Task_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9z6jHpGb8oaN"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xTClCeH0JQq0",
    "outputId": "4188be1e-1f56-448c-fb4e-8727042acb6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XoIKbOcmlppo"
   },
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ytRNt9Yslppp"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.functional as F\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import transforms\n",
    "from torchvision import models\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "import os\n",
    "import time\n",
    "from PIL import Image\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zypD7FV0lppq"
   },
   "source": [
    "### Set Seed and Device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ciPn53AOlppq",
    "outputId": "075bf2e5-8fa2-408d-b136-23bc097a4f57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "elif torch.backends.mps.is_available():\n",
    "    device = torch.device('mps')\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mS56vpRUlppr"
   },
   "source": [
    "### Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "CBJJQt2glppr"
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
    "])\n",
    "\n",
    "train_dataset = torchvision.datasets.ImageFolder(root='/content/drive/MyDrive/Dataset/dataset_horse_vs_panda/train', transform=transform)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "test_dataset = torchvision.datasets.ImageFolder(root='/content/drive/MyDrive/Dataset/dataset_horse_vs_panda/test', transform=transform)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4EZgRqLVlppr"
   },
   "source": [
    "### Define the train and test functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "A9qVWdsRlpps"
   },
   "outputs": [],
   "source": [
    "def test(model, data_loader, criterion, device):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        running_loss = 0.0\n",
    "        running_corrects = 0\n",
    "        for inputs, labels in data_loader:\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            running_loss += loss.item() * inputs.size(0)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            running_corrects += torch.sum(preds == labels.data)\n",
    "        accuracy = running_corrects.double() / len(data_loader.dataset)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "aTr7U870LZEN"
   },
   "outputs": [],
   "source": [
    "def log_test_images(model, test_loader, writer, epoch, device):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (images, labels) in enumerate(test_loader):\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            img_grid = torchvision.utils.make_grid(images.cpu())\n",
    "            writer.add_image(f'Test Images/epoch_{epoch}', img_grid, epoch)\n",
    "            predictions_str = \" \".join([str(pred.item()) for pred in preds])\n",
    "            writer.add_text(f'Predictions/epoch_{epoch}', predictions_str, epoch)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "kW__boSSlpps"
   },
   "outputs": [],
   "source": [
    "def train(model, train_loader, criterion, optimizer, device, num_epochs=10, model_name=\"model\"):\n",
    "    writer = SummaryWriter(log_dir=f\"runs/{model_name}\")\n",
    "\n",
    "    step=0\n",
    "    model.train()\n",
    "    loss_history = []\n",
    "    train_accuracy_hist = []\n",
    "    test_accuracy_hist = []\n",
    "    for epoch in range(num_epochs):\n",
    "        running_loss = 0.0\n",
    "        for batch_idx, (images, labels) in enumerate(train_loader):\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            running_loss += loss.item()\n",
    "            train_accuracy = test(model, train_loader, criterion, device)\n",
    "            test_accuracy = test(model, test_loader, criterion, device)\n",
    "            writer.add_scalar(\"Loss/train\", loss.item(), step)\n",
    "            writer.add_scalar(\"Accuracy/train\", train_accuracy, step)\n",
    "            writer.add_scalar(\"Accuracy/test\", test_accuracy, step)\n",
    "            if (epoch==num_epochs):\n",
    "                log_test_images(model, test_loader, writer, epoch, device)\n",
    "            step += 1\n",
    "        loss_history.append(running_loss / len(train_loader))\n",
    "        train_accuracy_hist.append(train_accuracy)\n",
    "        test_accuracy_hist.append(test_accuracy)\n",
    "        print(f\"Epoch {epoch + 1}/{num_epochs} Loss: {running_loss / len(train_loader)} Training Accuracy: {train_accuracy} Test Accuracy: {test_accuracy}\")\n",
    "    writer.close()\n",
    "    return loss_history, train_accuracy_hist, test_accuracy_hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2SX6pmt9yN1"
   },
   "source": [
    "### Function for Plotting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "w3UVOtW8lppt"
   },
   "outputs": [],
   "source": [
    "def plot_data(loss):\n",
    "  for i in range(len(loss[0])):\n",
    "      loss[1][i] = loss[1][i].item()\n",
    "      loss[2][i] = loss[2][i].item()\n",
    "  plt.figure(figsize = (10,5))\n",
    "  plt.subplot(1,2,1)\n",
    "  plt.plot(loss[0])\n",
    "  plt.xlabel(\"Epoch\")\n",
    "  plt.ylabel(\"Loss\")\n",
    "  plt.title('Loss vs Epochs')\n",
    "  plt.subplot(1,2,2)\n",
    "  plt.plot(loss[1], label='train')\n",
    "  plt.plot(loss[2], label='test')\n",
    "  plt.xlabel(\"Epoch\")\n",
    "  plt.ylabel(\"Accuracy\")\n",
    "  plt.title('Accuracy vs Epoch')\n",
    "  plt.legend()\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5sdEhu918dIi"
   },
   "source": [
    "# VGG (1 Block)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9h4ujkw8xpg"
   },
   "source": [
    "### Define Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P3BOwpGrlppr",
    "outputId": "ec3998b4-3c3a-4bf2-86ff-d44f579ab103"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vgg_1_block(\n",
      "  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (relu1): ReLU()\n",
      "  (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=401408, out_features=128, bias=True)\n",
      "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
      ")\n",
      "Number of parameters: 51381506\n"
     ]
    }
   ],
   "source": [
    "class vgg_1_block(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(vgg_1_block, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.fc1 = nn.Linear(32 * 112 * 112, 128)\n",
    "        self.fc2 = nn.Linear(128, 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.maxpool1(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "vgg1b = vgg_1_block().to(device)\n",
    "print(vgg1b)\n",
    "num_params_vgg1b = sum(p.numel() for p in vgg1b.parameters() if p.requires_grad)\n",
    "print(f'Number of parameters: {num_params_vgg1b}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Uhlv24G8250"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o19Se_hWlpps",
    "outputId": "42d0353d-c060-4916-a4d6-057bcc411da7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10 Loss: 36.705815601348874 Training Accuracy: 0.7625000000000001 Test Accuracy: 0.675\n",
      "Epoch 2/10 Loss: 6.4073793411254885 Training Accuracy: 0.8375 Test Accuracy: 0.7000000000000001\n",
      "Epoch 3/10 Loss: 5.023668873310089 Training Accuracy: 0.9437500000000001 Test Accuracy: 0.75\n",
      "Epoch 4/10 Loss: 0.7585530616342695 Training Accuracy: 0.9625 Test Accuracy: 0.8250000000000001\n",
      "Epoch 5/10 Loss: 1.010403851023875 Training Accuracy: 0.9812500000000001 Test Accuracy: 0.8250000000000001\n",
      "Epoch 6/10 Loss: 0.31349697411060334 Training Accuracy: 0.9875 Test Accuracy: 0.8250000000000001\n",
      "Epoch 7/10 Loss: 0.07664954257197679 Training Accuracy: 0.95625 Test Accuracy: 0.75\n",
      "Epoch 8/10 Loss: 0.22403884520740291 Training Accuracy: 1.0 Test Accuracy: 0.8250000000000001\n",
      "Epoch 9/10 Loss: 0.037602495402091304 Training Accuracy: 1.0 Test Accuracy: 0.8\n",
      "Epoch 10/10 Loss: 9.561210632114125e-06 Training Accuracy: 1.0 Test Accuracy: 0.8250000000000001\n",
      "Training time: 66.70412063598633\n",
      "Test accuracy of VGG 1 Block = 0.8250000000000001\n",
      "Train accuracy of VGG 1 Block = 1.0\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg1b.parameters(), lr=0.001)\n",
    "start_time = time.time()\n",
    "vgg1b_items = train(vgg1b, train_loader, loss_fn, optimizer, device, num_epochs=10, model_name = \"vgg1b\")\n",
    "end_time = time.time()\n",
    "vgg1b_time = end_time - start_time\n",
    "print(f\"Training time: {vgg1b_time}\")\n",
    "vgg1b_test_accuracy = test(vgg1b, test_loader, loss_fn, device)\n",
    "print(f\"Test accuracy of VGG 1 Block = {vgg1b_test_accuracy}\")\n",
    "vgg1b_train_accuracy = test(vgg1b, train_loader, loss_fn, device)\n",
    "print(f\"Train accuracy of VGG 1 Block = {vgg1b_train_accuracy}\")\n",
    "torch.save(vgg1b.state_dict(), 'vgg1b.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "dhAsjL19rGMO",
    "outputId": "9f7edaf7-e4aa-4586-aa48-101cec8a3d5d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 469,
       "width": 842
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(vgg1b_items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xonkQSY0-RUG"
   },
   "source": [
    "# VGG (3 Block)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CMKkO85q-akq"
   },
   "source": [
    "### Define Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z0lv6iJelppt",
    "outputId": "968f5508-8b7b-4498-a004-0b2ebcb92d48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vgg_3_block(\n",
      "  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (relu1): ReLU()\n",
      "  (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (relu2): ReLU()\n",
      "  (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (relu3): ReLU()\n",
      "  (maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=100352, out_features=128, bias=True)\n",
      "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
      ")\n",
      "Number of trainiable parameters: 12938690\n"
     ]
    }
   ],
   "source": [
    "class vgg_3_block(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(vgg_3_block, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.fc1 = nn.Linear(128 * 28 * 28, 128)\n",
    "        self.fc2 = nn.Linear(128, 2)\n",
    "\n",
    "    def forward(self,x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.maxpool1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.relu2(x)\n",
    "        x = self.maxpool2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.relu3(x)\n",
    "        x = self.maxpool3(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "vgg3b = vgg_3_block().to(device)\n",
    "print(vgg3b)\n",
    "print(f\"Number of trainiable parameters: {sum(p.numel() for p in vgg3b.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y_4JQGamBG_V"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mTToBQmRlppt",
    "outputId": "e6220028-86cb-4f58-e4b4-0329053f227b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10 Loss: 2.854408836364746 Training Accuracy: 0.6375000000000001 Test Accuracy: 0.675\n",
      "Epoch 2/10 Loss: 0.5712152004241944 Training Accuracy: 0.7312500000000001 Test Accuracy: 0.625\n",
      "Epoch 3/10 Loss: 0.4858014523983002 Training Accuracy: 0.80625 Test Accuracy: 0.8\n",
      "Epoch 4/10 Loss: 0.37473438382148744 Training Accuracy: 0.90625 Test Accuracy: 0.775\n",
      "Epoch 5/10 Loss: 0.2325326532125473 Training Accuracy: 0.925 Test Accuracy: 0.8\n",
      "Epoch 6/10 Loss: 0.15102850645780563 Training Accuracy: 0.93125 Test Accuracy: 0.8250000000000001\n",
      "Epoch 7/10 Loss: 0.14471316635608672 Training Accuracy: 0.9875 Test Accuracy: 0.8500000000000001\n",
      "Epoch 8/10 Loss: 0.06661008410155773 Training Accuracy: 0.9750000000000001 Test Accuracy: 0.875\n",
      "Epoch 9/10 Loss: 0.05058839954435825 Training Accuracy: 1.0 Test Accuracy: 0.8250000000000001\n",
      "Epoch 10/10 Loss: 0.02330785132944584 Training Accuracy: 1.0 Test Accuracy: 0.8500000000000001\n",
      "Training time: 72.6794114112854\n",
      "Test accuracy of VGG 3 Block = 0.8500000000000001\n",
      "Train accuracy of VGG 3 Block = 1.0\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg3b.parameters(), lr=0.001)\n",
    "start_time = time.time()\n",
    "vgg3b_items = train(vgg3b, train_loader, loss_fn, optimizer, device, num_epochs=10, model_name = \"vgg3b\")\n",
    "end_time = time.time()\n",
    "vgg3b_time = end_time - start_time\n",
    "print(f\"Training time: {vgg3b_time}\")\n",
    "vgg3b_test_accuracy = test(vgg3b, test_loader, loss_fn, device)\n",
    "print(f\"Test accuracy of VGG 3 Block = {vgg3b_test_accuracy}\")\n",
    "vgg3b_train_accuracy = test(vgg3b, train_loader, loss_fn, device)\n",
    "print(f\"Train accuracy of VGG 3 Block = {vgg3b_train_accuracy}\")\n",
    "torch.save(vgg3b.state_dict(), 'vgg1b.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "taqYg6ihuO8r",
    "outputId": "91fdce33-278c-42dc-98c6-f91f5da947ee"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 469,
       "width": 846
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(vgg3b_items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ANUKlAeP_iQm"
   },
   "source": [
    "# VGG (3 Block) with Data Augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "30_JZCfHA9nX"
   },
   "source": [
    "### Create Data Loader with Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "S9uTl7wKlppt"
   },
   "outputs": [],
   "source": [
    "transform_augmented = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomVerticalFlip(),\n",
    "    transforms.RandomRotation(20),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
    "])\n",
    "\n",
    "train_dataset_augmented = torchvision.datasets.ImageFolder(root='/content/drive/MyDrive/Dataset/dataset_horse_vs_panda/train', transform=transform_augmented)\n",
    "train_loader_augmented = DataLoader(train_dataset_augmented, batch_size=32, shuffle=True)\n",
    "\n",
    "vgg3b_aug = vgg_3_block().to(device)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg3b_aug.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1W7Mk6oBAYe"
   },
   "source": [
    "### Training Model on Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_GUqW_qklppt",
    "outputId": "6c1feddf-c12e-4087-c47d-84e1faccc953"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10 Loss: 3.1688533782958985 Training Accuracy: 0.6687500000000001 Test Accuracy: 0.6000000000000001\n",
      "Epoch 2/10 Loss: 0.6434512495994568 Training Accuracy: 0.6625000000000001 Test Accuracy: 0.47500000000000003\n",
      "Epoch 3/10 Loss: 0.5210174202919007 Training Accuracy: 0.7937500000000001 Test Accuracy: 0.775\n",
      "Epoch 4/10 Loss: 0.33364967703819276 Training Accuracy: 0.8562500000000001 Test Accuracy: 0.7250000000000001\n",
      "Epoch 5/10 Loss: 0.2255726546049118 Training Accuracy: 0.9 Test Accuracy: 0.8250000000000001\n",
      "Epoch 6/10 Loss: 0.18489379137754441 Training Accuracy: 0.9812500000000001 Test Accuracy: 0.8500000000000001\n",
      "Epoch 7/10 Loss: 0.10548855587840081 Training Accuracy: 0.9875 Test Accuracy: 0.8500000000000001\n",
      "Epoch 8/10 Loss: 0.07878597751259804 Training Accuracy: 0.99375 Test Accuracy: 0.9\n",
      "Epoch 9/10 Loss: 0.03509223349392414 Training Accuracy: 1.0 Test Accuracy: 0.9\n",
      "Epoch 10/10 Loss: 0.02192441876977682 Training Accuracy: 1.0 Test Accuracy: 0.9\n",
      "Training time: 72.79175353050232\n",
      "Test accuracy of VGG 3 Block with data augmentation = 0.9\n",
      "Train accuracy of VGG 3 Block with data augmentation = 1.0\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg3b_aug.parameters(), lr=0.001)\n",
    "start_time = time.time()\n",
    "vgg3b_aug_items = train(vgg3b_aug, train_loader, loss_fn, optimizer, device, num_epochs=10, model_name = \"vgg3b_aug\")\n",
    "end_time = time.time()\n",
    "vgg3b_aug_time = end_time - start_time\n",
    "print(f\"Training time: {vgg3b_aug_time}\")\n",
    "vgg3b_aug_test_accuracy = test(vgg3b_aug, test_loader, loss_fn, device)\n",
    "print(f\"Test accuracy of VGG 3 Block with data augmentation = {vgg3b_aug_test_accuracy}\")\n",
    "vgg3b_aug_train_accuracy = test(vgg3b_aug, train_loader, loss_fn, device)\n",
    "print(f\"Train accuracy of VGG 3 Block with data augmentation = {vgg3b_aug_train_accuracy}\")\n",
    "torch.save(vgg3b_aug.state_dict(), 'vgg3b_aug.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "RfI2kCDGlppt",
    "outputId": "3794c753-afbd-4c7b-f9c1-634693d8349a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 469,
       "width": 846
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(vgg3b_aug_items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_J8m8XYCBLuS"
   },
   "source": [
    "# VGG 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ns1eag4BNrp"
   },
   "source": [
    "## VGG 16 (Fine-Tuning all layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Nd3NrsUBVPX"
   },
   "source": [
    "### Setup the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bLGP6dYhlppu",
    "outputId": "2d47ec9a-c2f4-4faa-cb12-dedf0cf5272e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n",
      "100%|██████████| 528M/528M [00:03<00:00, 143MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=2, bias=True)\n",
      "  )\n",
      ")\n",
      "Number of trainable parameters: 134268738\n"
     ]
    }
   ],
   "source": [
    "vgg16_all = models.vgg16(pretrained=True)\n",
    "vgg16_all.classifier[6] = nn.Linear(4096, 2)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg16_all.parameters(), lr=0.001)\n",
    "vgg16_all.to(device)\n",
    "print(vgg16_all)\n",
    "vgg16_params = sum(p.numel() for p in vgg16_all.parameters() if p.requires_grad)\n",
    "print(f\"Number of trainable parameters: {vgg16_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h553Dk7Vlppu",
    "outputId": "a3a8d22a-c485-459f-c567-6392e0191064"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25 Loss: 42.7006019949913 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 2/25 Loss: 1.1842856645584106 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 3/25 Loss: 1.289179253578186 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 4/25 Loss: 12.673251104354858 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 5/25 Loss: 3.848039388656616 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 6/25 Loss: 0.7343393683433532 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 7/25 Loss: 0.6905558347702027 Training Accuracy: 0.6937500000000001 Test Accuracy: 0.675\n",
      "Epoch 8/25 Loss: 0.7223543524742126 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 9/25 Loss: 0.6954092144966125 Training Accuracy: 0.6875 Test Accuracy: 0.7250000000000001\n",
      "Epoch 10/25 Loss: 0.679206371307373 Training Accuracy: 0.70625 Test Accuracy: 0.8\n",
      "Epoch 11/25 Loss: 0.5928566336631775 Training Accuracy: 0.71875 Test Accuracy: 0.7250000000000001\n",
      "Epoch 12/25 Loss: 0.5701894760131836 Training Accuracy: 0.74375 Test Accuracy: 0.65\n",
      "Epoch 13/25 Loss: 0.5315627932548523 Training Accuracy: 0.7375 Test Accuracy: 0.7250000000000001\n",
      "Epoch 14/25 Loss: 0.771137124300003 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 15/25 Loss: 0.691971731185913 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 16/25 Loss: 0.6933342337608337 Training Accuracy: 0.5125000000000001 Test Accuracy: 0.525\n",
      "Epoch 17/25 Loss: 0.690766429901123 Training Accuracy: 0.5125000000000001 Test Accuracy: 0.525\n",
      "Epoch 18/25 Loss: 0.6857305884361267 Training Accuracy: 0.525 Test Accuracy: 0.525\n",
      "Epoch 19/25 Loss: 0.6823207020759583 Training Accuracy: 0.6937500000000001 Test Accuracy: 0.675\n",
      "Epoch 20/25 Loss: 0.6665484547615051 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 21/25 Loss: 0.6960962772369385 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 22/25 Loss: 0.8636711120605469 Training Accuracy: 0.55625 Test Accuracy: 0.55\n",
      "Epoch 23/25 Loss: 0.7054017663002015 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 24/25 Loss: 0.6970030546188355 Training Accuracy: 0.5 Test Accuracy: 0.5\n",
      "Epoch 25/25 Loss: 0.6940552234649658 Training Accuracy: 0.6312500000000001 Test Accuracy: 0.65\n",
      "Training time: 327.3699164390564\n",
      "Test accuracy of VGG 16 Block with fine-tuning all layers = 0.65\n",
      "Train accuracy of VGG 16 Block with fine-tuning all layers = 0.6312500000000001\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg16_all.parameters(), lr=0.001)\n",
    "start_time = time.time()\n",
    "vgg16_all_items = train(vgg16_all, train_loader, loss_fn, optimizer, device, num_epochs=25, model_name = \"vgg16_all\")\n",
    "end_time = time.time()\n",
    "vgg16_all_time = end_time - start_time\n",
    "print(f\"Training time: {vgg16_all_time}\")\n",
    "vgg16_all_test_accuracy = test(vgg16_all, test_loader, loss_fn, device)\n",
    "print(f\"Test accuracy of VGG 16 Block with fine-tuning all layers = {vgg16_all_test_accuracy}\")\n",
    "vgg16_all_train_accuracy = test(vgg16_all, train_loader, loss_fn, device)\n",
    "print(f\"Train accuracy of VGG 16 Block with fine-tuning all layers = {vgg16_all_train_accuracy}\")\n",
    "torch.save(vgg16_all.state_dict(), 'vgg16_all.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "8YCz4u1Slppu",
