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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/ram-kumar-m/EVA_4/blob/master/S5/EVA4S5_5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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": "UpB7lwAJ4iQ8",
        "colab_type": "text"
      },
      "source": [
        "Target:\n",
        "\n",
        "1. Add GAP\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Results: \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Parameters: 4.28k\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Best Train Accuracy: 97.21 at 15th epoch\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Best Test Accuracy: 98.16 at 11th epoch\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Analysis:\n",
        "\n",
        "\n",
        "1.   The model is underfitting a bit.\n",
        "2.   Need to increase capacity\n"
      ]
    },
    {
      "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.Resize((28, 28)),\n",
        "                                      #  transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\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.Resize((28, 28)),\n",
        "                                      #  transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\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"
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "_4A84rlfDA23",
        "colab_type": "code",
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            "340b6c55e5df4fcf993360a69b8022bc"
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      },
      "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": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
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          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "1bdc814f-e71d-41a0-c763-599a3b3f9af3",
        "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",
        "# dataloader arguments - something you'll fetch these from cmdprmt\n",
        "dataloader_args = dict(shuffle=True, batch_size=128, num_workers=8, 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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CUDA Available? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TFjoFekE_va",
        "colab_type": "text"
      },
      "source": [
        "# Data Statistics\n",
        "\n",
        "It is important to know your data very well. Let's check some of the statistics around our data and how it actually looks like"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWZPPo3yEHDW",
        "colab_type": "code",
        "outputId": "64512f84-dedf-4a06-c74c-8da51bc3a79e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        }
      },
      "source": [
        "# We'd need to convert it into Numpy! Remember above we have converted it into tensors already\n",
        "train_data = train.train_data\n",
        "train_data = train.transform(train_data.numpy())\n",
        "\n",
        "print('[Train]')\n",
        "print(' - Numpy Shape:', train.train_data.cpu().numpy().shape)\n",
        "print(' - Tensor Shape:', train.train_data.size())\n",
        "print(' - min:', torch.min(train_data))\n",
        "print(' - max:', torch.max(train_data))\n",
        "print(' - mean:', torch.mean(train_data))\n",
        "print(' - std:', torch.std(train_data))\n",
        "print(' - var:', torch.var(train_data))\n",
        "\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "print(images.shape)\n",
        "print(labels.shape)\n",
        "\n",
        "# Let's visualize some of the images\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:55: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Train]\n",
            " - Numpy Shape: (60000, 28, 28)\n",
            " - Tensor Shape: torch.Size([60000, 28, 28])\n",
            " - min: tensor(-0.4242)\n",
            " - max: tensor(2.8215)\n",
            " - mean: tensor(0.0009)\n",
            " - std: tensor(1.0000)\n",
            " - var: tensor(1.0001)\n",
            "torch.Size([128, 1, 28, 28])\n",
            "torch.Size([128])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f330016d940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YgSAIOxAEYQeCIOxAEIQdCOL/AYXSPzsanQusAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7l9lNaWYKuik",
        "colab_type": "text"
      },
      "source": [
        "## MORE\n",
        "\n",
        "It is important that we view as many images as possible. This is required to get some idea on image augmentation later on"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXXAg8hbK16u",
        "colab_type": "code",
        "outputId": "335b8984-8a59-4ff9-ce2d-16a050b50c16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "figure = plt.figure()\n",
        "num_of_images = 60\n",
        "for index in range(1, num_of_images + 1):\n",
        "    plt.subplot(6, 10, index)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(images[index].numpy().squeeze(), cmap='gray_r')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3N5dGjhyp0IpEllavXs1VMm8d+fn5Sob7/fffs+vT09PZazk+Pt5gOuTRZLrj\nx4+nhoYGfXSIYy+IiIiIGBV1bqzt6VBaWkpnz56lGzdusMu2b99Or776Knl5eZGXlxedOHGCiIgO\nHTpEDMOQubm5wlOMx9NBeaNmUa6vry/dv39fW94KnD17liQSiVEj3YyMDK6tFnTSkZeXx0YrYWFh\nnDQJVb3wr3/9ixiGoQ8++IBPNKCEoSPd7OxsAkB9+/alvn37Unl5uS4yDR7pvvrqqwaLdDMzM9V+\nyDNmpLtw4UK2lcLkyZOV/OHs2bNka2tr1CZjmiLd3NxcPlmpPDc6fUh78OABRowYgdzcXOzYsQMv\nvfQSgKZBOkJCQtheRrIPBDY2NgCaPlZ17dpV1+eDWqysrLB+/Xq0bt2a8z6XL1/G+++/j2fPngEA\nXnzxRcF1qUL+g1tQUBAyMjKMUq46Hj16hKysLAD/f5505ejRo2AYBlOmTIGpqalOeRQUFKCkpAQA\nMH78eL30qOO9994DAISHhwOAYB+nhKK+vh5RUVG4du1aUx0ggN69ews60Evfvn2RkZGBIUOGoKqq\nCoDxr8f79+/jyy+/BABs3LgRdXV1yMnJwdatW9GnTx8AgJubGxwdHVFZWYnCwkJkZ2fDx8fHoLqm\nTJli0Px1inQ//vhjYhhGa3MkGbKeUa+++qquTwclvLy82Gixc+fOnHTIOHv2LA0YMIDdv1evXlRX\nV6eTDj40j3I1NBPTWQffSPfu3bvsR7dTp07ppQP/1wTp5Zdf1ta2VCVlZWXk4eHBRlpqemPpdV7u\n3LlDUqmUTExM6NatW3Tr1i3eOoXQoY78/HyaPn26QuTp5eWlamwQTTo4a7l37x4VFRVRUVERVVVV\nsc3ojBHpPnz4UGHwKW3Jw8ODrly5IriO5ri4uBg00uVtuo8ePSIHBwcKDg7W2m6uvr6ezp49S56e\nnmRqakrbtm3TVagS8q0XGIah7777TmMngKdPn9Lt27cpISGBLCws2H3d3Nzo6tWrOuvgg3zbXG3d\nhHXV8fjxY/Ly8uJsuklJSazp6lu94OTkxBr+yy+/TMuWLeNUzVBXV0dXr16luLg4dv927drprEMT\n06dPZ6uj6uvrqb6+ns/uOukoLCyk1NRU2rVrFx07dkxthunp6UodJry9vWnPnj18deh8rcq6KRur\nemHDhg3k6elJ9vb2lJycTOPGjaNx48ZRcnIyJScnU3BwMHvPBAQEaAsMdNYhjyrTtbOzo/T0dGps\nbOSTlTCmu3r1amIYhiIiIujJkycqSyorK6OysjKKjY1lbyIvLy99hCqRmZlJDg4OCk/CBQsW0LFj\nx6i8vJwaGhro2LFjbAoPD5wUgM0AACAASURBVFf59Lx27ZpeOrjQvDMED8PVSYfst7q6ump9MM6f\nP5/VpW/rhaSkJHJ0dFRowRIeHs723ikqKqLa2lo23bp1i06dOkVDhgxhtzcxMaGoqChNPX70Oi9+\nfn4EgCZMmMBnN511rF+/npycnNio1cLCgtzc3MjNzY3Gjh1LiYmJ5OfnR35+fmxnEtmN3qNHDy5d\n5wU1XW9vb/L29jaa6RI1dYi5e/euynU1NTV06NAhsre3p5dffpm2bNliMB1ETaOaOTg4KJmulnuD\njw7+prtu3Tr2Amp+Y5SXl9P58+fJ3d2d3N3d2RvJ3d1dXTTJVahKtmzZovY1RL76QVWaOHEi5ebm\n0rNnz/TWoYnmVQpNNTq84K1Dvmtvdna2Uk+w69ev05o1ayg4OJi90Tt16kQXLlzQW8elS5fI09OT\nTExM2PPv6elJEydOJCcnJxo8eDANGjSIBg0apNSW293dncvbkM7nRfZB18LCgi5evMh1N511yHp+\nyffq0vS3bFnXrl05V92p0aGz6Rr7QxpXXn31VaN8SPP391dZrRAZGcmlRx4XHbrV6bq6urLRa3R0\nNJtky+Wjlvbt2/MxXHVCVdLY2EinT5+mtm3baq0PkvVjj42NpV9//ZXLa4LeF5Cqlgoc63H10iHf\nDdjOzo48PDyoY8eObLK1tVU4T3Z2dvTLL78IqmP69Onk5OSkYL6qkqmpKTk5OdH06dO5Xic6nxdZ\n29zw8HCuu+ilo6SkhI1y1XXxlf+7R48etHv3bnWtFPjo0NnsZOPpyp+j58V0zczMuB4bnXV8/fXX\nZG1trbaNbmlpKR/ZKs8NQ0Qav7OpWrh+/XpERUXhyZMnSusYhoGZmRkAIDo6GsuWLdOUvyp4T59c\nVVWFzZs3Y//+/SrXjxw5Eu3atUP//v0NqkMpg2bdKzMyMnTp7quTjrKyMrz00kvsl2mFnYnAMAxM\nTEzQs2dPLFu2DIGBgQbRsXnzZlbDjRs3kJWVhX79+gEAnjx5gp49e2Ls2LHastFbR1VVFby9vXHr\n1i1s27YN77zzDp8yddaxcOFCLF26tGnl/x13dmMiODk5seMHL1iwgL139NShUgsXHBwcADSN6ytj\n1apViI6O1lWLTjqa889//hNXr17F9u3bERERYVAdUqlU5Ri+s2bNwqJFi9hjxAHV50adG2t7OuTk\n5FB8fDz78eSVV16h+Ph4rnUufJ8OLYFeOuR7nWVkZOgS4eqto7S0lDw9PWncuHHk7OxM/fr1o379\n+tHYsWNp3LhxRu1aKSA66aivryc/Pz8aPHgw39HE9NJRV1dHx48fJzs7O4XIdtq0aXT79m0qKSkx\nhA6dz41sZL7nrXohJyeHFixYwHVsX710HDlyRCnKnTVrFu9+AGp06BbpGhiDPS15orOOzMxMdnAf\nAdo+/uWPh8CIOhQRNNJdvXo1AODDDz9klz0PkS5P9NLx7NkzHD16FGFhYZg4cSKApmNga2srhA5h\nRxkTaRpVTH70sJbu/CAiIsIPiUSCAQMGoLGx0SD5i5GuenTSIV9np2MdriA6DICoQ5HnWQfw/GgR\ndTRDHPBGYGQmK5DhioiI/M3QFumKiIiIiAiIGOmKiIiIGBHRdEVERESMiGi6IiIiIkZENF0RERER\nIyKaroiIiIgREU1XRERExIiIpisiIiJiRETTFRERETEi2sZeeF66zok6FBF1KCLqUOZ50SLqaIYY\n6YqIiIgYEXGUMREREZatW7cCaBp8fvjw4YiKimoRHbt27QIAnD9/HgUFBUhLSwPQNHV8WFgYZs+e\nDXNz8xbRpjfqBtrlO/AvEbFTN6enp1N6ejpdv36dbxbqBv5tCUQdAunIyMhgB3WPi4trsUHd5amv\nr6eUlBRKSUmhDh06EADasGGD0XUIgGCDmOfn51Pbtm2pbdu27ODdH3/8sb5aeLNlyxaVc7XJT3HU\nv39/KiwsNKiO5gQHB7O6Tpw4wWUXledGMNMtLCykUaNGkampKZu6dOnCJwtNQluCv4WO2tpa+vTT\nT2nmzJls6tOnD/Xo0YOkUinNnDlT04zIgunIyMhQmBFZlnjOjKy3DhkPHz5Umq/N19fX4Dqqq6up\npqaGUwHz5s0jAJScnMxXh07HZNGiRUom5+HhoXamXo5aeHHkyBGysLBQMN0ePXrQ9u3baenSpQpG\nfOnSJYPpUIX81OwHDx7ksovKcyNY9UJVVZXKeYWMyYMHD3DgwAEUFxer3cbCwgIjRoyAnZ0dLC0t\njaiuZfjss8/YQdVV8eWXX2LDhg04cuQIevfubTAdQUFBCAoKwokTJ5CZmckuX7JkCU6cOGH0wd6P\nHDli1PIAoKGhAW+88Qbc3d2xc+dOrdueOHECDMPg8uXLBteWlpaG+Ph4pXn9PDw88MILLxi8fAC4\ne/cu9uzZg4aGBnbZsGHDsGXLFtjY2AAATExMMH/+fKPokaeurg7Pnj0DADg6OiIkJET3zNS5sS5P\nh8WLF7dYpHvkyBF2Hiqomdpa/u8uXbrQ+fPnBdchz5o1a0gqlbKz8FpYWHB9Qgqio7a2lnr27Kl1\npmQAtGjRIoPpkK9eUJeCgoK4ZidIFDNw4ECjR7onT55kp6TXNjeabFs7OzsqKCjgq4PXMSkvL6eg\noCCVr/NpaWl8stJLR3h4OFuurIqytrZWYZt79+6x22zcuNEgOlSxevVqttzExESuuxk20n0ekEql\nqKioAND0MPHw