQDp06MAtt9xCYmJi+X4nak9z/vnnY7PZXNaVtYBatGgRs2bNok+fPgQGBpb3ys/KyuKZZ55h+PDhxMXF4evrS1xcHDfccAO7d++u9nFOpdYhQ4bg7e3N4cOHqz3GDTfcgM1mY/ny5cd97cpaHM2cOROANm3alLc6qtxWaunSpVx66aVERkbi7+9P586defzxx8nPz69yzLJ5Ag4ePMgNN9xAbGwsdrudRYsWHbeOEwkJCSkPQ37//XcAZs2ahc1mY9asWXz11VcMHjyYkJAQl59bUVERL7zwAn369CEoKIiQkBCGDh3Kl19+6XL81q1b884771R5/pXnOjjZc/r555+ZPn06nTp1Ijg4mODgYPr168cbb7xR7XOq7nes8u/R7Nmz6dWrFwEBATRr1ox77rmHo0ePuuxf9rN74oknWLVqFRdeeCEhISGEhYUxYcKE47YF27t3LzfddBOtWrXCz8+PZs2aMW3atOO+x7744gv69+9PQEAAMTEx3HzzzWRkZFS775kaN24cc+fOBeDhhx+u0jbNsizefvttBg8eTGhoKIGBgfTr14+33367yrFO93UE+PTTTxk+fDjR0dH4+/sTFxfHqFGj+PTTT6vsu2HDBq655hqaNWuGr68v8fHx3HXXXaSlpbnst2/fPmw2G9OmTWPXrl1MmDCBiIgIgoKCGDVqFOvXr6/2tUhJSeG+++6jffv2+Pn50aRJEyZOnMimTZuqHDshIYGEhASXFmXVBU/Hqvz7PGXKFJo0aUJISAiXXnope/bsAWDr1q2MHz+eyMhIQkJCuPLKK0lOTq72eF999RUjRowgLCyMgIAAevbsyQsvvEBJSUm1+//vf/+jW7du+Pv707JlSx566CEKCgqOW29OTg6PP/44Xbt2JSAggPDwcEaPHl3lb7yIiIiIVE8jRkRERETquS1btrBixQouueQSYmJiuOGGG1i4cCEzZ86s9oTfv//9b+677z4CAgKYMGECrVq14uDBg/z666/MnTuXIUOGAOB0Opk0aRJz586lefPmTJ48mdDQUPbt28fHH3/MmDFjaNWq1VnV/txzz/Hzzz8zbtw4LrroIry8vABzgvGxxx5jxIgRTJgwgaCgILZt28bs2bP5+uuvWbNmDfHx8eXHOdVab7nlFpYuXcrMmTP585//7FJLZmYmc+fOpWvXrgwaNOi4Nbdu3ZrHH3+cefPmsX79eu655x7Cw8MByr9/8sknTJ48GT8/PyZNmkR0dDTff/89f/3rX1mwYAGLFi3C39/f5bhpaWkMGjSIyMhIrrnmGgoKCggNDT2r1xeoEkp98sknfP/994wdO5bbb7+d7OxsAAoLC7n44otZtGgRvXr1YsaMGRQXF/P1118zbtw4Xn75Ze68804A7r33XmbNmlXl+R8bXJzoOT3zzDPs2rWLgQMHMmHCBDIzM/nuu++45ZZb2L59O//85z9P+Tm+8sorfPfdd4wbN46RI0fy3Xff8dJLL5GamsoHH3xQZf/ff/+dZ599lhEjRnDLLbewdu1a5s2bx8aNG9m0aZPLz2blypWMHj2avLw8xo4dS4cOHdi3bx8ffPAB3377LcuXL6dt27bl+7/77rtMnTqV0NBQrr/+esLDw5k/fz6jRo2iqKioRtuanX/++QwdOpQlS5bw008/cdlllwEmFLn22muZM2cOHTp0YMqUKfj6+vLDDz8wY8YMtmzZwvPPP3/Gr+Prr7/O7bffTrNmzZgwYQJRUVEkJSXx22+/8fnnnzNx4sTyfb/88kuuvvpq7HY748aNo2XLlmzZsoVXXnmFBQsWsHLlSiIiIlzq2LdvHwMHDqRr165Mnz6d3bt388UXXzBixAi2bt1KTExM+b67d+/m/PPP58CBA1x00UWMHz+elJQUPv30UxYsWMDChQsZMGAA4eHhPP7447z44ouA+R2u/DqeioyMDIYMGUJsbCxTp05lx44dzJ8/n23btvHFF18wdOhQ+vbty/Tp01m9ejWffvop6enp/PTTTy7HeeGFF3jggQeIjIxkypQpBAUF8eWXX/LAAw+wZMkSPvvsM5f37d/+9jcee+yx8pDNx8eHjz76iK1bt1ZbZ3p6OsOGDWPz5s0MHjyYW2+9lezs7PLX8JNPPmH8+PGn9JxFREREPJYlIiIiIvXa/fffbwHWnDlzLMuyrJycHCsoKMhq1aqV5XA4XPZdt26dZbfbrbi4OGvv3r0u25xOp3Xw4MHy2y+//LIFWBdccIGVn5/vsm9+fr6VlpZWfjs+Pt6Kj4+vtr7hw4dbx/6z8vHHH7cAKygoyNqwYUOV+2RmZrocv8xPP/1k2e1266abbnJZf6q1Hj161IqMjLTatm1rOZ1Ol/1eeeUVC7BefPHFap/HsaZOnWoBVV7HrKwsKywszPLz87PWr