Tiger_classification(Inception).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e73b73f2",
   "metadata": {},
   "source": [
    "# Tiger/Cat Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54446340",
   "metadata": {
    "papermill": {
     "duration": 0.004697,
     "end_time": "2023-04-19T09:54:49.521324",
     "exception": false,
     "start_time": "2023-04-19T09:54:49.516627",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beef2d9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:54:49.531423Z",
     "iopub.status.busy": "2023-04-19T09:54:49.531111Z",
     "iopub.status.idle": "2023-04-19T09:54:59.010904Z",
     "shell.execute_reply": "2023-04-19T09:54:59.009857Z"
    },
    "papermill": {
     "duration": 9.4878,
     "end_time": "2023-04-19T09:54:59.013434",
     "exception": false,
     "start_time": "2023-04-19T09:54:49.525634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python Projects For Github\\Tiger Classifier\\tiger\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from torchinfo import summary\n",
    "from collections import defaultdict\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision.models import list_models\n",
    "from torchvision.models import inception_v3, Inception_V3_Weights\n",
    "\n",
    "import torchvision.transforms as trans\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b2f5c9",
   "metadata": {
    "papermill": {
     "duration": 0.004238,
     "end_time": "2023-04-19T09:54:59.022306",
     "exception": false,
     "start_time": "2023-04-19T09:54:59.018068",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## PreProcessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "377c0e63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:54:59.032707Z",
     "iopub.status.busy": "2023-04-19T09:54:59.031851Z",
     "iopub.status.idle": "2023-04-19T09:54:59.148167Z",
     "shell.execute_reply": "2023-04-19T09:54:59.147178Z"
    },
    "papermill": {
     "duration": 0.124329,
     "end_time": "2023-04-19T09:54:59.150857",
     "exception": false,
     "start_time": "2023-04-19T09:54:59.026528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "DATA_PATH_TRAIN = Path(\"tiger_dataset/train\")\n",
    "DATA_PATH_TEST = Path(\"tiger_dataset/valid\")\n",
    "DATA_PATH_VAL = Path(\"tiger_dataset/test\")\n",
    "\n",
    "IMAGE_SIZE = (342, 342)\n",
    "EPOCHS = 10\n",
    "LR = 0.01\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "12a5f309",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:54:59.161574Z",
     "iopub.status.busy": "2023-04-19T09:54:59.160607Z",
     "iopub.status.idle": "2023-04-19T09:54:59.166141Z",
     "shell.execute_reply": "2023-04-19T09:54:59.165155Z"
    },
    "papermill": {
     "duration": 0.012957,
     "end_time": "2023-04-19T09:54:59.168184",
     "exception": false,
     "start_time": "2023-04-19T09:54:59.155227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "simple_transform = trans.Compose([\n",
    "    trans.Resize(IMAGE_SIZE),\n",
    "    trans.ToTensor(),\n",
    "    trans.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7eb65e82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:54:59.178171Z",
     "iopub.status.busy": "2023-04-19T09:54:59.177897Z",
     "iopub.status.idle": "2023-04-19T09:54:59.226742Z",
     "shell.execute_reply": "2023-04-19T09:54:59.225869Z"
    },
    "papermill": {
     "duration": 0.055949,
     "end_time": "2023-04-19T09:54:59.228657",
     "exception": false,
     "start_time": "2023-04-19T09:54:59.172708",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data = ImageFolder(DATA_PATH_TRAIN, transform = simple_transform)\n",
    "train_loader = DataLoader(train_data, batch_size = BATCH_SIZE, shuffle = True)\n",
    "\n",
    "eval_data = ImageFolder(DATA_PATH_VAL, transform = simple_transform)\n",
    "eval_loader = DataLoader(eval_data, batch_size = BATCH_SIZE, shuffle = True)\n",
    "\n",
    "test_data = ImageFolder(DATA_PATH_TEST, transform = simple_transform)\n",
    "test_loader = DataLoader(test_data, batch_size = BATCH_SIZE, shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "251415fd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Before and after transform')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import random\n",
    "mean = [0.485, 0.456, 0.406]\n",
    "std = [0.229, 0.224, 0.225]\n",
    "\n",
    "\n",
    "\n",
    "random_samples_idx = random.sample(range(len(train_data)), k=7)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(24, 8))\n",
    "fig,ax = plt.subplots(2,7)\n",
    "\n",
    "\n",
    "for i, targ_sample in enumerate(random_samples_idx):\n",
    "    targ_image, targ_label = train_data[targ_sample][0], train_data[targ_sample][1]\n",
    "\n",
    "    targ_image_adjust = targ_image.permute(1, 2, 0)\n",
    "\n",
    "        \n",
    "    ax[0,i].imshow(targ_image_adjust)\n",
    "    ax[0,i].axis(\"off\")\n",
    "    title = f\"{train_data.classes[targ_label]}\"\n",
    "    ax[0,i].set_title(title, fontsize = 7)\n",
    "    \n",
    "    trans_img = (targ_image_adjust.numpy() * std + mean).clip(0,1)\n",
    "    ax[1,i].imshow(trans_img)\n",
    "    ax[1,i].axis(\"off\")\n",
    "    ax[1,i].set_title(f\"{title}\",fontsize = 7)\n",
    "    \n",
    "fig.suptitle(\"Before and after transform\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb785414",
   "metadata": {
    "papermill": {
     "duration": 0.153496,
     "end_time": "2023-04-19T09:55:01.655295",
     "exception": false,
     "start_time": "2023-04-19T09:55:01.501799",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Setup and Fine adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "469645e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:55:01.958447Z",
     "iopub.status.busy": "2023-04-19T09:55:01.957842Z",
     "iopub.status.idle": "2023-04-19T09:55:03.423540Z",
     "shell.execute_reply": "2023-04-19T09:55:03.422414Z"
    },
    "papermill": {
     "duration": 1.623401,
     "end_time": "2023-04-19T09:55:03.426397",
     "exception": false,
     "start_time": "2023-04-19T09:55:01.802996",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "weights = Inception_V3_Weights.DEFAULT\n",
    "model = inception_v3(weights = weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e684e02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:55:03.730385Z",
     "iopub.status.busy": "2023-04-19T09:55:03.729751Z",
     "iopub.status.idle": "2023-04-19T09:55:03.739615Z",
     "shell.execute_reply": "2023-04-19T09:55:03.738586Z"
    },
    "papermill": {
     "duration": 0.166371,
     "end_time": "2023-04-19T09:55:03.741863",
     "exception": false,
     "start_time": "2023-04-19T09:55:03.575492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Feature Extractor -- Frozen\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Head/ Classifier -- Trainable\n",
    "model.fc = nn.Linear(in_features = 2048, out_features = len(train_data.classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4913da1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:55:04.043690Z",
     "iopub.status.busy": "2023-04-19T09:55:04.043064Z",
     "iopub.status.idle": "2023-04-19T09:55:16.745882Z",
     "shell.execute_reply": "2023-04-19T09:55:16.744784Z"
    },
    "papermill": {
     "duration": 12.861608,
     "end_time": "2023-04-19T09:55:16.749721",
     "exception": false,
     "start_time": "2023-04-19T09:55:03.888113",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python Projects For Github\\Tiger Classifier\\tiger\\lib\\site-packages\\torchinfo\\torchinfo.py:477: UserWarning:\n",
      "\n",
      "TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "\n",
      "D:\\Python Projects For Github\\Tiger Classifier\\tiger\\lib\\site-packages\\torch\\storage.py:665: UserWarning:\n",
      "\n",
      "TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "============================================================================================================================================\n",
       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #                   Trainable\n",
       "============================================================================================================================================\n",
       "Inception3                               [1, 3, 342, 342]          [1, 10]                   3,326,696                 Partial\n",
       "├─BasicConv2d: 1-1                       [1, 3, 342, 342]          [1, 32, 170, 170]         --                        False\n",
       "│    └─Conv2d: 2-1                       [1, 3, 342, 342]          [1, 32, 170, 170]         (864)                     False\n",
       "│    └─BatchNorm2d: 2-2                  [1, 32, 170, 170]         [1, 32, 170, 170]         (64)                      False\n",
       "├─BasicConv2d: 1-2                       [1, 32, 170, 170]         [1, 32, 168, 168]         --                        False\n",
       "│    └─Conv2d: 2-3                       [1, 32, 170, 170]         [1, 32, 168, 168]         (9,216)                   False\n",
       "│    └─BatchNorm2d: 2-4                  [1, 32, 168, 168]         [1, 32, 168, 168]         (64)                      False\n",
       "├─BasicConv2d: 1-3                       [1, 32, 168, 168]         [1, 64, 168, 168]         --                        False\n",
       "│    └─Conv2d: 2-5                       [1, 32, 168, 168]         [1, 64, 168, 168]         (18,432)                  False\n",
       "│    └─BatchNorm2d: 2-6                  [1, 64, 168, 168]         [1, 64, 168, 168]         (128)                     False\n",
       "├─MaxPool2d: 1-4                         [1, 64, 168, 168]         [1, 64, 83, 83]           --                        --\n",
       "├─BasicConv2d: 1-5                       [1, 64, 83, 83]           [1, 80, 83, 83]           --                        False\n",
       "│    └─Conv2d: 2-7                       [1, 64, 83, 83]           [1, 80, 83, 83]           (5,120)                   False\n",
       "│    └─BatchNorm2d: 2-8                  [1, 80, 83, 83]           [1, 80, 83, 83]           (160)                     False\n",
       "├─BasicConv2d: 1-6                       [1, 80, 83, 83]           [1, 192, 81, 81]          --                        False\n",
       "│    └─Conv2d: 2-9                       [1, 80, 83, 83]           [1, 192, 81, 81]          (138,240)                 False\n",
       "│    └─BatchNorm2d: 2-10                 [1, 192, 81, 81]          [1, 192, 81, 81]          (384)                     False\n",
       "├─MaxPool2d: 1-7                         [1, 192, 81, 81]          [1, 192, 40, 40]          --                        --\n",
       "├─InceptionA: 1-8                        [1, 192, 40, 40]          [1, 256, 40, 40]          --                        False\n",
       "│    └─BasicConv2d: 2-11                 [1, 192, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-1                  [1, 192, 40, 40]          [1, 64, 40, 40]           (12,288)                  False\n",
       "│    │    └─BatchNorm2d: 3-2             [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-12                 [1, 192, 40, 40]          [1, 48, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-3                  [1, 192, 40, 40]          [1, 48, 40, 40]           (9,216)                   False\n",
       "│    │    └─BatchNorm2d: 3-4             [1, 48, 40, 40]           [1, 48, 40, 40]           (96)                      False\n",
       "│    └─BasicConv2d: 2-13                 [1, 48, 40, 40]           [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-5                  [1, 48, 40, 40]           [1, 64, 40, 40]           (76,800)                  False\n",
       "│    │    └─BatchNorm2d: 3-6             [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-14                 [1, 192, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-7                  [1, 192, 40, 40]          [1, 64, 40, 40]           (12,288)                  False\n",
       "│    │    └─BatchNorm2d: 3-8             [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-15                 [1, 64, 40, 40]           [1, 96, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-9                  [1, 64, 40, 40]           [1, 96, 40, 40]           (55,296)                  False\n",
       "│    │    └─BatchNorm2d: 3-10            [1, 96, 40, 40]           [1, 96, 40, 40]           (192)                     False\n",
       "│    └─BasicConv2d: 2-16                 [1, 96, 40, 40]           [1, 96, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-11                 [1, 96, 40, 40]           [1, 96, 40, 40]           (82,944)                  False\n",
       "│    │    └─BatchNorm2d: 3-12            [1, 96, 40, 40]           [1, 96, 40, 40]           (192)                     False\n",
       "│    └─BasicConv2d: 2-17                 [1, 192, 40, 40]          [1, 32, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-13                 [1, 192, 40, 40]          [1, 32, 40, 40]           (6,144)                   False\n",
       "│    │    └─BatchNorm2d: 3-14            [1, 32, 40, 40]           [1, 32, 40, 40]           (64)                      False\n",
       "├─InceptionA: 1-9                        [1, 256, 40, 40]          [1, 288, 40, 40]          --                        False\n",
       "│    └─BasicConv2d: 2-18                 [1, 256, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-15                 [1, 256, 40, 40]          [1, 64, 40, 40]           (16,384)                  False\n",
       "│    │    └─BatchNorm2d: 3-16            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-19                 [1, 256, 40, 40]          [1, 48, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-17                 [1, 256, 40, 40]          [1, 48, 40, 40]           (12,288)                  False\n",
       "│    │    └─BatchNorm2d: 3-18            [1, 48, 40, 40]           [1, 48, 40, 40]           (96)                      False\n",
       "│    └─BasicConv2d: 2-20                 [1, 48, 40, 40]           [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-19                 [1, 48, 40, 40]           [1, 64, 40, 40]           (76,800)                  False\n",
       "│    │    └─BatchNorm2d: 3-20            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-21                 [1, 256, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-21                 [1, 256, 40, 40]          [1, 64, 40, 40]           (16,384)                  False\n",
       "│    │    └─BatchNorm2d: 3-22            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-22                 [1, 64, 40, 40]           [1, 96, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-23                 [1, 64, 40, 40]           [1, 96, 40, 40]           (55,296)                  False\n",
       "│    │    └─BatchNorm2d: 3-24            [1, 96, 40, 40]           [1, 96, 40, 40]           (192)                     False\n",
       "│    └─BasicConv2d: 2-23                 [1, 96, 40, 40]           [1, 96, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-25                 [1, 96, 40, 40]           [1, 96, 40, 40]           (82,944)                  False\n",
       "│    │    └─BatchNorm2d: 3-26            [1, 96, 40, 40]           [1, 96, 40, 40]           (192)                     False\n",
       "│    └─BasicConv2d: 2-24                 [1, 256, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-27                 [1, 256, 40, 40]          [1, 64, 40, 40]           (16,384)                  False\n",
       "│    │    └─BatchNorm2d: 3-28            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "├─InceptionA: 1-10                       [1, 288, 40, 40]          [1, 288, 40, 40]          --                        False\n",
       "│    └─BasicConv2d: 2-25                 [1, 288, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-29                 [1, 288, 40, 40]          [1, 64, 40, 40]           (18,432)                  False\n",
       "│    │    └─BatchNorm2d: 3-30            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-26                 [1, 288, 40, 40]          [1, 48, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-31                 [1, 288, 40, 40]          [1, 48, 40, 40]           (13,824)                  False\n",
       "│    │    └─BatchNorm2d: 3-32            [1, 48, 40, 40]           [1, 48, 40, 40]           (96)                      False\n",
       "│    └─BasicConv2d: 2-27                 [1, 48, 40, 40]           [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-33                 [1, 48, 40, 40]           [1, 64, 40, 40]           (76,800)                  False\n",
       "│    │    └─BatchNorm2d: 3-34            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-28                 [1, 288, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-35                 [1, 288, 40, 40]          [1, 64, 40, 40]           (18,432)                  False\n",
       "│    │    └─BatchNorm2d: 3-36            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-29                 [1, 64, 40, 40]           [1, 96, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-37                 [1, 64, 40, 40]           [1, 96, 40, 40]           (55,296)                  False\n",
       "│    │    └─BatchNorm2d: 3-38            [1, 96, 40, 40]           [1, 96, 40, 40]           (192)                     False\n",
       "│    └─BasicConv2d: 2-30                 [1, 96, 40, 40]           [1, 96, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-39                 [1, 96, 40, 40]           [1, 96, 40, 40]           (82,944)                  False\n",
       "│    │    └─BatchNorm2d: 3-40            [1, 96, 40, 40]           [1, 96, 40, 40]           (192)                     False\n",
       "│    └─BasicConv2d: 2-31                 [1, 288, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-41                 [1, 288, 40, 40]          [1, 64, 40, 40]           (18,432)                  False\n",
       "│    │    └─BatchNorm2d: 3-42            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "├─InceptionB: 1-11                       [1, 288, 40, 40]          [1, 768, 19, 19]          --                        False\n",
       "│    └─BasicConv2d: 2-32                 [1, 288, 40, 40]          [1, 384, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-43                 [1, 288, 40, 40]          [1, 384, 19, 19]          (995,328)                 False\n",
       "│    │    └─BatchNorm2d: 3-44            [1, 384, 19, 19]          [1, 384, 19, 19]          (768)                     False\n",
       "│    └─BasicConv2d: 2-33                 [1, 288, 40, 40]          [1, 64, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-45                 [1, 288, 40, 40]          [1, 64, 40, 40]           (18,432)                  False\n",
       "│    │    └─BatchNorm2d: 3-46            [1, 64, 40, 40]           [1, 64, 40, 40]           (128)                     False\n",
       "│    └─BasicConv2d: 2-34                 [1, 64, 40, 40]           [1, 96, 40, 40]           --                        False\n",
       "│    │    └─Conv2d: 3-47                 [1, 64, 40, 40]           [1, 96, 40, 40]           (55,296)                  False\n",
       "│    │    └─BatchNorm2d: 3-48            [1, 96, 40, 40]           [1, 96, 40, 40]           (192)                     False\n",
       "│    └─BasicConv2d: 2-35                 [1, 96, 40, 40]           [1, 96, 19, 19]           --                        False\n",
       "│    │    └─Conv2d: 3-49                 [1, 96, 40, 40]           [1, 96, 19, 19]           (82,944)                  False\n",
       "│    │    └─BatchNorm2d: 3-50            [1, 96, 19, 19]           [1, 96, 19, 19]           (192)                     False\n",
       "├─InceptionC: 1-12                       [1, 768, 19, 19]          [1, 768, 19, 19]          --                        False\n",
       "│    └─BasicConv2d: 2-36                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-51                 [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-52            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-37                 [1, 768, 19, 19]          [1, 128, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-53                 [1, 768, 19, 19]          [1, 128, 19, 19]          (98,304)                  False\n",
       "│    │    └─BatchNorm2d: 3-54            [1, 128, 19, 19]          [1, 128, 19, 19]          (256)                     False\n",
       "│    └─BasicConv2d: 2-38                 [1, 128, 19, 19]          [1, 128, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-55                 [1, 128, 19, 19]          [1, 128, 19, 19]          (114,688)                 False\n",
       "│    │    └─BatchNorm2d: 3-56            [1, 128, 19, 19]          [1, 128, 19, 19]          (256)                     False\n",
       "│    └─BasicConv2d: 2-39                 [1, 128, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-57                 [1, 128, 19, 19]          [1, 192, 19, 19]          (172,032)                 False\n",
       "│    │    └─BatchNorm2d: 3-58            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-40                 [1, 768, 19, 19]          [1, 128, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-59                 [1, 768, 19, 19]          [1, 128, 19, 19]          (98,304)                  False\n",
       "│    │    └─BatchNorm2d: 3-60            [1, 128, 19, 19]          [1, 128, 19, 19]          (256)                     False\n",
       "│    └─BasicConv2d: 2-41                 [1, 128, 19, 19]          [1, 128, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-61                 [1, 128, 19, 19]          [1, 128, 19, 19]          (114,688)                 False\n",
       "│    │    └─BatchNorm2d: 3-62            [1, 128, 19, 19]          [1, 128, 19, 19]          (256)                     False\n",