8AAAvPLKKyBqmon19OnTqKysRG5uLl577TWsXbsW06dPF1THvXv3EBkZiYyMDDx9\n+pSNvBmGwciRI/Hhhx9i4sSJcHV1FbTc5gwdOhRnz54FADg7O2PMmDEAmo6HTOeGDRtw584dbNy4\nEVOnTsWLL74ouA7ZfHEyZIO79+3bl43CZfPK/TdMb1RRUYH6+nqN25SUlAAAQkND4e7ublA9s2fP\nxsmTJxWWWVtb4/PPP8dbb71l0LLlmTZtGn744Qe8+eabGDRokMptampqmupFjcTDhw8BAFlZWewy\nvSN/dW6sy9NB9iFNll5++WWu89Rrezpw4vHjx3T69Gk6ffo0bdu2jSorK6myslJhm7KyMiouLiZf\nX19iGIakUqm6OiuddJSVlVG7du2Uomw0i7QnTZrE9WfppKOoqIgcHBzYstXN0nz37l3q1KkTAaD4\n+HhBdaiqw1X1Aa35zMla+MtGujNnziSGYWjUqFEat6uqqiJ/f39O26rRwfmYnD17lq2rlI90vby8\nuGahTQtn7t69SykpKUrRrTxz5swxap3uyZMn2bcOhmHI2tqabty4wXV3ledG0Ha6nTt3hpubG1q1\naoVWrVrh1q1bmDt3rpBFaMTKygr+/v7w9/fHO++8AxsbG7YuSMY//vEPuLi4YObMmQCanmQFBQWC\naUhJSUFRUREAwMXFBR9++CFmzJiBGTNmKGyXm5srWJnNefbsGb788kuUl5fD3Nwc8fHxGDt2rMpt\n27Rpg/DwcADAn3/+KWgUceLECfb/QUFBICKVUxjJT22kqf7Z0ERERBgs77y8PGzfvp3TttevX8eZ\nM2cAQO15E4qysjKV32KMGeHKaNOmDcaOHQsLCwujl62OXbt2YdeuXWAYBgzD4J133sFLL72kX6bq\n3FjXp4N8CwaJREIRERF8sxC8PkYV7777LjEMQ1ZWVpSbmyuYDqlUyj4Vr1y5QkREWVlZlJWVZbRI\nNzc3lwCQRCKhH374QWsBt2/fJgDUpUsXevr0qSA65KNXri0UwK1+V+/r4969e2wkKZ+uXr3KJxte\nOrKzs9lytB2Pbdu2EcMw5OrqSvn5+bro4HxM9u/fr/BG5uHhQR4eHuy1yxOD37vOzs4kkUiobdu2\nBm8ydubMGbK0tCRLS0s2ut69ezefLFSem/9K001NTWVv8BEjRgiqQ5bvsGHDiIiouLiYYmJiKCYm\nRuE1+5dffuEqV+fXem9vb04F1NXVUd++fQkANTQ06K2jebUCV+T304De10dKSoqS4RradKdOnUoM\nw1BwcDDV1dVpzHjkyJHEMAzFxsbqqoOX6cq3gbWxsSEbGxtat24d1yy0aRGMs2fPslUgoaGhBtcx\ndOhQhaZr9vb2fKtLW8Z0HRwcaO3atfoKFYySkhLq1asXAaC2bdtqigR10iFrY2hra0vjxo2jkSNH\nKtXpOjk5afsirbOO+vp6srCwIAC8OqeEh4eTubm54KbLtx0uh/0MYrqxsbHU2NjISypXHTdv3iRL\nS0tiGEap9cratWspPDycwsPDSSqVklQqZbfNysrSVQfnYzJ06FCljgcSiYRsbW0pLCzsubp3pVIp\nASAzMzPKzMw0mI6amhr66KOPFNoLd+zYkVJSUvhKNo7pvv322+yJk73iLlmyRF+hgrB9+3ZydXVl\nX6X2798vuI7FixerbK4mb7pantJ66SgsLCQA5ODgQKWlpZwKePDgAbVr146mTZsmiA753md84bCv\n3tdHjx49lM4RhyZzOuuQRbmy6ixZNGljY8PeJ/LXhywZw3Q7duyo0nRlydvbm+7fv881O846Kisr\n6cKFC0pJXceRPXv2kJWVFTEMQwMGDBBMhypKSkqU7l0fHx/OnVq06BAHvBERERExJoK305VIJGjV\nqhX7d6tWrZR6uRiao0ePory8HO3btwcAlJeX4/z581i8eLFC+8iEhAR88cUX8PT0xNdffy1I2ZMm\nTcL27dtx/fp1tdts3LhRkLJUcfHiRQBNrQWcnJw47ZOTk4Pbt28LpkG+xxlf+vbtq9f+XDh37pxR\nr8mGhoam10oA1dXVarczNTVle2OZmJgo3EeGICkpSeE6lWmU58KFC9i3bx8mTpwoWLlVVVWIjIzE\nDz/8wJYrOx+9evWCtbU11q1bB6CpR9x3332H9957D7W1tfD29saWLVsE06KKxMREpevjww8/FKwH\n61+2c0RVVRUaGhpw/vx5/Oc//0F2djZ+//13XnlkZGTAw8MDkyZNEkyXq6srjh8/jsWLF6s1V0OO\njiRr/mZlZcV5n/j4eMHKN7RhGoIXX3wRkZGRBsvf3t5eZUeYqKgoWFlZwcSk6TZs27YthgwZAgAI\nCQlBr169DKYJAMaPH4/Vq1ejsLCQXfb+++8DaGp2+M033wAAoqOj8fLLL6NPnz56l1lbW4vx48dj\n79697LLXXnsNjx49wq1bt3D69GkAwOuvvw4AGDhwIHbv3o2qqioAwCeffII2bdrorUMddXV12LRp\nE/u3rCPRiBEjBCvjL2u6o0aNwpkzZ1BeXs4+KWVPJ/knpwwiQpcuXWBlZcUewNdeew2vvfaaUlte\nfXF1dUVycjKSk5MBAD///DMAIDAwUNByVCGL5MPCwjhtX1xczLYJ1as/uUC0RDtdc3NzeHp6Giz/\nxMREJCYmat3u3r177P9l7cgNiZ2dHczMzBSWxcTEAGjqEScz3erqatTW1gpS5vLlyxUMt0ePHjh2\n7BiePn2K+vp6zJ8/H99++y3bi1PeADdt2oQ333xTEB2qqK6uRmRkJB48eMAukwVkpqamgpXzlzTd\nBQsW4NixY3jy5AmAJpNzcHBQG+k6ODggIyMD//znP9mooiUw5istl4ukuLgYb775Jurq6hAZGYnQ\n0FC9yw0KCkJcXJxO5vlXjJINgaenJ7y9vY1S1ltvvYXly5ezf3/yyScAFDu2AMB3330nyEO5urqa\nrcZo3bo1nJ2d4eLignfeeQdr1qzB2LFjsXv3bjx+/FhpXyGuT03k5+djz5497N8RERH497//LXg5\ngn9Ie/bsGRobG9HY2Mj+X1VdkT5s2rSJNVwAuHPnjpLhOjg4YMqUKZgyZQry8vLQtWvXFjVcY3Pp\n0iU0NjaqXFdTU4OioiKsW7cOOTk5AIAhQ4YI1hNo8eLFAJqiVj5G2nyMhv9W7O3tIZVKjVJWYGCg\nwpf1b7/9Ft9++y0KCgoUlg8fPlyQ8pycnNi30mPHjmHfvn04duwYPD09MXPmTPTv3x81NTXsNvJp\n1qxZuHjxIvLy8gA09fKT9wF9kb01y5LePc/Uoa5ZA99mFjKat9M1NTUVfCDkt99+W+VIVRYWFjRt\n2jQ6cuSILtJ56+DKjh07aMeOHWy7vwMHDhhMx86dO9njsWDBAnZciSNHjlBcXBzFxcWRr6+vwnHr\n1q0b3blzR1AdfHqk8RxnV+/zgmbNs9q3b883C0F0NGfFihXEMAz5+Pjoq4OzlnPnzpFUKlXbZEy2\nTI/xYxWoq6uj/v37k0QioYCAAFqwYAEFBQUplStru7xt2zZycnJS0GNnZ0chISFkZ2enbrQ2nY6H\n/ODt48ePp8ePH3PZTRMqz81f0nTr6uqouLiYNmzYQCtXrqSVK1dSRkYG53apQungi5eXFzEMQytW\nrDCYjvr6egXzcnV1pU6dOpGJiYnKB5WXlxfdu3dPcB1EpHU4R01JSB1KGTynpjtr1iyjmy4R0d69\nezWarq2tLZ08eVJXLUocPXpUpbFLJBIaOnQonT59WmH748eP0/jx49l2urJuwJ999hk9efJEZx3N\nkdehQ0cIrjoMb7qBgYF8+3ELfjHriEFMFwD179+fKioqDKLj2bNnlJOTo9XU7O3tafr06VwNl7cO\nGaqiWE2JQ4eKv53pNjQ0UENDA/Xv358YhqH169frq4OXlvLycnYqmuamGxQURPv27dNHixINDQ10\n5swZmjt3LtnY2JC1tTXNnTuXzpw5o85EiYiotLSUiouLqaSkhMrLy/XWIU9RURH7+zt16qTL6Ihc\ndQhvukVFRXTlyhW6cuUKdezYkYqKioQQ2hIIpuP27dt0+/ZtcnFxYW/0Dz74wGA68vPzydvbW62x\n+fv707Zt2/j+DL2Oh2y6nri4OAoKCmKjYNn/ecyZ9rczXdkQpDI9QgyqoqsWPfnL6jhx4gR17dqV\nOnbsqGkgHSF0gCHS+JFL2C9g3Hie56zXSYesyZh8O8fFixdj0aJFBtPx+PFjLF26FI8fP8alS5fQ\ntWtX9O/fHwDQv39/XT6a/e3Oi54IpkPWzK9Pnz64desWMjIy0LlzZ3106KxFT/5250ZPVJ4b0XTV\nI5iO7OxsAEBAQADq6+vRsWNH5Obmcm3797c7Hnoi6lBENF1lnmcdf812un81fHx8ADQ1eL906RLG\njBkjaGNrERGRvw5ipKseUYciog5FnmcdwPOjRdTRfKEW0xURERERERBxaEcRERERIyKaroiIiIgR\nEU1XRERExIiIpisiIiJiRETTFRERETEioumKiIiIGBHRdEVERESMiGi6IiIiIkZEWzfg56UXh6hD\nEVGHIqIOZZ4XLaKOZoiRroiIiIgREU1X5Lng/PnzCAkJQb9+/VpaioiIQRFN18AsW7YMFhYWsLCw\nAMMwaNOmjaCT6XEhNzcXL774IjvBX5s2bfDll1+qnbiyJRg2bBgOHz5s1BmTRZ4vMjMzsXjxYixe\nvBjBwcFskk10+rdB3ejmXEZbNxB6Tz1y4cIFhcRjahzBdNTU1FBcXBzZ2NgozQW1efNmImqaz6y+\nvt6gOp48ecJOA9M8bdmyhU9WBr0+2rZtSwBo4MCBLaqDB8+zjudJCyf4zKfHYZYRnXXIZjiRlaXD\nzCbadPA33YsXL1J8fDy9++67Sgdj5MiRQkx1odMBKy4upqioKAoLC1Myl4iICKPpkBEZGcma7ZAh\nQ2jIkCH0zTffkI+PD+Xl5VFOTg4NGjSIsrKyDKrjl19+YY9Dr169qFevXnTw4EHq3r07ubq6Ul5e\nHtesDHpj+/v705o1awz+ECIiKisro507d9Lbb7+tMLO0r68vrVq1ims2z7PR6aTl2rVr9PHHH9PH\nH39M7dq1IwD07rvv0jfffKOPFq3oMoFpUFCQ4DqISOv8fTzNVz/TzcvLo169epGJiYnKqEmWJk2a\nxEcUV6FqOXPmDFlbW5O5ublaTRKJhEaMGMF3SmWdT9yBAwfIwcGBJBIJSaVSys7OpuzsbHZ9Tk4O\nubm5kUQiMarppqWlUVpaGhERzZkzhwYMGKB2v5MnTzafUNSgJqNmKm1V6KVD9uBrfo3g/+ZNMzEx\noUWLFhlchzqKioqoqqqKzy6CmK6mGaMZhqE2bdrQ8uXL6dKlS3y1aETdxKWy+fRkiafx6nQ8uE6i\nqud8ftxN98qVKySVSqlDhw4UGhpKq1atot27d1N1dTVVV1dTcnIyMQxDjo6O9OTJE42zeuogVCVZ\nWVnk6uqq8SEgnzIyMqiyslJwHc3p06cPG+X+8ssvCusePHjAGu7AgQPp4cOHBtNBRPT48WOVpvvF\nF1/Q6tWrlbbfu3cvBQcHU+vWren48eOC6VBHTU0N31100nHgwAGKj48nOzs7AkB2dnY0efJkmjx5\nMh08eJDWrl1Lc+fOJXd3d2IYhosuvY7HpUuXaMuWLbRp0yby9/dnk1QqpU6dOtGCBQu4ZqW36W7c\nuJGcnZ05GY6zszNdvHiRjxbN4lWYrToyMjK4GqBBIl35xDU7VYmz6RIR3b17l54+faoy9xUrVhg9\n0vXz8+NsuLI0dOhQ+vDDD6m0tFQwHc2Rma67u7uCqT558oRmzpxJEomEvLy8uBiuXjqImkzX0tKS\nGIah2NhYio2NVbnd4cOHqU+fPmRvb092dnZ04sSJ5g9OwU13zpw5FB4eTo8ePeKzG28d06ZNIwsL\nC/YamDdvHhUUFKjcNiEhgRiGocjISMF0VFRU0JYtWygmJoYcHBzI0tKSTE1NVd7MZmZmJJVKqVu3\nbtrK16SD87m5cuUKOTo6KukIDw+n6dOnk5ubm9I6Nzc3Plo0i9chimxe5yqEDnVkZGRQRkaGUhWI\nluoNTTqE+5D26quvEsMwfOp/+AhV4ubNm9ShQwclU3V3d6f09HQaNWoUm1RFw76+vmzUp4+O5hQW\nFpKnpydJJBLq168f1dbWsus+//xzkkgkZGdnR/Pnz+d0MHTVIc/mzZuJYRgKDAykwMBAevDgARER\nNTQ0UHp6Ok2YMIFMTU3ZY9OpUydVdfOCXciyiNvCwoIA0IYNG/jszktHamqqws3i6upKR44cUbt9\nSkoKAaA2bdpoexhw0nH48GHq1KmTgoaAgAAaMWIEjRgxghYvXkxHjx5l0+nTp+nevXuUn5+vqWxt\nOjidm5SUFLbuVma0586do3PnzrF169euXSMvLy8F/SEhIXodExnNI1c+aDFqwQMEVVG2OuSidcOZ\nbm5uLtna2pKLiwtlZWXR/v372XTjxg2+v4+TjvXr1ysZqaOjI505c0Zp25ycHDpy5Aj5+Piwr4+y\n+rtly5ap+3ijd6QrkUiouLiYbUUhq+dNTEzkmpVeOmTITNfCwoIsLCzo7NmzlJSUpPJNYe7cuVRU\nVGQQHUREBQUF5OLiQi4uLgSAZs6cScXFxXyy4KyjpKSENXbZ79u3b5/GzO/du0cMw1BwcDA1Njbq\nrWPatGlsdJiSkkL379+nuro6jRp4ovO9Kx/huru7Kzxo7927RykpKdSnTx8Fo7G0tKSjR4/y0aKS\n5iamqVpBFfLRrj46+KBDFYPhTDczM5O9qG1tbRVuYgcHB5o+fTqVlZVx/m1cdDQ3XWdnZzp9+rTW\nzMvLy5WM5uOPP9ZZhyrkTXfQoEFs6wVzc3NKT0/nW4+p9wU0Y8YMhd9rZ2dHZmZm7N8WFhb06aef\nUmlpqaa6eEEu5DFjxrAX7dChQ/l8QOOto7S0VMF0+/btq7VOf8OGDcQwjKZojpcOmenu3r1bW366\nIojp2tra0po1a2jXrl20a9cuCggIUFn94erqyleLSuRNk6/hEim2eNBHBx+eK9MtKSmh8PBwtk2b\nfJJFlj169NBUAa9NqBKHDh0iBwcH1jhGjx7NSWtjYyNt3bqVevfuze5rbm5OKSkpOulQRW5uLkml\nUtZ4ZcnW1pZrFoLoICKqq6ujl19+WWX9drt27SguLo5OnTplcB1ERAcPHiRzc3P2otVSvSOIjpCQ\nENZ0o6KiKDIykqZNm0YhISEUExNDMTExtHXrVrp58yY9fPiQ/P39iWEYmjZtmiA6/Pz8CABVV1cT\nEdGdO3eob9++5O7uTu7u7jRv3jxaunQpLV26lPLy8nSJggUxXS7Jx8eHtm3bxleL6g151uNq2l8f\nHVzhU72gRYdwdbrqKCkpoeDgYGIYhry8vNj6RJ5CVSJ7PbaysqJff/2Vl65Tp06RVCplDUhF+0y9\njseePXvYDhGyZGNjw0ujEDrmzZun0nDbt29Pt27dMpoOIqLg4GACQMOGDaNhw4ZxaZOrt46tW7fy\nMhY9byglPvvsMwJAkyZNoocPH1Lfvn21GtuOHTv4tP4xiul6e3s3b83CVYsS+ka5RIYz3bi4OE7t\nhvX5kCZ2AxYRERExJurcmM/TgQtjxoxhWw3o8HRQ4vz589SuXTtiGIZ27dqlk6ba2lqF6E8XHerI\nzs5mP5zJkrm5OZ/2l3rpyM/Pp86dOytE21ZWVmRlZUUMw9C7775rFB0yDhw4QGZmZgSAVq9erbKN\nsCF01NTUKHxIk/32zz77jK1rl+8cwTAMjRo1SjAdOTk5Ch+rTExMyMPDgzZt2kRXrlxRSGPGjGGb\nkvn6+tLNmzd11cHp3AwbNkyhukddWr58OddvMrwjXV2B5kiZ9/Hg2yvuuWgypo2bN2+Sp6cnWVhY\naOt6ykmH7EPaq6++Sg0NDbz1PHz4kNLT09kbrXPnzjrpUEdqaqpSnS7DMGRtbc2lF5peOv7880+F\nelwbGxv6/PPPydfXl3x9fVvEdGXdxmXVCjpWLeikQ77uX2awnTt3JldXV7Y5obzpfvbZZ4LpKCsr\nI39/f4UPUZqaVT58+JBiY2PZdt4cmmDqde/+8ssvdPz4cTp+/DilpaWxx0Sm18bGhs/1ykmHlo9g\nWpGvXxXCdHXphqwucdBhPNMlavr4xTAM+fj4KLRf5SBUCVlXzoSEBCovL6eKigqqqamhZ8+esds0\nNDRQQ0MDlZeXU3l5Sw5RAwAAIABJREFUOU2dOpVGjhxJI0eOZOuZZUlFW1Gdj0dFRQWFh4ez3YCj\noqIoKiqKGKapS3JoaCiVl5dzzY6Xjq+//pratm3L/q6EhAS6du0aHTx4kExNTcnU1JScnZ1VNq0T\nUoc8Bw4cYCM4ns3lBNFx6dIltoNIc4OVr+OWNTsUMqojajJeWSsKNS1llDhx4gS1a9eOTE1Nac2a\nNXx18L53Hzx4QCtXrlQyES1lc9GihL6mq8HkeOlQlZ8+iWN7YeOa7qVLl9gmZfp2JWx+47i4uFBQ\nUBDFx8dTQkICJSQk0IQJE2jChAlae6m5urqqMiGdj8f+/ftVdgMePXo0u5xHEyLOOkpLS8nT05MY\nhiGpVEq7d+9m25ru3LmT/b3+/v5cy9ZJR3MiIyMJADk6OnLpCWgQHampqbR161bKysqirVu3Unp6\nOrsuKyuLPD09CQBJpVKD6Fi7di0lJSXxGv/j+vXr5OHhQQzTNFgRDx287t2HDx9SQkKCgoHY2NjQ\n4MGD+TbpM7jpcuxQobPpykYU4xr9BgUFafoY2PKmS0TUvXt3QUw3Pj5eq5lqS6+++iotWbKE/vzz\nT511qELedOUb/l+5coVdzvEVlpeO1NRU9reNGTNGYV1sbGyLmO7BgwdJIpEQAIqOjtalXEF0qOLP\nP/+kP//8kwYOHMg+xGXDbhpThyYKCgpo0qRJJJFI1EWdet+7q1evVjKT9evX6yKXk47m3Xi5NBnj\n2RXXIJEux3pcbTqMb7orV64UxHTz8vLUtj9tnszNzcnS0pIsLS3J0dGRUlJSKCUlRVuTKcFNt7Ky\nkl3u4+PDNTvOOg4fPkxubm5