    "outputId": "a208cfbc-9952-46a3-839c-5a0a962b3674"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABqAAAAOrCAYAAAA4VUcwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzdd3hUZfr/8c+k94QkgDRpoSkqSBNQAbsoCCtrAyn2dXXVZcVddVddd3WVtbs/FUEQG3ZEsaAYEBGkiNJ7EwgloaRnksz5/ZFvjjlnJv1MJuX9uq5c15lnnnnOnWGic+ae+7ldhmEYAgAAAAAAAAAAABwSFOgAAAAAAAAAAAAA0LiQgAIAAAAAAAAAAICjSEABAAAAAAAAAADAUSSgAAAAAAAAAAAA4CgSUAAAAAAAAAAAAHAUCSgAAAAAAAAAAAA4igQUAAAAAAAAAAAAHEUCCgAAAAAAAAAAAI4iAQUAAAAAAAAAAABHkYACAAAAAAAAAACAo0hAAQAAAAAAAAAAwFEkoAAAAAAAAAAAAOAoElAAAAAAAAAAAABwFAkoAAAAAAAAAAAAOIoEFAAAAAAAAAAAABxFAgoAAAAAAAAAAACOIgEFAAAAAAAAAAAAR5GAAgAAAAAAAAAAgKNIQAEAAAAAAAAAAMBRJKAAAEC9t2jRIrlcLrlcLg0dOjTQ4QAAAAAAHFZ6zedyuQIdCgCHkIACAD8ZOnSo+cbp4YcfDnQ4CKCyyZOa/OzevTvQvwIAAACamHHjxlnekz7xxBOBDgmo12pzzTdr1qxAhw8AfkECCgAAAAAAAKasrCx9/PHHlrHXX389QNEAAICGKiTQAQAA0NT88Y9/rNb8uLg4P0UCAAAAeHv//feVm5trGdu0aZNWrlypfv36BSgqoOEYNWqU2rRpU+X5PXr08GM0ABA4JKAAAKhjL774YqBDAAAAAMpVttopMjJSeXl55jgJKKByd911F71rAUBswQcAAAAAAID/s2vXLi1ZskRSSU+b//73v+Z977zzjtxud6BCAwAADQwJKAAAAAAAAEiSZs+eLcMwJElDhgzRLbfcoubNm0uSjh49qs8++yyQ4QEAgAaEBBQANCBfffWVbrjhBnXt2lVxcXGKjIxU+/btNXr0aM2aNUuFhYVVWqewsFBvvvmmfve736lTp06KiYlRSEiIYmNjlZKSoosvvlj/+Mc/tGLFigrXWblype644w6deeaZatasmUJCQhQZGalWrVrprLPO0h/+8Ae99957ysnJqdHve/jwYYWGhsrlcik4OFj79++v8mO7desml8sll8ul999/3+t+wzA0d+5cXXfdderWrZvi4uIUHBys6OhodejQQeedd57uu+8+paamyuPx1Ch+f5o1a5b5+02cOFFSyb/r66+/rgsvvFBt27ZVeHi42rZtq1GjRumTTz6p9jmcer2V9cUXX+jWW29Vz549lZSUpNDQUCUkJOjMM8/Urbfeqnnz5qmoqKjK63377be65ppr1KlTJ0VERCgpKUnnnnuuXnzxxSrHt3nzZk2ZMkVnnXWWkpOTFRYWpoiICLVo0UJ9+vTRpEmT9Prrr+vYsWPV/n0BAAAaEsMwNHv2bPP29ddfr5CQEF1zzTXmWNnt+aqquLhY7733nsaPH69u3bqpWbNmCg0NVVJSkgYMGKC77rpLCxcuNBNfFVm7dq3++te/asCAATrppJMUFhammJgYdevWTVdffbVmzJihEydOeD1u9+7d5vvnDh06VCnuDh06mI/ZvXt3lefs2LFDDzzwgHr37q3mzZsrKChIvXr18nrspk2b9Mwzz+h3v/udunXrptjYWIWGhqp58+bq27ev7rnnHm3cuLFKsZZVk+d7zZo15u/RrFkz5efnV+lcWVlZiomJMR+7du3aasd7+umnm49/5513qvy4W265xXxceX12/X3N6k/lvWYXLlyoa6+9Vp07d1ZkZKSaN2+uc845Ry+++KIKCgqqdY4NGzbo3nvvVe/evZWcnKzw8HC1bt1aQ4cO1RNPPKGMjIxqx13Tv9HybNmyRXfffbd69OihmJgYxcXF6YwzztDf/vY3paenVzs+AHXMAAD4xZAhQwxJhiTjoYceqtVahw4dMs4//3xzvfJ+unTpYqxcubLCtbZs2WL06NGj0rVKf7Zt2+a1RmFhoXHLLbdUeY0HHnigxr/7pZdeaq7z5JNPVukxP/74o/mY+Ph4Iy8vz3L/wYMHjYEDB1Y5/q+//rrG8RuGYaSmplrWc8LMmTPN9SZMmGAcOHDAGDRoUIW/x4gRI4zs7OxK13by9VZq/fr1Rt++fav0fF999dVejy/7HA4ZMsQoKCgwbr755grXOfPMM40jR45UGNdDDz1kBAcHVymusWPHVul3BQAAaKi+++47871PRESEceLECcMwDGPFihXmeGhoqHH48OFqrdm1a9cqvd+67777yl3n2LFjxtVXX224XK5K12nZsqXX43ft2mXe3759+yrF3r59e/Mxu3btqtKcV155xYiIiPCK6YwzzrA87ve//32VnhOXy2XcfffdRlFRUZVirs3z3adPH/O+N998s0rne/XVV83H9OvXr0qPsXviiSfMNYYPH16lx+Tn5xvNmjUzH/fDDz9Y7q/La9ZSZddLTU2t9Xr216zb7a70d+rRo4exZcuWStcuLCw07rzzzkqvhRISEoxZs2ZVKd7a/o0ahvU5NAzDeOmll4zw8PBy10lKSqryNSmAwAgRAKBeO3TokAYPHqwdO3aYY507d9aAAQMUHh6ujRs36scff5Qkbdu2TcOGDdOXX36pwYMHe62VlZWlCy64QL/++qskKSgoSL179za/SZSbm6v9+/frl19+qfCbRPfee6+mTZtm3m7Tpo369++v5s2by+PxKCMjQxs3btSWLVtq/fuPGzdOX3zxhSTprbfe0r333lvpY9566y3zeMyYMYqIiDBvFxcX67LLLtPq1avNsZ49e6pnz55KSEhQfn6+Dh48qF9++UVpaWm1jr8uFBYWavTo0frxxx8VHBysc845R507d1ZWVpYWL16sQ4cOSZI+/fRTjRgxQgsWLFBIiO+3AE6+3kotWrRII0eOVFZWljl28sknq3///kpMTFROTo62bNmiX375RYWFhVX6tuUtt9yi119/XUFBQRowYIC6d+8uj8ej5cuXm6+7n376SePHj9fnn3/uc43nnntOjzzyiHk7OTlZZ511llq1aiWXy6WjR49q8+bN2rRpk4qLiyuNCQAAoKErW910xRVXKC4uTpLUr18/de/eXZs3b1ZhYaHefvtt3XXXXZWuN2fOHI0fP95Smd61a1f17t1b8fHxyszM1IYNG7RhwwZ5PJ5y3wceOHBA5513nuX6IiEhQYMHD1arVq1UWFiovXv3avXq1crMzKxy9Y7T3n//fU2ZMkWS1Lp1aw0ePFjx8fE6cOCAjh49apm7d+9eSVJISIhOOeUUdenSRQkJCQoODtbhw4e1cuVK7d+/X4Zh6Nlnn1VBQYH+3//7fxWev7bP9y233KJbb71VkjRjxgyNHTu20t95xowZ5vFNN91U6XxfrrvuOv3tb3+Tx+PRggULdOTIEXPbx/J8/vnn5g4FKSkpGjhwoOX+urxmrSv33Xef+Tudfvrp6tWrlwzD0OrVq81KuU2bNum8887TsmXL1K5dO5/reDweXXnllZo3b545lpiYqKFDhyoxMVG//vqrUlNT5Xa7dfz4cU2cOFHHjx+v8G/eH3+js2bN0h/+8AdJJTuc9O3bV5GRkdq8ebOWLl0qwzCUkZGhkSNHatOmTYqPj6/8SQRQ9wKcAAOARsupCqiyFUDR0dHGO++84zVn5cqVRqdOncx57dq1M44dO+Y179lnnzXnnHLKKcbmzZt9ntPj8RgrVqww/vCHPxh79+613Jeenm6EhIQYkozg4GBj1qxZhsfj8bnOgQMHjOeff96YPn169X/x/5OTk2PExMSYca9fv77C+UVFRUaLFi3K/ebZ3LlzzftatWplLF++vNy11q9fb9x3333Gjz/+WOP4DcP/FVBhYWGGVFLxs3XrVsu8oqIi49FHH7Wc/7HHHit3XSdfb4ZhGHv37jWSk5PNuR07djS++OILn3OPHj1qvPzyy8Zf/vIXr/vKPoel34Dr16+fsWnTJss8j8djeZ1LMhYvXuy1XmFhoSWuxx9/3HC73T7jysjIMF577TXjiSee8Hk/AABAY5Cbm2vExcWZ748+++wzy/3//ve/zft69+5d6Xo//fSTpRKod+/e5b73TktLM6ZOnerz/VZhYaExePBgc53IyEjjxRdf9PneraCgwJg3b54xatQor/vqogIqJCTECAsLM6ZNm+Z1jZSfn2+5/de//tV47733zCozO4/HY8ybN89o3ry5uf6SJUvKjdWJ5zsrK8u89nK5XMaOHTvKPZ9hGMaGDRss1w6ZmZkVzq/IsGHDzLVeeOGFSuf/7ne/K/d6u66vWUuVvQZxugIqNDTUrPj56quvvObOmzfP8vd78cUXl7tu2YozScZf//pXo6CgwDInLS3NuOiiiyyv7fJeT079jRqG9TkMDw83mjdv7vP6cfHixZbf95FHHin39wUQWCSgAMBPnEhAffvtt5Y3YPaLwLJ27dplxMfHV/gG7MorrzTvr+m2cp9++qm5Rl1tSTZ+/HjLm+OKfPHFF+bck08+2etCY/Lkyeb9r776qj/DNtkTUH/84x+r/PPGG2/4XLNsAkqS0aZNGyM9Pb3cGB588EHLxaGvC12nX2+GYRhjx461XOgfPHiwkmfLN/tz2KVLFyMrK6vc+WPGjDHn3nbbbV73r1u3zrx/8ODBNYoJAACgMXnrrbfM90fNmzc3CgsLLffv3r3bsrXW2rVrK1yv7AfSffv2rfC9W0XKbvEWGhpqfPfddzVapy4SUFLVt66rquXLl5trX3XVVeXOc+r5LrvNdWXb0v35z382506aNKlG5yv12muvmWudddZZFc49fvy4ZVs2+7bxgbhmNQxr8mTUqFFVvub7xz/+4XO9sq9ZSUZQUJCxdOnScs//9ddfW+YvXLjQa86JEycsX/D09eW/Uvn5+Ua/fv3MucOGDfM5z6m/UcPwTkD98ssv5c598cUXzbndu3ev8TkB+BcJKADwEycSUFdffbW5xsiRIyudX/abTK1atfJKvlx44YXm/T///HONYip7YXr33XfXaI3qWrBgQYVJpbLKJjx8JavKXlDNnTvXn2Gb7MmT6vxMmDDB55r2BNSMGTMqjCE/P99o1aqVOf/ll1/2muP0623fvn3mNw8llVv5VBX25/DDDz+scP7nn39uzj3zzDO97l+6dKnl4hAAAKCpK1vt8Kc//cnnnLLXOJMnTy53rbJJE5fLZWzYsKHGcXXv3t1cq6IeUZWpiwRU//79axxfRUp7+CYlJfm838nnu2y/rzZt2pTbe8rtdluqsypKjFTFiRMnjMjISHO97du3lzu3bMLDV7IqENeshmFNnlTnp7zXoz0Bdf3111caQ9nKsGuuucbr/pdeesm8v2XLlkZubm6F65XtryzJ5y4qTv2NGob1ObzzzjsrnJuZmWleb7pcrnKrCQEEVpAAAPVWamqqeXzDDTdUOn/SpEkKCir5T3taWprXftZl94B++eWXaxRT2TU++ugjHT58uEbrVMf555+vVq1aSSrZK33JkiU+5+Xk5Gju3Lnm7XHjxnnNKRv/q6++2ih6+4SHh+uqq66qdM4111xj3i772vI15sTr7ZtvvlFRUZEkqUuXLrrkkksqXbMqIiIiNGLEiArn9O7d2zzevXu31/1lXwepqanaunWrI7EBAAA0RPv379c333xj3r7++ut9zhs/frx5/NZbb5X7XvrLL780j88//3ydcsopNYprz5492rx5s3n7jjvuqNE6daXs++3q2Lp1q95++209+uij+stf/qI777xTd9xxh/lz4sQJSVJGRobZz7csp55vqaTfV69evSSVvC6++uorn/PmzZunI0eOSJJOOeUUDRo0qMbnlKS4uDjLe/yyfX3tyt5X2TVfXV2z1oWyf3/lmTBhgnns65rv22+/NY+vvfZaRUZGVrhe//79ddppp5W7pj//Rn//+99XeH9sbKw6d+4sSTIMQ3v27HHs3ACcQwIKAOqp/fv3W94oV+UNffPmzdW1a1fz9k8//WS5v2yS4uWXX1b//v31v//9T9u3b69yXGeddZb5hn7v3r069dRTNWXKFC1ZskRut7vK61RHUFCQrr32WvP2m2++6XPe3LlzlZOTI6kkAXHqqad6zRkzZoyZNJk/f7569uypqVOnav369TIMww/RezNKKpCr9DNr1qxK1zvttNMUExNT6byyjXnXrFljuc8fr7fly5ebx0OHDq10varq1q2bQkNDK5yTlJRkHmdmZnrd365dO5111lmSpBMnTqhPnz66/fbb9fXXXys3N9exWAEAABqCN998Ux6PR5LUvXt39e3b1+e8MWPGKCIiQpJ08ODBcpMTZd8HDhs2rMZxlV2nS5cuatu2bY3Xqgt9+vSp1vz58+frzDPPVLdu3TR27Fj94x//0FNPPaUXX3xR//vf/8yfAwcOmI9JT0/3Wsep57vULbfcYh7PmDHD55yy4zfeeGOtzylZk0nlJaD27dunxYsXS5JCQ0N19dVXe80JxDWrXWpqapWv+Xx9Yc7O5XJpwIABlc4re8136NAhpaWlWe4vex1Y1aTh4MGDzeOKrvmc/hstm/gqT2XXfQACjwQUANRTpd8mk6TIyEg1b968So/r0KGDeWy/OLn44ot15513mrdXrlypO+64Q126dNFJJ52kMWPG6MUXX9S+ffvKXT80NFRvvPGGmfBIT0/X1KlTde655yo+Pl7nnHOOHnjgAS1dutTRhE7Zi5EPPvjA54VDZd+Ek6QePXroySeflMvlkiRt3rxZU6ZM0WmnnaakpCRdfvnlmjp1aoOqiDn55JOrPa/s68t+26nX26FDh8zjTp06VWm9qoiPj690TtkEVWkVlt2MGTPUsmVLSVJ2drZeeuklXXTRRYqPj1e/fv00efJkLViwoFFUyQEAAFTk9ddfN4/Lq36SSqpUrrjiCp+PK8up94H+ej/pL1V9Dy1JDz/8sC6//HKvL4ZVJisry2vM6edp7Nixio6OliR9+umnXtcO+/btM5OPYWFhVarMqYpLLrlEycnJkkqqwlauXOk15+233zavM8vOLytQ16z+1KxZM8XGxlY6r3nz5maSWKr4uq99+/ZVOncgrvmk6l/3FRYWOnp+AM4gAQUA9VR2drZ5XPrmvyrKzvV1cfL888/ro48+Uv/+/S3jhw4d0ocffqg777xTJ598ssaMGaO9e/f6PMeQIUP0yy+/aPz48ZaS/fz8fH3//fd67LHHdPbZZ6t79+6WLfFqo2xF07FjxzR//nzL/YcPH9bXX38tSQoODtZ1111X7lqTJ09Wamqqzj//fDMRVXbdKVOmqFu3brrgggu0bt06R+L3p6ioqCrNq+i14Y/XW9nbVanQqqqy/2a1ccopp+iXX37RnXfeabm4KSoq0qpVq/T000/r4osvVvv27TV9+nRHzgkAAFDfrFy5Ups2bZJU8j5r7NixFc4vm6CaN2+ejh8/7jXHqfeB/no/6S+VbWdW6uuvv9Yjjzxi3h44cKCmTZumNWvWKD09Xfn5+ZYKmSFDhphzSyvVynL6eYqLizMriwoLCzV79mzL/bNmzTLjuOKKK3wmgWrCXtHka+eLsmMVJUsDcc3qT1W95pOcv+4LxDWf5Nx1H4DAIgEFAPVU2TdvpdvKVUXZueV9Q2r06NH68ccftWfPHr3++uu69dZbLfuEG4ahDz/8UGeeeWa5lUCdOnXS66+/riNHjujLL7/Ugw8+qGHDhlne3G/dulWjR4/W008/XeX4K1LRlgzvvvuuWely/vnn66STTqpwrSFDhuibb75RWlqa3n33Xf3pT3/SmWeeaW7PJ0kLFy7UgAEDtHTpUkfi95eqbhlX0WvDH6+3srfLXujUJy1bttTzzz+vQ4cOadGiRXr00Ud16aWXKi4uzpyzf/9+3XzzzfrTn/4UwEgBAAD8o2wVk2EY6tChg1wuV7k/l19+uTk/Pz9f7777rteaTr0PDPT7SV/JHidMnTrVPL7hhhu0dOlS3XzzzerVq5eSkpIUHh5ume/ri4Vl+eN5uvnmm83jstvtGYahmTNnmrdvuukmR85Xquw137vvvmvZjWDdunXmFwTj4+Mr7QsbiGtWf6nONuFOX/c19Gs+AIFFAgoA6qmy2zfk5eX53Ovbl7L7R1f2TbSTTz5Z48eP18svv6wNGzZo7969euSRR8xvV2VkZOjPf/5zhWtER0fr4osv1qOPPqpvv/1WGRkZev/99y37Nf/tb3/T/v37qxR/RcaOHWt+C+qzzz4zm/FKVf8mnF3Lli111VVX6bnnntPq1at18OBBPfvss+Ze0nl5ebr11ltrHbs/lVepZle2YbH9teGP11vp9naStGvXriqtFyjh4eEaMmSIHnzwQX3++edKT0/XF198obPPPtuc88ILL/jcBgQAAKChcrvdeuedd2q1hq9t+Jx6H+jk+8mqbNFsV/Z6wynFxcVmD6OgoCA9/vjjlVZ6VPZ+3x/vu8866yydfvrpkqRNmzZp2bJlkkp6G+3cuVNSyRZuF1xwgSPnK3velJQUSSW7dJTuciFZr/nK9iOrTF1es/rLsWPHqpTgKa2gK1XRdV9VryMbyzUfgMAgAQUA9VSbNm3UokUL8/YPP/xQ6WPS09MtFUtnnnlmtc7Zrl07/eMf/9C0adPMsQULFqigoKDKa0RGRmrMmDFatGiR+WbU7XaX26C4uvGde+65kqSCggJ98MEHkqTt27drxYoVkkouLkaPHl3jczRv3lx33XWXPvnkE3Nsw4YN5kVWfbR+/foqfXut9KJR8n5t+OP1dtZZZ5nHqampla5Xn4SGhuqSSy7RN998o549e5rjn376aQCjAgAAcNZnn32mo0ePSpJCQkI0YMCAKv3069fPXGPZsmVeuyaUfR/47bff1ji+suts3bq1wl61lSlb4X7s2LFKe//s3btXmZmZNT5fedLT081+ti1atLC8B/dl48aNlX45zKnn285XFVTZaqhJkyZZdpBwStltIEt3vjAMw5IsLa/nb1X485rVXwzD0I8//ljpvLLXfC1btlTr1q0t9/fu3ds8rso1n31eRdd8tf0bBdA4kYACgHps2LBh5vGsWbMqnV92L+7WrVurW7duNTrvyJEjzePCwkLzorQ6EhMTNXjwYPN22eaktVH2QqP0G3Blvwk3atSoavUwKs/gwYOVmJho3nYqfn/Iz8/X+++/X+Ect9tt2R6l7GvL15gTr7cLL7xQISEhkqRt27bV6wu68oSHh+uiiy4yb9fn1wEAAEB1la1euvTSS7V8+fIq/axYscLyJR17j6BLL73UPF64cKHZY6q62rdvrx49epi3//e//9VoHalkq7DS9/e5ubnlbjVe6r333qvxuSpSNmGTl5dX6fyXXnqp0jlOPd9248aNM7ere/fdd7Vv3z599NFHkkp+jxtuuMGR8/g6b6m5c+cqNzdXixcvNnd0aNeunaUvVk3565rVX954441K55T9W/R1zXfeeeeZx3PmzLFUS/myatUqrV27ttw1nfwbBdA4kYACgHqs7NZvH3/8cYUf4O/Zs0f//ve/LY+1b+VQ1W3Vym7VFhQUZG5HJ5Vsy1dVZdep7Jt9VfX73//e3Grhu+++0759+yz9oCrbfq+qz8Hx48ctWxw4Fb+//OMf/9CxY8fKvf+xxx4zt5SIjo7Wtdde6zXH6ddb69atLU2Eb7311npzUXfs2LEq7+nvj9cxAABAoB05ckRffPGFebu6FSVl57/xxhuWiqL+/fubH+wbhqHx48fXuD9M2S3Bn3rqKS1ZsqRG60jSgAEDzOOKvnC1b98+Pf744zU+T0WSkpIUHx8vqWSLv9Lt+HxZunRplRJQTj7fZSUkJOiqq66SVNLfZ8yYMWbC4qKLLlK7du1qfQ5fUlJSzMqa7OxszZ0713LNV3Zrdl8Cfc3qL2+++WaFVVCpqan68MMPzdu++nNdd911Zh+otLQ0PfLII+Wu53a7deedd5q3hw0b5vNLrk7+jQJohAwAgF8MGTLEkGRIMh566KEar3PppZea68TExBjvvfee15xVq1YZKSkp5rx27doZx44d85qXkpJiXHvttcbnn39uFBQU+Dzfli1bjL59+5prXXjhhZb7H3roIeOMM84w/t//+39GWlqazzWysrKM+++/31wjODjY2Lt3b/V/+XKMGTPGXPvKK680j0866SSjqKiowscOGzbMuOyyy4z333/fyMnJ8Tln3759lue9a9eutYo3NTXVXMup//XOnDnTXC8sLMyQZPTt29fYvn27ZV5RUZHx2GOPGS6Xy5z/r3/9q9x1nXy9GYZh7N2710hMTDTnduzY0fjyyy99zj127JjxyiuvGPfee6/XfWWfwyFDhpT/xJRR0XM+c+ZMo3PnzsbUqVONXbt2+Xx8fn6+8cILL1ieu6VLl1bp3AAAAPXds88+a77HiY2NNXJzc6v1+D179ljeJy1cuNBy/+rVq43w8HDz/t69exvLly/3uVZaWpoxdepU48knn/S6r7Cw0Bg0aJC5TlRUlPG///3PcLvdXnMLCgqMefPmGaNGjfJ5njfeeMPyHvqDDz7wmrNs2TKjU6dOhsvlMt9nSyr3PWP79u0rnWN37bXXmo/p0KGD8eOPP3rNeffdd424uDhDkhEdHW3OT01N9bmmU8+33ffff295X1364+u5c9KLL75onuu8884zEhISzNvr16+v8LGBumYt+/yU9+9UHbt27TLXCw0NNSQZycnJxtdff+0197PPPjPi4+PLvY4v64knnrDE+uCDD3p9PnDw4EHjkksuMeeEhISU+3py8m+0utfNZT93ceI5B+A8l2FUsuktAKBGhg4dan6brWXLljrppJOq/Nh//vOf5jZ4hw4d0uDBg7Vjxw7z/i5dumjAgAEKCwvTxo0b9eOPP5rfOIyOjtZXX31l2UqgVIcOHbRnzx5JJften3766erUqZPi4uJ07Ngx7dy5U6tWrTLnR0ZGavny5WbzWUl6+OGHzW9JuVwude7cWT179lRycrIKCwuVlpamH374wfKNuwceeED/+te/qvz7V+aTTz7RqFGjvMbvuecePf300xU+tuy/S1hYmE499VR17dpV8fHxysrK0t69e7Vs2TKzOiY4OFifffaZLrnkkhrHu2jRIstWBX/84x+r9fgxY8Zo6NChlrFZs2Zp0qRJkkq+xVbaByskJETnnHOOOnfurKysLH333XdKS0szH3fuuedq4cKF5tZ4dk6+3kotXLhQo0aNsrwm2rdvr/79+ysxMVHZ2dnaunWrfv75ZxUWFuqKK67Q3LlzLWuUfQ6HDBmiRYsWVfq8lf1WpP3tTtnnT5JOPvlknX766ea3Hg8ePKjly5dbtp8cO3asZbtHAACAhuzMM8/UmjVrJEkTJkyo0hbMdkOGDNF3330nSRo/frxlSz+ppH/PxIkTVVRUZI5169ZNvXv3Vnx8vE6cOKGNGzdq/fr18ng8uuuuu/Tss896nWffvn0677zztG3bNnMsISFBgwcPVqtWrVRUVKQ9e/Zo9erVyszMVHx8vI4fP+61TlFRkfr27atffvnF8jz06tVLxcXFWrt2rfmcPPzww5o5c6Z5/bRr1y516NDBa82y11jlzbHbuHGj+vbta27B53K5NHDgQHXt2lVut1vLli3Trl27JJX0Ydq6dat5DZOamup1bVDKqefbrmfPntqwYYN5u0WLFtq3b59CQ0MrfWxNpaenq3Xr1iosLLSM9+7dWz/99FOFjw3UNWvZ649Ro0apTZs2VX7sGWecYem5JUm7d+9Wx44dJZVcP40ePdr89zrjjDPUq1cvGYah1atXW/59WrVqpWXLlql9+/Y+z+XxeDRq1ChLf9ukpCQNGzZMzZo106+//qrU1FRLL+hnnnlGd999d7nxO/U3WtE1nC9lr+8r+tsAEEABTH4BQKNW9ps41f2ZOXOmZa2DBw8a5513XqWPS0lJMVasWFFuTD179qxyDB07dvRZ7fHf//63ymuEhYUZjzzyiNNPreF2uy1VNaU/q1evrvSxl19+eZXjb9GihTF37txax2uvgKruzzPPPOO1ZtkKqAkTJhj79+83zjrrrArXueyyy4ysrKxK43Xq9VbWzz//bJxxxhlV+n3Hjh1b4XPoRAXU+++/b/nGbkU/QUFBxu233+7zG3wAAAAN0dq1ay3vd3xVVFTFtGnTzDWio6N9vtdcuHCh0bFjxyq973rggQfKPVdGRoYxevToKq3Tpk2bctfZuXOn0alTp3If63K5jAceeMDweDxVqm6qSQWUYRjG3LlzjaioqAp/j1tuucXIz8+vVpWHU893WWWr5SQZf/nLX6r8e9aGr2u3p556qtLHBeqatTbXfFdccYXXemUroNq3b2+43W7jxhtvrHCdbt26GZs2bao01sLCQuOOO+4wgoODK1wvPj7e6/OJ8jjxN1rRNZwvVEAB9Z/vrz8DAOqVli1bauHChfryyy/17rvv6vvvv9fBgwdVWFioFi1aqHfv3ho1apTGjRtX4bfQfv75Zy1fvlypqalasWKFtmzZogMHDig3N1dRUVE66aST1KtXL40cOVJXXXWVwsPDvdaYPHmyrrzySn399df64YcftG7dOu3evVuZmZkKCgpSQkKCevToofPOO0/jx48v91tXtREaGqqrr77ash96jx49dOaZZ1b62Hnz5mnNmjVauHChfvzxR23atEn79u1TTk6OwsPD1bx5c51++ukaPny4rrvuOsXFxTkevz+0bt1aixcv1ptvvqm33npLmzdvVnp6upKSktSvXz9NmjTJZ9WYL0693so644wztGbNGs2dO1dz587VsmXLdOjQIeXk5CguLk6dOnVS//79NWLECF188cW1eCaqZsyYMUpLS9OCBQu0dOlS/fLLL9q5c6f5Lbz4+Hh17dpVZ599tsaPH69TTjnF7zEBAADUlbKVSq1atdJ5551Xo3XGjBmjO++8UwUFBcrJydEHH3ygiRMnWuacd9552rJli+bMmaPPPvtMq1at0uHDh1VQUKD4+HilpKRo4MCBGj16tM4555xyz5WYmKiPPvpIK1eu1Ntvv61FixZp3759OnbsmCIjI9W2bVv16tVLl1xyicaMGVPuOh07dtTatWv1wgsv6KOPPtLWrVtVUFCg1q1b65xzztEf/vAHS68of7niiiu0fv16Pf3001qwYIH27t2rkJAQtW7dWoMHD9bEiRN17rnnVntdp57vsn73u99Zql989Rbyh+uvv16fffaZeTs4ONhnL1u7+nDN6g+hoaGaPn26fv/732vGjBlauXKl0tLSFB0drR49eujqq6/WLbfc4vM63i4kJEQvvPCCbrvtNr322mtauHChfv31V2VlZSkxMVFdu3bV8OHDdfPNN1t6QlfEqb9RAI0LW/ABANAAld1CrqZbpgAAAABAZV5//XUzsXj22WdryZIlgQ2oibBvwbd79+7ABgQANRAU6AAAAAAAAAAA1E8zZswwj+19igAAqAgJKAAAAAAAAABe1qxZY1Y8JSYm6qqrrgpwRACAhoQEFAAAAAAAAACL/Px83Xnnnebt2267TREREQGMCADQ0IQEOgAAAAAAAAAAgffiiy9q+/btOn78uBYuXKh9+/ZJkpKTkzV58uQARwcAaGhIQAEAAAAAAADQBx98oMWLF1vGgoODNWPGDCUmJgYoKgBAQ8UWfAAAAAAAAAAsmjVrpssuu0yLFy/WyJEjAx0OAKABchmGYQQ6CAAAAAAAAAAAADQeVEABAAAAAAAAAADAUSSgAAAAAAAAAAAA4CgSUAAAAAAAAAAAAHAUCSgAAAAAAAAAAAA4igQUAAAAAAAAAAAAHEUCCgAAAAAAAAAAAI4KCXQAqJ38/HytW7dOktS8eXOFhPBPCgAAgMatqKhIR44ckSSddtppioiICHBEqO+4bgIAAEBTUl+umXjX3cCtW7dO/fv3D3QYAAAAQECsWLFC/fr1C3QYqOe4bgIAAEBTFchrJrbgAwAAAAAAAAAAgKOogGrgmjdvbh6vWLFCrVq1CmA0AAAAgP+lpaWZ1Sxl3w8D5eG6CQAAAE1JfblmIgHVwJXdu7xVq1Zq27ZtAKMBAAAA6ha9fFAVXDcBAACgqQrkNRNb8AEAAAAAAAAAAMBRJKAAAAAAAAAAAADgKBJQAAAAAAAAAAAAcBQJKAAAAAAAAAAAADiKBBQAAAAAAAAAAAAcRQIKAAAAAAAAAAAAjiIBBQAAAAAAAAAAAEeRgAIAAAAAAAAAAICjSEABAAAAAAAAAADAUSSgAAAAAAAAAAAA4KiQQAcAAAAAAAAAAEB94/F4lJ2drczMTLndbhUXFwc6JDQRwcHBioqKUkJCgiIiIgIdTo2RgAIAAAAAAAAAoIysrCzt379fhmEEOhQ0QUVFRSooKNCxY8cUHx+vVq1ayeVyBTqsaiMBBQAAAAAAAADA//GVfHK5XAoODg5gVGhKioqKzOMTJ04oLCxMycnJAYyoZkhAAQAAAAAAAACgkm33yiafYmJilJiYqKioqAZZgYKGqbi4WMePH9fhw4clSUeOHFFcXJzCwsICHFn1BAU6AAAAAAAAAAAA6oPs7GxL8qlt27aKjo4m+YQ6FRwcrKSkJCUlJZlj2dnZAYyoZkhAAQAAAAAAAAAgKTMz0zxOTEwk8YSAiouLM49zcnICGEnNkIACAAAAAAAAAECS2+2WVNLzKSoqKsDRoKkLDw83k6Clr82GhAQUAAAAAAAAAAAq6b0jlWyBRvUTAs3lcik4OFhSSX+yhoYEFAAAAAAAAAAAABxFAgoAAAAAAAAAAACOIgEFAAAAAAAAAAAAR5GAAgAAAAAAAAAAgKNIQAEAAAAAAAAAAMBRJKAAAAAAoIHas2ePJk+erO7duys6OlqJiYnq16+fpk6dqtzcXEfOsXv3bt13333q06ePEhISFBoaqsTERA0aNEj//Oc/dfjw4Sqtk5ubqyeffFL9+vVTYmKioqOj1b17d02ePFl79uxxJFYAAADASS6XSy6XSw8//HCgQ2mQQgIdAAAAAACg+j799FONGzdOmZmZ5lhubq5WrVqlVatWafr06Zo/f75SUlJqfI433nhDt956q/Ly8izjx44d07Jly7Rs2TI999xzmjNnji688MJy19m+fbuGDx+ubdu2Wca3bNmiLVu2aPr06Xrrrbd0+eWX1zhWAAAAAPULFVAAAAAA0MCsWbNGV199tTIzMxUTE6N///vf+uGHH7Rw4ULdfPPNkqStW7fqsssuU1ZWVo3OsXTpUk2cOFF5eXkKCgrSpEmTNHfuXK1YsUIffPCBRowYIUk6evSorrjiCu3cudPnOllZWbrsssvM5NPNN9+shQsX6ocfftC///1vxcTEKDMzU1dffbV+/vnnGsUKAACAhmvWrFlmpdHu3bsDHQ4cRAUUAAAAADQwd911l/Ly8hQSEqIFCxZo4MCB5n3nnXeeunTpoilTpmjr1q166qmnarRlyOOPPy6PxyNJeuGFF3T77beb9/Xr109XXnmlJk+erKefflp5eXl6+umn9eKLL3qtM3XqVG3dulWS9OSTT+ree+817xs4cKCGDh2qIUOGKDc3V3fffbcWLVpU7VgBAAAAfzAMI9AhNGhUQAEAAABAA7JixQotWbJEknTjjTdakk+lJk+erB49ekiSnnvuORUWFlb7PD/88IMkKSkpyZJ8Kusf//iHebxs2TKv+wsLC/X8889Lknr06KHJkyd7zRk0aJBuvPFGSdLixYu1cuXKascKAAAAoP4hAQUAAAAADcjcuXPN40mTJvmcExQUpPHjx0uSjh8/rtTU1Gqfx+12S5I6duxY7pz4+HglJydb5peVmpqqEydOSJImTJigoCDfl6ATJ040jz/++ONqxwoAAACg/iEBBQAAAAANyPfffy9Jio6OVp8+fcqdN2TIEPN46dKl1T5Pt27dJEm7du0qd05mZqbS09Mt833Fao/Hrm/fvoqKiqpxrAAAAGh4Fi1aJJfLZflSVceOHc1+UKU/pVs0T5w4US6XSx06dJAkpaWl6b777tOpp56q2NhYy1xJOnbsmGbOnKlx48bplFNOUUxMjMLCwnTSSSfp4osv1rRp03x+iaqs0hh8bWlt713l8Xg0bdo0DRo0SM2aNVN0dLROP/10/fvf/1Zubm5tn64GiR5QqLFcd5Ee/3yz8gqLlVdYrHx3sZ666gwlRIUFOjQAAACg0dq0aZMkKSUlRSEh5V/Sde/e3esx1XHbbbfplltuUUZGhl5++WXddtttXnMeffRRy3y7jRs3+ozHLiQkRCkpKVq7dm2NYt23b1+F96elpVV7TQCQJB3aIP38tpTUWTpzghQUHOiIAACSli9frhEjRphfhvKld+/e2rNnj9f4oUOHtGDBAi1YsEAvv/yyPv/8c5100km1iic3N1cXXXSRFi5caBlft26d1q1bp3nz5unbb79VdHR0rc7T0JCAQq28sdz6B5yVX0QCCgAAAPCT/Px88yK7bdu2Fc4t/dZlTk6Ofv3112qf64YbbtD333+v2bNn649//KNWr16tkSNHqlWrVtq7d6/eeOMNczvABx54QBdccIHXGqWJoejoaCUkJFR4vnbt2mnt2rU6cuSICgoKFB4eXuVY27VrV+W5AFBl2UekV8+XivJKbuekS0OmBDYmAGhE+vXrp3Xr1umTTz7Rgw8+KEn66quv1Lp1a8s8+5bQ2dnZuvLKK5Wfn68HHnhAF154oaKiorRu3Tq1atXKnFdcXKwBAwbo8ssvV+/evdWyZUu53W7t2rVLb775pr788kutWbNG11xzjaVyqiZuvvlmLV++XBMmTNBVV12lk046SXv37tWTTz6pZcuWacWKFfrXv/6lxx9/vFbnaWhIQKHGIkK8v/WTV1gcgEgAAACApiErK8s8jomJqXR+aQIqOzu72ucKDg7W66+/rhEjRuixxx7T9OnTNX36dMucYcOG6f777/eZfCobb1VjLZWdnV2tBBQA+MXmz35LPknSug9IQAEweTyGjuVWvH1bY9IsKkxBQS5H14yOjlbPnj21atUqc6xr167mFnvlycjIUExMjL7//nudccYZ5ni/fv0s87799lt16dLF6/GDBg3S2LFjNXPmTN1www1avHixFi5cqPPPP7/Gv8sPP/ygN954Q+PGjTPHzjzzTF166aXq27ev1q9fr1dffVWPPvpohbsYNDZN5zeF44KCXAoPCVJBkcccyycBBQAAAPhNfn6+eRwWVvnOA6VJnLy8vEpm+rZp0ybNnj1b69at83n/smXLNGPGDPXo0UNt2rQpN97qxFqTeCur8EpLS1P//v2rtSYAKH2b9fbRnVJxkRTMx2kApGO5bvX51zeBDqPOrH7wAiXF1J8vCE2ZMsWSfPLFV/KprEmTJun555/Xzz//rLlz59YqAfW73/3OknwqFR4erjvuuEO33XabMjIytHHjRp1++uk1Pk9Dw/8xUSuRYcGWBFSemwQUAAAA4C8RERHmcWUNkyWpoKBAkhQZGVntcy1ZskQjRozQiRMn1L59e/3rX//ShRdeqMTERB06dEjz5s3T3//+d82ZM0ffffedFixYoFNPPdVnvNWJtSbxVrYdIQDUSIYtAeUplI7vKekHBQAIqLFjx1ZrvmEYOnTokDIzMy3vTdu0aaOff/5Zv/zyi9/i6dOnj3m8c+dOElBAVUWGBuu4Cs3bbMEHAAAA+E9sbKx5XJVt9XJyciRVbQu8sgoKCnTttdfqxIkTOumkk7R8+XJLY+a2bdvq9ttv15AhQ9S3b18dOHBAEyZMsGyfUjbe6sRak3gBwC/sFVClYySgACCgYmJi1KlTpyrNnT9/vl566SV99913lu2s7Ur7rNZU9+7dy70vMTHRPK4ohsYoKNABoGGLDLX2gWILPgAAAMB/IiIilJSUJEnat29fhXOPHTtmJnXatWtXrfN8+eWX2r9/vyTpzjvvtCSfyjr11FPNrUZWr17t9c3R0sqknJwcHT9+vMJzlm6j17x5c/o/AQi8ooKSaic7e1UUAKDOJSQkVDrHMAzddNNNuvzyyzV//vxKEz813bK6VFRUVLn3BQX9loYpLm5an59TAYVaCbcloKiAAgAAAPzrlFNO0ZIlS7R9+3YVFRWV28R48+bN5nGPHj2qdY5NmzaZx2eeeWaFc/v06aPp06eb5yy7F/8pp5yiDz/80LzvrLPO8rlGUVGRduzYUaNYAcAvju6SDI/3uK+qKABNUrOoMK1+8IJAh1FnmkVV3tOzrgQHB1c657XXXtOMGTMkSb169dLdd9+tAQMGqE2bNoqKijLXGD9+vN544w0ZhuHXmJsqElColchQaxFdntvHmzMAAAAAjjn77LO1ZMkS5eTkaPXq1RowYIDPeYsXLzaPBw8eXK1zlE1qFRUVVTi3sPC3LbntybCzzz7bEk95CahVq1aZ1VrVjRUA/KK8SqeM7XUbB4B6KyjIpaQYqrbrq1dffVWSlJKSoh9++KHcHqNHjx6ty7CaHLbgQ61EhlEBBQAAANSlUaNGmcczZ870Ocfj8Wj27NmSSrYoGTZsWLXO0bFjR/N4yZIlFc4tm+gq+zhJGjp0qOLj4yVJr7/+ernfLJ01a5Z5PHr06GrFCgB+UV6lExVQAOA4l8vl+JobNmyQJI0cObLc5JNhGPrpp58cPzd+QwIKtUIPKAAAAKBu9e/fX+ecc44kacaMGVq2bJnXnKeeesrcRu+uu+5SaGio5f5FixbJ5XLJ5XJp4sSJXo8///zzzX3sX3rpJa1bt85nLF988YU+/vhjSVKbNm3Uq1cvy/1hYWH605/+JKlkW7///ve/XmssW7bM3B5lyJAh6tevX3m/OgDUnfIqnXIOS/kn6jYWAGjkIiIizOOCggJH1iyt4i+tsvflk08+UVpamiPng28koFArEfYeUG4SUAAAAIC/Pffcc4qMjFRRUZEuuugiPf7441q+fLlSU1N16623asqUKZKkrl27avLkydVePyEhQX/9618lSVlZWRo0aJDuv/9+paam6ueff9ZXX32l22+/XSNHjpTHU7IN93/+8x9Lg+VS9957r7p27SpJmjJlim699ValpqZq+fLlevzxx3XRRRepqKhIkZGRevbZZ2v4jACAwyqqdEpnGz4AcFKrVq3M49K+oLXVpUsXSdKnn37qc5u9HTt26I9//KMj50L56AGFWrFXQLEFHwAAAOB/vXv31rvvvqtx48YpMzNT999/v9ecrl27av78+YqNja3ROR588EEdPXpUzz33nLKzs/X444/r8ccf95oXGhqqxx57TOPGjfO5TmxsrObPn6/hw4dr27ZtmjZtmqZNm2aZExcXp7feesurggoAAqa8HlCl97XtU3exAEAj17t3b0VERCg/P19///vfFRoaqvbt25tfbmrTpk252+iVZ/z48br33nt14MABDRw4UPfdd5969uyp/Px8ffvtt3r22WdVUFCgM888k234/IgKKNQKPaAAAACAwBgxYoTWrl2re+65R127dlVUVJQSEhLUt29fPfHEE1qzZo1SUlJqvL7L5dIzzzyjlStX6rbbblPPnj0VGxur4OBgxcfHq0+fPvrzn/+s9evX6y9/+UuFa6WkpGjNmjV64okn1LdvXyUkJCgqKkrdunXTPffco7Vr1+ryyy+vcawA4KicDCnvWPn30wcKABwVGxtrbtv8008/6aKLLlK3bt3UpUsXdenSRT/++GO117zrrrt00UUXSZK2bt2qG2+8UQMGDNCQIUP0yCOPyO12a/bs2TrttNMc/V1gRQUUaoUeUAAAAEDgtG/fXk8//bSefvrpaj1u6NChMgyjSnP79OmjPn1q/03/6OhoTZkyxdweEADqrYqqn6pyPwCg2v7zn/+oS5cumj17tjZs2KATJ06ouLjmnzWHhoZq/vz5eumllzR79mxt3LhRhmGoTZs2uuCCC3TXXXepe/fumj9/voO/BexIQKFW7D2gSEABAAAAAIAGrbIKJ3pAAYDjXC6XbrrpJt10003lzpk1a5ZmzZpV5TVDQkJ055136s4776zxmhV9aWvixImaOHFipXF06NChyl/+amzYgg+14rUFn5sEFAAAAAAAaMDsFU6RidbbR3dIHk/dxQMAQANFAgq1Yt+Cjx5QAAAAAACgQbNXOHW9xHq7KF868WvdxQMAQANFAgq1EhFqfQnlFfINIAAAAAAA0IDZK6DaD5LC4yqeAwAAvJCAQq149YBiCz4AAAAAANBQFRdJR3dZx5K7SEkp1jH6QAEAUCkSUKgVtuADAAAAAACNxvE9kqfQOpbUpSQJVRYVUAAAVIoEFGolMowEFAAAAAAAaCTSbYmlyGZSdFJJEqqieQAAwAsJKNSKvQKKLfgAAAAAAECDZa9sKk08Jdu24MtgCz4AACpDAgq1Yu8BRQUUAAAAAABosOyVTaVb79kroDL3S+6cuokJAIAGigQUasW+BV+Rx1BhsSdA0QAAAAAAANSCvbLJTEB1luSqeC4AALAgAYVasW/BJ1EFBQAAAAAAGih7BVRp5VNopBTfruK5AADAggQUasVXAiqfBBQAAAAAAGho8k9IOYetY8ldfB9LVEABAFAJElCoFfsWfJKU72YLPgAAAAAA0MCk2xJKrmCpWcffbtsTUFRAAQBQIRJQqJXwEO+XEFvwAQAAAACABid9q/V2s/ZSSNhvt5NSKp4PAAAsSEChVlwul9c2fCSgAAAAAABAg5NRTv+nUl5b8O2QDMO/MQEA0ICRgEKtRYRaX0Z5bhJQAAAAAACggbFvqWdPONkTUoU5UuYB/8YEAEADRgIKtWavgMqnAgoAAAAAADQ0GbYeUPYt9+JaS6HRtsfQBwoAgPKQgEKtRYSxBR8AAAAAAGjAPMUlW+qVZa+AcrmkpM7WMXvVFAAAMJGAQq159YBiCz4AAAAAANCQnPhVKi6wjtm33JN89IHa7j0HAABIIgEFB3gloKiAAgAAAAAADUm6LZEUHifFtPCeZ09KUQEFAEC5SECh1iLD6AEFAAAAAAAaMHsvp6SUki337LwqoEhAAQBQHhJQqLUItuADAAAAAAANmb2SyZ5oKpWUYr19/FepMM8/MQEA0MCRgPLhvvvuk8vlMn8WLVpU6WO++OILjR49Wm3btlV4eLjatm2r0aNH64svvvB/wAFm34Ivv4gEFAAAAAAAaEC8KqCqmICSIR3d6ZeQAKCpmDVrlvlZ/O7duwMdTrlKY3z44YcDHUqDQQLK5ueff9bTTz9d5fkej0c33XSThg8frrlz52r//v1yu93av3+/5s6dq+HDh+vmm2+Wx+PxY9SB5dUDyt14f1cAAAAAANAI2XtAJdsTTf8nPEaKbW17LNvwAQDgCwmoMjwej2655RYVFRWpRQsfjSZ9eOCBBzRjxgxJUu/evfXOO+9oxYoVeuedd9S7d29J0vTp0/Xggw/6Le5As/eAyqMHFAAAAAAAaCgKsqWsA9ax8iqgJO/kFH2gAADwiQRUGc8//7xWrlyp7t2768Ybb6x0/tatW/Xf//5XktS3b18tXbpU11xzjfr166drrrlG33//vfr27StJmjp1qrZv317Rcg2WvQdUPgkoAAAAAADQUGTYP69xSUmdy59vT07Zq6cAAIAkElCmvXv36u9//7sk6eWXX1ZYWFilj3n22WdVVFQkSXrhhRcUGRlpuT8qKkovvPCCJKmoqEjPPPOMw1HXD95b8JGAAgAAAAAADYQ9ARXfTgqN9D1XkpJtCSgqoACgRhYtWiSXy6VJkyaZYx07djR7LZX+LFq0yOuxc+fO1e9//3udfPLJioiIUEJCgvr27atHHnlEx44dq/C8W7du1Z133qmePXsqNjZWYWFhat26tXr16qUbbrhB7777rgoKCsz5HTp0kMvlMm8/8sgjXjFOnDix1s9HY0QC6v/88Y9/VHZ2tiZMmKAhQ4ZUOt8wDH3yySeSpO7du+uss87yOe+ss85St27dJEmffPKJDMNwLuh6IiLU+jJiCz4AAAAAANBg2Hs4ldf/qZSvCqhG+HkPANRHx44d0/nnn6/Ro0frgw8+0K+//qqCggKdOHFCq1ev1sMPP6zu3btr+fLlPh///vvv67TTTtOLL76oDRs2KDs7W4WFhUpLS9Mvv/yimTNn6pprrtG2bXy5wAkhgQ6gPnjvvff02WefKTEx0dxSrzK7du3SgQMl+wNXlrAaMmSItmzZov3792v37t3q2LFjrWOuT+gBBQAAAAAAGix7BVNF/Z8k7wRVwQkp54gUU7V+4gCAEv369dO6dev0ySef6MEHH5QkffXVV2rdurVlXunn6QUFBbrgggv0008/KTg4WNddd52GDx+ujh07qrCwUN99952efvppHT58WMOHD9eaNWvUvn17c51Dhw5p0qRJcrvdatGihe644w6dddZZSk5OVl5enrZv367Fixdr7ty5lvMvWLBAbrdbp512miTpD3/4g26//XbLnGbNmjn99DQKTT4Bdfz4cd11112SpCeeeELJyclVetzGjRvN4+7du1c4t+z9mzZtanQJKHpAAQAAAACABsurAqqSBFR8Oyk4XCr+bXsmpW8jAQU0NR6PlHc00FHUnchEKcjZDdWio6PVs2dPrVq1yhzr2rWrOnTo4HP+P//5T/30009KSEjQN998oz59+ljuP/vsszV27FgNHDhQaWlpuv/++/XWW2+Z98+fP185OTmSpIULF6pnz56Wxw8aNEjjx4/Xiy++aBnv2rWr5XaLFi28HgvfmnwCasqUKTp48KAGDx6sG2+8scqP27dvn3nctm3bCue2a9fOPP7111+rFV/Z8/iSlpZWrfX8gR5QAAAAAACgQTIMKWOHdSypki34goKlpM7S4d++nKyMbVKHwc7HB6D+yjsqTe0c6Cjqzr07pOiqFW/4Q3Z2tv73v/9Jkh599FGv5FOp9u3b6+9//7tuv/12vf/++5o2bZqio6MlSQcPHpRUUq1UUQIpMrKCPoColiadgFqyZImmT5+ukJAQvfzyy5ZGYpXJysoyj2NiYiqcW/oCl0r+UKqjbPKqvvJKQFEBBQAAAAAAGoLMA1JhjnWssgooqSRJVTYBZa+iAgA4avHixTpx4oQkacyYMRXOPffccyVJhYWFWr16tXm7VatWkkr6SH3yySe64oor/BgxpCacgHK73brllltkGIbuueeeapfM5efnm8dhYWEVzg0PDzeP8/LyqhdoA2DvAcUWfAAAAAAAoEGw938KjZJiW/ueW5Y9SZWx3bmYAABeym7TV5pIqorSqidJGjlypBISEnT8+HGNHj1aQ4cO1YgRI3TuueeqV69eCg4OrmAl1ESTTUA99thj2rx5s04++WQ99NBD1X58RESEeex2uyucW1Dw257A1S3fq2zLvrS0NPXv379aazrN3gOKLfgAAAAAAECDYK9cSkqpWo+TJFsCigooAPCrw4cP1+hxubm55nFSUpLmzZuna6+9Vvv371dqaqpSU1MlSXFxcTr//PN1ww036PLLL3ckZjTRBNTmzZv1+OOPS5JeeOEFyxZ5VRUbG2seV7atXmljM6ny7frsKusvVR/Yt+DLL/LIMIxqbWkIAAAAAABQ5+yVS1XZfs/XvGO7pSK3FFLxLjkAGpHIxJK+SE1FZGJAT19c/FvRw08//aTQ0NAqPc7++fo555yj7du368MPP9Tnn3+u7777Tvv27VNmZqY+/vhjffzxx7r44ov10UcfKSoqytHfoSlqkgmoZ555Rm63W506dVJubq7mzJnjNWf9+vXm8bfffmuW6o0YMULR0dGWF+6+ffsqPF/ZKqaG0NOpuuxb8BV7DBUWGwoLIQEFAAAAAADqMa8KqComoJJSrLeN4pIkVPOujoQFoAEICpKikwMdRZORlJRkHjdv3rxWhRsREREaO3asxo4dK0natWuX5s+frxdeeEFbt27VV199pQceeEDPPPNMreNu6ppkAqp0S7ydO3fq2muvrXT+o48+ah7v2rVL0dHROuWUU8yxzZs3V/j4svf36NGjuuHWe/YKKEnKKyxWWEgVStYBAAAAAAACxd4DqqoVUJEJUnRzKeeIdS0SUABQbVXZSat3797m8dKlS3X11Vc7dv6OHTvqjjvu0Pjx43Xqqadq3759eu+990hAOYAMQQ117NhRrVuXNKVcvHhxhXO/++47SVKbNm3UoUMHf4dW53wloPIL6QMFAAAAAADqscI86bit97a9sqki9IECAEdERESYx6XFI3YXXHCBuSXe888/L8MwHI8jLi5O/fr1kySlp6eXG2d5McJbk0xAzZo1S4ZhVPjz0EMPmfNTU1PN8dIEksvl0hVXXCGppMJp+fLlPs+1fPlyswLqiiuuaJR9kcJDvV9GeW4SUAAAAAAAoB7L2CHJ9gFmdRJQyba5JKAAoEZatWplHu/Y4buvVkJCgu644w5J0g8//KB77rlHHo+n3DUPHTqk6dOnW8a++uorpaWllfuYEydOaMWKFZJKClDKi7O8GOGtSW7B55S7775b06ZNU3Fxse6880599913ioyMNO/Py8vTnXfeKUkKCQnR3XffHaBI/Ss8JEgul1Q26ZxHBRQAAAAAAKjP7NvvxbaWwmOq/vhk23Z79vUAAFXSu3dvRUREKD8/X3//+98VGhqq9u3bKyiopPChTZs2ioyM1D//+U8tXrxYP/74o5577jktWrRIN998s3r16qXo6GgdO3ZMGzZs0DfffKMvvvhCp512mm666SbzPO+8845GjBihCy+8UBdddJF69uypxMREZWVlaf369XrxxRe1f/9+SdJtt93mFeegQYO0a9cuzZs3T6+88ooGDx5sVkXFxcWpRYsWdfBsNSwkoGqha9euuvfee/Wf//xHq1at0uDBg3Xfffepc+fO2rFjh5544gmtWbNGknTvvfeqS5cq7iPcwLhcLkWGBiu3TNUTCSgAAAAAAFCvpW+33rZXNFWGLfgAwBGxsbH605/+pCeffFI//fSTLrroIsv9qampGjp0qMLDw/X1119r4sSJ+uijj/TLL7+YVVG+xMXFeY0VFhbq888/1+eff17u42677Tb96U9/8hr/y1/+og8++EAFBQVeCaoJEyZo1qxZlfymTQ8JqFr697//rcOHD+u1117TmjVrdM0113jNufHGG/Wvf/0rANHVHXsCKp8t+AAAAAAAQH1mr1iyJ5Qqk2ybn3dUyj0qRSXWLi4AaIL+85//qEuXLpo9e7Y2bNigEydOqLjY+zPm2NhYffjhh/r+++/1+uuva8mSJTpw4IDy8vIUFxenzp07q3///rrsssu8ElnPPPOMLrzwQn377bdau3at0tLSdOTIEQUHB6tdu3YaOHCgbrrpJp199tk+Y+zVq5eWLVumqVOnaunSpTp06BD9oCpBAqqWgoKCNGPGDF155ZWaNm2aVq5cqfT0dCUnJ6tfv3669dZbdemllwY6TL+LCA223KYCCgAAAAAA1Gv2iiV7QqkyCe2loFDJU2hd8+QBtY8NAJoYl8ulm266ybJlXkXOPvvschNF5WnWrJnGjh2rsWPH1iRESSXbBb799ts1fnxTQwKqHA8//LAefvjhKs8fPny4hg8f7r+A6rnIMBJQAAAAAACggTAMKcO2BV91K6CCQ6TEjlL61t/GMkhAAQBQKijQAaBxiLRXQLEFHwAAAAAAqK+yD0sFmdax6vaAkugDBQBABUhAwRH2BFR+kSdAkQAAAAAAAFTC3v8pOFyKb1f9dexJK3tVFQAATRgJKDgiwrYFXz4VUAAAAAAAoL6yVyoldZaCgn3PrQgVUAAAlIsEFBwRGWp9KdEDCgAAAAAA1Fte/Z9qsP2eJCXbElBHd0rFRTVbCwCARoYEFBzh1QOKBBQAAAAAAKiv7JVK9kRSVdkroDyF0vE9NVsLAIBGhgQUHBFp24Ivjy34AAAAAABAfWXvAWVPJFVVdJIU2cy2Nn2gAACQSEDBIeEhth5QVEABAAAAAID6qMgtHbNVKdW0AkqiDxQAAOUgAQVHeFVAkYACAAAAAAD10bFdkmH73KKmPaAk7+SVvboKAIAmigQUHOHVA4ot+AAAAAAAQH1kr1CKbi5FJtR8PXvyKp0t+AAAkEhAwSFeCSgqoAAAAAAAQH3kVP+nUlRAAQDgEwkoOCIijB5QAAAAAACgAbBXKCXXYvs9yTuBlX1Iys+s3ZoAAiY4uORzzqKiIhUX8xknAsvj8Zivw9LXZkNCAgqOoAIKAAAAAAA0CE5XQCV2lFy2j9ioggIarKioKPP4+PHjgQsEkJSdnS3DMCRJkZGRAY6m+khAwRH0gAIAAAAAAA2CvQeUfQu96goJlxLa285BHyigoUpISDCPDx8+rMOHDys/P99MAgB1wePxKDMzUwcPHjTHYmNjAxhRzYQEOgA0DpFh1lxmfqEnQJEAAAAAAACUI/eolHfUOpbctfbrJneRju367TYVUECDFRERofj4eJ04cUKSlJGRoYyMDLlcrga5BRoapuLiYkvSMzIyUtHR0QGMqGZIQMEREaH0gAIAAAAAAPWcvfopKNS7eqkmkrpI2xaUfx4ADUqrVq0UFhamI0eOmGOGYaioqCiAUaGpioyM1MknnyyXyxXoUKqNBBQcQQ8oAAAAAABQ79krkxI7SsEOfDyWnGI7D1vwAQ2Zy+VScnKy4uLilJ2drZycHLndbnk87PqEuhEcHKzIyEjFxsYqOjq6QSafJBJQcEhkmHcCyjCMBvuHAQAAAAAAGiF7ZVJSLfs/lbdOxg7J45GCaL8ONGRhYWFKTExUYmJioEMBGiT+LwhHRIRYE1CGIRUU8Y0AAAAAAABQj9grk+yVSzWVbEtAFeVJmfucWRsAgAaKBBQcYa+AkugDBQAAAAAA6hl/VUDFtJTCYis+FwAATQwJKDgiItQ7AUUfKAAAAAAAUG8UF0lHd1rH7JVLNeVyeVdTkYACADRxJKDgiEhfCSg3CSgAAAAAAFBPHN8jeQqtY05VQPlaK4MEFACgaSMBBUeEBrsUHOSyjFEBBQAAAAAA6g17RVJkMyk6ybn17dVUVEABAJo4ElBwhMvl8qqCogcUAAAAAACoN+wVSU5WP0neCaiM7c6uDwBAA0MCCo6x94HKc3sCFAkAAAAAAICNvSLJqf5PpewJrcz9kjvH2XMAANCAkICCYyLDrC8nKqAAAAAAAEC9Ya9ISkpxdv2kzpKs7QmoggIANGUkoOAY+xZ89IACAAAAAAD1hr8roEIjpfh2FZ8TAIAmhAQUHEMCCgAAAAAA1Ev5J6Scw9Yxp3tASVKyraqKCigAQBNGAgqOsfeAYgs+AAAAAABQL6TbEkGuICmxo/PnsSe1qIACADRhJKDgmMgwWwWUmwQUAAAAAACoBzJsiaCE9lJIuPPnsW/rZz8vAABNCAkoOCYihC34AAAAAABAPeTv/k+lkuxb8O2QDMM/5wIAoJ4jAQXHeFVAkYACAAAAAAD1gb0SyR/9nyTvxJY7W8pK88+5AACo50hAwTFePaDYgg8AAAAAANQH9h5QySm+59VWbGspNMp2brbhAwA0TSSg4JjIUCqgAAAAAABAPePxSEd3WMf8VQEVFCQldbaO0QcKANBEkYCCYyLDrC+nvEJPgCIBAAAAAAD4Pyd+lYryrWP+6gEleSe37NVXAAA0ESSg4BivCii24AMAAAAAAIFmr0AKi5ViWvrvfPbkFhVQAIAmigQUHOPVA4ot+AAAAAAAQKD56v/kcvnvfF4VUCSgAABNEwkoOCYyjAQUAAAAAACoZ+wVSP7q/1QqOcV6+/heqTDf91wAABoxElBwjNcWfCSgAAAAAABAoNkrkJK7+vd8SbYElAzp6E7/nhMAgHqIBBQcQwIKAAAAAADUOxk+tuDzp/BYKbaVLQa24QMAND0koOCYCPsWfG4SUAAAAAAAIIDcOVLmfuuYv7fgk7yroOgDBQBogkhAwTFUQAEAAAAAgHrFXv0kl5TU2f/nTbYlubziAACg8SMBBcdEkIACAAAAAAD1ib3yKL6dFBrp//Paq6yogAIANEEkoOAYewVUfqFHHo8RoGgAAAAAAECTV9f9n8zz2CugtkkGn5EAAJoWElBwjD0BJUkFRZ4ARAIAAAAAACDvyqO66P8kefeAyj8h5aTXzbkBAKgnSEDBMRFh3i8ntuEDAAAAAAABk77VettemeQvCSdLweEVxwIAQCNHAgqO8VUBRQIKAAAAAAAEhGFIGTusY/bKJH8JCpYSO1nHMugDBQBoWkhAwTERvhJQbhJQAAAAAAAgADIPSIU51rG6qoCSvPtN2bcDBACgkSMBBceEBgcpNNhlGcunAgoAAAAAAASCveIoNEqKbV1357f3m8rYXnfnBgCgHiABBUfZq6BIQAEAAAAAgICwVxwldZaC6vCjMHu1FRVQAIAmhgQUHGXvA0UPKAAAAAAAEBD2iiN7RZK/2c93bLdU5K7bGAAACCASUHBUZJgtAUUPKAAAAMBv9uzZo8mTJ6t79+6Kjo5WYmKi+vXrp6lTpyo3N7fG6+7evVsul6taPx06dPC51tChQ6u8BgA4yl5xVJf9nyTvHlBGcUkSCgCAJiIk0AGgcaECCgAAAKgbn376qcaNG6fMzExzLDc3V6tWrdKqVas0ffp0zZ8/XykpKRWs4pxu3brVyXkAoMrsPaDqugIqspkU3VzKOWKNqXnXuo0DAIAAIQEFR4XTAwoAAADwuzVr1ujqq69WXl6eYmJi9Le//U3Dhg1TXl6e5syZo1dffVVbt27VZZddplWrVik2NrZa67dp00br1q2rdN7jjz+ut99+W5I0YcKECuf27dtXM2fOrFYcAFBjhXnS8V+tY/aKpLqQ1MWagKIPFACgCSEBBUdFhlp3dWQLPgAAAMB5d911l/Ly8hQSEqIFCxZo4MCB5n3nnXeeunTpoilTpmjr1q166qmn9PDDD1dr/dDQUPXs2bPCOcXFxVq0aJEkKTY2VqNHj65wfnR0dKVrAoBjju6UZFjHkgKQgEpOkfb+8Ntte1UWAACNGD2g4CjvLfg8AYoEAAAAaJxWrFihJUuWSJJuvPFGS/Kp1OTJk9WjRw9J0nPPPafCwkLH4/jmm2904MABSdKYMWMUGRnp+DkAoMbslUaxraTw6lWDOsK+7V/69rqPAQCAACEBBUdFhtEDCgAAAPCnuXPnmseTJk3yOScoKEjjx4+XJB0/flypqamOxzF79mzzuLLt9wCgznn1fwpA9ZMkJdsSUFRAAQCaEBJQcFQEPaAAAAAAv/r+++8llWxp16dPn3LnDRkyxDxeunSpozFkZWWZibAOHTro3HPPdXR9AKg1e6WRPRFUV+wVULkZUu7RwMQCAEAdIwEFR3ltwUcPKAAAAMBRmzZtkiSlpKQoJKT8tr7du3f3eoxTPvjgA+Xm5kqSrr/+erlcrkofs3nzZg0YMEAJCQmKiIhQ27ZtdcUVV2j27Nl+2SIQQBPnVQEVoARUs/ZSkO2/1RlswwcAaBrKv1oBasC7BxQJKAAAAMAp+fn5Sk9PlyS1bdu2wrnNmjVTdHS0cnJy9OuvvzoaR9nt90q3+qvMoUOHdOjQIfP2/v37tX//fs2bN09PPPGEPvjgA7NvVXXt27evwvvT0tJqtC6ABsow6k8FVHCo1KyjNSGWvk1q1z8w8QAAUIdIQMFR9h5QbMEHAAAAOCcrK8s8jomJqXR+aQIqOzvbsRj27t2rxYsXS5IGDRqklJSK+6oEBQXp/PPP1/Dhw3XGGWcoKSlJWVlZ+umnn/TKK69o06ZN2rhxo4YNG6YVK1bo5JNPrnZM7dq1q9HvAqCRyjkiFZywjgWqB5RUkvwqm4CiDxQAoIkgAQVH0QMKAAAA8J/8/HzzOCwsrNL54eHhkqS8vDzHYnjzzTdlGIakqlU/ffTRR0pISPAaP+ecc3T77bfr5ptv1uuvv65Dhw7p7rvv1kcffeRYrACaqHRbgic4XEqofnLbMfbklz0+AAAaKRJQcBRb8AEAAAD+ExERYR673e5K5xcUFEiSIiMjHYvhjTfekFSS3Lr66qsrne8r+VQqNDRU06dP1/Lly7VlyxZ9/PHH2r9/v9q0aVOtmCrbYjAtLU39+7PdFdBkePV/6iwFBfueWxfs2//RAwoA0ESQgIKj7Fvw5blJQAEAAABOiY2NNY+rsq1eTk6OpKpt11cVK1as0ObNmyVJI0eOrDC5VFUhISG68cYbNWXKFEnS4sWLdd1111Vrjcr6YQFoYuwVRoHcfk+SkmwJqKM7JU9xYJNiAADUgaBAB4DGxbsCyhOgSAAAAIDGJyIiQklJSZKkffv2VTj32LFjZgLKqR5Js2fPNo+rsv1eVZ1yyinm8f79+x1bF0ATZa8wslcg1TX7+Yvd0vE9gYkFAIA6RAIKjooItb6k6AEFAAAAOKs0WbN9+3YVFRWVO6+0UkmSevToUevzFhYWas6cOZKkFi1a6JJLLqn1mqVcLpdjawGAdwVUgBNQUUlSRIJ1LJ1t+AAAjR8JKDgqwl4BxRZ8AAAAgKPOPvtsSSXb661evbrceYsXLzaPBw8eXOvzzp8/XxkZGZKk6667TiEhzu3ovnHjRvO4devWjq0LoAkqckvHdlvHAl0B5XL56AO1zfdcAAAaERJQcJT3FnwkoAAAAAAnjRo1yjyeOXOmzzkej8fcLi8hIUHDhg2r9XnLbr83YcKEWq9XqqioSK+99pp5+9xzz3VsbQBN0LHdkmH7LCLQPaAk7yose5UWAACNEAkoOCoyjAQUAAAA4E/9+/fXOeecI0maMWOGli1b5jXnqaee0qZNmyRJd911l0JDQy33L1q0SC6XSy6XSxMnTqz0nEePHtX8+fMlSaeddpp69epVpVhTU1N1/Pjxcu8vLCzUTTfdZMY6YsQIx/pVAWii7JVF0c2lyISAhGKRbEuC2ftUAQDQCDm3ZwIg7wood5FHxR5DwUHs6Q4AAAA45bnnntPgwYOVl5eniy66SPfff7+GDRumvLw8zZkzR9OmTZMkde3aVZMnT671+ebMmSO32y2petVPr7/+ukaOHKmRI0dq6NCh6tatm+Li4pSdna3Vq1dr2rRp5vZ7LVq00HPPPVfrWAE0cfWt/1MpKqAAAE0QCSg4yt4DSpLyC4sVHc5LDQAAAHBK79699e6772rcuHHKzMzU/fff7zWna9eumj9/vmJjY2t9vtLt94KDgzV27NhqPTY7O1tvv/223n777XLnnHbaaZozZ446duxYqzgBwCuxY688ChR7D6jsg1J+phQRF5h4AACoA2QF4Cj7FnwSCSgAAADAH0aMGKG1a9fqueee0/z587Vv3z6FhYUpJSVFv//973XHHXcoKiqq1ufZtm2bfvzxR0nShRdeqJNOOqnKj73vvvvUq1cvLVu2TBs3btSRI0d09OhRhYeHq2XLlurbt6/GjBmj0aNHKzjY+1oCAKrNvgVffamASuwkuYIkw/PbWMY2qU2fwMUEAICfkRWAo+xb8En0gQIAAAD8pX379nr66af19NNPV+txQ4cOlWEYVZrbpUuXKs+169Gjh3r06KG77767Ro8HgGrzqoCqJwmokHAp4WTp2O7fxtK3k4ACADRqQYEOAI1LeVvwAQAAAAAA+FXuUSnvqHWsvlRASd6x2Ku16ohhGFq955i+23qEz2wAAH5FBRQcFRzkUlhIkNxFv5WU57k9FTwCAAAAAADAAfbqp6AQqVn7wMTiS3IXafvXv922x1tH/rtgi/6XukOS1Do+QpMv6qbRvdsoKMgVkHgAAI0XFVBwnH0bPrbgAwAAAAAAfmevKGrWUQoODUwsviSlWG9nbK/zEH49mquXFu0wbx84ka/J7/+iy174Xt9tPVLn8QAAGjcSUHAcCSgAAAAAAFDn6mv/p1L2eDJ2SJ663TVmxve75PHR1m9TWqbGv7ZC18/4URsOnKjTmAAAjRcJKDguItT6sspzk4ACAAAAAAB+Zq8oslccBZq9B1RRnpS5r85OfyzHrXdX/lrhnCXb0nX5C9/rz+/+rH3HcusoMgBAY0UCCo6LsFVA0dASAAAAAAD4XX2vgIo9SQqLsY7VYR+oN5fvsexSExzkUpcWMV7zDEP6aM1+nffUYj3++SadyC2ssxgBAI0LCSg4LjKMLfgAAAAAAEAdKi6Sju60jtkrjgLN5fKxDV/d9IHKLyzW68t2W8YuP72Vvrz7XD055nS1jAv3eoy7yKNXvtupc6em6tXvdvIFYwBAtZGAguO8ekCxBR8AAAAAAPCn43skj61Sp75VQEneSbE6qoD66Kf9Ss92W8ZuObeTgoNcuqpvOy36yzDde3E3xYSHeD32RF6h/v35Jp3/1GLNXbNfHl9NpAAA8IEEFBznlYDiGzIAAAAAAMCf7JVEEQlSVFJAQqmQVwWU/xNQHo+h6Uus1WFnpyTr1Nbx5u3IsGD9cViKFt87VBMHdVBIkMtrnf3H83T3uz9rxIvfa+n2dL/HDQBo+EhAwXERYfSAAgAAAAAAdchX/yeXdxIl4JJSrLfT/b8F39ebDmlneo5l7NYhnXzOTYoJ18MjT9U3fx6iy05v5XPOhgOZGjv9R014bYU2pWU6Hi8AoPEgAQXH2SugSEABAAAAAAC/slcS1bf+T6XsFVCZ+yR3ju+5Dnll8Q7L7VNaxenslOQKH9MhOVr/u+5MfXz7IPXvmOhzzuKtRzT8+SX6y/u/6MDxPMfiBQA0HiSg4Di24AMAAAAAAHXKXkmUnOJ7XqAldvYey9jhPeaQVbuP6qe9xy1jt5zbSa4qVof1PrmZ3r3lLE0f31cpLWK87jcM6YPV+zTsv4v0ny8260ReoY9VAABNFQkoOC7StgVfntsToEgAAAAAAECT0FAqoMKipPh21jE/9oF65Ttr76fW8RHlbq1XHpfLpQtOaakv7zpHj//uNDWPDfeaU1Dk0cuLd2jI1FTN+H6XCor4MjIAgAQU/CCCLfgAAAAAAEBdyc+Usg9Zx5K7BiaWqqijPlA7jmTrm03W5+WGszsqNLhmHweGBAfp2v4na/G9Q/XnC7sq2vYFZEk6nluoRz/bqAueXqxPft4vj8eo0bkAAI0DCSg4ji34AAAAAABAnbFXELmCpMSOgYmlKux9oPxUATV9yU4ZZfI/sREhuqb/ybVeNyosRH86v4sWTxmm8QPbKyTIezu/X4/m6a45P+uK/y3VDzvSa31OAEDDRAIKjosItb6s8twkoAAAAAAAgJ/YK4gS2ksh3tvE1Rv27QHTnU9AHckq0Ic/7beMjTurvWLCQxw7R3JMuP55RU8tuOdcXdrzJJ9z1u0/oete/VE3zlqpE7n0hwKApoYEFBxHBRQAAAAAAKgz9goie4VRfZNs24IvY7sspUoOeP2H3XIX/daTOyw4SJMGdXD0HKU6NY/RS+P66MM/DFLf9s18zlm4+bAemrfeL+cHANRfJKDguMgwekABAAAAAIA6Yq8gslcY1Tf2+NzZUtZBx5bPKSjSG8v3WMZG926jFnERjp3Dlz7tm+n92wbqlev7qFNytNf9K3cf8+v5AQD1DwkoOC6CCigAAAAAAFBXMmxb8NkrjOqbuDZSSKR1zME+UO+u/FUn8qzb3d18bt30xHK5XLr41JP01T3n6u4LrIm2HHdRncQAAKg/SEDBcV5b8NEDCgAAAAAA+IPHI2XssI7V9wqooCApyZYkc6gPVFGxRzO+32UZu6BHC6W0iHVk/aoKDQ7S+d1bWsZyC/h8CACaGhJQcJx9Cz4qoAAAAAAAgF9k7pOK8qxj9b0HlOS7D5QD5q9L0/7j1ufjlnM7O7J2dUWFWz8fchd7LH2pAACNHwkoOM5eAVVQyJsLAAAAAADgB/bKobBYKaal77n1ib1Ky4EKKMMwNO27nZaxXu0S1K9Ds1qvXRMx4SFeY7lswwcATQoJKDjO3gPKXexRUTFJKAAAAAAA4DB74iY5RXK5AhNLddirtNK31nrJH3ZkaMOBTMvYred2kitAz0eUbYccScqhTQMANCkkoOA4+xZ8kpRPiTUAAAAAAHBahi0BVd/7P5Wy94A6vlcqzK/Vkq/Yqp86JEXpolNPqtWatREV5l0BlVNABRQANCUkoOA4+xZ8kpTHN1wAAAAAAIDTvCqgGmgCSoZ0dKfPqVWxKS1T3209Yhm78ZxOCg4KXDVYcJDL6zMiElAA0LSQgILjfCWg8gtJQAEAAAAAAIdlbLfe9krs1FMRcVKMrTrJXs1VDa/aqp8So8P0+z5ta7yeU6LDrZ8R5fIFZQBoUkhAwXHhId4vqzwSUAAAAAAAwEnuHClzv3WsoVRAST76QNUsAXXgeJ7m/XLAMjZhYAevHt2BYN+GL5sKKABoUkhAwXFBQS6vJBRb8AEAAAAAAEfZq58kKbFz3cdRU/ZqLV+/TxW89v0uFXkM83ZEaJCuH9i+NpE5JjrcmoDKdZOAAoCmhAQU/CIyzPotGyqgAAAAAACAo+wVQ/HtpLCowMRSEw5UQJ3IK9Q7K/Zaxq7q206J0WG1icwx0WH2HlB8PgQATQkJKPiFvQ8UCSgAAAAAAOCohtr/qVSSLQGVsU0yDN9zy/H2j3uVU2bXmSCXdNPZnZyIzhFRtgqoHLbgA4AmhQQU/MKegMpnCz4AAAAAAOAke8VQQ+r/JEnJtoRZ/gkpJ73KDy8oKtbMpbssY5f2bKWTk+pPFVhMuK0Cis+HAKBJIQEFv7A3uqQCCgAAAAAAOCrDloCyVxTVdwntpWDbVnn236kCn/x8QIezCixjt5xbf6qfJCkqzNYDigooAGhSSEDBL+w9oPILPQGKBAAAAAAANDqGIWXssI7ZK4rqu6BgKdGWMKpiHyiPx9Cr3+20jA3omKgz2iU4FJwzvHpAuUlAAUBTQgIKfkEPKAAAAAAA4DdZaZI72zrW0CqgJO++VVWsgFq09bC2Hbb+/rcOqV/VT5IU7dUDis+HAKApIQEFv7BvwZdPAgoAAAAAADjFXikUEinFtQlMLLWR3NV6O317lR72ymJr9VPXljEa2rWFU1E5xp6AyqUCCgCaFBJQ8Av7Fnx5NJkEAAAAAABO8er/lCIFNcCPuZJtVVtVqID6+dfj+nHXUcvYzed0UlCQy8nIHBFl+3womx5QANCkNMD/M6MhiAy1vrTYgg8AAAAAADjGXilkT+Q0FPZtA4/tlooLK3zItO+sva9axoXril71s/rLuwKKz4cAoCkhAQW/oAcUAAAAAADwG3ulUENNQCXbekB5ikqSUOXYk5GjL9cftIxNGtxRYSH18yO+6DB7DygqoACgKamf/3dCg+fVA4pvuAAAAAAAAKfYe0DZK4kaishmUlSydcz+u5UxfckueYzfbseEh+i6ASf7Kbjaiwq3fj6UU8DnQwDQlJCAgl/YE1BUQAEAAAAAAEcU5kvH91rH7JVEDUkV+0AdzXHr/dW/Wsau7d9OcRGh/oqs1mJsW/DluKmAAoCmhAQU/CIyjAQUAAAAAADwg6M7JRnWsaQGnICyx15OBdTsZbuVX+gxb4cEuTRpcEd/RlZrUbbPh3LdxTIMo5zZAIDGhgQU/MKrBxRb8AEAAAAAACfYK4RiW0nhsYGJxQleFVDbvabkuYs1e9key9jIM1qrdUKkPyOrNXsPqGKPoYIiTzmzAQCNDQko+IU9AZVPBRQAAAAAAHCCV/+nBlz9JHn3r/JRAfXBT/t0NMdtGbtlSCd/RuWIaNsWfJKUU8A2fADQVJCAgl9EsAUfAAAAAADwB3uFkL2CqKGxx5+bLuUdM28WewxNX7LTMmVI1+bqflJcXURXK9HhwV5jueySAwBNBgko+IV3BRTl1QAAAAAAwAFeFVANPAHVrIMUZKsUSv8tyfbVhoPak5FrufvWc+t/9ZMkRWbu1v0hb2lC8FcKVkniKZsKKABoMrzrYAEHePWAogIKAAAAAADUlmF494Bq6BVQwaElSaiylV0Z26R2/WQYhl75zlr91LNNnAZ2TqrbGGuiIEuu6efrlpDjkqTWrnQ9XjRWuW4SUADQVFABBb+IDLO+tPIprwYAAAAAALVVkCXln7COJTaMaqAKefWB2ipJWrHrqH759bjlrlvO7SyXy1VHgdXCtq+l/OPmzYuCVkmScgr4jAgAmgoSUPCLCCqgAAAAAACA0wqyvMeiEus+Dqclp1hv/982g9Ns1U9tm0VqeM+T6iqq2rFtlRjvypEk5bAFHwA0GSSg4Bf2LfiKPIYKi+kDBQAAAAAAasGd7T0WFlv3cTjNXgGVsV3bDmVp4ebDluEbz+6okOAG8nGebavEaOVLknLYJQcAmowG8n8sNDSRYcFeY1RBAQAAAACAWimwJaBCIqTgRtDi3N7H6uhOTf/OVkEUGaqr+rarw6BqyVYBFe4qUqiK6AEFAE0ICSj4RUSIdwKKPlAAAAAAAKBW3LYt+MJiAhOH0+wVUMVurfr5F8vQ+IHtFR3eQJJthiFlbPcajlaestmCDwCaDBJQ8AsqoAAAAAAAgOPsFVDhjSQBFZ0sRcRbhtoZ+83jsJAgjR/YoY6DqoWsgz63S4xx5Su3gM+HAKCpIAEFvwgP8X5pkYACAAAAAAC1Yk9qNIb+T5LkcnlVQXV2pZnHV57ZVs1jw+s6qpqz9X8qFa085bAFHwA0GSSg4Bcul0uRodYqqDy24AMAAAAAALVRYNuCr7FUQElefaA6/V8CyuWSbj6nYyAiqrn08hJQ+cphCz4AaDJIQMFv7NvwUQEFAAAAAABqxasCqhEloJJSLDdLE1AX9mipTs0b2O/po/+TJMW48pTDF5QBoMkgAQW/sVdAFRR6AhQJAAAAAABoFBprDyjJuwIq6IAk6dYhnQIRTe1UUAGVSwUUADQZJKDgNxGh1pcXFVAAAAAAAKBWGnEFlGGrgGrpOq6z24WpT/vEAEVUC+X0gIpx5SmngM+HAKCpIAEFv/Hago8SawAAAAAAUBteFVCxgYnDD74/Gi+P4bKM/fH0AAVTG0UF0vG9Pu+KVr5y3FRAAUBTQQIKfmPfgo8KKAAAAAAAUCvuLOvtRlQB9crS/dpnJFvGBsQdDVA0tXB0p2T4bsMQrXzl8gVlAGgySEDBbyJsCah8ElAAAAAAAKA2GmkPqPX7T+j77enaabS2jAdlbA9QRLVQTv8nqXQLPiqgAKCpIAEFv/GqgOIbLgAAAAAAoDYaYQ+o9OwC3fH2T5KkHbYEVHm9lOq1CmKOVj4JKABoQkhAwW+8ekBRAQUAAAAAAGqjkfWAysov1MSZK7Q7I1eStNNoZZ2Q3hAroMqPOdqVp9zCYnk8Rh0GBAAIFBJQ8JuIEBJQAAAAAADAQY2oB1R+YbFumb1a6/dnmmNeCaiM7ZLHdz+lequCCqgY5cswpPwiPiMCgKaABBT8xl4BRQ8oAAAAAABQK42kB1Sxx9A97/6sZTszLOO5cZ2sE4vypMz9dRhZLRlGhT2gopUnScpmGz4AaBJIQMFvIugBBQAAAAAAnNQIekAZhqEH567XF+sPWsaTY8L07I2XeP9ODakPVG6GlH+83LtjXPkl0wr4jAgAmgISUPCbSHsCigooAAAAAABQU0VuqdhtHWuAPaCe/nqr3lmx1zIWEx6iWZP6q2PzGCkpxfqAhtQHqoLqJ0mKVkkCKsdNBRQANAUkoOA3kWHWl1deYQPbsxgAAAAAANQf9uonqcFVQM1cuksvfGtNKIUFB2na+D7q2Sa+ZCC5i/VBDakCqpJYo10lW/DlUAEFAE0CCSj4jb0Cih5QAAAAAACgxgqyvMcaUA+oT37er0c+3WgZC3JJz1/bS4M6J/82mGRLQFVSVVSv2GONSrbcjKECCgCaFBJQ8Bt7DygSUAAAAAAAoMZ8VUCFRtd9HDWwaMthTX7vF6/xf406TZf0bGUdTLZtwZfRgLbgs8fa5kzLzWjlSTLoAQUATQQJKPhNZJitB5SbNxcAAAAAAKCGCmwJqLAYKaj+f7T1095j+sObP6nIY1jG/3JRV1034GTvB9groE78Krlz/Rihg+wVUK17W24GuwxFyK2cAiqgAKApqP//l0aDZd+CL48KKAAAAAAAUFNu2xZ8DaD/07ZDWbph1kqvz0QmDe6gPw5L8f2gpM7eY0d3+CE6hxUXSsd2WcdsCSipZBs+tuADgKaBBBT8hh5QAAAAAADAMfYKqHre/2n/8TyNf22FjucWWsav6NVaf7/sFLlcLt8PDIuW4tpaxxpCH6hjeySPLbHkIwEV7cpTLrvkAECT0CQTUJmZmZozZ44mT56sIUOGKCUlRfHx8QoLC1OLFi00dOhQPfnkk8rIyKjSej/88IPGjRun9u3bKyIiQieddJIuvvhivfPOO37+Teq3CLbgAwAAAAAATrH3gKrHFVBHc9waP+NHpZ3It4wP6dpcU8ecoaCgcpJPpRpiH6j0rdbbUUlSTEspKMQyHKN8ZbMFHwA0CSGVT2l8VqxYoWuvvdbnfUeOHNHixYu1ePFiTZ06VW+++aYuvvjictd6+OGH9eijj8rj8Zhjhw4d0oIFC7RgwQK99dZb+uCDDxQREeH471HfRYR4b8FnGEb53/ABAAAAAAAoj1cFVGxg4qhETkGRJs1aqR1HcizjvU9O0EvjzlRYSBW+D57URdq56LfbDaECKsMWY1IXyeUqSRTmHzeHo5WnXBJQANAkNMkKKElq166dxo8fr+eee04fffSRli1bpqVLl+rdd9/V73//ewUHBys9PV0jR47UL7/84nONV155RY888og8Ho86d+6sGTNmaMWKFZo7d66GDRsmSZo/f75uuOGGuvzV6o1IWwWUx5DcxZ5yZgMAAAAAAFSgAfSAchd5dNubq/XLr8ct411axGjmxH6KCqvid8GTu1hv25M79ZE9SVZaxWVLFEa78pXDLjkA0CQ0yQqoYcOGae/eveXef9VVV2nu3LkaPXq03G63HnnkEX300UeWOUePHtV9990nSTr55JO1fPlyJScnm/dffvnlGj16tD799FO98847uuWWWzR06FC//D71lb0HlCTluz0KD/EeBwAAAAAAqFA97wHl8Rj683s/a8m2dMt46/gIzb6xvxKiwqq+WJJtC7707ZJhlFQU1Vf2bQKT/i+JZksUxihPOVRAAUCT0CQroIKDK0+AjBo1St26dZMkLVmyxOv+6dOn68SJE5KkJ554wpJ8Kj3H//t//88819SpU2sbdoPjKwGVV8g3XAAAAAAAQA3U4x5QhmHokU836LO1aZbxZlGhmn3jALWKj6zegvYKKHeWlHWwllH6mVcF1P/9DrZEIRVQANB0NMkEVFXFxpaUCOfn53vdN3fuXElSXFycfve73/l8fNu2bXXBBRdIkhYuXKisrCyf8xqriDDvlxcJKAAAAAAAUCP1uALqhW+36/VleyxjUWHBmjmpv1Ja1CDOuLZSiC1pVZ+34cs7JuVaK7/Kq4CiBxQANB0koMqxZcsW/fzzz5Kk7t27W+5zu91asWKFJGngwIEKCyu/hHrIkCGSpIKCAq1atco/wdZTYcFBCrJVhueTgAIAAAAAADVRYO8BFet7Xh17Y/kePf31VstYaLBLr1zfR73aJdRs0aAgKamzdcxeYVSfpNu233MFS806lByH27fgy1c2CSgAaBJIQJWRm5urbdu26emnn9aQIUNUVFTyP8O7777bMm/r1q0qLi5JpNiTU3Zl79+0aZOzAddzLpfLaxs+KqAAAAAAAECNuG0JqHpQATV/bZr+8cl6y5jLJT1zdS+d06V57Ra394Gy91iqT+zVWc06SCH/94VtW6IwxpWnXLbgA4AmISTQAQTarFmzNGnSpHLv/+tf/6rrrrvOMrZv3z7zuG3bthWu365dO/P4119/rXZ8Zc/lS1paWoX3B1pkWLBlX9983mAAAAAAAICasG/BF+AeUN9vS9fd766RYVjH/znyVF1+euvan8DeB6peV0CV0/9J8u4BpTzluqmAAoCmoMknoMrTq1cvTZs2Tf369fO6r2wvp5iYit/sREdHm8fZ2dkVzPStbAKrIYqgAgoAAAAAADjBXX96QK3dd1y3vrFKhcXW7NNd53fR9QM7OHOSJFsCqj73gLLHVrZ6y5YojHGxBR8ANBVNfgu+UaNGad26dVq3bp1WrFihd955R6NHj9bPP/+sa6+9Vp999pnXY/Lz883jivo/SVJ4eLh5nJeX51zgDQRb8AEAAAAAAEd4VUAFpgfUjiPZmjhzpWXHF0m6/qz2uvuCLuU8qgaSbVvwHd8rFRU4t76T7D2gkrv+duxVAZWv/EKPij220jEAQKPT5CugEhISlJCQYN7u16+frrnmGr3xxhuaMGGCrrjiCs2YMUMTJ04050RERJjHbre7wvULCn57YxAZGVnt+Crbti8tLU39+/ev9rp1JTLMloBiCz4AAAAAAFAT9aAC6uCJfI2fsUJHc6yfB112eis9PPJUuVwu505mr4AyPNLRnVKLHs6dwwme4pK4yiq7BZ+PHlCSlOsuUmxEqL+jAwAEUJNPQJXn+uuv12effab33ntPd9xxh0aOHKnExERJUmzsb//jrGxbvZycHPO4su36fKmsx1R9Z9+CL58KKAAAAAAAUF2G4SMBVbcVUMdz3Rr/2o/af9y6w83ZKcl6+qozFBzkYPJJkiLipJiWUvah38bSt9W/BNTxvVKxrTIrqaIeUCU7C+UUFJOAAoBGrslvwVeRK664QlJJEunLL780x8smhfbt21fhGmUrmBp6P6eaoAcUAAAAAACotcLckgqgssLqrgIqz12sG2at1NZD1iTY6W3j9fL1fRQeElzOI2up7FZ2Uv3sA5Vh234vIl6KTv7tdpg9AVWSwMtx0wcKABo7ElAVaN68uXm8Z88e87hr164KDi55Y7F58+YK1yh7f48e9ewbKnUgMtT6Estze8qZCQAAAAAAUA57/yepziqgCos9uv2t1fpp73HLeKfkaM2c2E8x4X7cYCjJ1gfK3mupPki3JcWSukhltyK0VUDFuEoqoHIL+JIyADR2JKAqsH//fvO47PZ5YWFhZt+lZcuWVdgHavHixZKk8PBw9e3b10+R1l+RVEABAAAAAIDasm+/J9VJBZTHY2jKB2uVuuWIZfykuAjNvrG/kmLC/RtAsq0PVL2sgLLFZI/Z1gOqdAu+7AIqoACgsSMBVYH333/fPD7ttNMs940aNUqSlJmZqY8++sjn4/ft26dvvvlGknT++edbekc1FZFh9IACAAAAAAC1VJBlvR0UIoX4Ofkj6bHPN+njNfstY/GRoZp9Y3+1bRbl9/NbeilJJdVGhuH/81aHVwWUrWrLVgEV5SpQkDzKZQs+AGj0mmQCatasWcrPz69wzjPPPKPPP/9cktSxY0edc845lvtvuukmxcfHS5L++te/KiMjw3J/cXGxbr/9dhUXlyRc7r33XqfCb1C8ekC5SUABAAAAAIBqsldAhcVYt3nzg282HtL073dZxiJCg/TaxH7q2rKOvmScbEvm5B+XcjN8Tg0Yew8orwoo70q1aOUrh8+IAKDR8+MmtfXXww8/rMmTJ+vKK6/U2Wefrc6dOysmJkZZWVlat26d3nrrLS1dulRSyXZ706ZNM3s+lUpMTNQTTzyh2267TXv27NGAAQP0wAMP6LTTTtOBAwf07LPPKjU1VZJ07bXXaujQoXX9a9YL9i348ot4cwEAAAAAAKrJ3gPKz/2f8tzFemjeBstYSJBLL43roz7tm/n13BYJ7aXgMKm4TPuH9G1SdHLdxVCRgiwpK806Zq/aCveVgMpTDlvwAUCj1yQTUJJ09OhRvfrqq3r11VfLndO2bVu99tpruuCCC3zef+utt+rAgQN69NFHtWPHDt1www1ec4YPH67XXnvNsbgbGq8eUHy7BQAAAAAAVJevCig/ejF1m/Yfz7OM/fOKnhrWrYVfz+slKFhK7CQd2fzbWMY2qf3Auo2jPPbqJ7lK4i0rzDtZGO3KJwEFAE1Ak0xAffXVV5o/f76WLl2q7du369ChQ8rIyFBkZKRatGihXr166fLLL9dVV12lqKiK9/N95JFHdPHFF+t///uflixZokOHDikhIUFnnHGGJk2apGuvvbaOfqv6yd4DKo8eUAAAAAAAoLrsPaB8VNU4ZfvhbE37bqdlrH/HRF3bv53fzlmhpBRrAsrecymQ0m0JqISTpdAI61hwiBQSIRX91g4jRnnK5UvKANDoNckEVLdu3dStWzf9+c9/dmS9QYMGadCgQY6s1djYe0Dlk4ACAAAAAADVVUcVUIZh6B+frFdhsWGOhQS59K9RPeXyc8+pctl7KnlVHQVQhi0ZZo+1VFiMJQFFBRQANA1BgQ4AjZvXFnwkoAAAAAAAQHV59YDyTwJq3i8H9MOODMvYjed0VNeW/u05VSF7T6V6VQFli8Ueaynbv1eM8pTjJgEFAI0dCSj4ldcWfJRXAwAAAI7Zs2ePJk+erO7duys6OlqJiYnq16+fpk6dqtzc3Bqvu3v3brlcrmr9dOjQocI1c3Nz9eSTT6pfv35KTExUdHS0unfvrsmTJ2vPnj01jhVAE+FVAeV8Qigzv1D/mr/JMtY6PkJ/Oq+cpEpdsVcVHdslFRcGJhY7rwqoFN/zbP9e0cpXbgGfEQFAY9ckt+BD3YkIteY48ws9AYoEAAAAaFw+/fRTjRs3TpmZmeZYbm6uVq1apVWrVmn69OmaP3++UlLK+TDQYd26dSv3vu3bt2v48OHats36QeWWLVu0ZcsWTZ8+XW+99ZYuv/xyf4cJoKGqgx5QTy/YqiNZBZaxh0aequjwAH98lmT777inSDq2p/xkT13xeLx7QFWxAirala/DbMEHAI0eCSj4lb0HFFvwAQAAALW3Zs0aXX311crLy1NMTIz+9re/adiwYcrLy9OcOXP06quvauvWrbrsssu0atUqxcZWr1KgTZs2WrduXaXzHn/8cb399tuSpAkTJvick5WVpcsuu8xMPt1888265pprFBkZqdTUVD3++OPKzMzU1VdfraVLl6pXr17VihVAE+HnHlDr95/Q7GW7LWPndW+hi05p6eh5aiQqUYpKknLLbA2YsS3wCajM/VJRnnWsoh5QZcQoT7vZJQcAGj0SUPArrx5QvLkAAAAAau2uu+5SXl6eQkJCtGDBAg0cONC877zzzlOXLl00ZcoUbd26VU899ZQefvjhaq0fGhqqnj17VjinuLhYixYtkiTFxsZq9OjRPudNnTpVW7dulSQ9+eSTuvfee837Bg4cqKFDh2rIkCHKzc3V3Xffba4JABZ+7AHl8Rh6YO56eYwyy4cE6eERp8rlcjl2nlpJ6mJNQKVvk7pdGrh4JO/t98JipNhWvuf6qICiBxQANH70gIJfefWAKiyWYRjlzAYAAABQmRUrVmjJkiWSpBtvvNGSfCo1efJk9ejRQ5L03HPPqbDQ+V4h33zzjQ4cOCBJGjNmjCIjI73mFBYW6vnnn5ck9ejRQ5MnT/aaM2jQIN14442SpMWLF2vlypWOxwqgEfBjD6g5K3/VL78et4zdMSxFJydFOXaOWrNXO9mTP4Hgtf1eZ6m8hJ2PCqgctuADgEaPBBT8yl4BJUkFRfSBAgAAAGpq7ty55vGkSZN8zgkKCtL48eMlScePH1dqaqrjccyePds8Lm/7vdTUVJ04ccKcExTk+xJ04sSJ5vHHH3/sXJAAGg8/9YDKyC7QE19utox1TI7WLUM6ObK+Y+y9lezJn0CwJ8HK6/8kSeHWhGG08pVTwC45ANDYkYCCX/lKQOXTBwoAAACose+//16SFB0drT59+pQ7b8iQIebx0qVLHY0hKyvLTIR16NBB5557rs95pbHa47Hr27evoqJKKg2cjhVAI+GnHlD/+WKzTuRZq0QfvaKnwkO8P88IKHtvpXpRAWWLobz+T5LXv1e0K0+5bMEHAI0eCSj4VUSY9xu2PBJQAAAAQI1t2rRJkpSSkqKQkPLb+nbv3t3rMU754IMPlJubK0m6/vrry+2RsnHjRp/x2IWEhCglpWR7KadjBdBI+KEH1MrdR/X+6n2WsRFntNbZXZJrvbbj7NVFOUekvOMBCcWUYd+CL8X3PMnr3yuGCigAaBLKv1oBHOCrAirPzRsMAAAAoCby8/OVnp4uSWrbtm2Fc5s1a6bo6Gjl5OTo119/dTSOstvvlW7158u+fSUf7EZHRyshIaHCNdu1a6e1a9fqyJEjKigoUHh4eJXjKT1PedLS0qq8FoB6yuEeUIXFHj348XrLWEx4iB68rEet1vWbZh0kV7BklPlMJWO71LZvYOJx50onbP9vqVYFVL7cxR65izwKC+H78QDQWJGAgl+FBgcpJMilIo9hjlEBBQAAANRMVtZvPVBiYir/9n9pAio7O7vSuVW1d+9eLV68WJI0aNAgs3LJl9J4qxprqezs7GoloNq1a1fluQAaIE+xVJhrHatlBdSspbu15ZC1r9SfL+yqlnERtVrXb0LCSpJQR3f8Npa+LXAJqLJxlKqwAsreAypPUsmXlElAAUDjxX/h4Xf2Kih6QAEAAAA1k5+fbx6HhYVVOr80iZOXl+dYDG+++aYMo+QLZhVVP0m/xVudWCVn4wXQCNirn6Ra9YBKO5GnZ77Zahnr0SpO4we2r/GadcJeYZS+1fe8umDv/xTXRgqL9j1X8vr3ilHJ/x+y6QMFAI0aFVDwu4iwYGUV/PaGIs/tCWA0AAAAQMMVEfHbN/Pdbnel8wsKCiRJkZGRjsXwxhtvSCpJGF199dUVzi2NtzqxStWPt7ItBtPS0tS/f/9qrQmgHrH3f5JqVQH16GcblWtrD/CvUT0VElzPv6dtrzDK2OZ7Xl2oTv8nyevfK9pV8kWD3AISUADQmJGAgt/ZK6DYgg8AAAComdjY37Ywqsq2ejk5OZKqtgVeVaxYsUKbN2+WJI0cObLSvk6l8VYnVqn68VbWDwtAA+ezAqpmPaAWbTmsz9cdtIxd06+d+rRvVqP16pRXBdR23/Pqgr0CKrlrxfPtPaD+rwIqhz7hANCo1fOvdqAxiAi1vsxIQAEAAAA1ExERoaSkJEnSvn37Kpx77NgxM6njVI+k2bNnm8eVbb8n/ZYYysnJ0fHjxyucW1rF1Lx582r1fwLQBNgroEIipODqf6c6v7BYD83bYBlrFhWq+y7pXpvo6k6SLQF1dGdJf6xAsFdf2ZNjdrYeUOGuIoWqSDlUQAFAo0YCCn7n1QOKb7cAAAAANXbKKadIkrZv366iovI/uCutVJKkHj161Pq8hYWFmjNnjiSpRYsWuuSSSyp9TGms9njsioqKtGNHSUN7J2IF0Mi4s6y3a9j/6aVFO7QnI9cy9rdLe6hZdOV96uoFe5KnuEA6vrfu4zAM7+qryrbg8/FvFq08ElAA0MiRgILfRbAFHwAAAOCYs88+W1JJVdHq1avLnbd48WLzePDgwbU+7/z585WRkSFJuu666xQSUnn1QWms9njsVq1aZVZrORErgEbGXgFVg/5Pu9Jz9NLiHZaxPu2baUyfBrSFZ3RzKSLeOmbvxVQXsg95JwUrrYDy/jeLceV79eICADQuJKDgd5FhJKAAAAAAp4waNco8njlzps85Ho/H3C4vISFBw4YNq/V5y26/N2HChCo9ZujQoYqPL/mw9PXXX5dhGD7nzZo1yzwePXp0zYME0DjZe0BVs/+TYRh6aN4GuYs85lhwkEv/GtVTQUEuJyKsGy6X9zZ89l5MdcF+zpBIKa6SRF5otNdQtPKUTQUUADRqJKDgd/Yt+PL4dgsAAABQY/3799c555wjSZoxY4aWLVvmNeepp57Spk2bJEl33XWXQkNDLfcvWrRILpdLLpdLEydOrPScR48e1fz58yVJp512mnr16lWlWMPCwvSnP/1JkrRp0yb997//9ZqzbNkyzZgxQ5I0ZMgQ9evXr0prA2hCCmzVNtWsgPpi/UF9t/WIZWzioA7q0SqutpHVPXulkb0XU12wnzOpsxRUyUeMQUFe2/BFK1+5bhJQANCYVb9jI1BNXj2gikhAAQAAALXx3HPPafDgwcrLy9NFF12k+++/X8OGDVNeXp7mzJmjadOmSZK6du2qyZMn1/p8c+bMkdvtllT16qdS9957r959911t3bpVU6ZM0fbt23XNNdcoMjJSqampeuyxx1RUVKTIyEg9++yztY4VQCPkVQFV9QRUdkGR/vnpRstYy7hw3XNhVyciq3v2XksBqYCqZv+nUmExln/LGFeecgr4jAgAGjMSUPC7CNsWfPlUQAEAAAC10rt3b7377rsaN26cMjMzdf/993vN6dq1q+bPn6/Y2OptVeVL6fZ7wcHBGjt2bLUeGxsbq/nz52v48OHatm2bpk2bZibISsXFxemtt96qcmUVgCamFj2gnv16qw5m5lvG/nH5qYoJb6AfiXlVQAWgB5S9Aqqy/k+lwmOkMv+U0cpXDlvwAUCjxhZ88DuvLfjoAQUAAADU2ogRI7R27Vrdc8896tq1q6KiopSQkKC+ffvqiSee0Jo1a5SSUsVvpVdg27Zt+vHHHyVJF154oU466aRqr5GSkqI1a9boiSeeUN++/5+9O49zrCzz/v89qaoslVRq6WJpaGgQaLoRVBQYkaXRGXCG1RYUGRl2hhmVAWXA7ZnRcXkUWRUe+bkgm7izCqIOyC6MgiAy3UCDNEt3s3R3VaUq+3J+f6SSyrlPUmv2fN6vl6+pnJwkN1Bzquq+zve69tHAwIB6e3u1++6765Of/KSeeuopHXnkkQteK4A2Nc8E1JqNEV3z+3WOYwftNqzD95r7daxpmDOgxje6WxTWmpm6MtdUifHfLWTFFeUmZQBoay16uwdaibsAlatwJgAAAIC5WLp0qS699FJdeumlc3rdIYccItu2Z3XubrvtNutzpxMMBnXBBRfoggsuWPB7AegwrgTUzMnOXM7W/7n1aWVzU9cvb5dHXzpmT1mWVe0V1s/QWyRZkkquy5ufl7bbuz6fn0lKoy85jw3P8mYH479bUAmNMAMKANoaCSjUXMBowRfn7hYAAAAAAOriobWb9KmfPanvPvCCMtkWvSE0ZSR8ZpGA+sWfXtXjL404jv3LIbto5+FgNVdWfz1+aWBH5zFzJlMtbXlRso3vo3kmoGjBh1pbtymqz9z0lL5yx2qNxdKNXg7QkUhAoeb8RgIqQQs+AAAAAABq7q9vTujEq/+n+Ni2pbNW7tLAFc3THGdAjURT+tqv1jiO7TjUq48d0oL/7OUM7+ZMIZkzmWrJ/KzQNpI/PLvX+sq04EuyR4TayOZsnfC9R7VxLD8D7oU3J3TNqfs1eFVA5yEBhZpjBhQAAAAAAPV3/3NvOh7f++wbDVrJAs1xBtQ3fvOsRoy0w38d81bXDbIty0wcmTOZamm+85+ksgmoGC34UCPPvBYpFp+