Regression_Overlap_Calculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a27b4040-e828-4af5-bd0f-3b59a3ed713b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x14a14b7de4f0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import copy\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tqdm\n",
    "import argparse\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import timedelta\n",
    "\n",
    "# Pytorch Related Library\n",
    "import torch\n",
    "from torchvision import transforms\n",
    "from torch.optim import lr_scheduler\n",
    "import torch.distributed as dist\n",
    "\n",
    "from pytorch_grad_cam import GradCAM, HiResCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, EigenGradCAM,FullGrad\n",
    "from pytorch_grad_cam.utils.image import show_cam_on_image\n",
    "\n",
    "random_state = 0\n",
    "\n",
    "np.random.seed(random_state)\n",
    "random.seed(random_state)\n",
    "torch.manual_seed(random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1722206c-37d6-450f-97b6-69810e54d165",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_name = \"1041220_21015_0_0.png\"\n",
    "class_index = 5\n",
    "output_index = 0\n",
    "thres = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a8b0cd-9424-4617-854d-97407b453ef5",
   "metadata": {},
   "source": [
    "# Creating a CAM heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ce4bc0b-af4f-4631-863f-6477605f945e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SwinforRegression(torch.nn.Module):\n",
    "    def __init__(self, model_name_or_path='microsoft/swin-large-patch4-window12-384-in22k', n_label=7):\n",
    "        super().__init__()\n",
    "        model = SwinForImageClassification.from_pretrained(model_name_or_path,num_labels=n_label,ignore_mismatched_sizes=True)\n",
    "        self.swin = model.swin\n",
    "        self.regressor_age = torch.nn.Linear(1536, 1)\n",
    "        self.regressor_education = torch.nn.Linear(1536, 1)\n",
    "        self.regressor_sleep = torch.nn.Linear(1536, 1)\n",
    "        self.regressor_BMI = torch.nn.Linear(1536, 1)\n",
    "        self.regressor_dbp = torch.nn.Linear(1536, 1)\n",
    "        self.regressor_sbp = torch.nn.Linear(1536, 1)\n",
    "        self.regressor_HbA1C = torch.nn.Linear(1536, 1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        y_swin = self.swin(x).pooler_output\n",
    "        y_age = self.regressor_age(y_swin)\n",
    "        y_education = self.regressor_education(y_swin)\n",
    "        y_sleep = self.regressor_sleep(y_swin)\n",
    "        y_BMI = self.regressor_BMI(y_swin)\n",
    "        y_dbp = self.regressor_dbp(y_swin)\n",
    "        y_sbp = self.regressor_sbp(y_swin)\n",
    "        y_HbA1C = self.regressor_HbA1C(y_swin)\n",
    "    \n",
    "        return y_age, y_education, y_sleep, y_BMI, y_dbp, y_sbp, y_HbA1C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01fb6912-df47-4abc-b226-cd7f3c87fb60",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SingleOutputModel(torch.nn.Module):\n",
    "    def __init__(self, model, output_index=0):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.output_index = output_index\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)[self.output_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad6b0093-be0e-4e81-a134-cc3fb0c3a8b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of SwinForImageClassification were not initialized from the model checkpoint at microsoft/swin-large-patch4-window12-384-in22k and are newly initialized because the shapes did not match:\n",
      "- classifier.weight: found shape torch.Size([21841, 1536]) in the checkpoint and torch.Size([7, 1536]) in the model instantiated\n",
      "- classifier.bias: found shape torch.Size([21841]) in the checkpoint and torch.Size([7]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SwinforRegression(\n",
       "  (swin): SwinModel(\n",
       "    (embeddings): SwinEmbeddings(\n",
       "      (patch_embeddings): SwinPatchEmbeddings(\n",
       "        (projection): Conv2d(3, 192, kernel_size=(4, 4), stride=(4, 4))\n",
       "      )\n",
       "      (norm): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.0, inplace=False)\n",
       "    )\n",
       "    (encoder): SwinEncoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): SwinStage(\n",
       "          (blocks): ModuleList(\n",
       "            (0-1): 2 x SwinLayer(\n",
       "              (layernorm_before): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
       "              (attention): SwinAttention(\n",
       "                (self): SwinSelfAttention(\n",
       "                  (query): Linear(in_features=192, out_features=192, bias=True)\n",
       "                  (key): Linear(in_features=192, out_features=192, bias=True)\n",
       "                  (value): Linear(in_features=192, out_features=192, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "                (output): SwinSelfOutput(\n",
       "                  (dense): Linear(in_features=192, out_features=192, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (drop_path): SwinDropPath(p=0.1)\n",
       "              (layernorm_after): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
       "              (intermediate): SwinIntermediate(\n",
       "                (dense): Linear(in_features=192, out_features=768, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output): SwinOutput(\n",
       "                (dense): Linear(in_features=768, out_features=192, bias=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "          (downsample): SwinPatchMerging(\n",
       "            (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
       "            (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "        )\n",
       "        (1): SwinStage(\n",
       "          (blocks): ModuleList(\n",
       "            (0-1): 2 x SwinLayer(\n",
       "              (layernorm_before): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
       "              (attention): SwinAttention(\n",
       "                (self): SwinSelfAttention(\n",
       "                  (query): Linear(in_features=384, out_features=384, bias=True)\n",
       "                  (key): Linear(in_features=384, out_features=384, bias=True)\n",
       "                  (value): Linear(in_features=384, out_features=384, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "                (output): SwinSelfOutput(\n",
       "                  (dense): Linear(in_features=384, out_features=384, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (drop_path): SwinDropPath(p=0.1)\n",
       "              (layernorm_after): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
       "              (intermediate): SwinIntermediate(\n",
       "                (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output): SwinOutput(\n",
       "                (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "          (downsample): SwinPatchMerging(\n",
       "            (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
       "            (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "        )\n",
       "        (2): SwinStage(\n",
       "          (blocks): ModuleList(\n",
       "            (0-17): 18 x SwinLayer(\n",
       "              (layernorm_before): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              (attention): SwinAttention(\n",
       "                (self): SwinSelfAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "                (output): SwinSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (drop_path): SwinDropPath(p=0.1)\n",
       "              (layernorm_after): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "              (intermediate): SwinIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output): SwinOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "          (downsample): SwinPatchMerging(\n",
       "            (reduction): Linear(in_features=3072, out_features=1536, bias=False)\n",
       "            (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "        )\n",
       "        (3): SwinStage(\n",
       "          (blocks): ModuleList(\n",
       "            (0-1): 2 x SwinLayer(\n",
       "              (layernorm_before): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
       "              (attention): SwinAttention(\n",
       "                (self): SwinSelfAttention(\n",
       "                  (query): Linear(in_features=1536, out_features=1536, bias=True)\n",
       "                  (key): Linear(in_features=1536, out_features=1536, bias=True)\n",
       "                  (value): Linear(in_features=1536, out_features=1536, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "                (output): SwinSelfOutput(\n",
       "                  (dense): Linear(in_features=1536, out_features=1536, bias=True)\n",
       "                  (dropout): Dropout(p=0.0, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (drop_path): SwinDropPath(p=0.1)\n",
       "              (layernorm_after): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
       "              (intermediate): SwinIntermediate(\n",
       "                (dense): Linear(in_features=1536, out_features=6144, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output): SwinOutput(\n",
       "                (dense): Linear(in_features=6144, out_features=1536, bias=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (layernorm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
       "    (pooler): AdaptiveAvgPool1d(output_size=1)\n",
       "  )\n",
       "  (regressor_age): Linear(in_features=1536, out_features=1, bias=True)\n",
       "  (regressor_education): Linear(in_features=1536, out_features=1, bias=True)\n",
       "  (regressor_sleep): Linear(in_features=1536, out_features=1, bias=True)\n",
       "  (regressor_BMI): Linear(in_features=1536, out_features=1, bias=True)\n",
       "  (regressor_dbp): Linear(in_features=1536, out_features=1, bias=True)\n",
       "  (regressor_sbp): Linear(in_features=1536, out_features=1, bias=True)\n",
       "  (regressor_HbA1C): Linear(in_features=1536, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import ViTForImageClassification, SwinForImageClassification, AutoFeatureExtractor\n",
    "\n",
    "device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "model = SwinforRegression(model_name_or_path='microsoft/swin-large-patch4-window12-384-in22k', n_label=7)\n",
    "checkpoint = torch.load('/red/ruogu.fang/yyang/ADRiskFactorPrediction/swin/regression/savedmodel/Swin_regression/Swin_regression6loss0.13acc0.19.pth', map_location=torch.device('cpu'))\n",
    "model.load_state_dict(checkpoint)\n",
    "model.to(device)\n",
    "\n",
    "model_for_gradcam = SingleOutputModel(model, class_index)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ee8f18e5-34d3-48fe-8ba7-8d366454f5e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ClassifierOutputTarget:\n",
    "    def __init__(self, category):\n",
    "        self.category = category\n",
    "\n",
    "    def __call__(self, model_output):\n",
    "        return model_output[self.category]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ec4ef75-1a00-4cbb-a9c3-7dd57e168fc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/blue/ruogu.fang/leem.s/conda/envs/monai-0.9.1/lib/python3.8/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "img = Image.open(os.path.join(\"/red/ruogu.fang/share/UKB/data/Eye/21015_fundus_left_1_good\", img_name))\n",
    "img = img.resize((576,576))\n",
    "img = np.array(img)\n",
    "img = img.astype(np.float32)\n",
    "img = img/255\n",
    "convert_tensor = transforms.Compose([transforms.ToTensor(),\n",
    "                                     transforms.Resize((576,576))\n",
    "                                    ])\n",
    "input_tensor = convert_tensor(img).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e02a7fc3-6f3a-4ee1-aa9a-45a44701355e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def reshape_transform(tensor, height=18, width=18):\n",
    "    result = tensor[:, : , :].reshape(tensor.size(0),\n",
    "        height, width, tensor.size(2))\n",
    "\n",
    "    # Bring the channels to the first dimension,\n",
    "    # like in CNNs.\n",
    "    result = result.transpose(2, 3).transpose(1, 2)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93c0fa81-712e-47a9-b6f2-4757f59c0f9f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_ori = model(input_tensor.unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43b916c9-dc2b-44c1-8b48-f9d86de48e3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 96/96 [00:57<00:00,  1.67it/s]\n",
      "100%|██████████| 96/96 [00:57<00:00,  1.68it/s]\n",
      "100%|██████████| 96/96 [00:56<00:00,  1.69it/s]\n",
      "100%|██████████| 96/96 [00:56<00:00,  1.69it/s]\n",
      "100%|██████████| 96/96 [00:56<00:00,  1.69it/s]\n",
      "100%|██████████| 96/96 [00:56<00:00,  1.69it/s]\n"
     ]
    }
   ],
   "source": [
    "targets = [ClassifierOutputTarget(0)]\n",
    "target_layers = [model_for_gradcam.model.swin.encoder.layers[-1].blocks[-1].layernorm_before]\n",
    "#target_layers = [model_for_gradcam.model.swin.layernorm]\n",
    "\n",
    "cam = ScoreCAM(model=model_for_gradcam, target_layers=target_layers, reshape_transform=reshape_transform)\n",
    "\n",
    "pred = model_for_gradcam(input_tensor.unsqueeze(0))\n",
    "output = cam(input_tensor=input_tensor.unsqueeze(0), targets=targets, aug_smooth = True, eigen_smooth=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d3b8ec4-26cd-4584-8cdc-6f56b1fe636d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cam = output[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c83253e-93a7-4dc5-a52c-c23422758a84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(576, 576)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14a11c79cd00>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FgKU01WnKuexPn33/ZzdC3oFRAMXIfL3UQk25glYLXa2P22FNPZSvz5F7M1RVcUceVhQaFu9xA3pgHBW2DnR7sIi9fERQ3VJ7aUHqiq6+RUWvRvVZty/hRRUlpa69GnHnAHUgLIIgemACGtUEVDgt4ObAs6hTcsppAazk99mmWXXvHQOijm2qJze/S2k1EOKmLqtCiHaByu+zOSyHvAyA5NP7pH0wySZ6HY8AFRW9Yu7BLViBYeqqByxzo82yU1VV2r4KuSoqpFqIavY5XKDw7nWDJrZUkUw37cB69VUjdRZVlFNXFldirxLVffVUlbcerLbcgFqV3Q1bx74GwR5K8iFc0slEyi2orqqm9naNdM31Ui8tSF2j+ZfMXH1eRWnYubn+9FcAdaiuvlIvBXtpRooJQFc1NdvYNAX4hHqoNTefA5ulR9i/m++u8+tXoLWWfgRBwDGhdwwPnM4yoBTOo3qoJkR9BKretFtGYX1TXmMJphggMXTpJTmvkHYBowZUaKCsrj3bTo/vgMUJoqZgqioh11NMVBWVBUuUh6XUTwnoYt3HHrWzBqsGVGgBtkNNLTv4rRG1ZplF6Ya0a26+0foGVq6Qb9x4o7B1LJXVqL5KEzNXmFBaggr9/V277ap73Fc/+dKB1HWrqLhf7/P2br4pgsrBKRHyobZ3Ktu7F2kPmGwby2hVUw5CTT2Ug0/t86+FY6PAFsfw518ntwC17upDe14RWGGfNS/cbLdURivuvt4yrjMxMhAOXva6h313IxFDvszVtwIqas49gKq3raYXWJU6J1RIwU0HYNk5ybXiCVJo6scLARcZGamCaqquP3D50CJVYP4dyOXd4U3nacdiQET89Rd6rW4rmsItbKPvnb7SLOB4ULAaBlfscQEqlCKogDB9ewIm1F46kLpO04fbAifqX6OitIujiayzV3YqKh/q8rJf93XkoaTrYsEdweTXKXiaMPR6jIVa6rj54vRIGXXh1Ek3vp6d86MBmARKy+ARyYsutsKZO7BBU1DXZQ4QcdkGvHpwquHdMGAoGPeAqqeowGjC03VeQVjB5C6IZsqfi01Ts5zlJIpy4hJoAQYOaQkqJBBnAxRLoAQirIDmI/BSvXB3QGWh9gPb00t6dbGTu1fistNI2p2wWrj6ppJvBtk9q9tiPRJwVF/lXYBRVXVBVV1vt7V+6gQp4HIqym8TOoeNffGxvATm2ksBWto7OTBUSu08bayvgBq58bCxfuk+RPOWHVOP1E0/sgioHpw6y5v8WWGLSi0PJQ+cZlkvnftd7FP/2G1TMtF04UQVaEyw+h8FlVeL6+BatqOCrveA0l87TQrn04GWW0ySMS0TkyYWUDFrO6lSsBKS5GGW0HV1+aG+E73CcQ0gMbzcL1uAym2X0Ubt9Sy8P90uraD3cQespnrx1tTVIsCiBx/U81mtrwIWqqoLKtuW2vqky9ZLPQR7aUDqOrtBavZLLcC8orLeJGo9FBygsqqrA6y+qOyj/LTqZBtKavbiabReCJSIvUrEIIouqCgUBOGYa8u6cPIVNr1jAS7s3cEpLCv5cpBqDtz+evXQQiYqqj68zG0of8YBnTcocbPv5rgCK3PtcD2vqpiOABXXgswDqgnqiKBlXwDXUy7zbPPlMSak2fqUKBtPVDmQ5MDWA73Cig1W0S6lotZAtWXOtceVuHXbXji8q8w8ClZAVVeO8hVKZd8LWEX4jOqr/DlpWqeqGlDpfhKw6IU+uPwedXX10oDUFWzXS+VdfY1P3sNBlRNqPZD0CpEntx9EQOHIZWTztU4Ky0CJuM6Bouvm2wkltT3KaZH/DqBaN6UUHgngRkF1QLVQRw4e7ncBr5Gi6kTHwZUFzO0+udmmhZWHzpUUlVyrxrUItB/IEVJuuoEzYAEjQDlOBZQHlTvGJNdiStLGKstHvH8HCNpeqrHRe9WJ5gMwBhXchbuELRrSw8GnAysAizqrZhufr1GIegxr9/VVneoie1aafWMDVAFMQAl2yY8+lKKdILW3h4mReSjJS8k2XVTU0s0Hq4/KAqo18Kwvp35ahU1CA6GmPVTCeFr345XWwHa78oC2VAzbq9qIgCrnwEvlRPJi+9/FDoEmHHoHvEhlicEGLZhGwFKFpdMGGBaw1EP1QLVbUbEWcCHs3O88XG676uHyL9jhyn0FFHMpHFmhxJBp+YgjiYYLgOJUxq26kkVQAW0wBdDv12/NFEju3ffP8AJWZambkuVTBc8qsJyCaqHUqa86xgUYQeVN66OsF/oOtG6J3W5IUcKVXHlrla6jB147j/XHjgpKXXlamMZ6qKlVVSxKahNE3XXj5T0ILVx6oZ7K9uFAZ7+Xsb3dNjTHXQGUwohkucxTKn+AKAIPIpVJzfLBMg8uWwYbvSKCibIEZohaMtcZHFAc2EjqLNYU1RhMMDdjCUV3w3zE6yiXqXvfBrfERy0yCTMZyBOQINdxYosERGKp7CdTUwVYBNIGvJ0ousV87MU7uvLi/KgbpD0uwN67ZB967b4MVuzW+XYNXN9NXwx1gQVg3FGtA5gtk99O+6ohqLyaOkItHRVQsbet1DXa7YbUsRag1A+I6EDPp0syHMekv1P5MpNl7LpGMhWl9T8BDAsA6HvQ1HPVyU2VFZepgrICn/qgcr+1Mnl5GY6yy/QpFI8Zz3cwT2Fd6bqnliT6+rNJHb+jsCyW1Ho9ElsknP8qtrohlqHsPYT0Q9ipoxKhRy10vIIagUr35+elkKQBdayeKlrn3kaglYJOnhk5N5Y6KBZ6kXxo0UTWtggaip5JPujkxKZpeVBgHSqXjQL0J5hRlIxf38BI/kXYaRb89fMFuZtsXJm9Avw63GtXCXTwdVMp13Gpcq5gc+e/CSwtRx8QrG43pFTB7DQKsJGFy2WLtP7tFUAdDsDhABwm8GGqCsuFm9tXmoGAugXEOMPlZytgYgGpAMTYfVKv7qnZvpcVdRvtzX/zGXpJ8+UNu3lxP5mnjpUtsfCp+eAw30sztF5hTyMXnoPQlqpSdaKA0jR+m267Kf26h33NL+p8fN532GJr9/woPA1Yk+aXigpgksCgZIEhOLCAm0E8FWhtFWrHut73vvtybcrlKudAILAEfNToR+5uV6bdYVeg1IWauu/8tuwekByW9/a9BQ4LBNlRNxUtBFL0Pt674AoNoG/KbjekdtgqmGR+MS6U1+6+yyNZR1MyQFU4pWVEnwUoEOqIuUtQ+a/YRUNeWpnWdCtpm4ERO6DSfYxU2uILO77HW+UE0zqo/PYOSosINUJpi6PuFtaDS0L9Ig4ZW4XTlvlMEELNUtlRD1Td5fohrEDV/LLr4SEqJ399JCvkDxRAdSVr7jFXSHk4mdqraqoMKSFqilEb9XKyRsE8yefDdfXYu1Vg55Kv/jpY4EIDKmlrVo/RbjYC01hp+fQdCK0BarSfkR3jgqOOmtLAC2+hwe+m0rpBN+CthpSGyW6ah1MPTL5uyXbcLrNONIHiuhBA8WESNdUqKQVEhITaauEeQRNhMpiv6akpZJp0nWWb9U767HXWH62w9pgDW7f9D0kaBywWZWK74LA/fx5xOhp11tsuVkDVgKhfV1UB6zNKVUExbJwnvS96uhFQW/zftME9K8Bjy6d2DRfVlA+i4Ew1JF2aWxRYTdBsN4ceFWijQtkv3/POe/WUyYEpzreKyg5xRSgt9tOB0/p6Xi67jLtvEUAR9hF7oLDoQQetAbCW3SxRzfs12q2GlAUwbJlTTqRhsYB8VQQw9aDkh+AAimpSFXWYnHpqG+7aMO/k8rCzMF8FVA8sx8IJdflu889e2O5mYAVXQjN6CsKrqTbggdr9xP320vWsE31QBvkLoJJ8ki2BqL8WVoAr9CHKJDGQqekrtIYdo6mPAtUseZdfze/66SzPr7+YSXpAl7xYhJ+Ci6kEUXhXYAxJT5BACw0XZbf/cFMaiq9AzKf3TT+OtYGiKuuuAUqj9HvgdB1GUmc6ivpTNaUqKo7i6/M0ANaD6sHidkOqFznUM6+eVDlZRF5VTAYmhZIeo9f10TQJrCqgeg13QWgi/ayOSPe96zxXYBPhlJYQ6roMewBETbcwDusG6moYURZ3t5WmBySguPx8sINWlMchx9fAtCcTsfDXZbKvBlSW1sFKC3NZV8ZVkmmIiyyhqicIqHqA8scgLK/L2rXcuM6jS6DKSYNSdLr8Ujl/hZOoKUDvT4GYdKzUZqNXGPtgBlmvXQQBAqywfjNi0K+SQJdGTRH1QXXdUBql7wJsZd0xMOj1zzfohaIBleU3AKvZpl9/xWG/u2znF+3thtQeU/UE9AHVg9MITD7M/DC1w8CnhHwo+zM15VWUwkRt5f6M3HENhCKcZN0mgCKssHxW4nzjcot53yok16y3ndufVwyxfirCC+ZrC/ta7H+Q2V5aO47LQMPBqKjcGg8rnzUlFZdCslVPJbEBitwpKjD0mvQg6vN9xKl307vnzdx+IzWl0zKEBgFg6bvJ6qcOaOtf2lbE5TcMFGir1Vvfg9Wa9YDm3YARVIsPm2sGk0+zJ0Cic44LhRmvV5gnrT8c9UKh3VfBgaY37LzPT1yvx7khVXW7IbXX3QeIupmOghO7YeD9ut4ou3EQQ2sH5dtL+b+R9YAxApSf74W1U7vPqJYWhdYKNLpd7URw6OKopkLlyVEeNoORlMqEpZrSDC0KmZ30XHu3RqBS+KB+iS8Vjh/LippzK+2bHKgAgRPLMya7kfop//HRuFb9pd342OilGabT+iXmRk0hSWHE7leDKA4JpQtat73WT6GELmp0HbS9kKooCcJolHHMZw9WIwvKuqopNKAC0Kisuj2abWsmrkldaR630l2l4F8AzEX6+c5nJehhERzRg9WKqropu9WQKiOH7iiIVEH1AKXgUjiltFRNAivr+8u79whdQIHggAdYRNRO6wVIVCBRXaZw8pDyLj+4/QCbBVd3vQNVk8bD6SqKqrd9UxoveVRcZmFFpvU8XPZ9H4GqyS676xSABdjLrfVTZRdLUGlGKVPrbpNj6nyjIrfgdOR6EEzd+WequC3dM6bKSuuiwBbtZ/VTB6voacEksLAyL/cGC+zAikWtyfJd9VGy3x6o9PStJ3NdtuaWk9NZpF1LD3TP5Zj5zXo6DxHfS7pXU6Ne0kPQw2LoeQA2YKLPWzzODaipWw2pXda4+BygTFVVCC3Uk4eTDLuh0yUwovZkXrYN9VApqCigugC9xQLa0qIWEjYdAJVWYDWyzrrddURAX+mspbms9cBVKdUW0o0/8gbMg8rygsG15HAtqL1GXBsba8Fs1TLufjf1QVFNDbK56+NkBVJ2WganUvCZ21IUEnHt5FbdfkpT7TJJe00vY09J+dgDEzO6XQQBsB4VOvVWu8yDz4MKaNQTjfZ/HS68UZ5H57Hl5jvm/BtwOejbx4CACljAalHfFAMsHhCobjek9jTm9XVQEVDWlknUk7r9enDSXwenOg8X1QdXL4Ua4efqi4B+Ad6ts3IFU9MxbARUgNXoGLutt+0eUG3Y0bEK3GGRHNN1s3ocqPbktYFjWOZhBZepESCaAk2gpRGJqSwsFfeAeMVgbj8PK58FtxyM4+G0kab2pAEHJ/lLsHop7/4DXP3UAcAFavsp3Sjr+ZdzZMYAVqGX8ACqxiuh742HDNGy+yQHKgBdWF2tHmqnovK2UaBvqqfuRrTYbx1Cnqp6akClKgvwLsBmW2AMqhu02w2pvX33iZtvE1AKp5Rat94anMipJ0JN7wDVqCBgd4HeKCIPqxSmE9r6Kgeqxi7zPPltlAE9UPn0V4FjBMJifQuiPqg61kDFL++lHcwP8zb6Iu7Di5nNXVk7NWIrvK0ADurK6rOo3ecmoHoQ2lhGmldCLei9y88pLJ0GXP0UCNYbxQGlHz