kYmKi0PPqypUr1K5dO7ZH2sGDB7mWrZMOedauXctetLp+8BRCR3Mq\nKyvp3XffpXfffZetchgzZgyfjitGuV+ImiJxU1NTMjExodzcXC46OGvZs2cPWVtbKxlMTEyMLlI5\n61AVSTYfWUzTKGNamprxOh6q2uAKYLjqdGidmFJQbt26haSkJEHy6tChA6ZOnYrbt29r3Xby5Mno\n3LmzIOXyZefOnbC1tQUAZGRksMunTZsmeFm+vr544YUXAAAXLlxA586d8eTJE4wePRpFRUXsMejf\nv7/gZatjz549AICePXtixIgRRitXG5GRkfj+++/Zv319fbFp0yaYmZm1oCrV9O7dG++//z6++OIL\n3Lx5U7Bree/evRgzZgxqa2uV1u3cuROPHj3CBx98YJB7JyMjA8HBwcjMzGSXLVmyhPP+QUFBgmkJ\nCgpCUFCQgpbm5cjfu3qjzo21PR34Ul9fT5GRkcQwDM2ePVvTYCJGiyC0oLOOuro66tq1q8rWCxKJ\nhEaPHq3pQ6JeOkJCQlRG++PGjaO6ujp9+v3rdDxiY2MJAK1cuVLXcgXRIc+UKVMUohhXV1euzbME\n1cGH0tJSAkDTp0/nooOTllGjRmmM7kxNTenYsWN8ZPLWwXUMW3CLcHXWIT9jhKyeVsfZIrTpMI7p\n1tbWsh+0+vTpo63/+1/edIma2hG3bt1aZTvd8+fPG0xHVlYWBQYGsmZrZmZGP/zwgy7j1uqlw4Do\npUNmNAzTNKhNcHAwXb161eg6+PL48WMCQJ6enlx0cNLSvEeaqakp2drakq2tLX3wwQeUmprKV6bO\nx0RWjSCrdpD/W6AZG1oCleeGISKNgbAu0XN+fj4A4N69e9i8eTMOHjyIO3fu4I033sCePXtgY2Oj\naffnec56UYcifykd3377LWbPno2KigrY2dlh//79AIDXX3/dqDp0obq6GtbW1k2FKN6z6mby1Krl\n3XffRUpKCvv3K6+8gmPHjgEAXF1ddZH5l79GBEbluTFIne6hQ4cAAO+//z4AYODAgVi8eDFGjBih\nzXBFRAyGjY0NKisr0bt3byQnJ8PLy6ulJXFGIpGgV69egtavbt26FQCQkpKCLl26YOHChbqarQgP\nDBLp6snz/JQSdSgi6lDkedEBPD9aRB3NEAe8ERERETEi2iJdEREREREBESNdERERESMimq6IiIiI\nERFNV0RERMSIiKYrIiIiYkRE0xURERExIqLpioiIiBgR0XRFREREjIhouiIiIiJGRNvYC89L1zlR\nhyKiDkVEHco8L1pEHc0QI10RERERIyKaroiIiIgREU1XRERExIgYdY40kf9enj17ht9//x2rV69G\nRUUFgKZxlz09PfH222/j9ddfxxtvvAGGUVdFKSLy90DwSLe6uhq3bt3CrVu3EB0djZiYGAwYMAAS\niQQSiQQRERG4c+eO0MU+V1RVVeH48eN4//33ERYWhjt37vztf7Mmzp49i86dO8PHxwc//fQTXnjh\nBbzwwgtYuHAhpk6dCgCYOXMmvvzyyxZWKtLSxMfHg2EYNllaWraYlgsXLmD48OEYPnw4unXrhpMn\nTwqSr2CRbmNjIxYuXIiMjAycPXsWQNO0IrLIRfZvamoqLl++jEOHDuHFF18Uqvjnhp9//hmTJ0/G\nH3/8AaDpGPTu3RsAkJaWhtdee81oWq5evYpt27Zh//79+Mc//oEOHToAAN555x1Wk6HJzs5GSEgI\n2rZti6ysLPj7+6vc7uHDh2wE/N9OQUEB5syZg1mzZiEwMNCgZSUlJbHH/b333lN5T5aVleGNN95A\nt27d2NkmDMXatWsV3nZa4s0nOzsbiYmJ+O6779DQ0AAACAkJUTlrsk6omzyNeEzmduPGDZo8ebLa\n2W9VpeDgYD6TubUEvHVs3bpV40ymvr6+VFZWZnAdxcXFFBERQSYmJmRvb0/h4eE0depU6tevH/Xr\n149MTEwoPDycKioqDKqDiOiLL74gKysrOn36tMbt7t27Z1AdfCguLqaBAwcSANq7d69RddTV1VFo\naCgxDEMDBw7ksotO9252drbSxJTLli1Tue3Vq1fZWZN10MILqVSqMIu1paUl3yz00rFmzRqytLQk\niURCJiYmFBsbS7GxsfTkyROF7err6+mLL76guLg4unLlCh8d+ptuYWEheXh4sLPdBgQE0P79+2n/\n/v1UXV1N1dXV9NVXX5FEIiELCwuKjo6m8PBwQU13586dZGtrSwDI29ubhg8fTsOHD6fly5fT8uXL\nKSUlhU0TJkyg0NBQqqqq0pYtLx0NDQ0UHBzMzjRrbm5O3t7etHLlSnJ3dyd3d3diGIbmz5+vrVy9\ndBAR9ezZk8zNzSkqKooePHigtD4hIYGsrKwoJCRE6WISUgcRUUREBI0ZM4aImma0LS0tVUhPnz7l\nWr5eOoiI9uzZQ7du3VK5rra2lioqKuj8+fMklUoJAEkkEjpw4IDgOjQRFRXFGk7v3r257KLTvTtu\n3DjWbCMjI2ndunVqz8WYMWOMZrrdunVrMdMtLy8nT09PYhiGRo4cSfv27VPapqSkhFasWEHBwcEk\nkUjI2tqavvvuOz469Dfd06dPk0QiIVNTU1q5cqXKbeLj40kikVCvXr24ZMlLx71798jOzk5jhKkq\nqbv5dNVRWVlJnp6eBIDMzc1pzZo17LoDBw7QgQMH2AuJJ7x07N27l2xtbenQoUMaM924cSMBoJ07\ndxpEh4xZs2bRlClTaN26deTh4aF0Hnr27EnJycn0+PFjg+o4fPgwe26GDh1Kc+bMoaFDh9LQoUNp\nyJAh5O3tzZqtLEVERAiuQxWy4GTRokVkYWFBDMOQg4MDnT17lsvuvO/dCxcukJWVFQGghIQEjQ++\n3NxcMjc3N5rpzpo1q8VMd82aNSSRSCg8PFzl9XjixAny9/dn39bHjBlDN27c4KtD/zrd1NRUAICl\npSX+/e9/K60/efIkNmzYAAAYNWqUvsUpcfnyZVRUVMDNzQ1DhgxRWNfY2AgA2L9/P/r374/09HSU\nlJQIrgEATExMYGNjg969e2PChAmYOHEiuy45OZn9/0svvYRbt26hffv2gmsgIixduhQjRozAgAED\nNG47YMAAWFhYoKCgQHAdMsrLy7FlyxZUVlbC09MT48aNQ2hoKLv+4MGD+OWXXxATE4Nz585h7dq1\naNWqleA6fvzxR8yePRsAYGpqioCAAACAk5MTgP+vd3dzc0O3bt0QHByMiooKLFq0SHAtqujVqxcA\nIDc3l1321ltvoUePHgYpLzQ0FNXV1XjllVcQGRmp8Zg3Njaivr4eADBnzhyD6JFH5ifGpqqqCklJ\nSSAi9OvXD1ZWVgrrb9y4gZkzZ+Ly5ctwd3fH0aNH8dJLL+lWmDo35vp0SExMZKsOcnJyFNY1NjbS\noEGDSCKRkI2NDV28eJFLlrx0JCQkEACKj4/XmvHBgwcJAHl6eqp87dZHhzri4uIUoqd169bxzYKz\njosXLxIAKi0t5ZRx586dKS4uTnAdMhoaGmjy5Mk0c+ZMjdU5mzZtIltbW9q4caPgOgoKCqh9+/YE\ngAIDA+nw4cMaM9+7dy8BoAkTJgiqQxUXLlxQiq5lafPmzVyz4X3vWltbEwBKSkripFGmicP50fuY\ntFSdbklJCRvBfvXVVwrLS0pKyM3NjRiGoeDgYMrPz9dHh/6mW1JSQlOnTmXDcvmb68CBA+wPGTt2\nrD5C1cLHdMPCwggApaWl/W97Zx4UxZn/4W93cBhWgRkWOUqCQMmABSq6EuPihYU3rkIUtVyNJIix\nXEFcza4/VFQKo8aEKOUaj4iJCTFSEZX1SFmIikZdIxApiRI8wBsBkUskyuf3h5kuhrm6e3oGk/RT\nNVU60/P2Q0/3p99++33fltzDEPv27YODgwO3AwUGBuLZs2c6y2zbtg3Xr1+XxEN7CV1bW8vLb/Lk\nyXBwcDB1I0CUhxgyMjLg6OiIu3fvSurxt7/9jQtcc1RXVyM0NBREhGvXrknq0Z4XL15g/fr1cHV1\nBcMwcHJygpOTE7p06QKGYRAUFITm5ma+xQk6drdv3w6GYTBjxgxe7fkdQ7elpQVlZWVCXATRMXSV\nSiXf/dMij8bGRoSGhoJlWUyYMAGlpaV49OgR1Go11Go1WJaFj48PfvrpJ0s9pOm98ODBA8TExIBl\nWQwYMAARERGIiIjgei9ERkZaenYwijZ0c3JyTBZaVlYGBwcH9OrVCy0tLZJ7dOT58+eIioribqwx\nDGOw9pKUlAQnJyfExsYaO9CtErrV1dVQqVQgIhw/fpzPn2TV0D158iSICDt27JDUY9KkSejSpYtO\nG7sx9u7dy7Uz80DU9igsLMTKlSt1gqWiogIVFRUIDAwEwzDYsGEDn6JMeRh1ef3110FEmD9/Pq/C\n24fuli1b8MUXX8Df31+IiyA6hi7DMJg7d67QYkR5VFVVITo6GgzDoGfPnpg0aZJOjysz7bd8PaQJ\nXeDl2XvGjBl6XcZ8fHxw9epVS0WNcvLkSYSFhZkN9YkTJ0px2WYSbY21trYWixcvBsMwICK4uLjA\nxcUFlZWVet/RdjPTntUvXbok2qOsrAxEhFOnTpn948rLy7mD6fz582aXF+IhhsbGRqhUKslD98KF\nC9i6davZ9be0tGDQoEFwcnLiW5sRvD0eP36Mvn37cmESEBCA0tJS3Lx5Ezdv3uRqvvn5+XzWb8rD\nqEtQUBCICJGRkThy5IjZGnV2dja3n/To0YO7GXn69Gm+LoI4duyYXuiOGjWKd5OZFB5paWkGu7lm\nZWUJKcaYhzz3goyMjIxNMZbGQs8OABAXF6dzhqJfa3DTp0/H48ePLTk7WERZWRns7e3h6uoq5IzJ\n2+Ojjz7CwIEDjXZPO3funMnBAUOGDOGW9fPzE+3R1taGcePGITg42FCNWQdtl7GXuwAvrFrTvXDh\nAhiGkbymy5fVq1eDiLBw4UK+XxHs8e9//5s7JubOnYs7d+4AeHkjcdeuXWAYBsOHD0dra6sQdUHH\n7tdffw07Ozvutx8wYAAGDx5s9KVWq3X2ZYVCgfT0dMm2SUfy8vL0aroMw+DEiRNCirHY49NPP9Vz\nICL4+/ujoqLCEg/pQresrIwbJKF9hYWFcf/m2UfXmKhFzJ8/H0SEtWvXCvkaL48VK1bAzs5O70Sj\nffXo0QN1dXWoq6vT+25xcTGio6O5m2329vZYsmSJKA8tjx8/Rnh4OPz8/LB//36jB/DKlStBRHw7\n4Av2EIq12nT5cPr0aXh4eGDYsGFCAk+wx7Vr1xAWFoakpCSd9zUaDTQajbkRcEI8TLqMGjUKXbp0\nEdSvPSAgABs3bkRBQYFQF0GUlpZybdvtb0BLcANLENrBDzExMYiJiUFGRgb8/f3Bsiz69+9viYc0\noVteXg5fX1/uDt+WLVuwZcsWPHv2DGvXrsXw4cOhUCjw4YcfihUVRXl5OcrLy8GyLFQqlVXahTqG\nLP3a5uXh4QGlUgkiQmJiIhITE1FfX4/m5mbcv38fa9as4cJWW0ZkZKRoj/a0tLQgOTkZzs7O0Gg0\nWL58OY4fP8695s+fDycnJ3h5efG+oSLU45dffsGFCxf4lo1NmzbB2dkZDx8+lNSDD9ohvzz3T0k9\ntN386Ne2/ytXrggtQtSxm5eXh7S0NISGhhp9JSQkcG579uwR6yKYd955p1NupGl5+PAhfH194ebm\nhpaWFu7Ge0VFBYKCguDo6IgffvhBrIfloXv16lWuhqtSqQxeemzevJkbtXbx4kUxoqKYPHkyJk+e\nDCLCunXrhH6dl8e8efN0dpAhQ4Zw/Rmzs7Ph5+fHfebr64u+ffsaDOp169ahvr5etIcx1q1bh3Hj\nxmHgwIFcjSo6Oho//vgjRo8eLWS7CPLIy8uDq6srr4IPHz4MBwcHTJs2TXIPU6SkpHD9qMeMGcNn\naLikHvX19Rg7dqxO7wARWFRhMkViYuIfMnRLSkrAsiw8PDz0Ptu3bx9YlsWsWbPEelgWupWVlRgz\nZgzXfGCov+nx48e5fm7Ozs4oKSkRIyqYmzdvcjsMwzDG7rZa7HH37l0kJSVhzJgxyM7ONvh5XFwc\n4uLi9C7ZlEol3nvvPcn66ZqjtbWVu3xubW2Ft7e31UJ3+/btvEK3rq4OPXr0gEqlMtsOLcbDGLdv\n3+Z6lQQGBgoNXEk8zp8/z/Xw8fHx4TNgh6+HJKE7ZswYrtcCz3ZMSTwqKipgb28Pe3t7MAyD7t27\n8x0SLYnHihUrwLIsFi1apPeZNnQNHes8PSwL3SlTpoBlWfj6+hqcsSorKwsDBw4Ey7JwcHDA/v37\nxYoKZtGiRVy4eXp6iilC8rA7ceIENm7ciJCQELzzzjs4duyYTT3ac/36dat2odu+fTucnZ1NBkld\nXR2GDh0KIsL69eut4mEM7Sg1iWuYgoiKigLDMIiNjeUzAk6Ih8X7yNWrV+Hp6QkiQmBgoCUuolAq\nldw8FAzDCGkGs9hjwYIFYBgGiYmJOu8/ffoUsbGx6NKli9m5TUx4iA/dzMxM2NnZYfDgwVzg3rlz\nB3FxcfDw8OA6OWtnH1uzZg3fv9niH66pqQmenp7cD8ZzBJrkHhJh1dDl2TYl2KOwsBBEhNDQUJ12\nymvXrnEzvoWEhICIkJaWhhcvXljFwxA7d+6EQqHAxIkTMXHiRDx48EBoERZ7FBYWcm3+165d4zMC\nToiHxftIQUEBiAhdunTB6tWrLXERRcfQ5THZjmQeCxYsMNi8oJ24y1CzgwAPcaHb0NAAjUaDbt26\n4cCBA7h8+TISEhIQGBioNzhi2LBh5u548hEVxEcffQQiwrBhwzBs2DChX5fMQyKs4qEdHGGt0G1p\nacGyZctA9HKKxL59+8Ld3R0sy3I1TGdnZ6Snp6OtrU2IukXbo6SkBEQEBwcHXL58GZcvXxbydck8\nampqoFKpwDDMKxm677//PogI/fr1s9RFFJ0ZukVFRVAqlXBzc+PmXsjLy+Pm2TUxlSMfD3GzjF24\ncIHKy8uJiCg6OlrnM3d3d4qPjyciomHDhlHfvn3J1dVVzGpEk52dTURECQkJNl3vbwlrz8hvb29P\naWlp9H//93+Unp5O2dnZpFarafz48dwMaMHBwRQcHGxVj/bcuXOH218zMzOpT58+Nlt3R7766it6\n8uQJxcXFkUaj6TSPjpSVlRERUVZWVqd6hISEENHLrOmMdaekpFBycjJ5enpy7wOgv/71rzRhwgTL\nVmAsjU2dHZqbm3Hjxg0MGTIELMsiMDAQSUlJyM3NFXupZu7swJuzZ89ytan8/HyhQyol85CQ32Tz\nghUR7fHVV1+BiBATE6M38ZAtPZ4+fYr+/fuDYRicPXvWGh6if5u8vDzk5eVxVyOdVdOtrKxEZWUl\n3njjDTAMg7S0NJt6PHnyBP/4xz90rtyjoqJw+/ZtSz2kHZEmERZ5LF68GEQEd3f3TvWQEKuGrrUn\nMbcCojwyMjKgVCrh5+cnReCK9tBy7NgxBAYGCn1kEl8P0b+Ndh4IX19feHl5Ydu2bZa6dAavssfv\n7xHs2gmXrf1Av986fn5+L8+6fxD27t1Lzs7OlJOTQwqForN1aMyYMfTTTz91toYePj4+RER048aN\nzhX5HfO7C13t00w7PkVC5o/NmTNnOltBRoaIiJg/Um1HRkZGprORp3aUkZGRsSFy6MrIyMjYEDl0\nZWRkZGyIHLoyMjIyNkQOXRkZGRkbIoeujIyMjA2RQ1dGRkbGhpgbHNEZnXgNzcQie+gie+gie+jz\nqrjIHh2Qa7oyMjIyNkQOXRkZGRkbIoeujE0oKysjX19fYlmWoqOj9eZhlpHJycmhESNGEMMwxLIs\nsSxL7777Lm3dupWOHDnS2XqSYW7uhVelHeQ35/HkyROaPn06Xb9+nZvwXaVS0YoVK2j8+PEUEBBg\nEw8JEe2xb98+Sk5O1pu56sWLFzb1kJhX2YPo1XHh5bF582Zavnw5NTU1EQC9SfadnJwoPT2d5syZ\nY1WP9jQ1NVFbWxv3/++//56amprohx9+ICKiadOmUXBwML322mtCPCyfT3fWrFnco85nz55trTko\nLaa+vh5r167lJmc28wReiz20Tw3t+PgilmWhVCrx97//ne9cqq/y3KBmuXnzJvd3d3x98sknNvPg\n68kwDOLj463q8fnnn2Pz5s3YvHkzNm3aBDc3N7i5ucHd3R2bN28Wqi7q2H3+/DmWLFkChmEQEhIi\n9knEfFx44enpCYVCAbVaDZVKBbVaDbVaDaVSye0vTk5OOHTokNU8amtrce/ePaxfvx4JCQnc45S0\nL/r1yeLtX2YeRSbtJObV1dWIioqCQqGAnZ0d7Ozs0L17d9y5c4fP3ydU1CKqq6sRHh6u8/hza4bu\nrVu30Lt3b25n6dq1K7p27YqpU6di9erVmDBhAliWxe7du/kU95sN3erqagQGBuqErpubG/fI8ZEj\nR2LdunVoaWmxqoc5GhoaEBsby+3HdnZ2ePTokeQebW1tWLZsmU6QGDoh+fv74+jRo3z1RYXuypUr\nddZp6HHjIhD92xQVFWHTpk1673/++eeco1qtxsmTJyX3qKqqQnp6Onx8fPRC1c/PD35+ftBoNNi1\naxcyMzPxr3/9y/ah++DBA0ybNk1nJw0PD0d8fDwyMjL4bBShoqKoqqpCVVUVRo8erRO41g5d7dNE\nVSoVsrOzUVhYiMLCQu7zhw8fgmVZaDQaPsVJHjLNzc3cv9va2lBTU4OmpiYcOHCAeyS5gafzCvbI\nyMjQCRY/Pz+UlZXpLOPt7Y1ly5YJ0Zd8e0RFRensy3Z2dnweiS7Yo6qqyujVT8f3PDw8+Aav4NBt\naWnBiBEjXqnQ7UhNTQ1qamoQFRXFOfr4+Ejucf78efj7+3Mh6ujoiP79+2PmzJnIzs7G06dP8fTp\nU27527dvY9KkSWAYBuHh4eZOztKFbkFBgV7gdjyYLECyH27nzp3YuXOnTth6eXnh+PHjOhtSSo+s\nrCwMHz7c5I6sDV2WZfkUKcpj1apVmDt3Lu7fv49Hjx7hk08+wdy5czF37lwMGjSI+3dsbCx3idn+\nDF9bW2uRx927d7kyiQiBgYG4deuW3nL79