05_CodingDrill/CodingDrill_05.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satyajitghana/TSAI-DeepVision-EVA4.0/blob/master/05_CodingDrill/CodingDrill_05.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXuPMu9zb_s3",
        "colab_type": "text"
      },
      "source": [
        "# Coding Drill 05"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HR3_w_wrb7Bs",
        "colab_type": "text"
      },
      "source": [
        "## 1. Target\n",
        "\n",
        "- Fine Tune the Transforms, set rotation to `-10deg to 10deg`\n",
        "- Use the OneCycleLR Scheduler since the PyTorch Documentation says\n",
        "\n",
        "> Sets the learning rate of each parameter group according to the 1cycle learning rate policy. The 1cycle policy anneals the learning rate from an initial learning rate to some maximum learning rate and then from that maximum learning rate to some minimum learning rate much lower than the initial learning rate. This policy was initially described in the paper **Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.**\n",
        "\n",
        "> The 1cycle learning rate policy changes the learning rate after every batch. step should be called after a batch has been used for training.\n",
        "\n",
        "## 2. Result\n",
        "\n",
        "- Params: `9,962`\n",
        "- Best Train Accuracy: `98.12%`\n",
        "- Best Test Accuracy : `99.43%`\n",
        "\n",
        "## 3. Analysis\n",
        "\n",
        "- Now the learning is consistent, the accuracy increases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x93nIsZUcGgi",
        "colab_type": "text"
      },
      "source": [
        "## Layer Added Model\n",
        "\n",
        "- Convolution Block 1\n",
        "    - `Conv2D:  1, 14, 3; Out: 26x26x14`\n",
        "    - `Conv2D: 14, 30, 3; Out: 24x24x30`\n",
        "\n",
        "- Transition Block\n",
        "    - `Conv2D: 30, 10, 1; Out: 24x24x10`\n",
        "    - `MaxPool2D: 2x2;    Out: 12x12x10`\n",
        "\n",
        "- Convolution Block 2\n",
        "    - `Conv2D: 10, 14, 3; Out: 10x10x14`\n",
        "    - `Conv2D: 14, 15, 3; Out: 8x8x15`\n",
        "    - `Conv2D: 15, 15, 3; Out: 6x6x15`\n",
        "\n",
        "- GAP\n",
        "    - `AvgPool2D: 6x6;    Out: 1x1x15`\n",
        "    - `Conv2D: 15, 15, 1; Out: 1x1x15`\n",
        "    - `Conv2D: 15, 10, 1; Out: 1x1x10`\n",
        "\n",
        "\n",
        "Legend\n",
        "- `Conv2D (input_channels), (output_channels), (kernel_size); Out: (output_dim)`\n",
        "- `MaxPool2D: (kernel_size); Out: (output_dim)`\n",
        "- `AvgPool2D: (kernel_size); Out: (output_dim)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-7t1Y7-hV4",
        "colab_type": "text"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kH16rnZ7wt_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ky3f_Odl-7um",
        "colab_type": "text"
      },
      "source": [
        "## Data Transformations\n",
        "\n",
        "We first start with defining our data transformations. We need to think what our data is and how can we augment it to correct represent images which it might not see otherwise. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtssFUKb-jqx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train Phase transformations\n",
        "train_transforms = transforms.Compose([\n",
        "                                       transforms.RandomRotation((-10.0, 10.0), fill=(1,)),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.1307,), (0.3081,)) # The mean and std have to be sequences (e.g., tuples), therefore you should add a comma after the values. \n",
        "                                       # Note the difference between (0.1307) and (0.1307,)\n",
        "                                       ])\n",
        "\n",
        "# Test Phase transformations\n",
        "test_transforms = transforms.Compose([\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                                       ])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQciFYo2B1mO",
        "colab_type": "text"
      },
      "source": [
        "# Dataset and Creating Train/Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4A84rlfDA23",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = datasets.MNIST('./data', train=True, download=True, transform=train_transforms)\n",
        "test = datasets.MNIST('./data', train=False, download=True, transform=test_transforms)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgldp_3-Dn0c",
        "colab_type": "text"
      },
      "source": [
        "# Dataloader Arguments & Test/Train Dataloaders\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8OLDR79DrHG",
        "colab_type": "code",
        "outputId": "4578d126-420b-42a3-b28a-026c5b6e0411",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "SEED = 1\n",
        "\n",
        "# CUDA?\n",
        "cuda = torch.cuda.is_available()\n",
        "print(\"CUDA Available?\", cuda)\n",
        "\n",
        "# For reproducibility\n",
        "torch.manual_seed(SEED)\n",
        "\n",
        "if cuda:\n",
        "    torch.cuda.manual_seed(SEED)\n",
        "\n",
        "# note about pin_memory\n",
        "# If you load your samples in the Dataset on CPU and would like to push it\n",
        "# during training to the GPU, you can speed up the host to device transfer by\n",
        "# enabling pin_memory. This lets your DataLoader allocate the samples in\n",
        "# page-locked memory, which speeds-up the transfer.\n",
        "\n",
        "# dataloader arguments - something you'll fetch these from cmdprmt\n",
        "dataloader_args = dict(shuffle=True, batch_size=128, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n",
        "\n",
        "# train dataloader\n",
        "train_loader = torch.utils.data.DataLoader(train, **dataloader_args)\n",
        "\n",
        "# test dataloader\n",
        "test_loader = torch.utils.data.DataLoader(test, **dataloader_args)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CUDA Available? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubQL3H6RJL3h",
        "colab_type": "text"
      },
      "source": [
        "# The model\n",
        "Let's start with the model we first saw"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FXQlB9kH1ov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn.functional as F\n",
        "dropout_value = 0.1\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        # Input Block\n",
        "        self.convblock1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=1, out_channels=14, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(14),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 26\n",
        "\n",
        "        # CONVOLUTION BLOCK 1\n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=14, out_channels=30, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(30),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 24\n",
        "\n",
        "        # TRANSITION BLOCK 1\n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=30, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "        ) # output_size = 24\n",
        "        self.pool1 = nn.MaxPool2d(2, 2) # output_size = 12\n",
        "\n",
        "        # CONVOLUTION BLOCK 2\n",
        "        self.convblock4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=10, out_channels=14, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),            \n",
        "            nn.BatchNorm2d(14),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 10\n",
        "        self.convblock5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=14, out_channels=15, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),            \n",
        "            nn.BatchNorm2d(15),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 8\n",
        "        self.convblock6 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=15, out_channels=15, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),            \n",
        "            nn.BatchNorm2d(15),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 6\n",
        "        \n",
        "        # OUTPUT BLOCK\n",
        "        self.gap = nn.Sequential(\n",
        "            nn.AvgPool2d(kernel_size=6)\n",
        "        ) # output_size = 1\n",
        "\n",
        "        self.convblock7 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=15, out_channels=15, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(15),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "        )\n",
        "\n",
        "        self.convblock8 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=15, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "        )\n",
        "\n",
        "        self.dropout = nn.Dropout(dropout_value)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.convblock1(x)\n",
        "        x = self.convblock2(x)\n",
        "        x = self.convblock3(x)\n",
        "        x = self.pool1(x)\n",
        "        x = self.convblock4(x)\n",
        "        x = self.convblock5(x)\n",
        "        x = self.convblock6(x)\n",
        "        x = self.gap(x)        \n",
        "        x = self.convblock7(x)\n",
        "        x = self.convblock8(x)\n",
        "\n",
        "        x = x.view(-1, 10)\n",
        "        return F.log_softmax(x, dim=-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3-vp8X9LCWo",
        "colab_type": "text"
      },
      "source": [
        "# Model Params\n",
        "Can't emphasize on how important viewing Model Summary is. \n",
        "Unfortunately, there is no in-built model visualizer, so we have to take external help"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5skB97zIJQQe",
        "colab_type": "code",
        "outputId": "266dd711-d8a9-4db0-bde7-6a5d70d5cf27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "!pip install torchsummary\n",
        "from torchsummary import summary\n",
        "use_cuda = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "print(device)\n",
        "model = Net().to(device)\n",
        "summary(model, input_size=(1, 28, 28))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\n",
            "cuda\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 14, 26, 26]             126\n",
            "              ReLU-2           [-1, 14, 26, 26]               0\n",