/pNLUw2AFrBCvfn50H1THWAxXQh1W4j9suvx1QOiK/lwLSmilQ5Lw3QQVUWHVUVRdU8Vi4fjV1yyFF+8Idm4i+ACidnlKAUmpUVHTrRddbo548lAilsaNPu2Ja9+TTsduX7TstAeXnlzvGonBdRjPtmB+BarD/4fluXIfazRHbIXWzdkkA1WjfTQkcgHXER8Pm8gaAgxc1k/UFaBGCLPfN1wVpGgWTHqsHHzffhVZctwEzhpRfqUJJ65Ks/GFXGOnPpDviZuyp8v3gwGYH4NpT+gJMA1BZJtldmGB+nQcV6nHjJVps35teCxMP09cOnXgsb6aGApC3QAXALuqGquqCaqCm9sQK7H3tbjek9ph38zVRfANABfUUgyJK4UEVTISqnCKcUpi39W6b1by3f23wBIaAKsdtCWHl5UhQeAD1shJhtFa4X1VNrRnvBFVd0dkHjQGy2P7IgmbveSuoIIEGCbV+Su6x1gt5QNnlJ/frrvcxgFrAyS1v+hJ0v+rua9x+tjNRTsH/qKCC5JWl13eLcJywUFVDcDUfIxsXO4IKaGG1tp1lfr9aunS90YOyLqicctpSVRFUYb83Fe13uyGlwFlNk2oknyovqqqoCyjfe4RrmGvRexEyAU7AAFBHnZtXVQ56ye+7Dygbc6lUBkiG9LdkpG20GAq7+Axy/e2Bak1NbZ3j6uqemqLluro7ktPZASsPKp/vppAfEXs939vpqi5krcdRAqnbzz5OpKClmuUGUL3jXAFOOt8DEpiAxBVYgIOSnI+6+qb2YciADUliz4ycnqmqOdd7OHVAZQWrXsYdIPDqCej36BA9D5dVSz334LG2Q4HsPkYPHntApfuP6yKoTiHoO+yY8aS0zsl6hUi7AZWnVNs8aXi5vdhLMC2W+5d/UVh2uBC/hnW7ACY7ngfU5EazZVcOM6xUsELRldOoq7ugGgLpJlRTLcObZU22rKBbZtYKOh914bar+wwXoEnnv9h76wd57+wvljuWZy2ctR6nqb+EA1X5a/bjAaP3uLO+C6g1OJFbpWkDsLQNl7koQVZocu/h0EuSUOqm5FkkoFVVU5FmCqc2itGposVX1I4HcFSoa4BFL90xamnNPbhVJeHz31Mpx1qoe1sDFVA/OJoov15QxRqoblBN3W5ISTDDpnklFf66gIqj6R5c41wNktAb66HUzPvjh2VrD96oIPJ/qU57dx8nrqoK7bNqwLKP3gorLXA0j035HBRXAyq06XarKf9Oxv3UrNT9Opo23FkBVVkdYHUZqPbuh8+cTq6si9uWbJAV1kwyNpN+SCSnpjzAdGNa7nOrrqmnntY+ntiOjQAqB1gX4KHLmesZNg8EoTTPIYBm9wARQLOAz7n/aAYwATxLlqOamrlz0S9hGqjRW66XZ0stbbkH1/Lacz/2oLXnXHsh5n560N5pl/vPL3/AdrshtcNiXZR38+kAhUNAubGhWIZ590EQasMw38U66i/vZlzTURNMsfgzV580qnSBE/7Zt2c2wMpV3ctGnbx0QNVM9wr+61BYzbHkQAamak2h31MyoDGo2O1wAZrO9AhMHeVFAzVm2VAoad5lLCpLoPCQR9Z/7DTl2F4AhX2sfVAt4KTPmm7QhG3XjTNY2KX3y19wBydut7VboKDSvhEVTuT2FyP1enaMy61X8X+Ey2/TPajzETRRxekHdw9Ma0EiI+uBCjje/RfPZUtNXbPdakjt6nEi1EU10X3N+E+h3kn+8kRAArJTU70vz8UxO7a3XsreYy1I/F8K01oH1dRLcS2MWTuRdB9PiGWRgOqI52tVTW2c1y5zmawqbR1UZTM9cbeCO6A6xgKgKMw3KiksW6TVfKoS0Q+GBIvkK/e2nLTCyT8Lvj0xhdONYF0D1FDt6z51jKXkTkHmra6QHbxkR7Vi3QGKGJipQkgv01x6kEAGaKqgQqLS4StRhXcE1shGBeVg+SIKrwebY4IpRu6u3qjflgkHrBGs9sDZqyndtrefToey5cNpAKqErttveJ7X6PK71ZDaFTgRVZRXTwoqgkXyDQE1FUBlifBbtUt8TMRCtwEUYIVKrJNaTrP1SF1UUwWQPasIZbUvU/bkv6emjj7h8X6bIsiVcQ2oBtmVorNdE0G1le+eAuoBykA0BpNN+r4Lk6/7Jwcq6dkhqKnyJwWUg0mTfTfT1Dvpug1A9eqqVD35rpD8Lhna1qu9Bxmx/3NufwlSwMmBdNoNU2LZYADMYApwOiaAIqzfBaU4vaWWOmMyKaztQ3qe68ORXGhkBEkPVh4wW7YGKp33+3JKaAgqv1+3bBhEsQZktZ39xN1uSO01r6LcNFu9lDbohYWXN/OuPmrRULb3zGhhtfY8HQGyddXGoTBr86AusEYpjT5E2S1nmFtQf5uCTpcTOpBo58vuJEjcJ2jg4zZWUMVlHlRNX3kwr5kIrfaaRSDtAWtTiDtALYDkYVXTL+BEXNdnebkNPrI/F8W3uJekx2//2C8HlhDqzA+DKQIA7TK7QAmfvwiUPaBiUkjUZWXfoqiAUh9lx1mqqfKCrhTC3rbqltamt+qiVsDkt7dr5POp2/ZgtVpn3TnXFZdckybmPyqrtca4N+TK22MvDUjtsF4gWGNx5Urio+Fk5VhpZ2JQ0IpmLYzU/UJwjRpd8aBf4ZPOy760PzTtUZtlX359s1z27JY3YAmFWwMFPx8KSv1S8wVQu36533jJFgDqHKsp/N3+OS5DLx03eWng0gPUwKwYZrJ0m++4XQcpqPRXo+pYC2g5wOS2G12HOL03PSr87AOgC6Q61wMVEUv0epkuw0JUQPkvBprLtWcZ+r2sTWVP9i4I6FJZ7g4mJxVUhytcfce0XbsMoNwyK9jjMdR96SLqFmaBMfKbqL88TmseehBac8mNzsefBwBTUb1l3jyw97r6dgZhvPQglXnRLkJdYs20FghhmQ7qVrfdON7G+kZJ6LyAA/rV2TygnX1rwZW4REZJHq8DTM28HH8Bpw64WiXi5pv1ZNsqWLwysAAwLWfi+03t8WohG0HjANXkawmkBhTApQAVrQwA2M4vgOVLeH9OGrbt6q/MJAKugY6db2f/IU13WfhVNdVcY3ePyjKyaxfVHqcSTNHCyU+3oALkmU+u7E31+WYkEHKJ+vMqxMKo5dcDaq/6ULsuQGkhTGkfqNRGgOpttwLlbtretDuf/nl0lqlKXAsWuSZ76UFKrXdBXQefttbDQ+aHj9iOe9Qr2/y+G/eaKid1j+iBdR/6ZSuBESwvgXfPEAOUqYFOz5UXgbUAmb6rHioYAMUXVp1tfDor0JIsDLCqrhJ3y0Y3oKPMOC7zaQOQ/Pyu4IhRXqRkb4pj/eDR5RE4tilXV2ACfANW7Yy2OWjeCaStdS5N4+7T6yhpbL5TP9U/oZrCp26nl6CKKct8qie86IlCzCr4A6Dkd6im1grnYwEVFQLLWCwjW1NLrt7dIpO1risOAtmDck9N7QUU5+V14Y7iuoyKOsJuN6Q0EOIqpp/o9lsWq6JRF5wvyPd+LxwbLaeVxJCxhAwaJaM1f1wXWXQVoxRq2dWbdVSRP9YCWMN5tsOXwouGcDKWRgiNlgNWOd/AKpXCnKxwrumbKELq77NRT/DLw7LOfC84opxjuKEjWAKoEiQUyRr54tP19imuPru3hDrmlJeVcp8X+RkdYlcaqcN019bqH92HUaOsXMRfex38wXaAiiGDH6KFot0b7cuwgspORQtor6q2FNWW6lgBVK/+qese0wdqT8NeBZFupwFfbt6yaPdiDOVV2wLU6NwyLwHVg/aG7RVetxtSlzV5yc2N13RAGUAFV1D65+4IAI3zoDtz8AlAab8v65zCSdtJkXg+dLgHyyMvfxduPFlnbj8BU4RW/QrnWnhRgZaeRlNwAe3XeGe5/yK30WgJ0JFuNeKN3WVaXEoPG4UT0EILbZrRfAyO6Lr1NsqZNmN1f35PC1jF/bvr24y1pKCyvNPyPFfy2T3k6GGmcP90l5p1vV+Ay1sAFYcdDkBVClpIODrDOvtrUkv9lHPFa3uthapyCnR/od0Bk627JKBsPqipGCChQV1+vgeojstPG0LvAlQHLkNAMXcBtbnPa7aXFqR2oHsJrrCcm6avlzpGdxst6FnmmewL2QoHv07fcYGIjUWk0aEUoeN+oxuP5SHfSDeGT4UW4MBlEPPL0S7XfWs9OKP2mGEBA3JMX74hFLghLz5dz53XmzdAWX7dvnZYLHfqClqkW5jlg8zlB3IRb+iBqpzv3p7BOlkZmrWd0+uayvOkx22eM82PhZBXAC0DkgKc5OZq7+l2niW35ZIs6qcSGLn27Qe0PVLEIIJQaG8GUFjCZYG+sBGgeo1091gPRn6ZD73X44nibkCl262VR1uAsnQtoNbcfKeReW/CgnoCWiDZOleAdzswvaI1rkSFRtMWBLW+jDWUm1pAieKj+o5vqCceLK+/3uXnO8v1gRKxHqpMc1i/hFY9n3p+WhjqKUvLjdr2a3HhYGBZDY4Iaf18dO/5dk+juuqR7XpH9XxHpufg6leKymwbwnovWbPvazDBZTm2/zBxHxSmhD04oWCSjPEyi7qk7IvQnogDjY/OlkxFtx/ID47YAZVeQzt0p+DuVf6vqIPNgt0vb9RTBnKSd3QbXFYH5QEV3Zl6XA+q3nlGt+UeJZgZm4BaA/fmCe5L99KFVHCLsfv1y6yA9rC6geyYqpBjMtBWjOo7LwV7eQa59lziBl50H6J9KDlo9dyBpJAMyxrFJOAhICxzIGpgxmE9tYDy71Nyx2S5OJr39nuiXhv/B2BTPUmarntPL/ngRh8LrppI01J/G1deM4liJ5QeRLzrMEsmXFRmPMba8Ue28Pqxu1+oUPL1hDbtQWVg8l/1i6O5r25/4nquMq/vgEC7vB+SoQkAD8ag8hd3T8PfrYI1uvn2AEp/c6p1rSMLQBoCyu/DR+Evqg+2H0juufMuA6gbVFHASxlSAHwFdHXz2aJG3ZSKYu+KuP686CitJAAtuWMgkxWu3u1mhTYVqCzqiCKUNuG0TE/q09fLYl+wA/AAtqyBpoOWKi3OqJXkvUsCd84KLVshaQIE5cTddH++FxyhgGrWdTO28xnobF57mBgRUMmAopvU5ZwlMo3b+20Fth7P7zYcf1FGhvVNUwhoNsKHkq0EGjegB5Uqez1f1OfKH7Ip0zTvoqzsOZ/qvCkr7TtOM7Q1WOLewnOkoi4LqL09oK/BKwDK3jFTjw7AQKumRue3WL7iqjwWUHtV1BF2dGjcT/3UT+Ev/sW/iNe+9rUgIvzQD/1Qs56Z8c3f/M34xE/8RLzsZS/DG9/4RvzP//k/mzS/9Vu/ha/4iq/A448/jle96lX46q/+anz0ox+90onstr2+mvgC6cvLbv66/tzxPUjqH8sfQDOQZvm9AOii/U0XwHTOmM4Z6Rz9P0vL4a/dRzpfLl/bJl0w0sygufwu14f8zgDNZWgGmsM5c/0zcMb70oBHp7nOJ27nPaCoriPiFlCyLvDVHddnaPQs+dtK9tcFcq+MCh8CpW9Gtp7uedI/7v+5NNavI7k/1OnFiYaPDJtO4dcvF1ftcmwzWK8teYrrqDtd9kku0lN6ffFKY0rm7jPXYKM8YB9rR9l1Akp/OR9VeHNPNQmgFud5rImrz1SUhpnrvC3LlweUbr/nb4cdDamPfexj+KzP+ix8z/d8T3f9d3zHd+C7v/u78X3f9334mZ/5GbziFa/Am970JrzwwguW5iu+4ivwS7/0S/jxH/9xvO9978NP/dRP4Wu+5muOzcp+W20nEOajrzaCyxWea39HWVMIj0CFAgABVpLCPc1LCFCA1m7AuL+ybwFV/Gu2H+9T96HQagDrfxXAuYVVURB63UcFvIcQo3bAu4RTCygYoLzKa+Ak8NI6qi6sBvfTJk3queXamNefUzwPPWCCgIkNNkgeSFj+pQIvJHZg4wYcti8PLX+SHliJF8q4GXhTAGggXEBKz4M6ECO3DRmgdLSBNo0fdBTwI2M3dTXNPdJ1VyjY1Y4E1ML9taXoegETOkirnl8XyG46ugove27H1EHdsB3t7nvzm9+MN7/5zd11zIzv+q7vwjd90zfhi77oiwAA//Jf/ks8+eST+KEf+iF8+Zd/Of7H//gf+NEf/VH83M/9HD7ncz4HAPAP/+E/xF/4C38Bf+/v/T289rWvvcLpXMGcNLd2QUxNvVRxu3Vcfp1nYQ1UXU8P193oaK21MqAeh9z2TXCCywdrZXJUIvbLneVsYPTzDUD1WMMCS65Ps77vHtTCSKONsz9R2WdUrZvvR3IXPhT43bonm9brywYnnVeze8MayrFtDaAAu3EGqJ7ZYi00qMLC3FEA2b65Xhd3nIVrVGbMhU1138391VMn91wB9txbx9hADXJh1HpEOz/brBwru3Vop72fsRl+fmrdfhoJqHVUTJD8VNdfk+de/ZSe3NodXAs3jxdqo3BnlqYc1umzm/FtPHvRfH46heULFyfKuFXR1s5VVdReQHlbU1HXbEcrqTX7tV/7NTzzzDN44xvfaMueeOIJfN7nfR4++MEPAgA++MEP4lWvepUBCgDe+MY3IqWEn/mZn+nu9969e3j++eebv6saqcT11lFB3uU0NF756x67/Yv7sXWi5MzdN7MoqqpEKCgpc9VtKiiZDiqJVEnp+plBFxnpPJdfUUZkrjpND1FgUTV5FyAaFbVYJooKTj0urmW8pgo/u7Bo1JS91xvqqQeoxqtisGM9bD1mfBbQB5Q9cmv1UbpzD1nnmjR1NYk6ci4+qGvPXH7V9WeqybnU1B2qSmYhFf0HheTFK6Zmf3Jarcuu90fD5baN//Bxbr+iolo1xeoO1Hx7lWHnEeZ7FsuDLTdfs+2Own1n4d20i/IqSpatujR3qqcmYOKoc2iXja4NZ979t8euNXDimWeeAQA8+eSTzfInn3zS1j3zzDN4zWte02bicMCrX/1qSxPtPe95D77lW77lOrNqRvYl6m6uBk8w2spPhcgKsRZ+8E6husxD3Ektx5aKbPl1392vg6t9STLGSknVVeZ2OyuzNQTd/8IKiBoQgbZw66gsDcFlLgrKehGY3T40nN6VHQt1QO634y47Rj2V9NxsWzuGJRG15cYQcYWQbbv85lm492S6WReNwsnpYdiv03l/LJL8wd0zknlu4GjXVC6ZXljfApC5XiMGnIJyikqfJ7h75bPujhNOEvB51W2TzktDe21gz1SA6tJi0iCjmh87TuLSryFRnUbZr6oNTlQ6tI3WVQ0BUMcEGOQMTqlc2cwSCBJsq68+hbSCqWm47G6U3fg19RRAvCeKrznnB6eg1G5FdN+73/1uvOtd77L5559/Hp/8yZ983E42fEVaUC+ApdsymctvMQyK/5gJxzkaWq5A7udz4+vDrV4Dkh2jA6VyniEdxy5pMIZWIoNG6b1aCvYkhbm4TfMEJPU3ERVvqrYLEiXVgIq1l5Bw/YLyWIvcK9P1ApOuQ7st+f2AF6DSbXt3owFYBNQQTn4HNX/d9T1j/0eSD72fFVaAv5YBVgogO48OqNiBARUszb5yXW7r4vkJeBRs9t5ZHhVktTcKUw/yEakfRJrv8sFR9uGj4Mz9PbJBnfVuN9+x9TcjWAEOTHJHgnJS8BqovEvT9bAxhLCdR4DKnnPYAai96ugYu1ZIPfXUUwCAZ599Fp/4iZ9oy5999ll89md/tqX58Ic/3Gx3cXGB3/qt37Lto929exd37969cv4o8xIwPXN1Nr5W2b+EAOq+VsBzLLSa9BE4x2zDrpzboZL04TaINetq3QBZlBUqtMQNUwBUCviuykotsBKTKSlfBzw7MDW/WDEPqJ1wquvZ1lNnWnHkQUUIMIrmYFHm63O0yHfvXEbrUM9hebzeX1VY1tGwbNAAyQ63ASoogNj2rz1R2DZRVfn8yI7Kejdooq43FSXpCCVC0akpg1Mq+yjvRimkm9F8VU3Zjtxvz/aoqMGybuGec613ylzrpXoW3Xb6a65UBytgfH49cwpyYXshuyPUnI9VVTcV3bdmn/Ipn4KnnnoK73//+23Z888/j5/5mZ/B008/DQB4+umn8du//dv4+Z//eUvzH//jf0TOGZ/3eZ93ndnpm0lcru0LjlWqWoCH6LtmfXwxxYi5+1VnyxUiGuVm4dm+XopDfZDUIWnk3f1Sh5TOM9L9jOm+/J5nJK1fsvUz6L7UN11k0Pks285I9y9A5zPoPDe/SdLQnEs+zsM+L7LUd2XJn9Rl+fouzWunPirNNdrP97ixWjcYABXDynWdCS5iUMq2Hdk2ftr/eWgd8aiED50a0+2eleY85M/C5xmU3N+U3bT/y6CJy1hiSf/8fmD1VE2UoET9eRetqWL5Y5evZrmmXQlB13R+efzt/hFq/RWVaD+to7K6KVP0QKy7AdwH4TGlXE9F7egqqEwHQOmyntusd+jQd19PRfloRnbrmnq4+Bvz58Pj5VxXw8zXAOXqoZrrco12tJL66Ec/iv/1v/6Xzf/ar/0afuEXfgGvfvWr8brXvQ5f93Vfh7/9t/82PvVTPxWf8imfgr/xN/4GXvva1+KLv/iLAQCf/umfjj//5/88/spf+Sv4vu/7Ppyfn+Md73gHvvzLv/zhRPb5r6sMgFxXQ/oVqO4GdTeR/yJF/VL3z6J/MbhNB1T10ygrdvvgmqZx1UmeVRmVfS3Tk4fwhkKyB1K2aYJK/DJ9WVIqiigXNaUvD6mrT9x5IAIl5w5M0i+dq+imVP7SrN26yKEV/nJNYge/5drBClVTUklPUH6Cehq59jRtnW7dfXWnRTNYjCfLkPQd8HQB5TM/culFl2VzPp3zs2OxNfw2ZZI1D7IuoboBc9VRpT87PTtZtqGo9DQaVcX12IvphFY16Q5YwGnpyJ7ZZlpVuH10oORRd0po1VRUGHvratSawrhTQHsFMugNvOn+yEf2qUWQqDvBLWf3jtn5oaMWgaqa1s61dz4r9U92jvH8I6BuyI6G1H/5L/8Ff/pP/2mb17qit73tbfjn//yf46/+1b+Kj33sY/iar/ka/PZv/zb+xJ/4E/jRH/1RPPbYY7bNv/7X/xrveMc78Gf/7J9FSglvfetb8d3f/d3XcDrXb9bjsrxY5V1wD+IxwOql82DxdUiaXuG04rLruuvg0ug2kHUOYAak0TLAlCfpC5QcsPzX3kTVBejcgvrSNcAiqauRr2MiLiNpS2FJWYIT2ATFAhXtjXJ/WMKpLOO6rAFSXT9y961VaZgxqgtw5ApsTsKBKgLKLe+5LJtzQjku234FSMpOjZLUacVQ4lJfJe6yPaBSZerhY6dlFUwDWPlrwH5a61TcNlQ/TEieEQ1JhyoroB0gEdrPoD6ftRAvl9rBak3R9BQTsF4PZRcIJVjCKTISpTLsmDqCSs21i1J4MVENu/fnlVCBv2Lx3EaA7XZ1tAWoUS/w48zsSkZ8Ux0u3aA9//zzeOKJJ/DG1/6/cUgbdVVWgKaiAOQXUwKnVCowqbgQynpp3Z5KoavTtjwUht4W9U1hdi3gQiGlfeaN4FT71FuCibIDUk8J6fwOIPnttZuVbn9ioqo8sHiqadpgCueuSECeEvKdhHxGmO9Q+T0j5DPIn0wf5O8MyGcMPgPygZHPAD7LwIGBs4x0yLU/z1iQd4Hk1neWaRSfjqTrp3MmQOfLzZdmJ/pBI2DLdb4pnO054mY6uiw1beOuVDegS8NM4Extft2xbdr/AvJskIGsGSBT8+uiAm2de27btHBpqd1G3dfht+l5JCxLtkybQ6Bxd1uPLDPbs2rzOYsiz8DM5ZcZmIsLi+Z2HvMs15JbIDXvRnWZDeuh/D4SgaYJmCbQlIBpApKfTqtlU/0tvWzoFxpxPcfmN56fP+d5But5Mpd5n0ZtDVCjIIljAQXggs/xE/j3eO655/D4448P092K6L4rmfdTBLPw89GXDFBfPB9hpMuBBjTmCvTbujRrARd+CPcIqPLwKVBQX8gRnKSSdAmgHqzCPNw+fVpIYUauRbuO70NSYBPK8PUSSo7EoJTEPYEaRJEA5KKsIEq1VYejAr38sS2rBTwRCqB8QQ7shpOmWS7zmaih50VAMxrFJF/xHJateV1WAeXglAKYfL1ZuUXS9VBWQKVS35SpvgN6j9jnSwIrUgEUJ3YuQtT7HyDEHkCShFDTMNz7pc/zvHzPquKi+kyTe1ZI9mtfHPWYpM+L21e81pvRsMeadU6IomQe9De+uvG4zmvEbaMWUT4qqaeuKAE847bY7YZUZmxGPfhW3b7QXoEXgFovtRaRA9QX3rbjpaKKabSOZXFMbl4yX++k+yEDhp4PwjyjV6dU9++XrwDKjtmCqrlmHE5scUIdZRl97bLc2lWpi5Dqcnbrm0p7qZjXQIEaLLEEU3++BzC2rNZ8VxCQlqLgOh/rpbS0XptvIBsAleo8AaCUF3BK7nwJQBYFlRKQcwKnuYCLRFlJXVR5rgVYjEZZWW/r8iwV0NQPBivrtG7WQaGBTTMP80CWgA8q0dVzuQYZLq6BpdBntE0XyG6BAU8/3PxHmz37MTBKlwPdqLR6n12BroMvUvkQA3Nd5tYTidrJuWwv02V5gQeI5FfaYHiPhHoYUjvfc/X5h1Ld5rFa09JGOEneShMOvWmpXN/Fubtz6F0PhbTUj9203W5I7TF9eDagVNNLuh1JzTqgAoCFqgqgAtB0LVO3h1MvMDjF9ku+zqlx462pJ/3dApR/wBfTx1wc2cxfC335ZHrRYekKmJq+57Rg12g3qoETx4DJ/8KdXZJlWVx6ZFJBgQX4cHStniQDGKRAsBX1AB1AURJIGZBKiZwEWsnBKsmfd0vOOYEBpFTmcxY3EaN1B04CVueeVHBV1STLVdnajQTgl3mAiUxb1EcBRTlTuaZZgh3SBYBJQMVlmhVQ+l40D1H90FLXt3fzqcdj4e627TvL7KaTm5TC2UNJy5BEQJaCegeomo/ka7CiLAOAtD7KN1zWPOsf3HkoxHSZhck7l91OUC1so77vMna7IeUL2JGtuvK4RqK5iLTL5QWLsnvL/QeEF5FrGq+iGoXl3XwCG3spfWSeV096rvFrcgtQg+tLmUu9k+5Lp/372EzXE65xBVTBZDtuwWShyAanWrCXZVzcVPouplKg2+4GYOqua7Jb11X3Xr159XuH6zwAHQOqcnw5b0eSc1wAKrXqySsmnZ5kfpL5RIzMhCmxfK8UYKUkcHJ1ajnXeaRap6Z1Vos6NS3M9NlUeNkyByu/zMML8pwTkImQRF5lkD0mCrVSB1yeX733zWsl4OsBinjlXdhTcCpomNvC2brbOBJUfr9J1VCq8PIQcX+8+AWaEPP6oNaPBr08ptocLBowxe0lWMaBqZ5nB1Rqck1MTa2B64p2uyG111Q5EMs4PNwWrkfvD30xsQdUmg5t2rZBrsKppo0h5YuvxvBSbqqn8NsFlJq5OHA0xFsIdWAGNOqpqiaqKgpuOSBKqgJLXX2+QI+HbJe5aflNo/XEMrhr+YipLj8YwNQtV+fR1k1ZRQvqvObd16t1AJVsOoMATCkbnKaUi5qCPg4FOhFYuoyxhNZaIEg5RyzgVZdTfRYdrLrqS9x3CtRkaeVXmiL4+qhotWf8WmfaACq6/ew9Wr5bqyaFtLrsGvWwB1RADUbYcvUBbfuoHWbXx5+WHqdxyVDb84R3+Wm9FCUAs1OPG6Dy6vIB2YsfUtcsPa3q4UhQARjDys1b+biqopYKyQdJLOqe3P6iegIwBlTv2qmMqHJimaRbwuhvLcjbeih5YQOsFn/Jz3NRUolBKVuBXg6zDiVgHUydk3LrrqimJO/k4aRMTrlx76VUXXtJAJUEUJP8WhYFRgokD63y24eWbhvBZYrLp5Fr0cBLL4NPb8+QLBc3ekZCQnH5JRaQi3uvunKpgTdQ34uioiBRewKoOTd1so3L23/YucK1qXf1PTLEQlrXHwMqX9h7FdWz+sCO66Ni+kY5oYWTuf7cOW25/EZ53wJVVFM3ZLcaUk1L6YHRoCDdazpir/nWd2VMN17uy5KMgKUgcstURTW+9qCiui+opNP99n43AXWVh8/AUwHk531lcA9MjavP/RZXIDcFfEpVSdnhQ3ZGUOrN+zJlZpgCiGoKBiAJedeCnERNWX2U+3oxYKvyK8evARItoKaUzb2ncDqkbEoqyYOS4SAlF7sHLQBdcNlyLGEFW3ZJeGkjY+QCKnmvMjNSlvoxgRS5e9+YPO/ENdS8eQ9GHoVYRvTKDH0mOT4X/YJ5ASoASHlR2Dflz5arb2TaQ7xPIm5ACsqpyX+EU3T5Uar1WDtBtWrx2lyT3WpI7bEynou7cOFPo5Tqb0mnLxtQe5kwFWU7l9/R87Wyvt81UmfawUvn13zv1hbkWDj10sY0GTLcuytw91pw9bWBFO0yBRKaXxYlxc10LOinDfiMlsUP3bYHdLaszzyI8BMkaSiaqSdgOa+Xw/7qOaiLzwPK4OTU0yFlTFR+DaDAElIOXHo+OQAprqvAqrDaq7zKOiC6DjkDGZOBhmUZplKu8Vwh1Xys+HviPAn6jNt7MLfehOa90PehB6yehQJ6H6hkuSjbpq55xdXXnuCyPmqZN0hEZniYXNBEdP0tQtE7UX5l+RKyUVXudvvtEgcE7LgdtxtS2UgytiP9vZeyrTLbZ3EDaBYwYTDi1mUBNPDofjV22jj5h3qXe68HsUWeefwwerW0WCebU3mh2rqo+ssundVH+QIsuPomK+z7eR57XMbnWOqkSke4OuBhLyRdL5OFpMt5dtUUBbgCFvRxLKDupIsAqVLgKJzKshZSvfVeSdkyt24PvDykFm7DrMedSiGXgXxAUVGpBOJoV2RNwEysZ8lBSXWB1P8I3GVROVwGVM79twiYCMdahJ4Hs2swcqH74UdivVQMRdf9cFiv9W66rAcqoB9I8QBcfrcbUsfYNcrP/v7ld4uJPWDpMv1a4eVyXxfVuPls/eCLMSont6yrorbaSK2BaWSavKOeWmABi6CJAKX6KwW9fqRqAe8PsQIftbQjTZYM+iAKttPZUFMliZuvdVEgODefU1MdQJ3J74GygepOuihKyrn7gLkBTfubXLreeqeoOhAb1XfZNivwytkpsAwwJyBTGSY+w9pNeZduY/b8oxTKmat6KjteAkuX71FRbpiLS4EKqAFB3v2nD6gdJ7j6FvkArD4qmksvj1id7tVL9eCkYLJ3WQIo1urXdB/x+j2gIIoXP6Qy17vZi/Ajhna8eS3Wg9COtN2ACWAJIvfCxXBzexHdC7kbTu7Y62nJ9su96MjwIsVp673ap+0ByYBVf9UNyC5gQtsRJamz2QOdcugjXi4mKcOWIemrDXzdsUxNAa2Ksl8P2hq91wPUnTTjkGYDlXdxzgFIZVpVDC2XcW9Zq8a8C3HkPlwL1gCAi3myY+QsX90TF0jNoqak13M/VlnfVV6ev9V+J0vmWq+CLKNYsPoC2H+IbYEKqEAauv8UYA5Mobdz/Vt2q0ZNvS0gaim69nbAadXlF+vXvBKUtFFNtfl04ec3EIp+uyHFefuC0E76sMDKV3Bf1XrlYG/X+kxxnOe6PKZxARI9FbWvUa7ua0eaNVtRV82Ll/xyD512eePuI9ThJTRk26ZhUW+qPvZCaq9llrBpAAkEHqgpQIGFTTUFAFoXpcBKqqYAi+JrXHyUcTbNOFDGIc0GqrtpRgqtXgtkZjfvQOQewJnbexbBFiG1F14eXHrq85QLxDLhPBf3H2cGZlFTE5phOnp1UqqmKpjyGE7lJNple5/nHaAqu9yhqgA0ARPuGI2rzy/vqCwmLD+ke3C1fcg1WXP56bFzAppeNSqorA1VOMaD7BLqdkNqj3EuhUfOixs/NO114kbys766AVUvUq+nooKC6vW7t5jW/fXWLdJJSTBNdf1lr08sfAj2UnlALaL84rZOSZX3X0Dl6meuwxRQbQh6mR818O11l2RqSkLQzaPZfGAHN18HUHemCxwo467+pgscUgHS6Lw9fGxZ52vJQytzahTYLJAawSsqrAivOSfMOSHnhDxn67C5jH8F6UAVwDnaHkjK5S0/jFofJR3MlnvRgVNvubfesl4PEz1QyfZd9x/QqqpY3+RveOP+pgWEap1cXL4cnqSplxqAa+Hyg8AGmvfs0ldF1DRUBoKyqtfspuqlbjek9nwdxZvccYkNN2W5T6mz/LoZpkoIqAETqNCyuiiggglYqCjK4dzWwBTXj+AUt99qBN374ov3Ibgxhu49DytxBaGBU20/NFFtUzSyPQDzaUoZUNzDqqZ8SLphqnH/OTUlp29qStO6IA9VUd5l2QRJCKgOaTZA3UkXOKOMu+kch9T3JhwD617aGamBUARVD1y6rwivi5xwkRPmTJjnAqt5ZvAsQRMJ5vLzgNLfZviaLCoqfsCpxWffPdcLV5+tcAX4GqiAvqqy+igHK+TWk9MLkBjBapRGvnP02nTrpYjQjDW14vLDPC/PuRNIYeldj+kPSk3dbkhdhwkQeM3Fx1gooGv2KrXHsunwYo5UlAdTBPAalEbLRu0hegqKGUu/DLpKS+ujWvce2RfjQkkBNVjC1okScb1MTFTrbyKgYuE7OZjssWyg4RCCjsurKVMIrj7KwUrhdEgZZ1L3pC4+BdTddIEzmstv2tejddqS8f68QZgFQBVWdbpcm/3wusgJMxdQnc8z5pmQJyqAmgRO+hGiHyTxFsmzXyJgUYafkOU+TW+6WzcL9F1mI1DptluqqlwcaJ3Uom0U4EYSkF306qV6ncraOozrpfzyLVVFxQFdetZw9Une7YcOkGLd1KlbpIE59TE0/SqgDeW0B1aZq4/mmk2rW0pesAiYWFVR0c3nr8teV8camEa25faLaslv6sDTQojCvPwlP6S5gMqrEeJOpFsbvdcAa+AWZKmD8qb1UgqoqKZiA9+tzmeB+hj5/CdC4+bzYeYKqgNlnFE2QCmsAGBa9Mgab8d4/RQANqOAZjYQJbuma/Aages8T7ifDzg/TLh/MWE+JMwXXAAlQ97reG2L4eybmwF51vPyme08qwvltMfzMgJVs35FVXlY6TYjZeR7mXDrF/W0rn0hgeEk/DJvOm2qiupovR2XX3v+HbeftJ9aXCO1G47yu92Quoz5CL9eTAUzyLlxHoj5L77o6lMVZXnrqCjdR3D9xX03ttZ6fAtMptT2wdpeMi10hg2W6l8trNiFnstHgrWPEiWlaopqw1Zf6Kpq0h641VIHVPGq6CNSAVXS+Ppx38A3qik9LT+Uh4dT/NN6qYMA6kA1iu9uUjCdG6AeS+c4ozZ4IgInwmnqDG/Tc5FW+KQKrQG8tsB1nhLu5wnn84S7Z+V3mjJ4SsXVN9XnZBQ4Ud4HLgWu8xYMXXhqe8EU5xtV5JTUDlVVJhlNwIQAyfrq26iX2hpEtaohNCCqQRNYd/lp3nJ2aspBzs8Dy7opXRav7zWrqhc/pDIDJMETcZ0W6B1YbaqqY23rRcnu60jTR9eFV9cjFRXrpLa6MtnIlz6ARFT2NW0NsLXTnOvP6p6a9aiwAoKKcvVRwdXn66QMQA5Gk4MHsASXt+xcdiRpZwMeFg1896opfw5EEOVUgyU0BL2CStRTmheAuiuQUvB42HgYeZWVmuW8SKs2IxVFhAIlhc5lwHWRE+4fDnhhPsPZxRnOphnn0wSaMjglieAk6U2kKgm7VVp2sjz7s4w8e13mwaPzVndzpKqS/ZCHUBdIArPeOj89cJ0T2rG2/LRBqefys322npDaoW6ATE9NAf1w9Buw2w0pzlh++wbzFZejCL+RKjhCLSy322/NRywD1k8fUF19x9RFzeqUPi4fa5Wg9lV4DdYERAANkEZBE1WFoY4d5ZWH/Hl3n4dVA66goJI/bft4dj12u229mvINfEdqyuZR1BSBGlefhZ73VJS691xdlNZTKaAeo3M8ls7Leci7EMGjSsorLIVW3WaZZjbgKKQUSsfD61464F4+w+8dznDvcMD9ecL9Q8bFBZcIvwm1rZQ9B1KQqsXnvakTOeLZXEu7cJnhOFUFtOWM1kVF9eTh5Vx5rRuwZqsZIBTUKiU4NyBRGzTRc/nFZSEgoglLD2rKzikOOa+Xzw/dsWkJe1xWtxtSe6wXntetkwG6baT2gupIIEAP56YXXbkwWtedHCcOCz9SUQ98aOtg7F7IXtAEgAZKi3oIB6c6nlQp8TVoQoewSGAcaK5gkV1kTl211HQl5Jfbl2hdpk+QbzMVG/iO1BRkOfl5oLr4ZFoj+kagOtCMMxI40UUDqMfofAEiD6EIoB6Y6vb1anjoGKwuCa4zPuB3pzt42XQHv3u4wJ2LuajflJEn7jTmxeK1M/c3c1FRTeHpCthoEUpbdalbqgrYByu/P30PYji6T2OwclntgQqAj/Jr3JMxFN03mfMuP19HBbRuv165IWqqaTfVS3MDwRO3G1K+N4mRJaoFuE8bCvq6TyxgZcNlaF3VjsL/qOg/5+qLQ24sAia2VFTO44foKpWbeh13gm9P8Fy3TpCWL2mzLwGUv8DqHvPzgEIlF1CBTV3Feqjesmh7w7nbgRI7l8vlta07d0NyUIFGQgHXWSrwPaMZCYyzdIEJZV7/RlDyQDJIUTYYtYDipZIigRA5pUQCKtoPrcQZr5zu4WPT3RI+P804TDOm6YCLxOXrIAZM+A+X5mag/ywStc+4AiUqI122qPPp3bDBujUX4OiYfnrk6tPsNWn7WWqOHaHlepKwPv18jxQdsOm2BFS330hNWd4SKOe2Puua7XZD6jLmgGXdI83aaWKBBDNqQ0Go06ZYuhgXwEeBKb5f2s3LXDvPtEHcZolmij099+qi1K77gfHto+L165i6KFm+fomxqOPTahJjDpdlJO5OygDJuGw0o/ROcEHIKYESMM+M8zTh/pwxpUO3Pip2rArUyL9el0DcWTZz25NCGfl22UddHfXWdazKAMuw7pD5nACS9ERlX0QJ5+r2w4REjPtZlU/nGk8AstQbEZV6LeQCHBbgUK6QKl27FoiBcI4JExgzZUwSaJKBMi/H8267eVBSTnrT5CWZAMx2nIRZYIoMnNGMO+mi9JZBMjZWyqBUR1pmKUz98CwGq0lcYNqOaE0N9RRVBFMPSAtvRvyI5SPS5uruM6i6j+aJrLwpgEW5gJBySPPqlm+Z1Uv5oAnf9ZEpuvIMloO1amoMKqemVlx+120vDUj54InRsB25DGYt39v2q+ZBte+bGgsQjczGi8rl4bVRRqXbFx1+g9QP32u4qw+LvgAP0nwfiPo7CEapkRAwgHk4IddpmoE0l3Y0NBNYx725SMiJcUETiBj3mmAF93HRAU6c9leqWe4gt9V5KnM73Rvllu2c6xd0li/cC/HfJ2KcawYu0LydGtYNiBsOhHO6wAzCOU0CqAIqhZOCq+w7V3BxNmXlpwFRYh2XX5lWaCe3vl6zhDJW1FRKMUDykTnhjC5wJuHyhzTXYBeJ0jQl5Rr1+uFaang6ASmBpwSad7+J1dYK1TUg9db3oBTXa3kjHhqLKvbvrL43Ui5ZdLF+3FH15tiHHrtjbFkPWICDE1Wf91owRKI69pSvy2rUVAKlfK09T7z4IZW5/QoxKAU1kBnAAFSZgUkq+fbUB/Zs42FajDKac6ukPKB8a3uvoryr77p9w5SwGJtrzdQ1SVR77lCXhLQ3KwqKbFhwElDTTI2Colnc/BdAlq9APk8oZVT7CHt3m1oPPnVdzDYt5regBFuHdTjpegBZwKSCYAZwPofMXAB5ImtrpG2PLtKEi5RwniZkkIWgq+uu/o7BtQdaHkAeTHnlJfCgmmS7SdpxVdekDNro3JuzwckpCFNUtQNaSAPgJrR7z7O4Zr0CdQtIQB9KzawgxX9ArqgpYpbBIeXd0bHbGLIN5L1RuPmDdU7BRfbVOioydylL33xF7bmyRM5toaaYrd5pq7cJ2jFEEjG15zCwWw0pZjdw18AsIs09FAhqiuZs35EgBs2ooMoZNBFKFXnpOXgcIXjsCbh8Mi+htAIomgOofB5uouW3jlDXLOPmrzeA5OpVYXVzkkCaqoryauqCkFOJLiSBFaO0r8nEuDivPWxv9Tghh10u66UzF2CFUjsPg5Ltt6OaDE5yYMqlF4pMhELacaFf+8RLAizXswNK3Y/WSSVxrZX2YktondGMSSBUYMSYWNIy44wuMEkd1PkgP/PAj6D1YDOnJsTdt9E6k8AWDQTRARu1zZvVRSmcmj9xAyrVpwRcOEWwBpXe+mPcdsAmkMqi/jLSphuxjaa+O05peTUFdoOtyutX21CuvFm9OjKvoLRuClDB63Nc0i3aT224/W7QbjWkdpsbAtQiseyrQfzGCgIASFSVFJHBSaX3sDGq2pFSV+tv4EEVpuGg1ABKpzXvaz1NrGbiEq6TPbvltl7KGj3pB6KcOzWAEjWVnJq6kHDvcypFIAF8TsgOnNHdF22rK6TRJRsOlw408GkU02JdXV4KY5KP44TZ4gfr8fLk6rq4qqnMhAtOOOeEu2nCRZqK+wzSz5+qJ4FSUrWkkHIqy4AmwJpBmLity1qzXg8XcZkPxLBAD+nqSaP7krr7zLUHi/BjaTtVAmhI5qvLr15ganv+jwEM3tY6oO0VuDuBNHzvVaXovqKasuhP1PaZzhOhK3v1usNzjOH7CeDsyj73vjegmlLtbmoEqt5nXnD5nRrzHmFFcpNTUUFNqa94Lq3fy0NabgYhiT+4jG1tquoy/tZNl0OteyrtpJZwWgDKufnAXF+cS6m6jW00csq7BaK7NNZLUX3Rui6/nnIKKkrHjFNgIRVglbetfEgoqK4Kqe42NlFVlK5olJKkaaCk6+KypG4dCJ4SMlVQ+TPITDhzARsXKeFOmpEnwkWecDElHHJpO5VyVUzqRuupLHUPnvMBZ1RG9p041wAMUVdAv20VUNx66gL0y336ttEw4Q5JnRRpWH0InkjcBkw0dVKw4TyQqEasOdWgHknLzjGAUjh1XHZd67oI+wWz1YWn1L435s4LUM0kqgnyXqOWVaqmxGW3GqwVwaSmkX4uuwYq7xIEWlD5MH+vpnrH3ttOaoe96CEFoNZLcVFOpqbmucjwOQNTKgCgWp+AzKCpvDUGKKa2Ffdlredi8HCK6mnRkLEDqMz1RVk0wLuiUmLGZdo1r5nyziL5MoOy1kdV9x6lElUJInG6lvdYfUOMjMwT+JCvPY9mATRdGA3S1nlNQM2shDaApU2fdy/OqdTBTDI/ccacEy444UDZfpOGqoOLsqLSbuweHQxaDaRQQtnPeTJozUiYOBe3nxQg2QVezDxVCBEWaqsJvoiQY1FSneAJC9QzRUWtqrJpKoCSuilMWug79xZrXYvmc8UV6D0Q/iNPbfQx2oPRWnngm7/4d9gA6T7wUnWbV3ChDaC4jBHZx5G9Ij1QQYMh0ILKekt3br/ZrVcIz/s6Oz7GbjeksoaErZhVruZygUUJlRuC+qDMudyEVGDVNLrTnowVUGsfAJdQucshsNsHuaueRoAyN2bM186H+1iYhZeuVy9lX4P6rjJEdWhvGuKHz7RUUfpuEEAXQCJCptLJa9aaZC5BHepFOdr2bNODELAOKb/eW3YZFS9gEZmlAGA7HwenlJCnGVNOmFOB01maDVKHNON+ngqw8qG41GhCoowL+004pIxzTDhLM87nAqg5FTdfptTAaqJsdVATUon606AKeYEiqBRQvTZZZ6qkUunmyYInpNeNGt0HC6DwoIK5/0ofctRU+Ifb5ZVB937W96fMhvdm5K46Rlmp+cjiFTXVdMWmdVQaQGHHpqPLGCYPph2gMsUPU2xNWLo1Di5qyrezql0rXfEj3tnthtQei/3NiZpCLhXvNM8FRtMkNyuLe6pAilV+p1RhdSyI9nz9RDCtdRzbA9TimHmn5O7ko2eJsBhAMvi2V/fZcfmZklIF5aczQDMLlJyHgmCASpDhyeWP7ctjp635Svx+esn2uA7jdjpvDUFrqLGuy0h2XxW8ZdBAxpwJhyljZhJYJTes/FSDEgReqqouRGldpBkHLvVCqqByogZWZ5gtZHwWMJWQcnmeGAYqb9YGK0QXxsbCWi+VROkpqMjaSxU1peNKNSHo4g7Uh6EtfFHfyzWQdNYtvBBbqqhna3Uw7Fzlun//513pnXD0hctP9mF12T2zD+wA8B2Kqn2nJS++XRQZvSS5dDzrVdSej92d7vcXP6RQHsLi8hc1lYAGVIDIWXGIU3ErdGF1nQEG/gGT6e6ghXFZD1BeRdnLtkHTy0Bs7VxG9VLhmlmbjwxYvRSHuihSBVXcfqamBHCJCZkZiQFtx1SP1zvX3rLlwlHj5C2jvdAilDYkDlS6Siey9O7BmZETWfdPOsKtjt57Ie4yP0DiORhTmmoP6pwqpDjhQFOBFc3IRA2sciZkKtGCGkSRKOMMJTAjoyirOwBgbaGWndNGQJUGv7MpKQ2e0C6gLHhC1FSM7GvcfYSiPlICpgyesa4KeuA61gth2628T2sfd7GdZoz0a6DkPBGmqtxHHoVzkWdm1XpBEyugstB0y3/dlSklbS+VnbS6oeCrlwSkzDKvg8oPjzyAFYDa1b6zzeECtqwHJr+85zuPX3/H5qH30o3A5Xuc8Mfzbou4q4HLT4sV7WmiqikUFUVFTUFg5SNnAYAnDTgoHw/lthL40AJxwQ0aTLu0pA24rmqj95XlRQfXxpGmBCucKBVYlWuYkfMUYMVIlOqoxAKrQ8pSb5VxkRIOnK1Pw7Yeq4VV6bmi/p2lC1OrAGB9H1LpUQIMa//kzfoOFEDd0WhDJtyhuQ2ekBD0SYZcUSXVgklUFGmvFGQNe4moBO0WBhbzLqoNWwBqrT5l490aBVjYx7FXUwqKqKgWrr7wkScfd8D6I8r+EfaBJbQTVNojhfbvl7DsiNarKYEaAzXKb4/3aKfdbkjVUm9sREAuwwHUNlMBVDPAfhRNdQEqrHQ/Cqy97r5jbtRa1JH3m2va6JrwimrthdqS4Wvg8mqpuy03X4PNl1XP5SfbUIZ8ZZYXg4iQ5tKOiGYNjSgbqLs76budqRTwkzTi1VvsDjGCk0GMOKSldv3iegyWb6xrjyf5TlIYKagyAKekKAHEJdRLlZV2rlt+kymSkbqaSJRVzrgzXRTIpVl+SfrgK7DKRKaqSoBFhZMFd6G4/hLaBru1fVZuAHWGjExUlZQGT5C4B63nCUhEX+geyf9NEDWVwFM2OPHkQOXbBPXKB9+djwdU573ZVdiutBPilEp5QUFNEcmJdCJkST/iwkeevD8uc+v58i55cfs1LlKgD3lf5vl92WRQUz6A4hQ4cQlz9Sbm9gOqotIv2gArLWwBVGDJV8aN5FEthMJyD0J+m7h8S01dJupvrX4r+tbtOBi7/MSfzkz1w3Iu6Uk/AggCKFVNDlTukAasXIYIYFem2tvYBZNPE1xuvX34bS1duJYDdWarqU4wWPIgw8tblzNoYFVAxfJ1K2qTuLhBDVSMOYmqooTDJKBy7sGZM3KakUGNG/AuCJlyaYMlkCoXucDnTG+CnF8CSxDFuDDSjmoVUHckAOPMufx0yBFr0Ct/TfdIWo77gAoicfeVD0YPJ/tGib0q+J48/HuV3UOUe9F9O79GV+uw5AOZO2rKeyfce7SM7uNa9riPu/Jbfjaj/hRUkA87tI+r+75rPEZNWHpPTbFEOAWX355hffaWpC9+SKlJZI2Ni6SuP6CFFc9WurAWzKmosesaT6lnzQvSizLaWu+W9b7+hnnvQa3bOWdG0+OE/xqsB158DeryXgUwJRb+yf2Yy8CAaV72oKigUvXEUpcP+eUZtQNStDDyv17NxHRRecV9NKfUrAjHgnvpPcjIryOrXyiRo4CN3quXtZy0talCqvUXJMCCtIcqoxQnZA2s6MAqT2RuwDs8O2jlRlUBsJ4syr0u+ZikVdoMVVGlA9mkXS4hdLkE6cWdgMSMO5hxx/Xcbm2l7ByyQMm3mSIDVQ2eqP34UfZw4n6nqnDL5Hksv7Xull091VG2BTI/cm9PTXlX36heN3brtnq88i6Zy8+DTo9nAc8OVt71p5BXt19HWZnL2iqJqUb5XXMvFC8NSOkNkgvHGpY+60B+cytbDU6qVgBrP7U32OCyLa4XX3Nh3u83rNsatNDbKnD9fiOwNMIvQijUS+2rAJYXKYt7h1Hq9qgOIGigcuqJkwytId8ZnFEaeooCa74KUZeV80Yz0YAowIk6INNfGixHOa1WpXX2a+UHBVhJIW1fzS4YxCLbqACtbF8KdEoAp4ycqYHVIZXw9cOk7r0sEXyEOyBkLkETgNQ7pQsgn2FGVVATZekqaapDiDQNZao1KooyzqRdWyKYktLeLoqSCu2lFE5NOHqrqphQFEgiu9Y8S8FpwKImNHph+oxnVVIaOLH/vd3jCrRACUAeVFqqqU697rApB7mHbCt/CiqgAspDy8GKkjR/yPpuc7tNVFOzKzPll7SMvGa73ZDa004KgHV7BDSwAlpgldUCLaDeADWrLLxmu0TU0LglfNhXHIBtsP2m0lK10wue6LgsWleFFrRuaAIDFCxGpRbqXIMiQEjMBUIaxZfKlzln+boWJdW4iFbg00ALy/U+Ly5epk0n9Vjs9o94TPeB3OxXAGuRiAIrA2x2+yB3jKwZ5Ao3CTSgVK6Rh9U0lWFNppQtfL18ExDOphna/VKW58+GN7GQ8VwUlKwvvVRM0l5K1JYDVRyXqigp4I5cwDOJFKxKarYRlTV4Am5gyxZUdWh52LwGAkB8vlTVlDevqLwVWV7eBWbw3vqUIwKUihLhWtWwpqY2Xedol+vprX2cRlD1TFWUugSZttWUASy4/AxgOz7muwPKLe12Q2qvNVF74YZqkITY4nY3F/IGALXHriKdLwGtLrC01w6d9m4s77Lw6617l2WHsz6Awhr4ZpSJCwIOVUnZEBgTI7E08MxaiHH5CLYOSGs2IkAWkArzFNNB3sUAn8KZMkHNsbjZT9yvXirdr4KGtFC2tOTyyPV+RGg5qBdYE2iSOpCpXPMk8NJulUrD4OLey1Nx9+VUCmf9xXQBZC0azouCSozMCRlcIgDZuQOdJQl3T6KgzgCcycmX+qm5HbajCZ7wY0uFc20CKAja0ay/RhrxZ5F/+ufdfBp5pgFGqqK0A+c1u4x3JEscPFHbXnOkpnwARac+148zNYSTH5AR7fNctsMSWAonoF6/GEQR1FTxknC9xrpu9FFwSXtpQErtMoX9DUSr7LI9XyJb1oWNuwaXARZzLdx1XhY0bT5Q4TSsAJYAipb98sBfEDDJi8MMbRtldRWp9kaQ5EszFmzcK9zRpunNewh04dVAws9ThdoIWJq2+WCmsD9ewNDn08LkdbmqjIwywOJU3UecpOeKKUM7qz2o9wlFnapZmDkATBdInJB4QmJG4tqnXxmdt4y8OxPhDEvTuqgzIpyJi/wOXZSoP+volkNns35sqRDhJ+cee0Yvl1ICE7ya2vOqexWl4FokuVphS0lUk6opdfnFV7OjpkajCuzqGsl7gGK1QIQWHJyABlgKHE5kLsc2apCWUX7XbC8JSB0Vs3+N3XlcybSbpo4NXXMROvG843ZHAKtU7tfgiWYIAj2Wh1HP5TfqNFMK7RqFVRYQo3YsmtseB9jVXSy6z0EFxQhAVvgvljuQDJbrdBdaa8AKsLSPfP2N98lApcvZqTKqyzV0e9JG5wyeyj3IU2ntxJmQplyuJQqcDqFtWxzWRIevV0id8QwdPj6HgnZyDXgnMM7AmABMICQkTFRC0O84d5/2IXiggsek59e4+dg+SJqe0WVcqVKXV7/+GzUaGqI25t8NUVHsm3Zck3FOopQ1eEI/MDrh6Js7E/ge23XZRnrO0iTCuf2Km1DUkYeTr69SU5cfUOul1uq864a7sn+rIVU+sI+EyqgLocWifemu1UJQxqIXA1nf9Easq0JdWxc6/sW8BLDqflykX69eqgsnD6aShuT+Wd2UDxfWtOLLb8YVIt9bdjvvAdTUTwXoKDha6FBX+TSASXU9+X17YKV2e1OOHlZw6RE+rsN6nbUAFP+jedD2LlygrkqNWcZAm3a8JxNwkevw9ffywVx31pWSgAoE9AY/LN0jlbqoiQiJCGdUnpUzwIWhX1jgRJJ+/DTCr9s9Uq9uiiADA6KM1KtqKgdYRWuCJpz7T9tMXasV5dRVU8e4/HyU31pg08B4DRoTAXMOdVJBTfm6qZHLz9dLnaL7LmFrUXLoQMmvv/YHd2Ac6sdmNOCi5PJEqQlqaBQPsB9YO12C3SFPtF7Kuygy6kumlcYzyguWCUAukURcw8955jY4gVHVwQBWZEETVS1ZtznOFbYGLgOQHncErs42+lW/gJjmH2ggFvdledPTdnV8zbdJkybcQleGWDuxTNBOSU1VZUbOtW4qZ0LOEgGYS1DFPGVc5ISLqfQJeMEJF6k0/L2YZLh6npC5/M4gnPMBMyecpwMyEs7pgDklPIZz5HyBc5pxn2ZkugcA+B0mfIzv4HfzXfxuvosX8hnu5wPu5wPOc3Efci6qr4zaStBGzvbnzr18VFS3agsmXl6wFWNtM3UDxpn3qam9Lj9/WjvbPfq6rN35JjfaQ6xn8vNNg94KsO0DnAInql0FUA/aYmTPMF2uANO+4CAwOUYlrakrv6323qxDnhCqy8/17Gz79ONypSR9rNlAG6BCMFgkEcl20tZJPzBNmbAoLYOVgMvBqbyIMHeFd5eRUzFeMUW1VdPr8grBHrTsN0ALnTTNMePllpmuonLpFq4/yLvOslILc21DJh8HCjCaS11Vzox5JkxTwpwTzueiZg7ThPvTjLM542w64IV5xmPTOX5vOsOddIGPpbt42XQfH53v4m66wGPpHHfTOV6e7uMxOsdj6RyP0Tleke6VeTrHYzJm1UfmV+CZi1fh2fMn8Oz54/jNe6/E/7n3cnz0/l387r07uH//gPk8AfcT6JyQZFRm69NRCuvYhnrLrFGqXnB1A7rQ6fKhF78CrgFasQ73MlZfJbnv/R0yYVxdkdaHF7JrmmHn3aRflBMdePlQ9FPgxBH2KAHoilZ6Hxg88QFUAJawAvYDq+yg2c5azmvEUhbQ6JegG5drMYCkAxWYoKMeW+AVuWffqSh7BxRctAIrqweq9RR6HbybrQEP0CgtzcvQ3bcXWvqH+sG4gBfQHrPOLtVWAJsPCinXQcP0RVVN4iLKBExFtWIuy3mmEv03EfIMzCkjTRkXiTFNGedpQkoZZ1MBVwHUjLNpxp10gTvTXdxJF7ibZtxNF7ibzuW3QssD67FUBqP/fy5eid+8+Dh8+P7H4bfOX4H/594r8Py9x/D8C3fxwv0znN87gO9PoHOSP5ThWS5QOh2eIT0guO8hUVMV6PIQWaEeLpxvXKvzs44Lw235sPaxeN2qa83lpy9BVDPeiFbhvRkA4pQbgFqXt3aecn3K+9WGou/rcWIfvW83pNb6kdtpj5SK6tlql0RhnQMVsAEr4HJ1WD015dPqMT2o2D3MpZFTcVukOl3G8qoVtqXBMKrLL5Edk0RVaVqDlQxBXtQQNVCq0w5agINVCx+ggqsfeCHLgoswAm8VXjENsNiO0EkPNOdT1KYAP7HUAWqovrgAk4OVuFhL90KMPBFoTkjS99/FVDqyPZ+0DdMBh2nGmUTheWAdZP5uusDBoFX+zmg2aAHA/7l4Bf7P+cvxm/deiefPH8Pz9x7DR+/dwe/du4P79w7I9yeQqqiLoqKSwmlmA9RuWwCKUEbikUKVSlADi/uZxN3YtVgu+HfqssByYOqu67n8ZGDERV42YDIqJpmCitJj9363jKgNW9+yneluN6SOscuGlz6o+qidtlBTG6ACKqzUNhUW0FdZGs0X1FSpIxK336xtQtxIx1NdXoCmkMh1mhlSM1sLXKqwMp+6wkquw0JZ2dcdm4JRxaPTAHfAQ25alzOWcHLgAtCFkAPeAl6yTdxvnO7BkeI6OzdRUwrvjKKkEso1UqU1U5mf2EAFi5KUiMCUMFuv5BmTACulQwluEIU1pWzQ0jDyA5UObA+UcVd/BV4A8Dvnj+G588fwO/cfw0fP7+CjL9zFvfsHnN8/IL8wFTfffULSP1FSqqCq22/s0tLeSxZPeASWppdulBRcvt63KR/8O9YD1hHlhAVPNP0doQET/BloAJKqKn/4hG21s+bmA7UqqufqW7M4aq+pqV0b70r10oDUoxK9t9divVQA0S5QAcNK1M2Gu8AQWs3YXMUJXkClrWysB4/i+gNQRzpWiElv86aqpKTlnMtp7IBVzw1YK6Tl3OQUvEvIg6uugymirnJxdVw23tQCIhvwkmOM0vh9jNyOfptWGaLWSxWBICH7qF0mTQr0UuiXkO7yVW7QumCJomPkxKCpAItSadN0X+A1TVk6s2UbKuRsmkPP6xl3ptqrBAB89Pwufvf8Dj52/w5eOD/g3r0DLu4fwPdLPVS6lwxO6bzks3X16UB/y8d1Yeb2C0TXP18vxam2h7Qe/3PzjnWBFV2Dewr2TppFcw6gdflN1Kgp2y4Cq2dRdY3ylDvTQFNd0gxH5Lud8ueuauoam/LcbkhpD6NHb/YIA2qnbYIK2BWqur97JA2QcC+TRPCtgkp6q9Ze+HwlD8kXorn9PKymCqgerJo6K636KpHRDVzKbwVL6W1dzj3UZ1iwkYeGbL8EC0kaTd9TZ24/awBzEFvWh43D4s21qdfCRz+STpdCg2ffJ14FVu1iCNAeyDkp0Eu/gEhAlga2pH8ASBrg1r8WXH6MKwB44eKA37t/hvvnB5yfT8j3J/D9BDpPoHuEdE5I94Hpvrj7ziFuPy5uvwwbd2ytbkYp5hVVO5Cfq5fSrs58bzSZF8pJ37UFrI4AlbnG1bLctNicw9dD6Z8CiSoMttTOJsRUhak69SrKzQ/NnW8dNl7U1J7Q+LwjDW47pC5hR7Ugf5RcfR0IdUEF9OX2zlDVPT2or450jFweciIZgRTyZZVhoxwnV9Lqy8apFLweVkRDZSXjyEPbJDXdLGk+5YW2AsafBtXw5TKPOqaOB42fT2FdJ91CfQFWsPTrthzAevDaExafa96aEW25qiZyPXRYuzIHLL8tLfrJ0zywbV8UWylQSceCotojuw4houNeAcDFxVTgdJ7A9yfgogRIpPtU6p/uA+mcSrCEAIouuHH3xVB0vdbNQg2asHVuOtZLJe1SiWpUmr4b+s501BX7dZdRVL6bMaDdxrv8euDac4w9ENNe0D2UelBbAxZRdfn5ZXtsZ7qXFKSGgHoUVVR0+QH7QDVIt7C9Q3SggoucmrLB3NZGOtaOJrV0VVXVg1Uu7iWto+q6ATUAwAVNNJ3Xalc6QAurbIscJLgu826dUKb5Zb06obEKW0lLqBBbCbxgd+nMxUdAN8JQ0pq7j5bgIs2ngskDSFUXufyZonLLvcoK+UFizHbvYACz875I4PNU4KSA0iAJhZMC6hxI51zmXeDEap3UyNTFN5pXl5+63YAxrORhIm2jF5YfbQoh3xhe4GQ9o/sAil7gRO98j4GYA1QLrB1qyu8PqGpq1zanOimzVfU0ergeJRXl7RhQRbsOcB0BKquM1a+tTVgpbKjvBnRgsrZQSfcnm/v2G3ba7Xk1FesE860vKty9AgrXg6k9zjI6r+6z7k+Ob4omKqe2Z/M1taUuPYVEdfFRkwaD32bfDnCtYqoqCyRaJdU8NnlKdRl8vqieN80FTiUgQiL3LkgUk7r2dJptvrr7tHLfLq+7toRGTZEU5jmkWcCJYFF++pwCY1gFVbUbVD5QItQ/6XGsXkqh1bj6RLWiKqCuqQvvGLvusk4CWK7LbjWkWCsXL7fx9WbmJqynpoAhqACM21HpdtGOAFcpCN3+R6DSgSMpqKrdsNLlqpRKP20eVhVeqBGNTkU1BVZ8qXvPjNZPqfkqiQY0HJZXpVCW6fY64V2KDmhUu/zxwGrhpXVHEQhkrs0mcCICywNose/OcVH3v1CA5PIteVum0X7b2vQGKW2YewGkmSxyr8AKFiiRxMXXBE2oklrpxJTjPdRrHvvwk+ewBOF43yDqx5J8iOnzbrAKUGpAFfexZQqu2M2Y/2DznTbrsl7whT6je4q1Hsguq6L0uHq9EMC+vvGONLccUpe2LUA9qirK28CltwtWcT8j6wRiNOHmQAsqVDh5WBXfP11CWdX1tk7rqfJy3tRVz9z18EkqgGJBEyZjvRy5JM3XeW/f7f60Q9wawCA7I9eZKkXF4+Hhe3yXPtWyA5Zso0DxaVuAjBSdm0aYNuIsIabXpV1W74lvkFvbP9W/NNf6p3SBopwUYhI00a2Tivclqiq5R9aTd2pOqM5zWjb0BQxWC1DprhVUe9x+opYW3YwBtdzx9VEKJ60vGn1g7SyzRsEUw2CJtfqoeC0cqPbY3pHOdzoPi73nPe/B537u5+LjPu7j8JrXvAZf/MVfjF/91V9t0rzwwgt4+9vfjo//+I/HK1/5Srz1rW/Fs88+26T50Ic+hLe85S14+ctfjte85jX4hm/4BlxcXByTlcuZjTm+luYRA9RaflbOh6XPtvh33LFz+2dZ4hqiLtPMMtyB9iQ9z/VrLM82z3MGZ1l/cSF/c/3LGTSXPxvnZ5a/nEE5l05oZ+7Pz4O/i/5fOp/bv4tc/s7D3/0ZdF7/0n2ftmxLFxl0v/6l+3P9uzcj3c/yN2O6V6Yn+Uv3c6mDOWek+/V3us/O9cXuT1TH3FnWuM1qHc+k+3fHme4x0j1gCn/pHjC9IPMvAIffAw4v1L9m2e8Bh98FJvm1v4+V37OPlb/D7wJnv1vTTr8HTC8wDi9IPu4D032Ucz5nCZ6QczOA7esWqY43RYsPDDP3gVRcfvLxpHAKsLJCVT94drcH2mHW6W1u57nzG4ESl43+AOlWavnX7K+joqinFv10b37P3w47Skn95E/+JN7+9rfjcz/3c3FxcYG//tf/Or7gC74Av/zLv4xXvOIVAICv//qvxw//8A/jB37gB/DEE0/gHe94B77kS74EP/3TPw0AmOcZb3nLW/DUU0/hAx/4AH7jN34DX/VVX4WzszN827d92zHZwWVD0Pv7esTg5G3k9rP1K1F9i6Tr57npLsxFFZkPHajRPST1SLP2H+iUVRkQSvajbqEE61PM1BVb4UG5Psws9VQ6T26+UVfH2s4XBahiaNRR5/Dyx/TivuQLqnU8E4kqErU0kSgmqu2aCKXrI2qDGpoABui0/lJnGcKy6pLzdUg2v/fCbBkDvlFuO1/g08xbmgooGWfk+DqPXgHpXX7S67e1rfNh6sH91yiqNbdfNF/XZPNyPlQ+/si7Ci1gia2O14o7fU+PCGxY/+DtAGrPvhsXKNl1uk4jvkJPgB/5yEfwmte8Bj/5kz+JP/kn/ySee+45fMInfALe+9734ku/9EsBAL/yK7+CT//0T8cHP/hBfP7nfz5+5Ed+BF/4hV+IX//1X8eTTz4JAPi+7/s+fOM3fiM+8pGP4M6dO5vHff755/HEE0/gT9EX40C9IdeOtEcZUNH2FqrX+ZVnuwxfkDJfvzCP/AJ1X7HawNcedJtuCxYO8+1+LgGpa7DdvUtbvQ8ZqJBK6D1PyaDTBxZqmyei0nHsAlJu/2iBM54OEENd157k+LSGyiZ+fGvQQ4Y1yiWBjs3nNl1J4+d1aBeuMJtLJBwyF9Wci7KmnN1vNqUNN495Bs+5qH31Akiha0Vjzq4QdwEVpnZkmZ/3ZUqayrtDqUSwpgRME7RjW0xTuWe6jPyyJM+KeyeOfc53tVlaUW5wSioquLVtN+wi38P//ev/BM899xwef/zxYbor1Uk999xzAIBXv/rVAICf//mfx/n5Od74xjdamk/7tE/D6173OoPUBz/4QXzGZ3yGAQoA3vSmN+Frv/Zr8Uu/9Ev4o3/0jy6Oc+/ePdy7d8/mn3/++atku9htApM3zffWg7onMORIkFkUodaH6cusbIp9Aaq6snl54GvvoAKo8hXLUVnpdg5YC3UV6q4ae0DdXh2NRi18UgLSXDrfnbKdE09JgiEEWJlMPZUAEikHE4FC/ZScVAskl8klyNz5hzS2yiexZdxZ1kvnl7HNW8Nadut8el4eq/YxN7hnBDR9GDK53/KcLOqlyE1DlFEqoKqqKVVQeaUQ1NTQel0gxUbxOvxNHJctqqljjWjfe6DH7PxShJDmy1SmXge3fG/edtilIZVzxtd93dfhj//xP44/8kf+CADgmWeewZ07d/CqV72qSfvkk0/imWeesTQeULpe1/XsPe95D77lW77lslmtdlvB1DN/LpdVEMdGOFJygRluW2kXwdoNDQDMoSdkl18rTFlGLbVSUVyJXl2NAi2AvjuwOb/+/V741zfSX9tzo+dCCiI5LwMWKrCcwiqNa0V5zWzT1o7KCq8wZlC8HL6QC/JnsZ2HhweLKhtIGkuHFibZTe+9fJcVwuol3n0cp0iiy0+hE0Gl2+k5+uWjY3TW+14nLHiizMBcfjUxdGwwsPRNOfX3e0wehubTRkBtHcODaq/dNKTe/va347//9/+O//Sf/tNld7Hb3v3ud+Nd73qXzT///PP45E/+ZLmQLyLwXNZ6D+JNuL5c3Zf3v/eABSyhVbLVgos1r8kBq6OuLBx44A5cjGLsjrE6H1/C3rXcetG3fPBxeyLQNBmcCogEViSRkwKq8gVN0mGvqCdVVworH0G9MdjfuI5JC+I627jifMCCgkp/vRuuWc4NvPTcfZh+2+muqrs6re5NTdOk19+N26O9SVRXWfjA82oqoQ8qX0fVU1PAvnqpaL7nidiWSp/zDiRXzT4CL1E2eoW891w8qIBrbSMFXBJS73jHO/C+970PP/VTP4VP+qRPsuVPPfUU7t+/j9/+7d9u1NSzzz6Lp556ytL87M/+bLM/jf7TNNHu3r2Lu3fvXiarL127CdVoD39bKLfDGwiMDE4hW97F6DqbBYsKUHU1QwCUDES7gAW0L0nn67Ckyf3lQPuFvIBYB0i9a73WP+Q0lXqKydVNdIBVVBW16koABQ+rBZT23/th6HIPQNqY1sNnDtFhWdy+Wmfj11l9S71nPFF/WlQkSFSL1MdZvd3miVE/HJ2Cy89c0FTbTG2BCpAPiOxU1o7wc7XYmFcDksTlZ20LgaqmJHCCtr6H0pHqqWNdOHU+tLreHA+r7SPtSnUUpJgZ73znO/GDP/iD+Imf+Al8yqd8SrP+DW94A87OzvD+978fb33rWwEAv/qrv4oPfehDePrppwEATz/9NP7O3/k7+PCHP4zXvOY1AIAf//Efx+OPP47Xv/71x2TnZA/aRoqt18t8r9GlDIjWLFK15CKbTF1xUFfIC2A1w1u7di29PNeKcA+uTtrR+rBu4e5ZAd9i7C5VUodDAdUswMoCK/kt81xcmfJLEVYj5RTy0HWHdZWjU0LWjKDCh2ZZNiuMsksngQfSfIB1nkvBTBri3YBYznVKBQQSUEJzBTOYwEygqeSPVwq40jNJWEgtlKLLrxluYgAqu3c+kAJoI9xGecpc8u6v+wJWDPHvlXmvpkb3Ss9FJ69ZxawCr6fsHra77+1vfzve+9734t//+3+Pj/u4j7M6pCeeeAIve9nL8MQTT+Crv/qr8a53vQuvfvWr8fjjj+Od73wnnn76aXz+538+AOALvuAL8PrXvx5f+ZVfie/4ju/AM888g2/6pm/C29/+9pNauo12lM97SS6WEsVg1VNXM8QdSK07MGNRf9VVQQqaNQj1Irp65+iWDxWXb1PWW04JdFbghDmXwjmqq8O0DisWVTmltkTWMniQt17gQ3t+bnttg6ZAMkjlFkYaIccSNce5zM+l3ZutmybQNIEEzDgcQFOSeQGXnDcdpgJiGcARiYBDqh8wIy5T6HWC5HrNbt6rKxdAQYDre24JqjJml4dTUFN2zP2qqqmXsnugvYY4OKma0nNod7LrWAu7jv1cpVrhJiD1j//xPwYA/Kk/9aea5d///d+Pv/SX/hIA4Du/8zuRUsJb3/pW3Lt3D29605vwvd/7vZZ2mia8733vw9d+7dfi6aefxite8Qq87W1vw7d+67cek5WTvZiMMzjLgHPNuFjhSxIohYZ1weTcj9pfoKbR5UAohMO6EZyOBZPfJwZw8pa51D+FPtza9SuFBgvYm2XlhzowXsDJ0sClceuiYtLwbVUAOh0BNc/lGs0z2BpwzwBn67mD51nauc3SDky6zKLgHszSaTCL4tZLIvVexT08OIeeOTiZy0+X+259tkA1VFPlGe7VS3XbH+qH12L5QB0bYC9pW9teF/yu2a7UTuphmbWTwhddTzupkz08s4rwZG1JmpdUIeVf6FBXpdsv6qRGCioAqiQZQCoERXRfl416q1hokbSNocNBAijE9afuMB9UIcoCzhVWfwl8CJXrXkSE894C1Rakuioqux5EBE4GJg+szEVFiYIk7+pMOh3Od5rq+R7ExTmlGumoeZ7bOrOabzZF2NSVze5cInQlTdN7ik7rvc26vm7j03N8zuKzrW2g9Jn2baVim0FL69yTNxEUdYxdEzIu8j383/+/f3Sz7aROdrKbMmt9v7HMKuSveCwAfRUVrBt6HOEY2p810Y+yniYpjBMZdC0knUKBpAVUQvgtxy39COo1qLBi2t+nm9me9KpI5Lpb0IENHa5DtJSGtDYsu2/8LdfIzrl3GOZa98QMaH92XmTqpe25ctfyz9qbh5zHpK7mSygqdftJ+pGa6iqkRMvnxdeV+TwfCahdASYd2xV23hzoZnXOCVIne3FZ/Iq90r6O3MeoMXEshCS6zQrr+KWswSFuWeyLjgnw/dJZsICqKuFI7TKKl2mOMduv/GoQgg860BBu6V7Ihr5gLoW3P79O4dw15goraYdjA1zaesniqJ7Nn0NZ6RQ4LddvgepIo9659lx93hug+XEfI8DlwXOMxWPshtYNweoEqZO9uG2Hq+9Y6ym8ro0KFItwu6KKEi4YqERFkfWgjer+2xGB1tgePkdw9dSUNheQiL2Rampctz2TOrjiiqvn5euofNpFPpthWAgaWt8MKz8pUOs50Tx3QLVXTXXONdax9s79GBV13a6/zrO8G1o3BKsTpE52ey1zC4JLgGdvXdSa7R1ywG2wX0UB6ypKwQWUyD/m6v4TUDGq268Jzx4pqq1Cxm/nXX6qULyaUiW1dd5rJkEUIC5wETVl6xht+62eJYCzOEVD3nWoE5/WDz/fV1QdUG3YpqsvugN7Kuqm66P8/gfPgUJrE1bHHGvFTpA62e0wP4bVAzSF2G4QbeVRv5L3qCht9LqmohKsMS9piHoEFVq3n+XjMi6/ZuRjrufs2xmpmtJC3LvR4nmPTKMbMzXd3qlysqxf5qNdr6VvZTAC1TSVLr4wAJVv9xfUlK2Lbrzo6nN1dLq+cUdGkN8kqPwzsQGsTVhdkz34t/5kJ7sO2+zQ09mxYee+p+st841TI6AidARGx6ioZZ1Uq6JsIENoHRCWBVoo03YNvzGwxvUT6k/I59mrBLk2zXnbPjpFULzu7Hq1ACSKDmhC7n0vGHuUYLz21Olp3Fytrg5xpH4650GJlqHnzb1p4WV1dpqup6IehJIauRUHx77perKTkjrZo2fRjbfHeiMVX6NvvBtZ2INStJDGCrotFSXLDUgDFaX92RFcvUwCkIv7beH+OrZuqmcdd9+w1wZMpR2UHlvvawfE3UP5CL8MgLhRiGV5p4/A3jeMuvx8RKgqJjknnqSnC7dNCQhxwRTerSluP2iUoKqpIPEWIGsA3wmY8NM7rtO12B4VpcvDu1Xq9k6BEyd7qdo1hJlfu60Byq0b1kNsqajkCtEVUyVVim5u6p/KenH7ddZdyey82gKMENxivpNhPW/v6lu7r3rfDU5SNlIL2gbEo/NzYLXQfArXWAV1BJUHcc4tiDVYhXTATwdk9QRuufr8tD47vk4y1kXt/YA7FhprYOoFozwgUJ0gdbKXpF2Lq0/NF2K2qFOoqNKLDXcH7qatgIk4vEYDKlc/tWmhfuYo00g5Pf8Yup0Y1jO+V1Ga3s/3rIkepFZN+TT+NzZObvJb1i9A5dYBaKP+/DlCgYTaCXJT/xQUkreRqy8FV+kqxI74WDvWG5EDmIClYt5QVTcBqhOkTnb7LLoD15TWVUPPe67HGB7slq3Cyc03bj7tt04LqJ0BE2V4i1rHVPTSElQyB1NTCaUHCHQKnp65kO1yvkcCPLlpcu2l/PWLpo134Vx+7NRUNz36sPWQY7b8cK55qMAqvVMgJTBK4Ed17aEFsXZ6LGqRMmyAz4XLb83V10szcvNdl0ehC3DZd4TVkapqbx0V76wcPUHqZC8u6/XXt9eiitrbiDECaggnt37k5tPhwneoKJbl11LPpMYlzLsJVPDnG9sWAa0SGzWE1evhVVQscOO8H6k2o6gyVVOpc757+vDzX/+OCQosSpJfgRMn1D7+fP2U1r9FNRVdfrmty+y5+oYBE/pcxOXXYT3Y6TWLsNqrqk6NeU92smu2Hd0g7bVVQFG7bDVYQsG0U0UhybhJiUQdddRUb9iUnl32OnilAQxAherW6sFpVVFVlWhqKo7C7He31ttELGjVBHqcNVoPJlGbOioHk56asgAK26+f7qiptYAJ/R25Aq/D1tx38eNnj6q6ATtB6mS3yhZRdsdEAsbQ8x1ph8fo1EM1gPJfwNQppHsqKvQusUdFaY8TDK6giqdHzuV3TYVKo6Z6BV0EFZxi6A1Uucecgrp0UT0q5M0VyGAUd53159e4CmnZHZSqqbmco7r8TFH54/oQ9p6rLwZM2Po6vRzk0h3mUm3fdMcO4GuqagSqG4LWCVIne7TtmEa8o7ZTe/rxu0zAhBj5wnYPoLZUlKXDpopSiFXtVNY1aqqJwMNy2fEnPAATapdCQAWVpo/nvsdydcuZguq5+nw+dli3oCcqcErsKvqo1k/5ZZoe6HeuO8+wzoh7z+9INelvnPbPx7HntWHUg8yaqlqD0Q2Aaufbf7KTPSC7jo5h1dbqafa4+jiv52cRht4BlLQNWgDqmlQUC7CsDlrcf43UUNg1eb+kFnH7WYRGN7B25+uGW1m4wna4+vy8H9J+0XC3t8xv7q5fzUP7x6mtE4wNfptz7KifeF279VGjqD7/66fTOL+rfzutuSbxfsT71Vt+CTAeYycldbLba70GvMAqeNaU0u6ACWeNiqoLG2DtUVAtmKgEDOxQUQ2MyKkpYNHItzus+jE2cu0sACi/TXBFUFA9wHnzEZuqpuSYpqj0WKN6FQ/UCKbRcVVFJV7UT5mLMyodZtho0eryW+w3uPhGrj7ZZxMwoct6IFv7yNoLKv1eIxqrqi1FNZq+BjspqZO9NGyPqtpjI9ejH4RxC1A6oOFARVU1tVEXZcdGX02Jte2pfMG9/7Tbcw1qyi/rgcj/+eVhX11rukJiW0ZeVemfTz+yJhw+5Nl/JKRU70fMf1BTbcReBz5REfXuR+83Lusp4pWPnlWVGq+JhuWPPhz2KKpj87VTgZ0gdbLbY2swGRVOq338bbj6RqaF2IYNAwWI2r7YoooiF1qu8BnURWm3SLaOHKDIFTqNetjI996vYNn3EFR6vqkzHdP7/cTGuRFU7s9gBQxBtbgGIZ9MkFF/9SNieT+a++bPUW3RK0RqfheuPr9sdB0cDBfuuGMK/L2gcKDqqrYRqHrrr9FO7r6TPVrW6TG6fkm64bZjiHfj9pPSqgl/ztZmxSqzdfjvnUMtLLLq8xJU1Bqg3A5aFRWMtwqfNdMoNAA2rhSRFeDmCrT912nfOLgGPEiyUWyKiobRZYwqYFQwd9YdbVsFtj+uQl9sMZSJD+H3BbtG+jE119WWaSj6KHCiV681yvve6zFav/XR0eQfy3vs1z8EO0HqZA/fKNXRS72bzFW4rwHB0utLyqmASOsyODelD3EBFmsB4Qatk81lyIlrDOJYM+Z2SAoOA/wRSeNSFNhkBgglRo9R652USZrGH0Kg1PTlJ4P80QxgknIoE4i4HhvUdjOkJbheXy3A5LcLK18/dAycOupjCRlNs3N9sw77jfTiu3lXcJd6qBrlRyT9TfhzcH0Xds9zcbzBumPN7+O6ovKyew7ivvfuZ2e6E6RO9shZGcl12oRTV60ALgosS2FLXWA1sNJQd1VU1ovANZnWn3hFoQX91OZbG636Af6sR3MBSYEMakmr/NiqSO90k4QJ0stEuS7++KQ9Myi0tGeGHrCABlbkw9G99dxavd/B+jXw7EpzReNEoNmdr28zxQKzRCYvd7n6OudwI7YHWNH2wOdYQB1hJ0id7KGbV1GmoCKgprSEk3bOGkOZtc4ipyWwZgB7hgH3L5sPFT7WFH5he8pc6kAkvy2cCjj8AH8EKsKOVDmRU1V+P+gHiRAW/fkZAEWtMeDGZorAogZMprq8mwjUHLtRVU5NXRucOoX6ZhRf7z4e0yDcKw6vrqLLT6P81C3oo/qiq2+UL283ET3g35djLKqo8Lu3g9m9dZ8nSJ3skTHv4jNAWS/hCWQdrybUUV6lXYtOa4V65lI6BrcUAVVZkVNV87wEVUJb4Pv2Plcx7/4qhAhw8m4+roWhMKKAA+VrfiLryNQDyoea+9DiMkKvHDPXD3/dhhklOpA7wOooqQZWqrqiqw9YB9QxcDoGTCFduw26tjtEfwErLFx+wNyqKJeuyZevExv0MnGjFly2u2wLUHv2c4LUyW6F9TphVQD58YdkeIuioCos2IHKd+HCVMb9WcAKKHVPFFSVDM7HMYhiUFDsUlYeRmvrg8uv1kHVrozKWFCQpmEszCrbkm+7gpWCVg+hE5N+zTpg+a6TpL0Qsrr+yEjG2Q2WkeI8GlCp668bGdcpxPfAqevOs/Ok9fmRDRRA034omgaojFx+ANqw9ADNAUQfqvWCJ9R61+gygDrCTpA62cO30TDj4uIzQDn1xF5Vac/hUylsiXRgOgerOdfSm6h0WzOloqrmGdpg0w+3oH2vDYHk3TjHmldQqvKk8G8G+APKOYlILPmTyDypYxqH37tJdw6+S6SmsOeaouRFllGBlaormjsqSubJBvrDAlRd995ao9URnKK7L9pKYT8MltjjolIgqbKNCsR+ExbjaJF7VqIaj7DaOIcbsS0V1VvnA0dGLsA1Oympk90mUxfIYnj1EaAUTjrE+lRGRqUkhauH1ZxLWdt5kWiea0/WVOqowKntew3o1itdyrxqUusAy9RUKu42Dm4/ZILWT9U+JvaZtataS8PFLUjaM5S6Gj041Q04193x1AHVKLqvo6wWENpYvn2uu5KFjY5QAh5YEJcf3L2ydK4+1W/7ANsbjfr1O7pT2l5En58+KamTvehtNLx6D1ATtb/MwEwGKx+FxZRr2x+g9HBNVBTVDPv6JWJ7oZfDv6+0eXG26K1dj+sg1ARPCLwWA/zZNFW3X5bouRngadlZ7KIwitnolVW9wnKudVgeVmWfki8Xvq5wWoCKaQmq4N4bqafx8s45PAiT824i/NR6PaPrNn77wQfPoi9EXX4JaB2zzao701vuw4lCbyCnEPSTvXiMqNSnaL2SqSVqh1c3IEmaw1TqjkQ9ld8y/DoxF8jkDEICk0BLGu4wIK4/KvsFlnUJQA3C6PTCfqlIv1H9lK9812nf03czLW4/rZ+CQCK5QonKl3GvkPLdJi2WmWKRWS7TNJcJptJ+ikTIaf0Ypj6cWMVaPO8RoNbUUw9Olw0quM6hzfWeJWoUIwFVnQN9FeX3saYMj3zWLgO0xfEuC5lrVlBqJ0id7JExq4syt19t/+QVVAOoQ1FRnCTCjwlMUngTAzODkMsYQXIcca4VAMlxSsEudQnaY0CbuXb+qpFX9kKTmxf1pnVTk5t2br8y/DvK+FHgUi+kg/YVH+ACVN1+/Xyh735ZC6oshWeW4AmNKoQcHx1QKZxiB625Hn8LUAv15AGaqAGynPTQFgrBBdd0beTK8oX32rQ/TmwQHp7pzWfoQQNq10Ha69aoKF2/90NgZ7oTpE720K0JyW2i+shUlAFqmpaAmpIoKSkwZpR9ZFFVFxiDCqhf+9bNjXP5NYMargRJrBUQK2NiLVx+vsDWqCnABVSMQQWguDlB8NCp+df+/gKcFEwEa9vEoAKezKKi5KrJhk2TYA+qBk5cXX1y/F2AWiyDFei1n0KvztD5qkdNr8l8mhARWdZjv/k6J82zRvlpd1vAuorq7XMvwIJdGlD+A6KnokYBE710I8CP7OTuO9mts+Re0KiiFBg9QB2qkmKWbn0u6hcsIS1BpfUKLCDU6Do35ELdgSvomsLxEpF9zKVQ8METzcsa1JQbidbKvQgqA03dvjmkKRDUQr8HJzdfyl9Xd1bj111OA6iIi4tW49lZAefAA83DCqCie0/VU08BWl7C9fTrHbCODhJoDuLABCzrpcJzsjkisYMSX1WZH2Odx/Yy12ahooDaCfA12glSJ3s0LCVRUKm+1JMLNZ/UrSegcoDKUwInlCAChgAmSbgwA8hLUE1az8O1AMkaug5YtzY6BPhVG/BG64DJ8pPq0gZUc6n3KescqJQqRFJvVUfkjV/YEVA81WmFlXVzSDZh28T2M20nSwmYy0i8pd/VoKZW1ZIDVKh78upJQVvW1fywg0cXWD1QOTW1W0X5jns1//VgkqY8Sw8KO7tV1OgRDs0FlgfYuDiqomIAxZadlNTJboVRWgLAuz0UUObyI/AhVeWUyhdrWeY8d0ApnC5QXVRMBVA8l9I4swVbqHqzuhwLS+8UAJftFWAQPKHRembaxx+WoEKjrlpQaTBF01mtHtoX7hFQqaormxYlpa4+0jqp1lEqu+N6aolKkujqi/VNcVkEVMe9Z+fgAkXIXxc9V98Dh7qwRqAa2Or6nlsMqB84vfRbKuqm65Ou+o11TMDJSUmd7MVoTfso63FCp516UjCZihJoTUA+SD2KiqELSGEtwDkkEEo7KM65tMFyX/j1r/9pea1RfVs2UFSUiyutFOQS5s6oYeAYh2f78acioDhBCks42JPrh80DqgOqrPtGcfs5NWXBFGoNkPqAaoIjeoAiPad6/nVcKRJX4xhU69d+RxrJX+lNA8uCuems2AFqa5+XqI/atOtyAqhiYq6uvqii7B7suIinwImT3SoTKHn/fYno8wPPpSZIglMLqDJoHQMX5SV3gsTUVAlvl7ooqY9iqY9qChGp7GYgBE9o4dl589fqqHzwhNZLueQkbYjrggCBXh1VCE+H1SdJqDi5c24KfVRAiXvPpqVuytI2JXsAlU5SVW4EyA4BPxS7trnaDSgPJ70eDlC9HjSKmhS4i7Ky3uP3gOqYNj7ROi7IYZs6/0G0tr/rUFdrgNL9X3fo+CXGZluzE6RO9vDNCtDUvsDm7hMoSSGm8/lQClYFVJ7Kl3uCgIprHQ6QSjDFlEoBngBM2nmq+4JNCWWARN0sSRav6et2RVmtuf2AJZgahRXHn+oeQFSUFPbKaQ8onSYNnIDurwVVuYZ6HAGBKDCt6/Eq6tKA6sFJFRtQO9wFrH4MgPT2LvuJoMI+l9+VzcDonq9wb5oRmju2Vd+0un4PoHRaozKvck20XeGpMe/JXlQWlYm4+vTlZdfzuKkoVVLuL08QJaVle/nMt3KeAZoUXKWPPmsHJPUClKuaYy1UYnupJq+a9ysALLbFicvWQMXq7ittw5rxp8J2tnGjpDqAmlp3X1qoqDLNSRQMU4WT8lc/JlRNecW3dimOAZTWWeXaKZT1aajTPVB17OjQ8+ANNpefmq6PgPL70LqouO/rshGg9rgcj4FMDJi4ATtB6mQP3/wQ2o2CSqWQEhAhKZBq3ZSqqaqkgGRwki9+cTmxVlIQgVKqaipTA6yFq827za7T/DhG2iYqy9d1BBXQ1FFV9xnb9qamQn2RKiYAVUWJiukCyhVwEhdZd6TXdJKcaCNkOZdmmBHpKDcqirVAib2AMiWVBEu5diO1GJbEA0RV4ZFlahnZGNuFcc/t59fF5yvO94IorqtOaS8EG+XcWb0