u0DwzDIzs7m82cJ9uADwzA2Cd3ExESDAZuSkoKUlBQE\nBwfrBHFgYCAffcGh27GW26dPH7NP7C4sLERMTAzu378v1EUUBw8exMGDB3U8N2zYwPfrvD3aNxmE\nh4fj/PnzRgt9/vw5pkyZwoWzqWVNeIgL3cOHD3M7Z2hoqBQPozQnKpjTp09DpVJBpVJxgevh4YEr\nV65Y1SMoKAhEhAEDBujUKNuTnZ0NhmEwYsQIq3loA693797QaDR6l02mXu7u7obamwV5bN26lTtY\nAgMDcePGDYPLff/993Bzc8OKFSv4/FmCPUyxY8cO7Nixw2ah29bWBpZlERkZqfP+smXLsGzZMr3a\nrzVC986dO/D09NQJs59//tnkCurq6rgap5kHREry25w7dw4eHh7w8PDgHIOCglBeXs63CEGhq73H\nYq5JSdsG3q1bNyxcuFCsh7jQbb+TStQAb05UMNonv2pfKpUKJSUlVvcIDg4GwzCYNm2awc/z8/Ph\n6+sLlmVx/Phxq3js3bsXjo6OXIhqd6oTJ07ovY4cOaIXuidOnLDIo7q6Gn379uUOGEM13Pbk5ubC\ny8sLgwcPNvenCfIwRWpqqskbJNbwaGtrQ3l5Oe7du8e9d/PmTXh5ecHLy0svdPfs2SPWw6hLWVmZ\nTuBGREQYrRxomTdvHre8LUI3NjZW74pg4MCBuHv3Lt8ieHsUFRXxyoXS0lJuP5k0aZIlHr/f0J09\ne7Zes4ItPEJDQ8EwDFJTU/U+u3XrFmJiYmBnZ4dly5bhl19+kdyjpKREJ3BDQkJM1u4bGhp0wiYx\nMdHYjS3eHu17bri4uJj9AwEgPj4eLMsiIyMDz58/N7WoJPuHr68vtw93rOkmJyfzKcJij6qqKgQF\nBekFjFqtRlRUFJ+becY8jLocOnSIW0+PHj1w4cIFk4XX1taif//+fGvFkvw2S5cuNdjbZceOHXyL\nkMQDAHbv3o3du3fD1dUVRIRRo0YJeYJz54RuWFgYhg4diqFDh9qsi1RRUZFOswIR8a01WOxRXl4O\nhmHQs2dPHD1NDqMSAAALOElEQVR6FPn5+cjIyEBGRgb8/f25g6qoqMgqHhcuXOACtF+/fmYP3FOn\nTnHLDxkyxNTjyUWFbnp6usn1a6msrORqeVu2bDG1qMX7R35+PlxcXAyGbq9evVBcXMynGIs8qqqq\nuCagjuFy6tQpIUUJOnbb1yInTJhgtvDCwkIhTRGShF1JSQmWLl2qF74qlQrfffcdnyIk8bhy5Qo8\nPT3h6ekJhmGgUCgk+W1EhW5BQQF3oPbu3Zt7/9KlSygoKMDMmTMxc+ZMo5duDMNArVajoKDAUBcz\nizfYnDlzdAJ35MiROHv2rNBiRHts3boVr7/+usk71Y6Ojpg+fbqkHrm5udyBHBISYjZwCwoKoFQq\nOa+UlBRJPL755hswDANnZ2e+XXwAAElJSSAio00zQj0M8fTpU73uYe1Dd8GCBXyLsshj3LhxBo+P\nqVOnCinGmAev0HVwcDDbbbH98gEBAWLu1ltEc3Mz3nzzTZ1txKPyZrFHaWkpPDw8dNbb2NgotBhp\nQ1e7k3p4eGDDhg3YsGEDPDw89G5IdLx069+/P0JCQrj/T5w4sWPwWrzBFAqFTujybDvtiGiPkpIS\neHl5GTzR7N+/H83NzUhOToZSqTTYDCHWY+7cudy6zNXWmpub4ebmxi2/ZMkSyTy0NV0BXXwAvLxh\no71RcenSJYs9DLF8+XKj+2h0dDQaGhr4FmVx6HZGP90dO3borMve3h5OTk5wcnLC2LFjkZaWhrS0\nNKxduxZLly6Fvb09tyyPSoLkoQsAe/bsgYODA+dh7dC9fPky3N3dQUQYPHgwBg8ejPz8fDHqBn8b\nee4FGRkZGVtiLI1NnR1qamr0LtGMvRiGQUxMDHJzc5Gbm4vi4mIUFRXB29ubW2b48OHmzg682bVr\nF3fZRp1U0x07dix3Vg4LC0NqaipSU1Ph7++Pzz77jFsuODgY3bp1M9e+y8ujoaGB6xrWr18/k23t\n9fX1XAdvhmFMdukS6gGIr+kCwKJFi8CyLGbMmGGxhyFGjBihdwVCRPD19RWqapHH0aNH4eLiYnJw\nxNtvv43Hjx+L8TB57A4YMMBod0HtMWPos969e6O6ulqoiySsWrWK2z48uqiK9vjPf/6DHj16gGEY\njBo1CqdOnRLajmvOw+wk5gZxcXGh3r1781r27NmzpFQq6U9/+pPO+wqFQmcZKWhra6Pi4uKX7SZE\npFQqiYjI3t5ekvLNcevWLVq6dCl99913xDAMRUdH05dffsmtf9GiRdwEGq2trfT8+XNqamqiM2fO\nUEhIiEXrVigU5OfnRw8ePKDk5GRycXHRW6a+vp6IiObMmUOHDh0iIiJ/f386evQo+fj4WLR+Q9TU\n1NB///tfioyM5P0db29vIiK6ffu25D6NjY307NkzvQlKXrx4oTfBirUZO3Ys5eTk0OXLl7n3qqur\nKTU1lYiIqqqqaM+ePVRZWUknTpyQbL0uLi507Ngxeuutt+jixYvU2tpqcDlD26OyspIaGhroz3/+\ns2Q+fPnLX/7C/Xv8+PF08OBB8vLykqz81tZW+t///kcffPAB3bt3jxwdHWnHjh3Us2dPydbBYSyN\nzZ0djh49ivj4eHTt2tVsTddcbXjy5Mnmzg68+OKLL3RquOPGjcO4ceP4fr0jgjyePXuGuLg4rqYS\nFhZmck6B/Px8blkzN5t4eyxatMhk22xBQYHOTdBevXrhm2++MbVuUR7tey+Eh4fzLR8AuOGpQ4YM\nsdijI5mZmUb30cWLFwvytMTDbMEdapoiPHi5HD58GJmZmUhKSkJAQAACAgKg0Wig0WigVqt1at+O\njo7Yu3evGBdJyM3N5Vy8vb1RWloqmUdraysOHDigs81Hjx7Ne06Q2tpaFBYWIjY2FjExMeY8LJ9l\n7OOPP7YodGfMmIGHDx+aE+XFvHnzdEK3uLiYb/cfQwjyaD+018XFxWT/x+bmZkRERHA3TczcFeXt\nYerSr6CgABEREYiIiOB2rKSkJFPrFe1RVFTEdbNhGAbDhw9Hbm6uycLr6+vx8ccfc4ETFhZmsUdH\nunfvbnAfXLlyJZ9h4ZJ5GKOxsRFxcXF6TQ5mhkiLPnZNkZCQIKaPrFmPBQsWYOLEiZg4cSKKi4vN\nDsx48eIFKioqdJrsEhMTLfZoT3p6usHmFCODhDhKSkqQmZmpN4TejIe45oX2LFiwgCIjI+mDDz6g\no0eP6nxWV1dHr732GqnVanry5Ak5OTlxly3Ozs6Um5tLrq6upFarLdWgmzdvUmZmJvf/mJgYCg4O\ntrhcIeDXZo3Vq1fTG2+8YXCZJ0+eUHJyMuXl5RERUUJCAnXt2lWS9Ru77GtqaqJ//vOfdPHiRe69\noKAgev/99yVZb0dCQkJo165dNGHCBCIiOn36NF26dIlcXFzo7bffpj59+ugsf+vWLdq0aRPdv3+f\nGIbhXlJTXV1tcO7T1atXS74uQwCg8vJy+vbbb+mtt96i7t27k0qlonfffZeIiG7cuEGnT5/W+96U\nKVNs4mcKqfbRM2fOUElJCRERHT58mKZOnUp+fn5Gl29paaFNmzbpvLdmzRpJXIiISkpKaOPGjQY/\n+/LLLyk8PFznvdLSUqqoqKCcnBz69ttv6fHjx9xnPXv25H5LkxhLY3NnBz5ox5QDwNq1a1FfX8/n\na6I8cnJydGq5Jm7E8EV0TddU5/6ZM2dyy2k0Gj5TYVr0u9TW1kKhUOicibt06WLu8sxij/r6ep3m\nFkMjjIzdRHJzc8Phw4cl8dCi7bttqEYjEsEe2rkX2je9JCQkcHPHdtwuHh4efGbts0lNd9CgQZJ0\n1erXr5/BfcHcfqF9mZrTRIiHFnd3d50unUuWLOHGGLi6uiIkJETn1b6bpfY1ZswYpKam8h1zYN3Q\nFYkoj6ioKJ3QPXfunE09Xrx4gdTUVLAsi1mzZqGurg7Ay76nDx8+xMOHDxEeHg6lUsn1b7b2yKeT\nJ0/qBK5SqYRSqcT27dv5FmGRR1NTE2bPng1vb28oFAreofvee+9J6gGAm02t43BfnkN+JfFoa2sz\n2FvA0Htz5szhe2K0SeiyLIuvv/5ajIsO2dnZXID5+fnxDl2VSoVt27bh9u3bfPR5bw/t9p88eTIq\nKysBvOyn6+rqavK3cnV1RUpKCn7++WdTbb9/nNB1cXHp2E5sE4+qqip4e3uDYV4OA46JiYG3t7fe\nzmSr+WNTUlJ0Ajc2NpbP7FmSewDAwoULeR1c6enp5gYoSBK62uG+tmrzB/Rruh23gZOTEyIjI7Fu\n3Tq0trZa4mGV0JV6cMStW7fw6aefci+NRmNwvxg3bhyOHDkiRF9Q6Hbr1k1vmsaCggKMGjWKO37m\nz5+PhIQE5Obm4v79++a6zpny+P2E7qxZs7jQFRhqknqUl5djxIgRBocBr1y5EleuXOE70Y1FHgC4\nEwDDMFi1apWQdUrqITGShO7IkSNt7tHW1ob58+frhW5kZCQiIyNx+vRpqTysEroffvihGJfOgLdH\nY2MjmpqaDH7W2tqKxsZGNDY2oq2tTSqP30/oPn78GG+++SaIyNSkLVb3sAKiPGbOnMnNqxAWFsZ3\nxirJPayAKI+CggLEx8dzoZuVldUpHsXFxdyTRViWxYgRI1BXV8c1R0nkYfFvU1NTg/DwcCxfvhzL\nly/HZ599Zm72N2MuncGr7CE/DdgEv2mP/v37048//kgKhYIePHhAKpWqUzysgOyhy+/iacAS8yp7\nyHMv/N7JysqSInBlZGQkQq7pGkf20EX20OVV9iB6dVxkj45vmgldGRkZGRkJkZsXZGRkZGyIHLoy\nMjIyNkQOXRkZGRkbIoeujIyMjA2RQ1dGRkbGhsihKyMjI2ND/h+nUTvin3bx8AAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": [
        "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=4, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(4),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 26, RF = 3\n",
        "\n",
        "        # CONVOLUTION BLOCK 1\n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=4, out_channels=4, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(4),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 24, RF = 5\n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=4, out_channels=8, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 22, RF = 7\n",
        "\n",
        "        # TRANSITION BLOCK 1\n",
        "        self.pool1 = nn.MaxPool2d(2, 2) # output_size = 11, RF = 8\n",
        "        self.convblock4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 11, RF = 8\n",
        "\n",
        "        # CONVOLUTION BLOCK 2\n",
        "        self.convblock5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 9, RF = 12\n",
        "        self.convblock6 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "        ) # output_size = 7, RF = 16\n",
        "\n",
        "        # OUTPUT BLOCK\n",
        "        self.convblock7 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.BatchNorm2d(10),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_value)\n",
        "\n",
        "        ) # output_size = 7, RF = 20\n",
        "        self.gap = nn.Sequential(\n",
        "            nn.AvgPool2d(kernel_size=7),\n",
        "           \n",
        "        ) # output_size = 1, RF = 32\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.convblock7(x)\n",
        "        x = self.gap(x)\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": "2789f529-1bba-4ccc-fb72-481de0c16b1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 814
        }
      },
      "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": 11,
      "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, 4, 26, 26]              36\n",
            "       BatchNorm2d-2            [-1, 4, 26, 26]               8\n",
            "              ReLU-3            [-1, 4, 26, 26]               0\n",
            "           Dropout-4            [-1, 4, 26, 26]               0\n",
            "            Conv2d-5            [-1, 4, 24, 24]             144\n",
            "       BatchNorm2d-6            [-1, 4, 24, 24]               8\n",
            "              ReLU-7            [-1, 4, 24, 24]               0\n",
            "           Dropout-8            [-1, 4, 24, 24]               0\n",
            "            Conv2d-9            [-1, 8, 22, 22]             288\n",
            "      BatchNorm2d-10            [-1, 8, 22, 22]              16\n",
            "             ReLU-11            [-1, 8, 22, 22]               0\n",
            "          Dropout-12            [-1, 8, 22, 22]               0\n",
            "        MaxPool2d-13            [-1, 8, 11, 11]               0\n",
            "           Conv2d-14            [-1, 8, 11, 11]              64\n",
            "      BatchNorm2d-15            [-1, 8, 11, 11]              16\n",
            "             ReLU-16            [-1, 8, 11, 11]               0\n",
            "          Dropout-17            [-1, 8, 11, 11]               0\n",
            "           Conv2d-18             [-1, 16, 9, 9]           1,152\n",
            "      BatchNorm2d-19             [-1, 16, 9, 9]              32\n",
            "             ReLU-20             [-1, 16, 9, 9]               0\n",
            "          Dropout-21             [-1, 16, 9, 9]               0\n",
            "           Conv2d-22             [-1, 16, 7, 7]           2,304\n",
            "      BatchNorm2d-23             [-1, 16, 7, 7]              32\n",
            "             ReLU-24             [-1, 16, 7, 7]               0\n",
            "          Dropout-25             [-1, 16, 7, 7]               0\n",
            "           Conv2d-26             [-1, 10, 7, 7]             160\n",
            "      BatchNorm2d-27             [-1, 10, 7, 7]              20\n",
            "             ReLU-28             [-1, 10, 7, 7]               0\n",