       "│    └─BasicConv2d: 2-42                 [1, 128, 19, 19]          [1, 128, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-63                 [1, 128, 19, 19]          [1, 128, 19, 19]          (114,688)                 False\n",
       "│    │    └─BatchNorm2d: 3-64            [1, 128, 19, 19]          [1, 128, 19, 19]          (256)                     False\n",
       "│    └─BasicConv2d: 2-43                 [1, 128, 19, 19]          [1, 128, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-65                 [1, 128, 19, 19]          [1, 128, 19, 19]          (114,688)                 False\n",
       "│    │    └─BatchNorm2d: 3-66            [1, 128, 19, 19]          [1, 128, 19, 19]          (256)                     False\n",
       "│    └─BasicConv2d: 2-44                 [1, 128, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-67                 [1, 128, 19, 19]          [1, 192, 19, 19]          (172,032)                 False\n",
       "│    │    └─BatchNorm2d: 3-68            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-45                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-69                 [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-70            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "├─InceptionC: 1-13                       [1, 768, 19, 19]          [1, 768, 19, 19]          --                        False\n",
       "│    └─BasicConv2d: 2-46                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-71                 [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-72            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-47                 [1, 768, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-73                 [1, 768, 19, 19]          [1, 160, 19, 19]          (122,880)                 False\n",
       "│    │    └─BatchNorm2d: 3-74            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-48                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-75                 [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-76            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-49                 [1, 160, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-77                 [1, 160, 19, 19]          [1, 192, 19, 19]          (215,040)                 False\n",
       "│    │    └─BatchNorm2d: 3-78            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-50                 [1, 768, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-79                 [1, 768, 19, 19]          [1, 160, 19, 19]          (122,880)                 False\n",
       "│    │    └─BatchNorm2d: 3-80            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-51                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-81                 [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-82            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-52                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-83                 [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-84            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-53                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-85                 [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-86            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-54                 [1, 160, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-87                 [1, 160, 19, 19]          [1, 192, 19, 19]          (215,040)                 False\n",
       "│    │    └─BatchNorm2d: 3-88            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-55                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-89                 [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-90            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "├─InceptionC: 1-14                       [1, 768, 19, 19]          [1, 768, 19, 19]          --                        False\n",
       "│    └─BasicConv2d: 2-56                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-91                 [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-92            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-57                 [1, 768, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-93                 [1, 768, 19, 19]          [1, 160, 19, 19]          (122,880)                 False\n",
       "│    │    └─BatchNorm2d: 3-94            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-58                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-95                 [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-96            [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-59                 [1, 160, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-97                 [1, 160, 19, 19]          [1, 192, 19, 19]          (215,040)                 False\n",
       "│    │    └─BatchNorm2d: 3-98            [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-60                 [1, 768, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-99                 [1, 768, 19, 19]          [1, 160, 19, 19]          (122,880)                 False\n",
       "│    │    └─BatchNorm2d: 3-100           [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-61                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-101                [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-102           [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-62                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-103                [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-104           [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-63                 [1, 160, 19, 19]          [1, 160, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-105                [1, 160, 19, 19]          [1, 160, 19, 19]          (179,200)                 False\n",
       "│    │    └─BatchNorm2d: 3-106           [1, 160, 19, 19]          [1, 160, 19, 19]          (320)                     False\n",
       "│    └─BasicConv2d: 2-64                 [1, 160, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-107                [1, 160, 19, 19]          [1, 192, 19, 19]          (215,040)                 False\n",
       "│    │    └─BatchNorm2d: 3-108           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-65                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-109                [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-110           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "├─InceptionC: 1-15                       [1, 768, 19, 19]          [1, 768, 19, 19]          --                        False\n",
       "│    └─BasicConv2d: 2-66                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-111                [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-112           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-67                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-113                [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-114           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-68                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-115                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-116           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-69                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-117                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-118           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-70                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-119                [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-120           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-71                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-121                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-122           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-72                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-123                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-124           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-73                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-125                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-126           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-74                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-127                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-128           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-75                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-129                [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-130           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "├─InceptionD: 1-16                       [1, 768, 19, 19]          [1, 1280, 9, 9]           --                        False\n",
       "│    └─BasicConv2d: 2-76                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-131                [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-132           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-77                 [1, 192, 19, 19]          [1, 320, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-133                [1, 192, 19, 19]          [1, 320, 9, 9]            (552,960)                 False\n",
       "│    │    └─BatchNorm2d: 3-134           [1, 320, 9, 9]            [1, 320, 9, 9]            (640)                     False\n",
       "│    └─BasicConv2d: 2-78                 [1, 768, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-135                [1, 768, 19, 19]          [1, 192, 19, 19]          (147,456)                 False\n",
       "│    │    └─BatchNorm2d: 3-136           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-79                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-137                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-138           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-80                 [1, 192, 19, 19]          [1, 192, 19, 19]          --                        False\n",
       "│    │    └─Conv2d: 3-139                [1, 192, 19, 19]          [1, 192, 19, 19]          (258,048)                 False\n",
       "│    │    └─BatchNorm2d: 3-140           [1, 192, 19, 19]          [1, 192, 19, 19]          (384)                     False\n",
       "│    └─BasicConv2d: 2-81                 [1, 192, 19, 19]          [1, 192, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-141                [1, 192, 19, 19]          [1, 192, 9, 9]            (331,776)                 False\n",
       "│    │    └─BatchNorm2d: 3-142           [1, 192, 9, 9]            [1, 192, 9, 9]            (384)                     False\n",
       "├─InceptionE: 1-17                       [1, 1280, 9, 9]           [1, 2048, 9, 9]           --                        False\n",
       "│    └─BasicConv2d: 2-82                 [1, 1280, 9, 9]           [1, 320, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-143                [1, 1280, 9, 9]           [1, 320, 9, 9]            (409,600)                 False\n",
       "│    │    └─BatchNorm2d: 3-144           [1, 320, 9, 9]            [1, 320, 9, 9]            (640)                     False\n",
       "│    └─BasicConv2d: 2-83                 [1, 1280, 9, 9]           [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-145                [1, 1280, 9, 9]           [1, 384, 9, 9]            (491,520)                 False\n",
       "│    │    └─BatchNorm2d: 3-146           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-84                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-147                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-148           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-85                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-149                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-150           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-86                 [1, 1280, 9, 9]           [1, 448, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-151                [1, 1280, 9, 9]           [1, 448, 9, 9]            (573,440)                 False\n",
       "│    │    └─BatchNorm2d: 3-152           [1, 448, 9, 9]            [1, 448, 9, 9]            (896)                     False\n",
       "│    └─BasicConv2d: 2-87                 [1, 448, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-153                [1, 448, 9, 9]            [1, 384, 9, 9]            (1,548,288)               False\n",
       "│    │    └─BatchNorm2d: 3-154           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-88                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-155                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-156           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-89                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-157                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-158           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-90                 [1, 1280, 9, 9]           [1, 192, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-159                [1, 1280, 9, 9]           [1, 192, 9, 9]            (245,760)                 False\n",
       "│    │    └─BatchNorm2d: 3-160           [1, 192, 9, 9]            [1, 192, 9, 9]            (384)                     False\n",
       "├─InceptionE: 1-18                       [1, 2048, 9, 9]           [1, 2048, 9, 9]           --                        False\n",
       "│    └─BasicConv2d: 2-91                 [1, 2048, 9, 9]           [1, 320, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-161                [1, 2048, 9, 9]           [1, 320, 9, 9]            (655,360)                 False\n",
       "│    │    └─BatchNorm2d: 3-162           [1, 320, 9, 9]            [1, 320, 9, 9]            (640)                     False\n",
       "│    └─BasicConv2d: 2-92                 [1, 2048, 9, 9]           [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-163                [1, 2048, 9, 9]           [1, 384, 9, 9]            (786,432)                 False\n",
       "│    │    └─BatchNorm2d: 3-164           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-93                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-165                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-166           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-94                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-167                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-168           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-95                 [1, 2048, 9, 9]           [1, 448, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-169                [1, 2048, 9, 9]           [1, 448, 9, 9]            (917,504)                 False\n",
       "│    │    └─BatchNorm2d: 3-170           [1, 448, 9, 9]            [1, 448, 9, 9]            (896)                     False\n",
       "│    └─BasicConv2d: 2-96                 [1, 448, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-171                [1, 448, 9, 9]            [1, 384, 9, 9]            (1,548,288)               False\n",
       "│    │    └─BatchNorm2d: 3-172           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-97                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-173                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-174           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-98                 [1, 384, 9, 9]            [1, 384, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-175                [1, 384, 9, 9]            [1, 384, 9, 9]            (442,368)                 False\n",
       "│    │    └─BatchNorm2d: 3-176           [1, 384, 9, 9]            [1, 384, 9, 9]            (768)                     False\n",
       "│    └─BasicConv2d: 2-99                 [1, 2048, 9, 9]           [1, 192, 9, 9]            --                        False\n",
       "│    │    └─Conv2d: 3-177                [1, 2048, 9, 9]           [1, 192, 9, 9]            (393,216)                 False\n",
       "│    │    └─BatchNorm2d: 3-178           [1, 192, 9, 9]            [1, 192, 9, 9]            (384)                     False\n",
       "├─AdaptiveAvgPool2d: 1-19                [1, 2048, 9, 9]           [1, 2048, 1, 1]           --                        --\n",
       "├─Dropout: 1-20                          [1, 2048, 1, 1]           [1, 2048, 1, 1]           --                        --\n",
       "├─Linear: 1-21                           [1, 2048]                 [1, 10]                   20,490                    True\n",
       "============================================================================================================================================\n",
       "Total params: 25,132,754\n",
       "Trainable params: 20,490\n",
       "Non-trainable params: 25,112,264\n",
       "Total mult-adds (G): 7.28\n",
       "============================================================================================================================================\n",
       "Input size (MB): 1.40\n",
       "Forward/backward pass size (MB): 184.75\n",
       "Params size (MB): 87.22\n",
       "Estimated Total Size (MB): 273.38\n",
       "============================================================================================================================================"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(model = model, input_size = [1, 3, 342, 342],\n",
    "        col_names = [\"input_size\", \"output_size\", \"num_params\", \"trainable\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "63ca822d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:55:17.229067Z",
     "iopub.status.busy": "2023-04-19T09:55:17.228617Z",
     "iopub.status.idle": "2023-04-19T09:55:17.243750Z",
     "shell.execute_reply": "2023-04-19T09:55:17.242788Z"
    },
    "papermill": {
     "duration": 0.313933,
     "end_time": "2023-04-19T09:55:17.246295",
     "exception": false,
     "start_time": "2023-04-19T09:55:16.932362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1e5f21",
   "metadata": {
    "papermill": {
     "duration": 0.294152,
     "end_time": "2023-04-19T09:55:17.838762",
     "exception": false,
     "start_time": "2023-04-19T09:55:17.544610",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74702289",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:55:18.418780Z",
     "iopub.status.busy": "2023-04-19T09:55:18.418395Z",
     "iopub.status.idle": "2023-04-19T09:55:18.433301Z",
     "shell.execute_reply": "2023-04-19T09:55:18.432279Z"
    },
    "papermill": {
     "duration": 0.298332,
     "end_time": "2023-04-19T09:55:18.435558",
     "exception": false,
     "start_time": "2023-04-19T09:55:18.137226",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_step(model, loss_fn, optimizer, train_loader, device):\n",
    "    model.train()\n",
    "    model = model.to(device)\n",
    "    running_loss, running_acc = 0, 0\n",
    "    for x, y in train_loader:\n",
    "        x, y = x.to(device), y.to(device)\n",
    "        \n",
    "        y_logits = model(x).logits\n",
    "        y_probs = torch.argmax(torch.softmax(y_logits, dim = 0), dim = 1)\n",
    "        \n",
    "        loss = loss_fn(y_logits, y)\n",
    "        running_loss += loss.item()\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_acc += (y == y_probs).sum().item() / len(y_probs)\n",
    "    \n",
    "    running_loss /= len(train_loader)\n",
    "    running_acc /= len(train_loader)\n",
    "    \n",
    "    return running_loss, running_acc\n",
    "\n",
    "def test_step(model, loss_fn, test_loader, device):\n",
    "    model.eval()\n",
    "    model = model.to(device)\n",
    "    \n",
    "    running_loss, running_acc = 0, 0\n",
    "    with torch.inference_mode():\n",
    "        for x, y in test_loader:\n",
    "            x, y = x.to(device), y.to(device)\n",
    "\n",
    "            y_logits = model(x)\n",
    "            y_probs = torch.argmax(torch.softmax(y_logits, dim = 0), dim = 1)\n",
    "\n",
    "            loss = loss_fn(y_logits, y)\n",
    "            running_loss += loss.item()\n",
    "            running_acc +=(y == y_probs).sum().item() / len(y_probs)\n",
    "\n",
    "    running_loss /= len(test_loader)\n",
    "    running_acc /= len(test_loader)\n",
    "    \n",
    "    return running_loss, running_acc\n",
    "\n",
    "\n",
    "\n",
    "def fit(model, loss_fn, optimizer, train_loader, test_loader, epochs, device):\n",
    "    \n",
    "    model_results = defaultdict(list)\n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        \n",
    "        running_loss_train, running_acc_train = train_step(model,loss_fn, optimizer, train_loader, device)\n",
    "        running_loss_test, running_acc_test = test_step(model, loss_fn, test_loader, device)\n",
    "        \n",
    "        model_results[\"train_acc\"].append(running_acc_train)\n",
    "        model_results[\"test_acc\"].append(running_acc_test)\n",
    "        model_results[\"train_loss\"].append(running_loss_train)\n",
    "        model_results[\"test_loss\"].append(running_loss_test)\n",
    "        \n",
    "        print(f\"epoch: {epoch+1} / {epochs}, train_loss: {running_loss_train:.4f} | test_loss: {running_loss_test:.4f} | train_acc: {running_acc_train:.4f} | test_acc: {running_acc_test:.4f}\")\n",
    "        \n",
    "    return model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f5154a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T09:55:18.819932Z",
     "iopub.status.busy": "2023-04-19T09:55:18.816605Z",
     "iopub.status.idle": "2023-04-19T10:00:20.659480Z",
     "shell.execute_reply": "2023-04-19T10:00:20.657898Z"