GJWbwTmDtTXeC1MkeDfMNHv3LLIETTTTfJAXqoRSouVFSLICi8pF3EFXFZR6HBOIs4eoCp5RKYa8d0jbh8Gz5K3m7JhXl3X5hugsqoKoqSe9VFc1c1ZSOFxU298EIBq5GVcnfhGZb1nA/LhuzgolFSTHXfgeTqNSEsi67cPQdbr7dgJJzsUi+RKaqTFH5899QU6u2FqKt+egFUHTUk13Th6GienaMcgL6aU1RuYu0JyjiFDhxsltpDlBsf2V5CZZAH1gTIR8AyuXLv7wuZOWLflCXMp7KVGbgIHVOzGCeytdyZtBhAmuXNrGwsem0WL/o5RpoK9A9iNcugx9/SSGmARdAVVUCCpYuyklhzFVF2rDzqrIY1gdfU5Bz+NVZuf5EDA24gIbtq7JCuf7pAqVUySWTRc3kqlSBBZg2AyScEuxeK8Z2gXeVgIi4rXf5uRD7hdsvhN/XdLRcDiyes0W4/l7ruXo1P3E/8dxGwSOj5dfZF+LATpA62cMzckEJznfPUyquvokkSEJ6lbBw8+Lmy1NRSvlQCkh1VwGuGx+bhmvGkpAgffl4wOgW6noDwFP7Cb0IoFi4ZjpA6s3vVWKjwjWj5Fb3M0sXUMTSga4Aglhcgyh9/GmfhlmhQ7U5mJTHtAor+bPTlI8DDf2nErZOM0vv6QyeKYwUHK6Ha/fUhJa7eW/WHgwVUD7PBdxYBAEQ96+nhao3y5buvcUy3y5KXYyqJJsDO0D3AOXrrBbgCvsJeRzaGqhi3uTjZxE04eucevvpXctR+ivYCVIne6jWFPq+PYmqKWk7VcPOxc2n6slcfRAlVd/PbP0gUBP13YIqNS+VgUrnR+G0owJitHzLvdPZbqim/LwMwkdE4Jylx3eSnjRYghqk+x7dfqZaMGctpBVcFSLkgWWZ0j8CWzCE7Gwuv5xK7/YkHdPS1CkAMShk44d+TGJ1X3XelCIqoGp6dNTC8rCNRfceUZXiOg+3X38e3uWJNn3TaHcEKN3Niora3cNEDObomb82BqX+/WraR23t6xrtBKmTPXxLhBh+zuremzr1UamFEztFRRnm7ivvspbGTlFhp6ICmp4MVu0ydQo7tiEHUI4FTeMGJIAmEGXpR0/q2WY4xcTmPlMoMUnAhcEKTh1IHhyLrI/ZBFAWUIlKs7gU6dCjwIP6jV39frcsfpnHrow6gCLvGj3mWO5DYKmcUMO0m4dp6Ubzrj+OEFoDlHfzdcBy6Y5kN5oujaC0aKTbSxPro67ZTpA62aNhPrJP2kJB6yMmp6ImFDi5iL7i6qPg7mMHo6qofJXOUFGxDt/OoPmShcJVG4X2VnXdKKK2tM+8lMR9x7XwnAtISgfvorJW1JRXUj2Xn0K8DmgIgZ2otgRovRdYCmvrmspBdwEZfyAHIi3I1+AEPaZMOzdcTb9xT7xS9WoqKKemjZXVT3GbTuqomn3rr/c/x3VqqbPdDRkpaLyKWrtWa27AG7ATpE72cE0j6cR848a2f74Qfq6wckETrbtPu5I9BlRth3eEAzgNGiduvcSdypQ+ZNb2sS8dyaCECipQBqdUI/4IVdGommLJD4makuHomWBdHPWiA0lrg3S9iBcL2IDCSdRr44qTfHioZHcc76pj1xehbut+GyBFWIV6ovYkQtqeRTXVAIkF6k5lrYCq2af/HQCq6+brrTvSLtVIN3R9tOrqi8Y7lNWeNDhB6mSPiFnffc7dZ71LuNDoqqbIXH32Z1Fl1wgq/3WttggN31ivBd7U2Rc68NoDxRgUMGfYYIU+iELdXqqmXJBEE5Emmxq4dL82AVFM1V1o9UWOLRY1GELgTVUNf1nqzmqBKpjrnHvHtQcs4NRTZiPrBU90x1raApXPh7cRnNz0mpvvWDsKSj0VdUP1S5exE6RO9mhYqI+yIeEFVl5RtYCqKkr7ofNlxS5QsQbLCajUT5NRSnD/wjb1HM415AMaVEUpfDzoIvSson1QKMSKbcuHO55eOxc4wQ2sSL6E9WOgAom0RwZ2aop7OhDNwtFX/bBoc2ChXFRSubTijsukkqwEYEj+eqBaRO91lFPXxben3PUwimqq4w40UNkxuI3687ZHPQ3cfAuArYiQTUBtfXT5ZzmqKO/q8/VRzDWyb6dC2msnSJ3s4VsMv7X6KO/q66spc/mlZXSfdZFExyoqZzr4oSQk18MD+8JTR8b1Lzy5l117ZfDmCr4uqBrwoQVT/M3anZO0SZpLvRznLOHh2maJJZhCCthc8kla/2dqKrqeXLZ8qXzEcg8nA6S4IYkcrMASfCHXhGF1YepiNIUYFeVIPTW35QiV4GHkXXjM43W6HWDRiOXAO9XTHjffoIHx8Ny2lse6qAdU37THTpA62cM1e0kluk/77CN0Oj6lBai8q48n2MtuoJKyLHEHVLwSnh4KABK1VMFUv5at523mWhaIQmm+wHtfwXtB5QFly3LdfqZ6/TK7KL9JRh7hUoJboap51BGKAe0UVj11hHbQxNq+aTxd2zkt1xc46R8ZrNT1aG2/tN+IuW6/cMUJoBbLO65Bb2uAWg7t4e7bMaBqtqV1OAFL9RSnY7pjbdNdHdx8N9wX37F2gtTJHr6Fl7OEn6fyUjo4Za2LSq5O6uACKA4s5UbZ30JRDVx/kJ4p1EtRoCWKSgHSuJdqr98tmHwlO1eoZSm7R7DaqoOKI566nqZZ3TEpuVFxFVYYh6RrA1+uDXwbeKU+qGrXRfW3t6z2JhEhJW7H2cFK8lQ8kXJPdIgPrp3GlsvKVqCOXaT9xV1AabTjIvFOUAHQ/hNZ1XRvX5q1HpxCmlWQNXnHemj5Djit2par7wHZCVInezRMXHtWAW1Kqu34NCqo2pCXSwezDAFRABUvQVVgpIUioLDS8QZZ2g/pIHrQfuoSVWjlsl35Au8AixlIAVZAfcl9QbSnrqDn/6dUhrnXApV5PSSdNUhB5knmRU1Z9B+cIgJaCKXlvB+C3talCCkHJYGVfQxArx9VleTUmVlGNzw+2mUi2rqqTW1TNdX1CNk+Bk6r6Vds9Xybj57Odr26pi2Lz+MN2QlSJ3toRr7TVh804aYXffV5FdVx+2nB1QUVKqiY2ZY3sAK5D9QEuijd+xigtHGvQisJ4ARY2hO5jpS7gFV2Fe1KxDXbqqBmBnguLjsBlakpZgFRFlhxKYFdA1/W+qyoppTbCgq7aRFMkF5BdBrdaZBTT1TqCXUYlEwoff6BrLDTTnZJohLNdWrXni8XVt2zoKaGbj9gXVEB8INQroJmxbW3ud1egPSme4E/K/VVCxW1Zj5o4tg8rtgJUid7uBb7ukswVbUc7whW76TuP3Pzdd19Y1AVGHEzzVJvBXYd00LEChdlRQIlG7NJp32hpl/6QAdMPVDJ9EphMQTUPNdrZ8fPoNkB32DF5kKzfM4136xtqTQiUAprzVWsZ2oAFf5smVNWRBVSSABR6QUkzWzqNU+ExGyd2C47bXW/V6ncj91UhQbDZh5Uei+iYoqqCkB3/KwItDU4AWOwdaA5tL2Aiipq9CzuUVrXrKqOqor7x//4H+MzP/Mz8fjjj+Pxxx/H008/jR/5kR+x9S+88ALe/va34+M//uPxyle+Em9961vx7LPPNvv40Ic+hLe85S14+ctfjte85jX4hm/4BlxcXFzP2Zzsdpr0z2cKaiKrj7Ivdhcwkbsqig1WWaE1AXliC6rILsCiDjkvdV3WSS01w9Fn6dQ2Hwh80PB3QtZObw+126bax2Cy7puQUh1Z2IJDUAHiLc4H338DqDkXQEn/eJgz2C9TsM25AErVx6zpK2w9sGqYeA1K8G2i2N+P0Kg6H+T6SpOAfADyGZDvyO9ZTWNpfZs3Dzp189Gy8C59CjK0b8H4t8v0uvWeRTggx/vio/GSXpPWA2Dp4l9cjrC95QFtydx7VtasBwkX5LMKKH/YLSAdMwrvFewoJfVJn/RJ+PZv/3Z86qd+KpgZ/+Jf/At80Rd9Ef7bf/tv+MN/+A/j67/+6/HDP/zD+IEf+AE88cQTeMc73oEv+ZIvwU//9E8DAOZ5xlve8hY89dRT+MAHPoDf+I3fwFd91Vfh7OwM3/Zt33YjJ3iyW2LuJWZy7r0JrbISgDVwkvBznhiUUIaNaFTUUlHZLztFpeMmmbKSfOWiPpiLi5K1sJcugazyxhX8tu2cYbopcclJzhbEYOqxNwKwmhYGjYsvl/ojUVKlu6O5qLRcwtBLnZRkJjbw1fzSSE3JOcRsOXioWkL8cHCKN08s97AcJ12QH7XDymIrC1ngl7icKi0DJ1Yr79VdGWxtqI+RNfVTXklFVQUM66L6++2kGMmFEZz2uP10vY9Cjet69Z7cCUpZueY3WR8FAMRXPMKrX/1q/N2/+3fxpV/6pfiET/gEvPe978WXfumXAgB+5Vd+BZ/+6Z+OD37wg/j8z/98/MiP/Ai+8Au/EL/+67+OJ598EgDwfd/3ffjGb/xGfOQjH8GdO3d2HfP555/HE088gT+FL8KBzq6S/ZM9RKPDAfSyl4Fe/jLQyx4Dv+wu8mN3kF92wPzYAfNjqfzdIVzcJcx3gVl+8x3IL2O+y+A7DD7LKNFqBFwQ0gVAs1TUX5QCkmYtLCHrdR3bvE6nmaUeRdv06NdlVSA01164i/ooqqT68+XrNaoZBYcqnqxuOgVSXYacSw/nmYEsSmmeC6D09Z0m0OEAHA6gwwRME3CYwM10KmrukJBlyBM/9Il14HuAU4xB7ZhyivMdZeuXk7/uJNcc9psugHTObp4xncu9OGfQBWO6n5EuMug8I52X69I+UBvKFB1YRbefWiikV3uvGLjUynbchxJwOTD5440gdPT6oJhiXZSmjcuiCxqoH1E77CLfx/t/65/jueeew+OPPz5Md9nIe8zzjH/zb/4NPvaxj+Hpp5/Gz//8z+P8/BxvfOMbLc2nfdqn4XWvex0++MEPAgA++MEP4jM+4zMMUADwpje9Cc8//zx+6Zd+aXise/fu4fnnn2/+TvbiMJKKfnNpSJ2UD18eto3yBeGBgQMDE4OnMq3qykLVvRtQXU7O9VfnpRcL5/or7j4tnNU9VQt5VXla0Fu0Yqo9ui+CQzrtY5oRWxcFRK6AksKCFVZZXX1zAZoBMRvoDIri8rFxn3KFbe3NAZKuvV+90XyR2nuT5dpnnT5j5DsMPpPlZ1ybDjhXbFaVnNz+e+U16zVB+xfrUzquqt3uQHVBu/NuxsGKLsDoBnSuwMXykTtv5NY71t0H7AOUujyDwlr00echpNZz9V1zTxNqRwdO/OIv/iKefvppvPDCC3jlK1+JH/zBH8TrX/96/MIv/ALu3LmDV73qVU36J598Es888wwA4JlnnmkApet13cje85734Fu+5VuOzerJbptZIa5wikETyyAKngqYkBg0cXFvIZkXLovrDkzAwbn+WApHtC4nfc1qBKAPLyeJ2GZpPAtoyDappxD1lxjgmWVocwCTuNacq896Hx9ZztWdIm6+0uXRXNWWdMjHKZUCRsPRm2CKEqVYx5yq3SUhhJ/7oTYaAWH1UtSAJLsPCP8h0AS0JEYmDUgpx03iQuUJNuR8hV7p+FaPYUW0a3hKOTeFt7ZTspBNfab89Io1gRBqncg/vbeL5gNxvicB9sJmLd2aq6+zfAiouCy6+SLkO9BfOOK8OtuynYrraEj9oT/0h/ALv/ALeO655/Dv/t2/w9ve9jb85E/+5LG7Ocre/e53413vepfNP//88/jkT/7kGz3myR6gRYVB5IBES1glWF2UV1F0yNqpAgxU7EB1QcBUQVXrpWCFp9ZFlXeManseqrCysZOk3VHpY44qnGaJTssATQIqQkkjXRA1cCL5aue1wifXL191Ac7Z6qQwZ1CabV8saqlp4KtjTiWWejVtY8WmWkmi6jQEXLshqvcKrcJVWHWUbT4AOIiyTSX4IkOj9glZ68YYThERpsxt4+Bo2blM3TNkHdNGWIX6JOLlftueNWgJKjluTeMOHYG11y6zTdx+9QOns2xUL7UGo6isopv1hrtQOhpSd+7cwR/8g38QAPCGN7wBP/dzP4d/8A/+Ab7sy74M9+/fx2//9m83aurZZ5/FU089BQB46qmn8LM/+7PN/jT6T9P07O7du7h79+6xWT3Zi9H2vtfaEpTKtKmd8FcKZhMjVmmPZpnskzUDXKd1QEXfEBWQF7cUwAQp+DJbfxdlL9K0eEbr8mzcn0nqNZILH0/1y1dLS2sIHVxDfpyuaDHt6HqH6+WDJhYuP/2AmFBdrwIpMCTSEdYGWRtuw00vAKXH7JlCyMHI6oF8Q7gjgbAAFTAMU2/UldpVAXTdNoKOA9Kmmy+sa1SUd/VdM7QuXSellnPGvXv38IY3vAFnZ2d4//vfb+t+9Vd/FR/60Ifw9NNPAwCefvpp/OIv/iI+/OEPW5of//Efx+OPP47Xv/71V83KyW6refeN+LpJghZK8AJbZXoJaABIAyPOCXSfgPsJ+f6EfH8CzhNwnkAXBDonJPnrBVC0XfSgdtvDtdBZrZchVz/lGxsfkoWsZ50+S+A7CfnOhHxnAt+dwHcO7vcA3DkD3z0D370DvnsG3L0DPHYX9Nhd0GOP1b+XPYb0ssdK4MnLXlamH7sL3L0LunsXdOdO2dedM/DZATg7lGOcTeXvkEqovA+tP6RWvU6uD0VTTtSGiAMtvFbvs1NmwTW6mN5r3eAIp6R8mkuAqmuDYIvLRBBeyRwkupF7veUD6Gy6+dRiXVTc7gZU1VFK6t3vfjfe/OY343Wvex1+53d+B+9973vxEz/xE/ixH/sxPPHEE/jqr/5qvOtd78KrX/1qPP7443jnO9+Jp59+Gp//+Z8PAPiCL/gCvP71r8dXfuVX4ju+4zvwzDPP4Ju+6Zvw9re//aSUXqJmfc/pi5GLuyzNjDyXkXFTiQdAugD4HLWQtHoLkpEeSqlEF1TqXWZYdF9SKM0toCzCb0Y5dna/rjBdH6WWilpQtw+jfGmKC69E/ZW6DeLicrRghYmBnEBzBuUEC2/PGTRPUoek4M41AlADJWZpjjwli+JDSiWqL6US1TcReJrq8Cfatst+ffdTZO3HfJsldesNXXBiBIDVbemBZD11uGvpQeWmR8ENdlwJRqEOfPaMyfTAYfKgrIFHWD6Czpabz69zbr7ViL69ARQ3Mejhhz/8YXzVV30VfuM3fgNPPPEEPvMzPxM/9mM/hj/35/4cAOA7v/M7kVLCW9/6Vty7dw9vetOb8L3f+722/TRNeN/73oev/dqvxdNPP41XvOIVeNvb3oZv/dZvPSYbJ3uxmboZ5gySQphyQpoZfFFcR0kKUJoYKdWKe3PpyW7ALZxMIUU4zRVOFpY+VxVloeMdOIEIUgvlmkkJnCTagEEVdEnfYXH/ZQltnwRgmcGz64m8Cyxxr3hQmfpkYHJgmpKASkLOA5x0XsPMtdcIC0P3iknqBn3U3cL1pxZVkQeUdR8l19anidNrltxvxjag1lTUKPz8NtlKOb9wV66pqJHi6gBu1c2n89cYOHHldlIPw07tpF4cRodDcU29/OWgx+6CH7tT3FyPHZDvHjDfnTA/lpDvEC7ulvZS8x2UNlJ3yHoy0PZS+QDrxbu68YJrz69r3H1cwZUrtJovf7ivfPXjB7fVcp7b+djeyod/C7AgbkdIuysFVVme2/ZVoqQMTBMtfq1HDDddw+XR/gqgai8SZKHii57nZaDJGrrPbpnUS2ngBDHoIpX2a/dJ2kWhuGFtuvxN5yzTjOk+l/n7jHQ/YzrPoPsZ6f5FuS4IbrkNQDUqKnUA5zdfDUror9sd4n5Vc56HclxugeKiIC19py6KIox8dF6zbVVLi8ENo6ra6fK74Pt4//P/arOd1KnvvpM9NOPM8kXdurFYuu5JcwZflMI0aTSfdpUUgx+4jsWkcLKufrSOqVFW1bVnCirUTUVAAXosWMFXOqlQaVDFlKX3K8Rs4L+5uMZocsBiWNslyDQODMpJ5nNRXgovLVg8mAgoQ50EIIkyAjm11MDJXV+tW1MV5YMlnIIauc4sfN2un6pI7+4ju8b1ryqufs8R0n+HREg21gPUi9ECPJeKqZP+mNDyvW6+mBcHqD3aZ68+OkHqZA/X7ItQvtTE5ZcuMuaJQBeMlGrXOtXFx/BkyAyr91DQWKG4ABBMrbQD8VVAWeCE/PoCcVE4hgKx++pRu5IyQAfNp7r9BLQHdRW2KqtAKtUvYIEWgAokHTRS3XiECikDTevSA7llGiAR26Tp9tHdt2LWPZRXl+6a7lUdpWkCi/KpHwRmIzjF+3KEirp11gNXhEBM03Pz+ektN1+jugLIrtFOkDrZwzffIaq4s4qySqKmgJRSLUwlWAJaIxQKvwZOAVbwEHJBEqa2ArjUbCR4X56NwLW1nFX5ad4koIKLArReH2YCHRh1/KQOtDhZqDVr7xYJFVoKJiIDvY/WM1XlgiN67dKaniA8oAxabOcW/4hLHd5q0MSx5RpJPVdc1puO9+HFYD0Q7Fm2Ba9em6jF+nE9lAEqtqXqnsMNBE6c7GTXbjlDe1HQLzua51J3kTJyIiQdYoJQAAUtyEsUGWnhrv55DyaG+ef9cr++hUWd130XBRFKuVBQN4U29Otfp9FMNyCV82gDNlRRuWk7R27ViLpXJI8eStbIVqdTm66BjnOjLts+VRUVgyeGZhF+BB2WnjJVIdRTVHtgFe/F4r4sM7W7v77bZr0yvrtsCaZdbaK23Hx+/lhAHWEnSJ3s0TDXwSonUVMXuUTAEUBJxh7yX+yAUyDFXabrGij5r/oGBl6R+GUwQGmhyuBaAPo6MdRpgwFh+SvbWccUlg9qQakqyiutAOMmncx7gIyg1M8bNbCyX+vuKKiq3jlFs3tTpq0slPPtRfS1Hbj2H5HocqU1WIX0Zi8WQIn1gju6rj5grKIu6+bT+QZWLphiy051Uie7DVYVVK7uvpyLmkoAzuGGF5feGpjkTwt3Kr026HAXHkxoAdSCIC5HCyfPw07ZNoSTDy5w6kTTjo6/hJPPbx0xt4C2gqvNzzqQlmBtf1s1RW2PEF5N6T4G16YNnCiYX3wMBFitNaCO1520y6bB+q51AHXr6qP2uvl0eSfSbqGigKu5+QBRWkcA6gg7QepkD8/kIdd+5HiSNlKpqCnfVRBJ56TEhMypVUa5hEGTdvqqBTsAhMKwP83DNACgcRocCvgy3aoW3/FqXU41Qo4A7eevhdM6uEZQVRdn63rcUnw17WjdYsRdwkJNdYMnGNae2cOHQSH/QVFtmI4rZdF9i/UrGw97iLhlgFqzNTBsKavrcvN1tl/P86lO6mS3wbTDVDcMBaYEupibXgVKF2yMzKnUl2cC5VTgpJFxU91t6z7izrJO2mZZK1FqqDnpokZZ1F4w/Hxb0HsVsgWiBbg2tlnWidXpRbj4cL7CrTmv2JA3nH/XBETEpU/DvmpdqqldZp0Rr2zwYnHrHaNKcnhuO/vqqig9zlXdfNesoNROkDrZwzf9OtORZedcYKCjzrp0KXNpXzUn5AxQJuRZGoQedr4kx75LWoCGco81AEEVhRTmeRJ1FYZENzUl1gKn5su3I+ov7wAMQU2QX07d5WtRiDUAw+U9qCi/bVdRabmn+df6sxGo0Jn3NgLTkUC6FQrqKgV+TyHtTXMVN59svzcEfe8pniB1sodrUhdldVOZgYsLAOa5K1+APMlLJYWf705oko5RD2lZWI7Ko87ybuFFAEg7QfLL0KiNBlBaoIfh1K3AR6izwUAtdeabhrAIaVfK3lV3WG+dc2EarHquwa7vDfXmaXSizYc89855y6yebD9sbgWYgH0lt7wHu/c3cvfFNM0ivjk335F2gtTJHroVQOWinBKVMo6citLxj3gqfdjl0gUQHQRSKZcevS+477qCK6QTtfONyuBmG6s/goOK5pnaxq0eUHnC4tdANuk5644CsCKMQtYu6yLb23C2nl97bsNAi9HxJGtaH9UCivoqqsnAnjzeEvDssUsop2EU3yi9uvq8iloLlgBwrJtvAbc1O0X3nexWGWtXPwVOfOFewpRAPJUXICVQTqCUBFChnzqNYPPd5ljgghSdBBfpJgPsJXJwqtuWcaLG7rJeuyIDUxzyPqH0Y2fnLLsx6FCNKORRmsGyFYuAjelHoFiLAPTn73dgxzIZHKrGVpTiokNfrxCdW9XqKm+oDuSB21XPwzwMl9zPVrAEcK1uvmPtBKmTPTxjqV9yYWrqKSqry1caJekK6IJcR6qpFFRTMlARlSHUIT0p1MatOrQDbBk5cBGhDnAo7i0DXSpZ88Op98O2yYC0ANQByDpqrevCp55nnOgAqAO0brpOWl5ZhwgwWccLENUMb6ko3e+CI9ZIGi2s1hTVaPeEXtOoRR4eebvugj2qJPlrVBTQVzujYIlj3HyxDms1r6fovpPdFmMZnVZnIa6JqdRDlVFWJSQ9TwCVeqg61LwAK6XSfsbBSsFF1gUQgZJTXJKeElu/d2AuPYZDSuve+xzcYe3ItB1AHZyS0sI+AAdYBwvg2NAUFG7bqFDc/v16XtmmF2o/hhX367t0/3I5VXANo/t0m60yO9F+oq3k6yVt7B4ChcxasASAa3XzHWknSJ3s4RpncE6lXmlG+epDUSZgFvgQMOcCkYsLgBKQyNpR0TShGTo9lfU6tpJXWK3qEmAlAmsjWu3IFgAmMqVBHIIngOrqo9ArQw9QMpRFPiBIFQCo7bTY/jWrzYY9M4RIQCgLWa4pu30bJMjqjbzCiedYDlx/67IePd3pOVDZcXOnPmoPnMIx4Bomtxne2FF72R+uPWLuys1gCUuz4ubjDN4Jq1Mv6Ce7PeZAhZzA+cIgxClVv05KFVoAWGAl/SYBgI1QS37wv0Runktv6TIYYJlWN6JkZ6rDfjC5yn+1xs2HVkU1f9zUReUDyvhKjiYkJX7zuq5AilfSRPcZC4h8KLiCq7RdYjeNqhr9vt1p9+DUCz1vGj8HcC723wFwA2IixJOtDXs7oir6AG8CBNehGB4EoHquvugKvEywhO77sm6+I+0EqZM9XGO2goUzF1BRKr7vhBL1BxSo5CxqQGCFuZaSAiwpI+Xlm4CJQZxCuZvAyOV3ElAw14Kc0UaORaXRhUGts1oEKqydPrGBymVwCIommCAuWzPnYtRT47pYpotabHbdqJcBnKLa6q3v5TsYizjS32ZdQhl/S9cl2O0fRwh2VgzSDoMOVoB01HGvy/aEko8a7K7utpO+p6I2tjMVtQdYpzqpk71kjNJyWeosu8yuV4CzAJT/zZBeMbgoLZnmGW3HqGq9gwR4dQtFp1Ja9xk1ygrw7r+lq82U1A7r9tlH2+tUeZJLS6Gea7TPUc761yR+9cdttgvk4b57+99afpN21cg+bzdUp3RVO0HqZC9eu2o7mkXh1v6xTbPVabEIQYOXLhPh14S3xzJha35tm17jXnaFu4dVTNNxuY0O3Kuj6i7362SaLNCCDE6NevL7uOyt6wSU1FNwC0KBvLyuR4LoOjxdR3xXHdtG6oFb78NxYWmXF+B2Q4r8k71ij/LNPNnN2mVAldEWlAqbCeJSoaWrT8PUdSBFByXrcYGoDEESggwa8/NbSoqX0xTXeTCF5avh7Hsu2whQAU6WJbmm5ODURguWAS3JpW9U1FaeBoAawakNQuH+tLcBiLoqJi5beQ7NtRz3fz3OgOPsyHolImpcfpRod+DEXrvdkNprTf3CCVgnG9si8M6bK/BVPS0AlYKa0hF+NciCqLQL64Sdx+PE6UW2RukikIbrV/LQsZ5aapZ5OIVlzbxXUcEFqPtcuPguAahNOI3A1Cmnh+60PYDSZRFUpB8HNX1TF6r5iLC6mfiEvkVo3VDXR2v20oCUN3OIn2D1SJvvl83XLzXT1J++jGUpRHYAytdDWTh15hK4IYAyNUUAZlEIVqDH0jwcdgsuK2m6aYEuebfquKoy6mSUVgDVSyPzPSg1dVUxm8e6/lYAtYCTNWx1hzsGOtG2yu843H00HWsMHVhdt6pykX031VPEddlLD1JqJ1g9ckaP4vAKmYsLA049ocKq6Y3CAYqpToOANBMyiavvGAgN1x+nghq76iPfU0cYAKoBnPb2UeulojuvG0xxFfOAGsCJRoqqN++287YKNy1r5gCfFNY7tUXaiP26bU/k3yNWJr50IaX2YuoD7KVuN9jhaBdQPmAiSUGVqRa0GiEvoKJZCDUAVQ9CW+pptd+9o05wfXXcX+wayQDl66kEyhYmPnDz7eriyPIZLx4cfCSJQYg31VMTgBDLgWNhtLUM9ZKVXlRkxsPKga0B1XWoqZsu5ygdXae1x06QAk6q6sVuWyVgr75gtCsGSo/stAw5D0qAE5BmKYusANcSG+sg2lnHFCFVO8ZFmBjM6za8nma4nLYB5dM0KilerzCtgRR1GiAXstgDtAdUPUEHoaiePKRC+boA0m6338LXunBHL2CV3L6sLym6HkW1JxLwEQ0/B247pChhX6gjboTwJ3sEbO/9H5l2hdTbNbddITWuPV7+gvXLnKorED3+RQmyE0TdQtklp07BTe36RaPaJTPb+qhO1he9ofssdwBlf1kaC0f1dKya6pk/75GLb6SeLu3225HG0spveFztkmeq6/Wj6YiPp5HRIwyfvXa7IXWM7ZGiJ9ffrbBuY9jrPoZ8wapyavxdDaCqy6+nDsrO5C+7+QCeXSDaSOsbyfrjLNplUecwAqtj3YReRY0AZcdvro9rL+X3Ry7I5Aq3eRFevgKoRlX5X29bQFqb34rY84qp49ZbW7dq11WWPWTQvXQgBdSv7jVYnUD1YG0vcB4AmDZNoYTyjJBTTNAP3wwZeoSWkOrsz9smjLaApgCIx4yuOIHsAlZHgmo3oKwhMC1de6jT3Z4lCOjWQ3Ws1AkGNeTb8Oi8c+9166SuAiQs1Qt7daSWUVyAvTqojFJPdVk1tce9190u9MX3iNhLC1JqN1TBd7KHZNcZig7ICx4/8euvhp2zTbsAClJoucJllKUjYbSWLjaU1eMu6ny4TlsMR88N2LMNV98QUIP8AAFQHfXl9921TmHcuPmAFkY9QHm4XRFI3fxIUcOj9k66PNZBXQZQjwBcrrtB70sTUsA6qE5q6nbb3he7Vwh0ltWOY1VF1eVowITaW4UuHx67/e0C6Yg0FAAR/xpYoYWTTa/ZAEr9UPMAqEbR1XqpJm9hW81TbGd1lEV3nloPUCNX3xqU9gDNB0HkDqiIyvEdqDSP3rW3K4DiEmXWtbWRSnQaT6pnpGMKbdiQ6idF9Ujb6r09VjFdtQJaQSUqqfQ2USSMfblL5bcWskTLXsUbY6yrqBUFVetRNINyjgQbWXjR5kiXpQGo4Pbls9lRNa2bsHXzRUBxEtdokvHBqAJo2F+fm+bBdF2IpatvkUaBNABUx0W2qpK2AJXdzXLPaldVhejicl/8SV9CUW2ZH6LjBuCyp80jMe3qPeNWQ2qvXUp+ntTUS866X6scpuWv6WFcCx4t8JuovgAqo0Pdt4fQHiD1AMYEqcdApWSAFSdZrd02oSZvr8OKsgogWSidCB0DF4N8GzJVVUS1Dg1YgutIsw+GLpDWAXW0SlLbcvnFgAeSHvFTTONgxlwi/rRuyqdbnPMSsKu2lvaY/cjwOTdtLwlIASugOqmp22Nrw2/cRGCFluKZl6W5QUpKdNYvb5fGkWmhqDrAaRqjjoDUU1xyLIULkRSATBVU2regwMuDSqG55fYbut4crBYqKtRJITloOmB1lZX7Pbpx8pqNADVSSkOVtCOtbYNl1F50//k0NxUodBTMHo1y8SUDKeAEqhe9+TZTR77kVLlQbNB+SnuVMCJ4RRWghQxTR8w6qGILOq+ShopqC1hwwl8VFANIrv4HMFgZkJQfa8qpY5Y1BZLsG2uA0uMLRAtV635aNVWoeSUwrakoO5EAqFG91F6FFVWFuun04+omIHRMsbXqDr2m8u8GytKXFKSAS7r+TvZo2lUa8q404h0eTsux8LdQUL41LDVzsCL+CCiN0i16z2Y4QLnjCQFMTaHCKmYX8VQ8hAa/bV2UTldY1RB0VU5U3aH+T9O4X38sdVeOrHH1eVtx8y2GVgcuB6WQjh3wqIFVagMiVtRUcc2SdZtUrov/yDlGFd3eMu92Q2pPjxMdqndB1fsCONVLPXi7ag8SN2AWcq7c8WoqU20Ho416AVhpT24uAkhXrILIb8PoAcxcfAwwU1PoEWshWPJjSkrPCe30EFbDC+PS9YAW3X3OrWcuPrTTbdAE1QuwZr0Pz55y6qmmXpSf38aO0YfSIkKu006KdPtOo9xl1N81u/qOLcMesY/42w2pPTZowLsbVCe7eYtgGg3TASwj/q6rXZTbrw8cWLRbYUcdBkjpxSjd/qQAKp/fILZG7rs9YPL7IPhCLug2VUx+Wy2LNambNrVosNKFWJad3X6YOueq8zRYf+T0HjfgnuHUd0fv9dx4NunT+e21i4vwbJtaw9j11wtJ9/k+NlBij/myT8PJe+VhJ1giDnx43Xa7IZWoexMXphdcTS68hkk2sIo35kGqqUehV4UHbZTKfdD745sVpFSvSUpluUKJkpsmmydfB7Dj+Wj65/NjSjFL2VwBRImc+uAyTtQEaEQfEpagIle6R4+gru6pK7QFbbePPqoaQ5XIqC2Nb3Rc3ZSybQrKSpdDL5+DEaMGXlC5PsQsaoEEIDXkHKlmkmYCMtXRizOsrRl5aIfr0J6Inm/JEyll5aNTw1N4AmiWbVKJlKMkY6fo+U1lZEqaZcGkdUfu44IZmKY6rSYFNfnyYZJjM8P7kkvYPdXnWZ9jnV6xJsoP7pkYRSt2d6LvEFwD4gIbknwzM5Bd+KcWlzkBSc41y7kpqPw0sA63K9ithlS5x9sFe+vzXQJrAauovi4LqstC50iX12XGYXrY9XLkYQNsw0nS+PS1R4cALP+710RNkQQ4EMoAhpi0IBDlTbJOVVQuhaSBKYJK5uFcf+V47lrwcnlUAh5MzebBlaZp19RG19WXZXMNTZegj/aLnluXnI2XRXWHElXISYDBKLACKpgyVSBlNHAyj+Ha45nkYhBB4yat/VcAFbIOJJgrqFQuinuvnK9TJ80XwUApjeqGcq7Pq+2vA6iRdZQWAXXYF4PHQFGNVFYEleaFueaZuA+rrO9cdh82LagoZwEYLcvYoe0r5241pMqN3zhR/9BgBVgjWO0F1d5CccfN24TO1j789gMYUS9o4DojfPbaCpwArKunsIwioOSX4zZq6uYzGEnhrzAIaqoUFly+0rMGIljiMahEXSwqXPRYztbcVB488QnhpOqirhw1fFXxQXoJJM8GKqrqSkFW5qn20q0wIwUo2ZhampZlY9KMRBXlYEUOVnoNiMP1cOcGQK6zgEpVXQBVo6Kk3s7AZb5NlEAPr1x73SRtwWukuvzz2Kj/wfOo07HuCgBml9b/DgYztGfQ73uiuv8tWCG79EFVeVABN+b2u92Q2mO+TsMBy6gPLCRqU1+1Bao1OG0U1qswGm3b2WZVTU7LdeMHSV6wy6isS6i5o+Ckx6DOMk0fXX1lxfLAoQ6qWZbZXkpVU5RYhAIZb9hFUnRBZWAi9/Uqy46+UJpHl11q1yugTEXtAZTbZwMqr84s3/43KCf9wp9R3XtJ1xeIAADNAM1UXXwKrKik9G9g7M6XElvwCokixETAXE6knJeASncQ56fUggYCLTdfrpFb1rgDg3Ly7kI7Zv8DavX5VJCIO3NxXD1WyGfXesdR+IBqnhVWsp5Z1sGpKi0niSqobrBh7+2G1A6frpn6T4E+rDqqqgsqf+xunsZgGkKpt00cJK13vF7j1tFD75P4md6D1fjW1233sBmjhrh74eTTjtSTd6n49HHaW3zx1eWn7jlmieQD1Llk9VOiloAWVKXQJingZbmqEx8gtnXp4vrerbVz7cCpAytfB1eBK+uy28YBiFMBjIGgo6osve6DqYLKYI5WRTXqCUtQ7b02XlFJYc5TknOVvHZU1EJVAauF/25wxXSj53Dt3fGgAlx7KF6mW5sH2mfUvxv+GKaWlrBic/k5UKW8dH+q22+vI2VnQ7jbDak97j41LYw7sGpABRiU+hGA+0FzrVCKhfzW+pFFKPXcE3qIXvpjjrcFMA8mYB+cZPnCtefzszegRrPpC24Wd1FCrZtCdftpPZQViB1QmRuQ/DSgsALQBsaFrNq7y+26RVBEsw4NqDbff6+g3DZEbh7uMhLGqooh/fMJwHRfzgUIwlJFKZRco+emDO7Aqo5BxZJHgYwHlapa5/4rHx8rsALVeh5gCRpgCS5mcFBR5Av53jPYWcb+3fdg88ce7W9PFYSqw0V55J5b3c8guGIEqsbtd0N2qyFFtK+DWUAuJLCE1V5QrfaansJs7+EMhfoxUFpZtysk24NWoDRUST2IHePiDPmLNszvCEyddUNAdZQ1D4AVR921MXz8frn8824/A5UEUnRBxSXqzwIrCDWAgmgRBOE8h6Uw9/OapLkeuqzOL6Z7jyAr97g2/pWkjJqnRfdEW6rKAMxN2sYFKPtXFWX1UAooAxVXYAUr/fzpcPLlPpm6BarrT0Gl5byqY62rsnoXuMAKOGAB2llwWye2orbUMxPSkHs2jrLe+7kWELFmG2lY3dsKK62fm/NxoJrn1eNc1m41pI4JQa8gEurrl8oaqPbYFqD8+suCaQQlv7/RcTK3PSuoP1lLsXiu07QEmAfXMQDq5afZoJPntTqneqA2L43LTwuhHc+Ggimka9SUltJaoCmoFDhOURmAEgOzwimoqKZxKxlcCLAOakeg2oKTrQvLygo3zwJQoAaF6HaaLsIJVrZLYRXSaIKRC1CuN80BTk5BxXZg3Qg/PQd16+lHg5yLgYpKPkxV6fPgXFqNsgIWwKrXh219WTVQW1FFeaUVbcOVvnfY965rbaeCa5aJm867o5u6uhGoPJe8y+8aAyhuN6T29DgBNBEpQEdVjUC1pabcsS8Np2PAtAWlUeUo4F6KEByhAHPnRVqRavuo4LoSiEbpI5jc+oVqApZwitPHGguKmi5oqAFVEQncgiq4/mxpBshg1Lr/DAAEcyMW2C1B1c2qg9DCtee3Gd0OLVctWQsrwAGrA6cmBL0DM9sBo3EBAgooMkgt2ks5UK3WSVE9d9K8y7kwqDYbMFjpeVdYaX1VcfHpMzZQUqGuaai2dtRr2bx/Xnt1XJ11vTqupo5T36kmz4N3ZPG+Fv82k2s3JostxNCDSuFETk25ve3xcO19Y285pGhfVJmL8x+qqg1QLY89ANSKW+8YOK2Caa2Aj9fD3Jcyr+dikT1qUwum1J5ziZLqfY3tgFDMa2/70XXq1Tl1weWWyR97VXWsWXTfsn6qBypJ4NxiUoCJ+69RVT1YIYDKZ0XSDwHVg1RnPx5+vuFwERTu3hOs7qcLJw8lwsIFqNeBuCqaci2oqKqOimr+XP5WgyisAa+eSsmkuXI7sNJ92un6up9cTpBMHeoglytKStNIOLdBzQcjjLwWa2rD1Xst0vbqp4jWXYu+POkCjIt7D1g2ck4oD4B3/VFHTd1Q3dTth9SeAsjqAxKGrad7oPKH6gVRYAVQ1wWnI8C0UB36ogDufKYWXB7ADbg60NoR8LE4L5/PwXZdKAHjazV0ea48D1vPSVRTpAUSrOIdBpMlqMyx5OqpzP1nfRcFWCUHpggqYPipaYBy6mV3j+GdcnHhViOUc0oCK82Lh5PAq+cCZLesrJfr4Fx9baSf/FmBvMyr9fenLilz19f3chNWApNGXflH3r83gLkpiRU66EPJXcSuyvJBCbpfDzP5XbTN8tsu6r94+e5FUEU3uE5b/RlZnmhGce9pXRQKrAxU/mNxnuUjPgE813J15prmGmF1yyG1092nxAdgcf7SnqUBFdCCysNt1BeXz4tarwC+Kpz2gqlXZxRbwfe6b+mBK0Ir2pEwavLb28daBOMamOK0V1EreQGwqJeK3dCUgghSidwO2mflhVMVteDxy5ewKg2CBWQpgEpPgwN8aAmo1TZRA1vtzUGvie7TgiFoCCcLBGmuA0y56flRqJNSUKmCUrffnhB0f96xEX4XVuKC9MCy77Og3rrQ8sByiRf1Waai4IDmYAbUejqffnGCAigPJw8vElAmtPtYe7/Uw6Bwcuusd46cC6winMIyfV6h+5rLu21nsif6d2es+u2GVKJ+wePN2j9l+/JaBdUCWjQMpOhG8fUq+nt1KDcRft1TUsAyQq/T71jbE0czszxHnx87nw0Y+XzZNgNwd9MOwBTnFVCjtH4zLbyA5Uvei/YLwRS7VZVCS2ElgRU+AtBA5Qp3b73ovVG4eYwcbJZjubyXbgEelobKa3Dqgcqdj0GoUU+oKkrhZNDoyT5Rd1Q/LKyxsMJK7hU0rwqoeE1a3qxDa4fKGqqo2HgYaEHlTbeJ7j3mWj6p10fzEYCzMOf2bgCl85pXZDBSA6pGTWnbP2vAq2WpqCnMVWldo91qSO0JQbfCN+8AVdmpPSDR7bc6aCKwD1ARTm67o7v98T01RNiVhXLu3L5kHWjFjjLtvHOAUcxzPBefF0u7opC66TfWr8x3u0DacvXpiw5A3X4AQseeZT8x6s9ANWOoqgqYKqzKQITyNa+g8vU3ot6WdVN6Pq2iYu92ctsZrEZACoVxM6/ngDqtXR6xqssenHTbCCssAaXqyZQUY5inxvRR1v3Kgqb+qwMs3e8QWsnlAy20eirLB1+UfVdgLVSUhbwP4NszDQlnBua5vpMsY1RpG0f//K64u7uAUq/KDGgHvAYq/QhIVF2sWj8FKR9VTan7ceamDL0Ou9WQ2mNtIMQAVJqu4/arO3IuP9SX4EqA6u1jr3Lyy3o+aLWmAhcAqA2VBVpoqcrS7acW1ItzjMfb67LT8422R4F1lnXV0xqcepXPoZ5g4fpDKRAbUDGL+69VVVYIRli5/Mae1E1xRED52+kKft89kBa85nAJZURT6HsQjKb1OKKayM1bnZr2OtGDU5zX/Yb6qAIAXoakd87BX7/mmjSXagksTLSIamyH1Qg76yitnsraBaxOl0x2vDU15QGlId1ZMkJJ6uaye9+obusvji9TPKAmsulyjVpQgam6+EgbZfsyx6kpbQt3M82kbjmk/EVeSya/TYy/B5Xv4iN0mliW0dDlN7SrAspDbw+c3PJFNyjR9x377OrlG1gCS+0yUDpWHW0s34xiWlWYLrm6/PRrNAIZoqgALMLT5evRVJVcUnUPdWFFsApm367HyNRRUJaRBk4wCPjeK6wHBpJD+9Nx04uQ72YZ17xq1vQ4bt7qq+wLHWNYEQYBE9yEoEeXX7SiBspFKgoUQ2DpvV9A39XLLjbK4q71eXDHUNCuRQsuwtuJCwDsHGS6471ZuPlyBudcAcVcaIIJDOljLyXYGFQkwROhfRb78kHLC4WV5hUCKqI61EmSm9m0LSU0aoqqy49Izn1HudwpWbp2uyGVaF8FnUCnBkM4UAFoYv0bOC0bpnWj+aKK6gBqV91TVE974eRkeeP6cjCi6PLTB27UyaQ/j7XGvHugNEqz8SCvttTfA7VdL0oAFVBhJdfCCpeR+09hRQNYgbFwA0oJZ66n5m9EKp/pcn3M7QdXCMK5/6I5MC1/uaoZsICIKmxcsEYbKLEDVnrNAqD8eVPzt/FRSDBna1yu52mnHJe552KoWhkuaIF2Aau574zaUXFhSgVV4qI63HOwMF8HpYCasykpUC7lWFtxFq6FLzNQATVVQNkHrZ7bBHP3FTd1VVZF/ft9phrp56l9zdF9+8IrBvbt3/7tICJ83dd9nS174YUX8Pa3vx0f//Efj1e+8pV461vfimeffbbZ7kMf+hDe8pa34OUvfzle85rX4Bu+4RtwcXFxfAZ8Ib325wt5QFRLagt822Un/a6sXAOgJBCk6dHbd5o6paq43HJOVNo29LaTX55SSZfWrhPV/cfrF/MS8mB5SzLdS+Pz7PLT/ZvSOJ8RRKPl/n73zH98NHV0qG4Y