            "          Dropout-29             [-1, 10, 7, 7]               0\n",
            "        AvgPool2d-30             [-1, 10, 1, 1]               0\n",
            "================================================================\n",
            "Total params: 4,280\n",
            "Trainable params: 4,280\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.39\n",
            "Params size (MB): 0.02\n",
            "Estimated Total Size (MB): 0.41\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": [
        "from tqdm import tqdm\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)\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 backpropragation because PyTorch accumulates the gradients on subsequent backward passes. \n",
        "    # Because of this, when you start your training loop, ideally you should 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",
        "    # Update pbar-tqdm\n",
        "    \n",
        "    pred = y_pred.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "    correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "    processed += len(data)\n",
        "\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",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
        "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\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": "markdown",
      "metadata": {
        "id": "drokW8wWODKq",
        "colab_type": "text"
      },
      "source": [
        "# Let's Train and test our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMCFxeAKOB53",
        "colab_type": "code",
        "outputId": "7aff6aaa-6a6b-4bda-fb2a-11da5401218e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model =  Net().to(device)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "EPOCHS = 15\n",
        "for epoch in range(EPOCHS):\n",
        "    print(\"EPOCH:\", epoch)\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "EPOCH: 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.49749863147735596 Batch_id=468 Accuracy=71.53: 100%|██████████| 469/469 [00:12<00:00, 38.70it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.4993, Accuracy: 9017/10000 (90.17%)\n",
            "\n",
            "EPOCH: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.2874607741832733 Batch_id=468 Accuracy=92.18: 100%|██████████| 469/469 [00:12<00:00, 55.50it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.3386, Accuracy: 9192/10000 (91.92%)\n",
            "\n",
            "EPOCH: 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.1625577062368393 Batch_id=468 Accuracy=94.14: 100%|██████████| 469/469 [00:11<00:00, 39.26it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.2187, Accuracy: 9476/10000 (94.76%)\n",
            "\n",
            "EPOCH: 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.19028884172439575 Batch_id=468 Accuracy=95.11: 100%|██████████| 469/469 [00:11<00:00, 39.23it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1547, Accuracy: 9606/10000 (96.06%)\n",
            "\n",
            "EPOCH: 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.16679827868938446 Batch_id=468 Accuracy=95.52: 100%|██████████| 469/469 [00:11<00:00, 39.55it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1415, Accuracy: 9634/10000 (96.34%)\n",
            "\n",
            "EPOCH: 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.22129635512828827 Batch_id=468 Accuracy=95.84: 100%|██████████| 469/469 [00:12<00:00, 38.49it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1198, Accuracy: 9682/10000 (96.82%)\n",
            "\n",
            "EPOCH: 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.21984322369098663 Batch_id=468 Accuracy=96.20: 100%|██████████| 469/469 [00:12<00:00, 38.89it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1180, Accuracy: 9675/10000 (96.75%)\n",
            "\n",
            "EPOCH: 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.1580056995153427 Batch_id=468 Accuracy=96.42: 100%|██████████| 469/469 [00:11<00:00, 39.85it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1046, Accuracy: 9721/10000 (97.21%)\n",
            "\n",
            "EPOCH: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.09653440117835999 Batch_id=468 Accuracy=96.54: 100%|██████████| 469/469 [00:11<00:00, 39.36it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1178, Accuracy: 9683/10000 (96.83%)\n",
            "\n",
            "EPOCH: 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.10355141758918762 Batch_id=468 Accuracy=96.76: 100%|██████████| 469/469 [00:11<00:00, 39.54it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0813, Accuracy: 9785/10000 (97.85%)\n",
            "\n",
            "EPOCH: 10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.08339137583971024 Batch_id=468 Accuracy=96.89: 100%|██████████| 469/469 [00:12<00:00, 38.35it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0854, Accuracy: 9766/10000 (97.66%)\n",
            "\n",
            "EPOCH: 11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.13294221460819244 Batch_id=468 Accuracy=96.92: 100%|██████████| 469/469 [00:11<00:00, 39.98it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0833, Accuracy: 9780/10000 (97.80%)\n",
            "\n",
            "EPOCH: 12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.11903048306703568 Batch_id=468 Accuracy=96.98: 100%|██████████| 469/469 [00:11<00:00, 40.08it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0751, Accuracy: 9801/10000 (98.01%)\n",
            "\n",
            "EPOCH: 13\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.08561208099126816 Batch_id=468 Accuracy=97.13: 100%|██████████| 469/469 [00:11<00:00, 39.99it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0728, Accuracy: 9787/10000 (97.87%)\n",
            "\n",
            "EPOCH: 14\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Loss=0.04383723437786102 Batch_id=468 Accuracy=97.21: 100%|██████████| 469/469 [00:11<00:00, 39.64it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0655, Accuracy: 9816/10000 (98.16%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bThPVxrz92wW",
        "colab_type": "code",
        "outputId": "70b0512e-a349-48f2-bfee-22ff0dbb162c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Feb 22 15:54:29 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.48.02    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   52C    P0    24W /  75W |    595MiB /  7611MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87RaqGSEOWDe",
        "colab_type": "code",
        "outputId": "da65b7a5-0bc9-403a-b8dd-5ab00326ab80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "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)\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\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test Accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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79mDSoJ68sGyPJiQRkbCm0CYiIiKd1szJg9iRV8zyXQVelyIi4hmFNhEREem0\nLpvQj4QYTUgiIuFNoU1EREQ6rW7RkXz+tAG8tS6HXfnFXpcjIuIJhTYRERHp1P7f+cOJi/Jxx6yV\nlFZUel2OiEiHU2gLUl5Z5XUJIiIiUkf/nnH8+osTWb/vCD9/a5PX5YiIdLiwDW0/vDT9hH0X/HpB\nxxciIiIiJ3XR2FRuPm8of1u6m7fX5XhdjohIhwrb0Pa1c4eesC+74JgHlYiIiEhTfP/idCYO7Mn3\n/rWWvYdKvC5HRKTDhG1oi/LV/9G1DoyIiEjnFB0ZwZMzTwPgjhdWUlahYQ0iEh7CNrQ15If/Xu91\nCSIiItKAgb278eg1E1iTXcgv39nsdTkiIh1Coa0OrQMjIiItYWYXm9kWM8sys3vrOX6PmW00s7Vm\nNtfMBntRZyi4eHw/bjp7MH9espP3Nh7wuhwRkXan0FaP4tIKr0sQEZEuxMx8wFPAJcBYYKaZja1z\n2iogwzk3AXgF+FXHVhlafnjZGMYP6MF3Xl5DdoHGt4lIaFNoq8cdL6z0ugQREelaJgNZzrkdzrky\nYDZwZfAJzrn5zrnqdPERkNbBNYaUmEgfT86cRGWV465Zq7Rsj4iENIW2eszfkqfWNhERaY4BwN6g\n19mBfQ25GXi7oYNmdquZZZpZZl5eXhuVGHqGJHXnF184hZV7DvPr/27xuhwRkXYT1qEtISaywWNX\nPf1BB1YiIiLhwsy+BGQAjzZ0jnPuWedchnMuIzk5ueOK64Iun9ifG84cxB8X7mD+5lyvyxERaRdh\nHdreu2caP7m87pADv60HjnZwNSIi0oXtAwYGvU4L7KvFzC4EfgRc4Zwr7aDaQt4DnxtLet8E7nlp\nNTmFWnNVREJPWIe2vomxZAzp7XUZIiLS9S0HRprZUDOLBq4HXg8+wcxOA/6IP7CpSagNxUb5eOrG\nSZRWVHHXrFVUaHybiISYsA5tAGYNH/sgK7/jChERkS7LOVcB3AG8C2wCXnLObTCzB83sisBpjwLx\nwMtmttrMXm/gctICw5Pjefiq8SzfVcDv3t/mdTkiIm0q7ENbet8eXJtR/wReNz73cQdXIyIiXZVz\nbo5zbpRzbrhz7uHAvgecc68Hti90zqU6504N/FzR+BWlua46LY1rM9J4akEWi7dpAhcRCR1hH9p8\nEcavrpnY4PHCY+UdWI2IiIi0xk+vGM/IlHi+OXs1uUeOe12OiEibCPvQVu3zp/avd//En/63gysR\nERGRloqL9vHUDZMoLqvg7tmrqaxyXpckItJqCm0B545I8roEERERaQMjUxN48MrxLN1xkCfmaXyb\niHR9Cm0B/XvGeV2CiIiItGfg7KAAACAASURBVJEvnp7GF04bwO/nbuPD7ZpYTES6NoW2gMZmkRQR\nEZGuxcx46PPjGZbUnbtnryavSMviiUjXpdAWkBwf0+jxhVvzyC4o6aBqREREpLW6x0Ty5A2TOHKs\nnHteWk2VxreJSBel0BYwMjWhwWM/eHUdNz2/jM/+dlEHViQiIiKtNaZfD358+TgWb8vn6QVZXpcj\nItIiCm1NMGvZHgCKyyrZkXfU42pERESkOWZOHsjlE/vz2HtbWbbzkNfliIg0m0JbM03/zUKvSxAR\nEZFmMDN+ftV4BvXuxl2zVnGouMzrkkREmkWhLciw5O5elyAiIiLtICE2iidvmMSh4jKNbxORLkeh\nLcifbzqjSefN35zbzpWIiIhIWxs/IJH7PjeGBVvyeHbxDq/LERFpMoW2IEOTmtbStiRL672IiIh0\nRV8+azCXntKXR9/dwordGt8mIl2DQlsdpw7sedJzlmzLZ+n2g8zfohY3ERGRrsTMeOTqCfTvGcud\nL6zicInGt4lI56fQVsdrt5970nP2Fx5j5p8+4mt/Wc41z3xIeWVVzbFd+cVUqp+8iIhIp9UjNoon\nZ04i72gp33l5Lc7puS0inZtCWwsUHa+o2c7cXcAnhccB2HuohPN/vYBH393iVWkiIiLSBBMH9uTe\nS8bw/qYD/HnJTq/LERFpVLNDm5ldbGZbzCzLzO6t5/hXzSzPzFYHfm5pm1I7zhlDerXofblFpQB8\nvPNgW5YjIiIi7eB/zh3CRWNT+eU7m1m997DX5YiINKhZoc3MfMBTwCXAWGCmmY2t59QXnXOnBn6e\na4M6O9TLt53TrPN/+c5miksr2HuopJ0qEhERkbZmZjx6zQRSEmK544WVFB4r97okEZF6RTbz/MlA\nlnNuB4CZzQauBDa2dWFdyZtrc9h