    },
    "papermill": {
     "duration": 302.03565,
     "end_time": "2023-04-19T10:00:20.662311",
     "exception": false,
     "start_time": "2023-04-19T09:55:18.626661",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|████████▎                                                                          | 1/10 [00:22<03:26, 22.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 / 10, train_loss: 1.4115 | test_loss: 0.4277 | train_acc: 0.7077 | test_acc: 0.8200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 20%|████████████████▌                                                                  | 2/10 [00:43<02:53, 21.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 / 10, train_loss: 0.4313 | test_loss: 0.3709 | train_acc: 0.7724 | test_acc: 0.8400\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 30%|████████████████████████▉                                                          | 3/10 [01:06<02:36, 22.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2 / 10, train_loss: 0.4008 | test_loss: 0.3713 | train_acc: 0.8177 | test_acc: 0.8000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|█████████████████████████████████▏                                                 | 4/10 [01:29<02:13, 22.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 3 / 10, train_loss: 0.3672 | test_loss: 0.3708 | train_acc: 0.8099 | test_acc: 0.8200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████████████████████████████████████████▌                                         | 5/10 [01:49<01:48, 21.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 4 / 10, train_loss: 0.3393 | test_loss: 0.3546 | train_acc: 0.8239 | test_acc: 0.8200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|█████████████████████████████████████████████████▊                                 | 6/10 [02:10<01:25, 21.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 5 / 10, train_loss: 0.3927 | test_loss: 0.5762 | train_acc: 0.7908 | test_acc: 0.8600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 70%|██████████████████████████████████████████████████████████                         | 7/10 [02:31<01:03, 21.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 6 / 10, train_loss: 0.3290 | test_loss: 0.3689 | train_acc: 0.8418 | test_acc: 0.7800\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 80%|██████████████████████████████████████████████████████████████████▍                | 8/10 [02:51<00:41, 20.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 7 / 10, train_loss: 0.3220 | test_loss: 0.5291 | train_acc: 0.8326 | test_acc: 0.8200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|██████████████████████████████████████████████████████████████████████████▋        | 9/10 [03:12<00:20, 20.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 8 / 10, train_loss: 0.3141 | test_loss: 0.3773 | train_acc: 0.8116 | test_acc: 0.8000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [03:33<00:00, 21.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 9 / 10, train_loss: 0.3306 | test_loss: 0.4593 | train_acc: 0.8165 | test_acc: 0.8400\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model_result = fit(model, loss_fn, optimizer, train_loader, test_loader, EPOCHS, DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d17dd57",
   "metadata": {
    "papermill": {
     "duration": 0.147079,
     "end_time": "2023-04-19T10:00:20.958786",
     "exception": false,
     "start_time": "2023-04-19T10:00:20.811707",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Plot Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4f95a32c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T10:00:21.278129Z",
     "iopub.status.busy": "2023-04-19T10:00:21.277005Z",
     "iopub.status.idle": "2023-04-19T10:00:21.334125Z",
     "shell.execute_reply": "2023-04-19T10:00:21.332967Z"
    },
    "papermill": {
     "duration": 0.224561,
     "end_time": "2023-04-19T10:00:21.336741",
     "exception": false,
     "start_time": "2023-04-19T10:00:21.112180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'LOSS & ACCURACY')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainacc = model_results['train_acc']\n",
    "testacc = model_results['test_acc']\n",
    "trainloss = model_results['train_loss']\n",
    "testloss = model_results['test_loss']\n",
    "epoch = range(len(model_results['train_loss']))\n",
    "\n",
    "fig,(ax1,ax2) = plt.subplots(1,2, figsize=(15,7))\n",
    "\n",
    "#fig = make_subplots(rows = 1, cols = 2, subplot_titles = [\"accuracy\", \"loss\"])\n",
    "ax1.plot(epoch, trainacc,label = 'train acc')\n",
    "ax1.plot(epoch, testacc, label = 'test acc')\n",
    "ax1.set_title(\"Accuracy\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(epoch, trainloss, label='train loss')\n",
    "ax2.plot(epoch, testloss, label = 'test loss')\n",
    "ax2.set_title(\"Loss\")\n",
    "ax2.legend()\n",
    "plt.xlabel(\"Epoch\")\n",
    "\n",
    "fig.suptitle('LOSS & ACCURACY', fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5e0e58",
   "metadata": {
    "papermill": {
     "duration": 0.151454,
     "end_time": "2023-04-19T10:00:21.636520",
     "exception": false,
     "start_time": "2023-04-19T10:00:21.485066",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Prediction on random Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "fd2e7693",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T10:00:21.940689Z",
     "iopub.status.busy": "2023-04-19T10:00:21.940136Z",
     "iopub.status.idle": "2023-04-19T10:00:21.952937Z",
     "shell.execute_reply": "2023-04-19T10:00:21.951979Z"
    },
    "papermill": {
     "duration": 0.167913,
     "end_time": "2023-04-19T10:00:21.955188",
     "exception": false,
     "start_time": "2023-04-19T10:00:21.787275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def predict_image(data_path, transforms, class_names):\n",
    "    image = Image.open(data_path)\n",
    "    image_trans = transforms(image).to(DEVICE).unsqueeze(dim = 0)\n",
    "    \n",
    "    y_logits = model(image_trans)\n",
    "    y_preds = torch.softmax(y_logits, dim = 1)\n",
    "    y_preb_probs = torch.argmax(y_preds, dim = 1)  # not used anyway\n",
    "    \n",
    "    \n",
    "    fig,ax = plt.subplots(1,2)\n",
    "    fig.tight_layout(pad=6)\n",
    "    ax[0].imshow(image)\n",
    "    ax[0].set_title('Image')\n",
    "    x=y_preds.cpu().detach().numpy()[0] * 100\n",
    "    y=class_names\n",
    "    ax[1].barh(y,x)\n",
    "\n",
    "    fig.suptitle(\"Predict function for Inception V3\")\n",
    "    plt.xlabel(\"Confidence\")\n",
    "    plt.ylabel(\"Classes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ba3f1f52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-19T10:00:22.255657Z",
     "iopub.status.busy": "2023-04-19T10:00:22.255083Z",
     "iopub.status.idle": "2023-04-19T10:00:22.371232Z",
     "shell.execute_reply": "2023-04-19T10:00:22.370198Z"
    },
    "papermill": {
     "duration": 0.274945,
     "end_time": "2023-04-19T10:00:22.376154",
     "exception": false,
     "start_time": "2023-04-19T10:00:22.101209",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAGqCAYAAADeCefYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5gkVb34/3flqs49eWZzYpegoEtQVBYUXBBFEUH5ghIUQUVEwABeSQaUKwI/JVy9JK9kDHhRQVG4gmTJsLvsLptnZid37orn90f1NgyzS3JhBz2v5+kHuup0VXV173z6pM9RhBACSZIkSZImBXVbX4AkSZIkSS+QgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWXrDzJw5k6OPPrr5/O6770ZRFO6+++43/Nz/+Z//yezZs9E0jV122eUNP9/r8Wbej815K9yjyeql321J2ppkYP4XdfXVV6MoSvNh2zbbbbcdJ554Ihs3btzWl/ea/OEPf+Dss89+1eX/9Kc/8fWvf533vOc9XHXVVXz/+99/4y7uVbj00ku5+uqrt+k1vNSbfY+OPvpoUqnUG3qOre2+++7j7LPPZmxsbFtfCgAHHXQQiUSCUqm0xTJHHHEEpmkyPDwMwFe/+lXe+c530tLSQiKRYPvtt+fss8+mXC6/WZctvQ76tr4A6Y117rnnMmvWLOr1Ovfeey+XXXYZf/jDH3j66adJJBJv6rXstdde1Go1TNN8Ta/7wx/+wCWXXPKqg/Nf//pXVFXliiuueM3neiNceumltLW1Tahhvd77sTVMtns0Gd13332cc845HH300eRyuXH7li1bhqq+ufWaI444gv/93//lN7/5DZ/5zGcm7K9Wq9x6663sv//+tLa2AvDwww/zvve9j2OOOQbbtnnsscf4wQ9+wJ133snf/va3N/09SK+ODMz/4g444AB23XVXAD73uc/R2trKj3/8Y2699VYOP/zwzb6mUqmQTCa3+rWoqopt21v9uC81MDCA4ziTPuC8Wfdjc7b2PRJCUK/XcRxnqxxvsrMs600/50EHHUQ6nea6667bbGC+9dZbqVQqHHHEEc1t995774Ryc+bM4bTTTuOhhx7iXe961xt6zdLrI38u/Zt5//vfD8CqVauAF5oYV65cyYc+9CHS6XTzH3YURVx00UXsuOOO2LZNZ2cnxx9/PKOjo+OOKYTgu9/9LlOnTiWRSLDPPvvwzDPPTDj3lvpUH3zwQT70oQ+Rz+dJJpO8/e1v5+KLL25e3yWXXAIwrml+SxRF4aqrrqJSqTTLXn311axevbr5/5t7zYtr42effTaKorBixYpmbSmbzXLMMcdQrVYnvP6Xv/wlu+++O4lEgnw+z1577cWf/vQnIO6LfOaZZ/i///u/5vXsvffeL3s/br75ZhYuXIjjOLS1tXHkkUeyYcOGcWU2fW4bNmzgYx/7GKlUivb2dk477TTCMNzi/Xm5ewQQBAHf+c53mDNnDpZlMXPmTM444wxc1x13jJkzZ/LhD3+YO+64g1133RXHcfiv//qvlz3vS206xr333svuu++ObdvMnj2bX/ziFxPKjo2N8dWvfpWZM2diWRZTp07lM5/5DENDQ80yruty1llnMXfuXCzLYtq0aXz961+fcO2KonDiiSdy7bXXMn/+fGzbZuHChfztb39rljn77LP52te+BsCsWbOa92n16tXNa39pC8jzzz/PoYce2mw2fte73sXvf//7cWU2feY33XQT3/ve95g6dSq2bfOBD3yAFStWvOz9chyHj3/84/zlL39hYGBgwv7rrruOdDrNQQcd9LLHmTlzZvOeSpOTrDH/m1m5ciVAs6kL4j/Gixcv5r3vfS8/+tGPmk3cxx9/PFdffTXHHHMMJ510EqtWreKnP/0pjz32GH//+98xDAOAM888k+9+97t86EMf4kMf+hCPPvooH/zgB/E87xWv589//jMf/vCH6e7u5itf+QpdXV0sWbKE2267ja985Sscf/zx9Pb28uc//5n/+Z//ecXj/c///A8/+9nPeOihh/jv//5vAPbcc8/XfJ8ADjvsMGbNmsV5553Ho48+yn//93/T0dHBD3/4w2aZc845h7PPPps999yTc889F9M0efDBB/nrX//KBz/4QS666CK+/OUvk0ql+Na3vgVAZ2fnFs+56X7vtttunHfeeWzcuJGLL76Yv//97zz22GPjmlTDMGTx4sXsscce/OhHP+LOO+/kggsuYM6cOXzhC194Xffoc5/7HNdccw2f+MQnOPXUU3nwwQc577zzWLJkCb/5zW/GHWfZsmUcfvjhHH/88Rx33HHMnz//Nd/jFStW8IlPfILPfvazHHXUUVx55ZUcffTRLFy4kB133BGAcrnM+973PpYsWcKxxx7LO9/5ToaGhvjd737H+vXraWtrI4oiDjroIO69914+//nPs/322/PUU09x4YUX8txzz/Hb3/523Hn/7//+jxtvvJGTTjoJy7K49NJL2X///XnooYfYaaed+PjHP85zzz3H9ddfz4UXXkhbWxsA7e3tm30fGzduZM8996RarXLSSSfR2trKNddcw0EHHcQtt9zCwQcfPK78D37wA1RV5bTTTqNQKHD++edzxBFH8OCDD77s/TriiCO45ppruOmmmzjxxBOb20dGRrjjjjs4/PDDJ7RaBEHA2NgYnufx9NNP8x//8R+k02l23333V/UZSduAkP4lXXXVVQIQd955pxgcHBTr1q0TN9xwg2htbRWO44j169cLIYQ46qijBCC++c1vjnv9PffcIwBx7bXXjtt+++23j9s+MDAgTNMUBx54oIiiqFnujDPOEIA46qijmtvuuusuAYi77rpLCCFEEARi1qxZYsaMGWJ0dHTceV58rC996UvitXxVjzrqKJFMJsdtW7VqlQDEVVddNaE8IM4666zm87POOksA4thjjx1X7uCDDxatra3N58uXLxeqqoqDDz5YhGG4xevfcccdxaJFiyac96X3w/M80dHRIXbaaSdRq9Wa5W677TYBiDPPPHPcewTEueeeO+6Y73jHO8TChQsnnOulNnePHn/8cQGIz33uc+O2n3baaQIQf/3rX5vbZsyYIQBx++23v+K5tnS+Tcf429/+1tw2MDAgLMsSp556anPbmWeeKQDx61//esJxN93n//mf/xGqqop77rln3P7LL79cAOLvf/97cxsgAPHII480t61Zs0bYti0OPvjg5rb//M//FIBYtWrVhPPOmDFj3Hf75JNPFsC485dKJTFr1iwxc+bM5vdj02e+/fbbC9d1m2UvvvhiAYinnnpqwrleLAgC0d3dLd797ndv9n3ecccdE15z//33N98zIObPn9/8zkmTk2zK/he377770t7ezrRp0/jUpz5FKpXiN7/5DVOmTBlX7qU1rJtvvplsNst+++3H0NBQ87Fw4UJSqRR33XUXAHfeeSee5/HlL395XBPzySef/IrX9thjj7Fq1SpOPvnkCYNrXq65+s1ywgknjHv+vve9j+HhYYrFIgC//e1viaKIM888c8Igmtdz/Y888ggDAwN88YtfHNf3fOCBB7JgwYIJzaJbusbnn3/+NZ8b4kF2AKeccsq47aeeeirAhPPPmjWLxYsXv65zbbLDDjvwvve9r/m8vb2d+fPnj3sPv/rVr9h5550n1Drhhft88803s/3227NgwYJx39dNXTebvq+bvPvd72bhwoXN59OnT+ejH/0od9xxxyt2BWzOH/7wB3bffXfe+973NrelUik+//nPs3r1ap599tlx5Y855phx/fub7sErfXaapvGpT32K+++/v9msDnEzdmdnJx/4wAcmvGaHHXbgz3/+M7/97W/5+te/TjKZlKOyJznZlP0v7pJLLmG77bZD13U6OzuZP3/+hCCi6zpTp04dt2358uUUCgU6Ojo2e9xNfVxr1qwBYN68eeP2t7e3k8/nX/baNjWr77TTTq/+Db2Jpk+fPu75pvczOjpKJpNh5cqVqKrKDjvssFXOt+lebq5JeMGCBRMG8ti2PaFpNZ/PTxgD8FrOr6oqc+fOHbe9q6uLXC7XvL5NZs2a9brO82Ivvccw8T2sXLmSQw455GWPs3z5cpYsWbLFpuaX9sm+9PsKsN1221GtVhkcHKSrq+vVXH7TmjVr2GOPPSZs33777Zv7X/w9f7nv1is54ogjuPDCC7nuuus444wzWL9+Pffccw8nnXQSmqZNKJ/JZNh3330B+OhHP8p1113HRz/6UR599FF23nnnV/8mpTeNDMz/4nbffffmqOwtsSxrQrCOooiOjg6uvfbazb5mS38AJ6st1WBfrna0uT9yEA92mwy2dH3/rFdb298aI7C31j2Oooi3ve1t/PjHP97s/mnTpr3ma3sj/TPve+HChSxYsIDrr7+eM844g+uvvx4hxLjR2C/n4x//OJ/+9Ke54YYbZGCepGRgljZrzpw53HnnnbznPe952T/AM2bMAOIay+zZs5vbBwcHX/HX/5w5cwB4+umnm7/oN2drNGtvqpG8dCTqS2uBr8WcOXOIoohnn332ZTNnvdrr33Qvly1b1myC3WTZsmXN/W+UGTNmEEURy5cvb9b0IB7YNDY29oaff0vmzJnD008//YplnnjiCT7wgQ+8qvu9fPnyCduee+45EolE80fna/nezZgxg2XLlk3YvnTp0ub+remII47g29/+Nk8++STXXXcd8+bNY7fddntVr3VdlyiKKBQKW/WapK1H9jFLm3XYYYcRhiHf+c53JuzbNMoT4j5swzD4yU9+Mu7X/kUXXfSK53jnO9/JrFmzuOiiiyYEzBcfa9Oc6n9mekcmk6GtrW3clBiIk3+8Xh/72MdQVZVzzz2XKIrG7Xvp9b+aa991113p6Ojg8ssvHzfF549//CNLlizhwAMPfN3X+mp86EMfAiZ+dptqoW/0+bfkkEMO4YknnpgwKhxeuM+HHXYYGzZs4Oc///mEMrVajUqlMm7b/fffz6OPPtp8vm7dOm699VY++MEPNmuzr+V796EPfYiHHnqI+++/v7mtUqnws5/9jJkzZ2617o5NNtWOzzzzTB5//PHN1pbHxsbwfX/C9k0j8V+pJU3admSNWdqsRYsWcfzxx3Peeefx+OOP88EPfhDDMFi+fDk333wzF198MZ/4xCeac2fPO+88PvzhD/OhD32Ixx57jD/+8Y/NKSZboqoql112GR/5yEfYZZddOOaYY+ju7mbp0qU888wz3HHHHQDNQTonnXQSixcvbg6Aea0+97nP8YMf/IDPfe5z7Lrrrvztb3/jueeee+03p2Hu3Ll861vf4jvf+Q7ve9/7+PjHP45lWTz88MP09PRw3nnnNa//sssu47vf/S5z586lo6NjQo0YwDAMfvjDH3LMMcewaNEiDj/88OZ0qZkzZ/LVr371dV/rq7Hzzjtz1FFH8bOf/YyxsTEWLVrEQw89xDXXXMPHPvYx9tlnnzf0/Fvyta99jVtuuYVDDz2UY489loULFzIyMsLvfvc7Lr/8cnbeeWc+/elPc9NNN3HCCSdw11138Z73vIcwDFm6dCk33XRTc771JjvttBOLFy8eN10K4ulvm2z63n3rW9/iU5/6FIZh8JGPfGSzyXe++c1vcv3113PAAQdw0kkn0dLSwjXXXMOqVav41a9+tdUzbM2aNYs999yTW2+9FWCzgfnuu+/mpJNO4hOf+ATz5s3D8zzuuecefv3rX7Prrrty5JFHbtVrkraibTcgXHojbZou9fDDD79suc1NY3mxn/3sZ2LhwoXCcRyRTqfF2972NvH1r39d9Pb2NsuEYSjOOecc0d3dLRzHEXvvvbd4+umnJ0wpeen0oE3uvfdesd9++4l0Oi2SyaR4+9vfLn7yk5809wdBIL785S+L9vZ2oSjKK06d2tJ7qlar4rOf/azIZrMinU6Lww47TAwMDGxxutTg4OC412+6py+dPnPllVeKd7zjHcKyLJHP58WiRYvEn//85+b+/v5+ceCBB4p0Oi2A5tSpLd2PG2+8sXm8lpYWccQRRzSnt73Se9x07a9kS6/3fV+cc845YtasWcIwDDFt2jRx+umni3q9Pq7cjBkzxIEHHviK53m5823pGIsWLZowvWx4eFiceOKJYsqUKcI0TTF16lRx1FFHiaGhoWYZz/PED3/4Q7Hjjjs2P4uFCxeKc845RxQKhWY5QHzpS18Sv/zlL8W8efOEZVniHe94x2anEH3nO98RU6ZMEaqqjvvsX/rdFkKIlStXik984hMil8sJ27bF7rvvLm677bZxZTZ95jfffPO47S83nW9LLrnkEgGI3XfffbP7V6xYIT7zmc+I2bNnC8dxhG3bYscddxRnnXWWKJfLr/o80ptPEWKSjGSRJEl6EyiKwpe+9CV++tOfbutLkaTNkn3MkiRJkjSJyMAsSZIkSZOIDMySJEmSNInIUdmSJP1bkcNqpMlO1pglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkS0bf1BUiSJE0GURTR29tLOp1GUZRtfTnSvyAhBKVSiZ6eHlR1y/ViGZglSZKA3t5epk2btq0vQ/o3sG7dOqZOnbrF/TIwS5IkAel0Goj/aGYymW18NdK/omKxyLRp05rftS2RgVmSJAmazdeZTEYGZukN9UpdJXLwlyRJkiRNIjIwS5IkSdIkIgOzJEmSJE0iMjBLkiRJ0iQiA7MkSZIkTSIyMEuSJEnSJCIDsyRJkiRNIjIwS5IkSdIkIgOzJEmSJE0iMjBLkiRJ0iQiA7MkSZIkTSIyMEuSJEnSJCIDsyRJkiRNIjIwS5IkSdIkIgOzJEmSJE0iMjBLkiRJ0iQiA7MkSf8URVFe9nH22WezevVqFEXh8ccfH/faX/3qV7z//e8nn8/jOA7z58/n2GOP5bHHHmuWufrqqzd7XNu2m2WOPvro5nbDMJg1axZf//rXqdfrb9ZtkKStRt/WFyBJ0ltbX19f8/9vvPFGzjzzTJYtW9bclkqlGBoamvC6b3zjG1xwwQWcdNJJnHPOOcyYMYPBwUH++Mc/cvrpp3P77bc3y2YymXHHhPgHwYvtv//+XHXVVfi+zz/+8Q+OOuooFEXhhz/84dZ6q5L0ppCBWZKkf0pXV1fz/7PZLIqijNsGTAjMDzzwAOeffz4XX3wxJ510UnP79OnTWbhwIUKIceU3d8yXsiyrWWbatGnsu+++/PnPf5aBWXrLkYFZkqQ33fXXX08qleKLX/ziZve/tDb8Wj399NPcd999zJgxY4tlXNfFdd3m82Kx+E+dU5K2FtnHLEnSm+65555j9uzZ6PoLdYMf//jHpFKp5qNQKDT3FQqFcftSqRQHHHDAuGPedtttpFIpbNvmbW97GwMDA3zta1/b4jWcd955ZLPZ5mPatGlb/41K0usga8ySJE0Kxx57LAcddBAPPvggRx555Ljm7HQ6zaOPPjquvOM4457vs88+XHbZZVQqFS688EJ0XeeQQw7Z4vlOP/10TjnllObzYrEog7M0KcjALEnSm27evHnce++9+L6PYRgA5HI5crkc69evn1BeVVXmzp37ssdMJpPNMldeeSU777wzV1xxBZ/97Gc3W96yLCzL+iffiSRtfbIpW5KkN93hhx9OuVzm0ksvfUOOr6oqZ5xxBv/xH/9BrVZ7Q84hSW8UGZglSXrTvfvd7+bUU0/l1FNP5ZRTTuHee+9lzZo1PPDAA1xxxRUoioKqvvDnSQhBf3//hEcURVs8x6GHHoqmaVxyySVvxluSpK1GBmZJkraJH/3oR1x33XU89thjfPjDH2bevHkceuihRFHE/fffTyaTaZYtFot0d3dPeAwMDGzx+Lquc+KJJ3L++edTqVTejLckSVuFIl46YVCSJOnfULFYJJvNUigUxv0okKSt5dV+x2SNWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZIkaRKRgVmSJEmSJhEZmCVJkiRpEpGBWZIkSZImERmYJUmSJGkSkYFZkiRJkiYRGZglSZJeZKez7mDmN3+/rS9D+jcmA7MkSZIkTSIyMEuSJEnSJCIDsyRJkiRNIjIwS5IkSdIkIgOzJEmSJE0iMjBPEldffTWKovDII49s60uRJEmStiEZmCXpVRocHOQLX/gC06dPx7Isurq6WLx4MX//+9+bZWbOnImiKDzwwAPjXnvyySez9957j9s2MjLCySefzIwZMzBNk56eHo499ljWrl3bLHP55ZeTTqcJgqC5rVwuYxjGhOPdfffdKIrCypUrN3v9Z599NrvssssW39/ee++NoigTHieccMK4crfddhuLFi0inU6TSCTYbbfduPrqq8eVWb169bhjtLa28sEPfpDHHntsXLn7778fTdM48MADJ1zPS4/R0tLCokWLuOeeeya8r01ldF2nra2Nvfbai4suugjXdbf4fiVpspKBWZJepUMOOYTHHnuMa665hueee47f/e537L333gwPD48rZ9s23/jGN172WCMjI7zrXe/izjvv5PLLL2fFihXccMMNrFixgt12243nn38egH322YdyuTyuJeWee+6hq6uLBx98kHq93tx+1113MX36dObMmfO63+Nxxx1HX1/fuMf555/f3P+Tn/yEj370o7znPe/hwQcf5Mknn+RTn/oUJ5xwAqeddtqE491555309fVxxx13UC6XOeCAAxgbG2vuv+KKK/jyl7/M3/72N3p7ezd7TZuO8be//Y2enh4+/OEPs3HjxnFldtxxR/r6+li7di133XUXhx56KOeddx577rknpVLpdd8PSdomhDQpXHXVVQIQDz/8sBBCiKOOOkokk0mxZs0aceCBB4pkMil6enrET3/6UyGEEE8++aTYZ599RCKRENOnTxfXXnvtuOMNDw+LU089Vey0004imUyKdDot9t9/f/H4449POPfq1avFRz7yEZFIJER7e7s4+eSTxe233y4Acdddd40r+8ADD4jFixeLTCYjHMcRe+21l7j33nvfmJsyiYyOjgpA3H333S9bbsaMGeKkk04SpmmK3//+983tX/nKV8SiRYuaz0844QSRTCZFX1/fuNdXq1UxZcoUsf/++ze3dXd3i/POO6/5/Otf/7r40pe+JLbffvtxn89ee+0ljjrqqC1e21lnnSV23nnnLe5ftGiR+MpXvrLF/WvXrhWGYYhTTjllwr7/7//7/wQgHnjgASGEEKtWrRKAeOyxx5pl/v73vwtA3H777UIIIUqlkkilUmLp0qXik5/8pPje97437pibO8aTTz4pAHHrrbe+4vtasmSJME1TfOtb39rs+6nX66JQKDQf69atE4CYdvJNYsY3btvifZCk16tQKAhAFAqFly0na8yTWBiGHHDAAUybNo3zzz+fmTNncuKJJ3L11Vez//77s+uuu/LDH/6QdDrNZz7zGVatWtV87fPPP89vf/tbPvzhD/PjH/+Yr33tazz11FMsWrRoXM2kUqnw/ve/nzvvvJOTTjqJb33rW9x3332brfH99a9/Za+99qJYLHLWWWfx/e9/n7GxMd7//vfz0EMPvSn3ZFtJpVKkUil++9vfvmLz6KxZszjhhBM4/fTTiaJowv4oirjhhhs44ogj6OrqGrfPcRy++MUvcscddzAyMgLEtea77rqrWeauu+5i7733ZtGiRc3ttVqNBx98kH322eeffatbdMstt+D7/mZrxscffzypVIrrr79+i693HAcAz/MAuOmmm1iwYAHz58/nyCOP5Morr0QIscXX12o1fvGLXwBgmuYrXu+CBQs44IAD+PWvf73Z/eeddx7ZbLb5mDZt2iseU5LeFG/O7wTplWyuxgyI73//+80yo6OjwnEcoSiKuOGGG5rbly5dKgBx1llnNbfV63URhuG4c6xatUpYliXOPffc5rYLLrhAAOK3v/1tc1utVhMLFiwYV2OOokjMmzdPLF68WERR1CxbrVbFrFmzxH777bdV7sNkdsstt4h8Pi9s2xZ77rmnOP3008UTTzwxrsyMGTPEhRdeKAYGBkQ6nRa/+MUvhBDja8z9/f0CEBdeeOFmz/PrX/9aAOLBBx8UQgjx85//XCSTSeH7vigWi0LXdTEwMCCuu+46sddeewkhhPjLX/4iALFmzZotXv+rqTEbhiGSyeS4xy9/+UshRFzLz2azW3z929/+dnHAAQcIISbWdkdHR8XBBx8sUqmU6O/vF0IIseeee4qLLrpICCGE7/uira1tXAvApmM4jiOSyaRQFEUAYuHChcLzvFf1vr7xjW8Ix3E2u0/WmKU3m6wx/4v43Oc+1/z/XC7H/PnzSSaTHHbYYc3t8+fPJ5fLNfslASzLQlXjjzcMQ4aHh0mlUsyfP59HH320We72229nypQpHHTQQc1ttm1z3HHHjbuOxx9/nOXLl/P//t//Y3h4mKGhIYaGhqhUKnzgAx/gb3/722Zrh/9KDjnkEHp7e/nd737H/vvvz91338073/nOCQOfANrb2znttNM488wzmzXElxIvUzt8sb333ptKpcLDDz/MPffcw3bbbUd7ezuLFi1q9jPffffdzJ49m+nTp/8zb5EjjjiCxx9/fNzjxd+N12rPPfcklUqRz+d54oknuPHGG+ns7GTZsmU89NBDHH744QDous4nP/lJrrjiignHuPHGG3nsscf41a9+xdy5c7n66qsxDONVnV8IgaIom91nWRaZTGbcQ5ImA31bX4C0ZbZt097ePm5bNptl6tSpE/7YZLNZRkdHm8+jKOLiiy/m0ksvZdWqVYRh2NzX2tra/P81a9YwZ86cCcebO3fuuOfLly8H4Kijjtri9RYKBfL5/Kt8d29Ntm2z3377sd9++/Htb3+bz33uc5x11lkcffTRE8qecsopXHrppVx66aXjtre3t5PL5ViyZMlmz7FkyRIURWl+BnPnzmXq1KncddddjI6OsmjRIgB6enqYNm0a9913H3fddRfvf//7/+n3l81mJ3z2m2y33XYUCgV6e3vp6ekZt8/zPFauXDmhKf3GG29khx12oLW1lVwu19x+xRVXEATBuOMIIbAsi5/+9Kdks9nm9mnTpjFv3jzmzZtHEAQcfPDBPP3001iW9YrvZ8mSJcyaNevVvHVJmjRkjXkS0zTtNW1/cQ3s+9//Pqeccgp77bUXv/zlL7njjjv485//zI477vi6arabXvOf//mf/PnPf97sI5VKvebjvtXtsMMOVCqVze5LpVJ8+9vf5nvf+964kcGqqnLYYYdx3XXX0d/fP+41tVqNSy+9lMWLF9PS0tLcvs8++3D33Xdz9913j5smtddee/HHP/6Rhx566A3tX4a4xcAwDC644IIJ+y6//HIqlUqzBrzJtGnTmDNnzrigHAQBv/jFL7jgggvG1cyfeOIJenp6Xraf+hOf+AS6rk/4sbM5S5cu5fbbb+eQQw559W9SkiYBWWP+F3XLLbewzz77TGgaHBsbo62trfl8xowZPPvssxOa/FasWDHudZum4GQyGfbdd9838Monp+HhYQ499FCOPfZY3v72t5NOp3nkkUc4//zz+ehHP7rF133+85/nwgsv5LrrrmOPPfZobv/+97/PX/7yF/bbbz/OP/98dtppJ1atWsV//Md/4Ps+l1xyybjj7LPPPnzpS1/C9/1mjRlg0aJFnHjiiXie96oCc61W4/HHHx+3LZ1ONz/farU64ceCZVnk83mmT5/O+eefz6mnnopt23z605/GMAxuvfVWzjjjDE499dRx73FLbrvtNkZHR/nsZz87rmYMcfC/4oorJsyd3kRRFE466STOPvtsjj/+eBKJBBAH+/7+fqIoYnh4mLvvvpvvfve77LLLLnzta197xWuSpMlE1pj/RWmaNqEP8+abb2bDhg3jti1evJgNGzbwu9/9rrmtXq/z85//fFy5hQsXMmfOHH70ox9RLpcnnG9wcHArXv3kk0ql2GOPPbjwwgvZa6+92Gmnnfj2t7/Ncccdx09/+tMtvs4wDL7zne+Mm28McXfCAw88wD777MPxxx/PnDlzOOyww5gzZw4PP/wws2fPHld+n332oVarMXfuXDo7O5vbFy1aRKlUYv78+XR3d7/i+3juued4xzveMe5x/PHHN/f//Oc/p7u7e9zjxbXgk08+md/85jfcc8897Lrrruy0005cd911XHbZZfzoRz96xfND3Iy97777TgjKEAfmRx55hCeffHKLrz/qqKPwfX/cfX/mmWfo7u5m+vTp7L333tx0002cfvrp3HPPPf+WLTnSW5siXu0IFOkNdfXVV3PMMcfw8MMPs+uuu3L00Udzyy23TAiCe++9N0NDQzz99NPjts+cOZOddtqJ2267DYCzzjqLc889l6OPPpo999yTp556imuvvZZcLse0adO4++67gTiL1Nve9jY2btzIV77yFbq7u7n22mvxPI/HH3+cu+++u1lDu/vuuznggAPo6OjgmGOOYcqUKWzYsIG77rqLTCbD//7v/77xN0qS3iDFYjGeNnXyTahWgtU/mJiNTJL+GZu+Y4VC4WUHG8qm7H9RZ5xxBpVKheuuu44bb7yRd77znfz+97/nm9/85rhyqVSKv/71r3z5y1/m4osvJpVK8ZnPfIY999yTQw45BNu2m2X33ntv7r//fr7zne/w05/+lHK5TFdXF3vssce4WpckSZL0+skas7RZF110EV/96ldZv349U6ZM2daXI0lvOFljlt5or7bGLPuYJWq12rjn9Xqd//qv/2LevHkyKEuSJL3JZFO2xMc//nGmT5/OLrvsQqFQ4Je//CVLly7l2muv3daXJklvuqfPWSyTjUjblAzMEosXL+a///u/ufbaawnDkB122IEbbriBT37yk9v60iRJkv7tbNOm7EsuuYSZM2di2zZ77LHHv/xCCJPVySefzNNPP025XKZWq/GPf/xDBmVJkqRtZJsF5htvvJFTTjmFs846i0cffZSdd96ZxYsXMzAwsK0uSZIkSZK2uW0WmH/84x9z3HHHccwxx7DDDjtw+eWXk0gkuPLKK7fVJUmSJEnSNrdN+pg9z+Mf//gHp59+enObqqrsu+++3H///RPKu647bg3cKIoYGRmhtbV1iyvHSNLrJYSgVCrR09PTXKFLkiTpzbJNAvPQ0BBhGI5LLQjQ2dnJ0qVLJ5Q/77zzOOecc96sy5MkANatW8fUqVO39WVIkvRv5i0xKvv000/nlFNOaT4vFApxTty93kEmncSyTBRFIYogCgVRqFCru0SRIJ3OoGgqg4NDFEtlkskktpNkdHSUkdECnhsShYIgDBGAZpioKji2STqTwnZsDE1H1zQc2yKTSmDbJqqqYGgajpMgDD0KhTEUBWzbJAyhVq9jGTbJVApd0yGKj2+YBp4XoKoKpmlg2ylcL2Tj4CgDQ6PUXJ9KNb72MAwxDA3T0NA1Fcux0HWdZCJFe0cHHe2t2LZOFAVYpoaua4hIUC7XGB0tEoYCw7LQdBMRBRi6gqJEhGHI0meXs7F/GN8PURComsAPPPL5PJ0dWXJZBz9wGytZqXh+yGihQqFQZuPgKJ4boqIiUFBVDREFOI6JZZlkM2m6e7pJpZJEUcDo2AjDwwP4nksUxecPo7CZyzsMBZ7n4roeYRARRfEjCAKEEIRhfC9EFKEoAkMBTVHQVBXd0DBNDdPSMYz4c9JUUFXQVQVE2LhKgYgiCAOiMCQKAyAiikIQcS05CARhJPCDkH+sLpNOp7fF112SpH9z2yQwt7W1oWkaGzduHLd948aNdHV1TShvWdZm117VdQ1dV9G0eNUZXddBaHi1AFMLsRIm6UwSTdfQdWhry+MHgrGxCrWqTxgqaLqOqgnUUMc0LayEjaoppJI2yYRFMpnE0DU0NQ6OtmliGgaqpqApAk0TRJFA01Rs28ZxHOp1FxFBve6RSin4vkcmlUTXdSo1l/b2DgxDR1E0anWfoeE+1qztpe6FoGqYjoOIIiIhEKFA1TQiBUJhoAiDmqvSP1BirOhhOyamAemkSWdbjnTKIZNMMK2nA1XVcD0PBYUgDKjVKtTrVUqlCpri4rljoBiYlk0ilaJWcxkZKSBCD1NrJ5tLYZo6lWqNarXO0NAYQ0NFPDdEURRCIqJQoBsKqqpSKlWoVKoMj4zS17+RXD7P7Nmz6O6ejqJqDA/143l1BAKUuPsiiqLGjwUNXddRFdEMyJqmjV+iUlVQFIGKQFVAUxUMTcUydXRNRVNAUwS6pqKrCpqqoERxWYQgAgQaIowIowghBFHEC/daKEQCgkYuPNlNIknStrBNArNpmixcuJC//OUvfOxjHwPifuO//OUvnHjiia/6OJVSnXQyhaJoGIaJYdj4bkQ18hGKgqIrBKGLqpt0tOVxg5D+/hHGxoqUKy5hKNAtHU1VUVWNZDKFY1tYpkY2l8K2DDRNwdB1dFVHJf5/TdNACdE0BdetEwQBlmlhmja2YeNYCWpWHV2voCiCVCqJqmlUqnUioWCYDn4QsmF9L+s29FJ3fYIgJJFMopkaqXQCTdXQ1PjHguf5hCJCIa69BoEgCCPKVZeq66MSUKvoZJIJ8rksRAJLtxkZHWV4eJRiqUwQBHhenXJljHQ6wfTp3Vi2hh8IokjBMJOUy1WCKKJYrlGu1DFNg0JhFFXT6e3tZ2zMxXMBxUIoESoRKBFhEBAqIBSFSIn7ZEvVOm4wTLFUobu7k1mzZiCikIGBXqJIAFGjNhwHXkVRUFUFIeLtL84Uq6pxa8imQKkAihI/VBUUBIgIBVCFghJFgEoYRShECEA0WiAEgjBS8ALwg4AwjBAIIqHE3xlFww1lllpJkradbdaUfcopp3DUUUex6667svvuu3PRRRdRqVQ45phjXvUxyiWXqFPFNBPouo7vRQyPjOG6HplsmnQmia4rWJaJbZtUR4oEQRwEozCu6YZRhGHqqGpc+/Y9F10zEGFA4IeEAVhGXHM0Gs26hqahaxaKHgcSBARBCCjohoVuaKAoJJIOtmUjGvtNW8MPYO2GAQYHhhgaGsIPAjTdIJ1Lk8ykMQ0D2zaw7QTdXV1EAsqlMpVKBdf18MOIwBcIVMJQUKtWCSIPPZ0nmUxRrbiUikXK5Rp9/QMMDI9QLJQJowjfrWEaCvPmzyQUkM3lqVRrDA6OUBspkkhm0XUTr1ZlaGQERQuxLJUw8NF0DVQNRVNRVZ0w8hHErRZRFKHrOl7oIQQg4ubpes2nXvfwgwDDNGlvS5NMJimVSgihIUSEokAUvRCIhRCgjF8bWogIVW0EZSFQUFCUeHskoriJWot/EChKvF9EIQrxa0KxqSlcEAqBHwp8oVL3BX4YgaIQCRHPUVAgEHLAlyRJ2842C8yf/OQnGRwc5Mwzz6S/v59ddtmF22+/fcKAsJdTKteo10M8T+C6LoVCmeHhEdLpFNlsBidhoGngmCZ+EBD4AULENaUgDFFVFYU4uFimRdJx0DUwTZ1k0iGZsAijABUBUUDd8zBNG9f3CTQN3TSwbQfXdanVXAzDIBKCarUORKiKQhD6gIaiG6gCapUKa9f1Ui5Xse0kuYSN7ZgYlolu6tiWScpJYlsO1VIJ1/VwXZfQ97BNA+H6mHbcT+1WqgS1KrZjkM+mqdXqrBkYYM2atYwVy5RrHq4XoKgamqLhuiF1X7Chd4jOjjy2Y1AqVRgeLqCqJoYZkEol0dWQZErHtE0sR8eyEtR9lWJpBNfziYRAURVAQdN0hOejGzqKruDWPRRVBUUjCuMada1WY/Xq1djmTObMmcPatWsZHByMg7AQRI3+5rjWrKJpKgoKURQhREQYNgK1AJRGrRoFoUAkIkIRogkQIm4aV5UXatfhpmZrBBEKfgR1X+D5EW4gCMK4piwgvhYR4QXhVv++S5IkvVrbtGpw4oknsmbNGlzX5cEHH2SPPfZ4Ta+vuXUGhoYZGh3DdX2CICCRcMhmM6RSCWzbwbIsVFXDMCwioVIuV/E8D01VURTQVRVd0Ug6NralYxo6mXSSXCZDNp1CQyAaza26rmOaOpqhYTk2mqZTLleo1eo4TqK5RGJcw9PIZPJ0dPaQymSpuT4b+jby/Oq11DyfZDpNR1cn3T2dTJ3aw9SpXUyb1kUmncDQFUToE/oeURD/13PrBJ6LhkJxbIzB/n761q+jXi/T1dWBZdusXruWZ5cto3dgkEK5gh8EceCKfNywBrqC0BSGxwoMDI9QrXmEoYKhW4RBRBgGBH4NISISiRSqouPWA0aGC4yNlqhUa3hegOcHuF6I7wf4vo/nexRKBcrlMn4YxP3aioJmaADxYK8wIAxDHCdBS0seVVXiGm1cQUZXVUxdxzIMLMOM+4xVJf6vpjW6GxQUJX7E/dRK44dWiOv5+GGAH0Vxq0Ik8MMwfh5FcUAOIsp1j5rrU3MDvEAQROD6Ea4fUfcEdS/E82Vgfr2OPvro5mdkmiZz587l3HPPJQgCrr76anK53GZfpygKv/3tbwFYvXp1/P3RNDZs2DCuXF9fH7quoygKq1evnnCcxYsXo2kaDz/88FZ+Z5L05nlLjMreEsvSqFZKlAyVfCpFW2sOXddIp1MkHAvdUBEiJAgiPC9ojCA2UFUVgU8YRKiqgQgjdA0sU40HIAFuvYamRvieD41GUU3TCIK4dogiKJer+J5PKpUkmUxgmia+76PrBtlcjmQyhVv36O8fYtXqDYyMFbCdBB3traSSCRKOjWFotLbkqFTL+DUXz62jCgXDMHDdenxuRTRGijtUqjWKhQLFQglVE7R35ema2sbAxkGWLFvKWLGMplkoio6qqlgqBL5PQEBEiK7pWJaBYegYhoZtW7S3t1Gu1ECBUqlAGAS4tSSKiPcXC6OMjZXxvQAhNBBK3D8cRURB1LgnGoL4XKZpNZr4NzVHCywj/tETeB7ZTJbWllaGh4ZQdL3ZcrFpJLZK3GQdKipevKcxUls0PgulcdT4FCEgwhDhCSJdJ1AUNEUFARHxYLIgiltK6m5IEEWEQYSIFCIgihRQIIwgEnGfu/T67b///lx11VW4rssf/vAHvvSlL2EYBt3d3a/pOFOmTOEXv/jFuHwH11xzDVOmTGHt2rUTyq9du5b77ruPE088kSuvvJLddtvtn34vkrQtvKU702bO6CGXSWBbBplMkq7Odro622jJZ7AtHRGG+K6P7wcEQdznWa3UIB4ChKoqWKaJYzskHJuEY5NM2CSTTlwz1jTy+RbSqXSjD1rHMA10PQ7QiUSCtvY2Uul0PA3K9wijiFQqTSqVpVJxeeaZ53jq6aUMDAxh6DpTerqZMqWT1tYMjqOjEOBWy0SejxIKRAB+GDIwOEgQhtTdOigC33cZKwwzOjpIqTyKaalsN38Ws2b0MDYyxJo1qykUioShghAahpnAttOoqokQKrpq4tgpTN1BVUyy2RymqWM13o+CQr3uYugmLfkcqiqIwhC3XsfQdUxDQ1EjNDUCfDRFoCgQBH5cgxVxAFRVhSDw42lRgU8U+hCFJJMJEom42d+xHVryeUzDwNA1TNPANOKR1YauYRgGtmVh21bz+jRNiwN4Y9SXqmqNSVAqKBApEIQi7qYIQlzfp+bWqdbqVGoulVqNuhsQhBCFCkKo8WuFihAKYRCPOUCoqG/tfxbbnGVZdHV1MWPGDL7whS+w77778rvf/e41H+eoo47iqquuGrftqquu4qijjtps+auuuooPf/jDfOELX+D666+fsJypJL1VvKX/Ak2Z0sbc2VOZMa2LttYsmbRNwtFJJEw0VaFWrbOxb5j16zeybl0fGzYMMDZWJIoiNFXFcSzSmRRtbTly2QymoWIZOpqqEAYBCBE3X1txs7XnuYRhQK1eo1Qp4wcelm1jmhZ+EKEoGplsDjuRoLevj8eeeII16zYQhZBJp8lmsyRsi2w6gWMb6BroGtSqVVy3jlv3mnOxdd3E832KpSKr16yiUB6jVCkQhHWmT+tk4cIdWTB/NiLwWfP8aoYGhjGNBI6dRDdsdN0kjCJCIdBNA8OwCX0FTTGIoojiWJFatd6YTxzheS6mbpDP5Wlrb8OyTCIR4Tg2qVSSnu4OZkzrJpe1MYwIgYsfVEGJ8Dyv2V8cBgGB7+N7HoK4v1fTFExDp1aLuxEGBgYBjXxLG6ZpYejxHGzD0Btzu01MS28GbE1T0bR4Opyu6yiqhqpqKEpjhrKIR1MLIAiiRoCO4kFeYWPEtVARStwvvmnAd3zJ8TZQUFFfCP7SVuM4Dp7nvebXHXTQQYyOjnLvvfcCcO+99zI6OspHPvKRCWWFEFx11VUceeSRLFiwgLlz53LLLbe87PFd16VYLI57SNJk8JZuys7nHJIJG1VRMfQ4kBqmgaJE1Oo+G/rHWL9+gKHhAgLwPB8Ax05gmBqOY5FNp8ikbWxTbQQQLW4GxWz+alEUgevFv77rpVo8r9o00XQdPwwZK5YRkaBnyhTSmRTlSoXejf2MFYuksxlSyQwdba1ksxl0Pe4rLdcqhFFEEERUqlU0Tcc0VerVCr7nUigWSaVSqCrUalU01UFF0NHWwg4L5iMIqNZr9A70MzBUoFaHCLAcnTAMCXyXuNk3AhE0BlYJosjF1MG2TBShomoqgoBsLo2uaWTSKequx4a+YQLfY2ysEk/3UlTSKQtIYNsq9bpHta6hKSbFQgWEaAToeJSzruloxAHZNOMkIIqIqFddytW4adkwHXTLhciNpzgJQeD7RIRoqo7repimhqY51F23Mb0JNBQiEcV9mYK41hsJiIfpEQgVVVEQSoRQBJESEbGpaV2gKPH4gngaVTzNCjUeABaPCpeBeWsQQvCXv/yFO+64gy9/+cuv+fWGYXDkkUdy5ZVX8t73vpcrr7ySI488EsMwJpS98847qVarLF68GIAjjzySK664gk9/+tNbPL7MKChNVm/pwGybFo5to+sGumYRRaBpJqVShTVre1m1agOjY1WqNZcwDDFNA8Mw0A0D2zKwTYNkwsYy46k7mhr3vWpafFvq9TqWpmHbdmMAU4hhGCSTSXw/pFgsMzJaAFQ6OjqxHZuh4RGWLXuO0dFRstkcHR1dKIognU6QdCz8wMOt1xkZGSEMBaZpYhoWyXSaSqVKsVTEsUxSiQQiDFEVlbZ8K/V6nWw6Q093N4gIz3WpVuuUqx51L0TVjDhhhgjQDA1FCVEVDSEUfD9A1VR0HRQRksnm0Q2LSs3D83yqdZex0RF0TWXOrBl4XkC95lEqlykWqxgj5UYGsHgulJOwyWUzpFPxfRCRR+D78TQlVMIgRDciclmb1tY82WyKdDLRGIEtKBbK+KFCZ1c7tp0g8ENUEcRToVSNKIozlCmKguf7+H6IHuk08oQRRSKer6yojSQholHTFXH/NwrRpmZuIkQU14fDRjDXtHhAWrPqDKAojfnRMij/s2677TZSqRS+H3dp/L//9/84++yzufnmm1/zsY499lj23HNPvv/973PzzTdz//33EwTBhHJXXnkln/zkJ+MkQ8Dhhx/O1772NVauXMmcOXM2e+yXZhQsFotMmzbtNV+jJG1tb+nArGk6um5Sr8dTioQwEIpL/8aNrFy5lr6+IfwAdMPAcax40JNpkMmksC0dVYlTPMZB28JJOBhGPKfZ8zxUTSMMQ+r1eiNDldL8hx+GEdVKlRDB1KlT6ehsp1gssmzZcnp7B2htbWX6tOmk0wk0TUFEEaVKAV3XqdarCBTS6XQ8pzqKKJWqjI6MYGomqlBQdb2xWMcwpmUypaeT1rYW0qkU5XIN3/NYvaaXoeEKkdCJFMhkMkAYD3jy49STIgpJJBKgBCQTJulkC7blMDg4QrFUwfNDXM9FEOI4GqVqnaRl097WAWhomgmKSoSgUioBAtcr4dgG6ZRDwlRQMhaKYiFI4zgOiqJQr9doa29DhD666qMoPmEocKwkvu9Rrvgkk0ny+TSu4qPiAiFaqBAGKoYhGv3XKlHkomsi/l0Qga/GtVwhGgFWiZuzaTRLC+KR3jSaueOatGiUiUfYC0BVVERjYJkCKFo83SqS+UX+Kfvssw+XXXYZpmnS09PT/DeTyWSoVCrxlLYXLQ4yNjYGQDabnXCst73tbSxYsIDDDz+c7bffnp122onHH398XJmRkRF+85vf4Ps+l112WXN7GIZceeWVfO9739vsdW4po6AkbWtv6cBcq9VRVZVCqcrAQIF6XYCiUSqWKBaqiEjE/ZeGQjJpEUUhighQCOJUl4aGYWgkk4nGgK+4/7VeryGEIJGwMQyrWVsWIk6P6QcBAoFl2aSyafIteTyvzrp16ymVKrS2tDF75izaWnNoaoSi0viDHxFEPq7n4tZdwgBqtRphGMbzn2t1RODTns8ThC6lcpFkwmL23NlkMmmEAM+Nm6hdL6RWCajXfAzDRovA93xEFBKJkMD3m4OYVB3a27L09LQRRbBuXT+Dg0OEQsP1IhRNQ9M1okilVK6QdBxaWvMIFCo1jyCKm5itRJJMyiadNLEMBdNQUZWIIEzHCVC8AMPQ0HWjkaM6nmOsoBH6HqqmE4QuugFR5FMulWlpSWNZCUQk4s9FUwgVDSE2zTPXiATNxCWRGiE0jRCB2DSrScQJQhrTnFEbfcZRFGf9QlHjJCTE5aJGZrHm3GiAF2Ubk3Xmf04ymWTu3LkTts+fP58gCHj88cd55zvf2dz+6KOPArDddttt9njHHnssX/ziF8cF3Re79tprmTp1anO61SZ/+tOfuOCCCzj33HMbLT6S9Nbwlg7MiqLhByFjY0VWPr+WYsFH0eLmaEU1yOZzqCoIQmxbQ1F0HMvCNOM//IoS51N2HBvLMAiDkLoXT1GyLKv5jzkIQ8qVCkIIksQpRdva2ujs0NEtC1XTWLtuHSNjI7S3t9HV2UUykUAlRBECVYAfuIyNDOF7IZVKlWQyRSKRwtAUhkdHKJXKpFJp7HQCwzRImjYzZ89o9FkXeX7V86iqRmtLG5VqHREK7IRNSz5NqRz/kPBq8VxuTQVFBcPQyKSTtOQztLen8LwitapPsTBMvV5G1R1U3UBVlOZCD7ZtY9kmvu/i+TWCKEBECknHRNNVNCUin0/R1prBNjWq1RpjY2UCX6FUGqFSraHpPq5bp1r1UFRIpxI4tkoiaVAojmCaCroe4Xo1FEXDMlOEEXFfuFAJVQhDDxqtGYamE2ohoRKiayoILQ6kkUDTFPwgYNOUNjZVrBs14zjVdhyE48QnKiIKEY2pUUIhzhr2opzcssL8xthxxx354Ac/yLHHHssFF1zA7NmzWbZsGSeffDKf/OQnmTJlymZfd9xxx3HooYducQ70FVdcwSc+8Ql22mmncdunTZvG6aefzu23386BBx64td+OJL1h3tKBOZlMUHc9KtU6rhtRdwNQBJpu0JLP4DgmYeijKlFzCk4isalmHBCFcWYpt1ZFa0z90Y14mpSiKHieh27EU4GKxSLVapWWlhY6OzvjVZ9MC0UzGBoeZnRkDNuy6ehoJZtLEnoegR+hKaAYGsVCgcAPSKVS5HI5VEXF98M4cYaIaG3JkcvlSCYcEo6DYVqUqxUee2IJ/f0bKZVKWJbFjjuZcQ0/bdOuKWgGIGp4XoiRtlEUNa6dqtDe3oKmCRIJHcdUcCsemiJob83i+wGeL6hUK6imiWUYOLZJZ0c7+ZYs1VqVZDobJx7x4xp4FPlxP7UaoekqgQgxTJsgqjAwVKRQrOH5YZxkxK2hNLJ6JRI2qWSSnp42EikH09bp6x/Cr8UtB6lUDs8LUYQf9zMLgaJERCJoJhVRVTVePatRq93UtfDixS5eyLHdSD4SvZDmM+47jlOZooIQIXHABlCaA75END5Pt7R13XjjjZx11lkcf/zx9Pb2MnXqVA4++GC+/e1vb/E1uq7T1ta22X3/+Mc/eOKJJ/j5z38+YV82m+UDH/gAV1xxhQzM0luKIt6Cf4WKxSLZbJZvfPUoQhGxfMValixbS7HooSg62WyKrq4WDEPDD1wSltloqm4EtUQibp5uZMYytHiubCQiTMtq9H/Fg6b8IKRUKjE0NEytVqOlJc/cefPo7u4mCEJGR4ts6NuIEHFChFwuQ71WJvR9bNMiDAIq5QquHxAEAZlMlrGxsUbftYeqKPiBj5NwaGtvp16vUyhV2LC+n/6NQxRLNcIwbjZXNYVUyqGjo4V02iGZMFHViDD0CYMAVdUIfIFtWURRgG0biChAVVVs24rndIcCP1IYGhqlWnOp110cJ0EqmSKZTKEqGr6IyLTEi2Hk0mkCz6dSLlIpF9A1lVw+i2UbCCWit3eQ51duYHikgKJa1F0fL/AJ/Dqe74JorEClKuRySTo7Wkln0miawYYNA7S2djNt+lQ0NQDhoQgfFYHnewSBj+t61BoB3Pf9OPOY5+G6Hp7nNVeiCsIX0nrSzJctxtV+hQJ+GG8LwgDEi5eeDJsjsoWA0eEChUKh0W8v/TvY9HdFfu7SG+XVfsfe0jVm1w/RDQPDiNNuxrUqFdsxQBX4QTxS2DAMTMsklUximWYj8cAL825DLSShJ6CRBjCugcU1p7HRUYQQZDJpHCfBlClTcBJJXD+gVq2xobePWs2lu6ubfC5LGHgoIsKxTRKJBF7dp1SqoCgqiUSSer2O7/v4QdAItGl0wyCba2F0dJQnn17C4OAoxWKVIARF0TF0m1BoiAiGR0oMjYyhqwqZtMWUrjw9Pe2ESoSm6QReDctME0YqgR/i+yGe5+HYIs5K1ljisbvbwXVr6GrcOhAEcatCve4xuHGQgaEhVEBToK0lH6csNU1KpSKZbArLTOGHAaZpIwDLsilXA9A0DE1FURVC4iQliIgggsGhEvVaQC5Xo7WtFSeRYHB4gFxrjlwmGX+GwkcVIZoqiFS1+ZlGho4Q8XuIxTVjRVGaSziKTdsiaIZkpVG0kf5TbeTF1hQ1nkrV6N/ftARlHKhlL7MkSdvOWzow1z2fsOpRqdRRFBXT0OJm05RNGPpUKlUM3cAydHRDBRFR1/Q4k5Sqxv3AqoofBHHiCsA2TXRdx/U86vUag4OD2E6C7u4eWltbSaVSuK7L8OAwGweGKJXKzN9uPlN7uqlUytTKZfL5HKZpxrUvUyWVyVKvuXi+z+DQcDwiWI37PE07gR+G3P/gI2zY0EuhWCGM1HiurRrnm9Z0hTAK0HUDRagEXojvhYiwSjqZJOcG6JqOUFT8UGPlql6q1Xg5yjCM8D2Bqik4CQtV13ASCVryOfKZNKlEPFp9bHQsHsyWSpHJ5ah7ASuXr0Az9cbANYFhWSiVeO6wpihUalWymTSplEOpXEUQoms2mqGDY0EZAs/DE3FGNEU1KFVd6u4II6MlLNvCcmw2buwjlZyBoQo2dRRrWpzZKww1oij+mgoEQWAQhhFBoDabsiFe2/nFA7ia2xU1XoGKTQO7VGhMpNrU6h0nGhGNkdxyypQkSdvWWzowF4pVatU6Q4OjeK6HbVuk0g6WrTdqVo0BP7yQMyIe8BXPf61UKnGazcaUCbWR9lEIQbkcL7Vo21bc95tMNhJ+aPh+wIb1fYwVq0yZ0k1LSz4O+pVqPICs5lGp1AjDCNd3KZerDA2PoRAnC3F9j2w2Q09XD8VimSVLV7Bm3XqiECIRr0tsmia2baHpOoYRz1GuVquoioKmqeiahmlp5FtbCCOB73v09Q8xMlQmDOPAjgoChcATGLZJveyjahHlegXXE9TrHtvNmUEum2uODkcDTREIv0Y6ZVOtVdF0FSeZYGRkEN3QsR2Ler2CKkJcL8A0zXj9aCKIBEQCTdfIpOLR2oHXmMIV+XHNNlLwfZe655HTFYIozqaWzGcRfkgUxs3Tiqo0sn6phGG81KOmqY11mF/I0qWqKiIURETNvuRNnzVsmhYVb45zcsdLTSqNYKwK4h8OioKhGS+k+JYkSdoG3tKBeWBoJE6EUa2haTqZTIZUykFTFNAisukUhqGTTNrYtk0i4RD6AdVKpTmHMR6FbOMHAdVKlXq1jmnqoCi0trbS0dFJMpXGthP4vk+lUqK/r59iuUpbewct+VYCP6Ds+9Rcl+HhYZ577nk838cNPDzPxfc9DM2gUq3hJB223357svkWhodHeezxpxgZLQE6mq5h6Rq2paGoCulUgkQiQWtrGyOjY+iaSrlSIQxdbNti3tw5dPV0MDTUx+hogcHhEp4n0DULTdPjmqKIsFM2XhCgoKGpFkLRKFV8PLeA766kvzWLrsXTXBSh4NgWqcaylzXXRdUN/MBD03WSiTTVWpWUY5JI2FRHS/iej20nESLEC+IBX1EYoesqmVQGQqhGFQhDIkJ8z4/7w72QcrlCKl3FsmyCIEQEEYpQGhFUNOcdI8QLCUCUFwaEaZqKCOLAD3EQRhFEjVHWorHSRfy6TccR8dKQjRWr4kCuNXNyB5FcXUqSpG3nLR2Y+zeOICJBre4iQkFhbBTfreEkLFpaspjmphzLKioCVRFYSQfdiGtbqqbihR74KoEfMDA4SKlUYerUKfRM7SaRSGAYRpziMhCUyxU29A3guRG5bAtTp3Sh6yq+7zYSXahkcy0MjhQYLhRwPZdUOkFHRzstuQyZTI5cSwuoGstXrubZZ59jcGgEIRQsKwFCIZGw6OluIwyDePUpXSd0qzimhp7LgghB+LS3t9LR0Y5b9+jrLzAyOkoYglBUQiVCEIISr6ikiaiRYcyAKO5PDoVAQWesHDBWHkRTI1pyWTra28ilUyQTcYazgYEBKrUq63r7MC0b0zDI5HPYtoFhqGTQ6OwOcL0RXC9EVyLcoI5Q4uUTTcMgmU7FNWAXPK+OqoUI4YOq4no+1XKdWrlGqi1LLfJRRNCYDhUnSCEMEGG8GlQYRvHKyo22aA2NSBXNEdmK8kLTthCNQWCbYq8CiAhNE2h6vGCGosZBWtdUND2ulUehyqpt+L2WJOnf21s6MI8MD2MYjTSapgYiolIpI4SPaYBtG2RzGTKZdGMQ0aZFEqzGCN+45oYAz/OoVivU61XCKCThJEkl04RhRK1aZt26XtatXUfdD2ltaaOjs5VsxqFaqVCr1shmM8ybPQNFU0mkNFatjlN/TumZQj4bz6fWdYtK1eXhfzzO0mUrqdY9hFCxbYdEIoHjWLTkc3R3tlIYHaWjvQ3PcxFCoVyt079xkDAISKfT9PT04PsBS59dxvDYKJpm4Lo+mmYQNaYcCSEa61HHTcKGYVEpVzBtG92I17T1ojh4RiJkeKxEpeYCgqlTutA0g2y+BUU36OoUBKFAQYsTieg6CnGztKKohGGIoggiEaLrccY0TTdRNYMo9LCc+AdRqRQSiiieHkVIEAnq9XrcbWDFP6SEEPEyjmFjvWfPIxIQinjpyng6skDTdUQUxU3vopHZa1PNWVWateEoilDUuH1aV3QMXcUwNQxdQ9WUuHtABVWjsUyorDFLkrTtvKUDc+AHJBMOmWyGdCIZr2wUBDiWSTJlk8umSKYS2Hbch7yp6bped4FGNq5QEIVxWsbW1lZmzpzJlClTSacyBCEUxqo8//wa1qxZg+/7JNIpUmmHXC6F51bx61U62lqZPm0qtm1QrZXYfv505s7piddEVgwqVZexYpk1a1fz3PJVrFnXS6XiEUaCVDpNd1cnphlnIFMVQRQGWJaOQohjGZi2g6Jp9PX3k0wmyLe24Xs+zy19jkq52ngfPqqixdOHQtH4wRI/VDWeM23oEYZlYliNZCGNAW+eW0cBHMtG0TRWretjYHCYjvYWkgmTbDaLbTls7B+gUCiCAE1VCXWNat1FRApChIRBHVVR4kxijTWhPTfu60+mUkSBjef5eH6ApuoEgQ9EVMolRkdGSKdsWnIpPDdOPhIELkEY4Ac+ES8Ef11TiEIFTVeIhAKRFi9KIV4YqW0YOrqhoTSWp9QaAdhQwTRUTLPRVy3A0HU0TUHTIlRVwfPfcjMIJUn6F/KWDsypZIJUMkkqaZNNJxt9hDqWqSOiMO5/DKO4j9cwCAKfSiXA8+LBRbquEQabcmWbOE6C9o420uk0uh6n5xwaGqW/bxAh4oUq0rk0+XyWKApQBeTzLXR1dqKrKkIEcWKMwGsMgDKo12qsWb2BZStXsXz5KupugOdFhJHAtCw6Ozvo7OpAJcIwVUSjCTubdtDVeBGHwHfZsH4N1VqFqdNmUq5UeX5DH2OjY2iajlDj1ZyaA6GUeMCbbduN96njBx66aaAJPR40papxVi8R1zxVRUHVTYSi4HoB1ZESnu+TsHWSiTnUqzVUFHTNxPUCSqUqPVO6GSmWSKeStLflMYwShVIZP/IJQyVO7qLqQLwsYyaXImg0+Q8MDMS5rkVIFARUKhXqtTpWRxuh50IY4tXK+FFABAihQRRiKipC11BEGAdWTRCFWrzCFKAqWtxsb5lYpoGqxbVp3VDRFAVDidC1+IEIUYUS15xV0LUITVXwfDn6S5KkbectHZhz2SyBX6c4ViedtEk72UaAgnq1xthoEU3XyOazJBKJZjIK206gaRr1eh1NNbBtp9nMbZomYRgyOjrKhvX9PPHkM7iuy/TpM5gypQcnYaFo8cheEamUSnWGzDHa23IId1OmKotKtcLGjWtZtmQFTzy1hLIbByldN9ENg0TKoaUlh66BCH0MO/5BYWgmke9i2imymTTr1q2lVK5iGBoLFmzHxo1DrF/XS73moSganhegmyZoNFfOileZotmUHQQBqWQS0zRQVQXX89BUjdAL8QIf27IJo4i65yNCge8HBJ4fX1drmnq9ThD4WLaJG/hs6O1FUVX8MMKydFLJBN3dbSSSNsmiTblaY3SkiKlpeIH3Qu5sRaOtta3ZglEojOG5NVBU6nWXSrmG6wbkMjmqI33g11DwURWVMFIxFANViVeg0jUDMwoIIhVFNdBUA103sCwH23JQ1TgbWBD4CASaBo5lYuugKh6K4qFEHkoEhqag6yKuMSsKrqwxS5K0Db2lA3Mmk6Iw5sbTXhAYhopCnDQkCOOl4VQ1TjCi6zqVSqWZQATiEbu1Wo1arY5tW6TTGUzDRFVVisUx1m9YSxDEo7Q9v0ahOIqm5zFNE98LKRXLDGzso6M9i1BmkUqniCIYGBrl8ceeYuXK1RRGi/ihQLXidJlBJEgnbTraW8nns6gKOLaB45jkczmi0Cf0fYjCZv+153vMnTOHwdExSqUitVqNwIsIwwjDtJoJMoLGMnu6YRCFIYauNXN+b1qqUlXiOdwIES84oakEQYDv+Y0fG3GSjSj0SFg2ICiXi9im3ux7VVWN4ZFRgiAknTKxZ82gq7MNEQW05LNUazVGMiMUiiXKZZdKrQ6hQaEw2pzm1NXVRTKZZHCgH9+rxz+GCgWKpSq5dAfJRIJ6JcRUBYIA1Yib9jVNxTANIkwiVSNCQzMcbDuBEIKRkQK9vf0ApNNpfN+nWCyi6xptrTlIO2RSDgnbQcWHKEAVAboWomkRmqqiuTIw/zvb6aw7UK3Etr6Mt7zVP5BpUF+vt3RgTjo2URiv86vr8eAuEUUIVSGbyZDLZlEUgVAUarU6mqaTTCZIpVKYpkmlUqGvdyPFYpF8Pk8ul3vR6F5wHIOu7jZ03cBxkliW0dgPff0bWb++nyCok8k7lGoV3DBg5fPrefLJZfRuGKBec1EQWLaDqhioerwWcCplk0wYOLaGqeuommBsdITA81CJ8D2XhJ3A94vopoXq+yx9bhmREvermoYOUYBl2UQC1Eae7yAMSTgJdE2jpaWl0WQdL1Wp6zq1ao0oavwwEQLXdRtzhpV4jVshCP0IQUAiYTF9ejeOCYYOQgSEod9YDELDD0OqdRfb1qjVa5TXrY1TfgYF2trbyGamUq1WQNHYuHGUdesGqFSrCDU+v23bdHV1Y9sWhbERNAVsy2Z0rEAunSSTzVGvpFBVH12LF6vQNQ3DslENG81IESo2dVehUCyzatVaDMPAc300TcNxEs382vGPEpdKtYbvBxSLgulT2+lozaNEdURQRVdDUPw4UYkmA7MkSdvOWzswJy1yuakYpobeWL+4WolHVWdSaRIJB4HA9QPEplHDEc0cy74f55HOZrPxACfbbvYtCxGRSiXJ5/Momoqq6KRSWTTVoFyuMDw4RLVaJpWxsRMWuqkzPDLG0mUr6Osfou5HRKhoKni+j21YZLM5spkkjq0DIaauks9nsEyTsPGjoDA2hm1blKtVyuUSjpNgQ28fxXKJnmnTSCQSJBN1TCNE03SqntesyRqKRjabJYriml8qk8Hz45HaYeBjaBpo8SAqv+4336cQgjDw0VUV01RQVZ2WfALH1shnbAwNxkYLoKhUqlUUVSMSCnUvQCg6rh8SBj7JRAJRqdC7fgPZXJqWtjx+ENLelkXTDFasXEPN9YkUhXrdA1EhkUhi6Bq1Spm656OUSvRuHGD+jDZS2TyRX8LSA2xTxXIsdMtGs1IIJU1vf4WVz/dTLBexEzaOncCx48/XsgzSmRQDAxvjfmY/wvXcOOmLIiiVXab1dKACkR8vngFx/3so5zFLkrQNvaUDc7VWJWumSSaT6LpOsVBibKyA53kIAYlkAtMyqbsBhUKRIPBpaWkhkUg08i57GKZGJpONtztJoijC932EgHQ6g207CFRc16Neq1MoDNHfP0ChOEY6k2TG9B7aWvLUqy5Lly5ncHCEMIzX/tW0eBqObduYlsaU7g6SCZso8mnJZ+K51Qhs06AeBnGWLS1eTSebteIMWUGAaTqYpk+lXAUFOrtaEZHKyGgB1dCp1esoQuA4SZyEhW07jYxnjSbuwI9/hGgKdbeO0hi97XkehCFB4GOYGqEAXTfp6WrFNsGxNTQ1ol6vk86kCIVGzRWsXLOcStXFMATFkkci4dLakkEzNbJmHs8LQKiUCmUMy8C2TVKpkGw2SbV/CC+IME0V141/HFi2TUtbgsLoMH4QUa7WKXsR2bYu3LIgYbikbAPDSiB0h4qvsW7DAM88s4YgUMi2ZNE0qNbKKIpCrVbBMHNEwiKVSsZzk6MMpVIZUFAVldGxEpVqnZacTqQoRH78oy1OzykDsyRJ285WD8xnn30255xzzrht8+fPZ+nSpQDU63VOPfVUbrjhBlzXZfHixVx66aV0dna+5nNFUYBp6nHNWICiKkSRIAjCePWoKMTz4j+2qqKh6/EAKdu28f24ybOlpYVUKoWmaVSqZTw3IJFIYNs2ChrFQpUgjCiVyxTGCpTLVRKJJNOnTaO7p42enm4UYNWqdfRtGKBWcRFRnLAiztOt0dKax3YsDF0hmbTJZTtxLIMo9CkVC5QKBWzLIIwCQKDoGuVCld6+fsqVKqZlYVkOhUKBZDKJmYmbbB3HolxzyWQyOKZFMpmMpyJpKul0lmK5TL1eJwzjFJle4CEaSx36m/qjG/O7hYgIw4Ao8igUYNqCOajCp1QsEYYBrlegWgsRqo2q6jh2ElXTKVc8RkZrJFPJOIUmColkhpHREZIpB6VWBwQtrR10dNQZGStSrfi4rotuxClDVTUg4aTI5vNEYQCKio+OnU6iUiBjqdiGgWnl6Ruq8/iy5ynVfAIFnLRFKm2h6dDe3tZYtauGqqrssP0OjIwUGBsrNKZ0qdTdGoZuEAZ1hkdHyaRT6GpEREgURIRRgOt5//w/BEmSpNfpDakx77jjjtx5550vnER/4TRf/epX+f3vf8/NN99MNpvlxBNP5OMf/zh///vfX/N5WltbyGYzhGGAEGBbcXAyDB3btho5lRWy2RSaFtccE06isXpUSDabJp1OEwYRo6NjDA4Ooagq7e0dqOiUyzVGRsaoVGuUSkU0VaG1JcfMmTPJ59NomsDUVTZuHGbt2l7KJbcxZQd0QyOdTMZ93S0ZnKSF53rxAK3QJ5NuoVotgRKhqCG2ncS205SrNSrVOuvXr8O0TJLE6UJdt0Z3ZyddPd0knCRPP/UsGwdHMSybZDJFNpfBNA0ymk615hKGAfVaPV49KRIE9QDXc1FQiKJaY/S4ioaCokTUa3WC0ENEAXp7CqGE1Op1Bvo3omo6mmbhRwpBWI/XlM7bJJwUUSjw/BrDw0UyKYswDEkn0gg0yuUamh6nwiyuXYsIVdLJJJ5bwvUFlWqNRBI0HYqlEsmUTSKZIPA9ShUXzcxiJhwMy8DQHZ5fPcqzz23AU00sO4VpO7S15eloz2OaOpZlUyyWyOezCAEbN27E0C0y6SyFQpmWfCsjY0MURsdIOgYg4vW43Tp+vU4YCCIEnuduhX8FkiRJr4/6RhxU13W6urqaj02LnBcKBa644gp+/OMf8/73v5+FCxdy1VVXcd999/HAAw+85vNsmnYTL1ghUDXIZBO0d+RJpZx4neLQJwpdosDF1A0sw8L34z7IVMrBd2uMDg8zPDBEb28/QyMj+H6EohnU6h51N0DXNHq6O9lxh3m8bad5dHZkSCZ0WnJJVAUGBgbp6x+gWncJo4h0JkF3VxvTp/cwe/Z0Ottb4wU2UglMSycKA4aGNuK6ZVJJC10DVVep1OrxcpNhxKyZ09lpx+2YOrWdrq48lgGuW6VSKtHX10+1VicSUHf9uKatCjLZJI5jICKfUmmsmdLSdeu4dQ8NHSUSRFGApoMgwA9cqtUKtUqVoO7hmBau69Lb38/a9f2UawIvMMm3TcVJ5sjk8nR1ddDelqenq5WuzjyZVIJUIo2m2iQSaYSikM/nSSbTmGYC00ySzbSRSmbIZbJ0dnbS1d1BW3sLyVQCoSoIVSGIBH4YYlgOI0MFSiWPRLoN9Dxr+uo88tTzjNU8IkJMSyWRMNE1MAwNUKi7HuVKjTVrNvD4Y09SGivRu34DgecThgGlUolKsYymGtRdNz534BO4IaEfL2wRRj5Cibbiv4a3nnXr1nHsscfS09ODaZrMmDGDr3zlKwwPD48rt2LFCo455himTp2KZVnMmjWLww8/nEceeaRZRmnkNX/p44YbbgDg7rvvRlEUxsbGtng9t912G4sWLSKdTpNIJNhtt924+uqrm/vPPvvsLZ5n00OS3krekBrz8uXL6enpwbZt3v3ud3Peeecxffp0/vGPf+D7Pvvuu2+z7IIFC5g+fTr3338/73rXuzZ7PNd1cd0XajHFYhEAwzSaTbJxViiF9vZ2NE3F81yq1Wq8YIVpYNkWpuGgNBYv0FQVGisKmWY8t9jUDKZ0Tae1pYNqpdpI9JEkm0mRStkIQqLQi9cKjkKGR8v0bxzm+dXrKVdcDMMgk0yQzabiAV5KQL1epljw0Qydzq4ObMvAsQyq1RK6bpJMZfCDkCVLltPa1k5rawuqV6enu4dMOsWs2dMYKxaZO28WK1asZO261ZiWg6b65HJJEsk8ChG+69LZ0YHjWEybNpXRQokVK1YzPFJD0wwg7pNXlAiIKJd9fC9EQ42XkFQUQEXVTFTFQSFBPpdHVTUEcTKWbCaJH4aEUUTgB9RqBdKpNIYeUa3WKIYR6VQCRYnIZZP4QUS9WiGbzeMkUqgpnWQmz9DIKKVKjWQ6Xqci9AMsIx50Z+om2UwW01CJhEm57DI2NMby51YRCoVk2sFOWLR35NE1lYRtogjQNQM/iNA0kyCIiEKF1avXUSgUWPn8GpxkisAPIYpwXY9Zs7vI5VKE7mCcj1vES08pitbMt/3v6Pnnn+fd73432223Hddffz2zZs3imWee4Wtf+xp//OMfeeCBB2hpaeGRRx7hAx/4ADvttBP/9V//xYIFCyiVStx6662ceuqp/N///V/zmFdddRX777//uPPkcrlXdT0/+clPOPnkk/nGN77BZZddhmma3HrrrZxwwgk8/fTT/OhHP+K0007jhBNOaL5mt9124/Of/zzHHXfcVrknkvRm2+qBeY899uDqq69m/vz59PX1cc455/C+972Pp59+mv7+fkzTnPCPsrOzk/7+/i0e87zzzpvQbw000j2+ML1pUzKNej3uq43zRKuIKMIP/Hh5xNBHUxVUEjh2GsuyQegojMWDp8wEoRugKyqzpk3FSZgYpoZlaQglolIuU6u5lMsu63tHWLJ0BaVyjSAMsW2HfC7TqM1ZZNMZ3HqdlnyWlpY2UgmHkKCxepUglUwxViixctUG6l5Ie0cXmiawbQvPd0mkOsjnciSSDmFHO9OnTWV4ZJgnnniSgQGPYqmCbeSYOmU63T3dTJsepwWt1WvUvTqKJjBNHT+MawyWbeK5tTjxhh8iQgU3CDEME9tySKcStLW2kkolyGeSpFIOUeATihDf91BUnd4NfYQRKCikEklSiQyF0RKjYyVK1RqqopBOm/h+3GrR2dVO4McpQ8tund7+Iaq1OP+3omhYpknSSWKbOqapk2/JoxDfg8GhXoqjffjVIopikMvbqCZYCZOpU3poa20lYdmsXbuBKFKoVos88/RS1q9bh6ootOZbqFSqJFIqOdMinbEZHRrBMHUy6QSOpeO5IYqqoKgKAXHzvqq8IQ1Jbwlf+tKXME2TP/3pTziOA8D06dN5xzvewZw5c/jWt77FpZdeytFHH828efO455574ilmDbvssgtf+cpXxh0zl8vR1dX1mq9l3bp1nHrqqZx88sl8//vfb24/9dRTMU2Tk046iUMPPZQ99tiDVCrV3K9pGul0+nWdU5Img60emA844IDm/7/97W9njz32YMaMGdx0003Nf+iv1emnn84pp5zSfF4sFpk2bRq1Wp1kMoFpmggRxtOlqlWq1Sq6rpJKxasahVE8SjrwPQrFIoamkUw2klJEgnqtRBRBe1sHKSeBpigkUglsx8RxLHRdpVIr4YchvhvSv3GE51aspa9/jEq1ThSBYVh0dLSRzSTJ5ZMkExajI6PxKGtDxdBUqtUylVoVTddQVI2BoRJLnl3K6NgIO+20PT1Tunn++edIJhLMmTsHVVEYHh7CNA0sy8BxDBKOTi67B719fTz+6NMMDK6DsE69WiKbTqLkcyhotLZ2kE73Uym7+GEIioKqQhjFyy76XoCuxgt6TJnaQzaXxTB1fN+Lc2+LkCBw0dQIwgAI+cc/nmBsrIzvR+hanGpzhwUh5UqdQqFMzXNRVAXXq5DLpejp7gYEqhLR29vHxpFRyjUfFQMRKeiqjqfV8C2NdE87O2w/k1mzZ7Cxv5cnnniW9etXMW/2FIqjEbZh4rp1dEsjED7VaoUNtTqjwwV03aJarRMKSKezBMFaatUKXt3DsiwEFWynRFc6TUtbK6FfJ5Ww8erVeLUpVSFSFFRUFE3j37Uhe2RkhDvuuIPvfe97E/6tdnV1ccQRR3DjjTfy+c9/nmeeeYbrrrtuXFDe5NXWhl/JLbfcgu/7nHbaaRP2HX/88Zxxxhlcf/317LHHHq/r+FtqiZOkbe0Nny6Vy+XYbrvtWLFiBfvttx+e5zE2NjbuH+/GjRtf9tftprWTX2pkeAxdM3AchyhScN0qQkTN6VDVao0wDNB1HctycF0X0zRoy7eQSWepVuoMDg4xsHEEx0mTy+UxE411mh0LJ5kkiAKKhTJr1q5lQ28fg4NjDA6OUCzXUTQDQzdIpzN0d3WSSyfI51NkMg5B4JLLptF1E8O0CCKf4ZFhVM2kWqozNDRKb28fQkR0dHYxZ84sHFunq6uDRDKFk0iQTqUwtC6q1TKqEPGPD0uns6OFdMomn0myYvkqhoaLPPvMP1i1agWz58wn19KOomrkMi34nqBQKOJ6PmXPxdB0TEcn05Gira2NTCaHphkMDY8wMjpCuVIi9Gp0d2SZM3sqEI9yXrVqLevXrQIMvMb8b9PQWblyKUEgCEWIYaj4QUAYKYyOFnlerI8TvoQRq9etxQ19QiHQFANFqNiGRahDa64TXfcojPXz8INrWbt2LatWrWOP3d5BT08ewjLZdA5QQYVAeIShT6E0hoJBvebiOCkee+IJBgeHgU25wCP0Rh70TCaDrmuUikVUfERYJ/QCTDVeECPu3jBRdTMejfZvaPny5Qgh2H777Te7f/vtt2d0dJTly5cDcTfUq3H44Yc3s+1t8uyzzzJ9+vSXfd1zzz1HNpulu7t7wj7TNJk9ezbPPffcq7qGzdlSS5wkbWtv+F+gcrnMypUr+fSnP83ChQsxDIO//OUvHHLIIQAsW7aMtWvX8u53v/s1H9t1vcYc3Thjk6ZpBEHUWE0pnpJjGAaWZWEYOqZpYlkW6UQS23YYHS3Q2zdAf/8QXZ0aLW0qumWimgZeIFjfO8Cq1avp37iR3r4+ypUqvh8RBsRpMBWFpJUgl02TStikkjZWo3Yc+vEgrkJ5jEQqQzbfQirbwsaBEVY+v4ZioYyqQUd7nlw2RaVcwDRm4Loube2dWIZNGMQrKamqgYoAoWJoOroW55y2DJ1sJsXzz68hCgOWr1zPM888gaanMK0k6WyOfD6PZVg4ToJcLkcykSJh28yaMZWWtgxLly7h2Weeo1iqU6u71NwKnlumOLoBzy3Q0ZZncHCQStWju3MK5YpLoVhsrOYUMjo6TDKRIt+WJ4rCeJ1kIkRUZ3SkPw6IhkEmbVOuhdi2DSIklXRIJRJYpoWTcOhdv54nH38Sz/WxTYtpU3roam1lqL8XTYVKpUh3dw9hFKFZDoKQzo52bDvD8MYCG3oHME0by0lQq9VQNQ3LMmlpa6FerzE0NABC4CQcCKNGjvIAoTbWe9RUDC3OKhaF/96DheJVul7//pe68MILx40rAejp6XnN17W1baklTpK2ta0emE877TQ+8pGPMGPGDHp7eznrrLPQNI3DDz+cbDbLZz/7WU455RRaWlrIZDJ8+ctf5t3vfvcWB369nGQyQRCG+L6PbVsIIfA8D01TcRy7uT9OJhLgOA6maZBvycfZsESBSq1OEAh0w0I3HTQzgesHrF6zludXrqJ/Yz/VajVeEziKV0YyTQfHMUkmbbLpDPPmzCCTSmLoCo4dL7SgJVKMjBQoFatkch2YdppVa5bz+BPPUC5XSKcS8dKU+QRTprSjaypDg0PMnD6LSrXOwMYhdF0jk0kzMjyMroCuqximTmtrDtNIoGsVVAXmz5/baIGIWLGqlyhQ8VFYv6bI0EB8HNM2sR0Ht1Knr15FoUa1lmFgYA3V6gjDg2MEoSASPoriEykRgwMbSdg6tWoVvx6QybbR0dFJJEL6+nsxTQ3P82nJt9LR1UGlUokXskjYVMpF6rU6pmEyMDBMrVonk2rFNFSmT2snldKoVSvoukOlFjA2WqK3dwBDV9jlvXuyw3YzGRsdgCDk+RUr2G2PXenqbqPm1tENDU3XGBst43k+Y4USS597jvW9faiajm6auF48dS0MQtLpDLlcniAIGe7rpSVrkrBNVMVDKBphqKMZJpqRQNNton/Ttuy5c+eiKApLlizh4IMPnrB/yZIl5PN5tttuOwCWLl3KO97xjlc8bldXF3Pnzn3N17PddttRKBTo7e2dEMg9z2PlypXss88+r/m4m2ypJU6StrWtHpjXr1/P4YcfzvDwMO3t7bz3ve/lgQceoL29HYh/PauqyiGHHDIuwcjrkUylSDg2qqoRhvGAr7jJTDQfQRCv/0skMAydVKqNRCqN78UpOUEjm2slm28jlWkhCAVr129k+fI1jIwUqNV83E3LRBo6mUyatrZ2dE3Btg3aW9tpyWewLRNFRNTrFVRVxXIStLR2kM0pRIrOht5BVq1ZT6lSIZdNkc068aIJWkjg1xkdKuLVPObOXUAUDsdNsX7A2tVrcD2XwsgofX29dHd3MW/eXJKpBPWai6qZ8QINbTlmzZqC7disWL4e1w3QFIN6LSCKBHpVxwujRoYrj7FCP/PmTUPVBFOmtOHW4znbpqGRy7cgCOhsbyObzRL6PoET4SQtIgIs3WDGzClEgU+1Hmcr6+1dx7q161AUQU9XO7NnTaO9u51Suc7Q4DCFosvQYBVDh5ZsAsdKMmP6DIoll2UrljA4NIqua+yw/Sw++ME9eX7Z0/hehXKlRld3D+VymWeefYowEui6Ra3uURgrU6n6pNN5TMui7tZRNQMRRbieRxgEOLZDve5Rq3pxTU+N2HmHGei6QhjFec9VI4FiWKi6japZEPx7Zv5qbW1lv/3249JLL+WrX/3quH7m/v5+rr32Wj7zmc+wyy67sMMOO3DBBRfwyU9+ckI/80u7ql6vQw45hG984xtccMEFXHDBBeP2XX755VQqFQ4//PB/+jySNNls9cC8aX7ilti2zSWXXMIll1zyT58rbPwBDYOwsTBCPFArbNSSa7UarutRrdZRFOju7iabzcb5kMOIIBCYpkMq3UJbZzeW5bBm7Xo2rOujWq4iogjT0HGcNKmkTa4lg6Gpcb7qZBJd0+IBQ0Tous6aNavZuLGP6TOmk1V1dNNGeILhkQJPPbuM/r4NaCqIyMXQbLLZFEYjAGezeYIwZOnSJcyaNR3TtBqrYsFTTz3FU48/gZNMkm1pYUP/ALlsjtaWNGAQ+B61mksmkyKVSpFNp1mztpdaPUIoJsPDBfwwpFKpx4tVEFF1I6r1EvO3m4Vtm0yZkqenO4dX9xktldA0g8JYidJYkYRto2kapVKR3r6NVGsulumQyeQIQp9ICMrlEqEXkM8mIahTK4+SdvQ4CYsButFYJUqLCEQdQZIVz69lyZLVDA6XCQLo7OjkM58+knzGZEUUMDw8TN0LyLdkGBweIJFMkk7l6dswhBAKzy5dTqlcw/UCvCCg7nqEURXHdvD8iECJ2LhxCMeysC0fO+kwd94MWlqyIOpx+k3NIlJNUAw0xURVDMLX2FT7r+SnP/0pe+65J4sXL+a73/3uuOlSU6ZM4Xvf+x6KonDVVVex77778r73vY9vfetbLFiwgHK5zP/+7//ypz/9adx0qbGxsQmzLtLpOJXuJk899RTpdLr5XFEUdt55Z84//3xOPfVUbNvm05/+NIZhcOutt3LGGWdw6qmnvu6BX5I0mb2lR7nU6y6qGk8DQtEIQxXD0HEcp7mikhAwODhCNptpbg8Cn1K5TK3ukm9po71jCpqqMTg0xMDAAIHvY1k6tp0kmWwlkTBJJi0s06BSLiNEhIYglXBQiFcy8n0fPwiwnSTJdIZIUSkWyoyMlujrG6A4OkrguVhJi8ivoYoMGioaGv0bNjJ1+lQ0TWF0dIiWljSarmNoOsmETTqVxHIsavU61UqVWtKlWt3Ic8uX0tqaoqerk2Qyi66ZVCslEgkHJ2FQqXqk0i2sfH4dq9YO4o+61LwaYegjhILrmqxd18fOb98OyxREfsDGcpHiWIkg0ghcF1UETJ3axdveviOKImjryNO/cZAoVFAVk4HBCqEIWTB/Hp3tGTraMvheGUNXaW9vx0nkaG1rZ3iswlixREtLnqk9nfzx979ndLRIvrWTTC5JLpejrSVDPptmw9qVjBVcNg4WcX2XbFsGw7YZGhrj2adXYxoJbNtk5vSpVGs+lZpH1fVZs24dtVKFIKgQhSHJxkpbgR8xWBhGqNDamiEMOwnVCFXTCYWGgk4k4h9ZIlKI3pi8O28J8+bN45FHHuGss87isMMOY2RkhK6uLj72sY9x1lln0dLSAsDuu+/OI488wve+9z2OO+44hoaG6O7uZs899+Siiy4ad8xjjjlmwnnOO+88vvnNbzaf77XXXuP2x+NFAk4++WRmz57Nj370Iy6++GLCMGTHHXfksssu2+xxJelfwVs6MAsRkclkMEwNTQXD1NE0FdMwCcKw0RSnMG3qNFpbW8nn8iBgdHSU4aEiqmbT3tKGZlgMjY4yOlJAQdCSz9LensXzaqiNecepZIIg8FBVJV7sIhQEvsAwoF6vYRg6c+fNJogEpXKNFc+tZEPvAKBQrlSo1avYtklHWwuKEiBCn4RtYZomnu+zYcN6pk2bgufXKZYKdHV2E/jx4huGrtPa2obn+wwMDrBhQx+tbe1MndZNoVCkWllDa0uOrq4OUASeV2X7Hebhuh69fRvZfsEUSpUykcizau0Yvu+iKAZ1V7Cxb4jljslOO82js72Njs6p1Nwn2bBhGEO1SCdzzJszjwXbzUbTI4rlNpLJ3QlDjSVLn2ekUMSvhYyNjbHdvJm0dbRjWypR6DFrxjRqdY8nn3mW++57kCBUaW1tY0pXFwvmz8dyLLabP5++/gEee/QJBvp6eeShfzA6PMDKlevxfVA1DRQwTQvHCuloN1EVjUq1hGEYJFWdbEue/sERcrkMnufG4wlsm7aWFmzTJJlw0DUNRYloaUljmDohEYqiEUYaIlAwNB0NA4EO2r9vYAaYMWPGuMxaW7LddttxzTXXvGyZVxootvfee79imYMOOoiDDjroFa9nk9WrV7/qspI0Gb21AzNKPOLa1HFMHdsxCcMwnstcdxuJBrJ0tKdxLBtd0aiWKxRGixiGQ3dLB14A6zasp1ytYuomlm3Fax2bKkrSYHhokJHhAmEQ55oGSKcMHMdGVQyq1QqmZZBLJePpOJUaxUKJvr4hiuUatuMQAV7gk3KsuDZra6SS/z97fx5uWVrW98OfZ81r7Xk4c81jzwM9Ic2kgiL+RHkxiEoiivgaxTdcKij+FIMaOmo0icRcaq4maAaMGSBGlIBI2wgNdjfdTdPdVd3VdarqzGfPe695eJ73j3Uo0zLENmhb4Xyuq+rUXmvtvdfetarudd/PfX+/LuPpiEazweLKAnmeEUYxQRCSxClRmNBstun3+xRFwfETJ/jc5z7HoD8gCMr14Ml4xMqBRRoLLaI45fzqKo5r0WrW0A0TC0Wz4TEcjrjp+uP0hzGdjscDDz5EUUjqtQpFVrB+sYfKYLOzQ3euxenTRzly+DCXLmxQJBnDfo84WOGm513DpbUNZrOMwXDGIw8/xm5vRKVS5eyZC2ys7wKK5QPz2Jbk5V//Iu64/TZOHj9JEOSsrm4xnU4JZjNuv+MWbEcnz1IsPaeIY4a9EX/8kXuxbEEhc7I8puLYTMdTXNemVqsiixlFUeC6JktL87TabXojn0kwxfMsPM8hmAUIYHFxAdvS6XbbtFoNNrfXWFqeQwqDQglSqaEQWLpNrkx0YWJZDir/Ku3+2mefff5OcEUH5jSX5IXANR1000ShkxcJo3FIkqS0Ox1qtTa1ahUN8AOfKA6o1ZvUW0uEsWD9/AVms1k5RlXzsE2BkDmaBrJI6XQ6aJpgOBxTrVbxPI+iKFXFDNO67AWdpSmm6ZKlCdvbm0RRgGGY6Hrp8asbOpWqi1QFQjMYjgbs7GxjbVucOHkSAA3BwsLC3qz3jDjOGI/HrK+vY1kWW1vbxElMGIY4jku/PyTPc0zTwqtY1Koussjp9cY4joVl6hSFIgxCwiijWW/yotf9fziwuMj9Dz5KvdpiMhoDEiFTZBbRaixz4vhxLl3a4NGHP02n1eam553G9jTWN9c4e+48f/ShT5KmFuNJjOvWcQybueOn2NrawqvW2dieEYVjxtMP0Z1bIgwjbrvtVtLiz9n9zA6fefizTGYz/GBCs14jTXIct0qa7+LHM4xcoygSPM+hUnHJ84JKpcqRo0fI05yNjU1sx0UqWZqXWCYHlxeJgoDQ95FpiiY0/NkYu9XA0BSWKahWPTrzc6RZSoEOqvSezjUDy7AQpgOGBV+1EiP77LPP3wWu6MCc5ZIgTkDTSMwMQ9cIg6CUh5z5KAyazS6tpoEmNFyvQqVeo9NZIM1Nzq2eLUU/hMK2DBxLRxcFhm2SJTFFUWDbNo1GE00ry+SVSnVPb1tg2QatVoOiKLBMnSgKAUWn02IyCQmTgrwoQEksU8NxLNIkRqvZzHXaSJljGCZRGJFlGUarhWVZe2NdDp5XQQiNyWTGuXNPEscxvh8QBhFxnGGaJqPRCKUKmq06hw8vY+oag0GfNE3otJt0u20M3SJLA+Io5MKFVV728q9nZ3fIaDShO1ejUa/w0pe+kJUDCyRJhO+HLC4sctNN13HTjdexcmCO86tPsTsqzTqmvo/vS2rNDjdefxWb6xdpNBz6u6CJnNMnj/DYY49gWTYf+qM/ZjgaY1gux08cp9Zo8dhj51CYSJlwzz2fwNQNTpw4zrGThxFauXafpjEgcTyPWtWlXm/gOA56RcO0LKYznzzPqdXqVOsalUrIpYuXWF5aoNVoEgURIOn3d2m2qhw+dhNXeVex1dslSTI0rbz0hW6CboFpIUwLzbIQ4quzK3ufffb5u8EVHZht1yNJcyxTkuUxSRyTxqUOs66bl+cUyz87VKo1KvU6hmkzWOuzvd1HKWg2aliGQehP8TwT3w/Z3dnBtiyWF5ep1xtAKToRxzGj8QiE4vTpUywtLZPGGb3+LrpeanIvzHeRSqPXm7Kzuwsyp9Oqs7I8j23qjIa7VN1Sjapeb2CaJkmSoOsa/myGZdv0egOazRa2XTZ/RVFEEPhUKlUM3SQMIyaTMZqmUa9XWDmwxHg8AVlQq1ap12rYtoUsoFqpMRpPmcx8wnSN3f6UerPGdDbhxMmjrKwsMrcwxxNnnuKhhz7LYDhB1w0sS3C7Z7O1u0OzPY8Z5Fx9zQqIJh/+44/S6bpU6wIlQgyjynU3nKbfH9Fq1bnm6lNcf93VBLMpT55b5dLGEzxx9kluvvEWkghms4hbb70eTVPIogCt/KXpGrW6R5GXywQzf0arUSfPCzY2Nul0SmONhYUFoijF90Ns28ZzXea6Xba2d7HqNp7jMJ1O8aoemcwRuqA/HuEnGUqzyP8X0w5hOmiWi2a7CMNEI39Or+t99tnnq5srOjAvLMwBCsuxqXgueZqQxFG5BunYzM3N0WzUcF2XPFMgBLbj4QcRa+sbhFEpm1nxHPI0BpmjaSaOVTocGf+LNKNpll3eQkDFq2A5NtV6k82NHT7z4CP0B7vUGy4HDi6xsLDEwnwXw7Ap8pSRVlCtOsg8I1UZ1WoV13Xxw5DpdFqOXhk6RZYRxxFplpHnOb3eDt3uHI7rsLAwT78vOHr0KJ5X5eLFS+zu9Gg06ngVl83NDZrNKitLS2hCkKYJjXoZnLe2Sw3gyXjMZLZDlgl8Py7tHn2fyWhEHKXcd9/9DIZTDNtlMNjlxhuv5kP/889otxssL63w5NlV2p1FshR0YaDyHApoNdoURYYfDFjf2uGpC2tYlsHi4gIrSy2++ZVfx71/dh8XLlzi4Yce4MDKEWazEff+6Ue59ppTtFpN/NmUKAoxLZ1a1cP3Z7iuw4kTJ7ENkyxLUeSMRhOiKMIwbSrVJru9IXmSkEtJXkiq1Rqbm9voukGeF+R56Zw1nvqESYwSBmJPHlLTS2ERNBOhm3vZs4kqvnrHpfaBz73zG6nX68/1aezzVcwVHZiPHztOFEXousZct4tQkigs1bDq9RqWaWDoBkJozPwJhmXRzAqCIGIwGJFkCYoKmiZQKmc6GZGmIY1Gk1qlhuu42I7FZDIGJGkaU2/WWD6wjONU2Nkd8sgjn+P806tEUYgmUqIoxLYcvGodyxQ0Gh5FEWKYGrouqHgOUTAjjmMMw0BKSZompCmYusZsNsG0HRzbIY4TBoMejUaDO+98Pu1OhyzNeOSRz9Jq1TEMjQMHDnDg4DJpGjGbThhPRlRcFykLGs0G1YrLdDZGygJTDEj8GVmukYcJTc9jodvh8MElhr0tDh+c55bn3cBnPvsYQmuzurpGUUiuvqrK2TOfYjDo053bRSkIg4jZJObChXUqnsPWxuaeQIdBFM/wg5T777+fpW9+GYahuPNrbuLqq44SRylCaJjGIv1Bj7lugygMadWbjPojVGYxM6eYtomU8OTZc1Q9j6WlRRaXlojjkDjNefr8BVy3ThjEUEgcz8UwLBoNjzBI2d3dZTLzMR0bx6uSFhJ0A6FZFFLtSbha6KaNabulMAmluQhC/99devvss88+f2Nc0YG50+1iWxa6ZmCaBkoWtNtdLEMvtZxljq7pSCWYTn0c1yFNy/LndDpF08CyDIRQuI6NbVlsb2+TJinNRpNevwcokiTAMDXa7TZC7DV+GRYKg9HIJ8sVeS5x3bJsrusmFddjOpnSaddpt2rkRcp0PMKxLareHNPpFCEEWZZhWiaykOiiFFaI43BvPdtDSomuC0zLwLYN0jTi4qVV4ihhaWmJo0cPoVTBLIkpioxGo87RI0co8hxNF0ynU2q1CnmRkSYpluWyudljcb7LqZPHOXhwiTSLWFlp4VWrbO/0eMmLb+NP//RT7IZTrr3qeja3tri0donTVx/n1ltu4czZJ7Hsa1lf73H2yQtcfdVJNrf76JrJrbffQZzErK0/zZGD81imwrZ1HLdCtVpBUPpCG7rBynKH6WRCu1lj7dI2u9s9VpaXQSk0NDy3SpZKptMZvV6PND2N69kcPnyMue4yjz76GHGc0O10sCyb3mDIeDxhe2eX2WyGbhgYls3pq64hJ8fUDDTNRFfl96zrRmkyYph7jXo6qL8Qrtlnn332eS64ogNzHMe0mg0qlSpFISkKia5p6AKyJEUqQBhoAjRNx3NLm0elcgxDQ9cEjm1jWaWYR7vTKr2H04yiKAC5Z3zg0GzWqFarmJaLZVnkhSSKYvJComsGnudRa7h0OnNUqzWyLEMA7VaD4bBPniakScK2PyNNEyzLolqrI7yymUkTAn86QdMEumkymYwJ9KAURCkyunMtKhWPIPBZWJhnNg3oD/psbm0wPz9XNkbpglqtRn8woFGv0251SNKI3d4OWVbgVqqsVGpYtkW73cF1DXQTDHSkUqxvbtNstdnZ2uXooUVuufk6XLfChYtnOHR4kcOHV5j6I2r1GhubffqD0tt4PJ7i2DaB73PhwnmkAs/12NnZZmuzQaNZxzAdzp27SLVapdVuoItSgazTbRGGAUtL87hOhdnMR0oYDEZYjku9XieNI4pCZ2Njg+XlZSbjKb1en1arxfz8Ar1eHyUUi4sLxGlaSqc26iilWFlewvM8oixCGDpCgkSgaXq5jm4amIZZ+jHnOUpoFPn+GvM+++zz3HFlB+Z0gh/ZaBYIdAzdRNc1ZAEFgkKBqenomka9VsdxHJQssC0bx7bwpyGyyClyRRqX5ggVzyHVdYRQVKulr7JbsRGawnMr5IVGFCbs9Mace/o8URxhOiau63FwZZ5DR45gOx5bG5usr10iSwI2tzf2Op3n6bYX2NhYJy8KpFJkWUaapXiug0QRxzGepmEaNqPRGMdxSLOExx57nG63i2VZaJpGf9Bna3OL0WjEoUMHcV0bKQtazRbj4ZBbb72VQgm2tvoMRyNGoylepYIQcOzEUXRDI0szJtOA0XDCZx56lN5gzLd9+7cjNIulpS433Xwzm1u73H77LSSp5P77H2Q6m2FbLp7XwtAEp44ewDJyuvVlhtMphq3zucefKpvSPJv1tRGNWpfdwQ6fefBhogQ81+WG66/hyNEVoiTDrdSYTncZjPocOHAQIcCyXYIgQChJkkQsLMwxnZZLAP7UJwoj5ubn8f0ZeR5DUjAY9hgNdlFFim1aVGtVTp0+yed100sTEoEmNISmEEIhZUaWCzSpl1KtQFGkz/GVvc8++3w1c0UHZs3MifMZcpajaxa1ShPTLud3hQZ+niJ0DaUgSRN0Q1CtediWiee5KIZEcUieFSiZlRm0Y1GpuIRBBBQYukAWOfVaHZQgDCO2twc8fX6drV6fOAlxXZv5xQ7HThyl2y3XgdMkJU0zojii0WiQ5aULVl7k1Op1EKWmdyElQRCQxBGVikscF8hCEaYhSkFRFOi6i5SK++77FDfffDOWZaGkLJ+fS2ShcJ0KuqGxsbGDEBAnBR//s/tI0pjnP/92ZuMJtmUxno6YTidYmGhoLM7P49ouYRhyJM84cniB8XjMXKdLrz/mc4+d5eKFDfq9Ab2dLQ4cXObmm25ASg2dhFrVQOYZtZpDd75FhqDerKEJwWK3yeJ8kyCc0ppr8+KvezF/+If3sLE1IAg/S3844fCRJRYX5zHsCvVWgzCJiKOMM2ee4vjxI8RxRL1RpSgyQJGmGcPBkHqzyfzCPHPzbSxLZ219Dcc2ueGGa8izgsFgSLVWY2GuQ64KBOXMswSU0kAWFKIcZ9PyDMMwsSwLXQiybD8w77PPPs8dV3RgNmyF0BOSvMDQbKJUQ2gSx6pQSMV44pPnCtcx0HRKUwjdoN1wWVlcYDQYoWsKlCJNUsJgRrfbZH7+AM1mgaBUZ7QcCyE0CgV+GLO2sUMYJ1iGTqNRpV6vcPTIYZrNBghFs1nn6LHDNBoVsjzGD6YsLMwThRFBFGEY5XhUkiZIJZFFjjQEQRBi6DamYeK6JuPxhKKQKCmwLZfpZMbjj53h8KGj7Gz36PWGTCYzdnd7HD16FE3T8Dyf8XjCH3/0T7jxxuu5+ebnsby0zPkg4MLq6l7Xd8Lhw4dIkjIA7fZ2qNY9WraF7w/RdZ21zV3+6I8+wsWLa9x+6/M4dvQAWXyMG264lp3dPv3hmMOHl0nznMFgglfTCOOMnd0+Mz/gebfcyK233kgYTlCDHNMysGx48Uvu4Mmzazx5dpX7H3yU7Z0BYRRw+vRxvvZr72D13Hne/z8+ghDw6BPnuP2Wazh1cgXd0LFsG4TAq1To9Xap1j1cz+bgwSUOHlxhc2sbx/Hw/YClpQVcr0Kr1WAymxGEPplKSYqySxulI4SOpptYpoUsclSRYxgGSRo/txf2Pvvs81XNFR2YERK0Ah0BoiDNAkAShimDnRmXLm2yuDDHsaNLdOc66JoBCnRNY3G+w852neFwSL1eoTm/gGAOoRUYhk7FdbFMkyRLUcDMDxmOp2xuDpiFCfMLi5dFQ0xTp1GvEwYh/V6P5eUFTEvDdi1m/SnjyRRNE7iei6ZpjEdjeru7xHHE3HynbEADhNAY9AeoliTNS6tJXTdJ04w0LUeFtrd3uemmm7jqqqtZXb1IkqRYlsXFixepVCrouo6UBddddy2LS4vMzXWZ+TN83yfLEioVFxeHIAyJophWJ6e7ME8QhjSbTTqdOS5e3OKjH/skq6sbuJ7DsWMrzHc8bGOBdrPJxdUt7vvkZxCWjdBN4jhmPFujSBWtVofJaJcL59ZYmlukP9hhOhty7NgB5rpt5ucsHNvGcSzW13fZ2RkQRjNe/OI7GA76nH3yaSqew8u/9hZ2+hMeeuQs1113DMfxaLUrTKc+SZaCgN3dHSzbpFp1WV5a5vTp00RRRK3m7ekv6wihMT83z9SfEo5n5JSe2ppmoWkmssjRkAipIfOUVAgi339ur+t99tnnq5orOjArDYShoQkNColUOVmeMR0FnHnyIrs7QzShcejgIlKCLHIsQycIEixLsLzcRYiySatacak3quh6acKeFUXpWhSEDEcTtrZ3uLi+hZIGlVqTbneBdtPDsU0s20SpgieeeJyNtUuEvs/KyjKDwYiHH/4so9GAY8eP4LlVilximhadTpc8z7AsgyRNSOIUTSs7sX1/Sr3eJAhDXNdlZ2ebnZ1tarUao9GQhx/+DK94xSu49tprOX9+FSklYRiilKQ71+HkqeMoCmSRE858dne36ff7JFmC7dqkSYqUCoFGGIbUazWOHD6GEoKLF9f48/s/wyOPPornuDz/1ps4dGCJatXE0E3OnLnIdn9KLg2OHzzGQw89RlEoWq0ali3Ji4AXvug2ZtOQ//r+P2A8HlEUOYcPH+LrvvYFFHKMpklued51aOJz7O4OuHBxTBqHyKLCZBpy9PASnfYcSpkE/iPUKi00oTEajBCaRpokZGkMVRtdF7iuy2w2YzLxmZ+fZ3l5Ecdx2draJQwDbNfh0KED7PTWSfIEoWloeoYQBrpmovKUUs5NoaTEn+0H5n322ee544oOzEKAEAqhlc092t740WA4YHNrmzSWpHnBdBYzHvVB5SzNd0HkZHlKteZwzDtEkRXoe01ihVIEQUIQxAwGI5588mmGowmjyRSBVs4n2xl5luLYDTzXRqoC07Ro1GqcTxLCMCBJYiaTUgzD9wN2tndKVyopWVvbQNME83MdbMel0Wgym81IkgRZFNTrDXIpyfKc8XjCbDZDKUVRFCwvL+G6LhcvXuDGG2+g050jCn0ajTqtdoPpdELoB5w8dYqF+XmEACkVSimUgiiKcByHTru7J8CRM9ddYDKdESdlZv7ZRx8jThJOHjvC9ddejYZGEOR8+sEHGY0Cnjq3yk0334BjGSzOt7lwcYs0Tjl67BCup7O0soBpBWSfe4o4yShkwcVL23zmgc/ygq+5CattYpoOt99+E5sbO0ymM3Z7Ixo1j3PnLvFdf++VtFsd1td7mJZJEqfkWYZjW+VNU5qwuLiA0CQyz/BnPr4fEoYR0+mULD2AYZrU621cTyOJI/xgiiqKslEMgRAJQtPRhEEqBEiFVIqiyAhmwXN9ae+zzz5fxVzRgVnTNXRdo6xPl122WZQyHI2YTGdowsYPIibTgKefvkgUTtCuv5pa1SXLE2zbxLJsNEtgaAZpkvP4Y2d4+JHP4vsxQZSQZWVZ2XFcPNfDq3iYFsTRhCzxmGUBaZpSq1Wp1zwOHlyh19uhUnFYXJxnPB6gyJmb7yKEoNcbMhgMSdOEMAxZWJhD1wRKSZI0JQpDHMsmTksVsFqtRq1Wx3HsclzLNHEcmzAKWV29QLszj+c6jCcDjhw9wFVXnWQyGmNbFnmeMhqVJvX9/i7NZp2NzU2OHjlKGIU06k16gwFFUaCUQiA4f36VPM+oViucOnWc+fkOYeDzR3/0UR56/CmmQYTnOaAXLCx06LZv4w//8KOMRiN2tw0OHVlm7dIlzjyxjmHoXHPNaRaWOkR+QpGE6EIjShJ2dnaYziIWlw7wojvv4H988CN84pMPsrI0jyYgihOmQcBct4VUOV6lnDNPktItSinJ1J/Q7XYQmomUGq5bJY4TzjxxhvmFBXZ3B1i2h+u4ZEVCnufIIqUoMqSUKKEhlIaUspxfziR5mu01/u2zzz77PDdc0YG5zHzKzBkUuqZQSPwwJE4zTMMkCCPSNEWh2N7ZpdNpsbK0gGUZZKlEqQwdnd54SJrmbGzusrU1wA8iCiUwLYdms8X8QpeVlSXarQZ+MKW/u02/L/ZUuxKuuuo0i4tzNBpVnnrqSYoiI81illeWqNUreJ6HLODgQYeikGxtbRKGIVEcUal4uF4Fx/NQCuIoRtdMPNcgjjKKHDRhIGoaruuSJDGj0ZgwSnn+3DIYEiHK4K5UwcJClzRNGQ5GBL6PJjS63TmmsykLC0sUUpFmOX4YUK/XieIYJSXTyZg4Cjl08ACNZoObb7iGLI3Y3FznscefII5z5ubanDhxjMD3GY9M5ubaHDk6R7NZAU1SZJI4Cwj9CboBJ0+ucOTIAUbDEQaCOAmIktKM49FHz7CzO+HQocP84P/3DURhSJ4mmLaHHwQcPLRIp1MBkROEMbX6UqkIVihq1Qa2Y5NmGZ1uDct20TUD359hOwaFlGRZTuAnTPQZXsXGNAyCOCPLI7I8pZASgUaRSVRRBuYizYmC/eavffbZ57njig7MutDQ9jJmIcpydlHkxHECSpHnGUEwIQwnLC3OMezvcHF1jSItqDcbtNpN6pZLFEU8ceZcuU4rdSzbRYVlsMqyhCDw6fcVtarLiWMHOXCgy7BTJY9jstRAN+rouo7vz0jTjFqtjpQFhmUihKBS8TA0nc2tbbIkp+q5HFxZQTd14iRka2sL3/exLAfX8XBdD9AI/YA4ial4GiKMSbd7XFrbwHEcsiwlCCJWV1f5xm/8emb+It1uAykLxuMxUkqCMLjskBWEIbKAWq2BrmtlM5rr7r1WQr3ZoNlp0ey2uH40pdffYTzuo+oO/eEmzbbDtYeOcNvtd3L2iaf49Cc+xXrF4/obTtFq1liYX+Lppy/gTwPa7Sa3PO8U7U6dpaUutiWoH+hi6DpZlpMWGXmukWYajz+xSn8Q8OIX30m/N+bME2dZXlpmbr5JvenRnXPJihRN0xlPplQqNUzLxq1W0UyLpm3hei7FeApKw3E8LMuiXq+xsbFNf9BnOp1y6MgKpuUgVU5axORFRp6nZe9BBnkqkZkiTwricH9c6tnyhje8gfF4zAc+8IEv2HfkyBHe8pa38Ja3vOXytk9+8pP8wi/8Avfddx9RFHHy5Em+93u/l3/0j/5RqcC2hxCl5/rZs2c5fPjw5e3f9m3fRrPZ5L3vfS8AvV6Pd7zjHXzwgx9kZ2eHVqvFjTfeyDve8Q7uvPPOv6mPvc8+fyNc0YG5LL+C0LS9bDEnz1LiKEIWEsssx50gI4xi6rUK4+GExx8/g2nZHD12nAMHDuC5DtVamyBIWFyaw/Zszq9eYDAYUCgIwxlpGlCrOORpgl51aDVbRH7A7s4OUoE/CxiPJximQZZme1KaJqEfMZ2NGQ76zGZlc5Jl6xRSJ0liTEPHdRxq1RqeVyGOc5QS9AdDlFTkBUymAYPRmCxLcZzSbUrKAiXhwoWLZFnOsWPHSRKfKPRBaPhBgGFaVGs6tuPgVSo4rkcYhKRZQr1eQ9M0NF0nzTK2d3aYX1giTVJMXWO+2yWKfAzTYeXgAb7r1HHarS667jDa3uZ5N17PpQuXQMJCt8v51XXGgwmmZbGytIDjQLftYmoFtm5RFAV5njK/uMTUj9ENG6kMhLCJE8X73//7e5rk0B/4mJZOIROazQa33XItrmtSrbnkhUTkOVmWU61VcT0XVRTU63XyTF32vw6CCN9PuHRpi43NDbxqlaMnVtgenEeqgkwlFCrfm20W5LIgyxSyUGR59hxe1f/38/73v5/Xvva1fO/3fi8f+9jHaDab/PEf/zFve9vbuO+++/i93/s9RFkGA8rg/I53vIPf/u3f/pKv+ZrXvIY0Tfnt3/5tjh07xs7ODh/96EcZDAZ/Gx9pn32+olzRgVkAyLKbFiVREoosRxUSU9epVUo1roX5NsPBkL7MaTbrZFnG9k6PWRDRGwypVWvEUYTr1ZBKsrjUpdGsEEYhUiouXrjEpUtr5FnMZDwmz9Iy+0tTxuMZ6+vrNJtNoiRhOByUClJFzsrKCq1Wk26ni22ZxNF5wiCgUq3geRbduRZKSpIkJc8lvh/R6w2YBRFRFGGZNoZhUsiCoshJ04S8UCgJhqEjhCAMQrK95rUkyQmCECklo/EYFGR7a9UKgWU7pS63yrEsi/5gwNLyARAG1VqNra0ek8kUy7J44IGHCIOAq68+xcrKHEcOL1OvefR7Y267+VpuvPYqNjc3SNOEOEmoeDbzc13CMAWlUa97WIZB1atiGhb1WhXDMVC6QXeuS21jgqFbVKsVRqNdNA2e/4IX8+ef+jOEMIgTSZpm+P4mB1aWaLeqmKbNkcMHy25220IIKIqM0Pdx7HJUzLb3ZtjHAz73+DlWV9dJs5St7SHzi10yKVF7NySFylBCUhRQ6IpCFKAJpL7vLvU3RRAEvOlNb+JVr3oVv/Vbv3V5+/d///ezsLDAq171Kn7v936P7/iO77i8781vfjO/+qu/ylvf+lauu+66L3jN8XjMxz/+ce655x5e8pKXAHD48GFuv/32L3suSZKQJMnlx9Pp9P/04+2zz1eEKzowq7J6jSpkmTFLMA0DUzfxHMnRwwc4fuwQ1aqDpQvajRppkhOEB3nq3HmeXr3I1vYmfdMmDmOiMMBxNU6ePEa706RatZFFwdEjK7QaFQb9IaurT7O8tESWpViWSa1ZxR5aaIagU20DkiSJCaOCokgxLR3Xtem2D+HaJkmaYrs2jlNKaCoFs2nIcDBhPJ7Q6/WJkqzsGNZN0jhC0zTiOEYpRZqFJEmMbVu0W20qlQp5nu11XMdMpqWK2Pb2zl4X9zKuDnGcoFSB5TjksiDJsr2msgqTqc9T585y8dI6CkGSlBKgnu2haQ7Hjh5jZfkwQmXIFPzZjDwLsK0EU9eIgpRG3WM8CDBqNVAgENTqDYbjGaZusbC0RLNdJwhimk25p+vtMZ6mJHHA0oEjSFmulTdadXZ3h+iaRRQHXH311Qz6WyRxxGQ8ojvXwbEtxF5JHmAyHZNlUK0Izp27wKfv/8ye37aOQmM8CRhNfKTSELqBEAaapoMUaEJhaqU/s8wURqE9l5f1/9V8+MMfZjAY8OM//uNfsO9bvuVbOHXqFO973/ueEZjvvPNOnnzySX7yJ3+SP/iDP/iC51WrVarVKh/4wAd4/vOfj23bf6Vzueuuu3jnO9/51/8w++zzN8QVHZjzrBThkLJsekJKpJQYmqBZr3PwwBKua5GlKY5t4dgWAE4QYtgn8WoVBsMJw+GUKEnwg5DxOCLPUw6sLFKrubiOjWUYNJs1XMemUa/jehZ6ujeipWmcOHGUoiio1eosr8wxm83IsgypMhAFvj8lTyPmF+Yuy2JGYUihJM1mG6U0trf7hEFUlvAESArCKETXDXRNwzTNvaw5R6Gj6yZxnDAcjvZMMwqSJCHPC3TDwvOqVGtVNEPDMPUy6CHIi4IoCnHdDouLSzz6uSdIkpw/+djHyXOJVBpxEhHFKbrm4c8iqpUaqihtLy3TwnOrhP6MZqPOcDBGypRmo8qwPiNJFWE0QzcbpJlkNosYT3ZYXlmhWqtjmQ5xtFs2hE0i1tb7bG+d3xNT0RGahlexcZyy0S3v+dx407VonKJacSiylDiJy0x7PEIhcT0bITSkLHj44c/y0COPMRhOyTLK1xQ6vd6I9a0dzKpWBmdDR8dCUwVCU0gddEMjT3LQ9jPmvymefPJJAK6++uovuv+qq666fMz/yl133cUNN9zAxz/+cV70ohc9Y59hGLz3ve/lTW96E7/xG7/B8573PF7ykpfwute9jhtuuOFLnsvb3/52fvRHf/Ty4+l0ysGDB/86H2uffb6iPOvU4N577+VbvuVbWF5eRgjxBc0eSine8Y53sLRUztu+7GUv46mnnnrGMcPhkO/+7u+mXq/TbDZ54xvfiP/XUFsa9qakSSmALJAgcgxDYtmCZqvC4uIcqsiJ/RlZEqNrApTEc21ajSrXnD7OHbfexM03XMPhA/OYpgSVMxkPmUwHQE6aRgRBQOiHDHoDdnZ26PV2UKogiSO2Nja4eOECmhB4rkORZ8ymY/IsxtA10iSkt7vJpUurKJWTZjFJHOP7s72yeAaywLYMup0GQpNoOshcomSOlDlCKCpVh0rFpd1q0Go28CoeSghypXjqqad56qknieOQdrOODtSrDRzbQ9fMPSlSE1UIDF2n2aiTZSm9/oDpLORPP/5pBgMf122haw5gUq+3iKOM0WjC2qU1ZrMZUmqYtotbq2K6DgUCw7EwTB0/CEqHJpmiRMFoMsW0XIIgorfbo1ZvEIUppmmzMNel3a5iGAWOa+K6FWbTEZAjgEMrcywvtUBlnDp1nNlkQBDMmJvrcuLkSU4cP4Gh69RqVVrNJo7jEMYp6xs91rZ69AYzgignK0rLziQNMA2NpYUlPK9KKa1ioQsLXbMwTAvDMrEcE93W0W3zWV+L+zw7SmW2vzrXXHMN/+Af/AN+8id/8ovuf81rXsPm5ia///u/zyte8Qruuecenve8511uDvti2LZNvV5/xq999vm7wLMOzEEQcOONN/Lrv/7rX3T/L/3SL/Frv/Zr/MZv/Aaf/vSnqVQqfOM3fiNx/BcjKN/93d/NY489xkc+8hH+4A/+gHvvvZcf+IEfeNYn39+eEE5TilyUzkFIhCaxbY1mw6NRq1CvVqlVKtiWiZKlkUGWZqhCkiUxwXTE8nyTr7n1er7+xc/n+utOceLEIZaXujSaVWq1GlmWc/78RZ586jxPnVtlPJ6Spime69JqNUmTmNXz50mTGFkUDAd9hoM+Fdel1WiyvLzEyRPHsO2yCaooCubm5rj6qqupVapUK1UOrCxy8OASB5YXaDWqVCsW1YpLp9VgeXmBleVFDh1a4sCBRebm23iujZIFaBpxluEHAY7jkmUpplmKpVi6hWXaNOotHNtF0wzCIKS328MwDfwgYmNjl+HIJ0kVo3HpEd2sN6i6HrpukGUpk+kYhIZXqZNkObMwZGc44qHPnUUzLXTTAmESRimj8ZhCSUzLZjQaEYQBt916C7pWZsBZlmIaGt1OneNHDzIZD2m1F/FnE8488XgpwLK+xvb2LjPf5wd/4Hu46qpTnDhxnFqjgWXbeF6FhYV5Kl4FTdPJMtjaHvG5x89x5snzJLlC6GaZ+WoZtbrDNdecZn6uzeLiIqZhUeRAoWPgUPHqgMb8wiIveOGdnL721LO+Fvf5q3HqVPndPvHEE190/xNPPHH5mL/MO9/5Tj7zmc980c5vAMdxePnLX87P/MzP8MlPfpI3vOEN/OzP/uxX5Lz32edvk2ddyv6mb/omvumbvumL7lNK8S/+xb/gp3/6p/nWb/1WAH7nd36HhYUFPvCBD/C6172OJ554gg996EPcf//93HrrrQC8+93v5pWvfCX/7J/9M5aXl//K5zIaThkNZphGHdMtx6Y0NGrVGp1OC8OAatXBsTTSLCMMQ7IsJytyiqKgP+zz9FPn6Ha7VFyXxYUO1bpDkoYIAbZtkaYFhqkzm02ZzKZkeYZXqaAbOt1unWazzopaIkkyCnKqjQoHDh8gz1NylZNnORsbG9SrVRaUYDKdEIQhhVI0251S4MQ00UwNr+ahWwa7uwPCMMY0LbrdOQpZlM1prlN6Biu4tLZOlqVEUYjjVNBNl1qti64rAn+GaSfEUYSmCYJghuVYZLmkZbWJ44hOew6JhWFYOLaD53rIoqDbqSE0jQsX16hV61x/7XG63XY591vkSAmW6bK2tsvWxgSRX2R+rstkMqTRKN2hiiJnNpvimAYoSZqW405plqLrkGUJ08mY8XiI6zpMZwGNxhzVismhA8ugFC99yQt49be+ksWleWzHwXFtHNtEEwppW8hJQbvZoj8YMxn7PP3UJuvrOyRxRpFLiiJDiJy5uTq3PO9GWu0283Nz5JbH9u4GnU6H6WyE5RisrCxz8eIF+oMe/cEuWbbvx/w3xTd8wzfQbrf5lV/5FV7wghc8Y9/v//7v89RTT/HzP//zX/S5Bw8e5M1vfjM/9VM/xfHjx/+373XNNdd8ySC+zz5/l/mKrjGvrq6yvb3Ny172ssvbGo0Gd9xxB/fddx+ve93ruO+++2g2m5eDMsDLXvYyNE3j05/+NK9+9au/4HW/VPdkb2eIbTvkWUq762LbRqll7VZo1KqgCnRNA9NmPPHJckma5uSywHEsup0O/V4Pw9CZzsYkaUSalmXmopDUGnXq9QaNZp3uQofBaEgQRwxGI+p1D9tRWJaGaerU61VqNQ8pFfPzcyRJqeyVJCmVSo1KtUa92cb2PMajMVmesrm5ied5VCpVavUqYRgy123TbLZQhULoOnEU4zgOQRig6zrD4ZA0SVFFBhRIKYmTnLyIuXBxF8sR1Coupu3QaDSYzkZomoFjuyg5JU5Crr3uBubm5ukNJjQaNY4fO0SSxAgKbrnlJmazkJ2dHnEc0e/3SdPDSKlwXYcgCBmNhhxYXkZmikcffZhjR49Tq87o9UfYps2wP6TeqBOGEZWqh2EaTKczWu06aVqOfFUqHvMLXdrtIesbQxSwsLjA//PKl3FoZYlms47QNNbXtjBMk2q1guOYNJs1dF1HKY0oyNnc6POpTz3C9s4YXbMw9RChFEKWZfJbbrmRq646ieu61JsV1npbJEmE0Mo1+clsRK/XK5vIBECBkvIr+c/iq4bJZMLDDz/8jG2dTucZjyuVCr/5m7/J6173On7gB36AN7/5zdTrdT760Y/y1re+lW//9m/nta997Zd8j7e//e38m3/zb1hdXb3cIDYYDPh7f+/v8X3f933ccMMN1Go1HnjgAX7pl37pcoKwzz5XEl/RwLy9vQ3AwsLCM7YvLCxc3re9vc38/PwzT8IwaLfbl4/5y3yp7snezoAgiNnZ2ubg4XmWluapVZt4btmlKYRCKoiTnK2dIevra0hZ4Dg2zWaDhYUuNz/vZkASBT472zusrl5kNvOpN5u4FYiiBNuxufrqk3iezdPnLwIFcRLh2G0Mw0KIHBBMpz7tdhshNAzDYne3z2g0oihKLW0FVCpVsiwjCMo1tixLS4lLTWCYBmka02528P0ITdcx9Apzc3NkWcqFCxeoVir0o4giz7Etm2qtxm5vyNqlTaSU5EVMp1On22ly/XVXY+gK29KIooRKpcrKgWXa7Sae66IQnDh+iGuvvYpGvU4cBhw6tIJhuTy9eoGLq5fwgxlbW9ucOnkEpSTjSZ/FxS6QUa1YbG9eYGdnG6EJbMdmMBiTxpDGQFVnfq6FrmtEUYC/NiFJYgxDI05LTfEo9vH9MXmhmMwCzpx9mv/+gQ9y4eIalarH6RMnmE19ZJFTqTgsryziODaLi0skYcpnHvoc2/0JUupAKQIDEkNUOH3Vce6841a8ao0wiRlNe6xtPE1WhIy2twEFmig7+wFd19B1gSz2m7/+Otxzzz3cfPPNz9j2xje+8QuO+/Zv/3Y+9rGP8U/+yT/hRS96EXEcc/LkSf7f//f/5S1vecszZpj/Mu12m5/4iZ/gp37qpy5vq1ar3HHHHfzzf/7Pefrpp8myjIMHD/KmN73pGcfts8+VwhXRlf2luieHgxG+HzIaasxmE2aTiOXlnHZrDtfxEEIj8EPOX9jmM595lIuXLqIJgWXrHDt2lFa7xcJ8F5mnGLrGk0+d58KFTXr9EY6zzfLKEktLc8zNt6k3PE6dPopXcRiPZszPzWGZDrbtYts2vu+zdmmd4XDMysoKtm1z4MABOp0Om5sbxHHEeDy6rO3d7XZxHJsszzAtk/F4DHB5NKreqNLpzuP7AVmWM5lMcBwHIQS+71BvNDCSjEqtyvLyElvbfXw/ZDiasr3TQxeK1fOXuO66U7zkRbdjGgae69Fs1XAdEyHgROUIR48dI8/L2eg8TQhmM0zX5dbbbmRz81IpcRpERFHKxsYGjUaN4XCH4XCboii49rpTPH3uIq12l53dmNk0IEsUF86t4TknsO0IV0rOnDmDaVnkeYFAoVuCIi+IwhlR6KObNjvbPT7xiT9nbe0SnlvB9/sMBxOqThXD0AijkIcefgLHtbHMclwsy3Is12Xmh5imzsJcm4X5LkIpjh8/jGWZNFsN8OGRxz5Hf7RNEI/QDA2pAAmgoSQoYZT62c++9eKrnve+971fttHqL/OiF72ID33oQ//b475Yk9jb3/523v72t19+bNs2d911F3fddddf+f332efvMl/RwLy4uAjAzs4OS0tLl7fv7Oxw0003XT5md3f3Gc/L85zhcHj5+X8Z27a/6GyibZo4toPQNcIgZ2drSBwVyMMG117lADqD0S4PPfw4Z8+uEsXR3tyrRMpV2q0WMs9KRbDRiPPnzrM7GBFGGX6YMp6GrG1scOjgAkePHmBhfo52s0lvu8/ahYvki4s0601cy8Ntu8isIAx9xoMetXoN2zBxGjWKrINS0GzUQCnCMEAVBUIpZFYQzHws3cSyLPR5A9eroOsWcZIyC0pZzTTPmVtYIAojFjUT26mw0xuAbqJUTq3uEYQBpln6DCdxwm7PZ3t7jNAdunMtmvUGjqVjGOWae5rlJHGCUhpxlGBoGpZpkyQx115zmvs++WdsXFonPLDE2sYWQpN0uw22NrcYTyZYpkV/MMRyLPxgxu7uNnmRk2YFURzxxONPcvPzrmNjfRPPq1LkM/K8QBMK29VJi5Tjxw/z0GeeotlskSU5O9s9bMvlyJFrMEyNJ554iGazzeLyIru9Hts7u6VKW+iz1evRajboNB1anQqe6+DaJp5nUfNqVLwaoBFFMY+feZzVtYtgFbhOBaUESglkUf5Uspxj1gRI9teY99lnn+eOr2hgPnr0KIuLi3z0ox+9HIin0ymf/vSn+Yf/8B8C8DVf8zWMx2MefPBBbrnlFgD+5E/+BCkld9xxx7N6P8dzcV2PLM+Jwogo7NHvj9CESZLmKKWzudnj4sV1/CChkBJdE0iZMZkEXFrbwHUsHMcqMyYUhSyQlMIfeSEYDqf4/ozd3V2uOn2K48eOc9P1N7O+vk4cJ0ynUzqdNvVGnXanQZ5nZFlKlmVEUcR0Mi3XMHWDVrtDrVbDq1TIkow8k7iOh2GUMpKmaQNhGSjj0jM5yXJQijQv6A9HOLZDoSRJlmE5NpblcOjgCu1Oh4/dcy+z2bgcjTJtkjRjd3fIxUubpUsTBYXSyOKc0WjKeDxjNJpgOx47O9tsbqzhz6ZYjsvWbg+lBFGc8OlPPUAUBjSbLyFJfYSm027NceniOru7Q9rtNmtr6yglqdVckmRMxfOoNRoMhzMM3SXPtNIYpCiwDB2hCyr1GoZbKnalaUqWlZ31R4+eJs8lmi4oioJ63aXX26RSbeDNPJIsw3EdNM2i3vDodOpUKh7Nep12q87m+g66oaOE4MKFNXZHPbaGW1hVA7faBqHI84JCSlSuSuWvQiI/L1SzX8neZ599nkOedWD2fZ9z585dfry6usrDDz9Mu93m0KFDvOUtb+EXfuEXOHnyJEePHuVnfuZnWF5e5tu+7duAUljgFa94xWUxgCzLePOb38zrXve6Z9WRDRDFEVKV/6kmaUyeZdi2zXA4YjYLcBzFxUubjCc+hVQUhdoT2ihVsqbTWWnkYDtQr3PrbbcwixIuXdogjjOybM8OsBBs70wI/M8xm8ZcffpqnnfLbRhmKY3pei4SRTCdlfO8otxuO+U67tyCQNMMLMfDtL3S99kuyNOURqNBs9XCc12GwxG2V6FSrWHbLoZlEQQR555+mihNaTabGIZBEIYITaPTbtFstVlc7NJotqjVXT7xift4+twakzyjWvXQDYvNrT7bu0OiKKJaqXJxdZ0/+tBHWF/bIoxiprMZuiHI84QsiWnUW0xmPoocmQs0Q8f3Y+IkJQgCAj8g9BPiJEPXLYpCsbCwxHQWkecS2zVotmpous54PKPiVSklUyDPJbpmous2KyuHCeIU13Pwp2N03STLEuIwBj1jMByi6xpCy2m3qhimSa3usdsf4ro2rVoVz3PQEMRhSOJYWHaLxcU5Ll3c4ROfvI+LW+u05lpcf+t1VJoOaApNK8friiLf82jOyPO8/FnkFPvNX/vss89zyLMOzA888ABf+7Vfe/nx59d+v+d7vof3vve9vO1tbyMIAn7gB36A8XjMC1/4Qj70oQ/hOM7l5/yH//AfePOb38zXf/3Xo2kar3nNa/i1X/u1Z33yQRCWmZWmgwDd1JFIgihgNBlTyRXD4fjy7HCWZWhaaWwhJQS+z3AwwjAUhqZo1Ku88M472DqyQ5xkDEdTzj29ShInxHHMdBbxxNmnSNICzTQ4eHAZr1JDN3SC0Gf14jpPnDmz56PcYGF+kcXFJWr1OTQh0IRNlikKIUsHLBSzIMIPY0yzFLXwfZ/tnR5xmpPnEs/zqNZqnD59uix17znv5IWk2+2wvLRMEkfUKsvcdsuNzHWavO99/wV/NsV2Kghd46GHH2VjY42bb7yOJM6490//jPPnL6GExtVXX8PCAZMgnJEmEbpQ2KaJlBsMh32UUmiaQb3RolqtAxbb27uMRzM6rTlAw7ZtNM1ACYFhGQhdIWVOvdYkilJM22Y2m6BEgdB03EoV27YQmOzubFGrVQj8iCJP6c4t4HoGuSyQRUKzXsM2dSqeS3dhieFkguuaGKbGgZVlWs0G25sbbGys02xWWV6cw7QtVi9e4MLaJayKw9XXXUO9VUeY8rLGeCEzpASkoMghLxRFIclyDSie9bW4zz777POV4lkH5pe+9KVfVrVHCMHP/dzP8XM/93Nf8ph2u81//I//8dm+9Rd5M500y5EyJc9TDFPHsgyCwGf1wiqHD58gLyS2Ze8FQv5C8rKQ5HlOmiW4jouuS/q7uxgI5jtt3Godr1LnRS96CePJjKeeepK19UvEcUQQhzz86CMMxyOuu/46DMNgNBySSZ1afY71jTOcObOG7TxNtVLDdV0qFQfHdThx4jgHDqxQqTjM/BmKGE3TsF0XIUAzDGSSQCExTYOdnR12drZJ8xQpC8IwRNM0kqR8PB2N6e/2kTKn3W4xP9fku777NfzhBz9Mvd5FN2wee/wJRiOfP/jgR9nc2CJNc+r1BidOneLn/sm7CMKAM2ee4M8/9Sk+9YlPsLRgsbzSZHG5xngU0O9NmEx88kJw8OAhDMPmic+dJdv7/mrNBiidQwePsLGxgWU6GIbFaDRBCB0pc9IsBA1MwybLUgyjRppk7G73SNMc03YRmuDgwSUcS0fXDY4fWcY2dISUtNtdRtMp/V6PAoFpGIDO1sYuF85vMJvNaNabpGmOZRnkRUq9Weeq669h5dABNFdQEJaiLORoIkfTSylXYeSIPMdQEi2X7CfM++yzz3PJFdGV/aWwTA1D11FKomlm6TNsaliWyc7uLo7TRCoNiYZSGggNhUDKnEKVutqaBs1Wg1rVoeo5zGYRg+GY4WjC9naPrFB05+Z5yUtezHgy4uKFi1y8eJGiUEymPmfPPk2r2UQqha7bVKp1XK+GYQYEQUyWqbJjeFwaLvR6fY4cOcL1N1yLYRqMpyG2baEbIaZl0h+O0HQdTTcYjcYEYYimQZxElE1rkiLPkYXE9318BJOZz06vx/Fjx7jzhS/g0IEVXnjnHRSFIs8laTQEIXjq3AVs28X1LKo1j/nFDmfPPEKSxJw7+ziPPvQAo91dGo5kbs7FdkwWuot4Tp/NzR4P3P84hw69guPHTjLoDRkOSmUv07KxTIcs2wAEnc4cOzs9PLeK7ZgYpollO0RRiKFJgnCK0DqYtk1aSPK8wLKtPeGUDY4ePsjGxgaddouve8md9PsjNnf6PHnuPGBSq1SZn5vn3FPnGQ56FFlBo17h0KEDpVhKpUK706bRnePQscNohiDNQoSeldKtSgIZiAwlMpQqS+fIAk1PEXr85S+8ffbZZ5+/Qa7owFyt2OiajiwkjmNgOzaVWg00k6IoGAyHFFJhmBaWY5PLHKEKdF1DyZwgnDGZjDENA8PQqTfqVGs16q0m7nafySTADxPW19c49/RTGIZBq9nmqquuZW1tjfF4hmk67O72yYscoWk0Gg1uuvlm5ua3OHvmDLOZT5olqLAMqoPhkDhJWd/YYHFpEdd1aLdbTKsBmiZI0wTLtsmynCCMqdXqBMEMw9DJsgJN08iyrJy9FRpSSshzBoMhgR9Sq9W5/Y7b6LSb+P4EXdO58/nXE8YRCwsNHmtcZDyOWN9YZ2NtlY//6f9EFwXDXg9DTqlYBbYuyZKIRt2j1WqjCos0FTzw4KMcO36Yb/yGF7JyYIXZZIbrVen1BtRrdZRS1OuNsiEvLZ2iNL1cZlBKwzQ8LLMsm0dJgGma5EXObOajGRa6bpJEMQ888Blcx2V7Z4fzFy7QanYwTYc4LZibW8C2LTY3NwjCKQsLbRqNBral0+tvcv6cznU33sCRY4dx6w0yMgpSBClFHoPKgQIlMiQphYooZIxSOarIybKUON0PzPvss89zxxUdmFutBpZh7GW+GrZjYTsOWQFJEpDnCbajY1oC2zHIcwVSoWmKeq3Uaj5x/NhlhTC7WqGQ4FY0atUW/eGY8cTH9VzWNzZYX19nOvGZn1/AMAyazSa1Wo2iKJj5M8Iowvd9jh07TrvT5sabb2I6nrKxsc5sNiFNU4QQjEYjoihiNp3heQ7NZpN2u0UURVSqVZqtFkVR4Lous9mMJI3I86S0fUzTstktzhGaTlHkCDRkXqDqiq2tbSbTKa1WCykzkOX+6WzEQreOccMxHvjzxzB1OHf2LBVP0qjbtFoW9qll6jWNZqOGbgq68y0Mw2IaTlGaYubP+LNP3MfXvOAWWq15NP3p8jNpGsNBH9d1saxScWw4GKNpBgjw/ZA4TgFwvVLr2jSty53Ysihwax4oHaEkmnBp1RaxbIP17VUWuhU0obEwP4cQOZNJj26nyuFDXRxHx/enbG3tUq14zC3cghAaJ06fIMoStgebhOEYKSIUCVKGSFUabSiVkakEqTKUKlBFjswLsnR/jXmfffZ57riiA3O9WsV1S9GNJIlJs4TRaEAUJeTKYHFhmRPHDhL6U5AJMtOpVjzarTrHjh7i4IFFmvUqgnI917TKMStNtxAaOFGCESbohkG73SGKE3q9ARsbmyRJwtLSAvV6nTiO6XbnGAwHBEHA7u4O3e48c3Nz1Go1mq0GWZoQRTFJkpKmCVmWIqXEsiyUUgRBSJZl6HrMVEzIiwJ/NiOKQzRNlY5TUpJlGXkuAZ0iTVGAzHOUlIzHUyaTKZcurTMeD3Esg/m5DjLPeOD+z2LoBtdedx1LS20uXbpEmqSsnl9nZblDVncxDcGhoyt7hhVVao02jz/+NBfX1gmCjP5gyNyow5NPXuCGa0/jeVWk3Ma2TCqVCoEfY1k6vh9gWRZxnKIbOoZZOlylacp0MsNxLIQQCKHRaDTRNI16rYGuW/S3A2rVBu12iyxPkIXk5puup9ls8NS5x5Ey4fChRSpelTAI2NreYHd3m3q9zg033kirNV+uQes6tmlxrH2YKGkynfUYT3oM+gOyvLxWFKWTl0KiCQ3bqrKwMo+GwUf4s+f68t7nOeK6n/2faLb3N/LaF/7pN/+NvO4+/3dxRQfmLEvQtVLSE6GwLJMoCgFF1bMRIueG60/jWCYXL11AsES97nHowDIHDyzjeTamIahWq3ieh67r2I6i35+QFRAEEWEUoWk6tuMhhM7iwhJ5lhPHEb1en0qlgud5zGYzGvUGWZYxnU7RNJ3ZbIbjuuimgWZqVBsN8ixDKkWRlfPOhlZ6JUtVYJgmCkUQlhaYRVGQyxREQZqmGIZRBjTNoJSTLCsARVGaZcRFwmc/+zkKpXAcG5lldFptZCHY2hzQ2x0wHIYcO3GC5QMHSHLJ5maf0WjC0WMrLM53sd0aeVGwvRvwiU8+xtbWgLwQ9AcjEDpCWDz00OOcPH6cpaUltnY2S73yXFEUBVEUkyY5um4AKXGcYElFlmZA6Y+saRphEOH7AVKC51UYD3q4lTpRHHDV6Ws4dfwoUiX0hpu8+tXfwHQ6Jc36eK6DLBSXLm3x+ONniaKApeUup0+fpFZrIIVEszTiIqQ/3GZj+2lmwYAoHqNUjizSy7rYSoCSCqVUqaimQsYjnyTKnrNrep999tnnig7MuoBqxQMhSDKxt8ZZx05zqtU6lqHRatW4/fabOXBwrlS90iHLIjzXQNfKDtwoitA0Dc+tMJlMmc5mxHFOEIRsrG+RF5I4SbBth35vyGzqMzfXpV4vBVRs20aIslO41SpL0mEYMJ3u0O50sF0HwzbQkKALLNMi0wWZzEnynCxN0XWxNwqlQBUUMkcphVQFSklyWYDScG0Xy3Ew9LL7XOYFRV4gCgmFIghmPPnkWZaXlqlVPKbTGbZVBvIwjnni7Hk2d4Zcfe3VrBw6xMfuuZfxaMCFi1tcWttBF1ZZZp/N0DUb03QxTJOjR0+ycvAAC3Pz+P6MnZ0+tlOWrYXQKQoFCPxZiGUK+v3h3ohaju8HeJ6HEALXs7GMsqscBBWvyvzCMmtrFxj2Num0F7j1llu5/bZr+G8f+G9ce+1JIGQ82cJ1NITKyNIcIQStVptWq8mBg3PUGhWac1WwMjJyTFtjp3+Bnf4F0sxHqqT8/oVEqFKLWSpQSqBpAiVBIEjTeL+Uvc8++zynXNGB+dChQ1SrNfwwIh+NCIIAqUAIjTzPmYyHZEnI4uISupjHMARJFFAUOpoGQimyLCeKYnTdIE0LVi+sceaJJ5nOInZ2B0xnEZblEMcxtuOwtbGN4zis+hdwXQtNK0vMhqHjui6GZZaOT3GMEpI0z5hrLtBZ6OC6DqBI04w0yUjjhCiKSOKYosgxdJ0sTVBFjpA6CIVUkkIWtGp1avUaXsWj0+2gaRq721v4/pTIj0jjjCwpyLOCPE/pD7YxjDkmkwFXnz7JN73ya1m9cImHHzlLrz/i4x+/h0rV4/ChJWpVhyzNCMIQWYBtubQPzaFrDlIJCpljWCbVSoP5uSVmE5MwjJmfr9BstJASpFLMZsFe9ixJ0pQ4SQnDCMuyAQ0hwDItlMxxXJe8KJifn2dxacLcwgEmoykyT+n3+ximzve+4Tvw/RFbOxvEUUDVc0mznI3VNfr9MZWqR6NVo7vURPcUsRrRH83Y2LmE4egUMuPYycM8ff4scZRQIFGyLFsXUiHQUEoipYaSEqkkSqm9m4x99tlnn+eGKzowNxoNZn6pJW3ZLtNpQJpm6KZOGifEhkF/d4cDy0vomsA0dJRlMB5NkYZBnud7FoIK3/cJooRHH32MRx5+lOFoShAlmKZLnheEYZlVozTStGxkqjcqWFYpDOJ5LmEYYpomCoiTmDhLsWybdqfJ/NIChmkAgjhO9jJdSZqkZFm650BlUmQpWZJQ5FkpK6kBSuJ6LrVGDde1cFwLwxDUGg6jwYhBb0gcJqRhRpble3KTBbPZlJ2dHRbmOzSbFa69+iitZp2NzR5JmtHv9RmMhvjTCbVKnRPXXMPi0hxSKvwwocgAYTAajxkMhyAKzp49Q7NRZToLmJ+v02p1iKOEfG/WeGNji/FkSqVSJYoSDKNcX7YsC5CYlkXVa+J4FtPJjNHeuFgQpjSbdaLAZ+3iBR556LNce91x8jwhCWPiKGU8DpkFIUKYFApMzyCRPpEU2LbH+Y1VpuGIIJ5SjBWO41DIFMtyabbabG1toFRGLlU5OqdKGVYpizJ9pvylxH5g3meffZ47rujAHIYheZFfVm0qJCRZgasboAsEgs3NTW666SYqlUrpYiQMUDCZTFFK4noVvEoF3TQZbW4znUzo9XoMR1MKCXkxgT23IcdxieMAJUHTBLOZ3BtjyjBNk2rVw7ZtLLvsOJafn5U2y0AilAZC4FZroMrzU0pRijMLLMNEE+WacZomWKaOpguUKEuwihy0HKEn5KQIO8FrGgizQRykxGFGHGZkaUGe5ZimwTQImPoh9XqHMBgxm+yyfulpilzj0OHD3HzTDQyHE7Y2tum0qtx2y3UcPnIE3bBB6eimxcVL67znPf+OQW+XarVJkqYoJUiSjEqlhiZ0hG5imS6j0ZSZH6IEuJ5HISWWbVOr1QjDgIpXYW6uQxRFrO9cIkwKlDDp7WzQ720TxxGyKHjo0QdLOU4heNP3vJowyIiijFmQ0etP0EwTTMnCoQaaHbE12WISDYmLCEwFhSRKU9JRyPz8IgpFq9tlOOiVc+8CyuJ1KQOX5zma0BBC7QXpffbZZ5/nhis6MGdF2TAVRD6D0YiJ74MEz9XxPBfbsRgM+/T7fbqdDsFU4ro2lumS6jnbu9vEmzscPnqEhaUllpaXOXRowJNPPsVk6lMouaerLAFRqosZGprQSw1tqYiiGEPXEIAsyiYtXS9lH3VN3xtxKrM0gcA2bUzTRNfNsmlNCXStPFbTNHRNoImCLEvRdChkRprHFEVCVmRkxYw49UnzGVmekitFriv0iolj6NgVjyIT5KnCMx2a9QY7/TG1WpW5uQNkuc5slvLwI59j9MiYW/a8sIXMWFxcIIl9RqMerVaHRqOJ63qEQRPXMfF9H9dxEEIxHI9JkylznSa2aWE7NrGWIUTpUIWuo8cxummiGaVJhWXZ1Gp1lBKkaYrneSR5yPnVVdbX1jh69DhPPXWGY4cPM/N9huMRN15/ijhK2d0dkuUaw8mUWRjTmqvQnq9gVQsmSQ8/HyDNHMhRqkAzBEIJVC7Z6a3RmVtEMwTtbof+YEChSvtJTQNQ6KYoVeGUBvq+9Nc+++zz3HFFB2bXs9F1k8nUZzAcMeiPqFaqzM91qdZqoDKmkzFPPvkUtZtrZYl2FqJpBkUBmjAZj3t4/SFetRTIWFiY53m3PI+5+UXSXJJnBTM/KMVLvAr+zEfT9MvOS4ahIyh9lB3HQtN0gjAgiELSIkdKSRAEzKHj2B6WZWNZFoZuYBgWQmh7VpQACsMoM2ipCQqZkquEVAZkRUicjoiSPmHcI4pHJHlIIQtUZmBpFerePJry6C4tYyiHUX+GH/kYmkl/FNLpLrK4fJhao861N13N5kaPhx58DD+IuOH6qxlNdukNy9L6dDzl5IkTxGGMP5vgOg5zXYdOq4llmwg+f+MhsB2PPJcEQcjcwiKW4+EHwZ42uYZSZVlZIOj3eiRJTMV1ME2LVsti+75Pcettt9Ftdzl37iwHV1aoeFVWL51ne2fAiSMRpmUx832KomB+oUt70WHhYJUg3yCJA3KRkUoJmlFaOMoCKBAaaLrFaNqnUqkjlaQz32AyniCLHCnzsotQAmKvQ1vfz5gB3vCGNzAej/nABz5wedtdd93FT//0T/NP/+k/5a1vfesXPGd7e5u77rqLD37wg6yvr9NoNDhx4gSvf/3r+Z7v+R48rxxDEkLw/ve//7K5zZd7T4D3ve99vP71r+cHf/AH+fVf//Vn7Lvnnnueod/f7Xa57bbb+MVf/EWuv/76/7MvYZ99ngOu6MBsWx4IDdvy8Nwavp2g6Qae5+G6LkmqIM/Z3Njk5LETeJ7LYDigyMuRJT+ISJKCwI8Z9AdUa1U81+O6a6/h1ImTREnCbDrDDyNMw0TTdAaDMUopslwy831arTbhntsTqsygJaCbFn7ko5QkCkIMzcCxHGzHRtONMmvWjFIfWwgKVWZ6hcwoZESSBXuZcUAcTwmjMXE6Jk5HzPxdchmRyxzLdlhc7DIYjInULkUqGK9vYht1bKOB7dXRdZtML3jq0kWadQfH0ag3m3hOjYbX4vzT5zl75jEWlrqkSc4wn5CmBesbG+S5YuYndFpNsgy67Rq6bhDFPrrmomkGUZKSFwVRklJIhVQKx3WRwyG6YZClKUEYIADLNLFtG9erIjSBEoIkSVhZnieNUwxdL9fbLZ0jhw/y4T+5l3q9zvJSDSHWUUqRZxntzhJRNmQc9YjSCCkFmtDI8wJZlONPpfwqCJUjBEynY0zTRhcOFc9jOp2A0PaWEhRqr3Fwny/Ne97zHt72trfxnve85wsC8/nz57nzzjtpNpu8613v4vrrr8e2bR599FF+67d+i5WVFV71qlf9td737rvv5m1vexu/+Zu/ya/8yq88wxTn85w9e5Z6vc7m5iZvfetb+eZv/mbOnTu319+wzz5XDld0YG63OxSFIopyWo0mSZzsrfdaWKZTjhmlBZcurXPhwiWuv+E62u0OWRajKDPZ9c0tdno9JtMVrrv+GjRNx3UdDEMjin3muk3MsUZRSLxKlSLPUUowCwKS1MD3Z0RxTKc7R5FLiFP2oi2e64JQJHGMTHNsw8QyLYRRmjQIoRBKoigoZEQuE7IsIE6nRPGUJPVJ0vJnGI1JMh8pY5IsxHEtOvU51tY2uRStkeUJI7WLkmBqHoZWoep28USMsNskmsVoHDGJLGqeQ8OrQabRbreoVa7j0vp54jil3qiRZzmrFy/QbHdAaSRxTLtVjkV1O62yXK9lNOp1DKOUPxWaTqVaI0lTivEYy7KwbZs8y2g2m0RRQBwn5EVB022W689F2aDmeS6agIX5DtVqhanvc/z4ES5cGpfyqQryPKHbadBpdxmHU+rNKv1okyAOSdO8dIjKSo9lFORFgSYEQitlS0vTCo0kj0nCiGazietUyhsGIfaCskBJDVnsl7K/GH/6p39KFEX83M/9HL/zO7/DJz/5SV7wghdc3v9DP/RDGIbBAw88QKVSubz92LFjfOu3fuuXNb/5cqyurvLJT36S//pf/ysf+9jH+G//7b/xXd/1XV9w3Pz8PM1mk8XFRd7ylrfwqle9ijNnznDDDTf8td53n32eK67owDzXnWc6m5Vzv0WKZeqlvKauY9sOIAiDhMnU5+mLFzl+6iSVio2U5VrogQMrFArOnz/HxUurdOeaLC4sUq3WqNUqjMcTDKM0V6hWawRhBCjG4yGZlMxmPq7rYdsWURSQpilxEjOdjsnzvLRDNDSyNCPN0r21ZR2FAoqyjEpGXkTE6YQonRKEA+J4RJwEJKlPnkdoOgTRmDCcUam75EXKYDhlNB6UNwOYFEW5Dg4aWZ6T4dNoNtFMn43dTXb6HlWnySw2ifMaUVSjU1/ENHSKVEcKgwsXV+l0ckxDo9vtEkcxcZwShjGe51KpVCmKlDDykTKn0awz833iOEHTdCqVCrpusLx8gDzPybKMOI5BCBzHQymB67oUhSKJc/IiJ04yVpaWePKpp/maO27nxutv4GP33svTq6sEYcjpE8dIUkWWx2hCo96o0l5u47U0fByKMeQp5JlESkGeAUKglE6hFOx1WAtdUEiJUhKBYDQa47ouKI1CSjRhlNKmCjRhAOFzck3/Xebuu+/mO7/zOzFNk+/8zu/k7rvvvhyYB4MBH/7wh3nXu971jKD8vyKE+KLb/3f823/7b/nmb/5mGo0Gr3/967n77ru/aGD+PJPJhN/93d8F+LLZcpIkJEly+fF0Ov1rnd8++3yluaIDc73eJUkVaSZJsxzdMJBpyvZuD80wsUwTYVhots3G9g5bu7scWF5EN0wcz2NxaQmvWqPTbTEa92m2mjSbDYQQRFFMnmfMZjM8r0IQhPT6/bLUrDLSNAcB4/GYZrNJlqTIIidJQoLAxzQNLKuCUlDkOYHvk+cJhlXOJxdFglQpWT4jjEf44S5+OGAW9EjT6V5gjihkutd8BmmWEvb80qpQlIEGoUiSFE3TEWhYloNSsDA/T293vWxKUxLbqpAGIyzTYxoZzDcPYtoVEBWEZtDqLiI1nbxQTIYD6vU627u72KZNo9HcGxnz90RDEtrtNkkSM5vNAI0kCaiJMsjpuo7v+wyHY3TdKBXLgEazRRTFFFlBXoDjVAnChBMnTjObTej3x1xz7bVUK1W2tzexTIP5+TkmU78sf7suUkChMiQ6ruPSanTJ4x3SLKQoAFmWrxEGeyEZAJkXl4M0CLQc0iTEsk009L218LKBbz9h/kKm0yn/5b/8F+677z4AXv/61/OiF72If/kv/yXVapVz586hlOL06dPPeF632y1vzoAf/uEf5hd/8Ref1ftKKXnve9/Lu9/9bgBe97rX8WM/9mOsrq5y9OjRZxx74MABAIIgAOBVr3oVV1111Zd87bvuuot3vvOdz+p89tnnb4MrOjDbnkut2WB+cZFZFDLzA6SmMZiMyFG0Ox08z6XebpFnGYPxhJWVpb315QDTtKlUqlTDCITAMN09mUjJbBYQBBG6ppPvrSdLWbCz28NxXfr9EUGQMRwO6fV7FEVOtVqh0Whw6NDByyXYOIqJo4hhf5fAn6BbgCbJZUKS+QRhj1mwwyzcJUrGBOGQNAuQMicvMoqiQKly/RP0smtYCRQKQdn1bZkmlWqVOIqp1SqMxiPWNy4gRKkcZhgaOQVJNiPKTXThYscetWKBmi6wTA/DsAiimMl4iufVmE5n3HLLrViGTZFLwihEEzpRFJGmKbValel0zHA4pNFoYZoWum6gG4IgDIiiCMdxyrluJcs1QSEwDBNdmKA0wiglTnP8IAShMRwOieOY5aUl5ttt/GBKkqXUag2SNKc3nLDT77N8uM16v88k3WQS7hIn4Z6OuEQWOkLosLe+rJRC07XSF1pJhAa2bSJlga5DkZeezBSga6JsAGO/+esv8773vY/jx49z4403AnDTTTdx+PBh/tN/+k+88Y1v/JLP+/M//3OklHz3d3/3M7LTvyof+chHCIKAV77ylUAZ6F/+8pfznve8h5//+Z9/xrEf//jH8TyPT33qU7zrXe/iN37jN77sa7/97W/nR3/0Ry8/nk6nHDx48Fmf4z77fKW5ogPzONmlMBSdlSa5eYjd3hCnWSHPJZqm41Uq2JaFVXWReUEYJ8RZQdU1qVTq7O7ssrnVK00psoT+cMJcp0UUhTzx+OMopTh16hRKSmzbQGhg2Q5raxuMhlPiJMf3fQxTQ9c1QGLbFm3PxbZtQKChMZrO2NneYu3SKkesQ2AUJHlAEA2Z+bsE0YAgHpKkU9IsopCl2xFwuUQtpUIT5U8UKAQVr4Km60RhSBjEpGlEmkV7/tRl6VBDUMiizL5zidANDC1nEvZppVMSmZVNyVLQbi/QrM8jZcrhgyvUqlXGwxG9fg9ZFOR5ThiGVCoVJpMJvV6fKM5w7AqO65bSprrOeFyW8huNBrPZ7PK6v6brhGEpeTkZj4mjiMlsymQyRtPBn82oVCrsbm/T2cvIdVPn8LE5Vi+usbnVQwiJMnKEFRGpnCLXkblBnmeoQifPyu/r841cmg5IhW5o6BqgQVHke1WLAiHK/i+h2FsDVfz1Cq7/d3P33Xfz2GOPlSN+e0gpec973sMb3/hGTpw4gRCCs2fPPuN5x44dAyiXDf4XarUak8nkC95nPB7TaDSe8b7D4fAZz5dS8tnPfpZ3vvOdpejPHkePHqXZbHL69Gl2d3f5ju/4Du69994v+Zls2977d7rPPn+3uKID8xOrn8a0KuS5IBUKt+FgVhyUKLMmSRnMkApTM8A0COOkdJOybAbDMY8/dpZLa5tM/YBOp82hQyuYpiDNoV7zEEJhuxaTyZR+f0QcxUwnAZPpjGxPYUsUikrFo1Lx9jJERZIkaMIgSzPiMMSwdHa3N6g2dExP4MdD/HBIlExI0ilROqMo0rIrWJplWVUWKFn+x6OkoECWzgsAQuD7ASAQmkIm2d5MrgQkUik0oZdhRpSZtW6WEpSFSkhSn9F4l7Z3BMPyyCOJoWs0600qFQev4jKaTNnY3CHby3SyLAU0xuMpcZwgdA0lFeefPk93fo7O3BymENRqtcvSlpblIIROEEQUssBxXFQR8wd/+N//Sn/HQgg6c10mkwlyT0bTsepI3WDmT0hTgWZ4FHlEEmdA2VinaxqKAoFEiLLxWuyZV0gl0TUdilIoRkgQmoZSpWZ2eU/07LO7/1t59NFHeeCBB7jnnntot9uXtw+HQ1760pdy5swZrrrqKl7+8pfzr/7Vv+JHfuRHvuQ68+c5ffo0Dz74IN/zPd9zeVtRFDzyyCN8//d/P1CuW//3//7f+d3f/V2uvfbaZxz3whe+kA9/+MO84hWv+KKv/8M//MPcddddvP/97+fVr371/8nH32efv3Wu6MA8HA9x3QyFjZIWmmGhazpSCaQqS5IFqsxmNZ0wThjPZhh6A9vQcdwqsyAmCDOyDPr9MgteWuxy9MgJ2q0KnmciZY4QDba2d9jZ2trLtASmqeO6Fu12h3q9Rq1WRUpZSmIWBapImEynbG5sYA0tLFfQma/iFDphOiVOQuIkIstylNJR0qBQxV5Q1vZKqvpe9rw30kOpEvbMrK4sbUulyiCz12SjlLz8Z4FWBhwNhFB76+RlWTqWKUUicWp1PK+KEBDFGYEfEUcZ1UqFPC/XuqtVlziOWVvbYDyZMj83j9BgOp2wsLyA7VjEk3jPccsv/z40yCnIsxRdKzPpH/qH/z8cx2a3t8NufweB5OKFC1TcCt1WG8+u4Dg2syTgwoWLuF6VleUlBr0h4SzmupuvQthHeezJh5gEuygpsHRFVuSlxCblPLhSCiVVKRYjBIZhY+pWaVpSs7EsE9ctb6oarQ6NZhPb8vjc//ztv8Ur+e82d999N7fffjsvfvGLv2Dfbbfdxt13380v//Iv86//9b/mzjvv5NZbb+Uf/+N/zA033ICmadx///2cOXOGW2655fLzfvRHf5Q3vvGNlwN6EAS8+93vZjQaXQ7M/+7f/Ts6nQ6vfe1rv6Bx7JWvfCV33333lwzMnufxpje9iZ/92Z/l277t2/7ajWf77PNccEUH5jjU0DHQdRMwUUqUo0p7aEIv/yAEaBp5oegNhtQ8F8vwMO29mWLLQWKQ5RlBlDCdhSRZQaPZwrE1ZrMxui6Y65bjWb6fEMUxSZphmAaLC4tkWcbM94miCIGOEBDHKf7e2nQwmzHY7TMdzSM1l0wVFDkoaSCEjYZGoTTUXlNYIRVKSRRlJ/HnrQlB7clzqs8rhZZZ8d6yqFICXdfQRJkxgvyLMRWhoVRRqoyh0DQNmRcEcUDFqmFZFlIpbMthc2OdJ554nNl0zLVXX41j2+i6UYqjGCbNVpsLFy/hOg4nT54kyzMMXScMAqQskFIRhiH+LKQoZBn8qjWSOLmsUb69s8Uf/dEHeenXvRjTNDl67Cgyl2hSkGQJ//Oej3LdNdfSnuuSxBFPnn2MRr3FXOcIN133PIb+Nk+eewLXrpCkEWrPievz5WmlSklPTS8dpD5fxlYU6ErD99Py+9BLERS1N16VpfvdX1CWjDVN49//+3/PT/zET3zRY17zmtfwK7/yK7zrXe/i+PHjPPTQQ7zrXe/i7W9/O+vr69i2zTXXXMOP//iP80M/9EOXn/ed3/mdKKX41V/9VX7yJ38Sz/O45ZZbuPfee1lYWADKmelXv/rVXzSovuY1r+Hv//2/T7/f/5Ln/+Y3v5lf/dVf5T//5//Ma1/72v/Db2Offf72uKID8/aFGdUqVCoKx9Ux99aBlVJomkAzDZSAAkkuBYYSBEHEeDzBtSy8SoXllWW2dwYIXUeogizPGY7G9PsDwoMLgFGaTsiCWq1KXhQcOlRlMBwxnfl7IzoZ08mUjc0t0jRD03Qcx8FxnMsZNEoRhzHj4QyraqNZNkJlaEKh0CmkBhKUzMsbib01T8XnG78ot4kyW/4LrbDyd6XKrUL8xbo0osyey21FaeIBlwM9SrGxvoHI6xw7dALLsqjUKgjKOe0sy+n1B0ymExoHVojCiPFkShzHaJpGs93BME0qFZck1bEdB9fTuHjpEkoJZCEvd4ynaUYWlq/pz3yazQb3feqT3HDj9dTrNaIoolqtYhsOu5s7IBRHjx7h3PnzdCYzXNfAdQxOnzrC133tC0EVnH38LKoQhEGIJjR0TVGKYMs9FS8NqeTezdpePVsDXWPve1eYhk6e52WZW8m9Zrv97Apgd3eXEydOfNng97a3vY23ve1tlx8vLS3x7ne/+3IX9Zfju77ru77s2NNnP/vZL7nvta997eVg+9KXvvSLzkgfPHiQLNv31t7nyuOKDsypXxCrFNKAPJbUGw1s1wEliKIEoeU4VQ+MsktXUa7ATgOfTtHEsgxOX3WCza1dLq1vohsClZU6zn4QsL3TY75bCmsYhiAIfKoVF8PU8FybKI4ZDMaEQUyvPyQII7K0wLJMLMsBTcd2HUzfBCWxDIskSsljqDoVlKGBCkmkho5AalDIlEKWAUIqiaaXTUzlfzylStVfmB+pvRj++fBcZtTlrC57Up9lgNY0RS5zhAb63lrq1uYOYS9goX0c64SN6TgoAVvbW+imiVvx6Ha7zM3NMZlOefTRR2g0muiGjm5YnDp1ijwpM+CFxQV0XSCEhmM77Pb6SAVZnhOFMwyjNPZIkgTLKm+gdra3ef7X3E6z2cIwDLa3dpgmPlIqDFNgWxbT2ZQjR45z2203cvzoMvNzXfI8ZWenT6VS4ciho6SXZuyOtkDTsE2DMPT3bkooKw5SIfaqCJ7rYVulRadpmKRxRp7nmHs63m6lRhSmPMqn//Yv6L8jjEYjPvGJT3DPPffwgz/4g8/16eyzz1cdV3RgjqMUgU7ox0g1ZDQcUdtTowqThKzIcSsezYUupuNQaKCkxI8jRtMZNdtjYa7LHbffTJrEbG3voqPQBBiaIEsShoMhnmtRqbo0Gk2iMGEwGqPrFkUh2NrqMx77JEm2l9kKTMtG03X0va5o13WwbZNGq4Vju8gcRGHg6BX0z5fb9xTA0gyUVHuzymUjF5oonamUhiwKdENDymJvpbkM1krsyVCqohTY2Gv6knuzzmWnskDHLjNCaaEVBs16jUMHl6lWXLIkoR/N8Byba666lXv8GRdWz/Pp+x/k8MEDVKoNpBKYukWWF9SqDovdLuPxCNerksuC2dQv55TTgkwpdNtCpQlBEFDk+Z7nsY4fhMRxTOBHtK+aw7ZdhoMJ/Z0NHNum6jbwZ1OkLHj5N7yQw4dWsC2NQmZ8/M8+Sb3VoT3fYDLokakEy9Uo9maVT504zeLiMo89/gTDcR9FQV5kCFmgxQpdQGHmKJmX4iK6ga6Z2Hta5lH41d349X3f933cf//9/NiP/Rjf+q3f+lyfzj77fNXxrAPzvffeyy//8i/z4IMPsrW19QVC9G94wxv47d9+ZuPMN37jN/KhD33o8uPhcMiP/MiP8D/+x/9A0zRe85rXXBYqeDYEQch4PCHZy9rKOdmypNpoNqnWqhRpApqgNdfBchzQAUMv13CBNE05fOAAztd/HY9+7jHWLl2iWnE5duQg1arDZDwkzxKyPMNxXLJMMhn7hGHCTm9IHOekaYEQBgqFrgsMw6Ba81BFQbPRoFbzcDybaqWKZdiIDFI/BV2BoeHpVTzbI8libN0mVzWCaEBaBCiRgciRQpUdxnvNW6U9Yel0VajSuapUsNLRAMuwqNaqCBRREpBlKbpm4tgVZFaQ+ybznSMcW7mew8sn6XYWqdWqaDrIIqPdqvOSl7yQhYUu9933KR5+9LP4U58kjel2OywvL2FbBmsb6xw4cBC3UiNOUjQ9oygUcZISpglxkmLoBlLP0TTIilKYJYtjHNth1B+wtnoB1/NQhSLNclrtDkooTpw8Sm844P/P3p9HSXbW9/346z53r72qq/ee7tkXjaTRwiI2xyxGEMzv6x84sWOwwSZesOAY7GAfMMeLEkJM4pgQC7AdgeNfwCbx1xCCDRizGhAI7aPRbD0zPT29177duuvz/P64PR0GSQaxyYPrdU6d03Xrzq1bNbfqU89neb9nZsfo9bvce/o0tVqdTtdjBg3NlYTxgKnpCbZakn63SxLH1Btb9Hp9DMPkhS94MZdWlukO2vjhAK8/IPQjomCAIkYTAsMwMIo2hWKO8lgFJZMn+rH4geLDH/7wk30KI0b8k+YJB+bBYMCxY8f4uZ/7OV72spc95j4vetGLeP/7379z/xtnBV/xilewvr7Opz71KaIo4md/9mf5hV/4BT74wQ8+0dPBdd1tpa5hOqITJ/S7PVCQdV3sTJbE8xm2ehhlgWUZCAlRFKFcxYWlJeJIcfDwNbxgZprVS0sMBj1QEte2GPR6WLbJ0AvwhzGDgYdMkp3nLuQjwiAijGIc28a0BOVynoxroak0SJumi+0aJHEEMkbFMbrUELqO7/kEcUAsIyzHYqw4T6VaZBh26fa36A9r9IcN/KiPVBGxilGkzlgIDWQq2jE9M8fBg4fY2tzk5CMnkTHkM2WSJCEMFH6UoPSEofSwdJvp6Xmeeu3zmBk7SNbOIYSW1pizWQw9rXHPzs4yMzPNwsIutmpbrF5a4atf/Qrr62tIGZPJuKxtbFIcqxLHEtdx6RsDwjjG0HVkGOH3+miaRhSEKKGh6RqBP8BEUS2XefDBB5iplMnN7kLFCeVylemZOcbGXP78z/8X119/DcePP8z5c5fYWN/i4MFD3PSUaylWskSiSzsIyGZyVJnm2muO8fDx+0n9RGJUojj+yIMALCzMs1nf4qZjT2dl5RKDQY9d8zOAZGV1mXpji6/e8xUA4nAkMPJPmYd/91YKhcKTfRoj/gnzhAPzi1/8Yl784hf/g/vYts3U1NRjPnby5Ek+8YlP8LWvfY2nPOUpAPzX//pf+ef//J/zn/7Tf2JmZuZbPpfA98GyMYSOaaQzw0oqVCJRSUwcBgSDPplMBjkcohwbKS0kEA0DIjsmCCLuf/A46/Umz3veczl0ZD9bmxtsbmzgBz627WIYBmEoUTKdI3bdDPm8jWWnBghoijhJa8u5XIZSKZ+OakUJtm2jpEJIhWXZ6ChMASr20YSBrWkIYeBFEYNmj2AQYOAyPbObmeo+YjmkP2iwvLZImAywswatToNsLsuznnULKysXuftrd7O2uky7tYXv+8gkQCnY3LiEbjo7Fpfj1TKTk5N4XZ+CNUE2W04tMGWE0BWDQZ8w7JMkEl0I4jimWq0wMzvF7OwU11xzmJufeiMPPvAAjXoD180wHIY88sgpKpUKjiEIul26G5eIh0M030cGg1SmE52cZZMEQ3KZDPlMlqkbbuDDn/0MH/u7v+PAnj1ohkFlYpKNrTU++tG7SJKEXD7Pl770NSYnpzh24/XMzs1iWAZ+7OHJGsKJiGXMwGtx7lyT+YUFhNBZXlkmCHq0+3VMQ6fdMRAkPHD8bjJulqPXXsODDzyIZZtsbtZxs1luuvlprK6usrG+BfS+5etwxIgRI76bfE9qzJ/73OeYmJigXC7zvOc9j3/37/4dY2NjANx1112USqWdoAzwghe8ACEEX/3qVx9TDODxxOYNw0CqJB3rQGHooITAsnQKuSy2aSKjiHAwQAdCy0RLJLFS+MIgyUnm5uY5ffYC6+ubPPDAAxzYM4VpGuRyObz+gCRKiKIEQzfpeT18P0TTNGQSMlYZwzBMbMcmDEOq1QpSpuM4miYwsyZxFCM0PU0l6wZKybRJColjGemxlIVtGvQAP4rYWFlj0B6QzxdY2DPPs576FLLHi3hhA7ek8cDDX6NWv8T/+1cXdjq2E5ngB5DL5ZChydD3ME2BmzXx/JAg8tnYXGVzaxNbZDm8Z2/6IwbJcOhz8eIqlmmQz5dxbIfx8XHy+Szr66upWUaSMDc3i2WZ3HLLM7Asi3q9SaWyxPGHT3D2zCmOXXMYEYeUTJ3uUGI6Jl4vpjfwKBRKlF2LwngprbMDuufz1GuOct/JkzzwyCPA/53BPnz4EAcOHMDzhtx80y2MVUt0e01OnHyQ/Yf241gGuWKW2HBoNtex3QQwsTMWq6vrzMzN0O120A2N84unOb34CEom5PIlFCGf/fzfsnfvfuI44ciRI5w5u8iXv/wVQFId+78iGiNGjBjx/ea7Hphf9KIX8bKXvYw9e/Zw7tw53vKWt/DiF7+Yu+66K51d3dhgYmLiypMwDCqVChsbG495zMcTm1dILNNK5SeVhaHr6VYpybg2YrshSqCQUUiv1WZgDnAzGeIwIutkmNu1wNEjR7jnvgc48dDDBIMm+/btwbZsAt3HGw7QdR1/OCRJYgzDREqJpumYhkW+kKdYKhKEQyYnq/R6XQaDNH3reT66bhCGEZ4f0up0qJSK+H5APpfZGREyhI4/DJiemqE/GJDN5/hnP/Q8Hrj/IR6+/zjHH3gA29VwCxq1RhMhNUxdAAph6LiZDEMvIAgiPC9AJhFJLOkPu3SHbaQCXTdAaghNRxMGrVaLi8tLLCdb7J6Zo1yq4tgOhWKZUrFEEkf4fojjuMRxwsMPH+fee++lkC8wOTXNTTfdzN59+6lUximWSjzyyAn+5hOfIm/ZFDMulpPDVIr5SZPMbpckTkhkar241WzQ8Xwurm2wvL5BrlThmhtuYN/+vYyNldGFYH1tDSWhVC7jBz4Xli4QRAPm90wzOVtGdxSLyyfpeh36/S6uazLwuoTrAfsPHeLi0grdXg+lNJ7xzH/GqZMnGHpdBv6AoT9AKcWZxRPkcyXaps011xxhc2udrdomm5tr3+2PxYgRI0Z8y3zXA/NP/uRP7vx93XXXcf3117Nv3z4+97nP8fznP//bOubji82nmtCmLhBCYBoGKImmiR0LSKEbCCGwTAvdNNO51iQmHA5p1Gpk3SwH9+1jZXmV06dPczbxKWQKVMcrhGEIJPR6PUzTxHZM4kjiulm8oY8fhkRxhFQxrmsBCtu26PfTEadsNk8QBNiuTs8bEicJwzAgo1ximY49TU3NUiyWuOdr97F7YT9rG6s8/PCDXDx/Hse2iWNFrVEHDaZ2TVGZrDJdLlLMTlDrLDMIG3S7TdKxZI0o2la70raVrwBNmKjEII4Uum0yNzeHo+UZnxxnLLcLW5jomsb4xDitZod+dw3P61Mo5On128xMz1Eslnj44YfxPI8vfvEumq0ehw8dZmpmisPXXINtu+TzYzx0/AT3LC6RsywO756nXMwhQ59er0HPDwgSSb3TxYtj2kMfaZlM7Jplz4ED7Nu3h167xfLSEkJTZHNZ1jdXGAZDLNtkbKJIQkijtU626JDLFsjlS/QyBS6tLBKrCCU63Hf/XeRyZeZ2TSOEyenTi2TcMqXCBJgB586fRiYxURTQ7TXR0Ol0WkxNTTExMUYcl3iYxe/oczBixIgR3y7f83GpvXv3Uq1WWVxc5PnPfz5TU1NsbW1dsU8cxzSbzcetSz+e2LxI1UQwTRPLMtGF2A7Geup9LASa0NGFiW5uq1aZaaCWUtJsNhh6HkeOXMvTbr6JKBiytnaJtbV1QBIEqSCGYehksy5CmIRRgoaO7/usrG1gmAKhKyqVIsVijkwmh+f5O25LjUaDRrNFq90in8syPjGBocPzXvBc1tZWqdc2qdU2WN1Y5tLHL6ILgakLIn+AawmkjLFNnaEf0W32KeQK5MzCtiqZxXI9RA5DYhmi6QIZawgt1esWpBKfSSLIZyqUJstcf/1hjj/0AJFm4Q37VHKSIAoZK5e5ePECa2vraEpjMBgwP78L3x8yGHgIQ2PPngNYlsXTnvZsoijh3OIiSxcu4A8GzEzOkbFdnvLUpzG3a54H77uHdr/Ps57/zzhz4iFEXyceKgIpGZ8cJxZgNRvM2BMcPHSIQj5Pp10n8gNQEm/oI5Uik8kyv2+WybkCSvjEScDG1jr1Pswv7GF9vcbhwzcShtBobdDt10GLaLZWaLVXyWZL7N57EEO4LJ49j+nAkcPXcebMIpEKtuU7IxIZsbK6hJKCqcn578EnYcSIESO+Nb7ngXllZYVGo8H09DQAz3jGM2i329x777072rmf+cxnkFLy9Kc//QkdWwiBbqSB1jAMHNtMt+kCDQ3bdrZVnwSodDRKSYlppYHLNHSEgFptk/37D/L/eelLuPtrX2V9bZWtrU0Cf0h1vIJtZ/F9H8OAUrFMt9fHC4Z4/oBha0CplCeRuXSFrQniWGKagjCKkFLiuDZHrznC+voqT3nKjZxfPMsnP/HXxHFIIZ/DcRxyOYNGo4npZJCxhq7rdDsd4iS1d8w4GXSlkwQSCwf8hKnSHqRMWFp7mCFtNKHQDI0kUoCBPwzJ5gocu/5moiiiXtvkbz7+UUxTkLeh1a1zzYGbiXzJgw89yOb6Gmtra+SyebqdLvfffz+FQp6p6SkWdi/QbqUNUZNTU3S7XUqVMo5hsdQ9T73ZxMlmcGyT3XtncV2NTn2LM6tL9FWEci38bgfdMalMVkCHykSBbDaD7w/xejFDLyCXK7CwsJtMNsP45BS79+3Gk1s8ePJzrGycIpERjlPE0G0uXjpLuTzG1tYGExMz7Nmzj9X1JbqDLTZq51H49PobPHK6iWUU2bP7EL2+x/p6nZnpBZJEUtvawPd7RPEQITSkFFy4cOG7+REYcZVx7W9/EmFnnuzTGHGVsvQfXvIdH+MJB+Z+v8/i4v9N8124cIEHHniASqVCpVLhd3/3d3n5y1/O1NQU586d49d//dfZv38/t956KwBHjhzhRS96ET//8z/Pe9/7XqIo4nWvex0/+ZM/+YQ6siG1KozCGGSqgGVbFiiNJElrz9lsDqUUsVSEfrgjZakSmUoz6jqaphEEHvX6JrMzM9x80/UsV4ssLy8TahpDL8AyHRwnS6/fJ05aBGFIJuMyOTXBcNjHzdjk8lmGgY/tZPCGQ4Iw7Wh+zg89m4/874/g2CaGgL//7GeojJXIODZxrKFkguf1kUkEKiHwPQzNQlgC348IwhjHdrAdByUjVBJjKkHoRQhhUM7uouluEfWHxEQoJdB1C9tyObB/hpmZWc6fW2J1bQmpQjRNIYTB3NwC8wu76HSbbFyqcfbsIkkU4fsBSsLm5gamaaJp4GYybGxscsMNN2JZJmEU4fk+mqaxVtuiHwRgmpw9eYLpmUnCyMf3Bmla2PcpFIoITWPgh3hDH8cwyWTd1IrRNIldSRgl2GYW03KwbJeDB48wNTtFZSrPxdoaodak6a0ipSIjQ3KZIt1+lzDuYRo5dOFQLO5nenIObVOwsGsPpxcfpN3dJFERQ7/FA8e/Sj43zuz0PLpwWLqwzNTELgbDLmtry6nGdyJJkpGM44gRI548nnBgvueee3juc5+7c/9y7fdVr3oV73nPe3jooYf47//9v9Nut5mZmeGFL3wh//bf/tsrUtEf+MAHeN3rXsfzn//8HYGRd73rXU/45CvlCv5wQBKHBL5GYIfo22lsiSBRIIROEm13dGvpSlQpiT8MiJOIbD6H0mDoe7TaTXJ5m7FqGaUSzi+eZ3V1jX7fY2JiglqtwWZtizAKGR+vUihVmJoaR+iKOA5TTextFeu9e/dy6dJFPvPZz1AuFSAJmZ6oommK0PextAyumTo1SRStRp9+P8C1XXRbo9cbEESpAIZUCqXiVMBLJpiGgT/w6TV7WEXIuXm6nksUKSwzx6GDR6lUypw9c4IvfelvUy1oTaEQVKtzjI/NUCpN88ADD7J7Zki3MaBYLnF+8RwzM7Pc9eUvE4U+luVQKBTY2tri8JEjjI+P47ouUklyhTzdbpckluye30MSRVTHqmQLeXzfo1Or4UQxS+eWqK2tUNvaYG2rTqJgOXsaw7HIFUsUqmNYmQwXVlaZmZ9ncnaGvYf2ICwdqXvcf+I+jp/7e7Y6ywhLx+sNCcIG3X6LmelZOs0uiYoxdJv7H/wKB/YfIeNk6LVjnvnUf86DD99LvbVGp7eF0BO6/U3aj9Rw7BzXHDlGt+1hxzlmpw+ytnaJJB4i43/ayl8jRox4cnnCgfnxBOMv88lPfvKbHqNSqXxbYiLfSKmQR+Vc+v0eiZSoBCzHwrYcNE1LpSGJQaVjRGrbLUehyNkWg0GfdrNNGMSpNWAiCcI8puFgWS75QpEwjsgX8mRzDpVyASmjVOHLMBn0+5iGhm6k3sn1RovNrQb9nsfA83EdC9exkVGA4zoIDRzHpdVqMBQasTQRhiD0fDKZPIEviWJB4PvEMkaSoJsGQkosYTAzPcnc7BSJkgzDkI7XZSybB2Wiiwy7Zqe59vpjPPjQA5w99yC+30OSdpAbus3k5Ax79x7ivgfu5dzZC8xPXYdtmezZs5v6RoPJ8SonT53CMHQ0zWRqchI0jYMHDzI7N49pO6AJgiCV2HQtizgcMj93hOr4BKvrG2iGwd1f+Sr33vMgx3bvJuwOCDea6I0uVc2mG4eYkUToCkOCa9pYtkO1WuWWZ9zCxMwkui0ojTsMkzoPPfIVzq2cQOoekQopFApEYUKz2WZldYWJ6jRRpDBtiJKQh099jfHqDOPVWWr1OpXKOLlclr5X5eziqW2zZcnA6/DVu7/A3PQ+piZ20+kMmZ4w2Ni4RN8Lv+Nrc8SIESO+Xa5qreyMa2MYWUzTIIoibNsml8thmmmt+fINUnclNIii9EtX13VsO3V/CsOQzc0tOu0OvU6ZfD6LAizbYWpqilK5CCgKhRwIhe/7dLp9pBI0GnXCcEgiE3p9j5WVdZQS7Nm9h4nxMfpdH0MoVNbFMEzCKKTT7zHwB+TzudSfEIHv+0RRROAnxIkikTGVsTILuxdAU8zOzbG0dAE364IGl9YvodkaTiDIFXKUpg6QyVt85jN/gx95JDJASoWuOeTy41x39ChLF8/zxS9/ljgJMUQetIRcLkfOzlM+WCGJE6rj48RhSLfbYm52nnyhRKU6zlh1nF5vgBAa2axDsVDgi1/4PBtr6+QyOcI4QTMMLNth9979PPfZ/4yV02eYLlaoTU+xuniOduAT6IL+cIDuOljZDGbGIhGSG26+nsGwQ72RMLswxUb9PF+55++QwkMIhW5a9Ns9Qr+JlIJCvkgQhGxsrTM+NkWv3yWTcRn6AxrtVbq9FkeOXMdgGO50xx85eIxao8ba+iWUTEiSmAtLZ7lwfoWZqQWmJ+fIZRwunAfYfJKu6hEjRvxT56oOzJZpk8m4uK5DFEU7gdgwDBzHAdKAHMcxAIEfEAx9pExSWUtDxzQtLMsCQEpFr9cjSWKyORdd14miBG8wJJtLDQ66nTW2trbY3KrhRxEZ1ySby2xLWeo4ts1wGFKr1dCQZB0b4aap9U6vRxSFhHGAFimULtCFThDEhEmCsCxMLaboZPihH3ouS+eXUEpy+vTDrCwvMb97gTCKaHVatIdtitkMu/aMo/Qup88/wPojF4mSaNvSUaBrLpXSNDMz85w+c4pWa51ESQzdZbK6iz0LBygUCywvrqErg0p5DCkV62ur7N9/iIybodPtM4ag2WxQKORxnLQRrt1somkasVL8n7/5OMVSmWc+89lMTU8zNTFJo92m6Xto5TzO3nmqhQy5JGar1aC9tsIgCmlvrjCuTSBRWAWLQ9ccYmFhmna3Rqt3kaHXpjdsYmcsNhtrKE0jilM51HanjS5SC6lOt0Emk6fZbFMql9iqbYIacP9DX+XaIzdyaXkNpcA0dTJOnnJxkkZjCxkrklihpM+FC6dpNetk3SzPftYPc/yzH3pSrukRI0aMEE/2CXwn6IaBaVm4rgukY1dhGKKUIooiNE3DsmwymRyZTBbHcbBtG8tKDSUc67JfcoyGwDQsNE0wHPr0+14qTiJ0BgOfrc069VqLVqtDp9MjjmOSOCQMQzzPw/M8kIrx8SpTk5OUCgVMw8Q0TaIood3r0ex08YIIhMnQT9jYqHNmcZnl5Q2CUHHw0LU8/RnP5uanPY3Pfe4znF08w9lTpzA0A0MJ4ihmbWOVWmeTA9fu5od+5CZq3bPc8+CX2aqvg5BoQgICpRz27T1KoVDixCMPsFVfJ1YSXZgs7DrA4YPX4vkDPv+FT7Nt9JzKeG6uU65UME2Lz33hC9TrDeIkYjj0QEoC32M46CGTCCUjothnq77JF7/4ed777nfxuU//LadOHsfzu5Qmy6iCQ8/WuZQMCXIOopjDLOSpzEwxMTdNeazCgQP72L93gULGxO81aG5dYvXSIralcCwDFSfomkkSg4o1ZKKQMp3VTlREu9tGKonvxdz1N+f56l/V+dyfrfGZ/9853vcfP0qv4dNsNjh58jR/9z8eZuWRLvsWjpBzKujYyDhBaAmd9hbLS5f44B9/FIBqtcrCwgK/8iu/QqPRuOLa2717N+985zsfdU3+zu/8DjfccMPO/Ve/+tXbntgapmkyOTnJj/zIj/C+970vdf76hmNe3vfrb//hP/wHAJaWlq7Yns/nOXr0KLfddhtnz579pp8XTdP4yEc+8piPfe5zn3vM59Y07Qrhn2azyRve8AYWFhawLIuZmRl+7ud+juXl5SuO9/Wv27Is9u/fz+23377zI/kyt956K7qu87Wvfe1R5/SN792ePXv49V//dXzff9TrunzLZrMcOHCAV7/61dx7773f9D0ZMeIfI1f1ilmhiOMYwzTQdRNNJCgpt60OBaClildo2ytpC9tJm7MMQ0foOlJJ4jhBKZVaEkqNJElSURLbIoqSbdlLQTaXY//+g1Sr4zSadYIoAJUQx+lq3XbcbTMLgVJQyOeAmGEQMeh4qdGG1CgVS3iDhHqtRRglHDhwgB9+7vNptRo8dPwhfL+PShI0pZHJZEhkjG4bKD0ik9W56Yan4uRjjp/5PK3eFpGUSE2l4iLoKGVzzZHr8QYe5y+cIXWhTq0ejxy5iXxuDD/wOHPmLEV7Gj/oMTexj2ajQRSFtFpNPvaxj5HL5bnu+hKbmxtMz0wRxyH1egPTEDQaNUxTMDNVRSYhpp4QhxHnz53gxCP3cuTINZSrFTLZLJEMmZmbpZDLk0STZFyXMAyoHDqMaQg0JBnHQsYhta0tWo11dE0ioxhdaYSxxBY2UgNfRihNwvb/PVpqGHbx/AqnPxeCkFz7nGlKVYf19Q066wmf/n