91fa3KAoIV2WBUq5tCuxgjfLOvnDzKDZLdPCWOoomhDsgbWuPnLT7W+zvrujDqBU0Sz+2NalEJlXejUPaWdul/k0Ia3WSfl6qaj2euHxrfuT+n+pXh/7S+GPRCE2f/4ZdH+99S4NpMBnko8rOT6rWlFQABUWQPtsmsu9SjNmr6DyAlisMIv7sOsV3gmF1RQUVQr5FJjx6P3z5YS3rUC2S9illdTP/dzP4Z/8k3+Cz/zMz2yWf/3Xfz1++Id/GD/wAz+AJ554Au94xzvwJV/yJfjpn/5pAMA8z3jLW96Cp556Ch/4wAfwG7/xG/iqr/oqnJ2d4du+7duOy0TvIo3MN0TzCmqtZ99me2rqsIaNZzVfeky/fdyO3LLo3uupJ3++9iJ60FG7X//Q6zxzX2HtVVe9wn8EBze9GnF35AfBwta233IzuropD4lWWTmY6XoZ7NDamXSUlbnuCM1XNk2wfTMB1r+fgwYDXTXgXX0shaztN7sPfScCfB3PYloBZdMhjR2DofVQ5I7rVVOTVpWVwkGvkYOyHs8fd7XHCTmWuvyaLnw6NhqlWM+tB3EyOMBddJe2o7BMibtrza6OjXM5+eIaQ1VTWm81zCNXYM0zeM6irlJ5hljfT65pe0PzKIjsV6+hi4T1HwdIxZU4u3yKWuJEIG0PJeVUOf9UFSKwz8O1MwT9Ukrqox/9KL7iK74C//Sf/lP8vt/3+2z5c889h3/2z/4Z/v7f//v4M3/mz+ANb3gDvv/7vx8f+MAH8J//838GAPyH//Af8Mu//Mv4V//qX+GzP/uz8eY3vxl/62/9LXzP93wP7t+/f1xG9Itl6y8U/M1wEB1r1JRXQmvpgRYso6EnfJ7cMotUjOojAsqpkOa8vMqK++kpsKCwdqurnlrq/DVKqaesRursMn9rz8UeU7Xk76koJ1NPPl1c11FWNPNQXZX0sEKscW/p+z8qpPXX/e3pfaI9Nz2fCqilcqoKqqusoqrSuqbZK642LVwdVA9Q9Zq016J/Ej0VdMTfNPhzKkkVlyqoRnGoilJ1RXU7U0+TuGP9O7RV4sZgCVVPmZ37zy3rqanu9aKqkEzlyfm5IAqm/3975x5s2VHV/2/vO5khEGaGkGQmURKgeMQAQQmSXJWyyoxETOErf1CpFKaU0gIHCgQpQdEAVhkKq6TUn/CPCv+oKbAELV4SAgTBhEBIJAkagYpO1EyiUMkkAnmcvX5/7F7dq1ev7t373HNn5s7dq+rce053797d+9GfXqtXd7MJ2WVwG33vNkmWgtTBgwdx6aWX4sCBA0n4zTffjEcffTQJP/fcc3H22WfjhhtuAADccMMNeN7znod9+/aFNJdccgmOHDmCO+64wzzfww8/jCNHjiSflYg2+0kXcBYJqpBemeZkYwuE9NVdZWU+6lzJQ6ABZY1H6TTik2ljWvvy/81Z6tb10uJc9r3oaafz1p2JVciy41hGXLiGrVLpFcuGN4GS0NzHNINRqRWV0v+WQ4X2ABzAQanWJUBimwehxp0QzIDdonBMosURJLy19td8HZb8MMSCyVTkF6cAxAnXgOocQH/374Ru/JcV7dQlZWwcb4vKZHPfNddcg6985Sv40pe+lMUdPnwYO3fuxN69e5Pwffv24fDhwyGNBBTHc5wlV199Nd7+9rfnEX3JLqCk9eaxSU8uPw+Ad54cJrmJB0yOPy3pXh6OEXkk5bEa0FJvRr4A3pIZ0o+ZFWphYSVkivVS7tpJMVrWELOEj5nqSSkleGka16zyO2s4OiOtaHCSNBaoOahTx/jGzhyTsEQAhGRjLTUPHaaOGxUHsGlSTgTmRpXz88nEf7l7k4r3zmByDCnVluJ/c5sOBUpZjzDxd+SaJfVrFa5ETTKtG5kGGLTsRPNG+D5JOufdvDvzfYsFqVSUKHqR9kNaNkUOXqiFci5T3hXLJE3q7rvvxute9zr85V/+JR73uMdtVpkyectb3oIHHnggfO6+++72g6VrZ00lTjQXb4JbYw2qG+YSra1FrWltbTjGhyWak6FxjQJKlkNqYCK8uNRPrYGdKpkLLKXhPeVpLffZMdmEAdaiWNcXSDVOOaAu55CwWbTrohknDLy75BMGzh3CfY8aLRA8+2QPHRFoLSb6TMNQDbr21ONj5P9mjWRMDLBkE3arGlYZUFHDory+fC34+IZybqjOxvHBWUbHKUCFeOEMMSrSemM5WXVd0s6ENmos2wCd+B2+rFk5GbChXmR/pFlSSt+3fRpkkiZ1880347777sMLXvCCELZYLPC5z30O/+///T/8wz/8Ax555BHcf//9iTZ17733Yv/+/QCA/fv346abbkryZe8/TqNl165d2LVrVx5hjCWYMpaGJ+qyZsAaFRC0KkBoVnxYTWsaEqTnEHHFCbpWg1rSpFSYpQ1QskdOWaNyGj76AeX/zqUa1bGWkjeTVUZxfYraUC1NMZ3/0hnHyiIk9zavihb2P7C0KV48NfgoyMZSQmkqmMa0bp+XA6LrtdCogkbG5xSaVMlsaIfDBFRIL9q3dLuKerknaVVKLK0uxhE0oIZzKk2E40rtszNUOccwIpCGV0nU+OngB6UqT/yMxXTBxJuV16epwZbHyVYsk/rcF198MW677Tbceuut4fPCF74QV1xxRfh+0kkn4brrrgvH3HnnnTh06BDW19cBAOvr67jttttw3333hTTXXnstdu/ejfPOO29a6Ut01x8gUtv/LgJGj5c4rzWxZiU+gwPBWpImHR/qYI1hjS4Ka2hHk7SowhjLqOiHr6Y1WSs2W3lshuh7q+PGDpeak9aGnEs1JlOrihoTD6JLrSnRnACRN2tZsixWAeNXPU6UfDcb9lSLKuaXXZP0e2maVtlTkBuwqE0FjahVexoBVO06hfKNaVerejylR6QGFJdFlNs09Y12npVFxRwvn/CuS4D20pznyym0qMTUZ71vvREW4jY6uJrKJE3qiU98Ip773OcmYU94whPw5Cc/OYS/8pWvxBve8Aaceuqp2L17N1772tdifX0dF110EQDgJS95Cc477zy84hWvwLve9S4cPnwYb33rW3Hw4EFbW6pJTyh3SQwpPRSd1w5C70TkGRoUvXWF0njCd8PtHAKKOr0c19IPpTAj6bBELC1Ki9VDbmnk5YPImpTWqAw7+dLbZtek9lJrbbEwBkXWfXBqLE91IpIGW9XJ2tY9yVsfn5gaC/koSba6D73f9Lvpui1BAhVnnkilMXre7GYdtag067AKhDw+0aRIfE//I9TDBlSiRUlgy3Pwz2D9GKnzZoge19Hah27sLdFtinfvzoYUrPRGedC7sAI8EeDIxS1oyB6LSrQomVcRWKuFE8vKV5x497vfja7rcNlll+Hhhx/GJZdcgve85z0hfm1tDR/5yEfw6le/Guvr63jCE56AK6+8Eu94xztWXZRUxnotctA+ufmIF9/aCrp1B14LTDKuMG5SDLOcKmQDK8JIbxUwJtaLJdc0LIGgNWwZeJXKP6FeJRDJfXXSMau0rBlIdDVKYMri9L22CkvJCZL5U+E3gSia2TLtJORV1qCy0zoMhruR6ypBBVlSinXn5ZlKZsginGDFG4BKCs4FU+XsDVBRni5dDTxPn5SvpkVJk5mqmzQD2iu859c9WcSV922SG66OdU793KnE5MfztuR91mNRElbw9TLaBL2up1wEd1Qa311Hq9yM/ijJkSNHsGfPHhzY9yvY0e3cUF6Tq2+MdUzelsKK16q71KJUWOrKXm5k4+AmkKjtUlXnl0aHqQUus3rI82i3eP89c5eX9eTvprnAaiio/ltfy9p1sgCVhCGY74awwjlkcSpQApCa+ML8MZ5b48PCPB34uTx6pQMYHxdAUDKflU1qSCfyyv+1uhWuyWg6C1KADafwneL33giH0XkwypeBqgYpfy65bNMopBYU4xNwxcad02DBGlYv4ODn07GL+YL/+9XPF4v4XnKbsLY2tD3BiYun04i5j8HJYnD8kUsiyXd0qFfv5/f13smlj+Xo+2HLDi6bd3ygvh/KxitgLPrmrToeo0fw6e9cgwceeAC7d+8uptvSa/cR0STIWDAZ7YlokdpU6VjL06aUVjf0HDamSZXSLCEZoCwJPa4+usJu4JyTZIqZsrU8GlBKe4rjSiMwqp3TeAymzbsqfyKAhClwGXFRsUj+j71WliLAYQaY5Hc5OF+C0xBH8XsBUCEd1LXlyiwrDfVPtCgrrhfwDeVv6IABsQMX/vs5JaEzWrFjklH5JC+vTXWEsBZfYt4TnVofl+waLLUl2aHcBKcJYItDaqokC8WOQcOSMTfPsQaoJV6ahUyTnkojtSifR7K/0DKitSjfGQjXTG4RwA+3nj81JlKzqpV1DFDWb86zx9C7BDIg5b+RwckaS8qk1lYUevg8HpWsZj5BgpcfA4tbVAGvFi2qJrLurabCeHApPAdT+C3BJON6nTaNHwJjXAlUptlvoiRalBRt5oNIZ5nOAFSf+XBCr+W4QYsOSw6JcD1+mokAVhjbJL9uP8+bAqJ2pwFE6hPmTApTnzIBJv9r0jiGtbUh1TqZFxgfB6mZ51rMea3SMslVmspK5xv5TU7tYbOCvV7IP9yJOTFpFNLfm+I8kRYoORfgwa7L1XivMkDpDoCVvial6JJZrJLd0IAMak+yqWFUNkxAbboUGlsTbKTiRJoxOFlpknxLoCqWG9Xrnae3NCClRWnNSpjbg/TGMVJ0p01uHcTalLVSTU3ENh5yx2knTi+dJbRHX6JFyXoBETSb5DQBbHVIUd92cVxXn9sTTG4FMFkmuVWKGnMyx3L4/A1alCUBWq0altKistUmkg3XsLG5U2PalCyT8d1NNDOUtCgLUKPOEq3n09LoOhxAxEklgLxtLuRiaEmlcaZyWREdMYqJxLW3gDESlzbSIn3fnjZzlCiBagXalFWPuKwVohbF5ZValG70Qx5UNo85B30Dkm2GOrXOp5VFn+8plWhQDKqFfz61xmeBSYyfDXWn5H/czfsYuqBvWeE9WGoTPPUWGxpMY2p1cr4lurC1MSfnsp5T7iEmYYZpkwVluUcm6w27tCrtKeSr8lrRmFlyHvVdA8p8OUtlyMx8Yn21taG13tA6a+b5ocyIlaQWnNhC42+TvJt68q61GsJY2ZLG39SGKJ5L5duiIYVwA0jZMVaZK1rUUhoVpt1jZ9UNCFpUSBOukwCUHuupSefiOyx3cJgC2cQMh9ChzUAlNT5Li9JmQAggCfNmds4VydaGVOsNdy6CyhILUBacWh9maQ6bKNmk3TEYac1KxE1yPTfNGUKLEtcvmP20NiXzWUXjPlL2bJWMsfOGjof8HsehTEBtkvIsJ8yONpIBTgO1gus3V1mm8//TxtT+rsvDcckafgmMFKAKcCo5PGixHA7yglnHpeUugSrJY9n7KM+fPG/8P9eiTDPfiiRZCCDZq22JCgrTXwLQ0liUBlPF1Nfi0NbaNG19SLWYemSjVDJLaUAZcDJXfeBymOLqpih5nB6HkkBqMPMFyVxt3fjTkDUWZKvs1Adzg5mHdKBokVYznz7PWJilxbGfh7ivcVVriJcdAVDUoV6fZb2ZkufLl0vPUGAtSmhPEk4Ow/+kBAY0LO2mKhVFu5b3qKt4Kc+Rco2ZKfV1Cnk6W5tahckvMfUBuRbl44paVHO7lWtT4dme4kEsyyu0KcBrVFwn6RJfGosqmPqG71tkMu9xK0QIg44sljmqNldJhivAxHOo01rPEt9YCZ7aOJQ4j7liApCHZ+VbUqNiLUo1/Jk21ao5rWgib/YCJd9HzpFpUWz2Q1zaqOP4Sj5r/IK3VEIUs5ZnLc4Alryz4xpO+js5J6UaFKFSL0q/Z5qTCpsyJpafa4xSzgaVVeZVaMVSawKKWlSTma9mCbI6cHpb9hbrju6wsRMFZyHTaUAVtCjLq48K31chWxtS1KNJpy71/oFcZa4BKlGr1Y3nY4AcEOqmZb1mA1qZybGkLZXMf86lXn3ShdU6t6yHr0PyMIaHu6JNtYr1ck3Q+OqAEr8rY2JSi5L7/ITJsTzhVt6SQvHiuFG9/JmZyniUSseRTxfdiCOgksPHgLQiySaz1uBEhtv2RCmC3d/nDFQB6O1jU+WTp0CSpj7525wTZcw5GtWi5LsgtSmifGiC08hj+av0rpXmcAWqEF/6hHILLUqa+nojzQpla0Oqb+yidT2AtfZ8NaB41rZuXPkGromHCqhzUzae/hhzVQlZFj60MD5lmv820puRD9omupYC2HhZgdHj9Vhdct2UmQ8Ow0oPAV4yH/5in8eVaMP3OUCpcE+t42SjQzEPCa6iR10pvCKZNsX5kFxlgf8XAKXhZM0tQgU8UuSKCMscvxGpXTM9OdfSoixAAfXv8n3Q37XojmyxHpS/Z8GMyL+VFsXHSY8+q+Ma8lPtRIs5s/G939KQIiLQSFcxTkAVmoAFLKUxkV9KJNGw2NzG3nOOXyD1ELRAq9TTt8ahpJmv5ixhmPzMib2dG7Yw1+XRIu3OYTzKV6jvBk8/IHWgYLv3RnuvWkpalNEAmB5+2RiVi1qUNPP5MN4+3FzTrQAruZZdcmoFrwC/hkuUwMifU2+NkUkNTqXXRZRdOlFUpQSoXsGJkD2D5LWfrAxaFmTDqJPaE7VpU4G6S4h/7mQ9pKnP0qJMQFkOCJZwexLaG0rD/DUYk6BNkTo+lIfL3UdAaTNf0pZR2nHtjVV/VrzyxJaGVIsQDVtWVxUp1bgnJj4JKN62AT4/7iHDBWiZ3nSyt6IbS6unVJqo1+IssSLTxqSFIiv5LN0qTDl/Yxnl+JM5/8lB3G9OL44F0kbe5Y1vftLGHr9VJhlNuu2NZr9SeqssaRodkKcxRUKnBqg+xum19mrQ1yvLZ2ZSIDhAFUFVkSnOE7arvQQNEnOX6c6NNM1kkZaSoAEV2ohQ1tjWJKCyRGpQlrmvpEUZGx1uxlKwWxtSfY/RrTnD8j2CVH5lYAC59sH/9diUNA3J9MF27Bt39pYxy8LlNmDF+RnjUOVVuu0HNZpsfKtT06qmCGtTtXlIq5JVmAFbzhE0KGOpIpc2Zq5HemMpvQdh1W8fNwSq3xwmILiR5Xq4Ua5qPqW2yXoeBIszl3Sk50ncy2mIMwHFC6sCA0i4QF08PrmOosDJuoQuLVMGKl1nqVVNkKbdfoH0+Sx9B5o1C+q8N7DWmBLtiQtZaD8KYo5PifKaC0yLeCqAiQTENku2NqRWIdbDoG3Cagwpa0CNG5Q0AJmaLhLqnlHJxCfjgDxOSJOphsHVuaERYRCSA7puMJGE34jOEs6Bl2VJZr0bmmfxRRJ1S0yl/D/s75Ufzw16sSGRmnDFHBK2LuCGlXxdvRl0MKflrV8R8qVrvsmcDafRiqGGYkioj1OQTTIR6UNHR4CiA9wiTRdWrHD+GhL550cUQjyztRU9Snt16Z2Qlx6b8gBbyrmCtWx+XgmxI+jc0Gnlglnvu86LLRghrUsdqiyLS61sVrBcOoxFt2PazFdyhJAOE6X4MdkWa/dNFaFBJQumsnrux5Jcj8G0x+EuX04kHJfkn/7MQGWJsap667blo2I1qDUIy/zX1uAWC9Ca3+yRqTABUGXPSFFEvka6Xtq8oY+TsNJ10pqwuiaOfF/dA4p60Xtn7XMoXCPxJ4rWsloOCdoX/3fJb513EVoNwEpNc4ZGJQHE3x1F7asbNtRL0nZknisrUyWutvnkmOhxqWDy06CimD7PBEHri88LhyGDUwS2eD6T8WqXegarDjGptFOX/ypeCyufiikw0aK0lnUUZHtBSgv3FNbW4m+gqClloGrpLBhQsV6vzAONy4H8e02Liol0r1g0vqw1eQ2KOqGZSJdXBlVpzTANnzFASS1K9qblhMKS1qmvNffsw4usyiCvmeVu66fMkedv0JyGVmVotNZg36ySNHYg5KaA1R68czmEQpwCFyJA0pOl5wzpoNLKx903wgE63LZb36U2xWUGwfkxEL6u2XYiNTCp+ExTEoDSHpPJBO2JMskUzs8OhnvkGHgQcFrzj6blZAUMkVOhxdJQ1iaoyXGmVi3qKMv2g1R4atZUGBCebNam5BiSXvdqI0VQjXQiBTCF41hKx1taiTxGzpWS4GNQcdkkqPp+aJVCo1837xUBVRJ/vtCTt+pS06wsTaoTQORkFBvLQYPi/P39JsCt+XN4poc2wiq+7s2zycaEyoRnxtl5JFqUDDO+10BUTafbNdG3Sa+zDI+aldzZN1usdgKYdDkBZNfbcumfIlqbihmXj7HN+EP9B4uLG+C0EFlaTlb+eBNa/J1z6FUaWWdjfCl8Zfd9DSvrGAtQx4EWBWxHSLFYi6Xq2dja7KeWFNFir3ZtJLTSGWFL51fpZaVbeCBp9AeXcu4i+od1odbns8CjtadKumwnXC6vgFAGKym1nmeoVwWKYr0yBwcsetDa0FI5ONCChkN53pQ/TWjDdblDuLhEVmNaMvu2dHYd0ovhRJhVTcJkYOk0wZwHJLBJv3stSWtTnM6b/SDMY5PBBJThBKQdrVZWScVbgsr/TtLJMvC0DYeoITKUQdHMCQd0NKydKcsZTH6oQytLX3jms3oZMLPS6O9TNEgAPB6VOE1sota1tSHVdajuUNkifIHlPJ+SWs2gKozjZOabkmt4TUPSMla9JXuRpXwS0x8wbBrIPayCaW84Lv1dBVSpDgVYAeWOQTYnTZTNBKI+NcGD2JttuOH3Y5GZJqdgAXDvWsTJuT0l77OCZJpRBicnvufHZCa/MWBJE5WhRZE4ZkxL0tpU0Ft9nMy3VOcg5vilLt80QG10C4/sGnmHEAL5e+wGE3JPcN3QA2yDEFJoAcuZCENBKc3frIwC1JgW1SCb4X4ObHVIbZaIi509wHqmtrYdW6LGSJrGlPSxU2WqNiXqkIDKuahVaS0JsLUnIDUD+nNabvRJWRPAp+VPTKzyZZfHyTpoICYac5r/oFF57bEbesWDlxYAtvn13Ein4IqOHwjaxZAnl9trbbzEElEYx2gWASTzuwBOEVg1zakUJ88vwlu0qeHUwz0YtIsoxbq3gAkwOnl5eZtEgYrDkqWdsnNHLSqdVuCfGyhQEU2HENBmIuS00hSux3WJomu7BlMFUPm1mgasVcr2hhSb/AB71QQvZk9Lwko8FKG3pp0fSo21/K9loz0T63mSJj4JS+2Z2AGAcoMV/6twUmFmnaWMAQvIoSVfXCufwvnC/VGgGuIgNANuADS0EOGwQDQNigY7lI/5Lhr/5sVlw7UTdTa0qGDyq2hQo7Ay4hyiZiTLZ8Eq8/QLGSE1+0kpeOfVr08DxEYAVdtifqoMmhNFbQoGqAIF2yHE5Uy1J4TvnD4pNlF0epqiSRUcJapalLXKhIhbtWw/SMnlkSxTYbh5sEGlNQrOb0STMpc1cvUedXEtOFnO1nAubxhnUIO22jGBe2HBLV9MBtRmPaAMJ5m3Nv3JopXgVBPx4mb1rEgCqiGDWE6fV8ihH+rD4w+JWdAnHMa4fYMltQufi4PXqJL5KUDp9trag4pjULk0Psu7AVaWViXPK8E0hInaiWNZAzfNfq1aUnLucoLaNRoTE1RTRMA4uNeXQAWkDTebhv33BFpAG7iIhjEvDSsNKkuS4QsBKBEfACWPKawwcTRke0GqsPGhuYmfcpYAokkg0ap0D0k+/Jk2gNCIyzELc304IOt96oai5tlTFeFNl81R8hqUXiuM5PiMBSbxvwanJF4WfQws1cjpXWHT3dhYJZ61KLCTDbfYfN+IwjiWhFUcWOd763vWY9t/JIX0JXCNv2GAqAFWFqiy8RcXsytqU7K157Ihgqpcz/ELMg60aeEl9/+qG7qc/yXyN0HFzwNBbOnCAELm8DMJXPzeMqykg0bJDC4lQEcASi99BCB49EkZ8xTcBNlekNKiN/FjlZddrDWo2KRnjVOVJhey1qABJXaEDcXhRpAPTc5BaSPj8y4+JDz3Vg/Uhp4Y6p50YuZ74gJrrRlWglNrmjFRThStYjY4UzRQf+8HOPF9oxxWSTM9NFYJqJDe59ExzKQS/hBh6ht+I9emalpTA5SS4/x/c/wFSLSpBFpSmwqmT9StAkKmjtVtRPR6gvEHsvra0JYekCmohiRM8Jgm5DsVXPD5eDNhAiYNKjnO3CIaUMYisvJ3vqDs5mpU2xdSsqGQ7uhjoPKSj1M1vDEWoIRWFRsI0XPiQ8WcrbSRMUA19sxIUPn0GaySHp3Lj/dSBZP43jTHqyS18Swtwlxb0lCTdD5tlk6afX1Hw7E3V9fZsHIYNKUWUAEpPFgM7cjUoMT38PyIfKuwKkEJRmMMEZ5mYTfaoCKolpYNwqjpFBM0gmzlDTEOOaysMWg8QXtMTHyciQDXcLANLsCAlwPQl0E1NhZV8uTjc4W9oijVnGpbc2yibB9IyTEB+V2YAJOlkqTpD8i9+oA4jyrkiawhzdygLUBxQy57YD6E/wXTnF/XrtSYVEWCR/bOjNUfZJpij7PBxb4pfau0HCNs9gBSsybXQwDI6TD5YgZId4k25byrX9Csus6b8vhxUqCSJr8J1S2BivR3DZ6QQVlDyqCkw0TSYqG1NoUcVHLMqjYOd9zI2A3S75/UqFzsWEpYAf4+KFNctr6iBS4ghxd7DjpKJw0LKIYVZWrSCqiSFiXbhU00+20fSFkSzF/R7BfGp4DYW9Cw0u7n/F2LarjTnjDvWSUaUe6B+QdWwyou2FkBlaUVWGUqldtwRsiXtGnQrFi01jR2nQoyxQwUrpswa8aMFKASu7z4rV9G51JYrQ1LRJE/wfAdcamlMA4zaFthfEquwF+sgLyeScUSDYo/yVpyFVhloCrFKUmWSRKHD/9ts58+tl7fevRKZFLvYPiX18WHFUAFINGqAABr8l3kDm3Mb/iiNaU0OaeJJl8HLHpkk4a1NqWtLMZ3smBkhUmPvqO8XNL2gxRrTqxNKbNfAJUcw7G0Ku8JlqxKAZUfS6ZFcS8cQatie3VYiFPbtkVPbUPbbegyLRsPA0hAfbJuQ/5T5tBY5XBEqetvYawpAZSCU7JtAQB0XdyPx+/S7NClWtWa16zhG/DON+A9ojYl4DBtgq/LYKWPjebaFFYSPslvn8aM28jjxc+oNvv55/eYyBKASsQyqQtQAfKSxgA5XpXk7TuCzfDiMoABRtGJwwlQ+e9YcHoDVDLfkpOEAahMjiKwth+kLCFCeEJ6b/rzjW1Rq5IrEtQGwtlZkEHEDcya38PI7xCbdE8ZVt70FjyGkkFaFOcktEJsGWeE6nhSLb8pUCqslp7naeQHY46KqR7kgHKLPtWkOG6N4IZByyEnX2jXdaAFht7yAgOoKC4FFE0444AylwvK0iBxlJDPFFczn1ycgik0i4ZalGoGOnqCNiXTC1BthqykwwZkj0jJlJ5YL8TwQaa9InY8Y6ZpeZvhBSQA4/HwcL17n4lvi5L1BbVwm9ELADUA6lhpUcB2hZTWpoAIGhkHA1aLxbBqOsOJGyEgNeXoMRo1jyiOS8WGZxiz0PMvgGj759ZE1cfqLVnVHgGpKa3jQBOkBUxja7zlY38+mOItDO7CHaJ7uRyHYgj1fdSe+n4I7/v4QvIY11rntSShuQlQDZ2KPqw0EMx+PUQDNFFRceJTCtcwy0xP8dxFzUmHV8R0omgA1SRpvEhV55glz9FS/ySdXJUdSDuPKm0GpiRNQfMCEoARvIexQ1x9XWpTfKBcbzCpgADPMoBSv0kfv2I54SHlxhrQEqgAE1YA/KrgLmhTrick84i8WA0tm/nghsaO1lxoGHj5GBNUSpJ1wqxno6bdAeNjRQUIrFxqcJJRpXEaHYdh3lLceqMAcMu81/fDOn5EwGIBWgwX3q1RcpyjLm1jum643H0/jFG52LlIGiCqdBSsemkRnRqtmdvAqWhVFVDxuUra1ChAXH5MbW08E2Bj10KVYTKsjGRVOOmxXJTvlzlxGajCa8hPwUm+CtKKAt8B6nwHyFGEE5v8aKTjKsedKua9IqCOomxpSDnnxiEkpeDVF25Kp2+snCsgKUXjEIiFFA1K1KIohMc03CK4BRmgGtGmQtHIfJBGvexKEJi4udyyoucBVctk1CU0UmH9OKr3JLWTBANqsQAFbWrhkw/gcl0X9h6T7fnwu/PmPt+A+EaCiBO2XccIoFjnFngZ7do0UKkylM1dDWY/EvlAwC3LzP8bmZJQhZgBq1FQqehJXrJTnXxkWYKXXqEowdU8L1sCMDb1hRX7XdCm7Anpqhw6XM+JgoATYANKa1GbKFsaUhsWqTEBKayAIrCIXNSmZEM3csOkFgV+uJwLWwWE13/N1UFVOo1+mS2QWnOO/He5Csbw2wBHVqlKnJBSw2FqThacKmCSx/KUADaHJL1MWQ6pRTGUGFCLRQAWgAAmQnopHJehJ689OfV/KDebIJcZliFxb0JHRnwSj1ENIc4DdVDJ48e0qdzUXAAVdD6xQHGCeanS6qcxR1Ge3zqmKDVAhXIt1zEzOxPaY0+m0+9EDWISYP1AfyefAf+ck0PynzvG4Xy+QxZMfVqbQjugjpZsP0jpNdSAVKviNIBaa008wNwoFcxIcnM9Kc3jEZX9YoqmPcuDRzh1pAurivhNlBbTi7l9QpMjigGoSi+axDUIS0FJE8m0kaL05S/Fe6eYpHMSyiPKnnycERY/2tQnQaXb66RD739l41TF9Gl8+t/F+0R5GrDDgI/nNLHuLj2JvGykE+dpMhFpsvtPpXQy3Hh3kkKNvydj2m42jsX5brYm0tB5TpOvCEZNWyi1zerffpBatXhghXEpFoYVITX9LNJ3cBAXAcRjTDS8cENPHzGeGwZvsgqntOb9GKACECfs6lXAuWQLXhyUgimhyeZfGTitekvxeSB73JSUKcze563mfdmla3fTeETnT9oNrTt1brg/nRt2IeYzuy64GWNtMPO5rgN27Bh+d92wWWLXAWvdsArFml+NYkc3jDV23ntTfOdwOPgwJKbf9JOGl4AVIM3XGsZvXzF9vQj83edF8XewKnO4+DiZl4BROCn56+zjCeIZEAVLYWE8Z6r/UNqUMF0txE4D6PORHQ4FndJzJeDVbDJUeeXzHEfiuV2QY6qibXAibBIA/bPOwydENDzbDCoJVBHunItg4/dlxbL9IGWNsWx040QpjaafzEav4BRBhDqg9MPIxwBRY0rqWoGV0GAGc5BLgSWzqT2LYwO2SXFiC2uC0fesTVh1QhOrgUoNJhOPZTo3wAai4+C8KXexGM7UAc5DioGUAGrH2mBi7Lrkfw1Q1HlArQlQdbXvQznCb0vbAgSIkMAmg48GlrwvCWQErAxQxXQCVrDSOXWM74DIfOU98GXT91Lu+TQcr8lif88b+jKcxsJDFqxJ1sTQlJJ8a0DK4BqvrSMCFkMHdujEUnzv5f9q0RzIDct7ofdLpQjIBPiEKTc9mkC1CbL9INUqJswKOr0el0q0KLl0yeD9lZpMALcQ8GmFkzyv1KKk1GAF0SgsYg8WoKiFBw1s5FqVHtCKtSCa+ShOjGZwMBjZtCe60wmshFZlNrhA4jwR4MTAWuuGsSfnQN69PFTWOTgnxqQM7UmDaZWAqpr5Aqx8HLshQ9xTvhRIw8OVKQGLNSwFpTB/yoQUf4RK559Z4kuaHOMEoCjCVJa1AKoMUNWGXca1w6IocurBBIiVzlWCUkkrDOGL2KEFg0prUXwu693sujjeGjIXoPLHplpS1waqTdCmZkhtVIjfdPFbalN+3kww+y0iqIIwrFQvqEl7ylZPSB/ydN8kZCa+UAShRWXQAiK4CuCpmtoKceGcfL6OIhihYMUNKSI4yX+HX9WhKhJOQPA8wxqT0Juk5Lp9fA06Nw1Qax3QAf3a4IbV7xBLYElQGYAKUOrSj2nmE7AKd6uoNanwatoBWMNSTkhBxEAxIJUCKQeWCTZxLyOcIqiKz02xgVeJa1pTCRjycPmitjS+vpEfBdMIlJIwtaxXGBZY9LE9kFqUTxfakVK5vckbvDAAg4rjJoJKX4NRaZzXsr0g1Wrqa73IfR88v9hTLCyRxNqUNPvJiZ69QFuHVHPy+Uk4AUgBJWFUClM90QxYUmsSxwzn4rclgiuREpRqsLIA14lykjNhFbdgR6ZVSVBZOwwD3OgN92VwoQawhqjhrnWh3OSG3qBb9LFFqY0/JWAytKY1Bai14T5oQEU4iTgJJkPDkmNSltks3IkSsEScBBbAIDJAE34b2pWIy8yBPosAGJEHiXtZglOyWG24sSI+gVaacLKJzRXiCzIGMuscVTApKCV5iHZBAspcQFl+HzPHsQYUzBf9ZFBtltlve0FqI1Lz8OGVJvhBER6AwaLBEz3Rw/kJn8H0xy+8MGEU4QTYD6UGlPzPVTCKnqx6zifX4KrJVM0qM9/EdQzDcRJWQDTx8UrjwrssA1VNfJ5hEc41f+0ZwGtdMJGQfgml9uQ2ACi/pYcGVKJRaU1JjENpeCVjUnxJQ31HtCaf2AQUK60BRC48l4l2BZGOby+DpwAs17cvr5Sb8CLkmsEE1LWmsd8lEddrVDSISmCyND99jNSgFt681/dpu1Ay9YXM/fPtfM9Om+qk+a8FVCHb1YNqhtRGhaHEPX/p5ce9IF7XTYKq66LpLzQI/MAa406ArT3VwqUU3F0TMx+L0Jr0CgnN8BkLFwv8ppsK+vPAA9TQqqqgkj1MN6TL1vJTm8SRX9LIcW9BvuTsSt46/uRGAMUQUia+NA6JKS+MQ2l4SS0KglVCU6pqTSKsCC2K303tCiKN/z4OLBePJ4C3QGdQyWemNC6VNeDAhsDU4hmarRYyoS1uhVMRTLptkIDS2pQGVKFuw8r9fAIXQeXPUwVVzMQfZ5j9ViQzpLQ0Okwke0+Rf4slsACEPaYA741GKaicA6C8apKXvwAh/s/eoZaqr+eD1V5CC1Rc9bHjtFTmeGWyYAj4cyEFU4RXqlVloKqdI6uQASo3tMBESF3c2QSozHuDo8WIg0SAiXaSkJoUyoByEWbBtMfpGUziP1CAT7iwKo3WmpCHS2BlnniNwIppEYAEdvToxZJJbP7257G0KOgwoiqYQj0Kx1e9A61mYCMagoTNsmDiNBpKlLqkx3NSeTyKpXPRuy+xHrg6qMyxqM0x+20fSB0N13Nv8gu79vID2Q1aFMgFjYo1B8fHsoyByccBtXj1v0mWfKish3GKVuVBxBqLDSYBLxiggtembM4O0omxLW6BvLkvTg+g4Gk4FJWBJs18tnkPDqYHXzrOlI43xflSMQwCVjJejk1pU5+sd8LsVmgBNrg0ZBDDJIw0sAjcgKZpgwcfIb4jPa+xiKBkxfHDFDL8vMc5QT5iAphkPlbcaPgUSa6heHctOBlgCmmsjqnfsLC67YxZpsJL4gSo+FytoCqY/VouT4tsH0gtK7WLnfXE4u9hWR7xYLK2sODGiOIK2mLAvzqu1AqnFfZiTGkEU7EXx2m9aSyIvE7QYGoDlXm6AKDhJQuOFCzR98WXw2tTRMHUlpn3Em0KyCbpCu0pBVUEVDIeNQIoC1ThuKSu6h7oOEojxrQtC1hQYRpYw7U0tCw263FaASoev5XlMEWeZyPmvLH4CVJcNJgUnIAUrgU4lUz9IV1BmxrdF00Kaz3geyBgBCAx/00B1SaY/WZISWn16pMiN0QUjSwAuEXvxyU6uwFmk5/hzRPyg+75Gd8tTaz0e4pUjjUBNEWD8iZGR1FDybSqrhsH1dj5fKMXVjJg5yXAv3wMJMQOiW8lePWLRHtyyM17rBVVPPgSjUmY/CzolAClx6ECkKzHVgJHioKTpUkl8QJEJrBUGtPUF9Qkf98YTv57gC/i90Tzy+pGaWOu6hzrMBFMY21rwfAyCjhrWokvj+kgBQQIhfxVeO7ZWwFSOM6Oc7Kz2AtQHSeyfSG1rKlvynFs+gP3NEQDLJ0F9CnGoASUwVR7YUZeplH7datpzwoz8/Y9Ned3utVaFc8xs0DF29wrbcqUREuQGpRYfSM7AGFcL2hOFfNepkEJjSjz4NsIoOTYlS+q+QxZ10E+Ji3w8uBKXM1lPhaQqmnijYgLAPOnsnp5re+42WBaNm1ShsLUEqA43hTKOgYnnwdU2qBFjQk7GyWTcdUY1THWprYHpJbRkABUTX1apFquOCZ7jgCEw0Blq5HsZRrRkBo0qFHz20bCWzQrE2jOu373UaviCbY1UHHD6M1+YbPJkpQuc+0eM5y09iQApeGUwKhgvstWiwgQcgFCNQ0q1EWk0Y1xoiGpa1DWUBS8dB41DaqQLihQ4ZwCVCI4eBgKaMFRTOQ/TnwyU5moh5RRMG2yebx5eoll0vPlzYGlboKITwBVq5uCS+Lw4CaCysp7TMb2aPGyPSDVIsuCjCg1+fHDZoAKIGHW8i3ClDGvWngrlFqOXSWErB5VeBHccN380kS8+oNb9HVQhcm7MZ9hsVRX7nUXrnPV2cIf06Q9CfMexHdz/El66YXvLtWSBKgyaDn7vwkfA1ZJnQ2IASIvCS5/Dg3AVmilJ/IaRVKHCKbq29grbcQ4x1QwNc11WlaIkABVupFzfG3uo4aTBpCEkxFuvpda65Gg0mNUJVBJqThRbFRmSE0RZepL3NAZVHyT+B52IfHQmHotivwcoWZtreHGN2lKY2CZqrHVIBR+FqDXeRMPr9qx6Mug4onRYpCdzX6OELWp0DUX5xLXmArhslOhtwGpaU+JU4QJJMvlHAJSbhxIhiY16oKupaRF6ceP27USiKxjqZBWJiahWfEJOorQ64Q2N17ecAAALT1JREFU5aPNN+N4ANPURlgCVS9tVls5xoKTAZ4MTvJ7rawFUAHIgWTBZ0ybWpFsT0i1jCtNNfU5l2pULF6DgoQS0WDamnqeKdIKppaxLQ2iVggliQyYLQByvLCrG2BVAhX3FuXp5YvD2pS+nqwNufT32L5U/HtoQOvaUw4qIDHvMXSUppSZ9ywg1QAlNC5dhxKsTM2xpk2x0lOAVnK4i7+lJsbpg4GBEOZKkYBTAFMJVCKvYVkg67nTdWsA02aa+xhQtaXNauY+5bEHqM7oMu+5em80qIZ3rKA5Fcx+Q9zmaFMnPqRazHgtacbApkFV7NXlwLLPtwJ4FaEz0uOSUPLhS4Oopql1DkA/rKG31g0rMytQRbs85ROjiVJtCjBBZQEqBZGCUigbQ8WFY0raUzreVDDvqfTa5GcBqAVQJng0bGpSAJcJJgaI0pL0eTS4/FSo4bgAMN9hY0DxvXA0/vhzHtb4/DJg2iRONe1cYP5WWhPR+FhTqS2xLCw1UAEwVzXXZr8kO6VNNbVfbW3ciQ+pTZZ85QmkjXynGlvnwtYR5o3srFZAyTIOHYA92Bri+iSMrOOAHESj5kKjFQnlcAP8ebFXA1RBm+p9P7xDhJNYG3HoIFC5w8HzncANYfzPRQku5lL7khAqjT1JqOjVI9R/U3syoJSY/2CnjWH1FrY67lZKyNweAVNIw0FkxxMQV5rwHyfqw2a/sUebHSaslRuydEndrEZdH7MJpBobf6oBSsNJmvsK55okst3SoKIRRwoZZmlTK5StDamu4aleRibmyTcygxVQvmGWtiUBVi3PSGMcC+bLUIBTC5goTZPEJUVS9VRpMk2sH1we3RqGtQItUAnzRljBA0oLZScKR6ruMDtqGlAaTgkkOI1pvtOTceuTcxPYCUhlWpIGGOw4CahmCI2JatlTJ4kIdHOtP8TfOrsAL564Toju6kKbCia/8N+BeJKvJeKZajbjbRRMU0GgyyOBtODnm1ePGIFTza18I+2gfJeUdmU6UgBt2tSKpM0H0Mvb3vY2OOeSz7nnnhviv/e97+HgwYN48pOfjFNOOQWXXXYZ7r333iSPQ4cO4dJLL8XjH/94nHHGGXjTm96Exx57bDW1aZFl5kdZxxgPKxFlH5Q+i0X68bvBhk/ycPb58b3xGQqRp8mO7ZM8Q1k5/WIxPIy9L8eiz+P4E8o/xNOiHz6c72IB4vrJOvprQAvOZ/ifXLNFLGN4Yf1/a9UNXoUg3Z4EQYsaBRSPG4WJuvCfIa7nOVBrcqJuBJb8IItPPf4SbUkeUwKU1rIkoDbpE4DsAOo8WRyBOv8R5Q/H+Xqk5k8jb6s+FXH6/vahOCmgtMZB6hPyUmNa1vtUeseystU/icYU3gNfhqmAWuZTE+7sJk5EnREmvifjuEbb2OJ+PkEma1LPec5z8KlPfSpmsCNm8eu//uv46Ec/ig9+8IPYs2cPXvOa1+AXfuEX8IUvfAEAsFgscOmll2L//v34p3/6J9xzzz34xV/8RZx00kn4/d///eml7/UTWhB5gakfBxX3KsaOqZmXQlbCHLgRycokejwTylOVkkdPYpcWvakxMca2knA50LqG+DKuOei9uhLTBPete8RJvQUZTE3imJpwFRF2NorhTmgW8rePd/4/iXxi+JBfGDcTSZy3YobfEGlczIM4LWsZHcDrv2/WeEoQRZCxlR5ybUWFJQ14/J0fQ/mx1vmAvDE20tiOFuWLtzLvP6lBbZZY7YGMK7VBNc1w2fIea3Pfjh07sH///iz8gQcewJ//+Z/jr/7qr/ATP/ETAID3ve99+IEf+AHceOONuOiii/DJT34SX/va1/CpT30K+/btww/+4A/i937v9/Cbv/mbeNvb3oadO3duvEYtoqHT0ri3wE1JFU6luFIvpBV0Yy7kOk9StmcLVECqc/c8uNPH68beS8uq/NRAnI1Ij2GbecRdixzg1/+LIREWBCz8gsAgoB80IcfPip+g7YBBg+xcAE6I81AZMvRnMDQKB/VbpwFSDzhJso10SmpigiAPGgWWDKPhmkg4BUD1Ir4X4RR/B2gBQlMyTrgBQDWBqeX5Tjpl2JwxqClSA1WLFD0KVwskSybrZV//+tdx1lln4elPfzquuOIKHDp0CABw880349FHH8WBAwdC2nPPPRdnn302brjhBgDADTfcgOc973nYt29fSHPJJZfgyJEjuOOOO4rnfPjhh3HkyJHkM0la7MhTGvcGMQHlXPxI6br4sTMrn2iZcsrz+LzZfDvEOz/e14mPKLeO57CQJaeztM/e/p5USb7gm60mDJL13MmbhRbie2hIh3TcoDofJhvZpLENv30D1KfHojeO02GU5hXiF8P3lo9bTPhY6fs8PDvHyCdPQ+IacuMt6ut/S20rM/WyGFqX6apumO4ys2GSbwUaNRMb17cEqFapjUVNkVoelqUD6l0czX5z3tVJkLrwwgvx/ve/H5/4xCfw3ve+F3fddRde/OIX48EHH8Thw4exc+dO7N27Nzlm3759OHz4MADg8OHDCaA4nuNKcvXVV2PPnj3h85SnPGVKsQdp8TrTkj2Qbb2GIqCktIDJAlqpnGaPsvGhcSlgMlglwFKwAkxQjRc5NjTJw127xht9CZJGUDSGag5LFVQBFpSCg8MEwIIWIBvhPgJNQ21DsLI+CjKT4VXKqwYhMj5I649eXt/8e3Ifsmcg5peEtcAJyNsBnVfIsxFKIV/jgxxQWV56ku5mSyuozHjZwTx6HclJ5r6XvvSl4fv555+PCy+8EOeccw4+8IEP4OSTT1554Vje8pa34A1veEP4feTIkeVBVRufWsbsp47JAGXBqRa/EZnyAGpXUeWZqOuRrJI8fEEyXtW6pldNeDxKlnmj14fUzr0hHJnZb1gBA3XTXz/kE0x/DtHU550FOI0/GMFkJ50L+FhXMAG2hAGpp52v17IyaQxm4nmipiSBJbRTC1Tg77nTRKkMxXGfKaY9C0xZfo3nVoDKzHwq7egiz6sQPX411sEtmfos2YTdeTfkgr53714861nPwje+8Q385E/+JB555BHcf//9iTZ17733hjGs/fv346abbkryYO8/a5yLZdeuXdi1a9dGitouGchW0FCyGGa2oyKlBytsMWLASh3P0ErcUfnNs1xUq8Wh8D+crad0SGoq4MxyI23AgTg2xWZOUqACpo1R+bJT53eo6j2ofJoIJQiIQcBmNbACao1u4ZJNTL8hYW1Hmvm0NqlMfXo8Ks0v/T0FTkN6q4wjjTWQwSlf3Nb4bQFKxrdAsVWmtFcmfCeY+qiSduq4XUU25Cv40EMP4Zvf/CbOPPNMXHDBBTjppJNw3XXXhfg777wThw4dwvr6OgBgfX0dt912G+67776Q5tprr8Xu3btx3nnnTT6/5fJtuoFLOYpmP9OtU4dLs5712Yi0mhGk6VF/VBmdUybAkIcrfJdaZ8lcoK6nOQ9rYy9yMqFSNIhDXGwcY4+de/okzFH5GFVq6pNaAsrjVKapzBivajQDdovhUzT3Fcx2RXNfzYS4go92ktBmUzb1yfGoeA/1fa2Y9QraU9sKFOrdUSa85LwaPoaJL1/ZPIdVcbmjoyEanFl84d0dy2sFMkmT+o3f+A287GUvwznnnIP//u//xlVXXYW1tTVcfvnl2LNnD175ylfiDW94A0499VTs3r0br33ta7G+vo6LLroIAPCSl7wE5513Hl7xilfgXe96Fw4fPoy3vvWtOHjw4NHTlIDVmP3GpBVQrfmssGcySbK1CP1unsHUx+5s3JJMcFH3+QVXc8D3BFWaJbTZZHkkebz4LldOD9+BTKtiTWmISzUqgjT1ERxrT8qsF02EQE0rWkazsuuvr+FIvJFmkozdHgEh6YQSw2VnQMSx+VkCKoQV4GQVr1S3UVOXzqfQmBeOqe7/pMNreU6RVVp/xoBU62yuSCZB6j//8z9x+eWX41vf+hZOP/10/NiP/RhuvPFGnH766QCAd7/73ei6DpdddhkefvhhXHLJJXjPe94Tjl9bW8NHPvIRvPrVr8b6+jqe8IQn4Morr8Q73vGO1dZKSdNcpTFQNdx48xwWoEqT5OS588zrD+0yD/QyD7LcdlqCCX1slEP2+bhU5rmXjEH12Ew39DjPiQZTXKfhNJRNbkTPLuqQY1EeVAOIUlMf3PDcEFIghcGjDEorgJWqYyJTYNXyCDU8MkVwSg0w0ZxkGCWaau7cwo27ynwVcNLhFmx0mhY4yf8aUJx+1WNRy4BqzNSX1HtzgGSJo81cY32T5MiRI9izZw9+4olXYIdrn1uVQUQDQs+DsgBizbbmXrYJITV72wJUCUAtZq9SWE2Wmb8lz0MEXrEirFRBXpsKK1/0gwYi0qLvY3o+3Vo3aFJdJ76vxe/ODYvNdp3fwr0bfjsH6jq/GeHwH86FVSQAJAvHyrqNrojuxG/WVvTKFE7EhxUW4rJKcpmlfLFYp36n/+04l8WFushbJh6FGoCqcJK/a+1cIa4IqQWiWdSbFTsOC7853octvMfkgnwcwT02PENuUW4oJ8NJx9WAs0zaXoWr/+bisSNgmyyldzucpw+/KTl3DM+WS+tl2n5S+R6jR/Dp71yDBx54ALt37y6m29pr902UTKPabLNfK6Dkf3mTjfWxMsnG3NSL27JEiakBWhqeypc1qg7R7Ke0qaL4a02lMQUgNwWOZVm6VV6J4d5lolE5B95DIm4lLzQrwgAoOem3d16biZN4E68+qQ35ib+WlpSmjZ+iZgWUj+N6eilCKElDhfDK9TTC9KrzrpAuH8dDqkV5rSoxAULFyTGeLH+jvKGQjXAC2gHVnK4BUKUyrVLDmjpksBFTX4vpr1Eb21aQMmUqqFZh760BSsNKg6pm9rMeDGuPKyk17TKLw4pfmoJ5j3oQORRNtNpMWBBzfykLVMBg/uPjOgUrNvUhX52C3dDD0hHC/JfEm0BqhxWfomgKnACiEsCypYlkMn255WMCY5FbC1LSzKfMe3LuWTY+xWkAWM5AK4MT0GayE+k2AidAAKpVUxoz+y8rqzT1HetlkY4r6dldaEREI73UWnrLaFQlLaoEKP4exnnE9xaNylonz6pnyXGjuICkBSr+XtCmlh2B732r1ACgycLaFJCAClCwAgKwHMivq+eR1Pt74iAWUY1pgxMFLPhsDFZABJY5bmWM1VgwMgFVARVfn3BH5K1x6VcqxIWgzLOPgpYkYRUcJghR29AaN7uwl2SD47flMSUVb6apgMfSnmowNAvn2tJNFRNOda8+sq7DimVrQ6pVlDYx2ew3RWoAtAAl07MmZYHKktbxqTFtyloJ2YJoSI8UVItFnl/fYdibwSp3rxwS+uE8Laa9iZps6uXHgUh7s6pFJdlq96665l9wqHCAY9gIrWrDsPI/5RiVhpWT5W2AURVe+vERgCpBKEBUhWegkm7oQXtCOsmXUoeJxGVcOFqYMtZI1uKbnB7G4peEU0vZLVmVVmVaYMY6xZsDJEu2B6RaZIrZr7WhLGlLOkzGaVCxtGhTWmrlHNPsxHFBW0jGpJBs3uigGnfOW++PJepAPQ37STWK6wnUomXpVUB4rEnDCsiAJTWsISmNgsqa+MtaFXsDhmPgG9opsCIdr2AVC5t91/CxYJSnUddL/DNBNQIvrkNptQkI8176oWjq2wwwAZmLeXbcRgBlmfWsMpVg1fK+r9j8t5Spb5Ncz1m2D6TGtKmmPKho9hvNyzKnORcAACAOvJPxv1ou4X2D+KA1168AKOr0bwEqC4CsQVFBg1pW2Ay4wbHArPEF8saPzXnhGAxQ4NN7M1+AiDL/NWtVATwjsAIq8Sk0xoBUglEx3vH14hPESyMB5lSaWKD0v1xZQmpJ0R2dogdgEu9Nff4T11AsvBctjXahXbXnXqk4+X+KU0QrnIDN11Q0WDZq6ivluwLZPpACVm/2K8W3TN41xqIGT7GJgBqTmjZVACfgAWVqVYBb+DxZmxJbTsf0EZRNNQgvdz+0eAymiea9ILUOReWaknNGr12s/1fQqrCYBitija2kPUktaQxmoV6huNOBpH7HTAsQkgAFp3EqTfpfalDpShsCUHIsSjpMjMkGwMRSc5RoBpSGUw1MpbDNBFQLRJYx9ak6tMxsam3atjakiDD6FI80cEfFLV3DoAQCCSpdh0azhnw4itriCDizcuk85MCtiHPeA65cRNlzZO3PgcibwkhOo12BLPGyW30O8qqP8yqVpVWRU2NVrGHVYAUgB088LsBBa1d8jP9Hrg6lJg2LG1fdpuqy+DK3gAsKWhJQNTNftnQRIR2P4nlDKwDTUOxKo9sbYRpQlvZkjUvpfMzyLgGo1s7sCKBWZepb9dTbrQ2pFtE98TEnglVJqWHnYhmAGnWasMalaoOeXWO99fiTU2ULGxvGNMNqCkqbWiz8OBTif0Nos00ZGxHlNca74IIiMLRWlY4ZeZA5xPEqQhlWQNnBAjDC7fhRKGWAokocQt5DUX1aJ8GUgyvXoJS2lThMUGr+43lRQouyTH1N0mhxqsJJ5OM0iAqAchVwlcvaUKdVNPo1QI2Z+sxDjGOOh2WRtqyMgGrTvP0sUJXgJaCUaFMTTH6TejCdKguPj2nNSoYxkNicVxo/c5swLrUiqbotsyRmHgRA8ZbtCODxSZ0wAQoNKo5XVTQrAMVxKyDXngIoJTQjoGSZk/oSjcCL4vfQGZHnSjU3BDd7LqfS/GD8JygTH5JxqMyjL2hOgndE+T3cCJQsscx8KrwZUBvtkB1tQFnlTSbtTtQMVyDbA1KWbBRUy46VbERa3FdLD9GUspZgKkBEnYtjUyUTn/Nu6Jab+iZImKCLERjVXqpCnINfdQLkl0jysGKtaoFgAgy+fh5gNc0qQIaQwao4bgWk2lUoe4OmVIESKG2UXY+wHJMDbRhagyalNCgJKB+PACyhRflTJFpM6xhTTUpjRmLFiwQ80pQHrs8KALVsQ181HbbDyTbFG2Ej+W/GKnvbB1LLQKUEKh3eDy9wgJwEoHM+fZo1uyfrMo6aDBIgjcwShwHfpG7Ir4nMJ/GCM8oiJTRacoNBim7oblhBgjrAkQP1EzRTae4RH0cEWgAOPQgdsFaA06jJZeT8nT+fh/cw7uS1KqKhqi43AQ7jVMplHfAL0Aq3dQ+iELpAqlkx7KS5T2psop4l54nMcULMuZbA4uPiBGJOVIAWGFQRWgBME2G2iCw7Sshw6UjRxzi+31zPkhTB1NoxsTz5LECtojGeksfoMzzyEFeOT9bp0+lbvfo2UbYPpIAcVC1u6TWNigjDW9TF4wEPpmmgKi6Pom3gIczo7egyWk4enN6A01AeBaYesdzigS16L0nx41LBw481KtfBddwadKHxz3YDJoKT6/f5hoscX5MeruuG2+qBNSbL9a695uSdHRwYvOSBk2tViQlQegEC6fwqDyCGFZsBJayI1xUsmQL9seCfEj6cr6UtkT6OwvegBQHhWSHW+gR0IpyGYyW4hmNivcO+XX0EVRifIpiAip0SUU5DRseXamFNE3kNQJU6jyy1uY1LaPOxvMsDaYi2rgHXyXA7F4vJFs+1iXOlthekLFkGVDqM+mF+UOfKoALSBt9LMJnVXg4dXpgXlfaEjHE0y3nCgpYOy0A98hJJpwlp8uv7oE2Fbeg7p1z2CxpWT3G5JG64egytVu/AwMpkGSgZMmhCLmhVgzlLaVUJdMh0V495Ka2qAKvEDAhE7QpIgSXBxN9HtCWue1zRIUIqlJNNeZ12lFB2VWnSQw6veF4BJxLaE8V4CSg5FlXejkN3KkZ+q/Z0qSWOGOjLjDlN0famOjxkSRrKp2HUpHVt7gReKdsPUpbZbyqotDbl3DRQARmsml+OEqDGRNc7aEnKXCS1KQkry+23dm7pPMErhQNA18H1vehx+3PVPC7DNaZYRhevi+u9uW/MbjfhvUon/hLQ+WfC0KqqY1V97lSRaVX+GklYsbMGnNCihgKlwBoKsZS2lEBJwkBeB74G/VAGC1wIYSjCK5YLET4KTuG/BJTWomQHxShv2TSeJhvdH8oyfa0CUJa0wKkFHGNSK68GVIsWdZRka0NqiQVmW6VJo+LfEgA+LICKy9l1xksjjpd5DgWQhYn56LjsOHsw02lAVTQn1/MgubPTl8qgJbio+9886Vc6UnRdzNtaQzA5n6FxLnpgbQAfA6TZi6uhLs7XPWhRyLUqc6zKABWS0KhVASmsQBTnPrGDha9+BixdVmA5MMnvuiMlTxNAJUxZGl7AMFdMFVFqbNPgRG2N5Aig2jYurMBJfG/e6n1sObNMy6vDaRRIk5w1CucqACrToqa+Z0vK1obUstKgTQ3JKqDS4z5ECG+D1KhYA+Abm23HbpRN/ueyqfhsh9tWyTQ7AV4Fq+Q392DZHGNJyYvPdUDXey0tmv1C3l1nO3fIMoSy+sP6OHDPoAImjE1MFBNMJa3KApUep2LYEFIvvgVFZUm6rgPhvmRODaGeZTNeyYkifOfxH0iYII5DifOji2Fyiamk38NgCH+QQVGfO1nqiFI4JVoURD7hhNZ7gzxtq9ZU+b6hRWJraaydDLCBdx0wO63Vci2jQflyTy5n43u5PSBVGotpBBUAmO7pFqik6Q+IZi15DqCs3Y3ASZYplCVEWG6iCkIljUp9z8x+rZ6R7Host+6wFkTX9e+ch5lxHnlfegxjU6GMPs2iby+jJaUXRngp2mCC0rLaxqkACG0qNsxFYAG5ZmM5vxCysaVkLAhKW5IwE2lC9eX54J+DMO5kAIyP68Tzyhq6hFLl3E40mNqlPjkeRpxKM2m/JyuvEpxK57WkpE1ttCO67BbupXKPWGOGNEdvLIple0CqJI2gGpIWXLlroOJwNMJqiqqfvTDGw1ObhCzrXgBZYvaTWtQymomhTQGoa1ElSRoOf2253NIJZRXC+QqnlxxMOagS7z9jnAoQDTmphlxrWHySsOp8KJxYDURqHIh5TQCT1lwSa2LoPPjnWXjtxRMLEZtIclwGHJJJUnDkaRXgLFFpNrKlRijXGFxKYVOew1ZAjUFpI899xbuvulzSUYDW9oFUaVmgEqiAuvnPcqRITH9AfFsqsFoskGgz6nx5PVrglNcpLbu4FhVtCs7l41PyHK0vBY9N9QpU8Boqa1ElIcq1KQEm5/etCLBaQkomzMQlvwSqxPwH74FX8P5jUAHBBDjkR1FTCoCSrbgsrNKG/TFNZjwzjYZWhG/SkRHnl8sGZ7vydp6youhZfeR0BnnpK5rMpu35xPWdMnbUGjflGA0oy0qyzLlaTYSGiS+FZWMZVuxksX0gBUwDVSF9E6iAZlgB/iUeU8GzMhd6MCWwWaY7S5vi47knmJgAG6AJPly4mnNZeSsPD6oQBvG9BldZPz24TzTAasXCUwTCklG8kdNGzH9I6xOKHdz2BQQEuIAKvKaASY/z+HLYHnMUzqvDoDtcLH1+H4p3pvjc28FVp5gaoCqu5E1z/lrKnBW2QZsam05S8rpbpRS25DABVcpildYLJdsLUsDmgAqIY1TAOKyAMGYFIPasS9Jie7Z6oFO1KQtmRKkDZYsGpV9O58emiBJQxTjYY1G6PqaJFID0ktzIuFRFshVCJpr/NKiSvAmmdkQO2VhetXqNZrzkHvaqUS/c2+y0YmwqT7w596AoNXNUCVBSe7LMfsdAJgNq2TGpkrQAagK4ViXbD1LAcqACsrlUAMpaFZDCCkjnRtFi+Zd5ystkjUtZ2lQtTP4fK4MYb4raFNmgCsf48waPPzbrUe4l6Zx9/8bWEVxWkk0pVwkqJUGDjUHZ6gpjPXMDTJYZr7wunaGFFJ/RvBxR2xq5B2MzQmrvhaVVA6kWVQKUqlsGqCkaUqvU7tnYqjEyrqfVQymUowAn4JgDCtiukALqoAImwSrTqoBUswrHa4+2iWXeiOg5XkC8BiWznwWoWq9VijyGvZsYVEBa94aXPgEVl51FaoSt0trQhA0enRr/YtNkNx1UMO5FqGgheARQ1fGlYgNeCl8O9G5MG2ZJnCqUti87Gvr9kNfAuH9Lr1heM6VZdSrdhw1okKPr5x2FSbVV817l2dvstfy2NKSGjmP9AlU9x2p7LNVMR8YKFdn59MPUAq1lZOIGjFWtRJv9xkDV8MI4N2hCCaio4C5uOU8IuCbXWabhuVmbYWbSzhp8ftaq/CRiODdMKsY4qIYyi95rQ7HH9jdeGkxJQ1jpiLRIS+c6aMui0kKTdvK9WwjtTHpwyjLqsiYNrdKOxgCl8+J3QUuxg1FpM6Qbegmm0sxXANRRWdx1DFC1ha03AaJbGlItUnQdZxkDFVDXqoAisFhc6WHHoh0ytTXttGTjShVXdBYNJAtUnK5YRp+OTX48puWvXzCVTYWzAfzRXUQbpMn1nb0v19bKoApp++BQQeiKoMrKsYr3ugqgknlPpdON+GZIeBYErEiBSzjQBAMia1+dAQJr3pTWSEqAssyBoVwGtIB0vHnVop0YjiagJsDpaMoJDymgEVTAcrCSx0sZAVcQ36Mulk+Pc0mpgcucFEv1MZ5WUHFeNZGgAmxY6bK1ytgEZpYRMFe1cDkWBfhdh7uhQV2L+ZbGqcJ6go2gMmVCm9AEpsL4TFG7KJZrYmMpNQw2m/YYwth0ivg7MbFy/SC0KkNjcVa5RX2KgEq0TLvjGcqm24EVwSprHzRkJaCOBiis+3+U9o6yZFtACogXdCWwYmnJq1GocE5zaUILXK07B28EVDKPFpGTdg1YDT+NF711dv3YSzK2d0ft/olV3KnzoArrDvaxEe266aCyijJal0r8smCqaVet556ybYQbrleAUQlYAQJINVcw6EfOV5pwbgFKNv7JM66AJQFlwWrq6iz6ulmOEdQrWPXjz3xNauUq5Vu4v01zOFck2wZSLKNaFVA3AaaZraZQQDqpl6XrUB3vqu0cLMvYavKrgaokFrhlmF5RQ/eAk6xEvUraUgmWY55PZt0L9Uqu6wIgB1pbS0EFeE1nqJND1wyqvBiFBlWLpUxrM6wFJu6gWWCyIFZpDCfN59P3hFcdCZAygKUdU8K8NOTvRwvYJZBHAGWOeXIHDsidjMaAZI3njpU/lE+N+/A9qXUKxtqsKe3VFDhtsmw7SAETQMUydRX1ZVXybPHZtAxVt3cgBVHJ5CfiMm2Kz2mBypLSAyvTl4DF51IaVm76qMwVKa3SDEtjNrSq0vWRSZ333lssQK6DW+tyUHkHiiKoiMqmPl1f3a6PaTX6umwQTKaXGWD08ht60iakHKhbG8bu+NkaM6PKZwRIryVrjxaALEBlZRb1Fp2/8K6FAhnWBl1363ka8cpMzpuVjcuVAqq82/bEdqe2JJtVzppsotfhtoQUEC960+B5yzp7jeeriauZGEQZkrJPsYsb2lQzqJaV2vUNDX3fdh49yVHCSd2PLCero7FIU5nPQtcPjh4dAPSDBZH3UWoFFe8grEFVA1LpO5A7Baj/GwKTtfxODT4qrjrPpmPIUw6rtbWyGZUoXuONenBKaGlAhecqTjBvNotvVNijj8tRaPT5+rZqNE3DG6Uitb73mwgooG7dnUUL39RGQJF/AVpvdtMs79FMGsduTPNMY9iqxHiB7EV8y+VPADXWY5YfQ8z7VZpEWTK9kBikr9VBDicSte91VJmcWgSUzKsVUD2p8/Xxo+Ky6yavc6Ll9GkjLBtjbWJsBfZGxAIU15WvCSY01iuWZjPfSB5T2qDk3C2yyYACZkjNMssss8xyHMsMqVlmmWWWWY5bmSE1yyyzzDLLcSszpGaZZZZZZjluZYbULLPMMsssx63MkJpllllmmeW4lRlSs8wyyyyzHLcyQ2qWWWaZZZbjVrbkihM80ewxenTDeU2eK06dPbnTSrrEPLekPMnSBS4LczJ1sikRr4QgwsI+PnmeyQTa8N0K2wTRqyFALRUj99ahPIyosErC2J01l2KXR6tr26/5dea6IZbXnHN+/Tnwb7/2HMS6dLweHXVxKQw1mTe7JtZ3a6UJ+Z0nBVuTebP16qg+mTcrU3lyeXEpqyytg6M1/6x2w8oOroOT13BYVsL/7kC8dh91xetYXRaJ0t96xQlzMi8g3hPxjuh3w6n/pXDz3OL68/lDOcVE6xDeh6yWlSlvcfN5NlAgbr/HJg5vSUh961vfAgD84yMfOsYlmWWWWWaZZSPy4IMPYs+ePcX4LQmpU089FQBw6NChauVOZDly5Aie8pSn4O6778bu3buPdXGOusz1n+s/139r15+I8OCDD+Kss86qptuSkOr8wp579uzZsjdoVbJ79+5tfQ3m+s/1n+u/devfomTMjhOzzDLLLLMctzJDapZZZpllluNWtiSkdu3ahauuugq7du061kU5ZrLdr8Fc/7n+c/23R/0dHavNUmaZZZZZZpllRLakJjXLLLPMMsv2kBlSs8wyyyyzHLcyQ2qWWWaZZZbjVmZIzTLLLLPMctzKDKlZZpllllmOW9mSkPrTP/1TPPWpT8XjHvc4XHjhhbjpppuOdZFWIp/73Ofwspe9DGeddRacc/jwhz+cxBMRfvd3fxdnnnkmTj75ZBw4cABf//rXkzTf/va3ccUVV2D37t3Yu3cvXvnKV+Khhx46irVYXq6++mr88A//MJ74xCfijDPOwM/93M/hzjvvTNJ873vfw8GDB/HkJz8Zp5xyCi677DLce++9SZpDhw7h0ksvxeMf/3icccYZeNOb3oTHHnvsaFZlKXnve9+L888/P6wisL6+jo9//OMh/kSuuyXvfOc74ZzD61//+hB2Il+Dt73tbXDOJZ9zzz03xJ/Ida8KbTG55ppraOfOnfQXf/EXdMcdd9Cv/Mqv0N69e+nee+891kXbsHzsYx+j3/7t36a//du/JQD0oQ99KIl/5zvfSXv27KEPf/jD9M///M/0Mz/zM/S0pz2Nvvvd74Y0P/VTP0XPf/7z6cYbb6R//Md/pGc84xl0+eWXH+WaLCeXXHIJve9976Pbb7+dbr31Vvrpn/5pOvvss+mhhx4KaV71qlfRU57yFLruuuvoy1/+Ml100UX0Iz/yIyH+scceo+c+97l04MABuuWWW+hjH/sYnXbaafSWt7zlWFRpkvz93/89ffSjH6V/+7d/ozvvvJN+67d+i0466SS6/fbbiejErruWm266iZ761KfS+eefT6973etC+Il8Da666ip6znOeQ/fcc0/4/M///E+IP5HrXpMtB6kXvehFdPDgwfB7sVjQWWedRVdfffUxLNXqRUOq73vav38//cEf/EEIu//++2nXrl3013/910RE9LWvfY0A0Je+9KWQ5uMf/zg55+i//uu/jlrZVyX33XcfAaDrr7+eiIb6nnTSSfTBD34wpPmXf/kXAkA33HADEQ2g77qODh8+HNK8973vpd27d9PDDz98dCuwAnnSk55Ef/Znf7at6v7ggw/SM5/5TLr22mvpx3/8xwOkTvRrcNVVV9Hzn/98M+5Er3tNtpS575FHHsHNN9+MAwcOhLCu63DgwAHccMMNx7Bkmy933XUXDh8+nNR9z549uPDCC0Pdb7jhBuzduxcvfOELQ5oDBw6g6zp88YtfPOpl3qg88MADAOKq9zfffDMeffTR5Bqce+65OPvss5Nr8LznPQ/79u0LaS655BIcOXIEd9xxx1Es/cZksVjgmmuuwf/93/9hfX19W9X94MGDuPTSS5O6Atvj/n/961/HWWedhac//em44oorcOjQIQDbo+4l2VKroP/v//4vFotFchMAYN++ffjXf/3XY1SqoyOHDx8GALPuHHf48GGcccYZSfyOHTtw6qmnhjRbRfq+x+tf/3r86I/+KJ773OcCGOq3c+dO7N27N0mrr4F1jTjueJfbbrsN6+vr+N73vodTTjkFH/rQh3Deeefh1ltvPeHrDgDXXHMNvvKVr+BLX/pSFnei3/8LL7wQ73//+/HsZz8b99xzD97+9rfjxS9+MW6//fYTvu412VKQmmX7yMGDB3H77bfj85///LEuylGVZz/72bj11lvxwAMP4G/+5m9w5ZVX4vrrrz/WxToqcvfdd+N1r3sdrr32WjzucY871sU56vLSl740fD///PNx4YUX4pxzzsEHPvABnHzyycewZMdWtpS577TTTsPa2lrm0XLvvfdi//79x6hUR0e4frW679+/H/fdd18S/9hjj+Hb3/72lro+r3nNa/CRj3wEn/nMZ/D93//9IXz//v145JFHcP/99yfp9TWwrhHHHe+yc+dOPOMZz8AFF1yAq6++Gs9//vPxR3/0R9ui7jfffDPuu+8+vOAFL8COHTuwY8cOXH/99fjjP/5j7NixA/v27Tvhr4GUvXv34lnPeha+8Y1vbIv7X5ItBamdO3figgsuwHXXXRfC+r7Hddddh/X19WNYss2Xpz3tadi/f39S9yNHjuCLX/xiqPv6+jruv/9+3HzzzSHNpz/9afR9jwsvvPCol3mqEBFe85rX4EMf+hA+/elP42lPe1oSf8EFF+Ckk05KrsGdd96JQ4cOJdfgtttuS2B97bXXYvfu3TjvvPOOTkVWKH3f4+GHH94Wdb/44otx22234dZbbw2fF77whbjiiivC9xP9Gkh56KGH8M1vfhNnnnnmtrj/RTnWnhtT5ZprrqFdu3bR+9//fvra175Gv/qrv0p79+5NPFq2qjz44IN0yy230C233EIA6A//8A/plltuof/4j/8gosEFfe/evfR3f/d39NWvfpV+9md/1nRB/6Ef+iH64he/SJ///Ofpmc985pZxQX/1q19Ne/bsoc9+9rOJG+53vvOdkOZVr3oVnX322fTpT3+avvzlL9P6+jqtr6+HeHbDfclLXkK33norfeITn6DTTz99S7jhvvnNb6brr7+e7rrrLvrqV79Kb37zm8k5R5/85CeJ6MSue0mkdx/RiX0N3vjGN9JnP/tZuuuuu+gLX/gCHThwgE477TS67777iOjErntNthykiIj+5E/+hM4++2zauXMnvehFL6Ibb7zxWBdpJfKZz3yGAGSfK6+8kogGN/Tf+Z3foX379tGuXbvo4osvpjvvvDPJ41vf+hZdfvnldMopp9Du3bvpl37pl+jBBx88BrWZLlbdAdD73ve+kOa73/0u/dqv/Ro96UlPosc//vH08z//83TPPfck+fz7v/87vfSlL6WTTz6ZTjvtNHrjG99Ijz766FGuzXT55V/+ZTrnnHNo586ddPrpp9PFF18cAEV0Yte9JBpSJ/I1ePnLX05nnnkm7dy5k77v+76PXv7yl9M3vvGNEH8i170m835Ss8wyyyyzHLeypcakZplllllm2V4yQ2qWWWaZZZbjVmZIzTLLLLPMctzKDKlZZpllllmOW5khNcsss8wyy3ErM6RmmWWWWWY5bmWG1CyzzDLLLMetzJCaZZZZZpnluJUZUrPMMssssxy3MkNqlllmmWWW41ZmSM0yyyyzzHLcyv8HOE+kwv0UJYMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.shape(cam))\n",
    "plt.imshow(cam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a91db1d-19b9-463e-b947-6a52725d9c04",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "th, im_th = cv2.threshold(cam, thres, 1.0, cv2.THRESH_BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f2af35d-ea24-4212-a202-1e4b708b9659",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14a11c4bd760>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im_th)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993a34e9-3b88-4c48-8aa4-6291853655fa",
   "metadata": {},
   "source": [
    "# Reading the Artery & Vein segmentation results & processing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "611446c0-4ff8-4daf-8b02-206f508de4ea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_seg_artery = Image.open(os.path.join(\"/red/ruogu.fang/leem.s/NSF-SCH/code/data/regression_segmentation/M2/artery_vein/artery_binary_process\", img_name))\n",
    "img_seg_vein = Image.open(os.path.join(\"/red/ruogu.fang/leem.s/NSF-SCH/code/data/regression_segmentation/M2/artery_vein/vein_binary_process\", img_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "615d5829-35cf-48e3-b43e-2120058191fc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_seg_artery = img_seg_artery.resize((576,576))\n",
    "img_seg_artery  = np.array(img_seg_artery)\n",
    "th, img_seg_artery = cv2.threshold(img_seg_artery, 0.1, 1.0, cv2.THRESH_BINARY)\n",
    "img_seg_artery = img_seg_artery.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c05b080-7a2a-4dcf-bc37-d403de204635",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14a11c229610>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+4WhRcNXk3YjcSnpXNlT71bad+nsm500qXb4bRcXvL2GKLPj7PccmVeJI22k4a6zjuRrueYHSPaMolW6/4oh7ZYqOoeesEXzQ5wd7hyKKkTy9JxUZGUl0dDStW7e27fPw8KBJkyaEh4fTvXt3wsPD8fT0tCUogNatW6PRaNi1axfPPvvsTc+blZVF1r8GKCYnJ+dl2MJOtDWqouo0XH7MixbPR+Q4tiRwEl5a53/tuXmBvavmNNanB932+c2qwlcTnsM5zkRMIz1vP28dbeqmyeBpl5tn7nbVOFLDAMcGTGdi10qcz7C2BjatbED51Qm285TIS5jz+G9QFxhAwiPBzG05C2eNAbNq4ac0LzxnuxXa+2a34pCosvpaHSDV3qGIYiJPk1R0tHU56YCAgBz7AwICbMeio6Px9/fPGYROh7e3t+2c/5owYQLvv/9+XoYq7OjCW83I8rGwtcskSmmdb1op1sr5pj21d/cgNcrdtu0SpaX053duYXip1uNBa2HBB9aEpgsMYPjY8tYTNLChw+dU0ue8Hzra+wxwxroxeBfmQTe6GOvt6Un6qRo5zlcsEPLJccwJCdwvXalAPJZnsSb4xqrJYQe64fPcJRzTd9/389mT34xwLs9U5H6UyDNForpv7NixjBw50radnJxMuXLl7BhR0ZVWVgWNFiz5f3dDUzOUbP8b94kc3rlC+4BD9Haf/E+33Z0LZX5Jc2Z3WiXbtt80Z0ptzMUie/98cJquRBPy2j9fiDRahtboSn2vC7bTGrucuaml9e9EerDxYmh889PP6+hPitna4juT6cfBsXVRTDc+rB3PxGI6f+N1FL0BU/OauLx/gUXBmwHrWK5J16qi/cEHS/qpB79We5IEJfJQniapwMBAAGJiYihVqpRtf0xMDHXr1rWdExsbm+NxJpOJa9eu2R7/Xw4ODjg4OORlqCXWxu4TGfDp0w+86N090WhRNAoJn5nYWXfOLU64+b7SSWMaT89/HVTFti/o9/Qcpcw6Im56XK5ZzJgfvcwetLZdu5p35Y3HrS25rNJGItvf28Cfl9z//Xd9HubtzXG82qxBlP/wim1bE1yOX76fabv/BND84HN4dYvDPVnWaBIC8jhJBQcHExgYyMaNG21JKTk5mV27djFw4EAAwsLCSExMJCIiggYNGgCwadMmLBYLTZoU0nrgIs6QkMkvac487ZKOs6KQ0bAihvV5m6S0nh4ktakGQHKPZKbU/pFahq38e3XYf5uZWIY/4qvZts9c86X8e7sLpIV3N5ptByi/zfpvpV4NuoS2vuP55Z2v8XmpfXd93lV9JnKh143uSr2yN0eCGnSpKeYf/TEnn3mwwIUohu47SaWmpnL69I1JLCMjIzlw4ADe3t4EBQUxfPhwPvzwQ0JCQmwl6KVLl7aNpapWrRpPPPEE/fr1Y+bMmRiNRoYMGUL37t2lsi+fqBFHGPbn8zzdbja+WhcyhiXisFGHasqb4aCKgwNZ9SuzfcrM/xy5kaBSLZkkWkw8faAv5o0+lNqWhBpxxHbcn6t5EkteU/cfIaXFnc85XqY0dboMAsBigHmDplBam00pXc7uzCp6F6rob52EHz70LK7PJ+EVX/ir+IQoSPedpPbu3cujjz5q275+r6h3797MmzePN954g7S0NPr3709iYiLNmzfnt99+s42RAli4cCFDhgyhVatWtsG8U6dOzYPLEffij9o/0KbzcNx+zF2XkrZGVaIf9ib1kTT+aGaddunfhlxqQni0tUDBuMWXsvNPEJB6HkvmiWK1KrDp0mUCv7wxtuqd754AHy8ufmZAr72RlDQKrKszF19tztZlpDGV9GWBOMVHFljMQhQViqoWvbucycnJeHh40JKO6BS9vcMpGprW5sxzzpzuYW3t1P/fQPxmPNi3do2jI5oAPyJ7lePooOk3HT+SncELn42i9G+XMZ09l5uoixdF4fx7YSx48UsaGLS2YoyFKT4sbFITc2KSnQMUouCYVCNbWEVSUhLu7u63PU+SVAmS0r0pOyZbk9SR7AzroNUFFzGdi7rn58js0JgLz5k4+Nh09IoWh/+8/7V398BjgTvOK3JRhVecKQpaT0/My11YX221bXflRQOo9LoUS4iS416TVJEoQRd5w3PjGZ4+9QS/hPxGDYMTNR4/ScZ391Y1qSsVyLWWFfj0o5n/LMOes0Kv9qRB+Bw1Uuavo0Vq8GmBU1XMCQnErqgGb93YbfE1ojg4oBbEqrpCFCGynlQJYo6L4+KSYBLM1jFAyyv9wdFxZW57vtbLi7OfhXHm86b4rEgn/PPrCeqGOrt7UGnpAMr+cgXDb3skQT2gU49/C9Ur2zsMIQodaUmVMH4zdxJWahTHX5kBwPjHVrKY/1RVarSYH6lDcqCBwz2n3tSlB1B1zkCCf0mlzKkozInHCu3Ep4VVqR9P8EhUf9bN+ApnjQGtoiG5qhtu++0dmRCFi7SkShpVpcIvKUxLsFbdNXE8x8nvGqIrVxY0Wk5NbcLpBbVZ+f10wj+feVOCanqgC9W+GUTwymTUPYfkZv8DMl+Nx/VwDKmq0bbvk49nktnhFlNZCFGCSZIqgdS9h5n8V1vMqoVqBmcin5hNVLcgkp5vxK9PT+HMY3NvOdt4jfCeeHePJej9HTnGOIkHYzp/kT4PP8/7cdUBeNgR4upK54YQ/ybVfSWUxsWFM3MqcaTFXPSK9pbnXDWn0eNkd+JWlQMVSi86nr/TKZVQV0Y24+Dr1lL+4VcacvIJL8xxcXaOSoj8da/VfdKSKqEsaWlUmKrwc5r3LY//lOrOc/2Go+sQT8DUHQRM2yEJKp+U3nqjy/TDgG0oLjcvSyJESSVJqoQ6PaUpHb7dQlfXnPeUdmcZqbR0ADP7drZW66XfvO6SyFuKyUKUybr+kpNiwGNhCunPyjyWQoAkqRJJaVCD4Y+vY6hXzpVn0y3ZvNe2O5VH7ELzl5SZFRTL38do+fPrgHVJkEXBm0ktc+suWCFKGklSJUj2E42ovU+h16LfbAkq1ZLJrKTSNB43kKdeGojl/EVZD8gOQr+Kpd5HgziWbW25vj70R05PbmrnqISwPyklKiGSezQlrVsSEwNztpB+TKnAT9UDbCvYSnqyD/Ops/ifOkv7RkM522YOPd3iufj4RrbUaYjl72P2Dk8Iu5GWVAmgPlSX7yZMtq4o+490SzZjYury3bsdpeVUiOgvGJiVZB1cPcbnFI0XHETRyXdJUXJJkiquNFp0FStwcm4DEsamU83gbDuUbslmW6YLfzd1wHWZTARbmFR4J5y5HzyNWbUA0M9rF3EvN7JzVELYj4yTKqaujGzG3KFTaOBgyLF/dHQ9jvSqgpKchunCRTtFJ+5E4+hIysrSbKu9AoArplRe7DUUzVYpZhHFh4yTKqG0Xl5EfhLGU7225UhQRtVM9a8HcfjFqpiPnJAEVYhZMjOJS3Llqtk6WW8pnStOH1whbkAYKIqdoxOiYElLqhjRenowKmIbzR0zc8y59/ixDlxJcidoYBzmmFg7RijulcbFhZR2Nfnw029p6WTt+kswp/N8+5exHDxu5+iEyD1pSZUwGkdH1PKlCdEn5UhQUaZUdKPcKNPpiCSoIsSSlobL8l2MOtqFSKN1oK+X1plTb948p6IQxZkkqWIiuUMdfloznyCdq21f4/3P0XXc61gOHLVjZCI3/J49S7v5o23bT1aRiX1FySJJqojTODtD09o0ejMCZ82Ne1DHstPx+MwVjx9kSfKiTDWZcIhXmJVUmgNZWbzuv5mqe/XQtLa9QxOiQEiSKuKym1Zj/YoFTCm117bvoimVl94ZJdVgxUTglzv4qZo/Q0e+RqJFx9TSe1A+jrd3WEIUCElSRZg2pCK1Jv6dY9+BrCyefW80nt+H2ykqkV+cf97FgcyyAMyt/CPN/s5G4+Zm56iEyF+SpIooXdkyVFkSlaMFVWvX8wx6cxje30mCKq4mT+9KvwsPUUrnynCfCC6+WkvK0kWxJkmqiIp5IihHgorIysbjBzfcfpR7UMVZwNQd7FhVhyRLBh4aJzYPm0jUOzJ+ShRfkqSKoNTnmvDNW1/m2Dfh4pO4/CRTHJUE5T6PYHOGHwC+Whdm9J6J1uP240yEKMokSRUx6Z2akNg9NcdsEuNianPozxA7RiUKkpqVxZdDutMgoivplmxaOJrIWu5BQu8we4cm7CC+bxiautXtHUa+kSRVxDgOvsyRsIW27XnJ/hzoGUqFt+Q+VEliWL8Xv2fPUuPXIWgVDRur/0LvMavtHZYoYNoAfz4eO5uLrT3tHUq+kSRVRCh6AxfHNaN94CHbvtHR9fhkaWfMR0/aMTJhL6rJRPBPZn5Js85wX9fxPNltG9o5KlGQjo2vgBEtvoey7R1KvpEkVURonBz5+dWJDPc6Z9v307bGlH9XWlAlmf6PCCYP74lRNfOQo4Zz3VW01avYOyxRANKfbcL8J2bx9pFnMKzfe/cHFFGSpIqImB418NPcqOA6mJ1JmS32i0cUHs47z9DxZAcAItvOoeL883aOSBSEp//3B2EOZkq9abZ3KPlKklQRYHqsAQHdz+OlvbFwYd8PRuC8Qqr5BJjjr0F3Ew8fehaA9wI3cWZiGEqjWnaOTIjckyRVyCk6HaNn/cDaqmtt+1441xL/zZftGJUobMwxsTi/7UqSJQNfrQune87A/EmivcMS+UBXKpBTC+pjUTXU/WooRF+1d0j5SpJUEXMgK4uEzo6YIqVLR/zH/mM8/1BXfkqVMVPFWVzbYE61mo1Z1VB2wg7MCQn2DilfSZIqxLQ1qpL4SwXqO1wDINWSyZHs0lgSEu0bmCiUVJMJU9RFzmZbB/q+WWEtUctqoXGUNaiKi7TOTfji3a85Y8pgyfxW9g6nQEiSKsQiO/uws+5y/LUuALQ9/Dw/NKyOJTPTzpGJQktV+eXd1gC0cjKztelMUtrXsXNQIq8kVdISb3ZleN2nKDW5ZFT2SpIqpLQ+3ozosdK2nWBOxzDFB0tKiv2CEkWC219nCXt9ACeNafhrXXj/s9mYW9a3d1gil05PbsqAl37lYEYQ5sREUFV7h1QgJEkVUqdHV6W/x43iiEzVglPEOfsFJIoMc1wcnisOkGixTp3VyslMpq/ezlGJ3CpTI4bBnhdY/94jJSZBgSSpQkkbUpGvusy2bY+OrseLvYZiKeY3SEXesWRl8VavfnQ5Y+36Cxu3G13FCvYNSjyw9E5NeL3ieh59uR9um0vWDDMlMkkpOh3xfQvv8gZnXgqgjbPRtn0+3RvN1v2oJpMdoxJFiqqi2XaAM4urkGrJZGLgfkKXRaGtHGzvyMR9utYnDEv/OEbv64LDxr+LfTXff5XYJPX2m9/T9lASaljhuqmsCy7Py0//YdtekuJF4uiydoxIFGWllp0ixWL9cvN5qX1kl/Oyc0TifiW1SefrqosJWOSIaiy+c/TdTolMUgCldQmM9D7L5dGmQtWiOte9DGN8TgFgVi28v7gHSvjfd3mUELempqTwyI5BmFULABljEknu0dTOUYl7oeh0qGF1GFZnE/2PvoDTqt32DskuSmySuu6vRt+S8XQje4dxS5MTQqjw+aG7nyjEbVgyM6n08mkqr++PWbWwvfYKrtYtPF/KxO2d/qQhXy2ZzqUsL/yGGu/+gGKqxCap41mlSLVk4qV1RjskBq2nh71D4txHYXTpvtW2HZnhJyXnItcs6elU+zieS+Z0APq3/x2lXg0Z5FuIaX28MXub0KKyZl5zTGfP2TskuymRScqSmcniRtVoe/h5AP6o/jPHPq5q56igdOPLvO93xLZ98o3iu9qmKFjmM+dovWg0AKO9z/DDL7O4OETGThVW1X5PpEPtvxn6eG9KTS++y3DcixKZpAAsKSkYvvAmwZyOVtHwVZsFKPVq2C0ejZsbXg7Wb7pZqpFKm/pg2H/GbvGI4kXRajE53Rhb46t1YWjflZz8phHakIqF6r5sSabodFwa04yXfbaz8adGmE+eKZHFEv9WYpMUgNO+82T+c0O5