            "       BatchNorm2d-3           [-1, 14, 26, 26]              28\n",
            "           Dropout-4           [-1, 14, 26, 26]               0\n",
            "            Conv2d-5           [-1, 30, 24, 24]           3,780\n",
            "              ReLU-6           [-1, 30, 24, 24]               0\n",
            "       BatchNorm2d-7           [-1, 30, 24, 24]              60\n",
            "           Dropout-8           [-1, 30, 24, 24]               0\n",
            "            Conv2d-9           [-1, 10, 24, 24]             300\n",
            "        MaxPool2d-10           [-1, 10, 12, 12]               0\n",
            "           Conv2d-11           [-1, 14, 10, 10]           1,260\n",
            "             ReLU-12           [-1, 14, 10, 10]               0\n",
            "      BatchNorm2d-13           [-1, 14, 10, 10]              28\n",
            "          Dropout-14           [-1, 14, 10, 10]               0\n",
            "           Conv2d-15             [-1, 15, 8, 8]           1,890\n",
            "             ReLU-16             [-1, 15, 8, 8]               0\n",
            "      BatchNorm2d-17             [-1, 15, 8, 8]              30\n",
            "          Dropout-18             [-1, 15, 8, 8]               0\n",
            "           Conv2d-19             [-1, 15, 6, 6]           2,025\n",
            "             ReLU-20             [-1, 15, 6, 6]               0\n",
            "      BatchNorm2d-21             [-1, 15, 6, 6]              30\n",
            "          Dropout-22             [-1, 15, 6, 6]               0\n",
            "        AvgPool2d-23             [-1, 15, 1, 1]               0\n",
            "           Conv2d-24             [-1, 15, 1, 1]             225\n",
            "      BatchNorm2d-25             [-1, 15, 1, 1]              30\n",
            "             ReLU-26             [-1, 15, 1, 1]               0\n",
            "          Dropout-27             [-1, 15, 1, 1]               0\n",
            "           Conv2d-28             [-1, 10, 1, 1]             150\n",
            "================================================================\n",
            "Total params: 9,962\n",
            "Trainable params: 9,962\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.96\n",
            "Params size (MB): 0.04\n",
            "Estimated Total Size (MB): 1.00\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1__x_SbrL7z3",
        "colab_type": "text"
      },
      "source": [
        "# Training and Testing\n",
        "\n",
        "Looking at logs can be boring, so we'll introduce **tqdm** progressbar to get cooler logs. \n",
        "\n",
        "Let's write train and test functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbkF2nN_LYIb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# this automatically selects tqdm for colab_notebook\n",
        "from tqdm.auto import tqdm, trange\n",
        "\n",
        "train_losses = []\n",
        "test_losses = []\n",
        "train_acc = []\n",
        "test_acc = []\n",
        "\n",
        "def train(model, device, train_loader, optimizer, epoch):\n",
        "  model.train()\n",
        "  pbar = tqdm(train_loader, ncols=\"80%\")\n",
        "  correct = 0\n",
        "  processed = 0\n",
        "  for batch_idx, (data, target) in enumerate(pbar):\n",
        "    # get samples\n",
        "    data, target = data.to(device), target.to(device)\n",
        "\n",
        "    # Init\n",
        "    optimizer.zero_grad()\n",
        "    # In PyTorch, we need to set the gradients to zero before starting to do \n",
        "    # backpropragation because PyTorch accumulates the gradients on subsequent \n",
        "    # backward passes. \n",
        "    # Because of this, when you start your training loop, ideally you should \n",
        "    # zero out the gradients so that you do the parameter update correctly.\n",
        "\n",
        "    # Predict\n",
        "    y_pred = model(data)\n",
        "\n",
        "    # Calculate loss\n",
        "    loss = F.nll_loss(y_pred, target)\n",
        "    train_losses.append(loss)\n",
        "\n",
        "    # Backpropagation\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    # Decay the Learning Rate\n",
        "    scheduler.step()\n",
        "    \n",
        "    # get the index of the max log-probability\n",
        "    pred = y_pred.argmax(dim=1, keepdim=True)\n",
        "    correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "    processed += len(data)\n",
        "\n",
        "    # Update pbar-tqdm\n",
        "    pbar.set_description(desc= f'Loss={loss.item()} Batch_id={batch_idx} Accuracy={100*correct/processed:0.2f}')\n",
        "    train_acc.append(100*correct/processed)\n",
        "\n",
        "def test(model, device, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            # sum up batch loss\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item()\n",
        "            # get the index of the max log-probability\n",
        "            pred = output.argmax(dim=1, keepdim=True)\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    test_losses.append(test_loss)\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))\n",
        "    \n",
        "    test_acc.append(100. * correct / len(test_loader.dataset))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aE5Le6FYHhc8",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "c287bf0611f540b1a152f8c311b57711"
          ]
        }
      },
      "source": [
        "from torch.optim.lr_scheduler import OneCycleLR\n",
        "\n",
        "model =  Net().to(device)\n",
        "\n",
        "EPOCHS = 15\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "scheduler = OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(train_loader), epochs=EPOCHS)\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    # Print Learning Rate\n",
        "    print(\"EPOCH:\", epoch+1, 'LR:', scheduler.get_lr())\n",
        "    # Train the model\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    # Validate the model\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "EPOCH: 1 LR: [0.0003999999999999993]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "679035d9adc64956b2dac6038d936506",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.5134, Accuracy: 9353/10000 (93.53%)\n",
            "\n",
            "EPOCH: 2 LR: [0.0015240079703465938]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b1be0d55e31749a281a92afb1e1abe26",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0846, Accuracy: 9801/10000 (98.01%)\n",
            "\n",
            "EPOCH: 3 LR: [0.004369617749135262]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "737f7c4ab09c4624b14126126d8b3505",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0564, Accuracy: 9848/10000 (98.48%)\n",
            "\n",
            "EPOCH: 4 LR: [0.0076041259730932256]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4ee7c8867a644d0b36ff1076c7a38af",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0405, Accuracy: 9879/10000 (98.79%)\n",
            "\n",
            "EPOCH: 5 LR: [0.009712693882270338]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41aecb87fa874e5e98edfaeb2030ac55",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0412, Accuracy: 9881/10000 (98.81%)\n",
            "\n",
            "EPOCH: 6 LR: [0.009943677941594807]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f777c54b33834c5b9d997d177fa968c8",
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            },
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              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0312, Accuracy: 9908/10000 (99.08%)\n",
            "\n",
            "EPOCH: 7 LR: [0.009503461424944768]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ef7d058b74d4c058f9ea1ba211027e6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0286, Accuracy: 9912/10000 (99.12%)\n",
            "\n",
            "EPOCH: 8 LR: [0.008663094374661208]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc94dedcf3b8403c87de9599cccc6382",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0349, Accuracy: 9890/10000 (98.90%)\n",
            "\n",
            "EPOCH: 9 LR: [0.00749724709105188]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b335daf9fce40cea5ba221cff79d68e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0281, Accuracy: 9916/10000 (99.16%)\n",
            "\n",
            "EPOCH: 10 LR: [0.006109510221502005]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ec7e3efe44a420392657b8535ac1d43",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0297, Accuracy: 9910/10000 (99.10%)\n",
            "\n",
            "EPOCH: 11 LR: [0.004623190276854037]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f60ee5c8d7a34a039db716fca45a9aea",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0245, Accuracy: 9925/10000 (99.25%)\n",
            "\n",
            "EPOCH: 12 LR: [0.0031703533067975903]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a2d3b086b8a4d8b85f24ea342e8cdb4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0225, Accuracy: 9938/10000 (99.38%)\n",
            "\n",
            "EPOCH: 13 LR: [0.0018800902517922089]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35e5ce6a00cd4897bf2697fffed34006",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0215, Accuracy: 9933/10000 (99.33%)\n",
            "\n",
            "EPOCH: 14 LR: [0.0008670466465012771]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2a8a724a344345d8a2df6f7d143cf2ab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0205, Accuracy: 9943/10000 (99.43%)\n",
            "\n",
            "EPOCH: 15 LR: [0.00022123586092353013]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74341ac1a9b14336a6dba60eca70dff4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0201, Accuracy: 9942/10000 (99.42%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drokW8wWODKq",
        "colab_type": "text"
      },
      "source": [
        "# Plot the model's learning progress\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87RaqGSEOWDe",
        "colab_type": "code",
        "outputId": "42311898-2ea6-405e-9278-2e94548e58a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n",
        "plt.style.use(\"dark_background\")\n",
