05_CodingDrill/CodingDrill_02.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_02.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 02"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HR3_w_wrb7Bs",
        "colab_type": "text"
      },
      "source": [
        "## 1. Target\n",
        "\n",
        "- Reduce the number of parameters\n",
        "- Add Transformations\n",
        "- By close examination of the dataset we see that `-7deg to 7deg` rotations should work for us\n",
        "\n",
        "## 2. Result\n",
        "\n",
        "- Params: `9,707`\n",
        "- Best Train Accuracy: `98.85%`\n",
        "- Best Test Accuracy : `99.32%`\n",
        "\n",
        "## 3. Analysis\n",
        "\n",
        "- We added transformations/augmentation to compensate the removal of params, now we need to train the remaining neurons harder\n",
        "- The model is still overfitting, which is fineee :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x93nIsZUcGgi",
        "colab_type": "text"
      },
      "source": [
        "## Slimmed 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, 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((-7.0, 7.0), 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": "10961d1a-b5ac-4f93-8dd9-cb307111d716",
        "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": 0,
      "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=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "        )\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",
        "\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": "fd1a5a92-5c6e-4c1a-9598-20016364bf51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        }
      },
      "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": 0,
      "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, 10, 1, 1]             150\n",
            "================================================================\n",
            "Total params: 9,707\n",
            "Trainable params: 9,707\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",
        "    # 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": []
    },
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            "c4628ceb26ca4033b8febeb89eff732f"
          ]
        }
      },
      "source": [
        "model =  Net().to(device)\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "\n",
        "EPOCHS = 15\n",
        "for epoch in range(EPOCHS):\n",
        "    # Print Learning Rate\n",
        "    print(\"EPOCH:\", epoch+1)\n",
        "    # Train the model\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    # Validate the model\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "EPOCH: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0827, Accuracy: 9783/10000 (97.83%)\n",
            "\n",
            "EPOCH: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0624, Accuracy: 9819/10000 (98.19%)\n",
            "\n",
            "EPOCH: 3\n"
          ],
          "name": "stdout"
        },
        {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0468, Accuracy: 9857/10000 (98.57%)\n",
            "\n",
            "EPOCH: 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0332, Accuracy: 9897/10000 (98.97%)\n",
            "\n",
            "EPOCH: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0498, Accuracy: 9848/10000 (98.48%)\n",
            "\n",
            "EPOCH: 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0350, Accuracy: 9885/10000 (98.85%)\n",
            "\n",
            "EPOCH: 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0284, Accuracy: 9918/10000 (99.18%)\n",
            "\n",
            "EPOCH: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "413412a651144a1cac501e09cc3b2072",
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              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0353, Accuracy: 9883/10000 (98.83%)\n",
            "\n",
            "EPOCH: 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "235c8d18ea514102861d2df8ddbe4386",
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              "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=469), HTML(value='')), layout=Layout(display=…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Test set: Average loss: 0.0312, Accuracy: 9890/10000 (98.90%)\n",
            "\n",
            "EPOCH: 10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            },
            "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",
            "\n",
            "Test set: Average loss: 0.0285, Accuracy: 9905/10000 (99.05%)\n",
            "\n",
            "EPOCH: 11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "696db748aa0a494ca0eef6c10a058f74",
              "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",
            "\n",
            "Test set: Average loss: 0.0279, Accuracy: 9911/10000 (99.11%)\n",
            "\n",
            "EPOCH: 12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c9d1b45ef3c4d8191b4abab30649bf5",
              "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",
            "\n",
            "Test set: Average loss: 0.0263, Accuracy: 9922/10000 (99.22%)\n",
            "\n",
            "EPOCH: 13\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51b137451e9c42beb05d083ebe8524bd",
              "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",
            "\n",
            "Test set: Average loss: 0.0257, Accuracy: 9915/10000 (99.15%)\n",
            "\n",
            "EPOCH: 14\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "07b67d9712e84ab4920aad0303ba38ef",
              "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",
            "\n",
            "Test set: Average loss: 0.0217, Accuracy: 9932/10000 (99.32%)\n",