19fvt7hcFiTJk2yAOuvf/2ry30AC7BuvPFGq6Sk5JQe37IqXseyn32ZnJwc65xzzrEA65133rEsy7JmzpxpAZbdbrd++OGHKsf685//bAHWX/7yF5fXJjs72+rXr5/l6+vr8vtxvOd/qs9pz549VdYVFxdbF154oeXl5WUlJCS4bKvud6zs+YeFhVnbtm0rX5+fn2917NjRstvtLjX//PPP5XV9+OGHLse6/vrrq7yWRUVFVuvWra2QkBBrzZo1LvsvWbLE8vLyssaOHVu+LisrywoNDbWCgoKs7du3uxxn2LBhFnDc98mxymq95ZZbTrjfX/7yl/KfW5k33nij/LUvKioqX19YWGhddtllFmCtWrWqfP3pvo59+vSxfH19reTk5Cr1pKamuiyHhoZazZs3t/bt2+ey35w5cyzAuvPOO8vX7d27t/zn8/TTT7vs/+ijj1qA9dRTT7msP++88ywvLy/ru+++c1m/fft2KyQkxOrevbvL+hP9rTqRsrruu+8+l/W33XabBVjh4eEufzucTqd1ySWXWIC1evXq8vW7du2yvL29rejoaCsxMbF8fUFBgTVkyBALsN59993y9Tt37rS8vb2t5s2bu7zeWVlZVqdOnSzAGj58uEtNU6ZMsQDrzTffdFmfnJxstWzZ0mratKl19OjR8vUney+LiIiIeCK10hIRERGpx4qLi3nvvfcIDQ0tvwI4ODiYCRMmkJiYyI8//uiy/3//+1+cTid///vfq1zdb7PZXObLeO211/Dy8uL1118nICDAZd+AgAAiIyPPuv4//OEPdO/evcr6sLCwao8/YsQIunbtWuV5nWqt/v7+TJ06lT179lS5ivutt97Cz8+P66+//qye0xdffEFWVhbTp0+nR48e5evtdjvPPvss3t7ezJo1q8r9fH19efbZZ8tHzZyOuXPn8sQTT/DEE09w22230alTJ7Zs2UK/fv1c5h4B04Jp1KhRLuucTievv/467dq148knn3S5Wj0kJITHHnuMoqIiPvvss9Oq60TPqU2bNlXWeXt7c+utt+JwOPj5559P+XHuueceOnXqVH47ICCAyZMn43Q6Wb16dZX9hw0bVmUOnrI5OspajwHMnz+fffv28cc//pHevXu77D9kyBDGjRvHN998Uz7qZt68eWRnZzN9+nQ6duxYvq+Pjw//+Mc/Tvn5nI6y92xqamr5uldeeYWgoCBeffVVfHx8ytf7+vqW1zFnzpwqxzqd19HHx8fl2GWioqLKl999912ys7N56qmnXEZ4AVxzzTX06dOHDz/8sMox2rRpwx//+EeXdTNmzABcfz5r165l2bJlTJ06ldGjR7vs37FjR26++ebyUUA1ITg4mL///e8u6yZPngyY53333XeXr7fZbOXvvcotwGbPnk1JSQkPPPAALVu2LF/v5+fHM888A+Dy96Fs//vvv5/o6Ojy9aGhoTz66KNVakxNTeWjjz5i5MiR3HTTTS7boqOj+eMf/8iRI0eq/A0VEREREVdqpSUiIiJSj33xxRccOXKEGTNmuLT/ueGGG3j//fd56623uOiii8rX//bbbwAu66qTm5vL1q1bad++PR06dKid4oFzzz33uNsWLVrEiy++yMqVK0lNTXXpvV+5HdHp1vqHP/yBf/3rX7z55ptccMEFAKxevZq1a9cyZcqUsw581q5dC1TfnqdVq1a0bduWHTt2kJOTQ0hISPm2Nm3a0KRJkzN6zE8//bR8bofAwEDatWvHH/7wBx588MEqrZuqe823b99ORkYGcXFxPPnkk1W2HzlyBIBt27adVl0nek45OTk8//zzzJs3j927d1eZI6NscvtT0bdv3yrrWrRoAZgWaWe6/4oVKwDz+lTXli4pKQmn08mOHTvo169f+QnwoUOHVtl30KBBeHvX/n+v8vPz2bhxI3FxceUn2isrLi4Gqv9Znurrcs011/DQQw/RrVs3pkyZwogRIxgyZEiV1m9lr9/KlSurnRuooKCA1NRUUlNTXX5PevXqhd3ueo3eiX4+ycnJ1f58yp7jtm3b6NatW5Xtp6tDhw4EBga6rGvWrBkAPXr0qNK2rmxb5d/lE/19GDRoEP7+/qxbt6583Yl+p6pb9/vvv+NwOCgsLKz2Ndm5cydgXpOxY8dW2S4iIiIihoIRERERkXrsrbfeAkwQUtkFF1xA8+bN+eKLL0hPTy8/2Z+VlYXNZis/YXc8WVlZADRv3rwWqq5Qea6Ayj755BMmTZpEcHAwo0ePpnXr1gQGBpZPHl550uvTrbVz584MHz6cefPmkZaWRlRUFP/73/8AuPnmm8/yGVE+euB4z61Zs2bs2LGD7Oxsl2DkePufijlz5lQZGXI81T1Oeno6AJs3b2bz5s3Hve+x4cWZPBaYSd7PP/981qxZQ+/evbn++uuJiorC29ubffv28c4771BYWHjKj1PdXCxlIYTD4Tjj/ctel+rmKams7HUp+12sfGV/GS8vL5fRFDWl7KR706ZNATMPhmVZHDx4sNqQq0x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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(num=2, figsize=(20, 16))  \n",