        "\n",
        "fig, axs = plt.subplots(2,2,figsize=(15,10))\n",
        "axs[0, 0].plot(train_losses)\n",
        "axs[0, 0].set_title(\"Training Loss\")\n",
        "axs[1, 0].plot(train_acc[4000:])\n",
        "axs[1, 0].set_title(\"Training Accuracy\")\n",
        "axs[0, 1].plot(test_losses)\n",
        "axs[0, 1].set_title(\"Test Loss\")\n",
        "axs[1, 1].plot(test_acc)\n",
        "axs[1, 1].set_title(\"Test Accuracy\")\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TOomIGiwmfERERE6Wk5ODY8eOISEhAQCQkJCA9PR0q+mcFWvVdu/eHZGRkcjIyHBqrD5u\nHvALZcJHRNRQcUpnBe5eXigpKhI7DCIiagQSExOxdu1avPHGG9Dr9ZgwYQIAYOfOnXjjjTfw66+/\nYtGiRYiLi4PRaERJSQnGjx+P7GznlkfwcfdAi1YtIVcqYDKwFh8RUUPDhK8ChZsSYL5HREROkJGR\ngd69e1sdHzp0qHl70qRJTozItubuHpArFGgR1Aq6TI3Y4RARUS1xSmcFrvDQJxERkT35uHkAYGkG\nIqKGigkfgJaeTQAAMjl/HERERBU1d2fCR0TUkDHDARDs3RQA0KJVS5EjISIikpambu4wGY3wCwup\nvjEREUkOEz4Av+vKHoDvene8yJEQERFJi0Imx+WsS/ANCRI7FCIiqgMmfADkN57dU7i5iRwJERGR\n9Og0Wk7pJCJqoJjwAZDjRsKn5KKlRERElek1Wk7pJCJqoJjwAVDcGOGTKxUiR0JERCQ9ukwNfAID\nOBOGiKgBYsIHwM/DCwBgMrKgLBERUWU6TRbkcjl8g1uJHQoREdUSEz4A8UERAIC7Jj4mciRERETS\no1OXFVz3C+VzfEREDQ0TPgBK1t8jIiKqkk6tBQD4cuEWIqIGh5kO+EMgIiK6lSuXcmAsNcAvlAu3\nEBE1NMx1AMhuLNpCRERE1gSTCfqsLE7pJCJqgJjw4WYdPiIiIrJNr2bCR0TUEDHhA8B0j4iI6NZY\nfJ2IqGFiwgdO6SQiIqqOTl1Wi0/p4SF2KEREVAtM+ADIOcZHRER0SzpN2UqdfiFBIkdCRES1wYQP\ngLfSzbzdJqabiJEQERFJky6TpRmIiBoiJnywrMN333NPixgJERGRNJlH+LhwCxFRg8KErxKlu1v1\njYiIiBqZgpxcGEpKmPARETUwTPgqCe7QXuwQiIiIJEcQBOg1WVypk4iogWHCV4ln0yZih0BERCRJ\nOo0WfqEhYodBRES1wITPhhZBrcQOgYiISHJ0ai18uUonEVGDwoTvhjcHDDNv90t4SMRIiIiIpEmn\n1qKZvx/cvbzEDoWIiGqICd8NpcXF5m25QiFiJERERNKk15SXZuAoHxFRQ8GE7waTwWDelsuZ8BER\nEVWWp9YAABduISJqQJjw3WAymszbbp4eIkZCREQkTXr1jVp8YVy4hYiooWDCd4OhpMS8HRjZWsRI\niIiIpKkgT4fS68Uc4SMiakCY8NnQvmes2CEQERFJUllpBiZ8REQNhcMSPj8/P+zcuRN///03/vjj\nD2zZsgUBAQFW7by8vLBp0yacOnUKf/31F4YOHeqokIiIiKiedBotfJnwERE1GA5L+ARBwNKlS9Gx\nY0d069YNZ86cweLFi63aTZs2Dfn5+YiKisLw4cPxySefoEkTFj8nIiKSIl2mhlM6iYgaEIclfHq9\nHvv27TPvHz58GBEREVbtRo8ejdTUVADA6dOncfToUQwePNhRYdWYbzCXnCYiIqpMr9GiSYvm8Gji\nLXYoRERUA055hk8mk+HZZ5/F9u3brd5r3bo1Lly4YN6/ePEiwsPDnRGWlZSnkszbYxe/KUoMRERE\nUpZXvlInp3USETUISmdc5IMPPsDVq1exatUqh5z//Pnz9T6HIAi4bjTgw7+OAgBie/8fBEGo93md\nrSHGXBn7IA3sgzS4Qh8A1+kHVSjNEBIM7T9nRI6GiIiq4/CEb9myZebn82zd8C9evIiIiAjk5uYC\nKBvx+/7772t1jcjISItRwtoSBAEymQwAsOL4IQCAvuS6+VhDUbEfDRX7IA3sgzS4Qh8A+/UjIiLC\nLl/wUf3oNOUjfKzFR0TUEDh0SufChQsRFxeHUaNGoaRCnbuKPv/8c0yePBkA0L59e/Ts2RNff/21\nI8MiIiKiOrqmv4ziwkKu1ElE1EA4LOHr3LkzZs2ahZCQEBw8eBDp6en48ssvAQDp6ekIDi67USxb\ntgwtWrTAqVOnsGPHDjzzzDO4evWqo8IiIiKietKptfBnwkdE1CA4bErnyZMnq5zCExMTY94uLCzE\no48+6qgw6iVmyL1I3/Wt2GEQEZELioqKwtq1a+Hv74+8vDxMmDABp0+fttm2Q4cOSE9Px4cffojp\n06c7OVJrOrUWvizNQETUIDhllc6GatySeWKHQERELiolJQXJycmIjo5GcnKyuURRZXK5HKmpqdi6\ndauTI6yaXqNlLT4iogaCCR8REZGTBQYGIjY2FmlpaQCAtLQ0xMbGIiAgwKrtjBkzsGPHDvzzzz/O\nDrNKOrUWXj7N4OXTTOxQiIioGkz4Kjn61W6xQyAiIhcXHh4OtVoNk8kEADCZTNBoNFZ1aLt164b7\n7rsP7777rhhhVkmn1gAAR/mIiBoAp9Tha0h2v5+KHsMHm/eV7u4wVLHCKBERkaMolUqsXr0ajz/+\nuDkxrAt71aqtKLvoGjaeOY7vDx9CVHO/ep/fGVylFqQr9MMV+gC4Rj/YB+lwZD+Y8FVyOSvbYl8m\nb/g1sIiISFpUKhVCQ0Mhl8thMpkgl8sREhIClUplbhMcHIx27dph165dAIAWLVpAJpPBx8fHXM6o\nJuxZq7acl08zLDjwLZ568XnsX7epzud2Fta0lA5X6APgGv1gH6Sjvv2ork4tE75quMiXBkREJCE5\nOTk4duwYEhISsHHjRiQkJCA9PR25ubnmNiqVCoGBgeb9uXPnomnTppJYpbMovwBFBVc5pZOIqAHg\nM3zVGDT5cbFDICIiF5SYmIikpCRkZGQgKSkJiYmJAICdO3ciLi5O5Oiqp9do4RcaInYYRERUDY7w\nVeOepydi9/spYodBREQuJiMjA71797Y6PnToUJvt582TVqkgnVoD//AwscMgIqJqcISPiIiIak2n\nzoJfKKd0EhFJHRM+IiIiqjWdWgMPb280adFc7FCIiOgWmPDVwKgZUyFXKMQOg4iISDJ0Gi0AwJcL\ntxARSRoTPhsOfb7VYv+OsY+ibdztIkVDREQkPTp1WcLnF8aFW4iIpIwJnw1fvLVE7BCIiIgkTX9j\nhI+lGYiIpI0JHxEREdXa9avXUHglnwu3EBFJHBO+GhJYgZ2IiMhCnlrDhI+ISOKY8FXhxPf7LQ8w\n4SMiIrKgV2u5aAsRkcQx4avCwU1fWuwr3d1FioSIiEiadBotn+EjIpI4JnxVuHIpx2L/mdT3RIqE\niIhImnRqLdy9PNHU31fsUIiIqApM+KqQdfqs2CEQERFJmrk0QyhLMxARSRUTvlv4YNwzYodAREQk\nWSzNQEQkfUz4biEvUy12CERERJJ1c4SPCR8RkVQx4buFgjyd2CEQERFJVklREa7q9JzSSUQkYUz4\niIiIqM50ai38QoLEDoOIiKrAhK8WXtz4idghEBERSYpOw1p8RERSxoSvFiK6dRE7BCIiIknRq7Xw\nCw2GTCYTOxQiIrKBCR8RERHVWZ5aA6W7O5oF+IsdChER2cCErxrvJTwpdghERESSZS7NwIVbiIgk\niQlfNS6dPW+xL1cqxAmEiIhIgm6WZuDCLUREUsSErxol169b7HPhFiIiopt0miwA4MItREQSxYSv\nGoLJhDcHDDPvh3fuKGI0RERE0mIoLkZ+bh78OaWTiEiSmPDVQEFuntghEBERSZZOrYFvKEf4iIik\niAlfHcgVfI6PiIioXHlpBiIikh4mfHUwaPLjYodAREQkGXlqLXyDgiCT858VRERSw9/MdRAU1U7s\nEIiIiCRDr9FC4aZE88AAsUMhIqJKmPDVgVzBHxsREVE5c2mGMC7cQkQkNcxc6iC67/+JHQIREZFk\n6NQaACzNQEQkRUz46sDNw0PsEIiIiCRDr82GyWSCPxduISKSHIcmfMuWLcPZs2chCAK6dOlis83c\nuXORnZ2N9PR0pKenY9WqVY4Mqc7WTHnNYj8gIlykSIiIiKTFWFqK/JxclmYgIpIghyZ8W7duRXx8\nPM6fP3/LduvWrUNMTAxiYmLwwgsvODKkOjvx3X6L/Zk7/iNSJERERNKjV2vhxymdRESS49CE78CB\nA8jMzHTkJUTl0cRb7BCIiIgkQafRwi+Ui7YQEUmNUuwAAGDMmDG49957kZWVhblz5+Lw4cO1+nx1\nI4g1IQhCtW3eOWEZ1w9nM9C7ZVi9r21PNemH1LEP0sA+SIMr9AFwnX5Q1XRqLW6//x7IFQqYjEax\nwyEiohtET/hSUlKwcOFCGAwG3HPPPdi2bRs6deoEnU5X43NERkbiwoULdY5BEATIZLJq27248RNE\ndLv5LOKiZUvx1fIP6nxde6tpP6SMfZAG9kEaXKEPgP36ERERYZcv+MgxdGotFEolWgS1NJdpICIi\n8Ym+Smd2djYMBgMAYM+ePVCpVOjatavIUdlWUlRksc/VOomIiMqwNAMRkTSJnvCFhNyc79+9e3dE\nRkYiIyNDxIiqtn3Z+2KHQEREJEk6zY3i61ypk4hIUhya8K1cuRIqlQphYWHYs2cPTpw4AQDYuXMn\n4uLiAACLFi3C8ePHcezYMXz88ccYP348srOzHRlWnWkyTokdAhERkSRdzsqGyWjkwi1ERBLj0Gf4\npkyZgilTplgdHzp0qHl70qRJjgzBofqNeQhfLlwudhhERNTAREVFYe3atfD390deXh4mTJiA06dP\nW7SZNGkSpk6dCpPJBIVCgY8//hgffCCd58YrMxmMuJx9iaUZiIgkRvQpnURERI1NSkoKkpOTER0d\njeTkZKSmplq12bJlC7p3746YmBj07dsXr7zyCm677TYRoq05vSaLUzqJiCSGCR8REZETBQYGIjY2\nFmlpaQCAtLQ0xMbGIiAgwKJdQUGBedvb2xtubm6SL2+hU2uY8BERSYzoZRkaOplMJvkbMBERSUd4\neDjUajVMJhMAwGQyQaPRIDw8HLm5uRZthw8fjrfffhvt2rXDzJkzzc/C14azatUCwMFsFQ7nqGEw\nGqGQS+s7ZVe5V7tCP1yhD4Br9IN9kA5H9oMJXz11GRCPE9/tEzsMIiJyQV999RW++uorhIeHY+vW\nrdi1axf++eefWp3DWbVqAaDnyCEYs+B1tIqMQJ4qs87XtDfWtJQOV+gD4Br9YB+ko779qK5OrbS+\nfmsAPnl+msW+mydr8RERUc2pVCqEhoZCfmMETC6XIyQkBCqV6pafOXLkCIYNG+asMOskT83SDERE\nUsOEr5b+2n/A8oCLDCMTEZFz5OTk4NixY0hISAAAJCQkID093Wo6Z8eOHc3b/v7+GDBgAI4fP+7U\nWGtLX57whQSJHAkREZVjwldPMgV/hEREVDuJiYlISkpCRkYGkpKSkJiYCMCyTu0zzzyDEydOID09\nHXv37sWqVavwv//9T8ywq3XlUg6MpQbW4iMikhA+w1dPw19Jwm87vhE7DCIiakAyMjLQu3dvq+MV\n69S+/PLLzgzJLkxGIy5nZ3NKJxGRhHB4qp58AvzFDoGIiEgydGotR/iIiCSECZ8duMLqQERERPag\nU2vhy2f4iIgkgwmfHSz/4yDkCoXYYRAREYlOp9GiectAKN3dxQ6FiIjAhK9O1r48y+pYzOBBIkRC\nREQkLeUrdXKUj4hIGpjw1cEf//se+TmWy2ezHh8RERGgU2sAAH4hXLiFiEgKmPDVkclkstiPHz9G\npEiIiIikQ6cpL77OhVuIiKSACV8d/S91jcV+q7aR4gRCREQkIfmXcmEoLYVfKKd0EhFJARO+Ovr1\nq91ih0BERCQ5giBAr8mCL6d0EhFJAhO+Oiq9Xmx1zCcwQIRIiIiIpEWvYS0+IiKpYMJnR21ju4sd\nAhERkejy1Br4hXKEj4hICpjw2dH45QvEDoGIiEh0enUWmvn7wd3LU+xQiIgaPSZ8REREZFflpRl8\ng7lwCxGR2JjwERERkV2Vl2bw5bROIiLRMeEjIiIiu9KpyxI+fy7cQkQkOiZ89fDft98ROwQiIiLJ\nKcjNQ+n1YpZmICKSACZ89fDTZ59bHWvi20KESIiIiKRFr83iSp1ERBLAhM/OPLy9xA6BiIhIdCzN\nQEQkDUz4iIiIyO70ai38OKWTiEh0TPjsTBAEsUMgIiISnU6jRRPfFvDw9hY7FCKiRo0JHxEREdld\n+UqdLM1ARCQuJnxERERkd+UJH6d1EhGJiwlfPeVlaiwPcEYnERERdOqy+yMXbiEiEhcTvnpa+diT\n+M+bb5v3OXWFiIgIuKa/jOLCIiZ8REQiY8JXT9f0l5H559/m/cffWyxiNERERNKh12jhFxoidhhE\nRI0aEz47qLgyp3dzHxEjIfG6UGUAACAASURBVCIikg4dSzMQEYmOCZ8dVC7FoHR3FykSIiIi6dBp\ntPANDRI7DCKiRo0Jn11YJnxLft0HLx+O9BERUeOmy9TA28cHns2aih0KEVGjxYTPLmRWR3wC/ESI\ng4iISDp0GpZmICISm8MSvmXLluHs2bMQBAFdunSxfXG5HKtWrcLp06dx6tQpPPnkk44Kx6HyL+VY\nH5RZJ4FERESNib484ePCLUREonFYwrd161bEx8fj/PnzVbYZO3Ys2rdvj6ioKPTp0wdvvvkmIiIi\nHBWSw1y7fMXqmEzOwVMiImrc8jLLEz6O8BERicVhWcmBAweQmZl5yzajR4/Gxx9/DEEQkJubi61b\nt+KRRx5xVEgOVVJ03WKfA3xERNTYFeXn4/rVa/AN4cItRERiEXUYqnXr1rhw4YJ5/+LFiwgPDxcx\norqTWWV4zPiIiIh0ag38OaWTiEg0SrEDsIdbTRutqcqlFWrry/N/4/zVy+b9Y8eOoaVXk/qGVWv1\n7YcUsA/SwD5Igyv0AXCdflDt6Vh8nYhIVKImfBcvXkRERASOHj0KwHrEr6YiIyPr9LlygiDYGKGr\nnY539MHTH75j3o/rEYfMkxn1Omdt2aMfYmMfpIF9kAZX6ANgv35ERETY5Qs+ci6dWov2veLEDoOI\nqNESdUrn559/jqeffhoymQwBAQEYNWoUvvjiCzFDqrtK317fNvAuceIgIiKSEJ1aC88mTeDdnPVp\niYjE4LCEb+XKlVCpVAgLC8OePXtw4sQJAMDOnTsRF1f2Td/69etx9uxZnDp1CocPH8Zbb73lMt/e\n3vPMJLFDICIiEt3N0gxcqZOISAwOm9I5ZcoUTJkyxer40KFDzdsmkwnPPfeco0JwqtNHfhM7BCIi\nakCioqKwdu1a+Pv7Iy8vDxMmTMDp06ct2syZMwdjxoyB0WhEaWkpZs2ahW+//VakiOsmL1MDoKwW\nn7MfdSAiIpGndLoSQ0mJ2CEQEVEDkpKSguTkZERHRyM5ORmpqalWbY4cOYKePXuie/fueOKJJ7B5\n82Z4enqKEG3d6bVZAAC/EI7wERGJgQkfERGRkwUGBiI2NhZpaWkAgLS0NMTGxiIgIMCi3bfffoui\noiIAwB9//AGZTAZ/f3+nx1sf1wuuojA/H35hXKmTiEgMTPiIiIicLDw8HGq1GiaTCUDZIw4ajeaW\ntWgnTJiAM2fOQK1WOytMu9Flall8nYhIJC5Rh08qrlzKQfOWgeZ9Lx8fFOXnixgRERG5gvj4eMyf\nPx+DBg2q9WelUKt2+8UM6Iuv4xMR6zG6Si1IV+iHK/QBcI1+sA/S4ch+MOGzo8p1pkKi2+PML1zM\nhYiILKlUKoSGhkIul8NkMkEulyMkJAQqlcqqbe/evbFhwwaMHDkS//zzT62vJYVatSOmv4jeD48S\nra4ka1pKhyv0AXCNfrAP0lHfflRXp5ZTOu1odeJUsUMgIqIGICcnB8eOHUNCQgIAICEhAenp6cjN\nzbVo16NHD2zevBkPP/ww0tPTxQjVLnRqDTy8vdDUz1fsUIiIGh0mfHak/cdyOe27nxgPmZw/YiIi\nspaYmIikpCRkZGQgKSkJiYmJACzr1X744Yfw8vJCamoq0tPTkZ6ejq5du4oZdp3o1DdW6mQtPiIi\np+OUTgfq2L83Bj0zCd+m/EvsUIiISGIyMjLQu3dvq+MV69X26tXLmSE5jE59oxZfSDAuHj8pcjRE\nRI0Lh5/s7H+r11jsh3TsIFIkRERE0qDXlI3w+XKEj4jI6Zjw2dnXH6y22JfJG/6DpERERPVRXFiI\na/rL8AtlLT4iImdjwudgMjDhIyIiylNr4BfCET4iImdjwudgbl6eYodAREQkOr0mi4u2EBGJgAmf\ng/kGtRI7BCIiItHpMjXwDQlyiZpZREQNCRM+BwuMbI0+jzwgdhhERESi0mm0cPPwQFN/P7FDISJq\nVJjwOUHbuO5ih0BERCQqnUYLAPDnwi1ERE7FhM8JTEaT2CEQERGJSpdZVouPpRmIiJyLCZ8TtO8V\nK3YIREREotJry2rxcaVOIiLnYsLnBC24cAsRETVypdeLkZ+bx5U6iYicjAmfk/Qb85DYIRAREYlK\nr9Yy4SMicjImfA7w1YpVVsd6PzxShEiIiIikQ6fRwo+LthARORUTPgf49avdVscEQRAhEiIiIunQ\nqbVoEdwKMjn/+UFE5Cz8jesABXk6sUMgIiKSHJ1GC6WbG3wC/cUOhYio0WDC5yDzBo4QOwQiIiJJ\nKS/NwGmdRETOw4TPQfIv5Vjsh3bsIFIkRERE0lBefJ2lGYiInIcJnwN9+sJ0sUMgIiKSjMvabACA\nXxhH+IiInIUJnwNdv3ZN7BCIiIgkw1BSgivZORzhIyJyIiZ8DiSrtB/coZ0ocRAREUmFTqOFb0iQ\n2GEQETUaTPgc6Kr+ssV+85aBSPz4A8zc+blIEREREYlLp9Zw0RYiIidSih2AK8s+c85i/+mP3hUp\nEiIiImnQabS4/f57IFcoYDIaxQ6HiMjlcYSPiIiInEav1kKhVKJ5q0CxQyEiahSY8BEREZHT6NQs\nzUBE5ExM+ETi5eMjdghEREROZ074QpnwERE5AxM+kdz/wtNih0BEROR0l7OyYTIauXALEZGTMOFz\nsM9mvWXzuExWuWgDERGR6zMaDLhyKQe+nNJJROQUTPgcLH3XtzaPt4yMQFjnjgCA2GH3YdjU550Z\nFhERkWh0Gi38wpjwERE5AxM+B6tqyemo3j0wdfMaAMDYt9/EgCfGOTMsIiIi0egytVy0hYjISRya\n8EVFReHgwYPIyMjAwYMH0b59e6s2c+fORXZ2NtLT05Geno5Vq1Y5MiQiIiISmV6jRfOWgVAoWQ6Y\niMjRHJrwpaSkIDk5GdHR0UhOTkZqaqrNduvWrUNMTAxiYmLwwgsvODIkIiIiEplOrYFcoUCLoFZi\nh0JE5PIclvAFBgYiNjYWaWlpAIC0tDTExsYiICDAUZeUrK+TP67yvSYtmjsxEiIiIvGxNAMRkfM4\nLOELDw+HWq2GyWQCAJhMJmg0GoSHh1u1HTNmDH7//Xd888036N27t6NCEs3/Uv6FvEy1zffe+vFr\nJ0dDREQkLp2mLOHjSp1ERI4n+uT5lJQULFy4EAaDAffccw+2bduGTp06QafT1fgc58+fr3ccgiDU\n+xy3YhRMWPnnEYfH4Oh+OAP7IA3sgzS4Qh8A1+kH2ceV7BwYDQau1ElE5AQOS/hUKhVCQ0Mhl8th\nMpkgl8sREhIClUpl0S47O9u8vWfPHqhUKnTt2hX79++v8bUiIyNx4cKFOscqCIJT6uKtOH7olu/3\nHDkUZ375DXptVp3O76x+OBL7IA3sgzS4Qh8A+/UjIiLCLl/wkfhMRiMuZ13iSp1ERE7gsCmdOTk5\nOHbsGBISEgAACQkJSE9PR25urkW7kJAQ83b37t0RGRmJjIwMR4UlaQkLX8fzaz8SOwwiIiKH06k1\n8AsNqb4hERHVi0NX6UxMTERSUhIyMjKQlJSExMREAMDOnTsRFxcHAFi0aBGOHz+OY8eO4eOPP8b4\n8eMtRv1cya6VKdW2aebv54RIiIiIxKXXZHGEj4jICRz6DF9GRobNRViGDh1q3p40aZIjQ5CUiydO\nVttG6e4Ody8vlBQVOSEiIiIiceg0WjRvFQiFmxuMpaVih0NE5LIcOsJHlmr6DMuDs6fVqJ2HtzeL\n1hIRNUBRUVE4ePAgMjIycPDgQbRv396qzaBBg/DLL7/g+vXrWLZsmQhROpYuUwMA8A0JEjkSIiLX\nxoTPiWTymv24m7cKrFG7RT/vxTOrV9YnJCIiEkFKSgqSk5MRHR2N5ORkpKamWrU5e/YsnnrqKZdM\n9oCbpRk4rZOIyLGY8DnRud9+r1G7iG5d4N3cB14+PtW2bd8ztr5hERGREwUGBiI2NhZpaWkAgLS0\nNMTGxiIgIMCi3ZkzZ/D777/DYDCIEabD6cuLr4dx4RYiIkfifEAnKikqwtHtu9FjxOBbtvPw9sb8\nn74BALxyWx9nhEZERE4SHh4OtVoNk8kEADCZTNBoNAgPD7daydoepFqr1iQIeP/kESx9fyXu+M9/\n7X7+ylylFqQr9MMV+gC4Rj/YB+lwZD+Y8DlZbRdjaeLbAiVFRSi9Xmxx/O4nJ9gzLCIiclFSrlU7\nc+fnWL3zG8S/Ntch5y/HmpbS4Qp9AFyjH+yDdNS3H9XVqeWUTicz1HIlsrf278aUzz7FiuOHEDN4\nEICy0g1DX3rWEeEREZGDqVQqhIaGQn7juW65XI6QkBCoVCqRI3M+1uIjInI8JnxOZjIYa/2Z4Kh2\nAIBxS98CAPQf+6hdYyIiIufJycnBsWPHkJCQAABISEhAenq6Q6ZzSp1OrYVvKBdtISJyJCZ8Tlbb\nEb7K+iU8jHuenljl+w/NmY57n32yXtcgIiLHSkxMRFJSEjIyMpCUlITExEQAwM6dOxEXFwcA6Nev\nH1QqFV5++WVMnjwZKpUK9957r5hh251Oo4VPgD/cPD3EDoWIyGXxGT4n++7TdbdM2Krz4KxXqnwv\nq+gq+o5+EADw7UefIvL2brh2+TJyzl+s8/WIiMj+MjIy0Lt3b6vjQ4cONW8fOHAA4eHhzgzL6XQ3\nVur0DQ7CpXN1f86QiIiqxhE+Jyu+VohNry9wyLk/O3PCYj9pfSpmfLW5yvaT3luMRT9/55BYiIiI\nqlNemqH1bV1EjoSIyHUx4RPBL1t3ih0CAOC2gXfCw9sLy479BJ/AgOo/QKLwDw9Dm9juYodBRGR3\n2lNnoNdmIWHh63j8/SUI7tBO7JCIiFwOE75GYvi0JLRq18bme3KFAtF9eyEoqh1eWJcKdy9Pm+2C\nO7RDcIf2jgyTbJi163O8sDZF7DCIiOyuuLAQy0aNxe4PUtEuLgYvf74O45bMQ0CEa09lJSJyJiZ8\nIvlx43/sdq4mLZqbSzbY0izAH3dNfKzapGHk9BfRJqYbIm/vZvP9aVs2YNqW9dXGE961M2KHutbC\nAmQfCqUSoZ06iB0GEUlIcWEh9qz+NxYOfgjffboene+6A69u/QyPvjkTLYJaiR0eEVGDx0VbRLJt\nyXtQ/5WBMQter/e53vrxa6tjK44fMm97NWsKAPBu7oNO8f1w7fJlZJ06a/UZQRAAoN4FLF9K+xQA\n8NvOb+t1noqCO7SH0t0dqhMn7XZOcr4Rr05B/4SHsWjII8hTZYodDhFJSFF+AXa/n4IfN27GwCcn\nou/oBxA3/H4c+nwr9n68FgV5OrFDJCJqkDjCJxJBEJBx6BenXOu17ZvM208lL8eUjZ9g3JJ5leIp\n/x8A9Uz4KpMrFXjzh524/f576nyOaVvWmxNJarjCu3QCAMSPYy1JIrLtap4e25a+h7eHPIJftu9C\n39EPYtbuLRj60rPwbu4jdnhERA0OEz4R5V/KwSu39cErt/Vx+rWj+1suBy6Xyy3yPYVSCb+wEPQb\n85DVZ2u7gIi3jw+a+fvhgZkv16j9iuOH8PjKxbW6hhi8m/vg/qRnIJPb//9GHt7eVsfcPD0gVyrs\nfi0x9H/sEbFDICKJu5x9CV/MW4IlIxJwfO8PuOvxcZi1ewsGJT4BjybWvyOJiMg2JnyNlNLNzWJ/\n9PzZ6HgjCewxfDCWpv+I2bu34MHZ0xDZ/TY08W1hbvvC2hSbiaBPYACC2re1vtiNAcOmfr4WyVFT\nP1+8sWc7gtq3RYugVgjr3BEf/XUUAND17jtxx7jRWHH8kM3k51baxHZ3yHNikz9+Hw/NmW7ef3D2\nNAx65nF07G/fhL1V20gs+nkveowYYnF88S8/YPLq9+16LWeQKxV46PVX4dMysN7ThYmo8clTZeKz\nmfOw4qHxOPXzUdz//NOYvXsL7pr4GJQeLNhORFQdPsMnEbmqTFz4/QS6DRoAN5FvYN0G3W2xn7Rh\ntVWbQYlP4MCmLRbHXv/fVsgV1iNQMtnNJG/Se29jzYuvwd3LCyOmv4jmrQJx54QE9HpgGACgyGgw\ntx3+ygsAgBZBLc3Heo0ahiNbd9iMe8SrU+Dt0ww9R5YVLrb3yGmH3j3RoXdPbFmwDEDZiBsAyBX2\n/d4kOKpsWfJO8X1xdPsui/fa94y167WcoUOfXuj76APo++gDYodCRA1Y1umzWDt1JsK7dMLgpGcw\nfFoS4ieMwZ7V/8bPW7bDaDBUfxIiokaII3wS8faQR/DZzHkOmR5YWwq36r8HaObvZ3XMVrIHwKJP\nXQfEAwDGLJiDuGH3A7i5WIxVHMqyOJQe7uZjI2e8VGVMd44fY072aqL8/PVl71Er2z+NhksGjuoR\nkf2o/vwLqxOnInnSs8hTqfHQnOl47avN6DlySJX3ISKixkz87IIsVJ5qKWW33XMXWnfrghXHD2Hg\n0xOt3i+/8drKhypO/azuBv3yf9aatz2bNMHApyZC6e4O//CwW35u/oFv0Mzfz2YSHdA6DEvTf7RZ\nPqLznf0RGNna4ljU//WwvkClRDUoqh1WHD8EXXHRLeOqzoTlC+r1eVvGLn4TS3/7sUZt7096Biv/\n/Lnads+t+RCDX0y0Oq708DBPw43sfhuaVxihrYsVxw/hgVmv1KitX1hIrc/fulsXPPvpKrt9AUBE\nznH212NInvQsVidOReGVKxiz4HVM/+9GdL9vIKePExFVwIRPYg5/sQ2A/acjOsKkd9/GlI2fAADu\nmviY1ftvfl829bLyCM/o+bPRqm2keb/nSMtn1aozZEoilvy6D7N2fY67Jo0FALSzMdXR28cHb/6w\nE0t+3Wc+5hMYgFm7t+C2gXfeuLb1iOCTq5Zhxleb4dm0iflY4icf3CKisv7FDilLHk/nly0drnBz\ng9LDA+17xWHF8UPwDwutVT/tKXbofVWO3Hr5NIO7l6d5f9Azj8NYxahrRe16xOAeG4n+jK82YdHP\newGUTQd+5I3X6hj1Tf0THjZv+wYH4cHZ06wS+TvGPorZu7fU+vnN0fNmoX2vOKtCz74hQVhx/BCC\nbkyxJSJpyjhwGO+NeQL/fmkGTEYjJixfgKn/+Tc6xfcTOzQiIklgwicxX7y1BK/Glk17PJf+h8jR\n1JytpbLLF3qZ9uUGi+O9Rg2z23XvmvQYOvTpiR4jBlfZRqFUovfDIwEAfUc/CP+wEAxOKhuZ6tCn\nF4a/koSm/r4AgJGv3ZwymrThYzT198WyYz/ZPG/lnMg3JMhif+lv+7Hk6A/mpLK2q5vWRXXfaq84\nfsiizYAnxmHBgW8xa/eWKj+jdHev8j0AVgv4+AYHVdHSPsYvm49+Yx7CAzNfRsLCN7Di+CE08/cz\n/3xtjfwq3Nwwbsm8WsV22z13AQCmf7kBPi0Drd73bNoE077cgOAO7RA/fgxGvzUbQNmCQyuOH0Lz\nVtafAcqe/Zy8eiVatomocSxVCYxsjb6jH6z3eerq/x4cjhat6jeCS2Qvx/fuw/KHxmPjzDfh0cQb\nTyUvR9L61WjfK07s0IiIRMWET2IEQYCxtBQAsGrCZGSezMC1y1dEjqru4sePgZdPM4edv5m/Hyav\nfr/afzw/MncGRrw6BYMmPw7A8jnFuyY9hodffw1DX3oW8eNGm48HtWuDeT/ssppy6ubpAblCgbY3\nEoyud8dDoVSaR/gAWIzmlSejHSuVwlB6eKCpvy9ih92HYVOft/lcZG08/++PsPyPg+b93g+PhH9Y\nKLx8LJNxRYVpw8OmPg/A9jOZABDWORpLft2Hznf2r/K6D86eVqd4w7t0skgm48ePQdzwqhP3cuX/\nPfUb85D5Z9uybSS631u22JCtpLdj/96IGXIvRs2cWuP4Ko5MR3TrYvV++149EBzVDvc99zRGvjrF\nvPBQ74dHAABCOkTZPG+7HjHo0KcXRr5688uFZgH+ePk/axHcoX2N4wOAKRs/sVg5trK7n5xQZeJZ\nX57NmuLRebMw+eOGt3IsuS7BZMJvO77BkhFj8Pm8xWgR3ArPfroKm8/+iX4JD8MvNFjsEImInI4J\nn8S9O3oSju/5wby/870PxQumDka+OsUp14nsflu1be4cP6bK93yDg3D3kxNqdK3Fv/yAZcd+Mo9g\n9hw5BEvTbz4fZzCZIJNbJx0xgwdh7vc7sPDwHoxbMg+TU9/DvB92Yezbb2LAE+Pw5g87LUZLZDKZ\nzWfSut4dj4nvvm2RpIV26oC2cbff/KxcjkfmzkDShtVYcOCbGvXLlohuXQFYJ6u2Emy5UlGr52Ze\n2vQvPPzGq5ArFVC6u2Pkq1Pw2KI3AJQlQN7NfSymmla8TmXP/Su5xtetzLtFcwBlSXpVJUBa39a5\nTs8HVjTytZfw4sZPLOpdlnt122cI7dQBz//7I4vPbDx93OIZ1nKezZrikbkzzMmvredgW7aJwNCX\nnsWkdxfDNyQIcoUCgxKfsPhvbOI7izBz5+fm/bDO0RZJuEwuR/f7BuK17Zsgk8nwwrpULP7lh7Jr\n3phSW7FkC5FUmAxGHP5iG94e8gi2LV2Ja4ZSPDjrFcz++ktM/+9GDJv6PNr2iHGZ2qZERLfCVQoa\ngIr/AMs8mSFiJK4rrHO03c51OEdtLtlQmU+APwAgZoj1YjEA8ML6VIv9iiUtyj2+cgkAoNs9d+HQ\nF1vx30XvWCQFFac32hq5u3NCAr7713oIJpPVexUXLpErFeYVVGUyGfzDQqFTayAIAl7bvsnqs8vS\nbU99vZW2cbdj6uZ/I6TCyJaHt7f5+c+CPJ3VZ2z9TGxZ888xzNr9BRYNvvn8n60VQ8v/Tp5cVVZu\no/z52YrJ691PjMfdT4zHvIEj8PCc6dg4480K8di+vlBpvdXy0WOv8mdDK3zQ+8YorFezppDJ5ea/\nm+zr12w+kzho8uPmacoAIFcqYTIaLdqUJ6+tb+uMOd/8F6qTfyO8c0fc//zTyD57HktHJqDboAEA\ngMjbuyHpxn97v2zbiU1zFqCpny/m7btZFsTN0wNtYrpZxdKkRXP0HDkEv2yzLCHSvFUgPLy9cenc\nBds/ICInMJSUYP/6Tdi3Lg2Bka3R+c5+6HxHP8SPH4MBT4xDUX4B/j5wGH/tP4i/fzrUoGfUEBFV\nhSN8DcD3a24+A6f682+Lf8CumVL/BTHI/ia9u7hOn6v4jJlcobBYrMSWPg+PwtLf9lsce/OHnZj4\nzqIqPzNkSiIW//I9Vhw/ZHHcLyzEYqTy/uefMSd8rdq3wazdX2DAE+NtjnRVXtW0pvzDQi2SPQBw\n9/Yyb9su/3HrX1vlaZS+5PrNqbU3+tEmrnu1z5yZkxobmdzcvdvRZcAd6DlqCGwV0LCVCFc2vppV\nWJf/fqDahWcqj6QqKo3wyWQyvLTpXxbHwjt3NG9XXDQJAMYve8u8Xf7MaeXnHasqnwIAYxa8bnXs\njT3ba/TzIHKW3Asq7F+3CSlPJ+H1O+7Dv6fOxPG9+9CuZywee3su3ty3C0kbVuOeZyYhJNr2lGwi\nooaII3wNQNbps5ge0x8tWrVEUX4+ivLzze+d+G7/LT5JYglofeuSETVRvpKoI9haiGV2pYVbBj41\nAV/MXwoAaBcXAwAY+tKzyD57zuqzM77abHVs+n831im2xFs8EzYl7dNqF14Zv3yBRVL1wtoU84in\nt48PXt+zDa/c1gdN/X1RfK3Q6vPR/XujQ9//Q6f4vlVe44GZN8tEhHXpZN5u2SYCpdeLAQA+AQEI\naB2G3IuZNs9xq/9GXv7PWouVev1Cg9H30Qex870PIQgCQjtaJoQyhRwe3t7o/9gj6DLgDpvPHN5K\ni6BWFvud4vth1GuWNS8rJpkrjh/Cn9/XrMwHkRQVXyvE8T0/4PieHyCTyRDaKRqd7+yHTvF9MThp\nMgYnTcbl7Ev468eD+Gv/QZw6fBQlRfUruUNEJBYmfA2EyWCETq21OPbDvz8DALwRPxit2rXBws3r\noCm8an4/5ekXb/mPZ6LqjJxu/QzmE+8vrdFnK9ZarI1bfa511861Pp+t1VG73Xs3Jq5YaLP9oGce\nr9X5K48Ylo/OjX5rFgBgyYgxNqc1+oeFIjCyNQIjbI+OVhyBnf31lwCA00d/QzN/P6tVB3sMv98i\nCa2JuGH3V/neU8nLrY51vMMyAe4y4A6bnw3rHI2s0ze/FHhhbQoyruTWKjYiZxIEAZkn/0bmyb/x\n7Uefopm/Hzre0Qed7uiLmPsHoc/Do2AoKcGZX37Dyf1lCWBeplrssImIakwGW/OSGoiIiAicP38e\nkZGRuHCh7s+JCILgEkVaz+TrsPXCzWf8psf0r9NzVURkP7kXM+0y4it1r8XdifjxozH0pedgLDVY\n1X20R21Re/3Obyx4j7yprn1QKJVoG3c7OsX3Rac7+poXrMo+ex5/7T+Ik/sP4Fz67zAZjNWcyT4a\n89+F1LhCP9gH6ahvP6r7fc8RPhcS4m1Z/sBkMEJ76gyCWTiaSDSNIdkDgCW/7jNvV072AODx95dg\nzYt85pgaFqPBgFM/H8Wpn49i+7L34R8ehs7xfdH5zn7oP/YR3DXpMRQVXEXGwZ+hPXUGcrkccoWi\nwqvy/i2OKxU3P69UQC63Pv7fC39jcNJkqDNOQZNxCnkXM2/5fC0REcCEz6V4KpSY1q2vRS22X3d8\nba61RkQklq4D4sUOgaje8lSZ+HHjf/Djxv/Aw9sbUb17onN8X3S8ow9uv28gAMBkMsFkNMJkMJb9\naaq4bbq5bfEymbeNRgMEowmlJaUwGo0QjCaYjAYAQH7XrhjwxDjzisrFhYXQnjoDTcZpaP4+Bc0/\np6D95wyfNyQiC0z4XIwgCHjltj7mYeHDX2zHkCnP4n8ffYr7nn9a5OiIiIhcQ3FhIU58tw8nvisb\n3VbcKI/iyBG3f734Gtw8PNCqXSRCoqPMr5j770HfRx8AUJZw5l3MhOaf01D//Q80GaehzTiFy9mX\nHBYXEUkbEz4XVX7DKcrPx/Tu/eDu5YWeo4bBLzQY2lNnkPJ0EkoKr+PtI9+ZP7PswXHIOXcBLYJa\noSBPhwnvLESn/pbP+5FTogAAIABJREFU3WQcOIzofpZFuImIiBo7o8HglOsYSkqg/usfqP/6x+K4\nb3AQQqLbI6RjB4R0aI/Qjh3Q/d67ze9fu3wFmhtTQTUZp6HJOIXsM+ecFre9yGQyNPX3Q4ugVvAN\nboUWQa3QIrgVWrRqCd/gIDRvFYjS4mLo1Fro1VroNGUvvVqLPLUWBTm5nAZLjQ4TvkaipKgIC+9/\n0Op4zgUVAiPC8cu2ncg+cw6CyWRefez3b/aaE74F9z6AKzk5MBmMGDVjKu4Y+2i11/x5y3Z889En\neGPPdvMxo8FgUdybiIiI6k+vzYJem4U/f7i5WJtHE28ER7VHaMcoBEe3R0iHKPR55AG4e3kCAAyl\npbh09jw0GaehzvgH2afPoTC/ACWFhSguLEJxYSGKCwudtigNAHg2a1qWzAVVSOaCypK5FkEt0bxV\nSyjd3Cw+U1xYhMtZ2biszYL21Bm4e3rANzQYneL7wicwwKKtoaQEem029Bot8tQa6NVZ5oRQp9Gi\nIDePCSG5HP7Lu5FLnpiI4A7t8M+hX6zeO7ptFwwlpTj29R4IJpPV+/99+x08MPNl8/7XyR/j/uef\nxi/bdmHTnPnm47s/SMXgpMlY/tB4PP3RO2jeMrBWMV46d8G8MhoRERHVTPG1Qpw/9gfOH/vDfEwm\nlyMwIrzClND2iOrdAz1GDK7yPIaSEnMCWFJYhOLCorI/i27uV3yv+FphFe8VQl9chPa94sqSuaCW\nNxK6mwmeZ9MmFtc2Ggy4kp2Dy1nZuPD7CVzOyoZem43LWZeg12bhctYli/rElSk9POAXEgTfkGD4\nhQTDLzQIfqEh8A0JRtcB8Wjm72fRvrS4GHpNFvQaLXSaLOjUGujUN0cJC/J0VV5LJpfDzcMdSvcb\nLw93uJn/9IDSwx1Kdzco3T3K2t1o6+bhceO4+43Pe0CAgKt5OhTk5iE/Jw/5uXkoyM3DNf1lJqRU\nayzLAC7pWlvlI3xbF7+L7LPnMHn1+9i/YTO2LXkP7XrE4MzR9Co/6x8Wiuh+/4efv/wKT36wFFsW\nrsCsXZ/jzNF0XMm+hKcnTMLcGTOR/vUezP56C/Z+vBbGUgOGTEm0OM+0bn0R0f02JK1PrXd/1k9/\nHeOXza++IRHVS31LM7AsQ+3wHnmTK/QBcGw/mvi2QMs2EfBs0gQe3l5w9/a+8acXPG5sezTxhrvX\nzf3y98r+LNuurYI8Xdno3I0E7krWJeizss2JXUFuns0vne3FzdMDvsFB8AsLKUsIQ4LhG1qeHAaj\nqZ+vRfvS68W4nH0J7aPa44JKBaWbmzmhs7VCcW0ZSkpQWlwCuUJu8+dpNBhwNU+P/LyyBLAgJ69s\nuzwpLN/PzUPp9eJbXssV/n/hCn0AWJaBJOj3b/bijrGP4tTPR5F1+qzFP+JulewBQF6mGgc3lxWR\nXp04FQAwo+ddMJSUQjCZsOG1udi3Lg0A8Fps2ap+Mvn/s3fn4U2V6d/Av0m6l24kLaU0TYGWfacd\nkMUFkFVWRcCCMCCOuP58Z1BUFEcQHXVkUBAcEAdZByuCSkGkgAiF0UJqWaQN0BVK6ZLSDdqmed4/\nSgOlC23T5DTp93Ndz9XkOSfn3He20ztneeRI/yMBrVr74InlbwGo+GDkpKXXup79azdAf+WqafDr\nSueOHEO3+wdj86tL0K5LJyQe/xWJx3/Fg3+OgLpblxqHsajM786BsM11eONWPDj7iSZbHhERkTmK\n9HlI0ueZtQyZTAZHFxc4u7uZCsDKYrCygPxywwZMGj22osjLzIKhpO6ixNLKbpbgWlIKriXV/KOI\nk6srfAL8KwrAW0WhVxtfDO3VF/uP/e9WgVYCQ0kpykpLUX6rYKss3MpLK/orpxtKSmAoLav2OENp\nGcpLS6vsvXNydYGHUgkPlRKevrf+qir+eqhaw1OlQrsuneChbA25QlEt9hsFhRV7CG/tHaz8W5Cd\ni/ysbFwtLkSbju1RdvMmym6WoPTWX2O59Q7hJeuwaMEXGhqKjRs3QqlUIicnB08++SQuXLhQZR65\nXI5PPvkEo0ePhhAC77//Pr744gtLhkVmStLGN8kgypXu+QuU0YiEYycAAEE9u2HIjMcqHldaapqn\ncqDn65lZ8Grjix9XrwMAU8G3/tm/4lpyKspLS2F49f9w5uDP0EbtNz1+13srMPODv+PTmU9DCIH2\nfXvBNzgI536uPnD9qtnP4PmNa033dSdiETowDLlXMtA6oC0AIPa7vUg88RtOfr8XTq4uePgvf8aw\neU/iXzPm4fq1LLRu648HZz+B/KzsaucXHFi3Ef0fGQWftv6mvn8/8zIcnBwxYMoEHNm0HZMWvVyl\nMN376ec4+f0+BHQJRU76FfgFB2H2x8sBAJ/O+kuT7AklIiKqixACpTduoPTGDRTUMk/3b/1w4deT\nVo3LHKU3biDzYhIyLyZV6d+08E2MW7Lcwuu+iZz0y6ZrK9RGJpfD3ccLHkolPH1V8FS1hodKdaso\nVMLDV4nArp3hoVJWOWR266UzeGXX1mrLKy8z3Cr+bpqKwNsFYdXi8M7+6n0lKC8rQ7nBUDHkiMEA\no6H8rvs19JWXo7zMYHMXFGrOLHpIZ3R0NDZs2IAtW7YgIiICc+fOxfDhw6vMM2vWLERERGDMmDFQ\nKpXQarUYMmRIvQ4/4eEqVdlDHg3NoXVgAG7kF6DsZgkMdxSAlfqPH4O8q5m4+NuppgwTPm390b5f\nL5TeuIkzB48gsFtnpJ9LAACkFV6HupVXvZYjVygw8ZWXcHDDJqi7d4Onr9K0BzR0YDh6PfwQEo79\nz3TZ70rO7m5w9/bCG/sq5q2pAHdp5Q53b2/kpF+GTC5HtwcGI7h3D3R7cCj8O7av8hhlYDt0DO+H\nuH0HAACXsjJx38CB6DQwHEIIaPfuR+dBAzB1ySLT+pxcXatc5TU7LR2b/rYYgAx9xz6M/Wu+wPIT\n0TCUluLV/g8AABZsWI2Q8H74YNITyEpORWC3zhjyxFT0f2Q0gIri39HFuV7P3YnI3ZArFPjT5Eeq\n9FeeS+rl6Ix3n/gzkk79bto7W1JcDGc3N3z50qvoGN4P98+cBgDISb+CvKuZ6BjWF8lxpxHcp6dp\neVcSLyCgU0iVdeSkX4YysF2VPkNpKb54/m/w8vNF+769EfdjNP7y75X1yqU2Dwd0wE9XLpm1jIb4\n4eNVeOT/Pd+wx6xYXWWsz9rOuV08eFSd59nUBw/pbBhuI2+zhxwA+8jDHnIAbDcPJ1dXeChbw9NX\niQM/H8bkxx6Dk4sLHF2c4ejifOu2S/U+Vxc4Ojub+p1u3a+ctykOZ61JlSLwjuKwsi+0YwjOnT1b\npb+ykDTdL6u8X17z9DuLzrvnKzNUjIcpjBBGUXF4sRAwiorbwihuTxOiynxCiIp5b92uMr8QpvnK\nDQZcPp8IuVze6OfpXt/3Fiv4fH19kZiYCKVSCaPRCLlcjpycHISGhiI7O9s03w8//IAvv/wS33zz\nDQDg008/RUpKCj766KN7roMbs6rsIQ/m0DDtunZCQY4e+deymnS5teXg7OYGV08P5F3NBFBxMryz\nmyuKajgMSCaT4c2fdiPqk7WI/S6q4vHubvDVqE3FcaUXNv0bwX164uCGTRg2dxbeHT0FuZczMODR\nCZDLFdD97zfcKChEkT4PrZQ+KMzRmx4b0DkUNwoKsPjHbwFUnN9Z8aV7O4feo4YjPysbqafPwdWz\nVZXH18avvQa9Rw3HT2s3YPxfX0Dx9XxcOhWHzItJKL1Zgn/EHkbsd3tx5uDP+OPoCRjLDdWuZOfq\n6YEew+7HxVgtZv9zOeQOCsTujsLPX22DKigQj7/zOjr274u/9R4MYTTi/329ES6tWuHb9z7GH0eO\nQQiBwK6dMeDRCUj5/QwKc/V4+vN/IWbHt9izYjX8O3ZAfnY23ti3E8cjd0F3Ihb9xj6M2O/3QeHg\ngJEL5uGfj87Cs//5DPlZOchI0CEl/iwuJyTCwdEJb/60yxTr1tffwcnv96LXyGHw8lUhSRuPdl1C\n8ftPhwAAk159GWU3b+Lb9z7GlMV/g3+H9og/cBhHNm3HqGefwsgF8wBUPyc2+ffTGDH0fkzUdLnn\nc34vLPgahtvI2+whB8A+8rCHHAD7yKMpc5A7KG4XhHcUhwpHR8gdFFA4OECuqPircFBAXvlXccdf\nx1v9ijv+Ot5x/45lVC5zxhNPIHLnzip9la36fQcoHGueXtMhsdY0IqA9eiv97z1jLSQr+Pr164ev\nvvoKPXr0MPWdPXsWM2fOhFZ7+zyv+Ph4zJ07F7GxsQCAhQsXIjAwEC+99NI911GZHBHZNqMQEBCQ\nQwYBQG4DG1GjEJABZm0shRAwQkAhq/+vetk3i+Hj7NKgx1hDfmkJPBydrPIPEAu++mHBd5s95ADY\nRx72kANgH3kwh9tkMllFMenoYCouqxSDMhlkchnkcnnF+mQyyG7dlsvlkMllkMnkt+eT3eq7Y/47\n55PJby1DJgeEQPzhX+BoRtHZIi7awo1ZBXvIgzk0D8yhebCHHICmy4M/8hERkSUIIUyHcUrBwYzD\nOevDYktPS0tDu3btTMejyuVyBAQEIC0trcp8qamp0Ghun+8RFBRUbR4iIiIiIiJqOIsVfFlZWYiL\ni8OMGTMAADNmzIBWq61y/h4AfP3115g/fz5kMhlUKhUmTZqEyMhIS4VFRERERETUYlh0/+EzzzyD\nF154AQkJCXjhhRfwzDMVg2fv2bMH/fv3BwBs2rQJly5dgk6nw4kTJ/DOO+/wkB0iIiIiIqImYNFz\n+BISEjBw4MBq/ePGjTPdNhqNePbZZy0ZBhERUbPCcWqJiMhamtdl3oiIiFqAtWvXYvXq1ejcuTNW\nr16Nzz//vNo8ERERCAkJQWhoKO677z68/fbbVc55JyIiqg8WfERERFbk6+uLfv36Ydu2bQCAbdu2\noV+/flCpVFXmmzZtGtatWwchBLKzs7Fr1y5MnTpVipCJiMiGseAjIiKyIrVajcuXL8NoNAKoOLXh\nypUrUKvVVeYLCgqqMuRQampqtXmIiIjuxS7G4SMiIqKaNcWF0IQQ5gciMXvIAbCPPOwhB8A+8mAO\nzYcl87Dpgk9xa0T6wMBAs5dlL+dF2EMezKF5YA7Ngz3kADRNHpXf9ZXf/bbqznFqjUbjPcepjY2N\nBVB9j9+9VD5PQ4YMQXp6eqPjTU5ORnBwcKMf3xzYQw6AfeRhDzkA9pEHc2g+zM0jMDAQR48erXX7\nKANgs2Xx4MGDcfToUanDICIiKxoyZAiOHTsmdRhmOXToENavX48tW7YgIiIC8+bNw7Bhw6rMM3v2\nbMyYMQNjxoyBUqmEVqvF0KFD673HjttIIqKWpbbto00XfE5OTggPD0dGRgbKy8ulDoeIiCxIoVCg\nbdu2+O2331BaWip1OGbp3LkzNm7cCB8fH+j1ejz55JNITEzEnj178NZbb+HkyZOQy+VYtWoVRo4c\nCQD4xz/+gXXr1tV7HdxGEhG1DPfaPtp0wUdERERERES141U6iYiIiIiI7BQLPiIiIiIiIjvFgo+I\niIiIiMhOseAjIiIiIiKyUyz4iIiIiIiI7BQLPiIiIiIiIjvFgo+IiIiIiMhOtfiCLzQ0FDExMUhI\nSEBMTAxCQkKkDgkA8OGHH+LSpUsQQqB79+6m/rribew0S2ndujX27NmD8+fPIz4+Ht988w1UKhUA\nYMCAAYiLi0NCQgJ+/PFH+Pr6mh7X2GmW8u233yIuLg6nTp3CkSNH0Lt3bwC29VpUeuutt6q8p2zp\ndUhKSsIff/wBrVYLrVZrGozalnJwdnbGZ599hsTERMTHx+Pzzz8HYDvvJY1GY3r+tVotkpKSkJOT\nY1M5UP3Zw2tT13bIFt39HW5Lavv+szXjxo3DqVOnoNVqERcXh8mTJ0sd0j015n/K5qimPGztM17b\na1HJkp9x0ZJbdHS0iIiIEABERESEiI6OljwmAGLw4MEiMDBQJCUlie7du9cr3sZOs1Tz8fERDzzw\ngOn+Bx98INavXy9kMpnQ6XRi8ODBAoB44403xBdffCEANHqaJZunp6fp9oQJE8TJkydt7rUAIPr2\n7SuioqJM7ylbex3u/iyYE6dUOaxcuVJ8/PHHpvt+fn42+V6qbCtWrBCffvqpTefAVnuzh9emtu2Q\n1HE1pt39HS51PA1ttX3/2VrLzc01Pf89e/YU+fn5QiaTSR5XXa0x/1M2x1ZTHrb2Ga/ttQAs/hmX\nPnmpmq+vr9Dr9UIulwsAQi6XC71eL1QqleSxVbY7X/S64m3sNGvmMmXKFPHTTz+JsLAwcfr0aVO/\nUqkUBQUFAkCjp1mrzZo1S/z2228291o4OTmJmJgYodFoTO8pW3sdavoCtKUc3N3dhV6vF+7u7lX6\nbe29VNkcHR3FtWvXRN++fW02B7bam72+NpXbIanjaGir6Ttc6pga0mr7/rPFlp2dLQYNGiQAiKFD\nh4qEhATJY6pvq+//lFLH2ZA87m628hm/OwdLf8Yd0IKp1WpcvnwZRqMRAGA0GnHlyhWo1WpkZ2dL\nHF11dcUrk8kaNc1aecpkMixYsADfffcdgoKCkJKSYpqWk5MDuVwOHx+fRk/T6/UWjX/dunUYOXIk\nZDIZRo8ebXOvxTvvvIPNmzdXef5s8XXYsmULZDIZjh49itdff92mcujYsSNycnKwZMkSPPTQQygs\nLMTixYtx48YNm3ovVZowYQIuX74MrVaLfv362WQOVDtb2z7Wx53bIVtT03e4Lant++/YsWNSh9Zg\njz/+OHbv3o2ioiJ4eHhg7NixUofUKPyMNy+W/oy3+HP4yDo+/fRTFBYWYtWqVVKH0ijz58+HRqPB\n66+/jg8//FDqcBpk4MCBCAsLw2effSZ1KGYZOnQo+vTpg/DwcMhkMpt7LykUCnTs2BFarRbh4eF4\n9dVXsXPnTrRq1Urq0Bpl7ty52LBhg9RhENWbrW6H7OE7vLbvPw8PD6lDaxCFQoHXXnsNEydORHBw\nMMaPH48dO3bA3d1d6tAI/Izfi+S7NaVqtrA72x4O6fzwww/Fjz/+KJycnARgW4fh1dSKi4uFn5+f\nzbwWr776qrh8+bJISkoSSUlJoqysTKSnp4tXXnnFZl+HHj16iEuXLtnUe0mpVIrS0tIqfWfPnhXh\n4eE2816qbAEBAaKwsFC0bt1aALb73cRWe7O31+bu7ZAttdq+wx9++GHJY6tvq+37r3///pLH1pDW\nv39/cfbs2Sp9586dE2FhYZLHVp9mz4d02tpn/M4crPQZlz5pKduhQ4eqnLB68OBByWOq7Q1xr3gb\nO82S7d133xUHDx4Urq6upj6ZTCYuXLhQ5YIZGzZsMGuapZq7u7sIDAw03X/kkUdEenq6Tb4Wd7+n\nbOl1cHNzq3LxnGXLlomdO3faVA4AxI8//mj6Ag8NDRVZWVnCy8vL5t5Lr732mvjvf/9bpc/WcmC7\nd7OX16am7ZAtN1s8hw+o/ftP6rga0tq0aSOuX78uOnXqJACILl26iJycHOHj4yN5bPVpDfmfsjm3\nu/Owxc94XZ9jXrTFAq1z587ixIkTIiEhQZw4ccL0IZa6rVy5UqSlpYmysjKRkZEhzpw5c894GzvN\nUq1bt25CCCHOnz8vtFqt0Gq1YufOnQKAuO+++0R8fLxITEwU+/fvr3K1rsZOs0Tz8/MTx48fF/Hx\n8UKr1Yro6GjRt29fm3st7mx3fpHYyuvQvn17cerUKfH777+LM2fOiB07dgh/f3+byqEyj0OHDon4\n+Hhx8uRJMXr0aJt8LyUkJIhRo0ZV6bO1HNju3ezhtalrO2SrzVYLvtq+/2ytPfHEEyI+Pl7ExcWJ\nuLg4MXHiRMljuldrzP+UzbHVlIetfcZrey3ubJb4jMtu3SAiIiIiIiI7w4u2EBERERER2SkWfERE\nRERERHaKBR8REREREZGdYsFHRERERERkp1jwERERERER2SkWfERERERERHaKBR8REREREZGdYsFH\nRERERERkp1jwERERERER2SkWfERERERERHaKBR8REREREZGdYsFHRERERERkp1jwERERERER2SkW\nfERERERERHaKBR8REREREZGdYsFH1AByuRwFBQVQq9VNOi8RERERkSWw4CO7VlBQYGrl5eUoLi42\n3X/iiScavDyj0QgPDw+kpaU16byNNW/ePAghMGXKFIutg4iIqKm3p5WOHz+OiIiIe87n5eWF4uJi\n7Ny5s9HrImqpWPCRXfPw8DC11NRUjB8/3nR/69at1eZXKBQSRNl4s2fPRk5ODp588kmrr1su59cH\nEVFL0dDtaVObNm0aiouLMXbsWLRu3dri67uTrf1vQHQ3/sdGLdrSpUuxfft2bN26Ffn5+Zg5cyYG\nDhyI48ePQ6/X48qVK1i5ciUcHBwAVHzpCyGg0WgAAJs2bcLKlSsRFRWF/Px8xMTEIDg4uMHzAsDo\n0aORkJCAvLw8fPLJJzh69Chmz55da+wdOnTA4MGD8fTTT2PMmDFQqVRVpk+ePBlarRbXr1+HTqfD\nww8/DABo3bo1vvzyS1y5cgW5ubmIjIwEULG38NChQ6bH1xT/qlWrsHfvXhQWFmLo0KEYP368aR0p\nKSlYvHhxlRiGDh2K48ePIy8vD6mpqabn9/Lly5DJZKb5pk6ditjY2Hq/bkRE1LzI5XIsXrwYFy9e\nRFZWFjZv3gwvLy8AgJubG7Zt24acnBzo9XqcOHEC3t7e+OijjxAeHo7169ejoKAAH330Ua3Lnz17\nNv71r3/h4sWLmDFjRpVpGo0Gu3btQlZWFrKysqosZ8GCBfjjjz+Qn5+P+Ph49OjRA87OzhBCoF27\ndqb5tm3bhjfeeAMAMGrUKOh0OixevBhXr17FmjVroFKpEBUVhWvXriEnJwe7du2Cv7+/6fFKpRIb\nN25ERkYGcnNzsX37dgCATqfDiBEjTPM5OzsjLy8PXbp0MePZJmo4wcbWElpSUpIYPnx4lb6lS5eK\nkpIS8cgjjwiZTCZcXFxEWFiY+NOf/iQUCoVo3769SEhIEM8995wAIBQKhRBCCI1GIwCITZs2iays\nLNG/f3/h4OAgtm/fLjZt2tTgeX19fUV+fr6YMGGCcHBwEC+//LIoLS0Vs2fPrjWft99+Wxw7dkwA\nEOfOnRMvvviiadp9990n9Hq9GDZsmJDJZCIwMFB06tRJABD79u0TW7ZsEd7e3sLBwUEMHTpUABDz\n5s0Thw4dMi2jpvhzc3PFwIEDhUwmE05OTuKhhx4S3bp1EzKZTPTq1UtkZWWJcePGCQAiODhYFBQU\niKlTpwqFQiGUSqXo3bu3ACDOnz8vRowYYVrXd999VyV+NjY2Nrbm22ranr7yyiviyJEjom3btsLZ\n2Vl8+eWXYsOGDQKAePHFF8XXX38tXFxchEKhEGFhYcLNzU0AEMePHxcRERF1ri80NFSUl5eLDh06\niNdff138+uuvpmkODg7i3Llz4r333hOurq7CxcVF3HfffQKAmDlzpkhOThZ9+vQRAESnTp1Eu3bt\nhLOzsxBCiHbt2pmWs23bNvHGG28IAGLUqFGirKxM/P3vfxeOjo7CxcVF+Pn5iQkTJggXFxfh6ekp\ndu3aJbZt22Z6/IEDB8RXX30lvLy8hKOjo2nb+uabb4r//Oc/pvkef/zxKvGzsVmpSR4AG5tVWm0F\nX3R0dJ2P++tf/yp27NghgJqLoDVr1pjmHT9+vDh9+nSD5/3zn/8sjhw5UmW9V65cqbPgu3TpkqkQ\nXbx4sYiNjTVNW79+vfjggw+qPSYwMFCUlZUJT0/PatPqU/B98cUXdT5Xn376qWm9ixcvNj1vd7fX\nX3/dtAFUKpWiqKhI+Pr6Sv4eYWNjY2O7d6tpe3rp0iUxaNAg0/3g4GBRVFQkAIgFCxaIw4cPi+7d\nu1dbVn0KvqVLl4rjx4+bliuEEF26dBEAxIMPPijS09OFTCar9rjDhw+Lp59+ulp/fQq+wsJC4eDg\nUGtMAwYMEFeuXDHFVFJSIlq1alVtPo1GI/Ly8oSLi4sAIL7//nvxwgsvSP4asrWsxkM6qcW7+6Iq\nnTt3xg8//ICMjAxcv34d77zzTrXDJe909epV0+3i4mK0atWqwfMGBARUiyM9Pb3W5dx///0IDAzE\nf//7XwDA1q1b0b9/f3Tv3h0AoFarcfHixWqPU6vVyM7ORn5+fq3LrsvdMQ4cOBCHDh3CtWvXkJeX\nh6eeesr0XNUWA1BxeOjEiRPh4uKC6dOn49ChQ8jKympUTEREJD21Wo2oqCjo9Xro9XpotVrI5XIo\nlUp88cUX+PnnnxEZGYm0tDS8++67DToPfNasWdiyZQsAIDk5GTExMaZTHtRqNZKSkiCEqDGm2rZD\n93L16lUYDAbT/VatWuGLL75ASkoKrl+/jv3791fZ3l27dg2FhYXVlpOSkgKtVotJkyZBpVJh2LBh\npsM9iayFBR+1eHdvJD7//HOcOXMGISEh8PLywltvvVXlfDNLyMjIQGBgYJW+O88tuNvs2bMhl8tx\n+vRpZGRk4NixYzAajaYNYFpaGjp27FjtcWlpaVCpVPDw8Kg2raioCG5ubqb7d56bUOnu52r79u34\n5ptvoFar4e3tjfXr15ueq9piqJx28uRJTJo0CbNmzcKmTZtqzZWIiJq/y5cvY9iwYfDx8TE1V1dX\n5OTkoLS0FEuWLEHXrl1x//33Y+rUqZg+fTqA6tuVuz300EPQaDR4++23kZGRgYyMDPTu3RszZ86E\nTCZDWloagoODa9xO17YdKi0tRWlpaZ3bvLvjWrRoEQIDAxEeHg4vLy+MHDmyyvbOz88P7u7uNeaw\nceNGzJw5E9OnT8fBgwf5AydZHQs+ort4eHjg+vXrKCoqQpcuXfCXv/zF4uv84Ycf0K9fPzzyyCNQ\nKBR46aWX4OvrW+O8rq6ueOyxxzBv3jz06dPH1F5++WVERERALpfjiy++wFNPPYUHH3wQMpkM7dq1\nQ6dOnZCeno5FQFZmAAAgAElEQVQDBw5g9erV8PLygoODA4YOHQoA+P3339GrVy/06NEDLi4uWLJk\nyT3j9vDwQG5uLkpKSjBgwADTBhwANm/ejNGjR2PKlClQKBRQKpXo1auXafpXX32F1157DV26dMHu\n3bvNfAaJiEhKa9euxfvvv2/68dLX1xePPPIIAGD48OHo2rUrZDIZ8vPzYTAYYDQaAQCZmZno0KFD\nrcudPXs2vv/+e3Tv3t20vevduzdat26N4cOH4+jRoygoKMDSpUvh6uoKFxcX3HfffQCA9evXY9Gi\nRaZtT2hoKNq1awchBE6fPm3aZo4fP970mNp4eHiguLgYeXl5UCqVVS5SlpycjCNHjmDVqlXw9PSE\no6OjadsKAJGRkRgyZAgWLFiAr776qhHPLpF5WPAR3eWvf/0rZs+ejYKCAnz++eemwyYt6dq1a5g2\nbRo+/vhj5OTkoGPHjtBqtSgpKak275QpU1BQUIDNmzcjMzPT1NatWwdXV1c8/PDDOH78OObPn49P\nPvkE169fx6FDh0wDwM+cORMAkJiYiMzMTLzwwgsAgD/++APLly/H4cOHkZCQgCNHjtwz7gULFuC9\n995Dfn4+Xn/9dezYscM0LTk5GePHj8err76K3NxcnDp1Cj179jRN/+abb9ChQwdERkbi5s2bZj1/\nREQkrQ8++AAHDhzAwYMHTVei7tevH4CKI1Z2796NgoICnDlzBlFRUaZt64oVK/Dkk08iNzcX//jH\nP6os093dHY8++ig++eSTKtu7ixcvYvv27Zg9ezYMBgPGjh2L3r17Iz09HampqZg8eTKAih8eP/74\nY0RGRqKgoACRkZHw9vYGADz//POYNm0a9Ho9Jk2ahB9++KHO/D766COoVCrk5OTg6NGjiIqKqjJ9\nxowZcHR0hE6nw9WrV7FgwQLTtMLCQnz//fcICAjAd999Z94TTdQIMlSczEdEzYhcLseVK1fw2GOP\n4ejRo1KHYzFJSUmYM2cOfv75Z6lDISIisph3330Xfn5+mD9/vtShUAvEPXxEzcSoUaPg5eUFJycn\nvPnmmygrK8Ovv/4qdVgW8/jjj6OkpITFHhER2TWVSoU5c+bg3//+t9ShUAvFgo+omRgyZAguXbqE\nrKwsjBo1CpMnT0ZpaanUYVnEL7/8gk8++QTPPfec1KEQERFZzHPPPYfk5GR8/fXX+O2336QOh1oo\nHtJJRERERERkpxykDsAcTk5OCA8PR0ZGBsrLy6UOh4iILEihUKBt27b47bff7Hbvd1PiNpKIqGW4\n1/bRpgu+8PBwu76gBRERVTdkyBAcO3ZM6jCaPW4jiYhaltq2jzZd8GVkZACoSC49Pb3Ry0lOTkZw\ncHATRdU8MUf7wBztA3NsnMDAQBw9etT03U914zbyNnvIAbCPPOwhB8A+8mAOzYe5edxr+2jT5/Bp\nNBrTE5SSktLo5QghIJPJmjCy5oc52gfmaB+YY+M01Xd+S8Ft5G32kANgH3nYQw6AfeTBHJoPc/O4\n1/c9r9JJRERERERkp1jwERERERER2SkWfERERERERHaKBR8REREREZGdYsFHRERERERkp+5Z8H34\n4Ye4dOkShBDo3r27qT80NBQxMTFISEhATEwMQkJC6jWtysrlcqxatQoXLlyATqfDvHnzmiAlIiIi\nIiIiAupR8O3atQv3338/kpOTq/SvXbsWq1evRufOnbF69Wp8/vnn9Zp2p4iICISEhCA0NBT33Xcf\n3n77bWg0GvMyIiIiIiIiIgD1GHi9ptHafX190a9fPzz88MMAgG3btmHVqlVQqVSQyWS1TsvOzq6y\nnGnTpmHdunUQQiA7Oxu7du3C1KlT8dFHHzVFbs2KpncPXP4jEYbSUqlDISIiIiKiOrh7e8GvvQZu\nXp4oLy+H0VAOY/mtZiiv6Cs3wFhurNJvLL9j2h2PKb91WxiNVs/lngVfTdRqNS5fvgzjrYCNRiOu\nXLkCtVoNmUxW67S7C76goKAqgwOmpqZCrVY3OJ679z42hhCWG3/+2o0ibL54GgN822Fwm4bn11Qs\nmWNzwRztA3O0Dy0hRyIisl1yBwVU6kD4BgfBLzgIfu2DK26318Dd28ti6y0vM8BorCgADaVluFSg\nt9i6gEYWfM1NbaPK15e5o9vfS+jAcDyz7hNs+n4Xhsx/0WLrqYulc2wOmKN9YI72wRI5ajSaJvmB\nj4iILEfuoEDbkI7wD+2I8rIyFF+/jiL9dRTp81B0/TrKbpZYPSY3L0/4tQ++VdRp4Ns+CH7BGigD\n20HheLscys/KxrXkVPy+/yCyklNxLTkFhTm5kMkVUCgUkDsoIFfcag4VfTL57dvyu+ZROCggVzhU\ne8yd8wHA8+EPWDT/RhV8aWlpaNeuHeRyOYxGI+RyOQICApCWlgaZTFbrtLulpqZCo9EgNjYWQPU9\nfkRERERE1DzJHRTw79gB6u5dENitorXt1BGOzs61Pqb0xk0U5eWhSH/9VjGYh6K867ebPq9KkViY\ndx2GknsXiXIHBZSB7W4XdcEa+LXXwC84CO4+3qb5DKWlyEpJQ4buIuJ/OoRryam4lpSCrOQU3Cws\napLnpaF8PvzEostvVMGXlZWFuLg4zJgxA1u2bMGMGTOg1WpNh2zWNe1OX3/9NebPn4+dO3dCqVRi\n0qRJGDp0qHkZERERERFRk6os7gK7dTEVeHcWdzcKCpF+7jyObo1E+rnzuJKgAwC4+3jD3dsb7t6e\nt2/7eMHNywvuPl7wCWgLd28vuHl51rru2orEnzNS8OeV78OvfXD1vXXZOchKTkX8gcO4lpxSUdQl\npSL3SoYk59FJ6Z4F38qVKzFlyhT4+/vjwIEDyMnJQY8ePfDMM89g48aNeOutt6DX6/Hkk0+aHlPX\ntD179uCtt97CyZMnsWnTJgwYMAA6XcUb4p133rHrw3VksO9DuIiIiIjI9t1Z3AV26wx1967VirvL\nfySYirv0c+eRk3a55nO3k+p39J5coYCbl2dF8eftZSoM3avc9oabt2dFkejjhbjcq1AFqXH1wqWK\nwu7Wnrpryam4WVDYlE+JTbtnwffSSy/hpZdeqtafkJCAgQMH1viYuqaNGzfOdNtoNOLZZ5+tb6y2\nixcuICIiIqJmSO6gQJsO7aHu3hWB3TojsFsXBHQOqVbcHdv2DdLPnUfa2T9qL+7MYCwvR2GuHoW5\n9b+AiRACsh411xx0m11ctKXZs/OLMxARERGRbVAFBaJ9316IvpKEF7esr1Lc3SwsQvqdxd2588hJ\nTedVl20cCz4rEuCHhYiIiIisQ+HggHbdOqN9n14I7tsL7fv2goeyNQDgj7xslJWU4Nj2b5B+lsWd\nPWPBZ0U8h4+IiIiILMXV0xPBvXtUFHf9eiGoezc4ulTsvctOS0fCsf8hKS4eydp4XEm8AHn3P0kc\nMVkDCz5r4C8lRERERHahOY2jqgxsh/b9eiO4b0+079ML/iEdAFQM7H35fCJiduxEkjYeyXGnUZCd\nU+WxzSkPsiwWfEREREREdZDJZAgdGIaBj01C94eG4tNzv2LRDztQkJ2DgpzcilZ5Ozu3Sn95WVmT\nxKBwcEC7rp0q9t7dOkTTU6UEANzIL0Dy76dxKmo/krXxSD1zTpIBzql5YsFHRERERE3C0cUZrh4e\ncPVoBRePVnD1aAVXDw+4tGoFV89WiMu5Ck9fFfKzqo/P3By1UvrgT5MewYBHJ0ClDkSRPg8nInfj\nxeefx68/7IOHSok2HdsjdEBYrePIFV/Pv10QZuegIEePgpycKgVifnYOivR5MJaXmx7n6ukBTe8e\npuIuqEc3OLm6AABy0i8j8fivSNaeRlJcPDIvXOK5d1QrFnzWxD3nRERE1EzJZDI4ublWFGyerSqK\ntFvF2933K4s5F4+qfQ6OjnWu42BGMt48sBtJp35H3L4DiD9wCIU59b8MvzXIZDKEDAjDfVMnocdD\n90Ph6IALv53CvlXrcPrAYRhKS7Hz3Y/w0KtLqjxO4egIj9Y+8FApK5ryztut4aFsjcDuXeGhbA2X\nVu7V1ms0GlGkz0NBTi7kcjn8OgRDLpej3FBxeOaJyN1I0v6O5LjTNlMwU/PAgo+IiEhiY8eOxdKl\nS+Ho6Ijc3FzMmTMHycnJtfYTNaUO/ftg8PRH0WPY/XBwcqpz3pLiG7hZUIgbhYW4WVCIIn0eslPT\ncSO/ADcLi3CjoAA3CgpxM7/ib+V8N27dz8zPw/T/ex59Rg3Ho4sXYvJr/w8XfjuF33+MxukDh1GU\nd91KWVfXqrUPwieNw8BHJ0IVFIiivOv4ZesO/O+b73CtHoOHl5eVIS/zGvIyr91zXidXF7S6VQR6\nKG8VhKrWpuJQrlBAu/cnJGnjkXbmHEpv3GyKFKmFYsFnTdzTTkREd/H29sbGjRsxaNAg6HQ6RERE\nYM2aNZgxY0aN/WPGjJE6ZLIDzm5u6PfIKAye/ijahnZEcX4+/rfze+SmXzEVbTduFWo3Cytu3ywo\nRLnBYNZ6Wzu74qe1G/DT2g3wD+mAPqNHoM+o4Zi6ZBGmvPE36E7EIu7HAzhz8Ahu5Bc0Uba1k8lk\nCPlTfwycOqmi4HV0xMVYLfatvr03zxJKb9xEbvoV5KZfscjyie7Egs+aeEgnERHdJSQkBJmZmdDp\ndACAqKgobN68udZ+pVKJnJycuhZJVKs2HYIxaPqjCBs/Bi6t3JF27jz+++a70O77yeoX+bh64RL2\nrfo39q36NwI6h1YUf6OHY/rSxTC8VYbEmF8Rt+8Azhw6gpKi4iZdd6vWPgifOBYDHp0IX40aRXnX\ncWxbJE5E7q7X3jwiW8KCzwp4Ei0REdUmMTER/v7+CAsLQ2xsLCIiIgAAbm5uNfYHBQWx4KMGkTso\n0OOh+zF4+qMI+VN/GEpLEbcvGse2RyL19DmpwwMAXEnQ4UqCDlEr10DdvSv6jB6B3qOGodsDg1FW\nUoLzR08gbt8BnPv5GEpv3GjUOmQyGTqG96s4N2/4AxV7805qsX/NesT/ZLm9eURSY8FHREQkofz8\nfEybNg0rVqyAi4sL9u7dC71eD71eX2O/oYGH1DXFOX/28MOlPeQANCyPwrJSnNZfQ3xuJooMZfB0\ndEKv1m3Qw8cPbv3uB17/uwUjrV19cxBCIONGIRKu58B7VCv0HP4AHGRytPfwRmcvJdp7eMNRrrjn\ncooNZTirz8Jp/TXkld6Es0KB7t6+6OnTBsoeA4HZCyyaR3PGHJoPS+bBgo+IiEhi0dHRiI6OBgD4\n+flh4cKFuHjxIk6fPl1jf0MEBwcjJaXxh6gJIWx+gGZ7yAGofx4dw/pi8IzH0GPY/ZDJ5Ug4dgLH\ntu/E+aPHIYxGK0Rau8a+FjKZDO379UbvUcOhHzkMOmUuSoqLcfbwUcTtO4CEY/+rtoeucm9ezxEP\nwsHREZdOxuF45C7E7z9k9t48e3hPMYfmw9w8NBpNnT/useAjIiKSWJs2bZCZmQmZTIbly5dj7dq1\nKC4urrWf6G7O7m4IGz8Gg6ZNgX9IBxTlXceRTf/F8R3fIif9stThmU0IgUsn43DpZBx2vb8CHcP6\nos/oEeg14kH0GzsSNwoKcfbQL4j/6SB8NUEY+NhE+AYHoTg/HzHbd+JE5C5kXkqWOg0iSbDgIyIi\nktiyZcswePBgODk5Yf/+/Vi0aFGd/USV/EM6YNC0Keg/fjRc3N2ReuYcti9eCu2+aBhKrHsRFmsR\nRiMu/HoSF349iZ3LP0JIeH/0GT0CPUc8gLAJFVexTTr1O376/Ev8/tMhu30eiOqLBR8REZHE5s+f\n36B+atnkDgr0HP4gBk9/FB3D+qKspARx+w7g2LZvkHb2D6nDsyqjoRyJx39F4vFf8c3SD9AhrC/y\ns7KReTFJ6tCImg0WfEREREQ2oKCsFKOefQoDH5sIT18VctIv4/t/rsKv336P4uv5UocnuXKDAboT\nv0kdBlGzw4LPiuzhpFIiIiKqndxBAUcnZzg4O8HRyQkOzk5wcHa+fdupojk6O90xzx3TnZ3g4Hhr\n+q0+R2dnuHp6YH3CKYz4y59x/pfjOPbf5Ug4esJurlBIRJbDgs8K+GVMRERkn7o9MATD5s5EQJdO\ncHR2glxx72EC6mIsL0dZSSkMpaUwlJSirLTk1t9S9Fe1xeTwQchNv9JE0RNRS8CCj4iIiKgBZHI5\neo8chuFPPYmAzqHIvZyB419/i9IbN02FmqG09FbhVlLxt6QUhrIyGEpKTAVd5XRDya3bZaUwGspr\nXe8KIVjsEVGDseCzAh7KSUREZPsUDg7oP34Mhs2bBV+NGpmXkrH19Xeg3bu/zkKNiEhKLPiIiIiI\n6uDo4oyBj07Eg3OegLd/G6SdO4///N8inDl4hKdtEFGzx4LPCrgxICIisj0uHq0weNqjuH/WNLRq\n7YOLsVrsWPIeEmL+J3VoRET1xoKPiIiI6A6tWvtgaMTjGDzjMbh6tMIfv8Qget1GJGnjpQ6NiKjB\nWPARERERAfBu44cH5jyBgY9OhIOzE04fOIzodRtx+Xyi1KERETUaCz5r4sVbiIiImh1VUCCGzZ2F\n/hPGQAYZTu7Zh0MbNuNaUorUoRERmY0FHxEREbVIbTt1xPB5T6L3qOEoNxhwInI3Dn+5BfqMq1KH\nRkTUZOTmLmDs2LE4efIk4uPjcfjwYQQHB9fZf7clS5YgMzMTWq0WWq0Wq1atMjek5osXbyEiIpKc\npncPzP30Q/ztm83oev9gHP7PFrw7agq+Xf5PFntEZHfM2sPn7e2NjRs3YtCgQdDpdIiIiMCaNWsw\nY8aMGvvHjBlT43K++uorLFy40JxQiIiIiOoUOjAcw596EqEDwlCUdx17V/0bx7ZF4kZ+gdShERFZ\njFkFX0hICDIzM6HT6QAAUVFR2Lx5c639SqUSOTk55kdtq3gOHxERkdVdyM/Fi1vWQ9OrO65nZmH3\nBytxInI3Sm/ckDo0IiKLM6vgS0xMhL+/P8LCwhAbG4uIiAgAgJubW439QUFBNRZ806dPx8iRI3H1\n6lUsWbIEJ06caFAcycnJ5qQBwLJj5aUVXsfXyX/gwQcewGcSHtbZEsYDZI72gTnah5aQIzVvrZQ+\nmLHsLXyXmgh3Hy98/ff38dvuKJSXlUkdGhGR1ZhV8OXn52PatGlYsWIFXFxcsHfvXuj1euj1+hr7\nDQZDtWWsXbsW7777LgwGA0aMGIHdu3eja9euyM3NrXccwcHBSElp/JW0hBCQWXDvW8ewvnj2y89w\n+OefMa1jD4utpy6WzrE5YI72gTnaB0vkqNFomuQHPmoZOg8eiBnvvgkXd3cMaxuM/n2GwFheLnVY\nRERWZ/ZFW6KjozF06FCEh4dj1apVcHV1xcWLF2vtv1tmZqapEDxw4ADS0tLQo4c0RRERERHZNoWj\nI8b/7QU8vXYFCnP1WDH9z+ij9GexR0QtltkFX5s2bQAAMpkMy5cvx9q1a1FcXFxr/90CAgJMt3v3\n7o3g4GAkJCSYGxYRERG1MCqNGi9uXocHZz+Bo9si8a8Z85B5MUnqsIiIJGV2wbds2TKcO3cOOp0O\npaWlWLRoUZ39ALBnzx70798fALB8+XKcPn0acXFxWLduHWbNmoXMzExzwyIiIqIWJHzSOPy/Hf+B\nT4A/Nrz4Cr5d/k8YSkqkDouISHJmD7w+f/78BvUDwLhx40y358yZY24IRERE1EK5tHLHY2++gr5j\nR+LCryex5bW/I/9altRhERE1G2YXfERERERS0PTugYj3/w5vfz9ErVyLgxs2QRiNUodFRNSssOCz\nInu/Kh8REZE1yORyDJs3C6OefQp5V69h1exnkBp/VuqwiIiaJRZ8VsCRqIiIiJqGVxtfPLF8CUL+\n1B+novbjm6Uf4GZhkdRhERE1Wyz4rID79YiIiMzX/aGhmPbOG3BwcsS2N5Yi9rsoqUMiImr2WPAR\nERFRs+bg7IwJf3sBg6c/irRz57H5lbeQnZImdVhERDaBBZ8V8JBOIiKixvEP6YCZH7yDtqEdcfg/\nWxH1yVqUl5VJHRYRkc1gwUdERETN0n2PT8bEhS/hZlER/v2X/0NCzP+kDomIyOaw4CMiIqJmxc3L\nE4///XX0HP4A/jh6HNsXL0Vhjl7qsIiIbBILPmvi1VuIiIjq1DGsL554/220au2D3R+uxC+b/gsh\neHIEEVFjyaUOoEXghoqIiOowduxYnDx5EvHx8Th8+DCCg4MBAOPGjcOpU6eg1WoRFxeHyZMnSxuo\nBckdFBj9wtN45otVKC2+gU8insKRr7az2CMiMhP38FkDB1wnIqJaeHt7Y+PGjRg0aBB0Oh0iIiKw\nZs0ajBkzBps2bcLQoUNx9uxZ9OzZE8eOHcOuXbvsrgjyCfDHzH+8g+A+PfHrtz/g2/c+RumNG1KH\nRURkF1jwWZN9bZ+JiKgJhISEIDMzEzqdDgAQFRWFzZs3Q6VSwWg0wsvLC0BFYZiRkWF3xV7vUcMx\ndckiAMCmhW8ibt8BiSMiIrIvLPisiTv6iIjoLomJifD390dYWBhiY2MREREBAFCr1Xj88cexe/du\nFBUVwcPDA2PHjpU42qbl7d8GMz94B2mnz2Hzq28h93KG1CEREdkdFnzWYGe/xhIRUdPJz8/HtGnT\nsGLFCri4uGDv3r3Q6/UwGAx47bXXMHHiRMTExGDQoEHYsWMHunXrhqKionovPzk52ewYLbVX8VKB\nHrtSEvDKpMexMuIpi6yjkr3sGbWHPOwhB8A+8mAOzYcl82DBR0REJLHo6GhER0cDAPz8/LBw4UL4\n+PggICAAMTExAICYmBgUFRWha9euiI2Nrfeyg4ODkZKS0ujYhBCQWehc9KERj2PSopfRRRNs0WEX\nLJmDNdlDHvaQA2AfeTCH5sPcPDQaTZ0/7vEqnURERBJr06YNAEAmk2H58uVYu3YtEhISEBgYiE6d\nOgEAunTpgjZt2uDixYtShtqkVBo1bhYWcYw9IiIL4h4+IiIiiS1btgyDBw+Gk5MT9u/fj0WLFqGk\npAQLFixAZGQkjEYjAGDu3LnQ6+2nOPINCkRWSqrUYRAR2TUWfERERBKbP39+jf1bt27F1q1brRyN\n9ag0aqTGn5U6DCIiu8ZDOomIiMjqFA4O8Gnrj6zUdKlDISKyayz4iIiIyOqU6naQKxTITkmTOhQi\nIrvGgs+KZByIj4iICACgClIDALJSWfAREVkSCz4rsJfxQYiIiJqKr6ai4OMePiIiy2LBR0RERFan\n0qhRfD0fxdfzpQ6FiMiuseCzAnsYEJKIiKgp+QapkcW9e0REFseCj4iIiKxOpQlENs/fIyKyOBZ8\nVsBz+IiIiG5zcHaGT1t/nr9HRGQFLPiIiIjIqlTqdgDAMfiIiKzA7IJv7NixOHnyJOLj43H48GEE\nBwfX2V8tALkcq1atwoULF6DT6TBv3jxzQyIiIqJmrHJIBu7hIyKyPAdzHuzt7Y2NGzdi0KBB0Ol0\niIiIwJo1azBjxowa+8eMGVNtGREREQgJCUFoaCiUSiW0Wi0OHDiAlJQUc0JrlnjxFiIiIsBXEwiA\nY/AREVmDWXv4QkJCkJmZCZ1OBwCIiorC6NGja+1XKpXVljFt2jSsW7cOQghkZ2dj165dmDp1qjlh\nERERUTOmClKjICcXNwsKpQ6FiMjumbWHLzExEf7+/ggLC0NsbCwiIiIAAG5ubjX2BwUFIScnp8oy\ngoKCquzNS01NhVqtblAcycnJ5qQBwLIXVkkvyseOpHMYMmQIPpXwAi4t4eIxzNE+MEf70BJypMZR\nadTI5vl7RERWYVbBl5+fj2nTpmHFihVwcXHB3r17odfrodfra+w3GAxNFXcVwcHBZh0CKoSw6OGW\n7fv1xvMb1+KXX37B9I49LLaeulg6x+aAOdoH5mgfLJGjRqNpkh/4SHq+QWoknvhV6jCIiFoEswo+\nAIiOjkZ0dDQAwM/PDwsXLsTFixdx+vTpGvvvlpqaCo1Gg9jYWADV9/jZE3v/B4+IiOhenFxd4NXG\nl4OuExFZidlX6WzTpg2AimJm+fLlWLt2LYqLi2vtv9vXX3+N+fPnQyaTQaVSYdKkSYiMjDQ3rOaF\nhzUREREBAJTqigu28JBOIiLrMLvgW7ZsGc6dOwedTofS0lIsWrSozn4A2LNnD/r37w8A2LRpEy5d\nugSdTocTJ07gnXfe4SE7REREdso3OAgAh2QgIrIWsw/pnD9/foP6AWDcuHGm20ajEc8++6y5YdgG\nHtJJREQtnG/lGHzcw0dEZBVm7+GjemChR0REBABQaQKRn5WNkhpO8yAioqbHgs+aeC4fERG1cL5B\nag64TkRkRSz4iIiIyGpUGjWyU3g4JxGRtbDgsyYe2klERC2Ys7sbPJStkc09fEREVsOCj4iIiKzC\nV1NxwZas5FSJIyEiajlY8BEREZFVqG5doTOLV+gkIrIaFnxWIOOhnERERFDd2sOXk8aCj4jIWljw\nERERkVX4BqmRdzUTZTdLpA6FiKjFYMFHREREVqHSBCIrhRdsISKyJhZ8REREZBW+QWpk8/w9IiKr\nYsFHREREFufq6Ql3H29kcw8fEZFVOUgdQEvCi7cQEVFNxo4di6VLl8LR0RG5ubmYM2cOhBDYtWuX\naR5vb294enpCqVRKGGnj+WoCAQBZHIOPiMiqWPARERFJyNvbGxs3bsSgQYOg0+kQERGBNWvWYMyY\nMejbt69pvhUrVsDBwXY325VX6OQePiIi6+IhndbAPXtERFSLkJAQZGZmQqfTAQCioqIwevToKnvy\nHB0dERERgQ0bNkgVptl8g9QwGo3ISb8idShERC0KCz4rEkJIHQIRETUziYmJ8Pf3R1hYGAAgIiIC\nABAUFGSaZ8KECbh8+TK0Wq0kMTYFlUaNvIxMGEpLpQ6FiKhFsd1jQ2wQz+EjIqK75efnY9q0aVix\nYgVcXFywd+9e6PV6GAwG0zxz585t9N695ORks2Nsih8st1w8DWe5g2Q/ftrLj672kIc95ADYRx7M\nofmwZLm4jSQAACAASURBVB4s+IiIiCQWHR2N6OhoAICfnx8WLlyIixcvAgACAgLwwAMPYNasWY1a\ndnBwMFJSUhodmxCiSX6wXHZsP05F7cfUdz8ye1kN1VQ5SM0e8rCHHAD7yIM5NB/m5qHRaOr8cY+H\ndBIREUmsTZs2ACqOBFm+fDnWrl2L4uJiAMDs2bOxZ88e5ObmShmiWdx9vOHq6cFB14mIJMCCj4iI\nSGLLli3DuXPnoNPpUFpaikWLFpmmzZkzx6Yv1gJUXLAF4BU6iYikwEM6rcD2dzQTEZElzZ8/v9Zp\nnTt3tmIkllE5JAPH4CMisj7u4SMiIiKLUmkCUW4wIPcyh2QgIrI2FnxERERkUb5BauivXIXRUC51\nKERELQ4LPitydneTOgQiIiKrUwWpeTgnEZFEWPBZUdvQjlKHQEREZHUqTSAv2EJEJBEWfERERGQx\nHiolXNzdkc09fEREkmDBR0RERBbjW3mFzpR0iSMhImqZWPBZg4wDMxARUcuk4hh8RESSMrvgGzt2\nLE6ePIn4+HgcPnwYwcHBAIBx48bh1KlT0Gq1iIuLw+TJk2t8/JIlS5CZmQmtVgutVotVq1aZGxIR\nERE1E76aQJSXGaDPuCp1KERELZJZA697e3tj48aNGDRoEHQ6HSIiIrBmzRqMGTMGmzZtwtChQ3H2\n7Fn07NkTx44dw65duyCEqLacr776CgsXLjQnFCIiImqGVEFq5KRfhrGcQzIQEUnBrD18ISEhyMzM\nhE6nAwBERUVh9OjRUKlUMBqN8PLyAlBRGGZkZNRY7BEREZH9UmnUyOLhnEREkjFrD19iYiL8/f0R\nFhaG2NhYREREAADUajUef/xx7N69G0VFRfDw8MDYsWNrXc706dMxcuRIXL16FUuWLMGJEycaFEdy\ncrI5aQCARYvR1MLriEz+w+LruZeWUHAzR/vAHO1DS8iR6iaTyaBSB0J34jepQyEiarHMKvjy8/Mx\nbdo0rFixAi4uLti7dy/0ej0MBgNee+01TJw4ETExMRg0aBB27NiBbt26oaioqMoy1q5di3fffRcG\ngwEjRozA7t270bVrV+Tm5tY7juDgYKSkpDQ6DyEEZBa8sErH8H54dsNqALDoeupi6RybA+ZoH5ij\nfbBEjhqNpkl+4CPr8fRTwcnVhXv4iIgkZPZFW6KjozF06FCEh4dj1apVcHV1hY+PDwICAhATEwMA\niImJQVFREbp27Vrt8ZmZmTAYDACAAwcOIC0tDT169DA3rGbF3v+xIyIiqonpCp2pHJKBiEgqZhd8\nbdq0AVBR1Cxfvhxr165FQkICAgMD0alTJwBAly5d0KZNG1y8eLHa4wMCAky3e/fujeDgYCQkJJgb\nFhEREUmscgw+DslARCQdswu+ZcuW4dy5c9DpdCgtLcWiRYuQmZmJBQsWIDIyEnFxcdi+fTvmzp0L\nvV4PANizZw/69+8PAFi+fDlOnz6NuLg4rFu3DrNmzUJmZqa5YZEVzFv1EZ77zxqpw2hSvsFBWP6/\naPQdO1LqUIiIbJ4qSI2ykhLkXeV2nYhIKmadwwcA8+fPr7F/69at2Lp1a43Txo0bZ7o9Z84cc0Mg\niXR7YLDUITQ5pbodnN3c0P+RUdBG7Zc6HCIim+arCURO2mVewIeISEJm7+EjsifCWPFPiUzGjwYR\nkblUQWpkp/JwTiIiKfG/WqI73foVWibnhXaIiMwhk8uhCgpEVgov2EJEJCUWfER3EKaCjx8NIiJz\nePv7wcHJiXv4iIgkxv9qrYDDMtgevmZEROapvEInx+AjIpIWCz6iGrDgIyIyz+0x+FjwERFJiQUf\n0R0qCz0e0klEZB6VRo2S4hvIv5YtdShERC0a/6slulNlwcc9fEREZvENUiMnLZ1DMhARSYwFH1EN\nWPDZjofmzoR3Gz94KFtj9AtPQ65QSB0SEQG3rtDJwzmJiKRm9sDrRPakss7jIZ22QRnYDo+8/Bx6\njxqOG/kF6DQwHA6OTvjh41VSh0bUoskVCigD2+F09M9Sh0JE1OLxv1pr4N4i28FDOm2Sm6cn2rQP\nBgA89OcISWMhIsCnrT8Ujg7I5h4+IiLJseAjqgkLPptgNJYDAOQKObza+EocDRFVUlUOycArdBIR\nSY4FH9EdZKi8SicLPlsgl1ecr8fz9oiaF19NIABwDx8RUTPAgo/oThyWwaY8NHdmxY07LgKYezlD\nmmCIyEQVpMbNoiIU5ORKHQoRUYvH/2qJalC5p4+at/umTgIACGFEUd51AEDrdm3RSukDBycnKUMj\natFUGjWyU9KlDoOIiMCCj6iK2wOvs+CzJUIIuHq0Mt3/++EoPLX6nxJGRNQwY8eOxcmTJxEfH4/D\nhw8jODgYAODs7IzPPvsMiYmJiI+Px+effy5toPXkq1Ejm+fvERE1CxyWgf5/e3ceFlX1/wH8PTOA\nLO6sImuClrmkQoloZuWeSy5hkWkWlZXt5pK5Z+VGlolJ+g23yn1J/WmiVq65gCAqoLIoGrIp+35/\nfwxcGRnWAe7M5f16nvM8zL13hs/hcmE+c879HCqDyzIYJhNT03L38bn39JAoGqKaadmyJYKCgtCr\nVy9ER0fD19cXAQEBGDx4MBYvXozc3Fy0b98eAGBjYyNxtFVTGRmhtX0bhBz4U+pQiIgITPgaBAs+\nGh4uy2BYLFq1BAAU5ufXyVTOTs/2RVZaGu5EX0duZpbOr0dUGTc3NyQmJiI6OhoAsH//fmzcuBFt\n27bFa6+9BgcHB/HYu3fvShVmtbV2sIdSpeKUTiIiPcGEj6gsrsNn0HIzs9C09YOEz9LRASk3tb/p\nbGJujrzsbPGxjaszpu35DWve+Rivr/hG3P5ZV2+4P9UDBbl5SLuTiHv/JdZfByrw1OjhcPPsjk3T\n5zb496b6FxUVBTs7O3h4eODcuXPw9VWvJdmuXTukpKRgzpw56NevHzIzMzFr1iycOHGiRq8fGxur\nc4yCIFR9UIkb6WnYFR+J3b/+BnvzZjp/77pSkz7oMzn0Qw59AOTRD/ZBf9RnP5jwEWnBKZ2GKSk2\nHk1btxIfz9y/FZ929ip33JjZ0+A1diSWvzQBCVeiAADT9vwGAHhrtb/GsYOnvI3n3nxNY1vQp18g\nNiQMVs6O6P3yGBibNsG2+d+iaetWaN3WHuGHj9Uo7lb2drh3J7HCP/YvzZ0BAEz4ZCo9PR0+Pj7w\n9/eHqakpDhw4gLS0NAiCgHbt2iEkJASff/45nnzySezduxdubm7IyMio9uu7uLggLi6u1vEJglCj\nD8GeHj8OIz7/EO3bOorFlKRW0z7oKzn0Qw59AOTRD/ZBf+jaD2dn50o/3OO7WqIyFFpG+Nx7emJZ\n+CnYuT0iVVhUTdu/WlJum+fIoeW2lVb39Bg2BHbu7WBs2qTcMVvnqUf5Hk72AGDCsq8w58hevPe/\nVeg64Fl0fNobsw/vwSdbgjDR/2ssCz+Fjn17i8dX9gFCv0mvYtbBnXhz1fIq+6cy4md0chUcHIw+\nffrA09MTK1euhJmZGeLi4lBQUIBff/0VAPDvv/8iOTlZvJ9PX1k5OSA7PV1vkj0iosaOCR/VuaEf\nv4uWdrZSh1FL5RO+dwK/BwBM3blJkoio+tKTUsptG7dgFpw6d9R6/NPjfTB1x0Z8c/YYAODfXX/g\nyvFTmOU9AFGnz2oc+3n3pxH47idIva25zt+pbbu0vvYbK5dgWfgpLAs/haUXT+DVxfMBAM2sLBGX\neR+9fEbhzVXL8MLH7wEAHu3dEz3HjsRTo4ahqWUrra/5iEe3ijtPBs3WVv03U6FQYNGiRVi9ejXi\n4+Nx9OhR9O/fHwDg7u4OGxsbXLt2TcpQq2TNJRmIiPQKPy6mOtX20fZ4dtJ4PDtpvNapdIaidESm\nbAGQ6NPnpAqHqik3IxOLR7yMFrbWuJ+YhM93q0dGPty8Vvx9dOykPfkDgO0LlqAwPx8AkJOegS+8\nnsdXpw4DAIoKCnD1n1P4auAouHbrAtfuXXF881bk5+Ri27xv0cLWGsZNmqAwvwCvLfsKzl0e13jt\nboP7o9tg9Rv37bFXMHrWVHHf0tHj8dn2DRg7exoA4Mja9dj3XUC5+LoOfBbRDyWiJA8LFy6Et7c3\nTExMcOjQIUyfPh0A8M4772DdunVYtmwZCgoKMH78eNy/r98jZ1ZOjogNDZM6DCIiKsGEr0EY/tzi\naivT1TdXLUNOegZ+nbUAxYVF0sVUAw+WZVB/0bSk+iMApCbcliIkqoGiwkIk3ohF4o3YCo/56Ne1\nAICdXy+Hnfsj8Bqjnt5ZkJsnJnulcjOzsGjIWGTdu6exPSYkDDEhmm9o7ycmiV9/7/smnLo8jjZu\nj+Def3dRWFCAd9f9WC6WAyvX4K+gzSjIzcOxXzbjmYmvaI05LzsHTczNcCdSv0d2qPb8/Py0bo+J\niUG/fv0aOJraMzIxQcs2tkjazTX4iIj0BRM+qlPGTUzFrx/r0wsAcGH/n4g8eRodvJ7ClX9OShVa\n9Yj38KlH+MxaNBd3KXn/lMGZ2fM5LDodjGv/ni+379TWXSgqKMAfy1bCvacnbpwP1foaFVX5rEp8\nWATiwyLEx5929oJrty64HXkNOZmZUKlUGkVa9i77AXuX/YDFF/5BcVGxxmvl56gTPhNzs1rFIiXz\nFs3R73VfRJ78V+t5IHmxdLCHUqnkoutERHqE72CpTpmYlS9+Ye2irmT4aO+eAGAYUz1LRvrMmjUV\nN5mYmVZwMEnlbkwcbFydAQBFBYXl9udlZeN21DXkZGQCgFjB89rZCygqKACgHsWraVXN2iodFVQo\nFJWXX36oUpeyZIpxEwvzeoutrt3NycJ7vwTgkR5PAACefeM1w7j2SSdWzo4AgCTew0dEpDdYtIXq\nxLLwU3j+rYkwNi2fFJm3aC4mewDQwbtnuWP0RWmxlgdvsC3EfYY4uiJ3ZdfRe3jaZSn79m7o/Fxf\nAMC8v/YDAHJrUNK+oQnQTAQVSqWY6DUx0/+Er8ewwVgWfgobr4fjkR5PiOdo59dVVyElw2ftpE74\nOMJHRKQ/mPCRzsxLpj0OfM8PJmblk6LuQwZoPH5rtb/+rnP38JTOZuqE735iEkf49FGZkbD7d5Mq\nORBo095N/Pr4r9vrLaS69tSoYWLxoKfH+1R4nFPnjhpFhupLqzZ2cO/pCQCwdGgrfkiiMjLCjH1b\n8cqi2eKxt6OuYVavAfi0sxeOb95a77GR9KycHZGVdg856fr7oQoRUWOj85TOIUOGYMGCBTA2NkZq\naiomTpyI2NhYDB06FAsWLIBCoYBCocC8efOwc+fOcs9XKpX4/vvvMWjQIAiCgG+++QZr167VNSxq\nQGWnmZloWc/M0qFtuW1LL57ALO8B+vumoCSPKB3hu5+UZBCjK42NAgrEXgxHM0tLBLzxvtZjjm/e\nit6vjMVn2zeI2/S90mXZZUH6TXpVY1/T1q2QmZqmsc22nSs+3Kz+u7n+s1kIO3QEvXxGobWDPfZ/\nF4CiwvLTXWvCtKmFWK20Or4dPg7/XY+BklM4Gx1rZ0ckcXSPiEiv6DTM0rJlSwQFBWHcuHHo0qUL\nAgMDERCgLiW+YcMGjB8/Ht26dcP48eMRFBSkdQV5X19fuLm5wd3dHV5eXpg7dy6cnZ11CYsaWOmo\nglKpxEvzZorbFw58UeM4f5+JGo9du3Wt99hqSlx4XalE28faw3vcaABAelIyR/j0kEKpQGZqGhYN\nHo28rGytx1z+64TG49+//KohQqu9h+/te+ihm2f3ck+x7+Aufv3a0oX4ZGsQRn3xGZ6Z8AreDHgw\nlXLmge1YFn4KTS1bQalSoYl51R9ivL/+pxole3OfGYq7MXFa/96T/FlxDT4iIr2j0wifm5sbEhMT\nER0dDQDYv38/Nm7cCCsrKxQXF6NFixYA1InhnTt3tBYp8PHxQWBgIARBQHJyMnbt2oWxY8di6dKl\nuoSmV+T+xsfIxLjctmkez6AwLw+RJ06L9+wlXI1GyP5D6FYyxbO5jVWDxlkTCoUCn2wJEh9n30vn\nPXw19PxbExF38RKiz9Tf+oUKhaJ8gvSQyJNncPHQEXQd8CwA4MrxU/UWT32wcnIAAIT9eRRd+vdD\nYUmxmbIcHuug8bhsAti+pydenPkpvMaOhKqk0uy8Y/s1jv/h1bcQezEcrt264MUZn2LTjLkAgO5D\nB8K1WxfxuH3frcLJ33egIDcPn2xbjzPb9+DCvoNQKBTISEmtk/6S4TI2bYKWtjYc4SMi0jM6JXxR\nUVGws7ODh4cHzp07B19fXwCAo6MjXnrpJezevRtZWVlo1qwZhgwZovU1nJycEBcXJz6Oj4+Ho6Nj\njeKIjY2tdR9KVVoxT0exGfewI+5qvX+fqtT1915+6TQA4EJIKDbfuKSxryA3FwBQJBRjRcS/AIDi\nIvVafMWCgO8izmDcnOk4+fuOOk2Ide1j5P1k7Lt5DdY2NsgtejAN7q033kRo6n84/l88vG1r9vtZ\nU4Ig4HZ2BuzMm0KlKD8IL+XvUEVKz6lKocAHHZ/Eybu3cCYpAQDQy8YBPW0cNI4XBAEbrocjOffB\nqFzrJqZ4za2ruL861keHoYXHk1j3wbQqj/W/dBoCgHt3k6DUgw9hKurjiogzmD59OvZ9twqFxcX4\n/rL6+lk2+UMEXQvDth3b0aGF5ocl66PDYG5khEdbWuFQwg1xe+smZkjNy0Hvl8dUGsuUjWvg2qwl\nknOzkVGQj893bdbY36+NC5oam+CTn9cDP69/sGPEy7XqI8lT6YcTybHxEkdCRERl6TSlMz09HT4+\nPvD398fZs2dhY2ODtLQ0FBYWYsaMGRgxYgRcXFwwbNgwbNmyBRZlKh7WJRcXF/Fewdo0ADo9v6o2\naNAgMdb6/D4N3cdSvXp7lzsnpccYKVX45aPp+O3LheI2VUnBliJBwPN+E/Sqj+PGqd/ApqSkaPRn\nzbqfAQBnkhIw448t6PTs03X+81QZG2HRmWD4R5zB7zGXsSLiXyy/dBqdn+sLhUIB+w5uWH7pNLoP\nGSDZ71FFzeOFQeI59Y84IyZ7AHDy7i3xOCtHByy/dBr+EWc0kj0ASM3LxXcRZ7D80mm4duta4fey\ncnSA7SPqaz78Ujh27txZrRg/7eqNaT36QqVUSv7zqux3NTc3F99++w0UCgWeHDZY/Pm4P9IOAPDa\nxNc1jm9pZ4PkvGz8HrQeb5ZZvHvbgsV4z/NpjZ/xtbMXcHTdRgBAxLHjSLn14DzFZNxDRoHmwvMA\nsHfpD+hu1QbtW1jWWR9r21xcXMrFR/rDqqRCJ0f4iIj0i85FW4KDgxEcHAwAsLGxwdSpU9GqVSvY\n29vj5En1ItsnT55EVlYWHnvsMZw7pzm9Kz4+Hs7OzuL2h0f8SP89XBnwty8XajwOD/6rwueWrZyo\nTyxathC/vn4uBEbGD6atWrs4wWfeTMw+OljbUzWYNrVAcVERzJs3R05mZoX3mQHA6C+mwlTLhyKv\nr/hW4/Gri+fj2tkLyEhOKXdsfXqsTy/Eh0cg6979cvte/XZeuW0nftuOR3v3hKVDWzh07IBblyMx\ndu50jWNObtkJE1NTtLK3QzuPbuL2KRt+wrm9B3Ar4ipGTv8Yf/70P3R+/hnYtXMVj4m5cBFt3Nsh\nqZqjCUJxMQrzyyc0eq3MCFl+Tg4AwKnr4/h31x/idqfOjwMAbl66gvuJdwGol6s4tUVdJOu7cZPw\n0W/rcPDHQBxavQ7WLk5o7WCPrfO+RU56OgDg0T5e8Fu1HIX5+Zj37DC0tLOFpWNb3Dgfiqw07ctd\nED3MumQNPt7DR0SkX3RO+GxtbZGYmAiFQoFFixZh9erViIyMhIODA9q3b4+oqCg8+uijsLW1xfXr\n18s9f+vWrfDz88OOHTtgaWmJkSNHok+fPrqGRQ1I9dA9fDnpmVU+Z5b3QCw8cRDdhwzApmlz6iu0\nGisdmSjr4Kqf4TVmhMa2Jk2rN1r9cLGLBf1HwnPkUAT/HITiwiJx+9trVqC915MAgH93/YHd334H\nu3aPYMrGNVpft//br2PHV/V7n+uoLz6D97jROLByDQa//5a4/dPOXrBzewT/XVNPHWzTXj3ylBx/\nC7u+8cfA9/2w+xt/xISEoe1j7fHJliC89dMKMYlOuXUb4YePIer0WUSeOK3xPVXGxth9PQKR91Pg\nMWwwPEpGuPq//Xq5+Fy7q6d/alsKxOCV/B4WFxeLm/Jy1NOkvcaMxLZ56g8BvF56EU6dOwJQ3+OX\nk56Bb4ePw92YBx+a3Yy4ghlP9kN+yfOTYuOx/tMvNL7d1X9OaSyKnn0/Hbcjo+uhYyRnVk6OSE9O\n0Vgfk4iIpKdzwrdw4UJ4e3vDxMQEhw4dwvTp05GXl4fJkydj27Zt4huWSZMmIS1NXUp83759mD17\nNs6fP48NGzbgqaeeEgu/zJ8/v07uyaOGU3b0CwDysrKqfE5Oejqy09Nh3rw5TJtaIDez6uc0CC0J\nX056Rrl1Ax/u88OsXZwwfe/v5ba/+79VsHSwx41zIbh+LgQOHTtg9Jefw6mT+k37V4NHI/XWbQBA\n7MVwfNrZC94vj4FZ82a4+s9JxF26gteWLID3uNHwHjcaxcXF4iLxW+Z+javHT+F+YuXr0VXEtp0r\nJn2/GKvfnILhUz9Al/79AEAj2QOAZeHqoie/f/kV/t31B56ZoL53d7XfFKTd/g9X/jkpHptwJQqA\n5ohpwBvvIe32f1pjKCoowFBHdyz46FO8NG8mbkZcwe3Ia3hq1DCEHT6GX2fOR35ODmxcndH/nUnq\n6a1K6e/Hq1Mlg3oqIyOxIufWed+gMC9P4zCHjh0w5svPxcelS5yUTfZKlSZ7RPXJytkByXGczklE\npG90Tvj8ytwzUtbmzZuxefNmrfuGDh0qfl1cXIx3331X1zD0m5YkQk7KJXzV/HR3y5yvMdH/a9i5\ntUNsaFh9hFYnMpJTkF3N9QKtnB0xcvrHFU7dtHSwBwCMnTsD//vgc3z8+y/ivq9feElM9so68es2\n8WulQqExxU5ZJhF9ae4MAMBnXb0hlBkZKo3Lrt0juHSk4um17/1vFSxatcSsQ+XXy9TaFycHTN25\nCXZuj+D+3aQKk7jAdz+B3yr10gCl1VurcmbHXpzZsVd8vGXOIo39d2PiEB8Wge4lFV/lqN8b49HL\nZxQA4PLfJzX2fXvhb2z47EspwiKqkLWTI64eP131gURE1KB0KtpCBACqh+7hq+w+tbKSS27sn7Lh\npzqPqba05eYZKan4Y/lKnNq6S2P7gHcmQalSAQBMmzXF8M8/xIw/tuCx3l54YuBzAID7d5PwaWcv\nLH9pgsZzrZ0d0f+dSeLjX79YUO1Pxv/wX4Www8fw94bfEX3mHLYtWIzLfz9Ya27pxRN4avRwjefM\n+GMLXl/xDZRGqgpf16JVywr33b+bhNVvTtHY9rzfBNi5PQIAyEiuuCT/1X9OYenoV/F596erlexV\nl5wrQCqgQDPL1uLjglz1z+1YkPpDNCNjY7y+4htx/5p3Pm7YAIke0sTcHM2trZDEET4iIr2j8wgf\n0cPr8OVl51TreXeiHtzTqVAoKn0D38TCHMamTZCZkla7IKurgtHYvKxs7F36A7zGjhS3DXzPDwPf\n88MP49/GmNmfo417O43nXDt7AQGT3gOgntqYfPMWrp05j7ycHPQdPw7dBvfHrcuROBTwMyKOHa92\niIV5eQj6eIbGttICHc++8RqGfjQZL82dgadf9cGSF301jlsSchyzvAeKxToedLt8v29evooVL79R\nbrRQm03TK78Ps+y5rjvyTPhKr4PM1Ae/67mZ6vtiw/88hmcmvFLuOQV1mEgT1Ya4JAMrdBIR6R0m\nfKSzLs/303hc3YQPgHgPmp37I5UmBYtOqyvBHgpYi4Orfq5doLWQfPNBtbm87Gx8N24SFColPtz0\nIIaKRijDDx/TePz1kLEAAKVKhWatW6H70IHYMHUWkuPrrqLdkbXrceNcCKZsXAM7t0fE++3KsnZ2\nQHz4ZY1tdu6PlDvuty8WlEv2fv1iAVrb20FpZISnRg3D10NfQkFuriSjbTIe4AOgOVJeeh7iL11G\nbGg4XJ7oLO77e8PviDkf2uDxEZVlVVKhkyN8RET6hwkf6ezR3j01HufXoEJb4Dsf4+01K/DkyGHY\nvfg7jX1NLMzRwsYa1i5O4rYBk9/AgMlvAAAC3ngfWffu1enokQKaI12lUzZL3Yy4Uu3XuvyX9lG7\n4qIibJo+F5umz61xfNURezEc854dhjlH9mrdX3YZDUtH9afyLWysAQDb5i9Gx2e8ceXvk2IVzrLO\n7dkvfv1/K7VXEG0wcs74FAoUFxWW21xcVIQfxr8FS0cHzNy/FWve+bhcpVMiKZSO8KXc5JIMRET6\nhgkf1ZlN0+fA++UxKCos/0a1IlGn/sXtyGi0cW+HJwY+h8hT/yInPQOt2tiVKx6SnpyC5laW4uPJ\na1cCAJaOHo87UdcAAGbNm4nVCh/Wpn07vL3me/zfj4E4/dD9eBWpqLrhnz/9D00tW8FrjHqK55a5\nXyNk/59w7PQYEq/HoJmVJVIT7lTre9SH9KRkjTL7GiN9ZaZvzty/FQCwdb66zP+Vv0/g1NbqFW2R\nmlzv4Svt1+Ap71R4TMrNWxrnl0hq1s5OuJ+YxIqwRER6iAlfA2v7aHskXI2SOox6cWHfIVzYd6jG\nz4sLi4DX2JFw7+kBAPisSy+tlSK/HjIG3YYMQMj+Q3hp7gx0K6nQ+Nn2DSjMz8eGa2FYeOIQDgcG\n4dq/5/Hywi/RwlY9chV6MFgspDJ29jQolUqc2rKzfNLw0L1sFa0R+H8r18DG1VlM+M5s3wMAuH72\nAgDN+6/0jbb79UZ+/hEAdVJtKIRieSZ8pUxL1npcN2WqxJEQVc3a2RFJvH+PiEgvsUpnAyj7/vqT\nrUHoWpJ4kFrpWm2lloZplqCPC4vAnL5DkJ+TizPb9yA/Jxcbp83BjCf7iWuOGZmYIClXPZX0eb8J\n68gGygAAHo9JREFUeCfwezHZAyAme/cS7wIARs+aiqVhJ7Es/BSWhZ/C+KULYeXsCKXqwSVx4Ief\nKl18+m5MHPZ9twoLnh9R4TH6SGVU/nMeY9MmANRTBg2FINOiLYBmUv7wkgxE+sjKiWvwERHpK47w\nScDW1VnqEPSKRasWWrf/+sUCnN97oMKpe/k5ufh2+Dh0fq4vJn6nLlEfd/ESnLt2AgCk3r6D1W9M\nwegvp8LUwgKXjv6DI2vX45EeT+C9XwI0XuuJgc+JSWGpK9V4o31k7YYqj9E3KmNjeI8bjax796UO\nRTcyndL5cPXR6lRJJZKSabOmaNq6FQu2EBHpKSZ8VCeKdXhT+veG3zF4yttYN2UqLv91QhzhK1sg\npDLhwX/h085eEAQByq7eMDE1RX5Ojpgornn7I43jb5wPxaedvaBQKqFQKqBQKDHovTfRdeBzsHRo\nW6ZPhjPaVRMqIxVGffFZue36PA1VG7newwcAHft6Sx0CUbVZO6krdHJJBiIi/cSETwIqY/W6dWbN\nmyE/O6dGRU70VfSpf2v93PycHI0CFD+Mf7vcOnHVJRQXI6+aVUKF4mIIxQBQhH3fBWDfdwEwNm2C\nL/5vB5pZtkZxoTwTPoVS+0xugyu2INOEr4m5OWw4C4AMCJdkICLSb7yHTwLPvzURALDwxCFMXPGN\ntMHUkcL8gjp7rdjQMCTeiK2z16uJgtw8FOSqF7HWZdRSn5Xer/ewn9/7tIEj0Y1M8z0ig2Pt5IDi\n4mKk3EyQOhQiItKCCZ9EWjvYAwA6Pi2PqVtyGKUUldTLkOsIX3NLy3Lbtsz9GonXYySIpvYE9fCs\n1qqjRNRwrJwdce+/RBTm50sdChERacGEryFoeUP6VoC/BIHUHzklfMqSKY9yvYfvMS33h0Uc/UeC\nSHTEIT4ivWDl5IjkeC64TkSkr5jwScTaxQkAcCf6usSR1I2iAvkkfKUJulAkzymdbp7dNR7PeWaI\nwRVsAR7ke3Iu3kJkCKydHbkkAxGRHmPCJ7E27u3gOXKo1GHoTE4jfKVTBOV6D1+puIuXcChgLTJT\nDC/ZA5joEekD8xbNYd6iORddJyLSY0z49MCzk8ZLHYLOZJXwlUzplHtCsWnGPBxc9bPUYdSezM8P\nAGSkpEodAlGlSit0JsdxSicRkb5iwqcHQg8GSx2CzorllPCVTukU5D3Cl5uRIXUIOhEg/4Qv6JOZ\nUodADWTIkCE4f/48wsLCcOzYMbi4uAAAYmJicOXKFYSEhCAkJAQDBgyQNtCHcA0+IiL9x3X49MCA\ndybhfuJd3DgfirsxcVAolXDt3hU3zoWUO9aiVUtkpd2TIMrKFRbU3bIMUhOrPso8n8jJzJQ6BJ0I\nxeoTJOcqnbkGfo6oelq2bImgoCD06tUL0dHR8PX1RUBAAAYPHgwAGDNmDCIiIiSOUjtrFycUFxVx\nSQYiIj3GET49MXbOdHy6bT0AYOhH7+K9/61C28faAwCGT/0A7wT+gF4+ozD/7wMYPvUDKUPVSk4j\nfKULkAsyvocvLzvb8JedaARTOg3+HFG1uLm5ITExEdHR0QCA/fv3Y9CgQbDUsoSKvrFyckDanf9k\nNa2fiEhuOMLXABTQHIGIPHEaHbx7AlBXt1QZq0+DkYkJlEYq9HvdFwDwyZYgLBoyFn1fexkA4N7T\nAwDQ97WXsWfJ9w0VfrUUFcnnjelqvw/Qpf8zyLp3X+pQ6k1OhuGPHMn9HktAXvfGUsWioqJgZ2cH\nDw8PnDt3Dr6+6v8BTk7qas6bNm2CQqHA8ePHMXPmTNy/X7O/TbGxsTrHWNH1tvFaOMyMjAziejSE\nGKtDDv2QQx8AefSDfdAf9dkPjvBJYPPM+eLXZ3fv09i3JOS4xuNpe37V+hrNrNSf/LZu2wYAYNqs\nKZQqFfwC/MWRQUA93cbEzKxO4n7Yf9duiF/LaQmDlJu3cHTdRqnDqFc56YZ9/x4AcYRPLn/otSmW\n0QcpVLH09HT4+PjA398fZ8+ehY2NDdLS0lBYWIg+ffrgiSeegKenJxQKBVauXFnj13dxcYFCoah1\nA1Dhvvjku9i05medXr8hWmV9MKQmh37IoQ9y6Qf7oD9N1364lNz3XRGO8EkgMzUNs7wHIDcjE6O/\n/LzSY1VGD05R6P8dRuL1GAx8zw9PjRqGwVPe1vqcR3v3xLYFizGm5LWT4m7iUtpdLAs/hRlPPov8\nnBy88Mn76P3KGBwL2oyDKwNr96ZZ8WDkUu5LGMhNbmaW1CHoTL5p3gMc4Ws8goODERysLuBlY2OD\nqVOn4vr168jOzgYA5OfnY9WqVdizZ4+UYWpoatkKpk0tkMQ1+IiI9BpH+CSSk54BQRDEaZpV2Txz\nPrbM/Rp//vQ/AKgw2Ss1pkwiae3siEMJ6tG4r/89gn6v+6Lf674wbtIE/d96HT4LZsHK2RGWjg7w\nGvsiloWfwpjZ06BQKqEyMoJZ8+Ywa94MANDeyxOmTS3g2Kkj7Nq5it+DIxGGJTdLBgmfjEf2SvEe\nvsbD1tYWgPoT3kWLFmH16tUAgObNm4vHjBs3DqGhoZLEpw0rdBIRGQaO8Emsibl5uW27vvHHY0/3\nwqUjf2P0rKlIio3H+b0HtD6/sKAARsbG4uPLf59Ax6e9xce3o67Bvr2bxnNe+OR9AOpROaVSCc8R\nQ+A5YojGMV5jR8Jr7Mhq90POBU7kKFcGUzpLf+cUCvlW6eQIX+OxcOFCeHt7w8TEBIcOHcL06dNh\nb2+P7du3Q6VSQaVS4fLly3j33XelDlVUugZfEtfgIyLSa0z4GtjFQ0c0Hv8VtBkvfPI+Nnw2C+OX\nLgQA/LNpC/7ZtAWAekpnePBfGs9ZOeEdDP7gbexctAx52Tl4ZsIr2L8iQJym98xEX6TcSkDClUg4\nPP4YJiz7CrZmFpjcsx/mHv0DAHB6225snfcNer8yFi/O+KRWfTm39wAyU9PwzIRXUCyje/gag6z7\n6VKHoLvGMMLHkfNGw8/Pr9y2mJgYdO/eXYJoqsfKyRFFBYVIu31H6lCIiKgSTPgaQiUjEP9s2orL\nf51A4o1YMeHT3L+l3LaYCxexauKDT3l3fLVUY/+xXzaJX6cm3MGMJ/shKzMLryanPHjOomUAgOOb\nt+LUlp0ANEcTegwbjLTbd3DjfCiaWJgjLysbbdq3w52o6xrfa/jnHwIAiov5xtSQZMsg4WsE+R5H\n+EivWTs7IjXhNj+YICLSc0z4JFaYn4/EG7EAgOVjJyAno+6n2uXn5MJIqb5dc2bP5wABKCqzULq2\nN5Vlp5DmZamLBjyc7AGASqUCIK8qnY1BYX6+1CHUgUZQpZP38JEes3JyQBLv3yMi0ns6F20ZMmQI\nzp8/j7CwMBw7dgwuLi5wdnZGSEiI2GJiYpCSkqL1+XPmzEFiYqJ4bG1KTstFwtUopCbU79SYvKxs\n5JVUfasLipJEkiMRhuHC/kMA5DFVUM6JXik5nCeSLysnByTz/j0iIr2n0whfy5YtERQUhF69eiE6\nOhq+vr4ICAjA4MGD0a1bN/E4f39/GBlV/K3Wr1+PqVOn6hIKSURppB7h4xtTw5BRMq1XFiNHMk/4\nfhj/dqNIaskwNbe2QhNzc1boJCIyADqN8Lm5uSExMRHR0dEAgP3792PQoEGwtLQUjzE2Noavry/W\nrVunW6Skl1QqdSIviwSiEVAo1Je8HBKJ4mJ1H+RapTMpLl7qEIgq9KBCJxM+IiJ9p1PCFxUVBTs7\nO3h4qNeS8/X1BQA4OTmJxwwfPhwJCQkICQmp8HXGjRuHixcv4uDBg+jZs2eN44iNjYUgCLVuAHR6\nflXtj717xVgXT/6wXr9XQ/dxwusTAQDr1q2TpF8NeR71odW2j6U+/EhdZMd/+XLJ+6JrH//4Q31d\nPf98f8ljrss+lkq6e1fyOBviPNakxcbG1vj/A9UPa2euwUdEZCh0SvjS09Ph4+MDf39/nD17FjY2\nNkhLS0Nhmfu5Jk2aVOno3urVq+Hq6oquXbtiyZIl2L17N1q3bl2jOFxcXKBQKGrdAOj0/KraC8OG\nAQD8fV6HnXnTev1eDd3HTZvUFUFfG/+qJP1qyPOoD622fSz1/fffAwA++ugjyfuiax+HDh0KADh8\n+E/JY67LPpaytLSSPM6GOI81aS4uLjX630D1x9rJEYX5+Ui7kyh1KEREVAWdi7YEBwejT58+8PT0\nxMqVK2FmZobr19XVHO3t7dG3b18xKdAmMTFRTBAPHz6MmzdvolOnTrqGpVfKvomTG2VJlU5O6TQM\npb+LZUeSDJYc+lApufePDJmVsyOSbyZAKGaFZiIifadzwmdrawtA/UZy0aJFWL16NbJLqkBOmDAB\n+/btQ2pqaoXPt7e3F7/u2rUrXFxcEBkZqWtYekp+b+DEhI//9A3C7Uj1/bZyuO+mNN+T6xtOufaL\n5MHKyYHTOYmIDITOCd/ChQtx+fJlREdHIz8/H9OnTxf3TZw4Uet0zn379qFHjx4AgEWLFiE8PByh\noaEIDAzE+PHjkZjIKSKGglU6DcuZHXuxbMxriDxxWupQdKYsWRJErh82CMXy+4CI5EGhUMDKkUsy\nEBEZCp0XXvfz86twX4cOHbRuL733BlAnhWS4Skf4ijil02CUjvIZutI1IOWaGMli2i3JUgtbGxib\nNuGi60REBkLnET5q3I6u24jC/HzEXQyXOhRqZJSq0hE+eX7YIAjyHLkkwydW6JTB1HAiosZA5xE+\nqppCKd+iLTfOh2Jaj75Sh0GNUOkIX3GRPBMjDvCRvrJyYsJHRGRImPDVI4VCgT7jfTBiqnrts4K8\nfIkjIpKP0unEci1uItd+keGzcnZAQW4e7t9NkjoUIiKqBiZ8dajbkAHwGjsSl/86gWGfvq+x79TW\nXUi8HiNRZETy82BJkMIqjjRQHOIjPWXt5Ijkm7d4nykRkYFgwldHPvx1LZw6dQQAtPPoprHvx4mT\nceN8qBRhEclW6VRp2Vbp5Jtp0lNWzo5IvBErdRhERFRNTPhqqaWdLV6c+QmOrN2ANu7txGRv3nPD\n8cLH70IQBKQm3MHBHwMljpRInpTK0imd8kyMOKWT9JFCqYSlgz0ijv0jdShERFRNTPhqSKlSwdi0\nCaZsXIOWtjbo1O9pcd+cZ4YgMyUNm2fMkzBCosbhwTp8cq3SKc9Elgxbqza2MDIxYcEWIiIDwoSv\nCs1trJF+Nwmt27bBF/+3o8LjDqxcg8yUtAaMjKhxU5QsyyDItEonkT6ydnYCACTFc9F1IiJDwYSv\nAp/t2Ig27u0q3J8UG49vhvlg/t8HUJCfj+A1vzRccEQkTumU6z18RPrIqmQNvqTYeIkjISKi6mLC\nV6K1gz26PPcMzv1xAGPnTNea7EUcO451U6ZCoVCI061mPz24oUMlIpRZeL1InlM6ifSRlZMDcrOy\nkJGcInUoRERUTUz4AOQUFuKLA9sBAMM+myJuP755Kw6v+QXdhw7E6W27kZedDYD31hDpBYW6SieL\nmxA1HGtnR6TEJ0gdBhER1UCjT/jaez2JgKvnym0/s30Pdn69HADw1/pfGzosIqrCvzv3ou2j7fF/\nP/4sdShEjYaVkyMSrkZJHQYREdVAo0/4bkdGi1+veedjtPfyxIlftyE14Y6EURFRVQpy87BlziKp\nwyBqNJRGKrRu2wYXDwZLHQoREdVAo0/4MlPTMMihHSa+/joiT5xG5InTUodERESkd1rbt4HKyAjJ\n8VySgYjIkCilDkAfdGxpjbO79kkdBhERkd4SK3TGcUkGIiJDwoSPiIiIqmTtpE74OMJHRGRYmPAR\nERFRlaycHZGTkYnM1DSpQyEiohpgwkdERERVsnZy4OgeEZEBYsJHREQksSFDhuD8+fMICwvDsWPH\n4OLiorF/9uzZEAQBjz/+uDQBQj3ClxzHhI+IyNAw4SMiIpJQy5YtERQUhHHjxqFLly4IDAxEQECA\nuL9bt27o2bMnYmNjJYtRZWSEVm3skBTPgi1ERIaGCR8REZGE3NzckJiYiOho9bqw+/fvx6BBg2Bp\naQkTExP8+OOPmDx5sqQxWjq2hVKlQlJcvKRxEBFRzTX6dfiIiIikFBUVBTs7O3h4eODcuXPw9fUF\nADg5OcHHxwcbN25EXFxcrV+/LkYGT4ZewO74KPzx+1a0MW+m8+tJQRAEqUOoE3Lohxz6AMijH+yD\n/qjPfjDhIyIiklB6ejp8fHzg7+8PU1NTHDhwAGlpaWjWrBk8PDwwffp0nV7fxcVFp4RREAS8MeV9\nDJ/6Adq1cUBOerpO8UhBEAQoFAqpw9CZHPohhz4A8ugH+6A/dO2Hs7NzpR/ucUonERGRxIKDg9Gn\nTx94enpi5cqVMDMzQ+/evfHYY48hJiYGMTExcHBwwMGDB9G/f/8Gj8/K2RFZ9+4bZLJHRNTYMeEj\nIiKSmK2tLQBAoVBg0aJFWL16NRYtWoS2bdvC1dUVrq6uuHXrFgYOHIg///yzweOzdmKFTiIiQ8WE\nj4iISGILFy7E5cuXER0djfz8fJ2ncdY1K2cHJHENPiIig8R7+IiIiCTm5+dX5TGurq4NEEl5BcXF\naNXGDslckoGIyCDpPMKnbbFYZ2dnhISEiC0mJgYpKSnaA1AqsXLlSly7dg3R0dF44403dA2JiIiI\n6sj9/FwA4JROIiIDpdMIX+lisb169UJ0dDR8fX0REBCAwYMHo1u3buJx/v7+MDLS/q18fX3h5uYG\nd3d3WFpaIiQkBIcPH9apohgRERHVjbSShC+JCR8RkUHSaYSvssViSxkbG8PX1xfr1q3T+ho+Pj4I\nDAyEIAhITk7Grl27MHbsWF3CIiIiojpyL69khI/38BERGSSdRvgqWyy2dArn8OHDkZCQgJCQEK2v\n4eTkpDGaFx8fD0dHxxrFUReLyspl0cbKsI/yUJs+Lr90utbPlYKhxKmLivpoaOeqMnLoA6lH+DJS\nUpGbmSV1KEREVAs6JXwVLRZbWFgoHjNp0qQKR/fqSl0sKiuHRRsrwz7KQ237uCz8FAAYxM+nsZ9H\nQzpXlamP81jVwrJUP+7l5bBgCxGRAdO5aIu2xWKvX78OALC3t0ffvn2xadOmCp8fHx8PZ2dn8bGT\nkxNu3uS0ESIiIn2Qlp/L6ZxERAZM54RP22Kx2dnZAIAJEyZg3759SE1NrfD5W7duhZ+fHxQKBays\nrDBy5Ehs27ZN17CIiIhIRyZmZsgqLEBSLBM+IiJDpXPCV9lisRMnTtQ6nXPfvn3o0aMHAGDDhg24\nceMGoqOjcfr0acyfP59TdoiIiPSAlZMDAHDRdSIiA6bzwuuVLRbboUMHrduHDh0qfl1cXIx3331X\n1zCIiIiojlk5q4uocQ0+IiLDpfMIHxEREcmTtVNJwseiLUREBosJHxEREWll5ewACyNj5OfkSB0K\nERHVEhM+IiIi0srayREtTUylDoOIiHSg8z18RKT/DgWsRfb9dKnDoGr4a8NvuBN1TeowiAAAl/8+\nicnDRkkdBhER6YAJH1EjcHDVz1KHQNW0Z/EKqUMgEh1Zux4dfw6SOgwiItIBp3QSERERERHJFBM+\nIiIiIiIimWLCR0REREREJFNM+IiIiIiIiGSKCR8REREREZFMMeEjIiIiIiKSKSZ8REREREREMsWE\nj4iIiIiISKYMeuF1lUoFAHBwcND5tZydnXV+DX3HPsoD+ygP7GPNlf6tL/3bT5Xj/0hNcugDII9+\nyKEPgDz6wT7oD136UdX/RwUAodavLjFvb28cP35c6jCIiKgB9e7dGydOnJA6DL3H/5FERI1LRf8f\nDTrhMzExgaenJ+7cuYOioiKpwyEionqkUqnQpk0bnD17Fvn5+VKHo/f4P5KIqHGo6v+jQSd8RERE\nREREVDEWbSEiIiIiIpIpJnxEREREREQyxYSPiIiIiIhIppjwERERERERyRQTPiIiIiIiIpliwkdE\nRERERCRTTPiIiIiIiIhkqtEnfO7u7jh58iQiIyNx8uRJuLm5SR1SrcTExODKlSsICQlBSEgIBgwY\nAAB46qmnEBoaisjISBw8eBDW1tbicyrbpw+WLFmCGzduQBAEPP744+L2ys5ZbfdJpaI+VnQ+AcM6\np61bt8a+fftw9epVhIWFYfv27bCysqoyVrn0URAEXLx4UTyPnTp1Ep/3wgsv4MqVK4iOjsZvv/0G\nMzOzau2Tys6dOxEaGooLFy7g77//RteuXQHI63okTXI4R5Vdn4Zo9uzZ5f5fGIomTZpg1apViIqK\nQlhYGH766SepQ6qVoUOH4sKFCwgJCUFoaChefPFFqUOqUm3eT+kjbf0wtGu8onNRqj6vcaExt+Dg\nYMHX11cAIPj6+grBwcGSx1SbFhMTIzz++OMa2xQKhRAdHS14e3sLAIQvvvhCWLt2bZX79KV5e3sL\nDg4O5fpW2Tmr7T5966O282mI57RVq1ZC3759xceLFy8Wfv7551r3w5D6CEAQBEGwsLAo9xwLCwvh\nzp07gpubmwBACAwMFL788ssq90nZmjdvLn49fPhw4fz58wIgr+uRTbPJ4RxVdn0aWuvWrZuwf//+\nCv8/6HtbsWKFsHz5cvGxjY2N5DHVpqWmpoo//86dOwvp6emCQqGQPK7KWm3eT+lj09YPQ7vGKzoX\nQL1f49J3XqpmbW0tpKWlCUqlUgAgKJVKIS0tTbCyspI8tpo2bb8cHh4eQnh4uPjY0tJSyMjIqHKf\nvrWyfavsnNV2n9T903b+KrrYDf2cjho1Svjzzz9r3Q9D6iNQccI3ZswYYe/eveLjHj16CJcuXapy\nn7608ePHC2fPnpXt9cgmr/+PZVvZ69OQmomJiXDy5EnB2dnZIBM+CwsLIS0tTevfQ0NrycnJQq9e\nvQQAQp8+fYTIyEjJY6puq+77KanjrEk/Hm6Gco0/3If6vsaN0Ig5OjoiISEBxcXFAIDi4mLcvn0b\njo6OSE5Olji6mtu0aRMUCgWOHz+OmTNnwsnJCXFxceL+lJQUKJVKtGrVqtJ9aWlpUoRfLZWdM4VC\nUat9+nquHz6f9+/fN+hzqlAoMHnyZOzZs6fW/TCkPpY6duwYjIyMcODAAcydOxf5+fnl+hEfHw9H\nR0cAqHSf1AIDAzFgwAAoFAoMGjSoUV2PjY3c/j8C2q9PQzF//nxs3LhR42+DIWnXrh1SUlIwZ84c\n9OvXD5mZmZg1axZOnDghdWg19tJLL2H37t3IyspCs2bNMGTIEKlDqhVe4/qlvq/xRn8Pn1z06dMH\nTzzxBDw9PaFQKLBy5UqpQyIdyPF8/vDDD8jMzJRFXyrycB8dHR3h6emJp59+Gh07dsSXX34pcYS6\n8fPzg7OzM2bOnIklS5ZIHQ5RjRjq36CePXvCw8MDq1atkjqUWlOpVGjXrh1CQkLg6emJadOmYceO\nHWjWrJnUodWISqXCjBkzMGLECLi4uGDYsGHYsmULLCwspA6NwGu8KpIPa0rVDHk4u7LWqVMn4caN\nGwY/Na60NcYpndrOZ1XnTZ/P6ZIlS4SDBw8KJiYmOvXDkPr4cHvhhReEI0eOCIDhT+kEIGRnZws2\nNjayvB7Z5Pf/sarrU5/btGnThISEBCEmJkaIiYkRCgoKhFu3bgn9+/eXPLbqNktLSyE/P19jW0RE\nhNCjRw/JY6tJ69GjhxAREaGx7fLly4KHh4fksVWnyXlKp6Fd42X70EDXuPSdlrIdPXpU44bV0jdk\nhtTMzc01CiosXLhQ2LFjh6BQKIRr165pFLhYt26dAKDSffrWHr6wKztntd0ndSvbx4rOZ1XnTV/P\n6VdffSUcOXJEMDMzE7fVth+G1MeWLVsKpqamAgBBpVIJv/zyi1iwoGnTpsJ///2nUZhl9uzZVe6T\nqllYWAgODg7i4xdeeEG4deuWAMjzemST1znSdn0acjPEe/gACAcPHhTfwLq7uwtJSUlCixYtJI+r\nJs3W1la4f/++0L59ewGA8OijjwopKSlCq1atJI+tOq0m76f0uT3cD0O8xiu7jlm0pR5ahw4dhNOn\nTwuRkZHC6dOnxYvYkJqrq6tw4cIF4eLFi8KlS5eELVu2CHZ2dgIAwcvLSwgLCxOioqKEQ4cOaVTF\nqmyfPrQVK1YIN2/eFAoKCoQ7d+6IoxyVnbPa7tOnPlZ2Pg3tnHbs2FEQBEG4evWqEBISIoSEhIjJ\na237YSh97Nmzp3Dx4kUhNDRUiIiIEAIDAzUKFgwfPly4evWqEB0dLWzZskUwNzev1j4pmo2NjXDq\n1CkhLCxMCAkJEYKDg4Vu3boJgLyuRzbNJodzVNnfIENthprwubq6CkePHhXCwsKE8+fPC4MGDZI8\nptq0V155RQgLCxNCQ0OF0NBQYcSIEZLHVFWrzfspfWza+mFo13hF56Jsq49rXFHyBREREREREckM\ni7YQERERERHJFBM+IiIiIiIimWLCR0REREREJFNM+IiIiIiIiGSKCR8REREREZFMMeEjIiIiIiKS\nKSZ8REREREREMsWEj4iIiIiISKb+H2AQ23uX5PGqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjO3RK9UEnvF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}