            "\n",
            "EPOCH: 15\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "284c3f4b2e694ed48fd59d57f628b822",
              "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",
            "\n",
            "Test set: Average loss: 0.0211, Accuracy: 9926/10000 (99.26%)\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": "09d23e8c-1560-49cd-e90e-96f72d8c83f1",
        "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": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RmRSLaOrUKpXYEYiIyAyFh4cjIiICCoUCERERCA8PBwDEx8cjMDAQALBy5UpkZGQgPT0d\niYmJmDdvnma55DvvvIMBAwbgxIkTSE5OxtmzZ7F06VKxygFw6yqYzdu05lUwiYhMiEWcU0dERKQP\nCoUCvXv3rvP8yJEjNY/VajWmT59+z/dnZGRg+PDhesvXEKf2HtBcBTMrRbxZQyIienQWMVNHRERE\nj4ZLMImITI/FNHWONrY4sXOv2DGIiIiMXs0STHnXTmJHISKiR2AxTZ21VAqplN84EhERPUzNEsxu\nI4aKHYWIiB6BxTR1UkgglfEUQiIioofhEkwiItNiMU2dRAJIZTKxYxAREZkELsEkIjIdFtPU5Zff\nRIf+da9QRkRERHXdeRVMIiIybhbT1KkEQewIREREJqO89AYUCUfY1BERmQCLaeqIiIiofngjciIi\n08CmjoiIiO6JSzCJiEwDmzoiIiK6p/KSUigSjsBv2GCxoxAR0QOwqSMiIqL7SvljN5xc28CdV8Ek\nIjJabOqIiIjovk7tPYDqqir480bkRERGi00dERER3Vd5SanmRuRERGScLK6ps2rUSOwIREREJoVL\nMImIjJtWTZ2TkxPi4+Nx5swZnDhxAhs2bICzs3OdcY0bN8bq1auRnp6O06dPY+TIkdocViuBo0aI\ndmwiIiJTpFmCOZxLMImIjJFWTZ0gCFi4cCE6dOgAPz8/nD9/Hp9//nmdcTNnzkRxcTF8fHwwevRo\n/Pjjj2jSpIk2h24wqVQmynGJiIhMVXlJKc4mHIHfcC7BJCIyRlo1dUVFRdi3b5/m58TERHh4eNQZ\nFxISgpiYGADAuXPncPToUTz99NPaHLrBZNZWohyXiIjIlCXv2MUlmERERkpnHY5EIsG0adOwefPm\nOq+5u7sjKytL83N2djbkcnm99p+ZmalVvq9SE2/97zffoGfsWq32JTZBEMSOoDXWYBxYg3EwhxoA\n86mD7u3OJZjZJ9PEjkNERHfQWVP33XffobS0FJGRkbraZS2enp61GsP6+vLkYQDArNkf4Y/o5bqK\nZXCCIEAikYgdQyuswTiwBuNgDjUAuqvDw8ND6y/xSD/uXIK55cvvxI5DRER30MnVLxctWgQfHx+E\nhITc85va7OzsWssy3d3doVQqdXHoRya7/cuGtS2vfklERNQQvAomEZFx0rqpW7BgAQIDAxEUFITK\nysp7jlm3bh1ef/11AIC3tzd69uyJ33//XdtD14tMcqtUKxs2dURERA2Rumc/r4JJRGSEtGrqOnXq\nhA8++ACurq5ISEhAUlISfv31VwBAUlIS2rRpA+DWTJ6joyPS09OxdetWvPbaaygtLdU+fT3ImzQF\nAJReLTLocYmIiMyFZgkmb0RORGRUtDqnLi0t7b7nUHTv3l3z+ObNm3jxxRe1OZTWerdsi/MlRcjL\nuCBqDiIiIlOW8sdudBrUD/IunaBM5QVTiIiMgU7OqTMFNefUSWW8Tx0REVFD1SzB7DaCSzCJiIyF\nxTR1UjZ1REREWisvKcXZw1yCSURkTCymqZPgdlNnxaaOiIhIGyk7dsPJrQ3kXXgVTCIiY2AxTZ30\n9ql/je3txQ1CRERk4v65CuYQsaMQEREsqKmrUKkAAGM/nClyEiIiItNWswSTTR0RkXGwmKauGe9P\nR0REOubj44OEhAQoFAokJCTA29u7zhipVIrIyEicO3cO6enpmDJliua1n376CUlJSZpNpVJh9OjR\nhiyhwU78wSWYRETGQqtbGpgS6X1uvUBERNRQ0dHRiIqKQmxsLMLCwhATE4OhQ2tfFTIsLAze3t7w\n8fFBixYtkJSUhJ07dyIrKwuTJk3SjPPz88Pu3buxY8cOQ5fRIKl7DmiWYPLWBkRE4rKYmbqaC6UQ\nERHpgouLCwICAhAXFwcAiIuLQ0BAAJydnWuNCwkJwdKlSyEIAgoKCrBx40YEBwfX2d+UKVMQGxuL\nyspKg+TXVllxCZdgEhEZCYuZqeNEHRER6ZJcLkdOTg7UajUAQK1WIzc3F3K5HAUFBZpx7u7uyMrK\n0vycnZ0NuVxea1/W1tYYP348nnzyyQZlyczMbND77iQIQr3fc6roCnbkZODSjRK0thP/QmQNqcEY\nmUMd5lADYB51sAbjoc86LKapk3KmjoiIjFRQUBCys7ORkpLSoPd7enrWahzrSxAESBrw7Wfjpg6Y\nuzceUz/6L7Z+Fdng4+tCQ2swNuZQhznUAJhHHazBeGhbh4eHxwO/wLOc5Zdm8JeBiIiMh1KphJub\nG6TSWx+lUqkUrq6uUCqVtcZlZ2fDw8ND87O7u3udMa+88gqWL1+u/9A6VlZcgvTEv7kEk4hIZBbT\n1BEREelSfn4+kpOTERoaCgAIDQ1FUlJSraWXALBu3TpMnToVEokEzs7OCAoKwvr16zWvu7m5YcCA\nAYiNjTVofl1J2bHr1lUwO3cUOwoRkcViU0dERNRA4eHhiIiIgEKhQEREBMLDwwEA8fHxCAwMBACs\nXLkSGRkZSE9PR2JiIubNm1drCc2kSZOwZcsWXLt2TYwStHbnVTCJiEgcFnNOHRERka4pFAr07t27\nzvMjR47UPFar1Zg+ffp99/Hpp5/qJZuhaJZgjhiKrV9HiR2HiMgicaaOiIiItJJScyNyLsEkIhIF\nmzoiIiLSSuru/VyCSUQkIjZ1REREpJWaJZh+bOqIiETBpo6IiIi0lvLHbrRo68olmEREIrCopi7j\nWLLYEYiIiMxS6u4DUFVVcwkmEZEILKqpu3wuAyWFV8WOQUREZHbKiotxNvEIl2ASEYnAopo6tVoN\nqUwmdgwiIiKzVLMEs22nDmJHISKyKJbV1KlUkEotqmQiIiKDqVmC2W3EULGjEBFZFIvqcJo4NkPj\npg6QWVuLHYWIiMjslBUX4+xfvAomEZGhWVRTFzjqKQBAn+DnRE5CRERknk7s4BJMIiJDs6imrsaY\n/84QOwIREZFZOrl7/62rYI7gbB0RkaFYZFNHRERE+lGzBNN/OM+rIyIyFDZ1REREpFNcgklEZFhs\n6oiIiEinuASTiMiw2NQRERGRTv2zBJNNHRGRIbCpIyIiIp27tQTTDW07+YodhYjI7LGpIyIiIp37\nZwkmL5hCRKRvbOqIiIhI58qKi5H+11EuwSQiMgA2dURERKQXKTt2cQkmEZEBsKkjIiIivdAsweRs\nHRGRXrGpIyIiIr3QLMHkeXVERHplUU3dnhWxYkcgIiKyKCl/1FwFkzciJyLSF4tq6tL2HwIAnNpz\nQOQkREREluHkrn2oKq9A7xeeEzsKEZHZsqimLue0AgBw/miSyEmIiIgsQ1lxMY7H70DgqKdg16yp\n2HGIiMySRTV1gloAADi7txU5CRERkeXYH7sWNo1tOVtHRKQnWjd1ixYtQkZGBgRBQOfOne85Zs6c\nOcjLy0NSUhKSkpIQGRmp7WEb6FZT1zdkrEjHJyIisjyX088jPfEo+o17HlIrmdhxiIjMjtZN3caN\nGzFw4EBkZmY+cNzPP/+M7t27o3v37njzzTe1PWyDCIIgynGJiIgs3f5Va+DYuhW6Dn1C7ChERGZH\n66bu0KFDuHjxoi6y6B17OiIi0iUfHx8kJCRAoVAgISEB3t7edcZIpVJERkbi3LlzSE9Px5QpU2q9\nHhwcjBMnTuDkyZM4ceIEWrZsaaj4BnV6/yEUZF/EwAkhYkchIjI7Bjunbty4cUhJScGOHTvQu3dv\nQx22FolElMMSEZGZio6ORlRUFHx9fREVFYWYmJg6Y8LCwuDt7Q0fHx/06dMHc+fOhYeHBwAgMDAQ\nc+fOxbBhw9C1a1f0798f169fN3QZBiEIAg7EroVnt66Qd+kkdhwiIrNikKYuOjoaXl5e8Pf3x6JF\ni7Bp0yY4OTnVax+ZmZkQBKHBGwCU3byp2Z82+xJzM+XsrMG4NtZgHJs51KDLOh62lN+YuLi4ICAg\nAHFxcQCAuLg4BAQEwNnZuda4kJAQLF26FIIgoKCgABs3bkRwcDAA4N1338XixYuRl5cHACguLkZF\nRYVhCzGgvzfFo7z0BgZOeFHsKEREZsUgTV1eXh6qq6sBADt37oRSqUSXLl3qtQ9PT09IJJIGbwBg\nJZWhpPAqjsfv0GpfYm4ARM/AGliDsWyswXg2XdXh6empxaeNYcnlcuTk5ECtVgMA1Go1cnNzIZfL\na41zd3dHVlaW5ufs7GzNmE6dOuGxxx7Dvn37cOzYMXz44YeGK0AEFTdu4q/ftsB/+FA0dXF++BuI\niOiRWBniIK6ursjNzQUA+Pv7w9PTEwqFwhCHrqOsuAQSqUXdyYGIiIyUTCaDn58fhg0bBhsbG/z+\n++/Izs7GypUr67UfXcxw1sy46tu1ynIsP5uMbSePo18r+cPfUA+GqkHfzKEOc6gBMI86WIPx0Gcd\nWjd1S5YswdixY9G6dWvs3LkThYWF6NKlC+Lj4zF79mwcO3YMn376KQIDA6FSqVBZWYmJEydqlpoY\nmqq6GjJra1GOTURE5kOpVMLNzQ1SqRRqtRpSqRSurq5QKpW1xmVnZ8PDwwNHjx4FUHvmLjs7G+vX\nr0dlZSUqKyuxadMm9OrVq95NnaenZ63ZwPoSBEEz42oIL3/7BUr9u+IJd29UV1bqZJ+GrkFfzKEO\nc6gBMI86WIPx0LYODw+PB36Bp/WU1dtvvw25XA5ra2u0adNGs6xy5MiROHbsGABg8uTJ6Nq1K7p1\n64ZevXph+/bt2h62wVRV1ZBZGWSCkoiIzFh+fj6Sk5MRGhoKAAgNDUVSUhIKCgpqjVu3bh2mTp0K\niUQCZ2dnBAUFYf369QCAX375BcOHDwcAWFlZYejQoUhJSTFsISLYv3IN7J2aI+CZ4WJHISIyCxa3\nDlFVzaaOiIh0Izw8HBEREVAoFIiIiEB4eDgAID4+HoGBgQCAlStXIiMjA+np6UhMTMS8efM037au\nXr0aV65cQVpaGpKTk3Hq1CksW7ZMrHIM5vzfx5GrSMeAiby9ARGRLlhcd6NmU0dERDqiUCjueZue\nkSNHah6r1WpMnz79nu8XBAEzZszAjBkz9JbRWB1YtRYh8z9Eu54BOP/3cbHjEBGZNIubqauuqoLM\nmk0dERGRmI5v+wOlV4swkLN1RERas7imjjN1RERE4quurMThdRvRaVB/tGjrJnYcIiKTZnFNnW+/\n3vDw7wKpTCZ2FCIiIouWsOZXCCo1+o8PFjsKEZFJs7imroaUs3VERESiKs4vQMofu9BrzCg0amIn\ndhwiIpNlsU0dzOQmhkRERKZs/6q1sLVvgp7PjXz4YCIiuifLbeqIiIhIdMrUNGQmn8SAsBchkfLX\nEiKihrDcfz3N4M70RERE5mD/qjVwdm+LjgP6ih2FiMgkWWxTx56OiIjIOJzctRfXLudh4ATe3oCI\nqCEsuKljV0dERGQM1NUqHIxbD5/ePdDap53YcYiITI7FNnUAmzoiIiJjkbh+MyrLyjEw7EWxoxAR\nmRyLbeo4U0dERGQ8yoqLcXTLdgSMHIEmjs3EjkNEZFIsrqkrzi8AALj7dRY5CREREd3pYOxaWNs2\nQu8XgsSOQkRkUiyuqWvq4gwAaOnlIXISIiIiulNeRiYUCX+h37jnIbWSiR2HiMhkWFxTV0OtUokd\ngYiIiO6yf9UaNGvlAv9hQ8SOQkRkMiy2qVNVVokdgYiIiO6iOJiIKxeyMIC3NyAiemQW19Rt+GQR\nACA/K1vkJERERHQ3QRBw8Jd18PDrzPPfiYgekcU1dQXZSrEjEBER0QP8vWkbyopLeHsDIqJHZHFN\nnSDcfsBbGhARERmlyrIy/PXrFvgNH4JmrVzEjkNEZPQsrqmzsrEBALRq5yVyEiIiIrqfg3HrIJFI\n0DfkebGjEBEZPYtr6tr36QkAGDRxnMhJiIiI6H6Kci8jdc8B9AkOgrVtI7HjEBEZNYtr6mquetm8\nTWuRkxAREdGDHFi1Bk0cmyFg5AixoxARGTWLa+qqKisBgN/6ERERGbmMY8nIOX0WA3jBFCKiB7K4\npo43HSciIjId+1etQRufdvDp3VPsKERERsvimjpBrRY7AhERET2ipO1/oqTwKmfriIgewOKaur9+\n3SJ2BCIiInpEqqoqJKz5FZ2f6A9n97ZixyEiMkoW19SVFBSKHYGIiIjq4fDa31BdVYX+44PFjkJE\nZJQsrqkjIiIi01JSeBXJ23eiZ9BI2No3ETsOEZHRYVNHRERERu9A7BrYNmmCXmNGix2FiMjosKkj\nIiLSgo+PDxISEqBQKJCQkJLdsLkAACAASURBVABvb+86Y6RSKSIjI3Hu3Dmkp6djypQpmtfmzJmD\nvLw8JCUlISkpCZGRkYaMbzIupimQcSwZ/ccHQyLlry9ERHfiv4pERERaiI6ORlRUFHx9fREVFYWY\nmJg6Y8LCwuDt7Q0fHx/06dMHc+fOhYeHh+b1n3/+Gd27d0f37t3x5ptvGjK+Sdm/ag1atHVF5yf6\nix2FiMiosKkjIiJqIBcXFwQEBCAuLg4AEBcXh4CAADg7O9caFxISgqVLl0IQBBQUFGDjxo0IDuZF\nP+rr1J4DuJp7ibc3ICK6i5XYAYiIiEyVXC5HTk4O1LfvgapWq5Gbmwu5XI6CggLNOHd3d2RlZWl+\nzs7Ohlwu1/w8btw4DB8+HJcvX8acOXOQmJhYrxyZmZnaFQJAEASt92EIRwtysd81G3k3S9Gyce2L\npphKDfciCAL2X87G5ZulJl1HDXOoATCPOliD8dBnHZypIyIiElF0dDS8vLzg7++PRYsWYdOmTXBy\ncqrXPjw9PSGRSBq8AdDq/YbcBrbrgIqbZXhr8WcmW8O9Nt++j+NY4SXsvZwlehZtN1P/szCnOliD\n8Wza1uHp6fnAzwE2dURERA2kVCrh5uYG6e0Ld0ilUri6ukKpVNYal52dXescOnd3d82YvLw8VFdX\nAwB27twJpVKJLl26GKgC01NWXIKjm7chYORw2Ds1FzuOzvR9cQwAIPdmCTy7+YmchohMDZs6IiKi\nBsrPz0dycjJCQ0MBAKGhoUhKSqq19BIA1q1bh6lTp0IikcDZ2RlBQUFYv349AMDV1VUzzt/fH56e\nnlAoFIYrwgQdiF0LKxsb9A4OEjuKTjRt6YLOgwfg0OoNsJVZYfDL48WOREQmhk0dERGRFsLDwxER\nEQGFQoGIiAiEh4cDAOLj4xEYGAgAWLlyJTIyMpCeno7ExETMmzdPcx7cp59+ipMnTyI5ORlLly7F\nxIkTkZeXJ1Y5JiE/MxunDx5Gv5CxkFmZ/uUBeo8dDZmVFfb9FIduTq3QZcggtPTyePgbiYhuM/1/\nCYmIiESkUCjQu3fvOs+PHDlS81itVmP69On3fP/kyZP1Fc2sHVi5Bq/FfAP/p4bi+NYdYsdpMKlM\nhsdfeA5nDiai8GIOurVojQPKDDwxaTzWzv1M7HhEZCK0mqlbtGgRMjIyIAgCOnfufO8DPOCGq2KT\nymRiRyAiIqIGUCT8hbyMTJO/vUGnQf3h2KolEtb+CgCws7LGkY1bETj6KTR1cX7Iu4mIbtGqqdu4\ncSMGDhz4wEspP+yGq2JiU0dERGS6DqxaC/cunUz6wiJ9Q8bg2uU8nN6foHlu309xkMpkGBDGexkS\n0aPRqqk7dOgQLl68+MAxxnzDVTvHZmJHICIiogY6tnU7bhYXY8AE05ytc3ZvC9++j+Pw+k1Qq1Sa\n5wsv5uDEn3vQJ3gMGjWxEzEhEZkKvV8o5WE3XBXT+E9nix2BiIiIGqiyrByJ6zeh69BBKK6sEDtO\nvfUJHgNVdTX+2rC5zmt7VsSicVMH9H7+ORGSEZGpMZkLpTxoieejqrmL+1epiQCAHv37meQd6k0x\n891Yg3FgDcbBHGoAzKcOMi2H4jZg0EuhSL56Wewo9WJlY4OeQSORuns/SgoK67x+Me0M0v86ioEv\njcPBX9ZBdftehkRE96L3mboH3XC1Pjw9PXV2F/caF7IyRb+7vKHvRm8MG2swjo01GMdmDjXosg5P\nT896fz6QZbt2OQ+pu/fj5NUrsGncWOw4j8x/+BA0cWyGw2t/u++YPSti4diqJbo/M9yAyYjIFOm9\nqXvQDVfFJpOZzEQlERER3ceeFbGoUKvQL/R5saM8sr4hY3HlQhbS/zp63zGKQ4nIPXsOT0wer/ny\nhIjoXrRq6pYsWQKlUom2bdti586dSE1NBfDoN1wVW7NWLmJHICIiIi0pU9Pgae+IwZPDTGK2rk17\nb3h264rD6zY+dOyeFavQxqcdOvTvY4BkRGSqtGrq3n77bcjlclhbW6NNmzbo0qULgFs3XD127BiA\nf2646u3tDW9vbyxdulT71ERERER36NPSDU2aO5rEbF3fkLGoKq/A35u2PXRs8u87UXTpMga/MsEA\nyYjIVOl9+SURERGRvrWxc8DpAwlGP1vXqIkdAkeNQNLvf6KsuPih49XVKuz7eTXa9egOd7/OBkhI\nRKaITR0RERGZhT++X2b0s3WBo55CIzs7JKy5/wVS7vbXhs24eb0YgyeH6TEZEZkyNnVERERkFrJP\nphn9bF3fkLFQpp2BMjXtkd9TWVaGQ2s2oMvQQXD2MI57/RKRcbHIpu7PH1aIHYGIiIj0wJhn67y6\n+6GNTzscXvNrvd978Jd1UFVV4YlJ4/WQjIhMnUU2deXFpWJHICIiIj0w5tm6viFjUVZcgqTtf9b7\nvaWFRfh70zb0ePZpOLRw0kM6IjJlFtnU5WfX/+bnREREZBqMcbauSXNH+A0bjKNbtqOyrLxB+9j3\n0y+QWVuj//hgHacjIlNnkU3d2cNHxI5AREREemKMs3W9xoyClY0NDq999Auk3K0g+yJO7tyLvuPG\nopGdnQ7TEZGps8imTlALYkcgIiIiPTKm2TqJRII+wUE49/dx5GVkarWvvf+LhV3Tpnj8+Wd1E46I\nzIJlNnUCmzoiIiJzZkyzdb79HkeLtm4NukDK3bJPpuHc38cx6KVxkFrJdJCOiMyBhTZ1arEjEBER\nkZ5pZuvGjRU1R98Xx6K4oBAnd+3Tyf72rFgFx9at0P2pYTrZHxGZPots6nDHRJ2tg714OYiIiEhv\nsk+m4fTBw3hCxNk6x9at0HFgXxz5dQtU1dU62eeZA4dxKf08Br8yQSf7IyLTZ5FNnaC+Y6aOSzGJ\niIjM1h/fL4O9U3PRZut6Bz8HSCRI3LBJp/vdsyIWbXzaoUP/3jrdLxGZJsts6u5o5Hh+HRERkfnK\nPnFKtNk6qZUMj499FmcOHEZR7mWd7jt5+5+4djkPg1/mbB0RWWhTVwt7OiIiIrMm1mxdlyGD0NS5\nBRJ0cIGUu6mqq7Fv5Wp49wqEvEsnne+fiEwLmzqJ2AGIiIhIn8Sarev74hhczbmEM4cS9bL/v9Zv\nRllxCQa/HKaX/ROR6bD4pk4iYVdHRERk7gw9W9fSywM+j/fA4XUba5/Lr0MVN28iYe1v6Dp0EFrI\n2+rlGERkGiy+qZNZWYkdgYiIiPTM0LN1fYLHoLqqCkd+26LX4xyIXQu1SoVBL43T63GIyLhZfFPn\nN3yI2BGIiIjIAAw1W2dt2wg9nnsaJ//cg9KrRXo9VklBIY5u3o5eQaNg79Rcr8ciIuNl8U2dVCYT\nOwIREZkwHx8fJCQkQKFQICEhAd7e3nXGSKVSREZG4ty5c0hPT8eUKVPqjGnfvj1u3LiBRYsWGSK2\nRTLUbF33p4bBrmlTJKz9TW/HuNPen36BzMYa/UJfMMjxiMj4WHxTp6917kREZBmio6MRFRUFX19f\nREVFISYmps6YsLAweHt7w8fHB3369MHcuXPh4eGheV0qlSImJgYbN240ZHSLZIjZuj4hY3Ap/Twy\njiXr7Rh3ys/Mxqk9B9A/9AXRbrJOROJiU8f71BERUQO5uLggICAAcXFxAIC4uDgEBATA2dm51riQ\nkBAsXboUgiCgoKAAGzduRHBwsOb1999/H1u3bsXZs2cNmt8S6Xu2rm2nDnDv0gmH1xm2Qd+zYhXs\nmjVFrzGjDHpcIjIOFtvUpe07BAC4dilP5CRERGSq5HI5cnJyoL696kOtViM3NxdyubzWOHd3d2Rl\nZWl+zs7O1ozx8/PDiBEj8PXXXxsuuIXT52xd35CxqLhZhmNbtut83w+SlZKKjGPJGDQpFFIrnlpC\nZGks9tKPe/8Xi06D+qGyvFzsKEREZKGsrKzwww8/4OWXX9Y0hg2RmZmpdRZzWLlSnxp+zTyN4H+9\ng9+X/gQbHZ1fX66qxg9njqOjozPKSkobvJ+G/lmcLy7CpmwFUvMvo6Oj88PfoEfm8PcJMI86WIPx\n0GcdFtvUqVUqAICM32YREVEDKZVKuLm5QSqVQq1WQyqVwtXVFUqlsta47OxseHh44OjRowD+mblr\n06YN2rVrh23btgEAHB0dIZFI0LRpU7z++uuPnMPT07PWTGB9CYJg8vdtrW8N7n6d8Xbsj3hq6iTs\nWRGrkwwDwl5E0Pvv4uUnn0bOmYYtpdXmz0IikWDmb7H48cxZfBU8qUH70AVz+PsEmEcdrMF4aFuH\nh4fHA7/As9jllzXfiEqkbOqIiKhh8vPzkZycjNDQUABAaGgokpKSUFBQUGvcunXrMHXqVEgkEjg7\nOyMoKAjr16+HUqmEi4sLvLy84OXlhW+++QZLly6tV0NHDaOPc+v6vDgGWSmpDW7otCUIAvb+LxZu\nHdqjfZ9eomQgInFYblNXfWumzsa2kchJiIjIlIWHhyMiIgIKhQIREREIDw8HAMTHxyMwMBAAsHLl\nSmRkZCA9PR2JiYmYN2+eTpZMknZ0eW5du54BaPWYp8FuY3A/x+P/wPW8fAx+ZYKoOYjIsCx2+WXN\nScSTv/kc/+rWX7Mck4iIqD4UCgV69+5d5/mRI0dqHqvVakyfPv2h+/r44491mo0e7M7ZukOrf0Vl\nWVmD