/+Bgr9+hbOhERka6kZ7donrjhNK79w1Ku/cNSLj2lH1NHJTEh\nrSe+CPO6PBERoPndIwcAe4NeZwf21XW1ma01s1fMbGB9FzKzW80s08wy8/LymllG51M3sO09VMKQ\ne9/infWfeFSRiIiINMWkQb147LpTiYmK4Hdzt3LV0x8y6aH3uP2fK3lx+R72Hz7mdYkiEuaa29LW\nFG8As5xzpWb2v8DfgOl1T3LOPQs8C5CRkdHppm3a/NDFpN//Tovfv35fIQD/XpXNxeP7tlVZIiIi\n0g6umNifKyb25+DRUpZk5bN4Wz6Lt+Xx1rocAEakxDN1ZDJTRiVx1tA+xEX7PK5YRMJJc0PbPiC4\n5SwtsK+Gcy54Fo7ngF+1rDRvxUb5WPqD6Zz9i3nNfu+qPYd5bfW+k58oIiIinUqf+BiuPHUAV546\nAOccWw8cZdHWPBZty+MfH+/m+Q92Eh0ZweQhvZkyMompo5JJ75uAmbpSikj7aW5oWw6MNLOh+MPa\n9cANwSeYWT/nXE7g5RXAplZX6ZF+iXEtfu+7Gw4AUFpRdcKxisoqio5X0Kt7dIuvLyIiIu3LzBjd\nN4HRfRP4+tRhHC+v5OOdh1gcCHG/eHszv3h7M8kJMUwZmcS0UcmcOyKJpPgYr0sXkRDTrNDmnKsw\nszuAdwEf8LxzboOZPQhkOudeB+4ysyuACuAQ8NU2rrlDZT18CSN+9HaL379gy4nj9e57bT2zl++l\nX2IsS38wozXliYiISAeJjfIxbVQy00YlA5BTeIzF2/JZtDWPeZtzeXWlv5fN+AE9/F0pRyZz+uBe\nREdqWVwRaZ1mj2lzzs0B5tTZ90DQ9g+AH7S+tM4h0tf6v2izco8yIiW+5vUba/YDkBNYlFtERES6\nnn6JcVybMZBrMwZSWeVYv6+QxdvyWLQ1n2cX7eDpBdvpFu3j7GF9mDoqmamjkhma1N3rskWkC2qP\niUikjgsfW8iuRy5j64EiXl+93+tyREREpI35IoyJA3sycWBP7pg+kqLj5SzdfpBFgRA3d3MuAJMG\n9eTWqcO4aGxfLSkgIk2m0NYEab3iyC5o3XS/lVWOz/x2URtVJCIiIp1ZQmwUnxnXl8+M888gvftg\nMe9vyuVvH+7itn+sZGhSd24+byjXnJ5GbJRmohSRxqmTdRPc/7mxrb5GaUVlG1QiIiIiXdHgPv6Q\nNv875/P0jZPoERfFfa+t55xH5vG797dyqLjM6xJFpBNTaGuCz47ry65HLmvVNcY+8G6zzn9jzX6O\nlla06p4iIiLSufgijEtP6cdr3ziHl/73bCYN6snv3t/GOY/M5b7X1rErv9jrEkWkE1Joa4aEmLbv\nTVpV9em64it2H6KyyrH1QBF3zlrF919Ze8L5b6zZzysrstu8DhEREek4Zsbkob157qYzeP+eqVw5\ncQAvLc/mgt8s4Lb/W8HKPQVelyginYhCm8e+9tflAKzaU8DVzyzld+9v5eMd/vXJ31qXw+trak9c\ncuesVXzn5TUdXqeIiIi0jxEpCfzymgksufcCvnH+cJbuOMgXnv6QL/7hQ/674ZNaX/CKSHhSaGuG\nxG5RbX7NhVvzeDlzL1c9/SEAT8zL4v7/bKg5ftesVVzw6wUnvG/Bllx+//42cov8ywYUHS9v89pE\nRESk46QkxPLdz6bz4b3T+fHlY8kpPM6t/7eCC3+7kFnL9nC8XOPjRcKVOef9tzcZGRkuMzPT6zJO\nav2+QhZuzeP2C0ZQUVnVqkW3m6t6TN2Qe9+qtX/KyCSuOT2Nu2ev5q27zmNc/8QOq0lEpCXMbIVz\nLsPrOrqKrvKMlLZXUVnF2+s/4dlFO1i3r5Ck+GhuOnsIXzprML26R3tdnoi0scaej2ppa4bxAxK5\n/YIRQNssut0cDYXrkrJKFm7NA2Dj/iMnHM8/WlqrW8XeQyXc/s+V+rZORESkk4v0RXD5xP68fse5\nvPD1Mxk/IJHfvLeVcx6Zx4//s569h0q8LlFEOohCWyv891tTO+xe7244QEED0wEb9S/OmV1QQsbP\n3ueZhduZvyWXSQ+9x/f/tZa31uWwZFt+e5YrIiIibcTMOGd4En/92mTe/eZULpvQjxeW7WHao/O5\n/YWVrNl72OsSRaSdKbS1wqjUBH56xbgOuddHOw5y0W8XnrC/uLSCf62sfzbJ/Yf9490WbMnla39Z\nzqHiMnYf9H8rd8vfM/nJ6xtqnf/+xgMUa5kBERGRTmt03wR+/cWJLPn+dG6dOpxFW/O48qkPuO6P\nS5m76YAmLREJUW0/h32Y+crZgymvrOJnb21q1/v89cNd9e7f/ElRzXZxaQXff2Ut3/7MKOJj6/+j\n3Xf4WK1rfmHSACak9SQr9yi3/D2Tyyf254mZpzW7vheX72Fg726cMzypWe97dWU24wckMio1odn3\nFBERCVepPWK595J07pg+gtnL9vD8kp3c/LdMBvaOo1+POGKjfcRGRhAb5SMuykdslH87ps7r6u2Y\nKB+xkT7iogPHqrcjfcRERRATGYFZ/T17RKT9KbS1kpkxtl8Pr8sA4CdvbATgxcy9ALzw9TMBaGyu\nmSue/ID/u3kyCbH+mTHfWLOf/zl3CKcN6tWse3//X+sAmr0I+T0vrWnR+8A/Xm/LJ0WcO6J5QVFE\nRCRUxMdEcsuUYdx0zhDmrMvhjTX7KTpeQeGxcnLLKzlWXsnx8kqOl1dxrLySsoqqFt3HDGIj/YEu\ntUcsU0clM21UMhlDehET6WvjT9U+SisqWbGrgIXb8ticU8TN5w1l6qhkr8sSaRKFtnYQ7YugrLJl\nfym2pf/3j5UAZO5ufIHOL/95GcFfnr25NodlOw/xUuZe5n77fADeXpdD7+7RREQYpw/qRUTEp2+Y\n+qv5DV57zd7DHCop44LRKc2uf3veUYYnxzd4/Lo/LmV7XnGLAl9nlZVbRLTPx6A+3bwuRUREupAo\nXwRXnjqAK08d0Oh5VVWO0ooqjtcJdMcrKjleVun/tTz4uH+7NOj19ryj/OWDnTy7aAfdon2cM7wP\n00Ylc/7oFAb27jzPL+ccuw6WsGhrHou25rF0x0FKyiqJjDB6dY/mpr8s467pI7lrxkh8EWpFlM5N\noa0t1Pn//GdXjed7r6z1ppYghceavnZbcGvcn5fsPOH4//vnyprt2y8Yzq1Th5MY52+d2xM0e9Vz\ni3cQHxPJ9ZMHAXDlUx8AzW9Je2ttDre/sJJnv3w6nxnXt95ztucVN+uaXcGFjy0CWtbyKCIicjIR\nEUZctL/rY/P61NRWXFrB0u0HWbg1jwVbc3l/Uy6wgWFJ3Zk6KpnzRydz1rA+xEZ1bCvc0dIKPszK\nZ9G2PBZtza/5N8qg3t24elIaU0clc/bwPvjM+NFr6/j93G2s3FPA7647lT7xMR1aq0hzKLS1gT7d\nP/2ffMNPP8uynYc8rKb9PTV/O39avJMtD118QjCsHtt376vrWPTdC2r2r9hdQFlFFQ++uZHXbj/n\npF0pNuYUArDlk6J6Q9uBI8drtp1zHdbP/tWV2XSLjuTi8fUHSRERkXDQPSaSC8emcuHYVJxz7Mwv\nZuHWPBZuzWPWsj389cNdxERGcOaw6la4ZIYldW/z53VVlWNjzhEWBlrTVuwuoKLK1bQA3jJlKFNH\nJjMkqfsJ7/3NFycyeUhvHnh9A597YglP3jCJ0we3JsqKtB+FtjYwum8Cr9x2NhPSehId+emEnGcP\n68N5I5N49N0tHlbXOnUX865WVlHFxb9bzJYDRfUeB8jc/Wl4vfqZDxme3J3tecXsOVjCyDoTj7yz\n/hO2HSjizhkja+2ftyX3hH3g7xpZrcqBz+DaPy4lNsrH3/9ncq1znXM8t3gnV5zan9QesRwqLiMu\nyv8t4/HyStLvf4fvXTyab5w/ouHfiICmjMGrqKzihuc+5pszRnJOmI+3q6pyvLF2P5+b0D+sup6s\n2F3A1c98yDvfnEJ6384x5lVEpL2YGcOS4xmWHM/Xzh3K8fJKPt55iIVb/K1wD725kYfehLRecTXd\nKM8e3of4mJb9MzT/aCmLAy1pi7flkX/UvyTSmH49uGXKMKaOSuL0wScfa2dmXD95EOMHJPKNf67k\nuj8u5YeXjuFr5w7RpCvS6Si0tZGMIb1rtmMCwS2lRwy3XzCCbtE+xvTrwfXPfuRVee2iscAGnwac\natV/AdY3L8pt/1gBwMSBPfnrh7uYtzkXgFV7DvPvVdl868U1rLr/Inp1jwYgu+DTWTD9C49bgy2c\nuw+W8PCcTTw8ZxMv3noW1z37ESNS4nn/nmkcCbQU/uWDXXzj/BHM35xLfGwkZwT9eTZX/tEylu08\nxDdfXM2yH13Y4HklZRWUlFWSdJLuGGuzDzMsOZ4jx8rpEx/t2YDv6gXem/Mge2VFNt/711ryikq5\nZcqw9iqtRfYeKmHp9oNce8bANr/2nHU5ACzemq/QJiJhJzbKx7TARCUPMJa9h0r83Si35PHvVfv4\n58d7iPIZGYN7c/7oZKaNTmZ0akKDz5fyyipW7C7wj03blsf6fUcA6N09mvNGJDFtVDJTRiWRkhDb\nonrHD0jkjTvP4zsvr+HBNzeSufsQv7x6Qs0kbSKdgUJbOzh7eB/u/9xYvpiRBsDXzh0KQNbDl/Dt\nl9fwn9X7vSzPM1m5RwH/+Lnyyipm1hNiv/L8shP2fetFf/g77aH36m3hamhyzEPFZWQXlNAj6C/d\n6wL3rK6lWl5RKT/89zpe+HgPcGJL2rGyylqtqNXXOHK8nF+9s5m//8+ZNcernzknWynn6meWsinn\nCPdcNIqE2Mia/06CHS+v5IonP+DsYX1YuuMgF4/ryx++fPpJrtw+/vrhLn76xsZa4flk8o6WAv5u\ns189ZwiRvqYvDbnlkyKGJ3dv8ns+2nGQO2etYv53zm/St7fX/XEp+wuP8/wHOzlUXNZowA43f/1g\nJ+9s+ITZt57tdSkiEgIG9u7Gl84azJfOGkxZRRWZuw7VdKX8xdub+cXbm+nbI9Yf9EYnc+6IJApL\nylm4LTCByPaDHC2twBdhTBrUk29fNIppo5MZ3z+x1sRorZEYF8WzXz6dZxft4FfvbmFzzgc8/aVJ\n+uJNOg2FtnZgZtx83on/AI/0RZDWK86DijqX/YXH+PrfM2tNYNJUR0sryC4ooSpo5pS6Sxr86p3N\nnDsiiRuf+xiAxd+7gKaoDmx1OecY88A7zJz8aYtMcMAD2HWwmFGpCWw9UMTrgVCeV1Ta6P025fi/\nKXzsva0AJ4S2XfnFNcFv5R7/DKD/3fhJvdcqq6hi1H1vkxAbSUlZJdt/fmnNseyCElISYmtC5dML\nsrhoTCojA/Wm9YqjW7T/r4LDJWUkxkXV+23ni8v9S0nkFB5vcmhzQX84r67ax7UZTWvV2pF3lM/+\nbhG3Th3GDy8dU+vY0dIK5m46cMIMaY++u4W8olI25xyp1fLdkOpAGbzWYVvpSp1qjhwvZ3d+Caek\nJdbsq14+RJrHzC4Gfg/4gOecc4/UOT4V+B0wAbjeOfdKx1cp4q3oyAjOGZHEOSOS+MGlY8gpPMai\nQICbsz6HFzP3Yvbps31Azzgun9ifaaOSOWdEn1pfxLY1M+N/pw3n1IE9uWPWKj7/1Ac8/PlTuPr0\ntHa7p0hTKbR1sMF9ThwIG25++97WFgU2gD8s2M6T87Nq7XtyfhZ3XPDpeLSnF2zn6QXba15/tONg\ns+8z4zcLeO9b04iIsJoHx6xle2uONxTwPvPbRfXuLy6tYNyP3+WpGyaxYX/hSWf23HOwhPN/vaDm\ntavza10/n+OfAKboeMUJ9z3vl/MZP6AH//7GuTgHv3pnC88s2M6cu6bU1LvivgtZkpXP3bNXc99l\nYzhlQCIHi8uYMjKJ+JjIWiHOnbQN8VPBgbo5awPtDvz3sbLOchUb9x/h63/PZN/hY4xIiWdc/8T6\n3t6g2cv2MK5/Yq2AUp+s3CJW7jnM8OTujO2XSFx007ukFpdWkHuSwH4yx8sr+f3cbdw9Y2S7z7z2\ntb8sZ8XuAnb8/NI2+8Y6HJmZD3gKuAjIBpab2evOueAEvAf4KvCdjq9QpHPqlxjHdWcM4rozBlFR\nWcWqvYdZsi2fxLgoprXT5CUnc+awPrx113ncNWsV3355DZm7D/Hjy8d1+EyYIsEU2jrYF09PY/Xe\nww3+oz8crM0ubPF76wY2gMfnbuPVldkNvue7jS2/0MBzYHteMW+ty+HOWatqxig2puh4BQ+9eWLr\nxMNvbeTuC0exK7+4ptaTjQUEeKXO56lusXIONuwvZFz/RH7w6jpmLduDL8KorKodpHKLjnP5E0tq\nlmVYv+8IP5+ziXsvSQf8oWBK0Pp6p//s/ZrteZtza2YBBbjxzEH8Z/X+WmsPvrP+E277xwpSe8Tw\n329Nq7lPXU2Pd7V9JzAecm9B7XB/6eOLa7aPlVWycGseZw7tXetB2tA9V+0p4N5X/YvAv3nneY3e\nv3rpBYDLJvTjqRsmNbn2y59Ywo78ky9HMWddDjPGpNQ7RvH5D3byzILtdI/2ccf0EyfiaUvVrbgt\n/bOSGpOBLOfcDgAzmw1cCdT8xeCc2xU45v1CniKdUKQvgjOG9G7VuPK2kpIQyz9uPpPH3tvK0wu2\nsza7kGduPF3rqIpnmj7ARNqEmXHm0E//MhqR4l88+v7PjfWqpJAQPDFJU32Qlc/kh+c2ePzOWasA\nKG1CC9HVz3xY7/p2f1q8k3v/tZbPPbHkpNeYtezTIP/43G21jgW3WL26ch8rdh+qOb9uYAP/BCAH\njpSy9cCnY/dW7z1cc53yyob/iV63u+k/P97D0dKKmpYyw/jnx7sBOHCklIVb8+q9zordBSzZlt/g\nfRpzsLis5voNWZNdyE3PL+Mnr28I1FXbE3O38cyC7by/8QAANwWNl8zc1fRlOTbsa9qXDMWlFRwv\nr2xSYPtwez7f+OdKfvn2iTPL5h8trZnIpKHWyazcIoqON30dxsZU/3lX1f2DB37w6jrKK5UvmmgA\nsDfodXZgX4uY2a1mlmlmmXl59f8/JiLtK9IXwfcuTufPN2Ww91AJlz2xmPcCzxSRjqbQ5oGIQDP/\n1ZPSeP+eaex65LJ6x8BJ+5q7KbdD7vPm2pya7cZa2X4QaAWqT0VQMCs6Xs7Vzyxt8Fzwd4GsTz3/\nLj/B0iZ0J63bvbW0ohLnHHsPlZBT6A/QVz/zIcuaEI6+OXsVQ+59i9KKSsA/q2awwyVl/PDf63gm\nqMsrUNOyuSO/mGNllTWho/oz/ua9rfzync3c8vdMSsoqOBLUdXTWsr0n/F4s2OL/7+Hg0dpBsSkt\nUGuzDzPux+8y7dH5tfYH9+g5VlbJ0VJ/DVsC4+j2H679ZUNW7lEyfvZ+zcxowfcO/n258LFF3PCn\nj5tQmf/P5pG3N1NcWnHCsR15n4b6+kLbrGV7mL+5Y/4/kdqcc8865zKccxnJyclelyMS1maMSeWt\nu6YwpE93vv73TH7x9iYq9IWWdDB1j/TAxeP78vUpQ7n9gtrrgp09rA+jUuP529Ldjb7/tEE9WbXn\ncHuWGBae/+DElrGu4KXMhruCNmbVnsPsyD968hNP4vInl9Rq3bsr0CL54JXjeOA//lav1B4nLmNQ\nX/ipqKzitcDELbsPluCLMN4KCrkAhcfKG+1OvGznIcY88E7N69tfWMmEAbXHrNUda7jlQNEJ68Z9\n9S/L+c0XJ/Ltl2svVVFf0D1eXsl3Xl7DQ1eOp1f3aK548gOg/pbBx/67hZQesdz32noAenWLoqDE\n30pWd5jGh9trt0zmFPoXkX9z7X7ueGEVc+6awtj+/pnM1tVpAXxv44GadY+2Hihiw/5CrjotjZcy\ns/nDwu38YeF2Hvr8eL581uCa9+w7HLx0xomfE/xhrqKyintfXcc3zh/OsOT4+k+UfUDwTDtpgX0i\nEgIG9u7Gy7edzUNvbuSPC3ewas9hnpx5Gik9WrbMgEhzKbR5IMoXwY8uO7E75KxbzwKoFdriYyK5\nc/oIfvH25pp9T984ibN/Ma/9C5WQc9njJ++meTL1dccEagIb1B9e7n9tPQ+9sZGyyirev2cqI1IS\napZgALjnpdWs33eEMf1qT698uKR53QDzikqZW6d16GQzeVarG9jAP/tmXZf8fjE784t5c20O1zUy\nI2bw2MBqBUGf5+31tWcDrRskq4NU9bqFG/YX1oS2YNvzjvL1v2eesP+q09IoD+pief9r62uFtloT\nxVRW8c76T7jy1P61rlFZBWMfeJeyyiq2HSjiP3c0Ph4wjC0HRprZUPxh7XrgBm9LEpG2FBvl4+Gr\nTiFjSC9++Op6Ln18CU/MPI2zh/fxujQJAwptndCg3t3Yc6iExd+7gLRecZhZrdDWLzGON+88j5jI\nCC767SLiYyK59JS+RJhRUlbJ62vCcx046fyqJzMJnuijWnWXwOqlEKpd+dQHrb5vfeMSGwqfdVU5\nuPdfa7lzxkgKissYPyCRnUHj1l7M3NvIu0/u36uyOW9EMskJMfjqNL19vPMgj723la2BbrXffWVt\nvRPrFATGANZVUlZxQgunc46Vewo4fXDtgf7fnL2aeZtzTxjDtv/wsZo/tzWtmEQo1DnnKszsDuBd\n/FP+P++c22BmDwKZzrnXzewM4N9AL+ByM/upc26ch2WLSAtcdVoa4/oncts/VnDjcx/xnc+O5rap\nwzUDr7Qrc00Z5NLOMjIyXGbmid8Sh6tjZZWUV1XVWoukvoWfC0vKmfjgf0mIjWTdTz5bc+6Qe9/q\n2IJFwsjDV43nR/9e3y7XvvLU/vxndfO+dPnCaQNYu6/whAXjG/LQ58dzf6CrZrdoHyVllc26X30L\n3DeXma1wzmW0+kJhQs9Ikc7paGkF9/5rLW+uzWFGegqPXXsqid3abx05CX2NPR81EUknFBftO2Hx\nyJ9fdcoJ5/l8/m90BvaqPf3sfZeNOeFcgHFB3aomDerZ2jJFwlJ7BTag2YEN/IuWNzWwATWBDWh2\nYBMRkU/Fx0TyxMzT+OkV41i0LY/LnljM2mzNOSDtQ90ju5juQYv8xsdE8ocvnU7GkF61zrllyjBu\nmTKM+Ztz6d8zjs/+zt8V7fU7zmP+5lzG9u9B/