vXMmJ4Y42W1l07huNVlReQMAq9J8CXnpEObEJLvEIoqfSyMac6zLNGrtep7g1f04aUyjv8dlIjt8y8e/L0bj6mrvEAWgqVSB3UOn8NS6YZT9pGRMe3Q3OnsHYE/m+Gu0mvUGv/f/jLI6VyLbzSZY05eqWxxQs7IKNpj/fJFVzcV7ITNRQDRa1CY1adN9J4ezVUpP0MLuPfTs8zpzxn9BbYMj1QwaWu6IZn9yOeLHBKFLzcZy4Ki9I78rXalAjMEBdz1Pe+hskbm3e/5jB5w1BiovMpaoWSXupEQnKSxmyn24g7bmNzg45Cu0iobTbb7lqZDnUQ8fL7g4FAX1X23aMZu6UUUtmeWmIm9p/XyY8+PXtAwfxK6ljXDZbV0o03tuOK9mDietWxIHGy+2DnvwOQVLYXeWke5rhwBQYZUZ/YaIWz95QX+I/tMlaXm4Lme6Gqhb6yxrKs+/68Nq7uxJ+pU7txQN8RrKv7cb/ulZyaGArtPYugEf1f6RvlHNMURdpcgM3f93V3E+vFclO0n9o8IPUSzs7c+L7tbFw2Kae1EqrXyBrdl0ckYjFrf5CtACELS6QF5WlAAnJpXhlMmVst/o0W3KuZKz++KdGFIaE1EnmwYOBtv+xg56zj77DQAHnszijNHvpudde602pz+6c2GPy9bjmJOTHzh2S4t6ZHta5xy06BRav/cXNZ0uUk6/m8YO9z4X4eGmC+96Tqolk/Xd/W957N15L+B76NYpw3X/JUwXL91zLLej9fNjxMwfaOKQwFuralP2/I5cP2e+0WjJeqI+qtaanALGnKGb/x4APpjxAoFf5G3siqoWvTZlcnIyHh4etKQjOiVvJs60NK/L2c6OnOk2E4DqXw+i3EcF84dyfmktjjf/HoC+Uc2J7uEjixqKPJH6XBM0JnD+edctj1ta1KPv7JV0d0sgeO0ruJy2JquK7c7yS8hvuXrt1j1fRr/rwXskQv40MrX0nlseizSm8tQ3b6DcouHzIDL9LJzuMfO+H1fv40EEfnfgrudZ0tNvf1CjRVe+LPP/XETjFaMIGb6r0HX1aRwdQWPt7tF4uPPVzuUE63O2TisvHkDFnzJQdtzb5AMm1cgWVpGUlIS7++0X6pSW1D802w5QyVwHulm3f+o3iX4nRuCy/Nb/ufNDkiWDnb/UpmxkIf4WJYoU12V3/vtNquRIdzfrXHBl12pxXmH924tKa0bLDs/c9fmresbwTdlb3+CfPm8a6ZYH/4ipolcAa9KceK0Say7Xsh2LTXYlaOLePKt804ZU5OGaz6JR7p4cfgj9gbI66wf0qjc+49qoO39RvmT24N2JfdD8J1Tnq2Yc10Rw9pPGrOw6mW2ZAVT+MaPQJChNzVCuNrJOo9Vj1Hoeczlm3a+oBOmcAeu0bTPPP4JFVagyMxbzqbN5HockqX/RnbpI84Od2FZ7BdUMzrgPvoDl1/wtolD0BhwM1q6ESKOGoCkHyKMvh+J2NFp0/r4AWJJT7vwtt4QKmLYDpt39vIvlytKw7UAAjK4K37w2DTfbp7EGvWIhVO+AVrn/QuJIYyod9/bGsNoT34hkHPbfGENYDsjLj3LzqbM4tb23c7t2f51sV4VrtVR+7fgFesVCsE6Ls8Zwy/Prkkn78TNu2v/a5UacOVyWlV0n03H7IAJWGnDbsz9Pr+tBKA4OaL08Od3dixMv/ztuB9It2USaLHQ+3Y6L31fE61QmDlv3A5BfpV7S3fcf8X3D8O0ZxW+hazCqZqqsHUDVgfk3A3n6s03446uvcFD0tDz8DI7tL5f4cRH5JfW5JiSX15LlrbL1hYloFYVH5owm6L2S23JNeCmM3R9bP4haDHkV5xW56znQuLjYuoUAFEXh2KSqGDzu/kVPpzOzo8lsjKqFh3e9StnPtWj2Hiu8/x80WjQu1hbF2TdrQuW0O57u6pzJ7vpLbAnbqJpJsmTiq3UhyZJBlmqhyZoRuJ7WEbTwLKYr0fl+Cf+V2aExF7saCW/5Fc6KFleNdVaShw524mqSK5xyoeKnh8FozPUK4fnS3TdhwgRWrFjB8ePHcXJyolmzZnz66adUrXpjtobMzExGjRrFkiVLyMrKom3btkyfPp2AgBulolFRUQwcOJDNmzfj6upK7969mTBhAjqd/Rt2PnPCyYhpDLNAr2j5o80XDK3QC/PpyHx5vXQ/DQ7/JNr0RaVwMJ7Ll9cpbrR+fuB956mszvTyo9Fjx2zbH5eahp82m0PZvuzL9uWXa/UIXhybb98ASyJL2s0f1FX63/q+0n9p3d2JOWShtFZL9nlXtPsPYCmsCQrAYraVtld46+5jmjTOzrRsOxAUUBVo9e42GrtYB+w3crCuohz59CwAxvSsy6UMT8LPBlP1f9bXUC/H5GkpvaI3oKkYBMDp3n40bnmM/oGzeNgRjKojv2e4MObbl/E6Zcbjj2O4Jlu78gq6p+e+ssLWrVsZPHgwjRo1wmQyMW7cONq0acPRo0dxcXEBYMSIEaxZs4Zly5bh4eHBkCFD6NSpE9u3bwfAbDbTvn17AgMD2bFjB1euXOHFF19Er9fz8ccf5/0VPgCXyCQ+vlqVcb4nqKR3pcbScxzpGYL52Km8fSGNlvGj715Cay/x/cJwvGbB5aeCuy93O+pDdTnX3sm23bjlMeaW//GOj9GgoFU0PH3qCQ4dC2L7wSr47NXi89318v5ssJzOx6gLP6/jacxKKp1jFWh7sWRl0e7nUXzQbhknuk8nxDCQkKH2/9vLK5b09BwFLDt/dmQn1lluYoY0IajzjWKVTwMOWE+qsAXjo9avUfV2vUj2cXcqz4/DfOLB/241bm6cfaMm2b5mjj/9NRo0tv8rADMTyzBpXQcqj9tPmSxrL4M9v8jlqrsvLi4Of39/tm7dysMPP0xSUhJ+fn4sWrSILl26AHD8+HGqVatGeHg4TZs2Zd26dTz11FNcvnzZ1rqaOXMmY8aMIS4uDoPh1v26/5af3X3XaWqGMnn1d1QzWJvzdT4dROCXedstpA2pyFvrl/OQo/WPo/G4gXjNK/hR5roKQWSX9eZ0Lz0Dmm2x7e/qvp9rFgN/pN6YLuq7la0JWp+B/tRlzDGxeRaD4uCAuVG1HPtO99ExoLF1wt06TlE84Xxzl1GSJYOnBw7DkHD7b9yGM9F26TopKpLXVSK8zk950t2XFywby7Gh2q8kWTIImzmKch+WjO5YXalAsisF2rYvDjNz7KHvbzpvYYoPF7O9+fnTVridy0S759hd75trvbww1ihPdDNn+vdewyDPyBz3CY2qmZYjBuN8ORN9fFrefyG/hQKp7ktKsk7b4+3tDUBERARGo5HWrVvbzgkNDSUoKMiWpMLDw6lVq1aO7r+2bdsycOBAjhw5Qr169W56naysLLL+9UtIzsXYi3ulHj9N5zmv06fbekZ7n2HlyM94rNpIqn8WmyczEusqVqD6j5G2BFXQtF5eHP+wCqpGpV3Dg0wv8wtg/WO9wYmyQG3vG2XEY/qegr7w+LEOnD7ZOM/i0bgaOf7Y7NseHxfTkNeW3bwOkmKCipsO3rKb6boiMyhSAJCwuCwPP/csf9b6mUz/ktMZa7oSjeZfX6b8fBtT5fRA23a/Dr8z3OskPd3igXjGfHYKs2qh6pa+WOINVH3r6E3dgdrKwRwb5Yd32UQiGswDrOvUWVA5kJVNjyXDAFAsUGnDccwJCYWu+/uBk5TFYmH48OE89NBD1KxZE4Do6GgMBgOenp45zg0ICCA6Otp2zr8T1PXj14/dyoQJE3j//fcfNNQHoppMlPvfDlY0q8to7zME612JfHoWjy/ugyaXSUpbLYSop/xYE7gyT2K9V7rAAFKalgcgw1vLsWem2u6HAaRbsunQeyAOV27f733+f3qOhC1kQ7VfodptT3sgIVv6EvLhrSvtlKRUgi/dupUp1ZDFi8/scLQ/efH25lq4lEtB6+6eq0HBRZXTyt0Er7yxvWV6NTa7NaDa96f5vNQ+AOssOY/OJcmSQZs9I3FIyZliEkJ0RHacnmNf3SlDKLfmKhhNBJ+68X+qsCWn6x44SQ0ePJjDhw+zbdu2vIznlsaOHcvIkSNt28nJyZQrVy7fXxfAstifyOqplNU5oVe0ZIxNwlymKe6Ldj7Q8+nKlKb6wtOsDVx20zHHHtEoCw15Vs0U+UkYlL/xoR9aOoY/Q2bZtrNUWJPuyJuzXrbusEDZ3Ycw3+HmrN+cRtTaNei2x9PKm9n99ORbHtuaUYr3Zr9w28eW/Tsb89GTtz0u8odlsT+ptXJXqZXXzAkJrPqhBYdGTid4Ur97Lr4ozkwXLgKwbUoYtUpZexXKPHGeH0KW4qYxsOvTm8vczaqFq+YM5iTW5YfvHweg3JqrRer/2QMlqSFDhrB69Wr+/PNPypYta9sfGBhIdnY2iYmJOVpTMTExBAYG2s7ZvTvnvHQxMTG2Y7fi4OCAg4PDg4Saa57f72TI750pvSqVb8ttZ1vtFdTp2gOv1ff/7U5TM5RzHbz5JWA1t5qAfn7o93TuOxrHRBW3JQ+WBLVVKhHbwjq9y5iOP9PX49at0yGXmrD55waU//YUpeNu9PnfrVXisHYPpdfe/riiN9D7f51ueUw1mSh9tWTcXyhKfNafIf0jM7ENNFRYYe9obii7IQHzCAsTHlnO/LrtisSktwXB8/twPP/5tzLDjd4unTg9pCIRL31hKxm/bnJCCFtaV0Y1Gikdb/8iiAdxX0lKVVWGDh3Kzz//zJYtWwgODs5xvEGDBuj1ejZu3Ejnzp0BOHHiBFFRUYSFhQEQFhbGRx99RGxsLP7+1g/TDRs24O7uTvXqhXCRP1XFFB3D6eTyXDGlUkrnyt+NF1Nxal+qDjp2zwNBtV5e1FhwgnWB+/l3gjqWnY4RDZ4aE8F6V/a9O4MrplS69emF62vae67iyXqyEZZhVwnzP8OnAT8BcMaYysFsHT1mjcQ5Omd9jP+mS5Q9tyPP/2BVYzam6Jg8flZRECZ3m8vUd2qApXB8jCnnL1N920scbD6Hd573oOLfSqGZjaGwsKSkYElJocK7V2l1YgSZPgrbX5+Mq8aR5gc7wbd+uETbvxgmN+6rum/QoEEsWrSIVatW5Rgb5eHhgZOTtTx44MCBrF27lnnz5uHu7s7QoUMB2LHjnyxuNlO3bl1Kly7NZ599RnR0NL169eKVV1655xL0gqju+y+NoyOn36vHqRetTWqjaqZOeG+8lrriuvTOrR5tjapEPudDxCtTcoxKH36lIae6lMESe5XMFtUZNm0xz7ik2o5XWjIA58says0+ctPaUlofby68HGodc6GB7179Msekm9MSyrPmpYdRjp61JlL5zy1uQ+vujutaPb0DtzO1SuFJUmBtmZ+aW4N9LafT8uNR+E+Xlvid6MqU5oedy/HSOvPUyXbELKiA93eFc12qe63uu68kpdxm9c65c+fy0ksvATcG8y5evDjHYN5/d+WdP3+egQMHsmXLFlxcXOjduzeffPLJPQ/mtUeSAtBWrUyZBVd4M3A9lf6ZXDEiK5sB/xuG99ydt0wEGkdHqm43MaXUjSWgzxhT6TLxDUptiM3RUlLD6nCmmxMznppDG2ejbf/wKw2JSvPizIoQ6nc/BICvIZWJgftt51w1p7EtM4CPTjyJ02xPXM6lSveIuGdXRjVjwsDvCl2SAlCb1WHI/GWU0SUy9M3XcPvxwbrCSwJFb+D0J/XZ03UyXlpn3o+rzo46dx/WYw/5kqQKC3slKQAUhZNfNyLymRsFCFdMqTzz9mg8F9z8jSXxxTB+/WgS/loX277glf2pMuj260Vp6lRj9dqFN813ZlYtt50DrcuZ1qQ8kmBdD6fo/UqFnRXmJAXWcXSjj+5l5NRX83y8YnEUtMuFb8ttlyRlL3ZNUoAuuDzRj5em+2u/85rXcRwUPUeyM1ibWpPvlre1zgWnKLhs9eV/Qb9Qw2DtCj2YncnHl54kpZf7HcdaaRwdMTcIRfO/OB72vfU9qb2JQaSMK8P1SZu1SZlYCnKhRlGsJLwUxtz3JtN5wUjKv1v4uocUnY7QXfCExyEG/tGbKgNkUdA7yezQmE0zZ5JgyaDV56MJnFL4ErskqQJwtX8Yq96eaJu2H2DUlfqsWd0Uz1MWFn40ydYtCBCy5SUqPn/ADpEKcXd+Ozwp5ZjEka4V8m2uytxI7dqU7VNm0iCiK74dzxTKFl9hoa1RleXrv8dZY+D5yEdJ6mwodAVN95qk7DPdQTER+Gsk/dv2IXjdK7aZGj4vtY+9fSfflKCmJFRAe9rpdk8lRKEwMXA/xjKe9g7jlhzjjfS78BBLa39H0/2ZKHYallIUWE6cocvjPZmTFMii4M3Ef+dm75AemCSpXDBdicZ87BTV37rASWO2LVG5ahxzJCijambRZ+0KZTeKENelGgv3h75uYwSX2upIsegZ6r2by4MbgEZr77AKJdVkwnzsFCkW67ipih7xdo7owRW7JGV5pB7xr4SR8FIYSgEt/WGKiWV0k44Mu/zQLY8/efwZvJfsK5BYhHhQma/7Y1TNRDcpvC1+c2ISg94cxlGjC1tHTML0aF17h1SofXO0hb1DyLVil6RO99Cz6/2v2fLhl6jrA8no2BjtHfo784SqYo6JZc+MepjVm+dsyDDq83V1XyHygnL8HHXCe9Pnxd/sHcoduf24kzfHDcBB0ZH+emL+//8uwsp8bb1nP6r0etK6NLFzNA+m2CWpam8cJyLbjLPGwPpqq1n79VR0vzqjK1f27g/OJb+d8VjsvvizEA/GkpKC+YQbdZzOY3nk5tUIChPPDScZfLEV22ov4+T4QjhTTSHTwMFAcvmi2TVa7JKUOSWFl78Zxso0V8yqBVeNI7+E/IZ5voqmZii6ihXsHaIQhZY+WaGhQyqne+hR9IVzfA2AOf4a+xfVItmSicXHKEUUt6HNNPFTqjtZqpEsb7VI3sMrdkkKVaXMJzv4pn49qiwdRMXf+2JWLfwWuobV6xdR/sd8XPwuPpGqKwaxM1NKY0XRVObTHUy7Vo+THWagCalg73DuKGDqDhovGsWpx7+F2lXsHU7htPMg39YIZXOGK1t6TUTr52PviO5b8UtS/7CkpFB5xE6qvRPDO7F1uWJKRatoeCfgD859GIa5ZX0szevCbaZ6ehDmmFhChu5ia1ponj2nEPagQcnT/xv5JeS7OLZk6omv5VogXfpFkWo2Y0bBX+vM+el+aP387B3SfSm2Seo604WLRNTT0G7iG3Q505pSOldOvDyDPxZ9x9cLv0br7WXvEIUoVIyqFgsqp3t52zuUuzKfOM2rK/ux58MZnO0bZO9wCi2zqkGraDgStpCrT1a2dzj3pdgnqesCpu0gvZNCg4iuvHj+YVItmVTSOTF8119ceLsZmrpy81UIgL0dK1F1Yz9atDxk71Duiet5DVdMqXc/saSymPlsbC+2ZBTNj/uiGfUDMsfF4dvhJHGPZrMpwxsTZto4Gzk6aDptF+5AaVgzX24WZxgLZryWEHnBdC4KUvToNOZCXTxxXcDUHcxJbIhivPu5JZXL8l28srM3AGYHilShSYlKUtdZMjOZGRZG7XmvMSamLgDDvc4xa8VMohZXIb1T3o0nSLVkov2p6N2sFCWbx1EtbwRs4MTM2mgcHe/+ADvb8Whpyn9xwN5hFGpV34jhpDGNVW9P5MRXte0dzj0rkUkKwHw1ngpvhXOoe2VqTxqEUTUTpHPlaLMfaPzWHnRly+TJjWNXjSOG7oVrYkch7sZ/ejgHskqzp82XaHyKwL2p+Gv3vEp2SWVJTOKs0ZuyOlecvDKKRCsZSnCSus584jRlF56m6sZ+NNrXFYDPAvcye8ePnPtfU66MalZg0ysJUZi8daAjzoqemHbl7R2KyAOWtDTGTu4LwN9h8zkxrS7GNg3tHNXdlfgkBf+Ujr+4D7/no2nZrx+1vx5iqwL8+bXPUBtWR+PictfnUfQGNLVD8dDKNzpRxKkq/gudcNYYcO16xd7RiDxS+pfzNH1jAFmqkcinZ3G+l7nQ35+S9aRuQVu9Ck0WH2a8n3X5dbNqofq2l7Ccd6HK9EvWG8u3edzPGxbi8K+YnjzxJFeWVcB/+v0tOpb1ZCMutbzRgtMYoeJHf9/UpXFpTDOyfG78Cj2PgfdcmW1d5J62RlWaLf6bP6JDcWhzzt7hiDyidXfHstKN30LXABD82ysELwH973sLNI57XU9K+rFuwXz0JLufrULd9i1ZPGoS1QzOnGixAFpAvXOD8Dnmiz78KJbMzJseq/lP4/TC2gqUvs8EBXAtVE94j4n4/rPs/FVzGp32jECfnHM2i3f7LqSra5Jt+5lTbUk53+COz22ITZNVfMVdmY+cYMGRJjStEMmVFvXQbDsARe87rfgPc3IyuuedaD6vE9tqryDyidkEm/tRdatDoZwIW1pSd5HYK4ykEBjXdRkvul8FrC2rp9o9j+Vgzg96bfUqrNqwGL1inR9rYYoP8/s8hbLj7wd67ehhN1pJ7dvv4uPAXTedsy7dizcX97Jt62slsbfx/BznaNDYYgKYmViGT7c/CYAuXkelt/fYjqkm0wPFKoqn5OebsuKTSVyzaHm9RmssaWn2DknkEa2fHyzVs7bqWmtv0dzBVHhnZ4F9EZHl4/PYyW8aMf6RVbzkHgtAn6gWxPTyw3zqrO2cpJ5N2f7ZdLSKtTX14vmHiQlLzpPX1wUGoLrdfF9MyTZiOn/Btq1xc0MJzDntSVpVXwLePAOAQWNmTvkNti7JdEs22zKtz5up6vng0944JN283AiAQ4IJ/R8ReXI9omjQuLgw4fAm9IpFklQxFDu4Ga37hjMxcD8J5nQa/TiSym/uLZAvq5Kk8oHarA7d5qynm9s5XDWODLnUhE2/NKDc/6zdeaV3ujE36C8AEszpPDbh9fu+F5XfFL2BmP4NsfxTfZrhp7LphYn8d27kUjrXmx4bkZVNr4iXb9qv/8udMnMPA9Z5wuSDrPhQdDpi+zVCVRQCZu2WlnYxpCtTmuwFWn4LXUWWaqL+d8Oo8MGefP9dS5LKJ1ovL459VpnI9t8CMCamLgd7hxLXxIs+I1Yz2NPaqqm0ZACVR+0q/H34Gi1ab8+c+7w8mLVxAWVvkahuJcmSwVWz9V7ZD4mNWTXjEdwumXH8dXceByuEyA9aTw/eiPiLlk4WUi2Z1P1xOJVG7wFL/q3oIEkqPzWuxdPzttDN7Ti+Whc+jQ9hpPdx232fLNVIvZnDbC2sokbR6Uh6riEW3d0HM4cMPMZrpTbYtt0UI9UMzuzMNDM+sqNt/+V1QZSeVDTfDyGKPUUhtUtj9P1imFP1B3y1Wnqc6ox5jA/szp85HCVJ5TPFwYFz31fhePPvbzo28Vol/qjtma/fQgoLRW9A0d6oaExrW5s/Z8y66bznIx/l7zXVCNyZhW6T3NcSojBSdDqiBzbmwNjpAFReNIBKr+/Ml9eSJFUAlEa1eP77dbaqv+s+jQ9hU627D/4tjrQ+3qQ3rmTbvtxCx4QuC+nsai0g+SXNmT+SagDw6956VBkgXYJCFCa6MqW5NtuJL0OXEKIzMj7mEU6/GIz56Mk8fR1JUgXk2sth7PlwRo59lZYOoPLw/Pn2URRp/fw48VYl5j49k4f/NVfpzMQyTFrbAYCQeYkydkuIQuTiuGYcGWJtUbV9pleed/vJYN58ZmleF6O7nn6jV+XY/2cmVJmXzK2LuEsmc1wclYfH8cm33fjY2cC16q60GbGNcb4RDHh+JgBrnnHkQrYPsyc/jUdkNobtR245WFoIUTDKzznN+92qM97vKEkhLnjYqdNDWlL3QNEb0Ab4cfS9UqAACvzRagqV9DdXvwX/0l+6sO6FRsulN5qQ5amyveckPDSGHNNJVdvei8zknHOKaZJ0hIzdD5Z7/5NVjdl5FrIQJc3VV8Mo1eMctT0uEVEvb6d6le6+PJLVrhFDv/yRQF0iDzlqiDKlMvzcsznOeaPsbzR1tFb2RZlS6ddlYL5VxBQ7ioIuuDzGAA8cP4lBp9woNtFpLHxXYQ2uGmsfYZZqJDzTATP3toSKWdUwZnI/nOPMuK3cLwlLiAdwZlJT3n5qBUuffQTzsVN59rzS3fcgFAWthzvGWhW5MsL6gfZhrcW0cbrGNUs2F00w7WoL0h6Oy/GwQb88z76GPwIQpHPl9FAdlXvd9OziVlQV09lzKGch6xH498xhGmdnth70pI7hRmFKZf39zS22962vMGGmee+eZGRbv9CkXXOi2ugzoFo7Zc1JyYV/PJsQdvSSeyyfdvMj6L28S1L3SpLUP5RGtYh82pXfXpyIowJT4puz4UJVPjjWnnG7fKgw85+b+mYzkJTjsYEjs7m6Oc02GWz/utvYRMms7stLlvR0vm4UBpoHXHxSo+X4pHJ4ellnwDDorCPofYOuMu3Ar2gAIwrP/+91fGbLzPFCFEYlIklp3d1R/jurAnD8tdJUrWdddqN9wBZaOJ/istkZgPVzmxEw9cbg0zuOeEpJY1tmAK2druKqcaSr+34WjXiDwC93lYixUvnJnJCQq8eH9I67aZ+2amUurXNFiwUtKl+N+4oPenbgxIEgQidfxHThYq5eUwiRd0pEkoocUZM9/SbftN9JMaBVNFw0pfLI1qGs/vQh1FORAAQYb55x/HbMMbHMrFmDX7fqmRO0jWC9K9tHTabr2hcwnzidZ9ch8ob5xGk+rtYEAEWr5cQntRjVei2/dl3NX0/r+LRGY6ksFKKQKBZJSlu1MhZnayXYyT6uPP1QzhkNBrovYGumJ3GmnDfnPl3chcBdRrRZZipv3persnE1K4sTk+pi/vJPtIrGWqn2oN1UIt9dXzdHBUJe28UvjVuSNHs3XTz2UX17Njs/bYrrUhnrJgRYxzRWnH8Je0wvXKSr+1p5v8TZcbWY/uxs2jgbc5yTYE6nwS8jULKtiaLq1zGYT0fma1xaPz9cf7awtOJGjKqZjq2752k1jMh/sYOaUeq5c/QsvYsFoUFSUCFKtCsjm1G7y1GCnK7ZrQS9SLekoqYGcrrVDK6a0xgT8xC/R4US8L9/LsmsEnJgt+1DpkDuDHm4Mqz0T0De/jJFwfGfvgN+8ueHgMdBlRkwRMn2cI8Iviqzi+ZDX8WFe78FkpeKdJJyWedGtVODcEiAgGk78Oc4dv3ea9DT2EG+eRd15phYiIm1dxhCFBouF9Lt9tpFOkl5LtyNr53n7vu3+IY+aO5xoKnWx5uENlUA8Nobm2OFXyGEsDdttRCqOu9mYYoP2qSMgumNugXpl8pD1Qcdti0d33RfD7gUc9tzFb0ej34XCP98Jue7BBZUiEIIcU/OP+tHI6ezzHvlabtWKUuSyicph30wJyff9rgpOoaY5eXpHvkYqwd8xuXRzYh/JQw0/13IXQghClbGM41R6ydzxuiPZtsBu8YiScqO/KfvIKWLgVHnn+XQiOnMfmsKGkeHuz9QCCHykdPQyxxt9gMfz+tm9wpXSVJ2ZroSTeSSEJ462Q5frZHIN+ugrRxs77CEEAK/v413PymfSZLKCxotSr0a+BpSAViQ7EvIt1fu+eH+03eg9lAZf/kJjr8yg4AfrqIrVza/ohVCiNvS+njj4ZDBvGR/dOn2n9ZNklQe0Pn7suCXWUwM3A/AxWwfTGfP3ddzmK5Es39eLdoee4q5QX9R9qd4Lo5tlg/RCiHErSkODlyaE8DrZX7jx7oV0W7ZZ++QJEnllX+/kas/evSBnsNvZjja7lm8HVuLb8qGs23QJE5/0RQa18qbIIUQ4g4s9UNZU/9be4eRgySpPGZUzbhefPDJSc1xcfz2VXOqzxjEjixvjnf9mhGLlnJqXgOiR0jLSgiRfy6OMlNW50q3TQOxZNv/fhRIkspzVf/oh2b30Vw9h8/scMr9bwfTH3mMalte4QnnLM62mUPj7n+T/HxT1LA6eRStEEJY6cqUpmag9V66/xZ9oVlmqEjPOFEYqem6PFum3HTpMhW+DaTe9kEEdz/FzOCV+E/azrxkf6aezNmlmHrUm+A3ZeE+IcSDSWxWjjUVvyHSmIrGVHimd5MkVchpt+zDfwtkzHOmS5sRZPe/xl91fuSlf5arv25yxYosuPxEjn1lVpzHdPFSwQUrhCjSrphSGdyqFx7n9tp3HtR/kSSVByy+Xmjvcc6+B36N9HScVu7GeZ0DLZ8ehPqvtapULTz/1jqmDp/OQw4W29RMo1+qx5lUP5LeC8Jh32nMiUm3e3ohRAmX5fnP3Z/EZFSTPVaOujVJUnkg4TMTXlrnAnktNSsL12U3T5m/Zqkf65zLc+yTUHCwLt/o5JHJ0WY/kPW9kbrfDCPogx0FEqMQomhRdDrefWO+vcO4JUlSuZT+bBPmVJ8COAEwv+0s3u7YH6dVuws0DtVkwpycTJVBN15X4+xMq7C+qApUPH3RLqtqCiGKBkelcFTz/dd9VffNmDGD2rVr4+7ujru7O2FhYaxbt852PDMzk8GDB+Pj44OrqyudO3cmJibnTOBRUVG0b98eZ2dn/P39GT16NKZC1LS8H4qDA9deSKWGwcm272FHaP/hZjtGdYMlPR3dxgj0f0RgOhdl73CEEIWYpZAWe99XVGXLluWTTz4hIiKCvXv38thjj9GxY0eOHDkCwIgRI/j1119ZtmwZW7du5fLly3Tq1Mn2eLPZTPv27cnOzmbHjh3Mnz+fefPm8e677+btVeU3RaH0Tjd6HTzNgaYLbjo8wOsgl8bImCYhRNGgmkyMndLX3mHckqKquZvi1tvbm4kTJ9KlSxf8/PxYtGgRXbp0AeD48eNUq1aN8PBwmjZtyrp163jqqae4fPkyAQEBAMycOZMxY8YQFxeHwWC4p9dMTk7Gw8ODlnREV8CLHkZ+Eoa5TCZ/PDyNYL3rbc977XIjNqxuRPkJEWA2F6obkUII8V+pzzVh+eef07d+R8xX4/P99UyqkS2sIikpCXd399ue98DtO7PZzJIlS0hLSyMsLIyIiAiMRiOtW7e2nRMaGkpQUBDh4dbxO+Hh4dSqVcuWoADatm1LcnKyrTV2K1lZWSQnJ+f4sZcXn9zMmVZzCda7YlTNdDnTmieOt+eJ4+05mH1jpomppfew55XJvHdsB+cWVbNbvEIIUZTdd5I6dOgQrq6uODg4MGDAAH7++WeqV69OdHQ0BoMBT0/PHOcHBAQQHR0NQHR0dI4Edf349WO3M2HCBDw8PGw/5cqVu9+w80RirzA6u1snXIw0plLl9/6kPZ6K+tgl1Mcu8fJHIziWnY5RtY7UdtU40tRRy5T6Szn3Y210ZUqj6KRWRQgh7tV9J6mqVaty4MABdu3axcCBA+nduzdHj+ZuGqC7GTt2LElJSbafCxcu5Ovr3UpKt6ZYusVTzeCMWbUw4LmBVO1/CEvmjdaTz+ydjKrbjmkJITke+4RzFidaLGB2+FIy29Yr6NCFEKLIuu8kZTAYqFy5Mg0aNGDChAnUqVOHL7/8ksDAQLKzs0lMTMxxfkxMDIGBgQAEBgbeVO13ffv6Obfi4OBgqyi8/lOQtO7u1H99P3vqLyXdks1vGc5oE9Nvnv5IVTEnJrFwalt+T9eTpeYs6Sylc6X5Rzvx2ObD+aW10IZUlJaVEKLQ8NU6cex/ldC4uNg7FJtc1xxaLBaysrJo0KABer2ejRs32o6dOHGCqKgowsLCAAgLC+PQoUPExsbaztmwYQPu7u5Ur149t6HkC42zMxfml+WrMtYBtDMTQ5kWWhPzyTO3fYzvrHAmV6tHjcVDCV7fl6vmNNuxD/0PsbTiRo43/56lmxdxalJDmTBWCFEo6BUtp5+eScKzhWd5oPv6Gj927FjatWtHUFAQKSkpLFq0iC1btrB+/Xo8PDzo27cvI0eOxNvbG3d3d4YOHUpYWBhNmzYFoE2bNlSvXp1evXrx2WefER0dzdtvv83gwYNxcHDIlwvMLUvtymxuOAOwfrP4Mz4E1RRz5wcBqjGbSq/vROPszAd/tqSmyyX6ul+0TVkE1ntWZ7rOZN4T/nw/uAO6NCO6mCRMkefz63KEEOKOtIoGVcnfad7ux30lqdjYWF588UWuXLmCh4cHtWvXZv369Tz++OMAfPHFF2g0Gjp37kxWVhZt27Zl+vTptsdrtVpWr17NwIEDCQsLw8XFhd69e/PBBx/k7VXloeTxafhqXThpTOPJZaOo+tXl+3q8JT2dEw3hlFslJr3VEaO3icinci4q9pJ7LC99PweAylteolLPKMjdyAAhhLgv7seSeD+uOuP9jpLPU5Hel1yPk7KHAhknpShcfDOM9QM/o6zOlTXpjkytUiPXa6zogstTeslV9Brr81R2imWk91nb8TPGVKZffZi94xtiSDGi2bo/V68nhBD36sykphzpMY0ZiSH8Vj8ANSsr317rXsdJSZK6Da2nBx/v/526/3RDVlz+KiHDdud5C0cbUpHKiy8wtfSem459eDWU7Y09c1QQCiFEflH0Bi4Na0itZ4+R+BSYExPzrVcn3wfzFme6UoEkLfGhlkFPqiWTJ088SeUfs/Lll2U+dZY/VjXi8WMd+PM/uWik90HGHt2JrmKFPH9dIYT4L9WYTelJO4jJcON/+9ZzaUyYvUOSJHUrCQ9XYHvtFWgVDRdMFtSnU1C2H8i31yv3vx1oWl1g2KHuxP6rEtBZY+AhBwtH3/BH6+uTb68vhBD/ZpoSSM89fXn35YVoA/xBo7VbLJKk/sP0WAPe+2iOXV47sEcUz7w+iu2ZFts+raLhZIcZfL73V5Kfb2qXuIQQJYvj6t34LXGmjC6BOXtWcPn1JnaLRZLUvyh6A5G9Vdo4WwfhDrnUhFeHD8eSll4gr29JT8ftx528+0o/mr/2Kj+lWvtp9YqWagZnXPtekq4/IfJRVvtGOG0NQBcYcPeTiznnFbt4t18/Wu0ewC+DP8PlTz80dQt+PKskqX9RG4RyqvVsALJUI5tXNMBp5e5cV/TdL92mCFyW72Lc0p68HXtjUN1voauYuvkHUrpLi0qIvKQLDODc/8L4ZNpMdBozalb23R9UAug2RuD1owspqo4VlTfQcdFWzv0vjKQXCu4zSObk+YeuTGniqrrYBttW+2kIVSbuxp6ljxXeDmffwlB2rjlAU0ctWkVDJb0rxheuoVnjhiUlxY7RCVF8JD1UgRN9ZzD5WggZzztiTrho75AKDddluxh9oT+JVVyY98HnDOg7g6dOtiP7zN1nytEdj8KckJCr15ck9Y/TA8pzou8MAFamuVJxhbFQrAFlPn6al78bSqcuf/Gh/yEA9tRfSs35PfFY4obr0p12jlCIokvr5cXx96vwbfvZdD3bihPLqxJ4YYe9wyp8dh5EV64JT/84CotBJbzL5/gsv7Ei+b9n0vm3Rvu6En+uim3b6ZKWshPu7/2VJAVoHB0xBt0YtDZmXycqbNlnx4j+RVUp9+EO9v0USuRv4baFFg83XUhNeuK+TlpUQjyo1IdDONb5K35O9Se1s5bAGElQt+O6bBeuywBF4aVv+6BqbiSmyOe8eaTdzRMP1Pe/AP4XeDNgA8F6VxLM6bRMf53AL+/9fS7xg3mz2jUi4dVU9jVaiFbR0PLwMzj3SMYcfy2Pos0bit7A5aEN6dlnA2N8Ttn2B6/uR9VB+wtFq0+IoiSrfSNGTlnIvCsPcW5RZfxmhts7pGLr2sthZPhb51oq/Wcayo6/73kwb4lvSV2rpudg48WAhhfPP0zqslI4xJ+zd1g3UY3ZlJq8gz/ah+ZIUsfbT6f+6GH33YQWoiTLat+Iz7/6GqOqJbMz+MVJgspP3t89+Ptboqv7dGXL8HCPCNv25XGV8fm2cP+xpn1bhoisbNtaVQ6Knp49NnJmov1HhgtRFFxvQc2MfZRh44dgvnrV3iGJOyjRSerCV+62daKKCrclO3m75qOMib6RlMb5nmDYk2vtMoZBiKLkegvqMadr7FhZB8/vw2XFgUKuRCYpRW8gY30ws2r/AMCspNI0f+1VDAci7RzZvbGkpbFrUkN+SXMm3WIdzzHU6zyN5/+NtoBXLRaiqLi0ogZvfrmAeLML9b8fQbmJu+0dkrgHJTNJaTVMqrKUpo7W+ajmj38al+W7cl3PX5DcF+/k66rVmJtcybZvpM9eYrvVsGNUQhROWU82Yn69uTzsmMKUJ54ieGy4FBsVESUuSVla1KPF7gRq6lWMqpkpCRUwJBXRP1aLmR/+154D/6z54qFx4vu3PiezQ2M7ByZE4RA3MIwWBzP54KtveT/qaTr2eBXL+Uv2DkvchxKXpE730TLO9wTOGgOnjVn8HhaEYf1ee4f1wNwX72TwmGGcMaYCUM3gTFzvdBRdiS/cFCWVRou5ZX1OTm9M5Z4nGetzlDAHM9kjfdD8tR/VKFMeFSUlK0kpCh8+9DMAB7Ky6PXRKMzFYCCs6/I9jL/0lG17WcNvoV41O0YkRMHT1KnGhbea0f/4KT6dO5NDT0/FQ5/JI8MG0rHN83Dw1N2fRBQ6Jebrti64PKcmeNDM8S/AlSEnehT6cvN7ZjFzbnJVsr7cgIOip4bBifbz/mRtjzAsB4/bOzoh8oWi06GpVIHT77kAMKDWX/TxOEycReWZ2aMxJELA17twseyiYKeIFnmpxCSpS0+V4eTD0wFXkiwZuI/SF6s/XNdfD1Cz4Wv8/cKXOGsMDPU6z4rSbTActHdkQuQtbZVKxD3kT/wj2axpOY1qBme6Rz7GTxfrsuH5xmjiEikXLaXlxUXxT1KKQtyrTVk8ahJgLdmu+/tQQs8ctndkeUrNyiLkk6Nc6ZFNJY0BgLShiThul7n9RNGm9fWx3WM9PaQiTz+5k4mBP5FgTueyWeFgdiaJQ0rhuv8IFsBy56cTRUyxn7tPV7EC87cuxFdr7RIYfqUhx5tQLMtPNc7OKGs8WVt1LQBm1ULVxYOo9LrMlC6KHq2PNxf6hPJe/x94xOmKdR8KLfb0Iztbi1O4K6Vn/w1YFwyVllPRInP3AVpPD0597G5LUMey09kbF4SL6aydI8sflvR0EubVIfbDNPy11rWxJnX8gVlz2mE+JjeNhf3pypVFdXPOsU/VaEj7PItKHjmnJ/I1JDDFZyJHswPYlmldKfeS0YugvpdtYxql1VT8Feskdal3jX/uQ1mr+YaMHoHLT8V7lLnX/HBalh3N34OmoVe0POOSykxXR3uHJQQAKbP1bKy5+Kb9ekVLuiWbGr8PgqwbRcfbtzbBbemenCdbis6ge5F7xTZJKQ1rMmrgUgAisrIZdaobLsuL1jx9Dyr4u7OsecmDZ1ysY6c6LdjIjK+fwf8rmSld2Ie2SiUudAxgZehnHDPqWJdSC4A/YkNhvA8AikWlys79YClOJU0it4pfklIUaFKLDt9t5kV3a/dBlw2DqdJ/z10eWHyYrkQzbt6LPDPY2ors73GZyX52DkqUaJEfOXPsoenU3Pkq7j+64faj9T6phgvABfsGJwq1YjeY99SUJvxv8RwGe1r/8PtEtaD6+yVvGhSXyyqjrtS3bS958Quu9QlDVyHIjlGJkkZTpxoXxzVjdeMZvHoxDN85zrYEJcS9KHZJCq9sGjtYK/5eu9yImF5+mC5dtnNQBc97bjhHXrkx60RdBwf2fDSDC53LonFxQePsfIdHC5EHFIXjQ104MmQ6T+4cRNTDKg5rSk6Phsgbxa+7D/gzE8ad7MS1bYGUO1Vy78Nok9KYllCeoV7nbfvWDvuMpKFajmcH8PGknij/dP/7rzhepGaBF4Wb0rAm559yZ1fbiTx1sgt+S52wZGbaOyxRBBW7cVKW5nUxueox/Cbf2MD6YRH5rBsn+sy443mP9nkF54MXMUXHyHgTkTsaLaemNORsl2+oEd6ToJeiZEC5uMm9jpMqdt19mm0HJEH9i7r3MGU255z1eWGKDyFbXiJky0vszLQ2pVbOmcbsXcs590FTWeFX5Io2tBIHO33JgawsPJbKjCcid4pdkhI302aabUt5AHx34SEqv3qGis8f4J0+rxC89hX0aCmlc+VE3xmce9rTWiUpxH3S+niTVtETJ8XAKx8PlyIJkWuSpEoAzbYDtN443La9sfovJDxtXcFXs3U/VfpFUP+7YYyLqQ3AX/0mEv9KU3uEKoq4C31D2fTNTEZcaULAhpJXVSvyniSpEsJwWc+SFC/bdts3/kLRWyeiRVUp/244B7qF8H5cdXy1Lnz/1ufEDmpmp2hFUaT18+OdVxayMMWfP1Y1wnQuyt4hiWJAklQJUeHtcKaO72bbHuC1ixNf1UFbtbJtn/nkGXb2rM34uBpUMzjz65ufce3lMHuEK4oYtVkdjk8qx1POccwZ3Yly/yshVbXSLZ7vJEmVUKV0rjSqcRYlLSPHfsvh4+zpXp2LplTK6lz56b2JxA6WFpW4s2vVnDn7+Hc8FPEizhuL1zI4t6I0qEFa5yZcXlENyyP17B1OsSZJqgTxjIilxrRBtiKKpRU3cmqSz03nWc5G8dSkNxgfV4MgnSs/vjGR80tryWwV4pYUBwe8nr9IkiWDpERn67IZxZCuYgXOL63F+aW16PD9n6ycMpmF9b4jw89g79CKtWI3Tkrc3eXXm3FopHVevy0ZGkZ9+ioBy24ezKupHcpPaxfg/M8iivU+HiST1IqbaFxcmHTkD3pOHkXpeYcxJyfbO6Q8pXFxIa5HbdLKKhzrb/1/k2rJpEuHvmguxmKOi7NzhEVTiR0nJe6u3C8xvB1rnYW6pZOFiPdmYKlY+uYTT0fR8q1hvB9nHTf1w+ufk9RTqv7EDWqzOlh+8aK8TkGfqha7BJX+bBMc1rqy6/2vWdVnIlX/epHG4wbSatwIlGNnJEEVgGI5LZK4M/PJM+xLKAf+h2z7LrTxoGxEzvMs6el4zQ9n+6lGsPwoNQxOmHtcI1ltivsiGf8iICPAkb+qrWZpagCeZ4rPtEfaaiGcf8YPGibxV8h6Jl4LYdMLTQg+fNS2qrcsuFgwpCVVQpnG+VFp6QASzNb7B6sHfEZ8v1tX8mkPneWRV/vzW7oDe+ovZfbHX5DWpUlBhltsaX190NQMRVM7lOR1laiw28n2c2ZiIa+sVBTS+iRiVi2M2/ssmq377R1RrmmcnTnzeVPaLN/DwSFf8UGtX6kxbRCbnm+E5cCNBCUKjrSkSigl/G8qh8OHLZrzeal9BOtd8eh2Ce0yD8yJSTnOtaSk4PjrbgZ2eJHIp76lhsGJxOdTcf3VATUry05XUDRpaodyuueN8WoVG15gbegiALRKzu+MH7ZNYL7lUQCqfHMF09lzBRbnvUqIdSNLNeEaXjxm1T/xaS3Odp4JQKN93fHtepGy6Tuk1WRHUjhRwmU92Yj3ps2hpZOFLNVIuz4D0f++95bnqg/V5eEZOxnpfRC9oqXKLwOpMnB3AUdcNCX2CqPHm+uoYLjKMy6pHMnOYHlSgzs+po5zlG115SUpXlw2erHsozZ4/3UB00X7z+Zw/v1m7O07mbo/DqfKrKuYT5y2d0gPTFujKq1/3ENb1yMsT2rAj6fqU2FgjNxzykf3WjghSUpw5vOmnO5h/fa4NNWDbwZ0Qbcp4pbnKg4OvHl0Dy2dLExLKM/qPg/DnsMyc/q/KLobHRTnxzUms6yR1W2mUsPghFE1Y8FCw929Kf3s0Ts+T+yQZuwc+6VtW4cWraLh+chHCT9SGSVbQ9WxR1EzMlDN5gL9HWhDKlJr6Vk+DThA7c8HUerzolv1qeh0nJrUkJPPTWfY5TBONZLegYIgSUrcM62vDy9u30d3N2sJ+pp0R75u2OSmbr/rUro3ZeLH03nIUcOBrCw6rxxG5RFSSAGgdXfn6Z1nqGiIBaCcLonxFzsAkG3WkTEmAG18KkpKmnVZlLs8FwG+tu1z3QKp+cQJNIrKN+XX4Ko4sCVTj1nVMGhVXyqNKrjfQUC4OwvK/0mVP1+kcv9zRbaqT1euLP02baGqPpYus16nwvdRmC5ctHdYJcK9Jim5JyUwX43nbJY/6S4xOGsMPOqYzOvDalD+o