99U3ZCxuXi9G8o5dOkxYf6qqKuxftQajZ7yJtp18cTFNIWoeIjIMi52pk0r/Kb3nc8+ImISI\niIjEoovZOgfnFug6ZBCObNyK6ooKHaZrmMT1G1FWUoonJoeJHYWIDMRim7o7Lz7z4scfiBeEiIiI\nRKOLc+seHzsaMmsrg9+b7n7KS2/g8Lrf4D98CJzauoodh4gMwGKbOjO4iA4RERHpgDazdRKpFL1f\neA5nE/9GQZby4W8wkAOr1kKtVmPQS6FiRyEiA7Dcpk5qsaUTERHRHbSZres4oC+at2mNhDW/6ild\nwxTnF+DYlt/RK2gUmjR3FDsOEemZxXY21VVVYkcgIiIiI9HQ2bq+IWNw/Uo+Tu09oKdkDbf3f7Gw\naWyLfuOeFzsKEemZxTZ12SdOiR2BiIiIjERDZuuc3NrAt19v/LVhs+ZWScbkyoUspO7Zj/6hL8Cm\nsa3YcYhIjyy2qSMiIiK6U81sXd+QR5ut6xMcBEGtRuKGTXpO1nB7lseiSXNH9AwaJXYUItIjNnVE\nREREuDVbd+ZgIga/HPbQmS2ZtTV6jRmNtH2HcD0v30AJ6y8z+QQuJJ3AoJdCIZXJxI5DRHrCpo6I\niIjotj+ia2brHnwemt+wwbB3am50F0i5lz0rVqFFW1f4DRssdhQi0hM2dURERES3ZaWkPtJsXd8X\nx6Ag+yLSE/82YLqGSdt7EFcuZOGJl3kzciJzxaaOiIiI6A4Pm61r7dMOjwV2w+G1v0EQBAOnqz9B\nELD3f7GQd+oAn8d7iB1HazIrK7EjEBkdNnVEREREd3jYbF3fF8egqqICRzZuFSFdwxzbugPF+QUY\nbOKzdS6e7vjw91/x3rqf4NnNT+w4REZD66bOx8cHCQkJUCgUSEhIgLe3d50xc+bMQV5eHpKSkpCU\nlITIyEhtD0tERESkN/ebrbNp3BiBo55Cyo7duHm9WKR09VddWYkDsWvh2683AkaNEDtOgzi7t8W0\nZZGQWslg16wpIlbGIGT+h7B3ai52NCLRad3URUdHIyoqCr6+voiKikJMTMw9x/3888/o3r07unfv\njjfffFPbw+pE5KRwzWOntq4iJiEiIiJjcr/ZuoBRI2Br3wQJa43/Ail3OxC7Ful/HUXogtnoGTRS\n7Dj10qKtG6Ytj4LMygrRr0Zg4XOh2PXjzwgYOQL/2bIa/cY9D4mUC9DIcmn1t9/FxQUBAQGIi4sD\nAMTFxSEgIADOzs46CadvN4quaR5P+GKeiEmIiIjI2Nxrtq7vi2OQc+YsslJSRUzWMFXlFVj25kyk\nJ/6NcfNnoXdwkNiRHomTWxtMWx4JaxsbRE+NwOVzGagsK8e2Jd/jy+cn4mKaAmM/nIl34pbD3a+z\n2HGJRKFVUyeXy5GTkwO1Wg0AUKvVyM3NhVwurzN23LhxSElJwY4dO9C7d29tDqsXHvxHgIiIiO5w\n92ydh38XuHVoj4S1v4kdrcGqyiuwPOLfSNt3CMGz/4P+418QO9IDNXdtjWnLotDIzg7RU9/CpbPn\na71+5UIWYqa+hZ9nzoJDCye8Hfsjgue8jyaOzURKTCQOg1w+KDo6GgsWLEB1dTWefPJJbNq0CR07\ndsTVq1cfeR+ZmZla57j7ClXXK8ux7GzyfV83VqaS80FYg3FgDcbBHGoAzKcOojv9Eb0Mb61air4h\nz6ONTzuUl95AUvwfYsfSSnVlJf73zvuYuPgTjPnvDFhZ22DvT7+IHasOx9atMG1ZJGwdmiD61Qjk\nKtLvOzZlxy6cOXAYw8JfwcAJIfAbNhjxS77HXxs2Q7g9+UBkzrRq6pRKJdzc3CCVSqFWqyGVSuHq\n6gqlUllrXF5enubxzp07oVQq0aVLF+zfv/+Rj+Xp6YmsrKwGZxUEARKJpNZzNo1t8dmRPZqf737d\nGN2rDlPDGowDazAO5lADoLs6PDw8dPIlHpGu1MzWDXllAmzsGuPIb1tRcfOm2LG0pqquxs8zP0TY\nZ3MxemYEZDbW2LX0J7FjaTRr5YJpyyNh17Qpoqe+hZzTZx/6noqbN7H1q0j8vSkeYz+YgeDZ/8Hj\nY0fj108WQ3nqtAFSE4lHq+WX+fn5SE5ORmhoKAAgNDQUSUlJKCgoqDXO1fWfi5D4+/vD09MTCoVC\nm0PrRHVlldgRiIiIyMj9Eb0MTZo7wrpRI5Neenk3dbUKse/PxdEt2/HMW+EYMf1VsSMBAJq6OGPa\nsig0cXTED+Hv4GLamXq9P+/8BXw/5U2s+vdsNGvpgrd++RHPf/Rv2DVrqqfEROLTevlleHg4fvrp\nJ8yePRtFRUV46aWXAADx8fGYPXs2jh07hk8//RSBgYFQqVSorKzExIkTa83eiUWtUokdgYiIiIxc\nVkoqTu7aBxvbRricfv7hbzAhapUKq2d9AnW1CsOnTYGVjTXiv/letDwOzi0wbVkkHJyd8MPr7yD7\nZFqD95W0/U+k7T+EEdNfRf/xwfAfNhjx3/wfjvy2lcvFyexo3dQpFIp7Xvhk5Mh/LpU7efJkbQ9D\nREREJJqf3v2v2TYCglqNtXM+RXVlJYZMeQkyGxtsXrjE4DnsWzTHtGWRaNbKBUvD39XJFUYrbtzE\n5kXf3l6SORMvfvwBHh/7LH79dDEupom/aoxIV3hDDyIiIqKHMNeGroYgCNjwySLsX7UGgyaOw9gP\nZxr0fF97p+aY9mMkHFu3wo/TZ+BC0gmd7v/S2fOImjwNv/z3Yzi1dcXbccsx9sOZaNzUQafHIRKL\nQa5+SURERETGb9MX30BVWYXBr0yAzMoK6+d9ofeGtoljM7y+9Fs4ubnix+nvIeNY8sPf1EDHtv6O\nU/sO3lqSGfoC/IcPwdavo3B00zazb9zJvHGmjoiIiIg0tn4dhT9jVqD3C88hZP4sSKT6+3XRrllT\nvL70W7i4y7E84l84fzRJb8eqUV5Sik1ffIOvQ15GfpYS4+bPwhs/RcPV10fvxybSF4tv6irLyjWP\nn3l7GsKXfidiGiIiIiLx/R75A7ZH/oCezz2DsM/mQGol0/kxGjd1wOs/fIuWXh5Y/ta/kf7XUZ0f\n40FyFemImhSO1bPmw9m9Ld5dswJB778LWwd7g+Yg0gWLX365Z/lKjHhjKgBg6KsviZyGiIiIyDjs\njFkBVWUlRr33JmTW1lj179lQVVfrZN+2DvZ4/YclaO3theVv/QdnDx/RyX7rSxAE/L1pG1L3HMDT\nEa+jX+gL6PbUk9jyZSTUXI5JJsTiZ+ouZ2SKHYGIiIjIKO1ZEYuNX3wDv2GDMemrTyGzttZ6n7b2\nTfBa9Ddo094b/3vnv1AcStRBUu2UFZfg1wWLsST0FVy9mIvxn87G8rNJGPrqJDi0cBI7HtFDWXxT\nl5l88p7PSyQSDH5lAqfgiYiIyKIdWLUG6+cvROfBA/DKtwth1ahRg/fVqIkdpkZ/DbeO7fHzex/g\n9IEEHSbV3sU0Bb6b+Bp+eu8DONrY4pm3w/HRn5vw0pcL4PN4D4NeEZSoPiy+qbsf3/69MerdNzDm\n/ffEjkJEREbKx8cHCQkJUCgUSEhIgLe3d50xUqkUkZGROHfuHNLT0zFlyhTNa5MnT0ZKSgqSkpJw\n4sQJREREGDI+0SM7vPY3rJn9Kdr37YVXIxfDprFtvffRyM4OU//vK8g7dcTKmR/h1N6DekiqPUEQ\ncOLPPXjBqxM+G/UiDsSuhXevQIT/+B3+s3k1Br0UiiaOzcSOSVQLm7r7aOrsDODWN0pERET3Eh0d\njaioKPj6+iIqKgoxMTF1xoSFhcHb2xs+Pj7o06cP5s6dCw8PDwDAhg0b4O/vj+7du6Nv376YMWMG\nunbtaugyiB7Jkd+2YPWH89GuZ3e8+v1XaGT36L8j2TS2xZT/Wwx3v85Y9Z/ZSN29T49JdacgS4kt\nX36HeUOfRex/56Kk8Cqe/ddbmL1rM8Z/Ngde3f3EjkgEgE0d7jWLbtWoEULmfWD4MEREZDJcXFwQ\nEBCAuLg4AEBcXBwCAgLgfPtLwRohISFYunQpBEFAQUEBNm7ciODgYABASUmJZpydnR2sra15rywy\nase2/o7Y/8yBp39XvBbzDWztmzz0Pda2jTAlcjG8uvnhl/fn4sSfewyQVLeqKytxfOsORE2ehkVj\nwpC4fhM6DeqPN3+OwcxfV6Ff6As8ZYdEZfFXv5Sgblf3xdG9/7zOpdNERHQPcrkcOTk5UKvVAAC1\nWo3c3FzI5XIUFBRoxrm7uyMrK0vzc3Z2NuRyuebn0aNH47PPPkO7du3w3//+F6mpqfXOkpmZ2fBC\nbjOHZtIcagBMo4704quIt7bGj8mHMdajIxpb1f6VsqaGKrUam7LOQHmjGE+19cbM33eKEbfBHvRn\nUaVWQXG9ECmNm6CNTzu8+OG/4NusBfydWqFV4yZGc/6dKfx9ehhzqAHQbx0W39RV3HGfunuRSG5N\nZnZ+oj8cW7fCodUbDBGLiIgsxJYtW7BlyxbI5XJs3LgR27Ztw9mzZ+u1D09Pz1qNY30JgmA0v4A2\nlDnUAJhWHR0H9sPkrz/FrN9+Qcxrb+HGtesA/qnBysYGr3y3ED69eyLuw3l4b+sOkRPXT33+LNw6\ntkef4DG4MXI4Tl3LhzLtDBLXbcTx+D9QWVam56T3Z0p/n+7HHGoAtK/Dw8PjgV/gWfzyy7Li4ge+\nXjOV/sp3izD2w5mGiERERCZAqVTCzc0NUumtj1KpVApXV1colcpa47KzszXn0AG3Zu7uHlOzvyNH\njmDUqFH6DU6kI6f3H8KyN/+Fll4emLY8CvYtmmtes7KxweQln8Ond0+s+WgBjptYQ1dfOafPYv28\nL/DxkNFYP38hZDIZgue8jzm7t2DshzPRpn3diygR6ZLFN3UP065Hd7EjEBGREcrPz0dycjJCQ0MB\nAKGhoUhKSqq19BIA1q1bh6lTp0IikcDZ2RlBQUFYv349AKBDhw6acS1atMDgwYNx8uS9b7VDZIzO\nHj6CH9+YASc3V0xf/n9o6uKMarUak776FB3798G6OZ/h6OZtYsc0mIobN3F47W/48oWXsCTsVZzc\ntRe9gkZh5oaViFj1A3o8+4xWt4Qguh82dQB2L/v5ga83dXF+4OtERGSZwsPDERERAYVCgYiICISH\nhwMA4uPjERgYCABYuXIlMjIykJ6ejsTERMybN0+zhOa1115DamoqkpKSsGvXLkRGRuLPP/8Uqxyi\nBjl35BiWTnsXzVq54I3/fY/N2WfRaVA/rPv4cxzZuFXseKLJPnEKq2d9go+HPouNn3+Nxg4OCF3w\nEebs2oxn//025J07wqGFE6QymdhRyQxIABj1mYc160f1eb7AyHenY8grEx9pPzO69mlwBl0wh3XF\nrME4sAbjYA41ALqrQ1f/5lsKQ3xGmgpzqAEw7Trc/Trjte+/RuOmDlg/fyEOr/1N7Eha0cefxWM9\nuqNvcBC6DhsMK2trzfM3iq6htOgaSq8W1dpuFF1DydUi3Kh5rugabl4vhnD7Ak1i1GBo5lADoLtz\n6u73773FXygF+OdiKI+iWSsXFOcXPvL/mYiIiIgsQfaJU/h2wlQkpiRhhok3dPqScTQJGUeTYP/F\nN3gssBvsnZrDvrkj7Fs4wd6pOZo0d0Srdl5o16M77Bybac7ZvZNapcKNa9drNX6lV4tuN3/XUHr1\nqqZBrObvqxaDTR2Ag7Fr4T98CJzc2jx07Oydm7F7+UrEf/1/BkhGREREZDquXMiCl0Pzhw+0cKVX\nix56vz6pTAY7x6awb978juavOZrU/Hz7OVdfH9g7NYdds6Z19hF1+m+8+VM0Mo6nION4MjKTT6K8\npFRfZZGI2NQBuJZ3BQueGosvTx5+pPFDXpmIxg4OWD/vCz0nIyIiIiJLpFapUFpYhNLCokcaL7Oy\nQpPmjmjS3BEOLW41fV//+APOy6R4YtJ4DH31JajValxOP4+M4ym4cCwZGUknUHwlX8+VkCGwqWug\nPsFB2Lbke/QLfQE7Y1bUuZlgzZrZO58fMf1V/LVhM67lXTFoViIiIiIyb6rqahTnF6A4vwCXbj83\nsLUHBk14Dda2jeDepRO8ArvhsQB/9Hj2afQPfQEAUHgxBxnHUnAhKQUXjqfgygWez2yK2NTdIWn7\nn+j+9LBHHj9u/ix0HjwAT70xtc4FVOYd+B2VZWWYPywIANCmvTeGT5uC9n174bsJr+k0NxERERHR\n/VSVV+D80SScP5oE4NbSTldfb3gF3GryOvTvjZ7PPQMAKCm8igtJJ241ecdSkHPmLNQqlZjx6RGw\nqbvDrwsW16upa+3zmOax1EoGdfU/f+HtmjWttba55kRX69v3JpHKZBjy6kvY/3McKsvKtY1ORERE\nRPRI1CoVLqYpcDFNzbreUwAAIABJREFUgQOr1gAAnD3keCygGx4L9IdXgD/8nnwCAFBx8yayUlJx\n4XgKMo6nIPvkKf7uaoTY1N2hqqKiXuNbtHXTPJ5/YAc+7PMkZFZWUFVX1xl79/LMHqOfxtNvvgZb\nOzts/TqqYYGJiIiIiHSgIEuJgiwljvy2BcCt+zR7BfjjsYBbTd6waVMglUqhqqrGxdMKXDh+a8lm\n4cUclBRexc1rxZzRExGbujtUlVcg41gyHgvsVu/32to3ga2DPRYk/IltS6LvO86tQ3s0b9Ma1o1t\nAQA2do1rvf7Um68hP0uJY1u21zuDuXDv2glVFRW4dPa82FGIiIiILFJxfgFSduxCyo5dAABbB3t4\n+nfRLNnsP/4FPDF5vGa8Wq3Gzdu3WigpvFrrfnt3/lxSeBWlhUWoLCsTqzSzxKbuLtsjf8AbKxp2\nu4IFCX8CAPqHBT9w3Bs/fY+9/4sFANg0rt3UDXv9ZQAwWFMn79IJytQ0gxzrUb39yzIA4t/onYiI\niIhuKS8pxZmDiThzMBEAYGVjA7eO7dGspYvmFgsOt++3Z+/UHG4d2sOhhRMaN3W45/4qy8of2gCW\nXr2KclXdFXBUF5u6u2TcPoFUG02dW2gedxkyCAXKi+g8eIDmOYcWTprHPZ97Bqtnza+zj/Z9euLa\n5Sv3vAKRRCpFq3ZeuJxev5msviFj0aF/HyyP+BcAoOPAfng1ajHWffw5Etdvqte+xNC+Ty+cP5oE\nVVWV2FF0pqWXB6xtGyHn9FmxoxARERE9surKSmSlpD50nMzaGvZOjnc0fE63b7nwTwPo2Kol2nb0\nhb1Tc8isa7cn0aePYfSMCPzx/TJU3Lypr3JMHpu6e/gzZoVmxkxbLy/5/P/Zu+uwKLY3DuDfXbq7\nBAQVsBUJxU4QA1EM9AeCiqjY3Y117VZsxa5rXbtF8VooWIBdKEiDQZ3fH1xGFpaOZZf38zzneXZn\nZmfes7M7s++eM2cQHxkFNV0dbpq0rCzM7WxzLes8eSz3eOiWtQCAyVatkJ6aijqtW+D1/UcAAAcf\nLzgMG4RlLu65EjsFVRX8TEgUGkvPmZnJXDsvD6T+/o3uUzK312XscGSkZ3B9qAsir6LM3bhSy8gQ\nMZ+/5LpmsLQZ16uDoVvW4Kb/IZxcujrXfL0a1aCgooJ3j4NLbZt8aSmwDAaWkZFrXnJaCrRNjPH9\n/Uehr1VUU4VRnZoIC7wPS8cOCL50TWg/8ymnDgKgVsmKQkVLE/bDBuHkX6uFXhtb3qRkZCAtI0Mn\nMUIIIWIrPTUV8d+iEP+tcPfDU1BV5e6zp6ypgWU7tyLNoy8adbbH6RXrEXT2YhlHLJ74og6gIrq+\ne3+pri97QpelXttW3GNZBXkM27YOrfq75lpu6aOb0DGtCq/1y9Bn/nQAQNV6dQAA6nqC6zW3s8WC\n2xdh0dQW1W0aQfq/kTZz6jLWh0vogMwExHX+dAzZvApLg25BTkkxz7oY1rLAwjuX0KRnN+hWM8H0\nc0fRbrBHPrUH5BQV4ThyCPjSUvkulx8lDTUAgE61qgLTeTwenCaOwuQT+zHK3w91Wrco9jZyWhYU\ngInH/LnnUjIyUNXRBgD4vXyEaWcO5/la702rMHTLWjhPGYv+y3zR2rNfobbJ4/NR/7/RpkqTnKIi\nuowbDinp0v8fp1qjBqjaoG6e8weuWYIVIYFQ19Mt9W0XhW41E7Tz6p/vMt2njUfzvj1Rp03pfY5K\nYvzhXVj07xVRh0EIIYSUm58JCYh8+x5vHj5G8KVrcDCsgbVu3oiPjIL7X/Pgs2MD9M2qF7yiSoaS\nOiF+JSZhcqOWBS9YShbfuwbzJjZ5zpdTzEyytIwzR9tkyGwVq9umJVaEBGJFSCAGrF4Cw1oWAIDm\n/XphxM6N6DVrcpHiqNncDlLS0lh0V/BHpJaRIQwszACA+7HbZ+40aBoaAADMG9tAWlYWAFDDphHa\nefXH/FvnoVvNBADQafRQ2A8dCKvOHYVut7q1JWy6dYa0rCz6LZxdpJir1DRHG88/F+k6TxkDozo1\n4TxlLJQ01LkYiyv7QcNtyVzMuXqau7F8dtm71AKZLYcA0Mo9M1FX1dYu1Paa9+2JAasWw6Zb5r1i\nzBpbY0VIIAwsahQr/iwdRwxGu0H9Ydu9S4nWI8zIPX4Ys2+bwDQDCzOMObAdsgoKqNeuNQDAqG6t\nUt92UYzc44cuY4dDXU9X4HYj2WXdeqSioJMWIYQQAnx8+hxr3bxxZN4SGJjXwPgju9Ft8hjIKyuJ\nOrQKo2L9gqlAKkLXqyx9F8wEABjXqYVj715A1zQzWWrm6sItU799a6SlZN6SIasV0NKxPeQUFbHk\nwXXUbtW82Nuffu4oJh7zh+eqxdDQ18s139zOBn89vAEAGL5zI7qMHQ4ldTVMOXUQzfv1gqx85kif\nUtla6hp374oVIYGw7d4FI3ZtQr+Fs1C/fWvYdOuUfzA5unnycvwI1zY2wrhDu9DK3RXzb57DjPPH\nC6xfe29PLvlTUldD24FuAvOzEtb67Vv/t1HBpK5RZwfMvf4PTBvWz3MbCqrKXDLY4n+9sPTRLaEH\noqzWLBVtTYFtTjy2FzLyuVteDWtbCJ1u17s7qjaoC1ffGajXrjVXBykZmTxjFEbYugvDafwIVK1X\nB9UaNeCmDVzzV7HWlRdLxw74/utPt0Q5JUWB+plaNoC2iTH3POtzOOvySfgGXACQOWptz5mTIPvf\naLQ5ySkpQllTI88YathawaJp7q7UOQ1cs4Tr/gwAGgb66DlrMvhSmd+JrD8BypKckmKerfeEEEJI\nRccyMnD36Eks6doH/x4/jZZufTDl9CFYdRXeaFDZUFKXj60+40QdAgDAwPxPK837pHhoGVURulxG\nuuC1XzJycug1ezJk5OQweMPyIm1T28SYawXM0qBDGzTp2e3PQjmSG2HdNq27dOReo6ShATU9HewK\nfwJX3xkAgL6+M7llNXPUS15ZCW0HuWPxvWtwmT4hcxuKiph6+hDX3U9Yq1lh8aWlMPH4XnQePQyD\nN66ErIICvP1Wo+v4kegybji3XM4Ldnl8wW1mJS6GtS3+LJMjLlvnLnDw8QIA9Jg2AVIy0qjdslne\nwWUlr9nWY9VF8KClqKaK8Yd3C23d7D17Csbs24bG3bti4Jol3PQq/7W4Foa5nS2W3L+ONgMyk1xL\nxw5cclgR9F/miz2v/lxDuejuFYzc8+d2IqP8/fLtIrsiJBDtvT3RzNUFrbO19mY39cxhzLtxNs91\nDN+xgbv+NT/12rVGM1cXaJsYY9i2dfBYsRDN+vSA+9L5eJ0Qg8kn9qON5//KpHtslkV3r2DiMX+0\n8ugLffOit/yq6enkGq0XyNwP2Y8ThBBCSFn6EZ+AY75LsaafF2K/fIXb4rkYvmtjiXs1iTtK6vLx\nMuAu/urWV9RhFFr2loAsOROBwsrvx3AWHgQTl5zdNgHBm67XadUMLjMmIua38PuSdB49TOC59+ZV\n6DpuBGQV5LkbvVe3toSOaVX0mDYe9dq1wtiDOwqMM4t5ExtUt2kEs8bWGLxxBZYFBXAJs151U/hs\nXw/jOpldBNsN+nPtlePIIWjYsT33fFlQgGAd/xtIJavVUK+6qdDtN7BvK/Dcfel87nFWV9UsBhZm\nUM/RKsrSM9Bt0mgMXPsX6ndow3XLbejQDirZRlwVJqu1ya6XM2ZfPoWOwwf/maelgZrNmuR6TVZi\n6zRhJNyWzEX/Zb4CyW6hCEm6eTwe+FJSMLCowf1x0LxvT66+2lWN4Hv7AjSq6AMAmvbugR7TJ0BB\nVQVK6mr5bi7retPCykqiHEd4C0y3HzoQppYNuJFsZRXk0cTFCStCAoUOcmTr3DnXPsySvQvltDOH\nYd7EBlXrZ8bZ0KEdTn7IHPnUaeIoLA26JXQdWftaWlYWRnWK3o0167OpY2IM50ljMP7wrjyXrd+h\nDXSrmYAvLSXQUjv78imM2ruFe25Y2wIrQgJh6dihyPEQQgghJfXp+Uusc/fG4TmLoF+9GsYd2gXn\nKWMhr6Is6tBEgka/LEDk2/fY6jMO3ptWiTqUCkdNv+CBL0wt/3RJrGbVsEjrz687Y9V6dQrdna9u\nmxZo3KMrd21XnuusLzwhaOXuyl0bl5NhLQsuoeJLScGyY3v0X75A6LL6ZtXRfarw1t8ppw5ikmUL\ntB3kDgCw7uoI666OiP70mVuGsQy09sgccKVe21a46X+Imzfh6B7c+/sMLm4WnuRmbzlU09PhWg2N\n69bmEuPJjVoKdDs2zpY8VK2f2TKa9V7Mad0Z826cxYWNgtfS8aWlBJJeBSEHVo+Vi9Agx2AwLjMm\nwmXGxMy+8hZmUFRVxcwLf2P3+OnoNTvz2tAW/XoBADYOGoEBqxcLrWd+hI3QyuP9+V+rVsum3KBG\nhrUsMMrfj5s39/pZyClmtlI16NAG4XfvC3T36LtgFgDho5gWdN/KwlBQUcbvHz8wZMtq1LBuhEt+\nO/HvsVOIjfgK43p18DMxUehIrAqqqpCSkUKDDoJ/KOTXIjhgVeZ7+/bRE1SzaihQp6yW3hb/64Ue\n0yYIvG5H2ONi148QQggpDsYY/j1+GsGXb6DTqCFo8b/eaNTJHmdWbsDD0+fKfHT2ioSSukLIusli\nlkWdemH6uaMiiqbi6D17iqhDKJRB65aV2brHH9nNPebz+XkmdFlauvXJc960f47kmpbVQgnkupxQ\nYLRUFS1NtB/sAVvnztwIndnxpXI3yofFRwu0dC68exl7J8/B06s3crcS5ritg/2wQQAg0OI348Jx\nPPpHcJjh/sty34MxZ0KXXe85UwWee65clGuZ4Ts25Jqmlm0k2GFb18HcLvfAQ8K66uqb/bmWzbqr\nI0wa1hMaV1ZCB2TeNqTLWB+088o96qtuNZNc95aUlilal9WsroxTbdtw05S1NNDKoy9qWDcCkNmS\naD90ICbUb4qxB7YD+JNQ6lYzQeMeTrDq4sAlqZf8dubajpS0NLw3rcLX12/Q0q0P5rVzQkLUd25+\n1p8wfRfMhK3znwF2FNVUcyV0ABCX8qtI9SSEEEJKy