k/PWwtCUngPqgAIWa8/c4v81owQcAAAAAQO2NGkWYsXiLbvbPYQbUn14e0Y//8LLj2D/sua3eu/vWtVhZY5gzl+qZgDILULOd/yS5ElBBK64JWvChRszrX84W329AA1CAQs2ZCSha8AEAAAAAUHvjiYzxuEVnoMxyBlQmm9P/ueVpx7Feb5f+48g9arWyxjBTR5tfkHJ1mu9lFrvmlIByFg5DSijGTcqokbG4+3rXstdAoIVRgELN+b204AMAAAAAoN7MzVazINUyZjkD6oZHX9LqjRHHsXP/bjdtNxCo1coaY9go+qRj0viG2n+ubZdJQM2hAGUmoBRXlEQKasRMQEktfA0EWhgFKNQcM6AAAAAAAKg/c7N1IpmRbbfgDJRZzIB6I5LQJb99znFs2TYhnXrAzrVcWWP0LXb/O6jHHKjYZikx6jy2aA4t+Iw1h6yEoqlsa35PoumVT0BRgALqjQIUao4WfAAAAAAA1N940rkBm83ZrdfyLJOSsinnsTIzoL5y5xrXfJevfGAv9XS14daXZUmLdnEe2/x87T/XLHJ5eqSBpbN/vSsBlVA2ZyuZqVP7QHQUWvABzaENfwqj2QS8ZgEqp1yOu1sAAAAAAKilSNx9t3+k1TZgzfST5ErSPPz8Jt3+Z2cLumPfuUT77TxUy5U1ljl7qR4JKHP+09BbpK7u2b/emAEVtOKS1HpFUbSEsXjKdazlrn9AG6AAhZozE1CSuLsFAAAAAIAaK3e3f8u1oEqOu4+VJGmSmaz+47anHU/3B3r0ucOX13pljWXOXjKLQ7WwkPlPkisBFVJCkpgDhZqgBR/QHChAoeb8ZQpQzIECAAAAAKC2ym22tlwLqnIJqJ5g8cvvPfBX/fXNqOPpC/5+dy0K+Wq9ssYyZy9tqkMLPrPN31zmP0mu5FpQcUm2oimKAqi+0RgFKKAZUIBCzZkt+CQKUAAAAAAA1Fq5zdZIq23AJo0ClDckefLbWa9siemK3zmLIm/fYUAf2XfHeq2uccz00dgrUjpe28+scgKqy7LlV0rRJHtEqL5yCSha8AH1RwEKNefvdn+bxenvCwAAAABAzSTSWaWy7vb3LZcASBkt+CZTNLZt6wu3/6+jxb/Hkr5yzJ7q8lj1XGFjuNJHtrT5hdp9XjYtjbxorGGOBShjBpSUb8NHCz7UAi34gOZAAQo1193lkbfL+a2WIAEFAAAAAEDNVNpobbkWfGYCajJF89+rX9fvnnnD8dQ/vXup9lrSX6+VNZY3KIW3dx6r5RyokZeknPE9tcAElCQFrbhitOBDDYzRgg9oChSgUBe+Hue3Gi34AAAAAAConUqFppbbgDVnQHlDiqUy+q9frnYcHg759KnDdq/jwppAPedAmcWtwJDUOzS39+j2S5ZzTEM+AcUeEaorm7M1XiZZ13IFeKANUIBCXQR6nL9g0IIPAAAAAIDaad8EVJ+u+N3zWj/qnHf0H0euUH+gp44LawJmAqmWCaiFzn+SJMuSfM42fEHFFSUBhSqLlGm/J7VgAR5oAxSgUBcBr1GAIgEFAAAAAEDNVC5AtdgGrDEDKtcT1I2PvuQ4tv9bFunot29Xz1U1B3MG06bnavdZZnFrPgUoyV2AskhAofpGKxagWqwAD7QBClCoCzMBxQwoAAAAAABqJ1Jho7VSMqBpGQmoVFevIkYR7T+O3EOWZdVzVc1huEwLPtuuzWeZ7f3M4tdseZ1zoEJiBhSqb6zCdS4S53sNqDcKUKgLPy34AAAAAACom3adAZXs6nWdssvWwXqtprkML3M+To1LE6/X5rOqloByFqCCVkITZWb1AAtRqQBFAgqoPwpQqAsSUAAAAAAA1E/btOAzElAJy1mA8nV75Ot27jl0jPASqTvgPGbOaqqG+KgUfdN5rEoJqKDiitGCD1U2GkuVPR5NZZXN1SglCKAsClCoC/cMqFyDVgIAAAAAQPsz29RNHW+xBICRgIpbzoJLONBTz9U0F49HWrSL85iZVKqGzUb7PatLGtxpfu/lM1vwJRSlBR+qbLpWoxOtVoQHWhwFKNSFmYCKk4ACAAAAAKBm2qYFX3Lc8TAqv+Nxn7+7nqtpPovKzIGqNjNVNbiT1O2d33t5+xwPg1ZCMcY0oMoqteCTWrAID7Q4ClCoC3MGFC34AAAAAAConcot+Fps89VIQE3ISED5OzgBJblnMdUkAVWl+U9SmQRUnBlQqLrRWOXrXMsV4YEWRwEKdRHwOr/V4tzdAgAAAABAzVQqNE0kM7LtFpqBYsyAGs/6HI87ugWf5J7FVIsZUOZ7mqmruTBnQFkJxWjBhyqbLgHVckV4oMVRgEJd0IIPAAAAAID6qXSXf86Woq10U6iRgBrNOQtQHd+Cb9goBo2+JGWS1f0McwZUFRNQQcUVS7bQ9yNawvQFKAqeQD1RgEJdUIACAAAAAKB+pttkbakEgJGAGjMTUJ3egs9MQNk5acuL1Xv/XFba/ML0nzkXxgyokJWgBR+qbnS6AlSyha5/QBugAIW68JkzoFrpbisAAAAAAFpMZJoiUyTeIhv+ti2lxh2HtqS9jsfhQIcnoPxhKbSN81g150CNvSJljURVtRNQ7BGhyiLTFKBa5voHtAkKUKgLElAAAAAAANRPWySg0vF8oqfEpjQJKJdazoEy38vXLwW3mv/7mTOglFA01WJzydD0mAEFNA8KUKiLgJcCFAAAAAAA9WDb9rSbrC0zA8WY/yRJm1LOglO402dASe45UObMpoUwC1DDu0qWNf/3MxJQISsh22afCNU1GmuD6x/QJihAoS5cCSji1QAAAAAA1EQyk1M6WzlRMl17vqaSHHcdeiPpLED1kYCqbQLKbOe3kPlPkmsGVFBxSVI0yT4RqiOZyU5b0IxQgALqigIU6sJvFKCSmVyFMwEAAAAAwELMVGBqmQSAmYCyurQ54UzfdPwMKMk9k6maM6DKJaAWwpwBZSVlKadYqkW+J9H0pmu/J9GCD6g3ClCoC1cLPhJQAAAAAADUxEwFppYpQCWNApQvpIiRlGEGlKRFRlEoPiJFN1fnvc12fgtOQIVch4JKaCLZIt+TaHqRGQtQfK8B9UQBCnXhasFHb18AAAAAAGpi5gJUiyQAjASU7e1zFSpowSdpYKnkMf49VCMFlRyXxjc6j5lpq7nylS9AxbhRGVVCAgpoLhSgUBcUoAAAAAAAqI+ZNlhbJgFgzIDK9QRdp9CCT1JXtzT0FuexasyBMtNPstyfM1fGDChJCllxRUlAoUpGY21y/QPaBAUo1EXA6/xWS2VyyuYqD0QFAAAAAADz064JqEy3uwBFAmpSLeZAbTIKUAM7SD2Bhb1nV7fU7XccCiqhaJIblVEdMyegKEAB9UQBCnXhNxJQkpQgBQUAAAAAQNXNNAMl0iobsEYCKt3d63jssaSg173f0JHMOVBm8Wg+zCLWQuc/FRhzoIJWQtFUi3xPoumZBShvt3P7O9IqBXigTVCAQl2YLfgk2vABAAAAAFALbZOASjoTUEmPswAVDvTIsqx6rqh51SQBZbzHQuc/FRhzoEKKK0YLPlSJ2YJvyYAztRdLZZXJ5uq5JKCjUYBCXQTK3JEUZ8AkAAAAAABV1zYzoIwWfAmjANXnZ/5TkZlO2vKilF3gf2dXAmrX8ufNlTEHKqiEouwRoUrMBNSSoV7XORMUPIG6oQCFuvB304IPAAAAAIB6MFvsDYd8jsctU4AyWvDFLSMBxfynKWY6KZeWRl+a//vlctLmF6b/jPkyE1BWXFEKAqgSswXpDoPuuWUtcw0E2gAFKNSFx2O5eq7Sgg8AAAAAgOozN1e3H/A7HrfMDBQjARWV85+DAlSJ3iEpMOQ8ZrbQm4vxDVI65jxWqxlQSihGAgpVMmoUoBb3++UxOnW2zDUQaAMUoFA35hwoWvABAAAAAFB9Zgu+7YwZKBPJjHI5u55Lmh9jBtSE7SxA0YLPUM05UGbxqicohbeb//uVKpOAoiUaqsVswTfQ61XI57xWkIAC6ocCFOrGVYAiAQUAAAAAQNWZm6tmAcq2pWiqBTZgjQRUxChAhQMkoBzMhNJCElCbnzfeexfJssqfO1dGAiqkuGKt8P2IlmAWoPoDPeoz0pIUoID6oQCFugl4nQWoRDrXoJUAAAAAANC+xpPTJ6CkFtmANWZAjWWds6xIQBmGd3U+NotIc2EWr4aXzf+9TL4+x8OgEoomuUkZ1TEaK1eAMhNQtOAD6oUCFOrG32MWoPjlAgAAAACAajOLS4v7/TOe05SMBNRoxlmAYgaUwSwSLSgBZRagqjT/SXLPgLLiitKCD1Vg27YirhZ8Pa5rRUtc/4A2QQEKdRPocX670YIPAAAAzW7PPffUZZddpjfffLPRSwGAWZvagLX1qe6f6W9//T5d57tYW2l06pxWSAAYM6C2ZLyOx7TgM5gt+KJvSPHR+b3XJrMF367lz5sPcwaUEooyJxxVkEjnlMpOdVz6e88ftOIX79VFWz6ht1kvFI+bRSoAtUMBCnVjtuCL88sFAAAAmtzq1av17//+71qyZImOPfZY3XHHHcrlaCUNoHnZtl28u38/6xn9W/et8kU3aKX1J/1L9y+L57VECyojAbU57SxA0YLPMLiTZDn3XubVhi8dl8ZecR6raQIqwQwoVMVoPFX8ulcJXdzz/6ln9K9amnpe/7fn6uJz4yTugLqhAIW6CRgt+EhAAQAAoNntvffesm1b6XRat956q4455hjtsMMO+uxnP6vnnnuu0csDAJdEOqdMzpYk7e1xFh/eUfK46VtQ5bJSOuY4tMkoQNGCz9DtzRehSs2nDd/mFyTZzmNVTUCZM6BowYfqGCtJNi2zXlXIShQf7+lZp27lv89aogAPtAkKUKgbZkABAACg1Tz++OP685//rHPOOUeLFi2SbdvauHGjvvGNb2jFihU68MADdc011ygajTZ6qQAgybmxGrac16awpgo6kWYvQBnpJ0l6I+FMPIVJQLmZSSVzltNsmK8Jby95g/Nfk8lbpgVfkj0iLNxYbOr6N2iNu54vXAOb/voHtBEKUKgbVwKKFnwAAABoAXvttZcuu+wybdiwQTfddJOOPPJIdXV1ybZtPfLIIzrjjDO0ePFinX766XrooYcavVwAHa50Y7VfzgJUf0lBqukTAEl3Aer1pDPxxAyoMsyk0nwSULWc/yS5ZkAFrbji6ayyObvCC4DZGS1JQA3KXYAqXAObPgEKtBEKUKgbMwFFCz4AAAC0ku7ubq1atUq33367XnnlFX3961/X7rvvLtu2NTExoWuvvVYrV67U7rvvrgsvvFAbN25s9JIBdKDSwlK/kYAqLUg1/QZsmQTUWM7veEwLvjJcCah5zIAyE1DVnP8kuWdAKd8mjTlQWKjSFnzlElCFa2DTF+CBNkIBCnUT8FKAAgAAQHvYZpttdMEFF2j16tXFFFRfX59s29batWv1uc99TkuXLtVRRx2lW2+9VblcrtFLBtAhxqdJQPmstHxKTZ7X5BuwRgLK7vIrK+e+Qh8t+NwWmQWoF/LztObCTE2Z77lQxgwon5VRjzKK0SkHC+RswecuYpOAAuqPAhTqhhlQAAAAaEd/8zd/o+9+97u68cYbte2228qyLElSJpPRr371Kx177LHacccd9a1vfUvZLL8DA6it0o3VsBVzPR9Wi2zAppzphWyPewYRBagyzLRSNimNvTL719u2OzU1XOUWfEYCSpKCiiuabPLvSTS90gTUUJkWfGESUEDdUYBC3TADCgAAAO3m5Zdf1pe+9CXtsssuOvroo/X666/Ltm15PB4ddthh2n777WXbtjZs2KBPfvKTeve7362RkZFGLxtAGyvdWA0bCSiphRIARgLKLEAFvV3q7mJbyyW4leTrdx4zZzpNZ+J1KRlxHqt6AspdgApZCUWT7BNhYWZswdcq1z+gjfCTGnUT6HF+uyXStCEBAABA60kkErrxxhv1d3/3d3rLW96i//qv/9KLL74o27b1lre8RV/96lf18ssv69e//rVeeukl3XXXXTrkkENk27b+9Kc/6b/+678a/Y8AoI1FppkBJU215YvEmzwBYMyASnX1Oh73Mf+pPMtyJ5bMmU7TMdvvdful/h0Wvq5SZdJsQcUVZQYUFmg0Pn0LvkJRPpbKKp1lXxKoBwpQqBtmQAEAAKCVPfroozrrrLO0ePFinXTSSbr33nuVy+Xk9Xp1wgkn6J577tHatWv12c9+VosXL5YkWZal97///frd736nj33sY7JtW7fffnuD/0kAtLOpO/tthVWmBd9kW76mTwAknekFswAVDtB+ryIzsWQWlaZjFquGdpE8Vd4+9HhcbfiCSihGAQoL5EhAlWvBV9KWdKLZr4FAm+CnNeqGGVAAAABoNRs3btQNN9yga6+9Vs8++6wkybZtSdJee+2lM844QyeeeKIGBwdnfK/TTz9d3/72t/XKK3OYxQEAc1QoLPUqqR7L/Xd3f6vMQDESUAmLBNSsLSgBVeP5TwXekOO/cciKa4IWfFigsRkSUP0lbUnHExkNBr11WRfQyShAoW5cM6AoQAEAAKDJ7bjjjsrlcsWiU19fnz7ykY/ojDPO0L777jun9wqHw5KkXI6WLwBqp9CCr7/M/CephWagGDOg4lbA8TjsZ0urIlcCag4zoMxiVbXnPxX4QlLJf+KgEoolm/x7Ek1vLJaa/Moum4AqbUsaafYiPNAm+GmNunG14EtRgAIAAEBzy2bzv7Puv//+OuOMM3T88cert7d3hleVt8022+iaa66p5vIAwKVQWCo3/0maKkxNpDLK5Wx5PFbd1jYnRgIqKqMAFSABVdGwUTQa35Av6PlC5c8vZbbrM9+rWowWfCErrij7RFigQgIqpPi0CVCpBYrwQJugAIW6IQEFAACAVvPJT35SZ5xxhlasWLHg9wqFQjr55JOrsCoAqKzQWi9cIQEVnixM2Xa+CBVu1lZ2RgJq3PY7HveRgKps6C2SLEn21LHNz0vbvWP612WS0uhLzmM1S0D1OR4GlVCUBBQWIJeziwWoQcudfpKmrn9SC7QhBdpElacIApUxAwoAAACt5pJLLqlK8QkA6mW2CajSc5tSyrmBbBagmrZw1gx6AtLADs5jm2fRhm/Li5JttImt5QyoEkElFE018fcjmt5EKqPcZM11UO75T1ILXf+ANkIBCnVjtuBLZ22ls/S/BwAAAACgWmYsQLVKAsBIQI1lfY7HfRSgpueaA7W2/HmlzPlPwa0lf3/11lTK527BF0tyozLmbyw2dT0bqpCAapnrH9BGKEChbswElEQKCgAAAM3ttdde02mnnabTTjtN69evn/H89evX67TTTtPpp5+uLVu21GGFAOBU2FTtr9CCz7kB28QJAGMG1GjW63gcDtCCb1rm7CazuFROveY/SeUTULTgwwIU2u9J0kCFBFSf4rKUvxm+qa9/QBuhAIW6MWdAScyBAgAAQHO74YYbdO211+rJJ5/U9ttvP+P522+/vZ588klde+21+uEPf1iHFQLAFNu2FZncVA1bsbLnhDV1PBJv4gSAkYDaknEmoGjBN4NFRuu8WSWgjDZ95ntUk5GAClpxWvBhQUoLUJUSUB7LVp/ikqQICSigLihAoW7KFaASKVrwAQAAoHn99re/lWVZOu6442b9muOPP162beuuu+6q4coAwC2ezio7OQQlXCEBFW6ZBJRzA3lzypmA6vOTgJqWKwH1gmTb07+mrgmoPsfDkBKKpbhJGfPnSEBVKEBJU9fApr7+AW2EAhTqxtft/nYjAQUAAIBm9vTTT0uS9ttvv1m/Zp999pEkPfXUUzVZEwBUUrqhWnEGlFpkBoqRgNqUciaewgESUNMyZ0Clo1Jkw/SvMdv0me9RTWYCSnFN0IIPC+BIQKlyAapwDaQABdQHBSjUjcdjyd/j/JZjBhQAAACa2ebNmyVJW2211axfMzw87HgtANRLaUGp0gyokJVQt/Ibr5Fm3oBN0YJvQcLbST1B57Hp5kBFN0vxEeexOs6AClkJxZLsEWH+RmOlCajyM6CkqeI8LfiA+qAAhboy2/CRgAIAAEAzC4XyG2RjY2Ozfk0kEpEkeb3eGc4EgOoqLSiFKySgJKlvcg5U0yYAMikpm3IcisrveBymBd/0LEtatIvz2HRzoMzilKdHGlha/XUVuBJQCWZAYUFmm4AKN/v1D2gzFKBQVxSgAAAA0EqWLFkiSXrkkUdm/ZqHH35YkrT99tvXZE0AUImjBV+FBJQ0lQBo2hZ8KXd6YcIOOB7Tgm8WXHOgnq98rlmcGnqL1FXDIp8xAypoxRWlBR8WoLQANTiLBFTTXv+ANkMBCnXl9zoLUAkGTAIAAKCJHXLIIbJtW1dccUUx2TSdSCSiK6+8UpZl6ZBDDqn9AgGghKMF3zQJqKafgZJ0pxdKE1A9XVbZOdMwDC9zPp5LAqqW7fckVwIqpISi7BFhAcbiU6nJQWu6GVD54lTTXv+ANsNPa9QVCSgAAAC0krPOOkuWZWnjxo064ogj9Prrr1c897XXXtMRRxyhDRs2yLIsnXXWWXVcKQA4N1QLbabKCVuFFlRNmgAok4CKaWoGVNjfI8uy6rmi1rRoV+fj6WZAbTLSUeZrq81rtuCLK5XJKp3N1fZz0bamElC2BlU5ATV1/aMABdQDDXNRVxSgAAAA0Ere+ta36pxzztHll1+u3//+99p11111/PHH66CDDtLixYslSRs3btQDDzygn/3sZ4rFYrIsSx//+Mf1jne8o7GLB9BxCgUlr9IKWKmK5zV/Asq5eZzp7pVdcg817fdmyUwxjb4ipeNST8B9boMTUF2WLb9SiiWz6u/lfnnM3Wgsf/3rVVI+q3JxvXD9i6fzBc+eLr7fgFqiAIW6Chgt+OLEqwEAANDkLr74Yo2Njemaa65RNBrVNddco2uuucZ1nm3bkqQzzjhDl19+eZ1XCQBSJJ4vKE03/0maas8XadoElLN9Vror6Hjc52c7a1ZcKSZb2vJXaZu3Og9nM9KWF43X1rgAZcyAkgpt+DLq76XAiLkrJKAGVbn9nuRsTzqeyGgo6K3puoBOR4kXdeXrNmZAkYACAABAk/N4PLr66qt16623av/995eULzaV/k+SDjjgAN1+++367ne/S2soAA1RSECFp5n/JLVeAirZ1et4HPZToJgVb1AKb+88Vm4O1OhLUs4oRtY5ASVJQSuuWKpJvyfR9IoFqGnmP0nOAn3TtiEF2gi3jKCuXAkoClAAAABoEUcffbSOPvpobdmyRU8++aQ2bdokSRoeHtbee++twcHBBq8QQKcrFJRmSkA1/QwUYwZUwnK2jCMBNQeLdpUi66celytAmccCQ1LvUG3X1e2XrC7JntoXCimhiST7RJi7bM4uXs8Grcrzn6Sp65/UxNdAoI3wExt1Fehxhu7iKYZLAgAAoLUMDQ3pfe97X6OXAQAukcnN1JkSUGHlN2gnkhllc7a6PE2W2jQSUHGLBNS8De8mvXj/1GNz1lO5Y7VOP0mSZeVTUImx4qGg4oolKQhg7iLxqSTTTC34wiUF+qZtQwq0EVrwoa4CPSSgAAAAAACohUI7qdnOgJLyRaimY8yAisrveBwOcD/1rJmznGaTgKr1/KcCYw5U0EooyqxwzMNYaQHKlYByFtjNGVAAaosCFOrKb7TgS1KAAgAAAACgKoot+GY5Ayr/miZMABgJqAnbbMFHAmrWhnd1Pt78vDQ5u7DILECZr6kVYw5USHFFm7EgiqY3WlKAGjJnQPXv4HyoqKT8/w9QgAJqj1tGUFckoAAAANCqxsfHdffdd+vPf/6zNm3apHg8LtvcxCthWZauvvrqOq4QQKcbT+Y3YcOKTXte089AMWZAjds+x+MwM6Bmz0wzJSPSxBtS3zZTx8wWfHVLQDkLUPkEVBN+P6LplSagBswWfIveIo29XHzYY2UVUFJx+ZuzAA+0GX5io64oQAEAAKDV5HI5ffnLX9Yll1yiaHT6VEGBbdsUoADUXcUElLfP0dbOmYBqwg1/IwE1ljVb8JGAmrX+HaRuv5RJTB3bvHaqABUflaJvOl9TjxlQkisBlZ8BxT4R5m5sugTU0Fukv97nONSv6GQBqgmvf0CbacoC1Ouvv6477rhDmzZt0s4776wjjzxSvb29M78QTS9gtOCL09sXAAAATe6UU07RjTfeKNu21dXVpUWLFumNN96QZVlasmSJRkZGNDGR3yy1LEvDw8P8/QKg7mzbnipAmTOgBneSXv9L8aFzBkoTJgCMBNRo1ut4TAu+OfB4pKFdpDf+d+rYprXSTgfmv978vPN8q0sa3Lk+a/OaLfgSzTmTDE1vLJYqfj1oJqAGd3Kd329F9Zq9qDmvf0CbqfsMqDVr1ujDH/6wjj/+eI2Ojrqev/3227XLLrvon//5n/W5z31OJ5xwglasWKEnn3yy3ktFDfiNBFSCBBQAAACa2G9+8xv98Ic/lJQvRL3xxhu6++67i8+/9NJLikQiWrNmjf7t3/5NHo9Hg4ODuuuuu/Tiiy82atkAOlAslVU2l28LWtpiT5I0uNTxsE8xWcpJkiLNuAGbdG4gb8k4C1C04JujcnOgCsz5T4NLpW7nv++a8fU5HgathGK04MM8lCagBi1nAVvBrSRfv+NQoUgfifP9BtRa3QtQt956q37xi19ow4YNGhgYcDz3xhtv6MQTT1QsFpNt28X/vfLKKzrqqKOKdxWiddGCDwAAAK3kmmuukSS99a1v1Q9+8AMNDg7KsizXebvvvrsuv/xy3XzzzXrhhRd0+OGHa2xsrN7LBdDBSltJlU1AleiybIWUcL2uaRgJqC1p5wwoElBzZM50Ki06NWr+k1QmARVXlE45mAdnAcpIQAWGpICzAFUo0hfm5gGonboXoO655x5ZlqUjjzzS9dy3v/1tTUxMqLu7W5deeqn+/Oc/6xvf+IY8Ho82bNig733ve/VeLqqMAhQAAABayaOPPirLsvTxj398VucfddRROvnkk/XSSy/pW9/6Vo1XBwBTSltJuWZAVWhBlX9dExagjBlQE7Y5A4oE1JyYM51Ki05mAqpe858k9wwoK6EoLfgwD6OxkgKUjABD7yLJXz4B1ZTXP6DN1L0A9fLLL0uS9t57b9dzN910kyzL0kknnaRzzz1Xe+21l/793/9dp59+umzb1u23317v5aLK3DOgcg1aCQAAADCzN954Q5K0bNmy4rGurqnfaZPJpOs1xx13nGzb1i233FL7BQLApEjJRmrYLECFt8vP9ilRbEHVjC34jATUhMwCFAmoOTFTTSMvSZnJmTnmDKhFRru+WjISUEHFFU1yozLmrpCA8iupgJVyPtk7JPkHHIcKBfgIBSig5upegCr8Abf11ls7jm/atEn/+7/5gYj/+I//6Hju6KOPliStXr26DitELfl7nN9yzIACAABAKxgaGip+3dc3NbOi8PdNqcLfOuvWrav5ugCgoDQBFTZb8AUGXQmAcAsloKJ2oPi1ZUkhLwmoOTFnQNlZaeRFKZeVNr9gnFvPBJRzBlSIGVCYp0IBypV+kvIFqMCA49DU9a8JC/BAm6l7ASoej0uSEomE4/hDDz0kSfJ6vTrwwAMdzy1evFiSNDo6WvsFoqb8ZVrw2bbdoNUAAAAA09tmm20kSVu2bHEc83rzA9qfeuop12teeuklSe6/eQCglgqFJI9yCltx55P+AfcGbLO2oLJtKeWc4RItSUCFfN3yeNyz+DANf78UdN4Irk1rpbFXpKyR5G3gDKh8AqrJvh/REgoFqCFz/pPlkXz97gRUs17/gDZU9wJU4c7BQiu+gnvuuUeStM8++xT/mCvIZPIXg1DI+YMJrcecAZXN2UpnKUABAACgOe21116SnN0Yuru7iy3Fr7nmGtdrrrrqKknS0qVL67BCAMgrbKS60k9SvgBhzkBp1gRAOi7Zznb9E5pKQIX9tN+bl3JzoDYZ7fd8YSlkFKpqyZwBpYSiKTrlYO4KBagBswAVGJI8nmkSoE12/QPaUN0LUG9/+9slST/60Y+Kx+LxuH7+85/Lsiy9733vc72mcAdh4e5DtC5zBpQkJTL8cgEAAIDmdMghh8i2bd19992O4yeeeGJxztPJJ5+sO++8Uz/72c90xBFH6O6775ZlWTrmmGMatGoAnaiwkdpvzn+S8umnVkkApNwttKL2VAKqz0/7vXkxZzttej5fhDLPseqYLjMSUCEroRgJKMxDMQElowDVO9lC2UiAFq5/iXRO6Szz6YFaqnsB6iMf+Yhs29Yvf/lLfeQjH9GVV16pww47TG+88YYsy9IJJ5zges3//M//SOIOwnZgJqAkKcHdLQAAAGhSq1atkiT9+te/1uuvv148ftZZZ+md73ynbNvWD3/4Qx199NE64YQT9Otf/1qStOOOO+rTn/50Q9YMoDNNJaBizic8PVJPb5kZKLHJ1zVZAiA57jrkSEAFSEDNS9kE1Nrpz6k1YwZUUHFNUIDCHKUyOcUm9xYHLKOA3bso/3+NAnzh+ic1YREeaDN1L0CddNJJOvDAA2Xbtn7+85/rnHPO0e9//3tJ0qmnnqrly5e7XnPzzTfLsiy95z3vqfdyUWXmDCgpPwcKAAAAaEY777yz/vrXv+rpp59WOBwuHu/u7tZ///d/66Mf/ai6u7tl23ZxtukRRxyhBx98UIODg41aNoAOFKmUgAoM5FMtFRJQkXiTbb4aCaicupTUVNEpTAJqfszZTpvWlklA1bkAZc6AspKKp9LMCsecFNJPUpkEVKCQgHL+TtZf0qo0Em+yIjzQZur+U9vj8eiuu+7SF77wBf385z/Xa6+9psWLF+vkk0/Wf/zHf7jOv+OOO7Ru3TpZlqXDDz+83stFlfm6PbKs/EzRAgpQAAAAaGY77bRT2eODg4O64YYb9O1vf1tr165VJpPRrrvuWpx7CwD1VLiLv9+cAVWYfdIqM6CSzgJUsqtX0lRbOGZAzZOZbopvkTY8aZxjtOmrNSMBJUm+XFypbE6+bvcNzEA5Y/FU8Wt3Amryd7IK1z+JBBRQaw25bSQYDOriiy/WxRdfPOO5BxxwgF588UVJtOBrB5ZlKdDTVYzGSlKcFnwAAABoYX19fXrnO9/Z6GUA6HCFQlLYTEAVkk8VZqBEU1llc7a6PHWc/TMdIwGV9AQcj2nBN08DS/PtGHMlBcdkxHlOvRNQvpDrUFAJRZNZClCYNUcCyqowA6pCAlRqwiI80Gbq3oJvrgYHB7V06VKKT23EnANFAgoAAADNyuPxqLu7W9/4xjcavRQAmFakUgKqUHhyzUCZOm+imRIAxgyomOUsQPXRgm9+urqloZ2nOcGSFu1St+VIkrzuBFTIiivKHCjMQWkBatBswVeYAWUU4ANWSl7lXxdppusf0IaavgCF9mPOgUpQgAIAAECT8nq9sm1bBx10UKOXAgDTKrbgm2MCSpqaH9UUjARUTEYCihZ88ze8rPJzAztIPYHKz9dCV7fsbr/jUFAJR9ccYCajsZIClJmACpRvwSdJYcUkkYACaq3uBah0Oq3Vq1dr9erVSiaTrucTiYTOO+887bDDDgoEAtpjjz10xRVX1HuZqKGA10hApXINWgkAAAAwve22206S1N3NHfcAmlthE3W2M6DCzToDxZgBNWE7CxQkoBZg0TQznurdfm+S5XW24QtaCU2QgMIcOBJQrhlQkwkoIwEqSf2T5zbV9Q9oQ3UvQN1yyy3aa6+9tHLlyrLPr1q1SpdffrnWr1+vZDKpZ555Rueee64+8YlP1HmlqBVa8AEAAKBVHHzwwZKkxx9/vMErKe+ll17Seeedp+XLlysYDGpoaEj77ruvLrroIsVisap+1t13361TTjlFu+66q4LBoPr7+7Vs2TIdd9xxuuqqqzQxMVH2dYcccogsy5rV/wDMX2ETNWwZ/79fqQWfYpLsydc2UQLASECN55wFKGZALcDwNEWm6Z6rJWMOVEhxxVIUBDB7s2rB1+2VenodTxWK9RSggNqqewHqN7/5jWzb1gc+8AH5fD7Hc3feead+85vfSJKWLFmiVatWafvtt5dt27rqqqv0+9//vt7LRQ34e5zfdhSgAAAA0KzOPvtsdXV16eKLL1YkEpn5BXX0y1/+Um9729t06aWX6tlnn1UsFtPIyIgee+wxXXDBBdp77731/PPPL/hzRkZG9IEPfECHHnqorrvuOr3wwguKxWKKRCJau3atbrrpJn3sYx+rymcBmB/btoupkbArATWQ/79GCz6flZFfKUlNtgFrzIAayzn3jmjBtwDTpZymS0fVkjEHKqiEokn2iTB7hRZ8PqUUtIxuW71DU1+75uDRgg+oh7rnlv/0pz/JsqyyCagf/OAHkqRly5bpD3/4g/r6+jQ2Nqb3vOc9euaZZ/T9739f73nPe+q9ZFSZawYUvX0BAADQpN71rnfpiiuu0Cc+8QmtXLlS/+///b+m+JvkiSee0PHHH694PK5QKKTPfvazeu9736t4PK6f/OQn+t73vqfnnntORxxxhB577DH19bkHvc/G2NiYDj300GICbNWqVTruuOO0yy67qKurS6+88oruv/9+3XTTTTO+1z777KNrrrlmXusAML1YKqtsLp9mcs+AKrTgG3C9rl9RJeTTeLKJNmCNBNRoxlmAogXfArRCAsqKK0oLPsxBZDIBNaAySexCAkrKXwvHNxQfhklAAXVR95/ab7zxhiRp112dd1bkcjndc889sixLZ599dvEPpP7+fn3iE5/Qxz/+cT3yyCP1Xi5qgBZ8AAAAaBWnnXaaJGn33XfXn//8Zx100EHaYYcd9La3vU2Dg4Pq6uqq+FrLsnT11VfXZF3nnHOO4vG4uru79dvf/lb7779/8bn3ve992m233XTBBRfoueee0yWXXKIvfvGL8/qcs88+W48//rh8Pp9+9rOf6eijj3Y8v88++2jVqlW67LLLlM1O/3t9MBjUnnvuOa91AJhepOQOftcMqGILPucMKClfrHrdHlIk3kQbsMYMqIhNC76q6R2SAkNSfIv7uQbNgJI5A0oJWvBhTgot+Fzzn2Q5r3tGCrRQrI+QgAJqqu4FqE2bNkmSAoGA4/iTTz6pSCQiy7J0xBFHOJ4r/JHyyiuv1GeRqKmA10hAUYACAABAk7r22muLs4ksy5Jt23r55Zdn/NvEtu2aFaD+8Ic/6MEHH5QknX766Y7iU8F5552na665RmvWrNE3v/lNff7zn1dPz9w2bR966CHdcMMNkqSvfOUrruJTKcuy1N1NKgFolNI7+MOuBNRA/v96uiRfWEpOtROdSgA00QaskYCaMApQJKAWaHg36ZX/cR7rCUrh7RqzHiMBFbTiitIpB3MwWixAGfOfAoP5615B2Tl4JKCAWqv7T22fz6dMJlMsRBU88MADkvKzn5YuXep4rpCGmumOOrQGElAAAABoFTvuuGOxANUsbr311uLXp556atlzPB6PTjrpJH32s5/V6Oio7r33Xh122GFz+pwrr7xS0lRXCgDNa6qAZBc3VYtK7/r3DzgKUP1WVLKbbAPWSEBF5byBmQLUAi0qU4BatIvUqJ91RgIqpIRGaMGHOSgmoGQUoErnP0kVE1BNVYAH2lDdf2ovXbpUq1ev1v/8z//ob//2b4vHf/nLX8qyLB188MGu12zZko8Gb7XVVnVbJ2rHNQOKAhQAAACa1Lp16xq9BJeHHnpIUr6l3bve9a6K55XO3X344YfnVIBKpVK67bbbJEmHHnqo/P58AiGbzWrDhg3KZrPadttti8cBNFZksoAUUlzdVs75ZGkLKn+/NDb1sNCuL9JMBaiUcxM5qqnrjL/HI1935danmIXhXcsca1D7PUnyOWcUBpXQq0n2iTB7hQLUkJmAKp3/JLnakPYzAwqoC0+9P/C9732vbNvWFVdcoTVr1kiSbr/9dt13332SpMMPP9z1mqefflqStHjx4rqtE7VjtuCLE60GAAAAZq3wd9Suu+46bdu75cuXu14zW3/+85+VSCQkSXvttZcikYjOPfdcDQ8Pa8cdd9TOO++s/v5+HXroocW/5WbyzDPP6G/+5m80MDAgv9+vJUuW6JhjjtH111+vdHphdx+/+uqr0/5v48aNC3p/oNkVNlBd858kZ9upVkgAJM0WfFMJqD4/858WrNysp0bNf5LcM6CsODOgMGu2bWsslr9+DciYARUwElBGC76pGVB8v6FGfnS89ON/lG77hPTf/ymNvtzoFTVE3RNQZ599tr773e/qjTfe0J577qnBwUGNjIzItm0tWbJExx57rOs1v/3tb2VZlt72trfVe7moAVrwAQAAAPOTSCSK7cyXLFky7bmDg4MKBoOKRqNznqe7evXq4te5XE777LOP1q5d6zgnlUrp7rvv1j333KOvfe1r+vSnPz3te77++ut6/fXXi4/Xr1+v9evX6/bbb9eFF16oX/ziF1qxYsWc1lmwww47zOt1QLsoFJDCltF+T1Z+7lOBkQBoyhkoKbMF31QCKkz7vYUrl3ZqaALK3YKPGVCYrUQ6p1Q2n/qcMQFlFOCbcgYe2odtS2v/W7JLrmd7HisN7Ni4NTVI3RNQu+22m2644Qb19vbKtm1t2bJFtm1rYGBAP/7xj+X1eh3nv/baa/rv//5vSdL73ve+ei8XNeAuQOUqnAkAAACg1Pj41OZKKBSa5sy8YDAoSZqYmJjhTKdCG3RJuvDCC7V27Vr9/d//vf7whz8okUjojTfe0FVXXaX+/n7Ztq3PfOYzxZZ9Jo/Ho7/927/VJZdcorvvvltPPPGEHnjgAV1++eXFgtPq1av13ve+Vy+/3Jl3hgILVUxAWUYCyt8veUq2flowARW1SwpQARJQCza4s+Qx/j0OL2vMWqSyCagoM6AwS4X2e5I06CpADTofV0hAJTM5pTLsTaLKEmPO4pPkLop2iIbcOvKhD31IK1eu1J133qnXXntNixcv1tFHH62hoSHXuU899ZT+8R//UVL59nxoPX6jBV+CO1sAAADQpBZaENlxx+re5VhoiyfJdfNeOT6fT5IUj8fn9DnR6NQmdiKR0KGHHqo77rhDXV353+W32mor/cu//Iv23HNPrVy5UrlcTp/97Gd19NFHyzIG2d98880aGBhwfcZBBx2kj33sYzrzzDN13XXX6fXXX9e5556rm2++eU5rlTRjwmvjxo3ab7/95vy+QKsoFJBcLfiMxFOlDdimSUDlclLa+c8wIVrwVVW3V3rHCdKfrs8/3m5vadu9GrceYwZUSAkKUJi10Xiq+PWg2YJvphlQJQX78URai0K+qq8PHSy22X3MbAvZIRqWXd5666116qmnznjeYYcdNqdhuWh+tOADAABAq9h5553n/VrLspTJVHcTze+fSgKkUqlpzsxLJpOSpEAgMMOZlT9HyqegCsWnUgceeKA++MEP6he/+IXWrFmjv/zlL67W6eWKTwU9PT36/ve/r0cffVTPPvusbrnlFq1fv17bb7/9nNY7UztCoN0VCkhhMwFlJJ7MAlTTteBLudOajgQULfiq48jLpaUHSMlx6e0nSMaNA3VlJqAUV4wblTFLhflPUpkElLnZX6EFn5S/BlKAQlXFtjgfdwckb29j1tJgdW/BB/h7nN92FKAAAADQrGzbXtD/qq2vb+pO8dm01SskmWbTrq/S52y11Vbae++9K577/ve/v/j1H//4xzl9jiR1d3fr9NNPLz6+//775/weQKeLTLahCrsSUAPOx+YG7GTBKtIsLfjKFKBIQNWAp0t6+0ek/c50zWCqO5/Zgo8EFGbP0YJPM8yAMgvwVlwe5VvvNc01EO0jbhSgejsz/SQ1MAFV6vXXX9fTTz9d7DM+NDSkPffcU9tss02DV4ZacCWguLMFAAAATeqaa66Z8ZxoNKrnnntON910k9avX68DDjhAZ5xxRk3W4/f7tWjRIm3evFmvvvrqtOeOjIwUC1A77LDDnD6n9PyZ0kWl57755ptz+pyCPfbYo/j1+vXr5/UeQCerOAPKlYAyWlBNFqxiqawy2Zy6uxp8n3KyTAJKpTOgmmIbC9VkJKBCSiiaogCF2Rl1zIAyW/AZG/5mS1JJfYppTKHmSYGifZgt+ChA1Z9t2/rud7+rK6+8UqtXry57zh577KGzzz5bZ555pquPOFqXWYBKkIACAABAkzr55JNnfe5FF12kT37yk7rqqqt0wAEH6Otf/3pN1rTHHnvowQcf1PPPP69MJqPu7vJ/1j3zzDPFr1esWDGnz3jrW99a/Dqbnf739dLnK61lJvy9ByxMsQA1zxlQkjSRzGigd+bZcjWVdCYYUpZXmZKtqzAJqPZjzIDyWWmlJtvHAjMppD97lFGfZcy7NBNQZkFe+WvgmB0qztEDqsZswdeh85+kBrXgGxkZ0cEHH6yPfexjWr16dcV2FatXr9a//uu/6uCDD9bo6Ggjlooa8HspQAEAAKD99PT06Morr9Qhhxyiiy66SL/5zW9q8jkHHnigpHzy6vHHH694XmkruwMOOGBOn7F06VLtuOOOkqR169ZN207whRdeKH4919lNBaU3JW633Xbzeg+gkxXaR4WtmPOJmVrwGTNQGi7lLEDF5ZxfxwyoNuQt0wIwNVGTNrZoP6OTM6AGzPZ7knvDv6dX8jiL2IWifaQZrn9oL64E1KLy53WAuhegbNvWMccco4cffli2bWtoaEj/+q//qmuvvVa//vWv9etf/1rXXnutPvaxj2nRokWybVu///3vdcwxx9R7qagRVwu+dJZfLAAAANA2zjrrLNm2rSuuuKIm7/+BD3yg+HWlFoG5XE7XX3+9JGlgYEDvfe975/w5xx57rCQpEononnvuqXjezTffXPy6UBybi0wmox/84AfFxwcffPCc3wPodBUTUK4WfM7HQSupbuVf2xQzUIwWfFHLKEAFSEC1nTIzqHrtuBLpXAMWg1ZTmAHlar8nSYFB52PLqjgHrykK8GgvzIAqqnsB6kc/+pEeeughWZalj370o/rrX/+q//f//p9OOukkHXbYYTrssMN00kkn6corr9Rf//pX/dM//ZNs29ZDDz2kH//4x/VeLmrALEDlbCmV5RcLAAAAtIfddttNkvTYY4/V5P33228/HXTQQZKkq6++Wo888ojrnEsuuURr1qyRJJ1zzjnq6XFu2t53332yLEuWZemUU04p+znnnnuu/P787JVPfepTikQirnN++MMf6r777pMkHXHEEa5ZU/fee++03SzS6bTOOOOM4lqPOuqoOc+rAqBi+yjXDChXCz73DJRC0aopNmBTzk3kCdvveNxHAqr99ARdh4IWc6AwO4UC1JBlJKD8/VJXmetFhTl4tOBD1ZGAKmpIAUqSVq5cqRtuuEF9fX0Vzw2FQrruuuu0cuVK2batH/7wh/VaJmooYLTgk6REigIUAAAA2sPY2Jjj/9bCN7/5TQUCAWUyGR122GH62te+pkcffVT33nuvzjrrLF1wwQWSpGXLlum8886b12fsuOOO+tKXviRJ+stf/qL99ttP11xzjR5//HHde++9Ovvss4vFq3A4rMsuu8z1Htddd5122GEHffSjH9X3vvc9PfDAA3ryySf10EMP6Zvf/Kbe8Y536LrrrpMkbb311vrmN785r7UCncy2bU0kK82AGnA+rjADRWqSApQxA2o85yxAMQOqDXk8so02fCHFFU02wfcjmt5ovNCCz0hAVdrsrzAHrymuf2gvsRHn4w6eAVX3W0f+9Kc/ybIsfeITn5j1a84++2zdf//9euKJJ2q4MtSLv8ddgIqns+oXv0gCAACg9RUKKosXL67ZZ+y999766U9/qhNPPFGRSESf+9znXOcsW7ZMd95557Q3/c3k/PPP15YtW3ThhRfq2Wef1WmnneY6Z+utt9att95aTH6ZJiYm9KMf/ah4M2I5e+21l37yk59o5513nvdagU4VTWWVm+xq75oBZRacun1Sd0DKxIuHwsq/pikSAEYCKpLzOR7Tgq9NeUOO//ZBK6FoknnhmFnFBFSlzX7XHLwmuv6hvZCAKqp7AWrLlnz/w7n8YVE4t/BatDazBZ+UL0ABAAAArWzt2rW65JJLdN1118myLB1++OE1/byjjjpKTz31lL75zW/qzjvv1Kuvviqv16tdd91VH/rQh/SJT3xCvb29C/6cr33tazr66KN11VVX6cEHH9TGjRvl9/u1bNkyHX300Tr77LPV3+9u6yVJn/70p/WOd7xDjzzyiFavXq0333xTW7Zskc/n0zbbbKN99tlHxx13nFatWqWuLvffCQBmVrpx6k5AGTNQpPwG7PhUAarfikq2FIk3wQasOQNKzhlQtOBrT5YvpNIAS1AJxWjBh1mIFBNQRgGKBBQazTUDqszP4w5R95/c/f392rx5szZs2KC99957Vq/ZuHGjpHxbB7S+ni5LXR5L2cItWpLiKQpQAAAAaD5vectbZjwnl8tpdHRU4+NTmx9bb721Pv/5z9dyaZKkpUuX6tJLL9Wll146p9cdcsghsm175hMn7b///tp///3nujytWLFCK1as0Lnnnjvn1wKYnUg8v3HqU0o+yygilZn5JH+/NL6x+LCVZkDRgq9NmS34rHixrSQwndFYSlKZBFRvhQRUhRlQERJQqCbbJgFVou4FqD333FP333+/rrnmGh1xxBGzes0111xTfC1an2VZCvR0OX6ZIAEFAACAZrRu3bo5v2b//ffXD37wg5q24AOAgkICKmymn6SyM5/MBEC4kABohg3/aRJQXR5LvWVmSqMN+JytYkOKK8aNypiBbduKTBbOB61ZzoAyrokkoFATyXEpZ3xPUYCqn+OOO0733XefbrnlFn3xi1/UF77wBVmWVfH8L3/5y7rppptkWZY+9KEP1XGlqCW/UYBKUIACAABAEzr55JNnPMfj8aivr08777yzVq5cqXe84x21XxgATCpsnBY2Uh3KJaBcM1AKG7BNkABIOVMME5pKQPX5u6fdP0ILMxJQQSUUbYaCKJraRDJT7K40aLbgC1Rod2YW4JspAYr2YaafpMpzyTpA3QtQZ555pq644go9++yz+vKXv6ybb75Zp5xyiv7mb/5GW2+9tSzL0uuvv67/+Z//0XXXXaenn35akrR8+XKdeeaZ9V4uasTf43E8pgUfAAAAmlGhGwMANKtC6yjX/CdvSOoq07LObEFlFVpQNcEGrJmAKmnBR/u9NuZzt+CjAIWZjMamiubzTUAVE6DNUIBH+zDnP3X5JG+wMWtpAnUvQPX09Oiuu+7S3/7t3+rFF1/U//7v/+r888+veL5t23rLW96iX/3qV+ruZthkuwj0OGPztOADAAAAAGDuKiagyqWfJFcCoJlnQJW24OvzsyfUtsokoMa5URkzGIuXFKDMBNScZ0A1wfUP7SNmFKB6h6QOTvB6Zj6l+nbaaSc99dRTOu+889Tf3y/btsv+r7+/X//+7/+uJ598UkuXLq3qGh577DF96Utf0mGHHaYlS5bI5/MpFApp2bJlOvXUU/XQQw/N6f3uuusurVq1qvheS5Ys0apVq3TXXXdVdd3tIuClAAUAAAAAwEIVCkdhxZxPGIWmIlcCIDb5Pk2QADASUBMkoDqDkYAKWnHFUhQEML1IaQHKMgtQFRJQZgF+snCfyuSUzLA3iSpxFaA6d/6T1IAEVEEwGNRFF12kr371q3r88cf19NNPa8uW/H+coaEh7bnnnnrXu94lr9erV199Vb/73e8kSUcfffSCP/vggw/Wgw8+6DqeSqW0du1arV27Vtdee61OOukkfe9735PX6634XrlcTv/8z/+sq6++2nF8/fr1Wr9+vW699VadccYZ+s53viOPpyH1vqbkNxJQSQpQAAAAaELZbFYPP/ywJOntb3+7+vsrJAomjY6O6qmnnpIkHXTQQcwrAVBzhcKRKwFlFJqKmjoB5dxELk1AhQMkoNqWt8/xMKSEokn2iTC90ckCVJey6reMAvxsW/ApJsmWZGk8kZEv1FXuVcDcmDOgKs0k6xAN/+nt9Xq1//77a//99694zj333KNTTz1VHo9HmczCfyHasGGDJGm77bbThz70IR100EHacccdlc1m9cgjj+iSSy7R+vXrdf311yudTutHP/pRxff6/Oc/Xyw+7b333rrgggu0yy676IUXXtA3vvENPfHEE/r+97+vrbbaSv/3//7fBa+9XdCCDwAAAK3g1ltv1Yc+9CEtWrRIL7300ozne71effCDH9TIyIhuu+02HXnkkXVYJYBOVmzBZ86AqpSAqjADqhkTUFFNJaD6SEC1LzMBJWZAYWaFFnwDmnA/GZhdC75uK6egEooqoPFERsMhX7WXiU5kzoDq8ARUS0VybNuuyvssX75cP/3pT/Xyyy/r8ssv17HHHqt9991X7373u/XJT35STz75pJYtWyZJ+vGPf6wHHnig7Ps899xzuvjiiyVJ++yzjx5++GF95CMf0b777quPfOQjeuihh7TPPvtIki666CI9//zzVVl/O3AVoFK5Bq0EAAAAqOyWW26RJH3oQx9Sb2/vjOf39vbq+OOPl23buummm2q9PABQpFICqtIMKCMBUJyBEm+CDf+U2YKvJAFFAap9GTOgQlZCMWZAYQaFApSr/Z40zQyoAdehqWtgExTh0R7MBFSl78cO0VIFqGq544479OEPf1hdXeVjlcPDw7rkkkuKj3/xi1+UPe/yyy8vJrKuuOIKBQIBx/O9vb264oorJEmZTEaXXXZZNZbfFpgBBQAAgFbwxz/+UZZl6X3ve9+sX1M499FHH63VsgCgaGoG1Pxa8IUnC1fxdFbpbANvDs2kpGzKcag0AUULvjbmSkAlNEECCjMYjU0WoMwElC8sdVUoWPvCkpztkadSoHzPoUqYAeXQkQWo2Xjve99b/PqFF15wPW/btm677TZJ+UTVu9/97rLv8+53v1u77767JOm2226rWoqr1ZkzoBIUoAAAANCEXnnlFUnSzjvvPOvX7LTTTo7XAkAtVZwBVakFn1GY6lNclvKFp4lGbsCm3