923vFHstmQn/Z/rxY4ms6nr4MTwNutY3fhhlL9Plqh7VBoXFxStFnNoeS68Yf2up9eZ6eS2DvM/3/0WJNUjqXn8vx4Vg/ken9+cnAz/+vAv9+FZkj60tmb/OBRAM8fLVNeDr9aJYU+sY0b5Fjivc8N/2REAVJMp7wfWKgpaNzfqul0kwZxO5fHpRTZBad3dOTG8LI86xTHtWn3KfrwDKYsofKQlJQDrEgvn+4eyddBEfLUupFoyqbdwBBXf3HnLbiStuzuJT1a3taiiTKk8/v1oKrwVbofo7cPlTz8+Kb8SR0UlSOcKQJZqpNb3r1H583/uz1jMmOOv5flra3194J9CixNjK9Gn9Rbe9j1OgjmdOIv199XjYB98O5zM29etWpmJ67/HjEKfCSPwnXXrv4/CTuvlxfA922jumEbtra9S5bXz+fJ7Ercng3nFfTHHX6PcpL2sTgsGwFXjyPfPfYXW3+/W5ycn47ZkJ6+/ZZ1mKUjnyqhOqzg5uyEaF5eCDL1AaZyd0dQOJWN9MP8L+oUqehdSLFq2Z1qo+8kgHnljKBXficAcF2f9yacPPvPVeNtrVB65k+0v1GVMTF1OmfSYVYUqehem1liCZWM5Ij8OQ3FwyN0LKgq6smU4/pY7NQxOfB37KL7fhBfJBAWgeHlQ3ZDARbMRyzWDJKhCTFpSIgdL87r0nL2Wl9yt91SC1/SjSr87r3R87n9hnOhrXZ7erFqoM20IZT4pujfSbyetcxNin8tgX/NZuGocOWNMpc2fQ6n6YQrq+YtYMu07mFXRG1C0GtSalTk9UsfOFl/jq3XBqJqpsv5VnE8bqLDg/ANVBl57OYyf35tIlgpt/hpK1Y/TMB/N21ZaQdFWC6HKD5HUcr7Ikn5PoN15WMY/2YEUTogHdvK7hpxuOwutoqFGeE/Kj8u6Y3mxrmIFYlqVYv5bk6lhcGJJihfjdj1L6LDIm+YDLEp0gQFYArw59YInT7Tcxws+O2jqqCVLNfJzqj+ffdEdv5mFtHtTUTC2qk/C0DS+qf09jR2s/08+vlqV71e0osLPCSiXYjFfjb/tU2irhWBxdcT582jeLLeWs9n+bEiowcWwtCLbgtJVCCLkp8t8Hribyr/1p0rfWw+3EPlPkpR4YFofb2bu/8V2n2VmYhl+rn7rbr9/S32uCbMmTqGGwQmABu8PtHYJFUHaAH+cl1v4seLvtkG2qZZMaq0Zij5BR8V3IlCN2XaO8t6YH63P2Wf1NKx/mqUVN1r3qRZaH32WCwdKo82E4A9yXk9a5yZ88/kUQvUOaBUNtXY9T5nuZ1Czs4tsggI4ObcBkW3nEGlMZXD9p6Wbz46kuk88OKOJVSk1GOp1HgCNcm8fSq7LdjG4Xw8+C1lGYwc9Q0f8xLyojjjvPlPoPwy01auQFehGtoeOpz7YRCn9Pl50v8r127YTr1UiMsOPamNOYk5Moih9TGs37yNkM5wc2oy3+8aiVSyM9NnL5hqroAYYVTNzOgdhVG98HLRwto7r+iXNmd1plfD4wa3IV26qYXXw9k0BYFVqTTBKF19RIC0pcUtKo1o8M38TAzwvMScpkKU1yoDl3oqn0zo3Ia5rBidaLACg2cgBuC/fWyj7/a8PvI1aEsrRZj/kONb1bCsObqyKYoHgqceKdNflf10Z1Yzs2395tQlan4ay4+/8D6gAdD0WTV+PaL5OLMevvVui7jl09weJfCPdfSLXHLYG8kvIb6Rbsqm5cQChQ0/f85iY7LYN+XDGLNuA33iLM8NnvkrZddewHD6ez5HfmalVAzJ99CSX1zBlwDcAVNcnUUrnyodXQzmQVBaAUyuqEPhF8SsAKYk0daox7KcVPO6UQeXVr1Ll1TsXA4n8VyAl6J988gmKojB8+HDbvszMTAYPHoyPjw+urq507tyZmJicI+ujoqJo3749zs7O+Pv7M3r0aEyF8Ft2SXd5QTBG1YyzxsDZx7/DUqncPT/WsH4vY0cMoMrW3tR1cKCVk5lDI6Zz8hVPu5Woa1xc0Hp6UO2Tw2yfMpNDI6bTyslMc8dMrlm01Jw6iB1dapDS4iopLa5KgipGTr7kwRPOWdTd/QKhI+88HZUoXB44Se3Zs4dvvvmG2rVr59g/YsQIfv31V5YtW8bWrVu5fPkynTp1sh03m820b9+e7OxsduzYwfz585k3bx7vvvvug1+FyBcBmy5jVO91foSbOa3aTeASBxpEdLUtnLi382Sa7kjg6qthaJwLZuZsY5uGxA0Mo+wmmPb3Gj4vvQ2AI9kZNIjoSvUfhzK6QXvKfLID88kzBRKTKHgHsrJw+9EdS1qavUMR9+GBuvtSU1OpX78+06dP58MPP6Ru3bpMmTKFpKQk/Pz8WLRoEV26dAHg+PHjVKtWjfDwcJo2bcq6det46qmnuHz5MgEBAQDMnDmTMWPGEBcXh8FguOvrS3dfwdAFl2fZX8tsy8e36tUX3cZbTzx7N5qaodT+/jjDfbZRSueKWbVQ+dcBVJ9wxXqC2ZInM3trA/xRHK0DVyNfKEdQq/MMDdpIe2frGKZYcxo/Jlfnh0+exDHRjOOvMot7cXdyViPGP7yKI+llOFi/yN3dKLbytbpv8ODBtG/fntatW/Phhx/a9kdERGA0GmndurVtX2hoKEFBQbYkFR4eTq1atWwJCqBt27YMHDiQI0eOUK9evZteLysri6x/VRYlF9G5woq6qp8c4UyjB3us5fBx/m5s4OkXRuPQLYZttVdwusNMMp6ylj0fNWp5ac4wUKH0jky0m/fd0/OmP9uE+Opa2/bgnr/S2/0UAA6KHr1y49hTJ9txaXkwAd9G4JlVNEvjxf3r1CCCl9xjqfjHs4Swy97hiPt030lqyZIl7Nu3jz17br7xGB0djcFgwNPTM8f+gIAAoqOjbef8O0FdP3792K1MmDCB999//35DFblkvhxN+76D6DR5A0O9zuOkyd24INWYjffccLS/ePNw8/7MnvoFVfTW+1ONHeDooOkArOnjyIakmvf0nN29ptPUUfufvY7W+FULjd4aiOsV6/1O5wNR+EfvKFLl4yJ3tH5++OijAKgyN1V+90XQfSWpCxcuMGzYMDZs2ICjo2N+xXSTsWPHMnLkSNt2cnIy5crd+0188WDUrCwM6/dyMdsLOJ9nz2uOv4bzmn081fR1zE7Wjw2Lm4nTT1hnuWjvnEl753udCcCaoJoe6ELcSd+ch1Souvq0beE6Kc0pec69GsJa3w2YVVlbt6i6ryQVERFBbGws9evXt+0zm838+eeffPXVV6xfv57s7GwSExNztKZiYmIIDAwEIDAwkN27c94HuF79d/2c/3JwcMAhtxNkikJFNZkIHnujy03RG2hf50VQlAd6Pu/TF/FIuHnqpgcv+xBFnaI3kFHWxMo0V2Y+/yzKkVPSkiqC7itJtWrVikOHcg6A69OnD6GhoYwZM4Zy5cqh1+vZuHEjnTt3BuDEiRNERUURFhYGQFhYGB999BGxsbH4+/sDsGHDBtzd3alevXpeXJMoglRjNuw9/MCPl2Qk/uv8Ww052WEaC1NKoe45JAmqiLqvJOXm5kbNmjnvFbi4uODj42Pb37dvX0aOHIm3tzfu7u4MHTqUsLAwmjZtCkCbNm2oXr06vXr14rPPPiM6Opq3336bwYMHS2tJCJFngh85l6NwRhRNeb6e1BdffMFTTz1F586defjhhwkMDGTFihW241qtltWrV6PVagkLC+OFF17gxRdf5IMPPsjrUIQQJVRWu0YML7cBgE+WdrZzNCI3cj3B7JYtW3JsOzo68vXXX/P111/f9jHly5dn7dq1uX1pIYS4paRgPW2cjQCU2m60czQiN2RlXnHPXHVZaO8w6E4IIfKaJClxz971PcT5Ifc2fkmIwuDT+BCcLqbYOwyRC5KkxF1tnWQtetEqGlT5ixFFxMHsTLa80BDzkRP2DkXkgnzkiLvyPJFq7xCEuD8KpFgMqMfP2jsSkUuSpMR9mfLSt+jKlLZ3GELcltbdnRFDl9o7DJFHJEmJ+9LMMQX0uS4KFSL/aLW0cDpn7yhEHpEkJe6LXtGS2KiUvcMQQpQQkqTE3ZlVIo2pmFULDoqegKFnUfR3X/dLCCFyS5KUuCv1wFEG13+aJal+ACyutJarLzWwc1RCiJJAkpS4O1XFHH+Ng+nW5VEcFD0ZvgpaPz87ByaEKO4kSYl7drB3KB9frQrAgSHTODcj4C6PEEKI3JEkJe6Z5eBxZu9/CLAWUAR4pMg0SUKIfCVJStwX/w0GLpqsg3s311hFYjtZA0wULmpGBs/s72fvMEQekSQl7ovnsv2cNznbOwwhbsuSmYnmdy97hyHyiCQpcV9Uo4kXNryKWbUA4NTvMvF9w+wclRCiuJIkJe6PxUzosEM8eti6kNzG6r8Q30zW6xGFj7cmk2s96qMN8Ld3KCIXJEmJ+2bJzMTyrT8J5nQAxjZbi6Z2qJ2jEiKnagZndn88g4y6QfYOReSCJCnxQNzWH2VwVHsA+ntc5tyz3naOSAhRHEmSEg/EkpLCkaXVbNtjeixH6yU3q4UQeUuSlHhgvoezqLK1N2bVwkvusZwaK11+Qoi8JUlKPDDdxggqv5NMlmoCwOSXjeLgYOeohLjhSHYGujSTvcMQuSBJSuROfCKdTj4LQGTbOZx9r76dAxLihgUJYehjU+wdhsgFSVIiV8wJCZz/s7xte8cLk4gd0syOEQlxw6cBB8gIlnulRZkkKZFrZf9Ip+KyAQD4al2o8fxRKaIQdhUQnsSUhAoARPUy2zcYkSuSpESuabYdIGRhmm17VtDvJD1e1Y4RiZJO3X+E32Ot80q+03CNnaMRuSFJSuQJ7dnL1NvTHaNqxllj4IMJ35LVrpG9wxIlWPx35clSZTaUok6SlMgT5qvxBHY7x0+pvgC0cjJz/jlVlpkXduP71yWMqnT1FXWSpESesWRmMmF6D9tSHkfaTMd3qzPmR6XiTwjxYCRJiTwV+OUOWv41FABnjYEfKmwhw1dv56hESaQmpfDyufb2DkPkkiQpkedCJmQw+VpF23a9Nw6ARmu/gESJZE5I4PB6KeAp6iRJiTxnOXycHz9ta9se5b+RpOeliELYR33HKE5Na4KubBl7hyIegCQpkS98Vh0heE0/5iX7E6x3ZcFHn3PtZVkcURS82gZHznb+BnOAp71DEQ9AkpTIF+bkZKr028MX33QBoIrehZdeX43GWZaeFwVHMWNbRVoUTZKkRL5yizKzO8s6VuUVj7O8f2Qr2upV7ByVKCmCvvyb8XF17B2GyAVJUiJfufy0izdOPUesOQ0HRU9jBz3H33BD4+ho79BECWBJSyPDLNWlRZkkKZHvnJ6JpfkPr9u2Tzw+i4uvydgpIcTdSZIS+c6Snk7leXG8ejGMSGMqekVL395r0daQ8mAhxJ1JkhIFwnziNOeaZtNj3OucNKYx3OscrX/cw4W3m8nUSaJAnO/gYe8QxAOQJCUKjsWMyVlhU5q1cGKk91nW9PsMpUZlOwd2a7pyZVGb1UFtVoe4gWG0OJjJmYX10Pp42zs0cR82XazCzkwzTz0dbu9QxAOQJCUKlM+34SwZ/aRtO1jvyrm3CtFsFIoCisKlN5sRuCyR35fP5/fl89n3zgzWXKqBcsERtVygvaMU98G/43F6rhpMe4+/yXpSBpUXNTp7ByBKHqfNR6i0dACHukzFWWOgkt9VUjo0xjkyGcvh4wUejzbAn7TGFQAwD77KiIp/8JjTdnZkedPvwkM3TlzgR8XF4ciom6KppZOF811Uqm50QM3Ksnc44h5JkhIFzpKeTpU3D3CsIzRwgNVV1sE3ELzuFUKH3hjsq2Zno5pM+ReIoqBxciLhsYqEfz4TgGPZ6XT+9nXGm8F/bxb6PyJsp7uzM/9iEQXiSJvpPFeqC6ZzUfYORdwjSVLCLlSzhd77+zC59lLaOFsH++5p8yUXj9z4k3x202B8t91+jIvv8sNYUlIeOIbo4WHMf+0L3DS/k6U60PHEM6R9VZZyK3Y88HOKwsnjhEKUKRVfjRTpFDWKqqqqvYO4X8nJyXh4eNCSjugUGahXFGnd3UGjEPltEMsbzUKvWKiid7nnxxtVM09264vh9JUc+1WzBXNc3J1f28cbRa+nw6bDDPC8xKgr9fnzqyb4LNon3UDFWNdj0fRwi6Lzky9iOVjw3coiJ5NqZAurSEpKwt3d/bbnSUtKFDhdhSCcvk/nYoonFTqd4nV9GzR+Ppz62B1FuXGeVmthe5NZeGlvnu9Pr2hZvPjrm+4PHcjyZNQ3/XC8quI999bVXK6rFL4qv4ILJj1V/nyFkDcT8T4XTpH7tibuy0cbO5L+6G9kfp6B4XF7RyPulbSkRIFL6B3G7gkzaHX0aXSt73BvQFHIbN+ITC8tr729FE9tmu1Qdf1VgvWut31olCmVNy48Tfy48uijc3YJ1lh8hj8uVMV7qgu6jRG3eQZRHJ3+oilO5VMo0+mIvUMp8aQlJQqljI6NmfHel8A93BtQVRxX78YRWLCwQo5DST0aEdPiRjtqfMuVvOQea9sO0rmyJHgTxkXmm5420pTJtklN0G2UQoiSpvLIXfYOQdwnSVKiQBmSTJhR7n7if1lyJhuPhTvxWHhje2n1liz0tnYLnn5ZR+QTswFrt+B/PbX4dYIXycDOEqnodRyVeJKkRIHRVQii6qRDNHbQk2rJJP6XsgSQN6XA5qMnbSPTK7g0pErMQADU8hkcfWROjmS1usckev89CvfF0pISorCTJCUKhLZKJS4+GcCa0r8wLqY2B56vSsCJ/Ol6MazfS/B66781zs50qN6bqT99Y6serKJ3Ib1bEs4xDdBtkntSQhRmkqREgTj7gj/HX5kOQKLJGfOxUwXyupb0dJQDx3lu8miyvWD9y59RSuvE340Xc2BOFl139kdzypny70r3nxCFkczdJ/Jd5lON0dZItm2vO1izQF9fNZkI/HIHQe+HM7BxZ0ZdaQpAXQcHTj4yn76dfpfVgoUopCRJiXylcXSk4tvHOBK2kDXpjjR6eyDVRhVMK+omqoopOoYdMxpyICuLLNU608Vo7zNU+f4suvLl7BOXEOK27itJvffeeyiKkuMnNDTUdjwzM5PBgwfj4+ODq6srnTt3JiYmJsdzREVF0b59e5ydnfH392f06NGY8nN+NmE3aZ2bcHZ+FWaV2wLA0B3P4/1dOObEJLvG5fPdTsbWfIzqm/rb9k0ptZeQFVeIea0ZaArRrOxClHD33ZKqUaMGV65csf1s27bNdmzEiBH8+uuvLFu2jK1bt3L58mU6depkO242m2nfvj3Z2dns2LGD+fPnM2/ePN599928uRpRqGS/fI0TLRagV7Q0PdCF0GGR9g7JSlWxpKVR9bVzVN7cx9aimlJqL9vemMz59xrbOUAhxHX3naR0Oh2BgYG2H19fXwCSkpKYM2cOkydP5rHHHqNBgwbMnTuXHTt2sHOntdT3999/5+jRo/zwww/UrVuXdu3a8b///Y+vv/6a7OzsvL0yYVeaOtXoHHQAsM7+oC72w5yQYN+g/sOckEDll46wOePGzBWuGkfat9+FtkolO0YmhLjuvpPUqVOnKF26NBUrVqRnz55ERVnHuURERGA0GmndurXt3NDQUIKCgggPt1ZOhYeHU6tWLQICAmzntG3bluTkZI4cuf00JVlZWSQnJ+f4EYXX2U/DeGXZGkZ6H2fitUp0+t9oPL8vnNVzqjGbKT26UvH3vqxMsyarz0vto+evW7jwTjP7BieEuL8k1aRJE+bNm8dvv/3GjBkziIyMpEWLFqSkpBAdHY3BYMDT0zPHYwICAoiOjgYgOjo6R4K6fvz6sduZMGECHh4etp9y5eQGd6GkKGhqhvLCE1vp7JrMSWM2m5uVwefbwpmgrlP3HKLKq4f54Fh7jKp1ZouebvG0eWY3uuDyco9KCDu6r3FS7dq1s/27du3aNGnShPLly7N06VKcnJzyPLjrxo4dy8iRI23bycnJkqgKoXMfNOW7nl/zkKOGA1lZvPLRSHxSisasDmp2NoGvm2jv8zIdZm9hqNd5ppTay5Y/9hFrdmN+25aYIs/bO0whSpxclaB7enpSpUoVTp8+TWBgINnZ2SQmJuY4JyYmhsDAQAACAwNvqva7vn39nFtxcHDA3d09x48oXHQVK9Cr42YectQQa07jlQ+H4zM7vOjMlaaqmE+eQQn/m1nz25NgTgesS453dkkgeqoD6c82sXOQQpQ8uUpSqampnDlzhlKlStGgQQP0ej0bN260HT9x4gRRUVGEhYUBEBYWxqFDh4iNvTFb9YYNG3B3d6d69eq5CUXY2cUOpXnb17qQXIv5r+PzXdFoQd1K2a8O0OOZfky+VhEAraIhosFSZn4xRRKVEAXsvpLU66+/ztatWzl37hw7duzg2WefRavV0qNHDzw8POjbty8jR45k8+bNRERE0KdPH8LCwmja1DrCv02bNlSvXp1evXrx999/s379et5++20GDx6Mg4NDvlygyD8aZ2c0daqR9ltFloyYBMC4mNpUXJJQdFpQt2BJT0eNOMKsn57gQFYWV83WdaxqGJx4Z+J3XP65OroKQSh6WYpciPx2X4sedu/enT///JP4+Hj8/Pxo3rw5H330EZUqWct1MzMzGTVqFIsXLyYrK4u2bdsyffr0HF1558+fZ+DAgWzZsgUXFxd69+7NJ598gk5377fHZNFD+1McHDj9XXUiHpmOh8Z6P3JMTF0OPReM+XQhGQ+VS4pOh2IwEPNiHfy7RvFb6BrbsQRzOo0XjaLimMJdFCJEYXWvix7KyrzivuhKBRLXNpjGg/fxRekdWLCwOs0HgHcWvEC5D3fYOcL8kdW+EUO/+JFy+ngaO1j/5q6a0xh7uQ2n3q+O8+6zmK/G2zlKIYoOSVIiX5ya14BTj3+LVtEw+VpFZq5uS8W396CazUW6i++eKAqW5nXp9e2vvOh+1bbbrFqoOWsIQe8XzwQtRH641yQlE8yKu9I4O2NuWR+v7d6sbPk1WkXDT6nufLWxDcFvhqOaTMU/QQGoKpq/9jN+xzO8H1edi6ZUwFpY8U3v6Vz9tQrmlvXR1qhq50CFKD6kJSXuTFGgSS3Wr1gAQJZqpPrSoYQsSkPdc8jOwdlX7KBmlO92hpUh63Psb7SvKz7PnC0ZrUshHpC0pESuaRwdOfl1I95aZE1QZ4yp9DjzJKGfRpb4BAXgP30H55ZX4vnIR1mT7mjb/0vt7xh6/DAXltUgrXMTNDVD7/AsQog7kZaUuCVFp+P0pw050X06WkVDy8PPkLkgEI8fiu74p/xkbNOQyE4adrX/An+tS45jH14NZe4fLan63lEAVFXFkpJijzCFKDSkcEI8MEVv4MTUuhzp8BXOGgPdIx/j9Nyq1hkkxG0pegPaMoGcn+xGgHsKv1dbiVaxdlZkqUbOGq1LglyzODJ4yhC0mSreJzLRbN1vz7CFsAtJUuKBaBwdOfVRXY50n4aDoufxYx3QP5+FOSb27g8WNhpnZ651rkPfcavo73H5tuctTfXg7YhnCHkjHvOVaGsRihAlgCQpcf8UhZPTG3Hy6RnoFS1PHG9P3JIgfGdJC+pBnfsoDFP5TAAUjcpfzb+ilM41xzlm1UKyJZNGS0fiGKeh3NeHpDtQFHuSpMR901UI4oPNy2ngYKDT6cfJfE6RFlReUhSMrepjcbB2Aab76vjr46k4/OtvON2STfu+g3C8Yi1vJ/KSJCxRLN1rkrqvpTpE8Xb0XX8aOBhIsmQQuTQE/5gHG5ya0r0pWR4Kvt9ICywHVUX/R4Rt09nLi2r1h4JGpVXjw3xbbjvOGgN/fDfLdk6t8BcxnnXD4ZpCmU9ksLAoeSRJiRsUa6N6blI1Si04jPkBnyamfRabH5lGt+TXcV8s1YC3Y05IIGSY9f25XK4sTR4dyLT3p9qmXQI42uwHaAY/pbozffdz6JOzUPcetlfIQhQ4SVLCpuyvOkIvDKTCr6moyQ8+DipgtQM8ApleGmTlr3tjunARzwUXGZU6hLi6Gjp12M54/whbV2Bn12Q6/zCHLRka+mzqi+tJPWW+2C2FFqLYk3tSIs8pegP+fzrS0ecAsxvUkXsqD0AXGIDq5sKxN71pVDWSCeVWUUl/o+DiqjmNtWnlmf5hF3w2nsN0JdqO0Qpx/2TGCWE3qjGbU9Oq08LpCpe+LwtNa9s7pCLHFB2D+dRZqvTdS1LzeJ5/ezRhf3fmiimVdEs2vloXXnS/ys7PZuK01ETMa81Ao7V32ELkOUlSIl94LNtHl6MvcLDxYlLLOdk7nCLP8/twPDtfoW/9jtT9q1+OY8sr/cHWNz7n4hhZNVgUP5KkRL5QjdnEJLrZO4xixZKejvlqPBWmQZMxA2l77Cl2ZxlJtWTioXFi3cDPYGNZTs5sbO9QhcgzkqREvqk88ipXTKlcaYl1NnWRJ5Qdf+P5fTjKE7GMr9aCV84/CUCQzpX11VYz4dFlXBzXjKSeTe0cqRC5J0lK5BtzTCzNV7zO/o5TqLZXi9Kghr1DKlZUYzaWzEwSRpWlZd9+9IlqAUB3twSODJnOnI++oOpePWc/DbNzpEI8OElSIt+oJhPBK7NZnx7IlFJ7Mbk72Duk4mnnQRzW7eHyaxVy7K5hcGJq6T288MRWosY3k9asKJIkSYl8pd2yj+WxDe0dRomgOXiKJzr2osmbA3k7thbplmwAxvsdJaLfFDofjSGxl7SqRNEi46REvovvG4Zb98ucO+dPlX577B1OyaDRcumNJjzT/S/G+x1Ar1jL0w9mZ9Jx4xAAgn7W4LT+AKrJKCsIiwInE8yKQiW+bxjJrdMJ7vG3vUMpUXSBAUR3rEj5nqdp73eIvh43Bv2eNKZxweTOgJ/6E/xzOkq4/G5EwZEkJYTIIerdZmx45TMASmmdbQsyAixI9mX8jmeoNvosarZRZgkR+U6SlBAiB42jI4qLMygKxyZWwMsnlQYBF/m23HbAuq5VlCmdL6+25FgDmRNQ5C+ZFkkIkYMlMxNz/DXMV+Op0icCv6dPcHhKLbZnWjhjTEWraAjWuzLMdwvazaU5ObshugpB6CoEoXF2tnf4ooSSlpQQJZmioBgMZDxeh6t90jjY9HtbN6BZtZChWisEH313BN7fyfpgIu9Id58Q4r5oHB058Wld3m27gkaO56lhuDHn4pykQA6ll81x/vpfG1N+VRKKxYLl0Emw3P8KZBpnZ9Sqwbc9ro1NwHTp8n0/ryj8JEkJIR5Y6nNNiG6m8MJjf/G+35E7n2vJpNYvr6HJ1OC7X8Hz+9u0uJrW5nTXnN2GSkAWpx+de9vnrrj8VUJe23Xf8YvCT5aPF0I8MNdlu6i8DPZWqES99o/Q5pUdNHY5S2fX5JvP1TgS+Yx1yfuTndPYMi7kls9ZybCQVk733tpKMKfjdEWWHynppCUlhLgnalgdLjzuctP+RzvsY3qZnQ/0nCvTXBn7/Yu3PGZIhsApOx7oeUXhJy0pIUSeUsL/JugWPXnnvyvDQ7M74eOUft/PmfF2KYL+kkQkbk+SlBAiV0wXL+H6BGQ9wGM1yLL34s5knJQQQohCS5KUEEKIQkuSlBBCiEJLkpQQQohCS5KUEEKIQkuSlBBCiEJLkpQQQohCS5KUEEKIQqtIDua9PpOTCSMUuUmdhBBCmDACNz7Pb6dIJqn4+HgAtrHWzpEIIYTIjZSUFDw8PG57vEgmKW9vbwCioqLueHHFWXJyMuXKlePChQt3nJyxuJLrl+uX6y/a16+qKikpKZQuXfqO5xXJJKXRWG+leXh4FNlfUF5xd3cv0e+BXL9cv1x/0b3+e2lkSOGEEEKIQkuSlBBCiEKrSCYpBwcHxo8fj4ODg71DsZuS/h7I9cv1y/WXjOsvkivzCiGEKBmKZEtKCCFEySBJSgghRKElSUoIIUShJUlKCCFEoSVJSgghRKFVJJPU119/TYUKFXB0dKRJkybs3r3b3iHliT///JMOHTpQunRpFEVh5cqVOY6rqsq7775LqVKlcHJyonXr1pw6dSrHOdeuXaNnz564u7vj6elJ3759SU1NLcCreHATJkygUaNGuLm54e/vzzPPPMOJEydynJOZmcngwYPx8fHB1dWVzp07ExMTk+OcqKgo2rdvj7OzM/7+/owePRqTyVSQl/JAZsyYQe3atW2zCISFhbFu3Trb8eJ87bfyySefoCgKw4cPt+0rzu/Be++9h6IoOX5CQ0Ntx4vztd+RWsQsWbJENRgM6nfffaceOXJE7devn+rp6anGxMTYO7RcW7t2rfrWW2+pK1asUAH1559/znH8k08+UT08PNSVK1eqf//9t/r000+rwcHBakZGhu2cJ554Qq1Tp466c+dO9a+//lIrV66s9ujRo4Cv5MG0bdtWnTt3rnr48GH1wIED6pNPPqkGBQWpqamptnMGDBiglitXTt24caO6d+9etWnTpmqzZs1sx00mk1qzZk21devW6v79+9W1a9eqvr6+6tixY+1xSffll19+UdesWaOePHlSPXHihDpu3DhVr9erhw8fVlW1eF/7f+3evVutUKGCWrt2bXXYsGG2/cX5PRg/frxao0YN9cqVK7afuLg42/HifO13UuSSVOPGjdXBgwfbts1ms1q6dGl1woQJdowq7/03SVksFjUwMFCdOHGibV9iYqLq4OCgLl68WFVVVT169KgKqHv27LGds27dOlVRFPXSpUsFFnteiY2NVQF169atqqpar1ev16vLli2znXPs2DEVUMPDw1VVtSZ6jUajRkdH286ZMWOG6u7urmZlZRXsBeQBLy8vdfbs2SXq2lNSUtSQkBB1w4YN6iOPPGJLUsX9PRg/frxap06dWx4r7td+J0Wquy87O5uIiAhat25t26fRaGjdujXh4eF2jCz/RUZGEh0dnePaPTw8aNKkie3aw8PD8fT0pGHDhrZzWrdujUajYdeuXQUec24lJSUBN2a9j4iIwGg05ngPQkNDCQoKyvEe1KpVi4CAANs5bdu2JTk5mSNHjhRg9LljNptZsmQJaWlphIWFlahrHzx4MO3bt89xrVAyfv+nTp2idOnSVKxYkZ49exIVFQWUjGu/nSI1C/rVq1cxm805fgkAAQEBHD9+3E5RFYzo6GiAW1779WPR0dH4+/vnOK7T6fD29radU1RYLBaGDx/OQw89RM2aNQHr9RkMBjw9PXOc+9/34Fbv0fVjhd2hQ4cICwsjMzMTV1dXfv75Z6pXr86BAweK/bUDLFmyhH379rFnz56bjhX333+TJk2YN28eVatW5cqVK7z//vu0aNGCw4cPF/trv5MilaREyTF48GAOHz7Mtm3b7B1KgapatSoHDhwgKSmJ5cuX07t3b7Zu3WrvsArEhQsXGDZsGBs2bMDR0dHe4RS4du3a2f5du3ZtmjRpQvny5Vm6dClOTk52jMy+ilR3n6+vL1qt9qaKlpiYGAIDA+0UVcG4fn13uvbAwEBiY2NzHDeZTFy7dq1IvT9Dhgxh9erVbN68mbJly9r2BwYGkp2dTWJiYo7z//se3Oo9un6ssDMYDFSuXJkGDRowYcIE6tSpw5dfflkirj0iIoLY2Fjq16+PTqdDp9OxdetWpk6dik6nIyAgoNi/B//m6elJlSpVOH36dIn4/d9OkUpSBoOBBg0asHHjRts+i8XCxo0bCQsLs2Nk+S84OJjAwMAc156cnMyuXbts1x4WFkZiYiIRERG2czZt2oTFYqFJkyYFHvP9UlWVIUOG8PPPP7Np0yaCg4NzHG/QoAF6vT7He3DixAmioqJyvAeHDh3Kkaw3bNiAu7s71atXL5gLyUMWi4WsrKwSce2tWrXi0KFDHDhwwPbTsGFDevbsaft3cX8P/i01NZUzZ85QqlSpEvH7vy17V27cryVLlqgODg7qvHnz1KNHj6r9+/dXPT09c1S0FFUpKSnq/v371f3796uAOnnyZHX//v3q+fPnVVW1lqB7enqqq1atUg8ePKh27NjxliXo9erVU3ft2qVu27ZNDQkJKTIl6AMHDlQ9PDzULVu25CjDTU9Pt50zYMAANSgoSN20aZO6d+9eNSwsTA0LC7Mdv16G26ZNG/XAgQPqb7/9pvr5+RWJMtw333xT3bp1qxoZGakePHhQffPNN1VFUdTff/9dVdXife238+/qPlUt3u/BqFGj1C1btqiRkZHq9u3b1datW6u+vr5qbGysqqrF+9rvpMglKVVV1WnTpqlBQUGqwWBQGzdurO7cudPeIeWJzZs3q8BNP71791ZV1VqG/s4776gBAQGqg4OD2qpVK/XEiRM5niM+Pl7t0aOH6urqqrq7u6t9+vRRU1JS7HA19+9W1w6oc+fOtZ2TkZGhDho0SPXy8lKdnZ3VZ599Vr1y5UqO5zl37pzarl071cnJSfX19VVHjRqlGo3GAr6a+/fyyy+r5cuXVw0Gg+rn56e2atXKlqBUtXhf++38N0kV5/egW7duaqlSpVSDwaCWKVNG7datm3r69Gnb8eJ87Xci60kJIYQotIrUPSkhhBAliyQpIYQQhZYkKSGEEIWWJCkhhBCFliQpIYQQhZYkKSGEEIWWJCkhhBCFliQpIYQQ/2+vjgUAAAAABvlbT2NHSbQlKQC2JAXAlqQA2Ara3UNwHvZBSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_seg_artery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "59d8ce5f-a11a-4d21-9366-20b2eaaf79b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_artery = img_seg_artery * im_th\n",
    "plt.imshow(overlay_artery)\n",
    "im = Image.fromarray(overlay_artery*255).convert('RGB')\n",
    "im.save(img_name[:-4] + '_'+\"Artery\"+\".jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "449f5696-cf4e-4c06-97e8-59d7bb9e464b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of artery used for the inference is 9.10%\n"
     ]
    }
   ],
   "source": [
    "print(\"The percentage of artery used for the inference is {:.2f}%\".format(np.count_nonzero(overlay_artery)/np.count_nonzero(img_seg_artery)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0d7168a-8501-4ea3-945d-637ab2caf81e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_seg_vein = img_seg_vein.resize((576,576))\n",
    "img_seg_vein  = np.array(img_seg_vein)\n",
    "th, img_seg_vein = cv2.threshold(img_seg_vein, 0.1, 1.0, cv2.THRESH_BINARY)\n",
    "img_seg_vein = img_seg_vein.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f109d3a0-e355-42b0-b78a-462f64ff3749",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14a11c111f40>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_seg_vein)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7ada6fc7-aaeb-4e1b-b84e-70d29a152fd0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_vein = img_seg_vein * im_th\n",
    "plt.imshow(overlay_vein)\n",
    "im = Image.fromarray(overlay_vein*255).convert('RGB')\n",
    "im.save(img_name[:-4] +\"_\"+\"Vein\"+\".jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e9a9426a-ce0d-4a96-95c2-c373da73b470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of vein used for the inference is 10.00%\n"
     ]
    }
   ],
   "source": [
    "print(\"The percentage of vein used for the inference is {:.2f}%\".format(np.count_nonzero(overlay_vein)/np.count_nonzero(img_seg_vein)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb982d54-e511-49d1-9c36-f0220ad2424a",
   "metadata": {},
   "source": [
    "## Reading the optic cup and disc & processing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a9716190-8ba8-4cb1-89ee-a7deee5a0621",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_seg_optic_disc_cup = Image.open(os.path.join(\"/red/ruogu.fang/leem.s/NSF-SCH/code/data/regression_segmentation/M2/optic_disc_cup/raw\", img_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "824a125a-ebc3-4c47-9776-d0da3c88c6d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_seg_optic_disc_cup = img_seg_optic_disc_cup.resize((576,576))\n",
    "img_seg_optic_disc_cup  = np.array(img_seg_optic_disc_cup)\n",
    "img_seg_optic_disc_cup = img_seg_optic_disc_cup.astype(np.float32)\n",
    "img_seg_optic_disc_cup = img_seg_optic_disc_cup/255\n",
    "#th, img_seg_vessels = cv2.threshold(img_seg_vessels, 0.1, 1.0, cv2.THRESH_BINARY)\n",
    "img_seg_optic_disc_cup = img_seg_optic_disc_cup.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "baa271be-6fb0-4785-be64-43b8efc196e4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14a136d00760>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_seg_optic_disc = img_seg_optic_disc_cup[:, :, 0]\n",
    "#img_seg_optic_disc[350:550, 50:250]=0\n",
    "plt.imshow(img_seg_optic_disc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c8f0e6a-3697-4185-9b21-99f3ceb8cb42",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_optic_disc = img_seg_optic_disc * im_th\n",
    "plt.imshow(overlay_optic_disc)\n",
    "im = Image.fromarray(overlay_optic_disc*255).convert('RGB')\n",
    "im.save(img_name[:-4] +\"_\"+\"Disc\"+\".jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eba7190c-c603-41f9-8ffc-1ddc1fe37d98",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of disc used for the inference is 0.00%\n"
     ]
    }
   ],
   "source": [
    "print(\"The percentage of disc used for the inference is {:.2f}%\".format(np.count_nonzero(overlay_optic_disc)/np.count_nonzero(img_seg_optic_disc)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4801b5ff-86c4-4429-877a-b27955e8dc6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14a136d18700>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_seg_optic_cup = img_seg_optic_disc_cup[:, :, 2]\n",
    "#img_seg_optic_cup[350:550, 50:250]=0\n",
    "plt.imshow(img_seg_optic_cup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "93285b76-0b86-4b70-9ad1-a73e4d0cfaf9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_optic_cup = img_seg_optic_cup * im_th\n",
    "plt.imshow(overlay_optic_cup)\n",
    "im = Image.fromarray(overlay_optic_cup*255).convert('RGB')\n",
    "im.save(img_name[:-4] +\"_\"+\"Cup\"+\".jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "501b35b9-df5a-49d6-95ff-ad10cdc54413",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of cup used for the inference is 0.00%\n"
     ]
    }
   ],
   "source": [
    "print(\"The percentage of cup used for the inference is {:.2f}%\".format(np.count_nonzero(overlay_optic_cup)/np.count_nonzero(img_seg_optic_cup)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02f6ed78-007c-4076-a8a5-43f4678dc55f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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