55x/GLOJnrERfHou/XPNBjslAGJJ0x8ICIiIhLO\nzIybzhnChLREbv/nSq55Zik/vmIsN0we1KJFwSurHAUlZRw8WsbBo6UcLP701/y6+46WUVHl6Bbt\nIy7aF/g1km5Rvlr7ukVH+rejqvdF1jnuIy4q8tPtwDl1x1mLt9Q9sgvZsL+Q5PiYZs9UlFN4jJW7\nD3PZhH71Hq/uTrnrkcvq7Vp5z0WjuGvGSHW7FBF1j/SAnpEiXUNBcRnffHE1C7fmcdVpA3j4qvHE\nRfkoKq2oE8KCg5g/fB0s9v96qKSs3tl8fRFGr27RJMVH0yc+mj7dY+jdPZoon38+g2NllZSUVVJS\nXsmxsora+8oqOFZe2egarfWJjoyge7SPQX26Mzo1nlGpCYzum8Co1ARSEmJaFEqlcY09H9XS1oWM\n65948pPq0S8xjssmxDXp3CkjkxiW1J3/d/4ItucdpVu0j4lp/q6UPWIja611teaBzzDxwf8CDQc+\ngGsz0lo8TT3Axz+cwZk/b3gRbBERERGv9eoezV++egZPzs/it+9v5b2NByirqKqZzKmuxLioQACL\nZlhSPGcMiaZPfIw/mHWPoU98dM12YlxUqyc6Ka+sCgpzgWBXXhnYVxEIeMEBsIKjxyvYmV/MvM15\ntf4tlxgXxejUBEb19Ye5UakJjE5NoFf36FbVKA1TaBOeuXES0ZH+4Y3/d/OZNfv7JtZu0fvqOUN4\nfF5Wzeu6g21/eGk6b67NYW1ghrnBfbrxyBcmkDGkV6tCW2qP2EZDoYiIiEhnEBFh3DVjJBmDe/HG\n2v0kxtVuHfMHsRh6dYuu+bdXR4nyRZAYF0FiXMsmSzl4tJStB46y9UARWw4UsfWTIv6zej9FQV/o\nJyfEMKq6VS41gVF9ExiZEk9CrCZoaS2FNuGSU+rvNlnXty4axZ0zRrJqz+GagbZfOmtQzWQGt04d\nzv+cO5QRP3obgLn3HihdSwAAIABJREFUTCPSV/9fSG/ccR4jU+NJv/+dE4794Uunc9s/VjRYx4r7\nLuT0n71f8/qFr5/JhLSefOm5j1m9t/UDgN+/Z2q9U9KLiIiINMU5I5I4Z0SS12W0qT7xMZwdH1Nr\nXTrn3P9n787j46rr/Y+/PjOTPWnTbF3TpnsL3ejG2rJDKSJcQAFBFgW8il70il5EUbzXBVAvyP25\ngYCAsgkiKPsOsrS00JXSfadp06RJm3UyM9/fH+ckTdukWZpkMun7+XjMIzPnnDnn853MzHc+57sc\ntu+ua0ziVm33bo/O30xN/d7JrgZnp3nJ3AA/meufxaiCTFKTgs0dSpqhpE3azMxIChozh+cwc7h3\njaefnLfvrJahYICvnzyKORMGNJuwDc5OY2t5DROHeF09B/RJpXh3beP6fulJzJkwgKyUEHvqIgxq\n0tr31ndOZlNZNbmZKaz47zn832urueGMsY3dBX5y3gSuuG8+pf41q5b/+EzeXr2TPmkhvnDPPL5x\nyihyM5K55R8fA97U7ZcePWyfFrw3bjiJorwMjhmRw/vryvaJfXhexj7X55K2mTo0mw83aTYtERGR\n3sbMGNA3lQF9UzlxTH7j8ljMsWVXjZfM+beVxXt4Z01pY3fRgMGw3AxG5GWQlRoiI8W7pScHyUwJ\nkZ4cIiMlSEZyiPSU/ZaleBOutNQ40BspaZNOd8OZYw9YduyIXGaPyedLJxTtc1Hj9753CsO/9xzg\nfXi/N9e7XMGSW87gFy+u5MJpQxq3HZqbztBc7/IGaclBvjtn3D7HmDC4LwtvPp21JZXU1kfJSAkx\nZ8IAYN/JE95fV8YLy4uZNsybdfPVb5+IASPyMxu3yUje96Nx2wUTOW18f6b95BW+ccoorj91NJff\nN59+GcncdsEk7nlrHb9+dXXj9mlJwcYzTD84ezx1kRifFO/hH/td+Pzha47mC/fM22fZyWPzeX1l\nyQGv4bDcdL552mi+9djixmX3XzWDNz7ZwQPvbTxg+57ib187Xl1bRUREDiOBgDX+bjv9iP6Ny+uj\nMTaWVrFqeyUr/Za5DaXVVO2IUB2OUFkXoba++TGAzUkJBbxkzk/umk36UkLkZiQzMj+TEfkZDOmX\nnpAzYyppk27xyLXHNLvczDh70kDOmTSoMcFqWL5/UtZWI5skX825/XOTmDNhAOMG9Glx+8zUfT8a\nF80YCsCiH55On1RvMPDD1+wt07WzR7C2pJJzJg/iKw8t5Kf/NoEPNuzikfmbuHrWiMbtmiZtt10w\nkeNG5pGdnkR5dT0XThtCQVYK3zxtDDsr6zju1tcAWPCD0/jRM8u57YJJZKaEGpO2eTedSv8+qZw8\ntmCfpO3qE4Yze0w+l983H4DxA/vw2FeOYdIt3qQxS245g+RgoNmuqR3VNElt6r4rvQmQzp44kGeX\nbuu04730rdmccYe6sIqIiCSSpGCAUQVZjCrIYm4Lw3OiMUdVOEJ1XZSqcISqughVddHGpK46HG15\nWTjCntoI23fXUtXk+U1nzkwOBSjKTW9M4ry/3v39r5Pck2jKf5Fm7KoK8+tXV/O1k0eSEgp2eNDu\n/uqjMQJmxJwjyW/Sr62P8j///JjvnDmW7PS9sy69v66UytoIpzU5QwVQUVNPMGBkpuxNLOevL+Pz\nf3iPz0wayP/7wlQAjvjhC1SHoyz78ZnEnGPSLS+RmRJi2Y/PBOCuV1dz77/W89HNp/PmqhKu+tMH\nzP/+qdRHHYs2lfPn9zfy3rpSAKYUZu8zXvCRa46hoqae40bl8tU/L+T2CydzvJ9k3n/VDGaPzt/n\nLNb1j37U4oWjzzyyP8eOyOXU8f2ZdfvrzW6z4daz+fbji3nywy2Nj69+YAGvrNjOq98+kVN/9eZB\nX/eVP5nD2B94SepnJw/imcXNxzJ7TD5vrTqwlbMjRuZnMHN4Lo/M39Su551/1GD+9tHWZte11Arb\n1NyJA3huafEByy+ZObTdsTRHU/53P9WRIiKHpqwqzLqSStaVVLG2pJK1JVWsK6lkY1n1Pj3A8rNS\nGJmf4SVxeRmMLMhkZF4mg/uldUvr3MHqRyVtIr3Ev1bvZOqwbNL9rp0TfvQilXURltxyBgCTbnmJ\nrJQQS/2krS2eXrSV372xln984wTuenU1n5tWSL+MpGZngfrGIx+xsbSKZ75+wgHr1uzYw1V/+oAH\nv3Q0j32wmd+/uRaA7501jq+cOLJxu0vufp+Zw3OYM2EA97y1rjF5aUgUVm/fQ5+0JPr3SSUWc0T9\n5Lel7pd3XXIUg7NTmTYsh9LKOqIxR0GfVF7+eDuLN5fz/173ZkOdXJhNNBbjn9+YxZw73+KT4j2M\n7Z/Fyu17ADh/6mD+9uHeRGrDrWfz6ortfPmB5r+3fn3xFM6dMhjYex3EM4/sT2pSkIqaet5oJvG6\nae44xg7ow4lj8qmtj3LCba+xszK8zzZ/vHw6Vz+4gMmF2SxuZtKd40bm8vA1xxzwejS8frGY4xuP\nfsSzS7xWz6OH5zBvfdkB+zkYJW3dT3WkiEjXCEdibCqrZl2TRG7dTi+xK6+ub9wuORRgeG4GIwsy\nGJGX2fh3RH5Gp86MqaRN5DD0l3kb+f5Ty1jz07OojcSY8KMXGZydxjs3nhLv0NhWUcP1jy7iD5dN\nO+g1XVZs2826kqoWLwzfoKGssDcR+d2lUw86M6pzrnE85Yc3n05OM3F845GP+MfiT3n++lmMH9iH\nHXtqwXHABe6dc/zn44t5yk8yf3/ZtMbuvr98cSXJoQD/cerofZ7z5qoSrrhvPhdNLyQtOchNc8fv\nM/3z9t21rCze09jNdeLgvtx35Qxm/PQVHvryTGaNzufmvy/jofe9rrGDs9N49dsnkpoUpOjGZ0kO\nBnj668ezfXctJ40taNzvEwu3cMNfF/P9ueO5etZwnIMRN3mvw7ybTuXOV1Zx2THDKMxJZ3tFLWt2\nVALw1b98SMBg3c+VtHU31ZEiIt2vrCrM2pLKfRO6kqoDWucKslIYkZ/BhdMK95mLoSOUtIkID723\ngZPGFlCYkx7vULpEJBpjZ2WYvMxk3lhZckC30ubsqgqzYOOufQZJN1VZF+HNlSWtJo0NNuys4uan\nl/GHL05rbPFsiXOOF5dv59TxBY1dZZuzZkclQ/qltTgtsnOO6rA38U6DjaVVZKSEyMtMOWD7WMzx\nz6XbOHviwMauHh9sKCPHH6TdknAkRsy5TpmeWUlb+6iOFBHpORpa59Y26W65rqSSz04exJXHDz+k\nfXdq0mZmc4BfA0Hgj865W/dbnwI8CEwDSoGLnHMbDrZPVUgiIocPJW3tozpSROTwcLD6sV0XNzCz\nIPAb4CzgCOASMztiv82+DOxyzo0C7gBua3/IIiIiIiIiAu1M2oCZwBrn3DrnXBh4FDh3v23OBR7w\n7z8BnGpmiXcxBBERERERkR6gvUnbYGBzk8db/GXNbuOciwAVQO7+OzKza81sgZktKCnpnCm2RURE\nREREepv2Jm2dxjl3t3NuunNuen5+frzCEBERERER6dHam7RtBQqbPB7iL2t2GzMLAX3xJiQRERER\nERGRdmpv0vYBMNrMhptZMnAx8Mx+2zwDXOHfvxB4zfWE6wqIiIiIiIgkoINfSGg/zrmImX0deBFv\nyv/7nHPLzey/gQXOuWeAe4GHzGwNUIaX2ImIiIiIiEgHtCtpA3DOPQc8t9+yHza5Xwt87tBDExER\nERERkbhNRCIiIiIiIiKts54w3MzMSoCNh7ibPGBnJ4QTb72hHCpDz6Ay9Ay9oQzQueUY5pzTtMFt\npDqyUW8oA/SOcvSGMkDvKIfK0HN0RjlarB97RNLWGcxsgXNuerzjOFS9oRwqQ8+gMvQMvaEM0HvK\ncbjqDf+/3lAG6B3l6A1lgN5RDpWh5+jqcqh7pIiIiIiISA+mpE1ERERERKQH601J293xDqCT9IZy\nqAw9g8rQM/SGMkDvKcfhqjf8/3pDGaB3lKM3lAF6RzlUhp6jS8vRa8a0iYiIiIiI9Ea9qaVNRERE\nRESk11HSJiIiIiIi0oP1iqTNzOaY2UozW2NmN8Y7nqbM7D4z22Fmy5osyzGzl81stf+3n7/czOwu\nvxxLzGxqk+dc4W+/2syu6OYyFJrZ62b2sZktN7PrE60cZpZqZvPNbLFfhh/7y4eb2Tw/1sfMLNlf\nnuI/XuOvL2qyr+/5y1ea2ZndVYYmxw+a2Udm9s9ELIOZbTCzpWa2yMwW+MsS5r3U5PjZZvaEmX1i\nZivM7NhEKoeZjfX/Bw233Wb2zUQqg7TOenD92FbWQh2UiPb//k5EzX33xTum9jKzb/nvpWVm9oiZ\npcY7prawdvym7KlaKMMv/PfTEjN7ysyy4xljWzRXjibrvm1mzszyOvWgzrmEvgFBYC0wAkgGFgNH\nxDuuJvHNBqYCy5osux240b9/I3Cbf38u8DxgwDHAPH95DrDO/9vPv9+vG8swEJjq388CVgFHJFI5\n/Fgy/ftJwDw/tseBi/3lvwe+6t//GvB7//7FwGP+/SP891gKMNx/7wW7+T31n8DDwD/9xwlVBmAD\nkLffsoR5LzWJ+QHgav9+MpCdiOXw4wgCxcCwRC2Dbi3+X3ts/diOcjRbB8U7rg6WZZ/v70S8Nffd\nF++Y2hn/YGA9kOY/fhy4Mt5xtTH2Nv+m7Km3FspwBhDy79/W08vQUjn85YXAi8DG/X/rHOqtN7S0\nzQTWOOfWOefCwKPAuXGOqZFz7i2gbL/F5+J96eH/Pa/J8ged530g28wGAmcCLzvnypxzu4CXgTld\nH73HObfNOfehf38PsALvSy9hyuHHUuk/TPJvDjgFeKKFMjSU7QngVDMzf/mjzrk659x6YA3ee7Bb\nmNkQ4Gzgj/5jI8HK0IKEeS8BmFlfvC/sewGcc2HnXHmilaOJU4G1zrmNJG4Z5EA9un5sq4PUQQll\n/+/vRHSQ775EEwLSzCwEpAOfxjmeNmnnb8oeqbkyOOdecs5F/IfvA0O6PbB2auF/AXAH8F2835id\nqjckbYOBzU0eb6Hnf5n3d85t8+8XA/39+y2VpceU0bwudkfhtVQlVDn8bimLgB14PyzXAuVNviia\nxtMYq7++Asgl/v+LO/G+DGL+41wSrwwOeMnMFprZtf6yhHov4bVQlgD3+12d/mhmGSReORpcDDzi\n30/UMsiBet3/Zr86KNHs//2diFr67ksYzrmtwC+BTcA2oMI591J8ozokLX1nJ6ov4fXqSDhmdi6w\n1Tm3uCv23xuStoTmvLbUhLjugpllAk8C33TO7W66LhHK4ZyLOuem4J3BmQmMi3NI7WJmnwF2OOcW\nxjuWQ3SCc24qcBZwnZnNbroyEd5LeGdppwK/c84dBVThdUtplCDlwLwxkJ8F/rr/ukQpgxweDlYH\n9XS96Pu71e++ns4f83UuXgI6CMgws8viG1XnSPTvbDP7PhAB/hLvWNrLzNKBm4AfdtUxekPSthWv\n/2iDIf6ynmy7360I/+8Of3lLZYl7Gc0sCa+y/Itz7m/+4oQrB4DfleN14Fi8Ll6hZuJpjNVf3xco\nJb5lOB74rJltwOvmdArwaxKrDA1nOXHO7QCewkugE+29tAXY4pxrONv/BN4PmUQrB3jJ84fOue3+\n40QsgzSv1/xvWqiDEskB399m9uf4htQhLX33JZLTgPXOuRLnXD3wN+C4OMd0KFr6zk4oZnYl8Bng\nUj/5TDQj8U4ELPY/50OAD81sQGcdoDckbR8Ao82bQS8Zr5vPM3GOqTXPAA0zrF0BPN1k+eX+LG3H\n4DXZb8Mb0HiGmfXzzxCd4S/rFv44qHuBFc65/22yKmHKYWb5DbMRmVkacDreuIjXgQtbKEND2S4E\nXvO/RJ4BLjZvZsbhwGhgfneUwTn3PefcEOdcEd77/DXn3KWJVAYzyzCzrIb7eO+BZSTQewnAOVcM\nbDazsf6iU4GPE60cvkvY2zWyIdZEK4M0LxHrxwMcpA5KGC18fydc685BvvsSySbgGDNL999bp+L9\nHkhULX1nJwwzm4PXdfizzrnqeMfTEc65pc65Audckf8534I3gVJxZx4k4W94s5qtwhuj9P14x7Nf\nbI/g9Zmu9/+BX8YbV/QqsBp4BcjxtzXgN345lgLTm+znS3gTRqwBrurmMpyA19y+BFjk3+YmUjmA\nScBHfhmWAT/0l4/AS1jW4HUPS/GXp/qP1/jrRzTZ1/f9sq0EzorT++ok9s4emTBl8GNd7N+WN3xe\nE+m91OT4U4AF/nvq73gzJyZUOYAMvNbXvk2WJVQZdGv1f9xj68d2lKHZOijecR1CeRq/vxPx1tx3\nX7xj6kAZfgx8gvd74KGGerOn32jHb8qeemuhDGvwxt82fL5/H+84O1KO/dZvoJNnjzR/xyIiIiIi\nItID9YbukSIiIiIiIr2WkjYREREREZEeTEmbiIiIiIhID6akTUREREREpAdT0iYiIiIiItKDKWkT\nERERERHpwZS0iYiIiIiI9GBK2kRERERERHowJW0iIiIiIiI9mJI2ERERERGRHkxJm4iIiIiISA+m\npE1ERERERKQHU9ImIiIiIiLSgylpExERERER6cGUtImIiIiIiPRgStpERERERER6MCVtIiIiIiIi\nPZiSNhERERERkR5MSZscdswsaGaVZja0M7cVEREREekKStqkx/OTpoZbzMxqmjy+tL37c85FnXOZ\nzrlNnbltR5nZ1WbmzOyCrjqGiIhIZ9enTfb7vpld1obtsv1jPtXRY4kcrpS0SY/nJ02ZzrlMYBNw\nTpNlf9l/ezMLdX+Uh+QKoAy4vLsPbGbB7j6miIjER3vr0y5wEVANzDWz3G44XqME/G0gsg8lbZLw\nzOwnZvaYmT1iZnuAy8zsWP/MX7mZbTOzu8wsyd8+5LdsFfmP/+yvf97M9pjZe2Y2vL3b+uvPMrNV\nZlZhZv9nZu+Y2ZUHiX0kcDxwLXCWmeXvt/58M1tkZrvNbI2ZneEvzzWzP/ll22VmT/rLrzazN5o8\nv7n4f2NmL5hZFTDLzD7b5BibzOzm/WKY7b+WFWa22cy+6L++n5pZoMl2nzezhe3414mISA/iDwm4\n2czWmdlOM/uLmWX76zLM7FEzK/Pr1nlm1s/MfgXMAP7ot9j96iCHuAK4E1gLXLLfsYvM7Gn/uDub\n7sfMvmZmn/j17lIzm2hmqX79NqTJdo+a2Q/8+3P8evNmM9sO/M7M8v36u8Qvx9NmNrDJ8/PM7EEz\nK/br1sf85WvM7PQm26X6deL4Q3i5RdpFSZv0Fv8GPAz0BR4DIsD1QB5eUjQH+MpBnv8F4GYgB+/s\n4/+0d1szKwAeB77jH3c9MLOVuC8H3nfOPYlXiX2hYYWZHQfcB3wbyAZOBjb6qx8GkoEjgALg160c\nZ//4fwxkAe8BlcCl/jHOAa43s8/4MQwHngP+F8gFjgKWOufeA/YApzbZ7xeBB9sRh4iI9Cw3AGcA\nJwBDgHrgDn/d1UAIGIxXx30dCDvnvg18AFztt9h9u7kdm9kY4Bi8+usveAlcw7ok4HlgBTAUKAQa\nTkZ+EfgvvCSvD3AhsKuN5SkCkvz9/Qfe797f+8doOOF6R5PtHwMMGAf0B37jL38QaNr981xglXNu\nRRvjEDlkStqkt/iXc+4fzrmYc67GOfeBc26ecy7inFsH3A2ceJDnP+GcW+Ccq8erTKZ0YNvPAIuc\nc0/76+4Adra0EzMzvKTtYX/Rw+zbRfLLwD3OuVf9cm12zq00s0K8ZOmrzrldzrl659xbB4l3f085\n597z91nnnHvNObfcf7wYeJS9r9VlwPPOucf913Knc26Rv66xEjOzPD+mR9oRh4iI9Cz/DtzonPvU\nOVeLd4LvIr++qgfygZF+ffCBc66qHfu+HJjvnFuLV99Nb9JSdQJeQnaTc67ar8ff9dddDfzMOfeR\n86x0zm1p4zHrgP9xzoX9fW736+ga51wF8HP8+s4/STkL+Jpzrtx/TkPd+iBwnpml+Y+/CDzUjrKL\nHDIlbdJbbG76wMzGmdmzfheH3cB/450ZbElxk/vVQGYHth3UNA7nnAMOVrHMxjuT+Zj/+GFgqplN\n8B8X4rW+7a8Q2OlXOB2x/2t1rJm94XcXqcCrIBteq5ZiAK/COtevxC4GXnfO7ehgTCIiEkd+YlYI\nPOd3fywHPsL7rZgL3Au8CTxhZlvM7GfWxnHR/r6/iHeiE+fceryeHg2tbYXAeudcrJmnH6weak2x\nfxK1IY4sM7vPHwqwG3iJfeu7Hc65PfvvxDm3Ae+1OM8fxnAK3glOkW6jpE16C7ff4z8Ay4BRzrk+\nwA/xujx0pW14SRjQWEkNPsj2V+B9BpeaWTHwDl45GiqxzcDIZp63Gcgzsz7NrKsC0ps8HtDMNvu/\nVo/idUMpdM71Bf7I3teqpRjwZ9RcCJyHzjqKiCQ0/0TjVuAU51x2k1uq38uizjn3Q+fcOLyTjp/D\nO2EHB9Yr+zsZr0viLf7J1GJgMt4Y9ABeXVPUdJx0Ey3VQ2G81r+D1Xn7x3UjXj09w/9tcAb71ncF\nZtbSSdsH8HqXXAy8ppOU0t2UtElvlQVUAFV+94uDjWfrLP/Eayk7x7xZqq7H60pyADNLx+uX/2W8\n7pUNt28Bl/pnL+8Frjazk80sYGZDzGysc24z8ArwG/OmT04ys9n+rhcDk/xB2mnAj9oQdxZQ5pyr\nNbNj2FsJA/wZmGNmF5g3qUmemU1usv5B4Ht4/f+fbsOxRESk5/o9cKvfDR8zKzCzc/z7p5nZEX5i\ntRtv7HhDy9h2YMRB9nsFXh15JHvru8l4Y8NPBf6FN076f8ws3czS/HHd4J1IvNHMJptnjJkN8Vvl\nluLXmWb2WeDYVsqXhddDptzv1v+DhhV+699bwP8zs75mltykbgV4Aq8b51fR+G2JAyVt0lt9G6+S\n2IPX6vbYwTc/dM657XjTGf8vUIp3ZvAjvD71+zvfj+3PzrnihhtwD5AGnO73578GuAsvAX0dr/sG\n7B0QvQqvsvyGH8PHwM+AN4CVeBVQa74K/Ny8mTdvwptMpaFM6/EmJ/kvvMsSfAhMbPLcJ/Eq6iec\nczVtOJaIiPRct+OdFHzNrxPeBab66wbjnZzbg9eT5Tn21q13AJf7My7e3nSHfsvVBcBdTes759wa\nvJ4eV/hdGOfiJXJb8Cb5+jcA59xDePXqE/6xn8CbOAu8yVAuwpuY5Dy8xPBgfonXHbIUL1F8br/1\nl+BNXLIabyjEVxtW+N0m/4E3FOKZVo4j0unMaw0Xkc7mt5Z9ClzonHs73vF0Bb8L6HrgSufcG3EO\nR0REpMuY2c+AAufc1fGORQ4/amkT6UT+dWGyzSwF77IA9cD8OIfVlT6P15L4ZrwDERER6Sr+BCRX\n4s1GLdLtlLSJdK4TgHVACXAm8G/Ouea6RyY8M/sXXtfN65ya7EVEpJcys68DG4C/Oud684lY6cHU\nPVJERERERKQHU0ubiIiIiIhIDxaKdwAAeXl5rqioKN5hiIhIN1i4cOFO51yzl8OQA6mOFBE5PBys\nfuwRSVtRURELFiyIdxgiItINzGxjvGNIJKojRUQODwerH9U9UkREREREpAdT0iYiIiIiItKDKWkT\nERERERHpwVpN2szsPjPbYWbLmizLMbOXzWy1/7efv9zM7C4zW2NmS8xsalcGLyIiIiIi0tu1paXt\nT8Cc/ZbdCLzqnBsNvOo/BjgLGO3frgV+1zlhioiIiIiIHJ5aTdqcc28BZfstPhd4wL//AHBek+UP\nOs/7QLaZDeysYEVERERERA43HR3T1t85t82/Xwz09+8PBjY32W6Lv+wAZnatmS0wswUlJSUdDENE\nRERERKR3O+SJSJxzDnAdeN7dzrnpzrnp+fm6xqqIiIiIiCQmLyXqOh1N2rY3dHv0/+7wl28FCpts\nN8RfJiIiwJurSrjxySXxDkNEREQO0baKGu5/Zz2f+/27PPR+i9fF7hShDj7vGeAK4Fb/79NNln/d\nzB4FjgYqmnSjFJEe6JZnllOYk86XTxge71AOC1fcNx+An58/ETPr1H2HIzGWbq1g2rB+nbpfERER\n8XxaXsNzS7fx/LJiFm7cBcDY/llkpnQ0rWqbVvduZo8AJwF5ZrYF+BFesva4mX0Z2Ah83t/8OWAu\nsAaoBq7qgphFpBP96d0NAEraulltfYy05GCn7vPH/1jOX+Zt4oVvzmLcgD6dum9pPzO7HrgGMOAe\n59ydZjYF+D2QCkSArznn5scxTBERacXmsmpeWFbMs0u3sWhzOQDjB/bhhjPGMGfCQEYVZHZ5DK0m\nbc65S1pYdWoz2zrgukMNSkT29fk/vMeJY/K57uRR8Q5FOslLHxdz7pRm52nqsHfW7ARgzp1vs/hH\nZ/Dskm2MKsikMCeNfunJJAcDBAKd27onzTOzCXgJ20wgDLxgZv8Ebgd+7Jx73szm+o9PilugIiLS\nrE2l1Ty3bBvPL93G4i0VAEwY3IfvnDmWuRMHMjwvo1vj6dp2PJEe5v11peRnpTAyv/PPiFRU17Nq\nxx5mFOV0+r7nry9j/vqyLk3aim58llEFmTzx78eSnZ7cKft86L0NPLe0mEeuPWaf5bGY2yd5iMYc\n9dEY4WiMcCRG0IyMlBDJoZaH3e6uree837zDBVOHMGfCANaXVJEcCmAG1eEo1eEIA/umUZSbQUFW\nSuPxojHH7pp6tlXUUh+N0Sctib5pSURiMXB7Z1XKyUgmKRhga3kN1XURSqvCVNTUU1UXIejvKzcj\nhdzMZAJmVIUjVNdFiTpHOBIjEo1hZgQDRsAgEDACTbpDXv/oIq5/dBHThvVj5vAc6iNe+Wvro4zM\nz6RPWhJJwQAGFO+upSYcpV9GMrX1USrrIqQnBanyy1lZF6G2PsqG0urG/U/+8UsHvGahgJGT4f1v\nM1JCpCcHaQipqi5KTTiKmfcaxZy3fUpSwEv2zAgFjWf/Y1Zb//2Hu/HAPOdcNYCZvQmcj/cWa2gG\n7Qt8Gp/wRERF100/AAAgAElEQVTiJxZzrCmpZP76MtbvrKKwXxqjCrIYVZBJ/z4pnT58oK027Kzi\nuWXbeG7pNpZt3Q3ApCF9ufGscZw1YQDDcrs3UWtKSZv0OEU3PgvAT86bwGXHDOvUfV989/sArPvZ\n3E5vcZj8396P5B+cPZ4zjhjA0Nz0Tt1/S+qjMarrogSDRnVdhN21EZxzhIIB0pODBANGKGDURWJU\n1NSTHAw0JiaRaGyffa3ZUcmU/36Zwpw0MlOSSE0KUB+NkZkSIi8zhWDAqKip93/sG31Sk4jGYkRi\njmjMEYk6IrGYn4Q53lzlXc4jHIlx3zvrKa6oZeHGXSzdWkFWaoj6aIz6qPfc5qQlBQlHY83OyNTw\nlF+8uJJfvLjyoK9RUtBITQpSF/GSwraYPqwfJ4zO485XVrdp+476cNMuFm0uJ2hGekqQoBmPL9hy\nwHYB21vmUMCIxBzJwQAZKUHSk0OkJQcpyk1nT62XYILXdePfTxxBaWWY6nCE6nCU0sowZlAVjlJV\nFyEacwQDRkZeiLSkADEHQTOCQWtMJMORGDHnSAoGqItESQl1brfOXmoZ8FMzywVq8IYOLAC+Cbxo\nZr/EmwzsuOaebGbXAtcCDB06tFsCFhHpKvXRGMu2VvDBhjLmr9/Fgo1llFfXA5AcCuxTN2elhBhR\nkMnI/AxGFWQyKj+TUQWZDM1JJxQ85InvD7CupJLnlm7juaXFfLzNS9SmFGZz09xxnDVhIIU53fN7\nrjXW1dNTtsX06dPdggUL4h2GtMObq0pIDQU4ekRup+7XOcfw7z3X+Pi08QXcc/n0Tjvj0pAQNhhd\nkMn23bVML8phzoQBBM3IyUxmVH4mmSkhKmrqKa2qo7IuinOO6nCU+uiBP/rNjP945KN9ls0anYdz\nUF4T9sYvJQWpqotQVh2mPhIjJSlIQVYKlXURYjFHOOqNcUpP8s6lNCQ/UefYXFYDwEc3n85R//Ny\n4zH6pIbYXRvplNcG4N9PHMnK4t0s2LCLQdlpZKcnkZYcpLQy7P3Ad46M5BC1kSixmKMqHCUUsMbE\nMBQM7PO4oTtBU9npSeRkJDN7dD7JIa8VJykYIDkUICloJIcCRGOOytqIl2SGAo0tW03932tr9nn8\ng7PHk5eZQk5GMqlJQdKTg2worWJXVZit5bXU1kdJCQVITw6RkRJkcHYayaEAFTX1VNTUEwoYZoYZ\n/OX9TY1f3ACzx+Rz7uRBjCrIJCs1hAOq66JUhSOUVYW9FrvUJDJSQgQDkBz0kmWHw7mGliuv9co5\nR2VdhNH9s8j1Y23KOceu6nrCkRj1US8h7t8nhZRQkMraCClJAVKTgoQjMZKCFrezkYfCzBY656bH\nO46u5o/7/hpQBSwH6vAStTedc0+a2eeBa51zpx1sP6ojRSTR1ISjfLRpF/M3lPHBhjI+3FhOTX0U\ngOF5Gcwo6seMohxmDs9haE46JXvqWLOjkrUllazZUcka/+/23XWN+0wKGkW5fiJXkMlIP5kbkZ9B\nenL72qHW7NjDc0uLeW7pNj4p3gPA1KHZzJ04kLMmDmRwdlrnvRjtcLD6UUmbtFtxRS3H/PxVADbc\nenan7ru2Psq4m1/YZ9nEwX25ZOZQLplZuM8P1HAkRnl1mLLqMBXV9Ti8hCASddT5P3hjzlFXH6Mu\nEmV3bYTvPrF3qvWzJw2krj5KUjDAgo27KNlTR1eYPKQvORnJxBxkpYbIyUgmGDBq66PsrAyTlRIi\nEDCSggFq673uboaX+DTcnvrowCtnzJ04gNSkILkZyfTvk8p7a0s5ZkQuBX63Ai+pijS2eqWEAmSn\nJ+2TdAYDAWIxx/Gj8sjPSun0su+fJN9/5Qxmj8lvNgnr6L47+z3YdN8Aj157DMd08smJw93hkrQ1\nZWY/A7YAPweynXPOvC+0CufcQWeNUR0pIj1deXWYBRt28cGGMuatL2PZ1goiMYcZjB/Qh5nDc5hR\nlMOM4f0oyEpt835319azrqTKS+T829qSSjaWVtG0k87g7LR9ErmGW8OQAOccq3dU8uySbTy/bBur\ntldi5vWqOWvCQM6aOICBfeOTqDV1sPpR3SPjLBG7Gn1SvLcF4uWPt3PNgwsY0CeVB788k8J+6azZ\nUcnW8hoyUoKNEx/UR2ON42Xq6mPsrq2nrKq+MbEKR2JUh6PsrPQSpxvOGMO5UwYz6/bXWbq1gqVP\nLeWmp5YyMj+D3bURItEYu/xm9Y5Y9MPT9xm3FYnG2FBaRTAQoLSyjhXbdlMXiZGdnkxeZjJZqd5H\nJSMlRCjgjZsyvMEp3nkPx2n/+xbQNUlE06TtzCP784cvHvh5vnrWiE4/7qF68ZuzuezeeZTsqWP1\nT88iqQu6NXQ1JWzSUWZW4JzbYWZD8cazHQN8AzgReAM4Beja/rciEjfhSOygY7MT2baKGuav91rR\nPli/i5Xbvdaq5GCASUP6cs3sEcwcnsO0Yf3ok5rU4eP0SU1iSmE2Uwqz91leF4mysbT6gGRu3vpS\nauv3npzul57EqIJMyqrCrC2pwgxmFuXw488eyZwJA+jfp+0JZLwpaYujd9fs5At/nMfjXzmWmcM7\nf/KKyroIoYA3lqdh4oeYf1oi4Lf0JAf3TtwQjsSIOseWXTXsrqlnx546rztfVZhozFEXiVIXiTVO\ndQpwzYPe2d/i3bWcccdbHY41FDDSk4NkpSYxtn8WJ48roDAnnQ23nk19NMZvX1/Lwk27SA4GyMv0\nWqoKslLJzUwmJyOZvmlJOAelVXWkJQUbu91hkJoUJDUUJCUpwKm/epPjR+UeMNFGKBhgVEEW4DXb\nT+/gZCJdPeXr+p/PTajucGMHZPHB9w/a86vD3rnxFKrqOq9raHPevfGULt2/9HpP+mPa6oHrnHPl\nZnYN8GszCwG1+OPWRKT3cM7xo2eW89D7Gxmem8GUodkcVZjNlMJ+jBuYlXAnMJ1zrNtZxQfry5i/\nwZsYbcsub9hGRnKQaUU5nDN5IDOKcphcmH1At/+ukBIKMqZ/FmP6Z+2zPBZzbC2vYU1JJWubdLcc\n2DeNK48fzplH9m9XS19PoqQtjt5Z603PPW9daacnbdXhCBN+9GKz6/IykzEzSvbUkZniTQZR18rk\nDAHzPiCpSd4YoKZOG1/A5ccWcderq4nEHBdMG8LEwX2prfeSvJjzuual+R/i1KRg44x9KSFvVrqD\ndZdLCga4/rTR7XwFmvfPb5xAURdN0fr89bMY1IVN62YkVMLW1bqyv/nCH5xGWVWYQXHq0y69g3Pu\ngKk2nXP/AqbFIRwR6Sa/f3MdD763kbMmDKA+6nhrVQl/+9DrMZMSCjBxcF+OGuolcVOGZjOob2qP\nqd+rwxHW7qhiTcke1u6oYtX2PXy4aRc7K70JrnIzkplRlMNVxw9nZlEO4wdmdcnkIB0VCBiFOekU\n5qRz8tiCeIfTqZS0xZHRNR/QqrrIPl0YG4zMz2B4XiZ1kSjZ6ckMzUljT22EpGCgcfIGAwb3867p\n1L9PCpkpIfqmJR3wgVz+aQU5Gcms2VHJrNH5gDdZQ083YXDfLtv3+IFddzHjt797MumdfCFmaVlu\nZgq5mZ0/xk9ERHq3Z5ds47YXPuEzkwZy18VHEQgYzu/FtGhzOR9tKmfR5l088N5G7nl7PQAFWSle\nF8Ch2RxV2I9JQ/qSkdK1P9FLK+v2mfBjbUkVa/3hLQ2CAWNYTjqzR+czY7g3aciIvIwek2AebpS0\nxVHDe/6F5cVcdsww+qYlNbam1Edj7K6ppzocpX+fVKrDET4p3tM48LK0so6a+ii7ayKU7KmjpLKO\nnZV1lOypozocbTxGdnoSC75/WqefBTlykJf89IRBm4eDnjLdrIiIiDRv4cZdfOtx7/qbv/zc5MZL\nC5ntbf05Z/IgwBvvtmLbbhZtLveTuV289PF2wOvdNKZ/lt8a57XIjSrIbPckXvt3FWw69qvpvABp\nSUFGFmQwvagfF+cXNk7iMSw3o9eOyUtEStriaI8/VfvyT3fvM437/swaJrvYVzBgZKaEKMhKId8/\nS5OfmUJeVgpLt1bw7JJtnDK2oEc1W4uIiIj0NptKq7nmwQUM7JvKPZdPb3VcV3IowOTCbCYXZnOF\nv2xXVZhFWxpa48p5dsk2Hpm/GYDMlBCThvRlSmE2Rw3t5/3m82d9rotE2bCzeu90+f5t3c7KfSbl\nyMnwLmk0Z8LAvddAK8hkUN+0Tr92rXQ+JW1xlNlC0/fUodkkhwJMG9aPgN8cl5ESYmz/rMaJLvKz\nUg76hfDo/E08u2Rbp0ytLiIiIiLNK68Oc+Wf5hNzjvuvnNE4zXx79ctI5uSxBY1jsWIxx/rSKhb5\nSdxHm3dx91vriPiTyjVcb3RTWTXRJvPfN0x/f+zI3H2mwe9oXNIzKGmLo4bpS68/dTTXnTyKgNFp\nrWINH92A+h2LiIiIdIlwJMZXHlrIlrIa/nz10YzI77xZpAMBY2S+l3RdMG0I4F3PdtnWCq81bks5\nsZjjM5MGNiZnHbnQtCQG/VfjIBZzlNfUs3DTLgBOP6K/+gyLiIiIJBDnHDc+uYR568u486IpXXL5\npv2lJgWZXpTT4UsTSeJS0taKPbX1/NeTS/jvcyeQ187Z5PbU1rOxtJqNpdWs2r6HbRU1rN9ZxfJP\ndzdOFhIKGNnpHb/ooIiIiIh0v7teXcPfPtrKf54+hvOOGhzvcKSXU9LWir8u2MJzS4vJy0zhW6eN\noV9GMrGY481VJQQDRjTmWFtSSXU4ytqSSpZtraC0KkzQjNKqcON+zCA/M4XCnHQ+N20IRXkZDM5O\nY8rQ7C65yF/DxCXqHSkiIiLSuZ76aAt3vLKKC6YO4RunjIp3OHIYUNLWipQkr9vig+9t5MH3Nh50\n27SkIDOG5zA8L5OcjCRG5GcyzJ/idVRBZrdcIX5/StpEREREOs+8daV894klHDMih5+fP1HXLZNu\noaStFSmh5hOtqUOzSQoGGJSdxldPGklORjK5Gck95oPraOYaASIiIiLSYWtLKrn2oYUMzUnnD5dN\n15wE0m2UtLUipcmH8Y6LJnPimALSk4NxaTVrj1PGFRAMGJcePSzeoYiIiIgkvNLKOq66/wNCAeP+\nK2fSV3MSSDdS0taKpknbvx01JI6RtM/Avmms/dnceIchIiIikvBq66Nc+9BCtu+u5ZFrj2Fobnq8\nQ5LDjJK2VqQl9+wWNRERERHpOrGY49t/XczCjbv47aVTmTq0X7xDksOQOuK2Ism/2PVvL50a50hE\nREREpLv98qWVPLtkG987axxzJw6MdzhymFLS1oqGqfN1LTURERGRw8uj8zfx2zfWcsnMoVw7e0S8\nw5HDmJK2NjJ6xqyQIiIiIr1ZJBojFov/LNhvry7h+39fxuwx+fzPuUf2mBnC5fCkMW2t0NT5IiIi\nIl1vT2099/5rPX98ez1905K4/NhhXDxjaFxmaVxZvIev/flDRhdk8psvHEUoqHYOiS8lba3xczad\nXBERERHpfLX1UR56byO/fWMNu6rrOf2I/uyuqefnz3/Cna+s5vypg7nyuCJG98/qlnh27KnlS3/6\ngLTkIPddOYOsVA2RkfhT0taKhnY25WwiIiIinac+GuPxBZu569XVbN9dx6zRedxwxlgmF2YDsPzT\nCh54dwN/XbiFv8zbxKzReVx1fBEnjSkgEOiaX2bV4QhXP7CAsqowj3/lWAZlp3XJcUTaS0lbG6kf\ns4iIiMihi8Yc/1j8KXe8soqNpdVMG9aPOy86imNH5u6z3ZGD+nL7hZP5rznjeGT+Jh56fyNf+tMC\ninLTueK4Ii6cNqRTW8GiMcf1jy5i2dYK7v7idCYO6dtp+xY5VEraWuE0pE1ERNrJzK4HrsHrqHGP\nc+5OM3sMGOtvkg2UO+emxCtGke7mnOPlj7fzq5dWsXL7HsYP7MN9V07n5LEFBz05npuZwtdPGc1X\nThzJ88uKuf+d9fz4Hx/zq5dW8bnpQ7ji2CKK8jIOOb6fPbeClz/ezi3nHMFpR/Q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HIiIi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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktJl_O0VmE2H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}