G20ShNQtOBrY8YMqKAVVyxFMQDTKySghswE1HRpE4/HlQ7Nz4FqkjakaA+uGVAkoFBGMpksfl0uKfXiiy8WZ0mtXLly2vcqPL9+/XqtW7eueotsYe4WfBSgAAAA0LzmciNZ4dxqzK8FgJkUZ0DNtgWfcdxj2eorbsA28LqVdLfRciSg/CSg2paRgAopoWiSfSJMr9Ayb8Ayitczbfa75uDlX08CClUTH3E+JgGFcu6///7i1ytWrHA9v3r16uLXy5cvn/a9Sp9fs2ZNFVbX+gJe57ceLfgAAADQjLbaaitJ0jPPPDPr1xTOHR4ersmaAKDUQlvwSVPFq0gjEwBlElAx+Ypfk4BqY16zBV9c0SRpFExvNJ5v2TkkMwE1w2a/OQevGa5/aC/MgHLg9pEycrmcvv71rxcff/jDH3ad8+qrrxa/XrJkybTvt8MOOxS/nmsbjtLPKWfjxo1zer9m4UpAUYACAABAE9p33331yiuv6Prrr9eJJ544q9dce+21sixL73znO2u8OgCY2jQNWzHnE5Va8HmDkqdbyk3d7R9uigSUswAVlV92yX3TzIBqY0YCqsuylUnGG7QYtIqxYgJqDi34JPccvGa4/qF92DYFKAM/vcu47LLL9Ic//EGS9MEPflDvete7XOeMj09d3EKhkOv5UsFgsPj1xIT7jp7plBav2gkzoAAAANAKjjvuON1888265557dMkll+i8886b9vxLLrlEv/vd72RZlj70oQ/VaZUAOlUuZ2siOdmCb7YJKMvKb8DGNhUP9VtRyW7wDJSUcxN5wvY7HodJQLUvYwaUJHnS7paMQKnR2OQMqAUmoMKTCSgKUKiKVFTKppzHmAGFUvfff78+85nPSJK23nprXXXVVWXPSyQSxa+9Xu+07+nzTUXG43Hu4JDcBShmQAEAAKAZHX/88Xr7298u27Z1wQUX6LjjjtNDDz3kmO+UyWT04IMP6thjj9UFF1wgy7K05557zjoxBQDzFU1lZNtStzIKWQnnk5VmQJV5rlC8aqYE1IQdcDymANXGfO4buz3pqHK52c9fROcpJKAGzQTUXGdAFa9/tOBDFZjpJ6njZ0DVNAH1pS99qSrv8+STT1blfWbyv//7v1q1apUymYz8fr9+/vOfa+utty57rt8/dSdOKpUqe05BMpksfh0IBKY5022mln0bN27UfvvtN6f3bAa04AMAAEArsCxLt9xyiw444ABt3LhRt9xyi2655Rb19PRoaCi/wbFlyxal0/lNC9u2td122+m2226TZVmNXDqADlAoGPUp5n6yUgs+qUwCoNCCqnlmQEVlJKBowde+uv2yrS5Z9tTeUEgJxdJZhXz8d4dbNmcXr3+DltFtao4t+PpJQKGa4lucjz3dks+d8uwkNb2Kf/GLX2yZP7pefPFFHXbYYRoZGVFXV5d+8pOf6OCDD654fl/f1DfOTG31otGpGPxM7fpMM82XalUBLy34AAAA0Bp22mknPfHEE/qXf/kX3XbbbbJtW6lUSq+99prjPMuy9MEPflDf/va3K97IBgDVVJj/VNhAdajUgk9yb8BqYvL9micBFTUSUBQi2phlyfaGZCXHioeCiiuWzPDfHWVF4lPF8kFXC74ZClBmAX6ygB8hAYVqcM1/WpRvfdvBan4Vt+3mj8tu2LBBf/d3f6cNGzbIsiz94Ac/0DHHHDPta0oLQ6+++uq055ammNp1ptNcmQmoRDrXoJUAAAAAM9t66611880367nnntOdd96pJ554Qps25eenDA8P653vfKeOOOII7bbbbg1eKYBOUrhj3zX/qcsn9UzTgcVsQWU1QQsqcwZUSQIq6O1SdxdTJNqZ5QtJpQUoK6GJZEbczoFyxkoLUK4E1Aztzipe/0hAoQpiI87HHT7/SapxAeree++t5dtXxaZNm3TooYfqr3/9qyTpiiuu0EknnTTj6/bYY4/i188888y055Y+v2LFinmutL24ZkCRgAIAAEALWLZsmZYtW9boZQCApKmCkSsBNd38J8mVAGjGGVClLfjCAeY/tTvLaFEVUlwx5oWjgkIByqOcuwA/4wyoAcdDZkChqsoloDpcTQtQK1eurOXbL9jY2Jje//73a/Xq1ZKkr3/96/r4xz8+q9fuvPPO2m677bRhwwbdf//90577wAMPSJK233577bTTTgtac7swW/DF+aUCAAAAAIA5KRSMwuYMqOna70muDdipGVANLECZM6BKWvD1+WnD1va8zpEVQSuhaJJECsobnSxA9WtCHsvovjXThr9ZgCcBhWoyZ0D1DjZmHU2kY/PLsVhMRxxxhP70pz9Jkj7/+c/r05/+9Kxfb1lWsU3fM888o0cffbTseY8++mgxAXXMMce0zEysWjNb8MXT2ZZo1wgAAAAAQLMozGxyJ6AGpn9hhQRUQ2egGAmo0hZ8YT8JqLbnMwpQiiuaoiCA8goJqCFr3P3kTDOg/M6CQLgZEqBoHySgXDqyAJVKpbRq1So9/PDDkqRzzjlHX/nKV+b8Pueee666uvKFlLPPPlvxeNzxfDwe19lnny1J6u7u1rnnnruwhbcRswAlSckMc6AAAADQXH7/+9+rq6tLgUBA69evn/H89evXy+/3q7u7W48//ngdVgigkxVb8JktqGZqwdeMM1CmSUDRgq8DGAmokBKKJumWg/IKBagBGfOfvCGp2zf9i43rn99Ky6eUUtmcEowIwULFjAQUM6A6swB1wgkn6Le//a0k6X3ve59OP/10Pf300xX/99xzz5V9n2XLlun888+XJD322GM64IAD9NOf/lSPPfaYfvrTn+qAAw7QY489Jkk6//zzGUhcwu91f+vRhg8AAADN5ic/+Yls29aRRx6p7bfffsbzt99+ex111FHK5XL60Y9+VIcVAuhkxRZ8ZgJqri34mmEGStKZZCidAUULvg5gzIAKWgnFSEChgrFYSlKZBNRsNvvLXB9JQaFqSEC5dORP8Jtvvrn49e9+9zu97W1vm/b8pUuXat26dWWf++pXv6o33nhDP/jBD/TEE0/oIx/5iOuc008/fV4Jq3ZWLgEVT2dFV0wAAAA0k4ceekiWZekf/uEfZv2aI444QjfddFNxFiwA1EqhYBR2JaAGpn+hsQHbjDOgJlSSgKIFX/tzJaDimiABhQqKCSjLSEDN1H5PKpsQ7beietMe1Hgira36ZkhQAdNxzYAiAdWRCahq8ng8uvrqq3XnnXfqmGOO0XbbbSev16vttttOxxxzjH71q1/p+9//vjwe/lWX8lcoQAEAAADN5IUXXpAk7bHHHrN+zfLlyyVJzz//fE3WBAAF45VmQM0xAZVv4Wc3tgCVNFvwkYDqKOYMKCuhWJI0CsorzoCSkYCazWZ/V4+r4BlWExTh0R7MFnwkoDozAWXbdtXf8/DDD9fhhx9e9fdtVz1dHnV7LGVyU/8taMEHAACAZpNIJCRJfr9/hjOn+Hz5O2ej0egMZwLAwkTi1ZkB1WNl1aukYmlL6WxOPV0NuInWlYCauu4yA6oDGAWBoOKKsk+ECkZj+WvfoCsBNcvNfn+/45rTb0UlW4o0sg0p2gMzoFyI5aBhzDZ8DPoDAABAsxkayv/R+PLLL8/6Na+++qokaWBgoBZLAoCiqRlQMecTc2zBJ00VsRqWAHAloGjB11GMGVAhK6EoCShUUEhADZoJqNlu9pdNgZKAwgKlYlIm7jxGCz4KUGgcv9dZgKIFHwAAAJpNofXe7bffPuvX3HrrrZKk3XffvRZLAoCiYgs+MwE1Uws+X78ky3EobBU2YBuQALBtKeXcSI6KFnwdpWwCimIAyisWoCyzBd8sE1CuOXgNvP6hfZjznyRa8IkCFBrInYDKNWglAAAAQHmHH364bNvW9ddfrwcffHDG8x944AHdcMMNsixLRx55ZB1WCKCTFTZLXTOgZkpAeTySL+w41NAEQDou2c49gQmVJKBowdf+zBlQSiiW5EZllFe5AEUCCg0U2+x4aFtdM7fE7QAUoNAwZgGKBBQAAACazVlnnaXh4WFls1kdfvjhuvLKK4tzoUolEgl961vf0hFHHKFMJqPBwUH967/+awNWDKCTjCcyspRTWGYLvllseAWc5xSKWA2ZgWLMf5KkqE0CqqMYCaiQlSABhYqmWvCZM6BmW4CqdP3jew4LYMx/2mKHtOcXf6uVF92rY6/6vVZviDRoYY3FT3A0jNmCL8FwSQAAADSZUCikH/3oRzr88MMVi8V0zjnn6HOf+5ze9a53afHixZKkjRs36rHHHlMsFpNt2+ru7taPf/xjhcPhGd4dAOYvl7M1kcoopIQ8lu18cqYWfNJkAmBqvl2hiNWQBEBy3HXIkYBiBlT7M2ZABRVnBhTKSmVyik3uIVarBd9UAooWfFgAIwG1JRfSRCqjiWRGL22OybIqvK7NUYBCwwR6nAE8ElAAAABoRn/3d3+n3/zmN/qnf/onbdiwQRMTE3rggQcc59h2fvN3++231w033KBDDjmkASsF0EkmUhnZdpn2e9LMLfgk9was1cAWVEYCKmN7lNRU0SkcYPuq7ZkzoKyk4kmKAXArpJ8s5TRgJqAC82vBF7YaWIBH+4iPOB5ukbOwvijkredqmgY/wdEwtOADAABAq3jve9+rF154Qddff73uuOMOPfHEE9q0aZMkaXh4WO985zt11FFH6cQTT5TP52vwagF0gsJGaeHO/SLL40qTlFWhBVVDEgBJ5yZyVH5JU7eKk4DqAMYMKEnKJt2tGYFCASqsmLrM9OdsE1DNdP1D+zASUKP21HXNsqShXgpQQF0FjBZ8cVrwAQAAoIn5fD6deeaZOvPMM2c894knntD111+vyy67rA4rA9CJChulYTMB5e/XrPr8mAkANU8CqrT9nrfLI183I8zbntddgLLKzAYDxuIpSWXa70mznwFVsQUfCSgsgGsG1NTNIIO9XnV3debPss78p0ZT8BsJqAQJKAAAALSwjRs36qKLLtLb3vY27bPPPvrWt77V6CUBaGOReH6jNGwmoGbTfk9ybcAWClmReCMSUM6N5KjtL34dDnTL6tTBGZ2kTGrPQwEKZRQSUEMyClA9vVJPoMwrynC14Ju8/pGAwkKYCShNFdYXBTsz/SSRgEID0YIPAAAArS4ej+vmm2/W9ddfr9/97nfK5XKS8jOh2DAFUEuFBJRrBpRRWKrI2IBtaAIgZbbgm9pE7qP9Xmfo6lGuyydPNlk81JONKZPNdWxqAOUVClAD1jznP0nuAryYAYUqiFdOQA2HOrdFNwUoNIyrAEULPgAAALSIe++9V9dff71uvvlmTUzkN0BsOz+HYPHixVq1apWOPfbYRi4RQJurOAPKmG1SUaUZKMnGz4CaKE1A+dm66hS2NyjFpwpQQSuhaCqr/gAFKEwZjU0moMwWfLNtvye5rn99VlxdylKAwsJMl4AKkYAC6s5swUcCCgAAAM3smWee0fXXX68bb7xRr776qqSpotOSJUt07LHH6rjjjtN73vMe0k8Aaq5iAmrWLfgGHQ9JQKHRLG+fI0EQUlyxVEb9Ab4HMKWYgJKRgJpTAWrAdSisqMYT3aTYMX/TzIAiAQU0QMDLDCgAAAA0t82bN+vHP/6xrr/+ej3++OOSpopOAwMDGh0dlWVZuvjii/XhD3+4kUsF0GEiicIMqJjziXm24AtbMcf71pU5A0rOGVDoDJYv5HgcVELRJIkUOBVnQLkSUItm/yZlrpP9VlQj2bCSmZzrpnlgVowC1IijAEUCCqg782KeSOcatBIAAABgSjqd1i9/+Utdf/31+vWvf610Ol0sOnm9Xh1++OE68cQTdcQRRygQmOWwawCosmILvvkmoMwWfMUEVANa8KWma8FH+qVTWL4+x+OQFVc0yc3KcBqLFRJQRgFqLjOgegJSl08qmTlWKOZHEmkKUJi7dEJKO38ej2jqmraIBBRQf64ZUCSgAAAA0ECPPvqorr/+ev3sZz/TyMiIJBXbsBxwwAE68cQT9eEPf1iDg4MzvBMA1F6xBd98Z0AZCYCAlZJX6ca04EtO14KPrauOUS4BlSIBBaepBJTZgm8OCSgpf62MvlF82G9FJTtf3N+6b5rXAeXEt7gOjdglM6CCJKCAugt4nUMk4ykKUAAAAGicwuymQtpp991314knnqiPfvSj2mmnnRq7OAAwFApFYTMBNc8WfFI+ATCeaMBd2iSgIEleowBFAgplFApQg64WfHNIQEn5a2VpAaqRc/DQ+mKbHQ+ztqWIgsXHJKCABjATUMyAAgAAQDPo6+vTt771LZ188smNXgoAVFQ5ATUwuzcok5Tqtya0Kd2vVCYnb7enzItqhAQUJFcCKqSEYiSgYBgtFKDMFnxzTkANOB4W5uA1pA0pWp8x/2lMQeU09XN0qw4uQNXxtwnAyeynSgs+AAAANJpt25qYmNBpp52md77znbr00ku1cePGRi8LAFwiC01AdXulnl7HoYbNgUo5N5KjKklABUhAdQyvs+9ZUAkSUHCpmIAKzLFFsnGtLFz/InGKnpgHIwE1YjuvZ4tCnduCjwIUGoYZUAAAAGgm9913n0455RSFQiHZtq0nn3xS559/vnbccUcdeuihuv766zUxMTHzGwFAHeSLRPb8Z0BJZRIADWpBlTRb8E0loGjB10HMGVBWXNEkxQBMsW17sgBla1BVmAFVot9qUAEe7cGYATWiqQKUv8ejXm+X+YqOQQEKDRMw/h+PGVAAAABopIMPPlg/+MEP9Prrr+vGG2/U+9//fnk8HmWzWf3ud7/Tqaeeqm233VYnnHCCfvWrXymb5fdXAI0znsjIr5S8lnEtmm0LPsmVAAir0IKqzpv+KbMF31QCihZ8HcTrbsEXpQUfSiTSOaUyOYUVU7eVcz451xlQZgF+sqDFDCjMi9GCrzQBNRzyybKseq+oaVCAQsOYCahkJqdczm7QagAAAIA8v9+vE044QXfddZdeeeUVfeMb39Bee+0l27YVi8X0s5/9TEcddZQWL17c6KUC6GDjiYw7/STNrQ2VsQHbsASAOQPKpgVfRyqTgIpxszJKFNrvDVhlEulzTUCZBXhmQGEhXAWoqevZog6e/yRRgEIDmTOgJCmR4RcLAAAANI9tt91W//7v/64nn3xSTzzxhM4991xtvfXWsm1bmzZtKt7N+KlPfUrnnHOOHnzwwQavGEAnyOZsTSQzxYKRgy88+zcyW1AVZqDUMwGQy0lp5z/HhEpa8FGA6hzGDKiQEpqgBR9KFApQQzLmP3X5XDPtZtQM1z+0D2MG1JaSFnzDwc6d/yRRgEIDlStA0YYPAAAAzertb3+7Lr30Ur366qu644479OEPf1g+n0+2bWvDhg268sordcghh2jx4sX62Mc+pnvuuafRSwbQpgqb8q4ElLdP6ppDyzojAdCQBFTKnWQoTUDRgq+D+JwFqKDiilGAQonRWEqSNGAZBajeRdJcW5xVTIDyPYd5MGZAjZYkoIZJQAGNYc6AkqR4mgIUAAAAmltXV5cOP/xw/eQnP9Frr72m73znOzrwwAMl5Ydjv/766/rOd76j97///Q1eKYB2VSgQhc0ElFFQmpFrBkoDZkCVKUAVElCWJYW8FKA6hqsFX0JRblRGiYoJqLnOf5LcBXg1qAUp2sM0CahFIRJQQEP4u93ffgkKUAAAAGgh4XBYZ555ph544AG98MIL+sIXvqBddtlFtm3LtplvCqA2CgUiVwLKKCjNyGxB1YgEQLJMAkr5BFTI1y2Pp3MHt3ccr7MAFVJCURJQKFEoQA2aM6DmU4AyC/BWAwrwaB+xygkoZkABDdLd5ZG3y/ktmEjnGrQaAAAAYGF22mknfeELX9DatWv14IMP6swzz2z0kgC0qWIBykxAGQWlGTVDAiDlTDIk7R5llE89hf3Mf+ooRgs+n5VWIpls0GLQjKYKUEYCKjCfApTzehlWVJZyGk+SgMI8GAWoLXbJDKgOT0CRY0ZD+Xs8SmWnik604AMAAEA7OOCAA3TAAQc0ehkA2lRkchO20DKvaIEt+AoFrUg9C1BGAmpCU/OfwgEKUB3FSEBJkpLj7mPoWMUClMwE1KK5v5lxveyybIWUUCROAgpzlEm5bqYYUWkBigQU0DDmHKg4vX0BAAAAAJhW4Q59dwJqYG5vZGzAhhvRgs+YARW1pwpQfX7um+4oPgpQmN5orEICqgot+KT8NZUZUJiz+BbXoRGbGVAFFKDQUIEeowBFAgoAAAAAgGlVbME35wSUuwVV6fvXhZGAiiowtR5a8HWWnqDrkJWKljkRnaqqCShfn2Q5t8bDimo8kWGOJ+bGaL+Xsy2Naep6tihIAgpoGL9RgEpQgAIAAAAAYFqFAlGhYFQ01xlQRgIgbMXlUa6hM6AcLfhIQHUWj0dZowjlyUxUOBmdqKozoCzLdc3st6LK5Gxm1GNuYpsdDyPqVVb5PW/LkoaCJKCAhqEFHwAAAAAAc1OY0RS2jBlQC2zBJ0l9ijU2AWWXJKCYAdVx7B5nGz5/Lq5khr0i5I1WKkDNJwEluYvwxRQobfgwB0YLvi0l7feGer3q8lj1XlFToQCFhqIFHwAAAAAAc1NswWcmoObcgs99fr8VVaSBM6BIQHU4Yw5UUAnFkuwVIS8ST0uyNagqzICSyszByxf163oNROszElCjmrqOdfr8J4kCFBqMAhQAAAAAAHNTcQbUXBNQPQHJ40wZ9Sta37v/p0lA9TEDquNYRgEqZMUVTVEMQN5YPK2gEvJaxv7hfAtQxjWznwQU5iNWOQE1HOrs+U8SBSg0mN9owZegBR8AAAAAANMqbI66ElBznQFlWa4EQL8VVTKTUypTpxkoxgyoaGkCKkACqtN4fH2Ox0ElFCUBBUm2bWssnna335MW0ILPPQNKUn3bkKL1GQWoUU1dxxZRgKIAhcbyd5OAAgAAAABgLsYTGfUoo14r6Xxiri34pMbPQElO14KPBFSnsVwFKBJQyJtIZpTN2RqU85ohT4/kDZV/0UzMArwoQGEeppkBtShICz4KUGiogNf5LUgBCgAAAACA6UXi6WKhyGGuLfiksgkoqY4zUFK04EMJVws+ZkAhbyyeL4oPmQmo3kX5NOd8mAX44vWPFnyYA3MGlD11HduqjwQUBSg0lDkDKpGuU8QfAAAAAIAWNZ7IuOc/SXNvwVfmNXWfgWLOgKIFX2czkixBxTWRJI0CaTSWvyYNyCxAzXP+k9T46x/ag1GA2iISUKUoQKGhzAIUCSgAAAAAAKY3nki75z91+6Uef/kXTMdIANR9BoqRgJogAdXZyiWgaMEH5ZOfUoUE1HxVSIDSgg9zYsyAGilJQDEDigIUGszvNRJQKQpQAAAAAABUks3ZiqayClsx5xPzab8nuTZgw8q/b/0SUM7N5AlNFaDCfhJQHcdrzoBKKMpeETTVgm/AMmZABQbn/6ZmAZ4ZUJgPVwFq6jo2HCIBRQEKDUUCCgAAAACA2ZuY3Bh1JaCMQtKsmS2oGj0DqqQFHwmoDuRzt+CL0oIPkkYLCShXC77qJaAKhX1mQGHWsmkpOeY4NKLSAhQJKApQaCgKUAAAAAAAzF5hYzRszoCaz/wnyZUACNc7AWAmoCZb8Pl7PPJ2s23VcbxlWvBRgIKmElCDZgKqijOg8tc/mwQUZi8+4jpUmoBaRAKKAhQaK2C04IsTqwYAAAAAoKLCxmjYTEBVqwVfcQZKHRIAmZSUTTkOFRJQYdJPnalcAoq9IqikAFXNBJRx3fRZGfmVql8LUrQ+o/2eJI0qKCkfvOj10kqWAhQaym8koBIkoAAAAAAAqKiwMdpvJqDm3YLP+bq6zkAx2u9JUwmoPuY/dSZzBpSVUCxFGgXSaGyyBZ9lFKACC0hAlZkf1a8oCSjMXmyz42HE7lVG+Z9fw32knyQKUGgwWvABAAAAADB745VmQM03AVVhBlRdEgBG+z2pJAEVIAHVkYwEVEhxTSTZK4IUmUxADbha8C0gAeULuw6FrRgFKMxe3JmAGrGnrmGLgsx/kihAocFowQcAAAAAwOxFKiWg5jsDymzBp5gkW5F4YxJQMeU37GjB16G8Zgu+hGK0Q4MKLfhsDbla8C0gAdXV7Urd9WuieJ0FZmQkoEY09f00zPwnSRSg0GBmAiqRzjVoJQAAAAAANL+pGVAx5xNVasHXbeUUVELjyXokoJwFqAnbL3tyq4oWfB3KSEB1WzmlUrEKJ6OTjMZTCigpn2VcmxZSgJJc185+K9+Cz7bthb0vOkOscgJqOEQCSqIAhQbz9zi/BVPZnDJZilAAAAAAAJRTcQbUfFvwlSlc1W0GSsqZZCi035NowdexjDSKJNkJd1IOnWcsnnann6SFzYCSXNfOsGLK5mzGhGB2jATUlpIE1CISUJIoQKHB/EYCSpISGQpQAAAAAACUU3EG1HwTUN4+SZbjUCEBUHOuBFSg+DUt+DqUkYCS5CpUojONxtLu+U9W1/zbjxZUnIPHHCjMQnzE8XDULilAMQNKEgUoNJjZgk+SEtxhAAAAAABAWZFCAapaM6A8nrIbsOP1mIFizIAqTUDRgq9DdftlW869IisZrXAyOkU2Z2s8kdGQVWb+k2WVf9FsmS34VChAMQcKs2AmoEoKUMN9FKAkClBosIDXXYCKpyhAAQAAAABQzngiLUs5hRR3PjHfFnySawM2rGix0FVTRgIqWpqAogVfZ7IsZXuCjkOeNC34Ol2hGDQo43uhd9HC39y4dhaK+3W5BqL1GTOgRlUyAypICz6JAhQazN9NAgoAAAAAgNkaT2TUp5g8lu18Yr4t+KSyCahUJqdkpsZ/nxut1SZKZ0CRgOpYdo+zDV9XZkK2bVc4G51gNDZZgDITUAud/yS5C/BWTBIt+DBL0ySgFoVIQEkUoNBgHo8lX7fz25AhfwAAAAAAlDeeSLvb70kLm4NiJADCqtMMFDMB5ShAkYDqVLYxB6rXTijJvPCONhavUIDqrUIByizA04IPcxGfJgEVIgElUYBCEzDb8NGCDwAAAACA8sYTGYUVcx60uiRvqPwLZsOcgWLVqQBlzoBytOAjAdWpLF+f43HIimsiSRqlkxULUKpFAWrA8TBcr+sfWl8uK8VHHYcKCSiPJQ30UoCSKEChCQR6jAIUCSgAAAAAAMqKlEtABQYky5r/m7oSUPkCVyRe4wSAkYAqbcHXRwKqY3mMBFRQccWS7BV1stFiAqoGM6DMAvxkAqrm1z+0vvioJGd70JHJAtRQ0KsuzwJ+LrcRClBoOLMAxQwoAAAAAADKG09kihukRUYBac7KzIAqfFZNTZeAogDVsTx+IwGlhKIp0iidrGICqhozoEhAYb6M+U/SVAu+RUHmPxVQgELD+UlAAQAAAAAwo0w2p1gq605ALWT+k1QxAVDzGShJ52Zy1JGAogVfpzJb8AWthKK04OtokVomoJgBhfky5j+N2wGllL95YriP9nsFFKDQcO4ZUAyWBAAAAADAVJiDEzYTUEYBac5cCYB8C756J6AmlE9AdXks9Rp7BeggxjyzkOKKMi+8o43GUpKkQasGM6CM62fQSqpbGRJQmJmRgBq1p65dJKCmUIBCw/l7nN+GJKAAAAAAAHArbIi6E1ADC3vjSjNQap6AMlvw5RNQYX+3rIXMtEJrM2dAWQnFSEB1tKkWfLVIQA24DvUrqggFKMwk5kxAbdFUenNRiARUAQUoNBwzoAAAAAAAmFmhIOSaAbXgBFRzzICamGzB18f8p85mJKCCihfTf+hMY/G0fEqp10o6n6hBCz4pnwKlBR9mNE0CajhEAqqAAhQazpwBRQEKAAAAAAC3ygmoBc6A8g86Hk7NQKnxpr8rAZVvwRcOMP+poxkzoEJWQjFa8HW00Vhagxp3PxEYdB+bqx6/1O13HOpXlBZ8mFm8cgJqmARUEQUoNJyZgIrzSwUAAAAAAC6FDdGwYs4nqtyCz2fl0wY1TQDYtpRybihHVWjBRwKqo5VJQEVTFAM62Vg8rSFz/pPlWfi1r8B4n34rqvEkCSjMgBlQs0IBCg0XMAaLMgMKAAAAAAC3QkHIlYCqcgs+SQrXOgGQjkt2znFoQvkEVJ+fBFRHM2dAKaEoLfg6WiSe1oBlzH8KDEqeKm1tG9fQml//0B7MGVB2SQKqjwJUAQUoNJwrAUUBCgAAAAAAl0g8X4AKmzOgFpoCKFOA6reixZlTNWHMf5KkqE0CCnIloEJWQtEke0WdbDSe1pDZgq8a858KyszBG09kZNt29T4D7ccoQI2UtOBbFKQFXwEFKDQcM6AAAAAAAJhZzWZAdfW4Nv1rPgMl6Z7nMpWAogDV0YwZUEHFFaMFX8dKZ3OKpbJlElBD1fsQo4gfVlTZnM3sMUzPaME3UpqACpGAKqAAhYZzteDj4g4AAAAAgMt4MiPJds+AWmgLPsm9AWvFajsDykhAZWyPksoXnsIBWvB1NHMGlJVUrJbfi2hqY5PJz5omoIxraKHITxs+TCtuJqDy166gt8u1393JKECh4WjBBwAAAADAzMYTafUqqR7L+Lt5oS34JHcLqponoJwFqKj8kixJtODreMYMKEnKlEnMoTOMxvIFqEHLLEANVu9DXAmofJG/pkV4tLZcToqPOA4VElCLSD85UIBCw7kLULkKZwIAAAAA0LkiiYz6zflP0sJb8EllEwA1LUAZCahC+z1J6vOTgOpoXncByk64Z4ahMxQSUO4CVG1nQEn5ay5QVmJUsp172CN2/tq1KMT8p1IUoNBwfiOSmKAFHwAAAAAALuOJjMLm/CepOgWoMjNQUtlc7eY0G4mWqO2f+uwACaiOZsyAkiSbBFTHGounJJVpwVfNGVBmAV6FFnwkoFBBbIvr0IgmE1BBElClKECh4WjBBwAAAMzPSy+9pPPOO0/Lly9XMBjU0NCQ9t13X1100UWKxWIzv8Ec3H333TrllFO06667KhgMqr+/X8uWLdNxxx2nq666ShMT09+dHovF9I1vfEP77ruvhoaGFAwGtXz5cp133nl66aWXqrpWoF2NJ9LuBJSvX/JUYdZEvWegpMwWfCSgMKmrR1mPM0FgpUhAdapCAmrAMr4HqpqAGnA8ZAYUZmTMf4rZPiWVv25t1UcCqhQ/0dFw/h5nHZQCFAAAADCzX/7ylzrxxBMViUSKx2KxmB577DE99thj+v73v68777xTu+6664I+Z2RkRKeeeqpuu+0213ORSERr167VTTfdpP3331/veMc7yr7H888/r8MPP1xr1651HH/22Wf17LPP6vvf/75uvPFGHXnkkQtaK9DuxhMZ7WwmoAJVSD9JZWdA5T8zra36anA3tzEDaqI0AcUMqI6X6wmqK5kqPvakyyT/0BHGJmdADbla8NUuARUWBSjMILbZ8XCLppKbJKCcSECh4cwEFC34AAAAgOk98cQTOv744xWJRBQKhfTVr35Vv//973XPPffozDPPlCQ999xzOuKIIzQ+Pv+2RWNjYzr00EOLxadVq1bpxhtv1KOPPqo//vGPuvnmm3XOOedoyZIlFd9jfHxcRxxxRLH4dOaZZ+qee+7R73//e331q19VKBRSJBLR8ccfryeffHLeawU6wXgiXbwzv6ga7fek+icApklA9dOCr+PlepxzoLrSJKA61WghAaVaJqDKz4CiBR8qMlrwjdpT1yxmQDmRgELD+c0CVIYCFAAAADCdc845R/F4XN3d3frtb3+r/fffv/jc+973Pu2222664IIL9Nxzz+mSSy7RF7/4xXl9ztlnn63HH39cPp9PP/vZz3T00Uc7nt9nn320atUqXXbZZcpmy/8ef9FFF+m5556TJH3jG9/Q+eefX3xu//331yGHHKKVK1cqFovp3HPP1X333TevtQKdIBLPKCyjvaZROJo3MwFg5T8nUqsNWHMGlKYSULTgg+0LqbTe0J2JKZez5fFYjVsUGmIsnpZXaYWshPOJas6AMq6jfYrLUq521z+0PjMBZU8loIZDJKBKkYBCwwW8zgJUOmsrnc01aDUAAABAc/vDH/6gBx98UJJ0+umnO4pPBeedd55WrFghSfrmN7+pdHruGygPPfSQbrjhBknSV77yFVfxqZRlWerudm8Yp9Npfetb35IkrVixQuedd57rnPe85z06/fTTJUn333+//vjHP855rUAnSGdziqez7gSUUTiat4ot+OqTgCptwRfyUYDqdJbXmYAKWXFGNnSosXjanX6SqpuAMq6jHstWn2K04ENlxgyoUZGAqoQCFBrObMEnSQl+qQAAAADKuvXWW4tfn3rqqWXP8Xg8OumkkyRJo6Ojuvfee+f8OVdeeaUkqb+/X5/4xCfmvlBJ9957r8bGxiRJJ598sjye8n+CnnLKKcWvb7nllnl9FtDuJiY3QguzSYqqlYAy3idc6xZUyfIt+EK+bnV3sV3V6Sx/n+NxUAlFkxQDOtFYLO2e/ySresV3qex1NGxRgMI0SEDNGj/R0XDlClDc1QIAAACU99BDD0mSgsGg3vWud1U8b+XKlcWvH3744Tl9RiqVKs59OvTQQ+X355MJ2WxWr7zyitatW6dEIjHdWzjWaq7HtM8++6i3t3deawU6RWEjtGYzoIzN3EYloGi/B0nqMgtQVlxRZoZ3pLF4WoNmASowIHnc+4nz5g1KlvP9+hVlBhQqM2dAlSSgKEA5UYBCw5kt+CQpkaIFHwAAAFDOmjVrJEm77rpr2bZ3BcuXL3e9Zrb+/Oc/FwtMe+21lyKRiM4991wNDw9rxx131M4776z+/n4deuih085sWr16ddn1mLq7u7XrrrvOa61ApyjMInEloKrWgs/5PiEroS5lFalVAapCAirs76nN56GleHzOAlSIBFTHGounNSizAFXF+U+SZLkTVf1WtHbXP7Q+owBVSEB5LGkgwM+xUtxWgobzdbvroCSgAAAAALdEIqFNmzZJkpYsWTLtuYODgwoGg4pGo3rllVfm9DmlhaNcLqd99tlHa9eudZyTSqV0991365577tHXvvY1ffrTn3a9z6uvviopn9YaGBiY9jN32GEHPfXUU3rzzTeVTCbl883+7tHC51SycePGWb8X0KwqJ6AGqvMBZZJU4VomAFLODeWoSEBhiuWjBR/yRuNpDVrGDKhqzn8q8A842qqFFdNLFKBQiTkDarIANRT0yeOxGrGipsVPdTScZVkK9HQ5ik4UoAAAAAC38fGpDdtQKDTNmXmFAtTERJnh3dPYsmXqj+oLL7xQiURCf//3f68vfelLetvb3qZIJKKbbrpJn/nMZzQ2NqbPfOYzWr58uY455piy653tWgsmJibmVIDaYYcdZn0u0KoKhaB+VwJqsDofUCZJ1W9Fa9eCL2m24JtMQHHnOCTJ5/y5EbTiitGCryOVTUD1VjkBJbmK8PnrHy34UIE5A0r5AtRwyNuI1TQ1WvChKZht+OL8UgEAAAC4lM5d8npn/gO3UMSJx+Nz+pxodGqDO5FI6NBDD9Udd9yhfffdVz6fT1tttZX+5V/+RXfccYc8nvyflZ/97Gdl23bZ9c5lrfNZL9AJCoWgsBVzPlGtGVA9AanLWfit6QyUlNmCL5+ACpOAgiR5nQWokBKKpkijdJpEOqtUJlefBJRrDt5E7QrwaG227WrBN2Lnr1nMf3LjpzqaQqDHWYBKkIACAAAAXPx+f/HrVCo14/nJZFKSFAgE5v05Uj4F1dXlnt164IEH6oMf/KB+8YtfaM2aNfrLX/6it73tba73mcta57PemVoMbty4Ufvtt9+c3hNoNhUTUNVqwSflN2AnXi8+DFuxuiWgonahBR8JKKhsAup1WvB1nNFY/ro3aJkzoKqU/CxlXEsLCSjbtmVZtFRDicSYZDv3rkcmW/AtIgHlQgEKTcHX4wzj0YIPAAAAcOvrm5qJMZu2eoUk02xa4FX6nK222kp77713xXPf//736xe/+IUk6Y9//KOjAFV4n7msdT7rnWkeFtAOIomMvEorYBkF3TKt8+bN3+8oQPUrqnW1SEDlclLaWUibUKEFH1tVkOR1zoAKKaFokr2iTjMWnyxAuVrw1T4BFVZMOVuKprIK+bguoYQx/0mSRpT/3XVRkASUiRZ8aApmAooWfAAAAICb3+/XokX5TZdXX3112nNHRkaKRZ25zkgqPX+m4k7puW+++abjucJro9GoRkdHp32fQoppq622mtP8J6BTjCfS7vSTVL0WfFKFBEANUicpd1G6kIAKk4CC5E5AKa4YLfg6TrEAZSag6jQDShJzoOBmtN9L2D2KK/+763AfCSgTBSg0BVcLvgwFKAAAAKCcPfbYQ5L0/PPPK5OpvBn3zDPPFL9esWLFnD7jrW99a/HrbHb6381Ln+/udt4hXFiruR5TJpPRCy+8MK+1Ap1iPJFR2CpXgBqo3oeUSQDUqwBVSEDRgg+SXDOgglZCEySgOs5oLJ/4HFQdZkCZBXgVClAUPmEwClBb1Ccp36ZxmASUCwUoNIWAlwQUAAAAMBsHHnigpHyq6PHHH6943v3331/8+oADDpjTZyxdulQ77rijJGndunWybbviuYXCkSRtv/32Zddqrsf02GOPFdNac10r0CnGExl3AqqnV+qu4t3W08xAqapkmQSUJhNQtOCD5EpAhZQgAdWBKiegat+CjwQUKoptdjwctadahjIDyo0CFJqC30xAMQMKAAAAKOsDH/hA8etrrrmm7Dm5XE7XX3+9JGlgYEDvfe975/w5xx57rCQpEononnvuqXjezTffXPy6tOAkSYcccoj6+/Mtba677rqKm9jXXntt8etVq1bNea1AJ4gk0u4EVDXTT5KrBVVYE0pnbSUzuep+Tsq5mZy0e5SZHFNOAgqSXDOgfFZa8USyQYtBo4zF0+pWRmEr7nwiUIsWfAOOh32KScrP3wMcjBlQW+ypgvlwiASUiQIUmoJrBhQFKAAAAKCs/fbbTwcddJAk6eqrr9YjjzziOueSSy7RmjVrJEnnnHOOenqcG7r33XefLMuSZVk65ZRTyn7OueeeK78/n0j41Kc+pUgk4jrnhz/8oe677z5J0hFHHOGaNeX1evVv//ZvkqQ1a9bo4osvdr3HI488oquvQ/ws+wAAr+xJREFUvlqStHLlSu27776V/tGBjlY2AVXN+U9SxQRApNoJACMBNTGZfpKksJ8EFORKQElSNu7+OYT2NhZPu9vvSTVqwVdpBhQFKBjMBJRIQE2HAhSagqsAlary3VUAAABAG/nmN7+pQCCgTCajww47TF/72tf06KOP6t5779VZZ52lCy64QJK0bNkynXfeefP6jB133FFf+tKXJEl/+ctftN9+++maa67R448/rnvvvVdnn312sXgVDod12WWXlX2f888/X8uWLZMkXXDBBTrrrLN077336tFHH9XXvvY1HXbYYcpkMgoEArr88svntVagE4wn0gpbMedBo2C0YEYCIDyZAKj6BqwxAypqlxSgAiSgINcMKEnKJcfLnIh2NhZPa8AqU4AKDFb/w8wCvKKSbFrwwc0oQG0pacFHAsqN20rQFFwzoEhAAQAAABXtvffe+ulPf6oTTzxRkUhEn/vc51znLFu2THfeeaf6+vrKvMPsnH/++dqyZYsuvPBCPfvsszrttNNc52y99da69dZbtdtuu5V9j76+Pt155506/PDDtXbtWn33u9/Vd7/7Xcc54XBYN954o97xjnfMe61AuyufgBqo7ofUKwFgJKCiChS/7iMBBalsAcosXKL9jcbSGpJRePT3S101uE4Y19MeK6uAkiSg4BZztuAbUf56FfJ1u8bMgAQUmgQzoAAAAIC5Oeqoo/TUU0/pk5/8pJYtW6be3l4NDAxon3320YUXXqgnnnhCu+6664I/52tf+5oefvhh/dM//ZN22mkn+Xw+9ff3a99999WXv/xlPffcc9p///2nfY9dd91VTzzxhC688ELts88+GhgYUG9vr3bffXd98pOf1FNPPaUjjzxywWsF2tl4IlMsCBVVOwFVNgGg6icAjBlQzhZ8JKAgyeNRpivgOGQlKUB1mnwCyihA1WL+k1T2etqvKAkouJkFqMkEFO33yuO2EjQFdws+ClAAAADATJYuXapLL71Ul1566Zxed8ghh8i27Vmfv//++89YZJpJMBjUBRdcUGwPCGD20tmc4ums+rtrPAPKSAAUZ0DFa5yAsvOFBm+Xh7vHUZTtCak7Gy8+ttIUoDrNWDyt7cwCVC3mP0mSL+w61G9Fq3/9Q+uLmwWofAJqUZACVDkkoNAUAl7ntyIt+AAAAAAAyCu0gHLNgKp2Cz4jAdCnmCzlapCAchYSCgmocID7pDEl1xN0PO5KRyuciXY1Fk9rUEbhsbdGCShPl+Qz2pCSgEI5xgyoEeUTUMx/Ko8CFJqCKwFFAQoAAAAAAElTLfBcM6Cq3YLPSFR1WbZCStR+BtRkAor2eyhlG3OgukhAdZyxeFqD9UpASVLAeQ0MWzFmQMHJtqdpwUcBqhwKUGgKPmZAAQAAAABQVmED1DUDqsYt+AqfWesZUNHJBFSfnwQUplg+ZwHKl4srk801aDWoN9u2JwtQRuGxVjOgJNc1NZ+AogCFEslxKef8mTiVgKIFXzkUoNAUmAEFAAAAAEB5kckCUNhVgBqo7gf5+iTL+fd5WFFFapyAmmrBRwIKUyxfn+NxUHHFuGG5Y0wkM8rmbA3KTEDVsgA14HjYb0WL119Akmv+k8QMqJlQgEJTMAtQiQy/UAAAAAAAIJXMgKp1Cz7LcicArBokAFK04MPMPH5nASpkJRRNkkbpFGPxfOFnyNWCr4YFKOOaWpPrH1qbMf8paXcXU7zDfbTgK4cCFJpCwGsmoIhUAwAAAAAg5QtQHuUUtuLOJ6qdgJJcBaiwatCCz5wBRQs+lNEVcCegokluWO4UhQLUgCsBVcMZUMY1tSbXP7S22Ijj4ahCkixJ0qIgBahyKEChKfiZAQUAAAAAQFnjibQ7/SRVfwaUVJ8EgJGAmigkoGjBhxJdPncCKpYijdIpxmIVElB1nAEVtqKaSGZk23btPhOtxUhAbbGnrlPMgCqPAhSagmsGVDrLxR0AAAAAAOUTUGEr5n6i2i34pDIJgJjGk9VOQDk3lIsJKB8JKJTwhR0Pg0poghZ8HWMsnlaXsuo3r321TECZBXhFlbOlKLPqUWDMgBp1FKBIQJVDAQpNwWzBl83ZSmcpQAEAAAAAEImn1W8moDw9Uk9v9T+szAyoSLzGCSiRgEIZvpDjYVBxxWjB1zHG4mkNaML9RC1nQBkF+H4rf92NxGnDh0lmAkr561SXx1I/P8PKogCFpmAmoKR8CgoAAAAAgE43nsgUN0KLAgOSZVX/w8okAGo+A8rOJ6DCARJQKOF1FqBCVkJRWvB1jNF4WgNWmQJULVvwBQYdD8PKp6+q3oYUrStWPgE1FPTK46nBz+Q2QAEKTaFcAYo5UAAAAAAASOPJMgmoWsx/ksomAMYTVZyBkk1L2aTj0FQLPu4eR4kyCagoCaiOMRZPa0jG/Cdvn9Rdwzk7ZRKgkqpfhEfrciWg8gUo2u9VRgEKTcHvdX8rxumvCgAAAADA5AwoswA1UJsPMxJQYUWVydlKpHPVeX9j/pMkTdi04EMZ3j7Hw6CVUIwEVMcYi6c1aBnXi1q235PcBXgVClB832GSawZUvlA+HKphYbTFUYBCU/B2eWSmFGnBBwAAAACAFElk3Akoo1BUNbVOAKTcLbWKCSg/LfhQwkhAhUhAdZSxWFqDZgu+WhegjOtqwErJq7QiJKBQYLTg2zLZgm9RkAJUJRSg0BQsy3K14aMABQAAAABAvvjjmgFVqwRUhQRApFoJgGTlAhQJKDh4zRZ8CUWTFAI6xVg8rUGzBV/votp+aJnWpmHFSEBhilGAGlEhAUULvkooQKFp+I0CVIIWfAAAAAAA5FvwKeY8WKsZUGYLvhonoKK2T/bk9lSYBBRKGQmobiunZCJW4WS0m7It+AL1bcEnSf3WBAUo5Nm2awbUSCEBRQGqIgpQaBquAlSGAhQAAAAAAGUTUDVrwed833zhy67eBqwxAyqq/Pwny5KCXgpQKGHMgJKkXMI9QwztaTSe0qDMFnw1TkB1e6WeXsehfkWrV4BHa0vHpGzScWhEhQIULfgqoQCFphHwGi34UlUacAoAAAAAQItKZXJKpHMKmzOgataCz5ms8lkZ+ZWqXgHKSEBN2JPzn3zd8pjDodHZjASUJGUpQHWM/AwoswVfjRNQkrsIb9GCD5OM9JM0lYAapgBVEQUoNI1OnQE1nkjrzfHkzCcCAAAAADpO4c579wyoWrXgG3Qd6ldUkWolAIwZUMx/QkXdfuUs516RmaBDe8rmbEUSGQ1aZgKqHgUo57U1XM3rH1qbMf8pbXdpfDLFywyoyihAoWl0YgHq109v1H5fvUf7fvVuffmO1Y1eDgAAAACgyRTuvHcloGrVgs8Xdh3KJwBqNANqcvOuz08BCgbLUqbL2Q7NLGCiPRWuN4Oq8wwoyXVt7beiJKCQZySgRhWSlE/uMgOqMgpQaBp+owVfItX+Baiv3/VMsdB29UMv6oU3+UUKAAAAADClsPHpTkAN1OYDu7pds3f6NVGzGVDjdr4AFfYz/wlumR5nGz4rxb5JJxiLTxagXC34ajwDSnJdW5kBhaL4iOPhFnvqZ+WiIC34KqEAhaYR6HF+O7Z7Amo8kda6zTHHsSdeHm3MYgAAAAAATSm/8WkrLOffjzVLQJV576omAFwJqMkZUCSgUEauJ+h43JWJVjgT7WQ0lpZHOQ2Yyc96FKCM6x8zoFBUNgGVn2HoNzp7YQoFKDSNTmvB95JRfJKkZzZGGrASAAAAAECziiQyCimubivnfKJWM6CksgmAms2AKiSgAiSg4GZ7nQkoClCdYSyeVlhReSzb+UQDZkDlE1AUoCBXAaqQgFoUIv00HQpQaBoBowVfvM1b8JUrQK15jQIUAAAAAGDKeCLtTj9JtWvBJ9U2AWAkoCYmE1BhElAoxyhAdVOA6ghj8bSGzPZ7Un1mQJkFeKuKBXi0ttgWx8MRO399Yv7T9ChAoWmYUcVEmyeg1m12/9K0ZuO4bNsuczYAAAAAoBONJzLu+U+yJF+4dh9aNgFQrQSUc1M5ygwoTMPjd84jC+TiSmVyFc5GuxiNpzUgY95XT1Dq8df+w80WpIpqIplRLsd+XcczElAjyl+fhklATYsCFJpGp7XgW7fJXYDaEk3pzfFkA1YDAAAAAGhGZQtQ/n7JU8MtnTIJgKoloMwCVCEBFSABBTezABW04oqlaIfW7iLlElD1mP8kua5/YSsq25aifN8hbiagCi34SEBNhwIUmoaZgOrEFnyStJo5UAAAAACASfkWfGUKULVkJgCqWYByteDLJ6D6SEChjC6jABVSQhNJCgHtbiye1qCrADVYnw83E6CTNwAwBwquBNRkAWo4SAJqOhSg0DTMBFSizSPV5VrwSdIzr5XpcQsAAAAA6EiRRNqdgDIKRFVnJgAUq94MlKSzABW1mQGFyroDzlaT+QRUe9+wDGk0ltKgGpSAMmfgTd4AwBwoKDbieDii/Ayo4T4SUNOhAIWm4fcaBag2/oUilsrojQqt9taQgAIAAAAATBpPZMokoAZq+6FGAiA8mYCqysxiVwKKFnyozOMLOR4HlVCUBFTbyyegjBlQgaH6fLirBV9cHuVIQKFiAmpRkALUdChAoWl00gyoSu33JApQAAAAAIApZWdA1ToBZbbgU1TZnF2dv9NdCSha8GEaPncLvmiyffeLkDcWTzcuAVWmxWmfYhonAdXZUjEpE3ccKiSgFoVowTcdClBoGp1VgCrffk+SXngzqmSmff/ZAQAAAACzl58BZdzEWOsZUK4EQJVmoNi2lHJuKkdFCz5Mw2skoKy4oimSKO1uNFYmAdVbpwRUmQJ/VefgoTXFt7gObSnMgKIANS0KUGgaAa/z2zHexi34XtxUOQGVzdla+/pExecBAAAAAJ2jbAKqzi34+lUoQC0wAZCOS7Zz3vOE8gkoWvChrDIt+GIUoNpeJJ7WoNWgBFRPr+RxXo/6FVWEAlRnizkLUFnb0rh6JUnDIVrwTYcCFJqG30hAJTo0ASXRhg8AAAAAkBdJZIoFoKI6t+ALWkl1K7PwDdiU+2bLqJ1PQNGCD2UZCaiQFdcELfja3lg8rSGzBV9gsD4fblmua2B+Dh4t+DqaOf9JfbLlUbfHIsE7AwpQaBqd1IJv3QwFqGdeG5/2eQAAAABAZxhPpMskoOrbgk/KJwAW3IIq6f5bd0IBBXq61NPFFhXKMGZABZVQLEkSpZ2lszlFU1kNuFrw1SkBJZVNgdKCr8MZLfhG7XxxfCjolcdjNWJFLYOf7mgaAa+7AGXbdoNWU1svbXa24Nt9G+cvVCSgAAAAAACpTE7JTK7MDKiB2n5wmQJX2IotPAFgJKAytkdJ9ZB+QmWuBFRC0SRJlHY2Fk/LUk6DZgKqXjOgJNc1tp8EFIwWfFtUmP9E+72ZUIBC0zATULYtJTO5Cme3rkQ6q41jCcexv99zW8fjNRsjbVt8AwAAAADMTmHD05WAqnULvh6/1O13HKpOAspZgIrKL8li/hMqM2ZASVImTteYdjYWT6tPMXVZxr5YPRNQxjWWBBTMAlQhAbUo5G3EaloKBSg0DbMAJbXnHKiXt8Rcx8wC1EgsrTfGk/VaEgAAAACgCRVmLrlmQPnrMAulTAIgEq9uAmpCAUlSmAQUKvGWKUAlKEC1s9FYWkNWmf/GgcYloMJWbOHXP7Q2YwbUFpsE1GxRgELT8HvdBah2nAO1bpPzD4et+3zafZs+BY1//tW04QMAAACAjjaeSMunlHyWsfFZ6xlQUm0SAMYMqKidT1n1McAdlRgzoCQpRwGqrUXiaQ3KmP/UHZC8vfVbBDOgYDJnQBVb8JGAmgkFKDQNf3eZAlSqDQtQm50FqJ0WBeXxWNp9W+cvVc9s5BcqAAAAAOhk44mMwmb6Sap9Cz6pTAKgCjNQUmYLvskEFC34UElXjzKWc4PXTrJf0s5G4ykNmgmoes5/ktwFeIsCVMdzJaAKLfhIQM2EAhSaRk+XpS6P5TiWSLffDKh1m50t+JYuyt/BsWJx2HF8DQkoAAAAAOho44m0e/6TVJ8ElPEZ4RrMgJooJqBowYfKMt1Bx2Pb+D5CexmLlUlA1bsAZRbgVYUCPFqbMQNqZDIBtShIAmomFKDQNCzLcs2BascWfC+ZCajh/C9SFKAAAAAAAKUiiYx7/pM3JHXVITFUJgEQWWgBqlICihZ8mEamx1mA6kqRgGpnY/GMOwFVz/lPkuv6FyYBBbMAVZgB1UcCaiYUoNBU/EYBKtGGBah1myoloJwt+P66KdqW//wAAAAAgNkZT2QUtpx/Q9Yl/SS5EgD91UgAmDOglE9AhQMkoFBZzihAedJlUoFoG+Vb8C2q7yLKzICaSGWUy9n1XQeaR9wsQOVb8A0HKUDNhAIUmkrA6/yWbLcZUMlMVhvG4o5jOy3K/yK1+7bOBFQ2Z+v5N4iVAwAAAECnGk+k3QkoozBUM64EQGzhCYBUpRZ8JKBQmd0TcjzuogDV1sbiaQ2qwTOgzAK8FZVtSxMpUlAdKZN0/fwqtuAL0YJvJhSg0FTavQXfK1viso2bJQoJqJCvu/h1wWra8AEAAABAxxpPZNwzoIzCUM2USQCMJxeagKrUgo8EFCqzfc4CVHeWAlQ7i8TTGrTMGVB1TkCZBXjFJNm04etURvs9aSoBNcQMqBlRgEJTafcC1LpNzl+ShkNex51ey7d1tuF7ZiN9jQEAAACgU5VPQDWoBV81ZqBUSECFAySgUJllFKC82Zhs8+5etI3RWFpDjZ4BZVxnu62cgkosvA0pWlNss+NhzrY0ppD6/N2ucTJwowCFptLuM6DWbXb+4bB0kbOP8YrFzjZ8a0hAAQAAAEDHisQzCpsJqEa14FO+ALWgjX8SUJiHLr/zZt1eO65kJteg1aDWxuJpDajBCagy19l+RRWJk4DqSMb8pzEFlZNHwyHmP80GBSg0lYDXSEC12QyolzY7h8eaLffMAtQzr0W4qwcAAAAAOtR4skwCqlEt+KyosjlbsYX8nZ5yphqimkxAMQMK0/D4nXslQSUUTVIIaFdj8TIJqHrPgPKFJVmOQ/kUKAmojmQkoLbYk/OfaL83KxSg0FTavgWfkYDayUxAbev8pWokltbrkWTN1wUAAAAAaD5lZ0DVKwFlfE6f4rKUW1gbvqTZgi+fgOqjAIVp9AScCaigFV9YIRRNbTSeKpOAqnMByuNxFeHDijEDqlMZBagR5a9JJKBmhwIUmkq7F6BmSkAtGQwo5HO2HqANHwAAAAB0pvIFqDrNgDKSVh7LVp9iC0sApMwWfIUZULTgQ2XdAefNuiElNEECqi0l0ln5MlH1WMZ+YL1b8EllUqATJKA6VWzE8XDEzs+lWxQiATUbFKDQVPxGC75EG93Rksrk9OqIswC187AzAeXxWNp9W+edPWteowAFAAAAAJ1oPJFWWM6/I+vXgs/9OWErpkgVE1BR269uj+W6GRUoZflCjsf5BBQFqHY0Fk9rwGy/J0mBOiegJNe1tt+KLuz6h9ZlJqAKLfhIQM0KBSg0FX+3UYBKt89QyfWjceWMcU5Lh4Ku81YsNgpQG8v84AUAAAAAtL1IIqNwo1rweYOS5fwbvV8LmIGSy0lp5z/LhALq83fLsqwKLwIkeZ37JCElFE22zw3LmDIaS2tIzn0wu8uXvx7Vm3GtDStKC75OFd/ieDiifFF8KxJQs0IBCk0l4HV+S7ZTC751m5y/aA/29qi/193nesViZ7ScFnwAAAAA0HmSmaxSmZz6ZRSg6pWAsqyyCYB5b8Aa7fekfAIqHGD+E2ZgJqAUV5QWfG1pLJ7WoOW8Vli9Q/nrUb0Z17+wtcAWpGhdJKAWhAIUmko7z4Bat9n5R8PSReXv3li+rbMA9dc3J5Roo38PAAAAAICZjScy6lJWISvhfKJeM6AkVwKgfyEJgDIFqEICCpiW12zBl1C0jUY2YMpYPK1BIwHVkPlPknsGFAmozhUzE1CTBaggCajZoACFpuLvMVvwtc8vFC9tdvbt3mlRb9nzlm/b57ixI2dLa193/6IOAAAAAGhf44mMwmb6SapfCz6pugmAZJkElPwK+0lAYQZGAiqkBDOg2tRoLOVKQCkw2JjFmAV4awEtSNHaXAmo/DVpuI8E1GxQgEJTCXiNBFQb3dEy2wRU0NetpUPO4hRt+AAAAACgs0TiafWb85+k+rXgk8okACYUme8GbMqZakjaPcqomwIUZmbMgPJZacXi8QYtBrWUb8HXJAkoswWpooqQgOpM8RHHw0ILvuEgBajZ6NgC1BtvvKE77rhD//mf/6l/+Id/0PDwsCzLkmVZOuWUU+b8fnfddZdWrVqlJUuWyOfzacmSJVq1apXuuuuu6i++jbVzCz5XAmq4fAJKcrfhW/MaBSgAAAAA6CTjiYx7/lOXT+oJ1G8RZRMA89yANRJQE/JLEi34MDMjASVJmdh4mRPR6iJlW/ANNWYxxvWPGVAdKpOSks592S3qU7fHUjjAz6/Z6Nh/S9tss01V3ieXy+mf//mfdfXVVzuOr1+/XuvXr9ett96qM844Q9/5znfk8XRsvW/W2rUAlcnm9MoWswVf+QSUJK1YHNav//e14mMSUAAAAADQWcYTaYUt59+RdZ3/JLlb8Cmm56s0Aypq5wtQ4QAJKMzAW6YAlWCfpB2NxtPavVkSUMyAguRKP0nSqB3Soj6vrNIZKqiIioikHXfcUYcddti8Xvv5z3++WHzae++99eMf/1h/+MMf9OMf/1h77723JOn73/++/s//+T9VW2878xst+BJt0oJvw2hCmZztODZ9AcoZL3/mtXHZtl3hbAAAAABAuymbgKpn+z3JvQG7kBkoRgIqqnySixZ8mFGZAlQ2QQKqHY3F0xqUOQOqQQkoswXfQhKgaF3G/CdJGlVIwyHa781Wxyag/vM//1P77ruv9t13X22zzTZat26ddt555zm9x3PPPaeLL75YkrTPPvvogQceUCCQ/wVq33331dFHH62VK1fqscce00UXXaTTTjtNu+66a9X/WdpJuyagXjTmP4X93RrorfxL9orFzhZ8o7G0XosktLi/jq0WAAAAAAANE0mUmQFltISqObMF30JmoBgzoGjBh1nzeJTyBOTNTc19so2CJtrDaKyJZkD5Bx0Pw4pqIplRNmery0PypWPEtzgejtm9yqpLiyhAzVrHJqD+67/+S0ceeeSCWvFdfvnlymTyv3hdccUVxeJTQW9vr6644gpJUiaT0WWXXTb/BXeIdi1AvWQUoHYaDk4b01wyGFDI5/wlnDZ8AAAAANA5yiagGtyCr5ozoKL2ZAKKFnyYhXS30UUmSQKqHY3F0xpyFaAaNQPKeb31W2n5lNJEkhRURzESUFvsfNeq4aC3EatpSR1bgFoo27Z12223SZKWL1+ud7/73WXPe/e7363dd99dknTbbbfRRm0GAbMFXzqnXK71/52t2+Ts2710mvZ7kmRZlpZv62zDt2Yjv1wBAAAAQKcYT2TcM6Dq3oLP+Xl9is2/BZ8xA4oEFOYi293reGylohXORCuLxFIaMFvwNaoAVeZ6G9YC2pCiNcWcCahRTRag+khAzRYFqHl68cUXtWHDBknSypUrpz238Pz69eu1bt26Wi+tpfm7u1zHkplcA1ZSXa4E1KLeCmdOMdvwkYACAAAAgM4xnkgrbG7E1r0FX7kZUFVOQDEDCrOQ7XHOgfKkacHXjlLxcfks4xrTqBlQZRKnzIHqQBUSUItIQM0aBah5Wr16dfHr5cuXT3tu6fNr1qyp2Zragd/r/pZMtEEbvnWuAtT0CSiJAhQAAAAAdLLxRMY9A6reCSizBZ+iGk+k5tfdxZgBFZ1MQIUDJKAwM7vHuY/SlSYB1W5s25YnMeJ+olEzoLp6JK+z8BlWjAJUp4k7vydHlf+eYAbU7PFTfp5effXV4tdLliyZ9twddtih+PUrr7wy788pZ+PGjXN6v2ZnzoCS8nOgBsuc2yqyOVuvbIk7ju00PHMCavliZwu+FzdFlUhn5S/z7wgAAAAA0F4iiXTjZ0AZiaseKyu/nVQ0lXXNLZ5RsnwLPhJQmA3bKAR0ZShAtZtoKqt+23nzte3pluXrq/CKOvD3O9qH9ltRReK04OsolWZAhUhAzRYFqHkaH5+6cycUCk1zphQMTt2lMTExt4hwafGqE5QrrsRbPAG1YTSuVNbZRnCmGVCStHzbPlmWVLixLGdLz70+rrctGajBKgEAAAAAzaTsDKh6t+CbZgbKnAtQKVrwYf4sv7MI4c1SgGo3o7GUBi1nUtIODMmyrAatSPlrbmR98WG/ohpPUoDqKMYMqBE7XwcYJgE1a7Tgm6dEIlH82uudvuLp8019Q8bj8WnORE+XRz1dzh8s8VRrF6Be2uz8gyHk655Vn9Beb7erVR9t+AAAAACgM4yXS0DVuwWfL+w6NO8ZKOYMqMkEVMjPvdGYmZmC8eViyuXm0QoSTWssntagnAUoq1Ht9wrMNqTMgOo8RgJqRJMzoEhAzRo/5efJ7/cXv06lUtOem0wmi18HAoE5fc5MLfs2btyo/fbbb07v2ez8PV1KZ6cu5q0+A8qc/7R0Ue+s795Yvm2fXtw09fo1G8enORsAAAAA0C7KzoCqdwLK0yX5+qXkWPFQ/2QCas6MBNSEHVDI160uTwPTDWgZXUYCKqiE4umsgnNN4qFpjcXTGrSc14mGF6CMa24+AUoBqqPEzQTUZAEqSAJqtrhKz1Nf39QPvpna6kWjU78wztSuzzTTfKl2FOjpclzMW70F30tGAcpMNU1nxeKw7nr6teJjElAAAAAA0BkmEin1dZst+Oo8A0qSAkYByooqMq8ElPOGyqj8CpN+wix1B5xpvKASiqYyFKDayFgs7WrBp94GT4U3rrn9VlRvzqcAj9ZlJqDsPoX93fJ201hutvg3NU+lhaFXX3112nNLU0ydNtNpPgJe5xyoVm/Bt85owbd0Ue+sX7tisfMXrDUbI7JtIuYAAAAA0M4S6ay82Zi6LOPvv3q34JNcG7BhxeaXADATUAqoj/lPmKWeXjMBFVcs2dr7RXDKt+AzbvJvdALKbMFHAqqzZDNSYsxxaEQh5j/NEQWoedpjjz2KXz/zzDPTnlv6/IoVK2q2pnYR6DEKUO2WgBqefQJq+bbOX7AiiYw2jiUqnA0AAAAAaAdl2+9J9W/BV+Yz8zNQ5pEAMGdA2X6FA6RXMDvdfucNuiEroYkkhYB2MhZPa8hMQAWGGrOYgrLXP77vOkZ8xHVoxO5j/tMcUYCap5133lnbbbedJOn++++f9twHHnhAkrT99ttrp512qvXSWp7fKEC18gyoXM7WS0YCai4t+JYMBtRnxMlpwwcAAAAA7W08kVa/nAUo2/JIvr4Kr6ghMwEwnw3YbFrKJh2H8i34SEBhdiyfc6RFUHHFWrxjDpxG42kNyGzB1+gZUEYC1IrNrwCP1mTMf5KkURJQc0YBap4sy9IxxxwjKZ9wevTRR8ue9+ijjxYTUMccc4wsi+GaM3EloFr4F4rXIgklMznHsZ3m0ILPsiwtX+z8A+OZ18YrnA0AAAAAaAfjiYzCRgLK8vdLjdhTMBIAYc0jAZV0/x07YQfUxwwozJZRfA1a+RlQaB/5BJTZgq/BCSha8HU2Y/5TxA4orW4SUHNEAWoBzj33XHV15YslZ599tuLxuOP5eDyus88+W5LU3d2tc889t95LbEmuGVDpXIUzm986o/1eoKdLW/XNrUpuzoFaTQIKAAAAANpaJJFW2EhANaT9nlQmARBVJD7HDVhj/pM0mYAKkIDCLHmdCaiQ4orSgq+tjMXTGjBb8DU8ATXgeJi//pGA6hhGAWrUzl+HFgVJQM1Fx95q8tBDD+n5558vPt60aVPx6+eff17XXnut4/xTTjnF9R7Lli3T+eefr69//et67LHHdMABB+jTn/60dtllF73wwgu68MIL9cQTT0iSzj//fO222241+WdpN/4eZ120lVvwme33li7qnXMKzixA0YIPAAAAANpb2RlQxp34dVM2ATDXBFT5AhQJKMyaqwVfQjGSKG1lLJbWULO14DOuf2HFSEB1kpizBd8W5ZOYw3MMF/z/7f15nFx1mff/v0/t1V29pTshCekkQAgJiwGyQECECDIzbAqKOKPDIoqiKDrc6ohzq/fc98w4jqDo+EUQZBn1hpFxAaI/0bkhCkRCIJpgNkIW0tlIJ73WvpzfH52u9DlV3emllnOqX8/HI4/U+dSpcz6dVJ+qc65zXddkN2k/6R944AE98sgjRZ974YUX9MILL1jGigWgJOmf/umf9NZbb+kHP/iB1q1bpw984AMF69x88836P//n/0x4zpNFLfWAsmdAjaX/06AF061p5js7o4qnsgWZYgAAAACA2lCsB5Q9E6libBkA4+oBZcuAippBmfLQAwqjZ8uA8hk5JRLRYVaGGyVifQoZtuB2uKU6kxlkO+42GHHFEokqTQYVZ8uA6jKPBKDqKcE3FpTgmyCPx6MHH3xQK1eu1Lvf/W7NnDlTgUBAM2fO1Lvf/W798pe/1AMPPCCPh3/q0SroAeXiANSuTlsGVNvo+z8NOmV6g6XMd86Uth6gDxQAAAAA1KqBHlDW88mqleCzXQAeVwaArQdUVOGBbVGCD6Nl6wElSekYFWJqii3bRFL1M6CKHHe9qV5lc2bl54LKi1vfk11HMqBaI2RAjcWkzYB6+OGHC8rsTcRll12myy67rGTbm8wKAlAp9wag7BlQJ4wjA6ou4NMJrfXa3nl0W5v29WpRe/NEpwcAAAAAcKDeREZt9gyoapXgs2UANBlR9Y61BJ8tA6rfDEkSJfgwerYMKEnKJLg5t5Z4EtaL/abhlVGtzM9BRY67TUZU/YmMmuoIoNc8W1A0nwEVIQNqLEjLgePYS8u5NQPKNM0iPaDGHoCSpAUzrHf6bN7PlywAAAAAqFV9iXRhD6hqZUDZS/BpHCX4bD2gohoIQFGCD6PmDytnu4yZi5MBVSuyOVPBVLd1LNQijbGPesn5wzK91myXRsXGHoSHOxUEoAYC4WRAjQ0BKDhOrfSAeqsvWRA8mzuOEnyStHB6o2V54z6+ZAEAAABArepLZNTolB5QtgyAsJFSIhErvu5w7D2gjpTgIwMKo2YYSnmt11Syif5hVobb9CXSapb1Zmuz2v2fBhXJAh1zEB7uZO8BpQYFvB418tk1JgSg4Di10gNqZ6f1ZCHo8+i4htC4trVwhjUAtWlfr0yTerMAAAAAUIuKZkA5pASfJHmTYzwntfWAGizBRw8ojEXaFoBSiuowtaInnlaLYf3/9NS3VWk2Vobt2DuQBUoG1KRg7wFlRtQaCciodmaeyxCAguMUlOBzaQ+owvJ7dfJ4xneAspfg60tktLcnMe65AQAAAACcqy+RUVNBBlRzVeZSbL8N6ld0LOfqw2RAUYIPY5HxWdsaGKnoMGvCbXriabXIepzw1E+p0mxs7GVIyYCaPIpkQLXS/2nMCEDBcQozoHJVmsnE7Dxk/SI03v5PknR8c7ggvXPTXsrwAQAAAEAt6k2k1eiUDChfQKbfmnnSpKh642PIALD3gDqSAUUJPoxF1m8PQFGCr1Z0xwozoIy61irNxsZ27KUH1CSRy0rxbstQl9mg1nr6P40VASg4Tq30gLJnQJ3QNv4AlGEYWmArw7d5PwEoAAAAAKhFffG0GmXrs1StHlBSQQZAoxEbWwaALVDQr7ACPk/B+T8wkpw/Yln2pglA1YqBEny2/8+wUzKg6AE1KcW7JVlLzR42yYAaDwJQcJxaKcFXmAFVN8yao7NwurUM36Z91DoGAAAAgFqUTsQUNGwXOKtVgk+SYbsA2zjWHii2HlBRM0T5PYyZGbAGoHzZ2DBrwm0GSvDZrnM5JQPKHoBXPz2gJgNb/ydJ6lZEUyNkQI0VASg4TshnfVvGXZgBZZqmdnZaA1BzJ1CCT5IW2jKgNu0jAwoAAAAAao1pmvIkuwufCLdUfC5H991sWRxzBkBBBlSooMw8cCxGyHpjrj9DD6haMZABZQ9AOSQDaqLHP7iTrf9TvxlSSn4yoMaBABQcx54B5cYSfJ39qYKGrBPNgLKX4NtxKOra7DAAAAAAQHHJTE51uSKlxYKNhWOVYssAaFJ0bD1Q7D2gFFZDmAwojI0RtAagAmRA1YyiJfgckwFlzwCNqZcAVO2LWTOgujWQgUkPqLEjAAXHCdtqQCczOeVy5jBrO9MuW/m9gNejGU3hCW3zlOMa5DGOLpumtOUAZfgAAAAAoJb0JtJqlPWcMheISN4qZgzZMgAm2gNqoAQfGVAYG68tAypkxpR12fUiFNcTS2uKvQSfY3pANVsWBzKgKMFX82wZUIfNgeNPWwMBqLEiAAXHKdaENJFxV6bPzkPWu3Dap4TlHRo9GodwwKu5bdYyfpThAwAAAIDa0pfIqMmwBqCMKvZ/klSQAdCkMZagStpL8IXpAYUx84atWYD1SiiaIhOlFkSjfaozktZBp2RA2UvwjfX4B3ey9YDqNgczoCjBN1YEoOA49hJ8klxXas6eATXR/k+DFk63ftnaTAAKAAAAAGpKXyKjJlsGlFHN/k/SxDMAUtbMhqgZUmOYDCiMTcAWgIoooVjSXdeLUFzOlm0iyTk9oGzHv4EMUDKgap49A0pHMqAiZECNFQEoOI69BJ8kxV3WB8qeAWXPXBqvhTOs6eab9lGCDwAAAABqSV8iXZABZc9AqriJZACYZvEeUGRAYYz8dbYMKCNOBlStiHVZFk15qn/cG1TQAyqq/niqSpOprJd3Htb77n1RH7h/tTZ09FR7OpVl6wHVdaQE3xQyoMaMABQcp2gJPpcFoAozoOpKst2FM6xftjbt75VpUu8YAAAAAGpFbzyjRllvarQHgCquIAMgqt7RZgBkEpJpPafvFz2gMHa+sPWm3HolFE0SgKoF3oT1Yn860Ch5Cq8PVoXt+Os1TGUStV+RKJnJ6pM/elVrd3XpD9sP61P/91XlJlPPtSIBqKawXwEf4ZSx4l8MjuP1GAW/zPFUrkqzGTvTNLWj0xqAmlOiEnwLbAGovkRGe7rjJdk2AAAAAKD6imdANVdlLkf3b+sBZYwhA8qW/SRJ/SYZUBiHQMSyGDHiilKCryb4k92W5VzYIeX3pKLHX0+y9rOBXtvTq7f6jvbl2nkopu226501zdYDqksRtUbIfhoPAlBwJHsZPjeV4OuKpQu+iJeqB9TMpsK7xCjDBwAAAAC1oy+RKQxAVTsDyrb/Ro2hB1Sq8Jy1X2F6QGHsgoUZUDFK8LleOptTXdYa0DGdFIAKNsg0rJfQA+k+ZbLuuVl+PDZ0dBeO7Skcq1m2HlBdZgP9n8aJABQcyc0BqJ228ns+j6GZzaGSbNswjIIyfJv31X7aLwAAAABMFn2JdGEJvmr3QikowRdXNJ4svq6dLQMqY3qUlF+NZEBhrAoyoBLqT0yOXjy1rCeeVousgWpPfVuVZlOEYcgMFmaB9td4+cf1ewqzvNZPpj5Q9hJ8iqiNDKhxIQAFRwoHbAGolHsCUPb+T+1T6uTzlu5XrVgfKAAAAABAbehNZNTo8BJ8kpQbbQ+UlDUAFVVIkkEJPoxdMFIwlIoXlniEu/TE05piWANQvkhrlWYzDHsQXmMoQ+pSG4oEm4qN1aRcrrAEn9mg1noyoMaDABQcKWTLgEq4KQOq03qn2pzWupJuf+EMa8o5JfgAAAAAoHb0JTJqkrNL8EmSYevZMixbBlS/wpJECT6MXaAwAJWOTZIL4jWsJ55Ws2E9TnjrnRWAMuqaLctNRlS9oy1D6kLRZEbbDhYGd/+8t7fmSw9KkpI9kmn9ObvMBnpAjRMBKDhSyG99a7opAGXPgCpV/6dBC6ZbM6B2HopS8xgAAGCS2rVrl+644w4tWLBA9fX1mjJlipYuXap/+7d/UywWO/YGRvDwww/LMIxR/Xn44YeH3c5FF1006u0AGCjBV9ADqtoZUP46mR5rxpI/1atczjz2a209oKLmQIl6SvBhzGw9oCQpHeOmXLfriaU1xVaCT3UO6gElySjIgIrVdAbUn/f2yixyeI+ns3rjYLTwiVpjK78nDZbgIwNqPAhAwZHc3ANqxyHrif7cEmdAnTK9QZ4h5+amKW3ZzxcuAACAyeapp57S2972Nt19993asmWLYrGYurq6tHbtWn3+85/XWWedpW3btlV7mgDGqC+RUaM9A6raPaAMQzlbD5QGRRUdzc2QSXsJvoEMqIYQGVAYI69facOagZBLcD3E7YplQKnOWRlQ9mNwk1HbJfjWd3SP67maETtkWYybASUUpAfUOPFpD0dycwDKngE1p620GVAhv1cntNVb7jjYtK9PZ81uKel+AAAA4Fzr1q3Tddddp3g8rkgkoi9+8YtasWKF4vG4HnvsMX3/+9/X1q1bdfnll2vt2rVqaCi8a3wsfv3rX2vmzJnDPj9r1qxjbmPJkiV66KGHJjQPYDKIJeKqN5LWwWqX4JOkcJMU78wvNh3pgXLMXk62HlD9ZkgeQ6oPcEkKY5fy1MmfTeWXc0kCUG7XHUsVZkCFnZUBZT8GDxz/arcE34Y9w5e23LCnR9cuaa/gbKrAlgF1WAPfo1vJgBoXPu3hSKGArQdUyh0BqO5YSt0x6wdQqUvwSdKCGY2WANTm/aNs/goAAICacPvttysej8vn8+mZZ57R8uXL88+9853v1Mknn6zPf/7z2rp1q+666y599atfndD+5s+fr7lz505oG/X19Tr99NMntA1gMjDj3YWD1S7BJ8kTtt70ONgDZeaRjKZh2QIEUYUVCfrk8VB2E2OX8tWpPtudXzaThX1q4C498YwLMqCaLYtNRlRd8RoOQHUMH4BaP8JzNcOWAdVtDvSfowTf+FCCD47k1gyoXbbye16PoeObj/FlfBxOnWHtA7VpHwEoAACAyWLNmjX6/e9/L0m6+eabLcGnQXfccYcWLlwoSbrnnnuUTtfuRRKg1ngS3YWD1S7BJ8komgEwmhJ89gBUSI1h+j9hfDI+202+ZEC5Xl80pgYjbh10XADKegxuHO3xz4V6E2lt7xy+z9PGfb1KZ3MVnFEVxG0ZUOZgBhQl+MaDABQcya0BqJ228nvHN4cV8JX+12zhDGsJlc37+mQW6w4IAACAmvPzn/88//imm24quo7H49H1118vSeru7tazzz5biakBmCDTNOVNWS+o57xByR+q0oyGsGUANBqx0ZWgKlKCr/FYZfuAYWRtAShPevgL5XCHdH9n4WCdw0vwGVH1JWszAPWarfyez5atmsrktPVAjQd+bRlQXWpQwOtRQ5BicuNBAAqOFLaV4Iun3BFZt2dAzS1x/6dBC6ZbM6D6khl1dMWHWRsAAAC15Pnnn5c0UNJu8eLFw6534YUX5h+/8MILZZ8XgIlLpHOKmNaATS7YXJ3J2NkyAEafAWX9eaIKqyHERTyMTy5gD0BRgs/1YtYAlCnDEWVHLewl+Gq4B5S9xN6pMxs1p7VuxHVqjq0HVJcZUVskIMOgdOx4EICCI4VsGVAJl2ZAzbUdoEtlRlNITbaSBZThAwAAmBw2bdokSZo3b558vuEv4i5YsKDgNeN10003aebMmQoEAmpra9O5556rf/iHf9CePXtGvY3NmzfrnHPOUXNzs0KhkGbNmqV3v/vdevTRRykRCBzRl0irSbaMDtud91VTJAOgdzQBqGIZUJTgwziZAWtFGF+GDCjXi3VZFlO+BsnrsCC17fjXaMRGd/xzIXv/p4vaenXhNOtN77UfgCrMgGql/9O4Oey3GRjg2hJ8thqpc1rLkwFlGIYWzmjQH7Yfjchv3t+nS0+bXpb9AQAAwBkSiYQ6OwfuFJ41a9aI67a0tKi+vl7RaFS7d++e0H6fe+65/ONDhw7p0KFDeumll3TXXXfpW9/6lj72sY8dcxsHDhzQgQMH8st79uzRnj179OSTT+pf//Vf9cQTT+T7Vo1VR0fHiM/v27dvXNsFKq03kVGjYT2v9ISr3/9JUmEJPkX15mgyAIpkQFGCD+NlBCKWZX8mNsyacAtvwpptkg62yHGX+ov1gIrX5s0z6/d05x9/wfd/devmpyRJ9d4P6N7sVZKkDUPWqUlxa1C0y2yg/9MEEICCI4X91uS8eModAaiCEnxlyoCSBsrwDQ1AkQEFAABQ+/r6jtbcj0QiI6w5YDAA1d8/vhJFJ554oq655hotX75c7e3tkqTt27frv/7rv/TEE08okUjo4x//uAzD0C233FJ0Gx6PRxdffLEuu+wyLVq0SK2trerr69Orr76q++67T5s2bdLGjRu1YsUKrVmzRrNnzx7zPAfnBrhdXyKtRtkDUC1Vmo2N/QKsMcoSfLaeVlGFKMGHcTOCtgBUlgwot/Mnuy3LuZBDjnlD2QLwQSOjZKL2yj92RVPafXgg22maunSL9+n8c5/xPaH/yF6iftVpy/4+JdLZggpWNcOeAWU2qI0MqHHjEx+OVNADygUZUL2JtA5FU5axcmVASdKpM6x9oAhAAQAA1L5EIpF/HAgc+07MYHDgZDkeH3u/0Kuvvlo33HBDQb37pUuX6rrrrtPTTz+ta665Rul0Wp/97Gd11VVXafr0woz8n/70p2pubi4Yv+CCC/SJT3xCH/3oR/XII4/owIED+sxnPqOf/vSnY54rUCv6Ehk1GS4pwTfaHihJewm+sGZSgg/j5A1Zr4WEcmRAuV0o3S0NuQxo1rVWbS7DKnYjQLz2ytBt2HP0Z1rq2SKvYeaXg0ZGZ3re0PO5M5TOmtqyv0+L2purMMsKsPeAUkSnkgE1bvSAgiO5sQfUm7bsJ8OQ2qeEy7a/BTOsdY93HY4pmqzN+rMAAAAYEAqF8o9TqdQIaw5IJpOSpHB47N9Lm5qaRmy2fMUVV+jLX/6yJCkWi+nBBx8sul6x4NMgv9+vBx54QKeccook6Wc/+9mY+koN2r1794h/1qxZM+ZtAtXQl8gU9oCy3XlfNbZ5NI06A8pegi+kRjKgME7esPVaSNiMK5XJVWk2mKhEOqsG03pDtafegQGoYGPBkJHorvw8ymxoAGqJZ0vB80PH1u+pvQCcJMk0pbg1AHXYbFBbPRlQ40UACo7kxgDUzkPWk4SZTWEFfeVLRZ1/XIM8Q64HmKa05UDf8C8AAACA6zU0HL3wNpqyetHowHfU0ZTrG49bbrklH6RatWrVuLbh8/l0880355fHs51Zs2aN+GfGjBnjmhtQab2JtBoNW0ZHyCE9oGwZUI2KqTd27EB4QQ8oM0QPKIybL2wNBNQbCcVS3IzrVj3xtFoM67Usf6StSrMZgdenrN/6XcqbrL1KROs7uvOPz/a8XvD82cbRsQ1D1q0pyV4pZz2mdJsRekBNAAEoOFLY774SfPb+Tye0la/8njQQpDtxqvXDjzJ8AAAAtS0UCqm1deDO4I6OjhHX7erqygegytUjadq0afn5jCdzadCpp56afzyR7QBu15dIF2ZAOaUEny0Q5jNySsePcRNkLielrT9Pv8JqDJMBhfEJ1FkzoCJKKOqSvuEo1BNPq0XWIHWg0YEBKEm5oPUYGMr2Kp2trey7DR0DWU1hJXSasbPg+bM82+TRwM+8vqNGM6Bs/Z8k6bDoATURBKDgSAU9oFzwZWJHp/VL9ZzWurLvc8F06xevzfvIgAIAAKh1g8Gabdu2KZMZ/q7vzZs35x8vXLiwbPMZqUxfJbcB1IKiPaAcWoJPknSsElSpwkzNqBlSAxlQGKdAvTUIUK+4YrQjcK3uWFpTbBlQjizBJxXtg9c/mjKkLnGwL6m9PQO9Rs/0vCGfURhcazDiWmC8KUl6/a1+V1yvHbNYl2UxafoVV5AMqAkgAAVHsmdAJdLOv6Ngl60E39zW8mZASdLCGdbUczKgAAAAat/b3/52SQPl9V555ZVh1xtayu78888vy1wOHjyozs5OSdLMmTPHvZ2NGzfmH09kO4DbFe0B5ZQMqGCjTFmDxZ5xBKD6FaYEH8bNG7TeiFtvJNRPAMq1BjKgbDdTh6dUZzLH4Am3WJYbjdjo+uC5xGtDejoNLbVnN1iaL5sztbEWr0PaMqAOq0GSQQbUBBCAgiPZe0ClsjllHJ7WutNWgq8SGVCn2gJQm/f3KZczy75fAAAAVM973vOe/OOHHnqo6Dq5XE6PPvqoJKm5uVkrVqwoy1zuv/9+mebA988LL7xwXNvIZDL6wQ9+kF9+xzveUZK5AW400APKngHlkB5QHo+yAes5qDd1jCocySIZUAqpIUQJPoxT0NqKIKK4YrWYhTFJDPSAsh0n6pyZAeWpsx6LmxRVbyJdpdmU3tCSeks8W4Zdb+hzNdkHKn7YsthtDhxzptSTATVeBKDgSPYSfJKUyDg3ABVNZnSwL2kZm1vmHlCStGCG9c6f/mRGe7rjZd8vAAAAqmfZsmW64IILJEkPPvigVq9eXbDOXXfdpU2bNkmSbr/9dvn91myD5557ToZhyDAM3XjjjQWv37lzp9atWzfiPJ5++mn94z/+oyQpHA7rpptuKljn2WefVXd397DbSKfT+shHPpKf65VXXlm2flWAG/THU2qQ7ZzOKSX4VNgDxZ/pGfkmSFuAKmn6lZFPjWEyoDBOAet1kJCRViyeqNJkMFE9/VE1GtYbulXnzAwoI2TNgGoyojWVAbVhT7ckyVAun+VUzOIh2VHr99RgHyh7BpTZoOY6v/xewijjxS0ncCR7CT5poA9UJOjMt+wuW/aTJM2eUv4MqOmNITXX+dUdO3rHxcZ9vWqvwL4BAABQPffcc4/OP/98xeNxXXrppbrzzju1YsUKxeNxPfbYY7r//vslSfPnz9cdd9wx5u3v3LlTK1as0PLly3XllVdq0aJFmjZtmiRp+/bteuKJJ/TEE0/ks5++8Y1v6Pjjjy/YziOPPKKrrrpKV111lS666CKdcsopamxsVH9/v1555RXdf//9+fJ706ZN0z333DPefxKgJmTj3fIYtoCOU0rwSQNz6Xszv9ikqPpTmeFL6tkyoPoVkiQyoDB+tgwoSUrFarAM2CSR7jtUOOjQDKiCHlBGVH01mAE1z9irJntQcIh2z0FNU5feUos2dNRiAMqaAdWliFrJfpoQPvHhSMUCUIm0c1Oq7f2fZjSFCsoIloNhGFo4vVGrtx/9wN68r09/cdr0su8bAAAA1XPWWWfp8ccf14c+9CH19vbqzjvvLFhn/vz5WrlypRoaGopsYXRWr15dNMNqUF1dnb75zW/qlltuGXad/v5+/fjHP9aPf/zjYdc544wz9Nhjj+mEE04Y91yBmhAvcjHPKSX4JHlsF2AbNZABMGwAytYDKmqGFPZ7uZMc4xcoDEAlCUC5Vqa/s3DQ1mvJMWzH4kbF1FsjGVAHehN660hlp8WerdYnG4+Xkn1S8ujv2WLPVv0qd462HexXNJlRvUMTBsbFlgHVZTbQ/2mCaujdgVoS9BV+GY07OABl7/80t7X85fcGLZjRYAlAbarFBoAAAAAocOWVV2r9+vW65557tHLlSnV0dCgQCGjevHm69tprddttt6mubnyZ8YsXL9YPf/hDrV69WmvXrtW+ffvU2dmpTCajlpYWnXbaabr44ov1kY98JJ8ZVcwXvvAFnXnmmVq9erU2btyogwcP6vDhwwoGgzruuOO0ZMkSve9979PVV18tr7f8N3ABTmckrQGonOGVp8gF92rx1jVblhuN2JEMgHDxF9gyoKIKqzHMpShMQJHfh2yc6yBulbNlmyS8EYW8Di3RaSuH2mhEtadGMqCs/Z9sAajZ50rxbumN/7as86vcOTJN6c97e7XsBGeWTRyXeGEGFAGoieFTH47k8RgK+T1KpI/2fYo7uKnkzk5rBtTctsqVwFs4w9oEdtN+vngBAABMFnPmzNHdd9+tu+++e0yvu+iii/Ll84ppaGjQBz/4QX3wgx+c0PwWLlyohQsX6jOf+cyEtgNMFh5bACobaJLHMKo0m0KGvQSVouqNj5ABYOsB1a/Q8NlSwGh4PEoYIYXMo32fMom+EV4AJzPi1myTpL/5SKFOByp2/KuRDKgNHd35x2cbtgBU+7lSotsSgBqaJbW+o7u2AlD2Enxmg1ojlOCbCHKe4Vj2MnzOzoCyBqDmVDAD6lRbAGrXoZiiydr4AAQAAACAycI0TflT1hsKc0HnlN+TVJABcMweKPYMKDNM/ydMWMprvenXJAPKtTzxbstyOujQ8ntS0QyoWukBtX7PwM0PrerRiZ791ifblw38GeI0Y6dCGijZt2FPjfWBKhKAIgNqYghAwbHsPZSc3QPKXoKvchlQ86ZF5PVY74jbvJ+7fwAAAADATeLprCKy3tzopP5PkopmAPSNlAFg6wHVr5Aaw2RAYWJSXutNvznb+wzuEUh1WZZzIScHoKzH42Me/1zCNE1tOFKCr6D/k79eOu506fglknE0jOA3slpkbJek/Gtrhr0HlMiAmigCUHAsewaUUwNQ8VRW+3sTlrFKZkCF/F6d2GbdH32gAAAAAMBd+hIZNcl6Id1j67lUdbYLsEd7QA2jaAYUAShMTMZnvenXSHITrluF0t3WgToHl3KzBeDrjaSi8UTxdV1kb09Ch6IpSdLZntetT85aLHl9UjAyEIgaYjBYtb0zqt4ayQSTaRb2gDIjaq0nA2oiCEDBsewZUE4twffm4VjB2JwKZkBJ0gJbGb7N9IECAAAAAFfpS6TVZFgzoLx1DssGsJfgO1YPFFsPqKhCaqQEHyYo64tYlg0yoFzJNE3VZazZM5761irNZhRsxz9JytnKtbnR+t3d+cdL7BlQ7ecefTz7XMtTQ7OlXquVLKhUv5RNWYYOq0FtZEBNCAEoOFY4YAtApXJVmsnI7P2fjmsMqi5Q2S/UC2c0WJY37ePuHwAAAABwk95ERk22EnwepwWg7CX4jGOUoEpSgg+ll/Vbq8B40tFh1oSTRVNZNcl6/crX0Fal2YxCkZKoZtz9gZfB/k8BpXXGkbJ6ee3nFH+sgWwpQznLNlzPVn5PkrrNCD2gJogAFBzLXoLPqRlQu2wBqEqW3xu00J4Bta9XuZxZ8XkAAAAAAManL5FRo2GrsOG0HlC2/iyNio5cgi9VrAQfGVCYGDNgzYDyZQhAuVFPPK0phjUAFWx0cADKH1LWYw1EGMnu6sylhAZ7OJ1u7FDQGHpDgSHNWnJ00RaAajH6daKxz7IN17NltKVMr/oVpgfUBBGAgmPZS/A5tQfUjk7rCcLcCpffk6SF060BqGgqq46ueMXnAQAAAAAYn75EuiADqljJp6qyBcRCRlrx2AgX/+09oBRSIz2gMFEEoGpCdyylZlvfu2DDtCrNZnQyQesx0Jt0dwsM0zS1vqNbkrWkniRp2kJr1mvTLKlhpmWVwdes39NdvklWki0A1a0GBXxeRYLcODERBKDgWIUl+JwZgHJCBtRxjUG11Fm/xG/c5+4PQQAAAACYTPoSmYIeUPaSd1VXZD65WNfw69syoPrJgEIJGCFrG4JAtrA3N5yvWAaUo3tAScrZAlD+tLuvvb15OJbv41fY/8ma8STDkGZbx5YYA6/ZfTiurqi1d5Irxa0BqMNmg6ZGgjIMo0oTqg0EoOBYYb/17encEnz2DKjKB6AMwygsw7ff3R+CAAAAADCZ9MbTanRZBpQkmYnu4ddPWi8uR+kBhRLwBG0BqBwBKDfqi8bVKNv/Xd2U6kxmtGzHwHC2T+msM3vWj8b6fOk8U2fbA1Czzy18Qbt1bGjW1IZa6ANl6wHVrQjl90qAABQcyw09oBLprPb2WEvdzalCCT5JWmArw7eJDCgAAAAAcA1X9IDy+pXxhi1DRmKEi472DCiFKcGHCfOFrQGoUC4m06QPttvEeg7JY9j+3+qcnQHlsWWBDvTByxRf2QUGg0ZzjAOaatiuI7YvK3yBbewkzz61qNeyLVeLFWZAtdYTgJooAlBwrJCtBF/CgSX4Orpisn/HmdtW+QwoSVo4w/oFbNO+vmHWBAAAAAA4TV88VdgDymkl+CRlAvYeKMNfdDTtPaDMkBopwYcJ8oWtN+DWK6Fkxr1ZKJNVqq+zcDDs7Awob32LZbnJiKovka7SbCZusP/TYCm9vPppUssJhS+Yfobkt954v9jzumVbrmbLgOoyG9QWCVZpMrWDABQcyw0ZUDs7rXentUWCVWtMZy/B9+bhmPqT7r0LAwAAAAAmk2S8T37Ddt7rtBJ8knKhUfZAyaZlZJOWIUrwoRT8ddYbcOsVV8yBNy1jZJl+awAq7qmTfM7ONvHW2QJQLs6AyuVMvbZn4Pi9uKD/07KBnk92Xr90/GLL0OBrN3TUQAaUrQdUlyJqJQA1YQSg4FiuCEAdst6dNrdK5fckad60iLwe64fDFvpAAQAAAIArZGNFLt45rQSfVBAUC2b6lM0VKX+WLKzK0W9Sgg8TF6y3/l7UGwlFuQHXdXK2cmcJnwOPdzaG7fjXaMTU69IMqB2Hovkb1wsCUMX6Pw1qP8eyOPjavT0JHexLFnuFexTNgHJ2UNQNCEDBsUK2AFTCBQGoOa3VKb8nDfx7nTTVuv+NlOEDAAAAAFcw412Fgw4MQBlFeqAUrb5h6/8kSUlPWCE/l6IwMQFbCb6I4oqmCEC5jRG3XuxP+purM5GxsB3/3JwBNZix1KioTvF0WJ+0BZlGem6RsV1+DfwbvOb2PlAx6+dwlxmhBF8J8KkPxyrMgHJePd9dh6wl+KqZASVJC6Zbv4Rt3kcGFAAAAAC4gSdhvXCX8jVIHu8wa1ePz16CargeKMnCAJQ31CCjWFknYAw8IXsJvoSiLg0CTGa+RLdlOR1sKb6ik9gyoAaOf+58760/EoA6+0gPpzxvUJqxaPgXti+1LAaNtE43dli26Vq2DKjDalArGVATRgAKjhUK2DKgHFjPtyADqq16GVBSYR+oTQSgAAAAAMAVvKluy3I20Fh8xSrz1Y+yB4otAypqBhUJcyEPJRCIWBZ9Rk7xWHSYleFU/qQ12yQXdkEAqkgGaNEAvAts2NMtSTrbXn5v5lmSb4Ssn3CLNHWBZWhwG4PbdCXTlGnrAdVtNqi1ngyoiSIABcdyeg+oVCanPV1xy1i1M6AWzrDeBbR5f59yxWpxAwAAAAAcxZe2llDPBZ1Xfk+SDNtF4kYjVjwAZesBFRX9n1AiwYaCoVSxHmpwtFC62zoQnlKVeYyJrSyqWzOgsjlTr+0ZuGl9iWHv/zRC+b1BtjJ8S44EoFydAZWOycgkLEOHRQ+oUiAABcdyegCqoysme2ynmj2gpMIMqFgqq91dsWHWBgAAAAA4gWmaCqStFSxMW6knxyhyAbY3XiQDwJYB1W+G1Bj2lXNmmCxsGVCSlIpRAcZt6rLWYIUn0lalmYyB7bjcoLh6Y8nqzGUC3jjYr3g6K58yOtPzhvXJ9nOPvYHZ1nUGAlCm3upL6kBvovhrnC52uGCo24xoSj0BqIkiAAXHCgesb0+nleCz93+aUh9QU7i6d3NNawgWHBgpwwcAAAAAzhZLZdUoawkxT11zdSZzLLYSVE2Kqi957B5QUYXUECQDCiXgDytnu6SZjnPtw01yOVORnPX/zB9prdJsxsB2/PMYpjIuzL4bzFRaYLypOsMWQGtfduwN2DKgpho9ajfesmzbdWz9nzKmR95wk3xewicTxb8gHCvk8AyoHZ22/k9VLr8nSYZhFJTh27Svb5i1AQAAAABO0JfIFASgvHUO7YdiywBoHK4Elb0HlMJkQKE0DEMJI2wZysa59uEmfYmMpsj6fxZsnFql2YxBkczUbLyrcD2H29DRLelo6by81nlS/Sgy0aacKNVZ1xss5Te4bdex9X/qUkStDaEqTaa2EICCY9lL8GVyptLZXJVmU2jXIevJwdwql98btGC6tQwfGVAAAAAA4Gx9ibQaDes5pr/eoQEoWwZAo0bXA6rfDKmBHlAokaTXehNwLkkAyk264ym1GNb/s3CTCwJQgXrlDOv1SjPeXZ25TMD6PQNZSovtAaj2UfR/kiTDKFh3cFuD23YdWwm+LpP+T6VCAAqOFQ54C8aclAW101aCzwkZUFJhH6hN+wlAAQAAAICT9SYyanJLCT5bD6gGI66+eLxwvWIZUASgUCIpewAqQQDKTXqiiYJjnisyoAxDKb/1upuRcFfAJZ3NaePegWuF4w5ASdLsYQJQHT0yTXNCc6wKewBKDWqNBKs0mdpCAAqOZc+AkpzVB8qpGVD2Eny7D8fVlyhSjxsAAAAA4Ah9ibSabBlQxUo9OUKReWWi3YXr2XtAmSFK8KFk0l7bNRjb+w3OFu09JK9hDVIYdS7oASUpE7AGoLzJ7upMZJy2HuhTMpPTDB3STMMadBlTAMq27ilGhxoU0+FoSnu6i9yU4HS2HlBdZoPa6smAKgUCUHAsew8oyTkZUOlsTh1d1oOpUzKg5k2LyOcxLGNb9nMnEAAAAAA4VV+RDCjHBqBsJfgkKRfrLlzPlgHVrzAl+FAyWb81AGWkCUC5Sbz7YOFg3ZTKT2QccgFrFqgv7a5rbhs6BjK2lni2WJ8INUtt80e/oRlnSt6jARqPYeosz+uWfbiKvQeUGVEbGVAlQQAKjhX0eWRY4yhKpJ3RA2pvd1yZnPVOjRPanJEBFfR5ddLUiGWMPlAAAAAA4Fx9iYwaDWuZ92KBHkfwhZQ1rIGkoj1QbD15omZIjSEyoFAa9gCUJ0UAyk1SfZ2W5YSCkj9cpdmMUdgagAqk3XXNbbBH09lHgkV57cskzxhCBf7QQBBqiMVHtunKPlD2DChK8JUMASg4lmEYCvmsWVBOyYCy939qCvvVXOectMwFtjJ8m8iAAgAAAADH6kuk3ZMBVbQHSnfhegUZUCE1hsmAQmmYAeuNt95MdJg14UTZfuvF/n5v0zBrOo8RbrEs1+f6lMo444b50Rg2A2os5fcG2ftAGVss+3AVew8oM6LWiHOu9boZASg4WjhgC0A5pAfUzk57/ydnlN8btHCG9WSADCgAAAAAcK5oLKqwkbIOOjUDSlLGVoLKkyy82Gjae0AprAYyoFAi9gCUnwCUq5i2bJOEzz0BKG+dNQDVpKhreq8nM1lt3t+rOiW00HjT+uTsc8e+wXbra87ybJNXWa3v6JZpmsO8yKGKZEBRgq80CEDB0cK2PlAJx2RAWb/YzGl1Rvm9QfYA1Jb9fcrlXHbgBwAAAIBJIh3tLhwMOfeCbM42N3+q8KbHXNESfGRAoTSMgLXyiz8bG2ZNOJFh67eTDDRXZyLj4K+3BqAajZj6EpkqzWZstuzvUzprapHnDfmMIVlbHp808+yxb7B9mWWx3khqgbFbvYmM3jzsrt/JXJEMqDYyoEqCABQcLeS3vkWdUoJvl60En+MyoKZbv4jFUlnXHfgBAAAAYLLIxboKB51agk+SgsfugWLPgOpXmAAUSsYTsl73CBCAchVvwnqxPx1sGWZN5/HVF8uAckcAav1g+T3DVn5v+tukwDiubUamSVNOtAwtPlLab73byvDZM6BMekCVCgEoOJpjS/A5PANqakNQrfXWKD1l+AAAAADAmcx4t2U57QlJPufeeW3UNVuWw7l+ZW1VNwx7CT4zpAgl+FAi3rC1BF8oF6/STDAe/lS3ZTkXmlKdiYyDYSuP2mS4pwTfYG+mxZ7XrU+Mp//TMK8d3PaGPS4KQKXj8mSsx5Cot1H1tuvSGB8CUHA0ewk+J2RAZXOmdtuyiea2OSsDyjAM+kABAAAAgEsYiW7LcsrfWHxFh7D3QGlUv/qHZgCYpjxpawDKDETk9RiVmB4mAV/I+jsSMmPu6zkziYXT3ZZlo849ASh7dmqDYup1SwbUnh4ZyulsewBqdikDUFsH9tXRPf5tVpqt/J4kGfVtMgw+s0qBABQcLeTAHlB7u+NKZ61fauY6LANKkhbYyvBt2t83zJoAAAAAgGry2HooZQLODkD5660XixuNmHqHZgBkEjJM2/l70JqxAkxEoM5aBrJeCUfctIzRqctYj3meSGuVZjIOth54bsmASqSz2nqgTycbe9Ro2EpWljADapbRqek6pNf29LqnH72t/F7WNBSKuKcspNMRgIKjFWRAOaAEn738XkPQpyn1ziuNQAYUAAAAALiDP2UtVZSz9VhyGn99s2W5oAeKrfyeJHltPXuAiQjUWd9PESOuaLL614wwOg056zHP39BWpZmMg70En6Lqizs/ALVxX6+yOVNLjmQo5TXNlhpnjn/DUxcUBOUWe15XfzKjHbZrqI4Vt2ZAdSuiKQ3hKk2m9hCAgqMV9IBywN0sOw9Z7xKY01bnyJRMewCqoytuvSMNAAAAAOAIgbS1YoVpK/HkNEbYemd4QQZAqrAChzdMAAqlE4wUZkBFk+4ogzbZpbM5Ncp6jAg2Tq3SbMbBdnz2G1klYs6vOnS0/5MtADWR8nuS5PFIs5ZZhgb3MbhPx7NlQHWbEbVFnJds4FYEoOBoTuwBtavTGr2f48Dye5J00rR6+Wz1tbdQhg8AAAAAHMU0TYXs5ahsd9g7TpEMgN4RMqCypqFQiBJ8KJ1A2HrTbcRIKJpMVWk2GIveWEotsh4jwk3TqjSbcShyfE5Huyo/jzFaPxiAMmwBqImU3xtmG0f7QLklAGXNgDqsBrVGglWaTO0hAAVHc2IPKHsG1NzWuirNZGRBn1fzplm/4FOGDwAAAACcJZrKqkHW80xvXXN1JjNatnJLjUbMlgFlvbgcVViNddxNjtIxgoUZdW7IQoHU23NIPiNnGatvcVEAKljYoy9rC2A40YY93WpTj+Z6DlifKEUAypZFdZqxU2EltGFP98S3XQm2/79us0GtDmy34lYEoOBohQGo3DBrVs6uQ+7IgJKkBdOtX8g27ePLGAAAAAA4SV8irSbDep7pr59SpdmMkq0EVaOi6osPyT6xZUD1K6SGkK8CE8OkUSwA1e+SbItJLtb9VsFYsMFFJfg8XiW81hu+zXh3deYyStFkRtve6i8svxeISNNOnfgOjl8sGUev4fqMnBZ5tuu1PQN9pxzP1gPqsNmgqQ1kQJUKASg4WkEJvlR1M6ByOVO7DtszoJwbgLL3gSIDCgAAAACcpS+RUZNsAaiIwwNQthJUXsNUMjrk4r+tB1TUDKsx5K/AxDBpBApLOqZjXPNwg0TPQeuyAlLAmdWFhpP2W6+3GQlnBz837utVzizS/2nWEslbgpsDAvXS9DMsQ4uNrYqns3rjYP8wL3IQWw+oLjWotZ4AVKkQgIKjhQPWt2i1e0Dt700olbFmYc1tc+6HpD0AtWV/nzvuPAAAAACASaIvkVajLQPK+SX4mguGMtEhd5An7SX4QmoMkwGFEvIFlJb1PZWOE4Byg1Rvp2W5zygsaed0mYB1zp6kswNQ+f5P9gBUKcrvDbMtN/WBytlK8HWZEbVGKMFXKgSg4GgFGVBVDkDt7LSeFNQFvJrq4KZ0C2ZYU9Lj6azetGVwAQAAAACqpzeRKSjBVyzA4yiBiHK2S0qZ2NAMKFsJPjOkBjKgUGJxj/WG4EyctgNukIlas02iXvcFoLJBax88f8rZwc8NHd0KKqXTjR3WJ0oZgJpdGIAylNOGju7S7aNMsv3WoOhhNajNwdd73YYAFBytsAdUlQNQh6zBmzmt9TIMo0qzObZpDSG12SL2lOEDAAAAAOfoS2TUaCvBZy9x5zgej5I+ew+UrqMLBRlQlOBD6SVtAahcggCUG5hR68X+mL+5OhOZCNtNAv60s6+1re/o0enGDgWNzJBRY6AEX6nYgllNRkwnGXv1JxdkQJm2DKgeRdRSx2dWqRCAgqOFA87qAbXrkPWkYG6rc8vvDaIPFAAAAAA4V388oUYjbh10egaUpFRBD5TuIU9aAwH9CqkhRAk+lFbKa+3JbSYJQLmBMTRYLSnlbxpmTecybDcJhLLOfe/1JtLa3hnVEnv5veNOk0Il/LdvmiU1zrIMLfFs1cZ9vUpnc8O8yBk8cWsAKh2cIp+XsEmp8C8JR3NcCT5bAGpOa/0wazrHgunWMnyb9jn3QxEAAAAAJptkf3fhYCkvCpZJJmCdoyc55GZHewaUGVZjmLvJUVppr/WmYAJQ7uBLWC/2Z4ItVZrJ+Nn79NWb/QU9453itT0V6P80yF6Gz9iqVCanrQcc/LuZScqXsV7vNcJTqjSZ2kQACo5mD0BVuwTfLlsJPjKgAAAAAAATkYkeLhx0egk+SbmA9VzTlzpaZilrK4UWVUiNZEChxDI+603BRio6zJpwEn+q27KcC7nvYr+v3ho0a1JUfYl0lWYzsg0dPZJMne153fpEOQJQ7YV9oI7OwaFihZ/BvkhbFSZSu/j0h6OFHFSCzzTNggyouW3Oz4CyB6D2dMfVm0hTfxsAAAAAHCATtZajyhg++fzOv9nRtJUJDAzpgZKJ92no2XyfGVYD56Aosazf2ofMk+4fZk0XW/cjadXXpL791Z5JyZydtQZqjHr3BaCCkVbLcpMRVW8io9ZIsEozGt76PT06wdivNsN2Q/rs8gegTvTsV6t6tH5Pjz5Q+r2Vhq38Xs40FGx033vSyQhAwdGKleAzTVOGYVR8Lgd6k0qkrem0c11Qgu+kqRH5vYbSWTM/tnlfn5adwMEUAAAAAKrNjHdblpPeBvmqcM47ViP1QMklrRc6owrTAwolZwas12Q86RrLgDqwUXryNsl0Zmm38bKX4/LUtxZdz8nsGVCNijk6A2qZvfxe5DipeU7pd3bc6ZK/Xhryu3i253Vt6Jhd+n2VSuyQZbFH9WptcP5NIG5CCT44mj0AlTOlVJUa19mzn0J+j6Y1OO/OBruAz6OTplrvCqIMHwAAAAA4g5GwliZK+RuGWdNZ7D1Q6nL9yhw5XzdtPaASnrBCtvN7YKLMgPVah6/WAlAvfrvmgk/FtLQvrPYUxswegG8youpLZKozmRF0x1J683BMi40i/Z/KcaOD1yfNWmwZWuzZqs37e5XMVLetyrBsJfi6zIjaIoEqTaY2EYCCo4UDhV9Q7VlIlbLLFoCaM6VeHo/z70qTpFNtZfg27ycABQAAAABO4ElaA1CZQFOVZjI2flvZrCZF1Z88cgHWFoAybaXSgFIwgtb3lT8bG2ZNF+rZI234SbVnUXZr6i7UiYveUe1pjJ2tBKlTe0Bt2DPw+bLYngFVjv5Pw2x7sWer0llTW/b3DfOCKrNlQHWpwZGlFN2M/Gc4WshXLACVVVO48rWjdx6yfpGZ0+qedMwFMxqkdUeXBz+AAAAAAADV5UtZz8+yQXcEoAINtgDUkQyA5rpAYS+eIAEolJ4RtGYLBrM1lAH10r1SbkhGjS8k/e3PJX+4alMar2Qmp//5i9f0573Wm6GnTpuh79x6VXkyccrNlgEVNlLqjzovALq+o0dN6td8zx7rE7PPLd9O263bfpuxQwGltb6jR2+b1Vy+/Y5XvDADqrWeDKhSIgAFRwsFCpP04qnqpGzaM6Dmtjm//9OgU2dYT2Be29Orl3ce1tK59IECAAAAgGoKpG13hdvurHeqQKQwA6r3SAaA114KLeiOsoJwF1/YWu0lmHNeAGBcEj3S2oetY2d+UJqzvCrTmYhMNqdP/vAV/XZPq6SjvZ7mttbp4Y+cp4ZQ5W8wL4lQ4Y0Cyf6uKkxkZBs6enSW53XroC8kTX9b+XY6a4kkQ9JAL/qgkdbpxg5t6DixfPucADN6SENDoF1mg05yQcsVN6EEHxwt4PXIXuUunq5OAGpnp3szoJbMbVGbLX30G7/eItM0qzQjAAAAAIAkBbPWrACjyIVNJ7LPs9GIDfRAyeUKSqF5yYBCGXhD1sBmyIxXaSYl9srDUmpoYNqQln+yWrMZN9M09fc/3aDfbnrLMj6tIaj/uPkcTXXzRf4iNwpkoocL16uyDXt6tMRefm/m2ZKvjBk+4WZpmrWv12LPVq13aDWmTLSwBF9bvYvfmw5EAAqOZhiGwrZGpdUIQJmmWZABdUKrezKgQn6vbltxkmXspR2H9eIbh4Z5BQAAAACg3EzTVF3WWq7OU9dSpdmMka0EVaOi6ounJXv2kyRfXWPBGDBRftv7qs6MK5tz+Y22mZT0h3utYwuvlFpPKr6+g33tV5v1xCsdlrHGkE+P3rxM7VPcc1N3Ub6AUkbIMpSNOSsDqrM/qT3d8cIA1Owy9n8aZOsDtcSzVVsP9ClRpaSCkWT6Oi3LXWaDWiOU4CslAlBwvHDAGoBKVKEE38H+pKK2/c5xUQk+Sfrrc2ZrZpP1w/Ebz5AFBQAAAADVEk1l1ShrwMYfcUmpdFsGQNDIKNrfJyX7C1b1hwlAofQC9dYsvHojoVgqM8zaLrHhJ1LfPuvY+bdXZy4TcN+qN3Tf77ZbxoI+jx68cakWTK+N40HCZystGndWAGrDnh75lNEi4w3rE+2VD0Cd7dmqbC6njft6h3lB9eRsGVBRb6Pqg3QtKiUCUHC8kAMyoHYdspYPCPg8mtEYGmZtZwr6vPrUxSdbxta92a3/t/mtYV4BAAAAACin3nhajYY1ABWod0sGVOE8U/2HpFRhACpY546ygnCXkC0DKqK4YlXqG14Spim9+B3r2OzzjvTUcY+frN2tf/nVZsuY12Pou39zdk31Ik/5rQEoM+6s4MqGjh6dauxS2EhZn5i1rPw7t2VZTTV6Ncc4oA0dzivDZ8StpRMzIZd8BrsIASg4nhNK8O3stJ4QzJ5SJ4+9OZULvG/xLM22pTnf9cxW5dyeog4AAAAALtSXyKjJlgEVbHTJBdpgYRZDOtotJfssY0nTr/o6d93ACXcI2jKgQkZa/fFElWZTAq//Rjq4yTrmsuyn3248oL//6YaC8X9979t0yanHVWFG5ZPxW99/3lR3dSYyjPUdPVpsL7/XerJU31r+nbecINVPtQwtNrZqvQMDUL6kNXPNDLvkM9hFCEDB8ewl+OJVuJvFngE1t9WdtWr9Xo8+c4k1C2rjvl79//68v0ozAgAAAIDJqy+RVpMtA8pbJLPIkbw+JTzWc+NsvKsgANWvkBrD/krODJNEoEhvsUS/8y5wj9qL37Yut50inXxpdeYyDmt2HNYnf/xqQR+uL122UO9bPKtKsyqfbNAagPKlHJYBtae7MABVif5PkmQYRftAbdjTXZn9j1Y2rUDGmrXrrW+r0mRqFwEoOJ69BF81GtbtPGQ9IZjT6q7+T0O9+8zjNW9axDJ292+2ur9RJwAAAAC4TF88rUZZb3hUuLkqcxmPhNdegqq7oARf1AypMUQACmUQiBQMJaMuDUDteVXa+Xvr2HmfkjzuuHS7aV+vbn7kZSUzOcv4xy48UR99x4lVmlV5mSFrACqQdk4A6kBvQgd6E1piD0BVov/TMPs62/O6tr3Vr2jSQX3aYocLhoKNBKBKzR1HMUxqTijBVysZUNJA3d2/e9d8y9i2t/r15J/2VGlGAAAAADA5xfp75DOsF2wVck+/JHsPFCV6pKQtAKWwGkI0dEcZFAlApWLOCQKMiT37KTJdetv7qzOXMdp9OKbrf7BGfQlrYOHaxbP093+5oEqzKj+P7WaBYKav+IpVsKGjR8erU9MNa3k5tZ9buUnMtu5rvtGhiBnVxn0O+h2NFwagwgSgSo4AFByvIACVyg2zZnmYplmQATW3zb0ZUJL0l6dN16kzrKnq3/rt60pnK/tvCwAAAACTWbK/8OKXQs0Vn8d4ZQLWYJkn2SOlKMGHCvF4FJe1v1g67qCL26N1eIe08RfWsXM+JvmC1ZnPGBzsS+pvH3xJB/uSlvFLFh6nf7nmDBmG+/qnj5anzlouNZx1TgBq/Z4eLfa8bh0Mt0it8yo3iRmLJO/R97DHMHWWZ5uz+kDFDlkWe806tTS6+5qvExGAguOF/Na3aSJT2Qyow9FUwV0cc11cgk+SPB5Dd1xqzYLadSimJ17pqNKMAAAAAGDyyUStd6fnZEjBwr42TlXYA6VHuYS9BB8ZUCifuCdsWc64MQPqD/+fZA65ITgQkZZ8uHrzGaXeRFo3PrRGO21Vg5bNnaJ//5uz5PPW9mVnX701ANVgRpWs8DXL4azv6NZizxbrYPs5lS3p6AtKM8+yDC32bNH6ju7KzeFYbCX4DpsNaosEqjSZ2lXbRwLUhHDAngFV2YO5/YPU7zU0oyk0zNru8c4F03Rme7Nl7Nv//XpVemwBAAAAwGSUjVovfsU9Edf0fJEkM9hsWQ6kewtKoPWLHlAon6TH2iIhm3BOFsqoRA9Jr/6HdezsGxzfCy6RzuqWR9fqz3utv+8Lpjfo+zcsKejnXosCkSmW5UYjWnADezWYpqkNHT3V7f80aLZ1n0uMrdrg4AyobkXUFnF+5qHbuOdbDSYt+4dWpQMku2zl99pb6mriLg7DMPQ/Lj3FMravJ6HH1rxZpRkBAAAAwORixq0X4pK+wp42TmaErRlQwXSf0rafKWqGKcGHsknZAlA5Ww8yx3v5ASkTP7pseKVzb63efEYhmzN1+2Pr9Ift1gB6+5SwHv3wMjVNkt/3UIM1ANXkkADU3p6EEtEeLTBs1/eqEYCy7fNMzxva1dmr3kS68nMpIhu1BqAOmw1qJQOq5Nx/FR01r6AHVIUDUPYMqDmtdcOs6T7nz2vVOSdYPzD//dk3Kp5lBgAAAACTUqLbspj0uaf8nlSkB0quX5m4NQMlppDqA7WfDYHqSPtsLRKSLsqASselNfdbx854n9TcXp35jIJpmvqHn2/Qr/98wDLeFgnoPz58jqY1ur9i0GgVZEApqt549QMrGzq6daZnm7yGmR8zPT7p+LMrPxlbAKrOSGqhsUuv7XFGFlSy96BluYsMqLIgAAXHKwhAVTg4Ys+AmuPy/k9DGYah//EX1iyozv6kHl29szoTAgAAAIBJxJO0XoTLBJqGWdOZAhFrACpiFgag0r56GYZRyWlhEsn4bDcJp1yUAfXHH0uxTuvYeZ+qzlxG6RvPbNH/XbPbMtYQ9Onhm5ZpblvtXC8blVCzZbHRiKsvlqzOXIZY39GjxcbrljFjxiLJHx7mFWVU3yZNOckytNjzumPK8KX6rL9/XWaDWurIgCo1AlBwvIIeUFXOgDqhxj5Ql86dogvnT7WMfW/VG+pzSDosAAAAANQqX8p6ES4XdFcGlD0DoElRZeLWnjBZf22dQ8NZsraylR63BKByWWn1v1vHTnqnNP2M6sxnFB58foe+++wblrGAz6P7r1+i0493V/C8JEKFP3Oi/3CRFStrw54eLfFssQ62n1udyUjSbOu+l3i2aL1DMqCy/dYSfAl/s7webpgoNQJQcLxq94Da2WnPgKqdEnyD7rh0vmW5K5bWD57fWZ3JAAAAAMAk4U9bs4VM2x31The09UBpNKLKJaw/U9bvrr5WcJdcwBrg9Kajw6zpMJtXSoe3W8fO+3R15jIKP1+3R//76Y2WMY8hffsDZ2n5Sa1VmlWVhZsLhpJ91Q1Amaap13Yf1pmebdYn2pdVZ0JF9n22gzKgjLj1/ysbbBlmTUwEASg4XjV7QHXHUuqx1W+dW0Ml+Aa9bVazLj31OMvYA7/fru5YqkozAgAAAIDaF8pYs4WMIhc0nSxYJAPKsGegBGrvHBrOYQasAU5vxgUBKNOUXvy2dWz6GdKJF1VlOsfy7Ja39D9+8qeC8X+++gz95enTqzAjh/DXKSOfZSgdrW4AavfhuI5L7lSjEbc+MbuKGVC27KvjjUNKH97tiGuO3oT1/ytXN2WYNTERBKDgeAUl+CrYA8pefs/rMXR8SxVqplbA3106X0PLcvclM7r/d9uHfwEAAAAAYELCOWu2kKfOXXdfG2HrfOuNpIIZ253twYYKzgiTjWF7f/mzLghAvfkHqeNl69h5t0sO7JX2yq4u3frDV5TJmZbxz/3FKfrAstlVmpVDGIZiXuv7Lxvrrs5cjli/p1uLPVstY2bzHKmhioHCtvkF2b2LPVu1wQFl+AK2Mrie+kmazVdmBKDgePYMqEQ6V7F97zpk/eIyqyUsv7c2f20WTG/UFW+baRl76IWd6uyvfgNFAAAAAKg1uZypupz1nNNX11ydyYxXkZKBzVnrHeXeEAEolI8RtGZABbKxYdZ0EHv2U1O7dNp7qjKVkWw90KcPP/xywXW4m99+gj5x0UlVmpWzJLzW918u3l2diRyxoaOnIABltJ9Tpdkc4fHIsJXhW+zZqvXVLsOXzSictWYh+yNtVZpMbavNK+moKfYeUJUswbez0/rFZU4Nlt8b6rOXnKyhvfbi6azufe6N4V8AAAAAABiXaCqjJlkDUIGIy8r/hJoKhryyXqz2hQlAoXw8tgyooNMDUAe3SFt+aR079xOS11+d+Qyjoyum6x9cU9CW4uqzjteXLlsow4HZWtWQ8jdalo0qB6DWd/RoibHFOji7ygEoSbIFwZZ4tlS/D1Siu2Ao3DS18vOYBAhAwfHsJfgSFQxA2TOg5rbWVWzf1XDi1Ijee/Ysy9h//GGX9vXEh3kFAAAAAGA8ehMZNRnWc85Qg8vK//hDSikw4iq+cGGQCigVX9gaAAiZDr9+8eJ3rMuhZuns66syleEc6k/q+gfXaH9vwjJ+0SlT9fX3vU0eD8GnQWlbAMqT7K7ORDSQVbt/z07N9hy0PlHtDKgic1hovKltHfurNJkjYocKhuqbp1VhIrWPABQcL+S3vk3j6axM0xxm7dLaWRCAqu0MKEn69MUny+89+mUilcnp3//ftirOCAAAAABqT18iXZABFWp0WQBKKuiBYhesaxzxeWAi/HXW91/YyQGovv3S+setY0tvlmxlBKupP5nRTQ+/rO2d1mPT2bOb9f998OyabUsxXtmgNcDuTfUNs2b57TwU1SnpTZaxXCAiTTu1SjMa4vjFMo2jCQY+I6epfX+ubtuPmLVcbJ8ZVkujc34XawlHDTievQeUaUrJTGX6QO08ZE3dnttW2xlQktQ+pU7XLW23jD3+8m7tPuzwNHYAAAAAcJH+/qiChrW8lTfcXJ3JTIC9B4pdqJ4MKJSPv876/qpXXOls5XqHj8lL90nZ1NFlb0Ba9rHqzccmmcnq4//xSkFvnvnHRfSDG5eqLuCr0swczBaACqR7h1mx/DbsKdL/adZSyeMd5hUVFKiTpr/NMrTY2KoNe6pXhs+MdVqWu8yI2iIjZ/RifDhywPHsAShpoAyfvTdUqfXE0zocTVnGar0H1KDbVpys/1zbodSRQF8mZ+qe/35d37h2UZVnBgAAAAC1IdHXWTjowgBUyt8opYZ/vq6BDCiUjz3AWa+EYomMmuoddiE52SetfdA6tugDUsNxI77sd1sP6v7fbVdXbIRfshLpS2T0pu3m4+Obw3r0w+eouc5h/54OYdQ1W5aDmeoFoNZ39OgKewBq9rlVmk0hY/a50r51+eUlnq1a39GjFadUp+xdsrdToSHLXWpQWyRYlbnUOgJQcLxQoDDQFE9n1Vzm/b5py37yGNKslnCZ9+oM05tC+ttz5+jB53fkx376aoduvegknTSVdFQAAAAAmKhU3+HCwZD7soXSgUbZKgnmRc2gGsNc0EP5BCPWAKffyCoajzovAPXqf0gJW7bH8k+N+JL/t/mAPvroK8rmKtOGwm5KfUCP3rxM05tCx155kvLUtViW67LVK8G3efdb+oKxwzrYvqw6kymmfZn00r35xbM9r+tHu7uqNp1Y91vWAJTZoJPJgCoLSvDB8YplQMVT2bLv197/aWZzWEGfA9JWK+TWi05S3ZDgX86UvvXb16s4IwAAAACoHalot2U5boQlr786k5mAXGD4oFlUYTWE3PczwT3CdYXvv0R/9cp6FZVNS3/4/6xjp1wmTZ0/7Ete2XVYn/jRq1ULPtUHvHr4pqXchHwMvnpbACo3TDS+zLI5U5596xQwjl4vNQ2PNGtpVeZTlC0bq9GIqb/jtSpNRkrZspD7PA2UmSwTAlBwPL/XI5/HsIzF0+UPQO2yBaDmTpLye4PaIkHddP5cy9hTf9qrTfuql04MAAAAALUia2uAHve480KvOULWVr8ZUmOYC3ooH1+4sMRjIuqw6xZ//rnUs9s6dt6nh119y/4+3fTQy0qkq9PLqi7g1X1/u0Rvm9Vclf27SaB+imW5Qf1KVOCapd32g/06LbvZMpZpO1UKNlR8LsNqnKl0wyzL0Nz4azrQm6jKdDL91gBU3N9clXlMBnwLgCuE/V71JTP55UoczHfaSvDNaa0r+z6d5pYLTtKjq3epL3H03/7u32zV969fUsVZAQAAAID75WLdluW410EXCsdihL5VUYU0iwwolJM/rKw88uposMaeXVhVpim9eI91bNbSgmyQQbsPx3T9D15S75DrMJJ0+dtm6JKF5e+V4/d6tGTOFMrujVKo0RqAajKi6ktkyt633u5PHT1abOv/5J/rnP5Pg3xzzpVeeyK/PNgH6l2nVv79ZtpuAskEmis+h8mCABRcIRSwBqDiqfLfBbKz05oBdULb5MqAkqSmOr8+esGJuvs3Rz/EfrPxgP60u1uL2purNzEAAAAAcDkj0W1ZTvkLMzncwN4DZaiowoqEuPSEMjIMxRRSg47eRJyOOygDavuz0v4N1rHzb5cMo2DVzv6krv/BGh3oTVrGL14wTd+67kz5vRSycppwY6tluVExvRlPaWpDZXvfbdjdpdttASi1Oy8AZcy2BqAWG1v1045uvevU4yo+F2/CGoDKhVuHWRMTxZELrmDvA1WJEnyFGVCTLwAlSTedP1ctddY71u76zdZh1gYAAAAAjIaRsF4kz/jdmQFl74EyVNwIc9EcZZcwrBVrMk4KQL3wbevylJMG+j/Z9Cczuumhl7XDdjP0kjkt+ve/OZvfI4cK2I5/PiOn/r7K9yDrfHOjphj91sH2ZRWfxzHZ5jTXc0A739xZlakEkl2WZaNuyjBrYqI4esEVKh2A6k9m1NlvveNk7iQswSdJDSG/Pn7hSZax3209qDU7Dg/zCgAAAADAsfhS1ouUmeDwvZScLDhCACrlnZzn0aishCdsWc4m+qo0E5t96wcyoIY67zbJY73Glcxkdcuja7Vhj/WYcMpxDXrwhqUKBypbzg1jEGouGEr0HaroFNLZnJoOvmIZS4aPk5pnV3QeozLtNGV81s+FwN6XZZpmxacSSlt/33wNbRWfw2RBAAquELJ92Ja7B9SuQ9Y7TgxDap8yeb84X798bkH68Dee2VKVDwgAAAAAqAX+tC1Lo8iFTDcINQxftijtnZyVRFBZ9kBnzikBqBe/Y12ua5MW/bVlKJsz9dnH/6gX37AGLY5vDuvRm5epqY4eao4WbFRO1nKKyb7K3rD9+oF+vc3cYhkz2pcVLfNYdV6f0tPPtgydnNqovT2Jys4jl1VdznqcCBKAKhsCUHCFsN/6Vi1/AMpafm9GY6jiDQSdJBzw6rYV8yxja3Yc1vPbOqs0IwAAAABwt1DGFoAKN1dlHhMVbhr+ol3WTwAK5WcPdOYS/cOsWUHdu6XX/ss6ds7HJP/RbC3TNPU/f/Gafrlhv2W11vqAfviRc3RcY6gSM8VEeDyKGtb3Xzpa2QDUhj3dWmLr/xQ44byKzmEsQida57bEs1UbOrorO4lEjzyy3lRf11L5PlSTBQEouII9+BNPlTcAtdOWATVZ+z8N9YFl7ZrZZP3y841ntpIFBQAAAADjEMpaL5J765qrM5EJCkaah30u549UbiKYtNI+6zUbI+WADKg/3CuZQ65d+eukpR+xrPLN376uH7/0pmUsEvTpkQ8v0wltXIdyi7jHepzLxroruv/Xd76peZ691sH2cyo6h7EwZlvndrqxQ39+863KTiJWWCaxoWVaZecwiRCAgitUugfUrk5rBtRcPvgV9Hn16YtPtoz9aXe3/ntThT8kAAAAAKAG1OesASjfCL2UnMwIjzDvAAEolF/WHoBKR4dZs0Li3dKrj1jHzvqQVDclv/jwCzv07f9+3bJKwOvR/X+7WKcf785+cJNVwtdgWc7Fuiq6/9zuNZbltCckzXhbRecwJrOWyhxStjBoZBTd+coILyi9TL+1olPUDGpKU2NF5zCZEICCK1Q6ALXDlgE1t3Xy9n8a6r2LZ2mO7d/irt9sVS5HFhQAAAAAjFYuZyoiawDKH5kyzNoOF4goM8zlJSNEAArllwtYA1DedJVL8K39gZQaMgfDIy3/ZH7xF3/co68+tdHyEsOQ7vnAmTpvHn1o3CbpswUuEt2V23cmq2ld6yxjsbZFktfBvcNCTeprmm8ZanjrlYpWWOrvOmBZ7lKD2iKBiu1/siEABVcIBawBqESZS/DtogRfUX6vR5+5xJoFtWlfr3712v5hXgEAAAAAsOtPZdQk63lnqKG1SrOZIMNQ1CgeaPIEG4qOA6Vk2jLtvOnYMGtWQCYpvfQ969ip75Fa5kqSVm09qDv+808FL/s/7zldf3XGjPLPDyWXDlgDUJ5k7zBrlt7W/f06y7D2fwqeuLxi+x8vj60M32nZTdp9OF6x/Ue7D1qWu8yImusIQJULASi4QiUzoGKpjA70Ji1jc9vIgBp01aLjdfI065e7u3+zRVmyoAAAAABgVPpiCUWMhGUs3ODSDChJMU/xAJQ3TEkjVIAtAOXPVrEE3/rHpX5rdoXO/7Qkad2bXfr4f7yijO36yR3vmq8PnjOnUjNEiWVtAShfsqdi+35t90EtMt6wjIVOPK9i+x+v+nnnW5bP9ryu9R2VK12Y7LEGoKLeRnk9xjBrY6IIQMEVCgNQubLt683DhXfKzJ5CAGqQ12Po795lTZV942BUP1+3p0ozAgAAAAB3iXYXNkCva3RpBpQKe6AM8ofJgEL52TPtAtUKQOVy0ovfsY7NvUCaeZa2vdWnmx5+ueCG6hvPm6vb3jmvgpNEqZmhZsuyP125DKhD29YqZKStg7OWVmz/42W0WzOg2oxedbzxWsX2b+8BFfc1V2zfkxEBKLhC2FaCL16mEnyJdFZ/3mP9oDiuMai6gK8s+3Orvzhtuk6bab3D41v/vVXpbPkCgwAAAABQK+J9hQEoT11LFWZSGgU9UI4I1DdVeCaYjIyQPQBVuVJeFq//Wuq0lkPT+bdrb3dcf/vgGnXHrIGCqxbN1JevOFWGQeaFq4WbLYvBTOUCUIG9L1uWu+pOkOpckE3bMlf9futNF+abL1Vs97mo9TM4FWiu2L4nI66qwxVCtgyoxDAl+EzTVDSVVU88rZ5YWj3xtHoTR/4+8qcnPjieyT8efD6ZKQyg0P+pkMdj6I5L5+vDD6/Nj+0+HNdP1nbob86ZXcWZAQAAAIDzJfsOW5flV9AfrtJsJi4TaJSKJJ2ECUChAny2TLuQWaUeUC9827o87VR1zXiH/va+1drXYy25+Y75U/WNaxfJQ9kv1/OGrTcPhLL9FdlvIp1Ve3SDJb0kPdP52U+SBnoHTlusyJ5n8kNTu/6oXM6syO+EkbCW+8uGXBC0czECUHAFewm+zft79ckfv5oPKPUOCSqVuhfRCQSgilpxyjSdNbtZ697szo995/+9rmvOPr4gYAgAAAAAOCodtV78ihr1ClZpLqVg74EyKBQhAIXy89t6jYXNKmRAdayV3nzRMpRc+gnd+MhavXHQGp09s71Z3/vQ2Qr4KExVC3z1zZbl+lxlAlCb9vbobGOLZazxlLdXZN+lED7pfGlIAOpt5mbtPBTViVOL9xQsJX/S1m+qzr0lcN2AIx1cIRywvlU7+1NauX6ffv96p9Z39GjnoZi6YumSB58k6ew5zSXfZi0wDEP/49JTLGP7ehL6v2verNKMAAAAAMAdslFrBlTM4+5eSfYeKIMiDcXHgVLy1VkDUHVmXKZZ+utDI3rhHsui2TBTH/vTCfrT7m7L+LxpET1041JaPdSQQMQavIiY/RV5/21/Y5OOM7otY6ETziv7fkul4WTrXOd79mjzjspcUwymuy3L3ggBqHIiAAVXqK/CB3NjyKe/OWe2rj5rVsX37RbnndSqc0+0pql+99ltiqUyVZoRAAAAADhfLt5tWU743B2AUpEAVNY0FIm4/OeCKwTrrJl2ESOhVKaC1yUOvSFtesoy9IvQVXpuW49lbGZTSI9+eJla6gOVmxvKLthgvS7WpGjRFh+llnjDmnHX722SWueVfb+lYsw4UynD+rvQs/XFYdYurfqM9Xcz2NhWkf1OVoTb4QpL5k5R2O9VfJjeT8UYhtQY8qsp7Fdj2KemsD//pzHkV+Pg4yHjA8/51Bj2y+8lPnssg1lQ7/ve6vxYZ39Kj7y4S7dedFIVZwYAAAAADpawXvxKuTwA5alrLhiLKqzGMBfaUX6hSGEJyGh/n4ItFerrsvq7ko5mvCQ89fqfu5dYVmmp8+vRm8/RzGb39npDcWFbACpopPVWf59CLc1l3W/jwVcsy4ennKWI4aKeYr6A3mo4TbN61+WHgvtelvSR8u43l1PE7LMMhRunlXefkxwBKLjClPqAHrvlXP3fNW8qlsoeDRYNCSwNDSo11fkVCfho5lgBS+ZO0UWnTNVzWw7mx7636g198NzZagz5qzgzAACA2rdr1y59+9vf1sqVK7V7924Fg0GddNJJev/7369PfvKTqqurG/e2H374Yd10002jWvehhx7SjTfeOOI6sVhM//7v/66f/OQneuONN5RMJtXe3q7LL79cn/70pzVnzpxxzxVwG0+i27KcHqaHklv46lsKxqIKabqfGztRfmFbDx5Jivd1S5UIQPUflP74I8vQw6kV6tPRz9+6gFcP3bRM86aVv7cNKq++qTB7JtZzSCpjACqWyuikxEZLbTPP7HPKtr9yyR6/RBoSgDq+f72yOVPeMl7PNRM98smaoRaZclzZ9gcCUHCRRe3NWtTeXO1poIg73nWKJQDVE0/rB8/v0GcumV/FWQEAUJxpmupNZNQTS+dvaDHcdLcgcMRTTz2lD33oQ+rt7c2PxWIxrV27VmvXrtUDDzyglStXat686pdj2bZtmy677DK9/vrrlvEtW7Zoy5YteuCBB/SjH/1IV1xxRZVmCFSWN9VrWc4EmoZZ0x0C9YUX+uNGHZ+vqIhwpPD3JxnrLbJmGbz8fSmTyC+mTK8eyvxlftnvNfS9Dy3WmVzPqlmBSGEAPt57SFL5KgNt2rlHZxrWfklTT72wbPsrl6ZTLpA2fT+//DZt0xv7uzR/ZvmCx/09b8mec9w0hQyociIABWDCzpjVpL847Tj9+s8H8mMP/n6Hblg+l9rGAICKSmVyeqsvoQO9Ce3vSepA75HHvQnt70norb6k9vckLGV9Az6PpjUENa0hqOMaQwOPG0OaahtrqQuQXQ3HWLduna677jrF43FFIhF98Ytf1IoVKxSPx/XYY4/p+9//vrZu3arLL79ca9euVUPDxMp7/frXv9bMmTOHfX7WrOH7pvb19enyyy/PB58++tGP6gMf+IDC4bCeffZZ/cu//It6e3t13XXX6YUXXtCZZ545obkCbhBIW0vwmSF3B6CCDYUN3BMeSo2hMjz+oFKmTwHjaN+nZLRnhFeUSComrfm+ZejJ3Pk6oIGL54Yh3fX+M/WO+VPLPxdUj9evqEKq19FAZLLvcFl3+damF+Q1jpZ9TMun4Oyzy7rPcmg++e2W5bCR0u5NazR/5l8O84qJ6zt0wBKAipsBtbYUBhFROgSgAJTEZ981X89sPCDzyOdfXzKj+3+/XV/4ywXVnRjgUrmcqX29Ce04GNX2zn7t6Iwqkc4pEvSqLuBTJOhTXdCrSNCn+sCQx0eW64+sV87UdaCSTNNUVyyt/T1HA0r54FJPQgd6B4JNh6KpMW87lcmpoyuujq74iOv5PEY+KDVtMFDVENK0RmugqjUS5HcPZXf77bcrHo/L5/PpmWee0fLly/PPvfOd79TJJ5+sz3/+89q6davuuusuffWrX53Q/ubPn6+5c+eO67X/9m//pq1bt0qSvv71r+tzn/tc/rnly5froosu0oUXXqhYLKbPfOYzeu655yY0V8ANApl+60CouSrzKJW6xsK71VPe8ZcABcYqZoQV0NG+LulYBQJQf/yRFLcGGu7PXJ5//NUrT9NVi4a/eQO1I2rUq948GoBK93eVdX/Gm3+wLO8Nn6I5fhcG/etbtd/frunp3fmh1PYXpYvLF4Dq73rLstytBs0IeMu2PxCAAlAiC6Y36sq3zdSTf9qbH7v3uTf0yIs7FfB5FPB6Bv4+8jjosy4HfB75j/wdLFjfe/Sxz6OgbVter6FM1lQ6m1M6m1MmayqTyymdNZXJ5pTJmfnH6dzQMeu6g8vpYZ7P5HIyZMjrMeT3Dvzt83iKLvu8hnweQ16PRz7b8tF1iy8fXdeQYRjyGoY8huTxGPIYhrweyWMMPjZkGBpYx3N0zGNY1/F6NGRbhjxHtjG43/qgT0GfhxIZVdAVTWl7Z1TbDw4EmYb+SWZyx97AMYT93oGgVNCbD0zV5wNVA48jQd+RoNZA0Ko+WBi4Mk3TulxkX2bBoHmM562sbz+jYMzIr2dYloeuZ11/cNC6DcMwjvx9dD3DOLJasbHB1xiyvE4aOmYM2f7Rfdt/pYo9N3QeR9ex/zzFnht4hefI3Lwe2+93kd/3wePF4PGlFEzTVCZnKms5bg4cM/OPs0ePowPLR4/FR4/XRx8fjiYLspfe6k0qlZ3478REZHKm9vUktK8nIWn4ixoeQ2qNWINSzXWBgWPx4PtmyPvQM+Tx0ffawLF88HHR1wx5/9lfIw35HRhYGPpXwe/RSO/HoYq9bkZTWIvncNdgJa1Zs0a///3vJUk333yzJfg06I477tBDDz2kTZs26Z577tGXvvQl+f2V78+ZTqf17W9/W5K0cOFC3XHHHQXrnHfeebr55pt13333adWqVXr55Ze1dOnSSk8VqKhw1toA3VvXXJ2JlEhdkR4oaW99FWaCySpuhNVsDglAxftGWLsEcllp9b9bhp7NLtJWs12S9OmLT9YN580t7xzgGDFPg5Q9lF/OxMqbAdXW/SfLcvS4xWXdXzl1tZ6l6fuPBqAaOl8t6/4SPdYAVJ+nUTPKukcQgAJQMp+55GSt3LBP2dzRq8yxVFaxVHaEV8Ep/F5DkaBPkdBABk1DyHdk2a/IkeyaSNCvSMh3ZHnwsS//usHH3PlvFU9ltfNQVNsPRrWjs1/bhwSZumPp8u47nVU8nVVn/7HXxeRTLGBtCWYdCYB7PYZMU7bA0tEAE6xypnSwL6mDfUn9eW+F+g9U0eVvm0EAqsJ+/vOf5x/fdNNNRdfxeDy6/vrr9cUvflHd3d169tlndemll1Zohkc9++yz6ukZCNjecMMN8ng8Rde78cYbdd9990mSfvaznxGAQs2rswWgfEV6KLlJKNJcMJb1E4BC5SQ8ddKQSw/ZcgegNj0pde20DN2fHehj+MFzZuuzl5xc3v3DURK+Bsv7z4x1l21ffbGETslssdxoWXfSeWXbX7l555wr7X8yvzwv8WelM1n5feXJSkr3HbIsx33uLoHrBgSgAJTMiVMjeu/Zx+s/13ZUeyoYh3R2oLxVVwkCImG/V5GQTw1DAlqRkE8hv1e+Ixe2fZ6BrK3BbK/BbCzLmGHI6zXyGRtH1/XI61E+w2zwNYPb9XoN+T2DmXVGPrvO7x1YHsye83uPjk00GySTHSjhtaMzeiTAdCSj6WBUe3sSx94AUAXZnHnkPKn2gkhBn0fHNYY0vTGk45pCOq4hqOlNIR3XGMqPt9T71R1L662+pA72DfSHeutIKb+3+pL58UPR1DEz+IBKev755yVJ9fX1Wrx4+DteL7zwaDPqF154oSoBqMG52udjt2TJEtXV1SkWi+mFF16oxNSAqoqYUcvFw4DLA1CG16c+1alBsfxYjgAUKihpC0DlkmW8Ccg0lfrdtzS04/Wfcidqde5UXXbGdP3ju0+nusgkk/Q1Ssmjy2aifCUgt/95rRYZ1tLhM84Y/juW0x132kXSS0eXpxuHtXX7Vs2fv7As+8tFrQGoZKC5LPvBUQSgAJTUF/9qof68t3dS3PGN4Q1m3RzsSx57ZYewB6oC3qPBK7/XI/+R8o9+39GxgNejZCarHZ1RvXk4pnS29Feo6wNenTg1ohPa6tUU9iuWyiqazCiaygz8ncwefZzKKlWCsn2AkxmG1BYJDgSWGoNHg0xHAk2D401h/6hO/BtCfrVPGblHRTqbU2f/QHBqIDCVOPI4YRnr7E9ZsoAnCy6vVN6mTZskSfPmzZPPN/wp3YIFR3txDr5mvG666SZt2bJFnZ2damxs1Lx583TJJZfo1ltv1fHHHz/s6zZu3Fh0PnY+n0/z5s3T+vXrxzXXjo6Rb4Dat2/fmLdZKmufuk96/Zmq7R/OdLailuVgg7sDUJLUb9SrwTwagDIDDSOsDZRW2lsnDbmXsm3LY1p790vDv2AC/LmUFvX/0TJ2f+YKnT+vTd+87kwqgkxCab/1eDdrz0qtvfuNsuyrLrbXsrzPM10zmt3ba6xp1kL1KKImHS3bknriY1obmlqW/U3v22hZzoao5FBuBKBKaNeuXfr2t7+tlStXavfu3QoGgzrppJP0/ve/X5/85CdVV0cDTtS+lvqAnv7U27X7cFx9ybRSmdzAn2zO8jiZyQ37XCoz5PlsTqlMtmC95JDlXM6UzzvQO8k/2EPJ65H/SO8lv3cgS2YwaDDQY2kguGB9PPiao1kxvvzywHqS8j1Mskd6S2UHl7Om0rmjy5nswDpDy1QN9kgZGD/aA2Xo8uB2c+bgHymXM5UdXM5JOXPgNYPP5x/n1xt4zeDzOLaBPmDVKRnp9xqaPaVOJ7RFdOLUep3QNvDnxKn1mhoJjunuuXQ2p1gyq/58gMoWpDoSqMqPJzPqT2UUG7JeLJVVbph0j2IzKTa/YWc83BNm4cOhfaeOjg0uD3nOtP5t2axtG6Y58Nqj2xl8nX3MtOwzv1xknaFzGti+ddKDWxq6/aFzs/9stWow09E/5Lg62Dtv8Bg82DOvKezXcQ3WgNJxjSFNbwqpLRKU31u8hFe5+L0ezWgKa0bTyI19szlTh6IDgaqDQwJVB/oS6ktk8u8P0zQt78WcaRZ5rvh4bshrc6akIY/NIcf8o+/DY/8e2X+Hiv0OjvS66Y2hY/8jomQSiYQ6OzslSbNmzRpx3ZaWFtXX1ysajWr37t0jrnsszz33XP7xoUOHdOjQIb300ku666679K1vfUsf+9jHir5uMDBUX1+v5ubmEffR3t6u9evX6+DBg0omkwoGg6OeX3t7+6jXrbRMxzqd2/vbak8DTmP7ThRudH8AaqAHysH8shGMVHE2mGzSPmvG3bzsG1JveQIAdm/mpqpj+iX60d8uUbBMZcPgbNmAtYzbnFyH5vRWpjrQvsZF7u5h5PHozfozdEZ0dX7o9NSfpFRldm+GWyuzo0mMAFSJPPXUU/rQhz6k3t6jWR+xWExr167V2rVr9cADD2jlypWaN29eFWcJVIZhGJrdSsDVKQYvWmaPBK0GHw8GrFLZnKLJrPoTGfUl0+pPDGTXDCwP/B1NDnlc5Lkofb5GZWZTSCfkA0wRnXgkyHR8c1i+El1Q93s9aqrzqKmu8o3mUTr54NQxgla5IUGJgUD00eBzdvB3P2cLXh/53bcEr4cEs82h2xoS7JYG3l9ej5EPynuPBPcHbwAYKH9p5G8K8B0JMA2Wyqx1Xo+haQ0hTWsgIIPy6es72tMiEjn2xd3BAFR///iaAZ544om65pprtHz58nyQZ/v27fqv//ovPfHEE0okEvr4xz8uwzB0yy23DDvf0c51UH9//5gCUICb5UxDDVOmVXsaExb1t0rZ7fllb6Q8d68DxaSDLVKV+t7+PHS1fvDhcxUJcpl1sjLrq3cMzx6/rGr7LpXocUuk7auPvWIZeBuPq8p+JxOOjCWwbt06XXfddYrH44pEIvriF7+oFStWKB6P67HHHtP3v/99bd26VZdffrnWrl2rhgbS0AFUjmEYMgzJI0P+4W7GmuBhKZsz8xk2wwWu+pMZpTIDGV85cyBDLGcOZIhlh2SBZYv8Gfqa7JEsr8yRbLNsTkf+tm5jIKMpp/SRTLlMhVLBmsL+fBbTiYOBpqn1mttar3CAu+EwOoNZZYXJZbUfxAEwskTiaF/BQCAwwpoDBoM48Xj8GGsWuvrqq3XDDTcUZLouXbpU1113nZ5++mldc801SqfT+uxnP6urrrpK06dPLzrfscx1PPM9VobXvn37tGyZ+y/QoDb9KXK+zoo0V3saE5Y6/f3S2pclSX1mWCddcG2VZ4TJpP7s9yn7zC/kNSpbVmC70a5rbv6CWiPcNDGZzTjnvYptv191RmXbEOxXq+Zd8uGK7rMc5lzyMXXf95CajcpGkfvMsOaee3VF9zkZEYAqgdtvv13xeFw+n0/PPPOMli9fnn/une98p04++WR9/vOf19atW3XXXXfpq1/9avUmCwBl4PUYagz51RjyS03HXr8acjlT6VxuIDCVORKYOhKcGgxWpbK5I88NLCePrJcPZB157dH1cpJhqL0lrBOnDmQ0tdQf+wIbAADjFQodzbBLpY5dmySZHLgQEg6PXEKymKamkT/Ur7jiCn35y1/W//yf/1OxWEwPPvigvvSlLxWd71jmOp75HqscYTWFT3mnVnvJTEZx3ikn6G2XFy9h6TZLrrhFrzVPV/+b6zXn/Gs1Y/rsak8Jk8jp51+pzcH/UteffiVlKxMEyNVP14JLblDrcW0V2R+ca86Cs7TjupXa/9ITUjp27BeUQDY8VSde+EG1tLi/hOuMme3adf1/688v/FieRFdldhps0KzzrlP77JMrs79JzDDtxd0xJmvWrNE555wjSfrYxz6m733vewXr5HI5nX766dq0aZOam5v11ltvye8vzQlIR0dHvhTG7t27HX3iBQAAAJTCZP4OnEgk8sGZyy+/XE8//fSI60ciEUWjUZ177rlavbr0pU3eeustTZ8+XaZp6l3vepeeeeYZy/PnnnuuXnrpJdXX1x+zDOAVV1yhlStXShr4OUtZgm8yv2cAAAAw+Tjl+29lOzjXoJ///Of5xzfddFPRdTwej66//npJUnd3t5599tlKTA0AAABAjQmFQmptHWiW3NExcnPrrq4uRaNRScqffJbatGnT8vPZs2dPwfODJ7rRaFTd3d0jbmuwjN7UqVPp/wQAAADUAAJQE/T8889LGmiYu3jx4mHXu/DCC/OPX3jhhbLPCwAAAEBtOvXUUyVJ27ZtUyaTGXa9zZs35x8vXLiwbPOx94gaanCu9vnYZTIZvfHGG5LKO1cAAAAAlUMAaoI2bdokSZo3b558vuFbai1YsKDgNQAAAAAwVm9/+9slDWQVvfLKK8Out2rVqvzj888/vyxzOXjwoDo7OyVJM2fOLHh+cK72+ditXbs2n61VrrkCAAAAqCwCUBOQSCTyJ1vHqqHY0tKi+vp6SUdLS4xGR0fHiH/27ds3/h8AAAAAgOu85z3vyT9+6KGHiq6Ty+X06KOPSpKam5u1YsWKsszl/vvv12Bb4aFVHwZddNFFampqkiQ98sgjGq4F8cMPP5x/fPXVV5d+ogAAAAAqjgDUBPT19eUfRyKRY64/GIA6VvPdodrb20f8s2zZsrFPHAAAAIBrLVu2TBdccIEk6cEHH9Tq1asL1rnrrrvylRduv/12+f1+y/PPPfecDMOQYRi68cYbC16/c+dOrVu3bsR5PP300/rHf/xHSVI4HC7aEzcQCOjTn/60pIFKEN/4xjcK1lm9erUefPBBSQNBrKVLl464XwAAAADuMHzNOBxTIpHIPw4EAsdcf7CRbjweL9ucAAAAANS+e+65R+eff77i8bguvfRS3XnnnVqxYoXi8bgee+wx3X///ZKk+fPn64477hjz9nfu3KkVK1Zo+fLluvLKK7Vo0SJNmzZNkrR9+3Y98cQTeuKJJ/IZTd/4xjd0/PHHF93W5z73OT3++OPaunWrPv/5z2vbtm36wAc+oHA4rGeffVb//M//rEwmo3A4rG9961vj+wcBAAAA4DgEoCYgFArlH6dSqWOun0wmJQ3cHThaxyrXt2/fPrKgAAAAgEnmrLPO0uOPP64PfehD6u3t1Z133lmwzvz587Vy5Uo1NDSMez+rV68ummE1qK6uTt/85jd1yy23DLtOQ0ODVq5cqcsuu0yvv/667r///nyAbFBjY6N+9KMf6cwzzxz3XAEAAAA4CwGoCRh6IjeasnqDTXVHU65v0LF6SwEAAACYnK688kqtX79e99xzj1auXKmOjg4FAgHNmzdP1157rW677TbV1dWNa9uLFy/WD3/4Q61evVpr167Vvn371NnZqUwmo5aWFp122mm6+OKL9ZGPfCSfGTWSefPmad26dfrud7+rn/zkJ9q2bZtSqZTa29t12WWX6fbbb9ecOXPGNVcAAAAAzmSYw3WBxai0tbXp0KFDWrRokf74xz8Ou15XV5emTJkiSbr22mv1n//5nyXZf0dHh9rb2yUNZEsRsAIAAECt4zswxor3DAAAACYTp3z/9VRlrzXk1FNPlSRt27ZNmUxm2PU2b96cf7xw4cKyzwsAAAAAAAAAAKBaCEBN0Nvf/nZJA+X1XnnllWHXW7VqVf7x+eefX/Z5AQAAAAAAAAAAVAsBqAl6z3vek3/80EMPFV0nl8vp0UcflSQ1NzdrxYoVlZgaAAAAAAAAAABAVRCAmqBly5bpggsukCQ9+OCDWr16dcE6d911lzZt2iRJuv322+X3+ys6RwAAAAAAAAAAgEryVXsCteCee+7R+eefr3g8rksvvVR33nmnVqxYoXg8rscee0z333+/JGn+/Pm64447qjxbAAAAAAAAAACA8iIAVQJnnXWWHn/8cX3oQx9Sb2+v7rzzzoJ15s+fr5UrV6qhoaEKMwQAAAAAAAAAAKgcSvCVyJVXXqn169frs5/9rObPn6+6ujo1NzdryZIl+td//VetW7dO8+bNq/Y0AQAAAAAAAAAAyo4MqBKaM2eO7r77bt19993VngoAAAAAAAAAAEDVkAEFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICS8lV7ApiYTCaTf7xv374qzgQAAACojKHfe4d+HwaGw3kTAAAAJhOnnDMRgHK5gwcP5h8vW7asijMBAAAAKu/gwYOaO3dutacBh+O8CQAAAJNVNc+ZKMEHAAAAAAAAAACAkjJM0zSrPQmMXyKR0IYNGyRJU6dOlc9X2aS2ffv25e8gXLNmjWbMmFHR/cOZeF/AjvcE7HhPoBjeF7Ab7j2RyWTyGS1nnHGGQqFQ1eYId+C8CU7DewJ2vCdQDO8L2PGeQDHF3hdOOWeiBJ/LhUIhLV26tNrTkCTNmDFDs2bNqvY04DC8L2DHewJ2vCdQDO8L2NnfE5Tdw1hw3gQn4z0BO94TKIb3Bex4T6CYoe8LJ5wzUYIPAAAAAAAAAAAAJUUACgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJUUACgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJWWYpmlWexIAAAAAAAAAAACoHWRAAQAAAAAAAAAAoKQIQAEAAAAAAAAAAKCkCEABAAAAAAAAAACgpAhAAQAAAAAAAAAAoKQIQAEAAAAAAAAAAKCkCEABAAAAAAAAAACgpAhAAQAAAAAAAAAAoKQIQAEAAAAAAAAAAKCkCEABAAAAAAAAAACgpAhAAQAAAAAAAAAAoKQIQGHcdu3apTvuuEMLFixQfX29pkyZoqVLl+rf/u3fFIvFqj09VJBhGKP6c9FFF1V7qiiBt956S08//bS+/OUv66/+6q/U1taW/z++8cYbx7y9X/3qV7r66qs1a9YsBYNBzZo1S1dffbV+9atflX7yKJtSvC8efvjhUR9PHn744bL+PJi4tWvX6h//8R916aWX5n+/I5GI5s+fr5tuuknPP//8mLbHsaI2lOJ9wbECbsN5EyTOmSYjzptgxzkTiuG8CXY1d85kAuPw5JNPmo2Njaakon/mz59vvv7669WeJipkuPeB/c+FF15Y7amiBEb6P77hhhtGvZ1sNmvefPPNI27vIx/5iJnNZsv3w6BkSvG+eOihh0Z9PHnooYfK+vNgYi644IJR/T9ef/31ZjKZHHFbHCtqR6neFxwr4CacN2EQ50yTD+dNsOOcCXacN8GuFs+ZfALGaN26dbruuusUj8cViUT0xS9+UStWrFA8Htdjjz2m73//+9q6dasuv/xyrV27Vg0NDdWeMirk1ltv1Sc+8Ylhn6+vr6/gbFAJs2fP1oIFC/TMM8+M+bVf+tKX9OCDD0qSzjrrLH3+85/XSSedpDfeeENf//rXtW7dOj3wwAOaOnWq/vmf/7nUU0cZTeR9MejXv/61Zs6cOezzs2bNGve2UX579+6VJM2cOVPXXnutLrjgAs2ePVvZbFarV6/WXXfdpT179ujRRx9VOp3Wj3/842G3xbGidpTyfTGIYwWcjPMmFMM50+TEeRPsOGeCxHkTCtXkOVNZw1uoSYORWJ/PZ7744osFz3/961/PR1C/8pWvVH6CqDj+vyeXL3/5y+ZTTz1l7t+/3zRN09yxY8eY79rasmWL6fP5TEnmkiVLzFgsZnk+Go2aS5YsyR9ruDPY+Urxvhh6h86OHTvKN1mU3eWXX24+/vjjZiaTKfr8wYMHzfnz5+f/v1etWlV0PY4VtaVU7wuOFXALzpswFP/Xkw/nTbDjnAl2nDfBrhbPmegBhTFZs2aNfv/730uSbr75Zi1fvrxgnTvuuEMLFy6UJN1zzz1Kp9MVnSOA8vpf/+t/6YorrtBxxx037m1861vfUiaTkSR95zvfUTgctjxfV1en73znO5KkTCajb37zm+OfMCqiFO8L1I6nn35a73//++X1eos+39bWprvuuiu//MQTTxRdj2NFbSnV+wJwA86bAHDeBDvOmWDHeRPsavGciQAUxuTnP/95/vFNN91UdB2Px6Prr79ektTd3a1nn322ElMD4BKmaeoXv/iFJGnBggU699xzi6537rnn6pRTTpEk/eIXv5BpmhWbI4DyW7FiRf7xG2+8UfA8x4rJ6VjvC8AtOG8CMFF8FwIgcd6EQm47ZyIAhTF5/vnnJQ3UpV68ePGw61144YX5xy+88ELZ5wXAPXbs2JGvaTv0WFHM4PN79uzRzp07yz01ABWUTCbzj4vd3cWxYnI61vsCcAvOmwBMFN+FAEicN6GQ286ZCEBhTDZt2iRJmjdvnnw+37DrLViwoOA1qH0/+clPdOqpp6qurk4NDQ06+eSTdcMNN3A3Jyw2btyYfzz0WFEMx5LJ66abbtLMmTMVCATU1tamc889V//wD/+gPXv2VHtqKJFVq1blHw+WoBqKY8XkdKz3hR3HCjgV500YDudMGC2+C+FY+B40OXDeBDu3nTMRgMKoJRIJdXZ2SpJmzZo14rotLS2qr6+XJO3evbvsc4MzbNy4UZs2bVI8Hld/f7+2bdumRx99VO985zt19dVXq6enp9pThAN0dHTkHx/rWNLe3p5/zLFkcnnuuee0b98+pdNpHTp0SC+99JL+6Z/+SfPmzdN9991X7elhgnK5nL72ta/ll9///vcXrMOxYvIZzfvCjmMFnIjzJoyEcyaMFt+FcCx8D6p9nDfBzo3nTMPfigXY9PX15R9HIpFjrl9fX69oNKr+/v5yTgsOUFdXp6uuukoXX3yxFixYoEgkooMHD2rVqlX63ve+p0OHDunnP/+53v3ud+s3v/mN/H5/taeMKhrLsWTwgowkjiWTxIknnqhrrrlGy5cvz3853r59u/7rv/5LTzzxhBKJhD7+8Y/LMAzdcsstVZ4txuub3/ym1qxZI0m65ppripan4lgx+YzmfTGIYwWcjPMmFMM5E8aK70IYDt+DJg/Om2DnxnMmAlAYtUQikX8cCASOuX4wGJQkxePxss0JzrBnzx41NzcXjL/rXe/Spz71Kf3VX/2V1q1bp1WrVunee+/Vpz/96cpPEo4xlmPJ4HFE4lgyGVx99dW64YYbZBiGZXzp0qW67rrr9PTTT+uaa65ROp3WZz/7WV111VWaPn16lWaL8Vq1apX+/u//XpI0bdo03XvvvUXX41gxuYz2fSFxrIDzcd6EYjhnwljxXQjF8D1o8uC8CXZuPWeiBB9GLRQK5R+nUqljrj/YEC0cDpdtTnCGYidSg4477jg98cQT+Tv4vvOd71RoVnCqsRxLhjZW5FhS+5qamgq+HA11xRVX6Mtf/rIkKRaL6cEHH6zU1FAif/7zn3X11Vcrk8koFArpJz/5iaZNm1Z0XY4Vk8dY3hcSxwo4H+dNKIZzJowV34VQDN+DJgfOm2Dn5nMmAlAYtYaGhvzj0aRpRqNRSaMrO4HaduKJJ+pd73qXJGnbtm3au3dvlWeEahrLsWTwOCJxLMGAW265Jf8lamjjTTjfjh07dOmll6qrq0ter1ePPfaY3vGOdwy7PseKyWGs74vR4liBauK8CePBORPs+C6E8eJ7kLtx3gQ7t58zEYDCqIVCIbW2tkqyNrgrpqurK39QG9rgDpPXqaeemn+8Z8+eKs4E1Ta0KeaxjiVDm2JyLIE0kGY++FnEscQ99u7dq0suuUR79+6VYRj6wQ9+oHe/+90jvoZjRe0bz/titDhWoJo4b8J4cc6EofguhPHie5B7cd4Eu1o4ZyIAhTEZ/EK8bds2ZTKZYdfbvHlz/vHChQvLPi8430hpn5hchp5YDz1WFMOxBMVwPHGXzs5Ovetd79L27dslDZQVuv7664/5Oo4VtW2874ux4FiBauK8CePBcQtD8V0IE8HxxH04b4JdrZwzEYDCmLz97W+XNJCy+corrwy73tC0vfPPP7/s84Lzbdy4Mf945syZVZwJqu2EE07IvweOleL7u9/9TpJ0/PHHa+7cueWeGlzg4MGD6uzslMSxxA16enr0F3/xF/nPgK997Wv65Cc/OarXcqyoXRN5X4wWxwpUG+dNGA/OmTAU34UwXnwPch/Om2BXS+dMBKAwJu95z3vyjx966KGi6+RyOT366KOSBhqtrlixohJTg4Pt2LFDv/nNbyRJJ510ko4//vgqzwjVZBhGPl148+bN+sMf/lB0vT/84Q/5u3Pe/e53cwcXJEn333+/TNOUJF144YVVng1GEovFdPnll+vVV1+VJH3pS1/SF77whVG/nmNFbZro+2K0OFag2jhvwlhxzgQ7vgthvPge5C6cN8Gu5s6ZTGCMLrjgAlOS6fP5zBdffLHg+a9//eumJFOS+ZWvfKXyE0RFPfnkk2Y6nR72+f3795tnnXVW/j1x1113VXB2qIQdO3bk/39vuOGGUb1my5YtptfrNSWZS5YsMWOxmOX5WCxmLlmyJH+s2bp1axlmjnIa6/tix44d5quvvjriOk899ZQZCARMSWY4HDY7OjpKNFuUWjKZNC+99NL8e+D2228f13Y4VtSWUrwvOFbATThvwiDOmWCanDehEOdM4LwJdrV4zuQrX2gLteqee+7R+eefr3g8rksvvVR33nmnVqxYoXg8rscee0z333+/JGn+/Pm64447qjxblNunPvUppdNpvfe979Xy5cs1d+5chcNhdXZ26rnnntN9992XT+d8+9vfXvJ0UVTe888/r23btuWXB/9/pYE+Bw8//LBl/RtvvLFgG/Pnz9fnPvc5fe1rX9PatWt1/vnn6wtf+IJOOukkvfHGG/rXf/1XrVu3TpL0uc99TieffHJZfhaUzkTfFzt37tSKFSu0fPlyXXnllVq0aJGmTZsmSdq+fbueeOIJPfHEE/m7c77xjW9wZ7CD/fVf/7WeeeYZSdI73/lO3XzzzXrttdeGXT8QCGj+/PkF4xwraksp3hccK+AmnDdhEOdMkxPnTbDjnAl2nDfBribPmcoW2kJNe/LJJ83GxsZ8NNb+Z/78+ebrr79e7WmiAubMmTPs+2Don/e+971mV1dXtaeLErjhhhtG9X8++Gc42WzW/PCHPzzia2+++WYzm81W8KfDeE30ffHss8+O6nV1dXXmfffdV4WfEGMxlveCJHPOnDnDbotjRe0oxfuCYwXchvMmmCbnTJMV502w45wJdpw3wa4Wz5nIgMK4XHnllVq/fr3uuecerVy5Uh0dHQoEApo3b56uvfZa3Xbbbaqrq6v2NFEBjzzyiFatWqXVq1dr+/bt6uzsVG9vryKRiNrb23Xeeefphhtu0PLly6s9VTiMx+PRgw8+qPe+9726//779fLLL6uzs1NtbW1aunSpPvaxj+mv/uqvqj1NVMjixYv1wx/+UKtXr9batWu1b98+dXZ2KpPJqKWlRaeddpouvvhifeQjH8nfuYPJgWMFhuJYAbfhvAkS50yYGL4LYRDfgzASjhUY5LRjhXEksgYAAAAAAAAAAACUhKfaEwAAAAAAAAAAAEBtIQAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAAAAAAAAAACAkiIABQAAAAAAAAAAgJIiAAUAAAAAAAAAAICSIgAFAIBL7Ny5U4ZhyDAMPfzww9WeDgAAAAA4CudMAOAsBKAAAI733HPP5U8iRvvnM5/5TLWnDQAAAAAVwTkTAMCJCEABAAAAAAAAAACgpHzVngAAAGNx66236hOf+MQx12tra6vAbAAAAADAWThnAgA4BQEoAICrTJs2Taeffnq1pwEAAAAAjsQ5EwDAKSjBBwAAAAAAAAAAgJIiAAUAmBTmzp0rwzB04403SpJefvll/fVf/7Xa29sVCoXU3t6um266SZs3bx7V9p566im9733v06xZsxQMBtXa2qrly5fra1/7mvr7+0e1jddee02f+tSndMYZZ6ilpUV+v1/Tp0/XJZdcoq9//evat2/fMbfxm9/8RldeeaWmT5+uYDCoE044Qbfeeqs6OjpGNQcAAAAAkDhnAgCUnmGaplntSQAAMJLnnntOK1askCR95Stf0Ve/+tUxb2Pu3LnatWuXbrjhBr3jHe/Qxz72MWUymYL1gsGg/uM//kPXXntt0e0kEgn9zd/8jX72s58Nu6+ZM2dq5cqVOvPMM4s+n81m9bnPfU7f+ta3NNLH8A033KCHH344v7xz506dcMIJkqSHHnpIW7Zs0de+9rWir506dapWrVqlhQsXDrt9AAAAALWBc6YBnDMBgLOQAQUAmFT++Mc/6uMf/7imTZum73znO3rppZe0atUqfeELX1AwGFQymdQHP/hBrV27tujrb7jhhvyJ1KJFi/Too4/q5Zdf1q9//WvddNNNMgxDe/fu1cUXX6w9e/YU3cYtt9yib37zmzJNUzNmzNA//dM/6dlnn9Wrr76qX//61/rf//t/a9GiRSP+HN///vf1ta99TRdeeKF+/OMfa+3atfrtb3+r66+/XpJ08OBBffjDH57AvxQAAACAyYhzJgBAqZABBQBwvKF389166636xCc+cczXnHLKKfL7/fnlwbv5JGnOnDn6wx/+oOnTp1te8+yzz+rSSy9VJpPR0qVLtWbNGsvzK1eu1BVXXCFJuvjii/XLX/5SgUDAss73v/993XLLLZKk97///Xr88cctzz/55JN697vfLUlavny5fvnLX6q5ubnoz7B79261t7fnl4fezSdJH/3oR3XffffJMAzL6z760Y/qgQcekCS9+uqrOuuss4puHwAAAEBt4JxpAOdMAOAsBKAAAI439GRqtHbs2KG5c+fml4eeTD3xxBN673vfW/R1n/jEJ3TvvfdKGqh5vmTJkvxzl112mX71q1/J7/frjTfesJzoDPWud71Lv/3tb+Xz+fTmm29qxowZ+efOO+88rV69WnV1dXr99dc1c+bMUf9MQ0+mZsyYoR07digYDBast2XLFi1YsECSdM899+jTn/70qPcBAAAAwH04ZxrAORMAOAsl+AAAk0pLS0v+brpihpZg+O1vf5t/nMlktGrVKknSpZdeOuyJlDRwN93ga5577rn8+KFDh/SHP/xBknTdddeN6UTK7n3ve1/REylp4E7GSCQiSdq+ffu49wEAAABg8uGcCQBQKgSgAACu8pWvfEWmaR7zz9A7+YY666yz5PP5ht3+mWeemS8RsWHDhvz49u3bFYvFJEnnnHPOiHMc+vxrr72Wf/zHP/4x30D3ggsuGPkHPYbBu/WG09LSIknq6+ub0H4AAAAAuAvnTAM4ZwKA6iMABQCYVKZNmzbi8z6fT1OmTJEkHT58OD8+9PGxtjG0TvrQ13V2duYfDy0xMR51dXUjPu/xDHzEZ7PZCe0HAAAAwOTCORMAoFQIQAEAJhV789lqbQMAAAAAnIhzJgBAqRCAAgBMKgcOHBjx+Uwmk78Db/CuPvvjY21j//79RV/X1taWf7xv377RTRgAAAAAKohzJgBAqRCAAgBMKn/84x+VyWSGff5Pf/qTUqmUJOn000/Pj5944on5Eg4vvfTSiPtYs2ZN/vHQbZx11ln5OwF/97vfjX3yAAAAAFBmnDMBAEqFABQAYFI5fPiwnnrqqWGf/8EPfpB/fMkll+Qf+3w+XXjhhZKk3/zmN+ro6Bh2Gw888ED+NRdddFF+fMqUKTrvvPMkSf/5n/+pvXv3jutnAAAAAIBy4ZwJAFAqBKAAAJPO3/3d3xUtCbFq1Srdf//9kqTFixdr6dKlluc/+clPSpJSqZRuvvlmpdPpgm384Ac/0DPPPCNJuuaaawoa537hC1+QJMViMV177bXq6ekZdp4jnbABAAAAQLlwzgQAKAVftScAAMBYvPXWW3rttdeOuV44HNZJJ51UML5o0SJt3LhRixcv1he/+EUtW7ZMyWRSv/zlL/XNb35TmUxGPp9P3/3udwtee/nll+vaa6/VT37yEz3zzDM699xz9Xd/93dasGCBurq69Nhjj+XvBpwyZYruvvvugm1ceeWVuvnmm/Xggw/qxRdf1KmnnqrbbrtN559/vhobG9XZ2am1a9fq8ccf16JFi/Twww+P/R8JAAAAwKTFORMAwCkIQAEAXOXee+/Vvffee8z1Fi1apD/+8Y8F42eeeaZuu+023XrrrbrtttsKng8EAnrkkUd0zjnnFN3uo48+qkwmo5/97Gd69dVX9aEPfahgnZkzZ2rlypU6/vjji27jvvvuUzgc1ne/+13t3btXd95557A/AwAAAACMBedMAACnoAQfAGDS+chHPqLf//73ev/736+ZM2cqEAjo+OOP1/XXX69169bpAx/4wLCvDYVC+ulPf6onn3xS11xzTf71LS0tOuecc/Qv//Iv2rJli84888xht+H1evWd73xHa9eu1S233KL58+ervr5efr9f06dP16WXXqq7775b3/jGN8rw0wMAAADAyDhnAgCUgmGaplntSQAAUG5z587Vrl27dMMNN1CiAQAAAABsOGcCAJQaGVAAAAAAAAAAAAAoKQJQAAAAAAAAAAAAKCkCUAAAAAAAAAAAACgpAlAAAAAAAAAAAAAoKQJQAAAAAAAAAAAAKCnDNE2z2pMAAAAAAAAAAABA7SADCgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJUUACgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJUUACgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJUUACgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJUUACgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJUUACgAAAAAAAAAAACVFAAoAAAAAAAAAAAAlRQAKAAAAAAAAAAAAJUUACgAAAAAAAAAAACX1/wftPrkh9BKRswAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 469,
       "width": 848
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(vgg16_all_items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y-TKLVruCf2C"
   },
   "source": [
    "## VGG (16 Block) with Fine-Tuning only Final Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kxu6dYn1CnoK"
   },
   "source": [
    "### Setup the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5GvR2cAZlppu",
    "outputId": "40357225-750c-4136-a7bb-6f2878d3b89e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=2, bias=True)\n",
      "  )\n",
      ")\n",
      "Number of trainable parameters: 8194\n"
     ]
    }
   ],
   "source": [
    "vgg16_final = models.vgg16(pretrained=True)\n",
    "for param in vgg16_final.parameters():\n",
    "    param.requires_grad = False\n",
    "vgg16_final.classifier[6] = nn.Linear(4096, 2)\n",
    "for param in vgg16_final.classifier[6].parameters():\n",
    "    param.requires_grad = True\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg16_final.parameters(), lr=0.001)\n",
    "vgg16_final.to(device)\n",
    "print(vgg16_final)\n",
    "vgg16_params = sum(p.numel() for p in vgg16_final.parameters() if p.requires_grad)\n",
    "print(f\"Number of trainable parameters: {vgg16_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "llrEzmRblppu",
    "outputId": "3a619af7-0687-41c1-d05c-af0131d39510"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1 Loss: 0.24668943956494332 Training Accuracy: 1.0 Test Accuracy: 1.0\n",
      "Training time: 11.80798888206482\n",
      "Test accuracy of VGG 16 Block with fine-tuning only final layers = 1.0\n",
      "Train accuracy of VGG 16 Block with fine-tuning only final layers = 1.0\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(vgg16_final.parameters(), lr=0.001)\n",
    "start_time = time.time()\n",
    "vgg16_final_items = train(vgg16_final, train_loader, loss_fn, optimizer, device, num_epochs=1, model_name = \"vgg16_final\")\n",
    "end_time = time.time()\n",
    "vgg16_final_time = end_time - start_time\n",
    "print(f\"Training time: {vgg16_final_time}\")\n",
    "vgg16_final_test_accuracy = test(vgg16_final, test_loader, loss_fn, device)\n",
    "print(f\"Test accuracy of VGG 16 Block with fine-tuning only final layers = {vgg16_final_test_accuracy}\")\n",
    "vgg16_final_train_accuracy = test(vgg16_final, train_loader, loss_fn, device)\n",
    "print(f\"Train accuracy of VGG 16 Block with fine-tuning only final layers = {vgg16_final_train_accuracy}\")\n",
    "torch.save(vgg16_final.state_dict(), 'vgg16_final.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cHjS0JoIEwin"
   },
   "source": [
    "# Comparing different models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UC5dUC5NE3_1"
   },
   "source": [
    "### Training Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iyyahSan6cHQ",
    "outputId": "0fb5d103-f034-45a5-9bad-294a7452b5f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time for VGG 1: 66.70412063598633\n",
      "Training time for VGG 3: 72.6794114112854\n",
      "Training time for VGG 3 with Augmentation: 72.79175353050232\n",
      "Training time for VGG 16 (Fine-Tuning all layers): 327.3699164390564\n",
      "Training time for VGG 16 (Fine-Tuning final layer): 11.80798888206482\n"
     ]
    }
   ],
   "source": [
    "print(\"Training time for VGG 1:\", vgg1b_time)\n",
    "print(\"Training time for VGG 3:\", vgg3b_time)\n",
    "print(\"Training time for VGG 3 with Augmentation:\", vgg3b_aug_time)\n",
    "print(\"Training time for VGG 16 (Fine-Tuning all layers):\", vgg16_all_time)\n",
    "print(\"Training time for VGG 16 (Fine-Tuning final layer):\", vgg16_final_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HBhphAmqGuI0"
   },
   "source": [
    "### Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hFaI_OHe590A",
    "outputId": "0f58df14-bcd9-46bf-d851-b790270811c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy for VGG 1: tensor(1., device='cuda:0', dtype=torch.float64)\n",
      "Training Accuracy for VGG 3: tensor(1., device='cuda:0', dtype=torch.float64)\n",
      "Training Accuracy for VGG 3 with Augmentation: tensor(1., device='cuda:0', dtype=torch.float64)\n",
      "Training Accuracy for VGG 16 (Fine-Tuning all layers): tensor(0.6313, device='cuda:0', dtype=torch.float64)\n",
      "Training Accuracy for VGG 16 (Fine-Tuning final layer): tensor(1., device='cuda:0', dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Accuracy for VGG 1:\", vgg1b_train_accuracy)\n",
    "print(\"Training Accuracy for VGG 3:\", vgg3b_train_accuracy)\n",
    "print(\"Training Accuracy for VGG 3 with Augmentation:\", vgg3b_aug_train_accuracy)\n",
    "print(\"Training Accuracy for VGG 16 (Fine-Tuning all layers):\", vgg16_all_train_accuracy)\n",
    "print(\"Training Accuracy for VGG 16 (Fine-Tuning final layer):\", vgg16_final_train_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IUEFLcI7HL2v"
   },
   "source": [
    "### Test Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Tpba4UuFGwh",
    "outputId": "431ef8bd-8371-4021-9b0a-9ffe5d27ad6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy for VGG 1: tensor(0.8250, device='cuda:0', dtype=torch.float64)\n",
      "Test Accuracy for VGG 3: tensor(0.8500, device='cuda:0', dtype=torch.float64)\n",
      "Test Accuracy for VGG 3 with Augmentation: tensor(0.9000, device='cuda:0', dtype=torch.float64)\n",
      "Test Accuracy for VGG 16 (Fine-Tuning all layers): tensor(0.6500, device='cuda:0', dtype=torch.float64)\n",
      "Test Accuracy for VGG 16 (Fine-Tuning final layer): tensor(1., device='cuda:0', dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Accuracy for VGG 1:\", vgg1b_test_accuracy)\n",
    "print(\"Test Accuracy for VGG 3:\", vgg3b_test_accuracy)\n",
    "print(\"Test Accuracy for VGG 3 with Augmentation:\", vgg3b_aug_test_accuracy)\n",
    "print(\"Test Accuracy for VGG 16 (Fine-Tuning all layers):\", vgg16_all_test_accuracy)\n",
    "print(\"Test Accuracy for VGG 16 (Fine-Tuning final layer):\", vgg16_final_test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tc-L1JV1HOhg"
   },
   "source": [
    "### Training Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UuyZcpAdFO41",
    "outputId": "04a28b55-ed95-4877-99bf-5a18db878dec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Loss for VGG 1: 9.561210632114125e-06\n",
      "Training Loss for VGG 3: 0.02330785132944584\n",
      "Training Loss for VGG 3 with Augmentation: 0.02192441876977682\n",
      "Training Loss for VGG 16 (Fine-Tuning all layers): 0.6940552234649658\n",
      "Training Loss for VGG 16 (Fine-Tuning final layer): 0.24668943956494332\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Loss for VGG 1:\", vgg1b_items[0][-1])\n",
    "print(\"Training Loss for VGG 3:\", vgg3b_items[0][-1])\n",
    "print(\"Training Loss for VGG 3 with Augmentation:\", vgg3b_aug_items[0][-1])\n",
    "print(\"Training Loss for VGG 16 (Fine-Tuning all layers):\", vgg16_all_items[0][-1])\n",
    "print(\"Training Loss for VGG 16 (Fine-Tuning final layer):\", vgg16_final_items[0][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hd3X__ApHQM_"
   },
   "source": [
    "### Number of Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kEUwLsbfFWi4",
    "outputId": "72de16b1-eefd-4910-ac79-ea4123e62f7a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of parameters in VGG 1: 51381506\n",
      "Number of parameters in VGG 3: 12938690\n",
      "Number of parameters in VGG 3 with Augmentation: 12938690\n",
      "Number of parameters in VGG 16 (Fine-Tuning all layers): 134268738\n",
      "Number of parameters in VGG 16 (Fine-Tuning final layer): 8194\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of parameters in VGG 1:\", sum(p.numel() for p in vgg1b.parameters() if p.requires_grad))\n",
    "print(\"Number of parameters in VGG 3:\", sum(p.numel() for p in vgg3b.parameters() if p.requires_grad))\n",
    "print(\"Number of parameters in VGG 3 with Augmentation:\", sum(p.numel() for p in vgg3b_aug.parameters() if p.requires_grad))\n",
    "print(\"Number of parameters in VGG 16 (Fine-Tuning all layers):\", sum(p.numel() for p in vgg16_all.parameters() if p.requires_grad))\n",
    "print(\"Number of parameters in VGG 16 (Fine-Tuning final layer):\", sum(p.numel() for p in vgg16_final.parameters() if p.requires_grad))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fURFMI_O1Zy2"
   },
   "source": [
    "# Tabular Statistics\n",
    "| Model            | Training Time (in sec) | Training Loss | Training Accuracy | Testing Accuracy | Number of Model Parameters |\n",
    "|------------------|---------------|---------------|-------------------|------------------|----------------------------|\n",
    "| VGG (1 Block)          |    66.70          |  0.000009561210632114125             |     1.0              |    0.8250              |  51381506                          |\n",
    "| VGG (3 Block)          |    72.67           |   0.02330785132944584            |    1.0               |   0.8750               |                    12938690        |\n",
    "| VGG (3 Block) with Augmentation         |    72.79           |       0.02192441876977682        |    1.0               |     0.9000             |        12938690                    |\n",
    "| VGG16 (Fine-Tuning all Layers)          |    327.36           |        0.6940552234649658       |      0.6313             |   0.6500               |              134268738              |\n",
    "| VGG16 (Fine-Tuning final layer)          |     11.80          |      0.24668943956494332        |      1.0             |  1.0                |                     8194       |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Are the results as expected? Why or why not?<br>\n",
    "-> Yes, the results are as expected i.e. as we increase the model complexity we are getting better accuracy on the test data. Moreover, training time also increase as the complexity of the model increase. There is only 1 exception about fine-tuning all layers of vgg16 for which we get the least accuracy both in training and testing. This may be as it is a ery large model, it may be overfitting the data and not giving expected results.<br><br>\n",
    "- Does data augmentation help? Why or why not?<br>\n",
    "-> Yes, data augmentation helps in increasing model preformance as model see new differently oriented images. So, it can generalise better.<br><br>\n",
    "- Does it matter how many epochs you fine-tune the model? Why or why not?<br>\n",
    "-> For fine-tuning whole, the number of epochs matter as the model complexity is very high. But for fine-tuning oly the final layer, we can get good results in less epochs also.<br><br>\n",
    "- Are there any particular images that the model is confused about? Why or why not?<br>\n",
    "Yes, the images where the background of the images is similar to the other class or where the main object is not completely visible, it may be classified incorrectly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WLMq51PXI3ln",
    "outputId": "c3ed355f-14b3-41e7-99d4-73bc9bc1c618"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP(\n",
      "  (fc1): Linear(in_features=150528, out_features=2048, bias=True)\n",
      "  (relu1): ReLU()\n",
      "  (fc2): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "  (relu2): ReLU()\n",
      "  (fc3): Linear(in_features=1024, out_features=1000, bias=True)\n",
      "  (relu3): ReLU()\n",
      "  (fc4): Linear(in_features=1000, out_features=2, bias=True)\n",
      ")\n",
      "Number of trainable parameters: 311408570\n"
     ]
    }
   ],
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(224*224*3, 2048)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(2048, 1024)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.fc3 = nn.Linear(1024, 1000)\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.fc4 = nn.Linear(1000, 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.relu1(self.fc1(x))\n",
    "        x = self.relu2(self.fc2(x))\n",
    "        x = self.relu3(self.fc3(x))\n",
    "        x = self.fc4(x)\n",
    "        return x\n",
    "\n",
    "mlp = MLP().to(device)\n",
    "print(mlp)\n",
    "print(f\"Number of trainable parameters: {sum(p.numel() for p in mlp.parameters() if p.requires_grad)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ataQfPbPQkxm",
    "outputId": "8495eeb8-d503-4079-aca4-bd64192cb1d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10 Loss: 14.148894608020782 Training Accuracy: 0.6000000000000001 Test Accuracy: 0.5750000000000001\n",
      "Epoch 2/10 Loss: 2.6202272534370423 Training Accuracy: 0.86875 Test Accuracy: 0.775\n",
      "Epoch 3/10 Loss: 0.8985718458890914 Training Accuracy: 0.9125000000000001 Test Accuracy: 0.775\n",
      "Epoch 4/10 Loss: 0.8409863322973251 Training Accuracy: 0.95625 Test Accuracy: 0.75\n",
      "Epoch 5/10 Loss: 0.2874776036478579 Training Accuracy: 0.925 Test Accuracy: 0.7250000000000001\n",
      "Epoch 6/10 Loss: 0.6313084788620472 Training Accuracy: 0.9875 Test Accuracy: 0.775\n",
      "Epoch 7/10 Loss: 0.07953344001434744 Training Accuracy: 0.99375 Test Accuracy: 0.775\n",
      "Epoch 8/10 Loss: 0.008618661004584282 Training Accuracy: 0.9875 Test Accuracy: 0.775\n",
      "Epoch 9/10 Loss: 0.017626477222074756 Training Accuracy: 0.9812500000000001 Test Accuracy: 0.775\n",
      "Epoch 10/10 Loss: 0.06666219720209483 Training Accuracy: 0.99375 Test Accuracy: 0.75\n",
      "Training time: 68.82343029975891\n",
      "Test accuracy of MLP = 0.75\n",
      "Train accuracy of MLP = 0.99375\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(mlp.parameters(), lr=0.001)\n",
    "start_time = time.time()\n",
    "mlp_items = train(mlp, train_loader, loss_fn, optimizer, device, num_epochs=10, model_name = \"mlp\")\n",
    "end_time = time.time()\n",
    "mlp_time = end_time - start_time\n",
    "print(f\"Training time: {mlp_time}\")\n",
    "mlp_test_accuracy = test(mlp, test_loader, loss_fn, device)\n",
    "print(f\"Test accuracy of MLP = {mlp_test_accuracy}\")\n",
    "mlp_train_accuracy = test(mlp, train_loader, loss_fn, device)\n",
    "print(f\"Train accuracy of MLP = {mlp_train_accuracy}\")\n",
    "torch.save(mlp.state_dict(), 'mlp.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "Ys_cMHTHRprS",
    "outputId": "bc8afc66-c90d-4a45-a4f3-b86cf09ce524"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 469,
       "width": 841
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(mlp_items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MLP model is of the complexity comparable to VGG 16 (alost double the number of parameters). The training time of the model is similar to VGG1 Block and 3 Block. However, it has more training loss and less accuracy. From this, we can conclude that mlp is not able to capture the features properly and hence giving worse results. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WdoiVJmZR6m2"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}