8+EBBxfuBz/Hj8FlxkT0W/hLNj1csbxhcvx\nJTRc1OGVC0rqCun2wWNo3rcnAAi0nhCSxWniqBK9vqCROh18BhW4DmEJHQChrZRnPgoe5GTk5DBw\nzRJsGDgcI3ZuFJiX85+urFaz7DSrGKCDt2eBMZa2ao0a4FdyMvc8Z0KnUUUfLtMnCh30xaJpY+6x\nVWeHQm2vUSf7POdl3Xfw+q79iPsWiaSYWNg6dy7UenNacv8699hrwwquK2h2wrp8ZsWQnbD7bjbp\n2Q3mdjbc+6VX3VQgqcuSPaEDwA0yQwghhFQ0n1+EYX3/obBx7owuY4dj3KGduHPoOM6t38LdY1lS\n8QBU6HZJExMTvHv3Dqampnj//n3BL8gDY6zEg2osCwpA1LsPWOLkikad7Llrok4vX1fiH/SEVGRR\n7z5Ax7RqwQsW4OPzlwLdOkvL++BnMMnnXnmVwYxm9jC1rA/vjSuL9fqwu/dhXLe20C6zhXXJbyfO\nr99S8IL5KK1jfmVRUc6RFYEk1AGQjHpIQh0AyahHZa6DgqoKHEd4o5mrC5Lj4nF29SbcP/mPyLpk\nlnRfFHS8p6SuCGTk5ZCRnoH01FQAgJKGOn4mJEJOSQkLbtO/14QQIuy6wqKgpK5oKtI5UtQkoQ6A\nZNRDEuoASEY9qA6Z9zR2mT4B1awa4t2TEBxfuByfX4SVYoSFU9ZJHY1+WQSpv35zCR0AJMfGISM9\nHT8TErBzTOa1QInRMdg7ZY6oQiSEEEIIIYT850toONZ7DsP+6fOhZWSIsQd3wmXGRCioqoo6tFJF\nSV0peXr1BnaPn46VfQYg6GzmYBFfwl7h4qbtWNHLA6eXr8OsFh1x1HcpAGDXuGncayvK/fAIIYQQ\nQgiRRA9Pn8MSJ1cE7D+Cpr27Y9qZQ2ji4iT2LZlZSjxQirm5OXbv3g0tLS1ER0fDw8MDr169EliG\nz+dj7dq1cHR0BGMMS5Yswfbt20u66Qon+NI17vEkyxZgGRlcv92skXcCD/+NwMN/C7zuZcBdboh4\nQgghhBBCSOn7lZiEk3+txr2/T8Nl+kT0mTcdTXo64+p2f8R9/YakmFgkxcQiLSVF1KEWWYmTus2b\nN2PDhg3Yt28f3Nzc4Ofnh/bt2wss4+bmBjMzM5ibm0NLSwtBQUG4fPmyRF8vkZGeXqjlXt1/BABI\nionFhoHDEfnmHZQ01NHQoR0ub92Fpr17wGV65vDhZ9duxkW/nZCVlUV6WhqqW1vCdf4MbBsxAdKy\nMvDZth5KGuo4uWwN+Dw+Il69wZDNf+6vl5GeDr6UVK4Yru3ch7YD3Uqh1oQQQgghhFRsEWGvsWGA\nD6y6doTT+JEYuGaJwPxfSclcgpcUE4PErMfRMUiKjv3zPCYWP+ITct36SRRKNFCKjo4OwsLCoKWl\nhYyMDPD5fERHR8Pc3Bzfv/8ZGvvMmTPYuXMnjh07BgBYt24d3r9/j+XLlxe4DUm+CFxdTxfJ8fFI\n/fU73+W6jh8JKRlpnPxrdb71kFdRhqq2Vq77ZFk0tUXc10hEvn0PeRVlVK1XB6/vP4KJZX38TEhE\nRNgryCsrQaOKPqy7dkL0p8/oMnY4zq3zQ8jl63Dw8cL5DVtgYWcLtyXzuPXePngMbx4+Rku3PtxN\nxk+vWA+nCSMFtr995CR4rf9zr7ghNa2gIisHBVUVGJjXwIhdmwAA+6bNRZ8503INPT+vnRPmXD0N\nAPh78UrER0ZxN0gWJjE6BipamgLTTixZlevG3zFfIqBZJfM2Ai8D7qJWC7tc6/oSGo47h/9Gr1mT\n89xeYVzftR9tBvxP6LxPz0NhVKdmidZPSEVBA6WUL0k+RxaVJNQBkIx6SEIdAMmoB9WhYLIK8tA3\nqw5lTU0oa2pkFi0NqGQ91tSAspYmlDXUhTaOZKSnIzku/r+kL1sS+N/jpJhYJHyPwfvgp+Dzi3/l\nW5mOfmllZYU9e/agXr0/N+x99uwZ3N3dERQUxE0LDg7GoEGD8ODBAwDApEmTYGRkhDFjxpS4AoUl\nCR9qoOLUgy8thYy03K2RPB4PNVvYwWP5AmwZMhZaxoZ4eOY8pOXkwONlDjZTUB2M6tREfOR3KKmr\n4eurNwAyb66tb1Yd4f8+4LaT1bW1ZrMmMK5XGzf9DyLlZ+YNkJv27oFesycj8u17XNm2Bw9OnYVG\nFX0Y160t0E3WomljqOvr4d7fmUljOy8PfP/wERZNG+PZ9QC8uHkbAKCur4emvbvj2s69yEjPwInQ\nYLyIz/zjYnlPd+jXqI5qVg2R8D0abx4+hqKqCl7cvINh29bhpv9BhFy5ATlFRaSnp8Nn2zqYWtbH\nhQ1bkRwXj9sHM//s0DGtiqmnD3G3zVDV0UbjHl3x7/HTSPwejar16+BDyHMAgFlja1g0bYyXt+8C\nyLzlgLmdDXRNTZAcG4eA/Uegb14d2lWN4TjCG/pm1QFkDlsfFngParo62DBrHhYc8selzTugoq0F\n97/+JOxLnfuhaoO66Os7EwAQG/EVl7fsQr32rRH75SvePQ6BtrEh7hw6jhnnj3OJ+Oq+g9DAoS3a\nDeqP67v34/uHT2jSsxsOz14EHp8H5yljuRto5+fr67eQlpWBrLw8Ds5cAGUtTVRr1ABNe3fHzjFT\nMHDNX7hz6Djmjp+Ii58zPyPxkVHwnzgTNVvYoY3H/4Tel07otl69gb5Zddw5dBx12rTA9hETYdXZ\nAfoWNXBi8UqhN4UvipArN1C/fWuE//sA5k1y3xQ9uxN/rUb3KWMLXOeXsFdIT02Fcd3aQuevch2A\nKhbmcPWdIXT+z8SkYt+64EvYK1SxMMtzPiV15YvOkX9IQh0AyaiHJNQBkIx6UB1KD4/Hg6KaqmCi\np6kBFa1syWC2kvM826FKNTTU0i/29gtzvGfFLVZWVuzp06cC0549e8YaNWokMC04OJjZ2NhwzydN\nmsTWrFlTqG2YmJgwQkjlkJqezlLT0/Ocn5GRUeh1pWdbNj0jI9drMzIyWEp6WtGDzCOujIwMlpae\nzn6npXHbjPn1U2hc2WMpSp2KKz0jg/1KS8017WXcd4Htp6ans4SU3/muKyMjg6tD1mujf/1gv9JS\nS+39ZIwxExOTYp+byruYm5uzO3fusNDQUHbnzh1mZmaWaxk+n8/Wr1/PXr16xcLDw5mXlxc3b+bM\nmezp06fsyZMn7MGDB8zBwaHQ2846R5b0/WKMifx9LGmRhDpISj0koQ6SUg+qg+iKtKwsU9fTZUZ1\najGLprYsJT29ROsr6HhfomvqPn78CENDQ/D5fK77ZZUqVfDx40eB5T58+AATExOupa5q1apF/keR\n/oXMJAn1oDpUDFSHikES6gCUXj2y/okUJyW9tvzevXtYsWIFfv78iQYNGuDGjRswMDDAr1+/RFQj\nQgghJZWWkoK4b5GI+xYJAJApQdfLwijR2qOiovD48WP069cPANCvXz8EBQUJXE8HAEeOHIG3tzd4\nPB60tbXRvXt3HD16tCSbJoQQQkROR0cHVlZWOHDgAADgwIEDsLKygra2tsByrq6u2Lp1Kxhj+P79\nO06cOIHevXsDAC5evIifP38CyLxcgcfjQUtLq3wrQgghRKyVePTLYcOGYffu3Zg9ezZiY2Ph4eEB\nAPjnn38we/ZsPHz4EP7+/mjSpAnCwzOH9Z8/f77Y/RNLCCGE5GRsbIzPnz8j47+RzzIyMvDlyxcY\nGxsL/MGZs4fKhw8fYGxsnGt9Hh4eeP36NT5//lykOErjnMr+u05ZnElCHQDJqIck1AGQjHpQHSqO\nsqxHiZO60NBQ2NnlHjWwS5cu3OOMjAwMHz68pJsihBBCJFarVq3g6+sLe3v7Ir+WLlGQjDoAklEP\nSagDIBn1oDpUHCWtR0GXJ5Rt505CCCFEgmW/thxAgdeWZ6latarAMnZ2dti7dy+6d++OsLCw8gme\nEEKIxKCkjhBCCCmm0ri23MbGBocOHUKvXr0EbgdECCGEFBYldYQQQkgJDBs2DKNGjUJoaChGjRqF\nYcOGAci8ttza2hoA4O/vjzdv3iA8PBx3794VuLZ848aNUFBQgJ+fH4KCghAUFCRw/1dCCCGkICW+\npo4QQgipzEp6bXnjxo3LLDZCCCGVA7XUEUIIIYQQQogYq/AtdVJSUgAAIyOjEq8r+0Xq4kwS6kF1\nqBioDhWDJNQBKJ16ZB3rs479JH90jhQkCXUAJKMeklAHQDLqQXWoOEpSj4LOjzwAFfrGD82bN0dA\nQICowyCEEFKOWrRogdu3b4s6jAqPzpGEEFK55HV+rPBJnaysLGxtbREREYH09HRRh0MIIaQMSUlJ\nwcDAAPfv30dKSoqow6nw6BxJCCGVQ0Hnxwqf1BFCCCGEEEIIyRsNlEIIIYQQQgghYoySOkIIIYQQ\nQggRY5TUEUIIIYQQQogYo6SOEEIIIYQQQsQYJXWEEEIIIYQQIsYoqSOEEEIIIYQQMUZJHSGEEEII\nIYSIsUqR1Jmbm+POnTsIDQ3FnTt3YGZmJuqQAADLli3DmzdvwBhD3bp1uen5xVvceWVFU1MT//zz\nD16+fIng4GAcO3YM2traAIAmTZrg8ePHCA0NxYULF6Cjo8O9rrjzysrff/+Nx48f49GjR7h58yYa\nNmwIQLz2RZbZs2cLfKbEaT+8ffsWL168QFBQEIKCguDg4CB2dZCTk8PGjRsRFhaG4OBg+Pn5ARCf\nz5KJiQn3/gcFBeHt27eIjo4WqzqQwpOEfZPfeUgc5TyGi5O8jn/ipkuXLnj06BGCgoLw+PFj9OjR\nQ9QhFag4vykrImH1ELfveF77IktZfseZpJcrV64wNzc3BoC5ubmxK1euiDwmAKx58+bMyMiIvX37\nltWtW7dQ8RZ3XlkVDQ0N1rp1a+750qVL2bZt2xiPx2Ph4eGsefPmDACbMWMG2759OwNQ7HllWVRV\nVbnH3bp1Yw8fPhS7fQGANWrUiJ09e5b7TInbfsj5XShJnKKqw5o1a9jKlSu557q6umL5Wcoqq1at\nYuvWrRPrOlDJu0jCvsnrPCTquIpTch7DRR1PUUtexz9xKzExMdz7X79+fZaQkMB4PJ7I48qvFOc3\nZUUswuohbt/xvPYFUObfcdFXviyLjo4Oi42NZXw+nwFgfD6fxcbGMm1tbZHHllWy79j84i3uvPKs\ni4uLC7t06RKzsbFhISEh3HQtLS2WmJjIABR7XnmV/v37s/v374vdvpCVlWV37txhJiYm3GdK3PaD\nsIOcONVBSUmJxcbGMiUlJYHp4vZZyioyMjIsMjKSNWrUSGzrQCXvIqn7Jus8JOo4ilqEHcNFHVNR\nSl7HP3Es379/Z82aNWMAWMuWLVloaKjIYypsKexvSlHHWZR65Czi8h3PWYey/o5LQ8IZGxvj8+fP\nyMjIAABkZGTgy5cvMDY2xvfv30UcXW75xcvj8Yo1r7zqyePx4OPjg1OnTqFq1ap4//49Ny86Ohp8\nPh8aGhrFnhcbG1um8W/duhUODg7g8XhwdHQUu30xf/587N27V+D9E8f9sG/fPvB4PAQEBGD69Oli\nVYcaNWogOjoac+bMQdu2bZGUlISZM2fi58+fYvVZytKtWzd8/vwZQUFBsLKyEss6kLyJ2/mxMLKf\nh8SNsGO4OMnr+Hf79m1Rh1Zkffr0wcmTJ5GcnAwVFRV07txZ1CEVC33HK5ay/o5XimvqSPlYt24d\nkpKSsH79elGHUize3t4wMTHB9OnTsWzZMlGHUyR2dnawsbHBxo0bRR1KibRs2RKWlpawtbUFj8cT\nu8+SlJQUatSogaCgINja2mLKlCk4fvw4lJWVRR1asQwaNAg7duwQdRiEFJq4nock4Rie1/FPRUVF\n1KEViZSUFKZNmwZnZ2eYmprCyckJhw8fhpKSkqhDI6DveEFE3jxZlkUcmp4lofvlsmXL2IULF5is\nrCwDxKvLnLA6Key6AAAgAElEQVTy48cPpqurKzb7YsqUKezz58/s7du37O3btyw1NZV9+vSJTZ48\nWWz3Q7169dibN2/E6rOkpaXFUlJSBKY9e/aM2drais1nKatUqVKFJSUlMU1NTQaI77GJSt5F0vZN\nzvOQOJW8juH29vYij62wJa/jn7W1tchjK0qxtrZmz549E5j2/PlzZmNjI/LYClMkufuluH3Hs9eh\nnL7joq90WZdr164JXCR69epVkceU104vKN7izivLsnDhQnb16lWmoKDATePxeOzVq1cCg1Ts2LGj\nRPPKqigpKTEjIyPuedeuXdmnT5/Ecl/k/EyJ035QVFQUGLBmwYIF7Pjx42JVBwDswoUL3EHa3Nyc\nRUVFMTU1NbH7LE2bNo0dOnRIYJq41YFKwUVS9o2w85A4F3G8pg7I+/gn6riKUvT09Fh8fDyzsLBg\nAFitWrVYdHQ009DQEHlshSlF+U1ZkUvOeojjdzy/7zENlFLMUrNmTXb37l0WGhrK7t69y31RRV3W\nrFnDPn78yFJTU1lERAR7+vRpgfEWd15ZlTp16jDGGHv58iULCgpiQUFB7Pjx4wwAa9q0KQsODmZh\nYWHs4sWLAqNgFXdeWRRdXV0WGBjIgoODWVBQELty5Qpr1KiR2O2L7CX7wUJc9kO1atXYo0eP2JMn\nT9jTp0/Z4cOHmb6+vljVIase165dY8HBwezhw4fM0dFRLD9LoaGhrGPHjgLTxK0OVAoukrBv8jsP\niWsR16Qur+OfuJX//e9/LDg4mD1+/Jg9fvyYOTs7izymgkpxflNWxCKsHuL2Hc9rX2QvZfEd5/33\ngBBCCCGEEEKIGKKBUgghhBBCCCFEjFFSRwghhBBCCCFijJI6QgghhBBCCBFjlNQRQgghhBBCiBij\npI4QQgghhBBCxBgldYQQQgghhBAixiipI4QQQgghhBAxRkkdIYQQQgghhIgxSuoIIYQQQgghRIxR\nUkcIIYQQQgghYoySOkIIIYQQQggRY5TUEUIIIYQQQogYo6SOEEIIIYQQQsQYJXWEEEIIIYQQIsYo\nqSOEEEIIIYQQMUZJHSE58Pl8JCYmwtjYuFSXJYQQQgghpCxQUkfEXmJiIlfS09Px48cP7vn//ve/\nIq8vIyMDKioq+PjxY6kuW1xeXl5gjMHFxaXMtkEIIYSU9vk0S2BgINzc3ApcTk1NDT9+/MDx48eL\nvS1CKitK6ojYU1FR4cqHDx/g5OTEPd+/f3+u5aWkpEQQZfF5enoiOjoaHh4e5b5tPp8OEYQQUlkU\n9Xxa2lxdXfHjxw907twZmpqaZb697MTttwEhOdEvNiLxfH19cfDgQezfvx8JCQlwd3eHnZ0dAgMD\nERsbiy9fvmDNmjWQlpYGkHlgZ4zBxMQEAODv7481a9bg7NmzSEhIwJ07d2BqalrkZQHA0dERoaGh\niIuLw9q1axEQEABPT888Y69evTqaN2+OIUOGoFOnTtDW1haY36NHDwQFBSE+Ph7h4eGwt7cHAGhq\namLnzp348uULYmJicPToUQCZrX7Xrl3jXi8s/vXr1+PcuXNISkpCy5Yt4eTkxG3j/fv3mDlzpkAM\nLVu2RGBgIOLi4vDhwwfu/f38+TN4PB63XO/evfHgwYNC7zdCCCEVC5/Px8yZM/H69WtERUVh7969\nUFNTAwAoKiriwIEDiI6ORmxsLO7evQt1dXUsX74ctra22LZtGxITE7F8+fI81+/p6YnVq1fj9evX\n6Nevn8A8ExMTnDhxAlFRUYiKihJYj4+PD168eIGEhAQEBwejXr16kJOTA2MMhoaG3HIHDhzAjBkz\nAAAdO3ZEeHg4Zs6cia9fv2LTpk3Q1tbG2bNnERkZiejoaJw4cQL6+vrc67W0tLB7925EREQgJiYG\nBw8eBACEh4ejQ4cO3HJycnKIi4tDrVq1SvBuE1J0jAoVSSlv375l7du3F5jm6+vLfv/+zbp27cp4\nPB6Tl5dnNjY2rHHjxkxKSopVq1aNhYaGshEjRjAATEpKijHGmImJCQPA/P39WVRUFLO2tmbS0tLs\n4MGDzN/fv8jL6ujosISEBNatWzcmLS3Nxo0bx1JSUpinp2ee9Zk7dy67ffs2A8CeP3/ORo8ezc1r\n2rQpi42NZe3atWM8Ho8ZGRkxCwsLBoCdP3+e7du3j6mrqzNpaWnWsmVLBoB5eXmxa9eucesQFn9M\nTAyzs7NjPB6PycrKsrZt27I6deowHo/HGjRowKKioliXLl0YAGZqasoSExNZ7969mZSUFNPS0mIN\nGzZkANjLly9Zhw4duG2dOnVKIH4qVKhQoVJxi7Dz6eTJk9nNmzeZgYEBk5OTYzt37mQ7duxgANjo\n0aPZkSNHmLy8PJOSkmI2NjZMUVGRAWCBgYHMzc0t3+2Zm5uz9PR0Vr16dTZ9+nR27949bp60tDR7\n/vw5W7x4MVNQUGDy8vKsadOmDABzd3dn7969Y5aWlgwAs7CwYIaGhkxOTo4xxpihoSG3ngMHDrAZ\nM2YwAKxjx44sNTWVzZs3j8nIyDB5eXmmq6vLunXrxuTl5Zmqqio7ceIEO3DgAPf6y5cvsz179jA1\nNTUmIyPDnVtnzZrFdu3axS3Xp08fgfipUCmnIvIAqFAptZJXUnflypV8XzdhwgR2+PBhBghPdDZt\n2sQt6+TkxEJCQoq87MCBA9nNmzcFtvvly5d8k7o3b95wyebMmTPZgwcPuHnbtm1jS5cuzfUaIyMj\nlpqaylRVVXPNK0xSt3379nzfq3Xr1nHbnTlzJve+5SzTp0/nTnJaWlosOTmZ6ejoiPwzQoUKFSpU\nCi7Czqdv3rxhzZo1456bmpqy5ORkBoD5+Piw69evs7p16+ZaV2GSOl9fXxYYGMitlzHGatWqxQCw\nNm3asE+fPjEej5frddevX2dDhgzJNb0wSV1SUhKTlpbOM6YmTZqwL1++cDH9/v2bKSsr51rOxMSE\nxcXFMXl5eQaAnT59mo0aNUrk+5BK5SrU/ZJUCjkHMqlZsybOnDmDiIgIxMfHY/78+bm6Nmb39etX\n7vGPHz+grKxc5GWrVKmSK45Pnz7luZ5WrVrByMgIhw4dAgDs378f1tbWqFu3LgDA2NgYr1+/zvU6\nY2NjfP/+HQkJCXmuOz85Y7Szs8O1a9cQGRmJuLg4DB48mHuv8ooByOzK6ezsDHl5efTt2xfXrl1D\nVFRUsWIihBAiesbGxjh79ixiY2MRGxuLoKAg8Pl8aGlpYfv27bhx4waOHj2Kjx8/YuHChUW6Lrt/\n//7Yt28fAODdu3e4c+cOd3mCsbEx3r59C8aY0JjyOg8V5OvXr0hLS+OeKysrY/v27Xj//j3i4+Nx\n8eJFgfNdZGQkkpKScq3n/fv3CAoKQvfu3aGtrY127dpxXTMJKS+U1JFKIeeJwM/PD0+fPoWZmRnU\n1NQwe/Zsgeu/ykJERASMjIwEpmXv65+Tp6cn+Hw+QkJCEBERgdu3byMjI4M7yX38+BE1atTI9bqP\nHz9CW1sbKioqueYlJydDUVGRe579WoEsOd+rgwcP4tixYzA2Noa6ujq2bdvGvVd5xZA17+HDh+je\nvTv69+8Pf3//POtKCCGk4vv8+TPatWsHDQ0NrigoKCA6OhopKSmYM2cOateujVatWqF3797o27cv\ngNznlZzatm0LExMTzJ07FxEREYiIiEDDhg3h7u4OHo+Hjx8/wtTUVOh5Oq/zUEpKClJSUvI95+WM\na+rUqTAyMoKtrS3U1NTg4OAgcL7T1dWFkpKS0Drs3r0b7u7u6Nu3L65evUp/YpJyR0kdqZRUVFQQ\nHx+P5ORk1KpVC0OHDi3zbZ45cwZWVlbo2rUrpKSkMGbMGOjo6AhdVkFBAb169YKXlxcsLS25Mm7c\nOLi5uYHP52P79u0YPHgw2rRpAx6PB0NDQ1hYWODTp0+4fPkyNmzYADU1NUhLS6Nly5YAgCdPnqBB\ngwaoV68e5OXlMWfOnALjVlFRQUxMDH7//o0mTZpwJ2kA2Lt3LxwdHeHi4gIpKSloaWmhQYMG3Pw9\ne/Zg2rRpqFWrFk6ePFnCd5AQQogobd68GUuWLOH+oNTR0UHXrl0BAO3bt0ft2rXB4/GQkJCAtLQ0\nZGRkAAC+ffuG6tWr57leT09PnD59GnXr1uXOdw0bNoSmpibat2+PgIAAJCYmwtfXFwoKCpCXl0fT\npk0BANu2bcPUqVO5c4+5uTkMDQ3BGENISAh3znRycuJekxcVFRX8+PEDcXFx0NLSEhgY7N27d7h5\n8ybWr18PVVVVyMjIcOdWADh69ChatGgBHx8f7NmzpxjvLiElQ0kdqZQmTJgAT09PJCYmws/Pj+vi\nWJYiIyPh6uqKlStXIjo6GjVq1EBQUBB+//6da1kXFxckJiZi7969+PbtG1e2bt0KBQUF2NvbIzAw\nEN7e3li7di3i4+Nx7do17ibo7u7uAICwsDB8+/YNo0aNAgC8ePECixYtwvXr1xEaGoqbN28WGLeP\njw8WL16MhIQETJ8+HYcPH+bmvXv3Dk5OTpgyZQpiYmLw6NEj1K9fn5t/7NgxVK9eHUePHsWvX79K\n9P4RQggRraVLl+Ly5cu4evUqN8KzlZUVgMyeJydPnkRiYiKePn2Ks2fPcufWVatWwcPDAzExMfjr\nr78E1qmkpISePXti7dq1Aue7169f4+DBg/D09ERaWho6d+6Mhg0b4tOnT/jw4QN69OgBIPPPxZUr\nV+Lo0aNITEzE0aNHoa6uDgAYOXIkXF1dERsbi+7du+PMmTP51m/58uXQ1tZGdHQ0AgICcPbsWYH5\n/fr1g4yMDMLDw/H161f4+Phw85KSknD69GlUqVIFp06dKtkbTUgx8JB5cR0hpJzx+Xx8+fIFvXr1\nQkBAgKjDKTNv377FgAEDcOPGDVGHQgghhJSZhQsXQldXF97e3qIOhVRC1FJHSDnq2LEj1NTUICsr\ni1mzZiE1NRX37t0TdVhlpk+fPvj9+zcldIQQQiSatrY2BgwYgC1btog6FFJJUVJHSDlq0aIF3rx5\ng6ioKHTs2BE9evRASkqKqMMqE7du3cLatWsxYsQIUYdCCCGElJkRI0bg3bt3OHLkCO7fvy/qcEgl\nRd0vCSGEEEIIIUSMSYs6gILIysrC1tYWERERSE9PF3U4hBBCypCUlBQMDAxw//59iW3FLk10jiSE\nkMqhoPNjhU/qbG1tJXoQCUIIIbm1aNECt2/fFnUYFR6dIwkhpHLJ6/xY4ZO6iIgIAJkV+PTpU7HX\n8+7dO5iampZSVBUT1VEyUB0lA9WxeIyMjBAQEMAd+0n+6Bz5hyTUAZCMekhCHQDJqAfVoeIoaT0K\nOj9W+GvqTExMuDfh/fv3xV4PYww8Hq8UI6t4qI6SgeooGaiOxVNax/zKgs6Rf0hCHQDJqIck1AGQ\njHpQHSqOktajoOM9jX5JCCGEEEIIIWKMkjpCCCGEEEIIEWOU1BFCCCGEEEKIGKOkjhBCCCGEEELE\nGCV1hBBCCCGEECLGKKkjhBBCCCGEEDFGSR0hhBBCCCGEiDFK6kilZGBhBlUdbShraog6FEIIIYQQ\nIiKqOtowrGUh6jBKTFrUARBS3hp1doD7X/O45xPqNxVhNIQQQgghRBS0jAwxYtcmqOnp4Nubd3hw\n6hwe/XMBcV+/iTq0IqOWOlLp6NeoJuoQCCGEEEKICKnr62HYtnWQlpPFqWVrkRQbiy5jfTDjwnEM\n27YOts6dIaeoKOowC41a6gghhBBCCCGVhqqONoZtWwcFFWVsGjwSn1+E4caeA9A0qgLrro6wceqE\nvgtmwWXGJDy9egMPTp1D2N37YBkZog49T5TUkUqHgYk6BEIIIYQQIgLKmhoYunUtVLQ14TdkDD6/\nCOPmxXz6gkubd+DS5h0waVgPNk6dYNmpA6y6dER8ZBQe/XMRD8+cQ0TYaxHWQDhK6gghhBBCCCES\nT0FVFUO3rIFmFQNs9RmHD8HP8lz2/ZOneP/kKU78tRp1WjeHTbdOaOXuirYD3fD5ZRgenD6HoH8u\nIjE6phxrkDdK6gghhBBCCCESTV5ZCUP8VkG3mgm2j5yINw8fF+p16ampCLl8HSGXr0NJQx2Wjh1g\n49QJzpPGwGn8SIQG3sPDU+fw9NpNpP76Xca1yBsldYQQQgghhBCJJauggMEbVsCwpgV2jZuGsMD7\nxVpPcmwcbh84itsHjkK3mgmsuzrC2skR7kvn41dSMp5cvIqHp8/hzcPHYKx8L/ehpI5Ueur6emI5\ndC0hhBBCCMmftJwcBq1bCpOG9eA/aRae3wgolfVGvn2Pc+v8cH79FlS3aQQbp05o2LEdmrg4IeZL\nBB6eOY+Hp88j6t2HUtleQSipI5VPjj9OGKu4IxkRQgghhJDikZKRwYDVi1HD1goHps9D8KVrpb4N\nxhhe33+E1/cf4fii5ajXrjWsnRzR3ssD9kMG4n3wMzw8fQ5pZTxyJiV1pNKTlpEVdQiEEEIIIaQU\n8aWl4LHcF7VbNMWh2Yvw6J+LZb7N1F+/EXT2IoLOXoSKthasOjvAplsnuMyYiBdx38t025TUkUpP\nWlZG1CEQQgghhJBSwuPz8b9Fc1CvXWscX7QC9/4+Xe4xJH6Pxo09B3BjzwFoGOhjzKfPZbo9fpmu\nnRAxIC37p6WOx+dDWk5OhNEQQgghhJDi4vF4cJ0/HY062eP0ivW4feCoqENCbMRXSPHLNu2ipI5U\nOjlHI5LK1lLnsXwB/npwHXxpqfIOixBCCCGElJDLjImwde6C8xu24vqufaIOp9wUKqnr3LkzHj58\niODgYFy/fh2mpqb5Ts+1ET4f69evx6tXrxAeHg4vL6/Sir9CGr1vG9oP9hR1GKSQsrfUNbBvCwCw\n6txRVOEQQgghhJBi6DZ5DJq5uuDKtj24tHmHqMMpVwUmderq6ti9ezf69u2LBg0aYOvWrdi0aVOe\n04Vxc3ODmZkZzM3N0bRpU8ydOxcmJialXpmKwqRBXXQeMwxK6mqiDoUUgrRM7mvqdEyMRRAJIYQQ\nQoj4UNfXE/hzXJQ6jRqK1v374qb/IZxdIzwnkWQFJnVmZmb49u0bwsPDAQBnz56Fo6NjntO1tLRy\nrcPV1RVbt24FYwzfv3/HiRMn0Lt371KuSsXj9tc8UYdAhMnR/VLYwajDkAHlFAwhRNyVtDfLzJkz\n8fTpUzx58gQPHjyAg4ND+QVPCCHFIKekiN5zpmLWpROYdekEHEcNgZqejsji6TBkADoMGYDAIydw\nculqkcUhSgWOfhkWFgZ9fX3Y2NjgwYMHcHNzAwAoKioKnV61alVER0cLrKNq1ap4//499/zDhw8w\nNi5aS8i7d++KtLww5XFnd8YYVj37FwBQs1mTcr+bfHlvTxRKWsc73z7ibtSfEYhOnDwJczVNgX1X\nGtspCdqPkoHqKPmyeq00a9YM4eHhcHNzw6ZNm9CvXz+h0zt16pRrHffu3cOKFSvw8+dPNGjQADdu\n3ICBgQF+/folghoRQkj+LJraos+86VDT1cHNvYegWUUf7Qd7ot2g/gi5cgMB+w7jbVBwucXT2qMf\nOo0aivsnz+KY79Jy225FU2BSl5CQAFdXV6xatQry8vI4d+4cYmNjERsbK3R6WlpamQRqamoqkBgW\nFWMMPB6vFCMSTl5ZCQsDLwMAQq7cAK9+0zLfZpbyqqMolUYdHXy80HH4YO55n76ueHz+MpTU1TD/\n1nkAwIeQ5+W677Kj/SgZqI7FY2JiUip/4pUXYb1W9u7dm+d0LS2tXH98Xrz4595JwcHB4PF40NLS\nwufPZTv8NSGEFIWcoiK6ThiJZn16IPLte6zrPwQfQp4DADQNDdDMtSea9HSCZcf2+PwiDAH7j+DR\nuUtI+/27zGJq3rcnuk0ajcfnL+PwnEWV+o/GQg2UcuXKFbRs2RK2trZYv349FBQU8Pr16zyn5/Th\nwweBa+iqVq2Kjx8/ll4tKpBW/ftyj2UV5EUYCSksffPqAABlTQ0AQFpKClR1cncjJqQi4fF4NEpr\nBZC9NwsAob1Zsk+vWrVqvuvz8PDA69evKaEjhFQoZo2tMfH4Xtj1csa1nfuworcnl9ABQMznCJxZ\nuR6+HZxxZN4S8KWl4Oo7A7MvnUDnMT5Q19cr9Zgad+8KlxkT8fTqDeybNhcZ6emlvg1xUqibj+vp\n6eHbt2/g8XhYtGgRNm/ejB8/fuQ5PacjR47A29sbx48fh5aWFrp3746WLVuWemUqguwtQDWbNRFh\nJKSw7IcMxPl1W6BnlpncvX0UDHM7G/RfvgCXt+zCxGP+uLxlF86t8xNxpIT84eo7A7bOXfDo7EV8\nfPoCN/0PijqkSqk0e7O0atUKvr6+sLe3L3Ic4nKJQlmThDoAklEPSagDIBn1KEkdUtLTcevbBzyJ\n+QZ1WXk4GtXAxBXNgBXrCtzmp+QEBKl+hfJgD3QY7AEzVU1YaunDSFGlyL08ctbhRdx3nPv0CibK\nahg9chJ2jJ5S5LqJQll+ngqV1C1YsADNmzeHrKwsLl68iKlTp+Y7HQD++ecfzJ49Gw8fPoS/vz+a\nNGnCdUOZP3++WHWvKSxhN61uYN8WwZeuiSAaUlgxnyPA4/PhuWIhACDxv65Rlh3bw7JjewCZF+BS\nUkcqCiV1Ndg6dwEAWHV2gFVnBzw+fxkJUd9FHFnldOXKFVy5cgUAoKuri0mTJuH169cICQkROl0Y\nOzs77N27F87OzggLCytyDOJyiUJZkoQ6AOJfD2k5OSQkJUJRpmKMiFgS4r4vgJLVobq1JVx9Z0DT\nsApu7TuMc2s3I/VX0btSahjoo5lrDyT1dEZ4Qgy+hL1CwL7DeHT2YqHWl7MO9Tu0Qf9lvnj76Amm\njphQrJhEoaSfp4IuTyhUUuft7V2k6QDQpUsX7nFGRgaGDx9emE2JNacJI3NNa+ziREldRZPjXxJN\nQwOMObCde37JbyesuuS+T92KkECs7ueFj0+f55pHSHnRMNDHzIt/55ruPHkMtKsaw6hOTXz/+AlL\nuvSRiH+YxUFJe7PY2Njg0KFD6NWrF4KCgkRQA0JKT1/fGdjzKhjScnJlei0VKTuyCvLoNHoYWrm7\n4vuHT9g4cDjePnpS7PXFRnzFP6s34cKmHbDq7IAW/+uFPvOmo+v4kfj32CncPnQMsV++FmpdtVs2\ng/vS+fgQ8hzbR04Sm4SuPBTqmjpSOHVaN881jS/m//AUhbyKMpr26SHqMIrFuE4tAMDhOYsQ+Tbv\nf7vHZkv+spOSkUHDju3Bl6JrnEjx6VYzQdtB7nnO7zJuuEBCN71Je0z4b0AfS8cOMKpTEwCgbWwE\np0mjsSIkEIM3rgCPz4eWsRGUNTXgOn8GFt69LLRnASmeBQsW4Pnz5wgPD0dKSopAbxZh04HM3izW\n1tYAgI0bN0JBQQF+fn4ICgpCUFAQ6tWrJ5K6EFIS+uY10KiTPZLTUtG4e5eCX0AqnGqNGmD8kT1o\n5e6KW/sOY0Wv/iVK6LJL+/0b9/4+jZW9PbFhgA/C/32AVh59Mf3sUQxYvQRmja3zfb25nS08Vy3C\nl9BwbBs+Hik/f5ZKXJKiUC11pHBksv1I2j1+OjxXLsKzG7dFGFHZ40tJgcfjIT0tDQvvXAIAZKSl\n4d/jp0UcWfFEffgEAHj3JASmDesDADZ7j0bzvj1Rv31rAMCIXZuwceBwgVaQpY9uZr7ucQjW9R9S\nzlFXPjweD/NunMXHZy+x1WecqMMpFg0DfcRGCP4zOfHYXkjJSOPW3sNIS0kRmKeipYl2g/pzz1/c\nuoPf/7X6XNuxN1cy2Pq/QZtqt2yG5U9yH4f+enAdaampmN3SEb+Tc7cekcIraW+Wxo0bl3pMhIiC\n/dCB+JmYBBMdXbQZ4Ia7R09W+sErxIW0nBw6jRqCVv37IvbLV2wcOByvH5Rdz4E3Dx/jzcPHUNfT\nRVNXFzTt5Yz67VsjIvw1Ag4cxaMz55Hy889tXapbW2LQ2qWIevcBW4aOw6+k5DKLTVxRS10pyp7U\nhf/7AABg17ObqMIpNZpGVaCiLXw0yCF+q7E06JbAqJ995k0vr9BKXVJ0DABgnfsQzGzeEX5DRiP8\n7n3sGjsVdw4dB5B5YNE0rMK9xsDCjHtsalkfC+9exui9W8s38Epm6Na1UNJQR60WdqIOpViM69bG\nzIt/w653d26arIICpGQy/2ebc+00+i9fIPCaum3/DC413a49tg2fwD0/s2oDJjVqgQn1m3Itd3nJ\nfiKUlpGBwzAvNOnZDfIqyuDx6ZRACCkefbPqsOzYHgH7j8BO1whaRlXQ8L/r0knFZtKwHiYc2Y02\nnv9D4OG/sdzFvUwTuuzivkXi3NrNmN/BGQdn+iI9NQ29Z0/BrMsn4TRxFDSNqiDiRyK8NixHbMRX\n+A0Zg58JCeUSm7ihlrpSlD2py/p3oUpNc1GFU2pmnDsGAEJ/LJo3yRyu23nymHKNqbT8vXgFekz7\n8+M4+uOfYcR/JiQgLPA+9zzgwFE0c3UBABjWtkB6WhomHNsDRVVVAMDHZy9gXLc25JWUYNKwHlaE\nBOLQrIW4d+JMOdWm8sj63Ikr3eqmADK7udw9cgIABFraFFVVuYF65rTuDJ8dG6BfoxoAYKptG6HX\nEGSk/fk3fLpde3Qc4Y0L67fi948fUFBVhaqOFr69fgsAkJKWhv2wQbAfOhBtBvwPANBn7jQAwOKu\nffD9feFuOZOSng5NQwPEfI6Amp4OfiYkUXcYQiop+6ED8SspGTf2HESNtZvx9fVbtPPqj6CzFwt+\nMREJaVlZOI7wRmvPfoj7FonNg0dxjRLlLS0lBfdPnsX9k2dhatkALf7XCy3d+qBV/7448vYFkqJj\nsXnwKCTFxIokPnFAf8uWoqx/2QEgPTWVeyyp11mJ64hQ2YeOCDp7iXu8b9pcpOcz3Pi312+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B0iiQSLukYwadcOZcXFsLSzxex3l8J99AjkpJ2Gy4C+de5f/siXw4Kbt9SPu7h1R15WNv5vxyaY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\nY1NHRERERESkYf8PmgkTSg+I9FoAAAAASUVORK5CYII=\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": []
    }
  ]
}