/v4//5+aeD0giDLWyrTLfdZ+/CYU6fOUUSKZI4IA4kG6cCTCv9vXr//fezvLzMm970Jj7+8Y/zla98hUrlict1XjZ1SZKEzc1NPvGJT/Arv/Ir/OVf/iUf/ehHMYz/+zG8/fbb+fmf//kr/n0+n7/i/t/93d9x9OhRPM/j+PHj/Jf/8l84duwY/+f//J9vW8TnMqdPn36UgcNlpb5ms8ktt9yCZVm8973v5ejRoywtLfHWt76Vpz71qdx1113s3bv3Ua87CAL+5m/+httuuw3TNHnzm98MwPLyMl/+8pd53etex/ve9z6e+tSnPu57F0UR9957L6961avQNI3f+73fu2K/97///bzoRS/C933OnDnDH//xH/P0pz+d973vffzMz/zMd/SejBjx/eaqDsy6bqAbBpom0ITA0g0gbeJKkoReEKJpHkEQInSB0DSiKMI0THRhIjUNXTcRhoFSEEcxmgBTt4gTiRbFGLqJbju4roPjuqApstkciVR0ui3CwEcjJonTlHkQBnjeEKUkcTzEtS3COKLf79Pv93HsDEmiKORLTE3OcuDgAdqdFl/44qfxfY/hcIAC4kCSy2ZRyQA3Y1KezrH70BT7js6x2jjLifOP4PltEhUSJnFq/SgskDrXXHM9Q8/jwtJp0CJAB2ly6OANlArTdLstLi4v0mv3mNq/i2uuP8ilc3VOnjqJrsH6+gaWZXPdddfS63epjleRMmJjcxV/2GfXrlnCYEin3ST0PfKO4JqDu/H9tNM9kX3CoMXWWheZgK5b9PseQy/AtCzWVlbJui7+mMf8/BymbdDr9XBcgWHpdHsdDDNGJAaOkSHwA2xhExMTkxDLBKkkSSIRugASLtwTkkjFM/+/07Q6HXQ7y9SucZxsk+y45JETD3Ng3xE0oVGv1clXbcpFjf17DnPu7GkGcZfa8hChaUwdMLn4YMCuXbs4evQoN954I/v27eM3f/M3ec973vOEr9OvN3WZnZ3lpptu4pZbbuH5z38+f/qnf8q//tf/emfffD7/uEI7lxkbG9vZZ+/evbz0pS/l+c9/Pq95zWs4d+4cuq4/4XO8zMTEBKVS6TEf+83f/E3W1tZYXFzcef75+Xk++clPcuDAAW677TY+/vGPP+brfu1rX8uHP/xhPvrRj+4E5ve///386I/+KK997Wu55ZZb+M//+T/vZL8e6xi7du3iBS94AZ/61KceFZhLpdLOfrt37+aFL3whr3rVq3jd617HS1/6Usrl8rf9nowY8f3mqk5lG4aJZdnpatW2sW0HIdJAbdsO09PTzO3axdz8AuOTk1iug5vLYmdcTNsCoRHJhCCK0E0TN5vDsm1sxyaXz+O4GRw3QyaTxXZcQKNYKDM7O8fcrnnGxyexnSxCt9A0neFwSBxHZLMupVIR2zYRAnzfR2iCscoYY2MlHFfnyNF9XH/DYVbXLnBx+Sxx0kfoIUqEDMMh3UEfLxmi5xXFGZuFo1Pc9OyDLG3ez6XNh0jUgFgqEqkRJyFSJkQhHDxwLYPBgMVzp9BEgqbpWCLLDdc9jWJxjOXlJXqDFt1eE90QzM3PsF5b5tL6EprQOHNmkVarxeTkBJVKmbFqhbFKkXzOxbYNKpUSly5dpNNtoZKIfq+DITQ21lYp5DIYAsqFDEJGdOubhIMeGVNnaqzK8sVlli+t0veGnDh5houX1li+tAro6KaNsCyanTaZXI5ctoyhHExl45o5RKyhKwMTgZB6mtKOUjEV34vobiqqu3XCOCGOYH2tRiFbodfzAYU38HjwgQfRhU4um6e2ucnW2gaGZjA1MYulZRl2FeVJE9cyr7jOpqameMUrXsGHPvShf3Ai4YnwvOc9j2PHjvFXf/VX3/GxhBD8yq/8ChcvXvyepW+llPzFX/wFr3jFKx71w8F1XX75l3+ZT37ykzSbzcc9huu62zK3oJTi/e9/P6985Ss5fPgw+/fv5y//8i//wXN4+OGH+fKXv7zTE/LNeOMb30iv1+NTn/rUYz4eBAHdbveK24gR/xi4qlfM+WIRx3EIwxDTSYiiiEI2gyYEjuOkspjCIJu3UEoSRRGu66Ra0io1u9BE2hUtpWToeVjbSmJCCJRSONt+z91un0RKJGr71/kM2VyeYrFMs1FH0xSxjOkP+vQHaZOXoZtkcwUsJ4Mf+ERhgKZB4A85feoUQz8kiiRSJXh+H0mMm9NxizHZMcXYhMHEZJkj1x1gz8Eqdx3/a4ZBk1j5RFGCjGU6B60UUaAYK09Qq9XZ3FxGCEBaCBz27juArussnj/Fnj2z3H/f3SANDh+9gU6vT6+1yNBXbGw1WVtfZ8/u3UxMTGAYOpVSkXKpiONYZFyLc+fOsnJpmWajjqHrbG7VyOfy+MOYRqNFGPpYhsag26Xb6eC4klxhjEurSzjZPJ1uD9dxUZpgbWOLrdoWuik4dGg3tg5er0cc+li2zez4HCurF7GEhYmFqSJiqSOUQlcglSCOErxuKgiiOxq65mAZkn6vz+LpCzztpmfzlS/fRRJFCE0ShhGukyE/VmBjfR0ZS2ZnZqmbWaBGLutiao/+vXrkyBFarRa1Wu1RJizfLocPH+ahhx66Yttv/MZv8Na3vvWKbR//+Md5znOe802PBWkd+mlPe9q3fU5zc3NX3F9YWODEiRPUajXa7TZHjhx5zH935MgRlFIsLi4+6vmVUnz605/mk5/8JK9//euBNB3ved6O8NArX/lK7rzzTn76p3/6in+bllRyaTYqCBBC8Id/+Iff0mv5+vfksXg8c5wRI55srurAXK5UyOVyJElMnMQITcOyTIRItayjOCaKIvwgwNB1spks1rZsZxiGJEmCaVk4tku90cAPAiItIEmS1PRCN4iTBMdx8IMAPwhQmkIqQINCsUQul6c6No5h6DiuTbvbZm19jVp9C98b4nkhjmtTLNp4gwFhGBH4If3Apz/wCKIAqUcII8KwEkqTWcbncjgZk1y+zOGjh3EzFifOfhk/qREkPlJCIgOiJCSRMWEgkbFJtTrBqdMnkDIGZWHoGQ4dOIppKu578GtMTk1wz71fwtRN9u67hrGxKXShc+/d9zE/eZjFC2eYnp5hYWEPNxw7RpxE2I6NlAlJkrB04RwnH3kk1Rlvtalv1lhevkSlUiWTyaKJPkJI9JxD1s4wOenSH0bEEh44foJ2p0+pVKFQrGA5WQJ/yDCJ2FjfZHZmDFO3kIGPlsRohkG70cCxHNo9D10aaLFAiwXEIBNA01ESkjAGFEkM3sDH93xkrOiHARcWV8lnxqjXN1FIlIL6Vp25PUfotNIfD0kYMT05zQMsoWs6hjK/yZX33UEptWNzeZk3velNvPrVr75i2+zs7Ld0LOBRx3ui/P3f//0VNW3TvPK9eCIZg8tBNdqWpv2pn/opfud3fgeA973vffzET/zETn39X/2rf8Wb3vQmzp07x759+3aO8dznPpf3vOc9DAYD/uAP/gDDMHj5y1/+LT3/N3tPHt8cZ8SIJ5erPDCX0XWdMAzRYoFh6FiWhecN6Q+8tDlIKZRUBEmCbqQB27YsoigijmNarRa6MAmCACnl9pe3QtMFfhCQeB5BGBJvB/k4SnZmoru9LnGYEAYhlmmCppPLFjl8qEK5vEGz3qLZaNDptDBtgzCQaFjs3bOfiYlp2p0Wa5srKCPAdCPKVZvyNBQmNSSKuV27GQy22FqtMQxaJElCFCYEvtw+n4A4UqjYYn5uL2dOnyWOIjRhoKTBvgMH0HWNE488xNRUhU6vgaZplEvT7JrbB2jcffeXOXr0Wh762imEGTO3a47Dhw6Ry+ZAwGDQo+ENaLdbfPWuL7O6colMxiVJEtZW16nX29QbPTKZDKZlMr9rBk2AZVlMz0yT8RN0y2Xo+yQJBH5Er+fRanfJZl10AX4YIoRG4A9RSYROQhz6zExOcml1GS2GJJRoiQFJKriiI1BSoUkNy1ZAwLCb0G51QEIYxCANVpbXOXrkCPXNBlImaEikVAz6A649eozTj5wiDBKazRoASaRjaO6jrrWTJ09SLpcZHx8HoFAo0Ol0HrVfu92mWCx+S9fvyZMn2bNnzxXbqtUq+/fvf2IfhO1jAY863hNlz549j1ljHh8fp1Qq7TzPYz2/pmlXnPvloHq5e/tyEG42m3z4wx8miqIravZJkvC+972Pt73tbTvbstnszjHf9773cezYMe68805e85rXfNPX8s3ek8czxxkx4snmqg7MkDZztVotdF0nn8+jaYJMJrMzUqGhEQcBYRShbdsyqkQipMIfeDS2ariZLKZl4/UHKBJ0XWfo+SSJ3Pm1HYYhhmHQHwxS60gtFSDRlEaz0WIwGDA+XsUPAjzfAzRUonDdEoaRTRvQtDQA5fMVZmbnue76G7FdCykiPL+FMBMawyU8tUG1kiP0Q+r1dfygTRQlhEFCEAqGXoQ/VISBQRRKhGYThAF+OEjdtjSXI4evwbYE9z94F9lsjk63g+97FHNVDuy7nq3NOr1Bi7HxLI3mBmE8YGHPLNffcC179+1FFxq9XpdWs0W72+JLX/oiJ088DCiOHbseoQlMs5b6TceKVqeHZZlo+hqTU2MowA9Sj2i/O0BXgJTEsWR9bZOB5xPFEeVSjqXli9xwbB8qoxj6fQwtRgDBMIMpBEkcpzPHkYREQyUCDdLMgNRAGbilkN6aIj8eYJg2SaTQgCQKWF1ep1So0mzWgRghBJ1Wj3I+oJArEQx9kDA+XmBjrcvM5JWd1xsbG3zgAx/gZ37mZ3auh0OHDj1mPfe+++7j0KFD3/TK/cxnPsPx48d54xvf+O1e/DtIKXnXu97Fnj17uPHGG7/j4z0WQgj+5b/8l3zgAx/g9ttvv6LOPBwOefe7382tt956Rdf61wfVr+cDH/gAc3Nzjxrd+tu//Vt+//d/n9tvv/0xG9iEELzlLW/hV3/1V/mpn/qpRzWKfSPvfOc7KRQKvOAFL3iCr3bEiCeXqzowr62towstre/GIblcluFwiGEY5LJZXNvGD3ziIEApSa/bYaXTwbIMDF0nihNcx6bf62LbDt6gh+cPMU2TMEgNHaIoIpfL4zouSRxjGAae45DNZTE0HSUlliEQuQyeNwChYVs2YRjSbLfQ0CgWy+m4VqTTbDa5774HOXXqLGPVCvv272Hf/j1kcyUMUzD0QnTyTGbHOHHyHmRUQksMkmCAPxwwCDyiRBKEEVEoiUPB7OwUUThEJaBrGfYsXINhWNz/0F1ILcCPBSoUWEaG3bv3s7K6wuzMDOcvrnLN0YMsnlnEtHX27Flgam4Oy3VQSUiSRHQ6baIopL61RbPZolQujiEUaAAAWQBJREFUMV6dRCUJvbEuvh/hBRHdbhdNU8g4ot1uM1YpoNCIpKTb63LkyGEeObmI50doQiKERpJEDIcDJqpFlFLoejqqJmWMkFCvtfA8DxlJSBQ6BjoSXUlAJ5EilVlNYsbnbVYe8Vk/DoWZCGEokDFBX1E7t8I/f/kz6Le7oIXIWCP0Ey4sXuTwgUOcW1xEtwQLC2M89NAKp8+kc7srKys741Kzs7NXrOTe+MY38pznPIe3ve1tvOxlLyNJEv78z/+cu+66i3e/+91XXKdBELCxsXHFuNTb3/52fvRHf/RRozy9Xu+KuWGATCZzxQhTo9FgY2MDz/N4+OGHeec738ndd9/NX//1X3/TjuzLpjNfz4EDB3b+3traetSc8NjYGKZp8u///b/n05/+ND/yIz/CO97xDq699louXLjAW9/6VqIo4o477vgHn/syd955Jz/+4z/Otddee8X2Xbt28eY3v5lPfOITvOQlj+3Q8y/+xb/gTW96E3fccQf/5t/8m53t7XabjY0NgiDgzJkz/NEf/REf+chH+LM/+7PH7TIfMeIfK1d1YJ5fWMD3PPr9LlIm2/UwjSiKWTy7SK/XJfCHtFttHMfCMk2UTOgPIpIoxrQtbNthc3OTeqNBrlBIZ5fjmCiMUhELpbAsiziOsSyTjOuCAn84JN6unSEglqlIyWXTCSEE+XwOXegkSTqu5feGaRqcVAylXquztbXBpeWLTE1Nks1mkSQcPHKAS4+sYQe7CAdZkAGOFhBGTbRhnSTqIGOJShKE0Ol0mvjDISida687hmlY3HPfFxFmCEIRhSG6cNm39yCtdodcrsTi+dNUx8ssLy/R7tQpupMUyzkKpRymbREFEWPVCvl8lvPnF5FJwr79e3jKzTdzYN8+vMGARm2LiclxLNshkQn1rU3Gxkq0W028wRiZjEupWMA2XZJ4k127pljbaBCGEqFpCF0wOV7kwL49CE3hewPiMEQlMboShH6XMAyRSYKGhlAahmaibzfvGUqgZIwuFZapmD5g0tlM6KxIkhiEnmA6GoWqYPXSKuNj46wwoLGe3gAeuX8FgMNHZpgYy/PMZxzkzNl1AG644Qampqb4sR/7MX77t3/7itXgM5/5TD7+8Y9z++238/u///sIIbjuuuv49Kc//aiA84lPfILp6WkMw6BcLnPs2DHe9a538apXvWpHMvYyv/Vbv8Vv/dZvXbHtF3/xF3nve9+7c//yCjCTybCwsMBzn/tc/viP//hbSoF/fU31Mn//93+/8/djrfbvuusubrnlFsbGxvjKV77C7bffzi/+4i+ysbFBpVLhxS9+Mf/jf/wP5ue/uY/1vffey4MPPsif/MmfPOqxYrHI85//fO68887HDcyGYfC6172Od7zjHbz2ta8lm80C8LM/+7MAOI7D7Owsz372s7n77ru56aabvuk5jRjxjw1NfbfmP76PdLtdisUi/+W//CFKJvS6HQaDAULXsWyLOJacPn2aBx94gMmJcaSUeF6ffD5PpVzCskwC3ycIAqIkJgxj2t0eQuiARNMEg0Ef27bJZwvk8jn8YbqKmJqYSFfUYUi316PT7WLbVioCYhgkiSSbze0oimUyWTqdDqZpYpo2QtPp9wcoJQmjgDgOMU0D27bJZrPMzc1iGGmd3LZs4kQyDIa4GYtE+GzWlxiENdB9QjUEU2Pv/t08+MBDHD16jGzG5d7778YwJUJPSLPuDtWxCcaqVZYurHDo0BFWVpaZnqly6tQJNE2Rtce4Zt+zefZT/h9ydgmhFFnHwTJ16rVNzp1fxA98LFNn5dIyMk64dHGZbCHPyuo6szOz9LpdLMug3thk7+4FNCGojk+gYZDEigSNza0GrVaXlZWL5HIOU5NVJscr5LOCoVentrmMJmFbQySt+2uKREpiKYmVxIsiIpkQKwijmEAmhHFEFEVpCj2MSJIEXegITWAbNnOzczi2Q6vVwLbSxj4dHdu0KObztJstAt+HbQWpz37+FJ1O51FiGyN+cLn8vbLrDf9z5Mc84tvmH/JjvnyNfbPvlqt6xey6DrlMBpKY5YsXabTa7Nu/n/HqJPv2HuDM6TM4ToZCKU+n1UYpSbffp1wuUyhX0DSNza0tnKxFvlyh1++ztb6BZVlMTEylettKEQQ+7U6TYrHIMByiGRqmbRK2IjK5LIah0+/36LXb5PN5Go06hmFSLBYJAp84jtC01DijP+iTyWQJw5BwECJlTBSlzWXtdgfbylDIF2m3OuTzeQrFApZh4Q0HZHI55icOsFFzaPfrGLqLFBFBHw7suZZSocRXv/b3aCIEBEmkITSB62Q5ePAwDz10P9XqOBcvnqdaLVGrbYKWbCugRdSal9hqXCK/q4itp37JjuMwPTONaQlWVpZpNGoUizk219Y5sH83A39IGBYp5Gwso0CpVEpNOxLFcOhRkzUmJ6bIZGySJObQwXmSBMbKDmgJhg66HuN5Pq1mHW8wREcgSNXTNE1DN/TtETeB0HQMDZQmQCliQEiwdR0t0Rn6Q4hAaAZCpUHZ0i2GPR9DM3BMG1PoWKaFQEMlMa5tMTR14lABEt8fuUuNGDHiyeOqDsxpmjhhY2ODS5cu4WZzabNREJDNZslmc2xubjIxWcWenCQKQ9bWVul1+xQKJbyht231OCSTy+E4GcbGqiSJpFodR9d1NjbWcR2HyclpNAGtVotGo0GpXMGybbrd7vbcs45hGHS7XWzLJpfNINDwg3C7Np3W/qIootFooFTCcOih64LL8pKmabG5ucnq2jooRbffJoiq6LpOEPhEkUOlWmLv/DWsb65Sa29gmhqV7AxBNODur3wVzUhnuZI41fmWUpHL5Tl3/iye12dqchdx7NMfdOn22qApkiQm0nzavU22Gqsc3n8MU5jEcYjQNTQEw36fOAowDZ0QcBwbQ9eZHK/iOBa9bp9cLtUjF8LA90M03SAMIgzTRAiN4XBIJuPgujblUp5ev4OuK7I5h/pmnXqtjq6BZDvVLQQ6GjIBTRgYuiKWMg3bSqEpia4EOsa2k5bCMjJopGUI3dCxTBvHshBCUClX6LVbCA1Mw0AlCk1XNGpblEpF+v0uSiUokifxqh4xYsQ/da7qwBwGAc1GndXVVX7kR15It+8RxQm5fA5D6GTcDN6gj9AEiYyJwoBOuwOKtKvYsIjjBMtyGK+MUW80qMztpt1uY1upR7BpmmysrWM7Npmsi2laZLM5ADzPY3JykiSOieI4dZbyBpRKJUqlEv1OH6dYZKJaJUwigiBkYrLKoD9k6HsIoaUraUPfVkRSDAOPTDaLLgS6KRh4fRQKoQmErtNtp49Pji1QKU3RHXQoGBMcX/wKujRIEg10HU0T6LqBZZgcvuYgX/ryF0Ajrf96HRb2zFKrrYFIZ2mligjiHr1hnSj20rGwMKBSyhGGAaap4w89guGQWq2WjqAR4flDgmhIp9Om11tlYmIGw3TY2qwRxRH5fJ4zp89y3XXXEQcxzXqDXKFEo95k6A+ojBUIfJ9mq4kfRphCw9RNlHbZOxs0DYTQkGjomoahQCkNqRIMAQmCWCpEorAMHUMYSNJ6pGWZmEZaKshlsyRhADJBaIJYRiSJJI4jiqUia+urKAnqOxsFHjFixIjviKs6MK+vr3HPPV/DtW3K5QpOJkcUxZRKJdbX1gj8IfOzsxSyGWq1GkNvQBT4JHGWOIrIZbOMlStIBTJJtrW0Y/L5AvlcgYybwZqcptfpoVBIqTEzsysdxxr6JJGkWMwjpaTX6zGUikgYuJbDoNtHaFDI5zEsEz/0d9yrbNNi6KV16SAMGPo+juOko17ZDKap0+12UUpHN3RkknpG68JgqAVpmhYT287ytBuO8sjpE0xW5kB4tAYJsfQwLB2hCw4dOcziuZOYpoahW5RKRQK/S7fd5rLuglQSjYQo8en06mw11on7Atsw2TUzgRAaui7IOC6nHjnJ4uI5GvUGuqYzNTNJoZTDdbNsbjVZurhMLlsATQdSSdAokbRabSwrg2EYJAmsrW/RbNbxhmNMTpWoN5vIJAGVmloYhkjfc9IfDgqVNoAJga6DQgOhoYAkliRSpnOySgMESktnqYUA0zSwrNTe09AFaFr6XKT2HhK4cPEiwjCIowTEKDKPGDHiyeOqDsyf+cyn6XQ6qSuNpqhWy/hBxKDvsby8RLlUZHJ8jH63QxgO0+9bpchlXAr5HLpuYJsmURQxHPTJ2C6lUoV6o4FKJN1Wh/X1VeqbW0zNTFMqFDF0i153gGma7N+/H13XEZqG1++l7kzFEsOhh9huIioU8tsuWCGJAMM0iEIFjoUfKgZeiC5ACNCAYj6P73vIOEGzFChJ4A9IEkkvDAitgMgP0aTO1MQUzXoTHR1TOQzbGoYooFsSIWIKxSJJGBNHAbmMi2PlqBTzhH6Bbr+NQNs2v0hngiMVsVlb4eLyOYrWJPOHDqFpCl0obMug3Wxw/tw5Tp06jWnY7FnYjW07lEsV+v0BlUqZlZUN6rUmcawYH59ACEGcRNz9tbuZmJjgwP4DDFptzp5dIkoi/MhnMOzQ73sYuoZm6kTItK6spQ5iYrsLG02hlIbQQNc1FBqmFMQSDF1La9BCR9dNdN1A6AJdF5iWga4L4jhC1wQIhdq2XFQSklgxCD3KY2VqtRqxvOr6IUd8F3n4d28dNf2NeFK5qgPzzNws+XyO2bkZEBpD3wc0EhlRLBZJQp9mvYY36KHpAssycTMOtmOlohWJJAoC0KBSrtBqdwijgKmJCWQSo1RCoZBHyoRCPk+5XCaKJcPhkH4/XREHQYDQ0m7tXC5Lu9Umigr0el0SmRDF6VyuoQvGZ2eJo4ROt4svNDIZB9sy8AMfz/OwTJtgOEBoAqSi3egyHIQEoU8YhQhNQxcDkkTR7faZm59lZfUicRKikBQzU/jSIdYUUnkU3TIGJpZuoRkWGTeLZeiUiyUgTRX3vAGJjEDTUFISxz7dXovx6TlMQ0cjIYx8kiRkbKzM0aNHmJmdBXQEGmHk0+10EbrOxESV8fEJvvylr9LtthBCo1zKky9kGBvbh5KpSEShWAJNEIUJ3sCn0YoRMkbGGrpmIkxIVCp9KjQtPTdIVdxQSKVte2ULNCHRdR1bpHrnQjcwDRtdNzAtI7X3FGlWwO976JcFS5IEmUiQoKQCBI6TQSqB0L81k4QRI0aM+F5wVQfmoedRLBbx/SGrq6vEcZKmonNZWo0aayvL+EOPqekpNKHRHwwwDJ18PocQ0KjXaLWazM7Oks/lWLm0Qrkyhm0bNBster0e+VyeQjGPrgsC38d2M1QqY6kJRhKjbRsetNpthNCI45h8Po9pW7RaLTLZLPl8lk6rydbmFplMllKxhJQxvUEP2y4hpaTf7+P7AUopCoUcQhh0O30My8aRGZSKsSwL0zQAnUK+xJe/8iWC0Etr34YDmBSy4+h2FkyffXN7OHnmIRw3i2EKLNNBCMhl88RxjJNx0Vsmfb9LsO36E8UBQ39AJpN6JieJS+APcCyTbNblwMG9DDyfZrONNxgShybD4SCtJ+eySAUvfvGPsLXVZHFxkSj20fUM5VIeQzeZmp6g1/OYnpri/IWLxHFM6CcYIkEYJnGcYOgaMlFIXaUmI1paa4bUeCSRoAmBUumqV2gaoGFaNoZuYRhWqnVu6OhCSy04VUQcRAhNkcQRMk5QsSSOYqIgBl3DG6TmIEkin4SrecSIESNSrurA3Ol2yRfyDH2fldVHsG0X27bxvQGXlpcRGszMzjAxNYFlWXQ6HTY2NrBsG8d1iGWqeZ3JZqk36piWgYZECIXrOtiWmepTR4pyqUAsFYN+n/GJSXq9HqaZmltsbW3gWjYz09OpP7QusCyLcqVCotKO69rWFrWtGvMLC4hcBj8IGQ76BGGwre/tEUUxpmUyDHwmZyYxHRvQiJII2zJIZIzrugjNZGJiinqzxtj4DBnXQUpBr9un12phZwWlyjiDpiJvV0ALMU0NoUlkHCM0A8d2MZROkMkQqxClNIKhJAwDZBIyNlaiUimRcS1sUWR9Y4V+v00cB2xsrFCrtYjChGI+R6VSRilJu9WgWCwzGPSQScBTn3IzSRKhkOgadNpNNA0OHDrM0WsOc3HpAkIIZJKQKElEjCH0bT9tSZykVWYRJ2jbdV+l0kCrZFpXBtA1PZ1LFvqOFahppJ3gutBSE44oJI4TlAx3TDmiKHUkS+IYlWgMh0GaKje+PyYWI0aMGPFYXNWB2bJtVtfXMS2La6+9jrFKlayb4dLyEhPjVQxTMDU5yeT4OEPfx9StVCmr1aVYKJHJ5tENi3qrTRwnVCensF0bzxug64IwCJBRTBzFxHGys0o7e/Y0tm0zt2sWXS8y7HeYmZpi9+7dXLiwxNZmjXqjzsGDB8gXcrQbdYbDIWNjFSzTIIkCDJEaIYRRiKYJ2p1U5cow0/+SoeelEpcyYaI6RjabJZGSTrvNxPgEq5cuMeh3UTKiKwRCN1EqNWhIIqiWp1lbvoTSTMYmJtCkh+3EWJogUTGuZRIpRSGfBSQkHloUIdDQZIBtRhSyEPTrtJt1Oo0tDF3SqG2yvrbOhQsrOE6GRr3O1OQEURLTbLQolnq4mQyNZpNao06n02Fqaorx6hiVSplut8PZM6e46aZrWL60yOK5RaIwQQqFMhKEFqe1ZBRoGobSUVKhG2I7lQ1JIpFKpg1g25biuia26/oghEITaru5LQGVoFSETBKiOHUVU5I0MAcxSSJRmkav10E3NBI1WjGPGDHiyeOqDsy5XGpPt3v3Hnbv3sP01DSDfp+zZ/xt5a2EZquZdgLHCUEYomRqSGFZDkr26XR7KDRa7TbFchHLMul12tiWhZTJdk3aIY4i+oMBtXoDy3FwHIt2q0XGsbn+uqN02l0ajQaum6FQLKHrOp43YHy8TDaXJV8sogvBMAg4f3EJwzDYNT+PY6QzuOMTk2hCRwiNMIzTOVxh4PsDQKPd7hDHCUIIWo0WrWaLcqkMKAYDD03EGKaJ67pUJ8ZIYhh6Mf3+gH4nIl8ymJjKkzEzID10wwCp4VqSxMkQDxXK0HHNPLtmZshYGvWNZTZWVqhvbhDHCe1unziOKRYL2/Z4OjJO6HY9svkcBw4exjTNtOHKNJmcnEQpydLSEvfffx/XHD7C/PwCK6urXLiwRLGQJ+PaDAYhcZzWvKVMiGMJmtpZTce6hpHosN2hHW8HZg2dtJIg0UQaTDWZkCSpDaQQGgKJkglKxiRJjJQxcRyRJIokltuGJGnXdywThKmj4lHz14gRI548nlBgfvvb385f/dVfcerUKVzX5ZnPfCa/93u/d4W+ru/7/Nqv/Rp/8Rd/QRAE3Hrrrbz73e9mcnJyZ5/l5WVe+9rX8tnPfpZcLserXvUq3v72t+/Ywn2r9HseruvQqDcJFwJcx2F56QLNZgOESoMPikazSRAEuG6GXD6PaZnkcjkGngeNBrWtLUzTot1sEfke3U6byclJ3IxDfXOL8fEJypUKU1OTZDIZYplQKJbodNqcfPghCvk8uWyOXK7A9HTqnTs5OUUun8F1HDY3NimWKyxfvIhpmli2S7Vaxc3kUFLR6nQxTAfHzRCFIbmcja7rWLZNo9mk1+szHPpMTIxTqVTIZfMEQUi73drW8LYZ+j79QZtsNoOmTRDHiihMMAwLXbeJQ52lxRaZbJ9KNU9prIgtLIQwkaZBaEKsGUxX5tg9vZvNlTWGvS4bq6tcOLdIt9cjUyiAMOkNQgYDj/X1LXqdAVIq3IzLzMwMBw8doFZrUatt0Wo1qJTLTE5UyWVdlpdWWVvdotvtIBUUSyWybgbLNAiCAYHfB0uQSCDS8FVEOt2kYej6zniXYnt8SlNouo5UCkNKDNPEJF0pK2WgaQqBQkOiVIKUEUkUEMdxWsOWCk2k6miaJlBCpWpj36BfPWLEiBHfT55QJPz85z/PbbfdxlOf+lTiOOYtb3kLL3zhC3nkkUd2xOTf+MY38td//df8r//1vygWi7zuda/jZS97GV/60peA1HP1JS95CVNTU3z5y19mfX2dn/mZn9lxr3kiWIZFpVSmtrnFhXPn8Pp9altb+L5HJpOlUMgxHHq02x18PyCMEsIoQjcM6o0Gw+GQ0PcxTZNyuUyv10PX0pnhC+fPc+jgQcaqVTxvQGWsQhzHGIYgDiJ63Ra2Y5PNZSnmCxQLRdxsDt0wKJXL5IsFlpbO42ZcrrvhRsIoZnbXAijFYDAgjmPGxqpYls3q6ioDb0gURRRLFXRdR6oE3TBQilRjWxiEYYxp2jRbLXTTwHZsXM0hjBNimeANPXq9AcsXV3BcCxAoKVAxSF0QDMHr+bTrAYYlqUzksFwN282R1Q1KEw63POWZ6Ak0a1vIOMYbeAhh4LhZSqUK5UqVIJIMhwFj5RL93pBYgiY0MpnsjvZ3uVzGMAwc2yaXydLr9Ni9MI+mpZ7YGxub+L7H9ddey675OQZelwfuv4f1jVVkokCl8piJSgNtLFKrTW27S1vKdKWrGyZoGlEcY6PSdL5Kfa+FdnnlmyDjEJVEKBmnq2gALV2Bp9lwhRJaWsseZbJHjBjxJPIdmVjUajUmJib4/Oc/zw/90A/R6XQYHx/ngx/8ID/+4z8OwKlTpzhy5MiOQ83HP/5xfvRHf5S1tbWdVfR73/tefuM3foNarYZlffNRlctC4K/8V68iikMmJqos7J6nUilRr9W5cO48ruswNTWJlAkXli7SbrUBWF9fx3VdFhZ2Y5oWUkoajSamaRElEYqEoZfOIQtNY2F+ARkndLodDNPE90P8IKBQLFCplAg8n2zGZbxaxclkyReKNNsdbMfdVsNKz1WiYdsWup6Kh2TdVCRfKRDCoNVqM+j3UcD0zDSNRoOh79FsNshmU6nLfr/P7OwsZ86cxjAMMhkXpRQbG5vYtkOr1QJSl56LyxcxDZ1cPofrOMRJiDAEw/6QKEp9poUh0YyYTEbHNOFZz7yFmckJim4OLYZOu0UQhDvvW6wkQRiz3XPF5mYN34945OQpgiBkdn4XU1OTdNpt/OGQQqHAWKXC3NwcXn/A9Ow0hm7gDX16vT66rtNpd0hkglIS09Q5fvwhlpeXUSpB6GK7gpzWjhVsN4FpxCrtiDdME7Xt5mVZVqryZZpYpoGuga6R1phljJRp0E4USJWaaiRKQ6bTaduBPrUDvfuL50cmFv/E+FYNBkaM+Hb5vphYdDodgB07vHvvvZcoiq4wJj98+DDz8/M7gfmuu+7iuuuuuyK1feutt/La176WEydOPKbRexAEBEFwxYsDmJub4dy5szRbNbI5B11oDIc+mWweyzKpN9okScJWrc7DDx8nn8sz6PepVqv4vo9l2fR6Pba2Ntm1a56FuQU2a1vk8nk8zyPrZMhkcwRhQNEwCKOY7mCI6TjohomuW1i2xLRMNjY3yBdK9AceCEEmm+f48YdpNBpcd9212BmXbjdBSkkumyMMAqIoxjBM4shDJXI78kAUR9i2yaWVGuvr6+i6YHZ2lmq1uiNPmSQRYShIkgTHSW0ps9ksQmjs3r1AHEe0Wi28gQdS4WQspIwpVypkMwXCMKbZaJLIiEEn5MYbj7Iwe4iMbaAlMZ1ek4EfUq832NraotVqEQSpg9PY2Fj642Z+niBKyBeKJEmCYRnpbDGSOOviOjbZnMNw2EPTFZ1Og0azycT4BIVChs3NGtMzUxSLRU6fPk273Wb//oMYhk2ttkEY+mga27KZCVIm6UpXiO1VsSROEgzDQGgacRhCIhFSIqMQQ2iYhkBTKhVw0TQMYZEuigWG0IgVxKkF13bgh0SOtLJHjBjx5PFtB2YpJW94wxt41rOeteM/u7GROjN9ozH55OTkjvn7xsbGFUH58uOXH3ss3v72t/O7v/u7j9quaYps1iGMQ7a2tgj8iHw+lYMceAGNRpNWq7kTEHq9Hkmc+jZ3Oh2kVKytrVGtVncCmRA6hUKR66+/Ad/3CYY+ShNYjkbS62OYFrNzc/R6PfoDD8vQWV1bYzj02Ou46KbF+to6sdTI5QssLV2k0+0yXyrQavXp9/uoJEHXNBzHIUzSemcUBpRLRXRDUK/XkEoiZUKxmCeKIs6fP0ccR0xNTTE3N8fGxhq12ha5XI5SqUgUxdTrDQzDwveHlEpFNA3a7RaGYVAslMnmMxhCUCgW6fc8slmXMIyI45hds3vJZ8eorV/C8zqcO3eGk6dOEfg+mUyWXrdHxnGZm5vDNExKxRL9Xp9avUVzx6TeJ5NxuemmG1AoLMvAcdJar64Lli4usbGxiWma5PMJw+GA9fVV1tZW0TSNa689imma7Nu/n163TbfXptPuIHSTfq9HvdHA833q9VoqDINCGKlgiDJNDKGnVlNJgqkL0HWE0tGNy4pgOkoz0myBJkg0ha7SijUaSBRSjYLyiBEjnly+7cB822238fDDD/PFL37xu3k+j8mb3/zmKwzeu90uu3btot7cIpYJuVyOytg4tuUiJZiWQ6PeIp8rsHzxIr1+jyRJ6AUBtp02SsVxBKTpU8/rE4Yh55cuMDUzh64bXLq0SnbbTKJYrhCGIYNhyPTsLhw3Q6fT4+i1R5BRxMaGhT/0MEyTdqfN5OQUhmFSqYyxe/ceGo0mhw4eIMoG+N4A29SZnpwiiiIuXVpBSkXGdZFxgB/GbG6ugaYxMTHOhQsXME2TqakJgmBIq9Xc1q7WqVTGaLc7SAnlcpk9e3bjODZRFFEuF4njkGajju97DL0s+WKJwaCLJjSKxSK2bSKERT6XZ2K8ysb6OmdPPcJDx+9nbWMdXejs3bcXkoTxsSoCDa/XZ3x8nHajSbPTZugH+MMBmYwNJOi6YPHcWcYnp1jYtQuhaVxYWqLf7yEMi0JpnK16B38YE8eSjY1LLC6e4frrr0chyeazZDJZ/HCIY7u0ZJdGo47tuExMzaKkxDAslpcvIGWMSiRKKWKlgUHq3RzHKMtM5Tx1DQ09NfYwLTRhAhpK09AATSmUtp0i1zTC0Cci/p5f0yNGjBjxeHxb7aeve93r+NjHPsZnP/tZ5ubmdrZPTU0RhiHtdvuK/Tc3N5mamtrZZ3Nz81GPX37ssbBtm0KhcMUNoN3poOs6mjDw/YDBYEijkdZZJ6YmCKIAN+sSRXHa6BVGREFAt9uh2Wxur/ICDNPEyWTI5bO4TmoTGAY+F86fo9Fs0O12aTSaVKtVjh69lr179/HsZz+H8fFJcoUC+XyBMIxYXV1FE4JcLvVbzufz3HDjjczOzHH//fcT+gHTU1MgFZVK6getC0GlVGRsrILtWDi2hW072LbN9NQUBw8cIPADNKVRLlUY9AcMhz5SgmU5ZDJZHCeD5/k0Gk1qtQaLi+e5uHQR0zAYG0vLDP1+D98bIjSdrY0aa6vrrK2u0+91qVSKeF6P8+dP0+u3mJwc5+abbuLGG4/hWCaapmGaOrtmZzmwdy8aCt8fEkYhq6uXmJwc49ix67jp5hs5eOgAs7O72L//AG6mwMnT5/n8F77CPfceZ/HsEo88cooTD5/kS3d9lcXF88RxTKlUwfMDwjih2+vT7w9wbJeLyyusrm+wVW8y9CMCPyKXK7J7YS8HDx7GshxAoJRGGCUM/RA/8PHDkChOiBNJkkrGIDUdJQRSEyghQDcQhoVh2limg2k4CM1AJToyuaqnCL8pGxsbvP71r2fv3r3Yts2uXbt46Utfyqc//WkAdu/ezTvf+c5H/bvf+Z3f4YYbbrjivratCf/1t8OHD+/s88M//MOPuc8v/dIv8ad/+qeP+djX35aWlgC466670HWdl7zk0Sb0S0tLaJrGAw888KjHfviHf5g3vOEN38nbNWLE950n9A2klOL1r389H/7wh/nc5z7Hnj17rnj85ptvxjRNPv3pT/Pyl78cgNOnT7O8vMwznvEMAJ7xjGfwtre9ja2tLSYmJgD41Kc+RaFQ4JprrnlCJ+9msmgiDcyW5WLbLq1Wh2arRTbjYpiC3Xvm6XYb6TiMjAmDkCROWFlZoVgspD7Klsnk1BS5JEsU+kRhwNbWJo+cPMn+/Qeojo8zO7cLXQjOnztHsVRi165dRGFAGEUoTaDpOqVymSiKOH7iBHv37OfixYvMzs6xf/9+WvUCJx55GNMwuPbao9TrdaSUaFpaq7ccG1DYts34eJVOt8vq6hp79+whDELOnz/PzMwslmmzvHIJpRSmmdpWOk4WDUVvOKRcHiNJEjKuk8qIBgEZN4PjuAilyOayzE3PsLa2hkDDNAWVSoHN9WUG/S6maZJxXXKZHEN/iK3rrLZahJZBEvnIWJHJZrBzGSpTExw6fBihCXq9PsIx6PcGtNpd2t0BMoFms8nExBRRHKdGFNtd074fsLq2hmEYHLv+esI45uHjJ2l3OpimydzcNIVcnkwmTxRCo96mWCqRy0qy2Tw3HruZG44d4+Mf/zjNVjNVA1MKpWsYQJjEaIlAlyqtMavUV1oYOprQMXQdpQQaoBBICZoU6ELBD3A6e2lpiWc961mUSiX+43/8j1x33XVEUcQnP/lJbrvtNk6dOvWEjnf06FH+7u/+7opt3zj2+PM///PcfvvtV2zLZDKYpsmLXvSinW0ve9nLuPbaa6/Yd3x8HIA777yT17/+9dx5552sra0xMzPzhM5zxIiriScUmG+77TY++MEP8r//9/8mn8/v1ISLxSKu61IsFnnNa17Dr/7qr1KpVCgUCrz+9a/nGc94BrfccgsAL3zhC7nmmmv46Z/+ad7xjnewsbHBW9/6Vm677TZs235iJ2/aCE3geUNqtSYz07PkCkVqtU3Gx8dIZESn0yQMQ4rFApVyBRnHhGGYrlZ1Hdd1CEKftbW1tDmqUOTC+bNsbm5RrZR3fHwL+Ry249ButbFMk1OnTjE2NkbGdalUq+TyWbqdLpdWVwmjiI2tTQrbTVHDoY9lOxw+fJhut8up02d42lNuplqtMhymOt8rKytpZ7HjUBqrUiyVsEyTJJG4bobZ2V34fsDE5CQShed5oFLjhSAIGBsrMz+/QLfXod/vg1L4Qx/LMpjYfp4w8MlmMvR7Axw7DdxZ10HXFMVSHiHmGfQ6yDgm8H0MXaff7XD44AFmp6fpdbp0/C6BLxnGEU42g2E4xFKj0erS6XRZuXSJIIqIE4lt2wwGA0qVMrl8jjAKsW0Lw9ApFgrbzlwCTTco5fJsbjXxvIjqWJEoVDhulumZOZrNNkms0h9RhkGjXscQGgsLcywsLNBqt4ll2hymaQaaDmGcoESMFseg6+hKoZN2Xeu6jqbpqYHFdgzWlEbaA2akqe8fUH75l38ZTdO4++67d0YcIQ2wP/dzP/eEj2cYxuNmui6TyWQedx/XdXf+tizrMfft9/t86EMf4p577mFjY4M//dM/5S1vecsTPtcRI64WnlBgfs973gOk6aGv5/3vfz+vfvWrAfiDP/gDhBC8/OUvv0Jg5DK6rvOxj32M1772tTzjGc8gm83yqle96lG/qL8VlIzJF0u02xH1Wo1+t4cQOpom2LAdoiikWCxTrVRZu7RCpVQkCHySxEHXDSYnpyiUS+hCp1weQ6mEjbVLmIZgbm4GIQS2Y2I7dqp4ZbsUigVMy6Lf72+n2PM7whe2m1AqVahUdDbWN6nMlRkrjxFFIUrX0HRBsVjiwoXz3H///czPz5PL5RifmGD50goAwg/I5ksIIQiGARMT45TLRQqFHOvr63Q7TQ4e2EccJySxoj8Y0u12CYKQKA5xHIeJ8QmQknwmgz/0iKOY6lgV181hmSadVpt6vU6+kKfX6XP+/AX27NmF0MHN2PiDPjoFer0es9OTyDjB83xqjRYIHdPJYgodLwzZqNfxBh6tZpuLF5fp9frkslkcywapGCuXUlvNOMbNZiiXS+zdt5uxcjEdTYpCet0BJ0+eo15v0Gr30HQb3dCZm9PIZrMsLMwzHA45d24Ry9IYNys4GQc3m6NcHkuFRlSSGlZokLBtciE0YiSGJlFCgZBIFaNrqbRqkiSoZLsGLdPgnKqIPeFL8aqg2WzyiU98gre97W1XBOXLfGPT5j8W/uf//J8cPnyYQ4cO8cpXvpI3vOENvPnNb05n0L8DHm/aY8SIJ5snnMr+ZjiOwx133MEdd9zxuPssLCzwN3/zN0/kqR+TUrG4vcpWTE5McPHiJbLZLOVymXK5jGkaZLIO994zoNPrslWrUy6lq/tYSiSK4XCIoVtkMlk8b0AYRARhRBTFoGnEUqPf9zh82KBcGSOTzdDtttnYWGffvn2Yponj2Fi2heu6jI1V6HS6TE9NIzSdtbU1ms0GUiXkc1mUlOzevRvbNDGMtDYulWJiYoKhHxCEERsbmxRLpbSzOp3zYRj4mJZFdbzKxsZGWlt2s+TyWQxT5+zZM6mbVcalUMhjGSa6oW/P9qamDIahI6WkvL0a7/W6BH7I8sUVdF2wd988oWlgGgbRcEgmKwmGQzY3N1GJxLRt8sUisYRe32NtbZVavY7ruvS6fQaDIYV8kUI+Tz6fQcpkp0NcSoXtOORzBaIgwh/6BMEw9Znue6AgCEJ8P6DVbFMu5oiimHa7TTbnsmt+DsPUaDZb2+NR6WvR9fRvwzCRqcJ2amwtFEpLPZzRJGgJad91urJGbbtIKYFAA7Wts62lHs4/iCwuLqKUuqIG/Hj8xm/8Bm9961uv2BaG4aPKTcePHyeXy12x7ZWvfCXvfe97d+6/+93v5r/9t/92xT5/9Ed/xCte8Ypv6bzvvPNOXvnKVwLwohe9iE6nw+c///lHLRCe+cxnIr5BtW04HF5RF/96Hm/aY8SIJ5urusvF84b0ev20jprNMDMzTbeb1kld1wEUy8sXefjEcaI4ptlqYplpM5PStFSSU9NYra8DGoVCMZXJRJDIAbl8gUqlQqfboVav0el2iJOEVPBCsbm5huuYOI5Dq9Wk2aizd+9edu+ep9Fo0e30ELpGvpBjc2ODsUqZfC5LLpOh3+uyublJpVpN1b0sm+nZXZimxVatvjOEfnbxHOPjVfL5Irbt7jQ6NVbWKJfL2y5J4fac8xjdblqjzeXyBL7P5tYW+VyOnGWhBJhCEHg+tmNxfqnO9MwMSZxwcWkFmUj27l1AszQG3R6DgZfOezsOYRhSqY4RRjG+P6TdbrB49hS2nUVJ2FzfYHJikkq5QhwFJEmEZZkI4aZ+1IZJq9XGG/pkXJdBb0ittoVSijBMCPyIMIiwLZvA9+n3+6yurtFoNojjiKHnk8tlmJ2dS1+jYTE+PsGhQ4d54MH7aTTr6EKh6Rpa2tuFZmxraIuEREUkSkeT6YpaytRaUlM6pNYdqdgLaVz/QeSJaAm96U1v2smCXeZd73oXX/jCF67YdujQIT760Y9ese0bhRNe8YpX8Ju/+ZtXbPvGkcnH4/Tp09x99918+MMfBtLU+U/8xE9w5513Piowf+hDH+LIkSOPeu7H4/GmPUaMeLK5qgMzpKlxwzBS3egoIZ/Po5RkMBhg2ybHH3qQ4dBHKYUfBLR7XSKZoGkaQRgyGAwQusn5pQvMzc6xZ/ceisUiGxsbxHHE5PQks9ocSilc12Vp6QKapjFerTDo99nc3MAwdM6fP0+tVtsR+tA0jTiWdLqpIUY2m6FUKhJHAXESMT45Qbla5eTJU8RJQrFUZnpmFsMwyRdKtFqtVDBET0VTDhw4wMbGBqura3gDD0O36PV6+L5HHMdsbW1SLpeBdMY8CHykTJhbmGdtZRUpNMpjZfyhxzDwCIOQiclxlAa27RIEQzqtPu3WgLFKNhVPsVwMI7WkNEwb23Xp9mtcuHCepYtLOLZFFEcM6kMyjotjmQhNYhoC27GJogCEIpPJYpkua+s13EyOIIiJgxgl07JDNmuxsdmm3x8QBBG27ZDL5fGGPoOhx8TkJI1GA8MwKJUqHNh/mF6vRxzHHDlylB//Fz/Jxz72UTa31pFajCYUQkgMQyB0CSQolRDLEGKQBkCaidCUQKoEgY6mtG2/qh/M0HzgwAE0TfuWGryq1Sr79++/YttlIaGvx7KsR+33jRSLxW+6z+Nx5513EsfxFc1eSqVNkn/4h39IsVjc2b5r165HPc/X17C/Edu2n3Bfy4gR3w+u6pxdu93BMAxcN0OxmNYsW61WuiJWkkTGCF1HkUouXg7E3W4X3/cRup6aWpgmpmniDYecOn2a02fOYFgmU9PTxHGMaZo7gcH3fQ4ePMjRo9ewd+/encd6vR5TU1NYlkUURYRhSLPZZDAYUCyV2Lt3D3EcoxsWmWyeMJZMTc9w3fXHyGRzHDhwGCF0li9dQkq5k3au1Wrs3r0bIQT1eh3XdSgUihw9epRKuczUxCTVytjOl6ZhpAIa1WqVqalpCvkis3Pz9HoDPvPZz9Fst/BDnyAKCKJ0njuKIuIoQdN0Tp04Ra87IJ8vUyhWqVSnKFUm2HfwMKVKFc8P6fX7RGFE4Pv0Oh3GxytMTI7jujaGriF0EALK5RJKpS5OiVKUKxWuve564liyVW9x+sx57r3/Ie659wFq9QaFQjF18pIxnj9E001y+RL1WpPlS2vESVpTHw4D8vnCdhd+i5tvehrXXHsMTTfRhMAwdIQu0lqxliC1mFhFhElIrKJ0/lnF2zaZ24piO1aPGtoPaGCuVCrceuut3HHHHQwGg0c9/o1jjk82cRzzZ3/2Z/z+7/8+DzzwwM7twQcfZGZmhj//8z9/sk9xxIjvCVf1irlUSpuk4tgnm82g6zq9Xo+1tRW8YR/DEPj+ELbTlFIqgjBtkFKA53m4rkuSpB2/ruuwubVFu9siiHx6vS6gKJfL7N+/n35/QC5XYDgMME2bTMbaeU5NEygF+XyBwWBAoVCg2WxTKpVSlbFsBiEE1WqVTqdDEKS15TiR3HjjzbiZLMIw2LtvL+cWF9nc3KA6VuXYseuxLJPTp08RxzGVyhiGbnDikRNYlsHU5AS5fB4pE2w7TTnXajW63V6a4s+4lCsVCqUSmoB+r8vF8+cwLQPbcvCGA7TAx7bsdDY5DFlausS+/XvJ5VxiGaP6PtlsiVaniW44mFaGXK5Iv9dnfDzPWKVEJpPFti3arSblQmV7DKyHYdj0Bz5J32fXwh7qzSatboeB55ErlljbrG1riYNpmViuja3ZaEKwsbnF7MwMYRCSzWY4d/4CU5NTZNwsjpshm81RKlXIFko85zk/zP0P3ku/3wItwtA1dJEOQ4GGVAqxPa6VjlZtd2KjoX/dGjmtM/9gBmaAO+64g2c961k87WlP4/bbb+f6668njmM+9alP8Z73vIeTJ08+oePFcfwoxT5N065IVXue96h9bNveyfA8Hh/72MdotVq85jWvuWJlDPDyl7+cO++8k1/6pV96Quc7YsTVwFUdmNfX1jh46CC5XI44TldAlcq2S5SeNvF0Om2S7dR1Ws8MieKYbC6Xmih0OkRRTKFQRAiNYimH42RwXZdWq8nY2BiO42ynXHP0en2iKMbzhgRBmziOGQ59JienUrESw+TQocNIKSkWUqnMZrNBNuMipdxW6kpA0zh56jTZbI7JyWmELrbrqivkMi5xHLFV2ySTdXBdm1KpyMrKSqrzHQyxLQvDNPCDgGp1DK8/SEecDJPxapXVtTV6/R6GaZDIPqVSmWKphD8M2LWwm06nhZQSz/dw3QyHDh9ic32NWEasb2yyeP4C+Xxhu+nKoFZvUygUKRbHqFQmaDe75PMl5nbNMj0zSRCEJFJiWBbV8Qna7TZRJHEzWerNDVwni5vJsLaxwcDzaDRbZDNZiuUypmnSbTToDwbYjsPE5AS2bWNa6Zx2r9cjjFK/6erYOO1Ol0p1nFgqJipjCMPiWc9+DpdWl/jgB96PMBRCJIhtdS/9ssoXAiUTpJZsW0OmOtpSpYZSYtudSv0AzzHv3buX++67j7e97W382q/9Guvr64yPj3PzzTfvTF08EU6cOMH09PQV22zbxvf9nft/8id/wp/8yZ9csc+tt97KJz7xiX/w2HfeeScveMELHhWUIQ3M73jHO3jooYdGhhMjfuD4jtylniwuO3T84s//AoNBn3whR38wII4Tcrk8UZSwtbWJ69ocP36cM4uL21/MAGmtuFKp4DgOuq4zP787XeG2mszMTuHaDhsbm2iaxvz87p0AvmvXAkLTSRKJ49isrq0QhuHO/OXa2hozU9PMzMwwNT0NSiGlJIpCwjCgWq2i6wa9Xp/F8+fQ0KmMVbC2Vw+elzZbuY5NuVyg2+2ytrZGGIaMj4/T6XTpdnuMjY0RR5JLl5ZJZCof2Wg0uOnGGykVS+i6zsWLSzRbDeIkwbZtstk8/f4A0zQY9HtEUcD09DSJTDh58iRxFDLodhmvVpES8rkCQRRhGIIgSDMS119/PZsb65w8+Qj9QY/DBw9SLhcQhkYiJbWtGrou8DwPy7YQmkG702V9vcax629gq1nn4vJFBgMPz0vFUFDQ6/VotVoMBgNMy6RcKpHP53HdDJ43QDdEWsN2bObn59mzZw+ZjMPBQweZmZklTiS+PySTNXnta19DGPUxrVRLXTc1hKGn3duGiSFAaAa6bgEGmkxvQunb41LgBwGf/cK9I5ehf2KM3KVGfK/5vrhLPdnYtg1aGqBM02Ju1zxRFKfiESja7Q65XD71N07SGuJlkYq0kaiEbdskSUwmk0HTNNZX13eCdj6fZzgckslkmJ6exnFsMm6WTrfD6toKjpMG1OFwSL/fxxB6WguubXHp4hKTk5O4rksQhhRLZWq1Ot1uD4liajKtX/t+wKA/IJfN4doOhq6TzbrEcUKtVqfRaABsN0aFqRGDhMFgkKbNhwNCP2B+1wIbG1usrqztNJplMhk6vS6DwWDHXtJxXCzLwrFtHMchm8uRzRa5cOE8Yckjl3Fp1JusrK5SHR/HMCwGAw9N6Txw/0PYlkmvNwBNJ1+oYGdcLDO9jIIAlJToRgapUl/rerNLvlQmVopLK6sMh/52wA1Sgwqh0+326fUGaTOOZaNp+raMahNd1wmChCRJSGRCu9ul2W6RqAKdbp9MtsPZs2coFLJMTVWZnphgeaUHcrtDG9JsNgqlEpTadqbSFEJoqbmFpqMSDSXT36jf6XzsiBEjRnwnXNWBeTDoY5g6MzPTNBpN+r0es3O7WFw8z9TUNFJKwihiYmKSfq+HkhLHcSiVCkxMTDBerVIdH2dpaZlut4umaXjeEMsaoJTCcVxs26BcLrO5ucGFCxd2gnmplKcyNkYcJdsOTjq7dy+gaRq2ZXL+3CKVchElE4rlctrIVa8zGAzQNEESKzY3NwnDkMnJSZI4pttJNbxBYm1rVB88cBDf92m1O+RyEEcxa6vrqXGDTNANAzdjYFk2jUZz204ygm3/5/HxCSYmIJstsLGxibHdxW6aJprQ6Q8CTNMl4+TQNR1N09g1v0C+UOTSpUtk3Ay5XJYkkXQ6XSzLREmBk3G5+5770i5YpahWxwkChZIKYboEQ4+BFyERuJksD584SZJIklgyjH18z9/uHg/xvCGJlECYBkclCMMI00wb4FKPbkEUJ3S6XYIwYGNjkyiOWVm5xPKl8xw5fJC///tPs7JyEUMXaCpJ5TaVQlMSpbb7DJLUuzlJYlA6QqjU6xGFpmnIy2bTI0aMGPEkcVUG5svZd0WaGoiigMFgQKfbp9f3ME2LdruDUpLq2Bi25dDrdYnjaLvppEgURiRJgmVazM3NEYZhmoK1bKIo3pZtFDtd16Zpkc9n0XWNer0GKDzfI/BDSsUScRzjug5RGNJutTh8+CBDf4g3qNPtdUmUhqbrBGGIoZtsbm7S6XRwMy6+P+T8+fM73eTZrItjOyRJgutkKJcrFAslTp06vW3W4eE6LkPPw7As1tfXmZqcwPcDet0O7XYb3/eZmBhnYmIC0zIoFIvEUYxpGghh4HlpOlkInSRJyOUKuG4GXVMkSYgfDJmammLoDVNFrlaLXDZLtVolny/gRyHlSpXzF5YBjZW1Gq1Wi1Tv26LVaQGSbC7L0tIyW1ubxFGETCTewCdJEqSSWLZNNpsjCiPiOCEMEuKoj+1aOI5FksSpLGouh2EYDAYei2fPYVgm586fR9NgamqMr91zN+fPnSFOYoSUaCKV4FRSIQyZNn4lqWCMoadlDU0ITF1HatsCI0pDJunq/OuvsxEjRoz4fnJVBubL6d33vf/9T/KZjPhO8b9OEvHr8fzH3Px9pdfrPWbj0YgRI0Z8L7kqA/Plmd3l5eXRFyf/V7Ho0qVLo6YVvvP3QylFr9cbORiNGDHiSeGqDMyX9XCLxeIoEH0dX+9VPeI7ez9GP/hGjBjxZHFVK3+NGDFixIgRP2iMAvOIESNGjBjxj4irMjDbts1v//ZvjwTotxm9H1cyej9GjBhxNXNVKn+NGDFixHebkfLXiO813+o1dlWumEeMGDFixIgfVEaBecSIESNGjPhHxCgwjxgxYsSIEf+IGAXmESNGjBgx4h8RV2VgvuOOO9i9ezeO4/D0pz+du++++8k+pe8JX/jCF3jpS1/KzMwMmqbxkY985IrHlVL81m/9FtPT07iuywte8ALOnj17xT7NZpNXvOIVFAoFSqUSr3nNa+j3+9/HV/Hd4e1vfztPfepTyefzTExM8GM/9mOcPn36in183+e2225jbGyMXC7Hy1/+cjY3N6/YZ3l5mZe85CVkMhkmJiZ405veRBzH38+XMmLEiBH/IFddYP7Qhz7Er/7qr/Lbv/3b3HfffRw7doxbb72Vra2tJ/vUvusMBgOOHTvGHXfc8ZiPv+Md7+Bd73oX733ve/nqV79KNpvl1ltvvcKk/hWveAUnTpzgU5/6FB/72Mf4whe+wC/8wi98v17Cd43Pf/7z3HbbbXzlK1/hU5/6FFEU8cIXvpDBYLCzzxv//+3de1RU9fo/8PdwmREEZrgoFwNvoCNqeiQh9BvYkbxmlpZoekqPZSaaipf0tEw7HRdmx7LM6qyzEI4tL6RprFileRQQTbQQROOSogQnQEtlwBsX5/n94XJ+bmeUQZEZ9f1aa6/V7Oczn/18PrOXD/vWnjsX33zzDbZs2YLMzExUVFRgzJgxpvjVq1cxcuRI1NfX44cffsB//vMfJCcn4+2337bFkGymqqoKs2bNQpcuXaDRaBAYGIhRo0Zh9+7dinYJCQlwdHTE+++/b9ZHcnIyVCoVVCoVHBwc4O/vj9jYWJSVlVncpl6vh0ajQVVVlcV4eno6RowYAW9vb7i6uiI0NBTz5s3Db7/91qy+Bg0ahDlz5lgxC0R2TO4z4eHhEhcXZ/p89epVCQgIkISEBBtmde8BkO3bt5s+G41G8fPzk/fff9+0rrq6WjQajWzatElERAoKCgSA/Pjjj6Y23333nahUKvntt99aLfd74cyZMwJAMjMzReTa2J2dnWXLli2mNoWFhQJADhw4ICIi3377rTg4OEhVVZWpzWeffSYeHh5SV1fXugOwkVOnTklAQICEhobK1q1bpbi4WI4dOyarVq2S7t27K9oGBwfLokWLRK/Xm/WTlJQkHh4eUllZKRUVFbJ//37p06ePhIeHm7XNysqSoKAgefHFF2XFihVm8c8//1wcHBxkypQpkp6eLqdOnZLMzEyZOnWqzJ07t1l9RUdHy+zZs5s5K9cYDAYBIAaD4Y6+T9QUa/ex+6ow19XViaOjo6JAiYi89NJL8swzz9gmqVZyc2EuKSkRAJKbm6toFxUVJW+88YaIiCQmJopOp1PEGxoaxNHRUbZt23avU76njh8/LgDk6NGjIiKye/duASDnz59XtAsKCpIPPvhARESWLFkiffr0UcRPnjwpAOTw4cOtkbbNDR8+XDp06CAXLlwwi904dxkZGdKhQwepr6+XgIAA2b9/v6JtUlKSaLVaxbqPP/7Y4j86kydPlkWLFsl3330n3bp1U8TKy8tFrVbLnDlzLOZ78+95u75EWJjJvlm7j91Xp7L/+OMPXL16Fb6+vor1vr6+tzxF9qC6Pt7bzUVVVRXat2+viDs5OcHLy+u+ni+j0Yg5c+Zg4MCB6NWrF4BrY1Wr1dDpdIq2N8+Hpfm6HnvQnTt3Djt27EBcXBzatm1rFr9x7hITEzFhwgQ4OztjwoQJSExMvG3fZ86cwfbt2+Ho6AhHR0fT+traWmzZsgWTJk3CU089BYPBgKysLFN8y5YtqK+vx8KFCy32e2NOTfXVXHV1daipqVEsRPbgvirMRAAQFxeHY8eOYfPmzbZO5b5y4sQJiAj0ev1t29XU1GDr1q2YNGkSAGDSpEn48ssvzW4aNBgMcHNzQ9u2beHr64v09HSzor9582aEhISgZ8+ecHR0xPjx4xVF/vjx4/Dw8IC/v3+T+TfVV3MlJCRAq9WalsDAwDvui6gl3VeF2cfHB46OjmZ32p4+fRp+fn42yso2ro/3dnPh5+dndlNcY2Mjzp07d9/O18yZM5GWlob09HQ88sgjpvV+fn6or69HdXW1ov3N82Fpvq7HHnRi5f99d9OmTejatSv69OkDAOjbty86duyIlJQURTt3d3fk5eXhp59+wqpVq9CvXz8sX75c0WbdunWmAg9cK/JbtmxBbW2tKSeVSmVVXk311VyLFy+GwWAwLeXl5XfUD1FLu68Ks1qtRlhYmOLuUaPRiN27dyMyMtKGmbW+zp07w8/PTzEXNTU1OHjwoGkuIiMjUV1djZycHFObPXv2wGg0IiIiotVzvhsigpkzZ2L79u3Ys2cPOnfurIiHhYXB2dlZMR/FxcUoKytTzMfRo0cVf6zs2rULHh4eCA0NbZ2B2FBISAhUKhWKiopu2y4xMRE///wznJycTEtBQQHWrVunaOfg4IDg4GD06NED8fHxePzxx/H666+b4gUFBcjOzsbChQtN/Tz++OO4dOmS6WxHt27dYDAYUFlZeducrOmruTQajemd3XyXOdmVVrje3aI2b94sGo1GkpOTpaCgQKZNmyY6nU5xp+2Dora2VnJzcyU3N1cAyAcffCC5ubny66+/iojIihUrRKfTSWpqquTn58vo0aOlc+fOcvnyZVMfw4YNkz/96U9y8OBB2bdvn4SEhMiECRNsNaQ79vrrr4tWq5WMjAyprKw0LZcuXTK1mT59ugQFBcmePXvkp59+ksjISImMjDTFGxsbpVevXjJkyBDJy8uTHTt2SLt27WTx4sW2GJJNDBs27LY3f+Xn54tKpZLMzEw5evSoacnMzBSVSiWFhYUiYvnmr7KyMnF2dpacnBwREYmPj5eoqChFP0ePHpX4+HiJiIgwfceam7+s6UuEN3+RfXsg78q+bs2aNRIUFCRqtVrCw8MlOzvb1indE+np6QLAbHn55ZdF5NojU0uWLBFfX1/RaDQyePBgKS4uVvRx9uxZmTBhgri5uYmHh4dMmTJFamtrbTCau2NpHgBIUlKSqc3ly5dlxowZ4unpKa6urvLcc89JZWWlop/S0lIZPny4uLi4iI+Pj8ybN08aGhpaeTS2U1JSIn5+fqbHpX755RcpKCiQjz76SPR6vcyePVtR6G4UHh4u8+fPFxHLhVlEZNy4cTJy5Eipr6+Xdu3ayWeffWbW5vpjfMeOHRMRkbVr14pKpZK//vWvkpGRIaWlpbJv3z6ZNm2axMfHN6uv6OhoefHFF01/0F5frPnDnYWZ7rUHujAT0Z2rqKiQuLg46dixo6jVaunQoYM888wzsnPnTvH29paVK1da/N57770n7du3l/r6+lsW5gMHDggAWbFihdkz4zfq0aOH4hnlXbt2ydChQ8XT01PatGkjer1e5s+fLxUVFbJ161ar+4qOjrb4B9y7777b5LywMNO9Zu0+xvcxExGB72Ome4/vYyYiIroPsTATERHZERZmIiIiO8LCTEREZEdYmImIiOwICzMREZEdYWEmIiKyIyzMREREdoSFmYiIyI6wMBMREdkRFmYiIiI7wsJMRERkR1iYiYiI7AgLMxERkR1hYSYiIrIjLMz0UDlw4AAcHR0xcuRIs1hpaSlUKpXZMmnSJItxLy8vREdHIysrS9HPsmXL0LdvX8W6mpoavPXWW9Dr9WjTpg38/PwQExODbdu24eZXom/atAmOjo6Ii4szyzEjIwMqlQo9e/bE1atXFTGdTofk5ORbjt1SXjcaNGiQxfFPnz5d0S4tLQ3R0dFwd3eHq6sr+vfvb7bdm+fK29sbQ4YMQW5urqJdc36P28339TZOTk7w8fFBVFQUVq9ejbq6uluOl8hesTDTQyUxMRGzZs3C3r17UVFRYbHNf//7X1RWVpqWtWvXWozv3bsXAQEBePrpp3H69OlbbrO6uhoDBgzA+vXrsXjxYhw+fBh79+5FbGwsFi5cCIPBYJbjwoULsWnTJly5csVinydPnsT69eubOfqmvfrqq4qxV1ZWYuXKlab4mjVrMHr0aAwcOBAHDx5Efn4+xo8fj+nTp2P+/Plm/V2fq507d+LChQsYPnw4qqurFWO19ve43Xz37NkTlZWVKCsrQ3p6Ol544QUkJCRgwIABqK2tbZnJIWotQvSQqK2tFTc3NykqKpLY2FhZvny5In7q1CkBILm5uRa/bymen58vACQ1NdW0bunSpdKnTx/T59dff13atm0rv/32m8WcGhoaTJ9PnjwpLi4uUl1dLREREbJhwwZF+/T0dAEgCxYskMDAQLly5YopptVqJSkp6Zbjvzmvm0VHR8vs2bNvGS8rKxNnZ2eJj483i3388ccCQLKzs0XE8lzt379fAMiOHTtMY2/u72HNfF9XWFgoarVa3nrrrVuO6UYGg0EAiMFgsKo9UXNZu4/xiJkeGl9++SX0ej26d++OSZMmYd26dWankZvj8uXLpqNWtVptsY3RaMTmzZsxceJEBAQEmMXd3Nzg5ORk+pyUlISRI0dCq9Vi0qRJSExMtNjvnDlz0NjYiDVr1txx/s21detWNDQ0WDwyfu211+Dm5oZNmzbd8vsuLi4AgPr6egDN/z2sme8b6fV6DB8+HNu2bbMYr6urQ01NjWIhsgcszPTQSExMNF0vHjZsGAwGAzIzM83aDRgwAG5ubqbl5uui1+Nt27bFP//5T4SFhWHw4MEWt/nHH3/g/Pnz0Ov1TeZnNBqRnJxsynH8+PHYt28fTp06ZdbW1dUVS5cuRUJCgtmp8Lvx6aefKsbu5uaGDRs2AAB++eUXaLVa+Pv7m31PrVajS5cu+OWXXyz2W11djXfffRdubm4IDw8H0Pzfw5r5vpler0dpaanFWEJCArRarWkJDAy0qk+ie42FmR4KxcXFOHToECZMmAAAcHJyQmxsrMUj0pSUFOTl5ZmW0NBQs3hubi6++uorBAcHIzk5Gc7Ozha325wj8l27duHixYsYMWIEAMDHxwdPPfUU1q1bZ7H91KlT4e3tjffee8/qbTRl4sSJirHn5eXhmWeeueP+rhdVT09PHDlyBCkpKfD19W3272HtfN9MRKBSqSzGFi9eDIPBYFrKy8vveJxELcmp6SZE97/ExEQ0NjYqTieLCDQaDT755BNotVrT+sDAQAQHB9+yr8DAQISEhCAkJASNjY147rnncOzYMWg0GrO27dq1g06nQ1FRkVU5njt3znTKF7h2FJ2fn4933nkHDg7Kv6OdnJywfPlyTJ48GTNnzmyyf2totdpbjr1bt24wGAyoqKgwOy1fX1+PkpISPPnkk4r1KSkpCA0Nhbe3N3Q6nWl9c38Pa+f7ZoWFhejcubPFmEajsaoPotbGI2Z64DU2NmL9+vVYtWqV4kjwyJEjCAgIuO110aY8//zzcHJywqeffmox7uDggPHjx2PDhg0W7zq+cOECGhsbcfbsWaSmpmLz5s2KHHNzc3H+/Hl8//33Fvt/4YUX0LNnT7zzzjt3PAZrjR07Fs7Ozli1apVZ7PPPP8fFixdNR8DXBQYGomvXroqifDe/R1PzfaOioiLs2LEDY8eOtX6QRHaAR8z0wEtLS8P58+cxdepUxZEYcK3YJCYmmj2ray2VSoU33ngDy5Ytw2uvvQZXV1ezNsuXL0dGRgYiIiKwfPlyPPbYY3B2dkZWVhYSEhLw448/4osvvoC3tzfGjRtndup1xIgRSExMxLBhwyzmsGLFCgwdOtSqfC9fvoy8vDzFOnd3d3Tt2hUAcOnSJVRVVSniGo0Gnp6eCAoKwsqVKzFv3jy0adMGf/nLX+Ds7IzU1FT87W9/w7x58xAREdFkDnfze9xqvhsbG1FVVQWj0YizZ88iIyMD//jHP9C3b18sWLDAqrkhshv3/P5wIht7+umnZcSIERZjBw8eFABy5MiRO3pcSkTk4sWL4unpKe+9956IWH58p7q6WhYtWiQhISGiVqvF19dXYmJiZPv27WI0GqV3794yY8YMi9tNSUkRtVotv//+u+lxqfPnzyvaDBkyRAA0+bgUALNl8ODBInLtcSlL8aFDhyr6SU1NlSeeeELatm0rbdq0kbCwMFm3bp1VcyVy97+Hpfm+nqujo6N4eXnJ//3f/8mHH36oeJysKXxciu41a/cxlchdPC9CRPSAqKmpgVarhcFggIeHh63ToQeQtfsYrzETERHZERZmIiIiO8LCTEREZEdYmImIiOwICzMREZEdYWEmIiKyIyzMREREdoSFmYiIyI6wMBMREdkRFmYiIiI7wsJMRERkR1iYiYiI7AgLMxERkR1hYSYiIrIjLMxERER2hIWZiIjIjjjZOgEiInsgIgCuvcye6F64vm9d39duhYWZiAjA2bNnAQCBgYE2zoQedLW1tdBqtbeMszATEQHw8vICAJSVld32H80HVU1NDQIDA1FeXg4PDw9bp9OqWmvsIoLa2loEBATcth0LMxERAAeHa7fcaLXah64w3cjDw+OhHX9rjN2aP/p48xcREZEdYWEmIiKyIyzMREQANBoNli5dCo1GY+tUbOJhHr+9jV0lTd23TURERK2GR8xERER2hIWZiIjIjrAwExER2REWZiIiIjvCwkxEBGDt2rXo1KkT2rRpg4iICBw6dMjWKbW4hIQE9O/fH+7u7mjfvj2effZZFBcXK9oMGjQIKpVKsUyfPt1GGbesZcuWmY1Nr9eb4leuXEFcXBy8vb3h5uaGsWPH4vTp062eJwszET30UlJSEB8fj6VLl+Lw4cPo06cPhg4dijNnztg6tRaVmZmJuLg4ZGdnY9euXWhoaMCQIUNw8eJFRbtXX30VlZWVpmXlypU2yrjl9ezZUzG2ffv2mWJz587FN998gy1btiAzMxMVFRUYM2ZMq+fIx6WI6KEXERGB/v3745NPPgEAGI1GBAYGYtasWVi0aJGNs7t3fv/9d7Rv3x6ZmZmIiooCcO2IuW/fvli9erVtk7sHli1bhq+//hp5eXlmMYPBgHbt2mHjxo14/vnnAQBFRUXo0aMHDhw4gMcff7zV8uQRMxE91Orr65GTk4OYmBjTOgcHB8TExODAgQM2zOzeMxgMAP7/Czyu27BhA3x8fNCrVy8sXrwYly5dskV698Tx48cREBCALl26YOLEiSgrKwMA5OTkoKGhQbEf6PV6BAUFtfp+wJdYENFD7Y8//sDVq1fh6+urWO/r64uioiIbZXXvGY1GzJkzBwMHDkSvXr1M61988UV07NgRAQEByM/Px5tvvoni4mJs27bNhtm2jIiICCQnJ6N79+6orKzEO++8gyeeeALHjh1DVVUV1Go1dDqd4ju+vr6oqqpq1TxZmImIHkJxcXE4duyY4horAEybNs30371794a/vz8GDx6MkpISdO3atbXTbFHDhw83/fejjz6KiIgIdOzYEV9++SVcXFxsmJkST2UT0UPNx8cHjo6OZnffnj59Gn5+fjbK6t6aOXMm0tLSkJ6ejkceeeS2bSMiIgAAJ06caI3UWpVOp0O3bt1w4sQJ+Pn5ob6+HtXV1Yo2ttgPWJiJ6KGmVqsRFhaG3bt3m9YZjUbs3r0bkZGRNsys5YkIZs6cie3bt2PPnj3o3Llzk9+5fqOUv7//Pc6u9V24cAElJSXw9/dHWFgYnJ2dFftBcXExysrKWn0/4KlsInroxcfH4+WXX8Zjjz2G8PBwrF69GhcvXsSUKVNsnVqLiouLw8aNG5Gamgp3d3fTtVOtVgsXFxeUlJRg48aNGDFiBLy9vZGfn4+5c+ciKioKjz76qI2zv3vz58/HqFGj0LFjR1RUVGDp0qVwdHTEhAkToNVqMXXqVMTHx8PLywseHh6YNWsWIiMjW/WObACAEBGRrFmzRoKCgkStVkt4eLhkZ2fbOqUWB8DikpSUJCIiZWVlEhUVJV5eXqLRaCQ4OFgWLFggBoPBtom3kNjYWPH39xe1Wi0dOnSQ2NhYOXHihCl++fJlmTFjhnh6eoqrq6s899xzUllZ2ep58jlmIiIiO8JrzERERHaEhZmIiMiOsDATERHZERZmIiIiO8LCTEREZEdYmImIiOwICzMREZEdYWEmIiKyIyzMRERkIiKYNm0avLy8oFKpkJeXh0GDBmHOnDm3/V6nTp2wevXqVsnxQcfCTER0n6iqqsKsWbPQpUsXaDQaBAYGYtSoUYoXL9ytHTt2IDk5GWlpaaisrESvXr2wbds2vPvuuy22Dbo9vsSCiOg+UFpaioEDB0Kn0+H9999H79690dDQgJ07dyIuLg5FRUUtsp3rb1saMGCAaZ2Xl1eL9E3W4REzEdF9YMaMGVCpVDh06BDGjh2Lbt26oWfPnoiPj0d2djYAoKysDKNHj4abmxs8PDwwbtw4xXumly1bhr59++KLL75Ap06doNVqMX78eNTW1gIAJk+ejFmzZqGsrAwqlQqdOnUCALNT2WfOnMGoUaPg4uKCzp07Y8OGDWb5VldX45VXXkG7du3g4eGBP//5zzhy5IjVuQDXXr+5cuVKBAcHQ6PRICgoCMuXLzfFy8vLMW7cOOh0Onh5eWH06NEoLS1tiem2KRZmIiI7d+7cOezYsQNxcXFo27atWVyn08FoNGL06NE4d+4cMjMzsWvXLpw8eRKxsbGKtiUlJfj666+RlpaGtLQ0ZGZmYsWKFQCAjz76CH//+9/xyCOPoLKyEj/++KPFfCZPnozy8nKkp6dj69at+PTTT3HmzBlFmxdeeAFnzpzBd999h5ycHPTr1w+DBw/GuXPnrMoFABYvXowVK1ZgyZIlKCgowMaNG+Hr6wsAaGhowNChQ+Hu7o6srCzs378fbm5uGDZsGOrr6+9sou1Fq7/PioiImuXgwYMCQLZt23bLNt9//704OjpKWVmZad3PP/8sAOTQoUMiIrJ06VJxdXWVmpoaU5sFCxZIRESE6fOHH34oHTt2VPQdHR0ts2fPFhGR4uJiRZ8iIoWFhQJAPvzwQxERycrKEg8PD7ly5Yqin65du8q//vUvq3KpqakRjUYj//73vy2O94svvpDu3buL0Wg0raurqxMXFxfZuXPnLefpfsBrzEREdk6seDtvYWEhAgMDERgYaFoXGhoKnU6HwsJC9O/fH8C1u6fd3d1Nbfz9/c2OdpvajpOTE8LCwkzr9Ho9dDqd6fORI0dw4cIFeHt7K757+fJllJSUmD7fLpfCwkLU1dVh8ODBFvM4cuQITpw4ofg+AFy5ckWxjfsRCzMRkZ0LCQmBSqVqkRu8nJ2dFZ9VKhWMRuNd93ujCxcuwN/fHxkZGWaxGwv47XJxcXFpchthYWEWr2+3a9eu+UnbEV5jJiKyc15eXhg6dCjWrl2LixcvmsWrq6vRo0cPlJeXo7y83LS+oKAA1dXVCA0NbbFc9Ho9GhsbkZOTY1pXXFyM6upq0+d+/fqhqqoKTk5OCA4OViw+Pj5WbSckJAQuLi63fBSsX79+OH78ONq3b2+2Da1We1djtDUWZiKi+8DatWtx9epVhIeH46uvvsLx48dRWFiIjz/+GJGRkYiJiUHv3r0xceJEHD58GIcOHcJLL72E6OhoPPbYYy2WR/fu3TFs2DC89tprOHjwIHJycvDKK68ojnBjYmIQGRmJZ599Ft9//z1KS0vxww8/4K233sJPP/1k1XbatGmDN998EwsXLsT69etRUlKC7OxsJCYmAgAmTpwIHx8fjB49GllZWTh16hQyMjLwxhtv4H//+1+LjdcWWJiJiO4DXbp0weHDh/Hkk09i3rx56NWrF5566ins3r0bn332GVQqFVJTU+Hp6YmoqCjExMSgS5cuSElJafFckpKSEBAQgOjoaIwZMwbTpk1D+/btTXGVSoVvv/0WUVFRmDJlCrp164bx48fj119/Nd1VbY0lS5Zg3rx5ePvtt9GjRw/ExsaarkG7urpi7969CAoKwpgxY9CjRw9MnToVV65cgYeHR4uPuTWpxJq7CoiIiKhV8IiZiIjIjrAwExER2REWZiIiIjvCwkxERGRHWJiJiIjsCAszERGRHWFhJiIisiMszERERHaEhZmIiMiOsDATERHZERZmIiIiO/L/AEzkXWZBNib1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_image(\"tiger_dataset/valid/CLOUDED LEOPARD/5.jpg\", simple_transform, train_data.classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aeea1af",
   "metadata": {},
   "source": [
    "## Accuracy on Validation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "65d032f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.utils.data.dataloader.DataLoader at 0x14e8058f190>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3110483d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def val_model(model: torch.nn.Module, \n",
    "              loader: torch.utils.data.DataLoader, \n",
    "              loss_fn: torch.nn.Module):\n",
    "    # Put model in eval mode\n",
    "    model.eval() \n",
    "    \n",
    "    # Setup test loss and test accuracy values\n",
    "    val_loss, val_acc = 0, 0\n",
    "    \n",
    "    # Turn on inference context manager\n",
    "    with torch.inference_mode():\n",
    "        # Loop through DataLoader batches\n",
    "        for batch, (data, targets) in enumerate(loader):\n",
    "            # Send data to target device\n",
    "            data, targets = data.to(device), targets.to(device)\n",
    "    \n",
    "            # 1. Forward pass\n",
    "            output_logits = model(data)\n",
    "\n",
    "            # 2. Calculate and accumulate loss\n",
    "            loss = loss_fn(output_logits, targets)\n",
    "            val_loss += loss.item()\n",
    "            \n",
    "            # Calculate and accumulate accuracy\n",
    "            output_labels = output_logits.argmax(dim=1)\n",
    "            val_acc += ((output_labels == targets).sum().item()/len(output_labels))\n",
    "            \n",
    "    # Adjust metrics to get average loss and accuracy per batch \n",
    "    val_loss = val_loss / len(loader)\n",
    "    val_acc = val_acc / len(loader)\n",
    "    return val_loss, val_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3dfd35fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "lossvalidation,accvalidation = val_model(model,eval_loader,loss_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "de07e133",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuarcy of EfficientNet custom Model on validation dataset: 0.9\n",
      "Loss of EfficientNet Custom model on validation dataset: 0.1566009372472763\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuarcy of EfficientNet custom Model on validation dataset: {accvalidation}\")\n",
    "print(f\"Loss of EfficientNet Custom model on validation dataset: {lossvalidation}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3cbe12",
   "metadata": {},
   "source": [
    "### Confusion Metrics and other Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49705b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = train_data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "27494c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "\n",
    "y_pred = []\n",
    "y_true = []\n",
    "\n",
    "model.eval()\n",
    "# iterate over test data\n",
    "with torch.inference_mode():\n",
    "    for batch,(inputs, labels) in enumerate(eval_loader):\n",
    "        \n",
    "        #inputs, labels = inputs.to(DEVICE),labels.to(DEVICE)\n",
    "        output = model(inputs) # Feed Network\n",
    "\n",
    "        output = torch.argmax(output, dim=1).cpu().numpy()\n",
    "        y_pred.extend(output) # Save Prediction\n",
    "\n",
    "        labels = labels.data.cpu().numpy()\n",
    "        y_true.extend(labels) # Save Truth\n",
    "\n",
    "# Build confusion matrix\n",
    "cf_matrix = confusion_matrix(y_true, y_pred)\n",
    "df_cm = pd.DataFrame(cf_matrix / np.sum(cf_matrix, axis=1)[:, None], index = [i for i in class_names],\n",
    "                     columns = [i for i in class_names])\n",
    "plt.figure(figsize = (12,7))\n",
    "s = sn.heatmap(df_cm, annot=True,cmap=\"Blues\")\n",
    "s.set(xlabel='Predicted Label', ylabel='True Label')\n",
    "plt.savefig('cmatrixInception.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0e44b1",
   "metadata": {},
   "source": [
    "## Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "bf902ddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "SAVE_DIR = 'Best_model/Inception'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "17d3a03d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if os.path.exists(SAVE_DIR):\n",
    "    print('path Found')\n",
    "else:\n",
    "    os.mkdir(SAVE_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a4707d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = os.path.join(SAVE_DIR, \"Inception_catClassifier\"+\".pth\")\n",
    "torch.save(model.state_dict(), model_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tiger",
   "language": "python",
   "name": "tiger"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 350.469686,
   "end_time": "2023-04-19T10:00:25.927251",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-04-19T09:54:35.457565",
   "version": "2.4.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "15524f64f7a7429c9ae0d13e81fd785c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "2271fe8397ff4234a3a81c13b0a3604a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_b7ff1e0c849f4accaed7439676d6b3a1",
       "placeholder": "​",
       "style": "IPY_MODEL_6717da901aab467b92a0cf36a1b7412a",
       "value": "100%"
      }
     },
     "4a87e70aee584877944cdf557543d202": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "4b37d52ec26f4ae28589056a63c072c3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "4d459b5752d245f6b0923d82ee7834e3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "5a25c95768c340298290ad2e0d8c38dc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "6717da901aab467b92a0cf36a1b7412a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "6dc1ce036e9c4b8491f58771c1c2c1bb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "733b4e8e27a542e19d641ba74b47dd57": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_a827a4bc924b44dfac8db2560deab7df",
       "placeholder": "​",
       "style": "IPY_MODEL_4d459b5752d245f6b0923d82ee7834e3",
       "value": " 104M/104M [00:00&lt;00:00, 191MB/s]"
      }
     },
     "7e17e09b970f44f0b4553d97541e0e17": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_c49ae302b2b94a0cbad4d1f8b6e48986",
       "placeholder": "​",
       "style": "IPY_MODEL_9b105f4192674f3488c331f216e0ae70",
       "value": "100%"
      }
     },
     "7f6dbc9e83aa4bf892cb5684b54daa86": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "9b105f4192674f3488c331f216e0ae70": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "a827a4bc924b44dfac8db2560deab7df": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "aa114ee8a5764c9e87697b12add2674d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "ad32db243c0a4d85846672f429933877": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_fead776ee54141a08093f610c6a7812a",
       "placeholder": "​",
       "style": "IPY_MODEL_4a87e70aee584877944cdf557543d202",
       "value": " 10/10 [05:01&lt;00:00, 29.17s/it]"
      }
     },
     "aead3570634941a8aa06226949e63b96": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_7f6dbc9e83aa4bf892cb5684b54daa86",
       "max": 108949747,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_15524f64f7a7429c9ae0d13e81fd785c",
       "value": 108949747
      }
     },
     "b1a128256e364f6e870edb0334cff26a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_6dc1ce036e9c4b8491f58771c1c2c1bb",
       "max": 10,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_aa114ee8a5764c9e87697b12add2674d",
       "value": 10
      }
     },
     "b7ff1e0c849f4accaed7439676d6b3a1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "c1c6156ad9b54fd98e7776fca57d3778": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_7e17e09b970f44f0b4553d97541e0e17",
        "IPY_MODEL_aead3570634941a8aa06226949e63b96",
        "IPY_MODEL_733b4e8e27a542e19d641ba74b47dd57"
       ],
       "layout": "IPY_MODEL_5a25c95768c340298290ad2e0d8c38dc"
      }
     },
     "c49ae302b2b94a0cbad4d1f8b6e48986": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "f1c08276195e46e99f11ab8e6b5b24e1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_2271fe8397ff4234a3a81c13b0a3604a",
        "IPY_MODEL_b1a128256e364f6e870edb0334cff26a",
        "IPY_MODEL_ad32db243c0a4d85846672f429933877"
       ],
       "layout": "IPY_MODEL_4b37d52ec26f4ae28589056a63c072c3"
      }
     },
     "fead776ee54141a08093f610c6a7812a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}