    "fig1 = fig.add_subplot(2, 1, 1)\n",
    "fig2 = fig.add_subplot(2, 1, 2)\n",
    "\n",
    "fig1.plot(total_loss_train, label='Training Loss')\n",
    "fig1.plot(total_loss_val, label='Validation Loss')\n",
    "fig1.set_ylim([0, 1])\n",
    "fig1.set_title('Loss for Pretrainned Densenet model', fontsize=14)  \n",
    "fig1.legend()\n",
    "fig2.plot(total_acc_train, label='Training Accuracy')\n",
    "fig2.plot(total_acc_val, label='Validation Accuracy')\n",
    "fig2.set_ylim([0, 100])\n",
    "fig2.set_title('Accuracy for Pretrainned Densenet model', fontsize=14)  \n",
    "fig2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "Best models accuracy 98.89985439249313\n",
      "-------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "best_model_state = torch.load('densenet_pret.pth',map_location='cpu')\n",
    "device = torch.device('cpu')\n",
    "model_des.load_state_dict(best_model_state)\n",
    "model_des.to(device)\n",
    "test_accuracy = Model_functions.test_model(model_des, test_loader, device)\n",
    "print('-------------------------------------------------------')\n",
    "print(f'Best models accuracy {test_accuracy}')\n",
    "print('-------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------\n",
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         bkl       0.98      0.98      0.98       945\n",
      "          nv       0.98      0.95      0.96       892\n",
      "          df       1.00      1.00      1.00       848\n",
      "         mel       0.96      0.99      0.98       840\n",
      "        vasc       1.00      1.00      1.00       899\n",
      "         bcc       0.99      1.00      1.00       855\n",
      "       akiec       0.99      1.00      1.00       902\n",
      "\n",
      "    accuracy                           0.99      6181\n",
      "   macro avg       0.99      0.99      0.99      6181\n",
      "weighted avg       0.99      0.99      0.99      6181\n",
      "\n",
      "-------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "Model_functions.conf_report(model_des, test_loader, 'densenet_conf_mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}